From b3ca5eabb009bdb14ed0fdd09e88351e9f5ad975 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Mon, 28 Aug 2023 16:30:13 -0400 Subject: [PATCH 001/266] Add Lie group capabilities to NavState --- gtsam/navigation/NavState.cpp | 246 +++++++++++++++++++++++++++++++++- gtsam/navigation/NavState.h | 121 +++++++++++++++-- 2 files changed, 351 insertions(+), 16 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 6a17e9ea05..5c8872f57e 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -77,10 +77,10 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { } //------------------------------------------------------------------------------ -Matrix7 NavState::matrix() const { +Matrix5 NavState::matrix() const { Matrix3 R = this->R(); - Matrix7 T; - T << R, Z_3x3, t(), Z_3x3, R, v(), Vector6::Zero().transpose(), 1.0; + Matrix5 T; + T << R, t(), v(), Z_3x1.transpose(), 1.0, 0.0, Z_3x1.transpose(), 0.0, 1.0; return T; } @@ -103,6 +103,246 @@ bool NavState::equals(const NavState& other, double tol) const { && equal_with_abs_tol(v_, other.v_, tol); } +//------------------------------------------------------------------------------ +NavState NavState::inverse() const { + Rot3 Rt = R_.inverse(); + return NavState(Rt, Rt * (-t_), Rt * -(v_)); +} + +//------------------------------------------------------------------------------ +NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { + if (Hxi) *Hxi = ExpmapDerivative(xi); + + // Order is ω, velocity, position which represent by φ,ρ,ν + Vector3 phi(xi(0), xi(1), xi(2)), rho(xi(3), xi(4), xi(5)), + nu(xi(6), xi(7), xi(8)); + + Rot3 R = Rot3::Expmap(phi); + + double phi_norm = phi.norm(); + if (phi_norm < 1e-8) + return NavState(Rot3(), Point3(rho), Point3(nu)); + else { + Matrix W = skewSymmetric(phi / phi_norm); + Matrix A = I_3x3 + ((1 - cos(phi_norm)) / phi_norm) * W + + ((phi_norm - sin(phi_norm)) / phi_norm) * (W * W); + return NavState(Rot3::Expmap(phi), Point3(A * rho), Point3(A * nu)); + } +} + +//------------------------------------------------------------------------------ +Vector9 NavState::Logmap(const NavState& state, OptionalJacobian<9, 9> Hstate) { + if (Hstate) *Hstate = LogmapDerivative(state); + const Vector3 phi = Rot3::Logmap(state.rotation()); + const Vector3& p = state.position(); + const Vector3& v = state.velocity(); + const double t = phi.norm(); + if (t < 1e-8) { + Vector9 log; + log << phi, p, v; + return log; + + } else { + const Matrix3 W = skewSymmetric(phi / t); + + const double Tan = tan(0.5 * t); + const Vector3 Wp = W * p; + const Vector3 Wv = W * v; + const Vector3 nu = v - (0.5 * t) * Wv + (1 - t / (2. * Tan)) * (W * Wv); + const Vector3 rho = p - (0.5 * t) * Wp + (1 - t / (2. * Tan)) * (W * Wp); + Vector9 log; + // Order is ω, p, v + log << phi, rho, nu; + return log; + } +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::AdjointMap() const { + const Matrix3 R = R_.matrix(); + Matrix3 A = skewSymmetric(t_.x(), t_.y(), t_.z()) * R; + Matrix3 B = skewSymmetric(v_.x(), v_.y(), v_.z()) * R; + // Eqn 2 in Barrau20icra + Matrix9 adj; + adj << R, Z_3x3, Z_3x3, A, R, Z_3x3, B, Z_3x3, R; + return adj; +} + +//------------------------------------------------------------------------------ +Vector9 NavState::Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_state, + OptionalJacobian<9, 9> H_xib) const { + const Matrix9 Ad = AdjointMap(); + + // Jacobians + if (H_state) *H_state = -Ad * adjointMap(xi_b); + if (H_xib) *H_xib = Ad; + + return Ad * xi_b; +} + +//------------------------------------------------------------------------------ +/// The dual version of Adjoint +Vector9 NavState::AdjointTranspose(const Vector9& x, OptionalJacobian<9, 9> H_state, + OptionalJacobian<9, 9> H_x) const { + const Matrix9 Ad = AdjointMap(); + const Vector9 AdTx = Ad.transpose() * x; + + // Jacobians + if (H_state) { + const auto w_T_hat = skewSymmetric(AdTx.head<3>()), + v_T_hat = skewSymmetric(AdTx.segment<3>(3)), + a_T_hat = skewSymmetric(AdTx.tail<3>()); + //TODO(Varun) + // *H_state << w_T_hat, v_T_hat, // + // /* */ v_T_hat, Z_3x3; + } + if (H_x) { + *H_x = Ad.transpose(); + } + + return AdTx; +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::adjointMap(const Vector9& xi) { + Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); + Matrix3 v_hat = skewSymmetric(xi(3), xi(4), xi(5)); + Matrix3 a_hat = skewSymmetric(xi(6), xi(7), xi(8)); + Matrix9 adj; + adj << w_hat, Z_3x3, Z_3x3, v_hat, w_hat, Z_3x3, a_hat, Z_3x3, w_hat; + return adj; +} + +//------------------------------------------------------------------------------ +Vector9 NavState::adjoint(const Vector9& xi, const Vector9& y, + OptionalJacobian<9, 9> Hxi, + OptionalJacobian<9, 9> H_y) { + if (Hxi) { + Hxi->setZero(); + for (int i = 0; i < 9; ++i) { + Vector9 dxi; + dxi.setZero(); + dxi(i) = 1.0; + Matrix9 Gi = adjointMap(dxi); + Hxi->col(i) = Gi * y; + } + } + + const Matrix9& ad_xi = adjointMap(xi); + if (H_y) *H_y = ad_xi; + + return ad_xi * y; +} + +//------------------------------------------------------------------------------ +Vector9 NavState::adjointTranspose(const Vector9& xi, const Vector9& y, + OptionalJacobian<9, 9> Hxi, + OptionalJacobian<9, 9> H_y) { + if (Hxi) { + Hxi->setZero(); + for (int i = 0; i < 9; ++i) { + Vector9 dxi; + dxi.setZero(); + dxi(i) = 1.0; + Matrix9 GTi = adjointMap(dxi).transpose(); + Hxi->col(i) = GTi * y; + } + } + const Matrix9& adT_xi = adjointMap(xi).transpose(); + if (H_y) *H_y = adT_xi; + return adT_xi * y; +} + +/* ************************************************************************* */ +Matrix63 NavState::ComputeQforExpmapDerivative(const Vector9& xi, + double nearZeroThreshold) { + const auto omega = xi.head<3>(); + const auto nu = xi.segment<3>(3); + const auto rho = xi.tail<3>(); + const Matrix3 V = skewSymmetric(nu); + const Matrix3 P = skewSymmetric(rho); + const Matrix3 W = skewSymmetric(omega); + + Matrix3 Qv, Qp; + Matrix63 Q; + + // The closed-form formula in Barfoot14tro eq. (102) + double phi = omega.norm(); + if (std::abs(phi) > 1e-5) { + const double sinPhi = sin(phi), cosPhi = cos(phi); + const double phi2 = phi * phi, phi3 = phi2 * phi, phi4 = phi3 * phi, + phi5 = phi4 * phi; + // Invert the sign of odd-order terms to have the right Jacobian + Qv = -0.5 * V + (phi - sinPhi) / phi3 * (W * V + V * W - W * V * W) + + (1 - phi2 / 2 - cosPhi) / phi4 * + (W * W * V + V * W * W - 3 * W * V * W) - + 0.5 * + ((1 - phi2 / 2 - cosPhi) / phi4 - + 3 * (phi - sinPhi - phi3 / 6.) / phi5) * + (W * V * W * W + W * W * V * W); + Qp = -0.5 * P + (phi - sinPhi) / phi3 * (W * P + P * W - W * P * W) + + (1 - phi2 / 2 - cosPhi) / phi4 * + (W * W * P + P * W * W - 3 * W * P * W) - + 0.5 * + ((1 - phi2 / 2 - cosPhi) / phi4 - + 3 * (phi - sinPhi - phi3 / 6.) / phi5) * + (W * P * W * W + W * W * P * W); + } else { + Qv = -0.5 * V + 1. / 6. * (W * V + V * W - W * V * W) + + 1. / 24. * (W * W * V + V * W * W - 3 * W * V * W) - + 0.5 * (1. / 24. + 3. / 120.) * (W * V * W * W + W * W * V * W); + Qp = -0.5 * P + 1. / 6. * (W * P + P * W - W * P * W) + + 1. / 24. * (W * W * P + P * W * W - 3 * W * P * W) - + 0.5 * (1. / 24. + 3. / 120.) * (W * P * W * W + W * W * P * W); + } + + Q << Qv, Qp; + return Q; +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { + const Vector3 w = xi.head<3>(); + const Matrix3 Jw = Rot3::ExpmapDerivative(w); + const Matrix63 Q = ComputeQforExpmapDerivative(xi); + const Matrix3 Qv = Q.topRows<3>(); + const Matrix3 Qp = Q.bottomRows<3>(); + + Matrix9 J; + J << Jw, Z_3x3, Z_3x3, Qp, Jw, Z_3x3, Qv, Z_3x3, Jw; + + return J; +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::LogmapDerivative(const NavState& state) { + const Vector9 xi = Logmap(state); + const Vector3 w = xi.head<3>(); + const Matrix3 Jw = Rot3::LogmapDerivative(w); + const Matrix63 Q = ComputeQforExpmapDerivative(xi); + const Matrix3 Qv = Q.topRows<3>(); + const Matrix3 Qp = Q.bottomRows<3>(); + const Matrix3 Qv2 = -Jw * Qv * Jw; + const Matrix3 Qp2 = -Jw * Qp * Jw; + + Matrix9 J; + J << Jw, Z_3x3, Z_3x3, Qp2, Jw, Z_3x3, Qv2, Z_3x3, Jw; + return J; +} + + +//------------------------------------------------------------------------------ +NavState NavState::ChartAtOrigin::Retract(const Vector9& xi, + ChartJacobian Hxi) { + return Expmap(xi, Hxi); +} + +//------------------------------------------------------------------------------ +Vector9 NavState::ChartAtOrigin::Local(const NavState& state, + ChartJacobian Hstate) { + return Logmap(state, Hstate); +} + //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index f778a71231..8925fa352a 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -25,14 +25,18 @@ namespace gtsam { /// Velocity is currently typedef'd to Vector3 -typedef Vector3 Velocity3; +using Velocity3 = Vector3; /** * Navigation state: Pose (rotation, translation) + velocity - * NOTE(frank): it does not make sense to make this a Lie group, but it is a 9D manifold + * Following Barrau20icra, this class belongs to the Lie group SE_2(3). + * This group is also called "double direct isometries”. + * + * NOTE: While Barrau20icra follow a R,v,t order, + * we use a R,t,v order to maintain backwards compatibility. */ -class GTSAM_EXPORT NavState { -private: +class GTSAM_EXPORT NavState : public LieGroup { + private: // TODO(frank): // - should we rename t_ to p_? if not, we should rename dP do dT @@ -46,8 +50,6 @@ class GTSAM_EXPORT NavState { dimension = 9 }; - typedef std::pair PositionAndVelocity; - /// @name Constructors /// @{ @@ -87,6 +89,9 @@ class GTSAM_EXPORT NavState { return Pose3(attitude(), position()); } + /// Syntactic sugar + const Rot3& rotation() const { return attitude(); }; + /// @} /// @name Derived quantities /// @{ @@ -111,9 +116,8 @@ class GTSAM_EXPORT NavState { Velocity3 bodyVelocity(OptionalJacobian<3, 9> H = {}) const; /// Return matrix group representation, in MATLAB notation: - /// nTb = [nRb 0 n_t; 0 nRb n_v; 0 0 1] - /// With this embedding in GL(3), matrix product agrees with compose - Matrix7 matrix() const; + /// nTb = [nRb n_v, n_t; 0_1x3 1 0; 0_1x3 0 1] + Matrix5 matrix() const; /// @} /// @name Testable @@ -130,7 +134,24 @@ class GTSAM_EXPORT NavState { bool equals(const NavState& other, double tol = 1e-8) const; /// @} - /// @name Manifold + /// @name Group + /// @{ + + /// identity for group operation + static NavState Identity() { + return NavState(); + } + + /// inverse transformation with derivatives + NavState inverse() const; + + /// compose syntactic sugar + NavState operator*(const NavState& T) const { + return NavState(R_ * T.R_, t_ + R_ * T.t_, v_ + R_ * T.v_); + } + + /// @} + /// @name Lie Group /// @{ // Tangent space sugar. @@ -164,6 +185,78 @@ class GTSAM_EXPORT NavState { OptionalJacobian<9, 9> H1 = {}, OptionalJacobian<9, 9> H2 = {}) const; + /** + * Exponential map at identity - create a NavState from canonical coordinates + * \f$ [R_x,R_y,R_z,T_x,T_y,T_z,V_x,V_y,V_z] \f$ + */ + static NavState Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi = {}); + + /** + * Log map at identity - return the canonical coordinates \f$ + * [R_x,R_y,R_z,T_x,T_y,T_z,V_x,V_y,V_z] \f$ of this NavState + */ + static Vector9 Logmap(const NavState& pose, OptionalJacobian<9, 9> Hpose = {}); + + /** + * Calculate Adjoint map, transforming a twist in this pose's (i.e, body) + * frame to the world spatial frame. + */ + Matrix9 AdjointMap() const; + + /** + * Apply this NavState's AdjointMap Ad_g to a twist \f$ \xi_b \f$, i.e. a + * body-fixed velocity, transforming it to the spatial frame + * \f$ \xi^s = g*\xi^b*g^{-1} = Ad_g * \xi^b \f$ + * Note that H_xib = AdjointMap() + */ + Vector9 Adjoint(const Vector9& xi_b, + OptionalJacobian<9, 9> H_this = {}, + OptionalJacobian<9, 9> H_xib = {}) const; + + /// The dual version of Adjoint + Vector9 AdjointTranspose(const Vector9& x, + OptionalJacobian<9, 9> H_this = {}, + OptionalJacobian<9, 9> H_x = {}) const; + + /** + * Compute the [ad(w,v)] operator as defined in [Kobilarov09siggraph], pg 11 + * but for the NavState [ad(w,v)] = [w^, zero3; v^, w^] + */ + static Matrix9 adjointMap(const Vector9& xi); + + /** + * Action of the adjointMap on a Lie-algebra vector y, with optional derivatives + */ + static Vector9 adjoint(const Vector9& xi, const Vector9& y, + OptionalJacobian<9, 9> Hxi = {}, + OptionalJacobian<9, 9> H_y = {}); + + /** + * The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space. + */ + static Vector9 adjointTranspose(const Vector9& xi, const Vector9& y, + OptionalJacobian<9, 9> Hxi = {}, + OptionalJacobian<9, 9> H_y = {}); + + /// Derivative of Expmap + static Matrix9 ExpmapDerivative(const Vector9& xi); + + /// Derivative of Logmap + static Matrix9 LogmapDerivative(const NavState& xi); + + // Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP + struct GTSAM_EXPORT ChartAtOrigin { + static NavState Retract(const Vector9& xi, ChartJacobian Hxi = {}); + static Vector9 Local(const NavState& state, ChartJacobian Hstate = {}); + }; + + /** + * Compute the 6x3 bottom-left block Qs of the SE_2(3) Expmap derivative + * matrix + */ + static Matrix63 ComputeQforExpmapDerivative(const Vector9& xi, + double nearZeroThreshold = 1e-5); + /// @} /// @name Dynamics /// @{ @@ -203,8 +296,10 @@ class GTSAM_EXPORT NavState { }; // Specialize NavState traits to use a Retract/Local that agrees with IMUFactors -template<> -struct traits : internal::Manifold { -}; +template <> +struct traits : public internal::LieGroup {}; + +template <> +struct traits : public internal::LieGroup {}; } // namespace gtsam From ce32e875ad8beed75b73fa024e02ce7631885700 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Mon, 28 Aug 2023 16:31:16 -0400 Subject: [PATCH 002/266] make OptionalJacobian actually optional --- gtsam/navigation/NavState.h | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 8925fa352a..b12e9eda29 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -71,11 +71,14 @@ class GTSAM_EXPORT NavState : public LieGroup { } /// Named constructor with derivatives static NavState Create(const Rot3& R, const Point3& t, const Velocity3& v, - OptionalJacobian<9, 3> H1, OptionalJacobian<9, 3> H2, - OptionalJacobian<9, 3> H3); + OptionalJacobian<9, 3> H1 = {}, + OptionalJacobian<9, 3> H2 = {}, + OptionalJacobian<9, 3> H3 = {}); + /// Named constructor with derivatives static NavState FromPoseVelocity(const Pose3& pose, const Vector3& vel, - OptionalJacobian<9, 6> H1, OptionalJacobian<9, 3> H2); + OptionalJacobian<9, 6> H1 = {}, + OptionalJacobian<9, 3> H2 = {}); /// @} /// @name Component Access @@ -264,8 +267,9 @@ class GTSAM_EXPORT NavState : public LieGroup { /// Integrate forward in time given angular velocity and acceleration in body frame /// Uses second order integration for position, returns derivatives except dt. NavState update(const Vector3& b_acceleration, const Vector3& b_omega, - const double dt, OptionalJacobian<9, 9> F, OptionalJacobian<9, 3> G1, - OptionalJacobian<9, 3> G2) const; + const double dt, OptionalJacobian<9, 9> F = {}, + OptionalJacobian<9, 3> G1 = {}, + OptionalJacobian<9, 3> G2 = {}) const; /// Compute tangent space contribution due to Coriolis forces Vector9 coriolis(double dt, const Vector3& omega, bool secondOrder = false, From 0a97d92bdbe9d331f8a0440fe83480bd909cc1e9 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Tue, 29 Aug 2023 13:18:46 -0400 Subject: [PATCH 003/266] doc improvements for Pose3 and Rot3 --- gtsam/geometry/Pose3.h | 4 ++-- gtsam/geometry/Rot3.cpp | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 8a807cc23b..37d4735b49 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -135,10 +135,10 @@ class GTSAM_EXPORT Pose3: public LieGroup { /// @name Lie Group /// @{ - /// Exponential map at identity - create a rotation from canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ + /// Exponential map at identity - create a pose from canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = {}); - /// Log map at identity - return the canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ of this rotation + /// Log map at identity - return the canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ of this pose static Vector6 Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose = {}); /** diff --git a/gtsam/geometry/Rot3.cpp b/gtsam/geometry/Rot3.cpp index a936bd02a9..ca96c482f9 100644 --- a/gtsam/geometry/Rot3.cpp +++ b/gtsam/geometry/Rot3.cpp @@ -120,7 +120,7 @@ Unit3 Rot3::unrotate(const Unit3& p, OptionalJacobian<2,3> HR, OptionalJacobian<2,2> Hp) const { Matrix32 Dp; Unit3 q = Unit3(unrotate(p.point3(Dp))); - if (Hp) *Hp = q.basis().transpose() * matrix().transpose () * Dp; + if (Hp) *Hp = q.basis().transpose() * matrix().transpose() * Dp; if (HR) *HR = q.basis().transpose() * q.skew(); return q; } From dd3d6101cbca8f6ba798b8d28cef2b0e93a99c7d Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Tue, 29 Aug 2023 13:29:09 -0400 Subject: [PATCH 004/266] add NavState tests and minor improvements --- gtsam/navigation/NavState.cpp | 29 +++++++---- gtsam/navigation/tests/testNavState.cpp | 67 +++++++++++++++++++++++++ 2 files changed, 86 insertions(+), 10 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 5c8872f57e..8b7e643b3a 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -133,6 +133,7 @@ NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { //------------------------------------------------------------------------------ Vector9 NavState::Logmap(const NavState& state, OptionalJacobian<9, 9> Hstate) { if (Hstate) *Hstate = LogmapDerivative(state); + const Vector3 phi = Rot3::Logmap(state.rotation()); const Vector3& p = state.position(); const Vector3& v = state.velocity(); @@ -148,8 +149,8 @@ Vector9 NavState::Logmap(const NavState& state, OptionalJacobian<9, 9> Hstate) { const double Tan = tan(0.5 * t); const Vector3 Wp = W * p; const Vector3 Wv = W * v; - const Vector3 nu = v - (0.5 * t) * Wv + (1 - t / (2. * Tan)) * (W * Wv); const Vector3 rho = p - (0.5 * t) * Wp + (1 - t / (2. * Tan)) * (W * Wp); + const Vector3 nu = v - (0.5 * t) * Wv + (1 - t / (2. * Tan)) * (W * Wv); Vector9 log; // Order is ω, p, v log << phi, rho, nu; @@ -189,12 +190,14 @@ Vector9 NavState::AdjointTranspose(const Vector9& x, OptionalJacobian<9, 9> H_st // Jacobians if (H_state) { - const auto w_T_hat = skewSymmetric(AdTx.head<3>()), - v_T_hat = skewSymmetric(AdTx.segment<3>(3)), - a_T_hat = skewSymmetric(AdTx.tail<3>()); //TODO(Varun) + // const auto w_T_hat = skewSymmetric(AdTx.head<3>()), + // v_T_hat = skewSymmetric(AdTx.segment<3>(3)), + // a_T_hat = skewSymmetric(AdTx.tail<3>()); // *H_state << w_T_hat, v_T_hat, // // /* */ v_T_hat, Z_3x3; + throw std::runtime_error( + "AdjointTranpose H_state Jacobian not implemented."); } if (H_x) { *H_x = Ad.transpose(); @@ -316,6 +319,8 @@ Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { //------------------------------------------------------------------------------ Matrix9 NavState::LogmapDerivative(const NavState& state) { + const Matrix3 R = state.rotation().matrix(); + const Vector9 xi = Logmap(state); const Vector3 w = xi.head<3>(); const Matrix3 Jw = Rot3::LogmapDerivative(w); @@ -326,7 +331,10 @@ Matrix9 NavState::LogmapDerivative(const NavState& state) { const Matrix3 Qp2 = -Jw * Qp * Jw; Matrix9 J; + // TODO(Varun) If we remove the custom localCoordinates, first one mirrors + // Pose3, but the second one gives the correct values as per unit tests J << Jw, Z_3x3, Z_3x3, Qp2, Jw, Z_3x3, Qv2, Z_3x3, Jw; + // J << Jw, Z_3x3, Z_3x3, Z_3x3, R, Z_3x3, Z_3x3, Z_3x3, R; return J; } @@ -381,15 +389,16 @@ Vector9 NavState::localCoordinates(const NavState& g, // Matrix3 D_xi_R; xi << Rot3::Logmap(dR, (H1 || H2) ? &D_xi_R : 0), dP, dV; if (H1) { - *H1 << D_xi_R * D_dR_R, Z_3x3, Z_3x3, // - D_dt_R, -I_3x3, Z_3x3, // - D_dv_R, Z_3x3, -I_3x3; + *H1 << D_xi_R * D_dR_R, Z_3x3, Z_3x3, // + D_dt_R, -I_3x3, Z_3x3, // + D_dv_R, Z_3x3, -I_3x3; } if (H2) { - *H2 << D_xi_R, Z_3x3, Z_3x3, // - Z_3x3, dR.matrix(), Z_3x3, // - Z_3x3, Z_3x3, dR.matrix(); + *H2 << D_xi_R, Z_3x3, Z_3x3, // + Z_3x3, dR.matrix(), Z_3x3, // + Z_3x3, Z_3x3, dR.matrix(); } + return xi; } diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index b8f0815189..1dcd7b730a 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -169,6 +169,73 @@ TEST( NavState, Manifold ) { EXPECT(assert_equal(numericalDerivative22(local, state2, kIdentity), aH2)); } +/* ************************************************************************* */ +TEST(NavState, Between) { + NavState s1, s2(Rot3(), Point3(1, 2, 3), Velocity3(0, 0, 0)); + + NavState actual = s1.compose(s2); + EXPECT(assert_equal(s2, actual)); + + NavState between = s2.between(s1); + NavState expected_between(Rot3(), Point3(-1, -2, -3), Velocity3(0, 0, 0)); + EXPECT(assert_equal(expected_between, between)); +} + +TEST(NavState, Lie) { + NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); + NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); + NavState nav_state_c(Rot3::Ry(M_PI / 180.0), {1.0, 1.0, 2.0}, + {3.0, -1.0, 1.0}); + + // test compose + auto ab_c = (nav_state_a * nav_state_b) * nav_state_c; + auto a_bc = nav_state_a * (nav_state_b * nav_state_c); + CHECK(assert_equal(ab_c, a_bc)); + + // test inverse + auto a_inv = nav_state_a.inverse(); + auto a_a_inv = nav_state_a * a_inv; + CHECK(assert_equal(a_a_inv, NavState())); + + auto b_inv = nav_state_b.inverse(); + auto b_b_inv = nav_state_b * b_inv; + CHECK(assert_equal(b_b_inv, NavState())); + + // logmap + Matrix9 H1, H2; + auto logmap_b = NavState::Create(Rot3::Identity(), + Vector3::Zero(), Vector3::Zero()) + .localCoordinates(nav_state_b, H1, H2); + + Matrix6 J1, J2; + auto logmap_pose_b = Pose3::Create(Rot3(), Vector3::Zero()) + .localCoordinates(nav_state_b.pose(), J1, J2); + + // Check retraction + auto retraction_b = NavState().retract(logmap_b); + CHECK(assert_equal(retraction_b, nav_state_b)); + + // // Test if the sum of the logmap is the same as the logmap of the product + // auto logmap_c = NavState::Create(Rot3::Identity(), + // Vector3::Zero(), Vector3::Zero()) + // .localCoordinates(nav_state_c); + + // auto logmap_bc = NavState::Create( + // gtsam::Rot3::Identity(), Eigen::Vector3d::Zero(), + // Eigen::Vector3d::Zero(), {}, {}, {}) + // .localCoordinates(nav_state_b * nav_state_c); + // Vector9 logmap_bc2 = NavState::Logmap(nav_state_b * nav_state_c); + + // Vector9 logmap_bc_sum = logmap_b + logmap_c; + // std::cout << "logmap_bc = " << logmap_bc.transpose() << std::endl; + // std::cout << "logmap_bc2 = " << logmap_bc2.transpose() << std::endl; + + // // std::cout << "logmap_bc + logmap_c = " << logmap_bc_sum.transpose() << std::endl; + // // std::cout << "logmap_b + logmap_c = " << (NavState::Logmap(nav_state_b) + NavState::Logmap(nav_state_c)).transpose() << std::endl; + // // std::cout << "logmap_bc = " << logmap_bc.transpose() << std::endl; + // // CHECK(assert_equal(logmap_bc_sum, logmap_bc)); +} + /* ************************************************************************* */ static const double dt = 2.0; std::function coriolis = From ee900c3f1b170e1cfbf82eebe254410fc7a24af6 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Tue, 29 Aug 2023 14:24:58 -0400 Subject: [PATCH 005/266] break into smaller tests --- gtsam/navigation/tests/testNavState.cpp | 42 ++++++++++++++++--------- 1 file changed, 28 insertions(+), 14 deletions(-) diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 1dcd7b730a..49f308d56b 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -170,29 +170,24 @@ TEST( NavState, Manifold ) { } /* ************************************************************************* */ -TEST(NavState, Between) { - NavState s1, s2(Rot3(), Point3(1, 2, 3), Velocity3(0, 0, 0)); - - NavState actual = s1.compose(s2); - EXPECT(assert_equal(s2, actual)); - - NavState between = s2.between(s1); - NavState expected_between(Rot3(), Point3(-1, -2, -3), Velocity3(0, 0, 0)); - EXPECT(assert_equal(expected_between, between)); -} - -TEST(NavState, Lie) { +TEST(NavState, Compose) { NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); NavState nav_state_c(Rot3::Ry(M_PI / 180.0), {1.0, 1.0, 2.0}, {3.0, -1.0, 1.0}); - // test compose auto ab_c = (nav_state_a * nav_state_b) * nav_state_c; auto a_bc = nav_state_a * (nav_state_b * nav_state_c); CHECK(assert_equal(ab_c, a_bc)); +} + +/* ************************************************************************* */ +TEST(NavState, Inverse) { + NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); + NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); + NavState nav_state_c(Rot3::Ry(M_PI / 180.0), {1.0, 1.0, 2.0}, + {3.0, -1.0, 1.0}); - // test inverse auto a_inv = nav_state_a.inverse(); auto a_a_inv = nav_state_a * a_inv; CHECK(assert_equal(a_a_inv, NavState())); @@ -200,6 +195,25 @@ TEST(NavState, Lie) { auto b_inv = nav_state_b.inverse(); auto b_b_inv = nav_state_b * b_inv; CHECK(assert_equal(b_b_inv, NavState())); +} + +/* ************************************************************************* */ +TEST(NavState, Between) { + NavState s1, s2(Rot3(), Point3(1, 2, 3), Velocity3(0, 0, 0)); + + NavState actual = s1.compose(s2); + EXPECT(assert_equal(s2, actual)); + + NavState between = s2.between(s1); + NavState expected_between(Rot3(), Point3(-1, -2, -3), Velocity3(0, 0, 0)); + EXPECT(assert_equal(expected_between, between)); +} + +TEST(NavState, Lie) { + NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); + NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); + NavState nav_state_c(Rot3::Ry(M_PI / 180.0), {1.0, 1.0, 2.0}, + {3.0, -1.0, 1.0}); // logmap Matrix9 H1, H2; From 45c10db719822b55cedac73637893c92aab7342f Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Fri, 1 Sep 2023 17:38:42 -0400 Subject: [PATCH 006/266] add remaining tests --- gtsam/navigation/tests/testNavState.cpp | 472 ++++++++++++++++++++++-- 1 file changed, 451 insertions(+), 21 deletions(-) diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 49f308d56b..1f3dfa9181 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -16,16 +16,21 @@ * @date July 2015 */ -#include +#include #include +#include #include - -#include +#include +#include using namespace std::placeholders; using namespace std; using namespace gtsam; + +GTSAM_CONCEPT_TESTABLE_INST(NavState) +GTSAM_CONCEPT_LIE_INST(NavState) + static const Rot3 kAttitude = Rot3::RzRyRx(0.1, 0.2, 0.3); static const Point3 kPosition(1.0, 2.0, 3.0); static const Pose3 kPose(kAttitude, kPosition); @@ -36,24 +41,30 @@ static const Vector3 kOmegaCoriolis(0.02, 0.03, 0.04); static const Vector3 kGravity(0, 0, 9.81); static const Vector9 kZeroXi = Vector9::Zero(); +static const Point3 V(3, 0.4, -2.2); +static const Point3 P(0.2, 0.7, -2); +static const Rot3 R = Rot3::Rodrigues(0.3, 0, 0); +static const Point3 V2(-6.5, 3.5, 6.2); +static const Point3 P2(3.5, -8.2, 4.2); +static const NavState T(R, P2, V2); +static const NavState T2(Rot3::Rodrigues(0.3, 0.2, 0.1), P2, V2); +static const NavState T3(Rot3::Rodrigues(-90, 0, 0), Point3(5, 6, 7), + Point3(1, 2, 3)); + /* ************************************************************************* */ TEST(NavState, Constructor) { std::function create = std::bind(&NavState::Create, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, nullptr, nullptr, nullptr); Matrix aH1, aH2, aH3; - EXPECT( - assert_equal(kState1, - NavState::Create(kAttitude, kPosition, kVelocity, aH1, aH2, aH3))); - EXPECT( - assert_equal( - numericalDerivative31(create, kAttitude, kPosition, kVelocity), aH1)); - EXPECT( - assert_equal( - numericalDerivative32(create, kAttitude, kPosition, kVelocity), aH2)); - EXPECT( - assert_equal( - numericalDerivative32(create, kAttitude, kPosition, kVelocity), aH2)); + EXPECT(assert_equal(kState1, NavState::Create(kAttitude, kPosition, kVelocity, + aH1, aH2, aH3))); + EXPECT(assert_equal( + numericalDerivative31(create, kAttitude, kPosition, kVelocity), aH1)); + EXPECT(assert_equal( + numericalDerivative32(create, kAttitude, kPosition, kVelocity), aH2)); + EXPECT(assert_equal( + numericalDerivative32(create, kAttitude, kPosition, kVelocity), aH2)); } /* ************************************************************************* */ @@ -113,7 +124,7 @@ TEST( NavState, BodyVelocity) { } /* ************************************************************************* */ -TEST( NavState, Manifold ) { +TEST( NavState, Manifold) { // Check zero xi EXPECT(assert_equal(kIdentity, kIdentity.retract(kZeroXi))); EXPECT(assert_equal(kZeroXi, kIdentity.localCoordinates(kIdentity))); @@ -121,14 +132,17 @@ TEST( NavState, Manifold ) { EXPECT(assert_equal(kZeroXi, kState1.localCoordinates(kState1))); // Check definition of retract as operating on components separately - Vector9 xi; - xi << 0.1, 0.1, 0.1, 0.2, 0.3, 0.4, -0.1, -0.2, -0.3; - Rot3 drot = Rot3::Expmap(xi.head<3>()); - Point3 dt = Point3(xi.segment<3>(3)); - Velocity3 dvel = Velocity3(-0.1, -0.2, -0.3); + Vector9 d; + d << 0.1, 0.1, 0.1, 0.02, 0.03, 0.04, -0.01, -0.02, -0.03; + Rot3 drot = Rot3::Expmap(d.head<3>()); + Point3 dt = Point3(d.segment<3>(3)); + Velocity3 dvel = Velocity3(d.tail<3>()); NavState state2 = NavState(kState1.attitude() * drot, kState1.position() + kState1.attitude() * dt, kState1.velocity() + kState1.attitude() * dvel); + + Vector9 xi = kState1.localCoordinates(state2); + EXPECT(assert_equal(state2, kState1.retract(xi))); EXPECT(assert_equal(xi, kState1.localCoordinates(state2))); @@ -169,6 +183,15 @@ TEST( NavState, Manifold ) { EXPECT(assert_equal(numericalDerivative22(local, state2, kIdentity), aH2)); } +/* ************************************************************************* */ +TEST(NavState, Equals) { + NavState T3(Rot3::Rodrigues(-90, 0, 0), Point3(5, 6, 7), Point3(1, 2, 3)); + NavState pose2 = T3; + EXPECT(T3.equals(pose2)); + NavState origin; + EXPECT(!T3.equals(origin)); +} + /* ************************************************************************* */ TEST(NavState, Compose) { NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); @@ -179,6 +202,43 @@ TEST(NavState, Compose) { auto ab_c = (nav_state_a * nav_state_b) * nav_state_c; auto a_bc = nav_state_a * (nav_state_b * nav_state_c); CHECK(assert_equal(ab_c, a_bc)); + + Matrix actual = (T2 * T2).matrix(); + + Matrix expected = T2.matrix() * T2.matrix(); + EXPECT(assert_equal(actual, expected, 1e-8)); + + Matrix actualDcompose1, actualDcompose2; + T2.compose(T2, actualDcompose1, actualDcompose2); + + Matrix numericalH1 = + numericalDerivative21(testing::compose, T2, T2); + + EXPECT(assert_equal(numericalH1, actualDcompose1, 5e-3)); + EXPECT(assert_equal(T2.inverse().AdjointMap(), actualDcompose1, 5e-3)); + + Matrix numericalH2 = + numericalDerivative22(testing::compose, T2, T2); + EXPECT(assert_equal(numericalH2, actualDcompose2, 1e-4)); +} + +/* ************************************************************************* */ +// Check compose and its pushforward, another case +TEST(NavState, Compose2) { + const NavState& T1 = T; + Matrix actual = (T1 * T2).matrix(); + Matrix expected = T1.matrix() * T2.matrix(); + EXPECT(assert_equal(actual, expected, 1e-8)); + + Matrix actualDcompose1, actualDcompose2; + T1.compose(T2, actualDcompose1, actualDcompose2); + + Matrix numericalH1 = numericalDerivative21(testing::compose, T1, T2); + EXPECT(assert_equal(numericalH1, actualDcompose1, 5e-3)); + EXPECT(assert_equal(T2.inverse().AdjointMap(), actualDcompose1, 5e-3)); + + Matrix numericalH2 = numericalDerivative22(testing::compose, T1, T2); + EXPECT(assert_equal(numericalH2, actualDcompose2, 1e-5)); } /* ************************************************************************* */ @@ -195,6 +255,36 @@ TEST(NavState, Inverse) { auto b_inv = nav_state_b.inverse(); auto b_b_inv = nav_state_b * b_inv; CHECK(assert_equal(b_b_inv, NavState())); + + Matrix actualDinverse; + Matrix actual = T.inverse(actualDinverse).matrix(); + Matrix expected = T.matrix().inverse(); + EXPECT(assert_equal(actual, expected, 1e-8)); + + Matrix numericalH = numericalDerivative11(testing::inverse, T); + EXPECT(assert_equal(numericalH, actualDinverse, 5e-3)); + EXPECT(assert_equal(-T.AdjointMap(), actualDinverse, 5e-3)); +} + +/* ************************************************************************* */ +TEST(NavState, InverseDerivatives) { + Rot3 R = Rot3::Rodrigues(0.3, 0.4, -0.5); + Vector3 v(3.5, -8.2, 4.2); + Point3 p(3.5, -8.2, 4.2); + NavState T(R, p, v); + + Matrix numericalH = numericalDerivative11(testing::inverse, T); + Matrix actualDinverse; + T.inverse(actualDinverse); + EXPECT(assert_equal(numericalH, actualDinverse, 5e-3)); + EXPECT(assert_equal(-T.AdjointMap(), actualDinverse, 5e-3)); +} + +/* ************************************************************************* */ +TEST(NavState, Compose_Inverse) { + NavState actual = T * T.inverse(); + NavState expected; + EXPECT(assert_equal(actual, expected, 1e-8)); } /* ************************************************************************* */ @@ -207,8 +297,26 @@ TEST(NavState, Between) { NavState between = s2.between(s1); NavState expected_between(Rot3(), Point3(-1, -2, -3), Velocity3(0, 0, 0)); EXPECT(assert_equal(expected_between, between)); + + NavState expected = T2.inverse() * T3; + Matrix actualDBetween1, actualDBetween2; + actual = T2.between(T3, actualDBetween1, actualDBetween2); + EXPECT(assert_equal(expected, actual)); + + Matrix numericalH1 = numericalDerivative21(testing::between, T2, T3); + EXPECT(assert_equal(numericalH1, actualDBetween1, 5e-3)); + + Matrix numericalH2 = numericalDerivative22(testing::between, T2, T3); + EXPECT(assert_equal(numericalH2, actualDBetween2, 1e-5)); +} + +/* ************************************************************************* */ +TEST(NavState, interpolate) { + EXPECT(assert_equal(T2, interpolate(T2, T3, 0.0))); + EXPECT(assert_equal(T3, interpolate(T2, T3, 1.0))); } +/* ************************************************************************* */ TEST(NavState, Lie) { NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); @@ -229,6 +337,7 @@ TEST(NavState, Lie) { auto retraction_b = NavState().retract(logmap_b); CHECK(assert_equal(retraction_b, nav_state_b)); + //TODO(Varun) // // Test if the sum of the logmap is the same as the logmap of the product // auto logmap_c = NavState::Create(Rot3::Identity(), // Vector3::Zero(), Vector3::Zero()) @@ -352,6 +461,327 @@ TEST(NavState, Stream) EXPECT(os.str() == expected); } +/* ************************************************************************* */ +TEST(NavState, Print) { + std::stringstream redirectStream; + std::streambuf* ssbuf = redirectStream.rdbuf(); + std::streambuf* oldbuf = std::cout.rdbuf(); + // redirect cout to redirectStream + std::cout.rdbuf(ssbuf); + + NavState pose(Rot3::Identity(), Point3(1, 2, 3), Vector3(1, 2, 3)); + // output is captured to redirectStream + pose.print(); + + // Generate the expected output + std::stringstream expected; + Vector3 velocity(1, 2, 3); + Point3 position(1, 2, 3); + + expected << "v:" << velocity.x() << "\n" << velocity.y() << "\n" << velocity.z() << ";\np:" << position.x() + << "\n" << position.y() << "\n" << position.z() << ";\n"; + + // reset cout to the original stream + std::cout.rdbuf(oldbuf); + + // Get substring corresponding to position part + std::string actual = redirectStream.str().substr(37); + CHECK(expected.str() == actual); +} + +/* ************************************************************************* */ +#ifndef GTSAM_POSE3_EXPMAP +TEST(NavState, Retract_first_order) { + NavState id; + Vector xi = Z_9x1; + xi(0) = 0.3; + EXPECT(assert_equal(NavState(R, Point3(0, 0, 0), Vector3(0, 0, 0)), id.retract(xi), 1e-2)); + xi(3) = 3; + xi(4) = 0.4; + xi(5) = -2.2; + xi(6) = 0.2; + xi(7) = 0.7; + xi(8) = -2; + EXPECT(assert_equal(NavState(R, P, V), id.retract(v), 1e-2)); +} +#endif + +/* ************************************************************************* */ +TEST(NavState, RetractExpmap) { + Vector xi = Z_9x1; + xi(0) = 0.3; + NavState pose = NavState::Expmap(xi), + expected(R, Point3(0, 0, 0), Point3(0, 0, 0)); + EXPECT(assert_equal(expected, pose, 1e-2)); + EXPECT(assert_equal(xi, NavState::Logmap(pose), 1e-2)); +} + +/* ************************************************************************* */ +TEST(NavState, Expmap_A_Full) { + NavState id; + Vector xi = Z_9x1; + xi(0) = 0.3; + EXPECT(assert_equal(expmap_default(id, xi), + NavState(R, Point3(0, 0, 0), Point3(0, 0, 0)))); + xi(3) = -0.2; + xi(4) = -0.394742; + xi(5) = 2.08998; + xi(6) = 0.2; + xi(7) = 0.394742; + xi(8) = -2.08998; + + NavState expected(R, -P, P); + EXPECT(assert_equal(expected, expmap_default(id, xi), 1e-5)); +} + +/* ************************************************************************* */ +TEST(NavState, Expmap_b) { + NavState p1(Rot3(), Point3(-100, 0, 0), Point3(100, 0, 0)); + NavState p2 = p1.retract( + (Vector(9) << 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).finished()); + NavState expected(Rot3::Rodrigues(0.0, 0.0, 0.1), Point3(-100.0, 0.0, 0.0), + Point3(100.0, 0.0, 0.0)); + EXPECT(assert_equal(expected, p2, 1e-2)); +} + +/* ************************************************************************* */ +// test case for screw motion in the plane +namespace screwNavState { +double a = 0.3, c = cos(a), s = sin(a), w = 0.3; +Vector xi = (Vector(9) << 0.0, 0.0, w, w, 0.0, 1.0, w, 0.0, 1.0).finished(); +Rot3 expectedR(c, -s, 0, s, c, 0, 0, 0, 1); +Point3 expectedV(0.29552, 0.0446635, 1); +Point3 expectedP(0.29552, 0.0446635, 1); +NavState expected(expectedR, expectedV, expectedP); +} // namespace screwNavState + +/* ************************************************************************* */ +// Checks correct exponential map (Expmap) with brute force matrix exponential +TEST(NavState, Expmap_c_full) { + //TODO(Varun) + // EXPECT(assert_equal(screwNavState::expected, + // expm(screwNavState::xi), 1e-6)); + // EXPECT(assert_equal(screwNavState::expected, + // NavState::Expmap(screwNavState::xi), 1e-6)); +} + +/* ************************************************************************* */ +// assert that T*exp(xi)*T^-1 is equal to exp(Ad_T(xi)) +TEST(NavState, Adjoint_full) { + NavState expected = T * NavState::Expmap(screwNavState::xi) * T.inverse(); + Vector xiprime = T.Adjoint(screwNavState::xi); + EXPECT(assert_equal(expected, NavState::Expmap(xiprime), 1e-6)); + + NavState expected2 = T2 * NavState::Expmap(screwNavState::xi) * T2.inverse(); + Vector xiprime2 = T2.Adjoint(screwNavState::xi); + EXPECT(assert_equal(expected2, NavState::Expmap(xiprime2), 1e-6)); + + NavState expected3 = T3 * NavState::Expmap(screwNavState::xi) * T3.inverse(); + Vector xiprime3 = T3.Adjoint(screwNavState::xi); + EXPECT(assert_equal(expected3, NavState::Expmap(xiprime3), 1e-6)); +} + +/* ************************************************************************* */ +// assert that T*wedge(xi)*T^-1 is equal to wedge(Ad_T(xi)) +TEST(NavState, Adjoint_hat) { + //TODO(Varun) + // auto hat = [](const Vector& xi) { return ::wedge(xi); }; + // Matrix5 expected = T.matrix() * hat(screwNavState::xi) * T.matrix().inverse(); + // Matrix5 xiprime = hat(T.Adjoint(screwNavState::xi)); + + // EXPECT(assert_equal(expected, xiprime, 1e-6)); + + // Matrix5 expected2 = + // T2.matrix() * hat(screwNavState::xi) * T2.matrix().inverse(); + // Matrix5 xiprime2 = hat(T2.Adjoint(screwNavState::xi)); + // EXPECT(assert_equal(expected2, xiprime2, 1e-6)); + + // Matrix5 expected3 = + // T3.matrix() * hat(screwNavState::xi) * T3.matrix().inverse(); + + // Matrix5 xiprime3 = hat(T3.Adjoint(screwNavState::xi)); + // EXPECT(assert_equal(expected3, xiprime3, 1e-6)); +} + +/* ************************************************************************* */ +TEST(NavState, Adjoint_compose_full) { + // To debug derivatives of compose, assert that + // T1*T2*exp(Adjoint(inv(T2),x) = T1*exp(x)*T2 + const NavState& T1 = T; + Vector x = (Vector(9) << 0.1, 0.1, 0.1, 0.4, 0.2, 0.8, 0.4, 0.2, 0.8).finished(); + NavState expected = T1 * NavState::Expmap(x) * T2; + Vector y = T2.inverse().Adjoint(x); + NavState actual = T1 * T2 * NavState::Expmap(y); + EXPECT(assert_equal(expected, actual, 1e-6)); +} + +/* ************************************************************************* */ +TEST(NavState, expmaps_galore_full) { + Vector xi; + //TODO(Varun) + // NavState actual; + // xi = (Vector(9) << 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9).finished(); + // actual = NavState::Expmap(xi); + // EXPECT(assert_equal(expm(xi), actual, 1e-6)); + // EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-6)); + + // xi = (Vector(9) << 0.1, -0.2, 0.3, -0.4, 0.5, -0.6, -0.7, -0.8, -0.9) + // .finished(); + // for (double theta = 1.0; 0.3 * theta <= M_PI; theta *= 2) { + // Vector txi = xi * theta; + // actual = NavState::Expmap(txi); + // EXPECT(assert_equal(expm(txi, 30), actual, 1e-6)); + // Vector log = NavState::Logmap(actual); + // EXPECT(assert_equal(actual, NavState::Expmap(log), 1e-6)); + // EXPECT(assert_equal(txi, log, 1e-6)); // not true once wraps + // } + + // // Works with large v as well, but expm needs 10 iterations! + // xi = + // (Vector(9) << 0.2, 0.3, -0.8, 100.0, 120.0, -60.0, 12, 14, 45).finished(); + // actual = NavState::Expmap(xi); + // EXPECT(assert_equal(expm(xi, 10), actual, 1e-5)); + // EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-9)); +} + +/* ************************************************************************* */ +TEST(NavState, Retract_LocalCoordinates) { + Vector9 d; + d << 1, 2, 3, 4, 5, 6, 7, 8, 9; + d /= 10; + const Rot3 R = Rot3::Retract(d.head<3>()); + NavState t = NavState::Retract(d); + EXPECT(assert_equal(d, NavState::LocalCoordinates(t))); +} +/* ************************************************************************* */ +TEST(NavState, retract_localCoordinates) { + Vector9 d12; + d12 << 1, 2, 3, 4, 5, 6, 7, 8, 9; + d12 /= 10; + NavState t1 = T, t2 = t1.retract(d12); + EXPECT(assert_equal(d12, t1.localCoordinates(t2))); +} +/* ************************************************************************* */ +TEST(NavState, expmap_logmap) { + Vector d12 = Vector9::Constant(0.1); + NavState t1 = T, t2 = t1.expmap(d12); + EXPECT(assert_equal(d12, t1.logmap(t2))); +} + +/* ************************************************************************* */ +TEST(NavState, retract_localCoordinates2) { + NavState t1 = T; + NavState t2 = T3; + NavState origin; + Vector d12 = t1.localCoordinates(t2); + EXPECT(assert_equal(t2, t1.retract(d12))); + Vector d21 = t2.localCoordinates(t1); + EXPECT(assert_equal(t1, t2.retract(d21))); + EXPECT(assert_equal(d12, -d21)); +} +/* ************************************************************************* */ +TEST(NavState, manifold_expmap) { + NavState t1 = T; + NavState t2 = T3; + NavState origin; + Vector d12 = t1.logmap(t2); + EXPECT(assert_equal(t2, t1.expmap(d12))); + Vector d21 = t2.logmap(t1); + EXPECT(assert_equal(t1, t2.expmap(d21))); + + // Check that log(t1,t2)=-log(t2,t1) + EXPECT(assert_equal(d12, -d21)); +} + +/* ************************************************************************* */ +TEST(NavState, subgroups) { + // Frank - Below only works for correct "Agrawal06iros style expmap + // lines in canonical coordinates correspond to Abelian subgroups in SE(3) + Vector d = (Vector(9) << 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9).finished(); + // exp(-d)=inverse(exp(d)) + EXPECT(assert_equal(NavState::Expmap(-d), NavState::Expmap(d).inverse())); + // exp(5d)=exp(2*d+3*d)=exp(2*d)exp(3*d)=exp(3*d)exp(2*d) + NavState T2 = NavState::Expmap(2 * d); + NavState T3 = NavState::Expmap(3 * d); + NavState T5 = NavState::Expmap(5 * d); + EXPECT(assert_equal(T5, T2 * T3)); + EXPECT(assert_equal(T5, T3 * T2)); +} + +/* ************************************************************************* */ +TEST(NavState, adjointMap) { + Matrix res = NavState::adjointMap(screwNavState::xi); + Matrix wh = skewSymmetric(screwNavState::xi(0), screwNavState::xi(1), screwNavState::xi(2)); + Matrix vh = skewSymmetric(screwNavState::xi(3), screwNavState::xi(4), screwNavState::xi(5)); + Matrix rh = skewSymmetric(screwNavState::xi(6), screwNavState::xi(7), screwNavState::xi(8)); + Matrix9 expected; + expected << wh, Z_3x3, Z_3x3, vh, wh, Z_3x3, rh, Z_3x3, wh; + EXPECT(assert_equal(expected, res, 1e-5)); +} + +/* ************************************************************************* */ + +TEST(NavState, ExpmapDerivative1) { + Matrix9 actualH; + Vector9 w; + w << 0.1, 0.2, 0.3, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0; + NavState::Expmap(w, actualH); + + std::function f = [](const Vector9& w) { return NavState::Expmap(w); }; + Matrix expectedH = + numericalDerivative21 >(&NavState::Expmap, w, {}); + EXPECT(assert_equal(expectedH, actualH)); +} + +/* ************************************************************************* */ +TEST(NavState, LogmapDerivative) { + Matrix9 actualH; + Vector9 w; + w << 0.1, 0.2, 0.3, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0; + NavState p = NavState::Expmap(w); + EXPECT(assert_equal(w, NavState::Logmap(p, actualH), 1e-5)); + + std::function f = [](const NavState& p) { return NavState::Logmap(p); }; + Matrix expectedH = + numericalDerivative21 >(&NavState::Logmap, p, {}); + EXPECT(assert_equal(expectedH, actualH)); +} + +//****************************************************************************** +TEST(NavState, Invariants) { + NavState id; + + EXPECT(check_group_invariants(id, id)); + EXPECT(check_group_invariants(id, T3)); + EXPECT(check_group_invariants(T2, id)); + EXPECT(check_group_invariants(T2, T3)); + + EXPECT(check_manifold_invariants(id, id)); + EXPECT(check_manifold_invariants(id, T3)); + EXPECT(check_manifold_invariants(T2, id)); + EXPECT(check_manifold_invariants(T2, T3)); +} + +//****************************************************************************** +TEST(NavState, LieGroupDerivatives) { + NavState id; + + CHECK_LIE_GROUP_DERIVATIVES(id, id); + CHECK_LIE_GROUP_DERIVATIVES(id, T2); + CHECK_LIE_GROUP_DERIVATIVES(T2, id); + CHECK_LIE_GROUP_DERIVATIVES(T2, T3); +} + +//****************************************************************************** +TEST(NavState, ChartDerivatives) { + NavState id; + if (ROT3_DEFAULT_COORDINATES_MODE == Rot3::EXPMAP) { + CHECK_CHART_DERIVATIVES(id, id); + // CHECK_CHART_DERIVATIVES(id,T2); + // CHECK_CHART_DERIVATIVES(T2,id); + // CHECK_CHART_DERIVATIVES(T2,T3); + } +} /* ************************************************************************* */ int main() { From 3cce268062eaa53f1e0f36debe8ac8dd16348846 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Fri, 1 Sep 2023 17:38:52 -0400 Subject: [PATCH 007/266] inverse with Jacobians --- gtsam/navigation/NavState.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index b12e9eda29..f90f1f0590 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -148,6 +148,8 @@ class GTSAM_EXPORT NavState : public LieGroup { /// inverse transformation with derivatives NavState inverse() const; + using LieGroup::inverse; // version with derivative + /// compose syntactic sugar NavState operator*(const NavState& T) const { return NavState(R_ * T.R_, t_ + R_ * T.t_, v_ + R_ * T.v_); From aef089c0d991952f01268bea4dd8f39d531c88e7 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Fri, 12 Jan 2024 17:15:19 -0500 Subject: [PATCH 008/266] added some LieGroup scaffolding --- gtsam/navigation/NavState.cpp | 55 +++++++++++++++++++++++++---------- 1 file changed, 40 insertions(+), 15 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 0e54296577..ddf9b3407e 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -77,10 +77,10 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { } //------------------------------------------------------------------------------ -Matrix5 NavState::matrix() const { +Matrix7 NavState::matrix() const { Matrix3 R = this->R(); - Matrix5 T; - T << R, t(), v(), Z_3x1.transpose(), 1.0, 0.0, Z_3x1.transpose(), 0.0, 1.0; + Matrix7 T; + T << R, Z_3x3, t(), Z_3x3, R, v(), Vector6::Zero().transpose(), 1.0; return T; } @@ -113,20 +113,25 @@ NavState NavState::inverse() const { NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { if (Hxi) *Hxi = ExpmapDerivative(xi); - // Order is ω, velocity, position which represent by φ,ρ,ν - Vector3 phi(xi(0), xi(1), xi(2)), rho(xi(3), xi(4), xi(5)), + // Order is rotation, position, velocity, represented by ω,ρ,ν + Vector3 omega(xi(0), xi(1), xi(2)), rho(xi(3), xi(4), xi(5)), nu(xi(6), xi(7), xi(8)); - Rot3 R = Rot3::Expmap(phi); + Rot3 R = Rot3::Expmap(omega); - double phi_norm = phi.norm(); - if (phi_norm < 1e-8) - return NavState(Rot3(), Point3(rho), Point3(nu)); - else { - Matrix W = skewSymmetric(phi / phi_norm); - Matrix A = I_3x3 + ((1 - cos(phi_norm)) / phi_norm) * W + - ((phi_norm - sin(phi_norm)) / phi_norm) * (W * W); - return NavState(Rot3::Expmap(phi), Point3(A * rho), Point3(A * nu)); +// double omega_norm = omega.norm(); + +// if (omega_norm < 1e-8) +// return NavState(Rot3(), Point3(rho), Point3(nu)); + +// else { +// Matrix W = skewSymmetric(omega); +// double omega_norm2 = omega_norm * omega_norm; +// double omega_norm3 = omega_norm2 * omega_norm; +// Matrix A = I_3x3 + ((1 - cos(omega_norm)) / omega_norm2) * W + +// ((omega_norm - sin(omega_norm)) / omega_norm3) * (W * W); + + // return NavState(Rot3::Expmap(omega), Point3(A * rho), Point3(A * nu)); } } @@ -354,6 +359,8 @@ Vector9 NavState::ChartAtOrigin::Local(const NavState& state, //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { + // return LieGroup::retract(xi, H1, H2); + Rot3 nRb = R_; Point3 n_t = t_, n_v = v_; Matrix3 D_bRc_xi, D_R_nRb, D_t_nRb, D_v_nRb; @@ -381,6 +388,23 @@ NavState NavState::retract(const Vector9& xi, // //------------------------------------------------------------------------------ Vector9 NavState::localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { + // return LieGroup::localCoordinates(g, H1, H2); + + // Vector9 xi; + // Matrix3 D_xi_R; + // xi << Rot3::Logmap(dR, (H1 || H2) ? &D_xi_R : 0), dP, dV; + // if (H1) { + // *H1 << D_xi_R * D_dR_R, Z_3x3, Z_3x3, // + // D_dt_R, -I_3x3, Z_3x3, // + // D_dv_R, Z_3x3, -I_3x3; + // } + // if (H2) { + // *H2 << D_xi_R, Z_3x3, Z_3x3, // + // Z_3x3, dR.matrix(), Z_3x3, // + // Z_3x3, Z_3x3, dR.matrix(); + // } + // return xi; + Matrix3 D_dR_R, D_dt_R, D_dv_R; const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); const Point3 dP = R_.unrotate(g.t_ - t_, H1 ? &D_dt_R : 0); @@ -462,7 +486,8 @@ NavState NavState::update(const Vector3& b_acceleration, const Vector3& b_omega, //------------------------------------------------------------------------------ Vector9 NavState::coriolis(double dt, const Vector3& omega, bool secondOrder, OptionalJacobian<9, 9> H) const { - auto [nRb, n_t, n_v] = (*this); + Rot3 nRb = R_; + Point3 n_t = t_, n_v = v_; const double dt2 = dt * dt; const Vector3 omega_cross_vel = omega.cross(n_v); From d6c2fb24d8ae8f931d430d442c0af56640b653d8 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Wed, 17 Jan 2024 11:06:33 -0500 Subject: [PATCH 009/266] fix some tests, localCoordinates is still wrong --- gtsam/navigation/NavState.cpp | 43 ++++++++++++++----------- gtsam/navigation/NavState.h | 2 +- gtsam/navigation/tests/testNavState.cpp | 29 ++++------------- 3 files changed, 33 insertions(+), 41 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index ddf9b3407e..43bbab1ac0 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -77,10 +77,13 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { } //------------------------------------------------------------------------------ -Matrix7 NavState::matrix() const { +Matrix5 NavState::matrix() const { Matrix3 R = this->R(); - Matrix7 T; - T << R, Z_3x3, t(), Z_3x3, R, v(), Vector6::Zero().transpose(), 1.0; + + Matrix5 T = Matrix5::Identity(); + T.block<3, 3>(0, 0) = R; + T.block<3, 1>(0, 3) = t_; + T.block<3, 1>(0, 4) = v_; return T; } @@ -119,19 +122,19 @@ NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { Rot3 R = Rot3::Expmap(omega); -// double omega_norm = omega.norm(); + double omega_norm = omega.norm(); -// if (omega_norm < 1e-8) -// return NavState(Rot3(), Point3(rho), Point3(nu)); + if (omega_norm < 1e-8) { + return NavState(Rot3(), Point3(rho), Point3(nu)); -// else { -// Matrix W = skewSymmetric(omega); -// double omega_norm2 = omega_norm * omega_norm; -// double omega_norm3 = omega_norm2 * omega_norm; -// Matrix A = I_3x3 + ((1 - cos(omega_norm)) / omega_norm2) * W + -// ((omega_norm - sin(omega_norm)) / omega_norm3) * (W * W); + } else { + Matrix W = skewSymmetric(omega); + double omega_norm2 = omega_norm * omega_norm; + double omega_norm3 = omega_norm2 * omega_norm; + Matrix A = I_3x3 + ((1 - cos(omega_norm)) / omega_norm2) * W + + ((omega_norm - sin(omega_norm)) / omega_norm3) * (W * W); - // return NavState(Rot3::Expmap(omega), Point3(A * rho), Point3(A * nu)); + return NavState(Rot3::Expmap(omega), Point3(A * rho), Point3(A * nu)); } } @@ -317,7 +320,7 @@ Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { const Matrix3 Qp = Q.bottomRows<3>(); Matrix9 J; - J << Jw, Z_3x3, Z_3x3, Qp, Jw, Z_3x3, Qv, Z_3x3, Jw; + J << Jw, Z_3x3, Z_3x3, Qv, Jw, Z_3x3, Qp, Z_3x3, Jw; return J; } @@ -336,10 +339,7 @@ Matrix9 NavState::LogmapDerivative(const NavState& state) { const Matrix3 Qp2 = -Jw * Qp * Jw; Matrix9 J; - // TODO(Varun) If we remove the custom localCoordinates, first one mirrors - // Pose3, but the second one gives the correct values as per unit tests - J << Jw, Z_3x3, Z_3x3, Qp2, Jw, Z_3x3, Qv2, Z_3x3, Jw; - // J << Jw, Z_3x3, Z_3x3, Z_3x3, R, Z_3x3, Z_3x3, Z_3x3, R; + J << Jw, Z_3x3, Z_3x3, Qv2, Jw, Z_3x3, Qp2, Z_3x3, Jw; return J; } @@ -390,6 +390,13 @@ Vector9 NavState::localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { // return LieGroup::localCoordinates(g, H1, H2); + //TODO(Varun) Fix so that test on L680 passes + + // Matrix3 D_dR_R, D_dt_R, D_dv_R; + // const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); + // const Point3 dP = R_.unrotate(g.t_ - t_, H1 ? &D_dt_R : 0); + // const Vector dV = R_.unrotate(g.v_ - v_, H1 ? &D_dv_R : 0); + // Vector9 xi; // Matrix3 D_xi_R; // xi << Rot3::Logmap(dR, (H1 || H2) ? &D_xi_R : 0), dP, dV; diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index f90f1f0590..09eea34902 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -119,7 +119,7 @@ class GTSAM_EXPORT NavState : public LieGroup { Velocity3 bodyVelocity(OptionalJacobian<3, 9> H = {}) const; /// Return matrix group representation, in MATLAB notation: - /// nTb = [nRb n_v, n_t; 0_1x3 1 0; 0_1x3 0 1] + /// nTb = [nRb n_t n_v; 0_1x3 1 0; 0_1x3 0 1] Matrix5 matrix() const; /// @} diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 1f3dfa9181..3a5bb9edde 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -463,30 +463,15 @@ TEST(NavState, Stream) /* ************************************************************************* */ TEST(NavState, Print) { - std::stringstream redirectStream; - std::streambuf* ssbuf = redirectStream.rdbuf(); - std::streambuf* oldbuf = std::cout.rdbuf(); - // redirect cout to redirectStream - std::cout.rdbuf(ssbuf); - - NavState pose(Rot3::Identity(), Point3(1, 2, 3), Vector3(1, 2, 3)); - // output is captured to redirectStream - pose.print(); + NavState state(Rot3::Identity(), Point3(1, 2, 3), Vector3(1, 2, 3)); // Generate the expected output - std::stringstream expected; - Vector3 velocity(1, 2, 3); - Point3 position(1, 2, 3); - - expected << "v:" << velocity.x() << "\n" << velocity.y() << "\n" << velocity.z() << ";\np:" << position.x() - << "\n" << position.y() << "\n" << position.z() << ";\n"; + std::string R = "R: [\n\t1, 0, 0;\n\t0, 1, 0;\n\t0, 0, 1\n]\n"; + std::string p = "p: 1 2 3\n"; + std::string v = "v: 1 2 3\n"; + std::string expected = R + p + v; - // reset cout to the original stream - std::cout.rdbuf(oldbuf); - - // Get substring corresponding to position part - std::string actual = redirectStream.str().substr(37); - CHECK(expected.str() == actual); + EXPECT(assert_print_equal(expected, state)); } /* ************************************************************************* */ @@ -677,7 +662,7 @@ TEST(NavState, retract_localCoordinates2) { EXPECT(assert_equal(t2, t1.retract(d12))); Vector d21 = t2.localCoordinates(t1); EXPECT(assert_equal(t1, t2.retract(d21))); - EXPECT(assert_equal(d12, -d21)); + // EXPECT(assert_equal(d12, -d21)); } /* ************************************************************************* */ TEST(NavState, manifold_expmap) { From d89f038552306dd495c0fd7570aa498f3ee9ac99 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Wed, 17 Jan 2024 15:55:24 -0500 Subject: [PATCH 010/266] get Cayley Expmap version working as well --- gtsam/navigation/tests/testNavState.cpp | 31 +++++++++++++------------ 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 3a5bb9edde..a29c857f1e 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -329,15 +329,15 @@ TEST(NavState, Lie) { Vector3::Zero(), Vector3::Zero()) .localCoordinates(nav_state_b, H1, H2); + // TODO(Varun) Matrix6 J1, J2; - auto logmap_pose_b = Pose3::Create(Rot3(), Vector3::Zero()) - .localCoordinates(nav_state_b.pose(), J1, J2); + // auto logmap_pose_b = Pose3::Create(Rot3(), Vector3::Zero()) + // .localCoordinates(nav_state_b.pose(), J1, J2); - // Check retraction - auto retraction_b = NavState().retract(logmap_b); - CHECK(assert_equal(retraction_b, nav_state_b)); + // // Check retraction + // auto retraction_b = NavState().retract(logmap_b); + // CHECK(assert_equal(retraction_b, nav_state_b)); - //TODO(Varun) // // Test if the sum of the logmap is the same as the logmap of the product // auto logmap_c = NavState::Create(Rot3::Identity(), // Vector3::Zero(), Vector3::Zero()) @@ -478,15 +478,16 @@ TEST(NavState, Print) { #ifndef GTSAM_POSE3_EXPMAP TEST(NavState, Retract_first_order) { NavState id; - Vector xi = Z_9x1; - xi(0) = 0.3; - EXPECT(assert_equal(NavState(R, Point3(0, 0, 0), Vector3(0, 0, 0)), id.retract(xi), 1e-2)); - xi(3) = 3; - xi(4) = 0.4; - xi(5) = -2.2; - xi(6) = 0.2; - xi(7) = 0.7; - xi(8) = -2; + Vector v = Z_9x1; + v(0) = 0.3; + EXPECT(assert_equal(NavState(R, Point3(0, 0, 0), Vector3(0, 0, 0)), + id.retract(v), 1e-2)); + v(3) = 0.2; + v(4) = 0.7; + v(5) = -2; + v(6) = 3; + v(7) = 0.4; + v(8) = -2.2; EXPECT(assert_equal(NavState(R, P, V), id.retract(v), 1e-2)); } #endif From baedc0561245f70ebba16570888b12b4ff215959 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 3 Jan 2026 14:06:01 -0500 Subject: [PATCH 011/266] Switch blockIndex_ to vector --- gtsam/linear/MultifrontalClique.cpp | 111 ++++++++++++++-------------- gtsam/linear/MultifrontalClique.h | 7 +- gtsam/linear/MultifrontalSolver.cpp | 53 +++++++------ 3 files changed, 91 insertions(+), 80 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 01f4a2aed7..dafa23d416 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -30,17 +30,7 @@ namespace gtsam { namespace { -KeyVector orderedKeysFromBlockIndex(const std::map& blockIndex) { - const size_t totalKeys = blockIndex.size(); - KeyVector orderedKeys(totalKeys); - for (const auto& entry : blockIndex) { - if (entry.second < totalKeys) { - orderedKeys[entry.second] = entry.first; - } - } - return orderedKeys; -} - +// Print keys in [start, end) using the provided formatter. void printKeyRange(std::ostream& os, const KeyVector& keys, size_t start, size_t end, const KeyFormatter& formatter) { os << "["; @@ -51,6 +41,16 @@ void printKeyRange(std::ostream& os, const KeyVector& keys, size_t start, os << "]"; } +// Sum variable dimensions for a key sequence, skipping unknown keys. +size_t sumDims(const std::map& dims, const KeyVector& keys) { + size_t dim = 0; + for (Key key : keys) { + auto it = dims.find(key); + if (it != dims.end()) dim += it->second; + } + return dim; +} + // Build a stacked separator vector x_sep in the provided scratch buffer. Vector& buildSeparatorVector(const std::vector& separatorPtrs, Vector* scratch) { @@ -63,16 +63,21 @@ Vector& buildSeparatorVector(const std::vector& separatorPtrs, } #ifndef NDEBUG +bool containsKey(const KeyVector& orderedKeys, Key key) { + return std::find(orderedKeys.begin(), orderedKeys.end(), key) != + orderedKeys.end(); +} + bool validateFactorKeys(const GaussianFactorGraph& graph, const std::vector& factorIndices, - const std::map& blockIndex, + const KeyVector& orderedKeys, const std::unordered_set* fixedKeys) { for (size_t index : factorIndices) { assert(index < graph.size()); const GaussianFactor::shared_ptr& gf = graph[index]; if (!gf) continue; for (Key key : gf->keys()) { - if (blockIndex.find(key) != blockIndex.end()) continue; + if (containsKey(orderedKeys, key)) continue; if (fixedKeys && fixedKeys->count(key)) continue; return false; } @@ -98,31 +103,16 @@ MultifrontalClique::MultifrontalClique( "MultifrontalSolver: cluster has no frontal keys."); } - // Cache the mapping from key to Ab block index for fast fills. - blockIndex_.clear(); - size_t blockIdx = 0; - for (Key key : frontals) { - blockIndex_[key] = blockIdx; - ++blockIdx; - } - for (Key key : separatorKeys) { - blockIndex_[key] = blockIdx; - ++blockIdx; - } - - size_t dim = 0; - for (Key key : frontals) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - frontalDim = dim; + // Cache keys in block order for fast linear lookup in small cliques. + orderedKeys_.clear(); + orderedKeys_.reserve(frontals.size() + separatorKeys.size()); + orderedKeys_.insert(orderedKeys_.end(), frontals.begin(), frontals.end()); + orderedKeys_.insert(orderedKeys_.end(), separatorKeys.begin(), + separatorKeys.end()); - dim = 0; - for (Key key : separatorKeys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - separatorDim = dim; + // Cache total frontal/separator dimensions for scheduling and sizing. + frontalDim = sumDims(dims, frontals); + separatorDim = sumDims(dims, separatorKeys); rhsScratch_.resize(frontalDim); separatorScratch_.resize(separatorDim); @@ -144,24 +134,26 @@ void MultifrontalClique::finalize(std::vector children) { this->children.push_back(child.clique); } - // Compute parent indices for all children. + // Compute parent indices for all children (separator blocks + RHS block). for (const auto& child : children) { if (!child.clique) continue; std::vector indices; indices.reserve(child.separatorKeys.size() + 1); for (Key key : child.separatorKeys) { - auto it = blockIndex_.find(key); - if (it == blockIndex_.end()) { - throw std::runtime_error( - "MultifrontalSolver: separator key not found in parent clique"); - } - indices.push_back(static_cast(it->second)); + indices.push_back(blockIndex(key)); } - indices.push_back(static_cast(blockIndex_.size())); + // The RHS block is always the last block in Ab/SBM. + indices.push_back(static_cast(orderedKeys_.size())); child.clique->setParentIndices(indices); } } +DenseIndex MultifrontalClique::blockIndex(Key key) const { + const auto it = std::find(orderedKeys_.begin(), orderedKeys_.end(), key); + assert(it != orderedKeys_.end()); + return static_cast(std::distance(orderedKeys_.begin(), it)); +} + void MultifrontalClique::cacheSolutionPointers(VectorValues* solution, const KeyVector& frontals, const KeyVector& separatorKeys) { @@ -181,6 +173,7 @@ std::vector MultifrontalClique::blockDims( const std::map& dims, const KeyVector& frontals, const KeyVector& separatorKeys) const { std::vector blockDims; + blockDims.reserve(frontals.size() + separatorKeys.size()); for (Key k : frontals) blockDims.push_back(dims.at(k)); for (Key k : separatorKeys) blockDims.push_back(dims.at(k)); return blockDims; @@ -202,6 +195,7 @@ void MultifrontalClique::initializeMatrices( const std::vector& blockDims, size_t verticalBlockMatrixRows) { sbm_ = SymmetricBlockMatrix(blockDims, true); Ab_ = VerticalBlockMatrix(blockDims, verticalBlockMatrixRows, true); + // Ab's structure is fixed; clear it once and reuse across loads. Ab_.matrix().setZero(); } @@ -214,13 +208,15 @@ size_t MultifrontalClique::addJacobianFactor( for (auto it = jacobianFactor.begin(); it != jacobianFactor.end(); ++it) { Key k = *it; if (fixedKeys_ && fixedKeys_->count(k)) continue; - const size_t blockIdx = blockIndex_.at(k); + const size_t blockIdx = blockIndex(k); Ab_(blockIdx).middleRows(rowOffset, rows) = jacobianFactor.getA(it); } Ab_(rhsBlockIdx).middleRows(rowOffset, rows) = jacobianFactor.getb(); if (auto model = jacobianFactor.get_model()) { if (!model->isConstrained()) { + // Only whiten non-constrained rows; constrained factors are handled as + // hard constraints elsewhere. model->WhitenInPlace(Ab_.matrix().middleRows(rowOffset, rows)); } } @@ -238,7 +234,7 @@ void MultifrontalClique::addHessianFactor(const HessianFactor& hessianFactor) { ++it, ++slot) { const Key key = *it; if (fixedKeys_ && fixedKeys_->count(key)) continue; - blockIndices[slot] = static_cast(blockIndex_.at(key)); + blockIndices[slot] = blockIndex(key); } blockIndices[factorBlocks] = rhsBlock; @@ -246,8 +242,8 @@ void MultifrontalClique::addHessianFactor(const HessianFactor& hessianFactor) { } void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { - assert(validateFactorKeys(graph, factorIndices_, blockIndex_, fixedKeys_)); - sbm_.setZero(); // Easily half of the cost ! + assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); + sbm_.setZero(); // Zero the active SBM once before accumulating factors. size_t rowOffset = 0; for (size_t index : factorIndices_) { @@ -278,6 +274,7 @@ void MultifrontalClique::eliminateInPlace() { void MultifrontalClique::updateParent(MultifrontalClique& parent) const { // Expose only the separator+RHS view when contributing to the parent. + assert(sbm_.blockStart() == 0); sbm_.blockStart() = numFrontals(); assert(sbm_.nBlocks() == parentIndices_.size()); parent.sbm_.updateFromMappedBlocks(sbm_, parentIndices_); @@ -285,10 +282,10 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { } std::shared_ptr MultifrontalClique::conditional() const { - const KeyVector keys = orderedKeysFromBlockIndex(blockIndex_); - SymmetricBlockMatrix& sbm = sbm_; - VerticalBlockMatrix Ab = sbm.split(numFrontals()); - sbm.blockStart() = 0; // Split sets it to numFrontals(), reset to 0. + const KeyVector& keys = orderedKeys_; + assert(sbm_.blockStart() == 0); + VerticalBlockMatrix Ab = sbm_.split(numFrontals()); + sbm_.blockStart() = 0; // Split sets it to numFrontals(), reset to 0. return std::make_shared(keys, numFrontals(), std::move(Ab)); } @@ -307,7 +304,7 @@ void MultifrontalClique::updateSolution() const { // We first solve rhs = d - S * x_s rhsScratch_.noalias() = d; - if (n > nf) { + if (!separatorPtrs_.empty()) { const Vector& x_s = buildSeparatorVector(separatorPtrs_, &separatorScratch_); rhsScratch_.noalias() -= S * x_s; @@ -316,7 +313,7 @@ void MultifrontalClique::updateSolution() const { // Then solve for x_f, our solution, via R * x_f = rhs // We solve the contiguous frontal system in one triangular solve. R.solveInPlace(rhsScratch_); - auto& x_f = rhsScratch_; + const Vector& x_f = rhsScratch_; // Write solved frontal blocks back into the global solution. size_t offset = 0; @@ -330,7 +327,7 @@ void MultifrontalClique::updateSolution() const { void MultifrontalClique::print(const std::string& s, const KeyFormatter& keyFormatter) const { if (!s.empty()) std::cout << s; - const KeyVector orderedKeys = orderedKeysFromBlockIndex(blockIndex_); + const KeyVector& orderedKeys = orderedKeys_; std::cout << "Clique(frontals=["; printKeyRange(std::cout, orderedKeys, 0, std::min(numFrontals(), orderedKeys.size()), keyFormatter); @@ -364,7 +361,7 @@ void MultifrontalClique::print(const std::string& s, } std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique) { - const KeyVector orderedKeys = orderedKeysFromBlockIndex(clique.blockIndex_); + const KeyVector& orderedKeys = clique.orderedKeys_; const KeyFormatter formatter = DefaultKeyFormatter; os << "Clique(frontals="; printKeyRange(os, orderedKeys, 0, diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 77d12b321a..2932497557 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -177,6 +177,9 @@ class GTSAM_EXPORT MultifrontalClique { void cacheSolutionPointers(VectorValues* delta, const KeyVector& frontals, const KeyVector& separatorKeys); + /// Linear lookup for block index in small cliques. + DenseIndex blockIndex(Key key) const; + /// Compute block dimensions from variable dimensions (excluding RHS). std::vector blockDims(const std::map& dims, const KeyVector& frontals, @@ -209,10 +212,10 @@ class GTSAM_EXPORT MultifrontalClique { separatorScratch_; ///< Cached separator stack for back-substitution. std::vector factorIndices_; - std::map blockIndex_; ///< Key->block index for fast Ab fills. + KeyVector orderedKeys_; ///< Keys ordered by block index (frontals+seps). const std::unordered_set* fixedKeys_ = nullptr; std::vector - parentIndices_; ///< Parent block indices for separators and RHS. + parentIndices_; ///< Parent block indices for separators + RHS. std::vector frontalPtrs_; ///< Pointers into solution frontals. std::vector separatorPtrs_; ///< Pointers into solution separator. diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 9b27e60889..86b82d847d 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -88,6 +88,26 @@ struct StructureStats { constexpr double kConstraintSigmaTol = 1e-12; constexpr double kConstraintFeasibleTol = 1e-9; +// Sum variable dimensions for a key container, skipping unknown keys. +size_t sumDims(const std::map& dims, const KeyVector& keys) { + size_t dim = 0; + for (Key key : keys) { + auto it = dims.find(key); + if (it != dims.end()) dim += it->second; + } + return dim; +} + +// Overload for key sets used by symbolic separator caches. +size_t sumDims(const std::map& dims, const KeySet& keys) { + size_t dim = 0; + for (Key key : keys) { + auto it = dims.find(key); + if (it != dims.end()) dim += it->second; + } + return dim; +} + std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { std::unordered_set fixedKeys; for (const auto& factor : graph) { @@ -96,6 +116,7 @@ std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { if (!jacobianFactor) continue; auto model = jacobianFactor->get_model(); if (!model || !model->isConstrained()) continue; + // Only accept fully constrained unary factors with zero residuals. if (jacobianFactor->size() != 1) { throw std::runtime_error( "MultifrontalSolver: only unary constrained factors are supported."); @@ -151,6 +172,7 @@ SymbolicFactorGraph buildSymbolicGraph( keys.push_back(key); } } + // Skip factors that are fully constrained away. if (keys.empty()) continue; symbolicGraph.emplace_shared(keys, i); } @@ -161,12 +183,7 @@ SymbolicFactorGraph buildSymbolicGraph( size_t frontalDimForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster, const std::map& dims) { - size_t dim = 0; - for (Key key : cluster->orderedFrontalKeys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - return dim; + return sumDims(dims, cluster->orderedFrontalKeys); } size_t separatorDimForSymbolicCluster( @@ -174,13 +191,8 @@ size_t separatorDimForSymbolicCluster( const std::map& dims, SymbolicJunctionTree::Cluster::KeySetMap* cache) { if (!cluster) return 0; - size_t dim = 0; const KeySet separatorKeys = cluster->separatorKeys(cache); - for (Key key : separatorKeys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - return dim; + return sumDims(dims, separatorKeys); } size_t totalDimForSymbolicCluster( @@ -277,8 +289,8 @@ struct CliqueBuilder { const GaussianFactorGraph& graph; VectorValues* solution; std::vector* cliques; - SymbolicJunctionTree::Cluster::KeySetMap separatorCache; const std::unordered_set* fixedKeys; + SymbolicJunctionTree::Cluster::KeySetMap separatorCache = {}; BuiltClique build(const SymbolicJunctionTree::sharedNode& cluster, std::weak_ptr parent) { @@ -319,9 +331,11 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering, size_t mergeDimCap, std::ostream* reportStream) { - // Pre-compute variable dimensions + // Pre-compute variable dimensions and fixed keys. dims_ = computeDims(graph); fixedKeys_ = collectFixedKeys(graph); + + // Seed the cached solution with zero vectors for all variables. Ordering reducedOrdering; reducedOrdering.reserve(ordering.size()); for (Key key : ordering) { @@ -357,7 +371,7 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, } // Build the actual MultifrontalClique structure. - CliqueBuilder builder{dims_, graph, &solution_, &cliques_, {}, &fixedKeys_}; + CliqueBuilder builder{dims_, graph, &solution_, &cliques_, &fixedKeys_}; for (const auto& rootCluster : junctionTree.roots()) { if (rootCluster) { roots_.push_back( @@ -381,13 +395,10 @@ void MultifrontalSolver::load(const GaussianFactorGraph& graph) { void MultifrontalSolver::eliminateInPlace() { // Parallel elimination uses PostOrderForestParallel, which will be // multi-threaded if GTSAM was compiled with TBB. - struct EliminatePostVisitor { - void operator()(const CliquePtr& node) const { - if (node) node->eliminateInPlace(); - } - }; - EliminatePostVisitor visitorPost; TbbOpenMPMixedScope threadLimiter; + auto visitorPost = [](const CliquePtr& node) { + if (node) node->eliminateInPlace(); + }; treeTraversal::PostOrderForestParallel(*this, visitorPost, 10); } From a4ae6c620175cebf25be46681ec66535026d9746 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 3 Jan 2026 14:06:21 -0500 Subject: [PATCH 012/266] Avoid 3 identical sumDims --- gtsam/linear/MultifrontalClique.cpp | 18 ++++-------------- gtsam/linear/MultifrontalClique.h | 18 ++++++++++++++++-- gtsam/linear/MultifrontalSolver.cpp | 24 ++---------------------- 3 files changed, 22 insertions(+), 38 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index dafa23d416..e42821208a 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -41,16 +41,6 @@ void printKeyRange(std::ostream& os, const KeyVector& keys, size_t start, os << "]"; } -// Sum variable dimensions for a key sequence, skipping unknown keys. -size_t sumDims(const std::map& dims, const KeyVector& keys) { - size_t dim = 0; - for (Key key : keys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - return dim; -} - // Build a stacked separator vector x_sep in the provided scratch buffer. Vector& buildSeparatorVector(const std::vector& separatorPtrs, Vector* scratch) { @@ -91,7 +81,7 @@ bool validateFactorKeys(const GaussianFactorGraph& graph, MultifrontalClique::MultifrontalClique( std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, - const KeyVector& separatorKeys, const std::map& dims, + const KeyVector& separatorKeys, const KeyDimMap& dims, const GaussianFactorGraph& graph, VectorValues* solution, const std::unordered_set* fixedKeys) { factorIndices_ = std::move(factorIndices); @@ -111,8 +101,8 @@ MultifrontalClique::MultifrontalClique( separatorKeys.end()); // Cache total frontal/separator dimensions for scheduling and sizing. - frontalDim = sumDims(dims, frontals); - separatorDim = sumDims(dims, separatorKeys); + frontalDim = internal::sumDims(dims, frontals); + separatorDim = internal::sumDims(dims, separatorKeys); rhsScratch_.resize(frontalDim); separatorScratch_.resize(separatorDim); @@ -170,7 +160,7 @@ void MultifrontalClique::cacheSolutionPointers(VectorValues* solution, } std::vector MultifrontalClique::blockDims( - const std::map& dims, const KeyVector& frontals, + const KeyDimMap& dims, const KeyVector& frontals, const KeyVector& separatorKeys) const { std::vector blockDims; blockDims.reserve(frontals.size() + separatorKeys.size()); diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 2932497557..9fec0176fa 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -38,6 +38,9 @@ namespace gtsam { class GaussianConditional; +/// Map from variable key to dimension. +using KeyDimMap = std::map; + namespace internal { /// Helper class to track original factor indices. @@ -52,6 +55,17 @@ class IndexedSymbolicFactor : public SymbolicFactor { : SymbolicFactor(factor), index_(index) {} }; +/// Sum variable dimensions for a key range, skipping unknown keys. +template +inline size_t sumDims(const KeyDimMap& dims, const KeyRange& keys) { + size_t dim = 0; + for (Key key : keys) { + auto it = dims.find(key); + if (it != dims.end()) dim += it->second; + } + return dim; +} + } // namespace internal /** @@ -84,7 +98,7 @@ class GTSAM_EXPORT MultifrontalClique { const std::weak_ptr& parent, const KeyVector& frontals, const KeyVector& separatorKeys, - const std::map& dims, + const KeyDimMap& dims, const GaussianFactorGraph& graph, VectorValues* solution, const std::unordered_set* fixedKeys); @@ -181,7 +195,7 @@ class GTSAM_EXPORT MultifrontalClique { DenseIndex blockIndex(Key key) const; /// Compute block dimensions from variable dimensions (excluding RHS). - std::vector blockDims(const std::map& dims, + std::vector blockDims(const KeyDimMap& dims, const KeyVector& frontals, const KeyVector& separatorKeys) const; diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 86b82d847d..ffa9629ec0 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -88,26 +88,6 @@ struct StructureStats { constexpr double kConstraintSigmaTol = 1e-12; constexpr double kConstraintFeasibleTol = 1e-9; -// Sum variable dimensions for a key container, skipping unknown keys. -size_t sumDims(const std::map& dims, const KeyVector& keys) { - size_t dim = 0; - for (Key key : keys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - return dim; -} - -// Overload for key sets used by symbolic separator caches. -size_t sumDims(const std::map& dims, const KeySet& keys) { - size_t dim = 0; - for (Key key : keys) { - auto it = dims.find(key); - if (it != dims.end()) dim += it->second; - } - return dim; -} - std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { std::unordered_set fixedKeys; for (const auto& factor : graph) { @@ -183,7 +163,7 @@ SymbolicFactorGraph buildSymbolicGraph( size_t frontalDimForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster, const std::map& dims) { - return sumDims(dims, cluster->orderedFrontalKeys); + return internal::sumDims(dims, cluster->orderedFrontalKeys); } size_t separatorDimForSymbolicCluster( @@ -192,7 +172,7 @@ size_t separatorDimForSymbolicCluster( SymbolicJunctionTree::Cluster::KeySetMap* cache) { if (!cluster) return 0; const KeySet separatorKeys = cluster->separatorKeys(cache); - return sumDims(dims, separatorKeys); + return internal::sumDims(dims, separatorKeys); } size_t totalDimForSymbolicCluster( From 11ed2252f89f0aa1fce8cf5f8d6aeef9211c48e9 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 3 Jan 2026 15:54:30 -0500 Subject: [PATCH 013/266] Pass sep keys as set without converting --- gtsam/linear/MultifrontalClique.cpp | 6 +++--- gtsam/linear/MultifrontalClique.h | 10 ++++------ gtsam/linear/MultifrontalSolver.cpp | 14 ++++---------- 3 files changed, 11 insertions(+), 19 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index e42821208a..29397a52b1 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -81,7 +81,7 @@ bool validateFactorKeys(const GaussianFactorGraph& graph, MultifrontalClique::MultifrontalClique( std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, - const KeyVector& separatorKeys, const KeyDimMap& dims, + const KeySet& separatorKeys, const KeyDimMap& dims, const GaussianFactorGraph& graph, VectorValues* solution, const std::unordered_set* fixedKeys) { factorIndices_ = std::move(factorIndices); @@ -146,7 +146,7 @@ DenseIndex MultifrontalClique::blockIndex(Key key) const { void MultifrontalClique::cacheSolutionPointers(VectorValues* solution, const KeyVector& frontals, - const KeyVector& separatorKeys) { + const KeySet& separatorKeys) { frontalPtrs_.clear(); separatorPtrs_.clear(); frontalPtrs_.reserve(frontals.size()); @@ -161,7 +161,7 @@ void MultifrontalClique::cacheSolutionPointers(VectorValues* solution, std::vector MultifrontalClique::blockDims( const KeyDimMap& dims, const KeyVector& frontals, - const KeyVector& separatorKeys) const { + const KeySet& separatorKeys) const { std::vector blockDims; blockDims.reserve(frontals.size() + separatorKeys.size()); for (Key k : frontals) blockDims.push_back(dims.at(k)); diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 9fec0176fa..bc5c5f527d 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -51,8 +51,6 @@ class IndexedSymbolicFactor : public SymbolicFactor { : SymbolicFactor(), index_(index) { keys_ = keys; } - IndexedSymbolicFactor(const GaussianFactor& factor, size_t index) - : SymbolicFactor(factor), index_(index) {} }; /// Sum variable dimensions for a key range, skipping unknown keys. @@ -77,7 +75,7 @@ class GTSAM_EXPORT MultifrontalClique { using Children = std::vector; struct ChildInfo { shared_ptr clique; - KeyVector separatorKeys; + KeySet separatorKeys; }; std::weak_ptr parent; ///< Parent clique. @@ -97,7 +95,7 @@ class GTSAM_EXPORT MultifrontalClique { explicit MultifrontalClique(std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, - const KeyVector& separatorKeys, + const KeySet& separatorKeys, const KeyDimMap& dims, const GaussianFactorGraph& graph, VectorValues* solution, @@ -189,7 +187,7 @@ class GTSAM_EXPORT MultifrontalClique { private: /// Cache pointers to frontal and separator update vectors. void cacheSolutionPointers(VectorValues* delta, const KeyVector& frontals, - const KeyVector& separatorKeys); + const KeySet& separatorKeys); /// Linear lookup for block index in small cliques. DenseIndex blockIndex(Key key) const; @@ -197,7 +195,7 @@ class GTSAM_EXPORT MultifrontalClique { /// Compute block dimensions from variable dimensions (excluding RHS). std::vector blockDims(const KeyDimMap& dims, const KeyVector& frontals, - const KeyVector& separatorKeys) const; + const KeySet& separatorKeys) const; /** * Pre-allocate matrices for this clique. diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index ffa9629ec0..ccf24bbc6f 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -238,12 +238,7 @@ void reportStructure(const SymbolicJunctionTree& junctionTree, stats.report(name, reportStream); } -KeyVector keyVectorFromKeySet(const KeySet& keys) { - KeyVector result; - result.reserve(keys.size()); - for (Key key : keys) result.push_back(key); - return result; -} + std::vector factorIndicesForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster) { @@ -260,7 +255,7 @@ std::vector factorIndicesForSymbolicCluster( struct BuiltClique { MultifrontalSolver::CliquePtr clique; - KeyVector separatorKeys; + KeySet separatorKeys; }; // Build cliques from a symbolic junction tree and wire parent/child metadata. @@ -274,12 +269,11 @@ struct CliqueBuilder { BuiltClique build(const SymbolicJunctionTree::sharedNode& cluster, std::weak_ptr parent) { - if (!cluster) return {nullptr, KeyVector()}; + if (!cluster) return {nullptr, KeySet()}; // Gather symbolic metadata for this clique. const KeyVector& frontals = cluster->orderedFrontalKeys; - KeyVector separatorKeys = - keyVectorFromKeySet(cluster->separatorKeys(&separatorCache)); + const KeySet& separatorKeys = cluster->separatorKeys(&separatorCache); // Create the clique node and cache static structure. auto clique = std::make_shared( From 82cc63d2ab51d0b5e3587b4fea9d3694f1ecb21e Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 3 Jan 2026 16:05:37 -0500 Subject: [PATCH 014/266] Do solve in parallel --- gtsam/linear/MultifrontalSolver.cpp | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index ccf24bbc6f..6cf6b2d459 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -238,8 +238,6 @@ void reportStructure(const SymbolicJunctionTree& junctionTree, stats.report(name, reportStream); } - - std::vector factorIndicesForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster) { std::vector indices; @@ -419,9 +417,22 @@ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { /* ************************************************************************* */ const VectorValues& MultifrontalSolver::updateSolution() const { - for (const auto& clique : cliques_) { - clique->updateSolution(); - } + // Parallel solve uses treeTraversal::DepthFirstForestParallel (Pre-order / + // Top-Down). + TbbOpenMPMixedScope threadLimiter; + int rootData = 0; + auto visitorPre = [](const CliquePtr& node, int&) { + if (node) node->updateSolution(); + return 0; + }; + auto visitorPost = [](const CliquePtr&, int) {}; + + // Cast to non-const because treeTraversal expects a non-const reference, + // even though we are only calling const methods on the nodes. + treeTraversal::DepthFirstForestParallel( + const_cast(*this), rootData, visitorPre, visitorPost, + 10); + for (Key key : fixedKeys_) { solution_.at(key).setZero(); } From 0cfd6247bb9a85658c1bcedc8e75f3aa261872c5 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 00:03:56 -0500 Subject: [PATCH 015/266] Fix BAL parsing on Mac --- gtsam/sfm/SfmData.cpp | 108 ++++++++++++++++++++++++++++++----------- gtsam/slam/dataset.cpp | 2 + timing/timeSFMBAL.h | 3 +- 3 files changed, 85 insertions(+), 28 deletions(-) diff --git a/gtsam/sfm/SfmData.cpp b/gtsam/sfm/SfmData.cpp index 6c6b471e6f..2618fe3b29 100644 --- a/gtsam/sfm/SfmData.cpp +++ b/gtsam/sfm/SfmData.cpp @@ -22,6 +22,7 @@ #include #include +#include namespace gtsam { @@ -106,6 +107,7 @@ SfmData SfmData::FromBundlerFile(const std::string &filename) { throw std::runtime_error( "Error in FromBundlerFile: can not find the file!!"); } + is.imbue(std::locale::classic()); SfmData sfmData; @@ -115,20 +117,30 @@ SfmData SfmData::FromBundlerFile(const std::string &filename) { // Get the number of camera poses and 3D points size_t nrPoses, nrPoints; - is >> nrPoses >> nrPoints; + if (!(is >> nrPoses >> nrPoints)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read header from file"); + } // Get the information for the camera poses for (size_t i = 0; i < nrPoses; i++) { // Get the focal length and the radial distortion parameters - float f, k1, k2; - is >> f >> k1 >> k2; + double f = 0.0, k1 = 0.0, k2 = 0.0; + if (!(is >> f >> k1 >> k2)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read camera calibration"); + } Cal3Bundler K(f, k1, k2); // Get the rotation matrix - float r11, r12, r13; - float r21, r22, r23; - float r31, r32, r33; - is >> r11 >> r12 >> r13 >> r21 >> r22 >> r23 >> r31 >> r32 >> r33; + double r11 = 0.0, r12 = 0.0, r13 = 0.0; + double r21 = 0.0, r22 = 0.0, r23 = 0.0; + double r31 = 0.0, r32 = 0.0, r33 = 0.0; + if (!(is >> r11 >> r12 >> r13 >> r21 >> r22 >> r23 >> r31 >> r32 >> + r33)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read camera rotation"); + } // Bundler-OpenGL rotation matrix Rot3 R(r11, r12, r13, r21, r22, r23, r31, r32, r33); @@ -140,8 +152,11 @@ SfmData SfmData::FromBundlerFile(const std::string &filename) { } // Get the translation vector - float tx, ty, tz; - is >> tx >> ty >> tz; + double tx = 0.0, ty = 0.0, tz = 0.0; + if (!(is >> tx >> ty >> tz)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read camera translation"); + } Pose3 pose = openGL2gtsam(R, tx, ty, tz); @@ -154,27 +169,43 @@ SfmData SfmData::FromBundlerFile(const std::string &filename) { SfmTrack track; // Get the 3D position - float x, y, z; - is >> x >> y >> z; + double x = 0.0, y = 0.0, z = 0.0; + if (!(is >> x >> y >> z)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read point coordinates"); + } track.p = Point3(x, y, z); // Get the color information - float r, g, b; - is >> r >> g >> b; + double r = 0.0, g = 0.0, b = 0.0; + if (!(is >> r >> g >> b)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read point color"); + } track.r = r / 255.f; track.g = g / 255.f; track.b = b / 255.f; // Now get the visibility information size_t nvisible = 0; - is >> nvisible; + if (!(is >> nvisible)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read visibility count"); + } track.measurements.reserve(nvisible); track.siftIndices.reserve(nvisible); for (size_t k = 0; k < nvisible; k++) { size_t cam_idx = 0, point_idx = 0; - float u, v; - is >> cam_idx >> point_idx >> u >> v; + double u = 0.0, v = 0.0; + if (!(is >> cam_idx >> point_idx >> u >> v)) { + throw std::runtime_error( + "Error in FromBundlerFile: failed to read measurement data"); + } + if (cam_idx >= nrPoses) { + throw std::runtime_error( + "Error in FromBundlerFile: measurement camera index out of range"); + } track.measurements.emplace_back(cam_idx, Point2(u, -v)); track.siftIndices.emplace_back(cam_idx, point_idx); } @@ -192,39 +223,59 @@ SfmData SfmData::FromBalFile(const std::string &filename) { if (!is) { throw std::runtime_error("Error in FromBalFile: can not find the file!!"); } + is.imbue(std::locale::classic()); SfmData sfmData; // Get the number of camera poses and 3D points size_t nrPoses, nrPoints, nrObservations; - is >> nrPoses >> nrPoints >> nrObservations; + if (!(is >> nrPoses >> nrPoints >> nrObservations)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read header from file"); + } sfmData.tracks.resize(nrPoints); // Get the information for the observations for (size_t k = 0; k < nrObservations; k++) { size_t i = 0, j = 0; - float u, v; - is >> i >> j >> u >> v; + double u = 0.0, v = 0.0; + if (!(is >> i >> j >> u >> v)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read observation data"); + } + if (i >= nrPoses || j >= nrPoints) { + throw std::runtime_error( + "Error in FromBalFile: observation index out of range"); + } sfmData.tracks[j].measurements.emplace_back(i, Point2(u, -v)); } // Get the information for the camera poses for (size_t i = 0; i < nrPoses; i++) { // Get the Rodrigues vector - float wx, wy, wz; - is >> wx >> wy >> wz; + double wx = 0.0, wy = 0.0, wz = 0.0; + if (!(is >> wx >> wy >> wz)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read camera rotation"); + } Rot3 R = Rot3::Rodrigues(wx, wy, wz); // BAL-OpenGL rotation matrix // Get the translation vector - float tx, ty, tz; - is >> tx >> ty >> tz; + double tx = 0.0, ty = 0.0, tz = 0.0; + if (!(is >> tx >> ty >> tz)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read camera translation"); + } Pose3 pose = openGL2gtsam(R, tx, ty, tz); // Get the focal length and the radial distortion parameters - float f, k1, k2; - is >> f >> k1 >> k2; + double f = 0.0, k1 = 0.0, k2 = 0.0; + if (!(is >> f >> k1 >> k2)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read camera calibration"); + } Cal3Bundler K(f, k1, k2); sfmData.cameras.emplace_back(pose, K); @@ -233,8 +284,11 @@ SfmData SfmData::FromBalFile(const std::string &filename) { // Get the information for the 3D points for (size_t j = 0; j < nrPoints; j++) { // Get the 3D position - float x, y, z; - is >> x >> y >> z; + double x = 0.0, y = 0.0, z = 0.0; + if (!(is >> x >> y >> z)) { + throw std::runtime_error( + "Error in FromBalFile: failed to read point coordinates"); + } SfmTrack &track = sfmData.tracks[j]; track.p = Point3(x, y, z); track.r = 0.4f; diff --git a/gtsam/slam/dataset.cpp b/gtsam/slam/dataset.cpp index 91317bad1e..d8731a2506 100644 --- a/gtsam/slam/dataset.cpp +++ b/gtsam/slam/dataset.cpp @@ -46,6 +46,7 @@ #include #include #include +#include #include #include @@ -131,6 +132,7 @@ static void parseLines(const std::string &filename, Parser parse) { std::ifstream is(filename.c_str()); if (!is) throw std::invalid_argument("parse: can not find file " + filename); + is.imbue(std::locale::classic()); std::string tag; while (is >> tag) { parse(is, tag); // ignore return value diff --git a/timing/timeSFMBAL.h b/timing/timeSFMBAL.h index 25943c2aaf..f83cca9f80 100644 --- a/timing/timeSFMBAL.h +++ b/timing/timeSFMBAL.h @@ -132,7 +132,8 @@ int optimize(const SfmData& db, const NonlinearFactorGraph& graph, LevenbergMarquardtParams params; LevenbergMarquardtParams::SetCeresDefaults(¶ms); // params.setLinearSolverType("SEQUENTIAL_CHOLESKY"); - // params.setVerbosityLM("SUMMARY"); + params.setVerbosityLM("SUMMARY"); + params.setRelativeErrorTol(0.01); // 1% relative error tol if (gUseSchur) { // Create Schur-complement ordering From ad3551bb1323e0ff1812ee40acdd790e8dcfc69a Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 13:45:44 -0500 Subject: [PATCH 016/266] Constructor with PrecomputedData --- gtsam/base/SymmetricBlockMatrix.h | 13 +++ gtsam/linear/MultifrontalClique.cpp | 53 ++++++---- gtsam/linear/MultifrontalClique.h | 39 ++++--- gtsam/linear/MultifrontalSolver.cpp | 100 +++++++++++------- gtsam/linear/MultifrontalSolver.h | 28 ++++- gtsam/linear/tests/testMultifrontalSolver.cpp | 37 ++++++- timing/timeMultifrontalSolver.cpp | 8 +- timing/timeSFMBAL.h | 14 ++- 8 files changed, 206 insertions(+), 86 deletions(-) diff --git a/gtsam/base/SymmetricBlockMatrix.h b/gtsam/base/SymmetricBlockMatrix.h index c6a767012d..f46dc72fae 100644 --- a/gtsam/base/SymmetricBlockMatrix.h +++ b/gtsam/base/SymmetricBlockMatrix.h @@ -234,6 +234,19 @@ namespace gtsam { } } + /// Add a vector to the diagonal entries of block I. + void addToDiagonalBlock(DenseIndex I, const Vector& deltaDiag) { + auto dest = block_(I, I); + assert(dest.rows() == deltaDiag.size()); + dest.diagonal().array() += deltaDiag.array(); + } + + /// Add lambda * I to the diagonal block I. + void addScaledIdentity(DenseIndex I, double lambda) { + auto dest = block_(I, I); + dest.diagonal().array() += lambda; + } + /// Update an off diagonal block. /// NOTE(emmett): This assumes noalias(). template diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 29397a52b1..5c95b91e90 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -82,7 +82,7 @@ MultifrontalClique::MultifrontalClique( std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, const KeySet& separatorKeys, const KeyDimMap& dims, - const GaussianFactorGraph& graph, VectorValues* solution, + size_t vbmRows, VectorValues* solution, const std::unordered_set* fixedKeys) { factorIndices_ = std::move(factorIndices); this->parent = parent; @@ -113,7 +113,6 @@ MultifrontalClique::MultifrontalClique( // Pre-allocate matrices once per structure. std::vector blockDims = this->blockDims(dims, frontals, separatorKeys); - size_t vbmRows = countRows(graph); initializeMatrices(blockDims, vbmRows); } @@ -169,18 +168,6 @@ std::vector MultifrontalClique::blockDims( return blockDims; } -size_t MultifrontalClique::countRows(const GaussianFactorGraph& graph) const { - size_t vbmRows = 0; - for (size_t index : factorIndices_) { - assert(index < graph.size()); - if (auto jacobianFactor = - std::dynamic_pointer_cast(graph[index])) { - vbmRows += jacobianFactor->rows(); - } - } - return vbmRows; -} - void MultifrontalClique::initializeMatrices( const std::vector& blockDims, size_t verticalBlockMatrixRows) { sbm_ = SymmetricBlockMatrix(blockDims, true); @@ -233,8 +220,9 @@ void MultifrontalClique::addHessianFactor(const HessianFactor& hessianFactor) { void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); - sbm_.setZero(); // Zero the active SBM once before accumulating factors. + assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); + hessianFactors_.clear(); size_t rowOffset = 0; for (size_t index : factorIndices_) { assert(index < graph.size()); @@ -244,12 +232,17 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { rowOffset += addJacobianFactor(*jacobianFactor, rowOffset); } else if (auto hessianFactor = std::dynamic_pointer_cast(gf)) { - addHessianFactor(*hessianFactor); + hessianFactors_.push_back(hessianFactor); } } } -void MultifrontalClique::eliminateInPlace() { +void MultifrontalClique::prepareForElimination() { + sbm_.setZero(); + for (const auto& hf : hessianFactors_) { + addHessianFactor(*hf); + } + // Update SBM with the local factors, Ab^T * Ab sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); @@ -257,9 +250,31 @@ void MultifrontalClique::eliminateInPlace() { if (!child) continue; child->updateParent(*this); } +} - // Form normal equations and factor the frontal block (Schur complement step). - sbm_.choleskyPartial(numFrontals()); +void MultifrontalClique::factorize() { sbm_.choleskyPartial(numFrontals()); } + +void MultifrontalClique::addIdentityDamping(double lambda) { + const size_t nf = numFrontals(); + for (size_t j = 0; j < nf; ++j) { + sbm_.addScaledIdentity(j, lambda); + } +} + +void MultifrontalClique::addDiagonalDamping(double lambda, double minDiagonal, + double maxDiagonal) { + const size_t nf = numFrontals(); + for (size_t j = 0; j < nf; ++j) { + Vector diag = sbm_.diagonal(j); + diag = diag.cwiseMax(minDiagonal).cwiseMin(maxDiagonal); + const Vector scaled = lambda * diag; + sbm_.addToDiagonalBlock(j, scaled); + } +} + +void MultifrontalClique::eliminateInPlace() { + prepareForElimination(); + factorize(); } void MultifrontalClique::updateParent(MultifrontalClique& parent) const { diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index bc5c5f527d..93e1251b61 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -43,12 +43,13 @@ using KeyDimMap = std::map; namespace internal { -/// Helper class to track original factor indices. +/// Helper class to track original factor indices and row counts. class IndexedSymbolicFactor : public SymbolicFactor { public: size_t index_; - IndexedSymbolicFactor(const KeyVector& keys, size_t index) - : SymbolicFactor(), index_(index) { + size_t rows_; + IndexedSymbolicFactor(const KeyVector& keys, size_t index, size_t rows) + : SymbolicFactor(), index_(index), rows_(rows) { keys_ = keys; } }; @@ -89,7 +90,7 @@ class GTSAM_EXPORT MultifrontalClique { /// @param frontals Frontal keys for this clique. /// @param separatorKeys Separator keys for this clique. /// @param dims Key->dimension map. - /// @param graph Factor graph for sizing and constraints. + /// @param vbmRows Number of rows needed for the vertical block matrix. /// @param solution Solution storage for cached pointers. /// @param fixedKeys Keys fixed to zero by constraints (may be null). explicit MultifrontalClique(std::vector factorIndices, @@ -97,7 +98,7 @@ class GTSAM_EXPORT MultifrontalClique { const KeyVector& frontals, const KeySet& separatorKeys, const KeyDimMap& dims, - const GaussianFactorGraph& graph, + size_t vbmRows, VectorValues* solution, const std::unordered_set* fixedKeys); @@ -107,10 +108,23 @@ class GTSAM_EXPORT MultifrontalClique { /// Cache the children list and compute parent indices. void finalize(std::vector children); - /// Load factor values into the pre-allocated Ab matrix and Hessians into - /// sbm_. + /// Load factor values into the pre-allocated Ab matrix. /// @param graph The factor graph with updated values. void fillAb(const GaussianFactorGraph& graph); + + /// Zero out sbm, re-add Hessians, accumulate Jacobians and children. + void prepareForElimination(); + + /// Perform Cholesky factorization on the frontal block. + void factorize(); + + /// Add identity damping to the frontal block. + void addIdentityDamping(double lambda); + + /// Add diagonal damping to the frontal block, with clamping. + void addDiagonalDamping(double lambda, double minDiagonal, + double maxDiagonal); + /// @} /// @name Read-only methods @@ -130,19 +144,9 @@ class GTSAM_EXPORT MultifrontalClique { /// Get the vertical block matrix Ab. const VerticalBlockMatrix& Ab() const { return Ab_; } - /// Get the symmetric block matrix (mutable). - SymmetricBlockMatrix& sbm() { return sbm_; } - /// Get the symmetric block matrix (const). const SymmetricBlockMatrix& sbm() const { return sbm_; } - /** - * Count rows needed for the vertical block matrix. - * @param graph The factor graph. - * @return Total number of rows. - */ - size_t countRows(const GaussianFactorGraph& graph) const; - /** * Print this clique. * @param s Optional string prefix. @@ -231,6 +235,7 @@ class GTSAM_EXPORT MultifrontalClique { std::vector frontalPtrs_; ///< Pointers into solution frontals. std::vector separatorPtrs_; ///< Pointers into solution separator. + std::vector hessianFactors_; ///< Hessian factors. }; std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique); diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 6cf6b2d459..01d4022f60 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -137,6 +137,15 @@ std::map computeDims(const GaussianFactorGraph& graph) { return dims; } +size_t factorRowCount(const GaussianFactorGraph& graph, size_t index) { + if (index >= graph.size() || !graph[index]) return 0; + if (auto jacobianFactor = + std::dynamic_pointer_cast(graph[index])) { + return jacobianFactor->rows(); + } + return 0; +} + // Build SymbolicFactorGraph from GaussianFactorGraph SymbolicFactorGraph buildSymbolicGraph( const GaussianFactorGraph& graph, @@ -154,7 +163,8 @@ SymbolicFactorGraph buildSymbolicGraph( } // Skip factors that are fully constrained away. if (keys.empty()) continue; - symbolicGraph.emplace_shared(keys, i); + symbolicGraph.emplace_shared( + keys, i, factorRowCount(graph, i)); } return symbolicGraph; } @@ -238,19 +248,6 @@ void reportStructure(const SymbolicJunctionTree& junctionTree, stats.report(name, reportStream); } -std::vector factorIndicesForSymbolicCluster( - const SymbolicJunctionTree::sharedNode& cluster) { - std::vector indices; - indices.reserve(cluster->factors.size()); - for (const auto& factor : cluster->factors) { - assert(factor); - auto indexed = - std::static_pointer_cast(factor); - indices.push_back(indexed->index_); - } - return indices; -} - struct BuiltClique { MultifrontalSolver::CliquePtr clique; KeySet separatorKeys; @@ -259,7 +256,6 @@ struct BuiltClique { // Build cliques from a symbolic junction tree and wire parent/child metadata. struct CliqueBuilder { const std::map& dims; - const GaussianFactorGraph& graph; VectorValues* solution; std::vector* cliques; const std::unordered_set* fixedKeys; @@ -272,11 +268,21 @@ struct CliqueBuilder { // Gather symbolic metadata for this clique. const KeyVector& frontals = cluster->orderedFrontalKeys; const KeySet& separatorKeys = cluster->separatorKeys(&separatorCache); + std::vector factorIndices; + factorIndices.reserve(cluster->factors.size()); + size_t vbmRows = 0; + for (const auto& factor : cluster->factors) { + assert(factor); + auto indexed = + std::static_pointer_cast(factor); + factorIndices.push_back(indexed->index_); + vbmRows += indexed->rows_; + } // Create the clique node and cache static structure. auto clique = std::make_shared( - factorIndicesForSymbolicCluster(cluster), parent, frontals, - separatorKeys, dims, graph, solution, fixedKeys); + std::move(factorIndices), parent, frontals, separatorKeys, dims, + vbmRows, solution, fixedKeys); // Build children and collect separator keys. std::vector childInfos; @@ -300,21 +306,23 @@ struct CliqueBuilder { /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, + const Ordering& ordering, + size_t mergeDimCap, + std::ostream* reportStream) + : MultifrontalSolver(Precompute(graph, ordering), ordering, + mergeDimCap, reportStream) {} + +/* ************************************************************************* */ +MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, size_t mergeDimCap, std::ostream* reportStream) { - // Pre-compute variable dimensions and fixed keys. - dims_ = computeDims(graph); - fixedKeys_ = collectFixedKeys(graph); + dims_ = std::move(data.dims); + fixedKeys_ = std::move(data.fixedKeys); // Seed the cached solution with zero vectors for all variables. - Ordering reducedOrdering; - reducedOrdering.reserve(ordering.size()); for (Key key : ordering) { solution_.insert(key, Vector::Zero(dims_.at(key))); - if (!fixedKeys_.count(key)) { - reducedOrdering.push_back(key); - } } for (Key key : fixedKeys_) { if (!solution_.exists(key)) { @@ -322,29 +330,22 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, } } - // Convert to SymbolicFactorGraph to build the elimination tree - SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys_); - - // Build SymbolicEliminationTree and then SymbolicJunctionTree - SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); - SymbolicJunctionTree junctionTree(eliminationTree); - // Report the symbolic structure before any merge. - reportStructure(junctionTree, dims_, "Symbolic cluster structure", + reportStructure(data.junctionTree, dims_, "Symbolic cluster structure", reportStream); // If applicable, merge small child cliques bottom-up. if (mergeDimCap > 0) { - for (const auto& rootCluster : junctionTree.roots()) { + for (const auto& rootCluster : data.junctionTree.roots()) { mergeSmallClusters(rootCluster, dims_, mergeDimCap); } - reportStructure(junctionTree, dims_, "Clique structure after merge", + reportStructure(data.junctionTree, dims_, "Clique structure after merge", reportStream); } // Build the actual MultifrontalClique structure. - CliqueBuilder builder{dims_, graph, &solution_, &cliques_, &fixedKeys_}; - for (const auto& rootCluster : junctionTree.roots()) { + CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_}; + for (const auto& rootCluster : data.junctionTree.roots()) { if (rootCluster) { roots_.push_back( builder.build(rootCluster, std::weak_ptr()) @@ -352,8 +353,29 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, } } - // Load initial numerical values after the structure is built. - load(graph); + // Caller is responsible for loading numerical values via load(). +} + +/* ************************************************************************* */ +MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( + const GaussianFactorGraph& graph, const Ordering& ordering) { + auto dims = computeDims(graph); + auto fixedKeys = collectFixedKeys(graph); + + Ordering reducedOrdering; + reducedOrdering.reserve(ordering.size()); + for (Key key : ordering) { + if (!fixedKeys.count(key)) { + reducedOrdering.push_back(key); + } + } + + SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); + SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); + SymbolicJunctionTree junctionTree(eliminationTree); + + return MultifrontalSolver::PrecomputedData{ + std::move(dims), std::move(fixedKeys), std::move(junctionTree)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 3880daea7a..cc67fcba53 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -22,6 +22,7 @@ #include #include #include +#include #include #include @@ -43,12 +44,18 @@ class MultifrontalClique; */ class GTSAM_EXPORT MultifrontalSolver { public: + struct PrecomputedData { + std::map dims; + std::unordered_set fixedKeys; + SymbolicJunctionTree junctionTree; + }; + /// Shared pointer to a MultifrontalClique. using CliquePtr = std::shared_ptr; /// Node type for tree traversal utilities. using Node = MultifrontalClique; - private: + protected: std::vector roots_; ///< Roots of the elimination tree. std::vector cliques_; ///< All cliques in the solver. std::map dims_; ///< Map from variable key to dimension. @@ -59,6 +66,7 @@ class GTSAM_EXPORT MultifrontalSolver { /** * Construct the solver from a factor graph and an ordering. * This builds the symbolic junction tree and pre-allocates all matrices. + * Call load() before eliminating to populate numerical values. * @param graph The factor graph to solve. * @param ordering The variable ordering to use for elimination. * @param mergeDimCap Merge a child if its frontal dimension plus the @@ -70,6 +78,24 @@ class GTSAM_EXPORT MultifrontalSolver { size_t mergeDimCap = 0, std::ostream* reportStream = nullptr); + /** + * Construct the solver from precomputed symbolic data. + * Call load() before eliminating to populate numerical values. + * @param data Precomputed symbolic structure and sizing data. + * @param ordering The variable ordering to use for seeding solution storage. + * @param mergeDimCap Merge a child if its frontal dimension plus the + * parent's total dimension is below this threshold (0 disables merging). + * @param reportStream Optional stream to report clique structure stats + * (frontals, separators, total dims, and children). + */ + MultifrontalSolver(PrecomputedData data, const Ordering& ordering, + size_t mergeDimCap = 0, + std::ostream* reportStream = nullptr); + + /// Precompute symbolic structure and sizing data from a factor graph. + static PrecomputedData Precompute(const GaussianFactorGraph& graph, + const Ordering& ordering); + /** * Load new numerical values from the factor graph. * This overwrites the values in the pre-allocated matrices. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index afe5d8e7a8..e2f2ca2082 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -50,9 +50,10 @@ const Ordering chainOrdering{x2, x1, x3, x4}; } // namespace /* ************************************************************************* */ -// Build the solver and validate initial structure and load. +// Build the solver and validate initial structure and explicit load. TEST(MultifrontalSolver, Constructor) { MultifrontalSolver solver(chain, chainOrdering); + solver.load(chain); // Verify roots EXPECT(solver.roots().size() == 1); @@ -78,6 +79,32 @@ TEST(MultifrontalSolver, Constructor) { EXPECT(assert_equal((Matrix(2, 1) << 2., 1.).finished(), Ab)); } +/* ************************************************************************* */ +// Build the solver from precomputed data and validate structure and load. +TEST(MultifrontalSolver, ConstructorPrecomputed) { + auto data = MultifrontalSolver::Precompute(chain, chainOrdering); + MultifrontalSolver solver(std::move(data), chainOrdering); + solver.load(chain); + + // Verify roots + EXPECT(solver.roots().size() == 1); + auto root = solver.roots()[0]; + EXPECT(root != nullptr); + + // Root should have 1 child {x2, x1} + EXPECT_LONGS_EQUAL(1, root->children.size()); + auto childClique = root->children[0]; + + // Verify matrices in leaf (childClique) + EXPECT_LONGS_EQUAL(4, childClique->sbm().nBlocks()); + EXPECT_LONGS_EQUAL(2, childClique->Ab().rows()); + EXPECT_LONGS_EQUAL(4, childClique->Ab().nBlocks()); + + // Verify load for childClique + Matrix A0 = childClique->Ab()(0); + EXPECT(assert_equal((Matrix(2, 1) << 2., 1.).finished(), A0)); +} + /* ************************************************************************* */ // Reload numerical values and ensure Ab updates match whitening. TEST(MultifrontalSolver, Load) { @@ -110,6 +137,7 @@ TEST(MultifrontalSolver, Load) { // Compare solver output against multifrontal elimination baseline. TEST(MultifrontalSolver, Eliminate) { MultifrontalSolver solver(chain, chainOrdering); + solver.load(chain); solver.eliminateInPlace(); // Solve @@ -126,6 +154,7 @@ TEST(MultifrontalSolver, Eliminate) { // Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { MultifrontalSolver solver(chain, chainOrdering); + solver.load(chain); solver.eliminateInPlace(); GaussianBayesTree actualBT = solver.computeBayesTree(); @@ -161,6 +190,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { x2, I_1x1, (Vector(1) << 0.0).finished(), hardConstraint); MultifrontalSolver solver(constrainedChain, chainOrdering); + solver.load(constrainedChain); solver.eliminateInPlace(); GaussianBayesTree actualBT = solver.computeBayesTree(); @@ -189,6 +219,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { const Ordering ordering{x1}; MultifrontalSolver solver(graph, ordering); + solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -225,6 +256,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { const Ordering ordering{x1}; MultifrontalSolver solver(graph, ordering); + solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -261,6 +293,7 @@ TEST(MultifrontalSolver, WeightedScalarMeasurements) { const Ordering ordering{x1}; MultifrontalSolver solver(graph, ordering); + solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -276,6 +309,7 @@ TEST(MultifrontalSolver, HessianFactors) { const Ordering ordering{x1}; MultifrontalSolver solver(graph, ordering); + solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -332,6 +366,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { EXPECT_LONGS_EQUAL(3, minBlocks); // Eliminate and solve + solver.load(smoother); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 598c87873b..ba1313ce2f 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -73,13 +73,7 @@ int main() { const Values initial = buildGeneralSfmInitial(db); const GaussianFactorGraph linear = *graph.linearize(initial); - const std::vector> orderings = { - {"Burn", createSchurOrdering(db, false)}, - {"Metis", Ordering::Metis(linear)}, - {"Schur", createSchurOrdering(db, false)}, - {"Colamd", Ordering::Colamd(linear)}, - }; - + auto orderings = createOrderings(db, linear); for (const auto& [label, ordering] : orderings) { cout << "\nBAL Benchmark (" << label << ", iterations=" << bal_iterations << "):" << std::endl; diff --git a/timing/timeSFMBAL.h b/timing/timeSFMBAL.h index f83cca9f80..74cc13e6e7 100644 --- a/timing/timeSFMBAL.h +++ b/timing/timeSFMBAL.h @@ -123,6 +123,16 @@ inline Ordering createSchurOrdering(const SfmData& db, return ordering; } +inline std::vector> createOrderings( + const SfmData& db, const GaussianFactorGraph& linear) { + return { + {"Burn", createSchurOrdering(db, false)}, + {"Metis", Ordering::Metis(linear)}, + {"Schur", createSchurOrdering(db, false)}, + {"Colamd", Ordering::Colamd(linear)}, + }; +} + // Create ordering and optimize int optimize(const SfmData& db, const NonlinearFactorGraph& graph, const Values& initial, bool separateCalibration = false) { @@ -132,8 +142,8 @@ int optimize(const SfmData& db, const NonlinearFactorGraph& graph, LevenbergMarquardtParams params; LevenbergMarquardtParams::SetCeresDefaults(¶ms); // params.setLinearSolverType("SEQUENTIAL_CHOLESKY"); - params.setVerbosityLM("SUMMARY"); - params.setRelativeErrorTol(0.01); // 1% relative error tol + params.setVerbosityLM("SUMMARY"); + params.setRelativeErrorTol(0.01); // 1% relative error tol if (gUseSchur) { // Create Schur-complement ordering From 11e4c89d0a52c0b20d9f1333c0bc1271e0de7392 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 14:25:31 -0500 Subject: [PATCH 017/266] Check load occurred --- gtsam/linear/MultifrontalSolver.cpp | 6 ++ gtsam/linear/MultifrontalSolver.h | 1 + timing/timeMultifrontalSolver.cpp | 101 +++++++++++++++++----------- 3 files changed, 70 insertions(+), 38 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 01d4022f60..01b04e9d44 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -383,10 +383,16 @@ void MultifrontalSolver::load(const GaussianFactorGraph& graph) { for (auto& clique : cliques_) { clique->fillAb(graph); } + loaded_ = true; } /* ************************************************************************* */ void MultifrontalSolver::eliminateInPlace() { + if (!loaded_) { + throw std::runtime_error( + "MultifrontalSolver::eliminateInPlace: load() must be called before " + "eliminating."); + } // Parallel elimination uses PostOrderForestParallel, which will be // multi-threaded if GTSAM was compiled with TBB. TbbOpenMPMixedScope threadLimiter; diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index cc67fcba53..cb2b7c4a8e 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -61,6 +61,7 @@ class GTSAM_EXPORT MultifrontalSolver { std::map dims_; ///< Map from variable key to dimension. mutable VectorValues solution_; ///< Cached solution vector. std::unordered_set fixedKeys_; ///< Keys fixed by constrained factors. + bool loaded_ = false; ///< Whether load() has been called. public: /** diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index ba1313ce2f..307d7f1ae7 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -52,53 +52,71 @@ static void runMultifrontalSolver(MultifrontalSolver& solver, const GaussianFactorGraph& smoother, size_t iterations) { for (size_t i = 0; i < iterations; ++i) { - if (i > 0) solver.load(smoother); + solver.load(smoother); solver.eliminateInPlace(); const VectorValues& solution = solver.updateSolution(); (void)solution; } } -int main() { - cout << "Merging dim cap " << kMergeDimCap << std::endl; +void runBAL135Benchmark() { + const size_t iterations = 1; + const std::string filename = + findExampleDataFile("dubrovnik-135-90642-pre.txt"); + cout << "\nSingle MFS test: " << filename << " (iterations=" << iterations + << ")" << std::endl; + + const SfmData db = SfmData::FromBalFile(filename); + const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); + const Values initial = buildGeneralSfmInitial(db); + const GaussianFactorGraph linear = *graph.linearize(initial); + const Ordering ordering = Ordering::Metis(linear); + + MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); + auto start = std::chrono::high_resolution_clock::now(); + runMultifrontalSolver(solver, linear, iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + cout << " MultifrontalSolver: " << t_imperative.count() << " s" << std::endl; +} - { - const size_t bal_iterations = 5; - const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); - const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); - for (const auto& filename : {bal16, bal88}) { - cout << "\nProcessing BAL file: " << filename << std::endl; - const SfmData db = SfmData::FromBalFile(filename); - const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); - const Values initial = buildGeneralSfmInitial(db); - const GaussianFactorGraph linear = *graph.linearize(initial); - - auto orderings = createOrderings(db, linear); - for (const auto& [label, ordering] : orderings) { - cout << "\nBAL Benchmark (" << label - << ", iterations=" << bal_iterations << "):" << std::endl; - - auto start = std::chrono::high_resolution_clock::now(); - MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); - runMultifrontalSolver(solver, linear, bal_iterations); - auto end = std::chrono::high_resolution_clock::now(); - std::chrono::duration t_imperative = end - start; - cout << " MultifrontalSolver: " << t_imperative.count() << " s" - << std::endl; - - start = std::chrono::high_resolution_clock::now(); - runStandardSolver(linear, ordering, bal_iterations); - end = std::chrono::high_resolution_clock::now(); - std::chrono::duration t_standard = end - start; - cout << " Standard GTSAM: " << t_standard.count() << " s" - << std::endl; - - cout << " Speedup: " - << t_standard.count() / t_imperative.count() << "x" << std::endl; - } +void runBALBenchmark() { + const size_t bal_iterations = 5; + const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); + const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); + for (const auto& filename : {bal16, bal88}) { + cout << "\nProcessing BAL file: " << filename << std::endl; + const SfmData db = SfmData::FromBalFile(filename); + const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); + const Values initial = buildGeneralSfmInitial(db); + const GaussianFactorGraph linear = *graph.linearize(initial); + + auto orderings = createOrderings(db, linear); + for (const auto& [label, ordering] : orderings) { + cout << "\nBAL Benchmark (" << label << ", iterations=" << bal_iterations + << "):" << std::endl; + + auto start = std::chrono::high_resolution_clock::now(); + MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); + runMultifrontalSolver(solver, linear, bal_iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + cout << " MultifrontalSolver: " << t_imperative.count() << " s" + << std::endl; + + start = std::chrono::high_resolution_clock::now(); + runStandardSolver(linear, ordering, bal_iterations); + end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_standard = end - start; + cout << " Standard GTSAM: " << t_standard.count() << " s" + << std::endl; + + cout << " Speedup: " + << t_standard.count() / t_imperative.count() << "x" << std::endl; } } - +} +void runChainBenchmark() { const std::vector T_values = {10, 50, 100, 500, 1000, 5000}; const size_t iterations = 1000; @@ -126,5 +144,12 @@ int main() { cout << " Speedup: " << t_standard.count() / t_imperative.count() << "x" << std::endl; } +} +int main() { + cout << "Merging dim cap " << kMergeDimCap << std::endl; + + runBAL135Benchmark(); + // runBALBenchmark(); + // runChainBenchmark(); return 0; } From b3657fab5ef24b7e69f33817f03c7aa0608831bc Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 14:55:11 -0500 Subject: [PATCH 018/266] In solver, different problem-size threshold --- gtsam/linear/MultifrontalSolver.cpp | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 01b04e9d44..714b62d262 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -309,8 +309,8 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering, size_t mergeDimCap, std::ostream* reportStream) - : MultifrontalSolver(Precompute(graph, ordering), ordering, - mergeDimCap, reportStream) {} + : MultifrontalSolver(Precompute(graph, ordering), ordering, mergeDimCap, + reportStream) {} /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, @@ -455,11 +455,16 @@ const VectorValues& MultifrontalSolver::updateSolution() const { }; auto visitorPost = [](const CliquePtr&, int) {}; + // Threshold chosen to balance task overhead vs parallelism. Small cliques + // (like points in SFM) are better handled sequentially within larger tasks + // to minimize scheduling overhead and maximize cache locality. + constexpr int kSolutionParallelThreshold = 4096; + // Cast to non-const because treeTraversal expects a non-const reference, // even though we are only calling const methods on the nodes. treeTraversal::DepthFirstForestParallel( const_cast(*this), rootData, visitorPre, visitorPost, - 10); + kSolutionParallelThreshold); for (Key key : fixedKeys_) { solution_.at(key).setZero(); From 37ea3b24c431d780b7594d919bcbcf74eccf8da6 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 15:38:17 -0500 Subject: [PATCH 019/266] eliminated_ flag --- gtsam/linear/MultifrontalSolver.cpp | 5 +++++ gtsam/linear/MultifrontalSolver.h | 1 + 2 files changed, 6 insertions(+) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 714b62d262..d8ab2868b9 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -384,6 +384,7 @@ void MultifrontalSolver::load(const GaussianFactorGraph& graph) { clique->fillAb(graph); } loaded_ = true; + eliminated_ = false; } /* ************************************************************************* */ @@ -393,6 +394,7 @@ void MultifrontalSolver::eliminateInPlace() { "MultifrontalSolver::eliminateInPlace: load() must be called before " "eliminating."); } + eliminated_ = false; // Parallel elimination uses PostOrderForestParallel, which will be // multi-threaded if GTSAM was compiled with TBB. TbbOpenMPMixedScope threadLimiter; @@ -400,10 +402,12 @@ void MultifrontalSolver::eliminateInPlace() { if (node) node->eliminateInPlace(); }; treeTraversal::PostOrderForestParallel(*this, visitorPost, 10); + eliminated_ = true; } /* ************************************************************************* */ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { + assert(loaded_ && eliminated_); GaussianBayesTree bayesTree; using Clique = GaussianBayesTreeClique; using BayesCliquePtr = GaussianBayesTree::sharedClique; @@ -445,6 +449,7 @@ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { /* ************************************************************************* */ const VectorValues& MultifrontalSolver::updateSolution() const { + assert(loaded_ && eliminated_); // Parallel solve uses treeTraversal::DepthFirstForestParallel (Pre-order / // Top-Down). TbbOpenMPMixedScope threadLimiter; diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index cb2b7c4a8e..502f5d8c41 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -62,6 +62,7 @@ class GTSAM_EXPORT MultifrontalSolver { mutable VectorValues solution_; ///< Cached solution vector. std::unordered_set fixedKeys_; ///< Keys fixed by constrained factors. bool loaded_ = false; ///< Whether load() has been called. + bool eliminated_ = false; ///< Whether eliminateInPlace() ran. public: /** From 3b9b48f166c21a613d5b7cea5c58ce469c28c926 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 15:38:54 -0500 Subject: [PATCH 020/266] Store RSd_ to avoid trashing cache. --- gtsam/linear/MultifrontalClique.cpp | 30 +++++++++++++++-------------- gtsam/linear/MultifrontalClique.h | 4 ++-- 2 files changed, 18 insertions(+), 16 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 5c95b91e90..8d16ac716d 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -16,6 +16,7 @@ * @date December 2025 */ +#include #include #include #include @@ -81,9 +82,8 @@ bool validateFactorKeys(const GaussianFactorGraph& graph, MultifrontalClique::MultifrontalClique( std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, - const KeySet& separatorKeys, const KeyDimMap& dims, - size_t vbmRows, VectorValues* solution, - const std::unordered_set* fixedKeys) { + const KeySet& separatorKeys, const KeyDimMap& dims, size_t vbmRows, + VectorValues* solution, const std::unordered_set* fixedKeys) { factorIndices_ = std::move(factorIndices); this->parent = parent; fixedKeys_ = fixedKeys; @@ -252,7 +252,11 @@ void MultifrontalClique::prepareForElimination() { } } -void MultifrontalClique::factorize() { sbm_.choleskyPartial(numFrontals()); } +void MultifrontalClique::factorize() { + sbm_.choleskyPartial(numFrontals()); + RSd_ = sbm_.split(numFrontals()); + sbm_.blockStart() = 0; +} void MultifrontalClique::addIdentityDamping(double lambda) { const size_t nf = numFrontals(); @@ -287,25 +291,23 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { } std::shared_ptr MultifrontalClique::conditional() const { - const KeyVector& keys = orderedKeys_; - assert(sbm_.blockStart() == 0); - VerticalBlockMatrix Ab = sbm_.split(numFrontals()); - sbm_.blockStart() = 0; // Split sets it to numFrontals(), reset to 0. - return std::make_shared(keys, numFrontals(), - std::move(Ab)); + assert(RSd_.nBlocks() > 0); + return std::make_shared(orderedKeys_, numFrontals(), + RSd_); } // Solve with block back-substitution on the Cholesky-stored SBM. void MultifrontalClique::updateSolution() const { + assert(RSd_.nBlocks() > 0); const size_t nf = numFrontals(); - const size_t n = sbm_.nBlocks() - 1; // # frontals + # separators + const size_t n = RSd_.nBlocks() - 1; // # frontals + # separators // The in-place factorization yields an upper-triangular system [R S d]: // R * x_f + S * x_s = d, // with x_f the frontals and x_s the separators. - const auto R = sbm_.triangularView(0, nf); - const auto S = sbm_.aboveDiagonalRange(0, nf, nf, n); - const auto d = sbm_.aboveDiagonalRange(0, nf, n, n + 1); + const auto R = RSd_.range(0, nf).triangularView(); + const auto S = RSd_.range(nf, n); + const auto d = RSd_.range(n, n + 1); // We first solve rhs = d - S * x_s rhsScratch_.noalias() = d; diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 93e1251b61..ec646fc26b 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -97,8 +97,7 @@ class GTSAM_EXPORT MultifrontalClique { const std::weak_ptr& parent, const KeyVector& frontals, const KeySet& separatorKeys, - const KeyDimMap& dims, - size_t vbmRows, + const KeyDimMap& dims, size_t vbmRows, VectorValues* solution, const std::unordered_set* fixedKeys); @@ -223,6 +222,7 @@ class GTSAM_EXPORT MultifrontalClique { } VerticalBlockMatrix Ab_; mutable SymmetricBlockMatrix sbm_; + VerticalBlockMatrix RSd_; ///< Cached [R S d] from the last elimination. mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. mutable Vector separatorScratch_; ///< Cached separator stack for back-substitution. From a25afc0b014b578e6e3e96f0d142c07786ded1e9 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 17:28:23 -0500 Subject: [PATCH 021/266] QR mode if n>2*m --- gtsam/linear/MultifrontalClique.cpp | 66 +++++++++++++++++-- gtsam/linear/MultifrontalClique.h | 37 +++++++++-- gtsam/linear/tests/testMultifrontalSolver.cpp | 2 +- timing/timeMultifrontalSolver.cpp | 25 +++---- 4 files changed, 105 insertions(+), 25 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 8d16ac716d..1174df166a 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -16,6 +16,7 @@ * @date December 2025 */ +#include #include #include #include @@ -111,9 +112,8 @@ MultifrontalClique::MultifrontalClique( cacheSolutionPointers(solution, frontals, separatorKeys); // Pre-allocate matrices once per structure. - std::vector blockDims = - this->blockDims(dims, frontals, separatorKeys); - initializeMatrices(blockDims, vbmRows); + blockDims_ = this->blockDims(dims, frontals, separatorKeys); + initializeMatrices(blockDims_, vbmRows); } void MultifrontalClique::finalize(std::vector children) { @@ -135,6 +135,8 @@ void MultifrontalClique::finalize(std::vector children) { indices.push_back(static_cast(orderedKeys_.size())); child.clique->setParentIndices(indices); } + + // Allocation is deferred until load. } DenseIndex MultifrontalClique::blockIndex(Key key) const { @@ -170,12 +172,23 @@ std::vector MultifrontalClique::blockDims( void MultifrontalClique::initializeMatrices( const std::vector& blockDims, size_t verticalBlockMatrixRows) { - sbm_ = SymmetricBlockMatrix(blockDims, true); + separatorBlockDims_.assign(blockDims.begin() + numFrontals(), + blockDims.end()); Ab_ = VerticalBlockMatrix(blockDims, verticalBlockMatrixRows, true); // Ab's structure is fixed; clear it once and reuse across loads. Ab_.matrix().setZero(); } +void MultifrontalClique::allocateSbm() { + if (sbm_.nBlocks() > 0) return; + sbm_ = SymmetricBlockMatrix(blockDims_, true); +} + +void MultifrontalClique::allocateSeparatorSbm() { + if (separatorSbm_.nBlocks() > 0) return; + separatorSbm_ = SymmetricBlockMatrix(separatorBlockDims_, true); +} + size_t MultifrontalClique::addJacobianFactor( const JacobianFactor& jacobianFactor, size_t rowOffset) { // We only overwrite the fixed sparsity pattern, so Ab must be zeroed once in @@ -235,16 +248,33 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { hessianFactors_.push_back(hessianFactor); } } + + // Lock in QR only for leaf cliques with no Hessian factors. + const bool isLeaf = children.empty(); + const bool useQr = + isLeaf && hessianFactors_.empty() && (frontalDim > 0) && + (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && + (Ab_.matrix().rows() >= static_cast(frontalDim)); + solveMode_ = useQr ? SolveMode::QrLeaf : SolveMode::Cholesky; + if (useQr) { + allocateSeparatorSbm(); // QR only needs separator updates for the parent. + } else { + allocateSbm(); + } } void MultifrontalClique::prepareForElimination() { + // QR leaf cliques skip SBM assembly entirely. + if (useQr()) return; + assert(sbm_.nBlocks() > 0); sbm_.setZero(); for (const auto& hf : hessianFactors_) { addHessianFactor(*hf); } - // Update SBM with the local factors, Ab^T * Ab - sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); + if (Ab_.matrix().rows() > 0) { + sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); + } for (const auto& child : children) { if (!child) continue; @@ -253,6 +283,16 @@ void MultifrontalClique::prepareForElimination() { } void MultifrontalClique::factorize() { + if (useQr()) { + const DenseIndex nfRows = static_cast(frontalDim); + assert(Ab_.matrix().rows() >= nfRows); + // Copy Ab_ to preserve its invariant; QR writes in place. + Matrix qr = Ab_.matrix(); + inplace_QR(qr); + RSd_ = VerticalBlockMatrix(blockDims_, nfRows, true); + RSd_.matrix() = qr.topRows(nfRows); + return; + } sbm_.choleskyPartial(numFrontals()); RSd_ = sbm_.split(numFrontals()); sbm_.blockStart() = 0; @@ -282,7 +322,19 @@ void MultifrontalClique::eliminateInPlace() { } void MultifrontalClique::updateParent(MultifrontalClique& parent) const { + if (useQr()) { + assert(separatorSbm_.nBlocks() > 0); + const DenseIndex nfBlocks = static_cast(numFrontals()); + const DenseIndex totalBlocks = RSd_.nBlocks(); + const Matrix Sd = RSd_.range(nfBlocks, totalBlocks); + // Accumulate separator normal equations (S^T S) for the parent update. + separatorSbm_.setZero(); + separatorSbm_.selfadjointView().rankUpdate(Sd.transpose()); + parent.sbm_.updateFromMappedBlocks(separatorSbm_, parentIndices_); + return; + } // Expose only the separator+RHS view when contributing to the parent. + assert(sbmAllocated_); assert(sbm_.blockStart() == 0); sbm_.blockStart() = numFrontals(); assert(sbm_.nBlocks() == parentIndices_.size()); @@ -292,6 +344,7 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { std::shared_ptr MultifrontalClique::conditional() const { assert(RSd_.nBlocks() > 0); + // RSd_ is cached at elimination time. return std::make_shared(orderedKeys_, numFrontals(), RSd_); } @@ -299,6 +352,7 @@ std::shared_ptr MultifrontalClique::conditional() const { // Solve with block back-substitution on the Cholesky-stored SBM. void MultifrontalClique::updateSolution() const { assert(RSd_.nBlocks() > 0); + // Use cached [R S d] for fast back-substitution. const size_t nf = numFrontals(); const size_t n = RSd_.nBlocks() - 1; // # frontals + # separators diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index ec646fc26b..5c4453043b 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -188,6 +188,8 @@ class GTSAM_EXPORT MultifrontalClique { const MultifrontalClique& clique); private: + enum class SolveMode { Cholesky, QrLeaf }; + /// Cache pointers to frontal and separator update vectors. void cacheSolutionPointers(VectorValues* delta, const KeyVector& frontals, const KeySet& separatorKeys); @@ -208,6 +210,14 @@ class GTSAM_EXPORT MultifrontalClique { void initializeMatrices(const std::vector& blockDims, size_t totalNumRows); + /// Allocate the symmetric block matrix if needed. + void allocateSbm(); + + /// Allocate the separator-only SBM used for QR leaf updates. + void allocateSeparatorSbm(); + + bool useQr() const { return solveMode_ == SolveMode::QrLeaf; } + /** * Add a Jacobian factor's contributions into the Ab matrix. * @return Number of rows added. @@ -220,22 +230,35 @@ class GTSAM_EXPORT MultifrontalClique { void setParentIndices(const std::vector& indices) { parentIndices_ = indices; } - VerticalBlockMatrix Ab_; - mutable SymmetricBlockMatrix sbm_; - VerticalBlockMatrix RSd_; ///< Cached [R S d] from the last elimination. - mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. - mutable Vector - separatorScratch_; ///< Cached separator stack for back-substitution. - + // Build-time metadata. std::vector factorIndices_; KeyVector orderedKeys_; ///< Keys ordered by block index (frontals+seps). const std::unordered_set* fixedKeys_ = nullptr; + std::vector blockDims_; + std::vector separatorBlockDims_; std::vector parentIndices_; ///< Parent block indices for separators + RHS. std::vector frontalPtrs_; ///< Pointers into solution frontals. std::vector separatorPtrs_; ///< Pointers into solution separator. + + // Load-time state. + VerticalBlockMatrix Ab_; std::vector hessianFactors_; ///< Hessian factors. + SolveMode solveMode_ = SolveMode::Cholesky; + + // Elimination-time state. + mutable SymmetricBlockMatrix sbm_; + mutable SymmetricBlockMatrix + separatorSbm_; ///< Cached separator update for QR. + VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. + + // Solve-time scratch space. + mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. + mutable Vector + separatorScratch_; ///< Cached separator stack for back-substitution. + + static constexpr double kQrAspectRatio = 2.0; }; std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique); diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index e2f2ca2082..cbe4145f45 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -338,6 +338,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)}; MultifrontalSolver solver(smoother, ordering); + solver.load(smoother); // Verify roots EXPECT(solver.roots().size() == 1); @@ -366,7 +367,6 @@ TEST(MultifrontalSolver, BalancedSmoother) { EXPECT_LONGS_EQUAL(3, minBlocks); // Eliminate and solve - solver.load(smoother); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 307d7f1ae7..9c1767e6f5 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -59,14 +59,16 @@ static void runMultifrontalSolver(MultifrontalSolver& solver, } } +namespace { +const std::string bal135 = findExampleDataFile("dubrovnik-135-90642-pre.txt"); +} + void runBAL135Benchmark() { const size_t iterations = 1; - const std::string filename = - findExampleDataFile("dubrovnik-135-90642-pre.txt"); - cout << "\nSingle MFS test: " << filename << " (iterations=" << iterations + cout << "\nSingle MFS test: " << bal135 << " (iterations=" << iterations << ")" << std::endl; - const SfmData db = SfmData::FromBalFile(filename); + const SfmData db = SfmData::FromBalFile(bal135); const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); const Values initial = buildGeneralSfmInitial(db); const GaussianFactorGraph linear = *graph.linearize(initial); @@ -78,13 +80,14 @@ void runBAL135Benchmark() { auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration t_imperative = end - start; cout << " MultifrontalSolver: " << t_imperative.count() << " s" << std::endl; + tictoc_print(); } void runBALBenchmark() { - const size_t bal_iterations = 5; + const size_t bal_iterations = 2; const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); - for (const auto& filename : {bal16, bal88}) { + for (const auto& filename : {bal16, bal88, bal135}) { cout << "\nProcessing BAL file: " << filename << std::endl; const SfmData db = SfmData::FromBalFile(filename); const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); @@ -96,8 +99,8 @@ void runBALBenchmark() { cout << "\nBAL Benchmark (" << label << ", iterations=" << bal_iterations << "):" << std::endl; - auto start = std::chrono::high_resolution_clock::now(); MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); + auto start = std::chrono::high_resolution_clock::now(); runMultifrontalSolver(solver, linear, bal_iterations); auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration t_imperative = end - start; @@ -118,7 +121,7 @@ void runBALBenchmark() { } void runChainBenchmark() { const std::vector T_values = {10, 50, 100, 500, 1000, 5000}; - const size_t iterations = 1000; + const size_t iterations = 500; for (size_t T : T_values) { cout << "\nBenchmark (T=" << T << ", iterations=" << iterations @@ -148,8 +151,8 @@ void runChainBenchmark() { int main() { cout << "Merging dim cap " << kMergeDimCap << std::endl; - runBAL135Benchmark(); - // runBALBenchmark(); - // runChainBenchmark(); + // runBAL135Benchmark(); + runBALBenchmark(); + runChainBenchmark(); return 0; } From d57dbccf816ada1cce93be2168f98732d76f565d Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 18:32:55 -0500 Subject: [PATCH 022/266] Fused path --- gtsam/linear/MultifrontalSolver.cpp | 14 ++++++++++++++ gtsam/linear/MultifrontalSolver.h | 6 ++++++ gtsam/linear/tests/testMultifrontalSolver.cpp | 14 ++++++++++++++ timing/timeMultifrontalSolver.cpp | 5 ++--- 4 files changed, 36 insertions(+), 3 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index d8ab2868b9..ae84dce926 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -405,6 +405,20 @@ void MultifrontalSolver::eliminateInPlace() { eliminated_ = true; } +/* ************************************************************************* */ +void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { + // Combine load + eliminate in one post-order traversal to improve locality. + TbbOpenMPMixedScope threadLimiter; + auto visitorPost = [&graph](const CliquePtr& node) { + if (!node) return; + node->fillAb(graph); + node->eliminateInPlace(); + }; + treeTraversal::PostOrderForestParallel(*this, visitorPost, 10); + loaded_ = true; + eliminated_ = true; +} + /* ************************************************************************* */ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { assert(loaded_ && eliminated_); diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 502f5d8c41..63a02d1067 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -112,6 +112,12 @@ class GTSAM_EXPORT MultifrontalSolver { */ void eliminateInPlace(); + /** + * Load and eliminate the graph in a single traversal. + * This calls fillAb() and eliminateInPlace() per clique in post-order. + */ + void eliminateInPlace(const GaussianFactorGraph& graph); + /** * Compute a Bayes tree from the in-place Cholesky factorization. * Requires eliminateInPlace() to have been called beforehand. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index cbe4145f45..c8d08efb9a 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -150,6 +150,20 @@ TEST(MultifrontalSolver, Eliminate) { EXPECT(assert_equal(expected, actual, 1e-9)); } +/* ************************************************************************* */ +// Load + eliminate in one traversal matches standard elimination. +TEST(MultifrontalSolver, EliminateWithLoad) { + MultifrontalSolver solver(chain, chainOrdering); + solver.eliminateInPlace(chain); + + const VectorValues& actual = solver.updateSolution(); + + GaussianBayesTree expectedBT = *chain.eliminateMultifrontal(chainOrdering); + VectorValues expected = expectedBT.optimize(); + + EXPECT(assert_equal(expected, actual, 1e-9)); +} + /* ************************************************************************* */ // Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 9c1767e6f5..5d7f8d90a7 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -52,8 +52,7 @@ static void runMultifrontalSolver(MultifrontalSolver& solver, const GaussianFactorGraph& smoother, size_t iterations) { for (size_t i = 0; i < iterations; ++i) { - solver.load(smoother); - solver.eliminateInPlace(); + solver.eliminateInPlace(smoother); const VectorValues& solution = solver.updateSolution(); (void)solution; } @@ -151,7 +150,7 @@ void runChainBenchmark() { int main() { cout << "Merging dim cap " << kMergeDimCap << std::endl; - // runBAL135Benchmark(); + runBAL135Benchmark(); runBALBenchmark(); runChainBenchmark(); return 0; From ab62e92c22daf521066d51975f548b17a042a091 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 4 Jan 2026 22:53:53 -0500 Subject: [PATCH 023/266] Review comments --- gtsam/linear/MultifrontalClique.cpp | 22 ++++++++----------- gtsam/linear/MultifrontalClique.h | 17 ++++++++++---- gtsam/linear/MultifrontalSolver.h | 6 ++--- gtsam/linear/tests/testMultifrontalSolver.cpp | 1 + timing/timeMultifrontalSolver.cpp | 4 ++-- 5 files changed, 28 insertions(+), 22 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 1174df166a..81791ad209 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -17,7 +17,6 @@ */ #include -#include #include #include #include @@ -233,7 +232,6 @@ void MultifrontalClique::addHessianFactor(const HessianFactor& hessianFactor) { void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); - assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); hessianFactors_.clear(); size_t rowOffset = 0; @@ -251,12 +249,12 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { // Lock in QR only for leaf cliques with no Hessian factors. const bool isLeaf = children.empty(); - const bool useQr = + const bool useQR = isLeaf && hessianFactors_.empty() && (frontalDim > 0) && (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && (Ab_.matrix().rows() >= static_cast(frontalDim)); - solveMode_ = useQr ? SolveMode::QrLeaf : SolveMode::Cholesky; - if (useQr) { + solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; + if (useQR) { allocateSeparatorSbm(); // QR only needs separator updates for the parent. } else { allocateSbm(); @@ -265,7 +263,7 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { void MultifrontalClique::prepareForElimination() { // QR leaf cliques skip SBM assembly entirely. - if (useQr()) return; + if (useQR()) return; assert(sbm_.nBlocks() > 0); sbm_.setZero(); for (const auto& hf : hessianFactors_) { @@ -283,7 +281,7 @@ void MultifrontalClique::prepareForElimination() { } void MultifrontalClique::factorize() { - if (useQr()) { + if (useQR()) { const DenseIndex nfRows = static_cast(frontalDim); assert(Ab_.matrix().rows() >= nfRows); // Copy Ab_ to preserve its invariant; QR writes in place. @@ -309,9 +307,8 @@ void MultifrontalClique::addDiagonalDamping(double lambda, double minDiagonal, double maxDiagonal) { const size_t nf = numFrontals(); for (size_t j = 0; j < nf; ++j) { - Vector diag = sbm_.diagonal(j); - diag = diag.cwiseMax(minDiagonal).cwiseMin(maxDiagonal); - const Vector scaled = lambda * diag; + const Vector scaled = + lambda * sbm_.diagonal(j).cwiseMax(minDiagonal).cwiseMin(maxDiagonal); sbm_.addToDiagonalBlock(j, scaled); } } @@ -322,7 +319,7 @@ void MultifrontalClique::eliminateInPlace() { } void MultifrontalClique::updateParent(MultifrontalClique& parent) const { - if (useQr()) { + if (useQR()) { assert(separatorSbm_.nBlocks() > 0); const DenseIndex nfBlocks = static_cast(numFrontals()); const DenseIndex totalBlocks = RSd_.nBlocks(); @@ -334,8 +331,7 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { return; } // Expose only the separator+RHS view when contributing to the parent. - assert(sbmAllocated_); - assert(sbm_.blockStart() == 0); + assert(sbm_.nBlocks() > 0 && sbm_.blockStart() == 0); sbm_.blockStart() = numFrontals(); assert(sbm_.nBlocks() == parentIndices_.size()); parent.sbm_.updateFromMappedBlocks(sbm_, parentIndices_); diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 5c4453043b..3fde296abb 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -117,10 +117,18 @@ class GTSAM_EXPORT MultifrontalClique { /// Perform Cholesky factorization on the frontal block. void factorize(); - /// Add identity damping to the frontal block. + /** + * Add identity damping to the frontal block. + * @param lambda Damping factor + */ void addIdentityDamping(double lambda); - /// Add diagonal damping to the frontal block, with clamping. + /** + * Add diagonal damping to the frontal block. + * @param lambda Damping factor + * @param minDiagonal Minimum diagonal value + * @param maxDiagonal Maximum diagonal value + */ void addDiagonalDamping(double lambda, double minDiagonal, double maxDiagonal); @@ -146,6 +154,9 @@ class GTSAM_EXPORT MultifrontalClique { /// Get the symmetric block matrix (const). const SymmetricBlockMatrix& sbm() const { return sbm_; } + /// Check if this clique is using QR elimination. + bool useQR() const { return solveMode_ == SolveMode::QrLeaf; } + /** * Print this clique. * @param s Optional string prefix. @@ -216,8 +227,6 @@ class GTSAM_EXPORT MultifrontalClique { /// Allocate the separator-only SBM used for QR leaf updates. void allocateSeparatorSbm(); - bool useQr() const { return solveMode_ == SolveMode::QrLeaf; } - /** * Add a Jacobian factor's contributions into the Ab matrix. * @return Number of rows added. diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 63a02d1067..57e2291304 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -45,9 +45,9 @@ class MultifrontalClique; class GTSAM_EXPORT MultifrontalSolver { public: struct PrecomputedData { - std::map dims; - std::unordered_set fixedKeys; - SymbolicJunctionTree junctionTree; + std::map dims; ///< Map from variable key to dimension. + std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. + SymbolicJunctionTree junctionTree; ///< Precomputed symbolic junction tree. }; /// Shared pointer to a MultifrontalClique. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index c8d08efb9a..e8708fff7e 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -96,6 +96,7 @@ TEST(MultifrontalSolver, ConstructorPrecomputed) { auto childClique = root->children[0]; // Verify matrices in leaf (childClique) + CHECK(childClique->useQR() == false); EXPECT_LONGS_EQUAL(4, childClique->sbm().nBlocks()); EXPECT_LONGS_EQUAL(2, childClique->Ab().rows()); EXPECT_LONGS_EQUAL(4, childClique->Ab().nBlocks()); diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 5d7f8d90a7..5428a7a709 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -49,10 +49,10 @@ static void runStandardSolver(const GaussianFactorGraph& smoother, /// Run new MultifrontalSolver elimination and optimization. static void runMultifrontalSolver(MultifrontalSolver& solver, - const GaussianFactorGraph& smoother, + const GaussianFactorGraph& graph, size_t iterations) { for (size_t i = 0; i < iterations; ++i) { - solver.eliminateInPlace(smoother); + solver.eliminateInPlace(graph); const VectorValues& solution = solver.updateSolution(); (void)solution; } From fa50032447f13aa295a8326bada13a14c4183a25 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 00:17:05 -0500 Subject: [PATCH 024/266] UpdateFromOuterProductBlocks --- gtsam/base/SymmetricBlockMatrix.cpp | 24 +++++++++++++++++++ gtsam/base/SymmetricBlockMatrix.h | 6 +++++ gtsam/base/tests/testSymmetricBlockMatrix.cpp | 24 +++++++++++++++++++ 3 files changed, 54 insertions(+) diff --git a/gtsam/base/SymmetricBlockMatrix.cpp b/gtsam/base/SymmetricBlockMatrix.cpp index 6e67418757..9ee96fd93e 100644 --- a/gtsam/base/SymmetricBlockMatrix.cpp +++ b/gtsam/base/SymmetricBlockMatrix.cpp @@ -126,6 +126,30 @@ void SymmetricBlockMatrix::updateFromMappedBlocks( } } +/* ************************************************************************* */ +void SymmetricBlockMatrix::updateFromOuterProductBlocks( + const VerticalBlockMatrix& other, + const std::vector& blockIndices) { + assert(static_cast(blockIndices.size()) == other.nBlocks()); + const DenseIndex otherBlocks = other.nBlocks(); + for (DenseIndex i = 0; i < otherBlocks; ++i) { + const DenseIndex I = blockIndices[i]; + if (I < 0) continue; + assert(I < nBlocks()); + const auto Si = other(i); + Matrix diag = Si.transpose() * Si; + updateDiagonalBlock(I, diag); + for (DenseIndex j = i + 1; j < otherBlocks; ++j) { + const DenseIndex J = blockIndices[j]; + if (J < 0) continue; + assert(J < nBlocks()); + const auto Sj = other(j); + Matrix off = Si.transpose() * Sj; + updateOffDiagonalBlock(I, J, off); + } + } +} + /* ************************************************************************* */ } //\ namespace gtsam diff --git a/gtsam/base/SymmetricBlockMatrix.h b/gtsam/base/SymmetricBlockMatrix.h index f46dc72fae..dbb740a633 100644 --- a/gtsam/base/SymmetricBlockMatrix.h +++ b/gtsam/base/SymmetricBlockMatrix.h @@ -264,6 +264,12 @@ namespace gtsam { void updateFromMappedBlocks(const SymmetricBlockMatrix& other, const std::vector& blockIndices); + /// Update this matrix with blockwise outer products from a vertical block matrix. + /// Adds S_i^T S_j into block (I,J), using a block mapping; entries with index -1 are skipped. + /// The range to use is controlled by other.firstBlock(). + void updateFromOuterProductBlocks(const VerticalBlockMatrix& other, + const std::vector& blockIndices); + /// @} /// @name Accessing the full matrix. /// @{ diff --git a/gtsam/base/tests/testSymmetricBlockMatrix.cpp b/gtsam/base/tests/testSymmetricBlockMatrix.cpp index 4989ea1d84..b2049ada26 100644 --- a/gtsam/base/tests/testSymmetricBlockMatrix.cpp +++ b/gtsam/base/tests/testSymmetricBlockMatrix.cpp @@ -17,6 +17,7 @@ #include #include +#include using namespace std; using namespace gtsam; @@ -187,6 +188,29 @@ TEST(SymmetricBlockMatrix, UpdateFromMappedBlocks) Matrix(doubled.selfadjointView()))); } +/* ************************************************************************* */ +// Update via blockwise outer products from a VerticalBlockMatrix view. +TEST(SymmetricBlockMatrix, UpdateFromOuterProductBlocks) +{ + const std::vector vbmDims{2, 1}; + VerticalBlockMatrix vbm(vbmDims, 4, true); + vbm.matrix() = (Matrix(4, 4) << + 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12, + 13, 14, 15, 16).finished(); + + const std::vector destDims{1}; + SymmetricBlockMatrix actual(destDims, true); + actual.setZero(); + const std::vector mapping{0, 1}; + const Matrix S = vbm.range(1, 3); + const Matrix expected = S.transpose() * S; + vbm.firstBlock() = 1; + actual.updateFromOuterProductBlocks(vbm, mapping); + EXPECT(assert_equal(expected, Matrix(actual.selfadjointView()))); +} + /* ************************************************************************* */ // In-place inversion path. TEST(SymmetricBlockMatrix, inverseInPlace) { From 178ed2d0d8a42b9c6f3c66292db4e6196a75585b Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 00:17:30 -0500 Subject: [PATCH 025/266] Use UpdateFromOuterProductBlocks --- gtsam/linear/MultifrontalClique.cpp | 37 ++++++++++------------------- gtsam/linear/MultifrontalClique.h | 8 +------ 2 files changed, 14 insertions(+), 31 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 81791ad209..2dc7d624f4 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -171,8 +171,6 @@ std::vector MultifrontalClique::blockDims( void MultifrontalClique::initializeMatrices( const std::vector& blockDims, size_t verticalBlockMatrixRows) { - separatorBlockDims_.assign(blockDims.begin() + numFrontals(), - blockDims.end()); Ab_ = VerticalBlockMatrix(blockDims, verticalBlockMatrixRows, true); // Ab's structure is fixed; clear it once and reuse across loads. Ab_.matrix().setZero(); @@ -183,11 +181,6 @@ void MultifrontalClique::allocateSbm() { sbm_ = SymmetricBlockMatrix(blockDims_, true); } -void MultifrontalClique::allocateSeparatorSbm() { - if (separatorSbm_.nBlocks() > 0) return; - separatorSbm_ = SymmetricBlockMatrix(separatorBlockDims_, true); -} - size_t MultifrontalClique::addJacobianFactor( const JacobianFactor& jacobianFactor, size_t rowOffset) { // We only overwrite the fixed sparsity pattern, so Ab must be zeroed once in @@ -254,9 +247,7 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && (Ab_.matrix().rows() >= static_cast(frontalDim)); solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; - if (useQR) { - allocateSeparatorSbm(); // QR only needs separator updates for the parent. - } else { + if (!useQR) { allocateSbm(); } } @@ -320,22 +311,20 @@ void MultifrontalClique::eliminateInPlace() { void MultifrontalClique::updateParent(MultifrontalClique& parent) const { if (useQR()) { - assert(separatorSbm_.nBlocks() > 0); + // Accumulate separator (and RHS) normal equations (S^T S) into the parent. + assert(RSd_.nBlocks() > 0 && RSd_.firstBlock() == 0); + assert(parent.sbm_.nBlocks() > 0); const DenseIndex nfBlocks = static_cast(numFrontals()); - const DenseIndex totalBlocks = RSd_.nBlocks(); - const Matrix Sd = RSd_.range(nfBlocks, totalBlocks); - // Accumulate separator normal equations (S^T S) for the parent update. - separatorSbm_.setZero(); - separatorSbm_.selfadjointView().rankUpdate(Sd.transpose()); - parent.sbm_.updateFromMappedBlocks(separatorSbm_, parentIndices_); - return; + RSd_.firstBlock() = nfBlocks; + parent.sbm_.updateFromOuterProductBlocks(RSd_, parentIndices_); + RSd_.firstBlock() = 0; + } else { + // Accumulate the S^T S part from this clique's SBM into the parent. + assert(sbm_.nBlocks() > 0 && sbm_.blockStart() == 0); + sbm_.blockStart() = numFrontals(); + parent.sbm_.updateFromMappedBlocks(sbm_, parentIndices_); + sbm_.blockStart() = 0; } - // Expose only the separator+RHS view when contributing to the parent. - assert(sbm_.nBlocks() > 0 && sbm_.blockStart() == 0); - sbm_.blockStart() = numFrontals(); - assert(sbm_.nBlocks() == parentIndices_.size()); - parent.sbm_.updateFromMappedBlocks(sbm_, parentIndices_); - sbm_.blockStart() = 0; } std::shared_ptr MultifrontalClique::conditional() const { diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 3fde296abb..435e3aa20f 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -224,9 +224,6 @@ class GTSAM_EXPORT MultifrontalClique { /// Allocate the symmetric block matrix if needed. void allocateSbm(); - /// Allocate the separator-only SBM used for QR leaf updates. - void allocateSeparatorSbm(); - /** * Add a Jacobian factor's contributions into the Ab matrix. * @return Number of rows added. @@ -244,7 +241,6 @@ class GTSAM_EXPORT MultifrontalClique { KeyVector orderedKeys_; ///< Keys ordered by block index (frontals+seps). const std::unordered_set* fixedKeys_ = nullptr; std::vector blockDims_; - std::vector separatorBlockDims_; std::vector parentIndices_; ///< Parent block indices for separators + RHS. std::vector frontalPtrs_; ///< Pointers into solution frontals. @@ -258,9 +254,7 @@ class GTSAM_EXPORT MultifrontalClique { // Elimination-time state. mutable SymmetricBlockMatrix sbm_; - mutable SymmetricBlockMatrix - separatorSbm_; ///< Cached separator update for QR. - VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. + mutable VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. // Solve-time scratch space. mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. From 0f7e2a638d6d2d4d89e79b6468d6c35486820f09 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 12:35:55 -0500 Subject: [PATCH 026/266] In-place split --- gtsam/base/SymmetricBlockMatrix.cpp | 20 +++++++++++++++----- gtsam/base/SymmetricBlockMatrix.h | 3 +++ 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/gtsam/base/SymmetricBlockMatrix.cpp b/gtsam/base/SymmetricBlockMatrix.cpp index 9ee96fd93e..96149ab538 100644 --- a/gtsam/base/SymmetricBlockMatrix.cpp +++ b/gtsam/base/SymmetricBlockMatrix.cpp @@ -89,20 +89,30 @@ void SymmetricBlockMatrix::choleskyPartial(DenseIndex nFrontals) { } /* ************************************************************************* */ -VerticalBlockMatrix SymmetricBlockMatrix::split(DenseIndex nFrontals) { +void SymmetricBlockMatrix::split(DenseIndex nFrontals, + VerticalBlockMatrix* RSd) { gttic(VerticalBlockMatrix_split); + assert(RSd); // Construct a VerticalBlockMatrix that contains [R Sd] - const size_t n1 = offset(nFrontals); - VerticalBlockMatrix RSd = VerticalBlockMatrix::LikeActiveViewOf(*this, n1); + const DenseIndex n1 = offset(nFrontals); + assert(RSd->rows() == n1); + assert(RSd->cols() == cols()); + assert(RSd->nBlocks() == nBlocks()); // Copy into it. - RSd.full() = matrix_.topRows(n1); - RSd.full().triangularView().setZero(); + RSd->full() = matrix_.topRows(n1); + RSd->full().triangularView().setZero(); // Take lower-right block of Ab_ to get the remaining factor blockStart() = nFrontals; +} +VerticalBlockMatrix SymmetricBlockMatrix::split(DenseIndex nFrontals) { + // Construct a VerticalBlockMatrix that contains [R Sd] + const DenseIndex n1 = offset(nFrontals); + VerticalBlockMatrix RSd = VerticalBlockMatrix::LikeActiveViewOf(*this, n1); + split(nFrontals, &RSd); return RSd; } diff --git a/gtsam/base/SymmetricBlockMatrix.h b/gtsam/base/SymmetricBlockMatrix.h index dbb740a633..97bf3bd817 100644 --- a/gtsam/base/SymmetricBlockMatrix.h +++ b/gtsam/base/SymmetricBlockMatrix.h @@ -336,6 +336,9 @@ namespace gtsam { */ VerticalBlockMatrix split(DenseIndex nFrontals); + /// I n-place version of split. + void split(DenseIndex nFrontals, VerticalBlockMatrix* RSd); + protected: /// Number of offsets in the full matrix. From e5e01f45acf6727ec6cb3a454a47ba8aa74cf4fc Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 12:36:15 -0500 Subject: [PATCH 027/266] No more mallocs in QR path --- gtsam/linear/MultifrontalClique.cpp | 36 ++++++++++++++++------------- gtsam/linear/MultifrontalClique.h | 1 + 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 2dc7d624f4..b0a797447c 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -174,6 +174,8 @@ void MultifrontalClique::initializeMatrices( Ab_ = VerticalBlockMatrix(blockDims, verticalBlockMatrixRows, true); // Ab's structure is fixed; clear it once and reuse across loads. Ab_.matrix().setZero(); + RSd_ = + VerticalBlockMatrix(blockDims, static_cast(frontalDim), true); } void MultifrontalClique::allocateSbm() { @@ -247,9 +249,11 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && (Ab_.matrix().rows() >= static_cast(frontalDim)); solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; - if (!useQR) { - allocateSbm(); - } + RSdReady_ = false; + assert((useQR && RSd_.matrix().rows() == + static_cast(Ab_.matrix().rows()) || + RSd_.matrix().rows() == static_cast(frontalDim))); + allocateSbm(); } void MultifrontalClique::prepareForElimination() { @@ -273,18 +277,17 @@ void MultifrontalClique::prepareForElimination() { void MultifrontalClique::factorize() { if (useQR()) { - const DenseIndex nfRows = static_cast(frontalDim); - assert(Ab_.matrix().rows() >= nfRows); // Copy Ab_ to preserve its invariant; QR writes in place. - Matrix qr = Ab_.matrix(); - inplace_QR(qr); - RSd_ = VerticalBlockMatrix(blockDims_, nfRows, true); - RSd_.matrix() = qr.topRows(nfRows); - return; + assert(RSd_.matrix().rows() == Ab_.matrix().rows()); + assert(RSd_.matrix().cols() == Ab_.matrix().cols()); + RSd_.matrix() = Ab_.matrix(); + inplace_QR(RSd_.matrix()); + } else { + sbm_.choleskyPartial(numFrontals()); + sbm_.split(numFrontals(), &RSd_); + sbm_.blockStart() = 0; } - sbm_.choleskyPartial(numFrontals()); - RSd_ = sbm_.split(numFrontals()); - sbm_.blockStart() = 0; + RSdReady_ = true; } void MultifrontalClique::addIdentityDamping(double lambda) { @@ -312,7 +315,8 @@ void MultifrontalClique::eliminateInPlace() { void MultifrontalClique::updateParent(MultifrontalClique& parent) const { if (useQR()) { // Accumulate separator (and RHS) normal equations (S^T S) into the parent. - assert(RSd_.nBlocks() > 0 && RSd_.firstBlock() == 0); + assert(RSdReady_); + assert(RSd_.firstBlock() == 0); assert(parent.sbm_.nBlocks() > 0); const DenseIndex nfBlocks = static_cast(numFrontals()); RSd_.firstBlock() = nfBlocks; @@ -328,7 +332,7 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { } std::shared_ptr MultifrontalClique::conditional() const { - assert(RSd_.nBlocks() > 0); + assert(RSdReady_); // RSd_ is cached at elimination time. return std::make_shared(orderedKeys_, numFrontals(), RSd_); @@ -336,7 +340,7 @@ std::shared_ptr MultifrontalClique::conditional() const { // Solve with block back-substitution on the Cholesky-stored SBM. void MultifrontalClique::updateSolution() const { - assert(RSd_.nBlocks() > 0); + assert(RSdReady_); // Use cached [R S d] for fast back-substitution. const size_t nf = numFrontals(); const size_t n = RSd_.nBlocks() - 1; // # frontals + # separators diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 435e3aa20f..f9e6c3029d 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -255,6 +255,7 @@ class GTSAM_EXPORT MultifrontalClique { // Elimination-time state. mutable SymmetricBlockMatrix sbm_; mutable VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. + mutable bool RSdReady_ = false; // Solve-time scratch space. mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. From e09072d5093bd3cb3cfb83b7e23f23f1350043d5 Mon Sep 17 00:00:00 2001 From: Fan Jiang Date: Sat, 27 Dec 2025 22:49:56 -0800 Subject: [PATCH 028/266] JacobianFactor now compute the error more efficiently by first assembling the X vector We also now directly assemble the new `GaussianConditional` instead of using the `copy-then-offset` strategy. During the implementation I also discovered some latent bugs related to the iteration order of the `VectorValues`. - Tighten `BayesTree::equals` logic and related `Values` handling. - Adjust Shonan averaging expected matrices and subgraph preconditioner test setup/verbosity. - Silence `Point2`/`Point3` constructor unused variable warnings. --- gtsam/base/ConcurrentMap.h | 4 +- gtsam/inference/BayesTree-inst.h | 31 +++- gtsam/linear/Errors.cpp | 6 +- gtsam/linear/GaussianConditional.cpp | 31 +++- gtsam/linear/JacobianFactor.cpp | 15 +- gtsam/linear/SubgraphPreconditioner.cpp | 16 +- gtsam/linear/VectorValues.cpp | 160 ++++++++++-------- gtsam/linear/VectorValues.h | 6 +- .../tests/testCombinedImuFactor.cpp | 2 +- gtsam/nonlinear/Values.cpp | 6 - tests/testSubgraphPreconditioner.cpp | 18 +- 11 files changed, 184 insertions(+), 111 deletions(-) diff --git a/gtsam/base/ConcurrentMap.h b/gtsam/base/ConcurrentMap.h index 8ce44dda39..0fb059653b 100644 --- a/gtsam/base/ConcurrentMap.h +++ b/gtsam/base/ConcurrentMap.h @@ -19,6 +19,7 @@ #pragma once #include +#include "gtsam/base/FastMap.h" // Change class depending on whether we are using TBB #ifdef GTSAM_USE_TBB @@ -41,8 +42,7 @@ using ConcurrentMapBase = tbb::concurrent_unordered_map< #else -// If we're not using TBB, use a FastMap for ConcurrentMap -#include +// If we're not using TBB, use a std::map template using ConcurrentMapBase = gtsam::FastMap; diff --git a/gtsam/inference/BayesTree-inst.h b/gtsam/inference/BayesTree-inst.h index fc6a407b01..9de5b3f107 100644 --- a/gtsam/inference/BayesTree-inst.h +++ b/gtsam/inference/BayesTree-inst.h @@ -269,8 +269,35 @@ namespace gtsam { /* ************************************************************************* */ template bool BayesTree::equals(const BayesTree& other, double tol) const { - return size()==other.size() && - std::equal(nodes_.begin(), nodes_.end(), other.nodes_.begin(), &check_sharedCliques); + // Compare number of cliques first. + if (size() != other.size()) + return false; + + // Compare number of variables (nodes index size). + if (nodes_.size() != other.nodes_.size()) + return false; + + // Compare cliques by key so equality does not depend on the + // iteration order of the underlying ConcurrentMap. + for (const auto& kv : nodes_) { + const Key key = kv.first; + const sharedClique& clique = kv.second; + + auto it = other.nodes_.find(key); + if (it == other.nodes_.end()) + return false; + + const sharedClique& otherClique = it->second; + + if (!clique && !otherClique) + continue; + if (!clique || !otherClique) + return false; + if (!clique->equals(*otherClique, tol)) + return false; + } + + return true; } /* ************************************************************************* */ diff --git a/gtsam/linear/Errors.cpp b/gtsam/linear/Errors.cpp index d36b647cec..921b660911 100644 --- a/gtsam/linear/Errors.cpp +++ b/gtsam/linear/Errors.cpp @@ -27,9 +27,9 @@ namespace gtsam { /* ************************************************************************* */ Errors createErrors(const VectorValues& V) { Errors result; - for (const auto& [key, e] : V) { - result.push_back(e); - } + // Use a key-sorted view of VectorValues so the resulting Errors + // order is deterministic and independent of the underlying map. + for (const auto& [key, e] : V.sorted()) result.push_back(e); return result; } diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp index 8d3b0fa61e..04bb7a5c95 100644 --- a/gtsam/linear/GaussianConditional.cpp +++ b/gtsam/linear/GaussianConditional.cpp @@ -305,23 +305,36 @@ namespace gtsam { const Vector x = frontalValues.vector(KeyVector(beginFrontals(), endFrontals())); - // Copy the augmented Jacobian matrix: - auto newAb = Ab_; + // Compute updated right-hand side: d - R * x + const auto RR = R().triangularView(); + const Vector rhs = d() - RR * x; - // Restrict view to parent blocks - newAb.firstBlock() += nrFrontals_; + // Collect parent dimensions + FastVector parentDims; + parentDims.reserve(nrParents()); + for (auto it = beginParents(); it != endParents(); ++it) { + parentDims.push_back(getDim(it)); + } - // Update right-hand-side (last column) - auto last = newAb.matrix().cols() - 1; - const auto RR = R().triangularView(); - newAb.matrix().col(last) -= RR * x; + // Build a VerticalBlockMatrix containing only parent blocks and RHS. + const DenseIndex m = rows(); + VerticalBlockMatrix newAb(parentDims, m, true); + + // Copy parent blocks (S matrices). + DenseIndex blockIndex = 0; + for (auto it = beginParents(); it != endParents(); ++it, ++blockIndex) { + newAb(blockIndex) = S(it); + } + + // Set the RHS block. + const DenseIndex lastBlock = newAb.nBlocks() - 1; + newAb(lastBlock).col(0) = rhs; // The keys now do not include the frontal keys: KeyVector newKeys; newKeys.reserve(nrParents()); for (auto&& key : parents()) newKeys.push_back(key); - // Hopefully second newAb copy below is optimized out... return std::make_shared(newKeys, newAb, model_); } diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index 9f4d6daf8c..d5739ac990 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -491,10 +491,17 @@ bool JacobianFactor::equals(const GaussianFactor& f_, double tol) const { /* ************************************************************************* */ Vector JacobianFactor::unweighted_error(const VectorValues& c) const { - Vector e = -getb(); - for (size_t pos = 0; pos < size(); ++pos) - e += Ab_(pos) * c[keys_[pos]]; - return e; + const Vector v = c.vector(keys_); + Vector w(v.size() + 1); + w.segment(0, v.size()) = v; + w(v.size()) = -1.0; + // Fast path when the active view is the full matrix (no row/column offsets). + if (Ab_.firstBlock() == 0 && Ab_.rowStart() == 0 && + Ab_.rowEnd() == Ab_.matrix().rows()) { + return Ab_.matrix() * w; + } + // Fallback that respects firstBlock/rowStart/rowEnd for subviews. + return Ab_.full() * w; } /* ************************************************************************* */ diff --git a/gtsam/linear/SubgraphPreconditioner.cpp b/gtsam/linear/SubgraphPreconditioner.cpp index 53ea94d6eb..4fe4a79966 100644 --- a/gtsam/linear/SubgraphPreconditioner.cpp +++ b/gtsam/linear/SubgraphPreconditioner.cpp @@ -139,8 +139,9 @@ Errors SubgraphPreconditioner::operator*(const VectorValues &y) const { void SubgraphPreconditioner::multiplyInPlace(const VectorValues& y, Errors& e) const { Errors::iterator ei = e.begin(); - for(const auto& key_value: y) { - *ei = key_value.second; + // Fill the identity-part errors in key-sorted order, to match createErrors. + for (const auto& [key, value] : y.sorted()) { + *ei = value; ++ei; } @@ -155,8 +156,9 @@ VectorValues SubgraphPreconditioner::operator^(const Errors& e) const { Errors::const_iterator it = e.begin(); VectorValues y = zero(); - for(auto& key_value: y) { - key_value.second = *it; + // Map the identity-part errors back into y using key-sorted order. + for (const auto& [key, value] : y.sorted()) { + y.at(key) = *it; ++it; } transposeMultiplyAdd2(1.0, it, e.end(), y); @@ -169,9 +171,11 @@ void SubgraphPreconditioner::transposeMultiplyAdd (double alpha, const Errors& e, VectorValues& y) const { Errors::const_iterator it = e.begin(); - for(auto& key_value: y) { + // Add the identity-part contribution in key-sorted order so it + // matches the layout produced by createErrors(). + for (const auto& [key, value] : y.sorted()) { const Vector& ei = *it; - key_value.second += alpha * ei; + y.at(key) += alpha * ei; ++it; } transposeMultiplyAdd2(alpha, it, e.end(), y); diff --git a/gtsam/linear/VectorValues.cpp b/gtsam/linear/VectorValues.cpp index 8a5f575f05..3035cbb701 100644 --- a/gtsam/linear/VectorValues.cpp +++ b/gtsam/linear/VectorValues.cpp @@ -67,8 +67,8 @@ namespace gtsam { } /* ************************************************************************ */ - std::map VectorValues::sorted() const { - std::map ordered; + std::map VectorValues::sorted() const { + std::map ordered; for (const auto& kv : *this) ordered.emplace(kv); return ordered; } @@ -149,13 +149,8 @@ namespace gtsam { /* ************************************************************************ */ GTSAM_EXPORT std::ostream& operator<<(std::ostream& os, const VectorValues& v) { - // Change print depending on whether we are using TBB -#ifdef GTSAM_USE_TBB - for (const auto& [key, value] : v.sorted()) -#else - for (const auto& [key,value] : v) -#endif - { + // Always print in key-sorted order for deterministic output. + for (const auto& [key, value] : v.sorted()) { os << " " << StreamedKey(key) << ": " << value.transpose() << "\n"; } return os; @@ -171,13 +166,18 @@ namespace gtsam { /* ************************************************************************ */ bool VectorValues::equals(const VectorValues& x, double tol) const { - if(this->size() != x.size()) - return false; - auto this_it = this->begin(); - auto x_it = x.begin(); - for(; this_it != this->end(); ++this_it, ++x_it) { - if(this_it->first != x_it->first || - !equal_with_abs_tol(this_it->second, x_it->second, tol)) + if (this->size() != x.size()) return false; + + // Compare in key-sorted order so equality is independent of + // the underlying (possibly unordered) container iteration order. + const auto thisOrdered = this->sorted(); + const auto xOrdered = x.sorted(); + + auto it1 = thisOrdered.begin(); + auto it2 = xOrdered.begin(); + for (; it1 != thisOrdered.end(); ++it1, ++it2) { + if (it1->first != it2->first || + !equal_with_abs_tol(it1->second, it2->second, tol)) return false; } return true; @@ -193,12 +193,9 @@ namespace gtsam { // Copy vectors Vector result(totalDim); DenseIndex pos = 0; -#ifdef GTSAM_USE_TBB - // TBB uses un-ordered map, so inefficiently order them: + // Always order by key so the concatenated vector is deterministic + // even if the underlying container is unordered. for (const auto& [key, value] : sorted()) { -#else - for (const auto& [key, value] : *this) { -#endif result.segment(pos, value.size()) = value; pos += value.size(); } @@ -239,26 +236,40 @@ namespace gtsam { /* ************************************************************************ */ bool VectorValues::hasSameStructure(const VectorValues other) const { - // compare the "other" container with this one, using the structureCompareOp - // and then return true if all elements are compared as equal - return std::equal(this->begin(), this->end(), other.begin(), other.end(), - internal::structureCompareOp); + if (this->size() != other.size()) return false; + + // Compare in key-sorted order so structure comparison is + // independent of the underlying container iteration order. + const auto thisOrdered = this->sorted(); + const auto otherOrdered = other.sorted(); + + auto it1 = thisOrdered.begin(); + auto it2 = otherOrdered.begin(); + for (; it1 != thisOrdered.end(); ++it1, ++it2) { + if (!internal::structureCompareOp(*it1, *it2)) return false; + } + return true; } /* ************************************************************************ */ double VectorValues::dot(const VectorValues& v) const { - if(this->size() != v.size()) - throw std::invalid_argument("VectorValues::dot called with a VectorValues of different structure"); + if (this->size() != v.size()) + throw std::invalid_argument( + "VectorValues::dot called with a VectorValues of different " + "structure"); + double result = 0.0; - auto this_it = this->begin(); - auto v_it = v.begin(); - for(; this_it != this->end(); ++this_it, ++v_it) { - assert_throw(this_it->first == v_it->first, - std::invalid_argument("VectorValues::dot called with a VectorValues of different structure")); - assert_throw(this_it->second.size() == v_it->second.size(), - std::invalid_argument("VectorValues::dot called with a VectorValues of different structure")); - result += this_it->second.dot(v_it->second); + for (const auto& [key, value] : *this) { + const auto it = v.find(key); + assert_throw(it != v.end(), std::invalid_argument( + "VectorValues::dot called with a " + "VectorValues of different structure")); + assert_throw( + value.size() == it->second.size(), + std::invalid_argument("VectorValues::dot called with a VectorValues " + "of different structure")); + result += value.dot(it->second); } return result; } @@ -280,19 +291,27 @@ namespace gtsam { /* ************************************************************************ */ VectorValues VectorValues::operator+(const VectorValues& c) const { - if(this->size() != c.size()) - throw std::invalid_argument("VectorValues::operator+ called with different vector sizes"); - assert_throw(hasSameStructure(c), - std::invalid_argument("VectorValues::operator+ called with different vector sizes")); + if (this->size() != c.size()) + throw std::invalid_argument( + "VectorValues::operator+ called with different vector sizes"); VectorValues result; - // The result.end() hint here should result in constant-time inserts - for(const_iterator j1 = begin(), j2 = c.begin(); j1 != end(); ++j1, ++j2) + for (const auto& [key, value] : *this) { + const auto it = c.find(key); + assert_throw( + it != c.end(), + std::invalid_argument( + "VectorValues::operator+ called with different vector sizes")); + assert_throw( + value.size() == it->second.size(), + std::invalid_argument( + "VectorValues::operator+ called with different vector sizes")); #ifdef TBB_GREATER_EQUAL_2020 - result.values_.emplace(j1->first, j1->second + j2->second); + result.values_.emplace(key, value + it->second); #else - result.values_.insert({j1->first, j1->second + j2->second}); + result.values_.insert({key, value + it->second}); #endif + } return result; } @@ -306,16 +325,22 @@ namespace gtsam { /* ************************************************************************ */ VectorValues& VectorValues::operator+=(const VectorValues& c) { - if(this->size() != c.size()) - throw std::invalid_argument("VectorValues::operator+= called with different vector sizes"); - assert_throw(hasSameStructure(c), - std::invalid_argument("VectorValues::operator+= called with different vector sizes")); - - iterator j1 = begin(); - const_iterator j2 = c.begin(); - // The result.end() hint here should result in constant-time inserts - for(; j1 != end(); ++j1, ++j2) - j1->second += j2->second; + if (this->size() != c.size()) + throw std::invalid_argument( + "VectorValues::operator+= called with different vector sizes"); + + for (auto& [key, value] : *this) { + const auto it = c.find(key); + assert_throw( + it != c.end(), + std::invalid_argument( + "VectorValues::operator+= called with different vector sizes")); + assert_throw( + value.size() == it->second.size(), + std::invalid_argument( + "VectorValues::operator+= called with different vector sizes")); + value += it->second; + } return *this; } @@ -342,19 +367,27 @@ namespace gtsam { /* ************************************************************************ */ VectorValues VectorValues::operator-(const VectorValues& c) const { - if(this->size() != c.size()) - throw std::invalid_argument("VectorValues::operator- called with different vector sizes"); - assert_throw(hasSameStructure(c), - std::invalid_argument("VectorValues::operator- called with different vector sizes")); + if (this->size() != c.size()) + throw std::invalid_argument( + "VectorValues::operator- called with different vector sizes"); VectorValues result; - // The result.end() hint here should result in constant-time inserts - for(const_iterator j1 = begin(), j2 = c.begin(); j1 != end(); ++j1, ++j2) + for (const auto& [key, value] : *this) { + const auto it = c.find(key); + assert_throw( + it != c.end(), + std::invalid_argument( + "VectorValues::operator- called with different vector sizes")); + assert_throw( + value.size() == it->second.size(), + std::invalid_argument( + "VectorValues::operator- called with different vector sizes")); #ifdef TBB_GREATER_EQUAL_2020 - result.values_.emplace(j1->first, j1->second - j2->second); + result.values_.emplace(key, value - it->second); #else - result.values_.insert({j1->first, j1->second - j2->second}); + result.values_.insert({key, value - it->second}); #endif + } return result; } @@ -412,12 +445,7 @@ namespace gtsam { ss << " \n \n"; // Print out all rows. -#ifdef GTSAM_USE_TBB - // TBB uses un-ordered map, so inefficiently order them: for (const auto& kv : sorted()) { -#else - for (const auto& kv : *this) { -#endif ss << " "; ss << "" << keyFormatter(kv.first) << "" << kv.second.transpose() << ""; diff --git a/gtsam/linear/VectorValues.h b/gtsam/linear/VectorValues.h index c1f332380d..fee921cccd 100644 --- a/gtsam/linear/VectorValues.h +++ b/gtsam/linear/VectorValues.h @@ -76,9 +76,6 @@ namespace gtsam { typedef ConcurrentMap Values; ///< Collection of Vectors making up a VectorValues Values values_; ///< Vectors making up this VectorValues - /** Sort by key (primarily for use with TBB, which uses an unordered map)*/ - std::map sorted() const; - public: typedef Values::iterator iterator; ///< Iterator over vector values typedef Values::const_iterator const_iterator; ///< Const iterator over vector values @@ -368,6 +365,9 @@ namespace gtsam { /** Element-wise scaling by a constant in-place. */ VectorValues& scaleInPlace(double alpha); + /** Sort by key (primarily for use with TBB, which uses an unordered map)*/ + std::map sorted() const; + /// @} /// @name Wrapper support diff --git a/gtsam/navigation/tests/testCombinedImuFactor.cpp b/gtsam/navigation/tests/testCombinedImuFactor.cpp index 54be1fb07c..55d806a999 100644 --- a/gtsam/navigation/tests/testCombinedImuFactor.cpp +++ b/gtsam/navigation/tests/testCombinedImuFactor.cpp @@ -198,7 +198,7 @@ TEST_PIM(CombinedImuFactor, PredictRotation) { // Predict const Pose3 x(Rot3::Ypr(0, 0, 0), Point3(0, 0, 0)), x2; - const Vector3 v(0, 0, 0), v2(0, 0, 0); + const Vector3 v(0, 0, 0); const NavState actual = pim.predict(NavState(x, v), bias); const Pose3 expectedPose(Rot3::Ypr(M_PI / 10, 0, 0), Point3(0, 0, 0)); EXPECT(assert_equal(expectedPose, actual.pose(), tol)); diff --git a/gtsam/nonlinear/Values.cpp b/gtsam/nonlinear/Values.cpp index e2579e5a35..7fc84098d1 100644 --- a/gtsam/nonlinear/Values.cpp +++ b/gtsam/nonlinear/Values.cpp @@ -107,14 +107,8 @@ namespace gtsam { assert(this->size() == delta.size()); auto key_value = values_.begin(); VectorValues::const_iterator key_delta; -#ifdef GTSAM_USE_TBB for (; key_value != values_.end(); ++key_value) { key_delta = delta.find(key_value->first); -#else - for (key_delta = delta.begin(); key_value != values_.end(); - ++key_value, ++key_delta) { - assert(key_value->first == key_delta->first); -#endif Key var = key_value->first; assert(static_cast(delta[var].size()) == key_value->second->dim()); assert(delta[var].allFinite()); diff --git a/tests/testSubgraphPreconditioner.cpp b/tests/testSubgraphPreconditioner.cpp index 96f5824065..e1c5fa2368 100644 --- a/tests/testSubgraphPreconditioner.cpp +++ b/tests/testSubgraphPreconditioner.cpp @@ -15,8 +15,7 @@ * @author Frank Dellaert **/ -#include - +#include #include #include #include @@ -25,8 +24,7 @@ #include #include #include - -#include +#include #include @@ -98,7 +96,7 @@ TEST(SubgraphPreconditioner, system) { // Eliminate the spanning tree to build a prior const Ordering ord = planarOrdering(N); auto Rc1 = *Ab1.eliminateSequential(ord); // R1*x-c1 - VectorValues xbar = Rc1.optimize(); // xbar = inv(R1)*c1 + VectorValues xbar = Rc1.optimize(); // xbar = inv(R1)*c1 // Create Subgraph-preconditioned system const SubgraphPreconditioner system(Ab2, Rc1, xbar); @@ -188,7 +186,7 @@ TEST(SubgraphPreconditioner, conjugateGradients) { // Eliminate the spanning tree to build a prior GaussianBayesNet Rc1 = *Ab1.eliminateSequential(); // R1*x-c1 - VectorValues xbar = Rc1.optimize(); // xbar = inv(R1)*c1 + VectorValues xbar = Rc1.optimize(); // xbar = inv(R1)*c1 // Create Subgraph-preconditioned system SubgraphPreconditioner system(Ab2, Rc1, xbar); @@ -202,9 +200,11 @@ TEST(SubgraphPreconditioner, conjugateGradients) { // Solve for the remaining constraints using PCG ConjugateGradientParameters parameters; - VectorValues actual = conjugateGradients(system, y1, parameters); - EXPECT(assert_equal(y0,actual)); + parameters.setVerbosity("ERROR"); + VectorValues actual = + conjugateGradients( + system, y1, parameters); + EXPECT(assert_equal(y0, actual)); // Compare with non preconditioned version: VectorValues actual2 = conjugateGradientDescent(Ab, x1, parameters); From e3bb6a14126ac65580cfba2b33f75cf92533acad Mon Sep 17 00:00:00 2001 From: Fan Jiang Date: Mon, 5 Jan 2026 23:35:49 -0800 Subject: [PATCH 029/266] Address comments --- gtsam/linear/JacobianFactor.cpp | 8 ++++---- gtsam/linear/VectorValues.h | 23 +++++++++++++++++++++++ 2 files changed, 27 insertions(+), 4 deletions(-) diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index d5739ac990..ae04d1f59a 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -491,10 +491,10 @@ bool JacobianFactor::equals(const GaussianFactor& f_, double tol) const { /* ************************************************************************* */ Vector JacobianFactor::unweighted_error(const VectorValues& c) const { - const Vector v = c.vector(keys_); - Vector w(v.size() + 1); - w.segment(0, v.size()) = v; - w(v.size()) = -1.0; + const DenseIndex totalDim = c.totalDim(keys_); + Vector w(totalDim + 1); + c.fillVector(keys_, w); + w(totalDim) = -1.0; // Fast path when the active view is the full matrix (no row/column offsets). if (Ab_.firstBlock() == 0 && Ab_.rowStart() == 0 && Ab_.rowEnd() == Ab_.matrix().rows()) { diff --git a/gtsam/linear/VectorValues.h b/gtsam/linear/VectorValues.h index fee921cccd..0e7d6599e4 100644 --- a/gtsam/linear/VectorValues.h +++ b/gtsam/linear/VectorValues.h @@ -280,6 +280,29 @@ namespace gtsam { /** Retrieve the entire solution as a single vector */ Vector vector() const; + /** Compute the total dimension of a subset of relevant keys. */ + template + DenseIndex totalDim(const CONTAINER& keys) const { + DenseIndex totalDim = 0; + for (Key key : keys) { + totalDim += static_cast(at(key).size()); + } + return totalDim; + } + + /** Fill a preallocated Eigen vector expression with a subset of relevant keys. */ + template + void fillVector(const CONTAINER& keys, + const Eigen::MatrixBase& result) const { + auto& writable = const_cast&>(result); + DenseIndex pos = 0; + for (Key key : keys) { + const Vector& v = at(key); + writable.segment(pos, v.size()) = v; + pos += v.size(); + } + } + /** Access a vector that is a subset of relevant keys. */ template Vector vector(const CONTAINER& keys) const { From 43dd4721d716d7cc069bdce91c3b06bf7c5f1f85 Mon Sep 17 00:00:00 2001 From: Fan Jiang Date: Mon, 5 Jan 2026 23:44:48 -0800 Subject: [PATCH 030/266] Fix typo --- gtsam/base/ConcurrentMap.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/base/ConcurrentMap.h b/gtsam/base/ConcurrentMap.h index 0fb059653b..474091c627 100644 --- a/gtsam/base/ConcurrentMap.h +++ b/gtsam/base/ConcurrentMap.h @@ -19,7 +19,7 @@ #pragma once #include -#include "gtsam/base/FastMap.h" +#include // Change class depending on whether we are using TBB #ifdef GTSAM_USE_TBB From 3df67f5e2b318a152642baeb225ef3073d25ed58 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Wed, 7 Jan 2026 20:17:41 -0500 Subject: [PATCH 031/266] transformTo and the jacobians for pushforward --- gtsam/geometry/Similarity3.cpp | 50 ++++++++++++++++++-- gtsam/geometry/Similarity3.h | 31 ++++++++++--- gtsam/geometry/tests/testSimilarity3.cpp | 58 +++++++++++++++++++++++- 3 files changed, 129 insertions(+), 10 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index 0f1011f512..5d4c81bbe5 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -145,9 +145,53 @@ Point3 Similarity3::transformFrom(const Point3& p, // return s_ * q; } -Pose3 Similarity3::transformFrom(const Pose3& T) const { - Rot3 R = R_.compose(T.rotation()); - Point3 t = Point3(s_ * (R_ * T.translation() + t_)); +Pose3 Similarity3::transformFrom(const Pose3& bTi, + OptionalJacobian<6, 7> Hself, OptionalJacobian<6, 6> H_bTi) const { + + // Similarity3 components and their derivatives with respect to the Similarity3. + Matrix37 DSimR_dSim, Dt_dSim; + Matrix17 Ds_dSim; + const double Sim_s = this->scale(Hself ? &Ds_dSim : nullptr); + const Point3 Sim_t = this->translation(Hself ? &Dt_dSim : nullptr); + const Rot3 Sim_R = this->rotation(Hself ? &DSimR_dSim : nullptr); + + // Pose3 components and their derivatives with respect to the Pose3. + Matrix36 Dp_dT, DTR_dT; + const Rot3 TR = bTi.rotation(H_bTi ? &DTR_dT : nullptr); + const Point3 p = bTi.translation(H_bTi ? &Dp_dT : nullptr); + + // Rotation component of the result. + Matrix3 DR_dSimR, DR_dTR; + const Rot3 R = Sim_R.compose(TR, Hself ? &DR_dSimR : nullptr, H_bTi ? &DR_dTR : nullptr); + + Matrix3 Dat_dSimR, Dat_dp; + const Point3 at = Sim_R.rotate(p, Hself ? &Dat_dSimR : nullptr, H_bTi ? &Dat_dp: nullptr); + const Point3 t = Sim_s * (at + Sim_t); + + if (Hself) { + Hself->setZero(); + // Rotation component - this is correct. + Hself->block<3, 7>(0, 0) = DR_dSimR * DSimR_dSim; + + // Translation component - this is not correct, needs to be fixed. + Hself->block<3, 3>(3, 0) = Sim_s * (Dat_dSimR * DSimR_dSim).block<3, 3>(0, 0); + + // Part 1 + const auto ds = (at + Sim_t) * Ds_dSim; // 3x7 + // part 2 + const auto dt = Sim_s * Dt_dSim; // 3x7 + Hself->block<3, 4>(3, 3) = (ds + dt).block<3, 4>(0, 3); + } + + if (H_bTi) { + H_bTi->setZero(); + // Rotation component - this is correct. + H_bTi->block<3, 6>(0, 0) = DR_dTR * DTR_dT; + + // H_bTi->block<3, 6>(3, 0) = Sim_s * Dat_dp * Dp_dT; // This does not work, dont know why. + H_bTi->block<3, 3>(3, 3) = Sim_s * I_3x3; // This works, dont know why. + + } return Pose3(R, t); } diff --git a/gtsam/geometry/Similarity3.h b/gtsam/geometry/Similarity3.h index 0a2a569905..ce4675b849 100644 --- a/gtsam/geometry/Similarity3.h +++ b/gtsam/geometry/Similarity3.h @@ -118,7 +118,9 @@ class GTSAM_EXPORT Similarity3 : public MatrixLieGroup { * This group action satisfies the compatibility condition. * For more details, refer to: https://en.wikipedia.org/wiki/Group_action */ - Pose3 transformFrom(const Pose3& T) const; + Pose3 transformFrom(const Pose3& T, + OptionalJacobian<6, 7> H1 = {}, // + OptionalJacobian<6, 6> H2 = {}) const; /** syntactic sugar for transformFrom */ Point3 operator*(const Point3& p) const; @@ -191,14 +193,31 @@ class GTSAM_EXPORT Similarity3 : public MatrixLieGroup { /// Calculate 4*4 matrix group equivalent Matrix4 matrix() const; - /// Return a GTSAM rotation - Rot3 rotation() const { return R_; } + /// Return a rotation + Rot3 rotation(OptionalJacobian<3, 7> Hself = {}) const { + if (Hself) { + Hself->setZero(); + Hself->block<3, 3>(0, 0) = I_3x3; + } + return R_; + } - /// Return a GTSAM translation - Point3 translation() const { return t_; } + /// Return a translation with pushforward + Point3 translation(OptionalJacobian<3, 7> Hself = {}) const { + if (Hself) { + *Hself << Z_3x3, rotation().matrix(), -t_; + } + return t_; + } /// Return the scale - double scale() const { return s_; } + double scale(OptionalJacobian<1, 7> Hself = {}) const { + if (Hself) { + Hself->setZero(); + (*Hself)(0, 6) = s_; + } + return s_; + } /// @} /// @name Deprecated diff --git a/gtsam/geometry/tests/testSimilarity3.cpp b/gtsam/geometry/tests/testSimilarity3.cpp index f48a710f18..edbf100de1 100644 --- a/gtsam/geometry/tests/testSimilarity3.cpp +++ b/gtsam/geometry/tests/testSimilarity3.cpp @@ -80,6 +80,39 @@ TEST(Similarity3, Getters) { EXPECT_DOUBLES_EQUAL(7.0, sim3.scale(), 1e-9); } +/* ************************************************************************* */ +// Check translation and its pushforward +TEST(Similarity3, translation) { + Matrix37 actualH; + EXPECT(assert_equal(Point3(3.5, -8.2, 4.2), T1.translation(&actualH), 1e-8)); + + std::function f = [](const Similarity3& T) { return T.translation(); }; + Matrix37 numericalH = numericalDerivative11(f, T1); + EXPECT(assert_equal(numericalH, actualH, 1e-6)); +} + +/* ************************************************************************* */ +// Check scale and its pushforward +TEST(Similarity3, scale) { + Matrix17 actualH; + EXPECT_DOUBLES_EQUAL(2.0, T5.scale(&actualH), 1e-8); + + std::function f = [](const Similarity3& T) { return T.scale(); }; + Matrix17 numericalH = numericalDerivative11(f, T5); + EXPECT(assert_equal(numericalH, actualH, 1e-6)); +} + +/* ************************************************************************* */ +// Check rotation and its pushforward +TEST(Similarity3, rotation) { + Matrix37 actualH; + EXPECT(assert_equal(Rot3::Rodrigues(0.3, 0.2, 0.1), T2.rotation(&actualH), 1e-8)); + + std::function f = [](const Similarity3& T) { return T.rotation(); }; + Matrix37 numericalH = numericalDerivative11(f, T2); + EXPECT(assert_equal(numericalH, actualH, 1e-6)); +} + /* ************************************************************************* */ TEST(Similarity3, HatAndVee) { // Create a few test vectors @@ -274,7 +307,7 @@ TEST(Similarity3, GroupAction) { Point3 q(1, 2, 3); for (const auto& T : { T1, T2, T3, T4, T5, T6 }) { - Point3 q(1, 0, 0); + // Point3 q(1, 2, 3); Matrix H1 = numericalDerivative21(f, T, q); Matrix H2 = numericalDerivative22(f, T, q); Matrix actualH1, actualH2; @@ -307,6 +340,29 @@ TEST(Similarity3, GroupActionPose3) { // objects now live in the world frame, instead of in the egovehicle frame EXPECT(assert_equal(expected_wTo1, wSe.transformFrom(eTo1))); EXPECT(assert_equal(expected_wTo2, wSe.transformFrom(eTo2))); + + Similarity3 wSe2(Rot3::RzRyRx(60 * degree, 50 * degree, 30 * degree), Point3(2, 3, 5), 2.0); + std::function + f = [](const Similarity3& S, const Pose3& T){ return S.transformFrom(T); }; + + { + Matrix H1 = numericalDerivative21(f, wSe2, eTo1); + Matrix H2 = numericalDerivative22(f, wSe2, eTo1); + Matrix actualH1, actualH2; + wSe2.transformFrom(eTo1, actualH1, actualH2); + EXPECT(assert_equal(H1, actualH1)); + EXPECT(assert_equal(H2, actualH2)); + } + + { + Pose3 eTo(Rot3::RzRyRx(20 * degree, -15 * degree, 10 * degree), Point3(1, 2, 3)); + Matrix H1 = numericalDerivative21(f, wSe2, eTo); + Matrix H2 = numericalDerivative22(f, wSe2, eTo); + Matrix actualH1, actualH2; + wSe2.transformFrom(eTo, actualH1, actualH2); + EXPECT(assert_equal(H1, actualH1)); + EXPECT(assert_equal(H2, actualH2)); + } } // Test left group action compatibility. From c51763995895b5e7cd41d9085923d0d2cf4143d2 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Thu, 8 Jan 2026 13:49:04 -0500 Subject: [PATCH 032/266] this works! --- gtsam/geometry/Similarity3.cpp | 36 +++++++++--------------- gtsam/geometry/tests/testSimilarity3.cpp | 2 +- 2 files changed, 15 insertions(+), 23 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index 5d4c81bbe5..7d0211f50a 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -149,10 +149,7 @@ Pose3 Similarity3::transformFrom(const Pose3& bTi, OptionalJacobian<6, 7> Hself, OptionalJacobian<6, 6> H_bTi) const { // Similarity3 components and their derivatives with respect to the Similarity3. - Matrix37 DSimR_dSim, Dt_dSim; - Matrix17 Ds_dSim; - const double Sim_s = this->scale(Hself ? &Ds_dSim : nullptr); - const Point3 Sim_t = this->translation(Hself ? &Dt_dSim : nullptr); + Matrix37 DSimR_dSim; const Rot3 Sim_R = this->rotation(Hself ? &DSimR_dSim : nullptr); // Pose3 components and their derivatives with respect to the Pose3. @@ -164,35 +161,30 @@ Pose3 Similarity3::transformFrom(const Pose3& bTi, Matrix3 DR_dSimR, DR_dTR; const Rot3 R = Sim_R.compose(TR, Hself ? &DR_dSimR : nullptr, H_bTi ? &DR_dTR : nullptr); - Matrix3 Dat_dSimR, Dat_dp; - const Point3 at = Sim_R.rotate(p, Hself ? &Dat_dSimR : nullptr, H_bTi ? &Dat_dp: nullptr); - const Point3 t = Sim_s * (at + Sim_t); + Matrix37 Dt_sim; + Matrix3 Dt_p; + const Point3 t = this->transformFrom(p, Hself ? &Dt_sim : nullptr, H_bTi ? &Dt_p : nullptr); + + Matrix63 Dresult_t; + // Dresult_R is identity subblock of R, so ignoring. + const Pose3 result = Pose3::Create(R, t, nullptr, Hself ? &Dresult_t : nullptr); if (Hself) { Hself->setZero(); - // Rotation component - this is correct. + // Rotation component. Hself->block<3, 7>(0, 0) = DR_dSimR * DSimR_dSim; - // Translation component - this is not correct, needs to be fixed. - Hself->block<3, 3>(3, 0) = Sim_s * (Dat_dSimR * DSimR_dSim).block<3, 3>(0, 0); - - // Part 1 - const auto ds = (at + Sim_t) * Ds_dSim; // 3x7 - // part 2 - const auto dt = Sim_s * Dt_dSim; // 3x7 - Hself->block<3, 4>(3, 3) = (ds + dt).block<3, 4>(0, 3); + // Translation component. + Hself->block<3, 7>(3, 0) = (Dresult_t * Dt_sim).block<3, 7>(3, 0); } if (H_bTi) { H_bTi->setZero(); - // Rotation component - this is correct. + // Rotation component. H_bTi->block<3, 6>(0, 0) = DR_dTR * DTR_dT; - - // H_bTi->block<3, 6>(3, 0) = Sim_s * Dat_dp * Dp_dT; // This does not work, dont know why. - H_bTi->block<3, 3>(3, 3) = Sim_s * I_3x3; // This works, dont know why. - + H_bTi->block<3, 6>(3, 0) = (Dresult_t * (Dt_p * Dp_dT)).block<3, 6>(3, 0); } - return Pose3(R, t); + return result; } Point3 Similarity3::operator*(const Point3& p) const { diff --git a/gtsam/geometry/tests/testSimilarity3.cpp b/gtsam/geometry/tests/testSimilarity3.cpp index edbf100de1..42998a86b6 100644 --- a/gtsam/geometry/tests/testSimilarity3.cpp +++ b/gtsam/geometry/tests/testSimilarity3.cpp @@ -95,7 +95,7 @@ TEST(Similarity3, translation) { // Check scale and its pushforward TEST(Similarity3, scale) { Matrix17 actualH; - EXPECT_DOUBLES_EQUAL(2.0, T5.scale(&actualH), 1e-8); + EXPECT_DOUBLES_EQUAL(10.0, T5.scale(&actualH), 1e-8); std::function f = [](const Similarity3& T) { return T.scale(); }; Matrix17 numericalH = numericalDerivative11(f, T5); From cda91e190e4802725eb81a6574b767b644e0ec0f Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 26 Dec 2025 17:38:42 -0500 Subject: [PATCH 033/266] Priority-based task scheduler --- gtsam/base/PriorityScheduler.h | 340 +++++++++++++++++++++ gtsam/base/tests/testPriorityScheduler.cpp | 125 ++++++++ 2 files changed, 465 insertions(+) create mode 100644 gtsam/base/PriorityScheduler.h create mode 100644 gtsam/base/tests/testPriorityScheduler.cpp diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h new file mode 100644 index 0000000000..6e98f49266 --- /dev/null +++ b/gtsam/base/PriorityScheduler.h @@ -0,0 +1,340 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file PriorityScheduler.h + * @brief Priority-based task scheduler with a lightweight recursive API. + * + * @details + * This header defines a small, thread-based scheduler that executes tasks in + * priority order. A lower numeric priority is executed before a higher numeric + * priority (min-heap behavior). Tasks return a value of type `Y`, and callers + * can wait on results via `std::future`. + * + * The scheduler also supports a recursive execution model via + * `RecursiveTaskContext`. A recursive function can enqueue child tasks with + * specific priorities and block until each child returns, while still allowing + * other worker threads to make progress on queued tasks. + * + * @par Example + * @code + * // Compute a sum over a tree where children are scheduled by priority. + * struct Node { std::vector children; int value; }; + * + * gtsam::PriorityScheduler scheduler(4); + * + * auto sum = scheduler.runRecursive( + * root, 0.0, // initialPriority + * [](Node node, gtsam::RecursiveTaskContext ctx) { + * int total = node.value; + * double childPriority = 1.0; + * for (const auto& child : node.children) { + * total += ctx.processChild(child, childPriority, + * [](Node c, gtsam::RecursiveTaskContext childCtx) { + * return childCtx.runSubTask(2.0, [c]() { return c.value; }); + * }); + * childPriority += 1.0; + * } + * return total; + * }); + * @endcode + * + * @note `runRecursive` blocks until the root task finishes. Each recursive + * call can enqueue more work and synchronously wait on its results. + * + * @author Frank Dellaert + * @date May, 2025 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include // For std::runtime_error +#include +#include // For std::is_void_v +#include + +namespace gtsam { + +/// Forward declaration for the recursive context helper. +template +class PriorityScheduler; + +/** + * @brief Helper passed into recursive tasks to schedule child work. + * + * @details This class is intentionally lightweight: it holds a raw pointer to + * the scheduler and offers convenience methods that enqueue a task and + * immediately wait for its result. + * + * @tparam Y Result type produced by scheduled tasks. + * @tparam Node node type used by the recursive user function. + */ +template +class RecursiveTaskContext { + PriorityScheduler* scheduler_; + + public: + /// Construct a context tied to a scheduler. + explicit RecursiveTaskContext(PriorityScheduler* scheduler) + : scheduler_(scheduler) {} + + /** + * @brief Schedule a recursive child task and wait for its result. + * + * @param childNode Node to pass to the recursive function. + * @param priority Lower values execute before higher values. + * @param recursiveFunc The user-provided recursive function. + * @return Result produced by the child task. + */ + Y processChild(Node childNode, double priority, + const std::function)>& + recursiveFunc) { + assert(scheduler_); + + // The lambda captures recursiveFunc by reference (as it's a const ref + // parameter) and scheduler by pointer. childNode is copied. + auto childJob = [childNode, scheduler = scheduler_, &recursiveFunc]() -> Y { + RecursiveTaskContext childContext(scheduler); + return recursiveFunc(childNode, childContext); + }; + + std::future future = scheduler_->schedule(priority, std::move(childJob)); + return future.get(); + } + + /** + * @brief Schedule an arbitrary subtask and wait for its result. + * + * @param priority Lower values execute before higher values. + * @param subTaskJob Callable representing the subtask. + * @return Result produced by the subtask. + */ + Y runSubTask(double priority, std::function subTaskJob) { + assert(scheduler_); + std::future future = + scheduler_->schedule(priority, std::move(subTaskJob)); + return future.get(); + } +}; + +/** + * @brief Thread pool scheduler that prioritizes tasks by numeric priority. + * + * @details + * - Lower numeric values are executed first. + * - Tasks are executed by worker threads created at construction. + * - `schedule` returns a `std::future` for the task result. + * - `runRecursive` executes a root task and allows it to spawn child tasks + * through `RecursiveTaskContext`. + * + * @tparam Y Result type returned by tasks. Use `void` for no return value. + */ +template +class PriorityScheduler { + struct Task { + double priority; + std::function job; + std::promise promise; + + Task(double p, std::function j, std::promise _promise) + : priority(p), job(std::move(j)), promise(std::move(_promise)) {} + }; + + using TaskPtr = std::shared_ptr; + + struct Compare { + bool operator()(const TaskPtr& a, const TaskPtr& b) const { + return a->priority > b->priority; + } + }; + + std::priority_queue, Compare> taskQueue_; + std::vector workers_; + mutable std::mutex queueMutex_; + std::condition_variable condition_; + std::atomic stop_{false}; + std::atomic activeTasks_{0}; + + /** + * @brief Worker thread function for processing tasks from the priority queue. + * + * This function runs in a loop, waiting for tasks to become available in the + * task queue. It uses a condition variable (`condition_`) to efficiently wait + * until either: + * - The scheduler is stopped (`stop_` is true), or + * - There is at least one task in the queue (`!taskQueue_.empty()`). + * + * The condition variable is notified whenever a new task is added or when the + * scheduler is stopped, allowing the worker thread to wake up and check the + * queue or exit if needed. The use of the condition variable ensures that + * worker threads do not spin-wait, but instead sleep efficiently until work + * is available or shutdown is requested. + * + * When a task is available, the worker thread pops the highest-priority task + * from the queue, executes it, and sets the result or exception in the + * associated promise. After processing a task, it decrements the count of + * active tasks and notifies all waiting threads via the condition variable, + * in case other threads are waiting for tasks to complete. + * + */ + void worker_thread() { + while (true) { + TaskPtr task; + { + std::unique_lock lock(queueMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_relaxed) || !taskQueue_.empty(); + }); + + if (taskQueue_.empty()) { + if (stop_.load(std::memory_order_relaxed)) return; + continue; + } + + // Pop the highest-priority task: + task = taskQueue_.top(); + taskQueue_.pop(); + } + + try { + // Execute the task and set the promise value: + if constexpr (std::is_void_v) { + task->job(); + task->promise.set_value(); + } else { + task->promise.set_value(task->job()); + } + } catch (...) { + // On exception, set the exception on the promise: + try { + task->promise.set_exception(std::current_exception()); + } catch (...) { /* ignore */ + } + } + + // Decrement active task count and notify waiting threads: + std::lock_guard lock(queueMutex_); + activeTasks_.fetch_sub(1, std::memory_order_relaxed); + condition_.notify_all(); + } + } + + public: + /** + * @brief Construct a scheduler with a fixed number of worker threads. + * + * @param numThreads Number of worker threads to create. If zero, a single + * thread is created. + */ + PriorityScheduler(size_t numThreads = std::thread::hardware_concurrency()) { + if (numThreads == 0) numThreads = 1; + for (size_t i = 0; i < numThreads; ++i) { + workers_.emplace_back(&PriorityScheduler::worker_thread, this); + } + } + + /** + * @brief Wait for all tasks to finish, then stop worker threads. + * + * @note The destructor calls `waitForAllTasks` before stopping workers. + */ + ~PriorityScheduler() { + waitForAllTasks(); + stop_.store(true, std::memory_order_relaxed); + condition_.notify_all(); + for (std::thread& worker : workers_) { + if (worker.joinable()) { + worker.join(); + } + } + } + + /** + * @brief Enqueue a task for execution. + * + * @param priority Lower values execute before higher values. + * @param job Callable returning a `Y`. + * @return `std::future` associated with the task. + */ + std::future schedule(double priority, std::function job) { + if (stop_.load(std::memory_order_relaxed)) { + std::promise err_promise; + err_promise.set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + return err_promise.get_future(); + } + + std::promise promise; + std::future future = promise.get_future(); + auto task = + std::make_shared(priority, std::move(job), std::move(promise)); + + { + std::lock_guard lock(queueMutex_); + taskQueue_.push(task); + activeTasks_.fetch_add(1, std::memory_order_relaxed); + } + condition_.notify_one(); + return future; + } + + /** + * @brief Execute a recursive task starting at a root node. + * + * @details The user function can schedule child tasks via the provided + * `RecursiveTaskContext`. This call blocks until the root task completes. + * + * @param rootNode The starting node for recursion. + * @param initialPriority Priority for the root task. + * @param userRecursiveFunc The recursive function implementation. + * @return Result of the root task. + */ + template + Y runRecursive( + Node rootNode, double initialPriority, + // This std::function is copied when topLevelJob is created. + std::function)> userRecursiveFunc) { + auto topLevelJob = [this, rootNode, + userRecursiveFunc_copy = + std::move(userRecursiveFunc)]() -> Y { + RecursiveTaskContext rootContext(this); + return userRecursiveFunc_copy(rootNode, rootContext); + }; + + std::future topLevelFuture = + schedule(initialPriority, std::move(topLevelJob)); + return topLevelFuture.get(); + } + + /** + * @brief Block until all queued and active tasks complete. + * + * @note If the scheduler is stopping, this returns early. + */ + void waitForAllTasks() { + std::unique_lock lock(queueMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_relaxed) || + (activeTasks_.load(std::memory_order_relaxed) == 0 && + taskQueue_.empty()); + }); + } + + template + friend class RecursiveTaskContext; +}; + +} // namespace gtsam diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp new file mode 100644 index 0000000000..d35dacbc21 --- /dev/null +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -0,0 +1,125 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file testPriorityScheduler.cpp + * @brief Unit tests for PriorityScheduler with simplified recursive API. + * @author Frank Dellaert + * @date May, 2025 + */ + +#include +#include + +#include // For void test counter +#include +#include // For A* closed set +#include +#include // For A* result +#include + +using namespace gtsam; + +// Common TreeNode definition (using shared_ptr) +struct TreeNode { + int value; + std::vector> children; + TreeNode(int v) : value(v) {} + void addChild(std::shared_ptr child) { children.push_back(child); } +}; + +namespace { +std::shared_ptr createSimpleTree() { + auto n5 = std::make_shared(5); + auto n3 = std::make_shared(3); + auto n8 = std::make_shared(8); + auto n2 = std::make_shared(2); + auto n1 = std::make_shared(1); + auto n4 = std::make_shared(4); + auto n7 = std::make_shared(7); + n5->children = {n3, n8, n2}; + n3->children = {n1, n4}; + n2->children = {n7}; + return n5; +} +} // namespace + +/* ************************************************************************* */ +// Test 1: Recursive Node Counting (returns size_t) +/* ************************************************************************* */ +namespace { +size_t countNodesRecursive( + std::shared_ptr node, + RecursiveTaskContext> context) { + if (!node) return 0; + size_t count = 1; + double childPriorityBase = static_cast(node->value); + + for (const auto& child : node->children) { + count += context.processChild(child, childPriorityBase - 1.0, + countNodesRecursive); + } + return count; +} +} // namespace + +TEST(PriorityScheduler, RecursiveNodeCounting) { + std::shared_ptr root = createSimpleTree(); + PriorityScheduler scheduler(std::thread::hardware_concurrency()); + + std::function, + RecursiveTaskContext>)> + countFunc = countNodesRecursive; + size_t totalCount = scheduler.runRecursive(root, 0.0, countFunc); + + const size_t expectedTotalNodes = 7; + EXPECT_LONGS_EQUAL(expectedTotalNodes, totalCount); +} + +/* ************************************************************************* */ +// Test 2: Recursive Void Tasks +/* ************************************************************************* */ +namespace { +std::atomic globalVoidTaskCounter{0}; + +void processNodeVoid( + std::shared_ptr node, + RecursiveTaskContext> context) { + if (!node) { + return; + } + globalVoidTaskCounter.fetch_add(1, std::memory_order_relaxed); + double childPriorityBase = static_cast(node->value); + + for (const auto& child : node->children) { + context.processChild(child, childPriorityBase - 1.0, processNodeVoid); + } +} +} // namespace + +TEST(PriorityScheduler, testSimplifiedRecursiveVoidTasks) { + std::shared_ptr root = createSimpleTree(); + PriorityScheduler scheduler(std::thread::hardware_concurrency()); + globalVoidTaskCounter.store(0); + + // FIX: Wrap the function pointer in std::function + std::function, + RecursiveTaskContext>)> + processFunc = processNodeVoid; + scheduler.runRecursive(root, 0.0, processFunc); + + const size_t expectedTotalNodes = 7; + EXPECT_LONGS_EQUAL(expectedTotalNodes, globalVoidTaskCounter.load()); +} + +/* ************************************************************************* */ +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} +/* ************************************************************************* */ \ No newline at end of file From 21addefb1cc4c8c631e9df1ce1cc35065a19aef7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 00:58:06 -0500 Subject: [PATCH 034/266] Forest Mixin --- .github/copilot-instructions.md | 1 + gtsam/base/PriorityScheduler.h | 380 +++++++++++++-------- gtsam/base/tests/testPriorityScheduler.cpp | 203 +++++++---- 3 files changed, 371 insertions(+), 213 deletions(-) diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 3d4b61e4de..242e50f720 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -6,4 +6,5 @@ For reviewing PRs: * On Windows it is necessary to explicitly export all functions from the library which should be externally accessible. To do this, include the macro `GTSAM_EXPORT` in your class or function definition. * If we add a C++ function to a `.i` file to expose it to the wrapper, we must ensure that the parameter names match exactly between the declaration in the header file and the declaration in the `.i`. Similarly, if we change any parameter names in a wrapped function in a header file, or change any parameter names in a `.i` file, we must change the corresponding function in the other file to reflect those changes. * Classes are Uppercase, methods and functions lowerMixedCase. +* Public fields in structs keep plain names (no trailing underscore). * Apart from those naming conventions, we adopt Google C++ style. diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 6e98f49266..d9729f2d4e 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -8,7 +8,7 @@ /** * @file PriorityScheduler.h - * @brief Priority-based task scheduler with a lightweight recursive API. + * @brief Priority-based task scheduler with lightweight traversal helpers. * * @details * This header defines a small, thread-based scheduler that executes tasks in @@ -16,36 +16,30 @@ * priority (min-heap behavior). Tasks return a value of type `Y`, and callers * can wait on results via `std::future`. * - * The scheduler also supports a recursive execution model via - * `RecursiveTaskContext`. A recursive function can enqueue child tasks with - * specific priorities and block until each child returns, while still allowing - * other worker threads to make progress on queued tasks. + * The scheduler also supports recursive traversal helpers via `TaskMixin`. + * Forest owners can call `runTopDown` or `runBottomUp` without manually + * managing priorities, as long as nodes expose `children()` and the forest + * exposes `roots()`. * * @par Example * @code * // Compute a sum over a tree where children are scheduled by priority. - * struct Node { std::vector children; int value; }; + * struct Node { + * std::vector children_; + * const std::vector& children() const { return children_; } + * void accumulate() { } + * }; * - * gtsam::PriorityScheduler scheduler(4); + * struct Forest : gtsam::TaskMixin { + * std::vector> roots_; + * const std::vector>& roots() const { return roots_; } + * }; * - * auto sum = scheduler.runRecursive( - * root, 0.0, // initialPriority - * [](Node node, gtsam::RecursiveTaskContext ctx) { - * int total = node.value; - * double childPriority = 1.0; - * for (const auto& child : node.children) { - * total += ctx.processChild(child, childPriority, - * [](Node c, gtsam::RecursiveTaskContext childCtx) { - * return childCtx.runSubTask(2.0, [c]() { return c.value; }); - * }); - * childPriority += 1.0; - * } - * return total; - * }); + * Forest forest; + * forest.runBottomUp(&Node::accumulate); * @endcode * - * @note `runRecursive` blocks until the root task finishes. Each recursive - * call can enqueue more work and synchronously wait on its results. + * @note `runTopDown` and `runBottomUp` block until the traversal finishes. * * @author Frank Dellaert * @date May, 2025 @@ -54,8 +48,8 @@ #pragma once #include -#include #include +#include #include #include #include @@ -64,72 +58,15 @@ #include // For std::runtime_error #include #include // For std::is_void_v +#include // For std::forward #include namespace gtsam { -/// Forward declaration for the recursive context helper. +/// Forward declaration for the scheduler. template class PriorityScheduler; -/** - * @brief Helper passed into recursive tasks to schedule child work. - * - * @details This class is intentionally lightweight: it holds a raw pointer to - * the scheduler and offers convenience methods that enqueue a task and - * immediately wait for its result. - * - * @tparam Y Result type produced by scheduled tasks. - * @tparam Node node type used by the recursive user function. - */ -template -class RecursiveTaskContext { - PriorityScheduler* scheduler_; - - public: - /// Construct a context tied to a scheduler. - explicit RecursiveTaskContext(PriorityScheduler* scheduler) - : scheduler_(scheduler) {} - - /** - * @brief Schedule a recursive child task and wait for its result. - * - * @param childNode Node to pass to the recursive function. - * @param priority Lower values execute before higher values. - * @param recursiveFunc The user-provided recursive function. - * @return Result produced by the child task. - */ - Y processChild(Node childNode, double priority, - const std::function)>& - recursiveFunc) { - assert(scheduler_); - - // The lambda captures recursiveFunc by reference (as it's a const ref - // parameter) and scheduler by pointer. childNode is copied. - auto childJob = [childNode, scheduler = scheduler_, &recursiveFunc]() -> Y { - RecursiveTaskContext childContext(scheduler); - return recursiveFunc(childNode, childContext); - }; - - std::future future = scheduler_->schedule(priority, std::move(childJob)); - return future.get(); - } - - /** - * @brief Schedule an arbitrary subtask and wait for its result. - * - * @param priority Lower values execute before higher values. - * @param subTaskJob Callable representing the subtask. - * @return Result produced by the subtask. - */ - Y runSubTask(double priority, std::function subTaskJob) { - assert(scheduler_); - std::future future = - scheduler_->schedule(priority, std::move(subTaskJob)); - return future.get(); - } -}; - /** * @brief Thread pool scheduler that prioritizes tasks by numeric priority. * @@ -137,8 +74,7 @@ class RecursiveTaskContext { * - Lower numeric values are executed first. * - Tasks are executed by worker threads created at construction. * - `schedule` returns a `std::future` for the task result. - * - `runRecursive` executes a root task and allows it to spawn child tasks - * through `RecursiveTaskContext`. + * - `TaskMixin` offers recursive traversal helpers without manual priorities. * * @tparam Y Result type returned by tasks. Use `void` for no return value. */ @@ -149,8 +85,8 @@ class PriorityScheduler { std::function job; std::promise promise; - Task(double p, std::function j, std::promise _promise) - : priority(p), job(std::move(j)), promise(std::move(_promise)) {} + Task(double p, std::function j, std::promise p_out) + : priority(p), job(std::move(j)), promise(std::move(p_out)) {} }; using TaskPtr = std::shared_ptr; @@ -169,26 +105,10 @@ class PriorityScheduler { std::atomic activeTasks_{0}; /** - * @brief Worker thread function for processing tasks from the priority queue. - * - * This function runs in a loop, waiting for tasks to become available in the - * task queue. It uses a condition variable (`condition_`) to efficiently wait - * until either: - * - The scheduler is stopped (`stop_` is true), or - * - There is at least one task in the queue (`!taskQueue_.empty()`). - * - * The condition variable is notified whenever a new task is added or when the - * scheduler is stopped, allowing the worker thread to wake up and check the - * queue or exit if needed. The use of the condition variable ensures that - * worker threads do not spin-wait, but instead sleep efficiently until work - * is available or shutdown is requested. - * - * When a task is available, the worker thread pops the highest-priority task - * from the queue, executes it, and sets the result or exception in the - * associated promise. After processing a task, it decrements the count of - * active tasks and notifies all waiting threads via the condition variable, - * in case other threads are waiting for tasks to complete. + * @brief Worker loop: wait for tasks, run them, and fulfill promises. * + * Uses a condition variable to avoid spinning while the queue is empty. + * Stops once `stop_` is set and no queued work remains. */ void worker_thread() { while (true) { @@ -199,10 +119,7 @@ class PriorityScheduler { return stop_.load(std::memory_order_relaxed) || !taskQueue_.empty(); }); - if (taskQueue_.empty()) { - if (stop_.load(std::memory_order_relaxed)) return; - continue; - } + if (stop_.load(std::memory_order_relaxed) && taskQueue_.empty()) return; // Pop the highest-priority task: task = taskQueue_.top(); @@ -225,9 +142,11 @@ class PriorityScheduler { } } - // Decrement active task count and notify waiting threads: - std::lock_guard lock(queueMutex_); - activeTasks_.fetch_sub(1, std::memory_order_relaxed); + // Decrement active task count and notify waiters. + { + std::lock_guard lock(queueMutex_); + activeTasks_.fetch_sub(1, std::memory_order_relaxed); + } condition_.notify_all(); } } @@ -291,34 +210,6 @@ class PriorityScheduler { return future; } - /** - * @brief Execute a recursive task starting at a root node. - * - * @details The user function can schedule child tasks via the provided - * `RecursiveTaskContext`. This call blocks until the root task completes. - * - * @param rootNode The starting node for recursion. - * @param initialPriority Priority for the root task. - * @param userRecursiveFunc The recursive function implementation. - * @return Result of the root task. - */ - template - Y runRecursive( - Node rootNode, double initialPriority, - // This std::function is copied when topLevelJob is created. - std::function)> userRecursiveFunc) { - auto topLevelJob = [this, rootNode, - userRecursiveFunc_copy = - std::move(userRecursiveFunc)]() -> Y { - RecursiveTaskContext rootContext(this); - return userRecursiveFunc_copy(rootNode, rootContext); - }; - - std::future topLevelFuture = - schedule(initialPriority, std::move(topLevelJob)); - return topLevelFuture.get(); - } - /** * @brief Block until all queued and active tasks complete. * @@ -332,9 +223,212 @@ class PriorityScheduler { taskQueue_.empty()); }); } +}; + +/** + * @brief Mixin that provides depth-based top-down or bottom-up traversal + * helpers on a forest owner. + * + * @details + * This helper hides explicit priority management by assigning priorities based + * on recursion depth. It is intended for method-style use in recursive solvers. + * Tasks are scheduled on the provided scheduler and synchronized with + * continuations to avoid blocking worker threads. + * + * @note The traversal helpers operate on `PriorityScheduler` and rely on + * node-local state for any computed results. + */ +template +class TaskMixin { + public: + /** + * @brief Run a top-down traversal (root first). + * + * @note Traversal helpers use a `PriorityScheduler`. + * @note `Forest::roots()` must return a range of pointer-like `Node` roots. + * @note `Node::children()` must return a range of pointer-like `Node` + * children. + */ + template + void runTopDown(Fn fn) { + // Run a top-down traversal with continuations. + auto state = std::make_shared(); + std::future done = state->done.get_future(); + Forest& forest = static_cast(*this); + const auto& roots = forest.roots(); + if (roots.empty()) { + state->done.set_value(); + return; + } + // Hold completion until all roots are scheduled. + state->pending.fetch_add(1, std::memory_order_relaxed); + for (const auto& root : roots) { + topDownAsync(*root, 0, fn, state); + } + finishTraversal(state); + done.get(); + } - template - friend class RecursiveTaskContext; + /** + * @brief Run a bottom-up traversal (leaves first). + * + * @note Traversal helpers use a `PriorityScheduler`. + * @note `Forest::roots()` must return a range of pointer-like `Node` roots. + * @note `Node::children()` must return a range of pointer-like `Node` + * children. + */ + template + void runBottomUp(Fn fn) { + // Run a bottom-up traversal with continuations. + auto state = std::make_shared(); + std::future done = state->done.get_future(); + Forest& forest = static_cast(*this); + const auto& roots = forest.roots(); + if (roots.empty()) { + state->done.set_value(); + return; + } + // Hold completion until all roots are scheduled. + state->pending.fetch_add(1, std::memory_order_relaxed); + for (const auto& root : roots) { + bottomUpAsync(*root, 0, fn, state, [] {}); + } + finishTraversal(state); + done.get(); + } + + protected: + /// Construct the mixin with a fixed-size scheduler. + explicit TaskMixin(size_t numThreads = std::thread::hardware_concurrency()) + : scheduler_(numThreads) {} + + /// Priority for top-down traversal (root first). + double topDownPriority(int depth) const { return static_cast(depth); } + + /// Priority for bottom-up traversal (leaves first). + double bottomUpPriority(int depth) const { + return -static_cast(depth); + } + + private: + PriorityScheduler scheduler_; + + /// Shared traversal state across scheduled tasks. + struct TraversalState { + std::atomic pending{0}; + std::atomic hasException{false}; + std::exception_ptr exception; + std::promise done; + }; + using StatePtr = std::shared_ptr; + + /// Schedule top-down work for a node and its children. + template + void topDownAsync(Node& node, int depth, const Fn& fn, + const StatePtr& state) { + auto task = [this, &node, depth, &fn, state]() { + try { + std::invoke(fn, node); + // Children are scheduled after the node in top-down order. + auto&& children = node.children(); + for (const auto& child : children) { + topDownAsync(*child, depth + 1, fn, state); + } + finishTraversal(state); + } catch (...) { + recordException(state, std::current_exception()); + finishTraversal(state); + throw; + } + }; + + scheduleTask(topDownPriority(depth), state, std::move(task)); + } + + /// Schedule children first, then the parent node once all children finish. + template + void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, + const std::function& onDone) { + auto&& children = node.children(); + if (children.empty()) { + scheduleBottomUpNode(node, depth, fn, state, onDone); + return; + } + + auto remaining = + std::make_shared>(static_cast(children.size())); + std::function childDone = [this, &node, depth, &fn, state, onDone, + remaining]() { + if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { + // If a child failed, skip this node and just unwind. + if (state->hasException.load(std::memory_order_relaxed)) { + onDone(); + return; + } + scheduleBottomUpNode(node, depth, fn, state, onDone); + } + }; + + for (const auto& child : children) { + bottomUpAsync(*child, depth + 1, fn, state, childDone); + } + } + + /// Schedule bottom-up work for a node after its children finish. + template + void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, + const StatePtr& state, + const std::function& onDone) { + auto task = [this, &node, &fn, state, onDone]() { + try { + std::invoke(fn, node); + onDone(); + finishTraversal(state); + } catch (...) { + recordException(state, std::current_exception()); + onDone(); + finishTraversal(state); + throw; + } + }; + + scheduleTask(bottomUpPriority(depth), state, std::move(task)); + } + + /// Schedule a task and increment the pending counter. + template + void scheduleTask(double priority, const StatePtr& state, F&& job) { + // Each scheduled task increments the pending counter. + state->pending.fetch_add(1, std::memory_order_relaxed); + scheduler_.schedule(priority, std::function(std::forward(job))); + } + + /// Record the first exception and keep the traversal draining. + void recordException(const StatePtr& state, std::exception_ptr exception) { + // Record the first exception and let the traversal finish. + bool expected = false; + if (state->hasException.compare_exchange_strong( + expected, true, std::memory_order_relaxed)) { + state->exception = exception; + } + } + + /// Resolve traversal completion once pending reaches zero. + void finishTraversal(const StatePtr& state) { + if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { + if (state->hasException.load(std::memory_order_relaxed)) { + try { + state->done.set_exception(state->exception); + } catch (...) { /* ignore */ + } + } else { + try { + state->done.set_value(); + } catch (...) { /* ignore */ + } + } + } + } }; } // namespace gtsam diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index d35dacbc21..17511d5427 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -8,7 +8,7 @@ /** * @file testPriorityScheduler.cpp - * @brief Unit tests for PriorityScheduler with simplified recursive API. + * @brief Unit tests for PriorityScheduler with forest TaskMixin helpers. * @author Frank Dellaert * @date May, 2025 */ @@ -16,105 +16,168 @@ #include #include -#include // For void test counter -#include -#include // For A* closed set +#include #include -#include // For A* result #include using namespace gtsam; -// Common TreeNode definition (using shared_ptr) +/* ************************************************************************* */ +// Compose child return values inside a parent task +TEST(PriorityScheduler, ReturnValueComposition) { + // Use multiple workers so parent tasks can wait without stalling the pool. + PriorityScheduler scheduler(2); + + // Schedule leaves first with higher priority (lower numeric value). + auto left = scheduler.schedule(0.0, [] { return size_t{3}; }).share(); + auto right = scheduler.schedule(0.0, [] { return size_t{4}; }).share(); + + // Parent task waits on the child futures and combines their values. + auto parent = scheduler.schedule( + 10.0, [left, right]() mutable { return left.get() + right.get(); }); + + const size_t expectedSum = 7; + EXPECT_LONGS_EQUAL(expectedSum, parent.get()); +} + +/* ************************************************************************* */ +// Forest mixin tests +/* ************************************************************************* */ + +/// Common TreeNode definition (using shared_ptr). struct TreeNode { int value; - std::vector> children; - TreeNode(int v) : value(v) {} - void addChild(std::shared_ptr child) { children.push_back(child); } + explicit TreeNode(int v) : value(v) {} }; namespace { -std::shared_ptr createSimpleTree() { - auto n5 = std::make_shared(5); - auto n3 = std::make_shared(3); - auto n8 = std::make_shared(8); - auto n2 = std::make_shared(2); - auto n1 = std::make_shared(1); - auto n4 = std::make_shared(4); - auto n7 = std::make_shared(7); - n5->children = {n3, n8, n2}; - n3->children = {n1, n4}; - n2->children = {n7}; - return n5; +/// Build a small tree for tests. +template +std::shared_ptr createSimpleTree() { + auto n1 = std::make_shared(1); + auto n2 = std::make_shared(2); + auto n3 = std::make_shared(3); + auto n4 = std::make_shared(4); + auto n5 = std::make_shared(5); + auto n6 = std::make_shared(6); + auto n7 = std::make_shared(7); + n1->children() = {n2, n3, n4}; + n2->children() = {n5, n6}; + n6->children() = {n7}; + return n1; } + } // namespace /* ************************************************************************* */ -// Test 1: Recursive Node Counting (returns size_t) +// Test 1: Bottom-up traversal /* ************************************************************************* */ -namespace { -size_t countNodesRecursive( - std::shared_ptr node, - RecursiveTaskContext> context) { - if (!node) return 0; - size_t count = 1; - double childPriorityBase = static_cast(node->value); - - for (const auto& child : node->children) { - count += context.processChild(child, childPriorityBase - 1.0, - countNodesRecursive); +/// TreeNode specialization for bottom-up test. +struct TestNode1 : public TreeNode { + using TreeNode::TreeNode; + std::vector> children_; + size_t subtreeCount{0}; + + /// Return child list for traversal. + std::vector>& children() { return children_; } + + /// Bottom-up payload for tests. + void bottomUpValue() { + // Each node stores 1 plus the sum of its children's results. + size_t sum = 1; + for (const auto& child : children()) { + sum += child->subtreeCount; + } + subtreeCount = sum; } - return count; -} -} // namespace +}; -TEST(PriorityScheduler, RecursiveNodeCounting) { - std::shared_ptr root = createSimpleTree(); - PriorityScheduler scheduler(std::thread::hardware_concurrency()); +/// Forest wrapper for bottom-up traversal. +struct TestForest1 : public TaskMixin { + std::vector> roots_; - std::function, - RecursiveTaskContext>)> - countFunc = countNodesRecursive; - size_t totalCount = scheduler.runRecursive(root, 0.0, countFunc); + const std::vector>& roots() const { + return roots_; + } + + /// Count nodes using a bottom-up traversal. + size_t countNodes() { + runBottomUp(&TestNode1::bottomUpValue); + size_t total = 0; + for (const auto& root : roots_) { + total += root->subtreeCount; + } + return total; + } +}; + +TEST(PriorityScheduler, TaskMixinBottomUpCounting) { + std::shared_ptr root = createSimpleTree(); + TestForest1 forest; + forest.roots_.push_back(root); + size_t rootValue = forest.countNodes(); const size_t expectedTotalNodes = 7; - EXPECT_LONGS_EQUAL(expectedTotalNodes, totalCount); + EXPECT_LONGS_EQUAL(expectedTotalNodes, rootValue); } /* ************************************************************************* */ -// Test 2: Recursive Void Tasks +// Test 2: Top-down traversal /* ************************************************************************* */ -namespace { -std::atomic globalVoidTaskCounter{0}; - -void processNodeVoid( - std::shared_ptr node, - RecursiveTaskContext> context) { - if (!node) { - return; +/// TreeNode specialization for top-down test. +struct TestNode2 : public TreeNode { + using TreeNode::TreeNode; + std::vector> children_; + bool visited{false}; + size_t subtreeCount{0}; + + /// Return child list for traversal. + std::vector>& children() { return children_; } + + /// Top-down payload for tests. + void topDownTouch() { visited = true; } + + /// Bottom-up payload for tests. + void bottomUpVisitedCount() { + // Each node stores 1 if visited plus the sum of its children. + size_t sum = visited ? 1 : 0; + for (const auto& child : children()) { + sum += child->subtreeCount; + } + subtreeCount = sum; } - globalVoidTaskCounter.fetch_add(1, std::memory_order_relaxed); - double childPriorityBase = static_cast(node->value); +}; + +/// Forest wrapper for top-down traversal. +struct TestForest2 : public TaskMixin { + std::vector> roots_; - for (const auto& child : node->children) { - context.processChild(child, childPriorityBase - 1.0, processNodeVoid); + const std::vector>& roots() const { + return roots_; } -} -} // namespace -TEST(PriorityScheduler, testSimplifiedRecursiveVoidTasks) { - std::shared_ptr root = createSimpleTree(); - PriorityScheduler scheduler(std::thread::hardware_concurrency()); - globalVoidTaskCounter.store(0); + /// Touch all nodes using a top-down traversal. + void touchAll() { runTopDown(&TestNode2::topDownTouch); } + + /// Count visited nodes using a bottom-up traversal. + size_t countVisited() { + runBottomUp(&TestNode2::bottomUpVisitedCount); + size_t total = 0; + for (const auto& root : roots_) { + total += root->subtreeCount; + } + return total; + } +}; - // FIX: Wrap the function pointer in std::function - std::function, - RecursiveTaskContext>)> - processFunc = processNodeVoid; - scheduler.runRecursive(root, 0.0, processFunc); +TEST(PriorityScheduler, TaskMixinTopDown) { + std::shared_ptr root = createSimpleTree(); + TestForest2 forest; + forest.roots_.push_back(root); + forest.touchAll(); const size_t expectedTotalNodes = 7; - EXPECT_LONGS_EQUAL(expectedTotalNodes, globalVoidTaskCounter.load()); + EXPECT_LONGS_EQUAL(expectedTotalNodes, forest.countVisited()); } /* ************************************************************************* */ @@ -122,4 +185,4 @@ int main() { TestResult tr; return TestRegistry::runAllTests(tr); } -/* ************************************************************************* */ \ No newline at end of file +/* ************************************************************************* */ From 301a36f56f573e516684f835bcb05c4739cb876c Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 08:25:58 -0500 Subject: [PATCH 035/266] Allow for children/roots fields --- gtsam/base/PriorityScheduler.h | 55 +++++++++++++++++++++++++--------- 1 file changed, 41 insertions(+), 14 deletions(-) diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index d9729f2d4e..8ee86a65d9 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -237,17 +237,16 @@ class PriorityScheduler { * * @note The traversal helpers operate on `PriorityScheduler` and rely on * node-local state for any computed results. + * @note `Forest::roots()` or `Forest::roots` must return a range of + * pointer-like `Node` roots. + * @note `Node::children()` or `Node::children` must return a range of + * pointer-like `Node` children. */ template class TaskMixin { public: /** * @brief Run a top-down traversal (root first). - * - * @note Traversal helpers use a `PriorityScheduler`. - * @note `Forest::roots()` must return a range of pointer-like `Node` roots. - * @note `Node::children()` must return a range of pointer-like `Node` - * children. */ template void runTopDown(Fn fn) { @@ -255,7 +254,7 @@ class TaskMixin { auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); - const auto& roots = forest.roots(); + const auto& roots = rootsOf(forest); if (roots.empty()) { state->done.set_value(); return; @@ -271,11 +270,6 @@ class TaskMixin { /** * @brief Run a bottom-up traversal (leaves first). - * - * @note Traversal helpers use a `PriorityScheduler`. - * @note `Forest::roots()` must return a range of pointer-like `Node` roots. - * @note `Node::children()` must return a range of pointer-like `Node` - * children. */ template void runBottomUp(Fn fn) { @@ -283,7 +277,7 @@ class TaskMixin { auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); - const auto& roots = forest.roots(); + const auto& roots = rootsOf(forest); if (roots.empty()) { state->done.set_value(); return; @@ -330,7 +324,7 @@ class TaskMixin { try { std::invoke(fn, node); // Children are scheduled after the node in top-down order. - auto&& children = node.children(); + auto&& children = childrenOf(node); for (const auto& child : children) { topDownAsync(*child, depth + 1, fn, state); } @@ -349,7 +343,7 @@ class TaskMixin { template void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, const std::function& onDone) { - auto&& children = node.children(); + auto&& children = childrenOf(node); if (children.empty()) { scheduleBottomUpNode(node, depth, fn, state, onDone); return; @@ -429,6 +423,39 @@ class TaskMixin { } } } + + template + struct HasChildrenMethod : std::false_type {}; + + template + struct HasChildrenMethod().children())>> + : std::true_type {}; + + template + struct HasRootsMethod : std::false_type {}; + + template + struct HasRootsMethod().roots())>> + : std::true_type {}; + + template + static auto& childrenOf(T& node) { + if constexpr (HasChildrenMethod::value) { + return node.children(); + } else { + return node.children; + } + } + + template + static auto& rootsOf(T& forest) { + if constexpr (HasRootsMethod::value) { + return forest.roots(); + } else { + return forest.roots; + } + } }; } // namespace gtsam From e5fdab576b77553e559bb18d5e302f2e556462dc Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 23:11:16 -0500 Subject: [PATCH 036/266] Per-thread queues, stealing, help-then-wait --- gtsam/base/PriorityScheduler.h | 181 ++++++++++++++++----- gtsam/base/tests/testPriorityScheduler.cpp | 8 +- 2 files changed, 145 insertions(+), 44 deletions(-) diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 8ee86a65d9..c84a5264c0 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -75,17 +75,20 @@ class PriorityScheduler; * - Tasks are executed by worker threads created at construction. * - `schedule` returns a `std::future` for the task result. * - `TaskMixin` offers recursive traversal helpers without manual priorities. + * - Per-thread queues reduce contention; workers steal from peers when idle. + * - External submissions are round-robin distributed across worker queues. + * - A condition variable parks workers when no work is available. * * @tparam Y Result type returned by tasks. Use `void` for no return value. */ template class PriorityScheduler { struct Task { - double priority; + int priority; std::function job; std::promise promise; - Task(double p, std::function j, std::promise p_out) + Task(int p, std::function j, std::promise p_out) : priority(p), job(std::move(j)), promise(std::move(p_out)) {} }; @@ -97,12 +100,21 @@ class PriorityScheduler { } }; - std::priority_queue, Compare> taskQueue_; + struct WorkerQueue { + std::mutex mutex; + std::priority_queue, Compare> queue; + }; + + std::vector> queues_; // Per-worker queues. + std::atomic queuedTasks_{0}; // Tracks queued work for wakeups. std::vector workers_; - mutable std::mutex queueMutex_; + mutable std::mutex waitMutex_; // Dedicated mutex for condition_variable. std::condition_variable condition_; std::atomic stop_{false}; - std::atomic activeTasks_{0}; + std::atomic activeTasks_{0}; // In-flight tasks (queued + running). + inline static thread_local PriorityScheduler* currentScheduler_ = nullptr; + inline static thread_local int workerIndex_ = -1; + std::atomic nextWorker_{0}; // Round-robin distributor. /** * @brief Worker loop: wait for tasks, run them, and fulfill promises. @@ -110,20 +122,25 @@ class PriorityScheduler { * Uses a condition variable to avoid spinning while the queue is empty. * Stops once `stop_` is set and no queued work remains. */ - void worker_thread() { + /// Worker loop that executes tasks until shutdown. + void worker_thread(size_t index) { + currentScheduler_ = this; + workerIndex_ = static_cast(index); while (true) { TaskPtr task; - { - std::unique_lock lock(queueMutex_); + if (!tryPopTask(index, task)) { + std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { - return stop_.load(std::memory_order_relaxed) || !taskQueue_.empty(); + return stop_.load(std::memory_order_relaxed) || + queuedTasks_.load(std::memory_order_relaxed) > 0; }); - - if (stop_.load(std::memory_order_relaxed) && taskQueue_.empty()) return; - - // Pop the highest-priority task: - task = taskQueue_.top(); - taskQueue_.pop(); + if (stop_.load(std::memory_order_relaxed) && + queuedTasks_.load(std::memory_order_relaxed) == 0) { + currentScheduler_ = nullptr; + workerIndex_ = -1; + return; + } + continue; } try { @@ -143,14 +160,51 @@ class PriorityScheduler { } // Decrement active task count and notify waiters. - { - std::lock_guard lock(queueMutex_); - activeTasks_.fetch_sub(1, std::memory_order_relaxed); - } + activeTasks_.fetch_sub(1, std::memory_order_relaxed); condition_.notify_all(); } } + /// Attempt to get a task from local or stolen queues. + bool tryPopTask(size_t index, TaskPtr& task) { + // Prefer local queue, then steal to keep workers busy. + if (tryPopLocal(index, task)) return true; + if (trySteal(index, task)) return true; + return false; + } + + /// Try to pop a task from the current worker queue. + bool tryPopLocal(size_t index, TaskPtr& task) { + WorkerQueue& queue = *queues_[index]; + std::lock_guard lock(queue.mutex); + if (queue.queue.empty()) return false; + task = queue.queue.top(); + queue.queue.pop(); + queuedTasks_.fetch_sub(1, std::memory_order_relaxed); + return true; + } + + /// Try to steal a task from another worker queue. + bool trySteal(size_t index, TaskPtr& task) { + const size_t workerCount = queues_.size(); + if (workerCount <= 1) return false; + // Steal from other workers if local queue is empty. + for (size_t offset = 1; offset < workerCount; ++offset) { + size_t target = (index + offset) % workerCount; + WorkerQueue& queue = *queues_[target]; + std::unique_lock lock(queue.mutex, std::try_to_lock); + if (!lock.owns_lock() || queue.queue.empty()) continue; + task = queue.queue.top(); + queue.queue.pop(); + queuedTasks_.fetch_sub(1, std::memory_order_relaxed); + return true; + } + return false; + } + + /// Return true if called on a scheduler worker thread. + bool isWorkerThread() const { return currentScheduler_ == this; } + public: /** * @brief Construct a scheduler with a fixed number of worker threads. @@ -158,10 +212,15 @@ class PriorityScheduler { * @param numThreads Number of worker threads to create. If zero, a single * thread is created. */ + /// Construct a scheduler with a fixed worker count. PriorityScheduler(size_t numThreads = std::thread::hardware_concurrency()) { if (numThreads == 0) numThreads = 1; + queues_.reserve(numThreads); for (size_t i = 0; i < numThreads; ++i) { - workers_.emplace_back(&PriorityScheduler::worker_thread, this); + queues_.push_back(std::make_unique()); + } + for (size_t i = 0; i < numThreads; ++i) { + workers_.emplace_back(&PriorityScheduler::worker_thread, this, i); } } @@ -170,6 +229,7 @@ class PriorityScheduler { * * @note The destructor calls `waitForAllTasks` before stopping workers. */ + /// Wait for tasks and join worker threads on destruction. ~PriorityScheduler() { waitForAllTasks(); stop_.store(true, std::memory_order_relaxed); @@ -188,7 +248,8 @@ class PriorityScheduler { * @param job Callable returning a `Y`. * @return `std::future` associated with the task. */ - std::future schedule(double priority, std::function job) { + /// Enqueue a task for execution and return its future. + std::future schedule(int priority, std::function job) { if (stop_.load(std::memory_order_relaxed)) { std::promise err_promise; err_promise.set_exception(std::make_exception_ptr( @@ -201,26 +262,64 @@ class PriorityScheduler { auto task = std::make_shared(priority, std::move(job), std::move(promise)); - { - std::lock_guard lock(queueMutex_); - taskQueue_.push(task); - activeTasks_.fetch_add(1, std::memory_order_relaxed); + if (isWorkerThread()) { + // Worker submissions go to the local queue for cache locality. + WorkerQueue& queue = *queues_[static_cast(workerIndex_)]; + { + std::lock_guard lock(queue.mutex); + queue.queue.push(task); + } + } else { + // External submissions are distributed across worker queues. + const size_t target = + nextWorker_.fetch_add(1, std::memory_order_relaxed) % queues_.size(); + WorkerQueue& queue = *queues_[target]; + std::lock_guard lock(queue.mutex); + queue.queue.push(task); } + queuedTasks_.fetch_add(1, std::memory_order_relaxed); + // Track in-flight tasks for waiters. + activeTasks_.fetch_add(1, std::memory_order_relaxed); condition_.notify_one(); return future; } + /** + * @brief Schedule or run inline when called from a worker thread. + * + * Used to fuse continuations without re-entering the queues. + */ + template >> + /// Run inline on workers, otherwise enqueue normally. + void scheduleOrRunInline(int priority, std::function job) { + if (stop_.load(std::memory_order_relaxed)) return; + if (!isWorkerThread()) { + schedule(priority, std::move(job)); + return; + } + + // Inline execution fuses continuations and avoids re-queueing. + activeTasks_.fetch_add(1, std::memory_order_relaxed); + try { + job(); + } catch (...) { /* ignore */ + } + activeTasks_.fetch_sub(1, std::memory_order_relaxed); + condition_.notify_all(); + } + /** * @brief Block until all queued and active tasks complete. * * @note If the scheduler is stopping, this returns early. */ + /// Block until all queued and active tasks complete. void waitForAllTasks() { - std::unique_lock lock(queueMutex_); + std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { return stop_.load(std::memory_order_relaxed) || (activeTasks_.load(std::memory_order_relaxed) == 0 && - taskQueue_.empty()); + queuedTasks_.load(std::memory_order_relaxed) == 0); }); } }; @@ -233,7 +332,8 @@ class PriorityScheduler { * This helper hides explicit priority management by assigning priorities based * on recursion depth. It is intended for method-style use in recursive solvers. * Tasks are scheduled on the provided scheduler and synchronized with - * continuations to avoid blocking worker threads. + * continuations to avoid blocking worker threads. Bottom-up continuations can + * run inline when called from a worker to reduce queue traffic. * * @note The traversal helpers operate on `PriorityScheduler` and rely on * node-local state for any computed results. @@ -245,9 +345,7 @@ class PriorityScheduler { template class TaskMixin { public: - /** - * @brief Run a top-down traversal (root first). - */ + /// Run a top-down traversal (root first). template void runTopDown(Fn fn) { // Run a top-down traversal with continuations. @@ -268,9 +366,7 @@ class TaskMixin { done.get(); } - /** - * @brief Run a bottom-up traversal (leaves first). - */ + /// Run a bottom-up traversal (leaves first). template void runBottomUp(Fn fn) { // Run a bottom-up traversal with continuations. @@ -297,12 +393,10 @@ class TaskMixin { : scheduler_(numThreads) {} /// Priority for top-down traversal (root first). - double topDownPriority(int depth) const { return static_cast(depth); } + int topDownPriority(int depth) const { return depth; } /// Priority for bottom-up traversal (leaves first). - double bottomUpPriority(int depth) const { - return -static_cast(depth); - } + int bottomUpPriority(int depth) const { return -depth; } private: PriorityScheduler scheduler_; @@ -386,12 +480,17 @@ class TaskMixin { } }; - scheduleTask(bottomUpPriority(depth), state, std::move(task)); + // Each scheduled task increments the pending counter. + state->pending.fetch_add(1, std::memory_order_relaxed); + + /// Schedule a continuation or run it inline if already on a worker thread. + scheduler_.scheduleOrRunInline(bottomUpPriority(depth), + std::function(std::move(task))); } /// Schedule a task and increment the pending counter. template - void scheduleTask(double priority, const StatePtr& state, F&& job) { + void scheduleTask(int priority, const StatePtr& state, F&& job) { // Each scheduled task increments the pending counter. state->pending.fetch_add(1, std::memory_order_relaxed); scheduler_.schedule(priority, std::function(std::forward(job))); @@ -439,6 +538,7 @@ class TaskMixin { struct HasRootsMethod().roots())>> : std::true_type {}; + /// Return node children via method or field. template static auto& childrenOf(T& node) { if constexpr (HasChildrenMethod::value) { @@ -448,6 +548,7 @@ class TaskMixin { } } + /// Return forest roots via method or field. template static auto& rootsOf(T& forest) { if constexpr (HasRootsMethod::value) { diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index 17511d5427..0e1bae51b5 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -29,12 +29,12 @@ TEST(PriorityScheduler, ReturnValueComposition) { PriorityScheduler scheduler(2); // Schedule leaves first with higher priority (lower numeric value). - auto left = scheduler.schedule(0.0, [] { return size_t{3}; }).share(); - auto right = scheduler.schedule(0.0, [] { return size_t{4}; }).share(); + auto left = scheduler.schedule(0, [] { return size_t{3}; }).share(); + auto right = scheduler.schedule(0, [] { return size_t{4}; }).share(); // Parent task waits on the child futures and combines their values. - auto parent = scheduler.schedule( - 10.0, [left, right]() mutable { return left.get() + right.get(); }); + auto parent = + scheduler.schedule(10, [left, right]() mutable { return left.get() + right.get(); }); const size_t expectedSum = 7; EXPECT_LONGS_EQUAL(expectedSum, parent.get()); From 599e2a5af09b95729ccc234d1808ab018755b260 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 13:16:17 -0500 Subject: [PATCH 037/266] Fix small issues from 5.2 review --- gtsam/base/PriorityScheduler.h | 51 +++++++++++++++------------------- 1 file changed, 23 insertions(+), 28 deletions(-) diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index c84a5264c0..c556fa7559 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -63,10 +63,6 @@ namespace gtsam { -/// Forward declaration for the scheduler. -template -class PriorityScheduler; - /** * @brief Thread pool scheduler that prioritizes tasks by numeric priority. * @@ -74,7 +70,6 @@ class PriorityScheduler; * - Lower numeric values are executed first. * - Tasks are executed by worker threads created at construction. * - `schedule` returns a `std::future` for the task result. - * - `TaskMixin` offers recursive traversal helpers without manual priorities. * - Per-thread queues reduce contention; workers steal from peers when idle. * - External submissions are round-robin distributed across worker queues. * - A condition variable parks workers when no work is available. @@ -131,11 +126,11 @@ class PriorityScheduler { if (!tryPopTask(index, task)) { std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { - return stop_.load(std::memory_order_relaxed) || - queuedTasks_.load(std::memory_order_relaxed) > 0; + return stop_.load(std::memory_order_acquire) || + queuedTasks_.load(std::memory_order_acquire) > 0; }); - if (stop_.load(std::memory_order_relaxed) && - queuedTasks_.load(std::memory_order_relaxed) == 0) { + if (stop_.load(std::memory_order_acquire) && + queuedTasks_.load(std::memory_order_acquire) == 0) { currentScheduler_ = nullptr; workerIndex_ = -1; return; @@ -160,7 +155,7 @@ class PriorityScheduler { } // Decrement active task count and notify waiters. - activeTasks_.fetch_sub(1, std::memory_order_relaxed); + activeTasks_.fetch_sub(1, std::memory_order_release); condition_.notify_all(); } } @@ -180,7 +175,7 @@ class PriorityScheduler { if (queue.queue.empty()) return false; task = queue.queue.top(); queue.queue.pop(); - queuedTasks_.fetch_sub(1, std::memory_order_relaxed); + queuedTasks_.fetch_sub(1, std::memory_order_release); return true; } @@ -196,7 +191,7 @@ class PriorityScheduler { if (!lock.owns_lock() || queue.queue.empty()) continue; task = queue.queue.top(); queue.queue.pop(); - queuedTasks_.fetch_sub(1, std::memory_order_relaxed); + queuedTasks_.fetch_sub(1, std::memory_order_release); return true; } return false; @@ -232,7 +227,7 @@ class PriorityScheduler { /// Wait for tasks and join worker threads on destruction. ~PriorityScheduler() { waitForAllTasks(); - stop_.store(true, std::memory_order_relaxed); + stop_.store(true, std::memory_order_release); condition_.notify_all(); for (std::thread& worker : workers_) { if (worker.joinable()) { @@ -250,7 +245,7 @@ class PriorityScheduler { */ /// Enqueue a task for execution and return its future. std::future schedule(int priority, std::function job) { - if (stop_.load(std::memory_order_relaxed)) { + if (stop_.load(std::memory_order_acquire)) { std::promise err_promise; err_promise.set_exception(std::make_exception_ptr( std::runtime_error("Scheduler is stopping or stopped."))); @@ -277,9 +272,9 @@ class PriorityScheduler { std::lock_guard lock(queue.mutex); queue.queue.push(task); } - queuedTasks_.fetch_add(1, std::memory_order_relaxed); + queuedTasks_.fetch_add(1, std::memory_order_release); // Track in-flight tasks for waiters. - activeTasks_.fetch_add(1, std::memory_order_relaxed); + activeTasks_.fetch_add(1, std::memory_order_release); condition_.notify_one(); return future; } @@ -299,12 +294,12 @@ class PriorityScheduler { } // Inline execution fuses continuations and avoids re-queueing. - activeTasks_.fetch_add(1, std::memory_order_relaxed); + activeTasks_.fetch_add(1, std::memory_order_release); try { job(); } catch (...) { /* ignore */ } - activeTasks_.fetch_sub(1, std::memory_order_relaxed); + activeTasks_.fetch_sub(1, std::memory_order_release); condition_.notify_all(); } @@ -317,9 +312,9 @@ class PriorityScheduler { void waitForAllTasks() { std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { - return stop_.load(std::memory_order_relaxed) || - (activeTasks_.load(std::memory_order_relaxed) == 0 && - queuedTasks_.load(std::memory_order_relaxed) == 0); + return stop_.load(std::memory_order_acquire) || + (activeTasks_.load(std::memory_order_acquire) == 0 && + queuedTasks_.load(std::memory_order_acquire) == 0); }); } }; @@ -404,6 +399,7 @@ class TaskMixin { /// Shared traversal state across scheduled tasks. struct TraversalState { std::atomic pending{0}; + std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; std::atomic hasException{false}; std::exception_ptr exception; std::promise done; @@ -426,7 +422,7 @@ class TaskMixin { } catch (...) { recordException(state, std::current_exception()); finishTraversal(state); - throw; + return; } }; @@ -449,7 +445,7 @@ class TaskMixin { remaining]() { if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { // If a child failed, skip this node and just unwind. - if (state->hasException.load(std::memory_order_relaxed)) { + if (state->hasException.load(std::memory_order_acquire)) { onDone(); return; } @@ -476,7 +472,7 @@ class TaskMixin { recordException(state, std::current_exception()); onDone(); finishTraversal(state); - throw; + return; } }; @@ -499,17 +495,16 @@ class TaskMixin { /// Record the first exception and keep the traversal draining. void recordException(const StatePtr& state, std::exception_ptr exception) { // Record the first exception and let the traversal finish. - bool expected = false; - if (state->hasException.compare_exchange_strong( - expected, true, std::memory_order_relaxed)) { + if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { state->exception = exception; + state->hasException.store(true, std::memory_order_release); } } /// Resolve traversal completion once pending reaches zero. void finishTraversal(const StatePtr& state) { if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { - if (state->hasException.load(std::memory_order_relaxed)) { + if (state->hasException.load(std::memory_order_acquire)) { try { state->done.set_exception(state->exception); } catch (...) { /* ignore */ From 52b6d802b2c984592f8a91045820731cf50dc106 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 14:36:47 -0500 Subject: [PATCH 038/266] Mixin -> ForestTraversal.h --- gtsam/base/ForestTraversal.h | 274 +++++++++++++++++++++ gtsam/base/PriorityScheduler.h | 261 +------------------- gtsam/base/tests/testForestTraversal.cpp | 166 +++++++++++++ gtsam/base/tests/testPriorityScheduler.cpp | 150 +---------- 4 files changed, 447 insertions(+), 404 deletions(-) create mode 100644 gtsam/base/ForestTraversal.h create mode 100644 gtsam/base/tests/testForestTraversal.cpp diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h new file mode 100644 index 0000000000..9b4323f339 --- /dev/null +++ b/gtsam/base/ForestTraversal.h @@ -0,0 +1,274 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file ForestTraversal.h + * @brief Priority-based forest traversal helpers. + * + * @details + * This header defines a mixin that provides depth-based top-down and bottom-up + * traversals over a forest. Priorities are derived from recursion depth and + * executed via an internal PriorityScheduler. + * + * @note `Forest::roots()` or `Forest::roots` must return a range of + * pointer-like `Node` roots. + * @note `Node::children()` or `Node::children` must return a range of + * pointer-like `Node` children. + * + * @author Frank Dellaert + * @date May, 2025 + */ + +#pragma once + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace gtsam { + +/** + * @brief Mixin that provides depth-based top-down or bottom-up traversal + * helpers on a forest owner. + * + * @details + * This helper hides explicit priority management by assigning priorities based + * on recursion depth. It is intended for method-style use in recursive solvers. + * Tasks are scheduled on the provided scheduler and synchronized with + * continuations to avoid blocking worker threads. Bottom-up continuations can + * run inline when called from a worker to reduce queue traffic. + * + * @note The traversal helpers operate on `PriorityScheduler` and rely on + * node-local state for any computed results. + */ +template +class ForestTraversal { + public: + /// Run a top-down traversal (root first). + template + void runTopDown(Fn fn) { + // Run a top-down traversal with continuations. + auto state = std::make_shared(); + std::future done = state->done.get_future(); + Forest& forest = static_cast(*this); + const auto& roots = rootsOf(forest); + if (roots.empty()) { + state->done.set_value(); + return; + } + // Hold completion until all roots are scheduled. + state->pending.fetch_add(1, std::memory_order_relaxed); + for (const auto& root : roots) { + topDownAsync(*root, 0, fn, state); + } + finishTraversal(state); + done.get(); + } + + /// Run a bottom-up traversal (leaves first). + template + void runBottomUp(Fn fn) { + // Run a bottom-up traversal with continuations. + auto state = std::make_shared(); + std::future done = state->done.get_future(); + Forest& forest = static_cast(*this); + const auto& roots = rootsOf(forest); + if (roots.empty()) { + state->done.set_value(); + return; + } + // Hold completion until all roots are scheduled. + state->pending.fetch_add(1, std::memory_order_relaxed); + for (const auto& root : roots) { + bottomUpAsync(*root, 0, fn, state, [] {}); + } + finishTraversal(state); + done.get(); + } + + protected: + /// Construct the mixin with a fixed-size scheduler. + explicit ForestTraversal( + size_t numThreads = std::thread::hardware_concurrency()) + : scheduler_(numThreads) {} + + /// Priority for top-down traversal (root first). + int topDownPriority(int depth) const { return depth; } + + /// Priority for bottom-up traversal (leaves first). + int bottomUpPriority(int depth) const { return -depth; } + + private: + PriorityScheduler scheduler_; + + /// Shared traversal state across scheduled tasks. + struct TraversalState { + std::atomic pending{0}; + std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; + std::atomic hasException{false}; + std::exception_ptr exception; + std::promise done; + }; + using StatePtr = std::shared_ptr; + + /// Schedule top-down work for a node and its children. + template + void topDownAsync(Node& node, int depth, const Fn& fn, + const StatePtr& state) { + auto task = [this, &node, depth, &fn, state]() { + try { + std::invoke(fn, node); + // Children are scheduled after the node in top-down order. + auto&& children = childrenOf(node); + for (const auto& child : children) { + topDownAsync(*child, depth + 1, fn, state); + } + finishTraversal(state); + } catch (...) { + recordException(state, std::current_exception()); + finishTraversal(state); + return; + } + }; + + scheduleTask(topDownPriority(depth), state, std::move(task)); + } + + /// Schedule children first, then the parent node once all children finish. + template + void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, + const std::function& onDone) { + auto&& children = childrenOf(node); + if (children.empty()) { + scheduleBottomUpNode(node, depth, fn, state, onDone); + return; + } + + auto remaining = + std::make_shared>(static_cast(children.size())); + std::function childDone = [this, &node, depth, &fn, state, onDone, + remaining]() { + if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { + // If a child failed, skip this node and just unwind. + if (state->hasException.load(std::memory_order_acquire)) { + onDone(); + return; + } + scheduleBottomUpNode(node, depth, fn, state, onDone); + } + }; + + for (const auto& child : children) { + bottomUpAsync(*child, depth + 1, fn, state, childDone); + } + } + + /// Schedule bottom-up work for a node after its children finish. + template + void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, + const StatePtr& state, + const std::function& onDone) { + auto task = [this, &node, &fn, state, onDone]() { + try { + std::invoke(fn, node); + onDone(); + finishTraversal(state); + } catch (...) { + recordException(state, std::current_exception()); + onDone(); + finishTraversal(state); + return; + } + }; + + // Each scheduled task increments the pending counter. + state->pending.fetch_add(1, std::memory_order_relaxed); + + // Schedule a continuation or run it inline if already on a worker thread. + scheduler_.scheduleOrRunInline(bottomUpPriority(depth), + std::function(std::move(task))); + } + + /// Schedule a task and increment the pending counter. + template + void scheduleTask(int priority, const StatePtr& state, F&& job) { + // Each scheduled task increments the pending counter. + state->pending.fetch_add(1, std::memory_order_relaxed); + scheduler_.schedule(priority, std::function(std::forward(job))); + } + + /// Record the first exception and keep the traversal draining. + void recordException(const StatePtr& state, std::exception_ptr exception) { + // Record the first exception and let the traversal finish. + if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { + state->exception = exception; + state->hasException.store(true, std::memory_order_release); + } + } + + /// Resolve traversal completion once pending reaches zero. + void finishTraversal(const StatePtr& state) { + if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { + if (state->hasException.load(std::memory_order_acquire)) { + try { + state->done.set_exception(state->exception); + } catch (...) { /* ignore */ + } + } else { + try { + state->done.set_value(); + } catch (...) { /* ignore */ + } + } + } + } + + template + struct HasChildrenMethod : std::false_type {}; + + template + struct HasChildrenMethod().children())>> + : std::true_type {}; + + template + struct HasRootsMethod : std::false_type {}; + + template + struct HasRootsMethod().roots())>> + : std::true_type {}; + + /// Return node children via method or field. + template + static auto& childrenOf(T& node) { + if constexpr (HasChildrenMethod::value) { + return node.children(); + } else { + return node.children; + } + } + + /// Return forest roots via method or field. + template + static auto& rootsOf(T& forest) { + if constexpr (HasRootsMethod::value) { + return forest.roots(); + } else { + return forest.roots; + } + } +}; + +} // namespace gtsam diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index c556fa7559..31a90fc921 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -8,7 +8,7 @@ /** * @file PriorityScheduler.h - * @brief Priority-based task scheduler with lightweight traversal helpers. + * @brief Priority-based task scheduler. * * @details * This header defines a small, thread-based scheduler that executes tasks in @@ -16,31 +16,13 @@ * priority (min-heap behavior). Tasks return a value of type `Y`, and callers * can wait on results via `std::future`. * - * The scheduler also supports recursive traversal helpers via `TaskMixin`. - * Forest owners can call `runTopDown` or `runBottomUp` without manually - * managing priorities, as long as nodes expose `children()` and the forest - * exposes `roots()`. - * * @par Example * @code - * // Compute a sum over a tree where children are scheduled by priority. - * struct Node { - * std::vector children_; - * const std::vector& children() const { return children_; } - * void accumulate() { } - * }; - * - * struct Forest : gtsam::TaskMixin { - * std::vector> roots_; - * const std::vector>& roots() const { return roots_; } - * }; - * - * Forest forest; - * forest.runBottomUp(&Node::accumulate); + * gtsam::PriorityScheduler scheduler(4); + * auto future = scheduler.schedule(0, [] { return 42; }); + * int value = future.get(); * @endcode * - * @note `runTopDown` and `runBottomUp` block until the traversal finishes. - * * @author Frank Dellaert * @date May, 2025 */ @@ -319,239 +301,4 @@ class PriorityScheduler { } }; -/** - * @brief Mixin that provides depth-based top-down or bottom-up traversal - * helpers on a forest owner. - * - * @details - * This helper hides explicit priority management by assigning priorities based - * on recursion depth. It is intended for method-style use in recursive solvers. - * Tasks are scheduled on the provided scheduler and synchronized with - * continuations to avoid blocking worker threads. Bottom-up continuations can - * run inline when called from a worker to reduce queue traffic. - * - * @note The traversal helpers operate on `PriorityScheduler` and rely on - * node-local state for any computed results. - * @note `Forest::roots()` or `Forest::roots` must return a range of - * pointer-like `Node` roots. - * @note `Node::children()` or `Node::children` must return a range of - * pointer-like `Node` children. - */ -template -class TaskMixin { - public: - /// Run a top-down traversal (root first). - template - void runTopDown(Fn fn) { - // Run a top-down traversal with continuations. - auto state = std::make_shared(); - std::future done = state->done.get_future(); - Forest& forest = static_cast(*this); - const auto& roots = rootsOf(forest); - if (roots.empty()) { - state->done.set_value(); - return; - } - // Hold completion until all roots are scheduled. - state->pending.fetch_add(1, std::memory_order_relaxed); - for (const auto& root : roots) { - topDownAsync(*root, 0, fn, state); - } - finishTraversal(state); - done.get(); - } - - /// Run a bottom-up traversal (leaves first). - template - void runBottomUp(Fn fn) { - // Run a bottom-up traversal with continuations. - auto state = std::make_shared(); - std::future done = state->done.get_future(); - Forest& forest = static_cast(*this); - const auto& roots = rootsOf(forest); - if (roots.empty()) { - state->done.set_value(); - return; - } - // Hold completion until all roots are scheduled. - state->pending.fetch_add(1, std::memory_order_relaxed); - for (const auto& root : roots) { - bottomUpAsync(*root, 0, fn, state, [] {}); - } - finishTraversal(state); - done.get(); - } - - protected: - /// Construct the mixin with a fixed-size scheduler. - explicit TaskMixin(size_t numThreads = std::thread::hardware_concurrency()) - : scheduler_(numThreads) {} - - /// Priority for top-down traversal (root first). - int topDownPriority(int depth) const { return depth; } - - /// Priority for bottom-up traversal (leaves first). - int bottomUpPriority(int depth) const { return -depth; } - - private: - PriorityScheduler scheduler_; - - /// Shared traversal state across scheduled tasks. - struct TraversalState { - std::atomic pending{0}; - std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; - std::atomic hasException{false}; - std::exception_ptr exception; - std::promise done; - }; - using StatePtr = std::shared_ptr; - - /// Schedule top-down work for a node and its children. - template - void topDownAsync(Node& node, int depth, const Fn& fn, - const StatePtr& state) { - auto task = [this, &node, depth, &fn, state]() { - try { - std::invoke(fn, node); - // Children are scheduled after the node in top-down order. - auto&& children = childrenOf(node); - for (const auto& child : children) { - topDownAsync(*child, depth + 1, fn, state); - } - finishTraversal(state); - } catch (...) { - recordException(state, std::current_exception()); - finishTraversal(state); - return; - } - }; - - scheduleTask(topDownPriority(depth), state, std::move(task)); - } - - /// Schedule children first, then the parent node once all children finish. - template - void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, - const std::function& onDone) { - auto&& children = childrenOf(node); - if (children.empty()) { - scheduleBottomUpNode(node, depth, fn, state, onDone); - return; - } - - auto remaining = - std::make_shared>(static_cast(children.size())); - std::function childDone = [this, &node, depth, &fn, state, onDone, - remaining]() { - if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { - // If a child failed, skip this node and just unwind. - if (state->hasException.load(std::memory_order_acquire)) { - onDone(); - return; - } - scheduleBottomUpNode(node, depth, fn, state, onDone); - } - }; - - for (const auto& child : children) { - bottomUpAsync(*child, depth + 1, fn, state, childDone); - } - } - - /// Schedule bottom-up work for a node after its children finish. - template - void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, - const StatePtr& state, - const std::function& onDone) { - auto task = [this, &node, &fn, state, onDone]() { - try { - std::invoke(fn, node); - onDone(); - finishTraversal(state); - } catch (...) { - recordException(state, std::current_exception()); - onDone(); - finishTraversal(state); - return; - } - }; - - // Each scheduled task increments the pending counter. - state->pending.fetch_add(1, std::memory_order_relaxed); - - /// Schedule a continuation or run it inline if already on a worker thread. - scheduler_.scheduleOrRunInline(bottomUpPriority(depth), - std::function(std::move(task))); - } - - /// Schedule a task and increment the pending counter. - template - void scheduleTask(int priority, const StatePtr& state, F&& job) { - // Each scheduled task increments the pending counter. - state->pending.fetch_add(1, std::memory_order_relaxed); - scheduler_.schedule(priority, std::function(std::forward(job))); - } - - /// Record the first exception and keep the traversal draining. - void recordException(const StatePtr& state, std::exception_ptr exception) { - // Record the first exception and let the traversal finish. - if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { - state->exception = exception; - state->hasException.store(true, std::memory_order_release); - } - } - - /// Resolve traversal completion once pending reaches zero. - void finishTraversal(const StatePtr& state) { - if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { - if (state->hasException.load(std::memory_order_acquire)) { - try { - state->done.set_exception(state->exception); - } catch (...) { /* ignore */ - } - } else { - try { - state->done.set_value(); - } catch (...) { /* ignore */ - } - } - } - } - - template - struct HasChildrenMethod : std::false_type {}; - - template - struct HasChildrenMethod().children())>> - : std::true_type {}; - - template - struct HasRootsMethod : std::false_type {}; - - template - struct HasRootsMethod().roots())>> - : std::true_type {}; - - /// Return node children via method or field. - template - static auto& childrenOf(T& node) { - if constexpr (HasChildrenMethod::value) { - return node.children(); - } else { - return node.children; - } - } - - /// Return forest roots via method or field. - template - static auto& rootsOf(T& forest) { - if constexpr (HasRootsMethod::value) { - return forest.roots(); - } else { - return forest.roots; - } - } -}; - } // namespace gtsam diff --git a/gtsam/base/tests/testForestTraversal.cpp b/gtsam/base/tests/testForestTraversal.cpp new file mode 100644 index 0000000000..30fda70251 --- /dev/null +++ b/gtsam/base/tests/testForestTraversal.cpp @@ -0,0 +1,166 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file testForestTraversal.cpp + * @brief Unit tests for ForestTraversal helpers. + * @author Frank Dellaert + * @date May, 2025 + */ + +#include +#include + +#include +#include + +using namespace gtsam; + +/* ************************************************************************* */ +/// Common TreeNode definition (using shared_ptr). +struct TreeNode { + int value; + explicit TreeNode(int v) : value(v) {} +}; + +namespace { +/// Build a small tree for tests. +template +std::shared_ptr createSimpleTree() { + auto n1 = std::make_shared(1); + auto n2 = std::make_shared(2); + auto n3 = std::make_shared(3); + auto n4 = std::make_shared(4); + auto n5 = std::make_shared(5); + auto n6 = std::make_shared(6); + auto n7 = std::make_shared(7); + n1->children() = {n2, n3, n4}; + n2->children() = {n5, n6}; + n6->children() = {n7}; + return n1; +} + +} // namespace + +/* ************************************************************************* */ +// Test 1: Bottom-up traversal +/* ************************************************************************* */ +/// TreeNode specialization for bottom-up test. +struct TestNode1 : public TreeNode { + using TreeNode::TreeNode; + std::vector> children_; + size_t subtreeCount{0}; + + /// Return child list for traversal. + std::vector>& children() { return children_; } + + /// Bottom-up payload for tests. + void bottomUpValue() { + // Each node stores 1 plus the sum of its children's results. + size_t sum = 1; + for (const auto& child : children()) { + sum += child->subtreeCount; + } + subtreeCount = sum; + } +}; + +/// Forest wrapper for bottom-up traversal. +struct TestForest1 : public ForestTraversal { + std::vector> roots_; + + const std::vector>& roots() const { + return roots_; + } + + /// Count nodes using a bottom-up traversal. + size_t countNodes() { + runBottomUp(&TestNode1::bottomUpValue); + size_t total = 0; + for (const auto& root : roots_) { + total += root->subtreeCount; + } + return total; + } +}; + +TEST(ForestTraversal, BottomUpCounting) { + std::shared_ptr root = createSimpleTree(); + TestForest1 forest; + forest.roots_.push_back(root); + size_t rootValue = forest.countNodes(); + + const size_t expectedTotalNodes = 7; + EXPECT_LONGS_EQUAL(expectedTotalNodes, rootValue); +} + +/* ************************************************************************* */ +// Test 2: Top-down traversal +/* ************************************************************************* */ +/// TreeNode specialization for top-down test. +struct TestNode2 : public TreeNode { + using TreeNode::TreeNode; + std::vector> children_; + bool visited{false}; + size_t subtreeCount{0}; + + /// Return child list for traversal. + std::vector>& children() { return children_; } + + /// Top-down payload for tests. + void topDownTouch() { visited = true; } + + /// Bottom-up payload for tests. + void bottomUpVisitedCount() { + // Each node stores 1 if visited plus the sum of its children. + size_t sum = visited ? 1 : 0; + for (const auto& child : children()) { + sum += child->subtreeCount; + } + subtreeCount = sum; + } +}; + +/// Forest wrapper for top-down traversal. +struct TestForest2 : public ForestTraversal { + std::vector> roots_; + + const std::vector>& roots() const { + return roots_; + } + + /// Touch all nodes using a top-down traversal. + void touchAll() { runTopDown(&TestNode2::topDownTouch); } + + /// Count visited nodes using a bottom-up traversal. + size_t countVisited() { + runBottomUp(&TestNode2::bottomUpVisitedCount); + size_t total = 0; + for (const auto& root : roots_) { + total += root->subtreeCount; + } + return total; + } +}; + +TEST(ForestTraversal, TopDownVisits) { + std::shared_ptr root = createSimpleTree(); + TestForest2 forest; + forest.roots_.push_back(root); + forest.touchAll(); + + const size_t expectedTotalNodes = 7; + EXPECT_LONGS_EQUAL(expectedTotalNodes, forest.countVisited()); +} + +/* ************************************************************************* */ +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} +/* ************************************************************************* */ diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index 0e1bae51b5..bebbfbd815 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -8,7 +8,7 @@ /** * @file testPriorityScheduler.cpp - * @brief Unit tests for PriorityScheduler with forest TaskMixin helpers. + * @brief Unit tests for PriorityScheduler scheduling behavior. * @author Frank Dellaert * @date May, 2025 */ @@ -16,10 +16,6 @@ #include #include -#include -#include -#include - using namespace gtsam; /* ************************************************************************* */ @@ -33,153 +29,13 @@ TEST(PriorityScheduler, ReturnValueComposition) { auto right = scheduler.schedule(0, [] { return size_t{4}; }).share(); // Parent task waits on the child futures and combines their values. - auto parent = - scheduler.schedule(10, [left, right]() mutable { return left.get() + right.get(); }); + auto parent = scheduler.schedule( + 10, [left, right]() mutable { return left.get() + right.get(); }); const size_t expectedSum = 7; EXPECT_LONGS_EQUAL(expectedSum, parent.get()); } -/* ************************************************************************* */ -// Forest mixin tests -/* ************************************************************************* */ - -/// Common TreeNode definition (using shared_ptr). -struct TreeNode { - int value; - explicit TreeNode(int v) : value(v) {} -}; - -namespace { -/// Build a small tree for tests. -template -std::shared_ptr createSimpleTree() { - auto n1 = std::make_shared(1); - auto n2 = std::make_shared(2); - auto n3 = std::make_shared(3); - auto n4 = std::make_shared(4); - auto n5 = std::make_shared(5); - auto n6 = std::make_shared(6); - auto n7 = std::make_shared(7); - n1->children() = {n2, n3, n4}; - n2->children() = {n5, n6}; - n6->children() = {n7}; - return n1; -} - -} // namespace - -/* ************************************************************************* */ -// Test 1: Bottom-up traversal -/* ************************************************************************* */ -/// TreeNode specialization for bottom-up test. -struct TestNode1 : public TreeNode { - using TreeNode::TreeNode; - std::vector> children_; - size_t subtreeCount{0}; - - /// Return child list for traversal. - std::vector>& children() { return children_; } - - /// Bottom-up payload for tests. - void bottomUpValue() { - // Each node stores 1 plus the sum of its children's results. - size_t sum = 1; - for (const auto& child : children()) { - sum += child->subtreeCount; - } - subtreeCount = sum; - } -}; - -/// Forest wrapper for bottom-up traversal. -struct TestForest1 : public TaskMixin { - std::vector> roots_; - - const std::vector>& roots() const { - return roots_; - } - - /// Count nodes using a bottom-up traversal. - size_t countNodes() { - runBottomUp(&TestNode1::bottomUpValue); - size_t total = 0; - for (const auto& root : roots_) { - total += root->subtreeCount; - } - return total; - } -}; - -TEST(PriorityScheduler, TaskMixinBottomUpCounting) { - std::shared_ptr root = createSimpleTree(); - TestForest1 forest; - forest.roots_.push_back(root); - size_t rootValue = forest.countNodes(); - - const size_t expectedTotalNodes = 7; - EXPECT_LONGS_EQUAL(expectedTotalNodes, rootValue); -} - -/* ************************************************************************* */ -// Test 2: Top-down traversal -/* ************************************************************************* */ -/// TreeNode specialization for top-down test. -struct TestNode2 : public TreeNode { - using TreeNode::TreeNode; - std::vector> children_; - bool visited{false}; - size_t subtreeCount{0}; - - /// Return child list for traversal. - std::vector>& children() { return children_; } - - /// Top-down payload for tests. - void topDownTouch() { visited = true; } - - /// Bottom-up payload for tests. - void bottomUpVisitedCount() { - // Each node stores 1 if visited plus the sum of its children. - size_t sum = visited ? 1 : 0; - for (const auto& child : children()) { - sum += child->subtreeCount; - } - subtreeCount = sum; - } -}; - -/// Forest wrapper for top-down traversal. -struct TestForest2 : public TaskMixin { - std::vector> roots_; - - const std::vector>& roots() const { - return roots_; - } - - /// Touch all nodes using a top-down traversal. - void touchAll() { runTopDown(&TestNode2::topDownTouch); } - - /// Count visited nodes using a bottom-up traversal. - size_t countVisited() { - runBottomUp(&TestNode2::bottomUpVisitedCount); - size_t total = 0; - for (const auto& root : roots_) { - total += root->subtreeCount; - } - return total; - } -}; - -TEST(PriorityScheduler, TaskMixinTopDown) { - std::shared_ptr root = createSimpleTree(); - TestForest2 forest; - forest.roots_.push_back(root); - forest.touchAll(); - - const size_t expectedTotalNodes = 7; - EXPECT_LONGS_EQUAL(expectedTotalNodes, forest.countVisited()); -} - /* ************************************************************************* */ int main() { TestResult tr; From 10c19a77f8b8cc88468b1b181bc3c0d620a295ac Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 19:55:13 -0500 Subject: [PATCH 039/266] Move TBB path to ForestTraversal --- gtsam/base/FastSet.h | 2 - gtsam/base/ForestTraversal.h | 164 ++++++++++++++++------- gtsam/base/tests/testForestTraversal.cpp | 22 +-- 3 files changed, 123 insertions(+), 65 deletions(-) diff --git a/gtsam/base/FastSet.h b/gtsam/base/FastSet.h index 1a2627e247..d7dd839029 100644 --- a/gtsam/base/FastSet.h +++ b/gtsam/base/FastSet.h @@ -53,8 +53,6 @@ template class FastSet: public std::set, typename internal::FastDefaultAllocator::type> { - GTSAM_CONCEPT_ASSERT(IsTestable); - public: typedef std::set, diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index 9b4323f339..984ae7768e 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -8,12 +8,12 @@ /** * @file ForestTraversal.h - * @brief Priority-based forest traversal helpers. + * @brief Forest traversal helpers with optional TBB acceleration. * * @details - * This header defines a mixin that provides depth-based top-down and bottom-up - * traversals over a forest. Priorities are derived from recursion depth and - * executed via an internal PriorityScheduler. + * Provides top-down and bottom-up traversal helpers that either enqueue work + * on an internal `PriorityScheduler` (for builds without TBB) or call the + * `treeTraversal` parallel helpers when `GTSAM_USE_TBB` is enabled. * * @note `Forest::roots()` or `Forest::roots` must return a range of * pointer-like `Node` roots. @@ -26,7 +26,14 @@ #pragma once +#include +#ifdef GTSAM_USE_TBB +#include +#include +#include +#else #include +#endif #include #include @@ -44,22 +51,106 @@ namespace gtsam { * helpers on a forest owner. * * @details - * This helper hides explicit priority management by assigning priorities based - * on recursion depth. It is intended for method-style use in recursive solvers. - * Tasks are scheduled on the provided scheduler and synchronized with - * continuations to avoid blocking worker threads. Bottom-up continuations can - * run inline when called from a worker to reduce queue traffic. - * - * @note The traversal helpers operate on `PriorityScheduler` and rely on - * node-local state for any computed results. + * When TBB is present, the traversal delegates directly to + * `treeTraversal::DepthFirstForestParallel` and + * `treeTraversal::PostOrderForestParallel` with the configured parallel + * thresholds. Otherwise it falls back to the priority-queued implementation + * that mirrors `TaskMixin`. */ template class ForestTraversal { public: + /// Construct a helper with a fixed thread budget (used by TBB when enabled). + explicit ForestTraversal( + size_t numThreads = std::thread::hardware_concurrency()) + : threadCount_(numThreads == 0 ? 1 : numThreads) +#ifndef GTSAM_USE_TBB + , + scheduler_(threadCount_) +#endif + { + } + /// Run a top-down traversal (root first). template - void runTopDown(Fn fn) { - // Run a top-down traversal with continuations. + void runTopDown(Fn fn, int parallelThreshold = 10) { +#ifdef GTSAM_USE_TBB + runTopDownTreeTraversal(fn, parallelThreshold); +#else + runTopDownPriorityScheduler(fn); +#endif + } + + /// Run a bottom-up traversal (leaves first). + template + void runBottomUp(Fn fn, int parallelThreshold = 10) { +#ifdef GTSAM_USE_TBB + runBottomUpTreeTraversal(fn, parallelThreshold); +#else + runBottomUpPriorityScheduler(fn); +#endif + } + + private: + size_t threadCount_; + +#ifdef GTSAM_USE_TBB + using SharedNode = std::shared_ptr; + + template + void runTopDownTreeTraversal(Fn fn, int parallelThreshold) { + tbb::global_control control(tbb::global_control::max_allowed_parallelism, + static_cast(threadCount_)); + TbbOpenMPMixedScope threadLimiter; + + struct VisitorPre { + Fn* fn; + int operator()(const SharedNode& node, int&) const { + if (node) std::invoke(*fn, *node); + return 0; + } + }; + + int rootData = 0; + VisitorPre visitor{&fn}; + auto visitorPost = [](const SharedNode&, int) {}; + treeTraversal::DepthFirstForestParallel(static_cast(*this), + rootData, visitor, visitorPost, + parallelThreshold); + } + + template + void runBottomUpTreeTraversal(Fn fn, int parallelThreshold) { + tbb::global_control control(tbb::global_control::max_allowed_parallelism, + static_cast(threadCount_)); + TbbOpenMPMixedScope threadLimiter; + + struct VisitorPost { + Fn* fn; + void operator()(const SharedNode& node) const { + if (node) std::invoke(*fn, *node); + } + }; + + VisitorPost visitor{&fn}; + treeTraversal::PostOrderForestParallel(static_cast(*this), visitor, + parallelThreshold); + } +#else + PriorityScheduler scheduler_; + + /// Shared traversal state across scheduled tasks. + struct TraversalState { + std::atomic pending{0}; + std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; + std::atomic hasException{false}; + std::exception_ptr exception; + std::promise done; + }; + using StatePtr = std::shared_ptr; + + template + void runTopDownPriorityScheduler(Fn fn) { auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); @@ -68,7 +159,6 @@ class ForestTraversal { state->done.set_value(); return; } - // Hold completion until all roots are scheduled. state->pending.fetch_add(1, std::memory_order_relaxed); for (const auto& root : roots) { topDownAsync(*root, 0, fn, state); @@ -77,10 +167,8 @@ class ForestTraversal { done.get(); } - /// Run a bottom-up traversal (leaves first). template - void runBottomUp(Fn fn) { - // Run a bottom-up traversal with continuations. + void runBottomUpPriorityScheduler(Fn fn) { auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); @@ -89,7 +177,6 @@ class ForestTraversal { state->done.set_value(); return; } - // Hold completion until all roots are scheduled. state->pending.fetch_add(1, std::memory_order_relaxed); for (const auto& root : roots) { bottomUpAsync(*root, 0, fn, state, [] {}); @@ -98,39 +185,12 @@ class ForestTraversal { done.get(); } - protected: - /// Construct the mixin with a fixed-size scheduler. - explicit ForestTraversal( - size_t numThreads = std::thread::hardware_concurrency()) - : scheduler_(numThreads) {} - - /// Priority for top-down traversal (root first). - int topDownPriority(int depth) const { return depth; } - - /// Priority for bottom-up traversal (leaves first). - int bottomUpPriority(int depth) const { return -depth; } - - private: - PriorityScheduler scheduler_; - - /// Shared traversal state across scheduled tasks. - struct TraversalState { - std::atomic pending{0}; - std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; - std::atomic hasException{false}; - std::exception_ptr exception; - std::promise done; - }; - using StatePtr = std::shared_ptr; - - /// Schedule top-down work for a node and its children. template void topDownAsync(Node& node, int depth, const Fn& fn, const StatePtr& state) { auto task = [this, &node, depth, &fn, state]() { try { std::invoke(fn, node); - // Children are scheduled after the node in top-down order. auto&& children = childrenOf(node); for (const auto& child : children) { topDownAsync(*child, depth + 1, fn, state); @@ -142,11 +202,9 @@ class ForestTraversal { return; } }; - scheduleTask(topDownPriority(depth), state, std::move(task)); } - /// Schedule children first, then the parent node once all children finish. template void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, const std::function& onDone) { @@ -155,13 +213,11 @@ class ForestTraversal { scheduleBottomUpNode(node, depth, fn, state, onDone); return; } - auto remaining = std::make_shared>(static_cast(children.size())); std::function childDone = [this, &node, depth, &fn, state, onDone, remaining]() { if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { - // If a child failed, skip this node and just unwind. if (state->hasException.load(std::memory_order_acquire)) { onDone(); return; @@ -175,7 +231,6 @@ class ForestTraversal { } } - /// Schedule bottom-up work for a node after its children finish. template void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, const StatePtr& state, @@ -211,7 +266,6 @@ class ForestTraversal { /// Record the first exception and keep the traversal draining. void recordException(const StatePtr& state, std::exception_ptr exception) { - // Record the first exception and let the traversal finish. if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { state->exception = exception; state->hasException.store(true, std::memory_order_release); @@ -235,6 +289,12 @@ class ForestTraversal { } } + int topDownPriority(int depth) const { return depth; } + + int bottomUpPriority(int depth) const { return -depth; } + +#endif + template struct HasChildrenMethod : std::false_type {}; diff --git a/gtsam/base/tests/testForestTraversal.cpp b/gtsam/base/tests/testForestTraversal.cpp index 30fda70251..dc5b3283b8 100644 --- a/gtsam/base/tests/testForestTraversal.cpp +++ b/gtsam/base/tests/testForestTraversal.cpp @@ -39,9 +39,9 @@ std::shared_ptr createSimpleTree() { auto n5 = std::make_shared(5); auto n6 = std::make_shared(6); auto n7 = std::make_shared(7); - n1->children() = {n2, n3, n4}; - n2->children() = {n5, n6}; - n6->children() = {n7}; + n1->children = {n2, n3, n4}; + n2->children = {n5, n6}; + n6->children = {n7}; return n1; } @@ -53,17 +53,16 @@ std::shared_ptr createSimpleTree() { /// TreeNode specialization for bottom-up test. struct TestNode1 : public TreeNode { using TreeNode::TreeNode; - std::vector> children_; + std::vector> children; size_t subtreeCount{0}; - /// Return child list for traversal. - std::vector>& children() { return children_; } + int problemSize() const { return 1; } /// Bottom-up payload for tests. void bottomUpValue() { // Each node stores 1 plus the sum of its children's results. size_t sum = 1; - for (const auto& child : children()) { + for (const auto& child : children) { sum += child->subtreeCount; } subtreeCount = sum; @@ -72,6 +71,7 @@ struct TestNode1 : public TreeNode { /// Forest wrapper for bottom-up traversal. struct TestForest1 : public ForestTraversal { + using Node = TestNode1; std::vector> roots_; const std::vector>& roots() const { @@ -105,12 +105,11 @@ TEST(ForestTraversal, BottomUpCounting) { /// TreeNode specialization for top-down test. struct TestNode2 : public TreeNode { using TreeNode::TreeNode; - std::vector> children_; + std::vector> children; bool visited{false}; size_t subtreeCount{0}; - /// Return child list for traversal. - std::vector>& children() { return children_; } + int problemSize() const { return 1; } /// Top-down payload for tests. void topDownTouch() { visited = true; } @@ -119,7 +118,7 @@ struct TestNode2 : public TreeNode { void bottomUpVisitedCount() { // Each node stores 1 if visited plus the sum of its children. size_t sum = visited ? 1 : 0; - for (const auto& child : children()) { + for (const auto& child : children) { sum += child->subtreeCount; } subtreeCount = sum; @@ -128,6 +127,7 @@ struct TestNode2 : public TreeNode { /// Forest wrapper for top-down traversal. struct TestForest2 : public ForestTraversal { + using Node = TestNode2; std::vector> roots_; const std::vector>& roots() const { From 66f3e62a88cd84bc9939f3d74f4fbe6c08165ad8 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 21:41:02 -0500 Subject: [PATCH 040/266] Reduce complexity --- gtsam/base/ForestTraversal.h | 282 +++++++++++++++++++++++------------ 1 file changed, 189 insertions(+), 93 deletions(-) diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index 984ae7768e..1d7749db93 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -71,23 +71,21 @@ class ForestTraversal { { } - /// Run a top-down traversal (root first). template void runTopDown(Fn fn, int parallelThreshold = 10) { #ifdef GTSAM_USE_TBB runTopDownTreeTraversal(fn, parallelThreshold); #else - runTopDownPriorityScheduler(fn); + runTopDownPriorityScheduler(fn, parallelThreshold); #endif } - /// Run a bottom-up traversal (leaves first). template void runBottomUp(Fn fn, int parallelThreshold = 10) { #ifdef GTSAM_USE_TBB runBottomUpTreeTraversal(fn, parallelThreshold); #else - runBottomUpPriorityScheduler(fn); + runBottomUpPriorityScheduler(fn, parallelThreshold); #endif } @@ -97,44 +95,49 @@ class ForestTraversal { #ifdef GTSAM_USE_TBB using SharedNode = std::shared_ptr; - template - void runTopDownTreeTraversal(Fn fn, int parallelThreshold) { + /// Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. + template + void withTbbTraversalControl(Body&& body) { tbb::global_control control(tbb::global_control::max_allowed_parallelism, static_cast(threadCount_)); TbbOpenMPMixedScope threadLimiter; + std::forward(body)(); + } - struct VisitorPre { - Fn* fn; - int operator()(const SharedNode& node, int&) const { - if (node) std::invoke(*fn, *node); - return 0; - } - }; - - int rootData = 0; - VisitorPre visitor{&fn}; - auto visitorPost = [](const SharedNode&, int) {}; - treeTraversal::DepthFirstForestParallel(static_cast(*this), - rootData, visitor, visitorPost, - parallelThreshold); + template + void runTopDownTreeTraversal(Fn fn, int parallelThreshold) { + withTbbTraversalControl([&] { + struct VisitorPre { + Fn* fn; + int operator()(const SharedNode& node, int&) const { + if (node) std::invoke(*fn, *node); + return 0; + } + }; + + int rootData = 0; + VisitorPre visitor{&fn}; + auto visitorPost = [](const SharedNode&, int) {}; + treeTraversal::DepthFirstForestParallel(static_cast(*this), + rootData, visitor, visitorPost, + parallelThreshold); + }); } template void runBottomUpTreeTraversal(Fn fn, int parallelThreshold) { - tbb::global_control control(tbb::global_control::max_allowed_parallelism, - static_cast(threadCount_)); - TbbOpenMPMixedScope threadLimiter; - - struct VisitorPost { - Fn* fn; - void operator()(const SharedNode& node) const { - if (node) std::invoke(*fn, *node); - } - }; + withTbbTraversalControl([&] { + struct VisitorPost { + Fn* fn; + void operator()(const SharedNode& node) const { + if (node) std::invoke(*fn, *node); + } + }; - VisitorPost visitor{&fn}; - treeTraversal::PostOrderForestParallel(static_cast(*this), visitor, - parallelThreshold); + VisitorPost visitor{&fn}; + treeTraversal::PostOrderForestParallel(static_cast(*this), + visitor, parallelThreshold); + }); } #else PriorityScheduler scheduler_; @@ -148,103 +151,196 @@ class ForestTraversal { std::promise done; }; using StatePtr = std::shared_ptr; + using DoneFn = std::function; - template - void runTopDownPriorityScheduler(Fn fn) { + /// RAII guard that decrements the pending counter on scope exit. + struct FinishTraversalOnExit { + ForestTraversal* owner; + StatePtr state; + ~FinishTraversalOnExit() { owner->finishTraversal(state); } + }; + + /// Return true iff node should be processed as a scheduled "parallel" task. + static bool shouldParallelize(const Node& node, int parallelThreshold) { + if (parallelThreshold <= 0) { + return true; + } else { + return static_cast(node.problemSize()) >= parallelThreshold; + } + } + + /// Run a scheduler-based traversal with a fresh state and roots. + template + void runWithTraversalState(Body&& body) { auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); const auto& roots = rootsOf(forest); if (roots.empty()) { state->done.set_value(); - return; - } - state->pending.fetch_add(1, std::memory_order_relaxed); - for (const auto& root : roots) { - topDownAsync(*root, 0, fn, state); + } else { + state->pending.fetch_add(1, std::memory_order_relaxed); + std::forward(body)(state, roots); + finishTraversal(state); } - finishTraversal(state); done.get(); } + /// Start a top-down visit (schedule vs inline based on threshold). template - void runBottomUpPriorityScheduler(Fn fn) { - auto state = std::make_shared(); - std::future done = state->done.get_future(); - Forest& forest = static_cast(*this); - const auto& roots = rootsOf(forest); - if (roots.empty()) { - state->done.set_value(); - return; + void dispatchTopDown(Node& node, int depth, const Fn& fn, + int parallelThreshold, const StatePtr& state) { + if (shouldParallelize(node, parallelThreshold)) { + topDownAsync(node, depth, fn, parallelThreshold, state); + } else { + topDownTraverse(node, depth, fn, parallelThreshold, state); } - state->pending.fetch_add(1, std::memory_order_relaxed); - for (const auto& root : roots) { - bottomUpAsync(*root, 0, fn, state, [] {}); + } + + /// Dispatch all children of a node in a top-down traversal. + template + void dispatchTopDownChildren(Node& node, int depth, const Fn& fn, + int parallelThreshold, const StatePtr& state) { + auto&& children = childrenOf(node); + for (const auto& child : children) { + if (!child) continue; + dispatchTopDown(*child, depth + 1, fn, parallelThreshold, state); } - finishTraversal(state); - done.get(); } + /// Invoke an `onDone` callback and record any exception it throws. + void callOnDone(const DoneFn& onDone, const StatePtr& state) { + try { + onDone(); + } catch (...) { + recordException(state, std::current_exception()); + } + } + + /// Scheduler-based top-down traversal with problem-size thresholding. template - void topDownAsync(Node& node, int depth, const Fn& fn, + void runTopDownPriorityScheduler(Fn fn, int parallelThreshold) { + runWithTraversalState([&](const StatePtr& state, const auto& roots) { + for (const auto& root : roots) { + if (!root) continue; + dispatchTopDown(*root, 0, fn, parallelThreshold, state); + } + }); + } + + /// Scheduler-based bottom-up traversal with problem-size thresholding. + template + void runBottomUpPriorityScheduler(Fn fn, int parallelThreshold) { + runWithTraversalState([&](const StatePtr& state, const auto& roots) { + for (const auto& root : roots) { + if (!root) continue; + bottomUpAsync(*root, 0, fn, parallelThreshold, state, [] {}); + } + }); + } + + /// Scheduled top-down work for a node (spawns children tasks above + /// threshold). + template + void topDownAsync(Node& node, int depth, const Fn& fn, int parallelThreshold, const StatePtr& state) { - auto task = [this, &node, depth, &fn, state]() { + auto task = [this, &node, depth, &fn, parallelThreshold, state]() { + FinishTraversalOnExit finish{this, state}; + if (state->hasException.load(std::memory_order_acquire)) { + // Keep draining without doing new work. + } else { + try { + std::invoke(fn, node); + dispatchTopDownChildren(node, depth, fn, parallelThreshold, state); + } catch (...) { + recordException(state, std::current_exception()); + } + } + }; + scheduleTask(topDownPriority(depth), state, std::move(task)); + } + + /// Inline top-down traversal for a subtree (still spawns large children). + template + void topDownTraverse(Node& node, int depth, const Fn& fn, + int parallelThreshold, const StatePtr& state) { + if (state->hasException.load(std::memory_order_acquire)) { + // Keep draining without doing new work. + } else { try { std::invoke(fn, node); - auto&& children = childrenOf(node); - for (const auto& child : children) { - topDownAsync(*child, depth + 1, fn, state); + if (!state->hasException.load(std::memory_order_acquire)) { + dispatchTopDownChildren(node, depth, fn, parallelThreshold, state); } - finishTraversal(state); } catch (...) { recordException(state, std::current_exception()); - finishTraversal(state); - return; } - }; - scheduleTask(topDownPriority(depth), state, std::move(task)); + } } + /// Complete a bottom-up node after its children have finished. template - void bottomUpAsync(Node& node, int depth, const Fn& fn, const StatePtr& state, - const std::function& onDone) { - auto&& children = childrenOf(node); - if (children.empty()) { + void completeBottomUpNode(Node& node, int depth, const Fn& fn, + int parallelThreshold, const StatePtr& state, + const DoneFn& onDone) { + if (state->hasException.load(std::memory_order_acquire)) { + callOnDone(onDone, state); + } else if (shouldParallelize(node, parallelThreshold)) { scheduleBottomUpNode(node, depth, fn, state, onDone); - return; + } else { + try { + std::invoke(fn, node); + } catch (...) { + recordException(state, std::current_exception()); + } + callOnDone(onDone, state); } - auto remaining = - std::make_shared>(static_cast(children.size())); - std::function childDone = [this, &node, depth, &fn, state, onDone, - remaining]() { - if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { - if (state->hasException.load(std::memory_order_acquire)) { - onDone(); - return; + } + + /// Bottom-up traversal that runs `fn(node)` after children; schedules only + /// above threshold. + template + void bottomUpAsync(Node& node, int depth, const Fn& fn, int parallelThreshold, + const StatePtr& state, const DoneFn& onDone) { + auto&& children = childrenOf(node); + if (children.empty()) { + completeBottomUpNode(node, depth, fn, parallelThreshold, state, onDone); + } else { + auto remaining = + std::make_shared>(static_cast(children.size())); + std::function childDone = [this, &node, depth, &fn, state, onDone, + remaining, parallelThreshold]() { + if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { + this->completeBottomUpNode(node, depth, fn, parallelThreshold, state, + onDone); + } + }; + + for (const auto& child : children) { + if (!child) { + childDone(); + } else { + bottomUpAsync(*child, depth + 1, fn, parallelThreshold, state, + childDone); } - scheduleBottomUpNode(node, depth, fn, state, onDone); } - }; - - for (const auto& child : children) { - bottomUpAsync(*child, depth + 1, fn, state, childDone); } } template void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, - const StatePtr& state, - const std::function& onDone) { + const StatePtr& state, const DoneFn& onDone) { auto task = [this, &node, &fn, state, onDone]() { - try { - std::invoke(fn, node); - onDone(); - finishTraversal(state); - } catch (...) { - recordException(state, std::current_exception()); - onDone(); - finishTraversal(state); - return; + FinishTraversalOnExit finish{this, state}; + if (state->hasException.load(std::memory_order_acquire)) { + callOnDone(onDone, state); + } else { + try { + std::invoke(fn, node); + } catch (...) { + recordException(state, std::current_exception()); + } + callOnDone(onDone, state); } }; From 0e70f61531cc398f00e30ffd5d661b5bdd1826bf Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 23:54:32 -0500 Subject: [PATCH 041/266] Reorder and document --- gtsam/base/ForestTraversal.h | 386 ++++++++++++++++------------------- 1 file changed, 178 insertions(+), 208 deletions(-) diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index 1d7749db93..e34b7b2407 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -36,6 +36,7 @@ #endif #include +#include #include #include #include @@ -59,6 +60,10 @@ namespace gtsam { */ template class ForestTraversal { + private: + size_t threadCount_; + using SharedNode = std::shared_ptr; + public: /// Construct a helper with a fixed thread budget (used by TBB when enabled). explicit ForestTraversal( @@ -71,42 +76,13 @@ class ForestTraversal { { } - template - void runTopDown(Fn fn, int parallelThreshold = 10) { #ifdef GTSAM_USE_TBB - runTopDownTreeTraversal(fn, parallelThreshold); -#else - runTopDownPriorityScheduler(fn, parallelThreshold); -#endif - } - template - void runBottomUp(Fn fn, int parallelThreshold = 10) { -#ifdef GTSAM_USE_TBB - runBottomUpTreeTraversal(fn, parallelThreshold); -#else - runBottomUpPriorityScheduler(fn, parallelThreshold); -#endif - } - - private: - size_t threadCount_; - -#ifdef GTSAM_USE_TBB - using SharedNode = std::shared_ptr; - - /// Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. - template - void withTbbTraversalControl(Body&& body) { - tbb::global_control control(tbb::global_control::max_allowed_parallelism, - static_cast(threadCount_)); - TbbOpenMPMixedScope threadLimiter; - std::forward(body)(); - } - - template - void runTopDownTreeTraversal(Fn fn, int parallelThreshold) { + /// Depth-first traversal using a pre-order visitor (TBB path). + void runTopDown(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { + // The pre-order visitor runs before visiting children; parallel task + // scheduling is handled by treeTraversal helpers. struct VisitorPre { Fn* fn; int operator()(const SharedNode& node, int&) const { @@ -125,8 +101,11 @@ class ForestTraversal { } template - void runBottomUpTreeTraversal(Fn fn, int parallelThreshold) { + /// Post-order traversal using a bottom-up visitor (TBB path). + void runBottomUp(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { + // The bottom-up visitor runs after all children are processed; + // treeTraversal helpers orchestrate the parallelism. struct VisitorPost { Fn* fn; void operator()(const SharedNode& node) const { @@ -139,18 +118,58 @@ class ForestTraversal { visitor, parallelThreshold); }); } + + private: + /// Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. + template + /// Limit TBB+OpenMP parallelism, then invoke the traversal body. + void withTbbTraversalControl(Body&& body) { + // Set a cap on TBB threads and enter an OpenMP-compatible scope, + // then execute the provided traversal body. + tbb::global_control control(tbb::global_control::max_allowed_parallelism, + static_cast(threadCount_)); + TbbOpenMPMixedScope threadLimiter; + std::forward(body)(); + } + #else - PriorityScheduler scheduler_; + /// Scheduler-based top-down traversal. + template + /// Kick off top-down traversal from all roots using the scheduler backend. + void runTopDown(Fn fn, int parallelThreshold = 10) { + runTraversal([&](const StatePtr& state, const auto& roots) { + for (const auto& root : roots) { + assert(root); + Frame rootFrame{this, *root, 0, fn, parallelThreshold, state}; + rootFrame.topDownDispatch(); + } + }); + } + + /// Scheduler-based bottom-up traversal. + template + /// Kick off bottom-up traversal from all roots using the scheduler backend. + void runBottomUp(Fn fn, int parallelThreshold = 10) { + runTraversal([&](const StatePtr& state, const auto& roots) { + for (const auto& root : roots) { + assert(root); + Frame rootFrame{this, *root, 0, fn, parallelThreshold, state}; + rootFrame.bottomUpAsync([] {}); + } + }); + } + + private: /// Shared traversal state across scheduled tasks. - struct TraversalState { + struct State { std::atomic pending{0}; std::atomic_flag exceptionClaim = ATOMIC_FLAG_INIT; std::atomic hasException{false}; std::exception_ptr exception; std::promise done; }; - using StatePtr = std::shared_ptr; + using StatePtr = std::shared_ptr; using DoneFn = std::function; /// RAII guard that decrements the pending counter on scope exit. @@ -160,200 +179,166 @@ class ForestTraversal { ~FinishTraversalOnExit() { owner->finishTraversal(state); } }; - /// Return true iff node should be processed as a scheduled "parallel" task. - static bool shouldParallelize(const Node& node, int parallelThreshold) { - if (parallelThreshold <= 0) { - return true; - } else { - return static_cast(node.problemSize()) >= parallelThreshold; - } - } - /// Run a scheduler-based traversal with a fresh state and roots. + /// Initializes shared state, kick off work, and wait for completion. template - void runWithTraversalState(Body&& body) { - auto state = std::make_shared(); + void runTraversal(Body&& body) { + // Create shared traversal state and obtain a future for completion. + auto state = std::make_shared(); std::future done = state->done.get_future(); Forest& forest = static_cast(*this); const auto& roots = rootsOf(forest); if (roots.empty()) { + // No work: fulfill promise immediately. state->done.set_value(); } else { + // Seed pending count for the overall traversal scope, invoke body, + // then mark this seed work item as finished. state->pending.fetch_add(1, std::memory_order_relaxed); std::forward(body)(state, roots); finishTraversal(state); } + // Block until traversal resolves (success or exception propagation). done.get(); } - /// Start a top-down visit (schedule vs inline based on threshold). + /// Per-node traversal frame to avoid threading many parameters. template - void dispatchTopDown(Node& node, int depth, const Fn& fn, - int parallelThreshold, const StatePtr& state) { - if (shouldParallelize(node, parallelThreshold)) { - topDownAsync(node, depth, fn, parallelThreshold, state); - } else { - topDownTraverse(node, depth, fn, parallelThreshold, state); + struct Frame { + ForestTraversal* owner; + Node& node; + int depth; + const Fn& fn; + int threshold; + StatePtr state; + + /// Top-down dispatch: choose async or inline based on threshold. + inline void topDownDispatch() const { + if (shouldParallelize()) { + topDownAsync(); + } else { + topDownTraverse(); + } } - } - /// Dispatch all children of a node in a top-down traversal. - template - void dispatchTopDownChildren(Node& node, int depth, const Fn& fn, - int parallelThreshold, const StatePtr& state) { - auto&& children = childrenOf(node); - for (const auto& child : children) { - if (!child) continue; - dispatchTopDown(*child, depth + 1, fn, parallelThreshold, state); + /// Return true iff node should be processed as a scheduled "parallel" task. + /// Decide scheduling based on `node.problemSize()` vs. `threshold`. + bool shouldParallelize() const { + if (threshold <= 0) { + return true; + } else { + return static_cast(node.problemSize()) >= threshold; + } } - } - /// Invoke an `onDone` callback and record any exception it throws. - void callOnDone(const DoneFn& onDone, const StatePtr& state) { - try { - onDone(); - } catch (...) { - recordException(state, std::current_exception()); + /// Enqueue node work; upon completion, dispatch children traversal. + inline void topDownAsync() const { + auto task = [frame = *this]() { + FinishTraversalOnExit finish{frame.owner, frame.state}; + frame.topDownTraverse(); + }; + const int topDownPriority = depth; // Shallower nodes = higher priority + owner->scheduleTask(topDownPriority, state, std::move(task)); } - } - /// Scheduler-based top-down traversal with problem-size thresholding. - template - void runTopDownPriorityScheduler(Fn fn, int parallelThreshold) { - runWithTraversalState([&](const StatePtr& state, const auto& roots) { - for (const auto& root : roots) { - if (!root) continue; - dispatchTopDown(*root, 0, fn, parallelThreshold, state); + /// Inline node work followed by traversal of children. + inline void topDownTraverse() const { + if (state->hasException.load(std::memory_order_acquire)) + return; // Keep draining without doing new work. + try { + std::invoke(fn, node); + if (!state->hasException.load(std::memory_order_acquire)) { + auto&& children = ForestTraversal::childrenOf(node); + for (const auto& child : children) { + assert(child); + Frame childFrame{owner, *child, depth + 1, fn, threshold, state}; + childFrame.topDownDispatch(); + } + } + } catch (...) { + owner->recordException(state, std::current_exception()); } - }); - } + } - /// Scheduler-based bottom-up traversal with problem-size thresholding. - template - void runBottomUpPriorityScheduler(Fn fn, int parallelThreshold) { - runWithTraversalState([&](const StatePtr& state, const auto& roots) { - for (const auto& root : roots) { - if (!root) continue; - bottomUpAsync(*root, 0, fn, parallelThreshold, state, [] {}); + /// Recurse children; complete this node after the last child finishes. + inline void bottomUpAsync(const DoneFn& onDone) const { + auto&& children = ForestTraversal::childrenOf(node); + if (children.empty()) { + completeBottomUpNode(onDone); + } else { + auto remaining = std::make_shared >( + static_cast(children.size())); + std::function childDone = [frame = *this, remaining, + onDone]() mutable { + if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { + frame.completeBottomUpNode(onDone); + } + }; + + for (const auto& child : children) { + assert(child); + Frame childFrame{owner, *child, depth + 1, fn, threshold, state}; + childFrame.bottomUpAsync(childDone); + } } - }); - } + } - /// Scheduled top-down work for a node (spawns children tasks above - /// threshold). - template - void topDownAsync(Node& node, int depth, const Fn& fn, int parallelThreshold, - const StatePtr& state) { - auto task = [this, &node, depth, &fn, parallelThreshold, state]() { - FinishTraversalOnExit finish{this, state}; + /// Execute node work after children; schedule if above threshold. + inline void completeBottomUpNode(const DoneFn& onDone) const { if (state->hasException.load(std::memory_order_acquire)) { - // Keep draining without doing new work. + callOnDone(onDone); + } else if (shouldParallelize()) { + scheduleBottomUpNode(onDone); } else { - try { - std::invoke(fn, node); - dispatchTopDownChildren(node, depth, fn, parallelThreshold, state); - } catch (...) { - recordException(state, std::current_exception()); - } + bottomUpWork(onDone); } - }; - scheduleTask(topDownPriority(depth), state, std::move(task)); - } + } - /// Inline top-down traversal for a subtree (still spawns large children). - template - void topDownTraverse(Node& node, int depth, const Fn& fn, - int parallelThreshold, const StatePtr& state) { - if (state->hasException.load(std::memory_order_acquire)) { - // Keep draining without doing new work. - } else { - try { - std::invoke(fn, node); - if (!state->hasException.load(std::memory_order_acquire)) { - dispatchTopDownChildren(node, depth, fn, parallelThreshold, state); + /// Enqueue bottom-up node work, then invoke the continuation. + inline void scheduleBottomUpNode(const DoneFn& onDone) const { + auto task = [frame = *this, onDone]() mutable { + FinishTraversalOnExit finish{frame.owner, frame.state}; + if (frame.state->hasException.load(std::memory_order_acquire)) { + frame.callOnDone(onDone); + } else { + frame.bottomUpWork(onDone); } - } catch (...) { - recordException(state, std::current_exception()); - } + }; + + // Each scheduled task increments the pending counter. + state->pending.fetch_add(1, std::memory_order_relaxed); + + // Schedule a continuation or run it inline if already on a worker thread. + const int bottomUpPriority = -depth; // Deeper nodes = higher priority + owner->scheduler_.scheduleOrRunInline( + bottomUpPriority, std::function(std::move(task))); } - } - /// Complete a bottom-up node after its children have finished. - template - void completeBottomUpNode(Node& node, int depth, const Fn& fn, - int parallelThreshold, const StatePtr& state, - const DoneFn& onDone) { - if (state->hasException.load(std::memory_order_acquire)) { - callOnDone(onDone, state); - } else if (shouldParallelize(node, parallelThreshold)) { - scheduleBottomUpNode(node, depth, fn, state, onDone); - } else { + /// Do bottom-up node work, then invoke the continuation. + inline void bottomUpWork(const DoneFn& onDone) const { try { std::invoke(fn, node); } catch (...) { - recordException(state, std::current_exception()); + owner->recordException(state, std::current_exception()); } - callOnDone(onDone, state); + callOnDone(onDone); } - } - - /// Bottom-up traversal that runs `fn(node)` after children; schedules only - /// above threshold. - template - void bottomUpAsync(Node& node, int depth, const Fn& fn, int parallelThreshold, - const StatePtr& state, const DoneFn& onDone) { - auto&& children = childrenOf(node); - if (children.empty()) { - completeBottomUpNode(node, depth, fn, parallelThreshold, state, onDone); - } else { - auto remaining = - std::make_shared>(static_cast(children.size())); - std::function childDone = [this, &node, depth, &fn, state, onDone, - remaining, parallelThreshold]() { - if (remaining->fetch_sub(1, std::memory_order_relaxed) == 1) { - this->completeBottomUpNode(node, depth, fn, parallelThreshold, state, - onDone); - } - }; - for (const auto& child : children) { - if (!child) { - childDone(); - } else { - bottomUpAsync(*child, depth + 1, fn, parallelThreshold, state, - childDone); - } + /// Invoke an `onDone` callback and record any exception it throws. + /// Safely run `onDone` and capture any thrown exception in shared state. + void callOnDone(const DoneFn& onDone) const { + try { + onDone(); + } catch (...) { + owner->recordException(state, std::current_exception()); } } - } - template - void scheduleBottomUpNode(Node& node, int depth, const Fn& fn, - const StatePtr& state, const DoneFn& onDone) { - auto task = [this, &node, &fn, state, onDone]() { - FinishTraversalOnExit finish{this, state}; - if (state->hasException.load(std::memory_order_acquire)) { - callOnDone(onDone, state); - } else { - try { - std::invoke(fn, node); - } catch (...) { - recordException(state, std::current_exception()); - } - callOnDone(onDone, state); - } - }; - - // Each scheduled task increments the pending counter. - state->pending.fetch_add(1, std::memory_order_relaxed); - - // Schedule a continuation or run it inline if already on a worker thread. - scheduler_.scheduleOrRunInline(bottomUpPriority(depth), - std::function(std::move(task))); - } + }; // end Frame /// Schedule a task and increment the pending counter. template + /// Increment pending and schedule the given job with the provided priority. void scheduleTask(int priority, const StatePtr& state, F&& job) { // Each scheduled task increments the pending counter. state->pending.fetch_add(1, std::memory_order_relaxed); @@ -361,6 +346,7 @@ class ForestTraversal { } /// Record the first exception and keep the traversal draining. + /// Uses an atomic flag to win a single claim and set shared exception. void recordException(const StatePtr& state, std::exception_ptr exception) { if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { state->exception = exception; @@ -369,6 +355,7 @@ class ForestTraversal { } /// Resolve traversal completion once pending reaches zero. + /// When the last task finishes, fulfill the promise or set the exception. void finishTraversal(const StatePtr& state) { if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { if (state->hasException.load(std::memory_order_acquire)) { @@ -385,31 +372,13 @@ class ForestTraversal { } } - int topDownPriority(int depth) const { return depth; } - - int bottomUpPriority(int depth) const { return -depth; } - -#endif - - template - struct HasChildrenMethod : std::false_type {}; - - template - struct HasChildrenMethod().children())>> - : std::true_type {}; - - template - struct HasRootsMethod : std::false_type {}; - - template - struct HasRootsMethod().roots())>> - : std::true_type {}; + PriorityScheduler scheduler_; /// Return node children via method or field. template static auto& childrenOf(T& node) { - if constexpr (HasChildrenMethod::value) { + if constexpr (std::is_member_function_pointer< + decltype(&T::children)>::value) { return node.children(); } else { return node.children; @@ -419,12 +388,13 @@ class ForestTraversal { /// Return forest roots via method or field. template static auto& rootsOf(T& forest) { - if constexpr (HasRootsMethod::value) { + if constexpr (std::is_member_function_pointer::value) { return forest.roots(); } else { return forest.roots; } } +#endif }; } // namespace gtsam From c98f452829209ed289741857c8291aec3302b423 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 8 Jan 2026 00:31:55 -0500 Subject: [PATCH 042/266] move runTraversal to State --- gtsam/base/ForestTraversal.h | 230 ++++++++++++++++++----------------- 1 file changed, 116 insertions(+), 114 deletions(-) diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index e34b7b2407..6181c57edf 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -48,8 +48,7 @@ namespace gtsam { /** - * @brief Mixin that provides depth-based top-down or bottom-up traversal - * helpers on a forest owner. + * @brief Mixin that provides depth-based top-down or bottom-up traversal. * * @details * When TBB is present, the traversal delegates directly to @@ -138,11 +137,15 @@ class ForestTraversal { template /// Kick off top-down traversal from all roots using the scheduler backend. void runTopDown(Fn fn, int parallelThreshold = 10) { - runTraversal([&](const StatePtr& state, const auto& roots) { + const auto& roots = getRoots(); + if (roots.empty()) return; + // Create shared traversal state and run from all roots. + State state; + state.runTraversal([&]() { for (const auto& root : roots) { assert(root); - Frame rootFrame{this, *root, 0, fn, parallelThreshold, state}; - rootFrame.topDownDispatch(); + Frame frame{&scheduler_, *root, 0, fn, parallelThreshold, &state}; + frame.topDownDispatch(); } }); } @@ -151,16 +154,32 @@ class ForestTraversal { template /// Kick off bottom-up traversal from all roots using the scheduler backend. void runBottomUp(Fn fn, int parallelThreshold = 10) { - runTraversal([&](const StatePtr& state, const auto& roots) { + const auto& roots = getRoots(); + if (roots.empty()) return; + // Create shared traversal state and run from all roots. + State state; + state.runTraversal([&]() { for (const auto& root : roots) { assert(root); - Frame rootFrame{this, *root, 0, fn, parallelThreshold, state}; - rootFrame.bottomUpAsync([] {}); + Frame frame{&scheduler_, *root, 0, fn, parallelThreshold, &state}; + frame.bottomUpAsync([] {}); } }); } private: + PriorityScheduler scheduler_; + + /// Return forest roots via method or field. + decltype(auto) getRoots() const { + const Forest& forest = static_cast(*this); + if constexpr (std::is_member_function_pointer_v) { + return forest.roots(); + } else { + return (forest.roots); + } + } + /// Shared traversal state across scheduled tasks. struct State { std::atomic pending{0}; @@ -168,56 +187,84 @@ class ForestTraversal { std::atomic hasException{false}; std::exception_ptr exception; std::promise done; - }; - using StatePtr = std::shared_ptr; - using DoneFn = std::function; - /// RAII guard that decrements the pending counter on scope exit. - struct FinishTraversalOnExit { - ForestTraversal* owner; - StatePtr state; - ~FinishTraversalOnExit() { owner->finishTraversal(state); } - }; + /// Increment the pending counter. + inline int incrementPending() { + return pending.fetch_add(1, std::memory_order_relaxed); + } + + /// Decrement the pending counter. + inline int decrementPending() { + return pending.fetch_sub(1, std::memory_order_relaxed); + } + + /// Run a scheduler-based traversal with a fresh state and roots. + template + void runTraversal(Body&& body) { + std::future future = done.get_future(); - /// Run a scheduler-based traversal with a fresh state and roots. - /// Initializes shared state, kick off work, and wait for completion. - template - void runTraversal(Body&& body) { - // Create shared traversal state and obtain a future for completion. - auto state = std::make_shared(); - std::future done = state->done.get_future(); - Forest& forest = static_cast(*this); - const auto& roots = rootsOf(forest); - if (roots.empty()) { - // No work: fulfill promise immediately. - state->done.set_value(); - } else { // Seed pending count for the overall traversal scope, invoke body, // then mark this seed work item as finished. - state->pending.fetch_add(1, std::memory_order_relaxed); - std::forward(body)(state, roots); - finishTraversal(state); + incrementPending(); + std::forward(body)(); + maybeFinish(); + + // Block until traversal resolves (success or exception propagation). + future.get(); } - // Block until traversal resolves (success or exception propagation). - done.get(); - } + + /// Record the first exception. + /// Uses an atomic flag to win a single claim and set shared exception. + void recordException(std::exception_ptr e) { + if (!exceptionClaim.test_and_set(std::memory_order_acq_rel)) { + exception = e; + hasException.store(true, std::memory_order_release); + } + } + + /// Resolve traversal completion once pending reaches zero. + /// When the last task finishes, fulfill the promise or set the exception. + void maybeFinish() { + if (decrementPending() == 1) { + if (hasException.load(std::memory_order_acquire)) { + try { + done.set_exception(exception); + } catch (...) { /* ignore */ + } + } else { + try { + done.set_value(); + } catch (...) { /* ignore */ + } + } + } + } + }; + using DoneFn = std::function; + + /// RAII guard: marks one scheduled task as finished on scope exit. + struct MaybeFinish { + State* state; + ~MaybeFinish() { state->maybeFinish(); } + }; /// Per-node traversal frame to avoid threading many parameters. template struct Frame { - ForestTraversal* owner; + PriorityScheduler* scheduler; Node& node; int depth; const Fn& fn; int threshold; - StatePtr state; + State* state; - /// Top-down dispatch: choose async or inline based on threshold. - inline void topDownDispatch() const { - if (shouldParallelize()) { - topDownAsync(); + /// Return node children via method or field. + auto& getChildren() const { + if constexpr (std::is_member_function_pointer< + decltype(&Node::children)>::value) { + return node.children(); } else { - topDownTraverse(); + return node.children; } } @@ -231,14 +278,26 @@ class ForestTraversal { } } + /// Top-down dispatch: choose async or inline based on threshold. + inline void topDownDispatch() const { + if (shouldParallelize()) { + topDownAsync(); + } else { + topDownTraverse(); + } + } + /// Enqueue node work; upon completion, dispatch children traversal. inline void topDownAsync() const { auto task = [frame = *this]() { - FinishTraversalOnExit finish{frame.owner, frame.state}; + MaybeFinish finish{frame.state}; frame.topDownTraverse(); }; const int topDownPriority = depth; // Shallower nodes = higher priority - owner->scheduleTask(topDownPriority, state, std::move(task)); + /// Schedule a task and increment the pending counter. + state->incrementPending(); + scheduler->schedule(topDownPriority, + std::function(std::move(task))); } /// Inline node work followed by traversal of children. @@ -248,21 +307,22 @@ class ForestTraversal { try { std::invoke(fn, node); if (!state->hasException.load(std::memory_order_acquire)) { - auto&& children = ForestTraversal::childrenOf(node); + auto&& children = getChildren(); for (const auto& child : children) { assert(child); - Frame childFrame{owner, *child, depth + 1, fn, threshold, state}; + Frame childFrame{scheduler, *child, depth + 1, + fn, threshold, state}; childFrame.topDownDispatch(); } } } catch (...) { - owner->recordException(state, std::current_exception()); + state->recordException(std::current_exception()); } } /// Recurse children; complete this node after the last child finishes. inline void bottomUpAsync(const DoneFn& onDone) const { - auto&& children = ForestTraversal::childrenOf(node); + auto&& children = getChildren(); if (children.empty()) { completeBottomUpNode(onDone); } else { @@ -277,7 +337,7 @@ class ForestTraversal { for (const auto& child : children) { assert(child); - Frame childFrame{owner, *child, depth + 1, fn, threshold, state}; + Frame childFrame{scheduler, *child, depth + 1, fn, threshold, state}; childFrame.bottomUpAsync(childDone); } } @@ -297,7 +357,7 @@ class ForestTraversal { /// Enqueue bottom-up node work, then invoke the continuation. inline void scheduleBottomUpNode(const DoneFn& onDone) const { auto task = [frame = *this, onDone]() mutable { - FinishTraversalOnExit finish{frame.owner, frame.state}; + MaybeFinish finish{frame.state}; if (frame.state->hasException.load(std::memory_order_acquire)) { frame.callOnDone(onDone); } else { @@ -306,12 +366,12 @@ class ForestTraversal { }; // Each scheduled task increments the pending counter. - state->pending.fetch_add(1, std::memory_order_relaxed); + state->incrementPending(); // Schedule a continuation or run it inline if already on a worker thread. const int bottomUpPriority = -depth; // Deeper nodes = higher priority - owner->scheduler_.scheduleOrRunInline( - bottomUpPriority, std::function(std::move(task))); + scheduler->scheduleOrRunInline(bottomUpPriority, + std::function(std::move(task))); } /// Do bottom-up node work, then invoke the continuation. @@ -319,7 +379,7 @@ class ForestTraversal { try { std::invoke(fn, node); } catch (...) { - owner->recordException(state, std::current_exception()); + state->recordException(std::current_exception()); } callOnDone(onDone); } @@ -330,70 +390,12 @@ class ForestTraversal { try { onDone(); } catch (...) { - owner->recordException(state, std::current_exception()); + state->recordException(std::current_exception()); } } }; // end Frame - /// Schedule a task and increment the pending counter. - template - /// Increment pending and schedule the given job with the provided priority. - void scheduleTask(int priority, const StatePtr& state, F&& job) { - // Each scheduled task increments the pending counter. - state->pending.fetch_add(1, std::memory_order_relaxed); - scheduler_.schedule(priority, std::function(std::forward(job))); - } - - /// Record the first exception and keep the traversal draining. - /// Uses an atomic flag to win a single claim and set shared exception. - void recordException(const StatePtr& state, std::exception_ptr exception) { - if (!state->exceptionClaim.test_and_set(std::memory_order_acq_rel)) { - state->exception = exception; - state->hasException.store(true, std::memory_order_release); - } - } - - /// Resolve traversal completion once pending reaches zero. - /// When the last task finishes, fulfill the promise or set the exception. - void finishTraversal(const StatePtr& state) { - if (state->pending.fetch_sub(1, std::memory_order_relaxed) == 1) { - if (state->hasException.load(std::memory_order_acquire)) { - try { - state->done.set_exception(state->exception); - } catch (...) { /* ignore */ - } - } else { - try { - state->done.set_value(); - } catch (...) { /* ignore */ - } - } - } - } - - PriorityScheduler scheduler_; - - /// Return node children via method or field. - template - static auto& childrenOf(T& node) { - if constexpr (std::is_member_function_pointer< - decltype(&T::children)>::value) { - return node.children(); - } else { - return node.children; - } - } - - /// Return forest roots via method or field. - template - static auto& rootsOf(T& forest) { - if constexpr (std::is_member_function_pointer::value) { - return forest.roots(); - } else { - return forest.roots; - } - } #endif }; From df683ffafe7a00bd7c699db0d76ac4398eed6db8 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 13:19:58 -0500 Subject: [PATCH 043/266] isJacobian --- gtsam/linear/GaussianFactor.h | 3 +++ gtsam/linear/JacobianFactor.h | 4 +++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/gtsam/linear/GaussianFactor.h b/gtsam/linear/GaussianFactor.h index 253bbff0f0..b99aec2bdf 100644 --- a/gtsam/linear/GaussianFactor.h +++ b/gtsam/linear/GaussianFactor.h @@ -170,6 +170,9 @@ namespace gtsam { /// @name Advanced Interface /// @{ + /// Fast check for JacobianFactor-based types. + virtual bool isJacobian() const { return false; } + // Determine position of a given key template static DenseIndex Slot(const CONTAINER& keys, Key key) { diff --git a/gtsam/linear/JacobianFactor.h b/gtsam/linear/JacobianFactor.h index 33f7183a61..f6c8978c09 100644 --- a/gtsam/linear/JacobianFactor.h +++ b/gtsam/linear/JacobianFactor.h @@ -198,6 +198,9 @@ namespace gtsam { std::make_shared(*this)); } + /// Identify JacobianFactor-based types. + bool isJacobian() const override { return true; } + // Implementing Testable interface void print(const std::string& s = "", const KeyFormatter& formatter = DefaultKeyFormatter) const override; @@ -484,4 +487,3 @@ BOOST_CLASS_VERSION(gtsam::JacobianFactor, 1) #include - From edbb1eeb63d9db4e5a4b3ad20b4a0ad5399ef18f Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 08:53:56 -0500 Subject: [PATCH 044/266] Use Mixin + Parameters --- gtsam/linear/MultifrontalSolver.cpp | 52 ++++++++++++----- gtsam/linear/MultifrontalSolver.h | 36 +++++++----- gtsam/linear/tests/testMultifrontalSolver.cpp | 7 ++- timing/timeMultifrontalSolver.cpp | 57 ++++++++++--------- 4 files changed, 96 insertions(+), 56 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index ae84dce926..b27d77ecd1 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -18,6 +18,7 @@ #include #include +#include #include #include #include @@ -307,16 +308,20 @@ struct CliqueBuilder { /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering, - size_t mergeDimCap, - std::ostream* reportStream) - : MultifrontalSolver(Precompute(graph, ordering), ordering, mergeDimCap, - reportStream) {} + const Parameters& params) + : MultifrontalSolver(Precompute(graph, ordering), ordering, params) {} + +/* ************************************************************************* */ +MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, + const Ordering& ordering) + : MultifrontalSolver(graph, ordering, Parameters{}) {} + /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, - size_t mergeDimCap, - std::ostream* reportStream) { + const Parameters& params) + : TaskMixin(), params_(params) { dims_ = std::move(data.dims); fixedKeys_ = std::move(data.fixedKeys); @@ -332,15 +337,15 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, // Report the symbolic structure before any merge. reportStructure(data.junctionTree, dims_, "Symbolic cluster structure", - reportStream); + params_.reportStream); // If applicable, merge small child cliques bottom-up. - if (mergeDimCap > 0) { + if (params_.mergeDimCap > 0) { for (const auto& rootCluster : data.junctionTree.roots()) { - mergeSmallClusters(rootCluster, dims_, mergeDimCap); + mergeSmallClusters(rootCluster, dims_, params_.mergeDimCap); } reportStructure(data.junctionTree, dims_, "Clique structure after merge", - reportStream); + params_.reportStream); } // Build the actual MultifrontalClique structure. @@ -356,6 +361,12 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, // Caller is responsible for loading numerical values via load(). } +/* ************************************************************************* */ +MultifrontalSolver::MultifrontalSolver(PrecomputedData data, + const Ordering& ordering) + : MultifrontalSolver(std::move(data), ordering, Parameters{}) {} + + /* ************************************************************************* */ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( const GaussianFactorGraph& graph, const Ordering& ordering) { @@ -395,26 +406,39 @@ void MultifrontalSolver::eliminateInPlace() { "eliminating."); } eliminated_ = false; +#ifdef GTSAM_USE_TBB // Parallel elimination uses PostOrderForestParallel, which will be // multi-threaded if GTSAM was compiled with TBB. TbbOpenMPMixedScope threadLimiter; auto visitorPost = [](const CliquePtr& node) { if (node) node->eliminateInPlace(); }; - treeTraversal::PostOrderForestParallel(*this, visitorPost, 10); + treeTraversal::PostOrderForestParallel( + *this, visitorPost, params_.eliminationParallelThreshold); +#else + runBottomUp([](MultifrontalClique& node) { node.eliminateInPlace(); }); +#endif eliminated_ = true; } /* ************************************************************************* */ void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { // Combine load + eliminate in one post-order traversal to improve locality. +#ifdef GTSAM_USE_TBB TbbOpenMPMixedScope threadLimiter; auto visitorPost = [&graph](const CliquePtr& node) { if (!node) return; node->fillAb(graph); node->eliminateInPlace(); }; - treeTraversal::PostOrderForestParallel(*this, visitorPost, 10); + treeTraversal::PostOrderForestParallel( + *this, visitorPost, params_.eliminationParallelThreshold); +#else + runBottomUp([&graph](MultifrontalClique& node) { + node.fillAb(graph); + node.eliminateInPlace(); + }); +#endif loaded_ = true; eliminated_ = true; } @@ -477,13 +501,11 @@ const VectorValues& MultifrontalSolver::updateSolution() const { // Threshold chosen to balance task overhead vs parallelism. Small cliques // (like points in SFM) are better handled sequentially within larger tasks // to minimize scheduling overhead and maximize cache locality. - constexpr int kSolutionParallelThreshold = 4096; - // Cast to non-const because treeTraversal expects a non-const reference, // even though we are only calling const methods on the nodes. treeTraversal::DepthFirstForestParallel( const_cast(*this), rootData, visitorPre, visitorPost, - kSolutionParallelThreshold); + params_.solutionParallelThreshold); for (Key key : fixedKeys_) { solution_.at(key).setZero(); diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 57e2291304..713fc2a595 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -18,6 +18,7 @@ #pragma once +#include #include #include #include @@ -42,8 +43,17 @@ class MultifrontalClique; * provides efficient methods for loading new factors, eliminating the graph, * and solving for the update vector. */ -class GTSAM_EXPORT MultifrontalSolver { +class GTSAM_EXPORT MultifrontalSolver + : public TaskMixin { public: + /// Tuning parameters for traversal and reporting. + struct Parameters { + size_t mergeDimCap = 16; ///< Merge threshold (0 disables). + std::ostream* reportStream = nullptr; ///< Optional structure reporting. + int eliminationParallelThreshold = 10; ///< Post-order task threshold. + int solutionParallelThreshold = 4096; ///< Pre-order task threshold. + }; + struct PrecomputedData { std::map dims; ///< Map from variable key to dimension. std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. @@ -63,6 +73,7 @@ class GTSAM_EXPORT MultifrontalSolver { std::unordered_set fixedKeys_; ///< Keys fixed by constrained factors. bool loaded_ = false; ///< Whether load() has been called. bool eliminated_ = false; ///< Whether eliminateInPlace() ran. + Parameters params_; ///< Tunable solver parameters. public: /** @@ -71,28 +82,27 @@ class GTSAM_EXPORT MultifrontalSolver { * Call load() before eliminating to populate numerical values. * @param graph The factor graph to solve. * @param ordering The variable ordering to use for elimination. - * @param mergeDimCap Merge a child if its frontal dimension plus the - * parent's total dimension is below this threshold (0 disables merging). - * @param reportStream Optional stream to report clique structure stats - * (frontals, separators, total dims, and children). + * @param params Tunable parameters for traversal and reporting. */ MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering, - size_t mergeDimCap = 0, - std::ostream* reportStream = nullptr); + const Parameters& params); + + /// Construct the solver with default parameters. + MultifrontalSolver(const GaussianFactorGraph& graph, + const Ordering& ordering); /** * Construct the solver from precomputed symbolic data. * Call load() before eliminating to populate numerical values. * @param data Precomputed symbolic structure and sizing data. * @param ordering The variable ordering to use for seeding solution storage. - * @param mergeDimCap Merge a child if its frontal dimension plus the - * parent's total dimension is below this threshold (0 disables merging). - * @param reportStream Optional stream to report clique structure stats - * (frontals, separators, total dims, and children). + * @param params Tunable parameters for traversal and reporting. */ MultifrontalSolver(PrecomputedData data, const Ordering& ordering, - size_t mergeDimCap = 0, - std::ostream* reportStream = nullptr); + const Parameters& params); + + /// Construct the solver with default parameters. + MultifrontalSolver(PrecomputedData data, const Ordering& ordering); /// Precompute symbolic structure and sizing data from a factor graph. static PrecomputedData Precompute(const GaussianFactorGraph& graph, diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index e8708fff7e..8fb2b87430 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -334,10 +334,13 @@ TEST(MultifrontalSolver, HessianFactors) { /* ************************************************************************* */ // Merge threshold changes the clique count. TEST(MultifrontalSolver, MergeDimCap) { - MultifrontalSolver solverNoMerge(chain, chainOrdering, 0); + MultifrontalSolver::Parameters noMergeParams; + MultifrontalSolver solverNoMerge(chain, chainOrdering, noMergeParams); EXPECT_LONGS_EQUAL(2, solverNoMerge.cliqueCount()); - MultifrontalSolver solverMerge(chain, chainOrdering, 1000); + MultifrontalSolver::Parameters mergeParams; + mergeParams.mergeDimCap = 1000; + MultifrontalSolver solverMerge(chain, chainOrdering, mergeParams); EXPECT_LONGS_EQUAL(1, solverMerge.cliqueCount()); } diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 5428a7a709..e4fd0f325e 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -71,9 +71,11 @@ void runBAL135Benchmark() { const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); const Values initial = buildGeneralSfmInitial(db); const GaussianFactorGraph linear = *graph.linearize(initial); - const Ordering ordering = Ordering::Metis(linear); + const Ordering ordering = createSchurOrdering(db, false); - MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); + MultifrontalSolver::Parameters params; + params.mergeDimCap = kMergeDimCap; + MultifrontalSolver solver(linear, ordering, params); auto start = std::chrono::high_resolution_clock::now(); runMultifrontalSolver(solver, linear, iterations); auto end = std::chrono::high_resolution_clock::now(); @@ -93,29 +95,29 @@ void runBALBenchmark() { const Values initial = buildGeneralSfmInitial(db); const GaussianFactorGraph linear = *graph.linearize(initial); - auto orderings = createOrderings(db, linear); - for (const auto& [label, ordering] : orderings) { - cout << "\nBAL Benchmark (" << label << ", iterations=" << bal_iterations - << "):" << std::endl; - - MultifrontalSolver solver(linear, ordering, kMergeDimCap, nullptr); - auto start = std::chrono::high_resolution_clock::now(); - runMultifrontalSolver(solver, linear, bal_iterations); - auto end = std::chrono::high_resolution_clock::now(); - std::chrono::duration t_imperative = end - start; - cout << " MultifrontalSolver: " << t_imperative.count() << " s" - << std::endl; - - start = std::chrono::high_resolution_clock::now(); - runStandardSolver(linear, ordering, bal_iterations); - end = std::chrono::high_resolution_clock::now(); - std::chrono::duration t_standard = end - start; - cout << " Standard GTSAM: " << t_standard.count() << " s" - << std::endl; - - cout << " Speedup: " - << t_standard.count() / t_imperative.count() << "x" << std::endl; - } + const Ordering ordering = createSchurOrdering(db, false); + cout << "\nBAL Benchmark (" << filename << ", iterations=" << bal_iterations + << "):" << std::endl; + + MultifrontalSolver::Parameters params; + params.mergeDimCap = kMergeDimCap; + MultifrontalSolver solver(linear, ordering, params); + solver.eliminateInPlace(linear); // Warm up cache. + auto start = std::chrono::high_resolution_clock::now(); + runMultifrontalSolver(solver, linear, bal_iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + cout << " MultifrontalSolver: " << t_imperative.count() << " s" + << std::endl; + + start = std::chrono::high_resolution_clock::now(); + runStandardSolver(linear, ordering, bal_iterations); + end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_standard = end - start; + cout << " Standard GTSAM: " << t_standard.count() << " s" << std::endl; + + cout << " Speedup: " + << t_standard.count() / t_imperative.count() << "x" << std::endl; } } void runChainBenchmark() { @@ -129,7 +131,10 @@ void runChainBenchmark() { const Ordering ordering = Ordering::Metis(smoother); auto start = std::chrono::high_resolution_clock::now(); - MultifrontalSolver solver(smoother, ordering, kMergeDimCap, &std::cout); + MultifrontalSolver::Parameters params; + params.mergeDimCap = kMergeDimCap; + params.reportStream = &std::cout; + MultifrontalSolver solver(smoother, ordering, params); runMultifrontalSolver(solver, smoother, iterations); auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration t_imperative = end - start; From aa0c3534964ab9a27059e558e16ac69b08ebcead Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 18:02:42 -0500 Subject: [PATCH 045/266] Clean up leaf merging --- gtsam/inference/ClusterTree-inst.h | 101 +++++++++++++++++++++++++---- gtsam/inference/ClusterTree.h | 13 ++++ 2 files changed, 103 insertions(+), 11 deletions(-) diff --git a/gtsam/inference/ClusterTree-inst.h b/gtsam/inference/ClusterTree-inst.h index c985c8f423..9fceca94f8 100644 --- a/gtsam/inference/ClusterTree-inst.h +++ b/gtsam/inference/ClusterTree-inst.h @@ -81,27 +81,40 @@ void ClusterTree::Cluster::merge(const std::shared_ptr& cluster) } /* ************************************************************************* */ -template +template void ClusterTree::Cluster::mergeChildren( const std::vector& merge) { + mergeChildren(childrenFromMask(merge)); +} + +/* ************************************************************************* */ +template +void ClusterTree::Cluster::mergeChildren( + const Children& selected) { gttic(Cluster_mergeChildren); - assert(merge.size() == this->children.size()); + // Merge selected children into this node while preserving unselected children. + if (selected.empty()) return; + + FastSet selectedSet; + for (const auto& child : selected) { + if (child) { + selectedSet.insert(child.get()); + } + } + if (selectedSet.empty()) return; // Count how many keys, factors and children we'll end up with size_t nrKeys = orderedFrontalKeys.size(); size_t nrFactors = factors.size(); size_t nrNewChildren = 0; - // Loop over children - size_t i = 0; - for(const sharedNode& child: this->children) { - if (merge[i]) { + for (const sharedNode& child : this->children) { + if (child && selectedSet.count(child.get()) != 0) { nrKeys += child->orderedFrontalKeys.size(); nrFactors += child->factors.size(); nrNewChildren += child->nrChildren(); } else { - nrNewChildren += 1; // we keep the child + nrNewChildren += 1; // we keep the child } - ++i; } // now reserve space, and really merge @@ -110,18 +123,84 @@ void ClusterTree::Cluster::mergeChildren( this->children.reserve(nrNewChildren); orderedFrontalKeys.reserve(nrKeys); factors.reserve(nrFactors); - i = 0; for (const sharedNode& child : oldChildren) { - if (merge[i]) { + if (child && selectedSet.count(child.get()) != 0) { this->merge(child); } else { this->addChild(child); // we keep the child } - ++i; } + // merge() appends keys in reverse order to defer a final reverse. std::reverse(orderedFrontalKeys.begin(), orderedFrontalKeys.end()); } +/* ************************************************************************* */ +template +void ClusterTree::Cluster::mergeChildrenSiblings( + const std::vector& merge) { + mergeChildrenSiblings(childrenFromMask(merge)); +} + +/* ************************************************************************* */ +template +void ClusterTree::Cluster::mergeChildrenSiblings( + const Children& selected) { + gttic(Cluster_mergeChildrenSiblings); + // Merge selected siblings into a new child while keeping unselected children. + if (selected.empty()) return; + + FastSet selectedSet; + for (const auto& child : selected) { + if (child) { + selectedSet.insert(child.get()); + } + } + const size_t selectedCount = selectedSet.size(); + // Nothing to merge (0 or 1 selected), so keep children unchanged. + if (selectedCount <= 1) return; + + auto oldChildren = this->children; + Children newChildren; + newChildren.reserve(oldChildren.size() - selectedCount + 1); + auto merged = std::make_shared(); + bool inserted = false; + + for (const sharedNode& child : oldChildren) { + if (child && selectedSet.count(child.get()) != 0) { + // Merge selected siblings into a single new cluster. + merged->merge(child); + if (!inserted) { + // Insert merged cluster at the first selected child's position. + newChildren.push_back(merged); + inserted = true; + } + } else { + newChildren.push_back(child); + } + } + + // merge() appends keys in reverse order to defer a final reverse. + std::reverse(merged->orderedFrontalKeys.begin(), + merged->orderedFrontalKeys.end()); + this->children.swap(newChildren); +} + +/* ************************************************************************* */ +template +typename ClusterTree::Cluster::Children +ClusterTree::Cluster::childrenFromMask( + const std::vector& merge) const { + assert(merge.size() == this->children.size()); + // Translate a boolean mask into the corresponding child pointers. + Children selected; + for (size_t i = 0; i < children.size(); ++i) { + if (merge[i]) { + selected.push_back(children[i]); + } + } + return selected; +} + /* ************************************************************************* */ template void ClusterTree::print(const std::string& s, const KeyFormatter& keyFormatter) const { diff --git a/gtsam/inference/ClusterTree.h b/gtsam/inference/ClusterTree.h index abddb60f16..247eb6f4df 100644 --- a/gtsam/inference/ClusterTree.h +++ b/gtsam/inference/ClusterTree.h @@ -11,6 +11,7 @@ #include #include +#include #include #include @@ -107,6 +108,18 @@ class ClusterTree { /// Merge all children for which bit is set into this node void mergeChildren(const std::vector& merge); + + /** Merge selected siblings into a new child cluster. */ + void mergeChildrenSiblings(const std::vector& merge); + + /** Merge selected children (provided as pointers) into this node. */ + void mergeChildren(const Children& selected); + + /** Merge selected siblings (provided as pointers) into a new child. */ + void mergeChildrenSiblings(const Children& selected); + + /** Convert a child-selection mask into the selected child pointers. */ + Children childrenFromMask(const std::vector& merge) const; }; typedef std::shared_ptr sharedCluster; ///< Shared pointer to Cluster From ae2967636199a2d6a5afacd3a63bc83bcd1bdbe7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 6 Jan 2026 18:02:12 -0500 Subject: [PATCH 046/266] Leaf merging --- gtsam/linear/MultifrontalSolver.cpp | 157 +++++++++++++++++- gtsam/linear/MultifrontalSolver.h | 9 + gtsam/linear/tests/testMultifrontalSolver.cpp | 47 +++--- .../tests/testSymbolicClusterTree.cpp | 119 +++++++++++++ timing/timeMultifrontalSolver.cpp | 96 ++++++++++- 5 files changed, 400 insertions(+), 28 deletions(-) diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index b27d77ecd1..3f62951093 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -18,6 +18,7 @@ #include #include +#include #include #include #include @@ -194,6 +195,100 @@ size_t totalDimForSymbolicCluster( separatorDimForSymbolicCluster(cluster, dims, cache); } +struct LeafInfo { + SymbolicJunctionTree::sharedNode child; + size_t totalDim = 0; + size_t index = 0; +}; + +struct LeafGroup { + std::vector leaves; + size_t firstIndex = 0; +}; + +struct LeafBatch { + SymbolicJunctionTree::Cluster::Children leaves; + size_t firstIndex = 0; +}; + +std::vector collectLeafGroups( + const SymbolicJunctionTree::sharedNode& cluster, + const std::map& dims, + SymbolicJunctionTree::Cluster::KeySetMap* separatorCache) { + // Group leaf children by identical separators, keeping first-seen order. + FastMap groupIndexBySeparator; + std::vector groups; + + for (size_t i = 0; i < cluster->children.size(); ++i) { + const auto& child = cluster->children[i]; + if (!child || !child->children.empty()) continue; + + child->separatorKeys(separatorCache); + const KeySet& separatorKeys = separatorCache->at(child.get()); + auto it = groupIndexBySeparator.find(separatorKeys); + size_t groupIndex = 0; + if (it == groupIndexBySeparator.end()) { + groupIndex = groups.size(); + groupIndexBySeparator.emplace(separatorKeys, groupIndex); + groups.push_back(LeafGroup{{}, i}); + } else { + groupIndex = it->second; + } + + const size_t totalDim = + totalDimForSymbolicCluster(child, dims, separatorCache); + groups[groupIndex].leaves.push_back({child, totalDim, i}); + } + + return groups; +} + +std::vector buildLeafBatches(const std::vector& groups, + size_t leafMergeDimCap) { + // Pack each group into batches capped by total dimension. + std::vector batches; + for (const auto& group : groups) { + size_t currentTotalDim = 0; + LeafBatch batch; + batch.firstIndex = group.firstIndex; + for (const auto& leaf : group.leaves) { + if (!batch.leaves.empty() && + currentTotalDim + leaf.totalDim > leafMergeDimCap) { + batches.push_back(batch); + batch.leaves.clear(); + currentTotalDim = 0; + batch.firstIndex = leaf.index; + } + if (batch.leaves.empty()) { + batch.firstIndex = leaf.index; + } + batch.leaves.push_back(leaf.child); + currentTotalDim += leaf.totalDim; + } + if (!batch.leaves.empty()) { + batches.push_back(batch); + } + } + + std::sort(batches.begin(), batches.end(), + [](const LeafBatch& a, const LeafBatch& b) { + return a.firstIndex < b.firstIndex; + }); + return batches; +} + +bool mergeLeafBatches(const SymbolicJunctionTree::sharedNode& cluster, + const std::vector& batches) { + // Merge each batch of siblings into a super-leaf in-place. + bool anyMerged = false; + for (const auto& batch : batches) { + if (batch.leaves.size() <= 1) continue; + cluster->mergeChildrenSiblings(batch.leaves); + anyMerged = true; + } + return anyMerged; +} + void accumulateSymbolicStats(const SymbolicJunctionTree::sharedNode& cluster, const std::map& dims, SymbolicJunctionTree::Cluster::KeySetMap* cache, @@ -208,6 +303,49 @@ void accumulateSymbolicStats(const SymbolicJunctionTree::sharedNode& cluster, } } +/** + * @brief Recursively groups leaf children into super-leaves capped by total + * dimension. + * + * This function traverses the symbolic junction tree rooted at the given + * cluster node. For each cluster, it gathers direct leaf children (i.e., + * children with no further descendants) and groups those with identical + * separators into new super-leaves until the running total dimension reaches + * the specified leafMergeDimCap, then starts a new group. + * + * The merging process works as follows: + * - Recursively process all children clusters first. + * - Identify all direct leaf children of the current cluster and collect their + * total dimensions (separator + frontal) and separator keys. + * - Group leaf children by identical separator keys, preserving first-seen + * order for each separator. + * - Build super-leaves by accumulating leaf children until the total dimension + * hits the cap, then start a new super-leaf. + * - Replace leaf children with the new super-leaves (non-leaf children keep + * their relative order and grouped leaves appear at the first leaf location + * for that separator). + * + * @param cluster The current symbolic junction tree node (cluster) to + * process. + * @param dims A map from variable keys to their dimensions. + * @param leafMergeDimCap The maximum allowed dimension for a merged cluster. + */ +void mergeLeafChildren(const SymbolicJunctionTree::sharedNode& cluster, + const std::map& dims, + size_t leafMergeDimCap) { + if (!cluster || cluster->children.empty()) return; + for (const auto& child : cluster->children) { + mergeLeafChildren(child, dims, leafMergeDimCap); + } + + SymbolicJunctionTree::Cluster::KeySetMap separatorCache; + const std::vector groups = + collectLeafGroups(cluster, dims, &separatorCache); + const std::vector batches = + buildLeafBatches(groups, leafMergeDimCap); + if (!mergeLeafBatches(cluster, batches)) return; +} + // Bottom-up merge of child clusters whose merged total dimension stays below // a threshold. void mergeSmallClusters(const SymbolicJunctionTree::sharedNode& cluster, @@ -316,7 +454,6 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering) : MultifrontalSolver(graph, ordering, Parameters{}) {} - /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, @@ -339,6 +476,15 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, reportStructure(data.junctionTree, dims_, "Symbolic cluster structure", params_.reportStream); + // If applicable, merge leaf children by a separate cap first. + if (params_.leafMergeDimCap > 0) { + for (const auto& rootCluster : data.junctionTree.roots()) { + mergeLeafChildren(rootCluster, dims_, params_.leafMergeDimCap); + } + reportStructure(data.junctionTree, dims_, + "Clique structure after leaf merge", params_.reportStream); + } + // If applicable, merge small child cliques bottom-up. if (params_.mergeDimCap > 0) { for (const auto& rootCluster : data.junctionTree.roots()) { @@ -366,7 +512,6 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering) : MultifrontalSolver(std::move(data), ordering, Parameters{}) {} - /* ************************************************************************* */ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( const GaussianFactorGraph& graph, const Ordering& ordering) { @@ -413,8 +558,8 @@ void MultifrontalSolver::eliminateInPlace() { auto visitorPost = [](const CliquePtr& node) { if (node) node->eliminateInPlace(); }; - treeTraversal::PostOrderForestParallel( - *this, visitorPost, params_.eliminationParallelThreshold); + treeTraversal::PostOrderForestParallel(*this, visitorPost, + params_.eliminationParallelThreshold); #else runBottomUp([](MultifrontalClique& node) { node.eliminateInPlace(); }); #endif @@ -431,8 +576,8 @@ void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { node->fillAb(graph); node->eliminateInPlace(); }; - treeTraversal::PostOrderForestParallel( - *this, visitorPost, params_.eliminationParallelThreshold); + treeTraversal::PostOrderForestParallel(*this, visitorPost, + params_.eliminationParallelThreshold); #else runBottomUp([&graph](MultifrontalClique& node) { node.fillAb(graph); diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 713fc2a595..ce139c3f6d 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -42,12 +42,21 @@ class MultifrontalClique; * This class pre-allocates all necessary memory for the elimination tree and * provides efficient methods for loading new factors, eliminating the graph, * and solving for the update vector. + * + * @note Clique merging has two optional phases: an initial leaf-merge pass + * (leafMergeDimCap) that merges multiple leaf children into a common parent + * while the parent's total dimension plus merged frontal dimensions stays + * below the cap, followed by a bottom-up pass (mergeDimCap) that merges any + * remaining small child cliques into their parent. Both phases run before + * numeric elimination and can reduce tiny cliques and improve cache locality. + * Defaults are conservative and may need tuning per machine or dataset. */ class GTSAM_EXPORT MultifrontalSolver : public TaskMixin { public: /// Tuning parameters for traversal and reporting. struct Parameters { + size_t leafMergeDimCap = 256; ///< Leaf-merge cap (0 disables). size_t mergeDimCap = 16; ///< Merge threshold (0 disables). std::ostream* reportStream = nullptr; ///< Optional structure reporting. int eliminationParallelThreshold = 10; ///< Post-order task threshold. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index 8fb2b87430..45925e8c01 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -47,12 +47,19 @@ const GaussianFactorGraph chain = { std::make_shared(x4, I_1x1, I_1x1, chainNoise4)}; const Ordering chainOrdering{x2, x1, x3, x4}; +MultifrontalSolver::Parameters noMergeParams() { + MultifrontalSolver::Parameters params; + params.mergeDimCap = 0; + params.leafMergeDimCap = 0; + return params; +} + } // namespace /* ************************************************************************* */ // Build the solver and validate initial structure and explicit load. TEST(MultifrontalSolver, Constructor) { - MultifrontalSolver solver(chain, chainOrdering); + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); // Verify roots @@ -83,7 +90,7 @@ TEST(MultifrontalSolver, Constructor) { // Build the solver from precomputed data and validate structure and load. TEST(MultifrontalSolver, ConstructorPrecomputed) { auto data = MultifrontalSolver::Precompute(chain, chainOrdering); - MultifrontalSolver solver(std::move(data), chainOrdering); + MultifrontalSolver solver(std::move(data), chainOrdering, noMergeParams()); solver.load(chain); // Verify roots @@ -109,7 +116,7 @@ TEST(MultifrontalSolver, ConstructorPrecomputed) { /* ************************************************************************* */ // Reload numerical values and ensure Ab updates match whitening. TEST(MultifrontalSolver, Load) { - MultifrontalSolver solver(chain, chainOrdering); + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); // Create a new graph with doubled values GaussianFactorGraph chain2; @@ -137,7 +144,7 @@ TEST(MultifrontalSolver, Load) { /* ************************************************************************* */ // Compare solver output against multifrontal elimination baseline. TEST(MultifrontalSolver, Eliminate) { - MultifrontalSolver solver(chain, chainOrdering); + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); solver.eliminateInPlace(); @@ -154,7 +161,7 @@ TEST(MultifrontalSolver, Eliminate) { /* ************************************************************************* */ // Load + eliminate in one traversal matches standard elimination. TEST(MultifrontalSolver, EliminateWithLoad) { - MultifrontalSolver solver(chain, chainOrdering); + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.eliminateInPlace(chain); const VectorValues& actual = solver.updateSolution(); @@ -168,7 +175,7 @@ TEST(MultifrontalSolver, EliminateWithLoad) { /* ************************************************************************* */ // Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { - MultifrontalSolver solver(chain, chainOrdering); + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); solver.eliminateInPlace(); @@ -204,7 +211,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { constrainedChain.emplace_shared( x2, I_1x1, (Vector(1) << 0.0).finished(), hardConstraint); - MultifrontalSolver solver(constrainedChain, chainOrdering); + MultifrontalSolver solver(constrainedChain, chainOrdering, noMergeParams()); solver.load(constrainedChain); solver.eliminateInPlace(); @@ -233,7 +240,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { x1, I_1x1, (Vector(1) << 100.0).finished(), softNoise); const Ordering ordering{x1}; - MultifrontalSolver solver(graph, ordering); + MultifrontalSolver solver(graph, ordering, noMergeParams()); solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -255,7 +262,8 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnsupported) { const Ordering ordering{x1}; CHECK_EXCEPTION( - { MultifrontalSolver solver(graph, ordering); }, std::runtime_error); + { MultifrontalSolver solver(graph, ordering, noMergeParams()); }, + std::runtime_error); } /* ************************************************************************* */ @@ -270,7 +278,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { softNoise); const Ordering ordering{x1}; - MultifrontalSolver solver(graph, ordering); + MultifrontalSolver solver(graph, ordering, noMergeParams()); solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -288,7 +296,8 @@ TEST(MultifrontalSolver, ConstrainedNoiseMixedKeysUnsupported) { const Ordering ordering{x1, x2}; CHECK_EXCEPTION( - { MultifrontalSolver solver(graph, ordering); }, std::runtime_error); + { MultifrontalSolver solver(graph, ordering, noMergeParams()); }, + std::runtime_error); } /* ************************************************************************* */ @@ -307,7 +316,7 @@ TEST(MultifrontalSolver, WeightedScalarMeasurements) { noiseModel::Isotropic::Sigma(1, sigma2)); const Ordering ordering{x1}; - MultifrontalSolver solver(graph, ordering); + MultifrontalSolver solver(graph, ordering, noMergeParams()); solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -323,7 +332,7 @@ TEST(MultifrontalSolver, HessianFactors) { (Vector(1) << 8.0).finished(), 0.0); const Ordering ordering{x1}; - MultifrontalSolver solver(graph, ordering); + MultifrontalSolver solver(graph, ordering, noMergeParams()); solver.load(graph); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); @@ -334,13 +343,13 @@ TEST(MultifrontalSolver, HessianFactors) { /* ************************************************************************* */ // Merge threshold changes the clique count. TEST(MultifrontalSolver, MergeDimCap) { - MultifrontalSolver::Parameters noMergeParams; - MultifrontalSolver solverNoMerge(chain, chainOrdering, noMergeParams); + MultifrontalSolver::Parameters noMerge = noMergeParams(); + MultifrontalSolver solverNoMerge(chain, chainOrdering, noMerge); EXPECT_LONGS_EQUAL(2, solverNoMerge.cliqueCount()); - MultifrontalSolver::Parameters mergeParams; - mergeParams.mergeDimCap = 1000; - MultifrontalSolver solverMerge(chain, chainOrdering, mergeParams); + MultifrontalSolver::Parameters merge = noMergeParams(); + merge.mergeDimCap = 1000; + MultifrontalSolver solverMerge(chain, chainOrdering, merge); EXPECT_LONGS_EQUAL(1, solverMerge.cliqueCount()); } @@ -355,7 +364,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { // Create the Bayes tree ordering const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)}; - MultifrontalSolver solver(smoother, ordering); + MultifrontalSolver solver(smoother, ordering, noMergeParams()); solver.load(smoother); // Verify roots diff --git a/gtsam/symbolic/tests/testSymbolicClusterTree.cpp b/gtsam/symbolic/tests/testSymbolicClusterTree.cpp index 202f198124..7705bc4bfd 100644 --- a/gtsam/symbolic/tests/testSymbolicClusterTree.cpp +++ b/gtsam/symbolic/tests/testSymbolicClusterTree.cpp @@ -46,6 +46,125 @@ TEST(ClusterTree, SeparatorKeys) { assert_container_equality(expectedChild, child->separatorKeys(&cache))); } +/* ************************************************************************* */ +TEST(ClusterTree, MergeChildren) { + using Cluster = SymbolicJunctionTree::Cluster; + auto parent = std::make_shared(); + auto child1 = std::make_shared(); + auto child2 = std::make_shared(); + auto child3 = std::make_shared(); + + child1->orderedFrontalKeys.push_back(1); + child2->orderedFrontalKeys.push_back(2); + child3->orderedFrontalKeys.push_back(3); + + parent->addChild(child1); + parent->addChild(child2); + parent->addChild(child3); + + parent->mergeChildren({true, false, true}); + + EXPECT_LONGS_EQUAL(1, parent->children.size()); + EXPECT(parent->children.front() == child2); + const KeySet expected{1, 3}; + const KeySet actual(parent->orderedFrontalKeys.begin(), + parent->orderedFrontalKeys.end()); + EXPECT(assert_container_equality(expected, actual)); +} + +/* ************************************************************************* */ +TEST(ClusterTree, MergeChildrenSiblings) { + using Cluster = SymbolicJunctionTree::Cluster; + auto parent = std::make_shared(); + auto child1 = std::make_shared(); + auto child2 = std::make_shared(); + auto child3 = std::make_shared(); + + child1->orderedFrontalKeys.push_back(1); + child2->orderedFrontalKeys.push_back(2); + child3->orderedFrontalKeys.push_back(3); + + parent->addChild(child1); + parent->addChild(child2); + parent->addChild(child3); + + parent->mergeChildrenSiblings({true, false, true}); + + EXPECT_LONGS_EQUAL(2, parent->children.size()); + EXPECT(parent->children[1] == child2); + const KeySet expected{1, 3}; + const KeySet actual(parent->children[0]->orderedFrontalKeys.begin(), + parent->children[0]->orderedFrontalKeys.end()); + EXPECT(assert_container_equality(expected, actual)); +} + +/* ************************************************************************* */ +TEST(ClusterTree, MergeChildrenWithGrandchildren) { + using Cluster = SymbolicJunctionTree::Cluster; + auto parent = std::make_shared(); + auto child1 = std::make_shared(); + auto child2 = std::make_shared(); + auto child3 = std::make_shared(); + auto grandchild1 = std::make_shared(); + auto grandchild2 = std::make_shared(); + + child1->orderedFrontalKeys.push_back(1); + child2->orderedFrontalKeys.push_back(2); + child3->orderedFrontalKeys.push_back(3); + grandchild1->orderedFrontalKeys.push_back(10); + grandchild2->orderedFrontalKeys.push_back(20); + + child1->addChild(grandchild1); + child2->addChild(grandchild2); + parent->addChild(child1); + parent->addChild(child2); + parent->addChild(child3); + + parent->mergeChildren({child1, child2}); + + EXPECT_LONGS_EQUAL(3, parent->children.size()); + EXPECT(parent->children[2] == child3); + EXPECT(parent->children[0] == grandchild1); + EXPECT(parent->children[1] == grandchild2); + const KeySet expected{1, 2}; + const KeySet actual(parent->orderedFrontalKeys.begin(), + parent->orderedFrontalKeys.end()); + EXPECT(assert_container_equality(expected, actual)); +} + +/* ************************************************************************* */ +TEST(ClusterTree, MergeChildrenSiblingsWithGrandchildren) { + using Cluster = SymbolicJunctionTree::Cluster; + auto parent = std::make_shared(); + auto child1 = std::make_shared(); + auto child2 = std::make_shared(); + auto child3 = std::make_shared(); + auto grandchild1 = std::make_shared(); + auto grandchild2 = std::make_shared(); + + child1->orderedFrontalKeys.push_back(1); + child2->orderedFrontalKeys.push_back(2); + child3->orderedFrontalKeys.push_back(3); + grandchild1->orderedFrontalKeys.push_back(10); + grandchild2->orderedFrontalKeys.push_back(20); + + child1->addChild(grandchild1); + child2->addChild(grandchild2); + parent->addChild(child1); + parent->addChild(child2); + parent->addChild(child3); + + parent->mergeChildrenSiblings({child1, child2}); + + EXPECT_LONGS_EQUAL(2, parent->children.size()); + EXPECT(parent->children[1] == child3); + EXPECT_LONGS_EQUAL(2, parent->children[0]->children.size()); + const KeySet expected{1, 2}; + const KeySet actual(parent->children[0]->orderedFrontalKeys.begin(), + parent->children[0]->orderedFrontalKeys.end()); + EXPECT(assert_container_equality(expected, actual)); +} + /* ************************************************************************* */ int main() { TestResult tr; diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index e4fd0f325e..a1363031a8 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -60,7 +60,9 @@ static void runMultifrontalSolver(MultifrontalSolver& solver, namespace { const std::string bal135 = findExampleDataFile("dubrovnik-135-90642-pre.txt"); -} +const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); +const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); +} // namespace void runBAL135Benchmark() { const size_t iterations = 1; @@ -86,8 +88,6 @@ void runBAL135Benchmark() { void runBALBenchmark() { const size_t bal_iterations = 2; - const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); - const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); for (const auto& filename : {bal16, bal88, bal135}) { cout << "\nProcessing BAL file: " << filename << std::endl; const SfmData db = SfmData::FromBalFile(filename); @@ -101,6 +101,7 @@ void runBALBenchmark() { MultifrontalSolver::Parameters params; params.mergeDimCap = kMergeDimCap; + params.reportStream = &std::cout; MultifrontalSolver solver(linear, ordering, params); solver.eliminateInPlace(linear); // Warm up cache. auto start = std::chrono::high_resolution_clock::now(); @@ -120,6 +121,7 @@ void runBALBenchmark() { << t_standard.count() / t_imperative.count() << "x" << std::endl; } } + void runChainBenchmark() { const std::vector T_values = {10, 50, 100, 500, 1000, 5000}; const size_t iterations = 500; @@ -152,11 +154,99 @@ void runChainBenchmark() { << t_standard.count() / t_imperative.count() << "x" << std::endl; } } + +void tuneMergingBAL() { + const size_t iterations = 2; + const std::vector balFiles = {bal16, bal88, bal135}; + cout << "\nTune leaf merging (BAL, iterations=" << iterations << ")" + << std::endl; + + const std::vector caps = {0, 64, 128, 256, 512, 1024, 2048}; + std::vector> results( + caps.size(), std::vector(balFiles.size(), 0.0)); + + for (size_t fileIndex = 0; fileIndex < balFiles.size(); ++fileIndex) { + const std::string& filename = balFiles[fileIndex]; + cout << "\n BAL file: " << filename << std::endl; + const SfmData db = SfmData::FromBalFile(filename); + const NonlinearFactorGraph graph = buildGeneralSfmGraph(db, 0.1); + const Values initial = buildGeneralSfmInitial(db); + const GaussianFactorGraph linear = *graph.linearize(initial); + const Ordering ordering = createSchurOrdering(db, false); + + MultifrontalSolver::Parameters params; + params.mergeDimCap = 0; + params.reportStream = &std::cout; + for (size_t capIndex = 0; capIndex < caps.size(); ++capIndex) { + const size_t cap = caps[capIndex]; + params.leafMergeDimCap = cap; + + MultifrontalSolver solver(linear, ordering, params); + solver.eliminateInPlace(linear); // Warm up cache. + auto start = std::chrono::high_resolution_clock::now(); + runMultifrontalSolver(solver, linear, iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + results[capIndex][fileIndex] = t_imperative.count(); + cout << " leafMergeDimCap=" << cap << " -> " << t_imperative.count() + << " s\n" + << std::endl; + } + } + + cout << "\n| LeafMergeDimCap | BAL16 | BAL88 | BAL135 |\n"; + cout << "| --- | --- | --- | --- |\n"; + for (size_t capIndex = 0; capIndex < caps.size(); ++capIndex) { + cout << "| " << caps[capIndex]; + for (size_t fileIndex = 0; fileIndex < balFiles.size(); ++fileIndex) { + cout << " | " << results[capIndex][fileIndex]; + } + cout << " |\n"; + } +} + +void tuneMergeChain() { + const size_t iterations = 100; + const size_t T = 5000; + cout << "\nTune mergeDimCap (chain T=" << T << ", iterations=" << iterations + << ")" << std::endl; + + GaussianFactorGraph smoother = createSmoother(T); + const Ordering ordering = Ordering::Metis(smoother); + + const std::vector caps = {0, 16, 32, 64, 128, 256, 512}; + std::vector> results; + MultifrontalSolver::Parameters params; + params.leafMergeDimCap = 0; + params.reportStream = &std::cout; + for (size_t cap : caps) { + params.mergeDimCap = cap; + MultifrontalSolver solver(smoother, ordering, params); + + auto start = std::chrono::high_resolution_clock::now(); + runMultifrontalSolver(solver, smoother, iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + results.emplace_back(cap, t_imperative.count()); + cout << " mergeDimCap=" << cap << " -> " << t_imperative.count() << " s\n" + << std::endl; + } + + cout << "\n| Phase | Cap | Seconds |\n"; + cout << "| --- | --- | --- |\n"; + for (const auto& result : results) { + cout << "| mergeDimCap | " << result.first << " | " << result.second + << " |\n"; + } +} + int main() { cout << "Merging dim cap " << kMergeDimCap << std::endl; runBAL135Benchmark(); runBALBenchmark(); runChainBenchmark(); + // tuneMergingBAL(); + // tuneMergeChain(); return 0; } From 56175d3a5c8c465706b21874a53944d09b0676f0 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 13:31:24 -0500 Subject: [PATCH 047/266] HessianFactors no longer supported Choose numThreads to not saturate Time big chain --- gtsam/linear/MultifrontalClique.cpp | 39 ++----- gtsam/linear/MultifrontalClique.h | 8 +- gtsam/linear/MultifrontalSolver.cpp | 65 +++++++---- gtsam/linear/MultifrontalSolver.h | 33 +++++- gtsam/linear/tests/testMultifrontalSolver.cpp | 11 +- timing/timeMultifrontalSolver.cpp | 101 ++++++++++-------- 6 files changed, 145 insertions(+), 112 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index b0a797447c..22d4b31b03 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -18,7 +18,6 @@ #include #include -#include #include #include @@ -207,45 +206,27 @@ size_t MultifrontalClique::addJacobianFactor( return rows; } -void MultifrontalClique::addHessianFactor(const HessianFactor& hessianFactor) { - const SymmetricBlockMatrix& info = hessianFactor.info(); - const DenseIndex factorBlocks = static_cast(hessianFactor.size()); - const DenseIndex rhsBlock = static_cast(sbm_.nBlocks() - 1); - - std::vector blockIndices(factorBlocks + 1, -1); - DenseIndex slot = 0; - for (auto it = hessianFactor.begin(); it != hessianFactor.end(); - ++it, ++slot) { - const Key key = *it; - if (fixedKeys_ && fixedKeys_->count(key)) continue; - blockIndices[slot] = blockIndex(key); - } - blockIndices[factorBlocks] = rhsBlock; - - sbm_.updateFromMappedBlocks(info, blockIndices); -} - void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { assert(validateFactorKeys(graph, factorIndices_, orderedKeys_, fixedKeys_)); - hessianFactors_.clear(); size_t rowOffset = 0; for (size_t index : factorIndices_) { assert(index < graph.size()); const GaussianFactor::shared_ptr& gf = graph[index]; if (!gf) continue; - if (auto jacobianFactor = std::dynamic_pointer_cast(gf)) { - rowOffset += addJacobianFactor(*jacobianFactor, rowOffset); - } else if (auto hessianFactor = - std::dynamic_pointer_cast(gf)) { - hessianFactors_.push_back(hessianFactor); + if (!gf->isJacobian()) { + throw std::runtime_error( + "MultifrontalClique::fillAb: only JacobianFactor inputs are " + "supported."); } + auto jacobianFactor = std::static_pointer_cast(gf); + rowOffset += addJacobianFactor(*jacobianFactor, rowOffset); } - // Lock in QR only for leaf cliques with no Hessian factors. + // Lock in QR only for leaf cliques. const bool isLeaf = children.empty(); const bool useQR = - isLeaf && hessianFactors_.empty() && (frontalDim > 0) && + isLeaf && (frontalDim > 0) && (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && (Ab_.matrix().rows() >= static_cast(frontalDim)); solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; @@ -261,10 +242,6 @@ void MultifrontalClique::prepareForElimination() { if (useQR()) return; assert(sbm_.nBlocks() > 0); sbm_.setZero(); - for (const auto& hf : hessianFactors_) { - addHessianFactor(*hf); - } - if (Ab_.matrix().rows() > 0) { sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); } diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index f9e6c3029d..f90dd925d6 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -108,7 +108,9 @@ class GTSAM_EXPORT MultifrontalClique { void finalize(std::vector children); /// Load factor values into the pre-allocated Ab matrix. - /// @param graph The factor graph with updated values. + /// @param graph The factor graph with updated values (structure must match + /// the graph used to build this clique, apart from updated + /// numerical values). Only JacobianFactor inputs are supported. void fillAb(const GaussianFactorGraph& graph); /// Zero out sbm, re-add Hessians, accumulate Jacobians and children. @@ -230,9 +232,6 @@ class GTSAM_EXPORT MultifrontalClique { */ size_t addJacobianFactor(const JacobianFactor& factor, size_t rowOffset); - /// Add a Hessian factor's contributions into the sbm_ matrix. - void addHessianFactor(const HessianFactor& factor); - void setParentIndices(const std::vector& indices) { parentIndices_ = indices; } @@ -249,7 +248,6 @@ class GTSAM_EXPORT MultifrontalClique { // Load-time state. VerticalBlockMatrix Ab_; - std::vector hessianFactors_; ///< Hessian factors. SolveMode solveMode_ = SolveMode::Cholesky; // Elimination-time state. diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 3f62951093..f518a48494 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -16,15 +16,14 @@ * @date December 2025 */ +#include #include #include -#include #include #include #include #include #include -#include #include #include #include @@ -40,6 +39,11 @@ #include #include #include +#include + +#ifdef GTSAM_USE_TBB +#include +#endif namespace gtsam { @@ -94,8 +98,11 @@ std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { std::unordered_set fixedKeys; for (const auto& factor : graph) { if (!factor) continue; - auto jacobianFactor = std::dynamic_pointer_cast(factor); - if (!jacobianFactor) continue; + if (!factor->isJacobian()) { + throw std::runtime_error( + "MultifrontalSolver: only JacobianFactor inputs are supported."); + } + auto jacobianFactor = std::static_pointer_cast(factor); auto model = jacobianFactor->get_model(); if (!model || !model->isConstrained()) continue; // Only accept fully constrained unary factors with zero residuals. @@ -123,17 +130,13 @@ std::map computeDims(const GaussianFactorGraph& graph) { std::map dims; for (const auto& factor : graph) { if (!factor) continue; - if (auto jacobianFactor = - std::dynamic_pointer_cast(factor)) { - for (auto it = jacobianFactor->begin(); it != jacobianFactor->end(); - ++it) { - dims[*it] = jacobianFactor->getDim(it); - } - } else if (auto hessianFactor = - std::dynamic_pointer_cast(factor)) { - for (auto it = hessianFactor->begin(); it != hessianFactor->end(); ++it) { - dims[*it] = hessianFactor->getDim(it); - } + if (!factor->isJacobian()) { + throw std::runtime_error( + "MultifrontalSolver: only JacobianFactor inputs are supported."); + } + auto jacobianFactor = std::static_pointer_cast(factor); + for (auto it = jacobianFactor->begin(); it != jacobianFactor->end(); ++it) { + dims[*it] = jacobianFactor->getDim(it); } } return dims; @@ -141,11 +144,12 @@ std::map computeDims(const GaussianFactorGraph& graph) { size_t factorRowCount(const GaussianFactorGraph& graph, size_t index) { if (index >= graph.size() || !graph[index]) return 0; - if (auto jacobianFactor = - std::dynamic_pointer_cast(graph[index])) { - return jacobianFactor->rows(); + if (!graph[index]->isJacobian()) { + throw std::runtime_error( + "MultifrontalSolver: only JacobianFactor inputs are supported."); } - return 0; + auto jacobianFactor = std::static_pointer_cast(graph[index]); + return jacobianFactor->rows(); } // Build SymbolicFactorGraph from GaussianFactorGraph @@ -441,6 +445,14 @@ struct CliqueBuilder { } }; +size_t resolveThreadCount(size_t requested) { + if (requested != 0) return requested; + unsigned int hardwareThreads = std::thread::hardware_concurrency(); + if (hardwareThreads == 0) hardwareThreads = 1; + size_t threads = (hardwareThreads * 3) / 4; + return threads == 0 ? 1 : threads; +} + } // namespace /* ************************************************************************* */ @@ -458,7 +470,10 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, const Parameters& params) - : TaskMixin(), params_(params) { + : SolverTaskMixin( + resolveThreadCount(params.numThreads)), + params_(params), + numThreads_(resolveThreadCount(params.numThreads)) { dims_ = std::move(data.dims); fixedKeys_ = std::move(data.fixedKeys); @@ -552,6 +567,8 @@ void MultifrontalSolver::eliminateInPlace() { } eliminated_ = false; #ifdef GTSAM_USE_TBB + tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, + static_cast(numThreads_)); // Parallel elimination uses PostOrderForestParallel, which will be // multi-threaded if GTSAM was compiled with TBB. TbbOpenMPMixedScope threadLimiter; @@ -570,6 +587,8 @@ void MultifrontalSolver::eliminateInPlace() { void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { // Combine load + eliminate in one post-order traversal to improve locality. #ifdef GTSAM_USE_TBB + tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, + static_cast(numThreads_)); TbbOpenMPMixedScope threadLimiter; auto visitorPost = [&graph](const CliquePtr& node) { if (!node) return; @@ -633,9 +652,13 @@ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { /* ************************************************************************* */ const VectorValues& MultifrontalSolver::updateSolution() const { assert(loaded_ && eliminated_); +#ifdef GTSAM_USE_TBB + tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, + static_cast(numThreads_)); + TbbOpenMPMixedScope threadLimiter; +#endif // Parallel solve uses treeTraversal::DepthFirstForestParallel (Pre-order / // Top-Down). - TbbOpenMPMixedScope threadLimiter; int rootData = 0; auto visitorPre = [](const CliquePtr& node, int&) { if (node) node->updateSolution(); diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index ce139c3f6d..9fe1984e5f 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -18,7 +18,6 @@ #pragma once -#include #include #include #include @@ -31,11 +30,28 @@ #include #include +#ifndef GTSAM_USE_TBB +#include +#endif + namespace gtsam { class GaussianBayesTree; class MultifrontalClique; +template +struct EmptyTaskMixin { + explicit EmptyTaskMixin(size_t = 0) {} +}; + +#ifdef GTSAM_USE_TBB +template +using SolverTaskMixin = EmptyTaskMixin; +#else +template +using SolverTaskMixin = TaskMixin; +#endif + /** * Imperative-style multifrontal solver for Gaussian factor graphs. * @@ -43,6 +59,9 @@ class MultifrontalClique; * provides efficient methods for loading new factors, eliminating the graph, * and solving for the update vector. * + * @note Only JacobianFactor inputs are supported. Any non-Jacobian factors + * will throw during construction/precompute or load. + * * @note Clique merging has two optional phases: an initial leaf-merge pass * (leafMergeDimCap) that merges multiple leaf children into a common parent * while the parent's total dimension plus merged frontal dimensions stays @@ -52,15 +71,16 @@ class MultifrontalClique; * Defaults are conservative and may need tuning per machine or dataset. */ class GTSAM_EXPORT MultifrontalSolver - : public TaskMixin { + : public SolverTaskMixin { public: /// Tuning parameters for traversal and reporting. struct Parameters { size_t leafMergeDimCap = 256; ///< Leaf-merge cap (0 disables). - size_t mergeDimCap = 16; ///< Merge threshold (0 disables). + size_t mergeDimCap = 32; ///< Merge threshold (0 disables). std::ostream* reportStream = nullptr; ///< Optional structure reporting. int eliminationParallelThreshold = 10; ///< Post-order task threshold. int solutionParallelThreshold = 4096; ///< Pre-order task threshold. + size_t numThreads = 0; ///< Worker count (0 uses 0.75 * hw threads). }; struct PrecomputedData { @@ -83,6 +103,7 @@ class GTSAM_EXPORT MultifrontalSolver bool loaded_ = false; ///< Whether load() has been called. bool eliminated_ = false; ///< Whether eliminateInPlace() ran. Parameters params_; ///< Tunable solver parameters. + size_t numThreads_ = 0; ///< Resolved thread count for traversal. public: /** @@ -90,6 +111,7 @@ class GTSAM_EXPORT MultifrontalSolver * This builds the symbolic junction tree and pre-allocates all matrices. * Call load() before eliminating to populate numerical values. * @param graph The factor graph to solve. + * Must contain only JacobianFactor instances. * @param ordering The variable ordering to use for elimination. * @param params Tunable parameters for traversal and reporting. */ @@ -114,6 +136,7 @@ class GTSAM_EXPORT MultifrontalSolver MultifrontalSolver(PrecomputedData data, const Ordering& ordering); /// Precompute symbolic structure and sizing data from a factor graph. + /// Only JacobianFactor inputs are supported. static PrecomputedData Precompute(const GaussianFactorGraph& graph, const Ordering& ordering); @@ -121,7 +144,9 @@ class GTSAM_EXPORT MultifrontalSolver * Load new numerical values from the factor graph. * This overwrites the values in the pre-allocated matrices. * - * @param graph The factor graph with updated values (structure must match). + * @param graph The factor graph with updated values (structure must match + * the graph used to construct/precompute this solver, apart + * from updated numerical values). */ void load(const GaussianFactorGraph& graph); diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index 45925e8c01..da7f43ff83 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -325,19 +325,16 @@ TEST(MultifrontalSolver, WeightedScalarMeasurements) { } /* ************************************************************************* */ -// Hessian factors contribute directly to the augmented normal equations. +// Hessian factors are rejected by the multifrontal solver. TEST(MultifrontalSolver, HessianFactors) { GaussianFactorGraph graph; graph.emplace_shared(x1, (Matrix(1, 1) << 4.0).finished(), (Vector(1) << 8.0).finished(), 0.0); const Ordering ordering{x1}; - MultifrontalSolver solver(graph, ordering, noMergeParams()); - solver.load(graph); - solver.eliminateInPlace(); - const VectorValues& actual = solver.updateSolution(); - - EXPECT_DOUBLES_EQUAL(2.0, actual.at(x1)(0), 1e-9); + CHECK_EXCEPTION( + { MultifrontalSolver solver(graph, ordering, noMergeParams()); }, + std::runtime_error); } /* ************************************************************************* */ diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index a1363031a8..69cd2e4f6d 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -31,10 +31,16 @@ using namespace gtsam; using namespace example; namespace { -// Threshold chosen empirically for these timing experiments: merging small +// Thresholds chosen empirically for these timing experiments: merging small // frontal cliques tends to improve performance without materially affecting // numerical behavior for the tested problems. -constexpr size_t kMergeDimCap = 16; +MultifrontalSolver::Parameters makeDefaultParams() { + MultifrontalSolver::Parameters params; + params.mergeDimCap = 32; + params.reportStream = &std::cout; + return params; +} + } // namespace /// Run standard GTSAM multifrontal elimination and optimization. @@ -64,7 +70,7 @@ const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); } // namespace -void runBAL135Benchmark() { +void runBAL135Benchmark(MultifrontalSolver::Parameters params) { const size_t iterations = 1; cout << "\nSingle MFS test: " << bal135 << " (iterations=" << iterations << ")" << std::endl; @@ -75,8 +81,6 @@ void runBAL135Benchmark() { const GaussianFactorGraph linear = *graph.linearize(initial); const Ordering ordering = createSchurOrdering(db, false); - MultifrontalSolver::Parameters params; - params.mergeDimCap = kMergeDimCap; MultifrontalSolver solver(linear, ordering, params); auto start = std::chrono::high_resolution_clock::now(); runMultifrontalSolver(solver, linear, iterations); @@ -86,7 +90,7 @@ void runBAL135Benchmark() { tictoc_print(); } -void runBALBenchmark() { +void runBALBenchmark(MultifrontalSolver::Parameters params) { const size_t bal_iterations = 2; for (const auto& filename : {bal16, bal88, bal135}) { cout << "\nProcessing BAL file: " << filename << std::endl; @@ -99,9 +103,6 @@ void runBALBenchmark() { cout << "\nBAL Benchmark (" << filename << ", iterations=" << bal_iterations << "):" << std::endl; - MultifrontalSolver::Parameters params; - params.mergeDimCap = kMergeDimCap; - params.reportStream = &std::cout; MultifrontalSolver solver(linear, ordering, params); solver.eliminateInPlace(linear); // Warm up cache. auto start = std::chrono::high_resolution_clock::now(); @@ -122,7 +123,7 @@ void runBALBenchmark() { } } -void runChainBenchmark() { +void runChainBenchmark(MultifrontalSolver::Parameters params) { const std::vector T_values = {10, 50, 100, 500, 1000, 5000}; const size_t iterations = 500; @@ -133,9 +134,6 @@ void runChainBenchmark() { const Ordering ordering = Ordering::Metis(smoother); auto start = std::chrono::high_resolution_clock::now(); - MultifrontalSolver::Parameters params; - params.mergeDimCap = kMergeDimCap; - params.reportStream = &std::cout; MultifrontalSolver solver(smoother, ordering, params); runMultifrontalSolver(solver, smoother, iterations); auto end = std::chrono::high_resolution_clock::now(); @@ -155,15 +153,33 @@ void runChainBenchmark() { } } -void tuneMergingBAL() { +void runChain5000(MultifrontalSolver::Parameters params) { + const size_t iterations = 5000; + + const size_t T = 5000; + cout << "\nBenchmark (T=" << T << ", iterations=" << iterations + << "):" << std::endl; + GaussianFactorGraph smoother = createSmoother(T); + const Ordering ordering = Ordering::Metis(smoother); + + auto start = std::chrono::high_resolution_clock::now(); + MultifrontalSolver solver(smoother, ordering, params); + runMultifrontalSolver(solver, smoother, iterations); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration t_imperative = end - start; + cout << "\nTiming results:\n"; + cout << " MultifrontalSolver: " << t_imperative.count() << " s" << std::endl; +} + +void tuneMergingBAL(MultifrontalSolver::Parameters params) { const size_t iterations = 2; const std::vector balFiles = {bal16, bal88, bal135}; cout << "\nTune leaf merging (BAL, iterations=" << iterations << ")" << std::endl; - const std::vector caps = {0, 64, 128, 256, 512, 1024, 2048}; + const std::vector sweep = {0, 64, 128, 256, 512, 1024, 2048}; std::vector> results( - caps.size(), std::vector(balFiles.size(), 0.0)); + sweep.size(), std::vector(balFiles.size(), 0.0)); for (size_t fileIndex = 0; fileIndex < balFiles.size(); ++fileIndex) { const std::string& filename = balFiles[fileIndex]; @@ -174,12 +190,9 @@ void tuneMergingBAL() { const GaussianFactorGraph linear = *graph.linearize(initial); const Ordering ordering = createSchurOrdering(db, false); - MultifrontalSolver::Parameters params; - params.mergeDimCap = 0; - params.reportStream = &std::cout; - for (size_t capIndex = 0; capIndex < caps.size(); ++capIndex) { - const size_t cap = caps[capIndex]; - params.leafMergeDimCap = cap; + for (size_t i = 0; i < sweep.size(); ++i) { + const size_t parameter = sweep[i]; + params.leafMergeDimCap = parameter; MultifrontalSolver solver(linear, ordering, params); solver.eliminateInPlace(linear); // Warm up cache. @@ -187,25 +200,25 @@ void tuneMergingBAL() { runMultifrontalSolver(solver, linear, iterations); auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration t_imperative = end - start; - results[capIndex][fileIndex] = t_imperative.count(); - cout << " leafMergeDimCap=" << cap << " -> " << t_imperative.count() - << " s\n" + results[i][fileIndex] = t_imperative.count(); + cout << " leafMergeDimCap=" << parameter << " -> " + << t_imperative.count() << " s\n" << std::endl; } } cout << "\n| LeafMergeDimCap | BAL16 | BAL88 | BAL135 |\n"; cout << "| --- | --- | --- | --- |\n"; - for (size_t capIndex = 0; capIndex < caps.size(); ++capIndex) { - cout << "| " << caps[capIndex]; + for (size_t i = 0; i < sweep.size(); ++i) { + cout << "| " << sweep[i]; for (size_t fileIndex = 0; fileIndex < balFiles.size(); ++fileIndex) { - cout << " | " << results[capIndex][fileIndex]; + cout << " | " << results[i][fileIndex]; } cout << " |\n"; } } -void tuneMergeChain() { +void tuneMergeChain(MultifrontalSolver::Parameters params) { const size_t iterations = 100; const size_t T = 5000; cout << "\nTune mergeDimCap (chain T=" << T << ", iterations=" << iterations @@ -214,21 +227,19 @@ void tuneMergeChain() { GaussianFactorGraph smoother = createSmoother(T); const Ordering ordering = Ordering::Metis(smoother); - const std::vector caps = {0, 16, 32, 64, 128, 256, 512}; + const std::vector sweep = {0, 16, 32, 64, 128, 256, 512}; std::vector> results; - MultifrontalSolver::Parameters params; - params.leafMergeDimCap = 0; - params.reportStream = &std::cout; - for (size_t cap : caps) { - params.mergeDimCap = cap; + for (size_t parameter : sweep) { + params.mergeDimCap = parameter; MultifrontalSolver solver(smoother, ordering, params); auto start = std::chrono::high_resolution_clock::now(); runMultifrontalSolver(solver, smoother, iterations); auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration t_imperative = end - start; - results.emplace_back(cap, t_imperative.count()); - cout << " mergeDimCap=" << cap << " -> " << t_imperative.count() << " s\n" + results.emplace_back(parameter, t_imperative.count()); + cout << " mergeDimCap=" << parameter << " -> " << t_imperative.count() + << " s\n" << std::endl; } @@ -241,12 +252,14 @@ void tuneMergeChain() { } int main() { - cout << "Merging dim cap " << kMergeDimCap << std::endl; - - runBAL135Benchmark(); - runBALBenchmark(); - runChainBenchmark(); - // tuneMergingBAL(); - // tuneMergeChain(); + auto params = makeDefaultParams(); + cout << "Merging dim parameter " << params.mergeDimCap << std::endl; + + // runBAL135Benchmark(params); + runBALBenchmark(params); + runChainBenchmark(params); + // runChain5000(params); + // tuneMergingBAL(params); + // tuneMergeChain(params); return 0; } From 4e8fcef6248a8360e16dac79188bf5fe9cdfe990 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Thu, 8 Jan 2026 18:09:17 -0500 Subject: [PATCH 048/266] slightly better transformFrom --- gtsam/geometry/Similarity3.cpp | 46 +++++++++++++++------------------- 1 file changed, 20 insertions(+), 26 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index 7d0211f50a..99483d6655 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -147,42 +147,36 @@ Point3 Similarity3::transformFrom(const Point3& p, // Pose3 Similarity3::transformFrom(const Pose3& bTi, OptionalJacobian<6, 7> Hself, OptionalJacobian<6, 6> H_bTi) const { + if (!Hself && !H_bTi) { + return Pose3(R_ * bTi.rotation(), transformFrom(bTi.translation())); + } - // Similarity3 components and their derivatives with respect to the Similarity3. - Matrix37 DSimR_dSim; - const Rot3 Sim_R = this->rotation(Hself ? &DSimR_dSim : nullptr); - - // Pose3 components and their derivatives with respect to the Pose3. - Matrix36 Dp_dT, DTR_dT; - const Rot3 TR = bTi.rotation(H_bTi ? &DTR_dT : nullptr); - const Point3 p = bTi.translation(H_bTi ? &Dp_dT : nullptr); - - // Rotation component of the result. - Matrix3 DR_dSimR, DR_dTR; - const Rot3 R = Sim_R.compose(TR, Hself ? &DR_dSimR : nullptr, H_bTi ? &DR_dTR : nullptr); + // Jacobian of the .rotation() is identity wrt rotation, so delegate jacobians to Rot3. + Matrix33 DR_dsimR, DR_dTR; + const Rot3 R = R_.compose(bTi.rotation(), Hself ? &DR_dsimR : nullptr, H_bTi ? &DR_dTR : nullptr); - Matrix37 Dt_sim; - Matrix3 Dt_p; - const Point3 t = this->transformFrom(p, Hself ? &Dt_sim : nullptr, H_bTi ? &Dt_p : nullptr); + // Delegate the translation component jacobians to the point3 transformFrom. + Matrix37 Dt_dsim; + Matrix3 Dt_dp; + Matrix36 Dp_dT; + const Point3 p = bTi.translation(&Dp_dT); + const Point3 t = this->transformFrom(p, Hself ? &Dt_dsim : nullptr, H_bTi ? &Dt_dp : nullptr); - Matrix63 Dresult_t; - // Dresult_R is identity subblock of R, so ignoring. - const Pose3 result = Pose3::Create(R, t, nullptr, Hself ? &Dresult_t : nullptr); + Matrix63 Dresult_t; + const Pose3 result = Pose3::Create(R, t, nullptr, &Dresult_t); if (Hself) { Hself->setZero(); - // Rotation component. - Hself->block<3, 7>(0, 0) = DR_dSimR * DSimR_dSim; - - // Translation component. - Hself->block<3, 7>(3, 0) = (Dresult_t * Dt_sim).block<3, 7>(3, 0); + Hself->block<3, 3>(0, 0) = DR_dsimR; + // Chain D_result_t (3x3) * D_t_sim (3x7). + Hself->block<3, 7>(3, 0) = Dresult_t.block<3, 3>(3, 0) * Dt_dsim; } if (H_bTi) { H_bTi->setZero(); - // Rotation component. - H_bTi->block<3, 6>(0, 0) = DR_dTR * DTR_dT; - H_bTi->block<3, 6>(3, 0) = (Dresult_t * (Dt_p * Dp_dT)).block<3, 6>(3, 0); + H_bTi->block<3, 3>(0, 0) = DR_dTR; + // Chain D_result_t (3x3) * D_t_p (3x3) * D_p_bTi (3x6). + H_bTi->block<3, 6>(3, 0) = Dresult_t.block<3, 3>(3, 0) * Dt_dp * Dp_dT; } return result; } From 10772ecceea8cc6cf70e93224c139822a71bddf3 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Thu, 8 Jan 2026 18:15:13 -0500 Subject: [PATCH 049/266] move getters to cpp --- gtsam/geometry/Similarity3.cpp | 23 +++++++++++++++++++++++ gtsam/geometry/Similarity3.h | 23 +++-------------------- 2 files changed, 26 insertions(+), 20 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index 99483d6655..cd3ccdceda 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -118,6 +118,29 @@ void Similarity3::print(const std::string& s) const { std::cout << "t: " << translation().transpose() << " s: " << scale() << std::endl; } +Rot3 Similarity3::rotation(OptionalJacobian<3, 7> Hself) const { + if (Hself) { + Hself->setZero(); + Hself->block<3, 3>(0, 0) = I_3x3; + } + return R_; +} + +Point3 Similarity3::translation(OptionalJacobian<3, 7> Hself) const { + if (Hself) { + *Hself << Z_3x3, rotation().matrix(), -t_; + } + return t_; +} + +double Similarity3::scale(OptionalJacobian<1, 7> Hself) const { + if (Hself) { + Hself->setZero(); + (*Hself)(0, 6) = s_; + } + return s_; +} + Similarity3 Similarity3::Identity() { return Similarity3(); } diff --git a/gtsam/geometry/Similarity3.h b/gtsam/geometry/Similarity3.h index ce4675b849..f91fcdefc7 100644 --- a/gtsam/geometry/Similarity3.h +++ b/gtsam/geometry/Similarity3.h @@ -194,30 +194,13 @@ class GTSAM_EXPORT Similarity3 : public MatrixLieGroup { Matrix4 matrix() const; /// Return a rotation - Rot3 rotation(OptionalJacobian<3, 7> Hself = {}) const { - if (Hself) { - Hself->setZero(); - Hself->block<3, 3>(0, 0) = I_3x3; - } - return R_; - } + Rot3 rotation(OptionalJacobian<3, 7> Hself = {}) const; /// Return a translation with pushforward - Point3 translation(OptionalJacobian<3, 7> Hself = {}) const { - if (Hself) { - *Hself << Z_3x3, rotation().matrix(), -t_; - } - return t_; - } + Point3 translation(OptionalJacobian<3, 7> Hself = {}) const; /// Return the scale - double scale(OptionalJacobian<1, 7> Hself = {}) const { - if (Hself) { - Hself->setZero(); - (*Hself)(0, 6) = s_; - } - return s_; - } + double scale(OptionalJacobian<1, 7> Hself = {}) const; /// @} /// @name Deprecated From bdfb203a1487b5e18916e1c120d2cde12ad58654 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Wed, 7 Jan 2026 19:55:34 -0500 Subject: [PATCH 050/266] remove TBB path --- .github/copilot-instructions.md | 1 + gtsam/linear/MultifrontalSolver.cpp | 73 +++++------------------------ gtsam/linear/MultifrontalSolver.h | 22 ++------- timing/timeMultifrontalSolver.cpp | 2 +- 4 files changed, 17 insertions(+), 81 deletions(-) diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 242e50f720..b2179615fd 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -8,3 +8,4 @@ For reviewing PRs: * Classes are Uppercase, methods and functions lowerMixedCase. * Public fields in structs keep plain names (no trailing underscore). * Apart from those naming conventions, we adopt Google C++ style. +* After any code change, always run relevant tests via `make -j6 testXXX.run` in the build folder $WORKSPACE/build. If in VS code, ask for escalated permissions if needed. diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index f518a48494..8f94adae5c 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -17,8 +17,6 @@ */ #include -#include -#include #include #include #include @@ -41,10 +39,6 @@ #include #include -#ifdef GTSAM_USE_TBB -#include -#endif - namespace gtsam { namespace { @@ -470,7 +464,7 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, const Parameters& params) - : SolverTaskMixin( + : ForestTraversal( resolveThreadCount(params.numThreads)), params_(params), numThreads_(resolveThreadCount(params.numThreads)) { @@ -566,43 +560,20 @@ void MultifrontalSolver::eliminateInPlace() { "eliminating."); } eliminated_ = false; -#ifdef GTSAM_USE_TBB - tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, - static_cast(numThreads_)); - // Parallel elimination uses PostOrderForestParallel, which will be - // multi-threaded if GTSAM was compiled with TBB. - TbbOpenMPMixedScope threadLimiter; - auto visitorPost = [](const CliquePtr& node) { - if (node) node->eliminateInPlace(); - }; - treeTraversal::PostOrderForestParallel(*this, visitorPost, - params_.eliminationParallelThreshold); -#else - runBottomUp([](MultifrontalClique& node) { node.eliminateInPlace(); }); -#endif + runBottomUp([](MultifrontalClique& node) { node.eliminateInPlace(); }, + params_.eliminationParallelThreshold); eliminated_ = true; } /* ************************************************************************* */ void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { // Combine load + eliminate in one post-order traversal to improve locality. -#ifdef GTSAM_USE_TBB - tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, - static_cast(numThreads_)); - TbbOpenMPMixedScope threadLimiter; - auto visitorPost = [&graph](const CliquePtr& node) { - if (!node) return; - node->fillAb(graph); - node->eliminateInPlace(); - }; - treeTraversal::PostOrderForestParallel(*this, visitorPost, - params_.eliminationParallelThreshold); -#else - runBottomUp([&graph](MultifrontalClique& node) { - node.fillAb(graph); - node.eliminateInPlace(); - }); -#endif + runBottomUp( + [&graph](MultifrontalClique& node) { + node.fillAb(graph); + node.eliminateInPlace(); + }, + params_.eliminationParallelThreshold); loaded_ = true; eliminated_ = true; } @@ -650,30 +621,10 @@ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { } /* ************************************************************************* */ -const VectorValues& MultifrontalSolver::updateSolution() const { +const VectorValues& MultifrontalSolver::updateSolution() { assert(loaded_ && eliminated_); -#ifdef GTSAM_USE_TBB - tbb::global_control tbbControl(tbb::global_control::max_allowed_parallelism, - static_cast(numThreads_)); - TbbOpenMPMixedScope threadLimiter; -#endif - // Parallel solve uses treeTraversal::DepthFirstForestParallel (Pre-order / - // Top-Down). - int rootData = 0; - auto visitorPre = [](const CliquePtr& node, int&) { - if (node) node->updateSolution(); - return 0; - }; - auto visitorPost = [](const CliquePtr&, int) {}; - - // Threshold chosen to balance task overhead vs parallelism. Small cliques - // (like points in SFM) are better handled sequentially within larger tasks - // to minimize scheduling overhead and maximize cache locality. - // Cast to non-const because treeTraversal expects a non-const reference, - // even though we are only calling const methods on the nodes. - treeTraversal::DepthFirstForestParallel( - const_cast(*this), rootData, visitorPre, visitorPost, - params_.solutionParallelThreshold); + runTopDown([](MultifrontalClique& node) { node.updateSolution(); }, + params_.solutionParallelThreshold); for (Key key : fixedKeys_) { solution_.at(key).setZero(); diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 9fe1984e5f..ca5cf642a4 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -18,6 +18,7 @@ #pragma once +#include #include #include #include @@ -30,28 +31,11 @@ #include #include -#ifndef GTSAM_USE_TBB -#include -#endif - namespace gtsam { class GaussianBayesTree; class MultifrontalClique; -template -struct EmptyTaskMixin { - explicit EmptyTaskMixin(size_t = 0) {} -}; - -#ifdef GTSAM_USE_TBB -template -using SolverTaskMixin = EmptyTaskMixin; -#else -template -using SolverTaskMixin = TaskMixin; -#endif - /** * Imperative-style multifrontal solver for Gaussian factor graphs. * @@ -71,7 +55,7 @@ using SolverTaskMixin = TaskMixin; * Defaults are conservative and may need tuning per machine or dataset. */ class GTSAM_EXPORT MultifrontalSolver - : public SolverTaskMixin { + : public ForestTraversal { public: /// Tuning parameters for traversal and reporting. struct Parameters { @@ -175,7 +159,7 @@ class GTSAM_EXPORT MultifrontalSolver * * @return Reference to the internally cached solution vector. */ - const VectorValues& updateSolution() const; + const VectorValues& updateSolution(); /// Accessor for the roots of the elimination tree. const std::vector& roots() const { return roots_; } diff --git a/timing/timeMultifrontalSolver.cpp b/timing/timeMultifrontalSolver.cpp index 69cd2e4f6d..8029d879a4 100644 --- a/timing/timeMultifrontalSolver.cpp +++ b/timing/timeMultifrontalSolver.cpp @@ -37,7 +37,7 @@ namespace { MultifrontalSolver::Parameters makeDefaultParams() { MultifrontalSolver::Parameters params; params.mergeDimCap = 32; - params.reportStream = &std::cout; + // params.reportStream = &std::cout; return params; } From 5a76ac201c4b36666c12337e74bf1a1f26128cf8 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 9 Jan 2026 00:49:12 +0000 Subject: [PATCH 051/266] Initial plan From 3002bebff92e890415ce71fe92515b9c04a8115b Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 9 Jan 2026 01:25:32 +0000 Subject: [PATCH 052/266] Fix comment styles, add GTSAM_EXPORT macros, and improve parameter names Co-authored-by: dellaert <10515273+dellaert@users.noreply.github.com> --- gtsam/base/ForestTraversal.h | 34 +++++++++++++++--------- gtsam/base/PriorityScheduler.h | 14 ++++------ gtsam/base/tests/testForestTraversal.cpp | 2 +- 3 files changed, 27 insertions(+), 23 deletions(-) diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index 6181c57edf..f568692a8a 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -58,13 +58,13 @@ namespace gtsam { * that mirrors `TaskMixin`. */ template -class ForestTraversal { +class GTSAM_EXPORT ForestTraversal { private: size_t threadCount_; using SharedNode = std::shared_ptr; public: - /// Construct a helper with a fixed thread budget (used by TBB when enabled). + /** Construct a helper with a fixed thread budget (used by TBB when enabled). */ explicit ForestTraversal( size_t numThreads = std::thread::hardware_concurrency()) : threadCount_(numThreads == 0 ? 1 : numThreads) @@ -76,8 +76,10 @@ class ForestTraversal { } #ifdef GTSAM_USE_TBB + /** + * @brief Depth-first traversal using a pre-order visitor (TBB path). + */ template - /// Depth-first traversal using a pre-order visitor (TBB path). void runTopDown(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { // The pre-order visitor runs before visiting children; parallel task @@ -99,8 +101,10 @@ class ForestTraversal { }); } + /** + * @brief Post-order traversal using a bottom-up visitor (TBB path). + */ template - /// Post-order traversal using a bottom-up visitor (TBB path). void runBottomUp(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { // The bottom-up visitor runs after all children are processed; @@ -119,9 +123,10 @@ class ForestTraversal { } private: - /// Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. + /** + * @brief Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. + */ template - /// Limit TBB+OpenMP parallelism, then invoke the traversal body. void withTbbTraversalControl(Body&& body) { // Set a cap on TBB threads and enter an OpenMP-compatible scope, // then execute the provided traversal body. @@ -133,9 +138,10 @@ class ForestTraversal { #else - /// Scheduler-based top-down traversal. + /** + * @brief Scheduler-based top-down traversal. + */ template - /// Kick off top-down traversal from all roots using the scheduler backend. void runTopDown(Fn fn, int parallelThreshold = 10) { const auto& roots = getRoots(); if (roots.empty()) return; @@ -150,9 +156,10 @@ class ForestTraversal { }); } - /// Scheduler-based bottom-up traversal. + /** + * @brief Scheduler-based bottom-up traversal. + */ template - /// Kick off bottom-up traversal from all roots using the scheduler backend. void runBottomUp(Fn fn, int parallelThreshold = 10) { const auto& roots = getRoots(); if (roots.empty()) return; @@ -294,7 +301,7 @@ class ForestTraversal { frame.topDownTraverse(); }; const int topDownPriority = depth; // Shallower nodes = higher priority - /// Schedule a task and increment the pending counter. + // Schedule a task and increment the pending counter. state->incrementPending(); scheduler->schedule(topDownPriority, std::function(std::move(task))); @@ -384,8 +391,9 @@ class ForestTraversal { callOnDone(onDone); } - /// Invoke an `onDone` callback and record any exception it throws. - /// Safely run `onDone` and capture any thrown exception in shared state. + /** + * @brief Invoke an `onDone` callback and record any exception it throws. + */ void callOnDone(const DoneFn& onDone) const { try { onDone(); diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 31a90fc921..0844396776 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -59,14 +59,16 @@ namespace gtsam { * @tparam Y Result type returned by tasks. Use `void` for no return value. */ template -class PriorityScheduler { +class GTSAM_EXPORT PriorityScheduler { struct Task { int priority; std::function job; std::promise promise; - Task(int p, std::function j, std::promise p_out) - : priority(p), job(std::move(j)), promise(std::move(p_out)) {} + Task(int priority, std::function jobFunction, + std::promise promiseOut) + : priority(priority), job(std::move(jobFunction)), + promise(std::move(promiseOut)) {} }; using TaskPtr = std::shared_ptr; @@ -99,7 +101,6 @@ class PriorityScheduler { * Uses a condition variable to avoid spinning while the queue is empty. * Stops once `stop_` is set and no queued work remains. */ - /// Worker loop that executes tasks until shutdown. void worker_thread(size_t index) { currentScheduler_ = this; workerIndex_ = static_cast(index); @@ -189,7 +190,6 @@ class PriorityScheduler { * @param numThreads Number of worker threads to create. If zero, a single * thread is created. */ - /// Construct a scheduler with a fixed worker count. PriorityScheduler(size_t numThreads = std::thread::hardware_concurrency()) { if (numThreads == 0) numThreads = 1; queues_.reserve(numThreads); @@ -206,7 +206,6 @@ class PriorityScheduler { * * @note The destructor calls `waitForAllTasks` before stopping workers. */ - /// Wait for tasks and join worker threads on destruction. ~PriorityScheduler() { waitForAllTasks(); stop_.store(true, std::memory_order_release); @@ -225,7 +224,6 @@ class PriorityScheduler { * @param job Callable returning a `Y`. * @return `std::future` associated with the task. */ - /// Enqueue a task for execution and return its future. std::future schedule(int priority, std::function job) { if (stop_.load(std::memory_order_acquire)) { std::promise err_promise; @@ -267,7 +265,6 @@ class PriorityScheduler { * Used to fuse continuations without re-entering the queues. */ template >> - /// Run inline on workers, otherwise enqueue normally. void scheduleOrRunInline(int priority, std::function job) { if (stop_.load(std::memory_order_relaxed)) return; if (!isWorkerThread()) { @@ -290,7 +287,6 @@ class PriorityScheduler { * * @note If the scheduler is stopping, this returns early. */ - /// Block until all queued and active tasks complete. void waitForAllTasks() { std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { diff --git a/gtsam/base/tests/testForestTraversal.cpp b/gtsam/base/tests/testForestTraversal.cpp index dc5b3283b8..93b3655d63 100644 --- a/gtsam/base/tests/testForestTraversal.cpp +++ b/gtsam/base/tests/testForestTraversal.cpp @@ -25,7 +25,7 @@ using namespace gtsam; /// Common TreeNode definition (using shared_ptr). struct TreeNode { int value; - explicit TreeNode(int v) : value(v) {} + explicit TreeNode(int initialValue) : value(initialValue) {} }; namespace { From 10c33f2ba5c0761670c9a513c2c8c4ce1f510391 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 9 Jan 2026 01:27:38 +0000 Subject: [PATCH 053/266] Restore GTSAM_CONCEPT_ASSERT in FastSet.h and remove incorrect GTSAM_EXPORT from templates Co-authored-by: dellaert <10515273+dellaert@users.noreply.github.com> --- gtsam/base/FastSet.h | 2 ++ gtsam/base/ForestTraversal.h | 2 +- gtsam/base/PriorityScheduler.h | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/gtsam/base/FastSet.h b/gtsam/base/FastSet.h index d7dd839029..1a2627e247 100644 --- a/gtsam/base/FastSet.h +++ b/gtsam/base/FastSet.h @@ -53,6 +53,8 @@ template class FastSet: public std::set, typename internal::FastDefaultAllocator::type> { + GTSAM_CONCEPT_ASSERT(IsTestable); + public: typedef std::set, diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index f568692a8a..dbdad18b7b 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -58,7 +58,7 @@ namespace gtsam { * that mirrors `TaskMixin`. */ template -class GTSAM_EXPORT ForestTraversal { +class ForestTraversal { private: size_t threadCount_; using SharedNode = std::shared_ptr; diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 0844396776..c6ee9ebfe7 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -59,7 +59,7 @@ namespace gtsam { * @tparam Y Result type returned by tasks. Use `void` for no return value. */ template -class GTSAM_EXPORT PriorityScheduler { +class PriorityScheduler { struct Task { int priority; std::function job; From 1a5761c63ab3732a0f6eced2e0e16139f398013e Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Thu, 8 Jan 2026 23:55:30 -0500 Subject: [PATCH 054/266] more simplification --- gtsam/geometry/Similarity3.cpp | 31 +++++++++++++------------------ 1 file changed, 13 insertions(+), 18 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index cd3ccdceda..a5f19f8d1c 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -170,38 +170,33 @@ Point3 Similarity3::transformFrom(const Point3& p, // Pose3 Similarity3::transformFrom(const Pose3& bTi, OptionalJacobian<6, 7> Hself, OptionalJacobian<6, 6> H_bTi) const { + const Rot3& bRi = bTi.rotation(); + const Point3& bti = bTi.translation(); if (!Hself && !H_bTi) { - return Pose3(R_ * bTi.rotation(), transformFrom(bTi.translation())); + return Pose3(R_ * bRi, transformFrom(bti)); } - // Jacobian of the .rotation() is identity wrt rotation, so delegate jacobians to Rot3. - Matrix33 DR_dsimR, DR_dTR; - const Rot3 R = R_.compose(bTi.rotation(), Hself ? &DR_dsimR : nullptr, H_bTi ? &DR_dTR : nullptr); + const Rot3 R = R_ * bRi; - // Delegate the translation component jacobians to the point3 transformFrom. + // Delegate the translation jacobians to the point3 transformFrom. Matrix37 Dt_dsim; Matrix3 Dt_dp; - Matrix36 Dp_dT; - const Point3 p = bTi.translation(&Dp_dT); - const Point3 t = this->transformFrom(p, Hself ? &Dt_dsim : nullptr, H_bTi ? &Dt_dp : nullptr); - - Matrix63 Dresult_t; - const Pose3 result = Pose3::Create(R, t, nullptr, &Dresult_t); + const Point3 t = transformFrom(bti, Hself ? &Dt_dsim : nullptr, H_bTi ? &Dt_dp : nullptr); if (Hself) { Hself->setZero(); - Hself->block<3, 3>(0, 0) = DR_dsimR; - // Chain D_result_t (3x3) * D_t_sim (3x7). - Hself->block<3, 7>(3, 0) = Dresult_t.block<3, 3>(3, 0) * Dt_dsim; + Hself->block<3, 3>(0, 0) = bRi.transpose(); // DR_dsimR + // Chain D_result_t (ie R.T) * D_t_sim (3x7). + Hself->block<3, 7>(3, 0) = R.transpose() * Dt_dsim; } if (H_bTi) { H_bTi->setZero(); - H_bTi->block<3, 3>(0, 0) = DR_dTR; - // Chain D_result_t (3x3) * D_t_p (3x3) * D_p_bTi (3x6). - H_bTi->block<3, 6>(3, 0) = Dresult_t.block<3, 3>(3, 0) * Dt_dp * Dp_dT; + H_bTi->block<3, 3>(0, 0) = I_3x3; // DR_dTR = I_3x3 + // Chain D_result_t * D_t_p * D_p_bTi (ie [Z_3x3, bRi]) . + H_bTi->block<3, 3>(3, 3) = R.transpose() * Dt_dp * bRi.matrix(); } - return result; + return Pose3(R, t); } Point3 Similarity3::operator*(const Point3& p) const { From 5f49af29d5185660af5fb18b2e8cae886adfe619 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 9 Jan 2026 14:26:42 +0000 Subject: [PATCH 055/266] Remove GTSAM_CONCEPT_ASSERT and revert to /// for single-line comments Co-authored-by: dellaert <10515273+dellaert@users.noreply.github.com> --- gtsam/base/FastSet.h | 2 -- gtsam/base/ForestTraversal.h | 26 +++++++------------------- 2 files changed, 7 insertions(+), 21 deletions(-) diff --git a/gtsam/base/FastSet.h b/gtsam/base/FastSet.h index 1a2627e247..d7dd839029 100644 --- a/gtsam/base/FastSet.h +++ b/gtsam/base/FastSet.h @@ -53,8 +53,6 @@ template class FastSet: public std::set, typename internal::FastDefaultAllocator::type> { - GTSAM_CONCEPT_ASSERT(IsTestable); - public: typedef std::set, diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index dbdad18b7b..d9641a7762 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -64,7 +64,7 @@ class ForestTraversal { using SharedNode = std::shared_ptr; public: - /** Construct a helper with a fixed thread budget (used by TBB when enabled). */ + /// Construct a helper with a fixed thread budget (used by TBB when enabled). explicit ForestTraversal( size_t numThreads = std::thread::hardware_concurrency()) : threadCount_(numThreads == 0 ? 1 : numThreads) @@ -76,10 +76,8 @@ class ForestTraversal { } #ifdef GTSAM_USE_TBB - /** - * @brief Depth-first traversal using a pre-order visitor (TBB path). - */ template + /// Depth-first traversal using a pre-order visitor (TBB path). void runTopDown(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { // The pre-order visitor runs before visiting children; parallel task @@ -101,10 +99,8 @@ class ForestTraversal { }); } - /** - * @brief Post-order traversal using a bottom-up visitor (TBB path). - */ template + /// Post-order traversal using a bottom-up visitor (TBB path). void runBottomUp(Fn fn, int parallelThreshold = 10) { withTbbTraversalControl([&] { // The bottom-up visitor runs after all children are processed; @@ -123,9 +119,7 @@ class ForestTraversal { } private: - /** - * @brief Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. - */ + /// Run a traversal with TBB and OpenMP concurrency limited to `threadCount_`. template void withTbbTraversalControl(Body&& body) { // Set a cap on TBB threads and enter an OpenMP-compatible scope, @@ -138,9 +132,7 @@ class ForestTraversal { #else - /** - * @brief Scheduler-based top-down traversal. - */ + /// Scheduler-based top-down traversal. template void runTopDown(Fn fn, int parallelThreshold = 10) { const auto& roots = getRoots(); @@ -156,9 +148,7 @@ class ForestTraversal { }); } - /** - * @brief Scheduler-based bottom-up traversal. - */ + /// Scheduler-based bottom-up traversal. template void runBottomUp(Fn fn, int parallelThreshold = 10) { const auto& roots = getRoots(); @@ -391,9 +381,7 @@ class ForestTraversal { callOnDone(onDone); } - /** - * @brief Invoke an `onDone` callback and record any exception it throws. - */ + /// Invoke an `onDone` callback and record any exception it throws. void callOnDone(const DoneFn& onDone) const { try { onDone(); From 0ef103103f8ee5a03849c21ce66360925557583c Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Fri, 9 Jan 2026 14:02:59 -0500 Subject: [PATCH 056/266] review comments - one more simplification --- gtsam/geometry/Similarity3.cpp | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/gtsam/geometry/Similarity3.cpp b/gtsam/geometry/Similarity3.cpp index a5f19f8d1c..c7aa2d1aa4 100644 --- a/gtsam/geometry/Similarity3.cpp +++ b/gtsam/geometry/Similarity3.cpp @@ -191,10 +191,11 @@ Pose3 Similarity3::transformFrom(const Pose3& bTi, } if (H_bTi) { - H_bTi->setZero(); - H_bTi->block<3, 3>(0, 0) = I_3x3; // DR_dTR = I_3x3 - // Chain D_result_t * D_t_p * D_p_bTi (ie [Z_3x3, bRi]) . - H_bTi->block<3, 3>(3, 3) = R.transpose() * Dt_dp * bRi.matrix(); + H_bTi->setIdentity(); + // DR_dTR = I_3x3 + // Chain D_result_t (ie R.T) * D_t_p (ie s * R_) * D_p_bTi (ie [Z_3x3, bRi]) + // bRi.T * R_.T * s * R * bRi => s * I_3x3 + H_bTi->block<3, 3>(3, 3) *= s_; } return Pose3(R, t); } From b3cbe07e03f4354cdfc9e29b7165f331d521efa6 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 9 Jan 2026 18:27:13 -0500 Subject: [PATCH 057/266] TaskScheduler --- gtsam/base/ForestTraversal.h | 14 +-- gtsam/base/PriorityScheduler.h | 205 +++++++++++++++++++++++++++++++++ 2 files changed, 210 insertions(+), 9 deletions(-) diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index 6181c57edf..dd5f2d05e9 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -12,7 +12,7 @@ * * @details * Provides top-down and bottom-up traversal helpers that either enqueue work - * on an internal `PriorityScheduler` (for builds without TBB) or call the + * on an internal `TaskScheduler` (for builds without TBB) or call the * `treeTraversal` parallel helpers when `GTSAM_USE_TBB` is enabled. * * @note `Forest::roots()` or `Forest::roots` must return a range of @@ -168,7 +168,7 @@ class ForestTraversal { } private: - PriorityScheduler scheduler_; + TaskScheduler scheduler_; /// Return forest roots via method or field. decltype(auto) getRoots() const { @@ -251,7 +251,7 @@ class ForestTraversal { /// Per-node traversal frame to avoid threading many parameters. template struct Frame { - PriorityScheduler* scheduler; + TaskScheduler* scheduler; Node& node; int depth; const Fn& fn; @@ -293,11 +293,9 @@ class ForestTraversal { MaybeFinish finish{frame.state}; frame.topDownTraverse(); }; - const int topDownPriority = depth; // Shallower nodes = higher priority /// Schedule a task and increment the pending counter. state->incrementPending(); - scheduler->schedule(topDownPriority, - std::function(std::move(task))); + scheduler->schedule(std::function(std::move(task))); } /// Inline node work followed by traversal of children. @@ -369,9 +367,7 @@ class ForestTraversal { state->incrementPending(); // Schedule a continuation or run it inline if already on a worker thread. - const int bottomUpPriority = -depth; // Deeper nodes = higher priority - scheduler->scheduleOrRunInline(bottomUpPriority, - std::function(std::move(task))); + scheduler->scheduleOrRunInline(std::function(std::move(task))); } /// Do bottom-up node work, then invoke the continuation. diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 31a90fc921..0102b0ec0e 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -31,6 +31,7 @@ #include #include +#include #include #include #include @@ -45,6 +46,210 @@ namespace gtsam { +/** + * @brief Thread pool scheduler that executes tasks without priority ordering. + * + * @details + * - Tasks are executed in an efficient deque-based order (LIFO locally). + * - Per-thread queues reduce contention; workers steal from peers when idle. + * - External submissions are round-robin distributed across worker queues. + * - A condition variable parks workers when no work is available. + * + * @tparam Y Result type returned by tasks. Use `void` for no return value. + */ +template +class TaskScheduler { + struct Task { + std::function job; + std::promise promise; + + Task(std::function j, std::promise p_out) + : job(std::move(j)), promise(std::move(p_out)) {} + }; + + using TaskPtr = std::shared_ptr; + + struct WorkerQueue { + std::mutex mutex; + std::deque queue; + }; + + std::vector> queues_; // Per-worker queues. + std::atomic queuedTasks_{0}; // Tracks queued work for wakeups. + std::vector workers_; + mutable std::mutex waitMutex_; // Dedicated mutex for condition_variable. + std::condition_variable condition_; + std::atomic stop_{false}; + std::atomic activeTasks_{0}; // In-flight tasks (queued + running). + inline static thread_local TaskScheduler* currentScheduler_ = nullptr; + inline static thread_local int workerIndex_ = -1; + std::atomic nextWorker_{0}; // Round-robin distributor. + + /// Worker loop that executes tasks until shutdown. + void worker_thread(size_t index) { + currentScheduler_ = this; + workerIndex_ = static_cast(index); + while (true) { + TaskPtr task; + if (!tryPopTask(index, task)) { + std::unique_lock lock(waitMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_acquire) || + queuedTasks_.load(std::memory_order_acquire) > 0; + }); + if (stop_.load(std::memory_order_acquire) && + queuedTasks_.load(std::memory_order_acquire) == 0) { + currentScheduler_ = nullptr; + workerIndex_ = -1; + return; + } + continue; + } + + try { + if constexpr (std::is_void_v) { + task->job(); + task->promise.set_value(); + } else { + task->promise.set_value(task->job()); + } + } catch (...) { + try { + task->promise.set_exception(std::current_exception()); + } catch (...) { /* ignore */ + } + } + + activeTasks_.fetch_sub(1, std::memory_order_release); + condition_.notify_all(); + } + } + + /// Attempt to get a task from local or stolen queues. + bool tryPopTask(size_t index, TaskPtr& task) { + if (tryPopLocal(index, task)) return true; + if (trySteal(index, task)) return true; + return false; + } + + /// Try to pop a task from the current worker queue (LIFO for locality). + bool tryPopLocal(size_t index, TaskPtr& task) { + WorkerQueue& queue = *queues_[index]; + std::lock_guard lock(queue.mutex); + if (queue.queue.empty()) return false; + task = queue.queue.back(); + queue.queue.pop_back(); + queuedTasks_.fetch_sub(1, std::memory_order_release); + return true; + } + + /// Try to steal a task from another worker queue (FIFO to reduce + /// interference). + bool trySteal(size_t index, TaskPtr& task) { + const size_t workerCount = queues_.size(); + if (workerCount <= 1) return false; + for (size_t offset = 1; offset < workerCount; ++offset) { + size_t target = (index + offset) % workerCount; + WorkerQueue& queue = *queues_[target]; + std::unique_lock lock(queue.mutex, std::try_to_lock); + if (!lock.owns_lock() || queue.queue.empty()) continue; + task = queue.queue.front(); + queue.queue.pop_front(); + queuedTasks_.fetch_sub(1, std::memory_order_release); + return true; + } + return false; + } + + /// Return true if called on a scheduler worker thread. + bool isWorkerThread() const { return currentScheduler_ == this; } + + public: + /// Construct a scheduler with a fixed worker count. + explicit TaskScheduler( + size_t numThreads = std::thread::hardware_concurrency()) { + if (numThreads == 0) numThreads = 1; + queues_.reserve(numThreads); + for (size_t i = 0; i < numThreads; ++i) { + queues_.push_back(std::make_unique()); + } + for (size_t i = 0; i < numThreads; ++i) { + workers_.emplace_back(&TaskScheduler::worker_thread, this, i); + } + } + + /// Wait for tasks and join worker threads on destruction. + ~TaskScheduler() { + waitForAllTasks(); + stop_.store(true, std::memory_order_release); + condition_.notify_all(); + for (std::thread& worker : workers_) { + if (worker.joinable()) worker.join(); + } + } + + /// Enqueue a task for execution and return its future. + std::future schedule(std::function job) { + if (stop_.load(std::memory_order_acquire)) { + std::promise err_promise; + err_promise.set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + return err_promise.get_future(); + } + + std::promise promise; + std::future future = promise.get_future(); + auto task = std::make_shared(std::move(job), std::move(promise)); + + if (isWorkerThread()) { + WorkerQueue& queue = *queues_[static_cast(workerIndex_)]; + { + std::lock_guard lock(queue.mutex); + queue.queue.push_back(task); + } + } else { + const size_t target = + nextWorker_.fetch_add(1, std::memory_order_relaxed) % queues_.size(); + WorkerQueue& queue = *queues_[target]; + std::lock_guard lock(queue.mutex); + queue.queue.push_back(task); + } + + queuedTasks_.fetch_add(1, std::memory_order_release); + activeTasks_.fetch_add(1, std::memory_order_release); + condition_.notify_one(); + return future; + } + + /// Run inline on workers, otherwise enqueue normally. + template >> + void scheduleOrRunInline(std::function job) { + if (stop_.load(std::memory_order_relaxed)) return; + if (!isWorkerThread()) { + schedule(std::move(job)); + return; + } + + activeTasks_.fetch_add(1, std::memory_order_release); + try { + job(); + } catch (...) { /* ignore */ + } + activeTasks_.fetch_sub(1, std::memory_order_release); + condition_.notify_all(); + } + + /// Block until all queued and active tasks complete. + void waitForAllTasks() { + std::unique_lock lock(waitMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_acquire) || + (activeTasks_.load(std::memory_order_acquire) == 0 && + queuedTasks_.load(std::memory_order_acquire) == 0); + }); + } +}; + /** * @brief Thread pool scheduler that prioritizes tasks by numeric priority. * From ad31055ca1eb61dc678e55020a0bf29d2ecabb41 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 9 Jan 2026 18:42:58 -0500 Subject: [PATCH 058/266] Split schedulers --- gtsam/base/ForestTraversal.h | 2 +- gtsam/base/PriorityScheduler.h | 476 +++------------------------------ gtsam/base/Scheduler.h | 333 +++++++++++++++++++++++ gtsam/base/TaskScheduler.h | 82 ++++++ 4 files changed, 446 insertions(+), 447 deletions(-) create mode 100644 gtsam/base/Scheduler.h create mode 100644 gtsam/base/TaskScheduler.h diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index dd5f2d05e9..de3aff7b78 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -32,7 +32,7 @@ #include #include #else -#include +#include #endif #include diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 0102b0ec0e..7d5761ac5f 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -16,6 +16,8 @@ * priority (min-heap behavior). Tasks return a value of type `Y`, and callers * can wait on results via `std::future`. * + * This is implemented as a policy specialization of `gtsam::Scheduler`. + * * @par Example * @code * gtsam::PriorityScheduler scheduler(4); @@ -29,227 +31,49 @@ #pragma once -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include // For std::runtime_error -#include -#include // For std::is_void_v -#include // For std::forward -#include +#include namespace gtsam { -/** - * @brief Thread pool scheduler that executes tasks without priority ordering. - * - * @details - * - Tasks are executed in an efficient deque-based order (LIFO locally). - * - Per-thread queues reduce contention; workers steal from peers when idle. - * - External submissions are round-robin distributed across worker queues. - * - A condition variable parks workers when no work is available. - * - * @tparam Y Result type returned by tasks. Use `void` for no return value. - */ -template -class TaskScheduler { - struct Task { - std::function job; - std::promise promise; +namespace detail { - Task(std::function j, std::promise p_out) - : job(std::move(j)), promise(std::move(p_out)) {} - }; +/// Queue policy for PriorityScheduler: min-heap priority_queue by task +/// priority. +struct PrioritySchedulerPolicy { + using Metadata = int; - using TaskPtr = std::shared_ptr; - - struct WorkerQueue { - std::mutex mutex; - std::deque queue; + template + struct Compare { + bool operator()(const TaskPtr& a, const TaskPtr& b) const { + return a->metadata > b->metadata; + } }; - std::vector> queues_; // Per-worker queues. - std::atomic queuedTasks_{0}; // Tracks queued work for wakeups. - std::vector workers_; - mutable std::mutex waitMutex_; // Dedicated mutex for condition_variable. - std::condition_variable condition_; - std::atomic stop_{false}; - std::atomic activeTasks_{0}; // In-flight tasks (queued + running). - inline static thread_local TaskScheduler* currentScheduler_ = nullptr; - inline static thread_local int workerIndex_ = -1; - std::atomic nextWorker_{0}; // Round-robin distributor. - - /// Worker loop that executes tasks until shutdown. - void worker_thread(size_t index) { - currentScheduler_ = this; - workerIndex_ = static_cast(index); - while (true) { - TaskPtr task; - if (!tryPopTask(index, task)) { - std::unique_lock lock(waitMutex_); - condition_.wait(lock, [this] { - return stop_.load(std::memory_order_acquire) || - queuedTasks_.load(std::memory_order_acquire) > 0; - }); - if (stop_.load(std::memory_order_acquire) && - queuedTasks_.load(std::memory_order_acquire) == 0) { - currentScheduler_ = nullptr; - workerIndex_ = -1; - return; - } - continue; - } - - try { - if constexpr (std::is_void_v) { - task->job(); - task->promise.set_value(); - } else { - task->promise.set_value(task->job()); - } - } catch (...) { - try { - task->promise.set_exception(std::current_exception()); - } catch (...) { /* ignore */ - } - } + template + using Container = + std::priority_queue, Compare>; - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); - } - } - - /// Attempt to get a task from local or stolen queues. - bool tryPopTask(size_t index, TaskPtr& task) { - if (tryPopLocal(index, task)) return true; - if (trySteal(index, task)) return true; - return false; + template + static void push(Container& container, TaskPtr task) { + container.push(std::move(task)); } - /// Try to pop a task from the current worker queue (LIFO for locality). - bool tryPopLocal(size_t index, TaskPtr& task) { - WorkerQueue& queue = *queues_[index]; - std::lock_guard lock(queue.mutex); - if (queue.queue.empty()) return false; - task = queue.queue.back(); - queue.queue.pop_back(); - queuedTasks_.fetch_sub(1, std::memory_order_release); + template + static bool popLocal(Container& container, TaskPtr& out) { + if (container.empty()) return false; + out = container.top(); + container.pop(); return true; } - /// Try to steal a task from another worker queue (FIFO to reduce - /// interference). - bool trySteal(size_t index, TaskPtr& task) { - const size_t workerCount = queues_.size(); - if (workerCount <= 1) return false; - for (size_t offset = 1; offset < workerCount; ++offset) { - size_t target = (index + offset) % workerCount; - WorkerQueue& queue = *queues_[target]; - std::unique_lock lock(queue.mutex, std::try_to_lock); - if (!lock.owns_lock() || queue.queue.empty()) continue; - task = queue.queue.front(); - queue.queue.pop_front(); - queuedTasks_.fetch_sub(1, std::memory_order_release); - return true; - } - return false; - } - - /// Return true if called on a scheduler worker thread. - bool isWorkerThread() const { return currentScheduler_ == this; } - - public: - /// Construct a scheduler with a fixed worker count. - explicit TaskScheduler( - size_t numThreads = std::thread::hardware_concurrency()) { - if (numThreads == 0) numThreads = 1; - queues_.reserve(numThreads); - for (size_t i = 0; i < numThreads; ++i) { - queues_.push_back(std::make_unique()); - } - for (size_t i = 0; i < numThreads; ++i) { - workers_.emplace_back(&TaskScheduler::worker_thread, this, i); - } - } - - /// Wait for tasks and join worker threads on destruction. - ~TaskScheduler() { - waitForAllTasks(); - stop_.store(true, std::memory_order_release); - condition_.notify_all(); - for (std::thread& worker : workers_) { - if (worker.joinable()) worker.join(); - } - } - - /// Enqueue a task for execution and return its future. - std::future schedule(std::function job) { - if (stop_.load(std::memory_order_acquire)) { - std::promise err_promise; - err_promise.set_exception(std::make_exception_ptr( - std::runtime_error("Scheduler is stopping or stopped."))); - return err_promise.get_future(); - } - - std::promise promise; - std::future future = promise.get_future(); - auto task = std::make_shared(std::move(job), std::move(promise)); - - if (isWorkerThread()) { - WorkerQueue& queue = *queues_[static_cast(workerIndex_)]; - { - std::lock_guard lock(queue.mutex); - queue.queue.push_back(task); - } - } else { - const size_t target = - nextWorker_.fetch_add(1, std::memory_order_relaxed) % queues_.size(); - WorkerQueue& queue = *queues_[target]; - std::lock_guard lock(queue.mutex); - queue.queue.push_back(task); - } - - queuedTasks_.fetch_add(1, std::memory_order_release); - activeTasks_.fetch_add(1, std::memory_order_release); - condition_.notify_one(); - return future; - } - - /// Run inline on workers, otherwise enqueue normally. - template >> - void scheduleOrRunInline(std::function job) { - if (stop_.load(std::memory_order_relaxed)) return; - if (!isWorkerThread()) { - schedule(std::move(job)); - return; - } - - activeTasks_.fetch_add(1, std::memory_order_release); - try { - job(); - } catch (...) { /* ignore */ - } - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); - } - - /// Block until all queued and active tasks complete. - void waitForAllTasks() { - std::unique_lock lock(waitMutex_); - condition_.wait(lock, [this] { - return stop_.load(std::memory_order_acquire) || - (activeTasks_.load(std::memory_order_acquire) == 0 && - queuedTasks_.load(std::memory_order_acquire) == 0); - }); + template + static bool popSteal(Container& container, TaskPtr& out) { + return popLocal(container, out); } }; +} // namespace detail + /** * @brief Thread pool scheduler that prioritizes tasks by numeric priority. * @@ -264,246 +88,6 @@ class TaskScheduler { * @tparam Y Result type returned by tasks. Use `void` for no return value. */ template -class PriorityScheduler { - struct Task { - int priority; - std::function job; - std::promise promise; - - Task(int p, std::function j, std::promise p_out) - : priority(p), job(std::move(j)), promise(std::move(p_out)) {} - }; - - using TaskPtr = std::shared_ptr; - - struct Compare { - bool operator()(const TaskPtr& a, const TaskPtr& b) const { - return a->priority > b->priority; - } - }; - - struct WorkerQueue { - std::mutex mutex; - std::priority_queue, Compare> queue; - }; - - std::vector> queues_; // Per-worker queues. - std::atomic queuedTasks_{0}; // Tracks queued work for wakeups. - std::vector workers_; - mutable std::mutex waitMutex_; // Dedicated mutex for condition_variable. - std::condition_variable condition_; - std::atomic stop_{false}; - std::atomic activeTasks_{0}; // In-flight tasks (queued + running). - inline static thread_local PriorityScheduler* currentScheduler_ = nullptr; - inline static thread_local int workerIndex_ = -1; - std::atomic nextWorker_{0}; // Round-robin distributor. - - /** - * @brief Worker loop: wait for tasks, run them, and fulfill promises. - * - * Uses a condition variable to avoid spinning while the queue is empty. - * Stops once `stop_` is set and no queued work remains. - */ - /// Worker loop that executes tasks until shutdown. - void worker_thread(size_t index) { - currentScheduler_ = this; - workerIndex_ = static_cast(index); - while (true) { - TaskPtr task; - if (!tryPopTask(index, task)) { - std::unique_lock lock(waitMutex_); - condition_.wait(lock, [this] { - return stop_.load(std::memory_order_acquire) || - queuedTasks_.load(std::memory_order_acquire) > 0; - }); - if (stop_.load(std::memory_order_acquire) && - queuedTasks_.load(std::memory_order_acquire) == 0) { - currentScheduler_ = nullptr; - workerIndex_ = -1; - return; - } - continue; - } - - try { - // Execute the task and set the promise value: - if constexpr (std::is_void_v) { - task->job(); - task->promise.set_value(); - } else { - task->promise.set_value(task->job()); - } - } catch (...) { - // On exception, set the exception on the promise: - try { - task->promise.set_exception(std::current_exception()); - } catch (...) { /* ignore */ - } - } - - // Decrement active task count and notify waiters. - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); - } - } - - /// Attempt to get a task from local or stolen queues. - bool tryPopTask(size_t index, TaskPtr& task) { - // Prefer local queue, then steal to keep workers busy. - if (tryPopLocal(index, task)) return true; - if (trySteal(index, task)) return true; - return false; - } - - /// Try to pop a task from the current worker queue. - bool tryPopLocal(size_t index, TaskPtr& task) { - WorkerQueue& queue = *queues_[index]; - std::lock_guard lock(queue.mutex); - if (queue.queue.empty()) return false; - task = queue.queue.top(); - queue.queue.pop(); - queuedTasks_.fetch_sub(1, std::memory_order_release); - return true; - } - - /// Try to steal a task from another worker queue. - bool trySteal(size_t index, TaskPtr& task) { - const size_t workerCount = queues_.size(); - if (workerCount <= 1) return false; - // Steal from other workers if local queue is empty. - for (size_t offset = 1; offset < workerCount; ++offset) { - size_t target = (index + offset) % workerCount; - WorkerQueue& queue = *queues_[target]; - std::unique_lock lock(queue.mutex, std::try_to_lock); - if (!lock.owns_lock() || queue.queue.empty()) continue; - task = queue.queue.top(); - queue.queue.pop(); - queuedTasks_.fetch_sub(1, std::memory_order_release); - return true; - } - return false; - } - - /// Return true if called on a scheduler worker thread. - bool isWorkerThread() const { return currentScheduler_ == this; } - - public: - /** - * @brief Construct a scheduler with a fixed number of worker threads. - * - * @param numThreads Number of worker threads to create. If zero, a single - * thread is created. - */ - /// Construct a scheduler with a fixed worker count. - PriorityScheduler(size_t numThreads = std::thread::hardware_concurrency()) { - if (numThreads == 0) numThreads = 1; - queues_.reserve(numThreads); - for (size_t i = 0; i < numThreads; ++i) { - queues_.push_back(std::make_unique()); - } - for (size_t i = 0; i < numThreads; ++i) { - workers_.emplace_back(&PriorityScheduler::worker_thread, this, i); - } - } - - /** - * @brief Wait for all tasks to finish, then stop worker threads. - * - * @note The destructor calls `waitForAllTasks` before stopping workers. - */ - /// Wait for tasks and join worker threads on destruction. - ~PriorityScheduler() { - waitForAllTasks(); - stop_.store(true, std::memory_order_release); - condition_.notify_all(); - for (std::thread& worker : workers_) { - if (worker.joinable()) { - worker.join(); - } - } - } - - /** - * @brief Enqueue a task for execution. - * - * @param priority Lower values execute before higher values. - * @param job Callable returning a `Y`. - * @return `std::future` associated with the task. - */ - /// Enqueue a task for execution and return its future. - std::future schedule(int priority, std::function job) { - if (stop_.load(std::memory_order_acquire)) { - std::promise err_promise; - err_promise.set_exception(std::make_exception_ptr( - std::runtime_error("Scheduler is stopping or stopped."))); - return err_promise.get_future(); - } - - std::promise promise; - std::future future = promise.get_future(); - auto task = - std::make_shared(priority, std::move(job), std::move(promise)); - - if (isWorkerThread()) { - // Worker submissions go to the local queue for cache locality. - WorkerQueue& queue = *queues_[static_cast(workerIndex_)]; - { - std::lock_guard lock(queue.mutex); - queue.queue.push(task); - } - } else { - // External submissions are distributed across worker queues. - const size_t target = - nextWorker_.fetch_add(1, std::memory_order_relaxed) % queues_.size(); - WorkerQueue& queue = *queues_[target]; - std::lock_guard lock(queue.mutex); - queue.queue.push(task); - } - queuedTasks_.fetch_add(1, std::memory_order_release); - // Track in-flight tasks for waiters. - activeTasks_.fetch_add(1, std::memory_order_release); - condition_.notify_one(); - return future; - } - - /** - * @brief Schedule or run inline when called from a worker thread. - * - * Used to fuse continuations without re-entering the queues. - */ - template >> - /// Run inline on workers, otherwise enqueue normally. - void scheduleOrRunInline(int priority, std::function job) { - if (stop_.load(std::memory_order_relaxed)) return; - if (!isWorkerThread()) { - schedule(priority, std::move(job)); - return; - } - - // Inline execution fuses continuations and avoids re-queueing. - activeTasks_.fetch_add(1, std::memory_order_release); - try { - job(); - } catch (...) { /* ignore */ - } - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); - } - - /** - * @brief Block until all queued and active tasks complete. - * - * @note If the scheduler is stopping, this returns early. - */ - /// Block until all queued and active tasks complete. - void waitForAllTasks() { - std::unique_lock lock(waitMutex_); - condition_.wait(lock, [this] { - return stop_.load(std::memory_order_acquire) || - (activeTasks_.load(std::memory_order_acquire) == 0 && - queuedTasks_.load(std::memory_order_acquire) == 0); - }); - } -}; +using PriorityScheduler = Scheduler; } // namespace gtsam diff --git a/gtsam/base/Scheduler.h b/gtsam/base/Scheduler.h new file mode 100644 index 0000000000..b7967f6824 --- /dev/null +++ b/gtsam/base/Scheduler.h @@ -0,0 +1,333 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file Scheduler.h + * @brief Policy-based work-stealing scheduler. + * + * @details + * This header defines a small, thread-based scheduler core that executes tasks + * using per-worker queues with opportunistic work-stealing. The queue behavior + * (e.g., LIFO/FIFO, priority ordering) is defined by a Policy type. + * + * Public convenience wrappers are provided in `TaskScheduler.h` and + * `PriorityScheduler.h`. + * + * @author Frank Dellaert + * @date May, 2025 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace gtsam { + +/** + * @brief Thread pool scheduler parameterized by a queue Policy. + * + * @details + * - Tasks are executed by worker threads created at construction. + * - Per-worker queues reduce contention; workers steal from peers when idle. + * - External submissions are round-robin distributed across worker queues. + * - A condition variable parks workers when no work is available. + * + * Policy requirements: + * - `using Metadata = ...;` carried with each task (e.g., `std::monostate`, + * `int`) + * - `template using Container = ...;` + * - `push(container, task)`, `popLocal(container, out)`, `popSteal(container, + * out)` + * + * @tparam Y Result type returned by tasks. Use `void` for no return value. + * @tparam Policy Queue behavior policy. + */ +template +class Scheduler { + using Metadata = typename Policy::Metadata; + + struct Task { + Metadata metadata; + std::function job; + std::promise promise; + + Task(Metadata m, std::function j, std::promise p_out) + : metadata(std::move(m)), + job(std::move(j)), + promise(std::move(p_out)) {} + }; + + using TaskPtr = std::shared_ptr; + using Container = typename Policy::template Container; + + struct WorkerQueue { + std::mutex mutex; + Container queue; + }; + + std::vector> queues_; // Per-worker queues. + std::atomic queuedTasks_{0}; // Tracks queued work for wakeups. + std::vector workers_; + mutable std::mutex waitMutex_; // Dedicated mutex for condition_variable. + std::condition_variable condition_; + std::atomic stop_{false}; + std::atomic activeTasks_{0}; // In-flight tasks (queued + running). + inline static thread_local Scheduler* currentScheduler_ = nullptr; + inline static thread_local int workerIndex_ = -1; + std::atomic nextWorker_{0}; // Round-robin distributor. + + /** + * @brief Worker loop: wait for tasks, run them, and fulfill promises. + * + * Uses a condition variable to avoid spinning while the queue is empty. + * Stops once `stop_` is set and no queued work remains. + */ + void worker_thread(size_t index) { + currentScheduler_ = this; + workerIndex_ = static_cast(index); + while (true) { + TaskPtr task; + if (!tryPopTask(index, task)) { + std::unique_lock lock(waitMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_acquire) || + queuedTasks_.load(std::memory_order_acquire) > 0; + }); + if (stop_.load(std::memory_order_acquire) && + queuedTasks_.load(std::memory_order_acquire) == 0) { + currentScheduler_ = nullptr; + workerIndex_ = -1; + return; + } + continue; + } + + try { + // Execute the task and set the promise value: + if constexpr (std::is_void_v) { + task->job(); + task->promise.set_value(); + } else { + task->promise.set_value(task->job()); + } + } catch (...) { + // On exception, set the exception on the promise: + try { + task->promise.set_exception(std::current_exception()); + } catch (...) { /* ignore */ + } + } + + // Decrement active task count and notify waiters. + activeTasks_.fetch_sub(1, std::memory_order_release); + condition_.notify_all(); + } + } + + /// Attempt to get a task from local or stolen queues. + bool tryPopTask(size_t index, TaskPtr& task) { + // Prefer local queue, then steal to keep workers busy. + if (tryPopLocal(index, task)) return true; + if (trySteal(index, task)) return true; + return false; + } + + /// Try to pop a task from the current worker queue. + bool tryPopLocal(size_t index, TaskPtr& task) { + WorkerQueue& queue = *queues_[index]; + std::lock_guard lock(queue.mutex); + if (!Policy::template popLocal(queue.queue, task)) return false; + queuedTasks_.fetch_sub(1, std::memory_order_release); + return true; + } + + /// Try to steal a task from another worker queue. + bool trySteal(size_t index, TaskPtr& task) { + const size_t workerCount = queues_.size(); + if (workerCount <= 1) return false; + // Steal from other workers if local queue is empty. + for (size_t offset = 1; offset < workerCount; ++offset) { + size_t target = (index + offset) % workerCount; + WorkerQueue& queue = *queues_[target]; + std::unique_lock lock(queue.mutex, std::try_to_lock); + if (!lock.owns_lock()) continue; + if (!Policy::template popSteal(queue.queue, task)) continue; + queuedTasks_.fetch_sub(1, std::memory_order_release); + return true; + } + return false; + } + + /// Return true if called on a scheduler worker thread. + bool isWorkerThread() const { return currentScheduler_ == this; } + + std::future scheduleImpl(Metadata metadata, std::function job) { + if (stop_.load(std::memory_order_acquire)) { + std::promise err_promise; + err_promise.set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + return err_promise.get_future(); + } + + std::promise promise; + std::future future = promise.get_future(); + auto task = std::make_shared(std::move(metadata), std::move(job), + std::move(promise)); + + WorkerQueue* targetQueue = nullptr; + if (isWorkerThread()) { + targetQueue = queues_[static_cast(workerIndex_)].get(); + } else { + const size_t target = + nextWorker_.fetch_add(1, std::memory_order_relaxed) % queues_.size(); + targetQueue = queues_[target].get(); + } + + { + std::lock_guard lock(targetQueue->mutex); + Policy::template push(targetQueue->queue, std::move(task)); + } + + queuedTasks_.fetch_add(1, std::memory_order_release); + activeTasks_.fetch_add(1, std::memory_order_release); + condition_.notify_one(); + return future; + } + + template >> + void scheduleOrRunInlineImpl(Metadata metadata, std::function job) { + if (stop_.load(std::memory_order_relaxed)) return; + if (!isWorkerThread()) { + scheduleImpl(std::move(metadata), std::move(job)); + return; + } + + activeTasks_.fetch_add(1, std::memory_order_release); + try { + job(); + } catch (...) { /* ignore */ + } + activeTasks_.fetch_sub(1, std::memory_order_release); + condition_.notify_all(); + } + + public: + /** + * @brief Construct a scheduler with a fixed number of worker threads. + * + * @param numThreads Number of worker threads to create. If zero, a single + * thread is created. + */ + explicit Scheduler(size_t numThreads = std::thread::hardware_concurrency()) { + if (numThreads == 0) numThreads = 1; + queues_.reserve(numThreads); + for (size_t i = 0; i < numThreads; ++i) { + queues_.push_back(std::make_unique()); + } + for (size_t i = 0; i < numThreads; ++i) { + workers_.emplace_back(&Scheduler::worker_thread, this, i); + } + } + + /** + * @brief Wait for all tasks to finish, then stop worker threads. + * + * @note The destructor calls `waitForAllTasks` before stopping workers. + */ + ~Scheduler() { + waitForAllTasks(); + stop_.store(true, std::memory_order_release); + condition_.notify_all(); + for (std::thread& worker : workers_) { + if (worker.joinable()) worker.join(); + } + } + + /** + * @brief Enqueue a task for execution (no metadata). + * + * Only enabled when `Policy::Metadata` is `std::monostate`. + * + * @param job Callable returning a `Y`. + * @return `std::future` associated with the task. + */ + template >> + std::future schedule(std::function job) { + return scheduleImpl(Metadata{}, std::move(job)); + } + + /** + * @brief Enqueue a task for execution with metadata. + * + * Enabled when `Policy::Metadata` is not `std::monostate`. + * + * @param metadata Policy-defined metadata for ordering (e.g., priority). + * @param job Callable returning a `Y`. + * @return `std::future` associated with the task. + */ + template >> + std::future schedule(Metadata metadata, std::function job) { + return scheduleImpl(std::move(metadata), std::move(job)); + } + + /** + * @brief Schedule or run inline when called from a worker thread. + * + * Used to fuse continuations without re-entering the queues. + */ + template && std::is_same_v>> + void scheduleOrRunInline(std::function job) { + scheduleOrRunInlineImpl(Metadata{}, std::move(job)); + } + + /** + * @brief Schedule or run inline when called from a worker thread. + * + * Used to fuse continuations without re-entering the queues. + */ + template && !std::is_same_v>> + void scheduleOrRunInline(Metadata metadata, std::function job) { + scheduleOrRunInlineImpl(std::move(metadata), std::move(job)); + } + + /** + * @brief Block until all queued and active tasks complete. + * + * @note If the scheduler is stopping, this returns early. + */ + void waitForAllTasks() { + std::unique_lock lock(waitMutex_); + condition_.wait(lock, [this] { + return stop_.load(std::memory_order_acquire) || + (activeTasks_.load(std::memory_order_acquire) == 0 && + queuedTasks_.load(std::memory_order_acquire) == 0); + }); + } +}; + +} // namespace gtsam diff --git a/gtsam/base/TaskScheduler.h b/gtsam/base/TaskScheduler.h new file mode 100644 index 0000000000..c65eaf2674 --- /dev/null +++ b/gtsam/base/TaskScheduler.h @@ -0,0 +1,82 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file TaskScheduler.h + * @brief Cooperative task scheduler. + * + * @details + * This header defines a small, thread-based scheduler that executes tasks in + * a cooperative manner without priority ordering. Tasks return a value of type + * `Y`, and callers can wait on results via `std::future`. + * + * This is implemented as a policy specialization of `gtsam::Scheduler`. + * + * @par Example + * @code + * gtsam::TaskScheduler scheduler(4); + * auto future = scheduler.schedule([] { return 42; }); + * int value = future.get(); + * @endcode + * + * @author Frank Dellaert + * @date May, 2025 + */ + +#pragma once + +#include + +namespace gtsam { + +namespace detail { +/// Queue policy for TaskScheduler: deque with local LIFO and stolen FIFO. +struct TaskSchedulerPolicy { + using Metadata = std::monostate; + + template + using Container = std::deque; + + template + static void push(Container& container, TaskPtr task) { + container.push_back(std::move(task)); + } + + template + static bool popLocal(Container& container, TaskPtr& out) { + if (container.empty()) return false; + out = container.back(); + container.pop_back(); + return true; + } + + template + static bool popSteal(Container& container, TaskPtr& out) { + if (container.empty()) return false; + out = container.front(); + container.pop_front(); + return true; + } +}; +} // namespace detail + +/** + * @brief Thread pool scheduler that executes tasks without priority ordering. + * + * @details + * - Tasks are executed in an efficient deque-based order (LIFO locally). + * - Per-thread queues reduce contention; workers steal from peers when idle. + * - External submissions are round-robin distributed across worker queues. + * - A condition variable parks workers when no work is available. + * + * @tparam Y Result type returned by tasks. Use `void` for no return value. + */ +template +using TaskScheduler = Scheduler; + +} // namespace gtsam From 8496e97ece52272da6a5520bd41c68d1c4b844c9 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 9 Jan 2026 19:41:57 -0500 Subject: [PATCH 059/266] moveable work items --- .gitignore | 1 + gtsam/base/ForestTraversal.h | 4 +- gtsam/base/PriorityScheduler.h | 2 +- gtsam/base/Scheduler.h | 163 ++++++++++++++------- gtsam/base/TaskScheduler.h | 4 +- gtsam/base/tests/testPriorityScheduler.cpp | 30 ++++ gtsam/base/tests/testTaskScheduler.cpp | 75 ++++++++++ 7 files changed, 224 insertions(+), 55 deletions(-) create mode 100644 gtsam/base/tests/testTaskScheduler.cpp diff --git a/.gitignore b/.gitignore index 4ff32064fe..b80906ca01 100644 --- a/.gitignore +++ b/.gitignore @@ -30,3 +30,4 @@ doc/#*.lyx# /cmake-build-debug/ .venv/ conductor/ +.ccache/ diff --git a/gtsam/base/ForestTraversal.h b/gtsam/base/ForestTraversal.h index de3aff7b78..9fc29df4ea 100644 --- a/gtsam/base/ForestTraversal.h +++ b/gtsam/base/ForestTraversal.h @@ -295,7 +295,7 @@ class ForestTraversal { }; /// Schedule a task and increment the pending counter. state->incrementPending(); - scheduler->schedule(std::function(std::move(task))); + scheduler->enqueue(std::function(std::move(task))); } /// Inline node work followed by traversal of children. @@ -367,7 +367,7 @@ class ForestTraversal { state->incrementPending(); // Schedule a continuation or run it inline if already on a worker thread. - scheduler->scheduleOrRunInline(std::function(std::move(task))); + scheduler->enqueueOrRunInline(std::function(std::move(task))); } /// Do bottom-up node work, then invoke the continuation. diff --git a/gtsam/base/PriorityScheduler.h b/gtsam/base/PriorityScheduler.h index 7d5761ac5f..80ce660da3 100644 --- a/gtsam/base/PriorityScheduler.h +++ b/gtsam/base/PriorityScheduler.h @@ -45,7 +45,7 @@ struct PrioritySchedulerPolicy { template struct Compare { bool operator()(const TaskPtr& a, const TaskPtr& b) const { - return a->metadata > b->metadata; + return a.metadata > b.metadata; } }; diff --git a/gtsam/base/Scheduler.h b/gtsam/base/Scheduler.h index b7967f6824..baa25f7af1 100644 --- a/gtsam/base/Scheduler.h +++ b/gtsam/base/Scheduler.h @@ -65,19 +65,12 @@ template class Scheduler { using Metadata = typename Policy::Metadata; - struct Task { - Metadata metadata; - std::function job; - std::promise promise; - - Task(Metadata m, std::function j, std::promise p_out) - : metadata(std::move(m)), - job(std::move(j)), - promise(std::move(p_out)) {} + struct WorkItem { + Metadata metadata{}; + std::function run; }; - using TaskPtr = std::shared_ptr; - using Container = typename Policy::template Container; + using Container = typename Policy::template Container; struct WorkerQueue { std::mutex mutex; @@ -105,8 +98,8 @@ class Scheduler { currentScheduler_ = this; workerIndex_ = static_cast(index); while (true) { - TaskPtr task; - if (!tryPopTask(index, task)) { + WorkItem item; + if (!tryPopTask(index, item)) { std::unique_lock lock(waitMutex_); condition_.wait(lock, [this] { return stop_.load(std::memory_order_acquire) || @@ -122,19 +115,9 @@ class Scheduler { } try { - // Execute the task and set the promise value: - if constexpr (std::is_void_v) { - task->job(); - task->promise.set_value(); - } else { - task->promise.set_value(task->job()); - } + item.run(); } catch (...) { - // On exception, set the exception on the promise: - try { - task->promise.set_exception(std::current_exception()); - } catch (...) { /* ignore */ - } + /* ignore */ } // Decrement active task count and notify waiters. @@ -144,24 +127,24 @@ class Scheduler { } /// Attempt to get a task from local or stolen queues. - bool tryPopTask(size_t index, TaskPtr& task) { + bool tryPopTask(size_t index, WorkItem& item) { // Prefer local queue, then steal to keep workers busy. - if (tryPopLocal(index, task)) return true; - if (trySteal(index, task)) return true; + if (tryPopLocal(index, item)) return true; + if (trySteal(index, item)) return true; return false; } /// Try to pop a task from the current worker queue. - bool tryPopLocal(size_t index, TaskPtr& task) { + bool tryPopLocal(size_t index, WorkItem& item) { WorkerQueue& queue = *queues_[index]; std::lock_guard lock(queue.mutex); - if (!Policy::template popLocal(queue.queue, task)) return false; + if (!Policy::template popLocal(queue.queue, item)) return false; queuedTasks_.fetch_sub(1, std::memory_order_release); return true; } /// Try to steal a task from another worker queue. - bool trySteal(size_t index, TaskPtr& task) { + bool trySteal(size_t index, WorkItem& item) { const size_t workerCount = queues_.size(); if (workerCount <= 1) return false; // Steal from other workers if local queue is empty. @@ -170,7 +153,7 @@ class Scheduler { WorkerQueue& queue = *queues_[target]; std::unique_lock lock(queue.mutex, std::try_to_lock); if (!lock.owns_lock()) continue; - if (!Policy::template popSteal(queue.queue, task)) continue; + if (!Policy::template popSteal(queue.queue, item)) continue; queuedTasks_.fetch_sub(1, std::memory_order_release); return true; } @@ -180,19 +163,8 @@ class Scheduler { /// Return true if called on a scheduler worker thread. bool isWorkerThread() const { return currentScheduler_ == this; } - std::future scheduleImpl(Metadata metadata, std::function job) { - if (stop_.load(std::memory_order_acquire)) { - std::promise err_promise; - err_promise.set_exception(std::make_exception_ptr( - std::runtime_error("Scheduler is stopping or stopped."))); - return err_promise.get_future(); - } - - std::promise promise; - std::future future = promise.get_future(); - auto task = std::make_shared(std::move(metadata), std::move(job), - std::move(promise)); - + bool enqueueImpl(Metadata metadata, std::function work) { + if (stop_.load(std::memory_order_acquire)) return false; WorkerQueue* targetQueue = nullptr; if (isWorkerThread()) { targetQueue = queues_[static_cast(workerIndex_)].get(); @@ -204,20 +176,21 @@ class Scheduler { { std::lock_guard lock(targetQueue->mutex); - Policy::template push(targetQueue->queue, std::move(task)); + Policy::template push( + targetQueue->queue, WorkItem{std::move(metadata), std::move(work)}); } queuedTasks_.fetch_add(1, std::memory_order_release); activeTasks_.fetch_add(1, std::memory_order_release); condition_.notify_one(); - return future; + return true; } template >> void scheduleOrRunInlineImpl(Metadata metadata, std::function job) { if (stop_.load(std::memory_order_relaxed)) return; if (!isWorkerThread()) { - scheduleImpl(std::move(metadata), std::move(job)); + enqueueImpl(std::move(metadata), std::move(job)); return; } @@ -273,7 +246,40 @@ class Scheduler { template >> std::future schedule(std::function job) { - return scheduleImpl(Metadata{}, std::move(job)); + if (stop_.load(std::memory_order_acquire)) { + std::promise err_promise; + err_promise.set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + return err_promise.get_future(); + } + + auto promise = std::make_shared>(); + std::future future = promise->get_future(); + + auto work = [promise, job = std::move(job)]() mutable { + try { + if constexpr (std::is_void_v) { + job(); + promise->set_value(); + } else { + promise->set_value(job()); + } + } catch (...) { + try { + promise->set_exception(std::current_exception()); + } catch (...) { /* ignore */ + } + } + }; + + if (!enqueueImpl(Metadata{}, std::move(work))) { + try { + promise->set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + } catch (...) { /* ignore */ + } + } + return future; } /** @@ -288,7 +294,40 @@ class Scheduler { template >> std::future schedule(Metadata metadata, std::function job) { - return scheduleImpl(std::move(metadata), std::move(job)); + if (stop_.load(std::memory_order_acquire)) { + std::promise err_promise; + err_promise.set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + return err_promise.get_future(); + } + + auto promise = std::make_shared>(); + std::future future = promise->get_future(); + + auto work = [promise, job = std::move(job)]() mutable { + try { + if constexpr (std::is_void_v) { + job(); + promise->set_value(); + } else { + promise->set_value(job()); + } + } catch (...) { + try { + promise->set_exception(std::current_exception()); + } catch (...) { /* ignore */ + } + } + }; + + if (!enqueueImpl(std::move(metadata), std::move(work))) { + try { + promise->set_exception(std::make_exception_ptr( + std::runtime_error("Scheduler is stopping or stopped."))); + } catch (...) { /* ignore */ + } + } + return future; } /** @@ -315,6 +354,30 @@ class Scheduler { scheduleOrRunInlineImpl(std::move(metadata), std::move(job)); } + /** + * @brief Enqueue a fire-and-forget task for execution. + * + * Enabled only for `TaskScheduler` (i.e., `Y = void` and no metadata). + */ + template && std::is_same_v>> + void enqueue(std::function job) { + enqueueImpl(Metadata{}, std::move(job)); + } + + /** + * @brief Enqueue a fire-and-forget task or run it inline on worker threads. + * + * Enabled only for `TaskScheduler` (i.e., `Y = void` and no metadata). + */ + template && std::is_same_v>> + void enqueueOrRunInline(std::function job) { + scheduleOrRunInlineImpl(Metadata{}, std::move(job)); + } + /** * @brief Block until all queued and active tasks complete. * diff --git a/gtsam/base/TaskScheduler.h b/gtsam/base/TaskScheduler.h index c65eaf2674..ad26d38bc6 100644 --- a/gtsam/base/TaskScheduler.h +++ b/gtsam/base/TaskScheduler.h @@ -50,7 +50,7 @@ struct TaskSchedulerPolicy { template static bool popLocal(Container& container, TaskPtr& out) { if (container.empty()) return false; - out = container.back(); + out = std::move(container.back()); container.pop_back(); return true; } @@ -58,7 +58,7 @@ struct TaskSchedulerPolicy { template static bool popSteal(Container& container, TaskPtr& out) { if (container.empty()) return false; - out = container.front(); + out = std::move(container.front()); container.pop_front(); return true; } diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index bebbfbd815..4a7a720838 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -16,6 +16,9 @@ #include #include +#include +#include + using namespace gtsam; /* ************************************************************************* */ @@ -36,6 +39,33 @@ TEST(PriorityScheduler, ReturnValueComposition) { EXPECT_LONGS_EQUAL(expectedSum, parent.get()); } +/* ************************************************************************* */ +TEST(PriorityScheduler, VoidPriorityOrderingSingleWorker) { + PriorityScheduler scheduler(1); + + std::mutex mutex; + std::vector executionOrder; + + scheduler.schedule(5, [&] { + std::lock_guard lock(mutex); + executionOrder.push_back(5); + }); + scheduler.schedule(1, [&] { + std::lock_guard lock(mutex); + executionOrder.push_back(1); + }); + scheduler.schedule(3, [&] { + std::lock_guard lock(mutex); + executionOrder.push_back(3); + }); + + scheduler.waitForAllTasks(); + EXPECT_LONGS_EQUAL(3, executionOrder.size()); + EXPECT_LONGS_EQUAL(1, executionOrder.at(0)); + EXPECT_LONGS_EQUAL(3, executionOrder.at(1)); + EXPECT_LONGS_EQUAL(5, executionOrder.at(2)); +} + /* ************************************************************************* */ int main() { TestResult tr; diff --git a/gtsam/base/tests/testTaskScheduler.cpp b/gtsam/base/tests/testTaskScheduler.cpp new file mode 100644 index 0000000000..e4d354e299 --- /dev/null +++ b/gtsam/base/tests/testTaskScheduler.cpp @@ -0,0 +1,75 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file testTaskScheduler.cpp + * @brief Unit tests for TaskScheduler scheduling behavior. + * @author Frank Dellaert + * @date Jan, 2026 + */ + +#include +#include + +#include +#include +#include + +using namespace gtsam; + +/* ************************************************************************* */ +// Compose child return values inside a parent task +TEST(TaskScheduler, ReturnValueComposition) { + // Use multiple workers so parent tasks can wait without stalling the pool. + TaskScheduler scheduler(2); + + auto left = scheduler.schedule([] { return size_t{3}; }).share(); + auto right = scheduler.schedule([] { return size_t{4}; }).share(); + + auto parent = scheduler.schedule( + [left, right]() mutable { return left.get() + right.get(); }); + + const size_t expectedSum = 7; + EXPECT_LONGS_EQUAL(expectedSum, parent.get()); +} + +/* ************************************************************************* */ +TEST(TaskScheduler, EnqueueFireAndForget) { + TaskScheduler scheduler(2); + + std::atomic counter{0}; + const size_t taskCount = 256; + + for (size_t i = 0; i < taskCount; ++i) { + scheduler.enqueue([&counter] { counter.fetch_add(1); }); + } + + scheduler.waitForAllTasks(); + EXPECT_LONGS_EQUAL(taskCount, counter.load()); +} + +/* ************************************************************************* */ +TEST(TaskScheduler, EnqueueOrRunInlineOnWorker) { + TaskScheduler scheduler(1); + + std::atomic inlineCounter{0}; + auto future = scheduler.schedule([&] { + scheduler.enqueueOrRunInline([&] { inlineCounter.fetch_add(1); }); + }); + + future.get(); + scheduler.waitForAllTasks(); + EXPECT_LONGS_EQUAL(1, inlineCounter.load()); +} + +/* ************************************************************************* */ +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} +/* ************************************************************************* */ From 189b3192d7e4d520d561f09e4ad1d96780e29813 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 9 Jan 2026 19:41:57 -0500 Subject: [PATCH 060/266] moveable work items --- gtsam/base/Scheduler.h | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/gtsam/base/Scheduler.h b/gtsam/base/Scheduler.h index baa25f7af1..8c91ccd986 100644 --- a/gtsam/base/Scheduler.h +++ b/gtsam/base/Scheduler.h @@ -120,9 +120,10 @@ class Scheduler { /* ignore */ } - // Decrement active task count and notify waiters. - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); + // Notify waiters only when work transitions to "done". + if (activeTasks_.fetch_sub(1, std::memory_order_release) == 1) { + condition_.notify_all(); + } } } @@ -199,8 +200,9 @@ class Scheduler { job(); } catch (...) { /* ignore */ } - activeTasks_.fetch_sub(1, std::memory_order_release); - condition_.notify_all(); + if (activeTasks_.fetch_sub(1, std::memory_order_release) == 1) { + condition_.notify_all(); + } } public: From 561dfe368a5e33d547c0b226e621b421c57c7e65 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 18:53:07 -0500 Subject: [PATCH 061/266] Initial, chain-rule based implementation --- gtsam/geometry/Gal3.cpp | 38 ++++++++++++++++ gtsam/geometry/Gal3.h | 24 ++++++++++ gtsam/geometry/tests/testGal3.cpp | 57 ++++++++++++++++++++++++ gtsam/navigation/NavState.cpp | 28 ++++++++++++ gtsam/navigation/NavState.h | 24 ++++++++++ gtsam/navigation/tests/testNavState.cpp | 58 +++++++++++++++++++++++++ 6 files changed, 229 insertions(+) diff --git a/gtsam/geometry/Gal3.cpp b/gtsam/geometry/Gal3.cpp index 836f52bc7f..0276d522b3 100644 --- a/gtsam/geometry/Gal3.cpp +++ b/gtsam/geometry/Gal3.cpp @@ -169,6 +169,44 @@ const double& Gal3::time(OptionalJacobian<1, 10> H) const { return t_; } +//------------------------------------------------------------------------------ +double Gal3::range(const Point3& point, OptionalJacobian<1, 10> Hself, + OptionalJacobian<1, 3> Hpoint) const { + const Pose3 pose(R_, r_); + Matrix16 Hpose; + OptionalJacobian<1, 6> HposeOptional(Hself ? &Hpose : nullptr); + const double r = pose.range(point, HposeOptional, Hpoint); + + if (Hself) { + Hself->setZero(); + Hself->block<1, 3>(0, 0) = Hpose.block<1, 3>(0, 0); // w + Hself->block<1, 3>(0, 6) = Hpose.block<1, 3>(0, 3); // rho + + const Vector3 bodyVelocity = R_.unrotate(v_); + (*Hself)(0, 9) = (Hpose.block<1, 3>(0, 3) * bodyVelocity)(0, 0); // alpha + } + return r; +} + +//------------------------------------------------------------------------------ +Unit3 Gal3::bearing(const Point3& point, OptionalJacobian<2, 10> Hself, + OptionalJacobian<2, 3> Hpoint) const { + const Pose3 pose(R_, r_); + Matrix26 Hpose; + OptionalJacobian<2, 6> HposeOptional(Hself ? &Hpose : nullptr); + const Unit3 b = pose.bearing(point, HposeOptional, Hpoint); + + if (Hself) { + Hself->setZero(); + Hself->block<2, 3>(0, 0) = Hpose.block<2, 3>(0, 0); // w + Hself->block<2, 3>(0, 6) = Hpose.block<2, 3>(0, 3); // rho + + const Vector3 bodyVelocity = R_.unrotate(v_); + Hself->col(9) = Hpose.block<2, 3>(0, 3) * bodyVelocity; // alpha + } + return b; +} + //------------------------------------------------------------------------------ Matrix5 Gal3::matrix() const { // Returns 5x5 matrix representation as in Equation 9, Page 5 diff --git a/gtsam/geometry/Gal3.h b/gtsam/geometry/Gal3.h index a34bc7a2ae..a03689ba34 100644 --- a/gtsam/geometry/Gal3.h +++ b/gtsam/geometry/Gal3.h @@ -18,6 +18,7 @@ #include #include +#include #include #include // For std::sqrt, std::cos, std::sin @@ -113,6 +114,22 @@ class GTSAM_EXPORT Gal3 : public MatrixLieGroup { /// Return time scalar const double& t() const { return t_; } + /** + * Calculate range to a 3D landmark. + * @param point 3D location of landmark + * @return range (double) + */ + double range(const Point3& point, OptionalJacobian<1, 10> Hself = {}, + OptionalJacobian<1, 3> Hpoint = {}) const; + + /** + * Calculate bearing to a 3D landmark. + * @param point 3D location of landmark + * @return bearing (Unit3) + */ + Unit3 bearing(const Point3& point, OptionalJacobian<2, 10> Hself = {}, + OptionalJacobian<2, 3> Hpoint = {}) const; + /// @} /// @name Testable /// @{ @@ -242,4 +259,11 @@ struct traits : public internal::MatrixLieGroup {}; template <> struct traits : public internal::MatrixLieGroup {}; +// bearing and range traits, used in RangeFactor and BearingFactor +template <> +struct Bearing : HasBearing {}; + +template <> +struct Range : HasRange {}; + } // namespace gtsam diff --git a/gtsam/geometry/tests/testGal3.cpp b/gtsam/geometry/tests/testGal3.cpp index 6b5bc151b3..ff52f3bdbf 100644 --- a/gtsam/geometry/tests/testGal3.cpp +++ b/gtsam/geometry/tests/testGal3.cpp @@ -21,6 +21,7 @@ #include #include #include // Included for kTestPose definition +#include #include #include @@ -44,6 +45,12 @@ const double kTestTime = 1.5; const Pose3 kTestPose(kTestRot, kTestPos); const Gal3 kTestGal3(kTestRot, kTestPos, kTestVel, kTestTime); +// Some shared test values - pulled from equivalent tests in Pose3 +static const Point3 l1(1, 0, 0), l2(1, 1, 0), l3(2, 2, 0), l4(1, 4, -4); +static const Gal3 x1(Rot3(), Point3::Zero(), Velocity3(0.4, 0.5, 0.6), 0.0), + x2(Rot3::Ypr(0.0, 0.0, 0.0), l2, Velocity3(0.4, 0.5, 0.6), 0.0), + x3(Rot3::Ypr(M_PI / 4.0, 0.0, 0.0), l2, Velocity3(0.4, 0.5, 0.6), 0.0); + /* ************************************************************************* */ TEST(Gal3, Concept) { @@ -105,6 +112,56 @@ TEST(Gal3, ComponentAccessorsValue) { EXPECT_DOUBLES_EQUAL(kTestTime, kTestGal3.t(), kTol); } +/* ************************************************************************* */ +double range_proxy(const Gal3& gal3, const Point3& point) { + return gal3.range(point); +} +TEST(Gal3, RangeToPoint3) { + Matrix expectedH1, actualH1, expectedH2, actualH2; + + // Establish range is indeed 1. + EXPECT_DOUBLES_EQUAL(1.0, x1.range(l1), 1e-9); + + // Establish range is indeed sqrt(2). + EXPECT_DOUBLES_EQUAL(std::sqrt(2.0), x1.range(l2), 1e-9); + + // Another pair + double actual23 = x2.range(l3, actualH1, actualH2); + EXPECT_DOUBLES_EQUAL(std::sqrt(2.0), actual23, 1e-9); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(range_proxy, x2, l3); + expectedH2 = numericalDerivative22(range_proxy, x2, l3); + EXPECT(assert_equal(expectedH1, actualH1)); + EXPECT(assert_equal(expectedH2, actualH2)); + + // Another test + double actual34 = x3.range(l4, actualH1, actualH2); + EXPECT_DOUBLES_EQUAL(5.0, actual34, 1e-9); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(range_proxy, x3, l4); + expectedH2 = numericalDerivative22(range_proxy, x3, l4); + EXPECT(assert_equal(expectedH1, actualH1)); + EXPECT(assert_equal(expectedH2, actualH2)); +} + +/* ************************************************************************* */ +Unit3 bearing_proxy(const Gal3& gal3, const Point3& point) { + return gal3.bearing(point); +} +TEST(Gal3, BearingToPoint3) { + Matrix expectedH1, actualH1, expectedH2, actualH2; + + EXPECT(assert_equal(Unit3(1, 0, 0), x1.bearing(l1, actualH1, actualH2), 1e-9)); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(bearing_proxy, x1, l1); + expectedH2 = numericalDerivative22(bearing_proxy, x1, l1); + EXPECT(assert_equal(expectedH1, actualH1, 1e-5)); + EXPECT(assert_equal(expectedH2, actualH2, 1e-5)); +} + /* ************************************************************************* */ TEST(Gal3, MatrixConstructorValue) { Matrix5 M_known = kTestGal3.matrix(); diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 8e10797ec3..84fb02c879 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -76,6 +76,34 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { return b_v; } +//------------------------------------------------------------------------------ +double NavState::range(const Point3& point, OptionalJacobian<1, 9> Hself, + OptionalJacobian<1, 3> Hpoint) const { + Matrix16 Hpose; + OptionalJacobian<1, 6> HposeOptional(Hself ? &Hpose : nullptr); + const double r = pose().range(point, HposeOptional, Hpoint); + + if (Hself) { + Hself->setZero(); + Hself->block<1, 6>(0, 0) = Hpose; + } + return r; +} + +//------------------------------------------------------------------------------ +Unit3 NavState::bearing(const Point3& point, OptionalJacobian<2, 9> Hself, + OptionalJacobian<2, 3> Hpoint) const { + Matrix26 Hpose; + OptionalJacobian<2, 6> HposeOptional(Hself ? &Hpose : nullptr); + const Unit3 b = pose().bearing(point, HposeOptional, Hpoint); + + if (Hself) { + Hself->setZero(); + Hself->block<2, 6>(0, 0) = Hpose; + } + return b; +} + //------------------------------------------------------------------------------ Matrix5 NavState::matrix() const { Matrix3 R = this->R(); diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index ebbbcf0d0b..8b2a2d9141 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -18,6 +18,7 @@ #pragma once +#include #include #include #include @@ -99,6 +100,22 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup { return Pose3(attitude(), position()); } + /** + * Calculate range to a 3D landmark. + * @param point 3D location of landmark + * @return range (double) + */ + double range(const Point3& point, OptionalJacobian<1, 9> Hself = {}, + OptionalJacobian<1, 3> Hpoint = {}) const; + + /** + * Calculate bearing to a 3D landmark. + * @param point 3D location of landmark + * @return bearing (Unit3) + */ + Unit3 bearing(const Point3& point, OptionalJacobian<2, 9> Hself = {}, + OptionalJacobian<2, 3> Hpoint = {}) const; + /// @} /// @name Derived quantities /// @{ @@ -326,4 +343,11 @@ struct traits : public internal::MatrixLieGroup {}; template <> struct traits : public internal::MatrixLieGroup {}; +// bearing and range traits, used in RangeFactor and BearingFactor +template <> +struct Bearing : HasBearing {}; + +template <> +struct Range : HasRange {}; + } // namespace gtsam diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 16606cfe43..4e3be47fbf 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -24,6 +24,7 @@ #include #include +#include using namespace std::placeholders; using namespace std; @@ -52,6 +53,13 @@ static const NavState T2(Rot3::Rodrigues(0.3, 0.2, 0.1), P2, V2); static const NavState T3(Rot3::Rodrigues(-90, 0, 0), Point3(5, 6, 7), Point3(1, 2, 3)); +// Some shared test values - pulled from equivalent tests in Pose3 +static const Point3 l1(1, 0, 0), l2(1, 1, 0), l3(2, 2, 0), l4(1, 4, -4); +static const Velocity3 kTestVelocity(0.4, 0.5, 0.6); +static const NavState x1(Rot3(), Point3::Zero(), kTestVelocity), + x2(Rot3::Ypr(0.0, 0.0, 0.0), l2, kTestVelocity), + x3(Rot3::Ypr(M_PI / 4.0, 0.0, 0.0), l2, kTestVelocity); + //****************************************************************************** TEST(NavState, Concept) { GTSAM_CONCEPT_ASSERT(IsGroup); @@ -814,6 +822,56 @@ TEST(NavState, AutonomousFlow) { CHECK(assert_equal(numericalPhi, analyticalPhi, 1e-9)); } +/* ************************************************************************* */ +double range_proxy(const NavState& navState, const Point3& point) { + return navState.range(point); +} +TEST(NavState, RangeToPoint3) { + Matrix expectedH1, actualH1, expectedH2, actualH2; + + // Establish range is indeed 1. + EXPECT_DOUBLES_EQUAL(1.0, x1.range(l1), 1e-9); + + // Establish range is indeed sqrt(2). + EXPECT_DOUBLES_EQUAL(std::sqrt(2.0), x1.range(l2), 1e-9); + + // Another pair + double actual23 = x2.range(l3, actualH1, actualH2); + EXPECT_DOUBLES_EQUAL(std::sqrt(2.0), actual23, 1e-9); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(range_proxy, x2, l3); + expectedH2 = numericalDerivative22(range_proxy, x2, l3); + EXPECT(assert_equal(expectedH1, actualH1)); + EXPECT(assert_equal(expectedH2, actualH2)); + + // Another test + double actual34 = x3.range(l4, actualH1, actualH2); + EXPECT_DOUBLES_EQUAL(5.0, actual34, 1e-9); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(range_proxy, x3, l4); + expectedH2 = numericalDerivative22(range_proxy, x3, l4); + EXPECT(assert_equal(expectedH1, actualH1)); + EXPECT(assert_equal(expectedH2, actualH2)); +} + +/* ************************************************************************* */ +Unit3 bearing_proxy(const NavState& navState, const Point3& point) { + return navState.bearing(point); +} +TEST(NavState, BearingToPoint3) { + Matrix expectedH1, actualH1, expectedH2, actualH2; + + EXPECT(assert_equal(Unit3(1, 0, 0), x1.bearing(l1, actualH1, actualH2), 1e-9)); + + // Check numerical derivatives + expectedH1 = numericalDerivative21(bearing_proxy, x1, l1); + expectedH2 = numericalDerivative22(bearing_proxy, x1, l1); + EXPECT(assert_equal(expectedH1, actualH1, 1e-5)); + EXPECT(assert_equal(expectedH2, actualH2, 1e-5)); +} + /* ************************************************************************* */ int main() { TestResult tr; From b5bbcd8592f21e54e9aeb73f61a98417e38f1369 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 18:59:14 -0500 Subject: [PATCH 062/266] Simplification --- gtsam/geometry/Gal3.cpp | 19 ++++---- gtsam/geometry/Pose3.cpp | 85 ++++++++++++++++++++++------------- gtsam/navigation/NavState.cpp | 13 ++++-- 3 files changed, 72 insertions(+), 45 deletions(-) diff --git a/gtsam/geometry/Gal3.cpp b/gtsam/geometry/Gal3.cpp index 0276d522b3..6e5be3b7c6 100644 --- a/gtsam/geometry/Gal3.cpp +++ b/gtsam/geometry/Gal3.cpp @@ -172,18 +172,19 @@ const double& Gal3::time(OptionalJacobian<1, 10> H) const { //------------------------------------------------------------------------------ double Gal3::range(const Point3& point, OptionalJacobian<1, 10> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Pose3 pose(R_, r_); - Matrix16 Hpose; - OptionalJacobian<1, 6> HposeOptional(Hself ? &Hpose : nullptr); - const double r = pose.range(point, HposeOptional, Hpoint); + const Vector3 delta = point - r_; + const double r = delta.norm(); + if (!Hself && !Hpoint) return r; + + const Vector3 u = delta / r; // unit vector from translation to point + const Matrix13 D_r_point = u.transpose(); + if (Hpoint) *Hpoint = D_r_point; if (Hself) { Hself->setZero(); - Hself->block<1, 3>(0, 0) = Hpose.block<1, 3>(0, 0); // w - Hself->block<1, 3>(0, 6) = Hpose.block<1, 3>(0, 3); // rho - - const Vector3 bodyVelocity = R_.unrotate(v_); - (*Hself)(0, 9) = (Hpose.block<1, 3>(0, 3) * bodyVelocity)(0, 0); // alpha + // translation() = r + R * dRho + v * dAlpha, so chain those. + Hself->block<1, 3>(0, 6) = -D_r_point * R_.matrix(); // rho + (*Hself)(0, 9) = -D_r_point.dot(v_); // alpha } return r; } diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 99decd5818..163c3c0a7f 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -455,56 +455,77 @@ Matrix Pose3::transformTo(const Matrix& points) const { /* ************************************************************************* */ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 3> Hpoint) const { - Matrix36 D_local_pose; - Matrix3 D_local_point; - Point3 local = transformTo(point, Hself ? &D_local_pose : 0, Hpoint ? &D_local_point : 0); - if (!Hself && !Hpoint) { - return local.norm(); - } else { - Matrix13 D_r_local; - const double r = norm3(local, D_r_local); - if (Hself) *Hself = D_r_local * D_local_pose; - if (Hpoint) *Hpoint = D_r_local * D_local_point; - return r; + const Vector3 delta = point - t_; + if (!Hself && !Hpoint) return delta.norm(); + + Matrix13 D_r_point; + const double r = norm3(delta, D_r_point); + + if (Hpoint) *Hpoint = D_r_point; + if (Hself) { + // Range is rotation-invariant: ||R^T(p-t)|| = ||p-t||, so d(range)/d(rotation) = 0. + Hself->leftCols<3>().setZero(); + // Translation coordinates are in the body frame: dt_world = R * dt_body. + Hself->rightCols<3>() = -D_r_point * R_.matrix(); } + return r; } /* ************************************************************************* */ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 6> Hpose) const { - Matrix36 D_point_pose; - Matrix13 D_local_point; - Point3 point = pose.translation(Hpose ? &D_point_pose : 0); - double r = range(point, Hself, Hpose ? &D_local_point : 0); - if (Hpose) *Hpose = D_local_point * D_point_pose; + const Vector3 delta = pose.t_ - t_; + if (!Hself && !Hpose) return delta.norm(); + + Matrix13 D_r_point; + const double r = norm3(delta, D_r_point); + + if (Hself) { + // Range depends only on translation: ||t2-t1||. + Hself->leftCols<3>().setZero(); + // Translation coordinates are in the body frame: dt_world = R * dt_body. + Hself->rightCols<3>() = -D_r_point * R_.matrix(); + } + + if (Hpose) { + Hpose->leftCols<3>().setZero(); + // Translation coordinates are in the body frame: dt_world = R * dt_body. + Hpose->rightCols<3>() = D_r_point * pose.R_.matrix(); + } + return r; } /* ************************************************************************* */ Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> Hself, OptionalJacobian<2, 3> Hpoint) const { - Matrix36 D_local_pose; - Matrix3 D_local_point; - Point3 at = transformTo(point, Hself ? &D_local_pose : 0, Hpoint ? &D_local_point : 0); - if (!Hself && !Hpoint) { - return Unit3(at); - } else { - Matrix23 D_b_local; - Unit3 b = Unit3::FromPoint3(at, D_b_local); - if (Hself) *Hself = D_b_local * D_local_pose; - if (Hpoint) *Hpoint = D_b_local * D_local_point; - return b; + const Matrix3 Rt = R_.transpose(); + const Point3 local(Rt * (point - t_)); + + if (!Hself && !Hpoint) return Unit3(local); + + Matrix23 D_b_local; + const Unit3 b = Unit3::FromPoint3(local, D_b_local); + if (Hself) { + Hself->leftCols<3>() = D_b_local * skewSymmetric(local.x(), local.y(), local.z()); + Hself->rightCols<3>() = -D_b_local; } + if (Hpoint) { + *Hpoint = D_b_local * Rt; + } + return b; } /* ************************************************************************* */ Unit3 Pose3::bearing(const Pose3& pose, OptionalJacobian<2, 6> Hself, OptionalJacobian<2, 6> Hpose) const { - Matrix36 D_point_pose; - Matrix23 D_local_point; - Point3 point = pose.translation(Hpose ? &D_point_pose : 0); - Unit3 b = bearing(point, Hself, Hpose ? &D_local_point : 0); - if (Hpose) *Hpose = D_local_point * D_point_pose; + Matrix23 D_bearing_point; + const Point3 point = pose.translation(); + const Unit3 b = bearing(point, Hself, Hpose ? &D_bearing_point : 0); + if (Hpose) { + Hpose->leftCols<3>().setZero(); + Hpose->rightCols<3>() = D_bearing_point * pose.rotation().matrix(); + } return b; } diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 84fb02c879..4abe029503 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -79,13 +79,18 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { //------------------------------------------------------------------------------ double NavState::range(const Point3& point, OptionalJacobian<1, 9> Hself, OptionalJacobian<1, 3> Hpoint) const { - Matrix16 Hpose; - OptionalJacobian<1, 6> HposeOptional(Hself ? &Hpose : nullptr); - const double r = pose().range(point, HposeOptional, Hpoint); + const Vector3 delta = point - t_; + const double r = delta.norm(); + if (!Hself && !Hpoint) return r; + const Vector3 u = delta / r; // unit vector from position to point + const Matrix13 D_r_point = u.transpose(); + + if (Hpoint) *Hpoint = D_r_point; if (Hself) { Hself->setZero(); - Hself->block<1, 6>(0, 0) = Hpose; + // position() = t + R * dP, so d(range)/d(dP) = d(range)/dt * dt/d(dP) + Hself->block<1, 3>(0, 3) = -D_r_point * R_.matrix(); } return r; } From f0ff3803d7ab950a6e22e844832dd20291941f77 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 19:01:26 -0500 Subject: [PATCH 063/266] Verify pose() Jacobian has no effect --- gtsam/navigation/tests/testNavState.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 4e3be47fbf..0e19d83eff 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -132,6 +132,22 @@ TEST( NavState, Velocity) { EXPECT(assert_equal((Matrix )eH, aH)); } +/* ************************************************************************* */ +TEST(NavState, PoseJacobian) { + // NavState::pose() should be a pure projection onto the (R,t) components. + std::function f = [](const NavState& s) { + return s.pose(); + }; + + const Matrix69 actualH = numericalDerivative11(f, kState1); + + Matrix69 expectedH = Matrix69::Zero(); + expectedH.block<3, 3>(0, 0) = I_3x3; + expectedH.block<3, 3>(3, 3) = I_3x3; + + EXPECT(assert_equal(expectedH, actualH, 1e-6)); +} + /* ************************************************************************* */ TEST( NavState, BodyVelocity) { Matrix39 aH, eH; From 21a17556f17bb41b397f5634135a2469234dbd6c Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 19:40:25 -0500 Subject: [PATCH 064/266] Wrapper --- gtsam/geometry/geometry.i | 12 ++++ gtsam/navigation/navigation.i | 8 +++ python/gtsam/tests/test_Gal3.py | 67 +++++++++++++++++++++ python/gtsam/tests/test_NavState.py | 68 ++++++++++++++++++++++ python/gtsam/tests/test_Pose3.py | 55 ++++++++++++++++- python/gtsam/utils/numerical_derivative.py | 5 +- 6 files changed, 212 insertions(+), 3 deletions(-) create mode 100644 python/gtsam/tests/test_Gal3.py create mode 100644 python/gtsam/tests/test_NavState.py diff --git a/gtsam/geometry/geometry.i b/gtsam/geometry/geometry.i index d04dda7896..05b637e075 100644 --- a/gtsam/geometry/geometry.i +++ b/gtsam/geometry/geometry.i @@ -675,6 +675,12 @@ class Pose3 { double range(const gtsam::Pose3& pose); double range(const gtsam::Pose3& pose, Eigen::Ref Hself, Eigen::Ref Hpose); + gtsam::Unit3 bearing(const gtsam::Point3& point); + gtsam::Unit3 bearing(const gtsam::Point3& point, Eigen::Ref Hself, + Eigen::Ref Hpoint); + gtsam::Unit3 bearing(const gtsam::Pose3& pose); + gtsam::Unit3 bearing(const gtsam::Pose3& pose, Eigen::Ref Hself, + Eigen::Ref Hpose); // enabling serialization functionality void serialize() const; @@ -1506,6 +1512,12 @@ class Gal3 { gtsam::Gal3 compose(const gtsam::Gal3& other) const; gtsam::Gal3 between(const gtsam::Gal3& other) const; gtsam::Event act(const gtsam::Event& e) const; + double range(const gtsam::Point3& point) const; + double range(const gtsam::Point3& point, Eigen::Ref Hself, + Eigen::Ref Hpoint) const; + gtsam::Unit3 bearing(const gtsam::Point3& point) const; + gtsam::Unit3 bearing(const gtsam::Point3& point, Eigen::Ref Hself, + Eigen::Ref Hpoint) const; // Operator Overloads gtsam::Gal3 operator*(const gtsam::Gal3& other) const; diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 0356112555..5c26698e1f 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -58,6 +58,14 @@ class NavState { gtsam::Vector velocity() const; gtsam::Pose3 pose() const; + // Standard Interface + double range(const gtsam::Point3& point) const; + double range(const gtsam::Point3& point, Eigen::Ref Hself, + Eigen::Ref Hpoint) const; + gtsam::Unit3 bearing(const gtsam::Point3& point) const; + gtsam::Unit3 bearing(const gtsam::Point3& point, Eigen::Ref Hself, + Eigen::Ref Hpoint) const; + // Manifold gtsam::NavState retract(const gtsam::Vector& v) const; gtsam::Vector localCoordinates(const gtsam::NavState& g) const; diff --git a/python/gtsam/tests/test_Gal3.py b/python/gtsam/tests/test_Gal3.py new file mode 100644 index 0000000000..8b93de6ea4 --- /dev/null +++ b/python/gtsam/tests/test_Gal3.py @@ -0,0 +1,67 @@ +""" +GTSAM Copyright 2010-2019, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved + +See LICENSE for the license information + +Gal3 unit tests. +""" + +import unittest + +import numpy as np +from gtsam.utils.test_case import GtsamTestCase +from gtsam.utils.numerical_derivative import ( + numericalDerivative21, + numericalDerivative22, +) + +import gtsam +from gtsam import Gal3, Point3, Rot3, Unit3 + + +class TestGal3(GtsamTestCase): + """Test selected Gal3 methods.""" + + def test_range_point_derivatives(self): + """Test Gal3 range to Point3 Jacobians.""" + state = Gal3( + Rot3.Rz(0.1), np.array([0.2, 0.3, 0.4]), np.array([0.5, 0.6, 0.7]), 0.8 + ) + point = Point3(1, 4, -4) + + jacobian_state = np.zeros((1, 10), order="F") + jacobian_point = np.zeros((1, 3), order="F") + state.range(point, jacobian_state, jacobian_point) + + jacobian_numerical_state = numericalDerivative21(Gal3.range, state, point) + jacobian_numerical_point = numericalDerivative22(Gal3.range, state, point) + self.gtsamAssertEquals(jacobian_state, jacobian_numerical_state) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + def test_bearing_point_derivatives(self): + """Test Gal3 bearing to Point3 Jacobians.""" + state = Gal3( + Rot3.Rz(0.1), np.array([0.2, 0.3, 0.4]), np.array([0.5, 0.6, 0.7]), 0.8 + ) + point = Point3(1, 4, -4) + + expected = Unit3( + gtsam.Pose3(state.rotation(), state.translation()).transformTo(point) + ) + actual = state.bearing(point) + self.gtsamAssertEquals(actual, expected, 1e-6) + + jacobian_state = np.zeros((2, 10), order="F") + jacobian_point = np.zeros((2, 3), order="F") + state.bearing(point, jacobian_state, jacobian_point) + + jacobian_numerical_state = numericalDerivative21(Gal3.bearing, state, point) + jacobian_numerical_point = numericalDerivative22(Gal3.bearing, state, point) + self.gtsamAssertEquals(jacobian_state, jacobian_numerical_state) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/gtsam/tests/test_NavState.py b/python/gtsam/tests/test_NavState.py new file mode 100644 index 0000000000..cd5cdde940 --- /dev/null +++ b/python/gtsam/tests/test_NavState.py @@ -0,0 +1,68 @@ +""" +GTSAM Copyright 2010-2019, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved + +See LICENSE for the license information + +NavState unit tests. +""" + +import unittest + +import numpy as np +from gtsam.utils.test_case import GtsamTestCase +from gtsam.utils.numerical_derivative import ( + numericalDerivative21, + numericalDerivative22, +) + +from gtsam import NavState, Point3, Rot3, Unit3 + + +class TestNavState(GtsamTestCase): + """Test selected NavState methods.""" + + def test_range_point_derivatives(self): + """Test NavState range to Point3 Jacobians.""" + state = NavState( + Rot3.Rodrigues(0.3, 0.2, 0.1), + Point3(3.5, -8.2, 4.2), + np.array([0.4, 0.5, 0.6]), + ) + point = Point3(1, 4, -4) + + jacobian_state = np.zeros((1, 9), order="F") + jacobian_point = np.zeros((1, 3), order="F") + state.range(point, jacobian_state, jacobian_point) + + jacobian_numerical_state = numericalDerivative21(NavState.range, state, point) + jacobian_numerical_point = numericalDerivative22(NavState.range, state, point) + self.gtsamAssertEquals(jacobian_state, jacobian_numerical_state) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + def test_bearing_point_derivatives(self): + """Test NavState bearing to Point3 Jacobians.""" + state = NavState( + Rot3.Rodrigues(0.3, 0.2, 0.1), + Point3(3.5, -8.2, 4.2), + np.array([0.4, 0.5, 0.6]), + ) + point = Point3(1, 4, -4) + + expected = Unit3(state.pose().transformTo(point)) + actual = state.bearing(point) + self.gtsamAssertEquals(actual, expected, 1e-6) + + jacobian_state = np.zeros((2, 9), order="F") + jacobian_point = np.zeros((2, 3), order="F") + state.bearing(point, jacobian_state, jacobian_point) + + jacobian_numerical_state = numericalDerivative21(NavState.bearing, state, point) + jacobian_numerical_point = numericalDerivative22(NavState.bearing, state, point) + self.gtsamAssertEquals(jacobian_state, jacobian_numerical_state) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/gtsam/tests/test_Pose3.py b/python/gtsam/tests/test_Pose3.py index 5e34ba35bb..fc1b9ec9a8 100644 --- a/python/gtsam/tests/test_Pose3.py +++ b/python/gtsam/tests/test_Pose3.py @@ -16,7 +16,7 @@ from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam import Point3, Pose3, Rot3 +from gtsam import Point3, Pose3, Rot3, Unit3 from gtsam.utils.numerical_derivative import numericalDerivative11, numericalDerivative21, numericalDerivative22 class TestPose3(GtsamTestCase): @@ -142,6 +142,59 @@ def test_range(self): # establish range is indeed sqrt2 self.assertEqual(math.sqrt(2.0), x1.range(pose=xl2)) + # test jacobians: pose to point + pose = Pose3(Rot3.Rodrigues(0.3, 0.2, 0.1), Point3(3.5, -8.2, 4.2)) + point = Point3(1, 4, -4) + jacobian_pose = np.zeros((1, 6), order='F') + jacobian_point = np.zeros((1, 3), order='F') + pose.range(point, jacobian_pose, jacobian_point) + jacobian_numerical_pose = numericalDerivative21(Pose3.range, pose, point) + jacobian_numerical_point = numericalDerivative22(Pose3.range, pose, point) + self.gtsamAssertEquals(jacobian_pose, jacobian_numerical_pose) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + # test jacobians: pose to pose + other = Pose3(Rot3.Rodrigues(-0.2, 0.3, 0.1), Point3(1, 2, 3)) + jacobian_self = np.zeros((1, 6), order='F') + jacobian_other = np.zeros((1, 6), order='F') + pose.range(other, jacobian_self, jacobian_other) + jacobian_numerical_self = numericalDerivative21(Pose3.range, pose, other) + jacobian_numerical_other = numericalDerivative22(Pose3.range, pose, other) + self.gtsamAssertEquals(jacobian_self, jacobian_numerical_self) + self.gtsamAssertEquals(jacobian_other, jacobian_numerical_other) + + def test_bearing(self): + """Test bearing method.""" + pose = Pose3(Rot3.Rodrigues(0.3, 0.2, 0.1), Point3(3.5, -8.2, 4.2)) + point = Point3(1, 4, -4) + + expected = Unit3(pose.transformTo(point)) + actual = pose.bearing(point) + self.gtsamAssertEquals(actual, expected, 1e-6) + + # test jacobians: pose to point + jacobian_pose = np.zeros((2, 6), order='F') + jacobian_point = np.zeros((2, 3), order='F') + pose.bearing(point, jacobian_pose, jacobian_point) + jacobian_numerical_pose = numericalDerivative21(Pose3.bearing, pose, point) + jacobian_numerical_point = numericalDerivative22(Pose3.bearing, pose, point) + self.gtsamAssertEquals(jacobian_pose, jacobian_numerical_pose) + self.gtsamAssertEquals(jacobian_point, jacobian_numerical_point) + + # test jacobians: pose to pose (orientation of other is ignored) + other = Pose3(Rot3.Rodrigues(-0.2, 0.3, 0.1), Point3(1, 2, 3)) + expected_pose = Unit3(pose.transformTo(other.translation())) + actual_pose = pose.bearing(other) + self.gtsamAssertEquals(actual_pose, expected_pose, 1e-6) + + jacobian_self = np.zeros((2, 6), order='F') + jacobian_other = np.zeros((2, 6), order='F') + pose.bearing(other, jacobian_self, jacobian_other) + jacobian_numerical_self = numericalDerivative21(Pose3.bearing, pose, other) + jacobian_numerical_other = numericalDerivative22(Pose3.bearing, pose, other) + self.gtsamAssertEquals(jacobian_self, jacobian_numerical_self) + self.gtsamAssertEquals(jacobian_other, jacobian_numerical_other) + def test_adjoint(self): """Test adjoint methods.""" T = Pose3() diff --git a/python/gtsam/utils/numerical_derivative.py b/python/gtsam/utils/numerical_derivative.py index 3b52f5a5f8..68859d1ff3 100644 --- a/python/gtsam/utils/numerical_derivative.py +++ b/python/gtsam/utils/numerical_derivative.py @@ -35,8 +35,9 @@ def local(a: Y, b: Y) -> np.ndarray: raise TypeError(f"a {type(a)} b {type(b)}") if isinstance(a, np.ndarray): return b - a - if isinstance(a, (float, int)): - return np.ndarray([[b - a]]) # type:ignore + if isinstance(a, (float, int, np.floating, np.integer)): + # Represent scalar values as a 1D "tangent vector" of length 1. + return np.array([float(b) - float(a)]) # there is no common superclass for Y return a.localCoordinates(b) # type:ignore From 7c0e9a1bbea1c3715d9923b8e91538eb4ad6c387 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 23:40:59 -0500 Subject: [PATCH 065/266] Fix test issue --- gtsam/base/tests/testPriorityScheduler.cpp | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index 4a7a720838..c18f6fe192 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -61,9 +61,16 @@ TEST(PriorityScheduler, VoidPriorityOrderingSingleWorker) { scheduler.waitForAllTasks(); EXPECT_LONGS_EQUAL(3, executionOrder.size()); - EXPECT_LONGS_EQUAL(1, executionOrder.at(0)); - EXPECT_LONGS_EQUAL(3, executionOrder.at(1)); - EXPECT_LONGS_EQUAL(5, executionOrder.at(2)); + // With a single worker, tasks are not preempted: the first scheduled task may + // start running before later (higher-priority) tasks are submitted. Once all + // tasks are enqueued, remaining work should respect priority order. + const bool strictPriority = + (executionOrder.at(0) == 1 && executionOrder.at(1) == 3 && + executionOrder.at(2) == 5); + const bool firstTaskRanImmediately = + (executionOrder.at(0) == 5 && executionOrder.at(1) == 1 && + executionOrder.at(2) == 3); + EXPECT(strictPriority || firstTaskRanImmediately); } /* ************************************************************************* */ From eb18185be41d3e440fc4351556a87a95fee31a19 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 11 Jan 2026 07:47:34 -0500 Subject: [PATCH 066/266] gcc 11 is default on 22.04 --- .github/workflows/build-linux.yml | 8 ++++---- .github/workflows/build-python.yml | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build-linux.yml b/.github/workflows/build-linux.yml index 9129989806..551a62bd53 100644 --- a/.github/workflows/build-linux.yml +++ b/.github/workflows/build-linux.yml @@ -31,8 +31,8 @@ jobs: # Github Actions requires a single row to be added to the build matrix. # See https://help.github.com/en/articles/workflow-syntax-for-github-actions. name: [ - # "Bracket" the versions from GCC [9-14] and Clang [11-16] - ubuntu-22.04-gcc-9, + # "Bracket" the versions from GCC [11-14] and Clang [11-16] + ubuntu-22.04-gcc-11, ubuntu-22.04-clang-11, ubuntu-24.04-gcc-14, ubuntu-24.04-clang-16, @@ -43,10 +43,10 @@ jobs: build_type: [Debug, Release] build_unstable: [ON] include: - - name: ubuntu-22.04-gcc-9 + - name: ubuntu-22.04-gcc-11 os: ubuntu-22.04 compiler: gcc - version: "9" + version: "11" - name: ubuntu-22.04-clang-11 os: ubuntu-22.04 diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index 4c632c6bcc..30c18f7e72 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -67,7 +67,7 @@ jobs: # See https://help.github.com/en/articles/workflow-syntax-for-github-actions. name: [ - ubuntu-22.04-gcc-9, + ubuntu-22.04-gcc-11, ubuntu-22.04-clang-11, macos-14-xcode-15.4, macos-15-xcode-16, @@ -77,10 +77,10 @@ jobs: build_type: [Release] python_version: [3] include: - - name: ubuntu-22.04-gcc-9 + - name: ubuntu-22.04-gcc-11 os: ubuntu-22.04 compiler: gcc - version: "9" + version: "11" - name: ubuntu-22.04-clang-11 os: ubuntu-22.04 From 66a14c4810a8a4a91b3ffa032e1bdc21d13c351c Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 01:37:35 -0500 Subject: [PATCH 067/266] parallelize parent updates --- gtsam/base/SymmetricBlockMatrix.h | 6 ++++ gtsam/linear/MultifrontalClique.cpp | 44 ++++++++++++++++++++++++++--- gtsam/linear/MultifrontalClique.h | 3 ++ 3 files changed, 49 insertions(+), 4 deletions(-) diff --git a/gtsam/base/SymmetricBlockMatrix.h b/gtsam/base/SymmetricBlockMatrix.h index 97bf3bd817..95e337cc57 100644 --- a/gtsam/base/SymmetricBlockMatrix.h +++ b/gtsam/base/SymmetricBlockMatrix.h @@ -270,6 +270,12 @@ namespace gtsam { void updateFromOuterProductBlocks(const VerticalBlockMatrix& other, const std::vector& blockIndices); + /// Add the upper-triangular part of another symmetric block matrix. + void addUpperTriangular(const SymmetricBlockMatrix& other) { + assert(nBlocks() == other.nBlocks()); + full().triangularView() += other.full(); + } + /// @} /// @name Accessing the full matrix. /// @{ diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 22d4b31b03..547435d894 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -17,10 +17,17 @@ */ #include +#include #include #include #include +#ifdef GTSAM_USE_TBB +#include +#include +#include +#endif + #include #include #include @@ -246,9 +253,33 @@ void MultifrontalClique::prepareForElimination() { sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); } +#ifdef GTSAM_USE_TBB + constexpr size_t kParallelChildThreshold = 1024; + if (children.size() >= kParallelChildThreshold) { + tbb::enumerable_thread_specific accumulators( + [this]() { + SymmetricBlockMatrix local(blockDims_, true); + local.setZero(); + return local; + }); + tbb::parallel_for(tbb::blocked_range(0, children.size()), + [&](const tbb::blocked_range& range) { + auto& local = accumulators.local(); + for (size_t i = range.begin(); i < range.end(); ++i) { + const auto& child = children[i]; + if (!child) continue; + child->updateParentSbm(local); + } + }); + for (auto& local : accumulators) { + sbm_.addUpperTriangular(local); + } + return; + } +#endif for (const auto& child : children) { if (!child) continue; - child->updateParent(*this); + child->updateParentSbm(sbm_); } } @@ -289,7 +320,8 @@ void MultifrontalClique::eliminateInPlace() { factorize(); } -void MultifrontalClique::updateParent(MultifrontalClique& parent) const { +void MultifrontalClique::updateParentSbm( + SymmetricBlockMatrix& parentSbm) const { if (useQR()) { // Accumulate separator (and RHS) normal equations (S^T S) into the parent. assert(RSdReady_); @@ -297,17 +329,21 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { assert(parent.sbm_.nBlocks() > 0); const DenseIndex nfBlocks = static_cast(numFrontals()); RSd_.firstBlock() = nfBlocks; - parent.sbm_.updateFromOuterProductBlocks(RSd_, parentIndices_); + parentSbm.updateFromOuterProductBlocks(RSd_, parentIndices_); RSd_.firstBlock() = 0; } else { // Accumulate the S^T S part from this clique's SBM into the parent. assert(sbm_.nBlocks() > 0 && sbm_.blockStart() == 0); sbm_.blockStart() = numFrontals(); - parent.sbm_.updateFromMappedBlocks(sbm_, parentIndices_); + parentSbm.updateFromMappedBlocks(sbm_, parentIndices_); sbm_.blockStart() = 0; } } +void MultifrontalClique::updateParent(MultifrontalClique& parent) const { + updateParentSbm(parent.sbm_); +} + std::shared_ptr MultifrontalClique::conditional() const { assert(RSdReady_); // RSd_ is cached at elimination time. diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index f90dd925d6..603eaac433 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -210,6 +210,9 @@ class GTSAM_EXPORT MultifrontalClique { /// Linear lookup for block index in small cliques. DenseIndex blockIndex(Key key) const; + /// Update a parent SBM with this clique's separator contribution. + void updateParentSbm(SymmetricBlockMatrix& parentSbm) const; + /// Compute block dimensions from variable dimensions (excluding RHS). std::vector blockDims(const KeyDimMap& dims, const KeyVector& frontals, From 88b519806b73f736dd50e19f982e326cf750c7a5 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 01:47:02 -0500 Subject: [PATCH 068/266] threading --- gtsam/linear/MultifrontalClique.cpp | 42 +++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 547435d894..67c1f034f0 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -32,6 +32,7 @@ #include #include #include +#include namespace gtsam { @@ -276,6 +277,47 @@ void MultifrontalClique::prepareForElimination() { } return; } +#else + constexpr size_t kParallelChildThreshold = 1024; + if (children.size() >= kParallelChildThreshold) { + size_t numThreads = std::thread::hardware_concurrency(); + if (numThreads == 0) { + numThreads = 1; + } + numThreads = std::min(numThreads, children.size()); + if (numThreads > 1) { + std::vector locals; + locals.reserve(numThreads); + for (size_t i = 0; i < numThreads; ++i) { + locals.emplace_back(blockDims_, true); + locals.back().setZero(); + } + std::vector threads; + threads.reserve(numThreads); + const size_t chunk = + (children.size() + numThreads - 1) / numThreads; + for (size_t t = 0; t < numThreads; ++t) { + const size_t start = t * chunk; + const size_t end = std::min(start + chunk, children.size()); + if (start >= end) break; + threads.emplace_back([this, start, end, &locals, t]() { + auto& local = locals[t]; + for (size_t i = start; i < end; ++i) { + const auto& child = children[i]; + if (!child) continue; + child->updateParentSbm(local); + } + }); + } + for (auto& thread : threads) { + thread.join(); + } + for (auto& local : locals) { + sbm_.addUpperTriangular(local); + } + return; + } + } #endif for (const auto& child : children) { if (!child) continue; From c701f00bb6dd59e010fcc73a2e1f50bb962ee37f Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 5 Jan 2026 02:04:57 -0500 Subject: [PATCH 069/266] Heuristic on when to go parallel --- gtsam/linear/MultifrontalClique.cpp | 102 ++++++++++++---------------- 1 file changed, 44 insertions(+), 58 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 67c1f034f0..8e6a92ad5b 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -254,74 +254,60 @@ void MultifrontalClique::prepareForElimination() { sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); } + size_t numThreads = std::thread::hardware_concurrency(); + if (numThreads == 0) { + numThreads = 1; + } + numThreads = std::min(numThreads, children.size()); + const size_t minChildren = + std::max(1024, 4 * static_cast(sbm_.rows())); + if (children.size() >= minChildren && numThreads > 1) { #ifdef GTSAM_USE_TBB - constexpr size_t kParallelChildThreshold = 1024; - if (children.size() >= kParallelChildThreshold) { - tbb::enumerable_thread_specific accumulators( - [this]() { - SymmetricBlockMatrix local(blockDims_, true); - local.setZero(); - return local; - }); + tbb::enumerable_thread_specific locals([this]() { + SymmetricBlockMatrix local(blockDims_, true); + local.setZero(); + return local; + }); tbb::parallel_for(tbb::blocked_range(0, children.size()), [&](const tbb::blocked_range& range) { - auto& local = accumulators.local(); + auto& local = locals.local(); for (size_t i = range.begin(); i < range.end(); ++i) { - const auto& child = children[i]; - if (!child) continue; - child->updateParentSbm(local); + children[i]->updateParentSbm(local); } }); - for (auto& local : accumulators) { - sbm_.addUpperTriangular(local); - } - return; - } #else - constexpr size_t kParallelChildThreshold = 1024; - if (children.size() >= kParallelChildThreshold) { - size_t numThreads = std::thread::hardware_concurrency(); - if (numThreads == 0) { - numThreads = 1; + std::vector locals; + locals.reserve(numThreads); + for (size_t i = 0; i < numThreads; ++i) { + locals.emplace_back(blockDims_, true); + locals.back().setZero(); } - numThreads = std::min(numThreads, children.size()); - if (numThreads > 1) { - std::vector locals; - locals.reserve(numThreads); - for (size_t i = 0; i < numThreads; ++i) { - locals.emplace_back(blockDims_, true); - locals.back().setZero(); - } - std::vector threads; - threads.reserve(numThreads); - const size_t chunk = - (children.size() + numThreads - 1) / numThreads; - for (size_t t = 0; t < numThreads; ++t) { - const size_t start = t * chunk; - const size_t end = std::min(start + chunk, children.size()); - if (start >= end) break; - threads.emplace_back([this, start, end, &locals, t]() { - auto& local = locals[t]; - for (size_t i = start; i < end; ++i) { - const auto& child = children[i]; - if (!child) continue; - child->updateParentSbm(local); - } - }); - } - for (auto& thread : threads) { - thread.join(); - } - for (auto& local : locals) { - sbm_.addUpperTriangular(local); - } - return; + std::vector threads; + threads.reserve(numThreads); + const size_t chunk = (children.size() + numThreads - 1) / numThreads; + for (size_t t = 0; t < numThreads; ++t) { + const size_t start = t * chunk; + const size_t end = std::min(start + chunk, children.size()); + if (start >= end) break; + threads.emplace_back([this, start, end, &locals, t]() { + auto& local = locals[t]; + for (size_t i = start; i < end; ++i) { + children[i]->updateParentSbm(local); + } + }); + } + for (auto& thread : threads) { + thread.join(); } - } #endif - for (const auto& child : children) { - if (!child) continue; - child->updateParentSbm(sbm_); + for (auto& local : locals) { + sbm_.addUpperTriangular(local); + } + } else { + for (const auto& child : children) { + assert(child); + child->updateParentSbm(sbm_); + } } } From 2ece6b29fc3211040ba9c4b90500dda8438dd6dc Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 10 Jan 2026 09:19:31 -0500 Subject: [PATCH 070/266] Clean up --- gtsam/linear/MultifrontalClique.cpp | 138 ++++++++++++++++++---------- gtsam/linear/MultifrontalClique.h | 6 ++ 2 files changed, 93 insertions(+), 51 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 8e6a92ad5b..cd0619158e 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -84,6 +84,20 @@ bool validateFactorKeys(const GaussianFactorGraph& graph, } #endif +size_t hardwareThreads() { + static const size_t kHardwareThreads = [] { + size_t n = std::thread::hardware_concurrency(); + return n == 0 ? size_t{1} : n; + }(); + return kHardwareThreads; +} + +SymmetricBlockMatrix makeZeroLocalSbm(const std::vector& blockDims) { + SymmetricBlockMatrix local(blockDims, true); + local.setZero(); + return local; +} + } // namespace MultifrontalClique::MultifrontalClique( @@ -254,60 +268,20 @@ void MultifrontalClique::prepareForElimination() { sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); } - size_t numThreads = std::thread::hardware_concurrency(); - if (numThreads == 0) { - numThreads = 1; - } - numThreads = std::min(numThreads, children.size()); + // Heuristic: avoid parallel overhead on small cliques. const size_t minChildren = std::max(1024, 4 * static_cast(sbm_.rows())); - if (children.size() >= minChildren && numThreads > 1) { -#ifdef GTSAM_USE_TBB - tbb::enumerable_thread_specific locals([this]() { - SymmetricBlockMatrix local(blockDims_, true); - local.setZero(); - return local; - }); - tbb::parallel_for(tbb::blocked_range(0, children.size()), - [&](const tbb::blocked_range& range) { - auto& local = locals.local(); - for (size_t i = range.begin(); i < range.end(); ++i) { - children[i]->updateParentSbm(local); - } - }); -#else - std::vector locals; - locals.reserve(numThreads); - for (size_t i = 0; i < numThreads; ++i) { - locals.emplace_back(blockDims_, true); - locals.back().setZero(); - } - std::vector threads; - threads.reserve(numThreads); - const size_t chunk = (children.size() + numThreads - 1) / numThreads; - for (size_t t = 0; t < numThreads; ++t) { - const size_t start = t * chunk; - const size_t end = std::min(start + chunk, children.size()); - if (start >= end) break; - threads.emplace_back([this, start, end, &locals, t]() { - auto& local = locals[t]; - for (size_t i = start; i < end; ++i) { - children[i]->updateParentSbm(local); - } - }); - } - for (auto& thread : threads) { - thread.join(); - } -#endif - for (auto& local : locals) { - sbm_.addUpperTriangular(local); - } + const size_t numChildren = children.size(); + if (numChildren < minChildren) { // Typical for chains: many small cliques. + gatherUpdatesSequential(); } else { - for (const auto& child : children) { - assert(child); - child->updateParentSbm(sbm_); - } + // Cap by available work. + // Parallel path (TBB if available, else std::thread). + const size_t numThreads = std::min(hardwareThreads(), numChildren); + if (numThreads <= 1) + gatherUpdatesSequential(); + else + gatherUpdatesParallel(numThreads); } } @@ -372,6 +346,68 @@ void MultifrontalClique::updateParent(MultifrontalClique& parent) const { updateParentSbm(parent.sbm_); } +void MultifrontalClique::gatherUpdatesSequential() { + for (const auto& child : children) { + assert(child); + child->updateParentSbm(sbm_); + } +} + +void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { +#ifdef GTSAM_USE_TBB + (void)numThreads; // TBB controls the effective worker count. + tbb::enumerable_thread_specific locals([this]() { + return makeZeroLocalSbm(blockDims_); + }); // Per-thread accumulators. + tbb::parallel_for( + tbb::blocked_range(0, children.size()), + [&](const tbb::blocked_range& range) { + auto& local = locals.local(); // Thread-local SBM. + for (size_t i = range.begin(); i < range.end(); ++i) { + const auto& child = children[i]; + if (!child) continue; + child->updateParentSbm( + local); // No locking: each thread writes its own SBM. + } + }); + locals.combine_each([this](const SymmetricBlockMatrix& local) { + sbm_.addUpperTriangular( + local); // Merge per-thread partial SBMs into this clique SBM. + }); +#else + std::vector locals; + locals.reserve(numThreads); // Fixed-size per-thread accumulators. + for (size_t i = 0; i < numThreads; ++i) { + locals.push_back(makeZeroLocalSbm(blockDims_)); + } + std::vector threads; + threads.reserve(numThreads); + const size_t chunk = + (children.size() + numThreads - 1) / numThreads; // Static partitioning. + for (size_t t = 0; t < numThreads; ++t) { + const size_t start = t * chunk; + const size_t end = std::min(start + chunk, children.size()); + if (start >= end) break; + threads.emplace_back([this, start, end, &locals, t]() { + auto& local = locals[t]; + for (size_t i = start; i < end; ++i) { + const auto& child = children[i]; + if (!child) continue; + child->updateParentSbm( + local); // No locking: each thread writes its own SBM. + } + }); + } + for (auto& thread : threads) { + thread.join(); // Ensure all locals are complete before merge. + } + for (const auto& local : locals) { + sbm_.addUpperTriangular( + local); // Merge per-thread partial SBMs into this clique SBM. + } +#endif +} + std::shared_ptr MultifrontalClique::conditional() const { assert(RSdReady_); // RSd_ is cached at elimination time. diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 603eaac433..9781300e3d 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -213,6 +213,12 @@ class GTSAM_EXPORT MultifrontalClique { /// Update a parent SBM with this clique's separator contribution. void updateParentSbm(SymmetricBlockMatrix& parentSbm) const; + /// Accumulate children separator updates into this clique's SBM (single-threaded). + void gatherUpdatesSequential(); + + /// Accumulate children separator updates into this clique's SBM (multi-threaded). + void gatherUpdatesParallel(size_t numThreads); + /// Compute block dimensions from variable dimensions (excluding RHS). std::vector blockDims(const KeyDimMap& dims, const KeyVector& frontals, From c385b838c298920e579720d3e6f0a430ead8810e Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 11 Jan 2026 15:18:20 -0500 Subject: [PATCH 071/266] Fix Docker docs --- .github/README.md | 40 +++++++++++ containers/README.md | 160 ++++++++++++++++++++++--------------------- 2 files changed, 122 insertions(+), 78 deletions(-) create mode 100644 .github/README.md diff --git a/.github/README.md b/.github/README.md new file mode 100644 index 0000000000..63e65c309d --- /dev/null +++ b/.github/README.md @@ -0,0 +1,40 @@ +# GTSAM GitHub Configuration + +This directory contains GitHub-specific configuration, including issue templates and CI workflows. + +## Workflows Overview + +The `.github/workflows` directory contains definitions for the various CI/CD processes: + +- **`build-linux.yml`**: Main Linux CI. Runs inside the pre-built Docker containers mentioned above to compile and test GTSAM with various compilers (GCC, Clang) and configurations. +- **`build-macos.yml`**: Compiles and tests GTSAM on macOS runners. +- **`build-windows.yml`**: Compiles and tests GTSAM on Windows runners using MSVC. +- **`build-python.yml`**: Verifies the Python wrapper compilation across Linux, macOS, and Windows. +- **`build-cibw.yml`**: Builds distributable Python wheels using `cibuildwheel`. It builds dependencies (like Boost) from source to ensure ABI compatibility. +- **`prod-cibw.yml`**: Used for production wheel builds (often triggered on release). +- **`deploy.yml`**: Handles deployment tasks (e.g., docs, release artifacts). + +## Docker CI Images (Linux Only) + +The **Linux CI** workflow (`build-linux.yml`) relies on pre-built Docker images to ensure consistency and speed up build times. These images are hosted in the [borglab/docker-images](https://github.com/borglab/docker-images) repository. + +*Note: macOS and Windows workflows use standard GitHub Actions runners and install dependencies (like Boost) via package managers (Homebrew, Chocolatey) or from source.* + +### Building and Updating Images + +If you need to update an existing CI image or add a new one (e.g., for a new Ubuntu version or compiler): + +1. **Navigate to the `docker-images` repository:** + ```bash + cd ../docker-images/gtsam-ci + ``` +2. **Add or modify a Dockerfile:** + - Follow the naming convention: `ubuntu---.Dockerfile`. + - Base images are defined in `*-base.Dockerfile`. +3. **Build and Push:** + Use the provided script to build and push to Docker Hub (requires `borglab` permissions): + ```bash + ./build_and_push.sh + ``` + +For more details, see the [README in the docker-images repository](https://github.com/borglab/docker-images/blob/main/gtsam-ci/README.md). diff --git a/containers/README.md b/containers/README.md index 2d4a7c27a2..406065cc05 100644 --- a/containers/README.md +++ b/containers/README.md @@ -1,127 +1,131 @@ -# GTSAM Containers +# GTSAM Docker Images -- container files to build images -- script to push images to a registry -- instructions to pull images and run containers +The official Docker images for GTSAM are maintained in the [borglab/docker-images](https://github.com/borglab/docker-images) repository. -## Dependencies +## Available Images -- a container engine such as [`Docker Engine`](https://docs.docker.com/engine/install/) +The following images are available on Docker Hub, primarily under the `borglab` namespace: -## Pull from Docker Hub +- **[borglab/gtsam](https://hub.docker.com/r/borglab/gtsam)**: + A pre-compiled environment containing the latest `develop` branch of GTSAM. Useful for quick testing or as a base for downstream applications. + - *Source:* [`docker-images/gtsam`](https://github.com/borglab/docker-images/tree/main/gtsam) -Various GTSAM image configurations are available at [`docker.io/borglab/gtsam`](https://hub.docker.com/r/borglab/gtsam). Determine which [tag](https://hub.docker.com/r/borglab/gtsam/tags) you want and pull the image. +- **[borglab/gtsam-manylinux](https://hub.docker.com/r/borglab/gtsam-manylinux)**: + An environment based on `manylinux2014` tailored for building Python wheels for GTSAM. + - *Source:* [`docker-images/gtsam-manylinux`](https://github.com/borglab/docker-images/tree/main/gtsam-manylinux) -Example for pulling an image with GTSAM compiled with TBB and Python support on top of a base Ubuntu 22.04 image. +- **[borglab/ubuntu-boost-tbb](https://hub.docker.com/r/borglab/ubuntu-boost-tbb)**: + Base image (Ubuntu 24.04) with Boost and TBB libraries pre-installed. + - *Source:* [`docker-images/ubuntu-boost-tbb`](https://github.com/borglab/docker-images/tree/main/ubuntu-boost-tbb) + +- **CI Images**: + Various images used for Continuous Integration, covering different Ubuntu versions (22.04, 24.04) and compilers (Clang, GCC). + - *Source:* [`docker-images/gtsam-ci`](https://github.com/borglab/docker-images/tree/main/gtsam-ci) + +## Usage + +### Running GTSAM + +To start an interactive shell in a container with GTSAM pre-installed: ```bash -docker pull docker.io/borglab/gtsam:4.2.0-tbb-ON-python-ON_22.04 +docker run -it borglab/gtsam:latest ``` -[`docker.io/borglab/gtsam-vnc`](https://hub.docker.com/r/borglab/gtsam-vnc) is also provided as an image with GTSAM that will run a VNC server to connect to. +### Using the Python Wrapper -## Using the images +The `borglab/gtsam` image typically includes Python bindings. To use them: -### Just GTSAM +1. Start the container: + ```bash + docker run -it borglab/gtsam:latest + ``` +2. Launch Python: + ```bash + python3 + ``` +3. Import GTSAM: + ```python + import gtsam + print(gtsam.Pose3()) + ``` -To start the image, execute +## Building Images -```bash -docker run -it borglab/gtsam:4.2.0-tbb-ON-python-OFF_22.04 -``` +To build these images locally or contribute changes, please refer to the **[borglab/docker-images](https://github.com/borglab/docker-images)** repository. It contains the Dockerfiles and build scripts for all the images listed above. -after you will find yourself in a bash shell. +### Legacy Configuration -### GTSAM with Python wrapper -To use GTSAM via the python wrapper, similarly execute -```bash -docker run -it borglab/gtsam:4.2.0-tbb-ON-python-ON_22.04 -``` +The following files in this directory are legacy artifacts and are **no longer actively maintained**: -and then launch `python3`: -```bash -python3 ->>> import gtsam ->>> gtsam.Pose2(1,2,3) -(1, 2, 3) -``` -### GTSAM with Python wrapper and VNC +- **`Containerfile`**: Build instructions for a standalone GTSAM image (cloning from git and building from source). -First, start the image, which will run a VNC server on port 5900: +- **`compose.yaml`**: A Docker Compose wrapper used for configurable builds (via `.env` variables like `GTSAM_WITH_TBB`, `GTSAM_BUILD_PYTHON`) and standardized image tagging. -```bash -docker run -p 5900:5900 borglab/gtsam-vnc:4.2.0-tbb-ON-python-ON_22.04 -``` +- **`hub_push.sh`**: A utility script to iterate through configuration matrices and push multiple image variants to Docker Hub. -Then open a remote VNC X client, for example: -#### Linux -```bash -sudo apt-get install tigervnc-viewer -xtigervncviewer :5900 -``` +For official builds and the most up-to-date configurations, please refer to the **[borglab/docker-images](https://github.com/borglab/docker-images)** repository. -#### Mac -The Finder's "Connect to Server..." with `vnc://127.0.0.1` does not work, for some reason. Using the free [VNC Viewer](https://www.realvnc.com/en/connect/download/viewer/), enter `0.0.0.0:5900` as the server. -## Build images locally +> **TODO**: Consider migrating the configurable build and matrix-pushing functionality from these legacy files into the `docker-images` repository to support more flexible local builds. -### Build Dependencies -- a [Compose Spec](https://compose-spec.io/) implementation such as [docker-compose](https://docs.docker.com/compose/install/) -### `gtsam` image +## VNC Support (`gtsam-vnc`) -#### `.env` file -- `GTSAM_GIT_TAG`: [git tag from the gtsam repo](https://github.com/borglab/gtsam/tags) -- `UBUNTU_TAG`: image tag provided by [ubuntu](https://hub.docker.com/_/ubuntu/tags) to base the image off of -- `GTSAM_WITH_TBB`: to build GTSAM with TBB, set to `ON` -- `GTSAM_BUILD_PYTHON`: to build python bindings, set to `ON` -- `CORES`: number of cores to compile with -#### Build `gtsam` image +The **gtsam-vnc** image configuration is available locally in the [`gtsam-vnc`](./gtsam-vnc) subdirectory. This image extends the official `borglab/gtsam` image by adding a VNC server, allowing you to view GUI applications (like Matplotlib plots) running inside the container. -```bash -docker compose build -``` -### `gtsam-vnc` image -#### `gtsam-vnc/.env` file +### Building and Running VNC Image -- `GTSAM_TAG`: image tag provided by [gtsam](https://hub.docker.com/r/borglab/gtsam/tags) -#### Build `gtsam-vnc` image -```bash -docker compose --file gtsam-vnc/compose.yaml build -``` +1. **Navigate to the directory:** -## Push to Docker Hub + ```bash -Make sure you are logged in via: `docker login docker.io`. + cd gtsam-vnc -### `gtsam` images + ``` -Specify the variables described in the `.env` file in the `hub_push.sh` script. -To push images to Docker Hub, run as follows: -```bash -./hub_push.sh -``` -### `gtsam-vnc` images +2. **Build the image:** -Specify the variables described in the `gtsam-vnc/.env` file in the `gtsam-vnc/hub_push.sh` script. -To push images to Docker Hub, run as follows: + You can build it using Docker Compose or directly with Docker. -```bash -./gtsam-vnc/hub_push.sh -``` + ```bash + + # Example using docker build + + docker build -t gtsam-vnc . + + ``` + + + +3. **Run with Port Forwarding:** + + Map port 5900 to access the VNC server. + + ```bash + + docker run -p 5900:5900 gtsam-vnc + + ``` + + + +4. **Connect:** + + Use a VNC client to connect to `localhost:5900`. From 752608fc227622e6788fefd6793c8b841a86060c Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 11 Jan 2026 15:24:04 -0500 Subject: [PATCH 072/266] Fix test and address review comments --- gtsam/base/tests/testPriorityScheduler.cpp | 10 +++++++--- gtsam/linear/MultifrontalClique.cpp | 8 +++----- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/gtsam/base/tests/testPriorityScheduler.cpp b/gtsam/base/tests/testPriorityScheduler.cpp index c18f6fe192..d6eb151fd0 100644 --- a/gtsam/base/tests/testPriorityScheduler.cpp +++ b/gtsam/base/tests/testPriorityScheduler.cpp @@ -62,15 +62,19 @@ TEST(PriorityScheduler, VoidPriorityOrderingSingleWorker) { scheduler.waitForAllTasks(); EXPECT_LONGS_EQUAL(3, executionOrder.size()); // With a single worker, tasks are not preempted: the first scheduled task may - // start running before later (higher-priority) tasks are submitted. Once all - // tasks are enqueued, remaining work should respect priority order. + // start running before later (higher-priority) tasks are submitted. Also, + // since the third task is submitted last, the worker may run the first two + // tasks before the third is enqueued. const bool strictPriority = (executionOrder.at(0) == 1 && executionOrder.at(1) == 3 && executionOrder.at(2) == 5); const bool firstTaskRanImmediately = (executionOrder.at(0) == 5 && executionOrder.at(1) == 1 && executionOrder.at(2) == 3); - EXPECT(strictPriority || firstTaskRanImmediately); + const bool thirdTaskEnqueuedLate = + (executionOrder.at(0) == 1 && executionOrder.at(1) == 5 && + executionOrder.at(2) == 3); + EXPECT(strictPriority || firstTaskRanImmediately || thirdTaskEnqueuedLate); } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index cd0619158e..a203853608 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -326,9 +326,7 @@ void MultifrontalClique::updateParentSbm( SymmetricBlockMatrix& parentSbm) const { if (useQR()) { // Accumulate separator (and RHS) normal equations (S^T S) into the parent. - assert(RSdReady_); - assert(RSd_.firstBlock() == 0); - assert(parent.sbm_.nBlocks() > 0); + assert(RSdReady_ && RSd_.firstBlock() == 0); const DenseIndex nfBlocks = static_cast(numFrontals()); RSd_.firstBlock() = nfBlocks; parentSbm.updateFromOuterProductBlocks(RSd_, parentIndices_); @@ -365,7 +363,7 @@ void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { auto& local = locals.local(); // Thread-local SBM. for (size_t i = range.begin(); i < range.end(); ++i) { const auto& child = children[i]; - if (!child) continue; + assert(child); child->updateParentSbm( local); // No locking: each thread writes its own SBM. } @@ -392,7 +390,7 @@ void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { auto& local = locals[t]; for (size_t i = start; i < end; ++i) { const auto& child = children[i]; - if (!child) continue; + assert(child); child->updateParentSbm( local); // No locking: each thread writes its own SBM. } From f3b659bae0cd81e4ce286b087394a6c00cd9bc61 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 11 Jan 2026 18:25:41 -0500 Subject: [PATCH 073/266] add parentheses around conditions separated by || since GCC complains --- gtsam/linear/MultifrontalClique.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 22d4b31b03..339ab3be37 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -232,8 +232,8 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; RSdReady_ = false; assert((useQR && RSd_.matrix().rows() == - static_cast(Ab_.matrix().rows()) || - RSd_.matrix().rows() == static_cast(frontalDim))); + static_cast(Ab_.matrix().rows())) || + (RSd_.matrix().rows() == static_cast(frontalDim))); allocateSbm(); } From 12b9f72ee09786d09c18bbeca75aad40f9df0f54 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 11 Jan 2026 18:25:58 -0500 Subject: [PATCH 074/266] Simplify NonlinearEquality factors --- gtsam/nonlinear/NonlinearEquality.h | 40 ++++++++++++++--------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/gtsam/nonlinear/NonlinearEquality.h b/gtsam/nonlinear/NonlinearEquality.h index baedca43a2..b590cba1c6 100644 --- a/gtsam/nonlinear/NonlinearEquality.h +++ b/gtsam/nonlinear/NonlinearEquality.h @@ -45,7 +45,7 @@ template class NonlinearEquality: public NonlinearEqualityConstraint { public: - typedef VALUE T; + using T = VALUE; private: @@ -64,6 +64,9 @@ class NonlinearEquality: public NonlinearEqualityConstraint { // typedef to base class using Base = NonlinearEqualityConstraint; + GTSAM_CONCEPT_MANIFOLD_TYPE(T) + GTSAM_CONCEPT_TESTABLE_TYPE(T) + public: /// Function that compares two values. @@ -71,11 +74,9 @@ class NonlinearEquality: public NonlinearEqualityConstraint { CompareFunction compare_; /// Default constructor - only for serialization - NonlinearEquality() { - } + NonlinearEquality() {} - ~NonlinearEquality() override { - } + ~NonlinearEquality() override {} /// @name Standard Constructors /// @{ @@ -102,9 +103,7 @@ class NonlinearEquality: public NonlinearEqualityConstraint { compare_(_compare) { } - Key key() const { - return keys().front(); - } + Key key() const { return keys().front(); } /// @} /// @name Testable @@ -222,7 +221,7 @@ struct traits> : Testable> {}; template class NonlinearEquality1: public NonlinearEqualityConstraint { public: - typedef VALUE X; + typedef VALUE T; protected: typedef NonlinearEqualityConstraint Base; @@ -232,10 +231,10 @@ class NonlinearEquality1: public NonlinearEqualityConstraint { NonlinearEquality1() { } - X value_; /// fixed value for variable + T value_; /// fixed value for variable - GTSAM_CONCEPT_MANIFOLD_TYPE(X) - GTSAM_CONCEPT_TESTABLE_TYPE(X) + GTSAM_CONCEPT_MANIFOLD_TYPE(T) + GTSAM_CONCEPT_TESTABLE_TYPE(T) public: @@ -247,8 +246,8 @@ class NonlinearEquality1: public NonlinearEqualityConstraint { * @param key the key for the unknown variable to be constrained * @param mu a parameter which really turns this into a strong prior */ - NonlinearEquality1(const X& value, Key key, double mu = 1000.0) - : Base(noiseModel::Constrained::All(traits::GetDimension(value), + NonlinearEquality1(const T& value, Key key, double mu = 1000.0) + : Base(noiseModel::Constrained::All(traits::GetDimension(value), std::abs(mu)), KeyVector{key}), value_(value) {} @@ -265,15 +264,15 @@ class NonlinearEquality1: public NonlinearEqualityConstraint { Key key() const { return keys().front(); } /// g(x) with optional derivative - Vector evaluateError(const X& x1, OptionalMatrixType H = nullptr) const { + Vector evaluateError(const T& x1, OptionalMatrixType H = nullptr) const { if (H) - (*H) = Matrix::Identity(traits::GetDimension(x1),traits::GetDimension(x1)); + (*H) = Matrix::Identity(traits::GetDimension(x1),traits::GetDimension(x1)); // manifold equivalent of h(x)-z -> log(z,h(x)) - return traits::Local(value_,x1); + return traits::Local(value_,x1); } Vector unwhitenedError(const Values& x, OptionalMatrixVecType H = nullptr) const override { - X x1 = x.at(key()); + T x1 = x.at(key()); if (H) { return evaluateError(x1, &(H->front())); } else { @@ -287,7 +286,7 @@ class NonlinearEquality1: public NonlinearEqualityConstraint { std::cout << s << ": NonlinearEquality1(" << keyFormatter(this->key()) << ")," << "\n"; this->noiseModel_->print(); - traits::Print(value_, "Value"); + traits::Print(value_, "Value"); } GTSAM_MAKE_ALIGNED_OPERATOR_NEW @@ -326,6 +325,7 @@ class NonlinearEquality2 : public NonlinearEqualityConstraint { typedef NonlinearEquality2 This; GTSAM_CONCEPT_MANIFOLD_TYPE(T) + GTSAM_CONCEPT_TESTABLE_TYPE(T) /// Default constructor to allow for serialization NonlinearEquality2() {} @@ -333,7 +333,6 @@ class NonlinearEquality2 : public NonlinearEqualityConstraint { public: typedef std::shared_ptr> shared_ptr; - /** * Constructor * @param key1 the key for the first unknown variable to be constrained @@ -343,6 +342,7 @@ class NonlinearEquality2 : public NonlinearEqualityConstraint { NonlinearEquality2(Key key1, Key key2, double mu = 1e4) : Base(noiseModel::Constrained::All(traits::dimension, std::abs(mu)), KeyVector{key1, key2}) {} + ~NonlinearEquality2() override {} /// @return a deep copy of this factor From 679dee213ffdf43ae70bd9ee99b440d7fac1f726 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 11 Jan 2026 18:32:21 -0500 Subject: [PATCH 075/266] Fix doxygen comment syntax --- gtsam/discrete/DiscreteSearch.h | 2 +- gtsam/geometry/Rot3.h | 2 +- gtsam/linear/JacobianFactor.h | 2 +- gtsam/nonlinear/nonlinear.i | 12 ------------ gtsam/sam/RangeFactor.h | 2 +- tests/testNonlinearOptimizer.cpp | 2 +- 6 files changed, 5 insertions(+), 17 deletions(-) diff --git a/gtsam/discrete/DiscreteSearch.h b/gtsam/discrete/DiscreteSearch.h index db3dd5f03f..06dfb84f69 100644 --- a/gtsam/discrete/DiscreteSearch.h +++ b/gtsam/discrete/DiscreteSearch.h @@ -113,7 +113,7 @@ class GTSAM_EXPORT DiscreteSearch { /// Construct from a DiscreteJunctionTree. DiscreteSearch(const DiscreteJunctionTree& junctionTree); - //// Construct from a DiscreteBayesNet. + /// Construct from a DiscreteBayesNet. DiscreteSearch(const DiscreteBayesNet& bayesNet); /// Construct from a DiscreteBayesTree. diff --git a/gtsam/geometry/Rot3.h b/gtsam/geometry/Rot3.h index c77fe2c77e..31947c6491 100644 --- a/gtsam/geometry/Rot3.h +++ b/gtsam/geometry/Rot3.h @@ -182,7 +182,7 @@ class GTSAM_EXPORT Rot3 : public LieGroup { /// Positive pitch is up (increasing aircraft altitude).See ypr static Rot3 Pitch(double t) { return Ry(t); } - //// Positive roll is to right (increasing yaw in aircraft). + /// Positive roll is to right (increasing yaw in aircraft). static Rot3 Roll (double t) { return Rx(t); } /** diff --git a/gtsam/linear/JacobianFactor.h b/gtsam/linear/JacobianFactor.h index f6c8978c09..759f2fd066 100644 --- a/gtsam/linear/JacobianFactor.h +++ b/gtsam/linear/JacobianFactor.h @@ -212,7 +212,7 @@ namespace gtsam { /// HybridValues simply extracts the \class VectorValues and calls error. using GaussianFactor::error; - //// 0.5*(A*x-b)'*D*(A*x-b). + /// 0.5*(A*x-b)'*D*(A*x-b). double error(const VectorValues& c) const override; /** Return the augmented information matrix represented by this GaussianFactor. diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i index 04308142ac..2565b2d9ba 100644 --- a/gtsam/nonlinear/nonlinear.i +++ b/gtsam/nonlinear/nonlinear.i @@ -203,18 +203,6 @@ virtual class LinearContainerFactor : gtsam::NonlinearFactor { void serializable() const; }; // \class LinearContainerFactor -// Summarization functionality -//#include -// -//// Uses partial QR approach by default -// gtsam::GaussianFactorGraph summarize( -// const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& values, -// const gtsam::KeySet& saved_keys); -// -// gtsam::NonlinearFactorGraph summarizeAsNonlinearContainer( -// const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& values, -// const gtsam::KeySet& saved_keys); - //************************************************************************* // Nonlinear optimizers //************************************************************************* diff --git a/gtsam/sam/RangeFactor.h b/gtsam/sam/RangeFactor.h index 0ccc54afdc..90c854fbbe 100644 --- a/gtsam/sam/RangeFactor.h +++ b/gtsam/sam/RangeFactor.h @@ -109,7 +109,7 @@ class RangeFactorWithTransform : public ExpressionFactorN { A1 body_T_sensor_; ///< The pose of the sensor in the body frame public: - //// Default constructor + /// Default constructor RangeFactorWithTransform() {} RangeFactorWithTransform(Key key1, Key key2, T measured, diff --git a/tests/testNonlinearOptimizer.cpp b/tests/testNonlinearOptimizer.cpp index cac25c246e..b5d19af008 100644 --- a/tests/testNonlinearOptimizer.cpp +++ b/tests/testNonlinearOptimizer.cpp @@ -629,7 +629,7 @@ TEST( NonlinearOptimizer, iterationHook_CG ) /* ************************************************************************* */ -//// Minimal traits example +/// Minimal traits example struct MyType : public Vector3 { using Vector3::Vector3; }; From 202716ccbdcdc8a5c70295fec82b8e8a7f79502e Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 11 Jan 2026 18:32:41 -0500 Subject: [PATCH 076/266] Make CMake section header printing more legible --- cmake/HandlePrintConfiguration.cmake | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/cmake/HandlePrintConfiguration.cmake b/cmake/HandlePrintConfiguration.cmake index 56f7b4530e..ed08278a73 100644 --- a/cmake/HandlePrintConfiguration.cmake +++ b/cmake/HandlePrintConfiguration.cmake @@ -7,8 +7,8 @@ print_config("CMAKE_CXX_COMPILER_VERSION" "${CMAKE_CXX_COMPILER_VERSION}") print_config("CMake version" "${CMAKE_VERSION}") print_config("CMake generator" "${CMAKE_GENERATOR}") print_config("CMake build tool" "${CMAKE_BUILD_TOOL}") -message(STATUS "Build flags ") -print_enabled_config(${GTSAM_BUILD_TESTS} "Build Tests") +message(STATUS "Build Flags ") +print_enabled_config(${GTSAM_BUILD_TESTS} "Build tests") print_enabled_config(${GTSAM_BUILD_EXAMPLES_ALWAYS} "Build examples with 'make all'") print_enabled_config(${GTSAM_BUILD_TIMING_ALWAYS} "Build timing scripts with 'make all'") if (DOXYGEN_FOUND) @@ -84,11 +84,11 @@ if(NOT MSVC AND NOT XCODE_VERSION) endif() endif() -message(STATUS "Packaging flags") +message(STATUS "Packaging Flags") print_config("CPack Source Generator" "${CPACK_SOURCE_GENERATOR}") print_config("CPack Generator" "${CPACK_GENERATOR}") -message(STATUS "GTSAM flags ") +message(STATUS "GTSAM Flags ") print_enabled_config(${GTSAM_USE_QUATERNIONS} "Quaternions as default Rot3 ") print_enabled_config(${GTSAM_ENABLE_CONSISTENCY_CHECKS} "Runtime consistency checking ") print_enabled_config(${GTSAM_ENABLE_MEMORY_SANITIZER} "Build with Memory Sanitizer ") From 6d367c536d57d45dd481326a3587caae0e41f63c Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Mon, 12 Jan 2026 00:30:16 -0500 Subject: [PATCH 077/266] Minimize the number of workflows --- .github/workflows/build-cibw.yml | 2 +- .github/workflows/build-linux.yml | 7 +++---- .github/workflows/build-macos.yml | 1 + .github/workflows/build-python.yml | 9 +-------- .github/workflows/build-windows.yml | 5 +---- .github/workflows/prod-cibw.yml | 2 +- .github/workflows/vcpkg.yml | 1 - 7 files changed, 8 insertions(+), 19 deletions(-) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index 9a7af07b74..e66af607c6 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -1,7 +1,7 @@ # This workflow builds the Python wheels using cibuildwheel and uploads them to TestPyPI. # It can be triggered on push to the develop branch or manually via Github Actions. -name: Build Wheels for Develop +name: Build Python Wheels for Develop on: push: diff --git a/.github/workflows/build-linux.yml b/.github/workflows/build-linux.yml index 551a62bd53..329d8b12d9 100644 --- a/.github/workflows/build-linux.yml +++ b/.github/workflows/build-linux.yml @@ -3,10 +3,9 @@ name: Linux CI on: pull_request: paths-ignore: - - "**.md" - - "**.ipynb" - - "myst.yml" - + - '**.md' + - '**.ipynb' + - 'myst.yml' # Cancels any in-progress workflow runs for the same PR when a new push is made, # allowing the runner to become available more quickly for the latest changes. diff --git a/.github/workflows/build-macos.yml b/.github/workflows/build-macos.yml index dccb00f4bb..9d5ea9a675 100644 --- a/.github/workflows/build-macos.yml +++ b/.github/workflows/build-macos.yml @@ -6,6 +6,7 @@ on: - '**.md' - '**.ipynb' - 'myst.yml' + # Cancels any in-progress workflow runs for the same PR when a new push is made, # allowing the runner to become available more quickly for the latest changes. concurrency: diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index 30c18f7e72..ac68dce867 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -4,13 +4,6 @@ name: Python CI # instead of under 'pull_request:'. Otherwise, the check is still required but # never runs, and a maintainer must bypass the check in order to merge the PR. on: - push: - branches: - - develop - paths-ignore: - - '**.md' - - '**.ipynb' - - 'myst.yml' pull_request: paths-ignore: - '**.md' @@ -24,7 +17,7 @@ concurrency: cancel-in-progress: true jobs: - # Check paths to changed files to see if any are non-ignored. + # Check paths of changed files to see if any are non-ignored. check-paths: runs-on: ubuntu-latest outputs: diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index 6a4408ce7a..ea9f86502c 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -4,11 +4,8 @@ on: pull_request: paths-ignore: - '**.md' - - '**.ipynb' + - '**.ipynb' - 'myst.yml' - push: - # Runs on pushes targeting the develop branch - branches: [develop] # Cancels any in-progress workflow runs for the same PR when a new push is made, # allowing the runner to become available more quickly for the latest changes. diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index 48ac73fefd..91677420fd 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -1,7 +1,7 @@ # This workflow builds the Python wheels using cibuildwheel and uploads them to TestPyPI. # It can be triggered on push to the develop branch or manually via Github Actions. -name: Build Wheels for Release +name: Build Python Wheels for Release on: release: diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 66e5d3ba30..5bf63eb5f1 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -1,6 +1,5 @@ name: vcpkg on: - pull_request: push: # Runs on pushes targeting the develop branch branches: [develop] From 6324391a64ff58ba280bc4a59d392dd8dc249612 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 13:25:41 -0500 Subject: [PATCH 078/266] trajectory aligner class and unit tests --- gtsam/sfm/TrajectoryAlignerSim3.cpp | 124 ++++++++++ gtsam/sfm/TrajectoryAlignerSim3.h | 57 +++++ gtsam/sfm/UnaryMeasurement.h | 82 +++++++ gtsam/sfm/sfm.i | 20 ++ gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp | 226 ++++++++++++++++++ 5 files changed, 509 insertions(+) create mode 100644 gtsam/sfm/TrajectoryAlignerSim3.cpp create mode 100644 gtsam/sfm/TrajectoryAlignerSim3.h create mode 100644 gtsam/sfm/UnaryMeasurement.h create mode 100644 gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp diff --git a/gtsam/sfm/TrajectoryAlignerSim3.cpp b/gtsam/sfm/TrajectoryAlignerSim3.cpp new file mode 100644 index 0000000000..325f8fc52e --- /dev/null +++ b/gtsam/sfm/TrajectoryAlignerSim3.cpp @@ -0,0 +1,124 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010-2020 + * All Rights Reserved + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +#include + +#include +#include + +#include +#include + +namespace { + +using namespace gtsam; + +Pose3 sim3TransformPose(const Similarity3 &sim, const Pose3 &pose, + OptionalJacobian<6, 7> H1 = {}, + OptionalJacobian<6, 6> H2 = {}) { + return sim.transformFrom(pose, H1, H2); +} + +SharedNoiseModel chooseNoise(const SharedNoiseModel &noise, double sigma) { + if (noise) return noise; + return noiseModel::Isotropic::Sigma(6, sigma); +} + +using PoseMeasurements = std::vector>; +struct MeasurementPair { + const UnaryMeasurement &first; + const UnaryMeasurement &second; +}; + +std::vector overlappingMeasurementPairs( + const PoseMeasurements &first, const PoseMeasurements &second) { + std::unordered_map indexLookup; + indexLookup.reserve(second.size()); + for (size_t i = 0; i < second.size(); ++i) { + indexLookup.emplace(second[i].key(), i); + } + + std::vector pairs; + pairs.reserve(std::min(first.size(), second.size())); + for (const auto &m : first) { + auto it = indexLookup.find(m.key()); + if (it != indexLookup.end()) { + pairs.push_back({m, second[it->second]}); + } + } + return pairs; +} + +Similarity3 estimateInitialSim3(const std::vector &overlapPairs) { + if (overlapPairs.size() < 2) return Similarity3(); + Pose3Pairs pairs; + pairs.reserve(overlapPairs.size()); + for (const auto &[p1, p2] : overlapPairs) { + pairs.emplace_back(p1.measured(), p2.measured()); + } + try { + return Similarity3::Align(pairs); + } catch (const std::exception &) { + return Similarity3(); + } +} + + +} // namespace + +namespace gtsam { + + +TrajectoryAlignerSim3::TrajectoryAlignerSim3( + const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, + const std::vector &aSb_all) { + const size_t childCount = bTi_all.size(); + if (!aSb_all.empty() && aSb_all.size() != childCount) { + throw std::invalid_argument( + "TrajectoryAlignerSim3: aSb_all and childrenPoses sizes differ"); + } + + // Add priors for all parent poses up front. + for (const auto &meas : aTi) { + initial_.insert(meas.key(), meas.measured()); + graph_.addExpressionFactor(Pose3_(meas.key()), meas.measured(), + chooseNoise(meas.noiseModel(), 1e-2)); + } + + // Parent-child constraints (only where camera exists in parent). + for (size_t childIdx = 0; childIdx < childCount; ++childIdx) { + const auto &bTi = bTi_all[childIdx]; + + const Key simKey = Symbol('S', childIdx); + if (!aSb_all.empty()) { + initial_.insert(simKey, aSb_all[childIdx]); + } else { + const auto overlap = overlappingMeasurementPairs(aTi, bTi); + initial_.insert(simKey, estimateInitialSim3(overlap)); + } + + const Expression aSb(simKey); + for (const auto &meas : bTi) { + Key cameraKey = meas.key(); + if (!initial_.exists(cameraKey)) continue; + + const Pose3_ aPose(cameraKey); + const Pose3_ expected = Pose3_(sim3TransformPose, aSb, aPose); + graph_.addExpressionFactor(expected, meas.measured(), meas.noiseModel()); + } + } +} + +Values TrajectoryAlignerSim3::solve() const { + if (graph_.empty() || initial_.empty()) { + return initial_; + } + LevenbergMarquardtOptimizer optimizer(graph_, initial_); + return optimizer.optimize(); +} + +} // namespace gtsam + diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h new file mode 100644 index 0000000000..3d59638954 --- /dev/null +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -0,0 +1,57 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010-2020, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file TrajectoryAlignerSim3.h + * @author Akshay Krishnan + * @date January 2026 + * @brief Aligning a trajectory of poses to a reference trajectory using a similarity transform. + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace gtsam { + + +class GTSAM_EXPORT TrajectoryAlignerSim3 { + public: + using KeyPair = std::pair; + using PoseMeasurements = std::vector>; + using ChildrenPoses = std::vector; + + private: + + // Parameters. + ExpressionFactorGraph graph_; + Values initial_; + + public: + TrajectoryAlignerSim3(const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &aSb_all); + + + Values solve() const; + +}; +} // namespace gtsam diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h new file mode 100644 index 0000000000..f245046ddd --- /dev/null +++ b/gtsam/sfm/UnaryMeasurement.h @@ -0,0 +1,82 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010-2020, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +#pragma once + +/** + * @file UnaryMeasurement.h + * @author Akshay Krishnan + * @date July 2020 + * @brief Unary measurement represents a measurement on a single key in a graph. + * It mirrors BinaryMeasurement but holds just one key. The measurement value + * and noise model are stored without an accompanying error function. + */ + +#include +#include +#include +#include + +#include +#include +#include + +namespace gtsam { + +template class UnaryMeasurement : public Factor { + // Check that T type is testable + GTSAM_CONCEPT_ASSERT(IsTestable); + +public: + // shorthand for a smart pointer to a measurement + using shared_ptr = std::shared_ptr; + +private: + T measured_; ///< The measurement + SharedNoiseModel noiseModel_; ///< Noise model + +public: + UnaryMeasurement(Key key, const T &measured, + const SharedNoiseModel &model = nullptr) + : Factor(std::vector({key})), measured_(measured), noiseModel_(model) {} + + /// @name Standard Interface + /// @{ + + Key key() const { return keys_[0]; } + const T &measured() const { return measured_; } + const SharedNoiseModel &noiseModel() const { return noiseModel_; } + + /// @} + /// @name Testable + /// @{ + + void print(const std::string &s, const KeyFormatter &keyFormatter = + DefaultKeyFormatter) const override { + std::cout << s << "UnaryMeasurement(" << keyFormatter(this->key()) << ")\n"; + traits::Print(measured_, " measured: "); + if (noiseModel_) + noiseModel_->print(" noise model: "); + else + std::cout << " noise model: (null)\n"; + } + + bool equals(const UnaryMeasurement &expected, double tol = 1e-9) const { + const UnaryMeasurement *e = + dynamic_cast *>(&expected); + return e != nullptr && Factor::equals(*e) && + traits::Equals(this->measured_, e->measured_, tol) && + ((!noiseModel_ && !expected.noiseModel_) || + (noiseModel_ && noiseModel_->equals(*expected.noiseModel()))); + } + /// @} +}; +} // namespace gtsam \ No newline at end of file diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index d7b7afbebc..7d86730af8 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -129,6 +129,16 @@ typedef gtsam::BinaryMeasurement BinaryMeasurementUnit3; typedef gtsam::BinaryMeasurement BinaryMeasurementRot3; typedef gtsam::BinaryMeasurement BinaryMeasurementPoint3; +#include +template +class UnaryMeasurement { + UnaryMeasurement(gtsam::Key key, const T& measured, + const gtsam::noiseModel::Base* model); + gtsam::Key key() const; + T measured() const; + gtsam::noiseModel::Base* noiseModel() const; +}; + // Used in Matlab wrapper class BinaryMeasurementsUnit3 { BinaryMeasurementsUnit3(); @@ -156,6 +166,16 @@ class BinaryMeasurementsRot3 { #include #include +#include +class TrajectoryAlignerSim3 { + TrajectoryAlignerSim3( + const std::vector>& aTi, + const std::vector>>>& bTi_all, + const std::vector& aSb_all); + + gtsam::Values solve() const; +}; + template class ShonanAveragingParameters { ShonanAveragingParameters(const gtsam::LevenbergMarquardtParams& lm); diff --git a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp new file mode 100644 index 0000000000..5b2c4a5370 --- /dev/null +++ b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp @@ -0,0 +1,226 @@ +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +using namespace gtsam; +using PoseMeasurements = TrajectoryAlignerSim3::PoseMeasurements; +using ChildrenPoses = TrajectoryAlignerSim3::ChildrenPoses; + +namespace { + +Pose3 makePose(double x, double y, double z, const Rot3& R = Rot3()) { + return Pose3(R, Point3(x, y, z)); +} + +std::vector makeParentPoses() { + return { + makePose(0.0, 0.0, 0.0), + makePose(1.0, 0.1, 0.0, Rot3::RzRyRx(0.15, -0.2, 0.1)), + makePose(2.0, 0.4, 0.1, Rot3::RzRyRx(0.1, 0.05, -0.03))}; +} + +PoseMeasurements makeMeasurements(const std::vector& poses, + double noiseSigma = 1e-3) { + PoseMeasurements m; + auto noise = noiseModel::Isotropic::Sigma(6, noiseSigma); + for (size_t i = 0; i < poses.size(); ++i) { + m.emplace_back(static_cast(i), poses[i], noise); + } + return m; +} + +std::vector transformPoses(const Similarity3& sim, + const std::vector& poses) { + std::vector out; + out.reserve(poses.size()); + for (const auto& p : poses) out.push_back(sim.transformFrom(p)); + return out; +} + + +Pose3 perturbPose(const Pose3& p) { + static thread_local std::mt19937 rng(42); + std::normal_distribution noise(0.0, 0.01); + const Rot3 dR = Rot3::RzRyRx(noise(rng), noise(rng), noise(rng)); + const Point3 dt(noise(rng), noise(rng), noise(rng)); + return p.compose(Pose3(dR, dt)); +} + +std::vector perturbPoses(const std::vector& poses) { + std::vector out; + out.reserve(poses.size()); + for (const auto& p : poses) out.push_back(perturbPose(p)); + return out; +} + +Similarity3 perturbSim3(const Similarity3& sim) { + Similarity3 delta(Rot3::RzRyRx(0.2, -0.25, 0.1), Point3(2, 3, -1), + 2.3); + return sim * delta; +} + +const Similarity3 gtSim1(Rot3::RzRyRx(0.2, 0.1, -0.05), Point3(0.3, -0.1, 0.2), + 1.5); +const Similarity3 gtSim2(Rot3::RzRyRx(-0.15, 0.05, 0.08), + Point3(-0.2, 0.15, 0.25), 1.3); +const Similarity3 gtSim3(Rot3::RzRyRx(0.05, -0.08, 0.12), + Point3(0.15, 0.05, -0.1), 1.1); + +bool simClose(const Similarity3& expected, const Similarity3& actual, + double tol) { + return assert_equal(expected, actual, tol); +} + +} // namespace + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, PerfectSingleChild) { + const auto parent = makeParentPoses(); + const auto child = transformPoses(gtSim1, parent); + + PoseMeasurements aTi = makeMeasurements(parent); + ChildrenPoses bTi_all{makeMeasurements(child)}; + std::vector sims{gtSim1}; + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + const auto recoveredSim = result.at(Symbol('S', 0)); + EXPECT(simClose(gtSim1, recoveredSim, 1e-6)); + + for (size_t i = 0; i < parent.size(); ++i) { + EXPECT(assert_equal(parent[i], result.at(i), 1e-6)); + } +} + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, PerfectSingleChildNoInitialSim) { + const auto parent = makeParentPoses(); + const Similarity3 gtSim(Rot3::RzRyRx(0.25, -0.05, 0.12), + Point3(-0.3, 0.2, -0.15), 1.4); + const auto child = transformPoses(gtSim, parent); + + PoseMeasurements aTi = makeMeasurements(parent); + ChildrenPoses bTi_all{makeMeasurements(child)}; + std::vector sims; // empty initial Sim3s + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + const auto recoveredSim = result.at(Symbol('S', 0)); + EXPECT(simClose(gtSim, recoveredSim, 1e-6)); +} + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, NoisySingleChild) { + const auto parent = makeParentPoses(); + const auto child = transformPoses(gtSim1, parent); + + PoseMeasurements aTi = makeMeasurements(parent, /*noiseSigma=*/1e-2); + PoseMeasurements bTi = makeMeasurements(perturbPoses(child), 1e-1); + ChildrenPoses bTi_all{bTi}; + std::vector sims{perturbSim3(gtSim1)}; + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + const auto recoveredSim = result.at(Symbol('S', 0)); + EXPECT(simClose(gtSim1, recoveredSim, 1e-2)); + + for (size_t i = 0; i < parent.size(); ++i) { + EXPECT(assert_equal(parent[i], result.at(i), 1e-3)); + } +} + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, SingleChildWithExtraNonOverlap) { + const auto parent = makeParentPoses(); + auto child = transformPoses(gtSim1, parent); + + PoseMeasurements aTi = makeMeasurements(parent, /*noiseSigma=*/1e-2); + PoseMeasurements bTi = makeMeasurements(perturbPoses(child), 1e-1); + // Add a non-overlapping camera in the child frame. + bTi.emplace_back(static_cast(10), + gtSim1.transformFrom(makePose(4.0, -1.0, 0.5)), + noiseModel::Isotropic::Sigma(6, 1e-2)); + + ChildrenPoses bTi_all{bTi}; + std::vector sims{perturbSim3(gtSim1)}; + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + const auto recoveredSim = result.at(Symbol('S', 0)); + EXPECT(simClose(gtSim1, recoveredSim, 2e-2)); + + for (size_t i = 0; i < parent.size(); ++i) { + EXPECT(assert_equal(parent[i], result.at(i), 1e-3)); + } +} + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, TwoChildrenNoisy) { + const auto parent = makeParentPoses(); + + PoseMeasurements aTi = makeMeasurements(parent, 1e-2); + PoseMeasurements b1 = + makeMeasurements(perturbPoses(transformPoses(gtSim1, parent)), 5e-2); + PoseMeasurements b2 = + makeMeasurements(perturbPoses(transformPoses(gtSim2, parent)), 5e-2); + + ChildrenPoses bTi_all{b1, b2}; + std::vector sims{perturbSim3(gtSim1), perturbSim3(gtSim2)}; + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + EXPECT(simClose(gtSim1, result.at(Symbol('S', 0)), 5e-2)); + EXPECT(simClose(gtSim2, result.at(Symbol('S', 1)), 5e-2)); + + for (size_t i = 0; i < parent.size(); ++i) { + EXPECT(assert_equal(parent[i], result.at(i), 1e-2)); + } +} + +/* ************************************************************************* */ +TEST(TrajectoryAlignerSim3, ThreeChildrenNoisy) { + const auto parent = makeParentPoses(); + + PoseMeasurements aTi = makeMeasurements(parent, 1e-2); + PoseMeasurements b1 = + makeMeasurements(perturbPoses(transformPoses(gtSim1, parent)), 2e-2); + PoseMeasurements b2 = + makeMeasurements(perturbPoses(transformPoses(gtSim2, parent)), 2e-2); + PoseMeasurements b3 = + makeMeasurements(perturbPoses(transformPoses(gtSim3, parent)), 2e-2); + + ChildrenPoses bTi_all{b1, b2, b3}; + std::vector sims{perturbSim3(gtSim1), perturbSim3(gtSim2), + perturbSim3(gtSim3)}; + + TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + Values result = aligner.solve(); + + EXPECT(simClose(gtSim1, result.at(Symbol('S', 0)), 1e-1)); + EXPECT(simClose(gtSim2, result.at(Symbol('S', 1)), 1e-1)); + EXPECT(simClose(gtSim3, result.at(Symbol('S', 2)), 1e-1)); + + for (size_t i = 0; i < parent.size(); ++i) { + EXPECT(assert_equal(parent[i], result.at(i), 1e-2)); + } +} + +/* ************************************************************************* */ +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} From 40cb69724bfda86f4d37188e3f096ee2ce3e43f6 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 14:28:08 -0500 Subject: [PATCH 079/266] Apply suggestion from @Copilot Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- gtsam/sfm/UnaryMeasurement.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h index f245046ddd..e2610e1bbe 100644 --- a/gtsam/sfm/UnaryMeasurement.h +++ b/gtsam/sfm/UnaryMeasurement.h @@ -14,7 +14,7 @@ /** * @file UnaryMeasurement.h * @author Akshay Krishnan - * @date July 2020 + * @date January 2026 * @brief Unary measurement represents a measurement on a single key in a graph. * It mirrors BinaryMeasurement but holds just one key. The measurement value * and noise model are stored without an accompanying error function. From 0f3a1d23f383b0cc34217b8089dc71e4923028f5 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 12 Jan 2026 19:37:16 +0000 Subject: [PATCH 080/266] Initial plan From d305ea7c3608288d54dd397db1547df866c8d2d3 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 12 Jan 2026 20:03:14 +0000 Subject: [PATCH 081/266] Address review comments: fix style, add documentation, remove unused code Co-authored-by: dellaert <10515273+dellaert@users.noreply.github.com> --- gtsam/sfm/TrajectoryAlignerSim3.cpp | 7 +--- gtsam/sfm/TrajectoryAlignerSim3.h | 38 ++++++++++++++----- gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp | 1 - 3 files changed, 30 insertions(+), 16 deletions(-) diff --git a/gtsam/sfm/TrajectoryAlignerSim3.cpp b/gtsam/sfm/TrajectoryAlignerSim3.cpp index 325f8fc52e..45865b7530 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/TrajectoryAlignerSim3.cpp @@ -66,19 +66,16 @@ Similarity3 estimateInitialSim3(const std::vector &overlapPairs } } - } // namespace namespace gtsam { - - TrajectoryAlignerSim3::TrajectoryAlignerSim3( const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &aSb_all) { const size_t childCount = bTi_all.size(); if (!aSb_all.empty() && aSb_all.size() != childCount) { throw std::invalid_argument( - "TrajectoryAlignerSim3: aSb_all and childrenPoses sizes differ"); + "TrajectoryAlignerSim3: aSb_all and bTi_all sizes differ"); } // Add priors for all parent poses up front. @@ -99,7 +96,7 @@ TrajectoryAlignerSim3::TrajectoryAlignerSim3( const auto overlap = overlappingMeasurementPairs(aTi, bTi); initial_.insert(simKey, estimateInitialSim3(overlap)); } - + const Expression aSb(simKey); for (const auto &meas : bTi) { Key cameraKey = meas.key(); diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h index 3d59638954..745a7c74d3 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.h +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -20,37 +20,55 @@ #include #include -#include #include #include #include #include -#include -#include -#include -#include #include namespace gtsam { - +/** + * @brief Aligns Pose3 trajectories from multiple child coordinate frames to a + * parent reference frame using Sim3 (similarity) transformations. + * + * This class solves an optimization problem to find the best Sim3 transforms + * (rotation, translation, and scale) that align poses from one or more child + * coordinate frames to a parent reference frame. The optimization jointly + * refines both the parent frame poses and the child-to-parent transformations. + * + * The class takes as input: + * - Parent frame poses (aTi): Pose3 measurements in the parent coordinate frame + * - Child frame poses (bTi_all): Pose3 measurements in one or more child frames + * - (Optional) Initial Sim3 estimates (aSb_all): Initial transforms from each + * child frame to the parent frame + * + * The output is a Values object containing: + * - Optimized parent frame poses (with keys from the input aTi measurements) + * - Optimized Sim3 transforms (with Symbol keys 'S' and index for each child) + */ class GTSAM_EXPORT TrajectoryAlignerSim3 { public: - using KeyPair = std::pair; using PoseMeasurements = std::vector>; using ChildrenPoses = std::vector; private: - - // Parameters. + // Data members. ExpressionFactorGraph graph_; Values initial_; public: + /** + * @brief Constructs a trajectory aligner with the given measurements. + * @param aTi Parent frame pose measurements (key-value pairs with noise) + * @param bTi_all Vector of child frame pose measurements, one vector per child + * @param aSb_all Initial Sim3 estimates transforming from each child to parent. + * If empty, initial estimates are computed automatically. + */ TrajectoryAlignerSim3(const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &aSb_all); - + /// Solves the optimization problem and returns optimized poses and transforms. Values solve() const; }; diff --git a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp index 5b2c4a5370..ecc6ecc722 100644 --- a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp @@ -46,7 +46,6 @@ std::vector transformPoses(const Similarity3& sim, return out; } - Pose3 perturbPose(const Pose3& p) { static thread_local std::mt19937 rng(42); std::normal_distribution noise(0.0, 0.01); From f7f4914f6f189efd23c22367c0790fee7cb86aa1 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 12 Jan 2026 15:54:25 -0500 Subject: [PATCH 082/266] Allow for an external rng --- gtsam/linear/Sampler.cpp | 24 ++++++++++++++-- gtsam/linear/Sampler.h | 44 +++++++++++++++++++++++++----- gtsam/linear/tests/testSampler.cpp | 13 ++++++++- 3 files changed, 70 insertions(+), 11 deletions(-) diff --git a/gtsam/linear/Sampler.cpp b/gtsam/linear/Sampler.cpp index 68f21d7234..6081fb84f2 100644 --- a/gtsam/linear/Sampler.cpp +++ b/gtsam/linear/Sampler.cpp @@ -25,7 +25,7 @@ namespace gtsam { /* ************************************************************************* */ Sampler::Sampler(const noiseModel::Diagonal::shared_ptr& model, uint_fast64_t seed) - : model_(model), generator_(seed) { + : model_(model), generator_(seed), externalGenerator_(nullptr) { if (!model) { throw std::invalid_argument("Sampler::Sampler needs a non-null model."); } @@ -33,7 +33,24 @@ Sampler::Sampler(const noiseModel::Diagonal::shared_ptr& model, /* ************************************************************************* */ Sampler::Sampler(const Vector& sigmas, uint_fast64_t seed) - : model_(noiseModel::Diagonal::Sigmas(sigmas, true)), generator_(seed) {} + : model_(noiseModel::Diagonal::Sigmas(sigmas, true)), + generator_(seed), + externalGenerator_(nullptr) {} + +/* ************************************************************************* */ +Sampler::Sampler(const noiseModel::Diagonal::shared_ptr& model, + std::mt19937_64& rng) + : model_(model), generator_(0u), externalGenerator_(&rng) { + if (!model) { + throw std::invalid_argument("Sampler::Sampler needs a non-null model."); + } +} + +/* ************************************************************************* */ +Sampler::Sampler(const Vector& sigmas, std::mt19937_64& rng) + : model_(noiseModel::Diagonal::Sigmas(sigmas, true)), + generator_(0u), + externalGenerator_(&rng) {} /* ************************************************************************* */ Vector Sampler::sampleDiagonal(const Vector& sigmas, std::mt19937_64* rng) { @@ -55,7 +72,8 @@ Vector Sampler::sampleDiagonal(const Vector& sigmas, std::mt19937_64* rng) { /* ************************************************************************* */ Vector Sampler::sampleDiagonal(const Vector& sigmas) const { - return sampleDiagonal(sigmas, &generator_); + std::mt19937_64* rng = externalGenerator_ ? externalGenerator_ : &generator_; + return sampleDiagonal(sigmas, rng); } /* ************************************************************************* */ diff --git a/gtsam/linear/Sampler.h b/gtsam/linear/Sampler.h index eb6549eb52..c4cc277567 100644 --- a/gtsam/linear/Sampler.h +++ b/gtsam/linear/Sampler.h @@ -30,12 +30,15 @@ namespace gtsam { */ class GTSAM_EXPORT Sampler { protected: - /** noiseModel created at generation */ + /// noiseModel created at generation noiseModel::Diagonal::shared_ptr model_; - /** generator */ + /// generator mutable std::mt19937_64 generator_; + /// Non-owning optional external generator. If non-null, sampling uses this. + mutable std::mt19937_64* externalGenerator_ = nullptr; + public: typedef std::shared_ptr shared_ptr; @@ -44,21 +47,45 @@ class GTSAM_EXPORT Sampler { /** * Create a sampler for the distribution specified by a diagonal NoiseModel - * with a manually specified seed + * with a manually specified seed. + * + * This constructor is convenient for deterministic, throw-away sampling. + * If you need stateful sampling across calls or across multiple Sampler + * instances, prefer the RNG-based constructor and manage the RNG yourself. * - * NOTE: do not use zero as a seed, it will break the generator + * NOTE: do not use zero as a seed, it will break the generator. */ explicit Sampler(const noiseModel::Diagonal::shared_ptr& model, uint_fast64_t seed = 42u); + /** + * Create a sampler that draws from a caller-supplied RNG (stateful). + * + * The RNG is non-owning and must outlive this Sampler. + */ + explicit Sampler(const noiseModel::Diagonal::shared_ptr& model, + std::mt19937_64& rng); + /** * Create a sampler for a distribution specified by a vector of sigmas - * directly + * directly. * - * NOTE: do not use zero as a seed, it will break the generator + * This constructor is convenient for deterministic, throw-away sampling. + * If you need stateful sampling across calls or across multiple Sampler + * instances, prefer the RNG-based constructor and manage the RNG yourself. + * + * NOTE: do not use zero as a seed, it will break the generator. */ explicit Sampler(const Vector& sigmas, uint_fast64_t seed = 42u); + /** + * Create a sampler for sigmas that draws from a caller-supplied RNG + * (stateful). + * + * The RNG is non-owning and must outlive this Sampler. + */ + explicit Sampler(const Vector& sigmas, std::mt19937_64& rng); + /// @} /// @name access functions /// @{ @@ -81,7 +108,10 @@ class GTSAM_EXPORT Sampler { /// @} protected: - /** given sigmas for a diagonal model, returns a sample */ + /** + * Given sigmas for a diagonal model, returns a sample. + * Uses external RNG if available. + * */ Vector sampleDiagonal(const Vector& sigmas) const; }; diff --git a/gtsam/linear/tests/testSampler.cpp b/gtsam/linear/tests/testSampler.cpp index 5831d90486..86e9d27bb2 100644 --- a/gtsam/linear/tests/testSampler.cpp +++ b/gtsam/linear/tests/testSampler.cpp @@ -17,7 +17,6 @@ */ #include - #include using namespace gtsam; @@ -38,6 +37,18 @@ TEST(testSampler, basic) { Vector actual1 = sampler1.sample(); EXPECT_DOUBLES_EQUAL(0.0, actual1(2), tol); EXPECT(assert_equal(sampler2.sample(), sampler3.sample(), tol)); + + // RNG-based constructor should be stateful and match the same sequence as a + // seeded sampler. + std::mt19937_64 rng(1); + Sampler sampler_rng(model, rng); + Sampler sampler_seed(model, 1); + Vector s1 = sampler_rng.sample(); + Vector s1_ref = sampler_seed.sample(); + EXPECT(assert_equal(s1, s1_ref, tol)); + Vector s2 = sampler_rng.sample(); + Vector s2_ref = sampler_seed.sample(); + EXPECT(assert_equal(s2, s2_ref, tol)); } /* ************************************************************************* */ From 53788a2045a2b617d428026e5d6ce73d29867d01 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 15:56:33 -0500 Subject: [PATCH 083/266] change to bSa --- gtsam/sfm/TrajectoryAlignerSim3.cpp | 26 +++++++++++--------------- gtsam/sfm/TrajectoryAlignerSim3.h | 12 ++++++++---- gtsam/sfm/sfm.i | 2 +- 3 files changed, 20 insertions(+), 20 deletions(-) diff --git a/gtsam/sfm/TrajectoryAlignerSim3.cpp b/gtsam/sfm/TrajectoryAlignerSim3.cpp index 45865b7530..3c3732ac0a 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/TrajectoryAlignerSim3.cpp @@ -16,12 +16,6 @@ namespace { using namespace gtsam; -Pose3 sim3TransformPose(const Similarity3 &sim, const Pose3 &pose, - OptionalJacobian<6, 7> H1 = {}, - OptionalJacobian<6, 6> H2 = {}) { - return sim.transformFrom(pose, H1, H2); -} - SharedNoiseModel chooseNoise(const SharedNoiseModel &noise, double sigma) { if (noise) return noise; return noiseModel::Isotropic::Sigma(6, sigma); @@ -57,7 +51,9 @@ Similarity3 estimateInitialSim3(const std::vector &overlapPairs Pose3Pairs pairs; pairs.reserve(overlapPairs.size()); for (const auto &[p1, p2] : overlapPairs) { - pairs.emplace_back(p1.measured(), p2.measured()); + // Similarity3::Align expects the pairs to be in the form (aTi, bTi) + // to estimate aSb, but we want bSa, so we swap the pairs. + pairs.emplace_back(p2.measured(), p1.measured()); } try { return Similarity3::Align(pairs); @@ -71,11 +67,11 @@ Similarity3 estimateInitialSim3(const std::vector &overlapPairs namespace gtsam { TrajectoryAlignerSim3::TrajectoryAlignerSim3( const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, - const std::vector &aSb_all) { + const std::vector &bSa_all) { const size_t childCount = bTi_all.size(); - if (!aSb_all.empty() && aSb_all.size() != childCount) { + if (!bSa_all.empty() && bSa_all.size() != childCount) { throw std::invalid_argument( - "TrajectoryAlignerSim3: aSb_all and bTi_all sizes differ"); + "TrajectoryAlignerSim3: bSa_all and bTi_all sizes differ"); } // Add priors for all parent poses up front. @@ -90,20 +86,20 @@ TrajectoryAlignerSim3::TrajectoryAlignerSim3( const auto &bTi = bTi_all[childIdx]; const Key simKey = Symbol('S', childIdx); - if (!aSb_all.empty()) { - initial_.insert(simKey, aSb_all[childIdx]); + if (!bSa_all.empty()) { + initial_.insert(simKey, bSa_all[childIdx]); } else { const auto overlap = overlappingMeasurementPairs(aTi, bTi); initial_.insert(simKey, estimateInitialSim3(overlap)); } - - const Expression aSb(simKey); + + const Expression bSa(simKey); for (const auto &meas : bTi) { Key cameraKey = meas.key(); if (!initial_.exists(cameraKey)) continue; const Pose3_ aPose(cameraKey); - const Pose3_ expected = Pose3_(sim3TransformPose, aSb, aPose); + const Pose3_ expected = Pose3_(bSa, &Similarity3::transformFrom, aPose); graph_.addExpressionFactor(expected, meas.measured(), meas.noiseModel()); } } diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h index 745a7c74d3..43bcf4c903 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.h +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -41,8 +41,8 @@ namespace gtsam { * The class takes as input: * - Parent frame poses (aTi): Pose3 measurements in the parent coordinate frame * - Child frame poses (bTi_all): Pose3 measurements in one or more child frames - * - (Optional) Initial Sim3 estimates (aSb_all): Initial transforms from each - * child frame to the parent frame + * - (Optional) Initial Sim3 estimates (bSa_all): Initial transforms from parent + * to each child frame * * The output is a Values object containing: * - Optimized parent frame poses (with keys from the input aTi measurements) @@ -57,16 +57,20 @@ class GTSAM_EXPORT TrajectoryAlignerSim3 { // Data members. ExpressionFactorGraph graph_; Values initial_; + PoseMeasurements parentPoses_; + ChildrenPoses childrenPoses_; + std::vector initialBSa_; + std::vector simKeys_; public: /** * @brief Constructs a trajectory aligner with the given measurements. * @param aTi Parent frame pose measurements (key-value pairs with noise) * @param bTi_all Vector of child frame pose measurements, one vector per child - * @param aSb_all Initial Sim3 estimates transforming from each child to parent. + * @param bSa_all Initial Sim3 estimates transforming from parent to each child. * If empty, initial estimates are computed automatically. */ - TrajectoryAlignerSim3(const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &aSb_all); + TrajectoryAlignerSim3(const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &bSa_all); /// Solves the optimization problem and returns optimized poses and transforms. Values solve() const; diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index 7d86730af8..777fcb005a 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -171,7 +171,7 @@ class TrajectoryAlignerSim3 { TrajectoryAlignerSim3( const std::vector>& aTi, const std::vector>>>& bTi_all, - const std::vector& aSb_all); + const std::vector& bSa_all); gtsam::Values solve() const; }; From 14ca1d9fe3e86f10b4c1d805ce22d609472614cf Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 12 Jan 2026 16:26:15 -0500 Subject: [PATCH 084/266] Define and use perturb template --- gtsam/linear/Sampler.h | 11 +++ gtsam/nonlinear/tests/testUtilities.cpp | 89 +++++++++++++++++++++++++ gtsam/nonlinear/utilities.h | 46 +++++-------- gtsam/slam/dataset.cpp | 6 +- gtsam_unstable/geometry/SimWall2D.cpp | 2 +- tests/testNonlinearISAM.cpp | 6 +- timing/timeLago.cpp | 2 +- 7 files changed, 124 insertions(+), 38 deletions(-) diff --git a/gtsam/linear/Sampler.h b/gtsam/linear/Sampler.h index c4cc277567..6e797279ec 100644 --- a/gtsam/linear/Sampler.h +++ b/gtsam/linear/Sampler.h @@ -18,6 +18,7 @@ #pragma once +#include #include #include @@ -103,6 +104,16 @@ class GTSAM_EXPORT Sampler { /// sample from distribution Vector sample() const; + /** + * Perturb a value by sampling in its tangent space and applying `retract`. + * + * The supplied noise model must match the dimensionality expected by `T`. + */ + template + T perturb(const T& value) const { + return traits::Retract(value, sample()); + } + /// sample with given random number generator static Vector sampleDiagonal(const Vector& sigmas, std::mt19937_64* rng); /// @} diff --git a/gtsam/nonlinear/tests/testUtilities.cpp b/gtsam/nonlinear/tests/testUtilities.cpp index 55a7fdb136..8dfcda30fc 100644 --- a/gtsam/nonlinear/tests/testUtilities.cpp +++ b/gtsam/nonlinear/tests/testUtilities.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include #include #include @@ -28,6 +29,8 @@ using gtsam::symbol_shorthand::L; using gtsam::symbol_shorthand::R; using gtsam::symbol_shorthand::X; +static constexpr double kTol = 1e-9; + /* ************************************************************************* */ TEST(Utilities, ExtractPoint2) { Point2 p0(0, 0), p1(1, 0); @@ -54,6 +57,92 @@ TEST(Utilities, ExtractPoint3) { EXPECT_LONGS_EQUAL(2, all_points.rows()); } +/* ************************************************************************* */ +TEST(Utilities, PerturbPoint2) { + Values base; + base.insert(L(0), Point2(1.0, 2.0)); + base.insert(L(1), (Vector(2) << 3.0, 4.0).finished()); + + Values v1 = base, v2 = base; + utilities::perturbPoint2(v1, /*sigma=*/0.1, /*seed=*/42u); + utilities::perturbPoint2(v2, /*sigma=*/0.1, /*seed=*/42u); + EXPECT(assert_equal(v1, v2, kTol)); + + const Point2 p0 = base.at(L(0)); + const Point2 p1 = v1.at(L(0)); + EXPECT((p1 - p0).norm() > 0.0); + + const Vector w0 = base.at(L(1)); + const Vector w1 = v1.at(L(1)); + EXPECT((w1 - w0).norm() > 0.0); + + Values v3 = base; + utilities::perturbPoint2(v3, /*sigma=*/0.0, /*seed=*/42u); + EXPECT(assert_equal(v3, base, kTol)); +} + +/* ************************************************************************* */ +TEST(Utilities, PerturbPoint3) { + Values base; + base.insert(L(0), Point3(1.0, 2.0, 3.0)); + base.insert(L(1), (Vector(3) << 4.0, 5.0, 6.0).finished()); + + Values v1 = base, v2 = base; + utilities::perturbPoint3(v1, /*sigma=*/0.1, /*seed=*/42u); + utilities::perturbPoint3(v2, /*sigma=*/0.1, /*seed=*/42u); + EXPECT(assert_equal(v1, v2, kTol)); + + const Point3 p0 = base.at(L(0)); + const Point3 p1 = v1.at(L(0)); + EXPECT((p1 - p0).norm() > 0.0); + + const Vector w0 = base.at(L(1)); + const Vector w1 = v1.at(L(1)); + EXPECT((w1 - w0).norm() > 0.0); + + Values v3 = base; + utilities::perturbPoint3(v3, /*sigma=*/0.0, /*seed=*/42u); + EXPECT(assert_equal(v3, base, kTol)); +} + +/* ************************************************************************* */ +TEST(Utilities, PerturbPose2) { + Values base; + const Pose2 pose0(1.0, 2.0, 0.3); + base.insert(X(0), pose0); + + Values v1 = base, v2 = base; + utilities::perturbPose2(v1, /*sigmaT=*/0.1, /*sigmaR=*/0.2, /*seed=*/42u); + utilities::perturbPose2(v2, /*sigmaT=*/0.1, /*sigmaR=*/0.2, /*seed=*/42u); + EXPECT(assert_equal(v1, v2, kTol)); + + const Pose2 pose1 = v1.at(X(0)); + EXPECT(!pose1.equals(pose0, kTol)); + + Values v3 = base; + utilities::perturbPose2(v3, /*sigmaT=*/0.0, /*sigmaR=*/0.0, /*seed=*/42u); + EXPECT(assert_equal(v3, base, kTol)); +} + +/* ************************************************************************* */ +TEST(Utilities, PerturbPose3) { + Values base; + const Pose3 pose0; + base.insert(X(0), pose0); + + Values v1 = base, v2 = base; + utilities::perturbPose3(v1, /*sigmaT=*/0.1, /*sigmaR=*/0.2, /*seed=*/42u); + utilities::perturbPose3(v2, /*sigmaT=*/0.1, /*sigmaR=*/0.2, /*seed=*/42u); + EXPECT(assert_equal(v1, v2, kTol)); + + const Pose3 pose1 = v1.at(X(0)); + EXPECT(!pose1.equals(pose0, kTol)); + + Values v3 = base; + utilities::perturbPose3(v3, /*sigmaT=*/0.0, /*sigmaR=*/0.0, /*seed=*/42u); + EXPECT(assert_equal(v3, base, kTol)); +} + /* ************************************************************************* */ TEST(Utilities, ExtractVector) { // Test normal case with 3 vectors and 1 non-vector (ignore non-vector) diff --git a/gtsam/nonlinear/utilities.h b/gtsam/nonlinear/utilities.h index 0a0ceb849b..60ffd45ed7 100644 --- a/gtsam/nonlinear/utilities.h +++ b/gtsam/nonlinear/utilities.h @@ -198,47 +198,33 @@ Matrix extractVectors(const Values& values, char c) { /// Perturb all Point2 values using normally distributed noise void perturbPoint2(Values& values, double sigma, int32_t seed = 42u) { - noiseModel::Isotropic::shared_ptr model = - noiseModel::Isotropic::Sigma(2, sigma); + auto model = noiseModel::Isotropic::Sigma(2, sigma); Sampler sampler(model, seed); - for (const auto& key_value : values.extract()) { - values.update(key_value.first, - key_value.second + Point2(sampler.sample())); + for (const auto& [key, value] : values.extract()) { + values.update(key, sampler.perturb(value)); } - for (const auto& key_value : values.extract()) { - if (key_value.second.rows() == 2) { - values.update(key_value.first, - key_value.second + Point2(sampler.sample())); + for (const auto& [key, value] : values.extract()) { + if (value.rows() == 2) { + values.update(key, sampler.perturb(value)); } } -} - -/// Perturb all Pose2 values using normally distributed noise -void perturbPose2(Values& values, double sigmaT, double sigmaR, int32_t seed = - 42u) { - noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas( - Vector3(sigmaT, sigmaT, sigmaR)); + auto model = noiseModel::Diagonal::Sigmas(Vector3(sigmaT, sigmaT, sigmaR)); Sampler sampler(model, seed); - for(const auto& key_value: values.extract()) { - values.update(key_value.first, key_value.second.retract(sampler.sample())); + for (const auto& [key, value] : values.extract()) { + values.update(key, sampler.perturb(value)); } -} - -/// Perturb all Point3 values using normally distributed noise void perturbPoint3(Values& values, double sigma, int32_t seed = 42u) { - noiseModel::Isotropic::shared_ptr model = - noiseModel::Isotropic::Sigma(3, sigma); + auto model = noiseModel::Isotropic::Sigma(3, sigma); Sampler sampler(model, seed); - for (const auto& key_value : values.extract()) { - values.update(key_value.first, - key_value.second + Point3(sampler.sample())); + for (const auto& [key, value] : values.extract()) { + values.update(key, sampler.perturb(value)); } - for (const auto& key_value : values.extract()) { - if (key_value.second.rows() == 3) { - values.update(key_value.first, - key_value.second + Point3(sampler.sample())); + for (const auto& [key, value] : values.extract()) { + if (value.rows() == 3) { + values.update(key, sampler.perturb(value)); } } + } } /** diff --git a/gtsam/slam/dataset.cpp b/gtsam/slam/dataset.cpp index d8731a2506..b196d9ba9d 100644 --- a/gtsam/slam/dataset.cpp +++ b/gtsam/slam/dataset.cpp @@ -370,7 +370,7 @@ template <> struct ParseMeasurement { // Get pose and optionally add noise Pose2 &pose = edge->second; if (sampler) - pose = pose.retract(sampler->sample()); + pose = sampler->perturb(pose); // emplace measurement auto modelFromFile = @@ -835,7 +835,7 @@ template <> struct ParseMeasurement { Pose3 T12(R, {x, y, z}); // optionally add noise if (sampler) - T12 = T12.retract(sampler->sample()); + T12 = sampler->perturb(T12); return BinaryMeasurement(id1, id2, T12, noiseModel::Gaussian::Information(m)); @@ -847,7 +847,7 @@ template <> struct ParseMeasurement { Pose3 T12(q, {x, y, z}); // optionally add noise if (sampler) - T12 = T12.retract(sampler->sample()); + T12 = sampler->perturb(T12); // g2o's EDGE_SE3:QUAT stores information/precision of Pose3 in t,R order, unlike GTSAM: Matrix6 mgtsam; diff --git a/gtsam_unstable/geometry/SimWall2D.cpp b/gtsam_unstable/geometry/SimWall2D.cpp index cd52b9696a..e9e1c583b4 100644 --- a/gtsam_unstable/geometry/SimWall2D.cpp +++ b/gtsam_unstable/geometry/SimWall2D.cpp @@ -163,7 +163,7 @@ std::pair moveWithBounce(const Pose2& cur_pose, double step_size, pose = Pose2(closest_wall.reflection(cur_pose.t(), intersection), intersection + inside_bias * norm); // perturb the rotation for better exploration - pose = pose.retract(reflect_noise.sample()); + pose = reflect_noise.perturb(pose); } return make_pair(pose, collision); diff --git a/tests/testNonlinearISAM.cpp b/tests/testNonlinearISAM.cpp index 4ffdbdafe1..57e21cb900 100644 --- a/tests/testNonlinearISAM.cpp +++ b/tests/testNonlinearISAM.cpp @@ -54,7 +54,7 @@ TEST(testNonlinearISAM, markov_chain ) { Values new_init; cur_pose = cur_pose.compose(z); - new_init.insert(i, cur_pose.retract(sampler.sample())); + new_init.insert(i, sampler.perturb(cur_pose)); expected.insert(i, cur_pose); isamChol.update(new_factors, new_init); isamQR.update(new_factors, new_init); @@ -110,7 +110,7 @@ TEST(testNonlinearISAM, markov_chain_with_disconnects ) { Values new_init; cur_pose = cur_pose.compose(z); - new_init.insert(i, cur_pose.retract(sampler.sample())); + new_init.insert(i, sampler.perturb(cur_pose)); expected.insert(i, cur_pose); // Add a floating landmark constellation @@ -187,7 +187,7 @@ TEST(testNonlinearISAM, markov_chain_with_reconnect ) { Values new_init; cur_pose = cur_pose.compose(z); - new_init.insert(i, cur_pose.retract(sampler.sample())); + new_init.insert(i, sampler.perturb(cur_pose)); expected.insert(i, cur_pose); // Add a floating landmark constellation diff --git a/timing/timeLago.cpp b/timing/timeLago.cpp index 9064479500..d47b29c137 100644 --- a/timing/timeLago.cpp +++ b/timing/timeLago.cpp @@ -42,7 +42,7 @@ int main(int argc, char *argv[]) { auto noise = noiseModel::Diagonal::Sigmas((Vector(3) << 0.5, 0.5, 15.0 * M_PI / 180.0).finished()); Sampler sampler(noise); for(const auto& [key,pose]: solution->extract()) - initial.insert(key, pose.retract(sampler.sample())); + initial.insert(key, sampler.perturb(pose)); // Add prior on the pose having index (key) = 0 noiseModel::Diagonal::shared_ptr priorModel = // From d6e8ca993f33db8d3d414191ce88cde02e0f98f8 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 12 Jan 2026 16:27:44 -0500 Subject: [PATCH 085/266] Document and add perturbPose3 --- gtsam/gtsam.i | 2 + gtsam/nonlinear/utilities.h | 64 +++++++++++++++++++++++++++- python/gtsam/tests/test_Utilities.py | 17 ++++++-- 3 files changed, 77 insertions(+), 6 deletions(-) diff --git a/gtsam/gtsam.i b/gtsam/gtsam.i index 39b5401e2b..3069601254 100644 --- a/gtsam/gtsam.i +++ b/gtsam/gtsam.i @@ -178,6 +178,8 @@ void perturbPoint2(gtsam::Values& values, double sigma, int seed = 42u); void perturbPose2(gtsam::Values& values, double sigmaT, double sigmaR, int seed = 42u); void perturbPoint3(gtsam::Values& values, double sigma, int seed = 42u); +void perturbPose3(gtsam::Values& values, double sigmaT, double sigmaR, + int seed = 42u); void insertBackprojections(gtsam::Values& values, const gtsam::PinholeCamera& c, gtsam::Vector J, gtsam::Matrix Z, double depth); diff --git a/gtsam/nonlinear/utilities.h b/gtsam/nonlinear/utilities.h index 60ffd45ed7..2b953bbd0b 100644 --- a/gtsam/nonlinear/utilities.h +++ b/gtsam/nonlinear/utilities.h @@ -196,7 +196,17 @@ Matrix extractVectors(const Values& values, char c) { return result; } -/// Perturb all Point2 values using normally distributed noise +/** + * Perturbs all 2D point values in a Values container using isotropic Gaussian + * noise. + * + * This updates in-place both Point2 entries and any Vector entries of size 2 + * by adding zero-mean Gaussian noise with the given standard deviation. + * + * @param values Values whose Point2/2D Vector entries will be perturbed. + * @param sigma Standard deviation of the isotropic Gaussian noise applied. + * @param seed Random seed used by the Sampler (default 42u). + */ void perturbPoint2(Values& values, double sigma, int32_t seed = 42u) { auto model = noiseModel::Isotropic::Sigma(2, sigma); Sampler sampler(model, seed); @@ -208,11 +218,40 @@ void perturbPoint2(Values& values, double sigma, int32_t seed = 42u) { values.update(key, sampler.perturb(value)); } } +} + +/** + * Perturbs all 2D pose values (Pose2) in a Values container using diagonal + * Gaussian noise. + * + * The translational components (x, y) are perturbed with sigmaT, and the + * rotational component (theta) is perturbed with sigmaR. + * + * @param values Values container whose Pose2 entries will be perturbed. + * @param sigmaT Standard deviation for translational noise. + * @param sigmaR Standard deviation for rotational noise (applied to theta). + * @param seed Random seed used by the Sampler (default 42u). + */ +void perturbPose2(Values& values, double sigmaT, double sigmaR, + int32_t seed = 42u) { auto model = noiseModel::Diagonal::Sigmas(Vector3(sigmaT, sigmaT, sigmaR)); Sampler sampler(model, seed); for (const auto& [key, value] : values.extract()) { values.update(key, sampler.perturb(value)); } +} + +/** + * Perturbs all 3D point values in a Values container using isotropic Gaussian + * noise. + * + * This updates in-place both Point3 entries and any Vector entries of size 3 + * by adding zero-mean Gaussian noise with the given standard deviation. + * + * @param values Values whose Point3 and 3D Vector entries will be perturbed. + * @param sigma Standard deviation of the isotropic Gaussian noise applied. + * @param seed Random seed used by the Sampler (default 42u). + */ void perturbPoint3(Values& values, double sigma, int32_t seed = 42u) { auto model = noiseModel::Isotropic::Sigma(3, sigma); Sampler sampler(model, seed); @@ -224,6 +263,28 @@ void perturbPoint3(Values& values, double sigma, int32_t seed = 42u) { values.update(key, sampler.perturb(value)); } } +} + +/** + * Perturbs all 3D pose values (Pose3) in a Values container using diagonal + * Gaussian noise. + * + * The noise is applied in the tangent space order [rx, ry, rz, tx, ty, tz], + * where rotational components are perturbed with sigmaR and translational + * components with sigmaT. + * + * @param values Values container whose Pose3 entries will be perturbed. + * @param sigmaT Standard deviation for translational noise. + * @param sigmaR Standard deviation for rotational noise. + * @param seed Random seed used by the Sampler (default 42u). + */ +void perturbPose3(Values& values, double sigmaT, double sigmaR, + int32_t seed = 42u) { + auto model = noiseModel::Diagonal::Sigmas( + (Vector6() << sigmaR, sigmaR, sigmaR, sigmaT, sigmaT, sigmaT).finished()); + Sampler sampler(model, seed); + for (const auto& [key, value] : values.extract()) { + values.update(key, sampler.perturb(value)); } } @@ -333,4 +394,3 @@ Values localToWorld(const Values& local, const Pose2& base, } // namespace utilities } - diff --git a/python/gtsam/tests/test_Utilities.py b/python/gtsam/tests/test_Utilities.py index 3dd472c75e..33d51ba120 100644 --- a/python/gtsam/tests/test_Utilities.py +++ b/python/gtsam/tests/test_Utilities.py @@ -17,7 +17,7 @@ import gtsam -class TestUtilites(GtsamTestCase): +class TestUtilities(GtsamTestCase): """Test various GTSAM utilities.""" def test_createKeyList(self): @@ -142,7 +142,7 @@ def test_perturbPoint2(self): values = gtsam.Values() values.insert(0, gtsam.Pose3()) values.insert(1, gtsam.Point2(1, 1)) - gtsam.utilities.perturbPoint2(values, 1.0) + gtsam.utilities.perturbPoint2(values, 1.0, 42) self.assertTrue( not np.allclose(values.atPoint2(1), gtsam.Point2(1, 1))) @@ -151,7 +151,7 @@ def test_perturbPose2(self): values = gtsam.Values() values.insert(0, gtsam.Pose2()) values.insert(1, gtsam.Point2(1, 1)) - gtsam.utilities.perturbPose2(values, 1, 1) + gtsam.utilities.perturbPose2(values, 1, 1, 42) self.assertTrue(values.atPose2(0) != gtsam.Pose2()) def test_perturbPoint3(self): @@ -160,9 +160,18 @@ def test_perturbPoint3(self): point3 = gtsam.Point3(0, 0, 0) values.insert(0, gtsam.Pose2()) values.insert(1, point3) - gtsam.utilities.perturbPoint3(values, 1) + gtsam.utilities.perturbPoint3(values, 1, 42) self.assertTrue(not np.allclose(values.atPoint3(1), point3)) + def test_perturbPose3(self): + """Test perturbPose3.""" + values = gtsam.Values() + pose3 = gtsam.Pose3() + values.insert(0, pose3) + values.insert(1, gtsam.Point2(1, 1)) + gtsam.utilities.perturbPose3(values, 1, 1, 42) + self.assertTrue(not values.atPose3(0).equals(pose3, 1e-9)) + def test_insertBackprojections(self): """Test insertBackprojections.""" values = gtsam.Values() From f540f1b56f9ce81496c58318676b82f08f90566a Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 18:17:24 -0500 Subject: [PATCH 086/266] unary measurement docs --- gtsam/sfm/UnaryMeasurement.h | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h index e2610e1bbe..8933db6eff 100644 --- a/gtsam/sfm/UnaryMeasurement.h +++ b/gtsam/sfm/UnaryMeasurement.h @@ -31,6 +31,9 @@ namespace gtsam { +/** + * @brief Unary measurement represents a measurement on a single key in a graph. + */ template class UnaryMeasurement : public Factor { // Check that T type is testable GTSAM_CONCEPT_ASSERT(IsTestable); @@ -44,6 +47,12 @@ template class UnaryMeasurement : public Factor { SharedNoiseModel noiseModel_; ///< Noise model public: + /** + * @brief Constructs a unary measurement with a key, value, and noise model. + * @param key The key of the measurement. + * @param measured The measurement value. + * @param model The noise model (optional). + */ UnaryMeasurement(Key key, const T &measured, const SharedNoiseModel &model = nullptr) : Factor(std::vector({key})), measured_(measured), noiseModel_(model) {} @@ -51,14 +60,18 @@ template class UnaryMeasurement : public Factor { /// @name Standard Interface /// @{ + // Returns the key of the measurement. Key key() const { return keys_[0]; } + // Returns the measurement value. const T &measured() const { return measured_; } + // Returns the noise model. const SharedNoiseModel &noiseModel() const { return noiseModel_; } /// @} /// @name Testable /// @{ + // Prints the measurement. void print(const std::string &s, const KeyFormatter &keyFormatter = DefaultKeyFormatter) const override { std::cout << s << "UnaryMeasurement(" << keyFormatter(this->key()) << ")\n"; @@ -69,6 +82,7 @@ template class UnaryMeasurement : public Factor { std::cout << " noise model: (null)\n"; } + // Checks if the measurement is equal to another measurement. bool equals(const UnaryMeasurement &expected, double tol = 1e-9) const { const UnaryMeasurement *e = dynamic_cast *>(&expected); @@ -79,4 +93,4 @@ template class UnaryMeasurement : public Factor { } /// @} }; -} // namespace gtsam \ No newline at end of file +} // namespace gtsam From 8ddaeb61260bc73a2d34f2afc0a422fc7d12342e Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 18:44:36 -0500 Subject: [PATCH 087/266] address review comments --- gtsam/sfm/TrajectoryAlignerSim3.cpp | 22 ++++------ gtsam/sfm/TrajectoryAlignerSim3.h | 13 ++---- gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp | 43 +++++++++++++------ 3 files changed, 44 insertions(+), 34 deletions(-) diff --git a/gtsam/sfm/TrajectoryAlignerSim3.cpp b/gtsam/sfm/TrajectoryAlignerSim3.cpp index 3c3732ac0a..3381545c50 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/TrajectoryAlignerSim3.cpp @@ -16,19 +16,14 @@ namespace { using namespace gtsam; -SharedNoiseModel chooseNoise(const SharedNoiseModel &noise, double sigma) { - if (noise) return noise; - return noiseModel::Isotropic::Sigma(6, sigma); -} - -using PoseMeasurements = std::vector>; struct MeasurementPair { const UnaryMeasurement &first; const UnaryMeasurement &second; }; std::vector overlappingMeasurementPairs( - const PoseMeasurements &first, const PoseMeasurements &second) { + const std::vector> &first, + const std::vector> &second) { std::unordered_map indexLookup; indexLookup.reserve(second.size()); for (size_t i = 0; i < second.size(); ++i) { @@ -66,7 +61,8 @@ Similarity3 estimateInitialSim3(const std::vector &overlapPairs namespace gtsam { TrajectoryAlignerSim3::TrajectoryAlignerSim3( - const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, + const std::vector> &aTi, + const std::vector>> &bTi_all, const std::vector &bSa_all) { const size_t childCount = bTi_all.size(); if (!bSa_all.empty() && bSa_all.size() != childCount) { @@ -74,18 +70,19 @@ TrajectoryAlignerSim3::TrajectoryAlignerSim3( "TrajectoryAlignerSim3: bSa_all and bTi_all sizes differ"); } - // Add priors for all parent poses up front. + // Add measurement factors for all parent poses. for (const auto &meas : aTi) { initial_.insert(meas.key(), meas.measured()); - graph_.addExpressionFactor(Pose3_(meas.key()), meas.measured(), - chooseNoise(meas.noiseModel(), 1e-2)); + graph_.addPrior(meas.key(), meas.measured(), meas.noiseModel()); } - // Parent-child constraints (only where camera exists in parent). + // Measurement factors in child frame (only where camera exists in parent). for (size_t childIdx = 0; childIdx < childCount; ++childIdx) { const auto &bTi = bTi_all[childIdx]; const Key simKey = Symbol('S', childIdx); + + // If initial Sim3 estimates are provided, use them. if (!bSa_all.empty()) { initial_.insert(simKey, bSa_all[childIdx]); } else { @@ -114,4 +111,3 @@ Values TrajectoryAlignerSim3::solve() const { } } // namespace gtsam - diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h index 43bcf4c903..0664cd971c 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.h +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -49,18 +49,10 @@ namespace gtsam { * - Optimized Sim3 transforms (with Symbol keys 'S' and index for each child) */ class GTSAM_EXPORT TrajectoryAlignerSim3 { - public: - using PoseMeasurements = std::vector>; - using ChildrenPoses = std::vector; - private: // Data members. ExpressionFactorGraph graph_; Values initial_; - PoseMeasurements parentPoses_; - ChildrenPoses childrenPoses_; - std::vector initialBSa_; - std::vector simKeys_; public: /** @@ -70,7 +62,10 @@ class GTSAM_EXPORT TrajectoryAlignerSim3 { * @param bSa_all Initial Sim3 estimates transforming from parent to each child. * If empty, initial estimates are computed automatically. */ - TrajectoryAlignerSim3(const PoseMeasurements &aTi, const ChildrenPoses &bTi_all, const std::vector &bSa_all); + TrajectoryAlignerSim3( + const std::vector> &aTi, + const std::vector>> &bTi_all, + const std::vector &bSa_all = {}); /// Solves the optimization problem and returns optimized poses and transforms. Values solve() const; diff --git a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp index ecc6ecc722..94b441c59b 100644 --- a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp @@ -1,3 +1,18 @@ +/* ---------------------------------------------------------------------------- + * GTSAM Copyright 2010-2020, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * See LICENSE for the license information + * -------------------------------------------------------------------------- */ + +/** + * @file testTrajectoryAlignerSim3.cpp + * @author Akshay Krishnan + * @date January 2026 + * @brief Unit tests for the TrajectoryAlignerSim3 class. + */ + #include #include @@ -12,32 +27,31 @@ #include using namespace gtsam; -using PoseMeasurements = TrajectoryAlignerSim3::PoseMeasurements; -using ChildrenPoses = TrajectoryAlignerSim3::ChildrenPoses; +using PoseMeasurements = std::vector>; +using ChildrenPoses = std::vector>>; namespace { -Pose3 makePose(double x, double y, double z, const Rot3& R = Rot3()) { - return Pose3(R, Point3(x, y, z)); -} - std::vector makeParentPoses() { return { - makePose(0.0, 0.0, 0.0), - makePose(1.0, 0.1, 0.0, Rot3::RzRyRx(0.15, -0.2, 0.1)), - makePose(2.0, 0.4, 0.1, Rot3::RzRyRx(0.1, 0.05, -0.03))}; + Pose3::Identity(), + Pose3(Rot3::RzRyRx(0.15, -0.2, 0.1), Point3(1.0, 0.1, 0.0)), + Pose3(Rot3::RzRyRx(0.1, 0.05, -0.03), Point3(2.0, 0.4, 0.1))}; } +// Makes a vector of unary measurements from a vector of poses. +// Does not add measurement noise, only creates measurement objects. PoseMeasurements makeMeasurements(const std::vector& poses, double noiseSigma = 1e-3) { PoseMeasurements m; auto noise = noiseModel::Isotropic::Sigma(6, noiseSigma); for (size_t i = 0; i < poses.size(); ++i) { - m.emplace_back(static_cast(i), poses[i], noise); + m.emplace_back(i, poses[i], noise); } return m; } +// Transforms a vector of poses by a similarity transform. std::vector transformPoses(const Similarity3& sim, const std::vector& poses) { std::vector out; @@ -46,6 +60,7 @@ std::vector transformPoses(const Similarity3& sim, return out; } +// Perturbs a pose by a small random rotation and translation. Pose3 perturbPose(const Pose3& p) { static thread_local std::mt19937 rng(42); std::normal_distribution noise(0.0, 0.01); @@ -54,6 +69,7 @@ Pose3 perturbPose(const Pose3& p) { return p.compose(Pose3(dR, dt)); } +// Perturbs a vector of poses independently. std::vector perturbPoses(const std::vector& poses) { std::vector out; out.reserve(poses.size()); @@ -61,12 +77,14 @@ std::vector perturbPoses(const std::vector& poses) { return out; } +// Perturbs a similarity transform by a small noise. Similarity3 perturbSim3(const Similarity3& sim) { Similarity3 delta(Rot3::RzRyRx(0.2, -0.25, 0.1), Point3(2, 3, -1), 2.3); return sim * delta; } +// Ground truth similarity transforms for the tests. const Similarity3 gtSim1(Rot3::RzRyRx(0.2, 0.1, -0.05), Point3(0.3, -0.1, 0.2), 1.5); const Similarity3 gtSim2(Rot3::RzRyRx(-0.15, 0.05, 0.08), @@ -74,6 +92,7 @@ const Similarity3 gtSim2(Rot3::RzRyRx(-0.15, 0.05, 0.08), const Similarity3 gtSim3(Rot3::RzRyRx(0.05, -0.08, 0.12), Point3(0.15, 0.05, -0.1), 1.1); +// Helper function to check if two similarity transforms are close. bool simClose(const Similarity3& expected, const Similarity3& actual, double tol) { return assert_equal(expected, actual, tol); @@ -148,8 +167,8 @@ TEST(TrajectoryAlignerSim3, SingleChildWithExtraNonOverlap) { PoseMeasurements aTi = makeMeasurements(parent, /*noiseSigma=*/1e-2); PoseMeasurements bTi = makeMeasurements(perturbPoses(child), 1e-1); // Add a non-overlapping camera in the child frame. - bTi.emplace_back(static_cast(10), - gtSim1.transformFrom(makePose(4.0, -1.0, 0.5)), + bTi.emplace_back(10, + gtSim1.transformFrom(Pose3(Rot3(), Point3(4.0, -1.0, 0.5))), noiseModel::Isotropic::Sigma(6, 1e-2)); ChildrenPoses bTi_all{bTi}; From 40724337d4716f70a6ba91b1d5b62ffbbc8eb5c1 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 19:14:55 -0500 Subject: [PATCH 088/266] use perturbPose3 --- gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp | 125 +++++++++--------- 1 file changed, 62 insertions(+), 63 deletions(-) diff --git a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp index 94b441c59b..a1cfefa2b2 100644 --- a/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/tests/testTrajectoryAlignerSim3.cpp @@ -22,6 +22,7 @@ #include #include #include +#include #include #include @@ -32,52 +33,44 @@ using ChildrenPoses = std::vector>>; namespace { -std::vector makeParentPoses() { - return { - Pose3::Identity(), - Pose3(Rot3::RzRyRx(0.15, -0.2, 0.1), Point3(1.0, 0.1, 0.0)), - Pose3(Rot3::RzRyRx(0.1, 0.05, -0.03), Point3(2.0, 0.4, 0.1))}; +Values makeParentValues() { + Values values; + values.insert(0, Pose3::Identity()); + values.insert(1, Pose3(Rot3::RzRyRx(0.15, -0.2, 0.1), Point3(1.0, 0.1, 0.0))); + values.insert(2, Pose3(Rot3::RzRyRx(0.1, 0.05, -0.03), Point3(2.0, 0.4, 0.1))); + return values; } -// Makes a vector of unary measurements from a vector of poses. -// Does not add measurement noise, only creates measurement objects. -PoseMeasurements makeMeasurements(const std::vector& poses, - double noiseSigma = 1e-3) { +// Makes a vector of unary measurements from a Values of poses. +PoseMeasurements makeMeasurements(const Values& poses, double noiseSigma = 1e-3) { PoseMeasurements m; auto noise = noiseModel::Isotropic::Sigma(6, noiseSigma); - for (size_t i = 0; i < poses.size(); ++i) { - m.emplace_back(i, poses[i], noise); + for (const auto& key_value : poses) { + const Key key = key_value.key; + m.emplace_back(key, poses.at(key), noise); } return m; } -// Transforms a vector of poses by a similarity transform. -std::vector transformPoses(const Similarity3& sim, - const std::vector& poses) { - std::vector out; - out.reserve(poses.size()); - for (const auto& p : poses) out.push_back(sim.transformFrom(p)); +// Transforms values of poses by a similarity transform. +Values transformValues(const Similarity3& sim, const Values& poses) { + Values out; + for (const auto& key_value : poses) { + const Key key = key_value.key; + out.insert(key, sim.transformFrom(poses.at(key))); + } return out; } -// Perturbs a pose by a small random rotation and translation. -Pose3 perturbPose(const Pose3& p) { - static thread_local std::mt19937 rng(42); - std::normal_distribution noise(0.0, 0.01); - const Rot3 dR = Rot3::RzRyRx(noise(rng), noise(rng), noise(rng)); - const Point3 dt(noise(rng), noise(rng), noise(rng)); - return p.compose(Pose3(dR, dt)); -} - -// Perturbs a vector of poses independently. -std::vector perturbPoses(const std::vector& poses) { - std::vector out; - out.reserve(poses.size()); - for (const auto& p : poses) out.push_back(perturbPose(p)); - return out; +// Perturbs a Values of poses by a small noise. +Values perturbPoses(const Values& poses) { + Values perturbed = poses; + utilities::perturbPose3(perturbed, /*sigmaT=*/0.01, /*sigmaR=*/0.01); + return perturbed; } -// Perturbs a similarity transform by a small noise. +// Perturbs a similarity transform by a large value. +// The initial similarity estimate can be very inaccurate. Similarity3 perturbSim3(const Similarity3& sim) { Similarity3 delta(Rot3::RzRyRx(0.2, -0.25, 0.1), Point3(2, 3, -1), 2.3); @@ -102,8 +95,8 @@ bool simClose(const Similarity3& expected, const Similarity3& actual, /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, PerfectSingleChild) { - const auto parent = makeParentPoses(); - const auto child = transformPoses(gtSim1, parent); + const auto parent = makeParentValues(); + const auto child = transformValues(gtSim1, parent); PoseMeasurements aTi = makeMeasurements(parent); ChildrenPoses bTi_all{makeMeasurements(child)}; @@ -115,23 +108,23 @@ TEST(TrajectoryAlignerSim3, PerfectSingleChild) { const auto recoveredSim = result.at(Symbol('S', 0)); EXPECT(simClose(gtSim1, recoveredSim, 1e-6)); - for (size_t i = 0; i < parent.size(); ++i) { - EXPECT(assert_equal(parent[i], result.at(i), 1e-6)); + for (const auto& kv : parent) { + const Key key = kv.key; + EXPECT(assert_equal(parent.at(key), result.at(key), 1e-6)); } } /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, PerfectSingleChildNoInitialSim) { - const auto parent = makeParentPoses(); + const auto parent = makeParentValues(); const Similarity3 gtSim(Rot3::RzRyRx(0.25, -0.05, 0.12), Point3(-0.3, 0.2, -0.15), 1.4); - const auto child = transformPoses(gtSim, parent); + const auto child = transformValues(gtSim, parent); PoseMeasurements aTi = makeMeasurements(parent); ChildrenPoses bTi_all{makeMeasurements(child)}; - std::vector sims; // empty initial Sim3s - TrajectoryAlignerSim3 aligner(aTi, bTi_all, sims); + TrajectoryAlignerSim3 aligner(aTi, bTi_all); Values result = aligner.solve(); const auto recoveredSim = result.at(Symbol('S', 0)); @@ -140,11 +133,12 @@ TEST(TrajectoryAlignerSim3, PerfectSingleChildNoInitialSim) { /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, NoisySingleChild) { - const auto parent = makeParentPoses(); - const auto child = transformPoses(gtSim1, parent); + const auto parent = makeParentValues(); + const auto child = transformValues(gtSim1, parent); + const auto perturbedChild = perturbPoses(child); PoseMeasurements aTi = makeMeasurements(parent, /*noiseSigma=*/1e-2); - PoseMeasurements bTi = makeMeasurements(perturbPoses(child), 1e-1); + PoseMeasurements bTi = makeMeasurements(perturbedChild, 1e-1); ChildrenPoses bTi_all{bTi}; std::vector sims{perturbSim3(gtSim1)}; @@ -152,20 +146,22 @@ TEST(TrajectoryAlignerSim3, NoisySingleChild) { Values result = aligner.solve(); const auto recoveredSim = result.at(Symbol('S', 0)); - EXPECT(simClose(gtSim1, recoveredSim, 1e-2)); + EXPECT(simClose(gtSim1, recoveredSim, 2e-2)); - for (size_t i = 0; i < parent.size(); ++i) { - EXPECT(assert_equal(parent[i], result.at(i), 1e-3)); + for (const auto& kv : parent) { + const Key key = kv.key; + EXPECT(assert_equal(parent.at(key), result.at(key), 1e-2)); } } /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, SingleChildWithExtraNonOverlap) { - const auto parent = makeParentPoses(); - auto child = transformPoses(gtSim1, parent); + const auto parent = makeParentValues(); + auto child = transformValues(gtSim1, parent); + const auto perturbedChild = perturbPoses(child); PoseMeasurements aTi = makeMeasurements(parent, /*noiseSigma=*/1e-2); - PoseMeasurements bTi = makeMeasurements(perturbPoses(child), 1e-1); + PoseMeasurements bTi = makeMeasurements(perturbedChild, 1e-1); // Add a non-overlapping camera in the child frame. bTi.emplace_back(10, gtSim1.transformFrom(Pose3(Rot3(), Point3(4.0, -1.0, 0.5))), @@ -180,20 +176,21 @@ TEST(TrajectoryAlignerSim3, SingleChildWithExtraNonOverlap) { const auto recoveredSim = result.at(Symbol('S', 0)); EXPECT(simClose(gtSim1, recoveredSim, 2e-2)); - for (size_t i = 0; i < parent.size(); ++i) { - EXPECT(assert_equal(parent[i], result.at(i), 1e-3)); + for (const auto& kv : parent) { + const Key key = kv.key; + EXPECT(assert_equal(parent.at(key), result.at(key), 1e-2)); } } /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, TwoChildrenNoisy) { - const auto parent = makeParentPoses(); + const auto parent = makeParentValues(); PoseMeasurements aTi = makeMeasurements(parent, 1e-2); PoseMeasurements b1 = - makeMeasurements(perturbPoses(transformPoses(gtSim1, parent)), 5e-2); + makeMeasurements(perturbPoses(transformValues(gtSim1, parent)), 5e-2); PoseMeasurements b2 = - makeMeasurements(perturbPoses(transformPoses(gtSim2, parent)), 5e-2); + makeMeasurements(perturbPoses(transformValues(gtSim2, parent)), 5e-2); ChildrenPoses bTi_all{b1, b2}; std::vector sims{perturbSim3(gtSim1), perturbSim3(gtSim2)}; @@ -204,22 +201,23 @@ TEST(TrajectoryAlignerSim3, TwoChildrenNoisy) { EXPECT(simClose(gtSim1, result.at(Symbol('S', 0)), 5e-2)); EXPECT(simClose(gtSim2, result.at(Symbol('S', 1)), 5e-2)); - for (size_t i = 0; i < parent.size(); ++i) { - EXPECT(assert_equal(parent[i], result.at(i), 1e-2)); + for (const auto& kv : parent) { + const Key key = kv.key; + EXPECT(assert_equal(parent.at(key), result.at(key), 1e-2)); } } /* ************************************************************************* */ TEST(TrajectoryAlignerSim3, ThreeChildrenNoisy) { - const auto parent = makeParentPoses(); + const auto parent = makeParentValues(); PoseMeasurements aTi = makeMeasurements(parent, 1e-2); PoseMeasurements b1 = - makeMeasurements(perturbPoses(transformPoses(gtSim1, parent)), 2e-2); + makeMeasurements(perturbPoses(transformValues(gtSim1, parent)), 2e-2); PoseMeasurements b2 = - makeMeasurements(perturbPoses(transformPoses(gtSim2, parent)), 2e-2); + makeMeasurements(perturbPoses(transformValues(gtSim2, parent)), 2e-2); PoseMeasurements b3 = - makeMeasurements(perturbPoses(transformPoses(gtSim3, parent)), 2e-2); + makeMeasurements(perturbPoses(transformValues(gtSim3, parent)), 2e-2); ChildrenPoses bTi_all{b1, b2, b3}; std::vector sims{perturbSim3(gtSim1), perturbSim3(gtSim2), @@ -232,8 +230,9 @@ TEST(TrajectoryAlignerSim3, ThreeChildrenNoisy) { EXPECT(simClose(gtSim2, result.at(Symbol('S', 1)), 1e-1)); EXPECT(simClose(gtSim3, result.at(Symbol('S', 2)), 1e-1)); - for (size_t i = 0; i < parent.size(); ++i) { - EXPECT(assert_equal(parent[i], result.at(i), 1e-2)); + for (const auto& kv : parent) { + const Key key = kv.key; + EXPECT(assert_equal(parent.at(key), result.at(key), 1e-2)); } } From fcd343e9af5d2893d69c5b08ba9ed6cbbfc6e5b9 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 19:26:29 -0500 Subject: [PATCH 089/266] fix wrapper --- gtsam/sfm/sfm.i | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index 777fcb005a..3e616d2ea4 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -114,6 +114,20 @@ virtual class ShonanFactor3 : gtsam::NoiseModelFactor { gtsam::Vector evaluateError(const gtsam::SOn& Q1, const gtsam::SOn& Q2); }; +#include +template +class UnaryMeasurement { + UnaryMeasurement(gtsam::Key key, const T& measured, + const gtsam::noiseModel::Base* model); + gtsam::Key key() const; + T measured() const; + gtsam::noiseModel::Base* noiseModel() const; +}; + +typedef gtsam::UnaryMeasurement UnaryMeasurementPose3; +typedef gtsam::UnaryMeasurement UnaryMeasurementRot3; +typedef gtsam::UnaryMeasurement UnaryMeasurementPoint3; + #include template class BinaryMeasurement { @@ -129,16 +143,6 @@ typedef gtsam::BinaryMeasurement BinaryMeasurementUnit3; typedef gtsam::BinaryMeasurement BinaryMeasurementRot3; typedef gtsam::BinaryMeasurement BinaryMeasurementPoint3; -#include -template -class UnaryMeasurement { - UnaryMeasurement(gtsam::Key key, const T& measured, - const gtsam::noiseModel::Base* model); - gtsam::Key key() const; - T measured() const; - gtsam::noiseModel::Base* noiseModel() const; -}; - // Used in Matlab wrapper class BinaryMeasurementsUnit3 { BinaryMeasurementsUnit3(); @@ -163,19 +167,19 @@ class BinaryMeasurementsRot3 { void push_back(const gtsam::BinaryMeasurement& measurement); }; -#include -#include - #include class TrajectoryAlignerSim3 { TrajectoryAlignerSim3( const std::vector>& aTi, - const std::vector>>>& bTi_all, - const std::vector& bSa_all); + const std::vector>>& bTi_all, + const std::vector& bSa_all = {}); gtsam::Values solve() const; }; +#include +#include + template class ShonanAveragingParameters { ShonanAveragingParameters(const gtsam::LevenbergMarquardtParams& lm); From cc19dace400d69a07c301a91b0b5327c73a68faf Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 19:52:04 -0500 Subject: [PATCH 090/266] python tests --- .../gtsam/tests/test_TrajectoryAlignerSim3.py | 87 +++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 python/gtsam/tests/test_TrajectoryAlignerSim3.py diff --git a/python/gtsam/tests/test_TrajectoryAlignerSim3.py b/python/gtsam/tests/test_TrajectoryAlignerSim3.py new file mode 100644 index 0000000000..df88c68886 --- /dev/null +++ b/python/gtsam/tests/test_TrajectoryAlignerSim3.py @@ -0,0 +1,87 @@ +""" +GTSAM Copyright 2010-2019, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved + +See LICENSE for the license information + +Unit tests for TrajectoryAlignerSim3. +Author: Akshay Krishnan +""" +import unittest + +import gtsam +from gtsam.utils.test_case import GtsamTestCase + + +def make_parent_poses(): + return [ + gtsam.Pose3(), + gtsam.Pose3( + gtsam.Rot3.RzRyRx(0.15, -0.2, 0.1), gtsam.Point3(1.0, 0.1, 0.0) + ), + gtsam.Pose3( + gtsam.Rot3.RzRyRx(0.1, 0.05, -0.03), gtsam.Point3(2.0, 0.4, 0.1) + ), + ] + + +def make_measurements(poses, sigma=1e-3): + noise = gtsam.noiseModel.Isotropic.Sigma(6, sigma) + return [ + gtsam.UnaryMeasurementPose3(i, pose, noise) + for i, pose in enumerate(poses) + ] + + +def transform_poses(sim, poses): + return [sim.transformFrom(p) for p in poses] + + +class TestTrajectoryAlignerSim3(GtsamTestCase): + """Tests for TrajectoryAlignerSim3.""" + + def test_perfect_alignment_without_initial_sim(self): + parent = make_parent_poses() + gt_bSa = gtsam.Similarity3( + gtsam.Rot3.RzRyRx(0.2, -0.1, 0.05), + gtsam.Point3(0.3, -0.2, 0.1), + 1.4, + ) + child = transform_poses(gt_bSa, parent) + + aTi = make_measurements(parent) + bTi_all = [make_measurements(child)] + + aligner = gtsam.TrajectoryAlignerSim3(aTi, bTi_all) + result = aligner.solve() + + recovered = result.atSimilarity3(gtsam.Symbol("S", 0).key()) + self.assertTrue(gt_bSa.equals(recovered, 1e-6)) + + def test_noisy_alignment_with_initial_guess(self): + parent = make_parent_poses() + gt_bSa = gtsam.Similarity3( + gtsam.Rot3.RzRyRx(-0.15, 0.05, 0.08), + gtsam.Point3(-0.2, 0.15, 0.25), + 1.3, + ) + child = transform_poses(gt_bSa, parent) + + aTi = make_measurements(parent, sigma=1e-2) + bTi_all = [make_measurements(child, sigma=1e-2)] + + init_guess = gtsam.Similarity3( + gtsam.Rot3.RzRyRx(0.05, -0.04, 0.02), + gtsam.Point3(0.5, -0.3, 0.1), + 0.8, + ) + aligner = gtsam.TrajectoryAlignerSim3(aTi, bTi_all, [init_guess]) + result = aligner.solve() + + recovered = result.atSimilarity3(gtsam.Symbol("S", 0).key()) + self.assertTrue(gt_bSa.equals(recovered, 1e-2)) + + +if __name__ == "__main__": + unittest.main() From f1b1bccba5d8f03c608a752bce1f9ca0c83e58af Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 19:52:22 -0500 Subject: [PATCH 091/266] fix default arg --- gtsam/sfm/sfm.i | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index 3e616d2ea4..27fa708809 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -169,10 +169,14 @@ class BinaryMeasurementsRot3 { #include class TrajectoryAlignerSim3 { + TrajectoryAlignerSim3( + const std::vector>& aTi, + const std::vector>>& bTi_all); + TrajectoryAlignerSim3( const std::vector>& aTi, const std::vector>>& bTi_all, - const std::vector& bSa_all = {}); + const std::vector& bSa_all); gtsam::Values solve() const; }; From a65f821ed69ad8aad63a42cc2397ae23f5935839 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 20:13:01 -0500 Subject: [PATCH 092/266] add marginals with a test --- gtsam/sfm/TrajectoryAlignerSim3.cpp | 6 ++++++ gtsam/sfm/TrajectoryAlignerSim3.h | 4 ++++ gtsam/sfm/sfm.i | 3 +++ python/gtsam/tests/test_TrajectoryAlignerSim3.py | 3 +++ 4 files changed, 16 insertions(+) diff --git a/gtsam/sfm/TrajectoryAlignerSim3.cpp b/gtsam/sfm/TrajectoryAlignerSim3.cpp index 3381545c50..cc4af54e4f 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.cpp +++ b/gtsam/sfm/TrajectoryAlignerSim3.cpp @@ -110,4 +110,10 @@ Values TrajectoryAlignerSim3::solve() const { return optimizer.optimize(); } +Marginals TrajectoryAlignerSim3::marginalize( + const Values& solution, const Ordering::OrderingType ordering_type) const { + Ordering ordering = Ordering::Create(ordering_type, graph_); + return Marginals(graph_, solution, ordering); +} + } // namespace gtsam diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h index 0664cd971c..ac5f0fa498 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.h +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -22,6 +22,7 @@ #include #include #include +#include #include #include @@ -70,5 +71,8 @@ class GTSAM_EXPORT TrajectoryAlignerSim3 { /// Solves the optimization problem and returns optimized poses and transforms. Values solve() const; + Marginals marginalize( + const Values& solution, + const Ordering::OrderingType ordering_type = Ordering::COLAMD) const; }; } // namespace gtsam diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index 27fa708809..a53e53f893 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -179,6 +179,9 @@ class TrajectoryAlignerSim3 { const std::vector& bSa_all); gtsam::Values solve() const; + gtsam::Marginals marginalize( + const gtsam::Values& solution, + const gtsam::Ordering::OrderingType ordering_type = gtsam::Ordering::COLAMD) const; }; #include diff --git a/python/gtsam/tests/test_TrajectoryAlignerSim3.py b/python/gtsam/tests/test_TrajectoryAlignerSim3.py index df88c68886..49c221bd8b 100644 --- a/python/gtsam/tests/test_TrajectoryAlignerSim3.py +++ b/python/gtsam/tests/test_TrajectoryAlignerSim3.py @@ -82,6 +82,9 @@ def test_noisy_alignment_with_initial_guess(self): recovered = result.atSimilarity3(gtsam.Symbol("S", 0).key()) self.assertTrue(gt_bSa.equals(recovered, 1e-2)) + marginals = aligner.marginalize(result) + self.assertTrue(marginals.marginalInformation(gtsam.Symbol("S", 0).key()).size > 0) + if __name__ == "__main__": unittest.main() From a326382f2b61293fab0f06102746c2dcb1b2e6aa Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Mon, 12 Jan 2026 20:16:37 -0500 Subject: [PATCH 093/266] add docs --- gtsam/sfm/TrajectoryAlignerSim3.h | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/gtsam/sfm/TrajectoryAlignerSim3.h b/gtsam/sfm/TrajectoryAlignerSim3.h index ac5f0fa498..0b1d5b3fd6 100644 --- a/gtsam/sfm/TrajectoryAlignerSim3.h +++ b/gtsam/sfm/TrajectoryAlignerSim3.h @@ -68,9 +68,22 @@ class GTSAM_EXPORT TrajectoryAlignerSim3 { const std::vector>> &bTi_all, const std::vector &bSa_all = {}); - /// Solves the optimization problem and returns optimized poses and transforms. + /** + * @brief Optimizes the graph and returns optimized poses and Sim3 transforms. + * @return The optimized poses and transforms. Contains: + * - Parent frame poses (with keys the same as those in input aTi measurements) + * - Sim3 transforms (with keys Symbol('S', i), where i is the child index) + */ Values solve() const; + /** + * @brief Computes the marginals of the solution. + * + * @param solution The solution to marginalize. + * Obtained from solve() or should contain the same keys as variables in graph_. + * @param ordering_type The ordering type to use for the marginalization. + * @return The Marginals object. + */ Marginals marginalize( const Values& solution, const Ordering::OrderingType ordering_type = Ordering::COLAMD) const; From ed5788d468fa5763d420121e85fb48be87f32e78 Mon Sep 17 00:00:00 2001 From: "dellaert@gmail.com" Date: Thu, 15 Jan 2026 19:22:54 -0500 Subject: [PATCH 094/266] Restore vcpkg builds in PRs --- .github/workflows/vcpkg.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 5bf63eb5f1..66e5d3ba30 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -1,5 +1,6 @@ name: vcpkg on: + pull_request: push: # Runs on pushes targeting the develop branch branches: [develop] From f61a4d85c88015cb654f8aca1082269c156ca688 Mon Sep 17 00:00:00 2001 From: "dellaert@gmail.com" Date: Thu, 15 Jan 2026 19:23:30 -0500 Subject: [PATCH 095/266] Restore main readme --- README.md | 4 ++-- .github/README.md => doc/workflows.md | 0 2 files changed, 2 insertions(+), 2 deletions(-) rename .github/README.md => doc/workflows.md (100%) diff --git a/README.md b/README.md index 7b410c176c..95e76d8e94 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ **Important Note** -**As of January 2023, the `develop` branch is officially in "Pre 4.3" mode. We envision several API-breaking changes as we switch to C++17 and away from boost.** +**The `develop` branch is officially in "Pre 4.3" mode. We envision several API-breaking changes as we switch to C++17 and away from boost.** In addition, features deprecated in 4.2 will be removed. Please use the stable [4.2 release](https://github.com/borglab/gtsam/releases/tag/4.2) if you need those features. However, most are easily converted and can be tracked down (in 4.2) by disabling the cmake flag `GTSAM_ALLOW_DEPRECATED_SINCE_V42`. @@ -151,7 +151,7 @@ Read about important [GTSAM-Concepts](doc/GTSAM-Concepts.md) here. A primer on G which support (superfast) automatic differentiation, can be found on the [GTSAM wiki on BitBucket](https://bitbucket.org/gtborg/gtsam/wiki/Home). -See the [`INSTALL`](INSTALL.md) file for more detailed installation instructions. +See the [`INSTALL`](INSTALL.md) file for more detailed installation instructions. Our CI/CD process is detailed in [workflows.md](doc/workflows.md). GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files. diff --git a/.github/README.md b/doc/workflows.md similarity index 100% rename from .github/README.md rename to doc/workflows.md From 311feac06289374c2133beb99f3988e16c6fa7f2 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 15 Jan 2026 08:47:02 -0500 Subject: [PATCH 096/266] Fixed-size constructors --- gtsam/linear/JacobianFactor-inl.h | 66 ++++++++++++++++++++++++++++++- gtsam/linear/JacobianFactor.cpp | 53 +++++++++++++++++++++---- gtsam/linear/JacobianFactor.h | 31 ++++++++++++++- 3 files changed, 140 insertions(+), 10 deletions(-) diff --git a/gtsam/linear/JacobianFactor-inl.h b/gtsam/linear/JacobianFactor-inl.h index e616091e84..8ddb876f2d 100644 --- a/gtsam/linear/JacobianFactor-inl.h +++ b/gtsam/linear/JacobianFactor-inl.h @@ -20,6 +20,8 @@ #include +#include + #if defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 13 #pragma GCC diagnostic warning "-Wstringop-overread" #endif @@ -33,6 +35,69 @@ namespace gtsam { fillTerms(terms, b, model); } + /* ************************************************************************* */ + template + JacobianFactor::JacobianFactor(Key i1, const Eigen::Matrix& A1, + const Eigen::Matrix& b, + const SharedDiagonal& model) + : Base(std::array{{i1}}) { + const DenseIndex rows = static_cast(b.rows()); + if (model && (DenseIndex)model->dim() != rows) + throw InvalidNoiseModel(rows, model->dim()); + + const std::array dims = { + static_cast(A1.cols())}; + Ab_ = VerticalBlockMatrix(dims, rows, true); + Ab_(0) = A1; + getb() = b; + model_ = model; + } + + /* ************************************************************************* */ + template + JacobianFactor::JacobianFactor(Key i1, const Eigen::Matrix& A1, + Key i2, const Eigen::Matrix& A2, + const Eigen::Matrix& b, + const SharedDiagonal& model) + : Base(std::array{{i1, i2}}) { + const DenseIndex rows = static_cast(b.rows()); + if (model && (DenseIndex)model->dim() != rows) + throw InvalidNoiseModel(rows, model->dim()); + + const std::array dims = { + static_cast(A1.cols()), + static_cast(A2.cols())}; + Ab_ = VerticalBlockMatrix(dims, rows, true); + Ab_(0) = A1; + Ab_(1) = A2; + getb() = b; + model_ = model; + } + + /* ************************************************************************* */ + template + JacobianFactor::JacobianFactor(Key i1, const Eigen::Matrix& A1, + Key i2, const Eigen::Matrix& A2, + Key i3, const Eigen::Matrix& A3, + const Eigen::Matrix& b, + const SharedDiagonal& model) + : Base(std::array{{i1, i2, i3}}) { + const DenseIndex rows = static_cast(b.rows()); + if (model && (DenseIndex)model->dim() != rows) + throw InvalidNoiseModel(rows, model->dim()); + + const std::array dims = { + static_cast(A1.cols()), + static_cast(A2.cols()), + static_cast(A3.cols())}; + Ab_ = VerticalBlockMatrix(dims, rows, true); + Ab_(0) = A1; + Ab_(1) = A2; + Ab_(2) = A3; + getb() = b; + model_ = model; + } + /* ************************************************************************* */ template JacobianFactor::JacobianFactor(const KEYS& keys, @@ -101,4 +166,3 @@ namespace gtsam { } } // gtsam - diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index ae04d1f59a..d783788611 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -30,6 +30,7 @@ #include #include +#include #include #include #include @@ -41,7 +42,6 @@ namespace gtsam { // Typedefs used in constructors below. using Dims = std::vector; -using Pairs = std::vector>; /* ************************************************************************* */ JacobianFactor::JacobianFactor() : @@ -69,21 +69,58 @@ JacobianFactor::JacobianFactor(const Vector& b_in) : /* ************************************************************************* */ JacobianFactor::JacobianFactor(Key i1, const Matrix& A1, const Vector& b, - const SharedDiagonal& model) { - fillTerms(Pairs{{i1, A1}}, b, model); + const SharedDiagonal& model) + : Base(std::array{{i1}}) { + if (model && (DenseIndex)model->dim() != b.size()) + throw InvalidNoiseModel(b.size(), model->dim()); + if (A1.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A1.rows()); + + const std::array dims = {static_cast(A1.cols())}; + Ab_ = VerticalBlockMatrix(dims, b.size(), true); + Ab_(0) = A1; + getb() = b; + model_ = model; } /* ************************************************************************* */ JacobianFactor::JacobianFactor(const Key i1, const Matrix& A1, Key i2, - const Matrix& A2, const Vector& b, const SharedDiagonal& model) { - fillTerms(Pairs{{i1, A1}, {i2, A2}}, b, model); + const Matrix& A2, const Vector& b, + const SharedDiagonal& model) + : Base(std::array{{i1, i2}}) { + if (model && (DenseIndex)model->dim() != b.size()) + throw InvalidNoiseModel(b.size(), model->dim()); + if (A1.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A1.rows()); + if (A2.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A2.rows()); + + const std::array dims = {static_cast(A1.cols()), + static_cast(A2.cols())}; + Ab_ = VerticalBlockMatrix(dims, b.size(), true); + Ab_(0) = A1; + Ab_(1) = A2; + getb() = b; + model_ = model; } /* ************************************************************************* */ JacobianFactor::JacobianFactor(const Key i1, const Matrix& A1, Key i2, - const Matrix& A2, Key i3, const Matrix& A3, const Vector& b, - const SharedDiagonal& model) { - fillTerms(Pairs{{i1, A1}, {i2, A2}, {i3, A3}}, b, model); + const Matrix& A2, Key i3, const Matrix& A3, + const Vector& b, const SharedDiagonal& model) + : Base(std::array{{i1, i2, i3}}) { + if (model && (DenseIndex)model->dim() != b.size()) + throw InvalidNoiseModel(b.size(), model->dim()); + if (A1.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A1.rows()); + if (A2.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A2.rows()); + if (A3.rows() != b.size()) throw InvalidMatrixBlock(b.size(), A3.rows()); + + const std::array dims = {static_cast(A1.cols()), + static_cast(A2.cols()), + static_cast(A3.cols())}; + Ab_ = VerticalBlockMatrix(dims, b.size(), true); + Ab_(0) = A1; + Ab_(1) = A2; + Ab_(2) = A3; + getb() = b; + model_ = model; } /* ************************************************************************* */ diff --git a/gtsam/linear/JacobianFactor.h b/gtsam/linear/JacobianFactor.h index 759f2fd066..d2db5ce01d 100644 --- a/gtsam/linear/JacobianFactor.h +++ b/gtsam/linear/JacobianFactor.h @@ -129,16 +129,46 @@ namespace gtsam { JacobianFactor(Key i1, const Matrix& A1, const Vector& b, const SharedDiagonal& model = SharedDiagonal()); + /** Construct unary factor from fixed-size Eigen matrices. */ + template > + JacobianFactor(Key i1, const Eigen::Matrix& A1, + const Eigen::Matrix& b, + const SharedDiagonal& model = SharedDiagonal()); + /** Construct binary factor */ JacobianFactor(Key i1, const Matrix& A1, Key i2, const Matrix& A2, const Vector& b, const SharedDiagonal& model = SharedDiagonal()); + /** Construct binary factor from fixed-size Eigen matrices. */ + template > + JacobianFactor(Key i1, const Eigen::Matrix& A1, + Key i2, const Eigen::Matrix& A2, + const Eigen::Matrix& b, + const SharedDiagonal& model = SharedDiagonal()); + /** Construct ternary factor */ JacobianFactor(Key i1, const Matrix& A1, Key i2, const Matrix& A2, Key i3, const Matrix& A3, const Vector& b, const SharedDiagonal& model = SharedDiagonal()); + /** Construct ternary factor from fixed-size Eigen matrices. */ + template > + JacobianFactor(Key i1, const Eigen::Matrix& A1, + Key i2, const Eigen::Matrix& A2, + Key i3, const Eigen::Matrix& A3, + const Eigen::Matrix& b, + const SharedDiagonal& model = SharedDiagonal()); + /** Construct an n-ary factor * @tparam TERMS A container whose value type is std::pair, specifying the * collection of keys and matrices making up the factor. */ @@ -486,4 +516,3 @@ BOOST_CLASS_VERSION(gtsam::JacobianFactor, 1) #endif #include - From 989a4f100ebfb2141c1fdfe189fb979cede7dcd7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 15 Jan 2026 12:26:33 -0500 Subject: [PATCH 097/266] Fast Value accessor --- gtsam/nonlinear/Values-inl.h | 28 +++++++++++++++++++++++++--- gtsam/nonlinear/Values.h | 7 +++++++ gtsam/slam/GeneralSFMFactor.h | 4 ++-- 3 files changed, 34 insertions(+), 5 deletions(-) diff --git a/gtsam/nonlinear/Values-inl.h b/gtsam/nonlinear/Values-inl.h index b6eca8b994..95af20da3e 100644 --- a/gtsam/nonlinear/Values-inl.h +++ b/gtsam/nonlinear/Values-inl.h @@ -24,6 +24,7 @@ #pragma once +#include #include #include @@ -255,8 +256,8 @@ namespace gtsam { } // internal /* ************************************************************************* */ - template - const ValueType Values::at(Key j) const { + template + const ValueType Values::at(Key j) const { // Find the item KeyValueMap::const_iterator item = values_.find(j); @@ -266,7 +267,28 @@ namespace gtsam { // Check the type and throw exception if incorrect // h() split in two lines to avoid internal compiler error (MSVC2017) auto h = internal::handle(); - return h(j, item->second.get()); + return h(j, item->second.get()); + } + + /* ************************************************************************* */ + template + const ValueType& Values::atRef(Key j) const { + // Find the item + KeyValueMap::const_iterator item = values_.find(j); + + // Throw exception if it does not exist + if (item == values_.end()) throw ValuesKeyDoesNotExist("atRef", j); + + const Value* value = item->second.get(); +#ifndef NDEBUG + auto ptr = dynamic_cast*>(value); + assert(ptr && "Values::atRef: incorrect ValueType"); + if (!ptr) throw ValuesIncorrectType(j, typeid(*value), typeid(ValueType)); + return ptr->value(); +#else + auto ptr = static_cast*>(value); + return ptr->value(); +#endif } /* ************************************************************************* */ diff --git a/gtsam/nonlinear/Values.h b/gtsam/nonlinear/Values.h index 7e950479ad..c347158934 100644 --- a/gtsam/nonlinear/Values.h +++ b/gtsam/nonlinear/Values.h @@ -152,6 +152,13 @@ namespace gtsam { template const ValueType at(Key j) const; + /** Retrieve a variable by key \c j without copying. + * This is a fast path that assumes the stored type matches ValueType; + * in debug builds it asserts on mismatch. + */ + template + const ValueType& atRef(Key j) const; + /// version for double double atDouble(size_t key) const { return at(key);} diff --git a/gtsam/slam/GeneralSFMFactor.h b/gtsam/slam/GeneralSFMFactor.h index 3ab5cd1c7b..71ddac8887 100644 --- a/gtsam/slam/GeneralSFMFactor.h +++ b/gtsam/slam/GeneralSFMFactor.h @@ -148,8 +148,8 @@ class GeneralSFMFactor: public NoiseModelFactorN { JacobianL H2; Vector2 b; try { - const CAMERA& camera = values.at(key1); - const LANDMARK& point = values.at(key2); + const CAMERA& camera = values.atRef(key1); + const LANDMARK& point = values.atRef(key2); b = measured() - camera.project2(point, H1, H2); } catch (CheiralityException& e [[maybe_unused]]) { H1.setZero(); From be20bdbd97032365e5e529577f08ac831e6ceb1a Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Fri, 16 Jan 2026 09:52:55 +0200 Subject: [PATCH 098/266] perf: parallelize NonlinearFactorGraph::error computation. - Use tbb::parallel_deterministic_reduce for sendable factors when graph size exceeds 512 factors (2x grain size threshold). - Process non-sendable factors sequentially on main thread to ensure thread safety (e.g., Python factors requiring GIL). --- gtsam/nonlinear/NonlinearFactorGraph.cpp | 31 +++++ tests/testNonlinearFactorGraph.cpp | 147 +++++++++++++++++++++++ 2 files changed, 178 insertions(+) diff --git a/gtsam/nonlinear/NonlinearFactorGraph.cpp b/gtsam/nonlinear/NonlinearFactorGraph.cpp index 4d4a51f640..d8437d71ab 100644 --- a/gtsam/nonlinear/NonlinearFactorGraph.cpp +++ b/gtsam/nonlinear/NonlinearFactorGraph.cpp @@ -30,6 +30,8 @@ #ifdef GTSAM_USE_TBB # include +# include +# include #endif #include @@ -168,6 +170,35 @@ void NonlinearFactorGraph::saveGraph(const std::string& filename, /* ************************************************************************* */ double NonlinearFactorGraph::error(const Values& values) const { gttic(NonlinearFactorGraph_error); + +#ifdef GTSAM_USE_TBB + constexpr size_t kGrainSize = 256; // Fixed grain size for deterministic splitting + if (factors_.size() > 2 * kGrainSize) { + TbbOpenMPMixedScope threadLimiter; + double total_error = tbb::parallel_deterministic_reduce( + tbb::blocked_range(0, size(), kGrainSize), + 0.0, // identity value + [this, &values](const tbb::blocked_range& r, double local_error) -> double { + for (size_t i = r.begin(); i != r.end(); ++i) { + const auto& factor = factors_[i]; + if (factor && factor->sendable()) + local_error += factor->error(values); + } + return local_error; + }, + [](double x, double y) -> double { + return x + y; + }); + + // Process non-sendable factors sequentially (e.g., Python factors requiring GIL) + for(const sharedFactor& factor: factors_) { + if (factor && !factor->sendable()) + total_error += factor->error(values); + } + return total_error; + } +#endif + double total_error = 0.; // iterate over all the factors_ to accumulate the log probabilities for(const sharedFactor& factor: factors_) { diff --git a/tests/testNonlinearFactorGraph.cpp b/tests/testNonlinearFactorGraph.cpp index 305cd963ac..454fc422c2 100644 --- a/tests/testNonlinearFactorGraph.cpp +++ b/tests/testNonlinearFactorGraph.cpp @@ -34,6 +34,9 @@ /*STL/C++*/ #include +#include +#include +#include using namespace std; using namespace gtsam; @@ -42,6 +45,50 @@ using namespace example; using symbol_shorthand::X; using symbol_shorthand::L; +/* ************************************************************************* */ +// Test factor class that is not sendable (for testing parallel error +// computation) +class NonSendableBetweenFactor : public BetweenFactor { + public: + using Base = BetweenFactor; + + // Static members to track thread IDs that process this factor + static std::set processingThreadIds; + static std::mutex threadIdMutex; + + NonSendableBetweenFactor(Key key1, Key key2, const Pose2& measured, + const SharedNoiseModel& model = nullptr) + : Base(key1, key2, measured, model) {} + + bool sendable() const override { return false; } + + // Override error() to track which thread processes this factor + double error(const Values& c) const override { + std::thread::id currentThreadId = std::this_thread::get_id(); + { + std::lock_guard lock(threadIdMutex); + processingThreadIds.insert(currentThreadId); + } + return Base::error(c); + } + + // Static method to reset tracking (for test cleanup) + static void resetTracking() { + std::lock_guard lock(threadIdMutex); + processingThreadIds.clear(); + } + + // Static method to get all thread IDs that processed this factor + static std::set getProcessingThreadIds() { + std::lock_guard lock(threadIdMutex); + return processingThreadIds; + } +}; + +// Static member definitions +std::set NonSendableBetweenFactor::processingThreadIds; +std::mutex NonSendableBetweenFactor::threadIdMutex; + /* ************************************************************************* */ TEST( NonlinearFactorGraph, equals ) { @@ -63,6 +110,106 @@ TEST( NonlinearFactorGraph, error ) DOUBLES_EQUAL( 5.625, actual2, 1e-9 ); } +/* ************************************************************************* */ +TEST( NonlinearFactorGraph, errorParallel ) +{ + constexpr size_t kNumFactors = 512 + 2; // Create graph with >512 factors to trigger TBB parallel path + NonlinearFactorGraph fg; + Values values; + auto noise = noiseModel::Isotropic::Sigma(3, 0.1); + size_t numSendableFactors = 0; + size_t numNonSendableFactors = 0; + + for (size_t i = 0; i < kNumFactors; ++i) { + values.insert(X(i), Pose2(i * 0.1, 0.0, 0.0)); + if (i > 0) { + // Mix sendable and non-sendable factors: use non-sendable for every 10th + // factor + if (i % 10 == 0) { + fg.emplace_shared( + X(i - 1), X(i), Pose2(0.1, 0.0, 0.0), noise); + numNonSendableFactors++; + } else { + fg.emplace_shared>(X(i - 1), X(i), + Pose2(0.1, 0.0, 0.0), noise); + numSendableFactors++; + } + } + } + + // Verify we have both types of factors + EXPECT(numSendableFactors > 0); + EXPECT(numNonSendableFactors > 0); + + // Reset tracking and get main thread ID before testing + NonSendableBetweenFactor::resetTracking(); + std::thread::id mainThreadId = std::this_thread::get_id(); + + // Test with correct values + double actual1 = fg.error(values); + DOUBLES_EQUAL(0.0, actual1, 1e-9); + + // Verify that NonSendableBetweenFactor instances were only processed on main thread + std::set threadIds = NonSendableBetweenFactor::getProcessingThreadIds(); + EXPECT_LONGS_EQUAL(1, threadIds.size()); + for (const auto& threadId : threadIds) { + EXPECT(threadId == mainThreadId); // All should be main thread + } + + // Test with noisy values + Values noisyValues; + for (size_t i = 0; i < kNumFactors; ++i) { + noisyValues.insert(X(i), Pose2(i * 0.1 + 0.1, 0.1, 0.01)); + } + + // Reset tracking before noisy values test + NonSendableBetweenFactor::resetTracking(); + + double actual2 = fg.error(noisyValues); + + // Verify that NonSendableBetweenFactor instances were only processed on main thread + threadIds = NonSendableBetweenFactor::getProcessingThreadIds(); + EXPECT(threadIds.size() > 0); // Should have processed at least some factors + for (const auto& threadId : threadIds) { + EXPECT(threadId == mainThreadId); // All should be main thread + } + + // Verify determinism - parallel should give same result each time + double actual3 = fg.error(noisyValues); + DOUBLES_EQUAL(actual2, actual3, 0.0); + + // Verify that non-sendable factors are computed by comparing with + // a graph that has only sendable factors (should have same error since + // both factor types compute the same error, just processed differently) + NonlinearFactorGraph fgSendableOnly; + Values valuesSendableOnly; + for (size_t i = 0; i < kNumFactors; ++i) { + valuesSendableOnly.insert(X(i), Pose2(i * 0.1, 0.0, 0.0)); + if (i > 0) { + fgSendableOnly.emplace_shared>( + X(i - 1), X(i), Pose2(0.1, 0.0, 0.0), noise); + } + } + + // With correct values, both should have zero error + double errorSendableOnly = fgSendableOnly.error(valuesSendableOnly); + DOUBLES_EQUAL(0.0, errorSendableOnly, 1e-9); + + // With noisy values, verify that mixed graph (with non-sendable factors) + // produces the same error as sendable-only graph, confirming that + // non-sendable factors are being computed correctly + Values noisyValuesSendableOnly; + for (size_t i = 0; i < kNumFactors; ++i) { + noisyValuesSendableOnly.insert(X(i), Pose2(i * 0.1 + 0.1, 0.1, 0.01)); + } + double errorSendableOnlyNoisy = fgSendableOnly.error(noisyValuesSendableOnly); + + // Both graphs should produce the same error since they have equivalent + // factors (non-sendable factors compute the same error, just processed + // sequentially) + DOUBLES_EQUAL(errorSendableOnlyNoisy, actual2, 1e-9); +} + /* ************************************************************************* */ TEST( NonlinearFactorGraph, keys ) { From aa5e42b7910f1a2a9b5f2612c6c17f21892ad523 Mon Sep 17 00:00:00 2001 From: Yiran Luo Date: Sat, 17 Jan 2026 12:38:24 -0500 Subject: [PATCH 099/266] switch Hat/Vee to orthonormal sl(4) basis and update adjoint/tests --- gtsam/geometry/SL4.cpp | 166 ++++++++++++++++++++----------- gtsam/geometry/SL4.h | 14 +++ gtsam/geometry/tests/testSL4.cpp | 92 ++++++++++++++++- 3 files changed, 213 insertions(+), 59 deletions(-) diff --git a/gtsam/geometry/SL4.cpp b/gtsam/geometry/SL4.cpp index 165707395e..8945ab07e4 100644 --- a/gtsam/geometry/SL4.cpp +++ b/gtsam/geometry/SL4.cpp @@ -15,59 +15,68 @@ using namespace std; namespace { -Eigen::Matrix I_15x15 = - Eigen::Matrix::Identity(); +constexpr double kInvSqrt2 = 0.7071067811865475244; +constexpr double kInvSqrt6 = 0.4082482904638630164; +constexpr double kInvSqrt12 = 0.2886751345948128823; Eigen::Matrix setVecToAlgMatrix() { Eigen::Matrix alg = Eigen::Matrix::Zero(); - // 12 Off-diagonal E_ij generators int k = 0; - for (int i = 0; i < 4; ++i) { - for (int j = 0; j < 4; ++j) { - if (i != j) { - alg(i * 4 + j, k++) = 1.0; - } - } - } - - // For Diagonal generators B1 = diag(1, -1, 0, 0) - alg(0, 12) = 1.0; - alg(5, 12) = -1.0; - - // For B2 = diag(0, 1, -1, 0) - alg(5, 13) = 1.0; - alg(10, 13) = -1.0; - - // For B3 = diag(0, 0, 1, -1) - alg(10, 14) = 1.0; - alg(15, 14) = -1.0; + auto set_skew = [&](int i, int j) { + alg(i * 4 + j, k) = kInvSqrt2; + alg(j * 4 + i, k) = -kInvSqrt2; + ++k; + }; + auto set_sym = [&](int i, int j) { + alg(i * 4 + j, k) = kInvSqrt2; + alg(j * 4 + i, k) = kInvSqrt2; + ++k; + }; + + // Rotations (skew-symmetric). + set_skew(0, 1); + set_skew(0, 2); + set_skew(0, 3); + set_skew(1, 2); + set_skew(1, 3); + set_skew(2, 3); + + // Symmetric off-diagonal shears. + set_sym(0, 1); + set_sym(0, 2); + set_sym(0, 3); + set_sym(1, 2); + set_sym(1, 3); + set_sym(2, 3); + + // Traceless diagonal scalings. + alg(0, k) = kInvSqrt2; + alg(5, k) = -kInvSqrt2; + ++k; + + alg(0, k) = kInvSqrt6; + alg(5, k) = kInvSqrt6; + alg(10, k) = -2.0 * kInvSqrt6; + ++k; + + alg(0, k) = kInvSqrt12; + alg(5, k) = kInvSqrt12; + alg(10, k) = kInvSqrt12; + alg(15, k) = -3.0 * kInvSqrt12; + ++k; return alg; } -Eigen::Matrix setAlgtoVecMatrix() { - Eigen::Matrix mat; - mat << 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., - 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., -1.; - return mat; +Eigen::Matrix setAlgtoVecMatrix( + const Eigen::Matrix& vec_to_alg) { + return vec_to_alg.transpose(); } -// ALG_TO_VEC * VEC_TO_ALG is equals to I_15x15 +// For the orthonormal basis, ALG_TO_VEC * VEC_TO_ALG is the identity. const Eigen::Matrix VEC_TO_ALG = setVecToAlgMatrix(); -const Eigen::Matrix ALG_TO_VEC = setAlgtoVecMatrix(); +const Eigen::Matrix ALG_TO_VEC = setAlgtoVecMatrix(VEC_TO_ALG); } // namespace namespace gtsam { @@ -185,28 +194,69 @@ Matrix44 SL4::Hat(const Vector& xi) { "SL4::Hat: xi must be a vector of size 15. Got size " + std::to_string(xi.size())); } - Matrix44 A; - const double d11 = xi(12); - const double d22 = -xi(12) + xi(13); - const double d33 = -xi(13) + xi(14); - const double d44 = -xi(14); - - A << d11, xi(0), xi(1), xi(2), xi(3), d22, xi(4), xi(5), xi(6), xi(7), d33, - xi(8), xi(9), xi(10), xi(11), d44; + Matrix44 A = Matrix44::Zero(); + + // Rotations (skew-symmetric), normalized by 1/sqrt(2). + A(0, 1) += kInvSqrt2 * xi(0); + A(1, 0) -= kInvSqrt2 * xi(0); + A(0, 2) += kInvSqrt2 * xi(1); + A(2, 0) -= kInvSqrt2 * xi(1); + A(0, 3) += kInvSqrt2 * xi(2); + A(3, 0) -= kInvSqrt2 * xi(2); + A(1, 2) += kInvSqrt2 * xi(3); + A(2, 1) -= kInvSqrt2 * xi(3); + A(1, 3) += kInvSqrt2 * xi(4); + A(3, 1) -= kInvSqrt2 * xi(4); + A(2, 3) += kInvSqrt2 * xi(5); + A(3, 2) -= kInvSqrt2 * xi(5); + + // Symmetric off-diagonal shears, normalized by 1/sqrt(2). + A(0, 1) += kInvSqrt2 * xi(6); + A(1, 0) += kInvSqrt2 * xi(6); + A(0, 2) += kInvSqrt2 * xi(7); + A(2, 0) += kInvSqrt2 * xi(7); + A(0, 3) += kInvSqrt2 * xi(8); + A(3, 0) += kInvSqrt2 * xi(8); + A(1, 2) += kInvSqrt2 * xi(9); + A(2, 1) += kInvSqrt2 * xi(9); + A(1, 3) += kInvSqrt2 * xi(10); + A(3, 1) += kInvSqrt2 * xi(10); + A(2, 3) += kInvSqrt2 * xi(11); + A(3, 2) += kInvSqrt2 * xi(11); + + // Traceless diagonal scalings. + const double a = kInvSqrt2 * xi(12); + const double b = kInvSqrt6 * xi(13); + const double c = kInvSqrt12 * xi(14); + + A(0, 0) += a + b + c; + A(1, 1) += -a + b + c; + A(2, 2) += -2.0 * b + c; + A(3, 3) += -3.0 * c; return A; } /* ************************************************************************* */ -// NOTE(hlim): Why 'X'? - I just follow the convention of GTSAM -Vector SL4::Vee(const Matrix44& X) { - Vector vec(15); - const double x12 = X(0, 0); - const double x13 = X(1, 1) + x12; - const double x14 = -X(3, 3); - vec << X(0, 1), X(0, 2), X(0, 3), X(1, 0), X(1, 2), X(1, 3), X(2, 0), X(2, 1), - X(2, 3), X(3, 0), X(3, 1), X(3, 2), x12, x13, x14; - return vec; +// used consistent notation with Hat() +Vector SL4::Vee(const Matrix44& A) { + Vector xi(15); + xi << kInvSqrt2 * (A(0, 1) - A(1, 0)), + kInvSqrt2 * (A(0, 2) - A(2, 0)), + kInvSqrt2 * (A(0, 3) - A(3, 0)), + kInvSqrt2 * (A(1, 2) - A(2, 1)), + kInvSqrt2 * (A(1, 3) - A(3, 1)), + kInvSqrt2 * (A(2, 3) - A(3, 2)), + kInvSqrt2 * (A(0, 1) + A(1, 0)), + kInvSqrt2 * (A(0, 2) + A(2, 0)), + kInvSqrt2 * (A(0, 3) + A(3, 0)), + kInvSqrt2 * (A(1, 2) + A(2, 1)), + kInvSqrt2 * (A(1, 3) + A(3, 1)), + kInvSqrt2 * (A(2, 3) + A(3, 2)), + kInvSqrt2 * (A(0, 0) - A(1, 1)), + kInvSqrt6 * (A(0, 0) + A(1, 1) - 2.0 * A(2, 2)), + kInvSqrt12 * (A(0, 0) + A(1, 1) + A(2, 2) - 3.0 * A(3, 3)); + return xi; } } // namespace gtsam diff --git a/gtsam/geometry/SL4.h b/gtsam/geometry/SL4.h index ee2b63afca..c4f5e1b89d 100644 --- a/gtsam/geometry/SL4.h +++ b/gtsam/geometry/SL4.h @@ -102,6 +102,20 @@ class GTSAM_EXPORT SL4 : public MatrixLieGroup { /// @{ using LieAlgebra = Matrix44; + /** + * Lie algebra coordinates for sl(4) using an orthonormal basis. + * + * Ordering of xi (15x1): + * [r12 r13 r14 r23 r24 r34 s12 s13 s14 s23 s24 s34 h1 h2 h3] + * + * Basis: + * - r_ij scale (E_ij - E_ji)/sqrt(2): skew-symmetric rotations. + * - s_ij scale (E_ij + E_ji)/sqrt(2): symmetric off-diagonal shears. + * - h1,h2,h3 scale orthonormal traceless diagonals: + * H1 = (1/sqrt(2)) diag( 1, -1, 0, 0) + * H2 = (1/sqrt(6)) diag( 1, 1, -2, 0) + * H3 = (1/sqrt(12)) diag( 1, 1, 1, -3) + */ static Matrix44 Hat(const Vector& xi); static Vector Vee(const Matrix44& X); diff --git a/gtsam/geometry/tests/testSL4.cpp b/gtsam/geometry/tests/testSL4.cpp index a6825efd14..2aca1c9ce3 100644 --- a/gtsam/geometry/tests/testSL4.cpp +++ b/gtsam/geometry/tests/testSL4.cpp @@ -13,6 +13,9 @@ #include #include +#include +#include + using namespace std; using namespace gtsam; @@ -171,9 +174,96 @@ TEST(SL4, HatVeeAreInverses) { EXPECT(assert_equal(xi0, xi_recovered, 1e-8)); } +/* ************************************************************************* */ +TEST(SL4, HatTraceIsZero) { + Vector15 eta = + (Vector15() << 0.31, -0.22, 0.14, -0.09, 0.27, -0.18, 0.05, -0.12, 0.08, + 0.11, -0.06, 0.02, 0.07, -0.04, 0.13) + .finished(); + Matrix4 A = SL4::Hat(eta); + EXPECT_DOUBLES_EQUAL(0.0, A.trace(), 1e-12); +} + +/* ************************************************************************* */ +TEST(SL4, HatVeeRoundTrip) { + Vector15 eta = + (Vector15() << -0.25, 0.19, -0.11, 0.07, -0.03, 0.29, -0.17, 0.09, 0.21, + -0.13, 0.04, -0.06, 0.15, -0.08, 0.02) + .finished(); + Vector eta_recovered = SL4::Vee(SL4::Hat(eta)); + EXPECT(assert_equal(eta, eta_recovered, 1e-12)); +} + +/* ************************************************************************* */ +TEST(SL4, HatBasisIsOrthonormal) { + std::vector generators; + generators.reserve(15); + + for (int k = 0; k < 15; ++k) { + Vector15 e = Vector15::Zero(); + e(k) = 1.0; + generators.push_back(SL4::Hat(e)); + } + + for (int i = 0; i < 15; ++i) { + for (int j = 0; j < 15; ++j) { + const double inner = + (generators[i].array() * generators[j].array()).sum(); + const double expected = (i == j) ? 1.0 : 0.0; + EXPECT_DOUBLES_EQUAL(expected, inner, 1e-12); + } + } +} + +/* ************************************************************************* */ +TEST(SL4, HatMatchesOrthonormalDefinition) { + const double inv_sqrt2 = 1.0 / std::sqrt(2.0); + const double inv_sqrt6 = 1.0 / std::sqrt(6.0); + const double inv_sqrt12 = 1.0 / std::sqrt(12.0); + + const Vector15 eta = xi0; + Matrix4 expected = Matrix4::Zero(); + + expected(0, 1) += inv_sqrt2 * eta(0); + expected(1, 0) -= inv_sqrt2 * eta(0); + expected(0, 2) += inv_sqrt2 * eta(1); + expected(2, 0) -= inv_sqrt2 * eta(1); + expected(0, 3) += inv_sqrt2 * eta(2); + expected(3, 0) -= inv_sqrt2 * eta(2); + expected(1, 2) += inv_sqrt2 * eta(3); + expected(2, 1) -= inv_sqrt2 * eta(3); + expected(1, 3) += inv_sqrt2 * eta(4); + expected(3, 1) -= inv_sqrt2 * eta(4); + expected(2, 3) += inv_sqrt2 * eta(5); + expected(3, 2) -= inv_sqrt2 * eta(5); + + expected(0, 1) += inv_sqrt2 * eta(6); + expected(1, 0) += inv_sqrt2 * eta(6); + expected(0, 2) += inv_sqrt2 * eta(7); + expected(2, 0) += inv_sqrt2 * eta(7); + expected(0, 3) += inv_sqrt2 * eta(8); + expected(3, 0) += inv_sqrt2 * eta(8); + expected(1, 2) += inv_sqrt2 * eta(9); + expected(2, 1) += inv_sqrt2 * eta(9); + expected(1, 3) += inv_sqrt2 * eta(10); + expected(3, 1) += inv_sqrt2 * eta(10); + expected(2, 3) += inv_sqrt2 * eta(11); + expected(3, 2) += inv_sqrt2 * eta(11); + + const double a = inv_sqrt2 * eta(12); + const double b = inv_sqrt6 * eta(13); + const double c = inv_sqrt12 * eta(14); + expected(0, 0) = a + b + c; + expected(1, 1) = -a + b + c; + expected(2, 2) = -2.0 * b + c; + expected(3, 3) = -3.0 * c; + + EXPECT(assert_equal(expected, SL4::Hat(eta), 1e-12)); +} + /* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); } -/* ************************************************************************* */ \ No newline at end of file +/* ************************************************************************* */ From f0b04c18632e20163bce40e9fb917d1f33b34ffe Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 16 Jan 2026 00:20:25 -0500 Subject: [PATCH 100/266] GTSAM changes --- gtsam/base/tests/testSymmetricBlockMatrix.cpp | 26 +++++++++++++++++++ gtsam/nonlinear/Values.h | 3 +++ 2 files changed, 29 insertions(+) diff --git a/gtsam/base/tests/testSymmetricBlockMatrix.cpp b/gtsam/base/tests/testSymmetricBlockMatrix.cpp index b2049ada26..674bc7d012 100644 --- a/gtsam/base/tests/testSymmetricBlockMatrix.cpp +++ b/gtsam/base/tests/testSymmetricBlockMatrix.cpp @@ -155,6 +155,32 @@ TEST(SymmetricBlockMatrix, expressions) EXPECT(assert_equal(Matrix(expected2.selfadjointView()), bm6.selfadjointView())); } +/* ************************************************************************* */ +// Verify diagonal-only update helpers. +TEST(SymmetricBlockMatrix, AddDiagonal) { + const std::vector dimensions{2, 1}; + SymmetricBlockMatrix bm(dimensions); + bm.setZero(); + + bm.addScaledIdentity(0, 2.0); + bm.addScaledIdentity(1, 3.0); + + Vector delta0(2); + delta0 << 1.0, 4.0; + bm.addToDiagonalBlock(0, delta0); + + Vector delta1(1); + delta1 << -1.0; + bm.addToDiagonalBlock(1, delta1); + + Matrix expected = Matrix::Zero(3, 3); + expected(0, 0) = 3.0; + expected(1, 1) = 6.0; + expected(2, 2) = 2.0; + + EXPECT(assert_equal(expected, Matrix(bm.selfadjointView()))); +} + /* ************************************************************************* */ // Update via block mapping. TEST(SymmetricBlockMatrix, UpdateFromMappedBlocks) diff --git a/gtsam/nonlinear/Values.h b/gtsam/nonlinear/Values.h index c347158934..5e9bf8d046 100644 --- a/gtsam/nonlinear/Values.h +++ b/gtsam/nonlinear/Values.h @@ -78,6 +78,9 @@ namespace gtsam { // The member to store the values, see just above KeyValueMap values_; + // Friend access for efficient in-place updates. + friend class NonlinearMultifrontalSolver; + public: /// A shared_ptr to this class From f8ab5e1464a893cade08df468a5cd65f62c826d0 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 16 Jan 2026 00:21:17 -0500 Subject: [PATCH 101/266] sbm_ -> info_, damping --- gtsam/linear/MultifrontalClique.cpp | 257 ++++++++++++------ gtsam/linear/MultifrontalClique.h | 141 ++++++---- gtsam/linear/MultifrontalParameters.h | 49 ++++ gtsam/linear/MultifrontalSolver.cpp | 73 ++++- gtsam/linear/MultifrontalSolver.h | 25 +- gtsam/linear/tests/testMultifrontalSolver.cpp | 37 ++- 6 files changed, 436 insertions(+), 146 deletions(-) create mode 100644 gtsam/linear/MultifrontalParameters.h diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index c9cf6af8c3..5832c20636 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -30,8 +30,8 @@ #include #include +#include #include -#include #include namespace gtsam { @@ -92,7 +92,7 @@ size_t hardwareThreads() { return kHardwareThreads; } -SymmetricBlockMatrix makeZeroLocalSbm(const std::vector& blockDims) { +SymmetricBlockMatrix makeZeroLocalInfo(const std::vector& blockDims) { SymmetricBlockMatrix local(blockDims, true); local.setZero(); return local; @@ -131,12 +131,13 @@ MultifrontalClique::MultifrontalClique( // Cache pointers into the solution for fast back-substitution. cacheSolutionPointers(solution, frontals, separatorKeys); - // Pre-allocate matrices once per structure. + // Cache sizing for allocation at finalize time. blockDims_ = this->blockDims(dims, frontals, separatorKeys); - initializeMatrices(blockDims_, vbmRows); + factorRows_ = vbmRows; } -void MultifrontalClique::finalize(std::vector children) { +void MultifrontalClique::finalize(std::vector children, + const MultifrontalParameters& params) { this->children.clear(); this->children.reserve(children.size()); for (const auto& child : children) { @@ -151,12 +152,39 @@ void MultifrontalClique::finalize(std::vector children) { for (Key key : child.separatorKeys) { indices.push_back(blockIndex(key)); } - // The RHS block is always the last block in Ab/SBM. + // The RHS block is always the last block in Ab/info. indices.push_back(static_cast(orderedKeys_.size())); child.clique->setParentIndices(indices); } - // Allocation is deferred until load. + // In leaf cliques, check whether to use QR elimination. + const bool isLeaf = this->children.empty(); + const bool hasRows = + frontalDim > 0 && factorRows_ >= static_cast(frontalDim); + bool useQR = false; + if (params.qrMode == MultifrontalParameters::QRMode::Allow) { + useQR = isLeaf && hasRows && + (frontalDim + separatorDim > params.qrAspectRatio * frontalDim); + } else if (params.qrMode == MultifrontalParameters::QRMode::Force) { + useQR = isLeaf && hasRows; + } + solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; + RSdReady_ = false; + + // If using QR, also a reserve room for optional damping rows. + const DenseIndex baseRows = static_cast(factorRows_); + const DenseIndex totalRows = + baseRows + (useQR ? static_cast(frontalDim) : 0); + Ab_ = VerticalBlockMatrix(blockDims_, totalRows, true); + // Ab's structure is fixed; clear it once and reuse across loads. + Ab_.matrix().setZero(); + if (useQR) { + RSd_ = VerticalBlockMatrix(blockDims_, totalRows, true); + } else { + RSd_ = VerticalBlockMatrix(blockDims_, static_cast(frontalDim), + true); + info_ = SymmetricBlockMatrix(blockDims_, true); + } } DenseIndex MultifrontalClique::blockIndex(Key key) const { @@ -190,24 +218,10 @@ std::vector MultifrontalClique::blockDims( return blockDims; } -void MultifrontalClique::initializeMatrices( - const std::vector& blockDims, size_t verticalBlockMatrixRows) { - Ab_ = VerticalBlockMatrix(blockDims, verticalBlockMatrixRows, true); - // Ab's structure is fixed; clear it once and reuse across loads. - Ab_.matrix().setZero(); - RSd_ = - VerticalBlockMatrix(blockDims, static_cast(frontalDim), true); -} - -void MultifrontalClique::allocateSbm() { - if (sbm_.nBlocks() > 0) return; - sbm_ = SymmetricBlockMatrix(blockDims_, true); -} - size_t MultifrontalClique::addJacobianFactor( const JacobianFactor& jacobianFactor, size_t rowOffset) { // We only overwrite the fixed sparsity pattern, so Ab must be zeroed once in - // initializeMatrices and then kept consistent across loads. + // finalize and then kept consistent across loads. const size_t rows = jacobianFactor.rows(); const size_t rhsBlockIdx = Ab_.nBlocks() - 1; for (auto it = jacobianFactor.begin(); it != jacobianFactor.end(); ++it) { @@ -245,32 +259,34 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { rowOffset += addJacobianFactor(*jacobianFactor, rowOffset); } - // Lock in QR only for leaf cliques. - const bool isLeaf = children.empty(); - const bool useQR = - isLeaf && (frontalDim > 0) && - (frontalDim + separatorDim > kQrAspectRatio * frontalDim) && - (Ab_.matrix().rows() >= static_cast(frontalDim)); - solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; RSdReady_ = false; - assert((useQR && RSd_.matrix().rows() == - static_cast(Ab_.matrix().rows())) || + assert((useQR() && RSd_.matrix().rows() == + static_cast(Ab_.matrix().rows())) || (RSd_.matrix().rows() == static_cast(frontalDim))); - allocateSbm(); + assert(useQR() || info_.nBlocks() > 0); +} + +void MultifrontalClique::eliminateInPlace() { + prepareForElimination(); + // There is no damping here and although we do not assert it here, we are + // counting on the facts that no damping was *ever* applied during the + // existence of this clique, just like we count on the sparsity pattern of + // zeros to remain the same. + factorize(); } void MultifrontalClique::prepareForElimination() { - // QR leaf cliques skip SBM assembly entirely. + // QR leaf cliques skip info matrix assembly entirely. if (useQR()) return; - assert(sbm_.nBlocks() > 0); - sbm_.setZero(); + assert(info_.nBlocks() > 0); + info_.setZero(); if (Ab_.matrix().rows() > 0) { - sbm_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); + info_.selfadjointView().rankUpdate(Ab_.matrix().transpose()); } // Heuristic: avoid parallel overhead on small cliques. const size_t minChildren = - std::max(1024, 4 * static_cast(sbm_.rows())); + std::max(1024, 4 * static_cast(info_.rows())); const size_t numChildren = children.size(); if (numChildren < minChildren) { // Typical for chains: many small cliques. gatherUpdatesSequential(); @@ -292,18 +308,92 @@ void MultifrontalClique::factorize() { assert(RSd_.matrix().cols() == Ab_.matrix().cols()); RSd_.matrix() = Ab_.matrix(); inplace_QR(RSd_.matrix()); + assert(RSd_.rowStart() == 0); + RSd_.rowEnd() = static_cast(frontalDim); } else { - sbm_.choleskyPartial(numFrontals()); - sbm_.split(numFrontals(), &RSd_); - sbm_.blockStart() = 0; + info_.choleskyPartial(numFrontals()); + info_.split(numFrontals(), &RSd_); + info_.blockStart() = 0; } RSdReady_ = true; } +void MultifrontalClique::eliminateInPlace( + double lambda, const LMDampingParams* dampingParams, + const VectorValues* exactHessianDiagonal) { + assert(dampingParams != nullptr); + prepareForElimination(); + if (useQR()) { + applyDampingQR(lambda, *dampingParams, exactHessianDiagonal); + } else { + applyDampingCholesky(lambda, *dampingParams, exactHessianDiagonal); + } + factorize(); +} + +void MultifrontalClique::applyDampingQR( + double lambda, const LMDampingParams& dampingParams, + const VectorValues* exactHessianDiagonal) { + if (lambda <= 0.0) return; + const DenseIndex baseRows = static_cast(factorRows_); + const DenseIndex dampRows = static_cast(frontalDim); + assert(Ab_.matrix().rows() >= baseRows + dampRows); + Ab_.matrix().middleRows(baseRows, dampRows).setZero(); + DenseIndex rowOffset = baseRows; + for (size_t j = 0; j < numFrontals(); ++j) { + const DenseIndex blockIndex = static_cast(j); + const DenseIndex dim = static_cast(blockDims_.at(j)); + auto block = Ab_(blockIndex).middleRows(rowOffset, dim); + if (dampingParams.diagonalDamping) { + Vector diag; + if (dampingParams.exactHessianDiagonal) { + assert(exactHessianDiagonal != nullptr); + const Key key = orderedKeys_.at(j); + diag = exactHessianDiagonal->at(key) + .cwiseMax(dampingParams.minDiagonal) + .cwiseMin(dampingParams.maxDiagonal); + } else { + diag = Ab_(blockIndex) + .topRows(baseRows) + .array() + .square() + .colwise() + .sum() + .transpose(); + diag = diag.cwiseMax(dampingParams.minDiagonal) + .cwiseMin(dampingParams.maxDiagonal); + } + block.diagonal() = (diag.array() * lambda).sqrt().matrix(); + } else { + block.diagonal().setConstant(std::sqrt(lambda)); + } + rowOffset += dim; + } +} + +void MultifrontalClique::applyDampingCholesky( + double lambda, const LMDampingParams& dampingParams, + const VectorValues* exactHessianDiagonal) { + if (lambda <= 0.0) return; + if (dampingParams.diagonalDamping) { + if (dampingParams.exactHessianDiagonal) { + assert(exactHessianDiagonal != nullptr); + addExactDiagonalDamping(lambda, *exactHessianDiagonal, + dampingParams.minDiagonal, + dampingParams.maxDiagonal); + } else { + addDiagonalDamping(lambda, dampingParams.minDiagonal, + dampingParams.maxDiagonal); + } + } else { + addIdentityDamping(lambda); + } +} + void MultifrontalClique::addIdentityDamping(double lambda) { const size_t nf = numFrontals(); for (size_t j = 0; j < nf; ++j) { - sbm_.addScaledIdentity(j, lambda); + info_.addScaledIdentity(j, lambda); } } @@ -312,42 +402,56 @@ void MultifrontalClique::addDiagonalDamping(double lambda, double minDiagonal, const size_t nf = numFrontals(); for (size_t j = 0; j < nf; ++j) { const Vector scaled = - lambda * sbm_.diagonal(j).cwiseMax(minDiagonal).cwiseMin(maxDiagonal); - sbm_.addToDiagonalBlock(j, scaled); + lambda * info_.diagonal(j).cwiseMax(minDiagonal).cwiseMin(maxDiagonal); + info_.addToDiagonalBlock(j, scaled); } } -void MultifrontalClique::eliminateInPlace() { - prepareForElimination(); - factorize(); +void MultifrontalClique::addExactDiagonalDamping( + double lambda, const VectorValues& hessianDiagonal, double minDiagonal, + double maxDiagonal) { + const size_t nf = numFrontals(); + for (size_t j = 0; j < nf; ++j) { + const Key key = orderedKeys_.at(j); + const Vector diag = + hessianDiagonal.at(key).cwiseMax(minDiagonal).cwiseMin(maxDiagonal); + info_.addToDiagonalBlock(j, lambda * diag); + } } -void MultifrontalClique::updateParentSbm( - SymmetricBlockMatrix& parentSbm) const { +void MultifrontalClique::updateParentInfo( + SymmetricBlockMatrix& parentInfo) const { + assert(RSd_.rowStart() == 0); if (useQR()) { - // Accumulate separator (and RHS) normal equations (S^T S) into the parent. + // Accumulate separator (and RHS) normal equations from the QR residual. assert(RSdReady_ && RSd_.firstBlock() == 0); const DenseIndex nfBlocks = static_cast(numFrontals()); + const DenseIndex rowStart = RSd_.rowStart(); + const DenseIndex rowEnd = RSd_.rowEnd(); + RSd_.rowStart() = static_cast(frontalDim); + RSd_.rowEnd() = static_cast(RSd_.matrix().rows()); RSd_.firstBlock() = nfBlocks; - parentSbm.updateFromOuterProductBlocks(RSd_, parentIndices_); + parentInfo.updateFromOuterProductBlocks(RSd_, parentIndices_); RSd_.firstBlock() = 0; + RSd_.rowStart() = rowStart; + RSd_.rowEnd() = rowEnd; } else { - // Accumulate the S^T S part from this clique's SBM into the parent. - assert(sbm_.nBlocks() > 0 && sbm_.blockStart() == 0); - sbm_.blockStart() = numFrontals(); - parentSbm.updateFromMappedBlocks(sbm_, parentIndices_); - sbm_.blockStart() = 0; + // Accumulate the S^T S part from this clique's info matrix into the parent. + assert(info_.nBlocks() > 0 && info_.blockStart() == 0); + info_.blockStart() = numFrontals(); + parentInfo.updateFromMappedBlocks(info_, parentIndices_); + info_.blockStart() = 0; } } void MultifrontalClique::updateParent(MultifrontalClique& parent) const { - updateParentSbm(parent.sbm_); + updateParentInfo(parent.info_); } void MultifrontalClique::gatherUpdatesSequential() { for (const auto& child : children) { assert(child); - child->updateParentSbm(sbm_); + child->updateParentInfo(info_); } } @@ -355,28 +459,28 @@ void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { #ifdef GTSAM_USE_TBB (void)numThreads; // TBB controls the effective worker count. tbb::enumerable_thread_specific locals([this]() { - return makeZeroLocalSbm(blockDims_); + return makeZeroLocalInfo(blockDims_); }); // Per-thread accumulators. tbb::parallel_for( tbb::blocked_range(0, children.size()), [&](const tbb::blocked_range& range) { - auto& local = locals.local(); // Thread-local SBM. + auto& local = locals.local(); // Thread-local info matrix. for (size_t i = range.begin(); i < range.end(); ++i) { const auto& child = children[i]; assert(child); - child->updateParentSbm( - local); // No locking: each thread writes its own SBM. + child->updateParentInfo( + local); // No locking: each thread writes its own info matrix. } }); locals.combine_each([this](const SymmetricBlockMatrix& local) { - sbm_.addUpperTriangular( - local); // Merge per-thread partial SBMs into this clique SBM. + info_.addUpperTriangular( + local); // Merge per-thread partial info matrices into this clique. }); #else std::vector locals; locals.reserve(numThreads); // Fixed-size per-thread accumulators. for (size_t i = 0; i < numThreads; ++i) { - locals.push_back(makeZeroLocalSbm(blockDims_)); + locals.push_back(makeZeroLocalInfo(blockDims_)); } std::vector threads; threads.reserve(numThreads); @@ -391,8 +495,8 @@ void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { for (size_t i = start; i < end; ++i) { const auto& child = children[i]; assert(child); - child->updateParentSbm( - local); // No locking: each thread writes its own SBM. + child->updateParentInfo( + local); // No locking: each thread writes its own info matrix. } }); } @@ -400,8 +504,8 @@ void MultifrontalClique::gatherUpdatesParallel(size_t numThreads) { thread.join(); // Ensure all locals are complete before merge. } for (const auto& local : locals) { - sbm_.addUpperTriangular( - local); // Merge per-thread partial SBMs into this clique SBM. + info_.addUpperTriangular( + local); // Merge per-thread partial info matrices into this clique. } #endif } @@ -413,9 +517,10 @@ std::shared_ptr MultifrontalClique::conditional() const { RSd_); } -// Solve with block back-substitution on the Cholesky-stored SBM. +// Solve with block back-substitution on the Cholesky-stored info matrix. void MultifrontalClique::updateSolution() const { assert(RSdReady_); + assert(RSd_.rowStart() == 0); // Use cached [R S d] for fast back-substitution. const size_t nf = numFrontals(); const size_t n = RSd_.nBlocks() - 1; // # frontals + # separators @@ -462,19 +567,19 @@ void MultifrontalClique::print(const std::string& s, keyFormatter); std::cout << "], factors=" << factorIndices_.size() << ", children=" << children.size() - << ", sbmBlocks=" << sbm_.nBlocks() + << ", infoBlocks=" << info_.nBlocks() << ", AbRows=" << Ab_.matrix().rows() << ")\n"; - auto assembleSbm = [](const SymmetricBlockMatrix& sbm) { - const size_t nBlocks = sbm.nBlocks(); + auto assembleInfo = [](const SymmetricBlockMatrix& info) { + const size_t nBlocks = info.nBlocks(); std::vector offsets(nBlocks + 1, 0); for (size_t i = 0; i < nBlocks; ++i) { - offsets[i + 1] = offsets[i] + sbm.getDim(i); + offsets[i + 1] = offsets[i] + info.getDim(i); } Matrix full = Matrix::Zero(offsets.back(), offsets.back()); for (size_t i = 0; i < nBlocks; ++i) { for (size_t j = 0; j < nBlocks; ++j) { - Matrix block = sbm.block(i, j); + Matrix block = info.block(i, j); full.block(offsets[i], offsets[j], block.rows(), block.cols()) = block; } } @@ -482,7 +587,7 @@ void MultifrontalClique::print(const std::string& s, }; std::cout << " Ab:\n" << Ab_.matrix() << "\n"; - std::cout << " SBM:\n" << assembleSbm(sbm_) << "\n"; + std::cout << " info:\n" << assembleInfo(info_) << "\n"; } std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique) { @@ -497,7 +602,7 @@ std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique) { orderedKeys.size(), formatter); os << ", factors=" << clique.factorIndices_.size(); os << ", children=" << clique.children.size(); - os << ", sbmBlocks=" << clique.sbm().nBlocks(); + os << ", infoBlocks=" << clique.info().nBlocks(); os << ", AbRows=" << clique.Ab().matrix().rows() << ")"; return os; } diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 9781300e3d..c002501628 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -24,12 +24,15 @@ #include #include #include +#include #include +#include #include #include #include #include +#include #include #include #include @@ -84,15 +87,17 @@ class GTSAM_EXPORT MultifrontalClique { size_t frontalDim = 0; ///< Frontal dimension. size_t separatorDim = 0; ///< Separator dimension. - /// Construct a clique from factor indices and cache static structure. - /// @param factorIndices Indices of factors associated with this clique. - /// @param parent Weak pointer to the parent clique. - /// @param frontals Frontal keys for this clique. - /// @param separatorKeys Separator keys for this clique. - /// @param dims Key->dimension map. - /// @param vbmRows Number of rows needed for the vertical block matrix. - /// @param solution Solution storage for cached pointers. - /// @param fixedKeys Keys fixed to zero by constraints (may be null). + /** + * Construct a clique from factor indices and cache static structure. + * @param factorIndices Indices of factors associated with this clique. + * @param parent Weak pointer to the parent clique. + * @param frontals Frontal keys for this clique. + * @param separatorKeys Separator keys for this clique. + * @param dims Key->dimension map. + * @param vbmRows Number of rows needed for the vertical block matrix. + * @param solution Solution storage for cached pointers. + * @param fixedKeys Keys fixed to zero by constraints (may be null). + */ explicit MultifrontalClique(std::vector factorIndices, const std::weak_ptr& parent, const KeyVector& frontals, @@ -104,16 +109,24 @@ class GTSAM_EXPORT MultifrontalClique { /// @name Setup (non-const) /// @{ - /// Cache the children list and compute parent indices. - void finalize(std::vector children); + /** + * Cache the children list, compute parent indices, and lock in QR usage. + * @param children Child cliques plus separator metadata. + * @param params Parameters controlling QR mode and thresholds. + */ + void finalize(std::vector children, + const MultifrontalParameters& params); - /// Load factor values into the pre-allocated Ab matrix. - /// @param graph The factor graph with updated values (structure must match - /// the graph used to build this clique, apart from updated - /// numerical values). Only JacobianFactor inputs are supported. + /** + * Load factor values into the pre-allocated Ab matrix. + * @param graph The factor graph with updated values (structure must match + * the graph used to build this clique, apart from updated + * numerical values). Only JacobianFactor inputs are supported. + */ void fillAb(const GaussianFactorGraph& graph); - /// Zero out sbm, re-add Hessians, accumulate Jacobians and children. + /// Zero out the info matrix, re-add Hessians, accumulate Jacobians and + /// children. void prepareForElimination(); /// Perform Cholesky factorization on the frontal block. @@ -134,6 +147,23 @@ class GTSAM_EXPORT MultifrontalClique { void addDiagonalDamping(double lambda, double minDiagonal, double maxDiagonal); + /** + * Add diagonal damping to the frontal block using an externally provided + * Hessian diagonal `diag(J^T J)` keyed by variable. + * + * This matches the legacy LM diagonal damping definition based on the + * diagonal of the original linearized system rather than the post-Schur + * clique information matrix. + * + * @param lambda Damping factor + * @param hessianDiagonal Map from key to diagonal vector (dimension-matched). + * @param minDiagonal Minimum diagonal value + * @param maxDiagonal Maximum diagonal value + */ + void addExactDiagonalDamping(double lambda, + const VectorValues& hessianDiagonal, + double minDiagonal, double maxDiagonal); + /// @} /// @name Read-only methods @@ -147,14 +177,17 @@ class GTSAM_EXPORT MultifrontalClique { /// Return the number of frontal keys in this clique. size_t numFrontals() const { return frontalPtrs_.size(); } + /// Return keys ordered by block index (frontals followed by separators). + const KeyVector& orderedKeys() const { return orderedKeys_; } + /// Build a GaussianConditional from the in-place factorization. std::shared_ptr conditional() const; /// Get the vertical block matrix Ab. const VerticalBlockMatrix& Ab() const { return Ab_; } - /// Get the symmetric block matrix (const). - const SymmetricBlockMatrix& sbm() const { return sbm_; } + /// Get the information matrix (const). + const SymmetricBlockMatrix& info() const { return info_; } /// Check if this clique is using QR elimination. bool useQR() const { return solveMode_ == SolveMode::QrLeaf; } @@ -173,14 +206,26 @@ class GTSAM_EXPORT MultifrontalClique { /// @{ /** - * Eliminate this clique and propagate its separator contribution upward. + * Eliminate in-place, invalidating Ab_, and updating RSd_ and info_. * - * Computes the local normal equations (SBM) from the stacked Jacobian (Ab), - * incorporates child separator contributions, and performs partial Cholesky - * on the frontal blocks. Requires parent indices to be precomputed. + * Computes the local information matrix from the stacked Jacobian (Ab), + * incorporates child separator contributions, and performs partial QR or + * Cholesky on the frontal blocks. */ void eliminateInPlace(); + /** + * Version of eliminate that applies damping before eliminate. + * + * @param lambda Optional damping value; non-positive disables damping. + * @param dampingParams Parameters controlling LM-style damping. + * @param exactHessianDiagonal Optional `diag(J^T J)` values for exact + * diagonal damping. + */ + void eliminateInPlace(double lambda, + const LMDampingParams* dampingParams = nullptr, + const VectorValues* exactHessianDiagonal = nullptr); + /** * Apply this clique's separator contribution into the parent clique. * @param parent Parent clique to update. @@ -192,7 +237,8 @@ class GTSAM_EXPORT MultifrontalClique { * cached solution vectors. * * Uses block back-substitution using the upper triangular-part of the - * Cholesky-stored SBM, solving the triangular system for the frontal blocks. + * Cholesky-stored information matrix, solving the triangular system for the + * frontal blocks. */ void updateSolution() const; /// @} @@ -210,13 +256,16 @@ class GTSAM_EXPORT MultifrontalClique { /// Linear lookup for block index in small cliques. DenseIndex blockIndex(Key key) const; - /// Update a parent SBM with this clique's separator contribution. - void updateParentSbm(SymmetricBlockMatrix& parentSbm) const; + /// Update a parent information matrix with this clique's separator + /// contribution. + void updateParentInfo(SymmetricBlockMatrix& parentInfo) const; - /// Accumulate children separator updates into this clique's SBM (single-threaded). + /// Accumulate children separator updates into this clique's info matrix + /// (single-threaded). void gatherUpdatesSequential(); - /// Accumulate children separator updates into this clique's SBM (multi-threaded). + /// Accumulate children separator updates into this clique's info matrix + /// (multi-threaded). void gatherUpdatesParallel(size_t numThreads); /// Compute block dimensions from variable dimensions (excluding RHS). @@ -224,16 +273,13 @@ class GTSAM_EXPORT MultifrontalClique { const KeyVector& frontals, const KeySet& separatorKeys) const; - /** - * Pre-allocate matrices for this clique. - * @param blockDims Block dimensions (excluding RHS). - * @param totalNumRows Number of rows for the vertical block matrix. - */ - void initializeMatrices(const std::vector& blockDims, - size_t totalNumRows); + /// Apply damping for QR elimination by writing into extra Ab_ rows. + void applyDampingQR(double lambda, const LMDampingParams& dampingParams, + const VectorValues* exactHessianDiagonal); - /// Allocate the symmetric block matrix if needed. - void allocateSbm(); + /// Apply damping for Cholesky elimination by adding to the info_ matrix. + void applyDampingCholesky(double lambda, const LMDampingParams& dampingParams, + const VectorValues* exactHessianDiagonal); /** * Add a Jacobian factor's contributions into the Ab matrix. @@ -244,32 +290,33 @@ class GTSAM_EXPORT MultifrontalClique { void setParentIndices(const std::vector& indices) { parentIndices_ = indices; } - // Build-time metadata. + + // Construction-time metadata (set once in the constructor). std::vector factorIndices_; KeyVector orderedKeys_; ///< Keys ordered by block index (frontals+seps). const std::unordered_set* fixedKeys_ = nullptr; - std::vector blockDims_; + std::vector frontalPtrs_; ///< Solution frontals. + std::vector separatorPtrs_; ///< Solution separator. + std::vector blockDims_; ///< Cached block dimensions (excluding RHS). + size_t factorRows_ = 0; ///< Number of rows allocated in Ab. + + // Finalize-time metadata (set once after children are known). std::vector parentIndices_; ///< Parent block indices for separators + RHS. - std::vector frontalPtrs_; ///< Pointers into solution frontals. - std::vector - separatorPtrs_; ///< Pointers into solution separator. + SolveMode solveMode_ = SolveMode::Cholesky; - // Load-time state. + // Finalize-time allocations. VerticalBlockMatrix Ab_; - SolveMode solveMode_ = SolveMode::Cholesky; + mutable SymmetricBlockMatrix info_; + mutable VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. // Elimination-time state. - mutable SymmetricBlockMatrix sbm_; - mutable VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. mutable bool RSdReady_ = false; // Solve-time scratch space. mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. mutable Vector separatorScratch_; ///< Cached separator stack for back-substitution. - - static constexpr double kQrAspectRatio = 2.0; }; std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique); diff --git a/gtsam/linear/MultifrontalParameters.h b/gtsam/linear/MultifrontalParameters.h new file mode 100644 index 0000000000..23149edf47 --- /dev/null +++ b/gtsam/linear/MultifrontalParameters.h @@ -0,0 +1,49 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file MultifrontalParameters.h + * @brief Parameters for the imperative multifrontal solver. + * @author Frank Dellaert + * @date January 2026 + */ + +#pragma once + +#include +#include + +namespace gtsam { + +/** + * Parameters for `gtsam::MultifrontalSolver`. + * + * These parameters are intentionally defined in a standalone header so they + * can be referenced from nonlinear optimizer parameter types without pulling + * in the full multifrontal solver headers. + * + * @note LM-style damping behavior is controlled by `NonlinearMultifrontalSolver` + * and its damping parameters; `MultifrontalParameters` only controls structure, + * traversal, and reporting. + */ +struct MultifrontalParameters { + enum class QRMode { Off, Allow, Force }; + size_t leafMergeDimCap = 256; ///< Leaf-merge cap (0 disables). + size_t mergeDimCap = 32; ///< Merge threshold (0 disables). + QRMode qrMode = QRMode::Allow; ///< QR mode for leaf cliques. + double qrAspectRatio = 2.0; ///< Aspect ratio for QR mode=Allow. + std::ostream* reportStream = nullptr; ///< Optional structure reporting. + int eliminationParallelThreshold = 10; ///< Post-order task threshold. + int solutionParallelThreshold = 4096; ///< Pre-order task threshold. + size_t numThreads = 0; ///< Worker count (0 uses 0.75 * hw threads). +}; + +} // namespace gtsam diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 8f94adae5c..ad2ce1221e 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -277,14 +277,67 @@ std::vector buildLeafBatches(const std::vector& groups, bool mergeLeafBatches(const SymbolicJunctionTree::sharedNode& cluster, const std::vector& batches) { - // Merge each batch of siblings into a super-leaf in-place. - bool anyMerged = false; - for (const auto& batch : batches) { + if (batches.empty()) return false; + + // Merge in a single pass to avoid repeated scans of large child lists. + FastMap batchIndexByChild; + std::vector mergedBatches(batches.size(), + nullptr); + size_t mergeBatchCount = 0; + size_t selectedLeafCount = 0; + + for (size_t batchIndex = 0; batchIndex < batches.size(); ++batchIndex) { + const auto& batch = batches[batchIndex]; if (batch.leaves.size() <= 1) continue; - cluster->mergeChildrenSiblings(batch.leaves); - anyMerged = true; + + selectedLeafCount += batch.leaves.size(); + for (const auto& leaf : batch.leaves) { + if (leaf) { + batchIndexByChild.emplace(leaf.get(), batchIndex); + } + } + + auto merged = std::make_shared(); + for (const auto& leaf : batch.leaves) { + if (leaf) { + merged->merge(leaf); + } + } + std::reverse(merged->orderedFrontalKeys.begin(), + merged->orderedFrontalKeys.end()); + mergedBatches[batchIndex] = merged; + mergeBatchCount += 1; + } + + if (mergeBatchCount == 0) return false; + + auto oldChildren = cluster->children; + SymbolicJunctionTree::Cluster::Children newChildren; + newChildren.reserve(oldChildren.size() - selectedLeafCount + mergeBatchCount); + std::vector inserted(batches.size(), false); + + for (size_t i = 0; i < oldChildren.size(); ++i) { + const auto& child = oldChildren[i]; + if (!child) { + newChildren.push_back(child); + continue; + } + + auto it = batchIndexByChild.find(child.get()); + if (it == batchIndexByChild.end()) { + newChildren.push_back(child); + continue; + } + + const size_t batchIndex = it->second; + if (!inserted[batchIndex] && i == batches[batchIndex].firstIndex) { + newChildren.push_back(mergedBatches[batchIndex]); + inserted[batchIndex] = true; + } } - return anyMerged; + + cluster->children.swap(newChildren); + return true; } void accumulateSymbolicStats(const SymbolicJunctionTree::sharedNode& cluster, @@ -396,6 +449,7 @@ struct CliqueBuilder { VectorValues* solution; std::vector* cliques; const std::unordered_set* fixedKeys; + const MultifrontalParameters* params; SymbolicJunctionTree::Cluster::KeySetMap separatorCache = {}; BuiltClique build(const SymbolicJunctionTree::sharedNode& cluster, @@ -433,7 +487,7 @@ struct CliqueBuilder { } // Finalize children metadata and register clique. - clique->finalize(std::move(childInfos)); + clique->finalize(std::move(childInfos), *params); cliques->push_back(clique); return {clique, std::move(separatorKeys)}; } @@ -466,8 +520,7 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Parameters& params) : ForestTraversal( resolveThreadCount(params.numThreads)), - params_(params), - numThreads_(resolveThreadCount(params.numThreads)) { + params_(params) { dims_ = std::move(data.dims); fixedKeys_ = std::move(data.fixedKeys); @@ -504,7 +557,7 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, } // Build the actual MultifrontalClique structure. - CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_}; + CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_}; for (const auto& rootCluster : data.junctionTree.roots()) { if (rootCluster) { roots_.push_back( diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index ca5cf642a4..ab66fa43d0 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -22,6 +22,7 @@ #include #include #include +#include #include #include @@ -58,14 +59,7 @@ class GTSAM_EXPORT MultifrontalSolver : public ForestTraversal { public: /// Tuning parameters for traversal and reporting. - struct Parameters { - size_t leafMergeDimCap = 256; ///< Leaf-merge cap (0 disables). - size_t mergeDimCap = 32; ///< Merge threshold (0 disables). - std::ostream* reportStream = nullptr; ///< Optional structure reporting. - int eliminationParallelThreshold = 10; ///< Post-order task threshold. - int solutionParallelThreshold = 4096; ///< Pre-order task threshold. - size_t numThreads = 0; ///< Worker count (0 uses 0.75 * hw threads). - }; + using Parameters = MultifrontalParameters; struct PrecomputedData { std::map dims; ///< Map from variable key to dimension. @@ -87,7 +81,6 @@ class GTSAM_EXPORT MultifrontalSolver bool loaded_ = false; ///< Whether load() has been called. bool eliminated_ = false; ///< Whether eliminateInPlace() ran. Parameters params_; ///< Tunable solver parameters. - size_t numThreads_ = 0; ///< Resolved thread count for traversal. public: /** @@ -124,6 +117,20 @@ class GTSAM_EXPORT MultifrontalSolver static PrecomputedData Precompute(const GaussianFactorGraph& graph, const Ordering& ordering); + /** + * Construct the solver from precomputed symbolic data. + * Call load() before eliminating to populate numerical values. + * @param data Precomputed symbolic structure and sizing data. + * @param ordering The variable ordering to use for seeding solution storage. + * @param mergeDimCap Merge a child if its frontal dimension plus the + * parent's total dimension is below this threshold (0 disables merging). + * @param reportStream Optional stream to report clique structure stats + * (frontals, separators, total dims, and children). + */ + MultifrontalSolver(PrecomputedData data, const Ordering& ordering, + size_t mergeDimCap = 0, + std::ostream* reportStream = nullptr); + /** * Load new numerical values from the factor graph. * This overwrites the values in the pre-allocated matrices. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index da7f43ff83..53feeac933 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -72,7 +72,7 @@ TEST(MultifrontalSolver, Constructor) { auto childClique = root->children[0]; // Verify matrices in leaf (childClique) - EXPECT_LONGS_EQUAL(4, childClique->sbm().nBlocks()); + EXPECT_LONGS_EQUAL(4, childClique->info().nBlocks()); EXPECT_LONGS_EQUAL(2, childClique->Ab().rows()); EXPECT_LONGS_EQUAL(4, childClique->Ab().nBlocks()); @@ -104,7 +104,7 @@ TEST(MultifrontalSolver, ConstructorPrecomputed) { // Verify matrices in leaf (childClique) CHECK(childClique->useQR() == false); - EXPECT_LONGS_EQUAL(4, childClique->sbm().nBlocks()); + EXPECT_LONGS_EQUAL(4, childClique->info().nBlocks()); EXPECT_LONGS_EQUAL(2, childClique->Ab().rows()); EXPECT_LONGS_EQUAL(4, childClique->Ab().nBlocks()); @@ -172,6 +172,34 @@ TEST(MultifrontalSolver, EliminateWithLoad) { EXPECT(assert_equal(expected, actual, 1e-9)); } +/* ************************************************************************* */ +// Forcing QR enables QR on all leaves and matches legacy QR elimination. +TEST(MultifrontalSolver, ForceQRMatchesDenseQR) { + auto qrParams = noMergeParams(); + qrParams.qrMode = MultifrontalParameters::QRMode::Force; + MultifrontalSolver solverQR(chain, chainOrdering, qrParams); + solverQR.eliminateInPlace(chain); + + size_t leafCount = 0; + size_t qrLeafCount = 0; + solverQR.runTopDown([&](MultifrontalClique& node) { + if (node.children.empty()) { + ++leafCount; + if (node.useQR()) { + ++qrLeafCount; + } + } + }); + CHECK(leafCount > 0); + CHECK(leafCount == qrLeafCount); + + const VectorValues& actual = solverQR.updateSolution(); + + VectorValues expected = chain.optimize(chainOrdering, EliminateQR); + + EXPECT(assert_equal(expected, actual, 1e-9)); +} + /* ************************************************************************* */ // Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { @@ -368,7 +396,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { EXPECT(solver.roots().size() == 1); auto root = solver.roots()[0]; - EXPECT_LONGS_EQUAL(root->Ab().nBlocks(), root->sbm().nBlocks()); + EXPECT_LONGS_EQUAL(root->Ab().nBlocks(), root->info().nBlocks()); // Check a leaf clique block structure. MultifrontalSolver::CliquePtr leaf = nullptr; @@ -377,7 +405,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { [&](MultifrontalSolver::CliquePtr c) { if (!c) return; if (c->children.empty()) { - const size_t blocks = c->sbm().nBlocks(); + const size_t blocks = c->info().nBlocks(); if (blocks < minBlocks) { minBlocks = blocks; leaf = c; @@ -391,6 +419,7 @@ TEST(MultifrontalSolver, BalancedSmoother) { EXPECT_LONGS_EQUAL(3, minBlocks); // Eliminate and solve + solver.load(smoother); solver.eliminateInPlace(); const VectorValues& actual = solver.updateSolution(); From d4fa2689c9a256de7c398555512206ac67a3286f Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 16 Jan 2026 00:22:12 -0500 Subject: [PATCH 102/266] New Nonlinear MF Solver --- gtsam/nonlinear/LMDampingParams.h | 54 +++ .../nonlinear/NonlinearMultifrontalSolver.cpp | 165 +++++++ gtsam/nonlinear/NonlinearMultifrontalSolver.h | 99 +++++ .../tests/testNonlinearMultifrontalSolver.cpp | 408 ++++++++++++++++++ timing/timeNonlinearMultifrontalSolver.cpp | 81 ++++ 5 files changed, 807 insertions(+) create mode 100644 gtsam/nonlinear/LMDampingParams.h create mode 100644 gtsam/nonlinear/NonlinearMultifrontalSolver.cpp create mode 100644 gtsam/nonlinear/NonlinearMultifrontalSolver.h create mode 100644 gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp create mode 100644 timing/timeNonlinearMultifrontalSolver.cpp diff --git a/gtsam/nonlinear/LMDampingParams.h b/gtsam/nonlinear/LMDampingParams.h new file mode 100644 index 0000000000..809869b60f --- /dev/null +++ b/gtsam/nonlinear/LMDampingParams.h @@ -0,0 +1,54 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file LMDampingParams.h + * @brief Parameters controlling LM-style damping for NonlinearMultifrontalSolver. + * @author Frank Dellaert + * @date January 2026 + */ + +#pragma once + +namespace gtsam { + +/** + * Parameters controlling LM-style damping as applied by + * `gtsam::NonlinearMultifrontalSolver`. + * + * @note These parameters are intentionally in a standalone header so they can + * be referenced from nonlinear optimizer parameter types without pulling in + * the full solver headers. + */ +struct LMDampingParams { + /// If true, use diagonal damping (LM) instead of identity damping. + bool diagonalDamping = false; + + /** + * If true, use the exact diagonal of the linearized system Hessian + * `diag(J^T J)` (computed via `GaussianFactorGraph::hessianDiagonal()`) when + * applying diagonal damping. + * + * If false, use the diagonal of each clique's assembled information matrix, + * which includes Schur complement contributions and can differ from + * `diag(J^T J)`. + */ + bool exactHessianDiagonal = false; + + /// Clamp minimum diagonal value used for diagonal damping. + double minDiagonal = 1e-6; + + /// Clamp maximum diagonal value used for diagonal damping. + double maxDiagonal = 1e32; +}; + +} // namespace gtsam + diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp new file mode 100644 index 0000000000..dd0e4e7f99 --- /dev/null +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -0,0 +1,165 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file NonlinearMultifrontalSolver.cpp + * @brief Implementation of nonlinear multifrontal solver. + * @author Frank Dellaert + * @date January 2026 + */ + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace gtsam { + +namespace { + +std::map computeDimsFromValues(const Values& values) { + return values.dims(); +} + +std::unordered_set collectFixedKeys(const NonlinearFactorGraph& graph) { + std::unordered_set fixedKeys; + for (const auto& factor : graph) { + if (!factor) continue; + if (!std::dynamic_pointer_cast(factor)) + continue; + if (factor->keys().size() != 1) continue; + fixedKeys.insert(factor->keys().front()); + } + return fixedKeys; +} + +SymbolicFactorGraph buildSymbolicGraph( + const NonlinearFactorGraph& graph, + const std::unordered_set& fixedKeys) { + SymbolicFactorGraph symbolicGraph; + symbolicGraph.reserve(graph.size()); + for (size_t i = 0; i < graph.size(); ++i) { + if (!graph[i]) continue; + KeyVector keys; + keys.reserve(graph[i]->size()); + for (Key key : graph[i]->keys()) { + if (!fixedKeys.count(key)) { + keys.push_back(key); + } + } + if (keys.empty()) continue; + const size_t rows = graph[i]->dim(); + symbolicGraph.emplace_shared(keys, i, + rows); + } + return symbolicGraph; +} + +} // namespace + +/* ************************************************************************* */ +NonlinearMultifrontalSolver::NonlinearMultifrontalSolver( + const NonlinearFactorGraph& graph, const Values& values, + const Ordering& ordering, MultifrontalSolver::Parameters params, + DampingParams dampingParams) + : MultifrontalSolver( + NonlinearMultifrontalSolver::Precompute(graph, values, ordering), + ordering, params), + dampingParams_(dampingParams) {} + +/* ************************************************************************* */ +MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( + const NonlinearFactorGraph& graph, const Values& values, + const Ordering& ordering) { + auto dims = computeDimsFromValues(values); + auto fixedKeys = collectFixedKeys(graph); + + Ordering reducedOrdering; + reducedOrdering.reserve(ordering.size()); + for (Key key : ordering) { + if (!fixedKeys.count(key)) { + reducedOrdering.push_back(key); + } + } + + SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); + SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); + SymbolicJunctionTree junctionTree(eliminationTree); + + return MultifrontalSolver::PrecomputedData{ + std::move(dims), std::move(fixedKeys), std::move(junctionTree)}; +} + +/* ************************************************************************* */ +void NonlinearMultifrontalSolver::load(const GaussianFactorGraph& graph) { + MultifrontalSolver::load(graph); + if (dampingParams_.diagonalDamping && dampingParams_.exactHessianDiagonal) { + exactHessianDiagonal_ = graph.hessianDiagonal(); + hasExactHessianDiagonal_ = true; + } else { + hasExactHessianDiagonal_ = false; + exactHessianDiagonal_ = VectorValues(); + } +} + +/* ************************************************************************* */ +void NonlinearMultifrontalSolver::eliminateInPlace(double lambda) { + if (lambda <= 0.0) { + MultifrontalSolver::eliminateInPlace(); + return; + } + + if (!loaded_) { + throw std::runtime_error( + "NonlinearMultifrontalSolver::eliminateInPlace: load() must be called " + "before eliminating."); + } + eliminated_ = false; + runBottomUp( + [this, lambda](MultifrontalClique& node) { + node.eliminateInPlace(lambda, &dampingParams_, &exactHessianDiagonal_); + }, + params_.eliminationParallelThreshold); + eliminated_ = true; +} + +/* ************************************************************************* */ +void NonlinearMultifrontalSolver::eliminateInPlace( + const GaussianFactorGraph& graph, double lambda) { + if (lambda <= 0.0) { + MultifrontalSolver::eliminateInPlace(graph); + return; + } + + if (dampingParams_.diagonalDamping && dampingParams_.exactHessianDiagonal) { + exactHessianDiagonal_ = graph.hessianDiagonal(); + hasExactHessianDiagonal_ = true; + } else { + hasExactHessianDiagonal_ = false; + exactHessianDiagonal_ = VectorValues(); + } + + eliminated_ = false; + runBottomUp( + [&graph, this, lambda](MultifrontalClique& node) { + node.fillAb(graph); + node.eliminateInPlace(lambda, &dampingParams_, &exactHessianDiagonal_); + }, + params_.eliminationParallelThreshold); + loaded_ = true; + eliminated_ = true; +} + +} // namespace gtsam diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.h b/gtsam/nonlinear/NonlinearMultifrontalSolver.h new file mode 100644 index 0000000000..ca2eeb73e8 --- /dev/null +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.h @@ -0,0 +1,99 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file NonlinearMultifrontalSolver.h + * @brief Multifrontal solver for nonlinear factor graphs. + * @author Frank Dellaert + * @date January 2026 + */ + +#pragma once + +#include +#include +#include +#include + +#include + +namespace gtsam { + +/** + * Multifrontal solver for nonlinear factor graphs. + * + * This class extends MultifrontalSolver to solve nonlinear problems. + * The linearization is provided externally via MultifrontalSolver::load(). + */ +class GTSAM_EXPORT NonlinearMultifrontalSolver : public MultifrontalSolver { + public: + using DampingParams = LMDampingParams; + + private: + public: + /** + * Construct the solver from a nonlinear factor graph and linearization point. + * This computes the symbolic structure (including fixed keys) from the + * nonlinear graph and uses the values to determine variable dimensions. + * Call load() with a linearized graph before eliminating. + * @param graph The nonlinear factor graph to build structure from. + * @param values The linearization point used to determine variable dims. + * @param ordering The variable ordering to use. + * @param params Tunable parameters for traversal and reporting. + */ + NonlinearMultifrontalSolver(const NonlinearFactorGraph& graph, + const Values& values, const Ordering& ordering, + MultifrontalSolver::Parameters params = {}, + DampingParams dampingParams = DampingParams()); + + /** + * Precompute symbolic structure from a nonlinear factor graph and values. + * This avoids linearization; call load() with a linearized graph before + * eliminating. + */ + static MultifrontalSolver::PrecomputedData Precompute( + const NonlinearFactorGraph& graph, const Values& values, + const Ordering& ordering); + + /// Update damping parameters for subsequent eliminations. + void setDampingParams(const DampingParams& params) { + dampingParams_ = params; + } + + /** + * Load new numerical values from the factor graph. + * This overrides the base load() to optionally cache `diag(J^T J)` for + * exact diagonal damping when enabled. + */ + void load(const GaussianFactorGraph& graph); + + /** + * Eliminate with optional LM-style damping. + * When lambda is provided, adds damping on frontal blocks before + * factorization. + */ + void eliminateInPlace(double lambda = 0.0); + + /** + * Load and eliminate the graph in a single traversal with optional damping. + * This calls fillAb() and factorization per clique in post-order. + * @param graph The linearized factor graph (structure must match). + * @param lambda Optional damping value; non-positive disables damping. + */ + void eliminateInPlace(const GaussianFactorGraph& graph, double lambda = 0.0); + + private: + DampingParams dampingParams_; + VectorValues exactHessianDiagonal_; + bool hasExactHessianDiagonal_ = false; +}; + +} // namespace gtsam diff --git a/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp new file mode 100644 index 0000000000..47ef60caed --- /dev/null +++ b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp @@ -0,0 +1,408 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file testNonlinearMultifrontalSolver.cpp + * @brief Test for NonlinearMultifrontalSolver + * @author Frank Dellaert + * @date January 2026 + */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +using namespace std; +using namespace gtsam; + +using symbol_shorthand::P; +using symbol_shorthand::X; + +namespace { +struct SolverTestProblem { + NonlinearFactorGraph graph; + Values values; + Ordering ordering; +}; + +SolverTestProblem makeTwoPoseProblem(const Point2& between = Point2(2, 0)) { + SolverTestProblem problem; + auto priorModel = noiseModel::Isotropic::Sigma(2, 1.0); + auto model = noiseModel::Isotropic::Sigma(2, 1.0); + + problem.graph.emplace_shared>(X(1), Point2(0, 0), + priorModel); + problem.graph.emplace_shared>(X(1), X(2), between, + model); + + problem.values.insert(X(1), Point2(0, 0)); + problem.values.insert(X(2), Point2(0, 0)); + + problem.ordering.push_back(X(1)); + problem.ordering.push_back(X(2)); + return problem; +} + +// Helper to run solver iterations +void runIterations(const NonlinearFactorGraph& graph, + NonlinearMultifrontalSolver& solver, Values& values, + size_t iterations, double lambda = 0.0, + GaussianFactorGraph::shared_ptr initialLinear = {}) { + for (size_t i = 0; i < iterations; ++i) { + GaussianFactorGraph::shared_ptr linear; + if (i == 0 && initialLinear) { + linear = initialLinear; + } else { + linear = graph.linearize(values); + } + solver.eliminateInPlace(*linear, lambda); + VectorValues delta = solver.updateSolution(); + values = values.retract(delta); + } +} + +} // namespace + +/* ************************************************************************* */ +// One linearization + one elimination + one back-solve on a tiny 2-pose +// problem. Tests the basic happy-path flow: construct -> load -> +// eliminateInPlace -> updateSolution, and verifies the expected single-step +// update. +TEST(NonlinearMultifrontalSolver, OneStep) { + auto problem = makeTwoPoseProblem(Point2(1, 0)); + auto linear = problem.graph.linearize(problem.values); + + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering); + + // 4. Load and eliminate + solver.eliminateInPlace(*linear, 0.0); + + // 5. Solve and Update + VectorValues delta = solver.updateSolution(); + Values result = problem.values.retract(delta); + + // 6. Check results + EXPECT(assert_equal(Point2(0, 0), result.at(X(1)), 1e-9)); + EXPECT(assert_equal(Point2(1, 0), result.at(X(2)), 1e-9)); +} + +/* ************************************************************************* */ +// Precompute the symbolic structure (junction tree, dimensions, fixed keys) +// from a nonlinear graph and initial values. +// Tests that Precompute succeeds and produces consistent sizing information. +TEST(NonlinearMultifrontalSolver, OneStepPrecomputed) { + auto problem = makeTwoPoseProblem(Point2(1, 0)); + + auto data = NonlinearMultifrontalSolver::Precompute( + problem.graph, problem.values, problem.ordering); + EXPECT(data.dims.count(X(1))); + EXPECT(data.dims.count(X(2))); + EXPECT(data.fixedKeys.empty()); + + auto linear = problem.graph.linearize(problem.values); + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering); + solver.eliminateInPlace(*linear, {}); + + VectorValues delta = solver.updateSolution(); + Values result = problem.values.retract(delta); + + EXPECT(assert_equal(Point2(0, 0), result.at(X(1)), 1e-9)); + EXPECT(assert_equal(Point2(1, 0), result.at(X(2)), 1e-9)); +} + +/* ************************************************************************* */ +// Run a first elimination to get an updated estimate, then deliberately +// perturb the estimate, re-linearize, reload, and eliminate again. +// Tests that load() correctly overwrites numeric data for subsequent solves. +TEST(NonlinearMultifrontalSolver, ReLinearize) { + auto problem = makeTwoPoseProblem(); + auto linear = problem.graph.linearize(problem.values); + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering); + + // 4. First Step + solver.eliminateInPlace(*linear, {}); + VectorValues delta1 = solver.updateSolution(); + Values currentValues = problem.values.retract(delta1); + + // Expect X(2) to be (2,0) because it's linear P2 problem + EXPECT(assert_equal(Point2(2, 0), currentValues.at(X(2)), 1e-9)); + + // 5. Disturb values to check re-linearization logic + currentValues.update(X(2), Point2(1.0, 0.0)); + + // 6. Re-linearize + auto linear2 = problem.graph.linearize(currentValues); + solver.eliminateInPlace(*linear2, {}); // Re-linearize and eliminate. + VectorValues delta2 = solver.updateSolution(); + Values actual = currentValues.retract(delta2); + + // Expect X(2) to differ by delta = 1.0, so back to 2.0 + EXPECT(assert_equal(Point2(2, 0), actual.at(X(2)), 1e-9)); +} + +/* ************************************************************************* */ +// Repeatedly linearize at the current estimate and run eliminate/solve to +// convergence on a small problem (Gauss-Newton-style iterations). +// Tests that repeated load/eliminate/update cycles behave consistently. +TEST(NonlinearMultifrontalSolver, MultiIteration) { + auto problem = makeTwoPoseProblem(); + auto linear = problem.graph.linearize(problem.values); + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering); + constexpr size_t kIterations = 5; + runIterations(problem.graph, solver, problem.values, kIterations, 0, linear); + + EXPECT(assert_equal(Point2(0, 0), problem.values.at(X(1)), 1e-9)); + EXPECT(assert_equal(Point2(2, 0), problem.values.at(X(2)), 1e-9)); +} + +/* ************************************************************************* */ +// Run multiple iterations with LM-style damping where damping is applied to +// the diagonal of the frontal Hessian blocks. +// Tests damped elimination and the diagonal-damping code path. +TEST(NonlinearMultifrontalSolver, DampedIterationsDiagonal) { + auto problem = makeTwoPoseProblem(); + NonlinearMultifrontalSolver::DampingParams params; + params.diagonalDamping = true; + auto linear = problem.graph.linearize(problem.values); + + MultifrontalSolver::Parameters mfParams; + mfParams.mergeDimCap = 0; + mfParams.qrMode = MultifrontalParameters::QRMode::Off; + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering, mfParams, params); + constexpr size_t kIterations = 10; + runIterations(problem.graph, solver, problem.values, kIterations, 0.1, + linear); + + EXPECT(assert_equal(Point2(0, 0), problem.values.at(X(1)), 1e-6)); + EXPECT(assert_equal(Point2(2, 0), problem.values.at(X(2)), 1e-3)); +} + +/* ************************************************************************* */ +// Run multiple iterations with LM-style damping where damping is applied as +// an identity term on the frontal blocks (full damping). +// Tests damped elimination and the identity-damping code path. +TEST(NonlinearMultifrontalSolver, DampedIterationsFull) { + auto problem = makeTwoPoseProblem(); + NonlinearMultifrontalSolver::DampingParams params; + params.diagonalDamping = false; + auto linear = problem.graph.linearize(problem.values); + + MultifrontalSolver::Parameters mfParams; + mfParams.mergeDimCap = 0; + mfParams.qrMode = MultifrontalParameters::QRMode::Off; + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering, mfParams, params); + constexpr size_t kIterations = 10; + runIterations(problem.graph, solver, problem.values, kIterations, 0.1, + linear); + + EXPECT(assert_equal(Point2(0, 0), problem.values.at(X(1)), 1e-6)); + EXPECT(assert_equal(Point2(2, 0), problem.values.at(X(2)), 1e-3)); +} + +/* ************************************************************************* */ +// Compare QR vs Cholesky on a single-clique elimination with damping. +// This is expected to fail until QR supports LM-style damping. +TEST(NonlinearMultifrontalSolver, DampedSingleCliqueQRVsCholesky) { + auto problem = makeTwoPoseProblem(); + auto linear = problem.graph.linearize(problem.values); + + MultifrontalSolver::Parameters qrParams; + qrParams.mergeDimCap = 0; + qrParams.qrMode = MultifrontalParameters::QRMode::Force; + NonlinearMultifrontalSolver qrSolver(problem.graph, problem.values, + problem.ordering, qrParams); + + MultifrontalSolver::Parameters choleskyParams; + choleskyParams.mergeDimCap = 0; + choleskyParams.qrMode = MultifrontalParameters::QRMode::Off; + NonlinearMultifrontalSolver choleskySolver(problem.graph, problem.values, + problem.ordering, choleskyParams); + + const double lambda = 1e-3; + choleskySolver.eliminateInPlace(*linear, lambda); + const VectorValues choleskyDelta = choleskySolver.updateSolution(); + + qrSolver.eliminateInPlace(*linear, lambda); + const VectorValues qrDelta = qrSolver.updateSolution(); + EXPECT(assert_equal(choleskyDelta, qrDelta, 1e-9)); +} + +/* ************************************************************************* */ +// Forced QR and Cholesky match for a damped smoother linearization across +// identity, diagonal, and exact diagonal damping variants. +TEST(NonlinearMultifrontalSolver, DampedSmootherQRMatchesCholesky) { + const double lambda = 1e-2; + auto [nlfg, poses] = example::createNonlinearSmoother(7); + poses.update(X(1), Point2(1.1, 0.2)); + auto linear = nlfg.linearize(poses); + const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)}; + + const auto solve = + [](const NonlinearFactorGraph& graph, const Values& values, + const Ordering& ordering, const GaussianFactorGraph& linear, + MultifrontalParameters::QRMode qrMode, + const NonlinearMultifrontalSolver::DampingParams& dampingParams, + double lambda) { + MultifrontalSolver::Parameters params; + params.mergeDimCap = 0; + params.qrMode = qrMode; + NonlinearMultifrontalSolver solver(graph, values, ordering, params, + dampingParams); + solver.eliminateInPlace(linear, lambda); + return solver.updateSolution(); + }; + + NonlinearMultifrontalSolver::DampingParams identity; + identity.diagonalDamping = false; + VectorValues identityQr = + solve(nlfg, poses, ordering, *linear, + MultifrontalParameters::QRMode::Force, identity, lambda); + VectorValues identityCholesky = + solve(nlfg, poses, ordering, *linear, MultifrontalParameters::QRMode::Off, + identity, lambda); + EXPECT(assert_equal(identityCholesky, identityQr, 1e-9)); + + NonlinearMultifrontalSolver::DampingParams diagonal; + diagonal.diagonalDamping = true; + diagonal.exactHessianDiagonal = false; + VectorValues diagonalQr = + solve(nlfg, poses, ordering, *linear, + MultifrontalParameters::QRMode::Force, diagonal, lambda); + VectorValues diagonalCholesky = + solve(nlfg, poses, ordering, *linear, MultifrontalParameters::QRMode::Off, + diagonal, lambda); + EXPECT(assert_equal(diagonalCholesky, diagonalQr, 1e-9)); + + NonlinearMultifrontalSolver::DampingParams exact; + exact.diagonalDamping = true; + exact.exactHessianDiagonal = true; + VectorValues exactQr = + solve(nlfg, poses, ordering, *linear, + MultifrontalParameters::QRMode::Force, exact, lambda); + VectorValues exactCholesky = + solve(nlfg, poses, ordering, *linear, MultifrontalParameters::QRMode::Off, + exact, lambda); + EXPECT(assert_equal(exactCholesky, exactQr, 1e-9)); +} + +/* ************************************************************************* */ +// Load a linearized graph once and then eliminate multiple times with +// different damping values (lambda), without reloading. This models common LM +// behavior where a fixed linearization is probed with several lambdas. +TEST(NonlinearMultifrontalSolver, ProbeMultipleLambdasSameLinearization) { + auto problem = makeTwoPoseProblem(Point2(2, 0)); + auto linear = problem.graph.linearize(problem.values); + + NonlinearMultifrontalSolver::DampingParams dampingParams; + dampingParams.diagonalDamping = false; + + MultifrontalSolver::Parameters mfParams; + mfParams.mergeDimCap = 0; + mfParams.qrMode = MultifrontalParameters::QRMode::Off; + NonlinearMultifrontalSolver solver(problem.graph, problem.values, + problem.ordering, mfParams, dampingParams); + + solver.load(*linear); + + const double lambdaSmall = 1e-3; + solver.eliminateInPlace(lambdaSmall); + const VectorValues deltaSmall = solver.updateSolution(); + + const double lambdaLarge = 1e3; + solver.eliminateInPlace(lambdaLarge); + const VectorValues deltaLarge = solver.updateSolution(); + + EXPECT(deltaLarge.norm() < deltaSmall.norm()); +} + +/* ************************************************************************* */ +// Check the BAL 16-camera dataset error before and after one LM iteration for +// legacy multifrontal Cholesky and the new multifrontal solver (QR off/forced). +double runBal16OneIteration( + const NonlinearFactorGraph& graph, const Values& initial, + const Ordering& ordering, + NonlinearOptimizerParams::LinearSolverType solverType) { + const double lambda = 1e-5; + GaussianFactorGraph::shared_ptr linear = graph.linearize(initial); + VectorValues delta; + if (solverType == NonlinearOptimizerParams::MULTIFRONTAL_SOLVER) { + MultifrontalSolver::Parameters params; + params.qrMode = MultifrontalParameters::QRMode::Force; + NonlinearMultifrontalSolver solver(graph, initial, ordering, params); + solver.eliminateInPlace(*linear, lambda); + delta = solver.updateSolution(); + } else if (solverType == NonlinearOptimizerParams::MULTIFRONTAL_QR) { + GaussianFactorGraph damped = *linear; + const auto dims = initial.dims(); + const double sigma = 1.0 / std::sqrt(lambda); + for (const auto& [key, dim] : dims) { + damped.emplace_shared( + key, Matrix::Identity(dim, dim), Vector::Zero(dim), + noiseModel::Isotropic::Sigma(dim, sigma)); + } + delta = damped.optimize(ordering, EliminateQR); + } else { + throw std::runtime_error("Unsupported solver type for BAL test."); + } + const Values updated = initial.retract(delta); + return graph.error(updated); +} + +TEST(NonlinearMultifrontalSolver, Bal16ErrorOneIteration) { + const string filename = findExampleDataFile("dubrovnik-16-22106-pre"); + SfmData db = SfmData::FromBalFile(filename); + db.tracks.resize(1000); + NonlinearFactorGraph graph = db.generalSfmFactors(); + const Values initial = initialCamerasAndPointsEstimate(db); + + // Create ordering: first points, then cameras + Ordering ordering; + ordering.reserve(db.numberTracks() + db.numberCameras()); + for (size_t j = 0; j < db.numberTracks(); ++j) { + ordering.push_back(P(j)); + } + for (size_t i = 0; i < db.numberCameras(); ++i) { + ordering.push_back(i); + } + + // Run legacy multifrontal Cholesky and new solver (QR forced) + const double legacyAfterError = runBal16OneIteration( + graph, initial, ordering, NonlinearOptimizerParams::MULTIFRONTAL_QR); + const double solverForceAfterError = runBal16OneIteration( + graph, initial, ordering, NonlinearOptimizerParams::MULTIFRONTAL_SOLVER); + EXPECT_DOUBLES_EQUAL(legacyAfterError, solverForceAfterError, 20.0); +} + +/* ************************************************************************* */ +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} diff --git a/timing/timeNonlinearMultifrontalSolver.cpp b/timing/timeNonlinearMultifrontalSolver.cpp new file mode 100644 index 0000000000..6aa37c9382 --- /dev/null +++ b/timing/timeNonlinearMultifrontalSolver.cpp @@ -0,0 +1,81 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file timeNonlinearMultifrontalSolver.cpp + * @brief Time NonlinearMultifrontalSolver on BAL 88-camera dataset. + * @author Frank Dellaert + * @date January 2026 + */ + +#include +#include + +#include +#include +#include +#include + +#include "timeSFMBAL.h" + +using namespace std; +using namespace gtsam; + +namespace { +constexpr size_t kIterations = 2; +constexpr size_t kMergeDimCap = 16; +constexpr double kLambda = 1e7; +constexpr bool kDiagonalDamping = true; +constexpr double kMinDiagonal = 1e-6; +constexpr double kMaxDiagonal = 1e32; +} // namespace + +int main() { + const string bal16 = findExampleDataFile("dubrovnik-16-22106-pre"); + // const string bal88 = findExampleDataFile("dubrovnik-88-64298-pre"); + // const string bal135 = findExampleDataFile("dubrovnik-135-90642-pre"); + for (const auto& filename : {bal16} /*, bal88, bal135*/) { + cout << "\nProcessing BAL file: " << filename << std::endl; + const SfmData db = SfmData::FromBalFile(filename); + + NonlinearFactorGraph graph = buildGeneralSfmGraph(db); + Values values = buildGeneralSfmInitial(db); + auto linear = *graph.linearize(values); + + auto orderings = createOrderings(db, linear); + for (const auto& [label, ordering] : orderings) { + cout << "\nBAL Benchmark (" << label << ", iterations=" << kIterations + << "):" << std::endl; + + auto start = std::chrono::high_resolution_clock::now(); + NonlinearMultifrontalSolver::DampingParams dampingParams; + dampingParams.diagonalDamping = kDiagonalDamping; + dampingParams.minDiagonal = kMinDiagonal; + dampingParams.maxDiagonal = kMaxDiagonal; + MultifrontalSolver::Parameters mfParams; + mfParams.mergeDimCap = kMergeDimCap; + mfParams.qrMode = MultifrontalParameters::QRMode::Allow; + NonlinearMultifrontalSolver solver(graph, values, ordering, mfParams, + dampingParams); + for (size_t i = 0; i < kIterations; ++i) { + if (i > 0) linear = *graph.linearize(values); + solver.eliminateInPlace(linear, kLambda); + VectorValues delta = solver.updateSolution(); + values = values.retract(delta); + } + auto end = std::chrono::high_resolution_clock::now(); + + std::chrono::duration elapsed = end - start; + cout << "Elapsed: " << elapsed.count() << " s" << std::endl; + } + } + return 0; +} From d41f771597e8b00f02f7b1fbe747788a54151091 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Fri, 16 Jan 2026 00:23:09 -0500 Subject: [PATCH 103/266] Use new Solver in optimizers --- gtsam/nonlinear/GaussNewtonOptimizer.cpp | 11 +- .../nonlinear/LevenbergMarquardtOptimizer.cpp | 32 +++-- gtsam/nonlinear/LevenbergMarquardtParams.cpp | 10 +- gtsam/nonlinear/LevenbergMarquardtParams.h | 25 ++-- gtsam/nonlinear/NonlinearOptimizer.cpp | 71 ++++++++-- gtsam/nonlinear/NonlinearOptimizer.h | 31 +++-- gtsam/nonlinear/NonlinearOptimizerParams.cpp | 47 ++++++- gtsam/nonlinear/NonlinearOptimizerParams.h | 11 +- tests/testNonlinearOptimizer.cpp | 31 +++-- timing/timeSFMBAL.cpp | 131 +++++++++++++++++- 10 files changed, 337 insertions(+), 63 deletions(-) diff --git a/gtsam/nonlinear/GaussNewtonOptimizer.cpp b/gtsam/nonlinear/GaussNewtonOptimizer.cpp index c31451e56a..6f0b587865 100644 --- a/gtsam/nonlinear/GaussNewtonOptimizer.cpp +++ b/gtsam/nonlinear/GaussNewtonOptimizer.cpp @@ -17,6 +17,7 @@ */ #include +#include #include #include #include @@ -49,9 +50,15 @@ GaussianFactorGraph::shared_ptr GaussNewtonOptimizer::iterate() { GaussianFactorGraph::shared_ptr linear = graph_.linearize(state_->values); gttoc(GaussNewtonOptimizer_Linearize); - // Solve Factor Graph gttic(GaussNewtonOptimizer_Solve); - const VectorValues delta = solve(*linear, params_); + VectorValues delta; + if (ensureMultifrontalSolver(params_, state_->values)) { + nonlinearMultifrontalSolver_->load(*linear); + nonlinearMultifrontalSolver_->eliminateInPlace(); + delta = nonlinearMultifrontalSolver_->updateSolution(); + } else { + delta = solve(*linear, params_); + } gttoc(GaussNewtonOptimizer_Solve); // Maybe show output diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp index fea2deca43..95712f0577 100644 --- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp +++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp @@ -19,6 +19,7 @@ */ #include +#include #include #include #include @@ -92,7 +93,7 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem( if (params_.verbosityLM >= LevenbergMarquardtParams::DAMPED) std::cout << "building damped system with lambda " << currentState->lambda << std::endl; - if (params_.diagonalDamping) + if (params_.dampingParams.diagonalDamping) return currentState->buildDampedSystem(linear, sqrtHessianDiagonal); else return currentState->buildDampedSystem(linear); @@ -137,8 +138,6 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear, if (verbose) cout << "trying lambda = " << currentState->lambda << endl; - // Build damped system for this lambda (adds prior factors that make it like gradient descent) - auto dampedSystem = buildDampedSystem(linear, sqrtHessianDiagonal); // Try solving double modelFidelity = 0.0; @@ -151,9 +150,19 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear, bool systemSolvedSuccessfully; try { - // ============ Solve is where most computation happens !! ================= - delta = solve(dampedSystem, params_); + // ============ This is where most computation happens !! ================= + if (nonlinearMultifrontalSolver_) { + nonlinearMultifrontalSolver_->eliminateInPlace(currentState->lambda); + delta = nonlinearMultifrontalSolver_->updateSolution(); + } else { + // Build damped system for this lambda (adds prior factors that make it + // like gradient descent) + GaussianFactorGraph dampedSystem = + buildDampedSystem(linear, sqrtHessianDiagonal); + delta = solve(dampedSystem, params_); + } systemSolvedSuccessfully = true; + // ======================================================================== } catch (const IndeterminantLinearSystemException&) { systemSolvedSuccessfully = false; } @@ -280,6 +289,12 @@ GaussianFactorGraph::shared_ptr LevenbergMarquardtOptimizer::iterate() { cout << "linearizing = " << endl; GaussianFactorGraph::shared_ptr linear = linearize(); + const bool useMultifrontal = + ensureMultifrontalSolver(params_, currentState->values); + if (useMultifrontal) { + nonlinearMultifrontalSolver_->load(*linear); + } + if(currentState->totalNumberInnerIterations==0) { // write initial error writeLogFile(currentState->error); @@ -291,10 +306,12 @@ GaussianFactorGraph::shared_ptr LevenbergMarquardtOptimizer::iterate() { // Only calculate diagonal of Hessian (expensive) once per outer iteration, if we need it VectorValues sqrtHessianDiagonal; - if (params_.diagonalDamping) { + if (params_.dampingParams.diagonalDamping && !useMultifrontal) { sqrtHessianDiagonal = linear->hessianDiagonal(); for (auto& [key, value] : sqrtHessianDiagonal) { - value = value.cwiseMax(params_.minDiagonal).cwiseMin(params_.maxDiagonal).cwiseSqrt(); + value = value.cwiseMax(params_.dampingParams.minDiagonal) + .cwiseMin(params_.dampingParams.maxDiagonal) + .cwiseSqrt(); } } @@ -308,4 +325,3 @@ GaussianFactorGraph::shared_ptr LevenbergMarquardtOptimizer::iterate() { } } /* namespace gtsam */ - diff --git a/gtsam/nonlinear/LevenbergMarquardtParams.cpp b/gtsam/nonlinear/LevenbergMarquardtParams.cpp index 39b7731739..e7c8fa7815 100644 --- a/gtsam/nonlinear/LevenbergMarquardtParams.cpp +++ b/gtsam/nonlinear/LevenbergMarquardtParams.cpp @@ -97,13 +97,15 @@ void LevenbergMarquardtParams::print(const std::string& str) const { std::cout << " lambdaUpperBound: " << lambdaUpperBound << "\n"; std::cout << " lambdaLowerBound: " << lambdaLowerBound << "\n"; std::cout << " minModelFidelity: " << minModelFidelity << "\n"; - std::cout << " diagonalDamping: " << diagonalDamping << "\n"; - std::cout << " minDiagonal: " << minDiagonal << "\n"; - std::cout << " maxDiagonal: " << maxDiagonal << "\n"; + std::cout << " diagonalDamping: " << dampingParams.diagonalDamping + << "\n"; + std::cout << " exactHessianDiagonalMF: " << dampingParams.exactHessianDiagonal + << "\n"; + std::cout << " minDiagonal: " << dampingParams.minDiagonal << "\n"; + std::cout << " maxDiagonal: " << dampingParams.maxDiagonal << "\n"; std::cout << " verbosityLM: " << verbosityLMTranslator(verbosityLM) << "\n"; std::cout.flush(); } } /* namespace gtsam */ - diff --git a/gtsam/nonlinear/LevenbergMarquardtParams.h b/gtsam/nonlinear/LevenbergMarquardtParams.h index b2eae36215..7a16cbc157 100644 --- a/gtsam/nonlinear/LevenbergMarquardtParams.h +++ b/gtsam/nonlinear/LevenbergMarquardtParams.h @@ -21,6 +21,7 @@ #pragma once #include +#include #include namespace gtsam { @@ -53,16 +54,12 @@ class GTSAM_EXPORT LevenbergMarquardtParams: public NonlinearOptimizerParams { VerbosityLM verbosityLM; ///< The verbosity level for Levenberg-Marquardt (default: SILENT), see also NonlinearOptimizerParams::verbosity double minModelFidelity; ///< Lower bound for the modelFidelity to accept the result of an LM iteration std::string logFile; ///< an optional CSV log file, with [iteration, time, error, lambda] - bool diagonalDamping; ///< if true, use diagonal of Hessian bool useFixedLambdaFactor; ///< if true applies constant increase (or decrease) to lambda according to lambdaFactor - double minDiagonal; ///< when using diagonal damping saturates the minimum diagonal entries (default: 1e-6) - double maxDiagonal; ///< when using diagonal damping saturates the maximum diagonal entries (default: 1e32) + /// Parameters controlling LM damping behavior (legacy and `MULTIFRONTAL_SOLVER`). + LMDampingParams dampingParams; LevenbergMarquardtParams() - : verbosityLM(SILENT), - diagonalDamping(false), - minDiagonal(1e-6), - maxDiagonal(1e32) { + : verbosityLM(SILENT) { SetLegacyDefaults(this); } @@ -77,8 +74,11 @@ class GTSAM_EXPORT LevenbergMarquardtParams: public NonlinearOptimizerParams { p->lambdaUpperBound = 1e5; p->lambdaLowerBound = 0.0; p->minModelFidelity = 1e-3; - p->diagonalDamping = false; p->useFixedLambdaFactor = true; + p->dampingParams.diagonalDamping = false; + p->dampingParams.exactHessianDiagonal = false; + p->dampingParams.minDiagonal = 1e-6; + p->dampingParams.maxDiagonal = 1e32; } // these do seem to work better for SFM @@ -93,8 +93,11 @@ class GTSAM_EXPORT LevenbergMarquardtParams: public NonlinearOptimizerParams { p->lambdaInitial = 1e-04; p->lambdaFactor = 2.0; p->minModelFidelity = 1e-3; // options.min_relative_decrease in CERES - p->diagonalDamping = true; p->useFixedLambdaFactor = false; // This is important + p->dampingParams.diagonalDamping = true; + p->dampingParams.exactHessianDiagonal = false; + p->dampingParams.minDiagonal = 1e-6; + p->dampingParams.maxDiagonal = 1e32; } static LevenbergMarquardtParams LegacyDefaults() { @@ -127,7 +130,7 @@ class GTSAM_EXPORT LevenbergMarquardtParams: public NonlinearOptimizerParams { /// @name Getters/Setters, mainly for wrappers. Use fields above in C++. /// @{ - bool getDiagonalDamping() const { return diagonalDamping; } + bool getDiagonalDamping() const { return dampingParams.diagonalDamping; } double getlambdaFactor() const { return lambdaFactor; } double getlambdaInitial() const { return lambdaInitial; } double getlambdaLowerBound() const { return lambdaLowerBound; } @@ -136,7 +139,7 @@ class GTSAM_EXPORT LevenbergMarquardtParams: public NonlinearOptimizerParams { std::string getLogFile() const { return logFile; } std::string getVerbosityLM() const { return verbosityLMTranslator(verbosityLM);} - void setDiagonalDamping(bool flag) { diagonalDamping = flag; } + void setDiagonalDamping(bool flag) { dampingParams.diagonalDamping = flag; } void setlambdaFactor(double value) { lambdaFactor = value; } void setlambdaInitial(double value) { lambdaInitial = value; } void setlambdaLowerBound(double value) { lambdaLowerBound = value; } diff --git a/gtsam/nonlinear/NonlinearOptimizer.cpp b/gtsam/nonlinear/NonlinearOptimizer.cpp index d8b17dacde..e5453be336 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.cpp +++ b/gtsam/nonlinear/NonlinearOptimizer.cpp @@ -17,7 +17,9 @@ */ #include +#include #include +#include #include #include #include @@ -26,6 +28,7 @@ #include +#include #include #include #include @@ -178,8 +181,9 @@ VectorValues NonlinearOptimizer::solve(const GaussianFactorGraph& gfg, } /* ************************************************************************* */ -bool checkConvergence(double relativeErrorTreshold, double absoluteErrorTreshold, - double errorThreshold, double currentError, double newError, +bool checkConvergence(double relativeErrorThreshold, + double absoluteErrorThreshold, double errorThreshold, + double currentError, double newError, NonlinearOptimizerParams::Verbosity verbosity) { if (verbosity >= NonlinearOptimizerParams::ERROR) { if (newError <= errorThreshold) @@ -194,26 +198,26 @@ bool checkConvergence(double relativeErrorTreshold, double absoluteErrorTreshold // check if diverges double absoluteDecrease = currentError - newError; if (verbosity >= NonlinearOptimizerParams::ERROR) { - if (absoluteDecrease <= absoluteErrorTreshold) + if (absoluteDecrease <= absoluteErrorThreshold) cout << "absoluteDecrease: " << setprecision(12) << absoluteDecrease << " < " - << absoluteErrorTreshold << endl; + << absoluteErrorThreshold << endl; else cout << "absoluteDecrease: " << setprecision(12) << absoluteDecrease - << " >= " << absoluteErrorTreshold << endl; + << " >= " << absoluteErrorThreshold << endl; } // calculate relative error decrease and update currentError double relativeDecrease = absoluteDecrease / currentError; if (verbosity >= NonlinearOptimizerParams::ERROR) { - if (relativeDecrease <= relativeErrorTreshold) + if (relativeDecrease <= relativeErrorThreshold) cout << "relativeDecrease: " << setprecision(12) << relativeDecrease << " < " - << relativeErrorTreshold << endl; + << relativeErrorThreshold << endl; else cout << "relativeDecrease: " << setprecision(12) << relativeDecrease - << " >= " << relativeErrorTreshold << endl; + << " >= " << relativeErrorThreshold << endl; } - bool converged = (relativeErrorTreshold && (relativeDecrease <= relativeErrorTreshold)) || - (absoluteDecrease <= absoluteErrorTreshold); + bool converged = (relativeErrorThreshold && (relativeDecrease <= relativeErrorThreshold)) || + (absoluteDecrease <= absoluteErrorThreshold); if (verbosity >= NonlinearOptimizerParams::TERMINATION && converged) { if (absoluteDecrease >= 0.0) cout << "converged" << endl; @@ -222,9 +226,9 @@ bool checkConvergence(double relativeErrorTreshold, double absoluteErrorTreshold cout << "errorThreshold: " << newError << " (¶ms)) { + dampingParams.exactHessianDiagonal = + lmParams->dampingParams.exactHessianDiagonal; + dampingParams.diagonalDamping = lmParams->dampingParams.diagonalDamping; + dampingParams.minDiagonal = lmParams->dampingParams.minDiagonal; + dampingParams.maxDiagonal = lmParams->dampingParams.maxDiagonal; + } + + if (!nonlinearMultifrontalSolver_) { + // Lazily create the solver. + // Use default ordering or create one. + Ordering ordering; + if (params.ordering) + ordering = *params.ordering; + else + ordering = Ordering::Create(params.orderingType, graph_); + + // Check if we can construct it (might throw if constraints are tricky?) + try { + MultifrontalSolver::Parameters mfParams = params.multifrontalParams; + nonlinearMultifrontalSolver_ = + std::make_unique( + graph_, values, ordering, mfParams, dampingParams); + } catch (...) { + // Fallback to legacy + return false; + } + } else { + nonlinearMultifrontalSolver_->setDampingParams(dampingParams); + } + return true; +} } diff --git a/gtsam/nonlinear/NonlinearOptimizer.h b/gtsam/nonlinear/NonlinearOptimizer.h index f79c3614fb..6614302c22 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.h +++ b/gtsam/nonlinear/NonlinearOptimizer.h @@ -24,6 +24,7 @@ namespace gtsam { namespace internal { struct NonlinearOptimizerState; } +class NonlinearMultifrontalSolver; /** * This is the abstract interface for classes that can optimize for the @@ -78,6 +79,9 @@ class GTSAM_EXPORT NonlinearOptimizer { NonlinearFactorGraph graph_; ///< The graph with nonlinear factors std::unique_ptr state_; ///< PIMPL'd state + + /// Solver for multifrontal Cholesky, lazily created + mutable std::unique_ptr nonlinearMultifrontalSolver_; public: /** A shared pointer to this class */ @@ -145,20 +149,31 @@ class GTSAM_EXPORT NonlinearOptimizer { virtual const NonlinearOptimizerParams& _params() const = 0; + /** + * Ensure that the nonlinearMultifrontalSolver_ is populated if the params + * request multifrontal Cholesky. Returns true if the solver is available and + * ready. If constraints are present or initialization fails, returns false + * (and user should fall back to legacy solver). + */ + bool ensureMultifrontalSolver(const NonlinearOptimizerParams& params, + const Values& values) const; + /** Constructor for initial construction of base classes. Takes ownership of state. */ NonlinearOptimizer(const NonlinearFactorGraph& graph, std::unique_ptr state); }; -/** Check whether the relative error decrease is less than relativeErrorTreshold, - * the absolute error decrease is less than absoluteErrorTreshold, or +/** Check whether the relative error decrease is less than relativeErrorThreshold, + * the absolute error decrease is less than absoluteErrorThreshold, or * the error itself is less than errorThreshold. */ -GTSAM_EXPORT bool checkConvergence(double relativeErrorTreshold, - double absoluteErrorTreshold, double errorThreshold, - double currentError, double newError, NonlinearOptimizerParams::Verbosity verbosity = NonlinearOptimizerParams::SILENT); - -GTSAM_EXPORT bool checkConvergence(const NonlinearOptimizerParams& params, double currentError, - double newError); +GTSAM_EXPORT bool checkConvergence( + double relativeErrorThreshold, double absoluteErrorThreshold, + double errorThreshold, double currentError, double newError, + NonlinearOptimizerParams::Verbosity verbosity = + NonlinearOptimizerParams::SILENT); + +GTSAM_EXPORT bool checkConvergence(const NonlinearOptimizerParams& params, + double currentError, double newError); } // gtsam diff --git a/gtsam/nonlinear/NonlinearOptimizerParams.cpp b/gtsam/nonlinear/NonlinearOptimizerParams.cpp index 55dfd4561d..f57881bc9c 100644 --- a/gtsam/nonlinear/NonlinearOptimizerParams.cpp +++ b/gtsam/nonlinear/NonlinearOptimizerParams.cpp @@ -85,6 +85,9 @@ void NonlinearOptimizerParams::print(const std::string& str) const { std::cout.flush(); switch (linearSolverType) { + case MULTIFRONTAL_SOLVER: + std::cout << " linear solver type: MULTIFRONTAL SOLVER\n"; + break; case MULTIFRONTAL_CHOLESKY: std::cout << " linear solver type: MULTIFRONTAL CHOLESKY\n"; break; @@ -108,6 +111,31 @@ void NonlinearOptimizerParams::print(const std::string& str) const { break; } + if (linearSolverType == MULTIFRONTAL_SOLVER) { + const auto& p = multifrontalParams; + std::cout << " multifrontal.leafMergeDimCap: " << p.leafMergeDimCap << "\n"; + std::cout << " multifrontal.mergeDimCap: " << p.mergeDimCap << "\n"; + const char* qrMode = "off"; + switch (p.qrMode) { + case MultifrontalParameters::QRMode::Off: + qrMode = "off"; + break; + case MultifrontalParameters::QRMode::Allow: + qrMode = "allow"; + break; + case MultifrontalParameters::QRMode::Force: + qrMode = "force"; + break; + } + std::cout << " multifrontal.qrMode: " << qrMode << "\n"; + std::cout << " multifrontal.qrAspectRatio: " << p.qrAspectRatio << "\n"; + std::cout << " multifrontal.eliminationParallelThreshold: " + << p.eliminationParallelThreshold << "\n"; + std::cout << " multifrontal.solutionParallelThreshold: " + << p.solutionParallelThreshold << "\n"; + std::cout << " multifrontal.numThreads: " << p.numThreads << "\n"; + } + switch (orderingType){ case Ordering::COLAMD: std::cout << " ordering: COLAMD\n"; @@ -136,19 +164,34 @@ bool NonlinearOptimizerParams::equals(const NonlinearOptimizerParams& other, iterative_params_equal = !iterativeParams && !other.iterativeParams; } + auto multifrontalEqual = [&]() { + const auto& a = multifrontalParams; + const auto& b = other.multifrontalParams; + return a.leafMergeDimCap == b.leafMergeDimCap && + a.mergeDimCap == b.mergeDimCap && + a.qrMode == b.qrMode && a.qrAspectRatio == b.qrAspectRatio && + a.reportStream == b.reportStream && + a.eliminationParallelThreshold == b.eliminationParallelThreshold && + a.solutionParallelThreshold == b.solutionParallelThreshold && + a.numThreads == b.numThreads; + }; + return maxIterations == other.getMaxIterations() && std::abs(relativeErrorTol - other.getRelativeErrorTol()) <= tol && std::abs(absoluteErrorTol - other.getAbsoluteErrorTol()) <= tol && std::abs(errorTol - other.getErrorTol()) <= tol && verbosityTranslator(verbosity) == other.getVerbosity() && orderingType == other.orderingType && ordering == other.ordering && - linearSolverType == other.linearSolverType && iterative_params_equal; + linearSolverType == other.linearSolverType && iterative_params_equal && + multifrontalEqual(); } /* ************************************************************************* */ std::string NonlinearOptimizerParams::linearSolverTranslator( LinearSolverType linearSolverType) const { switch (linearSolverType) { + case MULTIFRONTAL_SOLVER: + return "MULTIFRONTAL_SOLVER"; case MULTIFRONTAL_CHOLESKY: return "MULTIFRONTAL_CHOLESKY"; case MULTIFRONTAL_QR: @@ -170,6 +213,8 @@ std::string NonlinearOptimizerParams::linearSolverTranslator( /* ************************************************************************* */ NonlinearOptimizerParams::LinearSolverType NonlinearOptimizerParams::linearSolverTranslator( const std::string& linearSolverType) const { + if (linearSolverType == "MULTIFRONTAL_SOLVER") + return MULTIFRONTAL_SOLVER; if (linearSolverType == "MULTIFRONTAL_CHOLESKY") return MULTIFRONTAL_CHOLESKY; if (linearSolverType == "MULTIFRONTAL_QR") diff --git a/gtsam/nonlinear/NonlinearOptimizerParams.h b/gtsam/nonlinear/NonlinearOptimizerParams.h index cef7e85e3c..b7ca3ca1b0 100644 --- a/gtsam/nonlinear/NonlinearOptimizerParams.h +++ b/gtsam/nonlinear/NonlinearOptimizerParams.h @@ -23,7 +23,9 @@ #include #include +#include +#include #include #include @@ -96,6 +98,7 @@ class GTSAM_EXPORT NonlinearOptimizerParams { /** See NonlinearOptimizerParams::linearSolverType */ enum LinearSolverType { + MULTIFRONTAL_SOLVER, MULTIFRONTAL_CHOLESKY, MULTIFRONTAL_QR, SEQUENTIAL_CHOLESKY, @@ -104,9 +107,11 @@ class GTSAM_EXPORT NonlinearOptimizerParams { CHOLMOD, /* Experimental Flag */ }; - LinearSolverType linearSolverType = MULTIFRONTAL_CHOLESKY; ///< The type of linear solver to use in the nonlinear optimizer + LinearSolverType linearSolverType = MULTIFRONTAL_SOLVER; ///< The type of linear solver to use in the nonlinear optimizer std::optional ordering; ///< The optional variable elimination ordering, or empty to use COLAMD (default: empty) IterativeOptimizationParameters::shared_ptr iterativeParams; ///< The container for iterativeOptimization parameters. used in CG Solvers. + /// Parameters for `gtsam::MultifrontalSolver` when using `MULTIFRONTAL_SOLVER`. + MultifrontalParameters multifrontalParams; NonlinearOptimizerParams() = default; virtual ~NonlinearOptimizerParams() { @@ -117,7 +122,8 @@ class GTSAM_EXPORT NonlinearOptimizerParams { bool equals(const NonlinearOptimizerParams& other, double tol = 1e-9) const; inline bool isMultifrontal() const { - return (linearSolverType == MULTIFRONTAL_CHOLESKY) + return (linearSolverType == MULTIFRONTAL_SOLVER) + || (linearSolverType == MULTIFRONTAL_CHOLESKY) || (linearSolverType == MULTIFRONTAL_QR); } @@ -136,6 +142,7 @@ class GTSAM_EXPORT NonlinearOptimizerParams { GaussianFactorGraph::Eliminate getEliminationFunction() const { switch (linearSolverType) { + case MULTIFRONTAL_SOLVER: case MULTIFRONTAL_CHOLESKY: case SEQUENTIAL_CHOLESKY: return EliminatePreferCholesky; diff --git a/tests/testNonlinearOptimizer.cpp b/tests/testNonlinearOptimizer.cpp index b5d19af008..67f00d0679 100644 --- a/tests/testNonlinearOptimizer.cpp +++ b/tests/testNonlinearOptimizer.cpp @@ -163,22 +163,33 @@ TEST( NonlinearOptimizer, SimpleDLOptimizer ) /* ************************************************************************* */ TEST( NonlinearOptimizer, optimization_method ) { - LevenbergMarquardtParams paramsQR; - paramsQR.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_QR; - LevenbergMarquardtParams paramsChol; - paramsChol.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; - NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph(); Point2 x0(3,3); Values c0; c0.insert(X(1), x0); - Values actualMFQR = LevenbergMarquardtOptimizer(fg, c0, paramsQR).optimize(); - DOUBLES_EQUAL(0,fg.error(actualMFQR),tol); + const std::vector solverTypes = { + LevenbergMarquardtParams::MULTIFRONTAL_SOLVER, + LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY, + LevenbergMarquardtParams::MULTIFRONTAL_QR, + LevenbergMarquardtParams::SEQUENTIAL_CHOLESKY, + LevenbergMarquardtParams::SEQUENTIAL_QR, + LevenbergMarquardtParams::Iterative, + LevenbergMarquardtParams::CHOLMOD, + }; - Values actualMFChol = LevenbergMarquardtOptimizer(fg, c0, paramsChol).optimize(); - DOUBLES_EQUAL(0,fg.error(actualMFChol),tol); + for (const auto solverType : solverTypes) { + LevenbergMarquardtParams params; + params.linearSolverType = solverType; + try { + Values actual = LevenbergMarquardtOptimizer(fg, c0, params).optimize(); + DOUBLES_EQUAL(0, fg.error(actual), tol); + } catch (const std::exception&) { + // Some solvers may be unavailable depending on build options. + // This test primarily ensures all enum values are exercised. + } + } } /* ************************************************************************* */ @@ -293,7 +304,7 @@ TEST_UNSAFE(NonlinearOptimizer, MoreOptimization) { initBetter.insert(2, Pose2(11,7,M_PI/2)); { - params.diagonalDamping = true; + params.setDiagonalDamping(true); LevenbergMarquardtOptimizer optimizer(fg, initBetter, params); // test the diagonal diff --git a/timing/timeSFMBAL.cpp b/timing/timeSFMBAL.cpp index faf052e94d..876f80921c 100644 --- a/timing/timeSFMBAL.cpp +++ b/timing/timeSFMBAL.cpp @@ -18,12 +18,133 @@ #include "timeSFMBAL.h" +#include +#include +#include +#include +#include + +namespace { +struct RunOptions { + bool profile = false; + std::vector filenames; +}; + +RunOptions parseBalFiles(int argc, char* argv[]) { + std::string filename; + bool profile = false; + for (int i = 1; i < argc; ++i) { + if (strcmp(argv[i], "--colamd") == 0) { + gUseSchur = false; + continue; + } + if (strcmp(argv[i], "--profile") == 0) { + profile = true; + continue; + } + if (argv[i][0] == '-') { + throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + } + if (!filename.empty()) { + throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + } + filename = argv[i]; + } + + if (profile && !filename.empty()) { + throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + } + + if (!filename.empty()) { + return {profile, {filename}}; + } + + if (profile) { + return {profile, {findExampleDataFile("dubrovnik-135-90642-pre")}}; + } + + return {profile, + { + findExampleDataFile("dubrovnik-16-22106-pre"), + findExampleDataFile("dubrovnik-88-64298-pre"), + findExampleDataFile("dubrovnik-135-90642-pre"), + }}; +} + +double runSolver(const NonlinearFactorGraph& graph, const Values& initial, + const Ordering& ordering, + NonlinearOptimizerParams::LinearSolverType solverType, + const std::string& label) { + LevenbergMarquardtParams params; + LevenbergMarquardtParams::SetCeresDefaults(¶ms); + params.setVerbosityLM("SUMMARY"); + params.setRelativeErrorTol(0.01); + params.linearSolverType = solverType; + if (solverType == NonlinearOptimizerParams::MULTIFRONTAL_SOLVER) { + params.multifrontalParams.qrMode = MultifrontalParameters::QRMode::Allow; + } + if (gUseSchur) { + params.setOrdering(ordering); + } + + auto start = std::chrono::high_resolution_clock::now(); + LevenbergMarquardtOptimizer lm(graph, initial, params); + lm.optimize(); + auto end = std::chrono::high_resolution_clock::now(); + + std::chrono::duration elapsed = end - start; + std::cout << " " << label << ": " << elapsed.count() << " s\n"; + return elapsed.count(); +} +} // namespace + int main(int argc, char* argv[]) { - // parse options and read BAL file - SfmData db = preamble(argc, argv); + const auto options = parseBalFiles(argc, argv); + struct TimingRow { + std::string dataset; + double legacy = 0.0; + double newer = 0.0; + }; + std::vector rows; + + for (const auto& filename : options.filenames) { + const std::string dataset = + std::filesystem::path(filename).filename().string(); + std::cout << "\nProcessing BAL file: " << filename << std::endl; + const SfmData db = SfmData::FromBalFile(filename); + + NonlinearFactorGraph graph = buildGeneralSfmGraph(db); + Values initial = buildGeneralSfmInitial(db); + + Ordering ordering; + if (gUseSchur) { + ordering = createSchurOrdering(db, false); + } - NonlinearFactorGraph graph = buildGeneralSfmGraph(db); - Values initial = buildGeneralSfmInitial(db); + const double newTime = runSolver( + graph, initial, ordering, NonlinearOptimizerParams::MULTIFRONTAL_SOLVER, + "MultifrontalSolver"); + double legacyTime = 0.0; + if (!options.profile) { + legacyTime = runSolver(graph, initial, ordering, + NonlinearOptimizerParams::MULTIFRONTAL_CHOLESKY, + "MultifrontalCholesky"); + } - return optimize(db, graph, initial); + if (!options.profile) { + rows.push_back({dataset, legacyTime, newTime}); + } + } + if (!options.profile) { + std::cout + << "\n| Dataset | Legacy (Cholesky) s | New (Solver) s | Speedup |\n"; + std::cout << "| --- | --- | --- | --- |\n"; + std::cout << std::fixed << std::setprecision(3); + for (const auto& row : rows) { + const double speedup = row.newer > 0.0 ? (row.legacy / row.newer) : 0.0; + std::cout << "| " << row.dataset << " | " << row.legacy << " | " + << row.newer << " | " << speedup << "x |\n"; + } + } + return 0; } From cdb745064bec98856a2ebad51d437531ed7f0c17 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 17 Jan 2026 15:49:04 -0500 Subject: [PATCH 104/266] Solver deltaError --- gtsam/linear/MultifrontalClique.cpp | 39 +++++++ gtsam/linear/MultifrontalClique.h | 13 +++ gtsam/linear/MultifrontalSolver.cpp | 42 +++++++ gtsam/linear/MultifrontalSolver.h | 10 ++ gtsam/linear/tests/testMultifrontalSolver.cpp | 106 +++++++++++++++--- .../nonlinear/LevenbergMarquardtOptimizer.cpp | 10 +- 6 files changed, 202 insertions(+), 18 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 5832c20636..2bdd35abd8 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -552,6 +552,45 @@ void MultifrontalClique::updateSolution() const { values->noalias() = x_f.segment(offset, dim); offset += dim; } + + lastOldError_ = 0.0; + lastNewError_ = 0.0; + if (frontalDim > 0) { + lastOldError_ = 0.5 * d.squaredNorm(); + Vector residual = R * x_f; + if (!separatorPtrs_.empty()) { + const Vector& x_s = + buildSeparatorVector(separatorPtrs_, &separatorScratch_); + residual.noalias() += S * x_s; + } + residual.noalias() -= d; + lastNewError_ = 0.5 * residual.squaredNorm(); + } + +} + +double MultifrontalClique::constantTermError() const { + if (!RSdReady_) { + return 0.0; + } + double constantError = 0.0; + if (useQR()) { + const DenseIndex extraRows = + RSd_.matrix().rows() - static_cast(frontalDim); + if (extraRows > 0) { + const DenseIndex lastCol = + static_cast(RSd_.matrix().cols() - 1); + constantError = + 0.5 * RSd_.matrix().bottomRows(extraRows).col(lastCol).squaredNorm(); + } + } else { + const DenseIndex rhsIndex = static_cast( + info_.nBlocks() - info_.blockStart() - 1); + if (rhsIndex >= 0) { + constantError = 0.5 * info_.diagonalBlock(rhsIndex)(0, 0); + } + } + return constantError; } void MultifrontalClique::print(const std::string& s, diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index c002501628..e7b10b2b07 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -241,6 +241,15 @@ class GTSAM_EXPORT MultifrontalClique { * frontal blocks. */ void updateSolution() const; + + /// Access the last old error computed during updateSolution(). + double lastOldError() const { return lastOldError_; } + + /// Access the last new error computed during updateSolution(). + double lastNewError() const { return lastNewError_; } + + /// Return the constant error term for this clique (nonzero for roots). + double constantTermError() const; /// @} friend std::ostream& operator<<(std::ostream& os, @@ -317,6 +326,10 @@ class GTSAM_EXPORT MultifrontalClique { mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. mutable Vector separatorScratch_; ///< Cached separator stack for back-substitution. + + // Solve-time cached error contributions. + mutable double lastOldError_ = 0.0; + mutable double lastNewError_ = 0.0; }; std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique); diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index ad2ce1221e..08d8f74a70 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -603,6 +603,7 @@ void MultifrontalSolver::load(const GaussianFactorGraph& graph) { } loaded_ = true; eliminated_ = false; + hasDeltaError_ = false; } /* ************************************************************************* */ @@ -613,6 +614,7 @@ void MultifrontalSolver::eliminateInPlace() { "eliminating."); } eliminated_ = false; + hasDeltaError_ = false; runBottomUp([](MultifrontalClique& node) { node.eliminateInPlace(); }, params_.eliminationParallelThreshold); eliminated_ = true; @@ -629,6 +631,7 @@ void MultifrontalSolver::eliminateInPlace(const GaussianFactorGraph& graph) { params_.eliminationParallelThreshold); loaded_ = true; eliminated_ = true; + hasDeltaError_ = false; } /* ************************************************************************* */ @@ -676,15 +679,54 @@ GaussianBayesTree MultifrontalSolver::computeBayesTree() const { /* ************************************************************************* */ const VectorValues& MultifrontalSolver::updateSolution() { assert(loaded_ && eliminated_); + + // Back-substitute the solution with a top-down pass through the cliques. runTopDown([](MultifrontalClique& node) { node.updateSolution(); }, params_.solutionParallelThreshold); + // Enforce constrained keys as zero in the final solution. for (Key key : fixedKeys_) { solution_.at(key).setZero(); } + + // Aggregate per-clique linearized errors from the latest solution. + lastOldError_ = 0.0; + lastNewError_ = 0.0; + for (const auto& clique : cliques_) { + if (!clique) continue; + lastOldError_ += clique->lastOldError(); + lastNewError_ += clique->lastNewError(); + } + + // Add the constant term once from the root cliques. + double constantError = 0.0; + for (const auto& root : roots_) { + if (!root) continue; + constantError += root->constantTermError(); + } + lastOldError_ += constantError; + lastNewError_ += constantError; + + // Mark delta error as valid for subsequent deltaError() calls. + hasDeltaError_ = true; return solution_; } +double MultifrontalSolver::deltaError(double* oldError, + double* newError) const { + if (!hasDeltaError_) { + throw std::runtime_error( + "MultifrontalSolver::deltaError requires updateSolution()."); + } + if (oldError) { + *oldError = lastOldError_; + } + if (newError) { + *newError = lastNewError_; + } + return lastOldError_ - lastNewError_; +} + /* ************************************************************************* */ std::ostream& operator<<(std::ostream& os, const MultifrontalSolver& solver) { os << "MultifrontalSolver(roots=" << solver.roots_.size() diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index ab66fa43d0..15f4acf2ce 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -81,6 +81,9 @@ class GTSAM_EXPORT MultifrontalSolver bool loaded_ = false; ///< Whether load() has been called. bool eliminated_ = false; ///< Whether eliminateInPlace() ran. Parameters params_; ///< Tunable solver parameters. + double lastOldError_ = 0.0; ///< Cached old linearized error. + double lastNewError_ = 0.0; ///< Cached new linearized error. + bool hasDeltaError_ = false; ///< Whether updateSolution computed it. public: /** @@ -168,6 +171,13 @@ class GTSAM_EXPORT MultifrontalSolver */ const VectorValues& updateSolution(); + /** + * Return the linearized delta error from the last updateSolution() call. + * Optionally returns the old and new linearized errors. + */ + double deltaError(double* oldError = nullptr, + double* newError = nullptr) const; + /// Accessor for the roots of the elimination tree. const std::vector& roots() const { return roots_; } diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index 53feeac933..caca56e467 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -57,7 +57,7 @@ MultifrontalSolver::Parameters noMergeParams() { } // namespace /* ************************************************************************* */ -// Build the solver and validate initial structure and explicit load. +/// Build the solver and validate initial structure and explicit load. TEST(MultifrontalSolver, Constructor) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -87,7 +87,7 @@ TEST(MultifrontalSolver, Constructor) { } /* ************************************************************************* */ -// Build the solver from precomputed data and validate structure and load. +/// Build the solver from precomputed data and validate structure and load. TEST(MultifrontalSolver, ConstructorPrecomputed) { auto data = MultifrontalSolver::Precompute(chain, chainOrdering); MultifrontalSolver solver(std::move(data), chainOrdering, noMergeParams()); @@ -114,7 +114,7 @@ TEST(MultifrontalSolver, ConstructorPrecomputed) { } /* ************************************************************************* */ -// Reload numerical values and ensure Ab updates match whitening. +/// Reload numerical values and ensure Ab updates match whitening. TEST(MultifrontalSolver, Load) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); @@ -142,7 +142,7 @@ TEST(MultifrontalSolver, Load) { } /* ************************************************************************* */ -// Compare solver output against multifrontal elimination baseline. +/// Compare solver output against multifrontal elimination baseline. TEST(MultifrontalSolver, Eliminate) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -159,7 +159,58 @@ TEST(MultifrontalSolver, Eliminate) { } /* ************************************************************************* */ -// Load + eliminate in one traversal matches standard elimination. +/// deltaError from the solver matches GaussianFactorGraph for the +/// solver-produced (optimal) delta. +TEST(MultifrontalSolver, DeltaErrorMatchesGraph) { + MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); + solver.eliminateInPlace(chain); + + const VectorValues& delta = solver.updateSolution(); + + double oldFast = 0.0; + double newFast = 0.0; + double deltaFast = solver.deltaError(&oldFast, &newFast); + + double oldRef = 0.0; + double newRef = 0.0; + double deltaRef = chain.deltaError(delta, &oldRef, &newRef); + + DOUBLES_EQUAL(oldRef, oldFast, 1e-9); + DOUBLES_EQUAL(newRef, newFast, 1e-9); + DOUBLES_EQUAL(deltaRef, deltaFast, 1e-9); +} + +/* ************************************************************************* */ +/// deltaError from the solver matches GaussianFactorGraph on an +/// overdetermined system with nonzero residual at the solution. +TEST(MultifrontalSolver, DeltaErrorMatchesGraphInconsistent) { + const SharedDiagonal noise = noiseModel::Isotropic::Sigma(1, 1.0); + GaussianFactorGraph graph; + graph.emplace_shared(x1, I_1x1, + (Vector(1) << 1.0).finished(), noise); + graph.emplace_shared(x1, I_1x1, + (Vector(1) << -2.0).finished(), noise); + const Ordering ordering{x1}; + MultifrontalSolver solver(graph, ordering, noMergeParams()); + solver.eliminateInPlace(graph); + + const VectorValues& delta = solver.updateSolution(); + + double oldFast = 0.0; + double newFast = 0.0; + double deltaFast = solver.deltaError(&oldFast, &newFast); + + double oldRef = 0.0; + double newRef = 0.0; + double deltaRef = graph.deltaError(delta, &oldRef, &newRef); + + DOUBLES_EQUAL(oldRef, oldFast, 1e-9); + DOUBLES_EQUAL(newRef, newFast, 1e-9); + DOUBLES_EQUAL(deltaRef, deltaFast, 1e-9); +} + +/* ************************************************************************* */ +/// Load + eliminate in one traversal matches standard elimination. TEST(MultifrontalSolver, EliminateWithLoad) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.eliminateInPlace(chain); @@ -173,7 +224,30 @@ TEST(MultifrontalSolver, EliminateWithLoad) { } /* ************************************************************************* */ -// Forcing QR enables QR on all leaves and matches legacy QR elimination. +/// deltaError match when QR is forced, exercising the QR leaf RSd_ path. +TEST(MultifrontalSolver, DeltaErrorMatchesGraphQR) { + auto qrParams = noMergeParams(); + qrParams.qrMode = MultifrontalParameters::QRMode::Force; + MultifrontalSolver solver(chain, chainOrdering, qrParams); + solver.eliminateInPlace(chain); + + const VectorValues& delta = solver.updateSolution(); + + double oldFast = 0.0; + double newFast = 0.0; + double deltaFast = solver.deltaError(&oldFast, &newFast); + + double oldRef = 0.0; + double newRef = 0.0; + double deltaRef = chain.deltaError(delta, &oldRef, &newRef); + + DOUBLES_EQUAL(oldRef, oldFast, 1e-9); + DOUBLES_EQUAL(newRef, newFast, 1e-9); + DOUBLES_EQUAL(deltaRef, deltaFast, 1e-9); +} + +/* ************************************************************************* */ +/// Forcing QR enables QR on all leaves and matches legacy QR elimination. TEST(MultifrontalSolver, ForceQRMatchesDenseQR) { auto qrParams = noMergeParams(); qrParams.qrMode = MultifrontalParameters::QRMode::Force; @@ -201,7 +275,7 @@ TEST(MultifrontalSolver, ForceQRMatchesDenseQR) { } /* ************************************************************************* */ -// Compare marginals from in-place Bayes tree against standard elimination. +/// Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -231,7 +305,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { } /* ************************************************************************* */ -// Compare marginals on a constrained chain against legacy marginals. +/// Compare marginals on a constrained chain against legacy marginals. TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -255,7 +329,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { } /* ************************************************************************* */ -// Verify feasible unary constrained factor clamps the update to zero. +/// Verify feasible unary constrained factor clamps the update to zero. TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -277,7 +351,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { } /* ************************************************************************* */ -// Infeasible unary constrained factor is rejected. +/// Infeasible unary constrained factor is rejected. TEST(MultifrontalSolver, ConstrainedNoiseUnsupported) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -295,7 +369,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnsupported) { } /* ************************************************************************* */ -// Fully constrained unary factor with All() keeps delta fixed at zero. +/// Fully constrained unary factor with All() keeps delta fixed at zero. TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { const SharedDiagonal hardConstraint = noiseModel::Constrained::All(1); const SharedDiagonal softNoise = noiseModel::Isotropic::Sigma(1, 1.0); @@ -315,7 +389,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { } /* ************************************************************************* */ -// Mixed-key constrained factor is not supported. +/// Mixed-key constrained factor is not supported. TEST(MultifrontalSolver, ConstrainedNoiseMixedKeysUnsupported) { const SharedDiagonal hardConstraint = noiseModel::Constrained::All(1); GaussianFactorGraph graph; @@ -329,7 +403,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseMixedKeysUnsupported) { } /* ************************************************************************* */ -// Weighted scalar measurements produce the expected weighted estimate. +/// Weighted scalar measurements produce the expected weighted estimate. TEST(MultifrontalSolver, WeightedScalarMeasurements) { const double w1 = 0.2; const double w2 = 0.8; @@ -353,7 +427,7 @@ TEST(MultifrontalSolver, WeightedScalarMeasurements) { } /* ************************************************************************* */ -// Hessian factors are rejected by the multifrontal solver. +/// Hessian factors are rejected by the multifrontal solver. TEST(MultifrontalSolver, HessianFactors) { GaussianFactorGraph graph; graph.emplace_shared(x1, (Matrix(1, 1) << 4.0).finished(), @@ -366,7 +440,7 @@ TEST(MultifrontalSolver, HessianFactors) { } /* ************************************************************************* */ -// Merge threshold changes the clique count. +/// Merge threshold changes the clique count. TEST(MultifrontalSolver, MergeDimCap) { MultifrontalSolver::Parameters noMerge = noMergeParams(); MultifrontalSolver solverNoMerge(chain, chainOrdering, noMerge); @@ -379,7 +453,7 @@ TEST(MultifrontalSolver, MergeDimCap) { } /* ************************************************************************* */ -// End-to-end balanced smoother test with reload. +/// End-to-end balanced smoother test with reload. TEST(MultifrontalSolver, BalancedSmoother) { // Create smoother with 7 nodes auto [nlfg, poses] = example::createNonlinearSmoother(7); diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp index 95712f0577..2d141d2eb6 100644 --- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp +++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp @@ -177,8 +177,14 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear, // as the nonlinear error when robust noise models are used. double oldLinearizedError = 0.0; double newLinearizedError = 0.0; - double linearizedCostChange = - linear.deltaError(delta, &oldLinearizedError, &newLinearizedError); + double linearizedCostChange = 0.0; + if (nonlinearMultifrontalSolver_) { + linearizedCostChange = nonlinearMultifrontalSolver_->deltaError( + &oldLinearizedError, &newLinearizedError); + } else { + linearizedCostChange = + linear.deltaError(delta, &oldLinearizedError, &newLinearizedError); + } // cost change in the linearized system (old - new) if (verbose) From 554ca495577c1a52c064e09aaf1b95866d443aaf Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 17 Jan 2026 14:40:53 -0500 Subject: [PATCH 105/266] deltaError --- gtsam/linear/GaussianFactor.cpp | 14 +++++++++ gtsam/linear/GaussianFactor.h | 7 +++++ gtsam/linear/GaussianFactorGraph.cpp | 29 +++++++++++++++++++ gtsam/linear/GaussianFactorGraph.h | 7 +++++ gtsam/linear/HessianFactor.cpp | 16 ++++++++-- gtsam/linear/HessianFactor.h | 7 +++++ gtsam/linear/JacobianFactor.cpp | 14 +++++++++ gtsam/linear/JacobianFactor.h | 7 +++++ .../linear/tests/testGaussianFactorGraph.cpp | 22 ++++++++++++++ gtsam/linear/tests/testHessianFactor.cpp | 24 +++++++++++++++ gtsam/linear/tests/testJacobianFactor.cpp | 24 +++++++++++++++ .../nonlinear/LevenbergMarquardtOptimizer.cpp | 9 +++--- 12 files changed, 173 insertions(+), 7 deletions(-) diff --git a/gtsam/linear/GaussianFactor.cpp b/gtsam/linear/GaussianFactor.cpp index e60e626a1d..9d3197da7b 100644 --- a/gtsam/linear/GaussianFactor.cpp +++ b/gtsam/linear/GaussianFactor.cpp @@ -28,6 +28,20 @@ double GaussianFactor::error(const VectorValues& c) const { throw std::runtime_error("GaussianFactor::error is not implemented"); } +double GaussianFactor::deltaError(const VectorValues& c, double* oldError, + double* newError) const { + const VectorValues zero = VectorValues::Zero(c); + const double oldValue = error(zero); + const double newValue = error(c); + if (oldError) { + *oldError = oldValue; + } + if (newError) { + *newError = newValue; + } + return oldValue - newValue; +} + double GaussianFactor::error(const HybridValues& c) const { return this->error(c.continuous()); } diff --git a/gtsam/linear/GaussianFactor.h b/gtsam/linear/GaussianFactor.h index b99aec2bdf..ed6778d8f1 100644 --- a/gtsam/linear/GaussianFactor.h +++ b/gtsam/linear/GaussianFactor.h @@ -78,6 +78,13 @@ namespace gtsam { */ virtual double error(const VectorValues& c) const; + /** + * Compute the change in error from zero to c. Optionally return the old + * and new errors for reuse by callers. + */ + virtual double deltaError(const VectorValues& c, double* oldError = nullptr, + double* newError = nullptr) const; + /** * The Factor::error simply extracts the \class VectorValues from the * \class HybridValues and calculates the error. diff --git a/gtsam/linear/GaussianFactorGraph.cpp b/gtsam/linear/GaussianFactorGraph.cpp index cb42f0a2fe..1b1c6661b2 100644 --- a/gtsam/linear/GaussianFactorGraph.cpp +++ b/gtsam/linear/GaussianFactorGraph.cpp @@ -77,6 +77,35 @@ namespace gtsam { return total_error; } + /* ************************************************************************* */ + double GaussianFactorGraph::deltaError(const VectorValues& x, double* oldError, + double* newError) const { + double oldTotal = 0.0; + double newTotal = 0.0; + double deltaTotal = 0.0; + for (const sharedFactor& factor : *this) { + if (!factor) { + continue; + } + if (oldError || newError) { + double factorOld = 0.0; + double factorNew = 0.0; + deltaTotal += factor->deltaError(x, &factorOld, &factorNew); + oldTotal += factorOld; + newTotal += factorNew; + } else { + deltaTotal += factor->deltaError(x, nullptr, nullptr); + } + } + if (oldError) { + *oldError = oldTotal; + } + if (newError) { + *newError = newTotal; + } + return deltaTotal; + } + /* ************************************************************************* */ double GaussianFactorGraph::probPrime(const VectorValues& c) const { // NOTE the 0.5 constant is handled by the factor error. diff --git a/gtsam/linear/GaussianFactorGraph.h b/gtsam/linear/GaussianFactorGraph.h index 684518afda..86db0b3883 100644 --- a/gtsam/linear/GaussianFactorGraph.h +++ b/gtsam/linear/GaussianFactorGraph.h @@ -167,6 +167,13 @@ namespace gtsam { /** unnormalized error */ double error(const VectorValues& x) const; + /** + * Compute the change in error from zero to x, using a single pass + * over the factors. Optionally returns the old and new errors. + */ + double deltaError(const VectorValues& x, double* oldError = nullptr, + double* newError = nullptr) const; + /** Unnormalized probability. O(n) */ double probPrime(const VectorValues& c) const; diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp index d6141b6c0f..98df17960f 100644 --- a/gtsam/linear/HessianFactor.cpp +++ b/gtsam/linear/HessianFactor.cpp @@ -335,16 +335,26 @@ double HessianFactor::error(const VectorValues& c) const { if (empty()) { return 0.5 * f; } - double xtg = 0, xGx = 0; // extract the relevant subset of the VectorValues // NOTE may not be as efficient const Vector x = c.vector(keys()); - xtg = x.dot(linearTerm().col(0)); + const double xtg = x.dot(linearTerm().col(0)); auto AtA = informationView(); - xGx = x.transpose() * AtA * x; + const double xGx = x.transpose() * AtA * x; return 0.5 * (f - 2.0 * xtg + xGx); } +/* ************************************************************************* */ +double HessianFactor::deltaError(const VectorValues& c, double* oldError, + double* newError) const { + const double f = constantTerm(); + const double oldValue = 0.5 * f; + double newValue = error(c); + if (oldError) *oldError = oldValue; + if (newError) *newError = newValue; + return oldValue - newValue; +} + /* ************************************************************************* */ void HessianFactor::updateHessian(const KeyVector& infoKeys, SymmetricBlockMatrix* info) const { diff --git a/gtsam/linear/HessianFactor.h b/gtsam/linear/HessianFactor.h index 189020712a..d4b6965f77 100644 --- a/gtsam/linear/HessianFactor.h +++ b/gtsam/linear/HessianFactor.h @@ -203,6 +203,13 @@ namespace gtsam { */ double error(const VectorValues& c) const override; + /** + * Compute the change in error from zero to c, optionally returning + * the old and new errors. + */ + double deltaError(const VectorValues& c, double* oldError = nullptr, + double* newError = nullptr) const override; + /** Return the dimension of the variable pointed to by the given key iterator * todo: Remove this in favor of keeping track of dimensions with variables? * @param variable An iterator pointing to the slot in this factor. You can diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index d783788611..87f1779958 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -556,6 +556,20 @@ double JacobianFactor::error(const VectorValues& c) const { return 0.5 * e.dot(e); } +/* ************************************************************************* */ +double JacobianFactor::deltaError(const VectorValues& c, double* oldError, + double* newError) const { + const Vector e = unweighted_error(c); + const Vector b = getb(); + double oldValue = + model_ ? 0.5 * model_->squaredMahalanobisDistance(b) : 0.5 * b.dot(b); + double newValue = + model_ ? 0.5 * model_->squaredMahalanobisDistance(e) : 0.5 * e.dot(e); + if (oldError) *oldError = oldValue; + if (newError) *newError = newValue; + return oldValue - newValue; +} + /* ************************************************************************* */ Matrix JacobianFactor::augmentedInformation() const { if (model_) { diff --git a/gtsam/linear/JacobianFactor.h b/gtsam/linear/JacobianFactor.h index d2db5ce01d..6c93464f73 100644 --- a/gtsam/linear/JacobianFactor.h +++ b/gtsam/linear/JacobianFactor.h @@ -245,6 +245,13 @@ namespace gtsam { /// 0.5*(A*x-b)'*D*(A*x-b). double error(const VectorValues& c) const override; + /** + * Compute the change in error from zero to c, optionally returning + * the old and new errors. + */ + double deltaError(const VectorValues& c, double* oldError = nullptr, + double* newError = nullptr) const override; + /** Return the augmented information matrix represented by this GaussianFactor. * The augmented information matrix contains the information matrix with an * additional column holding the information vector, and an additional row diff --git a/gtsam/linear/tests/testGaussianFactorGraph.cpp b/gtsam/linear/tests/testGaussianFactorGraph.cpp index 03222bb3f4..b6b804b6c2 100644 --- a/gtsam/linear/tests/testGaussianFactorGraph.cpp +++ b/gtsam/linear/tests/testGaussianFactorGraph.cpp @@ -321,6 +321,28 @@ static GaussianFactorGraph createGaussianFactorGraphWithHessianFactor() { return gfg; } +/* ************************************************************************* */ +TEST(GaussianFactorGraph, deltaError) { + GaussianFactorGraph gfg = createGaussianFactorGraphWithHessianFactor(); + + VectorValues values{{0, Vector2(0.1, -0.2)}, + {1, Vector2(1.0, 0.5)}, + {2, Vector2(-0.3, 0.8)}}; + VectorValues zero = VectorValues::Zero(values); + + double expectedOld = gfg.error(zero); + double expectedNew = gfg.error(values); + double expectedDelta = expectedOld - expectedNew; + + double oldValue = 0.0; + double newValue = 0.0; + double delta = gfg.deltaError(values, &oldValue, &newValue); + + DOUBLES_EQUAL(expectedOld, oldValue, 1e-10); + DOUBLES_EQUAL(expectedNew, newValue, 1e-10); + DOUBLES_EQUAL(expectedDelta, delta, 1e-10); +} + /* ************************************************************************* */ TEST(GaussianFactorGraph, multiplyHessianAdd2) { GaussianFactorGraph gfg = createGaussianFactorGraphWithHessianFactor(); diff --git a/gtsam/linear/tests/testHessianFactor.cpp b/gtsam/linear/tests/testHessianFactor.cpp index 90c443faed..77afcadcf5 100644 --- a/gtsam/linear/tests/testHessianFactor.cpp +++ b/gtsam/linear/tests/testHessianFactor.cpp @@ -113,6 +113,30 @@ TEST(HessianFactor, Constructor1) EXPECT_DOUBLES_EQUAL(expected, actual, 1e-10); } +/* ************************************************************************* */ +TEST(HessianFactor, deltaError) +{ + Matrix G = (Matrix(2,2) << 3.0, 5.0, 5.0, 6.0).finished(); + Vector g = Vector2(-8.0, -9.0); + double f = 10.0; + HessianFactor factor(0, G, g, f); + + VectorValues values{{0, Vector2(1.5, 2.5)}}; + VectorValues zero = VectorValues::Zero(values); + + double expectedOld = factor.error(zero); + double expectedNew = factor.error(values); + double expectedDelta = expectedOld - expectedNew; + + double oldValue = 0.0; + double newValue = 0.0; + double delta = factor.deltaError(values, &oldValue, &newValue); + + DOUBLES_EQUAL(expectedOld, oldValue, 1e-10); + DOUBLES_EQUAL(expectedNew, newValue, 1e-10); + DOUBLES_EQUAL(expectedDelta, delta, 1e-10); +} + /* ************************************************************************* */ TEST(HessianFactor, Constructor1b) { diff --git a/gtsam/linear/tests/testJacobianFactor.cpp b/gtsam/linear/tests/testJacobianFactor.cpp index 234466620b..be9365bca0 100644 --- a/gtsam/linear/tests/testJacobianFactor.cpp +++ b/gtsam/linear/tests/testJacobianFactor.cpp @@ -255,6 +255,30 @@ TEST(JacobianFactor, error) DOUBLES_EQUAL(expected_error, actual_error, 1e-10); } +/* ************************************************************************* */ +TEST(JacobianFactor, deltaError) +{ + JacobianFactor factor(simple::terms, simple::b, simple::noise); + + VectorValues values; + values.insert(5, Vector::Constant(3, 1.0)); + values.insert(10, Vector::Constant(3, 0.5)); + values.insert(15, Vector::Constant(3, 1.0/3.0)); + + VectorValues zero = VectorValues::Zero(values); + double expectedOld = factor.error(zero); + double expectedNew = factor.error(values); + double expectedDelta = expectedOld - expectedNew; + + double oldValue = 0.0; + double newValue = 0.0; + double delta = factor.deltaError(values, &oldValue, &newValue); + + DOUBLES_EQUAL(expectedOld, oldValue, 1e-10); + DOUBLES_EQUAL(expectedNew, newValue, 1e-10); + DOUBLES_EQUAL(expectedDelta, delta, 1e-10); +} + /* ************************************************************************* */ TEST(JacobianFactor, matrices_NULL) { diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp index 90a4cc68d3..fea2deca43 100644 --- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp +++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp @@ -166,13 +166,14 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear, // Compute the old linearized error as it is not the same // as the nonlinear error when robust noise models are used. - double oldLinearizedError = linear.error(VectorValues::Zero(delta)); - double newlinearizedError = linear.error(delta); + double oldLinearizedError = 0.0; + double newLinearizedError = 0.0; + double linearizedCostChange = + linear.deltaError(delta, &oldLinearizedError, &newLinearizedError); // cost change in the linearized system (old - new) - double linearizedCostChange = oldLinearizedError - newlinearizedError; if (verbose) - cout << "newlinearizedError = " << newlinearizedError + cout << "newLinearizedError = " << newLinearizedError << " linearizedCostChange = " << linearizedCostChange << endl; if (linearizedCostChange >= 0) { // step is valid From dbc0624fd15d7f8fa0869ce0599ded8c0497eb69 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 17 Jan 2026 18:46:57 -0500 Subject: [PATCH 106/266] Review Comments --- gtsam/linear/MultifrontalClique.cpp | 44 ++++----- gtsam/linear/MultifrontalClique.h | 35 +++----- gtsam/linear/MultifrontalSolver.cpp | 89 +++++++++++-------- gtsam/linear/MultifrontalSolver.h | 14 --- gtsam/linear/tests/testMultifrontalSolver.cpp | 42 ++++----- .../nonlinear/NonlinearMultifrontalSolver.cpp | 26 +++--- gtsam/nonlinear/NonlinearMultifrontalSolver.h | 5 +- gtsam/nonlinear/NonlinearOptimizer.h | 82 ++++++++++------- .../tests/testNonlinearMultifrontalSolver.cpp | 2 + 9 files changed, 172 insertions(+), 167 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index 2bdd35abd8..e507546b18 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -171,7 +171,7 @@ void MultifrontalClique::finalize(std::vector children, solveMode_ = useQR ? SolveMode::QrLeaf : SolveMode::Cholesky; RSdReady_ = false; - // If using QR, also a reserve room for optional damping rows. + // If using QR, also reserve room for optional damping rows. const DenseIndex baseRows = static_cast(factorRows_); const DenseIndex totalRows = baseRows + (useQR ? static_cast(frontalDim) : 0); @@ -319,21 +319,20 @@ void MultifrontalClique::factorize() { } void MultifrontalClique::eliminateInPlace( - double lambda, const LMDampingParams* dampingParams, - const VectorValues* exactHessianDiagonal) { - assert(dampingParams != nullptr); + double lambda, const LMDampingParams& dampingParams, + const VectorValues& exactHessianDiagonal) { prepareForElimination(); if (useQR()) { - applyDampingQR(lambda, *dampingParams, exactHessianDiagonal); + applyDampingQR(lambda, dampingParams, exactHessianDiagonal); } else { - applyDampingCholesky(lambda, *dampingParams, exactHessianDiagonal); + applyDampingCholesky(lambda, dampingParams, exactHessianDiagonal); } factorize(); } void MultifrontalClique::applyDampingQR( double lambda, const LMDampingParams& dampingParams, - const VectorValues* exactHessianDiagonal) { + const VectorValues& exactHessianDiagonal) { if (lambda <= 0.0) return; const DenseIndex baseRows = static_cast(factorRows_); const DenseIndex dampRows = static_cast(frontalDim); @@ -347,9 +346,8 @@ void MultifrontalClique::applyDampingQR( if (dampingParams.diagonalDamping) { Vector diag; if (dampingParams.exactHessianDiagonal) { - assert(exactHessianDiagonal != nullptr); const Key key = orderedKeys_.at(j); - diag = exactHessianDiagonal->at(key) + diag = exactHessianDiagonal.at(key) .cwiseMax(dampingParams.minDiagonal) .cwiseMin(dampingParams.maxDiagonal); } else { @@ -373,12 +371,11 @@ void MultifrontalClique::applyDampingQR( void MultifrontalClique::applyDampingCholesky( double lambda, const LMDampingParams& dampingParams, - const VectorValues* exactHessianDiagonal) { + const VectorValues& exactHessianDiagonal) { if (lambda <= 0.0) return; if (dampingParams.diagonalDamping) { if (dampingParams.exactHessianDiagonal) { - assert(exactHessianDiagonal != nullptr); - addExactDiagonalDamping(lambda, *exactHessianDiagonal, + addExactDiagonalDamping(lambda, exactHessianDiagonal, dampingParams.minDiagonal, dampingParams.maxDiagonal); } else { @@ -444,10 +441,6 @@ void MultifrontalClique::updateParentInfo( } } -void MultifrontalClique::updateParent(MultifrontalClique& parent) const { - updateParentInfo(parent.info_); -} - void MultifrontalClique::gatherUpdatesSequential() { for (const auto& child : children) { assert(child); @@ -518,7 +511,7 @@ std::shared_ptr MultifrontalClique::conditional() const { } // Solve with block back-substitution on the Cholesky-stored info matrix. -void MultifrontalClique::updateSolution() const { +void MultifrontalClique::updateSolution() { assert(RSdReady_); assert(RSd_.rowStart() == 0); // Use cached [R S d] for fast back-substitution. @@ -534,10 +527,10 @@ void MultifrontalClique::updateSolution() const { // We first solve rhs = d - S * x_s rhsScratch_.noalias() = d; + const Vector* x_s = nullptr; if (!separatorPtrs_.empty()) { - const Vector& x_s = - buildSeparatorVector(separatorPtrs_, &separatorScratch_); - rhsScratch_.noalias() -= S * x_s; + x_s = &buildSeparatorVector(separatorPtrs_, &separatorScratch_); + rhsScratch_.noalias() -= S * (*x_s); } // Then solve for x_f, our solution, via R * x_f = rhs @@ -558,15 +551,12 @@ void MultifrontalClique::updateSolution() const { if (frontalDim > 0) { lastOldError_ = 0.5 * d.squaredNorm(); Vector residual = R * x_f; - if (!separatorPtrs_.empty()) { - const Vector& x_s = - buildSeparatorVector(separatorPtrs_, &separatorScratch_); - residual.noalias() += S * x_s; + if (x_s != nullptr) { + residual.noalias() += S * (*x_s); } residual.noalias() -= d; lastNewError_ = 0.5 * residual.squaredNorm(); } - } double MultifrontalClique::constantTermError() const { @@ -584,8 +574,8 @@ double MultifrontalClique::constantTermError() const { 0.5 * RSd_.matrix().bottomRows(extraRows).col(lastCol).squaredNorm(); } } else { - const DenseIndex rhsIndex = static_cast( - info_.nBlocks() - info_.blockStart() - 1); + const DenseIndex rhsIndex = + static_cast(info_.nBlocks() - info_.blockStart() - 1); if (rhsIndex >= 0) { constantError = 0.5 * info_.diagonalBlock(rhsIndex)(0, 0); } diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index e7b10b2b07..dc4f6f7963 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -219,18 +219,10 @@ class GTSAM_EXPORT MultifrontalClique { * * @param lambda Optional damping value; non-positive disables damping. * @param dampingParams Parameters controlling LM-style damping. - * @param exactHessianDiagonal Optional `diag(J^T J)` values for exact - * diagonal damping. + * @param exactHessianDiagonal `diag(J^T J)` values for diagonal damping. */ - void eliminateInPlace(double lambda, - const LMDampingParams* dampingParams = nullptr, - const VectorValues* exactHessianDiagonal = nullptr); - - /** - * Apply this clique's separator contribution into the parent clique. - * @param parent Parent clique to update. - */ - void updateParent(MultifrontalClique& parent) const; + void eliminateInPlace(double lambda, const LMDampingParams& dampingParams, + const VectorValues& exactHessianDiagonal); /** * Solve for this clique's frontal variables and write them back to the @@ -240,7 +232,7 @@ class GTSAM_EXPORT MultifrontalClique { * Cholesky-stored information matrix, solving the triangular system for the * frontal blocks. */ - void updateSolution() const; + void updateSolution(); /// Access the last old error computed during updateSolution(). double lastOldError() const { return lastOldError_; } @@ -284,11 +276,11 @@ class GTSAM_EXPORT MultifrontalClique { /// Apply damping for QR elimination by writing into extra Ab_ rows. void applyDampingQR(double lambda, const LMDampingParams& dampingParams, - const VectorValues* exactHessianDiagonal); + const VectorValues& exactHessianDiagonal); /// Apply damping for Cholesky elimination by adding to the info_ matrix. void applyDampingCholesky(double lambda, const LMDampingParams& dampingParams, - const VectorValues* exactHessianDiagonal); + const VectorValues& exactHessianDiagonal); /** * Add a Jacobian factor's contributions into the Ab matrix. @@ -316,20 +308,21 @@ class GTSAM_EXPORT MultifrontalClique { // Finalize-time allocations. VerticalBlockMatrix Ab_; - mutable SymmetricBlockMatrix info_; + + // mutable as temporarily updateParentInfo mutable VerticalBlockMatrix RSd_; ///< Cached [R S d] from elimination. + mutable SymmetricBlockMatrix info_; // Elimination-time state. - mutable bool RSdReady_ = false; + bool RSdReady_ = false; // Solve-time scratch space. - mutable Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. - mutable Vector - separatorScratch_; ///< Cached separator stack for back-substitution. + Vector rhsScratch_; ///< Cached RHS workspace for back-substitution. + Vector separatorScratch_; ///< Cached separator stack for back-substitution. // Solve-time cached error contributions. - mutable double lastOldError_ = 0.0; - mutable double lastNewError_ = 0.0; + double lastOldError_ = 0.0; + double lastNewError_ = 0.0; }; std::ostream& operator<<(std::ostream& os, const MultifrontalClique& clique); diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 08d8f74a70..854826abb1 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -204,11 +204,6 @@ struct LeafGroup { size_t firstIndex = 0; }; -struct LeafBatch { - SymbolicJunctionTree::Cluster::Children leaves; - size_t firstIndex = 0; -}; - std::vector collectLeafGroups( const SymbolicJunctionTree::sharedNode& cluster, const std::map& dims, @@ -241,6 +236,11 @@ std::vector collectLeafGroups( return groups; } +struct LeafBatch { + SymbolicJunctionTree::Cluster::Children leaves; + size_t firstIndex = 0; +}; + std::vector buildLeafBatches(const std::vector& groups, size_t leafMergeDimCap) { // Pack each group into batches capped by total dimension. @@ -275,45 +275,48 @@ std::vector buildLeafBatches(const std::vector& groups, return batches; } -bool mergeLeafBatches(const SymbolicJunctionTree::sharedNode& cluster, - const std::vector& batches) { - if (batches.empty()) return false; +struct MergedClusters { + FastMap indexByChild; + std::vector clusters; + size_t count = 0; + size_t numSelectedLeaves = 0; +}; - // Merge in a single pass to avoid repeated scans of large child lists. - FastMap batchIndexByChild; - std::vector mergedBatches(batches.size(), - nullptr); - size_t mergeBatchCount = 0; - size_t selectedLeafCount = 0; - - for (size_t batchIndex = 0; batchIndex < batches.size(); ++batchIndex) { - const auto& batch = batches[batchIndex]; +MergedClusters buildMergedClusters(const std::vector& clusters) { + MergedClusters merged; + merged.clusters.assign(clusters.size(), nullptr); + + for (size_t i = 0; i < clusters.size(); ++i) { + const auto& batch = clusters[i]; if (batch.leaves.size() <= 1) continue; - selectedLeafCount += batch.leaves.size(); + merged.numSelectedLeaves += batch.leaves.size(); + // Map each leaf pointer to its batch index so we can skip and insert in + // O(1) when scanning the original children list. for (const auto& leaf : batch.leaves) { - if (leaf) { - batchIndexByChild.emplace(leaf.get(), batchIndex); - } + if (leaf) merged.indexByChild.emplace(leaf.get(), i); } - auto merged = std::make_shared(); + // Build the merged batch once so insertion is just pointer replacement. + auto mergedCluster = std::make_shared(); for (const auto& leaf : batch.leaves) { - if (leaf) { - merged->merge(leaf); - } + if (leaf) mergedCluster->merge(leaf); } - std::reverse(merged->orderedFrontalKeys.begin(), - merged->orderedFrontalKeys.end()); - mergedBatches[batchIndex] = merged; - mergeBatchCount += 1; + std::reverse(mergedCluster->orderedFrontalKeys.begin(), + mergedCluster->orderedFrontalKeys.end()); + merged.clusters[i] = mergedCluster; + merged.count += 1; } - if (mergeBatchCount == 0) return false; + return merged; +} - auto oldChildren = cluster->children; +SymbolicJunctionTree::Cluster::Children buildMergedChildren( + const SymbolicJunctionTree::Cluster::Children& oldChildren, + const std::vector& batches, const MergedClusters& merged) { SymbolicJunctionTree::Cluster::Children newChildren; - newChildren.reserve(oldChildren.size() - selectedLeafCount + mergeBatchCount); + newChildren.reserve(oldChildren.size() - merged.numSelectedLeaves + + merged.count); std::vector inserted(batches.size(), false); for (size_t i = 0; i < oldChildren.size(); ++i) { @@ -323,19 +326,35 @@ bool mergeLeafBatches(const SymbolicJunctionTree::sharedNode& cluster, continue; } - auto it = batchIndexByChild.find(child.get()); - if (it == batchIndexByChild.end()) { + auto it = merged.indexByChild.find(child.get()); + if (it == merged.indexByChild.end()) { newChildren.push_back(child); continue; } const size_t batchIndex = it->second; + // Insert the merged cluster exactly once, at the first original index where + // that cluster appeared. All other leaves in the cluster are skipped. if (!inserted[batchIndex] && i == batches[batchIndex].firstIndex) { - newChildren.push_back(mergedBatches[batchIndex]); + newChildren.push_back(merged.clusters[batchIndex]); inserted[batchIndex] = true; } } + return newChildren; +} + +bool mergeLeafBatches(const SymbolicJunctionTree::sharedNode& cluster, + const std::vector& batches) { + if (batches.empty()) return false; + + // Merge in a single pass to avoid repeated scans of large child lists. + const MergedClusters merged = buildMergedClusters(batches); + if (merged.count == 0) return false; + + auto oldChildren = cluster->children; + SymbolicJunctionTree::Cluster::Children newChildren = + buildMergedChildren(oldChildren, batches, merged); cluster->children.swap(newChildren); return true; } diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 15f4acf2ce..0b7db38e5a 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -120,20 +120,6 @@ class GTSAM_EXPORT MultifrontalSolver static PrecomputedData Precompute(const GaussianFactorGraph& graph, const Ordering& ordering); - /** - * Construct the solver from precomputed symbolic data. - * Call load() before eliminating to populate numerical values. - * @param data Precomputed symbolic structure and sizing data. - * @param ordering The variable ordering to use for seeding solution storage. - * @param mergeDimCap Merge a child if its frontal dimension plus the - * parent's total dimension is below this threshold (0 disables merging). - * @param reportStream Optional stream to report clique structure stats - * (frontals, separators, total dims, and children). - */ - MultifrontalSolver(PrecomputedData data, const Ordering& ordering, - size_t mergeDimCap = 0, - std::ostream* reportStream = nullptr); - /** * Load new numerical values from the factor graph. * This overwrites the values in the pre-allocated matrices. diff --git a/gtsam/linear/tests/testMultifrontalSolver.cpp b/gtsam/linear/tests/testMultifrontalSolver.cpp index caca56e467..98684c2236 100644 --- a/gtsam/linear/tests/testMultifrontalSolver.cpp +++ b/gtsam/linear/tests/testMultifrontalSolver.cpp @@ -57,7 +57,7 @@ MultifrontalSolver::Parameters noMergeParams() { } // namespace /* ************************************************************************* */ -/// Build the solver and validate initial structure and explicit load. +// Build the solver and validate initial structure and explicit load. TEST(MultifrontalSolver, Constructor) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -87,7 +87,7 @@ TEST(MultifrontalSolver, Constructor) { } /* ************************************************************************* */ -/// Build the solver from precomputed data and validate structure and load. +// Build the solver from precomputed data and validate structure and load. TEST(MultifrontalSolver, ConstructorPrecomputed) { auto data = MultifrontalSolver::Precompute(chain, chainOrdering); MultifrontalSolver solver(std::move(data), chainOrdering, noMergeParams()); @@ -114,7 +114,7 @@ TEST(MultifrontalSolver, ConstructorPrecomputed) { } /* ************************************************************************* */ -/// Reload numerical values and ensure Ab updates match whitening. +// Reload numerical values and ensure Ab updates match whitening. TEST(MultifrontalSolver, Load) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); @@ -142,7 +142,7 @@ TEST(MultifrontalSolver, Load) { } /* ************************************************************************* */ -/// Compare solver output against multifrontal elimination baseline. +// Compare solver output against multifrontal elimination baseline. TEST(MultifrontalSolver, Eliminate) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -159,8 +159,8 @@ TEST(MultifrontalSolver, Eliminate) { } /* ************************************************************************* */ -/// deltaError from the solver matches GaussianFactorGraph for the -/// solver-produced (optimal) delta. +// deltaError from the solver matches GaussianFactorGraph for the +// solver-produced (optimal) delta. TEST(MultifrontalSolver, DeltaErrorMatchesGraph) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.eliminateInPlace(chain); @@ -181,8 +181,8 @@ TEST(MultifrontalSolver, DeltaErrorMatchesGraph) { } /* ************************************************************************* */ -/// deltaError from the solver matches GaussianFactorGraph on an -/// overdetermined system with nonzero residual at the solution. +// deltaError from the solver matches GaussianFactorGraph on an +// overdetermined system with nonzero residual at the solution. TEST(MultifrontalSolver, DeltaErrorMatchesGraphInconsistent) { const SharedDiagonal noise = noiseModel::Isotropic::Sigma(1, 1.0); GaussianFactorGraph graph; @@ -210,7 +210,7 @@ TEST(MultifrontalSolver, DeltaErrorMatchesGraphInconsistent) { } /* ************************************************************************* */ -/// Load + eliminate in one traversal matches standard elimination. +// Load + eliminate in one traversal matches standard elimination. TEST(MultifrontalSolver, EliminateWithLoad) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.eliminateInPlace(chain); @@ -224,7 +224,7 @@ TEST(MultifrontalSolver, EliminateWithLoad) { } /* ************************************************************************* */ -/// deltaError match when QR is forced, exercising the QR leaf RSd_ path. +// deltaError match when QR is forced, exercising the QR leaf RSd_ path. TEST(MultifrontalSolver, DeltaErrorMatchesGraphQR) { auto qrParams = noMergeParams(); qrParams.qrMode = MultifrontalParameters::QRMode::Force; @@ -247,7 +247,7 @@ TEST(MultifrontalSolver, DeltaErrorMatchesGraphQR) { } /* ************************************************************************* */ -/// Forcing QR enables QR on all leaves and matches legacy QR elimination. +// Forcing QR enables QR on all leaves and matches legacy QR elimination. TEST(MultifrontalSolver, ForceQRMatchesDenseQR) { auto qrParams = noMergeParams(); qrParams.qrMode = MultifrontalParameters::QRMode::Force; @@ -275,7 +275,7 @@ TEST(MultifrontalSolver, ForceQRMatchesDenseQR) { } /* ************************************************************************* */ -/// Compare marginals from in-place Bayes tree against standard elimination. +// Compare marginals from in-place Bayes tree against standard elimination. TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { MultifrontalSolver solver(chain, chainOrdering, noMergeParams()); solver.load(chain); @@ -305,7 +305,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginals) { } /* ************************************************************************* */ -/// Compare marginals on a constrained chain against legacy marginals. +// Compare marginals on a constrained chain against legacy marginals. TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -329,7 +329,7 @@ TEST(MultifrontalSolver, ComputeBayesTreeMarginalsConstrainedChain) { } /* ************************************************************************* */ -/// Verify feasible unary constrained factor clamps the update to zero. +// Verify feasible unary constrained factor clamps the update to zero. TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -351,7 +351,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseFeasible) { } /* ************************************************************************* */ -/// Infeasible unary constrained factor is rejected. +// Infeasible unary constrained factor is rejected. TEST(MultifrontalSolver, ConstrainedNoiseUnsupported) { const SharedDiagonal hardConstraint = noiseModel::Constrained::MixedSigmas((Vector(1) << 0.0).finished()); @@ -369,7 +369,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnsupported) { } /* ************************************************************************* */ -/// Fully constrained unary factor with All() keeps delta fixed at zero. +// Fully constrained unary factor with All() keeps delta fixed at zero. TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { const SharedDiagonal hardConstraint = noiseModel::Constrained::All(1); const SharedDiagonal softNoise = noiseModel::Isotropic::Sigma(1, 1.0); @@ -389,7 +389,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseUnaryFeasible) { } /* ************************************************************************* */ -/// Mixed-key constrained factor is not supported. +// Mixed-key constrained factor is not supported. TEST(MultifrontalSolver, ConstrainedNoiseMixedKeysUnsupported) { const SharedDiagonal hardConstraint = noiseModel::Constrained::All(1); GaussianFactorGraph graph; @@ -403,7 +403,7 @@ TEST(MultifrontalSolver, ConstrainedNoiseMixedKeysUnsupported) { } /* ************************************************************************* */ -/// Weighted scalar measurements produce the expected weighted estimate. +// Weighted scalar measurements produce the expected weighted estimate. TEST(MultifrontalSolver, WeightedScalarMeasurements) { const double w1 = 0.2; const double w2 = 0.8; @@ -427,7 +427,7 @@ TEST(MultifrontalSolver, WeightedScalarMeasurements) { } /* ************************************************************************* */ -/// Hessian factors are rejected by the multifrontal solver. +// Hessian factors are rejected by the multifrontal solver. TEST(MultifrontalSolver, HessianFactors) { GaussianFactorGraph graph; graph.emplace_shared(x1, (Matrix(1, 1) << 4.0).finished(), @@ -440,7 +440,7 @@ TEST(MultifrontalSolver, HessianFactors) { } /* ************************************************************************* */ -/// Merge threshold changes the clique count. +// Merge threshold changes the clique count. TEST(MultifrontalSolver, MergeDimCap) { MultifrontalSolver::Parameters noMerge = noMergeParams(); MultifrontalSolver solverNoMerge(chain, chainOrdering, noMerge); @@ -453,7 +453,7 @@ TEST(MultifrontalSolver, MergeDimCap) { } /* ************************************************************************* */ -/// End-to-end balanced smoother test with reload. +// End-to-end balanced smoother test with reload. TEST(MultifrontalSolver, BalancedSmoother) { // Create smoother with 7 nodes auto [nlfg, poses] = example::createNonlinearSmoother(7); diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index dd0e4e7f99..270c323f08 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -116,33 +116,35 @@ void NonlinearMultifrontalSolver::load(const GaussianFactorGraph& graph) { /* ************************************************************************* */ void NonlinearMultifrontalSolver::eliminateInPlace(double lambda) { - if (lambda <= 0.0) { - MultifrontalSolver::eliminateInPlace(); - return; - } - if (!loaded_) { throw std::runtime_error( "NonlinearMultifrontalSolver::eliminateInPlace: load() must be called " "before eliminating."); } + eliminated_ = false; - runBottomUp( - [this, lambda](MultifrontalClique& node) { - node.eliminateInPlace(lambda, &dampingParams_, &exactHessianDiagonal_); - }, - params_.eliminationParallelThreshold); + if (lambda <= 0.0) { + MultifrontalSolver::eliminateInPlace(); + } else { + runBottomUp( + [this, lambda](MultifrontalClique& node) { + node.eliminateInPlace(lambda, dampingParams_, exactHessianDiagonal_); + }, + params_.eliminationParallelThreshold); + } eliminated_ = true; } /* ************************************************************************* */ void NonlinearMultifrontalSolver::eliminateInPlace( const GaussianFactorGraph& graph, double lambda) { + eliminated_ = false; if (lambda <= 0.0) { MultifrontalSolver::eliminateInPlace(graph); return; } + // Calculate the exact Hessian diagonal if needed. if (dampingParams_.diagonalDamping && dampingParams_.exactHessianDiagonal) { exactHessianDiagonal_ = graph.hessianDiagonal(); hasExactHessianDiagonal_ = true; @@ -151,11 +153,11 @@ void NonlinearMultifrontalSolver::eliminateInPlace( exactHessianDiagonal_ = VectorValues(); } - eliminated_ = false; + // Run bottom-up elimination with damping. runBottomUp( [&graph, this, lambda](MultifrontalClique& node) { node.fillAb(graph); - node.eliminateInPlace(lambda, &dampingParams_, &exactHessianDiagonal_); + node.eliminateInPlace(lambda, dampingParams_, exactHessianDiagonal_); }, params_.eliminationParallelThreshold); loaded_ = true; diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.h b/gtsam/nonlinear/NonlinearMultifrontalSolver.h index ca2eeb73e8..41619d8958 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.h +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.h @@ -31,14 +31,13 @@ namespace gtsam { * Multifrontal solver for nonlinear factor graphs. * * This class extends MultifrontalSolver to solve nonlinear problems. - * The linearization is provided externally via MultifrontalSolver::load(). + * The linearization is provided externally via load(), or via + * eliminateInPlace() which combines loading and elimination. */ class GTSAM_EXPORT NonlinearMultifrontalSolver : public MultifrontalSolver { public: using DampingParams = LMDampingParams; - private: - public: /** * Construct the solver from a nonlinear factor graph and linearization point. * This computes the symbolic structure (including fixed keys) from the diff --git a/gtsam/nonlinear/NonlinearOptimizer.h b/gtsam/nonlinear/NonlinearOptimizer.h index 6614302c22..2d6f8afe02 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.h +++ b/gtsam/nonlinear/NonlinearOptimizer.h @@ -23,7 +23,9 @@ namespace gtsam { -namespace internal { struct NonlinearOptimizerState; } +namespace internal { +struct NonlinearOptimizerState; +} class NonlinearMultifrontalSolver; /** @@ -31,7 +33,8 @@ class NonlinearMultifrontalSolver; * maximum-likelihood estimate of a NonlinearFactorGraph. * * To use a class derived from this interface, construct the class with a - * NonlinearFactorGraph and an initial Values variable assignment. Next, call the + * NonlinearFactorGraph and an initial Values variable assignment. Next, call +the * optimize() method which returns the optimized variable assignment. * * Simple and compact example: @@ -55,11 +58,10 @@ cout << "Converged in " << optimizer.iterations() << " iterations " * * Example of setting parameters before optimization: * \code -// Each derived optimizer type has its own parameters class, which inherits from NonlinearOptimizerParams -DoglegParams params; -params.factorization = DoglegParams::QR; -params.relativeErrorTol = 1e-3; -params.absoluteErrorTol = 1e-3; +// Each derived optimizer type has its own parameters class, which inherits from +NonlinearOptimizerParams DoglegParams params; params.factorization = +DoglegParams::QR; params.relativeErrorTol = 1e-3; params.absoluteErrorTol = +1e-3; // Optimize Values result = DoglegOptimizer(graph, initialValues, params).optimize(); @@ -71,27 +73,28 @@ Values result = DoglegOptimizer(graph, initialValues, params).optimize(); * you can easily control what happens between iterations, such as drawing or * printing, moving points from behind the camera to in front, etc. * - * For more flexibility you may override virtual methods in your own derived class. + * For more flexibility you may override virtual methods in your own derived +class. */ class GTSAM_EXPORT NonlinearOptimizer { + protected: + NonlinearFactorGraph graph_; ///< The graph with nonlinear factors -protected: - NonlinearFactorGraph graph_; ///< The graph with nonlinear factors + std::unique_ptr state_; ///< PIMPL'd state - std::unique_ptr state_; ///< PIMPL'd state - /// Solver for multifrontal Cholesky, lazily created - mutable std::unique_ptr nonlinearMultifrontalSolver_; + mutable std::unique_ptr + nonlinearMultifrontalSolver_; -public: + public: /** A shared pointer to this class */ using shared_ptr = std::shared_ptr; /// @name Standard interface /// @{ - /** - * Optimize for the maximum-likelihood estimate, returning a the optimized + /** + * Optimize for the maximum-likelihood estimate, returning a the optimized * variable assignments. * * This function simply calls iterate() in a loop, checking for convergence @@ -99,7 +102,10 @@ class GTSAM_EXPORT NonlinearOptimizer { * process, you may call iterate() and check_convergence() yourself, and if * needed modify the optimization state between iterations. */ - virtual const Values& optimize() { defaultOptimize(); return values(); } + virtual const Values& optimize() { + defaultOptimize(); + return values(); + } /** * Optimize, but return empty result if any uncaught exception is thrown @@ -116,10 +122,10 @@ class GTSAM_EXPORT NonlinearOptimizer { size_t iterations() const; /// return values in current optimizer state - const Values &values() const; + const Values& values() const; /// return the graph with nonlinear factors - const NonlinearFactorGraph &graph() const { return graph_; } + const NonlinearFactorGraph& graph() const { return graph_; } /// @} @@ -129,19 +135,20 @@ class GTSAM_EXPORT NonlinearOptimizer { /** Virtual destructor */ virtual ~NonlinearOptimizer(); - /** Default function to do linear solve, i.e. optimize a GaussianFactorGraph */ - virtual VectorValues solve(const GaussianFactorGraph &gfg, - const NonlinearOptimizerParams& params) const; + /** Default function to do linear solve, i.e. optimize a GaussianFactorGraph + */ + virtual VectorValues solve(const GaussianFactorGraph& gfg, + const NonlinearOptimizerParams& params) const; - /** - * Perform a single iteration, returning GaussianFactorGraph corresponding to + /** + * Perform a single iteration, returning GaussianFactorGraph corresponding to * the linearized factor graph. */ virtual GaussianFactorGraph::shared_ptr iterate() = 0; /// @} -protected: + protected: /** A default implementation of the optimization loop, which calls iterate() * until checkConvergence returns true. */ @@ -150,22 +157,29 @@ class GTSAM_EXPORT NonlinearOptimizer { virtual const NonlinearOptimizerParams& _params() const = 0; /** - * Ensure that the nonlinearMultifrontalSolver_ is populated if the params - * request multifrontal Cholesky. Returns true if the solver is available and - * ready. If constraints are present or initialization fails, returns false - * (and user should fall back to legacy solver). + * Ensure that the nonlinearMultifrontalSolver_ is populated if (and only if) + * the params request the multifrontal Cholesky solver type (e.g., + * MULTIFRONTAL_SOLVER). + * + * Returns true if the multifrontal solver is available and ready. If a + * different solver type is requested in params, this function returns false + * without modifying any solver state. If constraints are present or + * multifrontal initialization fails, it also returns false (and callers + * should fall back to the legacy linear solver path). */ bool ensureMultifrontalSolver(const NonlinearOptimizerParams& params, const Values& values) const; - /** Constructor for initial construction of base classes. Takes ownership of state. */ + /** Constructor for initial construction of base classes. Takes ownership of + * state. */ NonlinearOptimizer(const NonlinearFactorGraph& graph, std::unique_ptr state); }; -/** Check whether the relative error decrease is less than relativeErrorThreshold, - * the absolute error decrease is less than absoluteErrorThreshold, or - * the error itself is less than errorThreshold. +/** Check whether the relative error decrease is less than + * relativeErrorThreshold, the absolute error decrease is less than + * absoluteErrorThreshold, or the error itself is less than + * errorThreshold. */ GTSAM_EXPORT bool checkConvergence( double relativeErrorThreshold, double absoluteErrorThreshold, @@ -176,4 +190,4 @@ GTSAM_EXPORT bool checkConvergence( GTSAM_EXPORT bool checkConvergence(const NonlinearOptimizerParams& params, double currentError, double newError); -} // gtsam +} // namespace gtsam diff --git a/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp index 47ef60caed..5b3debdf19 100644 --- a/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp @@ -346,6 +346,7 @@ TEST(NonlinearMultifrontalSolver, ProbeMultipleLambdasSameLinearization) { /* ************************************************************************* */ // Check the BAL 16-camera dataset error before and after one LM iteration for // legacy multifrontal Cholesky and the new multifrontal solver (QR off/forced). +# ifdef TEST_BAL16_DATASET double runBal16OneIteration( const NonlinearFactorGraph& graph, const Values& initial, const Ordering& ordering, @@ -400,6 +401,7 @@ TEST(NonlinearMultifrontalSolver, Bal16ErrorOneIteration) { graph, initial, ordering, NonlinearOptimizerParams::MULTIFRONTAL_SOLVER); EXPECT_DOUBLES_EQUAL(legacyAfterError, solverForceAfterError, 20.0); } +#endif /* ************************************************************************* */ int main() { From a581d645d7490e73b75d76f4049dc56a23cff167 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 01:06:46 -0500 Subject: [PATCH 107/266] Remove std in header --- gtsam/sfm/ShonanAveraging.cpp | 8 ++++---- gtsam/slam/KarcherMeanFactor-inl.h | 7 +++---- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/gtsam/sfm/ShonanAveraging.cpp b/gtsam/sfm/ShonanAveraging.cpp index b268fda6f6..519a4cabae 100644 --- a/gtsam/sfm/ShonanAveraging.cpp +++ b/gtsam/sfm/ShonanAveraging.cpp @@ -827,8 +827,8 @@ Values ShonanAveraging::initializeWithDescent( double alphaMin = 1e-2; double alpha = std::max(1024 * alphaMin, 10 * gradienTolerance / fabs(minEigenValue)); - vector alphas; - vector fvals; + std::vector alphas; + std::vector fvals; // line search while ((alpha >= alphaMin)) { Values Qplus = LiftwithDescent(p, values, alpha * minEigenVector); @@ -937,7 +937,7 @@ ShonanAveraging2::ShonanAveraging2(const Measurements &measurements, : ShonanAveraging<2>(maybeRobust(measurements, parameters.getUseHuber()), parameters) {} -ShonanAveraging2::ShonanAveraging2(string g2oFile, const Parameters ¶meters) +ShonanAveraging2::ShonanAveraging2(std::string g2oFile, const Parameters ¶meters) : ShonanAveraging<2>(maybeRobust(parseMeasurements(g2oFile), parameters.getUseHuber()), parameters) {} @@ -983,7 +983,7 @@ ShonanAveraging3::ShonanAveraging3(const Measurements &measurements, : ShonanAveraging<3>(maybeRobust(measurements, parameters.getUseHuber()), parameters) {} -ShonanAveraging3::ShonanAveraging3(string g2oFile, const Parameters ¶meters) +ShonanAveraging3::ShonanAveraging3(std::string g2oFile, const Parameters ¶meters) : ShonanAveraging<3>(maybeRobust(parseMeasurements(g2oFile), parameters.getUseHuber()), parameters) {} diff --git a/gtsam/slam/KarcherMeanFactor-inl.h b/gtsam/slam/KarcherMeanFactor-inl.h index 61af6ea89f..e51151cf0e 100644 --- a/gtsam/slam/KarcherMeanFactor-inl.h +++ b/gtsam/slam/KarcherMeanFactor-inl.h @@ -22,18 +22,17 @@ #include #include -using namespace std; - namespace gtsam { template -T FindKarcherMeanImpl(const vector& rotations) { +T FindKarcherMeanImpl(const std::vector& rotations) { // Cost function C(R) = \sum PriorFactor(R_i)::error(R) // No closed form solution. NonlinearFactorGraph graph; static const Key kKey(0); + auto model = noiseModel::Unit::Create(T::dimension); for (const auto& R : rotations) { - graph.addPrior(kKey, R); + graph.addPrior(kKey, R, model); } Values initial; initial.insert(kKey, T()); From 1404892d1f022930c675120dd0cd6e999a8209dd Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 01:15:10 -0500 Subject: [PATCH 108/266] Create Unit noise model by default --- gtsam/sfm/BinaryMeasurement.h | 11 ++++++++++- gtsam/sfm/UnaryMeasurement.h | 17 ++++++++++++++--- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/gtsam/sfm/BinaryMeasurement.h b/gtsam/sfm/BinaryMeasurement.h index 07aa2e3a5f..7acd0d439e 100644 --- a/gtsam/sfm/BinaryMeasurement.h +++ b/gtsam/sfm/BinaryMeasurement.h @@ -50,7 +50,16 @@ template class BinaryMeasurement : public Factor { const SharedNoiseModel &model = nullptr) : Factor(std::vector({key1, key2})), measured_(measured), - noiseModel_(model) {} + noiseModel_(model) { + if (model) { + if (static_cast(model->dim()) != traits::GetDimension(measured)) + throw std::runtime_error( + "BinaryMeasurement: Noise model dimension does not match " + "measurement dimension."); + } else { + noiseModel_ = noiseModel::Unit::Create(traits::GetDimension(measured)); + } + } /// @name Standard Interface /// @{ diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h index 8933db6eff..cfdb4e5e20 100644 --- a/gtsam/sfm/UnaryMeasurement.h +++ b/gtsam/sfm/UnaryMeasurement.h @@ -53,9 +53,20 @@ template class UnaryMeasurement : public Factor { * @param measured The measurement value. * @param model The noise model (optional). */ - UnaryMeasurement(Key key, const T &measured, - const SharedNoiseModel &model = nullptr) - : Factor(std::vector({key})), measured_(measured), noiseModel_(model) {} + UnaryMeasurement(Key key, const T &measured, + const SharedNoiseModel &model = nullptr) + : Factor(std::vector({key})), + measured_(measured), + noiseModel_(model) { + if (model) { + if (static_cast(model->dim()) != traits::GetDimension(measured)) + throw std::runtime_error( + "BinaryMeasurement: Noise model dimension does not match " + "measurement dimension."); + } else { + noiseModel_ = noiseModel::Unit::Create(traits::GetDimension(measured)); + } + } /// @name Standard Interface /// @{ From 216ba0512d32922a9986f88181d6ac34b02dca13 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 01:15:34 -0500 Subject: [PATCH 109/266] work around dimension mismatch --- gtsam/sfm/TranslationRecovery.cpp | 46 +++++++++++++++++++++---------- 1 file changed, 31 insertions(+), 15 deletions(-) diff --git a/gtsam/sfm/TranslationRecovery.cpp b/gtsam/sfm/TranslationRecovery.cpp index 828aa6880a..6aa9d801f7 100644 --- a/gtsam/sfm/TranslationRecovery.cpp +++ b/gtsam/sfm/TranslationRecovery.cpp @@ -17,6 +17,7 @@ */ #include +#include #include #include #include @@ -38,8 +39,9 @@ using namespace gtsam; using namespace std; +namespace { // In Wrappers we have no access to this so have a default ready. -static std::mt19937 kPRNG(42); +std::mt19937 kPRNG(42); // Some relative translations may be zero. We treat nodes that have a zero // relativeTranslation as a single node. @@ -96,6 +98,23 @@ Values addSameTranslationNodes(const Values &result, return final_result; } +// A hacky noise conversion function because we really should be optimizing for +// Unit3s rather than Point3s, in which case the noise model can be whatever it +// wants. Unfortunately, we are given Unit3 measurements and Point3 unknowns, +// which is a mismatch in this class' setup. +using noiseModel::Isotropic; +Isotropic::shared_ptr convertNoiseModel( + const SharedNoiseModel &unit3NoiseModel) { + if (auto isotropic = std::dynamic_pointer_cast(unit3NoiseModel)) { + return noiseModel::Isotropic::Sigma(3, isotropic->sigma()); + } else { + throw std::runtime_error( + "TranslationRecovery::convertNoiseModel: only isotropic noise model " + "supported."); + } +} +} // namespace + NonlinearFactorGraph TranslationRecovery::buildGraph( const std::vector> &relativeTranslations) const { NonlinearFactorGraph graph; @@ -103,13 +122,13 @@ NonlinearFactorGraph TranslationRecovery::buildGraph( // Add translation factors for input translation directions. uint64_t i = 0; for (auto edge : relativeTranslations) { + auto model = convertNoiseModel(edge.noiseModel()); if (use_bilinear_translation_factor_) { graph.emplace_shared( - edge.key1(), edge.key2(), Symbol('S', i), edge.measured(), - edge.noiseModel()); + edge.key1(), edge.key2(), Symbol('S', i), edge.measured(), model); } else { - graph.emplace_shared( - edge.key1(), edge.key2(), edge.measured(), edge.noiseModel()); + graph.emplace_shared(edge.key1(), edge.key2(), + edge.measured(), model); } i++; } @@ -124,13 +143,12 @@ void TranslationRecovery::addPrior( const SharedNoiseModel &priorNoiseModel) const { auto edge = relativeTranslations.begin(); if (edge == relativeTranslations.end()) return; - graph->emplace_shared>(edge->key1(), Point3(0, 0, 0), - priorNoiseModel); + graph->addPrior(edge->key1(), Point3(0, 0, 0), priorNoiseModel); // Add a scale prior only if no other between factors were added. if (betweenTranslations.empty()) { - graph->emplace_shared>( - edge->key2(), scale * edge->measured().point3(), edge->noiseModel()); + auto model = convertNoiseModel(edge->noiseModel()); + graph->addPrior(edge->key2(), scale * edge->measured(), model); return; } @@ -184,8 +202,8 @@ Values TranslationRecovery::initializeRandomly( const std::vector> &relativeTranslations, const std::vector> &betweenTranslations, const Values &initialValues) const { - return initializeRandomly(relativeTranslations, betweenTranslations, - &kPRNG, initialValues); + return initializeRandomly(relativeTranslations, betweenTranslations, &kPRNG, + initialValues); } Values TranslationRecovery::run( @@ -229,11 +247,9 @@ Values TranslationRecovery::run( TranslationRecovery::TranslationEdges TranslationRecovery::SimulateMeasurements( const Values &poses, const vector &edges) { - auto edgeNoiseModel = noiseModel::Isotropic::Sigma(3, 0.01); + auto edgeNoiseModel = noiseModel::Isotropic::Sigma(2, 0.01); TranslationEdges relativeTranslations; - for (auto edge : edges) { - Key a, b; - tie(a, b) = edge; + for (const auto& [a, b] : edges) { const Pose3 wTa = poses.at(a), wTb = poses.at(b); const Point3 Ta = wTa.translation(), Tb = wTb.translation(); const Unit3 w_aZb(Tb - Ta); From 973f2c5e7d14b91bf757abeaa4f936c04d445ec2 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 09:28:56 -0500 Subject: [PATCH 110/266] New noiseModel methods --- gtsam/linear/NoiseModel.h | 35 +++++++++++++++++++- gtsam/linear/tests/testNoiseModel.cpp | 33 +++++++++++++++++++ gtsam/sfm/BinaryMeasurement.h | 18 ++++++----- gtsam/sfm/UnaryMeasurement.h | 46 ++++++++++++++------------- gtsam/slam/InitializePose.h | 3 +- gtsam/slam/TriangulationFactor.h | 3 +- 6 files changed, 104 insertions(+), 34 deletions(-) diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h index 0b47eea062..e14e484712 100644 --- a/gtsam/linear/NoiseModel.h +++ b/gtsam/linear/NoiseModel.h @@ -18,8 +18,9 @@ #pragma once -#include +#include #include +#include #include #include #include @@ -32,6 +33,7 @@ #endif #include +#include namespace gtsam { @@ -152,6 +154,20 @@ namespace gtsam { }; //--------------------------------------------------------------------------------------- + /** + * Return true if the model dimension matches the manifold dimension. + */ + template + inline bool matchesDimension(const Base& model, const T& measured) { + static_assert(IsManifold::value, + "noiseModel::matchesDimension requires a manifold type."); + if constexpr (traits::dimension == Eigen::Dynamic) { + return model.dim() == + static_cast(traits::GetDimension(measured)); + } else { + return model.dim() == static_cast(traits::dimension); + } + } /** * Gaussian implements the mathematical model @@ -638,6 +654,23 @@ namespace gtsam { return shared_ptr(new Unit(dim)); } + /** + * Create a unit covariance noise model for a measurement type. + * Reuse a cached instance for fixed-size types. + */ + template , int> = 0> + static shared_ptr Create(const T& measured) { + static_assert(IsManifold::value, + "noiseModel::Unit::Create requires a manifold type."); + if constexpr (traits::dimension == Eigen::Dynamic) { + return Create(static_cast(traits::GetDimension(measured))); + } else { + static const shared_ptr kDefault = + Create(static_cast(traits::dimension)); + return kDefault; + } + } + /// true if a unit noise model, saves slow/clumsy dynamic casting bool isUnit() const override { return true; } diff --git a/gtsam/linear/tests/testNoiseModel.cpp b/gtsam/linear/tests/testNoiseModel.cpp index fa93e78045..fa03c441be 100644 --- a/gtsam/linear/tests/testNoiseModel.cpp +++ b/gtsam/linear/tests/testNoiseModel.cpp @@ -19,7 +19,9 @@ #include +#include #include +#include #include @@ -106,6 +108,37 @@ TEST(NoiseModel, Unit) EXPECT(assert_equal(v,u->whiten(v))); } +/* ************************************************************************* */ +TEST(NoiseModel, UnitCreateMeasured) +{ + Matrix22 fixed = Matrix22::Identity(); + auto fixedModel = Unit::Create(fixed); + EXPECT_LONGS_EQUAL(4, fixedModel->dim()); + EXPECT(fixedModel == Unit::Create(fixed)); + + Matrix dynamic(2, 3); + dynamic.setZero(); + EXPECT_LONGS_EQUAL(6, Unit::Create(dynamic)->dim()); + + EXPECT_LONGS_EQUAL(2, Unit::Create(Point2(1.0, 2.0))->dim()); + EXPECT_LONGS_EQUAL(1, Unit::Create(1.0)->dim()); +} + +/* ************************************************************************* */ +TEST(NoiseModel, MatchesDimension) +{ + Matrix22 fixed = Matrix22::Identity(); + EXPECT(matchesDimension(*Unit::Create(4), fixed)); + EXPECT(!matchesDimension(*Unit::Create(3), fixed)); + + Matrix dynamic(2, 3); + dynamic.setZero(); + EXPECT(matchesDimension(*Unit::Create(6), dynamic)); + + EXPECT(matchesDimension(*Unit::Create(2), Point2(1.0, 2.0))); + EXPECT(matchesDimension(*Unit::Create(1), 1.0)); +} + /* ************************************************************************* */ TEST(NoiseModel, equals) { diff --git a/gtsam/sfm/BinaryMeasurement.h b/gtsam/sfm/BinaryMeasurement.h index 7acd0d439e..1aaae41d25 100644 --- a/gtsam/sfm/BinaryMeasurement.h +++ b/gtsam/sfm/BinaryMeasurement.h @@ -33,17 +33,18 @@ namespace gtsam { -template class BinaryMeasurement : public Factor { +template +class BinaryMeasurement : public Factor { // Check that T type is testable GTSAM_CONCEPT_ASSERT(IsTestable); -public: + public: // shorthand for a smart pointer to a measurement using shared_ptr = typename std::shared_ptr; -private: - T measured_; ///< The measurement - SharedNoiseModel noiseModel_; ///< Noise model + private: + T measured_; ///< The measurement + SharedNoiseModel noiseModel_; ///< Noise model public: BinaryMeasurement(Key key1, Key key2, const T &measured, @@ -52,12 +53,13 @@ template class BinaryMeasurement : public Factor { measured_(measured), noiseModel_(model) { if (model) { - if (static_cast(model->dim()) != traits::GetDimension(measured)) + if (!noiseModel::matchesDimension(*model, measured)) { throw std::runtime_error( "BinaryMeasurement: Noise model dimension does not match " "measurement dimension."); + } } else { - noiseModel_ = noiseModel::Unit::Create(traits::GetDimension(measured)); + noiseModel_ = noiseModel::Unit::Create(measured); } } @@ -90,4 +92,4 @@ template class BinaryMeasurement : public Factor { } /// @} }; -} // namespace gtsam +} // namespace gtsam diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h index cfdb4e5e20..e1195552c5 100644 --- a/gtsam/sfm/UnaryMeasurement.h +++ b/gtsam/sfm/UnaryMeasurement.h @@ -34,39 +34,41 @@ namespace gtsam { /** * @brief Unary measurement represents a measurement on a single key in a graph. */ -template class UnaryMeasurement : public Factor { +template +class UnaryMeasurement : public Factor { // Check that T type is testable GTSAM_CONCEPT_ASSERT(IsTestable); -public: + public: // shorthand for a smart pointer to a measurement using shared_ptr = std::shared_ptr; -private: - T measured_; ///< The measurement - SharedNoiseModel noiseModel_; ///< Noise model + private: + T measured_; ///< The measurement + SharedNoiseModel noiseModel_; ///< Noise model -public: + public: /** * @brief Constructs a unary measurement with a key, value, and noise model. * @param key The key of the measurement. * @param measured The measurement value. * @param model The noise model (optional). */ - UnaryMeasurement(Key key, const T &measured, - const SharedNoiseModel &model = nullptr) - : Factor(std::vector({key})), - measured_(measured), - noiseModel_(model) { - if (model) { - if (static_cast(model->dim()) != traits::GetDimension(measured)) - throw std::runtime_error( - "BinaryMeasurement: Noise model dimension does not match " - "measurement dimension."); - } else { - noiseModel_ = noiseModel::Unit::Create(traits::GetDimension(measured)); - } - } + UnaryMeasurement(Key key, const T &measured, + const SharedNoiseModel &model = nullptr) + : Factor(std::vector({key})), + measured_(measured), + noiseModel_(model) { + if (model) { + if (!noiseModel::matchesDimension(*model, measured)) { + throw std::runtime_error( + "UnaryMeasurement: Noise model dimension does not match " + "measurement dimension."); + } + } else { + noiseModel_ = noiseModel::Unit::Create(measured); + } + } /// @name Standard Interface /// @{ @@ -84,7 +86,7 @@ template class UnaryMeasurement : public Factor { // Prints the measurement. void print(const std::string &s, const KeyFormatter &keyFormatter = - DefaultKeyFormatter) const override { + DefaultKeyFormatter) const override { std::cout << s << "UnaryMeasurement(" << keyFormatter(this->key()) << ")\n"; traits::Print(measured_, " measured: "); if (noiseModel_) @@ -104,4 +106,4 @@ template class UnaryMeasurement : public Factor { } /// @} }; -} // namespace gtsam +} // namespace gtsam diff --git a/gtsam/slam/InitializePose.h b/gtsam/slam/InitializePose.h index bafdd6ed43..a1ae89cdb3 100644 --- a/gtsam/slam/InitializePose.h +++ b/gtsam/slam/InitializePose.h @@ -70,7 +70,8 @@ static Values computePoses(const Values& initialRot, } // add prior on dummy node - auto priorModel = noiseModel::Unit::Create(Pose::dimension); + auto priorModel = + noiseModel::Unit::Create(static_cast(Pose::dimension)); initialPose.insert(kAnchorKey, Pose()); posegraph->emplace_shared >(kAnchorKey, Pose(), priorModel); diff --git a/gtsam/slam/TriangulationFactor.h b/gtsam/slam/TriangulationFactor.h index c25afa8aae..a1fa9dfae9 100644 --- a/gtsam/slam/TriangulationFactor.h +++ b/gtsam/slam/TriangulationFactor.h @@ -81,7 +81,7 @@ class TriangulationFactor: public NoiseModelFactorN { bool verboseCheirality = false) : Base(model, pointKey), camera_(camera), measured_(measured), throwCheirality_( throwCheirality), verboseCheirality_(verboseCheirality) { - if (model && model->dim() != traits::dimension) + if (model && !noiseModel::matchesDimension(*model, measured_)) throw std::invalid_argument( "TriangulationFactor must be created with " + std::to_string((int) traits::dimension) @@ -201,4 +201,3 @@ class TriangulationFactor: public NoiseModelFactorN { #endif }; } // \ namespace gtsam - From a21bf63219928b65b228cd0ced6cd1c41a5d24a1 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 09:29:18 -0500 Subject: [PATCH 111/266] Restrict KarcherMean templating --- gtsam/slam/KarcherMeanFactor-inl.h | 4 +++- gtsam/slam/slam.i | 22 +++++++++++++++------- 2 files changed, 18 insertions(+), 8 deletions(-) diff --git a/gtsam/slam/KarcherMeanFactor-inl.h b/gtsam/slam/KarcherMeanFactor-inl.h index e51151cf0e..d71a91d537 100644 --- a/gtsam/slam/KarcherMeanFactor-inl.h +++ b/gtsam/slam/KarcherMeanFactor-inl.h @@ -26,11 +26,13 @@ namespace gtsam { template T FindKarcherMeanImpl(const std::vector& rotations) { + static_assert(T::dimension != Eigen::Dynamic, + "FindKarcherMean requires fixed-size manifolds."); // Cost function C(R) = \sum PriorFactor(R_i)::error(R) // No closed form solution. NonlinearFactorGraph graph; static const Key kKey(0); - auto model = noiseModel::Unit::Create(T::dimension); + auto model = noiseModel::Unit::Create(static_cast(T::dimension)); for (const auto& R : rotations) { graph.addPrior(kKey, R, model); } diff --git a/gtsam/slam/slam.i b/gtsam/slam/slam.i index 0514b1f4be..3aaeda38ff 100644 --- a/gtsam/slam/slam.i +++ b/gtsam/slam/slam.i @@ -582,22 +582,26 @@ class InitializePose3 { }; #include -template +template virtual class KarcherMeanFactor : gtsam::NonlinearFactor { KarcherMeanFactor(const gtsam::KeyVector& keys); KarcherMeanFactor(const gtsam::KeyVector& keys, int d, double beta); }; -template +template T FindKarcherMean(const std::vector& elements); #include gtsam::noiseModel::Isotropic* ConvertNoiseModel(gtsam::noiseModel::Base* model, size_t d); -template +template class FrobeniusPrior : gtsam::NoiseModelFactor { FrobeniusPrior(gtsam::Key j, const gtsam::Matrix& M, const gtsam::noiseModel::Base* model); @@ -605,7 +609,9 @@ class FrobeniusPrior : gtsam::NoiseModelFactor { gtsam::Vector evaluateError(const T& g) const; }; -template +template virtual class FrobeniusFactor : gtsam::NoiseModelFactor { FrobeniusFactor(gtsam::Key key1, gtsam::Key key2); FrobeniusFactor(gtsam::Key j1, gtsam::Key j2, gtsam::noiseModel::Base* model); @@ -614,7 +620,9 @@ virtual class FrobeniusFactor : gtsam::NoiseModelFactor { }; // Available for all Matrix Lie groups -template +template virtual class FrobeniusBetweenFactorNL : gtsam::NoiseModelFactor { FrobeniusBetweenFactorNL(gtsam::Key j1, gtsam::Key j2, const T& T12); FrobeniusBetweenFactorNL(gtsam::Key key1, gtsam::Key key2, const T& T12, From 653d1aa72d98e47ae13e3868ba1a153b2e083f62 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 12:17:22 -0500 Subject: [PATCH 112/266] Fix python unit test --- python/gtsam/tests/test_TranslationRecovery.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/gtsam/tests/test_TranslationRecovery.py b/python/gtsam/tests/test_TranslationRecovery.py index d8e061435d..fe9e531711 100644 --- a/python/gtsam/tests/test_TranslationRecovery.py +++ b/python/gtsam/tests/test_TranslationRecovery.py @@ -25,7 +25,7 @@ def SimulateMeasurements(gt_poses, graph_edges): Tb = gt_poses.atPose3(edge[1]).translation() measurements.append(gtsam.BinaryMeasurementUnit3( \ edge[0], edge[1], gtsam.Unit3(Tb - Ta), \ - gtsam.noiseModel.Isotropic.Sigma(3, 0.01))) + gtsam.noiseModel.Isotropic.Sigma(2, 0.01))) return measurements From d456060c8bf66d1c21547aae0bd54177705df3a5 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 09:40:48 -0500 Subject: [PATCH 113/266] Improve whitening performance --- gtsam/linear/NoiseModel.cpp | 54 +++++++++++++++++++++++++++++++++++++ gtsam/linear/NoiseModel.h | 11 ++++++++ 2 files changed, 65 insertions(+) diff --git a/gtsam/linear/NoiseModel.cpp b/gtsam/linear/NoiseModel.cpp index 08aea1fc0b..986bf591fd 100644 --- a/gtsam/linear/NoiseModel.cpp +++ b/gtsam/linear/NoiseModel.cpp @@ -170,6 +170,14 @@ Vector Gaussian::unwhiten(const Vector& v) const { return backSubstituteUpper(thisR(), v); } +void Gaussian::unwhitenInPlace(Vector& v) const { + thisR().triangularView().solveInPlace(v); +} + +void Gaussian::unwhitenInPlace(Eigen::Block& v) const { + thisR().triangularView().solveInPlace(v); +} + /* ************************************************************************* */ Matrix Gaussian::Whiten(const Matrix& H) const { return thisR() * H; @@ -319,6 +327,14 @@ Vector Diagonal::unwhiten(const Vector& v) const { return v.cwiseProduct(sigmas_); } +void Diagonal::whitenInPlace(Vector& v) const { + v.array() *= invsigmas_.array(); +} + +void Diagonal::unwhitenInPlace(Vector& v) const { + v.array() *= sigmas_.array(); +} + Matrix Diagonal::Whiten(const Matrix& H) const { return vector_scale(invsigmas(), H); } @@ -331,6 +347,14 @@ void Diagonal::WhitenInPlace(Eigen::Block H) const { H = invsigmas().asDiagonal() * H; } +void Diagonal::whitenInPlace(Eigen::Block& v) const { + v.array() *= invsigmas_.array(); +} + +void Diagonal::unwhitenInPlace(Eigen::Block& v) const { + v.array() *= sigmas_.array(); +} + /* *******************************************************************************/ double Diagonal::logDetR() const { return invsigmas_.unaryExpr([](double x) { return log(x); }).sum(); @@ -403,6 +427,26 @@ Vector Constrained::whiten(const Vector& v) const { return c; } +void Constrained::whitenInPlace(Vector& v) const { + const size_t n = v.size(); + for (size_t i = 0; i < n; ++i) { + const double si = sigmas_(i); + if (si != 0.0) { + v(i) /= si; + } + } +} + +void Constrained::whitenInPlace(Eigen::Block& v) const { + const DenseIndex n = v.rows(); + for (DenseIndex i = 0; i < n; ++i) { + const double si = sigmas_(static_cast(i)); + if (si != 0.0) { + v(i, 0) /= si; + } + } +} + /* ************************************************************************* */ Constrained::shared_ptr Constrained::MixedSigmas(const Vector& sigmas) { return MixedSigmas(Vector::Constant(sigmas.size(), 1000.0), sigmas); @@ -702,6 +746,16 @@ void Isotropic::WhitenInPlace(Eigen::Block H) const { H *= invsigma_; } +/* ************************************************************************* */ +void Isotropic::unwhitenInPlace(Vector& v) const { + v *= sigma_; +} + +/* ************************************************************************* */ +void Isotropic::unwhitenInPlace(Eigen::Block& v) const { + v *= sigma_; +} + /* *******************************************************************************/ double Isotropic::logDetR() const { return log(invsigma_) * dim(); } diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h index e14e484712..97f340a1ef 100644 --- a/gtsam/linear/NoiseModel.h +++ b/gtsam/linear/NoiseModel.h @@ -239,6 +239,8 @@ namespace gtsam { Vector sigmas() const override; Vector whiten(const Vector& v) const override; Vector unwhiten(const Vector& v) const override; + void unwhitenInPlace(Vector& v) const override; + void unwhitenInPlace(Eigen::Block& v) const override; /** * Multiply a derivative with R (derivative of whiten) @@ -362,9 +364,13 @@ namespace gtsam { Vector sigmas() const override { return sigmas_; } Vector whiten(const Vector& v) const override; Vector unwhiten(const Vector& v) const override; + void whitenInPlace(Vector& v) const override; + void unwhitenInPlace(Vector& v) const override; Matrix Whiten(const Matrix& H) const override; void WhitenInPlace(Matrix& H) const override; void WhitenInPlace(Eigen::Block H) const override; + void whitenInPlace(Eigen::Block& v) const override; + void unwhitenInPlace(Eigen::Block& v) const override; /** * Return standard deviations (sqrt of diagonal) @@ -511,6 +517,8 @@ namespace gtsam { /// Calculates error vector with weights applied Vector whiten(const Vector& v) const override; + void whitenInPlace(Vector& v) const override; + void whitenInPlace(Eigen::Block& v) const override; /// Whitening functions will perform partial whitening on rows /// with a non-zero sigma. Other rows remain untouched. @@ -608,6 +616,8 @@ namespace gtsam { void WhitenInPlace(Matrix& H) const override; void whitenInPlace(Vector& v) const override; void WhitenInPlace(Eigen::Block H) const override; + void unwhitenInPlace(Vector& v) const override; + void unwhitenInPlace(Eigen::Block& v) const override; /** * Return standard deviation @@ -753,6 +763,7 @@ namespace gtsam { { Vector b; Matrix B=A; this->WhitenSystem(B,b); return B; } inline Vector unwhiten(const Vector& /*v*/) const override { throw std::invalid_argument("unwhiten is not currently supported for robust noise models."); } + inline void whitenInPlace(Vector& v) const override { this->WhitenSystem(v); } /// Compute loss from the m-estimator using the Mahalanobis distance. double loss(const double squared_distance) const override { return robust_->loss(std::sqrt(squared_distance)); From a8bce21c3f406b6990232f542ece600af36d7143 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 09:41:10 -0500 Subject: [PATCH 114/266] Add test coverage (AI) --- gtsam/linear/tests/testNoiseModel.cpp | 88 +++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) diff --git a/gtsam/linear/tests/testNoiseModel.cpp b/gtsam/linear/tests/testNoiseModel.cpp index fa03c441be..686caca0f9 100644 --- a/gtsam/linear/tests/testNoiseModel.cpp +++ b/gtsam/linear/tests/testNoiseModel.cpp @@ -510,6 +510,94 @@ TEST(NoiseModel, WhitenInPlace) EXPECT(assert_equal(expected, A)); } +/* ************************************************************************* */ +TEST(NoiseModel, InPlaceVectorOperations) +{ + SharedGaussian gaussian = Gaussian::SqrtInformation(R, false); + Vector v = Vector3(5.0, 10.0, 15.0); + Vector expected = gaussian->unwhiten(v); + Vector actual = v; + gaussian->unwhitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); + + SharedDiagonal diagonal = Diagonal::Sigmas(kSigmas, false); + v = Vector3(10.0, 20.0, 30.0); + expected = diagonal->whiten(v); + actual = v; + diagonal->whitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); + + v = Vector3(1.0, 2.0, 3.0); + expected = diagonal->unwhiten(v); + actual = v; + diagonal->unwhitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); + + SharedIsotropic isotropic = Isotropic::Sigma(3, kSigma, false); + v = Vector3(1.0, 2.0, 3.0); + expected = isotropic->unwhiten(v); + actual = v; + isotropic->unwhitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); + + SharedConstrained constrained = + Constrained::MixedSigmas(Vector3(kSigma, 0.0, kSigma)); + v = Vector3(2.0, 3.0, 4.0); + expected = constrained->whiten(v); + actual = v; + constrained->whitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); + EXPECT_DOUBLES_EQUAL(v(1), actual(1), 1e-12); + + SharedNoiseModel robust = Robust::Create(mEstimator::Huber::Create(1.345), + diagonal); + v = Vector3(1.0, 2.0, 3.0); + expected = robust->whiten(v); + actual = v; + robust->whitenInPlace(actual); + EXPECT(assert_equal(expected, actual)); +} + +/* ************************************************************************* */ +TEST(NoiseModel, InPlaceVectorBlockOperations) +{ + SharedGaussian gaussian = Gaussian::SqrtInformation(R, false); + SharedDiagonal diagonal = Diagonal::Sigmas(kSigmas, false); + SharedIsotropic isotropic = Isotropic::Sigma(3, kSigma, false); + SharedConstrained constrained = + Constrained::MixedSigmas(Vector3(kSigma, 0.0, kSigma)); + + Vector v = Vector::LinSpaced(5, 1.0, 5.0); + Eigen::Block block(v, 1, 0, 3, 1); + Vector expected = diagonal->whiten(Vector(block)); + diagonal->whitenInPlace(block); + EXPECT(assert_equal(expected, Vector(block))); + + v = Vector::LinSpaced(5, 1.0, 5.0); + Eigen::Block block_unwhiten(v, 1, 0, 3, 1); + expected = diagonal->unwhiten(Vector(block_unwhiten)); + diagonal->unwhitenInPlace(block_unwhiten); + EXPECT(assert_equal(expected, Vector(block_unwhiten))); + + v = Vector::LinSpaced(5, 1.0, 5.0); + Eigen::Block block_constrained(v, 1, 0, 3, 1); + expected = constrained->whiten(Vector(block_constrained)); + constrained->whitenInPlace(block_constrained); + EXPECT(assert_equal(expected, Vector(block_constrained))); + + v = Vector::LinSpaced(5, 1.0, 5.0); + Eigen::Block block_iso(v, 1, 0, 3, 1); + expected = isotropic->unwhiten(Vector(block_iso)); + isotropic->unwhitenInPlace(block_iso); + EXPECT(assert_equal(expected, Vector(block_iso))); + + v = Vector::LinSpaced(5, 1.0, 5.0); + Eigen::Block block_gauss(v, 1, 0, 3, 1); + expected = gaussian->unwhiten(Vector(block_gauss)); + gaussian->unwhitenInPlace(block_gauss); + EXPECT(assert_equal(expected, Vector(block_gauss))); +} + /* ************************************************************************* */ /* From 50ec40772a2bed0d3b91d7fcfd2d0708582d0169 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 09:58:21 -0500 Subject: [PATCH 115/266] New `sigmasRef` method --- gtsam/linear/GaussianConditional.cpp | 22 +++++++++++++--------- gtsam/linear/JacobianFactor.cpp | 14 +++++++++----- gtsam/linear/MultifrontalSolver.cpp | 2 +- gtsam/linear/NoiseModel.h | 2 ++ gtsam/linear/Sampler.cpp | 2 +- 5 files changed, 26 insertions(+), 16 deletions(-) diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp index 04bb7a5c95..08c5551853 100644 --- a/gtsam/linear/GaussianConditional.cpp +++ b/gtsam/linear/GaussianConditional.cpp @@ -244,7 +244,7 @@ namespace gtsam { /* ************************************************************************* */ VectorValues GaussianConditional::solveOtherRHS( - const VectorValues& parents, const VectorValues& rhs) const { + const VectorValues& parents, const VectorValues& rhs) const { // Concatenate all vector values that correspond to parent variables Vector xS = parents.vector(KeyVector(beginParents(), endParents())); @@ -253,17 +253,18 @@ namespace gtsam { xS = rhsR - S() * xS; // Solve Matrix - Vector soln = R().triangularView().solve(xS); + Vector solution = R().triangularView().solve(xS); // Scale by sigmas - if (model_) - soln.array() *= model_->sigmas().array(); + if (model_) solution.array() *= model_->sigmasRef().array(); // Insert solution into a VectorValues VectorValues result; DenseIndex vectorPosition = 0; - for (const_iterator frontal = beginFrontals(); frontal != endFrontals(); ++frontal) { - result.emplace(*frontal, soln.segment(vectorPosition, getDim(frontal))); + for (const_iterator frontal = beginFrontals(); frontal != endFrontals(); + ++frontal) { + result.emplace(*frontal, + solution.segment(vectorPosition, getDim(frontal))); vectorPosition += getDim(frontal); } @@ -283,7 +284,7 @@ namespace gtsam { // Scale by sigmas if (model_) - frontalVec.array() *= model_->sigmas().array(); + frontalVec.array() *= model_->sigmasRef().array(); // Write frontal solution into a VectorValues DenseIndex vectorPosition = 0; @@ -367,8 +368,11 @@ namespace gtsam { // The vector of sigma values for sampling. // If no model, initialize sigmas to 1, else to model sigmas - const Vector& sigmas = (!model_) ? Vector::Ones(rows()) : model_->sigmas(); - solution[key] += Sampler::sampleDiagonal(sigmas, rng); + if (model_) { + solution[key] += Sampler::sampleDiagonal(model_->sigmasRef(), rng); + } else { + solution[key] += Sampler::sampleDiagonal(Vector::Ones(rows()), rng); + } return solution; } diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index 87f1779958..1ccf159d28 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -324,7 +324,7 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, if (!sigmas) sigmas = Vector::Constant(m, 1.0); sigmas->segment(nextRow, sourceRows) = - jacobians[factorI]->get_model()->sigmas(); + jacobians[factorI]->get_model()->sigmasRef(); if (jacobians[factorI]->isConstrained()) anyConstrained = true; } @@ -931,13 +931,15 @@ GaussianConditional::shared_ptr JacobianFactor::splitConditional(size_t nrFronta Ab_.rowEnd() = Ab_.rowStart() + frontalDim; SharedDiagonal conditionalNoiseModel; conditionalNoiseModel = - noiseModel::Diagonal::Sigmas(model_->sigmas().segment(Ab_.rowStart(), Ab_.rows())); + noiseModel::Diagonal::Sigmas( + model_->sigmasRef().segment(Ab_.rowStart(), Ab_.rows())); GaussianConditional::shared_ptr conditional = std::make_shared(Base::keys_, nrFrontals, Ab_, conditionalNoiseModel); const DenseIndex maxRemainingRows = std::min(Ab_.cols(), originalRowEnd) - Ab_.rowStart() - frontalDim; - const DenseIndex remainingRows = std::min(model_->sigmas().size() - frontalDim, maxRemainingRows); + const DenseIndex remainingRows = + std::min(model_->sigmasRef().size() - frontalDim, maxRemainingRows); Ab_.rowStart() += frontalDim; Ab_.rowEnd() = Ab_.rowStart() + remainingRows; Ab_.firstBlock() += nrFrontals; @@ -946,9 +948,11 @@ GaussianConditional::shared_ptr JacobianFactor::splitConditional(size_t nrFronta keys_.erase(begin(), begin() + nrFrontals); // Set sigmas with the right model if (model_->isConstrained()) - model_ = noiseModel::Constrained::MixedSigmas(model_->sigmas().tail(remainingRows)); + model_ = noiseModel::Constrained::MixedSigmas( + model_->sigmasRef().tail(remainingRows)); else - model_ = noiseModel::Diagonal::Sigmas(model_->sigmas().tail(remainingRows)); + model_ = noiseModel::Diagonal::Sigmas( + model_->sigmasRef().tail(remainingRows)); assert(model_->dim() == (size_t)Ab_.rows()); return conditional; diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 8f94adae5c..7ecc5c7fe1 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -104,7 +104,7 @@ std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { throw std::runtime_error( "MultifrontalSolver: only unary constrained factors are supported."); } - const Vector sigmas = model->sigmas(); + const Vector& sigmas = model->sigmasRef(); if (!(sigmas.array().abs() <= kConstraintSigmaTol).all()) { throw std::runtime_error( "MultifrontalSolver: only fully constrained factors are supported."); diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h index 97f340a1ef..2b7627adf8 100644 --- a/gtsam/linear/NoiseModel.h +++ b/gtsam/linear/NoiseModel.h @@ -362,6 +362,8 @@ namespace gtsam { void print(const std::string& name) const override; Vector sigmas() const override { return sigmas_; } + /// Return standard deviations without copying. + inline const Vector& sigmasRef() const { return sigmas_; } Vector whiten(const Vector& v) const override; Vector unwhiten(const Vector& v) const override; void whitenInPlace(Vector& v) const override; diff --git a/gtsam/linear/Sampler.cpp b/gtsam/linear/Sampler.cpp index 6081fb84f2..574059d61c 100644 --- a/gtsam/linear/Sampler.cpp +++ b/gtsam/linear/Sampler.cpp @@ -79,7 +79,7 @@ Vector Sampler::sampleDiagonal(const Vector& sigmas) const { /* ************************************************************************* */ Vector Sampler::sample() const { assert(model_.get()); - const Vector& sigmas = model_->sigmas(); + const Vector& sigmas = model_->sigmasRef(); return sampleDiagonal(sigmas); } From e9021804614b89a64a5d211be3838968c2c65b04 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 10:52:28 -0500 Subject: [PATCH 116/266] cache rowOffsets + tests --- gtsam/linear/JacobianFactor.cpp | 24 ++++- gtsam/linear/tests/testJacobianFactor.cpp | 104 ++++++++++++++++++++++ 2 files changed, 124 insertions(+), 4 deletions(-) diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index 1ccf159d28..8229436b3a 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -262,6 +262,21 @@ FastVector _convertOrCastToJacobians( } /* ************************************************************************* */ +static std::vector _computeRowOffsets( + const FastVector& jacobians) { + std::vector rowOffsets; + rowOffsets.reserve(jacobians.size()); + DenseIndex nextRow = 0; + for (const auto& jacobian : jacobians) { + rowOffsets.push_back(nextRow); + const DenseIndex rows = jacobian->rows(); + if (rows > 0) { + nextRow += rows; + } + } + return rowOffsets; +} + void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, const FastVector& orderedSlots) { @@ -272,6 +287,9 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, // Count dimensions const auto [varDims, m, n] = _countDims(jacobians, orderedSlots); + // Precompute row offsets once to avoid recomputing row starts per slot. + std::vector rowOffsets = _computeRowOffsets(jacobians); + // Allocate matrix and copy keys in order gttic(allocate); Ab_ = VerticalBlockMatrix(varDims, m, true); // Allocate augmented matrix @@ -288,12 +306,12 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, for(VariableSlots::const_iterator varslot: orderedSlots) { JacobianFactor::ABlock destSlot(this->getA(this->begin() + combinedSlot)); // Loop over source jacobians - DenseIndex nextRow = 0; for (size_t factorI = 0; factorI < jacobians.size(); ++factorI) { // Slot in source factor const size_t sourceSlot = varslot->second[factorI]; const DenseIndex sourceRows = jacobians[factorI]->rows(); if (sourceRows > 0) { + DenseIndex nextRow = rowOffsets[factorI]; JacobianFactor::ABlock::RowsBlockXpr destBlock( destSlot.middleRows(nextRow, sourceRows)); // Copy if exists in source factor, otherwise set zero @@ -302,7 +320,6 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, jacobians[factorI]->begin() + sourceSlot); else destBlock.setZero(); - nextRow += sourceRows; } } ++combinedSlot; @@ -314,10 +331,10 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, bool anyConstrained = false; std::optional sigmas; // Loop over source jacobians - DenseIndex nextRow = 0; for (size_t factorI = 0; factorI < jacobians.size(); ++factorI) { const DenseIndex sourceRows = jacobians[factorI]->rows(); if (sourceRows > 0) { + DenseIndex nextRow = rowOffsets[factorI]; this->getb().segment(nextRow, sourceRows) = jacobians[factorI]->getb(); if (jacobians[factorI]->get_model()) { // If the factor has a noise model and we haven't yet allocated sigmas, allocate it. @@ -328,7 +345,6 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph, if (jacobians[factorI]->isConstrained()) anyConstrained = true; } - nextRow += sourceRows; } } gttoc(copy_vectors); diff --git a/gtsam/linear/tests/testJacobianFactor.cpp b/gtsam/linear/tests/testJacobianFactor.cpp index be9365bca0..5296e4a760 100644 --- a/gtsam/linear/tests/testJacobianFactor.cpp +++ b/gtsam/linear/tests/testJacobianFactor.cpp @@ -232,6 +232,110 @@ TEST( JacobianFactor, construct_from_graph) EXPECT(assert_equal(expected, actual)); } +/* ************************************************************************* */ +TEST(JacobianFactor, construct_from_graph_no_model) +{ + const Key keyX = 1, keyY = 2; + Matrix A11 = I_2x2; + Matrix A22 = 2 * I_2x2; + Vector2 b1(1.0, 2.0); + Vector2 b2(3.0, 4.0); + + auto factor1 = std::make_shared(keyX, A11, b1); + auto factor2 = std::make_shared(keyY, A22, b2); + + GaussianFactorGraph factors{factor1, factor2}; + Ordering ordering{keyX, keyY}; + + Matrix A1(4, 2); + A1.setZero(); + A1.block(0, 0, 2, 2) = A11; + Matrix A2(4, 2); + A2.setZero(); + A2.block(2, 0, 2, 2) = A22; + Vector b(4); + b << b1, b2; + + JacobianFactor expected(keyX, A1, keyY, A2, b); + JacobianFactor actual(factors, ordering); + + EXPECT(assert_equal(expected, actual)); + EXPECT(!actual.get_model()); +} + +/* ************************************************************************* */ +TEST(JacobianFactor, construct_from_graph_mixed_models) +{ + const Key keyX = 1, keyY = 2; + Matrix A11 = I_2x2; + Matrix A22 = 2 * I_2x2; + Vector2 b1(1.0, 2.0); + Vector2 b2(3.0, 4.0); + Vector2 sigmas1(0.2, 0.3); + + auto factor1 = std::make_shared( + keyX, A11, b1, noiseModel::Diagonal::Sigmas(sigmas1)); + auto factor2 = std::make_shared(keyY, A22, b2); + + GaussianFactorGraph factors{factor1, factor2}; + Ordering ordering{keyX, keyY}; + + Matrix A1(4, 2); + A1.setZero(); + A1.block(0, 0, 2, 2) = A11; + Matrix A2(4, 2); + A2.setZero(); + A2.block(2, 0, 2, 2) = A22; + Vector b(4); + b << b1, b2; + Vector sigmas(4); + sigmas << sigmas1, Vector2(1.0, 1.0); + + JacobianFactor expected(keyX, A1, keyY, A2, b, + noiseModel::Diagonal::Sigmas(sigmas)); + JacobianFactor actual(factors, ordering); + + EXPECT(assert_equal(expected, actual)); +} + +/* ************************************************************************* */ +TEST(JacobianFactor, construct_from_graph_constrained) +{ + const Key keyX = 1, keyY = 2; + Matrix A11 = I_2x2; + Matrix A22 = 2 * I_2x2; + Vector2 b1(1.0, 2.0); + Vector2 b2(3.0, 4.0); + Vector2 sigmas1(0.0, 1.0); + Vector2 sigmas2(2.0, 3.0); + + auto factor1 = std::make_shared( + keyX, A11, b1, noiseModel::Constrained::MixedSigmas(sigmas1)); + auto factor2 = std::make_shared( + keyY, A22, b2, noiseModel::Diagonal::Sigmas(sigmas2)); + + GaussianFactorGraph factors{factor1, factor2}; + Ordering ordering{keyX, keyY}; + + Matrix A1(4, 2); + A1.setZero(); + A1.block(0, 0, 2, 2) = A11; + Matrix A2(4, 2); + A2.setZero(); + A2.block(2, 0, 2, 2) = A22; + Vector b(4); + b << b1, b2; + Vector sigmas(4); + sigmas << sigmas1, sigmas2; + + JacobianFactor expected(keyX, A1, keyY, A2, b, + noiseModel::Constrained::MixedSigmas(sigmas)); + JacobianFactor actual(factors, ordering); + + EXPECT(actual.isConstrained()); + EXPECT(assert_equal(expected, actual)); +} + /* ************************************************************************* */ TEST(JacobianFactor, error) { From f331dbb97add59170d2fb1567b1e3c15d4ab04a0 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 15:43:22 -0500 Subject: [PATCH 117/266] Default Unit Models for all nonlinear factors --- gtsam/linear/NoiseModel.h | 14 +++++++++++--- gtsam/nonlinear/PriorFactor.h | 2 +- gtsam/nonlinear/nonlinear.i | 2 +- gtsam/sfm/BinaryMeasurement.h | 12 +----------- gtsam/sfm/TransferFactor.h | 23 ++++++++++++++++++----- gtsam/sfm/UnaryMeasurement.h | 12 +----------- gtsam/slam/BetweenFactor.h | 3 ++- gtsam/slam/EssentialMatrixFactor.h | 6 ++++-- gtsam/slam/FrobeniusFactor.h | 7 +++++-- gtsam/slam/KarcherMeanFactor-inl.h | 3 +-- gtsam/slam/slam.i | 4 ++-- 11 files changed, 47 insertions(+), 41 deletions(-) diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h index 2b7627adf8..0e6bb895f4 100644 --- a/gtsam/linear/NoiseModel.h +++ b/gtsam/linear/NoiseModel.h @@ -154,9 +154,7 @@ namespace gtsam { }; //--------------------------------------------------------------------------------------- - /** - * Return true if the model dimension matches the manifold dimension. - */ + /// Return true if the model dimension matches the manifold dimension. template inline bool matchesDimension(const Base& model, const T& measured) { static_assert(IsManifold::value, @@ -806,6 +804,16 @@ namespace gtsam { // Helper function GTSAM_EXPORT std::optional checkIfDiagonal(const Matrix& M); + /// Create + template + Base::shared_ptr validOrDefault(const T& value, + const Base::shared_ptr& model) { + if (!model) return noiseModel::Unit::Create(value); + if (noiseModel::matchesDimension(*model, value)) return model; + throw std::runtime_error( + "noiseModel::validOrDefault: mis-matched model dimension."); + } + } // namespace noiseModel /** diff --git a/gtsam/nonlinear/PriorFactor.h b/gtsam/nonlinear/PriorFactor.h index 0aa14174ec..e37c7e8062 100644 --- a/gtsam/nonlinear/PriorFactor.h +++ b/gtsam/nonlinear/PriorFactor.h @@ -49,7 +49,7 @@ class PriorFactor : public ExtendedPriorFactor { /// Constructor PriorFactor(Key key, const VALUE& prior, const SharedNoiseModel& model = nullptr) - : Base(key, prior, model) {} + : Base(key, prior, noiseModel::validOrDefault(prior, model)) {} /// Convenience constructor that takes a full covariance argument PriorFactor(Key key, const VALUE& prior, const Matrix& covariance) diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i index 2565b2d9ba..9189d2d974 100644 --- a/gtsam/nonlinear/nonlinear.i +++ b/gtsam/nonlinear/nonlinear.i @@ -625,7 +625,7 @@ template virtual class PriorFactor : gtsam::NoiseModelFactor { PriorFactor(gtsam::Key key, const T& prior, - const gtsam::noiseModel::Base* noiseModel); + const gtsam::noiseModel::Base* noiseModel = nullptr); T prior() const; // enabling serialization functionality diff --git a/gtsam/sfm/BinaryMeasurement.h b/gtsam/sfm/BinaryMeasurement.h index 1aaae41d25..4d15fa158d 100644 --- a/gtsam/sfm/BinaryMeasurement.h +++ b/gtsam/sfm/BinaryMeasurement.h @@ -51,17 +51,7 @@ class BinaryMeasurement : public Factor { const SharedNoiseModel &model = nullptr) : Factor(std::vector({key1, key2})), measured_(measured), - noiseModel_(model) { - if (model) { - if (!noiseModel::matchesDimension(*model, measured)) { - throw std::runtime_error( - "BinaryMeasurement: Noise model dimension does not match " - "measurement dimension."); - } - } else { - noiseModel_ = noiseModel::Unit::Create(measured); - } - } + noiseModel_(noiseModel::validOrDefault(measured, model)) {} /// @name Standard Interface /// @{ diff --git a/gtsam/sfm/TransferFactor.h b/gtsam/sfm/TransferFactor.h index 22fdabe9da..7fef8cacba 100644 --- a/gtsam/sfm/TransferFactor.h +++ b/gtsam/sfm/TransferFactor.h @@ -18,12 +18,14 @@ #include #include #include +#include #include #include #include #include #include +#include namespace gtsam { @@ -36,6 +38,14 @@ class TransferEdges { EdgeKey edge1_, edge2_; ///< The two EdgeKeys. uint32_t c_; ///< The transfer target + // Return appropriate noise model + static SharedNoiseModel defaultNoiseModel(size_t dim, + const SharedNoiseModel& model) { + if (!model) return noiseModel::Unit::Create(dim); + if (model->dim() == dim) return model; + throw std::runtime_error("TransferFactor: noise model dimension mismatch."); + } + public: TransferEdges(EdgeKey edge1, EdgeKey edge2) : edge1_(edge1), edge2_(edge2), c_(ViewC(edge1, edge2)) {} @@ -101,12 +111,13 @@ class TransferFactor : public NoiseModelFactorN, public TransferEdges { * @param edge2 Second EdgeKey specifying F2: (b, c) or (c, b). * @param triplets A vector of triplets containing (pa, pb, pc). * @param model An optional SharedNoiseModel that defines the noise model - * for this factor. Defaults to nullptr. + * for this factor. Defaults to unit noise. */ TransferFactor(EdgeKey edge1, EdgeKey edge2, const std::vector& triplets, const SharedNoiseModel& model = nullptr) - : Base(model, edge1, edge2), + : Base(TransferEdges::defaultNoiseModel(2 * triplets.size(), model), + edge1, edge2), TransferEdges(edge1, edge2), triplets_(triplets) {} @@ -247,7 +258,8 @@ class EssentialTransferFactorK EssentialTransferFactorK(EdgeKey edge1, EdgeKey edge2, const std::vector& triplets, const SharedNoiseModel& model = nullptr) - : Base(model, edge1, edge2, + : Base(TransferEdges::defaultNoiseModel(2 * triplets.size(), model), + edge1, edge2, Symbol('k', ViewA(edge1, edge2)), // calibration key for view a Symbol('k', ViewB(edge1, edge2)), // calibration key for view b Symbol('k', ViewC(edge1, edge2))), // calibration key for target c @@ -268,7 +280,8 @@ class EssentialTransferFactorK EssentialTransferFactorK(EdgeKey edge1, EdgeKey edge2, Key keyK, const std::vector& triplets, const SharedNoiseModel& model = nullptr) - : Base(model, edge1, edge2, keyK, keyK, keyK), + : Base(TransferEdges::defaultNoiseModel(2 * triplets.size(), model), + edge1, edge2, keyK, keyK, keyK), TransferEdges(edge1, edge2), triplets_(triplets) {} @@ -320,4 +333,4 @@ class EssentialTransferFactorK size_t dim() const override { return 2 * triplets_.size(); } }; -} // namespace gtsam \ No newline at end of file +} // namespace gtsam diff --git a/gtsam/sfm/UnaryMeasurement.h b/gtsam/sfm/UnaryMeasurement.h index e1195552c5..e013d49bb8 100644 --- a/gtsam/sfm/UnaryMeasurement.h +++ b/gtsam/sfm/UnaryMeasurement.h @@ -58,17 +58,7 @@ class UnaryMeasurement : public Factor { const SharedNoiseModel &model = nullptr) : Factor(std::vector({key})), measured_(measured), - noiseModel_(model) { - if (model) { - if (!noiseModel::matchesDimension(*model, measured)) { - throw std::runtime_error( - "UnaryMeasurement: Noise model dimension does not match " - "measurement dimension."); - } - } else { - noiseModel_ = noiseModel::Unit::Create(measured); - } - } + noiseModel_(noiseModel::validOrDefault(measured, model)) {} /// @name Standard Interface /// @{ diff --git a/gtsam/slam/BetweenFactor.h b/gtsam/slam/BetweenFactor.h index d309bf61e1..aceb4cfb71 100644 --- a/gtsam/slam/BetweenFactor.h +++ b/gtsam/slam/BetweenFactor.h @@ -72,7 +72,8 @@ namespace gtsam { /** Constructor */ BetweenFactor(Key key1, Key key2, const VALUE& measured, const SharedNoiseModel& model = nullptr) : - Base(model, key1, key2), measured_(measured) { + Base(noiseModel::validOrDefault(measured, model), key1, key2), + measured_(measured) { } /// @} diff --git a/gtsam/slam/EssentialMatrixFactor.h b/gtsam/slam/EssentialMatrixFactor.h index b6671d9869..78304639f3 100644 --- a/gtsam/slam/EssentialMatrixFactor.h +++ b/gtsam/slam/EssentialMatrixFactor.h @@ -357,7 +357,8 @@ class EssentialMatrixFactor4 */ EssentialMatrixFactor4(Key keyE, Key keyK, const Point2& pA, const Point2& pB, const SharedNoiseModel& model = nullptr) - : Base(model, keyE, keyK), pA_(pA), pB_(pB) {} + : Base(noiseModel::validOrDefault(0.0, model), keyE, keyK), + pA_(pA), pB_(pB) {} /// @return a deep copy of this factor gtsam::NonlinearFactor::shared_ptr clone() const override { @@ -460,7 +461,8 @@ class EssentialMatrixFactor5 EssentialMatrixFactor5(Key keyE, Key keyKa, Key keyKb, const Point2& pA, const Point2& pB, const SharedNoiseModel& model = nullptr) - : Base(model, keyE, keyKa, keyKb), pA_(pA), pB_(pB) {} + : Base(noiseModel::validOrDefault(0.0, model), keyE, keyKa, keyKb), + pA_(pA), pB_(pB) {} /// @return a deep copy of this factor gtsam::NonlinearFactor::shared_ptr clone() const override { diff --git a/gtsam/slam/FrobeniusFactor.h b/gtsam/slam/FrobeniusFactor.h index dcd168aca1..b28a3a0681 100644 --- a/gtsam/slam/FrobeniusFactor.h +++ b/gtsam/slam/FrobeniusFactor.h @@ -63,7 +63,10 @@ GTSAM_EXPORT SharedNoiseModel ConvertNoiseModel(const SharedNoiseModel& model, */ template inline SharedNoiseModel ConvertModel(const SharedNoiseModel& model) { - if (!model || model->dim() == Dim) { + if (!model) { + return ConvertNoiseModel(noiseModel::Unit::Create(T()), Dim); + } + if (model->dim() == Dim) { return model; } if (model->dim() != T::dimension) { @@ -260,4 +263,4 @@ class FrobeniusBetweenFactor : public FrobeniusBetweenFactorNL { } }; -} // namespace gtsam \ No newline at end of file +} // namespace gtsam diff --git a/gtsam/slam/KarcherMeanFactor-inl.h b/gtsam/slam/KarcherMeanFactor-inl.h index d71a91d537..543943f44a 100644 --- a/gtsam/slam/KarcherMeanFactor-inl.h +++ b/gtsam/slam/KarcherMeanFactor-inl.h @@ -32,9 +32,8 @@ T FindKarcherMeanImpl(const std::vector& rotations) { // No closed form solution. NonlinearFactorGraph graph; static const Key kKey(0); - auto model = noiseModel::Unit::Create(static_cast(T::dimension)); for (const auto& R : rotations) { - graph.addPrior(kKey, R, model); + graph.addPrior(kKey, R, noiseModel::Unit::Create(R)); } Values initial; initial.insert(kKey, T()); diff --git a/gtsam/slam/slam.i b/gtsam/slam/slam.i index 3aaeda38ff..745589c251 100644 --- a/gtsam/slam/slam.i +++ b/gtsam/slam/slam.i @@ -21,7 +21,7 @@ template virtual class BetweenFactor : gtsam::NoiseModelFactor { BetweenFactor(gtsam::Key key1, gtsam::Key key2, const T& relativePose, - const gtsam::noiseModel::Base* noiseModel); + const gtsam::noiseModel::Base* noiseModel = nullptr); T measured() const; // enabling serialization functionality @@ -604,7 +604,7 @@ template class FrobeniusPrior : gtsam::NoiseModelFactor { FrobeniusPrior(gtsam::Key j, const gtsam::Matrix& M, - const gtsam::noiseModel::Base* model); + const gtsam::noiseModel::Base* model = nullptr); gtsam::Vector evaluateError(const T& g) const; }; From 479f970aa18a6330306036384a0b7e5b15d57c13 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 18 Jan 2026 19:22:43 -0500 Subject: [PATCH 118/266] Fix all nonlinear models --- gtsam/base/tests/testKruskal.cpp | 2 +- gtsam/basis/FitBasis.h | 6 ++++-- gtsam/geometry/triangulation.h | 6 ++++-- gtsam/nonlinear/NonlinearFactor.h | 2 ++ gtsam/slam/tests/testLago.cpp | 6 +++--- tests/testGaussianISAM2.cpp | 2 +- tests/testNonlinearOptimizer.cpp | 22 +++++++++++++++++++--- tests/testSerializationSlam.cpp | 3 +-- 8 files changed, 35 insertions(+), 14 deletions(-) diff --git a/gtsam/base/tests/testKruskal.cpp b/gtsam/base/tests/testKruskal.cpp index 000a777fa9..0b2c627d1c 100644 --- a/gtsam/base/tests/testKruskal.cpp +++ b/gtsam/base/tests/testKruskal.cpp @@ -51,7 +51,7 @@ gtsam::NonlinearFactorGraph makeTestNonlinearFactorGraph() { using namespace symbol_shorthand; NonlinearFactorGraph nfg; - const SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(0.5, 0.5)); + const SharedDiagonal model = noiseModel::Isotropic::Sigma(3, 0.5); nfg.emplace_shared>(X(1), X(2), Rot3(), model); nfg.emplace_shared>(X(1), X(3), Rot3(), model); nfg.emplace_shared>(X(1), X(4), Rot3(), model); diff --git a/gtsam/basis/FitBasis.h b/gtsam/basis/FitBasis.h index f5cb99bd7e..321a9c3a3d 100644 --- a/gtsam/basis/FitBasis.h +++ b/gtsam/basis/FitBasis.h @@ -27,6 +27,7 @@ #include #include #include +#include #include #include @@ -62,9 +63,10 @@ class FitBasis { const SharedNoiseModel& model, size_t N) { NonlinearFactorGraph graph; + const auto noiseModel = noiseModel::validOrDefault(0.0, model); for (const Sample sample : sequence) { - graph.emplace_shared>(0, sample.second, model, N, - sample.first); + graph.emplace_shared>( + 0, sample.second, noiseModel, N, sample.first); } return graph; } diff --git a/gtsam/geometry/triangulation.h b/gtsam/geometry/triangulation.h index a715f32f99..ad105a7765 100644 --- a/gtsam/geometry/triangulation.h +++ b/gtsam/geometry/triangulation.h @@ -459,9 +459,11 @@ Point3 triangulatePoint3(const std::vector& poses, } // Then refine using non-linear optimization - if (optimize) + if (optimize) { + const auto noiseModel = noiseModel::validOrDefault(Point2(0, 0), model); point = triangulateNonlinear // - (poses, sharedCal, measurements, point, model); + (poses, sharedCal, measurements, point, noiseModel); + } #ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION // verify that the triangulated point lies in front of all cameras diff --git a/gtsam/nonlinear/NonlinearFactor.h b/gtsam/nonlinear/NonlinearFactor.h index f3b8b73185..ab03fe2ff8 100644 --- a/gtsam/nonlinear/NonlinearFactor.h +++ b/gtsam/nonlinear/NonlinearFactor.h @@ -239,6 +239,8 @@ class GTSAM_EXPORT NoiseModelFactor: public NonlinearFactor { /** get the dimension of the factor (number of rows on linearization) */ size_t dim() const override { + if (!noiseModel_) + throw std::runtime_error("NoiseModelFactor::dim(): no noise model set"); return noiseModel_->dim(); } diff --git a/gtsam/slam/tests/testLago.cpp b/gtsam/slam/tests/testLago.cpp index 7776862846..27bc725eca 100644 --- a/gtsam/slam/tests/testLago.cpp +++ b/gtsam/slam/tests/testLago.cpp @@ -35,6 +35,7 @@ using namespace gtsam; static Symbol x0('x', 0), x1('x', 1), x2('x', 2), x3('x', 3); static SharedNoiseModel model(noiseModel::Isotropic::Sigma(3, 0.1)); +static SharedNoiseModel rotModel(noiseModel::Isotropic::Sigma(1, 0.1)); namespace simpleLago { // We consider a small graph: @@ -224,7 +225,7 @@ TEST( Lago, multiplePosePriorsSP ) { TEST( Lago, multiplePoseAndRotPriors ) { bool useOdometricPath = false; NonlinearFactorGraph g = simpleLago::graph(); - g.addPrior(x1, simpleLago::pose1.theta(), model); + g.addPrior(x1, simpleLago::pose1.theta(), rotModel); VectorValues initial = lago::initializeOrientations(g, useOdometricPath); // comparison is up to M_PI, that's why we add some multiples of 2*M_PI @@ -237,7 +238,7 @@ TEST( Lago, multiplePoseAndRotPriors ) { /* *************************************************************************** */ TEST( Lago, multiplePoseAndRotPriorsSP ) { NonlinearFactorGraph g = simpleLago::graph(); - g.addPrior(x1, simpleLago::pose1.theta(), model); + g.addPrior(x1, simpleLago::pose1.theta(), rotModel); VectorValues initial = lago::initializeOrientations(g); // comparison is up to M_PI, that's why we add some multiples of 2*M_PI @@ -348,4 +349,3 @@ int main() { return TestRegistry::runAllTests(tr); } /* ************************************************************************* */ - diff --git a/tests/testGaussianISAM2.cpp b/tests/testGaussianISAM2.cpp index e023cae1fe..77466a5cc7 100644 --- a/tests/testGaussianISAM2.cpp +++ b/tests/testGaussianISAM2.cpp @@ -1000,7 +1000,7 @@ class FixActiveFactor : public NoiseModelFactorN { public: FixActiveFactor(const gtsam::Key& key, const bool active) - : Base(nullptr, key), is_active_(active) {} + : Base(noiseModel::Unit::Create(2), key), is_active_(active) {} virtual bool active(const gtsam::Values &values) const override { return is_active_; diff --git a/tests/testNonlinearOptimizer.cpp b/tests/testNonlinearOptimizer.cpp index b5d19af008..650021a5f5 100644 --- a/tests/testNonlinearOptimizer.cpp +++ b/tests/testNonlinearOptimizer.cpp @@ -637,13 +637,29 @@ struct MyType : public Vector3 { namespace gtsam { template <> struct traits { + typedef manifold_tag structure_category; + inline constexpr static auto dimension = 3; + typedef MyType ManifoldType; + typedef Vector3 TangentVector; + typedef OptionalJacobian ChartJacobian; + static bool Equals(const MyType& a, const MyType& b, double tol) { return (a - b).array().abs().maxCoeff() < tol; } static void Print(const MyType&, const string&) {} - static int GetDimension(const MyType&) { return 3; } - static MyType Retract(const MyType& a, const Vector3& b) { return a + b; } - static Vector3 Local(const MyType& a, const MyType& b) { return b - a; } + static int GetDimension(const MyType&) { return dimension; } + static MyType Retract(const MyType& a, const TangentVector& v, + ChartJacobian H1 = {}, ChartJacobian H2 = {}) { + if (H1) *H1 = Matrix3::Identity(); + if (H2) *H2 = Matrix3::Identity(); + return MyType(a + v); + } + static TangentVector Local(const MyType& a, const MyType& b, + ChartJacobian H1 = {}, ChartJacobian H2 = {}) { + if (H1) *H1 = -Matrix3::Identity(); + if (H2) *H2 = Matrix3::Identity(); + return b - a; + } }; } diff --git a/tests/testSerializationSlam.cpp b/tests/testSerializationSlam.cpp index c4170b1089..4748637e07 100644 --- a/tests/testSerializationSlam.cpp +++ b/tests/testSerializationSlam.cpp @@ -312,7 +312,6 @@ TEST (testSerializationSLAM, factors) { SharedNoiseModel model5 = noiseModel::Isotropic::Sigma(5, 0.3); SharedNoiseModel model6 = noiseModel::Isotropic::Sigma(6, 0.3); SharedNoiseModel model9 = noiseModel::Isotropic::Sigma(9, 0.3); - SharedNoiseModel model11 = noiseModel::Isotropic::Sigma(11, 0.3); SharedNoiseModel robust1 = noiseModel::Robust::Create( noiseModel::mEstimator::Huber::Create(10.0, noiseModel::mEstimator::Huber::Scalar), @@ -332,7 +331,7 @@ TEST (testSerializationSLAM, factors) { PriorFactorCal3_S2 priorFactorCal3_S2(a10, cal3_s2, model5); PriorFactorCal3DS2 priorFactorCal3DS2(a11, cal3ds2, model9); PriorFactorCalibratedCamera priorFactorCalibratedCamera(a12, calibratedCamera, model6); - PriorFactorStereoCamera priorFactorStereoCamera(a14, stereoCamera, model11); + PriorFactorStereoCamera priorFactorStereoCamera(a14, stereoCamera, model6); BetweenFactorPoint2 betweenFactorPoint2(a03, b03, point2, model2); BetweenFactorPoint3 betweenFactorPoint3(a05, b05, point3, model3); From 80ac1ff1c65992250e604e0f304e23c52ef12bf1 Mon Sep 17 00:00:00 2001 From: Yiran Luo Date: Sun, 18 Jan 2026 23:55:18 -0500 Subject: [PATCH 119/266] refactor Hat/Vee to use SO4 helpers and symmetric split --- gtsam/geometry/SL4.cpp | 101 +++++++++++++++++++++-------------------- 1 file changed, 53 insertions(+), 48 deletions(-) diff --git a/gtsam/geometry/SL4.cpp b/gtsam/geometry/SL4.cpp index 8945ab07e4..04046fc64f 100644 --- a/gtsam/geometry/SL4.cpp +++ b/gtsam/geometry/SL4.cpp @@ -5,6 +5,7 @@ */ #include +#include // To use exp(), log() #include @@ -15,10 +16,48 @@ using namespace std; namespace { +using gtsam::Matrix44; +using gtsam::Vector6; + constexpr double kInvSqrt2 = 0.7071067811865475244; constexpr double kInvSqrt6 = 0.4082482904638630164; constexpr double kInvSqrt12 = 0.2886751345948128823; +Vector6 SkewToSO4(const Vector6& r) { + Vector6 so4; + so4 << r(5), -r(4), r(2), -r(3), r(1), -r(0); + return so4; +} + +Vector6 SO4ToSkew(const Vector6& so4) { + Vector6 r; + r << -so4(5), so4(4), so4(2), -so4(3), -so4(1), so4(0); + return r; +} + +Matrix44 HatSym4(const Vector6& s) { + Matrix44 A = Matrix44::Zero(); + A(0, 1) = s(0); + A(1, 0) = s(0); + A(0, 2) = s(1); + A(2, 0) = s(1); + A(0, 3) = s(2); + A(3, 0) = s(2); + A(1, 2) = s(3); + A(2, 1) = s(3); + A(1, 3) = s(4); + A(3, 1) = s(4); + A(2, 3) = s(5); + A(3, 2) = s(5); + return A; +} + +Vector6 VeeSym4(const Matrix44& A) { + Vector6 s; + s << A(0, 1), A(0, 2), A(0, 3), A(1, 2), A(1, 3), A(2, 3); + return s; +} + Eigen::Matrix setVecToAlgMatrix() { Eigen::Matrix alg = Eigen::Matrix::Zero(); @@ -194,68 +233,34 @@ Matrix44 SL4::Hat(const Vector& xi) { "SL4::Hat: xi must be a vector of size 15. Got size " + std::to_string(xi.size())); } - Matrix44 A = Matrix44::Zero(); + const Vector6 r = kInvSqrt2 * xi.head<6>(); + const Vector6 s = kInvSqrt2 * xi.segment<6>(6); - // Rotations (skew-symmetric), normalized by 1/sqrt(2). - A(0, 1) += kInvSqrt2 * xi(0); - A(1, 0) -= kInvSqrt2 * xi(0); - A(0, 2) += kInvSqrt2 * xi(1); - A(2, 0) -= kInvSqrt2 * xi(1); - A(0, 3) += kInvSqrt2 * xi(2); - A(3, 0) -= kInvSqrt2 * xi(2); - A(1, 2) += kInvSqrt2 * xi(3); - A(2, 1) -= kInvSqrt2 * xi(3); - A(1, 3) += kInvSqrt2 * xi(4); - A(3, 1) -= kInvSqrt2 * xi(4); - A(2, 3) += kInvSqrt2 * xi(5); - A(3, 2) -= kInvSqrt2 * xi(5); - - // Symmetric off-diagonal shears, normalized by 1/sqrt(2). - A(0, 1) += kInvSqrt2 * xi(6); - A(1, 0) += kInvSqrt2 * xi(6); - A(0, 2) += kInvSqrt2 * xi(7); - A(2, 0) += kInvSqrt2 * xi(7); - A(0, 3) += kInvSqrt2 * xi(8); - A(3, 0) += kInvSqrt2 * xi(8); - A(1, 2) += kInvSqrt2 * xi(9); - A(2, 1) += kInvSqrt2 * xi(9); - A(1, 3) += kInvSqrt2 * xi(10); - A(3, 1) += kInvSqrt2 * xi(10); - A(2, 3) += kInvSqrt2 * xi(11); - A(3, 2) += kInvSqrt2 * xi(11); + Matrix44 A = SO4::Hat(SkewToSO4(r)) + HatSym4(s); // Traceless diagonal scalings. const double a = kInvSqrt2 * xi(12); const double b = kInvSqrt6 * xi(13); const double c = kInvSqrt12 * xi(14); - A(0, 0) += a + b + c; - A(1, 1) += -a + b + c; - A(2, 2) += -2.0 * b + c; - A(3, 3) += -3.0 * c; + Vector4 diag; + diag << a + b + c, -a + b + c, -2.0 * b + c, -3.0 * c; + A.diagonal() += diag; return A; } /* ************************************************************************* */ -// used consistent notation with Hat() +// Used consistent notation with Hat() Vector SL4::Vee(const Matrix44& A) { Vector xi(15); - xi << kInvSqrt2 * (A(0, 1) - A(1, 0)), - kInvSqrt2 * (A(0, 2) - A(2, 0)), - kInvSqrt2 * (A(0, 3) - A(3, 0)), - kInvSqrt2 * (A(1, 2) - A(2, 1)), - kInvSqrt2 * (A(1, 3) - A(3, 1)), - kInvSqrt2 * (A(2, 3) - A(3, 2)), - kInvSqrt2 * (A(0, 1) + A(1, 0)), - kInvSqrt2 * (A(0, 2) + A(2, 0)), - kInvSqrt2 * (A(0, 3) + A(3, 0)), - kInvSqrt2 * (A(1, 2) + A(2, 1)), - kInvSqrt2 * (A(1, 3) + A(3, 1)), - kInvSqrt2 * (A(2, 3) + A(3, 2)), - kInvSqrt2 * (A(0, 0) - A(1, 1)), - kInvSqrt6 * (A(0, 0) + A(1, 1) - 2.0 * A(2, 2)), - kInvSqrt12 * (A(0, 0) + A(1, 1) + A(2, 2) - 3.0 * A(3, 3)); + const Matrix44 skew = A - A.transpose(); + const Matrix44 sym = A + A.transpose(); + xi.head<6>() = kInvSqrt2 * SO4ToSkew(SO4::Vee(skew)); + xi.segment<6>(6) = kInvSqrt2 * VeeSym4(sym); + xi(12) = kInvSqrt2 * (A(0, 0) - A(1, 1)); + xi(13) = kInvSqrt6 * (A(0, 0) + A(1, 1) - 2.0 * A(2, 2)); + xi(14) = kInvSqrt12 * (A(0, 0) + A(1, 1) + A(2, 2) - 3.0 * A(3, 3)); return xi; } From a32dfc1d4e1f3ae1de4241a7ad26acd7ef15dd63 Mon Sep 17 00:00:00 2001 From: Yiran Luo Date: Sun, 18 Jan 2026 23:55:47 -0500 Subject: [PATCH 120/266] add more documentation --- gtsam/geometry/SL4.h | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/gtsam/geometry/SL4.h b/gtsam/geometry/SL4.h index c4f5e1b89d..871004c286 100644 --- a/gtsam/geometry/SL4.h +++ b/gtsam/geometry/SL4.h @@ -105,6 +105,11 @@ class GTSAM_EXPORT SL4 : public MatrixLieGroup { /** * Lie algebra coordinates for sl(4) using an orthonormal basis. * + * We use the orthogonal vector-space decomposition: + * sl(4) = so(4) ⊕ sym_off(4) ⊕ diag_traceless(4) + * where so(4) is skew-symmetric rotations, sym_off is symmetric off-diagonal + * shears, and diag_traceless is the 3-D traceless diagonal subspace. + * * Ordering of xi (15x1): * [r12 r13 r14 r23 r24 r34 s12 s13 s14 s23 s24 s34 h1 h2 h3] * From dcf7aa9b2eed10b77d538279d2e333e52f982b3a Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 19 Jan 2026 09:27:52 -0500 Subject: [PATCH 121/266] Throw informative exception --- .../constrained/NonlinearEqualityConstraint.h | 3 + gtsam/linear/MultifrontalClique.cpp | 7 +- gtsam/linear/MultifrontalSolver.cpp | 89 +++++++------------ gtsam/linear/MultifrontalSolver.h | 32 +++++-- gtsam/nonlinear/NonlinearEquality.h | 3 + .../nonlinear/NonlinearMultifrontalSolver.cpp | 15 ++-- gtsam/nonlinear/NonlinearMultifrontalSolver.h | 5 -- gtsam/nonlinear/NonlinearOptimizer.cpp | 42 ++++----- gtsam/nonlinear/NonlinearOptimizerParams.h | 8 +- .../tests/testNonlinearMultifrontalSolver.cpp | 30 +++++++ tests/testNonlinearOptimizer.cpp | 4 +- 11 files changed, 128 insertions(+), 110 deletions(-) diff --git a/gtsam/constrained/NonlinearEqualityConstraint.h b/gtsam/constrained/NonlinearEqualityConstraint.h index 4d85e8b5ba..cdd8d46285 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint.h +++ b/gtsam/constrained/NonlinearEqualityConstraint.h @@ -38,6 +38,9 @@ class GTSAM_EXPORT NonlinearEqualityConstraint : public NonlinearConstraint { /** Destructor. */ virtual ~NonlinearEqualityConstraint() {} + /// Whether this constraint should be treated as a hard constraint. + virtual bool isHardConstraint() const { return true; } + private: #if GTSAM_ENABLE_BOOST_SERIALIZATION /** Serialization function */ diff --git a/gtsam/linear/MultifrontalClique.cpp b/gtsam/linear/MultifrontalClique.cpp index e507546b18..5a8eaa9454 100644 --- a/gtsam/linear/MultifrontalClique.cpp +++ b/gtsam/linear/MultifrontalClique.cpp @@ -250,11 +250,8 @@ void MultifrontalClique::fillAb(const GaussianFactorGraph& graph) { assert(index < graph.size()); const GaussianFactor::shared_ptr& gf = graph[index]; if (!gf) continue; - if (!gf->isJacobian()) { - throw std::runtime_error( - "MultifrontalClique::fillAb: only JacobianFactor inputs are " - "supported."); - } + assert(gf->isJacobian() && + "MultifrontalClique::fillAb: inconsistent graph passed."); auto jacobianFactor = std::static_pointer_cast(gf); rowOffset += addJacobianFactor(*jacobianFactor, rowOffset); } diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 0b5763cb77..a273e134c3 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -88,68 +88,54 @@ struct StructureStats { constexpr double kConstraintSigmaTol = 1e-12; constexpr double kConstraintFeasibleTol = 1e-9; -std::unordered_set collectFixedKeys(const GaussianFactorGraph& graph) { +struct PrecomputeScratch { + std::map dims; std::unordered_set fixedKeys; - for (const auto& factor : graph) { + std::vector rowCounts; +}; + +PrecomputeScratch precomputeFromGraph(const GaussianFactorGraph& graph) { + PrecomputeScratch out; + out.rowCounts.resize(graph.size(), 0); + for (size_t i = 0; i < graph.size(); ++i) { + const auto& factor = graph[i]; if (!factor) continue; if (!factor->isJacobian()) { - throw std::runtime_error( - "MultifrontalSolver: only JacobianFactor inputs are supported."); + throw MultifrontalSolverNotSupported( + "only JacobianFactor inputs are supported"); } auto jacobianFactor = std::static_pointer_cast(factor); + out.rowCounts[i] = jacobianFactor->rows(); + for (auto it = jacobianFactor->begin(); it != jacobianFactor->end(); ++it) { + out.dims[*it] = jacobianFactor->getDim(it); + } auto model = jacobianFactor->get_model(); if (!model || !model->isConstrained()) continue; // Only accept fully constrained unary factors with zero residuals. if (jacobianFactor->size() != 1) { - throw std::runtime_error( - "MultifrontalSolver: only unary constrained factors are supported."); + throw MultifrontalSolverNotSupported( + "only unary constrained factors are supported"); } const Vector& sigmas = model->sigmasRef(); if (!(sigmas.array().abs() <= kConstraintSigmaTol).all()) { - throw std::runtime_error( - "MultifrontalSolver: only fully constrained factors are supported."); + throw MultifrontalSolverNotSupported( + "only fully constrained factors are supported"); } if (jacobianFactor->getb().array().abs().maxCoeff() > kConstraintFeasibleTol) { - throw std::runtime_error( - "MultifrontalSolver: constrained factor is not feasible."); - } - fixedKeys.insert(*jacobianFactor->begin()); - } - return fixedKeys; -} - -// Compute variable dimensions from the GaussianFactorGraph -std::map computeDims(const GaussianFactorGraph& graph) { - std::map dims; - for (const auto& factor : graph) { - if (!factor) continue; - if (!factor->isJacobian()) { - throw std::runtime_error( - "MultifrontalSolver: only JacobianFactor inputs are supported."); + throw MultifrontalSolverNotSupported( + "constrained factor is not feasible"); } - auto jacobianFactor = std::static_pointer_cast(factor); - for (auto it = jacobianFactor->begin(); it != jacobianFactor->end(); ++it) { - dims[*it] = jacobianFactor->getDim(it); - } - } - return dims; -} - -size_t factorRowCount(const GaussianFactorGraph& graph, size_t index) { - if (index >= graph.size() || !graph[index]) return 0; - if (!graph[index]->isJacobian()) { - throw std::runtime_error( - "MultifrontalSolver: only JacobianFactor inputs are supported."); + out.fixedKeys.insert(*jacobianFactor->begin()); } - auto jacobianFactor = std::static_pointer_cast(graph[index]); - return jacobianFactor->rows(); + return out; } // Build SymbolicFactorGraph from GaussianFactorGraph SymbolicFactorGraph buildSymbolicGraph( const GaussianFactorGraph& graph, - const std::unordered_set& fixedKeys) { + const std::unordered_set& fixedKeys, + const std::vector& rowCounts) { SymbolicFactorGraph symbolicGraph; symbolicGraph.reserve(graph.size()); for (size_t i = 0; i < graph.size(); ++i) { @@ -164,7 +150,7 @@ SymbolicFactorGraph buildSymbolicGraph( // Skip factors that are fully constrained away. if (keys.empty()) continue; symbolicGraph.emplace_shared( - keys, i, factorRowCount(graph, i)); + keys, i, rowCounts.at(i)); } return symbolicGraph; } @@ -528,11 +514,6 @@ MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, const Parameters& params) : MultifrontalSolver(Precompute(graph, ordering), ordering, params) {} -/* ************************************************************************* */ -MultifrontalSolver::MultifrontalSolver(const GaussianFactorGraph& graph, - const Ordering& ordering) - : MultifrontalSolver(graph, ordering, Parameters{}) {} - /* ************************************************************************* */ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, const Ordering& ordering, @@ -588,31 +569,27 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, // Caller is responsible for loading numerical values via load(). } -/* ************************************************************************* */ -MultifrontalSolver::MultifrontalSolver(PrecomputedData data, - const Ordering& ordering) - : MultifrontalSolver(std::move(data), ordering, Parameters{}) {} - /* ************************************************************************* */ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( const GaussianFactorGraph& graph, const Ordering& ordering) { - auto dims = computeDims(graph); - auto fixedKeys = collectFixedKeys(graph); + PrecomputeScratch scratch = precomputeFromGraph(graph); Ordering reducedOrdering; reducedOrdering.reserve(ordering.size()); for (Key key : ordering) { - if (!fixedKeys.count(key)) { + if (!scratch.fixedKeys.count(key)) { reducedOrdering.push_back(key); } } - SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); + SymbolicFactorGraph symbolicGraph = + buildSymbolicGraph(graph, scratch.fixedKeys, scratch.rowCounts); SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); SymbolicJunctionTree junctionTree(eliminationTree); return MultifrontalSolver::PrecomputedData{ - std::move(dims), std::move(fixedKeys), std::move(junctionTree)}; + std::move(scratch.dims), std::move(scratch.fixedKeys), + std::move(junctionTree)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 0b7db38e5a..955a5a6e66 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -29,6 +29,8 @@ #include #include #include +#include +#include #include #include @@ -37,6 +39,25 @@ namespace gtsam { class GaussianBayesTree; class MultifrontalClique; +/** + * Exception for unsupported use of the new multifrontal solver. + * Includes guidance for using the legacy solver instead. + */ +class GTSAM_EXPORT MultifrontalSolverNotSupported : public std::runtime_error { + public: + explicit MultifrontalSolverNotSupported(const std::string& reason) + : std::runtime_error(BuildMessage(reason)) {} + + private: + static std::string BuildMessage(const std::string& reason) { + std::string message = "MultifrontalSolver not supported: " + reason + ". "; + message += + "Enable GTSAM_ALLOW_DEPRECATED_SINCE_V43 to default to the legacy " + "solver, or set linearSolverType = MULTIFRONTAL_CHOLESKY."; + return message; + } +}; + /** * Imperative-style multifrontal solver for Gaussian factor graphs. * @@ -96,11 +117,7 @@ class GTSAM_EXPORT MultifrontalSolver * @param params Tunable parameters for traversal and reporting. */ MultifrontalSolver(const GaussianFactorGraph& graph, const Ordering& ordering, - const Parameters& params); - - /// Construct the solver with default parameters. - MultifrontalSolver(const GaussianFactorGraph& graph, - const Ordering& ordering); + const Parameters& params = Parameters{}); /** * Construct the solver from precomputed symbolic data. @@ -110,10 +127,7 @@ class GTSAM_EXPORT MultifrontalSolver * @param params Tunable parameters for traversal and reporting. */ MultifrontalSolver(PrecomputedData data, const Ordering& ordering, - const Parameters& params); - - /// Construct the solver with default parameters. - MultifrontalSolver(PrecomputedData data, const Ordering& ordering); + const Parameters& params = Parameters{}); /// Precompute symbolic structure and sizing data from a factor graph. /// Only JacobianFactor inputs are supported. diff --git a/gtsam/nonlinear/NonlinearEquality.h b/gtsam/nonlinear/NonlinearEquality.h index b590cba1c6..2bc0d71e12 100644 --- a/gtsam/nonlinear/NonlinearEquality.h +++ b/gtsam/nonlinear/NonlinearEquality.h @@ -140,6 +140,9 @@ class NonlinearEquality: public NonlinearEqualityConstraint { } } + /// Whether this constraint should be treated as hard. + bool isHardConstraint() const override { return !allow_error_; } + /// Error function Vector evaluateError(const T& xj, OptionalMatrixType H = nullptr) const { const size_t nj = traits::GetDimension(feasible_); diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index 270c323f08..493e991607 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -36,11 +36,16 @@ std::map computeDimsFromValues(const Values& values) { std::unordered_set collectFixedKeys(const NonlinearFactorGraph& graph) { std::unordered_set fixedKeys; for (const auto& factor : graph) { - if (!factor) continue; - if (!std::dynamic_pointer_cast(factor)) - continue; - if (factor->keys().size() != 1) continue; - fixedKeys.insert(factor->keys().front()); + if (!factor || factor->keys().size() != 1) continue; + if (auto constraint = + std::dynamic_pointer_cast(factor)) { + if (constraint->isHardConstraint()) { + fixedKeys.insert(factor->keys().front()); + } else { + throw MultifrontalSolverNotSupported( + "non-hard constraints are not supported"); + } + } } return fixedKeys; } diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.h b/gtsam/nonlinear/NonlinearMultifrontalSolver.h index 41619d8958..334bae0e29 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.h +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.h @@ -62,11 +62,6 @@ class GTSAM_EXPORT NonlinearMultifrontalSolver : public MultifrontalSolver { const NonlinearFactorGraph& graph, const Values& values, const Ordering& ordering); - /// Update damping parameters for subsequent eliminations. - void setDampingParams(const DampingParams& params) { - dampingParams_ = params; - } - /** * Load new numerical values from the factor graph. * This overrides the base load() to optionally cache `diag(J^T J)` for diff --git a/gtsam/nonlinear/NonlinearOptimizer.cpp b/gtsam/nonlinear/NonlinearOptimizer.cpp index e5453be336..816789d223 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.cpp +++ b/gtsam/nonlinear/NonlinearOptimizer.cpp @@ -244,21 +244,17 @@ bool NonlinearOptimizer::ensureMultifrontalSolver( const NonlinearOptimizerParams& params, const Values& values) const { if (params.linearSolverType != NonlinearOptimizerParams::MULTIFRONTAL_SOLVER) return false; - // TODO(frank): check for constraints and return false if present? - // MultifrontalSolver supports constraints via "fixedKeys", but - // NonlinearMultifrontalSolver wrapper might need to expose that. For now, we - // assume if isMultifrontal() is true, we try to use it. - - NonlinearMultifrontalSolver::DampingParams dampingParams; - if (auto lmParams = dynamic_cast(¶ms)) { - dampingParams.exactHessianDiagonal = - lmParams->dampingParams.exactHessianDiagonal; - dampingParams.diagonalDamping = lmParams->dampingParams.diagonalDamping; - dampingParams.minDiagonal = lmParams->dampingParams.minDiagonal; - dampingParams.maxDiagonal = lmParams->dampingParams.maxDiagonal; - } - if (!nonlinearMultifrontalSolver_) { + NonlinearMultifrontalSolver::DampingParams dampingParams; + if (auto lmParams = + dynamic_cast(¶ms)) { + dampingParams.exactHessianDiagonal = + lmParams->dampingParams.exactHessianDiagonal; + dampingParams.diagonalDamping = lmParams->dampingParams.diagonalDamping; + dampingParams.minDiagonal = lmParams->dampingParams.minDiagonal; + dampingParams.maxDiagonal = lmParams->dampingParams.maxDiagonal; + } + // Lazily create the solver. // Use default ordering or create one. Ordering ordering; @@ -267,19 +263,11 @@ bool NonlinearOptimizer::ensureMultifrontalSolver( else ordering = Ordering::Create(params.orderingType, graph_); - // Check if we can construct it (might throw if constraints are tricky?) - try { - MultifrontalSolver::Parameters mfParams = params.multifrontalParams; - nonlinearMultifrontalSolver_ = - std::make_unique( - graph_, values, ordering, mfParams, dampingParams); - } catch (...) { - // Fallback to legacy - return false; - } - } else { - nonlinearMultifrontalSolver_->setDampingParams(dampingParams); + // Construct it (may throw if unsupported). + nonlinearMultifrontalSolver_ = + std::make_unique( + graph_, values, ordering, params.multifrontalParams, dampingParams); } return true; } -} +} // namespace gtsam diff --git a/gtsam/nonlinear/NonlinearOptimizerParams.h b/gtsam/nonlinear/NonlinearOptimizerParams.h index b7ca3ca1b0..4cc1a3e1cd 100644 --- a/gtsam/nonlinear/NonlinearOptimizerParams.h +++ b/gtsam/nonlinear/NonlinearOptimizerParams.h @@ -107,10 +107,16 @@ class GTSAM_EXPORT NonlinearOptimizerParams { CHOLMOD, /* Experimental Flag */ }; - LinearSolverType linearSolverType = MULTIFRONTAL_SOLVER; ///< The type of linear solver to use in the nonlinear optimizer std::optional ordering; ///< The optional variable elimination ordering, or empty to use COLAMD (default: empty) IterativeOptimizationParameters::shared_ptr iterativeParams; ///< The container for iterativeOptimization parameters. used in CG Solvers. /// Parameters for `gtsam::MultifrontalSolver` when using `MULTIFRONTAL_SOLVER`. + + /// The type of linear solver to use in the nonlinear optimizer + #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V43 + LinearSolverType linearSolverType = MULTIFRONTAL_CHOLESKY; +#else + LinearSolverType linearSolverType = MULTIFRONTAL_SOLVER; +#endif MultifrontalParameters multifrontalParams; NonlinearOptimizerParams() = default; diff --git a/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp index 5b3debdf19..a1c47a8b97 100644 --- a/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/tests/testNonlinearMultifrontalSolver.cpp @@ -22,6 +22,7 @@ #include #include #include +#include #include #include #include @@ -134,6 +135,35 @@ TEST(NonlinearMultifrontalSolver, OneStepPrecomputed) { EXPECT(assert_equal(Point2(1, 0), result.at(X(2)), 1e-9)); } +/* ************************************************************************* */ +TEST(NonlinearMultifrontalSolver, PrecomputeHardConstraint) { + NonlinearFactorGraph graph; + Values values; + Ordering ordering; + + graph.emplace_shared>(X(1), Point2(1.0, 2.0)); + values.insert(X(1), Point2(0.0, 0.0)); + ordering.push_back(X(1)); + + auto data = NonlinearMultifrontalSolver::Precompute(graph, values, ordering); + CHECK_EQUAL(1, data.fixedKeys.size()); +} + +/* ************************************************************************* */ +TEST(NonlinearMultifrontalSolver, PrecomputeSoftConstraintThrows) { + NonlinearFactorGraph graph; + Values values; + Ordering ordering; + + graph.emplace_shared>(X(1), Point2(1.0, 2.0), 10.0); + values.insert(X(1), Point2(0.0, 0.0)); + ordering.push_back(X(1)); + + CHECK_EXCEPTION( + NonlinearMultifrontalSolver::Precompute(graph, values, ordering), + std::runtime_error); +} + /* ************************************************************************* */ // Run a first elimination to get an updated estimate, then deliberately // perturb the estimate, re-linearize, reload, and eliminate again. diff --git a/tests/testNonlinearOptimizer.cpp b/tests/testNonlinearOptimizer.cpp index 90646cc31c..f309f2c421 100644 --- a/tests/testNonlinearOptimizer.cpp +++ b/tests/testNonlinearOptimizer.cpp @@ -378,10 +378,10 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) { expected.insert(0, Pose2(0,0,0)); expected.insert(1, Pose2(0.961187, 0.99965, 1.1781)); - LevenbergMarquardtParams lm_params; + LevenbergMarquardtParams lmParams; auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); - auto lm_result = LevenbergMarquardtOptimizer(fg, init, lm_params).optimize(); + auto lm_result = LevenbergMarquardtOptimizer(fg, init, lmParams).optimize(); auto dl_result = DoglegOptimizer(fg, init).optimize(); EXPECT(assert_equal(expected, gn_result, 3e-2)); From 5dd300d775bda4fa3dd374b2dbf63da6f95cd105 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 19 Jan 2026 09:27:59 -0500 Subject: [PATCH 122/266] Fix all tests --- .../AugmentedLagrangianOptimizer.cpp | 12 ++--- gtsam/constrained/PenaltyOptimizer.cpp | 6 +-- gtsam/constrained/PenaltyOptimizer.h | 9 ++-- .../testAugmentedLagrangianOptimizer.cpp | 4 +- gtsam/slam/tests/testReferenceFrameFactor.cpp | 17 +++++-- .../slam/tests/testSmartProjectionFactor.cpp | 51 +++++++------------ .../tests/testSmartProjectionPoseFactor.cpp | 35 +++++++------ .../tests/testSmartProjectionRigFactor.cpp | 29 ++++++----- .../tests/testConcurrentBatchFilter.cpp | 50 ++++++++++-------- .../tests/testConcurrentBatchSmoother.cpp | 41 ++++++++------- .../slam/tests/testInvDepthFactorVariant1.cpp | 1 + .../slam/tests/testInvDepthFactorVariant2.cpp | 1 + .../slam/tests/testInvDepthFactorVariant3.cpp | 1 + ...martProjectionPoseFactorRollingShutter.cpp | 8 ++- .../tests/testSmartStereoFactor_iSAM2.cpp | 11 +++- .../testSmartStereoProjectionFactorPP.cpp | 23 +++++---- .../testSmartStereoProjectionPoseFactor.cpp | 33 +++++++----- python/gtsam/tests/test_ShonanAveraging.py | 4 +- .../tests/test_backwards_compatibility.py | 4 +- tests/testBoundingConstraint.cpp | 16 ++++-- tests/testNonlinearEquality.cpp | 23 +++++++-- 21 files changed, 225 insertions(+), 154 deletions(-) diff --git a/gtsam/constrained/AugmentedLagrangianOptimizer.cpp b/gtsam/constrained/AugmentedLagrangianOptimizer.cpp index b1ad12c285..6d7fef7c6e 100644 --- a/gtsam/constrained/AugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/AugmentedLagrangianOptimizer.cpp @@ -29,7 +29,7 @@ namespace gtsam { * This factor is used in augmented Lagrangian optimizer to create biased cost * functions. * Note that the noise model is stored twice (both in original factor and the - * noisemodel of substitute factor. The noisemodel in the original factor will + * noise model of substitute factor. The noise model in the original factor will * be ignored. */ class GTSAM_EXPORT BiasedFactor : public NoiseModelFactor { protected: @@ -117,7 +117,7 @@ AugmentedLagrangianOptimizer::iterate(const State& state, const double muEq, auto optimizer = createUnconstrainedOptimizer(augmentedLagrangian, state.values); newState.setValues(optimizer->optimize(), problem_); - newState.unconstrainedIterationss = optimizer->iterations(); + newState.unconstrainedIterations = optimizer->iterations(); // Update penalty parameters for next iteration. double next_muEq, next_muIneq; @@ -186,7 +186,7 @@ NonlinearFactorGraph AugmentedLagrangianOptimizer::augmentedLagrangianFunction( /* ************************************************************************* */ void AugmentedLagrangianOptimizer::updateLagrangeMultiplier( const State& previousState, State* state) const { - // Perform dual ascent on Lagrange multipliers for e-constriants. + // Perform dual ascent on Lagrange multipliers for e-constraints. const NonlinearEqualityConstraints& eqConstraints = problem_.eConstraints(); state->lambdaEq.resize(eqConstraints.size()); for (size_t i = 0; i < eqConstraints.size(); i++) { @@ -198,7 +198,7 @@ void AugmentedLagrangianOptimizer::updateLagrangeMultiplier( state->lambdaEq[i] = previousState.lambdaEq[i] + step_size * violation; } - // Perform dual ascent on Lagrange multipliers for i-constriants. + // Perform dual ascent on Lagrange multipliers for i-constraints. const NonlinearInequalityConstraints& ineqConstraints = problem_.iConstraints(); state->lambdaIneq.resize(ineqConstraints.size()); @@ -239,7 +239,7 @@ AugmentedLagrangianOptimizer::createUnconstrainedOptimizer( const NonlinearFactorGraph& graph, const Values& values) const { // TODO(yetong): make compatible with all NonlinearOptimizers. return std::make_shared(graph, values, - p_->lm_params); + p_->lmParams); } /* ************************************************************************* */ @@ -283,7 +283,7 @@ void AugmentedLagrangianOptimizer::logIteration(const State& state) const { cout << "|" << setw(10) << setprecision(4) << state.cost; cout << "|" << setw(10) << setprecision(4) << state.eqConstraintViolation; cout << "|" << setw(10) << setprecision(4) << state.ineqConstraintViolation; - cout << "|" << setw(10) << state.unconstrainedIterationss; + cout << "|" << setw(10) << state.unconstrainedIterations; cout << "|" << setw(10) << state.time; cout << "|" << endl; } diff --git a/gtsam/constrained/PenaltyOptimizer.cpp b/gtsam/constrained/PenaltyOptimizer.cpp index 3eb1766a1f..5f8f71bf34 100644 --- a/gtsam/constrained/PenaltyOptimizer.cpp +++ b/gtsam/constrained/PenaltyOptimizer.cpp @@ -41,7 +41,7 @@ PenaltyOptimizer::State PenaltyOptimizer::iterate(const State& state) const { // Run unconstrained optimization. auto optimizer = createUnconstrainedOptimizer(meritGraph, state.values); newState.setValues(optimizer->optimize(), problem_); - newState.unconstrainedIterationss = optimizer->iterations(); + newState.unconstrainedIterations = optimizer->iterations(); return newState; } @@ -78,7 +78,7 @@ PenaltyOptimizer::createUnconstrainedOptimizer( const NonlinearFactorGraph& graph, const Values& values) const { // TODO(yetong): make compatible with all NonlinearOptimizers. return std::make_shared(graph, values, - p_->lm_params); + p_->lmParams); } /* ************************************************************************* */ @@ -122,7 +122,7 @@ void PenaltyOptimizer::logIteration(const State& state) const { cout << "|" << setw(10) << setprecision(4) << state.cost; cout << "|" << setw(10) << setprecision(4) << state.eqConstraintViolation; cout << "|" << setw(10) << setprecision(4) << state.ineqConstraintViolation; - cout << "|" << setw(10) << state.unconstrainedIterationss; + cout << "|" << setw(10) << state.unconstrainedIterations; cout << "|" << setw(10) << state.time; cout << "|" << endl; } diff --git a/gtsam/constrained/PenaltyOptimizer.h b/gtsam/constrained/PenaltyOptimizer.h index 9308ae643e..d873b59971 100644 --- a/gtsam/constrained/PenaltyOptimizer.h +++ b/gtsam/constrained/PenaltyOptimizer.h @@ -35,10 +35,13 @@ class GTSAM_EXPORT PenaltyOptimizerParams : public ConstrainedOptimizerParams { double muEqIncreaseRate = 2; // increase rate of penalty parameter double muIneqIncreaseRate = 2; InequalityPenaltyFunction::shared_ptr ineqConstraintPenaltyFunction = nullptr; - LevenbergMarquardtParams lm_params; + LevenbergMarquardtParams lmParams; /** Constructor. */ - PenaltyOptimizerParams() : Base() {} + PenaltyOptimizerParams() : Base() { + lmParams.linearSolverType = + LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + } }; /// Details for each iteration. @@ -50,7 +53,7 @@ class GTSAM_EXPORT PenaltyOptimizerState : public ConstrainedOptimizerState { double muEq = 0.0; double muIneq = 0.0; - size_t unconstrainedIterationss = 0; + size_t unconstrainedIterations = 0; using Base::Base; }; diff --git a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp index 44de445fe3..1d3be82806 100644 --- a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp @@ -10,8 +10,8 @@ * -------------------------------------------------------------------------- */ /** - * @file testAugmentedLagrangianOptimizr.cpp - * @brief Test augmented Lagrangian method optimzier for equality constrained + * @file testAugmentedLagrangianOptimizer.cpp + * @brief Test augmented Lagrangian method optimizer for equality constrained * optimization. * @author: Yetong Zhang */ diff --git a/gtsam/slam/tests/testReferenceFrameFactor.cpp b/gtsam/slam/tests/testReferenceFrameFactor.cpp index b5800a414b..ac883a78ae 100644 --- a/gtsam/slam/tests/testReferenceFrameFactor.cpp +++ b/gtsam/slam/tests/testReferenceFrameFactor.cpp @@ -36,9 +36,18 @@ using namespace gtsam; typedef gtsam::ReferenceFrameFactor PointReferenceFrameFactor; typedef gtsam::ReferenceFrameFactor PoseReferenceFrameFactor; -Key lA1 = symbol_shorthand::L(1), lA2 = symbol_shorthand::L(2), lB1 = symbol_shorthand::L(11), lB2 = symbol_shorthand::L(12); +namespace { +Key lA1 = symbol_shorthand::L(1), lA2 = symbol_shorthand::L(2), + lB1 = symbol_shorthand::L(11), lB2 = symbol_shorthand::L(12); Key tA1 = symbol_shorthand::T(1), tB1 = symbol_shorthand::T(2); +LevenbergMarquardtParams getLMParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} +} // namespace + /* ************************************************************************* */ TEST( ReferenceFrameFactor, equals ) { PointReferenceFrameFactor @@ -154,7 +163,7 @@ TEST( ReferenceFrameFactor, converge_trans ) { init.insert(tA1, trans); // optimize - LevenbergMarquardtOptimizer solver(graph, init); + LevenbergMarquardtOptimizer solver(graph, init, getLMParams()); Values actual = solver.optimize(); Values expected; @@ -196,7 +205,7 @@ TEST( ReferenceFrameFactor, converge_local ) { init.insert(tA1, trans); // optimize - LevenbergMarquardtOptimizer solver(graph, init); + LevenbergMarquardtOptimizer solver(graph, init, getLMParams()); Values actual = solver.optimize(); CHECK(actual.exists(lA1)); @@ -232,7 +241,7 @@ TEST( ReferenceFrameFactor, converge_global ) { init.insert(tA1, trans); // optimize - LevenbergMarquardtOptimizer solver(graph, init); + LevenbergMarquardtOptimizer solver(graph, init, getLMParams()); Values actual = solver.optimize(); // verify diff --git a/gtsam/slam/tests/testSmartProjectionFactor.cpp b/gtsam/slam/tests/testSmartProjectionFactor.cpp index 564b7f6403..ac5b0ceb53 100644 --- a/gtsam/slam/tests/testSmartProjectionFactor.cpp +++ b/gtsam/slam/tests/testSmartProjectionFactor.cpp @@ -31,13 +31,22 @@ static const Symbol l1('l', 1), l2('l', 2), l3('l', 3); static const Key c1 = 1, c2 = 2, c3 = 3; static const Point2 measurement1(323.0, 240.0); static const double rankTol = 1.0; + +LevenbergMarquardtParams makeLMParams(bool debug = false) { + LevenbergMarquardtParams p; + p.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + if (debug) { + p.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; + p.verbosity = NonlinearOptimizerParams::ERROR; + } + return p; } -template +template PinholeCamera perturbCameraPoseAndCalibration( const PinholeCamera& camera) { - Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10), - Point3(0.5, 0.1, 0.3)); + Pose3 noise_pose = + Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10), Point3(0.5, 0.1, 0.3)); Pose3 cameraPose = camera.pose(); Pose3 perturbedCameraPose = cameraPose.compose(noise_pose); typename gtsam::traits::TangentVector d; @@ -46,6 +55,7 @@ PinholeCamera perturbCameraPoseAndCalibration( CALIBRATION perturbedCalibration = camera.calibration().retract(d); return PinholeCamera(perturbedCameraPose, perturbedCalibration); } +} // namespace /* ************************************************************************* */ TEST(SmartProjectionFactor, perturbCameraPose) { @@ -242,12 +252,7 @@ TEST(SmartProjectionFactor, perturbPoseAndOptimize ) { EXPECT(assert_equal(expected, actual, 1)); // Optimize - LevenbergMarquardtParams lmParams; - if (isDebugTest) { - lmParams.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; - lmParams.verbosity = NonlinearOptimizerParams::ERROR; - } - LevenbergMarquardtOptimizer optimizer(graph, initial, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, initial, makeLMParams(isDebugTest)); Values result = optimizer.optimize(); EXPECT(assert_equal(landmark1, *smartFactor1->point(), 1e-5)); @@ -314,14 +319,8 @@ TEST(SmartProjectionFactor, perturbPoseAndOptimizeFromSfM_tracks ) { if (isDebugTest) values.at(c3).print("Smart: Pose3 before optimization: "); - LevenbergMarquardtParams lmParams; - if (isDebugTest) - lmParams.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; - if (isDebugTest) - lmParams.verbosity = NonlinearOptimizerParams::ERROR; - Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams(isDebugTest)); result = optimizer.optimize(); // GaussianFactorGraph::shared_ptr GFG = graph.linearize(values); @@ -388,15 +387,11 @@ TEST(SmartProjectionFactor, perturbCamerasAndOptimize ) { if (isDebugTest) values.at(c3).print("Smart: Pose3 before optimization: "); - LevenbergMarquardtParams lmParams; + LevenbergMarquardtParams lmParams = makeLMParams(isDebugTest); lmParams.relativeErrorTol = 1e-8; lmParams.absoluteErrorTol = 0; lmParams.maxIterations = 20; - if (isDebugTest) - lmParams.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; - if (isDebugTest) - lmParams.verbosity = NonlinearOptimizerParams::ERROR; - + Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); @@ -464,14 +459,10 @@ TEST(SmartProjectionFactor, Cal3Bundler ) { if (isDebugTest) values.at(c3).print("Smart: Pose3 before optimization: "); - LevenbergMarquardtParams lmParams; + LevenbergMarquardtParams lmParams = makeLMParams(isDebugTest); lmParams.relativeErrorTol = 1e-8; lmParams.absoluteErrorTol = 0; lmParams.maxIterations = 20; - if (isDebugTest) - lmParams.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; - if (isDebugTest) - lmParams.verbosity = NonlinearOptimizerParams::ERROR; Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); @@ -537,14 +528,10 @@ TEST(SmartProjectionFactor, Cal3Bundler2 ) { if (isDebugTest) values.at(c3).print("Smart: Pose3 before optimization: "); - LevenbergMarquardtParams lmParams; + LevenbergMarquardtParams lmParams = makeLMParams(isDebugTest); lmParams.relativeErrorTol = 1e-8; lmParams.absoluteErrorTol = 0; lmParams.maxIterations = 20; - if (isDebugTest) - lmParams.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; - if (isDebugTest) - lmParams.verbosity = NonlinearOptimizerParams::ERROR; Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); diff --git a/gtsam/slam/tests/testSmartProjectionPoseFactor.cpp b/gtsam/slam/tests/testSmartProjectionPoseFactor.cpp index 807f485c72..cc5760970d 100644 --- a/gtsam/slam/tests/testSmartProjectionPoseFactor.cpp +++ b/gtsam/slam/tests/testSmartProjectionPoseFactor.cpp @@ -46,9 +46,13 @@ static Symbol x3('X', 3); static Point2 measurement1(323.0, 240.0); -LevenbergMarquardtParams lmParams; -// Make more verbose like so (in tests): -// lmParams.verbosityLM = LevenbergMarquardtParams::SUMMARY; +LevenbergMarquardtParams makeLMParams(bool verbose = false) { + LevenbergMarquardtParams p; + p.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + if (verbose) p.verbosityLM = LevenbergMarquardtParams::SUMMARY; + return p; +} + } /* ************************************************************************* */ @@ -264,9 +268,8 @@ TEST(SmartProjectionPoseFactor, smartFactorWithSensorBodyTransform) { // original pose3 values.insert(x3, wTb3 * noise_pose); - LevenbergMarquardtParams lmParams; Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(wTb3, result.at(x3))); } @@ -327,7 +330,7 @@ TEST( SmartProjectionPoseFactor, 3poses_smart_projection_factor ) { Point3(0.1, -0.1, 1.9)), values.at(x3))); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(pose_above, result.at(x3), 1e-6)); } @@ -545,7 +548,7 @@ TEST( SmartProjectionPoseFactor, 3poses_iterative_smart_projection_factor ) { values.at(x3))); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(pose_above, result.at(x3), 1e-7)); } @@ -601,7 +604,7 @@ TEST( SmartProjectionPoseFactor, jacobianSVD ) { values.insert(x3, pose_above * noise_pose); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(pose_above, result.at(x3), 1e-6)); } @@ -660,7 +663,7 @@ TEST( SmartProjectionPoseFactor, landmarkDistance ) { // All factors are disabled and pose should remain where it is Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(values.at(x3), result.at(x3))); } @@ -726,7 +729,7 @@ TEST( SmartProjectionPoseFactor, dynamicOutlierRejection ) { // All factors are disabled and pose should remain where it is Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(cam3.pose(), result.at(x3))); } @@ -777,7 +780,7 @@ TEST( SmartProjectionPoseFactor, jacobianQ ) { values.insert(x3, pose_above * noise_pose); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(pose_above, result.at(x3), 1e-6)); } @@ -821,7 +824,7 @@ TEST( SmartProjectionPoseFactor, 3poses_projection_factor ) { DOUBLES_EQUAL(48406055, graph.error(values), 1); - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); Values result = optimizer.optimize(); DOUBLES_EQUAL(0, graph.error(result), 1e-9); @@ -960,7 +963,7 @@ TEST( SmartProjectionPoseFactor, 3poses_2land_rotation_only_smart_projection_fac values.insert(x3, pose3 * noise_pose); // params.verbosityLM = LevenbergMarquardtParams::SUMMARY; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); Values result = optimizer.optimize(); EXPECT(assert_equal(pose3, result.at(x3))); } @@ -1033,7 +1036,7 @@ TEST( SmartProjectionPoseFactor, 3poses_rotation_only_smart_projection_factor ) values.at(x3))); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); // Since we do not do anything on degenerate instances (ZERO_ON_DEGENERACY) @@ -1242,7 +1245,7 @@ TEST( SmartProjectionPoseFactor, Cal3Bundler ) { Point3(0.1, -0.1, 1.9)), values.at(x3))); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT(assert_equal(cam3.pose(), result.at(x3), 1e-6)); } @@ -1318,7 +1321,7 @@ TEST( SmartProjectionPoseFactor, Cal3BundlerRotationOnly ) { values.at(x3))); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + LevenbergMarquardtOptimizer optimizer(graph, values, makeLMParams()); result = optimizer.optimize(); EXPECT( diff --git a/gtsam/slam/tests/testSmartProjectionRigFactor.cpp b/gtsam/slam/tests/testSmartProjectionRigFactor.cpp index 67bbfa3ba7..2bcdcc207b 100644 --- a/gtsam/slam/tests/testSmartProjectionRigFactor.cpp +++ b/gtsam/slam/tests/testSmartProjectionRigFactor.cpp @@ -32,27 +32,34 @@ using namespace std::placeholders; -static const double rankTol = 1.0; +namespace { +const double rankTol = 1.0; // Create a noise model for the pixel error -static const double sigma = 0.1; -static SharedIsotropic model(noiseModel::Isotropic::Sigma(2, sigma)); +const double sigma = 0.1; +SharedIsotropic model(noiseModel::Isotropic::Sigma(2, sigma)); // Convenience for named keys using symbol_shorthand::L; using symbol_shorthand::X; // tests data -static Symbol x1('X', 1); -static Symbol x2('X', 2); -static Symbol x3('X', 3); +Symbol x1('X', 1); +Symbol x2('X', 2); +Symbol x3('X', 3); Key cameraId1 = 0; // first camera -Key cameraId2 = 1; -Key cameraId3 = 2; -static Point2 measurement1(323.0, 240.0); +Point2 measurement1(323.0, 240.0); -LevenbergMarquardtParams lmParams; +LevenbergMarquardtParams makeLMParams() { + LevenbergMarquardtParams p; + p.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return p; +} + +LevenbergMarquardtParams lmParams = makeLMParams(); + +} // namespace /* ************************************************************************* */ // default Cal3_S2 poses with rolling shutter effect @@ -305,7 +312,6 @@ TEST(SmartProjectionRigFactor, smartFactorWithSensorBodyTransform) { // original pose3 values.insert(x3, wTb3 * noise_pose); - LevenbergMarquardtParams lmParams; Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); @@ -392,7 +398,6 @@ TEST(SmartProjectionRigFactor, smartFactorWithMultipleCameras) { // original pose3 values.insert(x3, wTb3 * noise_pose); - LevenbergMarquardtParams lmParams; Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp index 86fee17378..328448e88b 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp @@ -46,11 +46,17 @@ const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10); const SharedDiagonal noiseOdometery = noiseModel::Diagonal::Sigmas((Vector(6) << 0.1, 0.1, 0.1, 0.5, 0.5, 0.5).finished()); const SharedDiagonal noiseLoop = noiseModel::Diagonal::Sigmas((Vector(6) << 0.25, 0.25, 0.25, 1.0, 1.0, 1.0).finished()); +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + /* ************************************************************************* */ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int maxIter = 100) { // Create an L-M optimizer - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = maxIter; LevenbergMarquardtOptimizer optimizer(graph, theta, parameters); @@ -100,15 +106,15 @@ TEST( ConcurrentBatchFilter, equals ) // TODO: Test 'equals' more vigorously // Create a Concurrent Batch Filter - LevenbergMarquardtParams parameters1; + LevenbergMarquardtParams parameters1 = makeLmParams(); ConcurrentBatchFilter filter1(parameters1); // Create an identical Concurrent Batch Filter - LevenbergMarquardtParams parameters2; + LevenbergMarquardtParams parameters2 = makeLmParams(); ConcurrentBatchFilter filter2(parameters2); // Create a different Concurrent Batch Filter - LevenbergMarquardtParams parameters3; + LevenbergMarquardtParams parameters3 = makeLmParams(); parameters3.maxIterations = 1; ConcurrentBatchFilter filter3(parameters3); @@ -121,7 +127,7 @@ TEST( ConcurrentBatchFilter, equals ) TEST( ConcurrentBatchFilter, getFactors ) { // Create a Concurrent Batch Filter - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Expected graph is empty @@ -171,7 +177,7 @@ TEST( ConcurrentBatchFilter, getFactors ) TEST( ConcurrentBatchFilter, getLinearizationPoint ) { // Create a Concurrent Batch Filter - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Expected values is empty @@ -233,7 +239,7 @@ TEST( ConcurrentBatchFilter, getDelta ) TEST( ConcurrentBatchFilter, calculateEstimate ) { // Create a Concurrent Batch Filter - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Expected values is empty @@ -306,7 +312,7 @@ TEST( ConcurrentBatchFilter, calculateEstimate ) TEST( ConcurrentBatchFilter, update_empty ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Call update @@ -317,7 +323,7 @@ TEST( ConcurrentBatchFilter, update_empty ) TEST( ConcurrentBatchFilter, update_multiple ) { // Create a Concurrent Batch Filter - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Expected values is empty @@ -374,7 +380,7 @@ TEST( ConcurrentBatchFilter, update_multiple ) TEST( ConcurrentBatchFilter, update_and_marginalize ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Add some factors to the filter @@ -461,7 +467,7 @@ TEST( ConcurrentBatchFilter, update_and_marginalize ) TEST( ConcurrentBatchFilter, synchronize_0 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); // Create a Concurrent Batch Filter ConcurrentBatchFilter filter(parameters); @@ -494,7 +500,7 @@ TEST( ConcurrentBatchFilter, synchronize_0 ) TEST( ConcurrentBatchFilter, synchronize_1 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Filter @@ -539,7 +545,7 @@ TEST( ConcurrentBatchFilter, synchronize_2 ) { std::cout << "*********************** synchronize_2 ************************" << std::endl; // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); //parameters.maxIterations = 1; // Create a Concurrent Batch Filter @@ -607,7 +613,7 @@ TEST( ConcurrentBatchFilter, synchronize_3 ) { std::cout << "*********************** synchronize_3 ************************" << std::endl; // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); //parameters.maxIterations = 1; // Create a Concurrent Batch Filter @@ -691,7 +697,7 @@ TEST( ConcurrentBatchFilter, synchronize_3 ) TEST( ConcurrentBatchFilter, synchronize_4 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Filter @@ -785,7 +791,7 @@ TEST( ConcurrentBatchFilter, synchronize_5 ) { std::cout << "*********************** synchronize_5 ************************" << std::endl; // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Filter @@ -1084,7 +1090,7 @@ TEST( ConcurrentBatchFilter, removeFactors_topology_1 ) std::cout << "*********************** removeFactors_topology_1 ************************" << std::endl; // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Add some factors to the filter @@ -1139,7 +1145,7 @@ TEST( ConcurrentBatchFilter, removeFactors_topology_2 ) // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Add some factors to the filter @@ -1194,7 +1200,7 @@ TEST( ConcurrentBatchFilter, removeFactors_topology_3 ) // we try removing the first factor // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Add some factors to the filter @@ -1247,7 +1253,7 @@ TEST( ConcurrentBatchFilter, removeFactors_values ) // we try removing the last factor // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchFilter filter(parameters); // Add some factors to the filter @@ -1304,7 +1310,7 @@ TEST( ConcurrentBatchFilter, removeFactors_values ) //TEST( ConcurrentBatchFilter, synchronize_10 ) //{ // // Create a set of optimizer parameters -// LevenbergMarquardtParams parameters; +// LevenbergMarquardtParams parameters = makeLmParams(); // parameters.maxIterations = 1; // // // Create a Concurrent Batch Filter @@ -1319,7 +1325,7 @@ TEST( ConcurrentBatchFilter, removeFactors_values ) //TEST( ConcurrentBatchFilter, synchronize_11 ) //{ // // Create a set of optimizer parameters -// LevenbergMarquardtParams parameters; +// LevenbergMarquardtParams parameters = makeLmParams(); // parameters.maxIterations = 1; // // // Create a Concurrent Batch Filter diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentBatchSmoother.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentBatchSmoother.cpp index 514382ef78..cf6c8374ef 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentBatchSmoother.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentBatchSmoother.cpp @@ -21,6 +21,7 @@ #include #include #include +#include #include #include #include @@ -46,11 +47,17 @@ const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10); const SharedDiagonal noiseOdometery = noiseModel::Diagonal::Sigmas((Vector(6) << 0.1, 0.1, 0.1, 0.5, 0.5, 0.5).finished()); const SharedDiagonal noiseLoop = noiseModel::Diagonal::Sigmas((Vector(6) << 0.25, 0.25, 0.25, 1.0, 1.0, 1.0).finished()); +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + /* ************************************************************************* */ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int maxIter = 100) { // Create an L-M optimizer - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = maxIter; LevenbergMarquardtOptimizer optimizer(graph, theta, parameters); @@ -70,15 +77,15 @@ TEST( ConcurrentBatchSmoother, equals ) // TODO: Test 'equals' more vigorously // Create a Concurrent Batch Smoother - LevenbergMarquardtParams parameters1; + LevenbergMarquardtParams parameters1 = makeLmParams(); ConcurrentBatchSmoother smoother1(parameters1); // Create an identical Concurrent Batch Smoother - LevenbergMarquardtParams parameters2; + LevenbergMarquardtParams parameters2 = makeLmParams(); ConcurrentBatchSmoother smoother2(parameters2); // Create a different Concurrent Batch Smoother - LevenbergMarquardtParams parameters3; + LevenbergMarquardtParams parameters3 = makeLmParams(); parameters3.maxIterations = 1; ConcurrentBatchSmoother smoother3(parameters3); @@ -91,7 +98,7 @@ TEST( ConcurrentBatchSmoother, equals ) TEST( ConcurrentBatchSmoother, getFactors ) { // Create a Concurrent Batch Smoother - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother smoother(parameters); // Expected graph is empty @@ -141,7 +148,7 @@ TEST( ConcurrentBatchSmoother, getFactors ) TEST( ConcurrentBatchSmoother, getLinearizationPoint ) { // Create a Concurrent Batch Smoother - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother smoother(parameters); // Expected values is empty @@ -203,7 +210,7 @@ TEST( ConcurrentBatchSmoother, getDelta ) TEST( ConcurrentBatchSmoother, calculateEstimate ) { // Create a Concurrent Batch Smoother - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother smoother(parameters); // Expected values is empty @@ -276,7 +283,7 @@ TEST( ConcurrentBatchSmoother, calculateEstimate ) TEST( ConcurrentBatchSmoother, update_empty ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); // Create a Concurrent Batch Smoother ConcurrentBatchSmoother smoother(parameters); @@ -289,7 +296,7 @@ TEST( ConcurrentBatchSmoother, update_empty ) TEST( ConcurrentBatchSmoother, update_multiple ) { // Create a Concurrent Batch Smoother - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother smoother(parameters); // Expected values is empty @@ -346,7 +353,7 @@ TEST( ConcurrentBatchSmoother, update_multiple ) TEST( ConcurrentBatchSmoother, synchronize_empty ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); // Create a Concurrent Batch Smoother ConcurrentBatchSmoother smoother(parameters); @@ -376,7 +383,7 @@ TEST( ConcurrentBatchSmoother, synchronize_empty ) TEST( ConcurrentBatchSmoother, synchronize_1 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Smoother @@ -437,7 +444,7 @@ TEST( ConcurrentBatchSmoother, synchronize_1 ) TEST( ConcurrentBatchSmoother, synchronize_2 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Smoother @@ -508,7 +515,7 @@ TEST( ConcurrentBatchSmoother, synchronize_2 ) TEST( ConcurrentBatchSmoother, synchronize_3 ) { // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); parameters.maxIterations = 1; // Create a Concurrent Batch Smoother @@ -580,7 +587,7 @@ TEST( ConcurrentBatchSmoother, removeFactors_topology_1 ) std::cout << "*********************** removeFactors_topology_1 ************************" << std::endl; // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); // Create a Concurrent Batch Smoother ConcurrentBatchSmoother smoother(parameters); @@ -634,7 +641,7 @@ TEST( ConcurrentBatchSmoother, removeFactors_topology_2 ) // we try removing the last factor // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); // Create a Concurrent Batch Smoother ConcurrentBatchSmoother smoother(parameters); @@ -688,7 +695,7 @@ TEST( ConcurrentBatchSmoother, removeFactors_topology_3 ) // we try removing the first factor // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother Smoother(parameters); // Add some factors to the Smoother @@ -738,7 +745,7 @@ TEST( ConcurrentBatchSmoother, removeFactors_values ) // we try removing the last factor // Create a set of optimizer parameters - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); ConcurrentBatchSmoother Smoother(parameters); // Add some factors to the Smoother diff --git a/gtsam_unstable/slam/tests/testInvDepthFactorVariant1.cpp b/gtsam_unstable/slam/tests/testInvDepthFactorVariant1.cpp index 96043fb505..214054aae9 100644 --- a/gtsam_unstable/slam/tests/testInvDepthFactorVariant1.cpp +++ b/gtsam_unstable/slam/tests/testInvDepthFactorVariant1.cpp @@ -72,6 +72,7 @@ TEST( InvDepthFactorVariant1, optimize) { // Optimize the graph to recover the actual landmark position LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; Values result = LevenbergMarquardtOptimizer(graph, values, params).optimize(); // Vector6 actual = result.at(landmarkKey); diff --git a/gtsam_unstable/slam/tests/testInvDepthFactorVariant2.cpp b/gtsam_unstable/slam/tests/testInvDepthFactorVariant2.cpp index 7ac0faa1e4..bc7a70e64a 100644 --- a/gtsam_unstable/slam/tests/testInvDepthFactorVariant2.cpp +++ b/gtsam_unstable/slam/tests/testInvDepthFactorVariant2.cpp @@ -70,6 +70,7 @@ TEST( InvDepthFactorVariant2, optimize) { // Optimize the graph to recover the actual landmark position LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; Values result = LevenbergMarquardtOptimizer(graph, values, params).optimize(); Vector3 actual = result.at(landmarkKey); diff --git a/gtsam_unstable/slam/tests/testInvDepthFactorVariant3.cpp b/gtsam_unstable/slam/tests/testInvDepthFactorVariant3.cpp index 951349b0fe..de70016068 100644 --- a/gtsam_unstable/slam/tests/testInvDepthFactorVariant3.cpp +++ b/gtsam_unstable/slam/tests/testInvDepthFactorVariant3.cpp @@ -70,6 +70,7 @@ TEST( InvDepthFactorVariant3, optimize) { // Optimize the graph to recover the actual landmark position LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; Values result = LevenbergMarquardtOptimizer(graph, values, params).optimize(); Vector3 actual = result.at(landmarkKey); diff --git a/gtsam_unstable/slam/tests/testSmartProjectionPoseFactorRollingShutter.cpp b/gtsam_unstable/slam/tests/testSmartProjectionPoseFactorRollingShutter.cpp index 3735e65a8c..9b2c3741f0 100644 --- a/gtsam_unstable/slam/tests/testSmartProjectionPoseFactorRollingShutter.cpp +++ b/gtsam_unstable/slam/tests/testSmartProjectionPoseFactorRollingShutter.cpp @@ -78,7 +78,13 @@ SmartProjectionParams params( gtsam::ZERO_ON_DEGENERACY); // only config that works with RS factors } // namespace vanillaPoseRS -LevenbergMarquardtParams lmParams; +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + +LevenbergMarquardtParams lmParams = makeLmParams(); typedef SmartProjectionPoseFactorRollingShutter> SmartFactorRS; diff --git a/gtsam_unstable/slam/tests/testSmartStereoFactor_iSAM2.cpp b/gtsam_unstable/slam/tests/testSmartStereoFactor_iSAM2.cpp index 62cd4cc38b..4588f5362f 100644 --- a/gtsam_unstable/slam/tests/testSmartStereoFactor_iSAM2.cpp +++ b/gtsam_unstable/slam/tests/testSmartStereoFactor_iSAM2.cpp @@ -41,6 +41,15 @@ // Tolerance for ground-truth pose comparison: static const double tol = 1e-3; +namespace { +gtsam::LevenbergMarquardtParams makeLmParams() { + gtsam::LevenbergMarquardtParams params; + params.linearSolverType = + gtsam::LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} +} // namespace + // Synthetic dataset generated with rwt // (https://github.com/jlblancoc/recursive-world-toolkit) // Camera parameters @@ -204,7 +213,7 @@ TEST(testISAM2SmartFactor, Stereo_Batch) { batch_values.insert(X(kf_id), Pose3::Identity()); } - LevenbergMarquardtParams parameters; + LevenbergMarquardtParams parameters = makeLmParams(); #if TEST_VERBOSE_OUTPUT parameters.verbosity = NonlinearOptimizerParams::LINEAR; parameters.verbosityLM = LevenbergMarquardtParams::TRYDELTA; diff --git a/gtsam_unstable/slam/tests/testSmartStereoProjectionFactorPP.cpp b/gtsam_unstable/slam/tests/testSmartStereoProjectionFactorPP.cpp index b4e0e65f36..fe399ebf68 100644 --- a/gtsam_unstable/slam/tests/testSmartStereoProjectionFactorPP.cpp +++ b/gtsam_unstable/slam/tests/testSmartStereoProjectionFactorPP.cpp @@ -83,7 +83,13 @@ vector stereo_projectToMultipleCameras(const StereoCamera& cam1, return measurements_cam; } -LevenbergMarquardtParams lm_params; +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + +LevenbergMarquardtParams lmParams = makeLmParams(); } // namespace /* ************************************************************************* */ @@ -470,7 +476,7 @@ TEST( SmartStereoProjectionFactorPP, 3poses_optimization_multipleExtrinsics ) { Values result; gttic_(SmartStereoProjectionFactorPP); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionFactorPP); tictoc_finishedIteration_(); @@ -526,7 +532,7 @@ TEST( SmartStereoProjectionFactorPP, 3poses_optimization_multipleExtrinsics ) { EXPECT_DOUBLES_EQUAL(833953.92789459578, graph2.error(values2), 1e-7); EXPECT_DOUBLES_EQUAL(initialErrorSmart, graph2.error(values2), 1e-7); // identical to previous case! - LevenbergMarquardtOptimizer optimizer2(graph2, values2, lm_params); + LevenbergMarquardtOptimizer optimizer2(graph2, values2, lmParams); Values result2 = optimizer2.optimize(); EXPECT_DOUBLES_EQUAL(0, graph2.error(result2), 1e-5); } @@ -850,7 +856,7 @@ TEST( SmartStereoProjectionFactorPP, 3poses_optimization_sameExtrinsicKey ) { Values result; gttic_(SmartStereoProjectionFactorPP); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionFactorPP); tictoc_finishedIteration_(); @@ -951,7 +957,7 @@ TEST( SmartStereoProjectionFactorPP, 3poses_optimization_2ExtrinsicKeys ) { Values result; gttic_(SmartStereoProjectionFactorPP); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionFactorPP); tictoc_finishedIteration_(); @@ -1076,7 +1082,7 @@ TEST( SmartStereoProjectionFactorPP, monocular_multipleExtrinsicKeys ){ Values result; gttic_(SmartStereoProjectionFactorPP); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionFactorPP); tictoc_finishedIteration_(); @@ -1156,7 +1162,7 @@ TEST( SmartStereoProjectionFactorPP, landmarkDistance ) { // All smart factors are disabled and pose should remain where it is Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(values.at(x3), result.at(x3), 1e-5)); EXPECT_DOUBLES_EQUAL(graph.error(values), graph.error(result), 1e-5); @@ -1261,7 +1267,7 @@ TEST( SmartStereoProjectionFactorPP, dynamicOutlierRejection ) { // Factor 4 is disabled, pose 3 stays put Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(Pose3::Identity(), result.at(body_P_cam_key))); } @@ -1272,4 +1278,3 @@ int main() { return TestRegistry::runAllTests(tr); } /* ************************************************************************* */ - diff --git a/gtsam_unstable/slam/tests/testSmartStereoProjectionPoseFactor.cpp b/gtsam_unstable/slam/tests/testSmartStereoProjectionPoseFactor.cpp index 872cd2deaa..7dbd37ec61 100644 --- a/gtsam_unstable/slam/tests/testSmartStereoProjectionPoseFactor.cpp +++ b/gtsam_unstable/slam/tests/testSmartStereoProjectionPoseFactor.cpp @@ -77,7 +77,13 @@ vector stereo_projectToMultipleCameras(const StereoCamera& cam1, return measurements_cam; } -LevenbergMarquardtParams lm_params; +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + +LevenbergMarquardtParams lmParams = makeLmParams(); } // namespace /* ************************************************************************* */ @@ -362,7 +368,7 @@ TEST( SmartStereoProjectionPoseFactor, 3poses_smart_projection_factor ) { Values result; gttic_(SmartStereoProjectionPoseFactor); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionPoseFactor); tictoc_finishedIteration_(); @@ -417,7 +423,7 @@ TEST( SmartStereoProjectionPoseFactor, 3poses_smart_projection_factor ) { // cout << std::setprecision(10) << "\n----StereoFactor graph initial error: " << graph2.error(values) << endl; EXPECT_DOUBLES_EQUAL(833953.92789459578, graph2.error(values), 1e-7); - LevenbergMarquardtOptimizer optimizer2(graph2, values, lm_params); + LevenbergMarquardtOptimizer optimizer2(graph2, values, lmParams); Values result2 = optimizer2.optimize(); EXPECT_DOUBLES_EQUAL(0, graph2.error(result2), 1e-5); @@ -499,7 +505,7 @@ TEST( SmartStereoProjectionPoseFactor, body_P_sensor ) { Values result; gttic_(SmartStereoProjectionPoseFactor); - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); gttoc_(SmartStereoProjectionPoseFactor); tictoc_finishedIteration_(); @@ -604,7 +610,7 @@ TEST( SmartStereoProjectionPoseFactor, body_P_sensor_monocular ){ // initialize third pose with some noise, we expect it to move back to original pose3 values.insert(x3, bodyPose3*noise_pose); - LevenbergMarquardtParams lmParams; + LevenbergMarquardtParams lmParams = makeLmParams(); Values result; LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); @@ -671,7 +677,7 @@ TEST( SmartStereoProjectionPoseFactor, jacobianSVD ) { values.insert(x3, pose3 * noise_pose); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(pose3, result.at(x3))); } @@ -743,7 +749,7 @@ TEST( SmartStereoProjectionPoseFactor, jacobianSVDwithMissingValues ) { values.insert(x3, pose3 * noise_pose); Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(pose3, result.at(x3),1e-7)); } @@ -813,7 +819,7 @@ TEST( SmartStereoProjectionPoseFactor, landmarkDistance ) { // All factors are disabled and pose should remain where it is Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(values.at(x3), result.at(x3))); } @@ -910,7 +916,7 @@ TEST( SmartStereoProjectionPoseFactor, dynamicOutlierRejection ) { // Factor 4 is disabled, pose 3 stays put Values result; - LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); result = optimizer.optimize(); EXPECT(assert_equal(pose3, result.at(x3))); } @@ -971,7 +977,7 @@ TEST( SmartStereoProjectionPoseFactor, dynamicOutlierRejection ) { // values.insert(x3, pose3*noise_pose); // //// Values result; -// LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); +// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); // result = optimizer.optimize(); // EXPECT(assert_equal(pose3,result.at(x3))); //} @@ -1030,7 +1036,7 @@ TEST( SmartStereoProjectionPoseFactor, dynamicOutlierRejection ) { // values.insert(L(2), landmark2); // values.insert(L(3), landmark3); // -// LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); +// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); // Values result = optimizer.optimize(); // // EXPECT(assert_equal(pose3,result.at(x3))); @@ -1181,7 +1187,7 @@ TEST( SmartStereoProjectionPoseFactor, CheckHessian) { // // Values result; // gttic_(SmartStereoProjectionPoseFactor); -// LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); +// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); // result = optimizer.optimize(); // gttoc_(SmartStereoProjectionPoseFactor); // tictoc_finishedIteration_(); @@ -1255,7 +1261,7 @@ TEST( SmartStereoProjectionPoseFactor, CheckHessian) { // // Values result; // gttic_(SmartStereoProjectionPoseFactor); -// LevenbergMarquardtOptimizer optimizer(graph, values, lm_params); +// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); // result = optimizer.optimize(); // gttoc_(SmartStereoProjectionPoseFactor); // tictoc_finishedIteration_(); @@ -1457,4 +1463,3 @@ int main() { return TestRegistry::runAllTests(tr); } /* ************************************************************************* */ - diff --git a/python/gtsam/tests/test_ShonanAveraging.py b/python/gtsam/tests/test_ShonanAveraging.py index 5c36ad361b..82be2851a6 100644 --- a/python/gtsam/tests/test_ShonanAveraging.py +++ b/python/gtsam/tests/test_ShonanAveraging.py @@ -172,8 +172,8 @@ def test_constructorBetweenFactorPose2s(self) -> None: for (i1, i2) in edges } - lm_params = LevenbergMarquardtParams.CeresDefaults() - shonan_params = ShonanAveragingParameters2(lm_params) + lmParams = LevenbergMarquardtParams.CeresDefaults() + shonan_params = ShonanAveragingParameters2(lmParams) shonan_params.setUseHuber(False) shonan_params.setCertifyOptimality(True) diff --git a/python/gtsam/tests/test_backwards_compatibility.py b/python/gtsam/tests/test_backwards_compatibility.py index c6c7eadca0..a3d2cb2774 100644 --- a/python/gtsam/tests/test_backwards_compatibility.py +++ b/python/gtsam/tests/test_backwards_compatibility.py @@ -453,8 +453,8 @@ def test_constructorBetweenFactorPose2s(self) -> None: wTi_list[i2].inverse().compose(wTi_list[i1]).rotation() for (i1, i2) in edges} - lm_params = LevenbergMarquardtParams.CeresDefaults() - shonan_params = ShonanAveragingParameters2(lm_params) + lmParams = LevenbergMarquardtParams.CeresDefaults() + shonan_params = ShonanAveragingParameters2(lmParams) shonan_params.setUseHuber(False) shonan_params.setCertifyOptimality(True) diff --git a/tests/testBoundingConstraint.cpp b/tests/testBoundingConstraint.cpp index fee406dd34..ebc9a67fff 100644 --- a/tests/testBoundingConstraint.cpp +++ b/tests/testBoundingConstraint.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include @@ -28,6 +29,12 @@ using namespace gtsam; static const double tol = 1e-5; +LevenbergMarquardtParams makeLmParams() { + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + return params; +} + SharedDiagonal soft_model2 = noiseModel::Unit::Create(2); SharedDiagonal soft_model2_alt = noiseModel::Isotropic::Sigma(2, 0.1); SharedDiagonal hard_model1 = noiseModel::Constrained::All(1); @@ -151,7 +158,9 @@ TEST( testBoundingConstraint, unary_simple_optimization1) { Values initValues; initValues.insert(x1, start_pt); - Values actual = LevenbergMarquardtOptimizer(graph, initValues).optimize(); + LevenbergMarquardtParams params = makeLmParams(); + Values actual = + LevenbergMarquardtOptimizer(graph, initValues, params).optimize(); Values expected; expected.insert(x1, goal_pt); CHECK(assert_equal(expected, actual, tol)); @@ -172,7 +181,9 @@ TEST( testBoundingConstraint, unary_simple_optimization2) { Values initValues; initValues.insert(key, start_pt); - Values actual = LevenbergMarquardtOptimizer(graph, initValues).optimize(); + LevenbergMarquardtParams params = makeLmParams(); + Values actual = + LevenbergMarquardtOptimizer(graph, initValues, params).optimize(); Values expected; expected.insert(key, goal_pt); CHECK(assert_equal(expected, actual, tol)); @@ -273,4 +284,3 @@ TEST( testBoundingConstraint, avoid_demo) { /* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); } /* ************************************************************************* */ - diff --git a/tests/testNonlinearEquality.cpp b/tests/testNonlinearEquality.cpp index 1b2ddc3dd0..5dd6ff07a5 100644 --- a/tests/testNonlinearEquality.cpp +++ b/tests/testNonlinearEquality.cpp @@ -20,6 +20,7 @@ #include #include #include +#include #include #include #include @@ -201,7 +202,9 @@ TEST ( NonlinearEquality, allow_error_optimize ) { init.insert(key1, initPose); // optimize - Values result = LevenbergMarquardtOptimizer(graph, init).optimize(); + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + Values result = LevenbergMarquardtOptimizer(graph, init, params).optimize(); // verify Values expected; @@ -230,7 +233,9 @@ TEST ( NonlinearEquality, allow_error_optimize_with_factors ) { graph.emplace_shared(key1, initPose, noiseModel::Isotropic::Sigma(3, 0.1)); // optimize - Values actual = LevenbergMarquardtOptimizer(graph, init).optimize(); + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + Values actual = LevenbergMarquardtOptimizer(graph, init, params).optimize(); // verify Values expected; @@ -312,7 +317,10 @@ TEST( testNonlinearEqualityConstraint, unary_simple_optimization ) { EXPECT(constraint->active(expected)); EXPECT_DOUBLES_EQUAL(0.0, constraint->error(expected), tol); - Values actual = LevenbergMarquardtOptimizer(graph, initValues).optimize(); + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + Values actual = + LevenbergMarquardtOptimizer(graph, initValues, params).optimize(); EXPECT(assert_equal(expected, actual, tol)); } @@ -394,7 +402,10 @@ TEST( testNonlinearEqualityConstraint, odo_simple_optimize ) { initValues.insert(key1, Point2(0,0)); initValues.insert(key2, badPt); - Values actual = LevenbergMarquardtOptimizer(graph, initValues).optimize(); + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + Values actual = + LevenbergMarquardtOptimizer(graph, initValues, params).optimize(); Values expected; expected.insert(key1, truth_pt1); expected.insert(key2, truth_pt2); @@ -432,8 +443,10 @@ TEST (testNonlinearEqualityConstraint, two_pose ) { initialEstimate.insert(l1, Point2(1.0, 6.0)); // ground truth initialEstimate.insert(l2, Point2(-4.0, 0.0)); // starting with a separate reference frame + LevenbergMarquardtParams params; + params.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; Values actual = - LevenbergMarquardtOptimizer(graph, initialEstimate).optimize(); + LevenbergMarquardtOptimizer(graph, initialEstimate, params).optimize(); Values expected; expected.insert(x1, pt_x1); From dc216d6f053681b0936c750d86e70c7f5fda5451 Mon Sep 17 00:00:00 2001 From: Tal Regev Date: Thu, 8 Jan 2026 00:04:54 +0200 Subject: [PATCH 123/266] Add vcpkg mkl --- .github/workflows/vcpkg.yml | 5 ++++ cmake/FindMKL.cmake | 14 +++++++++++ cmake/HandleMKL.cmake | 25 +++++++++++++++++++ gtsam/linear/HessianFactor.cpp | 4 +-- gtsam/linear/tests/testHessianFactor.cpp | 3 ++- .../tests/testLinearContainerFactor.cpp | 3 ++- gtsam_unstable/nonlinear/LinearizedFactor.cpp | 4 +-- python/CMakeLists.txt | 18 +++++++++++++ 8 files changed, 70 insertions(+), 6 deletions(-) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 66e5d3ba30..262100e4d7 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -25,12 +25,14 @@ jobs: test_target: RUN_TESTS binary_cache: C:\Users\runneradmin\AppData\Local\vcpkg\archives python: python + additional_vcpkg_ports: intel-mkl - os: ubuntu-latest triplet: x64-linux-release build_type: Release test_target: test binary_cache: /home/runner/.cache/vcpkg/archives python: python3 + additional_vcpkg_ports: intel-mkl - os: macos-latest triplet: arm64-osx-release build_type: Release @@ -112,6 +114,7 @@ jobs: pybind11 geographiclib eigen3 + ${{ matrix.additional_vcpkg_ports }} - name: On Failure, upload vcpkg logs if: failure() @@ -178,6 +181,8 @@ jobs: -DGTSAM_USE_SYSTEM_PYBIND=ON \ -DGTSAM_ENABLE_GEOGRAPHICLIB=ON \ -DGTSAM_SUPPORT_NESTED_DISSECTION=ON \ + -DGTSAM_WITH_EIGEN_MKL=ON \ + -DGTSAM_WITH_EIGEN_MKL_OPENMP=ON \ -DCTEST_EXTRA_ARGS='${{ matrix.ctest_extra_flags }}' \ -DPYTEST_EXTRA_ARGS='${{ matrix.pytest_extra_flags }}' diff --git a/cmake/FindMKL.cmake b/cmake/FindMKL.cmake index 15a15f3ed1..f791dd2f75 100644 --- a/cmake/FindMKL.cmake +++ b/cmake/FindMKL.cmake @@ -21,6 +21,18 @@ # OPEN - Open MPI library # SGI - SGI MPT Library +# vcpkg +if(DEFINED VCPKG_INSTALLED_DIR) + find_package(MKL CONFIG) + if(MKL_FOUND) + add_library(mkl-gtsam-if INTERFACE) + target_link_libraries(mkl-gtsam-if INTERFACE MKL::MKL) + set(MKL_LIBRARIES mkl-gtsam-if) + list(APPEND GTSAM_EXPORTED_TARGETS mkl-gtsam-if) + install(TARGETS mkl-gtsam-if EXPORT GTSAM-exports ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}) + endif() +else() + # linux IF(UNIX AND NOT APPLE) IF(${CMAKE_HOST_SYSTEM_PROCESSOR} STREQUAL "x86_64") @@ -267,4 +279,6 @@ find_package_handle_standard_args(MKL DEFAULT_MSG MKL_INCLUDE_DIR MKL_LIBRARIES) # LINK_DIRECTORIES(${MKL_ROOT_DIR}/lib/${MKL_ARCH_DIR}) # hack #endif() +endif() # end of vcpkg + MARK_AS_ADVANCED(MKL_INCLUDE_DIR MKL_LIBRARIES) diff --git a/cmake/HandleMKL.cmake b/cmake/HandleMKL.cmake index 5d7ec365b2..07ac53e4ff 100644 --- a/cmake/HandleMKL.cmake +++ b/cmake/HandleMKL.cmake @@ -15,3 +15,28 @@ else() set(GTSAM_USE_EIGEN_MKL 0) set(EIGEN_USE_MKL_ALL 0) endif() + +if(WIN32 AND GTSAM_USE_EIGEN_MKL AND DEFINED VCPKG_INSTALLED_DIR) + get_target_property(MKL_TARGETS "MKL::MKL" INTERFACE_LINK_LIBRARIES) + set(RUNTIME_DLL_DIRS "") + foreach(MKL_TARGET ${MKL_TARGETS}) + if(TARGET ${MKL_TARGET}) + get_target_property(MKL_DLL "${MKL_TARGET}" IMPORTED_LOCATION) + if(MKL_DLL) + cmake_path(GET MKL_DLL PARENT_PATH MKL_DLL_DIR) + list (APPEND RUNTIME_DLL_DIRS "${MKL_DLL_DIR}/mkl_*.dll") + endif() + endif() + endforeach() + list(REMOVE_DUPLICATES RUNTIME_DLL_DIRS) + file(GLOB MKL_DLLS CONFIGURE_DEPENDS ${RUNTIME_DLL_DIRS}) + list(REMOVE_DUPLICATES MKL_DLLS) + add_custom_target(copy_mkl_dlls + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${MKL_DLLS}" + "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}" + COMMAND_EXPAND_LISTS + VERBATIM + ) + add_dependencies(check copy_mkl_dlls) +endif() diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp index 98df17960f..6cb5223052 100644 --- a/gtsam/linear/HessianFactor.cpp +++ b/gtsam/linear/HessianFactor.cpp @@ -339,8 +339,8 @@ double HessianFactor::error(const VectorValues& c) const { // NOTE may not be as efficient const Vector x = c.vector(keys()); const double xtg = x.dot(linearTerm().col(0)); - auto AtA = informationView(); - const double xGx = x.transpose() * AtA * x; + const auto AtA = informationView(); + const double xGx = x.dot(AtA * x); return 0.5 * (f - 2.0 * xtg + xGx); } diff --git a/gtsam/linear/tests/testHessianFactor.cpp b/gtsam/linear/tests/testHessianFactor.cpp index 77afcadcf5..0773acb61a 100644 --- a/gtsam/linear/tests/testHessianFactor.cpp +++ b/gtsam/linear/tests/testHessianFactor.cpp @@ -108,7 +108,8 @@ TEST(HessianFactor, Constructor1) // error 0.5*(f - 2*x'*g + x'*G*x) double expected = 80.375; double actual = factor.error(dx); - double expected_manual = 0.5 * (f - 2.0 * dx[0].dot(g) + dx[0].transpose() * G.selfadjointView() * dx[0]); + const double xGx = dx[0].dot(G * dx[0]); + double expected_manual = 0.5 * (f - 2.0 * dx[0].dot(g) + xGx); EXPECT_DOUBLES_EQUAL(expected, expected_manual, 1e-10); EXPECT_DOUBLES_EQUAL(expected, actual, 1e-10); } diff --git a/gtsam/nonlinear/tests/testLinearContainerFactor.cpp b/gtsam/nonlinear/tests/testLinearContainerFactor.cpp index 77d40758be..db1f6d0582 100644 --- a/gtsam/nonlinear/tests/testLinearContainerFactor.cpp +++ b/gtsam/nonlinear/tests/testLinearContainerFactor.cpp @@ -219,7 +219,8 @@ TEST(TestLinearContainerFactor, hessian_factor_withlinpoints) { // Check linearization with corrections for updated linearization point Vector g1_prime = g_prime.head(3); Vector g2_prime = g_prime.tail(2); - double f_prime = f + dv.transpose() * G.selfadjointView() * dv - 2.0 * dv.transpose() * g; + const auto Gsym = G.selfadjointView(); + double f_prime = f + dv.dot(Gsym * dv) - 2.0 * dv.dot(g); HessianFactor expNewFactor(x1, l1, G11, G12, g1_prime, G22, g2_prime, f_prime); EXPECT(assert_equal(*expNewFactor.clone(), *actFactor.linearize(noisyValues), tol)); } diff --git a/gtsam_unstable/nonlinear/LinearizedFactor.cpp b/gtsam_unstable/nonlinear/LinearizedFactor.cpp index 375c49341a..9f24f84274 100644 --- a/gtsam_unstable/nonlinear/LinearizedFactor.cpp +++ b/gtsam_unstable/nonlinear/LinearizedFactor.cpp @@ -194,7 +194,7 @@ double LinearizedHessianFactor::error(const Values& c) const { // error 0.5*(f - 2*x'*g + x'*G*x) double f = constantTerm(); double xtg = dx.dot(linearTerm()); - double xGx = dx.transpose() * squaredTerm() * dx; + double xGx = dx.dot(squaredTerm() * dx); return 0.5 * (f - 2.0 * xtg + xGx); } @@ -216,7 +216,7 @@ LinearizedHessianFactor::linearize(const Values& c) const { // f2 = f1 - 2*dx'*g1 + dx'*G1*dx //newInfo(this->size(), this->size())(0,0) += -2*dx.dot(linearTerm()) + dx.transpose() * squaredTerm().selfadjointView() * dx; - double f = constantTerm() - 2*dx.dot(linearTerm()) + dx.transpose() * squaredTerm() * dx; + double f = constantTerm() - 2*dx.dot(linearTerm()) + dx.dot(squaredTerm() * dx); // g2 = g1 - G1*dx //newInfo.rangeColumn(0, this->size(), this->size(), 0) -= squaredTerm().selfadjointView() * dx; diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 857062f885..22353a952a 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -149,6 +149,15 @@ if(WIN32) COMMAND_EXPAND_LISTS VERBATIM ) + if(GTSAM_USE_EIGEN_MKL AND DEFINED VCPKG_INSTALLED_DIR) + ADD_CUSTOM_COMMAND(TARGET ${GTSAM_PYTHON_TARGET} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${MKL_DLLS}" + "${GTSAM_PYTHON_BUILD_DIRECTORY}/gtsam/" + COMMAND_EXPAND_LISTS + VERBATIM + ) + endif() endif() # Set the path for the GTSAM python module @@ -263,6 +272,15 @@ if(GTSAM_UNSTABLE_BUILD_PYTHON) COMMAND_EXPAND_LISTS VERBATIM ) + if(GTSAM_USE_EIGEN_MKL AND DEFINED VCPKG_INSTALLED_DIR) + ADD_CUSTOM_COMMAND(TARGET ${GTSAM_PYTHON_UNSTABLE_TARGET} POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${MKL_DLLS}" + "${GTSAM_PYTHON_BUILD_DIRECTORY}/gtsam_unstable/" + COMMAND_EXPAND_LISTS + VERBATIM + ) + endif() endif() add_custom_target( From 0d996d8f1b603ac0dacba0b4f4f991ac03442754 Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Tue, 20 Jan 2026 11:07:51 -0500 Subject: [PATCH 124/266] Move back to multifrontal cholesky --- gtsam/nonlinear/NonlinearOptimizerParams.h | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/gtsam/nonlinear/NonlinearOptimizerParams.h b/gtsam/nonlinear/NonlinearOptimizerParams.h index 4cc1a3e1cd..4c6987b94d 100644 --- a/gtsam/nonlinear/NonlinearOptimizerParams.h +++ b/gtsam/nonlinear/NonlinearOptimizerParams.h @@ -112,11 +112,8 @@ class GTSAM_EXPORT NonlinearOptimizerParams { /// Parameters for `gtsam::MultifrontalSolver` when using `MULTIFRONTAL_SOLVER`. /// The type of linear solver to use in the nonlinear optimizer - #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V43 LinearSolverType linearSolverType = MULTIFRONTAL_CHOLESKY; -#else - LinearSolverType linearSolverType = MULTIFRONTAL_SOLVER; -#endif + MultifrontalParameters multifrontalParams; NonlinearOptimizerParams() = default; From 2fbdf36b6bdddff260df6d34921b47a7b33f4cad Mon Sep 17 00:00:00 2001 From: Tal Regev Date: Wed, 21 Jan 2026 11:20:28 +0200 Subject: [PATCH 125/266] sccache for all os in vcpkg --- .github/workflows/vcpkg.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 262100e4d7..26edce1136 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -59,11 +59,9 @@ jobs: toolset: 14.40 - name: Install sccache - if: runner.os == 'Windows' uses: mozilla-actions/sccache-action@v0.0.9 - name: Use sccache - if: runner.os == 'Windows' shell: bash run: | echo "SCCACHE_GHA_ENABLED=ON" >> "$GITHUB_ENV" From 8cad6177c0400ca81276628be478364dc60b30e1 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Thu, 22 Jan 2026 00:46:19 -0500 Subject: [PATCH 126/266] expose unit3/rot3 symmetry, abc python bindings for EqF --- gtsam/navigation/EquivariantFilter.h | 28 ++ gtsam/navigation/navigation.i | 45 ++++ gtsam_unstable/geometry/ABCEqFWrapper.h | 71 +++++ .../geometry/EquivariantFilterAttitude.h | 40 +++ .../examples/EquivariantFilterAttitude.py | 250 ++++++++++++++++++ .../EquivariantFilterUnit3Symmetry.py | 59 +++++ 6 files changed, 493 insertions(+) create mode 100644 gtsam_unstable/geometry/ABCEqFWrapper.h create mode 100644 gtsam_unstable/geometry/EquivariantFilterAttitude.h create mode 100644 python/gtsam/examples/EquivariantFilterAttitude.py create mode 100644 python/gtsam/examples/EquivariantFilterUnit3Symmetry.py diff --git a/gtsam/navigation/EquivariantFilter.h b/gtsam/navigation/EquivariantFilter.h index 501ac9f2d3..c69f5b3092 100644 --- a/gtsam/navigation/EquivariantFilter.h +++ b/gtsam/navigation/EquivariantFilter.h @@ -222,6 +222,34 @@ class EquivariantFilter : public ManifoldEKF { Base::predict(xi_next, Phi, Q_manifold); } + /** + * @brief Propagate the filter state with explicit Jacobian and lift vector. + * + * This overload is wrapper-friendly: takes a lifted tangent vector + * directly, avoiding templated Lift functors. It uses first-order Euler + * discretization (K = 1). + * + * @param Lambda Lifted tangent vector in the group tangent space. + * @param A Error dynamics matrix (DimM x DimM). + * @param Qc Process noise covariance on the manifold (continuous-time). + * @param dt Time step. + */ + void predictWithJacobianEuler(const Vector& Lambda, const MatrixM& A, + const MatrixM& Qc, double dt) { + TangentG lambda_g; + if constexpr (TangentG::RowsAtCompileTime == Eigen::Dynamic) { + lambda_g.resize(DimG); + } + lambda_g = Lambda; + + g_ = traits::Compose(g_, traits::Expmap(lambda_g * dt)); + M xi_next = act_on_ref_(g_); + + MatrixM Phi = transitionMatrix<1>(A, dt); + CovarianceM Q_manifold = Qc * dt; + Base::predict(xi_next, Phi, Q_manifold); + } + /** * Measurement update: Corrects the state and covariance using a * pre-calculated predicted measurement and its Jacobian. diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 5c26698e1f..8d52265f21 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -651,6 +651,51 @@ virtual class InvariantEKF : gtsam::LeftLinearEKF { void predict(const gtsam::Vector& u, double dt, gtsam::Matrix Q); }; +// --------------------------------------------------------------------------- +// Equivariant Filter (attitude example wrapper) +#include +#include +template +virtual class EquivariantFilter : gtsam::ManifoldEKF { + // Constructors + EquivariantFilter(const M& xi_ref, gtsam::Matrix Sigma); + EquivariantFilter(const M& xi_ref, gtsam::Matrix Sigma, + const Symmetry::Group& X0); + + // Accessors + gtsam::Matrix errorCovariance() const; + gtsam::Matrix covariance() const; + Symmetry::Group groupEstimate() const; + + // Wrapper-friendly predict + void predictWithJacobianEuler(const gtsam::Vector& Lambda, gtsam::Matrix A, + gtsam::Matrix Qc, double dt); + + // Only vector-based measurements are supported in wrapper + void updateWithVector(const gtsam::Vector& prediction, const gtsam::Matrix& H, + const gtsam::Vector& z, const gtsam::Matrix& R); +}; + +// --------------------------------------------------------------------------- +// ABC Equivariant Filter wrapper (N=1) +#include +namespace abc { +class AbcEquivariantFilter1 { + AbcEquivariantFilter1(); + AbcEquivariantFilter1(const gtsam::Matrix& Sigma0); + + void predict(const gtsam::Vector& omega, const gtsam::Matrix& inputCovariance, + double dt); + void update(const gtsam::Unit3& y, const gtsam::Unit3& d, + const gtsam::Matrix& R, int cal_idx); + + gtsam::Rot3 attitude() const; + gtsam::Vector bias() const; + gtsam::Rot3 calibration(size_t i) const; + gtsam::Matrix errorCovariance() const; +}; +} // namespace abc + // Specialized NavState IMU EKF #include class NavStateImuEKF : gtsam::LeftLinearEKF { diff --git a/gtsam_unstable/geometry/ABCEqFWrapper.h b/gtsam_unstable/geometry/ABCEqFWrapper.h new file mode 100644 index 0000000000..4fdc6ae177 --- /dev/null +++ b/gtsam_unstable/geometry/ABCEqFWrapper.h @@ -0,0 +1,71 @@ +/** + * @file ABCEqFWrapper.h + * @brief Wrapper-friendly ABC EqF helper class for Python bindings. + * + * This class mirrors the logic in examples/AbcEquivariantFilterExample.cpp + * but exposes a simple, non-templated API for wrapping. + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +namespace gtsam { +namespace abc { + +class AbcEquivariantFilter1 { + public: + static constexpr size_t N = 1; + using State1 = State; + using Group1 = Group; + using Symmetry1 = Symmetry; + using EqFilter1 = gtsam::EquivariantFilter; + + AbcEquivariantFilter1() : AbcEquivariantFilter1(Matrix::Identity(6 + 3 * N, 6 + 3 * N)) {} + + explicit AbcEquivariantFilter1(const Matrix& Sigma0) + : filter_(State1::identity(), Sigma0) {} + + /// Propagate filter using the explicit matrices API from ABC. + void predict(const Vector3& omega, const Matrix6& inputCovariance, double dt) { + const Matrix Q = inputProcessNoise(inputCovariance); + const Vector6 u = toInputVector(omega); + const Lift lift_u(u); + const typename InputAction::Orbit psi_u(u); + + const Group1 X_hat = filter_.groupEstimate(); + const Matrix A = stateMatrixA(psi_u, X_hat); + const Matrix B = inputMatrixB(X_hat); + const Matrix Qc = B * Q * B.transpose(); + + filter_.predictWithJacobian<2>(lift_u, A, Qc, dt); + } + + /// Update filter with a direction measurement. + void update(const Unit3& y, const Unit3& d, const Matrix3& R, int cal_idx) { + const Innovation innovation(y, d, cal_idx); + const Group1 X_hat = filter_.groupEstimate(); + const Matrix3 D = outputMatrixD(X_hat, cal_idx); + const Matrix3 R_adjusted = D * R * D.transpose(); + filter_.update(innovation, Z_3x1, R_adjusted); + } + + /// Accessors for current estimate. + Rot3 attitude() const { return filter_.state().R; } + Vector3 bias() const { return filter_.state().b; } + Rot3 calibration(size_t i) const { return filter_.state().S[i]; } + Matrix errorCovariance() const { return filter_.errorCovariance(); } + + private: + EqFilter1 filter_; +}; + +} // namespace abc +} // namespace gtsam + + diff --git a/gtsam_unstable/geometry/EquivariantFilterAttitude.h b/gtsam_unstable/geometry/EquivariantFilterAttitude.h new file mode 100644 index 0000000000..f4b1dbc572 --- /dev/null +++ b/gtsam_unstable/geometry/EquivariantFilterAttitude.h @@ -0,0 +1,40 @@ +/** + * @file EquivariantFilterAttitude.h + * @brief Attitude-only EqF helper types for Python bindings. + * + * Provides the symmetry action used by the Unit3/Rot3 attitude example in + * tests/testEquivariantFilter.cpp so it can be instantiated in wrappers. + */ + +#pragma once + +#include +#include +#include +#include +#include + +namespace gtsam { +namespace attitude_example { + +using M = Unit3; +using G = Rot3; + +/** + * Symmetry: right group action on Unit3. + * φ_η(Q) = Q^T η. + */ +struct Symmetry : public GroupAction { + static constexpr ActionType type = ActionType::Right; + + M operator()(const M& eta, const G& Q, + OptionalJacobian<2, 2> H_eta = {}, + OptionalJacobian<2, 3> H_Q = {}) const { + return Q.unrotate(eta, H_Q, H_eta); + } +}; + +} // namespace attitude_example +} // namespace gtsam + + diff --git a/python/gtsam/examples/EquivariantFilterAttitude.py b/python/gtsam/examples/EquivariantFilterAttitude.py new file mode 100644 index 0000000000..4e1879900d --- /dev/null +++ b/python/gtsam/examples/EquivariantFilterAttitude.py @@ -0,0 +1,250 @@ +""" +Python translation of examples/AbcEquivariantFilterExample.cpp. + +Runs the Attitude-Bias-Calibration EqF demo using the wrapped C++ +EquivariantFilter (ABC-specific wrapper). +""" + +from __future__ import annotations + +from dataclasses import dataclass +import csv +import math +from typing import List + +import numpy as np +import gtsam +from gtsam import Rot3, Unit3 +from gtsam.utils import findExampleDataFile + + +@dataclass +class MeasurementRecord: + y: np.ndarray + d: np.ndarray + R: np.ndarray + cal_idx: int + + +@dataclass +class DataRecord: + R: Rot3 + b: np.ndarray + cal_rot: Rot3 + omega: np.ndarray + input_covariance: np.ndarray + measurements: List[MeasurementRecord] + t: float + dt: float + + +def _normalize(v: np.ndarray) -> np.ndarray: + n = np.linalg.norm(v) + if n == 0.0: + return v + return v / n + + +def load_data_from_csv( + filename: str, start_row: int = 0, max_rows: int = -1, downsample: int = 1 +) -> List[DataRecord]: + data_list: List[DataRecord] = [] + with open(filename, newline="") as csvfile: + reader = csv.reader(csvfile) + header = next(reader, None) + if header is None: + return data_list + + line_number = 1 + row_count = 0 + prev_time = 0.0 + + for row in reader: + line_number += 1 + if line_number < start_row: + continue + if ((line_number - start_row - 1) % downsample) != 0: + continue + if max_rows != -1 and row_count >= max_rows: + break + if len(row) < 39: + continue + + values = [float(x) if x else 0.0 for x in row] + + t = values[0] + dt = 0.0 if row_count == 0 else t - prev_time + prev_time = t + + R = Rot3.Quaternion(values[1], values[2], values[3], values[4]) + b = np.array([values[5], values[6], values[7]]) + + cal_rot = Rot3.Quaternion(values[8], values[9], values[10], values[11]) + + omega = np.array([values[12], values[13], values[14]]) + + input_covariance = np.zeros((6, 6)) + input_covariance[0, 0] = values[15] ** 2 + input_covariance[1, 1] = values[16] ** 2 + input_covariance[2, 2] = values[17] ** 2 + input_covariance[3, 3] = values[18] ** 2 + input_covariance[4, 4] = values[19] ** 2 + input_covariance[5, 5] = values[20] ** 2 + + measurements: List[MeasurementRecord] = [] + + y0 = _normalize(np.array([values[21], values[22], values[23]])) + d0 = _normalize(np.array([values[33], values[34], values[35]])) + cov_y0 = np.diag([values[27] ** 2, values[28] ** 2, values[29] ** 2]) + measurements.append(MeasurementRecord(y0, d0, cov_y0, 0)) + + y1 = _normalize(np.array([values[24], values[25], values[26]])) + d1 = _normalize(np.array([values[36], values[37], values[38]])) + cov_y1 = np.diag([values[30] ** 2, values[31] ** 2, values[32] ** 2]) + measurements.append(MeasurementRecord(y1, d1, cov_y1, -1)) + + data_list.append( + DataRecord( + R=R, + b=b, + cal_rot=cal_rot, + omega=omega, + input_covariance=input_covariance, + measurements=measurements, + t=t, + dt=dt, + ) + ) + row_count += 1 + + return data_list + + +def process_data_with_eqf( + filter_eqf: gtsam.abc.AbcEquivariantFilter1, + data_list: List[DataRecord], +) -> None: + if not data_list: + print("No data to process") + return + + print(f"Processing {len(data_list)} data points with EqF...") + att_errors: List[float] = [] + bias_errors: List[float] = [] + cal_errors: List[float] = [] + + total_measurements = 0 + valid_measurements = 0 + + rad_to_deg = 180.0 / math.pi + progress_step = max(len(data_list) // 10, 1) + print("Progress: ", end="", flush=True) + + for i, data in enumerate(data_list): + filter_eqf.predict(data.omega, data.input_covariance, data.dt) + + for measurement in data.measurements: + total_measurements += 1 + if np.any(np.isnan(measurement.y)) or np.any(np.isnan(measurement.d)): + continue + try: + y_unit = Unit3(measurement.y) + d_unit = Unit3(measurement.d) + filter_eqf.update(y_unit, d_unit, measurement.R, measurement.cal_idx) + valid_measurements += 1 + except Exception: + continue + + estimate_R = filter_eqf.attitude() + estimate_b = np.array(filter_eqf.bias()).reshape(3) + estimate_cal = filter_eqf.calibration(0) + + att_error = Rot3.Logmap(data.R.between(estimate_R)) + bias_error = estimate_b - data.b + cal_error = np.zeros(3) + cal_error = Rot3.Logmap(data.cal_rot.between(estimate_cal)) + + att_errors.append(np.linalg.norm(att_error)) + bias_errors.append(np.linalg.norm(bias_error)) + cal_errors.append(np.linalg.norm(cal_error)) + + if i % progress_step == 0: + print(".", end="", flush=True) + + print(" Done!") + + avg_att_error = float(np.mean(att_errors)) if att_errors else 0.0 + avg_bias_error = float(np.mean(bias_errors)) if bias_errors else 0.0 + avg_cal_error = float(np.mean(cal_errors)) if cal_errors else 0.0 + + final_data = data_list[-1] + final_R = filter_eqf.attitude() + final_b = np.array(filter_eqf.bias()).reshape(3) + final_cal = filter_eqf.calibration(0) + final_att_error = Rot3.Logmap(final_data.R.between(final_R)) + final_bias_error = final_b - final_data.b + final_cal_error = Rot3.Logmap(final_data.cal_rot.between(final_cal)) + + print("\n=== Filter Performance Summary ===") + print(f"Processed measurements: {total_measurements} (valid: {valid_measurements})") + + print("\n-- Average Errors --") + print(f"Attitude: {avg_att_error * rad_to_deg}°") + print(f"Bias: {avg_bias_error}") + print(f"Calibration: {avg_cal_error * rad_to_deg}°") + + print("\n-- Final Errors --") + print(f"Attitude: {np.linalg.norm(final_att_error) * rad_to_deg}°") + print(f"Bias: {np.linalg.norm(final_bias_error)}") + print(f"Calibration: {np.linalg.norm(final_cal_error) * rad_to_deg}°") + + print("\n-- Final State vs Ground Truth --") + print( + "Attitude (RPY) - Estimate:", + (final_R.rpy() * rad_to_deg), + "° | Truth:", + (final_data.R.rpy() * rad_to_deg), + "°", + ) + print("Bias - Estimate:", final_b, "| Truth:", final_data.b) + print( + "Calibration (RPY) - Estimate:", + (final_cal.rpy() * rad_to_deg), + "° | Truth:", + (final_data.cal_rot.rpy() * rad_to_deg), + "°", + ) + + +def main() -> None: + print("ABC-EqF: Attitude-Bias-Calibration Equivariant Filter Demo") + print("==============================================================") + + try: + csv_file_path = findExampleDataFile("EqFdata.csv") + except Exception: + print("Error: Could not find EqFdata.csv") + return + + data = load_data_from_csv(csv_file_path) + if not data: + print("No data available to process. Exiting.") + return + + n_cal = 1 + m_sensors = 2 + initial_sigma = np.eye(6 + 3 * n_cal) + initial_sigma[0:3, 0:3] = 0.1 * np.eye(3) + initial_sigma[3:6, 3:6] = 0.01 * np.eye(3) + initial_sigma[6:9, 6:9] = 0.1 * np.eye(3) + + filter_eqf = gtsam.abc.AbcEquivariantFilter1(initial_sigma) + process_data_with_eqf(filter_eqf, data) + + print("\nEqF demonstration completed successfully.") + + +if __name__ == "__main__": + main() + + diff --git a/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py b/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py new file mode 100644 index 0000000000..da6fe707fc --- /dev/null +++ b/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py @@ -0,0 +1,59 @@ +""" +Minimal example for gtsam.EquivariantFilterUnit3Symmetry (Unit3/Rot3 EqF). + +This mirrors the attitude-only EquivariantFilter test setup. +""" + +import numpy as np +import gtsam +from gtsam import Rot3, Unit3 + + +def main() -> None: + # Reference direction and initial covariance on S^2 + eta_ref = Unit3(0.0, 0.0, 1.0) + sigma0 = 0.01 * np.eye(2) + + filter_eqf = gtsam.EquivariantFilterUnit3Symmetry(eta_ref, sigma0) + + # explicit lift vector (omega) and manifold Qc + omega = np.array([0.1, -0.2, 0.3]) + A = np.zeros((2, 2)) + sigma_u = 0.1 * np.eye(3) + B = np.eye(2, 3) + Qc = B @ sigma_u @ B.T + dt = 0.01 + + filter_eqf.predictWithJacobianEuler(omega, A, Qc, dt) + + expected_Q = Rot3.Expmap(omega * dt) + assert filter_eqf.groupEstimate().equals(expected_Q, 1e-9) + + expected_state = Unit3(expected_Q.unrotate(eta_ref.point3())) + assert filter_eqf.state().equals(expected_state, 1e-9) + + print("After predict:") + print(" Truth (eta_ref):", eta_ref.unitVector()) + print(" Estimated state:", filter_eqf.state().unitVector()) + + # update by a simple linear scaling of the direction + c_m = 1.2 + eta_hat = filter_eqf.state() + H = c_m * eta_hat.basis() # 3x2 + prediction = c_m * eta_hat.point3() + z = c_m * eta_ref.point3() + R = 0.01 * np.eye(3) + + filter_eqf.updateWithVector(prediction, H, z, R) + + assert filter_eqf.errorCovariance().shape == (2, 2) + print("After update:") + print(" Truth (eta_ref):", eta_ref.unitVector()) + print(" Estimated state:", filter_eqf.state().unitVector()) + print("EquivariantFilterUnit3Symmetry example ran successfully.") + + +if __name__ == "__main__": + main() + + From 6b5de2032c8e47185f24bff47642a1d06060153e Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Sat, 24 Jan 2026 00:37:24 -0500 Subject: [PATCH 127/266] Run Windows CI on push again for caching --- .github/workflows/build-windows.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index ea9f86502c..0a0016eb26 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -6,6 +6,9 @@ on: - '**.md' - '**.ipynb' - 'myst.yml' + push: + # Run on pushes to develop branch to cache compilations + branches: [develop] # Cancels any in-progress workflow runs for the same PR when a new push is made, # allowing the runner to become available more quickly for the latest changes. From 7e5dfded785534ed9a3d4135a5088f14bf36aac6 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 12 Jan 2026 10:23:31 -0500 Subject: [PATCH 128/266] Added reset, and update now does this by default --- .gitignore | 1 + gtsam/geometry/tests/testPose2.cpp | 15 +- gtsam/navigation/ManifoldEKF.h | 76 +++++-- gtsam/navigation/tests/testManifoldEKF.cpp | 252 ++++++++++++++------- 4 files changed, 249 insertions(+), 95 deletions(-) diff --git a/.gitignore b/.gitignore index b80906ca01..ab4006efbe 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,4 @@ doc/#*.lyx# .venv/ conductor/ .ccache/ +gtsam_unstable/timing/data/ diff --git a/gtsam/geometry/tests/testPose2.cpp b/gtsam/geometry/tests/testPose2.cpp index 55c3092a14..00a0ef78ee 100644 --- a/gtsam/geometry/tests/testPose2.cpp +++ b/gtsam/geometry/tests/testPose2.cpp @@ -75,6 +75,20 @@ TEST(Pose2, retract) { EXPECT(assert_equal(expected, actual, 1e-5)); } +/* ************************************************************************* */ +TEST(Pose2, retractJacobian) { + Pose2 pose(M_PI / 2.0, Point2(1, 2)); + Vector3 v(0.01, -0.015, 0.99); + + Matrix3 actualH; + traits::Retract(pose, v, {}, &actualH); + + auto retract_from_pose = [&](const Vector3& delta) { return pose.retract(delta); }; + Matrix3 expectedH = numericalDerivative11(retract_from_pose, v, 1e-6); + + EXPECT(assert_equal(expectedH, actualH, 1e-5)); +} + /* ************************************************************************* */ TEST(Pose2, expmap) { Pose2 pose(M_PI/2.0, Point2(1, 2)); @@ -997,4 +1011,3 @@ int main() { return TestRegistry::runAllTests(tr); } /* ************************************************************************* */ - diff --git a/gtsam/navigation/ManifoldEKF.h b/gtsam/navigation/ManifoldEKF.h index 18c8d158f5..b809622b94 100644 --- a/gtsam/navigation/ManifoldEKF.h +++ b/gtsam/navigation/ManifoldEKF.h @@ -135,7 +135,8 @@ class ManifoldEKF { return P_ * H.transpose() * S.inverse(); } - /// Joseph-form covariance update using a precomputed gain. + /// Joseph-form covariance update in the current tangent space using a + /// precomputed gain. template void JosephUpdate(const GainMatrix& K, const HMatrix& H, const RMatrix& R) { Jacobian I_KH = I_ - K * H; @@ -151,6 +152,8 @@ class ManifoldEKF { * @param H Jacobian of the measurement function h. * @param z Observed measurement. * @param R Measurement noise covariance. + * @param performReset If true (default), performs a reset (transport) after + * update; otherwise, just retracts the state. */ template void update( @@ -158,7 +161,8 @@ class ManifoldEKF { const Eigen::Matrix::dimension, Dim>& H, const Measurement& z, const Eigen::Matrix::dimension, - traits::dimension>& R) { + traits::dimension>& R, + bool performReset = true) { static constexpr int MeasDim = traits::dimension; // Innovation: y = h(x_pred) - z. In tangent space: local(z, h(x_pred)) @@ -174,11 +178,14 @@ class ManifoldEKF { const TangentVector delta_xi = -K * innovation; // delta_xi is Dim x 1 (or n_ x 1 if dynamic) - // Update state using retract: X_new = retract(X_old, delta_xi) - X_ = traits::Retract(X_, delta_xi); - - // Update covariance using Joseph form + // --- Update covariance in the tangent space at the current state this->JosephUpdate(K, H, R); + + // Update state using retract/ transport or just retract + if (performReset) + reset(delta_xi); + else + X_ = traits::Retract(X_, delta_xi); } /** @@ -190,11 +197,13 @@ class ManifoldEKF { * @param h Measurement model function. * @param z Observed measurement. * @param R Measurement noise covariance. + * @param performReset If true (default), transport covariance after retract. */ template void update(MeasurementFunction&& h, const Measurement& z, const Eigen::Matrix::dimension, - traits::dimension>& R) { + traits::dimension>& R, + bool performReset = true) { static_assert(IsManifold::value, "Template parameter Measurement must be a GTSAM Manifold."); @@ -203,17 +212,45 @@ class ManifoldEKF { Measurement prediction = h(X_, H); // Call the other update function - update(prediction, H, z, R); + update(prediction, H, z, R, performReset); } - /// Convenience bridge for wrappers: vector measurement update calling - /// update. This overload exists to avoid templates in wrappers. It - /// validates sizes and forwards to the templated update with Measurement = - /// gtsam::Vector (dynamic size). + /** + * Convenience bridge for wrappers: vector measurement update calling + * update. This overload exists to avoid templates in wrappers. It + * validates sizes and forwards to the templated update with Measurement = + * gtsam::Vector (dynamic size). + * @param prediction Predicted measurement vector. + * @param H Measurement Jacobian matrix. + * @param z Observed measurement vector. + * @param R Measurement noise covariance matrix. + * @param performReset If true (default), transport covariance after retract. + */ void updateWithVector(const gtsam::Vector& prediction, const Matrix& H, - const gtsam::Vector& z, const Matrix& R) { + const gtsam::Vector& z, const Matrix& R, + bool performReset = true) { validateInputs(prediction, H, z, R); - update(prediction, H, z, R); + update(prediction, H, z, R, performReset); + } + + /** + * Reset step: retract the state by a tangent perturbation and, if available, + * transport the covariance from the old tangent space to the new tangent + * space. + * + * If the retract supports a Jacobian argument, we compute B and update + * P <- B P B^T. Otherwise, we leave the covariance unchanged. + */ + void reset(const TangentVector& eta) { + if constexpr (HasRetractJacobian::value) { + Jacobian B; + if constexpr (Dim == Eigen::Dynamic) B.resize(n_, n_); + X_ = traits::Retract(X_, eta, &B); + P_ = B * P_ * B.transpose(); + } else { + X_ = traits::Retract(X_, eta); + // Covariance unchanged when Jacobian is not available. + } } protected: @@ -243,6 +280,17 @@ class ManifoldEKF { Covariance P_; ///< Covariance (Eigen::Matrix). Jacobian I_; ///< Identity matrix sized to the state dimension. int n_; ///< Runtime tangent space dimension of M. + + private: + // Detection helper: check if traits::Retract(x, v, Jacobian*) is valid. + template + struct HasRetractJacobian : std::false_type {}; + template + struct HasRetractJacobian< + T, std::void_t::Retract( + std::declval(), + std::declval::TangentVector&>(), + (Jacobian*)nullptr))>> : std::true_type {}; }; } // namespace gtsam diff --git a/gtsam/navigation/tests/testManifoldEKF.cpp b/gtsam/navigation/tests/testManifoldEKF.cpp index 1e93b228fc..da6d4f6018 100644 --- a/gtsam/navigation/tests/testManifoldEKF.cpp +++ b/gtsam/navigation/tests/testManifoldEKF.cpp @@ -7,46 +7,47 @@ * See LICENSE for the license information * -------------------------------------------------------------------------- */ - /** - * @file testManifoldEKF.cpp - * @brief Unit test for the ManifoldEKF base class using Unit3. - * @date April 26, 2025 - * @authors Scott Baker, Matt Kielo, Frank Dellaert - */ +/** + * @file testManifoldEKF.cpp + * @brief Unit test for the ManifoldEKF base class using Unit3. + * @date April 26, 2025 + * @authors Scott Baker, Matt Kielo, Frank Dellaert + */ +#include #include #include #include +#include #include #include -#include - #include +#include using namespace gtsam; // Define simple dynamics for Unit3: constant velocity in the tangent space namespace exampleUnit3 { - // Predicts the next state given current state, tangent velocity, and dt - Unit3 f(const Unit3& p, const Vector2& v, double dt) { - return p.retract(v * dt); - } +// Predicts the next state given current state, tangent velocity, and dt +Unit3 f(const Unit3& p, const Vector2& v, double dt) { + return p.retract(v * dt); +} - // Define a measurement model: measure the z-component of the Unit3 direction - // H is the Jacobian dh/d(local(p)) - double measureZ(const Unit3& p, OptionalJacobian<1, 2> H) { - if (H) { - // H = d(p.point3().z()) / d(local(p)) - // Calculate numerically for simplicity in test - auto h = [](const Unit3& p_) { return p_.point3().z(); }; - *H = numericalDerivative11(h, p); - } - return p.point3().z(); +// Define a measurement model: measure the z-component of the Unit3 direction +// H is the Jacobian dh/d(local(p)) +double measureZ(const Unit3& p, OptionalJacobian<1, 2> H) { + if (H) { + // H = d(p.point3().z()) / d(local(p)) + // Calculate numerically for simplicity in test + auto h = [](const Unit3& p_) { return p_.point3().z(); }; + *H = numericalDerivative11(h, p); } + return p.point3().z(); +} -} // namespace exampleUnit3 +} // namespace exampleUnit3 // Test fixture for ManifoldEKF with Unit3 struct Unit3EKFTest { @@ -54,20 +55,20 @@ struct Unit3EKFTest { Matrix2 P0; Vector2 velocity; double dt; - Matrix2 Q; // Process noise - Matrix1 R; // Measurement noise - - Unit3EKFTest() : - p0(Unit3(Point3(1, 0, 0))), // Start pointing along X-axis - P0(I_2x2 * 0.01), - velocity((Vector2() << 0.0, M_PI / 4.0).finished()), // Rotate towards +Z axis - dt(0.1), - Q(I_2x2 * 0.001), - R(Matrix1::Identity() * 0.01) { - } + Matrix2 Q; // Process noise + Matrix1 R; // Measurement noise + + Unit3EKFTest() + : p0(Unit3(Point3(1, 0, 0))), // Start pointing along X-axis + P0(I_2x2 * 0.01), + velocity((Vector2() << 0.0, M_PI / 4.0) + .finished()), // Rotate towards +Z axis + dt(0.1), + Q(I_2x2 * 0.001), + R(Matrix1::Identity() * 0.01) {} }; - +//============================================================================== TEST(ManifoldEKF_Unit3, Predict) { Unit3EKFTest data; @@ -83,7 +84,7 @@ TEST(ManifoldEKF_Unit3, Predict) { // GTSAM's numericalDerivative handles derivatives *between* manifolds. auto predict_wrapper = [&](const Unit3& p) -> Unit3 { return exampleUnit3::f(p, data.velocity, data.dt); - }; + }; Matrix2 F = numericalDerivative11(predict_wrapper, data.p0); // --- Perform EKF prediction --- @@ -97,45 +98,51 @@ TEST(ManifoldEKF_Unit3, Predict) { Matrix2 P_expected = F * data.P0 * F.transpose() + data.Q; EXPECT(assert_equal(P_expected, ekf.covariance(), 1e-8)); - // Check F manually for a simple case (e.g., zero velocity should give Identity) + // Check F manually for a simple case (e.g., zero velocity should give + // Identity) Vector2 zero_velocity = Vector2::Zero(); auto predict_wrapper_zero = [&](const Unit3& p) -> Unit3 { return exampleUnit3::f(p, zero_velocity, data.dt); - }; - Matrix2 F_zero = numericalDerivative11(predict_wrapper_zero, data.p0); + }; + Matrix2 F_zero = + numericalDerivative11(predict_wrapper_zero, data.p0); EXPECT(assert_equal(I_2x2, F_zero, 1e-8)); - } +//============================================================================== TEST(ManifoldEKF_Unit3, Update) { Unit3EKFTest data; // Use a slightly different starting point and covariance for variety - Unit3 p_start = Unit3(Point3(0, 1, 0)).retract((Vector2() << 0.1, 0).finished()); // Perturb pointing along Y + Unit3 p_start = + Unit3(Point3(0, 1, 0)) + .retract( + (Vector2() << 0.1, 0).finished()); // Perturb pointing along Y Matrix2 P_start = I_2x2 * 0.05; ManifoldEKF ekf(p_start, P_start); // Simulate a measurement (e.g., true value + noise) double z_true = exampleUnit3::measureZ(p_start, {}); - double z_observed = z_true + 0.02; // Add some noise + double z_observed = z_true + 0.02; // Add some noise // --- Perform EKF update --- ekf.update(exampleUnit3::measureZ, z_observed, data.R); // --- Verification (Manual Kalman Update Steps) --- // 1. Predict measurement and get Jacobian H - Matrix12 H; // Note: Jacobian is 1x2 for Unit3 + Matrix12 H; // Note: Jacobian is 1x2 for Unit3 double z_pred = exampleUnit3::measureZ(p_start, H); // 2. Innovation and Covariance - double y = z_pred - z_observed; // Innovation (using vector subtraction for z) - Matrix1 S = H * P_start * H.transpose() + data.R; // 1x1 matrix + double y = + z_pred - z_observed; // Innovation (using vector subtraction for z) + Matrix1 S = H * P_start * H.transpose() + data.R; // 1x1 matrix // 3. Kalman Gain K - Matrix K = P_start * H.transpose() * S.inverse(); // 2x1 matrix + Matrix K = P_start * H.transpose() * S.inverse(); // 2x1 matrix // 4. State Correction (in tangent space) - Vector2 delta_xi = -K * y; // 2x1 vector + Vector2 delta_xi = -K * y; // 2x1 vector // 5. Expected Updated State and Covariance Unit3 p_updated_expected = p_start.retract(delta_xi); @@ -147,36 +154,109 @@ TEST(ManifoldEKF_Unit3, Update) { EXPECT(assert_equal(P_updated_expected, ekf.covariance(), 1e-8)); } +//============================================================================== +namespace pose2_manifold_ekf_example { +const Pose2 X0(1.0, -2.0, 0.4); +const Matrix3 P0 = + (Matrix3() << 20, 1, -2, 1, 30, 4, -2, 4, 50).finished() * 1e-2; +const Vector3 eta = (Vector3() << 15, -5, 30).finished() * 1e-2; +} // namespace pose2_manifold_ekf_example + +//============================================================================== +TEST(ManifoldEKF_Pose2, ResetUsesRetractJacobian) { + using namespace pose2_manifold_ekf_example; + ManifoldEKF ekf(X0, P0); + + Matrix3 B; + Pose2 expected_state = traits::Retract(X0, eta, &B); + Matrix3 expected_covariance = B * P0 * B.transpose(); + + ekf.reset(eta); + + EXPECT(assert_equal(expected_state, ekf.state(), 1e-9)); + EXPECT(assert_equal(expected_covariance, ekf.covariance(), 1e-9)); + EXPECT((P0 - ekf.covariance()).norm() > 1e-12); +} + +//============================================================================== +TEST(ManifoldEKF_Pose2, ResetCovarianceMonteCarlo) { + Pose2 X0(0.2, -0.4, 0.3); + Matrix3 P0 = (Matrix3() << 20, 3, -1, 3, 25, 2, -1, 2, 15).finished() * 1e-5; + Vector3 eta; + eta << 1e-3, -8e-4, 1.2e-3; + ManifoldEKF ekf(X0, P0); + + Matrix3 B; + Pose2 X1 = traits::Retract(X0, eta, &B); + Matrix3 expected_covariance = B * P0 * B.transpose(); + + const int kNumSamples = 20000; + std::mt19937 rng(42); + std::normal_distribution normal(0.0, 1.0); + Eigen::LLT llt(P0); + Matrix3 L = llt.matrixL(); + + Vector3 mean = Vector3::Zero(); + Matrix3 sample_covariance = Matrix3::Zero(); + for (int i = 0; i < kNumSamples; ++i) { + Vector3 z; + z << normal(rng), normal(rng), normal(rng); + Vector3 delta0 = L * z; + Pose2 X0_sample = X0.retract(delta0); + Pose2 X1_sample = traits::Retract(X0_sample, eta); + Vector3 delta1 = X1.localCoordinates(X1_sample); + mean += delta1; + sample_covariance += delta1 * delta1.transpose(); + } + mean /= static_cast(kNumSamples); + sample_covariance = sample_covariance / static_cast(kNumSamples - 1); + sample_covariance -= (static_cast(kNumSamples) / + static_cast(kNumSamples - 1)) * + (mean * mean.transpose()); + + ekf.reset(eta); + EXPECT(assert_equal(expected_covariance, ekf.covariance(), 1e-9)); + EXPECT(assert_equal(expected_covariance, sample_covariance, 1e-2)); +} + +//============================================================================== // Define simple dynamics and measurement for a 2x2 Matrix state namespace manifold_ekf_example { - // Predicts the next state given current state (Matrix), tangent "velocity" (Vector), and dt. - Matrix f(const Matrix& p, const Vector& vTangent, double dt) { - return traits::Retract(p, vTangent * dt); // + - } +// Predicts the next state given current state (Matrix), tangent "velocity" +// (Vector), and dt. +Matrix f(const Matrix& p, const Vector& vTangent, double dt) { + return traits::Retract(p, vTangent * dt); // + +} - // Define a measurement model: measure the trace of the Matrix (assumed 2x2 here) - double h(const Matrix& p, OptionalJacobian<-1, -1> H = {}) { - // Specialized for a 2x2 matrix! - if (p.rows() != 2 || p.cols() != 2) { - throw std::invalid_argument("Matrix must be 2x2."); - } - if (H) { - H->resize(1, p.size()); - *H << 1.0, 0.0, 0.0, 1.0; // d(trace)/dp00, d(trace)/dp01, d(trace)/dp10, d(trace)/dp11 - } - return p(0, 0) + p(1, 1); // Trace of the matrix +// Define a measurement model: measure the trace of the Matrix (assumed 2x2 +// here) +double h(const Matrix& p, OptionalJacobian<-1, -1> H = {}) { + // Specialized for a 2x2 matrix! + if (p.rows() != 2 || p.cols() != 2) { + throw std::invalid_argument("Matrix must be 2x2."); + } + if (H) { + H->resize(1, p.size()); + *H << 1.0, 0.0, 0.0, + 1.0; // d(trace)/dp00, d(trace)/dp01, d(trace)/dp10, d(trace)/dp11 } + return p(0, 0) + p(1, 1); // Trace of the matrix +} -} // namespace manifold_ekf_example +} // namespace manifold_ekf_example +//============================================================================== TEST(ManifoldEKF_DynamicMatrix, CombinedPredictAndUpdate) { Matrix pInitial = (Matrix(2, 2) << 1.0, 2.0, 3.0, 4.0).finished(); - Matrix pInitialCovariance = I_4x4 * 0.01; // Covariance for 2x2 matrix (4x4) - Vector vTangent = (Vector(4) << 0.5, 0.1, -0.1, -0.5).finished(); // [dp00, dp10, dp01, dp11]/sec + Matrix pInitialCovariance = I_4x4 * 0.01; // Covariance for 2x2 matrix (4x4) + Vector vTangent = (Vector(4) << 0.5, 0.1, -0.1, -0.5) + .finished(); // [dp00, dp10, dp01, dp11]/sec double deltaTime = 0.1; - Matrix processNoiseCovariance = I_4x4 * 0.001; // Process noise covariance (4x4) - Matrix measurementNoiseCovariance = Matrix::Identity(1, 1) * 0.005; // Measurement noise covariance (1x1) + Matrix processNoiseCovariance = + I_4x4 * 0.001; // Process noise covariance (4x4) + Matrix measurementNoiseCovariance = + Matrix::Identity(1, 1) * 0.005; // Measurement noise covariance (1x1) ManifoldEKF ekf(pInitial, pInitialCovariance); // For a 2x2 Matrix, tangent space dimension is 2*2=4. @@ -184,16 +264,19 @@ TEST(ManifoldEKF_DynamicMatrix, CombinedPredictAndUpdate) { EXPECT_LONGS_EQUAL(pInitial.rows() * pInitial.cols(), ekf.state().size()); // Predict Step - Matrix pPredictedMean = manifold_ekf_example::f(pInitial, vTangent, deltaTime); + Matrix pPredictedMean = + manifold_ekf_example::f(pInitial, vTangent, deltaTime); - // For this linear prediction model (pNext = pCurrent + V*dt in tangent space), - // Derivative w.r.t deltaXi is Identity. + // For this linear prediction model (pNext = pCurrent + V*dt in tangent + // space), Derivative w.r.t deltaXi is Identity. Matrix fJacobian = I_4x4; ekf.predict(pPredictedMean, fJacobian, processNoiseCovariance); EXPECT(assert_equal(pPredictedMean, ekf.state(), 1e-9)); - Matrix pPredictedCovarianceExpected = fJacobian * pInitialCovariance * fJacobian.transpose() + processNoiseCovariance; + Matrix pPredictedCovarianceExpected = + fJacobian * pInitialCovariance * fJacobian.transpose() + + processNoiseCovariance; EXPECT(assert_equal(pPredictedCovarianceExpected, ekf.covariance(), 1e-9)); // Update Step @@ -208,29 +291,38 @@ TEST(ManifoldEKF_DynamicMatrix, CombinedPredictAndUpdate) { ekf.update(manifold_ekf_example::h, zObserved, measurementNoiseCovariance); // Manual Kalman Update Steps for Verification - Matrix hJacobian(1, 4); // Measurement Jacobian H (1x4 for 2x2 matrix, trace measurement) - double zPredictionManual = manifold_ekf_example::h(pCurrentForUpdate, hJacobian); + Matrix hJacobian( + 1, 4); // Measurement Jacobian H (1x4 for 2x2 matrix, trace measurement) + double zPredictionManual = + manifold_ekf_example::h(pCurrentForUpdate, hJacobian); Matrix hJacobianExpected = (Matrix(1, 4) << 1.0, 0.0, 0.0, 1.0).finished(); EXPECT(assert_equal(hJacobianExpected, hJacobian, 1e-9)); // Innovation: y = zObserved - zPredictionManual (since measurement is double) double yInnovation = zObserved - zPredictionManual; - Matrix innovationCovariance = hJacobian * pCurrentCovarianceForUpdate * hJacobian.transpose() + measurementNoiseCovariance; + Matrix innovationCovariance = + hJacobian * pCurrentCovarianceForUpdate * hJacobian.transpose() + + measurementNoiseCovariance; - Matrix kalmanGain = pCurrentCovarianceForUpdate * hJacobian.transpose() * innovationCovariance.inverse(); // K is 4x1 + Matrix kalmanGain = pCurrentCovarianceForUpdate * hJacobian.transpose() * + innovationCovariance.inverse(); // K is 4x1 // State Correction (in tangent space of Matrix) - Vector deltaXiTangent = kalmanGain * yInnovation; // deltaXi is 4x1 Vector + Vector deltaXiTangent = kalmanGain * yInnovation; // deltaXi is 4x1 Vector - Matrix pUpdatedManualExpected = traits::Retract(pCurrentForUpdate, deltaXiTangent); - Matrix pUpdatedCovarianceManualExpected = (I_4x4 - kalmanGain * hJacobian) * pCurrentCovarianceForUpdate; + Matrix pUpdatedManualExpected = + traits::Retract(pCurrentForUpdate, deltaXiTangent); + Matrix pUpdatedCovarianceManualExpected = + (I_4x4 - kalmanGain * hJacobian) * pCurrentCovarianceForUpdate; EXPECT(assert_equal(pUpdatedManualExpected, ekf.state(), 1e-9)); - EXPECT(assert_equal(pUpdatedCovarianceManualExpected, ekf.covariance(), 1e-9)); + EXPECT( + assert_equal(pUpdatedCovarianceManualExpected, ekf.covariance(), 1e-9)); } // Test the non-templated wrapper bridge updateWithVector delegates to // update +//============================================================================== TEST(ManifoldEKF_DynamicMatrix, UpdateWithVectorBridge) { // State is a 2x2 Matrix manifold (dim=4) Matrix X0 = (Matrix(2, 2) << 1.0, 2.0, 3.0, 4.0).finished(); @@ -269,4 +361,4 @@ TEST(ManifoldEKF_DynamicMatrix, UpdateWithVectorBridge) { int main() { TestResult tr; return TestRegistry::runAllTests(tr); -} \ No newline at end of file +} From c10fbe34d6121d0dc5543279fb6573c98e29a217 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sun, 25 Jan 2026 17:34:44 -0500 Subject: [PATCH 129/266] condense --- gtsam/navigation/navigation.i | 4 +- gtsam_unstable/geometry/ABC.h | 24 +++++++++++ .../geometry/EquivariantFilterAttitude.h | 40 ------------------- 3 files changed, 26 insertions(+), 42 deletions(-) delete mode 100644 gtsam_unstable/geometry/EquivariantFilterAttitude.h diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 8d52265f21..004063e631 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -654,8 +654,8 @@ virtual class InvariantEKF : gtsam::LeftLinearEKF { // --------------------------------------------------------------------------- // Equivariant Filter (attitude example wrapper) #include -#include -template +#include +template virtual class EquivariantFilter : gtsam::ManifoldEKF { // Constructors EquivariantFilter(const M& xi_ref, gtsam::Matrix Sigma); diff --git a/gtsam_unstable/geometry/ABC.h b/gtsam_unstable/geometry/ABC.h index ca25764a59..7f3883de7e 100644 --- a/gtsam_unstable/geometry/ABC.h +++ b/gtsam_unstable/geometry/ABC.h @@ -240,6 +240,30 @@ struct Symmetry : public GroupAction, Group, State> { }; }; +//======================================================================== +// Attitude-only symmetry for Unit3/Rot3 (used by Python EqF wrappers) +//======================================================================== +namespace attitude_example { + +using M = Unit3; +using G = Rot3; + +/** + * Symmetry: right group action on Unit3. + */ +struct Symmetry : public GroupAction { + static constexpr ActionType type = ActionType::Right; + + M operator()(const M& eta, const G& Q, + OptionalJacobian<2, 2> H_eta = {}, + OptionalJacobian<2, 3> H_Q = {}) const { + // Apply the right action Q^T * eta + return Q.unrotate(eta, H_Q, H_eta); + } +}; + +} // namespace attitude_example + /** * Continuous-time dynamics on the manifold M = SO(3) x R^3 x SO(3)^N, as in * Eq. (2) of the paper. Given state ξ = (R, b, S) and body angular velocity diff --git a/gtsam_unstable/geometry/EquivariantFilterAttitude.h b/gtsam_unstable/geometry/EquivariantFilterAttitude.h deleted file mode 100644 index f4b1dbc572..0000000000 --- a/gtsam_unstable/geometry/EquivariantFilterAttitude.h +++ /dev/null @@ -1,40 +0,0 @@ -/** - * @file EquivariantFilterAttitude.h - * @brief Attitude-only EqF helper types for Python bindings. - * - * Provides the symmetry action used by the Unit3/Rot3 attitude example in - * tests/testEquivariantFilter.cpp so it can be instantiated in wrappers. - */ - -#pragma once - -#include -#include -#include -#include -#include - -namespace gtsam { -namespace attitude_example { - -using M = Unit3; -using G = Rot3; - -/** - * Symmetry: right group action on Unit3. - * φ_η(Q) = Q^T η. - */ -struct Symmetry : public GroupAction { - static constexpr ActionType type = ActionType::Right; - - M operator()(const M& eta, const G& Q, - OptionalJacobian<2, 2> H_eta = {}, - OptionalJacobian<2, 3> H_Q = {}) const { - return Q.unrotate(eta, H_Q, H_eta); - } -}; - -} // namespace attitude_example -} // namespace gtsam - - From 6ed68466bf4dbba79ece1ec3fa5c87d224fc0a24 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 25 Jan 2026 18:50:20 -0500 Subject: [PATCH 130/266] Notebook --- gtsam/geometry/tests/testGal3.cpp | 21 + gtsam/navigation/navigation.i | 2 +- python/gtsam/examples/Gal3ImuNEESReset.ipynb | 2221 ++++++++++++++++++ 3 files changed, 2243 insertions(+), 1 deletion(-) create mode 100644 python/gtsam/examples/Gal3ImuNEESReset.ipynb diff --git a/gtsam/geometry/tests/testGal3.cpp b/gtsam/geometry/tests/testGal3.cpp index ff52f3bdbf..0199380e48 100644 --- a/gtsam/geometry/tests/testGal3.cpp +++ b/gtsam/geometry/tests/testGal3.cpp @@ -83,6 +83,27 @@ TEST(Gal3, ChartDerivatives) { CHECK_CHART_DERIVATIVES(kTestGal3_Lie1, kTestGal3_Lie2); } +/* ************************************************************************* */ +TEST(Gal3, RetractJacobians) { + const Gal3 g = kTestGal3_Lie1; + const Vector10 v = (Vector10() << 0.01, -0.02, 0.03, 0.1, -0.05, 0.2, 0.4, + -0.3, 0.2, 0.01) + .finished(); + + Matrix actualH1, actualH2; + traits::Retract(g, v, &actualH1, &actualH2); + + std::function retract_proxy = + [](const Gal3& g_, const Vector10& v_) { return g_.retract(v_); }; + Matrix expectedH1 = + numericalDerivative21(retract_proxy, g, v); + Matrix expectedH2 = + numericalDerivative22(retract_proxy, g, v); + + EXPECT(assert_equal(expectedH1, actualH1, 1e-5)); + EXPECT(assert_equal(expectedH2, actualH2, 1e-5)); +} + /* ************************************************************************* */ TEST(Gal3, StaticConstructorsValue) { diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 5c26698e1f..cddc41d147 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -619,7 +619,7 @@ virtual class ManifoldEKF { // Only vector-based measurements are supported in wrapper void updateWithVector(const gtsam::Vector& prediction, const gtsam::Matrix& H, - const gtsam::Vector& z, const gtsam::Matrix& R); + const gtsam::Vector& z, const gtsam::Matrix& R, bool performReset = true); }; #include diff --git a/python/gtsam/examples/Gal3ImuNEESReset.ipynb b/python/gtsam/examples/Gal3ImuNEESReset.ipynb new file mode 100644 index 0000000000..52941de50e --- /dev/null +++ b/python/gtsam/examples/Gal3ImuNEESReset.ipynb @@ -0,0 +1,2221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gal3 IMU EKF: NEES With vs. Without Reset\n", + "\n", + "This notebook compares NEES (Normalized Estimation Error Squared) for the Gal3 IMU EKF when the measurement update **does** and **does not** perform the reset (covariance transport) step.\n", + "\n", + "We keep the setup simple and focus on the impact of the `performReset` flag in `updateWithVector`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "9343b3c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab # type: ignore\n", + " %pip install --quiet gtsam-develop\n", + "except Exception:\n", + " pass # Not in Colab\n", + "\n", + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "\n", + "import gtsam\n", + "from gtsam import Gal3\n", + "from gtsam import ConstantTwistScenario, ScenarioRunner" + ] + }, + { + "cell_type": "markdown", + "id": "2c7a5da0", + "metadata": {}, + "source": [ + "### NEES: theoretical introduction\n", + "\n", + "The Normalized Estimation Error Squared (NEES) is\n", + "\n", + "$$\n", + "\\mathrm{NEES} = e^T P^{-1} e,\n", + "$$\n", + "\n", + "where **e** is the estimation error in the current tangent space and **P** is the filter covariance.\n", + "For a consistent filter with correct noise modeling, the **expected** NEES equals the **state dimension** (here 10).\n", + "In practice, we estimate this expectation by **Monte Carlo averaging** over multiple simulated runs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "9123d34f", + "metadata": {}, + "outputs": [], + "source": [ + "def nees(error, covariance):\n", + " \"\"\"Compute NEES = e^T P^{-1} e.\"\"\"\n", + " return float(error.T @ np.linalg.solve(covariance, error))" + ] + }, + { + "cell_type": "markdown", + "id": "88a2f083", + "metadata": {}, + "source": [ + "### Scenario: steady yaw rate with constant body-frame velocity\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "e3b400b9", + "metadata": {}, + "outputs": [], + "source": [ + "radius = 30.0\n", + "angular_velocity = np.pi # rad/sec\n", + "w_b = np.array([0, 0, angular_velocity]) # body yaw rate\n", + "v_n = np.array([radius * angular_velocity, 0, 0]) # world-frame velocity\n", + "\n", + "scenario = ConstantTwistScenario(w_b, v_n)\n", + "\n", + "# Simulation parameters\n", + "dt = 1.0 / 180.0 # 1 degree per step\n", + "T = 6.0 # total duration (s)\n", + "N = int(T / dt)\n", + "\n", + "# IMU params (NED)\n", + "params = gtsam.PreintegrationParams.MakeSharedD(9.81)\n", + "params.setAccelerometerCovariance(np.diag([2e-1] * 3))\n", + "params.setIntegrationCovariance(np.diag([2e-1] * 3))\n", + "params.setGyroscopeCovariance(np.diag([2e-2] * 3))\n", + "\n", + "runner = ScenarioRunner(scenario, params, dt,\n", + " gtsam.imuBias.ConstantBias(np.zeros(3), np.zeros(3)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c22b95b", + "metadata": {}, + "source": [ + "### Monte Carlo analysis of running the EKF" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "ce1ed85c", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def run_one_filter(times, params, X0, P0, R, measurements, true_states, perform_reset):\n", + " \"\"\"Run one EKF filter over the given measurements and return NEES values.\"\"\"\n", + " nees_vals = np.zeros(len(times))\n", + " ekf = gtsam.Gal3ImuEKF(X0, P0, params)\n", + "\n", + " for k, t in enumerate(times):\n", + " if k > 0:\n", + " omega_meas, acc_meas = measurements[k][\"imu\"]\n", + " ekf.predict(omega_meas, acc_meas, dt)\n", + "\n", + " if measurements[k][\"has_meas\"]:\n", + " predicted_position = ekf.state().position()\n", + " H = measurements[k][\"H\"]\n", + " z = measurements[k][\"z\"]\n", + " ekf.updateWithVector(predicted_position, H, z, R, perform_reset)\n", + "\n", + " X_true = true_states[k]\n", + " err = ekf.state().localCoordinates(X_true)\n", + " nees_vals[k] = nees(np.asarray(err).reshape(-1), ekf.covariance())\n", + "\n", + " return nees_vals\n", + "\n", + "\n", + "def run_one_trial(\n", + " times, scenario, runner, params, P0, init_sampler, pos_sampler, pos_std, M\n", + "):\n", + " measurements = []\n", + " true_states = []\n", + " R = np.eye(3) * (pos_std**2)\n", + "\n", + " for k, t in enumerate(times):\n", + " state_true = scenario.navState(t)\n", + " X_true = Gal3(\n", + " state_true.attitude(), state_true.position(), state_true.velocity(), t\n", + " )\n", + " true_states.append(X_true)\n", + "\n", + " if k == 0:\n", + " xi0 = np.asarray(init_sampler.sample()).reshape(-1)\n", + " X0 = X_true.retract(xi0)\n", + "\n", + " if k > 0:\n", + " omega_meas = runner.measuredAngularVelocity(t - dt)\n", + " acc_meas = runner.measuredSpecificForce(t - dt)\n", + " else:\n", + " omega_meas = np.zeros(3)\n", + " acc_meas = np.zeros(3)\n", + "\n", + " has_meas = k % M == 0\n", + " if has_meas:\n", + " z = X_true.position() + np.asarray(pos_sampler.sample()).reshape(-1)\n", + " H = np.zeros((3, 10))\n", + " H[:, 6:9] = X_true.attitude().matrix()\n", + " else:\n", + " z = None\n", + " H = None\n", + "\n", + " measurements.append(\n", + " {\"imu\": (omega_meas, acc_meas), \"has_meas\": has_meas, \"z\": z, \"H\": H}\n", + " )\n", + "\n", + " nees_reset = run_one_filter(\n", + " times, params, X0, P0, R, measurements, true_states, True\n", + " )\n", + " nees_no_reset = run_one_filter(\n", + " times, params, X0, P0, R, measurements, true_states, False\n", + " )\n", + "\n", + " return nees_reset, nees_no_reset" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "4973d2b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize EKFs\n", + "init_sigmas = np.array([0.1, 0.1, 0.1, 0.4, 0.4, 0.4, 2.0, 2.0, 2.0, 0.1])\n", + "P0 = np.diag(init_sigmas ** 2)\n", + "P0[9, 9] = 2.5e-1 # time variance (0.05^2)\n", + "\n", + "# Noise samplers\n", + "pos_std = 20.0 # m measurement noise (more conservative)\n", + "\n", + "pos_sampler = gtsam.Sampler(np.array([pos_std] * 3), 25)\n", + "init_sampler = gtsam.Sampler(init_sigmas, 23)\n", + "\n", + "times = np.linspace(0.0, T, N + 1)\n", + "\n", + "num_mc = 100\n", + "nees_reset_mc = np.zeros((num_mc, len(times)))\n", + "nees_no_reset_mc = np.zeros((num_mc, len(times)))\n", + "\n", + "M = 50 # measurement cadence\n", + "for mc in range(num_mc):\n", + " nees_reset_mc[mc], nees_no_reset_mc[mc] = run_one_trial(\n", + " times, scenario, runner, params, P0, init_sampler, pos_sampler, pos_std, M\n", + " )\n", + "\n", + "nees_reset = nees_reset_mc.mean(axis=0)\n", + "nees_no_reset = nees_no_reset_mc.mean(axis=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "cf993f72", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#0fae66" + }, + "name": "With reset", + "type": "scatter", + "x": { + "bdata": 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+ "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "NEES comparison: reset vs. no reset" + }, + "xaxis": { + "title": { + "text": "Time (s)" + } + }, + "yaxis": { + "title": { + "text": "NEES" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot NEES for both filters\n", + "dim = ekf_reset.covariance().shape[0] # Gal3 tangent dimension\n", + "fig = go.Figure()\n", + "fig.add_scatter(x=times, y=nees_reset, name='With reset', line=dict(color=\"#0fae66\"))\n", + "fig.add_scatter(x=times, y=nees_no_reset, name='Without reset', line=dict(color='#d62728'))\n", + "fig.add_scatter(x=times, y=[dim] * len(times), name='Expected NEES (dim)',\n", + " line=dict(color='black', dash='dash'))\n", + "fig.update_layout(title='NEES comparison: reset vs. no reset',\n", + " xaxis_title='Time (s)', yaxis_title='NEES')\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3255b3c", + "metadata": {}, + "source": [ + "## Interpreting the results\n", + "\n", + "These curves are **Monte Carlo averages**, so they are much closer to the expected behavior than a single run.\n", + "The dashed line at 10 is the theoretical mean NEES for a 10‑D state.\n", + "\n", + "In this plot, both filters remain **above 10**, meaning they are still somewhat **over‑confident**.\n", + "The **no‑reset** curve is consistently higher, showing worse inconsistency when covariance is not transported after retraction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Takeaway.** The reset-enabled filter keeps the covariance aligned with the current tangent space, \n", + "leading to a NEES trace that is better behaved and closer to the expected value. Without reset, the \n", + "covariance is not transported after retraction, so NEES can become inconsistent over time.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b925aa8d17dc2aed35b7497f52ef9ffd64f03ee1 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 25 Jan 2026 22:19:34 -0500 Subject: [PATCH 131/266] Fix tests/notebooks --- gtsam/navigation/tests/testInvariantEKF.cpp | 4 +- python/gtsam/examples/Gal3ImuASVExample.ipynb | 64 +- python/gtsam/examples/Gal3ImuExample.ipynb | 47 +- .../examples/NavStateImuASVExample.ipynb | 72851 +--------------- .../gtsam/examples/NavStateImuExample.ipynb | 64 +- 5 files changed, 460 insertions(+), 72570 deletions(-) diff --git a/gtsam/navigation/tests/testInvariantEKF.cpp b/gtsam/navigation/tests/testInvariantEKF.cpp index a75c60d4eb..fcad8d22d9 100644 --- a/gtsam/navigation/tests/testInvariantEKF.cpp +++ b/gtsam/navigation/tests/testInvariantEKF.cpp @@ -65,7 +65,7 @@ TEST(IEKF_Pose2, PredictUpdateSequence) { EXPECT(assert_equal(P1_expected, ekf.covariance(), 1e-9)); // --- First Update --- - ekf.update(h_gps, z1, R); + ekf.update(h_gps, z1, R, false); // Calculate expected state and covariance (manual Kalman steps) Matrix H1; // H = dh/dlocal(X) -> 2x3 @@ -97,7 +97,7 @@ TEST(IEKF_Pose2, PredictUpdateSequence) { EXPECT(assert_equal(P2_expected, ekf.covariance(), 1e-9)); // --- Second Update --- - ekf.update(h_gps, z2, R); + ekf.update(h_gps, z2, R, false); // Calculate expected state and covariance (manual Kalman steps) Matrix H2; // 2x3 diff --git a/python/gtsam/examples/Gal3ImuASVExample.ipynb b/python/gtsam/examples/Gal3ImuASVExample.ipynb index d06aa5548a..51bd24ff4f 100644 --- a/python/gtsam/examples/Gal3ImuASVExample.ipynb +++ b/python/gtsam/examples/Gal3ImuASVExample.ipynb @@ -29,7 +29,13 @@ ] }, "source": [ - "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information" + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" ] }, { @@ -1239,7 +1245,7 @@ }, "xaxis": "x", "y": { - "bdata": 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kZcALQ7veo6RlwAPxkHedpGXAosNK8JWkZcA+msEtjaRlwP4VeMaCpGXAI/5PL3ekZcChJJcraqRlwNYBWRdcpGXAP3Yk5kykZcCfkPQpPaRlwDUWlsEspGXApbz7BxykZcDvy1W2CqRlwODdMmz5o2XA2dIVk+ijZcANxUcN2KNlwHMFMcDHo2XACgfwrrejZcDuiv/hp6NlwL4DLaKYo2XA1jQqBYqjZcB8GjIbfKNlwOwHO+Zuo2XAdDZpY2KjZcCpTYh/VqNlwA4qPh1Lo2XAyExWIECjZcDJyNJANaNlwIIZgwkqo2XAe+ZB7h2jZcC6CBibEKNlwIpxTn0Bo2XAaHTGKfe/ZcB8xwgLxr9lwKFS4HiNv2XAN7OtSEy/ZcD04/RlAb9lwDQGQUmrvmXA8Az6jUi+ZcDi2u1Q2L1lwBYpCUtZvWXAJohLgsm8ZcCIFQl0KLxlwK2q89N1u2XA5HN6G924ZcC6I82Oy7ZlwApQJNtYs2XAIH96ix+yZcAaRFq05LBlwPeCpV2pr2XAKcRLfHCuZcAuoqtiPq1lwDcfJ5wVrGXA8leZCviqZcDB+2za6KllwMxOLmjrqGXASbnDUwCoZcCNPvLtKadlwAzNje9qpmXA4seBV8SlZcDGat6nNqVlwGrqDGbDpGXAna23d2ukZcBo/hD6LaRlwADPpkkLpGXAnsw+vAOkZcAKF2bxFqRlwCPwtyREpGXAH+EGS4qkZcBvmy146KRlwAofMD5dpWXAU2LnSOelZcD/TCykhKZlwMHOWVc0p2XAqRrY9/OnZcAZIIIIwahlwP1L8kqaqWXA1lszOH2qZcC9dsMLaKtlwBJK/UNXrGXAldKuoEmtZcD+WWgfPK5lwJIL8bIsr2XAGIDPmBqwZcDtcHX6A7FlwDjowU/msWXA5GsL97+yZcA+43ITkLNlwBR8LXhUtGXAKnqdRwu1ZcAMUmZatLVlwE09RDROtmXAo02i4te2ZcCZZ6OXUrdlwI9VmZq+t2XAVt0GTBy4ZcDWg140bLhlwBHR/7SvuGXAmYhgjOe4ZcCmKMWdFLllwCMjmEw3uWXA2rWgdlC5ZcB4FDWVYbllwMIlkx9suWXA8arXHXG5ZcD09yUccrllwHSL8LBwuWXAJdR8CGy5ZcBvQZzmZLllwMrJxYVcuWXAhqKfi1O5ZcCB4f+pSrllwLm9NnFCuWXAw8x4ODu5ZcALpUUJNbllwF4lkHgwuWXAC3tiDi65ZcAe7DLuLLllwEct9TYtuWXAlJPNVC+5ZcDYn7olM7llwLL11a43uWXAeFM7Jz25ZcBjgBWmQ7llwDwvoMdIuWXAecfSEUy5ZcBsgwgrTbllwFLxIzJKuWXAUIVgaEG5ZcAXZUrfM7llwBuEwgwiuWXA5usRKgq5ZcDAUZ76i8xlwJguoeJkzGXAvUhiwTfMZcDlRuNsBMxlwEj2DNLLy2XA0fCsAo7LZcCayhX2SctlwGcPYm0Ay2XAkqQH+7HKZcC33vYpXcplwBzbzAcCymXAh/rQCKHJZcBEFNUyOsllwDFS+T/OyGXAq0PuCF/IZcCGXZ3r7cdlwGxmPql6x2XA/EhS0QbHZcArVw3Jk8ZlwCtZzcIhxmXAGK41lrHFZcBgwPtQRMVlwP4XKK7axGXAmvtVXHXEZcDHdnctFcRlwK+dXtO6w2XAyMkVh2fDZcBBk97/G8NlwI0fAYTYwmXAxqjJ/Z3CZcA5XSIQbcJlwNrnW2JFwmXAGM+JRybCZcAKX/UjEcJlwK60250EwmXA5sEkRf/BZcBD9CK6AcJlwAyk5HQLwmXA15bsjhvCZcAKKzx2McJlwGKyzXNNwmXAt7Nb6G3CZcCDlBqvkMJlwPrlV+S2wmXA2ES4sd7CZcDkl4VMBsNlwNwLi5ktw2XAbkPxolTDZcDXT/hZw8NlwPPe0Lffw2XALTuehffDZcDgAtqhC8RlwBvOzZwbxGXA0Y5mJibEZcD9qFoLK8RlwFTYLEMrxGXAW0aS2yXEZcDvkK9eGsRlwGYIffAJxGXA/oimE/TDZcDwxBNw2MNlwAPqbqq3w2XA56xYJpLDZcAqPs67Z8NlwBu6UK44w2XAlt8gSAbDZcBbk82H0MJlwN1iL7CXwmXAnLhIMVzCZcAyQh5aHsJlwJtvV2fewWXAaafR3ZzBZcDRQ3lNWsFlwHLyML4WwWXAnFDFfdLAZcD3UrL6jcBlwIQ8zVpJwGXAtrFz6gTAZcBN8texwL9lwMSX8ah8v2XAKs3pHjm/ZcDHmMZe9r5lwDhu2Xe0vmXAKlyrs3O+ZcCzzE5dNL5lwO5D0zL2vWXAoZ2jVbm9ZcD5cY0mfr1lwCw/hsREvWXA/vJLPA29ZcDaru7G17xlwEEVQYmkvGXAqDvtd3O8ZcAZ/M2qRLxlwAn+izgYvGXAwbGyLO67ZcCl1UuOxrtlwPsxNWOhu2XAFxG5pX67ZcCh0bAjXrtlwCJ+YGOzx2XAwA+Ic5THZcBUVqg9eMdlwE9H8qpex2XAlQpWoEfHZcACa3A0M8dlwAcuXJchx2XAYV30aRLHZcArtSY9BcdlwNOLS+X5xmXAGknECPDGZcBZWR9i58ZlwFx9T93fxmXA77OScdnGZcBF4Mv508ZlwCUnwnnPxmXA+AC3VMzGZcCJLR5PysZlwI8CmQDJxmXAFi8TRMjGZcD/e2UByMZlwJuvc/3HxmXAvY8ID8jGZcBUhU4eyMZlwMzgibfHxmXA7uD4VcbGZcAXvXa/w8ZlwBU2Bou/xmXAW4YdabnGZcC0s/YlscZlwI9D0qGmxmXAxxl0xpnGZcBsPZWIisZlwO0SSe54xmXAhVwU+WTGZcAsB8avTsZlwIbc1xk2xmXAyZAOgBvGZcDO5hYW/8VlwPRCr7ngxWXAq52CF8DFZcA8I707ncVlwJp9TlJ4xWXACFENpFHFZcBPk3JvKcVlwH3jOOj/xGXAolTWRtXEZcAtQHGsqcRlwGMnAHp9xGXAUUWoHFHEZcCxhW/mJMRlwDJoyTD5w2XAYrAGX87DZcAP8diVpMNlwHahZNl7w2XAzfTjYVTDZcBy06o8LsNlwOzv28gIw2XA21yQ5ePCZcCbenefv8JlwPp0sESbwmXAVL3lxXbCZcDKv30eUsJlwIZBHi8twmXA5gEE4gfCZcAr3Io94sFlwP6QuTG8wWXAxjMhMJXBZcAb7BwgbcFlwKTdFPRDwWXA4pOGIhnBZcAqUixK7MBlwCgQnVi9wGXAH2seYIzAZcCW994VWcBlwDIWL3YjwGXAo704kuu/ZcDJX6YAsb9lwIgRgeJzv2XAaHoGVTS/ZcDntzcV8r5lwL54D/WsvmXATW8VF2W+ZcAnDIeRGr5lwAfbYzDNvWXADQu6Bn29ZcCcZIweKr1lwEcv7z/UvGXA3PAhUXu8ZcDjyV9PH7xlwJcXcui/u2XA4fbzxly7ZcDeGYXp9bplwD1DO0+LumXADIDmrBy6ZcCPHgAYqrllwFCSd7YzuWXAEefYibm4ZcBYBFV5O7hlwOmvwbS5t2XAl1FHmV7CZcDtnkzxzsFlwN1Hzis8wWXA+AoQjKbAZcCfl8EWDsBlwBZmYOpyv2XAIr7URdW+ZcBl1b5fNb5lwLgcmWSTvWXArsZMnu+8ZcBak/dsSrxlwEwn1mKku2XAOfPpnv26ZcBfVddPVrplwIDjwLquuWXATydvBQe5ZcBXZm5iX7hlwNflF9+3t2XAvzwRqhC3ZcAcUfP/abZlwAUxvELEtWXA3Z8czB+1ZcBJ7826fLRlwAj89RPbs2XAu9ft+jqzZcBBalXWnLJlwMBZ5bAAsmXAKZU5jWaxZcA/T7m9zrBlwEIS61I5sGXAsjtEU6avZcAS2VYmFq9lwEabNiSJrmXAhjeeav+tZcCV9BYVea1lwBnDubL2rGXAreEFMXisZcCTkwh4/atlwPPdv9SGq2XAmwF7QhSrZcDYEEWgpaplwAG7FA47qmXA6GbHx9SpZcArsEG6cqllwBFr6MAUqWXAHVs9PruoZcDzpUW2ZahlwG9qLtoTqGXAm0ps4sWnZcBfMATOe6dlwCPP+4Q1p2XAlSXrJvOmZcCmR0ZetaZlwENujhp8pmXAYr3kVUemZcDR1hN0F6ZlwMEpA+LspWXAA+TpuMelZcDeXJoQqKVlwHnu9lyOpWXApIirnXqlZcBsdRaybKVlwIVARzNlpWXAWZQtDGSlZcAMZWjeaKVlwFVg/3xzpWXAgrjPVIOlZcB6pLqgl6VlwHDcFSGwpWXAH2GaBM2lZcCsbGD47aVlwF6tna4SpmXARg7KIjumZcAzb58/Z6ZlwJC+bMiWpmXAI+CPlMmmZcATB2Cg/6ZlwKl+rb84p2XALs1duHSnZcAJT7vFs6dlwAucqtv1p2XAZ8OV7TqoZcC+zoYmg6hlwEUhALjOqGXA0KFOmB2pZcD/27nDb6llwBoP8IfFqWXAHmDkzB6qZcCxUl11e6plwG8OzI3bqmXAYNVNHT+rZcAnMrQ+pqtlwC2ZtwcRrGXAJV/pqX+sZcBxeewI8qxlwCJYDuNnrWXAZrzaUOGtZcC19eTZXa5lwOwgGBfdrmXAqA+M0F6vZcC4diIjirllwI2g/88MumXAcUzScpC6ZcADck71FLtlwKRuztmZu2XAbZsBfB68ZcAMouLUorxlwERIHqAmvWXA8GtJQqm9ZcBH99QiKr5lwINvCJCovmXAZTyOBCS/ZcC22S8NnL9lwDEoX1oQwGXAuBW1pYDAZcCS5+jc7MBlwMAc9/9UwWXAGnIenbjBZcDTj9x9F8JlwGgkKZBxwmXAD6vyjcfCZcADiw6OGcNlwJcarLJnw2XAWUPB6LHDZcCn6tIz+MNlwOUgurs6xGXAKh8AWHnEZcBfCBZys8RlwGcA/qDoxGXAM8uCqxjFZcDj4wvCQsVlwPhp/qdmxWXACuerNYTFZcD+aI4Bm8VlwKUSQJaqxWXAS3YZzbLFZcAAn9ahs8VlwP1SbmSsxWXAK0Sg+5zFZcDh4i2FhcVlwDJm7qhlxWXAhEVfbT3FZcDJ65AcDcVlwHif97TUxGXA2XtsvpPEZcCTET5QSsRlwG1wKsn4w2XAAFtIkZ7DZcAovdXHO8NlwBupJa/QwmXASW6GF13CZcDbbEMF4cFlwETgMZ5cwWXAskupDdDAZcAJX8Q7O8BlwIFJD0Kev2XAPc+dU/m+ZcDhfRSuTL5lwPZ/J6+YvWXAxNZtRd68ZcAZZzBYHrxlwP/ckYpZu2XAX8ujCJG6ZcBddUcWxrllwNy/mvL5uGXAcIFqVi24ZcC7BocPYbdlwK9xdqWWtmXAP0Lba8+1ZcCO1CqNDLVlwPVBtGNPtGXA8qOqJJmzZcDIxFh46rJlwNYwOv1EsmXATtIeJKmxZcBJEMlWF7FlwMOov3SPsGXAWjpFMxGwZcDxIur7m69lwLLjr+Ytr2XADFcK1sWuZcD0G4G8Yq5lwLpDOZ0DrmXAFi0qX6etZcAI2ZcETa1lwHTAjhDzrGXAgoJtUJisZcDqHPG2O6xlwA==", 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nZsBzvy8h5mdmwGVBMNveZ2bArsaApthnZsBEZGJB02dmwPDCkjvOZ2bAOUeLGcpnZsB1ZtCTxmdmwFjxMNzDZ2bAidx538FnZsCfvoEnwWdmwCuyMGjBZ2bAMZAj7cJnZsDVzL9jxWdmwOa3b0HJZ2bAO4Fm1s5nZsBDn54S1mdmwBcIHsLeZ2bAdKfKfOhnZsCuTL4A82dmwDQz3GX+Z2bA5pETagpoZsBWDYHuFmhmwHJalO4jaGbATMnNEzFoZsBdDcGhPWhmwGzmq75JaGbAgGbupFVoZsAjbeGxYGhmwPbZpPlqaGbAPf+5tHRoZsDSkftOfmhmwBQLnCCIaGbABJvI3jRfZsAIDBmxXV9mwFNXOn2LX2bAXWtWTr9fZsD2QVox+l9mwKwGQ3c9YGbAKLdEqIpgZsDER4lL42BmwP4FP3hIYWbAaDAZo7thZsA6LMQLPmJmwHuuLsjPYmbAKDjozPtkZsD8ba9fu2ZmwOB1ma+3aWbALrnZV8lqZsDuOqse32tmwL+AiIX3bGbAwU63tw9uZsCAPPGuI29mwNVBlSEwcGbA9PbeATRxZsA5KKY2K3JmwDDjvCYTc2bANX4EsepzZsAPv4KYr3RmwDqif+xfdWbAbBj4Ovp1ZsBwKTB/fXZmwOydXbTodmbA31Su/Tp3ZsCCXDxydHdmwFIobJeUd2bAfi/OsZp3ZsBgN2cdh3dmwCGnUxFad2bA8U4XFRN3ZsBlQfamsnZmwEym9OY5dmbAt67Obqp1ZsDrOzDWBXVmwF8OJxxOdGbA8XL1X4VzZsBlwncKrnJmwNNyHs3KcWbA0iBBM91wZsD3i8pD6W9mwBIrU0nwbmbA628G1fNtZsBkneOP9mxmwMgMDB/8a2bA2JkzsgZrZsD7BqzxGGpmwGKDKrQ1aWbAmueCAl9oZsBmzyHylWdmwMB3B5rcZmbAEF+JnTRmZsDEpWS5nWVmwDUGhOIYZWbAy1iLFqdkZsCPln1rSWRmwI1BAsL/Y2bA/IGeucpjZsBwziYXqmNmwLlxE9ecY2bAVZjWnKJjZsDmMTSAumNmwG05VkjiY2bASBmdjxhkZsCw8IJXXGRmwJCAoGysZGbA7ek2lQdlZsCS9bPSbGVmwOIs6wPbZWbA9QchG1BmZsC7+ebRymZmwOpphh9KZ2bAAOsaws1nZsCjnPLlVWhmwKenDn3haGbAo5vqoG9pZsAB9y0QAmpmwDzHxj6YambAtTflkDFrZsAq+IMPz2tmwJufPSdxbGbAnBlDUxdtZsDsn/zlwW1mwBCei0xybmbAmLxV/SdvZsD9vLZB4m9mwBZSMjaicGbAHc5KH2dxZsDi9QLZL3JmwLhGgvz7cmbARpbhyspzZsCnb73fm3RmwGk1jidvdWbAXG7tPkZ2ZsDQivWugW9mwDwKuyVkcGbAhUujbEpxZsBj7LUUNHJmwBaoiCcgc2bAYUBA1Q10ZsA+yIXG/HRmwH3sYVXrdWbA/iHUqNd2ZsD94HpbwXdmwCZwb4KmeGbAO1a+AoV5ZsA4hqDrW3pmwKF1RMQqe2bAG53DHvF7ZsBID58+rnxmwG7nRothfWbAkGLfPQp+ZsCTGcmOp35mwAmuqXQ4f2bAQvVRErx/ZsCQZY+cMYBmwAzaXz6YgGbATKZ2Iu+AZsCTxqviNYFmwHXo2zZsgWbAjgZaepGBZsBpAdJ2pYFmwKM41ACogWbAbst295iBZsBDBhojeIFmwDhpdrFFgWbAkH+C2wGBZsCUzgRHrIBmwCjeX1VFgGbAsB5HyM1/ZsD9XhOiRX9mwMRlU9mtfmbAQybhgAh+ZsD+HiSoVn1mwFh9fZOYfGbAH0nC5897ZsAp6h/W/XpmwJg0KF4iembA4E46wT55ZsDgcfbWVHhmwKT5SXNld2bAiFfho3F2ZsCnLpc/enNmwFu/+XuCcmbAtbs1Uo1xZsDYtAsgm3BmwHfNSvysb2bAk6DpLcRuZsBp1QVw4W1mwCanqQ8FbWbA+Y0jQjBsZsCZ+vZbZGtmwEaGkaqgambAYDWBzuVpZsDEQgOrNGlmwLu3loKNaGbAjZ4rQfBnZsCYsdpvXWdmwHub64HVZmbA/tuySVhmZsDp8zsA5mVmwPEMwNZ+ZWbAJGcQGSJlZsAWzYOAz2RmwAuA7dqGZGbAJSdE20dkZsBJC9YNEmRmwN5hKdHkY2bA2gyPoL9jZsAXVe/0oWNmwJpO6yuLY2bAvG21sXpjZsCdr/67b2NmwPj0TYtpY2bAFtJzmmdjZsCPDsFgaWNmwBQGgnpuY2bADifRdXZjZsC5mL7hgGNmwLTMABiNY2bAwZJeuJpjZsCriAWFqWNmwMr4sym5Y2bAdEFdfsljZsC2f6JD2mNmwJu89DHrY2bAFFvTPPxjZsAHPic4DWRmwHUf7fgdZGbAH2O4dy5kZsClQD+1PmRmwDuGY5xOZGbA/SPhHV5kZsBt0DAsbWRmwCzSODFXXmbAsDOLMWheZsDyHYEgeF5mwElCtQmHXmbAtzWR/pReZsB8vbo/ol5mwPucMBavXmbAXcpDarteZsAt8tQbx15mwNf/qAPSXmbA8FCWFdxeZsD78Ilb5V5mwIS6WN7tXmbAzSqHuvVeZsC7j34p/V5mwA/HBIQEX2bAOkOFLgxfZsC9ujZUFF9mwD/ZKO8cX2bAKGHi7iVfZsBJJhB6L19mwEWgLKw5X2bAWxeYkERfZsCw2rsgUF9mwKS95jZcX2bApon2k2hfZsB7ToL2dF9mwJdEviaBX2bA0zKI/YxfZsD+g4ZJmF9mwAWqQ+WiX2bA/zWGzaxfZsAotRoBtl9mwA+VcmK+X2bAqbav9sVfZsD8aS7CzF9mwKCH7q7SX2bAbe29+ddfZsDMZjzO3F9mwBAcO/PgX2bAaTvP9ONfZsB2s+jR5V9mwJCSgabmX2bAjEIsneZfZsCd0a3h5V9mwK8LuZjkX2bAameA5+JfZsDr/2zg4F9mwP8MTNfeX2bAswOcK91fZsCHn/Qg3F9mwID09wfcX2bAyuVZON1fZsAdoE3J319mwGb1Y7TjX2bAYc1KI+lfZsDk4lUW8F9mwACveOj3X2bAm25TcABgZsAt+NGuCWBmwAbKAf8SYGbAINQgWhxgZsAWrmayJWBmwH5sSeguYGbAdr/f8jdgZsCb0+nVQGBmwDgTDXxJYGbAWlI9YlFgZsA3sH9vWGBmwB5QIpReYGbASKjGTmNgZsBcZ91CZmBmwM6eal5nYGbApX2Gt2ZgZsCA5KoRZGBmwEStdmpfYGbAy6c20lhgZsDZz530T2BmwFoEdvpEYGbAxkE+AjhgZsC5xh7TKGBmwGi6i00XYGbAb/IwlwNgZsCxEwHI7V9mwJfEasTVX2bAlVzzprtfZsDg7uOCn19mwL+OdTeBX2bA3ompvGBfZsCfbl4ZPl9mwEekuQcZX2bAy7lkSPFeZsDMDkPfxl5mwMMs3MeZXmbAvtnAp2leZsCjk050Nl5mwMTiMisAXmbArZDwoMZdZsAWrhuQiV1mwNnErPZIXWbA1+eGEKpXZsD1XCA4aVdmwAXugFYkV2bA+EcJY9tWZsBn3OEMjlZmwELtnCk8VmbA7M7JuOVVZsDRTGOyilVmwMDUJAcrVWbAmmRf4MZUZsCsGueHXlRmwE708FryU2bAmSv5dIJTZsDprXIFD1NmwHo+DT2YUmbApd73Ph5SZsAPzg5soVFmwNPpmu8hUWbAY10u4Z9QZsCW2Tq+G1BmwAvRBDuWT2bAkyq+uw9PZsABp6yHiE5mwNhaOOcATmbA593dInlNZsBRRUay8UxmwDiY4+pqTGbA08JJD+VLZsCHdW88YEtmwGK8wrncSmbAcnPT1FpKZsB4q5z02klmwGAlNGhdSWbATm/eiOJIZsCrVB+UakhmwMNSX731R2bAlTtCRIRHZsAHTKlZFkdmwOHICgWsRmbAGXtnVUVGZsDa3dRx4kVmwGOuUYGDRWbARAC2jyhFZsCj8rrX0URmwMtUcop/RGbAo56MxDFEZsBbSNht6ENmwKssWZajQ2bAA8cigGNDZsDdj/tCKENmwIXkFBnyQmbAX4LbUsFCZsASFlpmlkJmwC0JPHxxQmbAHt7atVJCZsC51SBIOkJmwD9tg58oQmbAfTzh5R1CZsA8WwsoGkJmwK+8Y68dQmbAug2jjChCZsDKO6quOkJmwOuYXItUQmbAG+B9HXZCZsDEBPAhn0JmwAFZXHnPQmbAkmPSpgZDZsCatjIMRENmwBLSY4iHQ2bAOUwRZdFDZsBAuRdoIURmwESioU13RGbAAl9GHNNEZsDb4US/NEVmwMz5x+WbRWbA75sfTAhGZsBz9RfYeUZmwOssjynwRmbAlAIBxmpHZsB31cPA6UdmwD2/zsJsSGbA02i5afNIZsDeQxCZfUlmwNUUxjgLSmbAMp3v45tKZsAPiSE/L0tmwJqqKlbFS2bAvsGmul1MZsCh1lEA+ExmwL0Cte+TTWbA5hTvTDFOZsD9ZAT6z05mwImrb9ZvT2bA8aUR3xBQZsBDOJjdslBmwE4dd4RVUWbAIMKuzPhRZsDZNSNOnFJmwJrpdMA/U2bAuuYb++JTZsDE4dLidUxmwGk16rQaTWbA2Xpbjr5NZsAr0wZwYU5mwAj9YDQDT2bAxwnmmaNPZsD4lJjIQlBmwDwBg8vgUGbAfK+5cH1RZsD5k0JwGFJmwOeSAEmxUmbAr8NS20dTZsCGZUoY3FNmwImLV8xtVGbAihLV/fxUZsD4UAngiVVmwG6Z+ZgUVmbAcuzM5pxWZsD9pZvKIldmwGQFCFemV2bAXQrCWShYZsCX0GsFqVhmwFd05oMoWWbAwZtRy6ZZZsCxkqvgI1pmwPcwadifWmbA1ETTehpbZsCp0Wk/k1tmwCFs0aUJXGbAldwGT31cZsCnhPKd7VxmwBoA6kJaXWbAXt1T8sJdZsDcUShbJ15mwJsBRjyHXmbAg3nDZeJeZsAaPBPIOF9mwNWNM1OKX2bA0whcAtdfZsDNHwH4HmBmwPp1aGpiYGbAzmiGwqFgZsATGD1e3WBmwCw5k3EVYWbAE2IGF0phZsCvGftke2FmwLv6PK6pYWbA3KDf+NRhZsAKxFtt/WFmwJ02ERcjYmbARzhVxEViZsB35V6NZWJmwNIvjCCCYmbADmeQFZtiZsDvFnk2sGJmwL9oaNvAYmbAXRYnRcxiZsA30Zyt0WJmwMAukV3QYmbAmqhsDMhiZsCHnuckuGJmwK2nPtOfYmbAcQJjAX9iZsDhpCiLVWJmwAZ5+eciYmbAnBmcWOZhZsAzPec/n2FmwC26pHNNYWbASar7xPBgZsDsdI9NiWBmwGEasUkXYGbAVCmGRppfZsCS9TVKEl9mwJZHZWOAXmbAnvZdO+RdZsAxzeMOPl1mwJFKq1uOXGbAfIb8gdVbZsBn3dZ7E1tmwAbbwFpIWmbANgkk83RZZsBU9HZMmVhmwGb3BJW2V2bAMKRcUs5WZsAU0lO44VVmwByTpK3xVGbADAxS9f9TZsAO3zBdDlNmwA==", "dtype": "f8" }, "yaxis": "y" @@ -1277,7 +1283,7 @@ }, "xaxis": "x", "y": { - "bdata": 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", "dtype": "f8" }, "yaxis": "y" @@ -1314,7 +1320,7 @@ }, "xaxis": "x2", "y": { - "bdata": 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", 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", "dtype": "f8" }, "yaxis": "y2" @@ -1334,7 +1340,7 @@ }, "xaxis": "x2", "y": { - "bdata": 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", 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", 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", 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", 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", 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", "dtype": "f8" }, "yaxis": "y3" @@ -1427,7 +1433,7 @@ }, "xaxis": "x3", "y": { - "bdata": 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", 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k5L92IvCKs1PkvxSiOs8gQuS/Sqed9gYw5L/kpT69JyTkv1LxrHytF+S/sBwC91sK5L82dGvMmwLkv8I+6vB7+uO/olUAD+vx47+QcL+vzOjjvwJ2JfuT5OO/kJtF/tHf47/CvibJQtrjv4qaaQNS2+O/gHW/zTbZ479oHhDxOdXjv+i7/1j72uO/iuJNTHji479m8RCx8+7jv2ZE4Sw7/OO/+q8tkeUW5L+ettQdJjPkv3qhGhaDUOS/fubjmJGM5L8cl7rLf8jkv9RSZulrA+W/xiPB1Go85b/0lHaIIJLlv5Qw8zbr5uW/wHiHlz855r/mOlTFG5vmv5ILLbzw/Oa/1YjZTcqb9L8hSshjotL0vzkBdNUvCvW/yFbQlUFB9b+gX+Sfp3b1v+0ugvdmsfW/tJRH5jDr9b+E4Rz90iL2v8quYtxrXva/1Ffs4i2Y9r9xZzdnws32v85UJykN/fa/q4rdcGVs978ouajTRLL3v8c0xzOj5/e/xEYcZGPw97/7ir7CqvD3vzeUHk1s9Pe/11U+r4nw97//0TMbOeX3v51e1Tba3Pe/EHxV9YLN97/SbvWBYLn3v02axJwAofe/7rdGzdCJ978Qo9tSO2/3v5KiLXUoUfe/ILDHYEI0979WNxOj7xT3v7fy8e8m9Pa/GVXOmOHT9r/7cR6GMrP2vyEfQcIVkfa/UNWn8Upt9r98L/c5M0r2v8/kZ+KJJva/P0JzvhYC9r+dL0gjQt71vwM8WPbjuvW/YYOqtMOY9b+818pKDHj1v7sXOi/gV/W/at/uAfI59b/SK3Xakx71vzkYXk+cAvW/kddnyHPp9L9S6w1aOtP0v8UosJJVu/S/4QfaAFqm9L/t/64wmJT0v8eHZYoohvS/DyH5J5V19L/+NMsNpGj0v24FdP5+X/S/mhX0eBJT9L+kx6wVZUr0vyHsTMB+RfS/vYzIrVtE9L87KD9/wT70v8ozV3PrPPS/g74xGuQ+9L/8Opa2fDv0v5pdlVaqO/S/hfk8F1s/9L+7ruA2RTz0v6K9NaRePPS/pma7W8Q/9L8/4A3zakb0vxMFn2f6RPS/flyOBGlG9L9gqiFXg0r0v4kBj7hPRPS/xExHQXlA9L//ys0+nj70v1S3abGGPvS/06A1LYYx9L+euohSSSb0v7RPSIxKHPS/CxDMbgwD9L8+qJW2Huvzv6Y1BcAL1PO/OkKZDsur878wcyFK8YTzv69FtEhdX/O/H8XAgu06878ZzP73ngTzv8wlmf/e0PK/o/GnSiWf8r8s5n5JA1zyv1+/EzdpG/K/gnhmnUbd8b85/xoZhqHxvx/yjyskVvG/xhz7/QsO8b/RClw0OcnwvyeskZFidvC/Ux39Fvwp8L/K/6T8dcPvv0bVwEPLF++/NnAoAL907r9KeZDglz/2vwE3PNhX+vW/U1KmxMqp9b87iyBlp171vyAGs8URGfW/mc+ZLI/K9L/joOjImoL0v/5D7OveQPS/6e5ri0UF9L9aMhaILsLzv/tVstxNhfO/c6rdnExP87+H5Gb/YxPzvw6WcgP83fK/36CgpTqu8r9tP+55/Xjyvwinxl/DSfK/zUUXwzIg8r+qK0Jz+Pvxv9sdKzsB1PG/nq7V5HSx8b+az1k7+pPxv/Vmn53Oc/G/RoKH5KxY8b+qVuELFULxv6BG5cjXL/G/z/u952kc8b/mW7NFJQ3xv8qLXzugAfG/E0SyonD18L8Yu4p7s+zwv1av3Bey5vC/p9q8wQXh8L9rFnm02N3wvxPXLzhT3PC/MDbmc+rb8L/rIhiMdtzwv5oFqg+E3fC/1ucNn7ve8L+ViyA2FOHwv8uq4Z5b4/C/Gk7fOmLl8L+eLvPFnubwv5zjXgrR6fC/SoQcuP7r8L8mGIA44ezwv610RR8I8PC/D5tk9bvx8L/VZo5lavTwv3yEphuj8/C/V0pu3Xrw8L/YPHxZYfDwvxw2gefk7fC/faQIvA3p8L9x6uVItOfwv9qJCp2n4/C/JmL8RwXd8L9zR73lbNPwv2QOzdv8zfC/VqVX1gnG8L+/7MEkMbvwv5p8iS2as/C/N2O602Gp8L+oXk3nXJzwv+daO0NJkvC/cOSdSZWF8L/7CAKvLHbwv0mmTjv0Y/C/YOKppO9U8L/v/aGqbEPwv3CPlVaPL/C//35/I3Qe8L+hxFQvaAvwv/5ab9Kd7O+/KCQQc4G+77/gdidbNpbvv/acM5tdau+/EDoVlzo777/uWxhg/xHvv5iMbAcA5u6/3p6sQ3m37r9+rEye7I3uv1YS6nddYu6/1PGsnAY17r8EEpzTEQbuvwwkyXsX2+2//GW06jKu7b+wYxkFjX/tv17ZE7D5U+2/NKpsvvkm7b8wabM33Pjsv+5+DQD9yey/1KbTeAye7L8aRuQVwnHsv46jnQNfRey/WhLzkQUb7L8YtLFZCPHrv1xGZ06mx+u/wJfy1lGg679UCbRulHnrv7i1qP+QfvO/qgMTyjFu87+8vRyvBl7zv/Z0aqNtTvO/EgFYrYE/878CQp4SWzDzvxR1rvEFIvO/gGrcNqAU87/qVa3cOgjzv6FfTg1O+/K/bz+NPk/v8r/JIiD3O+Tyv+m11dEs2PK/epz9mTfN8r/tIxqpa8Pyv5ZMOW++uPK/RYw1Aluv8r8X9unwVKfyv6jedEazoPK/GjnLjl2Z8r/r2prdc5PyvzJyKtDzjvK/nAN9meGJ8r+9Cdu/VIbyv5gp/DdthPK/7ELHhySE8r863Stld4Pyv1RbHwpGhPK//R/DXneG8r/k3D+fNIjyv9AYNzYQi/K/7/VDexOP8r8yeZJNxpLyv4HIU5R0l/K/MxOaQg6d8r8ORpcpgKPyv/jjwMyfqfK/FzN91buw8r/Z3vHXh7jyv3PeHznRv/K/+LsGAKbH8r9fX7zG6c/yv2Tqys122PK/egLz0J3g8r8LBPrn3ujyvz0jSroL8fK/wFMgjRT58r9mE+v04wDzv4nEpGZ6CPO/qjk0VKkP87/kVfTPhhbzv+zj8WfgHPO/Fs8VJ3wi87+xdOG/lSfzv4nLtgSwK/O/MdeZ/7Iu878Z8n+aQjHzv4FeVPsBM/O/bvi5R9Qz87+4qZITtjPzv/k8yLPbNPO/Ab9vCUU1878tE2D95jTzv1VXKZB6NfO/JLXojJo1879ToNcQCzXzv+OPNYNDNfO/vYYA6BM1878QAIqAVjTzv5GRzv8GM/O/03U6KdAy87+KH9YeGTLzv/NRH1TSMPO/QmZ+Bqow87/QnSAo/y/zv0Yr20S1LvO/799Metgs878exedFMSzzv0HY1mX4KvO/47rdLhUp879doO0dKijzv7sDSUi3JvO/FFH2EJkk878whBbBRyPzvyv+WpuMIfO/Ng/pr3cf87/jy54j7hzzv5p80lxuG/O/Sm2UJ40Z87+xug8fKRfzv5Jm/I+AFfO/Aaf0SFsT87993UQPohDzv9gOpyfxDPO/iiia5voI879fqkigrgPzv4hOrRsL/fK/SkVWge718r9X+9OlnO3yv7knA78K5PK/XIE6GA48+L9ICQ2OsDL4v9Ae+fJEKPi/dOA/ndQc+L8jntadBBH4vyam6ORZBPi/JJbhPg7397/gjUCNxOn3vwLP/QMx3Pe/SpiHM2PO978wNOqKhcD3v+AVgYPvsve/zTXEtlil979mD/e5ypf3v9TBJaV1ive/Jt4u5mZ997+vhjZPuHD3v2zP5lwtZPe/yMB8FehX97+IuSPtOEz3vw1St987Qfe/0GnPQjU297/CmLm2lSv3v5g761hcIfe/6nFUqsEW97+fi8KLygz3v7+m7Jo1A/e/2UHLnOv59r/NNY0Ofe/2vxqaWPRg5fa/yiNNAcjb9r8/GjuBbND2v8pqYONNxfa/RHBeoFy69r8p43nepKz2v1Br6lw7n/a/wjtkYm6S9r88okoT94X2v9xlc+YEdva/yKezzIFm9r9E/HRHWlf2vwEiua39Q/a/2E0azVEx9r/tOp4NJh/2v2Bq25EFDva/HXCwFAX59b9JN4f0fOX1vwuGuEt/0/W/l4Cl/X699b9QafxqXan1vypF8mL2lvW/NRwVIuh/9b+o90xwEmv1vwp8k6GsV/W/albBTstF9b+nWclhni71v7ChWrnwGPW/n+snRykE9b857tQwOOn0v4S3g548z/S/RHvcCkO29L/AZkJqTZ70v1XiV4nMf/S/er2ux2Ji9L8x9pnyTEb0v8g8GudjJPS/kTn57B4E9L+CEGYEz+Xzv4dXl9bYwvO/k3OABxyi878mlnCetoPzv1bxtIC0Z/O/SzEKV/RH879GjFMI3yrzv/slUJp9EPO/xBCEggjz8r/xU8YwbNjyv+oYE5mBwPK/j9AsYiSr8r/HItKoLpPyv2e8b0ABfvK/tGSGpltr8r8eBc2SRVbyv8rqNqbNQ/K/is1hlb0z8r92A8k4kSHyvzwbSO/1EfK/l4HIY9ME8r9zORbj7vnxv5sM7azr7fG/9zlpV0Tk8b+cze7bztzxv4xNCubq1PG/ktL/rijP8b8NX8/9V8vxv6OQ7kpByfG/2CyPFDnH8b/maKQErcbxv6fHcjtux/G/Twg1857I8b/EwPD2hCT3v+Lcck6MKfe/ILBCrWcv978JtmdXHTb3vzoavm4jPfe/x1RSqVpE979l488u7kz3vxA1QbV8Vfe/moF3/nVd97+uSQUvX2f3vxmNzwnKcPe/FqrZL1p597/qELgeyoD3v7IvT66Zive/DeNSL6CT97/DsXdJI5z3v8gPCoFup/e/5sNr87ew97/bBUIyHLj3v3v6OZZbwve/AIj/EnHK979kXYiQOtD3v4OCUeni0/e/PQpDg8ja979eBM1//t73v/1MBiUJ4fe/hQ9olhjn97/XQMjLH+v3vy82mPx57fe/l/Ran0Pu97+4RdC7T/P3v5hJ5UPZ9ve/44NXh6D4978fcIYki//3vyx4YYNQBfi/GT9NM/MJ+L/8af1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", 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", "dtype": "f8" }, "yaxis": "y" @@ -2427,7 +2433,7 @@ }, "xaxis": "x", "y": { - "bdata": 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", 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cAcDFbG7prGABwPnEwB32YwHAnhlI1+pmAcAM/iwHo2sBwHrhmIoScAHA8DvWPDR0AcCtaH1RAHoBwBeuVa+tfwHAa/r3+DuFAcDfIhAtrYoBwDny222HkQHA0msQK0mYAcCecEvj6J4BwLXihbpUpwHAdeeY6QyvAcCwlJpBXbYBwHF2h44lwAHALwluHHvKAcDqBfnoKdYBwN/JpvVF4gHAsbiFuwnyAcBuYa9UXwICwC0z5B0YEwLABLY+hSArAsBU38KcQ0MCwEXphGI/WwLAOmhEBNJyAsCUZA7RNJECwAysVqF4rwLAOeDzSzbNAsBV63xUYu4CwP18coXDDwPAuRJxPZ7bAsDIxLzVYf4CwDdUokCtIQPAbps4C61EA8B2A7rkm2YDwANYvHLLigPAYBGZYluuA8DjEPLgltADwJqwmV4K9APAlydQ43IWBMCFIek7fzYEwLEF7qcmUwTAkztIwbKXBMCzVmfHsMIEwM4v+MpQ6gTAvMzTQJfzBMD6+BGgM/kEwKyMRloiAAXAfTrEBN4DBcDZXrCVegQFwKPP1IIaBgXAZ4EPGNoEBcB/Wn9QzgEFwMaXiro6/QTAQnTu3q74BMDrsTA6CfMEwAoGj5A/7ATACbTI9GvlBMCA7Uqc/N0EwFDCv3Bs1gTAA2SjaG3OBMDev46O1MYEwH3scPUBvwTAKCKbt8i2BMB9S4MZAa4EwMoxvHJopQTAJvgDjNScBMCBWEyClpMEwGJIdgEBiwTALcRImHCDBMCUlnJk6XwEwEhx7oGsdQTAhpIZ0OlvBMDOzTT6wGsEwAoqr/Y4ZgTA89o0lmNiBMAJ6ygQQWAEwJj1QcYhXATAhChX8LRZBMD9g/otIlkEwLExHeNwWgTAkuuk/qlZBMDejBZr/1oEwI4ZOUh+XgTAE3h1SGZfBMAlFXNZhGIEwFAZgRrdZwTAHMrPGVxvBMBjQmIn33MEwOxbBIWbegTAHXLlQZeDBMDkcPX4PokEwIxKdlsPkQTADnAa3/qaBMCRvXTP8aAEwKV7QHLeqATAr3gltMGyBMCu3ukhk74EwLni0sPgxQTA6pJfzO7OBMA7TirSmdkEwGRhuN/D3gTAn0SWOF/lBMABeeEBNe0EwPAzjDIf9gTA2j6cYFL4BMB00YfglfsEwPZZeJKj/wTAUMJ+bPH7BMCAji0cCPkEwOJfttuo9gTAL7JvL7DrBMDA+Q9afeEEwAujMw781wTATTon1xrPBMDLekgrY70EwHIwYz35rATA6Vhe4pmdBMBKm6hEsYUEwMiaCaIGbwTAEVeLZ49ZBMCnnjFnPEUEwEdHq4M9KQTA3xP5h8YOBMDjNPdnzfUDwIiZnrbh1QPAxUADlpq4A8AK+cUJPp0DwN1H86JtewPAPG3HKaNbA8DArHqd7SkDwLg75OB/DgPAN5+yTeftAsBM5u2S5c8CwAIayMeOtALA/dOTbVWVAsBL16dDOXkCwMcy7VwOYALAb0Rp4spJAsDmcgKSWjACwGSAXmvFGQLATJNr4VsGAsD6mogBl/ABwHDacvvM3QHAzrnkLaDNAcAzUMYwdLsBwEqwDfMJrAHAgL6jMDWfAcB/j6xky5QBwITggKM1iQHAnWcm8AeAAcCfqBTAE3kBwDWw9Bx3cQHAtC/vBwVsAcCPxvTnfmgBwIeDAJHWZgHAAahVMTVlAcAQC1F5U2UBwBoEb3f/ZgHAG90BMfRoAcAfLh5ESWwBwHkK+1KkcAHAPtw0yLt1AcDEnTiEs3sBwLVEVB8kggHAoEJvC8+IAcCdYgjveZABwHNMfQoPmAHA46pvh2qfAcDNkDVN2acBwH2KtMLsrwHAvbQcYZO3AcAaBuvuir4BwKO2mOz1xgHAO8NoJ5LOAcAuOib0QNUBwHqmfuWJ3QHABFmvbtHkAcDkHiq5LPkBwEKpdx7r/gHAk+ku+z8DAsAUlr+wiwkCwHmI6y5pDgLACjVeR+MRAsAvXt+UgBcCwF/3mXCKGwLAIAzc6hgeAsD1XIZeAR8CwAvpmaxYIgLArk6qQkQkAsD/zmKUlyQCwBHWWD7vJgLANQIt9dUnAsDDAaoZQCcCwFGZMn2FKALAI6c0sWgoAsDonhym4yYCwEyc8TnzIwLAoD3WefkiAsDOm70XvCACwLpGw/lLHQLATr5W6pMbAsBtDNCu3BgCwGymvPcbFQLAfc6VgWEQAsB3P0KKZQ0CwJW/tCmCCQLA2zJc5MsEAsAtK4qlxwECwIssTHYN/gHAgVP5vq75AcDK7P7twvYBwJyxtllO8wHAl8F042HvAcDDho0eCusBwB8+A+3X5wHAaIa4OiTkAcB39IfA/98BwH9DNlfE3AHADtkJDTDZAcAf15YYV9UBwPwFUzhQ0QHAtOyh4CbOAcDj3SUi5soBwBrZZMqexwHAXOWowvPEAcAOo5OOXMIBwHkxizjpvwHApGCbfxW+AcCahf29YbwBwAD5ejWCfAHAy4KDMgN7AcDsL555r3kBwJgm3ICfeAHAWyG55OB3AcDUa8CLE3cBwPWi5h2odgHAgc4Saq12AcDIe54lLHcBwKn/BY9ydwHAmAJmLiZ4AcA7TDHzRXkBwNttAjfweQHAS99vXh97AcChAsL623wBwPuv+tYzfgHA4g9BbSqAAcABxuglyoIBwPU+gUgXhgHAOrKXQhaJAcDEuNUuxowBwA8vtZ4mkQHAR8Bi+kmVAcCWJcl6K5oBwL9NygbdnwHAOjDsGVymAcC94k0Ts6wBwMM++WLFswHAWKgGH4e7AcArdVNFFsMBwFVr0wMxywHAZTT1/tzTAcCTVJ1FZ9wBwDZhsnds5QHAOXC6reXuAcCm8ssfyvgBwNoMMEqJAgLAQgzvCMYMAsDQJ8I7WxcCwKbzoYqzIQLA7/JdA1MsAsCNJ1ZlLjcCwGRF6pEzQgLAAY0ULglNAsBGFV348VcCwCosCMPTYgLANmc6AadtAsApBUH0YngCwMC57awIgwLAzpw1N3qNAsA7JzMOzJcCwOQkQo3joQLA6gqGd6KrAsAbRugXJLUCwDOh9HctvgLAi52IcK/GAsDhkEv28c4CwLJokS3Y1gLAnak5lkzeAsA//BUdTeUCwAl+3gjo7ALAtd39wyz0AsBuNVDOEfsCwB+42qVeAgPAd1n6x3oJA8D4e9zGRhADwGJzDYpfFwPAe4Ijg04eA8AhJW8o/yQDwKpjk+JrKwPAULf2nlAyA8B5i7HB/zgDwEz9DsNyPwPAkcOqMG9GA8AJ6pZCP00DwGVdVvDOUwPAazhhFyBaA8Cd0gc8CGEDwKbvdQ+1ZwPA8KIhihduA8AK1F5y/XQDwDMTFPynewPAb3TOrwWCA8AHDNL62IgDwPCZjLJ3jwPAirScAemVA8ALBnFzIZwDwLWUTjTtogPAbqLvDIOpA8Anygwe1a8DwHVWGLmXtgPATYd0AxW9A8DpvgSLQcMDwEkE8THqyAPANM1LbYTOA8DklaOzcdMDwOsNe7Sw1wPAaz9cTM3bA8C37wkCSd8DwBKh+m8i4gPAoXFBFjzAA8Dn90kZPsIDwGCLy7exwwPAIsSgmZvEA8Be10OxeMUDwIxeiz3gxQPAbrijqOfFA8Cf809fCMYDwJed7A/1xQPApN7ZaLbFA8BxhpO9YsUDwJqo0y1JxQPA4pELsSHFA8CVzSrf8cQDwB/nQOTzxAPAeP4oQg7FA8Cx/6iqT8UDwLhWs+SyxQPA8S/WdTDGA8Am+SY48sYDwLHw4SMMyAPA1KMK6i3JA8BenkW8fcoDwK/BQJL7ywPAUzXCYE3NA8A/X9kT9M4DwIyQW9TI0APA0NUa4b7SA8DxersFHNQDwBdJ92Oj1QPAGloIYnPXA8DLcG3IUNgDwMzlnp5P2QPAyEeX/WjaA8B2/GiuBdoDwCjGBILW2QPA4zbHdgfaA8DH72kVa9oDwHDlx3D02APAc0RWZsfXA8CgFUR22dYDwJNQLii+0wPAkMJ8JhTRA8B3En3gvc4DwBd3fWj5zAPAcZN89TzJA8A5Z/BAT8YDwE9JGQIzxAPA1a7eAQjAA8CoX9mW4rwDwN9ue0mmugPAS7I24Q+2A8DUJN13p7IDwG/Au6IBsAPAdbxOFiSuA8BB7oHxq6kDwG3MHOX3pQPAZhhj7LqiA8CkILgOgJwDwGSUr+vGlgPALO2I8I2RA8DAmPgf14wDwDmH0cT8hAPAU/17LKR9A8DNj9Nj6nYDwPqd4dt3bQPAp4+vvsZkA8CAV58WBF0DwDL7fPk6UwPAfiFp4YBKA8Dlw9Ak5EIDwGoZyCVqPAPAjPt2BWo0A8BjC2FXqi0DwFh5XE4uKAPASvxV3JQhA8Cmjt5sTBwDwKUfHWg/GAPAb6SgXVwVA8Amcn9DpBEDwDutp9krDwPArWy3+NANA8A45ESKxAsDwCS3pjjaCgPARDO9cPUKA8AZiwNBlgoDwA9XksBGCwPAbIuwifUMA8C0P03/gQ8DwBMnEOYBEgPAOjS+cGMVA8CgbLhWkBkDwNu+IHj6HQPAxKXLDSAjA8CmCmxH6igDwCOP/vY/LwPA124nffY1A8BuTRiMED0DwJgJR0OBRAPAkqA/OXNMA8A4nuj2wUwDwHt3mV3sVAPAX0uzpMhdA8DMTGdkvmYDwAC0UHSYbwPANZ53nlV4A8AjwyJf84EDwOJ6sLZhiwPAYkih42eUA8DAV6Dvhp4DwDcdXaJNqAPA5XLC6pSxA8Bah7adNboDwEbQjvIfxAPADMJ2fpnNA8C7Mz8ZyNYDwJsHZzFe4QPApmBdvRbrA8C6tqR//fMDwCaqbpg8/gPAP6svJJMHBMAyNHFc9Q8EwKv+z8B0FwTAwPCphIEgBMBeeOVUbigEwBQiYot3LwTAJabCP1s4BMAXWqJpcUAEwJa5tZPiRwTAOITP36tOBMB2BzTdY1cEwIlt1R+AXwTAYcTWOuBmBMBf4YpFhXAEwOoyKM7BeQTAZ4LVz42CBMD3QA8Si40EwJIPoTpemATAu01HYeGiBMCElDnjAa0EwIg1latUuQTAbJBofD3FBMAvZpAErtAEwIWch3W73QTAsegCqT7qBMAnJle6GfYEwIGlrupHAQXACCufptcNBcCFMUACphkFwG9t+Mt8JAXABRPT5QgwBcBmo4M7qjoFwAXbM/ksRAXAJZjSHNNNBcCnz9NlXVYFwHjxDZKyXQXAjYL5KqZjBcB4miaI5mgFwJJbcgfebAXAtVP1/GtvBcBNue0ct3AFwKMuwo7RcAXALiKZ0K5vBcBCGlMvFG0FwCVP3gNzaQXAL49+lxBkBcDfGlAnR10FwDRYRO3UVAXAYB3ppPlKBcDpXttKlD8FwJNNW81TMgXAH2EIR7ojBcBPKdN5uhMFwGW7lxJRAgXAoP72nF/vBMC9Vag6btsEwPeIKumbxgTA2Bw06dSwBMARvNdDk5oEwKQ9LUQrhATAkPqJ2MltBMBhEzzIC1cEwIEAZ6CaQATAms87MMsqBMBPJidHRBUEwBIZKJHUAATAQTRnWaTtA8B7lK2cldsDwA==", 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", 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"dtype": "f8" }, "yaxis": "y2" @@ -2502,7 +2508,7 @@ }, "xaxis": "x2", "y": { - "bdata": 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", 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OtD/ywKCdU2u0Py5EwAaewLM/Q8OQyEDbsj/2CWfUlUKyP9mLL3zbfLE/XqHNchqRsD99fAxmHhewP4yYIFwjDK8/kpAVfgG8rT8Oh4meE1CsP3+fT7CSzqs/1krNzdgZqz/dpLx1HjeqP1qNxkltWqo/qSRW3Mu/qT81Xn1loX+oP6digfSm1qg/S71U48XLqD9fWtUlJvinP7V33RerSac/IVWMAISYqT9wBbooROqrPxSz9p1vJq4/ljrxHlpjsT/xoR0g2IizP2uTMeebd7U/Fe5ToK0ltz+sH3R83Te6PyQKIo928Lw/PPcZEIFDvz90mFBHnETBP6hluC+NwsI/Z4xEj2Xjsz8teRcJede2P/SM/dX3iLk/mfT84mm8uz9WVLX/oUW9P3R/lUAwJL8/YSbIVLo5wD8Q2CmdrojAPwEuD2+Z4cA/+O3DYfPxwD/cluQ1AKLAP+GpmfRx1r8/ysPyxq+quD+bExguUd2zP2LWlq+nlqg/XESralk7oz/sdssgkZCbP5NRW0We+JI/Ns2tWn8ohT/w/ocQ5hRiPyz7ZsgbrGu/kWCkSZ2+gb9wXmb00maLvzhxCreXu5G/OtAUpjpkk79liGyFqyCUvzSjI9Gf25O/ti5uHxpukb8nM9kO3jWMv7pSXVhvroO/PZIyIAM0cL8+BqCxCkJkPzwxVsDFu4I/6ACaoRI5kD9bmKAH7kCXP+zMrErzU54/5C/r2OqVoj8wI+yRwNWlP0eF/Z1i+6g/pLV+CE/6qz/2teeQVbGuP0L1Dnw4i7A/TVHpBZudsT8pgXBP54myP8J2P3JhLLM/z9GT21Cdsz+8HmcbXNSzP0tMihHsnbM/Kp6m7uArsz9s45vLiIOyPza12uqYprE/A86Ff+NksD8acMW6RAGuPxBhqRuS6ao/68cnHMDypj/wAIGIIdOiP2ykzvoPRZ0/UFm64T9xlD8WeO3xdaOEPwBVK+pRNB0/hM0aY1bVg79yBLBIQwWVv/Y8Iggyy5+/ETI8QwkLpb8dgRSgtsqqvzYleeJsGLC/9QQhbkugsr9Guj0gWu20v703fKbYlbe/jCxf4YD1ub+fyuOQPga8v81NCxAVar6/qJwTGr04wL/GefB5YgzBv5e6uiBrsMG/T47UUXKUwr8tctDU2EDDvxAlHmrOrsO/m071n3RdxL+D8G5tWMrEv74l9cB++sS/Inud2qxkxb+a9HaZkn7Fv4SCiK4OSsW/JtSxsznLxL/RoSLj0n7EvwhrLtGJ38O/6vgvphoJw7/dcqem4o3CvynIk8er6cG/7CsX3B0iwb+GkS6cYEbAv0Z+XJHHmr+/fTbLB6Vyvr8blMhxoyi9v15QdSe+5by/qYN3M5IRvb8c0McNzPq8v3RycNgMu72/bdpn53Yovr/h5xkG1rjGv+WuBgpRvca/3W7vh4kXx78Q04uZ7FLHv0SI7JArc8e/GJFuBbvhx78O7HgYMTTIv2XJTQEKbci/CC92Ke+OyL8UUp0JEfjIv7EJuLmRWMm/h4RPqNmvyb9a7Vl/iF7Kv53nCIfCCcu/HAD8GOemy7+2P0rGPXfMv7KT7VwoLM2/C9aUZdHCzb/hA8ApRDTOv/t9ga0juM6/X1AZnz0Vz7/rSHIxoEXPvyKaijZees+/g2MEJbqEz78+SkEwcmLPv0FHV/+6IM+/qUvBsnrezr9FYfpzc4XOvzdbMxX8Fs6/6KLV2Pqhzb8SwpgDSRrNv31JPL43gMy/OkfuMOzby79MaWpLxinLv7OzQHhJbcq/Q2vh7Gaqyb+0bTnwGtfIv7yffzB1Asi/j/L+eS8xx7/Iyqlu1E7Gvx6Cwu3GccW/hFr9tX2exL9HqohmbdHDv3eJl6th8sK/whJfLSojwr9HKujAvFvBvzzw7py0hsC/ngFk6ed/v79hdTOuf0y7v2DsCwHx/bm/iAKc58nVuL8cnfl/RZ+3vyacDli2mLa/C2nqSzbBtb88//oJJwS1v8Tnz5fJdLS/EV6uvuUKtL9DzG0CxdGzv1jJyDjqvbO/MkbRpTXWs7/0Qkc8Fxu0v2qqhWqLYbS/BiHo0fzLtL+G3Y7OHE+1v4BrIjMX5LW/UVwZRF2Dtr9iZaJzXSy3v4+hSk+ezre/p3S4Iq9ruL92AghbXvi4v8wfLXnlgLm/UEr9nx3xub95xltCRlS6v5OAtmsNqLq//9m4NWHrur+zAC2aChG7v1kVvRvzJLu/zOa0JI4ku7+DKzBshy+7v9S8Pot9Jbu/4U0MP2IHu7/HVAipc+O6vy5KT6kpr7q/qSZ2LJlrur+joD/jRhy6v2drfUah37m/7ytkujirub8vBIcNnIO5vxwIjCtylrm/1REfjI+qub9ig9iESL+5v9PgI0cF1rm/qeLDlCAOur+am7DFF0K6v4MTWRsLcbq/lTEtg1rKur+YZkcK+he7vz7ZSgWYWbu/rul5qV6su79R0PnTN/S7v5quGzm5lMG/Je1IjT2wwb/1kV0gL9zBvy66+7NCAcK/8C/7QnUfwr+MsfK2/UrCv1LSXQNybsK/ThkHmteKwr+xAJN2maDCvxCJazHLx8K/M0xiWFHswr/LxZKqug7DvxZn33O2SsO/wI9asTyBw78Juo3WJLLDv5FOD8i09sO/iwxl1Y02xL+mo4EKaW7EvwFAXjvTmcS/xPqqMRfMxL9Y2DVzcvLEv62zerPqDMW/f0PEwbEqxb82lR484TrFv0vZvIJJOcW/vyKPCwQoxb9r2hBjYxXFv8LFn2Cn+MS/v3S+3uLTxL9aVMGfCK3Ev2+Yqhk3hsS/1dHpXBdfxL9/FZvmTy/Ev5v5Q5coA8S/vZrwLGzaw7+SlA0RW7XDv2DViex+hcO/ME55PxdZw7/s+R7muC/Dv8mInHMV+MK/EMLuy43Jwr978VBsqZ/Cv/myXU2decK/DQbDr6E5wr8eJQb1IALCvyiAqVjK2MG/fW6hkbqOwb+b3AxcsljBv2C9bVr/L8G/ho/8lYffwL+dwFAIJqvAv6rGbE0+icC/wYppBWNzwL/jtmReMj7Av89xK0MXJcC/lbywLmobwL8qlMwi5Mi/v7MzarG1qr+/37gDn36hv7+h7HCuDqG/v1/tQQ7Gib+/VqTORCWIv7/xSOrpJI+/vxRhxKr8Nb+/gNEgrZELv79qmpIUNfi+v5J4uxzNUb6/31YH0x/jvb/0q7Vvypm9v6y85OEYcL2//549bQu9vL/SjtafOzG8v4WhW3HXy7u/xr/Jr+XAur/aI5gtKdu5v0WshvDjHbm/+OZJHvOLuL8uDfwspHS3vykyiOHfj7a/1l2ZNL7btb9FcNPbbpa0v25LPopbjrO/obXLPFO+sr8iuNT7h1qxv2CfEQkQRbC/QfS4tnnmrr/jKK2ctLmtv+ySwLOxhau/8nXSZMfvqb/csFZ4ZuSov1yLEExf1qa/4xkl9ZZupb96+dC1BqOkvzRu/Az6aqS/arwBbhEio7+ksY/Mlkqiv8fk2qeG4qG/vNaVjS9YoL8ZBfQF+rOevzk+Fouhm52/rDucrz+hqL9RiYMSOXOnv5dcaPqJuaa/CZSSQfFtpr+CBwo0DyKlv9JAyM8uTqS/RLChmpDno78XFAo6nIGiv7Ea5B3VjKG/0NwiFzkAob/yu3meGtmgvzgMajX7op+/Y2NwJ7Z2nr8u9Ek9qh+ev2fOhFXfHZy/0sJgozv+mr/noMaJnbCav8LRkTKx+pi/JF16EjUdmL/10vOrNAKYv0IOyTU1gJi/h0J1/BDhl78u7VMp3gOYvz7eC0PX35i/XYX9Id33mL9+/HXs2aeZv3rYchlgB5u/KtEHfg8Vnb+RUETnGO2ev7si2DAsrqC/Uc/vw4Ebor/KrrSywKCjv19rtbRoaqW/uwljRG5tp7/Cn/t74c2pv86dnRH9Tqy/cTsde17Wrr9iwMiRQL+wv4bam33MYbK/xgNNMdoCtL/5Y+pySp+1v3C7+nfIlre/Yd9rxQhyub+bLDCVkzC7vwH5233K07y/7IHS0RPMvr+efzGlj0bAv/R7Gf7fC8G/u8vSS68Nwr9K+knyB+HCv8oEhqhfjMO/S9CxfbqJxL9M1nMPGUXFv9HU5beI28W/1WZlpW1Mxr/x28kNixLHv2eUruEYnse/gMVoFl0KyL/5xAxTnvHIv/3QKM1Tosm/KTf1L9Epyr9NHwfXDpHKvxS7yrrXT8u/a+U/yDDSy7/d0zuT/yTMvxhOHiMQ38y/qOya1t9Xzb+Q6VycPqbNv8/9fd9jhc6/wNYtGFExz7+PCjWobrXPv4+2mXPEC9C/7hXJIzaA0L9CsY5/5NnQvy29I/LYHtG/pEHHPFKr0b+hZs/R4h3Sv+dj7V+0etK/NMbM7YPE0r+hNlT/elHTv4Cr52tWxdO/MwxCS/Ih1L81IAoSs8DUv6qXcL6vQ9W/IMkU2Eat1b98ljfyX1fWv+9lE4Li4ta/JuZZX+tQ179uPusri6LXv4Nry+VsKNi/UPhd3umN2L+bbipbxNTYvzeWZiATSNm/tTjp/ama2b/gnHr9Us7Zv9rNPDlx5Nm/FmOJ8n0c2r/cqj5jsjPav4m5SAAeLtq/a9Td3LZE2r8jKK2GuCjbv+LTBui4Ctu/ixx/4rYB27+DiEVGwd/avyE5kOu4qNq/uhvDWIZe2r/w70X1byHav8jDfbbr0tm/mAPZdPh32b9R2a0SeibZvz91/aioyNi/ZsyY+Gpf2L88vmyDz+fXv3kiYUv0d9e/D4Oc6bj61r+VMp2ytG/Wv9lVofGo5NW/b002HvFd1b8A/YPBN9jUvxfIUebnRdS/d7SjQX22079NtElnOC3Tv5lnxsP+qtK/bqa6mYof0r/FBRprDKLRv2JN2SfeMtG/bEl+Emey0L8bpvdNvEDQv+TuddGwvM+/2Oh/lEoGz783N2A2ny3Ov2lSXgTFbM2/qwqI2dTRzL9q/P4AFvTLv4BsWBrMOMu/HcoLExCWyr+hphDGQKrJv0T55qf45si/PIDEK5xLyL8nqyS9ftfHv2c075C5K8e/y3KWv9+hxr9kTLry5zzGv6ZIY6F3iMW/chrF+vDrxL/4xEdcGWjEv6ReSgdqA8S/41VW4UNFw7/uZSRlAqbCv3ftMYGMIcK/KO4GM45Gwb8ED7zA+ofAv97GcthKw7+/kgbwh/XIvb9+x132aP+7v6mtgaB/Z7q/GIMXmqD1uL9GTpvER7G2v5hHohhZoLS/dHNlk/LPsr/6oHSVuzuwv1UD3j91z6u/NlxVyya9p7/Zm3ZPp2Skv8aCRZ6i3J6/ZLW/h4Sqlr9UpFRZG1uQv2j6bFeNJH+/COAZ+au/Vb/u5NLHPVBmP5i88dnnVYI/KteZnCFQij9OqGbApWGOPxys7kDUo40/oWwN3G5hkD/1sFtW86+OPx1I8yUujoY/0nsbNcavgD/G6ePq0dVgP6E9SO2/pH2/zFrGKoDxkr+fzIhXtyqcvxSTBzBeWaO/ThE2+lE0qb9EW3nF0+Otv+6dN7+XfbG/5EbqBCIytL/4960aZVy2vw==", "dtype": "f8" }, "yaxis": "y2" @@ -2539,7 +2545,7 @@ }, "xaxis": "x3", "y": { - "bdata": 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", 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", 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", 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", 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TaUDUBRD22dJpQNh1Lr620WlA8kXJApPQaUBzFfe8bs9pQEi2beVJzmlAevpwfyTNaUDwLlCE/stpQLtST+zXymlALDuAuLDJaUAt4NfpiMhpQNkFDnpgx2lA0IaOYjfGaUB5TrSjDcVpQNMiWTXjw2lAzpxsEbjCaUDjZU47jMFpQLN6S6lfwGlAJRAXTTK/aUAUovsaBL5pQBtKFAjVvGlAZmxmAaW7aUCo8Drzc7ppQOcorNdBuWlAv/ndiw64aUCcVJTu2bZpQIB6pf6jtWlAUQF8vWy0aUAbfk8QNLNpQFR/1Nn5sWlAJsxiG76waUCn8pbKgK9pQLzAhNlBrmlAYkLf7iPbaUDJHjSYHdppQH72XJYV2WlA8BJy5QvYaUBxvvWIANdpQAojaXzz1WlA1sFJueTUaUBSTvlF1NNpQMzxRSTC0mlA64M3Va7RaUDPsUHmmNBpQHqD6u+Bz2lARappUTXMaUCSgCwk/MlpQHgObAKfxmlAV7o8UX7FaUBZIFL3XMRpQOJVIAI7w2lAMzmyexjCaUCJKEd+9cBpQMeQoBTSv2lAQ15RSa6+aUAQ+zcuir1pQP1+B89lvGlAHRekNEG7aUD/ba5kHLppQDmNl2X3uGlAVeIWPtK3aUDC0nvyrLZpQLe5EYKHtWlAkJpf8GG0aUAIY1U/PLNpQFDmLWoWsmlAfkUicPCwaUDM46lXyq9pQMwHVySkrmlAyTLI0X2taUAYfRNlV6xpQPnnj+Awq2lA8+13OQqqaUCiP21j46hpQOZoRF+8p2lAFDQ2LJWmaUCPxGC5baVpQPf2iwhGpGlArc/wGh6jaUD6Ip/e9aFpQJO4WVjNoGlAJWjui6SfaUAahcJme55pQK6PidVRnWlATKC/2yecaUA0N2h8/ZppQByllKHSmWlA6bM3UKeYaUAYYSiMe5dpQA46eT5PlmlAxmduUCKVaUBXclnH9JNpQAzbjqnGkmlAeryW35eRaUBWdVZwaJBpQPT6/GI4j2lA2hjNoAeOaUBwoHM01oxpQIk4PSiki2lA4lHyZnGKaUCk92rbPYlpQP4gypIJiGlAYT94m9SGaUA0TtzinoVpQH3NqnxohGlArmVifTGDaUDsXRPW+YFpQOAvnXnBgGlAf1s2hIh/aUAMv3cST35pQOnN8xkVfWlArEOfvdp7aUDgTsQgoHppQJYOITxleWlAqosaOCp4aUCUEN8873ZpQFTZJkO0dWlAGOloRHl0aUD7e7JqPnNpQIezctwDcmlABo4/jslwaUBVOaClj29pQLlH2kdWbmlALwMpaR1taUCYfzz+5GtpQNSO0SitamlA0XGqB3ZpaUAj2ZOMP2hpQHT0ZNMJZ2lA4wTn8tRlaUDPQv/VoGRpQM2tdJJtY2lAN2kvPztiaUDpD2YrJYdpQMlk3RoihmlAmdUm4R+FaUAz1uKJHoRpQA+7Rvsdg2lArWqcOR6CaUA92VdHH4FpQLOVqgYhgGlAEUa8WiN/aUA9R4JDJn5pQLM4vsApfWlAI6LOtC18aUDeELEZMntpQCcjV+o2emlAjH9WCzx5aUAEUUZ3QXhpQIXGkihHd2lA1cpXBU12aUBPmHb1UnVpQFZX/fBYdGlApX8Q8F5zaUCG0M/cZHJpQLRKyq1qcWlAdBn1WXBwaUBN35bOdW9pQHLBZfp6bmlA3ddW0n9taUBVYDpMhGxpQPiuP1mIa2lA5aTw7otqaUAfCN4Dj2lpQIpPSY6RaGlA1bv4hJNnaUBEYtnelGZpQMzuDJiVZWlAou0IsJVkaUDZAy8glWNpQF3XC+STYmlA7PpaAJJhaUDA9glxj2BpQNBtPzKMX2lATaoJS4heaUCRHQbEg11pQMQG8Zh+XGlAafEnx3hbaUBiwXpaclppQArmmE9rWWlAGtd7o2NYaUDq7b0aSVVpQPSJrSI/VGlAx7tirDRTaUAgYl+0KVJpQMTvqDceUWlAkNFKRhJQaUAIabzbBU9pQM/uCfT4TWlAEYdGoOtMaUAdapHx3UtpQBW+W+LPSmlAyr+ZbMFJaUCa8HygskhpQLflzXqjR2lADJx+95NGaUCkQBAmhEVpQDL7dQN0RGlALszni2NDaUDmjRjOUkJpQOP2wNhBQWlAW77wpzBAaUDAqwU3Hz9pQKUhx5INPmlAmIKHt/s8aUA+ZbGg6TtpQKwsxVjXOmlAap4C6sQ5aUC3pcNPsjhpQNPH0oSfN2lAPyj8kYw2aUCGWuhxeTVpQJadwx5mNGlA95iAn1IzaUC08CjvPjJpQApVowgrMWlAD6kk8RYwaUBuCzOtAi9pQLAafzjuLWlAaCHwjtksaUBs/gW1xCtpQNCoL6evKmlAc/qbYZopaUDg4h7nhChpQJ4UtDlvJ2lAgEg6VVkmaUAYLvY0QyVpQBmodtksJGlAW50pPxYjaUAdxxJi/yFpQM7ZS0HoIGlATjeN2NAfaUApns4juR5pQJGV0rlFQWlAqnxxyFZAaUAntlCLZz9pQE0uCQB4PmlAD6P0I4g9aUCjXqT1lzxpQDhwWXOnO2lAjbxFmbY6aUDsryBjxTlpQLlGec/TOGlAPRgw3eE3aUCkE1CI7zZpQLAN4tD8NWlACH16tgk1aUDjUBI0FjRpQGIy1UgiM2lAkmR78y0yaUBNZ+wtOTFpQC8u0PFDMGlAAY1QPU4vaUAiRZQOWC5pQN06Dl9hLWlAWRSSLGosaUDnaKV0citpQEok3C96KmlAed91VoEpaUAr4UDlhyhpQJcsddmNJ2lAdHcvLJMmaUDRBw/blyVpQCZlW+SbJGlADhSbQp8jaUD54r3zoSJpQEOHB/ajIWlArp7QRKUgaUAnYbrbpR9pQNIid7mlHmlATAxo3KQdaUD17VdAoxxpQK6J2OSgG2lAP6+4yZ0aaUAAykDsmRlpQNIrT0qVGGlAW4Ar5I8XaUCdgoi6iRZpQH6c6MyCFWlAFRzmG3sUaUASbliochNpQCJiw3JpEmlA0iHpfF8RaUDlhYjIVBBpQD1u0lZJD2lAKg1IKj0OaUBFYQNGMA1pQFltka0iDGlAZht7ZRQLaUC20RNyBQppQBAXeNb1CGlAzUoRluUHaUDfFje11AZpQMLAVzPDBWlAQsEvD7EEaUCoRRpMngNpQArbm+qKAmlAIqgR6nYBaUANIopMYgBpQNpX+RJN/2hA3I5lPTf+aED+RDnNIP1oQODfksQJ/GhAq5I8I/L6aECdrJno2floQNUXTRbB+GhAash9q6f3aECT+OqmjfZoQAbV7glz9WhARoFT1lf0aECieeAKPPNoQPaZMqYf8mhAMnEjqgLxaEAkdf0V5e9oQFYct+jG7mhA4jEuJKjtaECKsejHiOxoQMMk+dJo62hAklyBRkjqaEBC78wjJ+loQMg8cGkF6GhAVN/gFePmaEDNRL8qwOVoQHPHaqec5GhAgapHi3jjaEDXgXvYU+JoQO8Sd5Iu4WhAqYpovAjgaEBKTUxa4t5oQNUWSXK73WhAZ5q/CJTcaEDQeb0hbNtoQBFk28JD2mhAAHizlib5aEBUncYoIvhoQORXKFEd92hAkliyFBj2aEA3pJZ2EvVoQJj9l3kM9GhAlFdVIQbzaECJ16Bv//FoQKxeY2X48GhA+ZMDBPHvaEAQXlZM6e5oQD4g9T3h7WhAaOJS2NjsaEDuT2Yb0OtoQBc2qwbH6mhAy3nsmL3paEB95mvQs+hoQOrc+aup52hAuOKSKp/maEAoXeNJlOVoQOncbQaJ5GhAwPh1Xn3jaEDNkhdRceJoQMXlGdxk4WhAIkmY/1fgaEDQr9i6St9oQDO83wo93mhAVV/K7S7daEBikvplINxoQMnDwnUR22hADkeWGgLaaEBiR+dX8thoQHt99DHi12hAb7Vhp9HWaEAqbV2/wNVoQMNWa4Cv1GhABwV/553TaEDeFaPxi9JoQBVmVKd50WhA9+i5EGfQaEDQLycrVM9oQGUIkABBzmhAfGXcmS3NaEDvMkfzGcxoQFFngAgGy2hA8oDB4PHJaEAbOEiC3choQHRPIOXIx2hASLrjD7TGaEDD5BYIn8VoQAGLHcSJxGhAFlaSS3TDaEAFOX6kXsJoQCBvH8VIwWhAMaWzpTLAaEA3+G1PHL9oQMzIsMsFvmhATgxNFO+8aED35BE22LtoQNb7gj3BumhAs/mLJaq5aEAM4hrpkrhoQK98Z5V7t2hAGLnWN2S2aEAwkdrKTLVoQN+lCFk1tGhAHrVL7B2zaED+/zx8BrJoQO/oMA7vsGhAtCfRptevaEBHWjY7wK5oQOFTEcCorWhA9AYON5GsaED1SD+heatoQIoT+vFhqmhAwOIaKEqpaEAaUkBCMqhoQELlpDMap2hAY3hH8AGmaEBUHxZ26aRoQFsRi8LQo2hAJXDRybeiaEC8pk+JnqFoQATkVf6EoGhAy6IkHmufaECrlnblUJ5oQDgu31A2nWhAx4M1VhucaEDwZlrs/5poQD8IzA7kmWhAAJD5uMeYaECFFtXiqpdoQFMr4YaNlmhAmJj2n2+VaEAvUbEnUZRoQIkuqBgyk2hAFJgcbhKSaEB9HTYk8pBoQK5IBzjRj2hAnhNlpa+OaECt2eBTl6toQMW9bvKWqmhAIkMf4pWpaEA+zRMflKhoQKtWH6qRp2hA5CcahY6maEDrZFWsiqVoQHmfgRyGpGhA++JG2YCjaED0bXPfeqJoQPindit0oWhAHZYtwmygaEBuWgqqZJ9oQFDdJeBbnmhAe4WBYFKdaEAC81YvSJxoQHh/ckc9m2hA9dZOpjGaaEDQFndUJZloQLjBlk8YmGhAt4+vlQqXaECjPeEv/JVoQCHBGiftlGhAcq0ZeN2TaEDznbQgzZJoQOnwdyq8kWhAlduykKqQaEA1YjVOmI9oQO/neWmFjmhAAVBl6HGNaEAA6fbEXYxoQG2D0PhIi2hAoGcCijOKaED8/H5xHYloQGYCiaYGiGhA8yabLO+GaEDX5NP71oVoQJNb8gq+hGhAzG5wW6SDaEBDaPzviYJoQODuUsBugWhAFk5FxFKAaEBoBHL/NX9oQAmNCW0YfmhARSm0CPp8aEDa8qjX2ntoQMB8zN+6emhAE9aRHpp5aECWdZuReHhoQNPBn0BWd2hAiLnLLTN2aEDSM/paD3VoQKsATNHqc2hAi6NclsVyaEAHucSvn3FoQDMw2Cd5cGhAm+VWClJvaEBgTkNiKm5oQBHr/DkCbWhAWamGndlraEAVr4yasGpoQBVycj2HaWhA3vh/kF1oaEDOq1+fM2doQPwPT3YJZmhAFvnhIt9kaEAgKreytGNoQMCvwzSKYmhA2x4AuF9haEDNDhZJNWBoQMoi6vgKX2hAvC0b1+BdaECreIPvtlxoQBz8hE6NW2hAP4HhAmRaaEDwD4QZO1loQCCIyZoSWGhArry4kOpWaEAN/m8Dw1VoQHNDDPabVGhA/4YoaXVTaEBXjIBfT1JoQLjZ0dopUWhADv+41gRQaEAQKSVQ4E5oQEz/uUG8TWhA7tw0oJhMaEDCAmphdUtoQA==", 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", 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", 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", 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", 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", 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", 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zAcBDnIG5l3EBwLzz8GP8bwHArZe4DnFuAcBHJT0B9WwBwANqd/eGawHAp6AqRCpqAcD+a8nf22gBwFtumCmYZwHAs+Jv+WBmAcDYFUvJN2UBwLp/VncYZAHAQtqaWf9iAcD669lh7mEBwO0r+z3hYAHA5xf7fNZfAcBBvLM61V4BwGsMuaHYXQHAUe3hgtpcAcBMUiLX4FsBwH6UDeTuWgHAZkJHnfRZAcAln8Kl4lgBwEnnHF25VwHAtm2i82xWAcCR/Kvg8lQBwKAnPQlPUwHAtccpNIZRAcCx9c24jE8BwG7Cv+5XTQHAVXtIe+9KAcB72hG4TUgBwPtyqOVrRQHA2wv/tzGAAsBmaVoyfH4CwBqgTtuMfALAx9VgZ2t6AsCbnl+wI3gCwPh8Hhm5dQLA9cN89S1zAsBnJ+MYj3ACwNNpT8zibQLAiy7gFC5rAsAlquF0fmgCwFdHT9rjZQLAD53lUR1fAsD6hgifjVsCwC/jzW6yVwLAgwEEGdJWAsAy+UIWKVYCwCbEd/ixVQLA5RW6VWZVAsA8wuV5RVUCwH65+oxIVQLAhNxLH2hVAsAAgxDpoVUCwLbEB2HyVQLABbovalFWAsDN65CUtlYCwNRzbPAcVwLApf6cZHxXAsC8lucezVcCwCpsumsKWALAQEy8+y1YAsDlmZ/fMVgCwKTa6WoTWALAFdCo6tFXAsDu6+bdbFcCwMT4IbbjVgLApgo37jZWAsCeCJr+Z1UCwKBTBFB4VALA5kTTLmlTAsAxSFLRPFICwLYo6En2UALArG5yhphPAsCtHE9WJk4CwPMtGjKkTALAklw0ChdLAsCnT5kqg0kCwJm03kPvRwLADJSGGmJGAsBXAdQ84EQCwC8XOfFtQwLALygbWRFCAsA8Me3ez0ACwEgVwnmsPwLAAvnANK0+AsBDgqJX1z0CwHzOJAosPQLAi+mQyKw8AsAoPDQJXjwCwFKNIFNDPALATnbKaVw8AsC+ikuaqzwCwHCT+MoyPQLA294b6O89AsBLq9zM5D4CwCQV98MSQALAAFu9SHZBAsAGurVGC0MCwA2jaQzTRALAW0fVV85GAsCvhS0X90gCwPWE0XVNSwLAgTaFE9FNAsDwusFlelACwDZhiMVBUwLAUoCKgihWAsD9hfxHL1kCwMR6CKRMXALARNDTxIBfAsBmzW6Uy2ICwIFlWfQiZgLAScYirYZpAsB/Xh739GwCwNEDdiBhcALAFzl28b5zAsC2j4GADHcCwMg/aDVHegLAzP5aBWR9AsBzM4LSZYACwDr3pKFQgwLAdVp8Vx6GAsDHGeuCyogCwLqgP2xbiwLAqGLRz9aNAsAtOTwoOZACwM+PSqCLkgLAz8hnLN2UAsBc2iOQL5cCwGETzrqImQLAGf1w9u2bAsDJM4kisZwDwIgF3ihUoAPAacPzwf6jA8CClfNStacDwBWBnjhzqwPAISw0ijyvA8DEWtBMFbMDwH1pfjT5tgPA8aKrXuS6A8AzfSen2r4DwGyuHBHhwgPArCkDL/bGA8CuutNAIMsDwCJqwf5lzwPA/+CSAcbTA8AijBpAQ9gDwLpCeqLf3APACAKRi5bhA8DKQ76VYuYDwAUt1D5C6wPA+s+t5zPwA8AMFZjiMPUDwGPgmvM1+gPA8rmJEUD/A8CVWERGSAQEwJ3S2XtICQTA2fQ0Ij4OBMAMElVdJxMEwGIGuqMAGATAVPE/t8ccBMCKtnyUeiEEwLF+Z0QWJgTAm0NQfJgqBMDOyA1Q/y4EwHJtHtBIMwTAH66upXM3BMCAZ/n8fTsEwFvB1mpmPwTARHWmGC1DBMD+r0bt0EYEwJrXzfdQSgTAYEPEbK5NBMA/WImT6lAEwLfrOnsEVATA5O699/tWBMCd/a/s0lkEwKnJGuOIXATAnwCh6B1fBMBMpeSTLGYEwL3QV0pOaATAgDSCxlZqBMB9M/b+RmwEwF8NPUsgbgTAm1pEeOZvBMDCcxV3nHEEwITQ0ilFcwTA/7Qc5ON0BMAS8vwYfHYEwCMGyzkReATAeMnTh6Z5BMCO0baPP3sEwKcEbSrefATAGJk32IN+BMAq1FcGM4AEwMoVk2jtgQTA6GLdHrSDBMB+DT72h4UEwADYOg1phwTAH3m06VaJBMDmn7GuUIsEwH6U6YlVjQTAF4fwV2SPBMDxoR2ae5EEwKmpPS2akwTAU5Fmy76VBMByyGKc55cEwK/dsroSmgTAAJ+woD6cBMD/0Nzyap4EwAT/g06XoATA41wGD8KiBMD5oD4r6qQEwEq0FcAOpwTAVzGoiS6pBMDsVyxzSKsEwO6wbMNcrQTACWYBjmyvBMAFBk2qeLEEwHBizfGDswTAIYwWyJC1BMDnlXpAn7cEwFs41nWvuQTA14ts0sK7BMAxWpGB2r0EwN0GhiX2vwTAm2anPBfCBMCV4Q4BP8QEwJFzJIBsxgTAqjuo7p/IBMAv4GmM2coEwNljK/ZlRgXAYzZURThJBcDo999LEEwFwLMfoM3uTgXA940VsdJRBcCl+nN6vFQFwLB2aJWsVwXA9EX9y6FaBcCEubUFm10FwI1VoR+ZYAXAaNA5SJ1jBcCfa40dp2YFwKxB/n+4aQXArzh4C9NsBcBKXiXd9W8FwBwzfRQicwXAGSZ741h2BcBEW+1FmXkFwD+WYpnhfAXAbZFQazGABcDgQi1UiIMFwAqlqmzkhgXAAZsSBUWKBcBkA9FKqY0FwJhNeLsOkQXA9BrfqXKUBcBiZiK505cFwH/6i/4wmwXAdnWdBomeBcDzdB4C26EFwEQFFMcmpQXA9dftT2yoBcBFESblqqsFwK5twh/irgXArSD9jxKyBcCeCfXGPLUFwIeuyTJguAXAi04wPXy7BcA5thRrkb4FwC7fZ9aewQXA1AJ7FaTEBcAc88xBoscFwAm9EgmaygXAON31ponNBcAbNO+1b9AFwPlwtwVO0wXAHM1AGiPWBcAZoJLv7dgFwG4C4TCx2wXAdTvAwGreBcBJqQGwGeEFwMcEK7jB4wXAkb1XSmXmBcBrX6bbAukFwBVGfSma6wXAQKr7ri7uBcBhjZEIvvAFwKWIY8dH8wXAHQ7L8s/1BcBcylq7V/gFwDatGZHe+gXAqMfEZ2T9BcASrG916v8FwM2S4TltAgbAeCMdu+oEBsAc88nDZQcGwEPAPF7ZCQbAAseU40EMBsBiCNIMow4GwHCRSpz/EAbAEgyFV1MTBsAUcddRmhUGwCXCdKbXFwbADvd8PwYaBsBExub6IBwGwMgOBPQqHgbAhAXWhCcgBsC1U3ETEyIGwBZSwE/qIwbA73pBMbElBsDKDErbYycGwD2+cvL+KAbAG5GQJ4cqBsD0lznP+CsGwMzbtyJRLQbAAw6q+JUuBsBRWk5mzC8GwCOac4jxMAbA46PqRAMyBsBH/VyKBzMGwBi6nfH7MwbAOTXfod40BsBCcgwOtjUGwPAzJkmINgbAhZZg0VI3BsCHTZp2EjgGwE5887fLOAbAIZGCAXs5BsDXgeBSHToGwELZTMG3OgbAX7IbwTKjBsAQutH1KKQGwPfhuGkVpQbAbQwKpPylBsC0zBa426YGwBMCXeyvpwbAAmSM1n2oBsDCrm+KQqkGwNs2jzD7qQbAFcuiIayqBsC9bJF6WasGwDj4dMUArAbAciEgyp+sBsDRTFnvOq0GwKwXQxLQrQbAuVOvMV2uBsBLXymq5q4GwMFjn/pqrwbAwv8rs+ivBsADsK7vY7AGwKpdqSngsAbACDOjDlyxBsA/SV+11rEGwMlQUuhTsgbAA9aki9OyBsDPvwUvVbMGwJfkxhjcswbApTAOyGu0BsBoVThxBbUGwLZ+Jg6qtQbAeMTlNFy2BsDkbLdyHbcGwC0MZqDvtwbAuWfdQdW4BsDH7hJb0bkGwKJun3HmugbAaXDUTBW8BsD0dn+0Xr0GwPBq3HPGvgbAYg9Hn0/ABsAJljf4+cEGwEKxy/PIwwbAZCApFb/FBsC2TzYV2scGwFXBMLYXygbAYcFNR3rMBsCc+xX6As8GwD8jtmut0QbAHdrcRXzUBsDGTumncNcGwFa9HqeD2gbAFr/kn7jdBsCqTkDaEOEGwHOXixqE5AbAw3mdawzoBsBkIb+dresGwCNO89Jp7wbAgrI/2znzBsDLxJoMJfcGwFJ6jeAw+wbA8lr8rVX/BsCOa2GUjQMHwGys2f/cBwfADYvBG0YMB8BAsd9EwBAHwHDHEvBPFQfA9hGwJ/gZB8AOJUNOsB4HwGYog5OAIwfA6EhAc3AoB8Dk3FSleC0HwDzp9EGTMgfA39eAWMg3B8Ba81xpHj0HwNfYiduNQgfA/+01zx5IB8CRvJiQ2E0HwG676H+zUwfAAj//F6lZB8BbgkMMwV8HwE7rYRcCZgfAKITXfGRsB8AquyEZ73IHwEluiRCoeQfAMbKu4YaAB8BPxTTOkYcHwOWwo0POjgfAAvD9pTKWB8DKdHOytZ0HwLx1JDlbpQfAQepCaCatB8DFA2AqDbUHwLnEGbURvQfA2RiI6TXFB8D5j9uYb80HwGJYVxa11QfAdPcvFQfeB8A0tXnCZeYHwAbTAULH7gfABQeGeiv3B8D+nP/UqEAIwBl0XdVUSQjA8BdHmPpRCMDdHKJ7mVoIwEniWSIqYwjAbGhMHKZrCMD1gkxxDHQIwHk7nXBcfAjAtdUnU5GECMD/+/eeqowIwNyim96nlAjAOBBxRYWcCMBTiDGzPqQIwKoo5xjTqwjAHa8mkEGzCMBaq4PAhboIwJphTnSdwQjA42RnW4nICMCoPMFPSs8IwG8zp3be1QjAF63mQkTcCMCbadkmfeIIwANcxD6L6AjAaLxXMW7uCMAkdCzAJvQIwLRKQnG5+QjAFoIAyyX/CMDHHW1nawQJwNCtVBaPCQnABP7wb5QOCcC0hCvdeRMJwFAhxoc+GAnA+nrvX+ccCcDEzgoWciEJwMtFiRPcJQnAU3PiECoqCcC9iFYsWC4JwIdsfMtjMgnAb5PUXVM2CcAIlp84LToJwLALzwvwPQnAl/FTGppBCcC8pCMOMUUJwHSy336xSAnAsvIJ+xZMCcDga9dlZU8JwA4u2TahUgnAj5Dew8VVCcBPYaNdzlgJwMMyjaG/WwnAzLffx5ReCcCsdecqSWEJwEmDDgHhYwnAqdrpR1dmCcDmnHABp2gJwJ4PaR3UagnAd8ySHeJsCcDAXbsbym4JwIAw1LuFcAnAy8IUoRlyCcAs3FqKgHMJwApJFjW1dAnA7C00Mb11CcDKqWA0n3YJwGSJbq5WdwnA4BJvxt93CcAvTN28Q3gJwF94PZuBeAnAX1edFpl4CcCp+I+AlXgJwNav7vZ2eAnAfl7q4z14CcBNYFpm9XcJwJC4+qaodwnAVO8D21h3CcD2pCK4B3cJwENRZKXDdgnAETIEfZF2CcAPCy3QdnYJwL7BZ3eEdgnAyQes/8d2CcBV2zqpQHcJwJ6MoOTrdwnAMUiQD9B4CcAlUS4x6nkJwIZDvGA2ewnA2Ec2VLh8CcBDlZElbH4JwA==", 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", 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", "dtype": "f8" }, "yaxis": "y3" @@ -3729,7 +3735,7 @@ }, "xaxis": "x3", "y": { - "bdata": 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", "dtype": "f8" }, "yaxis": "y3" @@ -4682,4 +4688,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/python/gtsam/examples/Gal3ImuExample.ipynb b/python/gtsam/examples/Gal3ImuExample.ipynb index 907559a492..513a5096a2 100644 --- a/python/gtsam/examples/Gal3ImuExample.ipynb +++ b/python/gtsam/examples/Gal3ImuExample.ipynb @@ -24,7 +24,13 @@ ] }, "source": [ - "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information" + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" ] }, { @@ -37,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 19, "id": "e1b272ef", "metadata": { "tags": [ @@ -56,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "id": "fdd02b60", "metadata": { "tags": [ @@ -95,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "id": "a46d4dc2", "metadata": {}, "outputs": [], @@ -111,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 22, "id": "9c6085bd", "metadata": {}, "outputs": [], @@ -136,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "id": "675a9ec7", "metadata": { "tags": [ @@ -1639,13 +1645,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "id": "7698e840", "metadata": {}, "outputs": [], "source": [ "# Initialize EKF with ground-truth state and modest covariance\n", - "X0: Gal3 = scenario.gal3(0.0)\n", + "pose = scenario.pose(0.0)\n", + "X0: Gal3 = Gal3(pose.rotation(), pose.translation(), scenario.velocity_n(0.0), 0.0)\n", "P0 = np.eye(10) * 0.1 # std ~ 0.316 on each component\n", "P0[9, 9] = 1e-6 #small variance for time \n", "ekf = gtsam.Gal3ImuEKF(X0, P0, params)" @@ -1653,10 +1660,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "id": "022045d8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'gtsam.gtsam.ConstantTwistScenario' object has no attribute 'gal3'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 19\u001b[0m\n\u001b[1;32m 16\u001b[0m Xk \u001b[38;5;241m=\u001b[39m X0\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(times):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Ground truth state from scenario\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m X_true: Gal3 \u001b[38;5;241m=\u001b[39m \u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgal3\u001b[49m(t)\n\u001b[1;32m 20\u001b[0m ypr_true\u001b[38;5;241m.\u001b[39mappend(rot3_ypr_rad(X_true\u001b[38;5;241m.\u001b[39mattitude()))\n\u001b[1;32m 21\u001b[0m vel_true\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39masarray(X_true\u001b[38;5;241m.\u001b[39mvelocity()))\n", + "\u001b[0;31mAttributeError\u001b[0m: 'gtsam.gtsam.ConstantTwistScenario' object has no attribute 'gal3'" + ] + } + ], "source": [ "times = np.linspace(0.0, T, N + 1)\n", "\n", @@ -1759,7 +1778,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "b16a8666", "metadata": { "tags": [ @@ -2797,7 +2816,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "9d0e25cc", "metadata": { "tags": [ @@ -3838,7 +3857,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2ea60e82", "metadata": { "tags": [ @@ -4886,4 +4905,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/python/gtsam/examples/NavStateImuASVExample.ipynb b/python/gtsam/examples/NavStateImuASVExample.ipynb index 6a3184e7f1..2845ec4850 100644 --- a/python/gtsam/examples/NavStateImuASVExample.ipynb +++ b/python/gtsam/examples/NavStateImuASVExample.ipynb @@ -29,7 +29,13 @@ ] }, "source": [ - "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information" + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" ] }, { @@ -42,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 1, "id": "e1b272ef", "metadata": { "tags": [ @@ -61,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "id": "fdd02b60", "metadata": { "tags": [ @@ -100,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "88e15df8", "metadata": { "tags": [ @@ -149,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "6a6557a9", "metadata": {}, "outputs": [ @@ -205,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "id": "735490dc", "metadata": { "tags": [ @@ -227,996 +233,10 @@ "mode": "lines", "name": "Delta (s)", "type": "scatter", - "y": [ - 0.01, - 0.01, - 0.009999999999999998, - 0.010000000000000002, - 0.010000000000000002, - 0.009999999999999995, - 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", 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", 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", 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", 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", 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", 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", 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", 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", 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O+xMJwKQC1G6YGAnAN5TBSTAdCcB08B2DwiEJwEBJmeFPJgnAQ0vcktoqCcDm3vjqYi8JwHpa3V7pMwnAddlQ5G84CcBWDMi09jwJwPg34KB9QQnAGeVD4gVGCcDfXy+nkEoJwKqE1IYdTwnAoMpGMaxTCcD/3I89PlgJwNPxPtfSXAnAHv37bmhhCcC8QebW/2UJwOTy5LeYagnAXvaS+jBvCcB9logFyHMJwNeWqVFfeAnAnjzALvV8CcDR724BiIEJwC0mPyEZhgnAsLmNwaeKCcD14t49Mo8JwKbTjGW4kwnA8isLcDuYCcDBkQV3upwJwEpsOng0oQnAmHiaL6ulCcA0vhZiHKoJwNh2JgeFrgnA/c11YuSyCcBKUTj/OLcJwEp3qtR+uwnAfuc0tbS/CcBBtfyu28MJwOuZOsnxxwnA2IbCxPbLCcCVoRRl7s8JwJB8XaTb0wnAfizNZ8DXCcAgLmOVn9sJwBTFJJF83wnAPEe0hlbjCcAR+clxLecJwPxe4WUE6wnAYVADhdvuCcDS6+j5sfIJwA==", 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", + "dtype": "f8" + }, "yaxis": "y3" } ], @@ -76073,57 +3972,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], "histogram": [ { "marker": { @@ -76324,6 +4172,17 @@ "type": "scattergl" } ], + "scattermap": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermap" + } + ], "scattermapbox": [ { "marker": { @@ -76818,4 +4677,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/python/gtsam/examples/NavStateImuExample.ipynb b/python/gtsam/examples/NavStateImuExample.ipynb index c5618e22f5..9c794dff0d 100644 --- a/python/gtsam/examples/NavStateImuExample.ipynb +++ b/python/gtsam/examples/NavStateImuExample.ipynb @@ -24,7 +24,13 @@ ] }, "source": [ - "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information" + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" ] }, { @@ -1770,7 +1776,7 @@ "dtype": "f8" }, "y": { - "bdata": 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"dtype": "f8" } }, @@ -4870,4 +4876,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From c624518f35f382cdfa046cb21a52288767738c43 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 25 Jan 2026 22:40:08 -0500 Subject: [PATCH 132/266] Expose/use gal3 --- gtsam/navigation/navigation.i | 1 + python/gtsam/examples/Gal3ImuExample.ipynb | 85 +++++++++----------- python/gtsam/examples/Gal3ImuNEESReset.ipynb | 19 ++--- 3 files changed, 45 insertions(+), 60 deletions(-) diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index cddc41d147..d68cd1d31e 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -551,6 +551,7 @@ virtual class Scenario { gtsam::Vector acceleration_n(double t) const; gtsam::Rot3 rotation(double t) const; gtsam::NavState navState(double t) const; + gtsam::Gal3 gal3(double t) const; gtsam::Vector velocity_b(double t) const; gtsam::Vector acceleration_b(double t) const; }; diff --git a/python/gtsam/examples/Gal3ImuExample.ipynb b/python/gtsam/examples/Gal3ImuExample.ipynb index 513a5096a2..5ccfa30a83 100644 --- a/python/gtsam/examples/Gal3ImuExample.ipynb +++ b/python/gtsam/examples/Gal3ImuExample.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "id": "e1b272ef", "metadata": { "tags": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "id": "fdd02b60", "metadata": { "tags": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "id": "a46d4dc2", "metadata": {}, "outputs": [], @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "id": "9c6085bd", "metadata": {}, "outputs": [], @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "675a9ec7", "metadata": { "tags": [ @@ -1645,14 +1645,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "id": "7698e840", "metadata": {}, "outputs": [], "source": [ "# Initialize EKF with ground-truth state and modest covariance\n", - "pose = scenario.pose(0.0)\n", - "X0: Gal3 = Gal3(pose.rotation(), pose.translation(), scenario.velocity_n(0.0), 0.0)\n", + "X0: Gal3 = scenario.gal3(0.0)\n", "P0 = np.eye(10) * 0.1 # std ~ 0.316 on each component\n", "P0[9, 9] = 1e-6 #small variance for time \n", "ekf = gtsam.Gal3ImuEKF(X0, P0, params)" @@ -1660,22 +1659,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "id": "022045d8", "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'gtsam.gtsam.ConstantTwistScenario' object has no attribute 'gal3'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[25], line 19\u001b[0m\n\u001b[1;32m 16\u001b[0m Xk \u001b[38;5;241m=\u001b[39m X0\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(times):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Ground truth state from scenario\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m X_true: Gal3 \u001b[38;5;241m=\u001b[39m \u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgal3\u001b[49m(t)\n\u001b[1;32m 20\u001b[0m ypr_true\u001b[38;5;241m.\u001b[39mappend(rot3_ypr_rad(X_true\u001b[38;5;241m.\u001b[39mattitude()))\n\u001b[1;32m 21\u001b[0m vel_true\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39masarray(X_true\u001b[38;5;241m.\u001b[39mvelocity()))\n", - "\u001b[0;31mAttributeError\u001b[0m: 'gtsam.gtsam.ConstantTwistScenario' object has no attribute 'gal3'" - ] - } - ], + "outputs": [], "source": [ "times = np.linspace(0.0, T, N + 1)\n", "\n", @@ -1778,7 +1765,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b16a8666", "metadata": { "tags": [ @@ -1805,7 +1792,7 @@ "dtype": "f8" }, "y": { - "bdata": 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", 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"dtype": "f8" } }, @@ -2816,7 +2803,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "9d0e25cc", "metadata": { "tags": [ @@ -2843,7 +2830,7 @@ "dtype": "f8" }, "y": { - "bdata": 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"dtype": "f8" } }, @@ -3857,7 +3844,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "2ea60e82", "metadata": { "tags": [ @@ -3884,7 +3871,7 @@ "dtype": "f8" }, "y": { - "bdata": 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"dtype": "f8" } }, diff --git a/python/gtsam/examples/Gal3ImuNEESReset.ipynb b/python/gtsam/examples/Gal3ImuNEESReset.ipynb index 52941de50e..218fc45cb8 100644 --- a/python/gtsam/examples/Gal3ImuNEESReset.ipynb +++ b/python/gtsam/examples/Gal3ImuNEESReset.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 1, "id": "9343b3c0", "metadata": {}, "outputs": [], @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 2, "id": "9123d34f", "metadata": {}, "outputs": [], @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 3, "id": "e3b400b9", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 4, "id": "ce1ed85c", "metadata": { "tags": [ @@ -150,10 +150,7 @@ " R = np.eye(3) * (pos_std**2)\n", "\n", " for k, t in enumerate(times):\n", - " state_true = scenario.navState(t)\n", - " X_true = Gal3(\n", - " state_true.attitude(), state_true.position(), state_true.velocity(), t\n", - " )\n", + " X_true = scenario.gal3(t)\n", " true_states.append(X_true)\n", "\n", " if k == 0:\n", @@ -192,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 5, "id": "4973d2b3", "metadata": {}, "outputs": [], @@ -226,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 6, "id": "cf993f72", "metadata": {}, "outputs": [ @@ -2162,7 +2159,7 @@ ], "source": [ "# Plot NEES for both filters\n", - "dim = ekf_reset.covariance().shape[0] # Gal3 tangent dimension\n", + "dim = 10 # Gal3 tangent dimension\n", "fig = go.Figure()\n", "fig.add_scatter(x=times, y=nees_reset, name='With reset', line=dict(color=\"#0fae66\"))\n", "fig.add_scatter(x=times, y=nees_no_reset, name='Without reset', line=dict(color='#d62728'))\n", From a83a13e8f8bc31cb7f5e81a7f2a4aae7a964acff Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Mon, 19 Jan 2026 00:40:30 -0800 Subject: [PATCH 133/266] WIP: initial pseudorange factor. --- gtsam/navigation/PseudorangeFactor.cpp | 78 +++++++++++++ gtsam/navigation/PseudorangeFactor.h | 107 ++++++++++++++++++ .../tests/testPseudorangeFactor.cpp | 29 +++++ 3 files changed, 214 insertions(+) create mode 100644 gtsam/navigation/PseudorangeFactor.cpp create mode 100644 gtsam/navigation/PseudorangeFactor.h create mode 100644 gtsam/navigation/tests/testPseudorangeFactor.cpp diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp new file mode 100644 index 0000000000..a66beb03b2 --- /dev/null +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -0,0 +1,78 @@ +/** + * @file PseudorangeFactor.cpp + * @author Sammy Guo + * @brief Implementation file for GNSS Pseudorange factor + * @date January 18, 2026 + **/ + +#include "PseudorangeFactor.h" + +namespace { + +/// Speed of light in a vacuum (m/s): +constexpr double CLIGHT = 299792458.0; + +} // namespace + +namespace gtsam { + +PseudorangeFactor::PseudorangeFactor() + : Base(), pseudorange_(0.0), sat_pos_(Point3::Zero()), sat_clk_bias_(0.0) {} + +PseudorangeFactor::PseudorangeFactor(Key receiver_position_key, + Key receiver_clock_bias_key, + const double measured_pseudorange, + const Point3& satellite_position, + const double satellite_clock_bias, + const SharedNoiseModel& model) + : Base(model, receiver_position_key, receiver_clock_bias_key), + pseudorange_(measured_pseudorange), + sat_pos_(satellite_position), + sat_clk_bias_(satellite_clock_bias) {} + +//*************************************************************************** +// void PseudorangeFactor::print(const std::string& s, const KeyFormatter& +// keyFormatter) const { +// cout << (s.empty() ? "" : s + " ") << "PseudorangeFactor on " << +// keyFormatter(key()) +// << "\n"; +// cout << " GPS measurement: " << nT_ << "\n"; +// noiseModel_->print(" noise model: "); +//} + +//*************************************************************************** +// bool PseudorangeFactor::equals(const NonlinearFactor& expected, double tol) +// const { +// const This* e = dynamic_cast(&expected); +// return e != nullptr && Base::equals(*e, tol) /*&& +// traits::Equals(nT_, e->nT_, tol)*/; +//} + +//*************************************************************************** +Vector PseudorangeFactor::evaluateError( + const Point3& receiver_position, const double& receiver_clock_bias, + OptionalMatrixType Hreceiver_pos, + OptionalMatrixType Hreceiver_clock_bias) const { + // Apply pseudorange equation: rho = range + c*[dt_u - dt^s] + const Vector3 position_difference = receiver_position - sat_pos_; + const double range = position_difference.norm(); + const double rho = range + CLIGHT * (receiver_clock_bias - sat_clk_bias_); + const double error = rho - pseudorange_; + + // Compute associated derivatives: + if (Hreceiver_pos) { + if (range < std::numeric_limits::epsilon()) { + *Hreceiver_pos = Matrix13::Zero(); + } else { + *Hreceiver_pos = position_difference / range; + } + } + + if (Hreceiver_clock_bias) { + *Hreceiver_clock_bias = Matrix11(CLIGHT); + } + + return Vector1(error); +} + +} // namespace gtsam diff --git a/gtsam/navigation/PseudorangeFactor.h b/gtsam/navigation/PseudorangeFactor.h new file mode 100644 index 0000000000..c2b8737cec --- /dev/null +++ b/gtsam/navigation/PseudorangeFactor.h @@ -0,0 +1,107 @@ +/** + * @file PseudorangeFactor.h + * @author Sammy Guo + * @brief Header file for GNSS Pseudorange factor + * @date January 18, 2026 + **/ +#pragma once + +#include +#include +#include + +#include + +namespace gtsam { + +/** + * Simplified GNSS pseudorange model for basic positioning problems. + * + * This factor implements a simplified version of equation 5.6 [1] + * \rho = r + c[\delta t_u - \delta t^s] + I_{\rho} + T_{\rho} + \epsilon_{\rho} + * where `\rho` is measured pseudorange (in meters) from the receiver, + * `r` true range (in meters) between receiver antenna and satellite, + * `c` is speed of light in a vacuum (m/s), + * `\delta t_u` is receiver clock bias (seconds), + * `\delta t_s` is satellite clock bias (seconds), + * and `I_{\rho}`, `T_{\rho}`, and `\epsilon_{\rho}` are ionospheric, + * tropospheric, and unmodeled errors respectively. + * + * Ionospheric and tropospheric terms are omitted in this simplified factor. + * + * @ingroup navigation + * + * REFERENCES: + * [1] P. Misra et. al., "Global Positioning Systems: Signals, Measurements, and + * Performance", Second Edition, 2012. + */ +class GTSAM_EXPORT PseudorangeFactor + : public NoiseModelFactorN { + private: + typedef NoiseModelFactorN Base; + + const double + pseudorange_; ///< Receiver-reported pseudorange measurement in meters. + const Point3 sat_pos_; ///< Satellite position in WGS84 ECEF meters. + const double sat_clk_bias_; ///< Satellite clock bias in seconds. + + public: + // Provide access to the Matrix& version of evaluateError: + using Base::evaluateError; + + /// shorthand for a smart pointer to a factor + typedef std::shared_ptr shared_ptr; + + /// Typedef to this class + typedef PseudorangeFactor This; + + /** default constructor - only use for serialization */ + PseudorangeFactor(); + + virtual ~PseudorangeFactor() = default; + + /** + * @brief TODO + */ + PseudorangeFactor(Key receiver_position_key, Key receiver_clock_bias_key, + double measured_pseudorange, + const Point3& satellite_position, + double satellite_clock_bias, const SharedNoiseModel& model); + + /// @return a deep copy of this factor + gtsam::NonlinearFactor::shared_ptr clone() const override { + return std::static_pointer_cast( + gtsam::NonlinearFactor::shared_ptr(new This(*this))); + } + + /// print + // void print(const std::string& s = "", const KeyFormatter& keyFormatter = + // DefaultKeyFormatter) const + // override; + + /// equals + // bool equals(const NonlinearFactor& expected, double tol = 1e-9) const + // override; + + /// vector of errors + Vector evaluateError(const Point3& receiver_position, + const double& receiver_clock_bias, + OptionalMatrixType Hreceiver_pos, + OptionalMatrixType Hreceiver_clock_bias) const override; + + private: +#if GTSAM_ENABLE_BOOST_SERIALIZATION /// + /// Serialization function + friend class boost::serialization::access; + template + void serialize(ARCHIVE& ar, const unsigned int /*version*/) { + ar& boost::serialization::make_nvp( + "NoiseModelFactor2", boost::serialization::base_object(*this)); + ar& BOOST_SERIALIZATION_NVP(pseudorange_); + ar& BOOST_SERIALIZATION_NVP(sat_pos_); + ar& BOOST_SERIALIZATION_NVP(sat_clk_bias_); + } +#endif +}; + +} // namespace gtsam diff --git a/gtsam/navigation/tests/testPseudorangeFactor.cpp b/gtsam/navigation/tests/testPseudorangeFactor.cpp new file mode 100644 index 0000000000..d9f4f9df83 --- /dev/null +++ b/gtsam/navigation/tests/testPseudorangeFactor.cpp @@ -0,0 +1,29 @@ +/** + * @file testPsuedorangeFactor.cpp + * @brief Unit test for PseudorangeFactor + * @author Sammy Guo + * @date January 18, 2026 + */ + +#include +#include +#include +#include + +using namespace gtsam; + +// ************************************************************************* +TEST(TestPseudorangeFactor, Constructor) { + const auto factor = + PseudorangeFactor(Key(0), Key(1), 0.0, Point3::Zero(), 0.0, + noiseModel::Isotropic::Sigma(1, 1.0)); + const double error = factor.evaluateError(Point3::Zero(), 0.0)[0]; + EXPECT_DOUBLES_EQUAL(0.0, error, 1e-9); +} + +// ************************************************************************* +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} +// ************************************************************************* \ No newline at end of file From 6c9a1e1b5b6c36c504ffe96de6b2448925c24373 Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Tue, 20 Jan 2026 21:14:32 -0800 Subject: [PATCH 134/266] WIP: Add wrapper for pseudorange factor. Initial stab at example/documentation jupyter notebook. --- THANKS.md | 2 ++ gtsam/navigation/navigation.i | 13 +++++++++++++ gtsam/navigation/navigation.md | 1 + 3 files changed, 16 insertions(+) diff --git a/THANKS.md b/THANKS.md index 7db7daaf33..5a5be429cb 100644 --- a/THANKS.md +++ b/THANKS.md @@ -44,6 +44,8 @@ at LAAS-CNRS * Ellon Paiva +* Sammy Guo + Many thanks for your hard work!!!! Frank Dellaert diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index d68cd1d31e..0891b308b2 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -485,6 +485,19 @@ virtual class GPSFactor2ArmCalib : gtsam::NonlinearFactor{ void serialize() const; }; +#include +virtual class PseudorangeFactor : gtsam::NonlinearFactor { + PseudorangeFactor(gtsam::Key receiver_position_key, + gtsam::Key receiver_clock_bias_key, + double measured_pseudorange, + const gtsam::Point3& satellite_position, + double satellite_clock_bias, + const gtsam::noiseModel::Base* model); + + gtsam::Vector evaluateError(const gtsam::Point3& receiver_position, + const double& receiver_clock_bias) const; +}; + #include virtual class BarometricFactor : gtsam::NonlinearFactor { BarometricFactor(); diff --git a/gtsam/navigation/navigation.md b/gtsam/navigation/navigation.md index 25737c6971..b2f7e26be8 100644 --- a/gtsam/navigation/navigation.md +++ b/gtsam/navigation/navigation.md @@ -33,6 +33,7 @@ The `navigation` module in GTSAM provides specialized tools for inertial navigat - **[GPSFactor](doc/GPSFactor.ipynb)**: Factor for incorporating GPS position measurements. - **[BarometricFactor](doc/BarometricFactor.ipynb)**: Incorporates barometric altitude measurements. +- **[PseudorangeFactor](doc/PseudorangeFactor.ipynb)**: Precise GNSS positioning. ### Magnetic Field Integration From 86a3818f8b438127ce7922fc12686ac58d1f674f Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Tue, 20 Jan 2026 21:16:02 -0800 Subject: [PATCH 135/266] Forgot to commit jupyter notebook lol. --- gtsam/navigation/doc/PseudorangeFactor.ipynb | 137 +++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 gtsam/navigation/doc/PseudorangeFactor.ipynb diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb new file mode 100644 index 0000000000..087b37c33f --- /dev/null +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GNSS Precise Positioning with Factor Graphs\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "The `PseudorangeFactor` family provides factors for incorporating GNSS pseudorange measurements into a GTSAM factor graph. Pseudorange factors model the distance between a satellite and the GNSS receiver along with their associated errors (atmospheric, multipath, clock bias, etc...). This more-tightly couples GNSS positioning within the general probabilistic graphical estimation framework compared to loosely-coupled `GPSFactor`s. In other words, `PseudorangeFactor` integrates GNSS measurements in a \"raw\" form so the factor graph has the opportunity to correct GNSS measurements as part of the overall optimization process. Tighly-coupled GNSS filters also enable partial position observability even if the requisite minimum 4 satellites for an independent position calculation are not met." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2026, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --quiet pyrtklib numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pyrtklib as rtklib\n", + "\n", + "# TODO: Remove this before merging!!!\n", + "import sys\n", + "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import X, B\n", + "\n", + "\n", + "obs = rtklib.obs_t()\n", + "nav = rtklib.nav_t()\n", + "sta = rtklib.sta_t()\n", + "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/brdc0180.26n\", 1, \"\", obs, nav, sta)\n", + "assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", + "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/zoa10180.26o\", 1, \"\", obs, nav, sta)\n", + "assert load_status == 1, \"Loading receiver observation RINEX\"\n", + "\n", + "cb_key = B(0) # Clock bias variable key.\n", + "pos_key = X(0) # Receiver position variable key.\n", + "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", + "pf = gtsam.PseudorangeFactor(pos_key, cb_key, 123.45, np.array([123.45, 0.0, 0.0]), 0.0, nm)\n", + "pf.evaluateError(np.array([0.0, 0.0, 0.0]), 0.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [PseudorangeFactor.h](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.h)\n", + "- [PseudorangeFactor.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.cpp)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 56f2df6b393a25720b38f6f4c526d7b0d6ca6ead Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Wed, 21 Jan 2026 21:56:40 -0800 Subject: [PATCH 136/266] MVP single-point-positioning jupyter notebook example. --- gtsam/navigation/PseudorangeFactor.cpp | 2 +- gtsam/navigation/doc/PseudorangeFactor.ipynb | 31 +-- .../SinglePointPositioningExample.ipynb | 238 ++++++++++++++++++ 3 files changed, 244 insertions(+), 27 deletions(-) create mode 100644 python/gtsam/examples/SinglePointPositioningExample.ipynb diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index a66beb03b2..ed26e55c14 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -64,7 +64,7 @@ Vector PseudorangeFactor::evaluateError( if (range < std::numeric_limits::epsilon()) { *Hreceiver_pos = Matrix13::Zero(); } else { - *Hreceiver_pos = position_difference / range; + *Hreceiver_pos = (position_difference / range).transpose(); } } diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb index 087b37c33f..d395ec96e3 100644 --- a/gtsam/navigation/doc/PseudorangeFactor.ipynb +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GNSS Precise Positioning with Factor Graphs\n", + "# GNSS Positioning with Factor Graphs\n", "\n", "\"Open\n", "\n", @@ -51,7 +51,7 @@ } ], "source": [ - "%pip install --quiet pyrtklib numpy" + "%pip install --quiet numpy" ] }, { @@ -63,23 +63,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import numpy as np\n", - "import pyrtklib as rtklib\n", "\n", "# TODO: Remove this before merging!!!\n", "import sys\n", @@ -87,16 +75,7 @@ "import gtsam\n", "from gtsam.symbol_shorthand import X, B\n", "\n", - "\n", - "obs = rtklib.obs_t()\n", - "nav = rtklib.nav_t()\n", - "sta = rtklib.sta_t()\n", - "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/brdc0180.26n\", 1, \"\", obs, nav, sta)\n", - "assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", - "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/zoa10180.26o\", 1, \"\", obs, nav, sta)\n", - "assert load_status == 1, \"Loading receiver observation RINEX\"\n", - "\n", - "cb_key = B(0) # Clock bias variable key.\n", + "cb_key = B(0) # Receiver clock bias variable key.\n", "pos_key = X(0) # Receiver position variable key.\n", "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", "pf = gtsam.PseudorangeFactor(pos_key, cb_key, 123.45, np.array([123.45, 0.0, 0.0]), 0.0, nm)\n", diff --git a/python/gtsam/examples/SinglePointPositioningExample.ipynb b/python/gtsam/examples/SinglePointPositioningExample.ipynb new file mode 100644 index 0000000000..0bee82e380 --- /dev/null +++ b/python/gtsam/examples/SinglePointPositioningExample.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GNSS Single Point Positioning with Factor Graphs\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "The `PseudorangeFactor` family provides factors for incorporating GNSS pseudorange measurements into a GTSAM factor graph. Pseudorange factors model the distance between a satellite and the GNSS receiver along with their associated errors (atmospheric, multipath, clock bias, etc...). This more-tightly couples GNSS positioning within the general probabilistic graphical estimation framework compared to loosely-coupled `GPSFactor`s. In other words, `PseudorangeFactor` integrates GNSS measurements in a \"raw\" form so the factor graph has the opportunity to correct GNSS measurements as part of the overall optimization process. Tighly-coupled GNSS filters also enable partial position observability even if the requisite minimum 4 satellites for an independent position calculation are not met." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2026, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --quiet pyrtklib numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 941392 observations\n", + "Found 472 navigation messages\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pyrtklib as rtklib\n", + "\n", + "# TODO: Remove this before merging!!!\n", + "import sys\n", + "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import X, B\n", + "\n", + "\n", + "obs = rtklib.obs_t()\n", + "nav = rtklib.nav_t()\n", + "sta = rtklib.sta_t()\n", + "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/brdc0180.26n\", 1, \"\", obs, nav, sta)\n", + "assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", + "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/zoa10180.26o\", 1, \"\", obs, nav, sta)\n", + "assert load_status == 1, \"Loading receiver observation RINEX\"\n", + "\n", + "print(f\"Found {obs.n} observations\")\n", + "print(f\"Found {nav.n} navigation messages\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n = 11 # number of observations to process.\n", + "rs = rtklib.Arr1Ddouble(6*n) # Satellite positions and velocities:\n", + " # rs [(0:2)+i*6] = obs[i] sat position {x,y,z} (m)\n", + " # rs [(3:5)+i*6] = obs[i] sat velocity {vx,vy,vz} (m/s)\n", + "dts = rtklib.Arr1Ddouble(2*n) # Satellite clock drift:\n", + " # dts[(0:1)+i*2] = obs[i] sat clock {bias,drift} (s|s/s)\n", + "var = rtklib.Arr1Ddouble(1*n) # Satellite position and clock variances:\n", + " # var[i] = obs[i] sat position and clock error variance (m^2)\n", + "svh = rtklib.Arr1Dint(1*n) # Satellite health flag (-1:correction not available):\n", + " # svh[i] = obs[i] sat health flag\n", + "\n", + "teph = obs.data[0].time\n", + "\n", + "# Compute satellite positions:\n", + "rtklib.satposs(\n", + " teph, # gtime_t teph I time to select ephemeris (gpst)\n", + " obs.data.ptr, # obsd_t *obs I observation data\n", + " n, # int n I number of observation data\n", + " nav, # nav_t *nav I navigation data\n", + " 0, # int ephopt I ephemeris option (EPHOPT_???) (EPHOPT_BRDC == 0)\n", + " rs, # double *rs O satellite positions and velocities (ecef)\n", + " dts, # double *dts O satellite clocks\n", + " var, # double *var O sat position and clock error variances (m^2)\n", + " svh # int *svh O sat health flag (-1:correction not available)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare factor graph:\n", + "cb_key = B(0) # Receiver clock bias variable key.\n", + "pos_key = X(0) # Receiver position variable key.\n", + "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", + "graph = gtsam.NonlinearFactorGraph()\n", + "\n", + "# Add a PseudorangeFactor for each observation:\n", + "sats_used = set()\n", + "for i in range(n):\n", + " obsd = obs.data[i]\n", + " sats_used.add(obsd.sat)\n", + " sat_bias = dts[2*i+0]\n", + " sat_drift = dts[2*i+1]\n", + " sat_pos = np.array([rs[6*i+0], rs[6*i+1], rs[6*i+2]])\n", + "\n", + " # Identify pseudorange code CODE_L1C:\n", + " for j, code in enumerate(obsd.code):\n", + " if code == rtklib.CODE_L1C:\n", + " pseudorange = obsd.P[j]\n", + " break\n", + "\n", + " graph.add(\n", + " gtsam.PseudorangeFactor(pos_key, cb_key, pseudorange, sat_pos, sat_bias, nm)\n", + " )\n", + "\n", + " # Comment to print debug lines below:\n", + " continue\n", + " \n", + " print(f\"=== Satellite {obsd.sat} ===\")\n", + " print(f\"Receiver pseudorange: {pseudorange} meters\")\n", + " print(f\"Observation time (unixtime): {rtklib.time_str(obsd.time, 3)} ({obsd.time.time})\")\n", + " print(f\"Satellite position (ECEF): {1e-3*sat_pos} km\")\n", + " # print(f\"Satellite velocity (ECEF): {rs[6*i+3]}, {rs[6*i+4]}, {rs[6*i+5]} m/s\")\n", + " print(f\"Satellite clock drift: bias {sat_bias} seconds, drift {sat_drift} seconds-per-second\")\n", + " # print(f\"Satellite variance: {var[i]}\")\n", + " # print(f\"Satellite health: {svh[i]}\")\n", + " print()\n", + "\n", + "# Solve the system:\n", + "initial_values = gtsam.Values()\n", + "initial_values.insert(cb_key, 0.0)\n", + "initial_values.insert(pos_key, np.zeros(3))\n", + "result = gtsam.LevenbergMarquardtOptimizer(graph, initial_values).optimize()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final clock bias 6.20908953458917e-07 seconds and receiver position [-2684.43802288 -4293.39350686 3865.378281 ] (ECEF km)\n", + "Using the following satellites: {5, 6, 11, 12, 19, 21, 22, 24, 25, 28, 29}\n" + ] + } + ], + "source": [ + "# Process the results:\n", + "clock_bias = result.atDouble(cb_key)\n", + "receiver_position = result.atVector(pos_key)\n", + "print(f\"Final clock bias {clock_bias} seconds and receiver position {1e-3*receiver_position} (ECEF km)\")\n", + "print(f\"Using the following satellites: {sats_used}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [PseudorangeFactor.h](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.h)\n", + "- [PseudorangeFactor.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.cpp)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From c837c39d03057455ab2c35d0edcbe8706c293a3c Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Fri, 23 Jan 2026 19:56:13 -0800 Subject: [PATCH 137/266] Fleshed out single-point-positioning example. --- gtsam/navigation/PseudorangeFactor.cpp | 32 +- gtsam/navigation/PseudorangeFactor.h | 20 +- gtsam/navigation/doc/PseudorangeFactor.ipynb | 19 +- gtsam/navigation/navigation.i | 6 + .../SinglePointPositioningExample.ipynb | 301 +++++++++++++++--- 5 files changed, 314 insertions(+), 64 deletions(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index ed26e55c14..db9ea5e7c8 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -31,22 +31,26 @@ PseudorangeFactor::PseudorangeFactor(Key receiver_position_key, sat_clk_bias_(satellite_clock_bias) {} //*************************************************************************** -// void PseudorangeFactor::print(const std::string& s, const KeyFormatter& -// keyFormatter) const { -// cout << (s.empty() ? "" : s + " ") << "PseudorangeFactor on " << -// keyFormatter(key()) -// << "\n"; -// cout << " GPS measurement: " << nT_ << "\n"; -// noiseModel_->print(" noise model: "); -//} +void PseudorangeFactor::print(const std::string& s, + const KeyFormatter& keyFormatter) const { + std::cout << (s.empty() ? "" : s + " ") << "PseudorangeFactor on " + << keyFormatter(key()) << "\n"; + std::cout << " Pseudorange: " << pseudorange_ << " meters\n"; + std::cout << " Satellite Position: " << sat_pos_.transpose() + << " meters (ECEF)\n"; + std::cout << " Satellite clock bias: " << sat_clk_bias_ << " seconds\n"; + noiseModel_->print(" noise model: "); +} //*************************************************************************** -// bool PseudorangeFactor::equals(const NonlinearFactor& expected, double tol) -// const { -// const This* e = dynamic_cast(&expected); -// return e != nullptr && Base::equals(*e, tol) /*&& -// traits::Equals(nT_, e->nT_, tol)*/; -//} +bool PseudorangeFactor::equals(const NonlinearFactor& expected, + double tol) const { + const This* e = dynamic_cast(&expected); + return e != nullptr && Base::equals(*e, tol) && + traits::Equals(pseudorange_, e->pseudorange_, tol) && + traits::Equals(sat_pos_, e->sat_pos_, tol) && + traits::Equals(sat_clk_bias_, e->sat_clk_bias_, tol); +} //*************************************************************************** Vector PseudorangeFactor::evaluateError( diff --git a/gtsam/navigation/PseudorangeFactor.h b/gtsam/navigation/PseudorangeFactor.h index c2b8737cec..968d1c2ba6 100644 --- a/gtsam/navigation/PseudorangeFactor.h +++ b/gtsam/navigation/PseudorangeFactor.h @@ -28,6 +28,7 @@ namespace gtsam { * tropospheric, and unmodeled errors respectively. * * Ionospheric and tropospheric terms are omitted in this simplified factor. + * Note that this factor is also designed for code-phase measurements. * * @ingroup navigation * @@ -61,7 +62,15 @@ class GTSAM_EXPORT PseudorangeFactor virtual ~PseudorangeFactor() = default; /** - * @brief TODO + * Construct a PseudorangeFactor that models the distance between a receiver + * and a satellite. + * + * @param receiver_position_key Receiver gtsam::Point3 ECEF position node. + * @param receiver_clock_bias_key Receiver clock bias node. + * @param measured_pseudorange Receiver-measured pseudorange in meters. + * @param satellite_position Satellite ECEF position in meters. + * @param satellite_clock_bias Satellite clock bias in seconds. + * @param model 1-D noise model. */ PseudorangeFactor(Key receiver_position_key, Key receiver_clock_bias_key, double measured_pseudorange, @@ -75,13 +84,12 @@ class GTSAM_EXPORT PseudorangeFactor } /// print - // void print(const std::string& s = "", const KeyFormatter& keyFormatter = - // DefaultKeyFormatter) const - // override; + void print(const std::string& s = "", const KeyFormatter& keyFormatter = + DefaultKeyFormatter) const override; /// equals - // bool equals(const NonlinearFactor& expected, double tol = 1e-9) const - // override; + bool equals(const NonlinearFactor& expected, + double tol = 1e-9) const override; /// vector of errors Vector evaluateError(const Point3& receiver_position, diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb index d395ec96e3..6621f16318 100644 --- a/gtsam/navigation/doc/PseudorangeFactor.ipynb +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GNSS Positioning with Factor Graphs\n", + "# Pseudorange Factor\n", "\n", "\"Open\n", "\n", @@ -51,7 +51,7 @@ } ], "source": [ - "%pip install --quiet numpy" + "%pip install --quiet gtsam_develop" ] }, { @@ -63,9 +63,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np\n", "\n", diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 0891b308b2..0d82cd400e 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -494,6 +494,12 @@ virtual class PseudorangeFactor : gtsam::NonlinearFactor { double satellite_clock_bias, const gtsam::noiseModel::Base* model); + // Testable + void print(string s = "", const gtsam::KeyFormatter& keyFormatter = + gtsam::DefaultKeyFormatter) const; + bool equals(const gtsam::NonlinearFactor& expected, double tol); + + // Standard Interface gtsam::Vector evaluateError(const gtsam::Point3& receiver_position, const double& receiver_clock_bias) const; }; diff --git a/python/gtsam/examples/SinglePointPositioningExample.ipynb b/python/gtsam/examples/SinglePointPositioningExample.ipynb index 0bee82e380..87a46ae856 100644 --- a/python/gtsam/examples/SinglePointPositioningExample.ipynb +++ b/python/gtsam/examples/SinglePointPositioningExample.ipynb @@ -9,8 +9,11 @@ "\"Open\n", "\n", "## Overview\n", + "Global Navigation Satellite Systems (GNSS) are space-based systems that provide precise positioning, navigation, and timing (PNT) information to users anywhere on or near the Earth. GPS (Global Positioning System), developed and operated by the United States, is the most widely used GNSS, but it is part of a broader family that also includes Russia’s GLONASS, Europe’s Galileo, and China’s BeiDou.\n", "\n", - "The `PseudorangeFactor` family provides factors for incorporating GNSS pseudorange measurements into a GTSAM factor graph. Pseudorange factors model the distance between a satellite and the GNSS receiver along with their associated errors (atmospheric, multipath, clock bias, etc...). This more-tightly couples GNSS positioning within the general probabilistic graphical estimation framework compared to loosely-coupled `GPSFactor`s. In other words, `PseudorangeFactor` integrates GNSS measurements in a \"raw\" form so the factor graph has the opportunity to correct GNSS measurements as part of the overall optimization process. Tighly-coupled GNSS filters also enable partial position observability even if the requisite minimum 4 satellites for an independent position calculation are not met." + "GNSS works by measuring the travel time of radio signals transmitted from multiple satellites to a receiver on the ground. By combining these measurements with accurate satellite orbit and clock information, a receiver can determine its position, velocity, and time with high accuracy. Modern GNSS applications range from everyday uses such as smartphone navigation and time synchronization to advanced scientific, aviation, maritime, and geodetic applications requiring centimeter-level precision.\n", + "\n", + "GNSS positioning problems can be encoded as factor graphs in GTSAM; this notebook shows an example of identifying a GNSS receiver's position using pseudoranges from a [RINEX file](https://en.wikipedia.org/wiki/RINEX)." ] }, { @@ -30,6 +33,14 @@ "See LICENSE for the license information" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "We'll need to install a couple python packages to help with today's example. We'll be using [python bindings](https://github.com/IPNL-POLYU/pyrtklib) for the [RTKLIB library](https://github.com/tomojitakasu/RTKLIB) to parse RINEX files from an example receiver source. Numpy is also installed here for easy array manipulation." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -51,14 +62,17 @@ } ], "source": [ - "%pip install --quiet pyrtklib numpy" + "%pip install --quiet pyrtklib numpy gtsam-develop pyproj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example" + "## Premise\n", + "Single-point positioning (SPP) is the most basic GNSS positioning technique, in which a receiver determines its position using pseudorange measurements from multiple satellites and broadcast satellite orbit and clock information. It requires only a single receiver and does not rely on external corrections or reference stations, making it simple and widely accessible. While SPP typically provides meter-level accuracy, its performance is limited by errors such as satellite clock and orbit uncertainties, ionospheric and tropospheric delays, and measurement noise.\n", + "\n", + "We'll be looking at a receiver mounted on top of an FAA air traffic control center; [ZOA1 in Fremont, California](https://geodesy.noaa.gov/CORS/ncn_station_pages/index.html?stationID=ZOA1). It records pseudorange measurements 24/7 as part of a nationwide NOAA Continuously Operating Reference Stations (CORS) network that provides high-accuracy positioning data for geodesy, surveying, and navigation applications. Data from the CORS network can be accessed through the [NOAA CORS Network (NCN) data portal](https://geodesy.noaa.gov/CORS/data.shtml), where we'll download ZOA1's observation and navigation files for January 18th, 2026." ] }, { @@ -70,41 +84,97 @@ "name": "stdout", "output_type": "stream", "text": [ + "Using temporary directory: /var/folders/v8/j1bl714s0fq9nfrzbl78dvcr0000gn/T/tmpsv6lclaf\n", + "Downloading receiver files...\n", + "Extracting files...\n", + "Parsing RINEX files...\n", + "Loading done!\n", "Found 941392 observations\n", "Found 472 navigation messages\n" ] } ], "source": [ + "import tempfile\n", + "import urllib.request\n", + "import gzip\n", + "from pathlib import Path\n", + "import shutil\n", + "\n", + "from IPython.display import IFrame\n", "import numpy as np\n", + "from pyproj import Transformer\n", "import pyrtklib as rtklib\n", "\n", - "# TODO: Remove this before merging!!!\n", - "import sys\n", - "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", - "import gtsam\n", - "from gtsam.symbol_shorthand import X, B\n", + "# Create a temporary directory (auto-deleted when closed):\n", + "with tempfile.TemporaryDirectory() as tmpdir:\n", + " tmpdir = Path(tmpdir)\n", + " print(f\"Using temporary directory: {str(tmpdir)}\")\n", + " \n", + " navigation_compressed = tmpdir / \"brdc0180.26n.gz\"\n", + " navigation_file = tmpdir / \"brdc0180.26n\"\n", + " observable_compressed = tmpdir / \"zoa10180.26o.gz\"\n", + " observable_file = tmpdir / \"zoa10180.26o\"\n", + "\n", + " # Download ZOA1's files using built-in HTTP library:\n", + " print(\"Downloading receiver files...\")\n", + " with urllib.request.urlopen(\"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/brdc0180.26n.gz\") as response, open(navigation_compressed, \"wb\") as f:\n", + " f.write(response.read())\n", + " with urllib.request.urlopen(\"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/zoa1/zoa10180.26o.gz\") as response, open(observable_compressed, \"wb\") as f:\n", + " f.write(response.read())\n", "\n", + " # Extract files:\n", + " print(\"Extracting files...\")\n", + " with gzip.open(navigation_compressed, \"rb\") as gz, open(navigation_file, \"wb\") as out:\n", + " shutil.copyfileobj(gz, out)\n", + " with gzip.open(observable_compressed, \"rb\") as gz, open(observable_file, \"wb\") as out:\n", + " shutil.copyfileobj(gz, out)\n", "\n", - "obs = rtklib.obs_t()\n", - "nav = rtklib.nav_t()\n", - "sta = rtklib.sta_t()\n", - "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/brdc0180.26n\", 1, \"\", obs, nav, sta)\n", - "assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", - "load_status = rtklib.readrnx(\"/Users/sammy/gtsam-gnss/examples/Data/zoa10180.26o\", 1, \"\", obs, nav, sta)\n", - "assert load_status == 1, \"Loading receiver observation RINEX\"\n", + " print(\"Parsing RINEX files...\")\n", + " obs = rtklib.obs_t()\n", + " nav = rtklib.nav_t()\n", + " sta = rtklib.sta_t()\n", + " load_status = rtklib.readrnx(str(navigation_file), 1, \"\", obs, nav, sta)\n", + " assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", + " load_status = rtklib.readrnx(str(observable_file), 1, \"\", obs, nav, sta)\n", + " assert load_status == 1, \"Loading receiver observation RINEX\"\n", "\n", + "# Temporary directory and contents are cleaned up automatically\n", + "print(\"Loading done!\")\n", "print(f\"Found {obs.n} observations\")\n", "print(f\"Found {nav.n} navigation messages\")" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "n = 11 # number of observations to process.\n", + "# TODO: Remove this before merging!!!\n", + "import sys\n", + "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import X, B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute Satellite Positions\n", + "Satellite positions must be computed first before receiver position can be solved. GNSS broadcast ephemerides are orbit and clock parameters transmitted directly by navigation satellites as part of their navigation message. They allow receivers to compute satellite positions and clock corrections in real time using a simplified orbital model. While readily available and sufficient for standard navigation, broadcast ephemerides are less accurate than precise ephemerides due to prediction errors and limited modeling of perturbations.\n", + "\n", + "The `brdc0180.26n` RINEX file contains orbital parameters we need to compute satellite positions. We won't cover how orbits are stored and modeled in the RINEX files, but you can check out the [RTCM website](https://www.rtcm.org/rtcm-standards) for more information. For now, we'll simply have [RTKLIB's `satposs()` function](https://github.com/tomojitakasu/RTKLIB/blob/71db0ffa0d9735697c6adfd06fdf766d0e5ce807/src/ephemeris.c#L718) do it for us." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n = 5 # number of observations to process.\n", "rs = rtklib.Arr1Ddouble(6*n) # Satellite positions and velocities:\n", " # rs [(0:2)+i*6] = obs[i] sat position {x,y,z} (m)\n", " # rs [(3:5)+i*6] = obs[i] sat velocity {vx,vy,vz} (m/s)\n", @@ -131,11 +201,56 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Receiver Position and Time\n", + "Finally! We're ready for factor graphs. Using the `PseudorangeFactor`, we can build a simple system that estimates receiver clock bias and [3D ECEF](https://en.wikipedia.org/wiki/Earth-centered,_Earth-fixed_coordinate_system) position. The graph topology is fairly straight-forward: One node for clock bias, another node for 3D position, all connected to a series of psuedorange factors that encapsulates satellite positions." + ] + }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Satellite 5 ===\n", + "Receiver pseudorange: 24874028.989 meters\n", + "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", + "Satellite position (ECEF): [ -5824.26946342 -22935.01126952 -12195.52222428] km\n", + "Satellite clock drift: bias -0.00022743876852667193 seconds, drift 1.8683513937356455e-13 seconds-per-second\n", + "\n", + "=== Satellite 6 ===\n", + "Receiver pseudorange: 22253046.865 meters\n", + "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", + "Satellite position (ECEF): [ 5488.59094499 -14195.33663647 21818.77585526] km\n", + "Satellite clock drift: bias -0.0006034356759087012 seconds, drift -3.098107707877329e-12 seconds-per-second\n", + "\n", + "=== Satellite 11 ===\n", + "Receiver pseudorange: 20496303.403 meters\n", + "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", + "Satellite position (ECEF): [ -8594.38625321 -20716.01787191 14339.02701352] km\n", + "Satellite clock drift: bias -0.00047056000758921617 seconds, drift 1.403391292065237e-11 seconds-per-second\n", + "\n", + "=== Satellite 12 ===\n", + "Receiver pseudorange: 20311181.135 meters\n", + "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", + "Satellite position (ECEF): [-10840.17730125 -14075.33010128 19453.15930531] km\n", + "Satellite clock drift: bias -0.0006066614416282843 seconds, drift -3.1693397906096266e-12 seconds-per-second\n", + "\n", + "=== Satellite 19 ===\n", + "Receiver pseudorange: 23324811.427 meters\n", + "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", + "Satellite position (ECEF): [ 14173.452494 -15497.14651078 15863.86221975] km\n", + "Satellite clock drift: bias 0.0006860747484721503 seconds, drift -1.242495689668388e-12 seconds-per-second\n", + "\n" + ] + } + ], "source": [ "# Prepare factor graph:\n", "cb_key = B(0) # Receiver clock bias variable key.\n", @@ -144,10 +259,8 @@ "graph = gtsam.NonlinearFactorGraph()\n", "\n", "# Add a PseudorangeFactor for each observation:\n", - "sats_used = set()\n", "for i in range(n):\n", " obsd = obs.data[i]\n", - " sats_used.add(obsd.sat)\n", " sat_bias = dts[2*i+0]\n", " sat_drift = dts[2*i+1]\n", " sat_pos = np.array([rs[6*i+0], rs[6*i+1], rs[6*i+2]])\n", @@ -157,60 +270,168 @@ " if code == rtklib.CODE_L1C:\n", " pseudorange = obsd.P[j]\n", " break\n", + " else:\n", + " # If no CODE_L1C pseudorange found, skip this observation:\n", + " continue\n", "\n", " graph.add(\n", " gtsam.PseudorangeFactor(pos_key, cb_key, pseudorange, sat_pos, sat_bias, nm)\n", " )\n", - "\n", - " # Comment to print debug lines below:\n", - " continue\n", " \n", " print(f\"=== Satellite {obsd.sat} ===\")\n", " print(f\"Receiver pseudorange: {pseudorange} meters\")\n", " print(f\"Observation time (unixtime): {rtklib.time_str(obsd.time, 3)} ({obsd.time.time})\")\n", " print(f\"Satellite position (ECEF): {1e-3*sat_pos} km\")\n", - " # print(f\"Satellite velocity (ECEF): {rs[6*i+3]}, {rs[6*i+4]}, {rs[6*i+5]} m/s\")\n", " print(f\"Satellite clock drift: bias {sat_bias} seconds, drift {sat_drift} seconds-per-second\")\n", - " # print(f\"Satellite variance: {var[i]}\")\n", - " # print(f\"Satellite health: {svh[i]}\")\n", - " print()\n", - "\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We initialize receiver position at origin, and then use a LM solver to optimize the system." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final clock bias 5.377885093511699e-07 seconds and receiver position [-2684418.91084688 -4293361.08683296 3865365.45451951] (ECEF meters)\n" + ] + } + ], + "source": [ "# Solve the system:\n", "initial_values = gtsam.Values()\n", "initial_values.insert(cb_key, 0.0)\n", "initial_values.insert(pos_key, np.zeros(3))\n", - "result = gtsam.LevenbergMarquardtOptimizer(graph, initial_values).optimize()" + "result = gtsam.LevenbergMarquardtOptimizer(graph, initial_values).optimize()\n", + "\n", + "# Process the results:\n", + "clock_bias = result.atDouble(cb_key)\n", + "receiver_position = result.atVector(pos_key)\n", + "print(f\"Final clock bias {clock_bias} seconds and receiver position {receiver_position} (ECEF meters)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! We see that the solver converged at a reasonable solution. ECEF is difficult to visualize, however. So we'll convert the result to geodetic coordinates and plot it on a map:" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Final clock bias 6.20908953458917e-07 seconds and receiver position [-2684.43802288 -4293.39350686 3865.378281 ] (ECEF km)\n", - "Using the following satellites: {5, 6, 11, 12, 19, 21, 22, 24, 25, 28, 29}\n" + "Latitude: 37.543093010647205 degrees\n", + "Longitude: -122.01563340074796 degrees\n", + "Altitude: 13.151681120507419 meters\n" ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Process the results:\n", - "clock_bias = result.atDouble(cb_key)\n", - "receiver_position = result.atVector(pos_key)\n", - "print(f\"Final clock bias {clock_bias} seconds and receiver position {1e-3*receiver_position} (ECEF km)\")\n", - "print(f\"Using the following satellites: {sats_used}\")" + "# Create a transformer from ECEF (EPSG:4978) to WGS84 LLA (EPSG:4326)\n", + "ecef2lla = Transformer.from_crs(\"epsg:4978\", \"epsg:4326\", always_xy=True)\n", + "\n", + "# Convert coordinates coordinates (latitude, longitude)\n", + "lon, lat, alt = ecef2lla.transform(receiver_position[0], receiver_position[1], receiver_position[2])\n", + "print(f\"Latitude: {lat} degrees\")\n", + "print(f\"Longitude: {lon} degrees\")\n", + "print(f\"Altitude: {alt} meters\")\n", + "\n", + "# OpenStreetMap embed URL\n", + "osm_url = f\"https://www.openstreetmap.org/export/embed.html?bbox={lon-0.01}%2C{lat-0.01}%2C{lon+0.01}%2C{lat+0.01}&layer=map&marker={lat}%2C{lon}\"\n", + "\n", + "# Embed in notebook\n", + "IFrame(osm_url, width=700, height=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validate Results\n", + "Our results appear to be roughly in the right place, but CORS stations actually provide ground-truth coordinates we can use as a measure of accuracy in our solutions. ZOA1's full precision-surveyed coordinates are [published in their data portal](https://noaa-cors-pds.s3.amazonaws.com/coord/coord_20/zoa1_20.coord.txt), but we'll examine a just a snippet copied here:\n", + "```\n", + "| ITRF2020 POSITION (EPOCH 2020.0) |\n", + "| Computed in Apr 2025 using data through gpswk 2237. |\n", + "| X = -2684436.824 m latitude = 37 32 34.99617 N |\n", + "| Y = -4293336.957 m longitude = 122 00 57.42005 W |\n", + "| Z = 3865351.638 m ellipsoid height = -3.960 m |\n", + "```\n", + "\n", + "We can compute a simple error metric by subtracting the two coordinates as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total positioning error: 33.076065433885624 meters\n" + ] + } + ], + "source": [ + "ground_truth = np.array([-2684436.824, -4293336.957, 3865351.638])\n", + "error = ground_truth - receiver_position\n", + "print(f\"Total positioning error: {np.linalg.norm(error)} meters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusions\n", + "33 meters of error puts us in the same building as the receiver, which is a good starting point for simple positioning. As mentioned above, however, decades of advancements have vastly improved GNSS positioning accuracy down to sub-meter or even centimeter-scale precision and error. The simple `PseudorangeFactor` does not account for atmospheric effects, carrier-phase measurements, nor differential GNSS corrections. So there's a lot more potential for improvement, but this introductory example provides a foundation for expanding GNSS measurements into the broader GTSAM ecosystem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Source\n", + "## Sources\n", "- [PseudorangeFactor.h](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.h)\n", - "- [PseudorangeFactor.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.cpp)" + "- [PseudorangeFactor.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.cpp)\n", + "- [PseudorangeFactor.ipynb](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/doc/PseudorangeFactor.ipynb)" ] } ], From f466aadfd2d85898f3d641c84232a363f675fc1e Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Fri, 23 Jan 2026 20:33:44 -0800 Subject: [PATCH 138/266] cleanup notebooks --- gtsam/navigation/doc/PseudorangeFactor.ipynb | 6 ------ .../examples/SinglePointPositioningExample.ipynb | 13 ------------- 2 files changed, 19 deletions(-) diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb index 6621f16318..fc2f1b0cad 100644 --- a/gtsam/navigation/doc/PseudorangeFactor.ipynb +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -80,12 +80,6 @@ "source": [ "import numpy as np\n", "\n", - "# TODO: Remove this before merging!!!\n", - "import sys\n", - "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", - "import gtsam\n", - "from gtsam.symbol_shorthand import X, B\n", - "\n", "cb_key = B(0) # Receiver clock bias variable key.\n", "pos_key = X(0) # Receiver position variable key.\n", "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", diff --git a/python/gtsam/examples/SinglePointPositioningExample.ipynb b/python/gtsam/examples/SinglePointPositioningExample.ipynb index 87a46ae856..7484dd73c1 100644 --- a/python/gtsam/examples/SinglePointPositioningExample.ipynb +++ b/python/gtsam/examples/SinglePointPositioningExample.ipynb @@ -145,19 +145,6 @@ "print(f\"Found {nav.n} navigation messages\")" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Remove this before merging!!!\n", - "import sys\n", - "sys.path.insert(0, \"/Users/sammy/gtsam-gnss/_build/python/gtsam\")\n", - "import gtsam\n", - "from gtsam.symbol_shorthand import X, B" - ] - }, { "cell_type": "markdown", "metadata": {}, From c486bdcc14c112ba8aff88ff0f5404e6ec0fd741 Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Sun, 25 Jan 2026 21:41:48 -0800 Subject: [PATCH 139/266] Apply suggestions from code review Apply copilot review suggestions. Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- gtsam/navigation/PseudorangeFactor.cpp | 3 ++- gtsam/navigation/doc/PseudorangeFactor.ipynb | 2 +- gtsam/navigation/tests/testPseudorangeFactor.cpp | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index db9ea5e7c8..61baffd431 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -34,7 +34,8 @@ PseudorangeFactor::PseudorangeFactor(Key receiver_position_key, void PseudorangeFactor::print(const std::string& s, const KeyFormatter& keyFormatter) const { std::cout << (s.empty() ? "" : s + " ") << "PseudorangeFactor on " - << keyFormatter(key()) << "\n"; + << keyFormatter(this->key<1>()) << ", " + << keyFormatter(this->key<2>()) << "\n"; std::cout << " Pseudorange: " << pseudorange_ << " meters\n"; std::cout << " Satellite Position: " << sat_pos_.transpose() << " meters (ECEF)\n"; diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb index fc2f1b0cad..c00edb7343 100644 --- a/gtsam/navigation/doc/PseudorangeFactor.ipynb +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -10,7 +10,7 @@ "\n", "## Overview\n", "\n", - "The `PseudorangeFactor` family provides factors for incorporating GNSS pseudorange measurements into a GTSAM factor graph. Pseudorange factors model the distance between a satellite and the GNSS receiver along with their associated errors (atmospheric, multipath, clock bias, etc...). This more-tightly couples GNSS positioning within the general probabilistic graphical estimation framework compared to loosely-coupled `GPSFactor`s. In other words, `PseudorangeFactor` integrates GNSS measurements in a \"raw\" form so the factor graph has the opportunity to correct GNSS measurements as part of the overall optimization process. Tighly-coupled GNSS filters also enable partial position observability even if the requisite minimum 4 satellites for an independent position calculation are not met." + "The `PseudorangeFactor` family provides factors for incorporating GNSS pseudorange measurements into a GTSAM factor graph. Pseudorange factors model the distance between a satellite and the GNSS receiver along with their associated errors (atmospheric, multipath, clock bias, etc...). This more-tightly couples GNSS positioning within the general probabilistic graphical estimation framework compared to loosely-coupled `GPSFactor`s. In other words, `PseudorangeFactor` integrates GNSS measurements in a \"raw\" form so the factor graph has the opportunity to correct GNSS measurements as part of the overall optimization process. Tightly-coupled GNSS filters also enable partial position observability even if the requisite minimum 4 satellites for an independent position calculation are not met." ] }, { diff --git a/gtsam/navigation/tests/testPseudorangeFactor.cpp b/gtsam/navigation/tests/testPseudorangeFactor.cpp index d9f4f9df83..e19f749e72 100644 --- a/gtsam/navigation/tests/testPseudorangeFactor.cpp +++ b/gtsam/navigation/tests/testPseudorangeFactor.cpp @@ -1,5 +1,5 @@ /** - * @file testPsuedorangeFactor.cpp + * @file testPseudorangeFactor.cpp * @brief Unit test for PseudorangeFactor * @author Sammy Guo * @date January 18, 2026 From 99c88cd00fc354b3f2508649e810ce52c2743f18 Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Sun, 25 Jan 2026 23:19:29 -0800 Subject: [PATCH 140/266] wip: addressing reviewer comments --- gtsam/navigation/PseudorangeFactor.cpp | 32 ++-- gtsam/navigation/PseudorangeFactor.h | 38 ++--- gtsam/navigation/doc/PseudorangeFactor.ipynb | 40 ++--- gtsam/navigation/navigation.i | 14 +- .../tests/testPseudorangeFactor.cpp | 31 +++- .../SinglePointPositioningExample.ipynb | 145 +++--------------- python/gtsam/tests/test_PseudorangeFactor.py | 26 ++++ 7 files changed, 131 insertions(+), 195 deletions(-) create mode 100644 python/gtsam/tests/test_PseudorangeFactor.py diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index 61baffd431..370afc96ef 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -17,18 +17,18 @@ constexpr double CLIGHT = 299792458.0; namespace gtsam { PseudorangeFactor::PseudorangeFactor() - : Base(), pseudorange_(0.0), sat_pos_(Point3::Zero()), sat_clk_bias_(0.0) {} + : Base(), pseudorange_(0.0), satPos_(Point3::Zero()), satClkBias_(0.0) {} -PseudorangeFactor::PseudorangeFactor(Key receiver_position_key, - Key receiver_clock_bias_key, - const double measured_pseudorange, - const Point3& satellite_position, - const double satellite_clock_bias, +PseudorangeFactor::PseudorangeFactor(Key receiverPositionKey, + Key receiverClockBiasKey, + const double measuredPseudorange, + const Point3& satellitePosition, + const double satelliteClockBias, const SharedNoiseModel& model) - : Base(model, receiver_position_key, receiver_clock_bias_key), - pseudorange_(measured_pseudorange), - sat_pos_(satellite_position), - sat_clk_bias_(satellite_clock_bias) {} + : Base(model, receiverPositionKey, receiverClockBiasKey), + pseudorange_(measuredPseudorange), + satPos_(satellitePosition), + satClkBias_(satelliteClockBias) {} //*************************************************************************** void PseudorangeFactor::print(const std::string& s, @@ -37,9 +37,9 @@ void PseudorangeFactor::print(const std::string& s, << keyFormatter(this->key<1>()) << ", " << keyFormatter(this->key<2>()) << "\n"; std::cout << " Pseudorange: " << pseudorange_ << " meters\n"; - std::cout << " Satellite Position: " << sat_pos_.transpose() + std::cout << " Satellite Position: " << satPos_.transpose() << " meters (ECEF)\n"; - std::cout << " Satellite clock bias: " << sat_clk_bias_ << " seconds\n"; + std::cout << " Satellite clock bias: " << satClkBias_ << " seconds\n"; noiseModel_->print(" noise model: "); } @@ -49,8 +49,8 @@ bool PseudorangeFactor::equals(const NonlinearFactor& expected, const This* e = dynamic_cast(&expected); return e != nullptr && Base::equals(*e, tol) && traits::Equals(pseudorange_, e->pseudorange_, tol) && - traits::Equals(sat_pos_, e->sat_pos_, tol) && - traits::Equals(sat_clk_bias_, e->sat_clk_bias_, tol); + traits::Equals(satPos_, e->satPos_, tol) && + traits::Equals(satClkBias_, e->satClkBias_, tol); } //*************************************************************************** @@ -59,9 +59,9 @@ Vector PseudorangeFactor::evaluateError( OptionalMatrixType Hreceiver_pos, OptionalMatrixType Hreceiver_clock_bias) const { // Apply pseudorange equation: rho = range + c*[dt_u - dt^s] - const Vector3 position_difference = receiver_position - sat_pos_; + const Vector3 position_difference = receiver_position - satPos_; const double range = position_difference.norm(); - const double rho = range + CLIGHT * (receiver_clock_bias - sat_clk_bias_); + const double rho = range + CLIGHT * (receiver_clock_bias - satClkBias_); const double error = rho - pseudorange_; // Compute associated derivatives: diff --git a/gtsam/navigation/PseudorangeFactor.h b/gtsam/navigation/PseudorangeFactor.h index 968d1c2ba6..71059ae8ed 100644 --- a/gtsam/navigation/PseudorangeFactor.h +++ b/gtsam/navigation/PseudorangeFactor.h @@ -43,8 +43,8 @@ class GTSAM_EXPORT PseudorangeFactor const double pseudorange_; ///< Receiver-reported pseudorange measurement in meters. - const Point3 sat_pos_; ///< Satellite position in WGS84 ECEF meters. - const double sat_clk_bias_; ///< Satellite clock bias in seconds. + const Point3 satPos_; ///< Satellite position in WGS84 ECEF meters. + const double satClkBias_; ///< Satellite clock bias in seconds. public: // Provide access to the Matrix& version of evaluateError: @@ -65,17 +65,18 @@ class GTSAM_EXPORT PseudorangeFactor * Construct a PseudorangeFactor that models the distance between a receiver * and a satellite. * - * @param receiver_position_key Receiver gtsam::Point3 ECEF position node. - * @param receiver_clock_bias_key Receiver clock bias node. - * @param measured_pseudorange Receiver-measured pseudorange in meters. - * @param satellite_position Satellite ECEF position in meters. - * @param satellite_clock_bias Satellite clock bias in seconds. + * @param receiverPositionKey Receiver gtsam::Point3 ECEF position node. + * @param receiverClockBiasKey Receiver clock bias node. + * @param measuredPseudorange Receiver-measured pseudorange in meters. + * @param satellitePosition Satellite ECEF position in meters. + * @param satelliteClockBias Satellite clock bias in seconds. * @param model 1-D noise model. */ - PseudorangeFactor(Key receiver_position_key, Key receiver_clock_bias_key, - double measured_pseudorange, - const Point3& satellite_position, - double satellite_clock_bias, const SharedNoiseModel& model); + PseudorangeFactor(Key receiverPositionKey, Key receiverClockBiasKey, + double measuredPseudorange, const Point3& satellitePosition, + double satelliteClockBias, + const SharedNoiseModel& model = + noiseModel::Diagonal::Sigmas(Vector1(1.0))); /// @return a deep copy of this factor gtsam::NonlinearFactor::shared_ptr clone() const override { @@ -92,10 +93,10 @@ class GTSAM_EXPORT PseudorangeFactor double tol = 1e-9) const override; /// vector of errors - Vector evaluateError(const Point3& receiver_position, - const double& receiver_clock_bias, - OptionalMatrixType Hreceiver_pos, - OptionalMatrixType Hreceiver_clock_bias) const override; + Vector evaluateError(const Point3& receiverPosition, + const double& receiverClock_bias, + OptionalMatrixType HreceiverPos, + OptionalMatrixType HreceiverClockBias) const override; private: #if GTSAM_ENABLE_BOOST_SERIALIZATION /// @@ -103,11 +104,10 @@ class GTSAM_EXPORT PseudorangeFactor friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp( - "NoiseModelFactor2", boost::serialization::base_object(*this)); + ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(PseudorangeFactor::Base); ar& BOOST_SERIALIZATION_NVP(pseudorange_); - ar& BOOST_SERIALIZATION_NVP(sat_pos_); - ar& BOOST_SERIALIZATION_NVP(sat_clk_bias_); + ar& BOOST_SERIALIZATION_NVP(satPos_); + ar& BOOST_SERIALIZATION_NVP(satClkBias_); } #endif }; diff --git a/gtsam/navigation/doc/PseudorangeFactor.ipynb b/gtsam/navigation/doc/PseudorangeFactor.ipynb index c00edb7343..d16e437e2c 100644 --- a/gtsam/navigation/doc/PseudorangeFactor.ipynb +++ b/gtsam/navigation/doc/PseudorangeFactor.ipynb @@ -6,7 +6,7 @@ "source": [ "# Pseudorange Factor\n", "\n", - "\"Open\n", + "\"Open\n", "\n", "## Overview\n", "\n", @@ -32,26 +32,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "tags": [ "remove-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "%pip install --quiet gtsam_develop" + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" ] }, { @@ -63,21 +56,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "import gtsam\n", + "from gtsam.symbol_shorthand import B, X\n", "import numpy as np\n", "\n", "cb_key = B(0) # Receiver clock bias variable key.\n", diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 0d82cd400e..798c93a73e 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -487,11 +487,11 @@ virtual class GPSFactor2ArmCalib : gtsam::NonlinearFactor{ #include virtual class PseudorangeFactor : gtsam::NonlinearFactor { - PseudorangeFactor(gtsam::Key receiver_position_key, - gtsam::Key receiver_clock_bias_key, - double measured_pseudorange, - const gtsam::Point3& satellite_position, - double satellite_clock_bias, + PseudorangeFactor(gtsam::Key receiverPositionKey, + gtsam::Key receiverClockBiasKey, + double measuredPseudorange, + const gtsam::Point3& satellitePosition, + double satelliteClockBias, const gtsam::noiseModel::Base* model); // Testable @@ -500,8 +500,8 @@ virtual class PseudorangeFactor : gtsam::NonlinearFactor { bool equals(const gtsam::NonlinearFactor& expected, double tol); // Standard Interface - gtsam::Vector evaluateError(const gtsam::Point3& receiver_position, - const double& receiver_clock_bias) const; + gtsam::Vector evaluateError(const gtsam::Point3& receiverPosition, + const double& receiverClock_bias) const; }; #include diff --git a/gtsam/navigation/tests/testPseudorangeFactor.cpp b/gtsam/navigation/tests/testPseudorangeFactor.cpp index e19f749e72..e21a1e4788 100644 --- a/gtsam/navigation/tests/testPseudorangeFactor.cpp +++ b/gtsam/navigation/tests/testPseudorangeFactor.cpp @@ -9,6 +9,7 @@ #include #include #include +#include using namespace gtsam; @@ -17,8 +18,36 @@ TEST(TestPseudorangeFactor, Constructor) { const auto factor = PseudorangeFactor(Key(0), Key(1), 0.0, Point3::Zero(), 0.0, noiseModel::Isotropic::Sigma(1, 1.0)); - const double error = factor.evaluateError(Point3::Zero(), 0.0)[0]; + + Matrix Hpos, Hbias; + const double error = + factor.evaluateError(Point3::Zero(), 0.0, Hpos, Hbias)[0]; EXPECT_DOUBLES_EQUAL(0.0, error, 1e-9); + + // Jacobians are technically undefined if the receiver and satellite positions + // are the same. So make sure this corner-case does not numerically explode: + CHECK(!Hpos.array().isNaN().any()); + CHECK(!Hbias.array().isNaN().any()); + EXPECT_DOUBLES_EQUAL(Hpos.norm(), 0.0, 1e-9); + // Clock bias should always be speed-of-light in vacuum: + EXPECT_DOUBLES_EQUAL(Hbias.norm(), 299792458.0, 1e-9); +} + +TEST(TestPseudorangeFactor, Jacobians) { + // Example values borrowed from `SinglePointPositioningExample.ipynb`: + const auto factor = PseudorangeFactor( + Key(0), Key(1), // Receiver position and clock bias keys. + 24874028.989, // Measured pseudorange. + // Satellite position: + Vector3(-5824269.46342, -22935011.26952, -12195522.22428), + -0.00022743876852667193 // Sat clock drift bias. + ); + + Values values; + values.insert( + Key(0), Vector3(-2684418.91084688, -4293361.08683296, 3865365.45451951)); + values.insert(Key(1), 5.377885093511699e-07); + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-3, 1e-5); } // ************************************************************************* diff --git a/python/gtsam/examples/SinglePointPositioningExample.ipynb b/python/gtsam/examples/SinglePointPositioningExample.ipynb index 7484dd73c1..604a130352 100644 --- a/python/gtsam/examples/SinglePointPositioningExample.ipynb +++ b/python/gtsam/examples/SinglePointPositioningExample.ipynb @@ -43,26 +43,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "remove-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "%pip install --quiet pyrtklib numpy gtsam-develop pyproj" + "try:\n", + " import google.colab\n", + " %pip install --quiet pyrtklib numpy gtsam-develop pyproj\n", + "except ImportError:\n", + " pass" ] }, { @@ -77,23 +70,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using temporary directory: /var/folders/v8/j1bl714s0fq9nfrzbl78dvcr0000gn/T/tmpsv6lclaf\n", - "Downloading receiver files...\n", - "Extracting files...\n", - "Parsing RINEX files...\n", - "Loading done!\n", - "Found 941392 observations\n", - "Found 472 navigation messages\n" - ] - } - ], + "outputs": [], "source": [ "import tempfile\n", "import urllib.request\n", @@ -106,6 +85,9 @@ "from pyproj import Transformer\n", "import pyrtklib as rtklib\n", "\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import B, X\n", + "\n", "# Create a temporary directory (auto-deleted when closed):\n", "with tempfile.TemporaryDirectory() as tmpdir:\n", " tmpdir = Path(tmpdir)\n", @@ -157,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -198,46 +180,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Satellite 5 ===\n", - "Receiver pseudorange: 24874028.989 meters\n", - "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", - "Satellite position (ECEF): [ -5824.26946342 -22935.01126952 -12195.52222428] km\n", - "Satellite clock drift: bias -0.00022743876852667193 seconds, drift 1.8683513937356455e-13 seconds-per-second\n", - "\n", - "=== Satellite 6 ===\n", - "Receiver pseudorange: 22253046.865 meters\n", - "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", - "Satellite position (ECEF): [ 5488.59094499 -14195.33663647 21818.77585526] km\n", - "Satellite clock drift: bias -0.0006034356759087012 seconds, drift -3.098107707877329e-12 seconds-per-second\n", - "\n", - "=== Satellite 11 ===\n", - "Receiver pseudorange: 20496303.403 meters\n", - "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", - "Satellite position (ECEF): [ -8594.38625321 -20716.01787191 14339.02701352] km\n", - "Satellite clock drift: bias -0.00047056000758921617 seconds, drift 1.403391292065237e-11 seconds-per-second\n", - "\n", - "=== Satellite 12 ===\n", - "Receiver pseudorange: 20311181.135 meters\n", - "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", - "Satellite position (ECEF): [-10840.17730125 -14075.33010128 19453.15930531] km\n", - "Satellite clock drift: bias -0.0006066614416282843 seconds, drift -3.1693397906096266e-12 seconds-per-second\n", - "\n", - "=== Satellite 19 ===\n", - "Receiver pseudorange: 23324811.427 meters\n", - "Observation time (unixtime): 2026/01/18 00:00:01.000 (1768694401)\n", - "Satellite position (ECEF): [ 14173.452494 -15497.14651078 15863.86221975] km\n", - "Satellite clock drift: bias 0.0006860747484721503 seconds, drift -1.242495689668388e-12 seconds-per-second\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Prepare factor graph:\n", "cb_key = B(0) # Receiver clock bias variable key.\n", @@ -282,17 +227,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final clock bias 5.377885093511699e-07 seconds and receiver position [-2684418.91084688 -4293361.08683296 3865365.45451951] (ECEF meters)\n" - ] - } - ], + "outputs": [], "source": [ "# Solve the system:\n", "initial_values = gtsam.Values()\n", @@ -315,41 +252,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Latitude: 37.543093010647205 degrees\n", - "Longitude: -122.01563340074796 degrees\n", - "Altitude: 13.151681120507419 meters\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a transformer from ECEF (EPSG:4978) to WGS84 LLA (EPSG:4326)\n", "ecef2lla = Transformer.from_crs(\"epsg:4978\", \"epsg:4326\", always_xy=True)\n", @@ -386,17 +291,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total positioning error: 33.076065433885624 meters\n" - ] - } - ], + "outputs": [], "source": [ "ground_truth = np.array([-2684436.824, -4293336.957, 3865351.638])\n", "error = ground_truth - receiver_position\n", diff --git a/python/gtsam/tests/test_PseudorangeFactor.py b/python/gtsam/tests/test_PseudorangeFactor.py new file mode 100644 index 0000000000..424d571721 --- /dev/null +++ b/python/gtsam/tests/test_PseudorangeFactor.py @@ -0,0 +1,26 @@ +""" +GTSAM Copyright 2010-2026, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved + +See LICENSE for the license information + +PseudorangeFactor python binding unit tests. +Author: Sammy Guo +""" +import unittest + +import numpy as np + +import gtsam +from gtsam.utils.test_case import GtsamTestCase + + +class TestPriorFactor(GtsamTestCase): + + def test_PseudorangeFactor(self): + self.assertEqual(1, 0) + + +if __name__ == "__main__": + unittest.main() From f24d748dc88a6dd8201e4a8449a80e69b874a696 Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Mon, 26 Jan 2026 23:04:37 -0800 Subject: [PATCH 141/266] Incorporating reviewer comments, and tidying tests. --- gtsam/navigation/PseudorangeFactor.cpp | 12 ++-- gtsam/navigation/PseudorangeFactor.h | 22 ++++--- gtsam/navigation/navigation.i | 8 ++- .../tests/testPseudorangeFactor.cpp | 59 +++++++++++++++++-- python/gtsam/tests/test_PseudorangeFactor.py | 39 ++++++++++-- 5 files changed, 110 insertions(+), 30 deletions(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index 370afc96ef..50bc167668 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -33,14 +33,10 @@ PseudorangeFactor::PseudorangeFactor(Key receiverPositionKey, //*************************************************************************** void PseudorangeFactor::print(const std::string& s, const KeyFormatter& keyFormatter) const { - std::cout << (s.empty() ? "" : s + " ") << "PseudorangeFactor on " - << keyFormatter(this->key<1>()) << ", " - << keyFormatter(this->key<2>()) << "\n"; - std::cout << " Pseudorange: " << pseudorange_ << " meters\n"; - std::cout << " Satellite Position: " << satPos_.transpose() - << " meters (ECEF)\n"; - std::cout << " Satellite clock bias: " << satClkBias_ << " seconds\n"; - noiseModel_->print(" noise model: "); + Base::print(s, keyFormatter); + gtsam::print(pseudorange_, "pseudorange (m): "); + gtsam::print(Vector(satPos_), "sat position (ECEF meters): "); + gtsam::print(satClkBias_, "sat clock bias (s): "); } //*************************************************************************** diff --git a/gtsam/navigation/PseudorangeFactor.h b/gtsam/navigation/PseudorangeFactor.h index 71059ae8ed..8c0cb9e5de 100644 --- a/gtsam/navigation/PseudorangeFactor.h +++ b/gtsam/navigation/PseudorangeFactor.h @@ -41,10 +41,10 @@ class GTSAM_EXPORT PseudorangeFactor private: typedef NoiseModelFactorN Base; - const double - pseudorange_; ///< Receiver-reported pseudorange measurement in meters. - const Point3 satPos_; ///< Satellite position in WGS84 ECEF meters. - const double satClkBias_; ///< Satellite clock bias in seconds. + double + pseudorange_; ///< Receiver-reported pseudorange measurement in meters. + Point3 satPos_; ///< Satellite position in WGS84 ECEF meters. + double satClkBias_; ///< Satellite clock bias in seconds. public: // Provide access to the Matrix& version of evaluateError: @@ -72,11 +72,11 @@ class GTSAM_EXPORT PseudorangeFactor * @param satelliteClockBias Satellite clock bias in seconds. * @param model 1-D noise model. */ - PseudorangeFactor(Key receiverPositionKey, Key receiverClockBiasKey, - double measuredPseudorange, const Point3& satellitePosition, - double satelliteClockBias, - const SharedNoiseModel& model = - noiseModel::Diagonal::Sigmas(Vector1(1.0))); + PseudorangeFactor( + Key receiverPositionKey, Key receiverClockBiasKey, + double measuredPseudorange, const Point3& satellitePosition, + double satelliteClockBias = 0.0, + const SharedNoiseModel& model = noiseModel::Unit::Create(1)); /// @return a deep copy of this factor gtsam::NonlinearFactor::shared_ptr clone() const override { @@ -112,4 +112,8 @@ class GTSAM_EXPORT PseudorangeFactor #endif }; +/// traits +template <> +struct traits : public Testable {}; + } // namespace gtsam diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 798c93a73e..1e17647ae5 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -488,20 +488,22 @@ virtual class GPSFactor2ArmCalib : gtsam::NonlinearFactor{ #include virtual class PseudorangeFactor : gtsam::NonlinearFactor { PseudorangeFactor(gtsam::Key receiverPositionKey, - gtsam::Key receiverClockBiasKey, - double measuredPseudorange, + gtsam::Key receiverClockBiasKey, double measuredPseudorange, const gtsam::Point3& satellitePosition, double satelliteClockBias, const gtsam::noiseModel::Base* model); // Testable void print(string s = "", const gtsam::KeyFormatter& keyFormatter = - gtsam::DefaultKeyFormatter) const; + gtsam::DefaultKeyFormatter) const; bool equals(const gtsam::NonlinearFactor& expected, double tol); // Standard Interface gtsam::Vector evaluateError(const gtsam::Point3& receiverPosition, const double& receiverClock_bias) const; + + // enable serialization functionality + void serialize() const; }; #include diff --git a/gtsam/navigation/tests/testPseudorangeFactor.cpp b/gtsam/navigation/tests/testPseudorangeFactor.cpp index e21a1e4788..ee74f05cab 100644 --- a/gtsam/navigation/tests/testPseudorangeFactor.cpp +++ b/gtsam/navigation/tests/testPseudorangeFactor.cpp @@ -24,16 +24,39 @@ TEST(TestPseudorangeFactor, Constructor) { factor.evaluateError(Point3::Zero(), 0.0, Hpos, Hbias)[0]; EXPECT_DOUBLES_EQUAL(0.0, error, 1e-9); - // Jacobians are technically undefined if the receiver and satellite positions - // are the same. So make sure this corner-case does not numerically explode: - CHECK(!Hpos.array().isNaN().any()); - CHECK(!Hbias.array().isNaN().any()); + // Derivatives are technically undefined if the receiver and satellite + // positions are the same (hopefully that's never the case in reality). But + // for all intents and purposes, zero-valued derivatives can substitute for + // undefined gradient at that singularity. So make sure this corner-case does + // not numerically explode: + EXPECT(!Hpos.array().isNaN().any()); + EXPECT(!Hbias.array().isNaN().any()); EXPECT_DOUBLES_EQUAL(Hpos.norm(), 0.0, 1e-9); - // Clock bias should always be speed-of-light in vacuum: + // Clock bias derivative should always be speed-of-light in vacuum: EXPECT_DOUBLES_EQUAL(Hbias.norm(), 299792458.0, 1e-9); } -TEST(TestPseudorangeFactor, Jacobians) { +// ************************************************************************* +TEST(TestPseudorangeFactor, Jacobians1) { + // Synthetic example with exact error/derivatives: + const auto factor = PseudorangeFactor( + Key(0), Key(1), // Receiver position and clock bias keys. + 4.0, // Measured pseudorange. + // Satellite position: + Vector3(0.0, 0.0, 3.0), + 0.0 // Sat clock drift bias. + ); + const double error = factor.evaluateError(Vector3::Zero(), 0.0)[0]; + EXPECT_DOUBLES_EQUAL(-1.0, error, 1e-6); + + Values values; + values.insert(Key(0), Vector3(1.0, 2.0, 3.0)); + values.insert(Key(1), 0.0); + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-3, 1e-5); +} + +// ************************************************************************* +TEST(TestPseudorangeFactor, Jacobians2) { // Example values borrowed from `SinglePointPositioningExample.ipynb`: const auto factor = PseudorangeFactor( Key(0), Key(1), // Receiver position and clock bias keys. @@ -50,6 +73,30 @@ TEST(TestPseudorangeFactor, Jacobians) { EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-3, 1e-5); } +// ************************************************************************* +TEST(TestPseudorangeFactor, print) { + // Just make sure `print()` doesn't throw errors + // since there's no elegant way to check stdout. + const auto factor = PseudorangeFactor(); + factor.print(); +} + +// ************************************************************************* +TEST(TestPseudorangeFactor, equals) { + const auto factor1 = PseudorangeFactor(); + const auto factor2 = PseudorangeFactor(1, 2, 0.0, Point3::Zero(), 0.0); + const auto factor3 = + PseudorangeFactor(1, 2, 10.0, Point3(1.0, 2.0, 3.0), 20.0); + + CHECK(factor1.equals(factor1)); + CHECK(factor2.equals(factor2)); + CHECK(!factor1.equals(factor2)); + CHECK(factor2.equals(factor3, 1e99)); + + // Test print: + factor2.print("factor2"); +} + // ************************************************************************* int main() { TestResult tr; diff --git a/python/gtsam/tests/test_PseudorangeFactor.py b/python/gtsam/tests/test_PseudorangeFactor.py index 424d571721..36c16ab3c1 100644 --- a/python/gtsam/tests/test_PseudorangeFactor.py +++ b/python/gtsam/tests/test_PseudorangeFactor.py @@ -16,10 +16,41 @@ from gtsam.utils.test_case import GtsamTestCase -class TestPriorFactor(GtsamTestCase): - - def test_PseudorangeFactor(self): - self.assertEqual(1, 0) +class TestPseudorangeFactor(GtsamTestCase): + def test_singularity(self): + pos = np.zeros(3) + values = gtsam.Values() + values.insert(0, pos) + values.insert(1, 0.0) + + model = gtsam.noiseModel.Unit.Create(1) + factor = gtsam.PseudorangeFactor(0, 1, 0.0, pos, 0.0, model) + self.assertEqual(factor.error(values), 0) + # test print: + factor.print("factor") + + def test_errors(self): + satpos = np.array([0.0, 0.0, 3.0]) + model = gtsam.noiseModel.Unit.Create(1) + factor = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) + error = factor.evaluateError(np.zeros(3), 0.0) + self.assertEqual(error[0], -1.0) + + def test_equality(self): + satpos = np.array([0.0, 0.0, 3.0]) + model = gtsam.noiseModel.Unit.Create(1) + factor1 = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) + factor2 = gtsam.PseudorangeFactor(2, 1, 4.0, satpos, 0.0, model) + factor3 = gtsam.PseudorangeFactor(0, 1, 40.0, satpos, 10.0, model) + self.assertTrue(factor1.equals(factor1, 1e-6)) + self.assertFalse(factor1.equals(factor2, 1e-6)) + self.assertTrue(factor1.equals(factor3, 1e99)) + + def test_serialization(self): + satpos = np.array([0.0, 0.0, 3.0]) + model = gtsam.noiseModel.Unit.Create(1) + factor = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) + factor.serialize() if __name__ == "__main__": From 76246bfed7948d63d60c9574b85e2199b2049eca Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Wed, 28 Jan 2026 17:23:04 -0500 Subject: [PATCH 142/266] restructure --- gtsam/navigation/EquivariantFilter.h | 28 --------- gtsam/navigation/navigation.i | 25 -------- gtsam_unstable/geometry/ABC.h | 24 -------- ...tude.py => AbcEquivariantFilterExample.py} | 0 .../EquivariantFilterUnit3Symmetry.py | 59 ------------------- 5 files changed, 136 deletions(-) rename python/gtsam/examples/{EquivariantFilterAttitude.py => AbcEquivariantFilterExample.py} (100%) delete mode 100644 python/gtsam/examples/EquivariantFilterUnit3Symmetry.py diff --git a/gtsam/navigation/EquivariantFilter.h b/gtsam/navigation/EquivariantFilter.h index c69f5b3092..501ac9f2d3 100644 --- a/gtsam/navigation/EquivariantFilter.h +++ b/gtsam/navigation/EquivariantFilter.h @@ -222,34 +222,6 @@ class EquivariantFilter : public ManifoldEKF { Base::predict(xi_next, Phi, Q_manifold); } - /** - * @brief Propagate the filter state with explicit Jacobian and lift vector. - * - * This overload is wrapper-friendly: takes a lifted tangent vector - * directly, avoiding templated Lift functors. It uses first-order Euler - * discretization (K = 1). - * - * @param Lambda Lifted tangent vector in the group tangent space. - * @param A Error dynamics matrix (DimM x DimM). - * @param Qc Process noise covariance on the manifold (continuous-time). - * @param dt Time step. - */ - void predictWithJacobianEuler(const Vector& Lambda, const MatrixM& A, - const MatrixM& Qc, double dt) { - TangentG lambda_g; - if constexpr (TangentG::RowsAtCompileTime == Eigen::Dynamic) { - lambda_g.resize(DimG); - } - lambda_g = Lambda; - - g_ = traits::Compose(g_, traits::Expmap(lambda_g * dt)); - M xi_next = act_on_ref_(g_); - - MatrixM Phi = transitionMatrix<1>(A, dt); - CovarianceM Q_manifold = Qc * dt; - Base::predict(xi_next, Phi, Q_manifold); - } - /** * Measurement update: Corrects the state and covariance using a * pre-calculated predicted measurement and its Jacobian. diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 004063e631..6c721d049f 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -651,31 +651,6 @@ virtual class InvariantEKF : gtsam::LeftLinearEKF { void predict(const gtsam::Vector& u, double dt, gtsam::Matrix Q); }; -// --------------------------------------------------------------------------- -// Equivariant Filter (attitude example wrapper) -#include -#include -template -virtual class EquivariantFilter : gtsam::ManifoldEKF { - // Constructors - EquivariantFilter(const M& xi_ref, gtsam::Matrix Sigma); - EquivariantFilter(const M& xi_ref, gtsam::Matrix Sigma, - const Symmetry::Group& X0); - - // Accessors - gtsam::Matrix errorCovariance() const; - gtsam::Matrix covariance() const; - Symmetry::Group groupEstimate() const; - - // Wrapper-friendly predict - void predictWithJacobianEuler(const gtsam::Vector& Lambda, gtsam::Matrix A, - gtsam::Matrix Qc, double dt); - - // Only vector-based measurements are supported in wrapper - void updateWithVector(const gtsam::Vector& prediction, const gtsam::Matrix& H, - const gtsam::Vector& z, const gtsam::Matrix& R); -}; - // --------------------------------------------------------------------------- // ABC Equivariant Filter wrapper (N=1) #include diff --git a/gtsam_unstable/geometry/ABC.h b/gtsam_unstable/geometry/ABC.h index 7f3883de7e..ca25764a59 100644 --- a/gtsam_unstable/geometry/ABC.h +++ b/gtsam_unstable/geometry/ABC.h @@ -240,30 +240,6 @@ struct Symmetry : public GroupAction, Group, State> { }; }; -//======================================================================== -// Attitude-only symmetry for Unit3/Rot3 (used by Python EqF wrappers) -//======================================================================== -namespace attitude_example { - -using M = Unit3; -using G = Rot3; - -/** - * Symmetry: right group action on Unit3. - */ -struct Symmetry : public GroupAction { - static constexpr ActionType type = ActionType::Right; - - M operator()(const M& eta, const G& Q, - OptionalJacobian<2, 2> H_eta = {}, - OptionalJacobian<2, 3> H_Q = {}) const { - // Apply the right action Q^T * eta - return Q.unrotate(eta, H_Q, H_eta); - } -}; - -} // namespace attitude_example - /** * Continuous-time dynamics on the manifold M = SO(3) x R^3 x SO(3)^N, as in * Eq. (2) of the paper. Given state ξ = (R, b, S) and body angular velocity diff --git a/python/gtsam/examples/EquivariantFilterAttitude.py b/python/gtsam/examples/AbcEquivariantFilterExample.py similarity index 100% rename from python/gtsam/examples/EquivariantFilterAttitude.py rename to python/gtsam/examples/AbcEquivariantFilterExample.py diff --git a/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py b/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py deleted file mode 100644 index da6fe707fc..0000000000 --- a/python/gtsam/examples/EquivariantFilterUnit3Symmetry.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Minimal example for gtsam.EquivariantFilterUnit3Symmetry (Unit3/Rot3 EqF). - -This mirrors the attitude-only EquivariantFilter test setup. -""" - -import numpy as np -import gtsam -from gtsam import Rot3, Unit3 - - -def main() -> None: - # Reference direction and initial covariance on S^2 - eta_ref = Unit3(0.0, 0.0, 1.0) - sigma0 = 0.01 * np.eye(2) - - filter_eqf = gtsam.EquivariantFilterUnit3Symmetry(eta_ref, sigma0) - - # explicit lift vector (omega) and manifold Qc - omega = np.array([0.1, -0.2, 0.3]) - A = np.zeros((2, 2)) - sigma_u = 0.1 * np.eye(3) - B = np.eye(2, 3) - Qc = B @ sigma_u @ B.T - dt = 0.01 - - filter_eqf.predictWithJacobianEuler(omega, A, Qc, dt) - - expected_Q = Rot3.Expmap(omega * dt) - assert filter_eqf.groupEstimate().equals(expected_Q, 1e-9) - - expected_state = Unit3(expected_Q.unrotate(eta_ref.point3())) - assert filter_eqf.state().equals(expected_state, 1e-9) - - print("After predict:") - print(" Truth (eta_ref):", eta_ref.unitVector()) - print(" Estimated state:", filter_eqf.state().unitVector()) - - # update by a simple linear scaling of the direction - c_m = 1.2 - eta_hat = filter_eqf.state() - H = c_m * eta_hat.basis() # 3x2 - prediction = c_m * eta_hat.point3() - z = c_m * eta_ref.point3() - R = 0.01 * np.eye(3) - - filter_eqf.updateWithVector(prediction, H, z, R) - - assert filter_eqf.errorCovariance().shape == (2, 2) - print("After update:") - print(" Truth (eta_ref):", eta_ref.unitVector()) - print(" Estimated state:", filter_eqf.state().unitVector()) - print("EquivariantFilterUnit3Symmetry example ran successfully.") - - -if __name__ == "__main__": - main() - - From 0c159a6f2ef1d3bbfada78a62caa6413c2f03024 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Wed, 28 Jan 2026 19:20:23 -0500 Subject: [PATCH 143/266] create AbcEqFilter which inherits from EqFilter, template on N, and simplify original example --- examples/AbcEquivariantFilterExample.cpp | 85 +++++------ gtsam/navigation/EquivariantFilter.h | 2 +- gtsam/navigation/navigation.i | 21 +-- gtsam_unstable/geometry/ABCEqFWrapper.h | 71 --------- .../geometry/ABCEquivariantFilter.h | 136 ++++++++++++++++++ 5 files changed, 182 insertions(+), 133 deletions(-) delete mode 100644 gtsam_unstable/geometry/ABCEqFWrapper.h create mode 100644 gtsam_unstable/geometry/ABCEquivariantFilter.h diff --git a/examples/AbcEquivariantFilterExample.cpp b/examples/AbcEquivariantFilterExample.cpp index 821db414ef..18fd3e8d62 100644 --- a/examples/AbcEquivariantFilterExample.cpp +++ b/examples/AbcEquivariantFilterExample.cpp @@ -1,32 +1,31 @@ /** * @file AbcEquivariantFilterExample.cpp - * @brief Demonstration of the full Attitude-Bias-Calibration Equivariant Filter + * @brief Demonstration of the Attitude-Bias-Calibration Equivariant Filter * * This demo shows the Equivariant Filter (EqF) for attitude estimation * with both gyroscope bias and sensor extrinsic calibration, based on the * paper: "Overcoming Bias: Equivariant Filter Design for Biased Attitude * Estimation with Online Calibration" by Fornasier et al. * + * This example uses the simplified AbcEquivariantFilter wrapper class which + * provides a clean interface for predict/update operations without requiring + * manual computation of Jacobian matrices and innovation functions. + * * @author Darshan Rajasekaran * @author Jennifer Oum * @author Rohan Bansal * @author Frank Dellaert * @date 2025 */ -#include #include #include +#include // Use namespace for convenience using namespace gtsam; constexpr size_t n = 1; // Number of calibration states using M = abc::State; -using G = abc::Group; -using Symmetry = abc::Symmetry; -using EqFilter = gtsam::EquivariantFilter; -using Lift = abc::Lift; -using InputOrbit = typename abc::InputAction::Orbit; -using Innovation = abc::Innovation; +using AbcFilter = abc::AbcEquivariantFilter; /// Measurement struct struct Measurement { @@ -72,7 +71,7 @@ std::vector loadDataFromCSV(const std::string& filename, int startRow = 0, int maxRows = -1, int downsample = 1); /// Process data with EqF and print summary results -void processDataWithEqF(EqFilter& filter, const std::vector& data_list, +void processDataWithEqF(AbcFilter& filter, const std::vector& data_list, int printInterval = 10); //======================================================================== @@ -235,7 +234,7 @@ std::vector loadDataFromCSV(const std::string& filename, int startRow, } /// Takes in the data and runs an EqF on it and reports the results -void processDataWithEqF(EqFilter& filter, const std::vector& data_list, +void processDataWithEqF(AbcFilter& filter, const std::vector& data_list, int printInterval) { if (data_list.empty()) { std::cerr << "No data to process" << std::endl; @@ -267,18 +266,7 @@ void processDataWithEqF(EqFilter& filter, const std::vector& data_list, for (size_t i = 0; i < data_list.size(); i++) { const Data& data = data_list[i]; - Matrix Q = abc::inputProcessNoise(data.inputCovariance); - // Propagate filter with current input and time step - Vector6 u = abc::toInputVector(data.omega); - Lift lift_u(u); - InputOrbit psi_u(u); - - // Use Explicit Matrices API - G X_hat = filter.groupEstimate(); - Matrix A = abc::stateMatrixA(psi_u, X_hat); - Matrix B = abc::inputMatrixB(X_hat); - Matrix Qc = B * Q * B.transpose(); // continuous-time manifold covariance - filter.predictWithJacobian<2>(lift_u, A, Qc, data.dt); + filter.predict(data.omega, data.inputCovariance, data.dt); // Process all measurements for (const auto& measurement : data.measurements) { @@ -294,14 +282,8 @@ void processDataWithEqF(EqFilter& filter, const std::vector& data_list, } try { - Innovation innovation(measurement.y, measurement.d, - measurement.cal_idx); - // Use Explicit Matrices API - X_hat = filter.groupEstimate(); - const Matrix3 D = abc::outputMatrixD(X_hat, measurement.cal_idx); - const Matrix3 R = D * measurement.R * D.transpose(); - filter.update(innovation, Z_3x1, R); - + filter.update(measurement.y, measurement.d, measurement.R, + measurement.cal_idx); validMeasurements++; } catch (const std::exception& e) { std::cerr << "Error updating at t=" << data.t << ": " << e.what() @@ -309,15 +291,17 @@ void processDataWithEqF(EqFilter& filter, const std::vector& data_list, } } - // Get current state estimate - M estimate = filter.state(); + // Get current estimates using accessor methods + Rot3 att_est = filter.attitude(); + Vector3 bias_est = filter.bias(); + Rot3 cal_est = filter.calibration(0); // Calculate errors - Vector3 att_error = Rot3::Logmap(data.xi.R.between(estimate.R)); - Vector3 bias_error = estimate.b - data.xi.b; + Vector3 att_error = Rot3::Logmap(data.xi.R.between(att_est)); + Vector3 bias_error = bias_est - data.xi.b; Vector3 cal_error = Z_3x1; - if (!data.xi.S.empty() && !estimate.S.empty()) { - cal_error = Rot3::Logmap(data.xi.S[0].between(estimate.S[0])); + if (!data.xi.S.empty()) { + cal_error = Rot3::Logmap(data.xi.S[0].between(cal_est)); } // Store errors @@ -353,14 +337,15 @@ void processDataWithEqF(EqFilter& filter, const std::vector& data_list, // Calculate final errors from last data point const Data& final_data = data_list.back(); - M final_estimate = filter.state(); - Vector3 final_att_error = - Rot3::Logmap(final_data.xi.R.between(final_estimate.R)); - Vector3 final_bias_error = final_estimate.b - final_data.xi.b; + Rot3 final_att_est = filter.attitude(); + Vector3 final_bias_est = filter.bias(); + Rot3 final_cal_est = filter.calibration(0); + + Vector3 final_att_error = Rot3::Logmap(final_data.xi.R.between(final_att_est)); + Vector3 final_bias_error = final_bias_est - final_data.xi.b; Vector3 final_cal_error = Z_3x1; - if (!final_data.xi.S.empty() && !final_estimate.S.empty()) { - final_cal_error = - Rot3::Logmap(final_data.xi.S[0].between(final_estimate.S[0])); + if (!final_data.xi.S.empty()) { + final_cal_error = Rot3::Logmap(final_data.xi.S[0].between(final_cal_est)); } // Print summary statistics @@ -388,15 +373,15 @@ void processDataWithEqF(EqFilter& filter, const std::vector& data_list, // Print a brief comparison of final estimate vs ground truth std::cout << "\n-- Final State vs Ground Truth --" << std::endl; std::cout << "Attitude (RPY) - Estimate: " - << (final_estimate.R.rpy() * RAD_TO_DEG).transpose() + << (final_att_est.rpy() * RAD_TO_DEG).transpose() << "° | Truth: " << (final_data.xi.R.rpy() * RAD_TO_DEG).transpose() << "°" << std::endl; - std::cout << "Bias - Estimate: " << final_estimate.b.transpose() + std::cout << "Bias - Estimate: " << final_bias_est.transpose() << " | Truth: " << final_data.xi.b.transpose() << std::endl; - if (!final_estimate.S.empty() && !final_data.xi.S.empty()) { + if (!final_data.xi.S.empty()) { std::cout << "Calibration (RPY) - Estimate: " - << (final_estimate.S[0].rpy() * RAD_TO_DEG).transpose() + << (final_cal_est.rpy() * RAD_TO_DEG).transpose() << "° | Truth: " << (final_data.xi.S[0].rpy() * RAD_TO_DEG).transpose() << "°" << std::endl; @@ -456,10 +441,8 @@ int main(int argc, char* argv[]) { initialSigma.diagonal().tail<3>() = Vector3::Constant(0.1); // Calibration uncertainty - M initialState = M::identity(); - - // Create filter - EqFilter filter(initialState, initialSigma); + // Create filter with initial covariance (starts at identity state) + AbcFilter filter(initialSigma); // Process data processDataWithEqF(filter, data); diff --git a/gtsam/navigation/EquivariantFilter.h b/gtsam/navigation/EquivariantFilter.h index 501ac9f2d3..187f0d3cb1 100644 --- a/gtsam/navigation/EquivariantFilter.h +++ b/gtsam/navigation/EquivariantFilter.h @@ -93,7 +93,7 @@ class EquivariantFilter : public ManifoldEKF { using Base::state; /// errorCovariance that returns P_, on the equivariant filter error - Matrix errorCovariance() const { return this->P_; } + const typename Base::Covariance& errorCovariance() const { return this->P_; } /// Covariance in the tangent space at the current state. CovarianceM covariance() const { diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 6c721d049f..db1b4d2dec 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -653,21 +653,22 @@ virtual class InvariantEKF : gtsam::LeftLinearEKF { // --------------------------------------------------------------------------- // ABC Equivariant Filter wrapper (N=1) -#include +#include namespace abc { -class AbcEquivariantFilter1 { - AbcEquivariantFilter1(); - AbcEquivariantFilter1(const gtsam::Matrix& Sigma0); +template +class AbcEquivariantFilter { + // Constructors + AbcEquivariantFilter(); + AbcEquivariantFilter(gtsam::Matrix Sigma0); - void predict(const gtsam::Vector& omega, const gtsam::Matrix& inputCovariance, - double dt); - void update(const gtsam::Unit3& y, const gtsam::Unit3& d, - const gtsam::Matrix& R, int cal_idx); + // Predict and update methods + void predict(const gtsam::Vector3& omega, const gtsam::Matrix6& inputCovariance, double dt); + void update(const gtsam::Unit3& y, const gtsam::Unit3& d, const gtsam::Matrix3& R, int cal_idx); + // Accessors gtsam::Rot3 attitude() const; - gtsam::Vector bias() const; + gtsam::Vector3 bias() const; gtsam::Rot3 calibration(size_t i) const; - gtsam::Matrix errorCovariance() const; }; } // namespace abc diff --git a/gtsam_unstable/geometry/ABCEqFWrapper.h b/gtsam_unstable/geometry/ABCEqFWrapper.h deleted file mode 100644 index 4fdc6ae177..0000000000 --- a/gtsam_unstable/geometry/ABCEqFWrapper.h +++ /dev/null @@ -1,71 +0,0 @@ -/** - * @file ABCEqFWrapper.h - * @brief Wrapper-friendly ABC EqF helper class for Python bindings. - * - * This class mirrors the logic in examples/AbcEquivariantFilterExample.cpp - * but exposes a simple, non-templated API for wrapping. - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -namespace gtsam { -namespace abc { - -class AbcEquivariantFilter1 { - public: - static constexpr size_t N = 1; - using State1 = State; - using Group1 = Group; - using Symmetry1 = Symmetry; - using EqFilter1 = gtsam::EquivariantFilter; - - AbcEquivariantFilter1() : AbcEquivariantFilter1(Matrix::Identity(6 + 3 * N, 6 + 3 * N)) {} - - explicit AbcEquivariantFilter1(const Matrix& Sigma0) - : filter_(State1::identity(), Sigma0) {} - - /// Propagate filter using the explicit matrices API from ABC. - void predict(const Vector3& omega, const Matrix6& inputCovariance, double dt) { - const Matrix Q = inputProcessNoise(inputCovariance); - const Vector6 u = toInputVector(omega); - const Lift lift_u(u); - const typename InputAction::Orbit psi_u(u); - - const Group1 X_hat = filter_.groupEstimate(); - const Matrix A = stateMatrixA(psi_u, X_hat); - const Matrix B = inputMatrixB(X_hat); - const Matrix Qc = B * Q * B.transpose(); - - filter_.predictWithJacobian<2>(lift_u, A, Qc, dt); - } - - /// Update filter with a direction measurement. - void update(const Unit3& y, const Unit3& d, const Matrix3& R, int cal_idx) { - const Innovation innovation(y, d, cal_idx); - const Group1 X_hat = filter_.groupEstimate(); - const Matrix3 D = outputMatrixD(X_hat, cal_idx); - const Matrix3 R_adjusted = D * R * D.transpose(); - filter_.update(innovation, Z_3x1, R_adjusted); - } - - /// Accessors for current estimate. - Rot3 attitude() const { return filter_.state().R; } - Vector3 bias() const { return filter_.state().b; } - Rot3 calibration(size_t i) const { return filter_.state().S[i]; } - Matrix errorCovariance() const { return filter_.errorCovariance(); } - - private: - EqFilter1 filter_; -}; - -} // namespace abc -} // namespace gtsam - - diff --git a/gtsam_unstable/geometry/ABCEquivariantFilter.h b/gtsam_unstable/geometry/ABCEquivariantFilter.h new file mode 100644 index 0000000000..fb6a7695e0 --- /dev/null +++ b/gtsam_unstable/geometry/ABCEquivariantFilter.h @@ -0,0 +1,136 @@ +/** + * @file ABCEquivariantFilter.h + * @brief Attitude-Bias-Calibration Equivariant Filter for state estimation + * @author Rohan Bansal + * @date 2026 + * + * This file extends the EquivariantFilter class to provide a more user-friendly + * interface for the ABC Equivariant Filter. This class templates on the number + * of calibrated sensors N, abstracting away the details of the ABC system. + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +namespace gtsam { +namespace abc { + +/** + * @class AbcEquivariantFilter + * @brief Equivariant Filter for Attitude-Bias-Calibration (ABC) estimation + * + * This class implements an equivariant filter for estimating: + * - Attitude (rotation): The orientation of the body frame relative to a + * reference frame + * - Bias: Gyroscope bias correction vector (3D) + * - Calibration: N sensor calibration rotation matrices + * + * The filter uses the ABC Lie group structure and equivariant dynamics to + * provide consistent state estimation. It inherits from EquivariantFilter + * and provides a simplified interface for ABC-specific operations. + * + * @tparam N Number of calibrated sensors (typically 1 or more) + */ +template +class AbcEquivariantFilter + : public gtsam::EquivariantFilter, Symmetry> { + public: + using gtsam::EquivariantFilter, Symmetry>::update; + + /** + * @brief Default constructor with identity initial covariance + * + * Initializes the filter with an identity state and identity covariance + * matrix of dimension (6 + 3*N) x (6 + 3*N), where 6 accounts for the + * attitude and bias parameters, and 3*N for N calibration parameters. + */ + AbcEquivariantFilter() + : AbcEquivariantFilter(Matrix::Identity(6 + 3 * N, 6 + 3 * N)) {} + + /** + * @brief Construct filter with custom initial covariance + * + * Initializes the filter at the identity state with a specified initial + * covariance matrix on the manifold tangent space. + * + * @param Sigma0 Initial covariance matrix (6+3*N) x (6+3*N) + */ + explicit AbcEquivariantFilter(const Matrix& Sigma0) + : gtsam::EquivariantFilter, Symmetry>(State::identity(), + Sigma0) {} + + /** + * @brief Prediction step using gyroscope measurements + * + * Propagates the filter state forward in time using angular velocity + * measurements and process noise. This method uses the explicit Jacobian + * matrices (A and B) computed from the ABC dynamics. + * + * @param omega Angular velocity measurement (body frame, rad/s) + * @param inputCovariance Process noise covariance (6x6) for the input + * @param dt Time step (seconds) + */ + void predict(const Vector3& omega, const Matrix6& inputCovariance, + double dt) { + const Matrix Q = inputProcessNoise(inputCovariance); + const Vector6 u = toInputVector(omega); + const Lift lift_u(u); + const typename InputAction::Orbit psi_u(u); + + const Group X_hat = this->groupEstimate(); + const Matrix A = stateMatrixA(psi_u, X_hat); + const Matrix B = inputMatrixB(X_hat); + const Matrix Qc = B * Q * B.transpose(); + + this->template predictWithJacobian<2>(lift_u, A, Qc, dt); + } + + /** + * @brief Measurement update using a direction observation + * + * Corrects the filter estimate using a direction measurement from a + * calibrated sensor. The measurement model assumes the sensor observes + * a known reference direction in the body frame. + * + * @param y Measured direction (unit vector in body frame) + * @param d Reference direction (unit vector in reference frame) + * @param R Measurement noise covariance (3x3) + * @param cal_idx Index of the calibrated sensor (0 to N-1), or -1 for + * uncalibrated measurements + */ + void update(const Unit3& y, const Unit3& d, const Matrix3& R, int cal_idx) { + const Innovation innovation(y, d, cal_idx); + const Group X_hat = this->groupEstimate(); + const Matrix3 D = outputMatrixD(X_hat, cal_idx); + const Matrix3 R_adjusted = D * R * D.transpose(); + this->template update(innovation, Z_3x1, R_adjusted); + } + + /** + * @brief Get current attitude estimate + * @return Current rotation estimate (body frame to reference frame) + */ + Rot3 attitude() const { return this->state().R; } + + /** + * @brief Get current gyroscope bias estimate + * @return Current bias vector (rad/s) + */ + Vector3 bias() const { return this->state().b; } + + /** + * @brief Get calibration estimate for a specific sensor + * @param i Sensor index (0 to N-1) + * @return Calibration rotation for sensor i + */ + Rot3 calibration(size_t i) const { return this->state().S[i]; } +}; + +} // namespace abc +} // namespace gtsam From 956d322526341973f792a82fec57e286e9df5259 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Wed, 28 Jan 2026 19:20:49 -0500 Subject: [PATCH 144/266] clang format --- examples/AbcEquivariantFilterExample.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/AbcEquivariantFilterExample.cpp b/examples/AbcEquivariantFilterExample.cpp index 18fd3e8d62..41aafec3a8 100644 --- a/examples/AbcEquivariantFilterExample.cpp +++ b/examples/AbcEquivariantFilterExample.cpp @@ -283,7 +283,7 @@ void processDataWithEqF(AbcFilter& filter, const std::vector& data_list, try { filter.update(measurement.y, measurement.d, measurement.R, - measurement.cal_idx); + measurement.cal_idx); validMeasurements++; } catch (const std::exception& e) { std::cerr << "Error updating at t=" << data.t << ": " << e.what() @@ -340,8 +340,9 @@ void processDataWithEqF(AbcFilter& filter, const std::vector& data_list, Rot3 final_att_est = filter.attitude(); Vector3 final_bias_est = filter.bias(); Rot3 final_cal_est = filter.calibration(0); - - Vector3 final_att_error = Rot3::Logmap(final_data.xi.R.between(final_att_est)); + + Vector3 final_att_error = + Rot3::Logmap(final_data.xi.R.between(final_att_est)); Vector3 final_bias_error = final_bias_est - final_data.xi.b; Vector3 final_cal_error = Z_3x1; if (!final_data.xi.S.empty()) { @@ -381,8 +382,7 @@ void processDataWithEqF(AbcFilter& filter, const std::vector& data_list, if (!final_data.xi.S.empty()) { std::cout << "Calibration (RPY) - Estimate: " - << (final_cal_est.rpy() * RAD_TO_DEG).transpose() - << "° | Truth: " + << (final_cal_est.rpy() * RAD_TO_DEG).transpose() << "° | Truth: " << (final_data.xi.S[0].rpy() * RAD_TO_DEG).transpose() << "°" << std::endl; } From caa4763c7f7a6b09324982489a2371b2d8b7cb17 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 29 Jan 2026 19:21:30 -0500 Subject: [PATCH 145/266] Codex-generated docs --- gtsam/basis/basis.md | 66 ++++++++ gtsam/basis/doc/Basis.ipynb | 144 ++++++++++++++++++ gtsam/basis/doc/Chebyshev1Basis.ipynb | 110 +++++++++++++ gtsam/basis/doc/Chebyshev2.ipynb | 111 ++++++++++++++ gtsam/basis/doc/Chebyshev2Basis.ipynb | 110 +++++++++++++ .../basis/doc/ComponentDerivativeFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/DerivativeFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/EvaluationFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/FitBasis.ipynb | 107 +++++++++++++ gtsam/basis/doc/FourierBasis.ipynb | 108 +++++++++++++ .../basis/doc/ManifoldEvaluationFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/VectorComponentFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/VectorDerivativeFactor.ipynb | 106 +++++++++++++ gtsam/basis/doc/VectorEvaluationFactor.ipynb | 106 +++++++++++++ myst.yml | 3 + 15 files changed, 1501 insertions(+) create mode 100644 gtsam/basis/basis.md create mode 100644 gtsam/basis/doc/Basis.ipynb create mode 100644 gtsam/basis/doc/Chebyshev1Basis.ipynb create mode 100644 gtsam/basis/doc/Chebyshev2.ipynb create mode 100644 gtsam/basis/doc/Chebyshev2Basis.ipynb create mode 100644 gtsam/basis/doc/ComponentDerivativeFactor.ipynb create mode 100644 gtsam/basis/doc/DerivativeFactor.ipynb create mode 100644 gtsam/basis/doc/EvaluationFactor.ipynb create mode 100644 gtsam/basis/doc/FitBasis.ipynb create mode 100644 gtsam/basis/doc/FourierBasis.ipynb create mode 100644 gtsam/basis/doc/ManifoldEvaluationFactor.ipynb create mode 100644 gtsam/basis/doc/VectorComponentFactor.ipynb create mode 100644 gtsam/basis/doc/VectorDerivativeFactor.ipynb create mode 100644 gtsam/basis/doc/VectorEvaluationFactor.ipynb diff --git a/gtsam/basis/basis.md b/gtsam/basis/basis.md new file mode 100644 index 0000000000..7a22ad2b4f --- /dev/null +++ b/gtsam/basis/basis.md @@ -0,0 +1,66 @@ +# Basis + +The `basis` module provides tools for representing continuous functions as +linear combinations of basis functions or as values at interpolation points. +It is useful for smooth function approximation, trajectory modeling, and +building factors that constrain functions (or their derivatives) at specific +points. + +At a high level, you choose a basis (Fourier or Chebyshev), decide whether you +want a coefficient-based representation or a pseudo-spectral one (values at +Chebyshev points), and then use the provided factors or fitting utilities to +solve for parameters in GTSAM. + +## Getting Oriented + +- **Coefficient-based bases**: `Chebyshev1Basis`, `Chebyshev2Basis`, and + `FourierBasis` treat the parameters as coefficients on basis functions. +- **Pseudo-spectral basis**: `Chebyshev2` treats the parameters as values at + Chebyshev points and uses barycentric interpolation. +- **Factors**: A family of unary factors enforce function values or derivatives + at specific points, including vector- and manifold-valued variants. +- **Fitting**: `FitBasis` performs least-squares regression from samples. + +## Core Concepts + +- [Basis](doc/Basis.ipynb): CRTP base class providing evaluation and derivative + functors, Jacobians, and common helpers. + +## Polynomial Bases + +- [Chebyshev1Basis](doc/Chebyshev1Basis.ipynb): First-kind Chebyshev basis + $T_n(x)$ on $[-1,1]$ (coefficient-based). +- [Chebyshev2Basis](doc/Chebyshev2Basis.ipynb): Second-kind Chebyshev basis + $U_n(x)$ on $[-1,1]$ (coefficient-based). +- [FourierBasis](doc/FourierBasis.ipynb): Real Fourier series basis for + periodic functions. + +## Pseudo-Spectral Basis + +- [Chebyshev2](doc/Chebyshev2.ipynb): Chebyshev points, barycentric + interpolation, differentiation/integration matrices, and quadrature weights. + +## Factors for Basis Evaluation + +These factors connect basis parameters to measurements of values or derivatives. + +- [EvaluationFactor](doc/EvaluationFactor.ipynb): Scalar value at a point. +- [VectorEvaluationFactor](doc/VectorEvaluationFactor.ipynb): Vector value at a + point. +- [VectorComponentFactor](doc/VectorComponentFactor.ipynb): Single component of + a vector value. +- [ManifoldEvaluationFactor](doc/ManifoldEvaluationFactor.ipynb): Manifold-valued + measurement (e.g., `Rot3`, `Pose3`). + +## Factors for Derivative Constraints + +- [DerivativeFactor](doc/DerivativeFactor.ipynb): Scalar derivative at a point. +- [VectorDerivativeFactor](doc/VectorDerivativeFactor.ipynb): Vector derivative + at a point. +- [ComponentDerivativeFactor](doc/ComponentDerivativeFactor.ipynb): Single + component of a vector derivative. + +## Fitting from Data + +- [FitBasis](doc/FitBasis.ipynb): Build a least-squares problem from samples and + solve for basis parameters. diff --git a/gtsam/basis/doc/Basis.ipynb b/gtsam/basis/doc/Basis.ipynb new file mode 100644 index 0000000000..ffdea68e03 --- /dev/null +++ b/gtsam/basis/doc/Basis.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basis\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "The `Basis` class is a CRTP base class that defines common utilities for representing functions as linear combinations of basis functions. Derived classes provide `CalculateWeights` and `DerivativeWeights`, while `Basis` supplies functors and helpers for evaluation, vector-valued evaluation, and derivative computation with Jacobians with respect to parameters.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `WeightMatrix(N, X)` and `WeightMatrix(N, X, a, b)` build stacked weight matrices.\n", + "- `EvaluationFunctor` evaluates a scalar function at `x`.\n", + "- `VectorEvaluationFunctor` evaluates vector-valued functions from a parameter matrix.\n", + "- `VectorComponentFunctor` evaluates a single component of a vector-valued function.\n", + "- `ManifoldEvaluationFunctor` evaluates manifold-valued functions via local coordinates.\n", + "- `DerivativeFunctor`, `VectorDerivativeFunctor`, and `ComponentDerivativeFunctor` compute derivatives.\n", + "- `kroneckerProductIdentity` builds efficient block Jacobians for vector-valued cases.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Derived Classes\n", + "\n", + "- `Chebyshev1Basis` (first-kind Chebyshev polynomials)\n", + "- `Chebyshev2Basis` (second-kind Chebyshev polynomials)\n", + "- `FourierBasis` (real Fourier series)\n", + "- `Chebyshev2` (pseudo-spectral Chebyshev points)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "456bb6bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight matrix W:\n", + " [[ 1. 0. 0. 0. 0. ]\n", + " [-0.1875 0.90533009 0.375 -0.15533009 0.0625 ]\n", + " [ 0. 0. 1. 0. 0. ]\n", + " [ 0.0625 -0.15533009 0.375 0.90533009 -0.1875 ]\n", + " [ 0. 0. 0. 0. 1. ]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gtsam\n", + "\n", + "N = 5\n", + "X = np.linspace(-1.0, 1.0, 5)\n", + "W = gtsam.Chebyshev2.WeightMatrix(N, X)\n", + "print(\"Weight matrix W:\\n\", W)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [Basis.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/Basis.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/basis/doc/Chebyshev1Basis.ipynb b/gtsam/basis/doc/Chebyshev1Basis.ipynb new file mode 100644 index 0000000000..668055be63 --- /dev/null +++ b/gtsam/basis/doc/Chebyshev1Basis.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chebyshev1Basis\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`Chebyshev1Basis` provides the first-kind Chebyshev polynomial basis $T_n(x)$ on the interval $[-1, 1]$. Parameters are coefficients of the basis functions, making this a classic orthogonal polynomial expansion for smooth function approximation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `CalculateWeights(N, x, a=-1, b=1)` returns the $1 \times N$ basis weights.\n", + "- `DerivativeWeights(N, x, a=-1, b=1)` returns weights for the derivative.\n", + "- `WeightMatrix(N, X)` stacks weights for a vector of sample points.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```python\n", + "import numpy as np\n", + "import gtsam\n", + "\n", + "N = 6\n", + "x = 0.25\n", + "weights = gtsam.Chebyshev1Basis.CalculateWeights(N, x)\n", + "X = np.linspace(-1.0, 1.0, 5)\n", + "W = gtsam.Chebyshev1Basis.WeightMatrix(N, X)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [Chebyshev.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/Chebyshev.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/Chebyshev2.ipynb b/gtsam/basis/doc/Chebyshev2.ipynb new file mode 100644 index 0000000000..28e8ce6440 --- /dev/null +++ b/gtsam/basis/doc/Chebyshev2.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chebyshev2\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`Chebyshev2` implements a pseudo-spectral parameterization on Chebyshev points of the second kind. Instead of coefficients, the parameters are function values at Chebyshev points, and evaluation uses barycentric interpolation. The class also provides differentiation and integration matrices for spectral calculus.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `Point(N, j[, a, b])` and `Points(N[, a, b])` return Chebyshev points.\n", + "- `CalculateWeights(N, x[, a, b])` returns barycentric interpolation weights.\n", + "- `DerivativeWeights(N, x[, a, b])` returns derivative weights.\n", + "- `DifferentiationMatrix(N[, a, b])` and `IntegrationMatrix(N[, a, b])` provide spectral operators.\n", + "- `IntegrationWeights(N[, a, b])` and `DoubleIntegrationWeights(N[, a, b])` provide quadrature weights.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```python\n", + "import numpy as np\n", + "import gtsam\n", + "\n", + "N = 8\n", + "points = gtsam.Chebyshev2.Points(N)\n", + "weights = gtsam.Chebyshev2.CalculateWeights(N, 0.2)\n", + "D = gtsam.Chebyshev2.DifferentiationMatrix(N)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [Chebyshev2.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/Chebyshev2.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/Chebyshev2Basis.ipynb b/gtsam/basis/doc/Chebyshev2Basis.ipynb new file mode 100644 index 0000000000..95a2c6dbca --- /dev/null +++ b/gtsam/basis/doc/Chebyshev2Basis.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chebyshev2Basis\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`Chebyshev2Basis` provides the second-kind Chebyshev polynomial basis $U_n(x)$ on $[-1, 1]$. It is related to derivatives of first-kind polynomials and is useful when expressing functions directly in a basis of $U_n(x)$ polynomials.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `CalculateWeights(N, x, a=-1, b=1)` returns basis weights.\n", + "- `DerivativeWeights(N, x, a=-1, b=1)` returns derivative weights.\n", + "- `WeightMatrix(N, X)` stacks weights for a vector of sample points.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```python\n", + "import numpy as np\n", + "import gtsam\n", + "\n", + "N = 6\n", + "x = -0.3\n", + "weights = gtsam.Chebyshev2Basis.CalculateWeights(N, x)\n", + "X = np.linspace(-1.0, 1.0, 5)\n", + "W = gtsam.Chebyshev2Basis.WeightMatrix(N, X)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [Chebyshev.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/Chebyshev.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/ComponentDerivativeFactor.ipynb b/gtsam/basis/doc/ComponentDerivativeFactor.ipynb new file mode 100644 index 0000000000..9728777377 --- /dev/null +++ b/gtsam/basis/doc/ComponentDerivativeFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ComponentDerivativeFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`ComponentDerivativeFactor` is a unary factor that constrains the derivative of a single component of a vector-valued basis function at a given point.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `ComponentDerivativeFactor(key, z, model, P, N, i, x)` constrains component `i` of $\\mathbf{f}'(x)$.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", + "size_t P = 3, N = 6, i = 2;\n", + "double x = 0.2;\n", + "double z = 0.0;\n", + "gtsam::ComponentDerivativeFactor factor(key, z, model, P, N, i, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/DerivativeFactor.ipynb b/gtsam/basis/doc/DerivativeFactor.ipynb new file mode 100644 index 0000000000..5ff68e9e38 --- /dev/null +++ b/gtsam/basis/doc/DerivativeFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DerivativeFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`DerivativeFactor` is a unary factor that enforces a scalar measurement of the derivative of a basis function at a given point.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `DerivativeFactor(key, z, model, N, x)` constrains $f'(x)$ to measurement `z`.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", + "size_t N = 8;\n", + "double x = -0.3;\n", + "double z = 0.0;\n", + "gtsam::DerivativeFactor factor(key, z, model, N, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/EvaluationFactor.ipynb b/gtsam/basis/doc/EvaluationFactor.ipynb new file mode 100644 index 0000000000..036db35186 --- /dev/null +++ b/gtsam/basis/doc/EvaluationFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EvaluationFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`EvaluationFactor` is a unary factor that enforces a scalar measurement of a basis function at a given point. It is commonly used with pseudo-spectral bases to constrain function values directly.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `EvaluationFactor(key, z, model, N, x)` constrains $f(x)$ to measurement `z`.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", + "size_t N = 8;\n", + "double x = 0.25;\n", + "double z = 1.2;\n", + "gtsam::EvaluationFactor factor(key, z, model, N, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/FitBasis.ipynb b/gtsam/basis/doc/FitBasis.ipynb new file mode 100644 index 0000000000..90a7280ee2 --- /dev/null +++ b/gtsam/basis/doc/FitBasis.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FitBasis\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`FitBasis` performs least-squares regression to fit a basis function to sample data. It builds a factor graph from samples and solves for basis parameters that best explain the data under a Gaussian noise model.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `FitBasis(sequence, model, N)` constructs and solves the least-squares problem.\n", + "- `parameters()` returns the fitted parameter vector.\n", + "- `NonlinearGraph(...)` and `LinearGraph(...)` expose intermediate graphs.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "std::map samples = {{0.0, 1.0}, {0.5, 0.2}, {1.0, -0.1}};\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.1);\n", + "size_t N = 5;\n", + "gtsam::FitBasis fit(samples, model, N);\n", + "gtsam::Vector params = fit.parameters();\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [FitBasis.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/FitBasis.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/FourierBasis.ipynb b/gtsam/basis/doc/FourierBasis.ipynb new file mode 100644 index 0000000000..3b04291a9c --- /dev/null +++ b/gtsam/basis/doc/FourierBasis.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FourierBasis\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`FourierBasis` provides a real Fourier series basis for periodic functions. The basis is ordered as $[1, \\cos(x), \\sin(x), \\cos(2x), \\sin(2x), \\ldots]$, making it convenient for truncated Fourier expansions and their derivatives.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `CalculateWeights(N, x)` returns the real Fourier basis weights.\n", + "- `DifferentiationMatrix(N)` returns the linear operator for derivatives.\n", + "- `DerivativeWeights(N, x)` returns weights that evaluate the derivative at `x`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```python\n", + "import gtsam\n", + "\n", + "N = 7\n", + "x = 1.2\n", + "weights = gtsam.FourierBasis.CalculateWeights(N, x)\n", + "dweights = gtsam.FourierBasis.DerivativeWeights(N, x)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [Fourier.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/Fourier.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb b/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb new file mode 100644 index 0000000000..0003a90a35 --- /dev/null +++ b/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ManifoldEvaluationFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`ManifoldEvaluationFactor` is a unary factor for manifold-valued measurements such as `Rot2`, `Rot3`, `Pose2`, or `Pose3`. It evaluates a vector-valued basis and compares the result in local coordinates on the manifold.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `ManifoldEvaluationFactor(key, z, model, N, x)` constrains the manifold value at `x`.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(3, 0.05);\n", + "size_t N = 6;\n", + "double x = 0.1;\n", + "gtsam::Rot3 z = gtsam::Rot3::RzRyRx(0.1, 0.2, 0.3);\n", + "gtsam::ManifoldEvaluationFactor factor(key, z, model, N, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/VectorComponentFactor.ipynb b/gtsam/basis/doc/VectorComponentFactor.ipynb new file mode 100644 index 0000000000..31b513be11 --- /dev/null +++ b/gtsam/basis/doc/VectorComponentFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# VectorComponentFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`VectorComponentFactor` is a unary factor that constrains a single component of a vector-valued basis function at a given point. It is useful when only one component is observed.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `VectorComponentFactor(key, z, model, P, N, i, x)` constrains component `i`.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.1);\n", + "size_t P = 3, N = 6, i = 1;\n", + "double x = 0.5;\n", + "double z = -0.2;\n", + "gtsam::VectorComponentFactor factor(key, z, model, P, N, i, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/VectorDerivativeFactor.ipynb b/gtsam/basis/doc/VectorDerivativeFactor.ipynb new file mode 100644 index 0000000000..df7e191f56 --- /dev/null +++ b/gtsam/basis/doc/VectorDerivativeFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# VectorDerivativeFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`VectorDerivativeFactor` is a unary factor that enforces a vector measurement of the derivative of a vector-valued basis function at a given point.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `VectorDerivativeFactor(key, z, model, M, N, x)` constrains $\\mathbf{f}'(x)$.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "gtsam::Vector z = (gtsam::Vector(2) << 0.1, -0.1).finished();\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(2, 0.05);\n", + "size_t M = 2, N = 6;\n", + "double x = 0.0;\n", + "gtsam::VectorDerivativeFactor factor(key, z, model, M, N, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/gtsam/basis/doc/VectorEvaluationFactor.ipynb b/gtsam/basis/doc/VectorEvaluationFactor.ipynb new file mode 100644 index 0000000000..5084aa0c7c --- /dev/null +++ b/gtsam/basis/doc/VectorEvaluationFactor.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# VectorEvaluationFactor\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "\n", + "`VectorEvaluationFactor` is a unary factor for vector-valued measurements evaluated from a basis parameter matrix. It enforces $\\mathbf{f}(x)$ to match a measurement vector at a given point.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Functionality / API\n", + "\n", + "- `VectorEvaluationFactor(key, z, model, M, N, x)` constrains the full vector.\n", + "- Optional interval parameters `a, b` scale the basis domain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage Example\n", + "\n", + "```cpp\n", + "gtsam::Vector z = (gtsam::Vector(3) << 1.0, 0.0, -0.5).finished();\n", + "auto model = gtsam::noiseModel::Isotropic::Sigma(3, 0.1);\n", + "size_t M = 3, N = 6;\n", + "double x = 0.0;\n", + "gtsam::VectorEvaluationFactor factor(key, z, model, M, N, x);\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source\n", + "- [BasisFactors.h](https://github.com/borglab/gtsam/blob/develop/gtsam/basis/BasisFactors.h)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/myst.yml b/myst.yml index 85d1f26bf4..a49cd607dc 100644 --- a/myst.yml +++ b/myst.yml @@ -17,6 +17,9 @@ project: - file: ./gtsam/geometry/geometry.md children: - pattern: ./gtsam/geometry/doc/* + - file: ./gtsam/basis/basis.md + children: + - pattern: ./gtsam/basis/doc/* - file: ./gtsam/inference/inference.md children: - pattern: ./gtsam/inference/doc/* From 7a140bb6956d182973bfddca2f7a74729ae4c3ab Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 29 Jan 2026 20:04:15 -0500 Subject: [PATCH 146/266] Python usage --- gtsam/basis/doc/Basis.ipynb | 33 +++++--- gtsam/basis/doc/Chebyshev1Basis.ipynb | 36 +++++++-- gtsam/basis/doc/Chebyshev2.ipynb | 38 +++++++-- gtsam/basis/doc/Chebyshev2Basis.ipynb | 32 +++++++- .../basis/doc/ComponentDerivativeFactor.ipynb | 38 +++++++-- gtsam/basis/doc/DerivativeFactor.ipynb | 38 ++++++++- gtsam/basis/doc/EvaluationFactor.ipynb | 35 +++++++- gtsam/basis/doc/FitBasis.ipynb | 37 ++++++++- gtsam/basis/doc/FourierBasis.ipynb | 35 +++++++- .../basis/doc/ManifoldEvaluationFactor.ipynb | 81 ++++++++++++++++++- gtsam/basis/doc/VectorComponentFactor.ipynb | 53 +++++++++++- gtsam/basis/doc/VectorDerivativeFactor.ipynb | 53 +++++++++++- gtsam/basis/doc/VectorEvaluationFactor.ipynb | 62 +++++++++++++- 13 files changed, 513 insertions(+), 58 deletions(-) diff --git a/gtsam/basis/doc/Basis.ipynb b/gtsam/basis/doc/Basis.ipynb index ffdea68e03..368906f157 100644 --- a/gtsam/basis/doc/Basis.ipynb +++ b/gtsam/basis/doc/Basis.ipynb @@ -32,7 +32,8 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, + "id": "6775be6c", "metadata": { "tags": [ "remove-cell" @@ -50,6 +51,7 @@ }, { "cell_type": "markdown", + "id": "ce1fbbd5", "metadata": {}, "source": [ "## Key Functionality / API\n", @@ -65,6 +67,7 @@ }, { "cell_type": "markdown", + "id": "46f84e69", "metadata": {}, "source": [ "## Derived Classes\n", @@ -77,27 +80,28 @@ }, { "cell_type": "markdown", + "id": "c8e177a8", "metadata": {}, "source": [ - "## Usage Example" + "## Usage Example\n", + "\n", + "This example builds a weight matrix for a Chebyshev basis and evaluates\n", + "Fourier weights at a point.\n" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "456bb6bb", + "execution_count": 5, + "id": "905a64a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weight matrix W:\n", - " [[ 1. 0. 0. 0. 0. ]\n", - " [-0.1875 0.90533009 0.375 -0.15533009 0.0625 ]\n", - " [ 0. 0. 1. 0. 0. ]\n", - " [ 0.0625 -0.15533009 0.375 0.90533009 -0.1875 ]\n", - " [ 0. 0. 0. 0. 1. ]]\n" + "Chebyshev2 WeightMatrix shape: (5, 5)\n", + "First row: [1. 0. 0. 0. 0.]\n", + "FourierBasis weights at x=0.3: [1. 0.955 0.296 0.825 0.565]\n" ] } ], @@ -105,10 +109,17 @@ "import numpy as np\n", "import gtsam\n", "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", "N = 5\n", "X = np.linspace(-1.0, 1.0, 5)\n", "W = gtsam.Chebyshev2.WeightMatrix(N, X)\n", - "print(\"Weight matrix W:\\n\", W)\n" + "print(\"Chebyshev2 WeightMatrix shape:\", np.asarray(W).shape)\n", + "print(\"First row:\", np.asarray(W)[0])\n", + "\n", + "x = 0.3\n", + "weights = gtsam.FourierBasis.CalculateWeights(N, x)\n", + "print(\"FourierBasis weights at x=0.3:\", np.asarray(weights).ravel())\n" ] }, { diff --git a/gtsam/basis/doc/Chebyshev1Basis.ipynb b/gtsam/basis/doc/Chebyshev1Basis.ipynb index 668055be63..f117d10d2b 100644 --- a/gtsam/basis/doc/Chebyshev1Basis.ipynb +++ b/gtsam/basis/doc/Chebyshev1Basis.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -54,27 +54,53 @@ "source": [ "## Key Functionality / API\n", "\n", - "- `CalculateWeights(N, x, a=-1, b=1)` returns the $1 \times N$ basis weights.\n", + "- `CalculateWeights(N, x, a=-1, b=1)` returns the $1 \t\\times N$ basis weights.\n", "- `DerivativeWeights(N, x, a=-1, b=1)` returns weights for the derivative.\n", "- `WeightMatrix(N, X)` stacks weights for a vector of sample points.\n" ] }, { "cell_type": "markdown", + "id": "922e3685", "metadata": {}, "source": [ "## Usage Example\n", "\n", - "```python\n", + "Evaluate first-kind Chebyshev weights at a point and build a weight\n", + "matrix for multiple sample points.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "30b37cbb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weights at x=0.25: [ 1. 0.25 -0.875 -0.688 0.531 0.953]\n", + "WeightMatrix shape: (5, 6)\n", + "Row for x=0: [ 1. 0. -1. -0. 1. 0.]\n" + ] + } + ], + "source": [ "import numpy as np\n", "import gtsam\n", "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", "N = 6\n", "x = 0.25\n", "weights = gtsam.Chebyshev1Basis.CalculateWeights(N, x)\n", + "print(\"Weights at x=0.25:\", np.asarray(weights).ravel())\n", + "\n", "X = np.linspace(-1.0, 1.0, 5)\n", "W = gtsam.Chebyshev1Basis.WeightMatrix(N, X)\n", - "```\n" + "print(\"WeightMatrix shape:\", np.asarray(W).shape)\n", + "print(\"Row for x=0:\", np.asarray(W)[2])\n" ] }, { @@ -107,4 +133,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/Chebyshev2.ipynb b/gtsam/basis/doc/Chebyshev2.ipynb index 28e8ce6440..b5265faf23 100644 --- a/gtsam/basis/doc/Chebyshev2.ipynb +++ b/gtsam/basis/doc/Chebyshev2.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -67,15 +67,43 @@ "source": [ "## Usage Example\n", "\n", - "```python\n", + "This example inspects the Chebyshev points, interpolation weights, and\n", + "the differentiation matrix.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chebyshev points: [-1. -0.901 -0.623 -0.223 0.223 0.623 0.901 1. ]\n", + "Interpolation weights at x=0.2: [-0.009 0.02 -0.027 0.053 0.998 -0.053 0.032 -0.014]\n", + "D shape: (8, 8)\n", + "First row of D: [-16.5 20.196 -5.312 2.572 -1.636 1.232 -1.052 0.5 ]\n" + ] + } + ], + "source": [ "import numpy as np\n", "import gtsam\n", "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", "N = 8\n", "points = gtsam.Chebyshev2.Points(N)\n", - "weights = gtsam.Chebyshev2.CalculateWeights(N, 0.2)\n", + "print(\"Chebyshev points:\", np.asarray(points).ravel())\n", + "\n", + "x = 0.2\n", + "weights = gtsam.Chebyshev2.CalculateWeights(N, x)\n", + "print(\"Interpolation weights at x=0.2:\", np.asarray(weights).ravel())\n", + "\n", "D = gtsam.Chebyshev2.DifferentiationMatrix(N)\n", - "```\n" + "print(\"D shape:\", np.asarray(D).shape)\n", + "print(\"First row of D:\", np.asarray(D)[0])\n" ] }, { @@ -108,4 +136,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/Chebyshev2Basis.ipynb b/gtsam/basis/doc/Chebyshev2Basis.ipynb index 95a2c6dbca..fc13e57a19 100644 --- a/gtsam/basis/doc/Chebyshev2Basis.ipynb +++ b/gtsam/basis/doc/Chebyshev2Basis.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -65,16 +65,40 @@ "source": [ "## Usage Example\n", "\n", - "```python\n", + "Evaluate second-kind Chebyshev weights and build a weight matrix for\n", + "multiple sample points.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weights at x=-0.3: [ 1. -0.6 -0.64 0.984 0.05 -1.014]\n", + "WeightMatrix shape: (5, 6)\n", + "Row for x=0: [ 1. 0. -1. -0. 1. 0.]\n" + ] + } + ], + "source": [ "import numpy as np\n", "import gtsam\n", "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", "N = 6\n", "x = -0.3\n", "weights = gtsam.Chebyshev2Basis.CalculateWeights(N, x)\n", + "print(\"Weights at x=-0.3:\", np.asarray(weights).ravel())\n", + "\n", "X = np.linspace(-1.0, 1.0, 5)\n", "W = gtsam.Chebyshev2Basis.WeightMatrix(N, X)\n", - "```\n" + "print(\"WeightMatrix shape:\", np.asarray(W).shape)\n", + "print(\"Row for x=0:\", np.asarray(W)[2])\n" ] }, { @@ -107,4 +131,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/ComponentDerivativeFactor.ipynb b/gtsam/basis/doc/ComponentDerivativeFactor.ipynb index 9728777377..537823b62b 100644 --- a/gtsam/basis/doc/ComponentDerivativeFactor.ipynb +++ b/gtsam/basis/doc/ComponentDerivativeFactor.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -62,15 +62,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", - "\n", + "## C++ Usage Example\n", "```cpp\n", "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", "size_t P = 3, N = 6, i = 2;\n", "double x = 0.2;\n", "double z = 0.0;\n", "gtsam::ComponentDerivativeFactor factor(key, z, model, P, N, i, x);\n", - "```\n" + "```\n", + "\n", + "## Python Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " keys = { c0 }\n", + "isotropic dim=1 sigma=0.05\n", + "FunctorizedFactor(c0)\n", + " measurement: 0\n", + " noise model sigmas: 0.05\n", + "\n" + ] + } + ], + "source": [ + "import gtsam\n", + "\n", + "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", + "key = gtsam.symbol(\"c\", 0)\n", + "factor = gtsam.ComponentDerivativeFactorChebyshev2(key, 0.0, model, 3, 6, 2, 0.2)\n", + "print(factor)" ] }, { @@ -103,4 +131,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/DerivativeFactor.ipynb b/gtsam/basis/doc/DerivativeFactor.ipynb index 5ff68e9e38..35f2b4644a 100644 --- a/gtsam/basis/doc/DerivativeFactor.ipynb +++ b/gtsam/basis/doc/DerivativeFactor.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", @@ -70,7 +70,37 @@ "double x = -0.3;\n", "double z = 0.0;\n", "gtsam::DerivativeFactor factor(key, z, model, N, x);\n", - "```\n" + "\n", + "```\n", + "\n", + "## Python Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " keys = { c0 }\n", + "isotropic dim=1 sigma=0.05\n", + "FunctorizedFactor(c0)\n", + " measurement: 0\n", + " noise model sigmas: 0.05\n", + "\n" + ] + } + ], + "source": [ + "import gtsam\n", + "\n", + "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", + "key = gtsam.symbol('c', 0)\n", + "factor = gtsam.DerivativeFactorChebyshev2(key, 0.0, model, 8, -0.3)\n", + "print(factor)\n" ] }, { @@ -103,4 +133,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/EvaluationFactor.ipynb b/gtsam/basis/doc/EvaluationFactor.ipynb index 036db35186..2e49b1e54c 100644 --- a/gtsam/basis/doc/EvaluationFactor.ipynb +++ b/gtsam/basis/doc/EvaluationFactor.ipynb @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.05);\n", @@ -70,7 +70,36 @@ "double x = 0.25;\n", "double z = 1.2;\n", "gtsam::EvaluationFactor factor(key, z, model, N, x);\n", - "```\n" + "```\n", + "\n", + "## Python Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " keys = { c0 }\n", + "isotropic dim=1 sigma=0.05\n", + "FunctorizedFactor(c0)\n", + " measurement: 1.2\n", + " noise model sigmas: 0.05\n", + "\n" + ] + } + ], + "source": [ + "import gtsam\n", + "\n", + "key = gtsam.symbol('c', 0)\n", + "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", + "factor = gtsam.EvaluationFactorChebyshev2(key, 1.2, model, 8, 0.25)\n", + "print(factor)" ] }, { @@ -103,4 +132,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/FitBasis.ipynb b/gtsam/basis/doc/FitBasis.ipynb index 90a7280ee2..a6a14f7371 100644 --- a/gtsam/basis/doc/FitBasis.ipynb +++ b/gtsam/basis/doc/FitBasis.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -63,7 +63,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "std::map samples = {{0.0, 1.0}, {0.5, 0.2}, {1.0, -0.1}};\n", @@ -71,7 +71,36 @@ "size_t N = 5;\n", "gtsam::FitBasis fit(samples, model, N);\n", "gtsam::Vector params = fit.parameters();\n", - "```\n" + "\n", + "```\n", + "\n", + "## Python Example" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FitBasisFourierBasis parameters: [ 2.242 -1.242 -1.986]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gtsam\n", + "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", + "sequence = {0.0: 1.0, 0.5: 0.2, 1.0: -0.1}\n", + "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.1)\n", + "fit = gtsam.FitBasisFourierBasis(sequence, model, 3)\n", + "params = fit.parameters()\n", + "print(\"FitBasisFourierBasis parameters:\", params)" ] }, { @@ -104,4 +133,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/FourierBasis.ipynb b/gtsam/basis/doc/FourierBasis.ipynb index 3b04291a9c..e4acd181a2 100644 --- a/gtsam/basis/doc/FourierBasis.ipynb +++ b/gtsam/basis/doc/FourierBasis.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "remove-cell" @@ -65,14 +65,41 @@ "source": [ "## Usage Example\n", "\n", - "```python\n", + "Evaluate a real Fourier basis, its derivative weights, and a slice of\n", + "the differentiation matrix.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weights: [ 1. 0.362 0.932 -0.737 0.675 -0.897 -0.443]\n", + "Derivative weights: [ 0. -0.932 0.362 -1.351 -1.475 1.328 -2.69 ]\n", + "D[1:3, 1:3]: [[ 0. 1.]\n", + " [-1. 0.]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", "import gtsam\n", "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", "N = 7\n", "x = 1.2\n", "weights = gtsam.FourierBasis.CalculateWeights(N, x)\n", "dweights = gtsam.FourierBasis.DerivativeWeights(N, x)\n", - "```\n" + "D = gtsam.FourierBasis.DifferentiationMatrix(N)\n", + "\n", + "print(\"Weights:\", np.asarray(weights).ravel())\n", + "print(\"Derivative weights:\", np.asarray(dweights).ravel())\n", + "print(\"D[1:3, 1:3]:\", np.asarray(D)[1:3, 1:3])" ] }, { @@ -105,4 +132,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb b/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb index 0003a90a35..e4762a7179 100644 --- a/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb +++ b/gtsam/basis/doc/ManifoldEvaluationFactor.ipynb @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ example\n", "\n", "```cpp\n", "auto model = gtsam::noiseModel::Isotropic::Sigma(3, 0.05);\n", @@ -70,7 +70,82 @@ "double x = 0.1;\n", "gtsam::Rot3 z = gtsam::Rot3::RzRyRx(0.1, 0.2, 0.3);\n", "gtsam::ManifoldEvaluationFactor factor(key, z, model, N, x);\n", - "```\n" + "```\n", + "\n", + "## Python Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available ManifoldEvaluationFactor wrappers:\n", + " ManifoldEvaluationFactorChebyshev1BasisPose2\n", + " ManifoldEvaluationFactorChebyshev1BasisPose3\n", + " ManifoldEvaluationFactorChebyshev1BasisRot2\n", + " ManifoldEvaluationFactorChebyshev1BasisRot3\n", + " ManifoldEvaluationFactorChebyshev2BasisPose2\n", + " ManifoldEvaluationFactorChebyshev2BasisPose3\n", + " ManifoldEvaluationFactorChebyshev2BasisRot2\n", + " ManifoldEvaluationFactorChebyshev2BasisRot3\n", + " ManifoldEvaluationFactorChebyshev2Pose2\n", + " ManifoldEvaluationFactorChebyshev2Pose3\n", + " ManifoldEvaluationFactorChebyshev2Rot2\n", + " ManifoldEvaluationFactorChebyshev2Rot3\n", + " ManifoldEvaluationFactorFourierBasisPose2\n", + " ManifoldEvaluationFactorFourierBasisPose3\n", + " ManifoldEvaluationFactorFourierBasisRot2\n", + " ManifoldEvaluationFactorFourierBasisRot3\n", + "Created ManifoldEvaluationFactorChebyshev1BasisRot2 with dim 1\n", + "Created ManifoldEvaluationFactorChebyshev1BasisPose2 with dim 3\n", + "Created ManifoldEvaluationFactorChebyshev1BasisRot3 with dim 3\n", + "Created ManifoldEvaluationFactorChebyshev1BasisPose3 with dim 6\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gtsam\n", + "\n", + "names = [n for n in dir(gtsam) if n.startswith(\"ManifoldEvaluationFactor\")]\n", + "print(\"Available ManifoldEvaluationFactor wrappers:\")\n", + "for name in names:\n", + " print(\" \", name)\n", + "\n", + "selected = None\n", + "kind = None\n", + "for preferred in [\"Rot2\", \"Pose2\", \"Rot3\", \"Pose3\"]:\n", + " for name in names:\n", + " if preferred in name:\n", + " selected = name\n", + " kind = preferred\n", + " break\n", + " if selected is None:\n", + " print(\"No ManifoldEvaluationFactor wrapper found; skipping execution.\")\n", + " else:\n", + " cls = getattr(gtsam, selected)\n", + " if kind == \"Rot2\":\n", + " z = gtsam.Rot2(0.3)\n", + " dim = 1\n", + " elif kind == \"Rot3\":\n", + " z = gtsam.Rot3.RzRyRx(0.1, 0.2, 0.3)\n", + " dim = 3\n", + " elif kind == \"Pose2\":\n", + " z = gtsam.Pose2(1.0, 0.0, 0.1)\n", + " dim = 3\n", + " else:\n", + " z = gtsam.Pose3(gtsam.Rot3.RzRyRx(0.1, 0.2, 0.3), np.array([1.0, 0.0, 0.0]))\n", + " dim = 6\n", + "\n", + " model = gtsam.noiseModel.Isotropic.Sigma(dim, 0.1)\n", + " key = gtsam.symbol('x', 0)\n", + " factor = cls(key, z, model, 6, 0.1)\n", + " print(f\"Created {selected} with dim {factor.dim()}\")\n" ] }, { @@ -103,4 +178,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/VectorComponentFactor.ipynb b/gtsam/basis/doc/VectorComponentFactor.ipynb index 31b513be11..91d678d64a 100644 --- a/gtsam/basis/doc/VectorComponentFactor.ipynb +++ b/gtsam/basis/doc/VectorComponentFactor.ipynb @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "auto model = gtsam::noiseModel::Isotropic::Sigma(1, 0.1);\n", @@ -70,7 +70,54 @@ "double x = 0.5;\n", "double z = -0.2;\n", "gtsam::VectorComponentFactor factor(key, z, model, P, N, i, x);\n", - "```\n" + "```\n", + "\n", + "## Python Example" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VectorComponentFactor wrappers:\n", + " VectorComponentFactorChebyshev1Basis\n", + " VectorComponentFactorChebyshev2\n", + " VectorComponentFactorChebyshev2Basis\n", + " VectorComponentFactorFourierBasis\n", + "Created VectorComponentFactorChebyshev1Basis with dim 1\n", + "Created VectorComponentFactorChebyshev2 with dim 1\n", + "Created VectorComponentFactorChebyshev2Basis with dim 1\n", + "Created VectorComponentFactorFourierBasis with dim 1\n" + ] + } + ], + "source": [ + "import gtsam\n", + "\n", + "candidates = [n for n in dir(gtsam) if n.startswith(\"VectorComponentFactor\")]\n", + "print(\"VectorComponentFactor wrappers:\")\n", + "for name in candidates:\n", + " print(\" \", name)\n", + "\n", + "cls = None\n", + "selected = None\n", + "for name in candidates:\n", + " if hasattr(gtsam, name):\n", + " cls = getattr(gtsam, name)\n", + " selected = name\n", + "\n", + " if cls is None:\n", + " print(\"No VectorComponentFactor wrapper found; skipping execution.\")\n", + " else:\n", + " model = gtsam.noiseModel.Isotropic.Sigma(1, 0.1)\n", + " key = gtsam.symbol('c', 0)\n", + " factor = cls(key, -0.2, model, 3, 6, 1, 0.5)\n", + " print(f\"Created {selected} with dim {factor.dim()}\")\n" ] }, { @@ -103,4 +150,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/VectorDerivativeFactor.ipynb b/gtsam/basis/doc/VectorDerivativeFactor.ipynb index df7e191f56..286031cbf8 100644 --- a/gtsam/basis/doc/VectorDerivativeFactor.ipynb +++ b/gtsam/basis/doc/VectorDerivativeFactor.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [ "remove-cell" @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "gtsam::Vector z = (gtsam::Vector(2) << 0.1, -0.1).finished();\n", @@ -70,7 +70,52 @@ "size_t M = 2, N = 6;\n", "double x = 0.0;\n", "gtsam::VectorDerivativeFactor factor(key, z, model, M, N, x);\n", - "```\n" + "```\n", + "\n", + "## Python Example" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VectorDerivativeFactor wrappers:\n", + " VectorDerivativeFactorChebyshev1Basis\n", + " VectorDerivativeFactorChebyshev2\n", + " VectorDerivativeFactorChebyshev2Basis\n", + " VectorDerivativeFactorFourierBasis\n", + "factor:\n", + " keys = { c0 }\n", + "isotropic dim=2 sigma=0.05\n", + "FunctorizedFactor(c0)\n", + " measurement: [\n", + "\t0.1;\n", + "\t-0.1\n", + "]\n", + " noise model sigmas: 0.05 0.05\n", + "\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gtsam\n", + "\n", + "candidates = [n for n in dir(gtsam) if n.startswith(\"VectorDerivativeFactor\")]\n", + "print(\"VectorDerivativeFactor wrappers:\")\n", + "for name in candidates:\n", + " print(\" \", name)\n", + "\n", + "z = np.array([0.1, -0.1])\n", + "model = gtsam.noiseModel.Isotropic.Sigma(2, 0.05)\n", + "key = gtsam.symbol('c', 0)\n", + "factor = gtsam.VectorDerivativeFactorChebyshev2(key, z, model, 2, 6, 0.0)\n", + "print(\"factor:\\n\", factor)" ] }, { @@ -103,4 +148,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/basis/doc/VectorEvaluationFactor.ipynb b/gtsam/basis/doc/VectorEvaluationFactor.ipynb index 5084aa0c7c..8d14268e0a 100644 --- a/gtsam/basis/doc/VectorEvaluationFactor.ipynb +++ b/gtsam/basis/doc/VectorEvaluationFactor.ipynb @@ -60,9 +60,10 @@ }, { "cell_type": "markdown", + "id": "c2c42d05", "metadata": {}, "source": [ - "## Usage Example\n", + "## C++ Usage Example\n", "\n", "```cpp\n", "gtsam::Vector z = (gtsam::Vector(3) << 1.0, 0.0, -0.5).finished();\n", @@ -70,7 +71,62 @@ "size_t M = 3, N = 6;\n", "double x = 0.0;\n", "gtsam::VectorEvaluationFactor factor(key, z, model, M, N, x);\n", - "```\n" + "```\n", + "\n", + "## Python Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c8e2e7c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VectorEvaluationFactor wrappers:\n", + " VectorEvaluationFactorChebyshev1Basis\n", + " VectorEvaluationFactorChebyshev2\n", + " VectorEvaluationFactorChebyshev2Basis\n", + " VectorEvaluationFactorFourierBasis\n", + "Created VectorEvaluationFactorChebyshev2 with dim 3\n", + "Created VectorEvaluationFactorChebyshev2Basis with dim 3\n", + "Created VectorEvaluationFactorChebyshev1Basis with dim 3\n", + "Created VectorEvaluationFactorFourierBasis with dim 3\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gtsam\n", + "\n", + "candidates = [n for n in dir(gtsam) if n.startswith(\"VectorEvaluationFactor\")]\n", + "print(\"VectorEvaluationFactor wrappers:\")\n", + "for name in candidates:\n", + " print(\" \", name)\n", + "\n", + "cls = None\n", + "selected = None\n", + "for name in [\n", + " \"VectorEvaluationFactorChebyshev2\",\n", + " \"VectorEvaluationFactorChebyshev2Basis\",\n", + " \"VectorEvaluationFactorChebyshev1Basis\",\n", + " \"VectorEvaluationFactorFourierBasis\",\n", + "]:\n", + " if hasattr(gtsam, name):\n", + " cls = getattr(gtsam, name)\n", + " selected = name\n", + "\n", + " if cls is None:\n", + " print(\"No VectorEvaluationFactor wrapper found; skipping execution.\")\n", + " else:\n", + " z = np.array([1.0, 0.0, -0.5])\n", + " model = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)\n", + " key = gtsam.symbol('c', 0)\n", + " factor = cls(key, z, model, 3, 6, 0.0)\n", + " print(f\"Created {selected} with dim {factor.dim()}\")\n" ] }, { @@ -103,4 +159,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From 1e32c74c8713151cb350dfce1b4cc0791d2a3e39 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 29 Jan 2026 20:42:21 -0500 Subject: [PATCH 147/266] Example notebook --- python/gtsam/examples/FitBasisExample.ipynb | 3681 +++++++++++++++++++ python/gtsam/examples/README.md | 49 +- 2 files changed, 3716 insertions(+), 14 deletions(-) create mode 100644 python/gtsam/examples/FitBasisExample.ipynb diff --git a/python/gtsam/examples/FitBasisExample.ipynb b/python/gtsam/examples/FitBasisExample.ipynb new file mode 100644 index 0000000000..6d54e57df5 --- /dev/null +++ b/python/gtsam/examples/FitBasisExample.ipynb @@ -0,0 +1,3681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# FitBasis Example (Fourier + Chebyshev)\n", + "\n", + "\"Open\n", + "\n", + "This notebook demonstrates how to fit a set of scalar measurements using\n", + "`FitBasis` for all four basis options exposed in the Python wrapper:\n", + "\n", + "- `FourierBasis`\n", + "- `Chebyshev1Basis`\n", + "- `Chebyshev2Basis`\n", + "- `Chebyshev2` (pseudo-spectral, parameters are values at Chebyshev points)\n", + "\n", + "We use a time domain from **t = 0 to 1** and fit each basis with **fewer\n", + "parameters than points**. For Chebyshev bases, we map time into the\n", + "Chebyshev interval `x = 2t - 1` to match the default `[-1, 1]` domain.\n", + "For the Fourier basis we use `x = 2pi t` to model periodic behavior.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "50e0e944", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b8cf1aff", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import numpy as np\n", + "import gtsam\n", + "import plotly.graph_objects as go\n", + "\n", + "np.set_printoptions(precision=3, suppress=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a040dcaf", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Sample data\n", + "rng = np.random.default_rng(42)\n", + "num_points = 30\n", + "t = np.linspace(0.0, 1.0, num_points)\n", + "\n", + "# A smooth, mildly periodic signal with a trend\n", + "y_clean = 0.7 * np.sin(2 * np.pi * t) + 0.3 * np.cos(4 * np.pi * t) + 0.2 * t\n", + "y = y_clean + 0.05 * rng.standard_normal(size=t.size)\n", + "\n", + "# Parameter count must be smaller or equal than number of points\n", + "N = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "dab48fee", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def plot_fit(t_samples, y_samples, t_dense, y_fit, title, extra=None):\n", + " fig = go.Figure()\n", + " fig.add_trace(go.Scatter(x=t_samples, y=y_samples, mode=\"markers\", name=\"Samples\", marker=dict(color=\"blue\", symbol=\"diamond\")))\n", + " fig.add_trace(go.Scatter(x=t_dense, y=y_fit, mode=\"lines\", name=\"Fit\"))\n", + " if extra is not None:\n", + " fig.add_trace(extra)\n", + " fig.update_layout(\n", + " title=title,\n", + " xaxis_title=\"time t\",\n", + " yaxis_title=\"value\",\n", + " template=\"plotly_white\",\n", + " width=900,\n", + " height=450,\n", + " )\n", + " fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "62f6a786", + "metadata": {}, + "source": [ + "\n", + "## FourierBasis Fit\n", + "\n", + "This cell:\n", + "1. Builds a `{x: y}` sample map using the periodic domain `x = 2pi t`.\n", + "2. Fits Fourier coefficients with `FitBasisFourierBasis`.\n", + "3. Evaluates the fit on a dense grid via `FourierBasis.WeightMatrix`.\n", + "\n", + "Copy the core fit + evaluation parts if you want to integrate this into\n", + "your own pipeline (plotting is in a helper function).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c05c36fa", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "symbol": "diamond" + }, + "mode": "markers", + "name": "Samples", + "type": "scatter", + "x": { + "bdata": "AAAAAAAAAACWexphuaehP5Z7GmG5p7E/YbmnEZZ7uj+WexphuafBP3waYbmnEcY/YbmnEZZ7yj9GWO5phOXOP5Z7GmG5p9E/Ccs9jbDc0z98GmG5pxHWP+5phOWeRtg/YbmnEZZ72j/UCMs9jbDcP0ZY7mmE5d4/3dMIyz2N4D+WexphuafhP08jLPc0wuI/Ccs9jbDc4z/Cck8jLPfkP3waYbmnEeY/NcJyTyMs5z/uaYTlnkboP6gRlnsaYek/YbmnEZZ76j8aYbmnEZbrP9QIyz2NsOw/jbDc0wjL7T9GWO5phOXuPwAAAAAAAPA/", + "dtype": "f8" + }, + "y": { + "bdata": 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600)\n", + "x_dense = 2.0 * np.pi * t_dense\n", + "W = np.asarray(gtsam.FourierBasis.WeightMatrix(len(params), x_dense))\n", + "y_fit = W @ params\n", + "\n", + "plot_fit(t, y, t_dense, y_fit, \"FourierBasis Fit (periodic beyond [0, 1])\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "34962068", + "metadata": {}, + "source": [ + "\n", + "## Chebyshev1Basis Fit\n", + "\n", + "This cell:\n", + "1. Maps time to the Chebyshev interval with `x = 2t - 1`.\n", + "2. Fits Chebyshev-1 coefficients with `FitBasisChebyshev1Basis`.\n", + "3. Evaluates the fit using `Chebyshev1Basis.WeightMatrix`.\n", + "\n", + "Copy the fit + evaluation steps to reuse with your own data or noise model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ddacb31c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "symbol": "diamond" + }, + "mode": "markers", + "name": "Samples", + "type": "scatter", + "x": { + "bdata": "AAAAAAAAAACWexphuaehP5Z7GmG5p7E/YbmnEZZ7uj+WexphuafBP3waYbmnEcY/YbmnEZZ7yj9GWO5phOXOP5Z7GmG5p9E/Ccs9jbDc0z98GmG5pxHWP+5phOWeRtg/YbmnEZZ72j/UCMs9jbDcP0ZY7mmE5d4/3dMIyz2N4D+WexphuafhP08jLPc0wuI/Ccs9jbDc4z/Cck8jLPfkP3waYbmnEeY/NcJyTyMs5z/uaYTlnkboP6gRlnsaYek/YbmnEZZ76j8aYbmnEZbrP9QIyz2NsOw/jbDc0wjL7T9GWO5phOXuPwAAAAAAAPA/", + "dtype": "f8" + }, + "y": { + "bdata": 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np.asarray(gtsam.Chebyshev1Basis.WeightMatrix(len(params), x_dense))\n", + "y_fit = W @ params\n", + "\n", + "plot_fit(t, y, t_dense, y_fit, \"Chebyshev1Basis Fit (extrapolation outside [0, 1])\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "88a6344a", + "metadata": {}, + "source": [ + "\n", + "## Chebyshev2Basis Fit\n", + "\n", + "This cell uses the second-kind Chebyshev basis (coefficient form):\n", + "\n", + "1. Map time to `x = 2t - 1`.\n", + "2. Fit with `FitBasisChebyshev2Basis`.\n", + "3. Evaluate with `Chebyshev2Basis.WeightMatrix`.\n", + "\n", + "These steps are the minimal pieces you need for non-plotting usage.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fc3d1235", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "symbol": "diamond" + }, + "mode": "markers", + "name": "Samples", + "type": "scatter", + "x": { + "bdata": "AAAAAAAAAACWexphuaehP5Z7GmG5p7E/YbmnEZZ7uj+WexphuafBP3waYbmnEcY/YbmnEZZ7yj9GWO5phOXOP5Z7GmG5p9E/Ccs9jbDc0z98GmG5pxHWP+5phOWeRtg/YbmnEZZ72j/UCMs9jbDcP0ZY7mmE5d4/3dMIyz2N4D+WexphuafhP08jLPc0wuI/Ccs9jbDc4z/Cck8jLPfkP3waYbmnEeY/NcJyTyMs5z/uaYTlnkboP6gRlnsaYek/YbmnEZZ76j8aYbmnEZbrP9QIyz2NsOw/jbDc0wjL7T9GWO5phOXuPwAAAAAAAPA/", + "dtype": "f8" + }, + "y": { + "bdata": 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np.asarray(gtsam.Chebyshev2Basis.WeightMatrix(len(params), x_dense))\n", + "y_fit = W @ params\n", + "\n", + "plot_fit(t, y, t_dense, y_fit, \"Chebyshev2Basis Fit (extrapolation outside [0, 1])\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d2ebb77", + "metadata": {}, + "source": [ + "\n", + "## Chebyshev2 (Pseudo-Spectral) Fit\n", + "\n", + "This variant treats the parameters as function values at Chebyshev points.\n", + "The steps here are slightly different conceptually:\n", + "\n", + "1. Fit values at Chebyshev points with `FitBasisChebyshev2`.\n", + "2. Evaluate by barycentric interpolation using `Chebyshev2.WeightMatrix`.\n", + "3. Plot the interpolation nodes alongside the fitted curve.\n", + "\n", + "If you don't need plots, copy the fit + evaluation and skip the marker trace.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "37db92b8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "symbol": "diamond" + }, + "mode": "markers", + "name": "Samples", + "type": "scatter", + "x": { + "bdata": "AAAAAAAAAACWexphuaehP5Z7GmG5p7E/YbmnEZZ7uj+WexphuafBP3waYbmnEcY/YbmnEZZ7yj9GWO5phOXOP5Z7GmG5p9E/Ccs9jbDc0z98GmG5pxHWP+5phOWeRtg/YbmnEZZ72j/UCMs9jbDcP0ZY7mmE5d4/3dMIyz2N4D+WexphuafhP08jLPc0wuI/Ccs9jbDc4z/Cck8jLPfkP3waYbmnEeY/NcJyTyMs5z/uaYTlnkboP6gRlnsaYek/YbmnEZZ76j8aYbmnEZbrP9QIyz2NsOw/jbDc0wjL7T9GWO5phOXuPwAAAAAAAPA/", + "dtype": "f8" + }, + "y": { + "bdata": 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"line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Chebyshev2 Pseudo-Spectral Fit (extrapolation outside [0, 1])" + }, + "width": 900, + "xaxis": { + "title": { + "text": "time t" + } + }, + "yaxis": { + "title": { + "text": "value" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n", + "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", + "fit = gtsam.FitBasisChebyshev2(sequence, model, N)\n", + "params = np.asarray(fit.parameters()).ravel()\n", + "\n", + "t_dense = np.linspace(-0.1, 1.1, 600)\n", + "x_dense = 2.0 * t_dense - 1.0\n", + "W = np.asarray(gtsam.Chebyshev2.WeightMatrix(len(params), x_dense))\n", + "y_fit = W @ params\n", + "\n", + "cheb_points = np.asarray(gtsam.Chebyshev2.Points(N)).ravel()\n", + "t_cheb = 0.5 * (cheb_points + 1.0)\n", + "y_cheb = params\n", + "markers = go.Scatter(x=t_cheb, y=y_cheb, mode=\"markers\", name=\"Chebyshev2 points\", marker=dict(color=\"red\"))\n", + "\n", + "plot_fit(t, y, t_dense, y_fit, \"Chebyshev2 Pseudo-Spectral Fit (extrapolation outside [0, 1])\", extra=markers)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf516bde", + "metadata": {}, + "source": [ + "The Chebyshev points above *are* the parameterization. They values at those points are moved up and down to make the blue points fit the polynomial as closely as possible. Because for every $N$ points we can exactly fit an $n=N-1$ degree polynomial, the values at these Chebyshev points *are* a parameterization of the polynomial.\n", + "\n", + "Chebyshev points are fixed nodes that cluster near the interval ends; this improves interpolation stability and reduces endpoint oscillations compared to equally spaced nodes (mitigating the Runge phenomenon)." + ] + }, + { + "cell_type": "markdown", + "id": "7d4c0b1d", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python/gtsam/examples/README.md b/python/gtsam/examples/README.md index be160f8475..73ad0bbf02 100644 --- a/python/gtsam/examples/README.md +++ b/python/gtsam/examples/README.md @@ -2,15 +2,15 @@ | C++ Example Name | Ported | |-------------------------------------------------------|--------| -| CameraResectioning | :heavy_check_mark: | +| [CameraResectioning](CameraResectioning.ipynb) | :heavy_check_mark: | | CombinedImuFactorsExample | :heavy_check_mark: | -| CreateSFMExampleData | :heavy_check_mark: | -| DiscreteBayesNetExample | :heavy_check_mark: | +| [CreateSFMExampleData](CreateSFMExampleData.ipynb) | :heavy_check_mark: | +| [DiscreteBayesNetExample](DiscreteBayesNetExample.ipynb) | :heavy_check_mark: | | DiscreteBayesNet_FG | none of the required discrete functionality is exposed through Python | -| easyPoint2KalmanFilter | ExtendedKalmanFilter not yet exposed through Python | -| elaboratePoint2KalmanFilter | GaussianSequentialSolver not yet exposed through Python | -| FisheyeExample | :heavy_check_mark: | -| HMMExample | :heavy_check_mark: | +| [easyPoint2KalmanFilter](easyPoint2KalmanFilter.ipynb) | ExtendedKalmanFilter not yet exposed through Python | +| [elaboratePoint2KalmanFilter](elaboratePoint2KalmanFilter.ipynb) | GaussianSequentialSolver not yet exposed through Python | +| [FisheyeExample](FisheyeExample.ipynb) | :heavy_check_mark: | +| [HMMExample](HMMExample.ipynb) | :heavy_check_mark: | | ImuFactorsExample2 | :heavy_check_mark: | | ImuFactorsExample | | | IMUKittiExampleGPS | :heavy_check_mark: | @@ -20,22 +20,22 @@ | LocalizationExample | :heavy_check_mark: | | METISOrderingExample | | | OdometryExample | :heavy_check_mark: | -| PlanarSLAMExample | :heavy_check_mark: | -| Pose2SLAMExample | :heavy_check_mark: | +| [PlanarSLAMExample](PlanarSLAMExample.ipynb) | :heavy_check_mark: | +| [Pose2SLAMExample](Pose2SLAMExample.ipynb) | :heavy_check_mark: | | Pose2SLAMExampleExpressions | ExpressionFactorGraph not yet exposed through Python | | Pose2SLAMExample_g2o | :heavy_check_mark: | | Pose2SLAMExample_graph | :heavy_check_mark: | | Pose2SLAMExample_graphviz | :heavy_check_mark: | | Pose2SLAMExample_lago | lago not yet exposed through Python | -| Pose2SLAMStressTest | :heavy_check_mark: | -| Pose2SLAMwSPCG | :heavy_check_mark: | +| [Pose2SLAMStressTest](Pose2SLAMStressTest.ipynb) | :heavy_check_mark: | +| [Pose2SLAMwSPCG](Pose2SLAMwSPCG.ipynb) | :heavy_check_mark: | | Pose3Localization | | | Pose3SLAMExample_changeKeys | | | Pose3SLAMExampleExpressions_BearingRangeWithTransform | | | Pose3SLAMExample_g2o | :heavy_check_mark: | | Pose3SLAMExample_initializePose3Chordal | :heavy_check_mark: | | Pose3SLAMExample_initializePose3Gradient | | -| RangeISAMExample_plaza2 | :heavy_check_mark: | +| [RangeISAMExample_plaza2](RangeISAMExample_plaza2.ipynb) | :heavy_check_mark: | | SelfCalibrationExample | :heavy_check_mark: | | SFMdata | :heavy_check_mark: | | SFMExample_bal_COLAMD_METIS | | @@ -48,8 +48,8 @@ | ShonanAveragingCLI | :heavy_check_mark: | | SimpleRotation | :heavy_check_mark: | | SolverComparer | | -| StereoVOExample | :heavy_check_mark: | -| StereoVOExample_large | :heavy_check_mark: | +| [StereoVOExample](StereoVOExample.ipynb) | :heavy_check_mark: | +| [StereoVOExample_large](StereoVOExample_large.ipynb) | :heavy_check_mark: | | TimeTBB | | | UGM_chain | discrete functionality not yet exposed | | UGM_small | discrete functionality not yet exposed | @@ -57,8 +57,29 @@ | VisualISAMExample | :heavy_check_mark: | Extra Examples (with no C++ equivalent) +- [FitBasisExample](FitBasisExample.ipynb) - DogLegOptimizerExample - GPSFactorExample - PlanarManipulatorExample - PreintegrationExample - SFMData + +Additional Notebook Examples + +- [BearingRange3DExample](BearingRange3DExample.ipynb) +- [DiscreteBayesTree](DiscreteBayesTree.ipynb) +- [DiscreteMotionModel](DiscreteMotionModel.ipynb) +- [DiscreteSwitching](DiscreteSwitching.ipynb) +- [EKF_SLAM](EKF_SLAM.ipynb) +- [EqF](EqF.ipynb) +- [Gal3ImuASVExample](Gal3ImuASVExample.ipynb) +- [Gal3ImuExample](Gal3ImuExample.ipynb) +- [Gal3ImuNEESReset](Gal3ImuNEESReset.ipynb) +- [LQRExample](LQRExample.ipynb) +- [NavStateImuASVExample](NavStateImuASVExample.ipynb) +- [NavStateImuExample](NavStateImuExample.ipynb) +- [NonlinearEqualityExample](NonlinearEqualityExample.ipynb) +- [RangeSLAMExample_plaza2](RangeSLAMExample_plaza2.ipynb) +- [SL4SLAMExample](SL4SLAMExample.ipynb) +- [SinglePointPositioningExample](SinglePointPositioningExample.ipynb) +- [iLQRExample](iLQRExample.ipynb) From 66781451c761e3c5548c9a1d75bcf22ed64635a1 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Fri, 23 Jan 2026 23:28:34 -0500 Subject: [PATCH 148/266] Remove all Cephes source files except those needed for igami --- gtsam/3rdparty/cephes/CMakeLists.txt | 65 +- gtsam/3rdparty/cephes/cephes.h | 116 -- gtsam/3rdparty/cephes/cephes/airy.c | 376 ------ gtsam/3rdparty/cephes/cephes/bdtr.c | 241 ---- gtsam/3rdparty/cephes/cephes/besselpoly.c | 34 - gtsam/3rdparty/cephes/cephes/beta.c | 258 ---- gtsam/3rdparty/cephes/cephes/btdtr.c | 59 - gtsam/3rdparty/cephes/cephes/cbrt.c | 117 -- gtsam/3rdparty/cephes/cephes/chbevl.c | 81 -- gtsam/3rdparty/cephes/cephes/chdtr.c | 186 --- gtsam/3rdparty/cephes/cephes/dawsn.c | 160 --- gtsam/3rdparty/cephes/cephes/dd_idefs.h | 198 --- gtsam/3rdparty/cephes/cephes/dd_real.c | 587 --------- gtsam/3rdparty/cephes/cephes/dd_real.h | 143 --- gtsam/3rdparty/cephes/cephes/dd_real_idefs.h | 557 --------- gtsam/3rdparty/cephes/cephes/ellie.c | 282 ----- gtsam/3rdparty/cephes/cephes/ellik.c | 246 ---- gtsam/3rdparty/cephes/cephes/ellpe.c | 108 -- gtsam/3rdparty/cephes/cephes/ellpj.c | 154 --- gtsam/3rdparty/cephes/cephes/ellpk.c | 124 -- gtsam/3rdparty/cephes/cephes/erfinv.c | 78 -- gtsam/3rdparty/cephes/cephes/exp10.c | 115 -- gtsam/3rdparty/cephes/cephes/exp2.c | 108 -- gtsam/3rdparty/cephes/cephes/expn.c | 224 ---- gtsam/3rdparty/cephes/cephes/expn.h | 19 - gtsam/3rdparty/cephes/cephes/fdtr.c | 216 ---- gtsam/3rdparty/cephes/cephes/fresnl.c | 219 ---- gtsam/3rdparty/cephes/cephes/gammasgn.c | 25 - gtsam/3rdparty/cephes/cephes/gdtr.c | 132 -- gtsam/3rdparty/cephes/cephes/hyp2f1.c | 569 --------- gtsam/3rdparty/cephes/cephes/hyperg.c | 362 ------ gtsam/3rdparty/cephes/cephes/i0.c | 180 --- gtsam/3rdparty/cephes/cephes/i1.c | 184 --- gtsam/3rdparty/cephes/cephes/incbet.c | 369 ------ gtsam/3rdparty/cephes/cephes/incbi.c | 275 ----- gtsam/3rdparty/cephes/cephes/j0.c | 246 ---- gtsam/3rdparty/cephes/cephes/j1.c | 225 ---- gtsam/3rdparty/cephes/cephes/jv.c | 841 ------------- gtsam/3rdparty/cephes/cephes/k0.c | 178 --- gtsam/3rdparty/cephes/cephes/k1.c | 179 --- gtsam/3rdparty/cephes/cephes/kn.c | 235 ---- gtsam/3rdparty/cephes/cephes/kolmogorov.c | 1147 ------------------ gtsam/3rdparty/cephes/cephes/nbdtr.c | 207 ---- gtsam/3rdparty/cephes/cephes/ndtr.c | 305 ----- gtsam/3rdparty/cephes/cephes/ndtri.c | 176 --- gtsam/3rdparty/cephes/cephes/owens_t.c | 364 ------ gtsam/3rdparty/cephes/cephes/pdtr.c | 173 --- gtsam/3rdparty/cephes/cephes/poch.c | 81 -- gtsam/3rdparty/cephes/cephes/psi.c | 205 ---- gtsam/3rdparty/cephes/cephes/rgamma.c | 128 -- gtsam/3rdparty/cephes/cephes/round.c | 63 - gtsam/3rdparty/cephes/cephes/scipy_iv.c | 654 ---------- gtsam/3rdparty/cephes/cephes/shichi.c | 305 ----- gtsam/3rdparty/cephes/cephes/sici.c | 276 ----- gtsam/3rdparty/cephes/cephes/sindg.c | 219 ---- gtsam/3rdparty/cephes/cephes/sinpi.c | 54 - gtsam/3rdparty/cephes/cephes/spence.c | 125 -- gtsam/3rdparty/cephes/cephes/stdtr.c | 203 ---- gtsam/3rdparty/cephes/cephes/struve.c | 408 ------- gtsam/3rdparty/cephes/cephes/tandg.c | 141 --- gtsam/3rdparty/cephes/cephes/tukey.c | 68 -- gtsam/3rdparty/cephes/cephes/unity.c | 41 +- gtsam/3rdparty/cephes/cephes/yn.c | 105 -- gtsam/3rdparty/cephes/cephes/yv.c | 46 - gtsam/3rdparty/cephes/cephes/zetac.c | 345 ------ 65 files changed, 5 insertions(+), 14905 deletions(-) delete mode 100644 gtsam/3rdparty/cephes/cephes/airy.c delete mode 100644 gtsam/3rdparty/cephes/cephes/bdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/besselpoly.c delete mode 100644 gtsam/3rdparty/cephes/cephes/beta.c delete mode 100644 gtsam/3rdparty/cephes/cephes/btdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/cbrt.c delete mode 100644 gtsam/3rdparty/cephes/cephes/chbevl.c delete mode 100644 gtsam/3rdparty/cephes/cephes/chdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/dawsn.c delete mode 100644 gtsam/3rdparty/cephes/cephes/dd_idefs.h delete mode 100644 gtsam/3rdparty/cephes/cephes/dd_real.c delete mode 100644 gtsam/3rdparty/cephes/cephes/dd_real.h delete mode 100644 gtsam/3rdparty/cephes/cephes/dd_real_idefs.h delete mode 100644 gtsam/3rdparty/cephes/cephes/ellie.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ellik.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ellpe.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ellpj.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ellpk.c delete mode 100644 gtsam/3rdparty/cephes/cephes/erfinv.c delete mode 100644 gtsam/3rdparty/cephes/cephes/exp10.c delete mode 100644 gtsam/3rdparty/cephes/cephes/exp2.c delete mode 100644 gtsam/3rdparty/cephes/cephes/expn.c delete mode 100644 gtsam/3rdparty/cephes/cephes/expn.h delete mode 100644 gtsam/3rdparty/cephes/cephes/fdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/fresnl.c delete mode 100644 gtsam/3rdparty/cephes/cephes/gammasgn.c delete mode 100644 gtsam/3rdparty/cephes/cephes/gdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/hyp2f1.c delete mode 100644 gtsam/3rdparty/cephes/cephes/hyperg.c delete mode 100644 gtsam/3rdparty/cephes/cephes/i0.c delete mode 100644 gtsam/3rdparty/cephes/cephes/i1.c delete mode 100644 gtsam/3rdparty/cephes/cephes/incbet.c delete mode 100644 gtsam/3rdparty/cephes/cephes/incbi.c delete mode 100644 gtsam/3rdparty/cephes/cephes/j0.c delete mode 100644 gtsam/3rdparty/cephes/cephes/j1.c delete mode 100644 gtsam/3rdparty/cephes/cephes/jv.c delete mode 100644 gtsam/3rdparty/cephes/cephes/k0.c delete mode 100644 gtsam/3rdparty/cephes/cephes/k1.c delete mode 100644 gtsam/3rdparty/cephes/cephes/kn.c delete mode 100644 gtsam/3rdparty/cephes/cephes/kolmogorov.c delete mode 100644 gtsam/3rdparty/cephes/cephes/nbdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ndtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/ndtri.c delete mode 100644 gtsam/3rdparty/cephes/cephes/owens_t.c delete mode 100644 gtsam/3rdparty/cephes/cephes/pdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/poch.c delete mode 100644 gtsam/3rdparty/cephes/cephes/psi.c delete mode 100644 gtsam/3rdparty/cephes/cephes/rgamma.c delete mode 100644 gtsam/3rdparty/cephes/cephes/round.c delete mode 100644 gtsam/3rdparty/cephes/cephes/scipy_iv.c delete mode 100644 gtsam/3rdparty/cephes/cephes/shichi.c delete mode 100644 gtsam/3rdparty/cephes/cephes/sici.c delete mode 100644 gtsam/3rdparty/cephes/cephes/sindg.c delete mode 100644 gtsam/3rdparty/cephes/cephes/sinpi.c delete mode 100644 gtsam/3rdparty/cephes/cephes/spence.c delete mode 100644 gtsam/3rdparty/cephes/cephes/stdtr.c delete mode 100644 gtsam/3rdparty/cephes/cephes/struve.c delete mode 100644 gtsam/3rdparty/cephes/cephes/tandg.c delete mode 100644 gtsam/3rdparty/cephes/cephes/tukey.c delete mode 100644 gtsam/3rdparty/cephes/cephes/yn.c delete mode 100644 gtsam/3rdparty/cephes/cephes/yv.c delete mode 100644 gtsam/3rdparty/cephes/cephes/zetac.c diff --git a/gtsam/3rdparty/cephes/CMakeLists.txt b/gtsam/3rdparty/cephes/CMakeLists.txt index a1179d0c31..e5aea56d35 100644 --- a/gtsam/3rdparty/cephes/CMakeLists.txt +++ b/gtsam/3rdparty/cephes/CMakeLists.txt @@ -8,85 +8,24 @@ project( set(CEPHES_HEADER_FILES cephes.h - cephes/dd_idefs.h - cephes/dd_real.h - cephes/dd_real_idefs.h - cephes/expn.h cephes/igam.h cephes/lanczos.h cephes/mconf.h cephes/polevl.h - cephes/sf_error.h - ) + cephes/sf_error.h) # Add header files install(FILES ${CEPHES_HEADER_FILES} DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/gtsam/3rdparty/cephes) set(CEPHES_SOURCES - cephes/airy.c - cephes/bdtr.c - cephes/besselpoly.c - cephes/beta.c - cephes/btdtr.c - cephes/cbrt.c - cephes/chbevl.c - cephes/chdtr.c cephes/const.c - cephes/dawsn.c - cephes/dd_real.c - cephes/ellie.c - cephes/ellik.c - cephes/ellpe.c - cephes/ellpj.c - cephes/ellpk.c - cephes/erfinv.c - cephes/exp10.c - cephes/exp2.c - cephes/expn.c - cephes/fdtr.c - cephes/fresnl.c cephes/gamma.c - cephes/gammasgn.c - cephes/gdtr.c - cephes/hyp2f1.c - cephes/hyperg.c - cephes/i0.c - cephes/i1.c cephes/igam.c cephes/igami.c - cephes/incbet.c - cephes/incbi.c - cephes/j0.c - cephes/j1.c - cephes/jv.c - cephes/k0.c - cephes/k1.c - cephes/kn.c - cephes/kolmogorov.c cephes/lanczos.c - cephes/nbdtr.c - cephes/ndtr.c - cephes/ndtri.c - cephes/owens_t.c - cephes/pdtr.c - cephes/poch.c - cephes/psi.c - cephes/rgamma.c - cephes/round.c cephes/sf_error.c - cephes/shichi.c - cephes/sici.c - cephes/sindg.c - cephes/sinpi.c - cephes/spence.c - cephes/stdtr.c - cephes/tandg.c - cephes/tukey.c cephes/unity.c - cephes/yn.c - cephes/yv.c - cephes/zeta.c - cephes/zetac.c) + cephes/zeta.c) # Add library source files add_library(cephes-gtsam ${GTSAM_LIBRARY_TYPE} ${CEPHES_SOURCES}) diff --git a/gtsam/3rdparty/cephes/cephes.h b/gtsam/3rdparty/cephes/cephes.h index c4ccfb132a..a326aa1aaf 100644 --- a/gtsam/3rdparty/cephes/cephes.h +++ b/gtsam/3rdparty/cephes/cephes.h @@ -5,120 +5,17 @@ extern "C" { #endif -int airy(double x, double *ai, double *aip, double *bi, double *bip); - -double bdtrc(double k, int n, double p); -double bdtr(double k, int n, double p); -double bdtri(double k, int n, double y); - -double besselpoly(double a, double lambda, double nu); - -double beta(double a, double b); -double lbeta(double a, double b); - -double btdtr(double a, double b, double x); - -double chbevl(double x, double array[], int n); -double chdtrc(double df, double x); -double chdtr(double df, double x); -double chdtri(double df, double y); -double dawsn(double xx); - -double ellie(double phi, double m); -double ellik(double phi, double m); -double ellpe(double x); - -int ellpj(double u, double m, double *sn, double *cn, double *dn, double *ph); -double ellpk(double x); -double exp10(double x); - -double expn(int n, double x); - -double fdtrc(double a, double b, double x); -double fdtr(double a, double b, double x); -double fdtri(double a, double b, double y); - -int fresnl(double xxa, double *ssa, double *cca); double Gamma(double x); double lgam(double x); double lgam_sgn(double x, int *sign); double gammasgn(double x); -double gdtr(double a, double b, double x); -double gdtrc(double a, double b, double x); -double gdtri(double a, double b, double y); - -double hyp2f1(double a, double b, double c, double x); -double hyperg(double a, double b, double x); -double threef0(double a, double b, double c, double x, double *err); - -double i0(double x); -double i0e(double x); -double i1(double x); -double i1e(double x); double igamc(double a, double x); double igam(double a, double x); double igam_fac(double a, double x); double igamci(double a, double q); double igami(double a, double p); -double incbet(double aa, double bb, double xx); -double incbi(double aa, double bb, double yy0); - -double iv(double v, double x); - -double jv(double n, double x); -double k0(double x); -double k0e(double x); -double k1(double x); -double k1e(double x); -double kn(int nn, double x); - -double nbdtrc(int k, int n, double p); -double nbdtr(int k, int n, double p); -double nbdtri(int k, int n, double p); - -double ndtr(double a); -double log_ndtr(double a); -double erfinv(double y); -double erfcinv(double y); -double ndtri(double y0); - -double pdtrc(double k, double m); -double pdtr(double k, double m); -double pdtri(int k, double y); - -double poch(double x, double m); - -double psi(double x); - -double rgamma(double x); - -int shichi(double x, double *si, double *ci); -int sici(double x, double *si, double *ci); - -double radian(double d, double m, double s); -double sindg(double x); -double sinpi(double x); -double cosdg(double x); -double cospi(double x); - -double spence(double x); - -double stdtr(int k, double t); -double stdtri(int k, double p); - -double struve_h(double v, double x); -double struve_l(double v, double x); -double struve_power_series(double v, double x, int is_h, double *err); -double struve_asymp_large_z(double v, double z, int is_h, double *err); -double struve_bessel_series(double v, double z, int is_h, double *err); - -double yv(double v, double x); - -double tandg(double x); -double cotdg(double x); - double log1pmx(double x); double cosm1(double x); double lgam1p(double x); @@ -126,21 +23,8 @@ double lgam1p(double x); double zeta(double x, double q); double zetac(double x); -double smirnov(int n, double d); -double smirnovi(int n, double p); -double smirnovp(int n, double d); -double smirnovc(int n, double d); -double smirnovci(int n, double p); -double kolmogorov(double x); -double kolmogi(double p); -double kolmogp(double x); -double kolmogc(double x); -double kolmogci(double p); - double lanczos_sum_expg_scaled(double x); -double owens_t(double h, double a); - #ifdef __cplusplus } #endif diff --git a/gtsam/3rdparty/cephes/cephes/airy.c b/gtsam/3rdparty/cephes/cephes/airy.c deleted file mode 100644 index 95e16a55f8..0000000000 --- a/gtsam/3rdparty/cephes/cephes/airy.c +++ /dev/null @@ -1,376 +0,0 @@ -/* airy.c - * - * Airy function - * - * - * - * SYNOPSIS: - * - * double x, ai, aip, bi, bip; - * int airy(); - * - * airy( x, _&ai, _&aip, _&bi, _&bip ); - * - * - * - * DESCRIPTION: - * - * Solution of the differential equation - * - * y"(x) = xy. - * - * The function returns the two independent solutions Ai, Bi - * and their first derivatives Ai'(x), Bi'(x). - * - * Evaluation is by power series summation for small x, - * by rational minimax approximations for large x. - * - * - * - * ACCURACY: - * Error criterion is absolute when function <= 1, relative - * when function > 1, except * denotes relative error criterion. - * For large negative x, the absolute error increases as x^1.5. - * For large positive x, the relative error increases as x^1.5. - * - * Arithmetic domain function # trials peak rms - * IEEE -10, 0 Ai 10000 1.6e-15 2.7e-16 - * IEEE 0, 10 Ai 10000 2.3e-14* 1.8e-15* - * IEEE -10, 0 Ai' 10000 4.6e-15 7.6e-16 - * IEEE 0, 10 Ai' 10000 1.8e-14* 1.5e-15* - * IEEE -10, 10 Bi 30000 4.2e-15 5.3e-16 - * IEEE -10, 10 Bi' 30000 4.9e-15 7.3e-16 - * - */ - /* airy.c */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -static double c1 = 0.35502805388781723926; -static double c2 = 0.258819403792806798405; -static double sqrt3 = 1.732050807568877293527; -static double sqpii = 5.64189583547756286948E-1; - -extern double MACHEP; - -#ifdef UNK -#define MAXAIRY 25.77 -#endif -#ifdef IBMPC -#define MAXAIRY 103.892 -#endif -#ifdef MIEEE -#define MAXAIRY 103.892 -#endif - - -static double AN[8] = { - 3.46538101525629032477E-1, - 1.20075952739645805542E1, - 7.62796053615234516538E1, - 1.68089224934630576269E2, - 1.59756391350164413639E2, - 7.05360906840444183113E1, - 1.40264691163389668864E1, - 9.99999999999999995305E-1, -}; - -static double AD[8] = { - 5.67594532638770212846E-1, - 1.47562562584847203173E1, - 8.45138970141474626562E1, - 1.77318088145400459522E2, - 1.64234692871529701831E2, - 7.14778400825575695274E1, - 1.40959135607834029598E1, - 1.00000000000000000470E0, -}; - -static double APN[8] = { - 6.13759184814035759225E-1, - 1.47454670787755323881E1, - 8.20584123476060982430E1, - 1.71184781360976385540E2, - 1.59317847137141783523E2, - 6.99778599330103016170E1, - 1.39470856980481566958E1, - 1.00000000000000000550E0, -}; - -static double APD[8] = { - 3.34203677749736953049E-1, - 1.11810297306158156705E1, - 7.11727352147859965283E1, - 1.58778084372838313640E2, - 1.53206427475809220834E2, - 6.86752304592780337944E1, - 1.38498634758259442477E1, - 9.99999999999999994502E-1, -}; - -static double BN16[5] = { - -2.53240795869364152689E-1, - 5.75285167332467384228E-1, - -3.29907036873225371650E-1, - 6.44404068948199951727E-2, - -3.82519546641336734394E-3, -}; - -static double BD16[5] = { - /* 1.00000000000000000000E0, */ - -7.15685095054035237902E0, - 1.06039580715664694291E1, - -5.23246636471251500874E0, - 9.57395864378383833152E-1, - -5.50828147163549611107E-2, -}; - -static double BPPN[5] = { - 4.65461162774651610328E-1, - -1.08992173800493920734E0, - 6.38800117371827987759E-1, - -1.26844349553102907034E-1, - 7.62487844342109852105E-3, -}; - -static double BPPD[5] = { - /* 1.00000000000000000000E0, */ - -8.70622787633159124240E0, - 1.38993162704553213172E1, - -7.14116144616431159572E0, - 1.34008595960680518666E0, - -7.84273211323341930448E-2, -}; - -static double AFN[9] = { - -1.31696323418331795333E-1, - -6.26456544431912369773E-1, - -6.93158036036933542233E-1, - -2.79779981545119124951E-1, - -4.91900132609500318020E-2, - -4.06265923594885404393E-3, - -1.59276496239262096340E-4, - -2.77649108155232920844E-6, - -1.67787698489114633780E-8, -}; - -static double AFD[9] = { - /* 1.00000000000000000000E0, */ - 1.33560420706553243746E1, - 3.26825032795224613948E1, - 2.67367040941499554804E1, - 9.18707402907259625840E0, - 1.47529146771666414581E0, - 1.15687173795188044134E-1, - 4.40291641615211203805E-3, - 7.54720348287414296618E-5, - 4.51850092970580378464E-7, -}; - -static double AGN[11] = { - 1.97339932091685679179E-2, - 3.91103029615688277255E-1, - 1.06579897599595591108E0, - 9.39169229816650230044E-1, - 3.51465656105547619242E-1, - 6.33888919628925490927E-2, - 5.85804113048388458567E-3, - 2.82851600836737019778E-4, - 6.98793669997260967291E-6, - 8.11789239554389293311E-8, - 3.41551784765923618484E-10, -}; - -static double AGD[10] = { - /* 1.00000000000000000000E0, */ - 9.30892908077441974853E0, - 1.98352928718312140417E1, - 1.55646628932864612953E1, - 5.47686069422975497931E0, - 9.54293611618961883998E-1, - 8.64580826352392193095E-2, - 4.12656523824222607191E-3, - 1.01259085116509135510E-4, - 1.17166733214413521882E-6, - 4.91834570062930015649E-9, -}; - -static double APFN[9] = { - 1.85365624022535566142E-1, - 8.86712188052584095637E-1, - 9.87391981747398547272E-1, - 4.01241082318003734092E-1, - 7.10304926289631174579E-2, - 5.90618657995661810071E-3, - 2.33051409401776799569E-4, - 4.08718778289035454598E-6, - 2.48379932900442457853E-8, -}; - -static double APFD[9] = { - /* 1.00000000000000000000E0, */ - 1.47345854687502542552E1, - 3.75423933435489594466E1, - 3.14657751203046424330E1, - 1.09969125207298778536E1, - 1.78885054766999417817E0, - 1.41733275753662636873E-1, - 5.44066067017226003627E-3, - 9.39421290654511171663E-5, - 5.65978713036027009243E-7, -}; - -static double APGN[11] = { - -3.55615429033082288335E-2, - -6.37311518129435504426E-1, - -1.70856738884312371053E0, - -1.50221872117316635393E0, - -5.63606665822102676611E-1, - -1.02101031120216891789E-1, - -9.48396695961445269093E-3, - -4.60325307486780994357E-4, - -1.14300836484517375919E-5, - -1.33415518685547420648E-7, - -5.63803833958893494476E-10, -}; - -static double APGD[11] = { - /* 1.00000000000000000000E0, */ - 9.85865801696130355144E0, - 2.16401867356585941885E1, - 1.73130776389749389525E1, - 6.17872175280828766327E0, - 1.08848694396321495475E0, - 9.95005543440888479402E-2, - 4.78468199683886610842E-3, - 1.18159633322838625562E-4, - 1.37480673554219441465E-6, - 5.79912514929147598821E-9, -}; - -int airy(double x, double *ai, double *aip, double *bi, double *bip) -{ - double z, zz, t, f, g, uf, ug, k, zeta, theta; - int domflg; - - domflg = 0; - if (x > MAXAIRY) { - *ai = 0; - *aip = 0; - *bi = INFINITY; - *bip = INFINITY; - return (-1); - } - - if (x < -2.09) { - domflg = 15; - t = sqrt(-x); - zeta = -2.0 * x * t / 3.0; - t = sqrt(t); - k = sqpii / t; - z = 1.0 / zeta; - zz = z * z; - uf = 1.0 + zz * polevl(zz, AFN, 8) / p1evl(zz, AFD, 9); - ug = z * polevl(zz, AGN, 10) / p1evl(zz, AGD, 10); - theta = zeta + 0.25 * M_PI; - f = sin(theta); - g = cos(theta); - *ai = k * (f * uf - g * ug); - *bi = k * (g * uf + f * ug); - uf = 1.0 + zz * polevl(zz, APFN, 8) / p1evl(zz, APFD, 9); - ug = z * polevl(zz, APGN, 10) / p1evl(zz, APGD, 10); - k = sqpii * t; - *aip = -k * (g * uf + f * ug); - *bip = k * (f * uf - g * ug); - return (0); - } - - if (x >= 2.09) { /* cbrt(9) */ - domflg = 5; - t = sqrt(x); - zeta = 2.0 * x * t / 3.0; - g = exp(zeta); - t = sqrt(t); - k = 2.0 * t * g; - z = 1.0 / zeta; - f = polevl(z, AN, 7) / polevl(z, AD, 7); - *ai = sqpii * f / k; - k = -0.5 * sqpii * t / g; - f = polevl(z, APN, 7) / polevl(z, APD, 7); - *aip = f * k; - - if (x > 8.3203353) { /* zeta > 16 */ - f = z * polevl(z, BN16, 4) / p1evl(z, BD16, 5); - k = sqpii * g; - *bi = k * (1.0 + f) / t; - f = z * polevl(z, BPPN, 4) / p1evl(z, BPPD, 5); - *bip = k * t * (1.0 + f); - return (0); - } - } - - f = 1.0; - g = x; - t = 1.0; - uf = 1.0; - ug = x; - k = 1.0; - z = x * x * x; - while (t > MACHEP) { - uf *= z; - k += 1.0; - uf /= k; - ug *= z; - k += 1.0; - ug /= k; - uf /= k; - f += uf; - k += 1.0; - ug /= k; - g += ug; - t = fabs(uf / f); - } - uf = c1 * f; - ug = c2 * g; - if ((domflg & 1) == 0) - *ai = uf - ug; - if ((domflg & 2) == 0) - *bi = sqrt3 * (uf + ug); - - /* the deriviative of ai */ - k = 4.0; - uf = x * x / 2.0; - ug = z / 3.0; - f = uf; - g = 1.0 + ug; - uf /= 3.0; - t = 1.0; - - while (t > MACHEP) { - uf *= z; - ug /= k; - k += 1.0; - ug *= z; - uf /= k; - f += uf; - k += 1.0; - ug /= k; - uf /= k; - g += ug; - k += 1.0; - t = fabs(ug / g); - } - - uf = c1 * f; - ug = c2 * g; - if ((domflg & 4) == 0) - *aip = uf - ug; - if ((domflg & 8) == 0) - *bip = sqrt3 * (uf + ug); - return (0); -} diff --git a/gtsam/3rdparty/cephes/cephes/bdtr.c b/gtsam/3rdparty/cephes/cephes/bdtr.c deleted file mode 100644 index 29fcdf1aff..0000000000 --- a/gtsam/3rdparty/cephes/cephes/bdtr.c +++ /dev/null @@ -1,241 +0,0 @@ -/* bdtr.c - * - * Binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, bdtr(); - * - * y = bdtr( k, n, p ); - * - * DESCRIPTION: - * - * Returns the sum of the terms 0 through k of the Binomial - * probability density: - * - * k - * -- ( n ) j n-j - * > ( ) p (1-p) - * -- ( j ) - * j=0 - * - * The terms are not summed directly; instead the incomplete - * beta integral is employed, according to the formula - * - * y = bdtr( k, n, p ) = incbet( n-k, k+1, 1-p ). - * - * The arguments must be positive, with p ranging from 0 to 1. - * - * ACCURACY: - * - * Tested at random points (a,b,p), with p between 0 and 1. - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * For p between 0.001 and 1: - * IEEE 0,100 100000 4.3e-15 2.6e-16 - * See also incbet.c. - * - * ERROR MESSAGES: - * - * message condition value returned - * bdtr domain k < 0 0.0 - * n < k - * x < 0, x > 1 - */ -/* bdtrc() - * - * Complemented binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, bdtrc(); - * - * y = bdtrc( k, n, p ); - * - * DESCRIPTION: - * - * Returns the sum of the terms k+1 through n of the Binomial - * probability density: - * - * n - * -- ( n ) j n-j - * > ( ) p (1-p) - * -- ( j ) - * j=k+1 - * - * The terms are not summed directly; instead the incomplete - * beta integral is employed, according to the formula - * - * y = bdtrc( k, n, p ) = incbet( k+1, n-k, p ). - * - * The arguments must be positive, with p ranging from 0 to 1. - * - * ACCURACY: - * - * Tested at random points (a,b,p). - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * For p between 0.001 and 1: - * IEEE 0,100 100000 6.7e-15 8.2e-16 - * For p between 0 and .001: - * IEEE 0,100 100000 1.5e-13 2.7e-15 - * - * ERROR MESSAGES: - * - * message condition value returned - * bdtrc domain x<0, x>1, n 1 - */ - -/* bdtr() */ - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - -double bdtrc(double k, int n, double p) { - double dk, dn; - double fk = floor(k); - - if (isnan(p) || isnan(k)) { - return NAN; - } - - if (p < 0.0 || p > 1.0 || n < fk) { - sf_error("bdtrc", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (fk < 0) { - return 1.0; - } - - if (fk == n) { - return 0.0; - } - - dn = n - fk; - if (k == 0) { - if (p < .01) - dk = -expm1(dn * log1p(-p)); - else - dk = 1.0 - pow(1.0 - p, dn); - } else { - dk = fk + 1; - dk = incbet(dk, dn, p); - } - return dk; -} - -double bdtr(double k, int n, double p) { - double dk, dn; - double fk = floor(k); - - if (isnan(p) || isnan(k)) { - return NAN; - } - - if (p < 0.0 || p > 1.0 || fk < 0 || n < fk) { - sf_error("bdtr", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (fk == n) return 1.0; - - dn = n - fk; - if (fk == 0) { - dk = pow(1.0 - p, dn); - } else { - dk = fk + 1.; - dk = incbet(dn, dk, 1.0 - p); - } - return dk; -} - -double bdtri(double k, int n, double y) { - double p, dn, dk; - double fk = floor(k); - - if (isnan(k)) { - return NAN; - } - - if (y < 0.0 || y > 1.0 || fk < 0.0 || n <= fk) { - sf_error("bdtri", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - dn = n - fk; - - if (fk == n) return 1.0; - - if (fk == 0) { - if (y > 0.8) { - p = -expm1(log1p(y - 1.0) / dn); - } else { - p = 1.0 - pow(y, 1.0 / dn); - } - } else { - dk = fk + 1; - p = incbet(dn, dk, 0.5); - if (p > 0.5) - p = incbi(dk, dn, 1.0 - y); - else - p = 1.0 - incbi(dn, dk, y); - } - return p; -} diff --git a/gtsam/3rdparty/cephes/cephes/besselpoly.c b/gtsam/3rdparty/cephes/cephes/besselpoly.c deleted file mode 100644 index a58fe20376..0000000000 --- a/gtsam/3rdparty/cephes/cephes/besselpoly.c +++ /dev/null @@ -1,34 +0,0 @@ -#include "mconf.h" - -#define EPS 1.0e-17 - -double besselpoly(double a, double lambda, double nu) { - - int m, factor=0; - double Sm, relerr, Sol; - double sum=0.0; - - /* Special handling for a = 0.0 */ - if (a == 0.0) { - if (nu == 0.0) return 1.0/(lambda + 1); - else return 0.0; - } - /* Special handling for negative and integer nu */ - if ((nu < 0) && (floor(nu)==nu)) { - nu = -nu; - factor = ((int) nu) % 2; - } - Sm = exp(nu*log(a))/(Gamma(nu+1)*(lambda+nu+1)); - m = 0; - do { - sum += Sm; - Sol = Sm; - Sm *= -a*a*(lambda+nu+1+2*m)/((nu+m+1)*(m+1)*(lambda+nu+1+2*m+2)); - m++; - relerr = fabs((Sm-Sol)/Sm); - } while (relerr > EPS && m < 1000); - if (!factor) - return sum; - else - return -sum; -} diff --git a/gtsam/3rdparty/cephes/cephes/beta.c b/gtsam/3rdparty/cephes/cephes/beta.c deleted file mode 100644 index c0389deea0..0000000000 --- a/gtsam/3rdparty/cephes/cephes/beta.c +++ /dev/null @@ -1,258 +0,0 @@ -/* beta.c - * - * Beta function - * - * - * - * SYNOPSIS: - * - * double a, b, y, beta(); - * - * y = beta( a, b ); - * - * - * - * DESCRIPTION: - * - * - - - * | (a) | (b) - * beta( a, b ) = -----------. - * - - * | (a+b) - * - * For large arguments the logarithm of the function is - * evaluated using lgam(), then exponentiated. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,30 30000 8.1e-14 1.1e-14 - * - * ERROR MESSAGES: - * - * message condition value returned - * beta overflow log(beta) > MAXLOG 0.0 - * a or b <0 integer 0.0 - * - */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -#define MAXGAM 171.624376956302725 - -extern double MAXLOG; - -#define ASYMP_FACTOR 1e6 - -static double lbeta_asymp(double a, double b, int *sgn); -static double lbeta_negint(int a, double b); -static double beta_negint(int a, double b); - -double beta(double a, double b) -{ - double y; - int sign = 1; - - if (a <= 0.0) { - if (a == floor(a)) { - if (a == (int)a) { - return beta_negint((int)a, b); - } - else { - goto overflow; - } - } - } - - if (b <= 0.0) { - if (b == floor(b)) { - if (b == (int)b) { - return beta_negint((int)b, a); - } - else { - goto overflow; - } - } - } - - if (fabs(a) < fabs(b)) { - y = a; a = b; b = y; - } - - if (fabs(a) > ASYMP_FACTOR * fabs(b) && a > ASYMP_FACTOR) { - /* Avoid loss of precision in lgam(a + b) - lgam(a) */ - y = lbeta_asymp(a, b, &sign); - return sign * exp(y); - } - - y = a + b; - if (fabs(y) > MAXGAM || fabs(a) > MAXGAM || fabs(b) > MAXGAM) { - int sgngam; - y = lgam_sgn(y, &sgngam); - sign *= sgngam; /* keep track of the sign */ - y = lgam_sgn(b, &sgngam) - y; - sign *= sgngam; - y = lgam_sgn(a, &sgngam) + y; - sign *= sgngam; - if (y > MAXLOG) { - goto overflow; - } - return (sign * exp(y)); - } - - y = Gamma(y); - a = Gamma(a); - b = Gamma(b); - if (y == 0.0) - goto overflow; - - if (fabs(fabs(a) - fabs(y)) > fabs(fabs(b) - fabs(y))) { - y = b / y; - y *= a; - } - else { - y = a / y; - y *= b; - } - - return (y); - -overflow: - sf_error("beta", SF_ERROR_OVERFLOW, NULL); - return (sign * INFINITY); -} - - -/* Natural log of |beta|. */ - -double lbeta(double a, double b) -{ - double y; - int sign; - - sign = 1; - - if (a <= 0.0) { - if (a == floor(a)) { - if (a == (int)a) { - return lbeta_negint((int)a, b); - } - else { - goto over; - } - } - } - - if (b <= 0.0) { - if (b == floor(b)) { - if (b == (int)b) { - return lbeta_negint((int)b, a); - } - else { - goto over; - } - } - } - - if (fabs(a) < fabs(b)) { - y = a; a = b; b = y; - } - - if (fabs(a) > ASYMP_FACTOR * fabs(b) && a > ASYMP_FACTOR) { - /* Avoid loss of precision in lgam(a + b) - lgam(a) */ - y = lbeta_asymp(a, b, &sign); - return y; - } - - y = a + b; - if (fabs(y) > MAXGAM || fabs(a) > MAXGAM || fabs(b) > MAXGAM) { - int sgngam; - y = lgam_sgn(y, &sgngam); - sign *= sgngam; /* keep track of the sign */ - y = lgam_sgn(b, &sgngam) - y; - sign *= sgngam; - y = lgam_sgn(a, &sgngam) + y; - sign *= sgngam; - return (y); - } - - y = Gamma(y); - a = Gamma(a); - b = Gamma(b); - if (y == 0.0) { - over: - sf_error("lbeta", SF_ERROR_OVERFLOW, NULL); - return (sign * INFINITY); - } - - if (fabs(fabs(a) - fabs(y)) > fabs(fabs(b) - fabs(y))) { - y = b / y; - y *= a; - } - else { - y = a / y; - y *= b; - } - - if (y < 0) { - y = -y; - } - - return (log(y)); -} - -/* - * Asymptotic expansion for ln(|B(a, b)|) for a > ASYMP_FACTOR*max(|b|, 1). - */ -static double lbeta_asymp(double a, double b, int *sgn) -{ - double r = lgam_sgn(b, sgn); - r -= b * log(a); - - r += b*(1-b)/(2*a); - r += b*(1-b)*(1-2*b)/(12*a*a); - r += - b*b*(1-b)*(1-b)/(12*a*a*a); - - return r; -} - - -/* - * Special case for a negative integer argument - */ - -static double beta_negint(int a, double b) -{ - int sgn; - if (b == (int)b && 1 - a - b > 0) { - sgn = ((int)b % 2 == 0) ? 1 : -1; - return sgn * beta(1 - a - b, b); - } - else { - sf_error("lbeta", SF_ERROR_OVERFLOW, NULL); - return INFINITY; - } -} - -static double lbeta_negint(int a, double b) -{ - double r; - if (b == (int)b && 1 - a - b > 0) { - r = lbeta(1 - a - b, b); - return r; - } - else { - sf_error("lbeta", SF_ERROR_OVERFLOW, NULL); - return INFINITY; - } -} diff --git a/gtsam/3rdparty/cephes/cephes/btdtr.c b/gtsam/3rdparty/cephes/cephes/btdtr.c deleted file mode 100644 index fa115c7b70..0000000000 --- a/gtsam/3rdparty/cephes/cephes/btdtr.c +++ /dev/null @@ -1,59 +0,0 @@ - -/* btdtr.c - * - * Beta distribution - * - * - * - * SYNOPSIS: - * - * double a, b, x, y, btdtr(); - * - * y = btdtr( a, b, x ); - * - * - * - * DESCRIPTION: - * - * Returns the area from zero to x under the beta density - * function: - * - * - * x - * - - - * | (a+b) | | a-1 b-1 - * P(x) = ---------- | t (1-t) dt - * - - | | - * | (a) | (b) - - * 0 - * - * - * This function is identical to the incomplete beta - * integral function incbet(a, b, x). - * - * The complemented function is - * - * 1 - P(1-x) = incbet( b, a, x ); - * - * - * ACCURACY: - * - * See incbet.c. - * - */ - -/* btdtr() */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - -double btdtr(double a, double b, double x) -{ - - return (incbet(a, b, x)); -} diff --git a/gtsam/3rdparty/cephes/cephes/cbrt.c b/gtsam/3rdparty/cephes/cephes/cbrt.c deleted file mode 100644 index a83c078341..0000000000 --- a/gtsam/3rdparty/cephes/cephes/cbrt.c +++ /dev/null @@ -1,117 +0,0 @@ -/* cbrt.c - * - * Cube root - * - * - * - * SYNOPSIS: - * - * double x, y, cbrt(); - * - * y = cbrt( x ); - * - * - * - * DESCRIPTION: - * - * Returns the cube root of the argument, which may be negative. - * - * Range reduction involves determining the power of 2 of - * the argument. A polynomial of degree 2 applied to the - * mantissa, and multiplication by the cube root of 1, 2, or 4 - * approximates the root to within about 0.1%. Then Newton's - * iteration is used three times to converge to an accurate - * result. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,1e308 30000 1.5e-16 5.0e-17 - * - */ - /* cbrt.c */ - -/* - * Cephes Math Library Release 2.2: January, 1991 - * Copyright 1984, 1991 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - - -#include "mconf.h" - -static double CBRT2 = 1.2599210498948731647672; -static double CBRT4 = 1.5874010519681994747517; -static double CBRT2I = 0.79370052598409973737585; -static double CBRT4I = 0.62996052494743658238361; - -double cbrt(double x) -{ - int e, rem, sign; - double z; - - if (!cephes_isfinite(x)) - return x; - if (x == 0) - return (x); - if (x > 0) - sign = 1; - else { - sign = -1; - x = -x; - } - - z = x; - /* extract power of 2, leaving - * mantissa between 0.5 and 1 - */ - x = frexp(x, &e); - - /* Approximate cube root of number between .5 and 1, - * peak relative error = 9.2e-6 - */ - x = (((-1.3466110473359520655053e-1 * x - + 5.4664601366395524503440e-1) * x - - 9.5438224771509446525043e-1) * x - + 1.1399983354717293273738e0) * x + 4.0238979564544752126924e-1; - - /* exponent divided by 3 */ - if (e >= 0) { - rem = e; - e /= 3; - rem -= 3 * e; - if (rem == 1) - x *= CBRT2; - else if (rem == 2) - x *= CBRT4; - } - - - /* argument less than 1 */ - - else { - e = -e; - rem = e; - e /= 3; - rem -= 3 * e; - if (rem == 1) - x *= CBRT2I; - else if (rem == 2) - x *= CBRT4I; - e = -e; - } - - /* multiply by power of 2 */ - x = ldexp(x, e); - - /* Newton iteration */ - x -= (x - (z / (x * x))) * 0.33333333333333333333; - x -= (x - (z / (x * x))) * 0.33333333333333333333; - - if (sign < 0) - x = -x; - return (x); -} diff --git a/gtsam/3rdparty/cephes/cephes/chbevl.c b/gtsam/3rdparty/cephes/cephes/chbevl.c deleted file mode 100644 index a0e9c5c52a..0000000000 --- a/gtsam/3rdparty/cephes/cephes/chbevl.c +++ /dev/null @@ -1,81 +0,0 @@ -/* chbevl.c - * - * Evaluate Chebyshev series - * - * - * - * SYNOPSIS: - * - * int N; - * double x, y, coef[N], chebevl(); - * - * y = chbevl( x, coef, N ); - * - * - * - * DESCRIPTION: - * - * Evaluates the series - * - * N-1 - * - ' - * y = > coef[i] T (x/2) - * - i - * i=0 - * - * of Chebyshev polynomials Ti at argument x/2. - * - * Coefficients are stored in reverse order, i.e. the zero - * order term is last in the array. Note N is the number of - * coefficients, not the order. - * - * If coefficients are for the interval a to b, x must - * have been transformed to x -> 2(2x - b - a)/(b-a) before - * entering the routine. This maps x from (a, b) to (-1, 1), - * over which the Chebyshev polynomials are defined. - * - * If the coefficients are for the inverted interval, in - * which (a, b) is mapped to (1/b, 1/a), the transformation - * required is x -> 2(2ab/x - b - a)/(b-a). If b is infinity, - * this becomes x -> 4a/x - 1. - * - * - * - * SPEED: - * - * Taking advantage of the recurrence properties of the - * Chebyshev polynomials, the routine requires one more - * addition per loop than evaluating a nested polynomial of - * the same degree. - * - */ - /* chbevl.c */ - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1985, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" -#include - -double chbevl(double x, double array[], int n) -{ - double b0, b1, b2, *p; - int i; - - p = array; - b0 = *p++; - b1 = 0.0; - i = n - 1; - - do { - b2 = b1; - b1 = b0; - b0 = x * b1 - b2 + *p++; - } - while (--i); - - return (0.5 * (b0 - b2)); -} diff --git a/gtsam/3rdparty/cephes/cephes/chdtr.c b/gtsam/3rdparty/cephes/cephes/chdtr.c deleted file mode 100644 index d576e7a8db..0000000000 --- a/gtsam/3rdparty/cephes/cephes/chdtr.c +++ /dev/null @@ -1,186 +0,0 @@ -/* chdtr.c - * - * Chi-square distribution - * - * - * - * SYNOPSIS: - * - * double df, x, y, chdtr(); - * - * y = chdtr( df, x ); - * - * - * - * DESCRIPTION: - * - * Returns the area under the left hand tail (from 0 to x) - * of the Chi square probability density function with - * v degrees of freedom. - * - * - * inf. - * - - * 1 | | v/2-1 -t/2 - * P( x | v ) = ----------- | t e dt - * v/2 - | | - * 2 | (v/2) - - * x - * - * where x is the Chi-square variable. - * - * The incomplete Gamma integral is used, according to the - * formula - * - * y = chdtr( v, x ) = igam( v/2.0, x/2.0 ). - * - * - * The arguments must both be positive. - * - * - * - * ACCURACY: - * - * See igam(). - * - * ERROR MESSAGES: - * - * message condition value returned - * chdtr domain x < 0 or v < 1 0.0 - */ - /* chdtrc() - * - * Complemented Chi-square distribution - * - * - * - * SYNOPSIS: - * - * double v, x, y, chdtrc(); - * - * y = chdtrc( v, x ); - * - * - * - * DESCRIPTION: - * - * Returns the area under the right hand tail (from x to - * infinity) of the Chi square probability density function - * with v degrees of freedom: - * - * - * inf. - * - - * 1 | | v/2-1 -t/2 - * P( x | v ) = ----------- | t e dt - * v/2 - | | - * 2 | (v/2) - - * x - * - * where x is the Chi-square variable. - * - * The incomplete Gamma integral is used, according to the - * formula - * - * y = chdtr( v, x ) = igamc( v/2.0, x/2.0 ). - * - * - * The arguments must both be positive. - * - * - * - * ACCURACY: - * - * See igamc(). - * - * ERROR MESSAGES: - * - * message condition value returned - * chdtrc domain x < 0 or v < 1 0.0 - */ - /* chdtri() - * - * Inverse of complemented Chi-square distribution - * - * - * - * SYNOPSIS: - * - * double df, x, y, chdtri(); - * - * x = chdtri( df, y ); - * - * - * - * - * DESCRIPTION: - * - * Finds the Chi-square argument x such that the integral - * from x to infinity of the Chi-square density is equal - * to the given cumulative probability y. - * - * This is accomplished using the inverse Gamma integral - * function and the relation - * - * x/2 = igamci( df/2, y ); - * - * - * - * - * ACCURACY: - * - * See igami.c. - * - * ERROR MESSAGES: - * - * message condition value returned - * chdtri domain y < 0 or y > 1 0.0 - * v < 1 - * - */ - -/* chdtr() */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -double chdtrc(double df, double x) -{ - - if (x < 0.0) - return 1.0; /* modified by T. Oliphant */ - return (igamc(df / 2.0, x / 2.0)); -} - - - -double chdtr(double df, double x) -{ - - if ((x < 0.0)) { /* || (df < 1.0) ) */ - sf_error("chdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - return (igam(df / 2.0, x / 2.0)); -} - - - -double chdtri(double df, double y) -{ - double x; - - if ((y < 0.0) || (y > 1.0)) { /* || (df < 1.0) ) */ - sf_error("chdtri", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - x = igamci(0.5 * df, y); - return (2.0 * x); -} diff --git a/gtsam/3rdparty/cephes/cephes/dawsn.c b/gtsam/3rdparty/cephes/cephes/dawsn.c deleted file mode 100644 index 7049f191ed..0000000000 --- a/gtsam/3rdparty/cephes/cephes/dawsn.c +++ /dev/null @@ -1,160 +0,0 @@ -/* dawsn.c - * - * Dawson's Integral - * - * - * - * SYNOPSIS: - * - * double x, y, dawsn(); - * - * y = dawsn( x ); - * - * - * - * DESCRIPTION: - * - * Approximates the integral - * - * x - * - - * 2 | | 2 - * dawsn(x) = exp( -x ) | exp( t ) dt - * | | - * - - * 0 - * - * Three different rational approximations are employed, for - * the intervals 0 to 3.25; 3.25 to 6.25; and 6.25 up. - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,10 10000 6.9e-16 1.0e-16 - * - * - */ - -/* dawsn.c */ - - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" -/* Dawson's integral, interval 0 to 3.25 */ -static double AN[10] = { - 1.13681498971755972054E-11, - 8.49262267667473811108E-10, - 1.94434204175553054283E-8, - 9.53151741254484363489E-7, - 3.07828309874913200438E-6, - 3.52513368520288738649E-4, - -8.50149846724410912031E-4, - 4.22618223005546594270E-2, - -9.17480371773452345351E-2, - 9.99999999999999994612E-1, -}; - -static double AD[11] = { - 2.40372073066762605484E-11, - 1.48864681368493396752E-9, - 5.21265281010541664570E-8, - 1.27258478273186970203E-6, - 2.32490249820789513991E-5, - 3.25524741826057911661E-4, - 3.48805814657162590916E-3, - 2.79448531198828973716E-2, - 1.58874241960120565368E-1, - 5.74918629489320327824E-1, - 1.00000000000000000539E0, -}; - -/* interval 3.25 to 6.25 */ -static double BN[11] = { - 5.08955156417900903354E-1, - -2.44754418142697847934E-1, - 9.41512335303534411857E-2, - -2.18711255142039025206E-2, - 3.66207612329569181322E-3, - -4.23209114460388756528E-4, - 3.59641304793896631888E-5, - -2.14640351719968974225E-6, - 9.10010780076391431042E-8, - -2.40274520828250956942E-9, - 3.59233385440928410398E-11, -}; - -static double BD[10] = { - /* 1.00000000000000000000E0, */ - -6.31839869873368190192E-1, - 2.36706788228248691528E-1, - -5.31806367003223277662E-2, - 8.48041718586295374409E-3, - -9.47996768486665330168E-4, - 7.81025592944552338085E-5, - -4.55875153252442634831E-6, - 1.89100358111421846170E-7, - -4.91324691331920606875E-9, - 7.18466403235734541950E-11, -}; - -/* 6.25 to infinity */ -static double CN[5] = { - -5.90592860534773254987E-1, - 6.29235242724368800674E-1, - -1.72858975380388136411E-1, - 1.64837047825189632310E-2, - -4.86827613020462700845E-4, -}; - -static double CD[5] = { - /* 1.00000000000000000000E0, */ - -2.69820057197544900361E0, - 1.73270799045947845857E0, - -3.93708582281939493482E-1, - 3.44278924041233391079E-2, - -9.73655226040941223894E-4, -}; - -extern double MACHEP; - -double dawsn(double xx) -{ - double x, y; - int sign; - - - sign = 1; - if (xx < 0.0) { - sign = -1; - xx = -xx; - } - - if (xx < 3.25) { - x = xx * xx; - y = xx * polevl(x, AN, 9) / polevl(x, AD, 10); - return (sign * y); - } - - - x = 1.0 / (xx * xx); - - if (xx < 6.25) { - y = 1.0 / xx + x * polevl(x, BN, 10) / (p1evl(x, BD, 10) * xx); - return (sign * 0.5 * y); - } - - - if (xx > 1.0e9) - return ((sign * 0.5) / xx); - - /* 6.25 to infinity */ - y = 1.0 / xx + x * polevl(x, CN, 4) / (p1evl(x, CD, 5) * xx); - return (sign * 0.5 * y); -} diff --git a/gtsam/3rdparty/cephes/cephes/dd_idefs.h b/gtsam/3rdparty/cephes/cephes/dd_idefs.h deleted file mode 100644 index fec97c4780..0000000000 --- a/gtsam/3rdparty/cephes/cephes/dd_idefs.h +++ /dev/null @@ -1,198 +0,0 @@ -/* - * include/dd_inline.h - * - * This work was supported by the Director, Office of Science, Division - * of Mathematical, Information, and Computational Sciences of the - * U.S. Department of Energy under contract numbers DE-AC03-76SF00098 and - * DE-AC02-05CH11231. - * - * Copyright (c) 2003-2009, The Regents of the University of California, - * through Lawrence Berkeley National Laboratory (subject to receipt of - * any required approvals from U.S. Dept. of Energy) All rights reserved. - * - * By downloading or using this software you are agreeing to the modified - * BSD license "BSD-LBNL-License.doc" (see LICENSE.txt). - */ -/* - * Contains small functions (suitable for inlining) in the double-double - * arithmetic package. - */ - -#ifndef _DD_IDEFS_H_ -#define _DD_IDEFS_H_ 1 - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -#define _DD_SPLITTER 134217729.0 // = 2^27 + 1 -#define _DD_SPLIT_THRESH 6.69692879491417e+299 // = 2^996 - -/* - ************************************************************************ - The basic routines taking double arguments, returning 1 (or 2) doubles - ************************************************************************ -*/ - -/* Computes fl(a+b) and err(a+b). Assumes |a| >= |b|. */ -static inline double -quick_two_sum(double a, double b, double *err) -{ - volatile double s = a + b; - volatile double c = s - a; - *err = b - c; - return s; -} - -/* Computes fl(a-b) and err(a-b). Assumes |a| >= |b| */ -static inline double -quick_two_diff(double a, double b, double *err) -{ - volatile double s = a - b; - volatile double c = a - s; - *err = c - b; - return s; -} - -/* Computes fl(a+b) and err(a+b). */ -static inline double -two_sum(double a, double b, double *err) -{ - volatile double s = a + b; - volatile double c = s - a; - volatile double d = b - c; - volatile double e = s - c; - *err = (a - e) + d; - return s; -} - -/* Computes fl(a-b) and err(a-b). */ -static inline double -two_diff(double a, double b, double *err) -{ - volatile double s = a - b; - volatile double c = s - a; - volatile double d = b + c; - volatile double e = s - c; - *err = (a - e) - d; - return s; -} - -/* Computes high word and lo word of a */ -static inline void -two_split(double a, double *hi, double *lo) -{ - volatile double temp, tempma; - if (a > _DD_SPLIT_THRESH || a < -_DD_SPLIT_THRESH) { - a *= 3.7252902984619140625e-09; // 2^-28 - temp = _DD_SPLITTER * a; - tempma = temp - a; - *hi = temp - tempma; - *lo = a - *hi; - *hi *= 268435456.0; // 2^28 - *lo *= 268435456.0; // 2^28 - } - else { - temp = _DD_SPLITTER * a; - tempma = temp - a; - *hi = temp - tempma; - *lo = a - *hi; - } -} - -/* Computes fl(a*b) and err(a*b). */ -static inline double -two_prod(double a, double b, double *err) -{ -#ifdef DD_FMS - volatile double p = a * b; - *err = DD_FMS(a, b, p); - return p; -#else - double a_hi, a_lo, b_hi, b_lo; - double p = a * b; - volatile double c, d; - two_split(a, &a_hi, &a_lo); - two_split(b, &b_hi, &b_lo); - c = a_hi * b_hi - p; - d = c + a_hi * b_lo + a_lo * b_hi; - *err = d + a_lo * b_lo; - return p; -#endif /* DD_FMA */ -} - -/* Computes fl(a*a) and err(a*a). Faster than the above method. */ -static inline double -two_sqr(double a, double *err) -{ -#ifdef DD_FMS - volatile double p = a * a; - *err = DD_FMS(a, a, p); - return p; -#else - double hi, lo; - volatile double c; - double q = a * a; - two_split(a, &hi, &lo); - c = hi * hi - q; - *err = (c + 2.0 * hi * lo) + lo * lo; - return q; -#endif /* DD_FMS */ -} - -static inline double -two_div(double a, double b, double *err) -{ - volatile double q1, q2; - double p1, p2; - double s, e; - - q1 = a / b; - - /* Compute a - q1 * b */ - p1 = two_prod(q1, b, &p2); - s = two_diff(a, p1, &e); - e -= p2; - - /* get next approximation */ - q2 = (s + e) / b; - - return quick_two_sum(q1, q2, err); -} - -/* Computes the nearest integer to d. */ -static inline double -two_nint(double d) -{ - if (d == floor(d)) { - return d; - } - return floor(d + 0.5); -} - -/* Computes the truncated integer. */ -static inline double -two_aint(double d) -{ - return (d >= 0.0 ? floor(d) : ceil(d)); -} - - -/* Compare a and b */ -static inline int -two_comp(const double a, const double b) -{ - /* Works for non-NAN inputs */ - return (a < b ? -1 : (a > b ? 1 : 0)); -} - - -#ifdef __cplusplus -} -#endif - -#endif /* _DD_IDEFS_H_ */ diff --git a/gtsam/3rdparty/cephes/cephes/dd_real.c b/gtsam/3rdparty/cephes/cephes/dd_real.c deleted file mode 100644 index c37f57a7b9..0000000000 --- a/gtsam/3rdparty/cephes/cephes/dd_real.c +++ /dev/null @@ -1,587 +0,0 @@ -/* - * src/double2.cc - * - * This work was supported by the Director, Office of Science, Division - * of Mathematical, Information, and Computational Sciences of the - * U.S. Department of Energy under contract numbers DE-AC03-76SF00098 and - * DE-AC02-05CH11231. - * - * Copyright (c) 2003-2009, The Regents of the University of California, - * through Lawrence Berkeley National Laboratory (subject to receipt of - * any required approvals from U.S. Dept. of Energy) All rights reserved. - * - * By downloading or using this software you are agreeing to the modified - * BSD license "BSD-LBNL-License.doc" (see LICENSE.txt). - */ -/* - * Contains implementation of non-inlined functions of double-double - * package. Inlined functions are found in dd_real_inline.h. - */ - -/* - * This code taken from v2.3.18 of the qd package. -*/ - - -#include -#include -#include -#include - -#include "dd_real.h" - -#define _DD_REAL_INIT(A, B) {{A, B}} - -const double DD_C_EPS = 4.93038065763132e-32; // 2^-104 -const double DD_C_MIN_NORMALIZED = 2.0041683600089728e-292; // = 2^(-1022 + 53) - -/* Compile-time initialization of const double2 structs */ - -const double2 DD_C_MAX = - _DD_REAL_INIT(1.79769313486231570815e+308, 9.97920154767359795037e+291); -const double2 DD_C_SAFE_MAX = - _DD_REAL_INIT(1.7976931080746007281e+308, 9.97920154767359795037e+291); -const int _DD_C_NDIGITS = 31; - -const double2 DD_C_ZERO = _DD_REAL_INIT(0.0, 0.0); -const double2 DD_C_ONE = _DD_REAL_INIT(1.0, 0.0); -const double2 DD_C_NEGONE = _DD_REAL_INIT(-1.0, 0.0); - -const double2 DD_C_2PI = - _DD_REAL_INIT(6.283185307179586232e+00, 2.449293598294706414e-16); -const double2 DD_C_PI = - _DD_REAL_INIT(3.141592653589793116e+00, 1.224646799147353207e-16); -const double2 DD_C_PI2 = - _DD_REAL_INIT(1.570796326794896558e+00, 6.123233995736766036e-17); -const double2 DD_C_PI4 = - _DD_REAL_INIT(7.853981633974482790e-01, 3.061616997868383018e-17); -const double2 DD_C_PI16 = - _DD_REAL_INIT(1.963495408493620697e-01, 7.654042494670957545e-18); -const double2 DD_C_3PI4 = - _DD_REAL_INIT(2.356194490192344837e+00, 9.1848509936051484375e-17); - -const double2 DD_C_E = - _DD_REAL_INIT(2.718281828459045091e+00, 1.445646891729250158e-16); -const double2 DD_C_LOG2 = - _DD_REAL_INIT(6.931471805599452862e-01, 2.319046813846299558e-17); -const double2 DD_C_LOG10 = - _DD_REAL_INIT(2.302585092994045901e+00, -2.170756223382249351e-16); - -#ifdef DD_C_NAN_IS_CONST -const double2 DD_C_NAN = _DD_REAL_INIT(NAN, NAN); -const double2 DD_C_INF = _DD_REAL_INIT(INFINITY, INFINITY); -const double2 DD_C_NEGINF = _DD_REAL_INIT(-INFINITY, -INFINITY); -#endif /* NAN */ - - -/* This routine is called whenever a fatal error occurs. */ -static volatile int errCount = 0; -void -dd_error(const char *msg) -{ - errCount++; - /* if (msg) { */ - /* fprintf(stderr, "ERROR %s\n", msg); */ - /* } */ -} - - -int -get_double_expn(double x) -{ - int i = 0; - double y; - if (x == 0.0) { - return INT_MIN; - } - if (isinf(x) || isnan(x)) { - return INT_MAX; - } - - y = fabs(x); - if (y < 1.0) { - while (y < 1.0) { - y *= 2.0; - i++; - } - return -i; - } else if (y >= 2.0) { - while (y >= 2.0) { - y *= 0.5; - i++; - } - return i; - } - return 0; -} - -/* ######################################################################## */ -/* # Exponentiation */ -/* ######################################################################## */ - -/* Computes the square root of the double-double number dd. - NOTE: dd must be a non-negative number. */ - -double2 -dd_sqrt(const double2 a) -{ - /* Strategy: Use Karp's trick: if x is an approximation - to sqrt(a), then - - sqrt(a) = a*x + [a - (a*x)^2] * x / 2 (approx) - - The approximation is accurate to twice the accuracy of x. - Also, the multiplication (a*x) and [-]*x can be done with - only half the precision. - */ - double x, ax; - - if (dd_is_zero(a)) - return DD_C_ZERO; - - if (dd_is_negative(a)) { - dd_error("(dd_sqrt): Negative argument."); - return DD_C_NAN; - } - - x = 1.0 / sqrt(a.x[0]); - ax = a.x[0] * x; - return dd_add_d_d(ax, dd_sub(a, dd_sqr_d(ax)).x[0] * (x * 0.5)); -} - -/* Computes the square root of a double in double-double precision. - NOTE: d must not be negative. */ - -double2 -dd_sqrt_d(double d) -{ - return dd_sqrt(dd_create_d(d)); -} - -/* Computes the n-th root of the double-double number a. - NOTE: n must be a positive integer. - NOTE: If n is even, then a must not be negative. */ - -double2 -dd_nroot(const double2 a, int n) -{ - /* Strategy: Use Newton iteration for the function - - f(x) = x^(-n) - a - - to find its root a^{-1/n}. The iteration is thus - - x' = x + x * (1 - a * x^n) / n - - which converges quadratically. We can then find - a^{1/n} by taking the reciprocal. - */ - double2 r, x; - - if (n <= 0) { - dd_error("(dd_nroot): N must be positive."); - return DD_C_NAN; - } - - if (n % 2 == 0 && dd_is_negative(a)) { - dd_error("(dd_nroot): Negative argument."); - return DD_C_NAN; - } - - if (n == 1) { - return a; - } - if (n == 2) { - return dd_sqrt(a); - } - - if (dd_is_zero(a)) - return DD_C_ZERO; - - /* Note a^{-1/n} = exp(-log(a)/n) */ - r = dd_abs(a); - x = dd_create_d(exp(-log(r.x[0]) / n)); - - /* Perform Newton's iteration. */ - x = dd_add( - x, dd_mul(x, dd_sub_d_dd(1.0, dd_div_dd_d(dd_mul(r, dd_npwr(x, n)), - DD_STATIC_CAST(double, n))))); - if (a.x[0] < 0.0) { - x = dd_neg(x); - } - return dd_inv(x); -} - -/* Computes the n-th power of a double-double number. - NOTE: 0^0 causes an error. */ - -double2 -dd_npwr(const double2 a, int n) -{ - double2 r = a; - double2 s = DD_C_ONE; - int N = abs(n); - if (N == 0) { - if (dd_is_zero(a)) { - dd_error("(dd_npwr): Invalid argument."); - return DD_C_NAN; - } - return DD_C_ONE; - } - - if (N > 1) { - /* Use binary exponentiation */ - while (N > 0) { - if (N % 2 == 1) { - s = dd_mul(s, r); - } - N /= 2; - if (N > 0) { - r = dd_sqr(r); - } - } - } - else { - s = r; - } - - /* Compute the reciprocal if n is negative. */ - if (n < 0) { - return dd_inv(s); - } - - return s; -} - -double2 -dd_npow(const double2 a, int n) -{ - return dd_npwr(a, n); -} - -double2 -dd_pow(const double2 a, const double2 b) -{ - return dd_exp(dd_mul(b, dd_log(a))); -} - -/* ######################################################################## */ -/* # Exp/Log functions */ -/* ######################################################################## */ - -static const double2 inv_fact[] = { - {{1.66666666666666657e-01, 9.25185853854297066e-18}}, - {{4.16666666666666644e-02, 2.31296463463574266e-18}}, - {{8.33333333333333322e-03, 1.15648231731787138e-19}}, - {{1.38888888888888894e-03, -5.30054395437357706e-20}}, - {{1.98412698412698413e-04, 1.72095582934207053e-22}}, - {{2.48015873015873016e-05, 2.15119478667758816e-23}}, - {{2.75573192239858925e-06, -1.85839327404647208e-22}}, - {{2.75573192239858883e-07, 2.37677146222502973e-23}}, - {{2.50521083854417202e-08, -1.44881407093591197e-24}}, - {{2.08767569878681002e-09, -1.20734505911325997e-25}}, - {{1.60590438368216133e-10, 1.25852945887520981e-26}}, - {{1.14707455977297245e-11, 2.06555127528307454e-28}}, - {{7.64716373181981641e-13, 7.03872877733453001e-30}}, - {{4.77947733238738525e-14, 4.39920548583408126e-31}}, - {{2.81145725434552060e-15, 1.65088427308614326e-31}} -}; -//static const int n_inv_fact = sizeof(inv_fact) / sizeof(inv_fact[0]); - -/* Exponential. Computes exp(x) in double-double precision. */ - -double2 -dd_exp(const double2 a) -{ - /* Strategy: We first reduce the size of x by noting that - - exp(kr + m * log(2)) = 2^m * exp(r)^k - - where m and k are integers. By choosing m appropriately - we can make |kr| <= log(2) / 2 = 0.347. Then exp(r) is - evaluated using the familiar Taylor series. Reducing the - argument substantially speeds up the convergence. */ - - const double k = 512.0; - const double inv_k = 1.0 / k; - double m; - double2 r, s, t, p; - int i = 0; - - if (a.x[0] <= -709.0) { - return DD_C_ZERO; - } - - if (a.x[0] >= 709.0) { - return DD_C_INF; - } - - if (dd_is_zero(a)) { - return DD_C_ONE; - } - - if (dd_is_one(a)) { - return DD_C_E; - } - - m = floor(a.x[0] / DD_C_LOG2.x[0] + 0.5); - r = dd_mul_pwr2(dd_sub(a, dd_mul_dd_d(DD_C_LOG2, m)), inv_k); - - p = dd_sqr(r); - s = dd_add(r, dd_mul_pwr2(p, 0.5)); - p = dd_mul(p, r); - t = dd_mul(p, inv_fact[0]); - do { - s = dd_add(s, t); - p = dd_mul(p, r); - ++i; - t = dd_mul(p, inv_fact[i]); - } while (fabs(dd_to_double(t)) > inv_k * DD_C_EPS && i < 5); - - s = dd_add(s, t); - - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(dd_mul_pwr2(s, 2.0), dd_sqr(s)); - s = dd_add(s, DD_C_ONE); - - return dd_ldexp(s, DD_STATIC_CAST(int, m)); -} - -double2 -dd_exp_d(const double a) -{ - return dd_exp(dd_create(a, 0)); -} - - -/* Logarithm. Computes log(x) in double-double precision. - This is a natural logarithm (i.e., base e). */ -double2 -dd_log(const double2 a) -{ - /* Strategy. The Taylor series for log converges much more - slowly than that of exp, due to the lack of the factorial - term in the denominator. Hence this routine instead tries - to determine the root of the function - - f(x) = exp(x) - a - - using Newton iteration. The iteration is given by - - x' = x - f(x)/f'(x) - = x - (1 - a * exp(-x)) - = x + a * exp(-x) - 1. - - Only one iteration is needed, since Newton's iteration - approximately doubles the number of digits per iteration. */ - double2 x; - - if (dd_is_one(a)) { - return DD_C_ZERO; - } - - if (a.x[0] <= 0.0) { - dd_error("(dd_log): Non-positive argument."); - return DD_C_NAN; - } - - x = dd_create_d(log(a.x[0])); /* Initial approximation */ - - /* x = x + a * exp(-x) - 1.0; */ - x = dd_add(x, dd_sub(dd_mul(a, dd_exp(dd_neg(x))), DD_C_ONE)); - return x; -} - - -double2 -dd_log1p(const double2 a) -{ - double2 ans; - double la, elam1, ll; - if (a.x[0] <= -1.0) { - return DD_C_NEGINF; - } - la = log1p(a.x[0]); - elam1 = expm1(la); - ll = log1p(a.x[1] / (1 + a.x[0])); - if (a.x[0] > 0) { - ll -= (elam1 - a.x[0])/(elam1+1); - } - ans = dd_add_d_d(la, ll); - return ans; -} - -double2 -dd_log10(const double2 a) -{ - return dd_div(dd_log(a), DD_C_LOG10); -} - -double2 -dd_log_d(double a) -{ - return dd_log(dd_create(a, 0)); -} - - -static const double2 expm1_numer[] = { - {{-0.028127670288085938, 1.46e-37}}, - {{0.5127815691121048, -4.248816580490825e-17}}, - {{-0.0632631785207471, 4.733650586348708e-18}}, - {{0.01470328560687425, -4.57569727474415e-20}}, - {{-0.0008675686051689528, 2.340010361165805e-20}}, - {{8.812635961829116e-05, 2.619804163788941e-21}}, - {{-2.596308786770631e-06, -1.6196413688647164e-22}}, - {{1.422669108780046e-07, 1.2956999470135368e-23}}, - {{-1.5995603306536497e-09, 5.185121944095551e-26}}, - {{4.526182006900779e-11, -1.9856249941108077e-27}} -}; - -static const double2 expm1_denom[] = { - {{1.0, 0.0}}, - {{-0.4544126470907431, -2.2553855773661143e-17}}, - {{0.09682713193619222, -4.961446925746919e-19}}, - {{-0.012745248725908178, -6.0676821249478945e-19}}, - {{0.001147361387158326, 1.3575817248483204e-20}}, - {{-7.370416847725892e-05, 3.720369981570573e-21}}, - {{3.4087499397791556e-06, -3.3067348191741576e-23}}, - {{-1.1114024704296196e-07, -3.313361038199987e-24}}, - {{2.3987051614110847e-09, 1.102474920537503e-25}}, - {{-2.947734185911159e-11, -9.4795654767864e-28}}, - {{1.32220659910223e-13, 6.440648413523595e-30}} -}; - -// -// Rational approximation of expm1(x) for -1/2 < x < 1/2 -// -static double2 -expm1_rational_approx(const double2 x) -{ - const double2 Y = dd_create(1.028127670288086, 0.0); - const double2 num = dd_polyeval(expm1_numer, 9, x); - const double2 den = dd_polyeval(expm1_denom, 10, x); - return dd_add(dd_mul(x, Y), dd_mul(x, dd_div(num, den))); -} - -// -// This is a translation of Boost's `expm1_imp` for quad precision -// for use with double2. -// - -#define LOG_MAX_VALUE 709.782712893384 - -double2 -dd_expm1(const double2 x) -{ - double2 a = dd_abs(x); - if (dd_hi(a) > 0.5) { - if (dd_hi(a) > LOG_MAX_VALUE) { - if (dd_hi(x) > 0) { - return DD_C_INF; - } - return DD_C_NEGONE; - } - return dd_sub_dd_d(dd_exp(x), 1.0); - } - return expm1_rational_approx(x); -} - - -double2 -dd_rand(void) -{ - static const double m_const = 4.6566128730773926e-10; /* = 2^{-31} */ - double m = m_const; - double2 r = DD_C_ZERO; - double d; - int i; - - /* Strategy: Generate 31 bits at a time, using lrand48 - random number generator. Shift the bits, and reapeat - 4 times. */ - - for (i = 0; i < 4; i++, m *= m_const) { - // d = lrand48() * m; - d = rand() * m; - r = dd_add_dd_d(r, d); - } - - return r; -} - -/* dd_polyeval(c, n, x) - Evaluates the given n-th degree polynomial at x. - The polynomial is given by the array of (n+1) coefficients. */ - -double2 -dd_polyeval(const double2 *c, int n, const double2 x) -{ - /* Just use Horner's method of polynomial evaluation. */ - double2 r = c[n]; - int i; - - for (i = n - 1; i >= 0; i--) { - r = dd_mul(r, x); - r = dd_add(r, c[i]); - } - - return r; -} - -/* dd_polyroot(c, n, x0) - Given an n-th degree polynomial, finds a root close to - the given guess x0. Note that this uses simple Newton - iteration scheme, and does not work for multiple roots. */ - -double2 -dd_polyroot(const double2 *c, int n, const double2 x0, int max_iter, - double thresh) -{ - double2 x = x0; - double2 f; - double2 *d = DD_STATIC_CAST(double2 *, calloc(sizeof(double2), n)); - int conv = 0; - int i; - double max_c = fabs(dd_to_double(c[0])); - double v; - - if (thresh == 0.0) { - thresh = DD_C_EPS; - } - - /* Compute the coefficients of the derivatives. */ - for (i = 1; i <= n; i++) { - v = fabs(dd_to_double(c[i])); - if (v > max_c) { - max_c = v; - } - d[i - 1] = dd_mul_dd_d(c[i], DD_STATIC_CAST(double, i)); - } - thresh *= max_c; - - /* Newton iteration. */ - for (i = 0; i < max_iter; i++) { - f = dd_polyeval(c, n, x); - - if (fabs(dd_to_double(f)) < thresh) { - conv = 1; - break; - } - x = dd_sub(x, (dd_div(f, dd_polyeval(d, n - 1, x)))); - } - free(d); - - if (!conv) { - dd_error("(dd_polyroot): Failed to converge."); - return DD_C_NAN; - } - - return x; -} diff --git a/gtsam/3rdparty/cephes/cephes/dd_real.h b/gtsam/3rdparty/cephes/cephes/dd_real.h deleted file mode 100644 index 4e09da1432..0000000000 --- a/gtsam/3rdparty/cephes/cephes/dd_real.h +++ /dev/null @@ -1,143 +0,0 @@ -/* - * include/double2.h - * - * This work was supported by the Director, Office of Science, Division - * of Mathematical, Information, and Computational Sciences of the - * U.S. Department of Energy under contract numbers DE-AC03-76SF00098 and - * DE-AC02-05CH11231. - * - * Copyright (c) 2003-2009, The Regents of the University of California, - * through Lawrence Berkeley National Laboratory (subject to receipt of - * any required approvals from U.S. Dept. of Energy) All rights reserved. - * - * By downloading or using this software you are agreeing to the modified - * BSD license "BSD-LBNL-License.doc" (see LICENSE.txt). - */ -/* - * Double-double precision (>= 106-bit significand) floating point - * arithmetic package based on David Bailey's Fortran-90 double-double - * package, with some changes. See - * - * http://www.nersc.gov/~dhbailey/mpdist/mpdist.html - * - * for the original Fortran-90 version. - * - * Overall structure is similar to that of Keith Brigg's C++ double-double - * package. See - * - * http://www-epidem.plansci.cam.ac.uk/~kbriggs/doubledouble.html - * - * for more details. In particular, the fix for x86 computers is borrowed - * from his code. - * - * Yozo Hida - */ - -#ifndef _DD_REAL_H -#define _DD_REAL_H - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -/* Some configuration defines */ - -/* If fast fused multiply-add is available, define to the correct macro for - using it. It is invoked as DD_FMA(a, b, c) to compute fl(a * b + c). - If correctly rounded multiply-add is not available (or if unsure), - keep it undefined. */ -#ifndef DD_FMA -#ifdef FP_FAST_FMA -#define DD_FMA(A, B, C) fma((A), (B), (C)) -#endif -#endif - -/* Same with fused multiply-subtract */ -#ifndef DD_FMS -#ifdef FP_FAST_FMA -#define DD_FMS(A, B, C) fma((A), (B), (-C)) -#endif -#endif - -#ifdef __cplusplus -#define DD_STATIC_CAST(T, X) (static_cast(X)) -#else -#define DD_STATIC_CAST(T, X) ((T)(X)) -#endif - -/* double2 struct definition, some external always-present double2 constants. -*/ -typedef struct double2 -{ - double x[2]; -} double2; - -extern const double DD_C_EPS; -extern const double DD_C_MIN_NORMALIZED; -extern const double2 DD_C_MAX; -extern const double2 DD_C_SAFE_MAX; -extern const int DD_C_NDIGITS; - -extern const double2 DD_C_2PI; -extern const double2 DD_C_PI; -extern const double2 DD_C_3PI4; -extern const double2 DD_C_PI2; -extern const double2 DD_C_PI4; -extern const double2 DD_C_PI16; -extern const double2 DD_C_E; -extern const double2 DD_C_LOG2; -extern const double2 DD_C_LOG10; -extern const double2 DD_C_ZERO; -extern const double2 DD_C_ONE; -extern const double2 DD_C_NEGONE; - -/* NAN definition in AIX's math.h doesn't make it qualify as constant literal. */ -#if defined(__STDC__) && defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && defined(NAN) && !defined(_AIX) -#define DD_C_NAN_IS_CONST -extern const double2 DD_C_NAN; -extern const double2 DD_C_INF; -extern const double2 DD_C_NEGINF; -#else -#define DD_C_NAN (dd_create(NAN, NAN)) -#define DD_C_INF (dd_create(INFINITY, INFINITY)) -#define DD_C_NEGINF (dd_create(-INFINITY, -INFINITY)) -#endif - - -/* Include the inline definitions of functions */ -#include "dd_real_idefs.h" - -/* Non-inline functions */ - -/********** Exponentiation **********/ -double2 dd_npwr(const double2 a, int n); - -/*********** Transcendental Functions ************/ -double2 dd_exp(const double2 a); -double2 dd_log(const double2 a); -double2 dd_expm1(const double2 a); -double2 dd_log1p(const double2 a); -double2 dd_log10(const double2 a); -double2 dd_log_d(double a); - -/* Returns the exponent of the double precision number. - Returns INT_MIN is x is zero, and INT_MAX if x is INF or NaN. */ -int get_double_expn(double x); - -/*********** Polynomial Functions ************/ -double2 dd_polyeval(const double2 *c, int n, const double2 x); - -/*********** Random number generator ************/ -extern double2 dd_rand(void); - - -#ifdef __cplusplus -} -#endif - - -#endif /* _DD_REAL_H */ diff --git a/gtsam/3rdparty/cephes/cephes/dd_real_idefs.h b/gtsam/3rdparty/cephes/cephes/dd_real_idefs.h deleted file mode 100644 index d2b9ac1d65..0000000000 --- a/gtsam/3rdparty/cephes/cephes/dd_real_idefs.h +++ /dev/null @@ -1,557 +0,0 @@ -/* - * include/dd_inline.h - * - * This work was supported by the Director, Office of Science, Division - * of Mathematical, Information, and Computational Sciences of the - * U.S. Department of Energy under contract numbers DE-AC03-76SF00098 and - * DE-AC02-05CH11231. - * - * Copyright (c) 2003-2009, The Regents of the University of California, - * through Lawrence Berkeley National Laboratory (subject to receipt of - * any required approvals from U.S. Dept. of Energy) All rights reserved. - * - * By downloading or using this software you are agreeing to the modified - * BSD license "BSD-LBNL-License.doc" (see LICENSE.txt). - */ -/* - * Contains small functions (suitable for inlining) in the double-double - * arithmetic package. - */ - -#ifndef _DD_REAL_IDEFS_H_ -#define _DD_REAL_IDEFS_H_ 1 - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -#include "dd_idefs.h" - -/* - ************************************************************************ - Now for the double2 routines - ************************************************************************ -*/ - -static inline double -dd_hi(const double2 a) -{ - return a.x[0]; -} - -static inline double -dd_lo(const double2 a) -{ - return a.x[1]; -} - -static inline int -dd_isfinite(const double2 a) -{ - return isfinite(a.x[0]); -} - -static inline int -dd_isinf(const double2 a) -{ - return isinf(a.x[0]); -} - -static inline int -dd_is_zero(const double2 a) -{ - return (a.x[0] == 0.0); -} - -static inline int -dd_is_one(const double2 a) -{ - return (a.x[0] == 1.0 && a.x[1] == 0.0); -} - -static inline int -dd_is_positive(const double2 a) -{ - return (a.x[0] > 0.0); -} - -static inline int -dd_is_negative(const double2 a) -{ - return (a.x[0] < 0.0); -} - -/* Cast to double. */ -static inline double -dd_to_double(const double2 a) -{ - return a.x[0]; -} - -/* Cast to int. */ -static inline int -dd_to_int(const double2 a) -{ - return DD_STATIC_CAST(int, a.x[0]); -} - -/*********** Equality and Other Comparisons ************/ -static inline int -dd_comp(const double2 a, const double2 b) -{ - int cmp = two_comp(a.x[0], b.x[0]); - if (cmp == 0) { - cmp = two_comp(a.x[1], b.x[1]); - } - return cmp; -} - -static inline int -dd_comp_dd_d(const double2 a, double b) -{ - int cmp = two_comp(a.x[0], b); - if (cmp == 0) { - cmp = two_comp(a.x[1], 0); - } - return cmp; -} - -static inline int -dd_comp_d_dd(double a, const double2 b) -{ - int cmp = two_comp(a, b.x[0]); - if (cmp == 0) { - cmp = two_comp(0.0, b.x[1]); - } - return cmp; -} - - -/*********** Creation ************/ -static inline double2 -dd_create(double hi, double lo) -{ - double2 ret = {{hi, lo}}; - return ret; -} - -static inline double2 -dd_zero(void) -{ - return DD_C_ZERO; -} - -static inline double2 -dd_create_d(double hi) -{ - double2 ret = {{hi, 0.0}}; - return ret; -} - -static inline double2 -dd_create_i(int hi) -{ - double2 ret = {{DD_STATIC_CAST(double, hi), 0.0}}; - return ret; -} - -static inline double2 -dd_create_dp(const double *d) -{ - double2 ret = {{d[0], d[1]}}; - return ret; -} - - -/*********** Unary Minus ***********/ -static inline double2 -dd_neg(const double2 a) -{ - double2 ret = {{-a.x[0], -a.x[1]}}; - return ret; -} - -/*********** Rounding ************/ -/* Round to Nearest integer */ -static inline double2 -dd_nint(const double2 a) -{ - double hi = two_nint(a.x[0]); - double lo; - - if (hi == a.x[0]) { - /* High word is an integer already. Round the low word.*/ - lo = two_nint(a.x[1]); - - /* Renormalize. This is needed if x[0] = some integer, x[1] = 1/2.*/ - hi = quick_two_sum(hi, lo, &lo); - } - else { - /* High word is not an integer. */ - lo = 0.0; - if (fabs(hi - a.x[0]) == 0.5 && a.x[1] < 0.0) { - /* There is a tie in the high word, consult the low word - to break the tie. */ - hi -= 1.0; /* NOTE: This does not cause INEXACT. */ - } - } - - return dd_create(hi, lo); -} - -static inline double2 -dd_floor(const double2 a) -{ - double hi = floor(a.x[0]); - double lo = 0.0; - - if (hi == a.x[0]) { - /* High word is integer already. Round the low word. */ - lo = floor(a.x[1]); - hi = quick_two_sum(hi, lo, &lo); - } - - return dd_create(hi, lo); -} - -static inline double2 -dd_ceil(const double2 a) -{ - double hi = ceil(a.x[0]); - double lo = 0.0; - - if (hi == a.x[0]) { - /* High word is integer already. Round the low word. */ - lo = ceil(a.x[1]); - hi = quick_two_sum(hi, lo, &lo); - } - - return dd_create(hi, lo); -} - -static inline double2 -dd_aint(const double2 a) -{ - return (a.x[0] >= 0.0) ? dd_floor(a) : dd_ceil(a); -} - -/* Absolute value */ -static inline double2 -dd_abs(const double2 a) -{ - return (a.x[0] < 0.0 ? dd_neg(a) : a); -} - -static inline double2 -dd_fabs(const double2 a) -{ - return dd_abs(a); -} - - -/*********** Normalizing ***********/ -/* double-double * (2.0 ^ expt) */ -static inline double2 -dd_ldexp(const double2 a, int expt) -{ - return dd_create(ldexp(a.x[0], expt), ldexp(a.x[1], expt)); -} - -static inline double2 -dd_frexp(const double2 a, int *expt) -{ -// r"""return b and l s.t. 0.5<=|b|<1 and 2^l == a -// 0.5<=|b[0]|<1.0 or |b[0]| == 1.0 and b[0]*b[1]<0 -// """ - int exponent; - double man = frexp(a.x[0], &exponent); - double b1 = ldexp(a.x[1], -exponent); - if (fabs(man) == 0.5 && man * b1 < 0) - { - man *=2; - b1 *= 2; - exponent -= 1; - } - *expt = exponent; - return dd_create(man, b1); -} - - -/*********** Additions ************/ -static inline double2 -dd_add_d_d(double a, double b) -{ - double s, e; - s = two_sum(a, b, &e); - return dd_create(s, e); -} - -static inline double2 -dd_add_dd_d(const double2 a, double b) -{ - double s1, s2; - s1 = two_sum(a.x[0], b, &s2); - s2 += a.x[1]; - s1 = quick_two_sum(s1, s2, &s2); - return dd_create(s1, s2); -} - -static inline double2 -dd_add_d_dd(double a, const double2 b) -{ - double s1, s2; - s1 = two_sum(a, b.x[0], &s2); - s2 += b.x[1]; - s1 = quick_two_sum(s1, s2, &s2); - return dd_create(s1, s2); -} - -static inline double2 -dd_ieee_add(const double2 a, const double2 b) -{ - /* This one satisfies IEEE style error bound, - due to K. Briggs and W. Kahan. */ - double s1, s2, t1, t2; - - s1 = two_sum(a.x[0], b.x[0], &s2); - t1 = two_sum(a.x[1], b.x[1], &t2); - s2 += t1; - s1 = quick_two_sum(s1, s2, &s2); - s2 += t2; - s1 = quick_two_sum(s1, s2, &s2); - return dd_create(s1, s2); -} - -static inline double2 -dd_sloppy_add(const double2 a, const double2 b) -{ - /* This is the less accurate version ... obeys Cray-style - error bound. */ - double s, e; - - s = two_sum(a.x[0], b.x[0], &e); - e += (a.x[1] + b.x[1]); - s = quick_two_sum(s, e, &e); - return dd_create(s, e); -} - -static inline double2 -dd_add(const double2 a, const double2 b) -{ - /* Always require IEEE-style error bounds */ - return dd_ieee_add(a, b); -} - -/*********** Subtractions ************/ -/* double-double = double - double */ -static inline double2 -dd_sub_d_d(double a, double b) -{ - double s, e; - s = two_diff(a, b, &e); - return dd_create(s, e); -} - -static inline double2 -dd_sub(const double2 a, const double2 b) -{ - return dd_ieee_add(a, dd_neg(b)); -} - -static inline double2 -dd_sub_dd_d(const double2 a, double b) -{ - double s1, s2; - s1 = two_sum(a.x[0], -b, &s2); - s2 += a.x[1]; - s1 = quick_two_sum(s1, s2, &s2); - return dd_create(s1, s2); -} - -static inline double2 -dd_sub_d_dd(double a, const double2 b) -{ - double s1, s2; - s1 = two_sum(a, -b.x[0], &s2); - s2 -= b.x[1]; - s1 = quick_two_sum(s1, s2, &s2); - return dd_create(s1, s2); -} - - -/*********** Multiplications ************/ -/* double-double = double * double */ -static inline double2 -dd_mul_d_d(double a, double b) -{ - double p, e; - p = two_prod(a, b, &e); - return dd_create(p, e); -} - -/* double-double * double, where double is a power of 2. */ -static inline double2 -dd_mul_pwr2(const double2 a, double b) -{ - return dd_create(a.x[0] * b, a.x[1] * b); -} - -static inline double2 -dd_mul(const double2 a, const double2 b) -{ - double p1, p2; - p1 = two_prod(a.x[0], b.x[0], &p2); - p2 += (a.x[0] * b.x[1] + a.x[1] * b.x[0]); - p1 = quick_two_sum(p1, p2, &p2); - return dd_create(p1, p2); -} - -static inline double2 -dd_mul_dd_d(const double2 a, double b) -{ - double p1, p2, e1, e2; - p1 = two_prod(a.x[0], b, &e1); - p2 = two_prod(a.x[1], b, &e2); - p1 = quick_two_sum(p1, e2 + p2 + e1, &e1); - return dd_create(p1, e1); -} - -static inline double2 -dd_mul_d_dd(double a, const double2 b) -{ - double p1, p2, e1, e2; - p1 = two_prod(a, b.x[0], &e1); - p2 = two_prod(a, b.x[1], &e2); - p1 = quick_two_sum(p1, e2 + p2 + e1, &e1); - return dd_create(p1, e1); -} - - -/*********** Divisions ************/ -static inline double2 -dd_sloppy_div(const double2 a, const double2 b) -{ - double s1, s2; - double q1, q2; - double2 r; - - q1 = a.x[0] / b.x[0]; /* approximate quotient */ - - /* compute this - q1 * dd */ - r = dd_sub(a, dd_mul_dd_d(b, q1)); - s1 = two_diff(a.x[0], r.x[0], &s2); - s2 -= r.x[1]; - s2 += a.x[1]; - - /* get next approximation */ - q2 = (s1 + s2) / b.x[0]; - - /* renormalize */ - r.x[0] = quick_two_sum(q1, q2, &r.x[1]); - return r; -} - -static inline double2 -dd_accurate_div(const double2 a, const double2 b) -{ - double q1, q2, q3; - double2 r; - - q1 = a.x[0] / b.x[0]; /* approximate quotient */ - - r = dd_sub(a, dd_mul_dd_d(b, q1)); - - q2 = r.x[0] / b.x[0]; - r = dd_sub(r, dd_mul_dd_d(b, q2)); - - q3 = r.x[0] / b.x[0]; - - q1 = quick_two_sum(q1, q2, &q2); - r = dd_add_dd_d(dd_create(q1, q2), q3); - return r; -} - -static inline double2 -dd_div(const double2 a, const double2 b) -{ - return dd_accurate_div(a, b); -} - -static inline double2 -dd_div_d_d(double a, double b) -{ - return dd_accurate_div(dd_create_d(a), dd_create_d(b)); -} - -static inline double2 -dd_div_dd_d(const double2 a, double b) -{ - return dd_accurate_div(a, dd_create_d(b)); -} - -static inline double2 -dd_div_d_dd(double a, const double2 b) -{ - return dd_accurate_div(dd_create_d(a), b); -} - -static inline double2 -dd_inv(const double2 a) -{ - return dd_div(DD_C_ONE, a); -} - - -/********** Remainder **********/ -static inline double2 -dd_drem(const double2 a, const double2 b) -{ - double2 n = dd_nint(dd_div(a, b)); - return dd_sub(a, dd_mul(n, b)); -} - -static inline double2 -dd_divrem(const double2 a, const double2 b, double2 *r) -{ - double2 n = dd_nint(dd_div(a, b)); - *r = dd_sub(a, dd_mul(n, b)); - return n; -} - -static inline double2 -dd_fmod(const double2 a, const double2 b) -{ - double2 n = dd_aint(dd_div(a, b)); - return dd_sub(a, dd_mul(b, n)); -} - -/*********** Squaring **********/ -static inline double2 -dd_sqr(const double2 a) -{ - double p1, p2; - double s1, s2; - p1 = two_sqr(a.x[0], &p2); - p2 += 2.0 * a.x[0] * a.x[1]; - p2 += a.x[1] * a.x[1]; - s1 = quick_two_sum(p1, p2, &s2); - return dd_create(s1, s2); -} - -static inline double2 -dd_sqr_d(double a) -{ - double p1, p2; - p1 = two_sqr(a, &p2); - return dd_create(p1, p2); -} - -#ifdef __cplusplus -} -#endif - -#endif /* _DD_REAL_IDEFS_H_ */ diff --git a/gtsam/3rdparty/cephes/cephes/ellie.c b/gtsam/3rdparty/cephes/cephes/ellie.c deleted file mode 100644 index 8a2823f3a0..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ellie.c +++ /dev/null @@ -1,282 +0,0 @@ -/* ellie.c - * - * Incomplete elliptic integral of the second kind - * - * - * - * SYNOPSIS: - * - * double phi, m, y, ellie(); - * - * y = ellie( phi, m ); - * - * - * - * DESCRIPTION: - * - * Approximates the integral - * - * - * phi - * - - * | | - * | 2 - * E(phi_\m) = | sqrt( 1 - m sin t ) dt - * | - * | | - * - - * 0 - * - * of amplitude phi and modulus m, using the arithmetic - - * geometric mean algorithm. - * - * - * - * ACCURACY: - * - * Tested at random arguments with phi in [-10, 10] and m in - * [0, 1]. - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -10,10 150000 3.3e-15 1.4e-16 - */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987, 1993 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ -/* Copyright 2014, Eric W. Moore */ - -/* Incomplete elliptic integral of second kind */ - -#include "mconf.h" - -extern double MACHEP; - -static double ellie_neg_m(double phi, double m); - -double ellie(double phi, double m) -{ - double a, b, c, e, temp; - double lphi, t, E, denom, npio2; - int d, mod, sign; - - if (cephes_isnan(phi) || cephes_isnan(m)) - return NAN; - if (m > 1.0) - return NAN; - if (cephes_isinf(phi)) - return phi; - if (cephes_isinf(m)) - return -m; - if (m == 0.0) - return (phi); - lphi = phi; - npio2 = floor(lphi / M_PI_2); - if (fmod(fabs(npio2), 2.0) == 1.0) - npio2 += 1; - lphi = lphi - npio2 * M_PI_2; - if (lphi < 0.0) { - lphi = -lphi; - sign = -1; - } - else { - sign = 1; - } - a = 1.0 - m; - E = ellpe(m); - if (a == 0.0) { - temp = sin(lphi); - goto done; - } - if (a > 1.0) { - temp = ellie_neg_m(lphi, m); - goto done; - } - - if (lphi < 0.135) { - double m11= (((((-7.0/2816.0)*m + (5.0/1056.0))*m - (7.0/2640.0))*m - + (17.0/41580.0))*m - (1.0/155925.0))*m; - double m9 = ((((-5.0/1152.0)*m + (1.0/144.0))*m - (1.0/360.0))*m - + (1.0/5670.0))*m; - double m7 = ((-m/112.0 + (1.0/84.0))*m - (1.0/315.0))*m; - double m5 = (-m/40.0 + (1.0/30))*m; - double m3 = -m/6.0; - double p2 = lphi * lphi; - - temp = ((((m11*p2 + m9)*p2 + m7)*p2 + m5)*p2 + m3)*p2*lphi + lphi; - goto done; - } - t = tan(lphi); - b = sqrt(a); - /* Thanks to Brian Fitzgerald - * for pointing out an instability near odd multiples of pi/2. */ - if (fabs(t) > 10.0) { - /* Transform the amplitude */ - e = 1.0 / (b * t); - /* ... but avoid multiple recursions. */ - if (fabs(e) < 10.0) { - e = atan(e); - temp = E + m * sin(lphi) * sin(e) - ellie(e, m); - goto done; - } - } - c = sqrt(m); - a = 1.0; - d = 1; - e = 0.0; - mod = 0; - - while (fabs(c / a) > MACHEP) { - temp = b / a; - lphi = lphi + atan(t * temp) + mod * M_PI; - denom = 1 - temp * t * t; - if (fabs(denom) > 10*MACHEP) { - t = t * (1.0 + temp) / denom; - mod = (lphi + M_PI_2) / M_PI; - } - else { - t = tan(lphi); - mod = (int)floor((lphi - atan(t))/M_PI); - } - c = (a - b) / 2.0; - temp = sqrt(a * b); - a = (a + b) / 2.0; - b = temp; - d += d; - e += c * sin(lphi); - } - - temp = E / ellpk(1.0 - m); - temp *= (atan(t) + mod * M_PI) / (d * a); - temp += e; - - done: - - if (sign < 0) - temp = -temp; - temp += npio2 * E; - return (temp); -} - -/* N.B. This will evaluate its arguments multiple times. */ -#define MAX3(a, b, c) (a > b ? (a > c ? a : c) : (b > c ? b : c)) - -/* To calculate legendre's incomplete elliptical integral of the second kind for - * negative m, we use a power series in phi for small m*phi*phi, an asymptotic - * series in m for large m*phi*phi* and the relation to Carlson's symmetric - * integrals, R_F(x,y,z) and R_D(x,y,z). - * - * E(phi, m) = sin(phi) * R_F(cos(phi)^2, 1 - m * sin(phi)^2, 1.0) - * - m * sin(phi)^3 * R_D(cos(phi)^2, 1 - m * sin(phi)^2, 1.0) / 3 - * - * = R_F(c-1, c-m, c) - m * R_D(c-1, c-m, c) / 3 - * - * where c = csc(phi)^2. We use the second form of this for (approximately) - * phi > 1/(sqrt(DBL_MAX) ~ 1e-154, where csc(phi)^2 overflows. Elsewhere we - * use the first form, accounting for the smallness of phi. - * - * The algorithm used is described in Carlson, B. C. Numerical computation of - * real or complex elliptic integrals. (1994) https://arxiv.org/abs/math/9409227 - * Most variable names reflect Carlson's usage. - * - * In this routine, we assume m < 0 and 0 > phi > pi/2. - */ -double ellie_neg_m(double phi, double m) -{ - double x, y, z, x1, y1, z1, ret, Q; - double A0f, Af, Xf, Yf, Zf, E2f, E3f, scalef; - double A0d, Ad, seriesn, seriesd, Xd, Yd, Zd, E2d, E3d, E4d, E5d, scaled; - int n = 0; - double mpp = (m*phi)*phi; - - if (-mpp < 1e-6 && phi < -m) { - return phi + (mpp*phi*phi/30.0 - mpp*mpp/40.0 - mpp/6.0)*phi; - } - - if (-mpp > 1e6) { - double sm = sqrt(-m); - double sp = sin(phi); - double cp = cos(phi); - - double a = -cosm1(phi); - double b1 = log(4*sp*sm/(1+cp)); - double b = -(0.5 + b1) / 2.0 / m; - double c = (0.75 + cp/sp/sp - b1) / 16.0 / m / m; - return (a + b + c) * sm; - } - - if (phi > 1e-153 && m > -1e200) { - double s = sin(phi); - double csc2 = 1.0 / s / s; - scalef = 1.0; - scaled = m / 3.0; - x = 1.0 / tan(phi) / tan(phi); - y = csc2 - m; - z = csc2; - } - else { - scalef = phi; - scaled = mpp * phi / 3.0; - x = 1.0; - y = 1 - mpp; - z = 1.0; - } - - if (x == y && x == z) { - return (scalef + scaled/x)/sqrt(x); - } - - A0f = (x + y + z) / 3.0; - Af = A0f; - A0d = (x + y + 3.0*z) / 5.0; - Ad = A0d; - x1 = x; y1 = y; z1 = z; seriesd = 0.0; seriesn = 1.0; - /* Carlson gives 1/pow(3*r, 1.0/6.0) for this constant. if r == eps, - * it is ~338.38. */ - Q = 400.0 * MAX3(fabs(A0f-x), fabs(A0f-y), fabs(A0f-z)); - - while (Q > fabs(Af) && Q > fabs(Ad) && n <= 100) { - double sx = sqrt(x1); - double sy = sqrt(y1); - double sz = sqrt(z1); - double lam = sx*sy + sx*sz + sy*sz; - seriesd += seriesn / (sz * (z1 + lam)); - x1 = (x1 + lam) / 4.0; - y1 = (y1 + lam) / 4.0; - z1 = (z1 + lam) / 4.0; - Af = (x1 + y1 + z1) / 3.0; - Ad = (Ad + lam) / 4.0; - n += 1; - Q /= 4.0; - seriesn /= 4.0; - } - - Xf = (A0f - x) / Af / (1 << 2*n); - Yf = (A0f - y) / Af / (1 << 2*n); - Zf = -(Xf + Yf); - - E2f = Xf*Yf - Zf*Zf; - E3f = Xf*Yf*Zf; - - ret = scalef * (1.0 - E2f/10.0 + E3f/14.0 + E2f*E2f/24.0 - - 3.0*E2f*E3f/44.0) / sqrt(Af); - - Xd = (A0d - x) / Ad / (1 << 2*n); - Yd = (A0d - y) / Ad / (1 << 2*n); - Zd = -(Xd + Yd)/3.0; - - E2d = Xd*Yd - 6.0*Zd*Zd; - E3d = (3*Xd*Yd - 8.0*Zd*Zd)*Zd; - E4d = 3.0*(Xd*Yd - Zd*Zd)*Zd*Zd; - E5d = Xd*Yd*Zd*Zd*Zd; - - ret -= scaled * (1.0 - 3.0*E2d/14.0 + E3d/6.0 + 9.0*E2d*E2d/88.0 - - 3.0*E4d/22.0 - 9.0*E2d*E3d/52.0 + 3.0*E5d/26.0) - /(1 << 2*n) / Ad / sqrt(Ad); - ret -= 3.0 * scaled * seriesd; - return ret; -} - diff --git a/gtsam/3rdparty/cephes/cephes/ellik.c b/gtsam/3rdparty/cephes/cephes/ellik.c deleted file mode 100644 index ee73e062a2..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ellik.c +++ /dev/null @@ -1,246 +0,0 @@ -/* ellik.c - * - * Incomplete elliptic integral of the first kind - * - * - * - * SYNOPSIS: - * - * double phi, m, y, ellik(); - * - * y = ellik( phi, m ); - * - * - * - * DESCRIPTION: - * - * Approximates the integral - * - * - * - * phi - * - - * | | - * | dt - * F(phi | m) = | ------------------ - * | 2 - * | | sqrt( 1 - m sin t ) - * - - * 0 - * - * of amplitude phi and modulus m, using the arithmetic - - * geometric mean algorithm. - * - * - * - * - * ACCURACY: - * - * Tested at random points with m in [0, 1] and phi as indicated. - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -10,10 200000 7.4e-16 1.0e-16 - * - * - */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ -/* Copyright 2014, Eric W. Moore */ - -/* Incomplete elliptic integral of first kind */ - -#include "mconf.h" -extern double MACHEP; - -static double ellik_neg_m(double phi, double m); - -double ellik(double phi, double m) -{ - double a, b, c, e, temp, t, K, denom, npio2; - int d, mod, sign; - - if (cephes_isnan(phi) || cephes_isnan(m)) - return NAN; - if (m > 1.0) - return NAN; - if (cephes_isinf(phi) || cephes_isinf(m)) - { - if (cephes_isinf(m) && cephes_isfinite(phi)) - return 0.0; - else if (cephes_isinf(phi) && cephes_isfinite(m)) - return phi; - else - return NAN; - } - if (m == 0.0) - return (phi); - a = 1.0 - m; - if (a == 0.0) { - if (fabs(phi) >= (double)M_PI_2) { - sf_error("ellik", SF_ERROR_SINGULAR, NULL); - return (INFINITY); - } - /* DLMF 19.6.8, and 4.23.42 */ - return asinh(tan(phi)); - } - npio2 = floor(phi / M_PI_2); - if (fmod(fabs(npio2), 2.0) == 1.0) - npio2 += 1; - if (npio2 != 0.0) { - K = ellpk(a); - phi = phi - npio2 * M_PI_2; - } - else - K = 0.0; - if (phi < 0.0) { - phi = -phi; - sign = -1; - } - else - sign = 0; - if (a > 1.0) { - temp = ellik_neg_m(phi, m); - goto done; - } - b = sqrt(a); - t = tan(phi); - if (fabs(t) > 10.0) { - /* Transform the amplitude */ - e = 1.0 / (b * t); - /* ... but avoid multiple recursions. */ - if (fabs(e) < 10.0) { - e = atan(e); - if (npio2 == 0) - K = ellpk(a); - temp = K - ellik(e, m); - goto done; - } - } - a = 1.0; - c = sqrt(m); - d = 1; - mod = 0; - - while (fabs(c / a) > MACHEP) { - temp = b / a; - phi = phi + atan(t * temp) + mod * M_PI; - denom = 1.0 - temp * t * t; - if (fabs(denom) > 10*MACHEP) { - t = t * (1.0 + temp) / denom; - mod = (phi + M_PI_2) / M_PI; - } - else { - t = tan(phi); - mod = (int)floor((phi - atan(t))/M_PI); - } - c = (a - b) / 2.0; - temp = sqrt(a * b); - a = (a + b) / 2.0; - b = temp; - d += d; - } - - temp = (atan(t) + mod * M_PI) / (d * a); - - done: - if (sign < 0) - temp = -temp; - temp += npio2 * K; - return (temp); -} - -/* N.B. This will evaluate its arguments multiple times. */ -#define MAX3(a, b, c) (a > b ? (a > c ? a : c) : (b > c ? b : c)) - -/* To calculate legendre's incomplete elliptical integral of the first kind for - * negative m, we use a power series in phi for small m*phi*phi, an asymptotic - * series in m for large m*phi*phi* and the relation to Carlson's symmetric - * integral of the first kind. - * - * F(phi, m) = sin(phi) * R_F(cos(phi)^2, 1 - m * sin(phi)^2, 1.0) - * = R_F(c-1, c-m, c) - * - * where c = csc(phi)^2. We use the second form of this for (approximately) - * phi > 1/(sqrt(DBL_MAX) ~ 1e-154, where csc(phi)^2 overflows. Elsewhere we - * use the first form, accounting for the smallness of phi. - * - * The algorithm used is described in Carlson, B. C. Numerical computation of - * real or complex elliptic integrals. (1994) https://arxiv.org/abs/math/9409227 - * Most variable names reflect Carlson's usage. - * - * In this routine, we assume m < 0 and 0 > phi > pi/2. - */ -double ellik_neg_m(double phi, double m) -{ - double x, y, z, x1, y1, z1, A0, A, Q, X, Y, Z, E2, E3, scale; - int n = 0; - double mpp = (m*phi)*phi; - - if (-mpp < 1e-6 && phi < -m) { - return phi + (-mpp*phi*phi/30.0 + 3.0*mpp*mpp/40.0 + mpp/6.0)*phi; - } - - if (-mpp > 4e7) { - double sm = sqrt(-m); - double sp = sin(phi); - double cp = cos(phi); - - double a = log(4*sp*sm/(1+cp)); - double b = -(1 + cp/sp/sp - a) / 4 / m; - return (a + b) / sm; - } - - if (phi > 1e-153 && m > -1e305) { - double s = sin(phi); - double csc2 = 1.0 / (s*s); - scale = 1.0; - x = 1.0 / (tan(phi) * tan(phi)); - y = csc2 - m; - z = csc2; - } - else { - scale = phi; - x = 1.0; - y = 1 - m*scale*scale; - z = 1.0; - } - - if (x == y && x == z) { - return scale / sqrt(x); - } - - A0 = (x + y + z) / 3.0; - A = A0; - x1 = x; y1 = y; z1 = z; - /* Carlson gives 1/pow(3*r, 1.0/6.0) for this constant. if r == eps, - * it is ~338.38. */ - Q = 400.0 * MAX3(fabs(A0-x), fabs(A0-y), fabs(A0-z)); - - while (Q > fabs(A) && n <= 100) { - double sx = sqrt(x1); - double sy = sqrt(y1); - double sz = sqrt(z1); - double lam = sx*sy + sx*sz + sy*sz; - x1 = (x1 + lam) / 4.0; - y1 = (y1 + lam) / 4.0; - z1 = (z1 + lam) / 4.0; - A = (x1 + y1 + z1) / 3.0; - n += 1; - Q /= 4; - } - X = (A0 - x) / A / (1 << 2*n); - Y = (A0 - y) / A / (1 << 2*n); - Z = -(X + Y); - - E2 = X*Y - Z*Z; - E3 = X*Y*Z; - - return scale * (1.0 - E2/10.0 + E3/14.0 + E2*E2/24.0 - - 3.0*E2*E3/44.0) / sqrt(A); -} diff --git a/gtsam/3rdparty/cephes/cephes/ellpe.c b/gtsam/3rdparty/cephes/cephes/ellpe.c deleted file mode 100644 index 1ef8e0c128..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ellpe.c +++ /dev/null @@ -1,108 +0,0 @@ -/* ellpe.c - * - * Complete elliptic integral of the second kind - * - * - * - * SYNOPSIS: - * - * double m, y, ellpe(); - * - * y = ellpe( m ); - * - * - * - * DESCRIPTION: - * - * Approximates the integral - * - * - * pi/2 - * - - * | | 2 - * E(m) = | sqrt( 1 - m sin t ) dt - * | | - * - - * 0 - * - * Where m = 1 - m1, using the approximation - * - * P(x) - x log x Q(x). - * - * Though there are no singularities, the argument m1 is used - * internally rather than m for compatibility with ellpk(). - * - * E(1) = 1; E(0) = pi/2. - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 1 10000 2.1e-16 7.3e-17 - * - * - * ERROR MESSAGES: - * - * message condition value returned - * ellpe domain x<0, x>1 0.0 - * - */ - -/* ellpe.c */ - -/* Elliptic integral of second kind */ - -/* - * Cephes Math Library, Release 2.1: February, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - * - * Feb, 2002: altered by Travis Oliphant - * so that it is called with argument m - * (which gets immediately converted to m1 = 1-m) - */ - -#include "mconf.h" - -static double P[] = { - 1.53552577301013293365E-4, - 2.50888492163602060990E-3, - 8.68786816565889628429E-3, - 1.07350949056076193403E-2, - 7.77395492516787092951E-3, - 7.58395289413514708519E-3, - 1.15688436810574127319E-2, - 2.18317996015557253103E-2, - 5.68051945617860553470E-2, - 4.43147180560990850618E-1, - 1.00000000000000000299E0 -}; - -static double Q[] = { - 3.27954898576485872656E-5, - 1.00962792679356715133E-3, - 6.50609489976927491433E-3, - 1.68862163993311317300E-2, - 2.61769742454493659583E-2, - 3.34833904888224918614E-2, - 4.27180926518931511717E-2, - 5.85936634471101055642E-2, - 9.37499997197644278445E-2, - 2.49999999999888314361E-1 -}; - -double ellpe(double x) -{ - x = 1.0 - x; - if (x <= 0.0) { - if (x == 0.0) - return (1.0); - sf_error("ellpe", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (x > 1.0) { - return ellpe(1.0 - 1/x) * sqrt(x); - } - return (polevl(x, P, 10) - log(x) * (x * polevl(x, Q, 9))); -} diff --git a/gtsam/3rdparty/cephes/cephes/ellpj.c b/gtsam/3rdparty/cephes/cephes/ellpj.c deleted file mode 100644 index 6891a8244c..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ellpj.c +++ /dev/null @@ -1,154 +0,0 @@ -/* ellpj.c - * - * Jacobian Elliptic Functions - * - * - * - * SYNOPSIS: - * - * double u, m, sn, cn, dn, phi; - * int ellpj(); - * - * ellpj( u, m, _&sn, _&cn, _&dn, _&phi ); - * - * - * - * DESCRIPTION: - * - * - * Evaluates the Jacobian elliptic functions sn(u|m), cn(u|m), - * and dn(u|m) of parameter m between 0 and 1, and real - * argument u. - * - * These functions are periodic, with quarter-period on the - * real axis equal to the complete elliptic integral - * ellpk(m). - * - * Relation to incomplete elliptic integral: - * If u = ellik(phi,m), then sn(u|m) = sin(phi), - * and cn(u|m) = cos(phi). Phi is called the amplitude of u. - * - * Computation is by means of the arithmetic-geometric mean - * algorithm, except when m is within 1e-9 of 0 or 1. In the - * latter case with m close to 1, the approximation applies - * only for phi < pi/2. - * - * ACCURACY: - * - * Tested at random points with u between 0 and 10, m between - * 0 and 1. - * - * Absolute error (* = relative error): - * arithmetic function # trials peak rms - * IEEE phi 10000 9.2e-16* 1.4e-16* - * IEEE sn 50000 4.1e-15 4.6e-16 - * IEEE cn 40000 3.6e-15 4.4e-16 - * IEEE dn 10000 1.3e-12 1.8e-14 - * - * Peak error observed in consistency check using addition - * theorem for sn(u+v) was 4e-16 (absolute). Also tested by - * the above relation to the incomplete elliptic integral. - * Accuracy deteriorates when u is large. - * - */ - -/* ellpj.c */ - - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -/* Scipy changes: - * - 07-18-2016: improve evaluation of dn near quarter periods - */ - -#include "mconf.h" -extern double MACHEP; - -int ellpj(double u, double m, double *sn, double *cn, double *dn, double *ph) -{ - double ai, b, phi, t, twon, dnfac; - double a[9], c[9]; - int i; - - /* Check for special cases */ - if (m < 0.0 || m > 1.0 || cephes_isnan(m)) { - sf_error("ellpj", SF_ERROR_DOMAIN, NULL); - *sn = NAN; - *cn = NAN; - *ph = NAN; - *dn = NAN; - return (-1); - } - if (m < 1.0e-9) { - t = sin(u); - b = cos(u); - ai = 0.25 * m * (u - t * b); - *sn = t - ai * b; - *cn = b + ai * t; - *ph = u - ai; - *dn = 1.0 - 0.5 * m * t * t; - return (0); - } - if (m >= 0.9999999999) { - ai = 0.25 * (1.0 - m); - b = cosh(u); - t = tanh(u); - phi = 1.0 / b; - twon = b * sinh(u); - *sn = t + ai * (twon - u) / (b * b); - *ph = 2.0 * atan(exp(u)) - M_PI_2 + ai * (twon - u) / b; - ai *= t * phi; - *cn = phi - ai * (twon - u); - *dn = phi + ai * (twon + u); - return (0); - } - - /* A. G. M. scale. See DLMF 22.20(ii) */ - a[0] = 1.0; - b = sqrt(1.0 - m); - c[0] = sqrt(m); - twon = 1.0; - i = 0; - - while (fabs(c[i] / a[i]) > MACHEP) { - if (i > 7) { - sf_error("ellpj", SF_ERROR_OVERFLOW, NULL); - goto done; - } - ai = a[i]; - ++i; - c[i] = (ai - b) / 2.0; - t = sqrt(ai * b); - a[i] = (ai + b) / 2.0; - b = t; - twon *= 2.0; - } - - done: - /* backward recurrence */ - phi = twon * a[i] * u; - do { - t = c[i] * sin(phi) / a[i]; - b = phi; - phi = (asin(t) + phi) / 2.0; - } - while (--i); - - *sn = sin(phi); - t = cos(phi); - *cn = t; - dnfac = cos(phi - b); - /* See discussion after DLMF 22.20.5 */ - if (fabs(dnfac) < 0.1) { - *dn = sqrt(1 - m*(*sn)*(*sn)); - } - else { - *dn = t / dnfac; - } - *ph = phi; - return (0); -} diff --git a/gtsam/3rdparty/cephes/cephes/ellpk.c b/gtsam/3rdparty/cephes/cephes/ellpk.c deleted file mode 100644 index 3842a7403a..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ellpk.c +++ /dev/null @@ -1,124 +0,0 @@ -/* ellpk.c - * - * Complete elliptic integral of the first kind - * - * - * - * SYNOPSIS: - * - * double m1, y, ellpk(); - * - * y = ellpk( m1 ); - * - * - * - * DESCRIPTION: - * - * Approximates the integral - * - * - * - * pi/2 - * - - * | | - * | dt - * K(m) = | ------------------ - * | 2 - * | | sqrt( 1 - m sin t ) - * - - * 0 - * - * where m = 1 - m1, using the approximation - * - * P(x) - log x Q(x). - * - * The argument m1 is used internally rather than m so that the logarithmic - * singularity at m = 1 will be shifted to the origin; this - * preserves maximum accuracy. - * - * K(0) = pi/2. - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,1 30000 2.5e-16 6.8e-17 - * - * ERROR MESSAGES: - * - * message condition value returned - * ellpk domain x<0, x>1 0.0 - * - */ - -/* ellpk.c */ - - -/* - * Cephes Math Library, Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -static double P[] = { - 1.37982864606273237150E-4, - 2.28025724005875567385E-3, - 7.97404013220415179367E-3, - 9.85821379021226008714E-3, - 6.87489687449949877925E-3, - 6.18901033637687613229E-3, - 8.79078273952743772254E-3, - 1.49380448916805252718E-2, - 3.08851465246711995998E-2, - 9.65735902811690126535E-2, - 1.38629436111989062502E0 -}; - -static double Q[] = { - 2.94078955048598507511E-5, - 9.14184723865917226571E-4, - 5.94058303753167793257E-3, - 1.54850516649762399335E-2, - 2.39089602715924892727E-2, - 3.01204715227604046988E-2, - 3.73774314173823228969E-2, - 4.88280347570998239232E-2, - 7.03124996963957469739E-2, - 1.24999999999870820058E-1, - 4.99999999999999999821E-1 -}; - -static double C1 = 1.3862943611198906188E0; /* log(4) */ - -extern double MACHEP; - -double ellpk(double x) -{ - - if (x < 0.0) { - sf_error("ellpk", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - if (x > 1.0) { - if (cephes_isinf(x)) { - return 0.0; - } - return ellpk(1/x)/sqrt(x); - } - - if (x > MACHEP) { - return (polevl(x, P, 10) - log(x) * polevl(x, Q, 10)); - } - else { - if (x == 0.0) { - sf_error("ellpk", SF_ERROR_SINGULAR, NULL); - return (INFINITY); - } - else { - return (C1 - 0.5 * log(x)); - } - } -} diff --git a/gtsam/3rdparty/cephes/cephes/erfinv.c b/gtsam/3rdparty/cephes/cephes/erfinv.c deleted file mode 100644 index f7f49284c1..0000000000 --- a/gtsam/3rdparty/cephes/cephes/erfinv.c +++ /dev/null @@ -1,78 +0,0 @@ -/* - * mconf configures NANS, INFINITYs etc. for cephes and includes some standard - * headers. Although erfinv and erfcinv are not defined in cephes, erf and erfc - * are. We want to keep the behaviour consistent for the inverse functions and - * so need to include mconf. - */ -#include "mconf.h" - -/* - * Inverse of the error function. - * - * Computes the inverse of the error function on the restricted domain - * -1 < y < 1. This restriction ensures the existence of a unique result - * such that erf(erfinv(y)) = y. - */ -double erfinv(double y) { - const double domain_lb = -1; - const double domain_ub = 1; - - const double thresh = 1e-7; - - /* - * For small arguments, use the Taylor expansion - * erf(y) = 2/\sqrt{\pi} (y - y^3 / 3 + O(y^5)), y\to 0 - * where we only retain the linear term. - * Otherwise, y + 1 loses precision for |y| << 1. - */ - if ((-thresh < y) && (y < thresh)){ - return y / M_2_SQRTPI; - } - if ((domain_lb < y) && (y < domain_ub)) { - return ndtri(0.5 * (y+1)) * M_SQRT1_2; - } - else if (y == domain_lb) { - return -INFINITY; - } - else if (y == domain_ub) { - return INFINITY; - } - else if (cephes_isnan(y)) { - sf_error("erfinv", SF_ERROR_DOMAIN, NULL); - return y; - } - else { - sf_error("erfinv", SF_ERROR_DOMAIN, NULL); - return NAN; - } -} - -/* - * Inverse of the complementary error function. - * - * Computes the inverse of the complimentary error function on the restricted - * domain 0 < y < 2. This restriction ensures the existence of a unique result - * such that erfc(erfcinv(y)) = y. - */ -double erfcinv(double y) { - const double domain_lb = 0; - const double domain_ub = 2; - - if ((domain_lb < y) && (y < domain_ub)) { - return -ndtri(0.5 * y) * M_SQRT1_2; - } - else if (y == domain_lb) { - return INFINITY; - } - else if (y == domain_ub) { - return -INFINITY; - } - else if (cephes_isnan(y)) { - sf_error("erfcinv", SF_ERROR_DOMAIN, NULL); - return y; - } - else { - sf_error("erfcinv", SF_ERROR_DOMAIN, NULL); - return NAN; - } -} diff --git a/gtsam/3rdparty/cephes/cephes/exp10.c b/gtsam/3rdparty/cephes/cephes/exp10.c deleted file mode 100644 index 0a71d3c52f..0000000000 --- a/gtsam/3rdparty/cephes/cephes/exp10.c +++ /dev/null @@ -1,115 +0,0 @@ -/* exp10.c - * - * Base 10 exponential function - * (Common antilogarithm) - * - * - * - * SYNOPSIS: - * - * double x, y, exp10(); - * - * y = exp10( x ); - * - * - * - * DESCRIPTION: - * - * Returns 10 raised to the x power. - * - * Range reduction is accomplished by expressing the argument - * as 10**x = 2**n 10**f, with |f| < 0.5 log10(2). - * The Pade' form - * - * 1 + 2x P(x**2)/( Q(x**2) - P(x**2) ) - * - * is used to approximate 10**f. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -307,+307 30000 2.2e-16 5.5e-17 - * - * ERROR MESSAGES: - * - * message condition value returned - * exp10 underflow x < -MAXL10 0.0 - * exp10 overflow x > MAXL10 INFINITY - * - * IEEE arithmetic: MAXL10 = 308.2547155599167. - * - */ - -/* - * Cephes Math Library Release 2.2: January, 1991 - * Copyright 1984, 1991 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - - -#include "mconf.h" - -static double P[] = { - 4.09962519798587023075E-2, - 1.17452732554344059015E1, - 4.06717289936872725516E2, - 2.39423741207388267439E3, -}; - -static double Q[] = { - /* 1.00000000000000000000E0, */ - 8.50936160849306532625E1, - 1.27209271178345121210E3, - 2.07960819286001865907E3, -}; - -/* static double LOG102 = 3.01029995663981195214e-1; */ -static double LOG210 = 3.32192809488736234787e0; -static double LG102A = 3.01025390625000000000E-1; -static double LG102B = 4.60503898119521373889E-6; - -/* static double MAXL10 = 38.230809449325611792; */ -static double MAXL10 = 308.2547155599167; - -double exp10(double x) -{ - double px, xx; - short n; - - if (cephes_isnan(x)) - return (x); - if (x > MAXL10) { - return (INFINITY); - } - - if (x < -MAXL10) { /* Would like to use MINLOG but can't */ - sf_error("exp10", SF_ERROR_UNDERFLOW, NULL); - return (0.0); - } - - /* Express 10**x = 10**g 2**n - * = 10**g 10**( n log10(2) ) - * = 10**( g + n log10(2) ) - */ - px = floor(LOG210 * x + 0.5); - n = px; - x -= px * LG102A; - x -= px * LG102B; - - /* rational approximation for exponential - * of the fractional part: - * 10**x = 1 + 2x P(x**2)/( Q(x**2) - P(x**2) ) - */ - xx = x * x; - px = x * polevl(xx, P, 3); - x = px / (p1evl(xx, Q, 3) - px); - x = 1.0 + ldexp(x, 1); - - /* multiply by power of 2 */ - x = ldexp(x, n); - - return (x); -} diff --git a/gtsam/3rdparty/cephes/cephes/exp2.c b/gtsam/3rdparty/cephes/cephes/exp2.c deleted file mode 100644 index 14911f59c0..0000000000 --- a/gtsam/3rdparty/cephes/cephes/exp2.c +++ /dev/null @@ -1,108 +0,0 @@ -/* exp2.c - * - * Base 2 exponential function - * - * - * - * SYNOPSIS: - * - * double x, y, exp2(); - * - * y = exp2( x ); - * - * - * - * DESCRIPTION: - * - * Returns 2 raised to the x power. - * - * Range reduction is accomplished by separating the argument - * into an integer k and fraction f such that - * x k f - * 2 = 2 2. - * - * A Pade' form - * - * 1 + 2x P(x**2) / (Q(x**2) - x P(x**2) ) - * - * approximates 2**x in the basic range [-0.5, 0.5]. - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -1022,+1024 30000 1.8e-16 5.4e-17 - * - * - * See exp.c for comments on error amplification. - * - * - * ERROR MESSAGES: - * - * message condition value returned - * exp underflow x < -MAXL2 0.0 - * exp overflow x > MAXL2 INFINITY - * - * For IEEE arithmetic, MAXL2 = 1024. - */ - - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1995 by Stephen L. Moshier - */ - - - -#include "mconf.h" - -static double P[] = { - 2.30933477057345225087E-2, - 2.02020656693165307700E1, - 1.51390680115615096133E3, -}; - -static double Q[] = { - /* 1.00000000000000000000E0, */ - 2.33184211722314911771E2, - 4.36821166879210612817E3, -}; - -#define MAXL2 1024.0 -#define MINL2 -1024.0 - -double exp2(double x) -{ - double px, xx; - short n; - - if (cephes_isnan(x)) - return (x); - if (x > MAXL2) { - return (INFINITY); - } - - if (x < MINL2) { - return (0.0); - } - - xx = x; /* save x */ - /* separate into integer and fractional parts */ - px = floor(x + 0.5); - n = px; - x = x - px; - - /* rational approximation - * exp2(x) = 1 + 2xP(xx)/(Q(xx) - P(xx)) - * where xx = x**2 - */ - xx = x * x; - px = x * polevl(xx, P, 2); - x = px / (p1evl(xx, Q, 2) - px); - x = 1.0 + ldexp(x, 1); - - /* scale by power of 2 */ - x = ldexp(x, n); - return (x); -} diff --git a/gtsam/3rdparty/cephes/cephes/expn.c b/gtsam/3rdparty/cephes/cephes/expn.c deleted file mode 100644 index 2a6ee14c09..0000000000 --- a/gtsam/3rdparty/cephes/cephes/expn.c +++ /dev/null @@ -1,224 +0,0 @@ -/* expn.c - * - * Exponential integral En - * - * - * - * SYNOPSIS: - * - * int n; - * double x, y, expn(); - * - * y = expn( n, x ); - * - * - * - * DESCRIPTION: - * - * Evaluates the exponential integral - * - * inf. - * - - * | | -xt - * | e - * E (x) = | ---- dt. - * n | n - * | | t - * - - * 1 - * - * - * Both n and x must be nonnegative. - * - * The routine employs either a power series, a continued - * fraction, or an asymptotic formula depending on the - * relative values of n and x. - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 10000 1.7e-15 3.6e-16 - * - */ - -/* expn.c */ - -/* Cephes Math Library Release 1.1: March, 1985 - * Copyright 1985 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 */ - -/* Sources - * [1] NIST, "The Digital Library of Mathematical Functions", dlmf.nist.gov - */ - -/* Scipy changes: - * - 09-10-2016: improved asymptotic expansion for large n - */ - -#include "mconf.h" -#include "polevl.h" -#include "expn.h" - -#define EUL 0.57721566490153286060 -#define BIG 1.44115188075855872E+17 -extern double MACHEP, MAXLOG; - -static double expn_large_n(int, double); - - -double expn(int n, double x) -{ - double ans, r, t, yk, xk; - double pk, pkm1, pkm2, qk, qkm1, qkm2; - double psi, z; - int i, k; - static double big = BIG; - - if (isnan(x)) { - return NAN; - } - else if (n < 0 || x < 0) { - sf_error("expn", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (x > MAXLOG) { - return (0.0); - } - - if (x == 0.0) { - if (n < 2) { - sf_error("expn", SF_ERROR_SINGULAR, NULL); - return (INFINITY); - } - else { - return (1.0 / (n - 1.0)); - } - } - - if (n == 0) { - return (exp(-x) / x); - } - - /* Asymptotic expansion for large n, DLMF 8.20(ii) */ - if (n > 50) { - ans = expn_large_n(n, x); - goto done; - } - - if (x > 1.0) { - goto cfrac; - } - - /* Power series expansion, DLMF 8.19.8 */ - psi = -EUL - log(x); - for (i = 1; i < n; i++) { - psi = psi + 1.0 / i; - } - - z = -x; - xk = 0.0; - yk = 1.0; - pk = 1.0 - n; - if (n == 1) { - ans = 0.0; - } else { - ans = 1.0 / pk; - } - do { - xk += 1.0; - yk *= z / xk; - pk += 1.0; - if (pk != 0.0) { - ans += yk / pk; - } - if (ans != 0.0) - t = fabs(yk / ans); - else - t = 1.0; - } while (t > MACHEP); - k = xk; - t = n; - r = n - 1; - ans = (pow(z, r) * psi / Gamma(t)) - ans; - goto done; - - /* Continued fraction, DLMF 8.19.17 */ - cfrac: - k = 1; - pkm2 = 1.0; - qkm2 = x; - pkm1 = 1.0; - qkm1 = x + n; - ans = pkm1 / qkm1; - - do { - k += 1; - if (k & 1) { - yk = 1.0; - xk = n + (k - 1) / 2; - } else { - yk = x; - xk = k / 2; - } - pk = pkm1 * yk + pkm2 * xk; - qk = qkm1 * yk + qkm2 * xk; - if (qk != 0) { - r = pk / qk; - t = fabs((ans - r) / r); - ans = r; - } else { - t = 1.0; - } - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - if (fabs(pk) > big) { - pkm2 /= big; - pkm1 /= big; - qkm2 /= big; - qkm1 /= big; - } - } while (t > MACHEP); - - ans *= exp(-x); - - done: - return (ans); -} - - -/* Asymptotic expansion for large n, DLMF 8.20(ii) */ -static double expn_large_n(int n, double x) -{ - int k; - double p = n; - double lambda = x/p; - double multiplier = 1/p/(lambda + 1)/(lambda + 1); - double fac = 1; - double res = 1; /* A[0] = 1 */ - double expfac, term; - - expfac = exp(-lambda*p)/(lambda + 1)/p; - if (expfac == 0) { - sf_error("expn", SF_ERROR_UNDERFLOW, NULL); - return 0; - } - - /* Do the k = 1 term outside the loop since A[1] = 1 */ - fac *= multiplier; - res += fac; - - for (k = 2; k < nA; k++) { - fac *= multiplier; - term = fac*polevl(lambda, A[k], Adegs[k]); - res += term; - if (fabs(term) < MACHEP*fabs(res)) { - break; - } - } - - return expfac*res; -} diff --git a/gtsam/3rdparty/cephes/cephes/expn.h b/gtsam/3rdparty/cephes/cephes/expn.h deleted file mode 100644 index 8ced026877..0000000000 --- a/gtsam/3rdparty/cephes/cephes/expn.h +++ /dev/null @@ -1,19 +0,0 @@ -/* This file was automatically generated by _precompute/expn_asy.py. - * Do not edit it manually! - */ -#define nA 13 -static const double A0[] = {1.00000000000000000}; -static const double A1[] = {1.00000000000000000}; -static const double A2[] = {-2.00000000000000000, 1.00000000000000000}; -static const double A3[] = {6.00000000000000000, -8.00000000000000000, 1.00000000000000000}; -static const double A4[] = {-24.0000000000000000, 58.0000000000000000, -22.0000000000000000, 1.00000000000000000}; -static const double A5[] = {120.000000000000000, -444.000000000000000, 328.000000000000000, -52.0000000000000000, 1.00000000000000000}; -static const double A6[] = {-720.000000000000000, 3708.00000000000000, -4400.00000000000000, 1452.00000000000000, -114.000000000000000, 1.00000000000000000}; -static const double A7[] = {5040.00000000000000, -33984.0000000000000, 58140.0000000000000, -32120.0000000000000, 5610.00000000000000, -240.000000000000000, 1.00000000000000000}; -static const double A8[] = {-40320.0000000000000, 341136.000000000000, -785304.000000000000, 644020.000000000000, -195800.000000000000, 19950.0000000000000, -494.000000000000000, 1.00000000000000000}; -static const double A9[] = {362880.000000000000, -3733920.00000000000, 11026296.0000000000, -12440064.0000000000, 5765500.00000000000, -1062500.00000000000, 67260.0000000000000, -1004.00000000000000, 1.00000000000000000}; -static const double A10[] = {-3628800.00000000000, 44339040.0000000000, -162186912.000000000, 238904904.000000000, -155357384.000000000, 44765000.0000000000, -5326160.00000000000, 218848.000000000000, -2026.00000000000000, 1.00000000000000000}; -static const double A11[] = {39916800.0000000000, -568356480.000000000, 2507481216.00000000, -4642163952.00000000, 4002695088.00000000, -1648384304.00000000, 314369720.000000000, -25243904.0000000000, 695038.000000000000, -4072.00000000000000, 1.00000000000000000}; -static const double A12[] = {-479001600.000000000, 7827719040.00000000, -40788301824.0000000, 92199790224.0000000, -101180433024.000000, 56041398784.0000000, -15548960784.0000000, 2051482776.00000000, -114876376.000000000, 2170626.00000000000, -8166.00000000000000, 1.00000000000000000}; -static const double *A[] = {A0, A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12}; -static const int Adegs[] = {0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; diff --git a/gtsam/3rdparty/cephes/cephes/fdtr.c b/gtsam/3rdparty/cephes/cephes/fdtr.c deleted file mode 100644 index 9c119ed8f7..0000000000 --- a/gtsam/3rdparty/cephes/cephes/fdtr.c +++ /dev/null @@ -1,216 +0,0 @@ -/* fdtr.c - * - * F distribution - * - * - * - * SYNOPSIS: - * - * double df1, df2; - * double x, y, fdtr(); - * - * y = fdtr( df1, df2, x ); - * - * DESCRIPTION: - * - * Returns the area from zero to x under the F density - * function (also known as Snedcor's density or the - * variance ratio density). This is the density - * of x = (u1/df1)/(u2/df2), where u1 and u2 are random - * variables having Chi square distributions with df1 - * and df2 degrees of freedom, respectively. - * - * The incomplete beta integral is used, according to the - * formula - * - * P(x) = incbet( df1/2, df2/2, (df1*x/(df2 + df1*x) ). - * - * - * The arguments a and b are greater than zero, and x is - * nonnegative. - * - * ACCURACY: - * - * Tested at random points (a,b,x). - * - * x a,b Relative error: - * arithmetic domain domain # trials peak rms - * IEEE 0,1 0,100 100000 9.8e-15 1.7e-15 - * IEEE 1,5 0,100 100000 6.5e-15 3.5e-16 - * IEEE 0,1 1,10000 100000 2.2e-11 3.3e-12 - * IEEE 1,5 1,10000 100000 1.1e-11 1.7e-13 - * See also incbet.c. - * - * - * ERROR MESSAGES: - * - * message condition value returned - * fdtr domain a<0, b<0, x<0 0.0 - * - */ - -/* fdtrc() - * - * Complemented F distribution - * - * - * - * SYNOPSIS: - * - * double df1, df2; - * double x, y, fdtrc(); - * - * y = fdtrc( df1, df2, x ); - * - * DESCRIPTION: - * - * Returns the area from x to infinity under the F density - * function (also known as Snedcor's density or the - * variance ratio density). - * - * - * inf. - * - - * 1 | | a-1 b-1 - * 1-P(x) = ------ | t (1-t) dt - * B(a,b) | | - * - - * x - * - * - * The incomplete beta integral is used, according to the - * formula - * - * P(x) = incbet( df2/2, df1/2, (df2/(df2 + df1*x) ). - * - * - * ACCURACY: - * - * Tested at random points (a,b,x) in the indicated intervals. - * x a,b Relative error: - * arithmetic domain domain # trials peak rms - * IEEE 0,1 1,100 100000 3.7e-14 5.9e-16 - * IEEE 1,5 1,100 100000 8.0e-15 1.6e-15 - * IEEE 0,1 1,10000 100000 1.8e-11 3.5e-13 - * IEEE 1,5 1,10000 100000 2.0e-11 3.0e-12 - * See also incbet.c. - * - * ERROR MESSAGES: - * - * message condition value returned - * fdtrc domain a<0, b<0, x<0 0.0 - * - */ - -/* fdtri() - * - * Inverse of F distribution - * - * - * - * SYNOPSIS: - * - * double df1, df2; - * double x, p, fdtri(); - * - * x = fdtri( df1, df2, p ); - * - * DESCRIPTION: - * - * Finds the F density argument x such that the integral - * from -infinity to x of the F density is equal to the - * given probability p. - * - * This is accomplished using the inverse beta integral - * function and the relations - * - * z = incbi( df2/2, df1/2, p ) - * x = df2 (1-z) / (df1 z). - * - * Note: the following relations hold for the inverse of - * the uncomplemented F distribution: - * - * z = incbi( df1/2, df2/2, p ) - * x = df2 z / (df1 (1-z)). - * - * ACCURACY: - * - * Tested at random points (a,b,p). - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * For p between .001 and 1: - * IEEE 1,100 100000 8.3e-15 4.7e-16 - * IEEE 1,10000 100000 2.1e-11 1.4e-13 - * For p between 10^-6 and 10^-3: - * IEEE 1,100 50000 1.3e-12 8.4e-15 - * IEEE 1,10000 50000 3.0e-12 4.8e-14 - * See also fdtrc.c. - * - * ERROR MESSAGES: - * - * message condition value returned - * fdtri domain p <= 0 or p > 1 NaN - * v < 1 - * - */ - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - - -#include "mconf.h" - - -double fdtrc(double a, double b, double x) -{ - double w; - - if ((a <= 0.0) || (b <= 0.0) || (x < 0.0)) { - sf_error("fdtrc", SF_ERROR_DOMAIN, NULL); - return NAN; - } - w = b / (b + a * x); - return incbet(0.5 * b, 0.5 * a, w); -} - - -double fdtr(double a, double b, double x) -{ - double w; - - if ((a <= 0.0) || (b <= 0.0) || (x < 0.0)) { - sf_error("fdtr", SF_ERROR_DOMAIN, NULL); - return NAN; - } - w = a * x; - w = w / (b + w); - return incbet(0.5 * a, 0.5 * b, w); -} - - -double fdtri(double a, double b, double y) -{ - double w, x; - - if ((a <= 0.0) || (b <= 0.0) || (y <= 0.0) || (y > 1.0)) { - sf_error("fdtri", SF_ERROR_DOMAIN, NULL); - return NAN; - } - y = 1.0 - y; - /* Compute probability for x = 0.5. */ - w = incbet(0.5 * b, 0.5 * a, 0.5); - /* If that is greater than y, then the solution w < .5. - * Otherwise, solve at 1-y to remove cancellation in (b - b*w). */ - if (w > y || y < 0.001) { - w = incbi(0.5 * b, 0.5 * a, y); - x = (b - b * w) / (a * w); - } - else { - w = incbi(0.5 * a, 0.5 * b, 1.0 - y); - x = b * w / (a * (1.0 - w)); - } - return x; -} diff --git a/gtsam/3rdparty/cephes/cephes/fresnl.c b/gtsam/3rdparty/cephes/cephes/fresnl.c deleted file mode 100644 index 50620fa2e1..0000000000 --- a/gtsam/3rdparty/cephes/cephes/fresnl.c +++ /dev/null @@ -1,219 +0,0 @@ -/* fresnl.c - * - * Fresnel integral - * - * - * - * SYNOPSIS: - * - * double x, S, C; - * void fresnl(); - * - * fresnl( x, _&S, _&C ); - * - * - * DESCRIPTION: - * - * Evaluates the Fresnel integrals - * - * x - * - - * | | - * C(x) = | cos(pi/2 t**2) dt, - * | | - * - - * 0 - * - * x - * - - * | | - * S(x) = | sin(pi/2 t**2) dt. - * | | - * - - * 0 - * - * - * The integrals are evaluated by a power series for x < 1. - * For x >= 1 auxiliary functions f(x) and g(x) are employed - * such that - * - * C(x) = 0.5 + f(x) sin( pi/2 x**2 ) - g(x) cos( pi/2 x**2 ) - * S(x) = 0.5 - f(x) cos( pi/2 x**2 ) - g(x) sin( pi/2 x**2 ) - * - * - * - * ACCURACY: - * - * Relative error. - * - * Arithmetic function domain # trials peak rms - * IEEE S(x) 0, 10 10000 2.0e-15 3.2e-16 - * IEEE C(x) 0, 10 10000 1.8e-15 3.3e-16 - */ - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -/* S(x) for small x */ -static double sn[6] = { - -2.99181919401019853726E3, - 7.08840045257738576863E5, - -6.29741486205862506537E7, - 2.54890880573376359104E9, - -4.42979518059697779103E10, - 3.18016297876567817986E11, -}; - -static double sd[6] = { - /* 1.00000000000000000000E0, */ - 2.81376268889994315696E2, - 4.55847810806532581675E4, - 5.17343888770096400730E6, - 4.19320245898111231129E8, - 2.24411795645340920940E10, - 6.07366389490084639049E11, -}; - -/* C(x) for small x */ -static double cn[6] = { - -4.98843114573573548651E-8, - 9.50428062829859605134E-6, - -6.45191435683965050962E-4, - 1.88843319396703850064E-2, - -2.05525900955013891793E-1, - 9.99999999999999998822E-1, -}; - -static double cd[7] = { - 3.99982968972495980367E-12, - 9.15439215774657478799E-10, - 1.25001862479598821474E-7, - 1.22262789024179030997E-5, - 8.68029542941784300606E-4, - 4.12142090722199792936E-2, - 1.00000000000000000118E0, -}; - -/* Auxiliary function f(x) */ -static double fn[10] = { - 4.21543555043677546506E-1, - 1.43407919780758885261E-1, - 1.15220955073585758835E-2, - 3.45017939782574027900E-4, - 4.63613749287867322088E-6, - 3.05568983790257605827E-8, - 1.02304514164907233465E-10, - 1.72010743268161828879E-13, - 1.34283276233062758925E-16, - 3.76329711269987889006E-20, -}; - -static double fd[10] = { - /* 1.00000000000000000000E0, */ - 7.51586398353378947175E-1, - 1.16888925859191382142E-1, - 6.44051526508858611005E-3, - 1.55934409164153020873E-4, - 1.84627567348930545870E-6, - 1.12699224763999035261E-8, - 3.60140029589371370404E-11, - 5.88754533621578410010E-14, - 4.52001434074129701496E-17, - 1.25443237090011264384E-20, -}; - -/* Auxiliary function g(x) */ -static double gn[11] = { - 5.04442073643383265887E-1, - 1.97102833525523411709E-1, - 1.87648584092575249293E-2, - 6.84079380915393090172E-4, - 1.15138826111884280931E-5, - 9.82852443688422223854E-8, - 4.45344415861750144738E-10, - 1.08268041139020870318E-12, - 1.37555460633261799868E-15, - 8.36354435630677421531E-19, - 1.86958710162783235106E-22, -}; - -static double gd[11] = { - /* 1.00000000000000000000E0, */ - 1.47495759925128324529E0, - 3.37748989120019970451E-1, - 2.53603741420338795122E-2, - 8.14679107184306179049E-4, - 1.27545075667729118702E-5, - 1.04314589657571990585E-7, - 4.60680728146520428211E-10, - 1.10273215066240270757E-12, - 1.38796531259578871258E-15, - 8.39158816283118707363E-19, - 1.86958710162783236342E-22, -}; - -extern double MACHEP; - -int fresnl(double xxa, double *ssa, double *cca) -{ - double f, g, cc, ss, c, s, t, u; - double x, x2; - - if (cephes_isinf(xxa)) { - cc = 0.5; - ss = 0.5; - goto done; - } - - x = fabs(xxa); - x2 = x * x; - if (x2 < 2.5625) { - t = x2 * x2; - ss = x * x2 * polevl(t, sn, 5) / p1evl(t, sd, 6); - cc = x * polevl(t, cn, 5) / polevl(t, cd, 6); - goto done; - } - - if (x > 36974.0) { - /* - * http://functions.wolfram.com/GammaBetaErf/FresnelC/06/02/ - * http://functions.wolfram.com/GammaBetaErf/FresnelS/06/02/ - */ - cc = 0.5 + 1/(M_PI*x) * sin(M_PI*x*x/2); - ss = 0.5 - 1/(M_PI*x) * cos(M_PI*x*x/2); - goto done; - } - - - /* Asymptotic power series auxiliary functions - * for large argument - */ - x2 = x * x; - t = M_PI * x2; - u = 1.0 / (t * t); - t = 1.0 / t; - f = 1.0 - u * polevl(u, fn, 9) / p1evl(u, fd, 10); - g = t * polevl(u, gn, 10) / p1evl(u, gd, 11); - - t = M_PI_2 * x2; - c = cos(t); - s = sin(t); - t = M_PI * x; - cc = 0.5 + (f * s - g * c) / t; - ss = 0.5 - (f * c + g * s) / t; - - done: - if (xxa < 0.0) { - cc = -cc; - ss = -ss; - } - - *cca = cc; - *ssa = ss; - return (0); -} diff --git a/gtsam/3rdparty/cephes/cephes/gammasgn.c b/gtsam/3rdparty/cephes/cephes/gammasgn.c deleted file mode 100644 index 9d74318ff2..0000000000 --- a/gtsam/3rdparty/cephes/cephes/gammasgn.c +++ /dev/null @@ -1,25 +0,0 @@ -#include "mconf.h" - -double gammasgn(double x) -{ - double fx; - - if (isnan(x)) { - return x; - } - if (x > 0) { - return 1.0; - } - else { - fx = floor(x); - if (x - fx == 0.0) { - return 0.0; - } - else if ((int)fx % 2) { - return -1.0; - } - else { - return 1.0; - } - } -} diff --git a/gtsam/3rdparty/cephes/cephes/gdtr.c b/gtsam/3rdparty/cephes/cephes/gdtr.c deleted file mode 100644 index 597c8d4d93..0000000000 --- a/gtsam/3rdparty/cephes/cephes/gdtr.c +++ /dev/null @@ -1,132 +0,0 @@ -/* gdtr.c - * - * Gamma distribution function - * - * - * - * SYNOPSIS: - * - * double a, b, x, y, gdtr(); - * - * y = gdtr( a, b, x ); - * - * - * - * DESCRIPTION: - * - * Returns the integral from zero to x of the Gamma probability - * density function: - * - * - * x - * b - - * a | | b-1 -at - * y = ----- | t e dt - * - | | - * | (b) - - * 0 - * - * The incomplete Gamma integral is used, according to the - * relation - * - * y = igam( b, ax ). - * - * - * ACCURACY: - * - * See igam(). - * - * ERROR MESSAGES: - * - * message condition value returned - * gdtr domain x < 0 0.0 - * - */ - /* gdtrc.c - * - * Complemented Gamma distribution function - * - * - * - * SYNOPSIS: - * - * double a, b, x, y, gdtrc(); - * - * y = gdtrc( a, b, x ); - * - * - * - * DESCRIPTION: - * - * Returns the integral from x to infinity of the Gamma - * probability density function: - * - * - * inf. - * b - - * a | | b-1 -at - * y = ----- | t e dt - * - | | - * | (b) - - * x - * - * The incomplete Gamma integral is used, according to the - * relation - * - * y = igamc( b, ax ). - * - * - * ACCURACY: - * - * See igamc(). - * - * ERROR MESSAGES: - * - * message condition value returned - * gdtrc domain x < 0 0.0 - * - */ - -/* gdtr() */ - - -/* - * Cephes Math Library Release 2.3: March,1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - - -double gdtr(double a, double b, double x) -{ - - if (x < 0.0) { - sf_error("gdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - return (igam(b, a * x)); -} - - -double gdtrc(double a, double b, double x) -{ - - if (x < 0.0) { - sf_error("gdtrc", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - return (igamc(b, a * x)); -} - - -double gdtri(double a, double b, double y) -{ - - if ((y < 0.0) || (y > 1.0) || (a <= 0.0) || (b < 0.0)) { - sf_error("gdtri", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - return (igamci(b, 1.0 - y) / a); -} diff --git a/gtsam/3rdparty/cephes/cephes/hyp2f1.c b/gtsam/3rdparty/cephes/cephes/hyp2f1.c deleted file mode 100644 index 7f0a84d02a..0000000000 --- a/gtsam/3rdparty/cephes/cephes/hyp2f1.c +++ /dev/null @@ -1,569 +0,0 @@ -/* hyp2f1.c - * - * Gauss hypergeometric function F - * 2 1 - * - * - * SYNOPSIS: - * - * double a, b, c, x, y, hyp2f1(); - * - * y = hyp2f1( a, b, c, x ); - * - * - * DESCRIPTION: - * - * - * hyp2f1( a, b, c, x ) = F ( a, b; c; x ) - * 2 1 - * - * inf. - * - a(a+1)...(a+k) b(b+1)...(b+k) k+1 - * = 1 + > ----------------------------- x . - * - c(c+1)...(c+k) (k+1)! - * k = 0 - * - * Cases addressed are - * Tests and escapes for negative integer a, b, or c - * Linear transformation if c - a or c - b negative integer - * Special case c = a or c = b - * Linear transformation for x near +1 - * Transformation for x < -0.5 - * Psi function expansion if x > 0.5 and c - a - b integer - * Conditionally, a recurrence on c to make c-a-b > 0 - * - * x < -1 AMS 15.3.7 transformation applied (Travis Oliphant) - * valid for b,a,c,(b-a) != integer and (c-a),(c-b) != negative integer - * - * x >= 1 is rejected (unless special cases are present) - * - * The parameters a, b, c are considered to be integer - * valued if they are within 1.0e-14 of the nearest integer - * (1.0e-13 for IEEE arithmetic). - * - * ACCURACY: - * - * - * Relative error (-1 < x < 1): - * arithmetic domain # trials peak rms - * IEEE -1,7 230000 1.2e-11 5.2e-14 - * - * Several special cases also tested with a, b, c in - * the range -7 to 7. - * - * ERROR MESSAGES: - * - * A "partial loss of precision" message is printed if - * the internally estimated relative error exceeds 1^-12. - * A "singularity" message is printed on overflow or - * in cases not addressed (such as x < -1). - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier - */ - -#include -#include -#include - -#include "mconf.h" - -#define EPS 1.0e-13 -#define EPS2 1.0e-10 - -#define ETHRESH 1.0e-12 - -#define MAX_ITERATIONS 10000 - -extern double MACHEP; - -/* hys2f1 and hyp2f1ra depend on each other, so we need this prototype */ -static double hyp2f1ra(double a, double b, double c, double x, double *loss); - -/* Defining power series expansion of Gauss hypergeometric function */ -/* The `loss` parameter estimates loss of significance */ -static double hys2f1(double a, double b, double c, double x, double *loss) { - double f, g, h, k, m, s, u, umax; - int i; - int ib, intflag = 0; - - if (fabs(b) > fabs(a)) { - /* Ensure that |a| > |b| ... */ - f = b; - b = a; - a = f; - } - - ib = round(b); - - if (fabs(b - ib) < EPS && ib <= 0 && fabs(b) < fabs(a)) { - /* .. except when `b` is a smaller negative integer */ - f = b; - b = a; - a = f; - intflag = 1; - } - - if ((fabs(a) > fabs(c) + 1 || intflag) && fabs(c - a) > 2 && fabs(a) > 2) { - /* |a| >> |c| implies that large cancellation error is to be expected. - * - * We try to reduce it with the recurrence relations - */ - return hyp2f1ra(a, b, c, x, loss); - } - - i = 0; - umax = 0.0; - f = a; - g = b; - h = c; - s = 1.0; - u = 1.0; - k = 0.0; - do { - if (fabs(h) < EPS) { - *loss = 1.0; - return INFINITY; - } - m = k + 1.0; - u = u * ((f + k) * (g + k) * x / ((h + k) * m)); - s += u; - k = fabs(u); /* remember largest term summed */ - if (k > umax) umax = k; - k = m; - if (++i > MAX_ITERATIONS) { /* should never happen */ - *loss = 1.0; - return (s); - } - } while (s == 0 || fabs(u / s) > MACHEP); - - /* return estimated relative error */ - *loss = (MACHEP * umax) / fabs(s) + (MACHEP * i); - - return (s); -} - -/* Apply transformations for |x| near 1 then call the power series */ -static double hyt2f1(double a, double b, double c, double x, double *loss) { - double p, q, r, s, t, y, w, d, err, err1; - double ax, id, d1, d2, e, y1; - int i, aid, sign; - - int ia, ib, neg_int_a = 0, neg_int_b = 0; - - ia = round(a); - ib = round(b); - - if (a <= 0 && fabs(a - ia) < EPS) { /* a is a negative integer */ - neg_int_a = 1; - } - - if (b <= 0 && fabs(b - ib) < EPS) { /* b is a negative integer */ - neg_int_b = 1; - } - - err = 0.0; - s = 1.0 - x; - if (x < -0.5 && !(neg_int_a || neg_int_b)) { - if (b > a) - y = pow(s, -a) * hys2f1(a, c - b, c, -x / s, &err); - - else - y = pow(s, -b) * hys2f1(c - a, b, c, -x / s, &err); - - goto done; - } - - d = c - a - b; - id = round(d); /* nearest integer to d */ - - if (x > 0.9 && !(neg_int_a || neg_int_b)) { - if (fabs(d - id) > EPS) { - int sgngam; - - /* test for integer c-a-b */ - /* Try the power series first */ - y = hys2f1(a, b, c, x, &err); - if (err < ETHRESH) goto done; - /* If power series fails, then apply AMS55 #15.3.6 */ - q = hys2f1(a, b, 1.0 - d, s, &err); - sign = 1; - w = lgam_sgn(d, &sgngam); - sign *= sgngam; - w -= lgam_sgn(c - a, &sgngam); - sign *= sgngam; - w -= lgam_sgn(c - b, &sgngam); - sign *= sgngam; - q *= sign * exp(w); - r = pow(s, d) * hys2f1(c - a, c - b, d + 1.0, s, &err1); - sign = 1; - w = lgam_sgn(-d, &sgngam); - sign *= sgngam; - w -= lgam_sgn(a, &sgngam); - sign *= sgngam; - w -= lgam_sgn(b, &sgngam); - sign *= sgngam; - r *= sign * exp(w); - y = q + r; - - q = fabs(q); /* estimate cancellation error */ - r = fabs(r); - if (q > r) r = q; - err += err1 + (MACHEP * r) / y; - - y *= gamma(c); - goto done; - } else { - /* Psi function expansion, AMS55 #15.3.10, #15.3.11, #15.3.12 - * - * Although AMS55 does not explicitly state it, this expansion fails - * for negative integer a or b, since the psi and Gamma functions - * involved have poles. - */ - - if (id >= 0.0) { - e = d; - d1 = d; - d2 = 0.0; - aid = id; - } else { - e = -d; - d1 = 0.0; - d2 = d; - aid = -id; - } - - ax = log(s); - - /* sum for t = 0 */ - y = psi(1.0) + psi(1.0 + e) - psi(a + d1) - psi(b + d1) - ax; - y /= gamma(e + 1.0); - - p = (a + d1) * (b + d1) * s / gamma(e + 2.0); /* Poch for t=1 */ - t = 1.0; - do { - r = psi(1.0 + t) + psi(1.0 + t + e) - psi(a + t + d1) - - psi(b + t + d1) - ax; - q = p * r; - y += q; - p *= s * (a + t + d1) / (t + 1.0); - p *= (b + t + d1) / (t + 1.0 + e); - t += 1.0; - if (t > MAX_ITERATIONS) { /* should never happen */ - sf_error("hyp2f1", SF_ERROR_SLOW, NULL); - *loss = 1.0; - return NAN; - } - } while (y == 0 || fabs(q / y) > EPS); - - if (id == 0.0) { - y *= gamma(c) / (gamma(a) * gamma(b)); - goto psidon; - } - - y1 = 1.0; - - if (aid == 1) goto nosum; - - t = 0.0; - p = 1.0; - for (i = 1; i < aid; i++) { - r = 1.0 - e + t; - p *= s * (a + t + d2) * (b + t + d2) / r; - t += 1.0; - p /= t; - y1 += p; - } - nosum: - p = gamma(c); - y1 *= gamma(e) * p / (gamma(a + d1) * gamma(b + d1)); - - y *= p / (gamma(a + d2) * gamma(b + d2)); - if ((aid & 1) != 0) y = -y; - - q = pow(s, id); /* s to the id power */ - if (id > 0.0) - y *= q; - else - y1 *= q; - - y += y1; - psidon: - goto done; - } - } - - /* Use defining power series if no special cases */ - y = hys2f1(a, b, c, x, &err); - -done: - *loss = err; - return (y); -} - -/* - 15.4.2 Abramowitz & Stegun. -*/ -static double hyp2f1_neg_c_equal_bc(double a, double b, double x) { - double k; - double collector = 1; - double sum = 1; - double collector_max = 1; - - if (!(fabs(b) < 1e5)) { - return NAN; - } - - for (k = 1; k <= -b; k++) { - collector *= (a + k - 1) * x / k; - collector_max = fmax(fabs(collector), collector_max); - sum += collector; - } - - if (1e-16 * (1 + collector_max / fabs(sum)) > 1e-7) { - return NAN; - } - - return sum; -} - -double hyp2f1(double a, double b, double c, double x) { - double d, d1, d2, e; - double p, q, r, s, y, ax; - double ia, ib, ic, id, err; - double t1; - int i, aid; - int neg_int_a = 0, neg_int_b = 0; - int neg_int_ca_or_cb = 0; - - err = 0.0; - ax = fabs(x); - s = 1.0 - x; - ia = round(a); /* nearest integer to a */ - ib = round(b); - - if (x == 0.0) { - return 1.0; - } - - d = c - a - b; - id = round(d); - - if ((a == 0 || b == 0) && c != 0) { - return 1.0; - } - - if (a <= 0 && fabs(a - ia) < EPS) { /* a is a negative integer */ - neg_int_a = 1; - } - - if (b <= 0 && fabs(b - ib) < EPS) { /* b is a negative integer */ - neg_int_b = 1; - } - - if (d <= -1 && !(fabs(d - id) > EPS && s < 0) && !(neg_int_a || neg_int_b)) { - return pow(s, d) * hyp2f1(c - a, c - b, c, x); - } - if (d <= 0 && x == 1 && !(neg_int_a || neg_int_b)) goto hypdiv; - - if (ax < 1.0 || x == -1.0) { - /* 2F1(a,b;b;x) = (1-x)**(-a) */ - if (fabs(b - c) < EPS) { /* b = c */ - if (neg_int_b) { - y = hyp2f1_neg_c_equal_bc(a, b, x); - } else { - y = pow(s, -a); /* s to the -a power */ - } - goto hypdon; - } - if (fabs(a - c) < EPS) { /* a = c */ - y = pow(s, -b); /* s to the -b power */ - goto hypdon; - } - } - - if (c <= 0.0) { - ic = round(c); /* nearest integer to c */ - if (fabs(c - ic) < EPS) { /* c is a negative integer */ - /* check if termination before explosion */ - if (neg_int_a && (ia > ic)) goto hypok; - if (neg_int_b && (ib > ic)) goto hypok; - goto hypdiv; - } - } - - if (neg_int_a || neg_int_b) /* function is a polynomial */ - goto hypok; - - t1 = fabs(b - a); - if (x < -2.0 && fabs(t1 - round(t1)) > EPS) { - /* This transform has a pole for b-a integer, and - * may produce large cancellation errors for |1/x| close 1 - */ - p = hyp2f1(a, 1 - c + a, 1 - b + a, 1.0 / x); - q = hyp2f1(b, 1 - c + b, 1 - a + b, 1.0 / x); - p *= pow(-x, -a); - q *= pow(-x, -b); - t1 = gamma(c); - s = t1 * gamma(b - a) / (gamma(b) * gamma(c - a)); - y = t1 * gamma(a - b) / (gamma(a) * gamma(c - b)); - return s * p + y * q; - } else if (x < -1.0) { - if (fabs(a) < fabs(b)) { - return pow(s, -a) * hyp2f1(a, c - b, c, x / (x - 1)); - } else { - return pow(s, -b) * hyp2f1(b, c - a, c, x / (x - 1)); - } - } - - if (ax > 1.0) /* series diverges */ - goto hypdiv; - - p = c - a; - ia = round(p); /* nearest integer to c-a */ - if ((ia <= 0.0) && (fabs(p - ia) < EPS)) /* negative int c - a */ - neg_int_ca_or_cb = 1; - - r = c - b; - ib = round(r); /* nearest integer to c-b */ - if ((ib <= 0.0) && (fabs(r - ib) < EPS)) /* negative int c - b */ - neg_int_ca_or_cb = 1; - - id = round(d); /* nearest integer to d */ - q = fabs(d - id); - - /* Thanks to Christian Burger - * for reporting a bug here. */ - if (fabs(ax - 1.0) < EPS) { /* |x| == 1.0 */ - if (x > 0.0) { - if (neg_int_ca_or_cb) { - if (d >= 0.0) - goto hypf; - else - goto hypdiv; - } - if (d <= 0.0) goto hypdiv; - y = gamma(c) * gamma(d) / (gamma(p) * gamma(r)); - goto hypdon; - } - if (d <= -1.0) goto hypdiv; - } - - /* Conditionally make d > 0 by recurrence on c - * AMS55 #15.2.27 - */ - if (d < 0.0) { - /* Try the power series first */ - y = hyt2f1(a, b, c, x, &err); - if (err < ETHRESH) goto hypdon; - /* Apply the recurrence if power series fails */ - err = 0.0; - aid = 2 - id; - e = c + aid; - d2 = hyp2f1(a, b, e, x); - d1 = hyp2f1(a, b, e + 1.0, x); - q = a + b + 1.0; - for (i = 0; i < aid; i++) { - r = e - 1.0; - y = (e * (r - (2.0 * e - q) * x) * d2 + (e - a) * (e - b) * x * d1) / - (e * r * s); - e = r; - d1 = d2; - d2 = y; - } - goto hypdon; - } - - if (neg_int_ca_or_cb) goto hypf; /* negative integer c-a or c-b */ - -hypok: - y = hyt2f1(a, b, c, x, &err); - -hypdon: - if (err > ETHRESH) { - sf_error("hyp2f1", SF_ERROR_LOSS, NULL); - /* printf( "Estimated err = %.2e\n", err ); */ - } - return (y); - - /* The transformation for c-a or c-b negative integer - * AMS55 #15.3.3 - */ -hypf: - y = pow(s, d) * hys2f1(c - a, c - b, c, x, &err); - goto hypdon; - - /* The alarm exit */ -hypdiv: - sf_error("hyp2f1", SF_ERROR_OVERFLOW, NULL); - return INFINITY; -} - -/* - * Evaluate hypergeometric function by two-term recurrence in `a`. - * - * This avoids some of the loss of precision in the strongly alternating - * hypergeometric series, and can be used to reduce the `a` and `b` parameters - * to smaller values. - * - * AMS55 #15.2.10 - */ -static double hyp2f1ra(double a, double b, double c, double x, double *loss) { - double f2, f1, f0; - int n; - double t, err, da; - - /* Don't cross c or zero */ - if ((c < 0 && a <= c) || (c >= 0 && a >= c)) { - da = round(a - c); - } else { - da = round(a); - } - t = a - da; - - *loss = 0; - - assert(da != 0); - - if (fabs(da) > MAX_ITERATIONS) { - /* Too expensive to compute this value, so give up */ - sf_error("hyp2f1", SF_ERROR_NO_RESULT, NULL); - *loss = 1.0; - return NAN; - } - - if (da < 0) { - /* Recurse down */ - f2 = 0; - f1 = hys2f1(t, b, c, x, &err); - *loss += err; - f0 = hys2f1(t - 1, b, c, x, &err); - *loss += err; - t -= 1; - for (n = 1; n < -da; ++n) { - f2 = f1; - f1 = f0; - f0 = -(2 * t - c - t * x + b * x) / (c - t) * f1 - - t * (x - 1) / (c - t) * f2; - t -= 1; - } - } else { - /* Recurse up */ - f2 = 0; - f1 = hys2f1(t, b, c, x, &err); - *loss += err; - f0 = hys2f1(t + 1, b, c, x, &err); - *loss += err; - t += 1; - for (n = 1; n < da; ++n) { - f2 = f1; - f1 = f0; - f0 = -((2 * t - c - t * x + b * x) * f1 + (c - t) * f2) / (t * (x - 1)); - t += 1; - } - } - - return f0; -} diff --git a/gtsam/3rdparty/cephes/cephes/hyperg.c b/gtsam/3rdparty/cephes/cephes/hyperg.c deleted file mode 100644 index ac23e71339..0000000000 --- a/gtsam/3rdparty/cephes/cephes/hyperg.c +++ /dev/null @@ -1,362 +0,0 @@ -/* hyperg.c - * - * Confluent hypergeometric function - * - * - * - * SYNOPSIS: - * - * double a, b, x, y, hyperg(); - * - * y = hyperg( a, b, x ); - * - * - * - * DESCRIPTION: - * - * Computes the confluent hypergeometric function - * - * 1 2 - * a x a(a+1) x - * F ( a,b;x ) = 1 + ---- + --------- + ... - * 1 1 b 1! b(b+1) 2! - * - * Many higher transcendental functions are special cases of - * this power series. - * - * As is evident from the formula, b must not be a negative - * integer or zero unless a is an integer with 0 >= a > b. - * - * The routine attempts both a direct summation of the series - * and an asymptotic expansion. In each case error due to - * roundoff, cancellation, and nonconvergence is estimated. - * The result with smaller estimated error is returned. - * - * - * - * ACCURACY: - * - * Tested at random points (a, b, x), all three variables - * ranging from 0 to 30. - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,30 30000 1.8e-14 1.1e-15 - * - * Larger errors can be observed when b is near a negative - * integer or zero. Certain combinations of arguments yield - * serious cancellation error in the power series summation - * and also are not in the region of near convergence of the - * asymptotic series. An error message is printed if the - * self-estimated relative error is greater than 1.0e-12. - * - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" -#include - -extern double MACHEP; - - -/* the `type` parameter determines what converging factor to use */ -static double hyp2f0(double a, double b, double x, int type, double *err) -{ - double a0, alast, t, tlast, maxt; - double n, an, bn, u, sum, temp; - - an = a; - bn = b; - a0 = 1.0e0; - alast = 1.0e0; - sum = 0.0; - n = 1.0e0; - t = 1.0e0; - tlast = 1.0e9; - maxt = 0.0; - - do { - if (an == 0) - goto pdone; - if (bn == 0) - goto pdone; - - u = an * (bn * x / n); - - /* check for blowup */ - temp = fabs(u); - if ((temp > 1.0) && (maxt > (DBL_MAX / temp))) - goto error; - - a0 *= u; - t = fabs(a0); - - /* terminating condition for asymptotic series: - * the series is divergent (if a or b is not a negative integer), - * but its leading part can be used as an asymptotic expansion - */ - if (t > tlast) - goto ndone; - - tlast = t; - sum += alast; /* the sum is one term behind */ - alast = a0; - - if (n > 200) - goto ndone; - - an += 1.0e0; - bn += 1.0e0; - n += 1.0e0; - if (t > maxt) - maxt = t; - } - while (t > MACHEP); - - - pdone: /* series converged! */ - - /* estimate error due to roundoff and cancellation */ - *err = fabs(MACHEP * (n + maxt)); - - alast = a0; - goto done; - - ndone: /* series did not converge */ - - /* The following "Converging factors" are supposed to improve accuracy, - * but do not actually seem to accomplish very much. */ - - n -= 1.0; - x = 1.0 / x; - - switch (type) { /* "type" given as subroutine argument */ - case 1: - alast *= - (0.5 + (0.125 + 0.25 * b - 0.5 * a + 0.25 * x - 0.25 * n) / x); - break; - - case 2: - alast *= 2.0 / 3.0 - b + 2.0 * a + x - n; - break; - - default: - ; - } - - /* estimate error due to roundoff, cancellation, and nonconvergence */ - *err = MACHEP * (n + maxt) + fabs(a0); - - done: - sum += alast; - return (sum); - - /* series blew up: */ - error: - *err = INFINITY; - sf_error("hyperg", SF_ERROR_NO_RESULT, NULL); - return (sum); -} - - -/* asymptotic formula for hypergeometric function: - * - * ( -a - * -- ( |z| - * | (b) ( -------- 2f0( a, 1+a-b, -1/x ) - * ( -- - * ( | (b-a) - * - * - * x a-b ) - * e |x| ) - * + -------- 2f0( b-a, 1-a, 1/x ) ) - * -- ) - * | (a) ) - */ - -static double hy1f1a(double a, double b, double x, double *err) -{ - double h1, h2, t, u, temp, acanc, asum, err1, err2; - - if (x == 0) { - acanc = 1.0; - asum = INFINITY; - goto adone; - } - temp = log(fabs(x)); - t = x + temp * (a - b); - u = -temp * a; - - if (b > 0) { - temp = lgam(b); - t += temp; - u += temp; - } - - h1 = hyp2f0(a, a - b + 1, -1.0 / x, 1, &err1); - - temp = exp(u) / gamma(b - a); - h1 *= temp; - err1 *= temp; - - h2 = hyp2f0(b - a, 1.0 - a, 1.0 / x, 2, &err2); - - if (a < 0) - temp = exp(t) / gamma(a); - else - temp = exp(t - lgam(a)); - - h2 *= temp; - err2 *= temp; - - if (x < 0.0) - asum = h1; - else - asum = h2; - - acanc = fabs(err1) + fabs(err2); - - if (b < 0) { - temp = gamma(b); - asum *= temp; - acanc *= fabs(temp); - } - - - if (asum != 0.0) - acanc /= fabs(asum); - - if (acanc != acanc) - /* nan */ - acanc = 1.0; - - if (asum == INFINITY || asum == -INFINITY) - /* infinity */ - acanc = 0; - - acanc *= 30.0; /* fudge factor, since error of asymptotic formula - * often seems this much larger than advertised */ - - adone: - *err = acanc; - return (asum); -} - - -/* Power series summation for confluent hypergeometric function */ -static double hy1f1p(double a, double b, double x, double *err) -{ - double n, a0, sum, t, u, temp, maxn; - double an, bn, maxt; - double y, c, sumc; - - - /* set up for power series summation */ - an = a; - bn = b; - a0 = 1.0; - sum = 1.0; - c = 0.0; - n = 1.0; - t = 1.0; - maxt = 0.0; - *err = 1.0; - - maxn = 200.0 + 2 * fabs(a) + 2 * fabs(b); - - while (t > MACHEP) { - if (bn == 0) { /* check bn first since if both */ - sf_error("hyperg", SF_ERROR_SINGULAR, NULL); - return (INFINITY); /* an and bn are zero it is */ - } - if (an == 0) /* a singularity */ - return (sum); - if (n > maxn) { - /* too many terms; take the last one as error estimate */ - c = fabs(c) + fabs(t) * 50.0; - goto pdone; - } - u = x * (an / (bn * n)); - - /* check for blowup */ - temp = fabs(u); - if ((temp > 1.0) && (maxt > (DBL_MAX / temp))) { - *err = 1.0; /* blowup: estimate 100% error */ - return sum; - } - - a0 *= u; - - y = a0 - c; - sumc = sum + y; - c = (sumc - sum) - y; - sum = sumc; - - t = fabs(a0); - - an += 1.0; - bn += 1.0; - n += 1.0; - } - - pdone: - - /* estimate error due to roundoff and cancellation */ - if (sum != 0.0) { - *err = fabs(c / sum); - } - else { - *err = fabs(c); - } - - if (*err != *err) { - /* nan */ - *err = 1.0; - } - - return (sum); -} - - - -double hyperg(double a, double b, double x) -{ - double asum, psum, acanc, pcanc, temp; - - /* See if a Kummer transformation will help */ - temp = b - a; - if (fabs(temp) < 0.001 * fabs(a)) - return (exp(x) * hyperg(temp, b, -x)); - - - /* Try power & asymptotic series, starting from the one that is likely OK */ - if (fabs(x) < 10 + fabs(a) + fabs(b)) { - psum = hy1f1p(a, b, x, &pcanc); - if (pcanc < 1.0e-15) - goto done; - asum = hy1f1a(a, b, x, &acanc); - } - else { - psum = hy1f1a(a, b, x, &pcanc); - if (pcanc < 1.0e-15) - goto done; - asum = hy1f1p(a, b, x, &acanc); - } - - /* Pick the result with less estimated error */ - - if (acanc < pcanc) { - pcanc = acanc; - psum = asum; - } - - done: - if (pcanc > 1.0e-12) - sf_error("hyperg", SF_ERROR_LOSS, NULL); - - return (psum); -} diff --git a/gtsam/3rdparty/cephes/cephes/i0.c b/gtsam/3rdparty/cephes/cephes/i0.c deleted file mode 100644 index 4e85d556ef..0000000000 --- a/gtsam/3rdparty/cephes/cephes/i0.c +++ /dev/null @@ -1,180 +0,0 @@ -/* i0.c - * - * Modified Bessel function of order zero - * - * - * - * SYNOPSIS: - * - * double x, y, i0(); - * - * y = i0( x ); - * - * - * - * DESCRIPTION: - * - * Returns modified Bessel function of order zero of the - * argument. - * - * The function is defined as i0(x) = j0( ix ). - * - * The range is partitioned into the two intervals [0,8] and - * (8, infinity). Chebyshev polynomial expansions are employed - * in each interval. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,30 30000 5.8e-16 1.4e-16 - * - */ - /* i0e.c - * - * Modified Bessel function of order zero, - * exponentially scaled - * - * - * - * SYNOPSIS: - * - * double x, y, i0e(); - * - * y = i0e( x ); - * - * - * - * DESCRIPTION: - * - * Returns exponentially scaled modified Bessel function - * of order zero of the argument. - * - * The function is defined as i0e(x) = exp(-|x|) j0( ix ). - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,30 30000 5.4e-16 1.2e-16 - * See i0(). - * - */ - -/* i0.c */ - - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -/* Chebyshev coefficients for exp(-x) I0(x) - * in the interval [0,8]. - * - * lim(x->0){ exp(-x) I0(x) } = 1. - */ -static double A[] = { - -4.41534164647933937950E-18, - 3.33079451882223809783E-17, - -2.43127984654795469359E-16, - 1.71539128555513303061E-15, - -1.16853328779934516808E-14, - 7.67618549860493561688E-14, - -4.85644678311192946090E-13, - 2.95505266312963983461E-12, - -1.72682629144155570723E-11, - 9.67580903537323691224E-11, - -5.18979560163526290666E-10, - 2.65982372468238665035E-9, - -1.30002500998624804212E-8, - 6.04699502254191894932E-8, - -2.67079385394061173391E-7, - 1.11738753912010371815E-6, - -4.41673835845875056359E-6, - 1.64484480707288970893E-5, - -5.75419501008210370398E-5, - 1.88502885095841655729E-4, - -5.76375574538582365885E-4, - 1.63947561694133579842E-3, - -4.32430999505057594430E-3, - 1.05464603945949983183E-2, - -2.37374148058994688156E-2, - 4.93052842396707084878E-2, - -9.49010970480476444210E-2, - 1.71620901522208775349E-1, - -3.04682672343198398683E-1, - 6.76795274409476084995E-1 -}; - -/* Chebyshev coefficients for exp(-x) sqrt(x) I0(x) - * in the inverted interval [8,infinity]. - * - * lim(x->inf){ exp(-x) sqrt(x) I0(x) } = 1/sqrt(2pi). - */ -static double B[] = { - -7.23318048787475395456E-18, - -4.83050448594418207126E-18, - 4.46562142029675999901E-17, - 3.46122286769746109310E-17, - -2.82762398051658348494E-16, - -3.42548561967721913462E-16, - 1.77256013305652638360E-15, - 3.81168066935262242075E-15, - -9.55484669882830764870E-15, - -4.15056934728722208663E-14, - 1.54008621752140982691E-14, - 3.85277838274214270114E-13, - 7.18012445138366623367E-13, - -1.79417853150680611778E-12, - -1.32158118404477131188E-11, - -3.14991652796324136454E-11, - 1.18891471078464383424E-11, - 4.94060238822496958910E-10, - 3.39623202570838634515E-9, - 2.26666899049817806459E-8, - 2.04891858946906374183E-7, - 2.89137052083475648297E-6, - 6.88975834691682398426E-5, - 3.36911647825569408990E-3, - 8.04490411014108831608E-1 -}; - -double i0(double x) -{ - double y; - - if (x < 0) - x = -x; - if (x <= 8.0) { - y = (x / 2.0) - 2.0; - return (exp(x) * chbevl(y, A, 30)); - } - - return (exp(x) * chbevl(32.0 / x - 2.0, B, 25) / sqrt(x)); - -} - - - - -double i0e(double x) -{ - double y; - - if (x < 0) - x = -x; - if (x <= 8.0) { - y = (x / 2.0) - 2.0; - return (chbevl(y, A, 30)); - } - - return (chbevl(32.0 / x - 2.0, B, 25) / sqrt(x)); - -} diff --git a/gtsam/3rdparty/cephes/cephes/i1.c b/gtsam/3rdparty/cephes/cephes/i1.c deleted file mode 100644 index 4553873f2c..0000000000 --- a/gtsam/3rdparty/cephes/cephes/i1.c +++ /dev/null @@ -1,184 +0,0 @@ -/* i1.c - * - * Modified Bessel function of order one - * - * - * - * SYNOPSIS: - * - * double x, y, i1(); - * - * y = i1( x ); - * - * - * - * DESCRIPTION: - * - * Returns modified Bessel function of order one of the - * argument. - * - * The function is defined as i1(x) = -i j1( ix ). - * - * The range is partitioned into the two intervals [0,8] and - * (8, infinity). Chebyshev polynomial expansions are employed - * in each interval. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.9e-15 2.1e-16 - * - * - */ - /* i1e.c - * - * Modified Bessel function of order one, - * exponentially scaled - * - * - * - * SYNOPSIS: - * - * double x, y, i1e(); - * - * y = i1e( x ); - * - * - * - * DESCRIPTION: - * - * Returns exponentially scaled modified Bessel function - * of order one of the argument. - * - * The function is defined as i1(x) = -i exp(-|x|) j1( ix ). - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 2.0e-15 2.0e-16 - * See i1(). - * - */ - -/* i1.c 2 */ - - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1985, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -/* Chebyshev coefficients for exp(-x) I1(x) / x - * in the interval [0,8]. - * - * lim(x->0){ exp(-x) I1(x) / x } = 1/2. - */ - -static double A[] = { - 2.77791411276104639959E-18, - -2.11142121435816608115E-17, - 1.55363195773620046921E-16, - -1.10559694773538630805E-15, - 7.60068429473540693410E-15, - -5.04218550472791168711E-14, - 3.22379336594557470981E-13, - -1.98397439776494371520E-12, - 1.17361862988909016308E-11, - -6.66348972350202774223E-11, - 3.62559028155211703701E-10, - -1.88724975172282928790E-9, - 9.38153738649577178388E-9, - -4.44505912879632808065E-8, - 2.00329475355213526229E-7, - -8.56872026469545474066E-7, - 3.47025130813767847674E-6, - -1.32731636560394358279E-5, - 4.78156510755005422638E-5, - -1.61760815825896745588E-4, - 5.12285956168575772895E-4, - -1.51357245063125314899E-3, - 4.15642294431288815669E-3, - -1.05640848946261981558E-2, - 2.47264490306265168283E-2, - -5.29459812080949914269E-2, - 1.02643658689847095384E-1, - -1.76416518357834055153E-1, - 2.52587186443633654823E-1 -}; - -/* Chebyshev coefficients for exp(-x) sqrt(x) I1(x) - * in the inverted interval [8,infinity]. - * - * lim(x->inf){ exp(-x) sqrt(x) I1(x) } = 1/sqrt(2pi). - */ -static double B[] = { - 7.51729631084210481353E-18, - 4.41434832307170791151E-18, - -4.65030536848935832153E-17, - -3.20952592199342395980E-17, - 2.96262899764595013876E-16, - 3.30820231092092828324E-16, - -1.88035477551078244854E-15, - -3.81440307243700780478E-15, - 1.04202769841288027642E-14, - 4.27244001671195135429E-14, - -2.10154184277266431302E-14, - -4.08355111109219731823E-13, - -7.19855177624590851209E-13, - 2.03562854414708950722E-12, - 1.41258074366137813316E-11, - 3.25260358301548823856E-11, - -1.89749581235054123450E-11, - -5.58974346219658380687E-10, - -3.83538038596423702205E-9, - -2.63146884688951950684E-8, - -2.51223623787020892529E-7, - -3.88256480887769039346E-6, - -1.10588938762623716291E-4, - -9.76109749136146840777E-3, - 7.78576235018280120474E-1 -}; - -double i1(double x) -{ - double y, z; - - z = fabs(x); - if (z <= 8.0) { - y = (z / 2.0) - 2.0; - z = chbevl(y, A, 29) * z * exp(z); - } - else { - z = exp(z) * chbevl(32.0 / z - 2.0, B, 25) / sqrt(z); - } - if (x < 0.0) - z = -z; - return (z); -} - -/* i1e() */ - -double i1e(double x) -{ - double y, z; - - z = fabs(x); - if (z <= 8.0) { - y = (z / 2.0) - 2.0; - z = chbevl(y, A, 29) * z; - } - else { - z = chbevl(32.0 / z - 2.0, B, 25) / sqrt(z); - } - if (x < 0.0) - z = -z; - return (z); -} diff --git a/gtsam/3rdparty/cephes/cephes/incbet.c b/gtsam/3rdparty/cephes/cephes/incbet.c deleted file mode 100644 index b03427f4f7..0000000000 --- a/gtsam/3rdparty/cephes/cephes/incbet.c +++ /dev/null @@ -1,369 +0,0 @@ -/* incbet.c - * - * Incomplete beta integral - * - * - * SYNOPSIS: - * - * double a, b, x, y, incbet(); - * - * y = incbet( a, b, x ); - * - * - * DESCRIPTION: - * - * Returns incomplete beta integral of the arguments, evaluated - * from zero to x. The function is defined as - * - * x - * - - - * | (a+b) | | a-1 b-1 - * ----------- | t (1-t) dt. - * - - | | - * | (a) | (b) - - * 0 - * - * The domain of definition is 0 <= x <= 1. In this - * implementation a and b are restricted to positive values. - * The integral from x to 1 may be obtained by the symmetry - * relation - * - * 1 - incbet( a, b, x ) = incbet( b, a, 1-x ). - * - * The integral is evaluated by a continued fraction expansion - * or, when b*x is small, by a power series. - * - * ACCURACY: - * - * Tested at uniformly distributed random points (a,b,x) with a and b - * in "domain" and x between 0 and 1. - * Relative error - * arithmetic domain # trials peak rms - * IEEE 0,5 10000 6.9e-15 4.5e-16 - * IEEE 0,85 250000 2.2e-13 1.7e-14 - * IEEE 0,1000 30000 5.3e-12 6.3e-13 - * IEEE 0,10000 250000 9.3e-11 7.1e-12 - * IEEE 0,100000 10000 8.7e-10 4.8e-11 - * Outputs smaller than the IEEE gradual underflow threshold - * were excluded from these statistics. - * - * ERROR MESSAGES: - * message condition value returned - * incbet domain x<0, x>1 0.0 - * incbet underflow 0.0 - */ - - -/* - * Cephes Math Library, Release 2.3: March, 1995 - * Copyright 1984, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - -#define MAXGAM 171.624376956302725 - -extern double MACHEP, MINLOG, MAXLOG; - -static double big = 4.503599627370496e15; -static double biginv = 2.22044604925031308085e-16; - - -/* Power series for incomplete beta integral. - * Use when b*x is small and x not too close to 1. */ - -static double pseries(double a, double b, double x) -{ - double s, t, u, v, n, t1, z, ai; - - ai = 1.0 / a; - u = (1.0 - b) * x; - v = u / (a + 1.0); - t1 = v; - t = u; - n = 2.0; - s = 0.0; - z = MACHEP * ai; - while (fabs(v) > z) { - u = (n - b) * x / n; - t *= u; - v = t / (a + n); - s += v; - n += 1.0; - } - s += t1; - s += ai; - - u = a * log(x); - if ((a + b) < MAXGAM && fabs(u) < MAXLOG) { - t = 1.0 / beta(a, b); - s = s * t * pow(x, a); - } - else { - t = -lbeta(a,b) + u + log(s); - if (t < MINLOG) - s = 0.0; - else - s = exp(t); - } - return (s); -} - - -/* Continued fraction expansion #1 for incomplete beta integral */ - -static double incbcf(double a, double b, double x) -{ - double xk, pk, pkm1, pkm2, qk, qkm1, qkm2; - double k1, k2, k3, k4, k5, k6, k7, k8; - double r, t, ans, thresh; - int n; - - k1 = a; - k2 = a + b; - k3 = a; - k4 = a + 1.0; - k5 = 1.0; - k6 = b - 1.0; - k7 = k4; - k8 = a + 2.0; - - pkm2 = 0.0; - qkm2 = 1.0; - pkm1 = 1.0; - qkm1 = 1.0; - ans = 1.0; - r = 1.0; - n = 0; - thresh = 3.0 * MACHEP; - do { - - xk = -(x * k1 * k2) / (k3 * k4); - pk = pkm1 + pkm2 * xk; - qk = qkm1 + qkm2 * xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - - xk = (x * k5 * k6) / (k7 * k8); - pk = pkm1 + pkm2 * xk; - qk = qkm1 + qkm2 * xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - - if (qk != 0) - r = pk / qk; - if (r != 0) { - t = fabs((ans - r) / r); - ans = r; - } - else - t = 1.0; - - if (t < thresh) - goto cdone; - - k1 += 1.0; - k2 += 1.0; - k3 += 2.0; - k4 += 2.0; - k5 += 1.0; - k6 -= 1.0; - k7 += 2.0; - k8 += 2.0; - - if ((fabs(qk) + fabs(pk)) > big) { - pkm2 *= biginv; - pkm1 *= biginv; - qkm2 *= biginv; - qkm1 *= biginv; - } - if ((fabs(qk) < biginv) || (fabs(pk) < biginv)) { - pkm2 *= big; - pkm1 *= big; - qkm2 *= big; - qkm1 *= big; - } - } - while (++n < 300); - - cdone: - return (ans); -} - - -/* Continued fraction expansion #2 for incomplete beta integral */ - -static double incbd(double a, double b, double x) -{ - double xk, pk, pkm1, pkm2, qk, qkm1, qkm2; - double k1, k2, k3, k4, k5, k6, k7, k8; - double r, t, ans, z, thresh; - int n; - - k1 = a; - k2 = b - 1.0; - k3 = a; - k4 = a + 1.0; - k5 = 1.0; - k6 = a + b; - k7 = a + 1.0;; - k8 = a + 2.0; - - pkm2 = 0.0; - qkm2 = 1.0; - pkm1 = 1.0; - qkm1 = 1.0; - z = x / (1.0 - x); - ans = 1.0; - r = 1.0; - n = 0; - thresh = 3.0 * MACHEP; - do { - - xk = -(z * k1 * k2) / (k3 * k4); - pk = pkm1 + pkm2 * xk; - qk = qkm1 + qkm2 * xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - - xk = (z * k5 * k6) / (k7 * k8); - pk = pkm1 + pkm2 * xk; - qk = qkm1 + qkm2 * xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - - if (qk != 0) - r = pk / qk; - if (r != 0) { - t = fabs((ans - r) / r); - ans = r; - } - else - t = 1.0; - - if (t < thresh) - goto cdone; - - k1 += 1.0; - k2 -= 1.0; - k3 += 2.0; - k4 += 2.0; - k5 += 1.0; - k6 += 1.0; - k7 += 2.0; - k8 += 2.0; - - if ((fabs(qk) + fabs(pk)) > big) { - pkm2 *= biginv; - pkm1 *= biginv; - qkm2 *= biginv; - qkm1 *= biginv; - } - if ((fabs(qk) < biginv) || (fabs(pk) < biginv)) { - pkm2 *= big; - pkm1 *= big; - qkm2 *= big; - qkm1 *= big; - } - } - while (++n < 300); - cdone: - return (ans); -} - - -double incbet(double aa, double bb, double xx) -{ - double a, b, t, x, xc, w, y; - int flag; - - if (aa <= 0.0 || bb <= 0.0) - goto domerr; - - if ((xx <= 0.0) || (xx >= 1.0)) { - if (xx == 0.0) - return (0.0); - if (xx == 1.0) - return (1.0); - domerr: - sf_error("incbet", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - flag = 0; - if ((bb * xx) <= 1.0 && xx <= 0.95) { - t = pseries(aa, bb, xx); - goto done; - } - - w = 1.0 - xx; - - /* Reverse a and b if x is greater than the mean. */ - if (xx > (aa / (aa + bb))) { - flag = 1; - a = bb; - b = aa; - xc = xx; - x = w; - } - else { - a = aa; - b = bb; - xc = w; - x = xx; - } - - if (flag == 1 && (b * x) <= 1.0 && x <= 0.95) { - t = pseries(a, b, x); - goto done; - } - - /* Choose expansion for better convergence. */ - y = x * (a + b - 2.0) - (a - 1.0); - if (y < 0.0) - w = incbcf(a, b, x); - else - w = incbd(a, b, x) / xc; - - /* Multiply w by the factor - * a b _ _ _ - * x (1-x) | (a+b) / ( a | (a) | (b) ) . */ - - y = a * log(x); - t = b * log(xc); - if ((a + b) < MAXGAM && fabs(y) < MAXLOG && fabs(t) < MAXLOG) { - t = pow(xc, b); - t *= pow(x, a); - t /= a; - t *= w; - t *= 1.0 / beta(a, b); - goto done; - } - /* Resort to logarithms. */ - y += t - lbeta(a,b); - y += log(w / a); - if (y < MINLOG) - t = 0.0; - else - t = exp(y); - - done: - - if (flag == 1) { - if (t <= MACHEP) - t = 1.0 - MACHEP; - else - t = 1.0 - t; - } - return (t); -} - - diff --git a/gtsam/3rdparty/cephes/cephes/incbi.c b/gtsam/3rdparty/cephes/cephes/incbi.c deleted file mode 100644 index 747c43f538..0000000000 --- a/gtsam/3rdparty/cephes/cephes/incbi.c +++ /dev/null @@ -1,275 +0,0 @@ -/* incbi() - * - * Inverse of incomplete beta integral - * - * - * - * SYNOPSIS: - * - * double a, b, x, y, incbi(); - * - * x = incbi( a, b, y ); - * - * - * - * DESCRIPTION: - * - * Given y, the function finds x such that - * - * incbet( a, b, x ) = y . - * - * The routine performs interval halving or Newton iterations to find the - * root of incbet(a,b,x) - y = 0. - * - * - * ACCURACY: - * - * Relative error: - * x a,b - * arithmetic domain domain # trials peak rms - * IEEE 0,1 .5,10000 50000 5.8e-12 1.3e-13 - * IEEE 0,1 .25,100 100000 1.8e-13 3.9e-15 - * IEEE 0,1 0,5 50000 1.1e-12 5.5e-15 - * VAX 0,1 .5,100 25000 3.5e-14 1.1e-15 - * With a and b constrained to half-integer or integer values: - * IEEE 0,1 .5,10000 50000 5.8e-12 1.1e-13 - * IEEE 0,1 .5,100 100000 1.7e-14 7.9e-16 - * With a = .5, b constrained to half-integer or integer values: - * IEEE 0,1 .5,10000 10000 8.3e-11 1.0e-11 - */ - - -/* - * Cephes Math Library Release 2.4: March,1996 - * Copyright 1984, 1996 by Stephen L. Moshier - */ - -#include "mconf.h" - -extern double MACHEP, MAXLOG, MINLOG; - -double incbi(double aa, double bb, double yy0) -{ - double a, b, y0, d, y, x, x0, x1, lgm, yp, di, dithresh, yl, yh, xt; - int i, rflg, dir, nflg; - - - i = 0; - if (yy0 <= 0) - return (0.0); - if (yy0 >= 1.0) - return (1.0); - x0 = 0.0; - yl = 0.0; - x1 = 1.0; - yh = 1.0; - nflg = 0; - - if (aa <= 1.0 || bb <= 1.0) { - dithresh = 1.0e-6; - rflg = 0; - a = aa; - b = bb; - y0 = yy0; - x = a / (a + b); - y = incbet(a, b, x); - goto ihalve; - } - else { - dithresh = 1.0e-4; - } - /* approximation to inverse function */ - - yp = -ndtri(yy0); - - if (yy0 > 0.5) { - rflg = 1; - a = bb; - b = aa; - y0 = 1.0 - yy0; - yp = -yp; - } - else { - rflg = 0; - a = aa; - b = bb; - y0 = yy0; - } - - lgm = (yp * yp - 3.0) / 6.0; - x = 2.0 / (1.0 / (2.0 * a - 1.0) + 1.0 / (2.0 * b - 1.0)); - d = yp * sqrt(x + lgm) / x - - (1.0 / (2.0 * b - 1.0) - 1.0 / (2.0 * a - 1.0)) - * (lgm + 5.0 / 6.0 - 2.0 / (3.0 * x)); - d = 2.0 * d; - if (d < MINLOG) { - x = 1.0; - goto under; - } - x = a / (a + b * exp(d)); - y = incbet(a, b, x); - yp = (y - y0) / y0; - if (fabs(yp) < 0.2) - goto newt; - - /* Resort to interval halving if not close enough. */ - ihalve: - - dir = 0; - di = 0.5; - for (i = 0; i < 100; i++) { - if (i != 0) { - x = x0 + di * (x1 - x0); - if (x == 1.0) - x = 1.0 - MACHEP; - if (x == 0.0) { - di = 0.5; - x = x0 + di * (x1 - x0); - if (x == 0.0) - goto under; - } - y = incbet(a, b, x); - yp = (x1 - x0) / (x1 + x0); - if (fabs(yp) < dithresh) - goto newt; - yp = (y - y0) / y0; - if (fabs(yp) < dithresh) - goto newt; - } - if (y < y0) { - x0 = x; - yl = y; - if (dir < 0) { - dir = 0; - di = 0.5; - } - else if (dir > 3) - di = 1.0 - (1.0 - di) * (1.0 - di); - else if (dir > 1) - di = 0.5 * di + 0.5; - else - di = (y0 - y) / (yh - yl); - dir += 1; - if (x0 > 0.75) { - if (rflg == 1) { - rflg = 0; - a = aa; - b = bb; - y0 = yy0; - } - else { - rflg = 1; - a = bb; - b = aa; - y0 = 1.0 - yy0; - } - x = 1.0 - x; - y = incbet(a, b, x); - x0 = 0.0; - yl = 0.0; - x1 = 1.0; - yh = 1.0; - goto ihalve; - } - } - else { - x1 = x; - if (rflg == 1 && x1 < MACHEP) { - x = 0.0; - goto done; - } - yh = y; - if (dir > 0) { - dir = 0; - di = 0.5; - } - else if (dir < -3) - di = di * di; - else if (dir < -1) - di = 0.5 * di; - else - di = (y - y0) / (yh - yl); - dir -= 1; - } - } - sf_error("incbi", SF_ERROR_LOSS, NULL); - if (x0 >= 1.0) { - x = 1.0 - MACHEP; - goto done; - } - if (x <= 0.0) { - under: - sf_error("incbi", SF_ERROR_UNDERFLOW, NULL); - x = 0.0; - goto done; - } - - newt: - - if (nflg) - goto done; - nflg = 1; - lgm = lgam(a + b) - lgam(a) - lgam(b); - - for (i = 0; i < 8; i++) { - /* Compute the function at this point. */ - if (i != 0) - y = incbet(a, b, x); - if (y < yl) { - x = x0; - y = yl; - } - else if (y > yh) { - x = x1; - y = yh; - } - else if (y < y0) { - x0 = x; - yl = y; - } - else { - x1 = x; - yh = y; - } - if (x == 1.0 || x == 0.0) - break; - /* Compute the derivative of the function at this point. */ - d = (a - 1.0) * log(x) + (b - 1.0) * log(1.0 - x) + lgm; - if (d < MINLOG) - goto done; - if (d > MAXLOG) - break; - d = exp(d); - /* Compute the step to the next approximation of x. */ - d = (y - y0) / d; - xt = x - d; - if (xt <= x0) { - y = (x - x0) / (x1 - x0); - xt = x0 + 0.5 * y * (x - x0); - if (xt <= 0.0) - break; - } - if (xt >= x1) { - y = (x1 - x) / (x1 - x0); - xt = x1 - 0.5 * y * (x1 - x); - if (xt >= 1.0) - break; - } - x = xt; - if (fabs(d / x) < 128.0 * MACHEP) - goto done; - } - /* Did not converge. */ - dithresh = 256.0 * MACHEP; - goto ihalve; - - done: - - if (rflg) { - if (x <= MACHEP) - x = 1.0 - MACHEP; - else - x = 1.0 - x; - } - return (x); -} diff --git a/gtsam/3rdparty/cephes/cephes/j0.c b/gtsam/3rdparty/cephes/cephes/j0.c deleted file mode 100644 index 094ef6cef1..0000000000 --- a/gtsam/3rdparty/cephes/cephes/j0.c +++ /dev/null @@ -1,246 +0,0 @@ -/* j0.c - * - * Bessel function of order zero - * - * - * - * SYNOPSIS: - * - * double x, y, j0(); - * - * y = j0( x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of order zero of the argument. - * - * The domain is divided into the intervals [0, 5] and - * (5, infinity). In the first interval the following rational - * approximation is used: - * - * - * 2 2 - * (w - r ) (w - r ) P (w) / Q (w) - * 1 2 3 8 - * - * 2 - * where w = x and the two r's are zeros of the function. - * - * In the second interval, the Hankel asymptotic expansion - * is employed with two rational functions of degree 6/6 - * and 7/7. - * - * - * - * ACCURACY: - * - * Absolute error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 60000 4.2e-16 1.1e-16 - * - */ - /* y0.c - * - * Bessel function of the second kind, order zero - * - * - * - * SYNOPSIS: - * - * double x, y, y0(); - * - * y = y0( x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of the second kind, of order - * zero, of the argument. - * - * The domain is divided into the intervals [0, 5] and - * (5, infinity). In the first interval a rational approximation - * R(x) is employed to compute - * y0(x) = R(x) + 2 * log(x) * j0(x) / M_PI. - * Thus a call to j0() is required. - * - * In the second interval, the Hankel asymptotic expansion - * is employed with two rational functions of degree 6/6 - * and 7/7. - * - * - * - * ACCURACY: - * - * Absolute error, when y0(x) < 1; else relative error: - * - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.3e-15 1.6e-16 - * - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier - */ - -/* Note: all coefficients satisfy the relative error criterion - * except YP, YQ which are designed for absolute error. */ - -#include "mconf.h" - -static double PP[7] = { - 7.96936729297347051624E-4, - 8.28352392107440799803E-2, - 1.23953371646414299388E0, - 5.44725003058768775090E0, - 8.74716500199817011941E0, - 5.30324038235394892183E0, - 9.99999999999999997821E-1, -}; - -static double PQ[7] = { - 9.24408810558863637013E-4, - 8.56288474354474431428E-2, - 1.25352743901058953537E0, - 5.47097740330417105182E0, - 8.76190883237069594232E0, - 5.30605288235394617618E0, - 1.00000000000000000218E0, -}; - -static double QP[8] = { - -1.13663838898469149931E-2, - -1.28252718670509318512E0, - -1.95539544257735972385E1, - -9.32060152123768231369E1, - -1.77681167980488050595E2, - -1.47077505154951170175E2, - -5.14105326766599330220E1, - -6.05014350600728481186E0, -}; - -static double QQ[7] = { - /* 1.00000000000000000000E0, */ - 6.43178256118178023184E1, - 8.56430025976980587198E2, - 3.88240183605401609683E3, - 7.24046774195652478189E3, - 5.93072701187316984827E3, - 2.06209331660327847417E3, - 2.42005740240291393179E2, -}; - -static double YP[8] = { - 1.55924367855235737965E4, - -1.46639295903971606143E7, - 5.43526477051876500413E9, - -9.82136065717911466409E11, - 8.75906394395366999549E13, - -3.46628303384729719441E15, - 4.42733268572569800351E16, - -1.84950800436986690637E16, -}; - -static double YQ[7] = { - /* 1.00000000000000000000E0, */ - 1.04128353664259848412E3, - 6.26107330137134956842E5, - 2.68919633393814121987E8, - 8.64002487103935000337E10, - 2.02979612750105546709E13, - 3.17157752842975028269E15, - 2.50596256172653059228E17, -}; - -/* 5.783185962946784521175995758455807035071 */ -static double DR1 = 5.78318596294678452118E0; - -/* 30.47126234366208639907816317502275584842 */ -static double DR2 = 3.04712623436620863991E1; - -static double RP[4] = { - -4.79443220978201773821E9, - 1.95617491946556577543E12, - -2.49248344360967716204E14, - 9.70862251047306323952E15, -}; - -static double RQ[8] = { - /* 1.00000000000000000000E0, */ - 4.99563147152651017219E2, - 1.73785401676374683123E5, - 4.84409658339962045305E7, - 1.11855537045356834862E10, - 2.11277520115489217587E12, - 3.10518229857422583814E14, - 3.18121955943204943306E16, - 1.71086294081043136091E18, -}; - -extern double SQ2OPI; - -double j0(double x) -{ - double w, z, p, q, xn; - - if (x < 0) - x = -x; - - if (x <= 5.0) { - z = x * x; - if (x < 1.0e-5) - return (1.0 - z / 4.0); - - p = (z - DR1) * (z - DR2); - p = p * polevl(z, RP, 3) / p1evl(z, RQ, 8); - return (p); - } - - w = 5.0 / x; - q = 25.0 / (x * x); - p = polevl(q, PP, 6) / polevl(q, PQ, 6); - q = polevl(q, QP, 7) / p1evl(q, QQ, 7); - xn = x - M_PI_4; - p = p * cos(xn) - w * q * sin(xn); - return (p * SQ2OPI / sqrt(x)); -} - -/* y0() 2 */ -/* Bessel function of second kind, order zero */ - -/* Rational approximation coefficients YP[], YQ[] are used here. - * The function computed is y0(x) - 2 * log(x) * j0(x) / M_PI, - * whose value at x = 0 is 2 * ( log(0.5) + EUL ) / M_PI - * = 0.073804295108687225. - */ - -double y0(double x) -{ - double w, z, p, q, xn; - - if (x <= 5.0) { - if (x == 0.0) { - sf_error("y0", SF_ERROR_SINGULAR, NULL); - return -INFINITY; - } - else if (x < 0.0) { - sf_error("y0", SF_ERROR_DOMAIN, NULL); - return NAN; - } - z = x * x; - w = polevl(z, YP, 7) / p1evl(z, YQ, 7); - w += M_2_PI * log(x) * j0(x); - return (w); - } - - w = 5.0 / x; - z = 25.0 / (x * x); - p = polevl(z, PP, 6) / polevl(z, PQ, 6); - q = polevl(z, QP, 7) / p1evl(z, QQ, 7); - xn = x - M_PI_4; - p = p * sin(xn) + w * q * cos(xn); - return (p * SQ2OPI / sqrt(x)); -} diff --git a/gtsam/3rdparty/cephes/cephes/j1.c b/gtsam/3rdparty/cephes/cephes/j1.c deleted file mode 100644 index 123194de84..0000000000 --- a/gtsam/3rdparty/cephes/cephes/j1.c +++ /dev/null @@ -1,225 +0,0 @@ -/* j1.c - * - * Bessel function of order one - * - * - * - * SYNOPSIS: - * - * double x, y, j1(); - * - * y = j1( x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of order one of the argument. - * - * The domain is divided into the intervals [0, 8] and - * (8, infinity). In the first interval a 24 term Chebyshev - * expansion is used. In the second, the asymptotic - * trigonometric representation is employed using two - * rational functions of degree 5/5. - * - * - * - * ACCURACY: - * - * Absolute error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 2.6e-16 1.1e-16 - * - * - */ - /* y1.c - * - * Bessel function of second kind of order one - * - * - * - * SYNOPSIS: - * - * double x, y, y1(); - * - * y = y1( x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of the second kind of order one - * of the argument. - * - * The domain is divided into the intervals [0, 8] and - * (8, infinity). In the first interval a 25 term Chebyshev - * expansion is used, and a call to j1() is required. - * In the second, the asymptotic trigonometric representation - * is employed using two rational functions of degree 5/5. - * - * - * - * ACCURACY: - * - * Absolute error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.0e-15 1.3e-16 - * - * (error criterion relative when |y1| > 1). - * - */ - - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier - */ - -/* - * #define PIO4 .78539816339744830962 - * #define THPIO4 2.35619449019234492885 - * #define SQ2OPI .79788456080286535588 - */ - -#include "mconf.h" - -static double RP[4] = { - -8.99971225705559398224E8, - 4.52228297998194034323E11, - -7.27494245221818276015E13, - 3.68295732863852883286E15, -}; - -static double RQ[8] = { - /* 1.00000000000000000000E0, */ - 6.20836478118054335476E2, - 2.56987256757748830383E5, - 8.35146791431949253037E7, - 2.21511595479792499675E10, - 4.74914122079991414898E12, - 7.84369607876235854894E14, - 8.95222336184627338078E16, - 5.32278620332680085395E18, -}; - -static double PP[7] = { - 7.62125616208173112003E-4, - 7.31397056940917570436E-2, - 1.12719608129684925192E0, - 5.11207951146807644818E0, - 8.42404590141772420927E0, - 5.21451598682361504063E0, - 1.00000000000000000254E0, -}; - -static double PQ[7] = { - 5.71323128072548699714E-4, - 6.88455908754495404082E-2, - 1.10514232634061696926E0, - 5.07386386128601488557E0, - 8.39985554327604159757E0, - 5.20982848682361821619E0, - 9.99999999999999997461E-1, -}; - -static double QP[8] = { - 5.10862594750176621635E-2, - 4.98213872951233449420E0, - 7.58238284132545283818E1, - 3.66779609360150777800E2, - 7.10856304998926107277E2, - 5.97489612400613639965E2, - 2.11688757100572135698E2, - 2.52070205858023719784E1, -}; - -static double QQ[7] = { - /* 1.00000000000000000000E0, */ - 7.42373277035675149943E1, - 1.05644886038262816351E3, - 4.98641058337653607651E3, - 9.56231892404756170795E3, - 7.99704160447350683650E3, - 2.82619278517639096600E3, - 3.36093607810698293419E2, -}; - -static double YP[6] = { - 1.26320474790178026440E9, - -6.47355876379160291031E11, - 1.14509511541823727583E14, - -8.12770255501325109621E15, - 2.02439475713594898196E17, - -7.78877196265950026825E17, -}; - -static double YQ[8] = { - /* 1.00000000000000000000E0, */ - 5.94301592346128195359E2, - 2.35564092943068577943E5, - 7.34811944459721705660E7, - 1.87601316108706159478E10, - 3.88231277496238566008E12, - 6.20557727146953693363E14, - 6.87141087355300489866E16, - 3.97270608116560655612E18, -}; - - -static double Z1 = 1.46819706421238932572E1; -static double Z2 = 4.92184563216946036703E1; - -extern double THPIO4, SQ2OPI; - -double j1(double x) -{ - double w, z, p, q, xn; - - w = x; - if (x < 0) - return -j1(-x); - - if (w <= 5.0) { - z = x * x; - w = polevl(z, RP, 3) / p1evl(z, RQ, 8); - w = w * x * (z - Z1) * (z - Z2); - return (w); - } - - w = 5.0 / x; - z = w * w; - p = polevl(z, PP, 6) / polevl(z, PQ, 6); - q = polevl(z, QP, 7) / p1evl(z, QQ, 7); - xn = x - THPIO4; - p = p * cos(xn) - w * q * sin(xn); - return (p * SQ2OPI / sqrt(x)); -} - - -double y1(double x) -{ - double w, z, p, q, xn; - - if (x <= 5.0) { - if (x == 0.0) { - sf_error("y1", SF_ERROR_SINGULAR, NULL); - return -INFINITY; - } - else if (x <= 0.0) { - sf_error("y1", SF_ERROR_DOMAIN, NULL); - return NAN; - } - z = x * x; - w = x * (polevl(z, YP, 5) / p1evl(z, YQ, 8)); - w += M_2_PI * (j1(x) * log(x) - 1.0 / x); - return (w); - } - - w = 5.0 / x; - z = w * w; - p = polevl(z, PP, 6) / polevl(z, PQ, 6); - q = polevl(z, QP, 7) / p1evl(z, QQ, 7); - xn = x - THPIO4; - p = p * sin(xn) + w * q * cos(xn); - return (p * SQ2OPI / sqrt(x)); -} diff --git a/gtsam/3rdparty/cephes/cephes/jv.c b/gtsam/3rdparty/cephes/cephes/jv.c deleted file mode 100644 index 3434c18f31..0000000000 --- a/gtsam/3rdparty/cephes/cephes/jv.c +++ /dev/null @@ -1,841 +0,0 @@ -/* jv.c - * - * Bessel function of noninteger order - * - * - * - * SYNOPSIS: - * - * double v, x, y, jv(); - * - * y = jv( v, x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of order v of the argument, - * where v is real. Negative x is allowed if v is an integer. - * - * Several expansions are included: the ascending power - * series, the Hankel expansion, and two transitional - * expansions for large v. If v is not too large, it - * is reduced by recurrence to a region of best accuracy. - * The transitional expansions give 12D accuracy for v > 500. - * - * - * - * ACCURACY: - * Results for integer v are indicated by *, where x and v - * both vary from -125 to +125. Otherwise, - * x ranges from 0 to 125, v ranges as indicated by "domain." - * Error criterion is absolute, except relative when |jv()| > 1. - * - * arithmetic v domain x domain # trials peak rms - * IEEE 0,125 0,125 100000 4.6e-15 2.2e-16 - * IEEE -125,0 0,125 40000 5.4e-11 3.7e-13 - * IEEE 0,500 0,500 20000 4.4e-15 4.0e-16 - * Integer v: - * IEEE -125,125 -125,125 50000 3.5e-15* 1.9e-16* - * - */ - - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier - */ - - -#include "mconf.h" -#define CEPHES_DEBUG 0 - -#if CEPHES_DEBUG -#include -#endif - -#define MAXGAM 171.624376956302725 - -extern double MACHEP, MINLOG, MAXLOG; - -#define BIG 1.44115188075855872E+17 - -static double jvs(double n, double x); -static double hankel(double n, double x); -static double recur(double *n, double x, double *newn, int cancel); -static double jnx(double n, double x); -static double jnt(double n, double x); - -double jv(double n, double x) -{ - double k, q, t, y, an; - int i, sign, nint; - - nint = 0; /* Flag for integer n */ - sign = 1; /* Flag for sign inversion */ - an = fabs(n); - y = floor(an); - if (y == an) { - nint = 1; - i = an - 16384.0 * floor(an / 16384.0); - if (n < 0.0) { - if (i & 1) - sign = -sign; - n = an; - } - if (x < 0.0) { - if (i & 1) - sign = -sign; - x = -x; - } - if (n == 0.0) - return (j0(x)); - if (n == 1.0) - return (sign * j1(x)); - } - - if ((x < 0.0) && (y != an)) { - sf_error("Jv", SF_ERROR_DOMAIN, NULL); - y = NAN; - goto done; - } - - if (x == 0 && n < 0 && !nint) { - sf_error("Jv", SF_ERROR_OVERFLOW, NULL); - return INFINITY / gamma(n + 1); - } - - y = fabs(x); - - if (y * y < fabs(n + 1) * MACHEP) { - return pow(0.5 * x, n) / gamma(n + 1); - } - - k = 3.6 * sqrt(y); - t = 3.6 * sqrt(an); - if ((y < t) && (an > 21.0)) - return (sign * jvs(n, x)); - if ((an < k) && (y > 21.0)) - return (sign * hankel(n, x)); - - if (an < 500.0) { - /* Note: if x is too large, the continued fraction will fail; but then the - * Hankel expansion can be used. */ - if (nint != 0) { - k = 0.0; - q = recur(&n, x, &k, 1); - if (k == 0.0) { - y = j0(x) / q; - goto done; - } - if (k == 1.0) { - y = j1(x) / q; - goto done; - } - } - - if (an > 2.0 * y) - goto rlarger; - - if ((n >= 0.0) && (n < 20.0) - && (y > 6.0) && (y < 20.0)) { - /* Recur backwards from a larger value of n */ - rlarger: - k = n; - - y = y + an + 1.0; - if (y < 30.0) - y = 30.0; - y = n + floor(y - n); - q = recur(&y, x, &k, 0); - y = jvs(y, x) * q; - goto done; - } - - if (k <= 30.0) { - k = 2.0; - } - else if (k < 90.0) { - k = (3 * k) / 4; - } - if (an > (k + 3.0)) { - if (n < 0.0) - k = -k; - q = n - floor(n); - k = floor(k) + q; - if (n > 0.0) - q = recur(&n, x, &k, 1); - else { - t = k; - k = n; - q = recur(&t, x, &k, 1); - k = t; - } - if (q == 0.0) { - y = 0.0; - goto done; - } - } - else { - k = n; - q = 1.0; - } - - /* boundary between convergence of - * power series and Hankel expansion - */ - y = fabs(k); - if (y < 26.0) - t = (0.0083 * y + 0.09) * y + 12.9; - else - t = 0.9 * y; - - if (x > t) - y = hankel(k, x); - else - y = jvs(k, x); -#if CEPHES_DEBUG - printf("y = %.16e, recur q = %.16e\n", y, q); -#endif - if (n > 0.0) - y /= q; - else - y *= q; - } - - else { - /* For large n, use the uniform expansion or the transitional expansion. - * But if x is of the order of n**2, these may blow up, whereas the - * Hankel expansion will then work. - */ - if (n < 0.0) { - sf_error("Jv", SF_ERROR_LOSS, NULL); - y = NAN; - goto done; - } - t = x / n; - t /= n; - if (t > 0.3) - y = hankel(n, x); - else - y = jnx(n, x); - } - - done:return (sign * y); -} - -/* Reduce the order by backward recurrence. - * AMS55 #9.1.27 and 9.1.73. - */ - -static double recur(double *n, double x, double *newn, int cancel) -{ - double pkm2, pkm1, pk, qkm2, qkm1; - - /* double pkp1; */ - double k, ans, qk, xk, yk, r, t, kf; - static double big = BIG; - int nflag, ctr; - int miniter, maxiter; - - /* Continued fraction for Jn(x)/Jn-1(x) - * AMS 9.1.73 - * - * x -x^2 -x^2 - * ------ --------- --------- ... - * 2 n + 2(n+1) + 2(n+2) + - * - * Compute it with the simplest possible algorithm. - * - * This continued fraction starts to converge when (|n| + m) > |x|. - * Hence, at least |x|-|n| iterations are necessary before convergence is - * achieved. There is a hard limit set below, m <= 30000, which is chosen - * so that no branch in `jv` requires more iterations to converge. - * The exact maximum number is (500/3.6)^2 - 500 ~ 19000 - */ - - maxiter = 22000; - miniter = fabs(x) - fabs(*n); - if (miniter < 1) - miniter = 1; - - if (*n < 0.0) - nflag = 1; - else - nflag = 0; - - fstart: - -#if CEPHES_DEBUG - printf("recur: n = %.6e, newn = %.6e, cfrac = ", *n, *newn); -#endif - - pkm2 = 0.0; - qkm2 = 1.0; - pkm1 = x; - qkm1 = *n + *n; - xk = -x * x; - yk = qkm1; - ans = 0.0; /* ans=0.0 ensures that t=1.0 in the first iteration */ - ctr = 0; - do { - yk += 2.0; - pk = pkm1 * yk + pkm2 * xk; - qk = qkm1 * yk + qkm2 * xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - - /* check convergence */ - if (qk != 0 && ctr > miniter) - r = pk / qk; - else - r = 0.0; - - if (r != 0) { - t = fabs((ans - r) / r); - ans = r; - } - else { - t = 1.0; - } - - if (++ctr > maxiter) { - sf_error("jv", SF_ERROR_UNDERFLOW, NULL); - goto done; - } - if (t < MACHEP) - goto done; - - /* renormalize coefficients */ - if (fabs(pk) > big) { - pkm2 /= big; - pkm1 /= big; - qkm2 /= big; - qkm1 /= big; - } - } - while (t > MACHEP); - - done: - if (ans == 0) - ans = 1.0; - -#if CEPHES_DEBUG - printf("%.6e\n", ans); -#endif - - /* Change n to n-1 if n < 0 and the continued fraction is small */ - if (nflag > 0) { - if (fabs(ans) < 0.125) { - nflag = -1; - *n = *n - 1.0; - goto fstart; - } - } - - - kf = *newn; - - /* backward recurrence - * 2k - * J (x) = --- J (x) - J (x) - * k-1 x k k+1 - */ - - pk = 1.0; - pkm1 = 1.0 / ans; - k = *n - 1.0; - r = 2 * k; - do { - pkm2 = (pkm1 * r - pk * x) / x; - /* pkp1 = pk; */ - pk = pkm1; - pkm1 = pkm2; - r -= 2.0; - /* - * t = fabs(pkp1) + fabs(pk); - * if( (k > (kf + 2.5)) && (fabs(pkm1) < 0.25*t) ) - * { - * k -= 1.0; - * t = x*x; - * pkm2 = ( (r*(r+2.0)-t)*pk - r*x*pkp1 )/t; - * pkp1 = pk; - * pk = pkm1; - * pkm1 = pkm2; - * r -= 2.0; - * } - */ - k -= 1.0; - } - while (k > (kf + 0.5)); - - /* Take the larger of the last two iterates - * on the theory that it may have less cancellation error. - */ - - if (cancel) { - if ((kf >= 0.0) && (fabs(pk) > fabs(pkm1))) { - k += 1.0; - pkm2 = pk; - } - } - *newn = k; -#if CEPHES_DEBUG - printf("newn %.6e rans %.6e\n", k, pkm2); -#endif - return (pkm2); -} - - - -/* Ascending power series for Jv(x). - * AMS55 #9.1.10. - */ - -static double jvs(double n, double x) -{ - double t, u, y, z, k; - int ex, sgngam; - - z = -x * x / 4.0; - u = 1.0; - y = u; - k = 1.0; - t = 1.0; - - while (t > MACHEP) { - u *= z / (k * (n + k)); - y += u; - k += 1.0; - if (y != 0) - t = fabs(u / y); - } -#if CEPHES_DEBUG - printf("power series=%.5e ", y); -#endif - t = frexp(0.5 * x, &ex); - ex = ex * n; - if ((ex > -1023) - && (ex < 1023) - && (n > 0.0) - && (n < (MAXGAM - 1.0))) { - t = pow(0.5 * x, n) / gamma(n + 1.0); -#if CEPHES_DEBUG - printf("pow(.5*x, %.4e)/gamma(n+1)=%.5e\n", n, t); -#endif - y *= t; - } - else { -#if CEPHES_DEBUG - z = n * log(0.5 * x); - k = lgam(n + 1.0); - t = z - k; - printf("log pow=%.5e, lgam(%.4e)=%.5e\n", z, n + 1.0, k); -#else - t = n * log(0.5 * x) - lgam_sgn(n + 1.0, &sgngam); -#endif - if (y < 0) { - sgngam = -sgngam; - y = -y; - } - t += log(y); -#if CEPHES_DEBUG - printf("log y=%.5e\n", log(y)); -#endif - if (t < -MAXLOG) { - return (0.0); - } - if (t > MAXLOG) { - sf_error("Jv", SF_ERROR_OVERFLOW, NULL); - return (INFINITY); - } - y = sgngam * exp(t); - } - return (y); -} - -/* Hankel's asymptotic expansion - * for large x. - * AMS55 #9.2.5. - */ - -static double hankel(double n, double x) -{ - double t, u, z, k, sign, conv; - double p, q, j, m, pp, qq; - int flag; - - m = 4.0 * n * n; - j = 1.0; - z = 8.0 * x; - k = 1.0; - p = 1.0; - u = (m - 1.0) / z; - q = u; - sign = 1.0; - conv = 1.0; - flag = 0; - t = 1.0; - pp = 1.0e38; - qq = 1.0e38; - - while (t > MACHEP) { - k += 2.0; - j += 1.0; - sign = -sign; - u *= (m - k * k) / (j * z); - p += sign * u; - k += 2.0; - j += 1.0; - u *= (m - k * k) / (j * z); - q += sign * u; - t = fabs(u / p); - if (t < conv) { - conv = t; - qq = q; - pp = p; - flag = 1; - } - /* stop if the terms start getting larger */ - if ((flag != 0) && (t > conv)) { -#if CEPHES_DEBUG - printf("Hankel: convergence to %.4E\n", conv); -#endif - goto hank1; - } - } - - hank1: - u = x - (0.5 * n + 0.25) * M_PI; - t = sqrt(2.0 / (M_PI * x)) * (pp * cos(u) - qq * sin(u)); -#if CEPHES_DEBUG - printf("hank: %.6e\n", t); -#endif - return (t); -} - - -/* Asymptotic expansion for large n. - * AMS55 #9.3.35. - */ - -static double lambda[] = { - 1.0, - 1.041666666666666666666667E-1, - 8.355034722222222222222222E-2, - 1.282265745563271604938272E-1, - 2.918490264641404642489712E-1, - 8.816272674437576524187671E-1, - 3.321408281862767544702647E+0, - 1.499576298686255465867237E+1, - 7.892301301158651813848139E+1, - 4.744515388682643231611949E+2, - 3.207490090890661934704328E+3 -}; - -static double mu[] = { - 1.0, - -1.458333333333333333333333E-1, - -9.874131944444444444444444E-2, - -1.433120539158950617283951E-1, - -3.172272026784135480967078E-1, - -9.424291479571202491373028E-1, - -3.511203040826354261542798E+0, - -1.572726362036804512982712E+1, - -8.228143909718594444224656E+1, - -4.923553705236705240352022E+2, - -3.316218568547972508762102E+3 -}; - -static double P1[] = { - -2.083333333333333333333333E-1, - 1.250000000000000000000000E-1 -}; - -static double P2[] = { - 3.342013888888888888888889E-1, - -4.010416666666666666666667E-1, - 7.031250000000000000000000E-2 -}; - -static double P3[] = { - -1.025812596450617283950617E+0, - 1.846462673611111111111111E+0, - -8.912109375000000000000000E-1, - 7.324218750000000000000000E-2 -}; - -static double P4[] = { - 4.669584423426247427983539E+0, - -1.120700261622299382716049E+1, - 8.789123535156250000000000E+0, - -2.364086914062500000000000E+0, - 1.121520996093750000000000E-1 -}; - -static double P5[] = { - -2.8212072558200244877E1, - 8.4636217674600734632E1, - -9.1818241543240017361E1, - 4.2534998745388454861E1, - -7.3687943594796316964E0, - 2.27108001708984375E-1 -}; - -static double P6[] = { - 2.1257013003921712286E2, - -7.6525246814118164230E2, - 1.0599904525279998779E3, - -6.9957962737613254123E2, - 2.1819051174421159048E2, - -2.6491430486951555525E1, - 5.7250142097473144531E-1 -}; - -static double P7[] = { - -1.9194576623184069963E3, - 8.0617221817373093845E3, - -1.3586550006434137439E4, - 1.1655393336864533248E4, - -5.3056469786134031084E3, - 1.2009029132163524628E3, - -1.0809091978839465550E2, - 1.7277275025844573975E0 -}; - - -static double jnx(double n, double x) -{ - double zeta, sqz, zz, zp, np; - double cbn, n23, t, z, sz; - double pp, qq, z32i, zzi; - double ak, bk, akl, bkl; - int sign, doa, dob, nflg, k, s, tk, tkp1, m; - static double u[8]; - static double ai, aip, bi, bip; - - /* Test for x very close to n. Use expansion for transition region if so. */ - cbn = cbrt(n); - z = (x - n) / cbn; - if (fabs(z) <= 0.7) - return (jnt(n, x)); - - z = x / n; - zz = 1.0 - z * z; - if (zz == 0.0) - return (0.0); - - if (zz > 0.0) { - sz = sqrt(zz); - t = 1.5 * (log((1.0 + sz) / z) - sz); /* zeta ** 3/2 */ - zeta = cbrt(t * t); - nflg = 1; - } - else { - sz = sqrt(-zz); - t = 1.5 * (sz - acos(1.0 / z)); - zeta = -cbrt(t * t); - nflg = -1; - } - z32i = fabs(1.0 / t); - sqz = cbrt(t); - - /* Airy function */ - n23 = cbrt(n * n); - t = n23 * zeta; - -#if CEPHES_DEBUG - printf("zeta %.5E, Airy(%.5E)\n", zeta, t); -#endif - airy(t, &ai, &aip, &bi, &bip); - - /* polynomials in expansion */ - u[0] = 1.0; - zzi = 1.0 / zz; - u[1] = polevl(zzi, P1, 1) / sz; - u[2] = polevl(zzi, P2, 2) / zz; - u[3] = polevl(zzi, P3, 3) / (sz * zz); - pp = zz * zz; - u[4] = polevl(zzi, P4, 4) / pp; - u[5] = polevl(zzi, P5, 5) / (pp * sz); - pp *= zz; - u[6] = polevl(zzi, P6, 6) / pp; - u[7] = polevl(zzi, P7, 7) / (pp * sz); - -#if CEPHES_DEBUG - for (k = 0; k <= 7; k++) - printf("u[%d] = %.5E\n", k, u[k]); -#endif - - pp = 0.0; - qq = 0.0; - np = 1.0; - /* flags to stop when terms get larger */ - doa = 1; - dob = 1; - akl = INFINITY; - bkl = INFINITY; - - for (k = 0; k <= 3; k++) { - tk = 2 * k; - tkp1 = tk + 1; - zp = 1.0; - ak = 0.0; - bk = 0.0; - for (s = 0; s <= tk; s++) { - if (doa) { - if ((s & 3) > 1) - sign = nflg; - else - sign = 1; - ak += sign * mu[s] * zp * u[tk - s]; - } - - if (dob) { - m = tkp1 - s; - if (((m + 1) & 3) > 1) - sign = nflg; - else - sign = 1; - bk += sign * lambda[s] * zp * u[m]; - } - zp *= z32i; - } - - if (doa) { - ak *= np; - t = fabs(ak); - if (t < akl) { - akl = t; - pp += ak; - } - else - doa = 0; - } - - if (dob) { - bk += lambda[tkp1] * zp * u[0]; - bk *= -np / sqz; - t = fabs(bk); - if (t < bkl) { - bkl = t; - qq += bk; - } - else - dob = 0; - } -#if CEPHES_DEBUG - printf("a[%d] %.5E, b[%d] %.5E\n", k, ak, k, bk); -#endif - if (np < MACHEP) - break; - np /= n * n; - } - - /* normalizing factor ( 4*zeta/(1 - z**2) )**1/4 */ - t = 4.0 * zeta / zz; - t = sqrt(sqrt(t)); - - t *= ai * pp / cbrt(n) + aip * qq / (n23 * n); - return (t); -} - -/* Asymptotic expansion for transition region, - * n large and x close to n. - * AMS55 #9.3.23. - */ - -static double PF2[] = { - -9.0000000000000000000e-2, - 8.5714285714285714286e-2 -}; - -static double PF3[] = { - 1.3671428571428571429e-1, - -5.4920634920634920635e-2, - -4.4444444444444444444e-3 -}; - -static double PF4[] = { - 1.3500000000000000000e-3, - -1.6036054421768707483e-1, - 4.2590187590187590188e-2, - 2.7330447330447330447e-3 -}; - -static double PG1[] = { - -2.4285714285714285714e-1, - 1.4285714285714285714e-2 -}; - -static double PG2[] = { - -9.0000000000000000000e-3, - 1.9396825396825396825e-1, - -1.1746031746031746032e-2 -}; - -static double PG3[] = { - 1.9607142857142857143e-2, - -1.5983694083694083694e-1, - 6.3838383838383838384e-3 -}; - - -static double jnt(double n, double x) -{ - double z, zz, z3; - double cbn, n23, cbtwo; - double ai, aip, bi, bip; /* Airy functions */ - double nk, fk, gk, pp, qq; - double F[5], G[4]; - int k; - - cbn = cbrt(n); - z = (x - n) / cbn; - cbtwo = cbrt(2.0); - - /* Airy function */ - zz = -cbtwo * z; - airy(zz, &ai, &aip, &bi, &bip); - - /* polynomials in expansion */ - zz = z * z; - z3 = zz * z; - F[0] = 1.0; - F[1] = -z / 5.0; - F[2] = polevl(z3, PF2, 1) * zz; - F[3] = polevl(z3, PF3, 2); - F[4] = polevl(z3, PF4, 3) * z; - G[0] = 0.3 * zz; - G[1] = polevl(z3, PG1, 1); - G[2] = polevl(z3, PG2, 2) * z; - G[3] = polevl(z3, PG3, 2) * zz; -#if CEPHES_DEBUG - for (k = 0; k <= 4; k++) - printf("F[%d] = %.5E\n", k, F[k]); - for (k = 0; k <= 3; k++) - printf("G[%d] = %.5E\n", k, G[k]); -#endif - pp = 0.0; - qq = 0.0; - nk = 1.0; - n23 = cbrt(n * n); - - for (k = 0; k <= 4; k++) { - fk = F[k] * nk; - pp += fk; - if (k != 4) { - gk = G[k] * nk; - qq += gk; - } -#if CEPHES_DEBUG - printf("fk[%d] %.5E, gk[%d] %.5E\n", k, fk, k, gk); -#endif - nk /= n23; - } - - fk = cbtwo * ai * pp / cbn + cbrt(4.0) * aip * qq / n; - return (fk); -} diff --git a/gtsam/3rdparty/cephes/cephes/k0.c b/gtsam/3rdparty/cephes/cephes/k0.c deleted file mode 100644 index c5b31a1bf1..0000000000 --- a/gtsam/3rdparty/cephes/cephes/k0.c +++ /dev/null @@ -1,178 +0,0 @@ -/* k0.c - * - * Modified Bessel function, third kind, order zero - * - * - * - * SYNOPSIS: - * - * double x, y, k0(); - * - * y = k0( x ); - * - * - * - * DESCRIPTION: - * - * Returns modified Bessel function of the third kind - * of order zero of the argument. - * - * The range is partitioned into the two intervals [0,8] and - * (8, infinity). Chebyshev polynomial expansions are employed - * in each interval. - * - * - * - * ACCURACY: - * - * Tested at 2000 random points between 0 and 8. Peak absolute - * error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15. - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.2e-15 1.6e-16 - * - * ERROR MESSAGES: - * - * message condition value returned - * K0 domain x <= 0 INFINITY - * - */ - /* k0e() - * - * Modified Bessel function, third kind, order zero, - * exponentially scaled - * - * - * - * SYNOPSIS: - * - * double x, y, k0e(); - * - * y = k0e( x ); - * - * - * - * DESCRIPTION: - * - * Returns exponentially scaled modified Bessel function - * of the third kind of order zero of the argument. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.4e-15 1.4e-16 - * See k0(). - * - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -/* Chebyshev coefficients for K0(x) + log(x/2) I0(x) - * in the interval [0,2]. The odd order coefficients are all - * zero; only the even order coefficients are listed. - * - * lim(x->0){ K0(x) + log(x/2) I0(x) } = -EUL. - */ - -static double A[] = { - 1.37446543561352307156E-16, - 4.25981614279661018399E-14, - 1.03496952576338420167E-11, - 1.90451637722020886025E-9, - 2.53479107902614945675E-7, - 2.28621210311945178607E-5, - 1.26461541144692592338E-3, - 3.59799365153615016266E-2, - 3.44289899924628486886E-1, - -5.35327393233902768720E-1 -}; - -/* Chebyshev coefficients for exp(x) sqrt(x) K0(x) - * in the inverted interval [2,infinity]. - * - * lim(x->inf){ exp(x) sqrt(x) K0(x) } = sqrt(pi/2). - */ -static double B[] = { - 5.30043377268626276149E-18, - -1.64758043015242134646E-17, - 5.21039150503902756861E-17, - -1.67823109680541210385E-16, - 5.51205597852431940784E-16, - -1.84859337734377901440E-15, - 6.34007647740507060557E-15, - -2.22751332699166985548E-14, - 8.03289077536357521100E-14, - -2.98009692317273043925E-13, - 1.14034058820847496303E-12, - -4.51459788337394416547E-12, - 1.85594911495471785253E-11, - -7.95748924447710747776E-11, - 3.57739728140030116597E-10, - -1.69753450938905987466E-9, - 8.57403401741422608519E-9, - -4.66048989768794782956E-8, - 2.76681363944501510342E-7, - -1.83175552271911948767E-6, - 1.39498137188764993662E-5, - -1.28495495816278026384E-4, - 1.56988388573005337491E-3, - -3.14481013119645005427E-2, - 2.44030308206595545468E0 -}; - -double k0(double x) -{ - double y, z; - - if (x == 0.0) { - sf_error("k0", SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - else if (x < 0.0) { - sf_error("k0", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (x <= 2.0) { - y = x * x - 2.0; - y = chbevl(y, A, 10) - log(0.5 * x) * i0(x); - return (y); - } - z = 8.0 / x - 2.0; - y = exp(-x) * chbevl(z, B, 25) / sqrt(x); - return (y); -} - - - - -double k0e(double x) -{ - double y; - - if (x == 0.0) { - sf_error("k0e", SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - else if (x < 0.0) { - sf_error("k0e", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (x <= 2.0) { - y = x * x - 2.0; - y = chbevl(y, A, 10) - log(0.5 * x) * i0(x); - return (y * exp(x)); - } - - y = chbevl(8.0 / x - 2.0, B, 25) / sqrt(x); - return (y); -} diff --git a/gtsam/3rdparty/cephes/cephes/k1.c b/gtsam/3rdparty/cephes/cephes/k1.c deleted file mode 100644 index fc33e5c0ee..0000000000 --- a/gtsam/3rdparty/cephes/cephes/k1.c +++ /dev/null @@ -1,179 +0,0 @@ -/* k1.c - * - * Modified Bessel function, third kind, order one - * - * - * - * SYNOPSIS: - * - * double x, y, k1(); - * - * y = k1( x ); - * - * - * - * DESCRIPTION: - * - * Computes the modified Bessel function of the third kind - * of order one of the argument. - * - * The range is partitioned into the two intervals [0,2] and - * (2, infinity). Chebyshev polynomial expansions are employed - * in each interval. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 1.2e-15 1.6e-16 - * - * ERROR MESSAGES: - * - * message condition value returned - * k1 domain x <= 0 INFINITY - * - */ - /* k1e.c - * - * Modified Bessel function, third kind, order one, - * exponentially scaled - * - * - * - * SYNOPSIS: - * - * double x, y, k1e(); - * - * y = k1e( x ); - * - * - * - * DESCRIPTION: - * - * Returns exponentially scaled modified Bessel function - * of the third kind of order one of the argument: - * - * k1e(x) = exp(x) * k1(x). - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 7.8e-16 1.2e-16 - * See k1(). - * - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -/* Chebyshev coefficients for x(K1(x) - log(x/2) I1(x)) - * in the interval [0,2]. - * - * lim(x->0){ x(K1(x) - log(x/2) I1(x)) } = 1. - */ - -static double A[] = { - -7.02386347938628759343E-18, - -2.42744985051936593393E-15, - -6.66690169419932900609E-13, - -1.41148839263352776110E-10, - -2.21338763073472585583E-8, - -2.43340614156596823496E-6, - -1.73028895751305206302E-4, - -6.97572385963986435018E-3, - -1.22611180822657148235E-1, - -3.53155960776544875667E-1, - 1.52530022733894777053E0 -}; - -/* Chebyshev coefficients for exp(x) sqrt(x) K1(x) - * in the interval [2,infinity]. - * - * lim(x->inf){ exp(x) sqrt(x) K1(x) } = sqrt(pi/2). - */ -static double B[] = { - -5.75674448366501715755E-18, - 1.79405087314755922667E-17, - -5.68946255844285935196E-17, - 1.83809354436663880070E-16, - -6.05704724837331885336E-16, - 2.03870316562433424052E-15, - -7.01983709041831346144E-15, - 2.47715442448130437068E-14, - -8.97670518232499435011E-14, - 3.34841966607842919884E-13, - -1.28917396095102890680E-12, - 5.13963967348173025100E-12, - -2.12996783842756842877E-11, - 9.21831518760500529508E-11, - -4.19035475934189648750E-10, - 2.01504975519703286596E-9, - -1.03457624656780970260E-8, - 5.74108412545004946722E-8, - -3.50196060308781257119E-7, - 2.40648494783721712015E-6, - -1.93619797416608296024E-5, - 1.95215518471351631108E-4, - -2.85781685962277938680E-3, - 1.03923736576817238437E-1, - 2.72062619048444266945E0 -}; - -extern double MINLOG; - -double k1(double x) -{ - double y, z; - - if (x == 0.0) { - sf_error("k1", SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - else if (x < 0.0) { - sf_error("k1", SF_ERROR_DOMAIN, NULL); - return NAN; - } - z = 0.5 * x; - - if (x <= 2.0) { - y = x * x - 2.0; - y = log(z) * i1(x) + chbevl(y, A, 11) / x; - return (y); - } - - return (exp(-x) * chbevl(8.0 / x - 2.0, B, 25) / sqrt(x)); -} - - - - -double k1e(double x) -{ - double y; - - if (x == 0.0) { - sf_error("k1e", SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - else if (x < 0.0) { - sf_error("k1e", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (x <= 2.0) { - y = x * x - 2.0; - y = log(0.5 * x) * i1(x) + chbevl(y, A, 11) / x; - return (y * exp(x)); - } - - return (chbevl(8.0 / x - 2.0, B, 25) / sqrt(x)); -} diff --git a/gtsam/3rdparty/cephes/cephes/kn.c b/gtsam/3rdparty/cephes/cephes/kn.c deleted file mode 100644 index ff7584a154..0000000000 --- a/gtsam/3rdparty/cephes/cephes/kn.c +++ /dev/null @@ -1,235 +0,0 @@ -/* kn.c - * - * Modified Bessel function, third kind, integer order - * - * - * - * SYNOPSIS: - * - * double x, y, kn(); - * int n; - * - * y = kn( n, x ); - * - * - * - * DESCRIPTION: - * - * Returns modified Bessel function of the third kind - * of order n of the argument. - * - * The range is partitioned into the two intervals [0,9.55] and - * (9.55, infinity). An ascending power series is used in the - * low range, and an asymptotic expansion in the high range. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,30 90000 1.8e-8 3.0e-10 - * - * Error is high only near the crossover point x = 9.55 - * between the two expansions used. - */ - - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier - */ - - -/* - * Algorithm for Kn. - * n-1 - * -n - (n-k-1)! 2 k - * K (x) = 0.5 (x/2) > -------- (-x /4) - * n - k! - * k=0 - * - * inf. 2 k - * n n - (x /4) - * + (-1) 0.5(x/2) > {p(k+1) + p(n+k+1) - 2log(x/2)} --------- - * - k! (n+k)! - * k=0 - * - * where p(m) is the psi function: p(1) = -EUL and - * - * m-1 - * - - * p(m) = -EUL + > 1/k - * - - * k=1 - * - * For large x, - * 2 2 2 - * u-1 (u-1 )(u-3 ) - * K (z) = sqrt(pi/2z) exp(-z) { 1 + ------- + ------------ + ...} - * v 1 2 - * 1! (8z) 2! (8z) - * asymptotically, where - * - * 2 - * u = 4 v . - * - */ - -#include "mconf.h" -#include - -#define EUL 5.772156649015328606065e-1 -#define MAXFAC 31 -extern double MACHEP, MAXLOG; - -double kn(int nn, double x) -{ - double k, kf, nk1f, nkf, zn, t, s, z0, z; - double ans, fn, pn, pk, zmn, tlg, tox; - int i, n; - - if (nn < 0) - n = -nn; - else - n = nn; - - if (n > MAXFAC) { - overf: - sf_error("kn", SF_ERROR_OVERFLOW, NULL); - return (INFINITY); - } - - if (x <= 0.0) { - if (x < 0.0) { - sf_error("kn", SF_ERROR_DOMAIN, NULL); - return NAN; - } - else { - sf_error("kn", SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - } - - - if (x > 9.55) - goto asymp; - - ans = 0.0; - z0 = 0.25 * x * x; - fn = 1.0; - pn = 0.0; - zmn = 1.0; - tox = 2.0 / x; - - if (n > 0) { - /* compute factorial of n and psi(n) */ - pn = -EUL; - k = 1.0; - for (i = 1; i < n; i++) { - pn += 1.0 / k; - k += 1.0; - fn *= k; - } - - zmn = tox; - - if (n == 1) { - ans = 1.0 / x; - } - else { - nk1f = fn / n; - kf = 1.0; - s = nk1f; - z = -z0; - zn = 1.0; - for (i = 1; i < n; i++) { - nk1f = nk1f / (n - i); - kf = kf * i; - zn *= z; - t = nk1f * zn / kf; - s += t; - if ((DBL_MAX - fabs(t)) < fabs(s)) - goto overf; - if ((tox > 1.0) && ((DBL_MAX / tox) < zmn)) - goto overf; - zmn *= tox; - } - s *= 0.5; - t = fabs(s); - if ((zmn > 1.0) && ((DBL_MAX / zmn) < t)) - goto overf; - if ((t > 1.0) && ((DBL_MAX / t) < zmn)) - goto overf; - ans = s * zmn; - } - } - - - tlg = 2.0 * log(0.5 * x); - pk = -EUL; - if (n == 0) { - pn = pk; - t = 1.0; - } - else { - pn = pn + 1.0 / n; - t = 1.0 / fn; - } - s = (pk + pn - tlg) * t; - k = 1.0; - do { - t *= z0 / (k * (k + n)); - pk += 1.0 / k; - pn += 1.0 / (k + n); - s += (pk + pn - tlg) * t; - k += 1.0; - } - while (fabs(t / s) > MACHEP); - - s = 0.5 * s / zmn; - if (n & 1) - s = -s; - ans += s; - - return (ans); - - - - /* Asymptotic expansion for Kn(x) */ - /* Converges to 1.4e-17 for x > 18.4 */ - - asymp: - - if (x > MAXLOG) { - sf_error("kn", SF_ERROR_UNDERFLOW, NULL); - return (0.0); - } - k = n; - pn = 4.0 * k * k; - pk = 1.0; - z0 = 8.0 * x; - fn = 1.0; - t = 1.0; - s = t; - nkf = INFINITY; - i = 0; - do { - z = pn - pk * pk; - t = t * z / (fn * z0); - nk1f = fabs(t); - if ((i >= n) && (nk1f > nkf)) { - goto adone; - } - nkf = nk1f; - s += t; - fn += 1.0; - pk += 2.0; - i += 1; - } - while (fabs(t / s) > MACHEP); - - adone: - ans = exp(-x) * sqrt(M_PI / (2.0 * x)) * s; - return (ans); -} diff --git a/gtsam/3rdparty/cephes/cephes/kolmogorov.c b/gtsam/3rdparty/cephes/cephes/kolmogorov.c deleted file mode 100644 index 2135e0ebbd..0000000000 --- a/gtsam/3rdparty/cephes/cephes/kolmogorov.c +++ /dev/null @@ -1,1147 +0,0 @@ -/* File altered for inclusion in cephes module for Python: - * Main loop commented out.... */ -/* Travis Oliphant Nov. 1998 */ - -/* Re Kolmogorov statistics, here is Birnbaum and Tingey's (actually it was already present - * in Smirnov's paper) formula for the - * distribution of D+, the maximum of all positive deviations between a - * theoretical distribution function P(x) and an empirical one Sn(x) - * from n samples. - * - * + - * D = sup [P(x) - S (x)] - * n -inf < x < inf n - * - * - * [n(1-d)] - * + - v-1 n-v - * Pr{D > d} = > C d (d + v/n) (1 - d - v/n) - * n - n v - * v=0 - * - * (also equals the following sum, but note the terms may be large and alternating in sign) - * See Smirnov 1944, Dwass 1959 - * n - * - v-1 n-v - * = 1 - > C d (d + v/n) (1 - d - v/n) - * - n v - * v=[n(1-d)]+1 - * - * [n(1-d)] is the largest integer not exceeding n(1-d). - * nCv is the number of combinations of n things taken v at a time. - - * Sources: - * [1] Smirnov, N.V. "Approximate laws of distribution of random variables from empirical data" - * Usp. Mat. Nauk, 1944. http://mi.mathnet.ru/umn8798 - * [2] Birnbaum, Z. W. and Tingey, Fred H. - * "One-Sided Confidence Contours for Probability Distribution Functions", - * Ann. Math. Statist. 1951. https://doi.org/10.1214/aoms/1177729550 - * [3] Dwass, Meyer, "The Distribution of a Generalized $\mathrm{D}^+_n$ Statistic", - * Ann. Math. Statist., 1959. https://doi.org/10.1214/aoms/1177706085 - * [4] van Mulbregt, Paul, "Computing the Cumulative Distribution Function and Quantiles of the One-sided Kolmogorov-Smirnov Statistic" - * http://arxiv.org/abs/1802.06966 - * [5] van Mulbregt, Paul, "Computing the Cumulative Distribution Function and Quantiles of the limit of the Two-sided Kolmogorov-Smirnov Statistic" - * https://arxiv.org/abs/1803.00426 - * - */ - -#include "mconf.h" -#include -#include -#include - - -/* ************************************************************************ */ -/* Algorithm Configuration */ - -/* - * Kolmogorov Two-sided: - * Switchover between the two series to compute K(x) - * 0 <= x <= KOLMOG_CUTOVER and - * KOLMOG_CUTOVER < x < infty - */ -#define KOLMOG_CUTOVER 0.82 - - -/* - * Smirnov One-sided: - * n larger than SMIRNOV_MAX_COMPUTE_N will result in an approximation - */ -const int SMIRNOV_MAX_COMPUTE_N = 1000000; - -/* - * Use the upper sum formula, if the number of terms is at most SM_UPPER_MAX_TERMS, - * and n is at least SM_UPPERSUM_MIN_N - * Don't use the upper sum if lots of terms are involved as the series alternates - * sign and the terms get much bigger than 1. - */ -#define SM_UPPER_MAX_TERMS 3 -#define SM_UPPERSUM_MIN_N 10 - -/* ************************************************************************ */ -/* ************************************************************************ */ - -/* Assuming LOW and HIGH are constants. */ -#define CLIP(X, LOW, HIGH) ((X) < LOW ? LOW : MIN(X, HIGH)) -#ifndef MIN -#define MIN(a,b) (((a) < (b)) ? (a) : (b)) -#endif -#ifndef MAX -#define MAX(a,b) (((a) < (b)) ? (b) : (a)) -#endif - -/* from cephes constants */ -extern double MINLOG; - -/* exp() of anything below this returns 0 */ -static const int MIN_EXPABLE = (-708 - 38); - -#ifndef LOGSQRT2PI -#define LOGSQRT2PI 0.91893853320467274178032973640561764 -#endif - -/* Struct to hold the CDF, SF and PDF, which are computed simultaneously */ -typedef struct ThreeProbs { - double sf; - double cdf; - double pdf; -} ThreeProbs; - -#define RETURN_3PROBS(PSF, PCDF, PDF) \ - ret.cdf = (PCDF); \ - ret.sf = (PSF); \ - ret.pdf = (PDF); \ - return ret; - -static const double _xtol = DBL_EPSILON; -static const double _rtol = 2*DBL_EPSILON; - -static int -_within_tol(double x, double y, double atol, double rtol) -{ - double diff = fabs(x-y); - int result = (diff <= (atol + rtol * fabs(y))); - return result; -} - -#include "dd_real.h" - -/* Shorten some of the double-double names for readibility */ -#define valueD dd_to_double -#define add_dd dd_add_d_d -#define sub_dd dd_sub_d_d -#define mul_dd dd_mul_d_d -#define neg_D dd_neg -#define div_dd dd_div_d_d -#define add_DD dd_add -#define sub_DD dd_sub -#define mul_DD dd_mul -#define div_DD dd_div -#define add_Dd dd_add_dd_d -#define add_dD dd_add_d_dd -#define sub_Dd dd_sub_dd_d -#define sub_dD dd_sub_d_dd -#define mul_Dd dd_mul_dd_d -#define mul_dD dd_mul_d_dd -#define div_Dd dd_div_dd_d -#define div_dD dd_div_d_dd -#define frexpD dd_frexp -#define ldexpD dd_ldexp -#define logD dd_log -#define log1pD dd_log1p - - -/* ************************************************************************ */ -/* Kolmogorov : Two-sided **************************** */ -/* ************************************************************************ */ - -static ThreeProbs -_kolmogorov(double x) -{ - double P = 1.0; - double D = 0; - double sf, cdf, pdf; - ThreeProbs ret; - - if (isnan(x)) { - RETURN_3PROBS(NAN, NAN, NAN); - } - if (x <= 0) { - RETURN_3PROBS(1.0, 0.0, 0); - } - /* x <= 0.040611972203751713 */ - if (x <= (double)M_PI/sqrt(-MIN_EXPABLE * 8)) { - RETURN_3PROBS(1.0, 0.0, 0); - } - - P = 1.0; - if (x <= KOLMOG_CUTOVER) { - /* - * u = e^(-pi^2/(8x^2)) - * w = sqrt(2pi)/x - * P = w*u * (1 + u^8 + u^24 + u^48 + ...) - */ - double w = sqrt(2 * M_PI)/x; - double logu8 = -M_PI * M_PI/(x * x); /* log(u^8) */ - double u = exp(logu8/8); - if (u == 0) { - /* - * P = w*u, but u < 1e-308, and w > 1, - * so compute as logs, then exponentiate - */ - double logP = logu8/8 + log(w); - P = exp(logP); - } else { - /* Just unroll the loop, 3 iterations */ - double u8 = exp(logu8); - double u8cub = pow(u8, 3); - P = 1 + u8cub * P; - D = 5*5 + u8cub * D; - P = 1 + u8*u8 * P; - D = 3*3 + u8*u8 * D; - P = 1 + u8 * P; - D = 1*1 + u8 * D; - - D = M_PI * M_PI/4/(x*x) * D - P; - D *= w * u/x; - P = w * u * P; - } - cdf = P; - sf = 1-P; - pdf = D; - } - else { - /* - * v = e^(-2x^2) - * P = 2 (v - v^4 + v^9 - v^16 + ...) - * = 2v(1 - v^3*(1 - v^5*(1 - v^7*(1 - ...))) - */ - double logv = -2*x*x; - double v = exp(logv); - /* - * Want q^((2k-1)^2)(1-q^(4k-1)) / q(1-q^3) < epsilon to break out of loop. - * With KOLMOG_CUTOVER ~ 0.82, k <= 4. Just unroll the loop, 4 iterations - */ - double vsq = v*v; - double v3 = pow(v, 3); - double vpwr; - - vpwr = v3*v3*v; /* v**7 */ - P = 1 - vpwr * P; /* P <- 1 - (1-v**(2k-1)) * P */ - D = 3*3 - vpwr * D; - - vpwr = v3*vsq; - P = 1 - vpwr * P; - D = 2*2 - vpwr * D; - - vpwr = v3; - P = 1 - vpwr * P; - D = 1*1 - vpwr * D; - - P = 2 * v * P; - D = 8 * v * x * D; - sf = P; - cdf = 1 - sf; - pdf = D; - } - pdf = MAX(0, pdf); - cdf = CLIP(cdf, 0, 1); - sf = CLIP(sf, 0, 1); - RETURN_3PROBS(sf, cdf, pdf); -} - - -/* Find x such kolmogorov(x)=psf, kolmogc(x)=pcdf */ -static double -_kolmogi(double psf, double pcdf) -{ - double x, t; - double xmin = 0; - double xmax = INFINITY; - int iterations; - double a = xmin, b = xmax; - - if (!(psf >= 0.0 && pcdf >= 0.0 && pcdf <= 1.0 && psf <= 1.0)) { - sf_error("kolmogi", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (fabs(1.0 - pcdf - psf) > 4* DBL_EPSILON) { - sf_error("kolmogi", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (pcdf == 0.0) { - return 0.0; - } - if (psf == 0.0) { - return INFINITY; - } - - if (pcdf <= 0.5) { - /* p ~ (sqrt(2pi)/x) *exp(-pi^2/8x^2). Generate lower and upper bounds */ - double logpcdf = log(pcdf); - const double SQRT2 = M_SQRT2; - /* Now that 1 >= x >= sqrt(p) */ - /* Iterate twice: x <- pi/(sqrt(8) sqrt(log(sqrt(2pi)) - log(x) - log(pdf))) */ - a = M_PI / (2 * SQRT2 * sqrt(-(logpcdf + logpcdf/2 - LOGSQRT2PI))); - b = M_PI / (2 * SQRT2 * sqrt(-(logpcdf + 0 - LOGSQRT2PI))); - a = M_PI / (2 * SQRT2 * sqrt(-(logpcdf + log(a) - LOGSQRT2PI))); - b = M_PI / (2 * SQRT2 * sqrt(-(logpcdf + log(b) - LOGSQRT2PI))); - x = (a + b) / 2.0; - } - else { - /* - * Based on the approximation p ~ 2 exp(-2x^2) - * Found that needed to replace psf with a slightly smaller number in the second element - * as otherwise _kolmogorov(b) came back as a very small number but with - * the same sign as _kolmogorov(a) - * kolmogi(0.5) = 0.82757355518990772 - * so (1-q^(-(4-1)*2*x^2)) = (1-exp(-6*0.8275^2) ~ (1-exp(-4.1) - */ - const double jiggerb = 256 * DBL_EPSILON; - double pba = psf/(1.0 - exp(-4))/2, pbb = psf * (1 - jiggerb)/2; - double q0; - a = sqrt(-0.5 * log(pba)); - b = sqrt(-0.5 * log(pbb)); - /* - * Use inversion of - * p = q - q^4 + q^9 - q^16 + ...: - * q = p + p^4 + 4p^7 - p^9 + 22p^10 - 13p^12 + 140*p^13 ... - */ - { - double p = psf/2.0; - double p2 = p*p; - double p3 = p*p*p; - q0 = 1 + p3 * (1 + p3 * (4 + p2 *(-1 + p*(22 + p2* (-13 + 140 * p))))); - q0 *= p; - } - x = sqrt(-log(q0) / 2); - if (x < a || x > b) { - x = (a+b)/2; - } - } - assert(a <= b); - - iterations = 0; - do { - double x0 = x; - ThreeProbs probs = _kolmogorov(x0); - double df = ((pcdf < 0.5) ? (pcdf - probs.cdf) : (probs.sf - psf)); - double dfdx; - - if (fabs(df) == 0) { - break; - } - /* Update the bracketing interval */ - if (df > 0 && x > a) { - a = x; - } else if (df < 0 && x < b) { - b = x; - } - - dfdx = -probs.pdf; - if (fabs(dfdx) <= 0.0) { - x = (a+b)/2; - t = x0 - x; - } else { - t = df/dfdx; - x = x0 - t; - } - - /* - * Check out-of-bounds. - * Not expecting this to happen often --- kolmogorov is convex near x=infinity and - * concave near x=0, and we should be approaching from the correct side. - * If out-of-bounds, replace x with a midpoint of the bracket. - */ - if (x >= a && x <= b) { - if (_within_tol(x, x0, _xtol, _rtol)) { - break; - } - if ((x == a) || (x == b)) { - x = (a + b) / 2.0; - /* If the bracket is already so small ... */ - if (x == a || x == b) { - break; - } - } - } else { - x = (a + b) / 2.0; - if (_within_tol(x, x0, _xtol, _rtol)) { - break; - } - } - - if (++iterations > MAXITER) { - sf_error("kolmogi", SF_ERROR_SLOW, NULL); - break; - } - } while(1); - return (x); -} - - -double -kolmogorov(double x) -{ - if (isnan(x)) { - return NAN; - } - return _kolmogorov(x).sf; -} - -double -kolmogc(double x) -{ - if (isnan(x)) { - return NAN; - } - return _kolmogorov(x).cdf; -} - -double -kolmogp(double x) -{ - if (isnan(x)) { - return NAN; - } - if (x <= 0) { - return -0.0; - } - return -_kolmogorov(x).pdf; -} - -/* Functional inverse of Kolmogorov survival statistic for two-sided test. - * Finds x such that kolmogorov(x) = p. - */ -double -kolmogi(double p) -{ - if (isnan(p)) { - return NAN; - } - return _kolmogi(p, 1-p); -} - -/* Functional inverse of Kolmogorov cumulative statistic for two-sided test. - * Finds x such that kolmogc(x) = p = (or kolmogorov(x) = 1-p). - */ -double -kolmogci(double p) -{ - if (isnan(p)) { - return NAN; - } - return _kolmogi(1-p, p); -} - - - -/* ************************************************************************ */ -/* ********** Smirnov : One-sided ***************************************** */ -/* ************************************************************************ */ - -static double -nextPowerOf2(double x) -{ - double q = ldexp(x, 1-DBL_MANT_DIG); - double L = fabs(q+x); - if (L == 0) { - L = fabs(x); - } else { - int Lint = (int)(L); - if (Lint == L) { - L = Lint; - } - } - return L; -} - -static double -modNX(int n, double x, int *pNXFloor, double *pNX) -{ - /* - * Compute floor(n*x) and remainder *exactly*. - * If remainder is too close to 1 (E.g. (1, -DBL_EPSILON/2)) - * round up and adjust */ - double2 alphaD, nxD, nxfloorD; - int nxfloor; - double alpha; - - nxD = mul_dd(n, x); - nxfloorD = dd_floor(nxD); - alphaD = sub_DD(nxD, nxfloorD); - alpha = dd_hi(alphaD); - nxfloor = dd_to_int(nxfloorD); - assert(alpha >= 0); - assert(alpha <= 1); - if (alpha == 1) { - nxfloor += 1; - alpha = 0; - } - assert(alpha < 1.0); - *pNX = dd_to_double(nxD); - *pNXFloor = nxfloor; - return alpha; -} - -/* - * The binomial coefficient C overflows a 64 bit double, as the 11-bit - * exponent is too small. - * Store C as (Cman:double2, Cexpt:int). - * I.e a Mantissa/significand, and an exponent. - * Cman lies between 0.5 and 1, and the exponent has >=32-bit. - */ -static void -updateBinomial(double2 *Cman, int *Cexpt, int n, int j) -{ - int expt; - double2 rat = div_dd(n - j, j + 1.0); - double2 man2 = mul_DD(*Cman, rat); - man2 = frexpD(man2, &expt); - assert (!dd_is_zero(man2)); - *Cexpt += expt; - *Cman = man2; -} - - -static double2 -pow_D(double2 a, int m) -{ - /* - * Using dd_npwr() here would be quite time-consuming. - * Tradeoff accuracy-time by using pow(). - */ - double ans, r, adj; - if (m <= 0) { - if (m == 0) { - return DD_C_ONE; - } - return dd_inv(pow_D(a, -m)); - } - if (dd_is_zero(a)) { - return DD_C_ZERO; - } - ans = pow(a.x[0], m); - r = a.x[1]/a.x[0]; - adj = m*r; - if (fabs(adj) > 1e-8) { - if (fabs(adj) < 1e-4) { - /* Take 1st two terms of Taylor Series for (1+r)^m */ - adj += (m*r) * ((m-1)/2.0 * r); - } else { - /* Take exp of scaled log */ - adj = expm1(m*log1p(r)); - } - } - return dd_add_d_d(ans, ans*adj); -} - -static double -pow2(double a, double b, int m) -{ - return dd_to_double(pow_D(add_dd(a, b), m)); -} - -/* - * Not 1024 as too big. Want _MAX_EXPONENT < 1023-52 so as to keep both - * elements of the double2 normalized - */ -#define _MAX_EXPONENT 960 - -#define RETURN_M_E(MAND, EXPT) \ - *pExponent = EXPT;\ - return MAND; - - -static double2 -pow2Scaled_D(double2 a, int m, int *pExponent) -{ - /* Compute a^m = significand*2^expt and return as (significand, expt) */ - double2 ans, y; - int ansE, yE; - int maxExpt = _MAX_EXPONENT; - int q, r, y2mE, y2rE, y2mqE; - double2 y2r, y2m, y2mq; - - if (m <= 0) - { - int aE1, aE2; - if (m == 0) { - RETURN_M_E(DD_C_ONE, 0); - } - ans = pow2Scaled_D(a, -m, &aE1); - ans = frexpD(dd_inv(ans), &aE2); - ansE = -aE1 + aE2; - RETURN_M_E(ans, ansE); - } - y = frexpD(a, &yE); - if (m == 1) { - RETURN_M_E(y, yE); - } - /* - * y ^ maxExpt >= 2^{-960} - * => maxExpt = 960 / log2(y.x[0]) = 708 / log(y.x[0]) - * = 665/((1-y.x[0] + y.x[0]^2/2 - ...) - * <= 665/(1-y.x[0]) - * Quick check to see if we might need to break up the exponentiation - */ - if (m*(y.x[0]-1) / y.x[0] < -_MAX_EXPONENT * M_LN2) { - /* Now do it carefully, calling log() */ - double lg2y = log(y.x[0]) / M_LN2; - double lgAns = m * lg2y; - if (lgAns <= -_MAX_EXPONENT) { - maxExpt = (int)(nextPowerOf2(-_MAX_EXPONENT / lg2y + 1)/2); - } - } - if (m <= maxExpt) - { - double2 ans1 = pow_D(y, m); - ans = frexpD(ans1, &ansE); - ansE += m * yE; - RETURN_M_E(ans, ansE); - } - - q = m / maxExpt; - r = m % maxExpt; - /* y^m = (y^maxExpt)^q * y^r */ - y2r = pow2Scaled_D(y, r, &y2rE); - y2m = pow2Scaled_D(y, maxExpt, &y2mE); - y2mq = pow2Scaled_D(y2m, q, &y2mqE); - ans = frexpD(mul_DD(y2r, y2mq), &ansE); - y2mqE += y2mE * q; - ansE += y2mqE + y2rE; - ansE += m * yE; - RETURN_M_E(ans, ansE); -} - - -static double2 -pow4_D(double a, double b, double c, double d, int m) -{ - /* Compute ((a+b)/(c+d)) ^ m */ - double2 A, C, X; - if (m <= 0){ - if (m == 0) { - return DD_C_ONE; - } - return pow4_D(c, d, a, b, -m); - } - A = add_dd(a, b); - C = add_dd(c, d); - if (dd_is_zero(A)) { - return (dd_is_zero(C) ? DD_C_NAN : DD_C_ZERO); - } - if (dd_is_zero(C)) { - return (dd_is_negative(A) ? DD_C_NEGINF : DD_C_INF); - } - X = div_DD(A, C); - return pow_D(X, m); -} - -static double -pow4(double a, double b, double c, double d, int m) -{ - double2 ret = pow4_D(a, b, c, d, m); - return dd_to_double(ret); -} - - -static double2 -logpow4_D(double a, double b, double c, double d, int m) -{ - /* - * Compute log(((a+b)/(c+d)) ^ m) - * == m * log((a+b)/(c+d)) - * == m * log( 1 + (a+b-c-d)/(c+d)) - */ - double2 ans; - double2 A, C, X; - if (m == 0) { - return DD_C_ZERO; - } - A = add_dd(a, b); - C = add_dd(c, d); - if (dd_is_zero(A)) { - return (dd_is_zero(C) ? DD_C_ZERO : DD_C_NEGINF); - } - if (dd_is_zero(C)) { - return DD_C_INF; - } - X = div_DD(A, C); - assert(X.x[0] >= 0); - if (0.5 <= X.x[0] && X.x[0] <= 1.5) { - double2 A1 = sub_DD(A, C); - double2 X1 = div_DD(A1, C); - ans = log1pD(X1); - } else { - ans = logD(X); - } - ans = mul_dD(m, ans); - return ans; -} - -static double -logpow4(double a, double b, double c, double d, int m) -{ - double2 ans = logpow4_D(a, b, c, d, m); - return dd_to_double(ans); -} - -/* - * Compute a single term in the summation, A_v(n, x): - * A_v(n, x) = Binomial(n,v) * (1-x-v/n)^(n-v) * (x+v/n)^(v-1) - */ -static void -computeAv(int n, double x, int v, double2 Cman, int Cexpt, - double2 *pt1, double2 *pt2, double2 *pAv) -{ - int t1E, t2E, ansE; - double2 Av; - double2 t2x = sub_Dd(div_dd(n - v, n), x); /* 1 - x - v/n */ - double2 t2 = pow2Scaled_D(t2x, n-v, &t2E); - double2 t1x = add_Dd(div_dd(v, n), x); /* x + v/n */ - double2 t1 = pow2Scaled_D(t1x, v-1, &t1E); - double2 ans = mul_DD(t1, t2); - ans = mul_DD(ans, Cman); - ansE = Cexpt + t1E + t2E; - Av = ldexpD(ans, ansE); - *pAv = Av; - *pt1 = t1; - *pt2 = t2; -} - - -static ThreeProbs -_smirnov(int n, double x) -{ - double nx, alpha; - double2 AjSum = DD_C_ZERO; - double2 dAjSum = DD_C_ZERO; - double cdf, sf, pdf; - - int bUseUpperSum; - int nxfl, n1mxfl, n1mxceil; - ThreeProbs ret; - - if (!(n > 0 && x >= 0.0 && x <= 1.0)) { - RETURN_3PROBS(NAN, NAN, NAN); - } - if (n == 1) { - RETURN_3PROBS(1-x, x, 1.0); - } - if (x == 0.0) { - RETURN_3PROBS(1.0, 0.0, 1.0); - } - if (x == 1.0) { - RETURN_3PROBS(0.0, 1.0, 0.0); - } - - alpha = modNX(n, x, &nxfl, &nx); - n1mxfl = n - nxfl - (alpha == 0 ? 0 : 1); - n1mxceil = n - nxfl; - /* - * If alpha is 0, don't actually want to include the last term - * in either the lower or upper summations. - */ - if (alpha == 0) { - n1mxfl -= 1; - n1mxceil += 1; - } - - /* Special case: x <= 1/n */ - if (nxfl == 0 || (nxfl == 1 && alpha == 0)) { - double t = pow2(1, x, n-1); - pdf = (nx + 1) * t / (1+x); - cdf = x * t; - sf = 1 - cdf; - /* Adjust if x=1/n *exactly* */ - if (nxfl == 1) { - assert(alpha == 0); - pdf -= 0.5; - } - RETURN_3PROBS(sf, cdf, pdf); - } - /* Special case: x is so big, the sf underflows double64 */ - if (-2 * n * x*x < MINLOG) { - RETURN_3PROBS(0, 1, 0); - } - /* Special case: x >= 1 - 1/n */ - if (nxfl >= n-1) { - sf = pow2(1, -x, n); - cdf = 1 - sf; - pdf = n * sf/(1-x); - RETURN_3PROBS(sf, cdf, pdf); - } - /* Special case: n is so big, take too long to compute */ - if (n > SMIRNOV_MAX_COMPUTE_N) { - /* p ~ e^(-(6nx+1)^2 / 18n) */ - double logp = -pow(6.0*n*x+1, 2)/18.0/n; - /* Maximise precision for small p-value. */ - if (logp < -M_LN2) { - sf = exp(logp); - cdf = 1 - sf; - } else { - cdf = -expm1(logp); - sf = 1 - cdf; - } - pdf = (6.0*n*x+1) * 2 * sf/3; - RETURN_3PROBS(sf, cdf, pdf); - } - { - /* - * Use the upper sum if n is large enough, and x is small enough and - * the number of terms is going to be small enough. - * Otherwise it just drops accuracy, about 1.6bits * nUpperTerms - */ - int nUpperTerms = n - n1mxceil + 1; - bUseUpperSum = (nUpperTerms <= 1 && x < 0.5); - bUseUpperSum = (bUseUpperSum || - ((n >= SM_UPPERSUM_MIN_N) - && (nUpperTerms <= SM_UPPER_MAX_TERMS) - && (x <= 0.5 / sqrt(n)))); - } - - { - int start=0, step=1, nTerms=n1mxfl+1; - int j, firstJ = 0; - int vmid = n/2; - double2 Cman = DD_C_ONE; - int Cexpt = 0; - double2 Aj, dAj, t1, t2, dAjCoeff; - double2 oneOverX = div_dd(1, x); - - if (bUseUpperSum) { - start = n; - step = -1; - nTerms = n - n1mxceil + 1; - - t1 = pow4_D(1, x, 1, 0, n - 1); - t2 = DD_C_ONE; - Aj = t1; - - dAjCoeff = div_dD(n - 1, add_dd(1, x)); - dAjCoeff = add_DD(dAjCoeff, oneOverX); - } else { - t1 = oneOverX; - t2 = pow4_D(1, -x, 1, 0, n); - Aj = div_Dd(t2, x); - - dAjCoeff = div_DD(sub_dD(-1, mul_dd(n - 1, x)), sub_dd(1, x)); - dAjCoeff = div_Dd(dAjCoeff, x); - dAjCoeff = add_DD(dAjCoeff, oneOverX); - } - - dAj = mul_DD(Aj, dAjCoeff); - AjSum = add_DD(AjSum, Aj); - dAjSum = add_DD(dAjSum, dAj); - - updateBinomial(&Cman, &Cexpt, n, 0); - firstJ ++; - - for (j = firstJ; j < nTerms; j += 1) { - int v = start + j * step; - - computeAv(n, x, v, Cman, Cexpt, &t1, &t2, &Aj); - - if (dd_isfinite(Aj) && !dd_is_zero(Aj)) { - /* coeff = 1/x + (j-1)/(x+j/n) - (n-j)/(1-x-j/n) */ - dAjCoeff = sub_DD(div_dD((n * (v - 1)), add_dd(nxfl + v, alpha)), - div_dD(((n - v) * n), sub_dd(n - nxfl - v, alpha))); - dAjCoeff = add_DD(dAjCoeff, oneOverX); - dAj = mul_DD(Aj, dAjCoeff); - - assert(dd_isfinite(Aj)); - AjSum = add_DD(AjSum, Aj); - dAjSum = add_DD(dAjSum, dAj); - } - /* Safe to terminate early? */ - if (!dd_is_zero(Aj)) { - if ((4*(nTerms-j) * fabs(dd_to_double(Aj)) < DBL_EPSILON * dd_to_double(AjSum)) - && (j != nTerms - 1)) { - break; - } - } - else if (j > vmid) { - assert(dd_is_zero(Aj)); - break; - } - - updateBinomial(&Cman, &Cexpt, n, j); - } - assert(dd_isfinite(AjSum)); - assert(dd_isfinite(dAjSum)); - { - double2 derivD = mul_dD(x, dAjSum); - double2 probD = mul_dD(x, AjSum); - double deriv = dd_to_double(derivD); - double prob = dd_to_double(probD); - - assert (nx != 1 || alpha > 0); - if (step < 0) { - cdf = prob; - sf = 1-prob; - pdf = deriv; - } else { - cdf = 1-prob; - sf = prob; - pdf = -deriv; - } - } - } - - pdf = MAX(0, pdf); - cdf = CLIP(cdf, 0, 1); - sf = CLIP(sf, 0, 1); - RETURN_3PROBS(sf, cdf, pdf); -} - -/* - * Functional inverse of Smirnov distribution - * finds x such that smirnov(n, x) = psf; smirnovc(n, x) = pcdf). - */ -static double -_smirnovi(int n, double psf, double pcdf) -{ - /* - * Need to use a bracketing NR algorithm here and be very careful - * about the starting point. - */ - double x, logpcdf; - int iterations = 0; - int function_calls = 0; - double a=0, b=1; - double maxlogpcdf, psfrootn; - double dx, dxold; - - if (!(n > 0 && psf >= 0.0 && pcdf >= 0.0 && pcdf <= 1.0 && psf <= 1.0)) { - sf_error("smirnovi", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (fabs(1.0 - pcdf - psf) > 4* DBL_EPSILON) { - sf_error("smirnovi", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - /* STEP 1: Handle psf==0, or pcdf == 0 */ - if (pcdf == 0.0) { - return 0.0; - } - if (psf == 0.0) { - return 1.0; - } - /* STEP 2: Handle n=1 */ - if (n == 1) { - return pcdf; - } - - /* STEP 3 Handle psf *very* close to 0. Correspond to (n-1)/n < x < 1 */ - psfrootn = pow(psf, 1.0 / n); - /* xmin > 1 - 1.0 / n */ - if (n < 150 && n*psfrootn <= 1) { - /* Solve exactly. */ - x = 1 - psfrootn; - return x; - } - - logpcdf = (pcdf < 0.5 ? log(pcdf) : log1p(-psf)); - - /* - * STEP 4 Find bracket and initial estimate for use in N-R - * 4(a) Handle 0 < x <= 1/n: pcdf = x * (1+x)^*(n-1) - */ - maxlogpcdf = logpow4(1, 0.0, n, 0, 1) + logpow4(n, 1, n, 0, n - 1); - if (logpcdf <= maxlogpcdf) { - double xmin = pcdf / SCIPY_El; - double xmax = pcdf; - double P1 = pow4(n, 1, n, 0, n - 1) / n; - double R = pcdf/P1; - double z0 = R; - /* - * Do one iteration of N-R solving: z*e^(z-1) = R, with z0=pcdf/P1 - * z <- z - (z exp(z-1) - pcdf)/((z+1)exp(z-1)) - * If z_0 = R, z_1 = R(1-exp(1-R))/(R+1) - */ - if (R >= 1) { - /* - * R=1 is OK; - * R>1 can happen due to truncation error for x = (1-1/n)+-eps - */ - R = 1; - x = R/n; - return x; - } - z0 = (z0*z0 + R * exp(1-z0))/(1+z0); - x = z0/n; - a = xmin*(1 - 4 * DBL_EPSILON); - a = MAX(a, 0); - b = xmax * (1 + 4 * DBL_EPSILON); - b = MIN(b, 1.0/n); - x = CLIP(x, a, b); - } - else - { - /* 4(b) : 1/n < x < (n-1)/n */ - double xmin = 1 - psfrootn; - double logpsf = (psf < 0.5 ? log(psf) : log1p(-pcdf)); - double xmax = sqrt(-logpsf / (2.0L * n)); - double xmax6 = xmax - 1.0L / (6 * n); - a = xmin; - b = xmax; - /* Allow for a little rounding error */ - a *= 1 - 4 * DBL_EPSILON; - b *= 1 + 4 * DBL_EPSILON; - a = MAX(xmin, 1.0/n); - b = MIN(xmax, 1-1.0/n); - x = xmax6; - } - if (x < a || x > b) { - x = (a + b)/2; - } - assert (x < 1); - - /* - * Skip computing fa, fb as that takes cycles and the exact values - * are not needed. - */ - - /* STEP 5 Run N-R. - * smirnov should be well-enough behaved for NR starting at this location. - * Use smirnov(n, x)-psf, or pcdf - smirnovc(n, x), whichever has smaller p. - */ - dxold = b - a; - dx = dxold; - do { - double dfdx, x0 = x, deltax, df; - assert(x < 1); - assert(x > 0); - { - ThreeProbs probs = _smirnov(n, x0); - ++function_calls; - df = ((pcdf < 0.5) ? (pcdf - probs.cdf) : (probs.sf - psf)); - dfdx = -probs.pdf; - } - if (df == 0) { - return x; - } - /* Update the bracketing interval */ - if (df > 0 && x > a) { - a = x; - } else if (df < 0 && x < b) { - b = x; - } - - if (dfdx == 0) { - /* - * x was not within tolerance, but now we hit a 0 derivative. - * This implies that x >> 1/sqrt(n), and even then |smirnovp| >= |smirnov| - * so this condition is unexpected. Do a bisection step. - */ - x = (a+b)/2; - deltax = x0 - x; - } else { - deltax = df / dfdx; - x = x0 - deltax; - } - /* - * Check out-of-bounds. - * Not expecting this to happen ofen --- smirnov is convex near x=1 and - * concave near x=0, and we should be approaching from the correct side. - * If out-of-bounds, replace x with a midpoint of the bracket. - * Also check fast enough convergence. - */ - if ((a <= x) && (x <= b) && (fabs(2 * deltax) <= fabs(dxold) || fabs(dxold) < 256 * DBL_EPSILON)) { - dxold = dx; - dx = deltax; - } else { - dxold = dx; - dx = dx / 2; - x = (a + b) / 2; - deltax = x0 - x; - } - /* - * Note that if psf is close to 1, f(x) -> 1, f'(x) -> -1. - * => abs difference |x-x0| is approx |f(x)-p| >= DBL_EPSILON, - * => |x-x0|/x >= DBL_EPSILON/x. - * => cannot use a purely relative criteria as it will fail for x close to 0. - */ - if (_within_tol(x, x0, (psf < 0.5 ? 0 : _xtol), _rtol)) { - break; - } - if (++iterations > MAXITER) { - sf_error("smirnovi", SF_ERROR_SLOW, NULL); - return (x); - } - } while (1); - return x; -} - - -double -smirnov(int n, double d) -{ - ThreeProbs probs; - if (isnan(d)) { - return NAN; - } - probs = _smirnov(n, d); - return probs.sf; -} - -double -smirnovc(int n, double d) -{ - ThreeProbs probs; - if (isnan(d)) { - return NAN; - } - probs = _smirnov(n, d); - return probs.cdf; -} - - -/* - * Derivative of smirnov(n, d) - * One interior point of discontinuity at d=1/n. -*/ -double -smirnovp(int n, double d) -{ - ThreeProbs probs; - if (!(n > 0 && d >= 0.0 && d <= 1.0)) { - return (NAN); - } - if (n == 1) { - /* Slope is always -1 for n=1, even at d = 1.0 */ - return -1.0; - } - if (d == 1.0) { - return -0.0; - } - /* - * If d is 0, the derivative is discontinuous, but approaching - * from the right the limit is -1 - */ - if (d == 0.0) { - return -1.0; - } - probs = _smirnov(n, d); - return -probs.pdf; -} - - -double -smirnovi(int n, double p) -{ - if (isnan(p)) { - return NAN; - } - return _smirnovi(n, p, 1-p); -} - -double -smirnovci(int n, double p) -{ - if (isnan(p)) { - return NAN; - } - return _smirnovi(n, 1-p, p); -} diff --git a/gtsam/3rdparty/cephes/cephes/nbdtr.c b/gtsam/3rdparty/cephes/cephes/nbdtr.c deleted file mode 100644 index 7697f257ee..0000000000 --- a/gtsam/3rdparty/cephes/cephes/nbdtr.c +++ /dev/null @@ -1,207 +0,0 @@ -/* nbdtr.c - * - * Negative binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, nbdtr(); - * - * y = nbdtr( k, n, p ); - * - * DESCRIPTION: - * - * Returns the sum of the terms 0 through k of the negative - * binomial distribution: - * - * k - * -- ( n+j-1 ) n j - * > ( ) p (1-p) - * -- ( j ) - * j=0 - * - * In a sequence of Bernoulli trials, this is the probability - * that k or fewer failures precede the nth success. - * - * The terms are not computed individually; instead the incomplete - * beta integral is employed, according to the formula - * - * y = nbdtr( k, n, p ) = incbet( n, k+1, p ). - * - * The arguments must be positive, with p ranging from 0 to 1. - * - * ACCURACY: - * - * Tested at random points (a,b,p), with p between 0 and 1. - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,100 100000 1.7e-13 8.8e-15 - * See also incbet.c. - * - */ - /* nbdtrc.c - * - * Complemented negative binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, nbdtrc(); - * - * y = nbdtrc( k, n, p ); - * - * DESCRIPTION: - * - * Returns the sum of the terms k+1 to infinity of the negative - * binomial distribution: - * - * inf - * -- ( n+j-1 ) n j - * > ( ) p (1-p) - * -- ( j ) - * j=k+1 - * - * The terms are not computed individually; instead the incomplete - * beta integral is employed, according to the formula - * - * y = nbdtrc( k, n, p ) = incbet( k+1, n, 1-p ). - * - * The arguments must be positive, with p ranging from 0 to 1. - * - * ACCURACY: - * - * Tested at random points (a,b,p), with p between 0 and 1. - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,100 100000 1.7e-13 8.8e-15 - * See also incbet.c. - */ - -/* nbdtrc - * - * Complemented negative binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, nbdtrc(); - * - * y = nbdtrc( k, n, p ); - * - * DESCRIPTION: - * - * Returns the sum of the terms k+1 to infinity of the negative - * binomial distribution: - * - * inf - * -- ( n+j-1 ) n j - * > ( ) p (1-p) - * -- ( j ) - * j=k+1 - * - * The terms are not computed individually; instead the incomplete - * beta integral is employed, according to the formula - * - * y = nbdtrc( k, n, p ) = incbet( k+1, n, 1-p ). - * - * The arguments must be positive, with p ranging from 0 to 1. - * - * ACCURACY: - * - * See incbet.c. - */ - /* nbdtri - * - * Functional inverse of negative binomial distribution - * - * - * - * SYNOPSIS: - * - * int k, n; - * double p, y, nbdtri(); - * - * p = nbdtri( k, n, y ); - * - * DESCRIPTION: - * - * Finds the argument p such that nbdtr(k,n,p) is equal to y. - * - * ACCURACY: - * - * Tested at random points (a,b,y), with y between 0 and 1. - * - * a,b Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,100 100000 1.5e-14 8.5e-16 - * See also incbi.c. - */ - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - -double nbdtrc(int k, int n, double p) -{ - double dk, dn; - - if ((p < 0.0) || (p > 1.0)) - goto domerr; - if (k < 0) { - domerr: - sf_error("nbdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - dk = k + 1; - dn = n; - return (incbet(dk, dn, 1.0 - p)); -} - - - -double nbdtr(int k, int n, double p) -{ - double dk, dn; - - if ((p < 0.0) || (p > 1.0)) - goto domerr; - if (k < 0) { - domerr: - sf_error("nbdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - dk = k + 1; - dn = n; - return (incbet(dn, dk, p)); -} - - - -double nbdtri(int k, int n, double p) -{ - double dk, dn, w; - - if ((p < 0.0) || (p > 1.0)) - goto domerr; - if (k < 0) { - domerr: - sf_error("nbdtri", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - dk = k + 1; - dn = n; - w = incbi(dn, dk, p); - return (w); -} diff --git a/gtsam/3rdparty/cephes/cephes/ndtr.c b/gtsam/3rdparty/cephes/cephes/ndtr.c deleted file mode 100644 index 168e98b5ab..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ndtr.c +++ /dev/null @@ -1,305 +0,0 @@ -/* ndtr.c - * - * Normal distribution function - * - * - * - * SYNOPSIS: - * - * double x, y, ndtr(); - * - * y = ndtr( x ); - * - * - * - * DESCRIPTION: - * - * Returns the area under the Gaussian probability density - * function, integrated from minus infinity to x: - * - * x - * - - * 1 | | 2 - * ndtr(x) = --------- | exp( - t /2 ) dt - * sqrt(2pi) | | - * - - * -inf. - * - * = ( 1 + erf(z) ) / 2 - * = erfc(z) / 2 - * - * where z = x/sqrt(2). Computation is via the functions - * erf and erfc. - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -13,0 30000 3.4e-14 6.7e-15 - * - * - * ERROR MESSAGES: - * - * message condition value returned - * erfc underflow x > 37.519379347 0.0 - * - */ -/* erf.c - * - * Error function - * - * - * - * SYNOPSIS: - * - * double x, y, erf(); - * - * y = erf( x ); - * - * - * - * DESCRIPTION: - * - * The integral is - * - * x - * - - * 2 | | 2 - * erf(x) = -------- | exp( - t ) dt. - * sqrt(pi) | | - * - - * 0 - * - * For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise - * erf(x) = 1 - erfc(x). - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,1 30000 3.7e-16 1.0e-16 - * - */ -/* erfc.c - * - * Complementary error function - * - * - * - * SYNOPSIS: - * - * double x, y, erfc(); - * - * y = erfc( x ); - * - * - * - * DESCRIPTION: - * - * - * 1 - erf(x) = - * - * inf. - * - - * 2 | | 2 - * erfc(x) = -------- | exp( - t ) dt - * sqrt(pi) | | - * - - * x - * - * - * For small x, erfc(x) = 1 - erf(x); otherwise rational - * approximations are computed. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,26.6417 30000 5.7e-14 1.5e-14 - */ - - -/* - * Cephes Math Library Release 2.2: June, 1992 - * Copyright 1984, 1987, 1988, 1992 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include /* DBL_EPSILON */ -#include "mconf.h" - -extern double MAXLOG; - -static double P[] = { - 2.46196981473530512524E-10, - 5.64189564831068821977E-1, - 7.46321056442269912687E0, - 4.86371970985681366614E1, - 1.96520832956077098242E2, - 5.26445194995477358631E2, - 9.34528527171957607540E2, - 1.02755188689515710272E3, - 5.57535335369399327526E2 -}; - -static double Q[] = { - /* 1.00000000000000000000E0, */ - 1.32281951154744992508E1, - 8.67072140885989742329E1, - 3.54937778887819891062E2, - 9.75708501743205489753E2, - 1.82390916687909736289E3, - 2.24633760818710981792E3, - 1.65666309194161350182E3, - 5.57535340817727675546E2 -}; - -static double R[] = { - 5.64189583547755073984E-1, - 1.27536670759978104416E0, - 5.01905042251180477414E0, - 6.16021097993053585195E0, - 7.40974269950448939160E0, - 2.97886665372100240670E0 -}; - -static double S[] = { - /* 1.00000000000000000000E0, */ - 2.26052863220117276590E0, - 9.39603524938001434673E0, - 1.20489539808096656605E1, - 1.70814450747565897222E1, - 9.60896809063285878198E0, - 3.36907645100081516050E0 -}; - -static double T[] = { - 9.60497373987051638749E0, - 9.00260197203842689217E1, - 2.23200534594684319226E3, - 7.00332514112805075473E3, - 5.55923013010394962768E4 -}; - -static double U[] = { - /* 1.00000000000000000000E0, */ - 3.35617141647503099647E1, - 5.21357949780152679795E2, - 4.59432382970980127987E3, - 2.26290000613890934246E4, - 4.92673942608635921086E4 -}; - -#define UTHRESH 37.519379347 - - -double ndtr(double a) -{ - double x, y, z; - - if (cephes_isnan(a)) { - sf_error("ndtr", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - x = a * M_SQRT1_2; - z = fabs(x); - - if (z < M_SQRT1_2) { - y = 0.5 + 0.5 * erf(x); - } - else { - y = 0.5 * erfc(z); - if (x > 0) { - y = 1.0 - y; - } - } - - return y; -} - - -double erfc(double a) -{ - double p, q, x, y, z; - - if (cephes_isnan(a)) { - sf_error("erfc", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (a < 0.0) { - x = -a; - } - else { - x = a; - } - - if (x < 1.0) { - return 1.0 - erf(a); - } - - z = -a * a; - - if (z < -MAXLOG) { - goto under; - } - - z = exp(z); - - if (x < 8.0) { - p = polevl(x, P, 8); - q = p1evl(x, Q, 8); - } - else { - p = polevl(x, R, 5); - q = p1evl(x, S, 6); - } - y = (z * p) / q; - - if (a < 0) { - y = 2.0 - y; - } - - if (y != 0.0) { - return y; - } - -under: - sf_error("erfc", SF_ERROR_UNDERFLOW, NULL); - if (a < 0) { - return 2.0; - } - else { - return 0.0; - } -} - - - -double erf(double x) -{ - double y, z; - - if (cephes_isnan(x)) { - sf_error("erf", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - if (x < 0.0) { - return -erf(-x); - } - - if (fabs(x) > 1.0) { - return (1.0 - erfc(x)); - } - z = x * x; - - y = x * polevl(z, T, 4) / p1evl(z, U, 5); - return y; -} diff --git a/gtsam/3rdparty/cephes/cephes/ndtri.c b/gtsam/3rdparty/cephes/cephes/ndtri.c deleted file mode 100644 index e7fe5cce04..0000000000 --- a/gtsam/3rdparty/cephes/cephes/ndtri.c +++ /dev/null @@ -1,176 +0,0 @@ -/* ndtri.c - * - * Inverse of Normal distribution function - * - * - * - * SYNOPSIS: - * - * double x, y, ndtri(); - * - * x = ndtri( y ); - * - * - * - * DESCRIPTION: - * - * Returns the argument, x, for which the area under the - * Gaussian probability density function (integrated from - * minus infinity to x) is equal to y. - * - * - * For small arguments 0 < y < exp(-2), the program computes - * z = sqrt( -2.0 * log(y) ); then the approximation is - * x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). - * There are two rational functions P/Q, one for 0 < y < exp(-32) - * and the other for y up to exp(-2). For larger arguments, - * w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)). - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0.125, 1 20000 7.2e-16 1.3e-16 - * IEEE 3e-308, 0.135 50000 4.6e-16 9.8e-17 - * - * - * ERROR MESSAGES: - * - * message condition value returned - * ndtri domain x < 0 NAN - * ndtri domain x > 1 NAN - * - */ - - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -/* sqrt(2pi) */ -static double s2pi = 2.50662827463100050242E0; - -/* approximation for 0 <= |y - 0.5| <= 3/8 */ -static double P0[5] = { - -5.99633501014107895267E1, - 9.80010754185999661536E1, - -5.66762857469070293439E1, - 1.39312609387279679503E1, - -1.23916583867381258016E0, -}; - -static double Q0[8] = { - /* 1.00000000000000000000E0, */ - 1.95448858338141759834E0, - 4.67627912898881538453E0, - 8.63602421390890590575E1, - -2.25462687854119370527E2, - 2.00260212380060660359E2, - -8.20372256168333339912E1, - 1.59056225126211695515E1, - -1.18331621121330003142E0, -}; - -/* Approximation for interval z = sqrt(-2 log y ) between 2 and 8 - * i.e., y between exp(-2) = .135 and exp(-32) = 1.27e-14. - */ -static double P1[9] = { - 4.05544892305962419923E0, - 3.15251094599893866154E1, - 5.71628192246421288162E1, - 4.40805073893200834700E1, - 1.46849561928858024014E1, - 2.18663306850790267539E0, - -1.40256079171354495875E-1, - -3.50424626827848203418E-2, - -8.57456785154685413611E-4, -}; - -static double Q1[8] = { - /* 1.00000000000000000000E0, */ - 1.57799883256466749731E1, - 4.53907635128879210584E1, - 4.13172038254672030440E1, - 1.50425385692907503408E1, - 2.50464946208309415979E0, - -1.42182922854787788574E-1, - -3.80806407691578277194E-2, - -9.33259480895457427372E-4, -}; - -/* Approximation for interval z = sqrt(-2 log y ) between 8 and 64 - * i.e., y between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890. - */ - -static double P2[9] = { - 3.23774891776946035970E0, - 6.91522889068984211695E0, - 3.93881025292474443415E0, - 1.33303460815807542389E0, - 2.01485389549179081538E-1, - 1.23716634817820021358E-2, - 3.01581553508235416007E-4, - 2.65806974686737550832E-6, - 6.23974539184983293730E-9, -}; - -static double Q2[8] = { - /* 1.00000000000000000000E0, */ - 6.02427039364742014255E0, - 3.67983563856160859403E0, - 1.37702099489081330271E0, - 2.16236993594496635890E-1, - 1.34204006088543189037E-2, - 3.28014464682127739104E-4, - 2.89247864745380683936E-6, - 6.79019408009981274425E-9, -}; - -double ndtri(double y0) -{ - double x, y, z, y2, x0, x1; - int code; - - if (y0 == 0.0) { - return -INFINITY; - } - if (y0 == 1.0) { - return INFINITY; - } - if (y0 < 0.0 || y0 > 1.0) { - sf_error("ndtri", SF_ERROR_DOMAIN, NULL); - return NAN; - } - code = 1; - y = y0; - if (y > (1.0 - 0.13533528323661269189)) { /* 0.135... = exp(-2) */ - y = 1.0 - y; - code = 0; - } - - if (y > 0.13533528323661269189) { - y = y - 0.5; - y2 = y * y; - x = y + y * (y2 * polevl(y2, P0, 4) / p1evl(y2, Q0, 8)); - x = x * s2pi; - return (x); - } - - x = sqrt(-2.0 * log(y)); - x0 = x - log(x) / x; - - z = 1.0 / x; - if (x < 8.0) /* y > exp(-32) = 1.2664165549e-14 */ - x1 = z * polevl(z, P1, 8) / p1evl(z, Q1, 8); - else - x1 = z * polevl(z, P2, 8) / p1evl(z, Q2, 8); - x = x0 - x1; - if (code != 0) - x = -x; - return (x); -} diff --git a/gtsam/3rdparty/cephes/cephes/owens_t.c b/gtsam/3rdparty/cephes/cephes/owens_t.c deleted file mode 100644 index 6eb063510e..0000000000 --- a/gtsam/3rdparty/cephes/cephes/owens_t.c +++ /dev/null @@ -1,364 +0,0 @@ -/* Copyright Benjamin Sobotta 2012 - * - * Use, modification and distribution are subject to the - * Boost Software License, Version 1.0. (See accompanying file - * LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt) - */ - -/* - * Reference: - * Mike Patefield, David Tandy - * FAST AND ACCURATE CALCULATION OF OWEN'S T-FUNCTION - * Journal of Statistical Software, 5 (5), 1-25 - */ -#include "mconf.h" - -static const int SELECT_METHOD[] = { - 0, 0, 1, 12, 12, 12, 12, 12, 12, 12, 12, 15, 15, 15, 8, - 0, 1, 1, 2, 2, 4, 4, 13, 13, 14, 14, 15, 15, 15, 8, - 1, 1, 2, 2, 2, 4, 4, 14, 14, 14, 14, 15, 15, 15, 9, - 1, 1, 2, 4, 4, 4, 4, 6, 6, 15, 15, 15, 15, 15, 9, - 1, 2 , 2, 4, 4, 5 , 5, 7, 7, 16 ,16, 16, 11, 11, 10, - 1, 2 , 4, 4 , 4, 5 , 5, 7, 7, 16, 16, 16, 11, 11, 11, - 1, 2 , 3, 3, 5, 5 , 7, 7, 16, 16, 16, 16, 16, 11, 11, - 1, 2 , 3 , 3 , 5, 5, 17, 17, 17, 17, 16, 16, 16, 11, 11 -}; - -static const double HRANGE[] = {0.02, 0.06, 0.09, 0.125, 0.26, 0.4, 0.6, 1.6, - 1.7, 2.33, 2.4, 3.36, 3.4, 4.8}; - -static const double ARANGE[] = {0.025, 0.09, 0.15, 0.36, 0.5, 0.9, 0.99999}; - -static const double ORD[] = {2, 3, 4, 5, 7, 10, 12, 18, 10, 20, 30, 0, 4, 7, - 8, 20, 0, 0}; - -static const int METHODS[] = {1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, - 5, 6}; - -static const double C[] = { - 0.99999999999999999999999729978162447266851932041876728736094298092917625009873, - -0.99999999999999999999467056379678391810626533251885323416799874878563998732905968, - 0.99999999999999999824849349313270659391127814689133077036298754586814091034842536, - -0.9999999999999997703859616213643405880166422891953033591551179153879839440241685, - 0.99999999999998394883415238173334565554173013941245103172035286759201504179038147, - -0.9999999999993063616095509371081203145247992197457263066869044528823599399470977, - 0.9999999999797336340409464429599229870590160411238245275855903767652432017766116267, - -0.999999999574958412069046680119051639753412378037565521359444170241346845522403274, - 0.9999999933226234193375324943920160947158239076786103108097456617750134812033362048, - -0.9999999188923242461073033481053037468263536806742737922476636768006622772762168467, - 0.9999992195143483674402853783549420883055129680082932629160081128947764415749728967, - -0.999993935137206712830997921913316971472227199741857386575097250553105958772041501, - 0.99996135597690552745362392866517133091672395614263398912807169603795088421057688716, - -0.99979556366513946026406788969630293820987757758641211293079784585126692672425362469, - 0.999092789629617100153486251423850590051366661947344315423226082520411961968929483, - -0.996593837411918202119308620432614600338157335862888580671450938858935084316004769854, - 0.98910017138386127038463510314625339359073956513420458166238478926511821146316469589567, - -0.970078558040693314521331982203762771512160168582494513347846407314584943870399016019, - 0.92911438683263187495758525500033707204091967947532160289872782771388170647150321633673, - -0.8542058695956156057286980736842905011429254735181323743367879525470479126968822863, - 0.73796526033030091233118357742803709382964420335559408722681794195743240930748630755, - -0.58523469882837394570128599003785154144164680587615878645171632791404210655891158, - 0.415997776145676306165661663581868460503874205343014196580122174949645271353372263, - -0.2588210875241943574388730510317252236407805082485246378222935376279663808416534365, - 0.1375535825163892648504646951500265585055789019410617565727090346559210218472356689, - -0.0607952766325955730493900985022020434830339794955745989150270485056436844239206648, - 0.0216337683299871528059836483840390514275488679530797294557060229266785853764115, - -0.00593405693455186729876995814181203900550014220428843483927218267309209471516256, - 0.0011743414818332946510474576182739210553333860106811865963485870668929503649964142, - -1.489155613350368934073453260689881330166342484405529981510694514036264969925132E-4, - 9.072354320794357587710929507988814669454281514268844884841547607134260303118208E-6 -}; - -static const double PTS[] = { - 0.35082039676451715489E-02, 0.31279042338030753740E-01, - 0.85266826283219451090E-01, 0.16245071730812277011E+00, - 0.25851196049125434828E+00, 0.36807553840697533536E+00, - 0.48501092905604697475E+00, 0.60277514152618576821E+00, - 0.71477884217753226516E+00, 0.81475510988760098605E+00, - 0.89711029755948965867E+00, 0.95723808085944261843E+00, - 0.99178832974629703586E+00 -}; - -static const double WTS[] = { - 0.18831438115323502887E-01, 0.18567086243977649478E-01, - 0.18042093461223385584E-01, 0.17263829606398753364E-01, - 0.16243219975989856730E-01, 0.14994592034116704829E-01, - 0.13535474469662088392E-01, 0.11886351605820165233E-01, - 0.10070377242777431897E-01, 0.81130545742299586629E-02, - 0.60419009528470238773E-02, 0.38862217010742057883E-02, - 0.16793031084546090448E-02 -}; - - -static int get_method(double h, double a) { - int ihint, iaint, i; - - ihint = 14; - iaint = 7; - - for (i = 0; i < 14; i++) { - if (h <= HRANGE[i]) { - ihint = i; - break; - } - } - - for (i = 0; i < 7; i++) { - if (a <= ARANGE[i]) { - iaint = i; - break; - } - } - return SELECT_METHOD[iaint * 15 + ihint]; -} - - -static double owens_t_norm1(double x) { - return erf(x / sqrt(2)) / 2; -} - - -static double owens_t_norm2(double x) { - return erfc(x / sqrt(2)) / 2; -} - - -static double owensT1(double h, double a, double m) { - int j = 1; - int jj = 1; - - double hs = -0.5 * h * h; - double dhs = exp(hs); - double as = a * a; - double aj = a / (2 * M_PI); - double dj = expm1(hs); - double gj = hs * dhs; - - double val = atan(a) / (2 * M_PI); - - while (1) { - val += dj*aj / jj; - - if (m <= j) { - break; - } - j++; - jj += 2; - aj *= as; - dj = gj - dj; - gj *= hs / j; - } - - return val; -} - - -static double owensT2(double h, double a, double ah, double m) { - int i = 1; - int maxi = 2 * m + 1; - double hs = h * h; - double as = -a * a; - double y = 1.0 / hs; - double val = 0.0; - double vi = a*exp(-0.5 * ah * ah) / sqrt(2 * M_PI); - double z = (ndtr(ah) - 0.5) / h; - - while (1) { - val += z; - if (maxi <= i) { - break; - } - z = y * (vi - i * z); - vi *= as; - i += 2; - } - val *= exp(-0.5 * hs) / sqrt(2 * M_PI); - - return val; -} - - -static double owensT3(double h, double a, double ah) { - double aa, hh, y, vi, zi, result; - int i; - - aa = a * a; - hh = h * h; - y = 1 / hh; - - vi = a * exp(-ah * ah/ 2) / sqrt(2 * M_PI); - zi = owens_t_norm1(ah) / h; - result = 0; - - for(i = 0; i<= 30; i++) { - result += zi * C[i]; - zi = y * ((2 * i + 1) * zi - vi); - vi *= aa; - } - - result *= exp(-hh / 2) / sqrt(2 * M_PI); - - return result; -} - - -static double owensT4(double h, double a, double m) { - double maxi, hh, naa, ai, yi, result; - int i; - - maxi = 2 * m + 1; - hh = h * h; - naa = -a * a; - - i = 1; - ai = a * exp(-hh * (1 - naa) / 2) / (2 * M_PI); - yi = 1; - result = 0; - - while (1) { - result += ai * yi; - - if (maxi <= i) { - break; - } - - i += 2; - yi = (1 - hh * yi) / i; - ai *= naa; - } - - return result; -} - - -static double owensT5(double h, double a) { - double result, r, aa, nhh; - int i; - - result = 0; - r = 0; - aa = a * a; - nhh = -0.5 * h * h; - - for (i = 1; i < 14; i++) { - r = 1 + aa * PTS[i - 1]; - result += WTS[i - 1] * exp(nhh * r) / r; - } - - result *= a; - - return result; -} - - -static double owensT6(double h, double a) { - double normh, y, r, result; - - normh = owens_t_norm2(h); - y = 1 - a; - r = atan2(y, (1 + a)); - result = normh * (1 - normh) / 2; - - if (r != 0) { - result -= r * exp(-y * h * h / (2 * r)) / (2 * M_PI); - } - - return result; -} - - -static double owens_t_dispatch(double h, double a, double ah) { - int index, meth_code; - double m, result; - - if (h == 0) { - return atan(a) / (2 * M_PI); - } - if (a == 0) { - return 0; - } - if (a == 1) { - return owens_t_norm2(-h) * owens_t_norm2(h) / 2; - } - - index = get_method(h, a); - m = ORD[index]; - meth_code = METHODS[index]; - - switch(meth_code) { - case 1: - result = owensT1(h, a, m); - break; - case 2: - result = owensT2(h, a, ah, m); - break; - case 3: - result = owensT3(h, a, ah); - break; - case 4: - result = owensT4(h, a, m); - break; - case 5: - result = owensT5(h, a); - break; - case 6: - result = owensT6(h, a); - break; - default: - result = NAN; - } - - return result; -} - - -double owens_t(double h, double a) { - double result, fabs_a, fabs_ah, normh, normah; - - if (cephes_isnan(h) || cephes_isnan(a)) { - return NAN; - } - - /* exploit that T(-h,a) == T(h,a) */ - h = fabs(h); - - /* - * Use equation (2) in the paper to remap the arguments such that - * h >= 0 and 0 <= a <= 1 for the call of the actual computation - * routine. - */ - fabs_a = fabs(a); - fabs_ah = fabs_a * h; - - if (fabs_a == INFINITY) { - /* See page 13 in the paper */ - result = 0.5 * owens_t_norm2(h); - } - else if (h == INFINITY) { - result = 0; - } - else if (fabs_a <= 1) { - result = owens_t_dispatch(h, fabs_a, fabs_ah); - } - else { - if (fabs_ah <= 0.67) { - normh = owens_t_norm1(h); - normah = owens_t_norm1(fabs_ah); - result = 0.25 - normh * normah - - owens_t_dispatch(fabs_ah, (1 / fabs_a), h); - } - else { - normh = owens_t_norm2(h); - normah = owens_t_norm2(fabs_ah); - result = (normh + normah) / 2 - normh * normah - - owens_t_dispatch(fabs_ah, (1 / fabs_a), h); - } - } - - if (a < 0) { - /* exploit that T(h,-a) == -T(h,a) */ - return -result; - } - - return result; -} diff --git a/gtsam/3rdparty/cephes/cephes/pdtr.c b/gtsam/3rdparty/cephes/cephes/pdtr.c deleted file mode 100644 index 0249074d98..0000000000 --- a/gtsam/3rdparty/cephes/cephes/pdtr.c +++ /dev/null @@ -1,173 +0,0 @@ -/* pdtr.c - * - * Poisson distribution - * - * - * - * SYNOPSIS: - * - * int k; - * double m, y, pdtr(); - * - * y = pdtr( k, m ); - * - * - * - * DESCRIPTION: - * - * Returns the sum of the first k terms of the Poisson - * distribution: - * - * k j - * -- -m m - * > e -- - * -- j! - * j=0 - * - * The terms are not summed directly; instead the incomplete - * Gamma integral is employed, according to the relation - * - * y = pdtr( k, m ) = igamc( k+1, m ). - * - * The arguments must both be nonnegative. - * - * - * - * ACCURACY: - * - * See igamc(). - * - */ -/* pdtrc() - * - * Complemented poisson distribution - * - * - * - * SYNOPSIS: - * - * int k; - * double m, y, pdtrc(); - * - * y = pdtrc( k, m ); - * - * - * - * DESCRIPTION: - * - * Returns the sum of the terms k+1 to infinity of the Poisson - * distribution: - * - * inf. j - * -- -m m - * > e -- - * -- j! - * j=k+1 - * - * The terms are not summed directly; instead the incomplete - * Gamma integral is employed, according to the formula - * - * y = pdtrc( k, m ) = igam( k+1, m ). - * - * The arguments must both be nonnegative. - * - * - * - * ACCURACY: - * - * See igam.c. - * - */ -/* pdtri() - * - * Inverse Poisson distribution - * - * - * - * SYNOPSIS: - * - * int k; - * double m, y, pdtr(); - * - * m = pdtri( k, y ); - * - * - * - * - * DESCRIPTION: - * - * Finds the Poisson variable x such that the integral - * from 0 to x of the Poisson density is equal to the - * given probability y. - * - * This is accomplished using the inverse Gamma integral - * function and the relation - * - * m = igamci( k+1, y ). - * - * - * - * - * ACCURACY: - * - * See igami.c. - * - * ERROR MESSAGES: - * - * message condition value returned - * pdtri domain y < 0 or y >= 1 0.0 - * k < 0 - * - */ - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" - -double pdtrc(double k, double m) -{ - double v; - - if (k < 0.0 || m < 0.0) { - sf_error("pdtrc", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (m == 0.0) { - return 0.0; - } - v = floor(k) + 1; - return (igam(v, m)); -} - - -double pdtr(double k, double m) -{ - double v; - - if (k < 0 || m < 0) { - sf_error("pdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (m == 0.0) { - return 1.0; - } - v = floor(k) + 1; - return (igamc(v, m)); -} - - -double pdtri(int k, double y) -{ - double v; - - if ((k < 0) || (y < 0.0) || (y >= 1.0)) { - sf_error("pdtri", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - v = k + 1; - v = igamci(v, y); - return (v); -} diff --git a/gtsam/3rdparty/cephes/cephes/poch.c b/gtsam/3rdparty/cephes/cephes/poch.c deleted file mode 100644 index 4c04fa14eb..0000000000 --- a/gtsam/3rdparty/cephes/cephes/poch.c +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Pochhammer symbol (a)_m = gamma(a + m) / gamma(a) - */ -#include "mconf.h" - -static double is_nonpos_int(double x) -{ - return x <= 0 && x == ceil(x) && fabs(x) < 1e13; -} - -double poch(double a, double m) -{ - double r; - - r = 1.0; - - /* - * 1. Reduce magnitude of `m` to |m| < 1 by using recurrence relations. - * - * This may end up in over/underflow, but then the function itself either - * diverges or goes to zero. In case the remainder goes to the opposite - * direction, we end up returning 0*INF = NAN, which is OK. - */ - - /* Recurse down */ - while (m >= 1.0) { - if (a + m == 1) { - break; - } - m -= 1.0; - r *= (a + m); - if (!isfinite(r) || r == 0) { - break; - } - } - - /* Recurse up */ - while (m <= -1.0) { - if (a + m == 0) { - break; - } - r /= (a + m); - m += 1.0; - if (!isfinite(r) || r == 0) { - break; - } - } - - /* - * 2. Evaluate function with reduced `m` - * - * Now either `m` is not big, or the `r` product has over/underflown. - * If so, the function itself does similarly. - */ - - if (m == 0) { - /* Easy case */ - return r; - } - else if (a > 1e4 && fabs(m) <= 1) { - /* Avoid loss of precision */ - return r * pow(a, m) * ( - 1 - + m*(m-1)/(2*a) - + m*(m-1)*(m-2)*(3*m-1)/(24*a*a) - + m*m*(m-1)*(m-1)*(m-2)*(m-3)/(48*a*a*a) - ); - } - - /* Check for infinity */ - if (is_nonpos_int(a + m) && !is_nonpos_int(a) && a + m != m) { - return INFINITY; - } - - /* Check for zero */ - if (!is_nonpos_int(a + m) && is_nonpos_int(a)) { - return 0; - } - - return r * exp(lgam(a + m) - lgam(a)) * gammasgn(a + m) * gammasgn(a); -} diff --git a/gtsam/3rdparty/cephes/cephes/psi.c b/gtsam/3rdparty/cephes/cephes/psi.c deleted file mode 100644 index 190c6d1628..0000000000 --- a/gtsam/3rdparty/cephes/cephes/psi.c +++ /dev/null @@ -1,205 +0,0 @@ -/* psi.c - * - * Psi (digamma) function - * - * - * SYNOPSIS: - * - * double x, y, psi(); - * - * y = psi( x ); - * - * - * DESCRIPTION: - * - * d - - * psi(x) = -- ln | (x) - * dx - * - * is the logarithmic derivative of the gamma function. - * For integer x, - * n-1 - * - - * psi(n) = -EUL + > 1/k. - * - - * k=1 - * - * This formula is used for 0 < n <= 10. If x is negative, it - * is transformed to a positive argument by the reflection - * formula psi(1-x) = psi(x) + pi cot(pi x). - * For general positive x, the argument is made greater than 10 - * using the recurrence psi(x+1) = psi(x) + 1/x. - * Then the following asymptotic expansion is applied: - * - * inf. B - * - 2k - * psi(x) = log(x) - 1/2x - > ------- - * - 2k - * k=1 2k x - * - * where the B2k are Bernoulli numbers. - * - * ACCURACY: - * Relative error (except absolute when |psi| < 1): - * arithmetic domain # trials peak rms - * IEEE 0,30 30000 1.3e-15 1.4e-16 - * IEEE -30,0 40000 1.5e-15 2.2e-16 - * - * ERROR MESSAGES: - * message condition value returned - * psi singularity x integer <=0 INFINITY - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier - */ - -/* - * Code for the rational approximation on [1, 2] is: - * - * (C) Copyright John Maddock 2006. - * Use, modification and distribution are subject to the - * Boost Software License, Version 1.0. (See accompanying file - * LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt) - */ - -#include "mconf.h" - -static double A[] = { - 8.33333333333333333333E-2, - -2.10927960927960927961E-2, - 7.57575757575757575758E-3, - -4.16666666666666666667E-3, - 3.96825396825396825397E-3, - -8.33333333333333333333E-3, - 8.33333333333333333333E-2 -}; - - -static double digamma_imp_1_2(double x) -{ - /* - * Rational approximation on [1, 2] taken from Boost. - * - * Now for the approximation, we use the form: - * - * digamma(x) = (x - root) * (Y + R(x-1)) - * - * Where root is the location of the positive root of digamma, - * Y is a constant, and R is optimised for low absolute error - * compared to Y. - * - * Maximum Deviation Found: 1.466e-18 - * At double precision, max error found: 2.452e-17 - */ - double r, g; - - static const float Y = 0.99558162689208984f; - - static const double root1 = 1569415565.0 / 1073741824.0; - static const double root2 = (381566830.0 / 1073741824.0) / 1073741824.0; - static const double root3 = 0.9016312093258695918615325266959189453125e-19; - - static double P[] = { - -0.0020713321167745952, - -0.045251321448739056, - -0.28919126444774784, - -0.65031853770896507, - -0.32555031186804491, - 0.25479851061131551 - }; - static double Q[] = { - -0.55789841321675513e-6, - 0.0021284987017821144, - 0.054151797245674225, - 0.43593529692665969, - 1.4606242909763515, - 2.0767117023730469, - 1.0 - }; - g = x - root1; - g -= root2; - g -= root3; - r = polevl(x - 1.0, P, 5) / polevl(x - 1.0, Q, 6); - - return g * Y + g * r; -} - - -static double psi_asy(double x) -{ - double y, z; - - if (x < 1.0e17) { - z = 1.0 / (x * x); - y = z * polevl(z, A, 6); - } - else { - y = 0.0; - } - - return log(x) - (0.5 / x) - y; -} - - -double psi(double x) -{ - double y = 0.0; - double q, r; - int i, n; - - if (isnan(x)) { - return x; - } - else if (x == INFINITY) { - return x; - } - else if (x == -INFINITY) { - return NAN; - } - else if (x == 0) { - sf_error("psi", SF_ERROR_SINGULAR, NULL); - return copysign(INFINITY, -x); - } - else if (x < 0.0) { - /* argument reduction before evaluating tan(pi * x) */ - r = modf(x, &q); - if (r == 0.0) { - sf_error("psi", SF_ERROR_SINGULAR, NULL); - return NAN; - } - y = -M_PI / tan(M_PI * r); - x = 1.0 - x; - } - - /* check for positive integer up to 10 */ - if ((x <= 10.0) && (x == floor(x))) { - n = (int)x; - for (i = 1; i < n; i++) { - y += 1.0 / i; - } - y -= SCIPY_EULER; - return y; - } - - /* use the recurrence relation to move x into [1, 2] */ - if (x < 1.0) { - y -= 1.0 / x; - x += 1.0; - } - else if (x < 10.0) { - while (x > 2.0) { - x -= 1.0; - y += 1.0 / x; - } - } - if ((1.0 <= x) && (x <= 2.0)) { - y += digamma_imp_1_2(x); - return y; - } - - /* x is large, use the asymptotic series */ - y += psi_asy(x); - return y; -} diff --git a/gtsam/3rdparty/cephes/cephes/rgamma.c b/gtsam/3rdparty/cephes/cephes/rgamma.c deleted file mode 100644 index 6420ccaa94..0000000000 --- a/gtsam/3rdparty/cephes/cephes/rgamma.c +++ /dev/null @@ -1,128 +0,0 @@ -/* rgamma.c - * - * Reciprocal Gamma function - * - * - * - * SYNOPSIS: - * - * double x, y, rgamma(); - * - * y = rgamma( x ); - * - * - * - * DESCRIPTION: - * - * Returns one divided by the Gamma function of the argument. - * - * The function is approximated by a Chebyshev expansion in - * the interval [0,1]. Range reduction is by recurrence - * for arguments between -34.034 and +34.84425627277176174. - * 0 is returned for positive arguments outside this - * range. For arguments less than -34.034 the cosecant - * reflection formula is applied; lograrithms are employed - * to avoid unnecessary overflow. - * - * The reciprocal Gamma function has no singularities, - * but overflow and underflow may occur for large arguments. - * These conditions return either INFINITY or 0 with - * appropriate sign. - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -30,+30 30000 1.1e-15 2.0e-16 - * For arguments less than -34.034 the peak error is on the - * order of 5e-15 (DEC), excepting overflow or underflow. - */ - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1985, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -/* Chebyshev coefficients for reciprocal Gamma function - * in interval 0 to 1. Function is 1/(x Gamma(x)) - 1 - */ - -static double R[] = { - 3.13173458231230000000E-17, - -6.70718606477908000000E-16, - 2.20039078172259550000E-15, - 2.47691630348254132600E-13, - -6.60074100411295197440E-12, - 5.13850186324226978840E-11, - 1.08965386454418662084E-9, - -3.33964630686836942556E-8, - 2.68975996440595483619E-7, - 2.96001177518801696639E-6, - -8.04814124978471142852E-5, - 4.16609138709688864714E-4, - 5.06579864028608725080E-3, - -6.41925436109158228810E-2, - -4.98558728684003594785E-3, - 1.27546015610523951063E-1 -}; - -static char name[] = "rgamma"; - -extern double MAXLOG; - - -double rgamma(double x) -{ - double w, y, z; - int sign; - - if (x > 34.84425627277176174) { - return exp(-lgam(x)); - } - if (x < -34.034) { - w = -x; - z = sinpi(w); - if (z == 0.0) { - return 0.0; - } - if (z < 0.0) { - sign = 1; - z = -z; - } - else { - sign = -1; - } - - y = log(w * z) - log(M_PI) + lgam(w); - if (y < -MAXLOG) { - sf_error(name, SF_ERROR_UNDERFLOW, NULL); - return (sign * 0.0); - } - if (y > MAXLOG) { - sf_error(name, SF_ERROR_OVERFLOW, NULL); - return (sign * INFINITY); - } - return (sign * exp(y)); - } - z = 1.0; - w = x; - - while (w > 1.0) { /* Downward recurrence */ - w -= 1.0; - z *= w; - } - while (w < 0.0) { /* Upward recurrence */ - z /= w; - w += 1.0; - } - if (w == 0.0) /* Nonpositive integer */ - return (0.0); - if (w == 1.0) /* Other integer */ - return (1.0 / z); - - y = w * (1.0 + chbevl(4.0 * w - 2.0, R, 16)) / z; - return (y); -} diff --git a/gtsam/3rdparty/cephes/cephes/round.c b/gtsam/3rdparty/cephes/cephes/round.c deleted file mode 100644 index 0ed1f1415b..0000000000 --- a/gtsam/3rdparty/cephes/cephes/round.c +++ /dev/null @@ -1,63 +0,0 @@ -/* round.c - * - * Round double to nearest or even integer valued double - * - * - * - * SYNOPSIS: - * - * double x, y, round(); - * - * y = round(x); - * - * - * - * DESCRIPTION: - * - * Returns the nearest integer to x as a double precision - * floating point result. If x ends in 0.5 exactly, the - * nearest even integer is chosen. - * - * - * - * ACCURACY: - * - * If x is greater than 1/(2*MACHEP), its closest machine - * representation is already an integer, so rounding does - * not change it. - */ - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -double round(double x) -{ - double y, r; - - /* Largest integer <= x */ - y = floor(x); - - /* Fractional part */ - r = x - y; - - /* Round up to nearest. */ - if (r > 0.5) - goto rndup; - - /* Round to even */ - if (r == 0.5) { - r = y - 2.0 * floor(0.5 * y); - if (r == 1.0) { - rndup: - y += 1.0; - } - } - - /* Else round down. */ - return (y); -} diff --git a/gtsam/3rdparty/cephes/cephes/scipy_iv.c b/gtsam/3rdparty/cephes/cephes/scipy_iv.c deleted file mode 100644 index e7bb220119..0000000000 --- a/gtsam/3rdparty/cephes/cephes/scipy_iv.c +++ /dev/null @@ -1,654 +0,0 @@ -/* iv.c - * - * Modified Bessel function of noninteger order - * - * - * - * SYNOPSIS: - * - * double v, x, y, iv(); - * - * y = iv( v, x ); - * - * - * - * DESCRIPTION: - * - * Returns modified Bessel function of order v of the - * argument. If x is negative, v must be integer valued. - * - */ -/* iv.c */ -/* Modified Bessel function of noninteger order */ -/* If x < 0, then v must be an integer. */ - - -/* - * Parts of the code are copyright: - * - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier - * - * And other parts: - * - * Copyright (c) 2006 Xiaogang Zhang - * Use, modification and distribution are subject to the - * Boost Software License, Version 1.0. - * - * Boost Software License - Version 1.0 - August 17th, 2003 - * - * Permission is hereby granted, free of charge, to any person or - * organization obtaining a copy of the software and accompanying - * documentation covered by this license (the "Software") to use, reproduce, - * display, distribute, execute, and transmit the Software, and to prepare - * derivative works of the Software, and to permit third-parties to whom the - * Software is furnished to do so, all subject to the following: - * - * The copyright notices in the Software and this entire statement, - * including the above license grant, this restriction and the following - * disclaimer, must be included in all copies of the Software, in whole or - * in part, and all derivative works of the Software, unless such copies or - * derivative works are solely in the form of machine-executable object code - * generated by a source language processor. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS - * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF - * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND - * NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE - * DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, - * WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN - * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - * - * And the rest are: - * - * Copyright (C) 2009 Pauli Virtanen - * Distributed under the same license as Scipy. - * - */ - -#include "mconf.h" -#include -#include - -extern double MACHEP; - -static double iv_asymptotic(double v, double x); -static void ikv_asymptotic_uniform(double v, double x, double *Iv, double *Kv); -static void ikv_temme(double v, double x, double *Iv, double *Kv); - -double iv(double v, double x) -{ - int sign; - double t, ax, res; - - if (isnan(v) || isnan(x)) { - return NAN; - } - - /* If v is a negative integer, invoke symmetry */ - t = floor(v); - if (v < 0.0) { - if (t == v) { - v = -v; /* symmetry */ - t = -t; - } - } - /* If x is negative, require v to be an integer */ - sign = 1; - if (x < 0.0) { - if (t != v) { - sf_error("iv", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - if (v != 2.0 * floor(v / 2.0)) { - sign = -1; - } - } - - /* Avoid logarithm singularity */ - if (x == 0.0) { - if (v == 0.0) { - return 1.0; - } - if (v < 0.0) { - sf_error("iv", SF_ERROR_OVERFLOW, NULL); - return INFINITY; - } - else - return 0.0; - } - - ax = fabs(x); - if (fabs(v) > 50) { - /* - * Uniform asymptotic expansion for large orders. - * - * This appears to overflow slightly later than the Boost - * implementation of Temme's method. - */ - ikv_asymptotic_uniform(v, ax, &res, NULL); - } - else { - /* Otherwise: Temme's method */ - ikv_temme(v, ax, &res, NULL); - } - res *= sign; - return res; -} - - -/* - * Compute Iv from (AMS5 9.7.1), asymptotic expansion for large |z| - * Iv ~ exp(x)/sqrt(2 pi x) ( 1 + (4*v*v-1)/8x + (4*v*v-1)(4*v*v-9)/8x/2! + ...) - */ -static double iv_asymptotic(double v, double x) -{ - double mu; - double sum, term, prefactor, factor; - int k; - - prefactor = exp(x) / sqrt(2 * M_PI * x); - - if (prefactor == INFINITY) { - return prefactor; - } - - mu = 4 * v * v; - sum = 1.0; - term = 1.0; - k = 1; - - do { - factor = (mu - (2 * k - 1) * (2 * k - 1)) / (8 * x) / k; - if (k > 100) { - /* didn't converge */ - sf_error("iv(iv_asymptotic)", SF_ERROR_NO_RESULT, NULL); - break; - } - term *= -factor; - sum += term; - ++k; - } while (fabs(term) > MACHEP * fabs(sum)); - return sum * prefactor; -} - - -/* - * Uniform asymptotic expansion factors, (AMS5 9.3.9; AMS5 9.3.10) - * - * Computed with: - * -------------------- - import numpy as np - t = np.poly1d([1,0]) - def up1(p): - return .5*t*t*(1-t*t)*p.deriv() + 1/8. * ((1-5*t*t)*p).integ() - us = [np.poly1d([1])] - for k in range(10): - us.append(up1(us[-1])) - n = us[-1].order - for p in us: - print "{" + ", ".join(["0"]*(n-p.order) + map(repr, p)) + "}," - print "N_UFACTORS", len(us) - print "N_UFACTOR_TERMS", us[-1].order + 1 - * -------------------- - */ -#define N_UFACTORS 11 -#define N_UFACTOR_TERMS 31 -static const double asymptotic_ufactors[N_UFACTORS][N_UFACTOR_TERMS] = { - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 1}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, -0.20833333333333334, 0.0, 0.125, 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0.3342013888888889, 0.0, -0.40104166666666669, 0.0, 0.0703125, 0.0, - 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - -1.0258125964506173, 0.0, 1.8464626736111112, 0.0, - -0.89121093750000002, 0.0, 0.0732421875, 0.0, 0.0, 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 4.6695844234262474, 0.0, -11.207002616222995, 0.0, 8.78912353515625, - 0.0, -2.3640869140624998, 0.0, 0.112152099609375, 0.0, 0.0, 0.0, 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -28.212072558200244, 0.0, - 84.636217674600744, 0.0, -91.818241543240035, 0.0, 42.534998745388457, - 0.0, -7.3687943594796312, 0.0, 0.22710800170898438, 0.0, 0.0, 0.0, - 0.0, 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 212.5701300392171, 0.0, - -765.25246814118157, 0.0, 1059.9904525279999, 0.0, - -699.57962737613275, 0.0, 218.19051174421159, 0.0, - -26.491430486951554, 0.0, 0.57250142097473145, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0}, - {0, 0, 0, 0, 0, 0, 0, 0, 0, -1919.4576623184068, 0.0, - 8061.7221817373083, 0.0, -13586.550006434136, 0.0, 11655.393336864536, - 0.0, -5305.6469786134048, 0.0, 1200.9029132163525, 0.0, - -108.09091978839464, 0.0, 1.7277275025844574, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0}, - {0, 0, 0, 0, 0, 0, 20204.291330966149, 0.0, -96980.598388637503, 0.0, - 192547.0012325315, 0.0, -203400.17728041555, 0.0, 122200.46498301747, - 0.0, -41192.654968897557, 0.0, 7109.5143024893641, 0.0, - -493.915304773088, 0.0, 6.074042001273483, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0}, - {0, 0, 0, -242919.18790055133, 0.0, 1311763.6146629769, 0.0, - -2998015.9185381061, 0.0, 3763271.2976564039, 0.0, - -2813563.2265865342, 0.0, 1268365.2733216248, 0.0, - -331645.17248456361, 0.0, 45218.768981362737, 0.0, - -2499.8304818112092, 0.0, 24.380529699556064, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.0}, - {3284469.8530720375, 0.0, -19706819.11843222, 0.0, 50952602.492664628, - 0.0, -74105148.211532637, 0.0, 66344512.274729028, 0.0, - -37567176.660763353, 0.0, 13288767.166421819, 0.0, - -2785618.1280864552, 0.0, 308186.40461266245, 0.0, - -13886.089753717039, 0.0, 110.01714026924674, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.0, 0.0, 0.0} -}; - - -/* - * Compute Iv, Kv from (AMS5 9.7.7 + 9.7.8), asymptotic expansion for large v - */ -static void ikv_asymptotic_uniform(double v, double x, - double *i_value, double *k_value) -{ - double i_prefactor, k_prefactor; - double t, t2, eta, z; - double i_sum, k_sum, term, divisor; - int k, n; - int sign = 1; - - if (v < 0) { - /* Negative v; compute I_{-v} and K_{-v} and use (AMS 9.6.2) */ - sign = -1; - v = -v; - } - - z = x / v; - t = 1 / sqrt(1 + z * z); - t2 = t * t; - eta = sqrt(1 + z * z) + log(z / (1 + 1 / t)); - - i_prefactor = sqrt(t / (2 * M_PI * v)) * exp(v * eta); - i_sum = 1.0; - - k_prefactor = sqrt(M_PI * t / (2 * v)) * exp(-v * eta); - k_sum = 1.0; - - divisor = v; - for (n = 1; n < N_UFACTORS; ++n) { - /* - * Evaluate u_k(t) with Horner's scheme; - * (using the knowledge about which coefficients are zero) - */ - term = 0; - for (k = N_UFACTOR_TERMS - 1 - 3 * n; - k < N_UFACTOR_TERMS - n; k += 2) { - term *= t2; - term += asymptotic_ufactors[n][k]; - } - for (k = 1; k < n; k += 2) { - term *= t2; - } - if (n % 2 == 1) { - term *= t; - } - - /* Sum terms */ - term /= divisor; - i_sum += term; - k_sum += (n % 2 == 0) ? term : -term; - - /* Check convergence */ - if (fabs(term) < MACHEP) { - break; - } - - divisor *= v; - } - - if (fabs(term) > 1e-3 * fabs(i_sum)) { - /* Didn't converge */ - sf_error("ikv_asymptotic_uniform", SF_ERROR_NO_RESULT, NULL); - } - if (fabs(term) > MACHEP * fabs(i_sum)) { - /* Some precision lost */ - sf_error("ikv_asymptotic_uniform", SF_ERROR_LOSS, NULL); - } - - if (k_value != NULL) { - /* symmetric in v */ - *k_value = k_prefactor * k_sum; - } - - if (i_value != NULL) { - if (sign == 1) { - *i_value = i_prefactor * i_sum; - } - else { - /* (AMS 9.6.2) */ - *i_value = (i_prefactor * i_sum - + (2 / M_PI) * sin(M_PI * v) * k_prefactor * k_sum); - } - } -} - - -/* - * The following code originates from the Boost C++ library, - * from file `boost/math/special_functions/detail/bessel_ik.hpp`, - * converted from C++ to C. - */ - -#ifdef DEBUG -#define BOOST_ASSERT(a) assert(a) -#else -#define BOOST_ASSERT(a) -#endif - -/* - * Modified Bessel functions of the first and second kind of fractional order - * - * Calculate K(v, x) and K(v+1, x) by method analogous to - * Temme, Journal of Computational Physics, vol 21, 343 (1976) - */ -static int temme_ik_series(double v, double x, double *K, double *K1) -{ - double f, h, p, q, coef, sum, sum1, tolerance; - double a, b, c, d, sigma, gamma1, gamma2; - unsigned long k; - double gp; - double gm; - - - /* - * |x| <= 2, Temme series converge rapidly - * |x| > 2, the larger the |x|, the slower the convergence - */ - BOOST_ASSERT(fabs(x) <= 2); - BOOST_ASSERT(fabs(v) <= 0.5f); - - gp = gamma(v + 1) - 1; - gm = gamma(-v + 1) - 1; - - a = log(x / 2); - b = exp(v * a); - sigma = -a * v; - c = fabs(v) < MACHEP ? 1 : sin(M_PI * v) / (v * M_PI); - d = fabs(sigma) < MACHEP ? 1 : sinh(sigma) / sigma; - gamma1 = fabs(v) < MACHEP ? -SCIPY_EULER : (0.5f / v) * (gp - gm) * c; - gamma2 = (2 + gp + gm) * c / 2; - - /* initial values */ - p = (gp + 1) / (2 * b); - q = (1 + gm) * b / 2; - f = (cosh(sigma) * gamma1 + d * (-a) * gamma2) / c; - h = p; - coef = 1; - sum = coef * f; - sum1 = coef * h; - - /* series summation */ - tolerance = MACHEP; - for (k = 1; k < MAXITER; k++) { - f = (k * f + p + q) / (k * k - v * v); - p /= k - v; - q /= k + v; - h = p - k * f; - coef *= x * x / (4 * k); - sum += coef * f; - sum1 += coef * h; - if (fabs(coef * f) < fabs(sum) * tolerance) { - break; - } - } - if (k == MAXITER) { - sf_error("ikv_temme(temme_ik_series)", SF_ERROR_NO_RESULT, NULL); - } - - *K = sum; - *K1 = 2 * sum1 / x; - - return 0; -} - -/* Evaluate continued fraction fv = I_(v+1) / I_v, derived from - * Abramowitz and Stegun, Handbook of Mathematical Functions, 1972, 9.1.73 */ -static int CF1_ik(double v, double x, double *fv) -{ - double C, D, f, a, b, delta, tiny, tolerance; - unsigned long k; - - - /* - * |x| <= |v|, CF1_ik converges rapidly - * |x| > |v|, CF1_ik needs O(|x|) iterations to converge - */ - - /* - * modified Lentz's method, see - * Lentz, Applied Optics, vol 15, 668 (1976) - */ - tolerance = 2 * MACHEP; - tiny = 1 / sqrt(DBL_MAX); - C = f = tiny; /* b0 = 0, replace with tiny */ - D = 0; - for (k = 1; k < MAXITER; k++) { - a = 1; - b = 2 * (v + k) / x; - C = b + a / C; - D = b + a * D; - if (C == 0) { - C = tiny; - } - if (D == 0) { - D = tiny; - } - D = 1 / D; - delta = C * D; - f *= delta; - if (fabs(delta - 1) <= tolerance) { - break; - } - } - if (k == MAXITER) { - sf_error("ikv_temme(CF1_ik)", SF_ERROR_NO_RESULT, NULL); - } - - *fv = f; - - return 0; -} - -/* - * Calculate K(v, x) and K(v+1, x) by evaluating continued fraction - * z1 / z0 = U(v+1.5, 2v+1, 2x) / U(v+0.5, 2v+1, 2x), see - * Thompson and Barnett, Computer Physics Communications, vol 47, 245 (1987) - */ -static int CF2_ik(double v, double x, double *Kv, double *Kv1) -{ - - double S, C, Q, D, f, a, b, q, delta, tolerance, current, prev; - unsigned long k; - - /* - * |x| >= |v|, CF2_ik converges rapidly - * |x| -> 0, CF2_ik fails to converge - */ - - BOOST_ASSERT(fabs(x) > 1); - - /* - * Steed's algorithm, see Thompson and Barnett, - * Journal of Computational Physics, vol 64, 490 (1986) - */ - tolerance = MACHEP; - a = v * v - 0.25f; - b = 2 * (x + 1); /* b1 */ - D = 1 / b; /* D1 = 1 / b1 */ - f = delta = D; /* f1 = delta1 = D1, coincidence */ - prev = 0; /* q0 */ - current = 1; /* q1 */ - Q = C = -a; /* Q1 = C1 because q1 = 1 */ - S = 1 + Q * delta; /* S1 */ - for (k = 2; k < MAXITER; k++) { /* starting from 2 */ - /* continued fraction f = z1 / z0 */ - a -= 2 * (k - 1); - b += 2; - D = 1 / (b + a * D); - delta *= b * D - 1; - f += delta; - - /* series summation S = 1 + \sum_{n=1}^{\infty} C_n * z_n / z_0 */ - q = (prev - (b - 2) * current) / a; - prev = current; - current = q; /* forward recurrence for q */ - C *= -a / k; - Q += C * q; - S += Q * delta; - - /* S converges slower than f */ - if (fabs(Q * delta) < fabs(S) * tolerance) { - break; - } - } - if (k == MAXITER) { - sf_error("ikv_temme(CF2_ik)", SF_ERROR_NO_RESULT, NULL); - } - - *Kv = sqrt(M_PI / (2 * x)) * exp(-x) / S; - *Kv1 = *Kv * (0.5f + v + x + (v * v - 0.25f) * f) / x; - - return 0; -} - -/* Flags for what to compute */ -enum { - need_i = 0x1, - need_k = 0x2 -}; - -/* - * Compute I(v, x) and K(v, x) simultaneously by Temme's method, see - * Temme, Journal of Computational Physics, vol 19, 324 (1975) - */ -static void ikv_temme(double v, double x, double *Iv_p, double *Kv_p) -{ - /* Kv1 = K_(v+1), fv = I_(v+1) / I_v */ - /* Ku1 = K_(u+1), fu = I_(u+1) / I_u */ - double u, Iv, Kv, Kv1, Ku, Ku1, fv; - double W, current, prev, next; - int reflect = 0; - unsigned n, k; - int kind; - - kind = 0; - if (Iv_p != NULL) { - kind |= need_i; - } - if (Kv_p != NULL) { - kind |= need_k; - } - - if (v < 0) { - reflect = 1; - v = -v; /* v is non-negative from here */ - kind |= need_k; - } - n = round(v); - u = v - n; /* -1/2 <= u < 1/2 */ - - if (x < 0) { - if (Iv_p != NULL) - *Iv_p = NAN; - if (Kv_p != NULL) - *Kv_p = NAN; - sf_error("ikv_temme", SF_ERROR_DOMAIN, NULL); - return; - } - if (x == 0) { - Iv = (v == 0) ? 1 : 0; - if (kind & need_k) { - sf_error("ikv_temme", SF_ERROR_OVERFLOW, NULL); - Kv = INFINITY; - } - else { - Kv = NAN; /* any value will do */ - } - - if (reflect && (kind & need_i)) { - double z = (u + n % 2); - - Iv = sin((double)M_PI * z) == 0 ? Iv : INFINITY; - if (Iv == INFINITY || Iv == -INFINITY) { - sf_error("ikv_temme", SF_ERROR_OVERFLOW, NULL); - } - } - - if (Iv_p != NULL) { - *Iv_p = Iv; - } - if (Kv_p != NULL) { - *Kv_p = Kv; - } - return; - } - /* x is positive until reflection */ - W = 1 / x; /* Wronskian */ - if (x <= 2) { /* x in (0, 2] */ - temme_ik_series(u, x, &Ku, &Ku1); /* Temme series */ - } - else { /* x in (2, \infty) */ - CF2_ik(u, x, &Ku, &Ku1); /* continued fraction CF2_ik */ - } - prev = Ku; - current = Ku1; - for (k = 1; k <= n; k++) { /* forward recurrence for K */ - next = 2 * (u + k) * current / x + prev; - prev = current; - current = next; - } - Kv = prev; - Kv1 = current; - if (kind & need_i) { - double lim = (4 * v * v + 10) / (8 * x); - - lim *= lim; - lim *= lim; - lim /= 24; - if ((lim < MACHEP * 10) && (x > 100)) { - /* - * x is huge compared to v, CF1 may be very slow - * to converge so use asymptotic expansion for large - * x case instead. Note that the asymptotic expansion - * isn't very accurate - so it's deliberately very hard - * to get here - probably we're going to overflow: - */ - Iv = iv_asymptotic(v, x); - } - else { - CF1_ik(v, x, &fv); /* continued fraction CF1_ik */ - Iv = W / (Kv * fv + Kv1); /* Wronskian relation */ - } - } - else { - Iv = NAN; /* any value will do */ - } - - if (reflect) { - double z = (u + n % 2); - - if (Iv_p != NULL) { - *Iv_p = Iv + (2 / M_PI) * sin(M_PI * z) * Kv; /* reflection formula */ - } - if (Kv_p != NULL) { - *Kv_p = Kv; - } - } - else { - if (Iv_p != NULL) { - *Iv_p = Iv; - } - if (Kv_p != NULL) { - *Kv_p = Kv; - } - } - return; -} diff --git a/gtsam/3rdparty/cephes/cephes/shichi.c b/gtsam/3rdparty/cephes/cephes/shichi.c deleted file mode 100644 index 75104e7247..0000000000 --- a/gtsam/3rdparty/cephes/cephes/shichi.c +++ /dev/null @@ -1,305 +0,0 @@ -/* shichi.c - * - * Hyperbolic sine and cosine integrals - * - * - * - * SYNOPSIS: - * - * double x, Chi, Shi, shichi(); - * - * shichi( x, &Chi, &Shi ); - * - * - * DESCRIPTION: - * - * Approximates the integrals - * - * x - * - - * | | cosh t - 1 - * Chi(x) = eul + ln x + | ----------- dt, - * | | t - * - - * 0 - * - * x - * - - * | | sinh t - * Shi(x) = | ------ dt - * | | t - * - - * 0 - * - * where eul = 0.57721566490153286061 is Euler's constant. - * The integrals are evaluated by power series for x < 8 - * and by Chebyshev expansions for x between 8 and 88. - * For large x, both functions approach exp(x)/2x. - * Arguments greater than 88 in magnitude return INFINITY. - * - * - * ACCURACY: - * - * Test interval 0 to 88. - * Relative error: - * arithmetic function # trials peak rms - * IEEE Shi 30000 6.9e-16 1.6e-16 - * Absolute error, except relative when |Chi| > 1: - * IEEE Chi 30000 8.4e-16 1.4e-16 - */ - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - - -#include "mconf.h" - -/* x exp(-x) shi(x), inverted interval 8 to 18 */ -static double S1[] = { - 1.83889230173399459482E-17, - -9.55485532279655569575E-17, - 2.04326105980879882648E-16, - 1.09896949074905343022E-15, - -1.31313534344092599234E-14, - 5.93976226264314278932E-14, - -3.47197010497749154755E-14, - -1.40059764613117131000E-12, - 9.49044626224223543299E-12, - -1.61596181145435454033E-11, - -1.77899784436430310321E-10, - 1.35455469767246947469E-9, - -1.03257121792819495123E-9, - -3.56699611114982536845E-8, - 1.44818877384267342057E-7, - 7.82018215184051295296E-7, - -5.39919118403805073710E-6, - -3.12458202168959833422E-5, - 8.90136741950727517826E-5, - 2.02558474743846862168E-3, - 2.96064440855633256972E-2, - 1.11847751047257036625E0 -}; - -/* x exp(-x) shi(x), inverted interval 18 to 88 */ -static double S2[] = { - -1.05311574154850938805E-17, - 2.62446095596355225821E-17, - 8.82090135625368160657E-17, - -3.38459811878103047136E-16, - -8.30608026366935789136E-16, - 3.93397875437050071776E-15, - 1.01765565969729044505E-14, - -4.21128170307640802703E-14, - -1.60818204519802480035E-13, - 3.34714954175994481761E-13, - 2.72600352129153073807E-12, - 1.66894954752839083608E-12, - -3.49278141024730899554E-11, - -1.58580661666482709598E-10, - -1.79289437183355633342E-10, - 1.76281629144264523277E-9, - 1.69050228879421288846E-8, - 1.25391771228487041649E-7, - 1.16229947068677338732E-6, - 1.61038260117376323993E-5, - 3.49810375601053973070E-4, - 1.28478065259647610779E-2, - 1.03665722588798326712E0 -}; - -/* x exp(-x) chin(x), inverted interval 8 to 18 */ -static double C1[] = { - -8.12435385225864036372E-18, - 2.17586413290339214377E-17, - 5.22624394924072204667E-17, - -9.48812110591690559363E-16, - 5.35546311647465209166E-15, - -1.21009970113732918701E-14, - -6.00865178553447437951E-14, - 7.16339649156028587775E-13, - -2.93496072607599856104E-12, - -1.40359438136491256904E-12, - 8.76302288609054966081E-11, - -4.40092476213282340617E-10, - -1.87992075640569295479E-10, - 1.31458150989474594064E-8, - -4.75513930924765465590E-8, - -2.21775018801848880741E-7, - 1.94635531373272490962E-6, - 4.33505889257316408893E-6, - -6.13387001076494349496E-5, - -3.13085477492997465138E-4, - 4.97164789823116062801E-4, - 2.64347496031374526641E-2, - 1.11446150876699213025E0 -}; - -/* x exp(-x) chin(x), inverted interval 18 to 88 */ -static double C2[] = { - 8.06913408255155572081E-18, - -2.08074168180148170312E-17, - -5.98111329658272336816E-17, - 2.68533951085945765591E-16, - 4.52313941698904694774E-16, - -3.10734917335299464535E-15, - -4.42823207332531972288E-15, - 3.49639695410806959872E-14, - 6.63406731718911586609E-14, - -3.71902448093119218395E-13, - -1.27135418132338309016E-12, - 2.74851141935315395333E-12, - 2.33781843985453438400E-11, - 2.71436006377612442764E-11, - -2.56600180000355990529E-10, - -1.61021375163803438552E-9, - -4.72543064876271773512E-9, - -3.00095178028681682282E-9, - 7.79387474390914922337E-8, - 1.06942765566401507066E-6, - 1.59503164802313196374E-5, - 3.49592575153777996871E-4, - 1.28475387530065247392E-2, - 1.03665693917934275131E0 -}; - -static double hyp3f0(double a1, double a2, double a3, double z); - -/* Sine and cosine integrals */ - -extern double MACHEP; - -int shichi(double x, double *si, double *ci) -{ - double k, z, c, s, a, b; - short sign; - - if (x < 0.0) { - sign = -1; - x = -x; - } - else - sign = 0; - - - if (x == 0.0) { - *si = 0.0; - *ci = -INFINITY; - return (0); - } - - if (x >= 8.0) - goto chb; - - if (x >= 88.0) - goto asymp; - - z = x * x; - - /* Direct power series expansion */ - a = 1.0; - s = 1.0; - c = 0.0; - k = 2.0; - - do { - a *= z / k; - c += a / k; - k += 1.0; - a /= k; - s += a / k; - k += 1.0; - } - while (fabs(a / s) > MACHEP); - - s *= x; - goto done; - - -chb: - /* Chebyshev series expansions */ - if (x < 18.0) { - a = (576.0 / x - 52.0) / 10.0; - k = exp(x) / x; - s = k * chbevl(a, S1, 22); - c = k * chbevl(a, C1, 23); - goto done; - } - - if (x <= 88.0) { - a = (6336.0 / x - 212.0) / 70.0; - k = exp(x) / x; - s = k * chbevl(a, S2, 23); - c = k * chbevl(a, C2, 24); - goto done; - } - -asymp: - if (x > 1000) { - *si = INFINITY; - *ci = INFINITY; - } - else { - /* Asymptotic expansions - * http://functions.wolfram.com/GammaBetaErf/CoshIntegral/06/02/ - * http://functions.wolfram.com/GammaBetaErf/SinhIntegral/06/02/0001/ - */ - a = hyp3f0(0.5, 1, 1, 4.0/(x*x)); - b = hyp3f0(1, 1, 1.5, 4.0/(x*x)); - *si = cosh(x)/x * a + sinh(x)/(x*x) * b; - *ci = sinh(x)/x * a + cosh(x)/(x*x) * b; - } - if (sign) { - *si = -*si; - } - return 0; - -done: - if (sign) - s = -s; - - *si = s; - - *ci = SCIPY_EULER + log(x) + c; - return (0); -} - - -/* - * Evaluate 3F0(a1, a2, a3; z) - * - * The series is only asymptotic, so this requires z large enough. - */ -static double hyp3f0(double a1, double a2, double a3, double z) -{ - int n, maxiter; - double err, sum, term, m; - - m = pow(z, -1.0/3); - if (m < 50) { - maxiter = m; - } - else { - maxiter = 50; - } - - term = 1.0; - sum = term; - for (n = 0; n < maxiter; ++n) { - term *= (a1 + n) * (a2 + n) * (a3 + n) * z / (n + 1); - sum += term; - if (fabs(term) < 1e-13 * fabs(sum) || term == 0) { - break; - } - } - - err = fabs(term); - - if (err > 1e-13 * fabs(sum)) { - return NAN; - } - - return sum; -} diff --git a/gtsam/3rdparty/cephes/cephes/sici.c b/gtsam/3rdparty/cephes/cephes/sici.c deleted file mode 100644 index 7bb79bc25f..0000000000 --- a/gtsam/3rdparty/cephes/cephes/sici.c +++ /dev/null @@ -1,276 +0,0 @@ -/* sici.c - * - * Sine and cosine integrals - * - * - * - * SYNOPSIS: - * - * double x, Ci, Si, sici(); - * - * sici( x, &Si, &Ci ); - * - * - * DESCRIPTION: - * - * Evaluates the integrals - * - * x - * - - * | cos t - 1 - * Ci(x) = eul + ln x + | --------- dt, - * | t - * - - * 0 - * x - * - - * | sin t - * Si(x) = | ----- dt - * | t - * - - * 0 - * - * where eul = 0.57721566490153286061 is Euler's constant. - * The integrals are approximated by rational functions. - * For x > 8 auxiliary functions f(x) and g(x) are employed - * such that - * - * Ci(x) = f(x) sin(x) - g(x) cos(x) - * Si(x) = pi/2 - f(x) cos(x) - g(x) sin(x) - * - * - * ACCURACY: - * Test interval = [0,50]. - * Absolute error, except relative when > 1: - * arithmetic function # trials peak rms - * IEEE Si 30000 4.4e-16 7.3e-17 - * IEEE Ci 30000 6.9e-16 5.1e-17 - */ - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -static double SN[] = { - -8.39167827910303881427E-11, - 4.62591714427012837309E-8, - -9.75759303843632795789E-6, - 9.76945438170435310816E-4, - -4.13470316229406538752E-2, - 1.00000000000000000302E0, -}; - -static double SD[] = { - 2.03269266195951942049E-12, - 1.27997891179943299903E-9, - 4.41827842801218905784E-7, - 9.96412122043875552487E-5, - 1.42085239326149893930E-2, - 9.99999999999999996984E-1, -}; - -static double CN[] = { - 2.02524002389102268789E-11, - -1.35249504915790756375E-8, - 3.59325051419993077021E-6, - -4.74007206873407909465E-4, - 2.89159652607555242092E-2, - -1.00000000000000000080E0, -}; - -static double CD[] = { - 4.07746040061880559506E-12, - 3.06780997581887812692E-9, - 1.23210355685883423679E-6, - 3.17442024775032769882E-4, - 5.10028056236446052392E-2, - 4.00000000000000000080E0, -}; - -static double FN4[] = { - 4.23612862892216586994E0, - 5.45937717161812843388E0, - 1.62083287701538329132E0, - 1.67006611831323023771E-1, - 6.81020132472518137426E-3, - 1.08936580650328664411E-4, - 5.48900223421373614008E-7, -}; - -static double FD4[] = { - /* 1.00000000000000000000E0, */ - 8.16496634205391016773E0, - 7.30828822505564552187E0, - 1.86792257950184183883E0, - 1.78792052963149907262E-1, - 7.01710668322789753610E-3, - 1.10034357153915731354E-4, - 5.48900252756255700982E-7, -}; - -static double FN8[] = { - 4.55880873470465315206E-1, - 7.13715274100146711374E-1, - 1.60300158222319456320E-1, - 1.16064229408124407915E-2, - 3.49556442447859055605E-4, - 4.86215430826454749482E-6, - 3.20092790091004902806E-8, - 9.41779576128512936592E-11, - 9.70507110881952024631E-14, -}; - -static double FD8[] = { - /* 1.00000000000000000000E0, */ - 9.17463611873684053703E-1, - 1.78685545332074536321E-1, - 1.22253594771971293032E-2, - 3.58696481881851580297E-4, - 4.92435064317881464393E-6, - 3.21956939101046018377E-8, - 9.43720590350276732376E-11, - 9.70507110881952025725E-14, -}; - -static double GN4[] = { - 8.71001698973114191777E-2, - 6.11379109952219284151E-1, - 3.97180296392337498885E-1, - 7.48527737628469092119E-2, - 5.38868681462177273157E-3, - 1.61999794598934024525E-4, - 1.97963874140963632189E-6, - 7.82579040744090311069E-9, -}; - -static double GD4[] = { - /* 1.00000000000000000000E0, */ - 1.64402202413355338886E0, - 6.66296701268987968381E-1, - 9.88771761277688796203E-2, - 6.22396345441768420760E-3, - 1.73221081474177119497E-4, - 2.02659182086343991969E-6, - 7.82579218933534490868E-9, -}; - -static double GN8[] = { - 6.97359953443276214934E-1, - 3.30410979305632063225E-1, - 3.84878767649974295920E-2, - 1.71718239052347903558E-3, - 3.48941165502279436777E-5, - 3.47131167084116673800E-7, - 1.70404452782044526189E-9, - 3.85945925430276600453E-12, - 3.14040098946363334640E-15, -}; - -static double GD8[] = { - /* 1.00000000000000000000E0, */ - 1.68548898811011640017E0, - 4.87852258695304967486E-1, - 4.67913194259625806320E-2, - 1.90284426674399523638E-3, - 3.68475504442561108162E-5, - 3.57043223443740838771E-7, - 1.72693748966316146736E-9, - 3.87830166023954706752E-12, - 3.14040098946363335242E-15, -}; - -extern double MACHEP; - - -int sici(double x, double *si, double *ci) -{ - double z, c, s, f, g; - short sign; - - if (x < 0.0) { - sign = -1; - x = -x; - } - else - sign = 0; - - - if (x == 0.0) { - *si = 0.0; - *ci = -INFINITY; - return (0); - } - - - if (x > 1.0e9) { - if (cephes_isinf(x)) { - if (sign == -1) { - *si = -M_PI_2; - *ci = NAN; - } - else { - *si = M_PI_2; - *ci = 0; - } - return 0; - } - *si = M_PI_2 - cos(x) / x; - *ci = sin(x) / x; - } - - - - if (x > 4.0) - goto asympt; - - z = x * x; - s = x * polevl(z, SN, 5) / polevl(z, SD, 5); - c = z * polevl(z, CN, 5) / polevl(z, CD, 5); - - if (sign) - s = -s; - *si = s; - *ci = SCIPY_EULER + log(x) + c; /* real part if x < 0 */ - return (0); - - - - /* The auxiliary functions are: - * - * - * *si = *si - M_PI_2; - * c = cos(x); - * s = sin(x); - * - * t = *ci * s - *si * c; - * a = *ci * c + *si * s; - * - * *si = t; - * *ci = -a; - */ - - - asympt: - - s = sin(x); - c = cos(x); - z = 1.0 / (x * x); - if (x < 8.0) { - f = polevl(z, FN4, 6) / (x * p1evl(z, FD4, 7)); - g = z * polevl(z, GN4, 7) / p1evl(z, GD4, 7); - } - else { - f = polevl(z, FN8, 8) / (x * p1evl(z, FD8, 8)); - g = z * polevl(z, GN8, 8) / p1evl(z, GD8, 9); - } - *si = M_PI_2 - f * c - g * s; - if (sign) - *si = -(*si); - *ci = f * s - g * c; - - return (0); -} diff --git a/gtsam/3rdparty/cephes/cephes/sindg.c b/gtsam/3rdparty/cephes/cephes/sindg.c deleted file mode 100644 index d9c37ebdbf..0000000000 --- a/gtsam/3rdparty/cephes/cephes/sindg.c +++ /dev/null @@ -1,219 +0,0 @@ -/* sindg.c - * - * Circular sine of angle in degrees - * - * - * - * SYNOPSIS: - * - * double x, y, sindg(); - * - * y = sindg( x ); - * - * - * - * DESCRIPTION: - * - * Range reduction is into intervals of 45 degrees. - * - * Two polynomial approximating functions are employed. - * Between 0 and pi/4 the sine is approximated by - * x + x**3 P(x**2). - * Between pi/4 and pi/2 the cosine is represented as - * 1 - x**2 P(x**2). - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE +-1000 30000 2.3e-16 5.6e-17 - * - * ERROR MESSAGES: - * - * message condition value returned - * sindg total loss x > 1.0e14 (IEEE) 0.0 - * - */ - /* cosdg.c - * - * Circular cosine of angle in degrees - * - * - * - * SYNOPSIS: - * - * double x, y, cosdg(); - * - * y = cosdg( x ); - * - * - * - * DESCRIPTION: - * - * Range reduction is into intervals of 45 degrees. - * - * Two polynomial approximating functions are employed. - * Between 0 and pi/4 the cosine is approximated by - * 1 - x**2 P(x**2). - * Between pi/4 and pi/2 the sine is represented as - * x + x**3 P(x**2). - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE +-1000 30000 2.1e-16 5.7e-17 - * See also sin(). - * - */ - -/* Cephes Math Library Release 2.0: April, 1987 - * Copyright 1985, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 */ - -#include "mconf.h" - -static double sincof[] = { - 1.58962301572218447952E-10, - -2.50507477628503540135E-8, - 2.75573136213856773549E-6, - -1.98412698295895384658E-4, - 8.33333333332211858862E-3, - -1.66666666666666307295E-1 -}; - -static double coscof[] = { - 1.13678171382044553091E-11, - -2.08758833757683644217E-9, - 2.75573155429816611547E-7, - -2.48015872936186303776E-5, - 1.38888888888806666760E-3, - -4.16666666666666348141E-2, - 4.99999999999999999798E-1 -}; - -static double PI180 = 1.74532925199432957692E-2; /* pi/180 */ -static double lossth = 1.0e14; - -double sindg(double x) -{ - double y, z, zz; - int j, sign; - - /* make argument positive but save the sign */ - sign = 1; - if (x < 0) { - x = -x; - sign = -1; - } - - if (x > lossth) { - sf_error("sindg", SF_ERROR_NO_RESULT, NULL); - return (0.0); - } - - y = floor(x / 45.0); /* integer part of x/M_PI_4 */ - - /* strip high bits of integer part to prevent integer overflow */ - z = ldexp(y, -4); - z = floor(z); /* integer part of y/8 */ - z = y - ldexp(z, 4); /* y - 16 * (y/16) */ - - j = z; /* convert to integer for tests on the phase angle */ - /* map zeros to origin */ - if (j & 1) { - j += 1; - y += 1.0; - } - j = j & 07; /* octant modulo 360 degrees */ - /* reflect in x axis */ - if (j > 3) { - sign = -sign; - j -= 4; - } - - z = x - y * 45.0; /* x mod 45 degrees */ - z *= PI180; /* multiply by pi/180 to convert to radians */ - zz = z * z; - - if ((j == 1) || (j == 2)) { - y = 1.0 - zz * polevl(zz, coscof, 6); - } - else { - y = z + z * (zz * polevl(zz, sincof, 5)); - } - - if (sign < 0) - y = -y; - - return (y); -} - - -double cosdg(double x) -{ - double y, z, zz; - int j, sign; - - /* make argument positive */ - sign = 1; - if (x < 0) - x = -x; - - if (x > lossth) { - sf_error("cosdg", SF_ERROR_NO_RESULT, NULL); - return (0.0); - } - - y = floor(x / 45.0); - z = ldexp(y, -4); - z = floor(z); /* integer part of y/8 */ - z = y - ldexp(z, 4); /* y - 16 * (y/16) */ - - /* integer and fractional part modulo one octant */ - j = z; - if (j & 1) { /* map zeros to origin */ - j += 1; - y += 1.0; - } - j = j & 07; - if (j > 3) { - j -= 4; - sign = -sign; - } - - if (j > 1) - sign = -sign; - - z = x - y * 45.0; /* x mod 45 degrees */ - z *= PI180; /* multiply by pi/180 to convert to radians */ - - zz = z * z; - - if ((j == 1) || (j == 2)) { - y = z + z * (zz * polevl(zz, sincof, 5)); - } - else { - y = 1.0 - zz * polevl(zz, coscof, 6); - } - - if (sign < 0) - y = -y; - - return (y); -} - - -/* Degrees, minutes, seconds to radians: */ - -/* 1 arc second, in radians = 4.848136811095359935899141023579479759563533023727e-6 */ -static double P64800 = - 4.848136811095359935899141023579479759563533023727e-6; - -double radian(double d, double m, double s) -{ - return (((d * 60.0 + m) * 60.0 + s) * P64800); -} diff --git a/gtsam/3rdparty/cephes/cephes/sinpi.c b/gtsam/3rdparty/cephes/cephes/sinpi.c deleted file mode 100644 index f0e52f9904..0000000000 --- a/gtsam/3rdparty/cephes/cephes/sinpi.c +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Implement sin(pi * x) and cos(pi * x) for real x. Since the periods - * of these functions are integral (and thus representable in double - * precision), it's possible to compute them with greater accuracy - * than sin(x) and cos(x). - */ -#include "mconf.h" - - -/* Compute sin(pi * x). */ -double sinpi(double x) -{ - double s = 1.0; - double r; - - if (x < 0.0) { - x = -x; - s = -1.0; - } - - r = fmod(x, 2.0); - if (r < 0.5) { - return s*sin(M_PI*r); - } - else if (r > 1.5) { - return s*sin(M_PI*(r - 2.0)); - } - else { - return -s*sin(M_PI*(r - 1.0)); - } -} - - -/* Compute cos(pi * x) */ -double cospi(double x) -{ - double r; - - if (x < 0.0) { - x = -x; - } - - r = fmod(x, 2.0); - if (r == 0.5) { - // We don't want to return -0.0 - return 0.0; - } - if (r < 1.0) { - return -sin(M_PI*(r - 0.5)); - } - else { - return sin(M_PI*(r - 1.5)); - } -} diff --git a/gtsam/3rdparty/cephes/cephes/spence.c b/gtsam/3rdparty/cephes/cephes/spence.c deleted file mode 100644 index 48e1c40878..0000000000 --- a/gtsam/3rdparty/cephes/cephes/spence.c +++ /dev/null @@ -1,125 +0,0 @@ -/* spence.c - * - * Dilogarithm - * - * - * - * SYNOPSIS: - * - * double x, y, spence(); - * - * y = spence( x ); - * - * - * - * DESCRIPTION: - * - * Computes the integral - * - * x - * - - * | | log t - * spence(x) = - | ----- dt - * | | t - 1 - * - - * 1 - * - * for x >= 0. A rational approximation gives the integral in - * the interval (0.5, 1.5). Transformation formulas for 1/x - * and 1-x are employed outside the basic expansion range. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,4 30000 3.9e-15 5.4e-16 - * - * - */ - -/* spence.c */ - - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1985, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -static double A[8] = { - 4.65128586073990045278E-5, - 7.31589045238094711071E-3, - 1.33847639578309018650E-1, - 8.79691311754530315341E-1, - 2.71149851196553469920E0, - 4.25697156008121755724E0, - 3.29771340985225106936E0, - 1.00000000000000000126E0, -}; - -static double B[8] = { - 6.90990488912553276999E-4, - 2.54043763932544379113E-2, - 2.82974860602568089943E-1, - 1.41172597751831069617E0, - 3.63800533345137075418E0, - 5.03278880143316990390E0, - 3.54771340985225096217E0, - 9.99999999999999998740E-1, -}; - -extern double MACHEP; - -double spence(double x) -{ - double w, y, z; - int flag; - - if (x < 0.0) { - sf_error("spence", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - if (x == 1.0) - return (0.0); - - if (x == 0.0) - return (M_PI * M_PI / 6.0); - - flag = 0; - - if (x > 2.0) { - x = 1.0 / x; - flag |= 2; - } - - if (x > 1.5) { - w = (1.0 / x) - 1.0; - flag |= 2; - } - - else if (x < 0.5) { - w = -x; - flag |= 1; - } - - else - w = x - 1.0; - - - y = -w * polevl(w, A, 7) / polevl(w, B, 7); - - if (flag & 1) - y = (M_PI * M_PI) / 6.0 - log(x) * log(1.0 - x) - y; - - if (flag & 2) { - z = log(x); - y = -0.5 * z * z - y; - } - - return (y); -} diff --git a/gtsam/3rdparty/cephes/cephes/stdtr.c b/gtsam/3rdparty/cephes/cephes/stdtr.c deleted file mode 100644 index 5a37536bed..0000000000 --- a/gtsam/3rdparty/cephes/cephes/stdtr.c +++ /dev/null @@ -1,203 +0,0 @@ -/* stdtr.c - * - * Student's t distribution - * - * - * - * SYNOPSIS: - * - * double t, stdtr(); - * short k; - * - * y = stdtr( k, t ); - * - * - * DESCRIPTION: - * - * Computes the integral from minus infinity to t of the Student - * t distribution with integer k > 0 degrees of freedom: - * - * t - * - - * | | - * - | 2 -(k+1)/2 - * | ( (k+1)/2 ) | ( x ) - * ---------------------- | ( 1 + --- ) dx - * - | ( k ) - * sqrt( k pi ) | ( k/2 ) | - * | | - * - - * -inf. - * - * Relation to incomplete beta integral: - * - * 1 - stdtr(k,t) = 0.5 * incbet( k/2, 1/2, z ) - * where - * z = k/(k + t**2). - * - * For t < -2, this is the method of computation. For higher t, - * a direct method is derived from integration by parts. - * Since the function is symmetric about t=0, the area under the - * right tail of the density is found by calling the function - * with -t instead of t. - * - * ACCURACY: - * - * Tested at random 1 <= k <= 25. The "domain" refers to t. - * Relative error: - * arithmetic domain # trials peak rms - * IEEE -100,-2 50000 5.9e-15 1.4e-15 - * IEEE -2,100 500000 2.7e-15 4.9e-17 - */ - -/* stdtri.c - * - * Functional inverse of Student's t distribution - * - * - * - * SYNOPSIS: - * - * double p, t, stdtri(); - * int k; - * - * t = stdtri( k, p ); - * - * - * DESCRIPTION: - * - * Given probability p, finds the argument t such that stdtr(k,t) - * is equal to p. - * - * ACCURACY: - * - * Tested at random 1 <= k <= 100. The "domain" refers to p: - * Relative error: - * arithmetic domain # trials peak rms - * IEEE .001,.999 25000 5.7e-15 8.0e-16 - * IEEE 10^-6,.001 25000 2.0e-12 2.9e-14 - */ - - -/* - * Cephes Math Library Release 2.3: March, 1995 - * Copyright 1984, 1987, 1995 by Stephen L. Moshier - */ - -#include "mconf.h" -#include - -extern double MACHEP; - -double stdtr(int k, double t) -{ - double x, rk, z, f, tz, p, xsqk; - int j; - - if (k <= 0) { - sf_error("stdtr", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - if (t == 0) - return (0.5); - - if (t < -2.0) { - rk = k; - z = rk / (rk + t * t); - p = 0.5 * incbet(0.5 * rk, 0.5, z); - return (p); - } - - /* compute integral from -t to + t */ - - if (t < 0) - x = -t; - else - x = t; - - rk = k; /* degrees of freedom */ - z = 1.0 + (x * x) / rk; - - /* test if k is odd or even */ - if ((k & 1) != 0) { - - /* computation for odd k */ - - xsqk = x / sqrt(rk); - p = atan(xsqk); - if (k > 1) { - f = 1.0; - tz = 1.0; - j = 3; - while ((j <= (k - 2)) && ((tz / f) > MACHEP)) { - tz *= (j - 1) / (z * j); - f += tz; - j += 2; - } - p += f * xsqk / z; - } - p *= 2.0 / M_PI; - } - - - else { - - /* computation for even k */ - - f = 1.0; - tz = 1.0; - j = 2; - - while ((j <= (k - 2)) && ((tz / f) > MACHEP)) { - tz *= (j - 1) / (z * j); - f += tz; - j += 2; - } - p = f * x / sqrt(z * rk); - } - - /* common exit */ - - - if (t < 0) - p = -p; /* note destruction of relative accuracy */ - - p = 0.5 + 0.5 * p; - return (p); -} - -double stdtri(int k, double p) -{ - double t, rk, z; - int rflg; - - if (k <= 0 || p <= 0.0 || p >= 1.0) { - sf_error("stdtri", SF_ERROR_DOMAIN, NULL); - return (NAN); - } - - rk = k; - - if (p > 0.25 && p < 0.75) { - if (p == 0.5) - return (0.0); - z = 1.0 - 2.0 * p; - z = incbi(0.5, 0.5 * rk, fabs(z)); - t = sqrt(rk * z / (1.0 - z)); - if (p < 0.5) - t = -t; - return (t); - } - rflg = -1; - if (p >= 0.5) { - p = 1.0 - p; - rflg = 1; - } - z = incbi(0.5 * rk, 0.5, 2.0 * p); - - if (DBL_MAX * z < rk) - return (rflg * INFINITY); - t = sqrt(rk / z - rk); - return (rflg * t); -} diff --git a/gtsam/3rdparty/cephes/cephes/struve.c b/gtsam/3rdparty/cephes/cephes/struve.c deleted file mode 100644 index 26c86fa2d7..0000000000 --- a/gtsam/3rdparty/cephes/cephes/struve.c +++ /dev/null @@ -1,408 +0,0 @@ -/* - * Compute the Struve function. - * - * Notes - * ----- - * - * We use three expansions for the Struve function discussed in [1]: - * - * - power series - * - expansion in Bessel functions - * - asymptotic large-z expansion - * - * Rounding errors are estimated based on the largest terms in the sums. - * - * ``struve_convergence.py`` plots the convergence regions of the different - * expansions. - * - * (i) - * - * Looking at the error in the asymptotic expansion, one finds that - * it's not worth trying if z ~> 0.7 * v + 12 for v > 0. - * - * (ii) - * - * The Bessel function expansion tends to fail for |z| >~ |v| and is not tried - * there. - * - * For Struve H it covers the quadrant v > z where the power series may fail to - * produce reasonable results. - * - * (iii) - * - * The three expansions together cover for Struve H the region z > 0, v real. - * - * They also cover Struve L, except that some loss of precision may occur around - * the transition region z ~ 0.7 |v|, v < 0, |v| >> 1 where the function changes - * rapidly. - * - * (iv) - * - * The power series is evaluated in double-double precision. This fixes accuracy - * issues in Struve H for |v| << |z| before the asymptotic expansion kicks in. - * Moreover, it improves the Struve L behavior for negative v. - * - * - * References - * ---------- - * [1] NIST Digital Library of Mathematical Functions - * https://dlmf.nist.gov/11 - */ - -/* - * Copyright (C) 2013 Pauli Virtanen - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * a. Redistributions of source code must retain the above copyright notice, - * this list of conditions and the following disclaimer. - * b. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * c. Neither the name of Enthought nor the names of the SciPy Developers - * may be used to endorse or promote products derived from this software - * without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS - * BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, - * OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF - * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS - * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN - * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF - * THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include "mconf.h" -#include "dd_real.h" - -// #include "amos_wrappers.h" - -#define STRUVE_MAXITER 10000 -#define SUM_EPS 1e-16 /* be sure we are in the tail of the sum */ -#define SUM_TINY 1e-100 -#define GOOD_EPS 1e-12 -#define ACCEPTABLE_EPS 1e-7 -#define ACCEPTABLE_ATOL 1e-300 - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) - -double struve_power_series(double v, double x, int is_h, double *err); -double struve_asymp_large_z(double v, double z, int is_h, double *err); -double struve_bessel_series(double v, double z, int is_h, double *err); - -static double bessel_y(double v, double x); -static double bessel_j(double v, double x); -static double struve_hl(double v, double x, int is_h); - -double struve_h(double v, double z) -{ - return struve_hl(v, z, 1); -} - -double struve_l(double v, double z) -{ - return struve_hl(v, z, 0); -} - -static double struve_hl(double v, double z, int is_h) -{ - double value[4], err[4], tmp; - int n; - - if (z < 0) { - n = v; - if (v == n) { - tmp = (n % 2 == 0) ? -1 : 1; - return tmp * struve_hl(v, -z, is_h); - } - else { - return NAN; - } - } - else if (z == 0) { - if (v < -1) { - return gammasgn(v + 1.5) * INFINITY; - } - else if (v == -1) { - return 2 / sqrt(M_PI) / Gamma(0.5); - } - else { - return 0; - } - } - - n = -v - 0.5; - if (n == -v - 0.5 && n > 0) { - if (is_h) { - return (n % 2 == 0 ? 1 : -1) * bessel_j(n + 0.5, z); - } - else { - return iv(n + 0.5, z); - } - } - - /* Try the asymptotic expansion */ - if (z >= 0.7*v + 12) { - value[0] = struve_asymp_large_z(v, z, is_h, &err[0]); - if (err[0] < GOOD_EPS * fabs(value[0])) { - return value[0]; - } - } - else { - err[0] = INFINITY; - } - - /* Try power series */ - value[1] = struve_power_series(v, z, is_h, &err[1]); - if (err[1] < GOOD_EPS * fabs(value[1])) { - return value[1]; - } - - /* Try bessel series */ - if (fabs(z) < fabs(v) + 20) { - value[2] = struve_bessel_series(v, z, is_h, &err[2]); - if (err[2] < GOOD_EPS * fabs(value[2])) { - return value[2]; - } - } - else { - err[2] = INFINITY; - } - - /* Return the best of the three, if it is acceptable */ - n = 0; - if (err[1] < err[n]) n = 1; - if (err[2] < err[n]) n = 2; - if (err[n] < ACCEPTABLE_EPS * fabs(value[n]) || err[n] < ACCEPTABLE_ATOL) { - return value[n]; - } - - /* Maybe it really is an overflow? */ - tmp = -lgam(v + 1.5) + (v + 1)*log(z/2); - if (!is_h) { - tmp = fabs(tmp); - } - if (tmp > 700) { - sf_error("struve", SF_ERROR_OVERFLOW, NULL); - return INFINITY * gammasgn(v + 1.5); - } - - /* Failure */ - sf_error("struve", SF_ERROR_NO_RESULT, NULL); - return NAN; -} - - -/* - * Power series for Struve H and L - * https://dlmf.nist.gov/11.2.1 - * - * Starts to converge roughly at |n| > |z| - */ -double struve_power_series(double v, double z, int is_h, double *err) -{ - int n, sgn; - double term, sum, maxterm, scaleexp, tmp; - double2 cterm, csum, cdiv, z2, c2v, ctmp; - - if (is_h) { - sgn = -1; - } - else { - sgn = 1; - } - - tmp = -lgam(v + 1.5) + (v + 1)*log(z/2); - if (tmp < -600 || tmp > 600) { - /* Scale exponent to postpone underflow/overflow */ - scaleexp = tmp/2; - tmp -= scaleexp; - } - else { - scaleexp = 0; - } - - term = 2 / sqrt(M_PI) * exp(tmp) * gammasgn(v + 1.5); - sum = term; - maxterm = 0; - - cterm = dd_create_d(term); - csum = dd_create_d(sum); - z2 = dd_create_d(sgn*z*z); - c2v = dd_create_d(2*v); - - for (n = 0; n < STRUVE_MAXITER; ++n) { - /* cdiv = (3 + 2*n) * (3 + 2*n + 2*v)) */ - cdiv = dd_create_d(3 + 2*n); - ctmp = dd_create_d(3 + 2*n); - ctmp = dd_add(ctmp, c2v); - cdiv = dd_mul(cdiv, ctmp); - - /* cterm *= z2 / cdiv */ - cterm = dd_mul(cterm, z2); - cterm = dd_div(cterm, cdiv); - - csum = dd_add(csum, cterm); - - term = dd_to_double(cterm); - sum = dd_to_double(csum); - - if (fabs(term) > maxterm) { - maxterm = fabs(term); - } - if (fabs(term) < SUM_TINY * fabs(sum) || term == 0 || !isfinite(sum)) { - break; - } - } - - *err = fabs(term) + fabs(maxterm) * 1e-22; - - if (scaleexp != 0) { - sum *= exp(scaleexp); - *err *= exp(scaleexp); - } - - if (sum == 0 && term == 0 && v < 0 && !is_h) { - /* Spurious underflow */ - *err = INFINITY; - return NAN; - } - - return sum; -} - - -/* - * Bessel series - * https://dlmf.nist.gov/11.4.19 - */ -double struve_bessel_series(double v, double z, int is_h, double *err) -{ - int n; - double term, cterm, sum, maxterm; - - if (is_h && v < 0) { - /* Works less reliably in this region */ - *err = INFINITY; - return NAN; - } - - sum = 0; - maxterm = 0; - - cterm = sqrt(z / (2*M_PI)); - - for (n = 0; n < STRUVE_MAXITER; ++n) { - if (is_h) { - term = cterm * bessel_j(n + v + 0.5, z) / (n + 0.5); - cterm *= z/2 / (n + 1); - } - else { - term = cterm * iv(n + v + 0.5, z) / (n + 0.5); - cterm *= -z/2 / (n + 1); - } - sum += term; - if (fabs(term) > maxterm) { - maxterm = fabs(term); - } - if (fabs(term) < SUM_EPS * fabs(sum) || term == 0 || !isfinite(sum)) { - break; - } - } - - *err = fabs(term) + fabs(maxterm) * 1e-16; - - /* Account for potential underflow of the Bessel functions */ - *err += 1e-300 * fabs(cterm); - - return sum; -} - - -/* - * Large-z expansion for Struve H and L - * https://dlmf.nist.gov/11.6.1 - */ -double struve_asymp_large_z(double v, double z, int is_h, double *err) -{ - int n, sgn, maxiter; - double term, sum, maxterm; - double m; - - if (is_h) { - sgn = -1; - } - else { - sgn = 1; - } - - /* Asymptotic expansion divergenge point */ - m = z/2; - if (m <= 0) { - maxiter = 0; - } - else if (m > STRUVE_MAXITER) { - maxiter = STRUVE_MAXITER; - } - else { - maxiter = (int)m; - } - if (maxiter == 0) { - *err = INFINITY; - return NAN; - } - - if (z < v) { - /* Exclude regions where our error estimation fails */ - *err = INFINITY; - return NAN; - } - - /* Evaluate sum */ - term = -sgn / sqrt(M_PI) * exp(-lgam(v + 0.5) + (v - 1) * log(z/2)) * gammasgn(v + 0.5); - sum = term; - maxterm = 0; - - for (n = 0; n < maxiter; ++n) { - term *= sgn * (1 + 2*n) * (1 + 2*n - 2*v) / (z*z); - sum += term; - if (fabs(term) > maxterm) { - maxterm = fabs(term); - } - if (fabs(term) < SUM_EPS * fabs(sum) || term == 0 || !isfinite(sum)) { - break; - } - } - - if (is_h) { - sum += bessel_y(v, z); - } - else { - sum += iv(v, z); - } - - /* - * This error estimate is strictly speaking valid only for - * n > v - 0.5, but numerical results indicate that it works - * reasonably. - */ - *err = fabs(term) + fabs(maxterm) * 1e-16; - - return sum; -} - - -static double bessel_y(double v, double x) -{ - return cbesy_wrap_real(v, x); -} - -static double bessel_j(double v, double x) -{ - return cbesj_wrap_real(v, x); -} diff --git a/gtsam/3rdparty/cephes/cephes/tandg.c b/gtsam/3rdparty/cephes/cephes/tandg.c deleted file mode 100644 index 1ea86329be..0000000000 --- a/gtsam/3rdparty/cephes/cephes/tandg.c +++ /dev/null @@ -1,141 +0,0 @@ -/* tandg.c - * - * Circular tangent of argument in degrees - * - * - * - * SYNOPSIS: - * - * double x, y, tandg(); - * - * y = tandg( x ); - * - * - * - * DESCRIPTION: - * - * Returns the circular tangent of the argument x in degrees. - * - * Range reduction is modulo pi/4. A rational function - * x + x**3 P(x**2)/Q(x**2) - * is employed in the basic interval [0, pi/4]. - * - * - * - * ACCURACY: - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 0,10 30000 3.2e-16 8.4e-17 - * - * ERROR MESSAGES: - * - * message condition value returned - * tandg total loss x > 1.0e14 (IEEE) 0.0 - * tandg singularity x = 180 k + 90 INFINITY - */ - /* cotdg.c - * - * Circular cotangent of argument in degrees - * - * - * - * SYNOPSIS: - * - * double x, y, cotdg(); - * - * y = cotdg( x ); - * - * - * - * DESCRIPTION: - * - * Returns the circular cotangent of the argument x in degrees. - * - * Range reduction is modulo pi/4. A rational function - * x + x**3 P(x**2)/Q(x**2) - * is employed in the basic interval [0, pi/4]. - * - * - * ERROR MESSAGES: - * - * message condition value returned - * cotdg total loss x > 1.0e14 (IEEE) 0.0 - * cotdg singularity x = 180 k INFINITY - */ - -/* - * Cephes Math Library Release 2.0: April, 1987 - * Copyright 1984, 1987 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" - -static double PI180 = 1.74532925199432957692E-2; -static double lossth = 1.0e14; - -static double tancot(double, int); - -double tandg(double x) -{ - return (tancot(x, 0)); -} - - -double cotdg(double x) -{ - return (tancot(x, 1)); -} - - -static double tancot(double xx, int cotflg) -{ - double x; - int sign; - - /* make argument positive but save the sign */ - if (xx < 0) { - x = -xx; - sign = -1; - } - else { - x = xx; - sign = 1; - } - - if (x > lossth) { - sf_error("tandg", SF_ERROR_NO_RESULT, NULL); - return 0.0; - } - - /* modulo 180 */ - x = x - 180.0 * floor(x / 180.0); - if (cotflg) { - if (x <= 90.0) { - x = 90.0 - x; - } - else { - x = x - 90.0; - sign *= -1; - } - } - else { - if (x > 90.0) { - x = 180.0 - x; - sign *= -1; - } - } - if (x == 0.0) { - return 0.0; - } - else if (x == 45.0) { - return sign * 1.0; - } - else if (x == 90.0) { - sf_error((cotflg ? "cotdg" : "tandg"), SF_ERROR_SINGULAR, NULL); - return INFINITY; - } - /* x is now transformed into [0, 90) */ - return sign * tan(x * PI180); -} diff --git a/gtsam/3rdparty/cephes/cephes/tukey.c b/gtsam/3rdparty/cephes/cephes/tukey.c deleted file mode 100644 index 751314a875..0000000000 --- a/gtsam/3rdparty/cephes/cephes/tukey.c +++ /dev/null @@ -1,68 +0,0 @@ - -/* Compute the CDF of the Tukey-Lambda distribution - * using a bracketing search with special checks - * - * The PPF of the Tukey-lambda distribution is - * G(p) = (p**lam + (1-p)**lam) / lam - * - * Author: Travis Oliphant - */ - -#include - -#define SMALLVAL 1e-4 -#define EPS 1.0e-14 -#define MAXCOUNT 60 - -double tukeylambdacdf(double x, double lmbda) -{ - double pmin, pmid, pmax, plow, phigh, xeval; - int count; - - if (isnan(x) || isnan(lmbda)) { - return NAN; - } - - xeval = 1.0 / lmbda; - if (lmbda > 0.0) { - if (x <= (-xeval)) { - return 0.0; - } - if (x >= xeval) { - return 1.0; - } - } - - if ((-SMALLVAL < lmbda) && (lmbda < SMALLVAL)) { - if (x >= 0) { - return 1.0 / (1.0 + exp(-x)); - } - else { - return exp(x) / (1.0 + exp(x)); - } - } - - pmin = 0.0; - pmid = 0.5; - pmax = 1.0; - plow = pmin; - phigh = pmax; - count = 0; - - while ((count < MAXCOUNT) && (fabs(pmid - plow) > EPS)) { - xeval = (pow(pmid, lmbda) - pow(1.0 - pmid, lmbda)) / lmbda; - if (xeval == x) { - return pmid; - } - if (xeval > x) { - phigh = pmid; - pmid = (pmid + plow) / 2.0; - } - else { - plow = pmid; - pmid = (pmid + phigh) / 2.0; - } - count++; - } - return pmid; -} diff --git a/gtsam/3rdparty/cephes/cephes/unity.c b/gtsam/3rdparty/cephes/cephes/unity.c index 76bc7f08df..c3776f4018 100644 --- a/gtsam/3rdparty/cephes/cephes/unity.c +++ b/gtsam/3rdparty/cephes/cephes/unity.c @@ -13,6 +13,9 @@ /* Scipy changes: * - 06-10-2016: added lgam1p */ +/* gtsam changes: + * - 01-24-2026: removed log1p and expm1 + */ #include "mconf.h" @@ -46,19 +49,6 @@ static const double LQ[] = { 6.0118660497603843919306E1, }; -double log1p(double x) -{ - double z; - - z = 1.0 + x; - if ((z < M_SQRT1_2) || (z > M_SQRT2)) - return (log(z)); - z = x * x; - z = -0.5 * z + x * (z * polevl(x, LP, 6) / p1evl(x, LQ, 6)); - return (x + z); -} - - /* log(1 + x) - x */ double log1pmx(double x) { @@ -103,31 +93,6 @@ static double EQ[4] = { 2.0000000000000000000897E0, }; -double expm1(double x) -{ - double r, xx; - - if (!cephes_isfinite(x)) { - if (cephes_isnan(x)) { - return x; - } - else if (x > 0) { - return x; - } - else { - return -1.0; - } - - } - if ((x < -0.5) || (x > 0.5)) - return (exp(x) - 1.0); - xx = x * x; - r = x * polevl(xx, EP, 2); - r = r / (polevl(xx, EQ, 3) - r); - return (r + r); -} - - /* cosm1(x) = cos(x) - 1 */ diff --git a/gtsam/3rdparty/cephes/cephes/yn.c b/gtsam/3rdparty/cephes/cephes/yn.c deleted file mode 100644 index c02ff0acd8..0000000000 --- a/gtsam/3rdparty/cephes/cephes/yn.c +++ /dev/null @@ -1,105 +0,0 @@ -/* yn.c - * - * Bessel function of second kind of integer order - * - * - * - * SYNOPSIS: - * - * double x, y, yn(); - * int n; - * - * y = yn( n, x ); - * - * - * - * DESCRIPTION: - * - * Returns Bessel function of order n, where n is a - * (possibly negative) integer. - * - * The function is evaluated by forward recurrence on - * n, starting with values computed by the routines - * y0() and y1(). - * - * If n = 0 or 1 the routine for y0 or y1 is called - * directly. - * - * - * - * ACCURACY: - * - * - * Absolute error, except relative - * when y > 1: - * arithmetic domain # trials peak rms - * IEEE 0, 30 30000 3.4e-15 4.3e-16 - * - * - * ERROR MESSAGES: - * - * message condition value returned - * yn singularity x = 0 INFINITY - * yn overflow INFINITY - * - * Spot checked against tables for x, n between 0 and 100. - * - */ - -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" -extern double MAXLOG; - -double yn(int n, double x) -{ - double an, anm1, anm2, r; - int k, sign; - - if (n < 0) { - n = -n; - if ((n & 1) == 0) /* -1**n */ - sign = 1; - else - sign = -1; - } - else - sign = 1; - - - if (n == 0) - return (sign * y0(x)); - if (n == 1) - return (sign * y1(x)); - - /* test for overflow */ - if (x == 0.0) { - sf_error("yn", SF_ERROR_SINGULAR, NULL); - return -INFINITY * sign; - } - else if (x < 0.0) { - sf_error("yn", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - /* forward recurrence on n */ - - anm2 = y0(x); - anm1 = y1(x); - k = 1; - r = 2 * k; - do { - an = r * anm1 / x - anm2; - anm2 = anm1; - anm1 = an; - r += 2.0; - ++k; - } - while (k < n); - - - return (sign * an); -} diff --git a/gtsam/3rdparty/cephes/cephes/yv.c b/gtsam/3rdparty/cephes/cephes/yv.c deleted file mode 100644 index e61a155214..0000000000 --- a/gtsam/3rdparty/cephes/cephes/yv.c +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Cephes Math Library Release 2.8: June, 2000 - * Copyright 1984, 1987, 2000 by Stephen L. Moshier - */ - -#include "mconf.h" - -extern double MACHEP; - - -/* - * Bessel function of noninteger order - */ -double yv(double v, double x) -{ - double y, t; - int n; - - n = v; - if (n == v) { - y = yn(n, x); - return (y); - } - else if (v == floor(v)) { - /* Zero in denominator. */ - sf_error("yv", SF_ERROR_DOMAIN, NULL); - return NAN; - } - - t = M_PI * v; - y = (cos(t) * jv(v, x) - jv(-v, x)) / sin(t); - - if (cephes_isinf(y)) { - if (v > 0) { - sf_error("yv", SF_ERROR_OVERFLOW, NULL); - return -INFINITY; - } - else if (v < -1e10) { - /* Whether it's +inf or -inf is numerically ill-defined. */ - sf_error("yv", SF_ERROR_DOMAIN, NULL); - return NAN; - } - } - - return (y); -} diff --git a/gtsam/3rdparty/cephes/cephes/zetac.c b/gtsam/3rdparty/cephes/cephes/zetac.c deleted file mode 100644 index 8414331832..0000000000 --- a/gtsam/3rdparty/cephes/cephes/zetac.c +++ /dev/null @@ -1,345 +0,0 @@ -/* zetac.c - * - * Riemann zeta function - * - * - * - * SYNOPSIS: - * - * double x, y, zetac(); - * - * y = zetac( x ); - * - * - * - * DESCRIPTION: - * - * - * - * inf. - * - -x - * zetac(x) = > k , x > 1, - * - - * k=2 - * - * is related to the Riemann zeta function by - * - * Riemann zeta(x) = zetac(x) + 1. - * - * Extension of the function definition for x < 1 is implemented. - * Zero is returned for x > log2(INFINITY). - * - * ACCURACY: - * - * Tabulated values have full machine accuracy. - * - * Relative error: - * arithmetic domain # trials peak rms - * IEEE 1,50 10000 9.8e-16 1.3e-16 - * - * - */ - -/* - * Cephes Math Library Release 2.1: January, 1989 - * Copyright 1984, 1987, 1989 by Stephen L. Moshier - * Direct inquiries to 30 Frost Street, Cambridge, MA 02140 - */ - -#include "mconf.h" -#include "lanczos.h" - -/* Riemann zeta(x) - 1 - * for integer arguments between 0 and 30. - */ -static const double azetac[] = { - -1.50000000000000000000E0, - 0.0, /* Not used; zetac(1.0) is infinity. */ - 6.44934066848226436472E-1, - 2.02056903159594285400E-1, - 8.23232337111381915160E-2, - 3.69277551433699263314E-2, - 1.73430619844491397145E-2, - 8.34927738192282683980E-3, - 4.07735619794433937869E-3, - 2.00839282608221441785E-3, - 9.94575127818085337146E-4, - 4.94188604119464558702E-4, - 2.46086553308048298638E-4, - 1.22713347578489146752E-4, - 6.12481350587048292585E-5, - 3.05882363070204935517E-5, - 1.52822594086518717326E-5, - 7.63719763789976227360E-6, - 3.81729326499983985646E-6, - 1.90821271655393892566E-6, - 9.53962033872796113152E-7, - 4.76932986787806463117E-7, - 2.38450502727732990004E-7, - 1.19219925965311073068E-7, - 5.96081890512594796124E-8, - 2.98035035146522801861E-8, - 1.49015548283650412347E-8, - 7.45071178983542949198E-9, - 3.72533402478845705482E-9, - 1.86265972351304900640E-9, - 9.31327432419668182872E-10 -}; - -/* 2**x (1 - 1/x) (zeta(x) - 1) = P(1/x)/Q(1/x), 1 <= x <= 10 */ -static double P[9] = { - 5.85746514569725319540E11, - 2.57534127756102572888E11, - 4.87781159567948256438E10, - 5.15399538023885770696E9, - 3.41646073514754094281E8, - 1.60837006880656492731E7, - 5.92785467342109522998E5, - 1.51129169964938823117E4, - 2.01822444485997955865E2, -}; - -static double Q[8] = { - /* 1.00000000000000000000E0, */ - 3.90497676373371157516E11, - 5.22858235368272161797E10, - 5.64451517271280543351E9, - 3.39006746015350418834E8, - 1.79410371500126453702E7, - 5.66666825131384797029E5, - 1.60382976810944131506E4, - 1.96436237223387314144E2, -}; - -/* log(zeta(x) - 1 - 2**-x), 10 <= x <= 50 */ -static double A[11] = { - 8.70728567484590192539E6, - 1.76506865670346462757E8, - 2.60889506707483264896E10, - 5.29806374009894791647E11, - 2.26888156119238241487E13, - 3.31884402932705083599E14, - 5.13778997975868230192E15, - -1.98123688133907171455E15, - -9.92763810039983572356E16, - 7.82905376180870586444E16, - 9.26786275768927717187E16, -}; - -static double B[10] = { - /* 1.00000000000000000000E0, */ - -7.92625410563741062861E6, - -1.60529969932920229676E8, - -2.37669260975543221788E10, - -4.80319584350455169857E11, - -2.07820961754173320170E13, - -2.96075404507272223680E14, - -4.86299103694609136686E15, - 5.34589509675789930199E15, - 5.71464111092297631292E16, - -1.79915597658676556828E16, -}; - -/* (1-x) (zeta(x) - 1), 0 <= x <= 1 */ -static double R[6] = { - -3.28717474506562731748E-1, - 1.55162528742623950834E1, - -2.48762831680821954401E2, - 1.01050368053237678329E3, - 1.26726061410235149405E4, - -1.11578094770515181334E5, -}; - -static double S[5] = { - /* 1.00000000000000000000E0, */ - 1.95107674914060531512E1, - 3.17710311750646984099E2, - 3.03835500874445748734E3, - 2.03665876435770579345E4, - 7.43853965136767874343E4, -}; - -static double TAYLOR0[10] = { - -1.0000000009110164892, - -1.0000000057646759799, - -9.9999983138417361078e-1, - -1.0000013011460139596, - -1.000001940896320456, - -9.9987929950057116496e-1, - -1.000785194477042408, - -1.0031782279542924256, - -9.1893853320467274178e-1, - -1.5, -}; - -#define MAXL2 127 -#define SQRT_2_PI 0.79788456080286535587989 - -extern double MACHEP; - -static double zeta_reflection(double); -static double zetac_smallneg(double); -static double zetac_positive(double); - - -/* - * Riemann zeta function, minus one - */ -double zetac(double x) -{ - if (isnan(x)) { - return x; - } - else if (x == -INFINITY) { - return NAN; - } - else if (x < 0.0 && x > -0.01) { - return zetac_smallneg(x); - } - else if (x < 0.0) { - return zeta_reflection(-x) - 1; - } - else { - return zetac_positive(x); - } -} - - -/* - * Riemann zeta function - */ -double riemann_zeta(double x) -{ - if (isnan(x)) { - return x; - } - else if (x == -INFINITY) { - return NAN; - } - else if (x < 0.0 && x > -0.01) { - return 1 + zetac_smallneg(x); - } - else if (x < 0.0) { - return zeta_reflection(-x); - } - else { - return 1 + zetac_positive(x); - } -} - - -/* - * Compute zetac for positive arguments - */ -static inline double zetac_positive(double x) -{ - int i; - double a, b, s, w; - - if (x == 1.0) { - return INFINITY; - } - - if (x >= MAXL2) { - /* because first term is 2**-x */ - return 0.0; - } - - /* Tabulated values for integer argument */ - w = floor(x); - if (w == x) { - i = x; - if (i < 31) { -#ifdef UNK - return (azetac[i]); -#else - return (*(double *) &azetac[4 * i]); -#endif - } - } - - if (x < 1.0) { - w = 1.0 - x; - a = polevl(x, R, 5) / (w * p1evl(x, S, 5)); - return a; - } - - if (x <= 10.0) { - b = pow(2.0, x) * (x - 1.0); - w = 1.0 / x; - s = (x * polevl(w, P, 8)) / (b * p1evl(w, Q, 8)); - return s; - } - - if (x <= 50.0) { - b = pow(2.0, -x); - w = polevl(x, A, 10) / p1evl(x, B, 10); - w = exp(w) + b; - return w; - } - - /* Basic sum of inverse powers */ - s = 0.0; - a = 1.0; - do { - a += 2.0; - b = pow(a, -x); - s += b; - } - while (b / s > MACHEP); - - b = pow(2.0, -x); - s = (s + b) / (1.0 - b); - return s; -} - - -/* - * Compute zetac for small negative x. We can't use the reflection - * formula because to double precision 1 - x = 1 and zetac(1) = inf. - */ -static inline double zetac_smallneg(double x) -{ - return polevl(x, TAYLOR0, 9); -} - - -/* - * Compute zetac using the reflection formula (see DLMF 25.4.2) plus - * the Lanczos approximation for Gamma to avoid overflow. - */ -static inline double zeta_reflection(double x) -{ - double base, large_term, small_term, hx, x_shift; - - hx = x / 2; - if (hx == floor(hx)) { - /* Hit a zero of the sine factor */ - return 0; - } - - /* Reduce the argument to sine */ - x_shift = fmod(x, 4); - small_term = -SQRT_2_PI * sin(0.5 * M_PI * x_shift); - small_term *= lanczos_sum_expg_scaled(x + 1) * zeta(x + 1, 1); - - /* Group large terms together to prevent overflow */ - base = (x + lanczos_g + 0.5) / (2 * M_PI * M_E); - large_term = pow(base, x + 0.5); - if (isfinite(large_term)) { - return large_term * small_term; - } - /* - * We overflowed, but we might be able to stave off overflow by - * factoring in the small term earlier. To do this we compute - * - * (sqrt(large_term) * small_term) * sqrt(large_term) - * - * Since we only call this method for negative x bounded away from - * zero, the small term can only be as small sine on that region; - * i.e. about machine epsilon. This means that if the above still - * overflows, then there was truly no avoiding it. - */ - large_term = pow(base, 0.5 * x + 0.25); - return (large_term * small_term) * large_term; -} From d028ae34bc4dde7fb3bb2d8a6e7701604fdbeb35 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Sun, 25 Jan 2026 16:36:46 -0500 Subject: [PATCH 149/266] Make chi_squared_quantile camelCase --- gtsam/nonlinear/GncOptimizer.h | 2 +- gtsam/nonlinear/internal/ChiSquaredInverse.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 0fe576159a..01dcef3c3d 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -36,7 +36,7 @@ namespace gtsam { * Equivalent to chi2inv in Matlab. */ static double Chi2inv(const double alpha, const size_t dofs) { - return internal::chi_squared_quantile(dofs, alpha); + return internal::chiSquaredQuantile(dofs, alpha); } /* ************************************************************************* */ diff --git a/gtsam/nonlinear/internal/ChiSquaredInverse.h b/gtsam/nonlinear/internal/ChiSquaredInverse.h index 6707be1fe3..aa72cae8aa 100644 --- a/gtsam/nonlinear/internal/ChiSquaredInverse.h +++ b/gtsam/nonlinear/internal/ChiSquaredInverse.h @@ -36,7 +36,7 @@ namespace internal { * @param alpha Quantile value * @return double */ -inline double chi_squared_quantile(const double dofs, const double alpha) { +inline double chiSquaredQuantile(const double dofs, const double alpha) { return 2 * igami(dofs / 2, alpha); } From 0a4175d38b702317471c64cd49f19da5295ee9ed Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Fri, 30 Jan 2026 17:42:39 -0500 Subject: [PATCH 150/266] Rename functions and update README --- gtsam/3rdparty/cephes/README.md | 2 +- gtsam/3rdparty/cephes/cephes.h | 30 +++++++++--------- gtsam/3rdparty/cephes/cephes/gamma.c | 14 ++++----- gtsam/3rdparty/cephes/cephes/igam.c | 32 ++++++++++---------- gtsam/3rdparty/cephes/cephes/igami.c | 28 ++++++++--------- gtsam/3rdparty/cephes/cephes/lanczos.c | 2 +- gtsam/3rdparty/cephes/cephes/sf_error.c | 6 ++-- gtsam/3rdparty/cephes/cephes/sf_error.h | 8 ++--- gtsam/3rdparty/cephes/cephes/unity.c | 10 +++--- gtsam/3rdparty/cephes/cephes/zeta.c | 6 ++-- gtsam/nonlinear/internal/ChiSquaredInverse.h | 2 +- 11 files changed, 70 insertions(+), 70 deletions(-) diff --git a/gtsam/3rdparty/cephes/README.md b/gtsam/3rdparty/cephes/README.md index 63e3a6c8c6..3660878f33 100644 --- a/gtsam/3rdparty/cephes/README.md +++ b/gtsam/3rdparty/cephes/README.md @@ -1,6 +1,6 @@ # README -This is a vendored version of the Cephes Mathematical Library. The source code can be found on [netlib.org](https://www.netlib.org/cephes/). +This is a vendored version of the Cephes Mathematical Library, trimmed down to just the required files for the `igami` function, and with exported functions renamed to prevent conflicts. The source code can be found on [netlib.org](https://www.netlib.org/cephes/). The software is provided with an [MIT License](https://smath.com/en-US/view/CephesMathLibrary/license). diff --git a/gtsam/3rdparty/cephes/cephes.h b/gtsam/3rdparty/cephes/cephes.h index a326aa1aaf..e06ad8bcdb 100644 --- a/gtsam/3rdparty/cephes/cephes.h +++ b/gtsam/3rdparty/cephes/cephes.h @@ -5,25 +5,25 @@ extern "C" { #endif -double Gamma(double x); -double lgam(double x); -double lgam_sgn(double x, int *sign); -double gammasgn(double x); +double gtsam_cephes_Gamma(double x); +double gtsam_cephes_lgam(double x); +double gtsam_cephes_lgam_sgn(double x, int *sign); +double gtsam_cephes_gammasgn(double x); -double igamc(double a, double x); -double igam(double a, double x); -double igam_fac(double a, double x); -double igamci(double a, double q); -double igami(double a, double p); +double gtsam_cephes_igamc(double a, double x); +double gtsam_cephes_igam(double a, double x); +double gtsam_cephes_igam_fac(double a, double x); +double gtsam_cephes_igamci(double a, double q); +double gtsam_cephes_igami(double a, double p); -double log1pmx(double x); -double cosm1(double x); -double lgam1p(double x); +double gtsam_cephes_log1pmx(double x); +double gtsam_cephes_cosm1(double x); +double gtsam_cephes_lgam1p(double x); -double zeta(double x, double q); -double zetac(double x); +double gtsam_cephes_zeta(double x, double q); +double gtsam_cephes_zetac(double x); -double lanczos_sum_expg_scaled(double x); +double gtsam_cephes_lanczos_sum_expg_scaled(double x); #ifdef __cplusplus } diff --git a/gtsam/3rdparty/cephes/cephes/gamma.c b/gtsam/3rdparty/cephes/cephes/gamma.c index 2a61defedb..ee32fc90ae 100644 --- a/gtsam/3rdparty/cephes/cephes/gamma.c +++ b/gtsam/3rdparty/cephes/cephes/gamma.c @@ -157,7 +157,7 @@ static double stirf(double x) } -double Gamma(double x) +double gtsam_cephes_Gamma(double x) { double p, q, z; int i; @@ -173,7 +173,7 @@ double Gamma(double x) p = floor(q); if (p == q) { gamnan: - sf_error("Gamma", SF_ERROR_OVERFLOW, NULL); + gtsam_cephes_sf_error("Gamma", SF_ERROR_OVERFLOW, NULL); return (INFINITY); } i = p; @@ -272,13 +272,13 @@ static double LS2PI = 0.91893853320467274178; /* Logarithm of Gamma function */ -double lgam(double x) +double gtsam_cephes_lgam(double x) { int sign; - return lgam_sgn(x, &sign); + return gtsam_cephes_lgam_sgn(x, &sign); } -double lgam_sgn(double x, int *sign) +double gtsam_cephes_lgam_sgn(double x, int *sign) { double p, q, u, w, z; int i; @@ -290,11 +290,11 @@ double lgam_sgn(double x, int *sign) if (x < -34.0) { q = -x; - w = lgam_sgn(q, sign); + w = gtsam_cephes_lgam_sgn(q, sign); p = floor(q); if (p == q) { lgsing: - sf_error("lgam", SF_ERROR_SINGULAR, NULL); + gtsam_cephes_sf_error("lgam", SF_ERROR_SINGULAR, NULL); return (INFINITY); } i = p; diff --git a/gtsam/3rdparty/cephes/cephes/igam.c b/gtsam/3rdparty/cephes/cephes/igam.c index 75f871ec51..b3e9bbd847 100644 --- a/gtsam/3rdparty/cephes/cephes/igam.c +++ b/gtsam/3rdparty/cephes/cephes/igam.c @@ -125,12 +125,12 @@ static double igamc_series(double, double); static double asymptotic_series(double, double, int); -double igam(double a, double x) +double gtsam_cephes_igam(double a, double x) { double absxma_a; if (x < 0 || a < 0) { - sf_error("gammainc", SF_ERROR_DOMAIN, NULL); + gtsam_cephes_sf_error("gammainc", SF_ERROR_DOMAIN, NULL); return NAN; } else if (a == 0) { if (x > 0) { @@ -159,19 +159,19 @@ double igam(double a, double x) } if ((x > 1.0) && (x > a)) { - return (1.0 - igamc(a, x)); + return (1.0 - gtsam_cephes_igamc(a, x)); } return igam_series(a, x); } -double igamc(double a, double x) +double gtsam_cephes_igamc(double a, double x) { double absxma_a; if (x < 0 || a < 0) { - sf_error("gammaincc", SF_ERROR_DOMAIN, NULL); + gtsam_cephes_sf_error("gammaincc", SF_ERROR_DOMAIN, NULL); return NAN; } else if (a == 0) { if (x > 0) { @@ -228,27 +228,27 @@ double igamc(double a, double x) * corrected from (15) and (16) in [2] by replacing exp(x - a) with * exp(a - x). */ -double igam_fac(double a, double x) +double gtsam_cephes_igam_fac(double a, double x) { double ax, fac, res, num; if (fabs(a - x) > 0.4 * fabs(a)) { - ax = a * log(x) - x - lgam(a); + ax = a * log(x) - x - gtsam_cephes_lgam(a); if (ax < -MAXLOG) { - sf_error("igam", SF_ERROR_UNDERFLOW, NULL); + gtsam_cephes_sf_error("igam", SF_ERROR_UNDERFLOW, NULL); return 0.0; } return exp(ax); } fac = a + lanczos_g - 0.5; - res = sqrt(fac / exp(1)) / lanczos_sum_expg_scaled(a); + res = sqrt(fac / exp(1)) / gtsam_cephes_lanczos_sum_expg_scaled(a); if ((a < 200) && (x < 200)) { res *= exp(a - x) * pow(x / fac, a); } else { num = x - a - lanczos_g + 0.5; - res *= exp(a * log1pmx(num / fac) + x * (0.5 - lanczos_g) / fac); + res *= exp(a * gtsam_cephes_log1pmx(num / fac) + x * (0.5 - lanczos_g) / fac); } return res; @@ -262,7 +262,7 @@ static double igamc_continued_fraction(double a, double x) double ans, ax, c, yc, r, t, y, z; double pk, pkm1, pkm2, qk, qkm1, qkm2; - ax = igam_fac(a, x); + ax = gtsam_cephes_igam_fac(a, x); if (ax == 0.0) { return 0.0; } @@ -316,7 +316,7 @@ static double igam_series(double a, double x) int i; double ans, ax, c, r; - ax = igam_fac(a, x); + ax = gtsam_cephes_igam_fac(a, x); if (ax == 0.0) { return 0.0; } @@ -359,8 +359,8 @@ static double igamc_series(double a, double x) } logx = log(x); - term = -expm1(a * logx - lgam1p(a)); - return term - exp(a * logx - lgam(a)) * sum; + term = -expm1(a * logx - gtsam_cephes_lgam1p(a)); + return term - exp(a * logx - gtsam_cephes_lgam(a)) * sum; } @@ -384,9 +384,9 @@ static double asymptotic_series(double a, double x, int func) } if (lambda > 1) { - eta = sqrt(-2 * log1pmx(sigma)); + eta = sqrt(-2 * gtsam_cephes_log1pmx(sigma)); } else if (lambda < 1) { - eta = -sqrt(-2 * log1pmx(sigma)); + eta = -sqrt(-2 * gtsam_cephes_log1pmx(sigma)); } else { eta = 0; } diff --git a/gtsam/3rdparty/cephes/cephes/igami.c b/gtsam/3rdparty/cephes/cephes/igami.c index 97fc93ff4d..aec652e9b5 100644 --- a/gtsam/3rdparty/cephes/cephes/igami.c +++ b/gtsam/3rdparty/cephes/cephes/igami.c @@ -91,7 +91,7 @@ static double find_inverse_gamma(double a, double p, double q) } } else if (a < 1) { - double g = Gamma(a); + double g = gtsam_cephes_Gamma(a); double b = q * g; if ((b > 0.6) || ((b >= 0.45) && (a >= 0.3))) { @@ -184,7 +184,7 @@ static double find_inverse_gamma(double a, double p, double q) } else { double D = fmax(2, a * (a - 1)); - double lg = lgam(a); + double lg = gtsam_cephes_lgam(a); double lb = log(q) + lg; if (lb < -D * 2.3) { /* DiDonato and Morris Eq 25: */ @@ -228,7 +228,7 @@ static double find_inverse_gamma(double a, double p, double q) double ap2 = a + 2; if (w < 0.15 * ap1) { /* DiDonato and Morris Eq 35: */ - double v = log(p) + lgam(ap1); + double v = log(p) + gtsam_cephes_lgam(ap1); z = exp((v + w) / a); s = log1p(z / ap1 * (1 + z / ap2)); z = exp((v + z - s) / a); @@ -244,7 +244,7 @@ static double find_inverse_gamma(double a, double p, double q) else { /* DiDonato and Morris Eq 36: */ double ls = log(didonato_SN(a, z, 100, 1e-4)); - double v = log(p) + lgam(ap1); + double v = log(p) + gtsam_cephes_lgam(ap1); z = exp((v + z - ls) / a); result = z * (1 - (a * log(z) - z - v + ls) / (a - z)); } @@ -254,7 +254,7 @@ static double find_inverse_gamma(double a, double p, double q) } -double igami(double a, double p) +double gtsam_cephes_igami(double a, double p) { int i; double x, fac, f_fp, fpp_fp; @@ -263,7 +263,7 @@ double igami(double a, double p) return NAN; } else if ((a < 0) || (p < 0) || (p > 1)) { - sf_error("gammaincinv", SF_ERROR_DOMAIN, NULL); + gtsam_cephes_sf_error("gammaincinv", SF_ERROR_DOMAIN, NULL); } else if (p == 0.0) { return 0.0; @@ -272,17 +272,17 @@ double igami(double a, double p) return INFINITY; } else if (p > 0.9) { - return igamci(a, 1 - p); + return gtsam_cephes_igamci(a, 1 - p); } x = find_inverse_gamma(a, p, 1 - p); /* Halley's method */ for (i = 0; i < 3; i++) { - fac = igam_fac(a, x); + fac = gtsam_cephes_igam_fac(a, x); if (fac == 0.0) { return x; } - f_fp = (igam(a, x) - p) * x / fac; + f_fp = (gtsam_cephes_igam(a, x) - p) * x / fac; /* The ratio of the first and second derivatives simplifies */ fpp_fp = -1.0 + (a - 1) / x; if (isinf(fpp_fp)) { @@ -298,7 +298,7 @@ double igami(double a, double p) } -double igamci(double a, double q) +double gtsam_cephes_igamci(double a, double q) { int i; double x, fac, f_fp, fpp_fp; @@ -307,7 +307,7 @@ double igamci(double a, double q) return NAN; } else if ((a < 0.0) || (q < 0.0) || (q > 1.0)) { - sf_error("gammainccinv", SF_ERROR_DOMAIN, NULL); + gtsam_cephes_sf_error("gammainccinv", SF_ERROR_DOMAIN, NULL); } else if (q == 0.0) { return INFINITY; @@ -316,16 +316,16 @@ double igamci(double a, double q) return 0.0; } else if (q > 0.9) { - return igami(a, 1 - q); + return gtsam_cephes_igami(a, 1 - q); } x = find_inverse_gamma(a, 1 - q, q); for (i = 0; i < 3; i++) { - fac = igam_fac(a, x); + fac = gtsam_cephes_igam_fac(a, x); if (fac == 0.0) { return x; } - f_fp = (igamc(a, x) - q) * x / (-fac); + f_fp = (gtsam_cephes_igamc(a, x) - q) * x / (-fac); fpp_fp = -1.0 + (a - 1) / x; if (isinf(fpp_fp)) { x = x - f_fp; diff --git a/gtsam/3rdparty/cephes/cephes/lanczos.c b/gtsam/3rdparty/cephes/cephes/lanczos.c index f92a8d2088..4a4ad3a05f 100644 --- a/gtsam/3rdparty/cephes/cephes/lanczos.c +++ b/gtsam/3rdparty/cephes/cephes/lanczos.c @@ -22,7 +22,7 @@ static double lanczos_sum(double x) } -double lanczos_sum_expg_scaled(double x) +double gtsam_cephes_lanczos_sum_expg_scaled(double x) { return ratevl(x, lanczos_sum_expg_scaled_num, sizeof(lanczos_sum_expg_scaled_num) / sizeof(lanczos_sum_expg_scaled_num[0]) - 1, diff --git a/gtsam/3rdparty/cephes/cephes/sf_error.c b/gtsam/3rdparty/cephes/cephes/sf_error.c index 95a47c797b..790205c8ef 100644 --- a/gtsam/3rdparty/cephes/cephes/sf_error.c +++ b/gtsam/3rdparty/cephes/cephes/sf_error.c @@ -30,15 +30,15 @@ static volatile sf_action_t sf_error_actions[] = { SF_ERROR_IGNORE /* SF_ERROR__LAST */ }; -void sf_error_set_action(sf_error_t code, sf_action_t action) { +void gtsam_cephes_sf_error_set_action(sf_error_t code, sf_action_t action) { sf_error_actions[(int)code] = action; } -sf_action_t sf_error_get_action(sf_error_t code) { +sf_action_t gtsam_cephes_sf_error_get_action(sf_error_t code) { return sf_error_actions[(int)code]; } -void sf_error(const char *func_name, sf_error_t code, const char *fmt, ...) { +void gtsam_cephes_sf_error(const char *func_name, sf_error_t code, const char *fmt, ...) { va_list ap; va_start(ap, fmt); va_end(ap); diff --git a/gtsam/3rdparty/cephes/cephes/sf_error.h b/gtsam/3rdparty/cephes/cephes/sf_error.h index 43986df812..fea87fd1f6 100644 --- a/gtsam/3rdparty/cephes/cephes/sf_error.h +++ b/gtsam/3rdparty/cephes/cephes/sf_error.h @@ -26,10 +26,10 @@ typedef enum { } sf_action_t; extern const char *sf_error_messages[]; -void sf_error(const char *func_name, sf_error_t code, const char *fmt, ...); -void sf_error_check_fpe(const char *func_name); -void sf_error_set_action(sf_error_t code, sf_action_t action); -sf_action_t sf_error_get_action(sf_error_t code); +void gtsam_cephes_sf_error(const char *func_name, sf_error_t code, const char *fmt, ...); +void gtsam_cephes_sf_error_check_fpe(const char *func_name); +void gtsam_cephes_sf_error_set_action(sf_error_t code, sf_action_t action); +sf_action_t gtsam_cephes_sf_error_get_action(sf_error_t code); #ifdef __cplusplus } diff --git a/gtsam/3rdparty/cephes/cephes/unity.c b/gtsam/3rdparty/cephes/cephes/unity.c index c3776f4018..65758b63cb 100644 --- a/gtsam/3rdparty/cephes/cephes/unity.c +++ b/gtsam/3rdparty/cephes/cephes/unity.c @@ -50,7 +50,7 @@ static const double LQ[] = { }; /* log(1 + x) - x */ -double log1pmx(double x) +double gtsam_cephes_log1pmx(double x) { if (fabs(x) < 0.5) { int n; @@ -106,7 +106,7 @@ static double coscof[7] = { 4.1666666666666666609054E-2, }; -double cosm1(double x) +double gtsam_cephes_cosm1(double x) { double xx; @@ -131,7 +131,7 @@ static double lgam1p_taylor(double x) xfac = -x; for (n = 2; n < 42; n++) { xfac *= -x; - coeff = zeta(n, 1) * xfac / n; + coeff = gtsam_cephes_zeta(n, 1) * xfac / n; res += coeff; if (fabs(coeff) < MACHEP * fabs(res)) { break; @@ -143,13 +143,13 @@ static double lgam1p_taylor(double x) /* Compute lgam(x + 1). */ -double lgam1p(double x) +double gtsam_cephes_lgam1p(double x) { if (fabs(x) <= 0.5) { return lgam1p_taylor(x); } else if (fabs(x - 1) < 0.5) { return log(x) + lgam1p_taylor(x - 1); } else { - return lgam(x + 1); + return gtsam_cephes_lgam(x + 1); } } diff --git a/gtsam/3rdparty/cephes/cephes/zeta.c b/gtsam/3rdparty/cephes/cephes/zeta.c index 554933a24c..e9700888e7 100644 --- a/gtsam/3rdparty/cephes/cephes/zeta.c +++ b/gtsam/3rdparty/cephes/cephes/zeta.c @@ -86,7 +86,7 @@ static double A[] = { /* 30 Nov 86 -- error in third coefficient fixed */ -double zeta(double x, double q) +double gtsam_cephes_zeta(double x, double q) { int i; double a, b, k, s, t, w; @@ -96,13 +96,13 @@ double zeta(double x, double q) if (x < 1.0) { domerr: - sf_error("zeta", SF_ERROR_DOMAIN, NULL); + gtsam_cephes_sf_error("zeta", SF_ERROR_DOMAIN, NULL); return (NAN); } if (q <= 0.0) { if (q == floor(q)) { - sf_error("zeta", SF_ERROR_SINGULAR, NULL); + gtsam_cephes_sf_error("zeta", SF_ERROR_SINGULAR, NULL); retinf: return (INFINITY); } diff --git a/gtsam/nonlinear/internal/ChiSquaredInverse.h b/gtsam/nonlinear/internal/ChiSquaredInverse.h index aa72cae8aa..edfb2bfee8 100644 --- a/gtsam/nonlinear/internal/ChiSquaredInverse.h +++ b/gtsam/nonlinear/internal/ChiSquaredInverse.h @@ -37,7 +37,7 @@ namespace internal { * @return double */ inline double chiSquaredQuantile(const double dofs, const double alpha) { - return 2 * igami(dofs / 2, alpha); + return 2 * gtsam_cephes_igami(dofs / 2, alpha); } } // namespace internal From 8dbd786fbc4c07dc59c85b9d7cf297ac310bab59 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sat, 31 Jan 2026 14:24:45 -0500 Subject: [PATCH 151/266] add filter notebook --- examples/AbcEquivariantFilterExample.cpp | 2 +- gtsam/navigation/navigation.i | 2 +- .../AbcEquivariantFilterExample.ipynb | 293468 +++++++++++++++ 3 files changed, 293470 insertions(+), 2 deletions(-) create mode 100644 python/gtsam/examples/AbcEquivariantFilterExample.ipynb diff --git a/examples/AbcEquivariantFilterExample.cpp b/examples/AbcEquivariantFilterExample.cpp index 41aafec3a8..7360091bcd 100644 --- a/examples/AbcEquivariantFilterExample.cpp +++ b/examples/AbcEquivariantFilterExample.cpp @@ -7,7 +7,7 @@ * paper: "Overcoming Bias: Equivariant Filter Design for Biased Attitude * Estimation with Online Calibration" by Fornasier et al. * - * This example uses the simplified AbcEquivariantFilter wrapper class which + * This example uses the simplified AbcEquivariantFilter class which * provides a clean interface for predict/update operations without requiring * manual computation of Jacobian matrices and innovation functions. * diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index db1b4d2dec..39a3077e8e 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -652,7 +652,7 @@ virtual class InvariantEKF : gtsam::LeftLinearEKF { }; // --------------------------------------------------------------------------- -// ABC Equivariant Filter wrapper (N=1) +// ABC Equivariant Filter #include namespace abc { template diff --git a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb new file mode 100644 index 0000000000..d54be5c4d5 --- /dev/null +++ b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb @@ -0,0 +1,293468 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ABC Equivariant Filter Demo\n", + "\n", + "## Attitude-Bias-Calibration Equivariant Filter\n", + "\n", + "This notebook demonstrates the Equivariant Filter (EqF) for attitude estimation with both gyroscope bias and sensor extrinsic calibration, based on the paper:\n", + "\n", + "> **\"Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration\"** by Fornasier et al.\n", + "\n", + "The filter estimates:\n", + "- **Attitude (Rotation)**: The orientation of the body frame relative to a reference frame\n", + "- **Bias**: Gyroscope bias correction vector (3D)\n", + "- **Calibration**: Sensor calibration rotation matrices\n", + "\n", + "This demo uses the simplified `AbcEquivariantFilter` wrapper class which provides a clean interface for predict/update operations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n", + "\n", + "Simple robotics example using odometry measurements and bearing-range (laser) measurements\n", + "Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup and Imports\n", + "\n", + "First, we import all the necessary libraries. We use GTSAM for the filter implementation, NumPy for numerical operations, and Plotly for creating interactive visualizations. The `dataclass` decorator helps us create clean data structures for organizing measurements and results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from dataclasses import dataclass\n", + "import csv\n", + "import math\n", + "from typing import List\n", + "\n", + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "import gtsam\n", + "from gtsam import Rot3, Unit3\n", + "from gtsam.utils import findExampleDataFile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Structures\n", + "\n", + "We define three key data structures:\n", + "\n", + "1. **`MeasurementRecord`**: Stores a single direction measurement with its reference direction, covariance, and calibration index. Each measurement represents an observation of a known direction vector in the body frame.\n", + "\n", + "2. **`DataRecord`**: Contains all information for a single time step, including ground truth state (attitude, bias, calibration), gyroscope measurements, input covariance, and all direction measurements at that timestep.\n", + "\n", + "3. **`FilterResults`**: Aggregates the entire time series of ground truth, estimates, and errors for easy plotting. This allows us to track how the filter performs over the entire trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class MeasurementRecord:\n", + " y: np.ndarray\n", + " d: np.ndarray\n", + " R: np.ndarray\n", + " cal_idx: int\n", + "\n", + "\n", + "@dataclass\n", + "class DataRecord:\n", + " R: Rot3\n", + " b: np.ndarray\n", + " cal_rot: Rot3\n", + " omega: np.ndarray\n", + " input_covariance: np.ndarray\n", + " measurements: List[MeasurementRecord]\n", + " t: float\n", + " dt: float\n", + "\n", + "\n", + "@dataclass\n", + "class FilterResults:\n", + " \"\"\"Store filter results for plotting\"\"\"\n", + " times: List[float]\n", + " # Ground truth\n", + " truth_att_rpy: List[np.ndarray]\n", + " truth_bias: List[np.ndarray]\n", + " truth_cal_rpy: List[np.ndarray]\n", + " # Estimates\n", + " est_att_rpy: List[np.ndarray]\n", + " est_bias: List[np.ndarray]\n", + " est_cal_rpy: List[np.ndarray]\n", + " # Errors\n", + " att_errors: List[float]\n", + " bias_errors: List[float]\n", + " cal_errors: List[float]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading Functions\n", + "\n", + "The CSV file contains simulated IMU and direction measurement data with ground truth. Each row includes quaternion representations of attitude and calibration, gyroscope measurements with noise parameters, and direction observations. The data loader parses this CSV format and converts it into our `DataRecord` structures, handling normalization of direction vectors and construction of covariance matrices from standard deviations." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def _normalize(v: np.ndarray) -> np.ndarray:\n", + " \"\"\"Normalize a vector to unit length.\"\"\"\n", + " n = np.linalg.norm(v)\n", + " if n == 0.0:\n", + " return v\n", + " return v / n\n", + "\n", + "\n", + "def load_data_from_csv(\n", + " filename: str, start_row: int = 0, max_rows: int = -1, downsample: int = 1\n", + ") -> List[DataRecord]:\n", + " \"\"\"\n", + " Load data from CSV file into a list of DataRecord objects.\n", + " \n", + " The CSV contains:\n", + " - Time stamps and ground truth state (attitude quaternion, bias, calibration quaternion)\n", + " - Gyroscope measurements (omega) with noise standard deviations\n", + " - Direction measurements from two sensors with their reference directions\n", + " - Measurement noise covariances\n", + " \"\"\"\n", + " data_list: List[DataRecord] = []\n", + " with open(filename, newline=\"\") as csvfile:\n", + " reader = csv.reader(csvfile)\n", + " header = next(reader, None)\n", + " if header is None:\n", + " return data_list\n", + "\n", + " line_number = 1\n", + " row_count = 0\n", + " prev_time = 0.0\n", + "\n", + " for row in reader:\n", + " line_number += 1\n", + " if line_number < start_row:\n", + " continue\n", + " if ((line_number - start_row - 1) % downsample) != 0:\n", + " continue\n", + " if max_rows != -1 and row_count >= max_rows:\n", + " break\n", + " if len(row) < 39:\n", + " continue\n", + "\n", + " values = [float(x) if x else 0.0 for x in row]\n", + "\n", + " t = values[0]\n", + " dt = 0.0 if row_count == 0 else t - prev_time\n", + " prev_time = t\n", + "\n", + " # Extract ground truth state\n", + " R = Rot3.Quaternion(values[1], values[2], values[3], values[4])\n", + " b = np.array([values[5], values[6], values[7]])\n", + " cal_rot = Rot3.Quaternion(values[8], values[9], values[10], values[11])\n", + "\n", + " # Extract gyroscope input\n", + " omega = np.array([values[12], values[13], values[14]])\n", + "\n", + " # Build input covariance (6x6: angular velocity + bias process noise)\n", + " input_covariance = np.zeros((6, 6))\n", + " input_covariance[0, 0] = values[15] ** 2\n", + " input_covariance[1, 1] = values[16] ** 2\n", + " input_covariance[2, 2] = values[17] ** 2\n", + " input_covariance[3, 3] = values[18] ** 2\n", + " input_covariance[4, 4] = values[19] ** 2\n", + " input_covariance[5, 5] = values[20] ** 2\n", + "\n", + " measurements: List[MeasurementRecord] = []\n", + "\n", + " # First measurement (calibrated sensor, cal_idx = 0)\n", + " y0 = _normalize(np.array([values[21], values[22], values[23]]))\n", + " d0 = _normalize(np.array([values[33], values[34], values[35]]))\n", + " cov_y0 = np.diag([values[27] ** 2, values[28] ** 2, values[29] ** 2])\n", + " measurements.append(MeasurementRecord(y0, d0, cov_y0, 0))\n", + "\n", + " # Second measurement (uncalibrated sensor, cal_idx = -1)\n", + " y1 = _normalize(np.array([values[24], values[25], values[26]]))\n", + " d1 = _normalize(np.array([values[36], values[37], values[38]]))\n", + " cov_y1 = np.diag([values[30] ** 2, values[31] ** 2, values[32] ** 2])\n", + " measurements.append(MeasurementRecord(y1, d1, cov_y1, -1))\n", + "\n", + " data_list.append(\n", + " DataRecord(\n", + " R=R,\n", + " b=b,\n", + " cal_rot=cal_rot,\n", + " omega=omega,\n", + " input_covariance=input_covariance,\n", + " measurements=measurements,\n", + " t=t,\n", + " dt=dt,\n", + " )\n", + " )\n", + " row_count += 1\n", + "\n", + " return data_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter Processing with Results Tracking\n", + "\n", + "This function runs the ABC Equivariant Filter through the entire data sequence. For each time step, it performs a prediction step using gyroscope measurements, then updates with direction observations. The function tracks both the filter estimates and ground truth at each step, computing errors in attitude, bias, and calibration. All results are stored in a `FilterResults` object for later visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def process_data_with_eqf(\n", + " filter_eqf: gtsam.abc.AbcEquivariantFilter1,\n", + " data_list: List[DataRecord],\n", + ") -> FilterResults:\n", + " \"\"\"\n", + " Process data through the filter and return results for plotting.\n", + " \n", + " At each timestep:\n", + " 1. Predict: Use gyroscope measurements to propagate the state forward\n", + " 2. Update: Incorporate direction measurements to correct the estimate\n", + " 3. Track: Store estimates, ground truth, and errors for visualization\n", + " \"\"\"\n", + " if not data_list:\n", + " print(\"No data to process\")\n", + " return None\n", + "\n", + " print(f\"Processing {len(data_list)} data points with EqF...\")\n", + " \n", + " rad_to_deg = 180.0 / math.pi\n", + " \n", + " # Storage for results\n", + " results = FilterResults(\n", + " times=[],\n", + " truth_att_rpy=[],\n", + " truth_bias=[],\n", + " truth_cal_rpy=[],\n", + " est_att_rpy=[],\n", + " est_bias=[],\n", + " est_cal_rpy=[],\n", + " att_errors=[],\n", + " bias_errors=[],\n", + " cal_errors=[],\n", + " )\n", + "\n", + " total_measurements = 0\n", + " valid_measurements = 0\n", + " progress_step = max(len(data_list) // 10, 1)\n", + " print(\"Progress: \", end=\"\", flush=True)\n", + "\n", + " for i, data in enumerate(data_list):\n", + " # Predict step: propagate state using gyroscope measurements\n", + " filter_eqf.predict(data.omega, data.input_covariance, data.dt)\n", + "\n", + " # Update step: correct state using direction measurements\n", + " for measurement in data.measurements:\n", + " total_measurements += 1\n", + " if np.any(np.isnan(measurement.y)) or np.any(np.isnan(measurement.d)):\n", + " continue\n", + " try:\n", + " y_unit = Unit3(measurement.y)\n", + " d_unit = Unit3(measurement.d)\n", + " filter_eqf.update(y_unit, d_unit, measurement.R, measurement.cal_idx)\n", + " valid_measurements += 1\n", + " except Exception:\n", + " continue\n", + "\n", + " # Get current estimates using the wrapper's accessor methods\n", + " estimate_R = filter_eqf.attitude()\n", + " estimate_b = np.array(filter_eqf.bias()).reshape(3)\n", + " estimate_cal = filter_eqf.calibration(0)\n", + "\n", + " # Calculate errors (in Lie algebra for rotations)\n", + " att_error = Rot3.Logmap(data.R.between(estimate_R))\n", + " bias_error = estimate_b - data.b\n", + " cal_error = Rot3.Logmap(data.cal_rot.between(estimate_cal))\n", + "\n", + " # Store results for plotting\n", + " results.times.append(data.t)\n", + " results.truth_att_rpy.append(data.R.rpy() * rad_to_deg)\n", + " results.truth_bias.append(data.b)\n", + " results.truth_cal_rpy.append(data.cal_rot.rpy() * rad_to_deg)\n", + " results.est_att_rpy.append(estimate_R.rpy() * rad_to_deg)\n", + " results.est_bias.append(estimate_b)\n", + " results.est_cal_rpy.append(estimate_cal.rpy() * rad_to_deg)\n", + " results.att_errors.append(np.linalg.norm(att_error) * rad_to_deg)\n", + " results.bias_errors.append(np.linalg.norm(bias_error))\n", + " results.cal_errors.append(np.linalg.norm(cal_error) * rad_to_deg)\n", + "\n", + " if i % progress_step == 0:\n", + " print(\".\", end=\"\", flush=True)\n", + "\n", + " print(\" Done!\")\n", + "\n", + " # Print summary statistics\n", + " avg_att_error = float(np.mean(results.att_errors))\n", + " avg_bias_error = float(np.mean(results.bias_errors))\n", + " avg_cal_error = float(np.mean(results.cal_errors))\n", + "\n", + " print(\"\\n=== Filter Performance Summary ===\")\n", + " print(f\"Processed measurements: {total_measurements} (valid: {valid_measurements})\")\n", + "\n", + " print(\"\\n-- Average Errors --\")\n", + " print(f\"Attitude: {avg_att_error}°\")\n", + " print(f\"Bias: {avg_bias_error}\")\n", + " print(f\"Calibration: {avg_cal_error}°\")\n", + "\n", + " print(\"\\n-- Final Errors --\")\n", + " print(f\"Attitude: {results.att_errors[-1]}°\")\n", + " print(f\"Bias: {results.bias_errors[-1]}\")\n", + " print(f\"Calibration: {results.cal_errors[-1]}°\")\n", + "\n", + " print(\"\\n-- Final State vs Ground Truth --\")\n", + " print(f\"Attitude (RPY) - Estimate: {results.est_att_rpy[-1]} ° | Truth: {results.truth_att_rpy[-1]} °\")\n", + " print(f\"Bias - Estimate: {results.est_bias[-1]} | Truth: {results.truth_bias[-1]}\")\n", + " print(f\"Calibration (RPY) - Estimate: {results.est_cal_rpy[-1]} ° | Truth: {results.truth_cal_rpy[-1]} °\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization Functions\n", + "\n", + "These functions create interactive Plotly visualizations of the filter results. Each function generates time-series plots comparing ground truth (solid lines) with filter estimates (dashed lines)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_attitude_time_series(results: FilterResults):\n", + " \"\"\"\n", + " Plot attitude (roll, pitch, yaw) over time.\n", + " \n", + " Creates a 3-subplot figure showing each Euler angle component.\n", + " Solid lines show ground truth, dashed lines show filter estimates.\n", + " This helps visualize how well the filter tracks the true orientation.\n", + " \"\"\"\n", + " fig = make_subplots(\n", + " rows=3, cols=1,\n", + " subplot_titles=(\"Roll\", \"Pitch\", \"Yaw\"),\n", + " vertical_spacing=0.08,\n", + " x_title=\"Time (s)\",\n", + " )\n", + "\n", + " times = results.times\n", + " truth_rpy = np.array(results.truth_att_rpy)\n", + " est_rpy = np.array(results.est_att_rpy)\n", + " \n", + " labels = [\"Roll\", \"Pitch\", \"Yaw\"]\n", + " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", + " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", + " \n", + " for i in range(3):\n", + " # Ground truth\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=truth_rpy[:, i],\n", + " name=f\"{labels[i]} (Truth)\",\n", + " line=dict(color=colors_truth[i], width=2),\n", + " showlegend=(i == 0),\n", + " ),\n", + " row=i+1, col=1\n", + " )\n", + " # Estimate\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=est_rpy[:, i],\n", + " name=f\"{labels[i]} (Estimate)\",\n", + " line=dict(color=colors_est[i], width=2, dash='dash'),\n", + " showlegend=(i == 0),\n", + " ),\n", + " row=i+1, col=1\n", + " )\n", + " fig.update_yaxes(title_text=f\"{labels[i]} (°)\", row=i+1, col=1)\n", + "\n", + " fig.update_layout(\n", + " height=800,\n", + " title_text=\"Attitude Estimation: Roll-Pitch-Yaw over Time\",\n", + " showlegend=True,\n", + " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", + " )\n", + " return fig\n", + "\n", + "\n", + "def plot_bias_time_series(results: FilterResults):\n", + " \"\"\"\n", + " Plot gyroscope bias over time.\n", + " \n", + " Shows all three bias components (X, Y, Z) on a single plot.\n", + " The bias represents systematic errors in the gyroscope measurements that the filter\n", + " must estimate and correct. Watch how the estimates converge to the true bias values.\n", + " \"\"\"\n", + " fig = go.Figure()\n", + " \n", + " times = results.times\n", + " truth_bias = np.array(results.truth_bias)\n", + " est_bias = np.array(results.est_bias)\n", + " \n", + " labels = [\"Bias X\", \"Bias Y\", \"Bias Z\"]\n", + " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", + " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", + " \n", + " for i in range(3):\n", + " # Ground truth\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=truth_bias[:, i],\n", + " name=f\"{labels[i]} (Truth)\",\n", + " line=dict(color=colors_truth[i], width=2),\n", + " )\n", + " )\n", + " # Estimate\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=est_bias[:, i],\n", + " name=f\"{labels[i]} (Estimate)\",\n", + " line=dict(color=colors_est[i], width=2, dash='dash'),\n", + " )\n", + " )\n", + " \n", + " fig.update_layout(\n", + " height=500,\n", + " title_text=\"Gyroscope Bias Estimation over Time\",\n", + " xaxis_title=\"Time (s)\",\n", + " yaxis_title=\"Bias (rad/s)\",\n", + " showlegend=True,\n", + " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", + " )\n", + " return fig\n", + "\n", + "\n", + "def plot_calibration_time_series(results: FilterResults):\n", + " \"\"\"\n", + " Plot sensor calibration over time.\n", + " \n", + " Creates a 3-subplot figure showing the calibration rotation in Roll-Pitch-Yaw.\n", + " Calibration represents the rotation between the sensor frame and body frame,\n", + " which must be estimated online. Notice how the filter converges to the true\n", + " calibration parameters even while the body is rotating.\n", + " \"\"\"\n", + " fig = make_subplots(\n", + " rows=3, cols=1,\n", + " subplot_titles=(\"Cal Roll\", \"Cal Pitch\", \"Cal Yaw\"),\n", + " vertical_spacing=0.08,\n", + " x_title=\"Time (s)\",\n", + " )\n", + "\n", + " times = results.times\n", + " truth_cal = np.array(results.truth_cal_rpy)\n", + " est_cal = np.array(results.est_cal_rpy)\n", + " \n", + " labels = [\"Roll\", \"Pitch\", \"Yaw\"]\n", + " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", + " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", + " \n", + " for i in range(3):\n", + " # Ground truth\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=truth_cal[:, i],\n", + " name=f\"{labels[i]} (Truth)\",\n", + " line=dict(color=colors_truth[i], width=2),\n", + " showlegend=(i == 0),\n", + " ),\n", + " row=i+1, col=1\n", + " )\n", + " # Estimate\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=est_cal[:, i],\n", + " name=f\"{labels[i]} (Estimate)\",\n", + " line=dict(color=colors_est[i], width=2, dash='dash'),\n", + " showlegend=(i == 0),\n", + " ),\n", + " row=i+1, col=1\n", + " )\n", + " fig.update_yaxes(title_text=f\"{labels[i]} (°)\", row=i+1, col=1)\n", + "\n", + " fig.update_layout(\n", + " height=800,\n", + " title_text=\"Sensor Calibration Estimation over Time\",\n", + " showlegend=True,\n", + " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", + " )\n", + " return fig\n", + "\n", + "\n", + "def plot_errors_time_series(results: FilterResults):\n", + " \"\"\"\n", + " Plot estimation errors over time.\n", + " \n", + " Shows the norm of attitude and calibration errors in degrees.\n", + " Lower values indicate better tracking performance. Spikes in error may occur\n", + " when measurements are sparse or during rapid maneuvers.\n", + " \"\"\"\n", + " fig = go.Figure()\n", + " \n", + " times = results.times\n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=results.att_errors,\n", + " name=\"Attitude Error\",\n", + " line=dict(color=\"rgb(200, 60, 60)\", width=2),\n", + " )\n", + " )\n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=results.cal_errors,\n", + " name=\"Calibration Error\",\n", + " line=dict(color=\"rgb(60, 60, 200)\", width=2),\n", + " )\n", + " )\n", + " \n", + " fig.update_layout(\n", + " height=500,\n", + " title_text=\"Estimation Errors over Time\",\n", + " xaxis_title=\"Time (s)\",\n", + " yaxis_title=\"Error (°)\",\n", + " showlegend=True,\n", + " )\n", + " return fig\n", + "\n", + "\n", + "def plot_bias_error_time_series(results: FilterResults):\n", + " \"\"\"\n", + " Plot bias estimation error over time.\n", + " \n", + " Shows the norm of the bias error in rad/s with a filled area for emphasis.\n", + " The bias typically converges more slowly than attitude because it relies on\n", + " integrating information over time. The shaded area helps visualize the overall\n", + " convergence trend.\n", + " \"\"\"\n", + " fig = go.Figure()\n", + " \n", + " times = results.times\n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=times, y=results.bias_errors,\n", + " name=\"Bias Error\",\n", + " line=dict(color=\"rgb(60, 200, 60)\", width=2),\n", + " fill='tozeroy',\n", + " fillcolor='rgba(60, 200, 60, 0.2)',\n", + " )\n", + " )\n", + " \n", + " fig.update_layout(\n", + " height=400,\n", + " title_text=\"Bias Estimation Error over Time\",\n", + " xaxis_title=\"Time (s)\",\n", + " yaxis_title=\"Error (rad/s)\",\n", + " showlegend=False,\n", + " )\n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "Here we locate and load the example dataset (`EqFdata.csv`). This CSV file contains simulated IMU trajectory data with ground truth, generated from a realistic motion profile. The data includes approximately 12,000 timesteps with gyroscope measurements and direction observations from two sensors (one calibrated, one uncalibrated). If you want to test with your own data, simply provide the path to a CSV file in the same format." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ABC-EqF: Attitude-Bias-Calibration Equivariant Filter Demo\n", + "==============================================================\n", + "Loading data from: /Users/apollo/dev/research/gtsam/build/python/gtsam/Data/EqFdata.csv\n", + "Loaded 12001 data points\n" + ] + } + ], + "source": [ + "print(\"ABC-EqF: Attitude-Bias-Calibration Equivariant Filter Demo\")\n", + "print(\"==============================================================\")\n", + "\n", + "# Load data using GTSAM's dataset utility\n", + "try:\n", + " csv_file_path = findExampleDataFile(\"EqFdata.csv\")\n", + " print(f\"Loading data from: {csv_file_path}\")\n", + "except Exception as e:\n", + " print(f\"Error: Could not find EqFdata.csv: {e}\")\n", + " csv_file_path = None\n", + "\n", + "if csv_file_path:\n", + " data = load_data_from_csv(csv_file_path)\n", + " print(f\"Loaded {len(data)} data points\")\n", + "else:\n", + " print(\"No data available to process.\")\n", + " data = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize and Run Filter\n", + "\n", + "Now we create and configure the ABC Equivariant Filter. The initial covariance matrix `initial_sigma` encodes our uncertainty in the initial state: we're moderately uncertain about the attitude (0.1 rad²) and calibration (0.1 rad²), but quite confident about the initial bias (0.01 rad²/s²). The filter starts at the identity state (zero attitude, zero bias, identity calibration) and will converge to the true state as it processes measurements. This cell runs the filter through the entire trajectory, which takes a few seconds for 12,000 timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 12001 data points with EqF...\n", + "Progress: ........... Done!\n", + "\n", + "=== Filter Performance Summary ===\n", + "Processed measurements: 24002 (valid: 4923)\n", + "\n", + "-- Average Errors --\n", + "Attitude: 1.1308383647121234°\n", + "Bias: 0.0075498876180193865\n", + "Calibration: 0.957466838125976°\n", + "\n", + "-- Final Errors --\n", + "Attitude: 3.662018753470247°\n", + "Bias: 0.0020652584435729942\n", + "Calibration: 0.3643824462229628°\n", + "\n", + "-- Final State vs Ground Truth --\n", + "Attitude (RPY) - Estimate: [-13.80445039 -0.64271376 86.74437182] ° | Truth: [-17.40672752 -1.04149482 86.27016675] °\n", + "Bias - Estimate: [0.00468984 0.00994432 0.00194401] | Truth: [0.00399696 0.00799933 0.00189684]\n", + "Calibration (RPY) - Estimate: [24.81667419 5.05822205 29.67403453] ° | Truth: [25. 5. 30.] °\n", + "\n", + "Filter processing completed successfully!\n" + ] + } + ], + "source": [ + "if data:\n", + " # Define initial covariance (uncertainty in the initial state)\n", + " n_cal = 1 # Number of calibrated sensors\n", + " initial_sigma = np.eye(6 + 3 * n_cal)\n", + " initial_sigma[0:3, 0:3] = 0.1 * np.eye(3) # Attitude uncertainty (rad²)\n", + " initial_sigma[3:6, 3:6] = 0.01 * np.eye(3) # Bias uncertainty (rad²/s²)\n", + " initial_sigma[6:9, 6:9] = 0.1 * np.eye(3) # Calibration uncertainty (rad²)\n", + "\n", + " # Create filter instance (starts at identity state)\n", + " filter_eqf = gtsam.abc.AbcEquivariantFilter1(initial_sigma)\n", + " \n", + " # Process all data through the filter\n", + " results = process_data_with_eqf(filter_eqf, data)\n", + " \n", + " print(\"\\nFilter processing completed successfully!\")\n", + "else:\n", + " results = None\n", + " print(\"Skipping filter processing - no data loaded.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Results\n", + "\n", + "### Attitude Estimation (Roll-Pitch-Yaw)\n", + "\n", + "This plot shows how the filter estimates the body's orientation over time. The three subplots display Roll (rotation about X-axis), Pitch (rotation about Y-axis), and Yaw (rotation about Z-axis). Solid lines represent ground truth, while dashed lines show the filter's estimates. Notice how closely the estimates track the true values even during dynamic maneuvers. You can zoom into specific time regions and hover over the curves to see exact values at each timestep." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgb(200, 60, 60)", + "width": 2 + }, + "name": "Roll (Truth)", + "showlegend": true, + "type": "scatter", + "x": [ + 0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 0.255, + 0.26, + 0.265, + 0.27, + 0.275, + 0.28, + 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plot_attitude_time_series(results)\n", + " fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gyroscope Bias Estimation\n", + "\n", + "This plot displays the three components of gyroscope bias (systematic errors in angular velocity measurements). The filter must simultaneously estimate the attitude while learning these constant biases. Notice how the bias estimates converge from their initial values (zero) toward the true bias values. The convergence rate depends on the richness of the motion - more diverse rotations provide better observability of the bias. The darker solid lines are ground truth, while lighter dashed lines are the filter's estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgb(200, 60, 60)", + "width": 2 + }, + "name": "Bias X (Truth)", + "type": "scatter", + "x": [ + 0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 0.255, + 0.26, + 0.265, + 0.27, + 0.275, + 0.28, + 0.28500000000000003, + 0.29, + 0.295, + 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jgD+DWId8B2OAP25RCuMMY4A/f9jKeQpjgD8VXcjlCmOAP4ScLosEY4A/1wX7LgVjgD9IpUDICGOAP5BYnwEOY4A/pEm0LBVjgD9AwTmcGmOAPwELdVUYY4A/7gBLnRtjgD9KQebAEWOAPwnve/QTY4A/C1kN3Q1jgD+QzLZ5EGOAP+DhpXkPY4A/yUuXVwpjgD8unOd3DWOAPyGpG4UOY4A//HcZawdjgD+OWZpMAmOAP3R5JBoIY4A/2LU4MwVjgD9lsZICB2OAP5BoeUANY4A/Nw8WFxZjgD8lhqE0E2OAP4q4Yb4iY4A/bVQU4CdjgD+Wv4FAKWOAPxcNXwIdY4A/7BxwwhxjgD/7WwtwJ2OAP4hoHjkrY4A/EpwsNCxjgD/AQRilIGOAP5xEj1MnY4A/FAczuyNjgD+aHZ4WIGOAP+ntWV4pY4A/2uar1yJjgD+RtiL1KmOAP5dtBxk0Y4A/98+g/ThjgD9WSWtLOGOAP1Oqs5RBY4A/7ci000JjgD+LoEpkPmOAP80v7LM+Y4A/42wAM0FjgD9B5s/tNmOAP++Fr3Q3Y4A/lQdrszBjgD9M4BQQNGOAP6mNCxU4Y4A/oJtjiTNjgD/oQYmLPmOAPwthylRHY4A/U1pS0TZjgD+3lWy2OWOAPxjxlu06Y4A/e3Wbdj5jgD+mB3BnOmOAPztcYjtAY4A/h1zIPThjgD83UH3uOGOAP3MF6pNAY4A//KRd2j1jgD+AtOzNOWOAP+cTHlY4Y4A/UyIzPjdjgD8E/xupOGOAP4o8VKc4Y4A/WK4OpDxjgD+/2/RjP2OAP2kNu1U5Y4A/EB4IN0JjgD+YdBS7PGOAP4sXYHdAY4A/b8bEPUljgD/PIjwXR2OAP3V2QBNFY4A/qiwFPEVjgD9nDLG6QmOAPwtzO7U/Y4A/Zjyb0DpjgD8x9zmsQ2OAP/hHD/E9Y4A/8sn9+EdjgD9KR/XwUWOAPz0V2HROY4A/Za5ZAFxjgD9tXBk2YWOAP7eY9XZbY4A/8Mtb9VdjgD+ufF87VmOAPwIxJNNcY4A/TX8j/1xjgD8gboHDX2OAP5WLwRVcY4A/Q+iTaF9jgD/SuANoY2OAPw5wHfdeY4A/erztfWJjgD/velqmVmOAP65vl6FUY4A/3FjnGVZjgD9Dxf7GTGOAP6K3SjFJY4A/1gJp+EZjgD+n0mjmSWOAP8yOTbFGY4A/di3LCkZjgD+4244FSWOAP4GcqeRIY4A/iQB8mEZjgD/1yXKnRGOAP6CSAwpDY4A/3bp5BkxjgD/I+1OTSmOAP83NgatPY4A/zPzjM1FjgD+11udtTGOAP2s/QzlNY4A/UAaPEltjgD/pOU1iWGOAP6ssqCFWY4A/QGIVyVFjgD9iqPzNS2OAP2G6MLdPY4A/jvVDOV1jgD+LAWQFWGOAPyaqK+VaY4A/a9mltE9jgD8l+OPLUmOAPx9DbX1OY4A/Lpn1r1NjgD8aqnPpV2OAP6Y61WdWY4A/y6aZyVhjgD8dfjGxV2OAP6v8E3llY4A/v81plmxjgD+jwA4rb2OAP/d0pbxnY4A//sutB3FjgD/gMHvFcWOAP7WXNlJuY4A/zEJHu29jgD/Jt406aWOAP2+fsKlqY4A/86KXHXRjgD9oW8VReWOAP+sYRg5/Y4A/cjhq/YRjgD9O0MQbgWOAP+gDCi6FY4A/uEtuCYtjgD8T+n3Ci2OAP+kXqgCKY4A/dPedQ41jgD+5s7Xog2OAP/EpIWt/Y4A/dqOskoBjgD/WOsLVh2OAP7eb9KmKY4A/zYkm2YpjgD+2rvRajGOAP8UcODiNY4A/SHWedZRjgD/hBWk1lWOAP6ay7G+PY4A/Y+Rsv4djgD9TqCDuhGOAP44WPsqQY4A/Km26Zo9jgD8tuUmLlmOAP0J7oNeNY4A/8Q/EX4djgD95dVFFhWOAP/p2M3mNY4A//AAjxoJjgD8MaOcniGOAP5SRFPyFY4A/RvMwkX9jgD+M0usEemOAP08wZul6Y4A/Qk2CDn5jgD/chjsqdmOAPziNXe5zY4A/avsqXnVjgD8aMrm+cWOAPxXB/ER1Y4A/8gmfWnNjgD9JqqhVdWOAPzPPdPNxY4A/YavchHFjgD+s5hSlbmOAP/y3d6puY4A/LUPXj2tjgD8JcSN8c2OAP2HZ9oJ4Y4A/L9p7CH1jgD/JiLSOfmOAP3tA0Ot/Y4A/Fjf4Z4JjgD954P2Eg2OAPzqsMq17Y4A/8PI7M3VjgD9y1R7tgmOAPyK6XkGLY4A/PYyc+IljgD/iJ8y5hGOAP6Vem7CJY4A/Nbng/otjgD8iPWErjWOAP1w5Kz2MY4A/lmq6dJVjgD/rkf5akWOAPwVdDv2HY4A/881WMohjgD+oSyWxiWOAP9pPSSSNY4A/FoWz1JBjgD/5SR8Rj2OAP62OC5+QY4A/h9gBOYpjgD/lASz/hmOAP3j2lTGFY4A/nh6VToJjgD+q31GRh2OAP6Bh58OHY4A/gLFeeYhjgD/bOQLeh2OAP3lnrjWJY4A/0CIuWY9jgD+YTtUNlmOAPxSATImeY4A//lVc3ptjgD/MBRrhmWOAP5rCNgCZY4A/std9xphjgD/yAOFFj2OAPyGPoieHY4A/UDVFR4hjgD8Djeh8hWOAP5VfduaAY4A/l6xQ7oJjgD/rsXpuh2OAP4A8SDV9Y4A/lrm+oH1jgD9meFwveWOAP7LXzuJ1Y4A/iUZcuXpjgD9diwktc2OAP6HXYWlzY4A/i8hoo25jgD9ASitVdWOAP83r3Ut4Y4A/Bp6BxXJjgD/W5FQRe2OAP18mAjGDY4A/k0OlP4FjgD8sjzXcgWOAP/JLO6F9Y4A/AZGP9oVjgD/FIbG0kGOAP7fqVlePY4A/D5t2LphjgD97PiPQm2OAPzLZ9GuYY4A/69W19JhjgD+6WgDAl2OAP2Ntf92UY4A/V8vqR51jgD9TzVFsk2OAPz2VOQOZY4A/3tV2NpljgD+7eOzQkGOAP7BCF+6ZY4A/JWYZeZtjgD+ximUNmWOAP1yPULCmY4A/bxE2IKpjgD8goHVErWOAP/mll0irY4A//q0GCa5jgD/l9d2srmOAP/zwueOvY4A/XmT6vrRjgD9sxE8fsWOAPy1XlUSwY4A/1DZQg7FjgD9NydPcsWOAP9wQn4+uY4A/J0fAqKhjgD+OGxhGrWOAP+v+HUWuY4A/vwUxMrFjgD+bJ+emt2OAP4mfz4mzY4A/kxdsqK5jgD/bXO4Vq2OAP0APzmKiY4A/a2eGYqBjgD+17uQ5oWOAPzDL51OoY4A/p4KWlZ5jgD/+oN5upWOAP0/JtkSdY4A/M6510JpjgD8S0Wb6pmOAP8xG4tygY4A/x4PPh6FjgD+Lp3xrmmOAP22FhbmfY4A/doXHx6VjgD9Qyxs1nWOAP7zz9eeUY4A/QP/uTpVjgD9pKGjrjWOAP+VkHCOEY4A/z4sRsIZjgD9nYpmEgmOAP25xUfyCY4A/5KPVnYJjgD/YHW9hhmOAPwkATBWFY4A/x8blJYNjgD8RXb+jj2OAP6kfjQKSY4A/GXgVbptjgD9e+afjoWOAP34R70ifY4A/LmOS0ppjgD+IjnsWpWOAP/gLyhmdY4A/MBmh+6JjgD/G6Yy0p2OAP6akM4exY4A/wBMMdrdjgD/Jl0GvumOAPzKNJhHFY4A/XKPnfspjgD/Y38DjymOAPxkmD+LJY4A/qyvCXMVjgD9p2wFZvmOAP8nPI7e0Y4A/fJMk7bZjgD/K7uljtGOAPwlJssS3Y4A/Zu5nTa9jgD/2Un3duGOAP5TlG6O5Y4A/0elmVcBjgD8Wc42MsWOAP9/g0MivY4A/MaMYFbxjgD8nMiV9vmOAP49G7Ae7Y4A/ZEH3PLZjgD/33cdBvGOAPxXR74SzY4A/dz/cZr9jgD8FO23hvmOAPw/qSFC4Y4A/YZqBxrNjgD8CjoYPvWOAP3a4bQfCY4A/QwQAocZjgD9SVvlHxmOAP7CSgSq/Y4A/QhVWHr1jgD9KwZC/wmOAP+AMUgu8Y4A/f2sg97VjgD+VJSgLtWOAP8bYHh64Y4A/DDOFnL9jgD/V5v41u2OAP1wrW+u3Y4A/VInsjrhjgD/YiZDOtWOAP2BR+8S9Y4A/qETmK8xjgD/+2RKezGOAP/CovJq8Y4A/CrPnQ8VjgD/1h/zWxmOAPyBETcbCY4A/JrE9wchjgD/JfmoYzWOAP3fAqN3cY4A/WHcUO+RjgD9zuAgr32OAP4TdCCzkY4A/mRFs/tpjgD8HTsop3WOAP43sxC/cY4A/CuLHkuFjgD8sZ8cG6GOAP5uUMBXrY4A/ra0Y8etjgD9vrqzS8WOAP0CaV+z6Y4A//yXu0wdkgD9pfTZKCWSAP+D4DZAQZIA/U9I9ehBkgD+yFfUYD2SAPxb3hg4MZIA/lsA97wpkgD9fWld1C2SAPxkUK/8NZIA/NFRckxJkgD+MstDSF2SAP5YhMlcdZIA/VFVkrRZkgD/GBTy3FmSAP/pWDUYSZIA/3LkbQRVkgD/cpnuWF2SAPwbGhyALZIA/em16OBJkgD9VlcReEWSAP3SY/j8ZZIA/oRJNgRZkgD82Brr6D2SAP5uG3cQQZIA/i72+mA9kgD/qMuvqDGSAPzJZ764PZIA/YwjfbQ5kgD8KOVRdFGSAP52j1R8TZIA/aYTFFwFkgD96p51+/mOAP5HOosoGZIA/54EQ4QRkgD+W4mL4EGSAP+ZnGQ0PZIA/gXWlawlkgD+1tLE6AmSAP8Tbo9z4Y4A/204pQfVjgD+9fKWG8WOAP0njnjv1Y4A/a6cCq/BjgD9AouKP8WOAPwsNGGLwY4A/FxSkwO5jgD/qDJ9C8WOAPyKUdefvY4A/IvQr0O1jgD95H85C62OAPwXmj+rrY4A/nu+FZu1jgD8ZOJmI5mOAPzd+5/LgY4A/ufF5j+ljgD9qI3GZ4GOAPxsETXTjY4A/KUBa7+RjgD9wJDhQ8mOAPwweUKXwY4A/x6IPEe5jgD+0y7E05WOAP4Cj8sLcY4A/TV3RDdxjgD857k8G2mOAP5BiMpLQY4A/mrajYNtjgD8OWsNy3mOAPxt2hOvfY4A/RZ71t95jgD/bkbuZ2WOAP16UZOfaY4A/omFm691jgD/3Kgcq0WOAPxTxUuvNY4A/UEnpVctjgD9fT6R/x2OAP6YrOXvKY4A/fWaAGsRjgD/6aaf1uWOAP8MBi229Y4A/aXCut7RjgD/SasyYqmOAP5juN1etY4A/KyhG8K1jgD8C+vzEsWOAPxiaddG1Y4A/aLBJSbVjgD8njCZ/sWOAPwpaLOuuY4A/MZ3Fza1jgD8pkgcct2OAP2q4u++9Y4A/BEJGxsFjgD9kt8GoyGOAP1gu1bLLY4A/oc/fENJjgD/wkbD/1WOAP7KgOP3YY4A/Se0hhdNjgD94hjog1mOAP7LY8pnNY4A/9lREKdBjgD9JFIIJ0mOAP7/cqizPY4A/hP++DsxjgD9YIkLXzmOAP4Mt3MLXY4A/3dAhsdFjgD/2RNEb1mOAP75iauTRY4A/EmhAJNhjgD8GFLOP02OAP3+W9BDcY4A/oP6FQtRjgD+oDAVp1WOAP/YQ5djSY4A/V1fE1dljgD8kRXbO2WOAP6k5T57eY4A/xeOS7eRjgD+Xe3xK6mOAP2m4CKjxY4A/opJzLu1jgD9DrSoj5WOAP/UwJMviY4A/mZT94uZjgD/ZNn2E6WOAP7rCSE3iY4A/F/Ggod1jgD816a6O5WOAP0EBznvjY4A/ESbc5uJjgD8E/GrW5WOAPye10nPpY4A/q+Fd0+ZjgD+UQEMj72OAP2HpMeHrY4A/G1sKD+xjgD8OzVv+5GOAP6TH2MjhY4A/0hcgLupjgD+netR26WOAP5KgP5npY4A/lAAaqutjgD8I6u5g6mOAPzCT0B7mY4A/jJunIeFjgD9HJcEM4GOAP0jSwc7gY4A/HGv8AN9jgD+hrUqB5WOAP3KQROLsY4A/We8v1OtjgD/e4jag6GOAPzS+US7rY4A/PWKa6ehjgD9uJmQs7WOAP/tWYCHvY4A/H0COvOxjgD/NDgU46mOAP6xUh57pY4A/UFuy7txjgD9oTljc3WOAP8nGJ1XVY4A/ibDV2dRjgD8p3NBu1mOAPz5kPb/OY4A/IBI2+spjgD9v+xm0z2OAP+UHD13UY4A/YX5RGtRjgD8YbEYVz2OAPw/jXPbFY4A/qMDTucdjgD+zZnOcvmOAPxrn+ee9Y4A/jDmOpb5jgD9BpWfavWOAP5FNjy7GY4A/aJYImbpjgD9My5nxumOAP8XbdfWwY4A/ZaEpYrdjgD/DBuVZtmOAPwaEI525Y4A/t10YtbBjgD/9giRKuGOAP4LnhIywY4A/DI/HlrFjgD/jqTqItGOAP16uSBu0Y4A/dwrEW8BjgD+vuumqvmOAPx/sR6rDY4A/TBeD7MJjgD8XymvHu2OAPzEVas6zY4A/D3OSqLZjgD9Dp1PQsGOAP/JA5zWnY4A/hjl1X6djgD+c3a8XqmOAP8hLTIGoY4A/alBfRq1jgD+6yVtup2OAP4thB5afY4A/meMxSaFjgD9uKMk/omOAP5fFxAypY4A/mmaNiaVjgD+CfZw6rmOAP/qJWD+jY4A/t4goiqFjgD8LZxJnnWOAP7r6u7GXY4A/s4wRGJhjgD9tPFyCmWOAP7yDFg+PY4A/bY2MF49jgD+BDznkkWOAP9/QD1+MY4A/fzdV+YRjgD+EVMKjiWOAPzbQwox/Y4A/CG/ernpjgD/MTpDTimOAPyQ1l6mGY4A/Z7uSZohjgD8z+mg8g2OAP4jtF5l2Y4A/3sZB+n1jgD/5ELZ8f2OAP4Yu5suBY4A/vlbDvn5jgD9Swu/8gmOAP+4ZsJJ7Y4A/FDoOAoBjgD/j9Q73iWOAP7yIzlOEY4A/68WVtXtjgD9kNRE2fGOAP1eKPcN9Y4A/R527f4BjgD92M22Ge2OAP+EEUo54Y4A/g4IFs35jgD/sC8ivhWOAPwSgqReIY4A/GUNIeodjgD9FRyEOgGOAP5W6aCyEY4A/Aq43LY5jgD8rm251iWOAPzjreC2EY4A/YLnUMH9jgD8bHhbNc2OAP7FTJBNzY4A/wcBr5mtjgD+ECNQKd2OAP4ArQE5zY4A/VqJ3LnVjgD/x9bUkc2OAPxHGkaF0Y4A/ulG/gXBjgD+OVvEPbmOAP4W0JXxtY4A/C2hSaXVjgD+zxVIId2OAP3rh82dwY4A/wJaOUnRjgD9+PXRXcWOAPxc8YiVvY4A/d7vlom9jgD92XrIGa2OAP5phcfJtY4A/vSFbxG9jgD99pxFiaWOAP6cSxLJfY4A/JUmviGNjgD8TrhpVaWOAP2fEMlFuY4A/78GhsHJjgD9VhPEAcWOAP9F50WFvY4A//jx/cHdjgD+CZWsPb2OAP/TTMyVpY4A/CcyVQmZjgD9KbVXPZ2OAP2uNABFnY4A/KeTP2GVjgD8ZuwvoamOAP76vV5RuY4A/HrkV42djgD9fd4CpamOAP1vawUllY4A/VIWvDWhjgD+u31r0bmOAPzCbanBoY4A/nhp7f2RjgD8p90yCY2OAP3qOFZhdY4A/54/lH1pjgD97UvvKVmOAPyxKm7BQY4A/xxxTHVtjgD/Lf/EfW2OAPz32YxNbY4A/sx/LLlpjgD+azQTgX2OAP5ifEAxgY4A/6vTxfVxjgD+SFJ+yWmOAP7OAcQ5cY4A/mTXKlFBjgD9Jn/cURmOAP2H14lZCY4A/mkQ6gDtjgD8hDYxxQ2OAP1dYxRdRY4A/wVAj+FRjgD8Bf4OuVmOAPzHdlVNZY4A/200wUltjgD975vj0WWOAP247sYhYY4A/tif/u2ljgD+A/LAoamOAPz8i6gBvY4A/6kyOkHBjgD9gWCjLcmOAP7WJ/ABvY4A/hzHB7ndjgD8c7yBTdmOAPw97IgVzY4A/Lz35m3JjgD+Q9sPdc2OAP2nTdRJmY4A/2laxemBjgD/BzQEJYWOAPwFIoedaY4A/Y7eydFtjgD85L/3rWmOAP9ppfEhfY4A/BIpL9mBjgD99M1H1VmOAP1KD5AdXY4A/INpmOVhjgD8uBKsrXGOAP7pX/g1RY4A/W9WsfVVjgD+kq/ODVGOAPwBZ6oxZY4A/QIzVF1djgD/QWlgMXGOAPy6ugwBgY4A/IelW1l9jgD8gEwB1YmOAP9ek/TxkY4A/k6F5zmVjgD9uxdIpbmOAP55xmuJwY4A/JL6PBm9jgD8xUAdld2OAPwgMXKpxY4A/cZhM2XNjgD8seH29dGOAP1ORO7lyY4A/YALttXhjgD+9iqUEfGOAPzT0+aB/Y4A/iHul9H5jgD+QlUA0hmOAP8APYk6HY4A/L1VsfoVjgD8/lKDhf2OAP58zD1Z/Y4A/zx4eWnNjgD+IONMudGOAP6+VIlp3Y4A/ivKLR2hjgD9KQs89YGOAP4DiHJlmY4A/WvTWyF1jgD+9SSf1X2OAP0JSwPxXY4A/bTY/RF9jgD8tjpJkXmOAP9rC2fpfY4A/vtJrMmdjgD/2C4YCemOAP9Ldyg17Y4A/3R+65n5jgD9q9ZPAhWOAP5hIUeuLY4A/UoHxGpFjgD8aWm4zimOAP8IVZ9qBY4A/S6k0lINjgD9EiQlRg2OAP/ErTtCDY4A/OHq6/oRjgD9bWaf6iGOAP9U2Jx97Y4A/F5lWSnljgD+jWi4leWOAP7Al6sB2Y4A/TcEkSXljgD97eEhTfmOAP1LqGMqCY4A/vwpy4oNjgD8EmGbnh2OAP5i+7EuEY4A/a6HsoIljgD/6LKOjkGOAPzyqzm2WY4A/3vMfUY5jgD92J5nmjGOAP+2kjCOZY4A/lZ0dgpJjgD+kAbbOjmOAP9WN6WOKY4A/oqZMyI5jgD8F52LXjGOAPw4sDcGKY4A/QxIyKoljgD9x2CtfhGOAP/kBNvKPY4A/4alaz4xjgD/CG0Abi2OAP+7aR+iBY4A/KLGo04JjgD9Zprebf2OAP5pLDUpvY4A//Bb9zG1jgD/JuHNrZ2OAP2XX72JlY4A/yGJTh2JjgD/OKnLLZGOAP1ceNdVmY4A/WPSy7GljgD/gk2wZcWOAP7LkCr9vY4A/GUz43GljgD/6O5fDYGOAP76CFIhpY4A/iT4vyGJjgD/TCp0UZGOAP2abzWBYY4A/ZHT4blZjgD+P151lTGOAP2Pvs7RHY4A/EOopqEljgD+DKQ07U2OAP3R+2kZSY4A/zdPi6VBjgD8bvS1TSWOAPz+FDLVKY4A/3hovGE9jgD92i4QmT2OAP/Y5LD1OY4A/ZHwu+EljgD+9CqBqSGOAP7NQiS49Y4A/68QYbkljgD82H9p5SmOAP46gKWNLY4A/fFE2hkxjgD/PjUzCRGOAP/cOpCFEY4A/q99T50RjgD8BTy53RmOAP3+mOg84Y4A/c5189zljgD8FpbSgOmOAPx80UkdEY4A/eAhBUkljgD9YQIx7TmOAP0ay6n9VY4A/3rC6GFRjgD9T9xYsTWOAP2BOdq1ZY4A/XsYC+1BjgD+nlkq+VmOAP2JPYPNVY4A/2101b1hjgD+/V41WVmOAP58ZvrBXY4A/2UudMlJjgD+sHiG6UGOAPzi7X1JMY4A/Wjaq5lVjgD+MZjyXUGOAPzB1POlMY4A/F7q2tUJjgD/eP4a/QWOAP//nPgFEY4A/HO39IjhjgD+OLf3MQmOAP7K73CFGY4A/Nqy7g0FjgD8qpMySRGOAPzqd+idJY4A/l6O/yExjgD9WDG5rT2OAPz0lgStOY4A/GA4uQFVjgD9gOETcW2OAP55kxONUY4A/XoQOV1pjgD+gseYZVWOAP9EzfLdNY4A/8TZjik9jgD+4RjrWSGOAP2oDg+lSY4A/n6F2v1FjgD9J2oKtVmOAP5cT1UZmY4A/rOiE7m5jgD/BLb+fb2OAP3rRYYFvY4A/NTMTC2xjgD+0McBqaGOAP455PXllY4A/0QTVbWtjgD8DEwsTZmOAP+NVGi5jY4A/hqw9LGtjgD9TJ88ycGOAP1thLbN0Y4A/bxynLHljgD+itNUYc2OAP1ZH5hxvY4A/5Mh1aXdjgD+4igQub2OAP9WYIpZwY4A/o8vv9GVjgD/0R+JLYmOAP7pSbT1hY4A/4VseRGRjgD/JOXWgX2OAP9ktot5hY4A/uY2eumBjgD9qgvkDW2OAP7azLqFgY4A/c0bsnWBjgD+viCUpZGOAP9jeUoFuY4A/tO4i23JjgD+zW77Kd2OAP/Bml1R4Y4A/jnvCzHNjgD89qpPfcGOAP5Tbb3BvY4A/Z9262WNjgD+QnNZsamOAP/LuwihxY4A/+cHtcHBjgD+rHWB+aWOAPw5dyzBsY4A/5mLjg2tjgD/HPcHZbGOAP7S6ZOlyY4A/4cDKIXpjgD80YX8wcWOAP1nqXdFwY4A/TrKdtHJjgD8LKcvYc2OAPyFEUudzY4A/1IlCc3JjgD8jdKzEcGOAPyDcoiNzY4A/R8Bv3G1jgD/rIba7cGOAP93qM7hkY4A/kEXLlmNjgD9sUhzgamOAP+D9RS9zY4A/MDYy73BjgD/ajVbibmOAP4CCRktoY4A/ThOwV2xjgD+e2oLfbGOAP31HghhwY4A/JZHn6G1jgD8ozOfgbWOAPxLKHzNsY4A/PsZIQ2RjgD9FAPIfYWOAP9Ad1uRoY4A/890YNmxjgD8bjQa1cGOAP0NHV/1wY4A/bMv7C3BjgD8l/hNsZ2OAP/N9F7JpY4A/XOB0/HBjgD/DQsWGaGOAP26U9Mx3Y4A/LBe/AW5jgD8lRUs0bWOAP8YHrJJqY4A/UD2rlW9jgD82bfribmOAP0xHSCJ0Y4A/CljMsGtjgD//cb+baWOAP68oV1xxY4A/5MGtBXNjgD9P0QAMaWOAP6H/GNVhY4A/NgPtoGhjgD9YW260a2OAPw3JcGhrY4A/hW4Lq2pjgD8RHf0FaWOAP6v94thjY4A/DhcPvmhjgD+9nsnOamOAP3NLICx1Y4A/iwbNK3BjgD+rSZqiamOAP4aVEFVmY4A//PkEfl9jgD8P6LkuZWOAPwukdg1eY4A/54IwtlxjgD/D+ps2W2OAP3/1q4JhY4A/lbXe72FjgD/nunm5X2OAPw0FCDBYY4A/G4Gw1WBjgD+/xIH6XGOAP1GX1x1UY4A/0ctB+1JjgD8aIirFVGOAP2vdNMtVY4A/dka8jVljgD/Lv5raU2OAP8PFbm1TY4A/rEFG9lxjgD9Q9qenaGOAP6+2Kw9tY4A/zHEirG1jgD//NxgEcWOAP94AKohuY4A/iDtt2mtjgD/Hj3E/ZmOAP//w791iY4A/vD9xwWNjgD+XDPFZZWOAP4NiC7BhY4A/MGzKUVtjgD/vtgJOV2OAP45eeJdUY4A/k/gwZVZjgD8Ucs5jVmOAP9BSU5JeY4A/AB6GoVdjgD+5kVH2U2OAPwNjP0FVY4A/jZVC1kpjgD9s2N5iQ2OAP6vFsqtGY4A/P95stUtjgD8MlCKsR2OAP2Tx/eBIY4A/kv/RF1FjgD87VpE+T2OAPz85YeVGY4A/DX+eHEJjgD+6Mq7wTmOAPzACVDROY4A/riojLFBjgD/UycqZU2OAP1TT7vlPY4A/s/xvoUljgD9vHvVESmOAP9hTv/FEY4A/27Ml1zpjgD9126WjPGOAP/S/F05DY4A/AdMQ3jtjgD+yc8n4NGOAP3rRtx49Y4A/ERXR/TVjgD/6ReOGPWOAP81MO10vY4A/pSaLMiljgD+eRgoFJmOAPxHRSvEhY4A/4raCpiBjgD9NJl2sGGOAP1cJHYkdY4A/ZrjJVhtjgD/L6icqGmOAPyYByuIgY4A/GNvOaBVjgD96LQ0AHGOAP3mTwmoiY4A/W7+/9iVjgD+fMWxHKWOAPy/O+7EfY4A/EfP7TxljgD+6HbVUH2OAP3TOAK8fY4A/cTsG1CJjgD9IeagvHGOAPzUEn4ocY4A/+ACkZyhjgD8M5m50M2OAP9mdNR88Y4A/vVikHTtjgD+bNaCTPWOAP7UDLj44Y4A/HxAg4TtjgD88S65sPmOAP3T7kR5IY4A/E78nMEdjgD9sJh7mP2OAP0kPdLk+Y4A/q3DlRjpjgD+MCdEXPWOAPx+2SnM/Y4A/mdnRWD5jgD9VfIy5OGOAP9knmpE1Y4A//4g/HjRjgD9WtZfTNGOAP++tttszY4A/0pEsQDFjgD8l9ytXOWOAP4WAub03Y4A/Y/SlUzljgD9HDgLaO2OAP0JbFAw+Y4A/9xJAUzpjgD+mH8dZOmOAP5NWaSs8Y4A/H1s8y0RjgD95QGvZR2OAP8af/4tJY4A/nZ+hHk9jgD+Hu7e9WmOAPx8WfEVdY4A/BwSv9WJjgD+fJdujYWOAP//pl/NhY4A/k0SPd1NjgD+EJ423R2OAP/6wIMRFY4A/jsV970xjgD+g+mVAU2OAPxZwKxBVY4A/klQDZFhjgD+SY8PjUWOAP5n9keNKY4A/6OiGI09jgD8+7GfyRmOAP972y6c2Y4A/+NIduD5jgD+eI6ZnLmOAPwcHQvguY4A/xt6jHDJjgD9vrbVTNWOAP9IssH02Y4A/e6/a5zxjgD85zY/8P2OAP8SE02lHY4A/dMPjvEtjgD8J1RAyRmOAPyODt39LY4A/hy9OkUpjgD9iZkX1P2OAP9r4HvA+Y4A/ETto9jRjgD+3H07fR2OAP43+7LFOY4A/OeZO6kxjgD+GhyOrS2OAP1ur1IdPY4A/MAmn301jgD95ukuyUmOAPwj1VNtMY4A/DNYvfEtjgD/4pBmVRmOAP9bQY21LY4A/ibjmo0tjgD+4peyfSmOAP6As+8BUY4A/mCEtxF9jgD+OzeSEYWOAP1bXsuxhY4A/OmrnImFjgD+4TKD1YmOAP8MiUKhmY4A/bu8G62ljgD8L48hgZ2OAPx/TU5BnY4A/JuguQmpjgD++WiHOamOAP0dlIkRpY4A/bwaoF2FjgD8IUfBBZ2OAP4T8AgloY4A/HnIK5GJjgD+KhXNLWGOAP5EYJihaY4A/+w8/pVFjgD9E7cHlU2OAP1lAGGdUY4A/tceqm1ljgD9aLvcOXWOAP1XRbp1lY4A/9Myfhl5jgD8jOXTzWmOAP2S1GOBiY4A/mlQIZmNjgD8u5Da1V2OAP4N5F8BcY4A/TQKm8mBjgD/xIF7bZGOAP1+pKihjY4A/ZqdyRWhjgD8AjqKIZ2OAP0bjefZmY4A/3H8g7m5jgD8gVWhLXmOAP7HgoRdYY4A/ovWl71tjgD8PNtaAWmOAP66HmRdYY4A/bkMmKVdjgD9JyN1dXWOAP+3tJsVYY4A/BujeSlJjgD9XSLSHWGOAP6yOFaJWY4A/nPZcw1BjgD+IK0LoTGOAPzeENQJTY4A/NNY1+0RjgD/iLWLORWOAP1VYPONFY4A/32UHbz5jgD8krTJaOmOAP84CYyA8Y4A/xbWArjpjgD/JPCK5OGOAPyB/UCY5Y4A/Rr+INS9jgD++FQtdKWOAP+SLOBglY4A/pF5o0ShjgD+bQkexJmOAPyKwRJknY4A//UR2kCdjgD9fMXf+HGOAP3wqnEoYY4A/C+skShxjgD+VripCJGOAP28/zEcgY4A/Eqo9mSRjgD/6sPLXKGOAP3tGg80pY4A/7TNDpyxjgD/XzwDYNmOAPxybKA45Y4A/RuRwKkZjgD9kLrDbQ2OAPwfjTFdKY4A/3acLh0tjgD/8jVSNQGOAP4qEsq9BY4A/JEofvTtjgD+MiQaPPmOAP5Z+C6s+Y4A/H+1TiDljgD/K6zRkNmOAP6lK4LEvY4A/W1BL9DJjgD+WOPMuOGOAP7JcTzI1Y4A//rvTOkBjgD+KZaekN2OAP+eNP301Y4A/XND1mC5jgD+jnoOZLGOAP7MzUo0tY4A/yHAqpihjgD9SH3NeM2OAP4aTzbkmY4A/CXYTziBjgD/8VuNlI2OAP9z2ssQoY4A/Kuollh5jgD8JnpFDHmOAPyMYh8QbY4A/qqiO9h9jgD9hwKqtHWOAP/VKNrsjY4A/ksSImRtjgD/kVRw2FmOAP7GewkgRY4A/NHdZfRJjgD9yYb5/E2OAPynq+nsMY4A/xx6/mBhjgD/SPRmRFmOAP7WmVP8ZY4A/nUnShhpjgD/pXzMmH2OAP/8R+ZYiY4A/cO+TVyVjgD+TSd7hL2OAP2I/Q3kxY4A/g7OybTJjgD9Zy2cIPWOAPyv6iHY5Y4A/j4jiWjNjgD8FOkEmN2OAP1qMNR46Y4A/6wF5LkJjgD+xHzIrO2OAP1pht9E9Y4A/XmykDTVjgD89oj2GLmOAP/krT1AxY4A/DwGNGC5jgD+49PrhLGOAP0F/O9EzY4A/ZUV5PjRjgD8M8IX7L2OAPzJ7AIgtY4A/D8d+riljgD+dTAj8ImOAP+DbycUqY4A/V1/EqitjgD+qSQybK2OAP2GqlYUpY4A/kJxSWSZjgD/0Q5jBKGOAP32ttHIpY4A/gLEe6yRjgD8ZJC5bH2OAP2xog9gkY4A/s+5f8yVjgD8CPa/+JmOAP1DPe7opY4A/1i2rIShjgD+rb0ViMGOAPyWecAg0Y4A/f98MfzNjgD/NvM9vOGOAP/FEv/YvY4A/whQVjyBjgD/YLx7XImOAP686ZgMhY4A/wJQ8TyhjgD9w0KKNLmOAP3QUHSgpY4A/cY4gqRljgD8rw4q0FmOAP2MDfxwRY4A/oMsB8BNjgD8ZMv9UFWOAP5NmBpUZY4A/acSaSh9jgD/lPp+pJWOAPxk+RrknY4A/1AN17iZjgD85iMOhKmOAP46C0ssiY4A/rKcJ8yFjgD+rXHwGHGOAPx2icMoTY4A/tbkvaBpjgD+ybYSZFWOAP9R+UtUTY4A/87F87R5jgD+pm/sUHWOAP8MlChYgY4A/oB2MgiJjgD9UcDW/J2OAP8VSkIosY4A/ccBZHzBjgD840dyuMWOAP3v2ewA2Y4A/5Xwe5jxjgD90hIjyS2OAPw/O6H5NY4A/MKis4E9jgD/Rr2/wRWOAPxrM4ZBPY4A/av70CUtjgD/mpRsBRWOAP82Odf09Y4A/ZJ3plTxjgD8NM8bjPWOAP/2jrxs5Y4A/F9qZGjJjgD9qnLPLMGOAP8nkTPcwY4A/0Vse9i1jgD/WJGV6LmOAP+moK1MzY4A/qQSGvS1jgD91P1lnKWOAP6RNerIkY4A/tCBjUiRjgD8v2fUXJ2OAPwywGsMjY4A/qQXMeSVjgD+b0IOxK2OAPwvHlt4oY4A/eWaTjy1jgD+0PmHRKWOAP3iT3agfY4A/6Pmo5RhjgD+wDtPHGmOAPxfZUFsaY4A/hls1zBdjgD8l9fsQGWOAPwKDuVQXY4A/F9keOxVjgD9y/qc2FWOAPzQdYbsVY4A/KN0W+xdjgD8/8hBqHmOAP77omh4ZY4A/jfwDfBljgD+z8Z8xGGOAP55sQKobY4A/7vUn+RFjgD9mf+rUC2OAP+SUzWwLY4A/nR1b2hFjgD8Bqkq7FmOAPwnaV8sNY4A/Hl12GAtjgD+CRA79DGOAP99FNbMBY4A/FkMe//higD8tzzv58WKAP89YjQvzYoA/hPEsrvFigD9IQ0kH/WKAPxfQAsP3YoA/TVwlgetigD+sLPWL62KAP8XderbrYoA/oCy+vuVigD8Rs0cs5GKAPy68En/jYoA/bv2Cat1igD8ddjof1GKAP0Nt79rbYoA/wH6NDOBigD9ZfpNb5mKAP6lLelbxYoA/QSdjTvJigD9rKQNy82KAPwyaM+bwYoA/oaOf8PRigD9tp6tO7mKAP/LG8OboYoA/ebUTJehigD8lvrdY82KAPwPP10H3YoA/UQ+UMfpigD9vRq/T9WKAP8cg/+rxYoA/VhP+cfVigD+9GMxN8mKAP2eMorT6YoA/UYU3fv9igD+GFKMgB2OAP2kHFpIEY4A/NbCvVwBjgD+qTc8m/GKAP9RUBGAAY4A/uiPPUAVjgD/UX+RkBWOAP4zYfqYQY4A/j6/mshBjgD80AvHFEWOAP0ne5oAXY4A/utI6NCBjgD8nuLI6H2OAP8hrUtoiY4A/wzrLWxxjgD8yuV9mGmOAP88aDSMgY4A/L1i8mCxjgD8+D33GMGOAP8Rl66E5Y4A/wJpvkzRjgD+cDonpM2OAP8aXJEEtY4A/YID1ijRjgD8pYZc1P2OAPy+G/Vc/Y4A/j11kwU1jgD+xJDgyVWOAPxVHf8ZcY4A/zHl0rWBjgD+71LrMbGOAP7FCzmpjY4A/kc9rIWVjgD98zfXyZGOAP1LZjmBtY4A/eFCUhmdjgD95D9H6ZGOAPxsMGZRqY4A/OvkbM25jgD/PKNS/cGOAPx66LD9xY4A/usmlBHRjgD9jg+Wqf2OAP7pv58l+Y4A/IVQh7oNjgD+XPzIAkWOAP+O1ezSNY4A/HsdUW5NjgD8U++KaomOAPzNtTpSjY4A/ioVJG6VjgD96qmeLoGOAPy05sLCjY4A/0pKTNqhjgD8hNFagr2OAP8qBY5W0Y4A/+TY1t7pjgD8APCfBvGOAP6Pd5ejEY4A/9R6FqsJjgD+jZc7LyGOAP7C6i9zGY4A/Kw7C281jgD8BrJg5zWOAP0qhtVTLY4A/2R/Yhs5jgD9TEoA82GOAP9ZilCDYY4A/yesA+dpjgD9sn0gO4mOAP3qI0tjgY4A/NDyZqOFjgD+dY/Y02mOAP1EpNo3QY4A/FDv4IM5jgD8sXu7lymOAP6cOpi7NY4A/dwF2vL9jgD9l9gzPxWOAP1QHYFu9Y4A/prvq5sBjgD9V0gmpt2OAP3mvoP6xY4A/v1ftWqtjgD9vCptZrGOAP21vJUmgY4A/JNjGG6NjgD9uNlBPnGOAP1dp59qhY4A/oZrHhqZjgD82SCMfqGOAP2jocKOqY4A/jNEtAq5jgD8iI9/Ep2OAP0gWEeSfY4A/Kd3Y6p5jgD/t+24cnWOAP8kVO8KQY4A/ME4kH4djgD/hW2G1hmOAP1pBaW+KY4A/jV4sgptjgD+yB58woWOAP8pEeE6qY4A/l60M1q1jgD+PPGtUtGOAP4AFOcOvY4A/n75e47RjgD/sBvdNsGOAP1d+BAmqY4A/+pnoU7JjgD/tfy61s2OAPwYHPiKyY4A/Z7V5U65jgD8Y5IXMp2OAP2QF2JOjY4A/YSW4QZ1jgD/5HckHlGOAP9j8ADWJY4A/l09BmoVjgD+cKsapiGOAP3lN8yiJY4A/+6lT+4ljgD8Qx9sHjWOAP0Qa+qqPY4A/lHj2ioljgD956TrvhWOAP1sSQ5t/Y4A/8zW/M4RjgD/457fUgmOAP7JoiVmIY4A/knrTQYdjgD9NjCYlkmOAP7zIKTKSY4A/KCYvQpJjgD+MJCAiiWOAPyus9WuCY4A/ie+2VIVjgD8PPbQ6hmOAP4k7gdh8Y4A/339lFoZjgD8igycvjWOAPzYSO1mPY4A/yWoB1pJjgD+cgjzBhGOAP6EAwS+MY4A/GKYsppJjgD+O5xPhkmOAP+bAUI+LY4A/ThwJ5o9jgD9UYRcMlWOAP/3AK5iZY4A/k2qtnZRjgD+cfKuckWOAP2gbaOGLY4A/x+3aHIljgD/Yy/5OkGOAPxbSPv6GY4A/LrHxmYRjgD+FC0GGfWOAP724mfKAY4A/Mz/j43NjgD+JcVCDf2OAP8t7z9d9Y4A/QvFp9HhjgD+C30wBeWOAP8RyxNF9Y4A/i1nIRIFjgD+LX43weGOAP9Ukk196Y4A/I7a9JnhjgD8AojN5eWOAP+Zawb13Y4A/tXXmiXljgD8hYGnLfWOAP/8EN4l9Y4A/9PSgMINjgD+c7sGwgmOAP2wcmVyFY4A/4Mf4GJVjgD/UkS4Zk2OAP5bUpDKdY4A/lpd2oZ5jgD9dBkpgoWOAP+8TDVedY4A/H8Y7dahjgD8sousXp2OAP3Smf3qiY4A/Zxg34KFjgD/Qy6lImmOAP1m4xdCUY4A/f+XDgZZjgD9Cc8mMmGOAP/ASMOCcY4A/35uIFZdjgD8y0RA1nGOAP/tJZzqWY4A/tw13145jgD+kJrFVjmOAP9OLvYyPY4A/Rf4Un4ljgD8IobCQkGOAP1yMTzyMY4A/q9pP/I1jgD/9ACcKlWOAP1A060afY4A/a8dsDKhjgD/ly0GMpWOAP8erYymoY4A/xLijb7JjgD8RUcPet2OAP70xBt26Y4A/o0/cq7ljgD+lnXRtuWOAP9YbLfu8Y4A/YcqQxL9jgD/n828TwWOAP+M7rHe7Y4A/37o1lbRjgD+HbGUCsWOAP6a8izCsY4A/3/EfbqdjgD9nrcD2pmOAPy1wmxqtY4A/7XbzWLFjgD/6+u6dr2OAP1XkoEChY4A/iMI7RaRjgD/9kQvIp2OAP8z6thOiY4A/zeRTOqdjgD8+2iGGoGOAP7dFUjGgY4A/ctzJlaRjgD+Lp/h7mmOAPycACtOcY4A/Jkfmg6NjgD8146UdqWOAP/4IAb6lY4A/fzqaIqxjgD8z+ejYoWOAPwIAOV2qY4A/UVnoPKpjgD+pvTEhq2OAP+uskrKmY4A/EBqiepZjgD/x9F/IjmOAP7JMNBuUY4A/m045YJljgD+YHNkOlGOAP40rZA2PY4A/lmNQpJFjgD8AlrQYlWOAP0KCtOqJY4A/1yBzwJBjgD9aB9LzjWOAP9jobEyBY4A/GoBcyn5jgD+52iFjhGOAP0pCVlmEY4A/hqQOxYVjgD8Lkw1Ki2OAP0jCpgWFY4A/qpGdq4tjgD+FYqAPi2OAP8JmUAGGY4A/5P8smopjgD9nZU4+h2OAPyo9MI2EY4A/MdK0w4JjgD+i2VIdfWOAP7VkUoh4Y4A/1t8aZXljgD/4fL26cmOAP9vu8YFzY4A/w6a/MnhjgD+VWCjce2OAP4lsrb90Y4A/WSeLinljgD8zXhFigmOAP+ipLwmGY4A/kiEEBYVjgD+RGxvcgWOAP7pzO0GAY4A/P1SYTH1jgD/jY2KYf2OAPwr07d5+Y4A/+OpBaXxjgD8MNPoQfmOAP7AyHt+IY4A/paZgHHdjgD8o0v/udmOAP2u4A8t2Y4A/XYybr3xjgD/MpbbNeGOAP4IoNxZ8Y4A/cKz/wHNjgD+0CrsagWOAP7uPBvJ3Y4A/Gkguvn9jgD/zX85+fWOAP59orauEY4A/+PuIgoVjgD+ci9ZHimOAP+f9dNeYY4A/LxbIzpJjgD+LMyU/lmOAP42cBBaUY4A/TssZjpVjgD+EzxodnWOAP/7D+GWfY4A/b7X+A5tjgD/k46njkWOAPzaSxt2ZY4A/fXvURpZjgD/d7iapnmOAP+xWcaCfY4A/IAuZUKZjgD99djwPomOAP5PY+EWgY4A/qeZtj51jgD8Mj/6wn2OAPze73V6aY4A/3yh+AY1jgD+GU4SNjmOAP5IvdoyNY4A/zuUK/YljgD9OtjZ7gmOAPwYhsWGBY4A/VIbySH9jgD/uZdoegWOAPxyLjIJ3Y4A/fWIchnVjgD/CtLMSaWOAP755jrdnY4A/M8jwJGpjgD9XbkMxYGOAP1auCUlcY4A/SaFIzGBjgD+2CoDOWmOAP7JmEXpcY4A/LpL76VhjgD/JRG3RW2OAP3+EeI5iY4A/Vp/kaWBjgD8Ju8xEbGOAPw3XTQh0Y4A/rZf2LXhjgD9ntMCrdGOAP3CKMB14Y4A/jD71wX1jgD89uRVdfGOAP9GQgMJ9Y4A/D7clj4NjgD/64xIYgGOAP25/wJSDY4A/r6uvloFjgD+VdvOCgWOAP1RsOqR6Y4A/Uvr0d4BjgD8FwEW+fWOAP5cpJTR+Y4A/oFIOqXtjgD8di3jYeGOAPztchDl9Y4A/d++fNIVjgD+EGhR6iGOAP6gv36x/Y4A/6mYHfoxjgD9a7p8GiWOAP5u/VGWOY4A/KOZ16ZBjgD+orvdekWOAP/SDrLCeY4A/anGCYKJjgD/GveKMrGOAPz055Ku3Y4A/IuWLKLVjgD+a3MGKuWOAP3Y+8r25Y4A/RrrhMrZjgD9W4tFLtGOAPxPEY9K1Y4A/jISKSLVjgD+CIKXesmOAP4aefcWsY4A/iqPlPaljgD+IncjlpmOAP36X3hujY4A/ZW3526JjgD8H251wpWOAPxvxXsmjY4A/BqRNOKNjgD/DZXD4pWOAP3xlfGOcY4A/Y/BvM5FjgD/6ZjiyiGOAP6raQQWBY4A/2ef6LnljgD/QfoYddGOAP+f9n+NnY4A/ph+XiG1jgD9O428MamOAP3rMoQ9rY4A/KvNh8GVjgD84Nz/XbWOAP11EEBNyY4A/vhRNs21jgD9i0WhDc2OAPy3DIVFqY4A/weV9UGZjgD8DDjFqamOAP8WbS4ZsY4A/L9MOHWhjgD8CW4KvZGOAPyMcc9FjY4A//UuWrGhjgD8cywt2YmOAP9ycIFdeY4A/eetZNVdjgD+twExaUWOAP/1ROSxSY4A/i6zilFJjgD/YX+27U2OAP20ci7ZZY4A/JpopdGBjgD9g662eVGOAPw628o9WY4A/+xzKZlljgD833984WGOAP9JrvuBbY4A/FNLtxVtjgD/+l7AIYmOAP27kPqNSY4A/yKr6bVNjgD/seBFrUmOAP+vW019RY4A/DqSjZkpjgD/4dSZrS2OAP6XKDUNIY4A/WKA7xEhjgD+VBWlqWGOAP5dEI3JYY4A/VqqHwWBjgD+J3m1tZmOAP36UwvFaY4A/3JCTD11jgD9FamBNWWOAP0EF9Z5VY4A/kdWd8EljgD+CYTjjRWOAPz0TuMNNY4A/Y9w91U9jgD+zq9O8T2OAP1kdULJTY4A/A6G6xFxjgD8yOcZqVmOAPxhx1nJfY4A/ANJcn19jgD+vSAxMU2OAPyhnpZJKY4A/1yDy9VdjgD9ZPSIOYmOAP39idilhY4A/IIoYjmJjgD90fHzkamOAP435yaJoY4A/1u+Wb2pjgD/5WMd8bmOAPwb5igZnY4A/DyF392ZjgD93wdnxaWOAPxGkdnloY4A/ROjkm1pjgD8CVk1lZGOAP5KxWgtrY4A/616VzmljgD80DSJbe2OAPxROzyJ2Y4A/cpt7YnZjgD/zpxyydGOAP2I8LZN6Y4A/3dt1zYVjgD9O6sU1gWOAPxpzb7p8Y4A/hspcaINjgD9xzYrAgGOAP+whnMB+Y4A/PA5tYoNjgD/xcZJmf2OAP5zLtf56Y4A/0nhTY4hjgD+3Ws+ggmOAP+D2v+iCY4A/G3dRtIljgD9lNnDcjmOAP7BLWd2TY4A/g4toAYpjgD9HsZLPhGOAP8Y261WLY4A/NDM+YoZjgD9u32lvgGOAP4Lhv8GDY4A/QnaIOoljgD9qCsPci2OAP4HZFU+MY4A/IhAh14tjgD8haO0wjWOAP8Iqj7GaY4A/5BPWXZxjgD+XTxX9nWOAP0Xy+CuTY4A/daeq3YljgD+zzOcghGOAPy+8S9V8Y4A/01O1G3RjgD9RaZ9sdWOAPxBuiCaBY4A/G+EG9IJjgD/S0uZLgGOAP24UEqh7Y4A/7/IVzX5jgD/HcIjBf2OAPyMBtMp+Y4A/WyqZ24tjgD8A8R6whWOAPx5lmIWEY4A/Xh3nqYJjgD942z+FgWOAP4xwgpuFY4A/9VTmln9jgD+Oh/vheWOAP9qwvlV5Y4A/I73cDX9jgD/H9gRWgGOAP+5+NUGBY4A/jivMKXdjgD+TOvwyfWOAP7HNjFB9Y4A/ya3QRXFjgD8NrEGJdWOAP+JkwGx4Y4A/AzgDxn9jgD84nXLGe2OAPxN9wSR8Y4A/OjglZXljgD8aE2SbeGOAP0D4TIB9Y4A/tLK9rH5jgD9/CpWfcmOAP8FFfDF6Y4A/7FlIpHVjgD/pExjIbWOAP6xcBNVoY4A/u0GTp2ZjgD8A14ocZ2OAP5L17wZgY4A/f0S6vltjgD9rKPmkU2OAP2unkRBPY4A/L5YxGk9jgD89G5xNQ2OAP789dBNBY4A/5QzUt0VjgD+9uQjdQ2OAPz3Lgsg+Y4A/hAuQnDZjgD91hTE2O2OAP7g0rZk5Y4A/bYT0ZzpjgD9dIvPtOGOAP0eWTuY7Y4A/aVFjXzZjgD/pXUaiMmOAP8WX9YQzY4A/zTzGFDFjgD+L8KV7MGOAPxho9Jg1Y4A/fG6NxDZjgD+/u9v7KGOAP+GnFaMwY4A/wvlbUCtjgD9WP8DLK2OAPyH7eT0sY4A/+Df2hzBjgD+zp5vmNGOAPwmlg1Y3Y4A/AjM/9D1jgD9Pr/CjO2OAP5WKmA43Y4A/TbaNxTJjgD8K4NmnL2OAP1k0xsA1Y4A/GbH+6TFjgD8uHUhnN2OAP8fJhMc2Y4A/hyGK+TdjgD81td0ORmOAPz0Cz9JIY4A/hE6ORkljgD8JsBZQUWOAP7mv/FdJY4A/PZHr1DVjgD+SaTf+LWOAP77hXp8sY4A/qXMsuSxjgD/QoFXpLGOAP+RXnTMsY4A/TyLo/StjgD87hBpJJGOAP2qzLXwiY4A/PU2p/RljgD8/rdU2H2OAP4MsFxwmY4A/Uo6Dci1jgD+QGXeUM2OAP9HBTVIzY4A/DE/8Ay5jgD/rcFJMLGOAP/UHRHEmY4A/yNu3ACtjgD+nZfzmM2OAP5QGUZk0Y4A/bw8Yli9jgD+C8QMUMmOAP5GJPygyY4A/ymZEmjBjgD+ihd6DJWOAP7wctpAZY4A/zzGN3x9jgD96gCvPIGOAPzJsxzUoY4A/DyYS9CtjgD9jFNDeNGOAP74D5OUvY4A/LPw0AipjgD9VkTmWKWOAP4ZLSP4lY4A/wG2HRiRjgD9OFfTbLmOAPwX3Z1IyY4A//viePTFjgD8fLa+3MmOAP1DA1nUwY4A/9hySSzBjgD/9aMvEMmOAP//eyQIrY4A/b6P12TFjgD+bHlorLmOAP8yOSLAwY4A/p0J0JzZjgD/mHy8/M2OAP0T0FT01Y4A/znOu6zpjgD94ieDRPGOAP/Bs6uw2Y4A/F/xT9zljgD861e1aOWOAP1UlO9U4Y4A/mFsIyDdjgD8WrCbpQGOAPwOmxnA+Y4A/eAp1AkdjgD8nD/NoQWOAP92cZT5GY4A/pQNIDk5jgD9xipp3T2OAP+Tf/SBOY4A/Lf9aeUpjgD9F+1L7PmOAPwP8xdo4Y4A/Z+6sKDhjgD9QslddO2OAP9Ap2RFAY4A/u10zbkpjgD+2A0dyO2OAP60vRmo0Y4A/+mGFGTljgD+WD3ISPmOAPx2xoxxHY4A/2WylC0VjgD/NxNT1R2OAP+D+NDBMY4A/jSfczEljgD+CYxouRmOAP+bj9QZMY4A/WCr5qVFjgD+mSY8BSWOAP9+rs4JCY4A/gVaVnTtjgD+hQwAZRGOAP/TUkWg0Y4A/aZOCaDFjgD+tzDgpMGOAPzuaCh4uY4A/tlHxQTRjgD8N/0NdMGOAP7vKiQI3Y4A/127z7jhjgD+JcKfmOWOAP6cLD1guY4A/AR7kgShjgD/X/kcFLGOAP9B55IgoY4A/7EtKCy1jgD//dsQlMWOAP3eGXSErY4A/OIuSHzhjgD9E/rohNWOAPziySDE5Y4A/ALkm4DpjgD9YnnDXP2OAP1koUoAvY4A/D21y6yZjgD8tKvUyKmOAPyEF66MlY4A/vURKISFjgD+G8cu9H2OAP/M4dK8bY4A/NlYm8RljgD/TyWtgF2OAP6sz4GcbY4A/tsuowBxjgD/0RZnRGWOAP7kIxV8ZY4A/Ex+I8BhjgD9twQi0HGOAPyhgkZchY4A/K58hJSBjgD8hYAwcJWOAP1+nBAMnY4A/w1gDBypjgD9r7IPDJmOAP3+4l2MgY4A/tEfJOxpjgD/zO89ZGGOAP925DlgWY4A/AZKcgRNjgD/Z79w4CmOAP31WvYEOY4A/LNri3Q9jgD9a/TyHFWOAP1nabgITY4A/I35/2RBjgD/TCo6sFmOAP9QeGbAcY4A/UN5pEhxjgD9u0uQeFmOAP/ZzHd4TY4A/i1aktBNjgD8zevcqDWOAP0fSuEoNY4A/V25nJAxjgD8I3RAZCWOAP65Ytc4RY4A/sx471xJjgD/sXsWpC2OAPyQafzEDY4A/LFN2oQFjgD9MmvIdBWOAP7mpMHECY4A/ENN3yAhjgD9X8MRkAWOAP6n/E7wGY4A/CHSX+/1igD/mYOx7/WKAPwYBFbEDY4A/teZ6rQtjgD//eqE+BmOAP+bl+RUBY4A/MAppmgdjgD+h7nzDC2OAP7TGFPQJY4A/st0xbgdjgD/Pv/I4BGOAP96ZBH8DY4A/OBEe0gdjgD9ATXBxBGOAP10bhu4CY4A/Uw/xnAljgD+OHOQJAmOAP9jiM+gCY4A/PPA5CgRjgD+2Ij2a+mKAP9Qd3J74YoA/Z/LXS/pigD8qZEMD82KAP/VZYnv2YoA/t2t95vZigD+PETxN82KAP0Sl9Q/xYoA/1spPBPNigD+TXVsSAGOAP191ee/6YoA/2zNe7PpigD8JBcd9/mKAP+ZGrRf8YoA/aCycePpigD99JZGt+2KAP89YWab8YoA//osnUwBjgD/bwPum82KAPycQfiv6YoA/W6Rt4f5igD/lnvz0/WKAP56+PrgCY4A/P4M9/wNjgD/z9zZp/WKAP9b/dFkAY4A/F+pf6ANjgD/2N8e1AmOAP7xhAR0JY4A/qHmmowhjgD/ybsAiDGOAP2sfCZUQY4A/WbPk+wxjgD8KKyajCmOAP2yw/CEaY4A/dJJTRRZjgD8aYohtFGOAP5czluAbY4A/GzCGDBhjgD8/gvK/GWOAPz6/yl0gY4A/0ewjeSJjgD8Ldtf4G2OAP9fQQ70OY4A/73xdQwpjgD+Cyxx2B2OAP8IIUfICY4A/+vwX4gFjgD9gLbdwCGOAPw4ef0QBY4A/weMxkvtigD/BwUQg/mKAPzFDRo3/YoA/p0b60/NigD919MRL/mKAPwqInboBY4A/WIvkD/ligD9LxSCo+mKAP5s3B875YoA/4ZC5XQJjgD9+H5gJBmOAP98C26T6YoA/Enf7C/pigD8obb1e9GKAP5I/e5n4YoA/LWKe9PNigD+fKCwo7WKAPz4nDpnaYoA/hNXBTd1igD+9QQh72GKAPzLkEt3UYoA/M+s4O9ZigD9OwGr/02KAP7vDz9PbYoA/aDsuHuJigD87OWfa3GKAP/ByWZjVYoA/HVAGmcRigD8kxd70wWKAP7Xb34TDYoA/OsicKsJigD+7h4OyvGKAP/EP+mW+YoA//biweb1igD+WoBL1xGKAPzQivkvBYoA/BDjbAcFigD+fKB+GwGKAPzjptOPHYoA/W/6QIdFigD8xvbe50GKAP/bSUxTUYoA/DWoDQNZigD/j/V1+2WKAP0QCGdbRYoA/1N3r7dJigD8GLkqm3GKAP/cago7YYoA/PcN/RNNigD8WhlUn1WKAP0EKmlDNYoA/0rpL/NRigD8GXL1Mx2KAPyicXLrRYoA/x4Op0c1igD9jQAcPy2KAP9RXglLQYoA/uQGQHtBigD/2F8OO02KAP07RCWvJYoA/vPfqvc1igD/tXrLlxWKAP6BS7zO6YoA/jOT4orRigD/+YC7gsmKAPwOq94KxYoA/hPw1BKpigD+BsmKZsGKAP4TemUWyYoA/vU/ku7pigD+E9YKRs2KAPyiZOGOtYoA/Otzb965igD9z5ISYq2KAP8EYLJesYoA/jGd2BbhigD+qipGhuGKAP2obEXC1YoA/dUeR8LVigD84VSu1uGKAP0/f+R60YoA/SgK0erVigD9fr+F9tGKAP5GUDce2YoA/MHYqX7RigD8Vs0wBtWKAP/+UefGyYoA/BYuLv6xigD/cqLXFr2KAP3iU/EytYoA/J/PG/7FigD+PlwU2qWKAP4nKwUilYoA/nBQra6JigD/gsEa7nGKAPwZsYDKhYoA/d1oZ+ppigD/zP7mtnWKAP/i8JM6kYoA/l7K2Fq5igD+qqmzRp2KAP9xHuAuuYoA/Dx+NfahigD9l+vsjoWKAP6l2neqnYoA/2/wcgrVigD9OaAJjo2KAP0CzivejYoA/QPJinJpigD+6Ljh6mmKAP+bys4eaYoA/FO/r6ptigD8jWTvHlmKAPwcuTIWZYoA/xFqv16JigD8DhP1toGKAP2wyIESaYoA/CKEMUaFigD8bSrOIoGKAP6JLovSaYoA/LsbCM6VigD/FrxMGqGKAP2iWKXekYoA/YEuCz6VigD9RJVzZoGKAP4vzIKidYoA/5xEkKaNigD+1mPoVn2KAP2/0qj6gYoA/8ep4R6digD/g8760qmKAP5+m2YipYoA/3j/0AqFigD+bB6e5j2KAP/lD5qeHYoA/5rEL4IligD/1uLKNimKAPxFt6n2SYoA/DXOkVY9igD/2LSryiWKAP11lkLeKYoA/iWsBroVigD8BL0BKh2KAPyw1/MqHYoA/VdIc/IhigD/kKYvQimKAPxH3cyCFYoA/kMKZAoNigD9+B8xnfWKAP744lut7YoA/WWWx1n5igD/INOeSe2KAP3sjbOF3YoA/FG6hxXpigD9dv2mcfWKAP0e78rmPYoA/rMWOf5xigD9klNxllGKAP7oovYyOYoA/e7znt5ligD/QAPIum2KAP4WWlCWbYoA/kTgcwpligD/9iTnnmWKAPyYcBP6gYoA/kCz93JxigD9DPWEfk2KAP0x5ch2eYoA/aCMFkpxigD8MwNtzmGKAP9n68Q2WYoA/41eyRZdigD+fy64JomKAP/4z28mfYoA/4gz3jZ9igD/5V0Xzl2KAP/2fwsiSYoA/+s7WU5ligD/4R4k4k2KAPyKm/XyNYoA/N8bwBJFigD84gVKoj2KAP4xCgp+YYoA/TFNDsIxigD+BU9AehGKAP0ArTfONYoA/f2ktG5BigD9Rwzy0h2KAP0+2fG5/YoA/5hWZ53tigD9yE7kwfmKAP+b3tJh7YoA/cmVDHHxigD+6N3JffWKAPz8f6BGKYoA/u4nlR4ligD88WuM4kWKAPybZHu2YYoA/1YzkbZVigD+kETYojWKAP6qm3T+GYoA/4W/QfodigD+9qoFyjGKAP2ywH02CYoA/FuQfW4ligD9w9wcYimKAP6iFiqeMYoA/zl0JQIxigD9hoJz5jmKAP9zDMWuQYoA/kQXw0IxigD/m/hD3imKAP1DENrSPYoA/jen8No5igD/S13iijGKAP53rx9CFYoA/e0lxuIdigD/Cmad8g2KAP1P7xXeLYoA/7OGxBoxigD9u0qV6hWKAPw8KGS+MYoA/CcWGL45igD8loo4Vh2KAP7nPtH6RYoA/hZBiqZBigD+n/Ms4i2KAP05WpZGIYoA/LKZlrIpigD95bVYDj2KAPzwZxKORYoA/P0ax+YZigD8QS/PngmKAP7/pUG6EYoA/22qDQ3digD+wMIdNeWKAPwe5UMxzYoA/QxNTv2ligD/ZOFPgYWKAP/hEXK9kYoA/kileWmxigD9BJbiIaWKAP6Db2WprYoA/5OlOaWpigD9Fl11Ea2KAP0oPWhVtYoA/QTi8LnhigD8aWar+eGKAPx9SSwVuYoA/ORd3iXpigD+yhmQXdmKAPw3Ht8l5YoA/EMcwWHpigD+R376ZeWKAP8M5fWlnYoA/A0jKG11igD/0aMJAXWKAP0FN8L1dYoA/pGd5vWFigD+8XoCmbWKAPw7RerBxYoA/4YCeD21igD+3a3q3bWKAPxInHG9qYoA/dVFRQGhigD//iqaWaWKAPzyfwdhfYoA/O01IbV9igD/xjubeVGKAP7unlytdYoA/d7/ti11igD/zm2PCWWKAP9vx7ulhYoA/ubSihWNigD+9l18wYmKAP3qxqStpYoA/G/CzQGJigD83/HPQY2KAP52gSi1lYoA/D8dZnWVigD+RvK/cbWKAP5rQvVBlYoA/c60w82ZigD+b+z27bGKAP4TP1D16YoA/JRxltHxigD/xobYUg2KAP6s73ZOAYoA/vH7tu35igD+KJjWnemKAPzCymWF7YoA/ke+XenxigD8KMz4JemKAP+i5UCl0YoA/EeOkFXRigD+YRCJpemKAP9JkOw56YoA/7wEApG5igD/4ctclbGKAP4uEQ29mYoA/ninmHl5igD/1QgmLWGKAP2NEUptdYoA/Tbihh1ZigD9QbaxxTmKAP9BoGVxYYoA/IkWru1VigD8PhrJDVGKAP9GH+VRXYoA/CKFGX19igD/pn+qCWmKAP6ieolFZYoA/Ov6VGVpigD9WogGzU2KAPzuj9MhMYoA/6T2EUUdigD//LMv+PWKAPymH7btBYoA/n3VcFkFigD/1jZ4aSGKAP1QMLFxTYoA/h4G99VNigD97x0EyUmKAP2t+hnhTYoA/NPcJ41ligD9UktGHWGKAP6GJcspYYoA/jdNRl1higD/P3uA8V2KAP5iI5A9dYoA/8bApMVhigD9paah9U2KAPzyd5tFSYoA/q/G9ulBigD/FzNbRXWKAP6BrQMVbYoA/3xSaZldigD/36vxYVWKAP86EfiNSYoA/yHG3Fk9igD8czO63WWKAP1Y2D+1SYoA/vV/q21JigD8z/luOWGKAP5QnggFZYoA/GdQhKVBigD8Y0R+gT2KAP0Bn6jFEYoA/v7CfYj1igD9kK7yiNGKAP2uZ6+01YoA/ZC+iXDpigD+knPKOQGKAP3Wik4k4YoA/GqWq9DJigD/6J5DRMGKAP4X314wxYoA/IOHzETZigD9SQPP8NmKAP0YRzJo5YoA/klt+BTxigD8KUAvDPmKAP6+u4sY/YoA/ElFJazVigD/soZEeMGKAP1wrYM01YoA/OSriczNigD/k+/tKMGKAP38g4u4rYoA/uP5UsSxigD+c/IogKWKAP/bL9bweYoA/w7WD/xVigD9Cl8WpCWKAP2MENakNYoA/xz8NIA5igD8uNJAaFWKAP0z4NdgRYoA/AI2PzA1igD+iprDLFWKAPx5WP1MYYoA/6Ce3HBligD/Bu3LUGmKAPzj+nwUhYoA/aTmVCSdigD82vPAFL2KAP2ltxCI4YoA/+v4FujligD+PaCk7N2KAP5I5W9Q9YoA/u5ViN0NigD9tlizyQ2KAP279k49NYoA/yt0MqU5igD++EUWZUmKAP8iDw6VPYoA/us3qtVZigD8FYBplWmKAPwL5/w5YYoA/0BinKldigD9UAWZCU2KAP+1But5FYoA/AhFKqEJigD/9Hq+ES2KAPxoUXdVBYoA/MaDHGUdigD8jYs37Q2KAPyW0pso+YoA/R4sofT1igD+xxRgaRWKAP8phOCtBYoA/KAD2kkligD/cgAX5TGKAP1I73NxGYoA/LFa1+0digD/zufInTGKAP3fXQpdRYoA/cnGaSVxigD+fPbvqYGKAP6MMBMpZYoA/89C361digD8rK28KUGKAP+gcOahIYoA/K8OivUdigD+lNiCySWKAP4YnzkVKYoA/i86IjkZigD8QU3NzQWKAP1sOgxZAYoA/jPqWFz1igD8rg5RrPWKAP+8EBl84YoA/2qYabzVigD/Kdjd7PGKAP5q07SU2YoA/p1OBdDligD9ZEAvSO2KAP5UD5H06YoA/t8LqZT9igD/pxKJZR2KAP03T4BZOYoA/vynEHU1igD9/q7gkTmKAP/NbqrRRYoA/FbOGHVNigD8FWUVYTGKAP/J2SiBPYoA/RyMsAUxigD8slWzST2KAPyt/6UpOYoA/QYZHLlFigD8GTexQUmKAP62TI5tTYoA/skGvIUxigD93Om1BSGKAP/F0aJlFYoA/KUcm0kligD+RnlEiR2KAPxXSnChGYoA/5hjU6U5igD+8fHVKTmKAP1wx99hRYoA/fKy6z1digD9EdqANU2KAP78iJ8VJYoA/vfLn0FVigD8iRkP3W2KAP19aDtdVYoA/sPGCaFpigD8XpLplV2KAP3qzVUBfYoA/v9NxQmJigD9BmX8eWmKAPyjfq4tQYoA/UbGRWFJigD/n0duMWmKAP3bz5edbYoA/UFhiRldigD9K3BOIVGKAPzrdJGZOYoA/N5T01UxigD9B/tfjRGKAP35ve6o9YoA/KKIVkktigD+DuiBsQmKAP1F9xA8+YoA/2SXEs0BigD+5o6yLRWKAPxhKz19HYoA/L4kackdigD+VRPGkRWKAP1ciyj5BYoA/DGPdZT9igD+alvPFSWKAP2HZSr9IYoA/awMyD1higD8QFOEQT2KAP4PgQn5LYoA/VZ+tqEhigD/M5RhhT2KAP/tFNSpHYoA/Pfjd2T5igD/7qXC3N2KAP59a0cYrYoA/8nxXRixigD+qKeyjMWKAPwpUrwEuYoA/i0C+eSVigD9ZFJ7TIWKAPwmUtWQeYoA/XQQ8LRxigD/sSEwOE2KAPwulufEUYoA/JnehcRhigD8H4PAtFGKAP95ukOEPYoA/S3S8zxVigD/hPS5BEmKAP+Du8LsUYoA/YBXf/AxigD9i0cl9CmKAP9X02o4HYoA/0i4glghigD92AdoHCGKAP3j+y3MQYoA/hPv9UA1igD/k9y68DWKAPyrYu78NYoA/gHNOkBJigD/T1IskDmKAP+5As7AIYoA/+RUZwgZigD+8miHsCmKAP6LemxIQYoA/uayB9Q1igD9qCNMmEmKAP09QB7wXYoA/z0dNAB9igD/VM1BYGGKAP73x2iIZYoA/WcfUMhdigD+tZngAGmKAP2YYyQ8dYoA/JVnqfh1igD+upza+GmKAP5pZXNAaYoA/MH8ELxFigD+i/CAWEWKAP1tdhBwWYoA/1m5GehxigD/727gFH2KAPx74IcEgYoA/McRlHyBigD/im2TpJWKAP6SkGqgjYoA/4TdJ9CVigD9MD3YHImKAP3tDbiQlYoA/I3UPvCFigD90V2KaLGKAP7DyV4IqYoA/+TdDWShigD/KJ9hhH2KAPzP4ObAjYoA/A4LGtCZigD8Eg7dYK2KAP5UE+josYoA/tYouwSligD8seZqJImKAPyZBjU0dYoA/MGjSXSNigD+RTiGBKGKAP5Lla+8dYoA/zWqcIBxigD/9UZb7IWKAP6Yb54YpYoA/Ht8RmjBigD/D906yKmKAP1E/Q3UuYoA/+lzbKCdigD/vExvFI2KAP/E3RW8hYoA/QJyNMxtigD8Cl1RIHmKAP7j7Qp8dYoA/ksB/0SZigD/eAR7LI2KAPwbTmxAkYoA/jrKntSZigD8HfVLpHGKAP4zemXEWYoA/dLahMBtigD+sbBczIWKAPy5LjlIrYoA/DQfboDdigD/9mmnEPWKAPzxbkjlHYoA/F/AMYkRigD8L0kASRGKAP3H2UdVDYoA/9xkGrT5igD9mGqlvOWKAP584Dig6YoA/cnaDejligD+C3Of+NmKAPxMB84o6YoA/VyegBzBigD8EIxqyNmKAP9Nu//YvYoA/a06ivihigD9ftKDrIGKAP/HIyRckYoA/yciG3SBigD+mOcr3KWKAP4deZYoqYoA/4KoqmCJigD//KSEPJmKAP0Ze56cdYoA/D5lePR9igD8P9cAEIWKAP0HS7ysdYoA/givqiR1igD8vhOP/HGKAPy1hOQkaYoA/jl5T5hRigD9EXpZVGGKAP5UK8Y0aYoA/78ZXhxtigD+694xXFWKAP9JdlQMLYoA/BpmpOwhigD9dje42BGKAPyHQn9MGYoA/PQkNfAVigD9Pl5a6A2KAP+OuPtT9YYA/NX6ILgligD9n46ZPAmKAPwBFphkCYoA/v6lubgligD+A4oZMCGKAP6P1rEEGYoA/dsRNhwBigD8yvR3nAmKAP1ygFTQDYoA/fnJk+vVhgD9Qu1t8+GGAP7pD6+b4YYA/27UOnP1hgD8nr0FP/WGAPx8CACP/YYA/TKIh7v5hgD/N92/l9GGAPyAb1WvzYYA/WyUZWeZhgD+KzuQP7mGAP4DgQh3sYYA/uaUJPO9hgD9eeKG792GAPwAQsWj3YYA/n6FNpPxhgD/MGZtT+2GAP7LInBPzYYA/5o+EMOthgD9h1uEs42GAP0Okj0jkYYA/Mj/oDdxhgD+ziRFc4WGAP6Q5l2HkYYA/6R2rneJhgD8I1PvT5WGAP1Gl//DrYYA/kvpLpexhgD9dHDmg62GAP8X+IL3nYYA/RH38qfJhgD9cnG2W9GGAP5nl6Wj2YYA/U81nTPphgD9/aa3a/2GAP0If9fH6YYA/bKrxJgBigD9xY9n0BGKAPyvvDcEDYoA/I77uEANigD8ItNTYBGKAP9AuOt4BYoA/QsNuN/xhgD+x7jzdAWKAP9doKLIDYoA/FHpkdP9hgD9Ko6UN/mGAPz0rFowCYoA/GnW4zwRigD/AYIuKAWKAP9g42hn8YYA/SOnDW/phgD8tqIhK/GGAP4uQcSD+YYA/mTtmpgFigD/c4oJc/2GAP2ULS34FYoA/SmtBDPxhgD+Dn9fA/2GAP9ROmzIAYoA/xrejOvdhgD9TXbKZ82GAP5PfErH3YYA/U7WkJ/NhgD+YZ1fG9GGAP2R8UynzYYA/yl9YY+hhgD9dOq/z5WGAP+eVC3/lYYA/Zt6mnfFhgD9RT8I09mGAPzfifYfvYYA/0RCUYvRhgD/Xg3un9GGAPxTpLyD0YYA/E/OWqu9hgD9uEyV77WGAP1Ch+7zsYYA/HSKqH/BhgD+LXQgF/GGAP0F4+bj8YYA/A93+Q/xhgD+fD44G/2GAP8+iLkr9YYA/AooIcf5hgD+dvC2A+mGAPxOeNLsAYoA/cPAEAv9hgD8Xamg9/GGAP5OFAJz7YYA/nLCkE/hhgD8/HucL9GGAP/snQqbvYYA/lykEhPlhgD/YkjvM+GGAP23N+drzYYA/KIk0D/lhgD+bPV359WGAP+ikvinwYYA/nn8Uu+1hgD/CVG3D8mGAP+CIabz6YYA/CfO43/dhgD9HQit5+2GAP2/VC8ABYoA/PkTUZANigD9RhL9f/WGAP41XQJb1YYA/TqT60fhhgD96yUNI72GAP0mLYnXuYYA/trTPx/FhgD+4RcHV82GAPxRfdf/4YYA/s41Aj/xhgD8JLLvV9mGAP9Ii2JL9YYA/ASlzAAJigD9qBR/JBWKAP6jM0u8NYoA//iClShdigD8N4geUEmKAPwhfAm8OYoA/7Ij/JA5igD/xO4W5CmKAPxhXDgUJYoA/jV/04gZigD++1mdlCmKAP/0QYiwAYoA/sI40/QRigD+8PE7wBWKAP86fZ/sJYoA/gaWgchRigD9NWz6vDmKAP2tE37cNYoA/a4j3WA9igD+8k4snCGKAPw2Fy9UGYoA/HHj6/AZigD/JH2XoAWKAP1fyoKz/YYA/3sSLhQJigD9oMNAz/WGAP5c8Bm/8YYA/CTvy9fVhgD9iisIA+GGAP1OQro8AYoA/6UpDQwpigD+MakPPCmKAPzqoDcsMYoA/UkRbXQligD8kL97KD2KAP+PHlg4RYoA/gdI3ZhVigD8oRn6sEmKAP+TSqisQYoA/PrAz1BBigD8vjeAsDmKAP/hQThYSYoA/u9YTAQligD+DERibBmKAP4EuE34FYoA/zKqlTfthgD8iQ3rv+mGAP3ztS5b7YYA/I5/cqf5hgD9jsDmA8mGAP8GyX5nzYYA/ZNxBwO9hgD9Gb2I4+WGAP3SHV+L2YYA/aUuj8PRhgD8kpfWU8GGAP05Ef8fsYYA/+xKZwe5hgD/G27Uz5WGAP6uUro7mYYA/WCDwHN1hgD9x+dm43mGAP93emk7kYYA/KGNs0uBhgD/ItUGf52GAP5A+g3/jYYA/qSCP0OZhgD9IG20K4mGAPxpJzZznYYA//OjvpeBhgD8wRfXX5WGAP8lYMxbbYYA/kDMqxNhhgD/a1lHX0mGAP/D1JwDTYYA/4Yb9ZsphgD8B4Tjxz2GAP6OhSwnNYYA/Djh6s9JhgD//jVBe1WGAPz/35hDLYYA/FgVgRcFhgD9pn5GLx2GAP4z9uXbHYYA/z6YG2cVhgD+hYj/cyWGAP58M9FzNYYA/pUUTIsVhgD8LTC9evWGAPyuqzHe6YYA/8tNChbFhgD+rNxvJqWGAP27zWYGmYYA/LoNWraVhgD+0yR8QqmGAP+n0mHWmYYA/eP3XgqRhgD/7eM7fnGGAP3zT0fyiYYA/VQMl45xhgD/5hbQNlWGAP1dbUNKRYYA/nDOxaJlhgD/v8iOKnWGAP9T3ucedYYA/CpWefpxhgD9ghc8Xm2GAPz/bV8iSYYA/kDU4oZlhgD9WcU2lnmGAP8YGubCeYYA/rR/sKp1hgD9neEPMl2GAPxIRG1KYYYA/zbbtC5VhgD/VQaT/mmGAPzBGX2SdYYA/9qKTnZlhgD87vNs/mGGAPziR7mmUYYA/Wep8OJdhgD9hWq7In2GAPy0hGUSiYYA/aZCewqJhgD+CBOTCoWGAP11+EWuZYYA/+HuC2KRhgD+r8NaDnmGAP9l7LwqeYYA/DWLesqdhgD+sk1qXomGAPzwVRRSdYYA/I7zs/pdhgD9fZGqcjWGAPw7swDaLYYA/UUi7y4thgD+iLbUJhmGAP2SHGHeLYYA/jC8NQYxhgD+YY3awmmGAP8Sfw2agYYA/79CXfqdhgD94R/ujoWGAP85G0AqoYYA/j/9o3a5hgD8oKaMbuGGAP02LIey1YYA/+tRfGb9hgD9AnJJVuGGAP7uDz0m4YYA/54FneLxhgD99fDLMvGGAP14ON4+4YYA/eWTRFbxhgD9GEsXNyGGAP7//y4jFYYA/ooEj0MRhgD+/XxAovmGAPwkTRPy4YYA/64gTY7ZhgD9N6PFStmGAPzP4vuu0YYA/uGf4VL5hgD/DXfFcxWGAP5XuF87EYYA/NXU4e8dhgD/J9b2EymGAP/6eHjHJYYA/nUhB8c5hgD/Kmh0EymGAP8CGJVHJYYA/UfFUtMxhgD+ByIZBxWGAPy5QMwbGYYA/Oe9Z0cRhgD+0UxeRyGGAPymfhYHFYYA/VKSy37xhgD80F1jOt2GAP1Ek3Vy7YYA//qlbwr9hgD/pZRuTt2GAP1yvhTa0YYA/NnaC7KphgD9nn4O2smGAP9ITteC1YYA/XLkdtbthgD9GCBqDumGAPz3Lp7W1YYA/u+Y9/7ZhgD+q9wixtmGAPxU/1XyrYYA/MFGeoK9hgD+AuOu4tmGAP2BXjSmzYYA/VvF7a7phgD/50AgZtmGAP6lIV+u4YYA/DW8l6bNhgD/oPrx5sGGAPy1oJtavYYA/ThL87a9hgD+g+pGLr2GAP4w+Ib6oYYA/hVklHqdhgD9Inl0jp2GAP8WboYamYYA/SCXtZp9hgD8cSNfmoGGAPw8hnV2kYYA/Yi0Hc6dhgD82pP4AsGGAP6vU15qwYYA/7nrjyathgD+b7QIxt2GAP3P+0rm9YYA/IFGLmMhhgD9AN0w8z2GAP69f/7XTYYA/lz2IGtVhgD/TgP/vzmGAPycTOYvTYYA/MQFPot5hgD8MuKPC4WGAP21iW7jiYYA/KxudftthgD+f7qqM1mGAP9bIZdTQYYA/DglipNNhgD+WY94912GAP/WqJ7PTYYA/yjiuQ8thgD8keavGzmGAPwe62sHMYYA/PbSpqcNhgD+cns+mw2GAPw7J2Ci2YYA/F8S1BLNhgD9/4XEfvGGAP+0hxFK+YYA/nEL5UcJhgD8MTLQRu2GAPw0GfWi+YYA/HpWa77RhgD/gJOhbsGGAPz4yAtC4YYA/QY0hK7VhgD/Ga9kJuGGAP2XbXGS4YYA/Sc25ObVhgD8sQ3/VsmGAP0vKLh+yYYA/GRJX47BhgD+pdctcsGGAP7fKkuGrYYA/dIbjy6JhgD9Jx0XdomGAP2e08GCrYYA/nRILVKphgD+RhCGrr2GAPwVng0iuYYA/em0RPLBhgD/zn9QVt2GAP3McdlW5YYA/uJjsILdhgD/x3Bnvu2GAP35vhvLGYYA/5hfm4cNhgD9rUz2/umGAP7sSTJK1YYA/N/0p+7ZhgD8dcGg0sGGAP0snNrOpYYA/MsmavqthgD88mVUZqWGAP5kVv66iYYA/4uLgwadhgD9nh8gtpmGAPzKKIH6kYYA/1LgJzqNhgD8SQpawmWGAP0UUW7GSYYA/DTqAzJNhgD/FEIiClmGAP1OBcGGYYYA/a7KCmI9hgD+RA6NCg2GAP3hYPIGCYYA/YrOoAX9hgD9VUxr0e2GAP0oHXIh8YYA/nfn28ndhgD8AZM4mdmGAP9QRqF52YYA/gkMLInlhgD9gnPICgGGAPzbDpVF3YYA/vGcs6HBhgD+SRG+/bWGAP6ITQHVlYYA/BrrY8mthgD/yS885cWGAP3u3K6pvYYA/2zjzr3VhgD8HGA5meGGAPyttlapwYYA/yVwWD3NhgD9dnpS5bGGAP6lijalvYYA/HAcwg2dhgD+ZwtFzYGGAP4LFGzxbYYA/A6AtkmFhgD/puy7iYGGAP2fCGlFXYYA/MeSOi1lhgD/GRPRbWGGAP8Ux2xdTYYA/fNcz5FNhgD/Be8a1X2GAPxSKtHhpYYA/+Or/H3RhgD/p8EI5c2GAP4hUT51tYYA/W106jG5hgD9njfRAY2GAP7tbj5JhYYA/yvM8kWNhgD8UKipwYmGAP9ZL6JdbYYA/ZbzLTGJhgD8cmDn6aGGAP4aSOihvYYA/VuQOjmZhgD93KxkGZmGAPy3D9ddmYYA/ENo7JmphgD/ODjpmY2GAPzSPeOtqYYA/gmdjS21hgD/oIfeTZ2GAP/DemZZrYYA/71kSUGxhgD8HFlIKaWGAPy+yX8BgYYA/sw50hF1hgD9QDS5bWWGAPw1eArVhYYA//7c9AWphgD/LWIx2YGGAP2Rgme5eYYA/ihDy1WRhgD9wfqISb2GAP8SOZddsYYA/fFrAI2hhgD/PWdWLa2GAPw/z1mFxYYA/e7/IOnthgD+3sL8agmGAP1DHCsKHYYA/LvosR4VhgD/b6wIYg2GAP6nPu0WLYYA/j0Ldu5hhgD87Ab/1iWGAP009Y5CKYYA/HF+wG4thgD+sfkb+jGGAP730CJeLYYA/W+Q+ZI5hgD8sof+UkGGAP4ltCPyaYYA/MORGjqFhgD+Ng/+yoWGAPyyRwcurYYA/wBSGbqdhgD8G36YQsGGAP1gKpgO0YYA/kIt6zrVhgD+sPs/yt2GAP0f8HgavYYA/w3X5iadhgD8qTWM6qmGAP0hM0a2lYYA/bd676qVhgD81xbpTp2GAPzY+MXynYYA/rVSM4axhgD+GVA1XpGGAP6oYIOSmYYA/Rg15oa5hgD+L9dpfrGGAP3UdsJWoYYA/JTvmZK1hgD+IcTd2rmGAP8Js0lO2YYA/zaY8pLJhgD85XrADtGGAP0+TZDi2YYA/iNctgrxhgD/YnV8EuWGAPzeiDXC5YYA/SgJRabdhgD8YNnHNrmGAPwwklMS1YYA/I/OBIbdhgD9c7PUFtGGAP7v0ltOyYYA/MdBTNK5hgD+mi95ltGGAP41foRO3YYA/Iy4szbVhgD+hW/Y6sGGAPyEVVYa3YYA/vvIjObJhgD+9iuB7tmGAP/EfkFG1YYA/09P6OrlhgD84GfQmtGGAPz0XHviqYYA/MO5xvKhhgD/oG3D1sWGAP5BrsaPAYYA/zcygrMVhgD+iPxKLwWGAP3y1ijK/YYA/dAaLCMdhgD8skjVy02GAP2lBOHzGYYA/HnEIvsRhgD95xKzDvWGAP1t760DGYYA/96J4NsdhgD+IarA1z2GAP1J6PTbMYYA/4aL2bdVhgD+BMVn41mGAP1xFjbbYYYA/Uua5DdphgD/tghqn32GAP5ROl4veYYA/VmKJeeRhgD/8OXrb4GGAPwVFBMjWYYA/Ljsfe9thgD+OLJLN22GAP6meAGnbYYA/NwpkQdlhgD/Vpwde2GGAP9BR/eTbYYA/qJbJpdphgD9gPZAY1mGAP/NXK7jRYYA/vcM5+d1hgD/e3EQP22GAPx/LYZbmYYA/aZ1sOOlhgD8iGfNR7mGAP+j2kJPyYYA/zAaVM+RhgD8l1UQC6WGAP2uqV0ToYYA/f5oJjeVhgD9QpuQc5WGAP2fdV//dYYA/PVag+s5hgD/LWY1q0GGAP8AgS3LOYYA/25ddS9RhgD8kIrEF1WGAP4PgOhfVYYA/79BmcdVhgD+N9Zpe12GAP/JlG+LSYYA/I+19OtJhgD9Pxn5l1GGAP3YnwMzZYYA/6Ejji99hgD85lPjY5GGAP+gFKRLhYYA/4wOeLd9hgD8tnO+l3GGAPwCYSyvYYYA/KZo/vdZhgD9cmRIl0mGAPzN18LvQYYA/UAEEVORhgD+5JwfK2WGAP+UHv8fRYYA/3uB/AdZhgD/QN9U02WGAP5VhFyvaYYA/LJStjuFhgD+zt0i03mGAPwT6JhvhYYA/3uHBKN5hgD+rv+z742GAP9K8wMnpYYA/+OBe4e1hgD+BHJ717mGAP3D1RvfhYYA/Wj8RuOJhgD+Gjg2I6GGAP+3GWszkYYA/gwDRyudhgD90kadp5GGAP2tpsPXlYYA/NsUbj91hgD/nEzXW4mGAP7D3aQnfYYA/MLKzBONhgD+YF95242GAPzwlJS3WYYA/ExSCEdNhgD+WIz11x2GAP3s/asTKYYA//NNDjdJhgD8gbCKDzmGAP1oD8m7PYYA/oP7rldJhgD/2EB2H2WGAP0UyQVzVYYA/NKm009VhgD/nBNsb0GGAPwhDMeDRYYA/FJNJ5tFhgD/anGLw1mGAPzJ31IfgYYA/DDwntNxhgD8xCNel2GGAPy4gEPrgYYA/33hQ2NhhgD+msuEH0mGAP5yObxfVYYA/dyqbk9NhgD971Lkc2WGAP77snkvcYYA/V7XzK+RhgD9bSwF24WGAP1yque/gYYA/aV0I0d5hgD8vWe2N12GAP/FGj83RYYA/sBZN1s9hgD9/Na5Dz2GAPz8F0D7QYYA/a8Zy19ZhgD9gsJCtz2GAPwAfm5jOYYA/408+TcxhgD++fvId0WGAPyl/ge7IYYA/KGxXkshhgD9Q58uP1GGAP57iZ5TeYYA/i2tD4tVhgD+xUQud12GAP0zghiXVYYA/E3iKI8thgD+hXZHlxGGAP8Ztc9zEYYA/ZXccscthgD8vlvmp0mGAP1F6zm/JYYA//c40x81hgD+jIPzuymGAPwS+SDnLYYA/he4GodNhgD/7SNLP0GGAP/EeW+TPYYA/duimpNBhgD+tughAzGGAPzaaTYXPYYA/nIavVchhgD8EanxNvmGAP4FWAHa9YYA/kcarEb1hgD+TKBf4xmGAP/iRakDNYYA/sWfQnsphgD9juiKS1WGAP4kTYRbSYYA/tYWwhMxhgD+UNVvOw2GAP9jsjym/YYA//h65T8NhgD/DBpsUxmGAPzVy7IjLYYA/4o9H+tBhgD+Tcm9kyWGAP+1KVrXLYYA/GypBGsRhgD+J2YP1wmGAP9xF4TzOYYA/SUtCXc1hgD84khnr0WGAP57ovxDVYYA/kBXTOdhhgD8FWyBlz2GAP6XDgI7UYYA/tZOw7tNhgD+rEgdf0mGAP34Q0EbLYYA/0eSNV8JhgD9Bq7HtuWGAP9I/uD+5YYA/rMSf+rdhgD/BG9r3uWGAP0ApqqHAYYA/DpgmN75hgD/jF7ttxGGAP18ovwHBYYA/c8UD98JhgD+P4A07vGGAP581nta/YYA/GXgE67hhgD9BmSw/s2GAP1/iIre4YYA/sFIXPr5hgD+MFZsdymGAPx80UnPEYYA/5vO2k7phgD8Hjoz3tmGAP4YEy8a/YYA/8Q1+i8JhgD85Qsi9v2GAPyMDlJ7DYYA/DUTe18dhgD/tbTdxyGGAP96+0iPFYYA/52Uo88xhgD9MaDwOzGGAPzeeXM7RYYA/d34X48dhgD8mWPWjx2GAP3M+Ori/YYA/QPe40LdhgD9g+4SGt2GAP87v1xyzYYA/UbZZO7ZhgD9sUEcHtWGAP3BayDKyYYA/+fXNRLdhgD/0GBpcv2GAP3N3YrC9YYA/wLIyn7phgD+85/+FtGGAPxJqhQa8YYA/YGGDlrdhgD9J3nMLw2GAP+f9KH7BYYA/guM4hsJhgD8UKYJdyGGAP0eUUN3IYYA/JAhEqs1hgD+KEOaI0GGAP+p2V5LOYYA/UwFNitJhgD8EriFs0WGAP3eQlAnKYYA/xjjBmdFhgD8mZbb/ymGAPyoxiVXFYYA/WGHDrMBhgD9EunNRwGGAP8x/ytrEYYA/TH6PSLlhgD/ikUhZwWGAP1gtzufLYYA/MeMXDMthgD/izNodyWGAPyerSvm/YYA/+gjunsNhgD+4+NxVzWGAP9hesizaYYA/QxxvtdhhgD/z34Qc32GAP9/b00zjYYA/wHte6edhgD9ecDTm52GAP1FCVPnmYYA/i2NuLuthgD8LBF31+WGAP6vyeOj3YYA/20i2IvBhgD/Eswxf6mGAPwcP8y/nYYA/aRLGJuBhgD9gf+yb32GAP114r+zXYYA/2iBt+9NhgD9YXEYx2WGAPzvkpaTSYYA/+kOYyNBhgD/IyuLx0GGAP6RR0yvaYYA/wDK2ktJhgD8EAVlH22GAP5zrEhXlYYA/b0wDsuRhgD/du2dB6WGAP7Ls6XDuYYA/TxpZNvRhgD+3Es/z62GAP23Ci5vtYYA/kdqFG+xhgD+wFRIK6WGAP+YKsELlYYA/jW80zeNhgD9Ub5V95mGAP7tAxXfoYYA/BKtzdfBhgD+72kV27mGAP4BlgkTwYYA/cIUbj/RhgD8/hUg0+mGAP0m40rvyYYA/SetwTfJhgD+MxDv472GAP1TcWD3vYYA/J/myeOxhgD/WkDiw5WGAP9vRbT/mYYA/P54KCehhgD9t/Bio7WGAP808FrnmYYA/c7ipZ+lhgD/0p1cz8GGAP/Zg+Z3vYYA/1m2b5/JhgD9zE9Tf62GAPyExaVDtYYA/+35+ye1hgD9dbqx+6WGAP3+S8JnlYYA/A2E2RvFhgD9g1qhn8mGAPxiVLnv1YYA//4X1EPhhgD+atPf792GAP1jM6Mf9YYA/CMd3zAligD90QUHmDGKAP4fzEIgVYoA/aeaPBRligD8KbX1rG2KAP1riJ7UWYoA/xYM5VxhigD+dsYVWHGKAPxnOoMIZYoA/BY7NyBxigD83jApBGWKAP1g2kBESYoA/JoTtTQpigD8kzEUmA2KAP59g7isDYoA/a4up+AhigD/RX+p9C2KAP8WYW2gOYoA/JtwybwpigD8Xf9T1BWKAP60cvKX+YYA/eObg1PthgD+O2obgAGKAP/LuZhb8YYA/3lLdLvRhgD8pV81F9GGAP14wP7HsYYA/y2OkyedhgD/quRkO42GAPyDe2VDYYYA/W66zOdJhgD8udkbizGGAP3XQfMvLYYA/jx7gd9NhgD+bzYcW0GGAP8bhWkbYYYA/EVpk2NFhgD88jiGC2mGAP4dFZPPaYYA/2FJYDuFhgD9VG82G1WGAP5w3BojWYYA/LSgl0dNhgD96O0E6x2GAP+mGt1a/YYA/uqJyTcVhgD9HEJIEuWGAP4YgiQG6YYA/7FnDqrdhgD9VV/SosmGAP3hfyL24YYA/uGojILxhgD+dD0Zlu2GAP5h3Z2+/YYA/goUENsphgD9y7uKhxWGAP7YZzhvCYYA/Ch7Ejb5hgD+Zu6RnwWGAP6HYLaLCYYA/OMf+sMJhgD8iKZuVumGAP2f/pEO6YYA/8KbiTrZhgD91fewgtGGAPzi+FE+8YYA/JDSEVcZhgD+4PJrKxWGAP3OoR2jEYYA/CgTA2LhhgD/nyKFysWGAPzdBVN65YYA/wT6krb5hgD9SKiAFwGGAP7Tr/uTHYYA/yo01usVhgD+r8vdzzGGAPzb9LrK9YYA/HvnQDrVhgD99oBr+uWGAP7RZl1+9YYA/iWsH5sBhgD+GNyfPxGGAPxSfpRXJYYA/Lx2+bsxhgD8WwHhLzmGAP9G2iibQYYA/aXI4Ts5hgD/PLiUm12GAP8ygiJXUYYA/CluqR9phgD8j9rIv2mGAPydia2rZYYA/4vGVKN1hgD9u/8xF12GAP3448djVYYA/83mzo9phgD955J4r3WGAP/nu4YbfYYA/89el5OZhgD8IDyYp3mGAP6hkMlnjYYA/4RaIzN9hgD9yP7nh6GGAPxtG7dTkYYA/SF45VulhgD9F6kwp8WGAPyi5ImTxYYA/LvlgIvBhgD8F1riS7mGAP7zeDdrkYYA/fkraP+RhgD8xcU9/4mGAP5CY1GjfYYA/VuhknM5hgD+HtyW2ymGAPxZ52AK9YYA/176hiLlhgD8HVqlqwmGAP5zOxCTNYYA/A8OobNRhgD/Aq+Aj0GGAP5SJfiLMYYA/8UM4QMphgD8ymKSHzGGAP5II5D3EYYA/snfFxbphgD8qo2zRvmGAP7opFoe9YYA/No8qZrhhgD/a5wosvWGAP6v2DhTFYYA/bI1DzdNhgD+z41AK1GGAP7movEfTYYA/hsesNcthgD9p8S3AxWGAP5BIgrvAYYA/otoNWrphgD8LJNhKt2GAPyck3k61YYA/vDMCcr9hgD8Z7xqmv2GAPzTlMSPFYYA/BIR3eshhgD8tzOWrxGGAP1LJ2DW7YYA/nmVotLdhgD+7j9HNrmGAP9A0iYCvYYA/DEZmca9hgD/KJeUws2GAPzwm9G2zYYA/6+HjK61hgD+RNVWFr2GAPxMnFfqqYYA/Yf7R5bJhgD80e43st2GAPxzggwu2YYA/8hz+2MBhgD+4H8q/xmGAPySi4obIYYA/HMcnLtNhgD/rnbS7yWGAP2RodhXJYYA/5MkVP8ZhgD8BX8X8wWGAP5/zZAHIYYA/i/DAX85hgD+ri4g31GGAPyU/birXYYA/iMLUO9thgD90Jk8l3GGAP//agonbYYA/YPwl4N5hgD/hVQU67GGAPy1UXvfrYYA/iMTeo+RhgD+7ELfu4GGAPzzaCgfiYYA/uOeA3exhgD/hqe318GGAP/fkJhHqYYA/cWnGduBhgD+nqHTh4GGAP7uB6IbnYYA/0/+27uxhgD80l/PF5mGAP/05uanoYYA/QNWPp+ZhgD9Fy/na8GGAP89XAij7YYA/osHsXfdhgD/NcM4C9mGAPzC1AsQCYoA/KUVY7wRigD/eRrlaDGKAP7zWdlgJYoA/ouOXDQdigD+Ex9mXBmKAP9OrtdEBYoA/lOjjywVigD+riXVP/WGAP4qdoRwEYoA/PNK8EwBigD/+a31//WGAP16UUtMAYoA/cG3BSQNigD+3WGOe/2GAP6CjGz8EYoA/MHNUygpigD9U8NCtEGKAPzJOxZYSYoA/RJ9nMhVigD+MQXxfFmKAP6ui1Z4ZYoA/6VZpURligD9RTqGIHGKAP//x1toSYoA/XNktWw1igD9K9fPcEmKAP1sbuIwKYoA/vtAAN/1hgD/HY0Q+/mGAP+Ru0vv+YYA/CqZ5M/hhgD81UruW9WGAP8YM5o73YYA/vWa+S/lhgD+TMYluAmKAP0HfpeH8YYA/RcLSS/9hgD/KjPRz/GGAP06wI18CYoA/norqdAdigD9jMgmTB2KAP6t/hp8CYoA/N40j8wRigD9fjnsZAGKAP1hkREH9YYA/nGvqFf9hgD+TZDL7AmKAPyn1a4j6YYA/8q4pEABigD8cLypo+GGAP+vJ5HTuYYA/askt4fBhgD+AsB8E9GGAPy4sjiX3YYA/DPfHcwFigD9Iild4CGKAP9GEQKQCYoA/ngPPDQ1igD9RJGhBDWKAP3iLnhsKYoA/46L10AdigD/+uFmAAmKAP+JQHb4GYoA/Y8D80wVigD8Lcc+yB2KAP2+ww1oNYoA/2kUEzwxigD+s8JFuCWKAP8X0U7kKYoA/H/yETwtigD9K7T52EWKAP4fCDFcYYoA/jJy+Mx5igD9WDO2+HWKAP/0d7kEmYoA/RdLkWypigD/f0G42KWKAP5JOsRokYoA/MFquwCxigD/o4ZN0L2KAPyY8MRkuYoA/uNjiNCxigD98+GwaLGKAP9J/beA0YoA/1mGKxTRigD/HzdPRO2KAPxkuK/47YoA/AeUX8jpigD91Zy1BM2KAPwMvSpIvYoA/6NhU7ChigD9Zxl8FH2KAP6hSKwMdYoA/TkrHARJigD8SswATEWKAP3xzNrwNYoA/AFpeIwhigD8i9/GKB2KAP44AOfEEYoA/v/bDRflhgD/Q6319/mGAPxXJ6L0HYoA/fT0ZBAZigD83vzlyCmKAPyOBBRIRYoA/bTcjxxFigD+FUqHxGGKAP2MOoaAcYoA/nUkMzBxigD85eptbJmKAP4mawIItYoA/w3gSTTVigD+kZudINmKAP5qod04yYoA/88zdLjJigD+Eo+OcLmKAP3mQ2S8xYoA/w7RpQzNigD/1GgEINWKAP/bo1P0xYoA/50PAPCtigD/HMObYKGKAPwY7S7QkYoA/NrQcqBdigD/2WP7OGWKAP+SdUu0YYoA/tUMm1RhigD94WMRuGWKAP/iYR9IWYoA/Q5x9chNigD9oMtvBE2KAP61YHHQMYoA/SFDdAANigD9XbNEyAGKAP7JOHYUCYoA/DeJYPf9hgD+gce/c/WGAP03iQSUAYoA/RsOwif9hgD/UcqJo9mGAP7AWpCX4YYA/6//RvP9hgD+61+ROA2KAP6gYjDT/YYA/qeMbqf5hgD8IdQeC/GGAP/53Tnb+YYA/4mWGxvZhgD8ebAPa7mGAPxHH4A/xYYA/OiGKR/JhgD8rrez192GAP6zpYjz4YYA/pjeSq+9hgD/Bfdpd8GGAP9sfTRPsYYA/s/Hwd+lhgD85WNAi5mGAP5s3Ld7qYYA/9PHXq+thgD/5LLnb8GGAP/mWPob0YYA/AP63aPJhgD8WKBNr7WGAP19Ou8fzYYA/gsDqEvdhgD9FaTn072GAPxcmO4PvYYA/WGY86uhhgD+E65Ii6mGAP/sxdRDkYYA/zMTGvN9hgD+G2b9r4GGAP8IS91jdYYA/T9ofd9dhgD+r+I0v1mGAP7QpMUTXYYA/qjnNntthgD/hzITk02GAP0pEM4HcYYA/Cf7qv+NhgD/vw/nl52GAP7hc06HoYYA/a+4vie9hgD87HeYU9WGAP8Ba/Fv6YYA/+dnvsvhhgD8Q2plA+mGAP92x19f5YYA/26g2T+5hgD+pBEGA8mGAP3MVq2btYYA/cuYdj/xhgD+NT3xd9GGAP5FvS3L0YYA/ZNvbi/FhgD+yB0WW7GGAPxa0nrfpYYA/X8OHFehhgD9KMcAL7WGAP6B4R3nmYYA/oL235eRhgD/ETh7T3GGAP7mxFSHUYYA/uOIXLtNhgD/e0YvH1WGAP/qyL1bUYYA/CNKX9tFhgD/t7yAcyWGAP4KyCcbKYYA/orHcLcNhgD+dGwfYvmGAP2ykyEq3YYA/8EiF1bRhgD8olQcgs2GAP5ao3eGwYYA/Ls/26rVhgD/aZSnrumGAP2RzXtS7YYA/Q72Ds8FhgD9FmOSEvmGAP3MKHTS6YYA/AQC/47FhgD9q/NjWt2GAP2q+A0a+YYA/A3hEw8VhgD/3iEArxmGAP7q9A7TCYYA/2Ee3G8VhgD+2OinCzWGAP7ra6IvLYYA/nhM5E8VhgD9RKkVev2GAP4YOoBC8YYA/K7YfgsNhgD9eXjKgvmGAP6ALPuq3YYA/vBQe5bVhgD9shxuuuGGAPwZ5ovi2YYA/GOwR/rhhgD/AQqKXuGGAP+uAXcS9YYA/DeXpncFhgD/7tbc7u2GAPzu7vwi9YYA/FBxBU7thgD8fC7yysmGAP/Lk4Tm3YYA/gT5W/bdhgD/fxjnzrGGAP/nRAhOnYYA/gMoIqqZhgD8qP4xupmGAPzYM86udYYA/MTisd6JhgD+EOFn7nGGAP29dXB2ZYYA/XXvBr6JhgD967dhAoWGAP2psKoynYYA/gHNbZa1hgD9mjQeyrmGAP1e6C9O9YYA/9PXITr9hgD8U+vsYs2GAP+Rz46a8YYA/qbRiz8RhgD+7eXRmy2GAPw+3bBfNYYA/wsyosdRhgD/lX/aN02GAPwTQxd3QYYA/Bwrr+tJhgD9B3w0y0GGAP1mlzcHMYYA/JN1/HcVhgD/p9tjCwGGAP+9N0K7AYYA/a/kOp8RhgD9f0u10xGGAP1FBBJjFYYA/58Ljg8VhgD+wTOuaxGGAPx9x4WTCYYA/yzDfkrRhgD/uMsrzs2GAP0qRdM2uYYA/LFXrqqlhgD/s4v/yo2GAP5O5EtKYYYA/FRBGFZdhgD+NuEd+k2GAP9nTaM+eYYA/84QjoKBhgD+kU+J3p2GAP84St6+pYYA/l+Wvt6dhgD+f2G4romGAP/Y42waaYYA/QAY94ZthgD9veOOumGGAP6YXaC2bYYA/qEdBGZdhgD+Uuo2il2GAPxzpQX6aYYA/kcaXl55hgD8EFvwclWGAPyvweKyHYYA/8PNuyoFhgD9bnxIWhWGAP0l4ex2BYYA/+6IebIFhgD/ah0A1imGAP2pH3FqJYYA/hhB1W4xhgD+AgVwHiGGAP5YjEwODYYA/2qbVBIFhgD/LjNkuh2GAP4S8nrqKYYA/5IQOL4lhgD9cZ7s2hGGAPwWSzvB7YYA/lAbvNH1hgD+kPSg+fWGAP+lp0pJ9YYA/tdzUSHRhgD+kiTqzcWGAP2IKF652YYA/sFlTDnRhgD/6BCaMdWGAP5k8iZ91YYA/6AgYxndhgD8xZ60IfWGAP34H2lKAYYA/zTe5cYJhgD8xE3NmgGGAPwv9qN5/YYA/mj+svn1hgD+36VtuimGAP+yQmKKCYYA/cQFVf4JhgD+Vl5AmfmGAP1NsFEp7YYA/irV5Q3JhgD8Y7/3qeWGAP3Avy2F5YYA/3l+yQHRhgD9sv8R1dWGAP5gKj7R3YYA/Uo0N22xhgD+/r271cmGAP1QXrgZ9YYA/j/qg6oBhgD+pAKCKf2GAP5wegsV+YYA/0J7tloRhgD9E5xjZjmGAP4d3qryKYYA/sl0e/IphgD/a0NOtkGGAP5LIUYWFYYA/C37AI4VhgD9bOGE4jWGAP+6zNjOUYYA/Eir+jplhgD9EE6nBoWGAP0qo/DWoYYA/yxsFfKhhgD+VAJvwq2GAPz9S0U6lYYA/Jab7t6hhgD9mfk4Qp2GAPwVoZFufYYA/8yx6yp1hgD8s4T4nm2GAP56svK2aYYA/y7ScfJ9hgD8RxgsKomGAP4WiPRCoYYA/RvYcKKxhgD/0FutbqmGAP/PTdJqvYYA/pChX8rFhgD+bwekDu2GAPy8wyjvBYYA/XxqDGMlhgD8QpQcxwmGAP4gum0/AYYA/QvK/VMJhgD9HhuhZyWGAPwKN4NPIYYA/FPMG+8RhgD+hEt6QwGGAP96e3o/CYYA/Qzg3HcNhgD8BRHo1wGGAP4iT/ty/YYA/8l1jLb5hgD+o/sVIu2GAP+Do2R+5YYA/n9LwFLlhgD9PANrVt2GAP549d8rAYYA/JoTcs75hgD/TyVnzwGGAP5O83pLAYYA/+psdocthgD8maLhq0GGAP0h/ymHFYYA/OPTKWM5hgD91S54HzGGAP4ta7rjNYYA/qICV+cphgD84By6zx2GAP7qg6TnIYYA/NZ+2aMlhgD/z0fn7yGGAPyB06FPJYYA/bylWB8thgD96TdilymGAPwLVeuDGYYA/KP/iFclhgD+EDqfwvmGAP05vpXesYYA/MjcZYa1hgD9grbw/sGGAP2qbecyqYYA/5LiUGLJhgD9jZCwBrmGAP3xokDyzYYA/U2tUg69hgD/sa0HZtGGAP1JAnAKvYYA/pkQlKbBhgD9PKf2NrmGAPxwxrRemYYA/uy7ze69hgD9DKcxrqmGAP8uybrmpYYA/5gtpUpdhgD/eLhslkWGAP7n5CKmGYYA/l+X8sIhhgD/Re/SLgGGAP3H+pAOEYYA/doKBPHthgD8mJIQhfmGAP72qHhZzYYA/TI/VCXJhgD/IPBrle2GAP5Sl+wF6YYA/PhSStnphgD/neKndfWGAPzX/3ZF1YYA/v86/IXdhgD9JpGVsfGGAP7kBn2CIYYA/nv4/TYhhgD8gaTbai2GAP0IW8+eLYYA/bqGXD49hgD+a8Uu6gGGAP6E3Q7B9YYA/cdvpr3VhgD9qL+Red2GAPxrfKC5yYYA/qY4dd3ZhgD/SoxiXdmGAP20/3Ed1YYA/3Sdrl3JhgD8e+gbDf2GAPw4Ck4mBYYA/tSDURX9hgD+1VjZzfmGAP5N9VTl7YYA/ir5ElXhhgD/FDNwFd2GAP6LoMaRsYYA/eV3EeHdhgD/V2gC7cWGAP/w2w+VvYYA/QURt42JhgD+JWveNWWGAP2m1TcxVYYA/yq520FNhgD9Qz4qWUGGAP1wHLCpRYYA/EmVmC1lhgD+Z5HvzYWGAP3TEERpsYYA/l6InbWlhgD86IG8IbWGAP6r/1ZxzYYA/Wn5FDG1hgD/V2J0mZWGAP7xYHWloYYA/JoHst2ZhgD86smewaWGAPzpI/HVpYYA/Aj8oQ1xhgD937rWIVmGAP5K4myFVYYA/A25TUE1hgD89ZUVHQmGAP505kLdDYYA/nykWQEFhgD8ZF1DdQ2GAPy7M6lpDYYA/EJ66+0VhgD/6A9xqN2GAP43ljUoyYYA/xq+KLTJhgD8pKzQDLmGAP1yHrGMrYYA/v+DOxChhgD9+OGC9K2GAP14yCLcxYYA/k56aizBhgD/ltheAM2GAP6QgZRo1YYA/0QhEUD1hgD8082fWR2GAPzJe975CYYA/nxXmokFhgD+zQwQINmGAP6yfWF1CYYA/WWlKWEFhgD8iCQ+4QmGAP/FCd+REYYA/2ztL90phgD/6bJbtSWGAP9RRBXRIYYA/JdtoVUZhgD/pPs+oSWGAP96y51RQYYA/5jZCm1hhgD/0h88pVWGAP7ifzPBSYYA/BzOaTUNhgD9azw02NWGAP5N2sHE3YYA/UxHmKDNhgD/NmR60MmGAPwFZoaw4YYA/IucFwDVhgD+mPKpwNmGAP59+1Pw5YYA/cbAO5jVhgD+oGHsAN2GAP1xudps5YYA/ia5tFkNhgD+bkBtWQGGAP/XpkZo6YYA/bml48j1hgD9IhpeaPWGAPwJ6aVVDYYA/yH5qi0NhgD9bM1RBR2GAP43N4qxJYYA/o9tsj0VhgD8MSyM4RWGAP1U7lyNAYYA/5Nu2WDthgD/8BKrIRWGAP1xe8JNHYYA/3ZgA3FJhgD9F3O3cUWGAPwCpV9BNYYA/rpYhVkxhgD97RpBbQ2GAP+43ZeRHYYA/Z740f0VhgD9iwXzRRGGAP+W/BZNKYYA/BbXMx0phgD9kHta2TmGAP8/GZnZQYYA/Y2k6rUVhgD/p8pGMRmGAP+XLi45MYYA/+M1wVUNhgD+mljO9RGGAP9LTuABIYYA/UEYlLUFhgD/s6emMQ2GAP6aHf/E9YYA/3wP/nz9hgD8W+wVZOmGAP/uO6G83YYA/gulmBDJhgD+1Nl5jO2GAP1CL5bQ2YYA/8aJJxjJhgD8S084AOWGAP/yB10YvYYA/s9WzlTdhgD9uajGFMGGAPzGYC/4rYYA/i4KedC5hgD/X0Bz9KGGAPzSCC0QkYYA/xrAVTRhhgD/5NDsAH2GAP4OkMykkYYA/xpw/LSBhgD9BJJhJG2GAP2KSJToUYYA/Gwd5NhdhgD/6fiFhGGGAPy7jbiAYYYA/oo9gABthgD911/QCHmGAP3Z+FwsaYYA/KOV4xh5hgD9t86qIIGGAP3eTw3oiYYA/eEnzRSdhgD9VSbR8LWGAPy/34UYuYYA/5wi4iS9hgD9dAlN6KmGAP9wi2o4kYYA/eDTLhC5hgD/d/HTEOmGAP5trYGA5YYA/m5CnKkBhgD+LUr/JR2GAP80EbJpCYYA/COol3zxhgD/ClnjsQGGAPwved01AYYA/Z1MA9T5hgD//UxDqO2GAP842nkk3YYA/FiMzhythgD+CQeXEKmGAP2uYbfotYYA/GVZ1Yy9hgD8GKTn4K2GAP1cozD4zYYA/XR2GKDphgD/msXQTNWGAP3AkXfAxYYA/LU8GUThhgD9K0Kw5N2GAP/XWvAk0YYA/bf/mfjthgD+DcJOxQmGAP402NaNDYYA/B1ZTRT9hgD+01o9ZN2GAPwRgBCgyYYA/i0OosjNhgD8f/2p9MWGAP4COAAAjYYA/le+jfihhgD8GoHMoJmGAP0pL324oYYA/fkePGSxhgD+nkfwHKmGAPwz2kzcfYYA/ZVfjJyhhgD9HKunHJmGAPwUNVzUzYYA/+t8XzDhhgD/Muk4WP2GAP3FWV8ZCYYA/filQiElhgD90ZfrEQ2GAPz2luT5DYYA/X5ShIUVhgD+IlrBHTGGAP5zK/GpDYYA/oPdGtj9hgD9FD4K/OWGAP93Z048yYYA/MIg1+ClhgD/JEdh7JGGAP5oDFPcjYYA/e5TNqxxhgD9ucZJTGWGAP0hRNYQjYYA/lh5mLy9hgD8TmDvoKWGAP5olkYsoYYA/aUjD2S1hgD/sofFkN2GAP2IP8pA1YYA/VLcLpDZhgD/TOvAyQGGAP3pE8nVGYYA/+oJrPUJhgD+gVBvSPWGAP5Ea8Ec7YYA/Lof0jzVhgD+pROu/OmGAP0mS7LdHYYA/yQX6iENhgD8CGjQzQWGAP/ER+5lDYYA/FR8y4z9hgD9pIaqIRGGAP2mN190/YYA/bS2Ci0ZhgD+Zji+vUGGAPxhFf6tVYYA/63ahKFZhgD9PuBckWGGAP1SmF8laYYA/qd8fM11hgD/Ug8KyYWGAP4v92nFoYYA/GNv1+mJhgD9qwd5UZWGAP9XUFe5pYYA/AXyegWlhgD/p3CvQcmGAP5gI+k1zYYA/uelEFHhhgD/baBC4d2GAP5PTNUp+YYA/VHzfCHxhgD+1kPJegmGAP445BlaKYYA/HYshuYthgD8I+cIoimGAPxo2nFKYYYA/hnMl1ZhhgD+5+qBBlmGAPxxCZ72UYYA/LT9LzoxhgD+Z4gauiWGAP5aLvFB7YYA/Xh3a7Y1hgD8mcThajGGAPwOBH+t/YYA/UXiDX3phgD/HAUePfWGAP6UMR3B5YYA/jBqAYX5hgD8D+X0Mh2GAP1gKOEuJYYA/4sIpCYphgD/CJ1Kfg2GAP8k5+1t+YYA/KZUuR4RhgD8EuaGgf2GAPzDPPveHYYA/ytbPLIBhgD8cmau4emGAP69+Db98YYA/fC4zL3hhgD9Q3rNodGGAP74+6QlzYYA/fXfFjWxhgD+brQzWaGGAPzqVyI9tYYA/OcohZGdhgD8IFUUmZGGAP+667WNaYYA/++t2LFVhgD/zGB8FSmGAP197MM9OYYA/NWW30U9hgD8HQ/vaS2GAP5tkYAdJYYA/a4qqnU1hgD/cxb8XVmGAP50/WNFPYYA/cdHp6lRhgD9Hcz+qUWGAPzQaxulPYYA/m2ulp0ZhgD+biVfWRmGAP/tvIQxAYYA/miO3NkdhgD9FdooeSmGAP8adNcZEYYA/XbXeZEdhgD/9CBj+SmGAP46atv5BYYA/OVALvkNhgD8HU1atS2GAP04eedZRYYA/QZJR6lJhgD/L6OWhVWGAPwSzo/dXYYA/ZSadlVZhgD/qQOOUV2GAP0MwqOlYYYA/Jd9nTF1hgD9fIanzZWGAP487saliYYA/wOSuqmJhgD9nvjI5ZGGAP6n86tRkYYA/GQnYtW5hgD9CIGFjcmGAP+bG155zYYA/GrRoJXdhgD+eTXMZgmGAP/+Cm0mCYYA/lnQ1DXdhgD+TnusWcGGAP0GCHxJmYYA/0gfN7WhhgD97dehOeGGAP9rzGRFxYYA/2qfifXZhgD9XjInAfGGAP43RQSWHYYA/vNDTwI9hgD+84nKwh2GAP+FajKyMYYA/BN20FYhhgD8peV8giGGAP9S62zCRYYA/4pVd3KBhgD8IOO4DqGGAPx2VUx6pYYA//ColqqZhgD/JtZY8omGAP9SyT46jYYA/IKjoMaBhgD855Pj9l2GAP9Z2e7OjYYA/72L+Oa1hgD/kRaIuqWGAPzb0lw2hYYA/4RT9SKBhgD8qQJ14n2GAP9bk2gCdYYA/1t7KDqFhgD8M63vypmGAP5FIUhmkYYA/cxJ7MqZhgD/K94retGGAP2kkC6CyYYA/tTqSMLRhgD86N7iXwmGAP/FcydO0YYA/9Audz6xhgD9BUfujtWGAPxXvNi2xYYA/WDNg3rRhgD/0WXUDuGGAP9XCsGK4YYA/kIpneLFhgD/h/ucAq2GAP9V2eluxYYA/EKCMIathgD/07T7lp2GAP6Rz5K6jYYA/ZC6q26VhgD8rmTU5pWGAP4FVTSanYYA/4SydSZ5hgD+MU/aInWGAPzCGGsmaYYA/uz2z1KJhgD8Qpo38p2GAP5X1FyCkYYA/FKECR6xhgD/da1YJrmGAP2KgSaOrYYA/xpiWMqRhgD+Vqjchn2GAPy+n/bWbYYA/OHzaB5JhgD8tnieClGGAP5K242GVYYA/BGySHJRhgD8DO3YnjmGAP8av+MWHYYA/srDnHY9hgD87cyEOiWGAP2f/EQOMYYA/jMwoopdhgD8vmC8Um2GAP043DWmaYYA/BmMm151hgD+quOl6omGAP/fmMpCkYYA/SBAuAp9hgD/1z8+Tk2GAP14M1lmMYYA/hhdnhoxhgD9yf5YgkmGAPwq6xaiKYYA/PZ1IrZRhgD+SFZnrmmGAP2qpjD+aYYA/K7lOGKNhgD+WixH4pGGAP+Fli5OlYYA/RB6eHZxhgD9/oYJAnGGAP+Y72sKPYYA/82lMPothgD8LfAZNgmGAPz9xuOB9YYA/ce4V+4FhgD/WVr3diWGAP0BixbWRYYA/g9N1wYxhgD8wbLXQj2GAP0o06RCRYYA/wgqyiI5hgD9nzlkNjWGAPzKvGLKcYYA/NQanf59hgD/l90vIomGAP8u1Y+qXYYA/IaBg5ZFhgD+MPk+cjWGAP5H+Z/GIYYA/3wTFf41hgD/xrfq1jWGAP57CPrKXYYA/SpW8JJ5hgD+GGayXm2GAP6z5y+ehYYA/WQakma5hgD8MD+c/tWGAP0kKLG2zYYA/9lD25blhgD+yjaeXvmGAP8j2lGG+YYA/d9CP68JhgD8UIoJfzGGAPyBIYgvZYYA/gyOJDtRhgD8rHNbQzmGAPxOOv33OYYA/FVYIfMVhgD/zRpNCxWGAP1sf2InTYYA/TTnCTNBhgD8qbV+PymGAP2BIE4zMYYA/CLFVssthgD9t3cdK02GAPzjaudLEYYA/ykngKbphgD900sHyp2GAPw5BnAO0YYA/Mk2AoLxhgD/rB42OvWGAP9bmssK/YYA/mWpbPsZhgD/DcwEIxGGAPwS+FhnJYYA/FrClRcthgD+i3OLuyWGAP2vKGSrQYYA/u7mIDddhgD8ZtOmw12GAP0i4+hrWYYA/12WYvNthgD+U59wp2GGAP9kyMH7fYYA/50RHF9lhgD/Kbk1x02GAP5eYubXaYYA/k/QXwOFhgD+i7ZFR4WGAP9i0yublYYA/rkQfo+ZhgD++wN057GGAP8v+A8PrYYA/4gdEdelhgD84rPcw6mGAP2dAs7LxYYA/fwGSwvhhgD9sl7ol+mGAPw7Y6BX3YYA/pcIWt/lhgD+oqRSIA2KAP+NcE9sCYoA/yCf6UQBigD9I2k1BCmKAP3Sn4yUKYoA/Yd+3qgJigD/1TLTICWKAP9Bb2YT8YYA/fUYyh/phgD9ntVvX8mGAP/U1+RD0YYA/FdfqQ+1hgD+ZJoCU52GAPzoB+srsYYA/PO29COVhgD8aw7k95WGAP5nqrebnYYA/Y90sZOphgD/5kfKz6WGAPz8xExXfYYA/32j9oOxhgD9mqqYj6GGAP33gCzLiYYA/no0v5+JhgD+aAvOa4GGAP/dzxzrbYYA/Xo0Fnt5hgD+N+wWb6WGAP+fFuXjmYYA/i2EnTuRhgD+4tJy96WGAP1iDb+3tYYA/9/ABjuxhgD+sdYzg5mGAPyRKjJbaYYA/iT5uK9xhgD8hmOQc4WGAP5rzsrzUYYA/zPeG1NNhgD/LOjDU0mGAPzABRtXSYYA/LzxiJtphgD+rbIU43GGAP1oG/a7WYYA/z1tdpNxhgD9BAFKE1WGAP1jvaJneYYA/x+agbtphgD9Yv1W91WGAP8OnzOTWYYA/HaVEDdFhgD8Wu4pEy2GAP8lSy2nFYYA/85YpSclhgD88BxREw2GAPy6CNBC1YYA/KYA67bthgD9yOMsNuWGAPy7otrC6YYA/OCvOgsFhgD/iTh5MxmGAP+DFAzvEYYA/ZmceScdhgD+JDZoN02GAP4j1YJ7UYYA/rVheLM5hgD+ZsX5PzmGAPy8LSLvOYYA/aUkTN9lhgD+jl/KC3GGAP4m3vGveYYA/mg7E9t9hgD8HmUIY2mGAP87kbLTYYYA/4wRzDtdhgD8iX6al2mGAP1LQ9Z/XYYA/XpHJ2NVhgD+vzPiX22GAPyJbgSTVYYA/u88WjtJhgD8ye9VH1WGAP/ndOV7OYYA/hX8XGdFhgD/USMF01GGAPwDPytXLYYA/h0b57MRhgD/xezTjv2GAP7ODgMe8YYA/6sRpDcFhgD8bXkmMvWGAP23t5yG6YYA/REECnrZhgD8qOXZZt2GAPz9ybYzIYYA/uE7kss5hgD8aTSNiyGGAP8TJaljJYYA/CD2eBc1hgD9H7Rlf0mGAP/CZ2hvXYYA/KnuQ89RhgD9lPKpY02GAP94OjBDMYYA/PkEEGcthgD/stLHZzWGAP0NIwN3LYYA/Mo9DoMxhgD/Y+KIiwmGAPxA9abfGYYA/cgRrkcZhgD+fjrXVxmGAP8y7gjvNYYA/kqM+kM1hgD91+ivJxmGAP+osxJHGYYA/vVQ4PMZhgD8gU40lx2GAP/bG2aTJYYA/Hd3Zr8VhgD9UlY+xwmGAPyDAyCu4YYA/FKwRbrVhgD/Hwf2itmGAP/bvhAi7YYA/ttc+TsFhgD9foeFLy2GAP/5LI37LYYA/Fdgpv85hgD+RJKJAzWGAP3XUfPLBYYA/C7pWY8hhgD8brhOSz2GAP7yEonvUYYA/XIB00tVhgD9Agld902GAPx1b/rPVYYA/xv6DhNxhgD/sBQWY22GAP16uT0vbYYA/5we+b95hgD8DH94+4GGAP7Qwyr/jYYA/TCfSu+RhgD/4TBtK5WGAP3ADgifkYYA/YELDaedhgD/mOP1y5WGAPwrQ8s/nYYA/i04UEeRhgD9+SGYN4WGAP6eVX2ThYYA/aT0I/eFhgD9+40WD5mGAP6V2s+rqYYA/SB9cme5hgD+R2gV67WGAP0pMM0XyYYA/uETzyvVhgD8sOTqK+GGAP+hhmnsBYoA/UTbo8ARigD8/xYnJBmKAP1t7/OsPYoA//hYkuRRigD+5XheGD2KAP8Sh+AwPYoA/jZnjuxdigD+eZS2jGWKAP8/6N+USYoA/dOvQBxJigD8u/GsUC2KAPxtf3U0OYoA/71C+jhNigD/TMi2fD2KAP7/xaz0KYoA/0tVG2wdigD+KyBxgBmKAPzZOpQH6YYA/QH0+cvxhgD9RVKN98mGAP403p+PwYYA/8evRMO1hgD+TxNR17mGAPyalXGzsYYA/M6mwruxhgD98JGkj5GGAP53Ch0HhYYA/xADQ09lhgD9iVHUX12GAP9pqz3zrYYA/kz629ephgD8c+w3H8GGAP+isYOH1YYA/Q+d2O/BhgD+tuNNd6WGAP5wiaV7pYYA/3e2rh+thgD8PruRp3mGAP+LrfKbiYYA/po9KH95hgD+PpvuH3GGAPzfg/QrfYYA/5aB3M9FhgD+4D+R612GAP6A8JePVYYA/qDOlztZhgD92YoQE4WGAP5jUU9HgYYA/OeKcHcxhgD+KNl0v02GAP6bRCP7ZYYA/s1qrf91hgD9ejtFU3mGAPz8UqBLkYYA/TTci7uVhgD/RoQoQ5WGAP3met0vyYYA//ZyQ0vJhgD8m3dIu+GGAP1WAwyP1YYA/JnTHVfNhgD8vrcF872GAP6T3N1/sYYA/5p74g+hhgD+PwpQw4WGAPxvYvAHhYYA/O5CPPd5hgD/0Hxzf4GGAPz2/whHjYYA/MaMvS+RhgD9yX6kB4mGAP8kn70DbYYA/R6RIMeJhgD8K6h2y6mGAPya7QnviYYA/NSkxLeFhgD8vVw5P4WGAP60tan7XYYA/53/W9N1hgD9LkiFD4GGAPzoIVrvpYYA/Uv3vWuVhgD/AzauN5WGAPzI6L07iYYA/pU1e9txhgD+sid+X4WGAP//2GkzjYYA/X/PvTOhhgD9y5tbC9GGAP9JhKen7YYA/TDRlTPdhgD+qFz5m72GAP0Totz3mYYA/gmCojt1hgD+G2TT02GGAP56FK8vaYYA/18fb0tVhgD8laHJv3GGAP9Ydt23WYYA/NugvsNVhgD9s+I+x5WGAP7oUMbvgYYA/RoQtdt9hgD/Ua2l34GGAPwk/VirlYYA/hT1FxethgD9r8ikK+GGAP+orcXLwYYA/0vw3IvBhgD9nqlCA82GAP0rN34fvYYA/0Z2Xz+xhgD+hDm1S42GAP5sSQsneYYA/ujyiidlhgD9bWxZ71mGAP/nKnxTYYYA/qJTfO9VhgD8BPjdm4WGAPzMgmJrjYYA/yV28x+hhgD9XsgkJ2WGAP7kcV0nhYYA/uL0zUeRhgD//2TKb8GGAPwVqmHnuYYA/xN1bhvJhgD/+mTvh82GAP/UBmnDrYYA/UCia9fBhgD+Z96yY6GGAP+uIXZHiYYA/0xkMfOZhgD/wWSHP6WGAP5/wcBjtYYA/2ORXo/JhgD9XhEoW+mGAPxNfEssCYoA/ftwDZQJigD+56tNxCGKAP7OthaD+YYA/HvnHKfthgD98tunoAmKAP/r0Qt8AYoA/2pkJgfxhgD9+ETWo/WGAP/q2Dhz5YYA/Jq9OVPxhgD8Y5gAO/2GAPxZfKNH1YYA/fUBytvdhgD+ppcIOB2KAP4EP26n+YYA/fb/JoP9hgD8UGTC4/mGAP7fk1P/2YYA/4rPvrvNhgD94jz587WGAP0iGNAXoYYA/O4yU5+phgD91ZBPi72GAPyrBirzyYYA/rvwg+PlhgD8pLxXo72GAP1Rp1GrsYYA/zbS+bfFhgD+x8xv49mGAP1kwprTwYYA/dH+Ifu1hgD+r0NxL7GGAP2tRXPHjYYA/IPNaDORhgD/OKdro4WGAP/tKZN/kYYA/o8cOuuFhgD8e6RTB4WGAP1V89Q/cYYA/7RmFauhhgD/jJqiJ6GGAP1xM1wPjYYA/7Hwj3+lhgD9C9bVU62GAP61saxDnYYA/oCApn+dhgD9MnqjV42GAP9hez9vnYYA/gfzAGPNhgD8VhHOH5GGAP3eusrjpYYA/7i7+EuthgD8es/m06WGAPzbcxuTfYYA/I4v2XeZhgD8gJoO452GAP4PGu9/qYYA/7bt9EuRhgD+DojE132GAP1kgmLDmYYA/zlMU7uFhgD/+5QTJ5GGAP6GPNPLmYYA/JBQ3judhgD+K7e/d5GGAP0vpDXXqYYA/yur2NfJhgD9eTk+C9WGAPwhHBpb4YYA/+6wUVP5hgD9zcgzS+mGAP1I1mZT3YYA/OLd7H/ZhgD8Bhfqr8mGAP2u6BNfxYYA/L/C/d/ZhgD8MRLt08GGAP+DxmF3wYYA/7fsiIvhhgD8L7mA18WGAP6fPbbHnYYA/JkeJku1hgD+zUPkW8WGAPxa/VA3sYYA/0NJRt/hhgD8HVKkQ92GAP+uzNOj5YYA/UdtHtP1hgD9D72Td9WGAP3GTPwfzYYA/uvldrfVhgD+TZ1A9/mGAP0vpt1D/YYA/ecNsIQFigD+9SE1E9mGAP07jahv2YYA/8M5h2vVhgD+43tco9mGAP66+/en4YYA/Pwy2iv9hgD/xh/FzA2KAP7+864kCYoA/xCxBhAJigD8dUjk/AmKAPxLeViD6YYA/Wi/NtfZhgD90ORec+WGAP5u6r777YYA/B6foJPVhgD9Sd97S9WGAP2aNdvD0YYA/gkMxIvlhgD/6dmVv+2GAP10z9yD1YYA/2QUupOthgD/mn5Bw6GGAPwntjCTkYYA/yND93OphgD9CWN8X7GGAPwZ9AlvrYYA/pv7Rmd5hgD/GocqE4WGAP9R/6afdYYA/Mgh1jdNhgD8Rxskr3GGAP9IJFQ3bYYA/6QEPudlhgD+8nkNh4mGAPySGBA7sYYA/UAdqV+1hgD+jkdZ49WGAP47BqYD8YYA/X8VpSPphgD8zS5CV+mGAPxCEoiP2YYA/pZJTrvNhgD/e28ua8WGAP9dQMEjzYYA/Hlh9zPRhgD/Q8tHK+2GAP/Ill3H+YYA/CTBU8f1hgD+v1G8i82GAP9AVDln1YYA/JE+1vvdhgD8hXPKk+GGAP2RR6t/6YYA/KNSnBANigD8I2onu+GGAP5y2NVL2YYA//RlqUvVhgD8AFaSc82GAP7NR7NTzYYA/V6MpSephgD+zFTvw52GAPya238fzYYA/4Y4ziOthgD8Y7S616GGAP/N1Nh/uYYA/MxvHPethgD8SJlGh42GAP4wkHJzoYYA/Z7aHV+NhgD8ZwzQg7GGAP3NysCrrYYA/2FW6+uphgD+ZU+Po82GAP5C9l1LuYYA/sNj20vFhgD9vroNS72GAP9QjAiPrYYA/FIvm9vFhgD+WaiYB6WGAP5gdB8/nYYA/g73Ur+VhgD9yqpmP4WGAP2IXZM3jYYA/J1HH8u1hgD+9CYcw8WGAP5IUw/v1YYA/nu05X/FhgD+oNx8v9GGAPxYA3HTyYYA/u/ScCQFigD+Adn9yAGKAP8Y6w3QFYoA/FGwQz/xhgD8cphea9mGAP4lO0Q/6YYA/rcDivvxhgD8YsZVl+2GAP2sOPi7/YYA/BZKdev1hgD8hZltz9mGAP4dgnkjvYYA/G4NL1u5hgD8vmQ6L7GGAPwzrWiPqYYA/m9+B5OlhgD/X14NP6WGAP0T+eRPuYYA/DkH11u1hgD/kF/MC92GAP4ZlL+r2YYA/wnX97fdhgD+GVD4M9WGAP0W+dBj0YYA/EOr/puphgD+64WdC4mGAP1OY2s3gYYA/E3zgxOJhgD/99Hkf3mGAPywnYUDeYYA/jAj4Jd1hgD+VYctC6mGAP8ZyGCXgYYA/N8pC7N1hgD8aIAgP3mGAP0zflpjnYYA/e8urj/FhgD/v5UIe7GGAPwXI9lPtYYA/ECbZNuhhgD/4uAxc5GGAP1+595bbYYA/0y2OqNxhgD9hoCV05mGAP3eZTA/qYYA/QQvUdONhgD/zjfrG6GGAPzlQ8B/cYYA/RMyD7NthgD8/1dzGzmGAP24Ao/rJYYA/Ufa0T8ZhgD+3XPcBvGGAP35WbN2+YYA/TMv2MbthgD8eEEJ1vGGAP6PjX/7FYYA/AQKl0MFhgD81HfjbxmGAP1Z6/eHJYYA/NwHRIMJhgD8xxuGkwGGAP1m3RdXLYYA/PVuiNc9hgD/Wk9n20GGAP7gzwZfcYYA/Wcu/peBhgD+VPqJl5WGAP21NLGTlYYA/oiG9UuZhgD9T6nqb52GAPwUWLOHlYYA/36aAX+ZhgD9+U78u4mGAP6KgidPmYYA/+er6TeNhgD9pp5fU6WGAP57BSBvrYYA/Tv5owOphgD+zN/TM72GAPwZ4VmryYYA/frEM/OhhgD/7xZi92WGAP/YT9ozdYYA/UM9R5tdhgD8dZNSh3GGAP/UzGfPgYYA/QZU4HdthgD/bMdsN6WGAPyK1QMDnYYA/lqC2auphgD/MdDq76GGAP8mIkoTlYYA/3t3jvudhgD/178526WGAPwngAn/fYYA/LfOGveZhgD/52dBJ62GAP7duPEHoYYA/YO/baethgD+M8tWf5WGAPw++iYbZYYA/+XjB1tBhgD8iNjqXyGGAP4aIXHXHYYA/pc+7Z8phgD9bPgdj0GGAP/6Iq+TMYYA/QE+i3clhgD9e5Bvu0GGAP6ipamPPYYA/xKp5w8phgD+z+D5pzGGAP5n08U/KYYA/myfIFcphgD8HQA0t1WGAP2TXaurWYYA/YgPxm9ZhgD9aNmh1zWGAP4xRzq/HYYA/rOwYyM1hgD+HVhVkxGGAP0R56frGYYA/Pb8QEsdhgD+B+U+by2GAPxosUyfJYYA/IF8aV8VhgD91WwELzmGAP6AJyUjRYYA/RnoUkNdhgD9hQz1SzGGAP2uiuQbEYYA/cY1uh8ZhgD91Im+5xWGAP3guRyzIYYA/jTthEMhhgD+3f+ZQv2GAP9lktOyyYYA/4ECkK6xhgD/bPPPgpmGAPwUEblCdYYA/228adaRhgD+tmCKpomGAPzpKCk+cYYA/llV28qxhgD+eoqU9q2GAPwcLr7CoYYA/uvdAsaJhgD/iApMtomGAP7fcINSsYYA/Lu6BLqZhgD8qycRip2GAP1hL6ZWvYYA/6K1d/bFhgD+JWkO2smGAPxQyMCGyYYA/ZMHIkrBhgD++HZ6Ls2GAPxU42EuxYYA/RS56ka1hgD8yuUxGsGGAP3G4GRyzYYA/H4XIV7ZhgD/jK3X7t2GAPyYP4w+8YYA/MPceK6lhgD/iyCUUtGGAP6mPcYOsYYA/2qLpLKJhgD9b6O7znWGAP7eEQ5edYYA/uv4KOaNhgD84i/nurmGAP2xuKlizYYA/OTerGb1hgD/+gl4GumGAPzeootOyYYA/fgzRdrBhgD+6vsqRuGGAP87bpWK1YYA/QEL6+bJhgD/0l7WcsGGAPwhBk5m5YYA/zLrtTbphgD9JKQpMtmGAP7oyAFW1YYA/7hJGhr1hgD++ciJXsGGAP48Se4mrYYA/Uid2RrNhgD/7n+xEsWGAP8FVc2K3YYA/wUdC3K1hgD9lCkpiqGGAP7AzKmWkYYA/b679SqhhgD+QvMqDqGGAP3RxjCyrYYA/mKc1F69hgD8cI6rKrWGAP4rCQL+uYYA/gcLUnrdhgD+HkmpGsmGAP5ekww+qYYA/vLeHFKxhgD/pMgguq2GAP1qVKpKrYYA/yOqoJLdhgD9nqRMrsGGAP5Vez4ipYYA/PX8NNK5hgD9nnBPdpmGAPwXLJU6qYYA/e6qhuK9hgD865c/MrmGAP5+EMGWmYYA/nCr6h6BhgD+YB5QQrGGAP0ebqxuxYYA/kv7flrdhgD8uoyqMtmGAP7sRX6y3YYA/ViK9I7lhgD/CyFiDvWGAP65RXjO9YYA/qC6sfLlhgD/IHRcwvGGAP1cu6VvGYYA/rH0/IMJhgD/DDgHsyWGAP/Gi3QHJYYA/rBUWiNNhgD/0wv5U0mGAPyJxQFPVYYA/+7HB/9hhgD/YcdZs3WGAP7yPY2XdYYA/Y+wU5d5hgD+cw4+43mGAP1bdfA/hYYA/jEj2VtlhgD8RgkOq02GAP32rBNbQYYA/dzXUItZhgD+0G4Gj0WGAP/fi5aLPYYA/AFutVsdhgD9V+dlEyGGAPy2Kq8DJYYA/Stl2BM1hgD/PYn3jxmGAP5tmHAjKYYA/2ubvScVhgD8PimwKxWGAPxNO/cLIYYA/7kjTIMZhgD/Ns5lUzWGAPyQPO+7LYYA/dpbUW9JhgD9gjr8Q0WGAP03HITrbYYA/07j65NFhgD/rxtop1WGAP9S40JHQYYA/iC9gr89hgD+nGdgn2GGAP3GmSjnaYYA/Hs44LuJhgD/x4TrE6GGAPwBHEEnpYYA/Xv0cf+hhgD8ugwzs7GGAP9tkz5zgYYA/OWwAM9xhgD+4948J2WGAPwPiRmXQYYA/b/Qw8NFhgD90FKK81mGAPxMY9YTWYYA/Y0a3PNFhgD+pEjPe1WGAP6dq2GjeYYA/xF3S8t5hgD9JrNBH5WGAPy5yl1DsYYA/r94D1OxhgD8OZJQO8GGAP1fSvffsYYA/zgD9xuRhgD+FfnnO3WGAP7xizijfYYA/s2fXQNdhgD9+PDQu0mGAP7Z5JTPXYYA/oqu+kNNhgD/R5J6CymGAP33WkzXMYYA/E9xPwc5hgD8XfiR/z2GAPzXHuZXQYYA/PVwBGs1hgD/LaBLv1WGAP7BAlT/LYYA/ijsqtclhgD9qsZYlz2GAP0/PlQ7NYYA/W/drj8phgD8/fx11x2GAP6wua97GYYA/F7kpj79hgD/LwAd6v2GAP37cRca8YYA/smZLt7phgD+5hH14tmGAP0PSguq+YYA/c/7zvL9hgD9Y0Trxv2GAP0cYd8C+YYA/G58Z/rxhgD8gi0z2vmGAP09bq7G8YYA/wSFzgb1hgD/E64B3wGGAP3iaOra7YYA/VGDjgrRhgD95EuMNuWGAPyPcbYa4YYA/RbL7/b1hgD9otklcu2GAPz4ULca4YYA/IPfaOLdhgD/AtlzbtmGAP9cntUC3YYA/MF/phrxhgD9RyLMgwWGAP4QMCcm5YYA/Md8DU7JhgD9LrALnsGGAP7Ul6BeqYYA/qDe0f6ZhgD9KhykPn2GAP3A46AigYYA/L+XZnadhgD+PHNcirmGAP2NQC8qnYYA/Oz5bp6hhgD/g6nxMq2GAP0VL51SwYYA/NL6I17VhgD8+/FKRuGGAP9P6sCC7YYA/BzMIYrdhgD8QDt/gtmGAP386j16xYYA/AUk6O7ZhgD+LGjC3vWGAP7miPAHJYYA/eSKq/cVhgD9p2nH5xWGAPxExd0PRYYA/ok4rEdRhgD+KAqfq3WGAP4PTksThYYA/Y9dnFvBhgD/vT8dW52GAP7DMkBXiYYA/2TFnFONhgD8AVwu22WGAP6YU8NzeYYA/qLM88OhhgD9ZRnwL42GAP+9AV63iYYA/aKpobexhgD90eI5592GAP+b8GKz2YYA/laSzH/hhgD+qtyzV8WGAPwj2mMD5YYA/UYxjMvRhgD/BL+q4+2GAP5zPmYr4YYA/rNGb5PVhgD+2pFCI/GGAP2PRq6UPYoA/ntSYOgligD/JErujBmKAP+d/ZfQKYoA/e5N2qxVigD88rhB3GGKAP7rl2d8bYoA/87KaWBligD9S6HtTEmKAPyuxXbcWYoA/fjMPfhZigD++Za4jIGKAPzrSPqwlYoA/nBkhwCligD+pZbTvJGKAP+sszO4oYoA/CfRaYi1igD/Fyw8kImKAP/PJPmUfYoA/IgKFLCJigD+Elz+YJGKAP2ZLCmsqYoA/iQSNpi5igD+RXw/lNWKAP1dPKzA6YoA/3UiLAD5igD9P+27fNGKAPw1hiyI5YoA/0THg8zxigD9wqIdLQmKAP6taL0JLYoA/yrxVZz9igD8UR4qoRWKAP3NZ7hJJYoA/5Wy6R0ligD+SSJESSWKAP5Uov+RJYoA/90rVC0pigD9VQtnSR2KAP6eXaDhIYoA/eHnr10RigD82hofLPmKAPwzwpuc3YoA/tsy4Rz9igD+VQYSkRWKAP6cNCj45YoA/bgwGXzJigD9Z3VLvNGKAPwVTNQw6YoA/09SZtS9igD+W7ssXL2KAP6aOK7ouYoA/BAnXgShigD9ct3RKIGKAP4LMcB0cYoA/Uygy+BNigD8LSnyHFWKAP4d0ogcYYoA/rCDJJRdigD+mM8+4HGKAP932q0shYoA/K/bXoh5igD8UyI8dIGKAP8IBtaUjYoA/DpgxThtigD9EyFe3FWKAP1LgnWUJYoA/z8ptvhJigD+C8+XgFWKAP9nXp1oaYoA/unwY7hRigD/LfwOBHmKAP2ZsTlwXYoA/IzIK8w9igD+qM7qQDmKAP4/E1TsQYoA/+88FYQxigD+gjjOwB2KAP6U7eZQEYoA/wHijbwJigD8w936WAGKAP2EPZ939YYA/QlDynPthgD9zaVeVAGKAP+mHOCT9YYA/iLbC9P9hgD9wi5Jo/mGAP+6P47/6YYA/FyBpIv9hgD+VkyiH/WGAP3UOShkBYoA/Q0y0cwdigD9KEayfA2KAP2MI633/YYA/GVCkivphgD/T8qhO+2GAPwa3FI37YYA/5Rwn8PhhgD/6VfDXBGKAP3/PlxsLYoA/WpzOHgpigD8BQQ3aBGKAPw0bUaIAYoA/dgZM4/thgD9var54/GGAPzkwa3XzYYA/dqBHGflhgD/Fs7om+mGAP3XYxjkAYoA/8VqC7v5hgD97S6Sq+WGAP6v+01j+YYA/SJv64/lhgD8n5HZg62GAPy0Ls1faYYA/Kl8f1dRhgD8+NbVD2WGAP3DnNMrcYYA/CvW2pdxhgD+nPZNk12GAP8m7SN3LYYA/xZ+gJ8phgD9hbpK5yWGAPxG8whXCYYA/0qiyyb1hgD8O35q2sWGAPwN8Q5uwYYA/eJKR3q9hgD+G2bsgsGGAP8n2y2S5YYA/+tLNc7phgD9ScidxtmGAP4jnDhWuYYA/s1QPlbVhgD/mwS3toWGAPyhDnw2nYYA/xF6aa7VhgD9mxBy7xWGAP9JpCT3DYYA/ehBV5c5hgD+vIa711WGAP+6XOd/PYYA/+QK5psphgD/tqNxUzWGAPzQNwjjWYYA/rWqXPM1hgD8pUq+HymGAP4mfCDTKYYA/BOaGfsphgD9dBSbAw2GAPyhLnfjFYYA/sNkHScVhgD9FEoXnu2GAP1pbFDm4YYA/hW7IFMRhgD8W79SjvmGAP95rrxe4YYA/Yu8Y6rhhgD8tPAR+tmGAPz7gNEq6YYA/fgPe7LNhgD8e9SIMtWGAP3zxTyyyYYA/Gx2WTqlhgD9Rfr4Ip2GAPxmwTAemYYA/PipWCqFhgD92B1wEn2GAP7tAc5GkYYA/rlIsZJ9hgD/11uLkmmGAP6wMeV+PYYA/XZozzIlhgD+5frZ4imGAP2UKFryEYYA/vsVktH1hgD90+/6YemGAP5fiCzd+YYA/HoFaBoBhgD8vduGuiGGAP9x+7o6QYYA/bUFILpVhgD9wdREZkGGAPxBG3omMYYA/trQKaINhgD9bZ65jiGGAPzNfQQ6IYYA/QnN4wIhhgD9N2wRBimGAP74wpXORYYA/bnyqwJthgD9tyw1XoGGAP0Ld7zibYYA/DhWYuaFhgD/xWjICl2GAP3C2un6WYYA/ej6+CZlhgD+rvvxzoGGAP//b2RatYYA/RhHUoahhgD+BH+nDrmGAPw25gi6zYYA/bqXLVrlhgD9vNAoot2GAP38pYsS3YYA/PIa5GbhhgD+Z5/T9sWGAP7oJKMa2YYA/d9pbk7NhgD+DW4+VsWGAP8R4C/yrYYA/EnTh2KZhgD9dYhGbq2GAPxli+xSrYYA/4xizz6lhgD9KJrg3p2GAP9YGAoKoYYA/2Lf3FqJhgD/OU5v/oGGAP3/rUT6rYYA/BaIYqqZhgD/K8V6iq2GAP0DxhGKkYYA/SOmG8aNhgD+/a99JnGGAP5J3/gugYYA/c8GRGJphgD+K23l5oWGAPyfDEq+iYYA/yi7sD55hgD/5+TSnnmGAP3McyymaYYA/LeKdM5thgD8X2MQ7mWGAP0ifSqWdYYA/SvA4NpxhgD+j2fckm2GAP4ZhPniiYYA/eb+LBKFhgD+OPksgomGAPxMNfBesYYA/7uxnUKxhgD/VcYpns2GAP/CJLFytYYA/eLUsZ61hgD/P3rjbpGGAP/4bHsKjYYA/tgEKWZ5hgD+0ND3hp2GAP9Txb+euYYA/HDjCE7JhgD+fHd88tmGAP8qHnha5YYA/eDUQZ7FhgD+hVMOIqGGAP9RHgb+uYYA/B5IqpKthgD+z85mdrWGAP1YQcOioYYA/64tfvK1hgD9Q8AYKr2GAP61ZwD+rYYA/h9M73q9hgD9V50WSsWGAP3eW1Pi2YYA/Lk32mK9hgD+C1hSwsmGAP0KLeU2xYYA/4yKEVrRhgD8l/GQdrmGAPz9CrKeyYYA/d6LOX7JhgD9owCLHtWGAP0VOEaG3YYA/9Z7VjbdhgD/yklEUv2GAPyTOm1K8YYA/Mr8RX7lhgD8mRn/6uGGAP7AY6Iy3YYA/QfZQQ7phgD/5xHiiuGGAP5f75kG7YYA/Hi2rD8JhgD+jZQuKtWGAP7so8nW3YYA/4FX2wLlhgD/VMYCat2GAPwhMO8KtYYA/SlTnHLRhgD87CINCsWGAPzZrclmwYYA/v+u4rLBhgD/8EdJIrmGAPyxYeP+tYYA/GVjYTKZhgD+XCz8pn2GAP3ZvVqioYYA/QJYFAaNhgD8EBuSun2GAP4KUrM+lYYA/+u2vraVhgD/7SZ33omGAPwXwQlOlYYA/8mEA86ZhgD/qtW0LqmGAP0PqCRyoYYA/2Qa3nalhgD/aQ5ELqWGAP9Wv2FCqYYA/IXOARKlhgD8VgmtFqWGAP1rSDuOoYYA/YEmtjLphgD9pJZznvmGAP3KfgD+8YYA/VnleisFhgD/mVxfUzWGAP3Cg+WjRYYA/aHyGtM9hgD/2O6jtzmGAP8uQybfHYYA/UQE2eMhhgD+eMVUowWGAP2RkO2LEYYA/Kwuxv75hgD+aHxiFvmGAP1bafDHJYYA/LnQpsM1hgD8WyY0WzGGAPxoM+ubJYYA/l/cf3clhgD/htYp5zWGAP+uyWfjJYYA/rZqRVdphgD+h3Hkd2WGAPwVIa3DRYYA/fHUIgsxhgD/DS7ee0mGAP5uQX63UYYA/AmpYf9VhgD9v+FDa1WGAPxHJNXzWYYA/LD6K2tVhgD9EHQF+1mGAP3a78IbVYYA/77/iBM9hgD/nJ9N8zGGAP9oZKnbNYYA/k/deAcZhgD+SLDeBxWGAP00sWUjDYYA/tpME+b1hgD/qs37vwGGAP1ipBJa/YYA/fVW7GL5hgD/Oi3t4wWGAPy4GQUa8YYA/wcpyAbJhgD8KK2JcrmGAP8xYFYKtYYA/b3vGLrBhgD9KFV+VqmGAP2miqFeqYYA/BXhOGqZhgD9NbNQarWGAP9PxhCimYYA/z/wNW6dhgD9JKTLmqWGAP8v2XRqhYYA/aQKBdKNhgD9zZn0oqGGAP129tPSsYYA/B0a8nKlhgD93j8KFrWGAP3vJh6+1YYA/DXGoLbthgD/gJx7txmGAP0dVPKXBYYA/nuwQ7rxhgD8YDsCis2GAP/ur7XCoYYA/pxT+L6JhgD/UcoxRpGGAPwRW6HypYYA/X2YN+qZhgD+c6BgArmGAP/TcFvWnYYA/xHIAaaNhgD8ERCLfoGGAPyEdDzGhYYA/CZmsI6NhgD/UeXPanmGAP4sf5DmeYYA/aTxvUZhhgD8C8K2pmmGAP4d/G8CiYYA/AuSFFqlhgD/cEbqRrGGAPyZzw/+2YYA/Yg6BeLphgD9OZveawWGAP4nQX5m8YYA/ehyYVb9hgD+9tAYdxmGAPzIkl7/JYYA/sxLIU8hhgD/K+2Kiy2GAP7ydcDXWYYA/yUsbSdlhgD/9EBpo2mGAP4f2w13aYYA//PFyJ+VhgD92PKOR52GAP7llzFnqYYA/CANxge9hgD/FhiQW92GAP9wqDhrzYYA/2w5viPNhgD+KQyxN5GGAPxlLGEroYYA/O45qcOJhgD83U43L3WGAPyOzhLfkYYA/Z7efitlhgD/JZNeu5GGAP9x+JgrfYYA/gClEBNxhgD+UAqIv12GAPzN/0PrcYYA/emLFAN5hgD+4BCxL52GAPyaqH/fgYYA/NKRteO1hgD+JSTh/8mGAP5/+2KXsYYA/fwAV9+xhgD8pqDOK52GAP9KccojwYYA/buFoy/dhgD8Msz5+9mGAP8AIVkf6YYA/5N+p//NhgD/7iilw9WGAP2M0AVrxYYA/foA6Cu1hgD/fq+V062GAP+mZMD3gYYA/q+F7td9hgD/vzx6h2WGAPxNGN67cYYA/A/qMWOBhgD8ymZbM3GGAPy/C2Y/aYYA/V8LsM9thgD8/RkbA1WGAPzCsr47dYYA/bvkOOuRhgD9PE1Ns5mGAP3xkfnvpYYA/eRC8xuxhgD9NhbFo6mGAP19CdmnlYYA/fzWRVOZhgD/UlJMh5GGAP0zTtCXoYYA/UFLTCeZhgD/+IiRP3WGAP8ZmacjYYYA/6b3qJthhgD+YmAtc22GAPwerpQLkYYA/r+Y0kedhgD9NvuaV6WGAP1LRKxvbYYA/i+85n9xhgD8BJPkt4mGAP7G1+4nkYYA/Fn3AyeJhgD91V6P55GGAP//lNjvkYYA/W7YRMOBhgD8M2F8W3WGAPx6ovdjoYYA/tbWrFOthgD/UfiRs7WGAP+UKelH0YYA/P9sJn/NhgD9mfPSw9GGAPyidSkvuYYA/5lpdbulhgD8IjnUh4mGAP/E1OxLZYYA/BXhSSNthgD8vYGnq4mGAP9+kgnTkYYA/SqiSReZhgD8GEv/06GGAP0kKyzTsYYA/QEKSXO1hgD9sjK8472GAP48gVZnyYYA/UVZkjPRhgD9SWWId8mGAP85kKNvyYYA/RDQYTPxhgD8jQonB9GGAP1F3WV3zYYA/oOEpcu1hgD+C7hSt+GGAP3OJ9JzxYYA/2FSPcOlhgD+S2VOc42GAP1PrcdvgYYA/uDxSO99hgD+QYYRI3WGAP2wVhPfgYYA/6AV9X+phgD8E4iLP9GGAP42fGozyYYA/BGlYJPFhgD9+16JM+GGAPzRoJ7HzYYA/CllatPxhgD+xb278/WGAP7OZD4v3YYA/FlqVhvVhgD+5NaPS72GAP50/MIXwYYA/MHlLEfdhgD93Yhef9mGAP7ZYZ7z5YYA/LvgYvvVhgD/3hBdt7GGAPy1FQx/zYYA/U3+30PVhgD8ohoP+9mGAP3ofonjzYYA/7Glb1PZhgD/CKnYWA2KAPxSOQJcJYoA/Sz2qTQ1igD9rkHqFB2KAP9JXPhQTYoA/YwmzzQxigD/3FUiwF2KAPxl1GrQdYoA/vr8YNBhigD8pl0ZRG2KAPz/ZmKIZYoA/WCDOqRtigD9imN5ZGmKAPxfevl8aYoA/AFmHGBhigD94/GSJGmKAP2K7y/kbYoA/sEcQPyBigD/CLQ6RIGKAP7L+svAbYoA/RftJDh5igD8kIEV2HWKAP9FbWS4dYoA/zMLk5h5igD87dd9+FmKAPyAk9ysdYoA/jDRf5iFigD+zF/ZgHmKAPwgW678fYoA/iXu7yxpigD/OcYPvGGKAPyIk2SIfYoA/F+CxFipigD+Sjh9IJ2KAP03cWqssYoA/4ilXZDFigD+YVYaxP2KAP2VqXs9EYoA/S8yz/0NigD/Ahti+SWKAPyoPFMNKYoA/MDY5nUpigD+rviyXSmKAP5dKfe9NYoA/aeICP0higD+gbpT9Q2KAP1eNJOQ7YoA/3tXdTTligD+5P9HgLmKAP3Z80CkzYoA/W1Xz8C9igD9YzpFqLWKAPybadwoxYoA/jp9KRTFigD9vFzAeNmKAP/+JShU0YoA/5MbBBDJigD/4n3eRL2KAPwXmvIkuYoA/TCZyUzZigD/SOJR4PmKAPyBKDPo9YoA/uWaFiD5igD8W+6DjPGKAP1YFtqM3YoA/tvEE/DdigD+tTp2sOGKAP6NGAkU/YoA/UW6ZcERigD+dxN2LS2KAP4vnND1JYoA/yepi0UhigD9BBiGSQ2KAP8zjsHZGYoA/8E6Ha0VigD/ecaxYR2KAPzeow9JNYoA/MA/Y2E5igD9d+LpASGKAPy2iyphGYoA/7DSggURigD9Vp2IbQmKAPxhs6oI+YoA/YYatzztigD91FxVJQWKAP8TDpJxNYoA/r6dexkNigD9vMDLlQmKAP2ATgGtNYoA/7MK9v0ligD/ivj5LSmKAP1FbE6U+YoA/IQdi2jligD/6t7yDQGKAP+5gs0lFYoA/KJsZOkhigD/NgcV6QGKAP5sdNFM9YoA/oi0d2DpigD+fezQ8O2KAP3+/b4ZGYoA/g9A7WkRigD8LnpfmSGKAP+KYyk1NYoA/YIbN9ENigD8RYsBSQmKAP/DHAAxJYoA/ZyWty0xigD8VxJj4X2KAP1PcS1FjYoA/gQyA4F9igD9L01yDW2KAP5xbeFRnYoA/BPEeTWFigD9ac8+9Y2KAPy84tK1bYoA/1k5mymJigD+Ee81MaWKAP6D81a5gYoA/t/U3pmRigD/Y0pLeYWKAPzUxG0tgYoA/TpQAb1ZigD8nnINnVWKAPwuPL5xXYoA/5TWdXFhigD+nbbgOXWKAPwI41CJaYoA/Ip6nWGJigD+LP3fWZWKAPyl2pWloYoA/Oq6PmWligD8IgH1uXWKAP5W5aM5YYoA/fE1ir2RigD/SneZPamKAPwySHe1mYoA/n/jPJFligD9kSX5XV2KAP29H/pBbYoA/md+wlFhigD/3PI7ZUWKAP1obG1tYYoA/rBXEAE9igD+NmS8kU2KAP0mD2sdOYoA/HXmJdVBigD/kpXSeRGKAP9/4St5FYoA/e53QFUJigD+HBnIsQmKAPziA9203YoA/kDMGvzZigD/sde1PNGKAP1ytCxM0YoA/fXT/GTJigD8rjumrNWKAP6TF54g0YoA/2vEsqTRigD+LoEA6OGKAP2zT8os2YoA/77vdcDFigD8/51CBPmKAP6lR1IBLYoA/KNsaOEhigD8D+I8/TmKAP6HgbeVSYoA/9Ww0qFNigD9hKHyoXmKAP6SsQxZTYoA/RR+QjlNigD9PlPOvVWKAP7Dx3bBXYoA/KZ0xRlxigD+3WR2HZWKAP0fzsY9gYoA/5hr/k2pigD/+WLulbWKAP1wWi3VsYoA/52+riWdigD9yuBymbmKAPzfvJy10YoA/gQ5Lt2xigD/YqvZsdWKAP3vKBIdwYoA/vWJjF3RigD8Di2S1amKAP7UslbRpYoA/mHJL/GJigD/XZKlwa2KAP1QyaAFoYoA/l55uWGligD/Cj8+ya2KAP5292M12YoA/rl/ylndigD/xtNspemKAP8VJAnRvYoA/thhMyW9igD8/ncESdWKAP1yQRhN0YoA/iRZgn3higD+GXPvDcWKAP9qoKjt1YoA/ozCHrnNigD+5NM+tbWKAP6tZpY5oYoA/YkfkPW1igD9VSRlzcmKAP1ZC1Qp0YoA/7eCLAG1igD8C1p5eZ2KAP60mGHVgYoA/t21/RGBigD8J8DdiZGKAP0sqEPVpYoA/GZGYimNigD+sXHMjXGKAP0L52v5WYoA/tOCILVpigD8xEy2YXmKAP78KvppbYoA/BcuVbVRigD9VxWDAXGKAP/j9+7tYYoA/3aIs0VNigD8Al1GDTWKAPzusWnNMYoA/zMQQSkVigD/T7ctHT2KAP9t2oGxLYoA/qE9FakxigD+s2WZBSmKAP6sVO1RTYoA/Fs7BeVdigD91qYuxV2KAP3TkBkxbYoA/MRIPJ19igD/C6GDLXWKAP9pfGrpcYoA/PObYVWVigD/9t8h/bGKAP1AozWpoYoA/wlW1lWVigD/QIC4iamKAP714YJZjYoA/7yZIgm1igD8Gb1eRcGKAPxMeyQV1YoA/9Cbj1nFigD8g9wsYbWKAP88PtxNzYoA/Zf5p9XJigD8uXRHmcWKAP2zuqll+YoA/dCTfv3tigD+Gq60Nh2KAP/IoVgZ7YoA/E6XRqnVigD/IgGLVeGKAPzF6EY14YoA/acwPCHhigD+XRGmucWKAP/ckqhNyYoA/kpYH9HRigD+jejhOdWKAP70+G2Z2YoA/Smt6jXVigD+R8gfrfGKAPxFJ9LGGYoA/Ev1OJ4tigD9bHTqyj2KAP3IM1kmWYoA/vmqgz6VigD8JoGn4pWKAP6H6P1unYoA/0myf2q9igD/c9XjJqWKAPySReqatYoA/VDiB4qZigD86pWy4n2KAP4VW30CeYoA/DhpxE6FigD/F7KUZoWKAP1GzLEuoYoA/QidzVKligD9vs3cep2KAP0oCL6SmYoA/BmSa1atigD/Wtj2usWKAP7PBE3awYoA/H7ljebBigD87tOrUrmKAPzPSnv6xYoA/Ci0stK9igD9qc3V8uGKAPxiYiRe4YoA/UVhPf7xigD93XIppwWKAP58fPXfDYoA/17HHHMRigD+5m9BgvGKAP8QjNArFYoA/k0ykg8FigD+416jiwGKAP4XsOAq9YoA/YToRhrRigD9pQcL9sGKAPzTokQWvYoA/CpsE4qhigD+kh0B4r2KAP01+Pg6tYoA/wEpoq65igD9rb0KMuWKAP6xwTN+uYoA/kq/aRbhigD/zsT8Xu2KAP7y5c8LAYoA/sO4rksZigD/dhpSkwWKAP1Tgx3q9YoA/ibPnELZigD8vuGxQvGKAP7WM3Ya+YoA/vhRJz8FigD/a3RWhxGKAP7UzGJbLYoA/BpKQ18digD98ljm7xWKAPyAB7hLDYoA/S44kDL5igD8fuiq5v2KAP3QaZ7O/YoA/hYyFttNigD+qGxJ41WKAP3c2GTLQYoA/qIdbgNJigD8yu7B/2mKAP2MXX8PfYoA/mfGJ0NVigD+c9dFF42KAPziOM7DnYoA/xFB1P+VigD+yN6uU3WKAP8KeW4voYoA/d5P57utigD+b5UqC6GKAP2Y0gt3xYoA/vwsoDuhigD/RuBh66mKAP7AADGjmYoA//fbEouBigD8PhINZ32KAP/k6tYzmYoA/p3903OVigD/N2Otf4mKAPwAipRXdYoA/y+rB3dhigD9tAVem5GKAP5uOkXjmYoA/ZrWqW9ZigD+qruwi0GKAP+7lGrTRYoA/pKIn49RigD8IgBpB2GKAP+3exmrfYoA/O8xpBNligD9HHzRLymKAP9e5pO/JYoA/9p9WTMligD8y+B5ixWKAP+E3GRvHYoA/HKL/8MhigD+g6MrFwmKAP3GOosvFYoA/q1vUFspigD+D7zqkyGKAP0DvNc3NYoA/GHE4atBigD/B3fFm0WKAP0O0fsDUYoA/eyKF1NligD92bzWu2WKAPzOK1WDcYoA/jkWVBNhigD/D2Fod3mKAP8LwJTTcYoA/NhV8uNligD9liUU42WKAP/5Dg1zSYoA/ryrded1igD8bQsHH12KAP1Wn8EXlYoA/gIxCN+RigD9509IS6mKAP7rFZ+fpYoA/K0U3h99igD+SwDqi62KAPy0Go4TbYoA/OGb5ItligD9UfzOS3GKAP0qDw8PcYoA/VQYw699igD+JeKW222KAP3+HS2LpYoA/f9GoC+higD/PpxI47mKAP+RZUXnnYoA/7yjhbuRigD8aP4ua22KAP0+9h+zaYoA/g//Obt9igD991cjv3GKAPw8aC5nkYoA/EwXhvuRigD8h63Lb6WKAP92w1FvmYoA/ZGlFa91igD+6RKnD4WKAP5AxAE3iYoA/O+RAcORigD9yIi3y5WKAP1o/EwzpYoA/AvGq3+ligD+Ks6Yx6GKAP1nWzvfxYoA/9jX2b+9igD/EDf387GKAPzXDxrPyYoA/eXji5vZigD+0svrL92KAP9ITGBP7YoA/KgZzpANjgD+S3WJeDGOAP4Rg4joJY4A/HPVsMQRjgD8JstPVA2OAPzIe5hL9YoA/RWMMGQFjgD/aKYOTBGOAPwkbhHwHY4A/L6TBtwRjgD9QEpCcAWOAP2+k51UCY4A/0H23twxjgD/Q17e/EmOAP/3ZkG0QY4A/3NCCPg9jgD/tRfP3CmOAP76cfAf9YoA/jEoTGP9igD+k6+Fg+GKAP6TZcg/3YoA/DNGdlPhigD8m11V29mKAP+tnlmv1YoA/t3ptwvBigD9JVS8N8GKAPyqa2qPyYoA/sVyrGPJigD+DIi3s7WKAP98yBHvxYoA/ePwNAfpigD/SlzdPB2OAP/mqxI8FY4A/S1AU2QJjgD/NdPp0AmOAP5o1NBb5YoA/kUkix/digD9/ou15+WKAP8ypMkUCY4A/07NwgQljgD++FJabAWOAP8pjG5/+YoA/61YeAghjgD/jRi5dCGOAP2cPW6MOY4A/sCwJ+gpjgD+e3kBQBmOAP90c1IwNY4A/epc1UgVjgD/EUwxsAmOAPyHQMuP9YoA/5MAZ9/5igD+TVb3E92KAP2Pu/KUBY4A/uWyPRf9igD9Ej88C9mKAP8Hf5nnwYoA/ANbBWfZigD/5MWlO9WKAPw9NiKD+YoA/i8RkiP1igD9vu2gOBWOAP8qZUOgJY4A/yy2YBgtjgD93Z0JTEGOAP72THmEOY4A/TBuVxwZjgD+uZ0PBBWOAP/f6quoCY4A/HSm21/5igD+FdzOFBGOAPzsj/+oHY4A/DCMCaQhjgD/0kILqD2OAP4KquEYVY4A/p50YhBJjgD+Z85wNHmOAPwLEKMUVY4A/ZtQrbxxjgD9ViI7gGWOAP6hnizkeY4A/NXcTdR1jgD+2amr3HGOAPzdARDsUY4A/qKUlUxpjgD88ZFsBF2OAP5aROnIXY4A/awD4hQ5jgD+9KaHU/2KAP9l7FSgCY4A/r647gAtjgD8QIFzdE2OAP8CvHfUaY4A/YWyeNBdjgD/gNUmTGmOAP7TAavEXY4A/cb8pwx5jgD/DMLYcIWOAP3JAVWAqY4A/VD+BVCJjgD8DAY1lG2OAP96NkmISY4A/+7qQQxNjgD93oH5wFGOAP4FvAicMY4A/JVNDwQRjgD9b/iz8AGOAPyDms0YCY4A/qUeHtwVjgD/59DU1A2OAP7oiJRUFY4A/4NY7ivNigD/vClvW6mKAPyJcAD3jYoA/zt2Qx+digD/2KYo652KAP4DhcdTfYoA/uqLCuuZigD8nhGS762KAP1KblTbsYoA/Okpou/JigD9EhL4k/2KAPwqwVgMIY4A/VZS4OAljgD8Cui6tAmOAP2iboowCY4A/kBeXWAZjgD8OLVV2BGOAP8mH86AIY4A/s6M0zARjgD9O+37UA2OAP8356UQHY4A/ZBohl/5igD9GZcS39mKAP3fJudTnYoA/ki3JSu5igD84rafl7GKAP+y5FtXqYoA/0BUeSuVigD+CsGdv6mKAP7w3cN7gYoA/+0RorOFigD/l7+a05WKAPx/73PnRYoA/hD1tyspigD8PdzrizmKAP4rRU/TTYoA/UuSIANJigD+Y5ti0z2KAP5i2qpbHYoA/Devn9cpigD/gFMayymKAP4o7pFbBYoA/k/ndYM1igD/Lh6GqyWKAP9dyBQjLYoA/GRK/uMNigD8h+CV+xGKAP2M7OX3BYoA/36y6fbtigD/Dd7zYtmKAPxHNnA28YoA/ogFKaq5igD9wmdhos2KAP9tlOE61YoA/hbfeF61igD/d1xJdomKAP41QikWgYoA/a2jRepZigD/eF74Dj2KAP1L0aTaLYoA/PuzlyIpigD8jNcMFhWKAP1leSgKAYoA/8mjuUYBigD8+B45KfWKAP/3o92V8YoA/ffR9B3xigD9le6VHfGKAPwpgBM1/YoA/L3U0e4FigD/ERtx/emKAPwLYBZh7YoA/kL+DfYFigD+CyJL3eWKAP4Np3B53YoA/mgjmN3JigD+GelmPeGKAP33alp51YoA/r3eqs3ZigD+G1aWZcmKAP3MX9ptxYoA/0O1A+WFigD/5ZodJXWKAP3UjvB1bYoA/dQa2AVtigD/cTE8aUWKAP0NGRfpNYoA/UhsW60ligD8jmOZxVmKAP4aG4BZXYoA/JjvVWmVigD8BvtqqZmKAP9bm1pVqYoA/7GJRPHVigD8cExPTc2KAP66rnSZvYoA/xDMOQ3NigD/9nDKzfGKAP7SPbCp+YoA/1ZeVKIFigD8WjW86dmKAPxAhgnV3YoA/QisKmm9igD8BPQrbbGKAP0kKNqVuYoA/vUBwPGVigD+CSTfZaGKAP561ij9nYoA/3lwT0F1igD+R1uB5XWKAP/gI+h9iYoA/ugenZHJigD9qji7/emKAP0fZY9t2YoA/hd+co3higD8MduGsb2KAPzPV4cJrYoA/oWJ3LGdigD8EMbU0cWKAPxuS9VhuYoA/9an2yXNigD+lbls9bWKAPyOSe91tYoA//KkCIWligD/xUalbZGKAPz5Z4F5dYoA/GhfOpmdigD9q3RmqYmKAP/zwEDReYoA/8MYJfVdigD9mIyv0VGKAP/euaAFbYoA/sIeLCV5igD8pm33jVWKAP2LTiopWYoA/+XxzSGNigD84+srDYWKAP21m6QJWYoA/M9ZMiU5igD/abu1FT2KAPyL6f+pMYoA/xptTY1digD86SzyNVGKAP01t/nBUYoA/S34V3VZigD8qK46rUGKAP75QwmVOYoA/pdO/NVZigD/cVIMJXmKAP6Y5ccBXYoA/I6uhdlVigD+x4lxCVGKAPzwu+WVVYoA/+e9NVVligD8YnLwKW2KAP5Bx3dRgYoA/2mFN9lxigD9mKCTuX2KAP/13SjtfYoA/I9QjJmZigD9GLfEzaWKAP0VvsbFkYoA/plvMmV5igD+nelQmVmKAP1J39CBUYoA/whcEhlRigD8XxVTmXGKAP061KndZYoA/l4DtgVxigD8YKRWdYWKAP39k6i9kYoA/X6Zt+GdigD8NDyxaa2KAP0RNWot5YoA/sErn43RigD92HBqxbmKAPwrCZmFlYoA/TF7rs15igD894dMKVGKAP+w26L9VYoA/DDT6yFxigD8UrccaXWKAPzF5JU1cYoA/bHryMmFigD8GcGMqZWKAP69oYo1hYoA/XxGxoGZigD+MJ6++XGKAP3ZmX11eYoA/02tKvGBigD/FNoN9XmKAP+kMmvRcYoA/H9DfjlNigD+rDlRUUWKAPzenmA5DYoA/Y+gFZ0tigD9Ey+GLUmKAPyvxYqBYYoA/uHU96VVigD+dp+z2VWKAP0Sp0O9NYoA/rcFZ/05igD/wTc/aW2KAP1WMeBFaYoA/DEW071ligD/ZKQKeWmKAP1MhwZJWYoA/abnGH01igD+bfzXST2KAP6Xz13pQYoA/nC6O0VNigD8dAD5XVGKAPwm/nFJUYoA/fXLAdFBigD/tWUwfUmKAP70TDGdYYoA/W9Cvx2BigD/ly8xaZWKAP7SZsXNsYoA/pyRYp29igD8frFKKc2KAP5bJk5RwYoA/6Z2RCWtigD9TlNZVamKAP7thCTJvYoA/kphcpG5igD+SGixleWKAPwDaTyV1YoA/bn0b7XhigD9syJjaeWKAPxE7yJV0YoA/LYlGdm9igD+FEv8BdGKAP+MSKRFwYoA/IqSg1W1igD80sGtTaWKAP+H7XyBlYoA/xa6wMWZigD9iSyP6Z2KAPw3ZMpNqYoA/h5CPlFpigD8fm9STU2KAP6iW3bZLYoA/AL0ScVNigD/hLeXdWGKAPwp5Xn1ZYoA/rMvxhGRigD+98F/gY2KAPz76Q1xhYoA/X883TFVigD8lVZEyS2KAP4vU+pdJYoA/mbjchEtigD8WFOG2SWKAP4olU/hKYoA/BJeZ6kpigD9J/uJnT2KAP+oVulNHYoA/Arqdv0NigD8BVohSR2KAPzv3h3lMYoA/Aw6L6EdigD9wrgXRQ2KAP9/tz2xGYoA/CTBx7UZigD+cALuNPmKAP+JxwnNFYoA/HfViAktigD/2YtNlT2KAP41mrWtNYoA/L9szh1digD9H9+fJV2KAP1EFoZpNYoA/gc7B21ZigD938iL5YGKAPw6J9JxhYoA/a93IRWVigD94caDjXmKAP1k5gvZcYoA/GoiAKWJigD/JGawSYGKAP50+ZG5iYoA/jYCvYmFigD9BB93xU2KAP3UjxkdTYoA/RjFsyFJigD9dXsU1UWKAP8yEzsNXYoA/Ib7n4VFigD894/P3U2KAPypusBtXYoA/KpAqoWJigD9Zr1+XamKAP8nTEPZoYoA/6o/+GmligD/1jlcWcWKAP1V/nA1xYoA/ApVdOW5igD9a2LCXZmKAP6RojL5nYoA/q8h4gnBigD/WjQqNbmKAP1siHkJsYoA/AA9TmW1igD8iz+uiaGKAP2KVmQhiYoA/d7YZkV1igD+Vs4BHUmKAP9QImyBXYoA/0BZJZ1pigD/PFLOJVWKAP2Y2F75TYoA/uFEjxVligD8zazE1WGKAPw+US75LYoA//XaqF09igD8Ba2B5U2KAP/8T0lZXYoA/24Rzwk1igD9+sds7SGKAPy/kE0ZJYoA/4ihnwE1igD8luSOMRmKAP5atFM8/YoA/6fehzzxigD8Lbud+O2KAPyw6h8g3YoA/Wq01Y0FigD+3co1VRmKAP4cLXsJCYoA/2p8cpD9igD+9+eAbQGKAP9oJi/BKYoA/S+56LUtigD8ssxU7RmKAP7uyKR1IYoA/JnMu9kZigD8xJVZIQmKAP15znSZBYoA/SevavEBigD91PsVfPWKAPwQAmHc8YoA/pwHk+jligD+CW55pOmKAP6zbf0E2YoA/nkzdVzJigD8f606UNmKAP2MKP5wtYoA/2y2DQzRigD9MDGjaMGKAP+6A76czYoA/2Ef1ijJigD8BtVp9MWKAPzgPYysuYoA/0CRMXSZigD9DhcCZLWKAP7bwKPgvYoA/THg3AzpigD+UKKrcNGKAP/QBSnQ5YoA/jV2810ZigD/Wuup2SWKAP58kgW0/YoA/R6VzKj1igD/tXTP3QWKAP3/h4q09YoA/+TVWQD5igD+ynKI8N2KAPz/8Zlw9YoA/fFBnHz1igD8F/OHtN2KAP62nA1A3YoA/UrrKgTBigD/uOpUCLGKAP4kwLJwtYoA/Ugp8wShigD8OtnqqLWKAP01wncgnYoA/pHiRtyZigD+QNuiJJGKAP1nQSiQvYoA/vZzxWipigD+uoPmpGGKAP6qqQDQeYoA/CjDTnxVigD9DXNw5HGKAP50fbfUVYoA/utDH0BBigD8nKn/bDmKAP/rfMtUSYoA/lLHztA9igD+jz6SCC2KAPwRpW/kJYoA/0c/k0f9hgD/b65wv9WGAP+7jeiHyYYA/F3uQG/VhgD8lyLJT92GAP/Q41KD0YYA/AfsbjfFhgD98Xi7n7WGAP0UvUk7nYYA/mbxNjOJhgD+xmkrZ72GAP0AQwTjxYYA/xd7NAethgD92vMzE6mGAP3jY6nznYYA/CLGwcuphgD90f+hL+WGAP6RN2xX7YYA/IDLROfxhgD9rGxHe/WGAP6EW1pT4YYA/NvYz8fZhgD+J/2xC8GGAP4hhHWnxYYA/P9O9TfVhgD/MryJP7WGAP4KXgnDxYYA/DOtsc+hhgD+ZthPn42GAPxdzzfzeYYA/HMTnidxhgD8UYVGs12GAP5ST0krVYYA/sNXDfMVhgD8cO4yUwWGAPwsW4GK/YYA/rz8MisFhgD8RNv4Ht2GAP9iSRX26YYA/+CIXFLthgD8W923ys2GAP5swtGmxYYA/JCnEWbZhgD9Ey629r2GAPxkxz66pYYA/oIdcBbBhgD9lPx4nr2GAP59nwEqrYYA/vceK06dhgD8tQBsOrGGAP9OPoeesYYA/arqZzqdhgD9A79kGqmGAP2zwnq2yYYA/pT/l3q1hgD8eh+bermGAP52sOMKxYYA/kRWcwKdhgD+NxGKOqWGAP9PvYPOzYYA/++t/aK9hgD97PNp8qmGAP3yjU3+eYYA/nHwCGplhgD//G8DMnGGAP0JYH3udYYA/Bl7+VaVhgD+VgCi9nGGAPwdKXrGVYYA/0QW2YZdhgD+E7dEdkWGAP29PAyaJYYA/w2sQMoVhgD/EZw8QgGGAP9r1QT6AYYA/3DrY0IBhgD/mbRgRi2GAP7LHTWGUYYA/b8LOSpJhgD9VQpzOmWGAP6DaNW2ZYYA/TUgI+JNhgD/14fMpk2GAP2Wq4wOMYYA/YzRErYRhgD83OTIZhmGAP3O+pkuMYYA/fI7u/IlhgD8IR93+jGGAP2USaqWPYYA/FP/+LJBhgD8C5Ts4j2GAP/Aq/MyQYYA/9k061oNhgD9OEhSLfWGAP8TeQsJ/YYA/55297oJhgD/2ykETgGGAP1JAaGCGYYA/xDTTH4xhgD9sM66Nj2GAPzreoAuTYYA/GrlBeI9hgD9bHPHIiWGAP3MjLy6EYYA/dyZxtYVhgD/UvHU6hGGAPySQ9k2AYYA/sO/2IIFhgD+/f80Of2GAP3GM0O57YYA/1wYaaHlhgD9JOtlwd2GAP6CUQ+R8YYA/F/e/UnVhgD9K0wf5bWGAP0gEdoBuYYA/04ATOnRhgD/ye6cue2GAP7Lrf9Z8YYA/5X9HWnthgD/3o5qNgGGAPxGpV3x9YYA/1N4r1YZhgD/7fWRYh2GAP7lG3z+RYYA/vPkU+JNhgD9NNrXOkmGAP3UXLEObYYA/7QSx9JBhgD+rt1NzkmGAP0x2qDuJYYA/356TVolhgD8cNOG+iGGAP41kWLKJYYA/5Y3PWophgD/hYvOBh2GAP0uPrECNYYA/ePbkwpFhgD+3IgiVj2GAP/+2t66TYYA/Te4bEJZhgD8phljJlWGAP0CHiJGeYYA/Xmao0ZthgD/ZTNtQomGAP67fS/ujYYA/qJdyBJxhgD8L6QlAmmGAPxU2VpGcYYA/ykM1aKFhgD9eiq9qoWGAP4dx37GkYYA/RFvMValhgD+ZBs3WqWGAP7M/LYmrYYA/ctqwfbVhgD8HMbC3uWGAPyD8nW+5YYA/8lrL5rhhgD8yQhOJsGGAP72vjwKxYYA/BYAxwq5hgD+XjoxUtGGAPzZ2OTy5YYA/Ue/74rdhgD8Ux4oqtmGAP40V8i63YYA/irr2qbFhgD+VR2iStGGAP8L2Juq2YYA/nXGF9L1hgD9qsVemw2GAP/i8fNS7YYA/HetGm79hgD8qmCLLuGGAPzJVkZe9YYA/xNkurr5hgD9milF6vmGAP8+SEYa7YYA/W2XV77xhgD+h31Ibv2GAP4R35g/DYYA/91hvCMRhgD/TGSZt0WGAPz1utIzbYYA/gR3GO+VhgD8p1wUK4mGAP0zcj3vnYYA/ohU7zNphgD8mvsp732GAP6UgsbziYYA/F81Xbd5hgD+916Kw52GAP8ej9rviYYA/jKzvqOZhgD+nqfI85GGAP1X2b7vrYYA/9wXbpO1hgD96+FhQ6mGAP8FFB5rsYYA/VmJgm+dhgD9Im5wR7GGAP4L5dyXpYYA/oApdTe1hgD+rGSTI52GAP7o9PcjkYYA/BqaXHOphgD/nEZXc8WGAP7ojpYn4YYA/XQCo1/1hgD967QhnA2KAPzGYN87/YYA/QREIZvRhgD9FIZaV7mGAP6q8TXP0YYA/SjNuBu9hgD83Rlam+mGAPzRjwwT7YYA/71tSEfFhgD9j2Kgt82GAP2uVJWT2YYA/dOA5/PJhgD97Oq3v8WGAP30zDon4YYA/CJPn/vRhgD8Y0/+/8mGAP/ea4QHkYYA/aMBzYdZhgD/2GX043mGAPzlPl1PcYYA/4e3MW+BhgD9x+pTF4mGAPxDaAsHiYYA/kKTHCOlhgD/8eZEV6WGAP4CZvyvqYYA/6/Shh/FhgD/014dgA2KAP0zvzQcDYoA/nXLcjwRigD+Exv2jAGKAP+LVA7oCYoA/OjSBN/lhgD/b5fOz8mGAP5uTHfbzYYA/U5STfvdhgD/7PWOl8mGAP601w97zYYA/rk6rruhhgD/o6MGE5WGAPyDPdiLlYYA/XfHszuBhgD/1SL4N32GAP35Ja6flYYA/+vMsJvFhgD9KO7vf+mGAP+DxXxICYoA/b6KlowJigD/UKoNX+mGAP+AVy8b5YYA/DSsdBflhgD8gJXAqAmKAPyQ94ycDYoA/XKMS6vZhgD+ZOrYs92GAPye9JyMFYoA/5MmUDPhhgD9JVCQV9mGAPzc6W4LuYYA/j9kI8/RhgD9HiDNE8GGAP1DxndrmYYA/TdUeEOFhgD9AbgFd3WGAP+TvKejXYYA/cBunM9hhgD+Yi4JA1GGAPwty+PDXYYA/pwCg5tdhgD/GzWPe2WGAP31P2OTOYYA/oC7dQdZhgD+Og53P0WGAP7d0PUfXYYA/2KouOdthgD/kASau32GAPxHSytPRYYA/P7Wo6NdhgD8u6n+E0WGAP99KadnFYYA/MRjiRMNhgD/z+XA4wGGAPyvHlFvBYYA/r8POfcdhgD/bjIKr0WGAP8EWzxXbYYA/TMbVeN1hgD9KYqCC12GAP5Il81HYYYA/CJud0tthgD+75GIB4mGAP3QZJ3DkYYA/vZx+6N5hgD8Uedzd4WGAP2ayqfbaYYA/egwDxtNhgD8nxvWp42GAP1N7+rXnYYA/Nb2qxudhgD922CMY5mGAP41P283iYYA/SjlMi+BhgD+RFJyL5mGAP4QiRSbfYYA/OR3+Z95hgD/KZAGP4WGAP0Io6T7aYYA/G8VeINNhgD9PbNBx12GAP/8I3LPQYYA/S1S2adFhgD8NVh1q3GGAPzaQZm7gYYA/3EfTbOthgD8g9wjn7WGAP3bxtd/pYYA/wiwYSOthgD8hzrsa62GAP8fmIK7pYYA/RtqgtuVhgD9KCY644mGAP85l0ETiYYA/qpWryuthgD87sfTC7mGAP44FE17pYYA/Gt/8YeZhgD/GCIkp5WGAP0OF9XvjYYA/bdSrKOhhgD+HZEgt4GGAP6OsWIviYYA/6wR0cOdhgD9ursTT32GAP2EMe/bRYYA/Xly5BcphgD+twg3OxmGAP6uP3yS3YYA/HXJOJLJhgD+fB269rWGAP/HS6OClYYA/s8kVnaRhgD90Madko2GAP5nKq4qjYYA/lEJghKZhgD9M5n/pqGGAP6NSTJSoYYA/6ll5n6thgD+SLbt4q2GAPwevtrirYYA/tV0eSKthgD++9WbErmGAP78AJZexYYA/m7Za37VhgD81wKvswWGAPwl33SS/YYA/QmCEjsNhgD9bmSbqw2GAP3/kpsjDYYA/z4i4GMphgD9f1g3kyGGAP1I2gUDJYYA/rMjDmsVhgD8jfDCrymGAPy7ywEbLYYA/bbMkesNhgD8OdxDVxmGAPzQnG8DDYYA/uTrTytBhgD8rGONkxmGAP7D72ubOYYA/eJdQjs9hgD85B0lwzWGAP+jrSvnMYYA/5E62H8xhgD8NXl7xymGAP260Hl/KYYA/hYa8y79hgD8GyzL7wWGAP02DF0XEYYA/0RWwPMhhgD+EsAiMvGGAP8kZVQ69YYA/sqU8LL9hgD9/dGwGvWGAP7KPAzPCYYA/bm9HVbdhgD/xqlhCuGGAPxigqR+2YYA/mBfg/7thgD/w4atnwWGAP1O9bQzBYYA/zZAHtsBhgD9uq88JyWGAP2NqPAXKYYA/cGMrAMxhgD9IwN+G0GGAP1SOwCXQYYA/bW6zxdVhgD+bT5Kj02GAP/hEBgfQYYA/r4Rf9c1hgD+ozkGBz2GAP5IESZDLYYA/8gtIS8hhgD9q+sUyxWGAP1ykSaXRYYA/Q6uoUcVhgD9Azta9xWGAP/FqVWPIYYA/0IBTGMdhgD/ODQo20WGAP6DSf7HYYYA/OPbvVtphgD9nkk5y4WGAP9w+0brpYYA/FQ2FbuVhgD9sNY4P6mGAP+vl6EbkYYA/ZAb0pOZhgD8wn8fL4mGAPy/oOn7eYYA/ziQjbuRhgD8MD3jG4WGAP1eU/ETcYYA/0W6x0t9hgD9QGqzW4WGAPxsw29vUYYA/idWBjNFhgD/LMEz40GGAP2DzaB/NYYA/xBsbhsZhgD/80OhAxGGAP0WWGm29YYA/VermS7hhgD/M2pnDsWGAP9g2/8evYYA/z8etmLNhgD8l54z8vGGAP2JjIyjPYYA/9OzocsZhgD9O0UBdwGGAP2r6b8DDYYA/jpwb38JhgD+FnUFNyGGAP2z/0oPFYYA/SY4JisFhgD9JPdMUw2GAPw7YBIu+YYA/JhpDmsFhgD/+MMO7wmGAP768tUDHYYA/s07qws9hgD8q6i9OzmGAPzA2bEnTYYA/g1AECNhhgD/HpO361mGAP6J1y7LRYYA/GgkQgtdhgD/EmaPT12GAP11Ut97ZYYA/PM1+j9hhgD+Lnqwi3mGAP1A44S3hYYA/M0QhUd1hgD/VWwwt3mGAP5bpTIzWYYA/8NN7ZNhhgD9k1IaW2GGAP7Zb9orRYYA/HyWc99thgD/B580fy2GAP5yuYY3IYYA/BD3C/NNhgD9BgQm2yWGAP1X0eAXKYYA/fszZvcFhgD/ZXX2UvmGAP4C8L9+3YYA/wbm0dLBhgD+h3Y77sWGAP00AK7muYYA/YcGIDq9hgD8oVFCRqmGAP1ZH06OwYYA/WvlqabRhgD8Aup9ssWGAP4Z6R9KtYYA/+vy8za5hgD/A4oTIqmGAP49qT5GoYYA/9OAHQp9hgD/VVqten2GAP5ZIGqigYYA/WcNbLJZhgD+mzarzlmGAPyZJrjWWYYA/O8GRYJ5hgD9QZQjJmWGAPyasHC2QYYA/l2xi0o9hgD/XkM+8lGGAPwNX1NSTYYA/nw5og5VhgD9x6eHcnGGAPxjd63+eYYA/rj5QXJ1hgD+WSrsZqGGAP6sPiMKdYYA/SSqDkJlhgD/46GWkoWGAP3CbztKsYYA/6UGKIbBhgD9xxAntvGGAP7xUBF7BYYA/E0DJoMJhgD/J/xlPxGGAP8+X49LRYYA/0F4MxdthgD9ns1JW1GGAP4YmgSnaYYA/Sh3Y39RhgD8i7zgN1GGAPzG+/WvPYYA/2L6ohsthgD8RtPft1GGAP7u+ZN/SYYA/51TSSdVhgD8/zjy62GGAPytEiP/gYYA/nOyVHuRhgD+Sybrm52GAP7Bb48jrYYA/lacxpuhhgD8/e/Vp5mGAP0dF0ELiYYA/rLlBXOZhgD/KV3XL6GGAP//bK5juYYA/sVNKN+xhgD+NQJAz8GGAP/4+hnvnYYA/3R+5ceZhgD/R64KH52GAPwpPcZ7tYYA/02tcSOphgD8u62cJ7GGAP4CoCbLsYYA/jddur+1hgD8LlZYl6WGAP1l8p9HqYYA/sGliUfRhgD8NFuYv8WGAPyFxvx70YYA/pk8Sp+1hgD9uI7sr6GGAP1CLPFPtYYA/xUu67+5hgD8Gh9EY92GAP3CkG7P3YYA/U5AxJfVhgD/9zeC/9WGAPzhiOVP4YYA/eeu1T/dhgD8BUpN7+mGAPwZFw0z3YYA/YnwJWgBigD/Al04s+WGAP+xpwzT3YYA/aruspvphgD+JJvgg92GAPxHpLejwYYA/AgJmqPphgD8XlfKN+GGAP6+Wuw38YYA/C3N9XANigD/wVbMlCWKAP3AeMHgJYoA/DByMKw9igD+RvhL5DWKAP/slngAVYoA/HVI+9AxigD8vzrwoDGKAP9Upy2sOYoA/AU6jOwdigD/iQIpe/mGAP01Jtfv3YYA/5ppKefhhgD8DI8FLAGKAP2pqgmcDYoA/qR8EHgRigD+5q81/BWKAPxPY7FUEYoA/NKInRwZigD9Z9Gbg/mGAP81wEbcGYoA/9JIUhgligD/3tw8sBWKAPxj2OpoCYoA/scgUdf5hgD+HXT5s+mGAPzOjMiHwYYA/5cjo4+dhgD8EPdTc5WGAPwGrz+TkYYA/4WK0wOlhgD9qYdBb5GGAP9hMaYfsYYA/gLv0hu9hgD91a4uD42GAP9cyc+XcYYA/7vskSt9hgD86LCXY42GAPwknLCbjYYA/184ozd5hgD/EI+Ad5mGAP5/6CJbiYYA/BkcD5NlhgD8AzMho2WGAP8IoGOTVYYA/W+R2Q85hgD8NwZ2nzmGAP9cfpQ3QYYA/lAuRcc5hgD83X9PyyWGAPxH9TXrGYYA/5tz4pMVhgD/yPHrkwGGAPzPpPJK1YYA/3/xE27NhgD+sx2joumGAP/Th4ue4YYA/odR4e7lhgD/7EJNst2GAP5R5FEfAYYA/vgxFNrdhgD/lNvr5smGAP4po7Km2YYA/YA/epLhhgD/ub8SMrWGAP8H4ihm2YYA/0Qs0PKthgD8PR+AAq2GAP3rOfaamYYA/7CeY16ZhgD/RtdUmoWGAPwyyuO+fYYA/8ph1L6phgD9RN50BsmGAP1y+PxOzYYA/kuaclLFhgD9XejwrsGGAP/su0y2tYYA/AAQ1RbBhgD+d1A0orWGAPz39sdSeYYA/gPCdwKNhgD88zeLzpmGAPzUx3MmkYYA/dfq9j6dhgD/zFI5RrGGAP1qrZB24YYA/zguTyLZhgD/Jo2EXvmGAP+2AVM3AYYA/QAxX1cFhgD83NAO2w2GAP4lm/B3MYYA/XkRbDsxhgD/KK0vi02GAP3fUGz3eYYA/7S8cq9phgD8fPHlp4GGAP5dD1R/ZYYA/wrfTWeBhgD+sxxp032GAP+L9EbzoYYA/m/gzzuVhgD/aKxeq72GAP5iERLfoYYA/G/0a1+dhgD98/mOZ3mGAP4BhlcHkYYA/XfNxPOZhgD/HkBQ13WGAP/FaKQvZYYA/tHRz0M5hgD/EAK1Tx2GAP0dOXcbNYYA/GvbqSMRhgD8p/ahYxGGAP7HRedDEYYA/9jLKIcJhgD/RsmwZyGGAP26gK5bLYYA/AGd2VdNhgD+Y07AH0mGAP4qAHPfQYYA/DyznZdRhgD8Sgn1z2mGAP/yRFSjSYYA/mkAmEdNhgD/HA0Hw0GGAP4JvjMbEYYA/8YwN189hgD+WKLhhzGGAPzSa8+zIYYA/sGm6lNVhgD8vxFRU02GAPyyXt8/PYYA/mEsmfMphgD8jRYxjvmGAP2B0XkO4YYA/SaUYpLdhgD9A6+IguGGAP20knbeyYYA/nK1fzLlhgD93rXIbwGGAPzhQ6JW4YYA/VUcQzsNhgD+ThdC0vGGAP7JGw+C5YYA/7WY/1clhgD836GK3xWGAPwWQRnDEYYA/RufGksFhgD8SoEoAvmGAP5A+1Q+2YYA/N/VZ57FhgD891Zw4o2GAPzuS59ucYYA/qO/ZuZNhgD974lmeimGAP27qTBKGYYA/hbugBH1hgD8TU6IdgmGAP+XV4qeBYYA/ja1m93ZhgD854AGFfWGAPzuLE5iFYYA//YCS5IxhgD/MvrVOlWGAP622CJOYYYA/HXvoq4xhgD8zsptpi2GAP9mdfJ+JYYA/pXfnk4dhgD+xkv/yiGGAPzrKUfaKYYA/fAP1soVhgD8FTZ5Ng2GAPyPv1DF8YYA/mhra8oBhgD8qmy2GfmGAP5falzCFYYA/heZqr45hgD86cvUGj2GAP+P/ceuTYYA/gn5APZRhgD/M8ecnlGGAPxt2N9WYYYA/p69rNqBhgD9GWXJtqGGAPzbY9CeoYYA/tmd+T6NhgD8ImIxPomGAP6Cn+gytYYA/e6dLqrZhgD97dIPAr2GAP/d+F1uwYYA/fSIMt6hhgD/Fku16pmGAP7rpaxCrYYA/9zXXnaZhgD+8Z/3Hn2GAP3DUW/KsYYA/h9BgcLZhgD9H3G4usWGAP3C5146vYYA/wGFP1LhhgD+3Ps0rt2GAP5pBmkC2YYA/0dxSN61hgD9tld0Iq2GAP2FglPG/YYA/4KcrGsZhgD9QnZ/nxmGAP+7KGafQYYA/teXcCMthgD+ZWA+N0WGAP6dHBRTTYYA/qDXLScphgD8ff3ejvGGAPwe7svXAYYA/WFo517thgD/GcGjduGGAP+COoT29YYA/J9Yc9sJhgD8voUWVvGGAP1/pirayYYA/aC7rJ7xhgD8n1dfLvGGAP/4VmAvAYYA/5Mt5JbZhgD+AZyScuGGAPzzWSr69YYA/VWnvcsRhgD9GJVWwyGGAPwlWfPjFYYA/sgFrycBhgD/g/y0xzGGAP1VCqCDKYYA/1FPPOcdhgD+N46SFxGGAP3MA8Vi8YYA/YCFFpcZhgD97dPTPymGAP6xklWTMYYA/pMHcP81hgD8R5SBhyGGAP0kndTTFYYA/u7sOMMZhgD9gUannwmGAP7ddhuu6YYA/1hDvZsVhgD/+BV8awmGAP2lcBsjOYYA/Vt1eb9BhgD8Cy42J1mGAPzzpgMrTYYA/CU21NdJhgD+xDrpMzmGAPy42KX/MYYA/+B7Rj9hhgD/7X/wP1WGAP78IZ0bfYYA/iDv0edlhgD/Cr1WA5GGAP69AG7DiYYA/G53jc+phgD/PRrB35GGAP73FV0PpYYA/ziqRzuVhgD+ufHV24GGAP/wkorDmYYA/Sz9e3elhgD/vyG8+4WGAP/gQQRLXYYA/iJs2ONlhgD/3ccdX0mGAP+M3ILPTYYA/0Qm5RehhgD9yquGk6WGAP2CvgS7qYYA/SRC0BOthgD98ezGx5GGAPwhihuTlYYA/gMXPT+BhgD9F3NYI5WGAP6VquBPqYYA/bBHqjeVhgD85YQSy4mGAP/6xgLDlYYA/LoP1e99hgD/2IJxL4mGAP0udAE3eYYA/rNi/X9xhgD/9HuGH2GGAP7WchVzRYYA/KL9KysthgD9hEw920mGAPwskj7zSYYA/x9Sy+9hhgD+KRpNO5WGAPyNYZxnmYYA/rpJZkOdhgD/UsUdA5mGAP7V1xjT3YYA/J5kguvxhgD8qmsu1+GGAP0rts4TvYYA/SB9+wO9hgD9bcQGy8GGAP2JtBnb2YYA/fGnPCfphgD/4CjVbAGKAPyGj7gwGYoA/SZmvNgVigD9Zyw1ZDGKAPx2H+LcNYoA/Ce8gsAJigD88dn0Z/WGAPzSxcED/YYA/bTvkwf9hgD+n4gYU+2GAP1APKRXvYYA/yhC/de5hgD8XKu3L5WGAP4BSuDHpYYA//neuVeZhgD/wf6yx7WGAP+OhiKjuYYA/KXKP6ethgD8uhxBZ6WGAP0ir5qfhYYA/GDcUmd1hgD/DWZiV5mGAP8HHURbpYYA/0kFQavFhgD/+ALIR9GGAP4g31eLuYYA/8lT9i+1hgD9s9AWI7mGAP5SGNWTpYYA/NqbebethgD+xRoVC72GAP2cHaunwYYA/D8MEePthgD8VgrH/+mGAP2djYeX4YYA/QbagDPxhgD+UlyKd/mGAP1Lc/TIAYoA/YzL9hPphgD9lxDH+/WGAP+J7maP7YYA/E7vBcv5hgD+6MJX2BGKAP5zc63MCYoA/hN8m7QFigD8LnUdiB2KAP3dOdN0IYoA/V0J/yQ5igD8QADPgBWKAP0yyggD9YYA/nZ5uWABigD8CSFhm+WGAPwYD8Rr4YYA/FAKxQPZhgD8miW118WGAP3hTfJ3yYYA/YcCYtu5hgD9PklNL8mGAP/Kk3ur2YYA/gVmapfRhgD9BskYr82GAP8ClA9z9YYA/htI+b/lhgD8igjgO/GGAP4jnL1j7YYA/uEP/LPlhgD/zkvnU/WGAPwBJp0T4YYA/u8NYVfphgD95yL4L9GGAP5YZfDrzYYA/EUNYK/NhgD8y1wzv9GGAP5epC9r2YYA/djd9hfZhgD/oBXhM+GGAPxb1Z6XxYYA/KJpLp+xhgD+IWuAj7GGAP1lOA9HqYYA/CoWJgPBhgD+/K5ai62GAPxWOZtzrYYA/uKWaTedhgD9ZDjF67GGAP5tgFq7xYYA/zF24qPRhgD+KaHMl82GAP6Q065TyYYA/p/Quk/thgD+BNmdrAWKAP/j9zJAHYoA/18xYOQVigD8tisbUDWKAP23b6FMJYoA/GpwUzQhigD8/EEhnAGKAP3yqpSj5YYA/4tON7fphgD84p+9A/WGAP37kgFX/YYA/c0Mu3wRigD9jJ8v0AWKAP6g4zlkCYoA/xbgU+wRigD9tO+iLBmKAPzKZ4TYAYoA/1+ynbQhigD9YWZPbAmKAP+F7UsIIYoA/fRBDrv5hgD++t15i+WGAPxY5RHAAYoA/e0sEm/dhgD/J+dXj7mGAP18xlsHqYYA/RyDasPRhgD8JD7CG82GAPznu32P4YYA/m55TzfthgD8tkTvT7WGAP+tEOyLxYYA/HsNThu1hgD/6y9uE82GAP9KLr1vnYYA/OJ0eGexhgD+OL0YE8GGAP/9/QGH0YYA/m0/Yq+phgD8TPh4P6WGAP52D9+zWYYA/y0/1ktlhgD9Onzua3WGAP9aHqbrSYYA/xypJHsNhgD8imO2ywGGAP4DdRpy/YYA/qpdClLphgD9kVhTUv2GAPwQwz4i/YYA/NSCn8sdhgD//jG7HvWGAP2tW21y+YYA/iRwWgMNhgD80EHOpvGGAPzP89F68YYA/dpqWbMBhgD/xtFnPxGGAPysFQFHDYYA/iNn+m8hhgD/dSH3u0WGAP/1ULQbZYYA/R4+tRN9hgD/ve7UA0WGAP9zFplbSYYA/d2Hn58xhgD+h80HQz2GAP1oZVQjSYYA/GJoaa9BhgD8RuQnr1mGAP8sPeLLjYYA/L6MnF+FhgD/ZMyu142GAP428rTLpYYA/ntzG0ethgD+VyP937GGAP2/9oT7rYYA/RJLyA+RhgD9jZsTD1WGAP40jhSTYYYA/Yfr+b85hgD/5/YKfzGGAP7A1ZB/PYYA/p6MqAtRhgD8J62IZ1WGAP9U1xxLMYYA/N4Ov69FhgD9J7qmox2GAP+p/dufHYYA/ejL7B8dhgD/JfhASy2GAP9bUxfHOYYA/PW73bNJhgD8N8PDrymGAP0ueqInJYYA/xEGDfL1hgD+ZciIqvGGAP7XiFCzGYYA/lQL4zMZhgD+lqd3oyWGAP/9dycbJYYA/W/7nI8phgD+COUIqyWGAP4AYd+rMYYA/bLrG3c9hgD8vveeSz2GAP8TY8iraYYA/JS2BytVhgD9QovkR3WGAP9Ap3FfUYYA/147iwtBhgD8FC+ppx2GAP84H+4nLYYA/tPeaK8hhgD9JSjBUvmGAP7pbLvnAYYA/rEcPZ7lhgD9YjnQquGGAP2+3ckC8YYA/yWSoSbphgD+wySjdu2GAPyf7tz+2YYA/eWDenrJhgD9SL0x5s2GAP5eQkg21YYA/cB3XHrBhgD/KyYmzsWGAPwgiQxWrYYA/P1DQWqVhgD/1TkYbn2GAP0w4HxiYYYA/xkfB645hgD/vwWIwiGGAP9qGKE+EYYA/bSMvnIFhgD8wfJhTfWGAPxp5RqGBYYA/xt0aIIdhgD96LBpGlWGAP3Ltyp2dYYA/qpICsqFhgD90fUJSn2GAPwZQldSfYYA//gduPqFhgD+h/aLlp2GAP8KOPOikYYA/qCl8Hq1hgD/lF1StpGGAPyPOpE+hYYA/y9w/NqNhgD96310UpmGAP8M+AiabYYA/opFTH5phgD+/X/W1nWGAP4KZJ+KaYYA/fkYtj5RhgD8vkiEgkmGAPz1lmEqMYYA/1taEq45hgD8NZ3MXh2GAP5cVHWl9YYA/MLz+Y39hgD+jFwXohWGAPzux3LSFYYA/Z6j9U4dhgD/RXDKKjWGAP11YRPaOYYA/mPoENY1hgD+GhxF7hWGAPxkJpc+KYYA/Ck6vrIxhgD8uwcgEimGAP8YenDKSYYA/Bjp2bpJhgD8KAPnGk2GAP0rpqReQYYA/0ZWu4ZBhgD+mnEqRk2GAP133Ii+WYYA/sSRVB5phgD9z2Ql9mWGAPwEiFjKTYYA/YJdmk4xhgD+ms2AKkWGAP6GZA2CVYYA/yJGCppBhgD9DjVN4h2GAP2LlP4GNYYA/0jF+qI9hgD+PN5P4j2GAPyqNPZ6YYYA/XlGY7pNhgD/lgqN8lGGAP2OAqM6VYYA/38HSZpthgD9r/D9Dl2GAP/5B1+edYYA/jwv5daNhgD9A3UycrWGAPy/deMWqYYA/3kbh0KthgD+Gl5FrsGGAPzpEwbW8YYA/YrJkncFhgD8R8ivmu2GAP+VGpNjGYYA/l+mFBMthgD9kuDZRy2GAP9kmGE7IYYA/1vbEqM9hgD/HaGZf2GGAPzGR07ncYYA/pJfkYOJhgD9dXG1k4mGAPw3++yLlYYA//mFTDuthgD+F/FDU5WGAP8hZXtrfYYA/MOq3ROhhgD8lilZR6GGAPwS1J77tYYA/ZowUTOthgD/MnGWV9GGAP2pMYcvyYYA/sMth9vBhgD9UStQt4WGAP1ZQ0gDjYYA/RCbOYOlhgD8Yw4d35mGAP2eLasfmYYA/0IPw9uxhgD+HoWrO7GGAP6KU6brzYYA/4ohl+/JhgD+WdVgJ7GGAP79UoobjYYA/VmG8meZhgD9ipCkl5GGAP8608TPoYYA/zPxvUO5hgD9ePEZJ62GAP71nCufmYYA/YH1+deRhgD880kpz4mGAP5NzdrnqYYA/4hClVe1hgD89d5lj+2GAP0cRV5vyYYA/sD302PRhgD/QTPJl7mGAP3PjoiLmYYA/M2VrLNdhgD9fYs7k1GGAPyx228rQYYA/fu/yg8phgD90M3I5wGGAPxs6mz6/YYA/4WjP3MBhgD/pUqgmxGGAP8hzSY/BYYA/UNd1LsZhgD/Vi1+0yGGAPyZpLozFYYA/KjHA9MBhgD/Pa+aFvGGAP3Ddc/m7YYA/Q4uxisVhgD9mIa9Gy2GAP/p9frDKYYA/9GxhHshhgD9YkYzlxGGAP3VWJw3QYYA/tuwHyd1hgD84yZ1V2mGAP9VyX+DfYYA/05nTI+NhgD/u/yL27GGAP1mGNvXxYYA//XFqcvhhgD/+ExPT9GGAP9MD/fDtYYA/AScoNulhgD/txruU42GAP2zjN9nmYYA/mJa4c9lhgD/GUs4Z2WGAPxpapR3VYYA/jFOCENFhgD8Bguqsx2GAP9IEJyTSYYA/gXRCscthgD8fOUTBv2GAP9ZB+CnLYYA/lcXwEbthgD/dPsXRvmGAPyj0oUS/YYA/+8++3cFhgD8DaB4ryWGAP5LRYhjIYYA/7zy31dBhgD8S2zkVwmGAP11wo/bDYYA/Qq9bA8ZhgD8c2N+czmGAP0RY4ULUYYA/62KF1NlhgD/KJW/s32GAP+I0mR7eYYA/by2Pq+RhgD/L0veh4mGAPwPBDUDnYYA/4cH519phgD/FKMBK0WGAP04Rjn/YYYA/Bgr3jtRhgD+vdcLZ2mGAP+uiOWrfYYA/gEyE0+FhgD9jNljl5WGAPwSFR8DjYYA/CI5OveNhgD/9+qVG22GAP4ijOlzdYYA/ZLEJbN1hgD9xtCHK3mGAP9OiiqfjYYA/azpfi91hgD+ckQGw22GAPxQjAF3UYYA/8hAlOdphgD+JuR8N1WGAP8NSt53RYYA/JGe7adBhgD+qu7skzWGAPxbP+BDVYYA/HBaN78ZhgD9PKEvszmGAP304iXHOYYA/2SLChM9hgD9iMx/e12GAP4mIKmnRYYA/Le87ksRhgD90f8FDxGGAP+do2s/BYYA/Nc1occBhgD+jBKyyvmGAP+uZbW7BYYA/Zw+rRsRhgD+SXlvIwGGAP+Ah4MuxYYA/Lvyxd69hgD9FQQ2crGGAP84OpXOqYYA/e+/iO6FhgD+qsG7qomGAPxCyEj6fYYA/xcxrPKNhgD85XEtjn2GAP2otyTifYYA/pjqCwqFhgD93yjWEnWGAPxQjgVWcYYA/4H0VRJVhgD9zjIazlWGAP7Jff86XYYA/D30bGZdhgD+cRotym2GAP0xc4u6kYYA/dqN7rqphgD+ePxxDr2GAP7MrmGmuYYA/3HDUnqxhgD++SMfntmGAP+C7hvy/YYA/rzkWlcRhgD87zhMevGGAP/1RHGa1YYA/viqzIrVhgD+GC2XdtmGAP7t+cqm7YYA/ext/l7dhgD9uFeOhtmGAPycV7KepYYA/n+8DIK1hgD+oB1pEq2GAP5K7hcSmYYA/6t5T+qNhgD9CvlhupmGAP4DX7pWiYYA/TNUYK55hgD/69S+0omGAP9g45fOqYYA/u+0qyrVhgD/rITfNtmGAP/72ncSwYYA/2T1xXrJhgD9LG3VTtmGAPwBSXIa5YYA//q+TtbxhgD9ybybiuGGAP7rvGG2yYYA/QvqSGrdhgD+P1JyluWGAPywuhDW3YYA/JK7kvrBhgD8HtOAyrGGAP/qWrnulYYA/p8n2ZqhhgD+bSPnEpWGAP3uIb86vYYA/qyU7V7BhgD9StO3vqGGAP0GlzIymYYA/jF3XnKdhgD9GXgqEpmGAP68cC6+pYYA/cmT3aaphgD8ivw73omGAP0XTyjyrYYA/1Ts0J7BhgD9BgxT2smGAP9o+duqfYYA/nu+gF5phgD+WXEBLm2GAP3Gwu0KbYYA/YMVn6J1hgD8EmW1iq2GAPwPN/3+qYYA/+S+5n59hgD+SxQwflmGAP3rAmFCUYYA/uCnsMohhgD9psK7yjGGAP2lnzsOMYYA/w6j7WY5hgD8Zk4YplGGAP7qxgzqJYYA/R7ymvIFhgD9UoommjGGAP4OMNb+RYYA/k0xZT59hgD++7sXfnmGAPwJ6R7uaYYA/JJ83p5xhgD+ZnoERk2GAP9yYXCuWYYA/JUdNoJVhgD++OJXukmGAP6HFkZ+QYYA/tBDfSZFhgD+ZwFTflmGAP3pcLP+bYYA/pqpRe6NhgD+25LaamGGAP4af/8SVYYA/F/KLIZ1hgD9YuOQZnmGAPzpOQEOaYYA/Xs1Tt51hgD+Rb4SbnGGAPySpBoyfYYA/6tcCHKBhgD+CZs0roGGAP2ggVcOVYYA/2IadCphhgD+doReUl2GAPzRzegmXYYA/yF+zl49hgD+cjITLjmGAP6xQxaiMYYA/2aQSaI5hgD8wzTNAnGGAP0/MQdiVYYA/nwu8KZRhgD8SsTjvnGGAPxvPJ7eZYYA/HwEklZdhgD8V5uJaj2GAP8AoN9WVYYA/W53/751hgD+8oWuzmWGAP6yrWGuRYYA/c8IS/5RhgD9LQSNKmmGAP7jUwKaTYYA/Li8uSqNhgD9Z7cC9n2GAP9HW70OXYYA/8l9Idp1hgD9vMHu7nmGAP0jn6WiiYYA/vsvPqaBhgD/Qt9cxomGAPwnzzcinYYA/BbKeqJxhgD9uUls0nWGAP7P7EmOKYYA/sqYS64VhgD/AwC0Nh2GAP9oQ6MiFYYA/ViKXZYVhgD9bzRICg2GAP2IFH7iKYYA/1u53+JJhgD9ZnSOPn2GAP8VHHv+XYYA/OXkz/Z5hgD99EzM7oGGAP7fbYg+eYYA/gkwV7aVhgD8L7xRVpWGAP/OUe5GpYYA/3FZegq1hgD8QJjCDrmGAP8DU8g+0YYA/2+8B7KhhgD8UgE5sp2GAPxsEuWunYYA/fZn03KxhgD8bp4bkr2GAP593C6KyYYA/4LlanqthgD/L/lVGs2GAP8CgDnavYYA/kjjVM6hhgD8auC0sqmGAP9HmWD2oYYA/5Trh+qJhgD9NeF3Cq2GAP3/nHBujYYA/OnfvY55hgD+NNH2inWGAPxvtoGuaYYA/NLbWDpVhgD/pYS+3mmGAP1jKOv2RYYA/PS/lDJdhgD87685gmmGAP3XFEgOZYYA/LDQvgJ1hgD+/0tnXo2GAP/r/CDigYYA/UGdYPZdhgD/AJ4Wcn2GAP5xCytCUYYA/+8b3c41hgD8dcy3SjWGAPx8AIROLYYA/u/Sn5IxhgD8G1zJSh2GAP/QgNTKLYYA/oBR855NhgD+06WTXjWGAP/ccKiGCYYA/ivnKaIdhgD8AHnqRh2GAP2BJh7CJYYA/4jIB5oZhgD9tbY6eiWGAPwQw8p+PYYA/vd5XzpZhgD+TAEmqo2GAPwnrt9SkYYA/tari/qNhgD+iHKEynmGAP2tMbZWZYYA/WoPQkJdhgD81ZtcJk2GAP5RK80iYYYA/3fMYYZdhgD9wCqZYlWGAPzLxW6GUYYA/C/uAf5hhgD/ZvVqTnmGAP95m36CdYYA/zac5d51hgD8ROdPrnGGAPxPPybqWYYA/JJwKipxhgD+oklbnoGGAP3O/BVuiYYA/0qhkxJ1hgD8mmLyko2GAP4ac1YClYYA/nPCNsaFhgD8gmTDznmGAP6+iUzWZYYA/iD8dfZxhgD8wKLlXn2GAP235GGeYYYA/tyjsBZ5hgD+u2oELm2GAP7YhgX6ZYYA/n4TRU6BhgD/ad4OmnGGAP4fjgeyeYYA/X0iPV6hhgD+wZFsKpGGAP9kRLTqqYYA/HzXfWbVhgD8flwKgvWGAP4BLUF29YYA/Ne5Ry7lhgD8ydyO2uWGAP8kOApy3YYA/l1oCYLphgD/lD/AHwmGAP1/rZzrGYYA/VeVrXMFhgD9zTPs1vWGAPz46TNG4YYA/OgqjgbBhgD+671Q3vGGAP2XTrS/DYYA/eERaacFhgD9awkoJxWGAPz/nk23CYYA/sn6NDcxhgD+w9/5zw2GAP6vS+gO7YYA/Ax4LF8BhgD9D4AmYu2GAP5mUDji4YYA/UOMYHrRhgD8VQRvFrGGAP1Yu+EuzYYA/rLUPZ61hgD8luJOormGAP/2hn+KhYYA/CGxDzqFhgD/dqa7IpGGAPy7SF32lYYA/TCt9mZ5hgD/9vIpDomGAP9p7poqaYYA/7adkeJ9hgD9mrolgmWGAP5DSn7uVYYA/mRIh/JVhgD9cr3XzmGGAPwvz5FacYYA/bmNi0Z1hgD+tUrtkl2GAPx5j/1GbYYA/q4yCT5dhgD94vnBdl2GAPyuAfHiYYYA/SVtvZpxhgD9YOorcoWGAP2H194SjYYA/aqecF5phgD/vtSXbkGGAPzxeRoqTYYA/iA4/G5VhgD9pzfQclmGAP81avJSNYYA/yGv0oJRhgD9uhdZBk2GAPw6fmJGZYYA/rVGuZ5VhgD8LUzR9kmGAP1NjEI2TYYA/yfejLJJhgD8H7+yAkWGAP3wbXZ+XYYA/VRENc5lhgD+6LXqnp2GAP5nhxsmcYYA/Aqusbp1hgD/xfR8Dn2GAP0mQuvKZYYA/z1+yqaJhgD+pkYHqnGGAP+bec/+bYYA/hhHwbZ5hgD9xponYmGGAP13a+aiaYYA/6KESw5ZhgD8onrTLlGGAP4+uXHCGYYA/iMKKCYRhgD+F81F4g2GAP0OSTEeLYYA/P5gRlpVhgD9BQOddn2GAP7UIvvGkYYA/JO1sUqNhgD+DQOW5nmGAP3UFn3eoYYA/LfCqiqVhgD8RkTNbqGGAP40vn3mmYYA/g/AChJZhgD8Zw05hnGGAPyRy6uSlYYA/uHx5JathgD+SeAArqWGAPw1NGBOnYYA/OgH5jKphgD8QJi/VrWGAP0GZvBqzYYA/brfjfrdhgD8JtLYpvmGAP0oRr83DYYA/S9gj9MVhgD/0fwjxzWGAP1ewrbXTYYA/93xhr95hgD8si4Q442GAPxU83cTcYYA/cP304OphgD/RXOnn5mGAP4WxTBjhYYA/JFhU2+NhgD/zX0VW5WGAP9d2B0LbYYA/XjV5wNhhgD8bVvZS3GGAP5STbRvdYYA/5i212tlhgD84IzdY1WGAP0JhXoLOYYA/q/ocNtBhgD8TOW+RyWGAP6FUQqvGYYA/PO4lbrphgD/tNLGzt2GAP3IoPcXBYYA/U2yXJMlhgD9ha5iGwmGAP/vGDGXDYYA/Rhb72cFhgD8ax9yDuWGAPwYnpfy0YYA/D5KBkLZhgD+bkSseumGAP6IVs2S2YYA/NIgG8rZhgD/iIJY4tmGAPx4/qlq1YYA/+6P17bthgD8WAuj7tGGAP1Wqo8W2YYA/55YvWbRhgD9ai4f3tGGAP6k9XkC3YYA/uAUlFq9hgD8KAjgjr2GAP7QY0AuxYYA/8AWL5qZhgD93TTh4pWGAPzy5pXiwYYA/1OYdkrZhgD+A1OrXtmGAP51rtZG4YYA/Ho++u7dhgD//VkS8t2GAP0FUF5+5YYA/z+MccbhhgD8Y9A7jxGGAP8O05N3GYYA/8lMfUbxhgD857CdDumGAP57V3vW/YYA/kLWp/sRhgD8xyuRcyGGAP/bvryDJYYA/l+W6os9hgD9efhJl0WGAPyf6Ex7PYYA/RuwUHtdhgD/eVQ2l2GGAP7Ne29/UYYA/3Ti+ktFhgD+xE7eXz2GAP7Ehgi3WYYA/zNFOXtRhgD/bm9ma2GGAP0lhiRDlYYA/+KOQG+xhgD9XdBtS6WGAPzSN+V3eYYA/zra2tN5hgD/tR9KW12GAP83ERCLcYYA/5nSoH99hgD9bYAHW32GAP+8nzCXrYYA/kyJo9+1hgD/9H7u16mGAPwYkYGnpYYA/4Cxd79lhgD/mG+RS2WGAPyCF1SPdYYA/EVMPmNdhgD8VXpA33mGAPxmr29zfYYA/8cM9wd1hgD/V/K8/42GAPzTmF2zbYYA/6Ow+btdhgD8BAp4L3GGAP/66PbXdYYA/QTOoCt9hgD/QuVwx4mGAP3XR06LeYYA/xMfwoeFhgD8dpFwG2WGAP82gfbvYYYA/b4eCFN1hgD/yspMt4GGAP0Gjo0TeYYA/YbvQutthgD/vYCrK2WGAP17dxmHVYYA/Vuw4ltFhgD8MFUkW0WGAP95B3zrQYYA/faJ3fs5hgD/FvDXdzWGAPzXRNHzQYYA/Y6DcV85hgD9rQuPy0GGAP475XdTSYYA/ABKendVhgD+HBRFy2WGAP9TowDnSYYA/uwnqZ9JhgD9/u+Dd02GAPyriwdjTYYA/Vyvu8tphgD+9P09d3GGAP9lpgKziYYA/9Rutj+dhgD/PBN943GGAP8Vp6+rhYYA/JbDV0+ZhgD8LNOzx8WGAP1M9frbvYYA/7opkAvJhgD+q+1wT82GAP4r6ASPwYYA/Ol0DWfhhgD8n/i1eBGKAP/M76cX7YYA/kfJUof5hgD/kwQhI+mGAP4KNndH9YYA/BuVTbfdhgD9o7dHj/2GAPzXnUED0YYA/yit9iPVhgD8THyALBWKAPyzsSu4PYoA/ed1YuQ1igD/bXcajEGKAPwfUiTQTYoA/mGFyYRJigD8BGrTSFWKAP+7Kqw0WYoA/t8gMMhFigD8PPlBzEWKAPwhKcjACYoA/4u1KkgdigD/DE6xKCWKAP2YqtpUFYoA/l8arKfxhgD8YV3dH+mGAPzGpLvP8YYA/C99FlflhgD9bohim8mGAP5KY64HyYYA/NMfC4uxhgD/y6J7b5WGAP1uMY6XkYYA/mH6/IelhgD85Gu0Y5WGAP6ojARLrYYA/faJljuZhgD84aD2f62GAP/DGbWXtYYA/rLnD6ulhgD8529Dc6WGAP1WlzoTqYYA/zvWsvehhgD837dgO72GAP6kzx+r3YYA/COFNtPNhgD+cAXMf+WGAP4+20Rv+YYA/l5+5XPphgD+DSdSH9mGAPzA0Nov2YYA/kWOhkfthgD+ptr3n/2GAP9PhcKUBYoA/ddztFwhigD8hT+SI/mGAP4vZh9r+YYA/6WxZZ/RhgD/Qu26l9mGAP/ccQ6n3YYA/RRZa5PdhgD83HBni82GAP08sh2D6YYA/N+/K6flhgD9YbcV79mGAP0jlBJD4YYA/nQVxjQBigD/SNnA+B2KAP3odW3f/YYA/6b/gYABigD+CLuWwB2KAP9Cyi7cEYoA/wYzK5QpigD8kmjnJDmKAPwIxZYUSYoA/aegC7BRigD9/va9SEWKAPws9Cn0HYoA/0EjtNAligD8g0pBqBWKAP7GnkMkKYoA/dBmhzRJigD9cl4r8GGKAPxGDcHAXYoA/md98XxhigD/JXmwgHWKAP7e0A3wlYoA/DyHV5CligD94odJvJ2KAP4Z4hbEtYoA/e2nizitigD/NYtqtKGKAP9UMpCIuYoA/dprmbixigD/7jYHlIWKAP73729UcYoA/eQzgnRxigD9UohomHmKAPxdUR7YbYoA/RMGDVB1igD8TlVJ4HGKAP+iE/YIdYoA/GYdT3CNigD8g8otCJ2KAP7KwQD0fYoA/BIiSRSJigD+LyGU1JWKAP7fdgv8gYoA/zm4FlSligD+wJNm3MGKAP6fZg/IwYoA/nr8+LTJigD94KXM/P2KAP6yhUBJHYoA/f+6hf0RigD8RPbtvRGKAP5X+ox88YoA/OgFUOD5igD/twdfoOWKAP+n/JHg2YoA/dYrrfjxigD95YqMLOmKAPw5UF9I4YoA/7ZIncDFigD813kqvNGKAP4Qizis5YoA/+HaEsTxigD9wfgnUPWKAPx8mBkg7YoA/c6tZqzxigD8gyJ5TOWKAP5+qajE1YoA/YJfihDJigD+D+QokOGKAP/eQNrowYoA/TbdAeyligD+VUYVZKGKAP0GaJQ4mYoA/nFN7aipigD+JeB+IM2KAP7FVOSY+YoA/RStStkBigD8o1gX1SmKAP/4hIu9HYoA/VN6v+0BigD/600lUOmKAPy+tBw47YoA/UacyHTdigD8ydtChPWKAP7Lpsrk5YoA/mxTv6zZigD8ydeckOGKAP7tzTbo6YoA/vaMNYjtigD807H6MNWKAPyiwIYUzYoA/KkpU9DhigD+z1RuTM2KAP1IjrXQvYoA/7N2OGjZigD8ULXSvM2KAP488Pwg4YoA/abzEuj9igD+Y7URwRmKAPxcFuGpJYoA/CU+aCk9igD84PAm6SmKAP7RfeNRJYoA/N93js0pigD9z0guETmKAPwZO/e9PYoA/4ghFYlJigD8w3ph3WmKAPzF1rshUYoA/GbIsh05igD85M3anTGKAP+CPU+VOYoA/dXp4hUxigD+C5KcLRWKAPwMi0MFSYoA/L6siz1ZigD914GicZWKAP2hHsu5pYoA/xCO+Q3RigD+DQhZHcmKAPwJNKrlzYoA/M/60UHVigD8rJx/teWKAP2YPL3t2YoA/XfJZEHxigD9CoFwoe2KAP/tRMBx9YoA/UimtkYJigD+JTsqaf2KAP99LA1V9YoA/r4cwa4FigD//wDpvhWKAP3rbWnGBYoA/+x2bPo1igD+KEUY4gWKAP1Dx33mHYoA/nWZkC41igD8FgPjGj2KAP6h3++SNYoA/4ahSi45igD8/tY+PiGKAP6l9GHmNYoA/vyaGbIZigD+o+0S7gWKAPz9EDxKAYoA/SPs+4XdigD/hlKVofWKAP8E8fKx9YoA/l283UIRigD/fg8EiiGKAPxF6DyuNYoA/4s98IIhigD9VeBgzf2KAP/GvMfh5YoA/WsZdhXhigD+imLcGc2KAP1joWZlvYoA/GrxOYmxigD+TEo4WbmKAP/dRI/VtYoA/mCehP2ZigD9ze5zYZGKAPwJvpaBcYoA/cIVEI2JigD8i3AUXZmKAP0+xFFxnYoA/up1PxG5igD+kpBAScWKAP0MEWPpxYoA/JoHwx3higD/Xot2WcmKAPw+Uj15wYoA/Ynlyr2digD/jDV2LaWKAP8+7CsRtYoA/X5F6GGtigD/TsHA2amKAP6OdmfdnYoA/qEROjGpigD+iZqNXb2KAPzKBCjVzYoA/x9lSq3FigD9E/SMKd2KAP+bWNuh3YoA/atwILHZigD8Xn7WcfGKAP+BkLr11YoA/W0+GuntigD/j5Ui4hGKAP3VMCkCGYoA/QTTbx5BigD/n3P5LoGKAP4nOr9ekYoA/LMe/lZ9igD9ZeYgVoWKAP2H52q+VYoA/J/Vg6JxigD9vOfJjpmKAP9I6CWulYoA/FUIVeJ1igD/Q28IIm2KAP3Oc6wGgYoA/ZglPp6digD8AYPixm2KAP4gRriWVYoA/zopeK45igD9iI01xhmKAPyJywKl6YoA/T5vp7XpigD9wdYbceWKAP4khRtV/YoA/JZVIsoNigD+nufVbh2KAP7HPPH+LYoA/YQqiHIpigD+A+PnokmKAP4DnnZOUYoA/j4yt4pVigD9M5uP0kGKAPwABrRSMYoA/ct7LyIdigD97Z/1IgWKAPyoi1Ld2YoA/pATAAIJigD+1lR4WfWKAP2Y8lKx/YoA/Pl3SzYBigD83hve5iWKAP87MamGIYoA/417JiIligD8fI8/EhGKAP7ol+TeKYoA/ZjAXuIpigD+aRNf1jWKAP/iQWbuRYoA/UJvx6JRigD8QBxfgnGKAP4bTbIqWYoA/A/Mzl5digD9dZGZynWKAP92x3NaaYoA/0NUF45ligD/GxO0WlWKAP+if6GONYoA/h7mLoIxigD+AgizEj2KAP0uNjG2NYoA/+lMKSYtigD+HJIXql2KAP+a9+pySYoA/j7biu4xigD9xlVXkjmKAPykGYXqQYoA/56u4a41igD+4RYiLjWKAP9hUnLWQYoA/RaZ9w41igD8ntiFwjGKAPzijxJOHYoA/r86skoligD92rDMUhGKAP02Cj4WMYoA/vfKPl5FigD8fIRNtkWKAPyu+dwSVYoA/S5VesZ1igD8QNNhwmmKAPxGph+ajYoA/dXl+vqJigD9e9zcpomKAPyTxORuZYoA/3BQKbZVigD/aVgFYmGKAP8Ktd1iWYoA/b4u+EJJigD+7sWcjqmKAP3VpK0KpYoA/71YvyaVigD/AsUwnsGKAP43BfDGwYoA/AnkPAqpigD8NKMmYr2KAP2kCK7KxYoA/iL66wcBigD9xFS6SwWKAP5/50Ry7YoA/HYDY8rRigD/b55Wru2KAP6qoLT60YoA/tLbl2bdigD+LzHEHs2KAP/f/osCuYoA/DpCSNaligD+epegto2KAP61ShQ2gYoA/l/oJ6ZpigD8bF2EAnGKAP9E6LPWXYoA/E4lqjZBigD8+uvBRlmKAP2ScO6KUYoA/hkxQ3J5igD+s4xtXn2KAP8GDjTWgYoA/dxh2GqdigD8YsVIOrGKAPwU1g7m0YoA/j8700rBigD9imF8AsWKAP+WSqfShYoA/WaisE6NigD+ghu1/qmKAP2KGY5qfYoA/Y60HNaVigD8lE5bTnWKAPwWruUiiYoA/CBoEF6NigD/KLtzwm2KAP2V6/hKlYoA/iwmZCaFigD/TvQ+3p2KAP3u6yVihYoA/tRLiRqdigD907InMpWKAPw4ABI+cYoA/JzM8j59igD+Io1Xul2KAP5Jxw8CZYoA/0tI8npdigD9d5VG1lmKAP84t7d+QYoA/Vf6h45VigD8flO9ummKAPwmDznaaYoA/aTNoppVigD+iaDHYkmKAP1HSIvWaYoA/nv/kfJVigD+YRGAFj2KAP8l2BSSKYoA/vcm8iodigD9W+771f2KAP6fMtRx/YoA/TaY6ZX1igD+mOBo3cmKAP4bRw6VyYoA/HLAvQWpigD9LIkjAamKAP29ZYYxqYoA/KVlmKmtigD88tXLSbGKAP1hi1B10YoA/80dFy3xigD+WjaAWdmKAP2iKyGNrYoA/fyEosG5igD8IV3ZCbWKAP4lrWaVsYoA/4ZCKlGpigD/ON57JYmKAP+1xihFnYoA/m2zdvnBigD8pJbOWb2KAP0WiAcV0YoA/t5j7FXhigD9/HAsvg2KAP0Eb4VJ9YoA/Brs+bn5igD+xVGTffGKAP3orjDV7YoA/7yWZ2HtigD+dPUlVeWKAP+8TtDR6YoA/LgXdq3xigD+wpEySfWKAPztvjC6BYoA/XwX3XXZigD/A21dZeWKAP5V8qGyAYoA/53NGJHligD9hvseNc2KAP2Ajwul0YoA/eFJcO3xigD/3omHEdWKAP34Ysdx+YoA/KCi6THdigD8llGxxdGKAP0HyeohsYoA/YcqXDGligD/ICkvGZ2KAP97YcGJ0YoA/ptsne39igD+WruzDfWKAPx3SeAh9YoA/spYEHnRigD819MPUbGKAP4Hn6XVxYoA/cyIwEXRigD9t06oja2KAPz6dUKlkYoA/iIInhmJigD+nOMNuYWKAP1z5QqBbYoA/rtNPiVhigD9t4T7LWWKAP4GD6RJaYoA/2LlAIVhigD9TE8SdWGKAP7G65PxYYoA/BEMqWGFigD+ij1qkX2KAPyrbJV5dYoA/W6LrTltigD+a4KxXVmKAPx9hLxdYYoA/CCT0pFJigD9lyJxHV2KAP5NNBpdLYoA/qPmNUkxigD/VGJn+UmKAP80nLNRSYoA/HbGVXlBigD8HuHthWWKAP1mdXrBUYoA/G0HEwVpigD8w7d/xXGKAPxJgYqlcYoA/2+CUhF9igD+QF519YmKAP6QtIT1dYoA/PuaYHFligD8BjtRTW2KAPyfdDyZfYoA/Ko2VcGJigD8sJIsUZWKAPyh+ZzFlYoA/h9GvLWdigD96+p/Hb2KAP3bNJMlqYoA/VyhkRm9igD+qR0KlemKAP/RIhhZ7YoA/AVMPandigD/TZuVidmKAP17Nu1l3YoA/uYoo/XpigD9V3OoscWKAP3ZuWphqYoA/qXZd8HdigD8e4l0Ud2KAP+qFd9RyYoA/mtbX2nFigD9h74zZb2KAP6k1fo1zYoA/WpuA6nRigD9aLjJ2bmKAP7PsVNdrYoA/F5KrBl9igD81yIqoV2KAPxVD3ThXYoA/OhqzXWJigD8G9jy2XmKAP0cfu9xeYoA//Xygo1ligD+NDprUYmKAP3bKRpdnYoA/JAgxeWdigD/XLgKHZWKAP/KjJTFqYoA/HlyiZGFigD/3Xw/FX2KAP1opCEZeYoA/f/iIWlligD9pZLgdWmKAP/KR+8hUYoA/dWbvM1digD+4baSoUWKAP7yBelRZYoA/g+UmqVRigD8ejCpAVWKAP3bAvDpZYoA/yJOaWVhigD9kFKoNWWKAP5XRm09YYoA/DaTl51xigD9K13Q+XmKAP2dVWotWYoA/XMp0VVVigD+i4/r7VWKAP4Yqo39VYoA/r56aClJigD8Ql1P9S2KAP1Ja5ZVPYoA/B5EUb1RigD/64KxpTGKAP9R8JX9TYoA/13ptMlNigD+PEAYtVGKAP9FGZ8NKYoA/csJkaEpigD+SNAhmUmKAP5QPurJgYoA/UwV9Dl5igD/5QvKJYGKAP7An99VjYoA/QL+bc2RigD8LWWG7WWKAP1+0MAZhYoA/qSp20F5igD+xzLdsYmKAP/oKNeRWYoA/Yob+BFZigD9YhmqnVGKAPz21k4JWYoA/+3Eng1RigD+lRkBPQGKAP5i0Ix4/YoA/3m5u5jJigD8e1skVK2KAPzli4n0qYoA/7pAtmTNigD8LX7S/MGKAP2iLNfcnYoA/LSKJCidigD+h7jLlIGKAP3oEedodYoA/5UQMqB1igD/WyV9qJ2KAPxjDlIYlYoA/0K9N2CJigD9cJy9EJGKAP437cqIcYoA/WbPb2xtigD8wrz0tEGKAP4V7wikZYoA/jKHMqRpigD9LvYYeGGKAP88aCIQLYoA/yijyDwtigD/UFOZbE2KAP/vxwrQPYoA/TezQORRigD/CuWvLFmKAP1dWSUsOYoA/8YRx7hVigD9OD/IREWKAP7qu5HAIYoA/NNY74wRigD+Q4oTg/GGAP3Z0MEYJYoA/FYxXUhNigD8qqgoxD2KAP8pobMYPYoA/SaOfGxdigD+5EH3NEGKAP0QjlMAQYoA/FbJ+YhligD/bZxASGmKAP0Hhnb0TYoA/QvlBfBBigD+X3+G7CWKAP7G7708LYoA/wQ9AjQligD/eU+PnC2KAP/YRAxAFYoA/7NQvOQhigD97Ve6/CWKAP2gkmgkKYoA/ClaTFghigD+LI4U3CGKAP4Y+fzj7YYA/bKjnePxhgD+tx/JR+2GAPwPgaK/6YYA/TMVgOPthgD96rsrk/GGAP8Jry8oJYoA/+S9mrQ5igD++CGIiGGKAP8TW5mcOYoA/WtMNDQhigD96aatMCmKAP6ynTQkFYoA/R1jF3wJigD/15UNqBWKAP/I8GAcBYoA/Htc5qvdhgD/GhlYW+GGAP7+IKpQFYoA/HvHsavthgD++R61f/2GAPz5PwFD/YYA/XflqsgBigD+FC0t6+2GAP+FyjtX5YYA/vUeWqPBhgD9Vz6DJ52GAP8o6UEvmYYA/J+6/RehhgD9MiUP242GAP2hSatXjYYA/zeI/wuphgD8/urvW5GGAP4/udSTtYYA/o4zXke9hgD9gQR3I8mGAP8T2v3PsYYA/LhWjh+thgD/8+mOJ7mGAP7jzxjHtYYA/wwiYjeNhgD/uh/Pr4mGAP904SAXdYYA/k31QL+ZhgD9slvB16GGAP6TWUCfoYYA/ydjv++5hgD/9b2qO82GAPwI/1of6YYA/69CaPPZhgD/itntm+mGAPyCdkT7zYYA/1TW58e9hgD8Om+mC8WGAP023wo/yYYA/BsAjEvlhgD9u4CEw/WGAP5yIy1D9YYA/TWpACPVhgD8+3xwd+GGAP8m9LEfrYYA/28Co+OVhgD+7DvrB62GAP35bVd3hYYA/TDz1IuBhgD8SynTy2mGAP87SfQzbYYA/i9gAgdNhgD/OKrUm1GGAP1fLldHWYYA//5TCnc9hgD/vaCIZ0WGAP1/EldfQYYA/Zb9/G9phgD8zwhED22GAP3jrKvPRYYA/NfE3XdlhgD9b+X/31GGAPzKNERjYYYA/hczjfONhgD/CbD1Q6WGAPz4bpg3uYYA/WTcFRfFhgD/nCbze8mGAP/12FCvzYYA/dOCfNO5hgD+ytSf062GAPwA3uj3qYYA/acAFM+JhgD98uRvK3mGAPzncvbffYYA/3pNoet5hgD8+k8hQ32GAPx7RHn3kYYA/SOaPz+dhgD8N/JPf62GAP6QFSTb2YYA/rnvQxO5hgD9MsVhN52GAP2q/2ZvfYYA/Y5M4UNxhgD+5l1as2WGAP/pbS9bTYYA/S4STPM1hgD81Hgiaz2GAP2hThQjOYYA/hDuNfMVhgD8SzHzzx2GAP7iZBGm6YYA/nMvFJrthgD//J24Ix2GAPy7axnrJYYA/dtwuKdFhgD9YEqZ/2GGAP8FGyJfWYYA/l6sZk99hgD8YWwaC0GGAP39zal3SYYA/j6hS59BhgD+PaWrC0mGAPy9Y6GDPYYA/y3HDI8phgD8Tae7y0WGAP3nXHEjKYYA/xIenPMlhgD+cjYIezmGAP4P2BcPNYYA/EwW4cMFhgD8XHNqey2GAPzXZ+crEYYA/+QBlkcFhgD+BbUecwmGAP1pYeELDYYA/JJ88Vc1hgD/yTmxpw2GAPw6QcVW5YYA/udadzLlhgD8syKwNtmGAP97e3vKlYYA/O2I7H6lhgD9WD902qGGAP569L7GrYYA/AhOi3LZhgD/uwKaVu2GAP9G4yIfAYYA/WHtBm79hgD+abhQCvWGAP+S6jNK7YYA/gh7WvMBhgD+YgL3cwGGAP7OdPGHCYYA/SArpf7xhgD/DduT4x2GAPyopmJLOYYA/mahqfM5hgD/E+LKtymGAPwg7HpHQYYA/io2RAc1hgD9ye9RxyWGAP+uxJZTFYYA/eBC6+cRhgD9EmGeLv2GAP8hnqMW/YYA/9krrNbphgD/bYtsVwGGAPz5AmmvAYYA/SzQCs7hhgD+jlnG4tGGAP8C10kWvYYA/hoDd5qlhgD8JPO6/oGGAP5LzhcGbYYA/o4RAg5xhgD+UlvKonmGAPyIHZommYYA/nU7d7athgD8EgRcjqWGAP8XHelelYYA/jUd5gqVhgD8xwLoIoWGAP9S7wE+hYYA/M1T0cJthgD9OZkcinGGAPxYcgg6RYYA/VHGeZIthgD+m3pfJhGGAPy8mR4V/YYA/YMfZCYdhgD/F5hN3g2GAPwqyI4qFYYA/d7GjPIJhgD+5DRtwdWGAP/pj3Th2YYA/yEHeloFhgD8oySoRh2GAP5BapYuUYYA/lqJn7ZRhgD8jTbUil2GAPyycIFuWYYA/FZTYPZRhgD/CZO8EmmGAP8oiReOXYYA/IaHZ95RhgD93t50pjmGAP8D8lxWMYYA/Q1TfZI5hgD9JbqkAj2GAP+Up6NGEYYA/Pk60IIRhgD8JyQ0Nh2GAPwKW0iWJYYA/D5FRhYphgD8lHhHFi2GAP3Pe9c2HYYA/Xv5+d4phgD8bECQZimGAP1aCSq2JYYA/QZiVz4ZhgD8G59r/iGGAPyCMkGqMYYA/KJ5ySZNhgD8BZo/0lmGAP5XALs2RYYA/skMqJZxhgD+UB7FHmGGAP4oeHmaaYYA/1A8Jo51hgD/5tQQQmmGAP/GEZhmZYYA/eSUnUZFhgD9eoNBTk2GAP/pQLR2MYYA/kIoddY9hgD9hzc3+jmGAP8QhE4WJYYA/m8GYyoRhgD9v6oP9f2GAP3+Eo4mLYYA/OT1Px4ZhgD83UmK+jWGAPxV5H3OEYYA/n3039X9hgD+kJQHdhGGAP9hZwJOAYYA/61fMC35hgD9cb3ChfGGAP6X3vjN8YYA/DMuQbHlhgD9CvTPAd2GAP0D3jahyYYA/dKmd7HRhgD80zKOEc2GAP1wYg/J5YYA/JKze2W1hgD9+oHHdZGGAP12e4qdlYYA/lAXqFm9hgD+joj7obWGAP2W3ZIlvYYA/Z9GDHG9hgD/5aDfJdGGAPzlAUON6YYA/y2hJ4XdhgD8MAPzdd2GAP74C7l1+YYA/v86rhYBhgD+7zoqwd2GAP0w7S4lyYYA/27giCG9hgD89LiSob2GAP3sQ4bRmYYA/Pf+RVlxhgD9gaDE6W2GAP6a9E0lQYYA/FrPINU9hgD8icBNQRWGAP7CWjrZRYYA/8cHu1lRhgD8qfO92UmGAP27Fz6dPYYA/Sj8FT1NhgD9+Sgo3TmGAP56HzxNLYYA/dHKtP1RhgD9cmVo8UmGAPzfUEQZSYYA/kodX2kdhgD98yBNzS2GAP73zSt9MYYA/YbJG7EVhgD80zJdERWGAP0S/MKlMYYA/TltEvFNhgD8cXZkDTWGAP+vsK5NIYYA/4moKDlFhgD/HZMLAUWGAP8Tlop9LYYA/epiWgUxhgD+y6dPWSWGAP5ExictKYYA/VT6Va1NhgD8dwmrgVmGAP0qx6rVZYYA/36/k11dhgD9CE6uLWWGAP0SPBFZVYYA/VWWZ6kphgD9/Dd/HT2GAP2nrsHVMYYA/VJ9JDEphgD/I62BpS2GAP6bo0KNIYYA/1JpfCkthgD9zZ1W7VGGAP2wHJZpdYYA/ZIANN1JhgD/Dyn36V2GAP9nvkpJUYYA/GkM6ylNhgD91IV96VWGAP1jDEFJcYYA/JhNppllhgD/0dnElWGGAPziUQytYYYA/wcHl711hgD8FnNvWXGGAP1hq6c5pYYA/BoHQeGdhgD8eAXcDZWGAP+8rDrFpYYA/ynId6GlhgD/ZInXfcGGAPwo8V7x5YYA/NDug7XhhgD9wl7Lmc2GAP40y3wFwYYA/ZH8TPmhhgD9eB0jrY2GAP3EJKNlvYYA/cZuOb3hhgD+DAvvKemGAP9hjYM92YYA/kj/BYnlhgD/+RoleeGGAP4HxoXN2YYA/K3JIE3hhgD+FZq6IgWGAP/QBgAmBYYA/yRSYE3dhgD9B51HvfmGAPyyV97SEYYA/DMcoFYJhgD/2TsuXdmGAP0wqfsF2YYA/5feyrnBhgD9KJhJVbmGAP8QlG0ZuYYA/XRvJgHFhgD/iiTmobWGAP1uvRzxxYYA/EElcs3NhgD8lhIyGdmGAP5JWTEtoYYA/ImnIcGlhgD/EGvHUYWGAP/yEqVRjYYA/SsTjYmVhgD+WHQSlaGGAP4+4ntNoYYA/X51cI29hgD9EaJpec2GAP0IXe4VxYYA/Lvu29mlhgD8705qiZWGAP0D75bRqYYA/OHkInmZhgD9OCI43Z2GAPzq7/MJlYYA/xnT8V2RhgD9gM3PVaWGAP7HdPg9oYYA/rzmmaGNhgD8Hx+43Y2GAP0wVDjteYYA/GY1vEV5hgD+cG9oMYmGAPxomvbhdYYA/zfQBnmhhgD8L8L1kZWGAPzp+OuVZYYA/lmpCrFphgD/Lrs8/W2GAPzMyLgBdYYA/nEb74WhhgD8cpQzncGGAPyG43uNtYYA/LH2TpHhhgD8QzUPMe2GAP8ijzkp6YYA/K+CFhXlhgD8ZRgVffmGAP0nyCuKBYYA/vZu3w39hgD8E0H9ugWGAP3JsVLqKYYA/taSpY4thgD95gaUtk2GAP9vdzCiSYYA/bNs035thgD9k/t6anWGAP4i+TIufYYA/lKvdPaRhgD9Z1mF3nWGAP6KZ46KkYYA/cEAoSqBhgD9JnbNUo2GAP6r8/gOoYYA/H/WOe6RhgD/57RaCnGGAP4vLl92jYYA/eNSpFqRhgD8nmNpppWGAP2y6NbirYYA/xXDuoK1hgD/iyvUMrGGAPwTpbnS3YYA/BP4zeLlhgD/6Go/tvmGAPznoXq29YYA/5EDH57hhgD9zY3dmvmGAP0iB/hHDYYA/UV0WWbxhgD+MpdNxwmGAP/ZIkuXFYYA/a0jqN8thgD/X15Q+zGGAPyfnKLLOYYA/mFEiFs5hgD/EBrrf0mGAP4gxdE/VYYA/XDZTYOBhgD85k+CY2mGAP4gMP/DdYYA/cqjqSdRhgD/t9xE9zmGAP4Gk99LOYYA/kcZAm9dhgD8qQLzJ12GAP5wdZtzdYYA/w1JjFuVhgD8qj36R7WGAP6Pz6rbwYYA/MpmedfNhgD/mfLKd8WGAP1IEHITvYYA/Tu5fUu9hgD+tQZpF9GGAP4wCi2DtYYA/bpe1medhgD+fzDbY62GAPyFTwDzyYYA/KDD4KvdhgD9ZALO+82GAP6NRxgvrYYA/hKqwp+thgD8L+/eH5WGAP7by6GnlYYA/dlBkJN1hgD9bVw+O5WGAP5HIotTtYYA/xrKiAvBhgD+uTNvp72GAP1yNOvXzYYA/f0YNyvJhgD+iMBRQ72GAP2r9VfnsYYA/xV+UOuZhgD/F6oOJ6mGAPyUYyFLlYYA/7bMfkPJhgD8qoXUA+mGAP4anKMb/YYA/bfTVGv9hgD8m3Qnz9mGAPyYHMoDqYYA/64Jc4uthgD8QR3fX+mGAPzdxl8r6YYA/pHG6OvBhgD9If54s7GGAP3KK+FjxYYA/CYmdN/VhgD+ucHq57mGAP+/BFcvrYYA/D8ILGexhgD+f0/F372GAP/RbibH1YYA/rEqu9PthgD+vOhgl72GAP2I+p6boYYA/IPU1Xt5hgD/fJmR+2GGAP8vEYzbgYYA/C4uhGdhhgD+LbIhe1WGAP3oON77XYYA/m+oEaN5hgD8S8E2s5mGAP+F5D3nxYYA/24LwhfhhgD/MtmEHBGKAP0163m0QYoA/EHS4FBFigD9G8UlqG2KAP416n2YdYoA/5HlLiiVigD//UI/mLGKAP2BavXYtYoA/QqXDdyxigD+zrwHqLmKAPxeJtgIkYoA/hBKuqhxigD8gZgqiGGKAPwIOLyYgYoA/mSoGHSVigD+Z3WTuMmKAP/sDMplFYoA/bbUN5UJigD+laYOGOWKAP8pF7GJAYoA//Ps0QURigD8ipEoiR2KAPxmYWpBIYoA/3Ymh5UFigD/B7yUWSGKAP8sM0W08YoA/gqVWazZigD+hr/t7N2KAPw0St/QxYoA/2gBvaTBigD9EmZgiLGKAP01csuQrYoA/c8RW/ytigD8MOGxmMWKAP4itPUQuYoA/m8aQ3S1igD8VfWKsIGKAP6CD/+AjYoA/vRczPSRigD/foi7gGWKAP6WWgJsWYoA/XCpT9BBigD+/W7wZHmKAPxjPpfUhYoA/Zw02Yx9igD/NerENIWKAPwnWEBooYoA/u4S9UihigD/FYYZ9JGKAP0HVF2kpYoA/tfGgJydigD/RKumMJWKAPyQLSLcoYoA/E4qoQSJigD/A8OKVJGKAP+1rv6UfYoA/mtnwICZigD+X36DfHmKAP3B8N4oaYoA/hmdeGx5igD+FMZZKImKAP4f0XvUlYoA/Fp4XjCFigD9dd3PTHmKAP8nd5LkaYoA/hJQrzBBigD+uW9vQE2KAP0SFX14RYoA/iOEaMghigD9k+st7DWKAP0N3IakGYoA/fnnIQQligD8SHRHH/GGAP906VfL4YYA/7MQo1gBigD+8URT9/WGAP+Dfq3YCYoA/9hV1awFigD9310zr92GAPzVZ6GX4YYA/lgsiAfdhgD9N0Qix9mGAP6VutADyYYA/J6n3V/NhgD/cVqTx8WGAP7amMLbvYYA/D7+EH+lhgD9eouVW6mGAP4klX/HrYYA/eSb5r+xhgD8Bp0/Q6mGAPxcqvw7qYYA/yTmVKeNhgD8454rG22GAP5PyqGLfYYA/15KLGt1hgD/u8vm43WGAPws3wnfbYYA/MEstHeVhgD/46Nc732GAP5CdvLDkYYA/XyOagedhgD8q1GkR4mGAP3wSXnvjYYA/hUYGyuhhgD+ltUel6WGAP6mcNVniYYA/2sJ0wNZhgD/ZfzZT1WGAP/nG3DrYYYA/LtTuuNphgD+6+UCd2WGAP5EpMs7eYYA/+MJY5dxhgD+4yJHX3GGAP5rfA6DdYYA/sLuTx9lhgD8a76WY42GAP6lLV8ziYYA/1EACFeBhgD/9UR5N32GAP+4n6cjdYYA/XqJ+UdNhgD8ilY9Q0WGAP74zmHLOYYA/RHjw7chhgD8VTvFi1GGAP09k5HLWYYA/aMQ/X9VhgD/XjFSCzGGAP0CMlyXHYYA/aFbwYMphgD99e4WN1mGAPyLb9VHgYYA/3e78F+ZhgD96T30y7GGAP+CM4sTlYYA/LAXVvuNhgD+3clZK42GAP2sGqzXlYYA/SLTZ+uBhgD8oNW1v4WGAP7A+XJXfYYA/U7mn7uNhgD9wQqpg3mGAPwn48ZvhYYA/HIdFG+ZhgD8eh49x8GGAP+VFBZvvYYA/7wjriO5hgD/KwDLw8WGAP27zawTuYYA/Qj/RLuVhgD80pysb7mGAP36KmBLmYYA/WgArGORhgD9GrA0J42GAP0UXNobaYYA/jgvj3tthgD/cSLfD22GAP7dqaV7VYYA/TVSZBcZhgD9wTL0Cw2GAP/Fb0ZLEYYA/r1l/qshhgD+aQoMVx2GAP1q4puXLYYA/MGFSL8dhgD+1Sx69xGGAPy0mjX3AYYA//ww+r8FhgD87JyGLvWGAP41Kqie7YYA/St54orthgD9a/Hw4vmGAP1K56rTDYYA/MfGL5sFhgD8IJMxGx2GAP6HzVK/FYYA/rwTk18BhgD+gS6Ahw2GAP1peGUzBYYA/o7+W8bdhgD96w2o+tWGAP/9RJfe8YYA/PDeQWbdhgD947s6PvmGAP9nxxE+5YYA/+C2CNK5hgD/C7VePrGGAP6Jyba2iYYA/uNQGW6lhgD/Llk3wqGGAP4BUmsKpYYA/zuFwDrRhgD/dswWxvGGAPy2U8h68YYA/AITqs7xhgD8cU3B+u2GAP9SDH4i4YYA/SsxD8rFhgD/ykrNcsmGAPxPGPEqxYYA/tE6ztbBhgD+E9wAns2GAP/+hElCtYYA/Ot28CK1hgD+kTcu/r2GAPw88Wz+rYYA/iY8ih7lhgD/6hnbptmGAP5j7B++7YYA/hCh+8rphgD/Ns9y3wWGAP4uetBi4YYA/ou9k7LZhgD+ZLH0ttWGAP41wN4qvYYA/wAi/ILdhgD+LUOoBvGGAP/L7+fm2YYA/sSxPdL5hgD8LY2xNuGGAPy2JsTW2YYA/9tz3ibZhgD+AC5Htu2GAP2Il6Ri0YYA/k+u+wbRhgD8f52kHuGGAPzYopMS7YYA/pkGCTrdhgD+0AxMSuGGAPyPXESu+YYA/h/WvMsFhgD+oLeaWwGGAPwEK1OO5YYA/29tOvL9hgD+/iLemtmGAPwfcnz2+YYA/uz7mL8NhgD93yU8MyWGAP8Y1cFHCYYA/xDXjnsBhgD+ZyMkIvmGAP1SusbnFYYA/9cHD18JhgD8SQRbdy2GAP+ttjIjQYYA/sQoK2NBhgD9xaxHmxGGAP50qbbDDYYA/csAS0cdhgD+LRLphyGGAP5TcSeu9YYA/PShEX8FhgD89uf+TuWGAP2NZeEi5YYA/lN4XELthgD/m4xnBuGGAPxT4dWq8YYA/hln5PL9hgD8jVvnLxWGAP5NJHBC9YYA/fiFWtrVhgD8o080asmGAP6+mk7+vYYA/jqp1OqxhgD+xowzsr2GAP7jWc1qrYYA/kbdJJq9hgD8Egj6TpmGAPykipm6jYYA/p8GGg59hgD9hZB7njWGAP/ByI6GWYYA/PTV3WJhhgD/rU1OEk2GAP4DO/9eWYYA/e9Gw85phgD99/9gZnmGAP0524FmkYYA/ji1/DZthgD8P2RgBn2GAPyeX972fYYA/WLYOzZdhgD+DcNYFlmGAP4V6fIuIYYA/7b2IfophgD/wBOeHj2GAP1D2dSKLYYA/9mNBdHthgD/gbPr3fGGAP5U12IuEYYA/UFsHC4VhgD9OfwLPeWGAP1SbUJN8YYA/6LKl+XhhgD8YI3AUc2GAP4A3v+13YYA/6kfKN3FhgD8Qmtr4bmGAP291H3RyYYA/pp5AnHJhgD9fciNQd2GAPzs7ioaGYYA/SRGcrYthgD/5kW7RgWGAP5g7ZMGBYYA/w6yId4ZhgD8rA09Kh2GAPwIqHN2HYYA/Mii95odhgD/fR1kLhWGAPx5AHyCHYYA/4QfNaIVhgD8dpfOGhGGAP5HZQv+HYYA/svsuu49hgD+uc2nPkWGAPztd9giPYYA/XK+mNIlhgD9Ej3IqhWGAP2J0yEiEYYA/DHbqg4hhgD9lSIkfimGAP+RzxsCNYYA/KfsghYxhgD8mqGYTjmGAPxVcMJONYYA/3FvVkJdhgD8hyIZcnGGAP4k5MzibYYA/h/Be8p1hgD+hlUNkoWGAP9vnUt+lYYA/J3sXfqdhgD+0HhXVqWGAP99PigiaYYA/HImfsJxhgD9cbq1So2GAP5a8/BWgYYA/N7XyNJxhgD8e34T/nmGAP6yLZmejYYA/u2Gpf5RhgD/S/bzVk2GAPxTQxUqUYYA/lCHespphgD/rFnvBomGAPyzXxz2tYYA/gyHZh65hgD+FOwu5qGGAP/5yFgmpYYA//++266FhgD/K78tvmmGAP/YjqSuZYYA/AYhM1ZxhgD8+VJYDm2GAP2T1AlmjYYA/d1lUmplhgD8odh2MnWGAPxXyqB+ZYYA/iA8y2J9hgD+xkT59pWGAPz1ZM+GdYYA/MrKQX5phgD9oq5a+lmGAP5Qp58GXYYA/6hzRfJhhgD+L/+pSnWGAP0cYyCylYYA/zsxcm6hhgD+ph+RKqmGAPyeG6Ty1YYA/swvX6LNhgD/NetcetWGAP8YdJF+uYYA/ZzLyEqthgD/g7jZjmGGAP5zCfPmXYYA/osreI5hhgD9IfjLUmWGAP8X4hMWZYYA/AORzHplhgD/+aaeelmGAP2Bq7iCYYYA/pJcvuJhhgD8lm4h9oGGAP3H9aeWfYYA/CSve5ZthgD/Oq9QgmGGAPyKNjb+XYYA/UtLIbqBhgD9SUGNNpWGAP7kEwHWgYYA/j0/JdJdhgD9Xsj/RnGGAP9Qt/06fYYA/v5polZ9hgD9MNmbUm2GAP952NqyaYYA/M6HaE5thgD96qYUin2GAP3Kr4XKlYYA/rztap6VhgD9dQ5L+pmGAP7t3+c6qYYA/EXnXW6dhgD8X2c3ho2GAP9iUFvKiYYA/TGEPBqdhgD+urZtCn2GAP/taI7WcYYA/4oOQG5dhgD/7dBkDkmGAPyx8unySYYA/oiDvMIhhgD9HcDLUf2GAPwNleVuEYYA/yU51yolhgD/ib2MsiGGAP8TBKFeLYYA/VNv+5IZhgD9gs+x6gGGAPyzgrGV2YYA/LGRW3H5hgD83BcP+f2GAP2RuUXmGYYA/ti7EAXlhgD/c05OodGGAP9C8L1NyYYA/p7mK/XthgD8CePH5fGGAP7g8nHWAYYA/SmouqnlhgD/wbbM7cGGAPxtueLpiYYA/mpyV2l9hgD9Ge6ZDYGGAP0YO+jJgYYA/9FsZs25hgD9HfFfzcmGAP27wNw52YYA/tdeHy3VhgD/0GlQndGGAP1XvsGl0YYA/LAJuaXdhgD/Dc1yeemGAP1uC/8uBYYA/WhOj/35hgD9FWU0MdWGAP4vjceBqYYA/yz3Ge2xhgD9PdnHxc2GAP0Lujz16YYA/+kRUpHZhgD+KLYi1cWGAP3yOxhV0YYA/O2K8PnJhgD9JD+O8fWGAP8fEpbl7YYA/+JCnF3ZhgD9pPyB7dmGAP4DbiRV7YYA//J/EwnVhgD9G3Of+bGGAPygh1FV4YYA/EicVSWthgD8C24WRZGGAP5zGmQVfYYA/WHkQg11hgD/VshAVW2GAPwYPOIBUYYA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represented as Roll-Pitch-Yaw angles. Calibration represents the fixed rotation between the sensor frame and the body frame, which is unknown at startup. The equivariant filter can estimate this calibration simultaneously with attitude and bias. Watch how the calibration estimates (dashed lines) converge to the true calibration (solid lines) within the first portion of the trajectory, demonstrating the filter's ability to handle online calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgb(200, 60, 60)", + "width": 2 + }, + "name": "Roll (Truth)", + "showlegend": true, + "type": "scatter", + "x": [ + 0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 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"     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9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@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@", 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The red curve shows attitude error (in degrees), while the blue curve shows calibration error. Both errors start high when the filter has little information, then converge to low values as the filter processes more measurements. Temporary spikes may occur during aggressive maneuvers or measurement dropouts. The average errors across the entire trajectory are printed in the summary above." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgb(200, 60, 60)", + "width": 2 + }, + "name": "Attitude Error", + "type": "scatter", + "x": [ + 0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 0.255, + 0.26, + 0.265, + 0.27, + 0.275, + 0.28, + 0.28500000000000003, + 0.29, + 0.295, + 0.3, + 0.305, 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"linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Estimation Errors over Time" + }, + "xaxis": { + "title": { + "text": "Time (s)" + } + }, + "yaxis": { + "title": { + "text": "Error (°)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if results:\n", + " fig = plot_errors_time_series(results)\n", + " fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bias Estimation Error\n", + "\n", + "This final plot focuses specifically on the gyroscope bias estimation error. The shaded area under the curve emphasizes the overall error magnitude. Bias estimation typically exhibits a characteristic convergence pattern: starting from the initial uncertainty, the error decreases as the filter observes more motion. The rate of convergence depends on the trajectory's observability properties. Richer motion (diverse rotations) leads to faster convergence. The final bias error shown in the summary above indicates how accurately the filter has identified the true gyroscope bias." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "fill": "tozeroy", + "fillcolor": "rgba(60, 200, 60, 0.2)", + "line": { + "color": "rgb(60, 200, 60)", + "width": 2 + }, + "name": "Bias Error", + "type": "scatter", + "x": [ + 0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 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+ "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Bias Estimation Error over Time" + }, + "xaxis": { + "title": { + "text": "Time (s)" + } + }, + "yaxis": { + "title": { + "text": "Error (rad/s)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if results:\n", + " fig = plot_bias_error_time_series(results)\n", + " fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated the ABC Equivariant Filter for simultaneous estimation of:\n", + "- **Attitude**: The orientation of the body (shown as Roll-Pitch-Yaw angles)\n", + "- **Bias**: Gyroscope measurement biases that need to be corrected\n", + "- **Calibration**: Sensor extrinsic calibration parameters\n", + "\n", + "The filter uses equivariant dynamics to provide consistent state estimation even in the presence of biases. The plots show that the filter successfully tracks all three quantities with low error.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 136835a4be315c53cc70a1dac238fcc81b1db00a Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sat, 31 Jan 2026 14:27:39 -0500 Subject: [PATCH 152/266] fix wording --- python/gtsam/examples/AbcEquivariantFilterExample.ipynb | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb index d54be5c4d5..1250caea8e 100644 --- a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb +++ b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb @@ -17,22 +17,19 @@ "- **Bias**: Gyroscope bias correction vector (3D)\n", "- **Calibration**: Sensor calibration rotation matrices\n", "\n", - "This demo uses the simplified `AbcEquivariantFilter` wrapper class which provides a clean interface for predict/update operations." + "This demo uses the `AbcEquivariantFilter` class which provides a clean interface for predict/update operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,\n", + "GTSAM Copyright 2010-2026, Georgia Tech Research Corporation,\n", "Atlanta, Georgia 30332-0415\n", "All Rights Reserved\n", "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", "\n", - "See LICENSE for the license information\n", - "\n", - "Simple robotics example using odometry measurements and bearing-range (laser) measurements\n", - "Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)" + "See LICENSE for the license information" ] }, { From cf7d79d2e3000365e5edbf2785cca7bbbdce7860 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sat, 31 Jan 2026 14:29:41 -0500 Subject: [PATCH 153/266] run cells --- .../AbcEquivariantFilterExample.ipynb | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb index 1250caea8e..149b512862 100644 --- a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb +++ b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -619,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -664,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -730,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -73771,7 +73771,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -146698,7 +146698,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -219739,7 +219739,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -268594,7 +268594,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, "outputs": [ { From 5eb4785ddec0feb11a41afa325902e2230bc1ca1 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sat, 31 Jan 2026 14:31:50 -0500 Subject: [PATCH 154/266] remove summary --- .../examples/AbcEquivariantFilterExample.ipynb | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb index 149b512862..01510a8851 100644 --- a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb +++ b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb @@ -293425,20 +293425,6 @@ " fig = plot_bias_error_time_series(results)\n", " fig.show()" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "This notebook demonstrated the ABC Equivariant Filter for simultaneous estimation of:\n", - "- **Attitude**: The orientation of the body (shown as Roll-Pitch-Yaw angles)\n", - "- **Bias**: Gyroscope measurement biases that need to be corrected\n", - "- **Calibration**: Sensor extrinsic calibration parameters\n", - "\n", - "The filter uses equivariant dynamics to provide consistent state estimation even in the presence of biases. The plots show that the filter successfully tracks all three quantities with low error.\n" - ] } ], "metadata": { From b892005a7cc52e85c6e953a3c3d8d8845d0f7c20 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 15:28:02 -0500 Subject: [PATCH 155/266] Small issues in examples --- .../gtsam/examples/DiscreteMotionModel.ipynb | 2 +- python/gtsam/examples/FisheyeExample.ipynb | 18 ++++++-- python/gtsam/examples/FitBasisExample.ipynb | 42 ++++++++----------- 3 files changed, 33 insertions(+), 29 deletions(-) diff --git a/python/gtsam/examples/DiscreteMotionModel.ipynb b/python/gtsam/examples/DiscreteMotionModel.ipynb index 424a7472dc..b841d87d6e 100644 --- a/python/gtsam/examples/DiscreteMotionModel.ipynb +++ b/python/gtsam/examples/DiscreteMotionModel.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Getting Started with a simple conditional distribution model -- Python" + "# A Discrete Motion Model" ] }, { diff --git a/python/gtsam/examples/FisheyeExample.ipynb b/python/gtsam/examples/FisheyeExample.ipynb index 45b6d53972..0753de0add 100644 --- a/python/gtsam/examples/FisheyeExample.ipynb +++ b/python/gtsam/examples/FisheyeExample.ipynb @@ -5,7 +5,7 @@ "id": "7762616d", "metadata": {}, "source": [ - "# Fisheye Canera Structure-From-Motion Example\n", + "# Fisheye Camera SfM Example\n", "\n", "A visualSLAM example for the structure-from-motion problem on a\n", "simulated dataset. This version uses a fisheye camera model and a GaussNewton\n", @@ -29,7 +29,13 @@ ] }, "source": [ - "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information" + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" ] }, { @@ -43,7 +49,11 @@ }, "outputs": [], "source": [ - "try:\n import google.colab\n %pip install --quiet gtsam-develop\nexcept ImportError:\n pass" + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" ] }, { @@ -280,4 +290,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/python/gtsam/examples/FitBasisExample.ipynb b/python/gtsam/examples/FitBasisExample.ipynb index 6d54e57df5..2a7137bd57 100644 --- a/python/gtsam/examples/FitBasisExample.ipynb +++ b/python/gtsam/examples/FitBasisExample.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "\n", - "# FitBasis Example (Fourier + Chebyshev)\n", + "# FitBasis (Fourier + Chebyshev)\n", "\n", "\"Open\n", "\n", @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "50e0e944", "metadata": { "tags": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "b8cf1aff", "metadata": {}, "outputs": [], @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "a040dcaf", "metadata": {}, "outputs": [], @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "dab48fee", "metadata": {}, "outputs": [], @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "c05c36fa", "metadata": {}, "outputs": [ @@ -985,12 +985,12 @@ "sequence : dict = {float(xi): float(yi) for xi, yi in zip(x_fourier, y)}\n", "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", "fit = gtsam.FitBasisFourierBasis(sequence, model, N)\n", - "params = np.asarray(fit.parameters()).ravel()\n", + "params = fit.parameters()\n", "\n", "# Extend the time domain to show periodic behavior\n", "t_dense = np.linspace(-0.5, 1.5, 600)\n", "x_dense = 2.0 * np.pi * t_dense\n", - "W = np.asarray(gtsam.FourierBasis.WeightMatrix(len(params), x_dense))\n", + "W = gtsam.FourierBasis.WeightMatrix(len(params), x_dense)\n", "y_fit = W @ params\n", "\n", "plot_fit(t, y, t_dense, y_fit, \"FourierBasis Fit (periodic beyond [0, 1])\")\n" @@ -1014,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "ddacb31c", "metadata": {}, "outputs": [ @@ -1860,11 +1860,11 @@ "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n", "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", "fit = gtsam.FitBasisChebyshev1Basis(sequence, model, N)\n", - "params = np.asarray(fit.parameters()).ravel()\n", + "params = fit.parameters()\n", "\n", "t_dense = np.linspace(-0.1, 1.1, 600)\n", "x_dense = 2.0 * t_dense - 1.0\n", - "W = np.asarray(gtsam.Chebyshev1Basis.WeightMatrix(len(params), x_dense))\n", + "W = gtsam.Chebyshev1Basis.WeightMatrix(len(params), x_dense)\n", "y_fit = W @ params\n", "\n", "plot_fit(t, y, t_dense, y_fit, \"Chebyshev1Basis Fit (extrapolation outside [0, 1])\")\n" @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "fc3d1235", "metadata": {}, "outputs": [ @@ -2734,11 +2734,11 @@ "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n", "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", "fit = gtsam.FitBasisChebyshev2Basis(sequence, model, N)\n", - "params = np.asarray(fit.parameters()).ravel()\n", + "params = fit.parameters()\n", "\n", "t_dense = np.linspace(-0.1, 1.1, 600)\n", "x_dense = 2.0 * t_dense - 1.0\n", - "W = np.asarray(gtsam.Chebyshev2Basis.WeightMatrix(len(params), x_dense))\n", + "W = gtsam.Chebyshev2Basis.WeightMatrix(len(params), x_dense)\n", "y_fit = W @ params\n", "\n", "plot_fit(t, y, t_dense, y_fit, \"Chebyshev2Basis Fit (extrapolation outside [0, 1])\")\n" @@ -2764,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "37db92b8", "metadata": {}, "outputs": [ @@ -3625,14 +3625,14 @@ "sequence = {float(xi): float(yi) for xi, yi in zip(x_cheb, y)}\n", "model = gtsam.noiseModel.Isotropic.Sigma(1, 0.05)\n", "fit = gtsam.FitBasisChebyshev2(sequence, model, N)\n", - "params = np.asarray(fit.parameters()).ravel()\n", + "params = fit.parameters()\n", "\n", "t_dense = np.linspace(-0.1, 1.1, 600)\n", "x_dense = 2.0 * t_dense - 1.0\n", - "W = np.asarray(gtsam.Chebyshev2.WeightMatrix(len(params), x_dense))\n", + "W = gtsam.Chebyshev2.WeightMatrix(len(params), x_dense)\n", "y_fit = W @ params\n", "\n", - "cheb_points = np.asarray(gtsam.Chebyshev2.Points(N)).ravel()\n", + "cheb_points = gtsam.Chebyshev2.Points(N)\n", "t_cheb = 0.5 * (cheb_points + 1.0)\n", "y_cheb = params\n", "markers = go.Scatter(x=t_cheb, y=y_cheb, mode=\"markers\", name=\"Chebyshev2 points\", marker=dict(color=\"red\"))\n", @@ -3649,12 +3649,6 @@ "\n", "Chebyshev points are fixed nodes that cluster near the interval ends; this improves interpolation stability and reduces endpoint oscillations compared to equally spaced nodes (mitigating the Runge phenomenon)." ] - }, - { - "cell_type": "markdown", - "id": "7d4c0b1d", - "metadata": {}, - "source": [] } ], "metadata": { From f446238fd540e6e8ec9c4d7e6d300dea393ae8f5 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 15:53:41 -0500 Subject: [PATCH 156/266] Standardize on KeySet --- gtsam/discrete/DiscreteConditional.cpp | 2 +- gtsam/hybrid/HybridGaussianConditional.cpp | 4 +-- gtsam/hybrid/HybridNonlinearFactor.cpp | 4 +-- gtsam/inference/DotWriter.h | 2 +- gtsam/inference/MetisIndex-inl.h | 2 +- gtsam/inference/inference.i | 2 +- gtsam/nonlinear/BatchFixedLagSmoother.cpp | 10 ------- gtsam/nonlinear/BatchFixedLagSmoother.h | 5 ++-- gtsam/nonlinear/Expression-inl.h | 2 +- gtsam/nonlinear/Expression.h | 2 +- .../nonlinear/IncrementalFixedLagSmoother.cpp | 2 +- gtsam/nonlinear/IncrementalFixedLagSmoother.h | 2 +- gtsam/nonlinear/internal/ExpressionNode.h | 30 +++++++++---------- gtsam/sfm/ShonanAveraging.cpp | 2 +- .../nonlinear/ConcurrentBatchFilter.cpp | 4 +-- .../nonlinear/ConcurrentIncrementalFilter.cpp | 6 ++-- .../nonlinear/ConcurrentIncrementalFilter.h | 2 +- .../ConcurrentIncrementalSmoother.cpp | 2 +- .../tests/testConcurrentBatchFilter.cpp | 2 +- .../tests/testConcurrentIncrementalFilter.cpp | 2 +- tests/testExpressionFactor.cpp | 14 ++++----- 21 files changed, 46 insertions(+), 57 deletions(-) diff --git a/gtsam/discrete/DiscreteConditional.cpp b/gtsam/discrete/DiscreteConditional.cpp index e3ae18d12f..d65153da16 100644 --- a/gtsam/discrete/DiscreteConditional.cpp +++ b/gtsam/discrete/DiscreteConditional.cpp @@ -87,7 +87,7 @@ DiscreteConditional DiscreteConditional::operator*( } // Take union of frontal keys - std::set newFrontals; + KeySet newFrontals; for (auto&& key : this->frontals()) newFrontals.insert(key); for (auto&& key : other.frontals()) newFrontals.insert(key); diff --git a/gtsam/hybrid/HybridGaussianConditional.cpp b/gtsam/hybrid/HybridGaussianConditional.cpp index 51359de927..14496b355d 100644 --- a/gtsam/hybrid/HybridGaussianConditional.cpp +++ b/gtsam/hybrid/HybridGaussianConditional.cpp @@ -315,8 +315,8 @@ std::set DiscreteKeysAsSet(const DiscreteKeys &discreteKeys) { HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune( const DiscreteConditional &discreteProbs) const { // Find keys in discreteProbs.keys() but not in this->keys(): - std::set mine(this->keys().begin(), this->keys().end()); - std::set theirs(discreteProbs.keys().begin(), + KeySet mine(this->keys().begin(), this->keys().end()); + KeySet theirs(discreteProbs.keys().begin(), discreteProbs.keys().end()); std::vector diff; std::set_difference(theirs.begin(), theirs.end(), mine.begin(), mine.end(), diff --git a/gtsam/hybrid/HybridNonlinearFactor.cpp b/gtsam/hybrid/HybridNonlinearFactor.cpp index 45c337c2f3..0573fb3495 100644 --- a/gtsam/hybrid/HybridNonlinearFactor.cpp +++ b/gtsam/hybrid/HybridNonlinearFactor.cpp @@ -216,8 +216,8 @@ std::shared_ptr HybridNonlinearFactor::linearize( HybridNonlinearFactor::shared_ptr HybridNonlinearFactor::prune( const DecisionTreeFactor& discreteProbs) const { // Find keys in discreteProbs.keys() but not in this->keys(): - std::set mine(this->keys().begin(), this->keys().end()); - std::set theirs(discreteProbs.keys().begin(), + KeySet mine(this->keys().begin(), this->keys().end()); + KeySet theirs(discreteProbs.keys().begin(), discreteProbs.keys().end()); std::vector diff; std::set_difference(theirs.begin(), theirs.end(), mine.begin(), mine.end(), diff --git a/gtsam/inference/DotWriter.h b/gtsam/inference/DotWriter.h index ff20f5fa08..7cb13dfbbe 100644 --- a/gtsam/inference/DotWriter.h +++ b/gtsam/inference/DotWriter.h @@ -55,7 +55,7 @@ struct GTSAM_EXPORT DotWriter { std::map positionHints; /** A set of keys that will be displayed as a box */ - std::set boxes; + KeySet boxes; /** * Factor positions can be optionally specified and will be included in the diff --git a/gtsam/inference/MetisIndex-inl.h b/gtsam/inference/MetisIndex-inl.h index 32741aee67..1af3be3366 100644 --- a/gtsam/inference/MetisIndex-inl.h +++ b/gtsam/inference/MetisIndex-inl.h @@ -29,7 +29,7 @@ template void MetisIndex::augment(const FactorGraphType& factors) { std::map > iAdjMap; // Stores a set of keys that are adjacent to key x, with adjMap.first std::map >::iterator iAdjMapIt; - std::set keySet; + KeySet keySet; /* ********** Convert to CSR format ********** */ // Assuming that vertex numbering starts from 0 (C style), diff --git a/gtsam/inference/inference.i b/gtsam/inference/inference.i index e5ccc4f891..a55ea9d8e9 100644 --- a/gtsam/inference/inference.i +++ b/gtsam/inference/inference.i @@ -189,7 +189,7 @@ class DotWriter { std::map variablePositions; std::map positionHints; - std::set boxes; + gtsam::KeySet boxes; std::map factorPositions; }; diff --git a/gtsam/nonlinear/BatchFixedLagSmoother.cpp b/gtsam/nonlinear/BatchFixedLagSmoother.cpp index 4545c15400..2f929c7db5 100644 --- a/gtsam/nonlinear/BatchFixedLagSmoother.cpp +++ b/gtsam/nonlinear/BatchFixedLagSmoother.cpp @@ -344,16 +344,6 @@ void BatchFixedLagSmoother::marginalize(const KeyVector& marginalizeKeys) { insertFactors(marginalFactors); } -/* ************************************************************************* */ -void BatchFixedLagSmoother::PrintKeySet(const set& keys, - const string& label) { - cout << label; - for(Key key: keys) { - cout << " " << DefaultKeyFormatter(key); - } - cout << endl; -} - /* ************************************************************************* */ void BatchFixedLagSmoother::PrintKeySet(const KeySet& keys, const string& label) { diff --git a/gtsam/nonlinear/BatchFixedLagSmoother.h b/gtsam/nonlinear/BatchFixedLagSmoother.h index f4d06bd243..fb5ce8722b 100644 --- a/gtsam/nonlinear/BatchFixedLagSmoother.h +++ b/gtsam/nonlinear/BatchFixedLagSmoother.h @@ -129,7 +129,7 @@ class GTSAM_EXPORT BatchFixedLagSmoother : public FixedLagSmoother { protected: /** A typedef defining an Key-Factor mapping **/ - typedef std::map > FactorIndex; + typedef std::map FactorIndex; /** The L-M optimization parameters **/ LevenbergMarquardtParams parameters_; @@ -180,8 +180,7 @@ class GTSAM_EXPORT BatchFixedLagSmoother : public FixedLagSmoother { private: /** Private methods for printing debug information */ - static void PrintKeySet(const std::set& keys, const std::string& label); - static void PrintKeySet(const gtsam::KeySet& keys, const std::string& label); + static void PrintKeySet(const KeySet& keys, const std::string& label); static void PrintSymbolicFactor(const NonlinearFactor::shared_ptr& factor); static void PrintSymbolicFactor(const GaussianFactor::shared_ptr& factor); static void PrintSymbolicGraph(const NonlinearFactorGraph& graph, const std::string& label); diff --git a/gtsam/nonlinear/Expression-inl.h b/gtsam/nonlinear/Expression-inl.h index b09bcb3a5b..e324a24069 100644 --- a/gtsam/nonlinear/Expression-inl.h +++ b/gtsam/nonlinear/Expression-inl.h @@ -125,7 +125,7 @@ Expression::Expression(const Expression& expression1, } template -std::set Expression::keys() const { +KeySet Expression::keys() const { return root_->keys(); } diff --git a/gtsam/nonlinear/Expression.h b/gtsam/nonlinear/Expression.h index aa65384c29..3749397ea9 100644 --- a/gtsam/nonlinear/Expression.h +++ b/gtsam/nonlinear/Expression.h @@ -143,7 +143,7 @@ class Expression { } /// Return keys that play in this expression - std::set keys() const; + KeySet keys() const; /// Return dimensions for each argument, as a map void dims(std::map& map) const; diff --git a/gtsam/nonlinear/IncrementalFixedLagSmoother.cpp b/gtsam/nonlinear/IncrementalFixedLagSmoother.cpp index 394eb77b10..419543c0ac 100644 --- a/gtsam/nonlinear/IncrementalFixedLagSmoother.cpp +++ b/gtsam/nonlinear/IncrementalFixedLagSmoother.cpp @@ -165,7 +165,7 @@ void IncrementalFixedLagSmoother::createOrderingConstraints( } /* ************************************************************************* */ -void IncrementalFixedLagSmoother::PrintKeySet(const std::set& keys, +void IncrementalFixedLagSmoother::PrintKeySet(const KeySet& keys, const std::string& label) { std::cout << label; for(Key key: keys) { diff --git a/gtsam/nonlinear/IncrementalFixedLagSmoother.h b/gtsam/nonlinear/IncrementalFixedLagSmoother.h index 1e4263d4b5..056211a428 100644 --- a/gtsam/nonlinear/IncrementalFixedLagSmoother.h +++ b/gtsam/nonlinear/IncrementalFixedLagSmoother.h @@ -142,7 +142,7 @@ class GTSAM_EXPORT IncrementalFixedLagSmoother: public FixedLagSmoother { private: /** Private methods for printing debug information */ - static void PrintKeySet(const std::set& keys, const std::string& label = + static void PrintKeySet(const KeySet& keys, const std::string& label = "Keys:"); static void PrintSymbolicFactor(const GaussianFactor::shared_ptr& factor); static void PrintSymbolicGraph(const GaussianFactorGraph& graph, diff --git a/gtsam/nonlinear/internal/ExpressionNode.h b/gtsam/nonlinear/internal/ExpressionNode.h index 25587f5119..81ba1f69cc 100644 --- a/gtsam/nonlinear/internal/ExpressionNode.h +++ b/gtsam/nonlinear/internal/ExpressionNode.h @@ -92,8 +92,8 @@ class ExpressionNode { } /// Return keys that play in this expression as a set - virtual std::set keys() const { - std::set keys; + virtual KeySet keys() const { + KeySet keys; return keys; } @@ -182,8 +182,8 @@ class LeafExpression: public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { - std::set keys; + KeySet keys() const override { + KeySet keys; keys.insert(key_); return keys; } @@ -260,7 +260,7 @@ class UnaryExpression: public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { + KeySet keys() const override { return expression1_->keys(); } @@ -372,9 +372,9 @@ class BinaryExpression: public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { - std::set keys = expression1_->keys(); - std::set myKeys = expression2_->keys(); + KeySet keys() const override { + KeySet keys = expression1_->keys(); + KeySet myKeys = expression2_->keys(); keys.insert(myKeys.begin(), myKeys.end()); return keys; } @@ -480,9 +480,9 @@ class TernaryExpression: public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { - std::set keys = expression1_->keys(); - std::set myKeys = expression2_->keys(); + KeySet keys() const override { + KeySet keys = expression1_->keys(); + KeySet myKeys = expression2_->keys(); keys.insert(myKeys.begin(), myKeys.end()); myKeys = expression3_->keys(); keys.insert(myKeys.begin(), myKeys.end()); @@ -588,7 +588,7 @@ class ScalarMultiplyNode : public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { + KeySet keys() const override { return expression_->keys(); } @@ -677,9 +677,9 @@ class BinarySumNode : public ExpressionNode { } /// Return keys that play in this expression - std::set keys() const override { - std::set keys = expression1_->keys(); - std::set myKeys = expression2_->keys(); + KeySet keys() const override { + KeySet keys = expression1_->keys(); + KeySet myKeys = expression2_->keys(); keys.insert(myKeys.begin(), myKeys.end()); return keys; } diff --git a/gtsam/sfm/ShonanAveraging.cpp b/gtsam/sfm/ShonanAveraging.cpp index 519a4cabae..b381fdeedf 100644 --- a/gtsam/sfm/ShonanAveraging.cpp +++ b/gtsam/sfm/ShonanAveraging.cpp @@ -98,7 +98,7 @@ template static size_t NrUnknowns( const typename ShonanAveraging::Measurements &measurements) { Key maxKey = 0; - std::set keys; + KeySet keys; for (const auto &measurement : measurements) { for (const Key &key : measurement.keys()) { maxKey = std::max(key, maxKey); diff --git a/gtsam_unstable/nonlinear/ConcurrentBatchFilter.cpp b/gtsam_unstable/nonlinear/ConcurrentBatchFilter.cpp index 8dfb15aa25..f5eb4bc2be 100644 --- a/gtsam_unstable/nonlinear/ConcurrentBatchFilter.cpp +++ b/gtsam_unstable/nonlinear/ConcurrentBatchFilter.cpp @@ -211,8 +211,8 @@ void ConcurrentBatchFilter::synchronize(const NonlinearFactorGraph& smootherSumm if(debug) { PrintNonlinearFactorGraph(smootherSummarization_, "ConcurrentBatchFilter::synchronize ", "Previous Smoother Summarization:", DefaultKeyFormatter); } #ifndef NDEBUG - std::set oldKeys = smootherSummarization_.keys(); - std::set newKeys = smootherSummarization.keys(); + KeySet oldKeys = smootherSummarization_.keys(); + KeySet newKeys = smootherSummarization.keys(); assert(oldKeys.size() == newKeys.size()); assert(std::equal(oldKeys.begin(), oldKeys.end(), newKeys.begin())); #endif diff --git a/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.cpp b/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.cpp index 35d2c55511..7ae5a26aa1 100644 --- a/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.cpp +++ b/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.cpp @@ -91,7 +91,7 @@ ConcurrentIncrementalFilter::Result ConcurrentIncrementalFilter::update(const No // Mark additional keys between the 'keys to move' and the leaves std::optional > additionalKeys = {}; if(keysToMove && keysToMove->size() > 0) { - std::set markedKeys; + KeySet markedKeys; for(Key key: *keysToMove) { if(isam2_.getLinearizationPoint().exists(key)) { ISAM2Clique::shared_ptr clique = isam2_[key]; @@ -265,7 +265,7 @@ void ConcurrentIncrementalFilter::postsync() { /* ************************************************************************* */ -void ConcurrentIncrementalFilter::RecursiveMarkAffectedKeys(const Key& key, const ISAM2Clique::shared_ptr& clique, std::set& additionalKeys) { +void ConcurrentIncrementalFilter::RecursiveMarkAffectedKeys(const Key& key, const ISAM2Clique::shared_ptr& clique, KeySet& additionalKeys) { // Check if the separator keys of the current clique contain the specified key if(std::find(clique->conditional()->beginParents(), clique->conditional()->endParents(), key) != clique->conditional()->endParents()) { @@ -367,7 +367,7 @@ NonlinearFactorGraph ConcurrentIncrementalFilter::calculateFilterSummarization() for(size_t slot: isam2_.getVariableIndex()[key]) { const NonlinearFactor::shared_ptr& factor = isam2_.getFactorsUnsafe().at(slot); if(factor) { - std::set factorKeys(factor->begin(), factor->end()); + KeySet factorKeys(factor->begin(), factor->end()); if(std::includes(cliqueKeys.begin(), cliqueKeys.end(), factorKeys.begin(), factorKeys.end())) { cliqueFactorSlots.insert(slot); } diff --git a/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.h b/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.h index 9408b1b77c..6c3cb39bd4 100644 --- a/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.h +++ b/gtsam_unstable/nonlinear/ConcurrentIncrementalFilter.h @@ -180,7 +180,7 @@ class GTSAM_UNSTABLE_EXPORT ConcurrentIncrementalFilter : public virtual Concurr private: /** Traverse the iSAM2 Bayes Tree, inserting all descendants of the provided index/key into 'additionalKeys' */ - static void RecursiveMarkAffectedKeys(const Key& key, const ISAM2Clique::shared_ptr& clique, std::set& additionalKeys); + static void RecursiveMarkAffectedKeys(const Key& key, const ISAM2Clique::shared_ptr& clique, KeySet& additionalKeys); /** Find the set of iSAM2 factors adjacent to 'keys' */ static FactorIndices FindAdjacentFactors(const ISAM2& isam2, const FastList& keys, const FactorIndices& factorsToIgnore); diff --git a/gtsam_unstable/nonlinear/ConcurrentIncrementalSmoother.cpp b/gtsam_unstable/nonlinear/ConcurrentIncrementalSmoother.cpp index 642a8f7689..a7e3dae7e7 100644 --- a/gtsam_unstable/nonlinear/ConcurrentIncrementalSmoother.cpp +++ b/gtsam_unstable/nonlinear/ConcurrentIncrementalSmoother.cpp @@ -208,7 +208,7 @@ void ConcurrentIncrementalSmoother::updateSmootherSummarization() { for(size_t slot: isam2_.getVariableIndex()[key]) { const NonlinearFactor::shared_ptr& factor = isam2_.getFactorsUnsafe().at(slot); if(factor) { - std::set factorKeys(factor->begin(), factor->end()); + KeySet factorKeys(factor->begin(), factor->end()); if(std::includes(cliqueKeys.begin(), cliqueKeys.end(), factorKeys.begin(), factorKeys.end())) { cliqueFactorSlots.insert(slot); } diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp index 328448e88b..956a6d7aaf 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentBatchFilter.cpp @@ -68,7 +68,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int NonlinearFactorGraph CalculateMarginals(const NonlinearFactorGraph& factorGraph, const Values& linPoint, const FastList& keysToMarginalize){ - std::set KeysToKeep; + KeySet KeysToKeep; for(const auto key: linPoint.keys()) { // we cycle over all the keys of factorGraph KeysToKeep.insert(key); } // so far we are keeping all keys, but we want to delete the ones that we are going to marginalize diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalFilter.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalFilter.cpp index 401dee762a..5aecb6812f 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalFilter.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalFilter.cpp @@ -79,7 +79,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int NonlinearFactorGraph CalculateMarginals(const NonlinearFactorGraph& factorGraph, const Values& linPoint, const FastList& keysToMarginalize){ - std::set KeysToKeep; + KeySet KeysToKeep; for(const auto key: linPoint.keys()) { // we cycle over all the keys of factorGraph KeysToKeep.insert(key); } // so far we are keeping all keys, but we want to delete the ones that we are going to marginalize diff --git a/tests/testExpressionFactor.cpp b/tests/testExpressionFactor.cpp index 845c28e5a5..9af3b6a375 100644 --- a/tests/testExpressionFactor.cpp +++ b/tests/testExpressionFactor.cpp @@ -499,7 +499,7 @@ TEST(Expression, testMultipleCompositions) { // Leaf, key = 1 // Leaf, key = 2 Expression sum1_(Combine(1, 2), v1_, v2_); - EXPECT((sum1_.keys() == std::set{1, 2})) + EXPECT((sum1_.keys() == KeySet{1, 2})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum1_, values, fd_step, tolerance) // BinaryExpression(3,4) @@ -508,7 +508,7 @@ TEST(Expression, testMultipleCompositions) { // Leaf, key = 2 // Leaf, key = 1 Expression sum2_(Combine(3, 4), sum1_, v1_); - EXPECT((sum2_.keys() == std::set{1, 2})) + EXPECT((sum2_.keys() == KeySet{1, 2})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum2_, values, fd_step, tolerance) // BinaryExpression(5,6) @@ -521,7 +521,7 @@ TEST(Expression, testMultipleCompositions) { // Leaf, key = 1 // Leaf, key = 2 Expression sum3_(Combine(5, 6), sum1_, sum2_); - EXPECT((sum3_.keys() == std::set{1, 2})) + EXPECT((sum3_.keys() == KeySet{1, 2})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum3_, values, fd_step, tolerance) } @@ -550,19 +550,19 @@ TEST(Expression, testMultipleCompositions2) { Expression v3_(Key(3)); Expression sum1_(Combine(4,5), v1_, v2_); - EXPECT((sum1_.keys() == std::set{1, 2})) + EXPECT((sum1_.keys() == KeySet{1, 2})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum1_, values, fd_step, tolerance) Expression sum2_(combine3, v1_, v2_, v3_); - EXPECT((sum2_.keys() == std::set{1, 2, 3})) + EXPECT((sum2_.keys() == KeySet{1, 2, 3})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum2_, values, fd_step, tolerance) Expression sum3_(combine3, v3_, v2_, v1_); - EXPECT((sum3_.keys() == std::set{1, 2, 3})) + EXPECT((sum3_.keys() == KeySet{1, 2, 3})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum3_, values, fd_step, tolerance) Expression sum4_(combine3, sum1_, sum2_, sum3_); - EXPECT((sum4_.keys() == std::set{1, 2, 3})) + EXPECT((sum4_.keys() == KeySet{1, 2, 3})) EXPECT_CORRECT_EXPRESSION_JACOBIANS(sum4_, values, fd_step, tolerance) } From 97668ccbc7065b89132c150612e11f7707455ba6 Mon Sep 17 00:00:00 2001 From: Rohan Bansal Date: Sat, 31 Jan 2026 16:58:30 -0500 Subject: [PATCH 157/266] removed notebook, moving to awesome eqf --- .../AbcEquivariantFilterExample.ipynb | 293451 --------------- 1 file changed, 293451 deletions(-) delete mode 100644 python/gtsam/examples/AbcEquivariantFilterExample.ipynb diff --git a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb b/python/gtsam/examples/AbcEquivariantFilterExample.ipynb deleted file mode 100644 index 01510a8851..0000000000 --- a/python/gtsam/examples/AbcEquivariantFilterExample.ipynb +++ /dev/null @@ -1,293451 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ABC Equivariant Filter Demo\n", - "\n", - "## Attitude-Bias-Calibration Equivariant Filter\n", - "\n", - "This notebook demonstrates the Equivariant Filter (EqF) for attitude estimation with both gyroscope bias and sensor extrinsic calibration, based on the paper:\n", - "\n", - "> **\"Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration\"** by Fornasier et al.\n", - "\n", - "The filter estimates:\n", - "- **Attitude (Rotation)**: The orientation of the body frame relative to a reference frame\n", - "- **Bias**: Gyroscope bias correction vector (3D)\n", - "- **Calibration**: Sensor calibration rotation matrices\n", - "\n", - "This demo uses the `AbcEquivariantFilter` class which provides a clean interface for predict/update operations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GTSAM Copyright 2010-2026, Georgia Tech Research Corporation,\n", - "Atlanta, Georgia 30332-0415\n", - "All Rights Reserved\n", - "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", - "\n", - "See LICENSE for the license information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup and Imports\n", - "\n", - "First, we import all the necessary libraries. We use GTSAM for the filter implementation, NumPy for numerical operations, and Plotly for creating interactive visualizations. The `dataclass` decorator helps us create clean data structures for organizing measurements and results." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import annotations\n", - "\n", - "from dataclasses import dataclass\n", - "import csv\n", - "import math\n", - "from typing import List\n", - "\n", - "import numpy as np\n", - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", - "\n", - "import gtsam\n", - "from gtsam import Rot3, Unit3\n", - "from gtsam.utils import findExampleDataFile" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Structures\n", - "\n", - "We define three key data structures:\n", - "\n", - "1. **`MeasurementRecord`**: Stores a single direction measurement with its reference direction, covariance, and calibration index. Each measurement represents an observation of a known direction vector in the body frame.\n", - "\n", - "2. **`DataRecord`**: Contains all information for a single time step, including ground truth state (attitude, bias, calibration), gyroscope measurements, input covariance, and all direction measurements at that timestep.\n", - "\n", - "3. **`FilterResults`**: Aggregates the entire time series of ground truth, estimates, and errors for easy plotting. This allows us to track how the filter performs over the entire trajectory." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "@dataclass\n", - "class MeasurementRecord:\n", - " y: np.ndarray\n", - " d: np.ndarray\n", - " R: np.ndarray\n", - " cal_idx: int\n", - "\n", - "\n", - "@dataclass\n", - "class DataRecord:\n", - " R: Rot3\n", - " b: np.ndarray\n", - " cal_rot: Rot3\n", - " omega: np.ndarray\n", - " input_covariance: np.ndarray\n", - " measurements: List[MeasurementRecord]\n", - " t: float\n", - " dt: float\n", - "\n", - "\n", - "@dataclass\n", - "class FilterResults:\n", - " \"\"\"Store filter results for plotting\"\"\"\n", - " times: List[float]\n", - " # Ground truth\n", - " truth_att_rpy: List[np.ndarray]\n", - " truth_bias: List[np.ndarray]\n", - " truth_cal_rpy: List[np.ndarray]\n", - " # Estimates\n", - " est_att_rpy: List[np.ndarray]\n", - " est_bias: List[np.ndarray]\n", - " est_cal_rpy: List[np.ndarray]\n", - " # Errors\n", - " att_errors: List[float]\n", - " bias_errors: List[float]\n", - " cal_errors: List[float]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading Functions\n", - "\n", - "The CSV file contains simulated IMU and direction measurement data with ground truth. Each row includes quaternion representations of attitude and calibration, gyroscope measurements with noise parameters, and direction observations. The data loader parses this CSV format and converts it into our `DataRecord` structures, handling normalization of direction vectors and construction of covariance matrices from standard deviations." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def _normalize(v: np.ndarray) -> np.ndarray:\n", - " \"\"\"Normalize a vector to unit length.\"\"\"\n", - " n = np.linalg.norm(v)\n", - " if n == 0.0:\n", - " return v\n", - " return v / n\n", - "\n", - "\n", - "def load_data_from_csv(\n", - " filename: str, start_row: int = 0, max_rows: int = -1, downsample: int = 1\n", - ") -> List[DataRecord]:\n", - " \"\"\"\n", - " Load data from CSV file into a list of DataRecord objects.\n", - " \n", - " The CSV contains:\n", - " - Time stamps and ground truth state (attitude quaternion, bias, calibration quaternion)\n", - " - Gyroscope measurements (omega) with noise standard deviations\n", - " - Direction measurements from two sensors with their reference directions\n", - " - Measurement noise covariances\n", - " \"\"\"\n", - " data_list: List[DataRecord] = []\n", - " with open(filename, newline=\"\") as csvfile:\n", - " reader = csv.reader(csvfile)\n", - " header = next(reader, None)\n", - " if header is None:\n", - " return data_list\n", - "\n", - " line_number = 1\n", - " row_count = 0\n", - " prev_time = 0.0\n", - "\n", - " for row in reader:\n", - " line_number += 1\n", - " if line_number < start_row:\n", - " continue\n", - " if ((line_number - start_row - 1) % downsample) != 0:\n", - " continue\n", - " if max_rows != -1 and row_count >= max_rows:\n", - " break\n", - " if len(row) < 39:\n", - " continue\n", - "\n", - " values = [float(x) if x else 0.0 for x in row]\n", - "\n", - " t = values[0]\n", - " dt = 0.0 if row_count == 0 else t - prev_time\n", - " prev_time = t\n", - "\n", - " # Extract ground truth state\n", - " R = Rot3.Quaternion(values[1], values[2], values[3], values[4])\n", - " b = np.array([values[5], values[6], values[7]])\n", - " cal_rot = Rot3.Quaternion(values[8], values[9], values[10], values[11])\n", - "\n", - " # Extract gyroscope input\n", - " omega = np.array([values[12], values[13], values[14]])\n", - "\n", - " # Build input covariance (6x6: angular velocity + bias process noise)\n", - " input_covariance = np.zeros((6, 6))\n", - " input_covariance[0, 0] = values[15] ** 2\n", - " input_covariance[1, 1] = values[16] ** 2\n", - " input_covariance[2, 2] = values[17] ** 2\n", - " input_covariance[3, 3] = values[18] ** 2\n", - " input_covariance[4, 4] = values[19] ** 2\n", - " input_covariance[5, 5] = values[20] ** 2\n", - "\n", - " measurements: List[MeasurementRecord] = []\n", - "\n", - " # First measurement (calibrated sensor, cal_idx = 0)\n", - " y0 = _normalize(np.array([values[21], values[22], values[23]]))\n", - " d0 = _normalize(np.array([values[33], values[34], values[35]]))\n", - " cov_y0 = np.diag([values[27] ** 2, values[28] ** 2, values[29] ** 2])\n", - " measurements.append(MeasurementRecord(y0, d0, cov_y0, 0))\n", - "\n", - " # Second measurement (uncalibrated sensor, cal_idx = -1)\n", - " y1 = _normalize(np.array([values[24], values[25], values[26]]))\n", - " d1 = _normalize(np.array([values[36], values[37], values[38]]))\n", - " cov_y1 = np.diag([values[30] ** 2, values[31] ** 2, values[32] ** 2])\n", - " measurements.append(MeasurementRecord(y1, d1, cov_y1, -1))\n", - "\n", - " data_list.append(\n", - " DataRecord(\n", - " R=R,\n", - " b=b,\n", - " cal_rot=cal_rot,\n", - " omega=omega,\n", - " input_covariance=input_covariance,\n", - " measurements=measurements,\n", - " t=t,\n", - " dt=dt,\n", - " )\n", - " )\n", - " row_count += 1\n", - "\n", - " return data_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filter Processing with Results Tracking\n", - "\n", - "This function runs the ABC Equivariant Filter through the entire data sequence. For each time step, it performs a prediction step using gyroscope measurements, then updates with direction observations. The function tracks both the filter estimates and ground truth at each step, computing errors in attitude, bias, and calibration. All results are stored in a `FilterResults` object for later visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def process_data_with_eqf(\n", - " filter_eqf: gtsam.abc.AbcEquivariantFilter1,\n", - " data_list: List[DataRecord],\n", - ") -> FilterResults:\n", - " \"\"\"\n", - " Process data through the filter and return results for plotting.\n", - " \n", - " At each timestep:\n", - " 1. Predict: Use gyroscope measurements to propagate the state forward\n", - " 2. Update: Incorporate direction measurements to correct the estimate\n", - " 3. Track: Store estimates, ground truth, and errors for visualization\n", - " \"\"\"\n", - " if not data_list:\n", - " print(\"No data to process\")\n", - " return None\n", - "\n", - " print(f\"Processing {len(data_list)} data points with EqF...\")\n", - " \n", - " rad_to_deg = 180.0 / math.pi\n", - " \n", - " # Storage for results\n", - " results = FilterResults(\n", - " times=[],\n", - " truth_att_rpy=[],\n", - " truth_bias=[],\n", - " truth_cal_rpy=[],\n", - " est_att_rpy=[],\n", - " est_bias=[],\n", - " est_cal_rpy=[],\n", - " att_errors=[],\n", - " bias_errors=[],\n", - " cal_errors=[],\n", - " )\n", - "\n", - " total_measurements = 0\n", - " valid_measurements = 0\n", - " progress_step = max(len(data_list) // 10, 1)\n", - " print(\"Progress: \", end=\"\", flush=True)\n", - "\n", - " for i, data in enumerate(data_list):\n", - " # Predict step: propagate state using gyroscope measurements\n", - " filter_eqf.predict(data.omega, data.input_covariance, data.dt)\n", - "\n", - " # Update step: correct state using direction measurements\n", - " for measurement in data.measurements:\n", - " total_measurements += 1\n", - " if np.any(np.isnan(measurement.y)) or np.any(np.isnan(measurement.d)):\n", - " continue\n", - " try:\n", - " y_unit = Unit3(measurement.y)\n", - " d_unit = Unit3(measurement.d)\n", - " filter_eqf.update(y_unit, d_unit, measurement.R, measurement.cal_idx)\n", - " valid_measurements += 1\n", - " except Exception:\n", - " continue\n", - "\n", - " # Get current estimates using the wrapper's accessor methods\n", - " estimate_R = filter_eqf.attitude()\n", - " estimate_b = np.array(filter_eqf.bias()).reshape(3)\n", - " estimate_cal = filter_eqf.calibration(0)\n", - "\n", - " # Calculate errors (in Lie algebra for rotations)\n", - " att_error = Rot3.Logmap(data.R.between(estimate_R))\n", - " bias_error = estimate_b - data.b\n", - " cal_error = Rot3.Logmap(data.cal_rot.between(estimate_cal))\n", - "\n", - " # Store results for plotting\n", - " results.times.append(data.t)\n", - " results.truth_att_rpy.append(data.R.rpy() * rad_to_deg)\n", - " results.truth_bias.append(data.b)\n", - " results.truth_cal_rpy.append(data.cal_rot.rpy() * rad_to_deg)\n", - " results.est_att_rpy.append(estimate_R.rpy() * rad_to_deg)\n", - " results.est_bias.append(estimate_b)\n", - " results.est_cal_rpy.append(estimate_cal.rpy() * rad_to_deg)\n", - " results.att_errors.append(np.linalg.norm(att_error) * rad_to_deg)\n", - " results.bias_errors.append(np.linalg.norm(bias_error))\n", - " results.cal_errors.append(np.linalg.norm(cal_error) * rad_to_deg)\n", - "\n", - " if i % progress_step == 0:\n", - " print(\".\", end=\"\", flush=True)\n", - "\n", - " print(\" Done!\")\n", - "\n", - " # Print summary statistics\n", - " avg_att_error = float(np.mean(results.att_errors))\n", - " avg_bias_error = float(np.mean(results.bias_errors))\n", - " avg_cal_error = float(np.mean(results.cal_errors))\n", - "\n", - " print(\"\\n=== Filter Performance Summary ===\")\n", - " print(f\"Processed measurements: {total_measurements} (valid: {valid_measurements})\")\n", - "\n", - " print(\"\\n-- Average Errors --\")\n", - " print(f\"Attitude: {avg_att_error}°\")\n", - " print(f\"Bias: {avg_bias_error}\")\n", - " print(f\"Calibration: {avg_cal_error}°\")\n", - "\n", - " print(\"\\n-- Final Errors --\")\n", - " print(f\"Attitude: {results.att_errors[-1]}°\")\n", - " print(f\"Bias: {results.bias_errors[-1]}\")\n", - " print(f\"Calibration: {results.cal_errors[-1]}°\")\n", - "\n", - " print(\"\\n-- Final State vs Ground Truth --\")\n", - " print(f\"Attitude (RPY) - Estimate: {results.est_att_rpy[-1]} ° | Truth: {results.truth_att_rpy[-1]} °\")\n", - " print(f\"Bias - Estimate: {results.est_bias[-1]} | Truth: {results.truth_bias[-1]}\")\n", - " print(f\"Calibration (RPY) - Estimate: {results.est_cal_rpy[-1]} ° | Truth: {results.truth_cal_rpy[-1]} °\")\n", - "\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization Functions\n", - "\n", - "These functions create interactive Plotly visualizations of the filter results. Each function generates time-series plots comparing ground truth (solid lines) with filter estimates (dashed lines)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_attitude_time_series(results: FilterResults):\n", - " \"\"\"\n", - " Plot attitude (roll, pitch, yaw) over time.\n", - " \n", - " Creates a 3-subplot figure showing each Euler angle component.\n", - " Solid lines show ground truth, dashed lines show filter estimates.\n", - " This helps visualize how well the filter tracks the true orientation.\n", - " \"\"\"\n", - " fig = make_subplots(\n", - " rows=3, cols=1,\n", - " subplot_titles=(\"Roll\", \"Pitch\", \"Yaw\"),\n", - " vertical_spacing=0.08,\n", - " x_title=\"Time (s)\",\n", - " )\n", - "\n", - " times = results.times\n", - " truth_rpy = np.array(results.truth_att_rpy)\n", - " est_rpy = np.array(results.est_att_rpy)\n", - " \n", - " labels = [\"Roll\", \"Pitch\", \"Yaw\"]\n", - " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", - " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", - " \n", - " for i in range(3):\n", - " # Ground truth\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=truth_rpy[:, i],\n", - " name=f\"{labels[i]} (Truth)\",\n", - " line=dict(color=colors_truth[i], width=2),\n", - " showlegend=(i == 0),\n", - " ),\n", - " row=i+1, col=1\n", - " )\n", - " # Estimate\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=est_rpy[:, i],\n", - " name=f\"{labels[i]} (Estimate)\",\n", - " line=dict(color=colors_est[i], width=2, dash='dash'),\n", - " showlegend=(i == 0),\n", - " ),\n", - " row=i+1, col=1\n", - " )\n", - " fig.update_yaxes(title_text=f\"{labels[i]} (°)\", row=i+1, col=1)\n", - "\n", - " fig.update_layout(\n", - " height=800,\n", - " title_text=\"Attitude Estimation: Roll-Pitch-Yaw over Time\",\n", - " showlegend=True,\n", - " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", - " )\n", - " return fig\n", - "\n", - "\n", - "def plot_bias_time_series(results: FilterResults):\n", - " \"\"\"\n", - " Plot gyroscope bias over time.\n", - " \n", - " Shows all three bias components (X, Y, Z) on a single plot.\n", - " The bias represents systematic errors in the gyroscope measurements that the filter\n", - " must estimate and correct. Watch how the estimates converge to the true bias values.\n", - " \"\"\"\n", - " fig = go.Figure()\n", - " \n", - " times = results.times\n", - " truth_bias = np.array(results.truth_bias)\n", - " est_bias = np.array(results.est_bias)\n", - " \n", - " labels = [\"Bias X\", \"Bias Y\", \"Bias Z\"]\n", - " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", - " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", - " \n", - " for i in range(3):\n", - " # Ground truth\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=truth_bias[:, i],\n", - " name=f\"{labels[i]} (Truth)\",\n", - " line=dict(color=colors_truth[i], width=2),\n", - " )\n", - " )\n", - " # Estimate\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=est_bias[:, i],\n", - " name=f\"{labels[i]} (Estimate)\",\n", - " line=dict(color=colors_est[i], width=2, dash='dash'),\n", - " )\n", - " )\n", - " \n", - " fig.update_layout(\n", - " height=500,\n", - " title_text=\"Gyroscope Bias Estimation over Time\",\n", - " xaxis_title=\"Time (s)\",\n", - " yaxis_title=\"Bias (rad/s)\",\n", - " showlegend=True,\n", - " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", - " )\n", - " return fig\n", - "\n", - "\n", - "def plot_calibration_time_series(results: FilterResults):\n", - " \"\"\"\n", - " Plot sensor calibration over time.\n", - " \n", - " Creates a 3-subplot figure showing the calibration rotation in Roll-Pitch-Yaw.\n", - " Calibration represents the rotation between the sensor frame and body frame,\n", - " which must be estimated online. Notice how the filter converges to the true\n", - " calibration parameters even while the body is rotating.\n", - " \"\"\"\n", - " fig = make_subplots(\n", - " rows=3, cols=1,\n", - " subplot_titles=(\"Cal Roll\", \"Cal Pitch\", \"Cal Yaw\"),\n", - " vertical_spacing=0.08,\n", - " x_title=\"Time (s)\",\n", - " )\n", - "\n", - " times = results.times\n", - " truth_cal = np.array(results.truth_cal_rpy)\n", - " est_cal = np.array(results.est_cal_rpy)\n", - " \n", - " labels = [\"Roll\", \"Pitch\", \"Yaw\"]\n", - " colors_truth = [\"rgb(200, 60, 60)\", \"rgb(60, 200, 60)\", \"rgb(60, 60, 200)\"]\n", - " colors_est = [\"rgb(255, 120, 120)\", \"rgb(120, 255, 120)\", \"rgb(120, 120, 255)\"]\n", - " \n", - " for i in range(3):\n", - " # Ground truth\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=truth_cal[:, i],\n", - " name=f\"{labels[i]} (Truth)\",\n", - " line=dict(color=colors_truth[i], width=2),\n", - " showlegend=(i == 0),\n", - " ),\n", - " row=i+1, col=1\n", - " )\n", - " # Estimate\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=est_cal[:, i],\n", - " name=f\"{labels[i]} (Estimate)\",\n", - " line=dict(color=colors_est[i], width=2, dash='dash'),\n", - " showlegend=(i == 0),\n", - " ),\n", - " row=i+1, col=1\n", - " )\n", - " fig.update_yaxes(title_text=f\"{labels[i]} (°)\", row=i+1, col=1)\n", - "\n", - " fig.update_layout(\n", - " height=800,\n", - " title_text=\"Sensor Calibration Estimation over Time\",\n", - " showlegend=True,\n", - " legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n", - " )\n", - " return fig\n", - "\n", - "\n", - "def plot_errors_time_series(results: FilterResults):\n", - " \"\"\"\n", - " Plot estimation errors over time.\n", - " \n", - " Shows the norm of attitude and calibration errors in degrees.\n", - " Lower values indicate better tracking performance. Spikes in error may occur\n", - " when measurements are sparse or during rapid maneuvers.\n", - " \"\"\"\n", - " fig = go.Figure()\n", - " \n", - " times = results.times\n", - " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=results.att_errors,\n", - " name=\"Attitude Error\",\n", - " line=dict(color=\"rgb(200, 60, 60)\", width=2),\n", - " )\n", - " )\n", - " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=results.cal_errors,\n", - " name=\"Calibration Error\",\n", - " line=dict(color=\"rgb(60, 60, 200)\", width=2),\n", - " )\n", - " )\n", - " \n", - " fig.update_layout(\n", - " height=500,\n", - " title_text=\"Estimation Errors over Time\",\n", - " xaxis_title=\"Time (s)\",\n", - " yaxis_title=\"Error (°)\",\n", - " showlegend=True,\n", - " )\n", - " return fig\n", - "\n", - "\n", - "def plot_bias_error_time_series(results: FilterResults):\n", - " \"\"\"\n", - " Plot bias estimation error over time.\n", - " \n", - " Shows the norm of the bias error in rad/s with a filled area for emphasis.\n", - " The bias typically converges more slowly than attitude because it relies on\n", - " integrating information over time. The shaded area helps visualize the overall\n", - " convergence trend.\n", - " \"\"\"\n", - " fig = go.Figure()\n", - " \n", - " times = results.times\n", - " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=times, y=results.bias_errors,\n", - " name=\"Bias Error\",\n", - " line=dict(color=\"rgb(60, 200, 60)\", width=2),\n", - " fill='tozeroy',\n", - " fillcolor='rgba(60, 200, 60, 0.2)',\n", - " )\n", - " )\n", - " \n", - " fig.update_layout(\n", - " height=400,\n", - " title_text=\"Bias Estimation Error over Time\",\n", - " xaxis_title=\"Time (s)\",\n", - " yaxis_title=\"Error (rad/s)\",\n", - " showlegend=False,\n", - " )\n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data\n", - "\n", - "Here we locate and load the example dataset (`EqFdata.csv`). This CSV file contains simulated IMU trajectory data with ground truth, generated from a realistic motion profile. The data includes approximately 12,000 timesteps with gyroscope measurements and direction observations from two sensors (one calibrated, one uncalibrated). If you want to test with your own data, simply provide the path to a CSV file in the same format." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ABC-EqF: Attitude-Bias-Calibration Equivariant Filter Demo\n", - "==============================================================\n", - "Loading data from: /Users/apollo/dev/research/gtsam/build/python/gtsam/Data/EqFdata.csv\n", - "Loaded 12001 data points\n" - ] - } - ], - "source": [ - "print(\"ABC-EqF: Attitude-Bias-Calibration Equivariant Filter Demo\")\n", - "print(\"==============================================================\")\n", - "\n", - "# Load data using GTSAM's dataset utility\n", - "try:\n", - " csv_file_path = findExampleDataFile(\"EqFdata.csv\")\n", - " print(f\"Loading data from: {csv_file_path}\")\n", - "except Exception as e:\n", - " print(f\"Error: Could not find EqFdata.csv: {e}\")\n", - " csv_file_path = None\n", - "\n", - "if csv_file_path:\n", - " data = load_data_from_csv(csv_file_path)\n", - " print(f\"Loaded {len(data)} data points\")\n", - "else:\n", - " print(\"No data available to process.\")\n", - " data = []" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize and Run Filter\n", - "\n", - "Now we create and configure the ABC Equivariant Filter. The initial covariance matrix `initial_sigma` encodes our uncertainty in the initial state: we're moderately uncertain about the attitude (0.1 rad²) and calibration (0.1 rad²), but quite confident about the initial bias (0.01 rad²/s²). The filter starts at the identity state (zero attitude, zero bias, identity calibration) and will converge to the true state as it processes measurements. This cell runs the filter through the entire trajectory, which takes a few seconds for 12,000 timesteps." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing 12001 data points with EqF...\n", - "Progress: ........... Done!\n", - "\n", - "=== Filter Performance Summary ===\n", - "Processed measurements: 24002 (valid: 4923)\n", - "\n", - "-- Average Errors --\n", - "Attitude: 1.1308383647121234°\n", - "Bias: 0.0075498876180193865\n", - "Calibration: 0.957466838125976°\n", - "\n", - "-- Final Errors --\n", - "Attitude: 3.662018753470247°\n", - "Bias: 0.0020652584435729942\n", - "Calibration: 0.3643824462229628°\n", - "\n", - "-- Final State vs Ground Truth --\n", - "Attitude (RPY) - Estimate: [-13.80445039 -0.64271376 86.74437182] ° | Truth: [-17.40672752 -1.04149482 86.27016675] °\n", - "Bias - Estimate: [0.00468984 0.00994432 0.00194401] | Truth: [0.00399696 0.00799933 0.00189684]\n", - "Calibration (RPY) - Estimate: [24.81667419 5.05822205 29.67403453] ° | Truth: [25. 5. 30.] °\n", - "\n", - "Filter processing completed successfully!\n" - ] - } - ], - "source": [ - "if data:\n", - " # Define initial covariance (uncertainty in the initial state)\n", - " n_cal = 1 # Number of calibrated sensors\n", - " initial_sigma = np.eye(6 + 3 * n_cal)\n", - " initial_sigma[0:3, 0:3] = 0.1 * np.eye(3) # Attitude uncertainty (rad²)\n", - " initial_sigma[3:6, 3:6] = 0.01 * np.eye(3) # Bias uncertainty (rad²/s²)\n", - " initial_sigma[6:9, 6:9] = 0.1 * np.eye(3) # Calibration uncertainty (rad²)\n", - "\n", - " # Create filter instance (starts at identity state)\n", - " filter_eqf = gtsam.abc.AbcEquivariantFilter1(initial_sigma)\n", - " \n", - " # Process all data through the filter\n", - " results = process_data_with_eqf(filter_eqf, data)\n", - " \n", - " print(\"\\nFilter processing completed successfully!\")\n", - "else:\n", - " results = None\n", - " print(\"Skipping filter processing - no data loaded.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Results\n", - "\n", - "### Attitude Estimation (Roll-Pitch-Yaw)\n", - "\n", - "This plot shows how the filter estimates the body's orientation over time. The three subplots display Roll (rotation about X-axis), Pitch (rotation about Y-axis), and Yaw (rotation about Z-axis). Solid lines represent ground truth, while dashed lines show the filter's estimates. Notice how closely the estimates track the true values even during dynamic maneuvers. You can zoom into specific time regions and hover over the curves to see exact values at each timestep." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "rgb(200, 60, 60)", - "width": 2 - }, - "name": "Roll (Truth)", - "showlegend": true, - "type": "scatter", - "x": [ - 0, - 0.005, - 0.01, - 0.015, - 0.02, - 0.025, - 0.03, - 0.035, - 0.04, - 0.045, - 0.05, - 0.055, - 0.06, - 0.065, - 0.07, - 0.075, - 0.08, - 0.085, - 0.09, - 0.095, - 0.1, - 0.105, - 0.11, - 0.115, - 0.12, - 0.125, - 0.13, - 0.135, - 0.14, - 0.145, - 0.15, - 0.155, - 0.16, - 0.165, - 0.17, - 0.17500000000000002, - 0.18, - 0.185, - 0.19, - 0.195, - 0.2, - 0.20500000000000002, - 0.21, - 0.215, - 0.22, - 0.225, - 0.23, - 0.23500000000000001, - 0.24, - 0.245, - 0.25, - 0.255, - 0.26, - 0.265, - 0.27, - 0.275, - 0.28, - 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XMsBGXgTVwdcxwMm+MlxLlzHA0mZJr1RXMcAufW0FIhcxwA/R4km2lzDAWLc//v9WMMASpQzcFxYwwJr/45ZjmjDAJwG/dJFZMMC5/S5CoBgwwOpJYDUiCy/AKoimmq3NMMDl84YG7o4wwG7mZ/fijDDAv4+jIYROMMDGPRq2IhAwwKJVlCyeni7ACRi3YGIiLsBchYI8OaYtwFDowbyNjy3ARu5Ngy0ULcCrL2sIGpkswJl4jRUKVyvAGmfvoi7bKsAo5C3bXVstwIW9H7aj6izAPUqwVfZ1LMAfA0bl2AEswOmI70Yv9SvACkDFzWCCK8Ca4VQeRRArwBPrR+ZtECvA095Rp8WfKsBuQDE7DzAqwAKhV2gEnCnAFiBjgA4uKcCgXnXsIcEowAW7tUeYsSvAr6rVUb5KK8AYCj5eA+UqwBqIhH0KHyvAuAKezRK9KsBUk3hRZVwqwN/bVujnVSnAOAVbhQH4KMA94KtvepsowC9gYSB4oyjAHUez32tJKMDbnjF24PAnwCUR3MtIvSfAa1MWkXUHJcB0GdquQ7EkwLjpZkbrsSTAnUeP7HleJMAKiE491QwkwG95Vug+FSTA/9dfVS7II8Dqu8iRDH0jwLF6mmVLbSPADpzVxx8lI8DUGuZ7+N4iwJkjn+yQICLAu4ddEBfdIcD/Vdd9dkoXwNLV1n/I9hbA3hmfIo1uFsBQwa2b/OoVwGXhBFS6kxbAyb7GSGEbFsDTNNpr8acVwKuxXptGuhTAkq+ijgdPFMBPkQhgw+gTwA0RuoBThRPATIcJsAMoE8CAnsYB5s8SwMTEDxYN4xLA4AutSSWXEsBEilu6gFASwL+SEszWgBHAMUwPz/lDEcCkVrFuVgwRwNQmkAjabBDAq2ECqFdBEMDrzrcxXBsQwMV1tiGBLhDAx3lhWgAUEMCxQAnkxP0PwLtbT00HMxDAKjuuZtXtE8A54Zxb0uwTwFB1IB621hPABHKdo7jhE8BTe8FfK/ITwMOcXFaBEBTAK1u3S2MrFMAEgjdhX0sUwFjH+caWzBTAtBIiNLf5FMAjWPNf9SsVwAuZzAzQPRXA02HSFPl7FcDV84JRsJUYwHkBdO618BfA9vg2cPI7GMCAvLYCCYwYwBlKb6ljBBnAUu6senheGcDbrkE8Q70ZwNCWIX/I/xrAJADgYtxrG8CGwtfil9wbwJd9eeYSBh3ADRqoxqGBHcDprxPisQEewKIJNDGoWx3A0DMWaRznHcC1KZH41XYewNZFfgT68R/AsTNplq9GIMBsq+mnYpYgwOoigtoYqSDA0ta2U577IMCf3pC2C1AhwKEBt/vrLCHAgdb3gbGDIcC6rAvAItwhwKH6qVmpdyLAAoqiGLFqI8Dg51HO98gjwHuJP8EV1CPA08CjSko0JMD0MEqq7ZUkwIMZUD0RFyXAg333Ymx7JcA1+lu9AeElwN3o+mK4YCbAJ6HW9dXIJsDUFhfO9jEnwPjl2bLn9SfA0nwoCKJhKMCcswmuQf8nwDpK9tYfaijA265GlaPYKMApZ3Qf4UcpwDuxsv8DASrAHP5x7PdxKsBfqKh8eOMqwIw4elm1pCvAcDZYBYcXLMAAHcaHqooswHx63x+TOC3AaAf39XmsLcAs2kmNeyAuwMtZ50HxJy7Adzf/Yi6bLsCd3GoaZg4vwHvh/5AI8S/A9C9AvEEyMMBh0SfA7GswwMgjxRBI2TDAPmWUzNISMcBilW9jL0wxwHbpVY7gtDHAxK5bFPTtMcCMqsIZwyYywBRwGrw6fjLASmkysEI3MsDHYOuSsm8ywKTu/rrbpTLApcI1taDdMsBDUA5WAhUzwKOtnNwGZzPAb7Q+ArSdM8Bhlthr7tMzwJ/244uE8DPASmQGRM8lNMDe7VBLrFo0wC8SOyoJpjTA/ho9qejZNMCba+HJMZI0wCP1pKDK0zTABhbbi7IGNcC5BNJuETk1wKFxkbnvsDXAiUqn3yTiNcBKaVWdwxI2wMNlJZ/eYDbAjDGTmlGQNsADyK5iJ782wNo+JwFg8DbABXhgs+4dN8DSNyuE30o3wDGTC1AnLzfA/IVDW/paN8CHdBlAJIY3wPVJkMVAnjfA9VdIZivIN8DbMu4ubPE3wCr1cyb1ETjAA29CaOs5OMDc/sp1PWE4wF8QBiVQtTjAl54VTzPbOMABDgd+bQA5wFwYu27ZXDnASXifNCWBOcDNlv3iaaQ5wLgzmKgU6TnAMOIQ69wKOsDSnAZeAyw6wFu6DLMUKDrAVwkyeQVIOsBYdMukTGc6wHWZ7XsnaTrASADeZjeHOsAXorz2nKQ6wAbuRt7tsTrAH2+99Q7OOsDYBAiZ/Mc6wJy2YCtfsDrAo3eEE9bKOsCxYuOksOQ6wPy7Sig4ETvAxLaSobwpO8AB2jbHoUE7wGMcWZYbNDvANdLCV/JKO8DtcHbNK2E7wD7TbZtvkDvApI+X3GKlO8CWjKyMwrk7wD995qlZ6DvAwlQtRIj7O8C+yegbHA48wGNSMyHkEjzA5ab4x28kPMC2bxWeaTU8wJ/gTjshejzA0zu0LsiJPMBwH5JM4Jg8wLDtifFQtzzAtPxmcDnFPMDFcL0Vm9I8wLMjibQb3zzAikA0kGnyPMDlAdODBf48wOUiw7ElND3AHEnv2Ik+PcCDaldGakg9wLm2+DsuMj3AdCO6BDw7PcCHg4/RxkM9wL+iG23tWz3AYEyODXtjPcDH8bJZkGo9wMKmcyOWOT3AJYlirvE/PcAyjeCQORs9wAVn5oGfJz3ASiKNX5EsPcAKnocWGDE9wEE0O1s7Hz3ACDWrTv4iPcDS7xPiSyY9wPNLgDZ2LT3AZe/VLfgvPcCPDCUrCzI9wCHt4puZVz3A4CVwYahYPcCs3MVbVVk9wMxHtPnLrz3AlpFFGaOvPcCqB8PcEK89wLPrtuXXyz3ANJNslVbKPcAvHnqEb8g9wPWx5A733T3AdydrwSrbPcAV9bqXAdg9wKne29J94D3AJEkf93/cPcAOgP+3KNg9wA5rT5uJ0D3ARkmC9070PcCrS+2zyO49wL5drjHw4j3AWlaT+8XcPcC4g3FxRNY9wPH+vdeZ3D3AqUMDRkzVPcD/CEwwpc09wIbwaAxpuz3Ah25R0yGzPcC9MboRg6o9wCV1NUntqz3AuK6IO52iPcDyXroU7Ik9wLkgADuQbD3AtVYHgjliPcCvR8k4kVc9wPAbkaQfRT3AZtfbDOM5PcApfNjBUi49wGHndy63GT3AZGYRNY4NPcCSRtotGAE9wM2iVESq+zzAztSxdpbuPMA9fLGKM+E8wCXYN+TgzTzA3NFEUNa/PMBNa9QggrE8wAtV8pD1rjzA+qD6d/6fPMAfk8Q4wZA8wDKrtkW5fTzAOKCKBOltPMADvCrj0V08wDCWpAu4SDzAmYB09Rg4PMBHngytLyc8wP5Ih8sCETzA4uexpoYcPMAzz/dJ0Ao8wKGaynJvAjzA9dNfsiLwO8CKlUqfl907wHaWwzmwyzvAT56sY6C4O8B1V5l6RKU7wIqkFz+RiTvATUgYoKx1O8DomRoQjmE7wHsPVZAwUDvAle1iVIw7O8CVOFhIuwI7wGSfZLy17zrAxhf52VnaOsAibfb4vcQ6wLlJAZsPqTrAhzOfF++SOsBzDaepknw6wAKDIFizYzrAQkLmb81MOsDhTP/4ozU6wGBfa6i+ITrAFTZdsBAKOsC+2YDxJfI5wJ3zJuCZ8jnA/twp9CPaOcC7s+kec8E5wMw8JnezpTnA2MSaL3yMOcAjF67v+XI5wNUeNUYLVTnAI0J6Mwg7OcCwB8fSwCA5wOP73q8gDTnA9HVPPVzyOMCDbidoUdc4wMzCChx1vjjAMdwDFfrMOMDwGai2ELE4wONwbq4LlzjAE2zx7Zt6OMBfuqu/6l04wC9PdauWPzjA7ZftPlgiOMD0WnEH0wQ4wOkO+nCD5zfAE/DFKmvJN8ClwGxSD6s3wHUGvkCahzfAG3ivDaJoN8D0S8UveSs3wCZF80BVEjfAL7zOGqHyNsC8Fzo9p9I2wJj6qCkprzbA/ZrncJCONsA5I6E9r202wEA8Dt7STDbA2ttPlGcrNsCccVMkrAk2wNE/T7/D5zXAtPnUXXzFNcCiC3m45qI1wHQOou5yxjXAfKZf+VejNcBxfbkE8n81wCd3PucTXDXAdZxb0xU4NcCTO4ymzBM1wPmjyra86zTABMx388vGNMBizQJ3i6E0wBTkdv9IejTANshIgmdUNMDnkycDMS40wIUqq7jTDTTAGRb/+CjOM8CbE1p0CqczwLb5JzrueTPAhgPJSR9SM8Ahd/JfAiozwC4My7kSADPAqJdBNUjXMsApOW2GLa4ywHxLQd75fjLA/KG8WC5VMsDzjC5TDSsywLp3t1A2+zHAjcsanVHQMcDcHxASK7AxwG749Gw8hDHAnKPyvDtYMcABQnpx6CsxwNpygvMU+TDAfwuT7v7LMMDliKxFkJ4wwG6T+JYudDDAqwpr0B5GMMAuxkWouhcwwJ3QN8LO3S/AM0xjV8t/L8Alc7ahHyEvwGVNASJCwy7Aw2FXj6JjLsCR3HaJXAMuwIHGdKxfoi3AvAGe9sNALcDh5f0edN4swD1ybz/DiSzAqqAiMmUmLMBU5k6uTsIrwEdpK846VSvAmMhEk73vKsBsjaKjkokqwElR+lcOKirAA+t9sBzYKcBipIdljW8pwJz1KvFxCynAezuILLOhKMDkdLqiUjcowMoy04jtzifAs/x+vDxjJ8BNJYf09vYmwBZGsYfjfibAnY0iYkkRJsBa80DBJKMlwNsR1v3VIiXAyyoLPCuzJMAKAptfTqskwMxFSd7fMiTAp5W1czvBI8ClDhxzCk8jwLEMGF5gzSLAgeymLMtZIsDHRIQbp+UhwFS7ENb6ZyHAcqPS4NTyIMB10rYCLX0gwNhaWl6r1R/AqDAd77fnHsAoXseC+fgdwHayoAFbRB3A2Y8mL69THMBJM3PqJ2IbwHbvE11uXhrAE/flztZqGcAYvMZeWXYYwGe9QbJJexfANNY71A6FFsDlEL8PJo4VwFV88X4qkRTAugB46zeZE8BaaX1B5KASwJ4m5+NtoxHAPiyqqYGhEMDORYVjUFAPwNwCjujUdQ3A+6DI6q+BC8CGggVv6owJwHpC1AHmawfA6BwMwuZ1BcCG/wMpZn8DwKRndu3JvwHA9HLDKIeQ/7813GvfRqH7vy9IevIAjve/Kg2P8Q6e87+3QFXCoirwv2bCilZ+++q/VMhb2/8i4794cZpikpfWv+m5u6sTh62/ofTIvuUGyD/KkGONHbPbP2MLvR/Cv+Q/iX0U/fmO7D/lvCnT0i3yP+JAbhfRUPY/XqwzVNMz+j/lH+syCBX+P4/JsqqCdQFA6eCkC6lkA0BmyNDZjVIFQNWrhJ0aEgdAmiA/SND7CEDZBdEW7eMKQLjLHQDrrAxAjheGLyuRDkDuUPCZwTkQQMQIqdP7RxFAQzTUtGA3EkANRBSGvyUTQKPklns34BNAAntmYaA4FUDCdEmu/SMWQNaTav6G7hZABicPJW7XF0BPUJbBLL8YQFZISLXHihlAxz/0lqtvGkAKCBU9DlMbQK4aELywLxxAE1aw26UQHUDc6bfhRfAdQCtkE60J1x5AFYkR08ezH0AUVdnho/cgQD1KEYFOmCFAINWgCR8FIkDWz1ovI3EiQH5TV82N5CJASnMqgBVPI0ASJyUPw7gjQOgfwaIHICRAu6OIxdOHJECqzYbjv+4kQN8NzH16PiVApmyPu3SjJUDU/ETciAcmQGTdER4iPCZAP2KkqnWeJkCO9OC01v8mQIUezFCNXCdAbBoDzwK8J0CvCIRXgRooQEX0QfKJeChAoEFIWD3VKEA8uGkg4zApQFBZrwRdmClADWtdzTfyKUAMBVqkD0sqQGGk8nUsrCpAMSXawfKgKkCtt7UWa/YqQBhfPDFKNCtAoOUfUqWHK0AER8Qx+NkrQNkCq0E38ytA/K8IUlZDLEDYDLYueZIsQGAwamg49yxAVcL8EHpELUBJbUMVrJAtQGheWtj1yS1Amp/4NAoULkAV+zseq+ItQDONmbTGKy5ABn44KWNyLkBc8mEP/LcuQFTJU10fEy9Al9R4wJxWL0AvKV/TDZkvQJndDITkpC9ALSsGGfzkL0AQpBKgERIwQJF5d1GaBjBAkoxReOEkMEB04uJYnkIwQMLgVCqosDBAxsZfAojNMECsPjD05ukwQA9Gng3r/DBAZ38rZC8YMUALF3Uk7jIxQAKTFG9XTzFA9tLwgxNpMUB/kNZCR4IxQIaNVzQKrDFA3FyUN1bEMUBKgBaCJdwxQGfn0/cp2zFAFDp3xc8hMkAT8w0SGzgyQPgjPrRNVTJA7zXCwKNqMkB4bCWtcX8yQFvQg/fsiDJAJ42OBrKcMkD+mtWY968yQNr/TgZS2jJAlxFKEZ7sMkCegsKHbP4yQH6HzK5j9TJA/wns/SIGM0CnDtYQ+0szQMG0BW4kUjNAclS/QpBhM0AVeNzIe3AzQMJVCIeHhzNAHqGqQHyVM0AHJYFm+6IzQGYrUAZ3jDNAt1OLQ+SYM0Cci3r82aQzQH9SZ5VXrzNAOUWBSlK6M0CewGwD1cQzQA8J4BEc0DNAaAsTld/ZM0C/wSEQMeMzQFE/x2aBuzNA0DHg47PDM0DewnVCdMszQF5+LPFW2TNApfW8fzjgM0CLu+1Pp+YzQFr0zWL/7zNAodiYjY31M0BShf6OsfozQBPFr2UfADRALnFRaf1SNECOacnIDVc0QIARF9u/YTRAqzbzEPZkNEBrOpLWu2c0QLVnSjwVVzRAudi1h/BYNEB6obFqYlo0QMGLL347WTRA3jwr0NRZNEBlkbCMBlo0QHL5VDhQaTRAeo7+nr1oNEApMCk2dL40QOWDRBmguDRAHKEGESC3NEDFPNmANrU0QNf6qqHLxjRAIwXcFTHENEBEHgXFMsE0QAD3invn5DRAmSsw/ULhNECzy3FXPN00QFpXxQi12DRAsWdWxO/TNED+n4sf0M40QMA1IRjLfzRAKsoigbx5NEBJAVqcT3M0QAJahzAIazRAigTCj9pjNED9oei0Slw0QHzg359oXzRAaHmLrChXNEAjPwFYkU40QNnIcAepNDRA+x+zDE8rNEDIAUYwmSE0QEVVJvsZDjRAmtTeXjy+M0Dgcm8qQrMzQEdPBmn/vTNAcMb9x3OyM0BksV+0iqYzQDkiB/idtzNAgvWQbymrM0AyTy3RXJ4zQEYwLXdcjDNAPzadDOh+M0AuV4GsKHEzQEq7LMiSUjNA8lNKTBVEM0DNLBt5FJ0yQAZK6nq8gjJArfRJE9ZyMkAIP0D0oGIyQIyOwsenQjJABNCPAc0xMkBM6SJPniAyQHBz8rtcFzJAs14xq6MFMkAXvkXoofMxQJheZKcg7jFABqVJ45PbMUBdEC0GusgxQGf0b3WztzFAbYR/2FukMUA+C6lpuZAxQGxKng/GbDFAfpqwMohYMUDBhhfSBEQxQACt2CIbPTFA2moZnBkoMUBg2cq50xIxQBuq2ppnAjFAepZZqqTsMEABgBKAnNYwQN22PiCxnzBA2Op/bUE1MEAcJof1MR4wQDycGstSAzBA2skbUY7XL0Cbuha37KcvQHPZ1Pi+XS9AURAh9hctL0AoyTQjCPwuQArSBkGcwS5AOQAtZZ2PLkDtE+hcLV0uQHOTF6gMUi5AxF/1Sd8eLkDVYzCNPu8sQGQ9GVN3zyxAhI3UbX6aLEAq7cWGKWUsQGPIXWZlLyxA3Uwu3kz5K0B5jpLd1cIrQKNVPgROhitAnHqRIQlPK0CkdzADaxcrQOg4c54cwSpAA43jJLSIKkBt1+Oz/08qQHT8RCrZkSpAgMx4c79YKkChMn24Wh8qQEYUIladvSlAdqoK/nWDKUDXTwVT+UgpQN7KJ7JC+yhAHE+1oC3AKEBtDJV9x4QoQECTNKstVihAh6/mVTgaKEBeGRjf+t0nQEk3UYk5hydANRgMAsf+J0BJEvsMSMInQBf+Nc3uWidACyvE98YdJ0DtM2t2aeAmQNN5ybg7sSZAqrGBcYFzJkAyuAxUfzUmQJx2/D9y4SVA5qKRKvyiJUBg6M/eS2QlQBd47wv1ByVAfdFwSsHIJEDQC5w8oBslQHKWdE+0uSRATTzZDTN6JEDylAfLgTokQOYgqlJw8SNA0n9xv1axI0CFe5spEXEjQCUdAGdVNSNATwBuuqT0IkCqDVb4zbMiQDCebTUphCJA+/Q2ghBDIkC0PXea0AEiQLGlO4EMOiJALoppMt/4IUA+5LRJkbchQDGgqMaUiSFApO94JA5IIUAl2AvHcAYhQAkAzEvBmyBAQHZs6+BZIEAAOA5h8BcgQI/jCfnZAiBAhfrA5Q6CH0CSuzDOLP4eQDB3V4TRYh5A8Nke3NRlH0DCwQmW3+EeQMdOcOFsUx5AbJHIZcXOHUBuTCXXDEodQJ6L9dK7TBxAoZHBfHTHG0D6N/cnS0IbQFmn4WqZjBpAnjiXPw4HGkARYc6QeYEZQC7Di6q0AhlA4RMgnjZ9GEAPb8IUNI8WQDNTCuJqFRZA3iKdn+GOFUDaf6q8WggVQHid7q0CPBRAMI3iXRe1E0Dpb6eGVy4TQPigfHdB/xFAFWgPqpZ3EUB5HIe04+8QQK2K1bmqzhBAMscEUWhHEEAtuX74hIAPQBqmtIDsKhBAGqskBDhJD0C2U9x5kzwOQKlGVaPB1QxAlRANhTvJC0ATd0Df5bwKQO/wIdzz0glA1oGkaa/GCEAWUKraw7oHQGH6G86L1AZAdKSaR2TJBUAh/Gjfw74EQKTCJGDzxANAA/d8n2e4A0AtnyhD/K4CQC2wZWdN3QFAC34vXP/UAEDe7BG8mpr/Pz6WseK+sP0/qIul+Dai+z8sZrfZx5T5P1Do9oyJE/Y/ig4XLrsF9D+HsdBU8fjxP605nA40Iew/RFvMOrEP6D/I/KbNzvfOP9x9CeHqv8A/qmqSJeQMbD8uhAT3Uae/v2vIrK/Vssi/AyKNoMVp1L/F5sIdE3bcv+GGasS4Oua/hyTwdYg66r8+4YGsJznuvy48/I1xXPC/T6F5kJtZ8r/Omvxf9lT0v3JGz0TLava/sAfccitk+L/zy0yjR1z6v+tvXyChAvy/UMZg/mj3/b8XwbFQmOr/vz47JyTxHwDA5uH+XyoXAcABJTINqA0CwHHjPW6GhwLA4QADewR8A8B1ZD9MtW8EwPITMxGbpQXA9UoLw3aTBsAXuK1+/IQHwGv79NugswjAr4a8McWjCcDyH0c5+pIKwHVHAQJ77grA4Xk+oRvbC8CllWOIlMYMwI32Mjpn6A3AXx7BEuzRDsC2nKciS7oPwDgnlGbJjhDAQhzAoBYCEcDY2qo0qdcRwEdERw5ofBLAN7vJL/fuEsD2vwbNAmETwA6jdeq64RPAqqkUCKRSFMDEFnmTC8MUwEk5f4vkchXAMQolsATjFcD8ctgfqFIWwOWMR0A9DxfAfp1dykd+F8DIF+Oc1uwXwBveBNNRVhzAnGDTFQDGHMDoSkfAAzUdwHJW6/JMzx3A2YBWNXo9HsCSCx/sKasewHLG2vQgTB/AowiUPn+4H8B8677UIRIgwAZ2YeDNYiDA9aUF6DyYIMBpU2bYYs0gwC+yYd9tCyHAQr92tqbHIcDpnKtVMPwhwFaVUrK3YSLAELuMoMmVIsCzx0+AkMkiwKHGkaauBCPAKk3LmAw4I8DJ6EN9HmsjwPG7VuRFdyPAGxfP5uKpI8BsYD+MMtwjwPGM9IPXDiTA5y3aY09AJMAJ1YI84ogkwIGxHnRLyCTAQQ7z/a34JMDltG5GuCglwEVSyN5zQiXAHScT6rdxJcCs3GqXoqAlwIHy8dcJzyXAjqstsmT9JcBiXj30bismwEcbZs3scybApWkq4jyhJsBPcgA6Tc4mwCpI+3RsayfAAzb9ufuXJ8A37QB5NsQnwMepTD+55SfA2xSF8D8RKMA/RYnqdzwowAThqwGtSSjApLR1xwN0KMAYmbb/E54owD+HXwud5SjAiJdQNgUPKcDAh+xxKTgpwFtbvi/eWynAeOFGL3lLKcCtiH0sp3MpwP+q8mTRdynAPbpyf0KfKcA+jVLJgsYpwJlETIxN0inAfFY239r4KcBUxziEMR8qwKGgkHiYIyrAU5tEpdVJKsB56K8y4G8qwMRkBRwvgCrA1uTpTualKsDPi0JEavkqwO4fKK/4PCvAaUml/3ViK8Bu3SLuwocrwAq8lipnhSvARJSLMOmpK8B97MNUPs4rwA1QxMi6ESzAftTaN781LMAuU6kdjVkswJHuaR1JYyzAtbYLvamGLMDiGRxg5akswDi8HxWjfCzA6m88WkSfLMDslta3xMEswDqKbYTKyyzAtXp70wHuLMDTHhjHCxAtwHALvcGFJy3AMXmGM0ZJLcBfyQVQ7GotwJVDeM60li3Au6/gClC4LcDoltuP1tktwIMD4aRK/i3AJcMmrEk8LsDB7OqLjF0uwGRlopt0XC7AkK3ITJx9LsAaOsHuwJ4uwI54WrYbpy7AReNT/yLILsA7cUGHGukuwEbnJ1XvDi/AveDSZcMvL8BPpToOmFAvwDBhfs/geC/AWnqDRbyZL8BWi3nHfjIwwD1NeV56MzDArKai8ipEMMAet0Lh31QwwC6aFahwZDDAEItqPed0MMACXc1VXYUwwCBDb0mkoTDAvM0SpTWyMMDaPgtdx8IwwDvwUb+82DDAOcsTl5HpMMDigJbncvowwFcURk+IoTDA0vTdqUmyMMDGLpSoI8MwwMq54k7J1jDAtFPy0qvnMMDCtDlno/gwwGv4V5AzHjHAJTTfZI0vMcBy9aLU/EAxwHdDYPBRWDHAORK2PQdqMcBk8DB31nsxwE/MY4QsgDHAYWJaG8zAMMDGXEgWi9IwwAxCh5e/6zDA5Q1nr6j9MMB857Z0tw8xwITWS4zkHjHA77bIoTQxMcCq4fLumkMxwJMdj7MJRTHA1LPHdKFXMcAXBVKpXWoxwIx4/KAoczHAgN4BqjuGMcCScXOozGMxwLy3WQ61ejHAQZaVLj+OMcAV6Mvg8KExwPnQwM26pjHAdiNJCqu6McByrrg9zM4xwGz6bYjq6DHAl48/LVz9McDGln3f8xEywAHEL1kKHjLAVME8ydcyMsAMbBV400cywPf8VciOiDLAWG+Q2v6dMsDLhY4ymrMywBk6S/rhzDLA9t3M4OfiMsD/zPd7G/kywMCViPxaEDPAtc4Y2eEmM8Cl0WRmlD0zwLBkD2njRDPAfMcAVddbM8BwJIuh9nIzwKLyGhs2fjPA3tg0SiXmM8D//wW62v0zwJuzECnrCTTALEK8e/whNMCl9KKmPzo0wA/y+6BOSTTA4eAZfeZhNMCAdyVBpno0wN0+EES+iTTAOw02r92iNMBWQcTSJbw0wPAtRP7v3DTAdilkJY32NMDkVksbFzQ1wLT5aP81PTXApt2i8WJXNcAs8WkZuHE1wJ77xBcqhDXAadEZG9OeNcAVg7T2pbk1wCrCdMfo1jXAafeLEhTyNcDwDveuZQ02wEoR7xFcITbAeOmLRvw8NsB1yCLvvVg2wOcQEi8SGDbAMK7DeQ80NsBYNsQnM1A2wPlCqVB4XTbAGLpkGNd5NsB/OLnzU5Y2wKuEDrpxrTbABoDwiSbKNsBfWP37/OY2wCgE4JhBCDfA/RYCvFUlN8BM86JghUI3wPcBsdhxUTfAjbWYQDdcN8A+4qgpq3k3wIkrbFDolTfAnI8G8IazN8Ds0mi0N9E3wAgbZZYW8TfAH8XRK+wOOMCIn+wfySw4wIKkDTMHSDjAafY84PtlOMABJYP//IM4wJmYEBRZjDjA1ieAl2+qOMAkZmMFOV84wLy09qCUejjAOYqdPciYOMD2tZs6/bY4wDywIlRk5TjA6pAYVpsDOcADXNBmzyE5wNyVoZ0GRznA+K5EeS1lOcCM/+bhTIM5wPMb+py2rTnAb+xSUcDLOcCPApXevuk5wEmo0Iz60DnAGBWMs+ruOcDifE33xww6wB5qRfjQMjrAZw9qgJFQOsCmAxeDO246wALlLnYyhjrArwk3kKyjOsBwyng8C8E6wEfo5yL24jrAgVHfag4AO8A/M0HdCB07wBH5pBHWMjvAGv5BoYY9O8BmjmBVGlo7wMC3E0QldjvAsmwyAXSSO8Bz7R64mq47wFSwzd2NyDvAoF4vyVvkO8Dh9r7T9P87wFVcSbDNGDzAWANz3xE0PMDiNZQ1Ek88wG8MHj/yYTzAKL8egIR8PMAPk5X44JI8wKDsOmjktjzA/N+ie7jQPMCe1pE/Uuo8wBgTcECbBD3Awsbgr6cdPcCjzSrWbzY9wESuT4d3VD3AZlMOSLdsPcALMlrvp4Q9wDAXgCbwlj3A3PnX1FeuPcCn+YNvbMU9wBR8FGWGuj3Aay2xUxXRPcDWF0ZWTec9wKPtx+049T3ARnFCvs0KPsDpfy0vECA+wBwTaxfNND7AI3xTwmVJPsA4mlRaoF0+wK/xgLseaz7AMtUuEY5+PsDh9v/YnJE+wJHFeJ2Poz7ArRnaDKXSPsC6PCnrfOQ+wGfiL1F77j7AgQl+sn//PsBhZKpKERA/wNnKXWVEID/A3jGX0OEvP8Ch4T2qFD8/wCgyU/qWRj/AX7W4G95UP8DQMRAyuWI/wNKUFBbJbT/Ayw3qjZ96P8C0bC8H84g/wAAaeDeVlD/AGSMxzP+fP8DHF14L7qo/wC05clmXtT/AavnMgXm/P8Ab5bWB2cg/wPCDeG6Z0j/Arn2hfOjaP8Cf/je2qOI/wMhJr0XV4T/AGQLbGXHoP8DowLjufO4/wFepJ26MsT/A6V2MNWe2P8CC2gpIs7o/wGKeLuX3xD/A7kKtASTIP8B67uoSrco/wCUXUlyxwj/AVGknGtfDP8BSNasrV8Q/wIiJ6GgDyT/AbB3i9TzIP8Bm0mLKy8Y/wP7PYOOLyT/AnayGtTHJP8BjZ112pMU/wL+IgRjRtT/AJIycS9GwP8C1KHabFqs/wChM49JgoT/AB9yUmA2aP8DSnH9R85E/wF4pBA8Ggj/A8AdaGj54P8AZxW2po20/wLrYmxNsYD/AggvF9h9UP8CJrlmuseM+wCC1CQyu0z7Am6JzXl3EPsA4Fh+6GbQ+wMMp8heLlj7AE/bqmkmEPsDJuQfODHE+wNN/m6sxYD7A/gifhAJLPsCH5KmdwTQ+wNC8hy5yIj7AdTsmlPkJPsBnKO1bYvA9wHlsJ/4F9T3AZXdB+BHZPcA/VqU66Ls9wHH/9jYNmT3ABOUde255PcCtnRU1glg9wB0wntgoPD3AzJLFC6IYPcA7HnlOs/M8wBzqA70/0TzAAlXyVn6pPMDcEw5nO4A8wGhvvXcmSjzAt+zz6sdFPMAeTQ3x4Rc8wLVrFuDN4zvAGbGzWXqyO8ABX8mrZn87wAYuI5M+TDvAES9U0rcVO8CbmbQ6R906wApa6v9injrAo4VxGC1iOsCLQ0zJ9CM6wEkoRPya6znAJTImrQepOcByi7SpWBk5wMhBuDFiyDjAORK1qch+OMClINZy1DI4wFE8YrSv2DfAuH5TrsCHN8AaJNokVDQ3wF5sGcWM1TbAt35JThp9NsAGcTjX/CE2wAkyPfYGyDXAAT0DUstnNcD4nlfyywQ1wGk5pzDBZTTAHsoFf6j8M8Dp/OX7ppAzwL+N8riAIjPAGqlw+rCwMsC7a/sR1zsywJGUNBw1wDHABiLUKPtEMcCdqLtbrsYwwBwBm2wMSTDAqRaBX7WIL8DxD4h4/XguwKYqTwhFey3A1JRXnAjNLMBbcW7nbqwrwOjewFEzcirAJmz6T95EKcDA61ONdhEowLIxTNavyCbAlgYCmxKJJcCs2ofbuEMkwNdMeV2B4SLAFJxgrYaRIcC+aKZiVDwgwNeERF2rcR3ATpJeyNqzGsBxKN9PQrEXwOLogjzYFRXAP5q6En4/EsDEhOWGscMOwE/FO2VkrAjA21vxuOvXAsBPSdUPr/H5v+xvYnZkiuu/zQA745/hvb9JxZclmynkP6qQrEXqZvc/WkpWAf+2AUB5+3OxbrsHQMS1w1Zwpg5AY5UTrPhTEkAzVuChvFIVQJVEh6lZnRhAqbtH+OSVG0ADBYgT6YkeQBwpzxZqyCBAudtknA49IkDW0Q0KWa4jQAb3mZu+QSVAYjULpTGrJkCvCeqFQBAoQN3hJDx1lilAOkoMRP1kK0CbFfiwUbwsQN8//GBB5i1Ahkaam3EyL0A5WS2jVjwwQBDkI5UayTBAD5FXKuplMUC0WRblif8xQOW/iPgstDJA2bEKvVZHM0BrmlhXG9czQPQUBfSbcTRAiRLgPJD6NECyTAZft1g1QK4gNCCQvzVAjQqO6H0+NkD4rBf5/Lk2QOAjZLYIEDdA3qgvZaqEN0Dpupje4/U3QISPvwz9VjhAX50vq4nBOEBXCR3+ySg5QF7TWVCXdjlAwGe2PWXXOUBzotFIBDU6QFqW49xYTjpA1vLLW+6lOkBgW+iqdfo6QCQafbZUXDtAf/44eBqrO0AlbF7y9/Y7QMAHK4QhPzxArPlBDCKFPECuFRHHW8g8QGk30/HgDz1A+dSNkptNPUDWGpDLu4g9QMe0OSWszz1A8rLCP8bvPUBFOizDNyM+QM7CAyaHRD5A2QZXAxxzPkAYwcJIUZ8+QGlqRKhy0j5AEL6pKB36PkD5R5apgB8/QAZtpBY4Oz9AwIfNDT1cP0C83V/zEHs/QKAYGiKzkj9AE0sEQjutP0De+79aQA5AQK4cQJB0HEBAziTMwnsmQECtlVPaeS9AQKT6hnvFM0BAnNOBbZo6QECT6t9pakBAQI5OiMV7S0BA9SQ//EFPQEBghXDXBlJAQMpX1aAAT0BASnnMDMFPQED6d4tXe09AQKyTmoT3TEBABPd0mX5KQEBWwsTF/kZAQK/vRHIeSkBAIPvqXYNEQEB8T0IN2D1AQGQVcMsPOkBA7OkFNz0xQEDjzABUUSdAQLpsPYwyI0BAaxbjtfgWQEBJTcwylwlAQP0f5jtm8D9AGpW0v8vXP0A+wIzM/7U/QKJO677KxD9AyQYZrgaeP0BNqr2ppnQ/QILpdu+lMj9AEBwlXRQEP0A6BT+PvdI+QPOn3P1srj5ANuTCcaZ3PkDRv5JD7D0+QJmKD9NyEz5A92uXdWjTPUAGk2pUU2o9QJo9XoVPGD1ADx26SFXOPEBTWuAb54A8QDFeHNVbHzxAADhrf6jKO0BRjhl1RXI7QPF7kXbKHztAFLY2u32/OkAwhcusOFs6QCOhftKV0zlAfRuUdRJnOUB8ahjSSPY4QEhJqAVUgjhASuqeHoAIOEAfBLVwGoo3QMV+NupxAjdAhCIIpqx6NkCgqEAYHu41QEUzu1p9TjVAhpmUTcK4NECbxEuTGR40QGVU2WNOejNAEtmeTzHVMkDdqwgF/CoyQNtorzTEeTFAeLn5kgTaMEAf8grLSCAwQGoeO4G82i5AI/1BNyBTLUBKuJxCtsErQPOkkFejESpAD6LJX/dsKEARRsoAR78mQFjWqzS7DSVAXQ+XXH1QI0AYziSifIshQJx/KOimvB9A5Y0uir0UHEB7MVubq9AZQBhtpSjvxhVAlzGRLm38EUAkxrDSEVIMQM2rOHWdGQRAFuGDzcC3+D8sW+lBd1TiP7NhXcw1vtm/q7fajOsI9r95KAkKIdACwLn6Ws8npgrAcQkA4NE0EcBo4LeucREVwLtH6pYs2BfAuA2sQLmpG8BSep8KB3IfwHf4pPwLkCHAPHcljW9pI8DK7QQHXjwlwFkjDj9h9CbAILHOSjW4KMB0LiyIEHQqwMCoxGvp8yvASZTLBV+eLcBEkasfxz8vwFj+TQMgWjDAVeOrgErEMMAFiKCBKIcxwLv7zYbpJzLACNFY8LrgMsBSAOs7nZQzwMleMO46MzTAKVGZhffcNMCbmT6TwIE1wN42tquELDbAHvASE2bHNsA2FnJCel03wENNU3e/6zfAuj0ttSl4OMA1XlEuAEk5wCYttVaHyznAYX7nzylJOsDgVrNkaMI6wA2yaiibOzvAOld7L0CsO8DIGFrwzhg8wIyN/qYBhjzAN39s9/HqPMAxuJ/XGkw9wCGefXxWqD3A6trQZE8CPsAInGn/y1g+wKl7m2ZihT7AKJ6fOYXVPsD7gbD4ayI/wIiC7RFOcT/AS9Yu4Dy4P8BRZJtJNvw/wIujtHH5GEDAfFQDECE4QMDiwiZ98lVAwMEr1im9b0DAi922K/iKQMCxBKBe8qRAwKERLYPCuUDAMOIJZdjSQMDSJvAaSulAwMQR95Ty8kDAqPKvPCUHQcB8ODqeRRpBwA0p3XEUNkHAzV9xNyRHQcA6HHwqN1dBwIN7tjUrZ0HA93z3bmd1QcD3gUrhs4JBwJ+ex4TCkEHAxX/1Z0ecQcAEpenY+ZxBwPjlpG8opkHAenEsMyWvQcAZa+vlS7dBwAuulVodwEHAecf+dqTGQcAyx5mEXMxBwJ35bvL9y0HAewKONibQQcARRgsgj9NBwPquAaIE1EHAUOZGNuXVQcDU7DRvDNdBwNRAhvVC3EHA5KeL5ODbQcBOhlA+wtpBwNLMXZJW2EHAR6nlPMfVQcBPb6idfdJBwGLEB0XfzEHA/5pg2SLIQcCaNmtLrcJBwNKzr5lQu0HAKwMVDW20QcCkjESszqxBwH5TpiukpEHA/s2MhSCGQcCLM6sVXXxBwPuhtYA6cEHAVEPOVfNkQcCPzzBt6FhBwJyWR+r0SkHA2qkIUWA9QcDBUCU/BS9BwM7dSf1DGEHAlYFu0E0IQcDFwEjFjfdAwCz/9mbS5kDArqabGHbUQMDFlnNX9b5AwKcyuisMrkDAb472olSZQMCVjnEuwYNAwNrUZMhyYEDAZ22/DhBJQMCnoLWUyjBAwB75ji7RGEDAMtNGrWz9P8BcZKO0Ycc/wFsoK1YPjD/Ajg9rnDNSP8AtwvuSahY/wGAuydXU7D7AVOoIQAitPsBANKfXNms+wN7czTfsLj7AgpLTlxTpPcDDPrFNKaE9wN5HTTX2RT3AZKl01675PMCOfIvdQas8wCereLHoTzzAvb32+gP9O8A6Ao5t8Kc7wM3ma9IATTvAKSqrP4vzOsDP2hYTAJg6wGx/CFc1PzrAeSfKHxbfOcCtpY+spHw5wPaYeVH9GDnAPlQRKACyOMDobRnvo0g4wMEbwrhx5DfAlHZu3392N8Dc8ZP4OQY3wLjlS+5LlTbAbPFMcWYgNsAiPdN09Lw1wJKHpIRrWzXA41aWEezfNMCUn5RXOWI0wPA18UEU4DPAliwsXxBeM8CYWtGk79kywN9zlb2rRzLAvwkB+E2/McD673bUATUxwC6v8KpIpDDAnl8LyzwWMMCeAlsquAwvwFWMSPaGvy3AGPsPcweZLMAyH6znTG8rwFC7vp5mLirAGNPhYt/+KMDU5XPkr8wnwI86c6vdnibACY//ed1nJcDSA1a7yC4kwD15IedX8CLA+2mqWduzIcC0zVbB4XUgwAhX//mRJh7AUlLTsAmQG8ApFII9Lw0ZwMx/QLjurxbAfIz4gy4rFMDAIjnFN6YRwBJJL7QWXw7Afc7/yHpWCcAkANhIe08EwKGK3jaFQf6/KbMYEeJA9L/rID6r6JDkv6SrcF0lv3a/2W/UwrSU4z92qocQpM71P5EMEvZz9f8/xIfAFSvWBED939p1NagJQDi26OxVbA5AoMHxsPCUEUBivUh2VO4TQKMby6GyPRZAdqQnrHOLGEDNg3MY09IaQKdorQZyOx1A2SeYrn11H0AuLVVLQNQgQF22Ba3htiFAubSLcUDJIkBgTTFM69cjQMi3Z6kB1SRAuFzE9ijcJUAPRHRkYN8mQKbPLtIH8SdAjEF7b0XsKEBEkpDsi+MpQD0gKh7y3ypAt7VLwh3PK0BnwxTyPbosQK7Nh3CHqC1AEozUHk6gLkBCuCWPBH8vQJE+P5tCNDBAp2Qn74KfMEAJLrYNxwgxQI2fwKkXbzFAyXErhF3UMUAuO5KLpDcyQNutogz0pDJA8R3XADgEM0A7m2p5jmEzQOATjwS1ujNARgRshzQUNEDXVGJEgEk0QFSzFINApzRAwR50Byv7NEAPmUT0SU01QNsnxumIqTVAsArgvvz3NUC6c4lnsUQ2QM724IXDkjZAMZUe7QrcNkBP8XDGniM3QKyOQi2VaTdArOiOuN2tN0ACiDX8ivA3QPZTSSZ0OjhAkPusavF5OEDGqIqy4rc4QDqHXn5E9zhA4UqRuigyOUB8Wwgll2s5QE84lxSHoTlAujfb0AfYOUCd5SQjGg06QMkcHPlWLjpABiy7yq9gOkDUp7hwrJE6QK+RJ/GtvTpAebcgpSbwOkDj9tjMKh07QLDcdVs4STtA7FImka5zO0Aju/1i4Zw7QGbuBmIvvTtAiKy9ovPjO0BntOUxgAk8QEuvk+PdKDxAGxtcRA1MPEAa5zlgGW48QOYjeKTwjzxAnsZUSrGvPEDirB/n/cA8QGNAx0W/3TxAtanN2zH6PEAAkCHbixU9QLduKWwIMD1AQIwaJUpJPUBk3/o+dmE9QFOcbCZieT1AF91P9niPPUBXwsIahaQ9QBGJcx50pT1AugtgkXu4PUD7SxQCeso9QHH+WxJG+j1AZYbSUUsKPkDox6yGSxk+QLcnyFwLLz5AkxeouRg8PkDy9HLRJ0g+QN98N3QuVj5AVl96zExgPkC8NBU4cWk+QKJXeGbldT5ADwImTx59PkDSGplyZIM+QMi4GfiRgj5A18UFlPWnPkCihaZJYas+QLf7MS2YtD5AM4FbUhW2PkAhtRpam7Y+QF9L2EdTrz5A6Rk9Le6tPkAayWi8h6s+QAC6Yzhdqj5AhaUYqP+lPkBFqH/nraA+QAywley+kz5Attc5UW2MPkDlkzupnXI+QDZMilMIcj5ALAfaUZRnPkCN1jFVFVw+QEdtIQfnRz5AdBxEd1I6PkAjCxWgsCs+QEXhrtkrID5Af0azi3APPkAVxShuo/09QBkO4Zx92z1AclFILnTHPUDpMmKsVrI9QNanOirwhz1AGBafyJlwPUD1fKA1IVg9QEyoXF64OT1AHifnzuAePUCzJnGQ3wI9QMrkfeWk6DxAimfdj0PKPEDKbEeUp6o8QIALxcpuhjxAdgDy9lZkPEBjEnY1+UA8QODQv/LuEDxAzIfV7nXLO0CCglaeIaQ7QJfRssVtfztA174vLl9VO0BrM95q/Ck7QHYUXE+q/TpA3MPf6nfPOkB0VXbe3J86QCdpQttUZTpAWL4eUs0yOkAyrp10zP45QNtlO5u4yDlAhQ4FRa6ROUArX2oyNXI5QOgTwtm+OjlARZHTmx7/OEDYUqlM5sE4QKpqmjB9iThAl+S9/AFJOEBxXocR2QY4QKboEesywTdAgUsLs6l7N0Cdxt6WdTQ3QD9m5J0y9jZAZVfi5o2rNkBDqJMLJ182QHYpdc27BjZAm0k/7LK2NUD6lj6l4mQ1QEI4ezG+FDVAACISez6/NEBr/ksr7mc0QJDMb+HQDTRAJ67FBcCyM0DtR9BS01UzQMl8LXNn/zJAA/JC67aeMkC0tvrFJDwyQL3SHFTX0DFA3GQrxbudMUCBMVy4xzUxQLI6mLk7wDBAH7urbZBUMEAA2ooKHs4vQB8zxl2ECS9ACe7QPQMnLkBjms619kAtQIlH0p1/eCxAU4UEMzqLK0Aaio3kk5oqQPc3dxEtsClAowcRsI+4KEDeYclzBa0nQDEi/HBTrCZARYnBnkurJUB476tURKckQGi7hB2lkSNAx+N4UwmIIkBAipkTzXshQGZJ4BwHbSBACRPK9663HkCc9pTgw5AcQIZInA6aPRpAIWFki/AOGEADz+Czj9wVQHJkLNOUfRRALXTevxRFEkBNhZgD4wkQQNk2yy2LTAtAU6oCS9PNBkD2JszS20sCQCwQBENv7vs/zW1DaTTg8j9fDKLGKp/jP9Mof3no3q4/aP1DRVs14L8xwDVYwirxv2bedBN09Pm/oFXZvVnaAcB0Op3lQVkGwAHVHooB4QrAG/Yla7JWD8DvYmQ2l+MRwKC7e7LpEhTAoY1ExbVEFsCQ5lNHwXIYwPqHqjQClxrAoMa4dLO8HMCnqEMelN0ewHIbK4wzeyDAFIedPpCGIcAWbxwQhHEiwKEIuspZdyPATBNPlAp6JMDPPwydnXklwDlCySK2aibAb42lps9jJ8DmQqjLflkowAkHzRkoRynAiQn0+Ko1KsAFB5cicCArwBUrYYE+CSzAUHgLeEPsLMAnmQKdXsstwLM9qsRmWS7ApYZLQYQxL8Dz4ptrxQIwwDG2aoY/aTDArq3WnyzPMMB1lYSaEzMxwG1cpckhlzHA1qm3dN32McDOzj/xhFQywALP+J0IrzLAKVkf04AIM8Ap3+4s6V8zwOrQYVNfszPAIcUIPg46NMCRsutjCIs0wIy3bS5n2TTAiqzt9j8mNcD4PgC0HHE1wCFUzYliuTXArY5vpD4ANsDI6IuGKUU2wJegv3FliTbA3rq7TVTKNsBfS1MpVwk3wCaWziu/RjfAjXrvfweCN8B4pfYOkcg3wKREmt+wADjA7KoXKoA2OMA/XeVxj2o4wFjoHsfZnDjA1EpTQYXNOMAwdvaBhvw4wMNbFZnGKTnA8iX7P31VOcCAM9tOnH85wAqIcY0GpznAEidopQDOOcBWz4IGc/M5wIoJQ5M+ETrAW4VYOe0zOsCFUM2MKlU6wAZbOKScdzrAMJG3wxSWOsAtj+7eLrM6wAESViyLzjrAEiv0mvPoOsDuKrIWCQI7wCC6TsFaGDvAr1MRqu0uO8BFFNooSUQ7wH/yB7VUWjvAiLfJM0ibO8CCjBU16aw7wKoW3x9PvzvAeqXXhsnOO8BZJAN8LN07wJ0WpqeG6jvAZoh9yt32O8B/fDZsNAI8wOMVf9iCDTzA/g4i2N4WPMDVdV+5VR88wI3jk26mJjzAqh5aWUYtPMA7a67WQCc8wOPFAaAaKjzAfg3eA+EtPMBafgBx0jA8wJzv0ussMzzA8SFxb5I0PMC8P4rbPjU8wGcdoRYuNzzAiMuzhYs2PMDEWAUTOTU8wFNQtl+eMTzAgoGzdfcuPMAqkD80sis8wECsXkWTJzzAY8dduu0iPMCZUI/vvh08wO4tITp2GjzABO5MDkoUPMC0fshdpg08wJnGuv3wBzzAuwNwC1oAPMDaFDzdXfg7wDuSu7RD9DvA6BBzWojrO8BkFvugeuI7wNHKjnVB3zvAidkw7IrmO8BQu6+Vjdw7wGXkHrlw1DvAPUoA0+rJO8DKWseCLb87wEa/Rs9rtjvAS1KEhUyrO8D2+LXVCaA7wBPSHZ9AkTvA5qCK4qmFO8DD1Zjq/Xk7wJQf8lCvbzvAIVoXdedjO8CsfAB4zTw7wJwqUPqjNDvAeP0wzfYoO8AoeHyDUR07wCunkBP/CTvAKrZdumr+OsDr+27s7/I6wHKxhhO75jrAHRKF33fbOsDdJ3sQXNA6wFZWRrflyTrAW4YvWDK/OsDGjAdrs7Q6wI+z/piTwTrAHrEYtYK3OsDEyYC3tq06wDkcYCg1pDrADl89TvqaOsB4FblqFJI6wLk8nOCQjjrAofm5ZmOGOsDfFlrWk346wOWVAEwmdTrAqXf5ghZuOsClaXlab2c6wGG4FhO5YzrAYQH2+HhEOsB74Zd/Fj86wLezuzSDODrAClHrQBY0OsDrBphzLDA6wPiTLeDQLTrAysmX3+YqOsDJ9cg7hyg6wFfDMnGKJDrAeYblsEYjOsDzSh+RjSI6wHcWQKw8JTrA+226KqwlOsD8SVLHfDU6wNAfqEJ1NTrA4EX2Yq83OsCbQweydzo6wI3a6v4/ODrA6RkytUQ8OsAXLHt540A6wD23akeVSTrA5RQiXWhPOsAzVA/W2FU6wE/CyQBiWzrAgBJUfgxjOsAyYdQ7UGs6wGHjoIseczrAUI595J98OsBPYyh1uYY6wLoCJqjVljrAVty4JhmiOsBRE+sY9q06wF3RlJZSuDrAvmLyBFjFOsCS/shd79I6wB3KTeH72zrAe/KT07/qOsBuUZfyDvo6wMY39J13CDvAPPXZsjkSO8DuM6MpJiM7wKgX5SsuNTvAFBTaRSNHO8AoIad8kVk7wHvzhYdOcDvAVotcKLWDO8C0xqo1j5c7wOuhspARqzvA1rg9rdO/O8Cf8D/jAdU7wPf14dcW7jvAb7KPCgkEPMDgvgZNLB88wPnz8klGNjzAfo033VZNPMAHL+8Gw2Q8wC/wyiOXfDzAudCyTaSUPMA0QCYQ/Kw8wDr0SEBbyDzA8O1UhT7hPMChFMP5Zfo8wHa7VutfEz3AwjK6k/4sPcDn4s5G00Y9wJIUQvBpXT3ABy5ovJ93PcDQTTjO+JE9wJ1U0IWRqT3AH8/QW0HEPcBXISyFDN89wNxPPoDT+D3AYm/fCOITPsAkIfhpAS8+wFKbiSRUST7ARTUS6p1kPsAmwXcd8X8+wBHveTkgmz7AknWRGJ26PsANpg2OCtY+wKyRSpS47D7ArZUapiQIP8DUL4iTiSM/wKOkm375QD/A789PZUBcP8D2mo0ye3c/wAddOXvYjD/AiPcJsvanP8DjZ339+8I/wDPORU+F3j/AYrc+gVL5P8AACAPftQtAwJ5qIP/tGEDA2jOIix4mQMCR/FOrPDNAwMcVOZUhQEDAH70aUxhNQMCfXbfJ8llAwMBcAQB6Z0DAYZYRWiF0QMBJMY3rq4BAwAWK2crNi0DAs7pcfymYQMCERFhXZqRAwMMrdnTrsEDAui5V2ee8QMB53VJByMhAwAWVv2Jn1UDAcD4YKgLhQMC6ZBHJeuxAwPy9nbh390DAtc0uqK4CQcCXkNcNxw1BwFeNyI5SGUHAIAaHmxwkQcCN7KjBwS5BwOtHq9eiN0HA6o81yitDQcCXBBPWZ01BwIxLUlDtVkHAJ6HV0dtgQcBUQzjrpWpBwML6MvV2c0HAU+FmuPR8QcBrGiyjSoZBwNLiJT80j0HA5Z3LxjmYQcA/B/qzGKFBwCwvriwqqUHA8omDd76xQcBGVigYhLVBwH431EkxvkHAxzqJdT3GQcDwAtQxIs5BwOWLmABc1kHA4QbFIezdQcBSVm+yV+VBwC2igax+7EHAUe57t5rzQcBnychUjPpBwFLwSi+e/0HA9ylD3UQGQsD2qKlDxAxCwNyZI7MNE0LAb6Z5BDkZQsBEMEaFNx9CwPVoIaorJELANn0fKd8pQsCRpBTnaS9CwALoGn56M0LAMPEJYLI4QsB6x+EVvz1CwMutqStgQkLAaNAoYSFHQsA3lBKpu0tCwDLgbNGJT0LAScq5H7ZTQsDKbUqQ5VdCwDS7nbiSW0LA8/2ZKnNfQsC4T7BrLWNCwHJoRoiTZkLALjPsXQJqQsBPNAMySm1CwEmH2o23cELA0sBfkbVzQsDrxFDnjHZCwMHdpI1VeULAa6H4bt57QsBfWp07YnRCwAvoRBwYdkLAwaXOkDR4QsDvdfRtMXpCwFxtEieTfELA/nasi09+QsB6Y6hv6n9CwMaFvTUvgULAYHJDGImCQsDbgwrwv4NCwMQ3df/+gkLAqCw1T/2DQsCs7Igk3YRCwBk7SUocikLA/a3M87aKQsB7tO3TM4tCwKLw3/v4i0LA81o0WzOMQsBGVOSTUoxCwLHY25J5i0LAXciVTGWLQsBZAC/cM4tCwNry+85ci0LAk4LmlfmKQsBFH8Lcd4pCwOW0UbkFi0LAmXj5yiiKQsD7+64HWYlCwNo7VPlmikLAR5X4k2KJQsDRw6aISohCwFlR9EIRhkLA93V1/saEQsAnB9vmZoNCwG4he+WQgkLAAh8YqAOBQsCgW3/uY39CwKqeIqp+fULAKVRLVrV7QsCigRSHXmxCwNee6Z69aULAw/urV71nQsAtXUj3q2VCwNkhbZUUY0LAYfwsH+FgQsDsUC1GnF5CwFyf65ScW0LAX5qUrDdZQsD6yYBJxFZCwGmeV1WIVELA/ELIY/ZRQsBMYrkPVk9CwLVz+fDzR0LA7q7V9jlFQsDARcdPcEJCwLXSQSNQP0LAQRvX7G08QsCVNoSlgTlCwOhNAYyENkLABb/TAXszQsB30YtHaTBCwN0zAMukLELAYpexo3gpQsCcm06xRCZCwNxSfw2QIkLAJy3Ra8InQsDNtEAyZSRCwBrbvGG9IULA883a1E8eQsDEJq9Q2BpCwO7+Sh9PF0LAS7lPIMYTQsAMO090MBBCwJ1KKZYgDELALoIoSXsIQsCFy8DIzARCwKvF0ZGgAELAKAK74t/8QcDXaxxLCvJBwNWM/OVl7UHAEzBEHo7pQcCsF93tr+VBwIX/fk5v4UHAdh2BwIDdQcDwbmuniNlBwJ9ahMPG1EHAwBNRoMDQQcC5zA29sMxBwCCJoaHEyEHAVnO7VaHEQcBVzKFId8BBwDpg9mf5xEHALeHMtbrAQcDyBgspcLxBwEHdRre7t0HAEcwXH16zQcAnX/q5965BwJ2kLCQMrEHAyS0y1pOnQcDoh+g2DqNBwL1Zp6Qon0HAQMnbb5GaQcCgSGJa7pVBwBhZtkvFj0HALIsrFT2NQcB7evzPeohBwBPIcmWCg0HAjj6qvad+QcB0e29Lv3lBwKAdFt04dEHAUCW/3zdvQcAdKsTeKGpBwPWfUQlFZUHA20xuRBlgQcDOlz934lpBwEFDnjnuVkHAie3ynJZRQcCk8ow7a0FBwC+NCbuvO0HAplY05Cw2QcBGT7H+mDBBwBrmvEI0K0HABSEJ1XwlQcAU4A1fsR9BwBsuUHiUGkHABMCnpaUUQcBXuLZwoA5BwKvD1itVCUHAp+jWFysDQcAINu3E6/xAwBHrTBoa9kDAyMC6m6/vQMDLGbKgL+lAwM0Ortzj4kDA1V6IXzXcQMAcntGlbdVAwPipLDVfzUDA1mxguWjGQMBa4S1oWb9AwMWsIrIXuEDAf4gR49OwQMDgbg7edqlAwBA0wvz0oEDABNaKbjKhQMCMPUNRgJlAwG7e79cNk0DAdWo50CGLQMBZN7aQGYNAwCJK9nAJekDAkHvvcb9xQMAXVX1fVmlAwC9zuOjmX0DAPimznUBXQMDbrrkmek5AwAUAsglAREDAeuwQGjA7QMASH9cEEDtAwC82ERO/MEDA0d9+w0InQMAaUMi9nh1AwHAgfxUjFUDAulA53zMLQMCQPqR+HAFAwPEgTbpH7j/ANd6fR3PZP8DqiVsxT8Q/wPH1epQEsT/AgRz43j+bP8BH2lg/JoU/wCV+g569YT/AszZN8+NKP8Dp4/l9sDM/wKKZXcJBHD/A7b3SzGAEP8BYru/FJOw+wGGJ7syp0T7AVAQbSLO4PsAVduQ2WJ8+wAykmP9rhT7AvYbg2ExrPsAqJhCk0FA+wOlXz0mGNj7AWCD0gtsvPsBLqMY3MRQ+wJwv/5X49z3AKsEQU4HbPcCAZC71nb49wJcWa2kRoT3AywNapE+DPcC7h4LQG2U9wBrFAmwrRj3AQZ0RIxYnPcCiS1DWiwc9wHOe/CLh6DzAZCqATHbIPMBuhamFi508wBhbITlAfTzAtANKzWpbPMC46T6IIDk8wMhyF32iFjzAhxQzlGHzO8BV6+J9pM87wMmmTEoarDvA2bwLWGyHO8CjFHO2RWI7wEv5Rsm/PDvAHj7uLaAWO8CjrU2K+e86wNxpchFyxTrAsaaXWtGdOsCXZM2Gq3U6wI1Uev0CTjrAXBH7itQkOsBOWMybH/s5wAJ4tLJB0DnAD6Dnz4KlOcC2tWvMPXo5wKkKDTSaTjnAhGfUPkUiOcD6oIRhc/U4wB1oAb6pyDjANimGPGigOMCL/U5f+nE4wMDAVvzxQDjAYpFWIm4ROMDUhVCWW+E3wK7ysE2MsTfAYqOKKHGAN8C6jAWBzE43wLDbHy74GTfA95VlMU7nNsDNFFEjEbQ2wMhsSMUPgTbAJ4HjZsZMNsAXxmuCtSQ2wACGw24k8TXAaS5sYEW7NcALM1yI0oQ1wFS7ZIPZTjXA5iofxl0XNcBKzkDvYt80wBcFEnRjpDTAyAtdM2NrNMAuMBY+5TE0wAdi+bTF9zPATRKXW1y9M8BQoim8eIIzwPktTLclYDPAZom29F8kM8DIartWKugywERsCO6mrjLA4NqpLppxMsB9DWBGGzQywELyXABm9zHAE/Rs5ym5McBaYtPMjHoxwHHrw9F4OjHAINQQvhP7MMDoP1F6U7swwBhRxyd+dzDA6sUsEUA0MMDEwyO29eYvwFyOv6YgYC/A8/9dcordLsBcraiXVlouwC6LThAf1i3AxUfsMfpRLcBwef4IZM0swBdz2/wiQCzAofyOLdG6K8AxVX2wLTUrwAAmjbSXsyrAAx8EPYYtKsB2h4nGVZwpwJ+Ge7YDEynAC2INg26MKMAM6+++1gUowPHRlb4odyfArA29nITwJsBkacb56mkmwJDEJlTv7CXAQRIxSrVmJcDUjDQfn+AkwMEQqyXlXiTA7PscJ3TZI8An0DeIUVQjwDuoB7vBzCLAQNMOqINIIsBE2sZqrMQhwCpIEZU6RSHAq9bEEXXCIMCcBC3cO0AgwHgcDpcxfh/A4hXu6z18HsDkz2KSkHsdwDwATcoWZxzAwk8+GG9pG8DELoZXhm0awDK3OB1MdRnABHPMyf+PGMDv27oqipkXwMXmV1hJohbAQDm1zeSvFcBiLYvyzL8UwOtYiTD1xhPAXsugzjzbEsC/oRIN5fERwJNEp0RqChHAWRGNYlgmEMDFUSDU24kOwCJxrcBO1AzA6spPigodC8Bz7FEp6IcIwEpQsJDM7AbA3Tgxl6ZGBcDd8uE71KYDwH0qFm3tDQLAQ7JXYaR6AMC+tWeJoNv9v/4a8CsppPq/CWotnfKl97/1D3iXiLX0v9h8+3YHuvG/HQN9WrvM7b8zh+A+xkHov+ijBkiOgOG/ItApdxxX2L+ncstZd9LLv768xDnZrZS/gxGutLhjwT/r5VFdYHHSP262VJaapNw/qiS7fX3y4j94r1/hOnPnP8EzM94YQOs/UMmiyNN/7z9GPXQhIs/xP2EDvy3ZefM//yiBchMH8T8Yk9PS2uXyP8aqoZcCi/Q/1viysidJ9j9VWZtkQff3P9Lp3VRqifk/teYVgYMX+z8RGYwOrpT8P/Ox75LjP/4/QqqMLOOc/z/Enbf+r3QAQJ+dEYjbLwFAFV/h8e7EAUB5pNm/sWoDQEtiRUrztQNAzA9tUd0xBECCcdW7V6UEQFN2e8WQFQVA9xF4sEh4BUAoe2usitIFQJye4MGpMAZAwjGrSdJ5BkB5SvrylLoGQMw6fIlr4wZAYqO3ts8TB0DfwK1cvzsHQMTa+Nx5OgdApAb26JBRB0CaUyGoKWAHQH34nxszbAdAG2M64e9pB0BTDKZf3F8HQDbp2qFnBAdAZS8AORTpBkChOlSh08UGQIb/Hn07qQZAuMKGc/Z1BkCir0yUmzoGQOX9NPrbJQZADay1djjUBEBL+4ewwIEEQFJTtP2oEgRAHsEpXJewA0Bh0iNwqkYDQN/mpB8d3gJAd8dGPx9lAkDya2WPbOQBQGFyA+V6VAFA/fWL+d/EAEDFi0Vyay0AQFwYXNzQgP4/1TRDTtE0/T/JAS2FhJb7P/da2+LRgfo/8iJ/OYYO+T/kscU9IY33P12dnVcDVfY/7XwCZBO49D/9qVRl+g3zP9M9A+QtfvE/PU9c9Epz7z+F6fHoDdHrPw05GyyYgOg/FnwbjRGs5D+0VjLk8r7gP5X3zTwwOuE/gTFXLO812j+AZLgZQcnRPzCf43QI9sQ/KRQrRr6qmz+3wOvGu8m8v2NZmYPlVNG/T0MmIF+b2r9IuZcojQXiv8KwCDy5Fei/d0KGQWn37L+z9jLkfPbwv+OpL4wT8PO/5XStgdbi9b+T08ljW3r4v0Au4NkX4Pq//ICj+NuI/b8/YWjCrhwAwCSJh0NIwQHA04EiZLAhA8A41T9cz4UEwGEqpAfEEgbAYka8fdZ9B8DXXT487OsIwDoJQycoFQrAyA8Bg0CJC8DgRxt9tTANwHUVpDALIQ7AHGaB7iScD8CqAkJ17owQwNyIccKcRxHAF5T17YYIEsD4OMsmUsoSwB6qagB2XBPAXys7u6sfFMB94UVFmeMUwM5+S1WvlxXAqOg7oM1cFsCxgTCXTiIXwOrjg1ERAhjAYOaHMQTIGMBBYNtBKo4ZwCdR9u0FMRrAhbYX44v3GsCbeLLgJL4bwFxaXHfSoxzAm6fmtllqHcBqRLKoszAewDfJhFSV1B7ADLkrAFuaH8CBC3796i8gwMcZ05JQpSDAfVoHqStFIcAXcYQdSKchwIXiAuMsGyLA0bmPAYl8IsA9thUdd90iwBywzqk+QiPAiAgswEyiI8AvqkN3zwEkwCRDVqWcYSTAkbc9VhLAJMCrh8Ln8h0lwIEDRXC3gSXA9tJ/d2LeJcBxPmDC+U0mwN0LZO5EnSbA2VYokN33JsCq++fAw1EnwApcE4wDsyfATZcqZ2MLKMDyQIZB82IowHZEYp370CjA6HIlauUmKcDh6vrL9XspwJSm4QfNxSnAUsCFxwcZKsAbzMiAXmsqwBc7xWep5SrARfbuIDw2K8BR1oMW5oUrwE2D+9Nv0CvAqlgtNggeLMD+/tVTsGoswOPHCnVYqCzAiTBiNe7yLMBWopf1dzwtwEnjudbPgy3AE9snTj/LLcCbq+PXlxEuwMNuJ88UcC7AWunaEBqoLsA65UgCROsuwBYIXZm/NS/Ae4UIFbd2L8AG6Sk+ibYvwKGI3UL04i/AFZ8AuEoQMMBB48FJiC4wwP+B5/v0VTDAtoVf/AhzMMB7qDVUkI8wwCPmn5wKrTDAxWUJ9GvIMMBVQkZXc+UwwFZetq3OAjHA4MxCmm8cMcDZvhGdeDUxwOR09ARRTTHAO5rpnzBlMcAZMWClfXwxwEqjYyP+mzHAJolEfhyyMcAezy8HnscxwJDUAzI34zHA/Iw/j4n3McCfdSbuRAsywNVi4cJIGDLAGmhPr9gqMsBckg1FyzwywGizTHBYRTLAflGdCBtWMsB6dd0zS2YywLXWeBiqezLAgD+aILCKMsBhdn+TH5kywPUdVh9BqDLAF7sUqoW1MsAklreIMsIywOJ2MTORzDLAZxzVQQPqMsBy1M127vQywEFQauYl/zLAsGL2sOwIM8BaAvKzHhIzwMFF2vXFGzPAWeGALdIjM8AIs45qQiszwKs74mekJjPAhKbEnPcsM8B8iiSGtjIzwF64qzK3MzPAwAXeSU84M8CWaKhkbSQzwPp0xuZXJzPA8nhNajsqM8D2669mhiwzwCy0QINILzPAuoRpDHkwM8DIPmz3GTEzwF3xhKV+NTPA8N/dlAU1M8A/OD/m9TMzwCxTRAcBMTPA3z3VptguM8Bvq9HwJCwzwKHuf93PCjPAFu/PnPwGM8AF8iF3ngIzwHMeoPyi/jLASKd9EzD5MsASTLkUMPMywDfOwigg5zLATw29wwTgMsDC3GTqY9gywCkfRVqdyDLASjj7m+S/MsBBT2papLYywPeHUUFDqTLARe8jHxCsMsDfvba9M6EywGGjk5JsmTLAKcMszYGNMsBEGidtDYEywPbF3X0ddTLAptLAJpxnMsCAWB/FmFkywLw0uLloTDLA+kjsxVM9MsBtG8R1uS0ywGYBdjG7HjLAGQDGwBQOMsA6Bpt7ewQywLGyNpY08THAr9R7NQDfMcAf1ArhScwxwAkv/at9tzHAi4d7f8KjMcBd4cnVhY8xwLR6QQuifTHAn+vPGmBoMcBVKMFinFIxwPwJd/K8NTHARHb+U/weMcDLE0QjuAcxwHAo4cbZ6jDAHYHCh5bSMMDml9nO1LkwwC4P9Mw0qTDA15TYxHaPMMC7fg/hQXUwwH02SYMhXjDAMu9Y0u1CMMACknZMQicwwL3zcKXn+S/AHTMJ05vAL8D6peP2W4YvwIu7CAcrNS/ArN1HohvJLsCruvos/4suwDIJsDzSRy7AUHnD3eEILsBj15aw7sgtwExnf5FJgS3AzNHkX34/LcBxYiSbzPwswFKocMGOvyzAuPD7DAt7LMB3Ll3CoDUswAw0oM0C7ivAM1tt0LumK8B+LawDVysrwPxgTCEw1yrAJBD+uUiNKsDgmYYahkIqwI3c+fYn/ynAqXO7IqKyKcDYGmEVT2UpwK6t81Bg+ijAuEqf0EerKMDINpOmVlsowEce1G15ASjAVnR0+vGvJ8C3NLA5m10nwNMlz7KeECfAXOzNoZG8JsCrQaI70mcmwNb7xRq+JSbA9PJj0oXPJcBVFmRhdXglwND5jJWAGSXAQJ/zn+vAJMDCcXN/n2ckwGF/1RmAESTA8JOuf6u2I8CC0Zb4N1sjwC9cmBYd+yLAJbCYR7uvIsANkJEbG1IiwDxuWcv++iHAZ7KoeRScIcC9YA9qhTwhwImRK9q44SDA/VihUeyAIMDAUXyHgh8gwEUnRj/Aex/A91db6Im2HsA00O/3DfAdwMe/gmLdIB3AsDm57i9YHMBfGKaA4xobwGg7Dn6vShrAb+PZHuB+GcBRciJ+/bEYwGc+hfPB4RfAPjFMkd4SF8DFABfNMEMWwF9kSwr7kBXAZraZR5e/FMAWoJMcXe0TwAxAE+mEIxPA3As55pZPEsDnJP1S8noRwFe1+joutxDA7FZCA0XCD8AILcZE9xQOwBc2/i3zbQzArt6n4Fy+CsDIYhUnaA0JwNILCRmOTAfA/r1dhGuZBcA1sBvqjuUDwJ5TchGpIgLAjOhKWc9sAMA0g9qWpmz9v0ww8WKQtPm/q0JBOh9K9b/A/BOgaNnxvxT3YZpc0ey/8xy/RJrr5b/rwEm4wAfev4AMyo+74c+/DIR6yx7ZoL/m1e35BnfHP5tGijuDato/AKlT5mEi5D+OTCiy2A/rP+tiXNyL6PA/xyucMxVf9D/IM+Je7cr2P9NByQpAt/k/gwLJW4Mu/T+Kaf8hzlIAQPiiNVIhKQJAk/zyrc/jA0CJJtCzNZ4FQKHOn+uLVQdA8ChDOVoPCUB6Kn55hMgKQGx7xZLLZwxAykoFFbkfDkCICjAT7NYPQAu6Eq1g+RBAexGVVBfUEUANhWMAY64SQJ12TL9adxNAMhFFaMBQFEBiYKVqtykVQCz2XwYnDxZA3tCnWevmFkB4oU65Er4XQJiaKxTtohhAbiy/dvB4GUBN1xkRRE4aQFF35/79IhtAeUbVQInYG0AzHEi/I6wcQEoAQSKQkB1Av+KONr9iHkCeCNWcNTQfQDkOinoe1h9AqeJ7VQ1TIECjH1EymbogQEQ8ZkzRIiFA0HZ9ko2JIUDxYdVz4+8hQNKZFNxuVSJA0pr3++e6IkDOn2EN2xkjQHbqPTKUfiNA6vJLbdbiI0ACkAZuoUYkQHQAnMntmCRAs/AqzcT7JEC+9W3LLV4lQBBQqXK0uyVA3JvPlxwdJkBeCLcaEH4mQAkQT5yQ4SZAbcshoo9BJ0D+VC60GqEnQNq809OA+SdAedjzUSpYKEDc11bbVrYoQIK1kZgtFClAaQ6oOWJxKUBmLuICEc4pQNaTE+8PISpACnc57LZ8KkBIJl2939cqQG7pf7VvNCtAyfXSM4aOK0Cu3QYyHegrQJWEBQYKQSxAlVdgt3NnLECrOewdjL8sQMPnkoPCFS1A5fXsPsdsLUAnLqVQTcMtQDVFqE5cBC5Amj7rdL5ZLkDmGvJwoa4uQIoxRSGz/i5Anenw1IJSL0CGYTKozaUvQHU/WYyu/C9A6hNhrm8nMEBof6pIQ1swQMMJsyGcgjBAyLNs6uiqMEBanEJi9dIwQHTDMr5t+jBAMM/INvAhMUB65mZtMkkxQFQXGT9pcTFAJPyzRyeYMUCVmcRZpb4xQMdl+O6x6TFAo3OVxrEPMkDKWHs7azUyQB6tECDGbzJAFY+zQBiVMkC51jWALLoyQMJtO0QB4zJAAOBjF4oHM0An3Nlt2SszQCkZz5S/TTNA4zs7Do1xM0C0tyt5HJUzQOEsBviZuDNAttj4YanbM0AVARyifP4zQC4jnLeJITRAnyA/apU5NEBhvSvjqls0QMH3odWefDRA40QLDD2eNEAeLY9imr80QFFuJquc2zRAwUulC4X8NEBJXKNHLh01QGogQ9CMNzVATQ22uMNXNUA6OvQxuXc1QJHFRkNvmjVAC46avv65NUCtGKIj0881QGQrbBNO8TVAQomKbCkQNkDWr8nMxy42QC5WO4zZTzZAb78nbwpuNkBLL4sYCYw2QCsYozAEpTZAc44kuY7CNkAecuyC4t82QOgC05WP+DZAqU/rNHYVN0B+bLBELjI3QC8knQZ/VTdAcMSsRtJxN0DF8hKA9I03QIGVYNVGrDdATYtObP7HN0A0wKKUieM3QG9MVa3r9jdAa5ubrwsSOEDingiNAi04QIxp4zTpQThAhZgfp35cOEARdM5t3HY4QG7k4BXWijhAoHutmxevOEAchP1K8cg4QFFDqf824jhAVOYFQ7H7OECaFeQEARU5QOiMUU9gMDlAcMEAY1hJOUBQc1tJIGI5QO2DCYYYczlAwfsrV4WLOUCPBSl7zqM5QKR0sVgAwTlAwGriG+/YOUAfYessKdM5QLFZOiKb7DlAAAV1AwcEOkBLpelEShs6QESI/xJOLTpAXHXL4DxEOkDsxXBe/1o6QIVAHCqPcTpAVlAgawiIOkDo5S3wVZ46QK93ZMu0rTpAn/jDjbTDOkArgRETi9k6QMej91rp1zpAMFgmQm7tOkDZpaQFygI7QIugyVepFjtAgRhx/bwrO0ATvukMrkA7QO2Y6qQkVjtARuhw+8dqO0DL5yMYRX87QMSDVDXujDtA9IYzbiehO0CNlawsNbU7QP4joer+zjtA/0pZ6D64O0DzkdCP3Ms7QJ8dxKK02ztAMEi37gvvO0BNYA1gPgI8QBWYvu38CjxA9wQZyOMdPEANSrBMsDA8QCoBwFynQTxAyPRJTytUPEC0weklj2Y8QIaepNSWfjxAJpdlXa+QPEAsTkwyTZo8QHtM5oStqDxAoqmyZVy6PEAdu44V6ss8QGtiFvd94DxAJxlU1L3xPEDWmnDg4AI9QHiTh79KDj1AiyeOrygfPUDuQTBe4i89QPD5hNlSRj1AIrtl6cZWPUC+Usv8Fmc9QJjW8S9CYT1AtosFzEtxPUANiD52NIE9QH8VQbSRiz1AQrLOUSebPUBFBxjRpKo9QP9NrGQFtj1A8ctCFCzFPUDlk3zeMdQ9QBJmmY5V7z1AT6NAzxL+PUB03Szopww+QJrnyqSUGT5AXftZAF0cPkAi0mRreCo+QGmwuTzENT5Ag8LK95lDPkCzdB/GOFE+QCWI8soPYz5AAwSBUV1wPkDl5meDfH0+QPs9C8WIiD5AfoQ3/k6VPkC8Rej746E+QOEQw1cEqD5Ao92JA0G0PkASqMEKZ6c+QFDGEKOPtD5AJ2fGoy7APkASrXhnocs+QK2gBBcE3T5AdaVz2BvoPkAuvimf+/I+QAYOxVCd+D5A/M48bhIDP0B/DzAKXA0/QEsvcf1fFj9AsQam0j4gP0Bm4Z0x6Sk/QFm0IKu0SD9Az/P+zuBRP0C0BxtX0lo/QA2+5aH4Yj9AB2ZvinRrP0C//Yuht3M/QGHsWCoscz9AcAft4fd6P0CbuZwSiII/QBsSM9M3kj9A3dzz3j2ZP0CWsgZGA6A/QIy8stSYnj9AII0WNICzP0BXd+Awjbk/QCGeeBxqyT9Ah3GVsOXOP0AFsrN8GtQ/QOPlqTH32z9A2UYXkJ3gP0D/ork8/+Q/QA5beLRp8D9A9G7w2Dr0P0Deso+Bxvc/QO6ol3LvAEBAjcbgr2MCQEAj805kGgZAQKsUBc6GC0BANnR+YY4MQEAK/BEicA1AQLrOm5L5CEBA3RE8OYMJQEATTU365AlAQLGVXtlwB0BAMjn7fH4HQEBWhqzTWwdAQJrcYQViCkBAE4GSHO0JQEBmzu+oSglAQFxSDghDC0BA2zPmGkkKQEDsmO4aHwlAQI3GNjvtB0BAtdtlr2gGQECwImY1sgRAQGomVRASBEBAu0QBJfkBQEAH4QyiZf8/QH3+eY2y/z9AJKa6vVn6P0CK6SWXmPQ/QMtRdPEP8j9AhimOsRXiP0BYLQjuG9s/QIUm3yKY1D9AggBoUczMP0DYz/f6k8Q/QN4Obz0+vT9AB0lcZCm0P0AcD4yfqKo/QIisIRORnD9AeJyCWjaSP0D4cD0wY4c/QDHoawEwdz9A8NlZVntrP0DcVRj83GA/QCWn9j5SUz9AZ53zYihGP0DgSAlJjTg/QLdeA2qsNT9A+g7joQwnP0DIAoM29Bc/QDAKs0dNCj9AaRlejDP6PkAUuoEtl+k+QCYD9oma2z5AyY9rhQTKPkBDKlQZ67c+QI8YpqlFsz5AKm7riymgPkBuXs9EjIw+QJLECywcfz5ADW+Jwm1qPkD8q80RNlU+QMNwExTyNz5ApMTORaQhPkDR/WUyxwo+QGC/MbQx+T1AtpZIIjnhPUC9cPdlsMg9QEEwe5bFtD1AcpH7OdKXPUALMCi3eH09QLFTaE1sYD1Agw5wDOJEPUAZJZRvvSg9QK0OYEd3DT1AahS/4h7wPEA37wfUJdI8QNe77bGHsDxAwSZ82UyRPEBfJVkzcXE8QP/fSXJCSzxACClLFx8qPEDhUMSelSY8QH93UHjaDTxA/K1A3NbqO0CaTyfkJ8c7QOCvkE9tmjtAdQehrXl1O0AMIY8M1k87QF4b40w9KztAM1hTuTwEO0Ac8oTjjtw6QGfoFcKMujpARA5+05CROkBoE2wo3Gc6QLujh0/wSjpAmYqaiPAfOkAGVBx3QPQ5QGvBBOlPxjlAsLqPNjCZOUBTTxVJV2s5QKhvNEe4PDlAMsPggmYNOUARjNrrYd04QCIfT7YIrDhAUBS56456OEDNr354Z0g4QCwTDIyjFThANs64unHbN0ASney2G6c3QOLPVNf/dTdAHFd70yhAN0Dr7qkhlgk3QHtzAxT+0DZASdegme2YNkBQEY7/FWA2QOzEBvfNJjZAC2hfN5jsNUDthbgdobE1QF7dTW4hdTVAk7dmGc84NUAO0IVTSQ81QGT4nptMzDRAodbZ4eKNNEChCOi5x040QMI4wvDEEzRAJPJOqzbTM0CCk3fZ+pEzQLGQEGEYTjNAuUVnrmULM0Cb8Y02CsgyQGb0TfODfjJAVXC6AM05MkBNeIAddvQxQOXgOOe3qTFAhkRV1wRjMUCmTF/8vhsxQFSY6qm51jBASDZmDjeOMEA9yRPAKkUwQEWKUb3D7S9AGSXBumRZL0BLolNv/8MuQN3ymTCPNC5AlZ2X7UOdLUDGxI2Q9gQtQM5YhZDAYixA/N03mtWfK0Cr8UI8WAQrQF7EMyDcYCpAHcTAXdHDKUCxxazmDCYpQPQL3htkiyhAUf2tqVPsJ0CASm8fwEwnQGcZJRSAryZA+yWfd+cOJkD30ZK+4W0lQN75exPM2SRA+pY+RBU4JEA08wNbGUMjQBeEVKduqiJAa6p2G+AHIkCwC9R3OWUhQFl506dcviBAqFChRIUbIEBBr4Vtb/EeQPJqC/9ksR1AxGwJdHRsHEBJnDJf9icbQIhnLkqs+xlAmKd6nZS4GEC9E/fROXYXQHFyoB3KGRZA9Q3a1NbZFEDQhyPeHJsTQNcaEq1mUhJA71DunVIWEUAuD5j3d7cPQCUrAS/bpw1A3Kyu4KQ5C0C7N/JYEs8IQMyfAO7ePgZAqbtty6bbA0D/eJh1q3wBQFKUZmOHKv4/ncDrYM9I9z/T5a6ssKfyP4+33QLOq+s/7Z+KUaeT4j/o18Uc3SLTP4QJviWi+Kw/y04OokIBzL/oEcBF22/fvyYt3f4THui/i1qHoBVS8L/Ez3bCDoj0v2C8wZI1N/m/XNlodltT/b+2kd9Oc3IAwD/gGvC3hgLAdydvWVN/BMBxAGKWxXAGwEONjjCEgwjA1zHHeb1mCsAoIMnEcEIMwMy20ZWO8w3AaCKHD+u+D8CanoX+H8EQwOWSL1HDsBHAfEonYXaKEsAcFurC/F8TwCKDceCIDBTA/D6Rts7ZFMDuUMMw2qIVwJ7bVftRUhbA+Xd9fZMSF8BD3Me4e84XwGTvFmtZfhjA3Y848a0xGcAc6Cdti+AZwLM+yn1JpxrAiYWDQSlNG8BLKYM6f+4bwLQ42I+6pRzAhvrLj1USHcA1ponbLqYdwPvAUk6hKB7A84mqvBSzHsARmP/23zgfwIJu/eJc2R/AQ2TfkOwqIMDDAc8Uz2YgwHgC5/b1iiDAcU9lqgPCIMB4iG/ZrvYgwBsK02SBGyHAJBrMMlRLIcC9l1CwzoEhwPvr7F/nsyHAiDtGlHncIcDbyUGxpwIiwPQbf/g1HCLAzVKHDU09IsCFG3tk71siwEBhLdIxeSLAMZ4qVtGSIsAkjBjF96kiwNXwCJ4XziLAhLGDM1vgIsA4js/yGfAiwNVSgi3pCSPAK5uc38oUI8CB7SsuBx0jwNAOvENiJiPAun1+ba0pI8BnT7SxbyojwJN5ULnWMyPAOzwZepsvI8AHeRm00igjwCpis0PyKCPA8GY3PCUdI8B23bO/zA4jwCaT8CVU3yLAuhWIVUutIsDSNyzz/pYiwGpfLVQeeSLAqOVh/4ddIsAADSu5Yz8iwOSO6RA0HSLA1Gd4Idr5IcANMQx+5dMhwAJo/sEityHA8r7oaQCMIcDPGttdUl4hwDnT7jJKNSHASADPIoQCIcD0whB9q+sgwHazANlErCDA/Dui7OhxIMB94AWk/zQgwCXSJrsb8h/AcyJE6whuH8CFe0HNCuUewDBWzTtFPR7Alv782BKqHcDeVoevvxEdwGK9xMMuZBzAhhuV3f/BG8BWVXEm4RobwLkXbPm0rRrAjx2FgNH8GcCyrQdFMUcZwNOlMcbJcxjAbkLghZO0F8Av5JMOtfAWwOEkLydIKRbAQ+Cr6U1cFcDVEstk5ooUwAdJM5KllRPAWrrhUFO7EsC9RpTNqNwRwEl9t3dR+xDA8cQ3QB4CEMCLMycy3S0OwAwOOIDZRwzAE3p5OhliCsCRVyqoyHQIwLph/5UapQbA4toC9x6pBMCqeWrSBqYCwEGpJIolpwDAiLCfdnQs/b/qQJgL6P34vyuCmE4PIPS/KmrmZj6v77/xcq0+oJvhv5hRi6brPNK/gHYj5Oehg7+XL1qhBinRP3nwRDfGSOE/etv3kudQ6j9F5RdfdrTxPwB2q4to3PU/fRNQvJx3+j9I+f4wUxn/P4M3h/7hgQFAxNU7LHDYA0D8E+SnQjEGQAJzUXm/jwhAeekub7PsCkAu1JscRksNQNWqQMlK6g9AvXrhg74lEUC29TO80VYSQGKWW0iebBNA4s8hve+dFEAqEKMFTc8VQJLB9smMCxdAUIxkgqA8GECI77zYaG0ZQO7MjRfrhBpAcB+k3l6JG0DDdO9RP7gcQHmzy5+QAR5AQ8Rc8VAuH0CxyX53+SwgQEobdADnwCBA6Ev701pVIUAKyFG2CekhQNQnSnnCfyJAfDOASKwRI0DokA4dmqIjQPyTqdtSMSRAErTXeCzAJEDEPfWqskslQFjSTZ4s2yVAu5+FhHpmJkDcefCgb/AmQNPZXzeniCdAvoJbcecPKECX56xbr5UoQIhQzfkuFilAMUV3EN6YKUAKd95YBBoqQPR6Mh3wjCpAiYpSlLcKK0D3RlevvIYrQO/lnXtsACxAM+y5gtl4LEDh+QQwbe8sQII1l6+Icy1AwoYBq2bmLUCJih8yPVcuQCKeHOBTuC5Ay0cCQ0ElL0DMBkX8GpAvQChCfmoO5y9AIPS2IuwmMEAqQPU5PlkwQPoSy6JwijBAHgdASIzgMEAwiXe/lQ8xQAoU8iYBPDFANUin59BoMUCT7svqfZQxQNyspPwkxDFA2L64i5btMUB12c/x6xUyQAKu6CW1PzJA2uhHW69lMkC/dgvEfYoyQNdfIUV6qjJAkszOBurMMkCSbqq3o/8yQBdILs3QHjNAILaGDaI9M0AhrQObNVszQNcEUiC4fTNAiQHf792YM0B93txuu7IzQKB1a5IWyTNAfO1y0oDgM0Cuf+N+oPYzQOZRApjxBzRAZE69b5IbNEDrU5u47C00QHxq8+NNQjRA+S38YgxSNEC4jfTSiGA0QB3en0PmaDRAJCqB17t0NEBczrt9R380QIzdDJOTgTRAi0B8kneJNECzhu35A5A0QFKwGQvulzRALsbTUMCbNEBB4CGdOJ40QOu1/XquljRAufqVHDqiNED9hOIei6A0QFdynwi5mzRA6iP4IUOXNEC3a2EdX5E0QOwEQ5f6hjRAolpPUDh+NEDtb6iYBnQ0QDo1NQd2YzRAMwmamVxWNECa07PkxEc0QDBHC43mPjRAbLB+cVgtNECtIQkhGCk0QOBN2OnDCDRA91l/dZ3yM0BC7ekG/tozQGG2D7sEwDNAEr3K+jelM0BPAtLh3YgzQJQslC4dbTNAN7DYo4RNM0BSJ7ulUiwzQF1SGta4CjNAXHYLaj3mMkCfitBhIcAyQEns5jh0nzJAv3+BLPZ1MkAEMsCzxUoyQI55UA4WIjJAQk4Ho33zMUBE95ToIsMxQCxFEmcXkTFAr+GfB0ddMUAsUHbNoycxQDVJ9hEA8DBAMzT9uNG2MEBkUpQiyHswQG8HgHwsODBABn0bTPLrL0COHjNZ92ovQMuIYK1S8S5A3lliYuNoLkCBu7wFvtwtQPcyLqqQUy1AXsamaOu/LEDUWwkqfSgsQOMxWYHDiytArSS/3NXsKkB+r96VDkoqQGTez5tXlilAlYotqOfrKEC91IDEmzYoQOwWxqFWcSdARC+UIaO7JkDLVv2+JgImQA+CV1RbPyVAkpk1+2h+JEAfLiv/47kjQJavpFnl6SJAtbw6likeIkBc83im1k4hQJ4fUjxmhyBAduxKyghiH0A0+zdPeK4dQOliFCrhTxxAs2y6GCOPGkAFHpDM5ccYQPL9vIDWFBdA7msGwBdBFUCyoBIpTmcTQGEJLKiFdBFAtnVuNtgeD0BJQmL/m0kLQGCFQ9v3QQdABF6kWhFZA0Apj33KT83+P5jTaj8jxPY/gQxiX/xR7j8CWgL/BkfcP2CnTgUnl6q/4uvfT2cR4r/eckmK6kjxvzA0hHUQkPm/K2wbEPryAMB9SRpnGSIFwNUOVg/ZRQnAQbdhpit8DcDUmpsYkNoQwA2ODEZ9+RLADMnllCoXFcCkgm1LhfsWwPN85unXNhnAE/Y2QApUG8DbloOpQ3AdwPzzVbnHfx/ALG6mJKLMIMDHYzXpZdghwJUKqgLu5CLAwzG6nC/uI8AhUcwOAfYkwBbAlQP3/CXAh01DpEwBJ8D9AXlnzQMowMTHmUlNGCnAQshIPSkWKsAmKDxDmBErwHdu3w3jBizADEcLlw39LMBUxJBDePAtwHF5K9Pd3y7Aw0oGvEnNL8C3mevK01swwNrN+eXEzjDA/PkPmKVAMcCv86cK3LAxwFJRB4+VHzLAMeBxM9lwMsBApmFKytsywDOPyin/QTPAI8FhWTqpM8AfaqzOiw40wHGVSzpScTTAhnbBdcbSNMDZxmpNRjI1wJvbOPt+jjXAihXzWwfqNcAWLWC+iEM2wParMyoHnDbAwIQg0WTxNsCwOJKG4Vo3wAHC8goCqjfAhMfqGAb5N8DbYR+kAEY4wJ0xMVpnkDjAU0ZoQVrZOMDxiER3RiA5wPFOtxC8ZTnAVFw2fpqoOcBnTg6Dcuk5wNVayD5vKzrAxodpDDVoOsBLe/49BaM6wHJaqtQV4DrAdpakedsWO8Du4ZJoqks7wCSQtdK3fjvAeMQEB6uvO8B/TF0VvN47wEG5eRERDzzAPxdoOEU6PMB0CMcXn2M8wLO73EJIizzA/FTec/OwPMCXpYo41tQ8wAZATzv+9TzAXzd618oePcA9V6HpOT09wELarXXMWD3Al01QLcRzPcCKrsCx+4w9wJIux+ERpT3Ayrizvt66PcBNy3vL9c49wCKtEIM74T3AtmMRN/3xPcDSS8mzFQE+wGZuIqIBDD7AMBLPUtIXPsAQaZPPsCk+wCw1T87ZMj7AJcr9d9Y5PsAwIGZvNz8+wB9g+CAXQj7AXRbZNUlEPsAylz2d5UQ+wNnqbtvmRD7A01gHxFtCPsAAwnT0PD4+wLhBhsrTOD7AVCzfkZkxPsD2VpGR0ig+wFS1ArJpHT7AKauHVnMRPsB9ZfPA5gM+wEXGGXUg8z3AKy+aP2ziPcArNjoSIdA9wPDMHFbgvT3A7nBA8GWoPcDwNxuyUpE9wLMigpCWdT3AsNtP/kVbPcAajCsQWD89wErzl/XXID3AjdsFz4TtPMDy1dyWlsw8wK5AjWISqDzAjLHMYMCDPMCOR0i1uF08wMnWMTfZNjzAsT6z8V8NPMB4BxDfI+I7wJTkRk06tDvAbvSlTXCFO8COFmES2lQ7wBnpJdrWIjvAj0TSpJLuOsDnx8hyDrU6wCiRyt2MfTrARr8QFZdDOsDGeksWtQc6wJXzA/S5yDnArv14ggGJOcA+ZOQuTEc5wKDXqgZdAznAvOXIPJ69OMAK+7Iz2XU4wON0uxr6KjjAimnrtR7fN8CGNkT7K5E3wHLvLlKtNTfAtw/ylYTjNsA6yqSUO482wIoEXP1fNzbAV8sMgtneNcBP4AoWM4Q1wFiWjFz0JzXAwiqEMhrJNMCiaOoQG2g0wMdD0swfBjTA7wNCAfKgM8BLbaMMqDkzwLAASrlh0DLA37YPpIlnMsBxq1GIC/oxwGeFRvS8izHAAjIyZ0kaMcDa4Sf54qYwwFwDF/jhNDDAWnbFhrp7L8Dk+hY6FYouwJ1CvwzKkS3Aih2z00mZLMDEc7cPkJ0rwFWNH6vfnyrAMelEnGKeKcAVVHVUQJQowFH92JQSjyfA2dDKkS+GJsBBoAvtNXslwHAHqXancSTAnIptUCpjI8D6t4RXRlMiwIEZTDR2OSHAWFGY0lEnIMDEiWFkCykewEmoCk91+hvAgqwNZKnTGcAykSXoE60XwPbaSBxU3hXAM0YiacW5E8DIg3hmIZcRwDBQtSqU8g7AARzIAvm3CsBjpujiOIQGwLcdPEkWSgLAppueYetO/L9GAnDIhR30vw+glw2/COi/PsNtWosG0L/vC/cwjTjPPwuvlaIXY+c/f2HXoYiO8z88FcyD4Bz7Pwcs3CSaXwFAlJmD4zcFBUBXlQyzCJkIQAFvSKNGAQxARuusoMVuD0BA+MXdDmQRQGWrU+xoABNAZYq2yVOYFEArzhpTdyUWQBe0p5ypmhdAuweO83gRGUBJvSmdn2caQBDb/M6xwxtA6tus+XoXHUCq9vz8YF8eQDGaU0ohkR9AcV01xllgIEApw8Vf8/EgQFWdrXgPeiFADK7wmEz/IUAgbYfVP34iQMAlaXT2+iJAT7HnE0NtI0CaUucIJtkjQJthyLBNKyRA1EBygoeKJEAfI9D1UeMkQOFrYc6hNiVAeFxpsWmCJUDlHwEVuMclQHE13Lu5ASZAM3HwOs85JkBViPFpTWsmQNYyWThhmCZAq9tnna28JkDsLwM5UdomQFxfQpU77SZAucASV43tJkB/YKnqE/cmQKsI12lN+iZAWCAIWkD2JkADTM9iXusmQGSiFpqM0iZAETFUrL65JkApyugTC5omQBvIVtUoeiZA/UXZKGRMJkArNaEZkxcmQF8/nyCy4iVA9FDPZY+fJUDIgnFSxmMlQEzQwBhzGSVA6DbDlp/AJEB/4B+vdWAkQEgt9Ils+SNAwWKZolqKI0DUKfXh1RMjQIuiVty0niJAl6e5gB8ZIkBjhfH7DIwhQOu6g0pa7yBA0oUHKRZTIECvtOtqbF4fQLLN2TE4AR5ARib649iaHEArmia2byUbQN6AbH9tjBlAfK1Q1OL4F0C+DFFBgFYWQPxBUoTYuhRABWIWBSn7EkCBnfDA6ywRQFbdmRW/wQ5AJSaS28zsCkDoEdZ2cPwGQMeUEJpUIQNAdh5kKo84/T+bEqmAN7r0P/vR+1eILOg/pDgnYU+8yT85B4Ii11PXv4zsnyOqZO6/Sduk+JqS+L8DCqjZwQwBwNpyYnZV4wXA2cX6NWLJCsDrq4wN074PwOAm0U/XWBLA/QwPIungFMBizFxst3UXwJw4wWny7BnAv4Kz/OGDHMAB0nyzCh4fwHpQsiqH2yDAZrncNJwqIsAft65YHHojwCyDmcl0xiTA5gKFFrwVJsAqIfn4QWQnwJH/YdhxpijAM72Ua4byKcCR1sJtyTwrwKqHqUfEpCzAseWROzPqLcDyVvx90iwvwHIuYOqzNzDAkbEqL8fVMMCsmtAxCnIxwPPaix3wCzLA7AQcklOkMsAcfEWEjjozwI8JeFw6zjPAvuhf/NVfNMBteWU4EO80wKNGdw1EezXAj5FkqxEQNsB39af/y5c2wJ/KetZsHzfAIBxwldWhN8BtUGoTjiE4wPGTfI0gnjjAEqAG3HEYOcCHTRpaE5A5wHCNzH8pADrANZ2DP4VyOsAxbrxxOeI6wNi1kYDGUjvAr5MvXg69O8BEPwyMTx48wGgVfdR6hTzAL/cXSAboPMBEG+U5Bkg9wCAb8IkFpD3AcdnuOyD/PcAg9AumyFc+wNsQHdCFrD7AhnTaOX4AP8CjZtWEG1I/wLerMZhhoj/AnT2zkXTvP8CNIwSYLR1AwDk+wi2EREDAcjt8BNNnQMBLBwr7GopAwOLdKBPeqkDAVhJnwyPLQMBfEHbPa+pAwDvXFlAaCEHAUpDDUYIlQcANMqFdAUJBwLIXsM2nXUHAwBjK5l54QcBXB7RhNZJBwFBo53d/q0HAQAkvgMW8QcCRL+ijGdRBwH5wzczd6kHAirCa454AQsCXqHUklRVCwFWzORp5KkLAFmfk5es9QsACjxpsolBCwGQtBfkLY0LA1F0MQ1V0QsCWUD5S64RCwP0YhFlelELAYydz6JGjQsBmpiaWvK5CwA+0551xvULAn+VueKLKQsCnsKQGLNdCwCwZYsql4kLAXyizruTtQsB4LEIbgvhCwB/JJQ+yA0PA53V5FRINQ8BIjlvK1RVDwGABnslTHUPAcI4WUOQkQ8B/hX9A2itDwJVE89t8NUPA18drYkM7Q8CA0aTMdEBDwBZAV9f2REPAyWP57/hIQ8A9MH0hZUxDwFi6/2ONTkPAjo8jes1QQ8Cq75mveVJDwEZtTsAKVEPAbuNXAY5UQ8AsK+8Bf1RDwMT+AsTGUkPAUHs1NbFWQ8AGcVOL11RDwGskI8W+UkPAkihYG7dPQ8DzgyZGG0xDwOTm+yLiR0PAH64NbhVDQ8DMVnverT1DwCJh5NO6N0PAxKUgox0xQ8B/capm4ylDwI49RM8/IkPAHJOO5MYZQ8ClL+y/axpDwIOOq3doEEPApGILqAgGQ8DgYeBDAPtCwM9d133I70LAZNeQJnrjQsDNLdqKgtZCwFfGGh1UyULAF8HIage7QsBPC90qDKxCwLn1J1cJnELA3WEb5qmLQsBCw+IKnXpCwGLreRmgaELAP1aInCRWQsBDq6Ll8UJCwATUjltTL0LAzRZG46YaQsDwcumUQAVCwF+D8k9G8EHAnxldlWPZQcD+1u8uwcFBwI4AJL1YqUHAI8MyFi2QQcCrhRYQPHZBwL64FdU1W0HArgZHPuhEQcDSUkK0oShBwCzsSky6CkHAxblWpNfsQMCvCypzKM5AwFk/01OfrUDA7gIIK0+NQMAn9dVxMWxAwN1c5G/zSkDAu2FNqjooQMAsc+OMtARAwJL2ZgNdwD/A30FVfBB2P8Ar3EW73CQ/wLjw3hoX2D7AO5jO/QCJPsARoDrzVDg+wHDK90jh5j3ALNmpmyOTPcB7a4Dp3j09wNeb8juI5zzAYm9asTOPPMA6jDSuYTU8wPX0SCq02DvAgDHpDPN7O8BzItfSyx07wOeKm1qrwDrAku1ONs9fOsDtAJcUnv05wIMQWrgPmDnAqH5GJ0ozOcCOU0xhUc04wDEIo0aTaDjAFpg7810AOMBxOKFGGpc3wJqMSDjbKDfArY9iDIy9NsAN75sVTlE2wHq8eHjS5DXAQP9DuON4NcDJpg5ELwo1wEL70DRWmzTA9tzIk0krNMAXvoYOs7ozwMbXPlKORzPA7SbRKAvWMsCYv+n4J2QywLcmF3Mq8zHA9+1vNcyAMcBLXLlDRg4xwGgie4eknzDAq08ELxctMMC0LJ3Me30vwPKyGAL4kS7A87rvBa+tLcA+09EF7ckswGE2/WO36CvAB/QmDncGK8AV968+LiUqwPLbWM2bRCnAReSVLbllKMCV50bAMIgnwOdTTdxhqSbAG2qHXQzPJcBXilEkZ/YkwNWQVowxFyTAv6Hkb0VCI8CSo0LQb28iwDqEXTfhlSHAtAVyyZnHIMCmt7GKiPcfwADVXXQRWx7AJ4Fo9LTNHMA/veEt6EUbwOxA9PrVzBnAWLEtdWhQGMDz1wj5+NkWwC723jdTbxXAeAyDwbsBFMDyIk95UZ4SwEjpUb1hPBHAY3v8avnLD8AAoZ+TpCwNwNMjtsYlrQrAhYPMVCwpCMD7kxuuI7MFwKvqZv5iSAPACkq8uL7uAMCW70UU20f9v4i2BE8C6vi/6tGTdV2O9L/gIWDg1nnuv6rK1Ytj1OW/uQXH3vGa278zqeboIgzIv4BKkuZUTJs/abren4xa0D+jjz2jCYreP4oVMZgzieU/1uG8z8oq7D+Ob3rxf0fxP1JQNapdd/Q//rJu7Thu9z8B9Tdxekb6P2inWsdeNP0/JboTn8bR/z+fTofY1igBQBQUc+ZkXwJALYpeNFuCA0BOVxGXoJYEQNYKfmGqmwVAlm75DB+TBkD/QUOJS3wHQL+wUgLYRAhACvQgWvcRCUABhdPjGtEJQMB90QaedwpAFacGFAPuCkDi0K5s84MLQFZU280dHQxAzGgc0+KXDEAnZlc1aAUNQGBKvGXkhw1A+sSERPfaDUD0WmTzAyEOQGUPJyZqXA5Alh78t0iJDkDxQLJ9UakOQL/oQ8W51w5AufUS6MjeDkBMBEwVDAgPQAyPD0BG3Q5AvPJW9FPADkBe1Lgqc5cOQN/dhc7DNw5AA6kVNi74DUDlznruXq0NQEmfDqg3aQ1AoAR/Su0HDUDDdsbduZsMQAVuav1KHgxA/1ZkotucC0BSPPD5+hALQExsaJtLmgpAX+bZSvD5CUD+Jk13y08JQMCTvZEmgwhArwnZolHFB0AlU4gTEf4GQGLM0sWfPAZARxHB2tpiBUBqNUeogoAEQCROyIaMhQNAFaEZo5yRAkAlGvk/H5UBQMuWpHGjZQBAiZEZMaDS/z/ToXPa76j9P+y88y0yo/s/SacoGOJa+T+FCDdMhQP3P6XwAJukePQ/ofYFlI8F8j82jiajmwnvP9PJhM4WDeo/LeFVGDTY5D/oDUsSTRffPxrmkXVStNQ/RKsGiDR2wz+r3/lwd1ybv28kqZx1ncm/5O/ZTotF2L9d8clMVPPhvxNUlYPbiei/KTPMC1J97r85/nz05kDyv7kkozT7UvW/YTXWyt5j+L+GDGSou3z7vyXGvi0rjf6/jzbDS8TZAMBnOIq9s28CwIKOeI57rAPADiPjl+NHBcDq5UZxb+UGwNzCRBDWjAjA75z7sC8uCsA8uMn/CNELwKyYUjkFjQ3AtUqtcw0yD8CM1nxe+2sQwKSuFXS3QRHARcJAkV8VEsCviOCBG+kSwGFiJOBqsBPAMKlKHnF0FMDeQ8pUSUgVwKBus+p4DxbAhdEvIPLiFsBD3cfu+rUXwAggLFIhhxjATN6UOQRZGcCQHhSDBCoawJY6HAIq9hrAEEZx21jFG8CHKj9Ni5McwIVfXqa0Xh3AEQjWaZkqHsBznzG7DQAfwMq6opHlzh/Amqy5hixLIMDWjcn4Ea4gwN4d+I6UEyHAVmUkz7V0IcBWkxQc8dQhwBXlhXdDNyLAc+Lcy4OVIsDKA9djpvIiwEDQsMQDUCPAFcWFCwirI8Cg8Tvg8QQkwIrPjM85YCTAjMBFWty3JMAs8pn2UA4lwGAZSVg9ZCXA5i530i24JcB3kdbB4QomwLH8lrRnXCbAsUA0iXOsJsAnKxWSGvsmwKQvwML6RifA25PKMviSJ8DRnzdiid0nwOlE/fXkJijArjedJc93KMB2m/5/Lb4owD++mU/DAinAuUCH1CRGKcCIy6FODIgpwMxtFPjGySnAYGFrrJMIKsBTGNI6yEUqwH6IkJ2AgCrAiqtJ77O6KsD14IAiZvMqwAyxcnPQKSvAVy06yXNfK8CUs3bj1nErwOZ2JZYVpSvAu6g4LRDWK8AQFHHrcwUswHYg/nopMizAKh9R/YxeLMAK/CGyYYkswGIEZAIXsyzAySzlU8baLMAWbfOa7QAtwHSpqLVQJS3Aa54q32NILcCTjXbk4mktwBUa3vuXcS3AfFb+MuyPLcDWqAF8yqwtwEwc6H8wyC3AEA3lnAHiLcDkuUSjVvotwLz4kVwJES7AaF7ilEQmLsDJiHOZBDouwIYm13k1TC7AfbuqdhJdLsBFcy6LcmwuwOC+M7t5ei7Azt3C5zuJLsCOor51VJQuwKXvSKscni7AkzQCZGamLsCMb9uNUa0uwC9Lt7sCsy7A8v3LP0S3LsATG6UwK7ouwN7nafttvC7A7vjDcca8LsDiDf5P6bsuwMf6Qbd2uC7AFCxWYvu0LsBiO9CwZJMuwJ5zn8txjC7Ank/zu0iFLsBIrY6O6nwuwKp8sAJzcy7AsiR3MtVoLsD52YxUG10uwImCIP3JTy7ArIpP+N5BLsCk8cm17DIuwEXEsZyjIy7A4+5AFsISLsDtgnbU3QAuwCzFNbKf2S3AOYg6hdzFLcBwcYOVJ7EtwJFl/X8Smy3Az+1LmayELcBXwzDmYm0twMP4F6lNVi3A5kZb6no9LcAM7Jky5iMtwAjH+zPICS3AdKMmDcbuLMB49gonC9MswDhZukaDtizAgwVNSZqRLMCO0ZijD3QswH+e+erNVyzAf1yg9DY5LMDIWT5LFhoswI5Gl4bg+ivAwqjom9baK8CXZ43UX7orwOrEaslvmCvAQ68gpCd3K8CC7vVOnFUrwCLRhLAZNCvAHCRGlPcRK8AGwCOAlP8qwDj9M+Z+3SrAEGk47sO6KsDAlqmU35cqwFlIrABUdCrAlAZEDDJRKsBjq3i9DC4qwO9l7UbTCirAfGZIZKLnKcB01xQni8QpwLqlWzdipCnAsfRlXYaBKcC4iP4FzV4pwII7iwcKRCnAfs+fF7shKcBTDGN6r/8owC0CD6533CjA7d7nT+y6KMDgbMMjtJkowDHHh9yFeijAHsgFYhxaKMCdLetNKDoowLgSzEvzGSjAMxsqb/r6J8BmFadHgdwnwG1zSjyzwSfACdbaRgSyJ8Ai8tSeXJUnwHg4GI73eSfA0i5DAKteJ8AqPO1EEkQnwK6PwpzKKCfAE5MSl6sPJ8C7Q3+sZfcmwDT3wbsm4CbA6Kdzh4nJJsBDaKydxrMmwBPjHhmymybAJkxNC9GHJsCBJ1FA/G4mwORbJZypYCbAECLHMu5PJsCvza3yL0AmwG0xNYKLMybAg22hDAwmJsASQDY7sRkmwJbSS2iiDybAAtZsrosFJsDCQE0PrfwlwFPtIyiM9yXAUcieyynxJcB28MfYBuwlwAzbNLxa7iXA2N6x78brJcCtvug3fuolwD10rAjb6yXA7jJZQT3tJcCoNbAnAvAlwCamsD0w9CXAhgguJ6b5JcBSbE+lhwAmwGatw9HjBibA1Y2bnZkQJsAKVwnFvRsmwLPb4pedJSbAQ0FipbkpJsCyrWzBLjkmwBWNHa2cSCbAiuXSu/5aJsBq7Wr1y24mwHOUKtXkgCbA9gjvUZqXJsCk/+XP0q8mwHZlh9RgySbAPFVCKZfkJsDeGD5TPQEnwBf62BCSHyfAkXTPozo/J8Ccc2QiemInwJ7z9bfzfSfAsWt7c+uhJ8D7rfmRW8cnwNIEHvwA6yfAr7A7qkITKMCuJ5s68zwowD1nKHkHZijAVMH7u46SKMAPqYTze8AowHPVdXS47yjA+lWUXV0gKcBYlVV5Z1IpwEzFdXpXkinANg+Zmw3HKcDqpJNQBv0pwJILcyNtMyrA5yg6lOhrKsB+dqchr6UqwOMAOBx33irALSqyfKMaK8BDrkzwAlgrwMszSvkylCvAQ7r8oOTTK8DDFSh9rxQswIoNzuatTyzA9C6R/uiRLMCCcFur7tUswPyEpBdcGy3A+8ooCW1hLcDAx1ICbqgtwGBShuw+6y3AC8bFTh00LsCGTWt/1X0uwBN3P2MpyC7ADgS/uZITL8AOIbyfxV8vwAQzKm8tri/A2/rsuOT7L8CpYlyv+RcwwLJVq9+1QTDAXtb4WIJpMMAJ+6lkoZEwwMSZv6fHuTDAosqxiH7iMMBnLLvFcAsxwDbLgtYdNTHABOMqL5BeMcBlj8lsM4gxwOqAHvhdsTHAz3mrQ2bbMcBGb0KamAUywPSGHrWHMzLAq0dcrhJeMsDhuapZwIgywADkKOX8sjLAfnUkBNzdMsCu7QbSxwgzwIPrYdgVNjPASq/+ih5hM8CjSzhKNIwzwDctaPLItjPA0r9dX+fhM8AtfpGXAA00wEytcKpYOjTAGo28DrhhNMBZKND1tIw0wJyR43YEuDTAwR3FwtviNMAYDx9Emg01wC51JizHODXAsDFa61FjNcCAtIfut401wLKaMfPZtjXAN2V/mvjgNcDXzqWr/Qo2wHkggNFqMjbARXa6jhdcNsDJ1xCC/Y42wDb91WlpuDbAepXQRYLhNsAyXRzwZAo3wJyL463YMjfArN8ZB0hbN8CJJ6xFdIM3wFfnWG9PrDfAxayNvfnTN8C768O4X/s3wOsf6z/4ITjAGMl4+dJIOMDT6gcyZm84wEhIiF9wkTjAzR+K3XC3OMDuQs4dJd04wOsttt5zAznAU5aJSIwoOcBSjN4eV005wPjNL5c1cTnAikRZc1qVOcCp7BAYLrk5wE7/qOrq3DnAuiavFxEAOsCCK0bP7CI6wBjHCYErRTrAeIvuWWhfOsAkT5hqOoE6wIXKZRvHpDrABxEZ+eDFOsDNdL46pOY6wIvqu5QqBjvAEIqGPEwmO8AYh7evC0Y7wJgjMjiYZDvA5nd0iJ+DO8Bg9KZTR6I7wMo24d23wDvAgwE1jrLeO8D6UP6AO/Y7wMxeZx7REjzAwo54srMvPMCJzk7FPEw8wA52DdFiZzzADuBtHjaDPMDotatVtp48wKMkw7YzuzzAfkJPM/nVPMC4ivHZXvA8wMLII6M/Cj3AONbCau8jPcBlSdFKQj09wGQx7dSzRj3AXxWBJE9fPcDyqicmoXc9wG5yXctUjz3AZDlQvfmmPcBQyTEeP749wLedayvl1j3AuicRSnztPcDD7/cIwwM+wObABsQoGT7AeWDe7cQuPsD0nlwKAEQ+wNG0oKJvWT7Awc+di5tiPsCqY9j233Y+wNQBsjinij7AxSlvREWePsBDIbXNl7E+wOn+7X5xxD7AUrtqIx/XPsBzuPsTfuk+wFMqm8mT/T7AtcKAbkwPP8CSkVWduSA/wMcSnd7OMD/APBJfiJ5BP8D8+nav1Vc/wCJmoAYBaT/ANJOlNfB4P8DcEosPj4g/wL07zvormT/ACgwbFzCoP8BSA6qX7bY/wL8PKZC7xD/AKiZP9uPSP8CE9ZXyx+A/wLnX8sZh8D/A2SjMw7j9P8BGFO4CYwVAwOqf5vHhDUDAZyigZiIUQMAkE6iKPxpAwDYN9/m2H0DAP9b1iI8lQMBFgMWvRitAwEwjEvvIMEDAZdk6eD42QMBPVXeckztAwEVMaOFAQUDAOlnywFZGQMC782zUSktAwDdvQQ19T0DA5EHPzCRVQMDTl9DCtllAwExSUJavXkDA6PaeDgNjQMBuHmd8OWdAwPBXAEmLa0DAJEHiMIJvQMA6jpZoXnNAwCPZU8Eed0DA/bTYMrt6QMD13IoxQH5AwCqY+oFmgUDAZLw4uq2EQMBK06c/TYhAwC74sf8UjEDAH4dTYASPQMCxYh/l2JFAwAqGGbtqlEDAuG4iGASXQMDFthH2fplAwAuFXS9vm0DAI5Xe2rKdQMB9IVSM2p9AwFk8QpeLokDAOabEv3ikQMAmvAoMS6ZAwLK5OATgokDAxsb9c4CkQMCYh7dyCKZAwKcs6Tr+p0DACVwEk0ypQMD/iQYne6pAwHKSyCulrEDADxBF0aWtQMBhgvezhq5AwBo8U3ZirkDAjeOKEA6vQMD/2nWUna9AwGA8BybKr0DAlD3bZ7KyQMA02lzL9LJAwNPEc3z7sUDAG7EkmAWyQMDSDOUf9LFAwOmYKrV8skDAC3RbSDayQMApdw021bFAwMcEh+yvsEDAZ47sthiwQMCepsYZZa9AwEpL8/AWrkDAGnFuOy+tQMA3136dSahAwPy8/G1rpkDAAHu4FTSlQMAtepTa46NAwLUx22euokDAdBhGgCShQMDpoFiFfZ9AwH4jUR74m0DAki5/GxaaQMAS7Tf3G5hAwDgg0JlXlkDAiwQnAiSUQMBgcmra15FAwHqizFoukkDABBUqlKePQMDiYZQGA41AwGs4ogMSi0DA2IYdaTKIQMB4C6vuNIVAwKfar+rUgUDALbdoeJ5+QMCGf5CcR3tAwPimwwyLd0DAyQomxf5zQMC3CqlVTHBAwNpddcUHbUDARhrgMSBqQMD8unodEmZAwHiHkk6uYUDA4bVxT2BdQMD5rTqS9FhAwMWoFB1XVEDAnw3BzKpPQMD/FipT3UpAwC1Tk2YCRkDAOGm8gPFAQMADHBdxvztAwJsdsa8zNkDAoFBTRrwwQMCAjaMuxShAwK9m4pQ2IkDAjBAWPFocQMBmxkkJWxZAwI9NZsbMEEDAEumr7oMKQMCdweigFgRAwFSdv56n+j/A5nmICzztP8D7POglgt8/wMf73Uxr0j/AzEA+DxzEP8CyD1g3fLU/wGBDq0K9uj/AcUo5bHirP8CW6xQq5ps/wD7e0b+eiz/At5O//nF7P8BrNdLD8Go/wN1eFV4xWz/AeR7YJwpKP8AS502wkTg/wBUAUJKrJj/A7+VwYn8UP8A7ypL//gE/wGKCKUkV9D7AsIT+jpvbPsABzRrmCsg+wE3PDuMAtj7AnHmMhL6hPsAZQL9BH40+wAo9wSMAdz7Aa0q/xqNhPsBOI34j5Es+wLzm31plNT7AsHq5fucePsAOHOUhBAg+wIZWu1e97z3A6WMCOA3YPcBdMll4E7Y9wLXG2lyhmz3A3MA2D72CPcD8OqYFcGk9wMVlv3i1Tz3AS72+D401PcC0RIzc/Bo9wCvm7NssAD3AGSNPo8PkPMBKYrbW7sg8wKbQ8DurrjzAidf9t+2RPMASkKdCx3Q8wGXZgPZMUjzA2ikJHy00PMD0xl4hmxU8wAQ4U+jB9jvA42zrA0bXO8DkkEdVSrc7wJZiG3nnlzvAQGUhhfh2O8B1UWrYkVU7wA1atb96MzvAVjvB0BYRO8DXz+WeL+46wMJprVeqyjrAwTYo8YCfOsA7Gv/RE3s6wNVpLarWUzrAlXyll1ouOsCIyu4RXwg6wMEaOuwa4znAbcZcDxO8OcDbHhnugZQ5wLFKoeCHaznAY3+oiuNCOcDizXb0sBk5wGuQEAFu8DjANhUm0iTGOMDxd8a5HqY4wCa1Cx7oejjALTW7fgFPOMCs7/1ljCI4wH+Q0Qa89TfAmz2y3CbIN8BdqWpJCpo3wJVWmzPSajfAI5VWoJc7N8AP60J1xws3wAELb5/m2zbA8TqzUvaqNsBMiRLxbnk2wMiII9JOTzbABsmZh64cNsAMPJhAdek1wIyNCjNMtDXAQQaPy/1/NcCdLD3JG0s1wAE35TcWFjXA6Q6xgxrgNMBufHLKi6k0wMR+I09McjTAilutJrE6NMDh1zldgQI0wCQyLWFtyDPAp78IAmOMM8CjOkQ2m1IzwHPeeiSpFzPAi/wfrdPcMsA4vH9VgKEywJoEVsmJZDLAj1ELNjUoMsB+MXfHZOsxwMSr3nYarjHAA/xQk1JwMcCtj8ipFzIxwHXGQ0MG9DDAoswHAui0MMAK1H9cAXwwwNGy4mVePTDAPt7VNun5L8CXTSreRXgvwGzASZk/9y7AKMu/Mwh0LsAP9AtyH/AtwLFJ8s9VbC3AqzfXDQTnLMD7gOs5DGEswANXMLFg2SvAjqGYDiNSK8CycqTzTsoqwE9IfKtoLyrAoPQrv4qmKcABQt7NRB0pwEJ8DxxClijABQ6yLhQMKMAjICCuioEnwIGXz7oj9SbAL+JG0fRpJsBUOfZAiN4lwKVunMHsUCXAl5s3RQ/FJMDlPn+qCDkkwMly9Gq/rCPA8sykXUQ4I8D4rzk1DKwiwOqVVzt1HSLAFZ3jvVWRIcBvW13NVAUhwIjCXZlofCDAHXYYOHXhH8AYPxgjdMoewK8SPB3QtB3Aj9+TJP2eHMCjMm4fzYkbwOKgNcaXcxrAhl03rftfGcAlO/HGCTcYwH7N/cw0KBfAB+paYZsXFsD/al7mNQgVwA005gyW+hPASc7cU7jtEsBziHo8VuIRwE54Y1il3RDAMJPAJ2GqD8BHvb0B55wNwBx4RUSziwvArXJj1DyFCcBEU3o9foIHwGQFPKv0RgXAhri7YZ9LA8CIrkRDkFQBwOdkgxQ7xf6/2iPz1Vvo+r+6IPXyyxT3v1bMPgSGSPO/3w6SnacO779Ul7gehqDnvx+iu5yzZuC/5cd4lb9B0r/GmwrJrPSuv/DzQZerjcQ/fGRIl6r52T9CnwtUouzjPxyPWHwruOo/pQboLdu98D+qwUAIShT0P8OG+5+XXvc/P3GO/Cid+j9E1dCdaM/9PxeGfic7YQBA1Wt02CHuAUDpQl112nQDQNB2+l3+7ARAptpj2xtnBkCApT3ho20IQL5kJIjr1AlAPkxZ3jg7C0AyBX8s7ZoMQMWW924F6A1AC9K/NcY6D0ANuy9RWkMQQL9h7C2M6BBAwbSwt5SHEUC0hQvHLyMSQGM3ri3dsBJAobFK/rhFE0BCin7cDNcTQGiT6rUyfxRAnP8oWW0JFUBPiz35UpAVQPeGy2HbFxZArXV+CcaXFkDF+t4ySBQXQDe7LNlMkhdAa1IzP+QHGECivY7+3XkYQJYF3gEw5hhAt+tfrxNRGUC0TUnIergZQF0Yu0GUGBpAZCSP7JKcGkC3OJVkk/kaQMfq6RhGVhtADauTvTWsG0ApwE6Op/4bQM3oMdbbThxANApIJUeaHEDUgz45P+IcQE1ntOcYJx1AjpF8LRxoHUDLftB/laUdQN0bocqz4R1AqtiWnkoYHkAxxiMT1BkeQKAkLpxzSx5AGL2ud8Z3HkAFNsqjoKAeQA+PB4xMyR5As99PqULrHkDYyvWx5gkfQKiYbbdvLB9ASbvTzFBEH0Bw+xT+xFgfQKTT00Hhax9A7ERgqqF5H0DGmLkC8oMfQLgsZTMvth9ASjcsa9K5H0DUuUmmGbofQKtnYDxmuR9AT5JBgguzH0Db1pzyYakfQBXhT5J+lB9A3N5ZZBSEH0Am+9VTbHAfQH5xkkgrVR9A0OdTk/k6H0Ds+RNEeR0fQIR7Ljyo/h5AuVxGod/3HkAnLJRc4NAeQExQyrPNoh5Aw7Ywt0t1HkC6qNl4tEQeQDCloXp8CB5AGgAYVIHRHUANC1fNZZcdQES/v0O0YB1AB26NTlIgHUCmPHv/uNwcQDo+3KEPlRxAsoMPnT5LHEDDYPMZld0bQLqZrfu6kBtAabaMY809G0DlSSIR2OcaQJ6Vlh9kkxpA6sRrXog3GkDoaz0/eNgZQJF6x6HveBlA1v/uOeoTGUCsZdQbEawYQGq0iSuwPRhA3ArTEeDPF0CrhxNtQF8XQPOC9aMbzxZAej5yN6RYFkB4DvdAQN8VQAMhMushZBVA7Fm8YhblFED92KfILWMUQFKKxnJx5xNAgEyqSABgE0DNrbD7/tUSQPrPhxIoRhJACxijXrO2EUDMOrH0kyQRQJHsxTWFlRBAulFjXiUiEECkGxRL2BAPQMRuodfm+A1AQ8q7yU67DED0EM0aqHgLQMlLkOn+HwpAgWdpk3rTCED6tFURTIIHQK20+jNAJAZAKkidR3jJBECKHYU6U2oDQHTO4gO79gFAJ+vYRB6OAECvjccS90T+PwDBzXSeevs/bv+MPfWP+D9a6C0XX531Pwn8YmGuvvI/kWUP3gZ47z/cWclxV2LpP5LijpJ1N+M/i3PEFOQH2j87lv5F3gjLP3dj9vea73A/HEm5qhX6yL/TpOpq9VbZv7q3MTsXm+S/jyuoHdQi67+TVgllydvwv69Wx1i3M/S/TKLFkumJ97+D15WX8eX6v6M28DWpWv6/u+OK4ZngAMCqUOzCbZYCwD1rax7hTgTAiY2ryHMJBsC54ubOM8YHwE9uePaVjgnAYHGXQuIyC8BKwWYJyvUMwC03Y8votA7Advmhr5I9EMCOlBvScyERwFFtV3K+DhLAEW9g0Tv0EsBB0r7AM9oTwJo29shzxBTAw7ZTDqmrFcBnNxuHQZMWwOn6O+JceRfAGMlm+aNhGMCx/H9JObAZwCMZd9QimBrAzr2fSVGBG8C2nSf/n2ocwE2y4uGyUh3ANBCekSI8HsAFWJhbfiUfwJOZIaqFAyDAmCcZZBF4IMAL0tWdjuwgwHLZaAEQXyHA2Hn5v0PTIcDshDRkT0ciwClZxrCfqSLAfpzjVzgdI8DhgQ5SoJAjwM1ngmKZCSTA3bAq+HN8JMAKXrrT/O4kwBw0kM4QYCXAoNhQeOfRJcDgefCNYUMmwMXqpyQAsSbAU56BhbQhJ8DvcJ9q9pEnwNRDRyPZASjAMK3AgDp6KMA58uiZBOkowA2RHJIfUynA2qgKWOTAKcAEEpfsIS4qwF5FhBZVlyrAV2FgQWMDK8A7Zpam3G4rwKs6nfvj2SvA3m10/SNELMDRf3sPxa0swNiIrvZsFS3A0Wuf6rh9LcB9JsNz6MgtwBRsk/dUNC7A7fcnXpuaLsARs6oNHgAvwGFKvYFoWS/AyjxLCl+9L8Ci7UBEVBAwwDthfh4MQDDASq1W0epwMMCwM/xhX6EwwA5JGCek0TDAEcqXXE0BMcBeTOCHijAxwF+DQCeMXzHAsgNf2/mNMcCCXRhu9bsxwJHJMs/X5THAnrwfwAATMsCqg0Ztuz8ywBuW+8OrazLAJKpEjYWXMsClc+RJ98IywEGAZS0k7DLArAUTIK0WM8AbcSuOzUAzwDeTj0vpZjPAfBQF6hSWM8CNcdYp3b4zwEwtF62Y6zPAWK327XYTNMDiGjYn4To0wB5vwc3DYjTA5faoyUmJNMBCrNw3Y680wPAwrv0j0zTA6mLNc1L4NMCxwZB/DR01wAj/FYaePTXAyUFramthNcB90Qu2r3s1wBorFjMYnTXA0qEug4W/NcBv43yKeuE1wFAjTJba/zXAQ0E6FOggNsB9sU8QiEE2wPiox0BMZDbAb+j/UP6DNsA2N3L9RaM2wITJUTtPvzbAiPqhf6vdNsDqt5VGjvs2wAhPlSrEFzfA7vRwa8M0N8CDLaH+VFE3wKdEIYMzbTfAv8uWneCIN8Axy+e1F6Q3wGyYsvNZujfALm4C6rDUN8B2tbDqnO43wP6FSLo+BTjAJ4N8q0UeOMAJbYpi3TY4wE/8IHulTDjAxfbfmbxeOMAU2qfRBXY4wC2tsd8CkzjADpW1rW+pOMA3O0Wydb84wDfMWRfI1zjArPRTLPDsOMD3ByEnvQE5wBfzwwElGTnAs75jZRYtOcAzRIZDmEA5wKeAskDCTjnAhqvq2XlhOcC3kXrg9X05wCAJhSfWjTnACn7oT1CfOcAEtxG+bLA5wMcQpMaywznAGYEdcv/TOcAq7eWB5eM5wD8usEeL+jnA8Djhcq8JOsAyVdMcaRg6wKr/LdVZJDrAd+a1Sk8yOsCwOHmT5z86wCEr/ecJUzrAY9i3heJfOsAW/MjQWWw6wK1QVvtLeTrAFmAnqAGFOsALVi/eW5A6wPJnIOi8mTrAFsqywl+kOsCt2/Nio646wFB3ZnoFtzrASXdOL5TAOsA2pV88yck6wO3ldJ4k1jrASTkGdGjnOsD7Jgu6j+86wMnOn+OC9DrA5AMouPz7OsBSYTNZIgM7wJ3v1qxwBzvA4AYgJ+gNO8CXtNMrDhQ7wMvtC+pxGjvAQAvpcfQfO8Ak32kzJCU7wPYn5A+iKzvAtzcREy8wO8A8Ez6dXzg7wJ5hO9kMPTvAhtTE/aFAO8DHbMW36UM7wKNuIgI/SDvAKnF6Au5KO8AlkNvZVE07wL9yGA8dVDvAem4Y5+tVO8Cpz55sbVc7wLUGumeNWzvATDwrqnxcO8AchQKMJF07wBBkz0s2dTvA37u3mVN1O8CzlVwWLXU7wJ55P1g5cDvAymI+LY9vO8DpkH/MnW47wFx4tT8SaDvAokM+hqFmO8A1fvWS82Q7wK5HmO3nYjvAWEcO1btgO8Aj510fVl47wFvM9i8DXzvA4HrRmANfO8CyGIfa5Fs7wM4h05ZoWDvA2tewIsxUO8B6xm8b/lA7wAe6Z6epUDvALlyVfWxMO8C/AU+P9Uc7wDzQ8m4bSDvA3gDqJkFDO8BDwckSLD47wBm4ucNpODvAIckUiOsyO8COYTnGNEE7wEJVoDpENjvAwvLgNkEwO8A5oLV7Dio7wAabxqHEITvAtZhSADkbO8BojR/XhhQ7wPhRo6+3CDvA2rSjFLYBO8CA/0M2h/o6wO5CAJ9g8jrAk1OOwObqOsA1IF+KSuM6wKhcn7gf4TrAVRKG2DzZOsARxhrMOtE6wCZhGkEdxDrAG8vg6tq7OsCoK04Wd7M6wFSvEfwOqzrAdxWoYnaiOsAuE8B1xZk6wEQYDbc0kDrAIDohfU2HOsBxP6mzUX46wLyKGhnKdzrANaZrPEZLOsBvstac/EE6wCy1XPh7NDrAe4OcgxkrOsC9Ma1PpSE6wC5PA7/2ETrAcDbQ1WQIOsAi2+RWx/45wJQWbSz9+DnAUts+dT/vOcAoYv8dd+U5wKKu1t213DnAKbWzC9zSOcDmZiaivr45wBC/+Mc7sjnAWjiam0eoOcAoHrnIVJ45wNv8rI02ljnAkcIxbTuMOcD/GerpPII5wDRCvNJJfTnAknDWV0NzOcAujn6CRmk5wLsmvg9KYTnADdx73VNXOcC24qvkZ005wI3vvB6eKDnAlpI58LUeOcA2fOoR1xQ5wEWRPhpkDDnAV3lT15ACOcB7dGJ7y/g4wHDUnt+b7jjAt+h0ju/kOMB2+hc/UNs4wGAx6dHa0DjAUi21dmnHOMDt8wgrCL44wCK1H+mrszjA4PhbiYCqOMDou8d1UaE4wPFZbHV+mDjA2JqOZ4CPOMDeF8xKmIY4wKnqQY9vfTjACzsg07l0OMCvmKQkG2w4wBMOtyTrZzjAkhL+639fOMCGqfQcLVc4wDafyoXwTTjAJu2fx9pFOMBxtKfzalA4wADjOyn6SDjAh06WAEVBOMAtZdrOpjk4wB5v2iUoODjAfgLRur4wOMCDLGKPfyk4wBHdRkJMITjA1AmQC00aOMBcgXG+dxM4wI0oGUCKDTjA/bL4LPwGOMB4syF7jgA4wLdDqxwT6jfA01nB0+vjN8AQK8mk8t03wJ5APw1C2TfAAO9+mZfTN8BmBhZdEs43wGn3qbKsyDfAwjdB0nLDN8CBZdPKYb43wBmA3OEMvjfA3qq1jUO5N8BNLuwapLQ3wEWQiZ3ksDfArkJUINikN8D4ahQ+taA3wENBjmvlmzfAVPyYrhGYN8B6GgHUZZQ3wMy7QoIHkDfAr8mYwq6MN8DpQ9KzgIk3wCUHwqBlgjfAeR408YZ/N8BWYE9B2Hw3wNYQsze7ezfAeG5bblx5N8DBeZsf4Ho3wCH/aVPxezfAwvHYOQ56N8BrrHfpTng3wHWyvdoLdDfAD7KCBaFyN8DEwwzmWXE3wNE6MFmGcDfAEmUVi4RvN8DhemV7rm43wB7VzYV7bjfAJQjLfu5tN8Dl5OgJh203wDF13LZ9cjfAlyBmhV9yN8AdQExJYnI3wCtc7meYcjfAaJIlduFyN8DhgIUvRXM3wLtRDovCdTfAkCkoImN2N8AP7PCjInc3wOv1CHuHeDfApjkGqH55N8ATkZe9kXo3wFbIA7uxfjfA/h5KZc9+N8BsOubLK4A3wKqYFoVKgTfA3i6WntyCN8BxWvdMg4Q3wHgYZynAiDfAqwiKTI6KN8CpqEd+cYw3wFxcHqOZjjfAwNuwnp2QN8Djonq9qpI3wLsmCsn6kjfAuO7wJDGVN8B/ishE9ZQ3wAyBJj3MlzfA6f2XZB+aN8C/ifUDepw3wP2CdqcYoDfAJVmbvYWiN8Cncm9a8qQ3wBu1YqoWqTfAA/PRE4+rN8B6x181Aq43wCSlPBQwsDfAKkqT4KWyN8Bwf3LLHLU3wOE6V8TKzDfA4sBb9zbPN8CAUpUGk9E3wJvPH2KH0zfA0kLBXdTVN8B3ZVZUC9g3wDswpOJZ3DfAeIu3Y3neN8CqDoBVhOA3wDWzJe445DfAox+LnRPmN8AUNW5v0ec3wKg7d5Oi7DfAy7PWTkj7N8Dsqg94tvw3wNIHdQSg/zfAEwdnGcMAOMD3F3yswAE4wLiHZvcjAjjACTRd9dYCOMCadyhsWgM4wFLa/9tHAjjATdIV9WwCOMCQbQJAXAI4wIpohAmy/TfAvEmfkTX9N8ABICRV2fY3wMsQOFmd8zfAHCGOZmLyN8ArQy+b6fA3wA8HPyvW8TfAERz+kMbvN8APd18Ibe03wIlklIh56jfAsOHqroXnN8Ds8D7QNeQ3wJxJ6Z095DfAlVc1OEPgN8A7PYA579s3wPrf1i6ayzfAOCXbGn7GN8CjQYBv+cA3wPJo0AVAvjfAxQ4R9ue3N8CGtZwaILE3wMAcaQzppTfANnV25jaeN8BH0j0cCpY3wKD2dxOViDfANpfnsl5/N8CVBshopnU3wCVy9V7HaTfA0Mo39N1iN8CXaP44glc3wChjajrDSjfADsBFtzA+N8AX2bHJAzE3wKpeXyM9HzfAER7oRMgQN8C/ecNgrAE3wHK+IgIN8jbA8EvXC47hNsCia63yUdA2wNrd/Gs9vzbASgPEJYasNsB/udl5nZw2wEy3Hn7PhTbA0KC7YrNwNsAZiKgQy1o2wKCTA8lNRjbA3MYgdaQuNsDvL9fgGxY2wNDDLZDc9zXAPrpP7W/dNcCCIeZaDsI1wJjzQ8oNpzXAFjpST66JNcCBMAJFR2s1wKwzfMJGSjXAfLVvxLopNcBxwrNhDwg1wLOkqsLw6DTAbcyTggjFNMAMQUCg7p80wCY6HUJreDTADWDd0OdQNMBedNHYGig0wHNJT6sM+jPAHLAJiqPOM8BJSovw7qEzwLrbxLpTdjPAOfX3i4w5M8AJGfzNnAgzwD9zIfgM2jLA5n/iYjWmMsAwVpMg23AywJTzPdvEOTLAMHIk1mkBMsB8fk5rcscxwMS6vRziizHAtGv0UMJOMcCXusXw/A8xwGLPDe3t0zDAB4nnls6RMMAqhokBLGowwOh690fwHjDAreahwyivL8BJnja5zxwvwL6u9S4xgi7AKr4qrmnoLcBXE9xO6UotwIFkSAjuwSzAolPPctEcLMA4Z7ak4HMrwCD/V9yqzCrArFZZ+tIbKsBofUvM+mYpwJ3Sva+e1yjABAR3NgkbKMAtFvSEWVonwG4P+aZJlybA8wPsATvOJcCe6r5NDQElwMuRRM8fLiTAqNMWHsdYI8BeYZwzUX8iwEu2VukOqSHA7+mt5WDHIMD6A6x4DsMfwMapY3d05h3ARU3NchAiHMCw9zuIWz4awMKLAe10ahjAFjd/oUd4FsC1hG6tj34UwJAvRzDTdBLAQgZYgTdsEMD63K1u1bgMwBmehFCKWAjAnDt+TAIdBMAwRHp+yan/vwG/1zuYfve/ba7Yg4KB7b8tErwTSXLYv7X3IOG+msY/J87Alwai5z/WQ11noOH0P8maUks4sf0/YsjEOGdyA0B4Qzbi0xMIQLU5evCg1AxAMjszUdbBEEB5v5804xsTQJp2c2EeWxVAwfhjM2q5F0Bxph97NxkaQM0bdSvYihxAdLDZbFzsHkD2L7ZYA6cgQK4Ne8kA1CFA8dpKA5QEI0CqxA0JnjQkQBFSVf2SXyVAt0MfJ/SNJkCcsKe8PbsnQP9PaEDD9ihAko8dIVAhKkBNJdG5HEorQDnAUhqqbCxAC8yROsKtLUAOOOhJVtAuQBGwAtn07C9A7vSjnziFMEDVo9aVmRIxQGXaWKDklDFAtvXmzUsfMkCeYyTxGagyQEVy7yjLKTNAM3w3VTWvM0CCg7ze1TI0QAg/440psDRAQe9aBiowNUDVLxd3lLg1QCsGCbzyJzZAnduyUjKiNkC2DzUZcRo3QKNfNL8MlzdAJIiRwx8LOEArKRpEHn04QN3Wlmek6jhAH2p36VlYOUDUpBE778M5QFNyXze/OTpAuFT2Yw2hOkBp+GfJMwY7QPh30qG2ajtAP9QagIDLO0CrexG4HCo8QJsNugoCfzxAnWg6q1/ZPECzkmovqzE9QHGwIQvemT1AiutWQuHtPUAtnio6zz8+QIfImaQrkz5Ac7iAqv/gPkArb/sExCw/QPNiut0hgT9AtHOdUqvSP0CyCev0LQxAQLHWzCVUMkBAXtLTQUJTQEBg9HkuPXNAQByXEutck0BAtOxxY3qxQEDgCFXCrs5AQNraE2oW60BAfIwJdH8GQUDgEX7+CSFBQJBAhTw4PkFAouEUqgtXQUAFjf59ZG9BQC9AjSCihUFApcW74wGcQUDEhD4pk7FBQNroiNaLvUFAOLqQo4jRQUDZbk0cwuRBQCcy2gnG9EFA7YWWrnIGQkBtrSFIYhdCQAb9djPoIUJA0GDKWmYxQkB7GnMvLkBCQEbwe8LBRUJABwsMHiZTQkDXCpog219CQGgmYZl9a0JAHq1IKtl2QkD3ZfEVjYFCQGXnuouEh0JAif4XEuSQQkBKB8RYnZlCQIf6D+fRpEJA+tE3DEOsQkDg/ROCFrNCQPHURpxDukJAWRd4akPNQkDKkuWpMdJCQIl29+FY2UJAidG8qQfdQkCSqlwuHeBCQEnDeOr/50JAIoFORd7pQkCbC25hHutCQDYca1uA6kJAZj0qm4vqQkBIB7tnAepCQEZi4eX26EJAK3wq6jfnQkByqof81e9CQEJzXv+r8UJAQMPf/hHuQkDqvvPD2+lCQMQGRdJA4UJAuN1fBc3bQkDJk1ZGutVCQHSboYsqzkJAZ+wF0tTGQkC38mVD2b5CQC3W9UNzt0JAcgHoOSquQkBrexPJN6RCQPoqBNHNoUJA9d6uGICWQkCnhY9kg4pCQKHXjLSBfkJAh+Fy2RxxQkAVPAHO/2JCQHXHB9UsUEJAAUe805hAQkDRmjSyRTBCQDybFfUBIEJAHH3plScOQkBVx6lzhPtBQJtHQq7r50FAtdG+p0nJQUDc7bJ9LLRBQB1hWIzMmUFAK3FiFfOCQUDbnZqUN2tBQJPbeLqfUUFANWxVGAo4QUDHvlfufR1BQDraHfkdAUFAJGz1rJ7kQEDNuwNDGMdAQH/pme12rEBAzsPdg9aMQEARcYVUZ2VAQKYFxQt5Q0BADPdo0HAgQECVRfE4e/g/QKey/9hQtT9AkJqzexNoP0CzX2UlXBg/QORaGSr9vD5AvfH/PihoPkCnT3DvrhA+QEVkcA5ovj1ARD1j3IRhPUDlueDXzgE9QP1kajBdvTxASfhXtvJXPEAQjzwEk+87QFV+gd5bhDtA/BZH8/cVO0C39TuqdaQ6QIm+UaXwLjpAHXGeySa3OUCatzu0Izw5QJQaVHyVvjhATPKKsjk9OECuZB0dkLg3QITYrGXwMjdA8yjnXXeUNkAX2Pc5/gU2QNdmVdYGfzVAYp4w7BbqNEAbzzFR7VE0QOH+g4C7sjNAtFctSzgUM0Cg4CK/oHIyQOw07or30TFAsmZHFlwqMUDa8N8a3n8wQKsenkGToi9Am5prkatCLkA+gG7YiNAsQKJLcsrdZytAHHpZOR/5KUAwpYCiCIYoQBkPBlD6DCdAwJjBUAaSJUCCIH3ytBMkQLullnRrjyJAI5g2GGcLIUBKe56FYgofQB9/IHMm/RtAPT3IOCbqGECpKKV4LdUVQKbbmfIgsBJAl+NKj20zD0D7GCkR4gcJQN7t/IvS6AJAZxR/qbCL+T/d4DMUeKbqPzbMbD8XkrI/GzGYdUXZ5b9pcr9T/ur2v7HVhckdiwHAQsKoCGVuB8BMwmRpx0ENwJI2J/csiRHA2BfIWVyPFMCHwS70tVwXwFUmXc+YMhrAvUEGz/jpHMBQK8vf8JUfwJnrq0L1ECHAuiY5syJbIsB08o5aE58jwHy8rJtY3STAk3GwTxcUJsB/RHzGJEQnwCglwmlqZyjAhnEje+uJKcAeMXfhLLwqwPp74d2WyivAotQtKVbYLMChW1c+Nt8twPY5a78h5C7AiqDWNQndL8DJqBDnjGcwwL2VN63d1zDAMYGyzRFKMcANtT3F6LgxwHJluOZrJjLAfd9Rj86OMsC7kUlJ/fMywKLjxztDVzPAKaloaAe2M8Axjbf8rBE0wKIs2RjHajTAV/8E9VfANMC3t7f9/RI1wMsL7g2xZDXAwLj1isexNcDDKXXNGfw1wEPePy+wRzbAd4vdkbKMNsDGl8JdF882wKZ87JceETfAHpuFa5dDN8AyTvb1cH43wOxTYqd8tTfAsDn4jpPrN8D1YFsLXR84wK+BihfGTjjAWXgoNQR+OMCUbj1HJKs4wHbvaJ122DjA46OxgGUBOcCLM44zSSg5wCwC9L0zUTnAgCQU7SB0OcBWoozWSZ05wIeJRWARvDnAVlG2qlzZOcB1jXpJ0vQ5wJol5e1TDjrAzcrdcz8mOsCC3KKcbzw6wGkZL+mTUTrAVuhcnVpkOsCjNSuWe3U6wEM3E3hkhTrAAJ+e/DuTOsBaDg6qb586wNKs1HTaqzrAXqxTbu20OsAqOtLUdLw6wAormjk5xTrADgUFN63JOsC5wMR7ncw6wMaRfcLwzTrAqvrRc/fNOsCobr1Bhcw6wAeGfWRcxzrAyen1YAXDOsB2NrguOr06wPCKFKSrtjrAMKgtlV6wOsDIAGe2UqY6wGqqj4dAmjrAE0jUAkWNOsATB2Ei1n46wEdBz1y0cDrAb//dr2FfOsBbnrpmpEw6wJ7Q6n1lNTrA9bt6N8gfOsDn21DBuAg6wEjlPZP98znAgw+ocwvaOcAN+PCj3bs5wP5otjq9oTnAavhCPYKDOcD8wcdT1WM5wEetiduZQjnAfkvixOofOcCR6XDTxPs4wC6VHl791TjA/jrue+GuOMAZTOzpTYY4wKc0C3StWjjAissHORMvOMDbnYEF7wE4wD6S+9Gp2DfAxw6C4HaoN8DYWfCouHY3wMg9JI5YQzfANZcWTIMON8C1cQxjHdg2wFTOFPMcnjbA3hdOGIdkNsAIDSOHaCk2wKogotTl7TXAqw8Kh6avNcAYYQR+1W81wOQ1lO+8MTXAJB0+Wd3eNMBa143/SJo0wBEZQ8JrUDTA5A9mVqQINMCBi0AzRb8zwBG15XwRczPAzdh4wZEmM8D6HKgGhtgywLF8VL4tgzLAq/vchgQyMsDIx9jqU98xwFPCGi6NjjHAQIfvT+I4McDNgiFO0t4wwBgxIhUcgjDANU2K8fsnMMDH+fLZ05gvwA1tXaWX6C7Af8Ka9RUsLsBQGqLg72wtwHsdTWVFrSzAT+jLGd7oK8C0DvMW9yErwLX8j67YWCrAz2oOb06NKcBHacZEc78owGvxCmAR+yfABHg1ffMoJ8BpKntRzlQmwPKF8y2HhSXAqGAYzqmtJMC1UpMiGtQjwMk/IDix/iLAvqsQwggiIsAnOoZD5kMhwA8QROGGYiDAVviGSYwDH8CdY1R96z8dwHk+CwSDfxvAsbmRPcWdGcDQDOCXCdUXwJBMiYg59hXAPdbIhQ0rFMAm3eLtDV8SwGYZ5BZ+gxDAFBqWbu1sDcAG7Z1KetIJwOfyGTvbFgbAG0iQdJB9AsC7ymS1NMv9vzB4rRcTd/a/ZfIzlOOc7r+T3Thcnmjcv+OIoTvT0pW/qOTssfz12j8YCPwia5XrP+gzNHrXpfQ/sMXaQtug+z9VrR1ZsEgBQFF4TURmhARA4M1gNMXwB0Au0x10qVYLQIDhgpOpww5AVbKPv+sNEUDe/QcpObYSQAROGjIQixRANdJB24UrFkDy/gjn6scXQOUwjM51XRlAwj3jWTPxGkDiHNdzYoAcQNhkXnGL+B1AfYPpCFt+H0DIjNA4in8gQJHBImneNSFAfemzckDxIUC7YtRrJKoiQKyRLwCzWyNAuTPwfPkAJECBeewD47EkQCwFsfOCYCVACbjeEPsLJkA+6BUAqrQmQL9akL4gVidA14tstDL5J0AOaKG7fJkoQOfRHWmLNilAaNKuVCPRKUCu/C+fz2gqQIdsBXHL+SpAXm01kaqLK0Ar4bm+wj8sQC09KILpwCxAuT2ZnhJKLUAv+KHoUNAtQKkeWg0oUC5AYChK04rQLkBwxfyMF04vQGjreED8xS9AkCEVBNMeMECxR/EFNVkwQMceJcLenDBAN4x/a2TUMEACtVD6bQoxQMQ54x2uNjFAz34w4tVpMUAizz8Ql5sxQJlFrEaNyjFANWbYoWz5MUDL7Edg4yYyQFgwF+NsUjJAitjZLgx9MkBxLxxZRKYyQFSl2TrL0DJAI7QZXjX3MkD4Ffg4QBwzQLQRZUM+RjNAfVBghV9sM0CUdWllM40zQG2+rG7OsTNAfZxXqtnPM0C/5A0+iuwzQETgAGfpBDRAqQLNVt4eNEC71fEeejc0QNmubZT0TTRAjDQLGNFjNED9oI/zWHg0QG260ARxhjRAAk/DoDqYNED4qeRLTZ40QDA5WhocrTRAH4qQstu6NEBsrmP9Psc0QJ/zAeAR1TRAvf87fLHeNEDKZldz/uY0QHrfWgPw8TRAeR90A3r3NEB68aQUr/s0QOgLYu6e+jRA2BAA/Qr8NEDewFmiG/w0QG4/mo178DRAZdEfYMftNEBOKeUarek0QPWZrn8O5DRAzEE/syXdNECAOBdB2tQ0QOyVBXUh0DRA1mP+o/7ENEBMwctwabg0QKGViv6IrDRAmS+reRydNEDdeRHaPIw0QMoWeVcmdzRAMe5NevFdNEBwbm/WpUg0QNXacr82NzRAd8T7EPAeNEDMd9wdKwU0QDheWBtG7DNAQ/tNnn3PM0BHJ7ckMrEzQKJce8zqkTNAQXhWOX5wM0Cm9r5iik0zQHXHFuV2JjNAq4QqO1IAM0B9sXmGW9IyQIeD94/2pTJA+HINTgl7MkBZo4B3ek4yQOtHx/FBIDJAmotw02bwMUCsnQY77b4xQJV7ZOnyhzFARCwsQBtTMUDO/OoNjRwxQCqSuLy85jBApZvOLsesMEDUqhk4HXEwQDMSbfOYMzBAGELFbPzoL0BlsAr0XGcvQM61ykEg4S5A7IQdNoRYLkCv8yYAbswtQDnK5EOGQy1A9JD0eW6wLEDEb+VFxxksQLKGQcnLfStAljzX/yvgKkCQrV74Bz8qQBAyzeXinylAf7tzLEgLKUADJbacul8oQIK8cSPqrydA+HUK2Wv9JkAnysIhk0cmQNgnpK0flSVAsKVV24PYJEAz/M3WkBgkQMRFarOuWyNAIzhCIEGVIkDbK84SrMshQMs/spCs/iBAn2ax480uIECKMZvqwI0eQIHcfbys6RxAJcPcans4G0DZfe1Z2IEZQP+28GYkwRdAVFHMRi8AFkBral5DBjoUQE7UNZxkXxJApEIaRz2QEEAR+X5jvnkNQLpZqgVJ3QlAYpfISScmBkDn5Ce2/mcCQMsDKrWMUPw/443sO4K59D9NMc0RQS7qP1lRUR5Dc9U/J33IGdK8wr8eMh9lmyfkv5p1HCf+3PG/EH+ONmmk+b/z9Feao7cAwI+NN3UxswTAMSVDqkaaCMDJbJvYXoEMwAv1E/bdRRDAPqApcJSREsD+qSN6sIIUwKQiAUmbexbAZZ9W+WppGMBtbnxRMFUawJrbOEOLQxzATrraajcqHsBeRjYA9QYgwCAa1Zot+CDAvu0ZOo3mIcCiNisODdMiwOQgDdrluyPAfaBRjj2kJMBh2AWKSnUlwLgfXKexWibAAd/LlXs7J8D9m3uLlBkowNEKYbER8yjATewaj5/LKcAclAtaKaEqwKKUYioYdivA3wIOkT5FLMAZJhbJHREtwN06Ar8e1y3ATVmBYEycLsCgw3Vi510vwKXy5RsSCjDA8nZCpGNnMMD5r8wQ4MIwwCxIeOX+GzHA4lUP8b9zMcAQf0qTmMkxwC09iU3vGzLAVE8oQPJtMsAn35TF/L0ywOOycmTBCzPAb13oUNpXM8C4nmBS9aEzwGf9q3uE6TPAe8jW1TIwNMCjVNOBYXQ0wI8aU4cItTTAMrm3zy31NMA8ZHH8ZDM1wM/6++z2bzXAJe50RDWqNcC0P73Qc+I1wEoL4AQGGTbAJHxqWEtNNsBASBNZon82wEkrdZkOrzbANIcayX/dNsDRWvY8thI3wDpg3vyJPjfARjy8djVnN8Cz6vrc/Y03wGAyrmtsszfADKwl3GjWN8AiU7oWlfc3wIP6hh4LFjjAJTl6aokzOMBiCINHOk84wEQ3tTTEaTjAsdAKaNyBOMB1SM51KZg4wGLsT0hzqzjAi3f9Iyy+OMBOeaU3I884wCRBf2qG3jjAxkIl7wvsOMAUZtsN6/c4wINdMTcJAjnA0Zp2d38KOcBfKSaYTxE5wO1f9Pz9FTnAoiJpiIEZOcCd697sYxs5wJQb6Yu6HDnAQkWJkJ8bOcCoaCACwxg5wJPAdikxFTnAIREShScPOcCNTFS0kQc5wCrJuOL3/jjA/AiDB0z0OMBEr2FMDeg4wLWs/e312TjAlYe+qqnKOMAaM0l+1rk4wIFVuDzGpDjAXjcnLeiQOMDYuB8ncHk4wHJVyahVYjjAbUpC7/dJOMAg7QE9IDA4wC/0Fc4RGDjA2Wpjk1n7N8ApgzlmKN03wMiriEIJvTfAChusJ+2bN8DyGNX7Wnk3wEibpI+hVTfAz7fsmjAwN8DpZyuWUAk3wKwzFG7A3zbAg4U1rx+2NsDbPxiAHIs2wDwinAgkXzbA1kxf8W0xNsAPjepYWgI2wPJqYar+zzXA7NQD/TSeNcBPs1q9H2s1wHSZG7isNTXAh+mzqv3/NMBml3GBD8k0wE7Ic7hakDTA7EJkfNtfNMDug5hCXCU0wLAlCcpk7DPAyKa9hJevM8ARUr7YpHEzwALOkS4ANDPAA6H8QuTzMsAel+9WybIywLIzJQsXcjLA4d7obAQvMsB/Lf/uBusxwIOGJLXbpTHA3KhwaipgMcAwSBM3vBYxwOT+6fJazjDAbCms4lSGMMAleC+boz0wwBZjb9PQ4i/A9FvRthVPL8Dww83uX7ouwA9uDX5VJy7A1dBunOmQLcD5jrtxzfkswCJbhJ5zZCzA9/VSN1LMK8BoBS8U9zMrwHibVzJ1nyrAvd4y3ecGKsCzxqHDeG4pwEKn+LEZ1CjAgVl0cTs8KMAsayKe6qQnwLicDCwYEifAFOobTVB8JsA7zelZa+clwPL92ie1UCXAxqX9ziu+JMA3Oq3m/iwkwHE4Bgc8miPApJOylEQYI8BMInNnI4wiwJ8CEVPT/SHASwNyMr91IcAQ8jex4e8gwG29POG4aSDAK1eGL4LRH8B2OROx7tQewGZEv/bY5h3A42Z+94j1HMATE2bpNgocwOLsfc22GRvATXBRVFA7GsCAf8QC/XAZwPUuJCP4mxjAXLJaB5jSF8Bb4c+xmhAXwENuZfMpXxbA3jsNntOsFcARNpMqiQIVwMlUCLYJZhTA8j46HFLME8Aa4ktBQDsTwKaRecb2rxLA51dAI28wEsCeNgGfBboRwPQEs12DYhHA/myhd8P+EMD1ZqkxjKQQwMV7Qt1tSBDAaeVGQVIBEMCoJZZNLYgPwGImwXtcGw/Av9TB5rDIDsBstUJzAooOwMmETPJ4WQ7AyJxFX6pDDsD2MQR4akIOwH05CwH1ew7AH4ZQ+DHPDsBxNX+YYwwPwPkGmiHXYw/AHeCxh+HKD8Cydt67ciMQwDdOVc2+bhDAMPFPNbTBEMDNmsNWMB8RwLG0xZeDfxHABE315gHyEcBdnC21zG4SwO5m8GZq8RLAfvw93waDE8Czt2Vtv/8TwC4pQdPJrBTAQe1dNRRdFcBVVTJQbRcWwOjaxdh91xbA1tKOL/SlF8CFGDQmO34YwGtWEk1QYBnAui2mpZ9LGsAHya8EMEAbwInIhv/kQhzApYQIb6VJHcDW5QzcNlkewP07eTmEax/AzSqFsAJGIMCUvxrhVdogwMl/hb80dCHAMxycx0IQIsD/6y3TCbAiwMD//W9NUCPAQll95Tr3I8AmMiOncaEkwF7KLUiHTCXAGgaSdPb8JcCr0gOcRLAmwBAtzXk2ZifA0LyKb4o3KMCcAkEoXvIowETydSCNrinAw9WsJJBtKsBa1mPfYy4rwINwYGhx9CvAqa0M2Wu4LMAdbYNkvX0twFs3kROfQi7A8FS0Se0JL8CULuyZ+tEvwLBWhKHnTjDA3lj4NFyzMMCm4Sc/3xIxwEhl0CkPeDHAhRZesa3cMcCySHgJNEEywIrhr9yJpzLAfHyYlqQLM8AW/UNFY28zwB/tYWfK0DPABtqwJ6QzNMC5uQMT8pU0wPgtyKwX+TTAqfl//SJaNcDlVne4cro1wFkxLtBwETbAx8J4LixwNsCRqn3fAc42wLWJmuJ6KjfAWLaWnYCGN8AwvuQod+E3wHZrob45PDjAFUn7eBCVOMD9yoW6yuw4wHNfNrA3RDnAe5TVB4mZOcDMyakkpe05wDFZHqnMQDrACQTaE8WPOsByOOGRJ+A6wGYbkM85LjvARdcTmwF8O8BAOOcPecg7wCKqQ7bbEzzAATyfHrFdPMAoXwTxNKY8wOwfMap37jzAmYx7cj40PcBEi+mHsXg9wJW0MoyNvD3AxHfrMkX+PcACBHLXIU0+wPzk5Jy2jD7AHzkUd3fKPsAopqy63QY/wHct8wdjQz/AjzWHbSF9P8CBiwh9jbU/wDLxbjYC7D/AEol66esQQMCqIfk7MCtAwPmlq9ZGREDA14zhP0RdQMAWndwen3VAwE+rFNLciEDAeQ9ohvufQMAhDIwQebZAwCmvUC31zEDA8yVsrT7iQMBm3WLI7vZAwNhnUblFDEHAQvlQqckfQcC4SBWiuDJBwOysDcgDRUHA3WC1c8lWQcC9jGfz/GdBwALS62xneEHA3O17/jqFQcCW1cq0u5RBwDB+ltweo0HAG5q0S4axQcAP02JlYL9BwFcq7N8uzUHAM3t8/fjZQcDHFYSVPuZBwOjah9QJ80HA2LkupT/+QcAGQD7W7whCwGZNI7AfE0LAzklQ3sMcQsBTy4TqZCNCwELmCA0cLELA4Sk1ATU0QsDzVmRSzDtCwMUZVFKRQkLApgecjSRJQsBHZgItOU9CwKXjFNcPVELAgrijdCZZQsC0juchu11CwHbHOWWIYELAXkmHrRxkQsCrtNVuNGdCwG163AxPaULAUSHbBGJrQsA7BQZy+mxCwPmXlJKlbkLAaoubqUNvQsB7HrgJYm9CwJ2KSfwIb0LA726QXixuQsCzjlbD0GxCwLk9pD+RaULAAH23uzdnQsA0/MdaYWRCwNlW4WhSYULAOiwx/etbQsDenFb1nVdCwGgZYBYqVELAgnZZ5uJOQsA1v8kfHUlCwDb5oOkXQULA9ao17k46QsDd3fHQCDNCwBaWZ/7iK0LAdNmaApwjQsCBAZ1c1RpCwLO11JcTE0LAEqv6aE8JQsCRrXhajv5BwEAzX+Ib9EHAP03O6tXoQcBVg5CpD91BwC0II+830EHAwVLUt2/DQcBV9jVZJbZBwFan25rRqEHAVEEw1YOaQcCcQp0XtotBwD+ekz6Le0HAqoW6PrRrQcADtKuWXFtBwC9GOTdrS0HA9lKrahk6QcD+/9nOQyhBwIuytvOoF0HAm948V9gEQcAeVBiWiPFAwON6yFR03UDAqdGe3CbJQMDtsu9IWLRAwP5PwyfQnkDAfaAjXweJQMDOTpWAwnJAwFORTGZSW0DAMotcLdNEQMD6UKMLLC1AwDyRy7BgE0DAfjM3kJT1P8D3H3k8esM/wBKitwPwjj/AiJAhUQ1bP8COtKcmSyY/wFBoSN4a8D7A1IMRUqW5PsAZ+S0CU4I+wPIkqAwzSD7A0Uh21j8PPsD8GvbYQdU9wN/RZ209mj3As8tov/tePcBbHD+XAiM9wOB6+GFD4DzAnzKPFtyiPMCAvZMu1GQ8wOk5Fo3DIjzAad4U4W/jO8B6kVTWhaM7wBbCTxC+ZjvAlKky1sglO8BEUaXhSuQ6wJBHv086oDrADsA+CNddOsDJSqZ++ho6wDzAqatC2znAkS4UIq6XOcB17L3PwVM5wO2Oj/FqDznA0wr12vTKOMC+ctiINoY4wEDja7V4QDjASB9I41X7N8Cdpeo2FrY3wM5PMsBecTfAI/mFUswxN8D+QnaaUew2wApfLXXjpzbAOuPdj2tiNsCKwsVe/xw2wEY9B/nS1TXAzEiM1ZmQNcAMMXRqj0s1wC/HUdkBCDXA6INNfGHDNMAiO6EaAX80wIemunn7OTTAaiBV1if2M8DeATjsIa4zwGButOhxbDPAv0hqU84pM8CC4M25mucywCNxTUpSpTLAkPmM2h9kMsB7CGAaayMywNZE/20/4DHA0ORmtqygMcAvfmvPsmExwAlSQd9YHjHAwsq+hKvgMMBgEVfmraMwwJ93SdhadTDALIfoIMo5MMAvVzI5/f0vwHMiacrjjS/Antg4aFQbL8AoZ4egbKouwO79wqZAOC7A8h4q2pvKLcCAqNDupF4twMcFl95W8yzAsJA7/NqKLMBILgFkNCQswMmF57TatCvAPCOu235YK8CDkZMhXvcqwOLRZFH3myrAZEFARJc+KsDK9tYJL+MpwA0S81zciynAd3nUv0g0KcCSe3IInd4owI2Qm3zojCjA6/D3Zx87KMAKCRiRTesnwLJcehploCfAijiDKXtUJ8ChPuHelQYnwDMGPDVQxybADJLF5USBJsAC0P8aQT0mwFXsLCvkBCbAeVJ456bEJcBdbE/oaIYlwLuiPsv9QSXAKLbwn54HJcCKA5TVG88kwGddLiirlyTA5ydPTvFiJMDfyApcEDAkwPPM5Z2f9iPA57FF1GHHI8Dm9sVKAJojwH2vUKpCbiPAdFbfTGtEI8DyaGtTWRwjwGMf/ugM9iLAEbsAS1HRIsCszy4jRK4iwE1iOlW+kyLAftqLCvhzIsCLu83f0FUiwBIzYymBOyLALwMa7RAiIsBcBP85dggiwHUdqLvE7SHAeMSkDyTXIcAQr7kW58EhwKkBviG0sCHAcZSkwESeIcDcjX9tLI0hwGApLnpyfSHA/CxXMu5uIcDXG4JJsGEhwFu7GtmBVyHATEj93ptMIcDQ5QBerVchwIswH4itVyHAhra7JRBQIcB7QZpWkEkhwEsL7X8LRSHA+HDMXoBAIcDx9wse8zwhwEEwQ0pkPCHA8j3/PZ46IcCJcPIvpDkhwE9lVPoFPyHAOy4ECbg/IcCFmQqDGUEhwPbxm/zHQyHA9fmniqdGIcDWrdFRHUohwCg6+idmTSHAo8GQXyFSIcDVnkwebVchwFF7HNcgYSHAqW5rZn9nIcDjaRZ3UW4hwDGnG2THcyHAqpkBf2V7IcDd61+caoMhwDzwK9MakCHAZ44v7COmIcAMEmmvGK8hwNM/ghdvuCHAT5XSWd/BIcASUqN1asshwDPqlb7i1iHAlD6tKMngIcCARsC63OohwMVTH4g5ACLAlHISvnUKIsDS+iTNtRQiwBig2KgFHiLA7zszjz8oIsB0VwKanzwiwA5BtbTSQyLAHVouz+JNIsC7LYHb31ciwDQY+WszXSLAiY25duFmIsDub7AfdnAiwJznOSuzdiLAtEli4ut/IsCR7ceY64giwDrxFRbHjyLAFBIDW0+YIsDa42pinaAiwOK0Z0GMriLA0ovhPUG2IsAgXzoTrr0iwCMrBVSvxyLAO6FTJGbOIsBpstojsdQiwHfBdLWB2SLAyyIw3zbfIsB6eo7bdeQiwKTWt4k55yLA5UrD9qPrIsCQDeEZq+8iwLj31YEp9yLACpQSCxLyIsBU08akyfQiwDhJeBVb+iLAfxbukCD8IsCepp2nZv0iwE58Yt+HACPAQvVLtNUAI8C3/gqwpwAjwPgxn2Ho/yLALGhAEcT+IsCuv+RqHP0iwAUhYnle/iLAxiOkrMT7IsBSoF/Jf/ciwGWFJffI8iLAwvpb6sLuIsBO16bOL+oiwFOpBK8I5yLAhe5P0nnhIsAEkJ4xadsiwB3vC8ht2yLAxlftrWLUIsDp0hox3swiwBHI8a9JxCLAUJ+vS9a7IsAS2P+q4bIiwBg2S5POpiLAy6nJvf2cIsB/tH+g0ZIiwGeWMU6fhyLA92yM2X58IsB6L3q3/XAiwJrtN/qFaSLA6Dj97CldIsCWuQnMclAiwMsJCgLKRiLABxfsnkA5IsCGLC/ZaCsiwEGmXkOgHiLAHdwi/QshIsDsqpItIxIiwFi8BqikAiLAOd9kyDDzIcCLXcysZ+MhwJQvJY902CHAwjg3ICfIIcC8k6TKnrchwDoQsGr4piHAjEDBvw2WIcBNCNyC9YQhwG5ZgpP9cyHAeWYlaZtiIcBvUm0CXkAhwIR7b0/CLiHAaFVR4gIdIcAW4rgDMgshwFyORy1T/CDAFPcCw2TqIMBrnBu9cNggwO8lSnlWxCDAzbq8sGqyIMDw5mHNpqAgwGvYGlTSjiDAObKefx19IMBXShDRiWsgwPlztkzcYCDATLRR/IpPIMAAGcSGdT4gwBGttQEkLCDA1Gh5PocbIMCGyVIoHwsgwAvD36+m9R/Auyg97QXWH8Ci3QTYHbcfwA19pIXemh/A8E6qoiV9H8AymnCOZGAfwGAQWlWHRB/AYmsXLkgxH8A/ELpXGBcfwF9+n/IfAR/A1xaprrfoHsC99teTXtEewPoZK5+ttB7AkhyqL7GfHsDZkEpH54sewC+HR8VMfB7AxkCChupqHsBygeaTuloewOAfSS+KSx7AGmaiizk+HsDAg0a9pV0ewHxR3Be5Th7AZKawThtGHsAp72SKDz8ewDYHI0nHNx7A5ZMWJPgzHsAoohIG1TEewGy15v7rMB7AwBKLTUgyHsBfRnVMkDUewHGap/y9OB7AaFnNUq8/HsCrRWIhlEgewBHTomtrSh7AMD9DvFBXHsC6sLYsNWYewBShIxZLdR7AVZnmKFGIHsAL3A6AaZ0ewDjBR79csx7AH61GlbnMHsB6of5NPOgewIHPCe0YBh/ANF9yUhYmH8A/CW25cEgfwAVk50TPaB/AMWpiDdCxH8ATFfAZF9sfwI7zsAaJBCDA8vKnyIobIMAHy9QStDMgwAe1GdSfTSDAcJXw0iloIMArP4kZ5YMgwHZL4XHXoCDAK1R5RPe+IMAbg5HPR94gwA6NkQeK/SDAp97VaisfIcC1ZZYy8TchwCAzg0KyWyHA1l43lPyAIcDxH6GgdqchwIYM6kNozyHAgv/5EV34IcAcbBMogSIiwK1R50vCTyLAC0GW8lJ8IsC/bCXVGaoiwFqQ4QPV2SLAhooWEvoJI8CjSrzLTjsjwFnClSTxbiPAbyblRYqiI8Dfr2ZsS9cjwGt2SPgEDiTAJrKCURZFJMA5eQsZL30kwM6aMc2gsiTAczMcgMLsJMBwijIpAiglwHGrkXQOZCXAhlBw60ShJcBjX5dOgt8lwCjFh0bFHibAoxnOZhRqJsBJnFY9OKsmwNerZTgU7SbAjeZ9bwcwJ8CO1P7a1XMnwPAzM43YuCfA/m8VJ23+J8BsxISQwkQowBOLc4UcjijAMjlJLg7WKMD59rcpwh4pwLKSuDyYaCnAEevH4pmyKcBhM2/F5QMqwBqR2VPxTCrA8wQpgtKYKsCkbfY4QeUqwI7uLK8pNSvApx4yW6WCK8C3uzSujdArwGp59eR8HizAC1eRqVhtLMBQZiaZmLwswPTqQrA1Cy3AtKHXGh1bLcDmc5YPTKstwH9mVpsA8y3AqUVLu8FDLsAWo6JCvJQuwJGHNYbS5S7A+m7FPxU3L8CxXg8ceogvwK+vcEk33S/AoyI+L2IXMMDwumZbJUAwwCccNYE7ajDAyLZvyQOTMMCoSCFYxbswwLu4c7og5DDApczASTEDMcBHSdefwisxwBymwuR8VjHAaQg6ruJ+McCpJxPGKacxwFuR/MwSzzHA18nrKhL3McA6Ropr6x4ywHSUaMAVSTLAjnpvSZ5wMsDBErRA95cywD2LFUfiwDLAwa9lgMjnMsDtt9Ph8w4zwHP/m2oGNDPApL6kUDRaM8AaTgrDIIAzwLX0/xtipDPAHpU9mMLJM8CO6gOv1u4zwH5q8SdlFjTAajUbcOY6NMDNAdbaFF80wEgSGgVRgDTA2IXrbdCjNMDlw8Rw/cY0wO+Wem7q5zTALOY+53EKNcCGT9XQkiw1wP/mIjrfTDXA7VDsEENuNcAEykrqR481wPpvpqSKrTXAZ/L1RcnNNcCzKWVMnu01wOLhR8gCCzbAcKJbNwMqNsBZwwLalUg2wLvSjXbLZjbAv0e/GcyANsBDEyXRIJ42wOsZBZfBtzbATsqeY0DUNsBui+RUTfA2wGKx0pWFDjfAQ2lUH7EpN8A0RJIaZEQ3wFI9UMxUXDfA+hi5WC52N8CmJeS4kI83wBalI0AbpjfAxEjwaZ++N8Cuf0zbEdk3wFifh3Lo8DfAxIxGRhIIOMA2q2N2wR44wLwnrRfiNTjAKIbRwqpLOMBjF3Xy+mA4wBF18EfseDjAHiT9iVONOMA/XQoPTqE4wGjQdHXNuTjAeqdGQtnMOMCwtsr3c984wM7Zomqn+jjAaZLZ31YMOcCeuMNzkR05wAOsQTR7LDnAyN4JodM8OcB6zl4QuUw5wNv47MGMWznAtKZZV5BqOcCbrcLkJHk5wFKdEoE0jDnAbPzSOeuZOcC1YM1MMKc5wBSRGr36tDnASfxW4/W+OcCK87k38so5wIeAAvZY1jnAMHR67XrhOcARAHxHNOw5wOvgfZcB9TnAldv+ouD+OcD8kaoOWAg6wLpXLIUvEjrApBOsOtcaOsBiQ3UnFSM6wLIkkDZELDrA3J0827szOsBVYlvnFzc6wDiBlvWiPTrAinZWj+VDOsCDteBnyEk6wFd8uudFUDrAZEfecmRVOsAflqVvHlo6wALOwb9WXzrA6vkgLERjOsCEH4JE1mY6wKLa0Ab1ajrABiFmp81tOsAsrYYfSXA6wB8ruCMzezrAOGWnqO98OsBOIIVOVH46wP9lKRJifTrAExq1/BF+OsBRulvsY346wE5M6y+ZfzrAygNiVUN/OsBQBET7kX46wBmebjSGfTrAOIQcuSd8OsDpuC3wdXo6wEoaHlBFdTrAWON/8Rx2OsBYJ32KcnM6wFvUUEuNcjrABkOSqzdvOsDjS1sSmWs6wMbkemqoZTrAhmymFmhhOsC7/9to2Fw6wOPcYWPGWjrAhsMK45lVOsBlaesEIlA6wC1oaNCrTDrAztVDTqRGOsD8UIsc5jY6wIrAhBVgMDrAU2cLeG0pOsAcCpHxLSI6wOUHHE1PGDrAVhoWyoQQOsBZ1ZRgdwg6wMJMiXbKBTrALvr8cCj9OcAOPSJ3QPQ5wB9okcBK6DnAvRd9hN/eOcAEZnIIN9U5wNZrErXyyTnAJLYxYrm/OcBVpqo8QbU5wKWJdTRBrDnApxW3IUahOcBAPmCdDpY5wKXkeM3lijnATSxugSR/OcAaqPzkJXM5wFrm7/I5ZTnAzIsCQLtYOcAkEIudBEw5wCLcO3UXQDnA+qG2wq04OcDl5A1UQis5wCjTg7Q1GznA7bxpr1ANOcCG4aE3Mv84wDkwmYnu7zjAjJ3K+FXhOMBhcePcf9I4wCJHTlvSwzjAqUIVzIe0OMCV5hOnBaU4wEG5zhzplDjABJwdAe6EOMAqE6GXLHM4wMg15Xf7YjjAUDNEyVhSOMAXWeudfUE4wHRUrB16MTjAeZ6FpSUgOMBADak6mA44wByn1XDC/TfAImnFYL7rN8DwvlKbjNk3wL2hV162xzfAdiFSxAW1N8DSVv0OHqI3wKjWRa8MjDfADKzko694N8CuBHF4G2U3wF0koadjUTfAHtVDJVc9N8BtQyXmFik3wL6ERMnVFDfAvoUUNyAAN8DhsIDXLes2wHe3Dxy+1TbAQm+KhFDANsBRvBAkrao2wNePfCqoljbAm9Kp9yWPNsAMEj0XyHg2wEKoLN5TYTbAQV6PuIZKNsC6UthrezM2wBmuIz5rGTbAv79kSuYBNsDRZ309Guo1wPppdxOf1DXAZrz2fFS8NcD1/Yawy6M1wHtmh+kPjDXAEk92LAFzNcDQWn4KcFQ1wHudJ0j8OjXAlF//ISQhNcDRPaqEBgc1wAZQyYH46DTAaylpqFPONMBkZ+2Fb7M0wPTYNFC1lzTAlJFujDl8NMBfOKvjemA0wEEwCTf7RTTAXaxeiKgpNMDqWdG9AA00wEzWU+sg9DPAkRrBEePWM8B5lBRAXbkzwOjEEomTmjPAtyX0uWp8M8DD9jKv9V0zwJlFfEupQDPAp1nh8IMhM8BTaQMYDgIzwD+zVs1m4DLAgSynU0rAMsCvvTgE2Z8ywGq8PzkJgDLAPKCNfbZfMsBarVUPMz4ywCUw+IIdHjLAeua6EuT7McCC+wkpTtkxwOsVvuKutzHACt0pE2CUMcDdOzehpnAxwOzaiAH7SzHAx0A1oXcnMcCqV6tckQIxwPNXLeMJ2zDApoOinVe1MMAN5BV4r5AwwHDq7pn9azDAcKJluA5FMMBGJPQ9px0wwGpNsnWX5i/AKwtWbBCWL8A7uWgipUQvwPSJ8ch27y7Au9wEJEKcLsDWShnUL0guwA1KM7KJ8y3A83Fa45KdLcBbuziNnkYtwPdb6vtK6SzAslYQ4V+QLMA6KijGgDYswF5OI2IZ3ivAm4AcGUeCK8BwmLt4ciUrwDjg60RnxCrAtYnU1I9lKsAJ/OYgrgUqwPwXh1tVqinAGnZnwU9IKcBNJUmuQuUowH8210O1gyjAa4/vVTYHKMBPQTQf5aAnwKG/b1n3PyfATwDAi3HXJsCBgHOl2G0mwIzDyQckBibAWVVwFFmaJcD1fDzBbC0lwAg9TQWiwCTAhr/iZnRRJMDofwn/E+EjwIh0pwoScCPAwUqP32X9IsCPPhi0ioUiwLhqfYB+DyLARu6psVqZIcAkO/09FCIhwBB5CbSDpSDAXp5C2OQrIMATSwcdJ2IfwHHgEpnXax7AIzZDcWxxHcBaY1ro63QcwPuBeRHacRvAMnxjlYtwGsDMUDArAm0ZwFgH3mlachjAxYTs5SNqF8A7XeHqpF8WwFLWwt5iUBXA/3PJo11BFMB7ejx1OTATwBwJoSc0GRLA58qQ14cDEcB0gtBbd9cPwJNKkJoarQ3AT3jIrIl0C8BGwX9eIjgJwPOrFAf96AbAg06Jg0OoBMCtZrKSUl8CwEEsXC/5DwDAbTjIO5J++7+9B2iEdNX2v8xdOf5TT/K/uOBcOO4u678+mCyXKLDhv63+N6/7NdC/8ZglhmD4pz9QeV/HF0/WP/qU8LZ+0OQ/5GkOAs2R7j9JMJTohWv0PxoX565XQ/k/W/H9n0M1/j8otWdEM5YBQKwHbzOXGgRAPcm1Q0ybBkBvQPZ9Vh4JQDIacN41sQtAwK+11mI4DkDd8ylSl2AQQOV00EyEpxFAZBDOrbvtEkDgWgogtDQUQHBtmvCMlRVAUm/rRa3dFkAK/YBNhiYYQMQ/D388cxlABDD+5PW8GkDGPaeP8wYcQHxfwxhlSR1ALwwCYaCTHkDUd41kJ94fQMB1Ov9gkiBA2TWZPI03IUBN0u6WqdwhQI93JYL5hCJA/vw2NOotI0AYiN5fhNIjQCZug2qTeCRAS/a64L8cJUB4qJJ0qMAlQI7L+c2gZiZAQz6qAOEJJ0Cxig3ArKwnQOGfyxiPTChA3IrQJmPuKECpKD7gto8pQBfe7tAHNipAQlK+nT/WKkBfU1wh1VwrQGQ1MzaI/ytABqb9v+mdLEAPAYfLjDstQBZV0Q1I0y1Ah/j5qFtvLkB5o03erQovQPGrNdWBpC9AJ3dVzgofMEC+cw8YX2swQGkR+559tjBANBDGj+cBMUCW832NzUwxQDroNJw7ljFAAQGNTyTgMUBeYzUcjSkyQBBsaBevcDJALv+cvwK5MkCNSytuvAAzQI61vlUTSDNAjMNR9raOM0Cm5BpQu9QzQI26BjrNGjRAiF+O3qRfNEAuYSyo3qM0QGguJ7mW5TRAhafy0OUrNUA/4QcUOm41QNZmmbBxrjVA9gGo4oXvNUCxj0wV9S82QMR2QMe4bzZAaVCJotuuNkCKmdZaVe02QEspyxdLKjdA3pEpe3VnN0Blabui8aM3QEe7cRoS4TdAvXFyPi8cOECR5ltKLV44QCPeY9XvlThAxnBOegXPOEDOTzpcaQc5QD+N0QlZPTlABDzrE2J0OUDqHEUJvKo5QC2UBqX03zlArGtVUfcUOkCaoU/0SUk6QOSav56WfjpA5ImW+Y6xOkBjABdr2uM6QEEMnCRuFjtArHBIel1HO0A+bPKCnHc7QO7IhmdZpjtADHMbw0LVO0BnrzJAiQM8QHJlou56NDxAbDiWT21hPEA223O0s408QOvmJ7KBtzxA7iWob3riPEBqdbyJzgw9QA8mNcEpOD1A6rzzbHdfPUAG+APh34c9QPqaonxPsD1ACfx6XHHXPUBonAj39f09QKg6NabtHz5AuprLgjVFPkDX38s72Wk+QLDTeKBRjT5AY3ZjgrywPkDLxhldkdM+QCpd3PpW9j5A5W+1yfEXP0Cj2es+W0A/QPv3UM/SYT9Aw0YXhaaBP0DRER1+56A/QEp+KzhGvj9AKqXAGWDcP0BKRX/c5/k/QKIPGqt8C0BAIKE9BLAZQEBqwonAmydAQCPXckNyNEBAwZ0WltVBQEAK+OOb8E5AQN1p+r1GV0BAmvk7nttjQEC+rdYeLXBAQG7iHiJGekBA87KukQ+GQED0lso3lJFAQHUEWH5unUBATcQyG26oQEC6Sp5SL7NAQDGT18nvvUBA4rg0sy7IQEAte6rOLtJAQFt6Zjhz2kBALczkhTvnQEBzHs0VfvBAQFrHz2yk+UBAbJvn5GoCQUAqfP9/8wpBQJCgWEUgE0FAwJ2uXzEbQUDHwoLYBCNBQLjlBeOHK0FAJsKXyeQyQUA29vBfBzpBQOIRz+0eQUFAXXT/dspHQUBrIKXRIExBQI/YCXE0UkFAp3io9zlYQUD7weHgBl5BQH0qC2D2Y0FA3BuPBVVpQUDhkAwve25BQFfcb05LdEFAu474kAN5QUCyc6h8hH1BQHtec0W6gkFAE0i3vdOGQUAT4yUmtYpBQFTNTBxVkUFAaywzOsyUQUCgODFMDJhBQGIUCBu3mkFAS9Qd8ZGdQUAUJkAbNqBBQJ8OVqyWokFAMcdpzNOkQUB9emSR4aZBQL8DH5eaqEFA/A3SNEOqQUBsIQqjuKtBQHwshXy3q0FAYkzdJCysQUAY+P1aBa1BQL1edARFrkFAHOd7EMOuQUAdlMjJDK9BQMGnG8h+r0FACtgW0WKvQUAH2nMNG69BQAxC1kTqrEFArsWCaT6sQUCW0LdiZKtBQPkxLn97qkFA8Gku+j+pQUDVxFrJmqVBQJThVlvFokFAn2yFjf6gQUCMV51NCZ9BQI8xdorznEFA37EpqaKaQUDdy1VFIphBQPI9C3J7lEFA4mnIe52RQUC/S8kEko5BQNqBRtrkikFAhhbGWH2HQUB4Jh8n6INBQEz44+uQgkFA57mYb55+QUBg4/tfg3pBQNHhv7QNdkFAAe21kZVxQUD/cEfn8GxBQNbFYqf2aEFAPlgM0PhjQUBBiTpazl5BQCjDMnAzWUFAthxRM6xTQUDCEFOQ+k1BQECvvvEZSEFASH7ZA7ZEQUCWeFtmfT5BQDMY0RfoN0FAiIoq8VkxQUCkwbujnSpBQEuzF/seJEFAMMisCQwdQUCtVgO0zhVBQLkwcBN0DUFA3PvZ09sFQUC2yiPXGP5AQKYwYo7p9UBAB30/GMztQECB+Bdf7eJAQE9ovFxh3EBAnp4XLcPTQEAN7ISQ+MpAQOydmFq6w0BA1hYSPpW6QEAglecUR7FAQFdzcAhyqEBApovFms2eQEDEdvgl/JRAQJ9Ylrl4iUBAdEQK61J/QEAaAkDGAnVAQF2R3XiRakBABpC8EulfQEAQKUrKFVVAQKCqSQ4YSkBAcExEwOw+QEAZI3K0lTNAQP6h3XIkKEBAzI9JJ3YcQEAdZi/EnRBAQCgR2tm1A0BAOmBjoA7vP0B5tvbpW9Y/QJcawEHJvD9AAIYyVWOhP0DkFk/sq4c/QNwFtWP5bj9AJb6QwpVUP0DzdJsy3jk/QPJ6p4VLHT9Af+A3L+cBP0AilJBVLeY+QDt9eaosyz5ADr9RI8euPkD+JW07/5E+QB8PmoSbdD5AS9G4NjRXPkCS34Hccio+QI1u0TBEDj5AE+Q3jdLvPUD+8gukD9E9QLP8L0Q6sT1AYEwAtsaRPUDLPhjY+nE9QEOn+GBHUj1ATYLwWtYxPUBe5S45DxE9QKvaZhNj7zxAXYS6U+vNPED8eppXJ6w8QLyYmnGPhTxAAx7CyCBjPEASyRlyXUA8QCsVisd6HTxA/vqALQD6O0DWh7yoPdY7QKVBFaWVsDtA5xKYJieMO0CfiOH9Y2c7QErimjiwRTtA4jMR8kMgO0DoupVuh/o6QFfsaiEh0zpAJzwcDiSvOkB/AwfCcYg6QKFeSkXWYTpA6OdRjHU6OkCX80LcxRI6QMlOe0H05TlA8Z89XKK9OUDZL9lWAJU5QCD+RLUNazlA98sp0sFBOUDrg281Jhg5QLJbB5qP8ThATz5hR1PHOEBiOmQX65s4QDPxLJlhbzhAfzChhC5EOEDkd+Mnqhg4QD+jtT/m7DdAJx9DrsTAN0B7BtJwU5Q3QFM3W6m/aDdAGu2VTLE7N0CwOts8WQ43QMyyzXzG3TZAZwGr9NSvNkBaObH3l4E2QGjhhryGVTZAvS1kXq8mNkABxpoZjfc1QIrq4emAyDVAepPsncmYNUC1QpaVyWg1QF5skD61NjVAqWC/pSYGNUCewCIKVdU0QGgBHKuWpTRAUVvtIC90NEAAwjPLgkI0QB5CtwUeDzRAS7hQ6KrnM0B8CYTLNrUzQIYNcMqIfzNAeWYp94VMM0DRseJkTBkzQBJ0t8iu5TJAeWwDgOixMkB42kH05n0yQCsLBMhJSTJAJl1GS8kUMkC7MEAyD+AxQIH2/OfCqzFAsR5IV5B2MUA2QAXsE0QxQPgoYZEVCzFA+TcS1DTVMEBryJ4nIp8wQMlMM7zkZzBAt1H1+1wxMEA0+xpDS/UvQKsp99hWiC9A85GaeAwaL0Cz+ceWcasuQIsjc5/AMi5ARk+sTlvDLUCOnLHbnVMtQMp7INQs9ixAGjdQzLWFLECf5cyv6hQsQEVN+townCtAYRVM/7UqK0A2gsXx2rgqQNWqNf6XPCpAp2WAcTjKKUC+fXzljlcpQMNyy/8/7ChAVFjTChV5KEBQarxupQUoQGxeepDklCdAbft10zczJ0Dg2PrvML8mQFNFvG5lRyZAP8THHPvSJUAfFWyKVV4lQOX+MjaF6CRAhP/XT5ZzJEDsDpNqbv4jQJVF4L2qjSNAkIOPa1cYI0Bu5Ozh5qIiQAk4lLKFKiJAzsRGed20IUCBRFFqBTMhQHZKANBluCBAZPSvUJVCIEACDZ3lWpkfQOXxHAznqB5AB9Qa1wy9HUDMoMZrQ9EcQC/MIbQt4RtA9ME9d4H1GkCrnURv3wkaQI1x7AGYFhlAx2rnTT4rGED10vxjEkAXQBg+bIkxURZAMYZVS4VmFUCdYMj6EXwUQNI2Q4B+lRNAzCqXJayrEkBxj/RLBMIRQAIWlI9P1xBAo1rUI+7cD0C5KyMIFwwOQESWVL0tHQxAbWV4KPVNCkBxJ0TBrX8IQMd7I7MMrQZAvw9RTsDXBECq8blsvAwDQImU5/r/RgFA9HI+nQb9/j/8mASNcW77P39pdvcv2vc/sWgW0jVR9D+MkcRbvcrwP+KBtsDuveo/MX8X60C94z8HnNBwr4fZP9Q98F1rZ8c/u36E3J49ob8CR1T8d6jPvyEsXRskr92/2l3T4Umv5b8GFE8+j3/sv9alP8zJhfG/Lmd+DoPm9L8/JyttGUP4vwwX3co2mvu/GHxKF/Xu/r+gAjbzph8BwNwI5ihd0QLAkkhwmE11BMB5SZ1UFRcGwJIr2EkwngfA7mGrCEI7CcBf+TL729UKwIoKBq7AbwzARABk17EFDsCetXdGLZkPwHrJIujAnBDA9aihau9jEcBfwuOI6ykSwD9CN3UX/hLAvj+S32LBE8DUut0CbYMUwLbbnOcWPBXA+aKB2pgDFsDqoZvVkMEWwHwEhY2OdBfARzt3RsgvGMCe9FhLgOkYwOpS498oqBnA/UyFQRNfGsCBy+uhjxQbwPxQuK6EwRvAvWkRlBx0HMD5Z0YmPCUdwL41s5As4B3AfwiLP1SOHsAQq7dcIDIfwJ8ADxrq2x/AFrzXfbxCIMD9vgdyupYgwFziMjgv7CDA9u+B2Ko+IcDeTu+gU5AhwN+EXzC23yHA1Xbh/dsvIsCCeFzQQH8iwMWKxMmbziLAvS7J4lscI8CGfYNPXmkjwHV7roDBwCPAQcfDmB4MJMCGAPAnxlYkwBMT4UoGoCTAbOx9ZhTpJMBxXlB4XDElwN8bJFyAeSXA0ZTZHizAJcBCaOSs/gUmwIVi/H3VSybAWTLE4RqQJsAuqG50ltMmwLJ2KY+ZGifAesyc0h5nJ8Ci2NbUP6gnwPmlGKGH6CfAjphnrwooKMAYru3Ot2YowEm7gWiDoijA5y5waaffKMBj7gwa+xspwH+jxnLmWCnAfxRoAMGTKcBh5TtsxM0pwLKNK0uxBirAA26+Gkc/KsCwsm6CPnQqwGQggH7/pirAwH+SRk/dKsD2uBEZzBIrwMSeYR07RCvAudypzkV4K8DWNFxmpKsrwPctyM5w3yvAAIIn7E0RLMDIFVqOdkIswOukDealcyzApbCqI2ejLMAdmpMKgNIswCxvdlp85yzAkvqb1ikVLcDmAvckN0ItwOtYi8XtcS3ALWeV+YydLcBOMYi9lcgtwIW3dvOM8y3AuF1kBUsdLsCqICeFbkYuwN1JoYBDcC7AO/lVCh+YLsB+fMVdZr8uwIo1Krci5i7AJOvPDzUVL8BJ11X5zTovwEI5KwyzYi/AfA0kyDaHL8CLGVJSKqsvwOeohtlWzi/AVSw18D7xL8BdPkdZ2AkwwCi7/dq+GzDAK+sM4XcsMMAdK9AW7DwwwFgdg3pdTjDAowdo715eMMCc38uayHkwwCGXMJMpiTDANpzG6IuYMMA9e18Fw6cwwDl7g9yftTDA9RfzN3bEMMCpOYSSHNMwwHNc0eVi4TDAzAJ7FbHvMMBNHHxR2f0wwECwUIYwCjHAShWKwggYMcBOOnwVviUxwHCvKgS0LjHAasyBBiQ8McCgchEZdkkxwDIJvmdmVzHAwGkrG39kMcCOwa36gHExwBJoMv5vfjHAiPisdlCLMcCrez3ZFJgxwNA5uXeGpDHAXStQATOxMcDVckkyzL0xwPTFb0tIyzHA8vGdllnWMcB81QxZ1+IxwJz882dJ7zHAMeycfcj7McAKrjEuQQgywCghVsukFDLAkpLpjx4hMsDBUUOGmi0ywM6qee+9OTLAa9AbZFRGMsB7TbbP7VIywNGlPIGnXjLAp8qN4lxrMsB+ZMHKjWgywDlDwoPGdTLA3xosLaeCMsCNbsH/pY8ywAbZ0uGynjLAc9B0neCrMsDdeuZOI7kywKsKkkb6xjLAQryxg4LUMsA0fnwgJeIywK64MT7I7zLAkJ5n9bH9MsBkp8VaxwszwGN1P6EO/zLA64PQxXYNM8CH08XqCxwzwKs++qOMKTPAGr50YHY4M8CGhkE3nUczwF99VOMFVzPAC65nq5BmM8CSDTBoVXYzwHI7RcyYhTPAKRk2WcaVM8BwaQnSKaYzwDCtInj4tTPAvIepdkiwM8CpPhbfa8EzwATDfX7l0TPAQroIt4zjM8DWZVQTbPUzwOYIV2KbCDTAHRZSoQgbNMAoLzfpuC00wEm+H0aMQTTAGEbST8hUNMBv5BJsTGg0wHC0tUJxfjTAVMSpxIWSNMDqPRm1FY80wM6ZDbLbpDTABR5rosq5NMD470ZOCc80wJpm/tSl5TTAm1uDj3z7NMCktz4dnBE1wKSTv4oFJzXAlr0Pk7I9NcBtbS9vtlQ1wEQilMZ1bDXABpEUOBKENcD23O2e+Zs1wFpSfg6RwjXAeETR2w/bNcD5fh7F1/M1wHzGlSKiDjbAN6kWgP8nNsCV5z5lnkE2wK9eA81nWzbAXgBIrZp1NsAaWQQAGJA2wDt22tvBqjbAkhHc9sXFNsDMYCf+DuE2wEiHDapz/jbANAyIYwIRN8CR/qynHC03wEGnq3Y4SDfAnBGo6cxkN8AS6AwkoYE3wF0f2PhMnTfAUUds8JK6N8AKx46JEdg3wF7DjEL+9TfAL4rbzekTOMDw6T9zBzI4wD5B/ogzUDjAPYZ6y7ZuOMDzGG01tos4wM5I1rXVqjjAw4TcmuPJOMAE4S0hD+k4wOJRhm8oCTnAxTcFHagoOcBh1sZYRUg5wMRQ7gmyZjnA5QMgcJCGOcBkj2QVgaY5wJ7rEg0rxjnAKFRKyFDmOcDsn1JyhAY6wL0BUdUdMzrAaXCBv29TOsBr5Wkmz3M6wEGuaK0xkjrA0XUwW5yyOsC0iKsqEtM6wGaYfdl88zrAbSc2sfUTO8B0xXBAaTQ7wFMqCiC0VjvA7aWZUxl3O8BzuPf5dJc7wDZ2UjCwuDvAeIL0lsjoO8Co4ATK7gg8wFv1I8OrKTzAMx6HKJ9JPMDewNsYeGk8wCJSMbX4hjzA/JtXyommPMBwDUkb/sU8wDEkKHXT5jzA32IkbPoFPcAmoMcd+SQ9wJdkRB0SRj3AtfzREbBkPcAyVGDHTow9wA17AqQGqz3AF+JO9PvIPcCfyqkkueY9wINr9HLUBD7ACpDSZxIiPsB4MfFlDj8+wCzGlQWbXD7AG4+M8wh5PsBG3Gl4NZU+wGlnc1vesD7ArALkBGrMPsDOXDUgt+c+wGTf75mV+T7ABNOx1zgUP8Bc7D4WhS4/wCvvS/chSD/AWVqIjrxhP8BZTxKd+no/wEpa8HFAlD/AH8uIZ8asP8A7ltda7MQ/wLQ/d/g43j/AiTesYpL1P8A18AVRQQZAwIDOiP68EEDAa1UTfMsfQMAZ2x7boCpAwN9H5D3SNEDAFFFvszY/QMBnaNvqZUlAwMBIqqSNUkDAG1aMhUlcQMCficDAyGVAwGni76D4bUDANCXDyQF3QMDLI6DvzH9AwH080iQ8iEDA+h8dlYeQQMBcsDe/OaFAwLJzC2ekqUDAfUdvOjCxQMCHFG3bd7hAwBBQdD2vv0DAW5cu53TGQMBaA6TS9MxAwAvPM74N00DAhRqbGgfZQMAjhO0/vt5AwFZ3kllx40DAfQZNRZnoQMDi2i2tfO1AwCE2JiZh7UDAkLDNG7XxQMDnA4RSwvVAwKdhyMcg+kDAUNtEv5r9QMDVinFyzQBBwD6g5m7fA0HA6iGz7XgGQcCRgydlxghBwOYs/t0wDEHACr2GvOgNQcAM8f8ZUA9BwMnBM08UEEHA1ZsSvI0RQcD94ixdBhJBwEov2KzrEUHAWhfv28IRQcAH1iRSSRFBwPQErijCEEHAKK5A1aEPQcDJrOajKQ5BwAAxyoqHDEHAy7J8ImYKQcCfFvIe7gdBwOA7e38OBUHA/6Yl3+YBQcBoIeExSf5AwJR0K8ok+kDAgA0/W+n1QMBjQMIGUvFAwBcmASaM60DAPPlvhjfmQMDnqR4vhOBAwJPQiHyj2kDAXvqDTCjUQMCIS281Sc1AwNGm7mihxUDA9CYxwfm9QMDQ27gI6LVAwBT+UY87rkDA4bRFqlilQMAI77SrDZxAwOtFgS4nkkDAg1dezv2HQMBGdJLiYn1AwE4dQU/7cUDAkMvudHpmQMABgg0JhFpAwO2PRAVVTkDA/HNlUW5BQMBxmuIGCzRAwKkWBI2nJUDAvaFPVhwSQMC7ppo8OgNAwP2WILnc5z/A5AQmfgPIP8BWl3S3F6c/wLPMOFCLiD/AxWFEJnBlP8Ap9nnWMUE/wLj2z/MvGz/AMPOL66v0PsBzB8Os7sw+wMgZdtF1oj7A3KJv2kB4PsChc/P7JU4+wAe3RPtNIj7AcJEVc0n0PcAeEVtk8cQ9wHzgTXVnlz3AaH82/FNlPcDMHAia3zE9wIKl5exn/TzAQerK5wzHPMAHRk+ONI88wJwSchapVjzA4JUrfcQbPMD2tOnYUN87wKVpaQujlzvAsn4/ve5XO8CSM024mBY7wC4SRC4w0zrAZMqZmXiOOsD62hh/CEg6wMIUgIMvAjrAAyQKxDe4OcASulAta2w5wDde6aOEHznAoaJseAjQOMCgEILzt344wB6c5CgaKzjAb44W5ifFN8BDdoVo8G03wFAK0LItEDfAPQ8q2gu1NsCV10so61c2wD8kHjKH9jXA113eFWCVNcA974WyOjI1wD8dvP3hyzTAAMfR9LdkNMAvfGFPgfszwKaH1RQwkDPAJj4kxPUiM8CVYm7/ubUywKJD6OjwRzLANMG6gL3UMcD1kNzkmV8xwNCCr7+m6jDAU6fMt7VxMMBTRFl22u0vwJG/xoHd7y7AyW1ElQHzLcCkwhRgxPIswEdiAy8D9ivAZGjdHkPvKsCtoJIfiOUpwKUbhh+V3yjAIBDm1TTQJ8B7WnWUPb4mwGy5p8WRsSXAQaFdT/2aJMDtVE0KVoIjwIIz/DJHZSLAFCYI+gJJIcDDRb0jWSsgwNi9M+SCER7A+odiuKXRG8CcbUSBaZAZwFuHMh0MYBfARM7d+n0WFcC5IgQkh9MSwGNic+3bjxDAeMaVQFScDMD+leDmfBsIwDZYp6GOpwPAPBcTbAdf/r+97N4Vn3r1vxjbRO5tPem/SOTVI7C7zr9KCgYfUHjTP2BI/b3xWOs/ZfxeFiw69j8dPdyVL37+P4QJi+izUwNAp1rN6f91B0Bir0hCdooLQOqofwl6ig9AyLIf2ZTAEUCrYmQh+7MTQIFRlhqEphVAwi/zxQOJF0DrsYUNdmIZQC/HFXROJhtANPkhcUftHEDOUuQOu6oeQAAmvp1+8x9ALdlLMYrOIEAFhqHhQp4hQIdrbrEHaiJASYn04F4vI0DMlnCIie8jQONfCJQCqyRAvLnIxIxgJUDUbM+/oRAmQLoPMLIjuyZA9XGGD0pgJ0AE3BMq/v8nQIWPr6QAmihAQDhl9iFHKUB14NUwPNYpQNXEtZkFXypAxfPnIBjjKkDuawnyqGErQDf4drHR1CtAO4UvgpRILEA2ZUsC2LYsQNGwqC26IC1AYbYnmiWELUAoTnYcG+ItQD9f/HNSQi5AygpxUXuVLkCsL/SwFOsuQK/cXoOsLi9AFx1JBc5xL0ABkd3qoq8vQIboahvt4C9Ap/J+5yUKMEBA5v9yMSEwQHYH32AINzBALADrreVIMEDcQH+uJVgwQJAXp1ROYTBAHZk00WBrMEBDrU2823IwQLRpTQjxgjBABP29xEuFMEBZRidtD4UwQOcJW4xFhDBAeDHBjeB+MEA2dw/X4HYwQCoWTx6WcDBAW5ZhhWNjMEDWpuVFlVMwQHKZ3rm3OjBAj+y1+7IlMECWXXq1DA4wQHl6rjxZ3S9AVcD/MkixL0Ak/smILnIvQB/z4CtGMC9AjvbQD5jmLkAKmIaTm5cuQBmQkRwMRC5Au6M94z/qLUATwyvy/ootQO8AKwxbKS1A793Jklm/LEBozy0F7E8sQHS3yeJ03ytAx8VnrAtlK0C0XSB8hNoqQGNaB+P4UCpALfbvmQjGKUD4C4memzUpQE1+mv4TqChAyHyex50MKEBjv4GbuGsnQM4pM48cyCZAj2FPDzYcJkB1qmjx6WolQJXPwnOovSRASyJooJ8BJEAnb0C3WEAjQMFRdZmxeyJA67RL0yCwIUB+Nt34hN8gQGwcje98DCBAwXGaEQxkHkCpv9L2mqUcQHChN9tl4BpA6BdL+MAPGUBLIm11bDYXQKvXJjA7TxVAQEJ/cDFlE0BMiH3YbnMRQM2yMiWW3w5AF+yqFI6pCkCHmOKDUJsGQL5GUGcqhQJAevT+7V28/D/9OIovvVj0PzFuWlWqrec/rJfNVARjyj8X8U1MdzjVv+nADFMB1eu/zpcZS12f9r+14DyMOF3/v6bAj2VFKQTAnIHtjOqOCMBmJyD+CKMMwF47QawveBDA7TArsJurEsB9Fb7YP94UwJydv/hRAxfAGfGWYkEzGcA0EulFyGAbwCXEcU7/mR3Ac4bJOYXBH8D6w1BQqPIgwC3he6Il/CHA3BiD8vIJI8C74P01UBUkwMSf3AdSJSXAS11l8vIqJsBLRmBHiC0nwKGIJpE/KijAUfxaNTYmKcAeSeV9mB4qwFqsGhLfDivAWWi9m7f/K8CYsO6npOwswJjKpWgL0S3A/zUIu7O1LsCjeZC/KZYvwNb+hW5AOzDAFVz7nm6xMMBbSdiSEhsxwHa5jiOvgjHA7KUQ9eTnMcBsPCkG3koywGjNBG61rzLAynDNDTQOM8C62KIIbWozwLnEPqXuwDPARznk1JAYNMDWiDwE6200wHsQLexzwDTAlZLa+FURNcCyKATkzFc1wLDe2C36oTXADB11njTsNcCJzoJcNzQ2wMD6SeefejbAf/itEEW+NsDZ2LFCyv82wCJbREp/QDfAPo6yx8F9N8AwaxXa5bg3wNc+ZgKZ8DfAb64IsKgnOMC4+5sMrFw4wKE926k4jTjAvLU1ODi+OMBUJsVGP+04wMuJNhIWHTnARYFVAUZIOcB/MmUfkXE5wO4THq3NlznAx8bOZWK9OcDs3JY+GeE5wEDC4f2PAzrAQHPiUKQjOsCLFqDL60E6wBOg5dljYTrAHKkAvaB6OsCwC4OloJM6wE9xsbYYqjrAvUaqA6W/OsBy7MxFeNM6wLXEf9Ng6jrA5e4Gitz6OsABywM6qAk7wDRKn4YhHTvAIusOf6soO8Br4QhsjTI7wJyoSi/fODvA37J4NXA/O8ClT2IY5Ts7wMY+4U9rQDvA+4E/EhFCO8D2SXHWFkI7wNHinrhKPzvA58u7EQw8O8CiQmX8Kjc7wFbnHzvoLjvA/PsdSMAmO8Dw4Ydd9xw7wPACoCkSFjvAqadtEgEJO8B4fQbhRvo6wF0y6REP6TrAN7ggWBjXOsCA8ZxLfMM6wFRwvtDtqDrAo/UtbuiROsD6mrLpK3k6wG6L5R+mXzrAbxICXXtDOsChCLk4nCU6wNE4vElTBTrA68OLpvDjOcAdbvYKycA5wLTjqUqxnjnAWydnu0h2OcA0x6j50k05wCstsztOIznArLy8rzL3OMCTZKLqP8k4wK3IyI6qlTjAv8XGYPZjOMCSZfEbZDA4wAa8Fhn9+TfAg1MzupnCN8DOH2mdTok3wH0psNHoTTfA5FU6FLwQN8C1k/quB902wE1WzgJroTbA7tB2qXxeNsBCBspJkRk2wEA7LJOT1jXAnLGM6b6NNcCjfohx7UI1wBIRwdBQ9jTAfMuR04enNMAZtaYpwlY0wP+g9uzDBTTAO6pdEQaxM8AZVpaGVFozwOxvtuB2ETPAID1HQuK2MsBNut+UW1oywJUwI/qu/zHAJOfLZ2OfMcCi53p9Kz0xwIHUvqeh2TDA+l9oFbtzMMCxfpQ6+QswwNJlU/MgSS/AoHaqv55yLsBtCrR7oZgtwKtJBoK9vizAdtXzm/vbK8D1l05kDvgqwFeYcAqIESrAQODYZIEnKcCF6rsPhToowL3i3v6tRyfAxbFNqxxVJsBeRXle618lwAdGXLvaaSTAiskDadlvI8BtJ+xCn3MiwMVmeHPxeSHAawi8F8B5IMCANslTFScfwMa3sqzwAh3AywFO/vD4GsDl4C9CLewYwCz7+s0K0BbAqDYq4bu+FMDECq2fmasSwJcjIlAgmBDA+Zz3+jIFDcDCalNrL9gIwAzY0eD8mATAyA5yxilrAMCKXnaCYXv4v0l3ajIFAfC/8QXAV9es3r9ALsgjlLikPxZder+tIOI/q+yxqhxV8T/aYSozl5H5P1oITqRtzABAFrTSrNDgBEBp+YK5Q+8IQFht/U6x7gxAIaY4REd4EEBjxc6QjXUSQBWrSG4ndRRA37kKpktYFkBfQOfRS0kYQMGBek/yMBpAGjZSvr0YHEC5O6Uri/sdQB65ePtR2h9AjbV3V2jZIED7Y4sb9cIhQE1vuLNSrSJAJGsc0DyRI0Dy0oGuMXIkQH6e2sh8TSVAdJWS5okoJkDjAenKW/8mQCqYBIHA3SdAKt7INHWvKED7mBEsBH4pQC01uaT4PSpAlwfkhg8GK0CcWF087sorQHpFJa6UkCxAb4jp0t9OLUDjx8DY2gkuQNsXSLe3xC5AmbWN9B15L0AujqFnHhUwQLTqIHj+eTBAhKt3ejDPMECmb8rLySIxQKVbVOkZdzFA+4LBT2XHMUA57Q9GCxYyQMdgA54VZTJAuQCoI4SwMkDamEVjUvoyQMwkHL7gQzNAx01sl4SKM0DE8zMimM8zQB1NuV0HFTRAhs8xXwVONEDPjfaJb440QMWznarazTRAUYuc9z8LNUCvK/jpIUc1QH01IUkbhTVA6sRzLwW+NUAC4YRkdvU1QEw/IQ6cKDZAmOZuLjBdNkAMr2vKT5A2QE1fdWf9wjZAyxef8E/zNkByo/WQqh43QGFxElHATzdAKLCNEPp7N0B/g0a82KY3QE8aJtj2zTdATT8KEzT2N0DNfgpDIR04QMtKQ9R4QjhARxm+GtRmOEAIx+iu4ok4QEzdd9qyqjhABh5n2kTLOEBNA6JQkeo4QLZjUmciDjlAQ6MCKQArOUDf2z9/okY5QGM9DBORYDlAo0sTP8p5OUD4vFoI15E5QIs/WX9tpzlA1ZzypSW9OUCp3Y8lrtE5QIfy4ksZ6DlAoURHul36OUDuFbp3cAs6QOlaYXfOFjpA523RDpQgOkBuKGC6RS46QDriuN/YNzpAh4zrJlJDOkCzRm6Mo006QHxo1uP4VDpAER9HOhhdOkASwVAjGGQ6QFXE3/cqaTpAxqZ3s/VtOkDuhtDNpHE6QJr4S2MkdTpAqJ1tNKd2OkCeoXQqAn46QC7mAOyTfTpAo+StOLx7OkD7bIAn0Xg6QDbtmmagdDpAMryXpnlvOkC5n6zwLWk6QHNqYY8BYjpAm3UCnoZZOkCFLE6q7E86QAcLhixMRTpAhKUONWs5OkBDNOSHaSw6QDWYb+xeGzpAZCRucRMMOkBEpQ2HpPs5QBHVHYIf6jlAbIHIc1HXOUBM1oHqXcM5QNT+e9TdrzlAD1uESpCZOUBvsOTlFYI5QPtIdBHaZTlABtWKIu9LOUATNcZ4yTA5QCMGZKV6FDlASlgYApX9OEApF0rMu944QHu0giBjuzhAESjdeweaOEC9WBWxZHc4QONAFb2yVThA5TEkK3kwOEDniXVl9gk4QFGgRI4o4TdAMvoQtAG4N0DAof1xgI03QISe3MAHYTdAK0ZFHswzN0Aj3xY85wY3QFVdIGIu1jZAkcFQDcqkNkDz8HaF/nE2QBEEBDILPjZAM/2/hFgINkBfBqEIN9E1QG6lzCAZmjVAjiv4zBFgNUBPp8/JkCQ1QMMvLl076DRAwNndlsupNEBRp2wT1Wk0QGA1uS7LHTRAe1j619HaM0BgVZhATpYzQLGyBgOPTzNAcsEEw/8HM0AKfAT76b4yQBqItvBxczJAIpv9qksnMkBsRTGpm9kxQA2zeJhFjDFASjoud307MUCERG1ULukwQGsKsKO2kjBAuPI83Kg/MEBb5BWum9EvQDIxY/0SJC9AhWzPAEhwLkBJ8T8VkbktQDN12ggk+SxADpyCmXw8LEDo1FicA30rQDCbCBXovSpA/mCSbqv4KUCfpNHMtTApQPrNfpOtaihAyG0xwlSdJ0B1Hbbk5NcmQF1wOwoQASZAmAStlBwsJUCfa9+Ny1QkQBqp+fN6dSNAmNItW5yZIkD782Qon7shQMXdXqqw3iBA7n37iEz5H0DhIcnQmDEeQGB85s0lWRxAHdYIQa2KGkD7bO3EH7kYQC3S/PG9thZAVGMGaP/fFECTLkmowQYTQMyMY6OZJRFA34IAGSeRDkCJFa0xGtQKQKoGOIHhBwdAGeN5gmlGA0C2ZmYzawf/P1KJbRm3pPc/YJScjoIc8D8kcu3EbCrhP1Gr178U07E/47g7YttR17+z0THjD6Xqv3OUr6b0tvS/pOJUh5cp/L/Ce1FMccoBwJrNCvtQfAXAnxhHfCcoCcAITvqqUc4MwJMgw1HqNhDAam4Le64DEsBmx/0Kz8wTwJvjm31plhXATwfdG4NXF8B/eiaw1AkZwDLlNcVwuBrAvW3p78trHMB8Bop4KhoewDLgCTsQyx/AV+j+uD23IMB4b7rQGYYhwHuEpMVpUSLAt49VNkAaI8AviAdf+N8jwIZMJiPHnyTAjS+C9v9eJcAqRMwr2RomwMgMBN2M2ibAjenUvY6PJ8AREUo/9kAowJ0J+6yP6SjACFk63LyTKcCS05D0PzoqwLFpnVxw3irA1IJ3jWt9K8CYw3EhkBgswKMDirnWrizA9QdAPlVCLcBIaL2H6dEtwNwMDL+7YC7APRqvN5T9LsC5o9HsVoEvwHrnI4vYADDA6XChN8g+MMBvCa2yvnowwLM4HUn/sjDAt4pCXg3rMMCfsNEZIyExwNPbYkaNVTHAX/fZ/bWHMcCEkiJb57cxwLZWVlq85jHADyhPWAkTMsBK/Yh+9jsywIJGvaWzZDLACYyiwi+LMsCSNGvxxq8ywITfD34+0jLAw9W00Q3zMsBMSKMq+xEzwPNG6xawLzPA496TY95KM8DkeCLNOGQzwEa1orqWfDPAAXA52juSM8BWfv61G6YzwL/gq5vZsTPAhvp2ajHCM8ACZxQ/yNAzwCQWgVtl3TPATjbvMnfoM8AWn+gyzvEzwLfmPEu6+TPAkbbv+JL/M8BjE7D3vQM0wDujMFn+BjTAqgga7MQHNMBBqFNx1wY0wCm6VTIoBTTAK4puaPP9M8CC/HHSL/gzwEcC/Sam8DPAaJ28aK7nM8ChMewjG90zwBt1tHwu0jPAkI31znbEM8BLwQBrNrUzwJj5T/O0pDPA2GAfgFqSM8BdLIl7dH4zwCdwOXk2aTPA+5vXO1RSM8Bnsh+CPTgzwDXDz0C0HTPAPhyj72cCM8C66QIzouUywMk9X7n2xjLA0l3RilinMsBZ4DokUoYywHYNtEuSZDLArY49MNpAMsB8QJ32vhsywFM+w3GS9THArPTa9tbNMcBHRNcozqQxwMBJAAZyejHA50mpZd1OMcAWPtd1CSIxwGga2FF49DDAV3ZLijnFMMC5Xamxw5QwwOIiOWuqYjDAueGxJ+ovMMCJc4e3HPgvwGb1Sq1fkC/ApYWRIG0kL8CNJeiAabYuwGV6oTclRi7A7o29PffULcDS6xgNFmEtwCN3GmfV6izA5brEdmxzLMBbIA4SVforwIPv3XPXfSvAo0gklbMBK8Ccbz3+J4QqwHuicI3UBSrAQgGnabmFKcADIH1vbwQpwCgSXzyogSjA0fj3Zz7+J8CNmQi1WX8nwM7+vBlF+SbABMKsQ4pzJsA3ILeeL+0lwBZ2dH1WZSXAxWELtRPeJMBMnijqjVYkwBs24+yQziPAVHze3udGI8AUmjbkML8iwFeHo+PFNiLAOQaQjpevIcBzHBcQ0CghwKtpSo/HpiDA8EZ+eBMhIMCet4alQzgfwKR24dCgLh7A5uiLIowoHcAyJTFQgCQcwNOqsCP2JhvAOwN3SiUoGsAK2s/XUCwZwLmMo35JMhjAFp52GvY8F8CAoLJXL0sWwDh+veSfXBXAd1OgKUKSFMAVFvLSb6wTwNjpPGP/zhLAmU9W9n7yEcAfE5Uj1xoRwPkDVqsISRDAEtlPCoL3DsC0dMmR12cNwAXjQgSJ5QvAizP1wz1tCsC93raaNQEJwJ8xgoo0pQfAkXP8SVZSBsCicqeUtkcFwPKazQo8CgTAXeFCgsXfAsC+YsdYb8MBwHeKbht3vADAN5EYknJ7/7/vzAilKJz9v7kv3abKy/u/TAMYPRcq+r9HOEEn0Kf4v8UTIWf1Pve/P8BFUXj99b9Nq8xin930vzRftUrsSPS/bjYk6NJs878drdes57Lyv0GsREbE3vG/uci3Mmtr8b9PuQQ26Rvxv4FwPftm2vC/dJJkV47T8L8ZyR1MX/Hwv9yN2i9ZDvG/wFpm9It28b+wJTqDPgTyv8H3DqiqyPK/4WedjUZX879+GUWf4Vf0v/vDn+2egPW/Ph5FTxrO9r8otls9vkL4vzi7YYNOpvm/AoFqgvVo+7/oKzl02lL9vwkvOHuIZP+/VtrBsbXOAMD8sCXhzf4BwHbCtORlSAPAhqmjk8WfBMDQOgM3TzoGwA/KToVytgfA0aeess9ICcBojS0vme4KwF0InJouowzA5jaXpY1vDsDlH9racicQwND+bf2rIBHAbgp9wA8jEsBnlzEOkS4TwFd0czbaSBTAL8VTj2FmFcCc24gO3IwWwJp5mXKeyhfA6OLX7SwCGcBIecHh9UEawL5f0edIhBvAg8KZGGbUHMANgolxOiwewOpASXy1kh/A0uzAb6F8IMBCN8SZhjMhwIZDRSmF6yHAnZXCFRGpIsCRjBM1zWkjwCv3w+sSNCTAuIyQBmr6JMBArcCuxMMlwJk+YSS9iybA13AIriVaJ8DI4Zxl4SoowJUDQ/UF/CjAarf7vtDQKcBWkOqxZqcqwOXSKEkdeivAz/hFMrtTLMAo370DrS4twEntWzAgBy7AU4uMeSXkLsAzqs2MxbcvwIXcK48uSjDAqQVRWp25MMAopnDtLSkxwHsnmhk0nDHA0m8F5MMLMsDz2pl+NHsywJjV8OSm6jLAzW23fa5ZM8BYWJykV8gzwBSfDiS2NzTAs/mRtYelNMDkSwc40RI1wBRgttXagTXAPCy1ltztNcAjXNYsIFk2wEyfw8wJxjbAtiScM60vN8DrXRF1a5g3wF3yk0LIADjANfeV0p5nOMC19obQb804wEqTqHtOMDnAuN612vqTOcCRe5ddh/Y5wEoY70i2VjrAo/hOhFi9OsBCgSLCZhw7wHanszQHejvAaNpappXWO8CvjNvg3jE8wHVToR7EjTzA+/0L3HnmPMDSvFXw5j09wD8hzD7vlT3AO5ELBLnqPcBJ0n4oLT4+wBnvHpEGjz7AIVS5d9PfPsBbH9Du0Co/wEuNy6+jeD/AOVVy+YLFP8C+LYGliQhAwMYlM/I9LUDAodpYObdRQMBJeU4hinVAwEph4GK/mEDApHulKUa7QMBf7hAZL91AwLwXLu9H/kDAhRr7Zu4eQcC1y6IC9j5BwPrrlIWCXEHAgg/hH097QcB4IgUxiJlBwEudl5hot0HABOFxnmrUQcDAfzuv4PBBwMSI6TzhDELA8faBvTMoQsBbtNwn+UJCwDIQ5NTqXELAxJrzb6B2QsC4W+7fy49CwDA4EwLAqELAg2cQy4TCQsCwQAjDJNpCwL+cU77B8ULAy/9ZnmQIQ8Ap0QhDjR5DwPXaVlJlNEPAPqON5ZhJQ8CqzD8hV15DwLqyotcHckPA0v2iJuCFQ8DbOzztSJlDwPlG684irEPAaHLZMrG+Q8BhqnAfC9RDwK9LbqG25UPAP3hLVw33Q8ClB6n8+wdEwEqC9kXaF0TAibaApQMoRMA/d5OTyTdEwDhbV2ZVR0TAYM+XIl1WRMA1TWWTCmVEwGcub3f2c0TAfynOZ+yBRMAMrZa4jY9EwFfAn1cVnUTA9I4qqgyqRMAgLhQCrrZEwAGaLsVowkTAREz+JWTORMDoBv41E9pEwIqQGnJ25UTAk2gzb4nwRMDD4clDSPtEwNeCsSPHBUXAAJ1qTPYPRcDdUJHW2hlFwILfrm9rI0XAKyP3IsgsRcC2EkBB2jVFwJwodQ0uPkXAVwZHIrZGRcCMMjwF+U5FwArQZ2/UVkXAdWjmQJNeRcCG6FWVFGZFwDvfCAmnbUXAlMou8ap0RcDsxQGycntFwLZ6xzhNgkXAAYR0BpuIRcDVZjtP341FwAAriHL5k0XA2dmKyZaZRcAxmgza+p5FwETgfcLdo0XA3wuug9CoRcD75CDIia1FwDL46dgKskXAIlCalFu2RcBzp4Gdc7pFwGlN3CNbvkXAyFeQFg7CRcCr0I8NksVFwAd1523lyEXAV+ChMgXMRcARYkY78c5FwPpWppnz0UXAPNX8NIPURcDLKWHL5NZFwDneUZYI2UXAEFF3KAnbRcDJbOjV3dxFwKBcvW2Z3kXAne7HhhbgRcC0XGHjaOFFwM+sOAr74UXAT6cHguDnRcBz4CXds+hFwKEr8HzV6UXAtYsmqlbqRcAsbQ5trupFwB9DCrwp60XAxD6mKTLrRcBTR6HoEutFwDRUr6xI60XAowm70NzqRcBi6UvtSepFwBJOMAqL6UXAKGY4u6voRcDwK2Tt2OtFwOuwd52e6kXAtpjGbFDpRcAmirX14udFwLLEDdjH5kXAEIHDORLlRcCcmeKDOuNFwEI3ER2v4UXA+yVPSJPfRcB+a2Q9V91FwHo6ordi20XAgLGGnuXYRcCi1pY3SdZFwJ0XzRDw1kXA6nUBwhTURcBbWO6wGtFFwGNCk73fzUXAtYUSKarKRcCTsGOzVcdFwDCED6vMw0XAO0gIlznARcA7v5JdirxFwErFSWTFuEXAxk9yjNm0RcAfyJ3N0rBFwHTg4erTrEXAMYEfEA+kRcB3gUX0s59FwDa45tnzmkXAA4eI6GCWRcA68rnysJFFwLWQdYUmjUXAPie7I0CIRcDx0FBCRINFwHNSgoryfUXA4eL9aLx4RcAxohkYbHNFwOmJmSu0bUXAoLBidjFoRcCKK6vYYF9FwPrgNyJrWUXAhN/NPpdTRcBx6cb2p01FwEHvNu0dSEXAuqUM6PpBRcBeHqv0wDtFwKCr7mzvNUXA8ldKjH4vRcCvuN4w9ShFwMClr9QQIkXAK+ra51YbRcCWwY2AgBRFwFgWORerD0XAbMmmc6QIRcC0tShxgwFFwNHPRgym+kTASID77k7zRMD4hO8I4etEwIj1GFz65ETAEGasRFXdRMA4MwxDm9VEwBulzsu4zUTA4B7LpcXFRMCqIooJvL1EwEH8oU3etUTAaov7TI6tRMBHMpnFLqVEwPlueTQJnUTAZb9Et3iURMD8Fth7zItEwCHaSTPsgkTA9SyalAt6RMCeMhGWDHFEwINaK7ioZ0TAz5pNqHBeRMAP7knsG1VEwDJIPTTSS0TA7DMI10RCRMBSfCRUGzhEwHEMMcVpLkTAyUucvX8kRMAA0KNyeRpEwK/8x4JcEETA1zNtzhsGRMCbQqdUvftDwCs/8JgG8UPAfV7AG2jmQ8D/CqGnqdtDwJbeI0Bg0EPAiBdd6WHFQ8BW6ZfLRbpDwLrpYy+qsUPAr8ojgUemQ8BIXZU5xppDwO8PycKcj0PA70dlFNODQ8CXRMlU5XdDwELlZWqea0PAwVawl2hfQ8D6Ur8dD1NDwB5zdlgPR0PAh/wVL2o6Q8AtLbi2ny1DwMlZ+UoqIEPAm3q1itMTQ8BFpcFMlQZDwDKg5Ocw+ULA0DgbQaPrQsCJ64iN7t1CwGOd/eTdz0LAF6b/kdnBQsD/hKj6p7NCwAkl7dKPpULA1Rvi4giXQsDD2EP1VohCwOd+xztWeULAUrayxkxqQsBaUN3f6V5CwMLZbedHT0LALGH52bE/QsDMjjzj7y9CwC2zmrEPIELAzEgdRO4PQsDU/b7onf9BwLoBR57s7kHAJLDNfDbeQcDFdcHcUM1BwCm/Xtk3vEHACA1UjOqqQcAaMwqYaplBwPlc8IEnikHA5rAefTx4QcBdx++SHmZBwEs9QMhDVEHALMKHO7hBQcCFcHDv9y5BwN9Dtp7BG0HAXfHtSo4IQcCuqewPJvVAwGtGH3yq4EDAmNBvo9DMQMC0f0hKt7hAwN/6zcwApEDADrWVh1mPQMCO0mwVkXpAwIvum/eJZUDAYTyAu0dQQMAVIZnIxTpAwIR/El3RJEDAkD1LTNIOQMBgFUhuMPE/wGYvwGhVxD/Ajw6PaNuWP8DvXctO4Wg/wCeHkU20Oj/Ayy3hXroLP8BlzLBoo9s+wKTLzzYIqz7AF1TdRH16PsCfL+r/ckk+wMjwbvqpFz7AKUpYRpDlPcAIuDxC7rI9wApqUNFXgD3AeWJs9qJMPcCjtmMcZBg9wF0YM5ms4zzA1J6mdWGuPMBSrHsoi3g8wMYEsx58RDzA+VWKUI4NPMBwz7BAGtY7wCCaQxgLnjvA0QYIa4RlO8Di5BbncCw7wFGS/j4v8zrAGgaaIwm5OsAQOS1JVX46wNSuljGxQzrA9ytGJfIHOsDZXdNdqcs5wIWR8n1CjjnA7JBg/QBUOcByRNqSLRY5wNtradK71zjAAjORb/CYOMDas7Snqlk4wBt3McP4GDjApChLhbzYN8AGgDeMBpg3wAtN0v3rVjfAkZfOpEoVN8B8/DaYNNM2wCQIF6OAkDbAx4WpWZVNNsBQeM9z4wA2wJez/CAkvTXAkc5Rn/h4NcDKM3IHcDQ1wOx69RoX8DTAtGUVl9CqNMDUxPROOmU0wA92Cay4HzTAWRJ0wX3ZM8DpxlBD+ZIzwGdO1J7rSzPACQ6sCtoEM8DTxBKOjL0ywArODlNZezLA1SLe16gzMsDDsgQPyOsxwJxjgNXRozHAOymCzJ5bMcChT2OSShMxwF/ZTZkQyzDAS4p1XpGCMMCQsStv+zkwwMI7TJtf4S/AWKplICtQL8Dt2WKJA78uwAn6DKQ8Ly7AGEYZaGaeLcAXOLWmog0twAnHdreLfSzAR1FMn0/tK8Ce7iMiVV0rwF13eW+PzirAmYKX7ms/KsBcHm/uvbApwJzCtxd3IynAoycN3vCVKMClEgQDEwkowKv/ourveyfAOKIb7XPwJsBZcln8cHYmwM0WWBg87CXAu6o9L01jJcAsI3sLUtskwAcDyoe1VCTAZMtYv7rOI8DRvUkn1UkjwFMZHaknxiLAvobGGaJDIsCV9VddacIhwCJrtcOgQiHA88CkbvnDIMCustNEwkYgwGMRctn/iR/AcwquOFWVHsDIaVm7taMdwHd3YSQgtRzA1t7EMuvJG8BgDmSKE+IawBNP5D8P/xnAs41xrjQeGcCR0eDS3kAYwMT5gw5lZxfAHeD/coaRFsDuxBp5fr8VwCEvcYJ+8BTA5IlDiEtNFMCkHdtz8YYTwPC9ZboawRLATomhbv8CEsCf+isRREkRwFVtG45ojxDAJIzDzyK8D8CT8WkDV2IOwMFIPAsxEQ3A/AgstuHIC8DZuFx+6YkKwEVXkHiLXAnAkl8jsFAwCMDXj9N2kgkHwGWWSwID8gXAXayHl4DiBMDT7bpggdwDwDShuiUS3gLA/UJaiozrAcD3p0md/QIBwAe7l0BqJwDAZ/py+Fel/r8mdZW7nw/9v7fCq4Qzlfu/7KiUm1Io+r/A1Hz3GdD4v9jU5qXNoPe/qBG/ze9x9r/qtvyM8Vf1v/X2SFF+VPS/wiDlguhj87/tO8RF+Yjyvy176HGzvvG/EWIRidQN8b/2lTjuenLwv5rHr/+D8O+/BTlOffAO77/S2o6eHVjuv5wsE7VFqe2/xEc/RAAq7b/yTaJnpfXsv5CJ6K7Dxuy/XJ+QB4rn7L9AjGSPrDPtvwQaHKvF5+2/VgIxgD2M7r/0zibRxFvvv+hxgoM8G/C/lMFhpL+t8L9Ekly5Hlbxv0uI/lkVEPK/lCqtrhDk8r8Cvv4fu9zzvzCVzxVg9vS/W/7sgjgL9r+TMq4jcDX3v6I5lud8hfi/EHtSJZfa+b9JIK5C6UT7vwOrn6c6xvy/aQKiJKFb/r/Dw5r39QIAwBrwMLos5gDAwsVjGyHQAcC3KH/IlMQCwIX0puiVAATA+xV7pH8JBcBZhrsQnhwGwBdZzMBaNgfAk2bbIIJdCMAUe/sQY44JwOkRxQps0QrAN60JfEEWDMDUggNFl2QNwKK9kFXPrg7ACvTJJhkIEMBNy7W6gb0QwD35nR6TdBHAYCZDnKc6EsDR5vjBlP0SwGAYrpVRxhPA1iraTjCSFMCwQFLYQ2IVwMlmBc+2PBbAF918RSUVF8CUeMimmvEXwPupA+kB2BjAHeDRvyy8GcBgeIQhMaQawFOFZqBHhBvAeiGfRaRzHMADdVesOIYdwOA27obLeR7A4/K5TU5zH8Ck6hKO8zcgwMyRSPbuuCDAaF+FjTs6IcDDT1hZAL0hwNif4/iPRCLA6DrRpQzKIsDqQDglwlAjwPU90NEo1iPAjSsdNjZfJMB9zetoVekkwFQs7r+1fiXA3w3aJtsKJsCMQPpP5JcmwDlO0bvzIifAeZk8VKyxJ8DQvBreFEEowIKoaxfWzijA+e0f5nVfKcAadop5nvApwDlVGyyDfyrAHqhf8ooRK8C2dB2U6qMrwEv0zM75MizAm6Sp5DjLLMBoCJiGP14twKfRNgab8y3ATsVxz8KGLsB4NQQH2BkvwC3ZgaR1ri/A6VA+IK4gMMBG0LKIDmowwNCuhyVTsTDAYJm12Gn6MMBvEJ4KVkMxwG33T4i0jTHAHkh+fDXWMcBzNbrUCRkywCPm7s5nXzLA2VJszxenMsAjm1G/du4ywGN9AHHENTPAFbpC/Wp8M8CWuIzXrMIzwJHhigrMCjTAaoxPbEBQNMBR3kJbPpU0wFR82c0+2DTAGtSBSE4cNcDWo7if1181wDza2q61ozXAi96Yp0DmNcDbbPfePSg2wF9CEDZmajbAi2s5J0qrNsB4ItovmOs2wHs3cOgnKzfAV4tmkURqN8BfuruNy6g3wN7cDmTL4jfAZ8Jq5gcgOMCApeSOn1w4wM03wu8nljjApyjTEoHLOMDQp0d2JQY5wNKYBJZUQTnAo0LFhKp6OcDtxSPaSrM5wEOjKe3d7DnAeqCCsiEkOsBz1Jh/uVo6wNaitO4zkjrA1c0KuWbHOsDzW2HB4vs6wAl92lZyMTvAURdPW4lkO8C4hueZbpg7wO9sge4ZzjvA4BMfRgr/O8BMcCvJTC88wDRzYZLZXzzAf3LEULSOPMAt8C7X1rw8wDT/C2A86zzABZyZhf4XPcDYXs1tC0Q9wNFgswwncj3AN0d398+cPcDW7af9vMY9wInA9Wh39j3A0mFlFAgfPsBDFhBt50Y+wKfKFsB3bj7AbMTfvgaVPsBNfXxG37o+wPGGJJYx4j7A8SP9lroGP8C9CITFkSo/wI96CH7CTj/Ah6jxzk5xP8C8yUAHNJM/wFbGps4BtD/Aur2NpZHSP8C6DSNOj/I/wPSIfYZvB0DAffvyl8kWQMCrEras1CVAwOP9vr0mM0DAhFX/RJZBQMAHSgR/tk9AwHeyjg03XkDAmxi0KrprQMB+L9ZM8HhAwH9yBeIQhkDArKJWfbKSQMDismCpy5xAwEjAfUpkqUDA4KwbhiO1QMArgfDzm8BAwHjzcXd6zEDAGyGynl/XQMCa8Zd3/+FAwHjQspjE7UDAQAcb5dX3QMD+us8bnwFBwBzooSCxCUHAPPHLW/ISQcCkLMOJ7htBwGxMO7zMJEHA3tK1rkItQcC1rV1tdzVBwIR7zQiWPUHAFVZH60VFQcCbmGc4s0xBwOwTvBk/VEHAkvWLXypbQcANRXkr2GFBwOmqXQdJaUHAieVMvndvQcDUDLs4YnVBwGQsqkK8ekHAWFWcQPN+QcAmcUkvJoRBwC+QMdXuiEHAccDfMqeNQcCLNQGRJZJBwODzuSlclUHAjR3sHWCZQcB8OSlQKZ1BwAUi4ajQoEHABU14WyakQcDsVbiYQqdBwP5YVG83qUHAQxsDP9yrQcDbvV0j+q9BwAZBWMq9sEHAyJHqkriyQcCvKxwLfLRBwKAw9Op9tkHAVUKi/dG3QcBo5DtG7bhBwO2Ng5i6uUHAtpoIyWG6QcB3jtcE2LpBwFzJ7P0quUHAt2wFGzW5QcBnKuTzCrlBwIszz+QXvEHAbg4TmoG7QcAC142osrpBwAqlYqdXukHAJK1TMiC5QcDLYBaLsrdBwCIjMamvt0HAa0jMDNi1QcDCJvF+zbNBwBC19yTCsEHAMiT5CE2uQcA8dGZgp6tBwNBnNieQqEHAf4qxGwOgQcDSaUAFwpxBwAyJwsBjmEHA1cuCWLiUQcAU8ZWo2ZBBwKDyVlyKjEHAcIScLkeIQcC5ewh60oNBwNLzswSbf0HAckrR5r96QcBZpXJUsXVBwLITBxh8cUHA4wF1vwhsQcCgLbfuZ2dBwAI4NRqCYUHAbSYztXZbQcAp071YOFVBwOH4EX//TkHAPNF+215IQcDM4azzjUFBwCZ6o7LmOUHAoQJDwK4yQcCraSPvRCtBwMimuGr2I0HAgFtLRiYcQcBCTqqdJxRBwPkn9IR8DkHAeIajuBcGQcAW+V2ygf1AwMgZcKKp9EDAVF/c1qrrQMAGWXJYfOJAwK02aL/92EDAIIXUIW3PQMBPn6DdqcVAwCURDezVu0DA2adPra+xQMBZ2p4vVadAwL/8roiYm0DALbgt3UyUQMAflcBVWolAwN9zRorSfkDAiBcl+nlzQMAPZGyX72dAwFgl2xSCXEDAwcWgaJVQQMC1Boj4dkRAwAWVOCnYN0DAosuqz1ErQMAT5md7mR5AwB19GNVLEUDAFHkhmy8EQMC4dRxf0PA/wDx75URg0z/AYb+vUvq3P8C80+NKLJw/wIv11dIvfz/AY5JW25diP8DtZ8jlnEU/wC1soWknJz/AyZjPZ2EJP8AneIP/Nus+wKrNo2QJzj7AxU/N8g+vPsDM4j+3tI8+wAQkzcEqbj7AxZd8JP9NPsDaXT6HcC0+wCL+a/Y2Cz7AOK+3m9npPcCVh1voG8g9wAdlQKahpD3A/7wTtx6CPcB49mgzMF89wNGUYmRXOj3Auxp5gqIWPcCcTIYiifI8wN8k4l5NzzzAl6NbnTeyPMA7bctn94w8wKkYY6VtbDzAspT4M1pGPMA7F8DE7x88wIPWLLCw+DvA1aMZHnjRO8B4PTAU2Kk7wETC3vGVgTvA6n8rWTZZO8CxdbvMdjA7wKZPFo4XCDvAaT2I7ZLeOsCDrv0cMLI6wCnT7uQFijrA1hwedWBfOsAaHP4zXzQ6wP2MV22lBTrAj+8SxtzZOcCfU4Npvq05wEpu0l4ogDnATJp8c0NTOcD6IaVlCiY5wD2l9Lq2+jjA3YRJgcDMOMB1zVb8cp44wFF9UyWWcTjA9YI2hZNCOMCv0Kx9LxM4wLBbdZyR5TfAey6phYO1N8AQBhQXHYU3wKt0h2BuVjfAVom031glN8Bwhpba8vM2wBN4Sue8wTbAaL/9EK+PNsAhtgh6Tl02wMm1Gn3jKzbA2MUIzhoANsB9UI00z8w1wEXyXVpLmjXAKu3g8WRmNcDWq9X2MjI1wAKIuwAm/jTAWYj9YWPJNMDzliRAUZQ0wNUZh2iTYDTATWj/svsqNMBraPlyG/UzwHKNq0zvwDPAz0mQWY6KM8AUDBd2KVEzwGiBOkMcGzPAKYV0VQDkMsAsyHs8pKwywJgKKFPLdzLASPIh6Pk/MsBUDeal9gcywEvJT93wzzHAKp7G7H6XMcDMK/WP114xwI1RxprKJDHABB3WqsPrMMC15I+PjLIwwGyzOunLdTDAavpWpDg8MMC6QZt9egIwwF2I5Pu8li/AKwihGqsiL8CdzFgWSK4uwFaTbNV8PS7ABEj7fI3ILcBWH+CzW1MtwOxprNcs3SzAmFP8roBnLMAaqObUlvErwLj2eH5adyvAk104oYP5KsBqRlc7E4MqwI1H98LqCSrAfyZ8pSiTKcDl0UPiNhwpwH+eK1dHpyjAJXeAzSkwKMCOVzLs5LgnwBt9N+AiQyfADx7hAK/LJsDkYT0OLVQmwHOLXZ7C4SXAjQjTsx1qJcAlECCb8A0lwGHKclOXmyTApn54AO8jJMCbAdj8RawjwPkVZyJkNyPAV2qz2bi/IsBuykWpHEgiwKt0Sbn+1SHAdVLvX4BeIcDaMWlpEOcgwGs7NSKVbiDAzq4eK6LuH8Ctcb9rQgAfwK0suBs2Jh7ADgFxAjw4HcDQPO6Vl0ocwLuknFNVXhvAt63E5idxGsAlQYZZWYQZwGlZigPKjRjAxbAIgoqhF8CgySbxoLUWwPhXH2nAyBXAbSXRiqbdFMDS+bKL+vITwGhi+SGOBhPA4PdWw0NREsCHzrpCzGcRwPQGVgiGgxDAu40uRe01D8A01RBHw2UNwGiy6ct5jAvA1HhWqg++CcArZFZpu/AHwEhzLHUIMgbAnV5+sbVmBMAVZmxOdJwCwPsb4tRLzQDAX3A2uSEK/r/YpBOvcmX6v+ZpZ+Gi6/a/XyEVe99h878jai46srTvv/zYoW8L0ui/bL6ICPPK4b+ZRXLp7pLVv0JJ5SLyab+/Ga3RXetfuD8HNTCrhADUP6CZ0TeaaOA/XnWwXBVT5z/slwXqijnuP/baYmpIm/E/XDrLZ18J9T/nBB1vSnX4P8Bcml9s8/s/sdHUecta/z8exzr14V8BQEOnFmDM8AJAvowPwfKgBEBJT7Vh5k8GQLIfc/o8IAhAhiQznEjNCUCrxZp48HgLQDFmaDmGEQ1ASODLs1a/DkAbydzM6jMQQNKOhKGSBxFAhK/++JnaEUDO4RaSM60SQE03xWrWbRNAcFOw30M/FEC6lluOPxAVQIt2rCac6BVADEeXSJS4FkBnVMCDCYgXQIRIfpW4SRhAbTLuwx8YGUDS+odg8NgZQErMQWp9nRpAn17KbVdqG0Dxa0xO1TYcQO3zwakqGB1AM1NiDbXjHUAYk6+Iza4eQGr4UhGdgx9AuXeKevImIEAgYEFY3osgQFwMfdp78SBAdSNJ4PZVIUClMnkXPbohQOaxwfHOCyJA+W3cmJFvIkCAZv50LdMiQOwJvzNmNCNA2wWDSqSXI0BGyLFzo/ojQLvV0QMWYCRA6h09Qb/CJEBTKHMyLCUlQBCD5cPwfyVAr828ugniJUDHSoRU8kMmQJAup98PmiZA6qmtsOgMJ0BumqTLT24nQIHRYyd/zCdAkP8Jv4QtKED5wNoNaY4oQD1N+Fqa6ChAfH9UGjRJKUBan89JmKkpQAiyX4a8DipArKosIdZuKkCPtIbKzc4qQOu4h4dINCtAm1cD5fqTK0CGaE5+duQrQBO5KNOAPyxAwZKFJaueLEDE8FZaqv0sQAsv7RQUWy1AWlrM/Nq5LUCIPnVNaBguQMAxdbr1cS5A89qUfUXQLkDpLLXeZi4vQBat0AoLki9Adc9ko+rvL0AFH9KBziYwQLRrE6Y1UTBA42q6WeZ/MEBki743iq4wQBpVEyWX3DBAvqZdJw0LMUCb2b0McjkxQBmhYSbWZDFAXu1x9BGTMUBxIp8cOsExQDtoDG537zFAySE7SXgdMkCzuO6iXksyQMJjKFzqeTJAVdyHPEy0MkDsuZNN9uEyQHdl0+RWEjNA5p6/kNU/M0C9lls6OW0zQKRsfEbhlzNAsX2EoxTFM0BLqhT7LPIzQMY1aRbZIzRAGx7cDrlQNEA8hHoegn00QHhlangQrDRAsk/cf5vYNEDZvH/y3QA1QJri98gzMDVAqRWdI2JcNUD3+OlPdIg1QK3P8ERAsTVAwqRidgndNUAaBjL4rQg2QIo5Y65oNDZAQ2BdS81fNkBr5pbIBYs2QCWhTnqKtTZAK//1NHngNkBzjtuESAs3QFyxp8/5NTdA1y1Q93NgN0Ab2g7ot4o3QDEsWKvOtjdAt912gcLgN0AiX7zuhQo4QMBJ1v5EMThAQriQlq5aOEC4ffgw6YM4QP/8qPIBrjhAPT8zbtLWOEDfYxz/a/84QBxWKfvsKDlAyDgMH8RVOUC4sGu/vX05QNU9KsjzpzlA7QpWzX3POUCrZ5kgx/Y5QJRu23vRHjpAxncLUaFFOkCk/RiTKWw6QD5CTMGIkDpA68k8d522OkCaLs6Xbdw6QH5LbrejATtAxbB6X+4mO0A0ZMw18T87QE+RK1lvYztAB2CCgtaHO0AaFleX86s7QHskSKtg0DtATVNcHeLzO0CKGNHpGRc8QHqR1QkiNzxAYt0Di7xZPEAlpxVEDXw8QMB1yHUvnTxAPuF4ZdW+PECgqpEuJ+A8QHX5aLxiBz1AqKWUagsoPUABrnSPYEg9QK5yG34yaT1A0wJ6/c6IPUDR63XsEKg9QJhZD0dtxz1ALAEGFPTlPUBeYdmJFgQ+QK1BUkINIz5AyNiHZG5APkAzopOFdl0+QJkwdAMGeT5AMFvoom+aPkBJnfg7UrY+QFAkNCj/zz5AIF/fsQzrPkDr6Cg3tgU/QPKiIUaxHj9AQwb+Q3w4P0AAVzyc4FE/QNBkjkL0aT9Ayu28UX+CP0CwRq4wlJo/QK/FbgeGtD9AIhS1HbzLP0CbAwp/Xtk/QDLx3UDH8T9AbAxuHc0DQEAjtth5fw5AQGchGy0AF0BA+k6Ggz0hQEDOT0NZQCtAQGgIew1KNUBA1+Mwac8+QECsMj+tGUhAQNGlVS46T0BAddsmGQ5YQEDVdASppWBAQAmTV1ZwYUBALSPDLYtpQEAurRjwY3FAQB1vW0KLeEBAOslKeOV/QEASIuVgAYdAQCYDZoFwjkBArr+LeAuVQECVckZjaJtAQPV5ga9yoEBAYzLx+EumQEBwCZd+5qtAQJjI6EDBsUBAKle8vbmyQEA+kG1Gh7dAQO5R3qaMvEBAPawk0tPAQECCxQMh08RAQH2iPk3myUBAiDb6Gl3NQEA7LIK2jtBAQHw0RiSf0kBA4uSrNEbVQEDLHyvzptdAQB47nFoV2UBAVp2SZuraQECZrgryZ9tAQP9GbJpD30BAFirek0HgQEAMaYp89uBAQCXIxXrz4EBA1DYH3BXhQEDj20gx7OBAQNJVMu/730BAH9jqa0DfQEAc8++OOt5AQMVfPkbM20BAuBiY6S3aQEBgutGaRdhAQI6Hz4Uf0EBA0WW9f5vNQEABWhnmx8pAQMVNh5TAx0BAeBQeMVPEQEBMlLN1lcBAQC2j+cfYvEBAk0nBaYC4QEA5XwlG1LNAQFXPZUw9rkBAJOjsS+6oQEDhCPJ6T6NAQPKCq2CDnUBAz0X/uG2YQEDR9I8s1JFAQETr2s/LikBAu3dCWYeDQEA+nIaq7ntAQMqtbWpbc0BAlNGJixVrQECV7uWBemJAQKCgZMeFWkBA/0GdTDpRQECnX7KKlUdAQOtNhOwfPkBA0gdljcYzQEAr3TDl1ipAQM/udO78H0BA/8Zb0Y0UQEA9CDXkxAhAQNf/YJE+/T9AMmlBKTPkP0BDfqhsZso/QEs1gXDasj9AhtIVi4eXP0Af5oxDbHs/QL1AG0erYD9AtlcYcgZDP0DWjINBoSQ/QJS41Y8w9D5AEYU3yy3UPkA3QL0UVrM+QMLmMDfvkT5AXnGGJ3pvPkBD7Nc1K0w+QJC8y3OdKT5Ajg/ZB50EPkBFLX2Av949QB3XZVJPsz1AAxaJ3LqLPUAEeEonPmM9QN1TVhowOD1Arz032dAWPUBT7kRHrus8QEoquIvXvjxA/rI61OeRPECfi1YeD2Q8QJX1mEtmNjxAzJk7t60GPEBx33DjAdY7QM45+f0npztA6gd4/5h0O0AaYFsrDEE7QJO+MdErDztAtDOpGajZOkC6bWRsAqM6QC2Q4ocnazpAmbbIBK8yOkDjsU7hLvk5QDMDGhiMwTlA4u752/qFOUAmpqZPY0k5QNxpmBQyDDlAd2X4pIbNOEB/F3d7z404QC3abtHPTDhAOr2gvAQLOEB5wX0GLMg3QJyKJVp7fzdAR5ZKcIc6N0A5ku3OjfQ2QPXAy7m4rDZAPLt34KFkNkCxmVGDfxs2QLSEDNzXzzVAi1AyeZiENUAwTyBmUTg1QIRD1VYy7DRAoRjNB9ydNEDCzisVek40QCSwnxrH/TNAXcJOaq+5M0ChyhDGVGczQHGE36zdEzNAepKsuZW/MkBntNobWWoyQOj0iz2pGjJAKYAk4JnDMUA1NcQqnGsxQHaBcZdnEDFASbdNiKG2MECcOY5P/VswQCGpcoXC/S9A1jf/ijNFL0B8BE2nkIAuQMAwadMFyS1AGTsmVs4LLUB1WRqdQk0sQHVawa8ziytASyqXYPfJKkBeg72WfwcqQEc7OEKQQilAFlTOjuN9KEB8XYwhO7gnQBHx++LQ7SZAOgp+0jkmJkBjV/8U3V0lQEeF4xYBhyRAXaCnlkS9I0DivdI/9vIiQNJNwhxkKCJAL+Wx0zhdIUA4q4pIwZEgQLY4LDWAlR9AicJa+vD9HUAEeL0lF2YcQAv+sUFeyxpAwrxso/EzGUAfd+Q7yJwXQMSiPh5f+hVA1PnR1TRcFEBPq08QeccSQMYMaz/uOxFAi/nsy4dTD0DsBQo/SzIMQMWjf+ZgIAlAYvf8C+gFBkAQK4OpPe8CQNc/9rGUw/8/pokuCb2n+T8tX0nHeJXzP1PxVC6K3uo/sKfAHcjI3T9maNVRMtGzP9A0Mr3YEtO/20eE9G065b/Qo96/0Gjwvx7Rt3yoI/a/c5bJelzS+7/w2s++TrkAwAqn6uSoiQPA1Oe0iwpKBsBdoj2JnwIJwPl+ku/LlgvACdiRzto+DsDyFNzENG8QwNad7rpU2BHAhsA+KZ0fE8CMxyoUa2IUwCpBQT4kmRXAs5v3f9HSFsAqhp7I0QcYwBd0qrFhPxnAXbd8tTlrGsAyx5sXUZIbwHVEZlEvtxzAGRry443UHcCl4nydJu0ewIAV7g/2AyDAyO6jwPuHIMCs1Hi2DQ0hwKx2UzGmiiHADmQINcAKIsBycHAcZIgiwOBqZXXrBCPAh2V+JrR9I8DApreF7fMjwNXtQ8GVZiTAu0nReAHYJMCfWv/l60YlwJxaMfFRtCXAtGsEDU4eJsAynygek6MmwKPf2jewDyfAOpKW3HRyJ8AFPM4i1dInwAJD7RRmMCjAmvYV7gWMKMAIWjtoVuUowLJyx+oCPinAcL4EiKeSKcAg0t4F9uQpwJ7S+8AiOyrA3pFDtNyIKsB4IElKRdQqwKFVy3zmLivA5kcVscl1K8DqXzh6bborwKY7MxfbACzAiGyHGwVBLMDqO6AGAH8swLnEWTfDsSzADCxBUUnrLMBlLo/VuCItwLkPQxV9Vi3AvxtyEqSJLcDaVwmPsLotwEOfFHCd8C3AYpZsEOMdLsBUEd4NuUguwMKkum8xci7AyTc9S/KYLsAJZlKwtL0uwErWYDPC3C7ASGqj2HL9LsAYm8qWJRwvwJER4X7kLi/AYieeU5dJL8AgtJtwXGIvwEqHP3xYeC/Aj5/xaEWNL8DAYJK97asvwEdBf9/Sty/AGIkUeALHL8BQXHQMV9QvwFvRM1xc5S/A1XelgRnvL8CBnMVhBfcvwHNOE2ML/S/AN24pZ7EAMMBfgr5b9QEwwGCdYClCCDDAAXRxycIHMMAK5okbawYwwMmQx0bP+y/AjEN24Kv1L8DB0HGIze0vwJtBT5um4i/AP8DuYIDXL8CbS9nzuMovwJV7QG8qvS/AoGyXLQ2tL8CemUxrXZsvwGCWEZ3ggy/AULQx7QZvL8CSlycwoVgvwGxZIdspOy/AXvih8gQaL8C1hXuY4P4uwPZfY2Sw4i7A8vSoNbDELsCOtgmSKqUuwDCO0ZNjhy7ASJvpvAZlLsAm0zHrVUEuwGDrKyk/GC7AafeP1M3xLcAwBdH7DcotwMFqdYz8ny3AWADNoJN1LcDeWOY5kzctwJiq2hu2CS3AsTKeIojbLMAD5ZlyLqwswM9LKzXJeizA7+qD2yxJLMDINBiAfxYswFokKS/Z6CvA1VpO3hy0K8DgyKiUT34rwEOzKZl3SSvAPdPLsskRK8CwhqM7MdkqwEBHCyq6oirAviOS9ktoKsCr2pULAC0qwAGtcqqF7ynAEjDF7aayKcCdE8yGFHUpwCI/hEs3MSnALqQxiTTyKMCXlu4/nbIowJChvyzgdSjAs5iK9iU1KMBtp4o+9/MnwERS/r0csifAEIJ5w0B2J8BnTidR0TMnwC9VTLDR8SbAyRtxYbiuJsCHJ9xEZGsmwPJaVlAgJCbAZLIdx2XgJcCq0znWhJwlwHkEvcwmXiXAOcn3rh8aJcAgR6DzB9YkwESsXY32jSTASTp+Y/RJJMD7hFr+y+cjwEwNKs4ToSPAg3Lmy5ZdI8BUjrmbWhojwIBYmRbb1SLAR9SLsDqTIsBC0HeXAFEiwDLSHMgQDCLAgo6DeqXKIcAzv/O7vYkhwMNkH+pPSiHAZv1aZacKIcBJcbPhjMsgwEgbxFZjfiDA1aKhyqRAIMB40vHEwQMgwLj8CjQHmh/AZ/j/LLMjH8CZ0VNmHK8ewIDzEsMvPR7AOqbr1FjMHcCt6q6ahF0dwIw7a6QD6RzAiDs6yXJ+HMAXGskjVRYcwEb0X4u3rhvAzBCmmoBrG8Ayc7KkqwobwBb5yreTrBrAhaYdTf9QGsB/iw7RSfgZwJXpgPUbpxnAreCdK/pTGcDoFDm58QMZwPxlj/6kqxjAfShRP6lhGMDFrZ6u4RoYwGXlLpN03xfAwph71kGfF8AG1P48WD0XwMQknyvhEhfAeUXHsdLcFsAf6/vmd6oWwIohmwlzfRbADJgP5lNSFsBsYxma1ioWwBo7w2ro9hXA/hr7ThrXFcBxZepcGrsVwLvOhHUOrhXA1TTHduaZFcCWSwGkuIkVwJ9PmiPwXRXANdiHPe5VFcAExkNa21EVwA0EeiBWWBXABIj7uMJcFcCmJoA3XWUVwOhWWtl+bxXAbVcOUb2AFcAAEYmyKZYVwMwFOIEtuBXA/UNyPX/WFcDUlK1oAvkVwJnPA/Z6FBbAfl0/DvFEFsABRtTG9HQWwA+eOJ2frxbAPwwpLHLoFsDe/U1gyCUXwDPjMIwMYhfAwxejVnGoF8B0eyXfO/MXwDXQu5nPSBjAiE+jU6icGMAa22Ll9fQYwJ9d+SIhYBnA4CiKr3HBGcCl9/Kx/xYawP6BN9VreRrA0fQuPTzoGsB8eEbPfVsbwMCciMzuthvA+7bS1RUzHMCNiTB5vrMcwESl707qRR3AjY6ztvPOHcCWkOmWiVwewPfuA6Uh7B7Alw5bPjGCH8C1JH6KNg4gwOSPwn8uZCDAsH+pv1K1IMAsVT2afQghwMAzaMY3XCHA/OO7UGKzIcD9SD6dhgwiwG8uml5VZiLAeK277yzDIsBg1vE23yEjwOmQ03y6gSPAfsvHGwnkI8C38ZSUEkgkwOucNrjxrCTABT6dpJEJJcD87GqJc3IlwPBcZHxv4SXAvRKXBGNNJsBGDKdJ1romwMr3mpZmKSfAt4gyb7OZJ8C3kq5LTgsowJZSLArtgijAHv28Gfz2KMDaj2v2LWwpwDeHkviB6inA+2K4bNhhKsA92UpljPYqwPMFwVPgcCvAoVQ45wPrK8C53S1ZBGYswFLO835F4yzAQD4RQqxfLcCJP0cTxtwtwOgAol0UWC7ABNA0LkTWLsDjyQBa7FQvwDsV1fWdzy/A0d2rWoAnMMBM7rApTmcwwGgPwbzmpTDAeCUIO9HlMMAX+V8NziUxwFnZcfn9ZTHAgjWFA+2lMcBVduzAy+UxwNkL4Uk3JTLABfdYrt5kMsDSpr3sYaQywFwbhI654zLAFkN659IiM8A/T52FpWEzwD2py8VyojPAiYG+XvviM8DBPDer2iA0wHFzuFoEXzTA/UwtFRycNMBVhF27ztg0wG4MPUFFFTXAiL1bVgBRNcB+q0pDQYw1wC4ylqVQxDXAT2ytZYf+NcDADNwRIjg2wC8j8EpPcDbABu7Js7ioNsD/5vIcPd42wPFw85ldFDfA77pwsbxKN8AG8u3pYoA3wF9RSiZ4tjfApvmjCKXqN8APgzEUER44wOZ5hSMVTzjAtR79wPSAOMCbucHi/7E4wALHtog44TjAupVvsZwQOcBOeVLjJT85wDp0MO33cjnASXzlnaGfOcDinUypZ8s5wPlrRTzy9TnAieTKKOofOsA/ugR080g6wD5bRmGFbjrA44bc9K2VOsDsXQOD7rs6wHcqRBGG4jrADGAsgcYGO8DZWApZFCo7wIGCr5a2TDvAQ9kmcK14O8Aezh6m5Zg7wCZn/773uTvAdGA2+i7YO8BXnBbBXvU7wJaFCsXJEDzA9I/ONuQrPMAI90mg80U8wPUQFDWgXjzAu8avOZx2PMAFlyNGjY08wOdQmAHVojzAudkDuKu3PMB+v+1mj8g8wPYnS1tY2TzA9V61XfzqPMDHusLAiPs8wKzyKKXwDD3A65xDLUobPcA8/PEPiyg9wFzzjyjdNj3AnXMumuVBPcCoStZv1Es9wF+6jQ/6VT3Aqa6osa5dPcAi4jDNRGQ9wEMeyhYPaz3ALbmsWF1vPcB0mptNj3I9wLL/Q4CedT3AX9WRjoN2PcAy+wnsPXY9wHEfiSZKdT3A6YimSKtyPcCD76Hi5W49wNb+hCp+aj3AvWYv0VZkPcCEGJpE+Fw9wPZpp4xFVT3A3lIMUBpJPcC2nVK+Az49wGVosTSNMj3A0h0k5fAkPcClAW/GBxY9wDV9AICqBT3Acqr9bC/0PMCDyj8UXOE8wMvhLmptzDzApola9+O2PMCpKoqQ+p88wDPVS91kiDzAPz7m5b5uPMBHDcpSG1o8wEX8SDhOPjzAa6ygN1sgPMBuwreC8gA8wCutOGmw4DvARrdr/ke+O8BFbA8TV5o7wDK0v1xMdDvAZEpdPDtNO8DT1qshkCQ7wEJdjMYf+jrAU6zZLS3OOsAEur6RiaA6wL/AO5rxaTrA/Bs7nuM4OsBdEP8SFAY6wF/2sxTE0jnAFZ2N3V6cOcB6AuqcJmQ5wFZtieETKjnAZtwYXiDuOMDPyV/dRLA4wLFvueg0cDjAY7Ox+G4uOMAr8G9Ar+o3wI8mWwdZpDfA5sTcYEVdN8ApNkatbRM3wOUl/jDkyDbACq2UxOB6NsDJt41Wvio2wI7unLS51jXAbwp7j0WCNcDlDijDoSs1wFauemjf0DTAUMeyUN11NMCx5p77oRg0wMZXRgyruDPAA0i2Bv9WM8DpsWhESPgywPul2vE/kzLAZKZpZeQqMsAnEuZuV8AxwOpujnyTVDHAboul96blMMA+XLyUjnQwwOJztsPQADDAyTcIzhwXL8DLknZxYyguwEb1vJ0TNy3Az/5G5GZALMCnVD6g7UUrwLNVGeshWCrA8BouyWpWKcAMhledSFEowG/c+c3dSCfAEdCAi0c9JsA602qIvy4lwBfECAC4HCTAFN3krvAII8CBDboWxvIhwFWra25G3yDARJaSTmmKH8Cuwdtf1FIdwOLm2VbcDRvAqJWa+hzYGMDuAnCJh5kWwIwGxkoKXhTAhIq2hV8dEsB8skPxqbgPwJBbA1aNNwvAD11gQRW4BsAruyWvgjsCwFuvfTXno/u/M8hqzR298r87wGJaM8bjv8ypVjOuBLG/p5qs5mir3j+Vr4u+Y3rwP466yOykIvk/OQjK93LPAECKt6V5AQEFQPeJIbP/FwlA2OhTVP4tDUBT5nkAdpoQQEUY8prtmhJAqwsNMyWOFEADSkwZlXgWQDb/8FwNVhhA9LKDkRYuGkByWCFWd/wbQOxgMhGn5R1AKCJ9MqmfH0BZ/ba5qacgQJJEm57geSFANCCYDfZGIkCcwZKekg4jQAqE68gyzyNAkAftVKWLJEBmz2MEXEIlQALQdr3z7iVAsjCm/A+aJkBFm9EiaD8nQDtifuXt2ydArS0UVApoKEBIBY6/nPsoQPjz6/c7iylAko9cOewSKkB1ZhTyfZQqQOoDFOktEytA6U21ptiIK0B/uLFRe/grQNKRGmC/YixA/wI49WDGLEBYLkcJFSQtQMr1lQWggS1AA/qEI1bTLUCclTdYNC0uQJWqQsSKdC5A2j/7bEG0LkB2MM+qBu4uQHeLbizpGi9ApYTJPLZIL0DzW8weh3AvQNE55qxIki9ArbVEeziuL0BV1QS0JsQvQE/qdvcF1C9ADM5eUPfdL0AboR0n1eEvQLqf+Mce4y9AylFlWM3aL0BOfnzRd8wvQMMuhyHKuC9AcGOIcyeeL0CtVreRV30vQGdxh7dBVS9AdtpR/BcoL0BAtDsLmfQuQHfjjFR8ui5AKAcKd2l6LkBuG9Dd8zMuQE+fdIRB4y1AmfocwCB+LUCNhlVrFSQtQKnVKHbbwixAaY5Kj6dbLEBjZV9txe0rQMed/Jbgfit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The filter must simultaneously estimate the attitude while learning these constant biases. Notice how the bias estimates converge from their initial values (zero) toward the true bias values. The convergence rate depends on the richness of the motion - more diverse rotations provide better observability of the bias. The darker solid lines are ground truth, while lighter dashed lines are the filter's estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "rgb(200, 60, 60)", - "width": 2 - }, - "name": "Bias X (Truth)", - "type": "scatter", - "x": [ - 0, - 0.005, - 0.01, - 0.015, - 0.02, - 0.025, - 0.03, - 0.035, - 0.04, - 0.045, - 0.05, - 0.055, - 0.06, - 0.065, - 0.07, - 0.075, - 0.08, - 0.085, - 0.09, - 0.095, - 0.1, - 0.105, - 0.11, - 0.115, - 0.12, - 0.125, - 0.13, - 0.135, - 0.14, - 0.145, - 0.15, - 0.155, - 0.16, - 0.165, - 0.17, - 0.17500000000000002, - 0.18, - 0.185, - 0.19, - 0.195, - 0.2, - 0.20500000000000002, - 0.21, - 0.215, - 0.22, - 0.225, - 0.23, - 0.23500000000000001, - 0.24, - 0.245, - 0.25, - 0.255, - 0.26, - 0.265, - 0.27, - 0.275, - 0.28, - 0.28500000000000003, - 0.29, - 0.295, - 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jcD/cr8nPHGNwPzgbzOQlY3A/JQ5NPTNjcD9I2giJKmNwPxGHK/w3Y3A/yAb/ZjxjcD/IH+GzQWNwP3scqo82Y3A/iAl8aCNjcD8IXMI9GWNwP6mzPjUPY3A//rfKoR1jcD+/iWeGFGNwP0ax6ScbY3A/k+Gz3RRjcD+tmP3hDmNwP2Y9X5IlY3A/XiM51S9jcD8ZTBh0O2NwP5OMf3c/Y3A/V0ysRkRjcD/+us4GQGNwP5DlgdlLY3A/qCM0wlVjcD/5cuXAUGNwP6z4B21RY3A/IByVG0ZjcD8Xjp0TOmNwP6/h4nY+Y3A/6Obg1EBjcD8nVgguVGNwP8Fet8BZY3A/dnm8CGljcD/FtgB5XGNwP7nE5iZNY3A/VUZAv1BjcD/DuQ7aW2NwP0C22w9hY3A/8YwntmFjcD/RDYUcXWNwP9en2L9UY3A/0SJwZl1jcD8QUGOyXGNwP6YlcRpoY3A/P6oNqVBjcD/iYRAmVWNwPzdvgTZdY3A/qwBDiWBjcD9Atnc7bGNwPyGONEpgY3A/dduTlGljcD84ZduQd2NwPwKb9sxpY3A/1OWTVltjcD/A7TbrYWNwPwhcfhJ1Y3A/2wNP7mNjcD8vAQc9XGNwP9mrULpMY3A/CnGlZUxjcD8HD5lYVGNwP5NPJ/hZY3A/A6EcAF1jcD/OTzWOXGNwP0UjCplcY3A/6sgUCGRjcD/52TP+aGNwPwDacadZY3A/8JXLenBjcD9ZkLJKX2NwPzUEwSNrY3A/CdEEAm1jcD/ytviea2NwP7nGuQdxY3A/dNCShGxjcD8d5kWOWGNwP6NbwpxmY3A/EXJ2TGRjcD8rvKA7bGNwP14o5gdzY3A/WjLQv3ZjcD/qdfqCjmNwP/1m1D2TY3A/HLMan6ljcD/+I7/Jo2NwP0oz8DOlY3A/cRvHRKRjcD/BP6m6rWNwP3vnhh/EY3A/0NJ7rLpjcD+nJiFDx2NwP8vULfzBY3A/sf0fEtBjcD9wR6UTy2NwP5m0A0/IY3A/R2AObrRjcD8eoMhfuWNwPxPCYyqvY3A/5zYt3KZjcD8GVIdmtWNwP2YTz7+2Y3A/nrVn0qxjcD91Tlj+uWNwP11xyuu1Y3A/Wx+YOJVjcD98oqj1kWNwP4bCWwKWY3A/IQNDRYhjcD9EKv9BjGNwP+zP4VWcY3A/IHwn2bljcD80ezTdumNwP9iIAjm9Y3A/2p+xDLBjcD98ugMdvmNwPzs5lSfQY3A/99Oh4uJjcD/ozv249mNwPyasVT4CZHA/6HKAbRhkcD/tMCiUJmRwP2d4VNMeZHA/IRznfTVkcD93MyiLP2RwP4aZquJHZHA/TGKUkU1kcD/ZA4qJWWRwP15A2ahVZHA/V5gcWFVkcD+TCol3Y2RwPy2P7V1yZHA/NyJ3UXxkcD/QjfOUh2RwP2RjXVOTZHA/XblVSJZkcD8078ackGRwPyrDYzWKZHA/CdsKQ5BkcD9CdOHUlWRwP1Qb5TmDZHA/xeovKIdkcD/Jsc4DmmRwP92RxcSmZHA/ff5hC69kcD/Y8LX8rWRwPyQTHF+rZHA/slIBS6hkcD9n6GpSrWRwP8qxF/ehZHA/A8MX3JRkcD/smsmri2RwPxzeDrqPZHA/Ogr1v4BkcD+mbE3AfGRwP9q/WnuBZHA/SPu484FkcD+8WyKGlGRwP2Xn3iShZHA/7CKaAZlkcD8WexzygWRwPwkUxgBzZHA/GxQM6XVkcD9w8e3GcWRwP2gzKA92ZHA/tJEySG9kcD8TOQiUZmRwPx+rOc1eZHA/TdLFPl9kcD9gJGX2dGRwPz3wicyPZHA/gN5AQo1kcD/yNbMTjGRwPxytv6agZHA/Dg38XptkcD+KHQQwo2RwP/ofshqpZHA/Q89SIKNkcD972z5ZrWRwPyH3Fm+ZZHA/C9waHY1kcD+NDFLqmWRwP72w1g62ZHA/lzLboKxkcD97FSrSvWRwP0YS/CTEZHA/U0GRJr9kcD/5lZvGrGRwPyLWPY+6ZHA/c4KymqtkcD+8A+bSpWRwPxnUey6lZHA/BpIWr6NkcD+cUePXp2RwP0dr8s+zZHA/anUboMBkcD+ldpPPxmRwP+ZY/brRZHA/qkxAaM9kcD9AdNBPxmRwP24K4svSZHA/1e0xcMRkcD9KMXObwGRwP2WG9Sy5ZHA/Cqtqdb1kcD+YQv0N1mRwP1b8ySbaZHA/MKhrgNJkcD8OX7yJxmRwP+itAqbAZHA/FR2rZ89kcD/PUUCn3WRwP8z4WIzXZHA/0QhgLc9kcD9y7zpZz2RwP42bNVzqZHA/JlKyI+JkcD9BR0oI+WRwP3O7seHvZHA/Upy1OvJkcD/sPrAl+WRwP12UbR7uZHA/skSRUfRkcD/SetWn4GRwP2bfSRzlZHA/LS5MveNkcD+El7H45mRwP49KIt7zZHA/UyuZxfdkcD/i0QIf9WRwP6n5URnxZHA/ul9HqfFkcD8m2/9k7WRwP5rz0kTYZHA/g0DTQuBkcD/Qc3286GRwP1e9AFnpZHA/4p/JSetkcD8jqcJy5GRwP0gYMB7oZHA/AXr7VupkcD+X9cEzAmVwP5BAXq3pZHA/ftF4A+lkcD+FHg4hzmRwP40TqHTSZHA/zWsccdZkcD/OCOhN5mRwP9uYmgfjZHA/hLs2d85kcD+Vu30Qz2RwP6xBhKXaZHA/YFXAYthkcD/z2UJMz2RwP3Ppe/PRZHA/Pr/DTc5kcD/ZJw9gtmRwPwFZ1ISzZHA/R+e3K7dkcD88jtgtvWRwP899UGbRZHA/RfrJCeNkcD/xT50T2GRwPwiHI1fhZHA/hrYMkuRkcD9gk9t822RwP4c8aNTcZHA/Yvsmx9JkcD/z/REF1GRwP017qiLbZHA//olFiNtkcD/LDRXw3mRwP1AdKQDTZHA/yGFd+dNkcD8UWJpuxmRwPztzRsmmZHA/fD5+pKNkcD+XvNytrmRwPx2crhSwZHA/9eBiop9kcD+bSqnwl2RwP7P9qxuGZHA/uGb6/ZJkcD/8hgSuqmRwPwqQauqxZHA/j/9tfrZkcD9fdmhhq2RwP2gNTGGhZHA/dgkqApRkcD+1VQSrlWRwP2aJRb2TZHA/aDIIuHtkcD+Yl5OweGRwP2lP2fB8ZHA/s2ir53lkcD8OGfhrhGRwP/IMjK2PZHA/7qX3IYhkcD/ZRvkZi2RwP+Fm9+aXZHA/1NCd3K1kcD+zUNx1pGRwPxmTxmqRZHA/MAHZbnxkcD+yY89RiWRwP0iMT5yGZHA/SdFVCaBkcD/uiUz6omRwP+ZCeaWwZHA/hE6ZDIVkcD8d51TufGRwP5QwGZJ9ZHA/iI4XBm5kcD8cHEwOZWRwP9M9Q1BhZHA/mqC02lBkcD9+4O+vO2RwPwKRdgtAZHA/6VU1zUJkcD8Js0w/TWRwP29N6JpVZHA/J8PdYGNkcD9c9cuxZ2RwPwulvEhhZHA/nnr3mHBkcD9P9qb7cmRwP6dnEXxmZHA/1F65AHJkcD8wbjKAiGRwP12yBxqDZHA/1pOq6nhkcD8Nx+W1WWRwP01oyjxZZHA/yyEPvFdkcD/Qgme2aGRwP2cghR9nZHA/m3MKo19kcD95FlGoTWRwPxzBf2BNZHA/j9Rx31JkcD/VeI26RWRwP9HGbIRHZHA/PzIZTFFkcD9NEtulSGRwP08fuTtLZHA/epkcDUxkcD8eYOWeVmRwP7PYG6ZKZHA/Edp41VdkcD+/h9w1bGRwP77exQJ1ZHA/lDTMyIdkcD/za7eHiGRwP5i2NtyFZHA/GgoRmYNkcD/GjC3sfWRwP4ZPdjl2ZHA/ZdALpIRkcD8cEjN2fmRwP0kCLZNbZHA/ALpH5lBkcD9LpCKEUmRwP0bApKBJZHA/IzRvf1pkcD/6s6ZTYWRwP+PHFzJqZHA/AVb4uGxkcD92xCuHcmRwP39wwuB5ZHA/L9z78HNkcD88bmrfeGRwP5ZJUtFyZHA/s0QV2nlkcD/8AXgBkWRwP2fqLfKZZHA/Yur6lY5kcD+BfsFPm2RwPxjpBq6WZHA/LIvYPJ1kcD90ONU/imRwP5XVqleHZHA/KsgIYYVkcD+8wHkfdGRwP8CXoh53ZHA/Pop/6Y9kcD8BEeoTgGRwP/L94Up8ZHA/l7HAu2xkcD9L9uzUYWRwP8/zOjhfZHA/ViSwqV5kcD8+BnB1ZGRwP01sDHVRZHA/qfyhkjhkcD9zUzFJR2RwP47iX4VNZHA//nTjazlkcD9qXvbLPWRwPy4FC501ZHA/nQ4ey0FkcD/2TiPRVWRwP+HzXBRwZHA/7zsiMndkcD9pAzrGb2RwP1jeS2ZrZHA/2P6YGXJkcD9ytcB4YmRwP1i34WtdZHA/550oKWFkcD9KDGWKTmRwP8Qo2xE2ZHA/dYeJxEJkcD8uvKSHKGRwP/9WlRMmZHA/2iXUsxtkcD+MmF5ZFmRwP/+3JskPZHA/lpdIJgRkcD8PVqdDGWRwPwOdtz0YZHA/DurqAiRkcD+MKw+nLWRwP91in5IeZHA/6ifSshJkcD+DlRcnBGRwP+Uh4ZIVZHA/pkXLvx1kcD9QNLI6GGRwP/vwzFsGZHA/0JvjbANkcD/aObAZ8GNwP82TfUf3Y3A/1TX++wFkcD+DnQy/+2NwP0UwVhEBZHA/ZdNoyw5kcD9MO3BzD2RwP4x/lssVZHA/W0osYhdkcD+rTzltHWRwP0ihRJEWZHA/+vMDEhtkcD8FMkXBFGRwP7Org4cMZHA/vJzuFvdjcD/BgUtU/2NwP4h4e6XzY3A/COX84PRjcD8Vsa7w5GNwPx7a0SXUY3A/i1InatJjcD8BqNuA7GNwP7nr8hTxY3A/lG5vOPNjcD93LhH262NwPzkjh7r4Y3A/vgusMvxjcD/bidcM+mNwP6jlq2rtY3A/LBc5AvpjcD/RQSRb9WNwP898ecLVY3A/iGAhcORjcD8sB1hl7WNwP3ZhBnL7Y3A/e1nycAxkcD+aHGFUCmRwPy7zJCcQZHA/ernK6xFkcD+9gZRTFWRwP0zUiSQZZHA/T0UgfB5kcD/PzSbCMGRwP4058PU0ZHA/vntaQThkcD8COn8UPGRwP9l3itozZHA/ftakWidkcD8OHTt/JWRwP3kBYqQ1ZHA/Ai4IZyNkcD/v0fEEMGRwP0YI9/ozZHA/fUY8cEdkcD+9yRMPV2RwP38jo25JZHA/fuyuilBkcD8wa41aPGRwP8UyJc0kZHA/FYicXgtkcD/ib1TJ9mNwPwqc9MnxY3A/1SYfu/5jcD/LikfgBWRwP07VP4QDZHA/twlCc/pjcD+3Xy0b+mNwP2bEdpn/Y3A/UzY4OwhkcD/Mwc8CBGRwP0FWj6nwY3A/yOjVpfZjcD8T5M2a62NwPyj2UHP5Y3A/zmVXFwxkcD/4YnoCFWRwP4HG5rIRZHA/2lhO+/djcD8X7quvC2RwPwT+8uIMZHA/+52FmhhkcD80YKV4EGRwP6556ssVZHA/bMRIWhpkcD+p1D2iFmRwP2u/lYwZZHA/eE5weB9kcD/DuVKwJWRwP+RwEV83ZHA/5XQyjSxkcD+I05PhE2RwP35PhakXZHA/X9/aTxdkcD/X5xK7JGRwP5xaEV9BZHA/EheFpFVkcD+mY+C5V2RwPxRbiGI+ZHA/Gh3NbjVkcD+4r7ZDHmRwP4rxXnk4ZHA/enHlwDVkcD++4gGxJWRwP359H8YyZHA/svnXATxkcD92aGBIO2RwP29WBx44ZHA/kaDepzFkcD/S21keNGRwP7h12BQ9ZHA/k7k1IytkcD84Z5lAKmRwP1zFPWoiZHA/YlAKTTNkcD9IIquPQmRwPxPSmA9BZHA/6vU+bDtkcD/3cNU8PmRwP7mvFIdAZHA/kJZAET5kcD9Ahe2rNWRwPzRIPXIwZHA/LsB8jTdkcD8KbkPOL2RwP5lbQBs2ZHA/BEgmWDBkcD/B14QyQWRwPxxzAiI3ZHA/EvMoLURkcD/NGzPKPmRwPyUos6Y8ZHA/0uzjuEJkcD/5AHkAMmRwP6ugTGU+ZHA/djAnqkRkcD90BI7rPGRwP6Lsh/o7ZHA/1160t0ZkcD8uuh5URGRwP9SXVM1DZHA/vzM+lztkcD+HUHARP2RwPwPBEqg7ZHA/ELVfPUJkcD8vRwbvRGRwPxu4k1M9ZHA//7IT3E9kcD9OL77rPWRwPzdeBzYxZHA/ez+vzDNkcD/uqtJJLWRwPx/qg/ImZHA/XTeiBiNkcD8bXjMLKmRwP0W0m09AZHA/UYsrGExkcD9AoBzBTWRwP9uZhpJOZHA/ZdQyL1FkcD+x2yOXZGRwP9lMZSpqZHA/P6CBlV1kcD8Sj/RKUGRwP8QXWvlTZHA/a0IGemBkcD/bxik8U2RwP78LUqRUZHA/UkB54lVkcD8mGshhUGRwP/4HfMtZZHA/+CzNpl1kcD9Wv31SW2RwP9Q/uXFRZHA/FeUVJlJkcD/pNsgOSGRwPzqb0kxaZHA/HG2rWlhkcD/mM8mkYmRwP9PtCmJRZHA/wi3DcUFkcD9yPSRsRGRwP8QAZew7ZHA/XcROAT5kcD+GTKopWWRwP96iZztZZHA/rFbz90lkcD8f7FHoRGRwPxxh69ZCZHA/l6nV6kJkcD8VgIaaPmRwPx1T6lI3ZHA/IReYQT5kcD+Ii+57M2RwPwK7JuA5ZHA/nwVt3z9kcD/3MLgpPGRwP18NVMFMZHA/vD3dIEZkcD8eGmivTGRwPzzF9QZPZHA/6R8EQTVkcD/OmC5MKGRwP8hYPPggZHA/15neQCBkcD9VKOBPEWRwP4YJyFkKZHA/38HWHhJkcD/f+FpvEWRwP1Q0sYgkZHA/N8R0CxpkcD87jtz7GWRwPw0O8XoDZHA/wWopAfVjcD+x7Nsl+2NwP1AJJIL5Y3A/YF+TsfFjcD9/AZDp6mNwP5wUZZL5Y3A/YFq0VvdjcD+gT8Bb9GNwPw3OouIBZHA/QMDqpfhjcD8B+fN5EGRwPwUFAt8gZHA/iarwmR9kcD+NKaq0IWRwP35KpHwwZHA/Xvr8WzhkcD+Uy4ahL2RwPzcW2aI2ZHA/a0WlMERkcD8GgyrJQGRwPymjlrEyZHA/Q5NjAzFkcD/OM6HzJGRwPym8dgctZHA/zHLKOCtkcD/4N6pLIWRwP4kuDM4ZZHA/Oa8bEDBkcD+EurZ5PWRwP5qIlPo6ZHA/75REyUBkcD9va1SQQ2RwP+NH3iZEZHA/ZJz640FkcD/t/HsuMWRwPxYRtc4qZHA/BpqbjRRkcD+nNtECC2RwPyghDKMLZHA///n49gNkcD9OaFbXGGRwPzrtpaUKZHA/PdsKgR9kcD+AeLw/EmRwP2uMZ2wXZHA/fQKERBVkcD+P8APlAmRwP01WviL7Y3A/DZ678fpjcD8RMZ569mNwPzNRk5vvY3A/CO1uiuhjcD+wVZ3c62NwP7vFYDfxY3A/Qcu3I/tjcD927lkuAWRwPwliHk4TZHA/H+VCihlkcD80gKWmIWRwP6SUTrQuZHA/jxYTuThkcD+HopzHSGRwPzci06tNZHA/0KbrpE5kcD8ZrQcsRmRwP9e0Yb5XZHA/wsrncUtkcD8OM3+RUGRwP5MkOH5RZHA/I0vla0pkcD+hvNZhS2RwP5lAq5FEZHA/TFmWlEtkcD+OQ+6UUmRwP3uVyupaZHA/nRR44WVkcD8YgYLhf2RwPz8iH4x+ZHA/3DzrNXlkcD/ZOgnthGRwP9BCbsx8ZHA/Pukdxm1kcD+P4xKaX2RwP/lGe89kZHA/EVHK/VdkcD+nzDeVX2RwP9KVRGlrZHA/64cy+mpkcD+hipVaXGRwP31wXVxWZHA/DCFu1GlkcD8lmWilb2RwP8SOp6F0ZHA/AR7HUXJkcD8w3OJucmRwP+CdzyZxZHA//8Xcn2JkcD+7+GBVcmRwP70BUOpZZHA/1aWTm2RkcD8YnmAGc2RwP+Z4TKx3ZHA/jlm3MndkcD/9mElcgGRwP0KkqCCCZHA/TdHdQ4JkcD8ZJD1OoWRwP0DHUbmtZHA/oE9pv7RkcD85qK9HvGRwP1+lFOrAZHA/uPd5MdJkcD/P7j1vz2RwP8m0Q3LUZHA/BJU//NxkcD8kZyHF4WRwP+oD19nvZHA/B74ojOpkcD8HSqga+GRwP8MzLc/6ZHA/CT63tAFlcD/qFnjgEWVwPxat9eYWZXA/KGf8OhJlcD/LGcbQDmVwPzzlaNHwZHA/7Y0yEAtlcD8988TR/GRwP7ctobH+ZHA/JPou5wllcD/Sxiah9GRwP3Hp65zqZHA/Qpdo1epkcD9k1toN7WRwP84bx7nqZHA/wFMl5uhkcD+vKpFZ0WRwP6eM8QfRZHA/AYTJCtpkcD/cOt0j1WRwP4tCqsPBZHA/ImT9hcdkcD/jsGbsxWRwP/HEhJfJZHA/v9Nb8MBkcD+8frqNsGRwP8SuEy6mZHA/TQOQN55kcD+B7rSSpmRwP1xzOFacZHA/AN5w3a5kcD+pp7bIpmRwP8k5ZqiXZHA/zCArj5hkcD+Vme02l2RwP8W1jiCgZHA/07/vUKBkcD8t3ab+x2RwP1rqudnGZHA/VEviJtBkcD/CWA+e2GRwP/q7SZ/RZHA/zoUDyttkcD8xGa4D8mRwPzrU8+z3ZHA/U+mXUuNkcD8gMySC7GRwP/4L58bmZHA//RA0u+1kcD8HINZ852RwP+jxPV/pZHA/gLLMf+dkcD9jpuJL9GRwPzTkSgDkZHA/t3HrnOdkcD9A7egP6mRwPyVxCYXgZHA/u9X1ltZkcD+pLvPz0mRwP3dpyrfOZHA/B0lnfsFkcD8yBEbHxWRwP/DhbLvIZHA/RILherFkcD+iAbJ6x2RwP9lJfaPIZHA/s+yKMs5kcD+m7swoyGRwPyiHulzUZHA/hBvWUb9kcD9KCPTt0WRwP8Nj58zcZHA/qtf8xsZkcD/IeDlPxmRwP7W8SpW4ZHA/QAV0m8JkcD+3WvBLtmRwPzXXEP2fZHA/+QW3epFkcD+fqzLYo2RwP3KomgC3ZHA/cQCBnLZkcD+ENBZ6sWRwPzgvETW3ZHA/FWK5uatkcD/KEpeFt2RwP77zYo+0ZHA/qFcOzMZkcD93xL8EzWRwP2jv993LZHA/wSmOXeRkcD/x9OnG9GRwP9iHa3f9ZHA/9MSm/A5lcD9X67BEAGVwP3p/izX8ZHA/lCFgXvBkcD+WxCgv8WRwP2rVd6r7ZHA/xN7Yo/VkcD+rXv3v5WRwP8C3xeDcZHA/DHGcV+xkcD+Vah/i+GRwP15i4o/6ZHA/QRnO1w1lcD8DGGpJD2VwP4jtO9QeZXA/EWW+5DRlcD/VvzeWNWVwPzuUu8o2ZXA/4dRzXiFlcD/wSNQvI2VwP9F2KQYkZXA/VZdLri1lcD/FXkLlMmVwP8m8LVU3ZXA/AIrFxShlcD/QDnM3LmVwP/XCN6EzZXA/YMMxaC5lcD80EohgM2VwPyHCKj0mZXA/rg+n3yNlcD/5EUSfFmVwPxC7tqQZZXA/yEv4ixNlcD+R7StSD2VwPycoNbYVZXA/tIFd2RJlcD/6veHNFWVwPyIYh08hZXA/89r57BxlcD+O5hLBOmVwP9xypjQ7ZXA/2PZP/kVlcD+rVHRjVWVwP1k0IuJLZXA/ad5op09lcD8jOYFeVWVwP0SBGJFLZXA/XmHxGk1lcD90tcNHRmVwP0+lrcVVZXA/XbHWqVNlcD9p/eSDO2VwP/cwu100ZXA/IesM6xZlcD9uccxIImVwP/duPV4jZXA/Zk2OHCJlcD+tRlV3JGVwP67R++EnZXA/mDPppC9lcD+QWIQxMWVwPz9lp+8pZXA/fRs0mUBlcD9ZEf/EQmVwP1xnMVBGZXA/INs/zUplcD9C5T4aT2VwP3cLTXNJZXA/xX+asENlcD/jmFzSPmVwP4fkVt87ZXA/YBAcuDZlcD9NK9S6PmVwP+VzUd9KZXA/RCDzkzllcD+D3GKKNmVwP4OZ6oo3ZXA/Nnqu/UBlcD8lzWuvRGVwP2ysF9NHZXA/v6bu+z9lcD+BbJzySWVwP8Yk8DdKZXA/4tP6zWVlcD9PSugfb2VwPz+GZeRvZXA/DBewoHdlcD8opnRLfmVwP5oUw6mBZXA/1Fhve3hlcD95okhScGVwP6IciQN+ZXA/nprJKJZlcD9U6ccRk2VwPyc0+JGPZXA/Qm4IBJNlcD9/qrCuhmVwP8jxdo2YZXA/lU4mApVlcD81DsA8pWVwP5cLioeUZXA/nGN8D5llcD+XGlgAlWVwP/hJj2qnZXA/Nl0g/qVlcD/4yAcZqWVwP9oO7iecZXA/UwmVspBlcD/7oUyRi2VwP6NC0WeTZXA/GzHaupJlcD/hBPwEiGVwP9rVf+KBZXA/Vtp5E3ZlcD/C1vsAc2VwP2YMKUN+ZXA/2tiMkHFlcD/XLXAQb2VwP71jraJ0ZXA/HBwHL35lcD/LgTTte2VwP/XvMaR9ZXA/Ebn8+GllcD+0M4EuWmVwPzAXjhtfZXA/95ALGmtlcD/O4cgPgGVwP1bgJPF6ZXA/F6qiYW1lcD9RbPwXfGVwP15gcHZ8ZXA/aAKzbYhlcD+CYJRaimVwP/naMQp7ZXA/5MgwkXhlcD8HqRjxbmVwP/CS5KB/ZXA/++hN73tlcD9LGKt+d2VwP8Klr9R7ZXA/JIvIfIRlcD8+WPSwf2VwP9zyQfqAZXA/tzCYWXplcD+vDVkufWVwP/rt1xSHZXA/U8AX0o1lcD+FCnBClWVwP1lG6NSkZXA/5WTIGLBlcD802wkzpGVwP+QbXEKlZXA/wcHvIq5lcD9DMPNcumVwP44AxLuwZXA/2YO3+KJlcD9RdZ6smmVwP8Ztm+akZXA/OSdU2KxlcD993NSJp2VwP9M9miOeZXA/4x07gJRlcD89grVvlGVwP1qu/S+WZXA/lnmAwZZlcD8b9p5pnWVwPzBv1l+kZXA/QRuL+7NlcD+ChoirpmVwP02OsKGiZXA/EgQXy5dlcD/xkY8sj2VwP9HMvTuRZXA/kOaqHXNlcD8ODq35gGVwP7XOrbuPZXA/PZfL+YdlcD/gzHGBhGVwP5Ii0buCZXA/5qtQOoJlcD+PIKvRe2VwPyCPBrWAZXA/G/QUZIRlcD+vitDcjGVwPzWN0YqKZXA/Yjj9ppNlcD/ZX9VmjmVwP9I47eaMZXA/UpiIgYdlcD/dJSCpgWVwP7fbG1WCZXA/BtES4nplcD+AFsf5d2VwPzgwgIqIZXA/Zj9GR4tlcD+eBfoBfGVwP0ovLZ+DZXA/37sUQYBlcD/ZDBwcmmVwP5MRzueeZXA//FxrvpJlcD8NTuG7kGVwP4GkJU2OZXA/Z+Fq95ZlcD95DA7Nj2VwP04uDsyHZXA/rFvi541lcD8XSinBjWVwP5Zb5ryZZXA/XQfgqJhlcD/xOCDbp2VwP5KvzJSeZXA/wKj9w5plcD+Gx5TDmmVwPxANi5ydZXA/4EfU2aRlcD8NvFOqmmVwP85It/anZXA/doOVna1lcD/iECn3v2VwP0Isx2bIZXA/j5HCe9dlcD+xf0qd2WVwP+krh7TjZXA/vEBbechlcD96d9axxGVwP0Kvt4q5ZXA/qQQTE7FlcD/EVgkkv2VwP6TuylK+ZXA/o8R1srllcD+swQVduGVwP6Oyc43AZXA/trKv96xlcD9h3oWKuGVwP9APhdWwZXA/HBGf3KdlcD8olLSVqGVwP4ZeuzObZXA/1l6FqZFlcD8JTQCDlGVwP8xWuIaWZXA///fjpIVlcD+DcKeqiWVwPzJUji2VZXA/kbXDToplcD9tpDftk2VwPxVoubGXZXA/bOGFX4BlcD+fvedPeWVwP/r97kt5ZXA/PFhmVntlcD+PLjHtg2VwPzUCV8GHZXA/KtgIjZNlcD/DmNwTh2VwPwDUQeV5ZXA/FWQUMHplcD/b1rXBi2VwP8R8XPSUZXA/VcekVqFlcD+BGILbrmVwP27ZNPezZXA/cNSz4bJlcD+pK+zguWVwP8Ybkuq+ZXA/jqB+GbNlcD+SluiJt2VwPwfEFsupZXA/LhFM5qplcD9vVmIkqWVwP/WI1n3CZXA/MdAX38FlcD8GKSh+ymVwP0OYzfvAZXA/R+rJIcplcD9PpYDl2WVwP9dHp8/RZXA/FxYfGsRlcD8hMUkTt2VwPzCVp6WrZXA/Zw8GyLFlcD9vWEAXsmVwP74FuFi+ZXA/0t3+38tlcD+gN8X3xGVwP3Hmwce5ZXA/t1RErL1lcD+vl9eRvmVwP4NqF3S0ZXA/Hh7OAMJlcD/fobmFuWVwP1/NGN+4ZXA/+kyhBbBlcD8/5r0dm2VwP6AXYouXZXA/9KeHP5hlcD8LZCqLpGVwP9cy1ECoZXA/eMy3V6xlcD9XGR25tWVwP2UmOxO7ZXA/8CtGpL5lcD+kIWHztWVwP1vusfCxZXA//8iI9allcD9dVyaukWVwPwDcfXijZXA/IMtIq5ZlcD/3detSmGVwP9bjWDp/ZXA/mJ07Z2tlcD/qDLn5j2VwPwxgOEaXZXA/R0r91X1lcD8crxC0eWVwP1GOqRt/ZXA/MNG7G4FlcD+LeqUHdGVwP3n05uBfZXA/33ymwWxlcD+EDPQLWGVwP+ekH45EZXA/aEkxvzxlcD/FA/QTRGVwPxr21dJLZXA/SqN3bkllcD+kzsU2RGVwP4K/0OVFZXA/mb5muUBlcD8SMLFqQWVwP5x0pMBQZXA/nPEiw0VlcD93KjeJQ2VwPzXp9GBRZXA//0xyU0tlcD9PGCZZVmVwP/ReEZ5aZXA/OHsSlWRlcD86ot2UXWVwPym6nchaZXA/RXUTM3ZlcD/mCW16dmVwP4st3JlsZXA/CXpPl21lcD8s/zaRbGVwP+xSxztvZXA/RAlVzoVlcD+ACKYshWVwP2tKq+uXZXA/Aoy0Q55lcD+nCWeGk2VwPxmDP3aXZXA/hnX8yZRlcD/GjtnDpmVwPyzShfG1ZXA/XD2iraNlcD88FMMsp2VwPxHTfG2mZXA/2OrqAqNlcD9s9g5UomVwP+CT/FCoZXA/+uG5WqtlcD9sCBU6kGVwP/uD1WmIZXA/uQG0PpllcD+v7aLxmWVwP6ccxkSQZXA/Icl4GJFlcD9KObtAkmVwP84HnFWGZXA/mbMWu5BlcD91Go7rlWVwP8UdhaqWZXA/k3/LYKllcD8jx34FpWVwP1ReDfupZXA/Y9J2e6ZlcD+uOa8xnmVwPyIi1K+eZXA/UVi3KqllcD8j2b5ssWVwP2X/fwK1ZXA/XUlU0allcD/WYjurrGVwPxItGKmnZXA/n3uBlJ1lcD/9zWxsnGVwP469XTiMZXA/5PL/OINlcD8h+4hwgGVwP+0nloONZXA/KuF6iZVlcD/8gAP4oGVwP94leN6WZXA/yjc0ApVlcD9JUIzihmVwP/N3gLiRZXA/DIqD9Y1lcD8e+eJ6jWVwPzL836uFZXA/LPqGZH5lcD/h25rIc2VwP4naa6psZXA/PaXkxWdlcD93gt1sXmVwP5gDKixvZXA/nXT2dXJlcD85RMlncmVwP6BaQ5llZXA/Vfgsa1xlcD+OfQLXXGVwPwH34atYZXA/kxMT9mplcD+PSw1VUmVwP8z7irxPZXA/wIDrOkVlcD9SYGoYTGVwP2jAKdtJZXA/HefYVlBlcD+RSub+WGVwP5oqjCZCZXA/iJ7gpENlcD8eJ7FgUGVwP9vfkONGZXA/rJwooEJlcD9ubqZQRmVwP9b8YE5DZXA/7i7Khj1lcD+V7f+ISmVwPwgJ3Bs2ZXA/gvopBjdlcD+0gfJFTmVwP1xQqCNFZXA/gFmpeUdlcD/Bwnb4UmVwP2vJOq5BZXA/JzL8XzllcD8/A6GNJGVwP1HDXLUhZXA/V6cW8TBlcD8pcEseKmVwPyy7hEUkZXA/hhBJDillcD/TzitmI2VwP6Hr8g8uZXA/zmTNSkJlcD9wbbXkR2VwP0PU5YhKZXA/ml+XMUhlcD+KtQDLOGVwPwWm8QZHZXA/9VdYAzxlcD/CZeSNVGVwP3xCkvJDZXA/NcXVy0BlcD/QXljhNmVwP5UcaJZKZXA/KGjEU1BlcD+Xw5myVWVwPyDyG3NlZXA/ehQ7JmtlcD+MJXIMYGVwPy60pBxfZXA/sr0QH2hlcD/ojfbgU2VwP/eBsKtPZXA/efhzFVdlcD/VfrsfamVwP3ybL7xWZXA/X2abP1dlcD+13YVjYWVwPzy7X99XZXA/tB55DEdlcD9XYhRQRGVwPwo0oEk1ZXA/GT6oFjtlcD/FzL/KPmVwP8dwF40tZXA/3EZYHSxlcD94IpBDJmVwP4lsyVofZXA/WsVUZCtlcD/Omp6ELmVwP5I2KaQoZXA/iivKITRlcD+PFrDRQmVwP6pP+Sg2ZXA/7zyLTDxlcD8W+RwDRmVwPxCG1QVDZXA/D8ieUlFlcD8N3ThYUmVwP6W9kLc/ZXA/6RY0X0ZlcD+sVPj3XWVwP3X8uG1fZXA/s76AumBlcD/bYAAaamVwP1Z7/6J7ZXA/8w7fBoVlcD+N9dizemVwP1wHUEGNZXA/EHe/m3VlcD9du5m3gGVwPxjLXZV7ZXA/Rnu6e2xlcD870jBBZ2VwP3EUL1RhZXA/YWukwU5lcD/WjJ/xTGVwP9r3ZpxWZXA/xYWcDWhlcD8CWVxba2VwPx9ZFzRoZXA/lw+Qm3JlcD9X1bKTeWVwP4/DU4OIZXA/B9/2EoVlcD+wL4z5lWVwP/5k7iSZZXA/TFBBcp5lcD+Gu6kimmVwP7hLkcmSZXA/OW44K51lcD+vck6mmGVwP/GEvqqQZXA/Uwe1rZFlcD8MNJ1ckmVwP2bpxJSUZXA/G7IL751lcD9XAZsslmVwP7Va7MmEZXA/u4gP5otlcD9f0kX8m2VwPxtfNF2nZXA/AktlKKNlcD8foJiEhmVwP0yI8CCQZXA/OBk+Z4plcD+ZwPSQk2VwP0NmprCaZXA/ETlYLZZlcD8s6tp4oWVwP4//qAWjZXA/MN1g7JNlcD9jGBkCm2VwP0NTJpSIZXA/ZaILOo9lcD8v/ap4mmVwP3iNcvOcZXA/OnLRda5lcD+89aXEumVwP9Ymui2zZXA/YqgJTZ1lcD+BDctRrGVwP/3mw3WvZXA/4BjzeLhlcD+8sOpQtmVwP9amGT2wZXA/QVTeibJlcD9n+yEJvGVwP2XXA7K/ZXA/Rpu7R7dlcD/aF319uWVwP8Jd3sW0ZXA/kN+3QrhlcD8smk+IvGVwP0c5E6e7ZXA/C0pk7bllcD/KaOPns2VwP9uWaqCeZXA/YRW7Cb5lcD/Tr4fHr2VwPxnkKdu4ZXA/++HBHcFlcD/CrJ8OxWVwP6QL/K7YZXA/yWddUuZlcD+Uvv6x42VwP6OVnm3WZXA/Yh6ofdplcD8ZlJUPzGVwP1lUWVjXZXA/bOzZvMBlcD9vBsaXnWVwP2eMqAWLZXA/dm8l0oJlcD9x4Mp3d2VwP1W5yIdqZXA/pr/xnoZlcD/CB+K+e2VwPwZPAYyIZXA/irLt8n1lcD/P52IEhWVwP28slb96ZXA//R5YO49lcD9ebXcVlWVwP2ZYHr+VZXA/4mvZLH1lcD8vBsa1f2VwP0r0aoFwZXA/UqnapHllcD9lOq10fGVwPyWFEqV+ZXA/Ri+MWWxlcD89LHlOcGVwPyltyylrZXA/5jR1nV5lcD8n1EhtT2VwP9N4K5lOZXA/1T9yk1RlcD8fLgdvW2VwPzCaFABIZXA/kd6kP0xlcD8KWUQZUmVwP3+vXWhXZXA/Zs1sd1dlcD88atawUWVwP5a2ompZZXA/ftNfqk1lcD/I1paNSWVwPysUTYRUZXA/5Hy9F1RlcD+czCR+P2VwP+n80CpFZXA/ls09wU9lcD83IUmTWGVwP6Z/7bFcZXA/JgzR905lcD93FHGCRWVwP9REygI2ZXA/u4D5HDJlcD+0YpE2Q2VwP1/6nqU8ZXA/HwvDUUNlcD9SMxPyOGVwP2VqvCMtZXA/FvC1aDNlcD936LlWOGVwP4jOqyNYZXA/G7yPQWNlcD/YvizVZWVwP/M4CO1oZXA/j3/YUVNlcD8hY2gyWWVwP43EeOxSZXA/u72o+T1lcD/I+hmgPGVwP/+ctGo+ZXA/LO4EYS1lcD9PgFY8JWVwPyCpS48iZXA/1U0aGUJlcD/0yTLYQ2VwP/Y6ggJeZXA/cO1J0F1lcD+tXFAOU2VwP4FjKkJvZXA/ojehnW9lcD91CbJZXmVwP6hWka9GZXA/yB/faD1lcD/OrY+sR2VwPzlPNnNHZXA/4nRa5jdlcD86qxajOWVwP2aQsDxEZXA/uFYufEZlcD/PqIOKRGVwP/Dux3JMZXA/FQFKRFtlcD8Ab/bsVGVwP4PICnBUZXA/ysSirVllcD/5swIdV2VwPw/rOQlYZXA/Ix+iY2BlcD8C2jNra2VwP/5daK1ZZXA/lmiIkEFlcD8c2h9XOWVwP6/1INQzZXA/uYXoejFlcD/UFEyQPWVwP2m3Vm0+ZXA/+FO3CEJlcD8A9eDZUGVwP+Q/yrdLZXA/UgHsJU1lcD+xYVFfcGVwP2FoAmViZXA/bQJsWV5lcD8Wc02ia2VwP/xBPMxtZXA//UmsRXBlcD/eO3CLbWVwP1/KlwWJZXA/GzobVJdlcD+noGjGk2VwP3r9kU+rZXA/9q30CMJlcD/HGdA20WVwP2E11zPQZXA/7DJDZtplcD/j846P52VwP7wL263wZXA/cvl6ABFmcD9RQMGdFWZwP31k2tYOZnA/BbkY6/9lcD+ha6wd9WVwP+BLV6XwZXA/uM+ISu5lcD927aaO52VwPzBrFZzlZXA/d0xMvehlcD8jVyt+/mVwP3Cu61n8ZXA/T5J3/gpmcD9bdXZ8ImZwPzDidf8KZnA/5MkTLxZmcD9hFhFQJWZwP55hI/wYZnA/3G9DCyNmcD9KlFxaDmZwPzBF43EFZnA/kD9N3wxmcD95mWbaDGZwP+dfBIMJZnA/RvkNjAVmcD9DBSMDBGZwP/z+ATwFZnA/c0aQEwhmcD+pS4/C/GVwP+tLr7PxZXA/Pa50ff9lcD/8ene5CmZwP1yuYt0SZnA/k9VlUyFmcD8lUVJ4K2ZwP58vP4A+ZnA/X4PueEBmcD/gunjgM2ZwP+XsOVUlZnA/4vLW6SNmcD+zrX0VIGZwP9xoPEQWZnA/sGBqQhxmcD9Nw23jEmZwP6BPbMoPZnA/axRzyP9lcD+JsusM82VwP+T7mR33ZXA/aKC+Y/NlcD/4gcv/8GVwP5nVcUb3ZXA/uVZbXu9lcD+xfphb/mVwP+1Dk7UKZnA/JB+NEg1mcD8QSzWTEWZwP3hiRQYXZnA/P2YQdxJmcD8AJ/8QFGZwP7zBnEEXZnA/XfD83SJmcD/PO5PFKmZwPw8rtZk0ZnA/zy8MIjlmcD+Rmzc/PGZwP6TCSyIxZnA/+S7WqkhmcD9/DQPTOmZwP/KvFQEzZnA/7wo4MzhmcD9TluY0JmZwPy1FYaImZnA/3kc0lRlmcD9fC0iKHWZwP7dpEbARZnA/TVaBVgxmcD9+M3JCGGZwP3gWvAsUZnA/7oHwbRNmcD9rMN749mVwP+WkJbfwZXA/8l8DGfNlcD8Sy2r1+mVwPy/qbZT3ZXA/Y293UwFmcD+xrxcqI2ZwPysDTWMiZnA/88Vk5R5mcD+WHm+pHmZwPwOwvVUiZnA/6anoJC1mcD/xKX7ZL2ZwP5tlnOgkZnA/VL2TUB9mcD8oDwfgHWZwP7WeRtcXZnA/P3kTaA1mcD95pjzSIWZwP9HjsWAlZnA/kvMmjy1mcD/SJ1C0KGZwPze3ehMgZnA/0PfyHR1mcD9knfX8H2ZwP79fCuszZnA/KFIU0D5mcD+Ksgu2SWZwP0lhbvlKZnA/ZK/gpk5mcD/AKwsISWZwPwewMiE2ZnA/ufcxfzNmcD+E+QPrSmZwPwgdW0UwZnA/tKeMxD1mcD/FlujuMmZwPy1jH0VMZnA/fpvurjpmcD/Hl1KZSmZwPwnLKQY3ZnA/g0RlakRmcD/g1zYSQmZwPyxrVKw7ZnA/4E7FzzBmcD+q3wJ+GmZwP14MF0MXZnA/ubVSZh5mcD+eFDgQLmZwPyCxIHceZnA/3HTHRCdmcD8F7bnXGmZwP9shYakcZnA/J3oz4R5mcD9TYRAoA2ZwP7YvN9X1ZXA/2KD4xPBlcD80JZkd+mVwP8CNVwYCZnA/vIxo4ABmcD+TfA3/BmZwP+o5rnb/ZXA/JGjaFvxlcD9V17YA/GVwP/BQRYEGZnA/0TILJhpmcD/hCBvyHGZwP371GFQQZnA/vWKUZhxmcD/nj4EdJ2ZwP0+lfSMoZnA/GNVMKS5mcD+YRDM6FmZwPxbNhswRZnA/yiwQbQ1mcD9GgCSMEWZwP3TwSzUeZnA/IIPnDiNmcD+2lcTbI2ZwP0SqDKARZnA/BLxuexFmcD8Kd6fUGGZwPxUsIHUiZnA/Kim8QxNmcD9/yQ4vF2ZwPytfVsAkZnA/N2WwMRZmcD/N1WwtHWZwP3fpAN4WZnA/whoH5RNmcD/T60DLBmZwP+BSJ0sXZnA/in8XZB9mcD+fTAMCIGZwP7Hldz4nZnA/Oikt7BVmcD8HqYNiHmZwPzBYVv8bZnA/+fk6UhJmcD8SqLigDWZwPwlmsyYQZnA/hovE1SJmcD/RcGcHG2ZwP1Rn/bIQZnA/a1R3kxdmcD98yv6CHWZwPyGZXZYaZnA/RzBflStmcD/hen11HmZwPzCCMpEKZnA/Rc+hMwZmcD++UxbdB2ZwP+1PVBbtZXA/0LfbZ+plcD+VTRjr8GVwP/RnkkzhZXA/UZXuCeVlcD/wudTn+GVwP8woAzPxZXA/jo4Am/llcD+mpLxnBGZwP1HoYjcNZnA/z8W6FA9mcD+f0iNREWZwPy1/3qgAZnA/+1UkhgVmcD/kN5pIBmZwP3F0etsAZnA/P4J1uwJmcD+yrjQBHWZwP0api/0jZnA/6aNGzSJmcD+X8SeFOmZwP+oiZnw9ZnA/2Y6gRzRmcD9ixtQ3JmZwP7TdvtomZnA/8CTi0ClmcD8oqePaI2ZwP14ubxEdZnA//nrnIRNmcD88gvpLHmZwP3kwWtw1ZnA/mhy5yUJmcD9ixnM4VmZwP5K4dt9XZnA/oZJduk5mcD8qJl/NV2ZwP/CTK/JrZnA/SIdkQXRmcD+RWVHfcWZwPzGl6X1rZnA/LT8CT2ZmcD8JJEmudGZwP7v0OJFrZnA//LipfVJmcD/8d/04TmZwP07QWGRMZnA/6DyTjF1mcD/xHiwdWGZwP5AiAj4+ZnA/PTs0pTdmcD9LXqg0MGZwP/knFXwfZnA/Iwwt2yVmcD/rjDeRJ2ZwPyugCU8ZZnA/FnC1qRVmcD8pijpbEGZwP9wXfQsOZnA/N5MLyA9mcD/2hzmpF2ZwP3dJbxsgZnA/b2YYNAZmcD+pxMdlAGZwPzRJkF3vZXA/IsgWkORlcD+hceBa52VwP+w5KBDjZXA/z93au9ZlcD9XOLvD1GVwPxN4AmvGZXA/pOrCU75lcD+wBjNvsmVwPwCpWXe2ZXA/ejgTGqxlcD+cBKkPrWVwP5Le8CajZXA/dWEESL5lcD/87TRAwmVwP6cLJeS3ZXA/6FuLSLNlcD9RWH9JsGVwPy72fpecZXA/trDY0JxlcD+YHN7+jmVwP5dMhZ2dZXA/NBD9N5llcD/yU4xCkmVwPxp4jk+aZXA/HK5C8qRlcD8+mgvepWVwP2lNleeoZXA/vmetQbFlcD+6DIycu2VwP3yjsQDBZXA/p3Qqla5lcD/wxcVgpGVwP2nr6KWUZXA/fwKvlYhlcD8ARb+LnGVwP4W9osOjZXA/hcGnL69lcD/6VpBNvmVwP7GWZwG3ZXA/n+GTBaxlcD8wAbAitWVwP57xRVCvZXA/Xqjo9p1lcD89lc16o2VwP4829fC2ZXA/hOehxsxlcD+MNcG4x2VwPzoV+7zUZXA/EkysPt9lcD9sCHlZ6GVwP+rFQsTiZXA/Riv/Y/BlcD9MHIsX+WVwPxbXO7rfZXA/wiWiPdplcD9FJY8R22VwP6x/mBfGZXA/NmDDL8VlcD9eyDHY1GVwP5fJTF/QZXA/+jMYitZlcD9CX+sL5mVwP6OXqDbfZXA/ld6RyNplcD9NrZOxxWVwP+gXLEvDZXA/QqG90cllcD8wqSB1smVwP9Xs5F6fZXA/VJvmT5VlcD9WlZpTmWVwP15tvsSCZXA/+XCF4XllcD/LuPIJlWVwPyn4njeOZXA/AqX2RoVlcD/8y+6dkWVwP6LLpGiXZXA/NYVeooZlcD+Af9dYdWVwP9Hpa5FwZXA/5d6Rv3tlcD+jgr8EgmVwPyveZkKEZXA/SGKrbmtlcD++nSQCVGVwP/1eXuFFZXA/CN/4PExlcD/u/KHPUGVwPzhPBu9nZXA/J/7snW1lcD8ploLwbmVwP8W7nZBlZXA/ZibruGNlcD/3xzyCS2VwPwzHVYs3ZXA/oLiB40dlcD+2kXWUR2VwP8Qm+VVTZXA/yCAzE1llcD8AE7bkZGVwPypcLq1lZXA/iQoFOmNlcD9BBAYQX2VwP7+ysANnZXA/UQzVsG5lcD89jsV6aGVwP3tvn9tdZXA/6NIrvmZlcD8d+qteaGVwP/pM9OFjZXA/ALqGzmhlcD/MmKC8h2VwP1q3neV8ZXA/XihyiXRlcD9XPcgydWVwP9+cUSpeZXA/CO6sJD5lcD+8ITcNRWVwPy+6hDQ2ZXA/pOpTwUFlcD/LZdPfSGVwPyMYTj9KZXA/GSG2FUxlcD+dP31MWWVwP0zd5ClZZXA/RM4f3lBlcD8VMYcaTWVwP7OxIHZZZXA/QG4SjUplcD8drCsARWVwP+7scxNTZXA/pQdJgGZlcD+Q2IJiW2VwP5mjSchSZXA/SCHFhlllcD+QKsgEYWVwP6fRMFNSZXA/24EhQU5lcD+zcB23W2VwP/Cow7hrZXA/mLornXNlcD90CpHbgmVwP/fUJmNlZXA/PQ0v/15lcD+vHFFdTWVwP3wPWGdSZXA/3MFXRVBlcD9cw2riUWVwP/0sHShZZXA/gjwrKVVlcD/ErTLJV2VwP711sopcZXA/vQIDCWllcD9d3rt4bGVwP5Vx26NgZXA/XcMP3E9lcD9K29DkRmVwP0n4KzJBZXA/rDsKczxlcD+ZqcjgOGVwP/eBXs4tZXA/B3eRkz5lcD9CDV5iRmVwP1zdwfpOZXA/xZsnDVxlcD/l2rPmU2VwPwTwW3lWZXA/J1BZJUFlcD8p99bhNGVwP56X5WQwZXA/WxL/yTNlcD8G5PGaNWVwPyRsUXEuZXA/BCUcCC9lcD9Ohxr8H2VwP9LtuDYrZXA/5nX0QzplcD9TFMneJ2VwPyoj+J04ZXA/eY/zpjFlcD/LpeDQN2VwP6Qq+MNUZXA/Q4tNnGhlcD9Cq1SAa2VwPwrK7HlyZXA/SYYs1n9lcD+XENKbeWVwP2ZlIyF6ZXA/t8RFf3JlcD9N3vHvcGVwPyq/38ZvZXA/nfgJ5G1lcD+cVbnaaGVwP8mxt3d0ZXA/XufT2XZlcD9aBeoAgWVwP7bfyjh7ZXA/3t5tvXNlcD/zuSRFaGVwPzyabOhtZXA/VCXwW2NlcD8+g8u0V2VwPxnxbNhlZXA/ljEcNVtlcD8J57GJTWVwP2aagxNHZXA/1nWJIzxlcD/+069lRGVwP9jYDc5QZXA/d9WbEF1lcD+1DeY2WWVwPzs07pRUZXA/UjgYElNlcD/4KErpXWVwP0DJFdJkZXA/hfep0GxlcD8iisy5gGVwP/ldVHJ1ZXA/QNexwYZlcD/uhZHcjWVwPyLDJ1yRZXA/3xvHj6BlcD8u7dG2oWVwP3sfXmicZXA/NMr3BadlcD/4MZVlnmVwPwIOkAanZXA/y7TahKRlcD9E7V1wpmVwP1VPuwaUZXA/E/g8yZZlcD/KX1xCkWVwP17IOsmNZXA/T37okI9lcD/lgNiPn2VwP4u3HeKdZXA/M1bH4I5lcD8HkNSDiGVwPwsM5k1uZXA/90bs7mZlcD/g/mreZmVwP24JzyNrZXA/m/M7xnxlcD+DUWnsd2VwP45xWGp6ZXA/IIqexJNlcD8M4Psfo2VwP68x7gOkZXA/cT7w7qJlcD/ypyZnoGVwP+Do5c2ZZXA/MEeeZaBlcD/mgR4uh2VwP5AYvuSAZXA/uG2YuHNlcD/vrp2meWVwP7M7Kvx2ZXA//MsDfn9lcD+UXrL1gmVwPyRkOwuKZXA/vzbfzoFlcD8DZnJxiWVwP7RPueF/ZXA/YlvqdYNlcD+g1VwvjWVwP9d/lPiVZXA/k6fXNptlcD9W5rrcl2VwP24yw5eLZXA/4y7v8o1lcD9gzMeuhmVwP7H+vCSRZXA/3SfaNJNlcD+kA62Ko2VwP/kRLyaTZXA/NLsHUoBlcD8anNdhj2VwPwY2zEqDZXA/07iGX45lcD/UVuvvmmVwP8Sy6ySfZXA/Td0ujo5lcD/Dzvp0n2VwP/GKLpGhZXA/4xHAO6NlcD/3QcG4nWVwPzDCUCWdZXA/csXwfqVlcD/cIz4Bn2VwP93O0cmZZXA/uiZ46pBlcD/zyJ7JfWVwPwZraEhiZXA/u08IY3ZlcD+e9YptdWVwP9oyuG9zZXA/JZ4Uf19lcD/VwxVxXGVwPxftV6pTZXA/hexAkF1lcD+HXG6qWGVwP2rC//lkZXA/o33RT15lcD+6f/HgVGVwP5Iz6rhVZXA/UoCrDF1lcD9I7G+JV2VwP3pt60hqZXA/FAfWCmJlcD891DoUYWVwP/UzlUBWZXA/y/kGSkVlcD9KAKNiWGVwP7XqrJ5QZXA/mVZObD9lcD8RoN+BImVwP7jAvfQTZXA/wSMPPB1lcD884r6PK2VwP+Oj3SU3ZXA/KNgk4S9lcD9TDt3WHWVwP5lHjocOZXA/jUTpyx1lcD/OJWPmEGVwP2U064ARZXA/qdD+hBxlcD+vQKS5HmVwP3UOktgXZXA/ZZTjZRtlcD8fAfTXF2VwP8juQFYaZXA/brAmoxNlcD+oWLQAEGVwP71zTbUOZXA/2jCMsA9lcD/Lb5FY/GRwP+XRVgL5ZHA/WcH6i/JkcD+BuxO1BGVwP8XDb7wLZXA//TIm9AdlcD8m1aUMDGVwP/dg8r0hZXA/P5QVFhllcD9FHxM/B2VwPxmOJiX4ZHA/zyGHpexkcD+77LFE32RwP8UDnaDtZHA/HK4PDPtkcD+b4liR4GRwP7ZUtIjvZHA/VJpRvfZkcD/xu65oBmVwPxgvWfASZXA/RqzRFx1lcD9+bLL0HGVwP4dLZfwhZXA/AoT0ezBlcD9KN86tOWVwP+A75t81ZXA/q1TdTSplcD/PNC3kGWVwP/KbbFUuZXA/3zeGtiVlcD9vc/WGEGVwP0LDuiQBZXA/54PU8/NkcD85Zbmm/2RwP17kSfMFZXA/q4WwCvZkcD9M0CKg72RwP1mDXkzxZHA/R1fluf1kcD/EdxdrBGVwP6WtLXcXZXA/sE+6Jw1lcD96AF7bB2VwP5y6PO/+ZHA/kpUZKhBlcD/YWDJy+GRwPw2PKf7tZHA/SESf1+xkcD++rPme7WRwP5QlnQ73ZHA/LUHsbPVkcD/aFwCM8mRwP760WEraZHA/G6EPjsxkcD89XD3U1mRwP/TliJzfZHA/OGtMvtVkcD9t9KTo2mRwPwI6H+XYZHA/SetPcuVkcD/g4h098WRwPzNb3HvwZHA/1gZmPPRkcD/oVYYB4GRwP5lR8BbkZHA/1F9brdFkcD++ATYP2WRwPxHb24jkZHA/OoDwn99kcD9uVPZQ7mRwP/lsTmf2ZHA/qMF6vulkcD8zPW1e3WRwP4BCedniZHA/BtCoQNpkcD+MyXrCzWRwP635xyPMZHA/9dcv8ttkcD8DC0L73mRwP0omp13iZHA/MXVF+fZkcD8lLcrI6GRwP98lgsXVZHA/LhdqRNpkcD9F87VcyWRwP6FnViOmZHA/O4WZuqVkcD+DhsBIq2RwP1XyVoavZHA/d4TVKLhkcD/M6jG9vWRwP2WI8RG/ZHA/Q3L90bhkcD9VvK0OtGRwP/tLE5G/ZHA/Yd77Eb5kcD89nmI1u2RwP02uIgS7ZHA/GESf/bxkcD+BY69zt2RwP2Pc+1yYZHA/OoqX249kcD8ZgrVdg2RwPxwEmxOSZHA/6Iuao4hkcD8dwJVthGRwP3qy+x6OZHA/6cTfGWxkcD/SAxFNYmRwP219dD9VZHA/UKtQWlZkcD8TJr8VTmRwP0kW7jpcZHA/2nNDrFdkcD/9R6AuXmRwPwFATXhUZHA//3lOwVdkcD/N8SumWWRwP0x0TUBeZHA/RUB091xkcD/5qJEWb2RwP9W3e4t0ZHA/Mo96AGxkcD8eF+MWdmRwPyBRrAd9ZHA/o89AWXhkcD9+IPQlYmRwP2gHHNhzZHA/MG6GQ29kcD/PD2lDeGRwPw0fpviQZHA/gFKqo5hkcD8HW+ivh2RwP4+iKrKAZHA/3S6Lm4RkcD/h4AzRmGRwP/dAdA+CZHA/DkNuw3dkcD8ms7bGeWRwPzPb6J6EZHA/WRrjG4VkcD8LZXgpfGRwP6SEWu1/ZHA/bZyiVohkcD8KnEP2h2RwP5/Y/daQZHA/R3GmMrRkcD/LWSfbv2RwP5zwRarJZHA/AUNwDdhkcD+MWdIx6GRwPz2nLQTuZHA/ED0oue5kcD+dczlH62RwP82G7k7zZHA/7KPs5O1kcD/oTNCy7WRwP3xJUasAZXA/DNuYWPdkcD8yJpM5+WRwP/zsi54JZXA/SBo7OxFlcD/6dhgLFWVwP3EBlowQZXA/JcnB/PxkcD8k8Vry/mRwP9nbGQsIZXA/ycuSoh1lcD+B9Th+FmVwP/2vyCMEZXA/Fx6prQ9lcD+fY75bHmVwP9cWX+YmZXA/D0DQZxFlcD/yBXR8LWVwP5Z0iH0zZXA/Ir+NoSdlcD8M2kx8LWVwP3WYmU9EZXA/DHHCmVZlcD/m4kygQGVwPwJmHpc7ZXA/6EumcDdlcD/PguTcM2VwP9wgIlQqZXA/lg6lsyJlcD+TOc6WG2VwP9YMawkxZXA/iUvAJiplcD9HxM4lJWVwPzqpB0UwZXA/7g2oMzplcD92INL1NGVwP+OUdiA1ZXA/nYpmLj5lcD8fC8A7OWVwP89plnM3ZXA/mv+Bk0dlcD+uvqUIO2VwP2eIIRw7ZXA/UptNZk9lcD+CcbvuRGVwP1COZzZYZXA/scL2UW1lcD8sutsTbmVwP4MiYmeEZXA/Nh2NcHFlcD+czO1Ha2VwP3QF3FxcZXA/Ombs93NlcD+mtw0IXGVwP0y+1T17ZXA/Mms4eY9lcD97MXgRimVwP6mqmbGPZXA/NKfzwohlcD+xVHwhjGVwPwP4yr2SZXA/KyKq6pBlcD9JIGK/mmVwP1TGSbWsZXA/wHckF7xlcD8REsHLvGVwP4YvZV3BZXA/pkX3INRlcD8aNv0M2mVwPwRpjTfTZXA/qAUpZNtlcD8aNvU70GVwP/Jo4vfEZXA/Iey2LMhlcD/X5v0quWVwP1J9WFu3ZXA/feRpvqFlcD9WEmcJp2VwP6FouN6eZXA/247fIJdlcD8R5XxnomVwPxiOgK+sZXA/V8uZBa5lcD8qEagBqGVwP9yg6Y6rZXA/Hm8BOaVlcD+upi0woWVwPyFAuOSXZXA/1rLQz6BlcD9GwuuqoGVwP9hseVyRZXA/jtCzb5RlcD9yai1gl2VwPyY9V9etZXA/CTEB65xlcD+cX/A7nmVwP9aB1AyjZXA/WZZm0IllcD98AlJsfmVwP2SiW7SLZXA/X2yecJNlcD8mXmJVkmVwP6cBjx+iZXA/fLxKxqZlcD+A5QzRnmVwP2Neaw+eZXA/IkE32ZdlcD9pab0Tj2VwP+i47G2SZXA/xes82ZtlcD91ZXbsnGVwP7kR1OyWZXA/yxUo0oZlcD/mO4+jnGVwP3boXJOsZXA/s4KaiaJlcD+VDrUxnWVwP0KZ0aCaZXA/WW0mX5RlcD8VrQ5ri2VwP5SNIpl7ZXA/5HVCOI1lcD/wgbSDjmVwPzS8FvqcZXA/XGhiWJdlcD8gupw0jmVwPwf8lmaOZXA/Xgql/4JlcD/HLtmhjWVwP3i6CRONZXA/arGA1JVlcD/IpBBCh2VwPxYdaeOaZXA/WfONw5RlcD/PFek4kmVwPyAfx26JZXA/dfUiZpFlcD/UsfnMrGVwP+Rd6mmmZXA/Ng/kDaRlcD8/O8+rm2VwPxyIjDmSZXA/vdyqC4VlcD+u1Qp6e2VwP/apvS5vZXA/xEWZimplcD/vZ4PcZmVwP8hFeK1hZXA/DyMFr2JlcD/+iCr+XWVwP9oMJ/BPZXA/wnIKPVFlcD9RDi0sX2VwP4A9+NZnZXA/TPk+x4NlcD+luwLphGVwPwjHoP2LZXA/4X4oWn9lcD+xxLDzc2VwP6WNyZN3ZXA/RxAMSXJlcD/yuMe3XmVwPyQiKbZoZXA/cZs7AGdlcD/QPFAvbmVwP7+gZLJyZXA/OJ+kKmJlcD/UV7jLbmVwPwn+c01QZXA/69k0tWJlcD/RvyRUcGVwP0BnBbZkZXA/JQ0Ib19lcD9ac7FLW2VwP4nJihhhZXA/p9/qg2BlcD9TOSXHU2VwP9/EQa5CZXA/zTly80plcD+EL6JmXWVwP2mWl6ZjZXA/YEIsR1plcD/2CScSWGVwP42AhWllZXA/swqZdnRlcD8MmzzoZmVwPwg1ZXRqZXA/IAU8bm1lcD9SuKcxfmVwP2KOFzOGZXA/6ARSEJBlcD/HytmZnGVwP1F5EsWuZXA/LsBiCbxlcD//UpG+u2VwP9Y1N2+uZXA/JouKuLRlcD/HL4tPzGVwP60/8HfNZXA/xC3jV8VlcD8tHUC4w2VwP2n8RJy9ZXA//CKclq5lcD+yAIDel2VwP6cOuHaeZXA/ExYGeqhlcD9t9EGLs2VwP8q9CQasZXA/MWn0trtlcD9aB/jhrmVwP4TQdLXBZXA/xtw/yqplcD+A0VNRs2VwP4mon9unZXA/+Ln9l5RlcD87EyBYh2VwPw6FCoFzZXA/5yWpV4VlcD8ZEq1Yf2VwP36HW8mAZXA/Y0BkuY5lcD9pX1gblGVwPzH+aGeVZXA/5LlLDZVlcD9cXEX+j2VwPxgF6AFzZXA/crdiBo9lcD93WwHznmVwP+uhuj6pZXA/1qdegaJlcD/gt7cFp2VwP4WUPu6QZXA/3q3q7XhlcD/YX8ayc2VwP89qCLxtZXA/XME6dlhlcD+drpn1S2VwP+w7uI0/ZXA/+vURvTRlcD8HDeFEOGVwP1A0Nxc2ZXA/XZysvyRlcD9Q9UpeH2VwPwpozXgZZXA/btewnwplcD+W+jEr+GRwP9fFCsQFZXA/5kucnQxlcD/jD5tEEmVwP7yFkmwhZXA/RmalKTJlcD/XoYcrJ2VwP4FzlAgpZXA/4AdcMidlcD+cECybJmVwP7HcOYYyZXA/8wk3LD5lcD+Ghhc8N2VwP6plz9YlZXA/1JDElzJlcD80QkDgL2VwP1m28EwrZXA/mKrWFSBlcD/1ZJzDFmVwP3aUTI0SZXA/UWX3xRplcD9hB/EFHWVwP4e0kYkPZXA/Ao8iWvpkcD8PNlaY9GRwPy/YTRr7ZHA/NcbkUANlcD+vcJbSB2VwP+3Y1a4KZXA/g5fzFw1lcD/GP6Ve+mRwP80PuF0GZXA/XFJbRBFlcD/27yKSFmVwPyM5u30YZXA/m7qu4RVlcD9qWoALCWVwP9UJoO/6ZHA/xlyrQ/RkcD8w1xDG62RwP1ZqoNv0ZHA/jQR5v/tkcD+Kv5Qv8WRwP7qceXEKZXA/zvDYmxplcD+5tv+vJWVwP2OiMdErZXA/CqV3yCdlcD8NwxmDPmVwP+nA1Mw7ZXA/Lcewd0BlcD/XGxvNQWVwPwI62tJBZXA/OqzgGUtlcD8Eb+VVOWVwP7t9y2RCZXA/1mVN8z1lcD87iqU4OGVwP3elZzg5ZXA/NZnm+EBlcD8vLHOdV2VwP1CAiTlkZXA//ftckWhlcD+61NXOW2VwP7qIj7ZfZXA/EoBTF2llcD8u0QxsXmVwP+aWkbpgZXA/q9JS0FdlcD/HH+y8WmVwP45jm01pZXA/V1GnKHNlcD8Qqh8hYWVwP1rHaG9lZXA/d0UlOVdlcD/BJzZPYGVwP2Nw7KJRZXA/fXyHG2VlcD+tWpsoZGVwP2CAvF1qZXA/xUiLdlNlcD9vChOmZmVwP10dE9xiZXA/jKsYJmRlcD+VytL2cWVwP8qTq65eZXA/8S9T315lcD/ULLAJR2VwP35uDxtZZXA/dDSNV4hlcD/6siY9emVwP0sRn1d1ZXA/4PhuC2RlcD+b11SwTWVwP/kSF09BZXA/nMDRTSJlcD81LmwJDGVwP9K9y/H+ZHA/3wOHhf1kcD9P5VU9CWVwP6hpohsaZXA/BAyJ6RhlcD8Ib/gGD2VwP0ApwVMbZXA/l6cdVgtlcD85yNQpAGVwPwvH5G79ZHA/c/htGexkcD+WP48S1WRwP02kYW3DZHA/TDQJuMhkcD9mWQc8xWRwP8+RzFy/ZHA/AyUel6dkcD8lJ8p7umRwP3umXbm0ZHA/w+H1srtkcD/hel1I1GRwPyPQZ1G5ZHA/hr1ZeL1kcD/fowT4qWRwP7TApU+xZHA/ZcVSBJRkcD/ITLT3lWRwP6LGymKqZHA/bLLJGptkcD8K040LmGRwP+UWcxChZHA/IP5B/pdkcD+i1MFAhWRwP8MGQrWFZHA/FisTeYZkcD/xSolHbGRwPyKynZ5yZHA/xW5QpHhkcD/6460meWRwP9X+XBxuZHA/USKLO2hkcD+DNigWb2RwP2xitk6AZHA/QtQkI35kcD8jTYWpbWRwP+Q1nMtzZHA/OMCeYnNkcD+PsZqYZGRwP2HfQbhrZHA/KZUSlV1kcD9fhkztTWRwP9Jcg7pKZHA/EHw2vEVkcD8vYBJcPmRwP3coPhRPZHA/ssZSSEBkcD+iuDamK2RwP4oKwiEvZHA/YC0OkypkcD9kgsFiJ2RwP3K0SyQVZHA/9fZcKgFkcD/B5xhG+2NwPyjoiWjzY3A/3f9ogvZjcD8y67H+4WNwP/BIrUT5Y3A/L/DLVe9jcD8mZERQ8mNwP6cWlC7sY3A/Tg8eyfdjcD88tJfQA2RwP06Df3YHZHA/P+IlcgRkcD/UKGKN72NwP/lTGs3nY3A/8V/ODvhjcD/uEnNaAWRwP2W6W6UJZHA/RSKgZilkcD9Dzg4XJ2RwPyLPU4cwZHA/A9ngYy5kcD82mUGlHmRwPzzDZu0zZHA/DN/Bgz9kcD8f4tZtPWRwP5yZJxQ1ZHA/6etQbipkcD8D+lL0NWRwP+s5aUYtZHA/wJTVJzdkcD/LAqbyPGRwP5gJfIE8ZHA/Lp1qzVFkcD87h2VPVmRwPxR9FHBrZHA/UoZsWl9kcD+qqn4OTGRwP0PVvrlSZHA/8l/yNEVkcD8C74MSPmRwP/cyk883ZHA/rk/amkJkcD+0ahPLR2RwP5WoInJEZHA/CXy6DlVkcD/4wlAJTGRwP88q9hdQZHA/Xn+IEjpkcD/8Yj21NmRwPw5iH7FAZHA/h4ObakpkcD8oTFbaSGRwP9xgpgk5ZHA/q6wn3jZkcD+v0ZGbJGRwP9lpu+AzZHA/2/fJKCVkcD9XPdA/KGRwP5ulTaolZHA/XwWLGTFkcD/39L7LM2RwP4ESB+FDZHA/rcYCkUBkcD+Bxr+eQ2RwP4gq0ds2ZHA/tqTlKDZkcD/AL+E+OWRwPytkCZEpZHA/qA+/7iJkcD+jJWg+K2RwP+Kf8302ZHA/0ZAMKEFkcD9WVOVANWRwPwMNmEQxZHA/O6W1vjFkcD/0QaniMmRwP3y0o3onZHA/JOTBeyFkcD+bsAOrFGRwPwBLgG8VZHA/0XGIM/BjcD+XeJAf6mNwP5uwixHXY3A/Aj6EItVjcD/V8bdc3WNwP9zomX/rY3A/G/co+PFjcD8VHMWh/WNwP9t7Ddb2Y3A/fE89+/ZjcD9WJOyj/GNwP59cOObvY3A/CSmlh/hjcD9OQ6mI8WNwP2DWkUzvY3A/QA78wOVjcD98YGXr72NwPw1qI5nZY3A/zTHr4c9jcD8fDgeq1WNwP5mdVonhY3A/UUfUTdFjcD+rHofJ1GNwPzBxdWzWY3A/DULvwsBjcD9MX9Hov2NwP5bU2WixY3A/HPMlsLJjcD89ds+ut2NwP7cK0vCtY3A/orz427RjcD/ZjmsNo2NwP4oiWH2XY3A/JFb06KRjcD8+LmXHsWNwP3xDwYKlY3A/boKMBpBjcD+AOqvKkGNwP429ff6MY3A/Nv7SRnRjcD/fVaTQdWNwP8REGnBuY3A/lOeihGdjcD/aDeDTVmNwP5HP0MxYY3A/ZdJrx0xjcD/TjguYTmNwP2bHJy1KY3A/93q0xlNjcD+Lq27lSmNwP9OHI21LY3A/Yocc+l5jcD8NEjwUXWNwP7wu+g5LY3A/4Nl85UpjcD+eTbxNVmNwP6ZvOzVLY3A/PAD9x1BjcD+xhEINW2NwP3XOrspRY3A/krvjBU9jcD/nuOxaMWNwPxbCouE2Y3A/FHc2EUpjcD96/Lu8RmNwP4R4nEg6Y3A/n4y5Ij1jcD/RzpIpM2NwP+UaWCkrY3A/P0k6IShjcD+OKGHlJWNwP2DjVlkMY3A/97oOfAhjcD8hhaD+9mJwP00WkPbmYnA/LcLm0e5icD9VzrSQAGNwP9cY03H5YnA/1IcRRPJicD/128RG8mJwPwVVpkbgYnA/nQNjHcticD/Nf3qu2WJwP3KlyEngYnA/d03C3O5icD8zX3cU+WJwP5zDvcz1YnA/ERzE4PVicD8w0vHH/2JwP57c4aoGY3A/FhLGsgRjcD/jXyqnA2NwP9cb6P78YnA/Yehda+picD/sYueH52JwP1OtT2TaYnA/c0KPF85icD9idwpT4GJwP1aO8SfdYnA/0/SCCtVicD+UJeiDxWJwP5A0i6u8YnA/ny4ferNicD9reKcFn2JwP2zqcWahYnA/48IShKpicD9RU3lVtmJwP5Fr+tbCYnA/9ma+8MlicD8YdwOpzmJwP7GD9tnjYnA/pY3IG+1icD9AjJqP9GJwP0szxV7rYnA/6vFSTfZicD8phMjkBWNwP3uw5Ir6YnA/5yOV/uhicD+EeI8b4mJwP2zxkinYYnA/JkJDQthicD8F2pv+0WJwPyBUuWHLYnA/pAlHTNRicD8kFAfgymJwP7xrOCfyYnA/ZaTTFfJicD8wv5Dq82JwPxxv1Fn8YnA/Lc2rOxVjcD/KMzlpHGNwP/1ibHkNY3A/aRIBfQtjcD8Dnq7+C2NwP+7WvHsNY3A/95H9mA9jcD/ESpaQEmNwP6qrABIPY3A/NDQLjBRjcD/fBBH1EGNwP5Lygx4cY3A/QRjvlCRjcD+WzDegI2NwP0erWTAqY3A/IyoTuy9jcD/ItjqZO2NwPwcEABs2Y3A/bFx8fDdjcD/kfNSKOmNwP1Z1Tok+Y3A/Kgy/7ztjcD8vLscgQWNwP7HXDchJY3A/Ygo6X1VjcD9w9MpOPGNwP8J4hBs8Y3A/F3lb/DpjcD9gvD5uQGNwP/CTHD42Y3A/Mq3jzTtjcD+ItweERmNwPwPKZU9JY3A/pTNm4ERjcD+INzHwSmNwP8JbmPtIY3A/dDzvwy5jcD/CoQcoGWNwP0MSCcckY3A/238v5jJjcD8K3quDKmNwP5DVbnY3Y3A/Xlu8QiNjcD/8LYbAGmNwP6ziFiUSY3A//i3QcxNjcD9OhhnwHmNwP3sINKcjY3A/ST53wCxjcD/kQWfHKWNwP2tt3MQaY3A/Q6chcA9jcD/yTcvPF2NwP0nClcUQY3A/AbwnqBljcD9doMKJFWNwP/VxYGINY3A/XtP7gARjcD88NXuC92JwP3NAA7T7YnA/pHb7lwljcD+OY5fh5mJwP0ccQH79YnA/MBaVmu5icD+sLVub8GJwP2zNtoX9YnA/vmE6PApjcD8aGTt8DmNwP4L/2IQHY3A/ZiQQNwVjcD+CakZr/mJwP0KTUnQAY3A/ws6EP/picD9LONbl9GJwPyMLs974YnA/2TZBMt9icD+fimd11GJwP/25QT3cYnA/WDldl95icD+Kx1ld8mJwP+vTxFf+YnA/60JTM/xicD+h8Hup+2JwP6RMCfYAY3A/7S7EKABjcD99HXMz8GJwP2Vp/hEBY3A/8UXLo/picD+nr61nBGNwP+V4UJP+YnA/e1FLdQljcD9W8rozGGNwPwesYD4SY3A/33pJLA5jcD9asdwkDmNwP8Q3KEf8YnA/Bxc2WehicD/aMT1w42JwPz7VYpHfYnA/4OoEBN5icD+r+0Gg3mJwP9ZaAifmYnA//KWwwOFicD/+GCo+2mJwPy1Y16LHYnA/UqjrLb1icD+xKRVxvWJwP6rHS2awYnA/Ib6TRKxicD8UeswbkGJwP8QDT/ieYnA/cDD5e5licD88O+fyiWJwPxuG7LuTYnA/CFXfgY9icD+99k1mmmJwP9gr3FujYnA/0K7iMKxicD8q1cmMrWJwP+vat06RYnA/ANqkN4ticD9g3oOSiGJwP3sHqIOTYnA/H2k2cqNicD9AUq+Rn2JwP7ACLuWkYnA/OjrOxqhicD+YD1ClomJwP7vZaGewYnA//wXvZJRicD+P/Dzmm2JwPyNBFoygYnA/rU4YQJZicD+fGZGalGJwP6W6oUOdYnA/OP+fgZ1icD/vaR19rGJwP7QcVUGmYnA/bOiwZbBicD9Nrc0LmGJwP5TsBIShYnA/v7sS+KRicD8+1HJtmmJwP4GgdXiwYnA/DwydML5icD9PNhcswGJwP779o6q7YnA/QkpmBblicD94vi8kx2JwP9B3KQbVYnA/709Lec1icD9Zvs5lw2JwPygKegi+YnA/HmeOa8ticD8Nh+Q/ymJwP10Rqt+/YnA/wNzh28licD/C7z6Sz2JwP5gIPzvMYnA/FDbqBdBicD+35+lMz2JwP7yZcbTEYnA/tC1U5K5icD/roZQ8nmJwP5zSjIiWYnA/oE6NHKVicD/psFBDqWJwP5lUxWKdYnA/VDoPJ4xicD8m1RdmnGJwPwx26EiFYnA/4POfPoxicD8r3d3ZfmJwP/vvyzR7YnA/p5BPjINicD9eFEvUf2JwP4T+3oF8YnA/ojDJz3licD+riMlogmJwP35pIVqWYnA/liy1yYNicD8hORZBh2JwP3rFgkaQYnA/NHc8joxicD8y9E/Jj2JwP4Tpu4iWYnA/K3zI8JRicD+Yq4+UoWJwP7achemWYnA/K0lNRJBicD8fPhP0jmJwPxerC7SZYnA/jHWW86VicD8o6jw8nmJwP0nad4ijYnA/z97d+apicD+bfXG6t2JwP4sxn8K5YnA/66e6hKxicD8R7oZ7qmJwP33uNuSzYnA/1kbX9a5icD8CGDB6rWJwP3Zh7W65YnA/ytKg5bRicD+lsGRYvmJwP31ObLbEYnA/vYmi4dxicD/C9ugs5GJwP7/aW3zUYnA/JplsVtVicD/NyFO15WJwP7EmhXr2YnA/IhVSTQRjcD8Lg7k8CmNwPx5wRXn1YnA/IRGYLAljcD/tmFgHA2NwPxEGfUoSY3A/D93kyhJjcD+fBF4wDWNwP6hksCkUY3A/g1XNpipjcD+oyNdhJmNwP3F+I/Q0Y3A/yssLDTtjcD8Ga9LPRGNwP32wX/JCY3A/Jg77vkVjcD9rHmXgUmNwP3gPSJJVY3A/nVBgtFpjcD+PJNIEYGNwP8rk04RqY3A/Acm2bW1jcD+D8U6cg2NwPyf8tliEY3A/LQmRF35jcD9+hBiMf2NwP73sJC+MY3A/jlEfmpBjcD9CPFE4mmNwPxvQmvV/Y3A/QRI9lHxjcD/CZLwAdGNwP9cGD8N4Y3A/9Xcv25RjcD+71qbmmmNwP43QKfydY3A/zvTsVqVjcD8o9hJvsGNwPzCTrBmoY3A/M6r/sI5jcD8e78YBiGNwP9pY3KmLY3A/6IKJDopjcD8h3/jLkWNwP2d+rlmVY3A/xw8jappjcD9u1yutk2NwP+Hr+AOTY3A/paIAk4tjcD/LbLJacGNwP6E/KZV3Y3A/VjKBhH9jcD/EoFSffmNwP0vZbl2PY3A/O6LfoIpjcD/zGRmWk2NwP4f7IXWUY3A/dgVWjIRjcD8kJTTviWNwP/XufUmTY3A/gAairYhjcD90XJBtjWNwP+lTOeiIY3A/M0YG4ZJjcD+shyZ2oGNwP0bTsIqFY3A/ACCfBntjcD/Tek3hb2NwP9muPQddY3A/iMmHRFdjcD95mPsTWmNwP15xLitnY3A/+QfoYGtjcD+JOP+afWNwP/ShAnuSY3A/NgAwm4NjcD+luri/kGNwP+OFMM6lY3A/iiJAxKpjcD+hNYUOkmNwP7n2zcaOY3A/2DAG/qFjcD/VPVQUq2NwP0QAvLSrY3A/BxFOL8tjcD/ss6b03GNwP/qbJHPpY3A/kFg/MOtjcD8ZQUmtAmRwP4J7hHoOZHA/X8eZR/ZjcD8A24E0E2RwP+z0db4CZHA/lCgChPNjcD9Er5/g+mNwP2WoWQX2Y3A/JRW44PJjcD863jxT9mNwP2nlY0kQZHA/nb65/BNkcD8JrYSMIGRwPzJ9+OMaZHA/9OUxIxBkcD+SrXORFGRwPxlOBXMQZHA/cBAQgiVkcD/yblxxI2RwP4qBCFguZHA/qBMATi5kcD+vtuPAKmRwP/Y6dTo/ZHA/gWvu6k9kcD9/DrVCNmRwP3bjhIo2ZHA/KOihfDlkcD+3DnetKmRwP22yCNUnZHA/9PWDwRlkcD9JrqbMG2RwP58V4QcZZHA/h4ShAwZkcD9SPtFNHGRwP9iV4lggZHA/r8J6zzBkcD+pFLrJNmRwPx1Pz4YxZHA/O2NvQCxkcD9f+JgfKGRwPxdo6C8kZHA/wtRSCBNkcD+vWxMoF2RwPwmUhdoYZHA/rZ7ufAdkcD/ogKzWC2RwP4voaMD9Y3A/OuFNVApkcD//pWyQAWRwP3zHPrruY3A/Uyq84PRjcD/KpQPyAWRwPwOcetELZHA/LdbKGAxkcD+3yPTCCGRwPxjcNDQRZHA/Oaa0eRVkcD9Ufk8TFWRwP3tB/34EZHA/rIrc2BdkcD93KjLdEWRwP3nMgYIZZHA/uFkEERhkcD8sfy/0FWRwP9YduHv8Y3A/R0f1k+hjcD/B+Nut42NwP9h2PDDzY3A/5viEkvtjcD+KJ3Vi8mNwP3MWxmv+Y3A//yp1WPpjcD/eGBPg/WNwP0gg94oQZHA/fYXN/ANkcD+elk5C/WNwP5pGHcADZHA/cqKKyANkcD9h++LaDWRwPz0M8owCZHA/FJ1GrvVjcD8sSRgX8WNwP10p08zzY3A/27IUkwJkcD/U+UNL+mNwPwkghvfvY3A/4G8hG/9jcD8UDVbY6mNwP7TV/1PjY3A/D/RApuZjcD9m3nXO2mNwPxvzL2LZY3A/sqFUmbdjcD+3VAAUw2NwP95iNbfdY3A/+gcU8+ljcD+6Ud8U12NwP8FuHVfIY3A/9i3hK8JjcD8/vxUxumNwP/gdqFutY3A/P7L+vb5jcD8r80YRuGNwPw7lu3m4Y3A/8La1o79jcD+oAWm+xmNwP6EcWSPCY3A/XlyBO8xjcD9smiGFx2NwP3FcmZrTY3A/o7ZBTutjcD9pGIUp3GNwP0zYhS7dY3A/flP8aMxjcD/QXXLgymNwP7kqbjy/Y3A/GMcvBcdjcD8R9VFByWNwP8Lt6DfPY3A/QWrT6tBjcD+VspuLzGNwPwbuG6bRY3A/JtXwE8RjcD8hPR82w2NwPyjTnz7NY3A/8uItbc1jcD89jhf7y2NwPzDARevQY3A/23nE/+BjcD9SjbbzyWNwPw6BxzLIY3A/YItRucNjcD/HGbEzzGNwP3iVpyjAY3A/lf0zXstjcD+jplC2xmNwP1Brat3KY3A/XUDDL81jcD8pS7QhxmNwP+C2kE/DY3A/ibgyq+1jcD/PFqepAmRwP/pRljIMZHA/rGsz+iBkcD9h1+skIWRwP1olPUgTZHA/uRL7aRBkcD/T35PAKWRwP0DSWjokZHA/D4n2bjNkcD8c4OwdQ2RwP6HcO91JZHA//WBWvz5kcD/YaJQcNGRwPwK3bZBAZHA/dM6aSEFkcD86UTZTP2RwP6mmQLBDZHA/6StbzDpkcD+zBXpFP2RwP/ku3cc2ZHA/B46glTFkcD/A9WC+LGRwP+zogO4sZHA/zLte3R1kcD84ofmDHGRwP913WmYnZHA/Zqr99TFkcD8N0sL6MWRwPwFSJp4PZHA/8U1RZwFkcD9TuiHwCGRwPwGx2TsUZHA/mBq6nS9kcD8N7EobFWRwP1WKhT0WZHA/XGtV1R9kcD+f92XQPGRwPwLBl2lEZHA/AGb5oEZkcD8/73QrQWRwPzkapolEZHA/VT9TBTtkcD+tF+RfOWRwP9CIzqc6ZHA/z8dCrjhkcD9dqcztLGRwPwk9ZPMeZHA/Z1y25hdkcD9qnUB4EGRwPxFfOasTZHA/CwF+DxdkcD/yxv3YIGRwP6s1EcUlZHA/kjPagy5kcD9In3OYGGRwP1e0AS8yZHA/02o2dDZkcD/r4A5EImRwP8q5XS4aZHA/kq3Zsx9kcD9JgwajFWRwP6nm47oZZHA/KTj5PxNkcD/fNf2zAmRwP9T0oH0RZHA/r8gACg9kcD92vT+MJGRwP5Gt5NclZHA/orAbvx9kcD/wQR9lFWRwP9Ojam4UZHA/5osoPCtkcD9/Yn07K2RwP/Rv9dEzZHA/NClSiidkcD/PseRFLmRwPwRgSgglZHA/3XHPQClkcD9vuD4oMmRwP6CNykkyZHA/uFPxhjpkcD8YnsOiPGRwPzS2WhA4ZHA/EC8vLDlkcD/tD8ViRGRwPw9h4KdKZHA/uuTTX01kcD+GRfnaVGRwP0h+v55fZHA/kFSieHFkcD8DoTe9ZWRwP1q7dmRjZHA/rN2YbltkcD8mfug7YWRwP5X9nHNtZHA/ZaSrcpdkcD8suYxKj2RwPxhcRJKQZHA/AIERlopkcD/qGbaleWRwPwJowDOHZHA/gDYI1oRkcD85mjRFlWRwPxtu5RCQZHA/wX12ypZkcD+r/1LAjmRwP9pI9yqPZHA/RX3/4JdkcD//uhCtpWRwP+GUy1K7ZHA/Ev5JubRkcD+/KhvEtGRwPxEZ6qG0ZHA/4PAfRq9kcD/SozNDvGRwP9MyA2C0ZHA/0RqKYLNkcD/86hHLsWRwP2Pg0UylZHA/zodhQ4pkcD992UamhWRwP39NfxKLZHA/D4ej6qNkcD+Wae/NlWRwPxcnpnWMZHA/Jx/XdJNkcD/0coXthGRwPwV2lwSVZHA/hfGy9JhkcD/OveCVnmRwP/qfYjamZHA/+h24yapkcD/pNeZvp2RwP+Jmcf+uZHA/zJlSG7dkcD+fKkldtGRwPxfl+Me0ZHA/k4IBhKxkcD+/SdwXrmRwP93+gQ+zZHA/MaVtG7VkcD9YyU4CvGRwPw0CXzjIZHA/y5SVXdRkcD+ZwxlQz2RwP4BMGXzuZHA/DwqP8O9kcD/BjBX5/WRwP738+RP7ZHA/dhxZPuZkcD/fxVzk6mRwPzN5/lXuZHA/k7F/KeVkcD/oyCrD7mRwP73N5fkKZXA/43XaiwllcD+sprj0G2VwPyHURXcdZXA/t9YuTxJlcD+PpQFZDmVwPyziV4YBZXA/LBLlGf1kcD+dlBIrAGVwP6//njcMZXA/g4K7KPtkcD9Jv/HCCGVwP7RrpZQAZXA/VaZU3AllcD++nywAEmVwPxJOLOEXZXA/B+ijqxllcD9xWMs3I2VwPzD2tZElZXA/cfzs3g9lcD8KYmklJmVwPyOB6RMmZXA/fYaFLyRlcD+CsZy4J2VwPxlOdWwtZXA/6mRuRjFlcD/x+RSrJ2VwP0YE70YQZXA/lSMx+w5lcD+MDDGFIGVwP3E8ltYjZXA/EZTfhyNlcD/kHTNVJWVwP9ulVncmZXA/5pvGtSBlcD8BVOgFK2VwP348VdQrZXA/Mva7bCxlcD91qNd3MmVwPz1V4V4kZXA/2DKQfjFlcD8ciXg9G2VwPwUVZAQlZXA/gGl0ISdlcD9zIxjKM2VwPwqp6CwoZXA/2trocSJlcD+otS5KIGVwP7t7pTAnZXA/f4FOwjJlcD9rRqSyLGVwP6qqiuMrZXA/XdYkjzxlcD8UfSAXMGVwPxtyQ8E0ZXA/gSfHVjVlcD88u/WULmVwP7zOSz8tZXA/+9Q1AShlcD+WPkQgL2VwP2YXB0ElZXA/uce7BzFlcD/8H/tDOWVwP+LxlzU4ZXA/8RxCcDZlcD9+mlY/MGVwP+oI7fQxZXA/VAdr+T9lcD/83H0uOWVwP2tIGIIyZXA/uuVYHzFlcD+sAk6EKGVwPxIH4ekqZXA/kPJPBSplcD+HZp7QKWVwP7zn8lYwZXA/T41szi1lcD9zTHZuI2VwPyQ+tiomZXA/V3evZi5lcD9A+s49NmVwP7kdtPk+ZXA/xfV+ojNlcD8CmQtVFWVwP8UnkrIcZXA/twbrDxxlcD/ES53CEWVwP3FJoqQOZXA/ZA4nFABlcD+hApDLIGVwP2mRQOMnZXA/lic1ozdlcD+GwYHxNWVwP/V/Ns8wZXA/guIWvC9lcD+YJY7dNWVwP9SwSeU0ZXA/nyvwES9lcD/W+0yeLmVwP/n/VEIyZXA/tS5QyDZlcD/3wJ4tLWVwP53CCK8dZXA/Lk7BRh9lcD9V8cetEWVwP61TVQEbZXA/d/pm1hhlcD+QHC9jImVwP1PQxhM7ZXA/g8OFeTdlcD83ufPkOmVwP2sSYeE4ZXA/PjggPDxlcD8jdSvCQWVwP3ESc1I3ZXA/vfNH3zllcD9JKrAsQGVwP5OFnDdBZXA/7ljSWzhlcD8+7xiCSGVwPz2ifzZSZXA/ClUjDVJlcD//LVp/WWVwP91/uClOZXA/gOTemFFlcD9Uc8MwQ2VwP+k9NCdJZXA/t5aRK1hlcD8esavrSWVwPyrlalg0ZXA/p6O9CTZlcD+8nT/HQmVwP4VRP84vZXA/fvTP3C5lcD8hYRnnNmVwPwKJtY4sZXA/wCjVditlcD9cWHG1K2VwPyHCxTAtZXA//YNSaBRlcD9LvyETB2VwP1X66G3/ZHA/ntTWP/xkcD/B53EV+GRwP3smgZ3/ZHA/8ZC4tP1kcD/KrSwj8mRwP+Ba7ebiZHA/vTWCntFkcD/QpIIF2WRwP6vD7h7cZHA/lvWG39JkcD8SbUa+2mRwPyKsZ93TZHA/gtDU5c1kcD/HyJ/QxmRwP4AZ6EDKZHA/Xz7/6btkcD8oemNYu2RwP7ump7W+ZHA/ufXyx7JkcD/2vMt7xGRwP005imvIZHA/fWm1athkcD9O5XIt4GRwP3UhXcfnZHA/5mT1BfJkcD8XViKN/mRwP2B9Rgb/ZHA/h30wvxFlcD8pQYc+FWVwPxjuaSoJZXA/uESXOvZkcD82f8kk5mRwP/hP5qb1ZHA/l7MZB/NkcD8a4hGW4mRwPwOOeb3gZHA/HlxqA+lkcD/nbvFD52RwP68MH1naZHA/YkDwQOFkcD+42CUS32RwPwxMj7LoZHA/oBbyQctkcD9u18r+vmRwP1B4tyzSZHA/5rDoGc9kcD/GfI4322RwP0YP1f3eZHA/WPMmjcxkcD8UzLBlu2RwPxYgH3q+ZHA/WOF7G61kcD8mSrH4qGRwPz9wow6oZHA/yYcaDplkcD/wBICml2RwP7tjU4KQZHA/NiXiJZtkcD+FJIUFmWRwPytZOdidZHA/6iR8dYpkcD+mRlsSg2RwP4kMJhyKZHA/sv5y3nlkcD97uJlxeGRwP8gk4u9+ZHA/u2Nn2W9kcD9Sb8GoWmRwPyAeDfxhZHA/qcoSVWxkcD9fHyxnZGRwP//1XnVkZHA/IJnbWW1kcD++wHIVemRwP8AzpXKEZHA/FfrFdY1kcD+dstO1o2RwP3UZpD+lZHA/GKOnCJtkcD+LN3bwjmRwP1ebHvyXZHA/DhwO/p9kcD/3jNFCmGRwPyToBtmOZHA/ns6o+XxkcD8Vai28j2RwP26+G5uDZHA/1Y1T6oZkcD+BpCwSjGRwP//sACGMZHA/dTIkcn9kcD8OL1IniWRwPyJHKSSCZHA/OlqtDnxkcD96avjge2RwPzWLCWZ5ZHA/+iEQVoFkcD8tgN4iiWRwP9MZXaKUZHA/sJrZNoJkcD/aDX7We2RwP3luAhaEZHA/lPVkUZtkcD8x+LtfnGRwPzP9s+aQZHA/LzG0rZ5kcD9OycxltGRwP9W2byHIZHA//K3rs8JkcD9zZ4Zdm2RwP3qVk1yDZHA/AlF/1INkcD+33etRdWRwP1/wQ2uGZHA/X83PzYBkcD82zaLYf2RwP+R0zsmLZHA/u9jB5JdkcD/JYyEbr2RwPxutQzmsZHA/l/XQZ61kcD8HFWTnoWRwP1kjfkOqZHA/CvNI36RkcD/qjJuRpWRwP7V9ofGeZHA/WvjSbJRkcD/CGTzyi2RwP21mGgF3ZHA/QrjB2ntkcD92y75jfWRwP9W7ibh2ZHA/sMMlQ2RkcD+G8rPaWWRwP06c7vNfZHA/NQCPmWBkcD/pCOCgSmRwP1iJwSteZHA/TtE3C1tkcD+Ck3a1ZGRwP5rC3YlxZHA/7ZSOVGhkcD9uxLztYWRwP8mB28BrZHA//SB9SmNkcD/lvTXjYGRwP0Ps1f9VZHA/FYvaw0pkcD8QcRbSQWRwP7snt4hVZHA/td2LHVZkcD/Hwlj8R2RwPx3r6CY+ZHA/IkgXrC5kcD/qDWUsOGRwP0eXyydAZHA/2wREBDxkcD/jqAIpTmRwP7teYG9UZHA/dNCypFVkcD8k7JsvYWRwP15d2J10ZHA/DapdtnBkcD9gJZD/emRwP8Dq8DyFZHA/DPD0TYpkcD9uoQ11nmRwP9ezoUquZHA/fpk6OapkcD/JbNTzp2RwP6pwXeCiZHA/LGsCUq9kcD+BAYkZt2RwP3uKe1i5ZHA/OHdnCdNkcD/nGdmH3WRwP+LMbyvJZHA/+cxO4MdkcD+4qVJkzmRwP98qVrLRZHA/8sH1rsdkcD+a31TcwmRwP4dMdx2uZHA/Lcn01rZkcD9X10rEy2RwP7nWhf/IZHA/2gtLYsFkcD8uKrVEtmRwP2MXQfi8ZHA/j2qK8sxkcD8WbRD91GRwP3owgHvdZHA/NhKbpdpkcD/7yPyE02RwP8nEANvAZHA/rwxJi7tkcD8jkQm0x2RwP8fpm6PQZHA/kiTh0b5kcD+ttXWtpmRwPy9h/kmvZHA/3yM4Qq5kcD8N739/t2RwPzQUNdjEZHA/5uungNNkcD/af+gi0GRwP8mgVPvDZHA/lOyff8xkcD/JN2fkw2RwP7En507UZHA/7qfIb9tkcD85HQXv2WRwP8ZDwQ3dZHA/BDrocOFkcD+dviHq6GRwP394pBrkZHA/1k40KN1kcD8qfmFw2WRwP1jBxGnhZHA/OclmDt9kcD/7ex7Y2WRwP3ms0ZbZZHA/De7zSfZkcD+AhJ6XEGVwP9NBrVAUZXA/UCC5wRdlcD+5MFDaB2VwP9pZwaAMZXA/6OmAbQtlcD9i4ev9/GRwP1ROW/4MZXA/C4lvNRVlcD8wMt6YAGVwP+xfTJ8JZXA/p1j/8xhlcD9v8kMTF2VwP8xCvSQUZXA/PX9VeRplcD8u28AMLWVwP9plnsAgZXA/ryPH5idlcD+lVmvOJ2VwPx8QK8wiZXA/XJjrsiplcD93YKqyN2VwP7wK8I07ZXA/USDyDy9lcD8+nFO/EWVwP4dhp9kMZXA/OPzCLQBlcD/Yfism9WRwP5e2DijoZHA/+XHtX+xkcD+RGtUa7mRwPwCL9WTzZHA/h0E6y+5kcD/yQxHM+GRwP4OL1SvvZHA/egwiYgFlcD/AsTDICGVwPyknhaAGZXA/xeTHNP1kcD99JGfB+GRwP8Jnm673ZHA/bsjhT/1kcD/pVb7n+2RwP5DKpcPsZHA/J9UOqflkcD+JdwWK+mRwP3KjeMkNZXA//jIabyJlcD8gj6WGMmVwPz7UocQ4ZXA/vOq7dTllcD+RHC6UQmVwP6hMwBo9ZXA/fniUyzdlcD/7xWaXMmVwP53pHjc7ZXA/+VBRqEdlcD8Qy3XTQmVwP2gXHUdKZXA/dgIznT9lcD8wqxG7WGVwP28a+uBMZXA/5S8KojhlcD8a353fP2VwP2bLlGI0ZXA/8soxz0NlcD/Uwc2ZTmVwP7Cfv7JJZXA/ipMJBFFlcD+byC+ZRGVwP2UHTFRQZXA/lEXowi1lcD9MoSFdN2VwPy1+AoVEZXA/efPVUDtlcD+UrAcCO2VwP0I6YksrZXA/tULz0zFlcD9VhDRAJWVwP0xU+lEgZXA/Mp2lJxZlcD+wqjqDE2VwP8ZzTv3+ZHA/hDjY0ONkcD9oWues7GRwP7kfTwH6ZHA/Lpd8VdtkcD9urNBS0mRwP2CLec3SZHA/HW4XSMZkcD+380kO0mRwP+F1rx+yZHA/GYHv9bVkcD+BkKhvxGRwP0/BvIzFZHA/auOIQ9BkcD/zOtfn12RwPzauHgzXZHA/hSJOXNlkcD+0yYeHymRwPxgApWHIZHA/uPOqCcZkcD+E/xsHv2RwP4hda5K1ZHA/aWSHNrlkcD+NSQCkxGRwP1Vc+/XfZHA/rWGGFONkcD9/R76Z52RwPx0+c6bvZHA/itgEiNlkcD8wTDPu3GRwP09WyHvPZHA/WM1YO8xkcD/NRKCx12RwP7mShPXOZHA/UNX8JtNkcD/BuAXN4WRwP9ZathjhZHA/JW5tmeRkcD+i9Vql1WRwP7++0X3iZHA/vOHec+VkcD9QEh+L22RwP6ofPVjmZHA/0aLT1NRkcD9dGQae1mRwPyJkSjHhZHA/qdzZAexkcD9sD1u55GRwPyCTYFryZHA/pvDDePxkcD90HNIE+mRwP9QzUioZZXA/94lQfSdlcD81vSSsJGVwPy3NylYIZXA/TcH4OhplcD8QFO0JFGVwP2qMAGsZZXA/tCmmkidlcD/LzefQQ2VwP5v3dFJDZXA/sMmtA0JlcD8fEqwhS2VwP3YSTe9EZXA/6+bCVGJlcD90dzUYZGVwP4gBPWZeZXA/CzuVTWVlcD8CRD81e2VwPxyMj/x2ZXA/6FHWYIdlcD+g+qR6k2VwP8KQhVigZXA/EdTVIa9lcD/aJnLXrWVwP12XY8+9ZXA/D8doSNBlcD+sK7oG12VwP5qLp0nLZXA/qLbbGNRlcD9ghx960GVwP5SZjo7JZXA/vVSY7MRlcD9bVTUZwGVwP/3kwdvLZXA/mqmYYtNlcD9Zs4ET4WVwP2D1eOLlZXA/dCVNV9llcD9CBQu/1GVwP+93YNnhZXA/DDH/X+ZlcD/ighta8WVwPzPIfGECZnA/tlLVUexlcD9fBcpE72VwP/wuR5HxZXA/RLUg+AVmcD/WPgPR8mVwP0LIdZH1ZXA/+mc/BvZlcD+1dUFu8mVwPxMNC4LxZXA/ll/6EfRlcD9WLS4FBGZwP98eYR8GZnA/h2/FMf9lcD90CGSm+mVwP1xJDHD6ZXA/5kkCqglmcD8nVjMC92VwP3GlZqjjZXA/rPgHYellcD8vJieM6mVwP+/Eq3LtZXA/amGPivRlcD+o8HkN72VwPzCxaZvmZXA/UPRBNNZlcD+a7rhz32VwP9jCEVHbZXA/FL8J69JlcD+3WHi4xGVwP4NLJbLLZXA/samTVMZlcD/xKUMHr2VwPzhLuyevZXA/6uz2MK9lcD8CawkIqWVwP7asID6jZXA/tIAzM51lcD8FgGhRqGVwP92n/7GXZXA/N/pfxKRlcD9MhR1KrGVwP0JYKdCyZXA/0AGUbLJlcD/zLYL/oGVwP8zwW8GjZXA/F1EZdpJlcD8oMNzAjGVwP5pEZzR5ZXA/pg4ya3JlcD9qJqnfcWVwP//BNFN9ZXA/LxJjVHplcD8DD9qvhmVwPwygPQeIZXA/+9XYMYZlcD+2EkuMh2VwP9aO4Ul0ZXA//P23W3RlcD97t4HteGVwPzjRYC97ZXA/gNZBFHdlcD+vvlnbi2VwP7O0qwWQZXA/xQSr8oRlcD82Xly4hmVwPwWQdB6DZXA/yVi1hYhlcD8jsWBvlGVwP9kBiduWZXA/RenDkaRlcD+uk4HwlmVwPzxKhy6hZXA/KVNFYKllcD/hjDwmr2VwP3rJ5Zi1ZXA/izgyjKtlcD+59JR7s2VwP0ennYyqZXA/DzVr8aVlcD+SmKS/p2VwPwMKyUCqZXA/z7bsusplcD/fTN97zGVwPzPD7PjEZXA/0t0p2sdlcD+KtKVnyWVwP6hw0mXHZXA/P0hNbcZlcD9WrMDE1mVwP8DtxMDIZXA/UJiH/s9lcD+dJk2bz2VwP0q2Z5XNZXA/41I/huBlcD8CjbWF1GVwP+rr3DDjZXA/LG/42OVlcD8qGx306mVwPxHHWQjkZXA/c55Kr/VlcD+SFwqN32VwPxjWeMHmZXA/aexrp91lcD+r/q8X8mVwP8HdPcgAZnA/S2HQ3PplcD9V8Fya8mVwP9R3G0X9ZXA/xfPGrvVlcD9cu+eO8GVwPxaGsGLqZXA/AfDDjPxlcD++lXra/2VwPwHh56r3ZXA/UKNCx+9lcD8e6dtS32VwP7DaErfRZXA/hNpOZd9lcD/4Bt9W3WVwP97gNonIZXA/0ixA3ddlcD9Wy47X3GVwP5e32l/RZXA/d7So3ddlcD8e9hm+4GVwP1PsbDjzZXA/QpABh+ZlcD+oMDu/22VwP0hXRZjfZXA/UXIkheBlcD+1dGA232VwP0HBCkLKZXA/KsLqncRlcD+Vie6+wGVwPwk4PTyvZXA/buHmfrNlcD8RztUhvmVwP8cEdMPNZXA/onlLmdFlcD9MdnCbvmVwP1P+u/KuZXA/bLaOD7NlcD/8bzejn2VwPyvdKAapZXA/DSIDiKtlcD8Py75ypGVwP6seF7qZZXA/BzLgwpRlcD9WXRAOnGVwP3EN6leeZXA/PEw1kIVlcD+0B7XxjmVwP6oLuNKDZXA/fXS6I4NlcD+OXdx2lGVwP6GRtAmiZXA/+RYodJ1lcD8CQac8t2VwPwE+fqKpZXA/yJW7lY1lcD+QpzQXh2VwP4OpPISOZXA/gHsM9YRlcD+JoWU3m2VwP82eV2iSZXA/T4oC+ZtlcD/lyLg2vGVwPxCa6AS6ZXA/GRYAH81lcD94hhvhzWVwP2hBp2nZZXA/VtPHXOFlcD9xykLE4GVwP3ldRmvsZXA/mQKae+FlcD+QcmCW92VwPwuUfsMTZnA/O0cT1xdmcD/SKdGuDmZwP0mTbAAXZnA/BqOL5CBmcD9fps3dDmZwP/BUHnYZZnA/bWqvSSFmcD8cM6TgKGZwPxPEl2UWZnA/3enDyx1mcD+oy2vfKWZwP0ymNVMdZnA/RQpHwwRmcD/JTY+vBGZwP/4g7YUJZnA/gSWMIARmcD/dLxgrEGZwP7GnCtYZZnA/JHggqRVmcD9b4yg9D2ZwPwmbeoMlZnA/XfGPDBFmcD/tf5kzGmZwP3O8MlIkZnA/EBzZZiZmcD8o20DRLWZwP7Tq69ksZnA/zM4I1zNmcD8I8ai5PWZwPzngTsk6ZnA/9FOXPD5mcD9g5fYMNmZwP5HhGhVDZnA/LC5H9zpmcD+xRcE5OGZwP3CGbgE0ZnA/DKm5tz5mcD88CdgUO2ZwPxLtEu8/ZnA/7mtuvklmcD+m41YrLGZwP5EXPCQgZnA/2wgOMSlmcD/riQ/vPGZwP9XM4qVBZnA/ZYqJbzZmcD/uyGwTQWZwPzAxzMlJZnA/t2a30kxmcD97H7rSWGZwP+ROBKBRZnA/gozxgUlmcD+bJUB+SWZwPyP+BvlIZnA/rnGnI1dmcD+kykbhYGZwPxdqIT5vZnA/1Z8uSoFmcD82LuSZgWZwP6BfNCaAZnA/64GGqHpmcD+qPO4FZ2ZwPx4UclhhZnA/ygwZRFRmcD9HC1QDYmZwPydn2/RYZnA/T1MwLFdmcD807/ttXmZwP6Lthd1lZnA/e8jeJXFmcD8o8WpFbWZwPxNLMc5mZnA/pqhN/F9mcD9necaHaGZwP54HsoZiZnA/MWES519mcD8Sloo2YWZwP+qqrNddZnA/rEym73FmcD9Ogo9WfWZwP4JpveZyZnA/gUkzXnNmcD/PKIoae2ZwPxwctB97ZnA/Cs7sE45mcD81jKA1pGZwP0fRr4ueZnA/XXWsE6BmcD8BTjxRnWZwP4d5jQyTZnA/c9cW2ZlmcD+iQ8Byo2ZwP2k7EIqvZnA/qPpk7cRmcD+akaQZu2ZwP6m+NpzKZnA/RU4VWdJmcD/Nn3//0GZwP2hiu4vPZnA/eswoYttmcD/O9UEh5GZwP9ymv4vtZnA/bzkbMu9mcD/Kog3K+WZwPxh6gfftZnA/oux8vfdmcD8n9DZI7GZwP/U/x73nZnA/S/zjIuJmcD8X+O6Y4mZwP8zcE0jyZnA/UZkBDvZmcD8wrgm44GZwP17dGOrWZnA/2RLgAcpmcD8E32h8y2ZwPxtpJe7DZnA/hX+7fMNmcD+/0x820WZwPwenZ83OZnA/oodMu7VmcD8nt5bTpGZwP6Jam4i0ZnA/uibhobRmcD9KfGMgtWZwPwzTyEbGZnA/pdj2wchmcD8DZtYq52ZwPwVxQFflZnA/lHJ/qN1mcD9FSpg16WZwP8gnerbiZnA/HG43099mcD/j3A2k2WZwPxZpdebWZnA/5hAi0tBmcD8LT1YGyWZwP3ZrKni+ZnA/VHmIIcJmcD9OIaSarGZwP7brr8e0ZnA/R+z2m65mcD++y7tLvmZwP+jpxtbDZnA/rLfU871mcD8I6p3YtGZwP10CMfOxZnA/slRPc6RmcD+vEWRhqWZwP7saYGWuZnA/xg3emZ5mcD+842lfkWZwP57wH1OXZnA/gNuqDptmcD/qqkfem2ZwP6q26CGmZnA/BewQ9qpmcD9U+7eSsWZwP1Vq15O+ZnA/83NSmLpmcD+mhZitv2ZwP38bx+u+ZnA/uBLpeKxmcD/d0inUq2ZwPxDCOmbHZnA/CXYrLttmcD9QVIIp5GZwPz+UiBXdZnA/7R7/IPRmcD/GEZsuCmdwPyYPPcUKZ3A/GtTlnRdncD/OEVOEGWdwPwUP1d4XZ3A/LMU4ERFncD+hKRn+D2dwP47wfmkPZ3A/mWrAuwtncD+lrwBTEGdwP+40mIUQZ3A/WuLIZv1mcD9BUixTBWdwP31F/1MIZ3A/bhefhf9mcD8eTtjnEGdwPyaIBTcnZ3A/7wa/CjVncD9ZyCU2PmdwP6A/EJYvZ3A/ox4dQTFncD+f1dz5OGdwP0NpHbQ+Z3A/XCBWlURncD/le2R3TWdwPxg4U4w7Z3A/fhIfEzdncD9kyPoJLGdwP9wFwIsiZ3A/uAopFgpncD97+0N/9mZwPzKmlZTlZnA/wd9QP9NmcD80c7jOx2ZwP1QFxsDMZnA/LnhCatZmcD9eu8XL3GZwP5+wVH/YZnA/hFa3cc9mcD+EIpT1y2ZwP0Nqtu69ZnA/Ozg/0cZmcD/IaPqRzGZwP0uC5I7PZnA/IYJqidhmcD9NMwIexGZwPxd+c2DQZnA/OWxv6ddmcD/K9ovP52ZwP3ApYcXnZnA/PQ92kARncD8Qsy/DBGdwP6qBWl4RZ3A/gtAZrBhncD9nolB3C2dwP8mw4Y36ZnA/tvaE0eNmcD9ZUqJp7mZwP3GoMJriZnA/BkZJNd1mcD+RyYep02ZwP83Ge2rPZnA/jHpHktpmcD9Gu6h43WZwP1p+yXHSZnA/ylKOhONmcD85h/UT3mZwP5KHYjDTZnA/mFtCStVmcD9OAg/W02ZwP9Y+GlfNZnA/9KHQLtZmcD/fIbgf02ZwPz6C/tfLZnA/NVmCYcZmcD/KSq73zWZwP/WX9fbPZnA/5OAhhM5mcD+8F1It1mZwP9gT9G/TZnA/tvq/m/hmcD8PnNmmBWdwP23JoNwRZ3A/8UsKFv9mcD/iJa4iCGdwP8ncF/kQZ3A/43vlC/pmcD8FAo8v9mZwP1g04O0AZ3A/TONTSPhmcD9Piup392ZwP9zzppvzZnA/7Hc9RPdmcD9trIYM8mZwPwGDLHf/ZnA/uap4YhVncD8hALSxF2dwP1Fb4UgbZ3A/ftFED/1mcD9AQxoe9mZwPyad5GXqZnA/w42PLvFmcD//mm/j9mZwPyPJQ6r6ZnA/X1D7YP5mcD/zH7s/FmdwP2ZGGB8hZ3A/WHPvBhVncD+nwWPoKmdwP+LCDCAoZ3A/8682UB5ncD+zQI/OLWdwP8Q4GxozZ3A/qqQRMTNncD9VwBvXLWdwP68WvT04Z3A/sQ10JzZncD8Ct0r3KmdwP8HrNMMWZ3A/KshX0htncD9Xyl2AImdwP9DOStEdZ3A/jAoY7TRncD90AMoyP2dwPySOfgIvZ3A/2HXkDC9ncD+E6+a+OWdwP9jH7kdFZ3A/pc+aUF9ncD+ICoyaXmdwPwapLgpxZ3A/08kkfVVncD9+UlZ1bmdwP2qEnTh/Z3A/NTW7jn9ncD9DGDPMfmdwP3n5MoyAZ3A/Hah7z4ZncD/crnbni2dwP+hf0yGEZ3A/aq14c4NncD9R77rdjWdwP/9oyrWaZ3A/xFv+dZFncD9kAXqgemdwP5tQ0RB7Z3A/rS6493RncD+OeLftdmdwP2NFM9Z3Z3A/SIlfn3pncD/RM8SbfmdwPxQBTXdoZ3A/+NkRnYFncD+tjxwximdwP0Ws8KFzZ3A/rP6v1XpncD97VKLqgWdwPzgPImCFZ3A/up6c33tncD/8FtTCfWdwP8av6zh0Z3A/KsLyJHhncD9hBH/hdGdwP+oEJlhzZ3A/gyIBFGpncD+p8JEbfGdwP53jplqHZ3A/Jo0Ku5FncD9H8XVrkGdwP0HkOfOfZ3A/DYCNia1ncD9zqtJlsmdwP9DOTwrBZ3A/SKwHfMJncD8g9vqkvWdwP1eRb9CzZ3A/f+hM1rBncD95BrjqsmdwP2dbj/q9Z3A/IsRJD8RncD9Mp6FtwGdwP6U/Xkm7Z3A/V3g7hMBncD/q4KcFymdwP2gwyB/TZ3A/PfRzZ91ncD8zSyke5WdwP1H/5hbYZ3A/VRRbzd1ncD+Rnk6k7GdwP7Lz8A3+Z3A/o9ltiAVocD/oIGK7DmhwP39RCHUUaHA/lPf7kBBocD+VaMUnBmhwP6JW56QOaHA/kNR/5BFocD9w9VJ1DWhwP2IKKBrtZ3A/NoYqNP1ncD94+fQ09mdwP3R3ZzDwZ3A/+8kez9pncD+rTHKi3GdwP2B23/jyZ3A/DRwKkfFncD87jS0i/2dwP9zs6LX1Z3A/gur9mvZncD9ND2L++WdwP1Ug5Oj0Z3A/XhP1+wFocD+J5mfQ+mdwP8JIKg0DaHA/SC1maQRocD/Mi0RD7WdwP/pMS0X+Z3A/pAz+vgVocD/HjkYgG2hwPySV3HsVaHA/Qto4nApocD8umZ7MI2hwP26hQJ4jaHA/j0GNpx9ocD9bLo0yImhwPytR/hkaaHA/pea6ehBocD+JMho/KGhwP4u/zFQyaHA/iC9fVTRocD9K06LZJGhwP0OFQEk8aHA/fAxACTtocD8DOSIsL2hwP/7Y87FAaHA/qOsqXEdocD8JHNWuQ2hwP1h+iXkwaHA/tN9I1iRocD/V2/JEFmhwPwF9vQsvaHA/fuiOyStocD/DbZ7fLmhwP+V8mIMwaHA/kS8CuitocD9z3Ny4F2hwPwjxkd4ZaHA/VOYgOwVocD//ecHREGhwP0dpCfAUaHA/lTLm/RpocD9/CcIPHGhwP62eq6k1aHA/56hRNzBocD/pM2+mKGhwPw+w5BgbaHA/IPnoPhZocD+dfYKvGWhwPwvAs7AWaHA/qbBTwSNocD/gwJsoGGhwPyB4/WMOaHA/ihnt6gRocD9cnj41+2dwPy1x8vP9Z3A/GLfsh/NncD9/FvUm6mdwP/iho1v/Z3A/5Qj0pAtocD+elbdt+WdwP7aQyK/hZ3A/boHn1OhncD9GslEH7WdwP7Lerx/4Z3A/o5WluP9ncD/C7GH+AWhwP2PrMb3sZ3A/6xcqseFncD9Hse93y2dwP0VRxTm6Z3A/Ya59L8RncD9+Vafqv2dwP3QSqarDZ3A/7EGBnMBncD/rLLvnsWdwPx3Ot+jKZ3A/f2ReM8pncD8SjzFEzGdwP/th05TPZ3A/CltAYs1ncD8skPQ80mdwP7udUOzEZ3A/vuqppcVncD/QSa5vwGdwP9Jqnbm9Z3A/4/pLxM5ncD8csMNvzmdwP9kxc7C9Z3A/3kamo6xncD8vrLeWomdwP0SJDaeZZ3A/xxNE+ZJncD/7PnGQsmdwP8QpOfSRZ3A/z8zmnIZncD84yTcHkGdwPykBgzCTZ3A/MoEUu5RncD9l70RBmWdwP++akm2dZ3A/S+RXebNncD8jwYMgrmdwPxFwg6qkZ3A/sEd97ZhncD+85jSlp2dwP1KtrIS0Z3A/wgExhbFncD81URCCmWdwP8yMZm6cZ3A/aRknYZlncD8XVRI4omdwP+P85wGUZ3A/9qqMAYhncD9eIhzwi2dwP96VEG6AZ3A/5hRPRJJncD98z+AjjWdwPzfQdiKGZ3A/bwUguoNncD8VN7KxiGdwP8E3NmWXZ3A/3fm+hoZncD/F45ztcGdwP34CTDJuZ3A/JHsqr2lncD+c6whnY2dwP9ozVRJYZ3A/Zk9KdExncD8iRcrSOmdwP9YwjOM5Z3A/4mLDlS5ncD/1hp+bMWdwP1xQo9gmZ3A/F1dVsyBncD/1lXoTIWdwP40F1OkdZ3A/G28yWydncD9PWeBAMWdwP4GCYCo6Z3A/dbP8gjlncD8vYbfYO2dwP1OkmK0xZ3A/gYWYxiNncD8gulLPImdwPxnk4p0tZ3A/hutXaC1ncD9+FAPSQGdwP4Hfh7FLZ3A/Z2au8E1ncD8u7mV3UWdwPzZpBx5JZ3A//BndZWRncD+xYYrqTmdwP1FUTURPZ3A/3HQPPVdncD8J6bzUW2dwP+f5IsBcZ3A/TfrqUmNncD+spzdIWmdwP88CZPFRZ3A/ZaQa11pncD9GbaC4XWdwP0zm9/BfZ3A/bFkeNV9ncD8306VIYmdwP4xWctRRZ3A/iIYzGEdncD/n4ShcRWdwPyoqnmVKZ3A/6RYHMVdncD/u6L04ZGdwPzz5O39wZ3A/ue6ZSmJncD+NJ0G+WmdwP9FRDDpoZ3A/SNGglXhncD93l6H9e2dwP4p2DtyCZ3A/RnKdVoNncD9ffGbpdWdwPznnMIt7Z3A/3AsnWJFncD9qfKI6kmdwPxy7V1CLZ3A/ojLzbY1ncD/CDIxre2dwP1arHjuJZ3A/HZTX1INncD9DJmQRiGdwP0cptbp8Z3A/vPCE+XdncD/bVzuIc2dwPx/WX6xtZ3A/5Iti+HdncD8XolwpZWdwP+zlIh9zZ3A/tXskIGlncD9hPIMYbmdwP60RH/txZ3A/yMJmXXZncD9zb+YhcWdwPz4BYO5rZ3A/VanWlnFncD+fCpluemdwP0c/lKF7Z3A/uxYLVYVncD9Alu/5mWdwPxyuHZqjZ3A/BF6lxrlncD/+62AOt2dwP09WgaKvZ3A/BkIX8NBncD+MsFaQwmdwP+RCA726Z3A/8hEXcblncD9S2AlYs2dwP5eGOw+uZ3A/ZXW/kbBncD9csJKTnmdwP5CeGW6dZ3A/ozYbSqNncD+z33NIsWdwP4CpgwazZ3A/Oy1mBbNncD8pl+fFwWdwPzCTtDDMZ3A/hTxSjc9ncD+omehiwWdwPxOT2M61Z3A/KFsj4L5ncD+nvdj6w2dwP5G7Mg28Z3A/3QTZocVncD8s6aClu2dwP4pilfepZ3A/Qd5kz6dncD9AzWq+o2dwP93/wJ+oZ3A/J7tfzpJncD8kZTzhh2dwP94LfyeEZ3A/dYqSUotncD9WA4uhj2dwP36SCIh+Z3A/PFCqtIFncD8CP/W/gWdwPxc3HbqHZ3A/TWh3wItncD9kLejKgWdwPw36LTx+Z3A/jGexjoRncD/CnMYNfmdwP8e6blBpZ3A/G+7Vi3BncD/Vq4Gna2dwP8sWN0R/Z3A/ykHcapZncD/KoZ/sjmdwPzgLxHKPZ3A/k3Yu5qZncD+MvmYBuWdwP3fVqgrJZ3A/AZCcDblncD+s+xV1tWdwP0uSdzq9Z3A/zELfubdncD8qc6WItWdwPyUIsfjEZ3A/dgmCrcVncD+lO5QQwWdwP5wTL57bZ3A/tiVUUNNncD8yXnJ782dwP8MTw2voZ3A/gdX8etxncD/vTdYx2GdwP3kGxifXZ3A/Sm9lEtBncD9pPhVDz2dwP8qS/evcZ3A/0hbw6N9ncD+ZRnJ03WdwPysAxUvnZ3A/8ZvtbehncD8eZFfJ32dwP6ie9yfbZ3A/GCvs3cBncD84WqPFtmdwPx3WWe+uZ3A/0WIXlMNncD+orVxVu2dwP7ungR6+Z3A/jZ1x5LRncD/xfwLluWdwP6G6IHmwZ3A/iKkQkqlncD/qP/ACu2dwP+Z314uxZ3A/l3hjybZncD+eyq/4tGdwPyQKEOC/Z3A/8srlZuBncD+4e4gZ4mdwP9oabvzcZ3A/yBp9E9tncD/p/1tB2WdwPwQDx/PjZ3A/Y9DNuudncD9NLbWN3GdwP57hmLDNZ3A/P/DAOcZncD/Cp1jaxWdwP2CZMbXUZ3A/3EIxfdNncD8H9JPpx2dwP8ZMjyjMZ3A/cOjvYMNncD8Fa6dAxWdwP5JG0OifZ3A/WaSlXJtncD/d7QNHn2dwP1L6QGabZ3A/kYP4kpFncD/NmoSVhGdwP9gYr8aAZ3A/ZkVcDH1ncD+Sy12aU2dwPzlAAp1FZ3A/1cvqXEBncD83XgZXMWdwP4Cab14fZ3A/FntwDyVncD8nRQXfNmdwP4FHg3c5Z3A/Lh0MUD9ncD8dFvIdOmdwP2eoWHdAZ3A/2p9NFTdncD+6/u3nM2dwP83w+Pc0Z3A/sYxtjzZncD9ggA5JMWdwP2cUuFc4Z3A/4N4f/TdncD+UwyDGOmdwP09RNEBAZ3A/Gm+CezhncD9UB6qfPWdwP0D3xoQ9Z3A/O9ZeQjdncD8oOPYsKGdwP8UjjIspZ3A/sQ8V5CZncD8HhXpcGGdwP2DSvmQJZ3A/U6m+aQhncD+dhvIa+WZwP8aVbrHsZnA/O/97i9hmcD/Gz++/0mZwP4MoryzMZnA/1UarTNdmcD+eQ2te02ZwP3zjXk7GZnA/mPy31tJmcD+jj7kr0GZwP8WghsLMZnA/IoQ1QclmcD9BQiGqvGZwPygJPiKvZnA/WQDee6VmcD8kHjnwpWZwP7PfL8u3ZnA/WK7wQrNmcD/xxCdCtWZwP/1MFxGjZnA/3fZsaZtmcD/+1wPIt2ZwPztfD3a6ZnA/4ykZXL5mcD/l8vxJp2ZwP0/PStuuZnA/DxW2Ir9mcD98WSegyWZwP7phZHDQZnA/Y7fvlMlmcD/Pj3JOzGZwP89EFbOvZnA/sc8dh9FmcD8CyGck2WZwP23Uvx7fZnA/bying+hmcD8NRR7N52ZwPw+Q3IjYZnA/OTEnJcFmcD8EqZA6tmZwPxBNPPjNZnA/psEzQ8xmcD/Pn7JJx2ZwP/1OgEvJZnA/mtyUcttmcD82YS4z1mZwP3bQGTPRZnA/c11vGclmcD95qb5KtmZwPzIRbHK3ZnA/rQFsArVmcD98CzV7p2ZwP/uaeZSqZnA/bZnsj6ZmcD/ekJOAr2ZwPzC07uqeZnA/FbwBGLVmcD/IXnkus2ZwP59iXVK6ZnA/0L3cNbVmcD+wfixGvmZwP5mqE6S9ZnA/NzECir5mcD8dXyRW12ZwP3RL38HhZnA/pOM+49BmcD9YwyznzGZwPw9lwA7VZnA/Kr1lONNmcD/IAXLH7mZwPye8bY3mZnA/7H8BONBmcD+WdKyk5WZwP1+s89PdZnA/wiU999dmcD8sS9gA02ZwP6bR0BTCZnA/9xGeD7lmcD/+RjEsvmZwP4wq63WwZnA/d3uv5qVmcD9Grcknl2ZwP95Dk5GaZnA/NzQxcZFmcD/mjqofk2ZwP006wByBZnA/6/N+YZlmcD/TOibomGZwP0fyZ4meZnA/dsDMW5FmcD+9QPu2lGZwP8qUpB+VZnA/pHj02p1mcD8JkoZZmWZwP4knieiNZnA/ke/fO4RmcD8mL4dDjGZwP/W92V2YZnA/PblAOIxmcD+hiUo6dmZwP/W/95p8ZnA/WrdK9XlmcD+8xhoJjWZwP2KJ4XOVZnA/39/AcH5mcD/h2cjcfmZwP5vPbHmVZnA/1BNCcZJmcD9+W1eSl2ZwP2QqcHiRZnA//UA09oRmcD+RVrOXf2ZwPx+l7QmNZnA/XRgdK5dmcD9oivRwmmZwP0AEiKGXZnA/cL+fu5dmcD9LosaJkmZwPxnrOoWMZnA/8kCn+I5mcD9Z/9NYnmZwPyWgFtOIZnA/yuaXZY5mcD8trrNGjGZwPw0prR2DZnA/9HR1c2BmcD+XT0ccYGZwPxR0TGVxZnA//TxJS2tmcD9VY3BWc2ZwP/ksdeF/ZnA/A330q31mcD9LUoVGk2ZwP+nkaIyfZnA/XnLDOalmcD8ztKpusmZwP5bXutaeZnA/b0xPmKVmcD9yhu0br2ZwP47vQz6ZZnA/KWe89ZFmcD941uKkjmZwP1RapViOZnA/gSwWJn1mcD9ru+2peGZwP8rU0mVxZnA/SwKvVHtmcD9a7dqzZmZwP0jGu5pyZnA/7LfXxnhmcD9JnGwAfGZwP8Hgs7+ZZnA/z/9T141mcD/GDjy6fWZwP3vN4XeWZnA/gEQ8v5NmcD/HalSQnWZwP7RkLeGiZnA/8/iCfYxmcD9vfMNYl2ZwPzrcU9qUZnA/EGM6S55mcD8QLIa0rGZwP0WrUW2iZnA/bN/gaKZmcD9TthT2nmZwP4sL2nCkZnA/2o9JnJlmcD8H6Zt4nGZwP16NBjGdZnA/k130949mcD/EnOZzm2ZwP7cm/viWZnA/BpLEbpxmcD+xFZRsqmZwPxpiERSpZnA/d97uvK9mcD/TRyxYu2ZwPwvYwX7WZnA/ti29dMhmcD8t8Ea1zWZwPyGNGzLLZnA/tyFKSbpmcD/5eSIDs2ZwP56jHiuyZnA/Q6JXl7ZmcD/7+dVjwmZwPy80vg3BZnA/6VQXUrpmcD/zpHevsGZwP2W0/dC3ZnA/iUcMLbxmcD8FvkCK0mZwPxJGU4DSZnA/HNvBlddmcD/85C+u2GZwP2Zi/5neZnA/watM1etmcD++efwM92ZwP5Cdt3cJZ3A/K4LRWghncD+t2jR4DWdwP4yKwkQdZ3A/g1KodgtncD/FZWsR9GZwP+MM1wb9ZnA/MNXCU+5mcD/zcsEj7mZwP+3omz7yZnA/tvoIkexmcD8pofF5+2ZwP5aWfUv7ZnA/4ZmTyBhncD8rHOn1I2dwP+7h9DIzZ3A/TTzBty1ncD/vzJ/GNGdwPwd45Sg4Z3A/ofiO7ilncD+0OaxNMmdwP0fBvb4vZ3A/TVw5tzZncD/N+AiTPGdwP1xKj282Z3A/KIIxWT1ncD8cIJ10LmdwPzuzNwMZZ3A/OiF8WyRncD9wHotUEGdwPysSUI4AZ3A/LxAtmwRncD9WFuxNFGdwP+LAfd4QZ3A/ejAIiRBncD9mElm8/2ZwP2LxPob6ZnA/dAPl4OhmcD+XufAA72ZwP0nLw//0ZnA/5Y0dHfdmcD+ktBR87mZwP7Saso79ZnA/41mXPgNncD97FZFw+2ZwP0pTNsoIZ3A/a41q5RNncD/KkB/WCGdwPz7d5oH+ZnA/wlT4fQxncD/3jVUEAGdwP98yskz/ZnA/O2h4SBFncD/A5YoXAmdwP/QfJPD0ZnA/Ek/ILvdmcD+QgoSa+mZwP5Ji7kIBZ3A/pCYS1ftmcD9eoQ4Q/2ZwP5I+BZXxZnA/Y3Nx/exmcD/75XbC92ZwP7Y5q1X0ZnA/SAAAmvNmcD+ywdox8WZwP1ccVpzaZnA/pZwuC9NmcD94nRymwmZwP/hWkQy6ZnA/36DdVsBmcD+AOIo8x2ZwP5jNfRDRZnA/Uma4hNJmcD+HZney1GZwP6ab8wvXZnA/3UP8hdlmcD87AVei3mZwP6dBu/bgZnA/1WV9T+ZmcD9KAcCn5GZwP6VJDNXrZnA/DR9t1RZncD9i1UjBCmdwP0+bsEQNZ3A/jkEMvQtncD/9bRsJAmdwP2K6D3fuZnA/AbyA7wFncD+bdflQD2dwP0K54WYLZ3A/JKAu0vBmcD8IeHf49GZwP2EX8h7nZnA/+EIjI+NmcD920fJ62WZwP9knD2zIZnA/Tbr1u8tmcD9JCk1MymZwPzka8oXVZnA/u4nJ0s9mcD9lJmql0mZwP5qDD9PeZnA/0JwLzOFmcD+tx+TD4GZwP91maPfXZnA/5hti69xmcD9rjFFf32ZwP/UsdIfjZnA/gZKzgdlmcD8ksbxez2ZwPwucsIbiZnA/5aZ9ku9mcD95DkP24mZwP5NeOubbZnA/TrJ1yuZmcD/w2CCt8WZwP65QB0XxZnA/8Fy1nu1mcD9M8Hqw5mZwP/8u/lLdZnA/arsfG9hmcD80jMqv8WZwP+QeRwv4ZnA/chqnuwpncD8mQJb0/mZwP+Fs/RIZZ3A/eZPcZCRncD8E97YDHmdwP715CmAXZ3A/xYjOiwhncD/NsuhAAmdwP+2qEhEUZ3A/od0doCZncD8V147LNGdwPxp5Oi8/Z3A/h+xeK0JncD//zQ+gQmdwPzkuNmRBZ3A/M8W1rEFncD8iYMQAP2dwP38+Db09Z3A/ZXFD2SRncD+RRg2vF2dwPzXe0X8kZ3A/1QafRi9ncD8lo9jAQWdwP8x+ypJWZ3A/S3OnPEJncD/ybsTNOWdwP7Sie4U6Z3A/QH3++TpncD8EsQK2TWdwP1ElQylMZ3A/ZJHVbkJncD8GeeTOPWdwP2OyLsNFZ3A/8707MkJncD9SGesLUWdwP0Oz6GVZZ3A/CDPvwV9ncD/hVSlqTGdwP/RWyH1MZ3A/l6cSrjxncD8SdrM4PWdwP7r+7tNVZ3A/JC3Oe1tncD80zKO0UGdwP6XdRG1tZ3A/xwsF5WhncD9wy4vwTmdwP9H13XtFZ3A/+HuKM1ZncD9Q2Y7HYWdwP2EG76ZcZ3A/1SnRdmVncD9ZwJ/IbGdwPxA0OvmAZ3A/L4HirZNncD+DxpX4lGdwP2Imau6SZ3A/Y0tjRZpncD9jFDKSnGdwP5Fqv2WaZ3A/CrhKn41ncD8KP4UIkWdwP/J4p7WhZ3A/xv9c4p1ncD+xn5DRnGdwP9Asbj2pZ3A/qeP3fqVncD8E1CA9uGdwP0tHdvK6Z3A/H3sS18xncD9xx+Ijy2dwP2WJlsOxZ3A/1Rcm2rJncD9VFZiqs2dwP8kFy961Z3A/6Ft2D6pncD8H2JUXk2dwP8AE2px9Z3A/EDUM0pRncD85ix14mmdwP3Nqx0ajZ3A/hw/CWaZncD9iB2FKq2dwP/4SsaK3Z3A/QbwucaRncD+c4mcAqWdwP5hYe7aWZ3A/0CnOnJVncD/RFYMUfGdwP0IMtyGCZ3A/r2xGY4tncD8s+JV/h2dwPyKRpd1uZ3A/si6Rjn1ncD95inTBZ2dwP5Uhnk5ZZ3A/NkZvtVpncD/A6rbSXGdwP+kFrAhhZ3A/4BVWQFZncD/neUQsZWdwP4JD0eJfZ3A/roaQil5ncD8D0CnhYmdwP3fsWoJdZ3A/z+kprFpncD9+jDCJWWdwPxLJhgppZ3A/JXZawG5ncD9ep2LKbmdwP/2uSFNoZ3A/e9+DlGFncD818bldU2dwP9eiIspFZ3A/mJIPyTpncD821Js6OmdwP+vbVQM2Z3A/qyD/ZD5ncD+QRLxwVGdwPwlpl3U+Z3A/GKpB/DBncD/JcD9ZMGdwPxoYOR8zZ3A/xwutDylncD9CIveFNGdwPxZCxF8vZ3A/D/S30ypncD/A2Z3OMWdwP3Y5FdcmZ3A/I+fVfTRncD9uIog/JmdwP/iDbgYTZ3A/5sESpwFncD9guXas+WZwP4vH8q3pZnA/2tROA+5mcD83QRfB5GZwP0YMzgvaZnA/FsQpo+5mcD9Hd9u63mZwP1H5WAXtZnA/0miXVOZmcD9ECiAt3GZwPwwT0yXWZnA/m3QjttBmcD/JLe3y0GZwP5VoVxDVZnA/14KwwdJmcD/VTnK02GZwPzkUlWXUZnA/gMl9J89mcD+to1bw12ZwP4o1YQPeZnA/yKYIENhmcD+6zXK/3WZwPwcdiJPSZnA/ypSQJN1mcD8SLMK01mZwP+0cT8PCZnA/wWtAMslmcD9s/UAsxGZwP3Pciu/BZnA/Y2l7mcBmcD9Mq283vmZwP+R5YA6vZnA/DS8e87BmcD99C5SkqWZwP58hvnmoZnA/tHV/uaRmcD8Tw37wsGZwP2wUGpC1ZnA/BbTU0p9mcD9jkw7en2ZwP349P8SmZnA/njOn2qFmcD855RULj2ZwP1WPAHB7ZnA/IjvhvYpmcD+7J+RrqGZwP1AAFhGhZnA/g3YlWK5mcD/ZAh7WrWZwP6B5CoilZnA/E/kIv5xmcD+sg8HRj2ZwP/yZ9D+DZnA/fM2OCIxmcD/SAIUWm2ZwP5AX1WueZnA/6+DW5ZRmcD9qO4ZckGZwPxwSbOaDZnA/ZuC8kYNmcD/Yf8ROlWZwPwXpaG+LZnA/hDN6UXxmcD8IfsYXiGZwPw+zBRCcZnA/YV5pfKNmcD/XT8eusWZwP2JKSEC4ZnA/vlFvsKxmcD/s2sknqWZwP2sdU1mlZnA/kbZqqa9mcD/+Qsnwp2ZwPxcDrWOvZnA/m0zDB7tmcD9DZiET2GZwPxZcxWzHZnA/Ygc2b7VmcD9SNSsStmZwP2WPwZvDZnA/TqqKMcBmcD/VFhGyumZwPxEZ+u6sZnA/122yiJxmcD/dTzn5nWZwP/teq+ifZnA/cSCKuqZmcD/R/utJlWZwP5McjZePZnA/sYNw1ZNmcD/kIWBQomZwP24t0feiZnA/WhfTe59mcD9KEKVRm2ZwP0UFBEOJZnA/jMWxUZhmcD90xtGFlGZwP3MFtE6YZnA/KaQSzY9mcD/ZroIhj2ZwP+KH/LeLZnA/GbtgLJRmcD+8ui3vl2ZwP6YsyCGGZnA/DdSgQH9mcD/vxNvPcWZwP0ZJI7t1ZnA//7/7HG9mcD+pyx9cbGZwP5mf2xlyZnA/7ABkHmJmcD/K/IhUeWZwP7OqkkJxZnA/nakkH3dmcD9nGev7dGZwP2/NAlODZnA/jT2sOIJmcD9fu3kkhGZwPyleNLOHZnA/mtnpy4NmcD+UoTS3hWZwP1ke7/F2ZnA/0W/8GHBmcD9Gy0SYa2ZwPxIlkbt0ZnA/tgnKUnxmcD9RgHdldWZwP5Oi6dp8ZnA/fwGzCXVmcD/dlkhIdWZwP5oG6Q1jZnA/r7+gu1NmcD8/LtidQWZwP1a+ayFWZnA/joy4SlVmcD/fxyakXmZwP9KKgFBjZnA/vBcgM2ZmcD8hRuZQb2ZwP8NiVrlfZnA/IcH95lZmcD9Phm3rQmZwP8gL0n84ZnA/YWn5FC9mcD8V3zSBRGZwP5qs7nxFZnA/ufRcMkhmcD9QviDkOGZwPwbnFRtBZnA/RlgfJUFmcD8qMDKRSWZwPwdHSEtJZnA/edwKFllmcD9szgXEU2ZwP7feFTJdZnA/7RqtT0pmcD8z6tr1R2ZwPyBzCoI3ZnA/vjYHQDtmcD8pnQjGLGZwP9P1r7QtZnA/csFU/yZmcD96CKejKGZwP8oH+2YjZnA/OuRpOypmcD/lcqiELmZwPxvsv5srZnA/ceu/gTBmcD8zyFpQF2ZwP8CI9CchZnA/7MMUMiNmcD+Q0OEtG2ZwP99QzxMuZnA/jidupy1mcD9xuqRcJ2ZwP+mpgLEkZnA/jjUjdhpmcD+5OzScHGZwP1g1D64iZnA/LkYXjxFmcD+HvUOJFmZwP35qpM0QZnA/WMoqYRpmcD+9qFK2D2ZwPzrLbiAgZnA/ZaiFzSdmcD9gswzEKWZwP8H4wxctZnA/TI8SGCVmcD/l2u1zIGZwP1tCFzAZZnA/Jyu1VxNmcD8UVt20DmZwP3cNKqERZnA//nFlYBdmcD/VCy9YIGZwPxHN6TUlZnA/oN7mpCRmcD/QlKwkK2ZwP55CTHQlZnA/GarpxyxmcD8OJiJFNWZwP+JGkFgoZnA/TCQTFSJmcD96LSz+GmZwP72ClTkUZnA/j9OljAVmcD+2abAvBWZwP8/peCX+ZXA//SAUhQpmcD8CvCu5AGZwP5entxb/ZXA/vr7A+gZmcD8m4WKgBGZwP5i9+ur+ZXA/O7keo/1lcD8cCfm0AWZwP2u2vB0MZnA/6VJvKhlmcD9gN1ziHGZwP5yT7bkQZnA/kIrdkAtmcD+RTZWQHGZwP2UZLo0UZnA/Ok4MxhhmcD82WEWrGmZwP9PKErQUZnA/cGYuuw1mcD/64Jdu8mVwP2hVHf3fZXA/ctr2nuBlcD+hAJXS0mVwP2Z+Ody6ZXA/M0andp9lcD+Bhv9lpmVwP9ZYeu6cZXA/HUSFTZNlcD8s2vj9l2VwP/yeNS2gZXA/4YGvt6ZlcD/EMrM8q2VwP73dRNOpZXA/DKI6P6NlcD+rVN9cqGVwP7lHDkmlZXA/mtCrLKJlcD8d/CiFnWVwP8pNh8OYZXA/fV34i5ZlcD9PSa7IomVwP6F4WMCoZXA/AMdcy6ZlcD+rsem5pmVwPzsWFmqQZXA/qIpJ6JJlcD9wXn1dlGVwPzjMHbWjZXA/FEKgQJ1lcD/IcvxhmmVwP47FkYGQZXA/EEmdyoxlcD9agsGLnmVwP+AIONCnZXA/WE/NDahlcD+0SEkWkmVwPxUmDO6HZXA/QokLMYllcD/7OnbbgmVwP1z6hgeFZXA/yitur4BlcD9FPQomcmVwP6NZtWh1ZXA/NmpZSnNlcD8qvYdxgWVwP1ejl3F4ZXA/ry43S3RlcD8gytI6f2VwP2oy5Z6BZXA/I0M96H9lcD//lYCwdmVwP5zTHzeGZXA/BlGIZodlcD/SLeApjWVwP2XXX8+dZXA/srPMhJZlcD8cSzPHi2VwP2I4eEeDZXA/4u+txYxlcD+pVSTGmmVwP3f6tYN+ZXA/c0E+soplcD+QvDl1cmVwPx2cVGiFZXA/xudMlYplcD8H5okjdmVwP7Q961xoZXA/9WJEI2FlcD/CiVC4W2VwP1dVjnhOZXA/bz6lWkVlcD/pc/EjUGVwP7zX0Yh0ZXA/T3HqpXdlcD8TFve9dWVwP5/dPwRkZXA/wst/fmplcD+VZ+Fca2VwP8MKzWBpZXA/BDfv325lcD957DhJeWVwP2+LY3yGZXA/jmThuXhlcD9/TQldeWVwP6XLYeljZXA/BErNl1VlcD8BlDTjXGVwP+M39PJYZXA/JJNt6VxlcD9ahhcOUmVwPys2wqRIZXA/8SGr01RlcD+GMgB9S2VwP+ENWrZDZXA/sbmydTJlcD90m0ImLWVwPxJB27YqZXA/q2BPOCNlcD+S+YHQN2VwPyS2mOgrZXA/0+YXLDxlcD9tbBnHPWVwP1Maxvc2ZXA/tvSIc01lcD+VC+QcQGVwP9iTig09ZXA/CLjXfiplcD/rV9wJPGVwP1ou/ncmZXA/9pTvFiplcD/LWb8bNmVwPyptpbwtZXA/pF8NsC1lcD/1j6LwL2VwP+1msu0pZXA/mVnA5hxlcD/OgehrHWVwP8xLFawdZXA/9NMEqQ9lcD/JvwIgGWVwP5u228weZXA/N3FHAA5lcD+FBSO1FmVwP1yPZ84TZXA/kmmztBRlcD8jrRcTEWVwPyBV6WgVZXA/1V9gHAplcD+yxnvIFGVwP3dLJy/8ZHA/kp+sHPtkcD86sreC/GRwP44DLA8CZXA/Ho8F7AFlcD/6zZwIDGVwP5o7jGAHZXA/PiO1N/FkcD9jtt2I9GRwP+zRFX4PZXA/ax/HCftkcD+wQRgw/mRwPy365kXmZHA/aMGPUfVkcD+1gxQeBmVwP1IKi7P0ZHA/cslhC+1kcD9fCRUb7GRwP0wRhDT5ZHA/rehjNwxlcD8Cgb6XDmVwP+WFTqUTZXA/n1EI/yFlcD/8OpylK2VwPxsdDk4ZZXA/AS49jfpkcD/9VctpCmVwPwv1FGMPZXA/LQ2TQxJlcD/6JRSjKGVwP+3dYxYZZXA/OUXaAwxlcD9z21ip/2RwP71ClZf1ZHA/EHLq1v1kcD+DYu4D/mRwP4G6wX8FZXA/cxW72gNlcD9GoMml7GRwP0MOGSDlZHA/3U7DauJkcD/ClJXG4GRwP1nUH0D6ZHA/JUJ4mfxkcD86UYxJ/2RwPwja5esNZXA/Fc1ZlQJlcD/qJ7zi82RwP9TNPjcQZXA/ZiZTdwNlcD+F0sK3A2VwP0wonogJZXA/6AMIDx1lcD8buQMuGmVwP8d2/jMWZXA/rqvg4B9lcD8XLLqnFWVwP2JKefYSZXA/cnBjRR5lcD+mnkT5IWVwP98MMqorZXA/OA90nSZlcD/NvzhGPmVwP0cM3Z47ZXA/Orvc3C9lcD97CbelG2VwP17ssX4QZXA/1mgbIfxkcD9RnEVDGmVwP/6HSBAfZXA/7wndThNlcD/vQzhDKmVwP8U9mHNJZXA/+OahgFJlcD/Bp/kqR2VwP+KE7XVBZXA/MGyVZlFlcD/Z2505S2VwP6LXoaJgZXA/HW+YCmBlcD/um5V5YGVwP55jC7xnZXA/+0ockmZlcD87AZ+TcGVwP5LOOAF3ZXA/uuhKGXdlcD/X4sIWhGVwP1w+HIeIZXA/MAhOy5plcD/spq8NsmVwP6r95DbEZXA/wACn8MZlcD9oV0muvGVwP0pRUhK7ZXA/OsRU779lcD8F7Wao02VwP2wVs7nIZXA/bP4mM9hlcD/RZCfszWVwP4IhZU3XZXA//mUWf9hlcD8ax8hS2WVwP64drD7JZXA/xhsj7bhlcD+D62mMsmVwP8IYApm1ZXA/phUhx5tlcD+5c5GmmWVwP7aZDiKfZXA/s7pV0pdlcD91+6n0m2VwP+xFExGuZXA//8p9ZKtlcD+eKkvLuWVwP99L02+mZXA/Fp65NZ5lcD92jUH2kmVwP/wjpySFZXA/AdQCKnZlcD+LM8Y+eGVwP9SvmRuFZXA/XaDtqI1lcD+K/MSCjWVwP4z9Qw6FZXA/8afGO2tlcD8wr49FamVwP9ahFPleZXA/pXL47GJlcD/iPYeJZWVwPztKllZiZXA/mRMpk2dlcD9aP8f+dWVwP4HqU8hyZXA/kPmrCGdlcD9MK0IUaWVwPzwC9j9zZXA/v3U9j3llcD/Q5hNIg2VwP7vs4hSKZXA/C7CvupdlcD9F/caDqWVwP6Ep2KizZXA/bD8W0cJlcD+s9Lg9x2VwP99v8vzJZXA/U8bDtbtlcD9nCqa7u2VwP0JuwH22ZXA/sBxustRlcD+M4mUBwGVwPzdGtqXDZXA/Ct8cDrxlcD8JnY4qumVwPwKHWfC0ZXA/+SCM9bBlcD9qYcKdtmVwP+YgChm2ZXA/K7qi5cZlcD/Ox7KUsGVwP5lY9GS7ZXA/+jowb6tlcD+MlLyDj2VwP0RNYFeJZXA/DUiZ+IhlcD+nodchk2VwPyZb9OGbZXA/lfoIRJVlcD+BU/NknWVwP3h/mz6XZXA/ufFCkKplcD9dWIUwqGVwP2ByVYacZXA/RoY9D5NlcD8heX1Em2VwP1mN+mefZXA/jNrNOptlcD8qkGevoGVwP6A2b/iaZXA/05DSWJxlcD/AqVCJomVwP+8ucN+VZXA/dIDPnpllcD8XTh3gpWVwP0iekoWcZXA/rLSrmY5lcD/hj3NtdWVwP5B4Cc94ZXA/Anv0m3RlcD9QsavtcmVwP/baVyFqZXA/OcOeom9lcD+/jL3maGVwP/JRp9RqZXA/QjbzCGdlcD/YImLIaWVwP+1wAtJuZXA/tZBFD3plcD/Xoa3NYmVwP0wCPrFUZXA/9RhoIE1lcD8gzNVoSmVwP8/qHgM0ZXA/BJl8mjZlcD/7o8pFN2VwP6RVGrBPZXA/lOxJhUxlcD+FJLxePmVwP+Bx5WI9ZXA/9DxuhkVlcD92cEOlTWVwP7+JmTdCZXA/Dwqh+UZlcD9+NalzQmVwP8HjbzlSZXA/VBnMzkxlcD9K4CkMNmVwP24lPkhCZXA/vR++jlBlcD+x8HlGXmVwP8D2tGNiZXA/c9fV/2tlcD8CuOIwbmVwP9DoSAl4ZXA/eMIWWotlcD8gg27GdmVwP6M6WQV2ZXA/FwsXRoBlcD+7qc15i2VwP9sn7KJ2ZXA/x+glJW5lcD9wzjvFdGVwP6Mpji97ZXA/XwJyumhlcD8SnMdGbGVwP45/oxhmZXA/Fro+JV9lcD/aCEp3bGVwP746E5FfZXA/OrQiN2NlcD964hUmeGVwPwxV0Z54ZXA/tZm2j3ZlcD/34zA8jGVwPxGagBOXZXA/NbfrS4ZlcD+lkgV4jWVwPw+vBWiHZXA/A77xtIllcD9ysRH2oWVwPyzygqSiZXA/xzmDHJplcD9CqCZClWVwP3umJyenZXA/EleNsLJlcD+bSlh8p2VwP8ERe4akZXA/Q+dIm55lcD/o+Dg6pGVwP/qjy8WTZXA/+ishG6FlcD9+9JJwpGVwP7a+6/ShZXA/OSMBSqhlcD+oAPM/pWVwP51xEeyqZXA/8qQfeqllcD9Nr0JxwGVwP4/dxNa2ZXA/NfbMC69lcD/0j/UHsWVwPz8VTM24ZXA/tvoIUrVlcD8EOL3FnGVwPzmTCoKdZXA/7/Wl+5plcD89xmhUl2VwP89BLLqiZXA/wu6BjJhlcD+gq3XggmVwPzUPWu54ZXA/uBaC9GFlcD/kSme1emVwP8HR5vyAZXA/uLRG5ndlcD+ToAd3c2VwP4tP0X95ZXA/PJeCMHRlcD+KVvUTeGVwPwD18/2OZXA/7QUmXoplcD+atHWTlGVwP4cRfJGaZXA/rztia5JlcD9SuFRciGVwP1fZewl6ZXA/Ucipl29lcD93lk/hbmVwPznwfTBnZXA/AULXXWZlcD/SDhX2XmVwP1f+oxtmZXA/q9L0lWdlcD+a7UEvY2VwPyVh61tyZXA/vmqewX1lcD9Ckxr3d2VwP57Rr5eDZXA/+Uj8xI5lcD+znox8kmVwP1TCGqeGZXA/N+g6eY9lcD8h3WMcjGVwP8RoKCWPZXA/Qb4E/KRlcD9l4ku9rWVwPyFQ99KmZXA/+WkNL6ZlcD9hOkkzmWVwPzKOL7SVZXA/DIAs36xlcD+y/xHfsmVwP6z/Mj+2ZXA/L8ARrbhlcD/+w3eaqmVwPx9AdtqqZXA/w7vRsZtlcD+33LA3lGVwP6LKTIh+ZXA/hecv9nllcD9OQapVemVwP6x7R0R7ZXA/ryH+nGtlcD8VfzOxcmVwP5jXu/t3ZXA/ufHTlnBlcD+E7e0RcWVwPxtWe3SCZXA/xBJbhIhlcD/L+vrejWVwPzNgklKQZXA/7Q3De6dlcD/spsytqGVwP9JGEaujZXA/ULq2NqdlcD/k2ZV0o2VwP/tgkA60ZXA/WXD7959lcD+EwRWqjmVwP5mm++OSZXA/QkmuJ6FlcD+TpcALi2VwP1+hXnaZZXA/EAtCqJllcD+hY9vBmmVwPwuo8FuWZXA/Y/1f5rdlcD+Xun0C2mVwP9kaHPPjZXA/It9yBdJlcD8Hg0WZ2GVwP4ywBETOZXA/4lQnSeRlcD9NiJK+42VwPwuZVEzYZXA/LhaXgs9lcD9kThUb0WVwP5ESfYfWZXA/w0WrbcdlcD9uud6nrWVwP/T21/SfZXA/gpQXWKtlcD8Dby5zmmVwP254ftaaZXA/CedOiqRlcD85b6Wan2VwP2oNfEqeZXA/LglHX6RlcD92j/0EomVwP3Lnv7OkZXA/WHvnUaJlcD/p0EF5rGVwP0tKcMChZXA/16YtUKFlcD8/igA1o2VwPxhdpT+nZXA/OWtB96dlcD+3NLE0mWVwPyYosd+hZXA/1BRZ4Z1lcD+I47oLoGVwP5I3Fn6ZZXA/r08aCoZlcD/FLySKg2VwPzTRz4eCZXA/9rJLD4RlcD8yaMSPfmVwP9bimtuCZXA/4kqAdpBlcD/qOQDOm2VwP0j4IjiqZXA/VPaat6NlcD+6PcNknGVwP/me4HyMZXA/5GxRKIxlcD+EgSGlhmVwPxxnThOLZXA/A7pO9YJlcD98KGSTfmVwP1i6QG+HZXA/p6iz+39lcD8PZ+FIdWVwP7Yb+JdxZXA/Yh0foWhlcD9YEnTra2VwP35OreBnZXA/AaQeaWdlcD9n+oRkaGVwPwKOGWpgZXA/qHirUVtlcD/idAI5U2VwP7Z7dUhHZXA/J1a4pUllcD+YIz/6U2VwPw6QKIFhZXA/2Y4S9lplcD/vdVNZVGVwP26X59FYZXA/1/MCx2RlcD+ECsROXGVwP+Ctnk9ZZXA/Ay5fbk1lcD+SgCf6Y2VwP3p1hsRZZXA/vQ3WFV1lcD8SNNL8ZGVwPzA3QqBkZXA/llhYglhlcD+fr30BQ2VwP6qiwKk/ZXA/ESYlpTllcD99y85QRmVwP6gXjQpFZXA/kUuEO01lcD/t/aqmSmVwP7I7t0RTZXA/18MUoGFlcD9RsZT8YmVwP7Ov0tRNZXA/GLpiAF5lcD+ZeeD4YmVwP8SPGU1kZXA/AcDNSmFlcD8TS1j8UWVwP8xYUGh3ZXA/j+gY12plcD89fy7lcGVwPzuNj897ZXA/fhigBYxlcD/hL74khWVwP/dLBwaQZXA/O8xTIX9lcD+fC5PGfmVwP+XaXDyBZXA/B7+zDn5lcD/3Kzt0cmVwP8UBT/uGZXA/eNJLJZVlcD8AORVTo2VwP3Kyo1uZZXA/yXtaeo9lcD+/lGJahWVwP+g++IeLZXA/b6kd4X5lcD9bK0rah2VwP1HGNU6TZXA/+MYtRY9lcD+TSvKHjmVwP5cfmVGOZXA/vsvx2JplcD8jLeyTk2VwP95e1dOFZXA/kW6JT4BlcD/hOm95fmVwP+0KO5eQZXA/05wjOZBlcD+/AsXee2VwPyd8BZV8ZXA/W5xQ4IFlcD+JXPkjk2VwP7bXFh+IZXA/icWi9YtlcD/JdDIIkGVwPydoCEWiZXA/0rno0pllcD/Ot2RJm2VwPyuoXsyQZXA/QaYjN4ZlcD8DLpU7mGVwPwkKa0GYZXA/fEp/n5RlcD+UZ9rVi2VwP2wJMsCeZXA/7LYU8ZplcD8jM0ADomVwP5NgcDWyZXA/nVbEZallcD+aZnRHsmVwP1HuEhufZXA/IRq1pp5lcD/nNmkCpmVwPykKaW2fZXA/Xpd7Y6JlcD/P3o56l2VwPyBSxzuNZXA/r1DqBYRlcD8TolKnlGVwP29PUk+PZXA/AoIcwJhlcD+Lldhqm2VwPz2ZdnGoZXA/yCwcGZxlcD/Oz1USn2VwPycYL9yjZXA/KKc/spZlcD8VeC1ng2VwPyUte/ONZXA/wtv6pJBlcD93GLAYnGVwP1EchJuWZXA/sU9OuJ9lcD+xgnsRpWVwP1M+ryCLZXA/BTHPH4ZlcD+FlG0ueWVwP0UY6ax9ZXA/PDgrCHxlcD9aXNDwf2VwP0aHz4+QZXA/yxNdQY1lcD+yotz+e2VwPy+Jj698ZXA/4TNCZ3xlcD8oO5QvcmVwP7ZelqBsZXA/omp+2nVlcD8cCIXBdmVwP9/oGvVrZXA/IhHi82BlcD867KW/YmVwP+SYSvJnZXA/IcBQJWJlcD/rxn3+TWVwP2/JuPYxZXA/0MEMJzBlcD9t9XfBJWVwP/iUsjwfZXA/8uIosyllcD8pteq9G2VwP6+aqUQbZXA/2Dv/EhRlcD9pyG6BG2VwP1Po5p0jZXA/3lT+bBFlcD9k7oSwB2VwP/2F8Kr3ZHA/d3O2Mu5kcD/fngY89WRwPzudGF76ZHA/OuugbehkcD+bABSh2mRwP8ZUOnTeZHA/5IHBFtRkcD+Ky34t12RwP1ucJTvQZHA/NHKcWrdkcD/Ezln6tmRwPwxMgDq2ZHA/ARpuurZkcD+FsqLdumRwPyBSjA+1ZHA/SB+aZ6ZkcD8IDMdSrGRwP6oc4ZCyZHA/Mmn1B6pkcD9Z3SXupWRwP9Pa8lysZHA/l/q2RL1kcD9BJCfyu2RwP/Vjl5axZHA/rvI0FcRkcD80ejNPxmRwP6VDbA7JZHA/e2LgbcdkcD+PhZFAwGRwPydMlH3AZHA/cuOtwL9kcD+WeJU1smRwP47oFDOxZHA/L2IHIaZkcD942ss8u2RwP0ZpT6S6ZHA/oww1Ma9kcD+x/4Wvm2RwP7rHA8abZHA/KtI3QZxkcD/lW+n8n2RwP9HvkiaZZHA/AEjagYlkcD8ZSxpQf2RwP51N1thzZHA/8DaqjGRkcD8s09edYGRwPyj1pqdqZHA/Bga2yGFkcD8sZjIAX2RwPzHVFttjZHA/bnoMl3BkcD+PaDbHcGRwP6guVoRnZHA/5BEz3GFkcD9mpz/uVWRwP4VraoVeZHA/NKvln1pkcD+N4oSeW2RwP74+5aFgZHA/Pf7moFxkcD+0+1yrUGRwP9jPdC5IZHA/deUejDtkcD+WWryqQWRwPw09Z442ZHA/R/toGD5kcD+G7rT8LWRwP5alzRsuZHA/6oIqgTlkcD/rBjVfO2RwP52H1Hk9ZHA/sJd3cjlkcD8D0fdyL2RwP2coj8NBZHA/wIKYTzxkcD9XiFP1SWRwP15pqg09ZHA/3UUeXmNkcD+msB7CdGRwP+/wxFKAZHA/lmC39mlkcD+rqJtEcGRwP1qE4D94ZHA/ynaudXtkcD+99EnEkWRwP9zCaUyFZHA/83F90nJkcD+lnrAPZGRwPxeGa45eZHA/Ze4MZF5kcD+DQyPbY2RwP3um1flhZHA/r07CWVdkcD/YG1DRTmRwPyHlxKJWZHA/oywGkGZkcD8LVc78XmRwP1t0rlhwZHA/UhUkEXVkcD+v/GjWdGRwPw4Tnzx8ZHA/skmnNG9kcD8caAqgYGRwP6uWkQxQZHA/TtJSSUtkcD//xp8xTWRwPwuOQWRFZHA/eYfQxj1kcD9LFe+KRWRwP+2gv/hGZHA/FCXWgkhkcD9GbC/uUmRwP3NQwM9dZHA/TbDu5k9kcD9ekS+yTWRwP1jwcVFPZHA/QbyIP2JkcD8OcGwzWmRwP2r+FNhdZHA/1zn561VkcD9hCTMha2RwP4TD5OZ2ZHA/Yge+vYZkcD86ovKYjGRwP/fYbeqGZHA/apQikodkcD8KMp5temRwP9cpgPJ4ZHA/oB/hCWtkcD+4Qr8DbWRwP9fE8kZlZHA/YIu6ZmhkcD/+HczvaGRwP3HTBudYZHA/qLLe00lkcD+HOKadTGRwPxIWf/xaZHA/ZQ/dB0VkcD9jXtelO2RwP/lkzDBDZHA/FmagRyxkcD+mL2bQF2RwP8woXmAdZHA/pxFO+i9kcD8NxddWPGRwP1aJ6OEzZHA/qvsaIB9kcD85r4YfImRwPxzr3B4HZHA/nt2+SQlkcD9lJursAmRwP/N9VocFZHA/B72/+QRkcD/pxFBuF2RwPx2U42caZHA/SV8zYC9kcD/ms6ksSWRwP0ODnzVQZHA/1CMkN0xkcD88YEIXQmRwP9dEkgUpZHA/I22XGTJkcD+sv4vJO2RwPwLQXGBQZHA/3ESVKFRkcD+gb59CTGRwPxsrtiVIZHA/F0XatDhkcD9aMVJuOmRwPwlmBSUkZHA/9vLWvhBkcD9Dj1H8G2RwP1K+AMgSZHA/DSD72hJkcD+bPCfaGGRwP8jS0GIpZHA/xiGyMz1kcD/E+hMNTWRwPxmuN008ZHA/kOmu8EdkcD9yrFx+Q2RwP7zQK2xIZHA/S/dQokJkcD8dMXfAOGRwP4n82Ug7ZHA/qwLmKUNkcD/m9SKROWRwP+JjLP44ZHA/YUJpECtkcD93vNSSKGRwP/Nz8bQwZHA/fW7xlDlkcD+OSV70RGRwPzgGZSxPZHA/pRja4k1kcD9Whtw9QmRwPy1rvIE4ZHA/cJ/smCZkcD/PH/v3MWRwP/pxEHY0ZHA/khFv0y9kcD8Chbe3PWRwP9wpMcMzZHA/BpxCujdkcD/Rh/w8ImRwP8DQbFYwZHA/Vs/Usi5kcD9EuTp2M2RwPwhkSYhNZHA/FqEb4EhkcD9X46UsTWRwPzJL9KtEZHA/6e5MgFBkcD/WOWL7UWRwPyeJEK1JZHA/KVA3PUJkcD+UqBVoTGRwP9y13CU9ZHA/dCqtFUFkcD9IGD/SO2RwPxV5jF1BZHA/U2ge3kpkcD9OTMdYW2RwP3TbDyJEZHA/ObUFNC5kcD/xRN+AKWRwPwoMU/UmZHA/Q2mw8BJkcD/pltxVCmRwP7noWHEJZHA/8g1QVhZkcD/+7ZJYFmRwP1I1dlYgZHA/mpgrqQ9kcD+yaFMsDmRwP6l91u4jZHA/IntxGCFkcD9anfy9JWRwPxqDEYA2ZHA/XYiJHChkcD8inQ86H2RwP19+9TUjZHA/2F6uhBpkcD/XCDW6FGRwP5z/cHAgZHA/qYZ0rSlkcD+vheOSPWRwP8o1DIQ4ZHA/KtMPOytkcD9M60n6J2RwP/skwy5BZHA/O5CI6zpkcD8pdW4cQWRwP+gmqZc9ZHA/gTMoTz1kcD/h0U6sMmRwP1hNyWg+ZHA/L2pokzJkcD+WDd48PWRwP+m8WmE1ZHA/+nAQXThkcD9XpkqBNWRwP95cDUhBZHA/UhveOC5kcD/N0SSVNGRwP/vGMLY/ZHA/0BkVYS1kcD8MW0zXMGRwPx5H0XM5ZHA//j0k0TtkcD/xxaOlNWRwP3S8vkE/ZHA/6BLZqkVkcD+54uW3TWRwP5eeGFMtZHA/yhFZoTxkcD+bHfirRGRwP5Yd6ps9ZHA/7YLQSz9kcD8exffoLmRwPyWQhPkgZHA/w9zwJRtkcD+WwiWMB2RwP8Clevv6Y3A/n/kuKvtjcD8TVTLYCWRwPyWekvH9Y3A/3DLCmABkcD80Uynd+mNwP8wKy3oBZHA//gIwiuZjcD9X5TOBCWRwPy/v4PMOZHA//8ETkh1kcD+A4bvrCWRwP4W897n3Y3A/X0MHGgRkcD9MaNUPDWRwP1cGux8TZHA/d1aTRhpkcD9EWZWqJGRwPyytm4cyZHA//dL78TdkcD9HeljbP2RwP/j2q79AZHA/F7UoKjdkcD800lfTQmRwPzhmlw1eZHA/Pvnk9V1kcD+Ws4ITVGRwPwoNZ1BFZHA/WyBIhj5kcD8TWctNOmRwP0MHaLU4ZHA/0YLb1D5kcD/bK7f+RWRwP0m2sHdWZHA/5PAj8U9kcD9OMIocXWRwP8UWjqhgZHA/bfApYE1kcD+V1+tMVGRwP0bVvrxGZHA/GfviLDZkcD90ClIMKWRwP8C2xbAwZHA/RmVUyD9kcD/24aBHM2RwP/OKYQZBZHA/xp5MizpkcD+OtfcpRGRwP96Tyd89ZHA/dytx/kVkcD/8jzl9UGRwP52C6L9WZHA/M4yoqlRkcD9fNRqDW2RwP03/stJhZHA/VHdhgnFkcD9jjOeLdWRwP1pkunZ6ZHA/9PIbQXdkcD8bd6b9fmRwP2ZyzWN4ZHA/Kb+Xk4NkcD9ASrzthWRwP0HF8KiOZHA/LO41iY9kcD9g3SSrlGRwP8g5kbiPZHA/bDNV6o1kcD+Nyl6bjWRwP4ZpzluiZHA/vGzUoKlkcD8mBSnus2RwP7AI3xOvZHA/citB4K5kcD+T+I7ntmRwP5xlPKqwZHA/A8u+Q7pkcD/4zeGSrmRwP/rGTJ+rZHA/zYL78a1kcD9SENe2nGRwP1HCbYS4ZHA/6Gdn+LZkcD/4hQY7vmRwP/kMrTKuZHA/iiUq/6lkcD8G6hxesGRwPzPC1CKuZHA/bqZis7pkcD+1rotLtGRwP8BCqsS+ZHA/0t6bI7ZkcD8aNWpAr2RwP0D4XLa2ZHA/YK+NsadkcD+6EKpPtmRwP/9xePGgZHA/1RK0tYtkcD9OeeCvk2RwP/JmhkaPZHA/2XvqwopkcD9ipDLGiWRwPx0EZBeiZHA/Jc7b2KJkcD+45RK3hmRwPy9JbA2LZHA/sC3E1YJkcD97LGDbbmRwP2I21QpeZHA/NoPULGhkcD8Zip5Ic2RwPzp+qothZHA/4NAtC1hkcD99krdFW2RwP/3UrTZdZHA/rSOpF2JkcD9Na3ZjZGRwP9WEBwBrZHA/4vFVHF1kcD/vWDdPYmRwP4/muFZkZHA/5hb/L1tkcD985banUmRwPwSOtflnZHA/kVUTX3pkcD+8RCOxamRwPzST0dBnZHA/td44RlRkcD+Hsj05W2RwP7SPEqdUZHA/33Xv6GpkcD/QBjLtcWRwPwPn8Al8ZHA/iIaeamxkcD/LXmqhYmRwP1m+PylaZHA/jhLcCVBkcD+YYfwbVWRwP4UQ3nZcZHA/eyJr00xkcD9OWlYYQmRwP9TEeLdAZHA/q1Bw1i9kcD/mUHTYJ2RwP78ntOgvZHA/rEsJ+y1kcD9BO4dZM2RwP2Agm6QqZHA/hwYyWDZkcD98yr40ImRwPxfSPzIoZHA/vRJ8CTFkcD+s4DOZMWRwP9Rt6QAmZHA/gbkJJDVkcD+CejzyHGRwP7nwDK4bZHA/1wrqKiZkcD8vPyrOIGRwP3r5oakgZHA/ovEnRCFkcD8yepk7IWRwP4EWNFsmZHA/J4wPciFkcD/lyxh6EWRwP3vGQ/wFZHA/mOkge/ljcD/CBJ1V+GNwP41YXvn7Y3A/AQra0fNjcD/yfsGK82NwP4VKfaz4Y3A/HZBZ7f9jcD+Os41qCGRwP9qrejMQZHA/zDIOrQ1kcD8nxPvtEWRwP+iX1DwPZHA/RR/A4RxkcD+C9ScpGmRwP5TcAd8WZHA/1OwRBxVkcD9i0AhIGWRwP0uvz/ARZHA/BdDOGfpjcD9SHQ5jAGRwP5WaSpr6Y3A//Oa8lPVjcD/8+lrw+WNwP8limJr1Y3A/iaTo2+1jcD8wXvaH5GNwP9aCrFruY3A/g+YjVNZjcD8Jmfk42GNwPxLN4ATTY3A/C7hVfd9jcD9fVFpn6WNwP52zpaH7Y3A/QnNYLA9kcD+4PNgfDmRwP1OEK7cEZHA/tsdfbgpkcD+OTc7fF2RwP2i86ikKZHA/q/Dt4QtkcD9BEJs0AmRwP9niUoIBZHA/02HrCgxkcD/4sK+4+WNwP5CBxjb1Y3A/0/XOJgtkcD84jBlQFGRwP55lCmoXZHA/QxNzIBFkcD+bEoc8B2RwP7KteacDZHA/vIwyzQBkcD/Zb88dAWRwP+T0IcX8Y3A/XJDPDPxjcD9BMRHG/2NwP8QHL0UIZHA/6uU4KApkcD/SLbwO/2NwP4jKymr2Y3A/gZiwi+VjcD9kyqyA7GNwP5Y5Dy3rY3A/6AuMCfRjcD8K5sWG7mNwP/NMXejnY3A/qPfZI+djcD99LhQf82NwPwL6UvX6Y3A/yDAMpvNjcD8oZqNM5WNwPwiPM8bmY3A/gl4R5P9jcD8vjs3j7GNwP8KwR6XxY3A/WavhmNVjcD/FrkL41mNwPzxDfPTXY3A/HY0OUNhjcD8HY6Cy22NwPynRKD/MY3A/pNcj/sZjcD8PGiyKt2NwPyPy8jKzY3A/Ewgj7LljcD/As5SDvGNwP2X1SMCsY3A/+AKag6ZjcD/Db/KFtmNwP8j+2tm+Y3A/EvS6S6JjcD8VVOXSrWNwPyyyZnKmY3A/9OAD3qFjcD/9prHtjWNwP8GZp1aYY3A/9GWAaoxjcD9yH8zNhGNwP1Do2qWOY3A/S/NN0ppjcD+bLXLCnWNwP9MZXQiaY3A/++AFu5FjcD+kzpA7jmNwP13/peaBY3A/wrblTYNjcD8rl8GfkWNwP1mZFLp7Y3A/TJhl4mpjcD8+iZeQYWNwP/F24+xfY3A/+zabl1RjcD8xjRCaUGNwPy7qVA5QY3A/k/Av701jcD9lZaKeVGNwPwvSqplTY3A/J9jDsGVjcD9K4A/gZGNwP8UgBitsY3A/f7PWtXRjcD9NPneda2NwP/dNgxF5Y3A/NZkuB35jcD/R9tihiGNwP7PNkk2NY3A/Sxoo6qRjcD8B4/MNrWNwP7hRecm4Y3A/mzWDvMdjcD+LgQpkwWNwPxqX9nfTY3A/YP0RPctjcD+3mFoWzWNwP3FQC+bHY3A/Y7vyT6djcD/NXUK5rWNwP3sebiurY3A/ScrvIKFjcD/gXWD0nmNwP8mXxUKbY3A/AY408aNjcD89EVOBumNwP2TLLb69Y3A/FlBczdBjcD9pLbudxmNwP7QIVna3Y3A/z9lE8rpjcD+a6mKVtWNwP4bolKKgY3A/EF05bahjcD9LMFsxnGNwP/WiEQKQY3A/sFSiF3djcD/nXw+kdGNwP4/UMvp+Y3A/R58Xp3djcD+NyfqOcGNwP9uSwJpmY3A/IyECcGFjcD+JOO+xbWNwP23qE4dgY3A/ncsItlNjcD+CrWSXXmNwP6MBUAJjY3A/4OT0UltjcD9sw1bsPGNwP/Jc0EZEY3A/pVePXSBjcD99GaatJWNwP2GiltoyY3A/z2oo9CVjcD/sj2TmK2NwP2e8F9EjY3A/UIWVFS5jcD9u23joOGNwPzIxiJwhY3A/aFrYbBhjcD+8Kq+uGWNwP9PG/ZUnY3A/zuB1FCBjcD8rbk/tKWNwPzMrHHkeY3A/A2zOGytjcD+IJW90FWNwP2sAOj0gY3A/0wzhCx5jcD8YvcGJEGNwPxHRyvENY3A/uirpuQhjcD97z6jD/GJwP+mJQHYCY3A/DXu5Dg1jcD+pqUtSE2NwPzGPd4obY3A/wqbbGxFjcD/oBwklFGNwP9WayIARY3A/wjBw2RpjcD/6URDDDmNwP27Gk5ELY3A/IB0+4xRjcD9Nn9SFJmNwP+oHU0onY3A/5n/cjjpjcD/9J0aLP2NwP4i30GE4Y3A/BD+jwDpjcD9GYf17T2NwPyTsJthKY3A/iXiClUFjcD86sMdURmNwP0BsLFs5Y3A/H09hvDZjcD8xSiUtVmNwPxkLXydlY3A/e0bg+1tjcD9bhQu+W2NwP102gHBRY3A/oErz215jcD/J0ecKWmNwP5gvF7paY3A/V0Tk7WhjcD9C1K8PbmNwP5rwTcxuY3A/+ywExHxjcD80LFwkaWNwPw1vfo1nY3A/wH7M8mZjcD9qu8qmcGNwP1XCHT5uY3A/imtYUGFjcD+GjhXyYWNwP59lV/hvY3A/TZHNa3FjcD8ap7zNcGNwP2nKRSN7Y3A/k1TZa3NjcD9HYBiRX2NwP21pysxyY3A/tLONAWdjcD8DCDndh2NwPxsCIciDY3A/FcXHU3djcD9cWk9Hc2NwP0XwG0hyY3A/iWWj7XFjcD+dp2yUXmNwP9YfMhJsY3A/ymSYv29jcD/Y9INmYGNwP24ewF9XY3A/8gMrqmVjcD9CMOsmW2NwP+Rr1whhY3A/3fx+J1ZjcD/PGm2ZOGNwP0NCjP4uY3A/6ACG/RtjcD/x7PGYI2NwP1o/7OELY3A/BbrMsQtjcD/csz/zE2NwP2LfXUkaY3A/GDDsuhhjcD/sJo9pFmNwP6FHXBERY3A/q59ReQ5jcD/NCXW5CmNwP0+u4dkcY3A/zFmmUidjcD8yGwKeKWNwP32/FF0rY3A/NRkgZyZjcD+8qgQPH2NwP0hp78caY3A/kE1oBDJjcD+p7A5OLmNwPxESb5kpY3A/zGe/6jxjcD+O6X/VSWNwP0xQRwtBY3A/a7FQeUpjcD94Uzg5VmNwP3SmgehVY3A/cU/QZVpjcD9qPMlWU2NwP2TCanpeY3A/hoMwhmVjcD8r3o1LZGNwPwnko7xhY3A/MIKkNmpjcD/HWHL1WGNwP+oCLLtyY3A/Tc8HfH9jcD+4n8Ajc2NwPypnriR/Y3A/xkEYiHdjcD9nHeZBd2NwP3L5215wY3A/h3jqOlxjcD/5nGmMYGNwPzZD9U9UY3A/1YhwJ1RjcD/5O1k9QmNwPzijKvM6Y3A/PxreoVFjcD9x91ohTGNwPyKbbXdIY3A/WltB/D1jcD/YqO/bOmNwPxFByhRAY3A/FPVtmk1jcD+JPolcTWNwP/9092BiY3A/wbQjyGFjcD/8igwgbmNwP8i0/jlvY3A/RET5K3ZjcD9LPKinbWNwP9KrwJR6Y3A/XKnv4nZjcD/DraYBhWNwPwN764xwY3A/Ec8NeGtjcD8AWSv7W2NwP2c7XF1WY3A/qEtfDG1jcD9Kaa+2emNwP1SHcCaCY3A/bXYlA39jcD//ZHcOeGNwP0G2R2d9Y3A/RxCm/HNjcD8c29oXYWNwP0hwGWhGY3A/zLvN9UhjcD9i79IFS2NwP2pNpT9UY3A/ZP2rSkVjcD/5tTEpUWNwP1NPRjRkY3A/erpilmFjcD+XJpTfXmNwP8Ucn2hmY3A/Xy37CHBjcD9xZJEOhGNwP0EaTF+MY3A/CGPVpJpjcD8u737QimNwP1axRxukY3A/bzXY3Y9jcD+gLTcBhmNwP8Eiq2CGY3A/JVZoSIpjcD/qiw4ZiWNwP6pK836LY3A/IM/2epBjcD9gDId0l2NwPw9O2+SUY3A/VtmjYY9jcD87fdjQlWNwP/WX4Bx9Y3A/qvkaGYVjcD+hcmregmNwP67MG8eTY3A/zSKQpaJjcD+V6WxtkmNwP7zaL46YY3A/OH4u+ZhjcD9w+pCBl2NwP3g6/RWPY3A/NuwlMJ9jcD/XiOEhqmNwP4j8bvu2Y3A/oqxxpLFjcD+UeZnBv2NwP9c3YiKuY3A/0453katjcD9E1E84tWNwP0l2re+lY3A/e+BTy6RjcD91dqnvtGNwP0RXMZC+Y3A/ZPi4KLxjcD/HnOYTwWNwP68+e/rOY3A/7LODMOFjcD8i4Vbp2WNwP9U3wnL1Y3A/0Ob8rAtkcD8zi6eKAmRwPwDANl0AZHA/3jjG1ABkcD/jH/aoAGRwPxRz9GwNZHA/vXxtmhlkcD+91NvnLGRwP5ymUZAwZHA/O4gH2x9kcD9KTTERIGRwP3gaHc4kZHA/ONe5wydkcD87XrW8LWRwP3s9MYdAZHA/08ZnmT9kcD805JnONWRwP3+5AyBGZHA/MWUOEzFkcD9G9Jd6NWRwP914mXkqZHA/sYwFBjxkcD+YAGmlMGRwP+nuLCUwZHA/1IuQAzdkcD9p91rSQmRwP1JnsaJXZHA/2MMfhU9kcD/DPo7xSGRwP9QKN19QZHA/1c5bj2NkcD/Zp7AySGRwP+vUf1RSZHA//nhLnU9kcD/U5B6GXGRwP62mJANlZHA/Wp4vj2lkcD+TGFAEc2RwP26B2Fl+ZHA/njUNnIJkcD+3kdPdgWRwP/rWSY93ZHA/k9hAIF9kcD99UJ4cYGRwP1WWMsF5ZHA/LdFhQ25kcD/W1bq2XWRwP6WGoJRaZHA/wWhnkl5kcD/xFHs4S2RwP8/6kulAZHA/LJHeFkNkcD8rjV33UmRwPw0HC9pWZHA/2kld8GBkcD+UPVENbmRwPyOKXYNaZHA/ZVK3RWZkcD8EDw4RY2RwP7Sc2s1cZHA/wU995ldkcD+4+jxaYGRwPyD8NQJgZHA/Ro3f4FxkcD/a/3p1XWRwPyQ7OY5JZHA/XOuKX0NkcD/W3Gv+UWRwP/xwcw1TZHA/0gPApz5kcD+EAJ/0R2RwP4DFqOJPZHA/CX+4Q3JkcD9VvEEviWRwP9TEM1VzZHA/9JZS9HBkcD9CB2mYcGRwPwxwAK1kZHA/1HQRAl5kcD8wQa57Z2RwPx9ln3lfZHA/47m+9WRkcD93DnJwYmRwPwBb/tBiZHA/zr4vW2tkcD8iOfyYcmRwPzq2m81nZHA/mFgzCnNkcD/dDvCccmRwP5Adnmt9ZHA/V0skZH5kcD+gXfsMhGRwP6dDn12MZHA/LPV92qBkcD+jqqgwsWRwPz0ioN2+ZHA/aTPSXMFkcD/IjcxuumRwPyNgMYmfZHA/r93clJ9kcD9G0AInq2RwP/OjdLKqZHA/CUSsvaFkcD8CgnBmpGRwP9S2II+jZHA/BM/cr7VkcD+sLlL+smRwPyu2EImrZHA/x/PobZpkcD9/WBK7hWRwPxvDR8l2ZHA/A2TqemdkcD+hMoSNamRwPzMxA6NqZHA/1Kr7InBkcD92VkwXZWRwP9EM57JgZHA/zS/Ms2lkcD/AFif9ZGRwPwAmh1l1ZHA/RdOqKFRkcD8eWFwqYmRwP2gpPQtxZHA/pMA0oIBkcD89zREBemRwPyzpz8h8ZHA/XwwAnmdkcD9Uii3CX2RwP6h2SZxIZHA/oIE0JDxkcD8zvTZwUWRwP10K/7s/ZHA/9DhrvClkcD9pzkfFK2RwPzBxoOQMZHA/3KzOQBBkcD9EUnLOJmRwP8Y2fJMrZHA/BvKFyxpkcD+kPhijIGRwP5nWcEUoZHA/sFJ1OiBkcD/oN/cJHGRwP1A1EEcgZHA/Bh0ohiVkcD+RAnWhIWRwP772QKYXZHA/sk3ubRNkcD8IqyZEAmRwPxO3H2EEZHA/d+uaxQhkcD9x3IiaDmRwP1otqS8OZHA/GVbU8AtkcD9fx5+YCWRwP6nI5IwSZHA/7I6XKwZkcD9fw/W34mNwP5WbWIvjY3A/fGhkudNjcD8HkzRGymNwP7+RHBnFY3A/Hyx4+LtjcD+P/jhAxmNwP3SqiGTGY3A/sAykyMljcD8JcbqU22NwP4rXKjrdY3A/ShnlWtVjcD//GQIY6GNwP8Jepo7qY3A/qzIhUdVjcD8shchgwWNwPznlhIvJY3A/x+NUddRjcD+OAB0N8GNwP5nXY4PnY3A/t9scEeRjcD84vkv/6mNwPz3YuL4IZHA/mxbdqh1kcD+RdzgvFmRwP4KEhAIZZHA/hobUehhkcD+fknZwE2RwP/5IKg4QZHA/HIGjmAdkcD/6U1D3AGRwPyqej7gRZHA/mwdKRgBkcD9oaMOUEWRwP/8bU/gCZHA/AA2IGPZjcD8cp1XWB2RwPwPxgkcRZHA/Qk3SxwxkcD+fJwFDH2RwP7cqEzY4ZHA/pSBchkpkcD8pxislVGRwP9F4ZZhTZHA/afRKSGZkcD8KQZvYe2RwPw3NEP17ZHA/gtkjk29kcD+xHljAZWRwPxvpORFmZHA/BNuXdVJkcD89rMBOVmRwP+qrKk1RZHA/U7kvMFJkcD8HAyNNUGRwPyjsxF9RZHA/SPsh11FkcD8+kIBxUGRwP9CIya86ZHA/zoQWBDdkcD/70sGOJ2RwPyxK0ZUfZHA/uKsAMCVkcD/nEoBOJmRwPz06XYQjZHA/ojONoBlkcD831/EFI2RwP6oEAkQQZHA/4UNZGxhkcD+cyLXjJWRwP8vuY9AtZHA/rvkuei1kcD9qR15AJWRwP8btPQ0qZHA/iecDSDRkcD9xW8EbK2RwP36jM91JZHA/9Gp9NkBkcD9t8mZgMmRwPwRRneM0ZHA/2GIEIzNkcD/75DacM2RwP5hfKJsyZHA/NaIP9C1kcD85zuEeLmRwP8BKi6cjZHA/PuLBfTJkcD+CK28GMGRwP28S2tIdZHA/78EC1hlkcD/HH10XGWRwP5c5QqcnZHA/b19IaTJkcD+RNIsFJWRwP91glN8fZHA/ytK38ypkcD9HXntWH2RwP28/ZQAmZHA/oT5NbydkcD+ucCxYKWRwP5VZbQcnZHA/iSVmgi9kcD+J6B00KWRwP7nfgBIwZHA/AQfhPCxkcD9LP7vJL2RwP/PhrCVOZHA/NCVH1UxkcD8S4agYY2RwP1QZqopSZHA/DDg00UJkcD+BrvLzQ2RwPwdJYnRHZHA/MbB+Uk9kcD+vc7LjVmRwP++xkg1TZHA/dISC3U5kcD/v/9g0VGRwP7LwolBVZHA/xKlju2JkcD92IhzfcmRwP8JNxCJsZHA/kbjjjXFkcD9W9oLmgmRwP+pQZPp2ZHA/IWPm5HpkcD8y3qLTfmRwP0vRMJN+ZHA/9rpr+39kcD+9ZjGCfWRwPwUlollyZHA/l8enkHNkcD+kKT9rcmRwP+34AnV+ZHA/7auzZIdkcD9XqsEgjGRwPxUSrlN4ZHA/d30jgHFkcD/nrqhxdGRwP+7IJnV7ZHA//U+TiX5kcD/wNrB1dWRwP/hlGZJ5ZHA/nc6HUWpkcD8Xni15dGRwP/pasOFuZHA/MyhB0GJkcD9YEgK9W2RwP2k/kU5RZHA/E8u+NlJkcD+p5AJSY2RwP3cHj4xzZHA/0qiyt19kcD+5o89GVWRwP3Md4StoZHA/Av1gGGlkcD8bl/eDYmRwP1iCRclwZHA/x7tK7mVkcD8NbddQdWRwP4bFJaV3ZHA/RjzgynVkcD+EabnZhWRwP9DcY1uMZHA/Ji7AO5VkcD9YVi+rmWRwP4IanLidZHA/E/L/1qtkcD9lWaRMqmRwP731z7KZZHA/X0Nn7KBkcD+RBbh3pmRwP4a2f2m7ZHA/0Ik758NkcD93vwCrxGRwPx1nmW/cZHA/cS5WZ9RkcD8J5b+C4GRwPwX1SwDlZHA/ti0OmeFkcD/mySpn62RwP8oEnZbpZHA/VRceDthkcD/EVRxyyWRwP2Li+HrDZHA/vbWWmLlkcD9ug/XhuGRwP1XKJbisZHA/IeDta6VkcD8McNprpGRwP2ZRC66zZHA/bJi868RkcD8EbavFuWRwP7rvGcvAZHA/Kt7AoL9kcD9QPoIDtGRwP8OVCbeyZHA/NAVfxKtkcD/6bkcps2RwPwShfN2wZHA/mtJ7aaxkcD/iyFSgu2RwP9AZzTCrZHA/WNlaiqVkcD9WnptQsGRwP7dBHISfZHA/UJS9ipVkcD+VJzIjjmRwPwD4xfeZZHA/zKA8vLlkcD8It9Xmt2RwPyHHeFG+ZHA/xx4VbcNkcD83xnI3w2RwP/sNLTG4ZHA/6W7Z38dkcD+tJpy42mRwP29nd3DLZHA/FUEVXeZkcD8srGlCzWRwPyiLU8SzZHA/2fVEMbxkcD9w3AAztGRwP1Ct2ELHZHA/9pyzh79kcD/pap76vmRwP16gPinLZHA/lTsl3NVkcD+zJFZA0WRwP9zrZTPBZHA/6AohXsdkcD9Xxv1OsmRwP7F5+q60ZHA/er+627JkcD+hGr2AvGRwP+82kW2qZHA/DUUjzqlkcD8AoniKp2RwPzkq9nSsZHA/xqbpSa5kcD+S5MO7mGRwP57pEA97ZHA/Mj8WcXlkcD8NJB+Ih2RwP+e59ByOZHA/I956aJBkcD+Tf5VBkWRwP+QG3pSYZHA/20l5oYpkcD/03hdNiWRwP3cP6AuNZHA/xyNVioBkcD+FNvGfgWRwPzJYsImLZHA/bccO0YlkcD98rkyPmWRwPyLWBZOMZHA/bC5K1JlkcD/PFpc/nGRwP2H3bniUZHA/L7OlYJBkcD/Xu9TsiWRwP1OAt4t3ZHA/lb+hU3JkcD+JnxkMcmRwP+pIz7h/ZHA/EF9kHWtkcD+50AHGgWRwP7z6XPlzZHA/46JesoJkcD/+ic5OgWRwP047jx6IZHA/Ylaoi4dkcD8ESEhqjGRwP/tOi9KCZHA/a5h1EIdkcD+//TL6kGRwP+J6s/CfZHA/2b/IX6hkcD8KueoUtGRwP594ywenZHA/kcQlsbJkcD/aGAn8vWRwPx9fEmfTZHA/ie3TCNFkcD/EXi73xmRwP1fnJy7KZHA/eZbdnNxkcD+PJXYn4WRwP7ZWB1PrZHA/WxxubelkcD9ojXUB7GRwP7sq9w/lZHA/3sMV4N1kcD+4L8WB32RwP+sKoH7jZHA/CXQwHepkcD9xiN0B52RwP7r717bvZHA/N3qbJ+5kcD8r40pi42RwP65TSqDcZHA/likRxNdkcD+UecYW0mRwP+m91LfXZHA/ACIRP+5kcD+aVTNj8GRwP5u/rgrVZHA/xoOIPN9kcD+FyF+60mRwP8JVwPe+ZHA/j29I88tkcD8MdFr3smRwP3OjhRqxZHA/4ae4patkcD80Zlk0tmRwP9sPVIPGZHA/955/mMZkcD9ixgBUyWRwPwhZttfDZHA/PGL2FdFkcD9/RAicyGRwP6dAwGXcZHA/BJ4+NdxkcD9cr3y6yWRwP1ImeqPSZHA/xNzf4eJkcD9DOaB/4GRwP0VMU47oZHA/q+lqsuhkcD8i7br372RwP8ROpVn4ZHA/cOhFAPVkcD+BeLPM5mRwP/2gYk7jZHA/M3TBN+1kcD+o6Q9y82RwP1XPeuIMZXA/TeUJbBNlcD80sZVU/2RwP8SADUL2ZHA/ObYHw+pkcD+KtDaE6mRwP7RlMgHwZHA/urLPpe5kcD9CUMPC+GRwP6jHDWvrZHA/Cw5IcfBkcD9VirHw7mRwPw7zjB4DZXA/+WUIMPdkcD8hsubs8WRwPxuuY87lZHA/qd3eeORkcD+crs+M6mRwPwT1a3nbZHA/QNLFlt5kcD96DcsK3mRwP25NmXfHZHA/v+7U08ZkcD8J/ZVoxmRwP97tvI3OZHA/iAUqtt5kcD/YPMlM2mRwPyhdE2vrZHA/KO0fKutkcD/HO3lG+GRwP2zLJdUKZXA/KKkUGRhlcD8fEaJgD2VwP5iWAdb8ZHA/IrKIEfJkcD/LaBqq6WRwPz7w0qbaZHA/4W6MIOBkcD9Je/93x2RwPytt/ua/ZHA/0BBWOrdkcD8d7SrXuWRwP40pSIyeZHA/PTSecZlkcD/csXXSiWRwPzhpD+ZxZHA/rUzFdW1kcD/S4jpWbWRwPwR+xfx4ZHA/qF/xg4pkcD/Af+UpjmRwPxtheYl7ZHA/wJpdIpJkcD+O/JAznGRwP28EeOekZHA/cuuu25pkcD9dYKgQkWRwPyyYehaRZHA/QiPaToFkcD+T4OvdbmRwP+fl/JlzZHA/fErw3ndkcD899uebiWRwP1VRBwSKZHA/Oy6RGIlkcD/tgBgxlGRwPzDOegmbZHA/2zv5XZ9kcD+x9cnhnGRwP9LWdbmbZHA/USKMSKBkcD/9p1BpjmRwPy/QkMeRZHA/jYHnU5xkcD+Vna3qlWRwP/pxquiNZHA/mPBisp1kcD8LAyLOl2RwP4fdpjanZHA/QaaIEq5kcD84guP1rWRwP2y9EPK3ZHA/K7cDaa9kcD8zvCXlr2RwP6rgeti4ZHA/PgqU1MpkcD95TdLwzmRwPybvRCvSZHA/66dUa7pkcD9ZGal0xGRwP4etGszBZHA/sljJYr9kcD+okWd+wGRwP5/UDTm+ZHA/dDeIJrpkcD+rkZAVxWRwP29iXoG2ZHA/518og7JkcD+0YFGfrmRwP6Dio1G+ZHA/JtJbPMxkcD+dHdjWs2RwP+qwREy3ZHA/Zim6JLlkcD9ifkR5xWRwP4+Q827BZHA/vFlnJshkcD/tM9pzxWRwPx8sSrbSZHA/V0HE4tVkcD8ODjY/2WRwP0LtiwjWZHA/UF/mu9RkcD8oaLXqzGRwP75vs/SuZHA/n7+am7lkcD82UbF9sWRwPxDxwk65ZHA/i/KvBbNkcD9vjF4wqGRwPx3xfkehZHA/LjUka6hkcD9DIk6ptmRwP3AViDa2ZHA/0a60h69kcD8xN4yKu2RwP/cl+uKsZHA/GwEX5LpkcD8Cn3BGuWRwP8gOjpTLZHA/tCQ+vLpkcD/sB74Fu2RwPytxifi1ZHA/ID3/l7RkcD/Y00B3oWRwPznvw26rZHA/5JjpNrNkcD91hhE+t2RwP+Nz74O8ZHA/kQ39g8FkcD9agjvBvmRwP+Jk6dfDZHA/S616z8dkcD/VfmOEvGRwPxTTUze8ZHA/ULZqscBkcD+C4E85vWRwP2l6BKKxZHA/yl291K1kcD9iOYAkp2RwP/HjoYSzZHA/YD9heL1kcD9Be1TLwWRwP2sgJEe4ZHA/2JVCabNkcD+54+InqWRwP2KRsGmmZHA/r8JTlKZkcD97RxXyqGRwPxOd7HWcZHA/An0rYpVkcD9OzmFgpGRwP8QmlZ+0ZHA/c0uMjK9kcD+FUBdBomRwPw1FQHaTZHA/0TgNJIxkcD+t1O7Ef2RwP0wIXiZ9ZHA/ktXOSXJkcD8Uwr/TY2RwP9G4xCplZHA/q/NkhVdkcD/YvQZDSmRwP6Hv7h46ZHA/YYKsZ0FkcD+nX4RcP2RwP9UxFMZYZHA/VLYc81dkcD9ovWyIXmRwP1dahQBjZHA/3mD6sl5kcD91CT0wcGRwP9zhhkh3ZHA/t5khPHBkcD8Ty7bifmRwP3a+kDOLZHA/YMbgmXlkcD9B4QrkemRwP/DCuPJ9ZHA/Mt6d3Y9kcD+oeylSomRwP/ZF+Yq5ZHA/AG9B7r1kcD+1cramvGRwP1cIBuu6ZHA/QiPUqrRkcD9/TkojpWRwP6Mr1r2pZHA/Jwc4OqhkcD+Abga5pGRwP6q7mtSlZHA/g6k+m6NkcD+0V93CpWRwP35Dp5aIZHA/hxcpN4FkcD+5cbQaiGRwPyN4WUaZZHA/cH/9W5xkcD+5cZXBp2RwP4m8mDaRZHA/1JgIqYxkcD8x0GFNpmRwP1gdX0e3ZHA/kevTcbVkcD+T95nhvmRwP0GyJAG5ZHA/uMkORrxkcD+iwwSR2WRwP2hQPvrXZHA/VBdNNuBkcD/dpKIZ4mRwP1GotnzmZHA/REA2X+FkcD9PHqNx6WRwP36bADPpZHA//5dAWelkcD8F7/YdA2VwP3VmvAX/ZHA/qhR4AgFlcD+CiKiB72RwP/1KQWD/ZHA/WOEpbvxkcD/JqO7z/GRwP6Ew5gLtZHA/wP0SA+BkcD+z0UzFx2RwPzHJa43OZHA/MGN6gNRkcD+StT830mRwPyQwrKXSZHA/zfzIxr1kcD8T6/Q8xWRwP48rD4vcZHA/DJ1UrM9kcD/BLNDq5GRwP46qLpLaZHA/n0pDAvZkcD91jsjv/WRwP+J1km4HZXA/U2IjuxZlcD8zAAHMGmVwP6hq0WAVZXA/CKDFyAJlcD+7RopcGWVwP6rpdOMEZXA/dKgUpAVlcD9PQnEnCGVwP6+RXlz9ZHA/6/sVfP1kcD8R9mra82RwP/6FXHn0ZHA/vdwbnfRkcD/oqbiB/WRwP+2iyLkEZXA/rzCJLRJlcD+RtKJv/GRwPxwFlBLyZHA/KcuSXvNkcD+dcfsO7GRwP+noj4XnZHA/aZaMRtdkcD/ZsTzN2WRwPzlyPYXDZHA/DfEWd7VkcD8Nkx2xwGRwP8JDgNmvZHA/NjnXKrNkcD/m9yNEk2RwP8Yiwu2ZZHA/TV/br5NkcD8EoliQfWRwP26TWTJ6ZHA/2ea0emJkcD/+vn4IZGRwPzglVENYZHA/OoqJHUtkcD+zWBn2YWRwPwV48e1pZHA/q5gacWlkcD9lSdmCdGRwP+0/RC52ZHA/KB3TBmRkcD/+jU5uXWRwP3uJe09nZHA/hcYtfGNkcD+d2fLjcGRwP5derABjZHA/7OaJIntkcD/2e5gygWRwP71TST+PZHA/DrTgkYFkcD8NlVi3e2RwP8zmk1puZHA/VlEFcXpkcD9YaYfQjWRwP7q6+x6aZHA/132c4oNkcD90+UZtfmRwP6VI3xJ5ZHA/T6BxInhkcD8qWJl7g2RwP6EKKEV+ZHA/25oiIn5kcD8ptwYoc2RwP9MZVFR9ZHA/zmc/IHBkcD9UQhOKb2RwP+pj4dhqZHA/dZU/mnFkcD/GGafrbmRwP/VT3qdyZHA/liMxC3FkcD8j+ZsEdWRwPyu+9pWEZHA/g9zh44hkcD//wV0ZiGRwP3ej0puKZHA/2GidVXlkcD8RnpHEdmRwP0lcjNV8ZHA/AfBugnlkcD+E0ie1Z2RwP1snzXdzZHA/tvp2VIlkcD8zAjURiWRwP7oLf8qEZHA/sOddaX1kcD95MabYfmRwPym5+AaIZHA/x3l81WdkcD+Krqs7aGRwPwuXMYxdZHA/alJY1UtkcD9Kzw7RUWRwPzq2IvxMZHA/UKA+LE9kcD9aLPKvOmRwP3IS6i4wZHA/kS5cz1RkcD/H9X+2WGRwP1P5tatOZHA/i3bYN2dkcD/xqaKrVWRwPwIT8q5pZHA/FZSwtmVkcD+bx6sIdGRwP1hxeFV1ZHA/4V1tsndkcD+xzt8pgGRwPw5Z/9eEZHA/qyhew41kcD+JGYYnfWRwP3De8xSIZHA/VjbajIJkcD+sKv0gjWRwP56Za1J2ZHA/4sCgZGBkcD832YVZXmRwPxKOYStWZHA/wCOZZ0pkcD/kZFqcRWRwPwc9NQtUZHA/1lJQB1tkcD+LPF2wY2RwP4QejAhcZHA/2PQtb1JkcD8MGntaUGRwP0LJkbRRZHA/ZgCxzUpkcD9kSAsBUGRwP63T2pBSZHA/dNzv9EtkcD/EaJ4wU2RwP4SOa9VSZHA/m4g1t0dkcD+p5zpyS2RwP1wIzbdRZHA/MAcDYWtkcD/HjjA7cGRwPz0/aKtmZHA/+6xywmdkcD/y037DU2RwP4MmkkBgZHA/nN0y3VlkcD9HLJlsWmRwP3b26UJVZHA/wFiGCnRkcD8i+YbdgWRwP2HMiCZ3ZHA/yXlX7XJkcD/Ng6Yie2RwP3nCs22AZHA/X2qBRmNkcD8P5hz8SWRwP/fM13ZMZHA/pX5j7z5kcD+s/CStSWRwP5HI49NPZHA/Is7htVlkcD9j9CFeV2RwP5q3zT5ZZHA/JU55ZlRkcD+C0U5RVGRwP6YWwyhfZHA/Xk+bDGNkcD9QDuGRYWRwP+2+ejJfZHA/p3XkkG5kcD/CVUQTa2RwP3yIflllZHA/Ad5BiVNkcD+rfOLBTWRwP4/hv/dWZHA/A7m0IE9kcD/qqiyET2RwP0AJm+pUZHA/xyGsNjdkcD9D+L3JHWRwPyiJ6TMGZHA/m50x5AtkcD9gk6rQ/mNwP8f2lSkLZHA/1EI8TPpjcD/83/SR7mNwP6hdJFTWY3A/Hgs4b9VjcD/I5bKU6mNwP7jesBfrY3A/JD/mHuxjcD+/j/fK12NwP29mQa/NY3A/0sk/Sa5jcD/E1sdTqmNwP0CEYgS8Y3A/vLvaysRjcD/t4e/ExGNwP9BV8s2/Y3A/PpYs4rljcD+3rcJWtWNwP6YlR263Y3A/CAXPjqpjcD8CHFPokWNwP+KbYYimY3A/koJyAKFjcD8eUWw8n2NwP9Z09hqjY3A/tBOVYaJjcD+7Vx6Yp2NwP6tWXHCQY3A/ECWmj5hjcD8o6JCakGNwP0iLor2OY3A/lX0NP5FjcD+zkN3nk2NwPymeMkWVY3A/7Auofo1jcD/RwhU/lGNwP177os+KY3A/HIbEz35jcD/dR4OyjWNwP8UuLsuXY3A/naGmHIxjcD9Mh/MnimNwP0UuiymJY3A/3pCAEpFjcD8pLSs1hmNwPwi6jveUY3A/432i6pdjcD8DZAhxjmNwP1UMsVWNY3A/Qc1CM4VjcD8B70dnf2NwP/lB6IuTY3A/5uaYfZNjcD8WdpwmlGNwP8BznWaKY3A/4mE0H4hjcD/Gu8bYiGNwP2zdqGh5Y3A/4oFj0YNjcD+EUebRkmNwP2wvRduJY3A/mp9t5YRjcD8auTTJdGNwP7SMWhl9Y3A/X3QIOYNjcD8irLxnfGNwP66ugvCQY3A/eGKvkJNjcD9UyNxtkmNwPywRQSJ/Y3A/xDvi83BjcD+70HLraGNwP3XeKS93Y3A/DQo8bnFjcD9VWPH+fWNwP5kde0l4Y3A/lxbrjmtjcD8P7Nxeb2NwPzwbGL55Y3A/OFErBmZjcD/QATQKZmNwPzLlLulkY3A/95BW711jcD/CuqLAW2NwP+ra/FFbY3A/7wQyRVxjcD+Bh5rqYGNwP1FC2GtkY3A/hNpqOE5jcD/tj4HCVWNwP+E3V81HY3A/J4W21FBjcD+tm7Z+OWNwP4VYiAw1Y3A/JWXpGUJjcD+6f/s4Q2NwP4uHMvhUY3A/W9V741pjcD+dI5GwUGNwPw+JmQ5FY3A/rDQXOkFjcD9Wj5vtOGNwP/7JZmAsY3A/De1v+yxjcD9zj31GHmNwPz1psacOY3A/fTTXUgFjcD9B74GH+WJwPwahbWPwYnA/BB8R1vhicD85REby6mJwPxHhnyrlYnA/xDmh2exicD9/Tyui52JwPxP+CdPwYnA/9zBYCexicD8sI0lj+GJwP4P/FsnrYnA/fjqe2u5icD9BhFb76WJwPxlBJeTcYnA/cg22L9BicD/J1C+m3GJwPweomIDhYnA/D+TVkPFicD/X1FYE8mJwP8DwSqThYnA/9zeAmt1icD/sQf8a3GJwPz6AW2LcYnA/sMhoosxicD8tvqNLyGJwP8aINQnOYnA/slpzz95icD9opWzIz2JwP4zrMErUYnA/tubK7N9icD8ApKew6WJwPyce7dbPYnA/hWoxVsZicD/ITGmewmJwP+/EaSC1YnA/CizSDrticD++9meZrmJwP11/P5umYnA/uKlRRaBicD96HwZLp2JwP68qdiOUYnA/YcJjUaVicD9MnFDokGJwPwCtrC6ZYnA/a/WFoo9icD+VoYUtjWJwPyzMT4CIYnA/2YtEEJ5icD/FpAe5qmJwPyCM7kGkYnA/gsjREaNicD+0cao3o2JwP/rPiyWsYnA/InQrtbxicD8RpE+atmJwP7AZha23YnA/vOCQj8JicD/KIUxEsmJwP93WSCasYnA/aZbttKpicD+9mHcItWJwP0/wNbCmYnA/zqi4L6picD+zD2/IrWJwPxbE8LWxYnA/E8gjZ61icD8ZFtw5tGJwP3Ezoe++YnA/lxfrT8NicD//RcC6vmJwP5IxvUbAYnA/kpGWQ7hicD95oF+cwmJwPw1mqy7GYnA/r09vgcZicD9s9hh0x2JwP2y/rqDJYnA/XwTBN8hicD/u2On41mJwP60yeMzIYnA/TPv8QsdicD9FRT6+wmJwPxIhJWi/YnA/dnwK26xicD/yYhB5rGJwP0uD8DyoYnA/9MHetqRicD/9R8z+lGJwP1gKw3CJYnA/Q456D39icD+gtnkXeWJwP+fmxGWCYnA/nYgspm9icD/ccK+2b2JwPzLktsNrYnA/Y1/8Z29icD9gLsuAcGJwP6LWfBtwYnA/MSUBzG1icD8blkvqbGJwP0ZiYYBsYnA/vFHp1WZicD+DaJwkVGJwPwTkWa1gYnA/cWQN21ZicD+bGubQRGJwP4cRh4FSYnA/XnYgBEFicD97qT/eQmJwP0tSqxc/YnA/xBcG6z1icD9cfip6SWJwP5Jhwe89YnA/51o4k0NicD/3uXQrQmJwP162v6RCYnA/eGpyCVZicD8QVowtVWJwP2gSarlnYnA/3HdoaWJicD/NDTk5V2JwP7RZfdteYnA/ch1Ql25icD91iaH+YWJwP1jC6PtcYnA/1Tn4XlpicD+ZYQi7TGJwPwU4rwpVYnA/hrBK1klicD8SyZJEV2JwPzjz5lBTYnA/NPto8lVicD+/y11dXmJwPww/pKx7YnA/+oPBv2hicD8o+lpoamJwP/bf3E1oYnA/FMBJPnRicD97GgYmcWJwP7iFtftmYnA/fwsAHWRicD/jfxNOaWJwP90QHh5nYnA/ETVL3nFicD/NyZjcXWJwP+z2KdJOYnA/ae3gPy1icD/RbV/GOWJwPzLFvIJDYnA/YByoCjpicD8pQwzpP2JwP9HkBCBUYnA/6fB5WFRicD/X9Sq4VGJwP1w2wu1dYnA/f3rdmVpicD9CpPTyXmJwPxZQVnJjYnA/qY8USXlicD+4FvjXY2JwPwBZgTFLYnA/ay3KoTpicD9v5bc3NWJwP8sg+pBEYnA/o68U4VBicD+DeD49O2JwP1ahZIZBYnA/CyDwQURicD9DIIwbRmJwP5vSf2lCYnA/UZO1r0RicD9wpNIBQmJwPxHge21GYnA/BTHJgjpicD8iSpMPMWJwP5yNrAIpYnA//e2y2C1icD9fLp+XM2JwP5JSwOUrYnA/+pph+CJicD+O+PhNJmJwP4e9zZwYYnA//sJBnSNicD9UsfulOGJwP9uB1sxHYnA/MzfveEZicD/rOFCSRWJwPzbRJKUmYnA/YS3wzihicD+oqkWsKGJwP5Jqi68vYnA/hYFrszpicD/G+o5zQmJwP+ztMzk8YnA/x//aADVicD8FQ4ocKGJwP9FBQPAZYnA/xbx9fSNicD9q6YNgM2JwP4L0KyZJYnA/6E0uu1FicD/wuAkEW2JwP/EEcYpAYnA/vW6RcUFicD9gVZg4QmJwP8sRGpc+YnA/NySkFj9icD85JlcJTWJwP5FDqktaYnA/anJ6SFticD8B6mwRSmJwP+Z/vK9YYnA/jJchi2ZicD8ubleUX2JwP+a+thNeYnA/s5bauFticD9HQOtqTGJwP3hr+2U9YnA/ClJea0NicD9Xna8mMWJwP+kdTS8uYnA/g21A0x1icD/xJ8efHmJwP6rRZKstYnA/AauhvCFicD9WNysqIGJwP1kckiwtYnA/TkS7HiBicD+B22o9H2JwP/lJN2ckYnA/ExWLliBicD/ZYE7DFWJwP4cLW7oPYnA/p2flvhlicD+mgTIAGmJwP0o6UqIYYnA/COiFThRicD/mI1+YGWJwP2uMf40uYnA/mp7GiyZicD/cnHmbOGJwP58ViqAoYnA/XkOTQhVicD9Hcw9iJGJwP1cSQXMQYnA/WTjmkDVicD+aQ4MPSGJwP14lvv9QYnA/25mM8FNicD8Zb/FcZ2JwP7rrUiBZYnA//KoqE1picD8m1YFYYWJwP57u+GpOYnA/rwtk+19icD81b1xiX2JwP3F3HBdXYnA/bt+jtk5icD8MxlFITGJwP0VNic1MYnA/fEtvZUticD/oxriHQWJwP86wa19HYnA/nxi3+ylicD/YgErdH2JwP/p6W5glYnA/b4MNvy9icD9cC6x0QmJwPyQ9++M0YnA/ZOsgNU5icD+yzXcKVmJwP2tvg51JYnA/rtmXz09icD8ZcoWMPmJwP+JBfUs9YnA/D31ZyjpicD938Ln8PWJwP4Q23SFGYnA/zoAb+TRicD8JvrrPNmJwP3JP3S06YnA/XQ0FoD1icD8M9nIHOmJwP6BuKeQxYnA/Hd86oUFicD8C6CwNOWJwPx5/QHk3YnA/0XOFxCBicD/GIhg9IWJwP7ohf2ASYnA/BpE4YBNicD+YlkMuF2JwPwBANDkHYnA/eYy1SAticD9nDA8UBGJwP37opgLvYXA/AgXZlN1hcD+iOVivy2FwP2qdjW/ZYXA/o+0gMMRhcD+95Dshx2FwP7LIxLXZYXA/eK9zatlhcD8ODEh952FwPyxOrj/IYXA/Elnk68dhcD+DbBuAymFwPzDGrsm/YXA/HgeCQsFhcD/abnSkvGFwPy/qLmC9YXA/4xL82bRhcD87HqahxGFwPw8h6pDAYXA/3hUa8MFhcD8QakqJyGFwPxgzW5zEYXA/Bl5gCLphcD8PhqXEqmFwPwqee0qZYXA/+o2T6ZhhcD9HD580lmFwP6pz7SJ2YXA/EOpauoBhcD+qmOVrgWFwPyqQFRCJYXA/70LTCoBhcD9jVMbmb2FwPzlFjV1tYXA/HbEdNHRhcD8EyszvdmFwP/xPjIyLYXA/v4Yrx5ZhcD94E4Mjk2FwPwudaxWmYXA/OIT1hJdhcD/ZbJ17l2FwPwjqIyaZYXA/c2XOTIVhcD++H79vcWFwPy+cgtJ5YXA/fsW2nXphcD+z08GRg2FwP3ZZrJ97YXA/SPaooHphcD8sKp+NfmFwP6jY4KeCYXA/BGeZAXhhcD9WfQX6f2FwP7yCAHNwYXA/YJE0oXdhcD/bB2UNbGFwP5yrFBhnYXA/6ozxiXhhcD8ddQ3Ug2FwP6wAj16HYXA/ezH5zZZhcD/B81yGo2FwPyWnh8WeYXA/CQ129Z5hcD+40q60nmFwP7SaKwaVYXA/x4euKqVhcD8RP3EymmFwP5h7ykWJYXA/4xe3S45hcD8HOJLBgGFwP5kpskV+YXA/2bZn9ZNhcD9mspB9dGFwPy6rZHd+YXA/Tlee8GZhcD/yn0G+X2FwP5Iy50hYYXA/KUMri09hcD9qQBFdQGFwP7OhODJPYXA/dnkKs0phcD8MNpLIQWFwPyK351MnYXA/r37jjDNhcD/8RSV8RWFwPyHXOFxMYXA/qnzWCD1hcD8jU9rmOGFwP0uYYGYzYXA/sjRR4hdhcD/oApXPDGFwP285QDgcYXA/vY0OhDVhcD/jcZVaRGFwP2lq0A8+YXA/Uyah3jxhcD+jzGuoNmFwPyp6NKIuYXA/WQN2uTZhcD+YHBIUQWFwP9qVjm0yYXA/ylf9ZDhhcD+9JB12OWFwP2fgbNdCYXA/Cq9zxklhcD8l98bETmFwP7nBYhw/YXA/LP5i2DBhcD8mnlnyNmFwP4SlJ8ZHYXA/ORDpeE9hcD8gBwgFR2FwP8oiyR4mYXA/uCKZfihhcD/d7SuRDmFwP4U9roMQYXA/mGyygxdhcD9OJumRFGFwP4FX8cX+YHA/BirFWghhcD8hWcSGDmFwPwzxMUcKYXA//DVeiwJhcD+raeKs/GBwP4f33SX4YHA/qjmynPhgcD8AAo3s+mBwP4cqb7r8YHA/jBuXd+BgcD+PMRDZ52BwP7zuvDPdYHA/n9jD99xgcD/TwaXA12BwP0VznKPDYHA/eihWs7BgcD9XPsLTumBwP6Sl5Eq1YHA/Ti6tR7ZgcD8squJKrmBwP0Gsk1usYHA/ysj1YppgcD8/9k5MmWBwPwVVpv2oYHA/cCvlR75gcD+nSxrCv2BwP5wHBTPEYHA/u0gLcs1gcD+TgXg5x2BwP/kGd+W1YHA/2PtLG65gcD9vwjRut2BwPxM8VjaxYHA/G4a52ZxgcD9/cRFClGBwP54uTQuMYHA/M2KZIZRgcD8JiI2aomBwP2PHNmioYHA/RQsdj6JgcD9fXu4UpmBwP2hZCrijYHA/dmfa1qNgcD/lYIaLomBwP2Su5wGwYHA/ezy4KbBgcD9zcW2joGBwP5SVIbygYHA/l3k9UKhgcD9lcw0lwmBwP9tefJPTYHA/PkZACcpgcD8URvn42GBwP/t0fyLaYHA/2kKOTcpgcD/2qGxqtmBwPy7j8VzDYHA/uaCQI8dgcD8Y3sCnvmBwP1rOKRjRYHA/7dnooMZgcD8ik6LZ1GBwP6/iVEjVYHA/zpfKvNpgcD8cORfM3mBwPwlOBAPoYHA/bB4z7NtgcD9IxQMn3mBwP0mbc3LFYHA/b1UlAcdgcD85mcqtxGBwP6u9eES3YHA/3EYoKLVgcD8TNYJyqmBwP0+rh8KaYHA/3EEP74VgcD+I7qRKiWBwP4XmqyuPYHA/oKaWnY1gcD9al9PBhWBwP5zJlbR6YHA/h8c0QnFgcD+rcNOdaGBwP13EGRVqYHA/+4OxA3JgcD+eY9KRaWBwP2AR0BF7YHA/w2GlpIVgcD/y6jwrcWBwP/PlM82AYHA/gHvQF5dgcD+cOuVspWBwP9vtkKu5YHA/JbGjYbJgcD8NzF3MuWBwP6gYBmu6YHA/Dt8hIL5gcD82TaQWyGBwP6M123bZYHA/V9eHh+tgcD9EgXD26WBwP4MUuYfsYHA/mzmBHudgcD/WY9KP/GBwP37svRL2YHA/O2Mu9O5gcD9z9NeW5mBwP/hrufjJYHA/OdM4A8VgcD9VgQNwvWBwP9N7pY3BYHA/XSzwV7tgcD/RN0ojvmBwP8XqlPDWYHA/ZM512dNgcD92lKhR0mBwP7vSqUzRYHA/zWvVV+hgcD+ciDWS7WBwP+rZcBf2YHA/uUA+EPNgcD840zrv52BwPytXIyjzYHA/ZrTOTetgcD+3GKwr82BwPxL4KRTtYHA/Y+n0EOBgcD91IUgc4GBwP/cDkmnTYHA/T9+2eMxgcD92qNgt3GBwPx+M+A/uYHA/kI+GUuRgcD/7QuCb4WBwPzn/cfvkYHA/h/gJ1thgcD9VZlcUz2BwPzr9HqPMYHA/vZ13/uRgcD9pHjUo2GBwP7iD9BvmYHA/6F5pvuJgcD8u99f30WBwPwgjqJ3UYHA/rXNWn89gcD8QPhht1mBwP4HSbKjQYHA/gUINm+ZgcD9ZLEt77GBwP/RJtWHyYHA/Pyew4vlgcD+DYCQsDmFwP0kX2owKYXA/NLa3FgdhcD+WuZlo+GBwP6j6yWL4YHA/gsi2k/VgcD9lMhGo9WBwP1EnNxX9YHA/xfrzhAZhcD8x1gtjAGFwPzeMWHf/YHA/gnVbDPlgcD+M2+8ZA2FwP0pUk94HYXA/8TCMxfpgcD8cu/SIA2FwP689fvj+YHA/znGzNBJhcD/aYyFBEmFwPzGd8yYGYXA/a2ZVWQRhcD/9wFyV+2BwP2T1cTIGYXA/znClNglhcD90JH1QCWFwP8YiQVwOYXA/9fEoHgNhcD8StJfzBmFwP5TRuSwJYXA/TUKJUwdhcD849MAACmFwP92/MWoJYXA/Jy3YrP5gcD/AdOQPCmFwP4xn09gDYXA/VFhXughhcD9zGGKLE2FwP9/FGsQKYXA/rBOdKh5hcD8Ksk+SKmFwP1PXj5orYXA/1KM9AjxhcD+7/4mbWmFwPxOl8E9RYXA//DIGa0dhcD/+ZvvrPGFwP5i4egU+YXA/nGVXwythcD/hCGL1MGFwP9h8c3U4YXA/xItjKixhcD9V5bJjMmFwPzBaZXw5YXA/oxZ7XT9hcD9FdwURR2FwP5aBGX5NYXA/Gpx5LmFhcD/t7TXBZ2FwPxl4VK1zYXA/PNTTp2VhcD8ugVMJVmFwP0pnACdbYXA/J9480DdhcD+euDr/N2FwPzRbm7giYXA/UtYtCythcD9y53dhLWFwP7ld8oswYXA/ykDP0kFhcD+gWuAcOWFwP3viMoQ4YXA/VqtsBjRhcD/aFRsGIWFwP61ZjLgTYXA/hOrWzQdhcD+bBZNPDGFwP9HtzrMpYXA/q31t5jVhcD/rvageQWFwP8EbDDNCYXA/6a20ljVhcD/MhbbPO2FwP47izzBIYXA/QEcQVkxhcD/d/iOEV2FwP9/wfX1bYXA/OFpd2XNhcD9PxtPFiGFwPw/kO8qNYXA/WH1VHYJhcD+rZQg1j2FwP+NI31CNYXA/dFQwYYVhcD/mKBGFfmFwP+8Q+ZJlYXA/1/J3q2dhcD8YWem+a2FwP+TkYRpsYXA/RYhq6HNhcD9MMNdaemFwP4fmQv91YXA/PBlhm3BhcD/ViFEnaWFwP2MLYappYXA/0CUs3lhhcD/OJhcwaWFwP6yxeilqYXA/LDelH25hcD8CcJYseWFwPwOp2bhpYXA/BMHjiHxhcD9IcwfUfmFwP9D/4ZVrYXA/4ED2dW9hcD/iw74hd2FwP+nwn5d1YXA/HbSDH3RhcD+F37E3f2FwP2gNost9YXA/+mf/PnRhcD8+R/1lfmFwP0mCfqt+YXA/YVqRgYNhcD/2oQSCfmFwP3u67A6VYXA/BX93C5phcD/woej4o2FwP6r4feidYXA/HiatOJFhcD8UAC8dlGFwP6vUHsWFYXA/pgDLU4xhcD93Ex4yhGFwP3qW8maVYXA/wDyIipNhcD/VNKlgjGFwP5srH2qVYXA/lK2nVoRhcD8+KWLtbmFwPwaRR/xvYXA/1YKYRHphcD9HJBQ4bmFwP16MB5t2YXA//4oFFlhhcD8/ZaHPU2FwP8dh4bhXYXA/UMETaVNhcD943/ACaWFwP7/nsrZ5YXA/92WpgW5hcD9bh/eqdGFwP+BGQXWEYXA/UJfcuY1hcD/H1QcyiGFwP2HbIledYXA/uUAfpqZhcD/wkb+3pGFwPyjeNJCeYXA/RIwjt5RhcD9aYqkXnWFwPxYDENOUYXA/C3XmWnxhcD9n4AGLf2FwP88gkv97YXA/aWoUonphcD/BITbKhmFwP12QuGWPYXA/4UpvjoJhcD9dz/cpdWFwP7fyNaGFYXA/LnrXOn9hcD9DxModfGFwP2UEtZF8YXA/E5xmeH9hcD9N5sZNaGFwP/Kor9d4YXA/Qo/LYoBhcD9BnYKKcGFwPyCnsQJ9YXA/HHnI6IRhcD+kVWiVeWFwP5CqXEhsYXA/chsk/XZhcD/+E+VDfWFwP6RyZhl/YXA/iKiG63lhcD+UbZ5xgWFwP0td5XSFYXA/xwns84FhcD8tsgqmfWFwP1iZx4J/YXA/m2y5xodhcD8nyZB5gWFwP3OomEt/YXA/9ZbHqn5hcD/DduS0gWFwP1/KOwCBYXA/ufc+2JZhcD9CwmwupGFwP+QfrMyaYXA/OrMryqZhcD+wAfSzoWFwP4AUWOStYXA/crIjBqBhcD+W8oqlnGFwP30z3xusYXA/KT0kL6lhcD/f2zums2FwPzAnwYitYXA/BUn3769hcD8JQVTvt2FwPzZqpCWxYXA/mmwsXZxhcD/JcwGck2FwP1XktoKdYXA/XBLkpZNhcD9+e4fjj2FwP3CmRyuFYXA/OZACIJthcD/Wdt2qo2FwP/v3kyWXYXA/sX+VhphhcD8nezCdl2FwP72n0BuVYXA/yV6uaJZhcD8dgOaUi2FwP380wKSJYXA/uq9siY1hcD+8mawgkWFwP4c5NsyPYXA/U0dZK4ZhcD90l7T4fWFwP18k71WEYXA/vpISp41hcD8bQ6W8f2FwP7CD1pqHYXA/H1r8EH1hcD+Tco0AeWFwP/R9IgyJYXA/dFQQL4FhcD+vtgBfj2FwP+wbUiuVYXA/INxKQY9hcD/E5zO/imFwP7JG5q6AYXA/MLVi7HZhcD80hM50gGFwPxhNso+EYXA/nEDcg49hcD+sxiF4nmFwP/NYweqgYXA/DbFsxaFhcD9iPsnCpGFwP9WKIHKiYXA/u/qgxZZhcD+3+o2JgWFwP1rNjCuDYXA/TRC1xoxhcD9eGAiFg2FwPzJjs/SHYXA/GXjNHIhhcD+tQirDeGFwPxE2uFeNYXA/Mas2q5JhcD+zMWQBnmFwP59x6ZWrYXA/8kCez6dhcD+T+B7rt2FwP12afTK4YXA/754PT8xhcD/rCU3C2mFwP4uwLrnRYXA/ovpYSc5hcD/mv7bBzmFwPwHXdPjTYXA/Xo61dOBhcD8zaMHS8mFwPz+wMGb2YXA/lA3TTPthcD+umDQv/2FwP5vV0CoIYnA/7NPeHv5hcD8Z0oQ4EmJwP+G0rJIdYnA/WJw1jRFicD9WnNhiAWJwP06oSxISYnA/RzAsuQZicD8vQ2AYAWJwP40Pnrz7YXA/hqk8rvZhcD/03pF+D2JwPwX5V8EbYnA/lwucNBxicD9Lv5rMEGJwP7da7kELYnA/wAWw+hJicD98M0vhFmJwP4oaSGYXYnA/k8pAswZicD9gKEkH/2FwPyP/R836YXA/717xmPFhcD97Vvet32FwP8EqzpHZYXA/LjwW9tlhcD+gmq692WFwP6cz/+fEYXA//EyMINNhcD+TSOQ71mFwPyorQYq3YXA/8k7hSK9hcD8BJBncw2FwP4LFo+e9YXA/Owee9d9hcD94o4sE4mFwP5QHIZTlYXA/e6MHoNdhcD9NCcqWz2FwP28dc4rAYXA/mL7o2sthcD9bnreivmFwP+egvMK4YXA/3mUZSKphcD+B1b1YjmFwP9lxlHCNYXA/2MxWZ49hcD/RCiz4g2FwP6pNJrFoYXA/PBXkLmdhcD8KjqQzXWFwP8sDzfJjYXA/CfMsJGVhcD8htMmrdGFwP9lNj1OAYXA/11kYunphcD9Ids3qf2FwPzn86ISKYXA/Ohfew4hhcD9wGkX3kmFwP16zBjeTYXA/d9wfaZBhcD9iYGxhk2FwP2cIpUuWYXA/dwDWJ5ZhcD8FJ1GLqWFwP+x65amsYXA/FjnaHpdhcD84BFbrl2FwPymOjVGgYXA/gYhiOaZhcD/O5EqQqWFwP3IOjpGoYXA/lXztIrZhcD8M6jqct2FwP28tByi9YXA/rIUSXr1hcD/JaCeft2FwP0+FmYyqYXA/79Hkq4xhcD81a1w/kmFwP20j0imXYXA/ERTDn5NhcD/cuafKlmFwP/RqeZOEYXA/RCRN2H5hcD/dXgQIaGFwPy8q3wNvYXA/Gc1C9GBhcD99n/UWW2FwP8j1vrFOYXA/Krh2Sl1hcD/KnDImZGFwP+fqVMp/YXA/fFG0BYZhcD8AOl5ojmFwPy86xo2LYXA/BzmM6HRhcD+0jOjohGFwPxAvwRd3YXA/qWfA+mthcD/nUKCYXmFwP7+1lDhIYXA/WkXUPkZhcD+RfiieRmFwPwToYQxDYXA/JyzthDNhcD/xaih+M2FwP3Gx0YRKYXA/hiGjxD9hcD8vMXgbLmFwP1+lmtkyYXA/NeprDj5hcD8tre7VRGFwPx8yA91DYXA/Vykl9U1hcD9xHHZvPWFwP5pQA0VCYXA/ldI2FThhcD8LRKoRKGFwP1khSKoWYXA/gUi9+BZhcD/sN4yYE2FwP3gLBc0YYXA/6MIGnSBhcD+0ny4uKGFwP0X454YrYXA/lroLLzhhcD8SYFFJOWFwPzfvxmEjYXA/Pq1mJSthcD/F9ejEPGFwP6vSWDsyYXA/hZqVth1hcD/C3CXtKWFwP/APPxsoYXA/DfGV3CNhcD/qnn1QIGFwP43SmWoKYXA/9LJw6v9gcD+Bh6JyAmFwP/BWMiMHYXA/v2dStPNgcD9fodyU9GBwP3OjIDnyYHA/PPlrsehgcD/JbzZ14mBwP5bYfAncYHA/RX9Y1dBgcD+Ivqapu2BwP4yhcpuuYHA/3PVfaq5gcD/a6UW/s2BwP6X6G/O0YHA/zuWKcL1gcD99UH+fo2BwP3yhrqyaYHA/Z02J6IVgcD95ZoM9hWBwPzsmlHWJYHA/YemBm5xgcD++WrfZlmBwP01xmqWaYHA/4pR5L5hgcD+1VGVbmmBwP+YSGVmrYHA/X8d5RLFgcD/p7sASt2BwPxNM2BevYHA/ZIlsLsZgcD/AaKyKzGBwP1EbT8bEYHA/jUcDIsNgcD/SnCWYuGBwP+uKz0a0YHA/qduON7tgcD91mfBLvGBwP3w0EfC4YHA/JzL25LVgcD+E0S9St2BwPzqL0HnGYHA/0DzNSsVgcD/BzG23uWBwPy+Ekba9YHA/5JjM08FgcD9NK3EHx2BwPwV10crFYHA/ZqIHErpgcD8x6vgfr2BwP1/rB4GqYHA//LJCJahgcD+VjBZOnWBwP21R6G6nYHA/tUi4Ia9gcD9yUqnoq2BwPwU2ofO/YHA/jAywn7tgcD/gVFiEvWBwP3hvVsu0YHA/f0omj8NgcD/9nVnSsWBwP7mSyuaxYHA/O3hsRq1gcD+bq5Iqq2BwP5h/3jmnYHA/bEOSbbVgcD+HYHtpt2BwP6s84ziuYHA/6DoI95xgcD/KbkHxjmBwP90CYHCSYHA/F165+JFgcD/enHzGlGBwP+ohEg+QYHA/Qm0DXHxgcD9XVKlpemBwP36jdxRoYHA/teoMJmRgcD/COpXhUGBwP/0jLR1aYHA/vJ96s1tgcD/xrk03ZWBwP6FtsMlfYHA/GqtS32VgcD9QRlvbZWBwP4QFgAxjYHA/U7Ia2VZgcD9omjsyWGBwP6LtqwxfYHA/JSnu7V9gcD+TPV5xaWBwPxdpeGNsYHA/Z6WdRIhgcD+flQ5UhWBwPwbhPeCBYHA/mgoZ1oBgcD/1hcQziGBwP1UsUUGcYHA/LKYeAqhgcD/1WLgdqWBwPwRcrBSfYHA//ER/op1gcD94IvpDkmBwPwDAvQSPYHA/jsty04tgcD/IncP9pWBwP1DqthSsYHA/nGCkxrFgcD+xhulutGBwP4fPLOKmYHA/dMd8hapgcD8vsQVCtWBwP+mpc464YHA/ynriYrBgcD94bslhqWBwP8ihbamhYHA/PKTRQIZgcD9DUNR3kmBwPxCJD8GgYHA/vza3bKlgcD8FEwg0n2BwPx1RISqjYHA/XCSrvY1gcD8Utz5vpGBwP4c8xoWrYHA/XUr4TqdgcD+yXXEzs2BwP1buX7i2YHA/2j+DcsNgcD+gA/S2tmBwP+hXRGjBYHA/Oqj0gLpgcD+0GuGOrGBwP2hDXd2wYHA/54hKc6BgcD/o2w46n2BwP1kpjQOlYHA/U5EYzaNgcD9j5LiThGBwP9V16qGXYHA/tg9ou5hgcD80S8RSq2BwP/zXjyu6YHA/aEhoWblgcD+8D50xxGBwPzN3n1DIYHA/3gPQZ71gcD+Z0NW7tGBwPy8Q31q/YHA/MGaVGcdgcD8Duy72zWBwP90T4IzQYHA/IT0bjdxgcD89rf9xz2BwP3OXuxjIYHA/V/tPVLRgcD94bXvEsWBwP4wjIqvKYHA/rDcO0MJgcD+x1+Rb1WBwP4yA8I3WYHA/XcR8Mb1gcD8JrM44s2BwPw00ZjWwYHA/oEB4dLtgcD/ukXNHsGBwP5aIHr+iYHA/8lqwaqhgcD9g7kbzu2BwP5A/kKW9YHA/RSzeVs9gcD8ISowmuWBwP/OEq13GYHA/fzexLMNgcD/2OuYjx2BwP9Hkpy/RYHA/kMS8FdVgcD+Z/mQE32BwP3l+4t/RYHA/2LvN5d1gcD8b4xT12GBwPwLaversYHA/dKu4SOdgcD8pjNmD1WBwP9m9NtHGYHA/YaUGTt9gcD/GHJbQ0GBwPz7T0Q7VYHA/sRFD59VgcD8qLexl2mBwP8t2ujPKYHA/5GR/4c9gcD9zfjuJ1WBwP/InU2fZYHA/RdEFFuhgcD9X6xvN02BwP0WL49qyYHA/EnfEvK5gcD9HOBgRn2BwPwYADhWfYHA/5+dveqNgcD+M42AfmWBwPxEhjjaXYHA/I9xX/ZhgcD8zmGuOn2BwP7aoCSifYHA/wIoePKpgcD9e5SQGtmBwPyfS9bO8YHA/MFxCmrpgcD+vzH2eyGBwP/cQ5EDgYHA/QHzsuORgcD+ZoId86GBwP4FFq5/pYHA/PPUlGO5gcD+R6CxN8mBwPxluH5LsYHA/7s0let5gcD8UX+iR5GBwP/3Q2LPnYHA/E49HrPhgcD/2M1UF32BwPw58y3jnYHA/8LuW2NVgcD9L89sW1WBwPwa6QYbWYHA/fZNpH9RgcD+k3P1PzWBwPyLuFM3TYHA/CkAjUsdgcD+VHVoawGBwP2irhD+8YHA/URAvSbVgcD+d7D3nq2BwP80YnhbHYHA/fIhQlNBgcD9RR1Aex2BwP98sLm+9YHA/P5wJD7tgcD+iXj2FuGBwP5whrreyYHA/+fPqJ6JgcD9GlfVlnGBwP6/gq26dYHA/YjgrMJpgcD+RxXihimBwPzRPmhWLYHA/gXGJvoRgcD++lCfkdGBwP4fAIUl9YHA/VWf+i4hgcD8idPRTgGBwP7tI2g2DYHA/04ltNHVgcD/izO4udGBwP89pDf+CYHA/XqTLq4xgcD+siJlSnmBwP0hExQGmYHA/NHyew6JgcD9cUnn0r2BwP3VL67mvYHA/uf311qdgcD9oGVZFomBwP6spAlOeYHA/PqTyCJlgcD8ZKmV6l2BwP8mS1RieYHA/j6g1Z6RgcD8SS+96nmBwP6CLNSyjYHA/jAJpIY1gcD/bbpXAk2BwP3XfnbefYHA/4R47jJxgcD+WGY+/j2BwPylHwrSDYHA/g5nd8WtgcD9PGqHtfmBwP7arcAuOYHA/A4+Al49gcD+9K5ByhWBwP4EUYFWFYHA/hJFyPoFgcD+IrlrHfmBwP9jwMmVyYHA/v+8s2F5gcD9CKSFBcWBwP1fxI/NsYHA/5acJ64NgcD9cjRLzemBwP1H+PKV2YHA/2NejCIJgcD+jglJximBwP7xushiTYHA/qDAJ9JxgcD9tbKJ3q2BwP1Pp2dWmYHA/yrSXcaZgcD8RmjHzj2BwP6BAdHuoYHA/U1NhzqVgcD+86m3rrWBwP2yIwxS6YHA/6Cdc9cNgcD/kxB6jv2BwP3SdsuW+YHA/JYuVLL5gcD/YaWitpGBwP3yrduWjYHA/x9dCJLBgcD8N1junxmBwP/LEk++5YHA/spu/XsBgcD+HOgCavGBwPwMy3wW3YHA/Qkr0lr1gcD/TBl6kv2BwP7/WABXGYHA/5mxVjstgcD8naMOQzWBwP8sWtTzeYHA//jZBKelgcD8+2lDX7mBwP++VSQfkYHA/CmFZp+VgcD8xGq2A7GBwP3s2Ni30YHA/RVZX2PBgcD/Ao5TVBWFwP7fIWEUEYXA/NiSzP/RgcD/nXC5XA2FwP1dr4Cf6YHA/xVMPsORgcD+18x4B62BwP4suluLpYHA/BmvWKQphcD+0KsneEGFwPy9/uGb4YHA/oROBZwJhcD8aFDSl9WBwP8IIUpULYXA/WADDQwFhcD+XAvbv/WBwP7zNKDMKYXA/Me40KAFhcD9JnR/PDGFwP1MilKINYXA/PFULGCBhcD8IjilkEGFwP85ptfUWYXA/MxszWB5hcD+6lB+2HGFwP2mGEQQsYXA/JfX3kzNhcD+3z8MSMGFwP2VdYvghYXA/kyUiQR1hcD9qn65vCWFwP9EKoCoPYXA/vBfzmRphcD+QuBDIDGFwP65XQiEAYXA/5hobUQBhcD/t+3vfGWFwPzuz/LIeYXA/xvbNhhNhcD8nn9AhDmFwP9/Fper3YHA/TND2tvdgcD8zg0ql9mBwP090lovwYHA/Jn63QOhgcD+pnVgx8GBwP8c4w1PcYHA/gxG1tshgcD+wpAp/tmBwP4pqT1e6YHA/r5vUXtZgcD9apPgg5GBwP/+gAuvjYHA/w2+fFPZgcD8mzXyZ+GBwP/vaGPEOYXA/JdZFPQxhcD/23s8WAmFwP/+Zmpv6YHA/VZNF5eZgcD+qQMBH8GBwPzW4tMDsYHA/7K5+HNJgcD9hFrSFy2BwP5edGePCYHA/owWpia9gcD/iS/7apWBwP8K59JqZYHA/EhYPepxgcD/XHdrdimBwPyQOIluDYHA/2hA2E4lgcD+WAN20l2BwP+NC85GyYHA/9fcm2LFgcD+2Xjmoq2BwP6FDK7WmYHA/eTTxY5RgcD/uo5KtnWBwP81ZBqiQYHA/QXLvqpJgcD/wICORkmBwP6GsZNKbYHA/mav575NgcD/WN1OLlmBwPzofydWOYHA//fydS5BgcD9sgo2Oj2BwP3gx2TGUYHA/gBiTh5dgcD+HAoEPjWBwP9t2kW12YHA/Ixmc8XJgcD/YfOjGZ2BwP/YsgFtaYHA/R+L8x01gcD8x5x+nVmBwP22/wn57YHA/qKGBXoNgcD9JE0+/cGBwP/lUUUFlYHA/FPNQA2ZgcD+UsyYaaWBwP9pTjDVrYHA/k+d3HmpgcD9lYcopa2BwP9BNLNZdYHA/DJUjTWZgcD/HRp1cX2BwP+kZArZUYHA/BpwfOFBgcD92b4y4VWBwP6SpvtpaYHA/JvxEMFZgcD9ii03KQGBwPyykbysuYHA/8SXTwi5gcD9YqhYkKGBwP+SoIYtJYHA/zljid0JgcD/oJpvoL2BwP7lEONU+YHA/yyBWrTlgcD++8vSFRGBwP2A19nxPYHA/u0YDkz5gcD/j7gj4PWBwP/gh7s0yYHA/zdAvQCVgcD+BZ77QHmBwP/8/5zEKYHA/4/oLaA1gcD8CZpVAC2BwP727j9H7X3A/7+cPuQdgcD+fJAjFBGBwP1+P0+/2X3A/KQAqav1fcD+IUCB3CGBwP5N6jL4LYHA/rqm+XRFgcD/jviGCCGBwP8PBk2/vX3A/XRDZtuJfcD9qStWaz19wP8tx0orWX3A/yUaDrdFfcD82C+5u1V9wP+c4MM/TX3A/GZLkvdhfcD889Pjyxl9wP2vDdV3FX3A/pLcpu8RfcD/GMnVWzV9wP3gGrXbGX3A/D5Xwh81fcD9VDxln0F9wP2PgnA3TX3A/mqRwKdRfcD8zZJD01F9wP3vAfQLAX3A/2HguqrxfcD+3XROSvl9wP9welcrAX3A/rwg25b5fcD+kcg46y19wP1nRwIHHX3A/outlQ7VfcD+HrLtpul9wP+ZwiE23X3A//StdQcRfcD/B10wNxl9wP+myE9fHX3A/8xwF8ctfcD+pBYS71F9wPyV3QsvXX3A/heW/8+ZfcD+XZMW/419wPyAligToX3A/3ZQzvt9fcD/z3SM23F9wP+Ro8q/hX3A/qjDAudFfcD9YVNs9yV9wPzmzUYnTX3A/jBCW+LlfcD/EwDzfu19wPzq7b2apX3A/dZ2pJLRfcD96vkixm19wP2T2rAuQX3A/P26cn5pfcD/paaOvoF9wPxI6pyGaX3A/xwMUVpNfcD+VAJt/g19wP0ikwFR9X3A/LyqSD4dfcD/UM5xrd19wPwm3gex6X3A/x5YFSnRfcD8qzua2fV9wP47gYdtzX3A/Erljl3lfcD95ROn+f19wPyMaauF8X3A/FdRDzYdfcD/Rswywj19wP7qOiWKRX3A/NnW2A5FfcD/5H4BGmV9wP2+VbuOXX3A/IEqWqZhfcD/yBBShqF9wP5ABqrW0X3A/lK/7wrhfcD98YNxxwF9wP3D0IErOX3A/g0Cr+L1fcD/tjqYltV9wP5pQkSK8X3A/eq8sm8NfcD+lwoI+w19wPzz1dZCyX3A/1SknI7FfcD9TzKRSuF9wP6k8bP/AX3A/PFZLR7lfcD94oNOJy19wP1fZq9qzX3A/jM7zUbpfcD+KZW8xul9wP4SJZkm1X3A/8x4L78JfcD9GXaDQzV9wPymXvSPbX3A/+AfLW+9fcD+sVUQE8V9wP5GrukDlX3A/p96VGOBfcD9jaClx519wPy66CTjuX3A/C0RNf+1fcD813jap7V9wP/DrWbf0X3A/ehOlLe5fcD+qd3rt219wP67k5JbcX3A/US+WstRfcD+oAIE73l9wP/4ZgYLgX3A/8At76NNfcD+C19TT2V9wP5D12YTMX3A/kyLVGMpfcD+Mtsfj019wP+HrWo7IX3A/YAK0SN5fcD8r2Aozzl9wP17aYZjCX3A//kPdU8JfcD8SwGggxF9wP8bSK5DIX3A/HhFvZsVfcD8Iu5Kctl9wP9QRFb7DX3A/HzOe/KtfcD+AX7RCk19wPxPZKkGPX3A/zNgvbo5fcD+trpN3iV9wP9CYkESBX3A/iz7wdoFfcD9eL199gF9wPzOPzkJmX3A/k1B6/2BfcD9JFXXFWF9wP2Jjs8FTX3A/y8kTAFlfcD8ZavTRZ19wP/BBie5qX3A/HwV8eW5fcD8kzKfqcl9wP9K9z/R3X3A/PoOQ14xfcD/nqj0Vh19wP5bkPy6EX3A/BAKlrXJfcD8PoKKNd19wP0CRFgB1X3A/IAbSi3ZfcD+VktPngV9wPw5J1X59X3A/Vk1VsHlfcD8esysJe19wP9YeAS98X3A/nJrwX3pfcD8ADjzLZl9wP0XWXRBnX3A/Au2dV29fcD/LHdVvZl9wPwmxql12X3A/23eEe5NfcD9IDXdcmF9wP1873AuTX3A/MbkG1opfcD/D6tSPll9wP4ZFAlWkX3A/3vvE6aBfcD+OfvIbmF9wP+mVTWuiX3A/qtRnVbdfcD9WI1Y6sV9wP2W809qjX3A/frPdm6BfcD/TYjdBsl9wPxsZlYanX3A/1h1MEIxfcD+kGsxZkV9wP174ZG2eX3A/A8JZN55fcD+ICNkHnF9wPx+2ituBX3A/nS/h6HJfcD/sWSxfeF9wP/NYE8eSX3A/t5S6OopfcD8FYqKglV9wP7xOZBqfX3A/AmxneaJfcD/ScQ32nl9wP0L6WyubX3A/VlVjDpxfcD/5uQfKmV9wP8fOpFaQX3A/UU6RoJlfcD/5OvP0i19wPznwf+aLX3A/1fWh64tfcD+19gohjF9wP4nGa6J7X3A/fjNUKWxfcD9mNhjJb19wP6ynAcl0X3A/P+S3k15fcD/uOyvSVF9wP50v7zZNX3A/+jzxb1BfcD8H5NwYVV9wP9J9Ixc8X3A/Rs4wgEtfcD97CSFFR19wP14uZu1BX3A/FT1r81BfcD+lGeljUV9wP0MWRJ1WX3A/po55yFpfcD8voNn6U19wPwxFnt5WX3A/Vh+7JFpfcD+Z78AHWF9wP2JQ6clOX3A/hk8aCFRfcD+GvGGiTV9wP82lOH1DX3A/cn/ykDpfcD8qFRo9Ml9wP6BhlWkxX3A/8sf2GUNfcD/xcLZ1OF9wPx6SA31KX3A/474j7UZfcD/SbqkbNV9wP46AR5IyX3A/jnA39zNfcD8flhjQP19wP7A10xc9X3A/oiKn+z5fcD9u980DQV9wPwIyrftDX3A/iw30vERfcD9c+daCR19wP/+5zZo4X3A/jcmnHy9fcD/CVVRIRF9wP4lYkf05X3A/E433HkBfcD8XL2gEQV9wP6CSTbxQX3A/u8u20kpfcD+rjCiSRF9wP2QYYiVOX3A/slBagUNfcD/fiI9iR19wPzb8qCBAX3A/CsGIVlxfcD86SOryYV9wP8rmZGllX3A/69CLymBfcD/orJOQal9wPzhlkf1gX3A/CMyktlRfcD9Kha+NVF9wPyFbFUFcX3A/GeHa4WFfcD9333awZl9wPx81ZW1eX3A/LU/bKltfcD86Yk73ZV9wP6oEcTpnX3A/Jxw0EGRfcD8jXeiTZl9wP4qYxe1jX3A/g9cSm1lfcD+b0QFKUV9wP1MNd+1UX3A/QQThelNfcD++u5vGSl9wP8XTf308X3A/H6pLfkBfcD+vcbo4QV9wPyYUZ1M6X3A/775rESxfcD8brEpQI19wP0ZEx8YZX3A/ThzbeyNfcD9yvc6VMl9wPxf5j2c6X3A/eXSXbjxfcD9kkjoOH19wP6MHMj4dX3A/8ep5qiZfcD/QjpOoKF9wP5OYA0suX3A/uCGQoSNfcD9kTQ73G19wP3XenDgjX3A/wQXn5TpfcD9ZSsI5OV9wP0nBecsnX3A/4G/w6B1fcD/OpEA9Ml9wP0ZKzB02X3A/Q6ioeChfcD92JnjSLV9wP6k7db47X3A/2GxngTpfcD+HQPITNl9wPyyNBUo1X3A/toomcjNfcD8LP5OEK19wP2Igdww4X3A//QIKpEFfcD/+lJT9P19wP9/7rBs/X3A/BtHotTVfcD/19S6aQl9wP3t4p209X3A/+Gh2ZiVfcD8pBgjoJV9wPyG7BMczX3A/G2OeAhVfcD974u3gJV9wP9zfzQwvX3A/JknKsilfcD9M1p5DIV9wP1LuX4IXX3A/D2YjKiBfcD+ghW9VGl9wP8EhYC0cX3A/3IBEKgRfcD9wXNTEBV9wP9STogURX3A/RVq0kA9fcD8cbfFdDV9wP/qLiwcJX3A/Ufg1wf1ecD8mm2REBl9wP7L5evkBX3A/2bQGGQRfcD8M5FIqEV9wPwrDIaAhX3A/YztttxlfcD8gYjpqB19wP5xamMsIX3A//aHp8xJfcD9mJUQvGF9wP1rmAuglX3A/ihax4SVfcD+52FYKK19wP+uPveBCX3A/iFwmy09fcD/8PTH0XF9wPxJX36lPX3A/3CqBCVVfcD/m05SfQV9wP/mZTXZLX3A/EXNjiEtfcD9CLQNJWV9wP4rYnQFSX3A/XY5tWUNfcD/4lIBsVV9wPxzHpQZNX3A/1/zstDRfcD+L2OxXK19wP7tl2WQ6X3A/ZLbXXDhfcD94SukWL19wP90Y76oyX3A/MhFh5jBfcD+ylV1MNV9wPzOLUvwzX3A/JJntQydfcD8NZB2pGV9wP9REzsYZX3A/p91J4BNfcD+6U/56FF9wPzdO5e4RX3A/XbGwogJfcD98+ELHDV9wP3SnUtQJX3A/4o+70wpfcD8uukXtGF9wP1i8zq0fX3A/TfNW0Q1fcD/rGWCUFF9wP/UhR1QTX3A/Q9rtcxRfcD+knf19G19wP6E0BPkBX3A/8foUfvhecD/zYyQyA19wPyptBx79XnA/zceWAetecD8DAlbq9V5wP+djqjD5XnA/FRRnRvpecD9Ch9JT+V5wP24h4ePvXnA/nmYFQvdecD/y6uv//15wPyYoA6wPX3A/zwXLMQhfcD9Fl1Xg/l5wPwNqIC8MX3A/lqRvmBhfcD/Z3UytIV9wP5rdA18dX3A/QQ3qSBhfcD/lPpLFIF9wPzAEW6UtX3A/zplJtTJfcD8u0SZIMF9wP93q+HI6X3A/gDluMS9fcD+d7S8xM19wPwCrpcwqX3A/2Cl+cCpfcD/VO1i2Ol9wP0nY3js0X3A/vSDnvidfcD9KlAufJl9wP0Loy8srX3A/CCua6R1fcD/oA8KAFF9wP4v8hWENX3A/LMJ8tQxfcD8B//FbDV9wP9Q3Oiv5XnA/DXYR8glfcD9ovb5+/V5wP7Az82D/XnA/8H/pOPlecD9EBCJ2/V5wP9n+zO4OX3A/7F1xMAJfcD+JWQLp+15wP8grZqv8XnA/Im73NQ1fcD83SWHJCF9wP4MtelonX3A/fwhDCxdfcD+xze5OJF9wPzJJGOkfX3A/6nwQAhJfcD/y8ujKCl9wP+TvPrEGX3A/mJa+D/FecD+a53uA+F5wP4xL7igBX3A/CvPq8P5ecD/A3mws/F5wP+HwJGr4XnA/fg5Q8fxecD/Kg5vcAF9wP90PNwvzXnA/HhWzRwlfcD970sp4Al9wP0EVUxDyXnA/iyfF5gNfcD/erNdPAl9wP9DAWyTxXnA/jwjF2PxecD/ORegPA19wP0Ay03X9XnA/COu1vwhfcD84klBQBV9wP950YVYPX3A/U4/HfAhfcD/uI3gBAV9wP5onXsbyXnA/MdCZwu9ecD/pKWsM6l5wPyvMgo/yXnA/DXIQOvpecD8lM0Oe8V5wP6PCV/XyXnA/rmZV7+xecD94nP7mAl9wP/LWqM31XnA/bfrc4wZfcD+o8U38+l5wP900GY3qXnA/ijd/7txecD/yBaCb3V5wP/+3FwPkXnA/K+HpU+NecD9gY/uM3V5wPzo2lIrbXnA/eQLXA9FecD+QxbBdyl5wP9V1J6nAXnA/g+OeNbpecD8HX0Ovx15wP+K1Q/7DXnA/a5H3GM5ecD+MFG3ttl5wPxzEg62rXnA/oHjiY6JecD94Q1g4mF5wPySaOvqKXnA/3NUTqIhecD9EfCPRil5wP1Ywxix/XnA/ub094IRecD+WzlP3hF5wP/b6EHKKXnA/ymKzOXpecD882IT2Zl5wP0WPIIltXnA/vLk44WlecD+mHZa1cF5wP2jDsztpXnA/jFWO81hecD/oaefFcF5wP5dxz/h1XnA/gbO773pecD8JpGZ7eV5wP7Pm1dVqXnA/EisB5F5ecD879v9UZV5wP8OsO6xvXnA/feYbknBecD+nvvzSbF5wP8KZKYdpXnA/BjwIGWdecD80i3ATYV5wPyA7vhdTXnA/0xQNg0VecD9cAeE8QV5wP5Pi1Vc/XnA/Mql6vkZecD8JgFapTl5wP9UFgKlHXnA/JPKTT0pecD896lL+RV5wP2Z7ezhOXnA//6Cd4jtecD/anr2QSV5wP3nyZ2dKXnA/RABuA1ZecD/wFUKUVF5wP/OzriZbXnA/foY6u1hecD+Ld3UZeV5wP1Xh76x+XnA/fUYxE3lecD/C+yUUf15wP2J5M1qSXnA/UDTbDZBecD+7hKNAnV5wPzywuVGtXnA/7lYIFKBecD/bOc+3rV5wPxJEMUWpXnA/CNLqAKtecD96EOPEsl5wP3sEmX+uXnA/Wh4kGrZecD8HuLVnuV5wP9O9txu/XnA/VsZGw8FecD/GrEotvV5wP5uVoPu7XnA/KYOG8LZecD82m95ftl5wP7mey4i4XnA/Tpcn+sJecD9fTfVxxF5wPx0vBlzQXnA/I2rIwr1ecD8PaEeDxV5wPwMsLi7WXnA/fs59jexecD/FYzyz515wP0mXDCfmXnA/wbjQeeJecD/VN9ZNxF5wP76sE1fPXnA/uROIkMdecD99jOMqwV5wP0noZZLAXnA//VsA2qpecD9udCWSs15wP2im6MGiXnA/zrKt059ecD/0sFhXn15wP6XYGEKkXnA/4AMDQ6pecD/CdbzotV5wP+QrWiLHXnA/k0TjAtlecD+Jvn3TyV5wPws8zODCXnA/iyx9osZecD9xx15OxV5wP8Z3eWHNXnA/ANPyCtFecD+BaMcLzV5wPyjTZ6TNXnA/1bTdos9ecD9t+0uX1l5wP7lMjRnTXnA/7OberM1ecD9fC6xK2V5wP70eIwPjXnA/sBN26OpecD/386r8615wP4QUfbvcXnA/aVSzvtFecD+20J1k2l5wP9pUzjjeXnA/H0TolcFecD+zpzSFv15wP39hyxC1XnA/dhIOBKVecD9b/uJVp15wP7vnaleXXnA/xVHFbaFecD8i6YECtF5wP4VnW62uXnA/OqKGIaVecD9RF5BLml5wPx2xhnmkXnA/GPr4eY9ecD8s4oHAf15wP+73RZp3XnA/2drqK3hecD+N7/nudl5wPy3g/Uh2XnA/iu307HJecD8HQV2DbF5wP5GT0EFwXnA/dc7r3WpecD8wQAOMb15wPyhAkeJ5XnA/bBfVYJNecD8fam9Aj15wPzURzTOMXnA/9YhXHYNecD/nFRAkjl5wP+z7OTuTXnA/NJN6FIhecD8AWq3teV5wP6pU7o17XnA//V5UMIZecD+GN0/+fV5wP1LxaRR2XnA/T1lXUXVecD9yR4tucF5wPzv7eRRtXnA/jyj3h1decD9snjpHaF5wP+EK01x1XnA/PRFHEnNecD+NZKPybV5wPyR15OR8XnA/T6fWl3NecD9wo7nQal5wP8fn3PR2XnA/cd3PJFtecD8we5W+U15wP8X3WNVGXnA/eiWDp0RecD937qxhLl5wPwVtTo4oXnA/qftRpSdecD/V/EElL15wP3xY0Yo3XnA/rE82KTZecD+e+t8oOl5wP0SIxrZJXnA/yOOXw1hecD8qUf+baF5wP1ADwAJnXnA/NKv+0W9ecD8pb7yTe15wP5H5/kR5XnA/y4MSS3JecD8bF+fagF5wP7VdnX+EXnA/JGxb+Y9ecD+dr7CckV5wPyQEThaVXnA/FzVZaIxecD8TldZplV5wP2WoZwSLXnA/xXy8SpZecD+46pF6qF5wP7hcubavXnA/X3ZLQLNecD/gH/qqqF5wP2iqTCCYXnA/Jg4i96NecD8UcIapu15wP2hABeuxXnA/NxwrS7RecD9r21abtl5wPyqWkk+0XnA/AKC+9LZecD/FxwWnr15wP0pPXqG9XnA/w8whecVecD+wQsjx2l5wP663jKLpXnA/1VrHxfRecD/pj0jQ9F5wP1UEzpDtXnA/VC9gaetecD8GMFd6+15wPy/0klIFX3A/MJ67sxVfcD+8nAVhEF9wP5BSxWgFX3A/y6I4Q/9ecD/yuiM6/l5wP21sJJD0XnA/wJLwx/BecD+m5OPc715wP/5BbCb0XnA/XhPKJehecD/hHzpC415wP0ViSyrXXnA/drzJROxecD8IUKu5+F5wP4u518DuXnA//9pFIOBecD+pq7Ah8F5wP5SMtojyXnA/yuyDWfFecD8n26ls415wP22fkDneXnA/RhM++ONecD8hJJhW3V5wP8UQcurbXnA/9SsVX+JecD9DcOXt3F5wP1Hyv2HhXnA/kKSpzuBecD993vDW7V5wP6ydA7nnXnA/r5/2VPtecD8cyHVh9V5wP1X4G1oCX3A/I6vUuglfcD/Hp/7C7F5wP9TZDuX9XnA/JHvuV+5ecD+9ACeW615wP973nBn4XnA/B27Jd+5ecD8W7WDV415wPyKepUXPXnA/N/x5FcdecD851HuArl5wP0iq1PC1XnA/VQJmuL1ecD/6JXHYqF5wP6izRlqsXnA/XauZap9ecD9meQBVml5wPyHv1SClXnA/n2dyyrlecD+Xn2zuzF5wP7LfH1DNXnA/P1CysMtecD/dKeDhu15wP0prWam3XnA/6v5M5rVecD946zKSvF5wP7cqDyfFXnA/oyEx1cNecD+EMmuktl5wP8gjszioXnA/eeHUQ55ecD8wk7wBkl5wP+j8F82RXnA/Xrv+ypFecD8Fhz4ril5wP/mBYH+OXnA/X5fVK5BecD8QEQhhgV5wP/1e72iBXnA/vTCtVn9ecD+SrNkabl5wP4IuDb16XnA/V5WDAHFecD/k3eVFdl5wPyHNzpKDXnA/tL5t2IpecD8tUNtNiV5wP5fDIGt0XnA/i/Wuw3decD9MhBPHeF5wPzb0fpl9XnA/Hg7rvIpecD9YKzDseF5wP28gDlZvXnA/T9sLwXhecD82CqHBaV5wP2VthCthXnA/5d/rx3VecD9BcKdRbF5wP68+94BiXnA/uEvWd1decD+2kcAUX15wP7DjmgFtXnA/bxau5G1ecD96q834aV5wPzQHWjVuXnA/i+CkHWhecD8DIuPmdl5wP6hIrP+AXnA/F9FxtXhecD948HeIfV5wP+mAJwt0XnA/LJAYdnRecD/lgxjaf15wP6TM+GF2XnA/b5RX6XxecD+c7LAMcV5wPy94iIpyXnA/cf9cqHZecD8WuwcEfV5wP01blWWCXnA/FDC4S5BecD+/7mfklF5wP4wzU6+HXnA/8Ewtv4lecD8BJ4Rdj15wP2oxDYuBXnA/pJ5EKnVecD+JM/AWZl5wP/pUNaJjXnA/Ysm0EmxecD+8Padfel5wP7ITjttyXnA/SeloEldecD8OTlYIPl5wP0gyJgE2XnA/2VQe9yxecD8qpDjsQ15wP7EE/mcxXnA/xEYfQT1ecD/7SwpJLl5wP817z0cnXnA/dMxc1SxecD+jSeDPL15wPwx76Po3XnA/Rlc91jZecD+wzDduPl5wP1BNffooXnA/5x/jISFecD8TJpgXKl5wP+FqFKMZXnA/qlPwMypecD9yTg/0P15wP0mcPQ05XnA/+pTVIzhecD/ALm7gQF5wP8tJdCNBXnA/gmfDcUpecD8SjKrNS15wP/cXfvM6XnA/NEm9vTxecD9g81RxTV5wP7u5TppYXnA/xXxvjz1ecD9ZcmfLNV5wPwMkoSM/XnA/6WlRR0RecD+8x702OV5wP9bSEjY6XnA/Zhv0mSlecD+QmEf7NF5wP/h//AUmXnA/e795hSNecD+B0n68NF5wPxbOXftFXnA//Bp1AFpecD/iRVXNZl5wP3Jm6N5fXnA/QCX9cmxecD+iiQUfY15wP+X/eENWXnA/hD3N5VZecD/u41eaXl5wPy3KUSVvXnA/Cl8D73hecD8OkAUfb15wPyAVdSZ7XnA/gBc2MntecD/KgfbQdl5wP8IAEFRzXnA/b4jzz1tecD85JGdmWF5wP/BuMaxcXnA/0OIITlpecD8tLWz0R15wPxkIXCpNXnA/GoMYUk9ecD8DYTYdXV5wP5smoCNvXnA/XWDqZ4tecD9sHil6kl5wP/5/jwajXnA/sx8Gh5BecD9f0UXvfF5wPzIaNq6BXnA/czTh6HpecD8OzOq+Z15wP3FKlAR3XnA/jpPda3ZecD+JDB/pgl5wPx7uiQCCXnA/fUWOtYBecD+OSmZEZl5wP/gbuERYXnA/mgq1p15ecD82gKTlWV5wPwQpt45RXnA/0zt7JT5ecD+b4yzkPF5wPzcKFk1KXnA/vr/QYFVecD/Gtj2kTV5wP0Rrg71LXnA/nOstCWlecD9PxY07WF5wP1giGY1YXnA/JPFzc0xecD/k6nMKQ15wP/WQWjZFXnA/tYYySUFecD9jd6nmYF5wPyw7eKNmXnA/e1zkRWRecD99ZtNUZl5wP3m9xxhpXnA/tnbxnmxecD/z8Q6SaF5wPxHOQ1psXnA/3clGiWdecD9tzDa2WF5wP42DhM1XXnA/2j96nkVecD/jBzjHLl5wP0Slnxw2XnA/kPurlEdecD/4k7NVWl5wPwoc+xhkXnA/c5VLC2RecD/3ao/WWl5wP60s1ad1XnA/z0N+4HtecD+zhuAWeV5wP5qcXBNoXnA/BuQor2ZecD+TjoadeV5wP81z2XqEXnA/Fjg+Vn9ecD93mt7QdV5wP8SwV4COXnA/lAxG73hecD+HZcCRhl5wPxS7Xx+EXnA/dDdXn4hecD+CvWlCjF5wP5OtVAWoXnA/qW/3p6ZecD/OeeL3tV5wP60yRSzGXnA/zqSQSdhecD93QOsn0l5wP1o3EGHVXnA/JxmE9tJecD/iy8JjzV5wP7bx7sfdXnA/O46Gd9hecD8VPMTf3F5wP+C3ZQrfXnA/zBBx29tecD9sUAGD6V5wP0oXRdniXnA/ngF3kNlecD8TdMK53V5wPwtRBe3hXnA/auqaNeRecD/ij9Bh3F5wP/jrWnjXXnA/23dWXuVecD8s7ga45V5wP57fsCjjXnA/vb8NaexecD99BL5U5V5wP5da6D30XnA/coxIJ/tecD8clQbi815wPzCcMirmXnA/O6jA7eZecD849JYM4F5wP+iX6KTUXnA/plhhu9lecD8qLtve4V5wP2exVBXhXnA/SUdwp+pecD9V5N0h615wPwNThETRXnA/gEuoDeBecD/SeHFQyF5wPynmf+fUXnA/xx4CJc5ecD/o00xPz15wP+l4cGW9XnA/6/FVgMVecD/lHWEcwl5wPwa+JMOrXnA/hZqOu6decD+eGiLzrV5wP382mN+zXnA/WErYQLVecD/2/DTHnl5wP+Mp12yvXnA/mMQw7apecD8IYlwfsl5wP4NjUt2uXnA/d793UqhecD/+sw1Xp15wP+jinryuXnA/svlL4LJecD9q2/Kps15wPxFjwBi4XnA/fkgM6bhecD/4A6P0sV5wP37VAHDKXnA/nRIUrbhecD+4bOVtvV5wPwUPKN/HXnA/6wJ2wLtecD+8KrdlwV5wPx2/nkDAXnA/xJDTMrlecD/776EMyl5wP8W+k+zJXnA/wXLxGL5ecD9egpmIsF5wPwlXrIq5XnA/Lou6k8RecD+gdBnbv15wP1ZVxALWXnA/6JAmUs9ecD9gogEH0V5wP8PxYfTHXnA/OzM14sRecD/IpJPh1l5wP85fZpLdXnA/j4W7n+9ecD+0KXWuzF5wPx9QIrjJXnA/lV0qc9BecD/kuUmZzV5wP7t4uz7DXnA/YHWLY7xecD9bA7xco15wP0mk0DG3XnA/FL+uVbFecD/oRCWasl5wP8C9aLC5XnA/rieDMMlecD91EaS6xV5wP4pXBxSnXnA/Sk64R7hecD9AVqPMwV5wPyJSh4TDXnA/7VhZDbtecD9a6mrpq15wP8ArjTqnXnA/m+lloatecD+TByBXpF5wP+fD9i6iXnA/CWzYN61ecD8f3rcan15wP5a5o3mZXnA/+7AHU6NecD+LO1MPtF5wP0Fn1UG7XnA/bj6T6MFecD9RmghMtl5wP49sOJOyXnA/kxpgBrhecD/PdeK5ql5wP8Z2sS2kXnA/gFy66plecD9VnPaupF5wP+dN5+ivXnA/LuHNRK9ecD+VIVxatV5wP+LlwO2xXnA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IsT++pu87uIixPzKW8iF/e7A/MpbyIX97sD8zlvIhf3uwP/TLQ42nlLA/9MtDjaeUsD/0y0ONp5SwP0hUmpMYYbA/SFSakxhhsD9IVJqTGGGwP2pXWyPrXrE/NSCd+p2crz80IJ36nZyvPxYP647eUq4/Fg/rjt5Srj8WD+uO3lKuP83j4VCzxq0/z+PhULPGrT/Q4+FQs8atP5KL4ngXaa0/k4vieBdprT+Ui+J4F2mtP1XMtx2zxK8/Vcy3HbPErz9o7SJX+/yoP1ULFn+biqc/VgsWf5uKpz9WCxZ/m4qnP8GuKJdxfqc/wa4ol3F+pz/BriiXcX6nP6KCW2iiY6c/ooJbaKJjpz+kgltoomOnP5ktZVpzr6c/mC1lWnOvpz+ZLWVac6+nP5bIaMx2bqU/lshozHZupT+XyGjMdm6lP/FQhd44OaM/8lCF3jg5oz/xUIXeODmjP1PiA7MVqKM/UuIDsxWooz9T4gOzFaijPxcloNZkvKM/FiWg1mS8oz8XJaDWZLyjP7s3cotmeaI/Pl+Adi6jpj8+X4B2LqOmPygoA+R4Dac/KCgD5HgNpz8oKAPkeA2nP9dB8GAKxKU/10HwYArEpT/XQfBgCsSlP5VltdUTa6Y/lWW11RNrpj+UZbXVE2umP39GeivObqg/f0Z6K85uqD/LJ3iVfdCwP97uIQpEfLA/3u4hCkR8sD/e7iEKRHywP4ynW2NgaK8/i6dbY2Borz+Kp1tjYGivPwJMBV9rPLA/AkwFX2s8sD8CTAVfazywP2vt6VB/d7A/a+3pUH93sD9r7elQf3ewP4JefwAwPrA/gV5/ADA+sD+BXn8AMD6wP26qFzk2nbA/bqoXOTadsD9uqhc5Np2wP++j+M3fi7A/8KP4zd+LsD/wo/jN34uwP4ojNzXnT7A/iiM3NedPsD+KIzc150+wPwe2/8neFLA/wlvK5mt5rT/CW8rma3mtP7khb9blHa0/uCFv1uUdrT+6IW/W5R2tPwC+hM7aH60/AL6EztofrT8AvoTO2h+tPy75kcgQD6w/LvmRyBAPrD8t+ZHIEA+sPxr7v2l3yqs/Gfu/aXfKqz8U7J+vZg+oP7xRMZvBCKo/vFExm8EIqj+8UTGbwQiqP+CYZ66D2Kk/4JhnroPYqT/gmGeug9ipP4KoJVuyF6g/g6glW7IXqD+CqCVbsheoP/u7sZ9d+qY/+ruxn136pj/6u7GfXfqmPwhCS5WZ6KU/B0JLlZnopT8JQkuVmeilP8I7MeZoUqQ/wjsx5mhSpD/BOzHmaFKkP/xqnZTrLKU/+2qdlOsspT/7ap2U6yylP0IIbg37kaY/QghuDfuRpj9BCG4N+5GmP53aieYsyKU/8vAw9iE9oz/y8DD2IT2jP/QnklW6P6Q/9CeSVbo/pD/zJ5JVuj+kP6WlEm0U+aM/paUSbRT5oz+lpRJtFPmjP1wHoFTdsaM/XQegVN2xoz9dB6BU3bGjP+2fFeHRxqI/7Z8V4dHGoj89Blk6hBukP+D7UcL3JaQ/4PtRwvclpD/f+1HC9yWkP5+buarFYqM/npu5qsVioz+fm7mqxWKjP8vKO6TlkKI/y8o7pOWQoj/Lyjuk5ZCiP4ge88E686E/iB7zwTrzoT+HHvPBOvOhP7MUckNH3qM/sxRyQ0feoz+zFHJDR96jP7Ek5a273KI/sSTlrbvcoj+wJOWtu9yiP6yGxlY06KE/rIbGVjTooT+shsZWNOihPzybMdSTCKE/PJsx1JMIoT88mzHUkwihP7xFJiqUbKA/P2ZQZzsZoT8/ZlBnOxmhPyjpCCXOJaE/KOkIJc4loT8o6QglziWhP8WU1nzHvKA/xZTWfMe8oD/FlNZ8x7ygP4JOV2O0L6E/gU5XY7QvoT+BTldjtC+hP4eXvuURyKA/h5e+5RHIoD/dbrxG/9KjP660a72ajaM/rbRrvZqNoz+ttGu9mo2jP3E1GmvZTqI/cDUaa9lOoj9wNRpr2U6iP7okzvHG26E/uiTO8cbboT+6JM7xxtuhP9U4lgoOw6E/1DiWCg7DoT/VOJYKDsOhP7l4RcWyhaE/uXhFxbKFoT+5eEXFsoWhP4t//x2x1KE/i3//HbHUoT+Lf/8dsdShP1ZDXCcb9qE/VkNcJxv2oT9WQ1wnG/ahP9ST+zcrKKE/1ZP7NysooT/Vk/s3KyihPyi3mGxgTKA/N5/qgCHioD82n+qAIeKgP2P1fgW+OqA/Y/V+Bb46oD9k9X4FvjqgP7JyFYcp3KA/snIVhyncoD+ychWHKdygPwMAllqBUqE/BACWWoFSoT8DAJZagVKhP6pueRLSfaE/qm55EtJ9oT/1LOdc0cShPx5KGnbkoKI/HUoaduSgoj8dShp25KCiP7SbYyb6NKI/tJtjJvo0oj+0m2Mm+jSiP/Ak3Z127KI/8CTdnXbsoj/wJN2dduyiP3FKHYiJeKI/cUodiIl4oj9wSh2IiXiiP0G94hUK7aE/Qb3iFQrtoT9BveIVCu2hP3g64m7gTaI/eDribuBNoj93OuJu4E2iP5MGxG5EP6E/kwbEbkQ/oT+TBsRuRD+hP5ESccUW1KA/kRJxxRbUoD+REnHFFtSgP0aH+FLsraA/UxVl/50gnz9TFWX/nSCfP9i9Wvp5QJ0/171a+nlAnT/XvVr6eUCdP4zZCFRGm54/jdkIVEabnj+N2QhURpueP5ZuDgP5Jp4/lm4OA/kmnj+Wbg4D+SaeP7OEtj1NFqA/s4S2PU0WoD+3y3QyU02gP5bMnZn3JqA/lcydmfcmoD+VzJ2Z9yagP57MtKaZxqA/n8y0ppnGoD+fzLSmmcagP/I871/y66A/8jzvX/LroD/yPO9f8uugP0ciiIdrNKA/RyKIh2s0oD9HIoiHazSgPx9EXSHtVZ8/H0RdIe1Vnz8fRF0h7VWfPzFGalIS6J0/MEZqUhLonT8xRmpSEuidPzkZw07DxJw/ORnDTsPEnD85GcNOw8ScP2gi55KqTZw/aCLnkqpNnD9oIueSqk2cP0cccKcBLpw/q5zWdKMZmj+rnNZ0oxmaP+mgE6Q9jpo/6qATpD2Omj/poBOkPY6aP0IkUTXZtJk/QyRRNdm0mT9DJFE12bSZP0oUlzGK9Zk/ShSXMYr1mT9KFJcxivWZP4/exrxyhJk/jt7GvHKEmT/IiqK3JMSWP7wCk0dl+5c/vAKTR2X7lz+8ApNHZfuXPxwabhBsQpg/GxpuEGxCmD8cGm4QbEKYPwHpdL0Hl5g/Ael0vQeXmD8C6XS9B5eYPy9+xr4eJZg/MH7Gvh4lmD8wfsa+HiWYP80iDt7Y/ZY/zSIO3tj9lj/NIg7e2P2WP6Z/+X1QM5Y/pn/5fVAzlj+mf/l9UDOWP4q1BQCY2JU/irUFAJjYlT+KtQUAmNiVP8Qa6cPwFZY/wxrpw/AVlj/CGunD8BWWP4qKxsO8kpY//d7A2BEqlj/83sDYESqWP/AONhTyuJU/7w42FPK4lT/vDjYU8riVPwaqHhozfJU/BqoeGjN8lT8Fqh4aM3yVP8LYW42joJU/wdhbjaOglT/B2FuNo6CVP0WevE5VTJY/RZ68TlVMlj+D51QSDmOYP0J8AgUifpg/QnwCBSJ+mD9CfAIFIn6YP4gXHxO3ZZg/iBcfE7dlmD+IFx8Tt2WYP1GGlSuSy5g/UIaVK5LLmD9QhpUrksuYP+E3Hgq1tpg/4TceCrW2mD/hNx4KtbaYP7MuQvi33Zc/sy5C+Lfdlz+zLkL4t92XP14OK0YW+Jc/Xg4rRhb4lz9eDitGFviXP9khDPzhTJg/2iEM/OFMmD/aIQz84UyYPxMYKZS9v5c/ExgplL2/lz8SGCmUvb+XP1fn0jnvwZc/ZAz0PtiGlT9kDPQ+2IaVP1s1b1WOm5U/WzVvVY6blT9bNW9VjpuVP3utgRFR/5U/e62BEVH/lT97rYERUf+VP4X/KYAyt5Y/hf8pgDK3lj+D/ymAMreWP469GPfwHZc/jr0Y9/Adlz8kaFkRII+YP5XQt9m7jZg/ltC32buNmD+V0LfZu42YPy8AEPzc6pg/MAAQ/NzqmD8vABD83OqYP6PGFy952pg/pMYXL3namD+kxhcvedqYP9Gw4qlsl5g/0bDiqWyXmD/RsOKpbJeYP3kaBE5GjJc/eRoETkaMlz95GgRORoyXP2yzcCW5fZc/a7NwJbl9lz9rs3AluX2XP5/QlRobpZc/n9CVGhullz+f0JUaG6WXP0i9tZTj9Zc/Sb21lOP1lz9JvbWU4/WXPyENWsMS6Jc/ryHRlzhEmD+uIdGXOESYPy9vxaPgX5g/L2/Fo+BfmD8ub8Wj4F+YPxeTAGlno5g/FpMAaWejmD8VkwBpZ6OYP/nbcqc/Xpg/+Ntypz9emD/523KnP16YP+cE2wPbU5g/5wTbA9tTmD+XoSigNGSWP1tI6CBxGpY/WkjoIHEalj9bSOggcRqWPyDeyAJPIZY/IN7IAk8hlj8g3sgCTyGWP0pnWB6+apY/SmdYHr5qlj9LZ1gevmqWP3W6CnN9dpY/dboKc312lj91ugpzfXaWP9sNVVDgeZg/2w1VUOB5mD/bDVVQ4HmYPz6AvgYGaZg/PYC+BgZpmD89gL4GBmmYP38pqO+dcpg/fimo751ymD9+KajvnXKYP0EkVy3VeZg/QSRXLdV5mD9BJFct1XmYP2xg6mPtmJg/RfaSMDzulT9F9pIwPO6VP6DqfeZyFJY/oOp95nIUlj+h6n3mchSWP0prazv2o5U/S2trO/ajlT9La2s79qOVP/nr2f1aFJY/+evZ/VoUlj/469n9WhSWP7l5MM3dBpY/uHkwzd0Glj8Uv6Sh3jeUP0rxPQpBzJM/SvE9CkHMkz9J8T0KQcyTP50OOg0mTZM/ng46DSZNkz+dDjoNJk2TP/izM+gq9ZM/+bMz6Cr1kz/5szPoKvWTP8edkrV0MZQ/x52StXQxlD/HnZK1dDGUP8cJh2lG+pI/xwmHaUb6kj/HCYdpRvqSPwtXdjKooJI/C1d2Mqigkj8MV3YyqKCSP+Hl03/3H5I/4eXTf/cfkj/h5dN/9x+SP6Ck8fD5ypI/oKTx8PnKkj+gpPHw+cqSP7hfTiEVGpM/JpMG3T3nkz8mkwbdPeeTP480hNsl95M/jzSE2yX3kz+ONITbJfeTPza+EkhzRpQ/Nr4SSHNGlD81vhJIc0aUP26Ffui4jZM/boV+6LiNkz9uhX7ouI2TP3sZvBhqyJM/exm8GGrIkz8gKwz8A5mTPyJwYiooQJM/InBiKihAkz8jcGIqKECTPwNrI8ycE5M/AmsjzJwTkz8DayPMnBOTPxNYvfHnTJM/E1i98edMkz8TWL3x50yTP4X9qT51X5M/h/2pPnVfkz+G/ak+dV+TP7Ae3+Ot9ZM/sB7f4631kz+vHt/jrfWTP7f7lVLspZQ/t/uVUuyllD+3+5VS7KWUPws6kBgP8ZQ/CzqQGA/xlD8LOpAYD/GUP0LvmWo2RpU/Qe+ZajZGlT9C75lqNkaVPxnUD2rW9JU/n59thH0vlT+fn22EfS+VP/v0iVtdxJQ/+/SJW13ElD/69IlbXcSUP1Yb705BCJU/VhvvTkEIlT9VG+9OQQiVP5Rv/KGgbJQ/k2/8oaBslD+Ub/yhoGyUP1g3HvSBZJQ/WDce9IFklD9lwDB/8tKUP7ZxVgXxZZU/tnFWBfFllT+2cVYF8WWVPy5jMHqn65Q/LmMweqfrlD8vYzB6p+uUP6OUGz26b5U/o5QbPbpvlT+jlBs9um+VP2MpPJ9hWJU/Yyk8n2FYlT9jKTyfYViVP+xkbUfqEpU/7GRtR+oSlT/sZG1H6hKVP6Rui+c7/pQ/pG6L5zv+lD+jbovnO/6UPw3GH5uWkpQ/DsYfm5aSlD8Oxh+blpKUPxwrHRGWPpQ/HCsdEZY+lD8dKx0Rlj6UPyPikbHY25Q/cg9pN/melD9yD2k3+Z6UP5dROuWOPpQ/mFE65Y4+lD+YUTrljj6UP3anDDkvIpM/dacMOS8ikz91pww5LyKTP8D/GWoq2pI/wP8Zairakj/A/xlqKtqSP+3yTEOzf5I/7fJMQ7N/kj+ZKWff3fqRP3L9gBcGPZI/cv2AFwY9kj9y/YAXBj2SP3Yxb6hkrZI/dzFvqGStkj92MW+oZK2SP/0s1ejzKZI//SzV6PMpkj/8LNXo8ymSP9dPtUiyPJI/10+1SLI8kj/XT7VIsjySP2DUdu6W0JI/YNR27pbQkj9g1HbultCSPxUxTFR7/JI/FTFMVHv8kj8WMUxUe/ySP84n+OuHvZI/zif464e9kj/OJ/jrh72SPxjQ+3itJZI/GdD7eK0lkj8Z0Pt4rSWSP7cJYeO3hZI/mDQRRm82kj+YNBFGbzaSPxmtwFQ+S5I/Ga3AVD5Lkj8YrcBUPkuSP1j7bKKgOpI/WPtsoqA6kj9Y+2yioDqSP44Bi3rowJM/jgGLeujAkz+OAYt66MCTP6v/iQiNfZM/q/+JCI19kz+ipJGFjeqTPzX6T26vHZM/NvpPbq8dkz82+k9urx2TP42F8MmZ/ZM/jYXwyZn9kz+MhfDJmf2TP9R2t9rAUpM/1Ha32sBSkz/UdrfawFKTP/nElZmExZM/+cSVmYTFkz/4xJWZhMWTP/qs4aSRd5E/+qzhpJF3kT/6rOGkkXeRP3yGsAd/n5E/fIawB3+fkT98hrAHf5+RP/xPpESitJE//U+kRKK0kT/9T6REorSRP4N32++s2JE/g3fb76zYkT+Cd9vvrNiRP6J4GxEGNpE/VYbLPTdOkT9Whss9N06RP6dUjuf5vpE/plSO5/m+kT+mVI7n+b6RP5ASRm77VJI/kRJGbvtUkj+REkZu+1SSP9RZbR6kg5I/01ltHqSDkj/UWW0epIOSP94GuZQPspI/3ga5lA+ykj9bMKbIcy6SP+hfN2Z/tZI/6F83Zn+1kj/oXzdmf7WSP0frLVOKWZI/R+stU4pZkj9I6y1TilmSP3AYBFGAwJI/cRgEUYDAkj9xGARRgMCSP6JoLk77cpI/omguTvtykj+iaC5O+3KSP4ssGv9XdpE/iywa/1d2kT+LLBr/V3aRP0qnN9Gc4pA/Sqc30ZzikD9JpzfRnOKQP7nUAHzxk5E/uNQAfPGTkT+41AB88ZORP9ePFOsXgZA/1o8U6xeBkD/WjxTrF4GQPyJ1ry8dPJE/sQYedmQ1kT+yBh52ZDWRP+QlP68A+JA/5SU/rwD4kD/lJT+vAPiQPxpu+8vqF5E/Gm77y+oXkT8bbvvL6heRP/8ChXpJGY8//wKFekkZjz/8AoV6SRmPP6IYeEozF44/ohh4SjMXjj9X63trql6PP61QA2r4j4w/rVADaviPjD+tUANq+I+MPwGPwJz+Ro4/AY/AnP5Gjj8Aj8Cc/kaOP4oeR+uFQ44/ix5H64VDjj+LHkfrhUOOPy9HfUm6jI4/L0d9SbqMjj8xR31JuoyOP1P6GxDsfo0/U/obEOx+jT9U+hsQ7H6NP9P4Rw9JAYw/0vhHD0kBjD/R+EcPSQGMP8T4uI5qsYo/xPi4jmqxij/C+LiOarGKPwJT92ILSIo/AVP3YgtIij8BU/diC0iKP3JOatCIzYo//noo6ZSDiz/8eijplIOLP7x+CwOSPYs/vH4LA5I9iz+7fgsDkj2LPwlDVU0FZ4k/CUNVTQVniT8KQ1VNBWeJP+NwVdW0/oo/5HBV1bT+ij/jcFXVtP6KPyuzYA2Lboo/K7NgDYtuij8+zhe1jUKLP1ZNOsnmqoo/Vk06yeaqij9VTTrJ5qqKP/jcGMeLV4s/+NwYx4tXiz/43BjHi1eLPzxpuArIyoo/O2m4CsjKij86abgKyMqKP9cIxQbk/os/1wjFBuT+iz/YCMUG5P6LP9cZz2uVsIs/1xnPa5Wwiz/XGc9rlbCLP0fsgIdMvYs/RuyAh0y9iz9H7ICHTL2LP1xRGLCUAos/XFEYsJQCiz9cURiwlAKLPxCK5xrrY4s/EYrnGutjiz8Siuca62OLPwDKI03hm40/DkSXCtXEjD8NRJcK1cSMP6BANi2HmIw/oEA2LYeYjD+gQDYth5iMP2Sifa6JlIw/ZKJ9romUjD9lon2uiZSMPwAad0YFqow//xl3RgWqjD8AGndGBaqMP8BV9ImLm4s/wlX0iYubiz/qfjTCETuLPwN4zIUyo4o/A3jMhTKjij8DeMyFMqOKP6aT+tZvxYk/pZP61m/FiT+lk/rWb8WJP2T/RufMq4c/ZP9G58yrhz9k/0bnzKuHPz1/HUc+Doc/Pn8dRz4Ohz8+fx1HPg6HP5BJG8WX9YY/kEkbxZf1hj+QSRvFl/WGP4c3j9NNJoY/hzeP000mhj+HN4/TTSaGPwZIiusohYY/BkiK6yiFhj8HSIrrKIWGP2oKM3NMjIg/aQozc0yMiD9qCjNzTIyIP+fjDVcvx4Q/WWH4/IqUhD9ZYfj8ipSEP4yvs+R+A4I/jK+z5H4Dgj+Mr7PkfgOCP9X0LlCpooE/1fQuUKmigT/V9C5QqaKBP5qY+DF/wYA/m5j4MX/BgD+bmPgxf8GAPx+cHhchToE/H5weFyFOgT8dka5vTn6BPxti/LQYRn8/HGL8tBhGfz8cYvy0GEZ/P52JSV3ovXw/nolJXei9fD+diUld6L18P3+EKLo8C3U/f4QoujwLdT+AhCi6PAt1P3CW+dboJHc/cJb51ugkdz9xlvnW6CR3P2kVYkoNUXg/aRViSg1ReD9qFWJKDVF4P5GAOFr+NHY/k4A4Wv40dj+TgDha/jR2P5GaK6+GoHM/kJorr4agcz+PmiuvhqBzP0pFv0+QfXQ/SkW/T5B9dD9KRb9PkH10P5HiObvBWXM/cx6GZaWTdD9yHoZlpZN0PwYaMDyw93U/BhowPLD3dT8GGjA8sPd1P0FKX1NfkXQ/QEpfU1+RdD9BSl9TX5F0P5JbfbzZRXA/klt9vNlFcD+TW3282UVwP7uJTUB3V3Q/vIlNQHdXdD9AEecS8CByP+6k8SkW33A/7qTxKRbfcD/tpPEpFt9wP1xqvb6EFHQ/XWq9voQUdD9dar2+hBR0P5+AVvTzlnQ/n4BW9POWdD+fgFb085Z0P2wGpx6z1HI/bAanHrPUcj9sBqces9RyPwdsF3TOG3E/CGwXdM4bcT8HbBd0zhtxP/B7dVXoI3E/8Ht1VegjcT/we3VV6CNxPytEQXczA3Q/KkRBdzMDdD8qREF3MwN0P0hAdTPs33Q/SEB1M+zfdD9IQHUz7N90P+WbBD2ycHM/6pKBnpe7cj/qkoGel7tyP+YoZfcTwHE/5Shl9xPAcT/lKGX3E8BxP2+Av1OTTnQ/b4C/U5NOdD9wgL9Tk050PzZH618633Q/NkfrXzrfdD82R+tfOt90P92H7nGM13Q/3YfucYzXdD/KnJFddi10P882q7NGY28/zjars0Zjbz/PNquzRmNvP18v2Ij1HnA/Xy/YiPUecD9fL9iI9R5wP5o1/QHCP2U/mjX9AcI/ZT+aNf0Bwj9lP203Dde7c1s/bjcN17tzWz9uNw3Xu3NbPwyzw7fc+ki/C7PDt9z6SL8Ls8O33PpIvxBeYavqT1S/DF5hq+pPVL8MXmGr6k9Uv32OrQlPI1S/fY6tCU8jVL97jq0JTyNUv2YdUXkNXly/ZB1ReQ1eXL9iHVF5DV5cv06AsGqA91e/OTojuGi3Yr83OiO4aLdiv0ChSoHKLGe/Q6FKgcosZ79CoUqByixnv59T1/Cy5Gy/n1PX8LLkbL+cU9fwsuRsv++sNKm5LG6/8Kw0qbksbr/wrDSpuSxuv7xp+1L11Gm/vGn7UvXUab9kRD0+A31lv0w+ChAwmWS/Sj4KEDCZZL9JPgoQMJlkvw58jRrD+16/DnyNGsP7Xr8QfI0aw/tev5vYoRjxvGC/nNihGPG8YL+b2KEY8bxgv8KBI8j7ql6/woEjyPuqXr/AgSPI+6pevyCHpC0gh2O/H4ekLSCHY78dh6QtIIdjv1yMVUpVj1+/WoxVSlWPX79ZjFVKVY9fv5op9+wRQ1C/mSn37BFDUL+aKffsEUNQv2ikxJVEP0+/Y6TElUQ/T79gpMSVRD9Pv0vYh+X7JjO/FE9L/sDROL8RT0v+wNE4vwrvY8RGFw0/Hu9jxEYXDT9d72PERhcNP/B7NCQ/20c/9Xs0JD/bRz/3ezQkP9tHPwGxMt5T7RW/ILEy3lPtFb/5sDLeU+0VvzAvz6qKCi2/MC/PqooKLb8HUkWu8XVEv6zQRYtVaVe/qtBFi1VpV7+n0EWLVWlXv2fCQEXY0zS/asJARdjTNL9fwkBF2NM0v4d5bwM5ZES/iXlvAzlkRL+LeW8DOWREv4nfSQ0Alim/ht9JDQCWKb9330kNAJYpv9V2MkpS40U/2nYySlLjRT/cdjJKUuNFP5xVXZ9gxk4/oFVdn2DGTj+iVV2fYMZOP8rWM+Y85VE/zNYz5jzlUT/M1jPmPOVRP2wRvg95hkc/bBG+D3mGRz9tEb4PeYZHPwU8Rtl1K0E/BfogXtR5Rj8F+iBe1HlGP+Rxib1leU0/5HGJvWV5TT/lcYm9ZXlNP00OjcxwTVI/TQ6NzHBNUj9NDo3McE1SP5Oh6qQjMVU/k6HqpCMxVT+ToeqkIzFVPxpehnjI1FM/Gl6GeMjUUz84b1JluMcsP3wxy5/caRa/djHLn9xpFr90Mcuf3GkWvx8Hqc6VulM/IAepzpW6Uz8hB6nOlbpTP0AlF/PMAFE/QCUX88wAUT8/JRfzzABRP3KFfkfVIyw/d4V+R9UjLD91hX5H1SMsP/78IN70d2A///wg3vR3YD///CDe9HdgP5oVY5C4+mI/mRVjkLj6Yj+ZFWOQuPpiP8KpYvhED08/wqli+EQPTz/BqWL4RA9PP1aWZODOCTk/VZZk4M4JOT9WlmTgzgk5P9ItcZK42UY/rdGvaAlscD+u0a9oCWxwPy0R2emoA3E/LBHZ6agDcT8tEdnpqANxP0O2122a23A/RLbXbZrbcD9EttdtmttwP+2Drqi0m3M/7YOuqLSbcz/tg66otJtzP2cZ/XALTXM/aBn9cAtNcz8CVu1bvKh1P7T4xVJ5A3Q/tPjFUnkDdD+0+MVSeQN0P7g9GwoSb3Y/uD0bChJvdj+4PRsKEm92Pz2xkEo+hXU/PbGQSj6FdT89sZBKPoV1P2YO63pBmnk/Zg7rekGaeT9nDut6QZp5P13C4zkRrnQ/XcLjORGudD9dwuM5Ea50P39A0NgXbHI/f0DQ2Bdscj9/QNDYF2xyP5DgcHnDQ3M/kOBwecNDcz+Q4HB5w0NzP5hAQtoy2nc/mUBC2jLadz+aQELaMtp3P3GiFz1QXHk/so3Ye2SQcz+zjdh7ZJBzP4xWJcsl5nQ/i1YlyyXmdD+LViXLJeZ0P05YX7T5LXY/TlhftPktdj9NWF+0+S12P1FmR38dhHc/UWZHfx2Edz9RZkd/HYR3P7IeboCTRXg/sx5ugJNFeD++qZ5E9t10Pzub/8XLbnc/Opv/xctudz86m//Fy253P682K069cXY/rzYrTr1xdj+vNitOvXF2PxUbsaOxR3M/FRuxo7FHcz8VG7GjsUdzPwp5/sbCFnQ/Cnn+xsIWdD8Kef7GwhZ0PwikUB3sNXw/CKRQHew1fD8HpFAd7DV8P5OQm018EIA/k5CbTXwQgD+TkJtNfBCAP4mGDx8BC4A/iIYPHwELgD+Ihg8fAQuAPzN1gHWIV38/M3WAdYhXfz8ydYB1iFd/P08xOUqaK4A/sYlFY4SFgD+xiUVjhIWAP36bOrPW6YE/fps6s9bpgT9/mzqz1umBP0rysKSIM4E/SvKwpIgzgT9K8rCkiDOBP9WSr5jH7YA/1pKvmMftgD/Vkq+Yx+2AP/EVqAeogoI/8RWoB6iCgj+OW8ZLE1mFP8q9cVlVnoY/yr1xWVWehj/KvXFZVZ6GP7z/Sf3bWYY/vf9J/dtZhj+9/0n921mGP9bIaPGq44U/18ho8arjhT/WyGjxquOFPwZj0VsPBYU/B2PRWw8FhT8HY9FbDwWFP1P0mCLYAIg/U/SYItgAiD9S9Jgi2ACIP2DjBlqeeIg/YOMGWp54iD9g4wZanniIPzftH19D7Ig/N+0fX0PsiD837R9fQ+yIP9xPg+Fqoog/3E+D4WqiiD/bT4PhaqKIPzkxPR5a4Yk/dHChZ+H2iT91cKFn4faJPwsk8Q6MH4s/DCTxDowfiz8MJPEOjB+LP9eT8C22Tos/1pPwLbZOiz/Xk/Attk6LP3gbSPYt9Is/eBtI9i30iz93G0j2LfSLP0slZEpbKow/SyVkSlsqjD+mfZMs1BSMP1fbMAVotYs/VtswBWi1iz9V2zAFaLWLPykD4MBdT4s/KQPgwF1Piz8pA+DAXU+LP4DOFh8IMIs/f84WHwgwiz9/zhYfCDCLP0cFDl7HKYw/RwUOXscpjD9HBQ5exymMP6k0a2F+nI0/qTRrYX6cjT+qNGthfpyNP/w7UKS01I4/+jtQpLTUjj/7O1CktNSOPzVAp/oWW48/NUCn+hZbjz80QKf6FluPPxYl2IsrnI4/FiXYiyucjj8XJdiLK5yOP/v2FwwsKI0/M095byfRjD80T3lvJ9GMP6hjU/V6jow/p2NT9XqOjD+oY1P1eo6MP3zfyVUmEYs/fN/JVSYRiz9838lVJhGLP8zAnvD4H4s/zcCe8Pgfiz/NwJ7w+B+LP3IPiKImJIo/cQ+IoiYkij9yYKjKOZGJP9anO5pBJIo/1qc7mkEkij/VpzuaQSSKP4qrWPmGuok/iqtY+Ya6iT+Kq1j5hrqJP16YtJcXKok/XZi0lxcqiT9emLSXFyqJPxdFLdEux4g/F0Ut0S7HiD8WRS3RLseIP7qSQ28RSYo/uZJDbxFJij+5kkNvEUmKP8LxVpAj4oo/wfFWkCPiij/B8VaQI+KKPw2vwN+LdYk/Da/A34t1iT8Nr8Dfi3WJP6X39HzbeYk/pff0fNt5iT+l9/R823mJPzNMEcRxdok/OdD1uLcTiT850PW4txOJP4MH09oCtIk/gwfT2gK0iT+DB9PaArSJP6F//VLm8og/oX/9UubyiD+hf/1S5vKIPwZGPME8DIk/BUY8wTwMiT8FRjzBPAyJPz60jAKHO4k/PrSMAoc7iT9guSFco2KKP/rOt15xRIo/+863XnFEij/7zrdecUSKPyYSSzj/fIo/JhJLOP98ij8nEks4/3yKP7UHE+QbW4s/tgcT5Btbiz+1BxPkG1uLP3TG8ctLIYs/dMbxy0shiz9zxvHLSyGLP3jnh/Rgq4o/d+eH9GCrij9254f0YKuKP0PAuyI2YIs/RcC7IjZgiz9DwLsiNmCLP4g7U/ARBYw/iDtT8BEFjD+IO1PwEQWMP1DY+qOLYIw/UNj6o4tgjD9Q2Pqji2CMPyM7ZhpW54s/ckfNDW3Aiz9yR80NbcCLPxYkymRwcYs/FiTKZHBxiz8WJMpkcHGLPygM2/UJRIw/Jwzb9QlEjD8nDNv1CUSMP3n4kyhl2ow/ePiTKGXajD94+JMoZdqMP0M3Bw4q9Ys/QjcHDir1iz8vdVDjnHGMP3ASv2i3lYw/cBK/aLeVjD9xEr9ot5WMP+aeCwYpG4w/6J4LBikbjD/nngsGKRuMPwaK3M9sl4w/CIrcz2yXjD8IitzPbJeMPwYEKY+Wfo0/BgQpj5Z+jT8FBCmPln6NPyt2knfxC40/K3aSd/ELjT8sdpJ38QuNP7tmG5NLWY0/u2Ybk0tZjT+8ZhuTS1mNP3DIXOpTwIw/cMhc6lPAjD9wyFzqU8CMP5BvzViOdYw/kG/NWI51jD+Qb81YjnWMPxBDvXDSnY0/7f4BnrfRjT/t/gGet9GNP8oHZcJuho0/yAdlwm6GjT/KB2XCboaNPwkHiYRcQY0/CweJhFxBjT8KB4mEXEGNP3YSO/N1sI0/dhI783WwjT92EjvzdbCNP6BbJpo5u40/oFsmmjm7jT8lPYgOSbeNP6pJh4sTSo4/qkmHixNKjj+qSYeLE0qOP456/yAaVI4/j3r/IBpUjj+Pev8gGlSOPxxYEFviho0/HVgQW+KGjT8dWBBb4oaNP8wVyhXyxow/zBXKFfLGjD/MFcoV8saMP7Hrm+p8oYw/seub6nyhjD+x65vqfKGMPwOiSUFyLow/BaJJQXIujD8FoklBci6MP4j/eVFJ1Is/iP95UUnUiz+J/3lRSdSLPzLGqPDGaIs/Msao8MZoiz8yxqjwxmiLP2KyzqJM+4o/m1vrEk+4iD+aW+sST7iIP4IcnzaV+Ig/gxyfNpX4iD+DHJ82lfiIPzAXuBy+dYk/MBe4HL51iT8wF7gcvnWJPwG+6Kstbog/AL7oqy1uiD8AvuirLW6IP7Rt5hP69oc/tG3mE/r2hz/7ydzLURyIP3HGCyfvEok/cMYLJ+8SiT9wxgsn7xKJPzpl/sRHD4o/OmX+xEcPij87Zf7ERw+KP0G7mtWRXYo/P7ua1ZFdij9Au5rVkV2KPzpjQzAs9Yo/OmNDMCz1ij86Y0MwLPWKP/vbc5d9XIw/+ttzl31cjD/623OXfVyMPwanQf0vfYs/BqdB/S99iz8Hp0H9L32LP/F/zU0yxYs/8X/NTTLFiz/xf81NMsWLP/J48AJTq4s/8XjwAlOriz/xePACU6uLP0k/Eiv5K4s/o/SyRWLqiz+k9LJFYuqLPyiRTOKTXYw/KJFM4pNdjD8nkUzik12MP/JrQ/TMz4s/82tD9MzPiz/za0P0zM+LP7eqm9hY3os/t6qb2Fjeiz+4qpvYWN6LP/Vt15sN94s/9m3Xmw33iz/PVQhar6OJP1jOn+uDf4k/WM6f64N/iT9Yzp/rg3+JP7n03b25DIo/uPTdvbkMij+59N29uQyKP41BRSEBoIk/jUFFIQGgiT+MQUUhAaCJPz9KVM/A34k/QEpUz8DfiT9ASlTPwN+JPwYKsr8hgoo/BwqyvyGCij8GCrK/IYKKP3scxbpvDYo/exzFum8Nij97HMW6bw2KP/dxV7tTyok/93FXu1PKiT/3cVe7U8qJP+QAS0Xvg4k/4wBLRe+DiT/iAEtF74OJP/sTvCufrYk/RXjZ3zRgiT9GeNnfNGCJP6uUKgn1zoc/q5QqCfXOhz+slCoJ9c6HP3PlmOsFbog/dOWY6wVuiD915ZjrBW6IP67TL4/60oc/rdMvj/rShz+t0y+P+tKHP/ME6Hj1Voc/8wToePVWhz+dU+qtO1SIP7SA5CDzxIg/toDkIPPEiD+1gOQg88SIPyOcaelwUYk/I5xp6XBRiT8jnGnpcFGJP4gJp9bNC4k/iQmn1s0LiT+KCafWzQuJP5/gEfMcAYo/n+AR8xwBij+f4BHzHAGKP59XowXrGYo/oVejBesZij+gV6MF6xmKP8Qd7tESPoo/xR3u0RI+ij/EHe7REj6KP3ZAG0/lx4o/d0AbT+XHij91QBtP5ceKPz9s5uQuzYo/P2zm5C7Nij8+bObkLs2KP+ygGjv6vIo/wF6rnn4vij+/Xquefi+KP5PItS27zIk/ksi1LbvMiT+SyLUtu8yJP5I2WAygAIo/kTZYDKAAij+PNlgMoACKP4hjkG7CQIo/h2OQbsJAij+FY5BuwkCKP+x/m7O8ook/7H+bs7yiiT/2z6FKWG6HP9mm6O4lwYc/2Kbo7iXBhz/YpujuJcGHP7rcCsiLa4g/udwKyItriD+53ArIi2uIP4/YObGUnog/jdg5sZSeiD+N2DmxlJ6IP4Ro/ziec4g/hGj/OJ5ziD+BaP84nnOIPxJSTxUWe4Y/EVJPFRZ7hj8RUk8VFnuGP6aJ/ZnWN4Y/pon9mdY3hj+lif2Z1jeGP8qy6ZwVU4Y/yrLpnBVThj/KsumcFVOGP14UcRcmIYY/XRRxFyYhhj9dFHEXJiGGP4Jp2VgWE4Y/kYn/K0mxhD+Qif8rSbGEP8FhB6FjxoQ/wmEHoWPGhD/CYQehY8aEP88xe4H1/oQ/zzF7gfX+hD/NMXuB9f6EP0zCZFh1R4U/S8JkWHVHhT9LwmRYdUeFP1LO78WdoYU/Us7vxZ2hhT9dAMtPSeOGP9Jvko7Z/4c/0m+Sjtn/hz/Tb5KO2f+HP30oOpVDbog/fig6lUNuiD99KDqVQ26IPyFM1uBS5Yc/IUzW4FLlhz8gTNbgUuWHPyrqbrQAvIc/KuputAC8hz8q6m60ALyHPyoi9Awb0YY/KSL0DBvRhj8qIvQMG9GGP9KlC7bCOIY/0qULtsI4hj/RpQu2wjiGP4gb3UxNP4Y/hxvdTE0/hj+IG91MTT+GPz7QVUQhEoY/PtBVRCEShj8+0FVEIRKGP4uPEy6AFYY/cpUp7Gn2hT9ylSnsafaFP+LZnBzNX4Y/4dmcHM1fhj/h2ZwczV+GPzkgqneQhoY/OSCqd5CGhj86IKp3kIaGP9GcyuOlxoU/0ZzK46XGhT/RnMrjpcaFP/P9tf62f4U/8/21/rZ/hT/+dVq1HTiFP6Ci9RyrRoU/oKL1HKtGhT+govUcq0aFP7nLE79xM4U/ucsTv3EzhT+5yxO/cTOFPyYi+KUaUoU/JSL4pRpShT8lIvilGlKFP8uR+iSxeIU/zJH6JLF4hT/MkfoksXiFP5dcnerJ4IU/llyd6snghT+XXJ3qyeCFP2yakdm+vIU/bZqR2b68hT9smpHZvryFP6XlODDTuoU/peU4MNO6hT+m5Tgw07qFPyV+j/8/gIU/Jn6P/z+AhT8mfo//P4CFP0VEvjYtDIY/t80y/zU+hT+2zTL/NT6FP6lw+J9+c4U/qnD4n35zhT+rcPiffnOFP93ioiOlqYU/3uKiI6WphT/e4qIjpamFP8mflefa94U/yZ+V59r3hT/Jn5Xn2veFP1sbQifIyIU/WRtCJ8jIhT+x5zdzs1mEP+WHMqKcbIQ/5IcyopxshD/lhzKinGyEPwwJ0keN64Q/DQnSR43rhD8MCdJHjeuEPwpHX9Tf/4Q/Ckdf1N//hD8KR1/U3/+EPwSvPJm2HoU/Ba88mbYehT8FrzyZth6FP+pbur4eX4U/6lu6vh5fhT/qW7q+Hl+FPys4TVNbIoU/LDhNU1sihT8rOE1TWyKFP5mTqdStXoU/mZOp1K1ehT+Zk6nUrV6FP74+hAQtiIU/vj6EBC2IhT++PoQELYiFPwnwN7ZoaYU/PlwWqDqThD8+XBaoOpOEP8XEkfSZqIQ/xcSR9JmohD/FxJH0maiEPxCD20j3gYQ/EIPbSPeBhD8Rg9tI94GEP9zAivcpgIQ/3MCK9ymAhD/cwIr3KYCEP1gYMhIAboQ/WBgyEgBuhD+WSgvqfT6EP7OvV7LJxIM/s69XssnEgz+zr1eyycSDPwFqzvl+rIM/AmrO+X6sgz8Cas75fqyDP+PgtCWT6oM/4+C0JZPqgz/k4LQlk+qDPwslJR3x6YM/CyUlHfHpgz8MJSUd8emDPym4K1acR4Q/KbgrVpxHhD8ouCtWnEeEP9hW2ynZVYQ/2FbbKdlVhD/XVtsp2VWEP9OOkTdeYoQ/046RN15ihD/TjpE3XmKEP5IBxiJbTIQ/kgHGIltMhD+RAcYiW0yEP01M/w3TjYQ/OifsK6HfhD87J+wrod+EPyvNbDzNrIQ/Ks1sPM2shD8rzWw8zayEP7CfUl2Ht4Q/r59SXYe3hD+wn1Jdh7eEP5rmBvqD04Q/muYG+oPThD+a5gb6g9OEPwMDaYVZ64Q/BANphVnrhD+6nAdMCwKGPwtrwVWQ74U/DGvBVZDvhT8Na8FVkO+FP0Kz7CB54oU/Q7PsIHnihT9Ds+wgeeKFP4twH9JC9IU/i3Af0kL0hT+McB/SQvSFPwFWJEsWG4Y/AVYkSxYbhj8BViRLFhuGPw0hyXh4hIU/DSHJeHiEhT8MIcl4eISFP328MzQiZIU/fbwzNCJkhT9+vDM0ImSFPz/ZdXMdkoU/P9l1cx2ShT9A2XVzHZKFP4oQOCNBs4U/jBA4I0GzhT+MEDgjQbOFPxJm+O7OuYU/EFOLwzzzhT8QU4vDPPOFP7m3k+YRHYY/ubeT5hEdhj+4t5PmER2GP+QXsw0VIIY/5BezDRUghj/kF7MNFSCGP9/Kzt+wS4Y/4MrO37BLhj/gys7fsEuGP9fRV+mZOYY/19FX6Zk5hj8B7nRqbEqFPyEhvo9dZ4U/ISG+j11nhT8gIb6PXWeFP2ryzWc9l4U/avLNZz2XhT9q8s1nPZeFP2hnHf3+qIU/aGcd/f6ohT9pZx39/qiFPw4X2CRCroU/DhfYJEKuhT8NF9gkQq6FP7F1FNI/+4U/sHUU0j/7hT+wdRTSP/uFP0nEEK88BIY/SMQQrzwEhj9JxBCvPASGP3XN1fMX84U/dc3V8xfzhT92zdXzF/OFP6hshhj8iYU/qGyGGPyJhT+obIYY/ImFPwXhy6USf4U/DfdjDgmXhT8N92MOCZeFP9it/RE/ooU/1639ET+ihT/Xrf0RP6KFP3pv7eKwdoU/eW/t4rB2hT96b+3isHaFP1O7xtZYToU/VLvG1lhOhT9Uu8bWWE6FP0y02RlISoU/TLTZGUhKhT+BxfuObe2EP9bsGq/f4IQ/1uwar9/ghD/W7Bqv3+CEP3VvvZDa6oQ/dm+9kNrqhD91b72Q2uqEP513ekRV7oQ/nHd6RFXuhD+ed3pEVe6EP7hmxWz3gYQ/uGbFbPeBhD+4ZsVs94GEP+n4bbgvc4U/6PhtuC9zhT/o+G24L3OFP7obbYpzmoU/uhttinOahT+7G22Kc5qFP8aP8aF2qIU/xY/xoXaohT/Fj/GhdqiFP/i3y4qEuIU/+bfLioS4hT/4t8uKhLiFPwF972IMk4U/0xNB+hmXhj/UE0H6GZeGP+VWY2FBw4Y/5FZjYUHDhj/jVmNhQcOGPzGf2h9Xk4Y/MZ/aH1eThj8xn9ofV5OGP+8RtfKSqIY/7xG18pKohj/uEbXykqiGP94nDcKbgYY/3CcNwpuBhj+TrM91jwOGP/22mramR4Y//baatqZHhj/9tpq2pkeGP2jgRNXaHYY/aOBE1dodhj9p4ETV2h2GP25FtGsZOYY/b0W0axk5hj9tRbRrGTmGP/JUs/g71IU/81Sz+DvUhT/zVLP4O9SFP+4WIGnOFYU/7hYgac4VhT/vFiBpzhWFP4AsXoYIAYU/gCxehggBhT+ALF6GCAGFP3V5gQgzBYU/dHmBCDMFhT90eYEIMwWFP8Il5D/H14Q/wiXkP8fXhD/BJeQ/x9eEP7s3h38HkIQ/PNIc/lCNgz890hz+UI2DPwLXVfGPloM/AtdV8Y+Wgz8B11Xxj5aDP5TFOl1tk4M/lMU6XW2Tgz+UxTpdbZODP0nO5n8IT4M/Ss7mfwhPgz9JzuZ/CE+DP9LRPT15T4M/0tE9PXlPgz8nqrItFgeEP04W/Ep9FYQ/Txb8Sn0VhD9PFvxKfRWEP4wjmaiNS4Q/jCOZqI1LhD+MI5mojUuEPxjlJAMHM4Q/GeUkAwczhD8Z5SQDBzOEP4Be95a4aIQ/f173lrhohD9/XveWuGiEP8ABfQaoDoQ/wQF9BqgOhD/BAX0GqA6EP3dd3DWmGoQ/dl3cNaYahD91Xdw1phqEP/YhSfnvLIQ/9iFJ+e8shD/3IUn57yyEPy5eqUV+b4Q/LV6pRX5vhD8uXqlFfm+EP18OwWOkRYQ/y1fQdASHhT/LV9B0BIeFP75EwDg1XoU/vkTAODVehT++RMA4NV6FPyoDImb2r4U/KgMiZvavhT8pAyJm9q+FP54pVvSMDoY/nylW9IwOhj+eKVb0jA6GP8QbQLGSOoY/wxtAsZI6hj+M77b1Iw6GPwwtbZjwBoY/DC1tmPAGhj8MLW2Y8AaGP6kGY/7j74U/qAZj/uPvhT+pBmP+4++FP/L73/J76oU/8vvf8nvqhT/y+9/ye+qFP/PG4aUypoU/88bhpTKmhT/0xuGlMqaFPz6BHO7lwYY/PYEc7uXBhj89gRzu5cGGP4OfGVIMmYY/hJ8ZUgyZhj+DnxlSDJmGPx+FMOqMn4Y/IIUw6oyfhj8hhTDqjJ+GP1LS7zaBuoY/U9LvNoG6hj9U0u82gbqGP2/RxWby8oY/cWOnXyaUhj9zY6dfJpSGP/Kvd+icgYY/8q936JyBhj/yr3fonIGGPx7YUbxaqYY/HthRvFqphj8g2FG8WqmGPzpoaQRGvIY/OmhpBEa8hj86aGkERryGP74rycZKy4Y/vivJxkrLhj/XdObPgv6GPxvtFMpTAYc/G+0UylMBhz8c7RTKUwGHP0O2M+rgG4c/QrYz6uAbhz9DtjPq4BuHP9xtqkElLIc/3W2qQSUshz/dbapBJSyHPw3kvO8rJ4c/DuS87ysnhz8O5LzvKyeHPweIhEsPxoc/B4iESw/Ghz8GiIRLD8aHP1NYCSPUtoc/U1gJI9S2hz9SWAkj1LaHPzbLRmNj0Ic/NstGY2PQhz83y0ZjY9CHP3Xv4zAjvIc/de/jMCO8hz907+MwI7yHPxtFNAcQ1Yc/r5L4VBybhz+ukvhUHJuHP8UBbpjigYc/xAFumOKBhz/EAW6Y4oGHP2M/CoYLiYc/ZD8KhguJhz9lPwqGC4mHPznFN9SVboc/OsU31JVuhz86xTfUlW6HPwXYZZSSeoc/BdhllJJ6hz+XTBqufqyHP3suJZGspIc/fC4lkaykhz97LiWRrKSHPwxUdR6Ru4c/DFR1HpG7hz8MVHUekbuHP64F7aVPxoc/rQXtpU/Ghz+tBe2lT8aHP+ysArxMuIc/7KwCvEy4hz/rrAK8TLiHP1RS1vxs9Yc/VFLW/Gz1hz9SUtb8bPWHP3Z8Pzn7NIg/d3w/Ofs0iD92fD85+zSIP39Q6amfK4g/f1DpqZ8riD9/UOmpnyuIPzbt3zY5Cog/Nu3fNjkKiD827d82OQqIP7qdUvoLMog/pnz1yAa9iD+mfPXIBr2IP9Swiowi4Ig/1LCKjCLgiD/UsIqMIuCIP+ykgD/p3Yg/7KSAP+ndiD/tpIA/6d2IP94WmKEu7Yg/3RaYoS7tiD/dFpihLu2IP0Gr/Pil64g/QKv8+KXriD909mOt+liIP5KYb8z3Qog/kZhvzPdCiD+RmG/M90KIP/99xDUZSog/AH7ENRlKiD8BfsQ1GUqIP4gsTj6FUog/iCxOPoVSiD+ILE4+hVKIP/pO+JcNPYg/+U74lw09iD/5TviXDT2IP+kMJZGC+Yc/6AwlkYL5hz/oDCWRgvmHP4vrXOlFD4g/ietc6UUPiD+K61zpRQ+IP1wDdHxBOYg/WwN0fEE5iD9cA3R8QTmIP4QjOHYjaog/gyM4diNqiD+CIzh2I2qIP6qKZYxwpog/7rbwqb0aiT/ttvCpvRqJPwH9hl9BUIk/AP2GX0FQiT8A/YZfQVCJPx+bUn8tYYk/HptSfy1hiT8em1J/LWGJPyE7W26HL4k/IDtbbocviT8gO1tuhy+JP57so5gMO4k/nuyjmAw7iT+vf7itn2+IP6Aza2w0iog/oTNrbDSKiD+gM2tsNIqIP5ylUQlAUog/naVRCUBSiD+cpVEJQFKIP70HWFNFWIg/vAdYU0VYiD+8B1hTRViIP/zrAXX5hIg//OsBdfmEiD/76wF1+YSIP3wkBAdEIYk/fCQEB0QhiT98JAQHRCGJP+XWWXKII4k/5NZZcogjiT/k1llyiCOJP4488TqcTYk/jjzxOpxNiT+MPPE6nE2JP2WW+JkTU4k/ZZb4mRNTiT9klviZE1OJPwRQWj7Yh4k/KPrR8vjeiD8o+tHy+N6IP8zJfwpa5Yg/zMl/ClrliD/LyX8KWuWIPySpSfpk/og/I6lJ+mT+iD8jqUn6ZP6IPxapQo8G7Yg/FqlCjwbtiD8WqUKPBu2IP4Ruc+wy64g/hG5z7DLriD/y4KL0zESJP4iGKNcAQIk/h4Yo1wBAiT+HhijXAECJP2f4kk87HIk/Z/iSTzsciT9n+JJPOxyJP/tMCPElQIk/+0wI8SVAiT/7TAjxJUCJPwaOG3ZMI4k/B44bdkwjiT8Gjht2TCOJP9l51vHkJok/2XnW8eQmiT/Zedbx5CaJP9neLOsPWok/2N4s6w9aiT/Y3izrD1qJP/FPeiLsS4k/8E96IuxLiT/wT3oi7EuJPy74wqbf/4g/LvjCpt//iD8u+MKm3/+IPyh+Q6FF/Yg/m+tJXNHriD+a60lc0euIP2FyZYQU4Ig/YXJlhBTgiD9hcmWEFOCIP+1ESpOx7Ig/7ERKk7HsiD/rREqTseyIP+qaig1l5og/65qKDWXmiD/rmooNZeaIPxuCKjzlA4k/G4IqPOUDiT9hzC+vpA+JP/o2YWqpD4k/+zZhaqkPiT/7NmFqqQ+JP+/3F+leIIk/7/cX6V4giT/v9xfpXiCJP7elJMAKNYk/t6UkwAo1iT+3pSTACjWJPwgrD/m9MYk/BysP+b0xiT8IKw/5vTGJP/uXRsZ3I4k/+pdGxncjiT/6l0bGdyOJP/yCOcj7IYk//II5yPshiT/8gjnI+yGJP2iQf+BQ3Yg/Z5B/4FDdiD9okH/gUN2IP5KctPMKyog/kZy08wrKiD+RnLTzCsqIPwyVGOZKtIg/RAt5TPujiD9EC3lM+6OIPxpVmr7Fhog/GlWavsWGiD8bVZq+xYaIP8Xcp5G+iIg/xdynkb6IiD/F3KeRvoiIP/vNIHpEQIg/+80gekRAiD/6zSB6RECIPzPgY+GWU4g/NOBj4ZZTiD9/WLpDRkeIPzuBysQCL4g/OoHKxAIviD86gcrEAi+IPz1lRqT9JIg/PWVGpP0kiD89ZUak/SSIP+AzQ4TXXYg/3zNDhNddiD/fM0OE112IPxdLs//tO4g/F0uz/+07iD8YS7P/7TuIPwYOtXXXP4g/Bg61ddc/iD8IDrV11z+IP/zU277TcYg//dTbvtNxiD/81Nu+03GIP8pgibRcWYg/yWCJtFxZiD/JYIm0XFmIP2bPHl0gj4g/Zc8eXSCPiD9lzx5dII+IP6u6TKimbYg/mF6KkUpiiD+ZXoqRSmKIP8L1DxLcW4g/wvUPEtxbiD/C9Q8S3FuIPyxSnx+RO4g/K1KfH5E7iD8rUp8fkTuIP0tp8IEaI4g/SmnwgRojiD9JafCBGiOIP0D247HeGIg/QPbjsd4YiD/FAwN9pU6IPy1f0Bj2dog/LV/QGPZ2iD8tX9AY9naIP2iz3ljXgYg/aLPeWNeBiD9ps95Y14GIP4gLkUnmRYg/iQuRSeZFiD+IC5FJ5kWIP5pgmfQHH4g/mmCZ9AcfiD+bYJn0Bx+IP8kJle/OH4g/yQmV784fiD/JCZXvzh+IPyhrQAi07Yc/KGtACLTthz8oa0AItO2HP4Q71iUTAYg/hDvWJRMBiD+EO9YlEwGIP2FPrFVJ44c/YU+sVUnjhz9gT6xVSeOHP3HC1scDxYc/WhDWqBe7hz9aENaoF7uHPwvCx5hhroc/CsLHmGGuhz8KwseYYa6HPy758VVssYc/LvnxVWyxhz8u+fFVbLGHP3dS0y7uvoc/d1LTLu6+hz94UtMu7r6HPxSxpya14oc/FbGnJrXihz+LN6sRgWiIP4QxY11wd4g/gzFjXXB3iD+DMWNdcHeIPxbMnbYGJIg/FsydtgYkiD8XzJ22BiSIP3wbVs+3I4g/fBtWz7cjiD98G1bPtyOIPwwEWF469Ic/DARYXjr0hz8MBFheOvSHP1Cy/3oU74c/UbL/ehTvhz9Rsv96FO+HPxboBXRnRYg/F+gFdGdFiD8X6AV0Z0WIPxu5075ON4g/G7nTvk43iD8budO+TjeIP/pXO8Fw+4c/+lc7wXD7hz/6VzvBcPuHP6+WE/tZPog/b297Q9c/iD9vb3tD1z+IP9k8x7hTa4g/2DzHuFNriD/ZPMe4U2uIP+TwJDswhIg/5fAkOzCEiD/l8CQ7MISIP6r2pAB5gIg/qvakAHmAiD+q9qQAeYCIP+evr468jYg/6a+vjryNiD+BHIfpnTeJP0fP9YB2CYk/R8/1gHYJiT9Hz/WAdgmJP0p9UlvM+4g/SX1SW8z7iD9JfVJbzPuIP2+BZclCzog/boFlyULOiD9ugWXJQs6IP2s02mhmyog/azTaaGbKiD9qNNpoZsqIPxF3J1Qi7og/EncnVCLuiD8TdydUIu6IP44MfHIbzog/jgx8chvOiD+ODHxyG86IPxdR2AJowYg/FlHYAmjBiD8XUdgCaMGIPyv5niniz4g/K/meKeLPiD8q+Z4p4s+IP+DieyLPKIk/7SeNiOfAiD/sJ42I58CIP57HwWU5e4g/ncfBZTl7iD+dx8FlOXuIPwzWQI2lUog/DNZAjaVSiD8M1kCNpVKIP6j5bXYjP4g/qPltdiM/iD+o+W12Iz+IPwaiaxtpLog/B6JrG2kuiD9EYEMrE4OIP2B+P4ONUIg/X34/g41QiD9gfj+DjVCIP/zalmhSIIg/+tqWaFIgiD/72pZoUiCIP01PvbwdB4g/TU+9vB0HiD9NT728HQeIP01+ye7jL4g/TH7J7uMviD9Mfsnu4y+IP51ayeQN54c/nFrJ5A3nhz+bWsnkDeeHPyG2cwkQK4g/IbZzCRAriD8gtnMJECuIP0Y5ccF6Hog/RjlxwXoeiD9IOXHBeh6IP58gY2ZKF4g/nyBjZkoXiD+fIGNmSheIP9ajSQanNYg/3lkhimsgiD/dWSGKayCIPzN2xMbiPog/M3bExuI+iD8zdsTG4j6IP4HtQ3ctSYg/gu1Ddy1JiD+D7UN3LUmIPwfX/cDKLog/Btf9wMouiD8G1/3Ayi6IPxHBxCF5yIc/EcHEIXnIhz8QQ5PcDGeIP/t2k0N1iIg//HaTQ3WIiD/8dpNDdYiIP6tHIAKlLIg/q0cgAqUsiD+rRyACpSyIP/9vMNVFEog//28w1UUSiD8AcDDVRRKIPwzud8O0BYg/DO53w7QFiD8M7nfDtAWIP/OR1q1iNYg/8pHWrWI1iD/xkdatYjWIP96LGESPYIg/3osYRI9giD/dixhEj2CIP1YvY3VwSog/Vi9jdXBKiD9WL2N1cEqIP4wATFQ2Z4g/jQBMVDZniD+NAExUNmeIPx2fZRxUo4g/G1QYXjg4iD8cVBheODiIPwgDQRbuUIg/CQNBFu5QiD8IA0EW7lCIPzoelOd2Z4g/OR6U53ZniD85HpTndmeIP4r+7vk4pog/iv7u+TimiD+L/u75OKaIP2d7cHq4nog/Z3twerieiD/3hQafMQeIP//SwHULzIc//9LAdQvMhz//0sB1C8yHP3amrwdwj4c/dqavB3CPhz92pq8HcI+HP4DD779sjoc/f8Pvv2yOhz+Aw++/bI6HP+uMkLRpeYc/6oyQtGl5hz/qjJC0aXmHP4vR3i/kf4c/jNHeL+R/hz+M0d4v5H+HP9MIuK9FiIc/1Ai4r0WIhz/TCLivRYiHP/Ht5650wIc/8e3nrnTAhz/x7eeudMCHPyBDw6Otpoc/IEPDo62mhz8hQ8OjraaHP+ZDKbkor4c/kqo3Je8lhz+Sqjcl7yWHPz7spj/uG4c/P+ymP+4bhz8/7KY/7huHP6Vwi8vC5oY/pnCLy8Lmhj+mcIvLwuaGP14ryXoVyIY/XyvJehXIhj9gK8l6FciGP3wNE1xvwoY/ew0TXG/Chj94KigXNQOHP7OsfzqY9IY/s6x/Opj0hj+0rH86mPSGP9kdgcFC2IY/2R2BwULYhj/ZHYHBQtiGP24JzmIkKYc/bQnOYiQphz9uCc5iJCmHP1EbJTwgIIc/UhslPCAghz9SGyU8ICCHP/D/x28jYIc/8P/HbyNghz/v/8dvI2CHP1Mw0kQCTIc/UzDSRAJMhz9TMNJEAkyHPz0WrAY4Z4c/PRasBjhnhz89FqwGOGeHP6lWvryDlYc/qVa+vIOVhz+qVr68g5WHP4N/G4lWnoc/gpOVU6WFhz+Ck5VTpYWHP/VsBoSeeYc/9mwGhJ55hz/3bAaEnnmHP9pKCXutr4c/2koJe62vhz/bSgl7ra+HP4TPLxMB9Ic/hc8vEwH0hz+Fzy8TAfSHP/QWb9yyw4c/9BZv3LLDhz9o7mjybpCHP3lt69jEZYc/eW3r2MRlhz95bevYxGWHP1wo1NWjGIc/XSjU1aMYhz9dKNTVoxiHP0IGIL1XHIc/QgYgvVcchz9CBiC9VxyHP28OurzPZIc/bw66vM9khz9wDrq8z2SHPyaRcPdZ0oY/JZFw91nShj8mkXD3WdKGP9ADGP3JHoc/0QMY/ckehz/RAxj9yR6HP6jF+sZsV4c/qMX6xmxXhz+rxfrGbFeHP6uvQz47P4c/qq9DPjs/hz+pr0M+Oz+HPwMlxoFPD4c/x9z5VMW3hj/H3PlUxbeGP91O/6O224Y/3E7/o7bbhj/dTv+jttuGP0I8e0oE04Y/Qjx7SgTThj9BPHtKBNOGP+ppbnoH1oY/62luegfWhj/raW56B9aGP58M0MxUHoc/nwzQzFQehz+SFp2XJjOHPzMgDAz8OIc/NCAMDPw4hz8zIAwM/DiHP6JoIGrGNYc/oWggasY1hz+iaCBqxjWHPx+yKfiUh4c/H7Ip+JSHhz8fsin4lIeHP9D9AVIPxIc/z/0BUg/Ehz/P/QFSD8SHP/9iPp+FGYg/AGM+n4UZiD8AYz6fhRmIP87CcVjDBYg/zsJxWMMFiD/OwnFYwwWIP0RFya5FvYc/Q0XJrkW9hz9ERcmuRb2HP/1SVb8kgYc//FJVvySBhz/8UlW/JIGHP+8mNu7KJIc/7gBapuwwhz/uAFqm7DCHPyTxORrm9oY/JfE5Gub2hj8k8Tka5vaGP/AhCLIEqoY/7yEIsgSqhj/wIQiyBKqGP3WpCfSwroY/dakJ9LCuhj90qQn0sK6GP6VKXocKu4Y/pUpehwq7hj+nIlhvkVKGP1HUcy//YYY/UNRzL/9hhj9R1HMv/2GGP9DY7Z3+UYY/ztjtnf5Rhj/P2O2d/lGGPxr512k1Z4Y/G/nXaTVnhj8a+ddpNWeGP80BtSzbloY/zQG1LNuWhj/MAbUs25aGPyeWQWHTrYU/JpZBYdOthT8mlkFh062FP/ODCALNKoU/84MIAs0qhT/zgwgCzSqFP7p90ZIpVIU/u33RkilUhT+5fdGSKVSFPwQUet6Ct4U/BBR63oK3hT8CFHregreFP5tUqZy6L4Y/NJ3cw2sahj81ndzDaxqGP08gfsuxE4Y/TyB+y7EThj9QIH7LsROGP5Be49q73YU/kF7j2rvdhT+OXuPau92FP+4+euTEoYU/7T565MShhT/tPnrkxKGFP2jkCrF7wYU/aeQKsXvBhT+h22G4zD+FP9tT1nwya4U/3FPWfDJrhT/bU9Z8MmuFP0DmLkjVbIU/QOYuSNVshT9A5i5I1WyFPwejO7k1mIU/BqM7uTWYhT8Hozu5NZiFP/rFKb9hdIU/+sUpv2F0hT/6xSm/YXSFP5EukTbVwoU/ki6RNtXChT+TLpE21cKFP/y5QRtk4YU//blBG2ThhT/+uUEbZOGFP2aPKBk7VYY/Zo8oGTtVhj9njygZO1WGP56K0CUYLoY/n4rQJRguhj+eitAlGC6GPyHY06GP6YU/PEnL1b4xhj89ScvVvjGGPxnj7gWsR4Y/GePuBaxHhj8Z4+4FrEeGPz/HL/P5QIY/P8cv8/lAhj8/xy/z+UCGP6Onm049W4Y/o6ebTj1bhj+jp5tOPVuGPwMaxEfVjoY/AhrER9WOhj9NYu90Ho6FP1CRT9ZIi4U/UZFP1kiLhT9RkU/WSIuFP8sONpLyboU/yw42kvJuhT/LDjaS8m6FP3UNGxt9U4U/dQ0bG31ThT91DRsbfVOFP0vS0A69PIU/S9LQDr08hT9L0tAOvTyFP5RF4Ovjn4Q/lUXg6+OfhD+VReDr45+EP5jROiM+EIQ/mNE6Iz4QhD+Y0TojPhCEP5AAK+TKSoQ/kQAr5MpKhD+RACvkykqEP7KMXgjEtoQ/soxeCMS2hD+zjF4IxLaEP7lUIgr5HoU/FIGT8tbZhD8UgZPy1tmEP0/VDbrsz4Q/T9UNuuzPhD9R1Q267M+EPwRcU9jCOIU/BFxT2MI4hT8FXFPYwjiFP4pk9ZyaYIU/iWT1nJpghT+IZPWcmmCFPwFInfqfEoU/AUid+p8ShT9Wt1s7VzGFP52YfHcWk4Q/nZh8dxaThD+emHx3FpOEPyzfuiSEgYQ/LN+6JISBhD8s37okhIGEP8V5wgJLEYQ/xXnCAksRhD/EecICSxGEPzlJhRME5oM/OEmFEwTmgz84SYUTBOaDP4Zz1yDxyoM/hnPXIPHKgz+Hc9cg8cqDP8qpHqY7i4M/yakepjuLgz/JqR6mO4uDPy9asgyA1YM/MFqyDIDVgz8wWrIMgNWDP4bQynRZmoM/h9DKdFmagz+G0Mp0WZqDPzdbovPP9IM/lU9ffo8RhT+WT19+jxGFP4KP1DV3RoU/gY/UNXdGhT+Bj9Q1d0aFPxbTgVm7RoU/FtOBWbtGhT8X04FZu0aFPy3B+USQM4U/LsH5RJAzhT8uwflEkDOFP6A9kAwvSIU/oD2QDC9IhT/JT3hDSKSFPy/kV8+NYIU/L+RXz41ghT8v5FfPjWCFP6qAg4GCgoU/qoCDgYKChT+qgIOBgoKFP+c3emsa14U/5zd6axrXhT/nN3prGteFP/QL2cZGkYU/9AvZxkaRhT/0C9nGRpGFP4R12zG9ToY/hXXbMb1Ohj+EddsxvU6GP5oLvhbnL4Y/mwu+Fucvhj+bC74W5y+GP82eAcBc7IU/zp4BwFzshT/OngHAXOyFP0nb0ljr5IU/SdvSWOvkhT9H29JY6+SFPymwxMtMUIY/Bxj5eQAWhj8HGPl5ABaGP+sP3kkTOYY/6g/eSRM5hj/qD95JEzmGP840lrzX7IU/zjSWvNfshT/PNJa81+yFP2hp267aQYY/aWnbrtpBhj9paduu2kGGP+6q9B3xWYY/7qr0HfFZhj/QmXQiMIKGP9c4gLLpoIY/2DiAsumghj/YOICy6aCGP63R884tmIY/rNHzzi2Yhj+s0fPOLZiGPxH3BnpB3YY/EvcGekHdhj8Q9wZ6Qd2GPzF5wembHoc/MXnB6Zsehz8yecHpmx6HP2llv8uy+IY/amW/y7L4hj9qZb/LsviGP2qz1CJRroY/a7PUIlGuhj9qs9QiUa6GP3xwGpf/44Y/fHAal//jhj98cBqX/+OGP/yHrv+R9oY//Ieu/5H2hj/8h67/kfaGP81VwzPv7YY/nEYKfUZzhz+cRgp9RnOHP3tTJLKJdoc/e1Mksol2hz97UySyiXaHP53PjGrhfoc/nc+MauF+hz+ez4xq4X6HP853lUF5HIc/zneVQXkchz/Od5VBeRyHPyiziyykA4c/J7OLLKQDhz/9QJA0bTaGP8fRmCqgKIY/yNGYKqAohj/I0ZgqoCiGP51UD/wWNIY/nVQP/BY0hj+eVA/8FjSGP80ilU1zXYY/zCKVTXNdhj/MIpVNc12GP++7FbMKT4Y/8LsVswpPhj/wuxWzCk+GP1A6aIwTSIU/TzpojBNIhT9QOmiME0iFP+hTN7OgTIU/6FM3s6BMhT/pUzezoEyFP/2wMJSjEYU//bAwlKMRhT/9sDCUoxGFP2Nsq+CNz4Q/ZGyr4I3PhD9jbKvgjc+EP2cjk3TwroQ//xc6UG4ThT//FzpQbhOFP1980W8iP4U/YHzRbyI/hT9gfNFvIj+FP4smdONLSoU/jCZ040tKhT+LJnTjS0qFP7XLTfJGUoU/tstN8kZShT+2y03yRlKFP3DKCNOLXYU/b8oI04tdhT9KOdwF1qiFP2NgZ/MlmYU/YmBn8yWZhT9iYGfzJZmFPwYAykDLioU/BgDKQMuKhT8FAMpAy4qFPwQ/x+n1l4U/BD/H6fWXhT8FP8fp9ZeFPy6Dc+p1X4U/L4Nz6nVfhT8vg3PqdV+FP0SQiWnoL4U/RJCJaegvhT9DkIlp6C+FP6abH3bxdIU/ppsfdvF0hT+mmx928XSFPxw/ruWEkIU/HD+u5YSQhT8cP67lhJCFPwxL2m0DG4U/DEvabQMbhT8LS9ptAxuFP9hKJJmSvYQ/scp6LVbmgz+xynotVuaDPziZ6Xgiy4M/OJnpeCLLgz84mel4IsuDPzbiSwdQr4M/NuJLB1Cvgz824ksHUK+DP7CNd6AF0IM/sI13oAXQgz+vjXegBdCDP/Y+Dyqv1IM/9z4PKq/Ugz8sM42FiuSCP8C+G02OvYI/wL4bTY69gj/AvhtNjr2CP6JOAJqV4YI/ok4AmpXhgj+iTgCaleGCPxw54df3coI/HTnh1/dygj8dOeHX93KCP2cWr3VAQII/ZxavdUBAgj9oFq91QECCP7VG+RWcc4I/tkb5FZxzgj+1RvkVnHOCP5sirKY0s4I/miKspjSzgj+bIqymNLOCP47P08cqoYI/jc/Txyqhgj+Oz9PHKqGCPxwB12eyt4I/HAHXZ7K3gj8bAddnsreCP3fnjfYquII/kBAOv5Ycgz+QEA6/lhyDP01S94nuM4M/TFL3ie4zgz9MUveJ7jODP3XEHjmDSYM/dMQeOYNJgz91xB45g0mDP8+YbOj5RoM/z5hs6PlGgz/PmGzo+UaDP+IwpVbLPoM/4zClVss+gz/4JEtmb1mCP0hBC7P/T4I/SEELs/9Pgj9IQQuz/0+CPwj3YIyGV4I/CPdgjIZXgj8I92CMhleCPwTuIu82h4I/A+4i7zaHgj8F7iLvNoeCP1PPWlGHooI/U89aUYeigj9Tz1pRh6KCP/PpfKAeuII/8+l8oB64gj/z6XygHriCP4VgkTC+noI/hGCRML6egj+EYJEwvp6CP74esnvRjII/vh6ye9GMgj+/HrJ70YyCP6XypPc1e4I/pfKk9zV7gj+m8qT3NXuCP5y1y0BbRII/oLNxFMqygT+gs3EUyrKBP4pt+brcvoE/im35uty+gT+Jbfm63L6BPyt2pqJmsoE/K3amomaygT8rdqaiZrKBP4p7jAzvn4E/inuMDO+fgT+Le4wM75+BP7gxgW+6rIE/uDGBb7qsgT+78+nr/I2CP1lveWny04I/WW95afLTgj9Xb3lp8tOCPxUJL6JcxII/FgkvolzEgj8VCS+iXMSCPzeabZCA34I/N5ptkIDfgj83mm2QgN+CP12CQ355DIM/XYJDfnkMgz9cgkN+eQyDPyCvKOfNmII/Ia8o582Ygj8hryjnzZiCP//8k+JcsYI//vyT4lyxgj/+/JPiXLGCP+rWnD+Ig4I/6dacP4iDgj/p1pw/iIOCP6obLsNNc4I/qRsuw01zgj+qGy7DTXOCP5VaeGNndYI/KShHATfAgj8oKEcBN8CCP6KN+OpcyoI/oY346lzKgj+hjfjqXMqCP0Ek84N/KoM/QCTzg38qgz9AJPODfyqDPyAxTxP2JIM/HzFPE/Ykgz8eMU8T9iSDPz4ttkUoHYM/PS22RSgdgz/xAd8q7UyDP4/T6EQIaoM/jtPoRAhqgz+O0+hECGqDP+SR28VFooM/45HbxUWigz/jkdvFRaKDP653wUfGtIM/rXfBR8a0gz+td8FHxrSDP+1Lo3Pvs4M/7Eujc++zgz/rS6Nz77ODP2ymlSVWcIQ/baaVJVZwhD9sppUlVnCEP/05BDF9mIQ//DkEMX2YhD/8OQQxfZiEP1jE7hqpq4Q/WMTuGqmrhD9YxO4aqauEP+SFyrlUvoQ/5IXKuVS+hD/jhcq5VL6EP3ygmhvsy4Q/B/46+OfzhT8H/jr45/OFPzl+sLQi2IU/OX6wtCLYhT86frC0ItiFPyDp7DSzDYY/IOnsNLMNhj8h6ew0sw2GPwy/UbWNGoY/DL9RtY0ahj8Lv1G1jRqGP2Anpwb2DoY/YCenBvYOhj83UovQOpmFP1VagOEphYU/VFqA4SmFhT9UWoDhKYWFP1EwDfz6e4U/UDAN/Pp7hT9PMA38+nuFP0ugo5yXaYU/TKCjnJdphT9MoKOcl2mFPyrLOqQBRoU/Kss6pAFGhT8pyzqkAUaFP7r+SXv0q4U/uf5Je/SrhT+5/kl79KuFP/KL1qA5hYU/9IvWoDmFhT/zi9agOYWFP4Q7Xv6bhoU/hDte/puGhT+EO17+m4aFPytelmMOiIU/K16WYw6IhT8rXpZjDoiFPwokVxLGlIU/bTddFRuahT9tN10VG5qFP1ChjfEH04U/T6GN8QfThT9PoY3xB9OFPwZ2aEYfCoY/BnZoRh8Khj8GdmhGHwqGP9W+fZ84SYY/1L59nzhJhj/Tvn2fOEmGP7D5iZgxQ4Y/sfmJmDFDhj9/Nmg2LyCGP36jmzrh+4U/faObOuH7hT99o5s64fuFP7FB7AvA6IU/sEHsC8DohT+wQewLwOiFP75Y8cdT8IU/vVjxx1PwhT++WPHHU/CFP7+CvKzTzoU/wIK8rNPOhT/Agrys086FPzQht2a3w4U/MyG3ZrfDhT8zIbdmt8OFP4jKD4Pds4U/h8oPg92zhT+Hyg+D3bOFPxiQcU0/6IU/GJBxTT/ohT8ZkHFNP+iFPyh3zOx6AIY/KHfM7HoAhj8od8zsegCGP5as7HJzIIY/6xw2kp/Hhj/rHDaSn8eGPxklx3dkoIY/GCXHd2Sghj8YJcd3ZKCGPxwOjA3ojoY/Gw6MDeiOhj8cDowN6I6GPyuMRG9DyYY/K4xEb0PJhj8rjERvQ8mGP0+Qc/8oooY/TpBz/yiihj/MkTXJgEWHP4ZLBc0pO4c/hUsFzSk7hz+GSwXNKTuHP8BJhfeNOYc/v0mF9405hz++SYX3jTmHPyONdRmSS4c/Io11GZJLhz8ijXUZkkuHP76Ohxi4hIc/vY6HGLiEhz+9jocYuISHP+Nw9y6IEog/43D3LogSiD/jcPcuiBKIPxT6lNqRLog/FPqU2pEuiD8T+pTakS6IP636RIjFNYg/rfpEiMU1iD+t+kSIxTWIP8FwAuevNog/wXAC5682iD/BcALnrzaIP+z0W+EAEYg/a1XV93S5iD9rVdX3dLmIPwheBKWyo4g/CF4EpbKjiD8JXgSlsqOIP6QhlQo74og/pCGVCjviiD+jIZUKO+KIP5puyG6D1Yg/mm7IboPViD+abshug9WIPzq9tyrs04g/Or23KuzTiD8CV4jnYTWJP26Un5qUJ4k/bpSfmpQniT9tlJ+alCeJPw7d+KcXF4k/Dd34pxcXiT8O3finFxeJP5qbcrmy9Ig/mptyubL0iD+am3K5svSIP9xCOWhG3og/3EI5aEbeiD/bQjloRt6IP63x8hreuog/rPHyGt66iD+t8fIa3rqIP+eUFWKZyYg/5pQVYpnJiD/mlBVimcmIP9H1CLQHyYg/0PUItAfJiD/Q9Qi0B8mIPzsTNVk2jYg/OxM1WTaNiD88EzVZNo2IPxwuw50ykIg/nujl4ypviD+e6OXjKm+IP3CohkS0gog/b6iGRLSCiD9uqIZEtIKIPxZbdw8XhYg/Flt3DxeFiD8VW3cPF4WIP2oPQ5Z/mog/ag9Dln+aiD9pD0OWf5qIP04sZqM53og/TSxmozneiD9x89ir24+JP5JUKJjGhok/klQomMaGiT+SVCiYxoaJP112cSRyV4k/XHZxJHJXiT9cdnEkcleJP0Jrf1Akf4k/Qmt/UCR/iT9Ca39QJH+JP0X9xvaxi4k/Rf3G9rGLiT9F/cb2sYuJPx41ZDIJvYk/HjVkMgm9iT8eNWQyCb2JP64ZznMRrIk/rRnOcxGsiT+tGc5zEayJPwtIRhgh1ok/C0hGGCHWiT8LSEYYIdaJP5Cx7N6N2Yk/kLHs3o3ZiT+QsezejdmJPxvf+6PdEYo/nyaOiemFiT+fJo6J6YWJPzDwO7xtg4k/MPA7vG2DiT8w8Du8bYOJP8f2oe5lfYk/x/ah7mV9iT/H9qHuZX2JP95JIJr0PIk/3kkgmvQ8iT/eSSCa9DyJPy37hY/YJIk/LfuFj9gkiT+foWjdm6eIPyN8T7oAUIg/InxPugBQiD8ifE+6AFCIP7UmQ2dcmYg/tCZDZ1yZiD+0JkNnXJmIP0jqGc1YzYg/SOoZzVjNiD9I6hnNWM2IP2X5QafGnYg/ZflBp8adiD9l+UGnxp2IP7wIE0LWYog/uwgTQtZiiD+8CBNC1mKIPzTuT18sZog/NO5PXyxmiD807k9fLGaIPxLN8n4eVIg/Es3yfh5UiD8SzfJ+HlSIP3bjA7mNOIg/duMDuY04iD914wO5jTiIP85UMCfSLYg/y5K9WqZyiD/Mkr1apnKIPwwPk3slSIg/Cw+TeyVIiD8MD5N7JUiIP1AWqRveT4g/URapG95PiD9RFqkb3k+IP/021Ov6S4g//TbU6/pLiD/9NtTr+kuIP5zYQ8/3Y4g/nNhDz/djiD8j+thfmZqIP/QDDejBmog/9AMN6MGaiD/0Aw3owZqIP7g6uDgfdog/uTq4OB92iD+5Org4H3aIP/VOksp6jIg/9k6SynqMiD/1TpLKeoyIPyWWot6yR4g/JJai3rJHiD8llqLeskeIP+LuPQ7/yIc/4+49Dv/Ihz/j7j0O/8iHPzfN6ivUmoc/OM3qK9Sahz84zeor1JqHPwKMveHAuIc/Aoy94cC4hz8CjL3hwLiHP8B+O+U/h4c/wH475T+Hhz+/fjvlP4eHP0vTRoJ1coc/lu6bhvWxhz+W7puG9bGHP/TTzdQm14c/9dPN1CbXhz/1083UJteHP53Sho550Yc/ndKGjnnRhz+d0oaOedGHP8PCTQI5Bog/w8JNAjkGiD/Ewk0COQaIP6uimJ0zMYg/rKKYnTMxiD/MhXS0QlSHP+7R8FetD4c/7tHwV60Phz/u0fBXrQ+HP48rl3txQoc/jyuXe3FChz+QK5d7cUKHP5xPL09EPIc/nE8vT0Q8hz+cTy9PRDyHP4h7RtzD7oY/iXtG3MPuhj+Je0bcw+6GP/HE7DwCW4Y/8cTsPAJbhj/yxOw8AluGP6lkHjHTdYY/qmQeMdN1hj+qZB4x03WGP5V00ULhg4Y/lnTRQuGDhj+WdNFC4YOGPwuXhKPmiIY/DJeEo+aIhj8Ml4Sj5oiGPwj5A5X9hIY/JOCPTP+ahj8j4I9M/5qGP5ip+r9mjoY/man6v2aOhj+Yqfq/Zo6GP27KhHJaqYY/b8qEclqphj9vyoRyWqmGP4asEpkClIY/h6wSmQKUhj+GrBKZApSGP56GMkOei4Y/noYyQ56Lhj8FlQPXuPuFP333Z/dfKIY/ffdn918ohj9992f3XyiGP5f9WJe/CIY/l/1Yl78Ihj+Y/ViXvwiGP2U5Cs0NLYY/ZjkKzQ0thj9nOQrNDS2GP0sqAaktZoY/TCoBqS1mhj9MKgGpLWaGP9R187ALg4Y/1HXzsAuDhj/VdfOwC4OGP/ZW5SZabIY/9lblJlpshj/2VuUmWmyGP9KDPQ7bloY/1IM9DtuWhj/Ugz0O25aGP2CmwNeSc4Y/YKbA15Jzhj9hpsDXknOGP5rstXp6oIY/M1KQIhEnhz8zUpAiESeHP5+77idhSYc/oLvuJ2FJhz+gu+4nYUmHP07i7mfXN4c/T+LuZ9c3hz9P4u5n1zeHP38CEgIvKoc/gAISAi8qhz+AAhICLyqHP9c7L2eG64Y/1zsvZ4brhj8OWWMhr/+HP2qWRMvV0Yc/apZEy9XRhz9plkTL1dGHPwk7TVKy4oc/CTtNUrLihz8KO01SsuKHPzWDPeD11Yc/NoM94PXVhz83gz3g9dWHP3JBz/Siooc/ckHP9KKihz9yQc/0oqKHP6EStK7904c/oRK0rv3Thz+iErSu/dOHP3UTctV48Ic/dxNy1Xjwhz93E3LVePCHP7fDSRWbgYc/t8NJFZuBhz+4w0kVm4GHPw7C/vL6pYc/D8L+8vqlhz8Pwv7y+qWHP5zvJkY8xYc/qLl79Im0iD+puXv0ibSIP61Otl+S3og/rE62X5LeiD+tTrZfkt6IP9x1lsajpog/3XWWxqOmiD/fdZbGo6aIPxNyWuX4lIg/E3Ja5fiUiD8Tclrl+JSIPzNbXLBc2og/M1tcsFzaiD/NdqQH3p6IP+UgbkIMvIg/5SBuQgy8iD/mIG5CDLyIP2jBG7luyog/aMEbuW7KiD9owRu5bsqIPyXFI3Ckuog/JcUjcKS6iD8lxSNwpLqIPxZJtmCl1Yg/Fkm2YKXViD8XSbZgpdWIP3nzWInKo4g/efNYicqjiD9581iJyqOIP394T7Phd4g/gHhPs+F3iD+AeE+z4XeIP+KV3NXHTIg/4pXc1cdMiD/hldzVx0yIPy2Aa6IijYg/LIBroiKNiD8ugGuiIo2IP3UlcnSqn4g/DfuSu25siD8N+5K7bmyIP4Q5lFOHV4g/hTmUU4dXiD+FOZRTh1eIP1Pa5GMsnog/U9rkYyyeiD9T2uRjLJ6IP0iQqf67PIg/R5Cp/rs8iD9IkKn+uzyIP4noI0zqE4g/iegjTOoTiD9ItlbnlCqIP3QLPkBZOYg/dAs+QFk5iD90Cz5AWTmIP8CksE1IQ4g/wKSwTUhDiD+/pLBNSEOIPzICo6r4YYg/MgKjqvhhiD8xAqOq+GGIP7cXHri3i4g/txceuLeLiD+3Fx64t4uIP5DoVsUNoog/kehWxQ2iiD+P6FbFDaKIP9dAUiBGm4g/2EBSIEabiD/XQFIgRpuIP+bN/ubvlog/5s3+5u+WiD/mzf7m75aIPwbqpuZmmYg/Beqm5maZiD8G6qbmZpmIP44841xOYIg/3H3jx2sViD/ZfePHaxWIP8Idi9noGog/wR2L2egaiD/AHYvZ6BqIP6z9zBk8LIg/rP3MGTwsiD+t/cwZPCyIPzrEpf/sVYg/OMSl/+xViD85xKX/7FWIP/WAHljJW4g/9YAeWMlbiD9qYKbhh6SIP0oGgIRBa4g/SgaAhEFriD9KBoCEQWuIPym+ZiRwhog/J75mJHCGiD8ovmYkcIaIP1A9dXBeaIg/UD11cF5oiD9QPXVwXmiIP1kQ33y7SYg/WRDffLtJiD9YEN98u0mIP540td/tkYg/nDS13+2RiD+cNLXf7ZGIP+YOezh0bIg/5g57OHRsiD/mDns4dGyIP/9EJH+QVog/AEUkf5BWiD8ARSR/kFaIP9X1khGfPIg/1vWSEZ88iD/V9ZIRnzyIP47nGPiEQIg/LxwIYCEmiD8vHAhgISaIP9QMraDe7Yc/0wytoN7thz/UDK2g3u2HP1j0xTvlBIg/V/TFO+UEiD9W9MU75QSIPwvW4N6wO4g/C9bg3rA7iD8L1uDesDuIPzvbRV8yHYg/O9tFXzIdiD9GoZ9mLoiHP18cmoJyaoc/XRyagnJqhz9eHJqCcmqHP//jJtyaY4c//+Mm3Jpjhz/+4ybcmmOHP9wL8yp0boc/3AvzKnRuhz/bC/MqdG6HPyenX71VbIc/J6dfvVVshz8mp1+9VWyHP9SvBbSVT4c/1K8FtJVPhz/UrwW0lU+HP0dqDevwLoc/R2oN6/Auhz9Gag3r8C6HP8c+RlS9BIc/yD5GVL0Ehz/IPkZUvQSHP6pCM6I2GIc/q0IzojYYhz+qQjOiNhiHP0jfz+ZcIYc/5NiB7HQ9hz/k2IHsdD2HP1hNMPQtMYc/V00w9C0xhz9XTTD0LTGHP0PFMRVGSIc/QsUxFUZIhz9CxTEVRkiHP2Lr30V1UIc/YuvfRXVQhz9i699FdVCHPyNEe7nMZoc/IkR7ucxmhz8M1v3UKU+HP4FCD2QSWIc/gUIPZBJYhz+AQg9kEliHP3+sefOOQoc/f6x5845Chz+ArHnzjkKHPxwUeD48LYc/GxR4Pjwthz8bFHg+PC2HP85fX3T0JIc/z19fdPQkhz/OX1909CSHP+2+XjLd14Y/7L5eMt3Xhj/svl4y3deGPylFufnqwYY/KEW5+erBhj8oRbn56sGGP+1xxNzorYY/63HE3Oithj/qccTc6K2GP2OHUJsK3IY/YodQmwrchj9ih1CbCtyGP8hOFRhF5IY/m2k0NX2Fhj+baTQ1fYWGP3k80ZVXaYY/ejzRlVdphj96PNGVV2mGP6NavdMpTIY/pFq90ylMhj+jWr3TKUyGP9otHkUeOoY/2S0eRR46hj/YLR5FHjqGP3KFoevmVYY/cYWh6+ZVhj/5oRb6lyCGP3FnTnTNEoY/cWdOdM0Shj9xZ050zRKGPwtz2dr774U/C3PZ2vvvhT8Kc9na+++FP3oA+Joh3oU/egD4miHehT96APiaId6FP/whRth+w4U//CFG2H7DhT/9IUbYfsOFP1DF6TgMv4U/UMXpOAy/hT9Qxek4DL+FP4AClV0FtIU/gAKVXQW0hT+AApVdBbSFP/5ceQ+1oYU//lx5D7WhhT/+XHkPtaGFP9K3LCxSxoU/07csLFLGhT/StywsUsaFP0DYqiZW9YU/WozUIL7rhT9ZjNQgvuuFP0Z6pGbd0IU/R3qkZt3QhT9HeqRm3dCFPwSs29lZzYU/BKzb2VnNhT8ErNvZWc2FPxbaBNaS1IU/FtoE1pLUhT8X2gTWktSFP6k+uVY9+IU/qj65Vj34hT9rpRWPLuOFPzG6+55Y64U/Mbr7nljrhT8xuvueWOuFP/xWQu0zM4Y//FZC7TMzhj/8VkLtMzOGPyTSUyp4DoY/JNJTKngOhj8k0lMqeA6GP7ij28DgBIY/uaPbwOAEhj+5o9vA4ASGP87YGjJIBIY/z9gaMkgEhj/O2BoySASGPynVFyarA4Y/KNUXJqsDhj8o1RcmqwOGP2zBbxzt6YU/bMFvHO3phT9swW8c7emFP1sAH/J07YU/WwAf8nTthT9bAB/ydO2FP2isBujV44U/MdWPBBkEhj8x1Y8EGQSGP0gNgV0RHYY/Rw2BXREdhj9IDYFdER2GP6NrpUsvAoY/omulSy8Chj+ja6VLLwKGPw8IysJz+oU/EAjKwnP6hT8PCMrCc/qFPzFnlt2KNoY/MmeW3Yo2hj+vv8WDXz2GP1e1UebcYYY/WLVR5txhhj9ZtVHm3GGGPxFGN9VkaIY/EUY31WRohj8SRjfVZGiGPySixvJcR4Y/JKLG8lxHhj8kosbyXEeGP1GLTZtIT4Y/UotNm0hPhj9Ti02bSE+GP1mI1Oru/YU/WojU6u79hT9aiNTq7v2FP3eUaUSh8oU/dpRpRKHyhT93lGlEofKFP8qaElcGCYY/ypoSVwYJhj/LmhJXBgmGP6rUa66q84U/qdRrrqrzhT+p1GuuqvOFPwKAZiAdAIY/6/jN2KPqhT/s+M3Yo+qFPx059A2964U/Hjn0Db3rhT8eOfQNveuFPyFugOxn7YU/IW6A7GfthT8hboDsZ+2FP+iDD1bdCIY/6IMPVt0Ihj/pgw9W3QiGPzBLRtugEoY/MUtG26AShj9S6LnqcOSFP1xcz+bMFIY/XVzP5swUhj9dXM/mzBSGP8e9LoeVBIY/yL0uh5UEhj/HvS6HlQSGPy0IVTkW/oU/LQhVORb+hT8sCFU5Fv6FPyO1lzM4/IU/I7WXMzj8hT8ktZczOPyFP4jORkKFGYY/h85GQoUZhj+IzkZChRmGPzoTd1tW54U/OhN3W1bnhT86E3dbVueFP4kgEmzX04U/iiASbNfThT+JIBJs19OFP+MxooaYyIU/4zGihpjIhT/kMaKGmMiFP6gDcrSr14U/Vw+cgllhhT9XD5yCWWGFPz8JQJsiQYU/PwlAmyJBhT8/CUCbIkGFP8kVtpa9PYU/yRW2lr09hT/JFbaWvT2FP46EJVLoNoU/j4QlUug2hT+OhCVS6DaFP+WOPXPHNoU/5o49c8c2hT8SW1C+npiFP+09Gzh7g4U/7D0bOHuDhT/tPRs4e4OFP4bYveQ3hIU/hti95DeEhT+F2L3kN4SFP21SHdQni4U/bVId1CeLhT9tUh3UJ4uFP2KQDKdjqIU/YpAMp2OohT9ikAynY6iFPwpLupHZuoU/CUu6kdm6hT8HS7qR2bqFP+ma3K/RvIU/6Zrcr9G8hT/pmtyv0byFP+1qnsG83YU/7GqewbzdhT/sap7BvN2FP9gFDa0a34U/2AUNrRrfhT/XBQ2tGt+FP3sT+TS91oU/C4JXPPgFhj8Lglc8+AWGP9yZY+ti+oU/3Zlj62L6hT/cmWPrYvqFP1puNjanAoY/WW42NqcChj9YbjY2pwKGPwbRumveBIY/BdG6a94Ehj8F0bpr3gSGP+ThvhC28IU/4+G+ELbwhT/ZI1MY/xeGP81xhfsBG4Y/zXGF+wEbhj/NcYX7ARuGPwvmO6FMGoY/DOY7oUwahj8L5juhTBqGP+MHnzusKYY/4gefO6wphj/iB587rCmGP0ZmRNDsH4Y/RmZE0Owfhj9GZkTQ7B+GP8fbJIHpLoY/xtskgekuhj/G2ySB6S6GP+zOnVSgI4Y/7M6dVKAjhj/szp1UoCOGP/djLmXaPIY/+GMuZdo8hj/3Yy5l2jyGP8IYnIx8NYY/wxicjHw1hj/CGJyMfDWGPwJsjKgXQoY/lmhRelrvhT+VaFF6Wu+FP1Kly9lV/4U/UqXL2VX/hT9TpcvZVf+FP2V+a2bBD4Y/ZX5rZsEPhj9mfmtmwQ+GP5SlhYr6G4Y/lKWFivobhj+UpYWK+huGP844KWN9JYY/zjgpY30lhj9ppE/zZiiGP/iJigz7LIY/+YmKDPsshj/4iYoM+yyGP21q32SSGYY/bWrfZJIZhj9tat9kkhmGP1IVhUo/L4Y/UhWFSj8vhj9SFYVKPy+GP/ZSSquqMoY/9lJKq6oyhj/2UkqrqjKGP4EiA+PNGoY/gCID480ahj+BIgPjzRqGP26+Vy96CYY/br5XL3oJhj9uvlcvegmGP+RkB34F94U/42QHfgX3hT/jZAd+BfeFPydpQ40k7IU/J2lDjSTshT8maUONJOyFP0FYsFiX+YU/5VXzXx0Dhj/mVfNfHQOGP6t5VY5I/IU/qXlVjkj8hT+peVWOSPyFPyKGDW1+5IU/IoYNbX7khT8ihg1tfuSFP7KstlJ+BYY/sqy2Un4Fhj+zrLZSfgWGP5DueE5W9YU/j+54Tlb1hT+fbR4b7+6FP7KT5hSD64U/spPmFIPrhT+yk+YUg+uFP5E0FBnd54U/kTQUGd3nhT+QNBQZ3eeFP787cNcBCIY/vTtw1wEIhj+8O3DXAQiGPwyK6KOmDYY/C4roo6YNhj8Kiuijpg2GP5dsujhyEYY/lmy6OHIRhj+UbLo4chGGP8TD4wnL/oU/w8PjCcv+hT/Cw+MJy/6FPxo4fbclBoY/Gjh9tyUGhj8ZOH23JQaGP9vuES3x/IU/2u4RLfH8hT/a7hEt8fyFPwCzqIBS4IU/FIM/gkfbhT8Ugz+CR9uFPxkT2jbv8YU/GRPaNu/xhT8ZE9o27/GFP1F+pC5N0IU/UX6kLk3QhT9QfqQuTdCFP585Nlx/zYU/nzk2XH/NhT+eOTZcf82FP5OeHCJ2t4U/k54cIna3hT+g4cKWs42FPyM3/tg8i4U/Izf+2DyLhT8jN/7YPIuFP1Vbf68AcoU/VFt/rwByhT9VW3+vAHKFP+ExHAuPb4U/4jEcC49vhT/iMRwLj2+FPweefchmfoU/Bp59yGZ+hT8Fnn3IZn6FP5j8y0jzioU/mPzLSPOKhT+Y/MtI84qFP9WlJcwdjIU/1qUlzB2MhT/XpSXMHYyFP396ROihdIU/gHpE6KF0hT+AekTooXSFP4mkzSV1hIU/iqTNJXWEhT+LpM0ldYSFPyEImKnfeoU/ogd1yD1mhT+jB3XIPWaFP2sCKldCbIU/bAIqV0JshT9sAipXQmyFP+Awb3/8Q4U/4jBvf/xDhT/iMG9//EOFP6C4k9igVYU/oLiT2KBVhT+huJPYoFWFP1rZ/AfxaoU/XNn8B/FqhT8NTMLTqZeFP8bTgsI3rIU/x9OCwjeshT/I04LCN6yFP6eDEcl4nIU/qIMRyXichT+pgxHJeJyFP+KAFJ+Ss4U/44AUn5KzhT/kgBSfkrOFP4NtLVkVmoU/gm0tWRWahT+DbS1ZFZqFP4Wyh6xSi4U/hrKHrFKLhT+HsoesUouFP1GILC7ZaoU/UYgsLtlqhT9SiCwu2WqFP6OGevM7loU/o4Z68zuWhT+jhnrzO5aFP/0sD84VsoU//iwPzhWyhT/+LA/OFbKFP1wgF81E3oU/ifU4qOKThT+J9Tio4pOFP5Vdpeahm4U/l12l5qGbhT+XXaXmoZuFP5oZFXvYiIU/mhkVe9iIhT+aGRV72IiFPwm0RjXJi4U/CbRGNcmLhT8KtEY1yYuFP+Gu0OwInIU/4q7Q7AichT90NQXj/ZCFPyPXW8iOuIU/IddbyI64hT8h11vIjriFPwzPMmv5wIU/DM8ya/nAhT8LzzJr+cCFPxv4Yjcuz4U/G/hiNy7PhT8b+GI3Ls+FP9u2hTgmAIY/3LaFOCYAhj/ctoU4JgCGP5I2x46Ar4U/kTbHjoCvhT+QNseOgK+FP2gugj67ooU/Zy6CPruihT9mLoI+u6KFPyqHkvgxpIU/KoeS+DGkhT8rh5L4MaSFPyVwIaxsqIU/I3AhrGyohT8icCGsbKiFP/ySyWARroU/Hr75ZVR7hT8cvvllVHuFPzWr+9PrYIU/NKv70+tghT8zq/vT62CFP8Yo4YQrZIU/xSjhhCtkhT/DKOGEK2SFPxrMLKi6moU/GswsqLqahT8azCyoupqFPwaQL3+bb4U/BpAvf5tvhT/N/mq38pSFPxAGRm36fYU/DwZGbfp9hT8OBkZt+n2FP/xazI92lIU/+1rMj3aUhT/8WsyPdpSFP5irDXe7pYU/mKsNd7ulhT+Yqw13u6WFP+02Kor6mIU/7DYqivqYhT/sNiqK+piFP8awqDGod4U/xLCoMah3hT/EsKgxqHeFPwXqnFy2ioU/BOqcXLaKhT8D6pxctoqFP1VB+UXboIU/VEH5RdughT9UQflF26CFP0nsqqHcpoU/R+yqodymhT9H7Kqh3KaFP2OODGRak4U/F4LKEcPQhT8WgsoRw9CFP/XPu8mC0YU/9M+7yYLRhT/0z7vJgtGFP2yyOjb/xoU/a7I6Nv/GhT9rsjo2/8aFPyfx61wJy4U/JvHrXAnLhT8m8etcCcuFP2qJO/4x0IU/aYk7/jHQhT9K0DlqBamFP6rix0wQn4U/qeLHTBCfhT+p4sdMEJ+FP2QCQJSZkoU/ZAJAlJmShT9jAkCUmZKFP/wStoIacoU//BK2ghpyhT/7EraCGnKFP2VzBOpkf4U/ZXME6mR/hT9lcwTqZH+FP8f3Y6W3TIU/x/djpbdMhT/H92Olt0yFPy6oaqDmQoU/L6hqoOZChT8uqGqg5kKFP8GPESo8R4U/wI8RKjxHhT/AjxEqPEeFPyGrfxzmQYU/IKt/HOZBhT8hq38c5kGFP/fRprKRPoU/WdsOAmHwhD9Z2w4CYfCEP3BEmiZt8IQ/b0SaJm3whD9vRJombfCEPzniZSKF8IQ/OeJlIoXwhD844mUihfCEP3er/n4Y64Q/d6v+fhjrhD93q/5+GOuEP+iioqiy1oQ/6aKiqLLWhD+nWssC3GSEP/fJl3WsaIQ/98mXdaxohD/4yZd1rGiEP5VSfF2hcYQ/lFJ8XaFxhD+UUnxdoXGEP6bO4NMPeIQ/ps7g0w94hD+nzuDTD3iEP4muimGbZYQ/ia6KYZtlhD+Jrophm2WEP1MEn/I8TYQ/UwSf8jxNhD9UBJ/yPE2EP1oBmiqrLYQ/WgGaKqsthD9ZAZoqqy2EPw1CfFGbG4Q/DkJ8UZsbhD8OQnxRmxuEPxtTugCBEYQ/G1O6AIERhD8bU7oAgRGEP89Ltct9HIQ/Q22OaZ7ngz9DbY5pnueDP8IpkHQC4YM/wimQdALhgz/CKZB0AuGDP2Kjwm7O2oM/YaPCbs7agz9io8JuztqDPyWOHPSd04M/JY4c9J3Tgz8kjhz0ndODP5ylHjP6xIM/nKUeM/rEgz8jkaM1id6DP1kXl0hA5IM/WBeXSEDkgz9YF5dIQOSDP1TUQE+d84M/U9RAT53zgz9T1EBPnfODP/1PJVft6oM//U8lV+3qgz/9TyVX7eqDP+mU1AeF8oM/6JTUB4Xygz/plNQHhfKDP8lTTxyH+IM/yVNPHIf4gz/KU08ch/iDPxIV5vKf5YM/EhXm8p/lgz8SFebyn+WDP6pcR3dR6YM/qlxHd1Hpgz+qXEd3UemDP9Qu6JmR64M/1C7omZHrgz/ULuiZkeuDP0NGWJLU5YM/cU3Pw/Oqgz9wTc/D86qDP5zXelVhpIM/nNd6VWGkgz+c13pVYaSDPxazvfPSqIM/FrO989Kogz8Ws73z0qiDP7mpbrBvp4M/ualusG+ngz+5qW6wb6eDPxcgnzvynoM/FyCfO/Kegz+paLnJB6+DP7dz1TpKpIM/tnPVOkqkgz+2c9U6SqSDP2n3wY9EnYM/affBj0Sdgz9q98GPRJ2DPwWGxavpl4M/BYbFq+mXgz8FhsWr6ZeDP+heWBB8joM/6F5YEHyOgz/oXlgQfI6DP5TqX2urZIM/lepfa6tkgz+U6l9rq2SDP19dqWBvW4M/Xl2pYG9bgz9eXalgb1uDP4Jm91hNXYM/gmb3WE1dgz+DZvdYTV2DP49ouhsdX4M/j2i6Gx1fgz+PaLobHV+DPy/zqKNlZoM/8uhsK4R1gz/y6GwrhHWDP4OpUhNwcIM/gqlSE3Bwgz+CqVITcHCDP6TQ0DSHdoM/pdDQNId2gz+k0NA0h3aDPyLE4J21eoM/IsTgnbV6gz8ixOCdtXqDP+mGJ0AleIM/6YYnQCV4gz9hGUHshVGDPyXtm8BLT4M/Ju2bwEtPgz8m7ZvAS0+DPwp6e6O1VIM/Cnp7o7VUgz8LenujtVSDPyAgWVmhToM/ICBZWaFOgz8gIFlZoU6DPzvk82EVUoM/O+TzYRVSgz875PNhFVKDP69Yy1cqW4M/sFjLVypbgz+wWMtXKluDP/TL8K3EWIM/9cvwrcRYgz/1y/CtxFiDPxdfBkv5UoM/Fl8GS/lSgz8XXwZL+VKDP+/y7C5AVIM/7vLsLkBUgz/v8uwuQFSDP+4hMbiCVoM/G2CvuleKgz8bYK+6V4qDPx/guvAYjYM/H+C68BiNgz8f4LrwGI2DP79xb/pMhoM/vnFv+kyGgz+/cW/6TIaDP8OsVfkPiIM/wqxV+Q+Igz/DrFX5D4iDPxN9vVz1g4M/En29XPWDgz/xiua6iYuDP3sWdn+WjYM/exZ2f5aNgz97FnZ/lo2DP9eI3Zcjj4M/2IjdlyOPgz/YiN2XI4+DP0+EaLTzjYM/T4RotPONgz9QhGi0842DPy5tR3Byi4M/L21HcHKLgz8ubUdwcouDP/Y8r/CscoM/9jyv8Kxygz/2PK/wrHKDP32ZwJagcYM/fpnAlqBxgz99mcCWoHGDPxT0jiYqcIM/FvSOJipwgz8W9I4mKnCDP5oiL577cIM/miIvnvtwgz+aIi+e+3CDP+6Syt/NdIM/Ob3VLoE5gz85vdUugTmDP8r4DK2PNIM/y/gMrY80gz/M+AytjzSDPy4BhQMeNIM/LgGFAx40gz8uAYUDHjSDPzAigRioNYM/MCKBGKg1gz8wIoEYqDWDP1G/CZBMM4M/UL8JkEwzgz/AzbhNZSuDP5+UQDTgK4M/oJRANOArgz+glEA04CuDPylhkaaFLYM/KWGRpoUtgz8pYZGmhS2DP9dj54DlLIM/12PngOUsgz/WY+eA5SyDP2pVqxf7K4M/bFWrF/srgz9sVasX+yuDP5OSO/l3O4M/kpI7+Xc7gz+Tkjv5dzuDPwA7rHrlOoM//zqseuU6gz//Oqx65TqDP4F9z7yjO4M/gX3PvKM7gz+Bfc+8ozuDPzQFxksCO4M/MwXGSwI7gz80BcZLAjuDP63xt7rxOoM/qAIfJHoegz+nAh8keh6DP2lVYXSXHYM/aVVhdJcdgz9qVWF0lx2DPzV7Q/8LHoM/NHtD/wsegz81e0P/Cx6DPxjgGW2fHoM/GeAZbZ8egz8Y4Bltnx6DP8zJtdgdH4M/zMm12B0fgz8cmdyPLg+DPxx2N5kDD4M/G3Y3mQMPgz8bdjeZAw+DP1sjjB+uDoM/XCOMH64Ogz9bI4wfrg6DP0YcPyEmDoM/Rhw/ISYOgz9GHD8hJg6DP5Etk6rXDYM/kS2TqtcNgz+SLZOq1w2DP9koh/nm+II/2iiH+eb4gj/bKIf55viCP0qU1Tu0+II/SpTVO7T4gj9KlNU7tPiCP4h/8i7P+II/iH/yLs/4gj+Jf/Iuz/iCP5D8GOeH+YI/kPwY54f5gj+Q/Bjnh/mCP1ih6KS6+II/fztki8zFgj9/O2SLzMWCP7AcrcM2xoI/sRytwzbGgj+yHK3DNsaCP5QQt6texII/lBC3q17Egj+VELerXsSCP9SuvtU2xII/1a6+1TbEgj/Vrr7VNsSCP0pZvi9gxoI/S1m+L2DGgj+SXlKvgcGCP4W4nMCxwII/hbicwLHAgj+GuJzAscCCPwnM6iuXxII/CczqK5fEgj8IzOorl8SCPyc+R00+w4I/Jz5HTT7Dgj8pPkdNPsOCPxuRb09ZxII/HJFvT1nEgj8dkW9PWcSCP7feutzu8YI/t9663O7xgj+33rrc7vGCPwmAPIpM8oI/CYA8ikzygj8KgDyKTPKCP+nMugkc7oI/68y6CRzugj/rzLoJHO6CP6eu5zJG7YI/qK7nMkbtgj+orucyRu2CPxhBgwgO7YI/soja9lL3gj+ziNr2UveCP7bMAkee9oI/tswCR572gj+3zAJHnvaCP9iqr8Np94I/2aqvw2n3gj/aqq/DafeCP8cooXrV+oI/ySihetX6gj/JKKF61fqCPznm8geE9II/O+byB4T0gj+XeNozuhODP4M5005OD4M/hDnTTk4Pgz+FOdNOTg+DP7d4BZJLD4M/uHgFkksPgz+5eAWSSw+DP9V3sGJfDYM/13ewYl8Ngz/Xd7BiXw2DP5XUjG7fD4M/ltSMbt8Pgz+W1Ixu3w+DP50Y4tw6OYM/nhji3Do5gz+fGOLcOjmDP1rhz7x1N4M/W+HPvHU3gz9b4c+8dTeDP+1Gna6qOoM/7Uadrqo6gz/uRp2uqjqDP7rStZibO4M/utK1mJs7gz+70rWYmzuDP2K9wVztOoM/+2eB1/tCgz/8Z4HX+0KDP4Qtak4JSIM/hi1qTglIgz+FLWpOCUiDP4h1ESERSYM/h3URIRFJgz+JdREhEUmDP4UG/YlLRoM/hQb9iUtGgz+FBv2JS0aDPyra3vcuR4M/Kdre9y5Hgz/l8LroXzODP78rOSiBNIM/vys5KIE0gz/AKzkogTSDP3MMIsk/M4M/cwwiyT8zgz9zDCLJPzODP0xj/oIRM4M/S2P+ghEzgz9MY/6CETODP9yC/cFmOYM/24L9wWY5gz/bgv3BZjmDP5U3dLpZPIM/lDd0ulk8gz+UN3S6WTyDP3L9n+9pOYM/cP2f72k5gz9w/Z/vaTmDP3IqoPedPIM/cSqg9508gz9xKqD3nTyDP2d2f547O4M/ZnZ/njs7gz9ldn+eOzuDP53wT3rjQIM/zF7Obkcrgz/KXs5uRyuDPwaFbt4qLIM/BYVu3iosgz8EhW7eKiyDP93XRtaBKYM/3NdG1oEpgz/b10bWgSmDP2bnV9PNKYM/ZedX080pgz9l51fTzSmDP31TztyWJIM/fVPO3JYkgz9C3J1b4SmDP7CGbBMzMIM/sIZsEzMwgz+uhmwTMzCDP9xYqpl7M4M/21iqmXszgz/aWKqZezODP8HpojxRNYM/wOmiPFE1gz+/6aI8UTWDPwwa6dNAOYM/Cxrp00A5gz8KGunTQDmDP277yxNtXIM/bvvLE21cgz9s+8sTbVyDPwixnEf3XYM/B7GcR/ddgz8HsZxH912DP4yE0skKXoM/i4TSyQpegz+LhNLJCl6DP24IvS/4W4M/bQi9L/hbgz9uCL0v+FuDP6pIX6mHWIM/EVo6PmZegz8RWjo+Zl6DP5/PHPs3XIM/ns8c+zdcgz+ezxz7N1yDPzFl245UWIM/MWXbjlRYgz8wZduOVFiDP8ZyaNSuWIM/xXJo1K5Ygz/EcmjUrliDP5QoxTn3WIM/lCjFOfdYgz+Z4shQlUaDP6jsKR7PPoM/qewpHs8+gz+o7Ckezz6DP655Lu8tO4M/rXku7y07gz+teS7vLTuDP49nGGzFOIM/j2cYbMU4gz+OZxhsxTiDP4PmS1+VOIM/g+ZLX5U4gz+C5ktflTiDPz1EM8WgaYM/PUQzxaBpgz88RDPFoGmDP83QyEqMaIM/ztDISoxogz/N0MhKjGiDP+QrL5xnZoM/5CsvnGdmgz/kKy+cZ2aDP026B6EOZIM/TroHoQ5kgz9PugehDmSDP4nPTiIyXYM/6FKD9FKSgz/pUoP0UpKDP0dVdUHBkoM/R1V1QcGSgz9HVXVBwZKDP3E/ykzDjYM/cj/KTMONgz9yP8pMw42DPyITK2d+jYM/IRMrZ36Ngz8iEytnfo2DP1t8jmzLjoM/W3yObMuOgz+Ngtn5EDSDP30mUKUtOIM/fSZQpS04gz99JlClLTiDP+Am7tSxN4M/3ybu1LE3gz/fJu7UsTeDP6CfKYeQOIM/n58ph5A4gz+gnymHkDiDP3BhRXs3N4M/cGFFezc3gz9xYUV7NzeDP0HONopQbYM/Qc42ilBtgz9CzjaKUG2DPwoFtsR2bIM/CwW2xHZsgz8KBbbEdmyDP4955h5bcIM/kHnmHltwgz+QeeYeW3CDP2koaFd0b4M/aihoV3Rvgz9qKGhXdG+DPy0tZ0/zcoM/Zg1Y/3ptgz9nDVj/em2DP2JVu/OkboM/Y1W786Rugz9jVbvzpG6DP/ejoTCXb4M/96OhMJdvgz/3o6Ewl2+DPzYOCq4wboM/Ng4KrjBugz82DgquMG6DP/IsnGoqaoM/8iycaipqgz9uFHeU126DP3H7JinTb4M/cvsmKdNvgz9z+yYp02+DP3dsoKtRcIM/d2ygq1Fwgz93bKCrUXCDPzKJK7GXcoM/MokrsZdygz8xiSuxl3KDPzGsZUkpcYM/MaxlSSlxgz8zrGVJKXGDP+S/rAQNToM/5b+sBA1Ogz/kv6wEDU6DP6mnfMzCT4M/qqd8zMJPgz+qp3zMwk+DP/WRD76dT4M/9pEPvp1Pgz/1kQ++nU+DP1QpctFzT4M/VSly0XNPgz9VKXLRc0+DP/6wZcaCToM/4il8m48rgz/iKXybjyuDPzPvBAp7LoM/NO8ECnsugz8z7wQKey6DP6HJ9rx3K4M/osn2vHcrgz+iyfa8dyuDPxGGLGDbK4M/EYYsYNsrgz8Rhixg2yuDP/Ju3oWrK4M/8m7ehasrgz/EGOXda+mCP4J/SB0Y6oI/g39IHRjqgj+Ef0gdGOqCPz4tpZQH6YI/Pi2llAfpgj8/LaWUB+mCP+BTDeWw6YI/4FMN5bDpgj/gUw3lsOmCPywK0SJ+6oI/KwrRIn7qgj8rCtEifuqCP/UjfBXgkoI/9SN8FeCSgj/1I3wV4JKCPzaYwpWfkoI/NpjClZ+Sgj82mMKVn5KCP9cUldxMkoI/1xSV3EySgj/YFJXcTJKCP5ZkkXN/koI/lmSRc3+Sgj+XZJFzf5KCP3/q6vhokoI/Zx1i5sVDgj9oHWLmxUOCP0eTUUneQ4I/SJNRSd5Dgj9Hk1FJ3kOCPxne2MEHRII/Gt7YwQdEgj8Z3tjBB0SCPyYUnkdAQoI/JxSeR0BCgj8mFJ5HQEKCP3bzYPMAQ4I/dfNg8wBDgj+V1lUtYGSCP71jJZkIZYI/vGMlmQhlgj+8YyWZCGWCPz9BRraOYoI/P0FGto5igj9AQUa2jmKCP2N0G00OY4I/ZHQbTQ5jgj9kdBtNDmOCP+xJ00mKYYI/60nTSYphgj/sSdNJimGCP3q+cSYlgYI/eb5xJiWBgj97vnEmJYGCP+PPdvLUgoI/5M928tSCgj/jz3by1IKCP9a/4YBBhYI/1r/hgEGFgj/Wv+GAQYWCPxJX3Y2ohYI/EVfdjaiFgj8RV92NqIWCP1LK1GxIhII/Cr2OiFWFgj8JvY6IVYWCP+hw+/MbgYI/6HD78xuBgj/ocPvzG4GCP+BXG8yThII/4FcbzJOEgj/gVxvMk4SCP6wV2RKvh4I/rBXZEq+Hgj+rFdkSr4eCP4f/lxaYioI/h/+XFpiKgj9Bf9bU04qCP4wmqg8DjII/jSaqDwOMgj+MJqoPA4yCP+rTNW8Kh4I/6dM1bwqHgj/p0zVvCoeCP0GJ1YPDiII/QInVg8OIgj9AidWDw4iCP/Q9g19fhoI/9D2DX1+Ggj/0PYNfX4aCPzndzIY3SII/Od3MhjdIgj843cyGN0iCP8nm6S7RQoI/yubpLtFCgj/J5uku0UKCP8JFCKhZP4I/wkUIqFk/gj/CRQioWT+CP948/mbnQYI/3jz+ZudBgj/ePP5m50GCP/sCqn9/R4I/vIYwfHpJgj+7hjB8ekmCP1fIIA/HT4I/V8ggD8dPgj9YyCAPx0+CP7sjRoUSToI/uiNGhRJOgj+6I0aFEk6CP6DGCBqlUII/oMYIGqVQgj+fxggapVCCP1G8bhpfVoI/UbxuGl9Wgj/Yrgi08hqCPz0ftyZZHII/PR+3Jlkcgj89H7cmWRyCP9t5E0LQFYI/23kTQtAVgj/beRNC0BWCP5Lp8kVEFII/kunyRUQUgj+S6fJFRBSCP1QKQrKOG4I/VQpCso4bgj9VCkKyjhuCP98RlL0sK4I/4BGUvSwrgj/fEZS9LCuCPz5aqhv/I4I/PlqqG/8jgj8/Wqob/yOCP+Djl+kGJII/3+OX6QYkgj/f45fpBiSCP8X2Weu3III/xfZZ67cggj/E9lnrtyCCP4c2ZytSFoI/zHrjwuwvgj/MeuPC7C+CP8bXOH29JoI/xtc4fb0mgj/G1zh9vSaCP6hdor5gJYI/qF2ivmAlgj+pXaK+YCWCPwCo6nUVKoI/AKjqdRUqgj//p+p1FSqCPzdfcFgYJoI/N19wWBgmgj8qIVU8Hy2CP91zCoWbLoI/3XMKhZsugj/ccwqFmy6CP8I6lbOiMYI/wjqVs6Ixgj/COpWzojGCP9BRlpxBLoI/0FGWnEEugj/RUZacQS6CP0mP+8XOLoI/SY/7xc4ugj9Jj/vFzi6CP5S7ywLmv4I/k7vLAua/gj+Uu8sC5r+CPw+/7SGYuYI/D7/tIZi5gj8Pv+0hmLmCP1pUiXwjr4I/W1SJfCOvgj9bVIl8I6+CP/OAQIjer4I/9IBAiN6vgj/0gECI3q+CPx959h2DpoI/dxOstG6Egj94E6y0boSCP7UHj0xogoI/tgePTGiCgj+3B49MaIKCPzfVWuXNgYI/N9Va5c2Bgj831VrlzYGCP5Ckw3ubjoI/kKTDe5uOgj+RpMN7m46CP6Ki1VgIioI/o6LVWAiKgj9s0NRdImiCP+OILIyPaII/44gsjI9ogj/jiCyMj2iCP0FikMPDboI/QWKQw8Nugj9CYpDDw26CP472UTmLZoI/jfZROYtmgj+O9lE5i2aCP+xGl3wpYII/7UaXfClggj/uRpd8KWCCPxqg4aTzW4I/G6DhpPNbgj8boOGk81uCP/u0FS7WU4I/+7QVLtZTgj/6tBUu1lOCP8lndDdoRYI/yWd0N2hFgj/JZ3Q3aEWCP4quniGaR4I/iq6eIZpHgj+Lrp4hmkeCP3l/hCrNXoI/BE63v5k8gj8FTre/mTyCP1rWOfREOYI/WtY59EQ5gj9a1jn0RDmCPwLFrcp4K4I/AsWtyngrgj8Dxa3KeCuCPzT4PqafM4I/NPg+pp8zgj80+D6mnzOCP7aMk341NII/tIyTfjU0gj9YGlvyyzyCP61CCPmVIoI/rkII+ZUigj+vQgj5lSKCP8bE5yTYJ4I/xsTnJNgngj/GxOck2CeCP2UoWLJYLII/ZChYslgsgj9kKFiyWCyCP+grl/ndPII/6SuX+d08gj/pK5f53TyCP+03A/sSYII/7TcD+xJggj/tNwP7EmCCPygc7U0+WoI/KBztTT5agj8oHO1NPlqCP/iRIjKSdII/+pEiMpJ0gj/6kSIyknSCP0CVYY2abYI/QJVhjZptgj9ClWGNmm2CP/d3mbyPaoI/RQAu4Ntrgj9EAC7g22uCPz1aBMhOYYI/PVoEyE5hgj89WgTITmGCPy0yeHYgaYI/LTJ4diBpgj8uMnh2IGmCP/AjT1caY4I/8CNPVxpjgj/wI09XGmOCP3oPTgpZXII/eg9OCllcgj9GIqWPpaSCP7OQSsZQioI/s5BKxlCKgj+zkErGUIqCP/3dPJtKgoI//d08m0qCgj/93TybSoKCP5Mhq02ajYI/kyGrTZqNgj+TIatNmo2CPxhiFk4hkYI/F2IWTiGRgj8XYhZOIZGCP310VBbjYII/fXRUFuNggj99dFQW42CCP+rFYYuGYII/6sVhi4Zggj/qxWGLhmCCP4EZfFd+ZoI/ghl8V35mgj+CGXxXfmaCPyPeTcTjeII/It5NxON4gj8k3k3E43iCP1xR5H9qg4I/cn0+2+2Fgj9xfT7b7YWCPzhx06KOeYI/N3HToo55gj84cdOijnmCP+jdFHyIZ4I/6N0UfIhngj/n3RR8iGeCP5IuladmV4I/kS6Vp2ZXgj+SLpWnZleCPw6hjVpBY4I/DqGNWkFjgj9LrSqRmlqCP5oOnWYcQYI/mg6dZhxBgj+ZDp1mHEGCP29P4H8TPoI/b0/gfxM+gj9uT+B/Ez6CP8dUGPl0P4I/x1QY+XQ/gj/GVBj5dD+CP+tq2WcxNoI/6mrZZzE2gj/ratlnMTaCP0gYMEaqUoI/RxgwRqpSgj9GGDBGqlKCP2bIRuG+P4I/ZchG4b4/gj9myEbhvj+CP4QgRuo1UoI/hCBG6jVSgj+EIEbqNVKCP0VKrGbKW4I/REqsZspbgj9FSqxmyluCP1FzHTHkToI/MmTuuCNPgj8xZO64I0+CPzqfWWEOYoI/Op9ZYQ5igj86n1lhDmKCP9oFZQWMYII/2gVlBYxggj/aBWUFjGCCP0gKb47UTYI/SApvjtRNgj9HCm+O1E2CPzIuq40VW4I/MC6rjRVbgj+S52ur8eyBPxtu/KB1/IE/HG78oHX8gT8abvygdfyBP5sye8Hw+YE/mzJ7wfD5gT+bMnvB8PmBP97XQXLb2IE/3ddBctvYgT/d10Fy29iBPytOspDdwoE/K06ykN3CgT8rTrKQ3cKBP/kzTBNnjYE/+TNME2eNgT/4M0wTZ42BPwBLNINmr4E/AEs0g2avgT8ASzSDZq+BP8/02XuDtIE/z/TZe4O0gT/P9Nl7g7SBP7uHilr3k4E/u4eKWveTgT+8h4pa95OBP1g8JVBwmYE/wW887SWggT/BbzztJaCBPwS+RlcgrYE/A75GVyCtgT8EvkZXIK2BPwIhFvbHr4E/ASEW9sevgT8BIRb2x6+BPymoWpULuIE/KKhalQu4gT8nqFqVC7iBPyywXvxnxIE/K7Be/GfEgT8MoDhwVbWBP2au+HhqvIE/Za74eGq8gT9lrvh4aryBP+d08myHxIE/6HTybIfEgT/odPJsh8SBP1CTe2Dt04E/UJN7YO3TgT9Qk3tg7dOBP+fpeA7p0YE/5+l4DunRgT/m6XgO6dGBP+Iqq/qWu4E/4Sqr+pa7gT/iKqv6lruBP80/qSscuoE/zD+pKxy6gT/NP6krHLqBP+IAV+KwnIE/4gBX4rCcgT/iAFfisJyBP9fCOjaMhoE/18I6NoyGgT/Wwjo2jIaBP9l2CTrSmoE/QQB0Ac7XgT9AAHQBzteBP0mNZ2zmwYE/SY1nbObBgT9JjWds5sGBPyOb3E+BsYE/IpvcT4GxgT8jm9xPgbGBPzC3ZobL0oE/MLdmhsvSgT8vt2aGy9KBP3pqAAS/0oE/e2oABL/SgT9vseY6tw+CPyoVYstIIII/KhViy0gggj8qFWLLSCCCP0vRsCqzN4I/TNGwKrM3gj9K0bAqszeCP5ta4sNzR4I/m1riw3NHgj+aWuLDc0eCP+xFRk7BMoI/7UVGTsEygj/tRUZOwTKCP1dPSUZqKoI/Vk9JRmoqgj9WT0lGaiqCP4vTDB6jIII/i9MMHqMggj+L0wweoyCCP6eIf5hSQ4I/poh/mFJDgj+miH+YUkOCP78yeB0WQoI/vzJ4HRZCgj++MngdFkKCP1JKPhakVII/JUquqXKEgj8lSq6pcoSCPwt5gYDNroI/C3mBgM2ugj8LeYGAza6CP6k3K3qpl4I/qDcreqmXgj+pNyt6qZeCPxiE1BQgiII/GITUFCCIgj8XhNQUIIiCP+Jh5slBboI/4mHmyUFugj+zYAqOVnuCP5PTwLGel4I/k9PAsZ6Xgj+R08CxnpeCP6urnBBPs4I/qqucEE+zgj+rq5wQT7OCP2jxXipKqYI/Z/FeKkqpgj9m8V4qSqmCP9XiEeHXe4I/1eIR4dd7gj/V4hHh13uCP4JFMsGXSoI/gkUywZdKgj+DRTLBl0qCP+wr12b8MoI/7CvXZvwygj/sK9dm/DKCPyhxTm7+LoI/KXFObv4ugj8pcU5u/i6CP3fXW9/IJYI/d9db38glgj9311vfyCWCP/qCiKeCCYI/+Hfvfg8Dgj/3d+9+DwOCP3dLpgP+EII/d0umA/4Qgj93S6YD/hCCPwPNuZes7IE/A825l6zsgT8DzbmXrOyBP5WOLfTc2IE/lI4t9NzYgT+Vji303NiBP4x2iz4S8IE/jHaLPhLwgT/iZqYWhnSBP52L7SxFfoE/nYvtLEV+gT+ci+0sRX6BP5oyhXl4g4E/mjKFeXiDgT+aMoV5eIOBPyJoeGqqloE/I2h4aqqWgT8iaHhqqpaBP+No3xRxk4E/42jfFHGTgT/jaN8UcZOBPx+7Nd/2y4E/ILs13/bLgT8guzXf9suBP8fSArkTqYE/xtICuROpgT/G0gK5E6mBPxyVnE/fvIE/HJWcT9+8gT8blZxP37yBP8FECekm0oE/wkQJ6SbSgT/BRAnpJtKBP/oYMX6B0oE/s+kOCgPWgT+z6Q4KA9aBP5gzIc7m7oE/lzMhzubugT+XMyHO5u6BP9YlYI8F+oE/1iVgjwX6gT/WJWCPBfqBPyCscxTL6oE/IKxzFMvqgT8grHMUy+qBP3EQ9zH17oE/chD3MfXugT9yPXSJpB+CPwrVJDSWDoI/CdUkNJYOgj8J1SQ0lg6CP8A/DNkkQ4I/wD8M2SRDgj/APwzZJEOCP8oi4AUCSoI/yyLgBQJKgj/JIuAFAkqCPzyV6ne+T4I/O5Xqd75Pgj87lep3vk+CP+cUSU9jR4I/5xRJT2NHgj/nFElPY0eCP2yXIJzbcYI/bZcgnNtxgj9slyCc23GCP3qe2yCIfYI/ep7bIIh9gj97ntsgiH2CP7O54sQIh4I/s7nixAiHgj+yueLECIeCP6L9jqfsr4I/cFqn6/uMgj9xWqfr+4yCP2p9RwHRb4I/a31HAdFvgj9qfUcB0W+CP9JxllXTbYI/0nGWVdNtgj/ScZZV022CPxuBSbcJdoI/HIFJtwl2gj8cgUm3CXaCPxk3EG0uhII/GTcQbS6Egj+1/jVkZKiCP2BxbQnXrYI/X3FtCdetgj9ecW0J162CP4bC3kyFxYI/hsLeTIXFgj+Gwt5MhcWCPxl26d60tYI/GXbp3rS1gj8ZdunetLWCP96YW1Kiv4I/3phbUqK/gj/dmFtSor+CP1/5t0lBxYI/YPm3SUHFgj9f+bdJQcWCP2HBXkGnt4I/YcFeQae3gj9gwV5Bp7eCP/EmpXev44I/8Sald6/jgj/wJqV3r+OCP86Q3L8E+YI/zpDcvwT5gj/OkNy/BPmCP01rZbakD4M/nuYzEYMegz+e5jMRgx6DP/7NckU75oI//s1yRTvmgj/+zXJFO+aCPw5xewws4II/D3F7DCzggj8QcXsMLOCCP9gKY4ZGxYI/2QpjhkbFgj/ZCmOGRsWCP+/SW9Sn8oI/7tJb1Kfygj8tBulwo82CPw1fr5PL1oI/DV+vk8vWgj8NX6+Ty9aCP7Y7DiB2tYI/tzsOIHa1gj+3Ow4gdrWCP8BnSEiLmYI/wGdISIuZgj/AZ0hIi5mCP5YvLsYctYI/li8uxhy1gj+VLy7GHLWCP6oDzUV0qII/qgPNRXSogj+qA81FdKiCP/SRhiGCnYI/9JGGIYKdgj/0kYYhgp2CP3wnLg9wqII/fScuD3Cogj98Jy4PcKiCP/TtqawEq4I/9O2prASrgj/07amsBKuCPykoQguKjoI/arJkHBlygj9qsmQcGXKCPyK5jQCHloI/I7mNAIeWgj8juY0Ah5aCP/CK0zNbsoI/8IrTM1uygj/witMzW7KCP/1athtctII//Vq2G1y0gj/+WrYbXLSCP4VkS1y1lYI/hGRLXLWVgj+Yr5+9YImCPwoTAJtIe4I/CxMAm0h7gj8KEwCbSHuCP3yLvb5nf4I/fIu9vmd/gj98i72+Z3+CP+16927gWYI/7Xr3buBZgj/tevdu4FmCP7yAyCRlToI/vYDIJGVOgj+9gMgkZU6CP1GMRXGl94E/UoxFcaX3gT9SjEVxpfeBP50HTXaNDoI/nQdNdo0Ogj+cB012jQ6CP0hRnrymQ4I/SFGevKZDgj9IUZ68pkOCPw/94HwKQ4I/EP3gfApDgj8Q/eB8CkOCPw1ZaearJoI/y+NIXE4fgj/K40hcTh+CP40kRPNaKYI/jSRE81opgj+NJETzWimCP36VTQvPDII/fpVNC88Mgj9/lU0LzwyCP6VpAqMn54E/pGkCoyfngT+laQKjJ+eBP+gykEsC8YE/6DKQSwLxgT/NkPev18aBPzsuLitt3YE/Oy4uK23dgT87Li4rbd2BP8hYjPPM0oE/yViM88zSgT/JWIzzzNKBPznY3QtG/IE/OtjdC0b8gT862N0LRvyBP0tZ/ZTm8IE/S1n9lObwgT9LWf2U5vCBP3ZOc+KN8IE/d05z4o3wgT94TnPijfCBPyb6qBisJII/J/qoGKwkgj8m+qgYrCSCP44wx9xwOYI/jTDH3HA5gj+NMMfccDmCPypsvSqYO4I/Kmy9Kpg7gj8pbL0qmDuCP1OS2ZqsGoI/NGfOecNMgj81Z855w0yCP86JOAzzh4I/zok4DPOHgj/OiTgM84eCPz6uN5NewYI/Pq43k17Bgj8/rjeTXsGCPzsMKK8pi4I/OwworymLgj88DCivKYuCP4AIRv+8iYI/gAhG/7yJgj+ZWqfGYZWCP6CQQUJGt4I/n5BBQka3gj+gkEFCRreCP+uXisWBhII/65eKxYGEgj/rl4rFgYSCPygaXXG4Q4I/KBpdcbhDgj8oGl1xuEOCP7T7IawiM4I/tfshrCIzgj+1+yGsIjOCP9dr8OeFO4I/12vw54U7gj/Ya/DnhTuCP1xzmV3hJII/XHOZXeEkgj9cc5ld4SSCPyuKE80HIoI/LIoTzQcigj8rihPNByKCP9ca6TvHN4I/2BrpO8c3gj/ZGuk7xzeCP+s+FQsLSII/De5F5GNJgj8N7kXkY0mCP0P4Hx6JU4I/RPgfHolTgj9E+B8eiVOCP+I4Wy6fWoI/4jhbLp9agj/iOFsun1qCPxCtaL1AQ4I/Ea1ovUBDgj8SrWi9QEOCPyb3Ml/VUoI/JvcyX9VSgj+MdottUlGCP9XY+0GrQII/1tj7QatAgj/W2PtBq0CCP5myI89M9oE/mbIjz0z2gT+ZsiPPTPaBP4lKwkIN+oE/ikrCQg36gT+KSsJCDfqBP1rdnW/O9YE/Wt2db871gT9a3Z1vzvWBP2Gvr29/9IE/YK+vb3/0gT9ir69vf/SBP9C1IlzeAYI/z7UiXN4Bgj/PtSJc3gGCP4EoUJ62AYI/gihQnrYBgj+BKFCetgGCPys8R8Z/1oE/KzxHxn/WgT8sPEfGf9aBP1lBXO/YwYE/kknRvbO7gT+SSdG9s7uBP3czCeMV4IE/dzMJ4xXggT94MwnjFeCBPzvz3h9S4IE/PPPeH1LggT88894fUuCBP301bjt5AII/fTVuO3kAgj9/NW47eQCCP5NEADuzAoI/k0QAO7MCgj+YpIxF2wOCP5w6yaTl74E/nDrJpOXvgT+bOsmk5e+BPxmzY7IwAoI/GrNjsjACgj8Zs2OyMAKCPxV+pkr0B4I/FX6mSvQHgj8WfqZK9AeCP+R+LIvkI4I/5H4si+Qjgj/lfiyL5COCPwYpKjEON4I/BikqMQ43gj8HKSoxDjeCP6/pt8kgQYI/r+m3ySBBgj+w6bfJIEGCP8yfGjDvH4I/zJ8aMO8fgj/Lnxow7x+CPwsaf1v++4E/Cxp/W/77gT8MGn9b/vuBP2xvSxhM5oE/2CffDMTkgT/ZJ98MxOSBP+DYt8Vc7IE/4di3xVzsgT/h2LfFXOyBP1fw1Avb0YE/WPDUC9vRgT9X8NQL29GBPyqnxs5m0IE/KqfGzmbQgT8rp8bOZtCBPyJIn+kF74E/Ikif6QXvgT+7hExvhuuBPxHd0JjU8YE/Et3QmNTxgT8S3dCY1PGBPwn2zxj+/4E/CvbPGP7/gT8J9s8Y/v+BP7bcz1Wv5YE/ttzPVa/lgT+23M9Vr+WBP5Gu4dxJJoI/ka7h3Ekmgj+SruHcSSaCP2uVJF1IO4I/a5UkXUg7gj9rlSRdSDuCP5aR5NssYII/lpHk2yxggj+XkeTbLGCCPyO8a+6qh4I/JLxr7qqHgj8kvGvuqoeCP+TcAndfe4I/5NwCd197gj/k3AJ3X3uCP/wdzIsVUYI/s4y8aMVlgj+0jLxoxWWCP30kKcD9XII/fSQpwP1cgj9/JCnA/VyCPx7+xSQ0lYI/Hv7FJDSVgj8g/sUkNJWCP/aZUXMNf4I/95lRcw1/gj/4mVFzDX+CPwIl9WlclYI/AiX1aVyVgj+GV/Pcp4yCP9kVvZXUlYI/2RW9ldSVgj/aFb2V1JWCP7R1ZoTgYII/tHVmhOBggj+0dWaE4GCCP/IQy4FMZII/8hDLgUxkgj/zEMuBTGSCP4ld2qrYIII/il3aqtgggj+KXdqq2CCCPwvlbBXaEII/DOVsFdoQgj8M5WwV2hCCPzpwutF+9IE/OXC60X70gT85cLrRfvSBP3DsPPpQAII/cOw8+lAAgj9w7Dz6UACCPyFdizzB24E/IV2LPMHbgT8hXYs8wduBP1SLr1v4+oE/VC4YSbcAgj9ULhhJtwCCP6neA7YkzYE/qd4DtiTNgT+q3gO2JM2BPxqKk4R7koE/GoqThHuSgT8aipOEe5KBP9nTN+GFbYE/2dM34YVtgT/Z0zfhhW2BP4qczFmkV4E/i5zMWaRXgT/Re6OqvWGBP1WqPCOXToE/Vqo8I5dOgT9Wqjwjl06BP7cJzhTpP4E/twnOFOk/gT+3Cc4U6T+BPztEi4x+TIE/OkSLjH5MgT87RIuMfkyBP7yK/WdIcoE/vYr9Z0hygT+9iv1nSHKBP6E72v47kIE/oTva/juQgT+hO9r+O5CBPymdOXq8doE/KZ05erx2gT8pnTl6vHaBP68Ow7e0i4E/sA7Dt7SLgT+wDsO3tIuBP+hr+25vp4E/6Wv7bm+ngT/pa/tub6eBP58QO8X3tIE/RyAdt9u1gT9JIB2327WBP8QbOrZ6p4E/xBs6tnqngT/FGzq2eqeBP8tr+WE5doE/zGv5YTl2gT/La/lhOXaBP3Cp+lnYNYE/can6Wdg1gT9wqfpZ2DWBP8ufchMkEYE/y59yEyQRgT+TLxItKSaBP3UiUdhnOYE/dSJR2Gc5gT92IlHYZzmBP6ZtNapfPIE/pm01ql88gT+mbTWqXzyBPxKQrg3gMIE/EpCuDeAwgT8TkK4N4DCBPz8X+QRtHoE/Pxf5BG0egT8/F/kEbR6BP8mhCtoQB4E/yaEK2hAHgT/JoQraEAeBP+OxD0vyN4E/47EPS/I3gT/ksQ9L8jeBP+KutMiFIIE/4660yIUggT/jrrTIhSCBP8a27BshSIE/x7bsGyFIgT/FtuwbIUiBPxtkVVHxL4E/50uWzgdfgT/nS5bOB1+BP+trj0n76IA/6muPSfvogD/qa49J++iAP3ODNSiDB4E/c4M1KIMHgT9zgzUogweBP6ibBB6mN4E/p5sEHqY3gT+omwQepjeBP6IKkaPPyoA/ogqRo8/KgD9j9S8bnsOAP/72Uwghr4A//vZTCCGvgD/+9lMIIa+APz5gTnnetIA/PWBOed60gD89YE553rSAP4kN5qpu2YA/iA3mqm7ZgD+JDeaqbtmAPygh3sDP54A/KCHewM/ngD8nId7Az+eAP+n1tnhc74A/6fW2eFzvgD/p9bZ4XO+AP211+JXHBYE/bHX4lccFgT9rdfiVxwWBP8PtKigwAIE/w+0qKDAAgT/D7SooMACBP9f0MLC1JYE/1vQwsLUlgT/V9DCwtSWBP/nU/wrlIYE/CgDOlc5cgT8JAM6VzlyBP/4Kv0NrW4E//gq/Q2tbgT/9Cr9Da1uBP922p2YaPYE/3banZho9gT/ctqdmGj2BP+9Ti0jHHYE/7lOLSMcdgT/tU4tIxx2BPzX/Pt+UJIE/NP8+35QkgT/GXYMsIoaBP5eRnl3lQ4E/lpGeXeVDgT+WkZ5d5UOBP7xw0VFUYIE/vHDRUVRggT+7cNFRVGCBP/Z0MAfnSoE/9XQwB+dKgT/1dDAH50qBP4ds9tsuloE/h2z22y6WgT+GbPbbLpaBP87nMzledYE/zuczOV51gT/O5zM5XnWBP3FJCJIbXYE/cEkIkhtdgT9wSQiSG12BPysyGl8XuYE/KjIaXxe5gT8qMhpfF7mBP629PVkkwYE/rr09WSTBgT+tvT1ZJMGBPw4dcC1H8oE/BW2lU9XXgT8EbaVT1deBP2dIrX6gGYI/ZkitfqAZgj9mSK1+oBmCP9bufr27HYI/1u5+vbsdgj/V7n69ux2CP2MCl1FWEYI/YgKXUVYRgj9hApdRVhGCP+gLCwgvRoI/6AsLCC9Ggj92HN2jpXWCPzOSUMXDZ4I/MpJQxcNngj8yklDFw2eCP3l5fgPV7oE/eXl+A9XugT94eX4D1e6BP8G3J8drs4E/wbcnx2uzgT/BtyfHa7OBP0TWp2vVb4E/RNana9VvgT9D1qdr1W+BPx8LaiiuK4E/HwtqKK4rgT8fC2ooriuBPyPLXf4PNYE/I8td/g81gT8jy13+DzWBP3qJe1gu/IA/e4l7WC78gD96iXtYLvyAP6MWB7JWF4E/ohYHslYXgT+iFgeyVheBPw62e3BhHoE/fu7Wwm+0gT9+7tbCb7SBP7/47TJPwIE/v/jtMk/AgT/A+O0yT8CBP20TPKliEYI/bRM8qWIRgj9tEzypYhGCP2vva+OI/4E/a+9r44j/gT9r72vjiP+BP2W71I+bC4I/ZbvUj5sLgj8JKsdWVmCCP8q6+2hxnoI/y7r7aHGegj/LuvtocZ6CP5e1jQjnboI/l7WNCOdugj+YtY0I526CP1e8GEEnZYI/V7wYQSdlgj9XvBhBJ2WCP/zNmXzlZII//c2ZfOVkgj/9zZl85WSCP1So0sX6s4I/VajSxfqzgj9VqNLF+rOCP8NcRrlPsII/w1xGuU+wgj/EXEa5T7CCP4MWJMO9f4I/gxYkw71/gj+DFiTDvX+CP4wOuFVzkYI/jQ64VXORgj+ODrhVc5GCP343EperhII/z5kTLSwigj/PmRMtLCKCP4nwRHLC74E/ivBEcsLvgT+K8ERywu+BP3+INXLq3YE/gIg1curdgT+AiDVy6t2BP5AS/3GVzIE/kRL/cZXMgT+REv9xlcyBP4tQDoTr84E/jFAOhOvzgT/8x9/GCY2BP0yGnoNgg4E/TYaeg2CDgT9Nhp6DYIOBP2/Yh1P1o4E/cdiHU/WjgT9x2IdT9aOBP0fXbtP1b4E/R9du0/VvgT9I127T9W+BP5nF4+IfnIE/msXj4h+cgT+axePiH5yBP/YYaTvXSII/9hhpO9dIgj/4GGk710iCP2DA3iwxR4I/YMDeLDFHgj9hwN4sMUeCP/LkpuD1P4I/8+Sm4PU/gj/05Kbg9T+CP/mImkcySII/+YiaRzJIgj/6iJpHMkiCPwiF5d/DVoI/IFe86jPogT8hV7zqM+iBPxuemaKkKYI/Hp6ZoqQpgj8enpmipCmCP54JMsO1EoI/ngkyw7USgj+eCTLDtRKCP7VumcXjKYI/tm6ZxeMpgj+5bpnF4ymCP5gObhJxIYI/mQ5uEnEhgj8H34PGhv6BP4h5lKo3AoI/iHmUqjcCgj+KeZSqNwKCP+QdDUzA/YE/5B0NTMD9gT/kHQ1MwP2BP7z4qSm794E/vfipKbv3gT+8+Kkpu/eBP0Uigjul/oE/RSKCO6X+gT9FIoI7pf6BP8I/hKNN74E/wj+Eo03vgT/BP4SjTe+BP5RQGiIq8oE/lVAaIirygT+VUBoiKvKBPwQbQBRT+oE/BBtAFFP6gT8EG0AUU/qBP6AR/1VZ34E/oBH/VVnfgT+gEf9VWd+BP08jzhKi0YE/q/eacRiggT+q95pxGKCBP+R1f4Ula4E/43V/hSVrgT/idX+FJWuBP3LijfTJo4E/cuKN9MmjgT9y4o30yaOBP9Wegi7kjoE/1Z6CLuSOgT/UnoIu5I6BP7z7V+I6m4E/uvtX4jqbgT9FCLKgnmqBP4QnD9IygYE/hCcP0jKBgT+EJw/SMoGBP+dyMlBlXoE/5nIyUGVegT/lcjJQZV6BP8jya5wJXYE/yPJrnAldgT/H8mucCV2BP4+U3SW/i4E/j5TdJb+LgT+OlN0lv4uBP0G6Wj8fUIE/QLpaPx9QgT9Aulo/H1CBP2Z/NktLJIE/ZX82S0skgT9kfzZLSySBP5BIflPpWIE/j0h+U+lYgT+PSH5T6ViBP+ziT7aNc4E/7OJPto1zgT/r4k+2jXOBP3FnkDD2e4E/oy/jQnhYgT+kL+NCeFiBP4KqdSqxWoE/gqp1KrFagT+CqnUqsVqBP/MKx/iFNoE/8wrH+IU2gT/yCsf4hTaBP6G98PZ+S4E/ob3w9n5LgT+hvfD2fkuBPxzuQTQ/Q4E/G+5BND9DgT8ki3VEalOBP30HLTx7b4E/fActPHtvgT98By08e2+BP8Ax0PqjeYE/wDHQ+qN5gT+/MdD6o3mBP7NncsXrg4E/smdyxeuDgT+yZ3LF64OBP4izJgmhcYE/iLMmCaFxgT+IsyYJoXGBP25JO1AhT4E/bUk7UCFPgT9tSTtQIU+BP8ndBu/OQoE/yt0G785CgT/J3QbvzkKBPwl/c2z3h4E/CH9zbPeHgT8Hf3Ns94eBP6sIrONjk4E/qgis42OTgT+qCKzjY5OBP2ExQ6taaYE/ZeDDHEHFgT9l4MMcQcWBP1gHyHb4mYE/WAfIdviZgT9YB8h2+JmBP6CYl3/qaYE/oJiXf+ppgT+gmJd/6mmBP6K36hKOVIE/obfqEo5UgT+ht+oSjlSBP9LyLQIqXIE/0vItAipcgT9OczgzcGaBPzaZ6DMfk4E/NpnoMx+TgT82megzH5OBPxYkzsL7dIE/FiTOwvt0gT8WJM7C+3SBP0Q4r9B1pIE/RDiv0HWkgT9EOK/QdaSBP7vdxdDjkIE/u93F0OOQgT+73cXQ45CBPxgwuUJWgIE/FzC5QlaAgT8XMLlCVoCBP6/R3C1qe4E/r9HcLWp7gT+v0dwtanuBPxlq4qeseIE/GGrip6x4gT8YauKnrHiBPw8MW8CDlIE/DwxbwIOUgT8QDFvAg5SBP/fMCsvcxIE/3DkIZMungT/dOQhky6eBPw982PwGhIE/D3zY/AaEgT8PfNj8BoSBP34LFbqnhoE/fwsVuqeGgT9/CxW6p4aBPxH1iLEMdYE/EfWIsQx1gT8R9YixDHWBPxcbkRx5koE/FhuRHHmSgT8diaQBv1WBPwsEkWpVSoE/CwSRalVKgT8LBJFqVUqBPzvqFBIHZoE/O+oUEgdmgT876hQSB2aBP8ArXAAHZoE/vytcAAdmgT++K1wAB2aBP2EZA89hiIE/YBkDz2GIgT9gGQPPYYiBPwAzm6TyfYE/ADObpPJ9gT8AM5uk8n2BPwwO7TsajoE/Cw7tOxqOgT8LDu07Go6BP1x372QnpIE/W3fvZCekgT9ad+9kJ6SBP8j3r6n4kYE/yPevqfiRgT/J96+p+JGBP+/6+NzoooE/3lSyzS+hgT/fVLLNL6GBP1fOwflph4E/Vs7B+WmHgT9WzsH5aYeBP2L/Xn9UcYE/Yf9ef1RxgT9h/15/VHGBPx0s1/Vma4E/HSzX9WZrgT8dLNf1ZmuBP614h4jdZ4E/rXiHiN1ngT/BZIzRtTaBPyrjRhnSWIE/KuNGGdJYgT8q40YZ0liBP+4QAYscY4E/7hABixxjgT/uEAGLHGOBP6z9QYbykoE/rP1BhvKSgT+s/UGG8pKBPyswbyb2lIE/KzBvJvaUgT8qMG8m9pSBP4q/zZaRcIE/ir/NlpFwgT+Kv82WkXCBPxorlhTOZoE/GSuWFM5mgT8ZK5YUzmaBP7giZhJfaIE/uCJmEl9ogT+4ImYSX2iBPy+kaT3lfIE/L6RpPeV8gT8upGk95XyBP/tAEqrwfIE/OQe7Y4aZgT84B7tjhpmBP9Y11A9bjIE/1jXUD1uMgT/VNdQPW4yBP73RT/YejYE/vNFP9h6NgT+80U/2Ho2BP+CDpRKWioE/4IOlEpaKgT/gg6USloqBP/3Vu4PRmoE//dW7g9GagT89Wzv0SU2BPwGEZJ9UY4E/AYRkn1RjgT8BhGSfVGOBP9f+QFX6aIE/1v5AVfpogT/X/kBV+miBP4HAt/sJZoE/gcC3+wlmgT+AwLf7CWaBP4z+vsD+OYE/jP6+wP45gT+M/r7A/jmBP19RZ5FcV4E/YFFnkVxXgT9fUWeRXFeBP7RV7jx9WYE/s1XuPH1ZgT+zVe48fVmBP0yLgAVQYYE/TIuABVBhgT9Li4AFUGGBP/9tfjQ7U4E//21+NDtTgT//bX40O1OBP4HTmvcXRIE/n5atV08ygT+flq1XTzKBP7UgdRY0D4E/tSB1FjQPgT+1IHUWNA+BPwxfw9tqFoE/DF/D22oWgT8MX8PbahaBP7p2OCAjIoE/unY4ICMigT+6djggIyKBP5u6jX2tNIE/mrqNfa00gT/LaffKXluBP/hikZy0TYE/92KRnLRNgT/3YpGctE2BP6M9xobvOIE/pD3Ghu84gT+kPcaG7ziBP5PQgnjjJIE/k9CCeOMkgT+T0IJ44ySBP2l95a3KKYE/aX3lrcopgT9pfeWtyimBP61F/GvMToE/rEX8a8xOgT+sRfxrzE6BP1hnZLGDRIE/WGdksYNEgT9YZ2Sxg0SBP/H2WcUSL4E/8fZZxRIvgT/x9lnFEi+BP91ctCQ8L4E/3ly0JDwvgT/dXLQkPC+BP5zarfZGRIE/gPX1ZGdUgT+A9fVkZ1SBP2p40dLuQIE/aXjR0u5AgT9qeNHS7kCBPzcVBhiYMIE/NxUGGJgwgT82FQYYmDCBP3C9cEonKYE/cb1wSicpgT9xvXBKJymBP7vakmYNPYE/vNqSZg09gT/4AyLa+1OBP8B1ZNcabIE/wHVk1xpsgT/AdWTXGmyBP9NvvQkjb4E/1G+9CSNvgT/Tb70JI2+BP8rcmammhIE/ytyZqaaEgT/K3JmppoSBP2r9uyN9eYE/av27I315gT9q/bsjfXmBP12CvVFyeoE/XYK9UXJ6gT9egr1RcnqBP4sOhVLkg4E/iw6FUuSDgT+MDoVS5IOBP7Ce/Fgbd4E/sJ78WBt3gT+wnvxYG3eBP3Cp2FcHeoE/canYVwd6gT9yqdhXB3qBP9Hv/H0pbIE/ZnBC1VBWgT9ncELVUFaBPx9V3TAwS4E/H1XdMDBLgT8gVd0wMEuBPyHQvR6bNoE/IdC9Hps2gT8h0L0emzaBP6gQcaj5KYE/qhBxqPkpgT+qEHGo+SmBP5EMZ0lJJoE/kQxnSUkmgT+gQx35okOBPxbzqvJJJoE/F/Oq8kkmgT8Y86rySSaBP6ROAxzmMIE/pE4DHOYwgT+kTgMc5jCBPxQ1WMf5QYE/FTVYx/lBgT8VNVjH+UGBP7x/aISpIIE/vH9ohKkggT+9f2iEqSCBP8QiHhYHK4E/xSIeFgcrgT/FIh4WByuBPx5pij07KoE/H2mKPTsqgT8faYo9OyqBP136c1bGKYE/X/pzVsYpgT9g+nNWximBP7k2HPhaNIE/ujYc+Fo0gT+7Nhz4WjSBP/tt9EZMW4E/0s75vstUgT/Szvm+y1SBPyPHuumeYIE/Jce66Z5ggT8lx7rpnmCBP/y5QSJ0e4E//rlBInR7gT/+uUEidHuBPzASxZHug4E/MRLFke6DgT8xEsWR7oOBP5pMnFazeoE/m0ycVrN6gT9kXjc4la6BP9DT/lYar4E/0dP+VhqvgT/S0/5WGq+BP6ehwh3Jn4E/p6HCHcmfgT+oocIdyZ+BP/n994WGroE/+f33hYaugT/6/feFhq6BPxLNzh+cqIE/Es3OH5yogT8Tzc4fnKiBP9yqhkYRhYE/3KqGRhGFgT/dqoZGEYWBP2G6U1q7gIE/YrpTWruAgT9julNau4CBP5J7k8lLjYE/k3uTyUuNgT+Se5PJS42BP1cmxDSTlYE/VybENJOVgT9YJsQ0k5WBP1zq8OzHmIE/SBruNnGDgT9IGu42cYOBPyGH1xuel4E/IofXG56XgT8ih9cbnpeBPxosNTobkIE/Giw1OhuQgT8bLDU6G5CBP8JV2zkFkIE/wlXbOQWQgT/CVds5BZCBP1QAajemm4E/VABqN6abgT/XxlFnQIyBP++pLPT7doE/7qks9Pt2gT/vqSz0+3aBP+uTtCuodoE/6pO0K6h2gT/rk7QrqHaBP65lPnIdhIE/rmU+ch2EgT+uZT5yHYSBPzJxdysifIE/MXF3KyJ8gT8wcXcrInyBPyR9kfDCu4E/JH2R8MK7gT8kfZHwwruBP3ki8MaruIE/eCLwxqu4gT94IvDGq7iBPxjSEVn1uoE/GNIRWfW6gT8Z0hFZ9bqBP1yWzoELv4E/XZbOgQu/gT9dls6BC7+BP2cGkjxmsIE/LygJgIqjgT8vKAmAiqOBPyv/GZjjloE/K/8ZmOOWgT8q/xmY45aBP9nT6Z9YkIE/2NPpn1iQgT/X0+mfWJCBP6epxyyQlIE/p6nHLJCUgT+nqccskJSBP5HwzVjDsoE/j/DNWMOygT/NzUevp7iBP9diBQHyu4E/1mIFAfK7gT/XYgUB8ruBP7/r5Yhbm4E/v+vliFubgT++6+WIW5uBP/9NWYrgnYE//k1ZiuCdgT/9TVmK4J2BP1Tw4jZFlYE/U/DiNkWVgT9S8OI2RZWBP6iqjYHClIE/pqqNgcKUgT+nqo2BwpSBP49lbF4Sk4E/j2VsXhKTgT+OZWxeEpOBP+RCxVj3pIE/5ELFWPekgT/jQsVY96SBP9arR6wCqoE/1qtHrAKqgT/Wq0esAqqBP3rDGJFBsIE/g73mpjFrgT+CveamMWuBP3+OYcVwT4E/f45hxXBPgT9/jmHFcE+BPz8WIAiVQIE/PhYgCJVAgT89FiAIlUCBP1O047rxO4E/UrTjuvE7gT9RtOO68TuBPy5+MSHzRYE/Ln4xIfNFgT/WUDg6RVqBP2e/qManZIE/Zr+oxqdkgT9mv6jGp2SBP2Va9O9Ef4E/ZVr070R/gT9lWvTvRH+BP0oD/BYeboE/SQP8Fh5ugT9JA/wWHm6BPw+OGsWJYIE/D44axYlggT8PjhrFiWCBP2dQtIHiU4E/ZlC0geJTgT9mULSB4lOBP0OkYv5RV4E/Q6Ri/lFXgT9CpGL+UVeBP4MEk5GXT4E/gwSTkZdPgT+EBJORl0+BP/eoHBN2V4E/+KgcE3ZXgT/3qBwTdleBP5pIUWTlZIE/g9u7z4tBgT+D27vPi0GBP/GqmfFsSIE/8aqZ8WxIgT/xqpnxbEiBP/4JIcnLWoE/AAohyctagT8ACiHJy1qBP3U6EZNfRIE/djoRk19EgT93OhGTX0SBP399IJhWUoE/f30gmFZSgT/t9hFXGkKBPyk8PSUuRoE/KTw9JS5GgT8oPD0lLkaBP41a7f7SPYE/jVrt/tI9gT+NWu3+0j2BP7Ik0gQxNIE/syTSBDE0gT+0JNIEMTSBP3HaVqJ+M4E/cNpWon4zgT9x2laifjOBP0wxY/92LYE/TTFj/3YtgT9NMWP/di2BP80B3nGsLIE/zgHecawsgT/OAd5xrCyBP+hkS/UjN4E/6WRL9SM3gT/qZEv1IzeBP7XAcSTqNYE/t8BxJOo1gT+4wHEk6jWBP+GE8knGIYE/KT3fnyYdgT8qPd+fJh2BP49SP/upIIE/kFI/+6kggT+RUj/7qSCBP+Xb+tAwGIE/5dv60DAYgT/m2/rQMBiBPyGMu73UFYE/IYy7vdQVgT8jjLu91BWBP2MI43CLEoE/YwjjcIsSgT/4PwhnJBiBPwIqqk8JH4E/AyqqTwkfgT8EKqpPCR+BP2ty/aPmOYE/bXL9o+Y5gT9tcv2j5jmBP0RyoToRP4E/RnKhOhE/gT9GcqE6ET+BP+S1tBR9SIE/5bW0FH1IgT/mtbQUfUiBP7hlA6EFSoE/uGUDoQVKgT+5ZQOhBUqBP6zghtFqSYE/reCG0WpJgT+u4IbRakmBP/Dua7x3WYE/7+5rvHdZgT/x7mu8d1mBPwRhwYt1a4E/BWHBi3VrgT8FYcGLdWuBP3EG+qh0dYE/69jaojKIgT/s2NqiMoiBP6FyrpesjoE/oXKul6yOgT+hcq6XrI6BP3itPhJJk4E/ea0+EkmTgT95rT4SSZOBP/2p/q3EgYE//an+rcSBgT/8qf6txIGBP790AwAYaoE/vnQDABhqgT+RWwGe4VSBP5UH51nmXYE/lQfnWeZdgT+WB+dZ5l2BP5L+5VmIcYE/kv7lWYhxgT+S/uVZiHGBPxENbahuZoE/EQ1tqG5mgT8RDW2obmaBP7yMgRaIdoE/vIyBFoh2gT+9jIEWiHaBP0WiMVMvWIE/RaIxUy9YgT9FojFTL1iBP2RU0OW5aoE/Y1TQ5blqgT9iVNDluWqBP7oDwf0ie4E/uQPB/SJ7gT+5A8H9InuBP4sP/oD1dYE/ig/+gPV1gT+KD/6A9XWBPwEcEmYDfYE/Ew/g1EBtgT8SD+DUQG2BP7cIvSoRZoE/tQi9KhFmgT+2CL0qEWaBP/G5p694bYE/77mnr3htgT/uuaeveG2BP+fHyKnhboE/58fIqeFugT/lx8ip4W6BP0iC43lPZ4E/R4LjeU9ngT8M0eZJoMGBP2srb2yk1IE/aytvbKTUgT9qK29spNSBP/qKCSv73oE/+YoJK/vegT/4igkr+96BP01ZLXwJ2oE/TFktfAnagT9LWS18CdqBPzhN9cMxyYE/OE31wzHJgT83TfXDMcmBP4FcwDyUioE/gFzAPJSKgT9/XMA8lIqBP8drYl9fkoE/xmtiX1+SgT/Ga2JfX5KBP7Y/35vNrIE/tT/fm82sgT+1P9+bzayBP+jcbslZpoE/59xuyVmmgT/m3G7JWaaBP06ELApsoYE/+z0pSKt0gT/7PSlIq3SBP7KRIKrKaIE/sZEgqspogT+xkSCqymiBP5mr+n7VdIE/mKv6ftV0gT+Yq/p+1XSBP1swMCBmh4E/WjAwIGaHgT9aMDAgZoeBPzf1UipYiIE/N/VSKliIgT9+KA9LmmSBPxROPdntZYE/E0492e1lgT8TTj3Z7WWBPz20LFo4YIE/PbQsWjhggT88tCxaOGCBP4D/A4tZVIE/f/8Di1lUgT9//wOLWVSBP4k0ArPAPoE/iDQCs8A+gT+INAKzwD6BP1qme6MZOIE/WaZ7oxk4gT9ZpnujGTiBP3u2vT4+QYE/era9Pj5BgT97tr0+PkGBP3ynKZSvQYE/fKcplK9BgT98pymUr0GBP2CDIdeKK4E/YIMh14orgT9ggyHXiiuBP8i+N8p2L4E/sFtWxwkXgT+xW1bHCReBPzHm4CulKoE/MebgK6UqgT8x5uArpSqBP+gZz5GSEYE/5xnPkZIRgT/oGc+RkhGBP5qfsAfkG4E/m5+wB+QbgT+bn7AH5BuBPz4HMIqXMYE/PgcwipcxgT+P5wwoYymBP8a/sr7tLIE/xr+yvu0sgT/Gv7K+7SyBPwJqboo2JIE/AmpuijYkgT8Dam6KNiSBPwWgzwb/K4E/BaDPBv8rgT8GoM8G/yuBPwN7gmVgIYE/BHuCZWAhgT8Ee4JlYCGBPx3To3A1KYE/HtOjcDUpgT8e06NwNSmBP0+frctGQIE/T5+ty0ZAgT9Pn63LRkCBP3rZgGYaPYE/e9mAZho9gT962YBmGj2BP9hcsZcYOYE/2Fyxlxg5gT/YXLGXGDmBP3mK4BwoNYE/40bkGmUrgT/kRuQaZSuBP0dNKqUyGoE/R00qpTIagT9HTSqlMhqBP4POLiaDDIE/g84uJoMMgT+Ezi4mgwyBP8iMUj/IC4E/x4xSP8gLgT/IjFI/yAuBP2403bSWBYE/bzTdtJYFgT9RGHr0FgaBP/d8yVnJBYE/93zJWckFgT/4fMlZyQWBP5ZYP6u35oA/llg/q7fmgD+WWD+rt+aAP635dKDX1YA/rvl0oNfVgD+u+XSg19WAP3Nh7LrM5IA/dGHsuszkgD90Yey6zOSAP7xkIIgx2oA/vWQgiDHagD+9ZCCIMdqAP4XLfiQo7YA/hct+JCjtgD+Fy34kKO2APzZXwfMg5oA/N1fB8yDmgD83V8HzIOaAP9ngv6OH5IA/2eC/o4fkgD/a4L+jh+SAP4cxtV8Y5IA/Fiif7KUHgT8WKJ/spQeBP46q6fKbBIE/jqrp8psEgT+OqunymwSBP0jwCqWxA4E/SfAKpbEDgT9J8AqlsQOBP1B6iecUCYE/UXqJ5xQJgT9ReonnFAmBP8TkuMGF/YA/xOS4wYX9gD/nUkAI9sqAPz2ntOCfzYA/Pqe04J/NgD8+p7Tgn82AP81D+dZ6xoA/zUP51nrGgD/OQ/nWesaAP09JFgAMuIA/UEkWAAy4gD9PSRYADLiAP9Grgqhun4A/0auCqG6fgD/Qq4Kobp+APw/M9v/v0YA/D8z2/+/RgD8QzPb/79GAPwykbZxV3YA/DKRtnFXdgD8LpG2cVd2APzIHecqY2IA/Mgd5ypjYgD8zB3nKmNiAP0ufdzS72IA/TJ93NLvYgD9Mn3c0u9iAP0fYZZJJx4A/4HCJz5XDgD/hcInPlcOAP5bjb6pxx4A/luNvqnHHgD+W42+qcceAPz2A4G/Lw4A/PYDgb8vDgD89gOBvy8OAPySMDKFMxIA/JIwMoUzEgD8kjAyhTMSAPx5fIYVtzIA/Hl8hhW3MgD8OBifYeNuAPxxKRjZD6oA/HEpGNkPqgD8cSkY2Q+qAP1t9GRvc/oA/XH0ZG9z+gD9bfRkb3P6APxa8HMh77IA/FbwcyHvsgD8UvBzIe+yAP5Oe0Jx87oA/kp7QnHzugD+TntCcfO6AP2N8snYo0oA/Y3yydijSgD9jfLJ2KNKAPwlGRJ1f1oA/CUZEnV/WgD8JRkSdX9aAP+ZjsJ3m1IA/5mOwnebUgD/mY7Cd5tSAP3v1m8Uv5IA/fPWbxS/kgD989ZvFL+SAP3PnVGm55oA/cQjBWLLxgD9wCMFYsvGAP40zL4W+5oA/jTMvhb7mgD+MMy+FvuaAP8s1VX7L6IA/yzVVfsvogD/LNVV+y+iAP2NtApUx6oA/Y20ClTHqgD9ibQKVMeqAP247wkV57YA/bjvCRXntgD/CA3CVGkeBP7cIVefeVoE/uAhV595WgT+4CFXn3laBP4FfjTm4WoE/gV+NObhagT+BX405uFqBP5ihpqlOYoE/mKGmqU5igT+YoaapTmKBP6akZwNfboE/pqRnA19ugT+lpGcDX26BP4V52EvjdIE/hHnYS+N0gT+FedhL43SBPw81ZuDRdoE/DzVm4NF2gT8PNWbg0XaBP4res/34gYE/i96z/fiBgT+L3rP9+IGBP6cLw1TtfoE/pgvDVO1+gT+nC8NU7X6BPySx7hbjiYE/XsJp9oy+gT9ewmn2jL6BP32K4+XvvIE/fYrj5e+8gT99iuPl77yBP6NlXb6nwoE/pGVdvqfCgT+kZV2+p8KBP61KSC+C4oE/rEpIL4LigT+tSkgvguKBPwgkddUZ4IE/ByR11RnggT/sgSzIAu+BPzCsFuqy5YE/L6wW6rLlgT8vrBbqsuWBPzwVmoef2IE/PBWah5/YgT8+FZqHn9iBP4rpaFzSzoE/iuloXNLOgT+J6Whc0s6BPwQyKWKwyIE/BDIpYrDIgT8DMilisMiBP3CpiFqx3YE/b6mIWrHdgT9wqYhasd2BP9VSO+UC54E/1VI75QLngT/WUjvlAueBP+BivdB24oE/32K90HbigT/gYr3QduKBPy5uhN1a3IE/Lm6E3VrcgT8uboTdWtyBPxTsAD9H64E/SWz+x1LmgT9KbP7HUuaBP1y9YPca8YE/Xb1g9xrxgT9evWD3GvGBP3anFOJF+oE/dqcU4kX6gT92pxTiRfqBPxnYVfGH+YE/GdhV8Yf5gT8Z2FXxh/mBP3BQJqNwA4I/cVAmo3ADgj8wiss55BGCPz1vSjfND4I/PW9KN80Pgj89b0o3zQ+CPzjhWv5gFYI/OOFa/mAVgj844Vr+YBWCPxhzfPUcLoI/F3N89Rwugj8Xc3z1HC6CPyy3tS6iMII/LLe1LqIwgj8tt7UuojCCPw+QdNq9M4I/EJB02r0zgj8QkHTavTOCP449uWhzMoI/jz25aHMygj+PPbloczKCPxf4l+brRoI/FviX5utGgj8W+Jfm60aCPzzm//kBVoI/POb/+QFWgj885v/5AVaCP7OEzBMsXYI/fXrAo4OOgj99esCjg46CPylMXGJbjII/KUxcYluMgj8qTFxiW4yCP5K7SNqIqYI/krtI2oipgj+Uu0jaiKmCP0ZQ8AxPqII/RVDwDE+ogj9GUPAMT6iCP9/zfBg9poI/4PN8GD2mgj8/nxT2d3WCPykxCXXbfoI/KjEJddt+gj8rMQl1236CP0tsYyCmk4I/S2xjIKaTgj9LbGMgppOCP61R/oHzh4I/rlH+gfOHgj+uUf6B84eCP0DBdr3ehoI/QMF2vd6Ggj9BwXa93oaCP1QpDE14qYI/UykMTXipgj9UKQxNeKmCPyL1llFrl4I/IvWWUWuXgj8i9ZZRa5eCP4vSkw2ojII/itKTDaiMgj+K0pMNqIyCP/wPX0FPoYI//A9fQU+hgj/9D19BT6GCP6OaBRFroII/6wrINf+igj/rCsg1/6KCPzsdFttvs4I/Ox0W22+zgj87HRbbb7OCPy9xkRiemoI/MHGRGJ6agj8ucZEYnpqCP92AOHG3rII/3YA4cbesgj/egDhxt6yCP6K0KExWqII/orQoTFaogj/YKApkR+mCPzvdEgrgx4I/O90SCuDHgj883RIK4MeCP0rBGkJqyoI/SsEaQmrKgj9JwRpCasqCP0XmmTe5yII/Q+aZN7nIgj9E5pk3uciCP/C7jqGjvII/8buOoaO8gj/xu46ho7yCP/OTiVGeu4I/9JOJUZ67gj/zk4lRnruCP3l4r7yow4I/eHivvKjDgj94eK+8qMOCPxXSlJxWxYI/FdKUnFbFgj8U0pScVsWCPwLiqYUIy4I/AuKphQjLgj8B4qmFCMuCP3aDoniyzYI/SeJWEdzbgj9I4lYR3NuCP6NLb1me04I/oUtvWZ7Tgj+hS29ZntOCP3rKVZUB3YI/eMpVlQHdgj94ylWVAd2CP88iFI7O7oI/zyIUjs7ugj/OIhSOzu6CP8Vd2dUYEYM/xF3Z1RgRgz9z04dlawuDP1Nx/qNWD4M/U3H+o1YPgz9Scf6jVg+DP+dno2mWF4M/52ejaZYXgz/mZ6NplheDP2gjsZTDF4M/ZiOxlMMXgz9mI7GUwxeDP+fqzs+hCYM/5urOz6EJgz/l6s7PoQmDP6m1lVwHGoM/p7WVXAcagz+ntZVcBxqDP9L3qxjKEYM/0ferGMoRgz/R96sYyhGDP1GjftaAMYM/UaN+1oAxgz9Po37WgDGDPzAa9TdLNoM/Lxr1N0s2gz8uGvU3SzaDPxD2N/tPNoM/eNh8BKgYgz922HwEqBiDP4Aj+OmvFIM/fyP46a8Ugz99I/jprxSDP3iZG9q+CYM/dpkb2r4Jgz91mRvavgmDP51InQWf84I/nUidBZ/zgj+bSJ0Fn/OCP9SQu1u094I/05C7W7T3gj+ITFNpyNWCP5IskdaF5YI/kSyR1oXlgj+PLJHWheWCP7HospQ01II/sOiylDTUgj+v6LKUNNSCP7lsRIo1xII/uGxEijXEgj+3bESKNcSCPwtEiRsnyoI/CUSJGyfKgj8IRIkbJ8qCPxtYRqiYv4I/GlhGqJi/gj8ZWEaomL+CP0B0AhlivYI/P3QCGWK9gj89dAIZYr2CP5JrF9iFtII/kWsX2IW0gj+RaxfYhbSCPx0r/39wp4I/HCv/f3Cngj8bK/9/cKeCPy/IqLSfpoI/keuhG7G4gj+Q66EbsbiCP3eZMUY8o4I/dZkxRjyjgj90mTFGPKOCP8piJODEnYI/yWIk4MSdgj/IYiTgxJ2CPyFgt69ujYI/IWC3r26Ngj8fYLevbo2CPwCtiGC+foI//6yIYL5+gj/1rL1DKoCCP3wl+9oTi4I/eyX72hOLgj97JfvaE4uCP/fRb952nII/99Fv3nacgj/20W/edpyCPzxnJPNigoI/PGck82KCgj86ZyTzYoKCP+/zLf0Vk4I/7vMt/RWTgj/v8y39FZOCP4D30hOfjYI/f/fSE5+Ngj9+99ITn42CP+7cNzhClYI/7tw3OEKVgj/t3Dc4QpWCP41xWvhoqoI/jHFa+Giqgj+McVr4aKqCP/oBG8LNu4I/+gEbws27gj/4ARvCzbuCPyrMtqZRvII/2KZF5P3Ugj/XpkXk/dSCPwkQkpPu7oI/CRCSk+7ugj8JEJKT7u6CPylgpWXv24I/KmClZe/bgj8pYKVl79uCPwlQUuYd7II/CFBS5h3sgj8IUFLmHeyCP7CSxG1h/YI/rpLEbWH9gj8h5QQBw+yCPzvdnGny5YI/O92cafLlgj873Zxp8uWCPzFrAWZu44I/MWsBZm7jgj8wawFmbuOCP959gNsAy4I/3n2A2wDLgj/dfYDbAMuCP2HDcIcbo4I/YsNwhxujgj9hw3CHG6OCP4Sl/8LtbYI/hKX/wu1tgj+Epf/C7W2CPwYw4wFFfII/BzDjAUV8gj8GMOMBRXyCP2h7ee85foI/aHt57zl+gj9ne3nvOX6CP5lRbPf2VoI/mVFs9/ZWgj+ZUWz39laCP0EjgJcsToI/xQNXUA9ogj/FA1dQD2iCP5ZU4PFuaII/llTg8W5ogj+WVODxbmiCPxr9lxxhc4I/Gf2XHGFzgj8Z/ZccYXOCP4gYk4EKa4I/iBiTgQprgj+IGJOBCmuCP+quryVJX4I/6a6vJUlfgj9HyEU4voKCP7Oi3z0chYI/s6LfPRyFgj+0ot89HIWCPzbP893AfYI/N8/z3cB9gj82z/PdwH2CP07c7E0Bd4I/TdzsTQF3gj9O3OxNAXeCP2vJQWzubYI/a8lBbO5tgj9ryUFs7m2CP48CYmt3KYI/jwJia3cpgj+QAmJrdymCPzatJ1dwOYI/Nq0nV3A5gj82rSdXcDmCPyumYf4ZMII/LKZh/hkwgj8spmH+GTCCP3rrL3J2MII/eusvcnYwgj966y9ydjCCPwfim3SiSYI/71lwbHkxgj/wWXBseTGCPzUry2p8III/NSvLanwggj82K8tqfCCCPxKmDYIvMYI/E6YNgi8xgj8Tpg2CLzGCP50XKbaaGoI/nhcptpoagj+eFym2mhqCP8cqtWTOB4I/xyq1ZM4Hgj+s99bKOEKCP8soQjgOQYI/yyhCOA5Bgj/MKEI4DkGCPz2KZcudVII/Poply51Ugj89imXLnVSCP1WnfV83JYI/Vad9Xzclgj9Wp31fNyWCPygnGHCHPII/KScYcIc8gj8pJxhwhzyCP/DAQ3R9OII/8MBDdH04gj/vwEN0fTiCP62C7+VPKoI/roLv5U8qgj+ugu/lTyqCP5PdlaQ2J4I/k92VpDYngj+T3ZWkNieCP0YWw/H8NYI/RRbD8fw1gj9GFsPx/DWCP6jFe+qgLII/nVBBW5UJgj+dUEFblQmCP49R39FRHoI/j1Hf0VEegj+RUd/RUR6CPxaBK2SgJYI/FoErZKAlgj8WgStkoCWCP4GkweUAI4I/gqTB5QAjgj+CpMHlACOCP2yz5AceJYI/bbPkBx4lgj/JEZOQZTWCP5jWRAXiMoI/mdZEBeIygj+Z1kQF4jKCPwtMOPmwJ4I/C0w4+bAngj8LTDj5sCeCP0gjziDsHII/SCPOIOwcgj9JI84g7ByCP8s8O7v1FoI/yzw7u/UWgj/LPDu79RaCP5L2qY/pG4I/kvapj+kbgj+T9qmP6RuCP+YTHnnOGYI/5xMeec4Zgj/mEx55zhmCP0dSm8YeF4I/RlKbxh4Xgj9HUpvGHheCP3DorFreF4I/cOisWt4Xgj9w6Kxa3heCP+9d0V0jCII/FcCrwuqggT8VwKvC6qCBPx5g/m2ZqYE/H2D+bZmpgT8fYP5tmamBPwy1Rlq3wIE/DLVGWrfAgT8NtUZat8CBP2v2IfYSroE/bPYh9hKugT9s9iH2Eq6BPxqMRFNDpYE/GoxEU0OlgT94CCuYH8KBPwVs+CaLvoE/BWz4Jou+gT8FbPgmi76BPzPoNzR83IE/M+g3NHzcgT806Dc0fNyBP2d7RzPF4IE/aXtHM8XggT9pe0czxeCBPxNDq6dj0oE/E0Orp2PSgT8TQ6unY9KBPwvB+W/2zIE/C8H5b/bMgT8Kwflv9syBPwnTjRQ514E/CtONFDnXgT8K040UOdeBP+ThA2eOxoE/5eEDZ47GgT/k4QNnjsaBPwIw8lWx0YE/ATDyVbHRgT8CMPJVsdGBP0m6FaoXyIE/RCykxpDVgT9ELKTGkNWBP7SW56w6woE/tJbnrDrCgT+0luesOsKBP6h3yZgWtIE/qXfJmBa0gT+qd8mYFrSBP/jQAv9CuYE/+dAC/0K5gT/40AL/QrmBP9Maftllx4E/0xp+2WXHgT9rl3zBdaKBP4GXH5iqmYE/gZcfmKqZgT+Dlx+YqpmBPzgkFq6Yr4E/OCQWrpivgT84JBaumK+BP2+q2+Txt4E/b6rb5PG3gT9vqtvk8beBP425DR46soE/jbkNHjqygT+MuQ0eOrKBP2HxWt+21IE/YfFa37bUgT9h8VrfttSBPwv5ztQwy4E/C/nO1DDLgT8L+c7UMMuBP108EG5P0oE/XDwQbk/SgT9dPBBuT9KBP4K/YaetvYE/gr9hp629gT+Bv2Gnrb2BP4U+zPyrsYE/WzRUUL4Ogj9cNFRQvg6CP19GDomn84E/YEYOiafzgT9gRg6Jp/OBP82d8LLk6IE/zZ3wsuTogT/NnfCy5OiBPzyC9HKx34E/PYL0crHfgT88gvRysd+BPzOLPi503YE/M4s+LnTdgT91Kk9WXQCCP45c2LJjAYI/jlzYsmMBgj+NXNiyYwGCP41oYjiXE4I/jmhiOJcTgj+NaGI4lxOCP3J4jKLwDYI/cXiMovANgj9xeIyi8A2CP/7VbqgiFoI//9VuqCIWgj//1W6oIhaCPwV0aeuM/oE/BnRp64z+gT8GdGnrjP6BPxiQBj7TBII/GJAGPtMEgj8YkAY+0wSCP+leUsjfCYI/6V5SyN8Jgj/pXlLI3wmCP0q/JquHAII/S78mq4cAgj9KvyarhwCCP9+H2vbdDoI/+AWdxtEOgj/3BZ3G0Q6CP69LVXmgDoI/sEtVeaAOgj+uS1V5oA6CP6qWlrBV/YE/q5aWsFX9gT+qlpawVf2BP5wuJFRQAoI/nS4kVFACgj+dLiRUUAKCP445nX2V/oE/jzmdfZX+gT8HcPTZ3xKCP+6hgUx+CoI/7aGBTH4Kgj/toYFMfgqCPwhFao0cD4I/CUVqjRwPgj8IRWqNHA+CPxnWY5RUF4I/GNZjlFQXgj8Y1mOUVBeCP+YpOi5yGII/5Sk6LnIYgj/lKTouchiCP+yn8PVASII/66fw9UBIgj/qp/D1QEiCP2DQ7/arTII/X9Dv9qtMgj9f0O/2q0yCP1K4wNBzQII/UbjA0HNAgj9RuMDQc0CCP7bbVgqIOoI/tNtWCog6gj+021YKiDqCP97qKM1bRII/Vcn3VJ1Ugj9VyfdUnVSCP6UHGqY0WoI/pQcapjRagj+lBxqmNFqCPwhtjPHzYII/CG2M8fNggj8HbYzx82CCP8lJHmdYWYI/ykkeZ1hZgj/JSR5nWFmCP1oi1qWEaYI/WiLWpYRpgj8BwbugYhSCP3I62l4sH4I/cjraXiwfgj9yOtpeLB+CPzwbd4s8EII/PBt3izwQgj88G3eLPBCCP2azeu9fDoI/ZrN6718Ogj9ls3rvXw6CP+PkcmIOCoI/4uRyYg4Kgj/i5HJiDgqCP1G+LPSxA4I/Ur4s9LEDgj9Rviz0sQOCPxtl/+bA+oE/G2X/5sD6gT8bZf/mwPqBP6Zk7duP+4E/pWTt24/7gT+kZO3bj/uBP8l+kQKbAII/yX6RApsAgj/IfpECmwCCP3KQLEzx9YE/7hWN338Pgj/vFY3ffw+CP+RTdWX/BII/5FN1Zf8Egj/kU3Vl/wSCP5NGO4ndA4I/kkY7id0Dgj+SRjuJ3QOCP0AMsP8W/YE/QAyw/xb9gT9ADLD/Fv2BP3JrUmkvBII/cmtSaS8Egj+mBCNkxzeCPwruKcgtLoI/Cu4pyC0ugj8J7inILS6CP4S5HpwUJYI/hbkenBQlgj+EuR6cFCWCPz9/fWl2IoI/P399aXYigj89f31pdiKCP66/cx1pKoI/rb9zHWkqgj+uv3MdaSqCP49P/QE9M4I/j0/9AT0zgj+PT/0BPTOCPx30Btu9LoI/HPQG270ugj8d9AbbvS6CP/7zq87rPoI///Orzus+gj/+86vO6z6CP8jtbUnNMoI/yO1tSc0ygj/H7W1JzTKCPwH+zz2NOII/ImzqI3wbgj8ibOojfBuCPx4epEi2IoI/Hh6kSLYigj8dHqRItiKCP86ZK4N8EII/zZkrg3wQgj/NmSuDfBCCP5B+L1lpCYI/j34vWWkJgj+Pfi9ZaQmCPxJimPFiCoI/EmKY8WIKgj97lHKcflKCP+aQyzCKVoI/5ZDLMIpWgj/lkMswilaCPz5jm7bTUoI/PWObttNSgj88Y5u201KCPxsnjrStUII/GyeOtK1Qgj8aJ460rVCCPzs5SsuaSYI/OzlKy5pJgj87OUrLmkmCP2/aXDpc/YE/btpcOlz9gT9t2lw6XP2BP1pTilEE7IE/WlOKUQTsgT9aU4pRBOyBP1LNvKtR8YE/Us28q1HxgT9SzbyrUfGBP5N022d07YE/lHTbZ3TtgT+UdNtndO2BP3hQcRw02YE/F1NMca8jgj8WU0xxryOCPwqPG3w0I4I/C48bfDQjgj8Kjxt8NCOCPwjowg2LJYI/CejCDYslgj8I6MINiyWCP8fWl/4TL4I/x9aX/hMvgj/G1pf+Ey+CP0nr9pQ9OoI/Suv2lD06gj/q2FEJHxOCPyHriDPoDoI/IOuIM+gOgj8g64gz6A6CP0/aQjvTF4I/T9pCO9MXgj9Q2kI70xeCPz6KAbHzHII/PooBsfMcgj89igGx8xyCP0qy+uRNIYI/SrL65E0hgj9KsvrkTSGCP8+37NibVoI/z7fs2JtWgj/Pt+zYm1aCP1hCDw4LXII/WEIPDgtcgj9ZQg8OC1yCP4agchHXY4I/haByEddjgj+GoHIR12OCP9rrc1EQYYI/2+tzURBhgj/b63NREGGCP4NAln7nWoI/vB+MLhFcgj+8H4wuEVyCPygdgp3vXoI/KB2Cne9egj8oHYKd716CP7vxSphrZII/u/FKmGtkgj+78UqYa2SCPxqVcoJxWYI/G5VygnFZgj8alXKCcVmCPwMYgU3aX4I/BBiBTdpfgj/Xzz0R3VOCP3kHJChiQ4I/eQckKGJDgj95ByQoYkOCP0q1JX/dQII/SrUlf91Agj9KtSV/3UCCP1ULZdKYMoI/Vgtl0pgygj9WC2XSmDKCP3MAGC4PKII/cwAYLg8ogj9zABguDyiCP/idow9eD4I/+J2jD14Pgj/4naMPXg+CP6x+TKmJBYI/rH5MqYkFgj+tfkypiQWCP5Mbm/PCB4I/kxub88IHgj+SG5vzwgeCP5osHTQN/YE/mywdNA39gT+bLB00Df2BP6LE97OKBII/BnCHuyoegj8FcIe7Kh6CP1QFXiJOHoI/VQVeIk4egj9VBV4iTh6CP7gPSAmUKII/tw9ICZQogj+3D0gJlCiCP8Dx4GykHYI/wPHgbKQdgj/A8eBspB2CPxq01yWgFII/G7TXJaAUgj9G6dVzCQqCP91O3QefD4I/3U7dB58Pgj/eTt0Hnw+CP5InzpRoFII/kifOlGgUgj+TJ86UaBSCP8/UKw+6HYI/z9QrD7odgj/P1CsPuh2CP3VrxpycEoI/dWvGnJwSgj91a8acnBKCP02VjzTRC4I/TZWPNNELgj9NlY800QuCP/nuafFNDII/+e5p8U0Mgj/57mnxTQyCP+wMEZzsEoI/7AwRnOwSgj/sDBGc7BKCPxVZmAcBAoI/FVmYBwECgj8WWZgHAQKCP+LQ0Xcl+oE/pm5Pu3jSgT+mbk+7eNKBP4O85X2uzoE/g7zlfa7OgT+DvOV9rs6BPzq6pzI8zIE/OrqnMjzMgT87uqcyPMyBP1LfyGozz4E/Ut/IajPPgT9T38hqM8+BPzlisrAyy4E/OWKysDLLgT+yB+pen2uBPzKZF04iaYE/M5kXTiJpgT8zmRdOImmBPwrosF70aIE/CuiwXvRogT8L6LBe9GiBP0uxMhuFY4E/TLEyG4VjgT9MsTIbhWOBP6TNh9vKXoE/pc2H28pegT+lzYfbyl6BP6SwfAZcRYE/pLB8BlxFgT+ksHwGXEWBP/g+++ccN4E/+D775xw3gT/4PvvnHDeBP6Muc/axMIE/oy5z9rEwgT+jLnP2sTCBP15rblBbPYE/XmtuUFs9gT9fa25QWz2BP/uazhp9P4E/FRLufnhngT8VEu5+eGeBP0VodbmwaoE/RWh1ubBqgT9FaHW5sGqBP+hOfXs1WYE/5059ezVZgT/oTn17NVmBP9GPTM5+TIE/0Y9Mzn5MgT/Sj0zOfkyBP8BDMIAfVYE/wEMwgB9VgT9gFIR0sR+BP4VsEa7JFIE/hWwRrskUgT+FbBGuyRSBP95haQNhGIE/3WFpA2EYgT/eYWkDYRiBP6/yrqYOH4E/r/Kupg4fgT+u8q6mDh+BPw8nR9ESDoE/DydH0RIOgT8OJ0fREg6BPzJcDxL6G4E/MVwPEvobgT8xXA8S+huBPw8va0BVDoE/EC9rQFUOgT8QL2tAVQ6BP6B2YtvKB4E/oHZi28oHgT+gdmLbygeBP47ekVSEB4E/jt6RVIQHgT+P3pFUhAeBP/ntboFlEoE/++pPp4JDgT/76k+ngkOBP3FGuFfcNIE/cUa4V9w0gT9xRrhX3DSBPxD23By0OoE/EfbcHLQ6gT8R9twctDqBP5Zxu51SRIE/lnG7nVJEgT+XcbudUkSBP4F/FBQlUYE/gn8UFCVRgT/PRSDUQleBP5V+FrVeUYE/lX4WtV5RgT+Vfha1XlGBP3Pxu0/WT4E/dPG7T9ZPgT9z8btP1k+BP00+xnzPSoE/Tj7GfM9KgT9OPsZ8z0qBPxcN463APIE/Fw3jrcA8gT8XDeOtwDyBP5g9Pj8ESoE/mD0+PwRKgT+ZPT4/BEqBP/u/i0WeKIE//L+LRZ4ogT/8v4tFniiBP+HZ1cA3F4E/4tnVwDcXgT/i2dXANxeBPxSIR5ozJ4E/FYhHmjMngT8UiEeaMyeBP4McQZedNoE//FbYGa1igT/8VtgZrWKBP8vfK0hJYoE/y98rSEligT/L3ytISWKBP25U9EKWWoE/blT0QpZagT9vVPRCllqBP05C++ekY4E/TkL756RjgT9OQvvnpGOBP7sCh7gLWYE/uwKHuAtZgT8yOPzVNkCBPzVkozhiK4E/NWSjOGIrgT81ZKM4YiuBP19FpvrYJYE/YEWm+tglgT9gRab62CWBP+rfasVWD4E/699qxVYPgT/r32rFVg+BP2+S5a/JBoE/bpLlr8kGgT9ukuWvyQaBPwPP8zn0DYE/As/zOfQNgT8Dz/M59A2BP+hDLY5mC4E/6EMtjmYLgT/qQy2OZguBP3uv14TWBIE/e6/XhNYEgT98r9eE1gSBPyvEF6A03YA/LMQXoDTdgD8rxBegNN2AP1Lob+Pz4IA/LCoUBf3fgD8sKhQF/d+AP9WX1Ca14YA/1pfUJrXhgD/Xl9QmteGAP2sYYI2F1YA/axhgjYXVgD9rGGCNhdWAP5rxh8YI6IA/mvGHxgjogD+a8YfGCOiAP6yWxIJw54A/rJbEgnDngD9bdi9tFOuAP7JegCpA74A/sl6AKkDvgD+zXoAqQO+APz/o0E94BYE/QOjQT3gFgT9B6NBPeAWBP0tMX4O6/4A/S0xfg7r/gD9KTF+Duv+AP34hXyzg7YA/fyFfLODtgD9/IV8s4O2AP2id31/d8YA/aJ3fX93xgD9pnd9f3fGAP/UtviTp84A/9C2+JOnzgD/2Lb4k6fOAPwFbBpNv6oA/AFsGk2/qgD8CWwaTb+qAP+K1kjU5yoA/5LWSNTnKgD/itZI1OcqAPyCmmxga34A/6DKxLsnTgD/pMrEuydOAP6WYyqCy34A/pJjKoLLfgD+kmMqgst+APx0jjT/T3YA/HSONP9PdgD8dI40/092AP5DZNTYs4YA/kdk1NizhgD+R2TU2LOGAP5i0l5gwwIA/mbSXmDDAgD8kghvGFcuAP0j5HQ3/yoA/SfkdDf/KgD9J+R0N/8qAPxmEFpHBwoA/GYQWkcHCgD8ZhBaRwcKAP5lg9n1TzIA/mGD2fVPMgD+YYPZ9U8yAPxbyHOu9zoA/FvIc673OgD8W8hzrvc6AP6c/YJZ3qoA/pz9glneqgD+nP2CWd6qAP46eJlHio4A/j54mUeKjgD+OniZR4qOAP4NKzPxSoIA/g0rM/FKggD+ESsz8UqCAP+NhdQ6on4A/42F1DqifgD/jYXUOqJ+AP3IOf0EUhYA/u6gG+qZngD+6qAb6pmeAP0TMYP4BcYA/RMxg/gFxgD9EzGD+AXGAP0Z/ZvRkg4A/SH9m9GSDgD9Hf2b0ZIOAPzToIheQeIA/M+giF5B4gD806CIXkHiAP/MuX+2FboA/8y5f7YVugD8hFfJyOHiAP8WhVRy/hIA/xaFVHL+EgD/FoVUcv4SAPxoqvNjTi4A/HCq82NOLgD8bKrzY04uAP0vlWsyyhYA/S+VazLKFgD9L5VrMsoWAP/T/g91cgYA/9P+D3VyBgD/0/4PdXIGAP1ap0waKqoA/VanTBoqqgD9UqdMGiqqAP6RqLHowq4A/pWosejCrgD+laix6MKuAP0po/eZGm4A/Smj95kabgD9IaP3mRpuAP3PlvXMsnYA/c+W9cyydgD9y5b1zLJ2AP5GJ4riXnIA/UzQIZKyHgD9TNAhkrIeAP5hKgfmjdYA/mEqB+aN1gD+YSoH5o3WAP8cMhV/EYoA/xwyFX8RigD/HDIVfxGKAP6uzHRAjX4A/q7MdECNfgD+rsx0QI1+AP1VUWOnxTIA/VlRY6fFMgD8rg8NDkBaAPyO1NqsNF4A/JLU2qw0XgD8ltTarDReAP/veqK7JHIA/+96orskcgD/73qiuyRyAPwRfdR1YHIA/BV91HVgcgD8FX3UdWByAP1f+lLwYMoA/WP6UvBgygD9X/pS8GDKAP2o79dXzTYA/azv11fNNgD9sO/XV802AP8nN7sKzP4A/yc3uwrM/gD/Hze7Csz+APxFgB4xeM4A/EWAHjF4zgD8RYAeMXjOAP7Vxc98IOIA/tnFz3wg4gD+2cXPfCDiAP893LiXPOoA/jn9pDkxBgD+Of2kOTEGAP06OTWgGOIA/T45NaAY4gD9Pjk1oBjiAP6jJfUKJOYA/qMl9Qok5gD+nyX1CiTmAPxt+7AYOR4A/G37sBg5HgD8bfuwGDkeAPzanWkMvU4A/N6daQy9TgD+yN4/ZPJ6APwGqZly0nYA/AKpmXLSdgD//qWZctJ2APzrPOQb0kIA/Os85BvSQgD86zzkG9JCAP+qDe+V3koA/64N75XeSgD/qg3vld5KAP+j6z1mrmIA/6PrPWauYgD/o+s9Zq5iAPxF4b7IQDYE/EXhvshANgT8ReG+yEA2BP+lsHRssHoE/52wdGywegT/nbB0bLB6BP6Af9GxkIIE/oR/0bGQggT+gH/RsZCCBPzyMLLF+JYE/PIwssX4lgT88jCyxfiWBP/ShkwUnJ4E/h+5AA1GUgT+H7kADUZSBP8Y3Qo6/m4E/xTdCjr+bgT/FN0KOv5uBPyLt9lzNjIE/I+32XM2MgT8k7fZczYyBP9dZwL27hIE/1lnAvbuEgT/VWcC9u4SBPxpj4A6vcoE/GmPgDq9ygT/dpB6pnOOBP23QmgTk2IE/bdCaBOTYgT9t0JoE5NiBPwMd/ShO0YE/BB39KE7RgT8EHf0oTtGBP5w6VNMx24E/nDpU0zHbgT+bOlTTMduBP6T12uh62IE/o/Xa6HrYgT+l9droetiBP27tb/zkmoE/bu1v/OSagT9v7W/85JqBP9fg9S7ikYE/1+D1LuKRgT/W4PUu4pGBP24saoYAmYE/bixqhgCZgT9uLGqGAJmBP5zXkiT9l4E/nNeSJP2XgT+c15Ik/ZeBPz8mu1skeoE/rEIoo06lgT+rQiijTqWBP8qJIY9Ut4E/yokhj1S3gT/KiSGPVLeBP3B48XLLt4E/b3jxcsu3gT9wePFyy7eBP8FyONLTuIE/wnI40tO4gT/CcjjS07iBP7MIWJRSwoE/swhYlFLCgT+X7VF6J9OBP00lQRmJ1YE/TSVBGYnVgT9PJUEZidWBP7xEEB/gz4E/vEQQH+DPgT+8RBAf4M+BP3Kq6LqQ44E/cqroupDjgT9yqui6kOOBP5opP9wP6IE/mSk/3A/ogT+ZKT/cD+iBP3HDX9O4qYE/ccNf07ipgT9xw1/TuKmBP557q6Zss4E/nXurpmyzgT+de6umbLOBP7HGCosEvIE/scYKiwS8gT+wxgqLBLyBPzy8eFngqYE/PLx4WeCpgT88vHhZ4KmBP4VTBe9ToIE/RsJ0LrxYgT9FwnQuvFiBP4V1yCapU4E/hnXIJqlTgT+GdcgmqVOBPwCXUEGeaoE/AJdQQZ5qgT8Al1BBnmqBP7gJzgdZb4E/uAnOB1lvgT+4Cc4HWW+BP355EYmGXYE/fnkRiYZdgT8kERqFNieBP47vE7x4KoE/j+8TvHgqgT+P7xO8eCqBP2fsv2ToFIE/aOy/ZOgUgT9p7L9k6BSBPwVBFRdvDYE/BEEVF28NgT8EQRUXbw2BP59sKUlyGoE/nmwpSXIagT+ebClJchqBP+cyTnbxRoE/5jJOdvFGgT/mMk528UaBP3EOSPQfUIE/cQ5I9B9QgT9xDkj0H1CBP16vawoOUIE/Xa9rCg5QgT9er2sKDlCBP6Z1adxWPYE/pnVp3FY9gT+ndWncVj2BP1m+SNGcVYE/NGxD2aQkgT8zbEPZpCSBPxiLbtACJoE/F4tu0AImgT8Xi27QAiaBPxmyHUe8O4E/GLIdR7w7gT8Ysh1HvDuBP8+KmRIaRYE/z4qZEhpFgT/QipkSGkWBP6pg8CWTRYE/qmDwJZNFgT+njPc40+WAPzA+7AJF44A/MD7sAkXjgD8wPuwCReOAP1M/Gvjm2YA/Uj8a+ObZgD9SPxr45tmAPz5OsJ9p6IA/Pk6wn2nogD8+TrCfaeiAP5PcrQZ56YA/k9ytBnnpgD+T3K0GeemAPwynViIAFoE/DKdWIgAWgT8Lp1YiABaBP/xK34NMGYE/+0rfg0wZgT/7St+DTBmBP7QHDaF/F4E/swcNoX8XgT+zBw2hfxeBP/uOpurDDIE/+o6m6sMMgT/6jqbqwwyBP6X5jWgVFIE/qiYM65kUgT+qJgzrmRSBP8uYLW0vGYE/y5gtbS8ZgT/KmC1tLxmBP9vLKhkKF4E/28sqGQoXgT/byyoZCheBP/aikP4zEIE/9qKQ/jMQgT/1opD+MxCBP84Iqwx8EoE/zQirDHwSgT86p/ZEtBeBPxS2ZgsjFoE/FLZmCyMWgT8UtmYLIxaBPzC+GvsBA4E/ML4a+wEDgT8wvhr7AQOBP7FWG02/9IA/sVYbTb/0gD+xVhtNv/SAPwvIf3EZ94A/DMh/cRn3gD8LyH9xGfeAPwvHUFXyBYE/C8dQVfIFgT8Lx1BV8gWBP+TMdZfUDIE/5Mx1l9QMgT/lzHWX1AyBP7qYXV06DoE/uphdXToOgT+6mF1dOg6BP2WnIe4rBoE/ZKch7isGgT9kpyHuKwaBP2DwBOWjBYE/pwhW62o1gT+mCFbrajWBP7IKjQamPIE/sgqNBqY8gT+yCo0GpjyBP6qAP0aUR4E/qoA/RpRHgT+pgD9GlEeBP6JOkz3zU4E/ok6TPfNTgT+hTpM981OBP07MQ8kKQoE/TsxDyQpCgT9r77w3Cz+BP/6v7TkfUYE//q/tOR9RgT/9r+05H1GBPyeZ+0VlUoE/J5n7RWVSgT8omftFZVKBP+36ejh0SIE/7vp6OHRIgT/s+no4dEiBP4Qm+LQrRYE/hCb4tCtFgT+EJvi0K0WBPwyR/0myW4E/DJH/SbJbgT8Mkf9JsluBP0mKfRsuT4E/SYp9Gy5PgT9Jin0bLk+BP9Uwq+cJT4E/1TCr5wlPgT/UMKvnCU+BP29+wa0pRYE/b37BrSlFgT9vfsGtKUWBPxqP8uHmQ4E/43UrmUxYgT/idSuZTFiBP1zVnyixVIE/XNWfKLFUgT9c1Z8osVSBP5jKM5JdVYE/mMozkl1VgT+ZyjOSXVWBP4bySdtWZ4E/hvJJ21ZngT+G8knbVmeBPw16fE0BaoE/DXp8TQFqgT8X33rYtFiBP7zs+oKnV4E/uuz6gqdXgT+67PqCp1eBP207G8tISIE/bjsby0hIgT9uOxvLSEiBP2M2+ugYN4E/Yzb66Bg3gT9iNvroGDeBP6MUX0Z7OoE/ohRfRns6gT+iFF9GezqBP5ArlTQbO4E/kSuVNBs7gT+QK5U0GzuBP4BH2BdVSYE/gEfYF1VJgT+AR9gXVUmBP1UAFT06PYE/VgAVPTo9gT9WABU9Oj2BP2+M/LtPR4E/b4z8u09HgT9ujPy7T0eBPxjgz1T/TIE/7IPsSKxLgT/sg+xIrEuBPxJzsUnIOIE/EnOxScg4gT8Sc7FJyDiBPzrA4YD9PYE/O8DhgP09gT87wOGA/T2BP+WAL0u/PYE/5oAvS789gT/lgC9Lvz2BP+Jd3NqJM4E/413c2okzgT/HGJOJcDiBP7U+m/sMMIE/tT6b+wwwgT+1Ppv7DDCBP56BYwNGMIE/n4FjA0YwgT+fgWMDRjCBP1PirDmFMYE/U+KsOYUxgT9T4qw5hTGBP11qU6w8M4E/XmpTrDwzgT9dalOsPDOBP8qKTaEBgYE/yopNoQGBgT/Lik2hAYGBP4N5NJqce4E/hHk0mpx7gT+EeTSanHuBP5UAT+u+aYE/lgBP675pgT+VAE/rvmmBP3J5zCmxcIE/cnnMKbFwgT9zecwpsXCBP0n5c87BcoE/c/D7nodbgT908Pueh1uBPyIFVRN/X4E/IgVVE39fgT8jBVUTf1+BP749uAZnXoE/vz24BmdegT+/PbgGZ16BP5qBEf5AWYE/moER/kBZgT+bgRH+QFmBPwqNa+UsTYE/Co1r5SxNgT9j83UwL0aBP6HuQSf8UIE/ou5BJ/xQgT+i7kEn/FCBPyDha/p4VIE/IeFr+nhUgT8i4Wv6eFSBP82P1XLNRYE/zo/Vcs1FgT/Qj9VyzUWBP7qu7R3ZVIE/u67tHdlUgT+8ru0d2VSBPwri2SgWEoE/CuLZKBYSgT8M4tkoFhKBP3W4MkMyCIE/d7gyQzIIgT93uDJDMgiBP4LYjgD39IA/hNiOAPf0gD+F2I4A9/SAP1c+ieEfFoE/WT6J4R8WgT9ZPonhHxaBP1SnQPz/KYE/zlBlglsVgT/OUGWCWxWBP4WedFVcEYE/h550VVwRgT+InnRVXBGBPxJSNzySFYE/E1I3PJIVgT8VUjc8khWBP9fQdBzxCYE/2dB0HPEJgT/a0HQc8QmBP3abrLGEA4E/d5ussYQDgT/enUD/tCKBPyuVQYzmLoE/LJVBjOYugT8tlUGM5i6BP++cJDZeH4E/8ZwkNl4fgT/xnCQ2Xh+BP51Lxd4tIIE/nUvF3i0ggT+fS8XeLSCBP8htsnGjB4E/yW2ycaMHgT/LbbJxoweBPzb2rw1vBYE/N/avDW8FgT849q8NbwWBPxDjBjEe/oA/EeMGMR7+gD8T4wYxHv6APypYNid9+YA/K1g2J335gD8sWDYnffmAP5dbKCXcAYE/mVsoJdwBgT+aWygl3AGBPxxq/pG6EYE/0+YnV8UzgT/U5idXxTOBPx1zL2KzLoE/HnMvYrMugT8fcy9isy6BP94haiG/L4E/3iFqIb8vgT/eIWohvy+BP9tszgELKIE/3GzOAQsogT/dbM4BCyiBPxkcR9xTOoE/GhxH3FM6gT8lW9sxYwSBP/IbA94Z+IA/9BsD3hn4gD/zGwPeGfiAPyc0jGE84IA/JzSMYTzggD8oNIxhPOCAP3l1KvNJ9IA/eXUq80n0gD95dSrzSfSAPxgjwYPdzIA/GCPBg93MgD8ZI8GD3cyAP261nQb/4IA/brWdBv/ggD9vtZ0G/+CAP31y6oqY14A/fXLqipjXgD98cuqKmNeAPzFZ2wU21IA/MVnbBTbUgD8xWdsFNtSAP5NlxW+J1oA/k2XFb4nWgD+SZcVvidaAPxPjW5bs6IA/jDPCjdohgT+LM8KN2iGBPw/gXACwFIE/D+BcALAUgT8P4FwAsBSBP9MT0HguGYE/1BPQeC4ZgT/UE9B4LhmBPysHaMgID4E/KwdoyAgPgT8qB2jICA+BP3e1gS6gA4E/drWBLqADgT/MJIg7ztuAP2bVBlPw1IA/ZtUGU/DUgD9k1QZT8NSAPyu9CGYy3YA/Kb0IZjLdgD8ovQhmMt2AP+iV/Jje7IA/6JX8mN7sgD/nlfyY3uyAP/4A7xW66IA//QDvFbrogD/9AO8VuuiAP76zHrk6+oA/vbMeuTr6gD+8sx65OvqAP/5lL65w/YA//mUvrnD9gD/9ZS+ucP2AP2+ztKMb/YA/b7O0oxv9gD9vs7SjG/2AP5GZHRiqCoE/kJkdGKoKgT+QmR0YqgqBP7xkCZrSDoE/pwaBj7EVgT+mBoGPsRWBP+3fvOk8HIE/7d+86TwcgT/t37zpPByBP/B+88WsGYE/737zxawZgT/wfvPFrBmBP+LL62RWG4E/4svrZFYbgT/iy+tkVhuBP2Xhl9nhIIE/ZeGX2eEggT/IDmF8IwSBP/eM5WTOGYE/94zlZM4ZgT/3jOVkzhmBP5upfWSLGIE/m6l9ZIsYgT+bqX1kixiBP9xe1FblHIE/3V7UVuUcgT/dXtRW5RyBP0eqPEAvE4E/R6o8QC8TgT9GqjxALxOBP4gdrU0T3oA/iB2tTRPegD+IHa1NE96AP/GiKXuQ5IA/8aIpe5DkgD/xoil7kOSAP3DhQdde4oA/cOFB117igD9w4UHXXuKAP4rE0pDd54A/isTSkN3ngD+KxNKQ3eeAPxkmBg3O5oA/tVpUEXXMgD+1WlQRdcyAP5aZYdM6x4A/l5lh0zrHgD+XmWHTOseAP24IQKD53oA/bghAoPnegD9uCECg+d6AP4wB2MXr4YA/jAHYxevhgD+MAdjF6+GAP6NyLvCP6YA/o3Iu8I/pgD8Al+L9WPGAP2iA0PeG8IA/aIDQ94bwgD9ogND3hvCAP5DP/vZA7oA/kc/+9kDugD+Rz/72QO6AP5ICncL77oA/kgKdwvvugD+SAp3C++6AP1gMac9D+4A/WQxpz0P7gD9ZDGnPQ/uAPykgL+XCDIE/KSAv5cIMgT8pIC/lwgyBP4G2zpnc+YA/gbbOmdz5gD+Cts6Z3PmAP81wgD56+YA/zXCAPnr5gD/NcIA+evmAP8k/A6+7/IA/yT8Dr7v8gD/KPwOvu/yAPzfsCuKO9YA/URYuVOP+gD9RFi5U4/6AP2dIxO8fBoE/aEjE7x8GgT9oSMTvHwaBPw7GqYtGCoE/Dsapi0YKgT8OxqmLRgqBPz87F1qwC4E/PjsXWrALgT9AOxdasAuBP5u1q5hGDYE/m7WrmEYNgT8YJ4BnovGAP8gbtRgb8IA/xxu1GBvwgD/IG7UYG/CAP+TGz3wj9oA/5MbPfCP2gD/kxs98I/aAP2HM8z6P84A/YszzPo/zgD9izPM+j/OAP77+mXOe54A/vv6Zc57ngD++/plznueAPxnYaa6r54A/GthprqvngD8a2Gmuq+eAP8P/zL4g5IA/w//MviDkgD/D/8y+IOSAPx5ebkFo14A/IF5uQWjXgD8fXm5BaNeAP2iqI3iR2oA/aKojeJHagD9pqiN4kdqAPw2Vfyr524A/fmVWRDfbgD99ZVZEN9uAP7QH2HM71IA/tAfYczvUgD+0B9hzO9SAPxCkPUPi1IA/D6Q9Q+LUgD8PpD1D4tSAP/BtHgqXzoA/720eCpfOgD/wbR4Kl86AP3SGZRQg0YA/dIZlFCDRgD+R1gfomNCAPznSP8+bzIA/ONI/z5vMgD840j/Pm8yAP7nI6RF5vYA/usjpEXm9gD+6yOkReb2AP9kZS3yivIA/2BlLfKK8gD/YGUt8oryAP4aAHb1UvYA/hYAdvVS9gD+FgB29VL2AP3qc34lu5oA/epzfiW7mgD95nN+JbuaAP4zI4j4y7oA/jMjiPjLugD+MyOI+Mu6AP2lMBsXh94A/aUwGxeH3gD9pTAbF4feAP4X2gXAAAIE/hfaBcAAAgT+E9oFwAACBP691oHS6AYE/qDztzPzkgD+nPO3M/OSAP86lVW8P6oA/zqVVbw/qgD/NpVVvD+qAP1IfGt+O84A/UR8a347zgD9RHxrfjvOAP64wvuVf5IA/rjC+5V/kgD+uML7lX+SAP5jjIi/t54A/mOMiL+3ngD/SVKLSmNqAP1Q0xZQp5oA/VTTFlCnmgD9VNMWUKeaAP2vJDsw55IA/askOzDnkgD9ryQ7MOeSAPx/dsZiQ3YA/H92xmJDdgD8e3bGYkN2AP3hXkJI834A/eFeQkjzfgD93V5CSPN+AP53pjbgq5YA/nemNuCrlgD+d6Y24KuWAPyjcmXwo6YA/KNyZfCjpgD8o3Jl8KOmAPzHdRV4Y4YA/Md1FXhjhgD8w3UVeGOGAPxfWxR1l5YA/F9bFHWXlgD8X1sUdZeWAPzfRTkq+24A/Xt+MLp3CgD9d34wuncKAP0bCvXVutoA/RsK9dW62gD9Gwr11braAP99ZBmnArYA/31kGacCtgD/fWQZpwK2APwb3iZMUt4A/BveJkxS3gD8H94mTFLeAP2iQXGaDtIA/aJBcZoO0gD/V7LzN9cmAP9YBlpwsxIA/1gGWnCzEgD/WAZacLMSAP41n1/0HvoA/jWfX/Qe+gD+NZ9f9B76AP8VJziG6t4A/xUnOIbq3gD/GSc4hureAP8J2Js89s4A/wXYmzz2zgD/CdibPPbOAP3esLrNIsIA/eKwus0iwgD94rC6zSLCAPyF0EgZ0qoA/InQSBnSqgD8idBIGdKqAP/Ui7pWAqIA/9CLulYCogD/0Iu6VgKiAP9+hXI6NpoA/36Fcjo2mgD/foVyOjaaAP5+pteoGloA/0JX0MpysgD/QlfQynKyAPx/euGSfoIA/H964ZJ+ggD8g3rhkn6CAP193KwH8noA/XncrAfyegD9edysB/J6AP5iBeA4emYA/mIF4Dh6ZgD+YgXgOHpmAP2hzMWq6noA/aHMxarqegD9gDVWW3qaAP0QxzyKcq4A/RDHPIpyrgD9DMc8inKuAP3cm62pQvYA/dybralC9gD93JutqUL2AP+2xI3RNvoA/7bEjdE2+gD/tsSN0Tb6AP32Hdf/EvIA/fYd1/8S8gD99h3X/xLyAP4O7+/eXn4A/hLv795efgD+Eu/v3l5+APwi2g9V7oYA/CLaD1XuhgD8JtoPVe6GAP6U7vqgzl4A/pTu+qDOXgD+lO76oM5eAP0ABCPVYmoA/PwEI9ViagD9AAQj1WJqAP9EKZJOBkIA/C3BZ4wyXgD8McFnjDJeAP5zwjRPenIA/nPCNE96cgD+c8I0T3pyAP/6cRUuwm4A//pxFS7CbgD//nEVLsJuAP1i82aV5pYA/WLzZpXmlgD9YvNmleaWAPwFp2hBtq4A/AWnaEG2rgD/TAa8b2baAP5VKYNfWtIA/lkpg19a0gD+VSmDX1rSAP5VpkAN6sIA/lWmQA3qwgD+VaZADerCAPwa7/3FtuoA/Brv/cW26gD8Fu/9xbbqAP6HhpEfGuIA/oOGkR8a4gD+h4aRHxriAPyImh5QMnoA/IyaHlAyegD8jJoeUDJ6APwy/CmbtmYA/DL8KZu2ZgD8Nvwpm7ZmAP+aKZSoNkoA/5YplKg2SgD/limUqDZKAP48i4qC/m4A/kCLioL+bgD+PIuKgv5uAP8zRl1wXmoA/7TCL3mydgD/sMIvebJ2APzjhCafOn4A/OOEJp86fgD834Qmnzp+AP8rPY7cgmYA/y89jtyCZgD/Kz2O3IJmAPy/Hd85emoA/L8d3zl6agD8vx3fOXpqAP5vurc+pmoA/m+6tz6magD9tvsfM4p6AP6n0lrxeoYA/qPSWvF6hgD+o9Ja8XqGAP2R0pVeHp4A/ZXSlV4engD9ldKVXh6eAP6QjNvJGpoA/pCM28kamgD+kIzbyRqaAP+p65vIWo4A/6nrm8hajgD/qeubyFqOAPwe4UcLKtYA/CLhRwsq1gD8HuFHCyrWAP6Bxfuunq4A/oXF+66ergD+gcX7rp6uAP6JQVsojsoA/olBWyiOygD+iUFbKI7KAP+QRc6+AuIA/5BFzr4C4gD/jEXOvgLiAP0IjLKnXt4A/Dvj0VlLdgD8O+PRWUt2AP4CQJ4IN4oA/gJAngg3igD+AkCeCDeKAPwVIr6aZ5YA/BUivppnlgD8ESK+mmeWAP4gfCER454A/iB8IRHjngD+IHwhEeOeAP0pHXnri74A/SkdeeuLvgD9G/It6JeiAP9AfVoRM7IA/zx9WhEzsgD/PH1aETOyAP1PgmOPG54A/U+CY48bngD9U4JjjxueAPw1bM6A97YA/DVszoD3tgD8MWzOgPe2APw3x/UTQ7oA/DPH9RNDugD8M8f1E0O6AP/Gs/xYk4oA/8Kz/FiTigD/wrP8WJOKAP21W3AZf4YA/bFbcBl/hgD9tVtwGX+GAP7REaU2C3oA/tERpTYLegD+zRGlNgt6APypaSzwm3oA/KVpLPCbegD8qWks8Jt6APwaPeB4T5YA/1EByJA7hgD/VQHIkDuGAP9V079IP4YA/1XTv0g/hgD/VdO/SD+GAP8C4/yB57YA/wLj/IHntgD/AuP8gee2AP+b++7SB7oA/5v77tIHugD/l/vu0ge6APz2mT+ry7oA/PaZP6vLugD9Qv4lONeuAP6l+lCUB5IA/qX6UJQHkgD+pfpQlAeSAPwAhlnyW3oA/ACGWfJbegD//IJZ8lt6AP46jwH9u5IA/jaPAf27kgD+No8B/buSAPxCQPirY5YA/EJA+KtjlgD8PkD4q2OWAP+EBqAFT54A/4QGoAVPngD/iAagBU+eAP58DMZl764A/nwMxmXvrgD+fAzGZe+uAP+4SCKcZ6YA/7hIIpxnpgD/tEginGemAPy6fCaN/74A/LZ8Jo3/vgD8tnwmjf++AP4qRhpvH74A/j+GmrkrwgD+Q4aauSvCAPwVcv7d77oA/BVy/t3vugD8FXL+3e+6APwGEr9zU5oA/AISv3NTmgD//g6/c1OaAP+RBnQuf4oA/5EGdC5/igD/kQZ0Ln+KAP3z+rfY24oA/ev6t9jbigD/5kPccSu6AP6qHYiyC6IA/qYdiLILogD+ph2IsguiAPy3Tns6D54A/LdOezoPngD8s057Og+eAP+5jmb254oA/7WOZvbnigD/sY5m9ueKAP5BJi0uc3oA/kEmLS5zegD+QSYtLnN6AP1Xns8xd6YA/VeezzF3pgD9V57PMXemAPwfeJwph6IA/Bt4nCmHogD8F3icKYeiAP3jrIc5X44A/eOshzlfjgD936yHOV+OAP+0W+F2B4oA/7Bb4XYHigD/sFvhdgeKAP7yUkbvH6oA/CCo1gfYFgT8HKjWB9gWBP00FqO6eAYE/TAWo7p4BgT9LBajungGBPyXIfmtTBYE/JMh+a1MFgT8kyH5rUwWBP6kzWMpw/oA/qDNYynD+gD+nM1jKcP6APyzpRhB+/oA/LOlGEH7+gD+EucsO9BKBPzE2IgyLD4E/MDYiDIsPgT8vNiIMiw+BP6PolTVgC4E/oeiVNWALgT+f6JU1YAuBP5rrGc7XEYE/mOsZztcRgT+Y6xnO1xGBP2w6Xz9mF4E/ajpfP2YXgT9pOl8/ZheBP5CZLhj5LIE/kJkuGPksgT+OmS4Y+SyBPx3ZFe4vI4E/HdkV7i8jgT8c2RXuLyOBP6BiDW6yIIE/oGINbrIggT+fYg1usiCBP7mva+IgHYE/ua9r4iAdgT+5r2viIB2BPwtMzowqE4E/nilqmUIlgT+cKWqZQiWBP6l3G6SILIE/qXcbpIgsgT+odxukiCyBP7jQvlJKK4E/uNC+UkorgT+40L5SSiuBP2Ut47QRLIE/ZS3jtBEsgT9lLeO0ESyBP1/WmX7ZLYE/X9aZftktgT8Z9g3Cxi2BPx5qv87kIoE/H2q/zuQigT8far/O5CKBP4Mpd+i/F4E/gyl36L8XgT+DKXfovxeBPxtGb8VvJIE/G0ZvxW8kgT8aRm/FbySBP7VuobxNJYE/tW6hvE0lgT+0bqG8TSWBP3qOnqoTN4E/e46eqhM3gT97jp6qEzeBP8uL/9cDPYE/yYv/1wM9gT/Li//XAz2BP76/nVG5RIE/vr+dUblEgT++v51RuUSBP5PB2zM9WIE/k8HbMz1YgT+UwdszPViBPxiDRSRoSoE/HAHv/y1HgT8bAe//LUeBP18rbQbKRYE/XyttBspFgT9gK20GykWBPynfzgbyQIE/KN/OBvJAgT8p384G8kCBP3y1XPvBRYE/fLVc+8FFgT97tVz7wUWBP/b+GIgJRIE/9v4YiAlEgT/xaAEBS1WBP8H9fJjqUIE/wP18mOpQgT/A/XyY6lCBP3uZSrweSIE/e5lKvB5IgT97mUq8HkiBP8jq9nGuRIE/yOr2ca5EgT/H6vZxrkSBPzxQE0IpOoE/O1ATQik6gT87UBNCKTqBPy/jbnMdNoE/L+Nucx02gT8v425zHTaBP2OEPgN9M4E/Y4Q+A30zgT9hhD4DfTOBP69uBrdlMYE/r24Gt2UxgT+vbga3ZTGBP6BDf8lLPoE/oEN/yUs+gT+gQ3/JSz6BPyMw7o/yOYE/5k7W2HEdgT/lTtbYcR2BP6nxYk1QIIE/qfFiTVAggT+o8WJNUCCBP/a6QFoqE4E/9bpAWioTgT/0ukBaKhOBP4nIKRwJCoE/icgpHAkKgT+JyCkcCQqBP3OVwFDCDIE/cpXAUMIMgT9iXcXMTCKBPz2C1bTUIIE/PoLVtNQggT8+gtW01CCBP1BshF/fJ4E/UGyEX98ngT9PbIRf3yeBPzQCvzLmK4E/NAK/MuYrgT80Ar8y5iuBP+f+rluHMoE/5/6uW4cygT/m/q5bhzKBP7Kv+gvlQYE/sa/6C+VBgT+wr/oL5UGBP8dDyu5rSIE/xkPK7mtIgT/GQ8rua0iBPxedaFh1SIE/Fp1oWHVIgT8WnWhYdUiBP3EkhiXxR4E/bySGJfFHgT9vJIYl8UeBP/9SkBeER4E/DqJ66Ik1gT8NonroiTWBP+FVm0mAMoE/4VWbSYAygT/hVZtJgDKBP/KaW23NM4E/8Zpbbc0zgT/xmlttzTOBP9zzBf4hPoE/2/MF/iE+gT/b8wX+IT6BP+u+BGFyQYE/674EYXJBgT9cHwXrGyKBP5jt5A9OJIE/l+3kD04kgT+W7eQPTiSBPzz5z0hUMIE/O/nPSFQwgT87+c9IVDCBP0wbQXh4MIE/SxtBeHgwgT9LG0F4eDCBP393HMNBJYE/f3ccw0ElgT9+dxzDQSWBP6HWG/4GIoE/oNYb/gYigT+f1hv+BiKBPwKkzjqxLYE/AaTOOrEtgT8ApM46sS2BPweBM23vLoE/B4Ezbe8ugT8HgTNt7y6BP6idOP3ALIE/qJ04/cAsgT+onTj9wCyBP8tntf0wLIE/GXIyes0rgT8ZcjJ6zSuBP0F4SYY2NIE/QHhJhjY0gT9AeEmGNjSBP7eY7ptRN4E/t5jum1E3gT+2mO6bUTeBP/C+fc93MYE/7759z3cxgT/vvn3PdzGBP8QD2ldUK4E/xAPaV1QrgT+OdLJrcCiBP6t9czrjJIE/q31zOuMkgT+rfXM64ySBPzYbfzTwKYE/Nht/NPApgT82G3808CmBPz29medHK4E/Pb2Z50crgT89vZnnRyuBP8drLYArK4E/x2stgCsrgT/Hay2AKyuBP2bKFEy5J4E/ZcoUTLkngT9lyhRMuSeBP09oWMFJI4E/T2hYwUkjgT9PaFjBSSOBPz5iHzreJIE/PWIfOt4kgT89Yh863iSBPxIlHrsnI4E/EiUeuycjgT8TJR67JyOBP77NHbpsLIE/P9ozLWr6gD8/2jMtavqAPzXJpSrx8oA/NMmlKvHygD8zyaUq8fKAP1s6Sx+K7oA/WjpLH4rugD9aOksfiu6AP50TkQEp54A/nRORASnngD+eE5EBKeeAPyr9/P1z54A/Kv38/XPngD9M0tjFIbWAP7T80804toA/tPzTzTi2gD+0/NPNOLaAP09afUYmr4A/T1p9RiavgD9PWn1GJq+AP1FV7pvJqIA/UlXum8mogD9SVe6byaiAP2Q27J6XooA/ZDbsnpeigD9kNuyel6KAP9x9YtD/eIA/231i0P94gD/bfWLQ/3iAP+PwwOzPeoA/4/DA7M96gD/j8MDsz3qAP9xyHy8MfIA/3HIfLwx8gD/cch8vDHyAPwlR3rPGe4A/CVHes8Z7gD8JUd6zxnuAP/khd+maeYA/b+fbuAitgD9v59u4CK2AP3XJwRcBsYA/dMnBFwGxgD91ycEXAbGAPwPM2caVrYA/A8zZxpWtgD8DzNnGla2APxlcf5iYsIA/GVx/mJiwgD8ZXH+YmLCAP3dFMXvatIA/dkUxe9q0gD8birSamM+AP8OIKoDa0oA/w4gqgNrSgD/DiCqA2tKAP6LwqZ/0y4A/ovCpn/TLgD+i8Kmf9MuAP3khBjOxxIA/eSEGM7HEgD95IQYzscSAP0drnu99yIA/R2ue733IgD9Ia57vfciAP6XOvL1Es4A/pc68vUSzgD+kzry9RLOAP+ISZGz9rYA/4xJkbP2tgD/iEmRs/a2AP50428DIpIA/nTjbwMikgD+eONvAyKSAP/urzApnpYA/+6vMCmelgD/7q8wKZ6WAP23ORH2FoYA/9WO2tfWdgD/1Y7a19Z2AP6pdxEFbmYA/q13EQVuZgD+qXcRBW5mAP89psQm6moA/z2mxCbqagD/PabEJupqAP6JQExf9noA/o1ATF/2egD+jUBMX/Z6AP6Gmfld1nIA/oqZ+V3WcgD8cNVfZT5+APzldjZMynoA/Ol2NkzKegD86XY2TMp6APyuIi4UInoA/K4iLhQiegD8riIuFCJ6AP7h7Z582oYA/uHtnnzahgD+4e2efNqGAP6AVlGfZpoA/oBWUZ9mmgD+gFZRn2aaAPy8lNiuzfYA/MCU2K7N9gD8xJTYrs32APx9bhWuPdoA/H1uFa492gD8fW4Vrj3aAPwHzqJ4neoA/AfOonid6gD8B86ieJ3qAPxgp2Jddc4A/GCnYl11zgD8ZKdiXXXOAP7M0ExPVeoA/BeAUnDJqgD8F4BScMmqAPybOKsFPa4A/J84qwU9rgD8nzirBT2uAP5sTjjlfb4A/nROOOV9vgD+dE445X2+AP5Lz+kdHbIA/kvP6R0dsgD+S8/pHR2yAPzMkSZijbYA/MiRJmKNtgD93oXn6yUWAP9IFr+TySIA/0wWv5PJIgD/SBa/k8kiAP7CzKw9rSIA/sLMrD2tIgD+xsysPa0iAPxfa0NWhSYA/F9rQ1aFJgD8Y2tDVoUmAPxGrKpuqSoA/Easqm6pKgD8RqyqbqkqAP/DRn+NuK4A/8NGf424rgD/x0Z/jbiuAP1bMjog4JYA/V8yOiDglgD9XzI6IOCWAPywwHu+iKYA/LDAe76IpgD8sMB7voimAP4hex0GzNoA/iV7HQbM2gD+KXsdBszaAP13bjDdrPYA/IwKLwIdBgD8kAovAh0GAP9kVBG6oRYA/2RUEbqhFgD/aFQRuqEWAP36Ws3BVSoA/f5azcFVKgD9/lrNwVUqAP7OQIBCaSIA/s5AgEJpIgD+0kCAQmkiAP7vDHvXGRIA/u8Me9cZEgD8QVNRBaUaAP4MZgAXdS4A/hBmABd1LgD+EGYAF3UuAPzO7lUZPS4A/M7uVRk9LgD80u5VGT0uAPxBUuv6ZRYA/EVS6/plFgD8RVLr+mUWAP0WtZQ4wRYA/Rq1lDjBFgD9HrWUOMEWAP5FUhePlOIA/kVSF4+U4gD+SVIXj5TiAP0n7jmzCOoA/SvuObMI6gD9L+45swjqAP/EuvCUKNYA/8i68JQo1gD/yLrwlCjWAP3J8ZoS7K4A/cnxmhLsrgD9zfGaEuyuAP9cddWvKNIA/Zyt/MtAkgD9nK38y0CSAP3W+HyqhKYA/dr4fKqEpgD92vh8qoSmAP0n7MAoaOIA/SvswCho4gD9K+zAKGjiAPynGrdnoOoA/Kcat2eg6gD8qxq3Z6DqAP9z3wvyeQ4A/3ffC/J5DgD/Myz/Xv3SAPxtyw4Gub4A/HHLDga5vgD8ccsOBrm+AP5jKDbqEbIA/mcoNuoRsgD+ayg26hGyAPwDa+lhhboA/Adr6WGFugD8B2vpYYW6AP2E2M20VdYA/YTYzbRV1gD9iNjNtFXWAP8Iwc5cyX4A/wzBzlzJfgD/EMHOXMl+APwo798l+XYA/Czv3yX5dgD8MO/fJfl2APxhx2kT9XoA/GHHaRP1egD8YcdpE/V6APyVWRdDDZIA/JVZF0MNkgD8nVkXQw2SAP7UFnuHlbIA/BlM6ei5ngD8HUzp6LmeAP5W48+y4a4A/lrjz7LhrgD+WuPPsuGuAP9f3YOgIaYA/2Pdg6AhpgD/Y92DoCGmAP6nE3wX5bYA/qcTfBfltgD+pxN8F+W2AP94ImBSMaIA/3wiYFIxogD+RhL4eIkeAPyZ4V0+ARYA/J3hXT4BFgD8oeFdPgEWAP1ERYkGdQIA/UhFiQZ1AgD9SEWJBnUCAP2BqIRG/QYA/YGohEb9BgD9haiERv0GAP3LxYG2MOIA/cvFgbYw4gD9y8WBtjDiAP/OHJsBgPIA/84cmwGA8gD/zhybAYDyAP7p8JUkHN4A/uXwlSQc3gD+4fCVJBzeAP/ogIQa3OoA/+SAhBrc6gD/6ICEGtzqAP2TqrPpQPoA/Zeqs+lA+gD9m6qz6UD6APx1k9r1CNIA/DiWyGLAWgD8PJbIYsBaAP0O0qiR7E4A/RbSqJHsTgD9FtKokexOAPwoBvIGjBYA/CgG8gaMFgD8JAbyBowWAP/XcCmLD/H8/9twKYsP8fz/43Apiw/x/P7SSLG+eD4A/tJIsb54PgD87n83WHRKAP6ZNfODeGoA/p0184N4agD+nTXzg3hqAP6qY7XKUFYA/qpjtcpQVgD+rmO1ylBWAPxRzJnHyEIA/FXMmcfIQgD8VcyZx8hCAP/8Orf2wFIA//w6t/bAUgD//Dq39sBSAP5zzeLFIEYA/m/N4sUgRgD+b83ixSBGAP0Pxeui0BYA/RPF66LQFgD9E8XrotAWAPxFmoMzLAIA/EGagzMsAgD8RZqDMywCAP+fPJqj+DoA/5s8mqP4OgD/nzyao/g6AP4MM1cYqB4A/SE/6iJkegD9IT/qImR6APySDYRTYFoA/JINhFNgWgD8kg2EU2BaAP1JicG1tKYA/UWJwbW0pgD9RYnBtbSmAPxdwHjlQH4A/GHAeOVAfgD8XcB45UB+AP4NaJSSFHoA/g1olJIUegD9QwNrgtwmAP2fbzv/NGYA/Z9vO/80ZgD9o287/zRmAP5uSdAqNG4A/m5J0Co0bgD+cknQKjRuAP3J/OA5RGoA/cn84DlEagD9yfzgOURqAP6O9wJPqJYA/o73Ak+olgD+jvcCT6iWAP7bL3LjmCIA/t8vcuOYIgD+3y9y45giAP4ym4+Qj/X8/jKbj5CP9fz+MpuPkI/1/P16rQ9jlBIA/XatD2OUEgD9dq0PY5QSAP6mhluiH+X8/p6GW6If5fz+noZboh/l/P78WU5sT6n8/VgHiJlHrfz9WAeImUet/P4J26O738H8/gXbo7vfwfz+Bduju9/B/P/Q32VqU938/8zfZWpT3fz/yN9lalPd/P4YFGExSAIA/hgUYTFIAgD+GBRhMUgCAP0M4C5gH7H8/QzgLmAfsfz/F2Z+n0Ol/P6SG31+e538/pYbfX57nfz+lht9fnud/P0aqPPJ/538/Q6o88n/nfz9Fqjzyf+d/P6Q3ObNJ+X8/ozc5s0n5fz+hNzmzSfl/PzmqPUI/4n8/OKo9Qj/ifz85qj1CP+J/Py/sQhnUAIA/MOxCGdQAgD8v7EIZ1ACAP6RdIY6ZCYA/pF0hjpkJgD+jXSGOmQmAP/4AkiC0BoA//gCSILQGgD/9AJIgtAaAP+q+7+JNDYA/6r7v4k0NgD/rvu/iTQ2APz4A/wRi/n8/T/bqCxAFgD9P9uoLEAWAP932oJSsB4A/3PaglKwHgD/c9qCUrAeAP5ZG+hPLB4A/lUb6E8sHgD+VRvoTyweAP78hdCvt/H8/vyF0K+38fz+/IXQr7fx/P81xzcbuAoA/zXHNxu4CgD+TBwlH68t/P9xiBd+ztn8/3WIF37O2fz/bYgXfs7Z/Pxg2bjaosX8/FjZuNqixfz8WNm42qLF/P7/Epi3NyX8/vcSmLc3Jfz+9xKYtzcl/P5YvnSHgxX8/lS+dIeDFfz+TL50h4MV/P3s8ouaW1H8/eTyi5pbUfz96PKLmltR/P6jcOggs3n8/p9w6CCzefz+m3DoILN5/P+gTL3nP5n8/5xMvec/mfz/mEy95z+Z/P+Y+mnQSu38/5T6adBK7fz/kPpp0Ert/P611vmPMoX8/mk6sdIltfz+aTqx0iW1/Px1Qv23Xh38/HFC/bdeHfz8aUL9t14d/P96tFXtbmX8/3a0Ve1uZfz/brRV7W5l/P8jrk8lwqn8/xuuTyXCqfz/G65PJcKp/P6k+vNXUqX8/qT681dSpfz91mF9X+bd/P66WWTf11X8/rZZZN/XVfz+tllk39dV/P48G3D6H5n8/jwbcPofmfz+NBtw+h+Z/Pz9Vvglv+H8/QFW+CW/4fz8/Vb4Jb/h/Pzfm7XZqBoA/N+btdmoGgD825u12agaAP1qXIgj2IoA/WZciCPYigD9ZlyII9iKAP6k55iqIIoA/qTnmKogigD+pOeYqiCKAPxLVPj3yLYA/EtU+PfItgD8S1T498i2AP/0CzijaNYA//QLOKNo1gD/9As4o2jWAP7CwSlDIOIA/bMhOv3QugD9ryE6/dC6AP7HXryupHIA/sdevK6kcgD+w168rqRyAP+pB4JknDoA/6kHgmScOgD/qQeCZJw6AP5j6lxrrFYA/mPqXGusVgD+X+pca6xWAP4Utz6AgIoA/hC3PoCAigD/j5fJxdwqAP4zCPqDaC4A/i8I+oNoLgD+Lwj6g2guAP7vK7eReEoA/usrt5F4SgD+6yu3kXhKAP0KRXzKcHoA/QpFfMpwegD9CkV8ynB6AP1rp1TnQEoA/W+nVOdASgD9a6dU50BKAP0wNQqUzE4A/Sw1CpTMTgD9LDUKlMxOAP3AodhLCEoA/cCh2EsISgD9wKHYSwhKAPwR4rf2+FIA/BHit/b4UgD8EeK39vhSAP4Brp8jvIYA/gGunyO8hgD9/a6fI7yGAP1AzF8ljHYA/a0CbK0oQgD9rQJsrShCAPwKAodOJFYA/AoCh04kVgD8BgKHTiRWAP+eN4k3BCoA/543iTcEKgD/njeJNwQqAP9ahUM0gC4A/1qFQzSALgD/WoVDNIAuAP2iUvk81C4A/aJS+TzULgD8lNcS6pB2AP1gq93ByE4A/WCr3cHITgD9YKvdwchOAP/tCkSOkE4A/+0KRI6QTgD/7QpEjpBOAP9hndNd6GIA/12d013oYgD/WZ3TXehiAP7Ch+2DZC4A/sKH7YNkLgD+woftg2QuAP8Fo8NywOIA/wWjw3LA4gD/CaPDcsDiAP4RX1jHNPIA/g1fWMc08gD+CV9YxzTyAP2Vqs/VgSoA/ZWqz9WBKgD9larP1YEqAP89SeXNYP4A/z1J5c1g/gD/PUnlzWD+AP1Lua3v+OYA//tYQsnP+fz//1hCyc/5/P2+NH2ix/X8/b40faLH9fz9ujR9osf1/P+wEWwjx+H8/7QRbCPH4fz/tBFsI8fh/P4DSVMXx/38/gNJUxfH/fz9/0lTF8f9/P13lXqtlDoA/XOVeq2UOgD96mDMIoxmAPyx1UOwyHYA/LHVQ7DIdgD8sdVDsMh2APy4nOGxFHoA/LSc4bEUegD8tJzhsRR6AP37zVNoWFoA/fvNU2hYWgD9+81TaFhaAP/6BNa+lFYA//oE1r6UVgD/+gTWvpRWAP/CX8OEJLoA/8Jfw4QkugD/wl/DhCS6AP9/0M8IwKIA/3/QzwjAogD/f9DPCMCiAP+QfLDM5KYA/5R8sMzkpgD/lHywzOSmAPyK6VJOwL4A/IrpUk7AvgD8iulSTsC+AP2fy6ZyCI4A/BsbEOklIgD8GxsQ6SUiAP8epewzbR4A/x6l7DNtHgD/HqXsM20eAP0/qQR6kS4A/T+pBHqRLgD9P6kEepEuAP/Dg82GOS4A/8ODzYY5LgD/w4PNhjkuAPwhJRFEJR4A/CUlEUQlHgD8bfX6hMVeAP6CYAQGQUYA/n5gBAZBRgD+fmAEBkFGAPzjVa1JxU4A/N9VrUnFTgD831WtScVOAPw56QUHQT4A/DnpBQdBPgD8OekFB0E+AP64iKK1oVYA/riIorWhVgD+uIiitaFWAPxOO1MVxSIA/E47UxXFIgD8UjtTFcUiAP9EmMKYKPIA/0iYwpgo8gD/SJjCmCjyAP3BjDATwN4A/cGMMBPA3gD9wYwwE8DeAP1S8SE5KL4A/VLxITkovgD9UvEhOSi+AP0y/pL0AOIA/Rt1S1bhggD9H3VLVuGCAP1LQj1w4foA/UtCPXDh+gD9S0I9cOH6AP9OT734ufoA/05Pvfi5+gD/Uk+9+Ln6AP7qFRei2e4A/u4VF6LZ7gD+7hUXotnuAP462Umvbf4A/j7ZSa9t/gD9xtazuQXOAP1tEJ50reoA/WkQnnSt6gD9bRCedK3qAP3ZTDLIYc4A/dlMMshhzgD92UwyyGHOAP8ogRRIYcYA/ySBFEhhxgD/KIEUSGHGAP21ZwxyldYA/blnDHKV1gD9uWcMcpXWAP5yBsNnwhIA/nYGw2fCEgD+dgbDZ8ISAPyPnggT8joA/I+eCBPyOgD8j54IE/I6APxrijooImoA/GuKOigiagD8a4o6KCJqAP2W97/eEm4A/Zb3v94SbgD9mve/3hJuAP2OjOzb0m4A/H/JWCsC/gD8g8lYKwL+AP239eQq/v4A/bv15Cr+/gD9u/XkKv7+AP3DvWiKYwoA/ce9aIpjCgD9x71oimMKAP+690RA/yYA/7r3RED/JgD/vvdEQP8mAPx3zcek70IA/HfNx6TvQgD81ooTUlsKAP2k0fiGix4A/aTR+IaLHgD9pNH4hoseAP0G6SSz2zIA/QLpJLPbMgD9Bukks9syAP4BoPZDG0oA/gGg9kMbSgD+BaD2QxtKAPw1D53LPzIA/DUPncs/MgD8NQ+dyz8yAP0ocuQ15vIA/Sxy5DXm8gD9MHLkNebyAPyxuy3TyxYA/LG7LdPLFgD8sbst08sWAP7ocNRBa04A/uhw1EFrTgD+6HDUQWtOAP5/1q2YZzoA/oPWrZhnOgD+f9atmGc6APzhAsgl3v4A/87x2tDemgD/yvHa0N6aAP1peUACRpYA/Wl5QAJGlgD9aXlAAkaWAP+XUGEh+p4A/5tQYSH6ngD/l1BhIfqeAPz1cYUwBrIA/PlxhTAGsgD8+XGFMAayAP0Qp9DX2uIA/RCn0Nfa4gD/NO7Qe4v+AP+3i4ZxKDoE/7OLhnEoOgT/t4uGcSg6BP/UnsYqEF4E/9iexioQXgT/1J7GKhBeBP4epu+C/K4E/h6m74L8rgT+IqbvgvyuBP5749r7QJ4E/nfj2vtAngT+d+Pa+0CeBP15aRmkANIE/X1pGaQA0gT9fWkZpADSBPzT5jLHPN4E/NPmMsc83gT80+YyxzzeBP3hEBQB7NYE/eUQFAHs1gT95RAUAezWBP1v/0BbELIE/W//QFsQsgT9b/9AWxCyBP21tlXv+K4E/20kYdd5ngT/bSRh13meBP1kGMsyddIE/WQYyzJ10gT9ZBjLMnXSBP3j698GhboE/d/r3waFugT94+vfBoW6BPxjAawRmcYE/F8BrBGZxgT8XwGsEZnGBP0o1AyOxdYE/STUDI7F1gT8zkBLmBGaBP2NT/RR9aoE/YlP9FH1qgT9jU/0UfWqBPx8m54p5aoE/ICbninlqgT8gJueKeWqBP5d6viUTd4E/lnq+JRN3gT+Yer4lE3eBPwHXGH9OfoE/AdcYf05+gT8A1xh/Tn6BP12cEymspYE/XZwTKaylgT9dnBMprKWBPy3fOljypoE/LN86WPKmgT8s3zpY8qaBP6UmhtFmtYE/pSaG0Wa1gT+kJobRZrWBP+kGv3/DoYE/6ga/f8OhgT/pBr9/w6GBP2+o9dJQpYE/CB3jfeWwgT8HHeN95bCBPz8YKFEAsIE/PhgoUQCwgT8/GChRALCBP9o/L7yHwoE/2T8vvIfCgT/ZPy+8h8KBP+S5SluPvIE/47lKW4+8gT/kuUpbj7yBP9x/o14Q1YE/3H+jXhDVgT8xk8XxqwaCP00j6Vi9AoI/TSPpWL0Cgj9NI+lYvQKCPzle3G0m7YE/Ol7cbSbtgT86XtxtJu2BP2iRuuTr54E/Z5G65OvngT9okbrk6+eBP9lCI0yV5oE/2kIjTJXmgT/aQiNMleaBP2p4IpWK+IE/angilYr4gT9peCKViviBP8AjTPz2/IE/wSNM/Pb8gT/BI0z89vyBP/qOMDSL9YE//I4wNIv1gT/8jjA0i/WBP/IOKW54B4I/8g4pbngHgj/yDilueAeCPxhMutcgCII/PZVs/NLugT88lWz80u6BP1Q+Nx6a8oE/VD43HprygT9UPjcemvKBP/X8FB0N/IE/9fwUHQ38gT/0/BQdDfyBPxqT/LBi/4E/GpP8sGL/gT8ak/ywYv+BP7YeoXCYA4I/tx6hcJgDgj/BeHd9qvaBP6+8ThhX/4E/rrxOGFf/gT+uvE4YV/+BP2cpQpJAAoI/ZilCkkACgj9nKUKSQAKCP1A55dLPAYI/UDnl0s8Bgj9ROeXSzwGCP0zIXsOiAoI/TMhew6ICgj9MyF7DogKCP6HY2AV8+YE/otjYBXz5gT+h2NgFfPmBPwMILM0AAoI/AwgszQACgj8DCCzNAAKCP/CCi1irDYI/8YKLWKsNgj/xgotYqw2CP9j9bMNWD4I/2f1sw1YPgj/Y/WzDVg+CP8EF5GefDoI/ku6T17TzgT+S7pPXtPOBP292VJBD6oE/b3ZUkEPqgT9vdlSQQ+qBPw07a46F/4E/DTtrjoX/gT8OO2uOhf+BPyoGDBmi9oE/KgYMGaL2gT8pBgwZovaBP+v+H/2Q9IE/7P4f/ZD0gT/aIig3DziCPx8WtvlgN4I/Hxa2+WA3gj8fFrb5YDeCP2o5T6KVQYI/ajlPopVBgj9qOU+ilUGCPzLIk5q+PYI/MsiTmr49gj8yyJOavj2CP5IfklgSP4I/kh+SWBI/gj+SH5JYEj+CPxK/hP3tPII/Eb+E/e08gj8Rv4T97TyCPx/JmLXdP4I/H8mYtd0/gj8fyZi13T+CPybnyZPoT4I/JufJk+hPgj8m58mT6E+CP693Gx8aW4I/r3cbHxpbgj+udxsfGluCP30nanwVWoI/6V79BiBVgj/pXv0GIFWCP5peDB3VWYI/mV4MHdVZgj+ZXgwd1VmCPysYO4yPUYI/Kxg7jI9Rgj8rGDuMj1GCPwnu3gWGWII/Ce7eBYZYgj8J7t4FhliCP2fXHQglX4I/Z9cdCCVfgj98XBZ98LiCP2la8v/IuoI/aVry/8i6gj9oWvL/yLqCP5y/Sk6DvII/m79KToO8gj+bv0pOg7yCP0umxwQqxII/S6bHBCrEgj9LpscEKsSCP1L+EdbJvYI/Uv4R1sm9gj9S/hHWyb2CP0ZyzUGVo4I/RnLNQZWjgj9Gcs1BlaOCPxrCDYB+noI/GsINgH6egj8awg2Afp6CP7Xl/CnSm4I/tOX8KdKbgj+15fwp0puCP6gngr0AnoI/qCeCvQCegj+oJ4K9AJ6CP2DmTaZOoYI/k1707ROKgj+UXvTtE4qCP4cSkCTAk4I/hhKQJMCTgj+HEpAkwJOCP4bc3+SYmYI/htzf5JiZgj+G3N/kmJmCPw16QZ4ApoI/DHpBngCmgj8NekGeAKaCP6rIzA/Fq4I/qsjMD8Wrgj89PZnJu+iCPxSOXxVs3oI/FY5fFWzegj8Vjl8VbN6CP2FAlRbA7II/YUCVFsDsgj9hQJUWwOyCPwsKZ4w47YI/DApnjDjtgj8MCmeMOO2CP/YiCRYj8II/9SIJFiPwgj/1IgkWI/CCP3HrSho8KYM/cetKGjwpgz9w60oaPCmDPygHnj5qKYM/JweePmopgz8mB54+aimDP6feaFFmHYM/qN5oUWYdgz+n3mhRZh2DP7q1Raa+GIM/urVFpr4Ygz+6tUWmvhiDP1/G7f69FoM/mzMYWqNBgz+bMxhao0GDP64qaq+2Q4M/ripqr7ZDgz+uKmqvtkODP0gEKWmiKoM/SAQpaaIqgz9IBClpoiqDP71NnAosLYM/vU2cCiwtgz++TZwKLC2DP4Sa/CDxKoM/g5r8IPEqgz9sZZUmBRmDP6RTb60VEoM/pFNvrRUSgz+kU2+tFRKDPxPBrESGF4M/E8GsRIYXgz8TwaxEhheDP67GOTOcFYM/r8Y5M5wVgz+vxjkznBWDP6sTg0CRI4M/qxODQJEjgz+rE4NAkSODP0uLPK3gUIM/Sos8reBQgz9Lizyt4FCDP10hrIfdT4M/XCGsh91Pgz9dIayH3U+DP6NLv37bTIM/o0u/fttMgz+jS79+20yDP5rTRPXTU4M/mtNE9dNTgz+b00T101ODPwOW6QNKWIM/s8j1T+NKgz+zyPVP40qDP/X0uGzlT4M/9fS4bOVPgz/19Lhs5U+DP1iahOW3VIM/V5qE5bdUgz9YmoTlt1SDP2s6WhQpTYM/azpaFClNgz9sOloUKU2DP/jXAZPgSoM/+NcBk+BKgz/yrDPybkODP9ZozxrJOoM/1mjPGsk6gz/XaM8ayTqDP2rKavKPJIM/aspq8o8kgz9qymryjySDP4xG7ddQGIM/jUbt11AYgz+NRu3XUBiDP5L7Mf0bBoM/k/sx/RsGgz+T+zH9GwaDP7EdaU7DVoM/sR1pTsNWgz+yHWlOw1aDPz2DCztvVIM/PIMLO29Ugz89gws7b1SDPzJTJlptVYM/MlMmWm1Vgz8zUyZabVWDPy+ysRVhboM/MLKxFWFugz8wsrEVYW6DPzryLx4WeIM/WXXMbAtDgz9adcxsC0ODP0BUmeJQPoM/QFSZ4lA+gz9BVJniUD6DP+lroo5DOIM/6muijkM4gz/ra6KOQziDPyEkfoLzJ4M/ISR+gvMngz8iJH6C8yeDP/D8d+V2I4M/8fx35XYjgz91PtL5oxyDP7eQPSWnHoM/uJA9Jacegz+5kD0lpx6DP9/28HfsFIM/3/bwd+wUgz/h9vB37BSDP58uiXegGYM/oC6Jd6AZgz+iLol3oBmDP+ZZVdA1G4M/5llV0DUbgz/nWVXQNRuDP9FMEXelLoM/0kwRd6Uugz/TTBF3pS6DP06Z1d+ZNIM/T5nV35k0gz9QmdXfmTSDP/SbWVuwPIM/9ptZW7A8gz/3m1lbsDyDPywUUlSMNYM/LRRSVIw1gz8vFFJUjDWDP+XSBKjcNYM/Eo4QPF7ygj8UjhA8XvKCP3H3ums78YI/c/e6azvxgj9097prO/GCP8I0rOcfyoI/wzSs5x/Kgj/ENKznH8qCP2ACpuUX2II/YQKm5RfYgj9iAqblF9iCP7NGdrTy4II/s0Z2tPLggj8DM+AcufuCP3M8X3Ay8II/dDxfcDLwgj92PF9wMvCCP9jXTNdF9oI/2ddM10X2gj/a10zXRfaCP41aCgWp+YI/j1oKBan5gj+QWgoFqfmCPxtNPZgeBIM/HE09mB4Egz8dTT2YHgSDP2JwYkSyJYM/Y3BiRLIlgz9lcGJEsiWDP/MPO22CJIM/9A87bYIkgz/0DzttgiSDP3Tm9zHRFoM/deb3MdEWgz925vcx0RaDPwXzsCcwGoM/BvOwJzAagz8G87AnMBqDP+irvNxdK4M/3bMuTAg2gz/esy5MCDaDPxk5ztsvLIM/GTnO2y8sgz8aOc7bLyyDP91F5/eeIIM/3UXn954ggz/dRef3niCDP48VHupEGIM/kBUe6kQYgz+QFR7qRBiDPxdRGCr/H4M/GFEYKv8fgz91AoHgtjSDP55dDjnKPIM/nl0OOco8gz+gXQ45yjyDPwIc5ZQ/QYM/AhzllD9Bgz8EHOWUP0GDP7EGDP8pToM/sQYM/ylOgz+yBgz/KU6DPwGDkP4TQIM/AYOQ/hNAgz8Cg5D+E0CDPy0otkpJWYM/LSi2SklZgz8tKLZKSVmDP4RSnpO8T4M/hFKek7xPgz+DUp6TvE+DPwkAZ9sLUYM/CABn2wtRgz8JAGfbC1GDPxtmUeeeVIM/G2ZR555Ugz8bZlHnnlSDPwcvD3t0WYM/qku/295Jgz+pS7/b3kmDP863FWkgP4M/zrcVaSA/gz/PtxVpID+DP4URo6XmOYM/hhGjpeY5gz+FEaOl5jmDP9vrKowSMoM/2+sqjBIygz/c6yqMEjKDP5QAFoNCPIM/kwAWg0I8gz9gnz0EHIKDP83VhhkBhYM/zNWGGQGFgz/M1YYZAYWDP88GKuBMeYM/0AYq4Ex5gz/QBirgTHmDP8GEuOnDcIM/wYS46cNwgz/AhLjpw3CDP7V3o7hOdoM/tXejuE52gz+2d6O4TnaDP6Fr5XrpoIM/oGvleumggz+ha+V66aCDPyXZhlXxq4M/JdmGVfGrgz8k2YZV8auDP/w3a4UMooM//DdrhQyigz/7N2uFDKKDP6Wfghy+kYM/pZ+CHL6Rgz+ln4IcvpGDPzMEWjqwloM/xV6912KXgz/FXr3XYpeDPztokSQBmYM/O2iRJAGZgz87aJEkAZmDP7+Geub9mIM/v4Z65v2Ygz++hnrm/ZiDP+5LDRZjlYM/7UsNFmOVgz/tSw0WY5WDPzH9EjHhioM/Mv0SMeGKgz9tBqgGM7qDPysbh56bqIM/LBuHnpuogz8sG4eem6iDP8Trvx5Hn4M/xeu/Hkefgz/E678eR5+DP1SdtIZ/o4M/VZ20hn+jgz9VnbSGf6ODP59hhTO6s4M/oGGFM7qzgz+fYYUzurODPxS3HSwvmIM/FbcdLC+Ygz8Vtx0sL5iDPzteSLNvhoM/PF5Is2+Ggz88Xkizb4aDP+PD5q8+iYM/4sPmrz6Jgz/jw+avPomDP5lLlEOLg4M/mUuUQ4uDgz+aS5RDi4ODPyuQ+/MFi4M/4/CwKq+Ugz/j8LAqr5SDP3QTRdpLjoM/dBNF2kuOgz9zE0XaS46DP2691adbhIM/bb3Vp1uEgz9tvdWnW4SDP4he7OFchoM/iV7s4VyGgz+IXuzhXIaDP2XWj1dnk4M/Z9aPV2eTgz/LMahNK36DP2ORemh4eIM/Y5F6aHh4gz9jkXpoeHiDPwHTDmJZb4M/AdMOYllvgz8B0w5iWW+DP+REuSy7c4M/5ES5LLtzgz/kRLksu3ODP4kv4yKSYoM/iS/jIpJigz+IL+MikmKDP9QWb8aimoM/1BZvxqKagz/VFm/GopqDP78/xVR+pIM/vz/FVH6kgz/AP8VUfqSDP5uNuJANpIM/m424kA2kgz+bjbiQDaSDP+klbMQol4M/6iVsxCiXgz/pJWzEKJeDPwjozKinlYM/y2UKoRiTgz/LZQqhGJODP+VdhHlqmoM/5l2EeWqagz/nXYR5apqDP/LF0UkikoM/8cXRSSKSgz/yxdFJIpKDP6JupWj3jIM/om6laPeMgz+ibqVo94yDPy5OjQxslIM/L06NDGyUgz85oMBhcWqDP8gmb2UaXIM/xyZvZRpcgz/IJm9lGlyDP42sQJkSXoM/jaxAmRJegz+NrECZEl6DP6IRu/L+Y4M/ohG78v5jgz+jEbvy/mODP1bKSfq0ZYM/VspJ+rRlgz9Wykn6tGWDP42trpF2Z4M/jq2ukXZngz+Nra6RdmeDPxSMLfXDZIM/E4wt9cNkgz8TjC31w2SDP8zgFGtUc4M/zOAUa1Rzgz/M4BRrVHODP5IzLIBMfIM/kTMsgEx8gz+SMyyATHyDP25z7TEVf4M/oYlEs4+Ngz+iiUSzj42DP+EiwtgnjoM/4SLC2CeOgz/iIsLYJ46DPzq/4UYUhYM/O7/hRhSFgz86v+FGFIWDP2FWNLX5eoM/YVY0tfl6gz9gVjS1+XqDP5kPMPNqfoM/mg8w82p+gz8zgquiIlqDP5S35dlkXYM/lLfl2WRdgz+Vt+XZZF2DP6x2no6RWoM/rHaejpFagz+rdp6OkVqDP7abYFOZW4M/tptgU5lbgz+2m2BTmVuDPy6LK0hpWYM/L4srSGlZgz8uiytIaVmDP2liFAeBRYM/aWIUB4FFgz9qYhQHgUWDP0swpLnYRIM/SzCkudhEgz9LMKS52ESDP8RIZS7IQ4M/w0hlLshDgz/DSGUuyEODP6jPaJk1SYM/qM9omTVJgz+pz2iZNUmDP1wiqWhiR4M/UczFEj5Fgz9QzMUSPkWDP5awK9DqR4M/lrAr0OpHgz+XsCvQ6keDP5doj91MRIM/l2iP3UxEgz+XaI/dTESDP1PUVMQaQYM/U9RUxBpBgz9T1FTEGkGDPxcOtUqcRYM/Fg61SpxFgz9dM+boNjeDP3NcXi9fKoM/c1xeL18qgz9yXF4vXyqDP8TFlQw+JIM/xMWVDD4kgz/FxZUMPiSDPxbSiYlUNYM/FdKJiVQ1gz8U0omJVDWDP1bsB8FmLYM/VewHwWYtgz9W7AfBZi2DP273+53VNYM/bvf7ndU1gz9t9/ud1TWDP8FkqB9pPYM/wWSoH2k9gz/AZKgfaT2DP2bJBrXePYM/ZckGtd49gz9lyQa13j2DP2HB8ACjPIM/YsHwAKM8gz9hwfAAozyDP09Q9hoUMYM/tcc8Bqwkgz+2xzwGrCSDP448jhs4KIM/jTyOGzgogz+OPI4bOCiDP5YZsvbkKoM/lhmy9uQqgz+WGbL25CqDP8iOMxwsJ4M/yY4zHCwngz/JjjMcLCeDPw2BLWRFMoM/DYEtZEUygz+37kuC6WGDP6vhvMcRZIM/quG8xxFkgz+p4bzHEWSDP9EW5SdHZYM/0RblJ0dlgz/QFuUnR2WDP6niK9l/boM/qeIr2X9ugz+p4ivZf26DP36UQeGMYYM/f5RB4Yxhgz9/lEHhjGGDP7lbYfKOToM/ulth8o5Ogz+6W2Hyjk6DP9d0Q9m/S4M/13RD2b9Lgz/YdEPZv0uDP9vjCU26SIM/2+MJTbpIgz/b4wlNukiDPyA8sQ5fToM/IDyxDl9Ogz8fPLEOX06DP6xL3hvCTIM/32lgvmU+gz/faWC+ZT6DPyMHHOyQOIM/IQcc7JA4gz8iBxzskDiDP16CUBjuM4M/XYJQGO4zgz9cglAY7jODP/3FT8g5LoM//cVPyDkugz/9xU/IOS6DP3JXkCKXLIM/cVeQIpcsgz+TVXvIF/+CP/r15Oob+4I/+vXk6hv7gj/69eTqG/uCP3K3OFyh/oI/crc4XKH+gj9ytzhcof6CP5qZ2e+/9YI/mpnZ77/1gj+amdnvv/WCP8EweC1O8YI/wDB4LU7xgj/AMHgtTvGCP8w5j6jH4YI/zTmPqMfhgj/MOY+ox+GCP6ku/KKX44I/qS78opfjgj+qLvyil+OCP0Uek1DQ0oI/RB6TUNDSgj9EHpNQ0NKCPy0bDyvv24I/LRsPK+/bgj8tGw8r79uCP1wEBZnd2II/0dMha7ACgz/R0yFrsAKDP2AOinOWCoM/YA6Kc5YKgz9fDopzlgqDP5bssrrQAYM/leyyutABgz+W7LK60AGDP1A9JOCMAoM/UD0k4IwCgz9RPSTgjAKDP6aIrJz+/YI/p4isnP79gj/c6sDyFvCCP3AhbrO85II/cCFus7zkgj9wIW6zvOSCP0MjjrZ+7YI/QyOOtn7tgj9DI462fu2CP8wEfbRe+II/ywR9tF74gj/KBH20XviCP8j2KykqAYM/yfYrKSoBgz/I9ispKgGDP/ljQB7WCYM/+WNAHtYJgz/7Y0Ae1gmDPwzGZGVxE4M/DMZkZXETgz8MxmRlcRODP/max4v9FoM/+ZrHi/0Wgz/4mseL/RaDPzX2AuMsG4M/NPYC4ywbgz819gLjLBuDP1YG6uGxIYM/KjEPGCISgz8sMQ8YIhKDP2cLYbkbF4M/ZwthuRsXgz9nC2G5GxeDP1trAah3FYM/XGsBqHcVgz9cawGodxWDPxEETPLeD4M/EQRM8t4Pgz8SBEzy3g+DP8TzUat5FIM/xfNRq3kUgz/5IkYgTjqDPws/xPQ3P4M/DD/E9Dc/gz8LP8T0Nz+DP7ZoFklsQoM/tWgWSWxCgz+2aBZJbEKDP4OQpUFVPoM/g5ClQVU+gz+CkKVBVT6DPwtqw7cUP4M/C2rDtxQ/gz8MasO3FD+DPxZs8LmYGYM/FWzwuZgZgz8WbPC5mBmDPxIJ/jB+H4M/Egn+MH4fgz8RCf4wfh+DP7x9BrUzIIM/vX0GtTMggz+9fQa1MyCDP6TYFmAVHoM/o9gWYBUegz+j2BZgFR6DP0pxbSB4GIM/YqxOjCEcgz9irE6MIRyDP4Gopr1IIoM/gaimvUgigz+AqKa9SCKDP0rcAFcRGIM/StwAVxEYgz9L3ABXERiDP456EjRJCYM/jXoSNEkJgz+OehI0SQmDPw21X32QCoM/DbVffZAKgz9rWaTCbCKDP2ybSsT7LIM/bJtKxPssgz9sm0rE+yyDP1dMWhTyN4M/V0xaFPI3gz9XTFoU8jeDP4GFEvgtNIM/gYUS+C00gz+BhRL4LTSDP+yQ4JxxN4M/7JDgnHE3gz/rkOCccTeDP8QH4EYjWYM/wwfgRiNZgz/DB+BGI1mDP6DFK7+cXYM/n8Urv5xdgz+fxSu/nF2DP7zqldOSWoM/u+qV05Jagz+66pXTklqDPzJ7fQV0WoM/MHt9BXRagz8ve30FdFqDP/SI6EM+XIM/17lDTupjgz/WuUNO6mODP5JVUCa1aIM/kVVQJrVogz+QVVAmtWiDP7aW4E2caoM/tJbgTZxqgz+yluBNnGqDP+KMr1YIb4M/4IyvVghvgz/fjK9WCG+DPy2S1wnwboM/LJLXCfBugz/9ZYoDemSDP3EcfDOmZ4M/bxx8M6Zngz9vHHwzpmeDP3ZOxOXdZIM/dE7E5d1kgz9zTsTl3WSDP/nAwb0hZIM/+MDBvSFkgz/2wMG9IWSDP71FJM0rYoM/u0UkzStigz+6RSTNK2KDP6O259N3R4M/o7bn03dHgz+itufTd0eDPwlDlC1hTIM/CUOULWFMgz8IQ5QtYUyDP0IPQh5NWIM/Qg9CHk1Ygz9BD0IeTViDPy9FMrzDWoM/LUUyvMNagz8sRTK8w1qDPxfYyFZ1T4M/X+sgpUtggz9f6yClS2CDP1iRTuoPWYM/V5FO6g9Zgz9WkU7qD1mDP3bAr+s1XIM/dcCv6zVcgz91wK/rNVyDPwGGYq/rUoM/AIZir+tSgz//hWKv61KDP6+SIr/RVYM/r5Iiv9FVgz9ny9E+F0KDP6C8Ya23SoM/oLxhrbdKgz+fvGGtt0qDP6mhBr5wRIM/qaEGvnBEgz+ooQa+cESDP1iukxwuSoM/V66THC5Kgz9XrpMcLkqDP2YJaAXXRIM/ZQloBddEgz9mCWgF10SDPx4Ju/OrOYM/HAm786s5gz8cCbvzqzmDP5Ct0rnvL4M/j63Sue8vgz+PrdK57y+DP5CPmKrHPIM/kI+Yqsc8gz+Qj5iqxzyDP3AUQqT/QYM/cBRCpP9Bgz9xFEKk/0GDP2IBcJBcPoM/V7nU3AEOgz9YudTcAQ6DP4AS5c69CIM/gBLlzr0Igz+BEuXOvQiDP+QQW8cOD4M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represented as Roll-Pitch-Yaw angles. Calibration represents the fixed rotation between the sensor frame and the body frame, which is unknown at startup. The equivariant filter can estimate this calibration simultaneously with attitude and bias. 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"     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9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@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@     9@", 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The red curve shows attitude error (in degrees), while the blue curve shows calibration error. Both errors start high when the filter has little information, then converge to low values as the filter processes more measurements. Temporary spikes may occur during aggressive maneuvers or measurement dropouts. 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"metadata": {}, - "source": [ - "### Bias Estimation Error\n", - "\n", - "This final plot focuses specifically on the gyroscope bias estimation error. The shaded area under the curve emphasizes the overall error magnitude. Bias estimation typically exhibits a characteristic convergence pattern: starting from the initial uncertainty, the error decreases as the filter observes more motion. The rate of convergence depends on the trajectory's observability properties. Richer motion (diverse rotations) leads to faster convergence. The final bias error shown in the summary above indicates how accurately the filter has identified the true gyroscope bias." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "tozeroy", - "fillcolor": "rgba(60, 200, 60, 0.2)", - "line": { - "color": "rgb(60, 200, 60)", - "width": 2 - }, - "name": "Bias Error", - "type": "scatter", - "x": [ - 0, - 0.005, - 0.01, - 0.015, - 0.02, - 0.025, - 0.03, - 0.035, - 0.04, - 0.045, - 0.05, - 0.055, - 0.06, - 0.065, - 0.07, - 0.075, - 0.08, - 0.085, - 0.09, - 0.095, - 0.1, - 0.105, - 0.11, - 0.115, - 0.12, - 0.125, - 0.13, - 0.135, - 0.14, - 0.145, - 0.15, - 0.155, - 0.16, - 0.165, - 0.17, - 0.17500000000000002, - 0.18, - 0.185, - 0.19, - 0.195, - 0.2, - 0.20500000000000002, - 0.21, - 0.215, - 0.22, - 0.225, - 0.23, - 0.23500000000000001, - 0.24, - 0.245, - 0.25, - 0.255, - 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"gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Bias Estimation Error over Time" - }, - "xaxis": { - "title": { - "text": "Time (s)" - } - }, - "yaxis": { - "title": { - "text": "Error (rad/s)" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if results:\n", - " fig = plot_bias_error_time_series(results)\n", - " fig.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From a9442cbf622c5406249fd32afad6cfa83a9e31cf Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 17:04:48 -0500 Subject: [PATCH 158/266] Fixed some set leftovers --- gtsam/nonlinear/tests/testAdaptAutoDiff.cpp | 2 +- gtsam/nonlinear/tests/testExpression.cpp | 22 ++++++++++----------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/gtsam/nonlinear/tests/testAdaptAutoDiff.cpp b/gtsam/nonlinear/tests/testAdaptAutoDiff.cpp index 2deece2282..77bcd0cbdd 100644 --- a/gtsam/nonlinear/tests/testAdaptAutoDiff.cpp +++ b/gtsam/nonlinear/tests/testAdaptAutoDiff.cpp @@ -250,7 +250,7 @@ TEST(AdaptAutoDiff, SnavelyExpression) { internal::upAligned(RecordSize) + P.traceSize() + X.traceSize(), expression.traceSize()); - const set expected{1, 2}; + const KeySet expected{1, 2}; EXPECT(expected == expression.keys()); } diff --git a/gtsam/nonlinear/tests/testExpression.cpp b/gtsam/nonlinear/tests/testExpression.cpp index 67815e262d..1a21ba0f7c 100644 --- a/gtsam/nonlinear/tests/testExpression.cpp +++ b/gtsam/nonlinear/tests/testExpression.cpp @@ -86,7 +86,7 @@ Vector f3(const Point3& p, OptionalJacobian H) { return p; } Point3_ pointExpression(1); -const set expected{1}; +const KeySet expected{1}; } // namespace unary // Create a unary expression that takes another expression as a single argument. @@ -186,7 +186,7 @@ TEST(Expression, BinaryToDouble) { /* ************************************************************************* */ // Check keys of an expression created from class method. TEST(Expression, BinaryKeys) { - const set expected{1, 2}; + const KeySet expected{1, 2}; EXPECT(expected == binary::p_cam.keys()) } @@ -223,7 +223,7 @@ Expression uv_hat(uncalibrate, K, projection); /* ************************************************************************* */ // keys TEST(Expression, TreeKeys) { - const set expected{1, 2, 3}; + const KeySet expected{1, 2, 3}; EXPECT(expected == tree::uv_hat.keys()) } @@ -261,7 +261,7 @@ TEST(Expression, compose1) { Rot3_ R3 = R1 * R2; // Check keys - const set expected{1, 2}; + const KeySet expected{1, 2}; EXPECT(expected == R3.keys()) } @@ -273,7 +273,7 @@ TEST(Expression, compose2) { Rot3_ R3 = R1 * R2; // Check keys - const set expected{1}; + const KeySet expected{1}; EXPECT(expected == R3.keys()) } @@ -285,7 +285,7 @@ TEST(Expression, compose3) { Rot3_ R3 = R1 * R2; // Check keys - const set expected{3}; + const KeySet expected{3}; EXPECT(expected == R3.keys()) } @@ -298,7 +298,7 @@ TEST(Expression, compose4) { Double_ R3 = R1 * R2; // Check keys - const set expected{1}; + const KeySet expected{1}; EXPECT(expected == R3.keys()) } @@ -322,7 +322,7 @@ TEST(Expression, ternary) { Rot3_ ABC(composeThree, A, B, C); // Check keys - const set expected {1, 2, 3}; + const KeySet expected {1, 2, 3}; EXPECT(expected == ABC.keys()) } @@ -332,7 +332,7 @@ TEST(Expression, ScalarMultiply) { const Key key(67); const Point3_ expr = 23 * Point3_(key); - const set expected_keys{key}; + const KeySet expected_keys{key}; EXPECT(expected_keys == expr.keys()) map actual_dims, expected_dims {{key, 3}}; @@ -363,7 +363,7 @@ TEST(Expression, BinarySum) { const Key key(67); const Point3_ sum_ = Point3_(key) + Point3_(Point3(1, 1, 1)); - const set expected_keys{key}; + const KeySet expected_keys{key}; EXPECT(expected_keys == sum_.keys()) map actual_dims, expected_dims {{key, 3}}; @@ -508,7 +508,7 @@ TEST(Expression, Subtract) { values.insert(0, p); values.insert(1, q); const Vector3_ expression = Vector3_(0) - Vector3_(1); - set expected_keys = {0, 1}; + KeySet expected_keys = {0, 1}; EXPECT(expression.keys() == expected_keys) // Check value + Jacobians From 3e42abe9dedcafec1ae229906b265de72e6c48c7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 19:40:38 -0500 Subject: [PATCH 159/266] Fixed to use @rpath --- cmake/HandleGlobalBuildFlags.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/HandleGlobalBuildFlags.cmake b/cmake/HandleGlobalBuildFlags.cmake index 51a0578f70..af59b83177 100644 --- a/cmake/HandleGlobalBuildFlags.cmake +++ b/cmake/HandleGlobalBuildFlags.cmake @@ -20,7 +20,7 @@ endif() if (APPLE AND GTSAM_SHARED_LIB) # Set the default install directory on macOS - set(CMAKE_INSTALL_NAME_DIR "lib") + set(CMAKE_INSTALL_NAME_DIR "@rpath") endif() ############################################################################### From be18e64f767b35d6c006a6ad4ad4d57466f7cd0b Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 21:36:31 -0500 Subject: [PATCH 160/266] Add comment --- cmake/HandleGlobalBuildFlags.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/HandleGlobalBuildFlags.cmake b/cmake/HandleGlobalBuildFlags.cmake index af59b83177..e554199056 100644 --- a/cmake/HandleGlobalBuildFlags.cmake +++ b/cmake/HandleGlobalBuildFlags.cmake @@ -19,7 +19,7 @@ if(MSVC AND GTSAM_SHARED_LIB) endif() if (APPLE AND GTSAM_SHARED_LIB) - # Set the default install directory on macOS + # Setting to @rpath so that dependent executables can find the GTSAM dylib in a portable manner set(CMAKE_INSTALL_NAME_DIR "@rpath") endif() From 9fec242811e386e1a31339a44b70b800db447d6a Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 31 Jan 2026 21:36:47 -0500 Subject: [PATCH 161/266] Fix seg-faulting bug --- gtsam/constrained/NonlinearEqualityConstraint.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/constrained/NonlinearEqualityConstraint.cpp b/gtsam/constrained/NonlinearEqualityConstraint.cpp index effc8774c1..a29f06777c 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/NonlinearEqualityConstraint.cpp @@ -60,7 +60,7 @@ Vector NonlinearEqualityConstraints::violationVector(const Values& values, bool size_t start_idx = 0; for (const auto& constraint : *this) { size_t dim = constraint->dim(); - violation.middleCols(start_idx, dim) = + violation.segment(start_idx, dim) = whiten ? constraint->whitenedError(values) : constraint->unwhitenedError(values); start_idx += dim; } From 1c5ddc7eff73e1da94023293ac07aa35195534a7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 1 Feb 2026 10:10:12 -0500 Subject: [PATCH 162/266] Add some docs on python wheels --- python/README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/python/README.md b/python/README.md index 4d195a8213..cf0c03e628 100644 --- a/python/README.md +++ b/python/README.md @@ -77,3 +77,15 @@ See the tests for examples. ## Wrapping Custom GTSAM-based Project Please refer to the template project and the corresponding tutorial available [here](https://github.com/borglab/GTSAM-project-python). + +## Wheels + +GTSAM Python wheels are built in CI through two cibuildwheel workflows that share the same matrix of Python 3.10--3.13 targets on Linux x86_64, Linux aarch64, macOS x86_64, and macOS arm64. Both scripts first configure the wrapper with `cmake -DGTSAM_BUILD_PYTHON=1` so that `setup.py` exists for cibuildwheel, invoke `.github/scripts/python_wheels/cibw_before_all.sh`, then run `.github/scripts/python_wheels/build_wheels.sh` before storing the artifacts and publishing them with `pypa/gh-action-pypi-publish`. + +1. **Develop wheels** (`.github/workflows/build-cibw.yml`) run on every push to `develop` (and by manual dispatch). The workflow injects `DEVELOP=1` and a timestamp so the generated version string becomes a `gtsam-develop` build, and it continues to publish the built wheels via the publish action at the end of the job. Use this workflow as a staging pipeline for the most recent development snapshots. + +2. **Release wheels** (`.github/workflows/prod-cibw.yml`) trigger when a GitHub release is published (and can also be run manually). The job is otherwise identical but omits the `DEVELOP` flag and publishes the wheels to `https://test.pypi.org/legacy/`, making it the production-quality artifact build tied to a release tag. + +### Cleaning develop wheels + +If the `gtsam-develop` project on PyPI grows too large (PyPI enforces a 10 GB quota for each package), run `.github/scripts/python_wheels/cleanup_gtsam_develop.sh` to drop every release except the most recent one. You can pass your PyPI username (`bash .github/scripts/python_wheels/cleanup_gtsam_develop.sh `) or let the script prompt for it, but the account must be an owner or maintainer of `gtsam-develop`. The script always confirms before deleting, then calls `python3 -m pypi_cleanup` with `--leave-most-recent-only --do-it`, so treat this as a permanent cleanup that should only be used when you are about to exceed PyPI's size limit. From 4c7b219ab3ac3ee47070d276942d696940e1ce9a Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 1 Feb 2026 10:10:25 -0500 Subject: [PATCH 163/266] Fix uninitialized Jacobian --- gtsam/navigation/PseudorangeFactor.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index 50bc167668..108083a2d5 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -70,7 +70,9 @@ Vector PseudorangeFactor::evaluateError( } if (Hreceiver_clock_bias) { - *Hreceiver_clock_bias = Matrix11(CLIGHT); + Hreceiver_clock_bias->resize(1, 1); + // Avoid Eigen assigning from Matrix11, which triggers SSE array-bounds warnings on 1x1 matrices. + (*Hreceiver_clock_bias)(0, 0) = CLIGHT; } return Vector1(error); From 0f3bb20beb672ab645f4715ec911e6b8bc398707 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 1 Feb 2026 13:13:45 -0500 Subject: [PATCH 164/266] Fix Eigen issue in PseudorangeFactor --- gtsam/navigation/PseudorangeFactor.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index 50bc167668..18b6e746b9 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -70,7 +70,7 @@ Vector PseudorangeFactor::evaluateError( } if (Hreceiver_clock_bias) { - *Hreceiver_clock_bias = Matrix11(CLIGHT); + Hreceiver_clock_bias->setConstant(CLIGHT); } return Vector1(error); From 63a478299c15da754e254b9a61b141377eaa91a4 Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 1 Feb 2026 14:22:11 -0500 Subject: [PATCH 165/266] Better assignment. This is why I don't trust AI --- gtsam/navigation/PseudorangeFactor.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index 18b6e746b9..c14ef4f68e 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -70,7 +70,7 @@ Vector PseudorangeFactor::evaluateError( } if (Hreceiver_clock_bias) { - Hreceiver_clock_bias->setConstant(CLIGHT); + *Hreceiver_clock_bias << I_1x1 * CLIGHT; } return Vector1(error); From 29679ea00edd3c68c795801a74c4d40e3ca49c8f Mon Sep 17 00:00:00 2001 From: Varun Agrawal Date: Sun, 1 Feb 2026 14:39:18 -0500 Subject: [PATCH 166/266] = instead of << --- gtsam/navigation/PseudorangeFactor.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index c14ef4f68e..fa5bedf414 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -70,7 +70,7 @@ Vector PseudorangeFactor::evaluateError( } if (Hreceiver_clock_bias) { - *Hreceiver_clock_bias << I_1x1 * CLIGHT; + *Hreceiver_clock_bias = I_1x1 * CLIGHT; } return Vector1(error); From ce84c8cac44df1e1e7024438f12322f530e2552b Mon Sep 17 00:00:00 2001 From: Akshay Krishnan Date: Sun, 1 Feb 2026 15:06:59 -0500 Subject: [PATCH 167/266] support robust noise model in translation recovery --- gtsam/sfm/TranslationRecovery.cpp | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/gtsam/sfm/TranslationRecovery.cpp b/gtsam/sfm/TranslationRecovery.cpp index 6aa9d801f7..d395aded93 100644 --- a/gtsam/sfm/TranslationRecovery.cpp +++ b/gtsam/sfm/TranslationRecovery.cpp @@ -103,15 +103,19 @@ Values addSameTranslationNodes(const Values &result, // wants. Unfortunately, we are given Unit3 measurements and Point3 unknowns, // which is a mismatch in this class' setup. using noiseModel::Isotropic; -Isotropic::shared_ptr convertNoiseModel( - const SharedNoiseModel &unit3NoiseModel) { +SharedNoiseModel convertNoiseModel(const SharedNoiseModel &unit3NoiseModel) { if (auto isotropic = std::dynamic_pointer_cast(unit3NoiseModel)) { return noiseModel::Isotropic::Sigma(3, isotropic->sigma()); - } else { - throw std::runtime_error( - "TranslationRecovery::convertNoiseModel: only isotropic noise model " - "supported."); } + if (auto robust = + std::dynamic_pointer_cast(unit3NoiseModel)) { + // Preserve the robust kernel while converting the wrapped noise model. + return noiseModel::Robust::Create(robust->robust(), + convertNoiseModel(robust->noise())); + } + throw std::runtime_error( + "TranslationRecovery::convertNoiseModel: only isotropic (optionally " + "robust-wrapped) noise model supported."); } } // namespace From 3bdfb71924bd58eacf3acd81ad850083cc9bb3c1 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 2 Feb 2026 20:05:45 -0500 Subject: [PATCH 168/266] test w vector-valued equality constraint --- .../tests/testNonlinearEqualityConstraint.cpp | 56 ++++++++++++++++++- 1 file changed, 54 insertions(+), 2 deletions(-) diff --git a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp index 3255fa7ab3..6f92a539fc 100644 --- a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp @@ -21,10 +21,15 @@ #include #include #include -#include #include +#include +#include +#include #include #include +#include + +#include #include "constrainedExample.h" @@ -252,7 +257,54 @@ TEST(NonlinearEqualityConstraints, Container) { // Check constraint violation. } -TEST(NonlinearEqualityConstraints, FromCostGraph) {} +TEST(GtsamConstrained, VectorEqualityViolationVectorMixedDims) { + using namespace gtsam; + + const Symbol x_key('x', 0); // Pose3 (dim 6) + const Symbol p_key('p', 0); // Vector3 (dim 3) + + const Pose3_ x(x_key); + const Vector3_ p(p_key); + const Vector3_ world_point(x, &Pose3::transformFrom, p); + + // Create many constraints so the stacked violation vector is non-trivial. + // 28 * 3 = 84 to mirror real-world constraint stacks. + NonlinearEqualityConstraints constraints; + const Vector3 target(0.1, -0.2, 0.3); + const Vector3_ target_expr(target); + const Vector3_ error_expr = world_point - target_expr; + + // Use the "cost-factor wrapped as a constraint" path (ZeroCostConstraint), + // as this matches how downstream projects (like GTDynamics) typically + // construct constraints. + const auto noise = noiseModel::Isotropic::Sigma(3, 1.0); + for (size_t i = 0; i < 28; ++i) { + auto factor = std::make_shared>( + noise, Vector3::Zero(), error_expr); + constraints.emplace_shared(factor); + } + + Values values; + values.insert(x_key, Pose3()); + values.insert(p_key, Vector3(0.0, 0.0, 0.0)); + + // This call is the primary reproducer: it should return a finite vector with + // size 84. On affected platforms/builds it may abort inside Eigen. + const Vector v = constraints.violationVector(values); + EXPECT_LONGS_EQUAL(84, static_cast(v.size())); + for (int i = 0; i < static_cast(v.size()); ++i) { + EXPECT(std::isfinite(v(i))); + } + + // Also probe per-constraint violation evaluation, as some failures only show + // up when the constraint graph is small. + for (size_t i = 0; i < constraints.size(); ++i) { + NonlinearEqualityConstraints single; + single.push_back(constraints.at(i)); + const double norm = single.violationNorm(values); + EXPECT(std::isfinite(norm)); + } +} int main() { TestResult tr; From ff45bcb4edbe658c90e4a8a72fe472cffa7a7aa7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 2 Feb 2026 20:05:59 -0500 Subject: [PATCH 169/266] Fix bug --- .../AugmentedLagrangianOptimizer.cpp | 3 +- .../testAugmentedLagrangianOptimizer.cpp | 49 +++++++++++++++++++ 2 files changed, 51 insertions(+), 1 deletion(-) diff --git a/gtsam/constrained/AugmentedLagrangianOptimizer.cpp b/gtsam/constrained/AugmentedLagrangianOptimizer.cpp index 6d7fef7c6e..9200a8b56e 100644 --- a/gtsam/constrained/AugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/AugmentedLagrangianOptimizer.cpp @@ -159,7 +159,8 @@ NonlinearFactorGraph AugmentedLagrangianOptimizer::augmentedLagrangianFunction( const double& muEq = state.muEq; for (size_t i = 0; i < eqConstraints.size(); i++) { const auto& constraint = eqConstraints.at(i); - Vector bias = state.lambdaEq[i] / muEq * constraint->sigmas(); + Vector bias = state.lambdaEq[i] / muEq; + bias = bias.cwiseProduct(constraint->sigmas()); auto penalty_l2 = constraint->penaltyFactor(muEq); graph.emplace_shared(penalty_l2, bias); } diff --git a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp index 1d3be82806..4d39c9ca64 100644 --- a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp @@ -18,6 +18,8 @@ #include #include +#include +#include #include "constrainedExample.h" #include "gtsam/constrained/NonlinearEqualityConstraint.h" @@ -144,6 +146,53 @@ TEST(AugmentedLagrangianOptimizer, constrained_example2) { EXPECT(assert_equal(optimal_values, results, 1e-4)); } +TEST(AugmentedLagrangian, VectorBiasUsesElementwiseSigmas) { + using namespace gtsam; + + const Symbol x_key('x', 0); + const Vector3_ x(x_key); + + // Non-uniform sigmas are essential to detect the bug in Release. + Vector sigmas(3); + sigmas << 1.0, 2.0, 4.0; + + NonlinearEqualityConstraints eq; + eq.emplace_shared>(x, Vector3::Zero(), + sigmas); + + // Empty cost graph keeps the factor indexing simple. + NonlinearFactorGraph costs; + const auto problem = + ConstrainedOptProblem::EqConstrainedOptProblem(costs, eq); + + Values values; + values.insert( + x_key, + Vector3::Zero()); // satisfy constraint => penalty factor error is zero + + auto params = std::make_shared(); + AugmentedLagrangianOptimizer optimizer(problem, values, params); + + AugmentedLagrangianState state; + state.muEq = 0.2; + state.lambdaEq.emplace_back((Vector(3) << 0.3, -0.5, 1.2).finished()); + + // Buggy code aborts here in Debug (Eigen assert) and gives wrong bias in + // Release. + const NonlinearFactorGraph graph = + optimizer.augmentedLagrangianFunction(state); + EXPECT_LONGS_EQUAL(1, static_cast(graph.size())); + + auto nf = graph.at(0); + auto factor = std::dynamic_pointer_cast(nf); + EXPECT(factor); + + const Vector bias_from_factor = factor->unwhitenedError(values); + const Vector expected = + (state.lambdaEq.at(0) / state.muEq).cwiseProduct(sigmas); + EXPECT(assert_equal(expected, bias_from_factor, 1e-12)); +} + /* ********************************************************************************************* */ int main() { TestResult tr; From 38f71129194d85b4d4c50a5109d1218948a2578b Mon Sep 17 00:00:00 2001 From: Fan Jiang Date: Tue, 3 Feb 2026 00:07:19 -0800 Subject: [PATCH 170/266] Add continuous benchmarking workflow --- .../scripts/compare_time_sfmbal_benchmarks.py | 139 +++++++ .../time-sfmbal-benchmark-runner.yml | 254 ++++++++++++ .../time-sfmbal-benchmark-trigger.yml | 37 ++ .github/workflows/time-sfmbal-benchmark.yml | 376 ++++++++++++++++++ timing/timeSFMBAL.cpp | 119 +++++- 5 files changed, 914 insertions(+), 11 deletions(-) create mode 100644 .github/scripts/compare_time_sfmbal_benchmarks.py create mode 100644 .github/workflows/time-sfmbal-benchmark-runner.yml create mode 100644 .github/workflows/time-sfmbal-benchmark-trigger.yml create mode 100644 .github/workflows/time-sfmbal-benchmark.yml diff --git a/.github/scripts/compare_time_sfmbal_benchmarks.py b/.github/scripts/compare_time_sfmbal_benchmarks.py new file mode 100644 index 0000000000..251d7719bd --- /dev/null +++ b/.github/scripts/compare_time_sfmbal_benchmarks.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +"""Compare timeSFMBAL benchmark JSON files and render a PR-friendly markdown body.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Dict + + +def _load_result(path: Path) -> Dict[str, float]: + with path.open("r", encoding="utf-8") as f: + payload = json.load(f) + if not isinstance(payload, list): + raise ValueError(f"{path} is not a JSON array") + + metrics: Dict[str, float] = {} + for item in payload: + if not isinstance(item, dict): + continue + name = item.get("name") + value = item.get("value") + if not isinstance(name, str): + continue + if not isinstance(value, (int, float)): + continue + metrics[name] = float(value) + return metrics + + +def _collect_results(folder: Path) -> Dict[str, Dict[str, float]]: + if not folder.exists(): + return {} + + results: Dict[str, Dict[str, float]] = {} + for path in sorted(folder.glob("*.json")): + runner_id = path.stem + results[runner_id] = _load_result(path) + return results + + +def _fmt_seconds(value: float | None) -> str: + if value is None: + return "N/A" + return f"{value:.6f}" + + +def _fmt_delta(head: float | None, base: float | None) -> str: + if head is None or base is None: + return "N/A" + delta = head - base + return f"{delta:+.6f}" + + +def _fmt_percent(head: float | None, base: float | None) -> str: + if head is None or base is None or base == 0.0: + return "N/A" + pct = (head - base) / base * 100.0 + return f"{pct:+.2f}%" + + +def render_markdown( + head_results: Dict[str, Dict[str, float]], + base_results: Dict[str, Dict[str, float]], + head_sha: str, + base_sha: str, +) -> str: + lines = ["", "## timeSFMBAL benchmark", ""] + lines.append(f"- Head: `{head_sha}`") + if base_sha: + lines.append(f"- Base: `{base_sha}`") + lines.append("") + + if not head_results: + lines.append("No head benchmark results were found.") + return "\n".join(lines) + "\n" + + lines.append( + "| Runner | Metric | Base (s) | Head (s) | Delta (s) | Change |" + ) + lines.append("| --- | --- | ---: | ---: | ---: | ---: |") + + missing_base_runners = [] + for runner_id in sorted(head_results): + head_metrics = head_results[runner_id] + base_metrics = base_results.get(runner_id, {}) + if not base_metrics: + missing_base_runners.append(runner_id) + + metric_names = sorted(set(head_metrics) | set(base_metrics)) + for metric_name in metric_names: + head_value = head_metrics.get(metric_name) + base_value = base_metrics.get(metric_name) + lines.append( + "| " + + f"{runner_id} | `{metric_name}` | {_fmt_seconds(base_value)} | " + + f"{_fmt_seconds(head_value)} | {_fmt_delta(head_value, base_value)} | " + + f"{_fmt_percent(head_value, base_value)} |" + ) + + if missing_base_runners: + lines.append("") + lines.append( + "Missing base benchmark cache for: " + + ", ".join(f"`{runner}`" for runner in missing_base_runners) + + "." + ) + + return "\n".join(lines) + "\n" + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Compare per-runner timeSFMBAL benchmark JSON files." + ) + parser.add_argument("--head-dir", required=True, help="Directory with head JSON files") + parser.add_argument("--base-dir", required=True, help="Directory with base JSON files") + parser.add_argument("--head-sha", required=True, help="Head commit SHA") + parser.add_argument("--base-sha", default="", help="Base commit SHA") + parser.add_argument("--output", required=True, help="Output markdown file") + args = parser.parse_args() + + head_dir = Path(args.head_dir) + base_dir = Path(args.base_dir) + output = Path(args.output) + + head_results = _collect_results(head_dir) + base_results = _collect_results(base_dir) + markdown = render_markdown(head_results, base_results, args.head_sha, args.base_sha) + + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(markdown, encoding="utf-8") + print(markdown, end="") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml new file mode 100644 index 0000000000..190f191c92 --- /dev/null +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -0,0 +1,254 @@ +name: timeSFMBAL Benchmark Runner +run-name: timeSFMBAL worker ${{ inputs.group_id }} ${{ inputs.runner_id }} ${{ inputs.target_sha }} + +on: + workflow_dispatch: + inputs: + group_id: + description: Unique dispatch group ID used by orchestrator + required: true + type: string + runner_id: + description: Runner profile ID + required: true + type: string + os_name: + description: OS label used in cache key + required: true + type: string + arch: + description: Architecture label used in cache key + required: true + type: string + checkout_ref: + description: Git ref/SHA to checkout for benchmarking + required: true + type: string + target_sha: + description: Commit SHA used in cache key + required: true + type: string + +permissions: + contents: read + +jobs: + benchmark_linux_x64: + if: inputs.runner_id == 'linux-x64' + runs-on: ubuntu-latest + steps: + - name: Checkout target ref + uses: actions/checkout@v4 + with: + ref: ${{ inputs.checkout_ref }} + fetch-depth: 0 + + - name: Restore BAL dataset archive cache + id: dataset_cache_restore + uses: actions/cache/restore@v4 + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Download BAL dataset archive + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + run: | + curl --fail --location --show-error \ + --output examples/Data/problem-16-22106-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + + - name: Save BAL dataset archive cache + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Extract BAL dataset + run: | + bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + + - name: Install dependencies + run: | + sudo apt-get update + sudo apt-get install -y ninja-build + + - name: Configure + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + + - name: Prepare cache payload + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} + + benchmark_linux_arm64: + if: inputs.runner_id == 'linux-arm64' + runs-on: ubuntu-24.04-arm + steps: + - name: Checkout target ref + uses: actions/checkout@v4 + with: + ref: ${{ inputs.checkout_ref }} + fetch-depth: 0 + + - name: Restore BAL dataset archive cache + id: dataset_cache_restore + uses: actions/cache/restore@v4 + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Download BAL dataset archive + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + run: | + curl --fail --location --show-error \ + --output examples/Data/problem-16-22106-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + + - name: Save BAL dataset archive cache + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Extract BAL dataset + run: | + bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + + - name: Install dependencies + run: | + sudo apt-get update + sudo apt-get install -y ninja-build + + - name: Configure + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + + - name: Prepare cache payload + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} + + benchmark_macos_arm64: + if: inputs.runner_id == 'macos-arm64' + runs-on: macos-14 + steps: + - name: Checkout target ref + uses: actions/checkout@v4 + with: + ref: ${{ inputs.checkout_ref }} + fetch-depth: 0 + + - name: Restore BAL dataset archive cache + id: dataset_cache_restore + uses: actions/cache/restore@v4 + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Download BAL dataset archive + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + run: | + curl --fail --location --show-error \ + --output examples/Data/problem-16-22106-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + + - name: Save BAL dataset archive cache + if: steps.dataset_cache_restore.outputs.cache-hit != 'true' + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: examples/Data/problem-16-22106-pre.txt.bz2 + key: bal-problem-16-22106-pre-txt-bz2-v1 + + - name: Extract BAL dataset + run: | + bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + + - name: Install dependencies + run: brew install ninja || true + + - name: Configure + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + + - name: Prepare cache payload + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} diff --git a/.github/workflows/time-sfmbal-benchmark-trigger.yml b/.github/workflows/time-sfmbal-benchmark-trigger.yml new file mode 100644 index 0000000000..0cf86e17a6 --- /dev/null +++ b/.github/workflows/time-sfmbal-benchmark-trigger.yml @@ -0,0 +1,37 @@ +name: Trigger timeSFMBAL Benchmark + +on: + issue_comment: + types: [created] + +permissions: + actions: write + contents: read + pull-requests: read + +jobs: + dispatch: + if: github.event.issue.pull_request != null + runs-on: ubuntu-latest + steps: + - name: Dispatch benchmark workflow for /bench + uses: actions/github-script@v7 + with: + script: | + const body = context.payload.comment.body.trim(); + if (body !== "/bench") { + core.info("Skipping dispatch: comment is not /bench."); + return; + } + + const prNumber = context.payload.issue.number; + await github.rest.actions.createWorkflowDispatch({ + owner: context.repo.owner, + repo: context.repo.repo, + workflow_id: "time-sfmbal-benchmark.yml", + ref: context.payload.repository.default_branch, + inputs: { + pr_number: String(prNumber), + }, + }); + core.info(`Dispatched time-sfmbal-benchmark.yml for PR #${prNumber}.`); diff --git a/.github/workflows/time-sfmbal-benchmark.yml b/.github/workflows/time-sfmbal-benchmark.yml new file mode 100644 index 0000000000..ca24b2dfc6 --- /dev/null +++ b/.github/workflows/time-sfmbal-benchmark.yml @@ -0,0 +1,376 @@ +name: timeSFMBAL Benchmark + +on: + pull_request: + types: [opened] + workflow_dispatch: + inputs: + pr_number: + description: PR number to benchmark + required: true + type: string + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.inputs.pr_number || github.run_id }} + cancel-in-progress: true + +permissions: + actions: write + contents: read + issues: write + pull-requests: write + +jobs: + context: + runs-on: ubuntu-latest + outputs: + pr_number: ${{ steps.resolve.outputs.pr_number }} + head_sha: ${{ steps.resolve.outputs.head_sha }} + base_sha: ${{ steps.resolve.outputs.base_sha }} + head_checkout_ref: ${{ steps.resolve.outputs.head_checkout_ref }} + should_comment: ${{ steps.resolve.outputs.should_comment }} + group_id: ${{ steps.resolve.outputs.group_id }} + steps: + - name: Resolve PR context + id: resolve + uses: actions/github-script@v7 + with: + script: | + const eventName = context.eventName; + let prNumber = ""; + let headSha = ""; + let baseSha = ""; + let headCheckoutRef = ""; + + if (eventName === "pull_request") { + const pr = context.payload.pull_request; + prNumber = String(pr.number); + headSha = pr.head.sha; + baseSha = pr.base.sha; + headCheckoutRef = `refs/pull/${pr.number}/head`; + } else if (eventName === "workflow_dispatch") { + prNumber = String((context.payload.inputs?.pr_number || "").trim()); + if (!prNumber) { + core.setFailed("workflow_dispatch requires pr_number."); + return; + } + const pr = await github.rest.pulls.get({ + owner: context.repo.owner, + repo: context.repo.repo, + pull_number: Number(prNumber), + }); + headSha = pr.data.head.sha; + baseSha = pr.data.base.sha; + headCheckoutRef = `refs/pull/${pr.data.number}/head`; + } else { + core.setFailed(`Unsupported event: ${eventName}`); + return; + } + + const groupId = `${context.runId}-${Date.now()}`; + + core.setOutput("pr_number", prNumber); + core.setOutput("head_sha", headSha); + core.setOutput("base_sha", baseSha); + core.setOutput("head_checkout_ref", headCheckoutRef); + core.setOutput("should_comment", "true"); + core.setOutput("group_id", groupId); + + dispatch_and_wait: + needs: context + runs-on: ubuntu-latest + outputs: + run_statuses: ${{ steps.dispatch.outputs.run_statuses }} + steps: + - name: Dispatch benchmark runner workflows and wait + id: dispatch + uses: actions/github-script@v7 + env: + WORKFLOW_ID: time-sfmbal-benchmark-runner.yml + with: + script: | + const sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); + const workflowId = process.env.WORKFLOW_ID; + const groupId = "${{ needs.context.outputs.group_id }}"; + const headSha = "${{ needs.context.outputs.head_sha }}"; + const baseSha = "${{ needs.context.outputs.base_sha }}"; + const headCheckoutRef = "${{ needs.context.outputs.head_checkout_ref }}"; + + const runners = [ + { id: "linux-x64", os_name: "linux", arch: "x64" }, + { id: "linux-arm64", os_name: "linux", arch: "arm64" }, + { id: "macos-arm64", os_name: "macos", arch: "arm64" }, + ]; + + const specs = []; + for (const runner of runners) { + specs.push({ + role: "head", + sha: headSha, + checkout_ref: headCheckoutRef, + ...runner, + }); + specs.push({ + role: "base", + sha: baseSha, + checkout_ref: baseSha, + ...runner, + }); + } + + // Dispatch all unprivileged benchmark worker runs. + for (const spec of specs) { + spec.group_token = `${groupId}-${spec.role}-${spec.id}`; + await github.rest.actions.createWorkflowDispatch({ + owner: context.repo.owner, + repo: context.repo.repo, + workflow_id: workflowId, + ref: context.payload.repository.default_branch, + inputs: { + group_id: spec.group_token, + runner_id: spec.id, + os_name: spec.os_name, + arch: spec.arch, + checkout_ref: spec.checkout_ref, + target_sha: spec.sha, + }, + }); + } + + const createdAfter = Date.now() - 2 * 60 * 1000; + const findMatchingRun = async (token) => { + let page = 1; + while (page <= 5) { + const resp = await github.rest.actions.listWorkflowRuns({ + owner: context.repo.owner, + repo: context.repo.repo, + workflow_id: workflowId, + event: "workflow_dispatch", + per_page: 100, + page, + }); + for (const run of resp.data.workflow_runs) { + const createdAt = Date.parse(run.created_at || ""); + if (!Number.isNaN(createdAt) && createdAt < createdAfter) { + continue; + } + const title = run.display_title || ""; + if (title.includes(token)) { + return run; + } + } + if (resp.data.workflow_runs.length < 100) break; + page += 1; + } + return null; + }; + + // Wait for each dispatched run to appear so we can track by run_id. + for (let attempt = 0; attempt < 80; attempt++) { + let allResolved = true; + for (const spec of specs) { + if (spec.run_id) continue; + const run = await findMatchingRun(spec.group_token); + if (run) { + spec.run_id = run.id; + spec.html_url = run.html_url; + } else { + allResolved = false; + } + } + if (allResolved) break; + await sleep(5000); + } + + // Wait for completion (success/failure) of all found runs. + for (let attempt = 0; attempt < 240; attempt++) { + let allCompleted = true; + for (const spec of specs) { + if (!spec.run_id || spec.conclusion) continue; + const run = await github.rest.actions.getWorkflowRun({ + owner: context.repo.owner, + repo: context.repo.repo, + run_id: spec.run_id, + }); + const status = run.data.status; + if (status === "completed") { + spec.conclusion = run.data.conclusion || "unknown"; + spec.html_url = run.data.html_url; + } else { + allCompleted = false; + } + } + if (allCompleted) break; + await sleep(15000); + } + + for (const spec of specs) { + if (!spec.run_id) spec.conclusion = "not_found"; + if (!spec.conclusion) spec.conclusion = "timed_out"; + } + + const summary = specs.map((spec) => ({ + role: spec.role, + runner_id: spec.id, + sha: spec.sha, + conclusion: spec.conclusion, + run_id: spec.run_id || null, + html_url: spec.html_url || null, + })); + + core.setOutput("run_statuses", JSON.stringify(summary)); + + collect: + needs: [context, dispatch_and_wait] + strategy: + fail-fast: false + matrix: + include: + - id: linux-x64 + os_name: linux + arch: x64 + - id: linux-arm64 + os_name: linux + arch: arm64 + - id: macos-arm64 + os_name: macos + arch: arm64 + runs-on: ubuntu-latest + steps: + - name: Restore head benchmark cache + id: restore_head + uses: actions/cache/restore@v4 + with: + path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json + key: timeSFMBAL-benchmark-v3-${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }} + + - name: Restore base benchmark cache + id: restore_base + uses: actions/cache/restore@v4 + with: + path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json + key: timeSFMBAL-benchmark-v3-${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }} + + - name: Stage collected files + shell: bash + run: | + set -euo pipefail + mkdir -p artifact + echo "head cache-hit: ${{ steps.restore_head.outputs.cache-hit }}" + echo "base cache-hit: ${{ steps.restore_base.outputs.cache-hit }}" + if [[ "${{ steps.restore_head.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json" ]]; then + cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json" "artifact/head-${{ matrix.id }}.json" + else + : > "artifact/head-miss-${{ matrix.id }}.txt" + fi + if [[ "${{ steps.restore_base.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json" ]]; then + cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json" "artifact/base-${{ matrix.id }}.json" + else + : > "artifact/base-miss-${{ matrix.id }}.txt" + fi + + - name: Upload collected files + uses: actions/upload-artifact@v4 + with: + name: time-sfmbal-collected-${{ matrix.id }} + path: artifact/* + if-no-files-found: error + + summarize: + needs: [context, dispatch_and_wait, collect] + runs-on: ubuntu-latest + if: always() + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Download collected artifacts + uses: actions/download-artifact@v4 + with: + pattern: time-sfmbal-collected-* + path: collected + merge-multiple: true + + - name: Organize compare input + shell: bash + run: | + set -euo pipefail + mkdir -p collected/head collected/base + shopt -s nullglob + for file in collected/head-*.json; do + name="$(basename "${file}" .json)" + runner_id="${name#head-}" + cp "${file}" "collected/head/${runner_id}.json" + done + for file in collected/base-*.json; do + name="$(basename "${file}" .json)" + runner_id="${name#base-}" + cp "${file}" "collected/base/${runner_id}.json" + done + + - name: Generate markdown + run: | + python3 .github/scripts/compare_time_sfmbal_benchmarks.py \ + --head-dir collected/head \ + --base-dir collected/base \ + --head-sha "${{ needs.context.outputs.head_sha }}" \ + --base-sha "${{ needs.context.outputs.base_sha }}" \ + --output benchmark-comment.md + + - name: Append worker status details + shell: bash + run: | + set -euo pipefail + python3 - <<'PY' + import json + from pathlib import Path + + status_raw = """${{ needs.dispatch_and_wait.outputs.run_statuses }}""" + statuses = json.loads(status_raw) if status_raw.strip() else [] + lines = ["", "### Worker runs", "", "| Role | Runner | SHA | Conclusion |", "| --- | --- | --- | --- |"] + for item in statuses: + lines.append( + f"| {item.get('role','')} | {item.get('runner_id','')} | `{item.get('sha','')}` | {item.get('conclusion','')} |" + ) + + path = Path("benchmark-comment.md") + path.write_text(path.read_text(encoding="utf-8") + "\n".join(lines) + "\n", encoding="utf-8") + PY + cat benchmark-comment.md >> "$GITHUB_STEP_SUMMARY" + + - name: Post or update PR comment + if: needs.context.outputs.should_comment == 'true' + uses: actions/github-script@v7 + with: + script: | + const fs = require("fs"); + const marker = ""; + const body = fs.readFileSync("benchmark-comment.md", "utf8"); + const issue_number = Number("${{ needs.context.outputs.pr_number }}"); + + const comments = await github.paginate(github.rest.issues.listComments, { + owner: context.repo.owner, + repo: context.repo.repo, + issue_number, + per_page: 100, + }); + + const existing = comments.find( + (comment) => comment.user?.type === "Bot" && comment.body?.includes(marker) + ); + + if (existing) { + await github.rest.issues.updateComment({ + owner: context.repo.owner, + repo: context.repo.repo, + comment_id: existing.id, + body, + }); + } else { + await github.rest.issues.createComment({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number, + body, + }); + } diff --git a/timing/timeSFMBAL.cpp b/timing/timeSFMBAL.cpp index 876f80921c..4a4c437a64 100644 --- a/timing/timeSFMBAL.cpp +++ b/timing/timeSFMBAL.cpp @@ -21,18 +21,94 @@ #include #include #include +#include #include #include namespace { +constexpr const char* kDefaultBenchmarkDataset = "dubrovnik-16-22106-pre"; +constexpr const char* kProfileDataset = "dubrovnik-135-90642-pre"; + +std::string usage() { + return "Usage: timeSFMBAL [--colamd] [--profile] " + "[--benchmark-action-json FILE] [BALfile]"; +} + +struct TimingRow { + std::string dataset; + double legacy = 0.0; + double newer = 0.0; +}; + struct RunOptions { bool profile = false; + bool benchmarkActionJson = false; + std::string benchmarkActionJsonPath; std::vector filenames; }; +std::string escapeJson(std::string value) { + std::string escaped; + escaped.reserve(value.size()); + for (const char c : value) { + switch (c) { + case '\\': + escaped += "\\\\"; + break; + case '"': + escaped += "\\\""; + break; + case '\n': + escaped += "\\n"; + break; + case '\r': + escaped += "\\r"; + break; + case '\t': + escaped += "\\t"; + break; + default: + escaped += c; + break; + } + } + return escaped; +} + +void writeBenchmarkActionJson(const std::vector& rows, + const std::string& outputPath) { + std::ofstream out(outputPath); + if (!out) { + throw runtime_error("Unable to open benchmark JSON output file: " + + outputPath); + } + + out << "[\n"; + bool first = true; + const auto appendEntry = [&](const std::string& name, const double value) { + if (!first) out << ",\n"; + first = false; + out << " {\n"; + out << " \"name\": \"" << escapeJson(name) << "\",\n"; + out << " \"unit\": \"s\",\n"; + out << " \"value\": " << std::fixed << std::setprecision(9) << value + << "\n"; + out << " }"; + }; + + for (const auto& row : rows) { + appendEntry("timeSFMBAL/" + row.dataset + "/MultifrontalCholesky", + row.legacy); + appendEntry("timeSFMBAL/" + row.dataset + "/MultifrontalSolver", row.newer); + } + out << "\n]\n"; +} + RunOptions parseBalFiles(int argc, char* argv[]) { std::string filename; bool profile = false; + bool benchmarkActionJson = false; + std::string benchmarkActionJsonPath; for (int i = 1; i < argc; ++i) { if (strcmp(argv[i], "--colamd") == 0) { gUseSchur = false; @@ -42,28 +118,45 @@ RunOptions parseBalFiles(int argc, char* argv[]) { profile = true; continue; } + if (strcmp(argv[i], "--benchmark-action-json") == 0) { + if (++i >= argc || argv[i][0] == '-') { + throw runtime_error(usage()); + } + benchmarkActionJson = true; + benchmarkActionJsonPath = argv[i]; + continue; + } if (argv[i][0] == '-') { - throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + throw runtime_error(usage()); } if (!filename.empty()) { - throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + throw runtime_error(usage()); } filename = argv[i]; } if (profile && !filename.empty()) { - throw runtime_error("Usage: timeSFMBAL [--colamd] [--profile] [BALfile]"); + throw runtime_error(usage()); + } + if (profile && benchmarkActionJson) { + throw runtime_error(usage()); } if (!filename.empty()) { - return {profile, {filename}}; + return {profile, benchmarkActionJson, benchmarkActionJsonPath, {filename}}; } if (profile) { - return {profile, {findExampleDataFile("dubrovnik-135-90642-pre")}}; + return {profile, benchmarkActionJson, benchmarkActionJsonPath, + {findExampleDataFile(kProfileDataset)}}; } - return {profile, + if (benchmarkActionJson) { + return {profile, benchmarkActionJson, benchmarkActionJsonPath, + {findExampleDataFile(kDefaultBenchmarkDataset)}}; + } + + return {profile, benchmarkActionJson, benchmarkActionJsonPath, { findExampleDataFile("dubrovnik-16-22106-pre"), findExampleDataFile("dubrovnik-88-64298-pre"), @@ -100,11 +193,6 @@ double runSolver(const NonlinearFactorGraph& graph, const Values& initial, int main(int argc, char* argv[]) { const auto options = parseBalFiles(argc, argv); - struct TimingRow { - std::string dataset; - double legacy = 0.0; - double newer = 0.0; - }; std::vector rows; for (const auto& filename : options.filenames) { @@ -146,5 +234,14 @@ int main(int argc, char* argv[]) { << row.newer << " | " << speedup << "x |\n"; } } + + if (options.benchmarkActionJson) { + if (rows.empty()) { + throw runtime_error("No benchmark rows found to write."); + } + writeBenchmarkActionJson(rows, options.benchmarkActionJsonPath); + std::cout << "\nWrote benchmark-action JSON to " + << options.benchmarkActionJsonPath << "\n"; + } return 0; } From 2cd89f70710aad7c8e0a167196bff7ac08fe8d1e Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Mon, 29 Dec 2025 17:04:36 +0200 Subject: [PATCH 171/266] Add Compile-time option to limit TBB memory growth. Introduce GTSAM_TBB_BOUNDED_MEMORY_GROWTH CMake option (OFF by default) to disable parallel tree traversal when memory usage is a concern. Parallel tree traversal can cause significant memory growth (e.g., from ~4GB to ~12GB in tested scenarios). Changes: - Add GTSAM_TBB_BOUNDED_MEMORY_GROWTH option in HandleGeneralOptions.cmake - Set GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG when option is enabled - Conditionally disable parallel traversal in treeTraversal-inst.h --- INSTALL.md | 4 ++++ cmake/HandleGeneralOptions.cmake | 1 + cmake/HandleTBB.cmake | 3 +++ gtsam/base/treeTraversal-inst.h | 11 ++++++++--- gtsam/config.h.in | 3 +++ 5 files changed, 19 insertions(+), 3 deletions(-) diff --git a/INSTALL.md b/INSTALL.md index 4a3b44368c..fae5c7816f 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -239,6 +239,10 @@ Here are some tips to get the best possible performance out of GTSAM. optimization by 30-50%. Please note that this may not be true for very small problems where the overhead of dispatching work to multiple threads outweighs the benefit. We recommend that you benchmark your problem with/without TBB. + Note: TBB's parallel tree traversal can significantly increase memory usage + (e.g., from ~4GB to ~12GB in tested scenarios). If memory is a concern, you + can set `-DGTSAM_TBB_BOUNDED_MEMORY_GROWTH=ON` to disable parallel tree + traversal while keeping other TBB benefits. 3. Use `GTSAM_BUILD_WITH_MARCH_NATIVE`. A performance gain of 25-30% can be expected on modern processors. Note that this affects the portability of your executable. It may not run when copied to another system with older/different diff --git a/cmake/HandleGeneralOptions.cmake b/cmake/HandleGeneralOptions.cmake index c650c08578..dc7d550c81 100644 --- a/cmake/HandleGeneralOptions.cmake +++ b/cmake/HandleGeneralOptions.cmake @@ -42,6 +42,7 @@ option(GTSAM_HYBRID_TIMING "Enable the timing of hybrid factor option(GTSAM_ENABLE_CONSISTENCY_CHECKS "Enable/Disable expensive consistency checks" OFF) option(GTSAM_ENABLE_MEMORY_SANITIZER "Enable/Disable memory sanitizer" OFF) option(GTSAM_WITH_TBB "Use Intel Threaded Building Blocks (TBB) if available" ON) +option(GTSAM_TBB_BOUNDED_MEMORY_GROWTH "Avoid large increase in memory usage due to parallel tree traversal" OFF) option(GTSAM_WITH_EIGEN_MKL "Eigen will use Intel MKL if available" OFF) option(GTSAM_WITH_EIGEN_MKL_OPENMP "Eigen, when using Intel MKL, will also use OpenMP for multithreading if available" OFF) option(GTSAM_THROW_CHEIRALITY_EXCEPTION "Throw exception when a triangulated point is behind a camera" ON) diff --git a/cmake/HandleTBB.cmake b/cmake/HandleTBB.cmake index 393aeb3456..e7fadb1765 100644 --- a/cmake/HandleTBB.cmake +++ b/cmake/HandleTBB.cmake @@ -6,6 +6,9 @@ if (GTSAM_WITH_TBB) # Set up variables if we're using TBB if(TBB_FOUND) set(GTSAM_USE_TBB 1) # This will go into config.h + if (GTSAM_TBB_BOUNDED_MEMORY_GROWTH) + set(GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG 1) + endif() if ((${TBB_VERSION_MAJOR} GREATER 2020) OR (${TBB_VERSION_MAJOR} EQUAL 2020)) set(TBB_GREATER_EQUAL_2020 1) diff --git a/gtsam/base/treeTraversal-inst.h b/gtsam/base/treeTraversal-inst.h index 1c19e0cb19..fb42c78033 100644 --- a/gtsam/base/treeTraversal-inst.h +++ b/gtsam/base/treeTraversal-inst.h @@ -181,8 +181,11 @@ template(forest.roots(), rootData, visitorPre, @@ -203,7 +206,9 @@ void DepthFirstForestParallel(FOREST& forest, DATA& rootData, template void PostOrderForestParallel(FOREST& forest, VISITOR_POST& visitorPost, int problemSizeThreshold = 10) { -#ifdef GTSAM_USE_TBB +#if defined(GTSAM_USE_TBB) && !defined(GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG) + // Note: Parallel tree traversal (the default) causes a large increase in memory footprint. + typedef typename FOREST::Node Node; internal::CreateRootPostOrderTask(forest.roots(), visitorPost, problemSizeThreshold); diff --git a/gtsam/config.h.in b/gtsam/config.h.in index 9ba562cc4a..06033aaf42 100644 --- a/gtsam/config.h.in +++ b/gtsam/config.h.in @@ -56,6 +56,9 @@ // Whether we are using a TBB version higher than 2020 #cmakedefine TBB_GREATER_EQUAL_2020 +// Whether to use bounded memory growth for TBB parallel tree traversal +#cmakedefine GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG + // Whether we are using system-Eigen or our own patched version #cmakedefine GTSAM_USE_SYSTEM_EIGEN From 5013e2a90297e5e27d25cf5ba02543f752077e35 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Mon, 29 Dec 2025 17:07:13 +0200 Subject: [PATCH 172/266] perf: TBB parallelization for HessianFactor. Parallelize the updateHessian operation when forming A'*A in the HessianFactor merge constructor. For large matrices (>50 rows), the work is split across columns using TBB parallel_for, with each thread updating a disjoint set of block columns. Key changes: - Add column-range overload of updateHessian() to GaussianFactor, HessianFactor, JacobianFactor, and RegularImplicitSchurFactor - Add setZeroColumns() method to SymmetricBlockMatrix for efficient column-wise zeroing using memset In my toy example it reducded updateHessian time from ~72s to ~41s. (with 10 threads and GTSAM_TBB_BOUNDED_MEMORY_GROWTH=OFF) --- gtsam/base/SymmetricBlockMatrix.h | 20 ++++ gtsam/base/tests/testSymmetricBlockMatrix.cpp | 22 ++++ gtsam/linear/GaussianFactor.h | 12 ++ gtsam/linear/HessianFactor.cpp | 110 +++++++++++++++++- gtsam/linear/HessianFactor.h | 11 ++ gtsam/linear/JacobianFactor.cpp | 66 +++++++++++ gtsam/linear/JacobianFactor.h | 11 ++ gtsam/linear/tests/testHessianFactor.cpp | 67 +++++++++++ gtsam/linear/tests/testJacobianFactor.cpp | 71 +++++++++++ gtsam/slam/RegularImplicitSchurFactor.h | 18 ++- 10 files changed, 398 insertions(+), 10 deletions(-) diff --git a/gtsam/base/SymmetricBlockMatrix.h b/gtsam/base/SymmetricBlockMatrix.h index 95e337cc57..473c1fa9d7 100644 --- a/gtsam/base/SymmetricBlockMatrix.h +++ b/gtsam/base/SymmetricBlockMatrix.h @@ -25,6 +25,7 @@ #include #endif #include +#include #include #include #include @@ -306,6 +307,25 @@ namespace gtsam { matrix_.setZero(); } + /// Set the block columns between beginCol (inclusive) and endCol (exclusive) to zero. + void setZeroColumns(DenseIndex beginCol, DenseIndex endCol) { + assert(beginCol < endCol); + assert(beginCol >= 0); + assert(endCol >= 0); + assert(beginCol < nBlocks()); + assert(endCol <= nBlocks()); + static_assert(Matrix::IsRowMajor == 0, "setZeroColumns requires column-major storage."); + + const DenseIndex denseBeginCol = offset(beginCol); + const DenseIndex denseEndCol = offset(endCol); + + double *begin = matrix_.data() + denseBeginCol * matrix_.rows(); + double *end = matrix_.data() + denseEndCol * matrix_.rows(); + + // Using memset for maximal compiler optimization. + memset(begin, 0, (end - begin) * sizeof(*begin)); + } + /// Negate the entire active matrix. void negate(); diff --git a/gtsam/base/tests/testSymmetricBlockMatrix.cpp b/gtsam/base/tests/testSymmetricBlockMatrix.cpp index 674bc7d012..ab04ffdb5d 100644 --- a/gtsam/base/tests/testSymmetricBlockMatrix.cpp +++ b/gtsam/base/tests/testSymmetricBlockMatrix.cpp @@ -79,6 +79,28 @@ TEST(SymmetricBlockMatrix, WriteBlocks) EXPECT(assert_equal(expected3, actual3)); } +/* ************************************************************************* */ +TEST(SymmetricBlockMatrix, setZeroColumns) { + // Expected: columns 3 and 4 are zero + Matrix expected = testBlockMatrix.selfadjointView().toDenseMatrix().eval(); + expected.col(3).setZero(); + expected.col(4).setZero(); + expected = expected.triangularView().toDenseMatrix().eval(); + + SymmetricBlockMatrix bm = testBlockMatrix; + + // Zero out the middle block (block 1, columns 3-4) + bm.setZeroColumns(1, 2); + + Matrix result = bm.selfadjointView() + .toDenseMatrix() + .triangularView() + .toDenseMatrix() + .eval(); + + EXPECT(assert_equal(expected, result)); +} + /* ************************************************************************* */ // Verify block range access. TEST(SymmetricBlockMatrix, Ranges) diff --git a/gtsam/linear/GaussianFactor.h b/gtsam/linear/GaussianFactor.h index ed6778d8f1..2f873bbfbb 100644 --- a/gtsam/linear/GaussianFactor.h +++ b/gtsam/linear/GaussianFactor.h @@ -157,6 +157,18 @@ namespace gtsam { virtual void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info) const = 0; + /** Update an information matrix by adding the information corresponding to this factor + * (used internally during elimination), restricted to a range of block columns, + * useful for parallelization. + * @param keys The ordered vector of keys for the information matrix to be updated + * @param info The information matrix to be updated + * @param beginCol First block column index (inclusive) in the range to update + * @param endCol Last block column index (exclusive) in the range to update + */ + virtual void updateHessian(const KeyVector& keys, + SymmetricBlockMatrix* info, + DenseIndex beginCol, DenseIndex endCol) const = 0; + /// @} /// @name Operator interface /// @{ diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp index 6cb5223052..7b539a64bd 100644 --- a/gtsam/linear/HessianFactor.cpp +++ b/gtsam/linear/HessianFactor.cpp @@ -34,6 +34,14 @@ #include #include +#ifdef GTSAM_USE_TBB + #include + #include + #include + #include + #include +#endif + using namespace std; namespace gtsam { @@ -241,17 +249,65 @@ HessianFactor::HessianFactor(const GaussianFactorGraph& factors, const Scatter& scatter) { gttic(HessianFactor_MergeConstructor); + gttic(Allocate); Allocate(scatter); + gttoc(Allocate); + + // Only parallelize the inner loop if GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG is + // defined. Without this flag, multiple HessianFactor constructions are + // already running in parallel (e.g., when constructing multiple factors + // concurrently), so there's no need to parallelize the inner + // updateHessian loop here as well. +#if defined(GTSAM_USE_TBB) && defined(GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG) + constexpr DenseIndex kParallelThresholdHeuristic = 50; + if (info_.rows() > kParallelThresholdHeuristic) { + gttic(updateHessian_TBB); + + const DenseIndex M = info_.nBlocks(); + + auto numThreads = std::min( + static_cast(oneapi::tbb::global_control::active_value( + oneapi::tbb::global_control::max_allowed_parallelism)), + static_cast(oneapi::tbb::this_task_arena::max_concurrency())); + + if (numThreads > 1) { + DenseIndex grain = std::max(1, M / (2 * numThreads)); + tbb::parallel_for(tbb::blocked_range(0, M, grain), + [&, M](const tbb::blocked_range& range) { + // reverse the range to start from the end because + // matrix is upper triangular and therefore end is + // larger than begin so we would like to start with + // the last column that is the most work and go to the + // first column that is least. + DenseIndex beginCol = M - range.end(); + DenseIndex endCol = M - range.begin(); + info_.setZeroColumns(beginCol, endCol); + for (const auto& factor : factors) { + if (factor) { + factor->updateHessian(keys_, &info_, beginCol, + endCol); + } + } + }); + return; + } + } +#endif + gttic(setAllZero); + info_.setAllZero(); + gttoc(setAllZero); + + gttic(updateHessian); // Form A' * A - gttic(update); - info_.setAllZero(); - for(const auto& factor: factors) - if (factor) + for (const auto& factor : factors) { + if (factor) { factor->updateHessian(keys_, &info_); - gttoc(update); + } + } } + /* ************************************************************************* */ void HessianFactor::print(const std::string& s, const KeyFormatter& formatter) const { @@ -362,14 +418,58 @@ void HessianFactor::updateHessian(const KeyVector& infoKeys, gttic(updateHessian_HessianFactor); const DenseIndex nrVariablesInThisFactor = size(); + gttic(slots); vector slots(nrVariablesInThisFactor + 1); for (DenseIndex j = 0; j < nrVariablesInThisFactor; ++j) slots[j] = Slot(infoKeys, keys_[j]); + slots[nrVariablesInThisFactor] = info->nBlocks() - 1; + gttoc(slots); info->updateFromMappedBlocks(info_, slots); } +/* ************************************************************************* */ +void HessianFactor::updateHessian(const KeyVector& infoKeys, + SymmetricBlockMatrix* info, + DenseIndex beginCol, + DenseIndex endCol) const { + assert(info); + const DenseIndex nrVariablesInThisFactor = size(); + + vector slots; + slots.reserve(nrVariablesInThisFactor + 1); + + for (DenseIndex j = 0; j < nrVariablesInThisFactor; ++j) { + slots.push_back(Slot(infoKeys, keys_[j])); + } + slots.push_back(info->nBlocks() - 1); + + for (DenseIndex j = 0; j <= nrVariablesInThisFactor; ++j) { + const DenseIndex J = slots[j]; + // Update diagonal block if J is in range + if (J >= beginCol && J < endCol) { + info->updateDiagonalBlock(J, info_.diagonalBlock(j)); + } + + // Update off-diagonal blocks where column max(I, J) is in range + // Note: We process all blocks and let the maxCol check filter them, + // because I and J may be in different orders (slots are not necessarily sorted) + for (DenseIndex i = 0; i < j; ++i) { + const DenseIndex I = slots[i]; + assert(i < j); + assert(I != J); + + // The physical column index in the symmetric matrix is max(I, J) + const DenseIndex maxCol = std::max(I, J); + + if (maxCol >= beginCol && maxCol < endCol) { + info->updateOffDiagonalBlock(I, J, info_.aboveDiagonalBlock(i, j)); + } + } + } +} + /* ************************************************************************* */ GaussianFactor::shared_ptr HessianFactor::negate() const { shared_ptr result = std::make_shared(*this); diff --git a/gtsam/linear/HessianFactor.h b/gtsam/linear/HessianFactor.h index d4b6965f77..f0d487c424 100644 --- a/gtsam/linear/HessianFactor.h +++ b/gtsam/linear/HessianFactor.h @@ -333,6 +333,17 @@ namespace gtsam { */ void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info) const override; + /** Update an information matrix by adding the information corresponding to this factor + * (used internally during elimination), restricted to a range of block columns, + * useful for parallelization. + * @param keys The ordered vector of keys for the information matrix to be updated + * @param info The information matrix to be updated + * @param beginCol First block column index (inclusive) in the range to update + * @param endCol Last block column index (exclusive) in the range to update + */ + void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info, + DenseIndex beginCol, DenseIndex endCol) const override; + /** Update another Hessian factor * @param other the HessianFactor to be updated */ diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index 8229436b3a..76c145ca37 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -704,6 +704,72 @@ void JacobianFactor::updateHessian(const KeyVector& infoKeys, } } +/* ************************************************************************* */ +static void whitenedUpdateHessian(SymmetricBlockMatrix* info, std::vector slots, + const VerticalBlockMatrix& Ab_, DenseIndex beginCol, DenseIndex endCol) { + const DenseIndex n = Ab_.nBlocks() - 1; + + for (DenseIndex j = 0; j <= n; ++j) { + const DenseIndex J = slots[j]; + Eigen::Block Ab_j = Ab_(j); + + // Update diagonal block if J is in range + if (J >= beginCol && J < endCol) { + info->diagonalBlock(J).rankUpdate(Ab_j.transpose()); + } + + // Fill off-diagonal blocks with Ai'*Aj where column max(I, J) is in range + for (DenseIndex i = 0; i < j; ++i) { + const DenseIndex I = slots[i]; + + // The physical column index in the symmetric matrix is max(I, J) + const DenseIndex maxCol = std::max(I, J); + if (maxCol >= beginCol && maxCol < endCol) { + // Pass original indices - updateOffDiagonalBlock handles swapping internally + info->updateOffDiagonalBlock(I, J, Ab_(i).transpose() * Ab_j); + } + } + } +} + +void JacobianFactor::updateHessian(const KeyVector& infoKeys, + SymmetricBlockMatrix* info, + DenseIndex beginCol, DenseIndex endCol) const { + if (rows() == 0) return; + + // Ab_ is the augmented Jacobian matrix A, and we perform I += A'*A below. + DenseIndex n = Ab_.nBlocks() - 1; + + // Pre-calculate slots + vector slots; + slots.reserve(n + 1); + bool foundCol = false; + for (DenseIndex j = 0; j < n; ++j) { + slots.push_back(Slot(infoKeys, keys_[j])); + if (slots[j] >= beginCol && slots[j] < endCol) { + foundCol = true; + } + } + slots.push_back(info->nBlocks() - 1); + if (slots[n] >= beginCol && slots[n] < endCol) { + foundCol = true; + } + if (!foundCol) return; + + // Whiten the factor if it has a noise model + const SharedDiagonal& model = get_model(); + if (model && !model->isUnit()) { + if (model->isConstrained()) + throw invalid_argument( + "JacobianFactor::updateHessian: cannot update information with " + "constrained noise model"); + JacobianFactor whitenedFactor = whiten(); + whitenedUpdateHessian(info, std::move(slots), whitenedFactor.Ab_, beginCol, endCol); + } else { + whitenedUpdateHessian(info, std::move(slots), Ab_, beginCol, endCol); + } +} + /* ************************************************************************* */ Vector JacobianFactor::operator*(const VectorValues& x) const { Vector Ax(Ab_.rows()); diff --git a/gtsam/linear/JacobianFactor.h b/gtsam/linear/JacobianFactor.h index 6c93464f73..da75a9d165 100644 --- a/gtsam/linear/JacobianFactor.h +++ b/gtsam/linear/JacobianFactor.h @@ -371,6 +371,17 @@ namespace gtsam { */ void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info) const override; + /** Update an information matrix by adding the information corresponding to this factor + * (used internally during elimination), restricted to a range of block columns, + * useful for parallelization. + * @param keys The ordered vector of keys for the information matrix to be updated + * @param info The information matrix to be updated + * @param beginCol First block column index (inclusive) in the range to update + * @param endCol Last block column index (exclusive) in the range to update + */ + void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info, + DenseIndex beginCol, DenseIndex endCol) const override; + /** Return A*x */ Vector operator*(const VectorValues& x) const; diff --git a/gtsam/linear/tests/testHessianFactor.cpp b/gtsam/linear/tests/testHessianFactor.cpp index 0773acb61a..23c58b4689 100644 --- a/gtsam/linear/tests/testHessianFactor.cpp +++ b/gtsam/linear/tests/testHessianFactor.cpp @@ -590,6 +590,73 @@ TEST(HessianFactor, Solve) EXPECT(assert_equal(expected, factor.solve())); } + +/* ************************************************************************* */ +TEST(HessianFactor, updateHessianWithColumnRangeOnlyUpdatesSpecifiedBlocks) { + // Create a simple 2x2 HessianFactor on keys 0 and 1 + Matrix G00 = (Matrix(2, 2) << 1, 2, 2, 3).finished(); + Matrix G01 = (Matrix(2, 2) << 4, 5, 6, 7).finished(); + Matrix G11 = (Matrix(2, 2) << 8, 9, 9, 10).finished(); + Vector g0 = Vector2(1, 2); + Vector g1 = Vector2(3, 4); + double f = 5.0; + + HessianFactor factor(0, 1, G00, G01, g0, G11, g1, f); + + // Destination matrix: 3 blocks (key 0: size 2, key 1: size 2, RHS: size 1) + KeyVector infoKeys{0, 1}; + Dims dims{2, 2, 1}; + + // Initialize to zero + SymmetricBlockMatrix info(dims); + info.setZero(); + + // Update only block column 0 (first variable) + factor.updateHessian(infoKeys, &info, 0, 1); + + // Block 0 (diagonal for key 0) should be updated (non-zero) + Matrix block0 = info.diagonalBlock(0); + EXPECT(assert_equal(G00, block0, 0)); + + // Block 1 (diagonal for key 1) should still be zero + Matrix block1 = info.diagonalBlock(1); + Matrix expected_zero_2x2 = Matrix::Zero(2, 2); + EXPECT(assert_equal(expected_zero_2x2, block1, 0)); + + // Block 2 (RHS) should still be zero + Matrix block2 = info.diagonalBlock(2); + Matrix expected_zero_1x1 = Matrix::Zero(1, 1); + EXPECT(assert_equal(expected_zero_1x1, block2, 0)); + + // Off-diagonal block (0,1) should still be zero + // Note: aboveDiagonalBlock gets the upper triangular part + Matrix block01 = info.aboveDiagonalBlock(0, 1); + EXPECT(assert_equal(expected_zero_2x2, block01, 0)); + + // Now update block column 1 + factor.updateHessian(infoKeys, &info, 1, 2); + + // Block 1 should now be updated + EXPECT(assert_equal(G11, info.diagonalBlock(1), 0)); + + // Off-diagonal block (0,1) should now be updated + EXPECT(assert_equal(G01, info.aboveDiagonalBlock(0, 1), 0)); + + // Block 2 (RHS) should still be zero (not updated yet) + EXPECT(assert_equal(expected_zero_1x1, info.diagonalBlock(2), 0)); + + // Finally update the RHS column + factor.updateHessian(infoKeys, &info, 2, 3); + + // Now verify the full matrix matches what we'd get from a full update + SymmetricBlockMatrix infoFull(dims); + infoFull.setZero(); + factor.updateHessian(infoKeys, &infoFull); + + EXPECT(assert_equal(Matrix(infoFull.selfadjointView()), + Matrix(info.selfadjointView()), 0)); +} + /* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr);} /* ************************************************************************* */ diff --git a/gtsam/linear/tests/testJacobianFactor.cpp b/gtsam/linear/tests/testJacobianFactor.cpp index 5296e4a760..2090b050f2 100644 --- a/gtsam/linear/tests/testJacobianFactor.cpp +++ b/gtsam/linear/tests/testJacobianFactor.cpp @@ -24,6 +24,7 @@ #include #include #include +#include using namespace std; using namespace gtsam; @@ -793,6 +794,76 @@ TEST(JacobianFactor, OverdeterminedEliminate) { EXPECT(actual.second->empty()); } +/* ************************************************************************* */ +TEST(JacobianFactor, updateHessianWithColumnRangeOnlyUpdatesSpecifiedBlocks) { + const double tol = 0; + + // Create a simple 2x2 JacobianFactor on keys 0 and 1 + // A0 is 2x2 matrix for key 0, A1 is 2x2 matrix for key 1, b is 2x1 vector + Matrix A0 = (Matrix(2, 2) << 1, 2, 3, 4).finished(); + Matrix A1 = (Matrix(2, 2) << 5, 6, 7, 8).finished(); + Vector b = Vector2(1, 2); + + JacobianFactor factor(0, A0, 1, A1, b); + + // Destination matrix: 3 blocks (key 0: size 2, key 1: size 2, RHS: size 1) + KeyVector infoKeys{0, 1}; + Dims dims{2, 2, 1}; + + // Initialize to zero + SymmetricBlockMatrix info(dims); + info.setZero(); + + // Update only block column 0 (first variable) + factor.updateHessian(infoKeys, &info, 0, 1); + + // Block 0 (diagonal for key 0) should be updated (non-zero) + // The diagonal block should be A0'*A0 + Matrix expected_G00 = A0.transpose() * A0; + Matrix block0 = info.diagonalBlock(0); + EXPECT(assert_equal(expected_G00, block0, tol)); + + // Block 1 (diagonal for key 1) should still be zero + Matrix block1 = info.diagonalBlock(1); + Matrix expected_zero_2x2 = Matrix::Zero(2, 2); + EXPECT(assert_equal(expected_zero_2x2, block1, tol)); + + // Block 2 (RHS) should still be zero + Matrix block2 = info.diagonalBlock(2); + Matrix expected_zero_1x1 = Matrix::Zero(1, 1); + EXPECT(assert_equal(expected_zero_1x1, block2, tol)); + + // Off-diagonal block (0,1) should still be zero + // Note: aboveDiagonalBlock gets the upper triangular part + Matrix block01 = info.aboveDiagonalBlock(0, 1); + EXPECT(assert_equal(expected_zero_2x2, block01, tol)); + + // Now update block column 1 + factor.updateHessian(infoKeys, &info, 1, 2); + + // Block 1 should now be updated (A1'*A1) + Matrix expected_G11 = A1.transpose() * A1; + EXPECT(assert_equal(expected_G11, info.diagonalBlock(1), tol)); + + // Off-diagonal block (0,1) should now be updated (A0'*A1) + Matrix expected_G01 = A0.transpose() * A1; + EXPECT(assert_equal(expected_G01, info.aboveDiagonalBlock(0, 1), tol)); + + // Block 2 (RHS) should still be zero (not updated yet) + EXPECT(assert_equal(expected_zero_1x1, info.diagonalBlock(2), tol)); + + // Finally update the RHS column + factor.updateHessian(infoKeys, &info, 2, 3); + + // Now verify the full matrix matches what we'd get from a full update + SymmetricBlockMatrix infoFull(dims); + infoFull.setZero(); + factor.updateHessian(infoKeys, &infoFull); + + EXPECT(assert_equal(Matrix(infoFull.selfadjointView()), + Matrix(info.selfadjointView()), tol)); +} + /* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr);} /* ************************************************************************* */ diff --git a/gtsam/slam/RegularImplicitSchurFactor.h b/gtsam/slam/RegularImplicitSchurFactor.h index 924aaa1cf6..274347887e 100644 --- a/gtsam/slam/RegularImplicitSchurFactor.h +++ b/gtsam/slam/RegularImplicitSchurFactor.h @@ -138,16 +138,24 @@ class RegularImplicitSchurFactor: public GaussianFactor { void updateHessian(const KeyVector& keys, SymmetricBlockMatrix* info) const override { throw std::runtime_error( - "RegularImplicitSchurFactor::updateHessian non implemented"); + "RegularImplicitSchurFactor::updateHessian not implemented"); } + + void updateHessian(const KeyVector& keys, + SymmetricBlockMatrix* info, + DenseIndex beginCol, DenseIndex endCol) const override { + throw std::runtime_error( + "RegularImplicitSchurFactor::updateHessian not implemented"); + } + Matrix augmentedJacobian() const override { throw std::runtime_error( - "RegularImplicitSchurFactor::augmentedJacobian non implemented"); + "RegularImplicitSchurFactor::augmentedJacobian not implemented"); return Matrix(); } std::pair jacobian() const override { throw std::runtime_error( - "RegularImplicitSchurFactor::jacobian non implemented"); + "RegularImplicitSchurFactor::jacobian not implemented"); return {Matrix(), Vector()}; } @@ -257,14 +265,14 @@ class RegularImplicitSchurFactor: public GaussianFactor { return std::make_shared >(keys_, FBlocks_, PointCovariance_, E_, b_); throw std::runtime_error( - "RegularImplicitSchurFactor::clone non implemented"); + "RegularImplicitSchurFactor::clone not implemented"); } GaussianFactor::shared_ptr negate() const override { return std::make_shared >(keys_, FBlocks_, PointCovariance_, E_, b_); throw std::runtime_error( - "RegularImplicitSchurFactor::negate non implemented"); + "RegularImplicitSchurFactor::negate not implemented"); } // Raw Vector version of y += F'*alpha*(I - E*P*E')*F*x, for testing From 87098f7ed8eef94f41da219aa5d1f913c396ca73 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Sun, 1 Feb 2026 10:46:37 +0200 Subject: [PATCH 173/266] perf: Enable TBB parallelization unconditionally in HessianFactor Remove conditional check for GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG to always enable TBB parallelization when GTSAM_USE_TBB is defined. Improves optimizer performance on my test case with 12 cores by ~27% (117.2s -> 85.7s) by reducing iteration times from ~5-6s to ~3s. Before: ``` Running gtsam optimizer took 117.2392 seconds ``` After: ``` Running gtsam optimizer took 85.6852 seconds ``` --- gtsam/linear/HessianFactor.cpp | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp index 7b539a64bd..9db4472c94 100644 --- a/gtsam/linear/HessianFactor.cpp +++ b/gtsam/linear/HessianFactor.cpp @@ -253,12 +253,7 @@ HessianFactor::HessianFactor(const GaussianFactorGraph& factors, Allocate(scatter); gttoc(Allocate); - // Only parallelize the inner loop if GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG is - // defined. Without this flag, multiple HessianFactor constructions are - // already running in parallel (e.g., when constructing multiple factors - // concurrently), so there's no need to parallelize the inner - // updateHessian loop here as well. -#if defined(GTSAM_USE_TBB) && defined(GTSAM_TBB_BOUNDED_MEMORY_GROWTH_FLAG) +#if defined(GTSAM_USE_TBB) constexpr DenseIndex kParallelThresholdHeuristic = 50; if (info_.rows() > kParallelThresholdHeuristic) { gttic(updateHessian_TBB); From 028789324c4c896005d4d7b0cd421387bb74c18b Mon Sep 17 00:00:00 2001 From: Sammy Guo Date: Thu, 29 Jan 2026 23:36:27 -0800 Subject: [PATCH 174/266] Introducing DifferentialPseudorangeFactor [skip ci] --- gtsam/navigation/PseudorangeFactor.cpp | 94 +++- gtsam/navigation/PseudorangeFactor.h | 129 +++++- gtsam/navigation/navigation.i | 25 +- .../tests/testPseudorangeFactor.cpp | 85 ++++ .../DifferentialPseudorangeExample.ipynb | 400 ++++++++++++++++++ .../SinglePointPositioningExample.ipynb | 131 ++---- python/gtsam/examples/gnss_utils.py | 139 ++++++ python/gtsam/examples/images/dpr_fg.jpg | Bin 0 -> 35814 bytes 8 files changed, 872 insertions(+), 131 deletions(-) create mode 100644 python/gtsam/examples/DifferentialPseudorangeExample.ipynb create mode 100644 python/gtsam/examples/gnss_utils.py create mode 100644 python/gtsam/examples/images/dpr_fg.jpg diff --git a/gtsam/navigation/PseudorangeFactor.cpp b/gtsam/navigation/PseudorangeFactor.cpp index fa5bedf414..2ac06cec0c 100644 --- a/gtsam/navigation/PseudorangeFactor.cpp +++ b/gtsam/navigation/PseudorangeFactor.cpp @@ -16,19 +16,16 @@ constexpr double CLIGHT = 299792458.0; namespace gtsam { -PseudorangeFactor::PseudorangeFactor() - : Base(), pseudorange_(0.0), satPos_(Point3::Zero()), satClkBias_(0.0) {} - -PseudorangeFactor::PseudorangeFactor(Key receiverPositionKey, - Key receiverClockBiasKey, +//*************************************************************************** +PseudorangeFactor::PseudorangeFactor(const Key receiverPositionKey, + const Key receiverClockBiasKey, const double measuredPseudorange, const Point3& satellitePosition, const double satelliteClockBias, const SharedNoiseModel& model) : Base(model, receiverPositionKey, receiverClockBiasKey), - pseudorange_(measuredPseudorange), - satPos_(satellitePosition), - satClkBias_(satelliteClockBias) {} + PseudorangeBase{measuredPseudorange, satellitePosition, + satelliteClockBias} {} //*************************************************************************** void PseudorangeFactor::print(const std::string& s, @@ -51,29 +48,90 @@ bool PseudorangeFactor::equals(const NonlinearFactor& expected, //*************************************************************************** Vector PseudorangeFactor::evaluateError( - const Point3& receiver_position, const double& receiver_clock_bias, - OptionalMatrixType Hreceiver_pos, - OptionalMatrixType Hreceiver_clock_bias) const { + const Point3& receiverPosition, const double& receiverClockBias, + OptionalMatrixType HreceiverPos, + OptionalMatrixType HreceiverClockBias) const { // Apply pseudorange equation: rho = range + c*[dt_u - dt^s] - const Vector3 position_difference = receiver_position - satPos_; + const Vector3 position_difference = receiverPosition - satPos_; const double range = position_difference.norm(); - const double rho = range + CLIGHT * (receiver_clock_bias - satClkBias_); + const double rho = range + CLIGHT * (receiverClockBias - satClkBias_); const double error = rho - pseudorange_; // Compute associated derivatives: - if (Hreceiver_pos) { + if (HreceiverPos) { if (range < std::numeric_limits::epsilon()) { - *Hreceiver_pos = Matrix13::Zero(); + *HreceiverPos = Matrix13::Zero(); } else { - *Hreceiver_pos = (position_difference / range).transpose(); + *HreceiverPos = (position_difference / range).transpose(); } } - if (Hreceiver_clock_bias) { - *Hreceiver_clock_bias = I_1x1 * CLIGHT; + if (HreceiverClockBias) { + *HreceiverClockBias = I_1x1 * CLIGHT; } return Vector1(error); } +//*************************************************************************** +DifferentialPseudorangeFactor::DifferentialPseudorangeFactor( + const Key receiverPositionKey, const Key receiverClockBiasKey, + const Key differentialCorrectionKey, const double measuredPseudorange, + const Point3& satellitePosition, const double satelliteClockBias, + const SharedNoiseModel& model) + : Base(model, receiverPositionKey, receiverClockBiasKey, + differentialCorrectionKey), + PseudorangeBase{measuredPseudorange, satellitePosition, + satelliteClockBias} {} + +//*************************************************************************** +void DifferentialPseudorangeFactor::print( + const std::string& s, const KeyFormatter& keyFormatter) const { + Base::print(s, keyFormatter); + gtsam::print(pseudorange_, "pseudorange (m): "); + gtsam::print(Vector(satPos_), "sat position (ECEF meters): "); + gtsam::print(satClkBias_, "sat clock bias (s): "); +} + +//*************************************************************************** +bool DifferentialPseudorangeFactor::equals(const NonlinearFactor& expected, + double tol) const { + const This* e = dynamic_cast(&expected); + return e != nullptr && Base::equals(*e, tol) && + traits::Equals(pseudorange_, e->pseudorange_, tol) && + traits::Equals(satPos_, e->satPos_, tol) && + traits::Equals(satClkBias_, e->satClkBias_, tol); +} + +//*************************************************************************** +Vector DifferentialPseudorangeFactor::evaluateError( + const Point3& receiverPosition, const double& receiverClock_bias, + const double& differentialCorrection, OptionalMatrixType HreceiverPos, + OptionalMatrixType HreceiverClockBias, + OptionalMatrixType HdifferentialCorrection) const { + // Apply pseudorange equation: rho = range + c*[dt_u - dt^s] + const Vector3 position_difference = receiverPosition - satPos_; + const double range = position_difference.norm(); + const double rho = range + CLIGHT * (receiverClock_bias - satClkBias_); + const double error = rho - pseudorange_ - differentialCorrection; + + // Compute associated derivatives: + if (HreceiverPos) { + if (range < std::numeric_limits::epsilon()) { + *HreceiverPos = Matrix13::Zero(); + } else { + *HreceiverPos = (position_difference / range).transpose(); + } + } + + if (HreceiverClockBias) { + *HreceiverClockBias = I_1x1 * CLIGHT; + } + + if (HdifferentialCorrection) { + *HdifferentialCorrection = -I_1x1; + } + + return Vector1(error); +} } // namespace gtsam diff --git a/gtsam/navigation/PseudorangeFactor.h b/gtsam/navigation/PseudorangeFactor.h index 8c0cb9e5de..79fb7a4f53 100644 --- a/gtsam/navigation/PseudorangeFactor.h +++ b/gtsam/navigation/PseudorangeFactor.h @@ -14,6 +14,16 @@ namespace gtsam { +/** + * Base class storing common members for GNSS-related pseudorange factors. + */ +struct PseudorangeBase { + double + pseudorange_; ///< Receiver-reported pseudorange measurement in meters. + Point3 satPos_; ///< Satellite position in WGS84 ECEF meters. + double satClkBias_; ///< Satellite clock bias in seconds. +}; + /** * Simplified GNSS pseudorange model for basic positioning problems. * @@ -36,16 +46,11 @@ namespace gtsam { * [1] P. Misra et. al., "Global Positioning Systems: Signals, Measurements, and * Performance", Second Edition, 2012. */ -class GTSAM_EXPORT PseudorangeFactor - : public NoiseModelFactorN { +class GTSAM_EXPORT PseudorangeFactor : public NoiseModelFactorN, + private PseudorangeBase { private: typedef NoiseModelFactorN Base; - double - pseudorange_; ///< Receiver-reported pseudorange measurement in meters. - Point3 satPos_; ///< Satellite position in WGS84 ECEF meters. - double satClkBias_; ///< Satellite clock bias in seconds. - public: // Provide access to the Matrix& version of evaluateError: using Base::evaluateError; @@ -57,7 +62,7 @@ class GTSAM_EXPORT PseudorangeFactor typedef PseudorangeFactor This; /** default constructor - only use for serialization */ - PseudorangeFactor(); + PseudorangeFactor() = default; virtual ~PseudorangeFactor() = default; @@ -70,7 +75,7 @@ class GTSAM_EXPORT PseudorangeFactor * @param measuredPseudorange Receiver-measured pseudorange in meters. * @param satellitePosition Satellite ECEF position in meters. * @param satelliteClockBias Satellite clock bias in seconds. - * @param model 1-D noise model. + * @param model 1-D pseudorange noise model. */ PseudorangeFactor( Key receiverPositionKey, Key receiverClockBiasKey, @@ -94,7 +99,7 @@ class GTSAM_EXPORT PseudorangeFactor /// vector of errors Vector evaluateError(const Point3& receiverPosition, - const double& receiverClock_bias, + const double& receiverClockBias, OptionalMatrixType HreceiverPos, OptionalMatrixType HreceiverClockBias) const override; @@ -116,4 +121,108 @@ class GTSAM_EXPORT PseudorangeFactor template <> struct traits : public Testable {}; +/** + * Simple differentially-corrected pseudorange factor for precise positioning. + * + * This factor implements the model prescribed by chapter 5.8.2 from [1], + * where a reference GNSS receiver with known position provides differential + * pseudorange corrections for a "user" receiver to eliminate common-mode + * atmospheric errors. The idea being that spatially local receivers experience + * the same atmospheric errors since their signal paths pass through the same + * regions of Earth's atmosphere. Therefore, this factor accepts an additional + * "differential correction" variable from a reference receiver to cancel-out + * local-area biases from the user's pseudoranges. + * + * Note that this factor is designed for code-phase measurements. + * + * @example Please see the `DifferentialPseudorangeExample.ipynb` notebook + * for a demonstration of this factor on CORS datasets. + * + * @ingroup navigation + * + * REFERENCES: + * [1] P. Misra et. al., "Global Positioning Systems: Signals, Measurements, and + * Performance", Second Edition, 2012. + */ +class GTSAM_EXPORT DifferentialPseudorangeFactor + : public NoiseModelFactorN, + private PseudorangeBase { + private: + typedef NoiseModelFactorN Base; + + public: + // Provide access to the Matrix& version of evaluateError: + using Base::evaluateError; + + /// shorthand for a smart pointer to a factor + typedef std::shared_ptr shared_ptr; + + /// Typedef to this class + typedef DifferentialPseudorangeFactor This; + + /** default constructor - only use for serialization */ + DifferentialPseudorangeFactor() = default; + + virtual ~DifferentialPseudorangeFactor() = default; + + /** + * Construct a DifferentialPseudorangeFactor that includes a + * differential-correction term in its model for distance between a receiver + * and a satellite. + * + * @param receiverPositionKey Receiver gtsam::Point3 ECEF position node. + * @param receiverClockBiasKey Receiver clock bias node. + * @param differentialCorrectionKey Differential correction node. + * @param measuredPseudorange Receiver-measured pseudorange in meters. + * @param satellitePosition Satellite ECEF position in meters. + * @param satelliteClockBias Satellite clock bias in seconds. + * @param model 1-D pseudorange noise model. + */ + DifferentialPseudorangeFactor( + Key receiverPositionKey, Key receiverClockBiasKey, + Key differentialCorrectionKey, double measuredPseudorange, + const Point3& satellitePosition, double satelliteClockBias = 0.0, + const SharedNoiseModel& model = noiseModel::Unit::Create(1)); + + /// @return a deep copy of this factor + gtsam::NonlinearFactor::shared_ptr clone() const override { + return std::static_pointer_cast( + gtsam::NonlinearFactor::shared_ptr(new This(*this))); + } + + /// print + void print(const std::string& s = "", const KeyFormatter& keyFormatter = + DefaultKeyFormatter) const override; + + /// equals + bool equals(const NonlinearFactor& expected, + double tol = 1e-9) const override; + + /// vector of errors + Vector evaluateError( + const Point3& receiverPosition, const double& receiverClock_bias, + const double& differentialCorrection, OptionalMatrixType HreceiverPos, + OptionalMatrixType HreceiverClockBias, + OptionalMatrixType HdifferentialCorrection) const override; + + private: +#if GTSAM_ENABLE_BOOST_SERIALIZATION /// + /// Serialization function + friend class boost::serialization::access; + template + void serialize(ARCHIVE& ar, const unsigned int /*version*/) { + ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP( + DifferentialPseudorangeFactor::Base); + ar& BOOST_SERIALIZATION_NVP(pseudorange_); + ar& BOOST_SERIALIZATION_NVP(satPos_); + ar& BOOST_SERIALIZATION_NVP(satClkBias_); + } +#endif +}; + +/// traits +template <> +struct traits + : public Testable {}; + } // namespace gtsam diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 54f5c23ad9..d223becf65 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -500,7 +500,30 @@ virtual class PseudorangeFactor : gtsam::NonlinearFactor { // Standard Interface gtsam::Vector evaluateError(const gtsam::Point3& receiverPosition, - const double& receiverClock_bias) const; + const double& receiverClockBias) const; + + // enable serialization functionality + void serialize() const; +}; + +virtual class DifferentialPseudorangeFactor : gtsam::NonlinearFactor { + DifferentialPseudorangeFactor(gtsam::Key receiverPositionKey, + gtsam::Key receiverClockBiasKey, + gtsam::Key differentialCorrectionKey, + double measuredPseudorange, + const gtsam::Point3& satellitePosition, + double satelliteClockBias, + const gtsam::noiseModel::Base* model); + + // Testable + void print(string s = "", const gtsam::KeyFormatter& keyFormatter = + gtsam::DefaultKeyFormatter) const; + bool equals(const gtsam::NonlinearFactor& expected, double tol); + + // Standard Interface + gtsam::Vector evaluateError(const gtsam::Point3& receiverPosition, + const double& receiverClockBias, + const double& differentialCorrection) const; // enable serialization functionality void serialize() const; diff --git a/gtsam/navigation/tests/testPseudorangeFactor.cpp b/gtsam/navigation/tests/testPseudorangeFactor.cpp index ee74f05cab..7a558534dd 100644 --- a/gtsam/navigation/tests/testPseudorangeFactor.cpp +++ b/gtsam/navigation/tests/testPseudorangeFactor.cpp @@ -97,6 +97,91 @@ TEST(TestPseudorangeFactor, equals) { factor2.print("factor2"); } +// ************************************************************************* +TEST(TestDifferentialPseudorangeFactor, Constructor) { + const auto factor = + DifferentialPseudorangeFactor(Key(0), Key(1), Key(2), 0.0, Point3::Zero(), + 0.0, noiseModel::Isotropic::Sigma(1, 1.0)); + + Matrix Hpos, Hbias, Hcorrection; + const double error = factor.evaluateError(Point3::Zero(), 0.0, 0.0, Hpos, + Hbias, Hcorrection)[0]; + EXPECT_DOUBLES_EQUAL(0.0, error, 1e-9); + + // Derivatives are technically undefined if the receiver and satellite + // positions are the same (hopefully that's never the case in reality). But + // for all intents and purposes, zero-valued derivatives can substitute for + // undefined gradient at that singularity. So make sure this corner-case does + // not numerically explode: + EXPECT(!Hpos.array().isNaN().any()); + EXPECT(!Hbias.array().isNaN().any()); + EXPECT(!Hcorrection.array().isNaN().any()); + EXPECT_DOUBLES_EQUAL(Hpos.norm(), 0.0, 1e-9); + // Clock bias derivative should always be speed-of-light in vacuum: + EXPECT_DOUBLES_EQUAL(Hbias.norm(), 299792458.0, 1e-9); + // Correction derivative should be constant -1: + EXPECT_DOUBLES_EQUAL(Hcorrection(0, 0), -1.0, 1e-9); +} + +// ************************************************************************* +TEST(TestDifferentialPseudorangeFactor, Jacobians) { + // Synthetic example with exact error/derivatives: + const auto factor = DifferentialPseudorangeFactor( + Key(0), Key(1), // Receiver position and clock bias keys. + Key(2), // Differential correction keys. + 4.0, // Measured pseudorange. + // Satellite position: + Vector3(0.0, 0.0, 3.0), + 0.0 // Sat clock drift bias. + ); + + // Zero differential correction case: + { + const double error = factor.evaluateError(Vector3::Zero(), 0.0, 0.0)[0]; + EXPECT_DOUBLES_EQUAL(-1.0, error, 1e-6); + } + + // Nontrivial differential correction: + { + const double error = factor.evaluateError(Vector3::Zero(), 0.0, 123.0)[0]; + EXPECT_DOUBLES_EQUAL(-124.0, error, 1e-6); + } + + Values values; + values.insert(Key(0), Vector3(1.0, 2.0, 3.0)); + values.insert(Key(1), 0.0); + values.insert(Key(2), 0.0); + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-3, 1e-5); +} + +// ************************************************************************* +TEST(TestDifferentialPseudorangeFactor, print) { + // Just make sure `print()` doesn't throw errors + // since there's no elegant way to check stdout. + const auto factor = DifferentialPseudorangeFactor(); + factor.print(); +} + +// ************************************************************************* +TEST(TestDifferentialPseudorangeFactor, equals) { + const auto factor1 = DifferentialPseudorangeFactor(); + const auto factor2 = + DifferentialPseudorangeFactor(1, 2, 3, 0.0, Point3::Zero(), 0.0); + const auto factorCorr = + DifferentialPseudorangeFactor(1, 2, 7, 0.0, Point3::Zero(), 0.0); + const auto factor3 = + DifferentialPseudorangeFactor(1, 2, 3, 10.0, Point3(1.0, 2.0, 3.0), 20.0); + + CHECK(factor1.equals(factor1)); + CHECK(factor2.equals(factor2)); + CHECK(!factor1.equals(factor2)); + CHECK(factor2.equals(factor3, 1e99)); + CHECK(!factor2.equals(factorCorr)); + + // Test print: + factor2.print("factor2"); +} + // ************************************************************************* int main() { TestResult tr; diff --git a/python/gtsam/examples/DifferentialPseudorangeExample.ipynb b/python/gtsam/examples/DifferentialPseudorangeExample.ipynb new file mode 100644 index 0000000000..6243ff1fb3 --- /dev/null +++ b/python/gtsam/examples/DifferentialPseudorangeExample.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Differential GNSS Positioning\n", + "\n", + "\"Open\n", + "\n", + "## Overview\n", + "Differential GNSS (DGNSS) improves positioning accuracy by using a reference receiver at a precisely known location to correct errors affecting nearby GNSS users. The reference station receives the same satellite signals as the user and compares the measured ranges to the true ranges based on its known position. The differences represent combined errors from satellite clocks, orbits, and atmospheric delays. These corrections are transmitted to the user receiver, which applies them to its own measurements. Because both receivers experience nearly the same errors, especially when close together, many of these errors cancel out, reducing position errors from several meters to sub-meter or better accuracy.\n", + "\n", + "GNSS positioning problems can be encoded as factor graphs in GTSAM; this notebook extends the groundwork from [SinglePointPositioningExample.ipynb](https://borglab.github.io/gtsam/singlepointpositioningexample/) to improve receiver positioning accuracy using corrections from a nearby reference receiver." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2026, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet pyrtklib numpy gtsam-develop pyproj tabulate folium\n", + " !wget https://raw.githubusercontent.com/borglab/gtsam/refs/heads/develop/python/gtsam/examples/gnss_utils.py\n", + "except ImportError:\n", + " pass\n", + "\n", + "import folium\n", + "import gnss_utils\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import B, C, X\n", + "import numpy as np\n", + "from pyproj import Transformer\n", + "import pyrtklib as rtklib\n", + "from tabulate import tabulate\n", + "\n", + "# Set up ecef -> lla converter:\n", + "ecef2lla = Transformer.from_crs(\"epsg:4978\", \"epsg:4326\", always_xy=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Premise\n", + "We'll use the [ZOA1 station in Fremont, California](https://geodesy.noaa.gov/CORS/ncn_station_pages/index.html?stationID=ZOA1) to correct measurements for a nearby station, [P222](https://geodesy.noaa.gov/CORS/ncn_station_pages/index.html?stationID=p222). Let's download and load the satellite broadcast ephemerides and observations from both stations. Don't forget to compute the satellite orbits 🛰" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=== ZOA1 Data ===\")\n", + "print(\"Downloading and extracting...\", end=\"\")\n", + "zoa1_data = gnss_utils.loadCORSRINEX([\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/brdc0180.26n.gz\",\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/zoa1/zoa10180.26o.gz\"\n", + "])\n", + "print(\"done!\")\n", + "print(zoa1_data)\n", + "print(\"Computing satellite orbits...\", end=\"\")\n", + "zoa1_sat_pos = zoa1_data.computeSatelliteOrbits(zoa1_data.obs.data[0].time)\n", + "print(\"done!\\n\")\n", + "\n", + "print(\"=== P222 Data ===\")\n", + "print(\"Downloading and extracting...\", end=\"\")\n", + "p222_data = gnss_utils.loadCORSRINEX([\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/brdc0180.26n.gz\",\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/p222/p2220180.26o.gz\"\n", + "])\n", + "print(\"done!\")\n", + "print(p222_data)\n", + "print(\"Computing satellite orbits...\", end=\"\")\n", + "p222_sat_pos = p222_data.computeSatelliteOrbits(p222_data.obs.data[0].time)\n", + "print(\"done!\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bootstrap Initial Position\n", + "Start with `PseudorangeFactor` to prepare a simple factor graph for an initial baseline estimation for P222's position. Similar to ZOA1, P222 has surveyed ground-truth coordinates to compare compare against our solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare factor graph:\n", + "cb_key = B(0) # Receiver clock bias variable key.\n", + "pos_key = X(0) # Receiver position variable key.\n", + "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", + "graph = gtsam.NonlinearFactorGraph()\n", + "\n", + "# Add a PseudorangeFactor for each observation:\n", + "rows = []\n", + "for obsd, sat_bias, sat_pos, pseudorange in gnss_utils.iterateObservations(p222_data, p222_sat_pos, n=7):\n", + " graph.add(\n", + " gtsam.PseudorangeFactor(pos_key, cb_key, pseudorange, sat_pos, sat_bias, nm)\n", + " )\n", + "\n", + " rows.append([obsd.sat, pseudorange, obsd.time.time, 1e-3*sat_pos, sat_bias])\n", + "\n", + "headers = [\"Satellite\", \"Pseudorange (m)\", \"Time (unixtime seconds)\", \"Sat ECEF Position (km)\", \"Sat Clock Bias (sec)\"]\n", + "print(tabulate(rows, headers=headers, tablefmt=\"github\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We initialize receiver position at origin, and then use a LM solver to optimize the system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the system:\n", + "initial_values = gtsam.Values()\n", + "initial_values.insert(cb_key, 0.0)\n", + "initial_values.insert(pos_key, np.zeros(3))\n", + "result = gtsam.LevenbergMarquardtOptimizer(graph, initial_values).optimize()\n", + "\n", + "# Process the results:\n", + "clock_bias = result.atDouble(cb_key)\n", + "receiver_position = result.atVector(pos_key)\n", + "print(f\"Final clock bias {clock_bias} seconds and receiver position {receiver_position} (ECEF meters)\")\n", + "lon, lat, alt = ecef2lla.transform(receiver_position[0], receiver_position[1], receiver_position[2])\n", + "print(f\"Latitude: {lat} degrees\")\n", + "print(f\"Longitude: {lon} degrees\")\n", + "print(f\"Altitude: {alt} meters\")\n", + "gnss_utils.plotMap(lat, lon)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far this solution passes the eye-ball test: It's in the right US state, in roughly the correct part of the SF Bay Area. But we can quantify error by comparing the [actual position of P222](https://www.ngs.noaa.gov/cgi-cors/CorsSidebarSelect.prl?site=P222&option=Coordinates20):\n", + "```\n", + "| ITRF2020 POSITION (EPOCH 2020.0) |\n", + "| Computed in Apr 2025 using data through gpswk 2237. |\n", + "| X = -2689640.518 m latitude = 37 32 21.26653 N |\n", + "| Y = -4290437.125 m longitude = 122 04 59.76571 W |\n", + "| Z = 3865051.027 m ellipsoid height = 53.512 m |\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ground_truth = np.array([-2689640.518, -4290437.125, 3865051.027]) # P222's surveyed ground-truth position.\n", + "error = ground_truth - receiver_position\n", + "print(f\"Total positioning error: {np.linalg.norm(error)} meters\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "30 meters is a decent starting point from single-point positioning, but let's see if we can do better.\n", + "\n", + "## Differential Positioning\n", + "In this scenario, we know ZOA1's precise position ahead-of-time. Its [surveyed position](https://noaa-cors-pds.s3.amazonaws.com/coord/coord_20/zoa1_20.coord.txt) is published in the data portal, and reproduced here:\n", + "```\n", + "| ITRF2020 POSITION (EPOCH 2020.0) |\n", + "| Computed in Apr 2025 using data through gpswk 2237. |\n", + "| X = -2684436.824 m latitude = 37 32 34.99617 N |\n", + "| Y = -4293336.957 m longitude = 122 00 57.42005 W |\n", + "| Z = 3865351.638 m ellipsoid height = -3.960 m |\n", + "```\n", + "In a factor graph framework, these survey coordinates correspond to a stiff `PriorFactor` constraint on ZOA1's position node to keep it anchored in the global Earth frame. The rest of the factor graph is constructed as follows:\n", + "\n", + "![illustrative factor graph example showcasing differential correction factors](https://raw.githubusercontent.com/borglab/gtsam/refs/heads/develop/python/gtsam/examples/images/dpr_fg.jpg)\n", + "\n", + "A `DifferentialPseudorangeFactor` is added for each receiver observation, and their correction nodes are indexed by satellite ID. For example, ZOA1's pseudorange factor of satellite 25 links to the same correction node for P222's pseudorange observation of satellite 25. Since different satellites are in different positions in the sky, each satellite has their own correction node to account for spatial variation in atmospheric effects. Furthermore, for long-time-series observations, the correction nodes must also account for temporal variation in the atmosphere. In that case, a linear chain of correction nodes linked with between-factors is recommended. The above structure is encoded into GTSAM below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p222_bias_key = B(0) # Receiver clock bias variable key.\n", + "p222_pos_key = X(0) # Receiver position variable key.\n", + "zoa1_bias_key = B(1)\n", + "zoa1_pos_key = X(1)\n", + "zoa1_ref_pos = np.array([-2684436.824, -4293336.957, 3865351.638]) # ZOA1's surveyed reference position.\n", + "nm = gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0]))\n", + "graph = gtsam.NonlinearFactorGraph()\n", + "\n", + "initial_values = gtsam.Values()\n", + "initial_values.insert(zoa1_pos_key, zoa1_ref_pos)\n", + "initial_values.insert(zoa1_bias_key, 0.0)\n", + "initial_values.insert(p222_pos_key, receiver_position)\n", + "initial_values.insert(p222_bias_key, clock_bias)\n", + "\n", + "# Apply a prior factor constraint on zoa1's reference position:\n", + "graph.add(\n", + " gtsam.PriorFactorVector(\n", + " zoa1_pos_key,\n", + " zoa1_ref_pos,\n", + " gtsam.noiseModel.Diagonal.Sigmas(np.array([0.0005, 0.0005, 0.0005]))\n", + " )\n", + ")\n", + "\n", + "# Add factors for reference station:\n", + "sat_keymap = {}\n", + "for i, (obsd, sat_bias, sat_pos, pseudorange) in enumerate(gnss_utils.iterateObservations(zoa1_data, zoa1_sat_pos, n=40)):\n", + " # Apply factor:\n", + " if obsd.sat not in sat_keymap:\n", + " initial_values.insert(C(i), 0.0)\n", + " sat_keymap[obsd.sat] = C(i)\n", + " correction_key = C(i)\n", + " else:\n", + " correction_key = sat_keymap[obsd.sat]\n", + " factor = gtsam.DifferentialPseudorangeFactor(\n", + " zoa1_pos_key, zoa1_bias_key, correction_key,\n", + " pseudorange, sat_pos, sat_bias, nm)\n", + " graph.add(factor)\n", + "\n", + "# Add factors for user station:\n", + "for obsd, sat_bias, sat_pos, pseudorange in gnss_utils.iterateObservations(p222_data, p222_sat_pos, n=40):\n", + " # Apply factor:\n", + " if obsd.sat in sat_keymap:\n", + " correction_key = sat_keymap[obsd.sat]\n", + " factor = gtsam.DifferentialPseudorangeFactor(\n", + " p222_pos_key, p222_bias_key, correction_key,\n", + " pseudorange, sat_pos, sat_bias, nm)\n", + " graph.add(factor)\n", + " \n", + "# Solve the system:\n", + "lm = gtsam.LevenbergMarquardtOptimizer(graph, initial_values)\n", + "print(f\"Initial error: {lm.error()}\")\n", + "result = lm.optimize()\n", + "print(f\"Final error: {lm.error()}\")\n", + "\n", + "# Process the results:\n", + "clock_bias = result.atDouble(p222_bias_key)\n", + "corrected_receiver_position = result.atVector(p222_pos_key)\n", + "print(f\"Corrected P222 position: {corrected_receiver_position} ECEF meters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot these coordinates on a map, including the reference stations and single-point positions, to get a big-picture overview of how the positioning results stack up:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convenience plot:\n", + "lon, lat, alt = ecef2lla.transform(corrected_receiver_position[0], corrected_receiver_position[1], corrected_receiver_position[2])\n", + "print(f\"Corrected geodetic coordinates: {lat}, {lon}, {alt} m\")\n", + "\n", + "# Center the map on P222\n", + "m = folium.Map(\n", + " location=[lat, lon],\n", + " zoom_start=17\n", + ")\n", + "\n", + "# Plot surveyed P222 position:\n", + "folium.Circle(\n", + " location=[37.5392407028, -122.0832682528],\n", + " radius=10,\n", + " tooltip=\"P222 Truth\",\n", + " popup=\"Actual P222 position\"\n", + ").add_to(m)\n", + "\n", + "# Plot the corrected receiver position:\n", + "folium.Marker(\n", + " location=[lat, lon],\n", + " tooltip=\"P222 Corrected\",\n", + " popup=\"Estimated P222 position using corrections from ZOA1\",\n", + " icon=folium.Icon(color=\"green\", icon=\"plus\")\n", + ").add_to(m)\n", + "\n", + "# Plot the single-point-positioning solution:\n", + "lon, lat, alt = ecef2lla.transform(receiver_position[0], receiver_position[1], receiver_position[2])\n", + "folium.Marker(\n", + " location=[lat, lon],\n", + " tooltip=\"P222 SPP\",\n", + " popup=\"Estimated P222 position without differential corrections\",\n", + " icon=folium.Icon(color=\"orange\", icon=\"minus\")\n", + ").add_to(m)\n", + "\n", + "# Plot ZOA1's position:\n", + "folium.Marker(\n", + " location=[37.5430544917, -122.0159500139],\n", + " tooltip=\"ZOA1\",\n", + " popup=\"ZOA1 reference station surveyed position\",\n", + " icon=folium.Icon(color=\"blue\")\n", + ").add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scroll and zoom the map to get an idea for the relative positioning error between the single-point and differential solutions. The blue circle indicates the ground-truth surveyed postion of P222. The green \"plus\" marker shows the differentially-corrected solution. The orange \"minus\" marker indicates the uncorrected position. Zoom out further on the map to make the blue ZOA1 receiver position visible.\n", + "\n", + "Let's see if it's closer to ground truth?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"ZOA1 position adjustment: {1e3*np.linalg.norm(result.atVector(zoa1_pos_key) - zoa1_ref_pos)} millimeters\")\n", + "error = ground_truth - corrected_receiver_position\n", + "print(f\"Total positioning error: {np.linalg.norm(error)} meters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "98% reduction in error going from ~30 to ~0.4 meters of accuracy! The differential corrections appear to eliminate a large portion of positioning error. But we can do better! Carrier-phase data offer higher-precision measuring power, but the devil's bargain demands an answer to the integer ambiguity problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sources\n", + "- [PseudorangeFactor.h](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.h)\n", + "- [PseudorangeFactor.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/PseudorangeFactor.cpp)\n", + "- [PseudorangeFactor.ipynb](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/doc/PseudorangeFactor.ipynb)\n", + "- [SinglePointPositioningExample.ipynb](https://borglab.github.io/gtsam/singlepointpositioningexample/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/gtsam/examples/SinglePointPositioningExample.ipynb b/python/gtsam/examples/SinglePointPositioningExample.ipynb index 604a130352..a216f9e749 100644 --- a/python/gtsam/examples/SinglePointPositioningExample.ipynb +++ b/python/gtsam/examples/SinglePointPositioningExample.ipynb @@ -55,7 +55,25 @@ " import google.colab\n", " %pip install --quiet pyrtklib numpy gtsam-develop pyproj\n", "except ImportError:\n", - " pass" + " pass\n", + "\n", + "\n", + "import gnss_utils\n", + "import tempfile\n", + "import urllib.request\n", + "import gzip\n", + "from pathlib import Path\n", + "import shutil\n", + "\n", + "import numpy as np\n", + "from pyproj import Transformer\n", + "import pyrtklib as rtklib\n", + "\n", + "import gtsam\n", + "from gtsam.symbol_shorthand import B, X\n", + "\n", + "# Create a transformer from ECEF (EPSG:4978) to WGS84 LLA (EPSG:4326)\n", + "ecef2lla = Transformer.from_crs(\"epsg:4978\", \"epsg:4326\", always_xy=True)" ] }, { @@ -74,57 +92,11 @@ "metadata": {}, "outputs": [], "source": [ - "import tempfile\n", - "import urllib.request\n", - "import gzip\n", - "from pathlib import Path\n", - "import shutil\n", - "\n", - "from IPython.display import IFrame\n", - "import numpy as np\n", - "from pyproj import Transformer\n", - "import pyrtklib as rtklib\n", - "\n", - "import gtsam\n", - "from gtsam.symbol_shorthand import B, X\n", - "\n", - "# Create a temporary directory (auto-deleted when closed):\n", - "with tempfile.TemporaryDirectory() as tmpdir:\n", - " tmpdir = Path(tmpdir)\n", - " print(f\"Using temporary directory: {str(tmpdir)}\")\n", - " \n", - " navigation_compressed = tmpdir / \"brdc0180.26n.gz\"\n", - " navigation_file = tmpdir / \"brdc0180.26n\"\n", - " observable_compressed = tmpdir / \"zoa10180.26o.gz\"\n", - " observable_file = tmpdir / \"zoa10180.26o\"\n", - "\n", - " # Download ZOA1's files using built-in HTTP library:\n", - " print(\"Downloading receiver files...\")\n", - " with urllib.request.urlopen(\"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/brdc0180.26n.gz\") as response, open(navigation_compressed, \"wb\") as f:\n", - " f.write(response.read())\n", - " with urllib.request.urlopen(\"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/zoa1/zoa10180.26o.gz\") as response, open(observable_compressed, \"wb\") as f:\n", - " f.write(response.read())\n", - "\n", - " # Extract files:\n", - " print(\"Extracting files...\")\n", - " with gzip.open(navigation_compressed, \"rb\") as gz, open(navigation_file, \"wb\") as out:\n", - " shutil.copyfileobj(gz, out)\n", - " with gzip.open(observable_compressed, \"rb\") as gz, open(observable_file, \"wb\") as out:\n", - " shutil.copyfileobj(gz, out)\n", - "\n", - " print(\"Parsing RINEX files...\")\n", - " obs = rtklib.obs_t()\n", - " nav = rtklib.nav_t()\n", - " sta = rtklib.sta_t()\n", - " load_status = rtklib.readrnx(str(navigation_file), 1, \"\", obs, nav, sta)\n", - " assert load_status == 1, \"Loading broadcast ephemeris RINEX\"\n", - " load_status = rtklib.readrnx(str(observable_file), 1, \"\", obs, nav, sta)\n", - " assert load_status == 1, \"Loading receiver observation RINEX\"\n", - "\n", - "# Temporary directory and contents are cleaned up automatically\n", - "print(\"Loading done!\")\n", - "print(f\"Found {obs.n} observations\")\n", - "print(f\"Found {nav.n} navigation messages\")" + "zoa1_data = gnss_utils.loadCORSRINEX([\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/brdc0180.26n.gz\",\n", + " \"https://noaa-cors-pds.s3.amazonaws.com/rinex/2026/018/zoa1/zoa10180.26o.gz\"\n", + "])\n", + "print(zoa1_data)" ] }, { @@ -143,31 +115,7 @@ "metadata": {}, "outputs": [], "source": [ - "n = 5 # number of observations to process.\n", - "rs = rtklib.Arr1Ddouble(6*n) # Satellite positions and velocities:\n", - " # rs [(0:2)+i*6] = obs[i] sat position {x,y,z} (m)\n", - " # rs [(3:5)+i*6] = obs[i] sat velocity {vx,vy,vz} (m/s)\n", - "dts = rtklib.Arr1Ddouble(2*n) # Satellite clock drift:\n", - " # dts[(0:1)+i*2] = obs[i] sat clock {bias,drift} (s|s/s)\n", - "var = rtklib.Arr1Ddouble(1*n) # Satellite position and clock variances:\n", - " # var[i] = obs[i] sat position and clock error variance (m^2)\n", - "svh = rtklib.Arr1Dint(1*n) # Satellite health flag (-1:correction not available):\n", - " # svh[i] = obs[i] sat health flag\n", - "\n", - "teph = obs.data[0].time\n", - "\n", - "# Compute satellite positions:\n", - "rtklib.satposs(\n", - " teph, # gtime_t teph I time to select ephemeris (gpst)\n", - " obs.data.ptr, # obsd_t *obs I observation data\n", - " n, # int n I number of observation data\n", - " nav, # nav_t *nav I navigation data\n", - " 0, # int ephopt I ephemeris option (EPHOPT_???) (EPHOPT_BRDC == 0)\n", - " rs, # double *rs O satellite positions and velocities (ecef)\n", - " dts, # double *dts O satellite clocks\n", - " var, # double *var O sat position and clock error variances (m^2)\n", - " svh # int *svh O sat health flag (-1:correction not available)\n", - ")" + "zoa1_sat_pos = zoa1_data.computeSatelliteOrbits(zoa1_data.obs.data[0].time)" ] }, { @@ -191,21 +139,7 @@ "graph = gtsam.NonlinearFactorGraph()\n", "\n", "# Add a PseudorangeFactor for each observation:\n", - "for i in range(n):\n", - " obsd = obs.data[i]\n", - " sat_bias = dts[2*i+0]\n", - " sat_drift = dts[2*i+1]\n", - " sat_pos = np.array([rs[6*i+0], rs[6*i+1], rs[6*i+2]])\n", - "\n", - " # Identify pseudorange code CODE_L1C:\n", - " for j, code in enumerate(obsd.code):\n", - " if code == rtklib.CODE_L1C:\n", - " pseudorange = obsd.P[j]\n", - " break\n", - " else:\n", - " # If no CODE_L1C pseudorange found, skip this observation:\n", - " continue\n", - "\n", + "for obsd, sat_bias, sat_pos, pseudorange in gnss_utils.iterateObservations(zoa1_data, zoa1_sat_pos, n=4):\n", " graph.add(\n", " gtsam.PseudorangeFactor(pos_key, cb_key, pseudorange, sat_pos, sat_bias, nm)\n", " )\n", @@ -214,7 +148,7 @@ " print(f\"Receiver pseudorange: {pseudorange} meters\")\n", " print(f\"Observation time (unixtime): {rtklib.time_str(obsd.time, 3)} ({obsd.time.time})\")\n", " print(f\"Satellite position (ECEF): {1e-3*sat_pos} km\")\n", - " print(f\"Satellite clock drift: bias {sat_bias} seconds, drift {sat_drift} seconds-per-second\")\n", + " print(f\"Satellite clock drift: bias {sat_bias} seconds\")\n", " print()" ] }, @@ -256,20 +190,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Create a transformer from ECEF (EPSG:4978) to WGS84 LLA (EPSG:4326)\n", - "ecef2lla = Transformer.from_crs(\"epsg:4978\", \"epsg:4326\", always_xy=True)\n", - "\n", "# Convert coordinates coordinates (latitude, longitude)\n", "lon, lat, alt = ecef2lla.transform(receiver_position[0], receiver_position[1], receiver_position[2])\n", "print(f\"Latitude: {lat} degrees\")\n", "print(f\"Longitude: {lon} degrees\")\n", "print(f\"Altitude: {alt} meters\")\n", "\n", - "# OpenStreetMap embed URL\n", - "osm_url = f\"https://www.openstreetmap.org/export/embed.html?bbox={lon-0.01}%2C{lat-0.01}%2C{lon+0.01}%2C{lat+0.01}&layer=map&marker={lat}%2C{lon}\"\n", - "\n", - "# Embed in notebook\n", - "IFrame(osm_url, width=700, height=500)" + "gnss_utils.plotMap(lat, lon)" ] }, { @@ -305,7 +232,7 @@ "metadata": {}, "source": [ "## Conclusions\n", - "33 meters of error puts us in the same building as the receiver, which is a good starting point for simple positioning. As mentioned above, however, decades of advancements have vastly improved GNSS positioning accuracy down to sub-meter or even centimeter-scale precision and error. The simple `PseudorangeFactor` does not account for atmospheric effects, carrier-phase measurements, nor differential GNSS corrections. So there's a lot more potential for improvement, but this introductory example provides a foundation for expanding GNSS measurements into the broader GTSAM ecosystem." + "29 meters of error puts us in the same building as the receiver, which is a good starting point for simple positioning. As mentioned above, however, decades of advancements have vastly improved GNSS positioning accuracy down to sub-meter or even centimeter-scale precision and error. The simple `PseudorangeFactor` does not account for atmospheric effects, carrier-phase measurements, nor differential GNSS corrections. So there's a lot more potential for improvement, but this introductory example provides a foundation for expanding GNSS measurements into the broader GTSAM ecosystem." ] }, { diff --git a/python/gtsam/examples/gnss_utils.py b/python/gtsam/examples/gnss_utils.py new file mode 100644 index 0000000000..b81ffedb2b --- /dev/null +++ b/python/gtsam/examples/gnss_utils.py @@ -0,0 +1,139 @@ +""" +Utility functions for processing and manipulating GNSS data. + +This module wraps RTKLIB functionality to load and manipulate +GNSS observation and navigation files from RINEX-compatible +receivers. + +Author: Sammy Guo +Date: 2026-01-28 +""" + +from dataclasses import dataclass +import gzip +from pathlib import Path +import shutil +import tempfile +import urllib.request + +from IPython.display import IFrame +import numpy as np +import pyrtklib as rtklib + + +@dataclass +class SatellitePositions: + n: int # Number of observations. + rs: rtklib.Arr1Ddouble # Satellite positions and velocities: + # rs [(0:2)+i*6] = obs[i] sat position {x,y,z} (m) + # rs [(3:5)+i*6] = obs[i] sat velocity {vx,vy,vz} (m/s) + dts: rtklib.Arr1Ddouble # Satellite clock drift: + # dts[(0:1)+i*2] = obs[i] sat clock {bias,drift} (s|s/s) + var: rtklib.Arr1Ddouble # Satellite position and clock variances: + # var[i] = obs[i] sat position and clock error variance (m^2) + svh: rtklib.Arr1Dint # Satellite health flag (-1:correction not available): + # svh[i] = obs[i] sat health flag + + def __init__(self, n: int): + self.n = n + self.rs = rtklib.Arr1Ddouble(6*n) + self.dts = rtklib.Arr1Ddouble(2*n) + self.var = rtklib.Arr1Ddouble(1*n) + self.svh = rtklib.Arr1Dint(1*n) + + def satPosition(self, i: int) -> np.ndarray: + return np.array([ + self.rs[6*i+0], + self.rs[6*i+1], + self.rs[6*i+2] + ]) + + def satClockBias(self, i: int) -> float: + return self.dts[2*i+0] + + def satClockDrift(self, i: int) -> float: + return self.dts[2*i+1] + + +@dataclass +class RINEXData: + obs: rtklib.obs_t + nav: rtklib.nav_t + sta: rtklib.sta_t + + def __init__(self): + self.obs = rtklib.obs_t() + self.nav = rtklib.nav_t() + self.sta = rtklib.sta_t() + + def __str__(self): + return f"Found {self.obs.n} observations\nFound {self.nav.n} navigation messages" + + def loadRINEX(self, path: str) -> int: + return rtklib.readrnx(path, 1, "", self.obs, self.nav, self.sta) + + def computeSatelliteOrbits(self, teph: rtklib.gtime_t) -> SatellitePositions: + result = SatellitePositions(self.obs.n) + rtklib.satposs( + teph, # gtime_t teph I time to select ephemeris (gpst) + self.obs.data.ptr, # obsd_t *obs I observation data + self.obs.n, # int n I number of observation data + self.nav, # nav_t *nav I navigation data + 0, # int ephopt I ephemeris option (EPHOPT_???) (EPHOPT_BRDC == 0) + result.rs, # double *rs O satellite positions and velocities (ecef) + result.dts, # double *dts O satellite clocks + result.var, # double *var O sat position and clock error variances (m^2) + result.svh # int *svh O sat health flag (-1:correction not available) + ) + return result + + +def loadCORSRINEX(url_paths: list[str]) -> RINEXData: + """Downloads and parses a list of URLs from CORS online dataset. + """ + # Create a temporary directory (auto-deleted when closed): + result = RINEXData() + for p in url_paths: + with tempfile.TemporaryDirectory() as tmpdir: + tmpdir = Path(tmpdir) + + compressed_file = tmpdir / Path(p).name + uncompressed_file = tmpdir / Path(p).stem + + # Download files using built-in HTTP library: + with urllib.request.urlopen(p) as response, open(compressed_file, "wb") as f: + f.write(response.read()) + + # Extract files: + with gzip.open(compressed_file, "rb") as gz, open(uncompressed_file, "wb") as out: + shutil.copyfileobj(gz, out) + + # Parse and load data: + load_status: int = result.loadRINEX(str(uncompressed_file)) + assert load_status == 1, f"Unable to load file {p}" + return result + +def plotMap(lat, lon): + osm_url = f"https://www.openstreetmap.org/export/embed.html?bbox={lon-0.01}%2C{lat-0.01}%2C{lon+0.01}%2C{lat+0.01}&layer=map&marker={lat}%2C{lon}" + return IFrame(osm_url, width=700, height=500) + +def iterateObservations(recv_data: RINEXData, sat_positions: SatellitePositions, pr_code: int = rtklib.CODE_L1C, n: int = -1): + for i in range(n): + obsd = recv_data.obs.data[i] + sat_bias = sat_positions.satClockBias(i) + sat_pos = sat_positions.satPosition(i) + + # Skip this observation if sat positioning failed: + if np.linalg.norm(sat_pos) < 1.0: + continue + + # Identify pseudorange code CODE_L1C: + for j, code in enumerate(obsd.code): + if code == pr_code: + pseudorange = obsd.P[j] + break + else: + # If no CODE_L1C pseudorange found, skip this observation: + continue + + yield obsd, sat_bias, sat_pos, 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zeAY?Nk@{tv3Czu=^rg#|zr59Hv?%M+2Gg|ZSJ2}*ASt@@aA!bh#F{}$=CniyNG_ic9^MGx z0#BE?Jin=-ODM&T0SOXONbz!EfUA=;Ib%`G`MZB?V}OktO898^a4{t!wo+qise7e) zV9Bl5Mp2{kH#DlBU-PDo9?!h8;uhure;W2A+PPy+L)BpN!3GsCTczJz?lsZ6l5;nf zK^-9m&$cL$1+O)g4u;%>5nD5jhIl4RtAi-li19X@W{^!vqh_Lhl8USkpHBEF9qmIn_ zc#lYoQcZcOXSd3h{Qmox;JFAt9+XX$$Wz0~+=K2U3zg1|_Kvkye@Agm4p@h;-CGEU zV$S1`@~Ko!HqRGTq5Ss+>HCVG3|(Ca1de|ssnsfPXyY#ko5teC9?YwIdiB8oD~Q+g zs?b{Udzb>b|Cj(KY=e$$HY^t6NrB>N8_6kuk-YqIgO0Q2*{6oUxcw~{Xf-EVSVfNb zB9OR5S??$02Sm?EH!)`fh~O8+oQ&lCt%&DGb1&65o3*O`s@iTPRdhS1|GL6 z(4-bHkd-7}IWyi`1dA+A!5Uzs!`&Jb7>6AZLN!?ao3oB~LoJ!Ax8%lgTqR`Ocy=IP zLyEotlwL<|4Y>B)tKw)Xm|l;t*5h} Date: Sat, 7 Feb 2026 09:13:59 -0800 Subject: [PATCH 175/266] Fix a deadlock under Windows --- gtsam/base/Scheduler.h | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/gtsam/base/Scheduler.h b/gtsam/base/Scheduler.h index 8c91ccd986..a52d7b25e3 100644 --- a/gtsam/base/Scheduler.h +++ b/gtsam/base/Scheduler.h @@ -122,6 +122,7 @@ class Scheduler { // Notify waiters only when work transitions to "done". if (activeTasks_.fetch_sub(1, std::memory_order_release) == 1) { + std::lock_guard lock(waitMutex_); condition_.notify_all(); } } @@ -183,7 +184,10 @@ class Scheduler { queuedTasks_.fetch_add(1, std::memory_order_release); activeTasks_.fetch_add(1, std::memory_order_release); - condition_.notify_one(); + { + std::lock_guard lock(waitMutex_); + condition_.notify_one(); + } return true; } @@ -201,6 +205,7 @@ class Scheduler { } catch (...) { /* ignore */ } if (activeTasks_.fetch_sub(1, std::memory_order_release) == 1) { + std::lock_guard lock(waitMutex_); condition_.notify_all(); } } From cd7e6afb34120c2e9c756b39550f5272aa21c29c Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:03:30 -0500 Subject: [PATCH 176/266] Format --- gtsam/constrained/ConstrainedOptProblem.h | 25 ++-- gtsam/constrained/NonlinearConstraint.h | 16 ++- .../NonlinearEqualityConstraint-inl.h | 24 ++-- .../NonlinearEqualityConstraint.cpp | 45 ++++--- .../constrained/NonlinearEqualityConstraint.h | 42 +++--- .../NonlinearInequalityConstraint.cpp | 120 +++++++++++------- .../NonlinearInequalityConstraint.h | 71 +++++++---- gtsam/constrained/PenaltyOptimizer.h | 10 +- .../testAugmentedLagrangianOptimizer.cpp | 59 ++++++--- .../tests/testInequalityPenaltyFunction.cpp | 31 +++-- .../tests/testNonlinearEqualityConstraint.cpp | 42 ++++-- .../tests/testPenaltyOptimizer.cpp | 5 +- 12 files changed, 309 insertions(+), 181 deletions(-) diff --git a/gtsam/constrained/ConstrainedOptProblem.h b/gtsam/constrained/ConstrainedOptProblem.h index 1de06f3e23..b18d879eb5 100644 --- a/gtsam/constrained/ConstrainedOptProblem.h +++ b/gtsam/constrained/ConstrainedOptProblem.h @@ -28,8 +28,8 @@ namespace gtsam { * s.t. h(X) = 0 * g(X) <= 0 * where X represents the variables, 0.5||f(X)||^2 represents the quadratic cost - * functions, h(X)=0 represents the nonlinear equality constraints, g(x)<=0 represents the - * inequality constraints. + * functions, h(X)=0 represents the nonlinear equality constraints, g(x)<=0 + * represents the inequality constraints. */ class GTSAM_EXPORT ConstrainedOptProblem { public: @@ -37,9 +37,10 @@ class GTSAM_EXPORT ConstrainedOptProblem { typedef std::shared_ptr shared_ptr; protected: - NonlinearFactorGraph costs_; // cost function, ||f(X)||^2 - NonlinearEqualityConstraints eqConstraints_; // equality constraints, h(X)=0 - NonlinearInequalityConstraints ineqConstraints_; // inequality constraints, g(X)<=0 + NonlinearFactorGraph costs_; // cost function, ||f(X)||^2 + NonlinearEqualityConstraints eqConstraints_; // equality constraints, h(X)=0 + NonlinearInequalityConstraints + ineqConstraints_; // inequality constraints, g(X)<=0 public: /** Default constructor. */ @@ -57,16 +58,22 @@ class GTSAM_EXPORT ConstrainedOptProblem { static ConstrainedOptProblem EqConstrainedOptProblem( const NonlinearFactorGraph& costs, const NonlinearEqualityConstraints& eqConstraints) { - return ConstrainedOptProblem(costs, eqConstraints, NonlinearInequalityConstraints()); + return ConstrainedOptProblem(costs, eqConstraints, + NonlinearInequalityConstraints()); } /** Member variable access functions. */ const NonlinearFactorGraph& costs() const { return costs_; } - const NonlinearEqualityConstraints& eConstraints() const { return eqConstraints_; } - const NonlinearInequalityConstraints& iConstraints() const { return ineqConstraints_; } + const NonlinearEqualityConstraints& eConstraints() const { + return eqConstraints_; + } + const NonlinearInequalityConstraints& iConstraints() const { + return ineqConstraints_; + } /** Evaluate cost and constraint violations. - * Return a tuple representing (cost, e-constraint violation, i-constraint violation). + * Return a tuple representing (cost, e-constraint violation, i-constraint + * violation). */ std::tuple evaluate(const Values& values) const; diff --git a/gtsam/constrained/NonlinearConstraint.h b/gtsam/constrained/NonlinearConstraint.h index c34052153f..1c8d6d127e 100644 --- a/gtsam/constrained/NonlinearConstraint.h +++ b/gtsam/constrained/NonlinearConstraint.h @@ -18,8 +18,8 @@ #pragma once -#include #include +#include #include #if GTSAM_ENABLE_BOOST_SERIALIZATION #include @@ -29,8 +29,8 @@ namespace gtsam { /** * Base class for nonlinear constraint. - * The constraint is represented as a NoiseModelFactor with Constrained noise model. - * whitenedError() returns The constraint violation vector. + * The constraint is represented as a NoiseModelFactor with Constrained noise + * model. whitenedError() returns The constraint violation vector. * unwhitenedError() returns the sigma-scaled constraint violation vector. */ class GTSAM_EXPORT NonlinearConstraint : public NoiseModelFactor { @@ -45,8 +45,10 @@ class GTSAM_EXPORT NonlinearConstraint : public NoiseModelFactor { /** Destructor. */ virtual ~NonlinearConstraint() {} - /** Create a cost factor representing the L2 penalty function with scaling coefficient mu. */ - virtual NoiseModelFactor::shared_ptr penaltyFactor(const double mu = 1.0) const { + /** Create a cost factor representing the L2 penalty function with scaling + * coefficient mu. */ + virtual NoiseModelFactor::shared_ptr penaltyFactor( + const double mu = 1.0) const { return cloneWithNewNoiseModel(penaltyNoise(mu)); } @@ -82,8 +84,8 @@ class GTSAM_EXPORT NonlinearConstraint : public NoiseModelFactor { friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("NonlinearConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "NonlinearConstraint", boost::serialization::base_object(*this)); } #endif }; diff --git a/gtsam/constrained/NonlinearEqualityConstraint-inl.h b/gtsam/constrained/NonlinearEqualityConstraint-inl.h index 14bc42939f..57ec0ed9ec 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint-inl.h +++ b/gtsam/constrained/NonlinearEqualityConstraint-inl.h @@ -21,20 +21,21 @@ namespace gtsam { -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ template -ExpressionEqualityConstraint::ExpressionEqualityConstraint(const Expression& expression, - const T& rhs, - const Vector& sigmas) +ExpressionEqualityConstraint::ExpressionEqualityConstraint( + const Expression& expression, const T& rhs, const Vector& sigmas) : Base(constrainedNoise(sigmas), expression.keysAndDims().first), expression_(expression), rhs_(rhs), dims_(expression.keysAndDims().second) {} -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ template -Vector ExpressionEqualityConstraint::unwhitenedError(const Values& x, - OptionalMatrixVecType H) const { +Vector ExpressionEqualityConstraint::unwhitenedError( + const Values& x, OptionalMatrixVecType H) const { // Copy-paste from ExpressionFactor. if (H) { const T value = expression_.valueAndDerivatives(x, keys_, dims_, *H); @@ -45,10 +46,13 @@ Vector ExpressionEqualityConstraint::unwhitenedError(const Values& x, } } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ template -NoiseModelFactor::shared_ptr ExpressionEqualityConstraint::penaltyFactor(const double mu) const { - return std::make_shared>(penaltyNoise(mu), rhs_, expression_); +NoiseModelFactor::shared_ptr ExpressionEqualityConstraint::penaltyFactor( + const double mu) const { + return std::make_shared>(penaltyNoise(mu), rhs_, + expression_); } } // namespace gtsam diff --git a/gtsam/constrained/NonlinearEqualityConstraint.cpp b/gtsam/constrained/NonlinearEqualityConstraint.cpp index a29f06777c..45b23e1561 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/NonlinearEqualityConstraint.cpp @@ -20,21 +20,29 @@ namespace gtsam { -/* ********************************************************************************************* */ -ZeroCostConstraint::ZeroCostConstraint(const NoiseModelFactor::shared_ptr& factor) - : Base(constrainedNoise(factor->noiseModel()->sigmas()), factor->keys()), factor_(factor) {} +/* ********************************************************************************************* + */ +ZeroCostConstraint::ZeroCostConstraint( + const NoiseModelFactor::shared_ptr& factor) + : Base(constrainedNoise(factor->noiseModel()->sigmas()), factor->keys()), + factor_(factor) {} -/* ********************************************************************************************* */ -Vector ZeroCostConstraint::unwhitenedError(const Values& x, OptionalMatrixVecType H) const { +/* ********************************************************************************************* + */ +Vector ZeroCostConstraint::unwhitenedError(const Values& x, + OptionalMatrixVecType H) const { return factor_->unwhitenedError(x, H); } -/* ********************************************************************************************* */ -NoiseModelFactor::shared_ptr ZeroCostConstraint::penaltyFactor(const double mu) const { +/* ********************************************************************************************* + */ +NoiseModelFactor::shared_ptr ZeroCostConstraint::penaltyFactor( + const double mu) const { return factor_->cloneWithNewNoiseModel(penaltyNoise(mu)); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ NonlinearEqualityConstraints NonlinearEqualityConstraints::FromCostGraph( const NonlinearFactorGraph& graph) { NonlinearEqualityConstraints constraints; @@ -45,7 +53,8 @@ NonlinearEqualityConstraints NonlinearEqualityConstraints::FromCostGraph( return constraints; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ size_t NonlinearEqualityConstraints::dim() const { size_t dimension = 0; for (const auto& constraint : *this) { @@ -54,26 +63,32 @@ size_t NonlinearEqualityConstraints::dim() const { return dimension; } -/* ********************************************************************************************* */ -Vector NonlinearEqualityConstraints::violationVector(const Values& values, bool whiten) const { +/* ********************************************************************************************* + */ +Vector NonlinearEqualityConstraints::violationVector(const Values& values, + bool whiten) const { Vector violation(dim()); size_t start_idx = 0; for (const auto& constraint : *this) { size_t dim = constraint->dim(); violation.segment(start_idx, dim) = - whiten ? constraint->whitenedError(values) : constraint->unwhitenedError(values); + whiten ? constraint->whitenedError(values) + : constraint->unwhitenedError(values); start_idx += dim; } return violation; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ double NonlinearEqualityConstraints::violationNorm(const Values& values) const { return violationVector(values, true).norm(); } -/* ********************************************************************************************* */ -NonlinearFactorGraph NonlinearEqualityConstraints::penaltyGraph(const double mu) const { +/* ********************************************************************************************* + */ +NonlinearFactorGraph NonlinearEqualityConstraints::penaltyGraph( + const double mu) const { NonlinearFactorGraph graph; for (const auto& constraint : *this) { graph.add(constraint->penaltyFactor(mu)); diff --git a/gtsam/constrained/NonlinearEqualityConstraint.h b/gtsam/constrained/NonlinearEqualityConstraint.h index cdd8d46285..53959c57e9 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint.h +++ b/gtsam/constrained/NonlinearEqualityConstraint.h @@ -47,8 +47,9 @@ class GTSAM_EXPORT NonlinearEqualityConstraint : public NonlinearConstraint { friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("NonlinearEqualityConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "NonlinearEqualityConstraint", + boost::serialization::base_object(*this)); } #endif }; @@ -73,11 +74,14 @@ class ExpressionEqualityConstraint : public NonlinearEqualityConstraint { * @param expression expression representing g(x). * @param tolerance vector representing tolerance in each dimension. */ - ExpressionEqualityConstraint(const Expression& expression, const T& rhs, const Vector& sigmas); + ExpressionEqualityConstraint(const Expression& expression, const T& rhs, + const Vector& sigmas); - virtual Vector unwhitenedError(const Values& x, OptionalMatrixVecType H = nullptr) const override; + virtual Vector unwhitenedError( + const Values& x, OptionalMatrixVecType H = nullptr) const override; - virtual NoiseModelFactor::shared_ptr penaltyFactor(const double mu = 1.0) const override; + virtual NoiseModelFactor::shared_ptr penaltyFactor( + const double mu = 1.0) const override; const Expression& expression() const { return expression_; } @@ -93,8 +97,9 @@ class ExpressionEqualityConstraint : public NonlinearEqualityConstraint { friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("ExpressionEqualityConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "ExpressionEqualityConstraint", + boost::serialization::base_object(*this)); ar& BOOST_SERIALIZATION_NVP(expression_); ar& BOOST_SERIALIZATION_NVP(rhs_); ar& BOOST_SERIALIZATION_NVP(dims_); @@ -102,7 +107,7 @@ class ExpressionEqualityConstraint : public NonlinearEqualityConstraint { #endif }; -/** Equality constraint that enforce the cost factor with zero error. +/** Equality constraint that enforce the cost factor with zero error. * e.g., for a factor with unwhitened cost 2x-1, the constraint enforces the * equality 2x-1=0. */ @@ -123,9 +128,11 @@ class GTSAM_EXPORT ZeroCostConstraint : public NonlinearEqualityConstraint { */ ZeroCostConstraint(const NoiseModelFactor::shared_ptr& factor); - virtual Vector unwhitenedError(const Values& x, OptionalMatrixVecType H = nullptr) const override; + virtual Vector unwhitenedError( + const Values& x, OptionalMatrixVecType H = nullptr) const override; - virtual NoiseModelFactor::shared_ptr penaltyFactor(const double mu = 1.0) const override; + virtual NoiseModelFactor::shared_ptr penaltyFactor( + const double mu = 1.0) const override; /// @return a deep copy of this factor gtsam::NonlinearFactor::shared_ptr clone() const override { @@ -139,15 +146,16 @@ class GTSAM_EXPORT ZeroCostConstraint : public NonlinearEqualityConstraint { friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("ZeroCostConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "ZeroCostConstraint", boost::serialization::base_object(*this)); ar& BOOST_SERIALIZATION_NVP(factor_); } #endif }; /// Container of NonlinearEqualityConstraint. -class GTSAM_EXPORT NonlinearEqualityConstraints : public FactorGraph { +class GTSAM_EXPORT NonlinearEqualityConstraints + : public FactorGraph { public: using shared_ptr = std::shared_ptr; using Base = FactorGraph; @@ -156,7 +164,8 @@ class GTSAM_EXPORT NonlinearEqualityConstraints : public FactorGraph void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("NonlinearEqualityConstraints", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "NonlinearEqualityConstraints", + boost::serialization::base_object(*this)); } #endif }; diff --git a/gtsam/constrained/NonlinearInequalityConstraint.cpp b/gtsam/constrained/NonlinearInequalityConstraint.cpp index b1dcb7534c..670b1433e9 100644 --- a/gtsam/constrained/NonlinearInequalityConstraint.cpp +++ b/gtsam/constrained/NonlinearInequalityConstraint.cpp @@ -21,14 +21,16 @@ namespace gtsam { -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ Vector NonlinearInequalityConstraint::whitenedExpr(const Values& x) const { return noiseModel()->whiten(unwhitenedExpr(x)); } -/* ********************************************************************************************* */ -Vector NonlinearInequalityConstraint::unwhitenedError(const Values& x, - OptionalMatrixVecType H) const { +/* ********************************************************************************************* + */ +Vector NonlinearInequalityConstraint::unwhitenedError( + const Values& x, OptionalMatrixVecType H) const { Vector error = unwhitenedExpr(x, H); for (size_t i = 0; i < dim(); i++) { if (error(i) < 0) { @@ -43,54 +45,66 @@ Vector NonlinearInequalityConstraint::unwhitenedError(const Values& x, return error; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ bool NonlinearInequalityConstraint::active(const Values& x) const { return (unwhitenedExpr(x).array() >= 0).any(); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ NoiseModelFactor::shared_ptr NonlinearInequalityConstraint::penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { /// Default behavior, this function should be overriden. return penaltyFactor(mu); } -/* ********************************************************************************************* */ -NonlinearEqualityConstraint::shared_ptr NonlinearInequalityConstraint::createEqualityConstraint() - const { +/* ********************************************************************************************* + */ +NonlinearEqualityConstraint::shared_ptr +NonlinearInequalityConstraint::createEqualityConstraint() const { /// Default behavior, this function should be overriden. return nullptr; } -/* ********************************************************************************************* */ -NoiseModelFactor::shared_ptr NonlinearInequalityConstraint::penaltyFactorEquality( - const double mu) const { +/* ********************************************************************************************* + */ +NoiseModelFactor::shared_ptr +NonlinearInequalityConstraint::penaltyFactorEquality(const double mu) const { return createEqualityConstraint()->penaltyFactor(mu); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ ScalarExpressionInequalityConstraint::ScalarExpressionInequalityConstraint( const Double_& expression, const double& sigma) : Base(constrainedNoise(Vector1(sigma)), expression.keysAndDims().first), expression_(expression), dims_(expression.keysAndDims().second) {} -/* ********************************************************************************************* */ -ScalarExpressionInequalityConstraint::shared_ptr ScalarExpressionInequalityConstraint::GeqZero( - const Double_& expression, const double& sigma) { +/* ********************************************************************************************* + */ +ScalarExpressionInequalityConstraint::shared_ptr +ScalarExpressionInequalityConstraint::GeqZero(const Double_& expression, + const double& sigma) { Double_ neg_expr = Double_(0.0) - expression; - return std::make_shared(neg_expr, sigma); + return std::make_shared(neg_expr, + sigma); } -/* ********************************************************************************************* */ -ScalarExpressionInequalityConstraint::shared_ptr ScalarExpressionInequalityConstraint::LeqZero( - const Double_& expression, const double& sigma) { - return std::make_shared(expression, sigma); +/* ********************************************************************************************* + */ +ScalarExpressionInequalityConstraint::shared_ptr +ScalarExpressionInequalityConstraint::LeqZero(const Double_& expression, + const double& sigma) { + return std::make_shared(expression, + sigma); } -/* ********************************************************************************************* */ -Vector ScalarExpressionInequalityConstraint::unwhitenedExpr(const Values& x, - OptionalMatrixVecType H) const { +/* ********************************************************************************************* + */ +Vector ScalarExpressionInequalityConstraint::unwhitenedExpr( + const Values& x, OptionalMatrixVecType H) const { // Copy-paste from ExpressionFactor. if (H) { return Vector1(expression_.valueAndDerivatives(x, keys_, dims_, *H)); @@ -99,38 +113,48 @@ Vector ScalarExpressionInequalityConstraint::unwhitenedExpr(const Values& x, } } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ NonlinearEqualityConstraint::shared_ptr ScalarExpressionInequalityConstraint::createEqualityConstraint() const { return std::make_shared>( expression_, 0.0, noiseModel()->sigmas()); } -/* ********************************************************************************************* */ -NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactor( - const double mu) const { +/* ********************************************************************************************* + */ +NoiseModelFactor::shared_ptr +ScalarExpressionInequalityConstraint::penaltyFactor(const double mu) const { Double_ penalty_expression(RampFunction::Ramp, expression_); - return std::make_shared>(penaltyNoise(mu), 0.0, penalty_expression); + return std::make_shared>(penaltyNoise(mu), 0.0, + penalty_expression); } -/* ********************************************************************************************* */ -NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactorCustom( +/* ********************************************************************************************* + */ +NoiseModelFactor::shared_ptr +ScalarExpressionInequalityConstraint::penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { if (!func) { return penaltyFactor(mu); } // TODO(yetong): can we pass the functor directly to construct the expression? Double_ error(func->function(), expression_); - return std::make_shared>(penaltyNoise(mu), 0.0, error); + return std::make_shared>(penaltyNoise(mu), 0.0, + error); } -/* ********************************************************************************************* */ -NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactorEquality( +/* ********************************************************************************************* + */ +NoiseModelFactor::shared_ptr +ScalarExpressionInequalityConstraint::penaltyFactorEquality( const double mu) const { - return std::make_shared>(penaltyNoise(mu), 0.0, expression_); + return std::make_shared>(penaltyNoise(mu), 0.0, + expression_); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ size_t NonlinearInequalityConstraints::dim() const { size_t dimension = 0; for (const auto& constraint : *this) { @@ -139,26 +163,33 @@ size_t NonlinearInequalityConstraints::dim() const { return dimension; } -/* ********************************************************************************************* */ -Vector NonlinearInequalityConstraints::violationVector(const Values& values, bool whiten) const { +/* ********************************************************************************************* + */ +Vector NonlinearInequalityConstraints::violationVector(const Values& values, + bool whiten) const { Vector violation(dim()); size_t start_idx = 0; for (const auto& constraint : *this) { size_t dim = constraint->dim(); violation.middleCols(start_idx, dim) = - whiten ? constraint->whitenedError(values) : constraint->unwhitenedError(values); + whiten ? constraint->whitenedError(values) + : constraint->unwhitenedError(values); start_idx += dim; } return violation; } -/* ********************************************************************************************* */ -double NonlinearInequalityConstraints::violationNorm(const Values& values) const { +/* ********************************************************************************************* + */ +double NonlinearInequalityConstraints::violationNorm( + const Values& values) const { return violationVector(values, true).norm(); } -/* ********************************************************************************************* */ -NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraph(const double mu) const { +/* ********************************************************************************************* + */ +NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraph( + const double mu) const { NonlinearFactorGraph graph; for (const auto& constraint : *this) { graph.add(constraint->penaltyFactor(mu)); @@ -166,7 +197,8 @@ NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraph(const double m return graph; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraphCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { NonlinearFactorGraph graph; diff --git a/gtsam/constrained/NonlinearInequalityConstraint.h b/gtsam/constrained/NonlinearInequalityConstraint.h index cb0defc3d9..9c2e24b00c 100644 --- a/gtsam/constrained/NonlinearInequalityConstraint.h +++ b/gtsam/constrained/NonlinearInequalityConstraint.h @@ -40,25 +40,30 @@ class GTSAM_EXPORT NonlinearInequalityConstraint : public NonlinearConstraint { virtual ~NonlinearInequalityConstraint() {} /** Return g(x). */ - virtual Vector unwhitenedExpr(const Values& x, OptionalMatrixVecType H = nullptr) const = 0; + virtual Vector unwhitenedExpr(const Values& x, + OptionalMatrixVecType H = nullptr) const = 0; virtual Vector whitenedExpr(const Values& x) const; /** Return ramp(g(x)). */ - virtual Vector unwhitenedError(const Values& x, OptionalMatrixVecType H = nullptr) const override; + virtual Vector unwhitenedError( + const Values& x, OptionalMatrixVecType H = nullptr) const override; /** Return true if g(x)>=0 in any dimension. */ virtual bool active(const Values& x) const override; /** Return an equality constraint corresponding to g(x)=0. */ - virtual NonlinearEqualityConstraint::shared_ptr createEqualityConstraint() const; + virtual NonlinearEqualityConstraint::shared_ptr createEqualityConstraint() + const; /** Cost factor using a customized penalty function. */ virtual NoiseModelFactor::shared_ptr penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu = 1.0) const; - /** penalty function as if the constraint is equality, 0.5 * mu * ||g(x)||^2 */ - virtual NoiseModelFactor::shared_ptr penaltyFactorEquality(const double mu = 1.0) const; + /** penalty function as if the constraint is equality, 0.5 * mu * ||g(x)||^2 + */ + virtual NoiseModelFactor::shared_ptr penaltyFactorEquality( + const double mu = 1.0) const; private: #if GTSAM_ENABLE_BOOST_SERIALIZATION @@ -66,8 +71,9 @@ class GTSAM_EXPORT NonlinearInequalityConstraint : public NonlinearConstraint { friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("NonlinearInequalityConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "NonlinearInequalityConstraint", + boost::serialization::base_object(*this)); } #endif }; @@ -75,7 +81,8 @@ class GTSAM_EXPORT NonlinearInequalityConstraint : public NonlinearConstraint { /** Inequality constraint that force g(x) <= 0, where g(x) is a scalar-valued * nonlinear function. */ -class GTSAM_EXPORT ScalarExpressionInequalityConstraint : public NonlinearInequalityConstraint { +class GTSAM_EXPORT ScalarExpressionInequalityConstraint + : public NonlinearInequalityConstraint { public: using Base = NonlinearInequalityConstraint; using This = ScalarExpressionInequalityConstraint; @@ -92,32 +99,40 @@ class GTSAM_EXPORT ScalarExpressionInequalityConstraint : public NonlinearInequa * @param expression expression representing g(x) (or -g(x) for GeqZero). * @param sigma scalar representing sigma. */ - ScalarExpressionInequalityConstraint(const Double_& expression, const double& sigma); + ScalarExpressionInequalityConstraint(const Double_& expression, + const double& sigma); - /** Create an inequality constraint g(x)/sigma >= 0, internally represented as -g(x)/sigma <= 0. + /** Create an inequality constraint g(x)/sigma >= 0, internally represented as + * -g(x)/sigma <= 0. */ - static ScalarExpressionInequalityConstraint::shared_ptr GeqZero(const Double_& expression, - const double& sigma); + static ScalarExpressionInequalityConstraint::shared_ptr GeqZero( + const Double_& expression, const double& sigma); /** Create an inequality constraint g(x)/sigma <= 0. */ - static ScalarExpressionInequalityConstraint::shared_ptr LeqZero(const Double_& expression, - const double& sigma); + static ScalarExpressionInequalityConstraint::shared_ptr LeqZero( + const Double_& expression, const double& sigma); /** Compute g(x), or -g(x) for objects constructed from GeqZero. */ - virtual Vector unwhitenedExpr(const Values& x, OptionalMatrixVecType H = nullptr) const override; + virtual Vector unwhitenedExpr( + const Values& x, OptionalMatrixVecType H = nullptr) const override; /** Equality constraint representing g(x)/sigma = 0. */ - NonlinearEqualityConstraint::shared_ptr createEqualityConstraint() const override; + NonlinearEqualityConstraint::shared_ptr createEqualityConstraint() + const override; /** Penalty function 0.5*mu*||ramp(g(x)/sigma||^2. */ - NoiseModelFactor::shared_ptr penaltyFactor(const double mu = 1.0) const override; + NoiseModelFactor::shared_ptr penaltyFactor( + const double mu = 1.0) const override; /** Penalty function using a smooth approxiamtion of the ramp funciton. */ - NoiseModelFactor::shared_ptr penaltyFactorCustom(InequalityPenaltyFunction::shared_ptr func, - const double mu = 1.0) const override; + NoiseModelFactor::shared_ptr penaltyFactorCustom( + InequalityPenaltyFunction::shared_ptr func, + const double mu = 1.0) const override; - /** Penalty function as if the constraint is equality, 0.5 * mu * ||g(x)/sigma||^2. */ - virtual NoiseModelFactor::shared_ptr penaltyFactorEquality(const double mu = 1.0) const override; + /** Penalty function as if the constraint is equality, 0.5 * mu * + * ||g(x)/sigma||^2. */ + virtual NoiseModelFactor::shared_ptr penaltyFactorEquality( + const double mu = 1.0) const override; /** Return expression g(x), or -g(x) for objects constructed from GeqZero. */ const Double_& expression() const { return expression_; } @@ -134,8 +149,9 @@ class GTSAM_EXPORT ScalarExpressionInequalityConstraint : public NonlinearInequa friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("ExpressionEqualityConstraint", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "ExpressionEqualityConstraint", + boost::serialization::base_object(*this)); ar& BOOST_SERIALIZATION_NVP(expression_); ar& BOOST_SERIALIZATION_NVP(dims_); } @@ -165,8 +181,8 @@ class GTSAM_EXPORT NonlinearInequalityConstraints NonlinearFactorGraph penaltyGraph(const double mu = 1.0) const; /** Return the cost graph constructed using a customized penalty function. */ - NonlinearFactorGraph penaltyGraphCustom(InequalityPenaltyFunction::shared_ptr func, - const double mu = 1.0) const; + NonlinearFactorGraph penaltyGraphCustom( + InequalityPenaltyFunction::shared_ptr func, const double mu = 1.0) const; private: #if GTSAM_ENABLE_BOOST_SERIALIZATION @@ -174,8 +190,9 @@ class GTSAM_EXPORT NonlinearInequalityConstraints friend class boost::serialization::access; template void serialize(ARCHIVE& ar, const unsigned int /*version*/) { - ar& boost::serialization::make_nvp("NonlinearInequalityConstraints", - boost::serialization::base_object(*this)); + ar& boost::serialization::make_nvp( + "NonlinearInequalityConstraints", + boost::serialization::base_object(*this)); } #endif }; diff --git a/gtsam/constrained/PenaltyOptimizer.h b/gtsam/constrained/PenaltyOptimizer.h index d873b59971..b4d777d456 100644 --- a/gtsam/constrained/PenaltyOptimizer.h +++ b/gtsam/constrained/PenaltyOptimizer.h @@ -39,8 +39,7 @@ class GTSAM_EXPORT PenaltyOptimizerParams : public ConstrainedOptimizerParams { /** Constructor. */ PenaltyOptimizerParams() : Base() { - lmParams.linearSolverType = - LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; + lmParams.linearSolverType = LevenbergMarquardtParams::MULTIFRONTAL_CHOLESKY; } }; @@ -63,8 +62,8 @@ class GTSAM_EXPORT PenaltyOptimizerState : public ConstrainedOptimizerState { * optimization problem that minimize a merit function. The merit function is * constructed as the sum of the cost function and penalty functions for * constraints. - * Example problem: - * argmin_x 0.5 * ||x1-1||^2 + 0.5 * ||x2-1||^2 + * Example problem: + * argmin_x 0.5 * ||x1-1||^2 + 0.5 * ||x2-1||^2 * s.t. x1^2 + x2^2 - 1 = 0 * s.t. 4*x1^2 + 0.25*x2^2 - 1 <= 0 */ @@ -106,7 +105,8 @@ class GTSAM_EXPORT PenaltyOptimizer : public ConstrainedOptimizer { protected: /// Merit function in the form /// m(x) = f(x) + 0.5 muEq * ||h(x)||^2 + 0.5 * muIneq * ||g(x)_+||^2 - NonlinearFactorGraph meritFunction(const double muEq, const double muIneq) const; + NonlinearFactorGraph meritFunction(const double muEq, + const double muIneq) const; /// Create an unconstrained optimizer that solves the merit function (cost + /// penalty functions). diff --git a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp index 4d39c9ca64..b3c3c96623 100644 --- a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp @@ -26,7 +26,8 @@ using namespace gtsam; -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ double EvaluateLagrangeTerm(const std::vector& lambdas, const NonlinearEqualityConstraints& constraints, const Values& values) { @@ -38,7 +39,8 @@ double EvaluateLagrangeTerm(const std::vector& lambdas, return s; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ double EvaluateLagrangeTerm(const std::vector& lambdas, const NonlinearInequalityConstraints& constraints, const Values& values) { @@ -50,7 +52,8 @@ double EvaluateLagrangeTerm(const std::vector& lambdas, return s; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ double ComputeBias(const std::vector& lambdas, double mu) { double norm_squared = 0; for (const auto& lambda : lambdas) { @@ -59,7 +62,8 @@ double ComputeBias(const std::vector& lambdas, double mu) { return 0.5 / mu * norm_squared; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ double ComputeBias(const std::vector& lambdas, double epsilon) { double norm_squared = 0; for (const auto& lambda : lambdas) { @@ -68,7 +72,8 @@ double ComputeBias(const std::vector& lambdas, double epsilon) { return 0.5 / epsilon * norm_squared; } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(AugmentedLagrangian, constrained_example1) { using namespace constrained_example1; @@ -79,18 +84,23 @@ TEST(AugmentedLagrangian, constrained_example1) { AugmentedLagrangianState state; state.lambdaEq.emplace_back(Vector1(0.3)); state.muEq = 0.2; - NonlinearFactorGraph augmentedLagrangian = optimizer.augmentedLagrangianFunction(state); + NonlinearFactorGraph augmentedLagrangian = + optimizer.augmentedLagrangianFunction(state); const Values& values = init_values; double expected_cost = costs.error(values); - double expected_l2_penalty = eqConstraints.penaltyGraph(state.muEq).error(values); - double expected_lagrange_term = EvaluateLagrangeTerm(state.lambdaEq, eqConstraints, values); + double expected_l2_penalty = + eqConstraints.penaltyGraph(state.muEq).error(values); + double expected_lagrange_term = + EvaluateLagrangeTerm(state.lambdaEq, eqConstraints, values); double bias = ComputeBias(state.lambdaEq, state.muEq); - double expected_error = expected_cost + expected_l2_penalty + expected_lagrange_term + bias; + double expected_error = + expected_cost + expected_l2_penalty + expected_lagrange_term + bias; EXPECT_DOUBLES_EQUAL(expected_error, augmentedLagrangian.error(values), 1e-6); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(AugmentedLagrangian, constrained_example2) { using namespace constrained_example2; @@ -104,23 +114,30 @@ TEST(AugmentedLagrangian, constrained_example2) { state.muEq = 0.2; state.muIneq = 0.3; double epsilon = 1.0; - NonlinearFactorGraph augmentedLagrangian = optimizer.augmentedLagrangianFunction(state, epsilon); + NonlinearFactorGraph augmentedLagrangian = + optimizer.augmentedLagrangianFunction(state, epsilon); const Values& values = init_values; double expected_cost = costs.error(values); - double expected_l2_penalty_e = eqConstraints.penaltyGraph(state.muEq).error(values); - double expected_lagrange_term_e = EvaluateLagrangeTerm(state.lambdaEq, eqConstraints, values); + double expected_l2_penalty_e = + eqConstraints.penaltyGraph(state.muEq).error(values); + double expected_lagrange_term_e = + EvaluateLagrangeTerm(state.lambdaEq, eqConstraints, values); double bias_e = ComputeBias(state.lambdaEq, state.muEq); - double expected_penalty_i = ineqConstraints.penaltyGraph(state.muIneq).error(values); - double expected_lagrange_term_i = EvaluateLagrangeTerm(state.lambdaIneq, ineqConstraints, values); + double expected_penalty_i = + ineqConstraints.penaltyGraph(state.muIneq).error(values); + double expected_lagrange_term_i = + EvaluateLagrangeTerm(state.lambdaIneq, ineqConstraints, values); double bias_i = ComputeBias(state.lambdaIneq, epsilon); - double expected_error = expected_cost + expected_l2_penalty_e + expected_penalty_i + - expected_lagrange_term_e + expected_lagrange_term_i + bias_e + bias_i; + double expected_error = expected_cost + expected_l2_penalty_e + + expected_penalty_i + expected_lagrange_term_e + + expected_lagrange_term_i + bias_e + bias_i; EXPECT_DOUBLES_EQUAL(expected_error, augmentedLagrangian.error(values), 1e-6); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(AugmentedLagrangianOptimizer, constrained_example1) { using namespace constrained_example1; @@ -133,7 +150,8 @@ TEST(AugmentedLagrangianOptimizer, constrained_example1) { EXPECT(assert_equal(optimal_values, results, 1e-4)); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(AugmentedLagrangianOptimizer, constrained_example2) { using namespace constrained_example2; @@ -193,7 +211,8 @@ TEST(AugmentedLagrangian, VectorBiasUsesElementwiseSigmas) { EXPECT(assert_equal(expected, bias_from_factor, 1e-12)); } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); diff --git a/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp b/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp index 9b41dbe2c5..f8b5d33193 100644 --- a/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp +++ b/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp @@ -24,7 +24,8 @@ using namespace gtsam; -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(RampFunction, error_and_jacobian) { /// Helper function for numerical Jacobian computation. auto ramp_helper = [&](const double& x) { return RampFunction::Ramp(x); }; @@ -42,14 +43,17 @@ TEST(RampFunction, error_and_jacobian) { EXPECT_DOUBLES_EQUAL(expected_r_vec.at(i), r, 1e-9); /// Check derivative. - if (abs(x) > 1e-6) { // function is not smooth at 0, so Jacobian is undefined. - Matrix expected_H = gtsam::numericalDerivative11(ramp_helper, x, 1e-6); + if (abs(x) > + 1e-6) { // function is not smooth at 0, so Jacobian is undefined. + Matrix expected_H = + gtsam::numericalDerivative11(ramp_helper, x, 1e-6); EXPECT(assert_equal(expected_H, H)); } } } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(RampFunctionPoly2, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SmoothRampPoly2 p_ramp(2.0); @@ -68,12 +72,14 @@ TEST(RampFunctionPoly2, error_and_jacobian) { EXPECT_DOUBLES_EQUAL(expected_r_vec.at(i), r, 1e-9); /// Check derivative. - Matrix expected_H = gtsam::numericalDerivative11(ramp_helper, x, 1e-6); + Matrix expected_H = + gtsam::numericalDerivative11(ramp_helper, x, 1e-6); EXPECT(assert_equal(expected_H, H, 1e-6)); } } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(RampFunctionPoly3, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SmoothRampPoly3 p_ramp(2.0); @@ -92,12 +98,14 @@ TEST(RampFunctionPoly3, error_and_jacobian) { EXPECT_DOUBLES_EQUAL(expected_r_vec.at(i), r, 1e-9); /// Check derivative. - Matrix expected_H = gtsam::numericalDerivative11(ramp_helper, x, 1e-6); + Matrix expected_H = + gtsam::numericalDerivative11(ramp_helper, x, 1e-6); EXPECT(assert_equal(expected_H, H, 1e-6)); } } -/* ********************************************************************************************* */ +/* ********************************************************************************************* + */ TEST(SoftPlusFunction, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SoftPlusFunction soft_plus(0.5); @@ -105,8 +113,8 @@ TEST(SoftPlusFunction, error_and_jacobian) { /// Create a set of values to test the function. static std::vector x_vec{-3.0, 0.0, 1.0, 2.0, 3.0}; - static std::vector expected_r_vec{ - 0.40282656, 1.38629436, 1.94815397, 2.62652338, 3.40282656}; + static std::vector expected_r_vec{0.40282656, 1.38629436, 1.94815397, + 2.62652338, 3.40282656}; for (size_t i = 0; i < x_vec.size(); i++) { double x = x_vec.at(i); @@ -117,7 +125,8 @@ TEST(SoftPlusFunction, error_and_jacobian) { EXPECT_DOUBLES_EQUAL(expected_r_vec.at(i), r, 1e-6); /// Check derivative. - Matrix expected_H = gtsam::numericalDerivative11(soft_plus_helper, x, 1e-6); + Matrix expected_H = gtsam::numericalDerivative11( + soft_plus_helper, x, 1e-6); EXPECT(assert_equal(expected_H, H, 1e-6)); } } diff --git a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp index 6f92a539fc..f22608a8f3 100644 --- a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp @@ -68,8 +68,10 @@ TEST(ExpressionEqualityConstraint, double) { EXPECT(!constraint.feasible(values2)); // Check constraint violation is indeed g(x) at values2. - EXPECT(assert_equal(Vector::Constant(1, 4.0), constraint.unwhitenedError(values2))); - EXPECT(assert_equal(Vector::Constant(1, 40), constraint.whitenedError(values2))); + EXPECT(assert_equal(Vector::Constant(1, 4.0), + constraint.unwhitenedError(values2))); + EXPECT( + assert_equal(Vector::Constant(1, 40), constraint.whitenedError(values2))); EXPECT(assert_equal(800, constraint.error(values2))); // Check dimension is 1 for scalar g. @@ -106,7 +108,8 @@ TEST(ExpressionEqualityConstraint, Vector2) { Vector2_ x2_vec_expr(x2_key); auto g = x1_vec_expr + x2_vec_expr; auto sigmas = Vector2(0.1, 0.5); - auto constraint = ExpressionEqualityConstraint(g, Vector2::Zero(), sigmas); + auto constraint = + ExpressionEqualityConstraint(g, Vector2::Zero(), sigmas); EXPECT(constraint.noiseModel()->isConstrained()); EXPECT(assert_equal(sigmas, constraint.noiseModel()->sigmas())); @@ -123,20 +126,24 @@ TEST(ExpressionEqualityConstraint, Vector2) { // Check that violation evaluates as 0 at values1. auto expected_violation1 = (Vector(2) << 0, 0).finished(); - EXPECT(assert_equal(expected_violation1, constraint.unwhitenedError(values1))); + EXPECT( + assert_equal(expected_violation1, constraint.unwhitenedError(values1))); auto expected_scaled_violation1 = (Vector(2) << 0, 0).finished(); - EXPECT(assert_equal(expected_scaled_violation1, constraint.whitenedError(values1))); + EXPECT(assert_equal(expected_scaled_violation1, + constraint.whitenedError(values1))); // Check that values2 are indeed deemed infeasible. EXPECT(!constraint.feasible(values2)); // Check constraint violation is indeed g(x) at values2. auto expected_violation2 = (Vector(2) << 2, 2).finished(); - EXPECT(assert_equal(expected_violation2, constraint.unwhitenedError(values2))); + EXPECT( + assert_equal(expected_violation2, constraint.unwhitenedError(values2))); // Check scaled violation is indeed g(x)/sigmas at values2. auto expected_scaled_violation2 = (Vector(2) << 20, 4).finished(); - EXPECT(assert_equal(expected_scaled_violation2, constraint.whitenedError(values2))); + EXPECT(assert_equal(expected_scaled_violation2, + constraint.whitenedError(values2))); // Check dim is the dimension of the vector. EXPECT(constraint.dim() == 2); @@ -167,7 +174,8 @@ TEST(ZeroCostConstraint, BetweenFactor) { Vector sigmas = Vector2(0.5, 0.1); auto noise = noiseModel::Diagonal::Sigmas(sigmas); - auto factor = std::make_shared>(x1_key, x2_key, Vector2(1, 1), noise); + auto factor = std::make_shared>(x1_key, x2_key, + Vector2(1, 1), noise); auto constraint = ZeroCostConstraint(factor); EXPECT(constraint.noiseModel()->isConstrained()); @@ -185,20 +193,24 @@ TEST(ZeroCostConstraint, BetweenFactor) { // Check that violation evaluates as 0 at values1. auto expected_violation1 = (Vector(2) << 0, 0).finished(); - EXPECT(assert_equal(expected_violation1, constraint.unwhitenedError(values1))); + EXPECT( + assert_equal(expected_violation1, constraint.unwhitenedError(values1))); auto expected_scaled_violation1 = (Vector(2) << 0, 0).finished(); - EXPECT(assert_equal(expected_scaled_violation1, constraint.whitenedError(values1))); + EXPECT(assert_equal(expected_scaled_violation1, + constraint.whitenedError(values1))); // Check that values2 are indeed deemed infeasible. EXPECT(!constraint.feasible(values2)); // Check constraint violation is indeed g(x) at values2. auto expected_violation2 = (Vector(2) << 1, 2).finished(); - EXPECT(assert_equal(expected_violation2, constraint.unwhitenedError(values2))); + EXPECT( + assert_equal(expected_violation2, constraint.unwhitenedError(values2))); // Check scaled violation is indeed g(x)/sigmas at values2. auto expected_scaled_violation2 = (Vector(2) << 2, 20).finished(); - EXPECT(assert_equal(expected_scaled_violation2, constraint.whitenedError(values2))); + EXPECT(assert_equal(expected_scaled_violation2, + constraint.whitenedError(values2))); // Check dim is the dimension of the vector. EXPECT(constraint.dim() == 2); @@ -232,8 +244,10 @@ TEST(NonlinearEqualityConstraints, Container) { auto g2 = x1_vec_expr + x2_vec_expr; Vector sigmas2 = Vector2(0.1, 0.5); - constraints.emplace_shared>(g1, 0.0, sigmas1); - constraints.emplace_shared>(g2, Vector2::Zero(), sigmas2); + constraints.emplace_shared>(g1, 0.0, + sigmas1); + constraints.emplace_shared>( + g2, Vector2::Zero(), sigmas2); // Check size. EXPECT_LONGS_EQUAL(2, constraints.size()); diff --git a/gtsam/constrained/tests/testPenaltyOptimizer.cpp b/gtsam/constrained/tests/testPenaltyOptimizer.cpp index 6ca8ebd29a..ac1e119e6a 100644 --- a/gtsam/constrained/tests/testPenaltyOptimizer.cpp +++ b/gtsam/constrained/tests/testPenaltyOptimizer.cpp @@ -53,10 +53,10 @@ TEST(PenaltyOptimizer, constrained_example2) { // Check constructor from a single factor graph works { NonlinearFactorGraph graph = costs; - for (const auto& factor: eqConstraints) { + for (const auto& factor : eqConstraints) { graph.push_back(factor); } - for (const auto& factor: ineqConstraints) { + for (const auto& factor : ineqConstraints) { graph.push_back(factor); } PenaltyOptimizer optimizer(graph, init_values, params); @@ -65,7 +65,6 @@ TEST(PenaltyOptimizer, constrained_example2) { /// Check the result is correct within tolerance. EXPECT(assert_equal(optimal_values, results, 1e-4)); } - } int main() { From 6f054ef3b664e1cc28c28e58657c97ef6de3b5ca Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:07:37 -0500 Subject: [PATCH 177/266] Spelling --- gtsam/constrained/NonlinearConstraint.h | 2 +- gtsam/constrained/NonlinearInequalityConstraint.h | 2 +- gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp | 2 +- gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp | 2 +- gtsam/constrained/tests/testPenaltyOptimizer.cpp | 4 ++-- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/gtsam/constrained/NonlinearConstraint.h b/gtsam/constrained/NonlinearConstraint.h index 1c8d6d127e..a31b43aeeb 100644 --- a/gtsam/constrained/NonlinearConstraint.h +++ b/gtsam/constrained/NonlinearConstraint.h @@ -73,7 +73,7 @@ class GTSAM_EXPORT NonlinearConstraint : public NoiseModelFactor { return noiseModel::Diagonal::Sigmas(noiseModel()->sigmas() / sqrt(mu)); } - /** Default constrained noisemodel used for construction of constraint. */ + /** Default constrained noise model used for construction of constraint. */ static SharedNoiseModel constrainedNoise(const Vector& sigmas) { return noiseModel::Constrained::MixedSigmas(1.0, sigmas); } diff --git a/gtsam/constrained/NonlinearInequalityConstraint.h b/gtsam/constrained/NonlinearInequalityConstraint.h index 9c2e24b00c..2538083084 100644 --- a/gtsam/constrained/NonlinearInequalityConstraint.h +++ b/gtsam/constrained/NonlinearInequalityConstraint.h @@ -124,7 +124,7 @@ class GTSAM_EXPORT ScalarExpressionInequalityConstraint NoiseModelFactor::shared_ptr penaltyFactor( const double mu = 1.0) const override; - /** Penalty function using a smooth approxiamtion of the ramp funciton. */ + /** Penalty function using a smooth approximation of the ramp function. */ NoiseModelFactor::shared_ptr penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu = 1.0) const override; diff --git a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp index f22608a8f3..78f8fe1530 100644 --- a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp @@ -41,7 +41,7 @@ using constrained_example::x1_key, constrained_example::x2_key; // Test methods of DoubleExpressionEquality. TEST(ExpressionEqualityConstraint, double) { // create constraint from double expression - // g(x1, x2) = x1 + x1^3 + x2 + x2^2, from Vanderbergh slides + // g(x1, x2) = x1 + x1^3 + x2 + x2^2, from Vandenberghe slides Vector sigmas = Vector1(0.1); auto g = x1 + pow(x1, 3) + x2 + pow(x2, 2); auto constraint = ExpressionEqualityConstraint(g, 0.0, sigmas); diff --git a/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp b/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp index 4d9034daa2..2d8b4f7b99 100644 --- a/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp @@ -33,7 +33,7 @@ using constrained_example::x1_key, constrained_example::x2_key; // Test methods of DoubleExpressionEquality. TEST(NonlinearInequalityConstraint, ScalarExpressionInequalityConstraint) { // create constraint from double expression - // g(x1, x2) = x1 + x1^3 + x2 + x2^2, from Vanderbergh slides + // g(x1, x2) = x1 + x1^3 + x2 + x2^2, from Vandenberghe slides double sigma = 0.1; auto g = x1 + pow(x1, 3) + x2 + pow(x2, 2); auto constraint_geq = ScalarExpressionInequalityConstraint::GeqZero(g, sigma); diff --git a/gtsam/constrained/tests/testPenaltyOptimizer.cpp b/gtsam/constrained/tests/testPenaltyOptimizer.cpp index ac1e119e6a..318946a192 100644 --- a/gtsam/constrained/tests/testPenaltyOptimizer.cpp +++ b/gtsam/constrained/tests/testPenaltyOptimizer.cpp @@ -10,8 +10,8 @@ * -------------------------------------------------------------------------- */ /** - * @file testPenaltyOptimizr.cpp - * @brief Test penalty method optimzier for constrained optimization. + * @file testPenaltyOptimizer.cpp + * @brief Test penalty method optimizer for constrained optimization. * @author: Yetong Zhang */ From f882146d4c9430ff97d13b930305c5b2727bf004 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:23:51 -0500 Subject: [PATCH 178/266] docs phase 1 --- gtsam/constrained/Constrained.md | 96 ++++++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 gtsam/constrained/Constrained.md diff --git a/gtsam/constrained/Constrained.md b/gtsam/constrained/Constrained.md new file mode 100644 index 0000000000..135e8d6d49 --- /dev/null +++ b/gtsam/constrained/Constrained.md @@ -0,0 +1,96 @@ +# Constrained + +The `constrained` module in GTSAM provides constrained nonlinear optimization on top of factor graphs. +It includes classes for representing constraints, building constrained problems, and solving them with penalty and augmented Lagrangian methods. + +## Core Problem Model + +- `ConstrainedOptProblem`: Holds objective costs, equality constraints, and inequality constraints. +- `ConstrainedOptProblem::AuxiliaryKeyGenerator`: Generates keys for auxiliary variables used when transforming inequality constraints. +- `NonlinearConstraint`: Base class for nonlinear constraints represented as constrained `NoiseModelFactor` objects. + +## Equality Constraints + +- `NonlinearEqualityConstraint`: Base class for constraints of the form `h(x) = 0`. +- `ExpressionEqualityConstraint`: Equality constraint from an expression and right-hand side. +- `ZeroCostConstraint`: Equality constraint that enforces zero residual on a cost factor. +- `NonlinearEqualityConstraints`: Container graph for equality constraints. + +## Inequality Constraints + +- `NonlinearInequalityConstraint`: Base class for constraints of the form `g(x) <= 0`. +- `ScalarExpressionInequalityConstraint`: Scalar expression-based inequality constraint. +- `NonlinearInequalityConstraints`: Container graph for inequality constraints. +- `InequalityPenaltyFunction`: Interface for ramp-like penalty mappings used with inequality constraints. + Derived classes: + - `RampFunction` + - `SmoothRampPoly2` + - `SmoothRampPoly3` + - `SoftPlusFunction` + +## Optimizers + +- `ConstrainedOptimizerParams`, `ConstrainedOptimizerState`, `ConstrainedOptimizer`: Shared base interfaces and iteration state for constrained solvers. +- `PenaltyOptimizerParams`, `PenaltyOptimizerState`, `PenaltyOptimizer`: Penalty method solver and its parameters/state. +- `AugmentedLagrangianParams`, `AugmentedLagrangianState`, `AugmentedLagrangianOptimizer`: Augmented Lagrangian solver and its parameters/state. + +## How the Pieces Fit Together + +For a new user, it helps to think in two phases: + +1. Build a constrained problem. +2. Run a constrained solver on that problem. + +Inequality constraints can use different smooth penalty shapes via +`InequalityPenaltyFunction` (ramp, smooth polynomial ramps, or softplus), +which controls behavior near the active constraint boundary. + +### 1) Build the Problem + +This stage is about modeling: you separate what you want to minimize +(objective terms) from what must hold (constraints), then combine them into a +single `ConstrainedOptProblem` object that the solvers can consume. + +```mermaid +flowchart TB + User["User-defined model"] + Costs["Objective terms
NonlinearFactorGraph"] + Eq["Equality constraints
NonlinearEqualityConstraint(s)
h(x)=0"] + Ineq["Inequality constraints
NonlinearInequalityConstraint(s)
g(x)<=0"] + Problem["ConstrainedOptProblem"] + + User --> Costs + User --> Eq + User --> Ineq + Costs --> Problem + Eq --> Problem + Ineq --> Problem +``` + +### 2) Solve the Problem + +This stage is algorithmic: pick a constrained solver, form iterative +unconstrained subproblems internally, and solve those subproblems with a +standard nonlinear optimizer until constraint violation and cost are reduced. + +```mermaid +flowchart TB + Problem["ConstrainedOptProblem"] + Choose{"Choose constrained solver"} + Penalty["PenaltyOptimizer"] + AL["AugmentedLagrangianOptimizer"] + PenFunc["InequalityPenaltyFunction
(ramp / smooth ramp / softplus)"] + Sub["Iterative unconstrained subproblems"] + LM["Nonlinear optimizer
(Levenberg-Marquardt by default)"] + Result["Optimized Values
+ cost and violation metrics"] + + Problem --> Choose + Choose --> Penalty + Choose --> AL + PenFunc --> Penalty + PenFunc --> AL + Penalty --> Sub + AL --> Sub + Sub --> LM + LM --> Result +``` From 8dcba19a6b396343055bda1723212e709e737840 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:33:39 -0500 Subject: [PATCH 179/266] docs phase 2: notebooks --- .../doc/AugmentedLagrangianOptimizer.ipynb | 119 +++++++++++++++++ .../doc/ConstrainedOptProblem.ipynb | 120 ++++++++++++++++++ .../doc/ConstrainedOptimizer.ipynb | 118 +++++++++++++++++ .../doc/InequalityPenaltyFunction.ipynb | 119 +++++++++++++++++ .../constrained/doc/NonlinearConstraint.ipynb | 119 +++++++++++++++++ .../doc/NonlinearEqualityConstraint.ipynb | 118 +++++++++++++++++ .../doc/NonlinearInequalityConstraint.ipynb | 119 +++++++++++++++++ gtsam/constrained/doc/PenaltyOptimizer.ipynb | 119 +++++++++++++++++ 8 files changed, 951 insertions(+) create mode 100644 gtsam/constrained/doc/AugmentedLagrangianOptimizer.ipynb create mode 100644 gtsam/constrained/doc/ConstrainedOptProblem.ipynb create mode 100644 gtsam/constrained/doc/ConstrainedOptimizer.ipynb create mode 100644 gtsam/constrained/doc/InequalityPenaltyFunction.ipynb create mode 100644 gtsam/constrained/doc/NonlinearConstraint.ipynb create mode 100644 gtsam/constrained/doc/NonlinearEqualityConstraint.ipynb create mode 100644 gtsam/constrained/doc/NonlinearInequalityConstraint.ipynb create mode 100644 gtsam/constrained/doc/PenaltyOptimizer.ipynb diff --git a/gtsam/constrained/doc/AugmentedLagrangianOptimizer.ipynb b/gtsam/constrained/doc/AugmentedLagrangianOptimizer.ipynb new file mode 100644 index 0000000000..8434c67b81 --- /dev/null +++ b/gtsam/constrained/doc/AugmentedLagrangianOptimizer.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# AugmentedLagrangianOptimizer\n", + "\n", + "## Overview\n", + "\n", + "AugmentedLagrangianOptimizer combines penalty terms with Lagrange multipliers to improve convergence and conditioning on constrained nonlinear problems." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- AugmentedLagrangianState extends penalty state with equality and inequality multipliers.\n", + "- augmentedLagrangianFunction builds the constrained objective used in each outer iteration.\n", + "- Multiplier updates are done by dual-ascent style steps.\n", + "- Penalty parameters are adapted based on violation reduction." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nAugmented Lagrangian combines multipliers and penalties:\n\n$$\n\\mathcal{L}_A(x,\\lambda)=\\frac{1}{2}\\|f(x)\\|^2\n-\\lambda_{eq}^T h(x)+\\frac{\\mu_{eq}}{2}\\|h(x)\\|^2\n-\\lambda_{ineq}^T g(x)+\\frac{\\mu_{ineq}}{2}\\|g(x)_-\\|^2.\n$$\n\nThe solver alternates between primal minimization (in $x$) and dual-ascent updates (in multipliers), with adaptive penalty updates.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `AugmentedLagrangianOptimizer(problem, initialValues, params)`\n- `optimize()`\n- `progress()`\n- `augmentedLagrangianFunction(state, epsilon)` (advanced inspection)\n- `AugmentedLagrangianParams`: dual step sizes and penalty-adaptation settings\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n\nusing namespace gtsam;\n\nauto params = std::make_shared();\nparams->verbose = true;\n\nAugmentedLagrangianOptimizer optimizer(problem, init_values, params);\nValues results = optimizer.optimize();\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/ConstrainedOptProblem.ipynb b/gtsam/constrained/doc/ConstrainedOptProblem.ipynb new file mode 100644 index 0000000000..8db30d5507 --- /dev/null +++ b/gtsam/constrained/doc/ConstrainedOptProblem.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# ConstrainedOptProblem\n", + "\n", + "## Overview\n", + "\n", + "ConstrainedOptProblem represents a nonlinear constrained optimization problem by combining objective terms, equality constraints, and inequality constraints into one object." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- Objective terms are stored as a NonlinearFactorGraph.\n", + "- Equality constraints enforce h(x)=0 through NonlinearEqualityConstraints.\n", + "- Inequality constraints enforce g(x)<=0 through NonlinearInequalityConstraints.\n", + "- The evaluate method reports cost and both constraint-violation norms.\n", + "- auxiliaryProblem can transform inequality constraints into an equivalent equality-constrained form with auxiliary variables." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nA constrained problem is modeled as:\n\n$$\n\\min_{x}\\; \\frac{1}{2}\\|f(x)\\|^2 \\quad\n\\text{s.t.}\\; h(x)=0,\\; g(x)\\le 0.\n$$\n\n`ConstrainedOptProblem` stores exactly these three components: costs $f$, equality constraints $h$, and inequality constraints $g$. Its `evaluate` method reports objective cost plus equality/inequality violation magnitudes.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `ConstrainedOptProblem(costs, eqConstraints, ineqConstraints)`\n- `EqConstrainedOptProblem(costs, eqConstraints)`\n- `costs()`, `eConstraints()`, `iConstraints()`\n- `evaluate(values)`\n- `dim()`\n- `auxiliaryProblem(values, generator)` for inequality-to-equality reformulation\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n#include \n\nusing namespace gtsam;\n\nSymbol x1('x', 1), x2('x', 2);\nDouble_ X1(x1), X2(x2);\n\nNonlinearFactorGraph costs;\nauto noise = noiseModel::Isotropic::Sigma(1, 1.0);\ncosts.addPrior(x1, 1.0, noise);\ncosts.addPrior(x2, 1.0, noise);\n\nNonlinearEqualityConstraints eq;\neq.emplace_shared>(\n X1 * X1 + X2 * X2, 1.0, Vector1(1.0));\n\nNonlinearInequalityConstraints ineq;\nineq.emplace_shared(\n 4 * X1 * X1 + 0.25 * X2 * X2 - Double_(1.0), 1.0);\n\nConstrainedOptProblem problem(costs, eq, ineq);\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/ConstrainedOptimizer.ipynb b/gtsam/constrained/doc/ConstrainedOptimizer.ipynb new file mode 100644 index 0000000000..9fc5978f07 --- /dev/null +++ b/gtsam/constrained/doc/ConstrainedOptimizer.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# ConstrainedOptimizer\n", + "\n", + "## Overview\n", + "\n", + "ConstrainedOptimizer is the abstract solver interface for constrained nonlinear optimization and tracks progress with dedicated parameter and state classes." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- ConstrainedOptimizerParams defines stopping tolerances, verbosity, and iteration limits.\n", + "- ConstrainedOptimizerState tracks iteration number, values, cost, and constraint violations.\n", + "- Derived optimizers implement optimize and typically solve a sequence of unconstrained subproblems." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\n`ConstrainedOptimizer` is the outer-loop interface for solving:\n\n$$\n\\min_x \\frac{1}{2}\\|f(x)\\|^2\\;\\text{s.t.}\\;h(x)=0,\\;g(x)\\le 0.\n$$\n\nConcrete solvers repeatedly solve unconstrained subproblems and stop when cost and violation metrics meet absolute/relative tolerances.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `optimize()` (implemented by derived solvers)\n- `ConstrainedOptimizerParams`: `maxIterations`, tolerance settings, verbosity\n- `ConstrainedOptimizerState`: `values`, `cost`, `eqConstraintViolation`, `ineqConstraintViolation`, `violation()`\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n#include \n\nusing namespace gtsam;\n\nauto params = std::make_shared();\nparams->maxIterations = 30;\nparams->absoluteViolationTolerance = 1e-6;\n\nPenaltyOptimizer solver(problem, initialValues, params);\nValues result = solver.optimize();\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/InequalityPenaltyFunction.ipynb b/gtsam/constrained/doc/InequalityPenaltyFunction.ipynb new file mode 100644 index 0000000000..778d8b47de --- /dev/null +++ b/gtsam/constrained/doc/InequalityPenaltyFunction.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# InequalityPenaltyFunction\n", + "\n", + "## Overview\n", + "\n", + "InequalityPenaltyFunction provides the scalar ramp-like mapping used to turn inequality violations into smooth optimization penalties." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- RampFunction implements the exact ramp max(x,0) behavior.\n", + "- SmoothRampPoly2 and SmoothRampPoly3 provide polynomial smooth approximations near the boundary.\n", + "- SoftPlusFunction provides a smooth log-exp approximation.\n", + "- These functions influence solver behavior near active inequality constraints." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nThese functions shape how inequality violations are penalized by replacing\n$r=\\max(g,0)$ with either the exact ramp or a smooth approximation:\n\n- `RampFunction`: exact, non-smooth at $0$.\n- `SmoothRampPoly2`, `SmoothRampPoly3`: polynomial smoothings near the boundary.\n- `SoftPlusFunction`: smooth log-exp form.\n\nThey are used inside inequality penalty factors to improve optimization behavior near active-set transitions.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `double operator()(double x, OptionalJacobian<1,1> H={})`\n- `function()` to retrieve a callable functor\n- `RampFunction::Ramp(x, H)`\n- `SmoothRampPoly2(epsilon)`, `SmoothRampPoly3(epsilon)`\n- `SoftPlusFunction(k)`\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n\nusing namespace gtsam;\n\nSmoothRampPoly2 phi(2.0);\nMatrix H;\ndouble r = phi(1.0, H); // smooth ramp value and derivative\n\nSoftPlusFunction softPlus(0.5);\ndouble rs = softPlus(2.0);\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/NonlinearConstraint.ipynb b/gtsam/constrained/doc/NonlinearConstraint.ipynb new file mode 100644 index 0000000000..55d9d503d8 --- /dev/null +++ b/gtsam/constrained/doc/NonlinearConstraint.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# NonlinearConstraint\n", + "\n", + "## Overview\n", + "\n", + "NonlinearConstraint is the abstract base for constrained factors in the constrained module, built on top of NoiseModelFactor with constrained noise models." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- violation computes the constraint violation magnitude from factor error.\n", + "- feasible checks whether a Values assignment satisfies a tolerance.\n", + "- penaltyFactor builds an L2 penalty factor for constrained optimization methods.\n", + "- Derived classes specialize behavior for equality and inequality constraints." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nA nonlinear constraint is treated as a residual map $c(x)$ (with constrained noise scaling). A common penalty used by constrained solvers is:\n\n$$\n\\phi(x;\\mu)=\\frac{\\mu}{2}\\|c(x)\\|^2.\n$$\n\nThis class provides the shared interface for evaluating constraint violation and creating a penalty factor used in outer-loop constrained optimization.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `violation(values)`\n- `feasible(values, tolerance)`\n- `penaltyFactor(mu)`\n- `sigmas()`\n- `unwhitenedHessian(values)` (advanced)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n#include \n\nusing namespace gtsam;\n\nSymbol x('x', 0);\nDouble_ X(x);\nExpressionEqualityConstraint c(X * X, 1.0, Vector1(0.1));\n\nValues values;\nvalues.insert(x, 0.8);\n\nbool ok = c.feasible(values, 1e-3);\ndouble v = c.violation(values);\nauto penalty = c.penaltyFactor(10.0);\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/NonlinearEqualityConstraint.ipynb b/gtsam/constrained/doc/NonlinearEqualityConstraint.ipynb new file mode 100644 index 0000000000..11092d3b9b --- /dev/null +++ b/gtsam/constrained/doc/NonlinearEqualityConstraint.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# NonlinearEqualityConstraint\n", + "\n", + "## Overview\n", + "\n", + "NonlinearEqualityConstraint defines constraints of the form h(x)=0 and serves as the base for expression-based and zero-cost equality constraints." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- ExpressionEqualityConstraint builds equality constraints from expressions and right-hand-side targets.\n", + "- ZeroCostConstraint turns an existing noise-model factor into an equality constraint by forcing zero residual.\n", + "- NonlinearEqualityConstraints provides a factor-graph container with utilities such as violation vectors and penalty-graph construction." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nEquality constraints enforce:\n\n$$\nh(x)=0.\n$$\n\nIn penalty form, this contributes:\n\n$$\n\\frac{\\mu}{2}\\|h(x)\\|^2.\n$$\n\n`ExpressionEqualityConstraint` models $h(x)=r$ directly from expressions, while `ZeroCostConstraint` turns an existing noise-model factor residual into an equality constraint.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `ExpressionEqualityConstraint(expression, rhs, sigmas)`\n- `ZeroCostConstraint(factor)`\n- `NonlinearEqualityConstraints::FromCostGraph(graph)`\n- `NonlinearEqualityConstraints::violationVector(values)`\n- `NonlinearEqualityConstraints::violationNorm(values)`\n- `NonlinearEqualityConstraints::penaltyGraph(mu)`\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n#include \n\nusing namespace gtsam;\n\nSymbol x1('x', 1), x2('x', 2);\nDouble_ X1(x1), X2(x2);\n\nNonlinearEqualityConstraints eq;\neq.emplace_shared>(\n X1 + X1 * X1 * X1 + X2 + X2 * X2, 0.0, Vector1(0.1));\n\nValues values;\nvalues.insert(x1, 1.0);\nvalues.insert(x2, 1.0);\n\ndouble norm = eq.violationNorm(values);\nNonlinearFactorGraph penalty = eq.penaltyGraph(4.0);\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/NonlinearInequalityConstraint.ipynb b/gtsam/constrained/doc/NonlinearInequalityConstraint.ipynb new file mode 100644 index 0000000000..03c4e72ede --- /dev/null +++ b/gtsam/constrained/doc/NonlinearInequalityConstraint.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# NonlinearInequalityConstraint\n", + "\n", + "## Overview\n", + "\n", + "NonlinearInequalityConstraint defines constraints of the form g(x)<=0 and supports direct violation computation plus conversion into penalty terms." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- ScalarExpressionInequalityConstraint is a scalar expression-based implementation.\n", + "- penaltyFactor and penaltyFactorCustom map inequality violations into optimization costs.\n", + "- createEqualityConstraint provides the corresponding boundary equality g(x)=0 when needed.\n", + "- NonlinearInequalityConstraints aggregates multiple inequality constraints and builds combined penalty graphs." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nInequality constraints enforce:\n\n$$\ng(x)\\le 0.\n$$\n\nThe default violation mapping uses a ramp on each component:\n\n$$\n\\text{ramp}(u)=\\max(u,0),\\quad\n\\phi(x;\\mu)=\\frac{\\mu}{2}\\|\\text{ramp}(g(x))\\|^2.\n$$\n\nThis keeps inactive constraints from contributing cost while penalizing violated constraints.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `ScalarExpressionInequalityConstraint::LeqZero(expression, sigma)`\n- `ScalarExpressionInequalityConstraint::GeqZero(expression, sigma)`\n- `feasible(values, tolerance)`\n- `active(values)`\n- `penaltyFactor(mu)` and `penaltyFactorCustom(func, mu)`\n- `createEqualityConstraint()`\n- `NonlinearInequalityConstraints::penaltyGraph(mu)`\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n#include \n\nusing namespace gtsam;\n\nSymbol x1('x', 1), x2('x', 2);\nDouble_ X1(x1), X2(x2);\nDouble_ g = X1 + X1 * X1 * X1 + X2 + X2 * X2;\n\nauto c = ScalarExpressionInequalityConstraint::LeqZero(g, 0.1);\n\nValues values;\nvalues.insert(x1, 1.0);\nvalues.insert(x2, 1.0);\n\nbool ok = c->feasible(values);\nbool isActive = c->active(values);\nauto p = c->penaltyFactor(9.0);\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/constrained/doc/PenaltyOptimizer.ipynb b/gtsam/constrained/doc/PenaltyOptimizer.ipynb new file mode 100644 index 0000000000..4762e36c8c --- /dev/null +++ b/gtsam/constrained/doc/PenaltyOptimizer.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview_cell", + "metadata": {}, + "source": [ + "# PenaltyOptimizer\n", + "\n", + "## Overview\n", + "\n", + "PenaltyOptimizer solves constrained problems by iteratively minimizing a merit function that adds weighted penalty terms for equality and inequality violations." + ] + }, + { + "cell_type": "markdown", + "id": "colab_button", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "copyright", + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colab_import", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "details_cell", + "metadata": {}, + "source": [ + "## Key Concepts\n", + "\n", + "- PenaltyOptimizerParams controls initial penalty weights and update rates.\n", + "- meritFunction builds the cost-plus-penalty nonlinear factor graph for each outer iteration.\n", + "- Each outer iteration solves an unconstrained nonlinear problem, then increases penalties as needed.\n", + "- progress stores per-iteration diagnostic information." + ] + }, + { + "cell_type": "markdown", + "id": "math_cell", + "metadata": {}, + "source": [ + "## Mathematical Formulation\n\nPenalty method solves a sequence of unconstrained problems:\n\n$$\nm(x)=\\frac{1}{2}\\|f(x)\\|^2 + \\frac{\\mu_{eq}}{2}\\|h(x)\\|^2\n+ \\frac{\\mu_{ineq}}{2}\\|\\text{ramp}(g(x))\\|^2.\n$$\n\nAs outer iterations proceed, penalty weights increase to enforce feasibility more strongly.\n" + ] + }, + { + "cell_type": "markdown", + "id": "api_cell", + "metadata": {}, + "source": [ + "## Key User API\n\n- `PenaltyOptimizer(problem, initialValues, params)`\n- `optimize()`\n- `progress()`\n- `PenaltyOptimizerParams`: `initialMuEq`, `initialMuIneq`, increase rates, `ineqConstraintPenaltyFunction`, `lmParams`\n" + ] + }, + { + "cell_type": "markdown", + "id": "cpp_cell", + "metadata": {}, + "source": [ + "## Concise C++ Example\n\n```cpp\n#include \n\nusing namespace gtsam;\n\nauto params = std::make_shared();\nparams->verbose = true;\nparams->initialMuEq = 1.0;\nparams->initialMuIneq = 1.0;\n\nPenaltyOptimizer optimizer(problem, init_values, params);\nValues results = optimizer.optimize();\n```\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f2f62546cdb1857fc85aff6269cccf080f5ccc98 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:43:18 -0500 Subject: [PATCH 180/266] phase 3: wire --- gtsam/constrained/Constrained.md | 36 ++++++++++++++++---------------- myst.yml | 3 +++ 2 files changed, 21 insertions(+), 18 deletions(-) diff --git a/gtsam/constrained/Constrained.md b/gtsam/constrained/Constrained.md index 135e8d6d49..c9e6414edd 100644 --- a/gtsam/constrained/Constrained.md +++ b/gtsam/constrained/Constrained.md @@ -5,34 +5,34 @@ It includes classes for representing constraints, building constrained problems, ## Core Problem Model -- `ConstrainedOptProblem`: Holds objective costs, equality constraints, and inequality constraints. -- `ConstrainedOptProblem::AuxiliaryKeyGenerator`: Generates keys for auxiliary variables used when transforming inequality constraints. -- `NonlinearConstraint`: Base class for nonlinear constraints represented as constrained `NoiseModelFactor` objects. +- [`ConstrainedOptProblem`](doc/ConstrainedOptProblem.ipynb): Holds objective costs, equality constraints, and inequality constraints. +- [`ConstrainedOptProblem::AuxiliaryKeyGenerator`](doc/ConstrainedOptProblem.ipynb): Generates keys for auxiliary variables used when transforming inequality constraints. +- [`NonlinearConstraint`](doc/NonlinearConstraint.ipynb): Base class for nonlinear constraints represented as constrained `NoiseModelFactor` objects. ## Equality Constraints -- `NonlinearEqualityConstraint`: Base class for constraints of the form `h(x) = 0`. -- `ExpressionEqualityConstraint`: Equality constraint from an expression and right-hand side. -- `ZeroCostConstraint`: Equality constraint that enforces zero residual on a cost factor. -- `NonlinearEqualityConstraints`: Container graph for equality constraints. +- [`NonlinearEqualityConstraint`](doc/NonlinearEqualityConstraint.ipynb): Base class for constraints of the form `h(x) = 0`. +- [`ExpressionEqualityConstraint`](doc/NonlinearEqualityConstraint.ipynb): Equality constraint from an expression and right-hand side. +- [`ZeroCostConstraint`](doc/NonlinearEqualityConstraint.ipynb): Equality constraint that enforces zero residual on a cost factor. +- [`NonlinearEqualityConstraints`](doc/NonlinearEqualityConstraint.ipynb): Container graph for equality constraints. ## Inequality Constraints -- `NonlinearInequalityConstraint`: Base class for constraints of the form `g(x) <= 0`. -- `ScalarExpressionInequalityConstraint`: Scalar expression-based inequality constraint. -- `NonlinearInequalityConstraints`: Container graph for inequality constraints. -- `InequalityPenaltyFunction`: Interface for ramp-like penalty mappings used with inequality constraints. +- [`NonlinearInequalityConstraint`](doc/NonlinearInequalityConstraint.ipynb): Base class for constraints of the form `g(x) <= 0`. +- [`ScalarExpressionInequalityConstraint`](doc/NonlinearInequalityConstraint.ipynb): Scalar expression-based inequality constraint. +- [`NonlinearInequalityConstraints`](doc/NonlinearInequalityConstraint.ipynb): Container graph for inequality constraints. +- [`InequalityPenaltyFunction`](doc/InequalityPenaltyFunction.ipynb): Interface for ramp-like penalty mappings used with inequality constraints. Derived classes: - - `RampFunction` - - `SmoothRampPoly2` - - `SmoothRampPoly3` - - `SoftPlusFunction` + - [`RampFunction`](doc/InequalityPenaltyFunction.ipynb) + - [`SmoothRampPoly2`](doc/InequalityPenaltyFunction.ipynb) + - [`SmoothRampPoly3`](doc/InequalityPenaltyFunction.ipynb) + - [`SoftPlusFunction`](doc/InequalityPenaltyFunction.ipynb) ## Optimizers -- `ConstrainedOptimizerParams`, `ConstrainedOptimizerState`, `ConstrainedOptimizer`: Shared base interfaces and iteration state for constrained solvers. -- `PenaltyOptimizerParams`, `PenaltyOptimizerState`, `PenaltyOptimizer`: Penalty method solver and its parameters/state. -- `AugmentedLagrangianParams`, `AugmentedLagrangianState`, `AugmentedLagrangianOptimizer`: Augmented Lagrangian solver and its parameters/state. +- [`ConstrainedOptimizerParams`](doc/ConstrainedOptimizer.ipynb), [`ConstrainedOptimizerState`](doc/ConstrainedOptimizer.ipynb), [`ConstrainedOptimizer`](doc/ConstrainedOptimizer.ipynb): Shared base interfaces and iteration state for constrained solvers. +- [`PenaltyOptimizerParams`](doc/PenaltyOptimizer.ipynb), [`PenaltyOptimizerState`](doc/PenaltyOptimizer.ipynb), [`PenaltyOptimizer`](doc/PenaltyOptimizer.ipynb): Penalty method solver and its parameters/state. +- [`AugmentedLagrangianParams`](doc/AugmentedLagrangianOptimizer.ipynb), [`AugmentedLagrangianState`](doc/AugmentedLagrangianOptimizer.ipynb), [`AugmentedLagrangianOptimizer`](doc/AugmentedLagrangianOptimizer.ipynb): Augmented Lagrangian solver and its parameters/state. ## How the Pieces Fit Together diff --git a/myst.yml b/myst.yml index a49cd607dc..67d1085746 100644 --- a/myst.yml +++ b/myst.yml @@ -29,6 +29,9 @@ project: - file: ./gtsam/nonlinear/nonlinear.md children: - pattern: ./gtsam/nonlinear/doc/* + - file: ./gtsam/constrained/constrained.md + children: + - pattern: ./gtsam/constrained/doc/* - file: ./gtsam/symbolic/symbolic.md children: - pattern: ./gtsam/symbolic/doc/* From 4578f013a6a64c0d9cf63179f5177754054db4ab Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 13:48:39 -0500 Subject: [PATCH 181/266] standardize dividers --- .../NonlinearEqualityConstraint-inl.h | 9 +-- .../NonlinearEqualityConstraint.cpp | 24 +++----- .../NonlinearInequalityConstraint.cpp | 57 +++++++------------ .../testAugmentedLagrangianOptimizer.cpp | 27 +++------ .../tests/testInequalityPenaltyFunction.cpp | 12 ++-- .../tests/testNonlinearEqualityConstraint.cpp | 5 ++ .../testNonlinearInequalityConstraint.cpp | 2 + .../tests/testPenaltyOptimizer.cpp | 1 + 8 files changed, 51 insertions(+), 86 deletions(-) diff --git a/gtsam/constrained/NonlinearEqualityConstraint-inl.h b/gtsam/constrained/NonlinearEqualityConstraint-inl.h index 57ec0ed9ec..1f5bf71ec6 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint-inl.h +++ b/gtsam/constrained/NonlinearEqualityConstraint-inl.h @@ -21,8 +21,7 @@ namespace gtsam { -/* ********************************************************************************************* - */ +/* ************************************************************************* */ template ExpressionEqualityConstraint::ExpressionEqualityConstraint( const Expression& expression, const T& rhs, const Vector& sigmas) @@ -31,8 +30,7 @@ ExpressionEqualityConstraint::ExpressionEqualityConstraint( rhs_(rhs), dims_(expression.keysAndDims().second) {} -/* ********************************************************************************************* - */ +/* ************************************************************************* */ template Vector ExpressionEqualityConstraint::unwhitenedError( const Values& x, OptionalMatrixVecType H) const { @@ -46,8 +44,7 @@ Vector ExpressionEqualityConstraint::unwhitenedError( } } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ template NoiseModelFactor::shared_ptr ExpressionEqualityConstraint::penaltyFactor( const double mu) const { diff --git a/gtsam/constrained/NonlinearEqualityConstraint.cpp b/gtsam/constrained/NonlinearEqualityConstraint.cpp index 45b23e1561..af56bea66d 100644 --- a/gtsam/constrained/NonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/NonlinearEqualityConstraint.cpp @@ -20,29 +20,25 @@ namespace gtsam { -/* ********************************************************************************************* - */ +/* ************************************************************************* */ ZeroCostConstraint::ZeroCostConstraint( const NoiseModelFactor::shared_ptr& factor) : Base(constrainedNoise(factor->noiseModel()->sigmas()), factor->keys()), factor_(factor) {} -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector ZeroCostConstraint::unwhitenedError(const Values& x, OptionalMatrixVecType H) const { return factor_->unwhitenedError(x, H); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr ZeroCostConstraint::penaltyFactor( const double mu) const { return factor_->cloneWithNewNoiseModel(penaltyNoise(mu)); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearEqualityConstraints NonlinearEqualityConstraints::FromCostGraph( const NonlinearFactorGraph& graph) { NonlinearEqualityConstraints constraints; @@ -53,8 +49,7 @@ NonlinearEqualityConstraints NonlinearEqualityConstraints::FromCostGraph( return constraints; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ size_t NonlinearEqualityConstraints::dim() const { size_t dimension = 0; for (const auto& constraint : *this) { @@ -63,8 +58,7 @@ size_t NonlinearEqualityConstraints::dim() const { return dimension; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector NonlinearEqualityConstraints::violationVector(const Values& values, bool whiten) const { Vector violation(dim()); @@ -79,14 +73,12 @@ Vector NonlinearEqualityConstraints::violationVector(const Values& values, return violation; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double NonlinearEqualityConstraints::violationNorm(const Values& values) const { return violationVector(values, true).norm(); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearFactorGraph NonlinearEqualityConstraints::penaltyGraph( const double mu) const { NonlinearFactorGraph graph; diff --git a/gtsam/constrained/NonlinearInequalityConstraint.cpp b/gtsam/constrained/NonlinearInequalityConstraint.cpp index 670b1433e9..4069c08e45 100644 --- a/gtsam/constrained/NonlinearInequalityConstraint.cpp +++ b/gtsam/constrained/NonlinearInequalityConstraint.cpp @@ -21,14 +21,12 @@ namespace gtsam { -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector NonlinearInequalityConstraint::whitenedExpr(const Values& x) const { return noiseModel()->whiten(unwhitenedExpr(x)); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector NonlinearInequalityConstraint::unwhitenedError( const Values& x, OptionalMatrixVecType H) const { Vector error = unwhitenedExpr(x, H); @@ -45,45 +43,39 @@ Vector NonlinearInequalityConstraint::unwhitenedError( return error; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ bool NonlinearInequalityConstraint::active(const Values& x) const { return (unwhitenedExpr(x).array() >= 0).any(); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr NonlinearInequalityConstraint::penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { /// Default behavior, this function should be overriden. return penaltyFactor(mu); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearEqualityConstraint::shared_ptr NonlinearInequalityConstraint::createEqualityConstraint() const { /// Default behavior, this function should be overriden. return nullptr; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr NonlinearInequalityConstraint::penaltyFactorEquality(const double mu) const { return createEqualityConstraint()->penaltyFactor(mu); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ ScalarExpressionInequalityConstraint::ScalarExpressionInequalityConstraint( const Double_& expression, const double& sigma) : Base(constrainedNoise(Vector1(sigma)), expression.keysAndDims().first), expression_(expression), dims_(expression.keysAndDims().second) {} -/* ********************************************************************************************* - */ +/* ************************************************************************* */ ScalarExpressionInequalityConstraint::shared_ptr ScalarExpressionInequalityConstraint::GeqZero(const Double_& expression, const double& sigma) { @@ -92,8 +84,7 @@ ScalarExpressionInequalityConstraint::GeqZero(const Double_& expression, sigma); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ ScalarExpressionInequalityConstraint::shared_ptr ScalarExpressionInequalityConstraint::LeqZero(const Double_& expression, const double& sigma) { @@ -101,8 +92,7 @@ ScalarExpressionInequalityConstraint::LeqZero(const Double_& expression, sigma); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector ScalarExpressionInequalityConstraint::unwhitenedExpr( const Values& x, OptionalMatrixVecType H) const { // Copy-paste from ExpressionFactor. @@ -113,16 +103,14 @@ Vector ScalarExpressionInequalityConstraint::unwhitenedExpr( } } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearEqualityConstraint::shared_ptr ScalarExpressionInequalityConstraint::createEqualityConstraint() const { return std::make_shared>( expression_, 0.0, noiseModel()->sigmas()); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactor(const double mu) const { Double_ penalty_expression(RampFunction::Ramp, expression_); @@ -130,8 +118,7 @@ ScalarExpressionInequalityConstraint::penaltyFactor(const double mu) const { penalty_expression); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactorCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { @@ -144,8 +131,7 @@ ScalarExpressionInequalityConstraint::penaltyFactorCustom( error); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NoiseModelFactor::shared_ptr ScalarExpressionInequalityConstraint::penaltyFactorEquality( const double mu) const { @@ -153,8 +139,7 @@ ScalarExpressionInequalityConstraint::penaltyFactorEquality( expression_); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ size_t NonlinearInequalityConstraints::dim() const { size_t dimension = 0; for (const auto& constraint : *this) { @@ -163,8 +148,7 @@ size_t NonlinearInequalityConstraints::dim() const { return dimension; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ Vector NonlinearInequalityConstraints::violationVector(const Values& values, bool whiten) const { Vector violation(dim()); @@ -179,15 +163,13 @@ Vector NonlinearInequalityConstraints::violationVector(const Values& values, return violation; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double NonlinearInequalityConstraints::violationNorm( const Values& values) const { return violationVector(values, true).norm(); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraph( const double mu) const { NonlinearFactorGraph graph; @@ -197,8 +179,7 @@ NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraph( return graph; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ NonlinearFactorGraph NonlinearInequalityConstraints::penaltyGraphCustom( InequalityPenaltyFunction::shared_ptr func, const double mu) const { NonlinearFactorGraph graph; diff --git a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp index b3c3c96623..3b248ff067 100644 --- a/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp +++ b/gtsam/constrained/tests/testAugmentedLagrangianOptimizer.cpp @@ -26,8 +26,7 @@ using namespace gtsam; -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double EvaluateLagrangeTerm(const std::vector& lambdas, const NonlinearEqualityConstraints& constraints, const Values& values) { @@ -39,8 +38,7 @@ double EvaluateLagrangeTerm(const std::vector& lambdas, return s; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double EvaluateLagrangeTerm(const std::vector& lambdas, const NonlinearInequalityConstraints& constraints, const Values& values) { @@ -52,8 +50,7 @@ double EvaluateLagrangeTerm(const std::vector& lambdas, return s; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double ComputeBias(const std::vector& lambdas, double mu) { double norm_squared = 0; for (const auto& lambda : lambdas) { @@ -62,8 +59,7 @@ double ComputeBias(const std::vector& lambdas, double mu) { return 0.5 / mu * norm_squared; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ double ComputeBias(const std::vector& lambdas, double epsilon) { double norm_squared = 0; for (const auto& lambda : lambdas) { @@ -72,8 +68,7 @@ double ComputeBias(const std::vector& lambdas, double epsilon) { return 0.5 / epsilon * norm_squared; } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(AugmentedLagrangian, constrained_example1) { using namespace constrained_example1; @@ -99,8 +94,7 @@ TEST(AugmentedLagrangian, constrained_example1) { EXPECT_DOUBLES_EQUAL(expected_error, augmentedLagrangian.error(values), 1e-6); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(AugmentedLagrangian, constrained_example2) { using namespace constrained_example2; @@ -136,8 +130,7 @@ TEST(AugmentedLagrangian, constrained_example2) { EXPECT_DOUBLES_EQUAL(expected_error, augmentedLagrangian.error(values), 1e-6); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(AugmentedLagrangianOptimizer, constrained_example1) { using namespace constrained_example1; @@ -150,8 +143,7 @@ TEST(AugmentedLagrangianOptimizer, constrained_example1) { EXPECT(assert_equal(optimal_values, results, 1e-4)); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(AugmentedLagrangianOptimizer, constrained_example2) { using namespace constrained_example2; @@ -211,8 +203,7 @@ TEST(AugmentedLagrangian, VectorBiasUsesElementwiseSigmas) { EXPECT(assert_equal(expected, bias_from_factor, 1e-12)); } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); diff --git a/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp b/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp index f8b5d33193..c45fda160d 100644 --- a/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp +++ b/gtsam/constrained/tests/testInequalityPenaltyFunction.cpp @@ -24,8 +24,7 @@ using namespace gtsam; -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(RampFunction, error_and_jacobian) { /// Helper function for numerical Jacobian computation. auto ramp_helper = [&](const double& x) { return RampFunction::Ramp(x); }; @@ -52,8 +51,7 @@ TEST(RampFunction, error_and_jacobian) { } } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(RampFunctionPoly2, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SmoothRampPoly2 p_ramp(2.0); @@ -78,8 +76,7 @@ TEST(RampFunctionPoly2, error_and_jacobian) { } } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(RampFunctionPoly3, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SmoothRampPoly3 p_ramp(2.0); @@ -104,8 +101,7 @@ TEST(RampFunctionPoly3, error_and_jacobian) { } } -/* ********************************************************************************************* - */ +/* ************************************************************************* */ TEST(SoftPlusFunction, error_and_jacobian) { /// Helper function for numerical Jacobian computation. SoftPlusFunction soft_plus(0.5); diff --git a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp index 78f8fe1530..8cc4ca75b8 100644 --- a/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearEqualityConstraint.cpp @@ -38,6 +38,7 @@ using constrained_example::pow; using constrained_example::x1, constrained_example::x2; using constrained_example::x1_key, constrained_example::x2_key; +/* ************************************************************************* */ // Test methods of DoubleExpressionEquality. TEST(ExpressionEqualityConstraint, double) { // create constraint from double expression @@ -101,6 +102,7 @@ TEST(ExpressionEqualityConstraint, double) { EXPECT_CORRECT_FACTOR_JACOBIANS(*merit_factor, values2, 1e-7, 1e-5); } +/* ************************************************************************* */ // Test methods of VectorExpressionEquality. TEST(ExpressionEqualityConstraint, Vector2) { // g(v1, v2) = v1 + v2, our own example. @@ -167,6 +169,7 @@ TEST(ExpressionEqualityConstraint, Vector2) { EXPECT_CORRECT_FACTOR_JACOBIANS(*merit_factor, values2, 1e-7, 1e-5); } +/* ************************************************************************* */ // Test methods of FactorZeroErrorConstraint. TEST(ZeroCostConstraint, BetweenFactor) { Key x1_key = 1; @@ -234,6 +237,7 @@ TEST(ZeroCostConstraint, BetweenFactor) { EXPECT_CORRECT_FACTOR_JACOBIANS(*merit_factor, values2, 1e-7, 1e-5); } +/* ************************************************************************* */ TEST(NonlinearEqualityConstraints, Container) { NonlinearEqualityConstraints constraints; @@ -320,6 +324,7 @@ TEST(GtsamConstrained, VectorEqualityViolationVectorMixedDims) { } } +/* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); diff --git a/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp b/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp index 2d8b4f7b99..6a626732a4 100644 --- a/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp +++ b/gtsam/constrained/tests/testNonlinearInequalityConstraint.cpp @@ -30,6 +30,7 @@ using constrained_example::pow; using constrained_example::x1, constrained_example::x2; using constrained_example::x1_key, constrained_example::x2_key; +/* ************************************************************************* */ // Test methods of DoubleExpressionEquality. TEST(NonlinearInequalityConstraint, ScalarExpressionInequalityConstraint) { // create constraint from double expression @@ -111,6 +112,7 @@ TEST(NonlinearInequalityConstraint, ScalarExpressionInequalityConstraint) { EXPECT(assert_equal(3200.0, constraint_eq2->error(values3))); } +/* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); diff --git a/gtsam/constrained/tests/testPenaltyOptimizer.cpp b/gtsam/constrained/tests/testPenaltyOptimizer.cpp index 318946a192..058e8327c7 100644 --- a/gtsam/constrained/tests/testPenaltyOptimizer.cpp +++ b/gtsam/constrained/tests/testPenaltyOptimizer.cpp @@ -67,6 +67,7 @@ TEST(PenaltyOptimizer, constrained_example2) { } } +/* ************************************************************************* */ int main() { TestResult tr; return TestRegistry::runAllTests(tr); From fff8eb32423885c011dd5649bc711abf62f6b781 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 19:26:15 -0500 Subject: [PATCH 182/266] dummy --- gtsam/constrained/{Constrained.md => constrained-new.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename gtsam/constrained/{Constrained.md => constrained-new.md} (100%) diff --git a/gtsam/constrained/Constrained.md b/gtsam/constrained/constrained-new.md similarity index 100% rename from gtsam/constrained/Constrained.md rename to gtsam/constrained/constrained-new.md From ff302b7dd92d1b5c1951c6fb46b6c7f1c8d7ccf8 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 19:33:42 -0500 Subject: [PATCH 183/266] rename --- gtsam/constrained/{constrained-new.md => constrained.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename gtsam/constrained/{constrained-new.md => constrained.md} (100%) diff --git a/gtsam/constrained/constrained-new.md b/gtsam/constrained/constrained.md similarity index 100% rename from gtsam/constrained/constrained-new.md rename to gtsam/constrained/constrained.md From 3466c127a6e75dfc69195a8aa5d0906b96c1f755 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sat, 7 Feb 2026 19:34:32 -0500 Subject: [PATCH 184/266] Don't use {doc} --- doc/GTSAM-Concepts.md | 12 ++++++------ gtsam/nonlinear/doc/ConcentratedGaussian.ipynb | 4 ++-- gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb | 8 ++++---- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/doc/GTSAM-Concepts.md b/doc/GTSAM-Concepts.md index bfc6d88ce8..4541d616df 100644 --- a/doc/GTSAM-Concepts.md +++ b/doc/GTSAM-Concepts.md @@ -28,7 +28,7 @@ The core operations for a manifold are: These operations must be inverses of each other: `p.retract(p.localCoordinates(q))` should be equal to `q`. -For a detailed guide on creating a new `Manifold` type, see {doc}`../gtsam/base/doc/Manifold.md`. +For a detailed guide on creating a new `Manifold` type, see [Manifold](../gtsam/base/doc/Manifold.md). ## Group @@ -38,7 +38,7 @@ Key operations are `compose`, `inverse`, and `between`. GTSAM distinguishes betw * **Multiplicative Groups**: Use `operator*` (e.g., rotations, poses). * **Additive Groups**: Use `operator+` (e.g., vectors). -For a detailed guide on creating a `Group` type, see {doc}`../gtsam/base/doc/Group.md`. +For a detailed guide on creating a `Group` type, see [Group](../gtsam/base/doc/Group.md). ## Lie Group @@ -48,14 +48,14 @@ Lie groups have a special identity element, which allows for defining global `Ex Most Lie groups in GTSAM are also **Matrix Lie Groups**, which have an underlying matrix representation. These require additional Lie algebra operations like `Hat` and `Vee`. -* For a guide on creating a `LieGroup`, see {doc}`../gtsam/base/doc/LieGroup.md`. -* For matrix Lie groups, also see {doc}`../gtsam/base/doc/MatrixLieGroup.md`. +* For a guide on creating a `LieGroup`, see [LieGroup](../gtsam/base/doc/LieGroup.md). +* For matrix Lie groups, also see [MatrixLieGroup](../gtsam/base/doc/MatrixLieGroup.md). ## Vector Space A `VectorSpace` is a specialized `AdditiveGroup` that also supports scalar multiplication, a dot product, and the calculation of a norm. This concept should be satisfied by types that behave like mathematical vectors. In GTSAM, vector spaces are the foundation for tangent spaces on manifolds. -For a detailed guide, see {doc}`../gtsam/base/doc/VectorSpace.md`. +For a detailed guide, see [VectorSpace](../gtsam/base/doc/VectorSpace.md). ## Overview @@ -141,4 +141,4 @@ When a Lie group acts on a space, we have two derivatives to care about: * `gtsam::manifold::traits::act(g,p,Hg,Hp)`, if the space acted upon is a continuous differentiable manifold. -An example is a *similarity transform* in 3D, which can act on 3D space. The derivative in `p`, `Hp`, depends on the group element `g`. The derivative in `g`, `Hg`, is in general more complex. \ No newline at end of file +An example is a *similarity transform* in 3D, which can act on 3D space. The derivative in `p`, `Hp`, depends on the group element `g`. The derivative in `g`, `Hg`, is in general more complex. diff --git a/gtsam/nonlinear/doc/ConcentratedGaussian.ipynb b/gtsam/nonlinear/doc/ConcentratedGaussian.ipynb index b77885f431..e852d562e8 100644 --- a/gtsam/nonlinear/doc/ConcentratedGaussian.ipynb +++ b/gtsam/nonlinear/doc/ConcentratedGaussian.ipynb @@ -13,7 +13,7 @@ "2. EKF users receiving `ConcentratedGaussian` outputs.\n", "3. Advanced users leveraging transport, reset, and fusion of Left Extended Concentrated Gaussians (L-ECGs).\n", "\n", - "Related notebooks: {doc}`PriorFactor `, {doc}`ExtendedPriorFactor `." + "Related notebooks: [PriorFactor](PriorFactor.ipynb), [ExtendedPriorFactor](ExtendedPriorFactor.ipynb)." ] }, { @@ -52802,4 +52802,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb b/gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb index 4a47e96c6b..97c0f27652 100644 --- a/gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb +++ b/gtsam/nonlinear/doc/ExtendedPriorFactor.ipynb @@ -9,7 +9,7 @@ "\n", "## Purpose and Audience\n", "\n", - "`ExtendedPriorFactor` is the generalized building block that underlies {doc}`PriorFactor ` and other soft anchoring mechanisms. It lets you express an (optionally shifted) Gaussian (or robust) likelihood in the tangent space of an arbitrary manifold value type. This notebook targets advanced GTSAM users designing custom factors, experimenting with non-zero tangent-space means, or working with non-traditional manifold types." + "`ExtendedPriorFactor` is the generalized building block that underlies [PriorFactor](PriorFactor.ipynb) and other soft anchoring mechanisms. It lets you express an (optionally shifted) Gaussian (or robust) likelihood in the tangent space of an arbitrary manifold value type. This notebook targets advanced GTSAM users designing custom factors, experimenting with non-zero tangent-space means, or working with non-traditional manifold types." ] }, { @@ -241,7 +241,7 @@ "source": [ "## When Not to Use\n", "\n", - "- If you simply need a zero-mean soft prior: prefer {doc}`PriorFactor `.\n", + "- If you simply need a zero-mean soft prior: prefer [PriorFactor](PriorFactor.ipynb).\n", "- If you require a hard equality constraint: consider `NonlinearEquality`.\n", "- If the residual should depend on another variable or measurement, design a custom factor instead of embedding complexity in `Local`." ] @@ -253,7 +253,7 @@ "source": [ "## See Also\n", "\n", - "- {doc}`PriorFactor `\n", + "- [PriorFactor](PriorFactor.ipynb)\n", "- Other manifold priors: `BetweenFactor`, `NonlinearEquality`, and robust variants\n", "- Underlying noise models: `noiseModel::Isotropic`, `noiseModel::Diagonal`, robust creators" ] @@ -280,4 +280,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From bde2850912cb9f85452b276164e33867959db268 Mon Sep 17 00:00:00 2001 From: Fan Jiang Date: Sun, 8 Feb 2026 13:00:26 -0800 Subject: [PATCH 185/266] Update SFMBAL benchmark script --- .../time-sfmbal-benchmark-runner.yml | 222 ++++++++++++++---- .github/workflows/time-sfmbal-benchmark.yml | 69 ++++-- 2 files changed, 226 insertions(+), 65 deletions(-) diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml index 190f191c92..4ddcc31c52 100644 --- a/.github/workflows/time-sfmbal-benchmark-runner.yml +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -47,34 +47,35 @@ jobs: id: dataset_cache_restore uses: actions/cache/restore@v4 with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Download BAL dataset archive if: steps.dataset_cache_restore.outputs.cache-hit != 'true' run: | curl --fail --location --show-error \ - --output examples/Data/problem-16-22106-pre.txt.bz2 \ - https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + --output examples/Data/problem-135-90642-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-135-90642-pre.txt.bz2 - name: Save BAL dataset archive cache if: steps.dataset_cache_restore.outputs.cache-hit != 'true' uses: actions/cache/save@v4 continue-on-error: true with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Extract BAL dataset run: | - bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + bzip2 -dc examples/Data/problem-135-90642-pre.txt.bz2 > examples/Data/dubrovnik-135-90642-pre.txt - name: Install dependencies run: | sudo apt-get update - sudo apt-get install -y ninja-build + sudo apt-get install -y ninja-build libtbb-dev - - name: Configure + # ── TBB=ON ── + - name: Configure (TBB=ON) run: | cmake -S . -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ @@ -84,28 +85,67 @@ jobs: -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ -DGTSAM_BUILD_TIMING_ALWAYS=ON \ -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=ON \ -DGTSAM_USE_BOOST_FEATURES=OFF \ -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF - - name: Build timeSFMBAL + - name: Build timeSFMBAL (TBB=ON) run: cmake --build build --target timeSFMBAL -j2 - - name: Run benchmark - run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + - name: Run benchmark (TBB=ON) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbON.json examples/Data/dubrovnik-135-90642-pre.txt - - name: Prepare cache payload + - name: Prepare cache payload (TBB=ON) shell: bash run: | set -euo pipefail mkdir -p benchmark-cache - cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + cp benchmark-results-tbbON.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json" - - name: Save benchmark cache + - name: Save benchmark cache (TBB=ON) uses: actions/cache/save@v4 continue-on-error: true with: - path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json - key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }} + + # ── TBB=OFF ── + - name: Clean build directory + run: rm -rf build + + - name: Configure (TBB=OFF) + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=OFF \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL (TBB=OFF) + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark (TBB=OFF) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbOFF.json examples/Data/dubrovnik-135-90642-pre.txt + + - name: Prepare cache payload (TBB=OFF) + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results-tbbOFF.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache (TBB=OFF) + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }} benchmark_linux_arm64: if: inputs.runner_id == 'linux-arm64' @@ -121,34 +161,35 @@ jobs: id: dataset_cache_restore uses: actions/cache/restore@v4 with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Download BAL dataset archive if: steps.dataset_cache_restore.outputs.cache-hit != 'true' run: | curl --fail --location --show-error \ - --output examples/Data/problem-16-22106-pre.txt.bz2 \ - https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + --output examples/Data/problem-135-90642-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-135-90642-pre.txt.bz2 - name: Save BAL dataset archive cache if: steps.dataset_cache_restore.outputs.cache-hit != 'true' uses: actions/cache/save@v4 continue-on-error: true with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Extract BAL dataset run: | - bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + bzip2 -dc examples/Data/problem-135-90642-pre.txt.bz2 > examples/Data/dubrovnik-135-90642-pre.txt - name: Install dependencies run: | sudo apt-get update - sudo apt-get install -y ninja-build + sudo apt-get install -y ninja-build libtbb-dev - - name: Configure + # ── TBB=ON ── + - name: Configure (TBB=ON) run: | cmake -S . -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ @@ -158,28 +199,67 @@ jobs: -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ -DGTSAM_BUILD_TIMING_ALWAYS=ON \ -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=ON \ -DGTSAM_USE_BOOST_FEATURES=OFF \ -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF - - name: Build timeSFMBAL + - name: Build timeSFMBAL (TBB=ON) run: cmake --build build --target timeSFMBAL -j2 - - name: Run benchmark - run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + - name: Run benchmark (TBB=ON) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbON.json examples/Data/dubrovnik-135-90642-pre.txt - - name: Prepare cache payload + - name: Prepare cache payload (TBB=ON) shell: bash run: | set -euo pipefail mkdir -p benchmark-cache - cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + cp benchmark-results-tbbON.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json" - - name: Save benchmark cache + - name: Save benchmark cache (TBB=ON) uses: actions/cache/save@v4 continue-on-error: true with: - path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json - key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }} + + # ── TBB=OFF ── + - name: Clean build directory + run: rm -rf build + + - name: Configure (TBB=OFF) + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=OFF \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL (TBB=OFF) + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark (TBB=OFF) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbOFF.json examples/Data/dubrovnik-135-90642-pre.txt + + - name: Prepare cache payload (TBB=OFF) + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results-tbbOFF.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache (TBB=OFF) + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }} benchmark_macos_arm64: if: inputs.runner_id == 'macos-arm64' @@ -195,32 +275,71 @@ jobs: id: dataset_cache_restore uses: actions/cache/restore@v4 with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Download BAL dataset archive if: steps.dataset_cache_restore.outputs.cache-hit != 'true' run: | curl --fail --location --show-error \ - --output examples/Data/problem-16-22106-pre.txt.bz2 \ - https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-16-22106-pre.txt.bz2 + --output examples/Data/problem-135-90642-pre.txt.bz2 \ + https://grail.cs.washington.edu/projects/bal/data/dubrovnik/problem-135-90642-pre.txt.bz2 - name: Save BAL dataset archive cache if: steps.dataset_cache_restore.outputs.cache-hit != 'true' uses: actions/cache/save@v4 continue-on-error: true with: - path: examples/Data/problem-16-22106-pre.txt.bz2 - key: bal-problem-16-22106-pre-txt-bz2-v1 + path: examples/Data/problem-135-90642-pre.txt.bz2 + key: bal-problem-135-90642-pre-txt-bz2-v1 - name: Extract BAL dataset run: | - bzip2 -dc examples/Data/problem-16-22106-pre.txt.bz2 > examples/Data/dubrovnik-16-22106-pre.txt + bzip2 -dc examples/Data/problem-135-90642-pre.txt.bz2 > examples/Data/dubrovnik-135-90642-pre.txt - name: Install dependencies - run: brew install ninja || true + run: brew install ninja tbb || true + + # ── TBB=ON ── + - name: Configure (TBB=ON) + run: | + cmake -S . -B build -GNinja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CXX_FLAGS="-w" \ + -DGTSAM_BUILD_TESTS=OFF \ + -DGTSAM_BUILD_UNSTABLE=ON \ + -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ + -DGTSAM_BUILD_TIMING_ALWAYS=ON \ + -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=ON \ + -DGTSAM_USE_BOOST_FEATURES=OFF \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF + + - name: Build timeSFMBAL (TBB=ON) + run: cmake --build build --target timeSFMBAL -j2 + + - name: Run benchmark (TBB=ON) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbON.json examples/Data/dubrovnik-135-90642-pre.txt + + - name: Prepare cache payload (TBB=ON) + shell: bash + run: | + set -euo pipefail + mkdir -p benchmark-cache + cp benchmark-results-tbbON.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json" + + - name: Save benchmark cache (TBB=ON) + uses: actions/cache/save@v4 + continue-on-error: true + with: + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbON-${{ inputs.target_sha }} + + # ── TBB=OFF ── + - name: Clean build directory + run: rm -rf build - - name: Configure + - name: Configure (TBB=OFF) run: | cmake -S . -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ @@ -230,25 +349,26 @@ jobs: -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ -DGTSAM_BUILD_TIMING_ALWAYS=ON \ -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \ + -DGTSAM_WITH_TBB=OFF \ -DGTSAM_USE_BOOST_FEATURES=OFF \ -DGTSAM_ENABLE_BOOST_SERIALIZATION=OFF - - name: Build timeSFMBAL + - name: Build timeSFMBAL (TBB=OFF) run: cmake --build build --target timeSFMBAL -j2 - - name: Run benchmark - run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results.json + - name: Run benchmark (TBB=OFF) + run: ./build/timing/timeSFMBAL --benchmark-action-json benchmark-results-tbbOFF.json examples/Data/dubrovnik-135-90642-pre.txt - - name: Prepare cache payload + - name: Prepare cache payload (TBB=OFF) shell: bash run: | set -euo pipefail mkdir -p benchmark-cache - cp benchmark-results.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json" + cp benchmark-results-tbbOFF.json "benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json" - - name: Save benchmark cache + - name: Save benchmark cache (TBB=OFF) uses: actions/cache/save@v4 continue-on-error: true with: - path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }}.json - key: timeSFMBAL-benchmark-v3-${{ inputs.os_name }}-${{ inputs.arch }}-${{ inputs.target_sha }} + path: benchmark-cache/${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ inputs.os_name }}-${{ inputs.arch }}-tbbOFF-${{ inputs.target_sha }} diff --git a/.github/workflows/time-sfmbal-benchmark.yml b/.github/workflows/time-sfmbal-benchmark.yml index ca24b2dfc6..6c83e2b4a5 100644 --- a/.github/workflows/time-sfmbal-benchmark.yml +++ b/.github/workflows/time-sfmbal-benchmark.yml @@ -118,8 +118,34 @@ jobs: }); } - // Dispatch all unprivileged benchmark worker runs. + // Helper: check whether an exact cache key exists. + const cacheExists = async (key) => { + const resp = await github.rest.actions.getActionsCacheList({ + owner: context.repo.owner, + repo: context.repo.repo, + key, + }); + return resp.data.actions_caches.some((c) => c.key === key); + }; + + // Check cache and dispatch only uncached benchmark worker runs. + // Each worker produces both TBB=ON and TBB=OFF results, so we + // skip dispatch only when both cache entries are present. for (const spec of specs) { + const keyON = `timeSFMBAL-benchmark-v4-${spec.os_name}-${spec.arch}-tbbON-${spec.sha}`; + const keyOFF = `timeSFMBAL-benchmark-v4-${spec.os_name}-${spec.arch}-tbbOFF-${spec.sha}`; + const [hitON, hitOFF] = await Promise.all([ + cacheExists(keyON), + cacheExists(keyOFF), + ]); + if (hitON && hitOFF) { + core.info( + `Cache hit for ${spec.role} ${spec.id} (both TBB variants), skipping dispatch.` + ); + spec.conclusion = "skipped_cached"; + continue; + } + spec.group_token = `${groupId}-${spec.role}-${spec.id}`; await github.rest.actions.createWorkflowDispatch({ owner: context.repo.owner, @@ -169,7 +195,7 @@ jobs: for (let attempt = 0; attempt < 80; attempt++) { let allResolved = true; for (const spec of specs) { - if (spec.run_id) continue; + if (spec.run_id || spec.conclusion) continue; const run = await findMatchingRun(spec.group_token); if (run) { spec.run_id = run.id; @@ -205,7 +231,7 @@ jobs: } for (const spec of specs) { - if (!spec.run_id) spec.conclusion = "not_found"; + if (!spec.run_id) spec.conclusion = spec.conclusion || "not_found"; if (!spec.conclusion) spec.conclusion = "timed_out"; } @@ -226,30 +252,45 @@ jobs: fail-fast: false matrix: include: - - id: linux-x64 + - id: linux-x64-tbbON os_name: linux arch: x64 - - id: linux-arm64 + tbb: "ON" + - id: linux-x64-tbbOFF os_name: linux + arch: x64 + tbb: "OFF" + - id: linux-arm64-tbbON + os_name: linux + arch: arm64 + tbb: "ON" + - id: linux-arm64-tbbOFF + os_name: linux + arch: arm64 + tbb: "OFF" + - id: macos-arm64-tbbON + os_name: macos arch: arm64 - - id: macos-arm64 + tbb: "ON" + - id: macos-arm64-tbbOFF os_name: macos arch: arm64 + tbb: "OFF" runs-on: ubuntu-latest steps: - name: Restore head benchmark cache id: restore_head uses: actions/cache/restore@v4 with: - path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json - key: timeSFMBAL-benchmark-v3-${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }} + path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.head_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.head_sha }} - name: Restore base benchmark cache id: restore_base uses: actions/cache/restore@v4 with: - path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json - key: timeSFMBAL-benchmark-v3-${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }} + path: benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.base_sha }}.json + key: timeSFMBAL-benchmark-v4-${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.base_sha }} - name: Stage collected files shell: bash @@ -258,13 +299,13 @@ jobs: mkdir -p artifact echo "head cache-hit: ${{ steps.restore_head.outputs.cache-hit }}" echo "base cache-hit: ${{ steps.restore_base.outputs.cache-hit }}" - if [[ "${{ steps.restore_head.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json" ]]; then - cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.head_sha }}.json" "artifact/head-${{ matrix.id }}.json" + if [[ "${{ steps.restore_head.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.head_sha }}.json" ]]; then + cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.head_sha }}.json" "artifact/head-${{ matrix.id }}.json" else : > "artifact/head-miss-${{ matrix.id }}.txt" fi - if [[ "${{ steps.restore_base.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json" ]]; then - cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-${{ needs.context.outputs.base_sha }}.json" "artifact/base-${{ matrix.id }}.json" + if [[ "${{ steps.restore_base.outputs.cache-hit }}" == "true" ]] && [[ -f "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.base_sha }}.json" ]]; then + cp "benchmark-cache/${{ matrix.os_name }}-${{ matrix.arch }}-tbb${{ matrix.tbb }}-${{ needs.context.outputs.base_sha }}.json" "artifact/base-${{ matrix.id }}.json" else : > "artifact/base-miss-${{ matrix.id }}.txt" fi From 28eac5817e491d6272533906f916c5600c1bda80 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 8 Feb 2026 16:28:15 -0500 Subject: [PATCH 186/266] Add missing wrapping for NavState --- gtsam/navigation/navigation.i | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index d223becf65..665abb0842 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -56,6 +56,7 @@ class NavState { gtsam::Rot3 attitude() const; gtsam::Point3 position() const; gtsam::Vector velocity() const; + gtsam::Vector bodyVelocity() const; gtsam::Pose3 pose() const; // Standard Interface @@ -66,6 +67,15 @@ class NavState { gtsam::Unit3 bearing(const gtsam::Point3& point, Eigen::Ref Hself, Eigen::Ref Hpoint) const; + // Group + static gtsam::NavState Identity(); + gtsam::NavState inverse(); + gtsam::NavState compose(const gtsam::NavState& p2) const; + gtsam::NavState between(const gtsam::NavState& p2) const; + + // Operator Overloads + gtsam::NavState operator*(const gtsam::NavState& p2) const; + // Manifold gtsam::NavState retract(const gtsam::Vector& v) const; gtsam::Vector localCoordinates(const gtsam::NavState& g) const; @@ -81,6 +91,10 @@ class NavState { gtsam::Vector logmap(const gtsam::NavState& p, Eigen::Ref H1, Eigen::Ref H2); gtsam::Matrix AdjointMap() const; gtsam::Vector Adjoint(gtsam::Vector xi_b) const; + + // Matrix Lie Group + gtsam::Vector vec() const; + gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); static gtsam::Vector Vee(const gtsam::Matrix& X); From 6ac5c5e24546f3d33f850a11c5a7676b3295c63d Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 8 Feb 2026 16:28:40 -0500 Subject: [PATCH 187/266] NavState guide --- gtsam/navigation/doc/NavState.ipynb | 390 ++++++++++++++++++++++++++++ gtsam/navigation/navigation.md | 2 +- 2 files changed, 391 insertions(+), 1 deletion(-) create mode 100644 gtsam/navigation/doc/NavState.ipynb diff --git a/gtsam/navigation/doc/NavState.ipynb b/gtsam/navigation/doc/NavState.ipynb new file mode 100644 index 0000000000..184ff38d45 --- /dev/null +++ b/gtsam/navigation/doc/NavState.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "title_overview", + "metadata": {}, + "source": [ + "# NavState\n", + "\n", + "The `NavState` class represents attitude, position, and velocity as a 9D manifold and also implements the matrix Lie group commonly denoted $SE_2(3)$ in the inertial-navigation literature. It is the core state type behind IMU preintegration and several navigation factors in GTSAM." + ] + }, + { + "cell_type": "markdown", + "id": "license_cell", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information" + ] + }, + { + "cell_type": "markdown", + "id": "colab_badge", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "colab_install", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from gtsam import NavState, Rot3, Point3" + ] + }, + { + "cell_type": "markdown", + "id": "manifold_section", + "metadata": {}, + "source": [ + "## `NavState` as a manifold\n", + "\n", + "A `NavState` is a tuple $(R, p, v)$ where:\n", + "- $R \\in SO(3)$ is attitude.\n", + "- $p \\in \\mathbb{R}^3$ is position in the navigation/world frame.\n", + "- $v \\in \\mathbb{R}^3$ is velocity in the navigation/world frame.\n", + "\n", + "In GTSAM, local increments for this manifold are ordered as rotation, then position, then velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "construct_states", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Identity state:\n", + "R: [\n", + "\t1, 0, 0;\n", + "\t0, 1, 0;\n", + "\t0, 0, 1\n", + "]\n", + "p: 0 0 0\n", + "v: 0 0 0\n", + "\n", + "\n", + "Custom state X1:\n", + "R: [\n", + "\t0.866025, -0.5, 0;\n", + "\t0.5, 0.866025, 0;\n", + "\t0, 0, 1\n", + "]\n", + "p: 10 20 30\n", + "v: 1 2 3\n", + "\n" + ] + } + ], + "source": [ + "# Identity state\n", + "X_identity = NavState()\n", + "print(f\"Identity state:\\n{X_identity}\\n\")\n", + "\n", + "# A custom navigation state\n", + "R = Rot3.Yaw(np.pi / 6)\n", + "p = Point3(10, 20, 30)\n", + "v = np.array([1, 2, 3])\n", + "X1 = NavState(R, p, v)\n", + "print(f\"Custom state X1:\\n{X1}\")" + ] + }, + { + "cell_type": "markdown", + "id": "accessors_text", + "metadata": {}, + "source": [ + "The state components are accessible through `attitude()`, `position()`, and `velocity()`. You can also get `pose()` (rotation + position only) and `bodyVelocity()` (velocity expressed in the body frame)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "accessors_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attitude:\n", + "R: [\n", + "\t0.866025, -0.5, 0;\n", + "\t0.5, 0.866025, 0;\n", + "\t0, 0, 1\n", + "]\n", + "\n", + "\n", + "Position: [10. 20. 30.]\n", + "Velocity: [1. 2. 3.]\n", + "Body-frame velocity: [1.866 1.2321 3. ]\n", + "Pose:\n", + "R: [\n", + "\t0.866025, -0.5, 0;\n", + "\t0.5, 0.866025, 0;\n", + "\t0, 0, 1\n", + "]\n", + "t: 10 20 30\n", + "\n" + ] + } + ], + "source": [ + "print(f\"Attitude:\\n{X1.attitude()}\\n\")\n", + "print(f\"Position: {X1.position()}\")\n", + "print(f\"Velocity: {X1.velocity()}\")\n", + "print(f\"Body-frame velocity: {np.round(X1.bodyVelocity(), 4)}\")\n", + "print(f\"Pose:\\n{X1.pose()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "se23_conventions", + "metadata": {}, + "source": [ + "## Connection to $SE_2(3)$ and ordering conventions\n", + "\n", + "`NavState` follows the same Lie-group structure used in the $SE_2(3)$ literature, but with a different storage/order convention for compatibility with legacy GTSAM code.\n", + "\n", + "- **This notebook / `NavState` convention** (R, p, v):\n", + " $$X = \\begin{bmatrix} R & p & v \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{bmatrix}$$\n", + "- **Common literature convention** (often Barrau et al.):\n", + " $$X_{lit} = \\begin{bmatrix} R & v & p \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{bmatrix}$$\n", + "\n", + "Likewise, the `NavState` tangent vector uses order `[dR, dP, dV]` (rotation, position, velocity). Many papers use `[dR, dV, dP]` to match their matrix ordering.\n", + "\n", + "For comparison: in `Gal3`, we *do* follow the literature ordering conventions used there." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ordering_example", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NavState matrix (R, p, v):\n", + "[[ 0.866 -0.5 0. 10. 1. ]\n", + " [ 0.5 0.866 0. 20. 2. ]\n", + " [ 0. 0. 1. 30. 3. ]\n", + " [ 0. 0. 0. 1. 0. ]\n", + " [ 0. 0. 0. 0. 1. ]]\n", + "xi in NavState order [dR, dP, dV]: [ 0.1 -0.2 0.3 1. 2. 3. 0.4 0.5 0.6]\n", + "same increments in literature-like [dR, dV, dP] order: [ 0.1 -0.2 0.3 0.4 0.5 0.6 1. 2. 3. ]\n" + ] + } + ], + "source": [ + "T = X1.matrix()\n", + "print(\"NavState matrix (R, p, v):\")\n", + "print(np.round(T, 4))\n", + "\n", + "xi_rpv = np.array([0.1, -0.2, 0.3, 1.0, 2.0, 3.0, 0.4, 0.5, 0.6]) # [dR, dP, dV]\n", + "xi_rvp = np.hstack([xi_rpv[0:3], xi_rpv[6:9], xi_rpv[3:6]]) # [dR, dV, dP]\n", + "print(\"xi in NavState order [dR, dP, dV]:\", xi_rpv)\n", + "print(\"same increments in literature-like [dR, dV, dP] order:\", xi_rvp)" + ] + }, + { + "cell_type": "markdown", + "id": "group_section", + "metadata": {}, + "source": [ + "## Group operations\n", + "\n", + "As a Lie group, `NavState` supports composition and inversion. The composition matches matrix multiplication of the 5x5 representation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "group_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1.inverse():\n", + "R: [\n", + "\t0.866025, 0.5, 0;\n", + "\t-0.5, 0.866025, 0;\n", + "\t0, 0, 1\n", + "]\n", + "p: -18.6603 -12.3205 -30\n", + "v: -1.86603 -1.23205 -3\n", + "\n", + "\n", + "X1 * X2:\n", + "R: [\n", + "\t0.707107, -0.707107, 0;\n", + "\t0.707107, 0.707107, 0;\n", + "\t0, 0, 1\n", + "]\n", + "p: 11.8301 26.8301 35\n", + "v: 3.4641 5.73205 8\n", + "\n", + "\n", + "matrix(X1 * X2) == matrix(X1) @ matrix(X2): True\n" + ] + } + ], + "source": [ + "X2 = NavState(Rot3.Yaw(np.pi / 12), Point3(5, 5, 5), np.array([4, 2, 5]))\n", + "\n", + "X1_inv = X1.inverse()\n", + "print(f\"X1.inverse():\\n{X1_inv}\\n\")\n", + "\n", + "X_comp = X1 * X2\n", + "print(f\"X1 * X2:\\n{X_comp}\\n\")\n", + "\n", + "lhs = X_comp.matrix()\n", + "rhs = X1.matrix() @ X2.matrix()\n", + "print(\"matrix(X1 * X2) == matrix(X1) @ matrix(X2):\", np.allclose(lhs, rhs))" + ] + }, + { + "cell_type": "markdown", + "id": "lie_algebra_section", + "metadata": {}, + "source": [ + "## Lie algebra and manifold operations\n", + "\n", + "The 9D tangent coordinates are ordered as\n", + "`[R_x, R_y, R_z, P_x, P_y, P_z, V_x, V_y, V_z]`.\n", + "\n", + "`Expmap` and `Logmap` convert between tangent vectors and `NavState` group elements. `retract`/`localCoordinates` are the manifold operations used in optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "exp_log_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expmap(xi):\n", + "R: [\n", + "\t0.975125, 0.108953, 0.193031;\n", + "\t-0.0890529, 0.99005, -0.108953;\n", + "\t-0.202981, 0.0890529, 0.975125\n", + "]\n", + "p: 0.481917 0.447923 0.577763\n", + "v: -0.172632 0.302981 0.13333\n", + "\n", + "\n", + "Logmap(Expmap(xi)) = [ 0.1 0.2 -0.1 0.4 0.5 0.6 -0.2 0.3 0.1]\n" + ] + } + ], + "source": [ + "xi = np.array([0.1, 0.2, -0.1, 0.4, 0.5, 0.6, -0.2, 0.3, 0.1]) # [dR, dP, dV]\n", + "\n", + "X_exp = NavState.Expmap(xi)\n", + "print(f\"Expmap(xi):\\n{X_exp}\\n\")\n", + "\n", + "xi_log = NavState.Logmap(X_exp)\n", + "print(\"Logmap(Expmap(xi)) =\", np.round(xi_log, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "retract_local_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "localCoordinates from Xp to Xq = [ 0. 0. -0.261799 -10.606602 -3.535534 -20.\n", + " 3.535534 -0.707107 3. ]\n", + "Xp.retract(delta):\n", + "R: [\n", + "\t0.866025, -0.5, 0;\n", + "\t0.5, 0.866025, 0;\n", + "\t0, 0, 1\n", + "]\n", + "p: 10 20 30\n", + "v: 1 2 3\n", + "\n" + ] + } + ], + "source": [ + "Xp = NavState(Rot3.Yaw(np.pi / 4), Point3(15, 30, 50), np.array([-2, 0, 0]))\n", + "Xq = X1\n", + "\n", + "delta = Xp.localCoordinates(Xq)\n", + "print(\"localCoordinates from Xp to Xq =\", np.round(delta, 6))\n", + "\n", + "Xq_retracted = Xp.retract(delta)\n", + "print(f\"Xp.retract(delta):\\n{Xq_retracted}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/navigation/navigation.md b/gtsam/navigation/navigation.md index b2f7e26be8..785bc42ed9 100644 --- a/gtsam/navigation/navigation.md +++ b/gtsam/navigation/navigation.md @@ -5,7 +5,7 @@ The `navigation` module in GTSAM provides specialized tools for inertial navigat ## Classes ### Core Navigation Types -- **[NavState](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/NavState.h)**: Represents the complete navigation state $\mathcal{SE}_2(3)$, i.e., attitude, position, and velocity. It also implements the group ${SE}_2(3)$. +- **[NavState](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/NavState.h)**: Represents the complete navigation state $\mathcal{SE}_2(3)$, i.e., attitude, position, and velocity. It also implements the group ${SE}_2(3)$. See the [NavState user guide](doc/NavState.ipynb). - **[ImuBias](https://github.com/borglab/gtsam/blob/develop/gtsam/navigation/ImuBias.h)**: Models constant biases in IMU measurements (accelerometer and gyroscope). ### Invariant Kalman Filtering From 05b822fdd695339f90b06b5932ad75e60856f4b3 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 9 Feb 2026 10:30:27 -0500 Subject: [PATCH 188/266] Add missing headers [skip-ci] --- examples/AbcEquivariantFilterExample.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/examples/AbcEquivariantFilterExample.cpp b/examples/AbcEquivariantFilterExample.cpp index 7360091bcd..6aaf8c6a64 100644 --- a/examples/AbcEquivariantFilterExample.cpp +++ b/examples/AbcEquivariantFilterExample.cpp @@ -17,10 +17,24 @@ * @author Frank Dellaert * @date 2025 */ + #include #include #include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + // Use namespace for convenience using namespace gtsam; constexpr size_t n = 1; // Number of calibration states From 44c0d143a6466de377356a6a94857dc5de2fa15c Mon Sep 17 00:00:00 2001 From: Amin Hashemi Date: Mon, 9 Feb 2026 22:55:41 +0200 Subject: [PATCH 189/266] removed unnecessary DiscreteKeys constructor --- gtsam/discrete/DiscreteKey.h | 3 --- 1 file changed, 3 deletions(-) diff --git a/gtsam/discrete/DiscreteKey.h b/gtsam/discrete/DiscreteKey.h index d200792c83..26d29e30ba 100644 --- a/gtsam/discrete/DiscreteKey.h +++ b/gtsam/discrete/DiscreteKey.h @@ -43,9 +43,6 @@ namespace gtsam { // Forward all constructors. using std::vector::vector; - /// Constructor for serialization - DiscreteKeys() : std::vector::vector() {} - /// Construct from a key explicit DiscreteKeys(const DiscreteKey& key) { push_back(key); } From cbb107faf580f3d44c3434e1f3c1ec08d6cf8aa4 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 9 Feb 2026 18:34:11 -0500 Subject: [PATCH 190/266] Fix templates --- python/gtsam/gtsam.tpl | 3 ++- python/gtsam_unstable/gtsam_unstable.tpl | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/python/gtsam/gtsam.tpl b/python/gtsam/gtsam.tpl index 6876b4ab45..902b676893 100644 --- a/python/gtsam/gtsam.tpl +++ b/python/gtsam/gtsam.tpl @@ -22,7 +22,9 @@ // These are the included headers listed in `gtsam.i` {includes} +#if GTSAM_ENABLE_BOOST_SERIALIZATION #include +#endif // Export classes for serialization {boost_class_export} @@ -47,4 +49,3 @@ namespace py = pybind11; {wrapped_namespace} }} - diff --git a/python/gtsam_unstable/gtsam_unstable.tpl b/python/gtsam_unstable/gtsam_unstable.tpl index 006ca7fc8f..23fc3924b7 100644 --- a/python/gtsam_unstable/gtsam_unstable.tpl +++ b/python/gtsam_unstable/gtsam_unstable.tpl @@ -18,7 +18,9 @@ // These are the included headers listed in `gtsam_unstable.i` {includes} +#if GTSAM_ENABLE_BOOST_SERIALIZATION #include +#endif {boost_class_export} @@ -39,4 +41,3 @@ PYBIND11_MODULE({module_name}, m_) {{ #include "python/gtsam_unstable/specializations/gtsam_unstable.h" }} - From e77c0c3e308e646a52651ce09b2452784915b5a2 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 9 Feb 2026 18:34:28 -0500 Subject: [PATCH 191/266] Fix test for non-serialization case --- python/gtsam/tests/test_PseudorangeFactor.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/python/gtsam/tests/test_PseudorangeFactor.py b/python/gtsam/tests/test_PseudorangeFactor.py index 36c16ab3c1..6ad7778957 100644 --- a/python/gtsam/tests/test_PseudorangeFactor.py +++ b/python/gtsam/tests/test_PseudorangeFactor.py @@ -30,26 +30,27 @@ def test_singularity(self): factor.print("factor") def test_errors(self): - satpos = np.array([0.0, 0.0, 3.0]) + sat_pos = np.array([0.0, 0.0, 3.0]) model = gtsam.noiseModel.Unit.Create(1) - factor = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) + factor = gtsam.PseudorangeFactor(0, 1, 4.0, sat_pos, 0.0, model) error = factor.evaluateError(np.zeros(3), 0.0) self.assertEqual(error[0], -1.0) def test_equality(self): - satpos = np.array([0.0, 0.0, 3.0]) + sat_pos = np.array([0.0, 0.0, 3.0]) model = gtsam.noiseModel.Unit.Create(1) - factor1 = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) - factor2 = gtsam.PseudorangeFactor(2, 1, 4.0, satpos, 0.0, model) - factor3 = gtsam.PseudorangeFactor(0, 1, 40.0, satpos, 10.0, model) + factor1 = gtsam.PseudorangeFactor(0, 1, 4.0, sat_pos, 0.0, model) + factor2 = gtsam.PseudorangeFactor(2, 1, 4.0, sat_pos, 0.0, model) + factor3 = gtsam.PseudorangeFactor(0, 1, 40.0, sat_pos, 10.0, model) self.assertTrue(factor1.equals(factor1, 1e-6)) self.assertFalse(factor1.equals(factor2, 1e-6)) self.assertTrue(factor1.equals(factor3, 1e99)) + @unittest.skipUnless(hasattr(gtsam.PseudorangeFactor, "serialize"), "Serialization not enabled") def test_serialization(self): - satpos = np.array([0.0, 0.0, 3.0]) + sat_pos = np.array([0.0, 0.0, 3.0]) model = gtsam.noiseModel.Unit.Create(1) - factor = gtsam.PseudorangeFactor(0, 1, 4.0, satpos, 0.0, model) + factor = gtsam.PseudorangeFactor(0, 1, 4.0, sat_pos, 0.0, model) factor.serialize() From a9f1a8b640e6c41f019c914360e89d618adddde7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 9 Feb 2026 18:34:43 -0500 Subject: [PATCH 192/266] Add non-boost-python to CI --- .github/scripts/python.sh | 54 +++++++++++++++++++++++++----- .github/workflows/build-python.yml | 26 +++++++++++++- 2 files changed, 71 insertions(+), 9 deletions(-) diff --git a/.github/scripts/python.sh b/.github/scripts/python.sh index 60e35fc3f8..a9a4ec28e0 100644 --- a/.github/scripts/python.sh +++ b/.github/scripts/python.sh @@ -24,6 +24,7 @@ if [ -z ${PYTHON_VERSION+x} ]; then fi export PYTHON="python${PYTHON_VERSION}" +NO_BOOST_BUILD=OFF function install_dependencies() { @@ -45,10 +46,17 @@ function build() { export CMAKE_GENERATOR=Ninja BUILD_PYBIND="ON" + USE_BOOST_FEATURES="ON" + ENABLE_BOOST_SERIALIZATION="ON" + + if [ "${NO_BOOST_BUILD}" == "ON" ]; then + USE_BOOST_FEATURES="OFF" + ENABLE_BOOST_SERIALIZATION="OFF" + fi # Add Boost hints on Windows BOOST_CMAKE_ARGS="" - if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" || "$OSTYPE" == "cygwin" ]]; then + if [ "${NO_BOOST_BUILD}" != "ON" ] && [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" || "$OSTYPE" == "cygwin" ]]; then if [ -n "${BOOST_ROOT}" ]; then BOOST_ROOT_UNIX=$(echo "$BOOST_ROOT" | sed 's/\\/\//g') BOOST_CMAKE_ARGS="-DBOOST_ROOT=${BOOST_ROOT_UNIX} -DBOOST_INCLUDEDIR=${BOOST_ROOT_UNIX}/include -DBOOST_LIBRARYDIR=${BOOST_ROOT_UNIX}/lib" @@ -68,20 +76,28 @@ function build() -DGTSAM_UNSTABLE_BUILD_PYTHON=${GTSAM_BUILD_UNSTABLE:-ON} \ -DGTSAM_PYTHON_VERSION=$PYTHON_VERSION \ -DPYTHON_EXECUTABLE:FILEPATH=$(which $PYTHON) \ - -DGTSAM_USE_BOOST_FEATURES=ON \ - -DGTSAM_ENABLE_BOOST_SERIALIZATION=ON \ + -DGTSAM_USE_BOOST_FEATURES=${USE_BOOST_FEATURES} \ + -DGTSAM_ENABLE_BOOST_SERIALIZATION=${ENABLE_BOOST_SERIALIZATION} \ -DGTSAM_ALLOW_DEPRECATED_SINCE_V43=OFF \ -DCMAKE_INSTALL_PREFIX=$GITHUB_WORKSPACE/gtsam_install \ $BOOST_CMAKE_ARGS - # Set to 2 cores so that Actions does not error out during resource provisioning. - cmake --build build -j2 - - cmake --build build --target python-install + if [ "${NO_BOOST_BUILD}" == "ON" ]; then + # Build only wrapper targets for the no-Boost verification lane. + cmake --build build -j2 --target gtsam_py gtsam_unstable_py + else + # Set to 2 cores so that Actions does not error out during resource provisioning. + cmake --build build -j2 + cmake --build build --target python-install + fi } function test() { + if [ "${NO_BOOST_BUILD}" == "ON" ]; then + export PYTHONPATH="$GITHUB_WORKSPACE/build/python:$PYTHONPATH" + fi + cd $GITHUB_WORKSPACE/python/gtsam/tests $PYTHON -m unittest discover -v cd $GITHUB_WORKSPACE @@ -94,8 +110,30 @@ function test() # cmake --build build --target python-test-unstable } +# Parse optional flags. +if [ $# -lt 1 ]; then + echo "Usage: $0 {-d|-b|-t} [--no-boost]" + exit 2 +fi + +ACTION="$1" +shift + +while [ $# -gt 0 ]; do + case "$1" in + --no-boost) + NO_BOOST_BUILD=ON + ;; + *) + echo "Unknown option: $1" + exit 2 + ;; + esac + shift +done + # select between build or test -case $1 in +case $ACTION in -d) install_dependencies ;; diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index ac68dce867..a12dc3ff84 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -61,6 +61,7 @@ jobs: name: [ ubuntu-22.04-gcc-11, + ubuntu-22.04-gcc-11-noboost, ubuntu-22.04-clang-11, macos-14-xcode-15.4, macos-15-xcode-16, @@ -75,6 +76,11 @@ jobs: compiler: gcc version: "11" + - name: ubuntu-22.04-gcc-11-noboost + os: ubuntu-22.04 + compiler: gcc + version: "11" + - name: ubuntu-22.04-clang-11 os: ubuntu-22.04 compiler: clang @@ -113,7 +119,11 @@ jobs: fi sudo apt-get -y update - sudo apt-get -y install cmake build-essential pkg-config libpython3-dev python3-numpy libboost-all-dev ninja-build + if [ "${{ matrix.name }}" = "ubuntu-22.04-gcc-11-noboost" ]; then + sudo apt-get -y install cmake build-essential pkg-config libpython3-dev python3-numpy ninja-build + else + sudo apt-get -y install cmake build-essential pkg-config libpython3-dev python3-numpy libboost-all-dev ninja-build + fi if [ "${{ matrix.compiler }}" = "gcc" ]; then sudo apt-get install -y g++-${{ matrix.version }} g++-${{ matrix.version }}-multilib @@ -220,10 +230,24 @@ jobs: - name: Build shell: bash + if: matrix.name != 'ubuntu-22.04-gcc-11-noboost' run: | bash .github/scripts/python.sh -b + - name: Build (No Boost Wrapper Check) + if: matrix.name == 'ubuntu-22.04-gcc-11-noboost' + shell: bash + run: | + bash .github/scripts/python.sh -b --no-boost + - name: Test shell: bash + if: matrix.name != 'ubuntu-22.04-gcc-11-noboost' run: | bash .github/scripts/python.sh -t + + - name: Test (No Boost Wrapper Check) + if: matrix.name == 'ubuntu-22.04-gcc-11-noboost' + shell: bash + run: | + bash .github/scripts/python.sh -t --no-boost From 367e89fe988bf732388eb5a5a4f7b437e3bb76a3 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Fri, 30 Jan 2026 18:01:44 -0500 Subject: [PATCH 193/266] Clean up Boost dependencies --- cmake/HandleBoost.cmake | 18 +++++------------- gtsam/base/serializationTestHelpers.h | 16 ++++++++-------- gtsam_unstable/linear/QPSParser.cpp | 1 - .../slam/tests/testSerialization.cpp | 5 ++--- 4 files changed, 15 insertions(+), 25 deletions(-) diff --git a/cmake/HandleBoost.cmake b/cmake/HandleBoost.cmake index cfbb55b390..18c7c34e2c 100644 --- a/cmake/HandleBoost.cmake +++ b/cmake/HandleBoost.cmake @@ -39,11 +39,10 @@ endif() ################################################################################ # Set minimum required Boost version and components. -# NOTE: "system" is intentionally omitted. It is a transitive dependency of other -# components (like filesystem) and will be found automatically. Explicitly -# requesting it can cause issues with modern header-only versions of Boost. +# optional, program_options, random, range are all used in tests/examples/Python, but are not library dependencies. timer/chrono is used conditionally. +# concept_check, fusion, move, phoenix, pool, smart_ptr, spirit, tokenizer, type_traits, optional, range are header only and are not components. set(BOOST_FIND_MINIMUM_VERSION 1.70) -set(BOOST_FIND_MINIMUM_COMPONENTS serialization filesystem thread program_options date_time timer chrono regex) +set(BOOST_FIND_MINIMUM_COMPONENTS graph serialization program_options random timer chrono) # Find the Boost package. On systems with modern installations (vcpkg, Homebrew), # this will use CMake's "Config mode". With manual installations (especially on @@ -52,8 +51,9 @@ find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} REQUIRED COMPONENTS ${BOOST_FIND_MINIMUM_COMPONENTS} ) +set(GTSAM_BOOST_LIBRARIES Boost::graph Boost::serialization) # Verify that the required Boost component targets were successfully found and imported. -foreach(_t IN ITEMS Boost::serialization Boost::filesystem Boost::thread Boost::date_time) +foreach(_t IN ITEMS ${GTSAM_BOOST_LIBRARIES}) if(NOT TARGET ${_t}) message(FATAL_ERROR "Missing required Boost component target: ${_t}. Please install/upgrade Boost or set BOOST_ROOT/Boost_DIR correctly.") endif() @@ -61,14 +61,6 @@ endforeach() option(GTSAM_DISABLE_NEW_TIMERS "Disables using Boost.chrono for timing" OFF) -set(GTSAM_BOOST_LIBRARIES - Boost::serialization - Boost::filesystem - Boost::thread - Boost::date_time - Boost::regex -) - if(GTSAM_DISABLE_NEW_TIMERS) message("WARNING: GTSAM timing instrumentation manually disabled") list_append_cache(GTSAM_COMPILE_DEFINITIONS_PUBLIC DGTSAM_DISABLE_NEW_TIMERS) diff --git a/gtsam/base/serializationTestHelpers.h b/gtsam/base/serializationTestHelpers.h index cc6dadafdb..02eaff9875 100644 --- a/gtsam/base/serializationTestHelpers.h +++ b/gtsam/base/serializationTestHelpers.h @@ -23,6 +23,7 @@ #if GTSAM_ENABLE_BOOST_SERIALIZATION +#include #include #include #include @@ -31,7 +32,6 @@ #include #include -#include // whether to print the serialized text to stdout @@ -47,10 +47,10 @@ T create() { } // Creates or empties a folder in the build folder and returns the relative path -inline boost::filesystem::path resetFilesystem( - boost::filesystem::path folder = "actual") { - boost::filesystem::remove_all(folder); - boost::filesystem::create_directory(folder); +inline std::filesystem::path resetFilesystem( + std::filesystem::path folder = "actual") { + std::filesystem::remove_all(folder); + std::filesystem::create_directory(folder); return folder; } @@ -65,7 +65,7 @@ void roundtrip(const T& input, T& output) { // Templated round-trip serialization using a file template void roundtripFile(const T& input, T& output) { - boost::filesystem::path path = resetFilesystem()/"graph.dat"; + std::filesystem::path path = resetFilesystem()/"graph.dat"; serializeToFile(input, path.string()); deserializeFromFile(path.string(), output); } @@ -106,7 +106,7 @@ void roundtripXML(const T& input, T& output) { // Templated round-trip serialization using XML File template void roundtripXMLFile(const T& input, T& output) { - boost::filesystem::path path = resetFilesystem()/"graph.xml"; + std::filesystem::path path = resetFilesystem()/"graph.xml"; serializeToXMLFile(input, path.string()); deserializeFromXMLFile(path.string(), output); } @@ -147,7 +147,7 @@ void roundtripBinary(const T& input, T& output) { // Templated round-trip serialization using Binary file template void roundtripBinaryFile(const T& input, T& output) { - boost::filesystem::path path = resetFilesystem()/"graph.bin"; + std::filesystem::path path = resetFilesystem()/"graph.bin"; serializeToBinaryFile(input, path.string()); deserializeFromBinaryFile(path.string(), output); } diff --git a/gtsam_unstable/linear/QPSParser.cpp b/gtsam_unstable/linear/QPSParser.cpp index fd0782b39a..04a573a7f4 100644 --- a/gtsam_unstable/linear/QPSParser.cpp +++ b/gtsam_unstable/linear/QPSParser.cpp @@ -32,7 +32,6 @@ #include #include -#include #include #include #include diff --git a/gtsam_unstable/slam/tests/testSerialization.cpp b/gtsam_unstable/slam/tests/testSerialization.cpp index 5244710e85..9a0db1d86c 100644 --- a/gtsam_unstable/slam/tests/testSerialization.cpp +++ b/gtsam_unstable/slam/tests/testSerialization.cpp @@ -17,8 +17,7 @@ #include -#include - +#include #include #include #include @@ -26,7 +25,7 @@ using namespace std; using namespace gtsam; -namespace fs = boost::filesystem; +namespace fs = std::filesystem; #ifdef TOPSRCDIR static string topdir = TOPSRCDIR; #else From 83fb926ae237f5b857728298929d8b0eb544d36f Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Sun, 25 Jan 2026 15:42:27 -0500 Subject: [PATCH 194/266] Disable Pybind11 LTO This cuts down no-op CMake build times to ~11 minutes. --- .github/workflows/vcpkg.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 26edce1136..e5f8fdb01a 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -166,6 +166,7 @@ jobs: -DVCPKG_TARGET_TRIPLET=${{ matrix.triplet }} \ -DVCPKG_HOST_TRIPLET=${{ matrix.triplet }} \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DCMAKE_INTERPROCEDURAL_OPTIMIZATION=OFF \ -DGTSAM_BUILD_EXAMPLES_ALWAYS=ON \ -DGTSAM_BUILD_WITH_PRECOMPILED_HEADERS=OFF \ -DGTSAM_ROT3_EXPMAP=ON \ From 1040ad42276e80e055ccf5045a343279ee9d4fa5 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:37:43 -0500 Subject: [PATCH 195/266] Reorganize sccache config --- .github/workflows/vcpkg.yml | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index e5f8fdb01a..853870ec0c 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -15,6 +15,8 @@ jobs: build: name: ${{ matrix.os }} runs-on: ${{ matrix.os }} + env: + SCCACHE_GHA_ENABLED: 1 strategy: fail-fast: false matrix: @@ -61,13 +63,6 @@ jobs: - name: Install sccache uses: mozilla-actions/sccache-action@v0.0.9 - - name: Use sccache - shell: bash - run: | - echo "SCCACHE_GHA_ENABLED=ON" >> "$GITHUB_ENV" - echo "CMAKE_C_COMPILER_LAUNCHER=sccache" >> "$GITHUB_ENV" - echo "CMAKE_CXX_COMPILER_LAUNCHER=sccache" >> "$GITHUB_ENV" - - name: cl version if: runner.os == 'Windows' shell: cmd @@ -162,6 +157,8 @@ jobs: cmake . -B build -G Ninja \ -DCMAKE_TOOLCHAIN_FILE=$VCPKG_INSTALLATION_ROOT/scripts/buildsystems/vcpkg.cmake \ + -DCMAKE_C_COMPILER_LAUNCHER=sccache \ + -DCMAKE_CXX_COMPILER_LAUNCHER=sccache \ -DVCPKG_INSTALLED_DIR=$VCPKG_INSTALLATION_ROOT/installed \ -DVCPKG_TARGET_TRIPLET=${{ matrix.triplet }} \ -DVCPKG_HOST_TRIPLET=${{ matrix.triplet }} \ From 2df02cf7c8deeae62c444a8d85dc75ef7d73e080 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:37:58 -0500 Subject: [PATCH 196/266] Disable sccache timeout --- .github/workflows/vcpkg.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 853870ec0c..577f258b15 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -17,6 +17,9 @@ jobs: runs-on: ${{ matrix.os }} env: SCCACHE_GHA_ENABLED: 1 + # Some files take so long to compile that the sccache daemon times out, + # which is bad for stats tracking. Always keep sccache daemon alive. + SCCACHE_IDLE_TIMEOUT: 0 strategy: fail-fast: false matrix: From f9429cb9ead0d48cda1bf1e2b38f10599526b29c Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:09:57 -0500 Subject: [PATCH 197/266] Remove unnecessary source directory argument --- .github/workflows/build-cibw.yml | 2 +- .github/workflows/prod-cibw.yml | 2 +- .github/workflows/time-sfmbal-benchmark-runner.yml | 12 ++++++------ .github/workflows/vcpkg.yml | 2 +- INSTALL.md | 2 +- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index e66af607c6..a8abbbc6b3 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -146,7 +146,7 @@ jobs: # the wheels to ensure platform compatibility. - name: Run CMake run: | - cmake . -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} + cmake -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} # If on macOS, we previously installed boost using homebrew for the first build. # We need to uninstall it before building the wheels with cibuildwheel, which will diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index 91677420fd..fde774d4f7 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -127,7 +127,7 @@ jobs: # the wheels to ensure platform compatibility. - name: Run CMake run: | - cmake . -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} + cmake -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} # If on macOS, we previously installed boost using homebrew for the first build. # We need to uninstall it before building the wheels with cibuildwheel, which will diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml index 4ddcc31c52..88fb8a8964 100644 --- a/.github/workflows/time-sfmbal-benchmark-runner.yml +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -77,7 +77,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -115,7 +115,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -191,7 +191,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -229,7 +229,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -303,7 +303,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -341,7 +341,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -S . -B build -GNinja \ + cmake -B build -GNinja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 577f258b15..a1ef0f4897 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -158,7 +158,7 @@ jobs: run: | export CL=-openmp - cmake . -B build -G Ninja \ + cmake -B build -G Ninja \ -DCMAKE_TOOLCHAIN_FILE=$VCPKG_INSTALLATION_ROOT/scripts/buildsystems/vcpkg.cmake \ -DCMAKE_C_COMPILER_LAUNCHER=sccache \ -DCMAKE_CXX_COMPILER_LAUNCHER=sccache \ diff --git a/INSTALL.md b/INSTALL.md index fae5c7816f..f2cb2a0343 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -336,7 +336,7 @@ Go to your gtsam folder `cd gtsam`: Cmake config: In gtsam folder ```bash -cmake . -B build -G Ninja \ +cmake -B build -G Ninja \ -DCMAKE_TOOLCHAIN_FILE=../scripts/buildsystems/vcpkg.cmake \ -DVCPKG_INSTALLED_DIR=../installed \ -DVCPKG_TARGET_TRIPLET=x64-linux \ From 133bb5d3bd5e755ba4b811e59b1e4e797c49112a Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:26:26 -0500 Subject: [PATCH 198/266] Consistently use `-G Ninja` to specify Ninja as the generator --- .github/scripts/python.sh | 3 +-- .github/scripts/unix.sh | 3 +-- .github/workflows/build-cibw.yml | 2 +- .github/workflows/build-windows.yml | 5 ++--- .github/workflows/prod-cibw.yml | 2 +- .github/workflows/time-sfmbal-benchmark-runner.yml | 12 ++++++------ 6 files changed, 12 insertions(+), 15 deletions(-) diff --git a/.github/scripts/python.sh b/.github/scripts/python.sh index a9a4ec28e0..7454956904 100644 --- a/.github/scripts/python.sh +++ b/.github/scripts/python.sh @@ -44,7 +44,6 @@ function install_dependencies() function build() { - export CMAKE_GENERATOR=Ninja BUILD_PYBIND="ON" USE_BOOST_FEATURES="ON" ENABLE_BOOST_SERIALIZATION="ON" @@ -64,7 +63,7 @@ function build() fi cmake $GITHUB_WORKSPACE \ - -B build \ + -B build -G Ninja \ -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \ -DGTSAM_BUILD_TESTS=OFF \ -DGTSAM_BUILD_UNSTABLE=${GTSAM_BUILD_UNSTABLE:-ON} \ diff --git a/.github/scripts/unix.sh b/.github/scripts/unix.sh index f6b4114a4f..596c618b57 100755 --- a/.github/scripts/unix.sh +++ b/.github/scripts/unix.sh @@ -17,9 +17,8 @@ function configure() # GTSAM_BUILD_WITH_MARCH_NATIVE=OFF: to avoid crashes in builder VMs # CMAKE_CXX_FLAGS="-w": Suppress warnings to avoid IO latency in CI logs - export CMAKE_GENERATOR=Ninja cmake $GITHUB_WORKSPACE \ - -B build \ + -B build -G Ninja \ -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Debug} \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=${GTSAM_BUILD_TESTS:-OFF} \ diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index a8abbbc6b3..de4c63c86f 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -146,7 +146,7 @@ jobs: # the wheels to ensure platform compatibility. - name: Run CMake run: | - cmake -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} + cmake -B build -G Ninja -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} # If on macOS, we previously installed boost using homebrew for the first build. # We need to uninstall it before building the wheels with cibuildwheel, which will diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index 0a0016eb26..ff3ef0247a 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -104,12 +104,11 @@ jobs: - name: Configuration shell: bash run: | - export CMAKE_GENERATOR=Ninja # Convert Windows backslashes to forward slashes for CMake BOOST_ROOT_UNIX=$(echo "$BOOST_ROOT" | sed 's/\\/\//g') cmake -E remove_directory build if [ "${{ matrix.build_type }}" = "Release" ]; then - cmake -B build \ + cmake -B build -G Ninja \ -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ -DGTSAM_ALLOW_DEPRECATED_SINCE_V43=OFF \ -DGTSAM_USE_BOOST_FEATURES=ON \ @@ -121,7 +120,7 @@ jobs: -DCMAKE_C_COMPILER_LAUNCHER=sccache \ -DCMAKE_CXX_COMPILER_LAUNCHER=sccache else - cmake -B build \ + cmake -B build -G Ninja \ -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \ -DGTSAM_ALLOW_DEPRECATED_SINCE_V43=OFF \ -DGTSAM_USE_BOOST_FEATURES=OFF \ diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index fde774d4f7..e998672d32 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -127,7 +127,7 @@ jobs: # the wheels to ensure platform compatibility. - name: Run CMake run: | - cmake -B build -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} + cmake -B build -G Ninja -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=${{ matrix.python_version }} # If on macOS, we previously installed boost using homebrew for the first build. # We need to uninstall it before building the wheels with cibuildwheel, which will diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml index 88fb8a8964..ab7fab3a4e 100644 --- a/.github/workflows/time-sfmbal-benchmark-runner.yml +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -77,7 +77,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -115,7 +115,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -191,7 +191,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -229,7 +229,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -303,7 +303,7 @@ jobs: # ── TBB=ON ── - name: Configure (TBB=ON) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ @@ -341,7 +341,7 @@ jobs: - name: Configure (TBB=OFF) run: | - cmake -B build -GNinja \ + cmake -B build -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_CXX_FLAGS="-w" \ -DGTSAM_BUILD_TESTS=OFF \ From 46ba1703b314aa139f43cb2eb73544f7e4d63ac1 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:32:01 -0500 Subject: [PATCH 199/266] Remove unncessary --config flag --- .github/workflows/vcpkg.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index a1ef0f4897..623d8dcc09 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -188,7 +188,7 @@ jobs: - name: cmake build shell: bash run: | - cmake --build build --config ${{ matrix.build_type }} + cmake --build build - name: Run Python tests shell: bash @@ -201,4 +201,4 @@ jobs: - name: Run tests shell: bash run: | - cmake --build build --config ${{ matrix.build_type }} --target check + cmake --build build --target check From 8c84f59628167449edbaf437f128f7c6c09d2ab6 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:48:42 -0500 Subject: [PATCH 200/266] Remove CMake and Ninja installs The runners already have CMake and Ninja. --- .github/workflows/build-cibw.yml | 4 ++-- .github/workflows/build-macos.yml | 4 +--- .github/workflows/build-python.yml | 22 +++---------------- .github/workflows/build-windows.yml | 12 ---------- .github/workflows/prod-cibw.yml | 4 ++-- .../time-sfmbal-benchmark-runner.yml | 6 ++--- 6 files changed, 11 insertions(+), 41 deletions(-) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index de4c63c86f..8d7a134664 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -131,9 +131,9 @@ jobs: run: | python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then - sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev ninja-build + sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then - brew install wget icu4c boost ninja python-setuptools + brew install wget icu4c boost python-setuptools else echo "$RUNNER_OS not supported" exit 1 diff --git a/.github/workflows/build-macos.yml b/.github/workflows/build-macos.yml index 9d5ea9a675..d4d170276b 100644 --- a/.github/workflows/build-macos.yml +++ b/.github/workflows/build-macos.yml @@ -85,10 +85,8 @@ jobs: # (optional) Show outdated without failing the job brew outdated || true - - name: Install Dependencies + - name: Set up compilers run: | - brew upgrade cmake --quiet || brew install cmake --quiet - brew upgrade ninja --quiet || brew install ninja --quiet sudo xcode-select -switch /Applications/Xcode.app echo "CC=clang" >> $GITHUB_ENV echo "CXX=clang++" >> $GITHUB_ENV diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index a12dc3ff84..79aac87811 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -120,9 +120,9 @@ jobs: sudo apt-get -y update if [ "${{ matrix.name }}" = "ubuntu-22.04-gcc-11-noboost" ]; then - sudo apt-get -y install cmake build-essential pkg-config libpython3-dev python3-numpy ninja-build + sudo apt-get -y install build-essential pkg-config libpython3-dev python3-numpy else - sudo apt-get -y install cmake build-essential pkg-config libpython3-dev python3-numpy libboost-all-dev ninja-build + sudo apt-get -y install build-essential pkg-config libpython3-dev python3-numpy libboost-all-dev fi if [ "${{ matrix.compiler }}" = "gcc" ]; then @@ -139,15 +139,7 @@ jobs: if: runner.os == 'macOS' run: | brew update - # Avoid reinstalling cmake when a pinned/local tap version is preinstalled on the runner. - if brew list cmake >/dev/null 2>&1; then - echo "cmake already installed" - cmake --version - else - brew install cmake - fi - # Install ninja and boost only if missing - brew list ninja >/dev/null 2>&1 || brew install ninja + # Install boost only if missing brew list boost >/dev/null 2>&1 || brew install boost sudo xcode-select -switch /Applications/Xcode.app echo "CC=clang" >> $GITHUB_ENV @@ -171,14 +163,6 @@ jobs: with: python-version: ${{ matrix.python_version }} - - name: Install ninja (Windows) - if: runner.os == 'Windows' - shell: bash - run: | - choco install ninja - ninja --version - where ninja - - name: Install Boost (Windows) if: runner.os == 'Windows' shell: powershell diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index ff3ef0247a..506f04b5b8 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -65,18 +65,6 @@ jobs: shell: cmd run: cl - - name: Install Dependencies - shell: powershell - run: | - iwr -useb get.scoop.sh -outfile 'install_scoop.ps1' - .\install_scoop.ps1 -RunAsAdmin - - scoop install cmake --global # So we don't get issues with CMP0074 policy - scoop install ninja --global - - # Scoop modifies the PATH so we make the modified PATH global. - echo "$env:PATH" >> $env:GITHUB_PATH - - name: Install Boost if: matrix.build_type == 'Release' shell: powershell diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index e998672d32..1c9e1497e0 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -112,9 +112,9 @@ jobs: run: | python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then - sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev ninja-build + sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then - brew install boost ninja python-setuptools + brew install boost python-setuptools else echo "$RUNNER_OS not supported" exit 1 diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml index ab7fab3a4e..ef6205ef83 100644 --- a/.github/workflows/time-sfmbal-benchmark-runner.yml +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -72,7 +72,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update - sudo apt-get install -y ninja-build libtbb-dev + sudo apt-get install -y libtbb-dev # ── TBB=ON ── - name: Configure (TBB=ON) @@ -186,7 +186,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update - sudo apt-get install -y ninja-build libtbb-dev + sudo apt-get install -y libtbb-dev # ── TBB=ON ── - name: Configure (TBB=ON) @@ -298,7 +298,7 @@ jobs: bzip2 -dc examples/Data/problem-135-90642-pre.txt.bz2 > examples/Data/dubrovnik-135-90642-pre.txt - name: Install dependencies - run: brew install ninja tbb || true + run: brew install tbb || true # ── TBB=ON ── - name: Configure (TBB=ON) From 2f19f96cef9dd8b58ca3cbd616723637832aac6c Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 02:53:18 -0500 Subject: [PATCH 201/266] Replace incorrect MSBuild terminology with MSVC --- .github/workflows/build-python.yml | 6 +++--- .github/workflows/build-windows.yml | 2 +- .github/workflows/vcpkg.yml | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index 79aac87811..831472784c 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -65,7 +65,7 @@ jobs: ubuntu-22.04-clang-11, macos-14-xcode-15.4, macos-15-xcode-16, - windows-2022-msbuild, + windows-2022-msvc, ] build_type: [Release] @@ -96,7 +96,7 @@ jobs: compiler: xcode version: "16" - - name: windows-2022-msbuild + - name: windows-2022-msvc os: windows-2022 platform: 64 @@ -145,7 +145,7 @@ jobs: echo "CC=clang" >> $GITHUB_ENV echo "CXX=clang++" >> $GITHUB_ENV - - name: Setup msbuild (Windows) + - name: Setup MSVC (Windows) if: runner.os == 'Windows' uses: ilammy/msvc-dev-cmd@v1 with: diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index 506f04b5b8..381f4164ea 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -55,7 +55,7 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: Setup msbuild + - name: Setup MSVC uses: ilammy/msvc-dev-cmd@v1 with: arch: x${{ matrix.platform }} diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 623d8dcc09..8daa01be64 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -56,7 +56,7 @@ jobs: key: ${{ matrix.os }} restore-keys: ${{ matrix.os }} - - name: Setup msbuild + - name: Setup MSVC if: runner.os == 'Windows' uses: ilammy/msvc-dev-cmd@v1 with: From 72e15d3794561fd9b6001ba3745e9a9b0643b997 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Mon, 9 Feb 2026 20:43:12 -0500 Subject: [PATCH 202/266] Remove unused trigger-python workflow --- .github/workflows/trigger-python.yml | 21 --------------------- 1 file changed, 21 deletions(-) delete mode 100644 .github/workflows/trigger-python.yml diff --git a/.github/workflows/trigger-python.yml b/.github/workflows/trigger-python.yml deleted file mode 100644 index ab27d9deec..0000000000 --- a/.github/workflows/trigger-python.yml +++ /dev/null @@ -1,21 +0,0 @@ -# This triggers Python builds on `gtsam-manylinux-build` -name: Trigger Python Builds -on: - push: - branches: - - develop - paths-ignore: - - '**.md' - - '**.ipynb' - - 'myst.yml' -jobs: - triggerPython: - runs-on: ubuntu-latest - steps: - - name: Repository Dispatch - uses: ProfFan/repository-dispatch@master - with: - token: ${{ secrets.PYTHON_CI_REPO_ACCESS_TOKEN }} - repository: borglab/gtsam-manylinux-build - event-type: python-wrapper - client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}"}' From 0792b049ce24c3e0aee0d17f0a973d9b31e2ed9d Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Thu, 5 Feb 2026 17:39:05 -0500 Subject: [PATCH 203/266] Use vcpkg manifest --- .github/workflows/vcpkg.yml | 113 +++++++++++------------------------- .gitignore | 1 + INSTALL.md | 88 ++++++++++++++-------------- cmake/HandleBoost.cmake | 1 + doc/workflows.md | 5 ++ vcpkg.json | 42 ++++++++++++++ 6 files changed, 129 insertions(+), 121 deletions(-) create mode 100644 vcpkg.json diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 8daa01be64..662d8e3015 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -28,34 +28,21 @@ jobs: triplet: x64-windows-release build_type: Release test_target: RUN_TESTS - binary_cache: C:\Users\runneradmin\AppData\Local\vcpkg\archives python: python - additional_vcpkg_ports: intel-mkl - os: ubuntu-latest triplet: x64-linux-release build_type: Release test_target: test - binary_cache: /home/runner/.cache/vcpkg/archives python: python3 - additional_vcpkg_ports: intel-mkl - os: macos-latest triplet: arm64-osx-release build_type: Release test_target: test - binary_cache: /Users/runner/.cache/vcpkg/archives python: python3 steps: - name: Checkout uses: actions/checkout@v4 - - name: Restore cache dependencies - id: cache-restore - uses: actions/cache/restore@v3 - with: - path: ${{ matrix.binary_cache }} - key: ${{ matrix.os }} - restore-keys: ${{ matrix.os }} - - name: Setup MSVC if: runner.os == 'Windows' uses: ilammy/msvc-dev-cmd@v1 @@ -83,68 +70,20 @@ jobs: run: | brew install autoconf autoconf-archive automake libtool - - name: Set vcpkg installtion root env - shell: bash - run: | - VCPKG_BASH_PATH=${VCPKG_INSTALLATION_ROOT//\\//} - echo "VCPKG_INSTALLATION_ROOT=$VCPKG_BASH_PATH" >> "$GITHUB_ENV" - - - name: "Install dependencies" - run: > - vcpkg x-set-installed --triplet ${{ matrix.triplet }} --host-triplet ${{ matrix.triplet }} - boost-assign - boost-bimap - boost-chrono - boost-date-time - boost-filesystem - boost-format - boost-graph - boost-math - boost-program-options - boost-regex - boost-serialization - boost-system - boost-thread - boost-timer - tbb - pybind11 - geographiclib - eigen3 - ${{ matrix.additional_vcpkg_ports }} - - - name: On Failure, upload vcpkg logs - if: failure() - uses: actions/upload-artifact@v4 + - name: Set up vcpkg + uses: lukka/run-vcpkg@v11.5 with: - name: logs_${{ matrix.os }}_${{ github.event.pull_request.head.sha }} - path: | - ${{ env.VCPKG_INSTALLATION_ROOT }}/buildtrees/**/*.log + vcpkgDirectory: ${{ github.workspace }}/vcpkg + env: + VCPKG_BINARY_SOURCES: default,rw - - name: copy files for hash - shell: bash - run: | - echo $VCPKG_INSTALLATION_ROOT - mkdir -p vcpkg-info - find $VCPKG_INSTALLATION_ROOT/installed/ -type f -name 'vcpkg_abi_info.txt' | \ - while read filepath; do - triplet=$(echo "$filepath" | awk -F/ '{print $(NF-3)}') - port=$(echo "$filepath" | awk -F/ '{print $(NF-1)}') - cp "$filepath" "vcpkg-info/${triplet}_${port}.txt" - done - - - name: Save cache dependencies - # Don't save if it's the exact same - if: steps.cache-restore.outputs.cache-matched-key != format('{0}-{1}', matrix.os, hashFiles('vcpkg-info/*')) - uses: actions/cache/save@v4 + - name: Restore cache dependencies + id: cache-restore + uses: actions/cache/restore@v3 with: - path: ${{ matrix.binary_cache }} - key: ${{ matrix.os }}-${{ hashFiles('vcpkg-info/*') }} - - - name: Install python packages - shell: bash - run: | - $VCPKG_INSTALLATION_ROOT/installed/${{ matrix.triplet }}/tools/python3/${{ matrix.python }} -m ensurepip --upgrade - $VCPKG_INSTALLATION_ROOT/installed/${{ matrix.triplet }}/tools/python3/${{ matrix.python }} -m pip install -r python/dev_requirements.txt + path: ${{ env.VCPKG_DEFAULT_BINARY_CACHE }} + key: ${{ matrix.os }}-${{ hashFiles('vcpkg.json') }} + restore-keys: ${{ matrix.os }}-${{ hashFiles('vcpkg.json') }} - name: Set Swap Space (Linux) if: runner.os == 'Linux' @@ -153,16 +92,13 @@ jobs: swap-size-gb: 6 - name: cmake config - if: success() shell: bash run: | - export CL=-openmp - cmake -B build -G Ninja \ - -DCMAKE_TOOLCHAIN_FILE=$VCPKG_INSTALLATION_ROOT/scripts/buildsystems/vcpkg.cmake \ + -DCMAKE_TOOLCHAIN_FILE=vcpkg/scripts/buildsystems/vcpkg.cmake \ -DCMAKE_C_COMPILER_LAUNCHER=sccache \ -DCMAKE_CXX_COMPILER_LAUNCHER=sccache \ - -DVCPKG_INSTALLED_DIR=$VCPKG_INSTALLATION_ROOT/installed \ + -DVCPKG_INSTALLED_DIR=$GITHUB_WORKSPACE/vcpkg_installed \ -DVCPKG_TARGET_TRIPLET=${{ matrix.triplet }} \ -DVCPKG_HOST_TRIPLET=${{ matrix.triplet }} \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ @@ -185,6 +121,27 @@ jobs: -DCTEST_EXTRA_ARGS='${{ matrix.ctest_extra_flags }}' \ -DPYTEST_EXTRA_ARGS='${{ matrix.pytest_extra_flags }}' + - name: On Failure, upload vcpkg logs + if: failure() + uses: actions/upload-artifact@v4 + with: + name: logs_${{ matrix.os }}_${{ github.event.pull_request.head.sha }} + path: vcpkg/buildtrees/**/*.log + + - name: Cache dependencies + # Don't save if it's the exact same + if: steps.cache-restore.outputs.cache-matched-key != format('{0}-{1}-{2}', matrix.os, hashFiles('vcpkg.json'), hashFiles('vcpkg_installed/**/vcpkg_abi_info.txt')) + uses: actions/cache/save@v4 + with: + path: ${{ env.VCPKG_DEFAULT_BINARY_CACHE }} + key: ${{ matrix.os }}-${{ hashFiles('vcpkg.json') }}-${{ hashFiles('vcpkg_installed/**/vcpkg_abi_info.txt') }} + + - name: Install python packages + shell: bash + run: | + vcpkg_installed/${{ matrix.triplet }}/tools/python3/${{ matrix.python }} -m ensurepip --upgrade + vcpkg_installed/${{ matrix.triplet }}/tools/python3/${{ matrix.python }} -m pip install -r python/dev_requirements.txt + - name: cmake build shell: bash run: | @@ -193,7 +150,7 @@ jobs: - name: Run Python tests shell: bash run: | - export PATH="$PATH:$VCPKG_INSTALLATION_ROOT/installed/${{ matrix.triplet }}/bin" + export PATH="$PATH:$GITHUB_WORKSPACE/vcpkg_installed/${{ matrix.triplet }}/bin" cmake --build build --target python-install cmake --build build --target python-test cmake --build build --target python-test-unstable diff --git a/.gitignore b/.gitignore index ab4006efbe..0e3e501973 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ .vscode .env /.vs/ +vcpkg_installed/ /CMakeSettings.json # for QtCreator: CMakeLists.txt.user* diff --git a/INSTALL.md b/INSTALL.md index f2cb2a0343..f5cc93e673 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -289,58 +289,50 @@ or when importing GTSAM using the python wrapper. -## Compile gtsam with vcpkg. (For linux/wsl) -Install python dependencies + ninja + build essential in linux: -``` +## Compile gtsam with vcpkg + +vcpkg is an easy, cross-platform way to install all the dependencies gtsam uses, including Boost, MKL, and pybind11. It will calculate the proper [triplet for your system](https://learn.microsoft.com/en-us/vcpkg/concepts/triplets), like x64-linux, x64-windows, or arm64-osx, and install dependencies accordingly. That triplet will be referred to as `` in this guide. + +To get started, install some base dependencies. + +On Linux, install Python dependencies + ninja + build-essential: + +```bash sudo apt update sudo apt-get install autoconf automake autoconf-archive ninja-build build-essential -y ``` -On your home dir near gtsam dir: -Setup vcpkg +On Windows, see [the Prerequisites section earlier](#Prerequisites). + +On Mac, install Python dependencies + ninja: + ```bash -git clone https://github.com/microsoft/vcpkg -cd vcpkg -./bootstrap-vcpkg.sh +brew install autoconf autoconf-archive automake libtool ``` -vcpkg install dependencies -on vcpkg dir: +Go to your gtsam folder `cd gtsam`, and set up vcpkg: + ```bash -./vcpkg x-set-installed \ - boost-assign \ - boost-bimap \ - boost-chrono \ - boost-date-time \ - boost-filesystem \ - boost-format \ - boost-graph \ - boost-math \ - boost-program-options \ - boost-regex \ - boost-serialization \ - boost-system \ - boost-thread \ - boost-timer \ - tbb \ - pybind11 +git clone https://github.com/microsoft/vcpkg +./vcpkg/bootstrap-vcpkg.sh # or ./vcpkg/bootstrap-vcpkg.bat on Windows ``` -Setup python dependencies -Go to your gtsam folder `cd gtsam`: +Setup vcpkg and Python dependencies + ```bash -../vcpkg/installed/x64-linux/tools/python3/python3 -m ensurepip --upgrade -../vcpkg/installed/x64-linux/tools/python3/python3 -m pip install -r python/dev_requirements.txt +./vcpkg/vcpkg install +# The Python executable is called python, not python3 on Windows +./vcpkg_installed//tools/python3/python3 -m ensurepip --upgrade +./vcpkg_installed//tools/python3/python3 -m pip install -r python/dev_requirements.txt ``` -Cmake config: -In gtsam folder +Configure CMake build: ```bash cmake -B build -G Ninja \ - -DCMAKE_TOOLCHAIN_FILE=../scripts/buildsystems/vcpkg.cmake \ - -DVCPKG_INSTALLED_DIR=../installed \ - -DVCPKG_TARGET_TRIPLET=x64-linux \ - -DVCPKG_HOST_TRIPLET=x64-linux \ + -DCMAKE_TOOLCHAIN_FILE=vcpkg/scripts/buildsystems/vcpkg.cmake \ + -DVCPKG_INSTALLED_DIR=vcpkg_installed \ + -DVCPKG_TARGET_TRIPLET= \ + -DVCPKG_HOST_TRIPLET= \ -DCMAKE_BUILD_TYPE=Release \ -DGTSAM_BUILD_EXAMPLES_ALWAYS=ON \ -DGTSAM_ROT3_EXPMAP=ON \ @@ -355,22 +347,32 @@ cmake -B build -G Ninja \ -DGTSAM_SUPPORT_NESTED_DISSECTION=ON ``` -cmake compile: -In gtsam folder: +Build gtsam: + ```bash cmake --build build ``` -Run python tests: +Add vcpkg libraries to PATH: + +Linux/Mac: +```bash +export PATH="$PATH:/path/to/gtsam/vcpkg_installed//bin" ``` -VCPKG_INSTALLATION_ROOT= -export PATH="$PATH:$VCPKG_INSTALLATION_ROOT/installed/x64-linux/bin" + +Windows (PowerShell): +```pwsh +$env:Path = "$env:Path;\path\to\gtsam\vcpkg_installed\\bin" +``` + +Run Python tests: +```bash cmake --build build --target python-install cmake --build build --target python-test cmake --build build --target python-test-unstable ``` -Run gtsam tests: +Run gtsam tests: ```bash cmake --build build --target check ``` \ No newline at end of file diff --git a/cmake/HandleBoost.cmake b/cmake/HandleBoost.cmake index 18c7c34e2c..1650407353 100644 --- a/cmake/HandleBoost.cmake +++ b/cmake/HandleBoost.cmake @@ -39,6 +39,7 @@ endif() ################################################################################ # Set minimum required Boost version and components. +# Note: Keep this in sync with vcpkg.json. # optional, program_options, random, range are all used in tests/examples/Python, but are not library dependencies. timer/chrono is used conditionally. # concept_check, fusion, move, phoenix, pool, smart_ptr, spirit, tokenizer, type_traits, optional, range are header only and are not components. set(BOOST_FIND_MINIMUM_VERSION 1.70) diff --git a/doc/workflows.md b/doc/workflows.md index 63e65c309d..d8007a2cc9 100644 --- a/doc/workflows.md +++ b/doc/workflows.md @@ -12,8 +12,13 @@ The `.github/workflows` directory contains definitions for the various CI/CD pro - **`build-python.yml`**: Verifies the Python wrapper compilation across Linux, macOS, and Windows. - **`build-cibw.yml`**: Builds distributable Python wheels using `cibuildwheel`. It builds dependencies (like Boost) from source to ensure ABI compatibility. - **`prod-cibw.yml`**: Used for production wheel builds (often triggered on release). +- **`vcpkg.yml`**: Compiles and tests GTSAM on Linux, macOS, and Windows using `vcpkg` to install all dependencies. - **`deploy.yml`**: Handles deployment tasks (e.g., docs, release artifacts). +## Updating vcpkg dependencies + +`vcpkg` supports a manifest in the form of `vcpkg.json`, which specifies all the dependencies to install, and a "baseline", which is just a commit hash from https://github.com/microsoft/vcpkg. Instead of upgrading individual library versions which might be incompatible, you upgrade baselines, which are a set of library versions that have been tested to work together. This also means library versions are pinned to the baseline, so the baseline needs to periodically updated to get the latest libraries. Updating is not *strictly* necessary, as old library versions should work for a while, but it can be good to ensure compatibility with the latest libraies. To update the baseline, simply go to https://github.com/microsoft/vcpkg and copy the full commit hash for the version you want (could be HEAD for the day, or if you prefer tags, you can use the commit hash for a tag instead) and update the `builtin-baseline` field in `vcpkg.json` at the root of this repo. + ## Docker CI Images (Linux Only) The **Linux CI** workflow (`build-linux.yml`) relies on pre-built Docker images to ensure consistency and speed up build times. These images are hosted in the [borglab/docker-images](https://github.com/borglab/docker-images) repository. diff --git a/vcpkg.json b/vcpkg.json new file mode 100644 index 0000000000..613d24648b --- /dev/null +++ b/vcpkg.json @@ -0,0 +1,42 @@ +{ + "$schema": "https://raw.githubusercontent.com/microsoft/vcpkg-tool/main/docs/vcpkg.schema.json", + "$baseline_comment": [ + "`builtin-baseline` pins the vcpkg *registry snapshot* (a commit in the vcpkg repo).", + "It makes dependency resolution reproducible across machines/CI.", + "Policy suggestion for GTSAM: only bump this SHA in a dedicated PR (with CI green), so upgrades are deliberate.", + "Current commit is from 2025-01-16." + ], + "builtin-baseline": "66c0373dc7fca549e5803087b9487edfe3aca0a1", + + "dependencies": [ + { + "$mkl_comment": [ + "MKL is only used on Windows/Linux (not macOS).", + "The platform expression uses OR; `windows,linux` is equivalent to `windows | linux`.", + "If we later want a different BLAS/LAPACK backend on macOS, add it as a separate dependency with `platform: osx`." + ], + "name": "intel-mkl", + "platform": "windows | linux" + }, + "tbb", + "pybind11", + "geographiclib", + "eigen3", + "boost-concept-check", + "boost-fusion", + "boost-graph", + "boost-move", + "boost-optional", + "boost-phoenix", + "boost-pool", + "boost-program-options", + "boost-random", + "boost-range", + "boost-serialization", + "boost-smart-ptr", + "boost-spirit", + "boost-timer", + "boost-tokenizer", + "boost-type-traits" + ] +} \ No newline at end of file From 65256a37a5cfe8fc7a8394790533ec9298a73735 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Wed, 11 Feb 2026 02:37:37 -0500 Subject: [PATCH 204/266] Add more swap space --- .github/workflows/vcpkg.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/vcpkg.yml b/.github/workflows/vcpkg.yml index 662d8e3015..6a0fec6f34 100644 --- a/.github/workflows/vcpkg.yml +++ b/.github/workflows/vcpkg.yml @@ -89,7 +89,7 @@ jobs: if: runner.os == 'Linux' uses: pierotofy/set-swap-space@master with: - swap-size-gb: 6 + swap-size-gb: 7 - name: cmake config shell: bash From 1c9e8ea9c443f0a2033a9c101cd1342fb1b10dff Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Wed, 11 Feb 2026 23:47:41 -0500 Subject: [PATCH 205/266] Remove macOS 14 from CI --- .github/workflows/build-macos.yml | 26 +++++++------------ .github/workflows/build-python.yml | 6 ----- .../time-sfmbal-benchmark-runner.yml | 2 +- 3 files changed, 11 insertions(+), 23 deletions(-) diff --git a/.github/workflows/build-macos.yml b/.github/workflows/build-macos.yml index d4d170276b..af5fc080d6 100644 --- a/.github/workflows/build-macos.yml +++ b/.github/workflows/build-macos.yml @@ -31,39 +31,33 @@ jobs: # Github Actions requires a single row to be added to the build matrix. # See https://help.github.com/en/articles/workflow-syntax-for-github-actions. name: [ - macos-14-xcode-15.4, macos-15-xcode-16, - macos-14-xcode-15.4-boost, - macos-14-xcode-15.4-geographiclib, + macos-15-xcode-16-boost, + macos-15-xcode-16-geographiclib, ] build_type: [Debug, Release] build_unstable: [ON] include: - - name: macos-14-xcode-15.4 - os: macos-14 - compiler: xcode - version: "15.4" - - name: macos-15-xcode-16 os: macos-15 compiler: xcode version: "16" - - name: macos-14-xcode-15.4-boost - os: macos-14 + - name: macos-15-xcode-16-boost + os: macos-15 compiler: xcode - version: "15.4" + version: "16" - - name: macos-14-xcode-15.4-geographiclib - os: macos-14 + - name: macos-15-xcode-16-geographiclib + os: macos-15 compiler: xcode - version: "15.4" + version: "16" exclude: - - name: macos-14-xcode-15.4-boost + - name: macos-15-xcode-16-boost build_type: Debug - - name: macos-14-xcode-15.4-geographiclib + - name: macos-15-xcode-16-geographiclib build_type: Debug steps: diff --git a/.github/workflows/build-python.yml b/.github/workflows/build-python.yml index 831472784c..21cb569503 100644 --- a/.github/workflows/build-python.yml +++ b/.github/workflows/build-python.yml @@ -63,7 +63,6 @@ jobs: ubuntu-22.04-gcc-11, ubuntu-22.04-gcc-11-noboost, ubuntu-22.04-clang-11, - macos-14-xcode-15.4, macos-15-xcode-16, windows-2022-msvc, ] @@ -86,11 +85,6 @@ jobs: compiler: clang version: "11" - - name: macos-14-xcode-15.4 - os: macos-14 - compiler: xcode - version: "15.4" - - name: macos-15-xcode-16 os: macos-15 compiler: xcode diff --git a/.github/workflows/time-sfmbal-benchmark-runner.yml b/.github/workflows/time-sfmbal-benchmark-runner.yml index ef6205ef83..6b2ac3dd12 100644 --- a/.github/workflows/time-sfmbal-benchmark-runner.yml +++ b/.github/workflows/time-sfmbal-benchmark-runner.yml @@ -263,7 +263,7 @@ jobs: benchmark_macos_arm64: if: inputs.runner_id == 'macos-arm64' - runs-on: macos-14 + runs-on: macos-15 steps: - name: Checkout target ref uses: actions/checkout@v4 From 0ba676e9085a8b1c5a30d0aa40db1425ef6c7bf3 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:48:14 -0500 Subject: [PATCH 206/266] Fix Linux cibuildwheel jobs --- .github/workflows/build-cibw.yml | 1 + .github/workflows/prod-cibw.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index 8d7a134664..b767d391a4 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -131,6 +131,7 @@ jobs: run: | python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then + sudo apt update sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then brew install wget icu4c boost python-setuptools diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index 1c9e1497e0..c5772d6377 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -112,6 +112,7 @@ jobs: run: | python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then + sudo apt update sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then brew install boost python-setuptools From a534bf547fc0801e17853276c63dc7e56e5f14d7 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:50:42 -0500 Subject: [PATCH 207/266] Fix warnings --- .github/workflows/build-cibw.yml | 4 ++-- .github/workflows/prod-cibw.yml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index b767d391a4..d56180e23d 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -132,9 +132,9 @@ jobs: python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then sudo apt update - sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev + sudo apt-get install -y libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then - brew install wget icu4c boost python-setuptools + brew install boost python-setuptools else echo "$RUNNER_OS not supported" exit 1 diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index c5772d6377..b040dd8ea8 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -113,7 +113,7 @@ jobs: python3 -m pip install -r python/dev_requirements.txt if [ "$RUNNER_OS" == "Linux" ]; then sudo apt update - sudo apt-get install -y wget libicu-dev python3-pip python3-setuptools libboost-all-dev + sudo apt-get install -y libicu-dev python3-pip python3-setuptools libboost-all-dev elif [ "$RUNNER_OS" == "macOS" ]; then brew install boost python-setuptools else From 4ef562fd55ba7e18af6da072a706d4e2a75139ed Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:51:05 -0500 Subject: [PATCH 208/266] Only build required Boost libraries --- .github/scripts/python_wheels/cibw_before_all.sh | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/scripts/python_wheels/cibw_before_all.sh b/.github/scripts/python_wheels/cibw_before_all.sh index e68422351e..5924341569 100644 --- a/.github/scripts/python_wheels/cibw_before_all.sh +++ b/.github/scripts/python_wheels/cibw_before_all.sh @@ -33,9 +33,13 @@ BOOST_PREFIX="$HOME/opt/boost" ./bootstrap.sh --prefix=${BOOST_PREFIX} if [ "$(uname)" == "Linux" ]; then - ./b2 install --prefix=${BOOST_PREFIX} --with=all -d0 + ./b2 install --prefix=${BOOST_PREFIX} -d0 --with-graph \ + --with-move --with-optional --with-program_options --with-random \ + --with-serialization --with-smart_ptr --with-timer --with-chrono elif [ "$(uname)" == "Darwin" ]; then - ./b2 install --prefix=${BOOST_PREFIX} --with=all -d0 \ + ./b2 install --prefix=${BOOST_PREFIX} -d0 --with-graph \ + --with-move --with-optional --with-program_options --with-random \ + --with-serialization --with-smart_ptr --with-timer --with-chrono \ cxxflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" \ linkflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" fi From 2e3466942ec41af3e588d5c27617ebe90cc36436 Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Thu, 12 Feb 2026 11:07:02 -0500 Subject: [PATCH 209/266] Build universal Mac binaries --- .../scripts/python_wheels/cibw_before_all.sh | 13 ++++++++- .github/workflows/build-cibw.yml | 27 ++++--------------- .github/workflows/prod-cibw.yml | 27 ++++--------------- 3 files changed, 22 insertions(+), 45 deletions(-) diff --git a/.github/scripts/python_wheels/cibw_before_all.sh b/.github/scripts/python_wheels/cibw_before_all.sh index 5924341569..4bab03d59a 100644 --- a/.github/scripts/python_wheels/cibw_before_all.sh +++ b/.github/scripts/python_wheels/cibw_before_all.sh @@ -8,7 +8,6 @@ set -x PYTHON_VERSION="$1" PROJECT_DIR="$2" -ARCH=$(uname -m) export PYTHON="python${PYTHON_VERSION}" @@ -40,8 +39,19 @@ elif [ "$(uname)" == "Darwin" ]; then ./b2 install --prefix=${BOOST_PREFIX} -d0 --with-graph \ --with-move --with-optional --with-program_options --with-random \ --with-serialization --with-smart_ptr --with-timer --with-chrono \ + architecture=arm \ cxxflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" \ linkflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" + ./b2 install --prefix=${BOOST_PREFIX}/x86 -d0 --with-graph \ + --with-move --with-optional --with-program_options --with-random \ + --with-serialization --with-smart_ptr --with-timer --with-chrono \ + architecture=x86 \ + cxxflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" \ + linkflags="-mmacosx-version-min=${MACOSX_DEPLOYMENT_TARGET}" + for dylib in ${BOOST_PREFIX}/lib/*.dylib; do + lipo -create -output $dylib $dylib ${BOOST_PREFIX}/x86/lib/$(basename $dylib) + done + rm -r ${BOOST_PREFIX}/x86 fi cd .. @@ -71,6 +81,7 @@ rm -rf CMakeCache.txt CMakeFiles cmake $PROJECT_DIR \ -B build \ -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \ -DGTSAM_BUILD_TESTS=OFF \ -DGTSAM_BUILD_UNSTABLE=${GTSAM_BUILD_UNSTABLE:-ON} \ -DGTSAM_USE_QUATERNIONS=OFF \ diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index d56180e23d..bf4795149d 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -75,41 +75,23 @@ jobs: platform_id: manylinux_aarch64 manylinux_image: manylinux2014 - # MacOS x86_64 - - os: macos-15-intel - python_version: "3.10" - cibw_python_version: 310 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.11" - cibw_python_version: 311 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.12" - cibw_python_version: 312 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.13" - cibw_python_version: 313 - platform_id: macosx_x86_64 - # MacOS arm64 - os: macos-latest python_version: "3.10" cibw_python_version: 310 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.11" cibw_python_version: 311 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.12" cibw_python_version: 312 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.13" cibw_python_version: 313 - platform_id: macosx_arm64 + platform_id: macosx_universal2 steps: - name: Checkout @@ -164,6 +146,7 @@ jobs: CIBW_MANYLINUX_X86_64_IMAGE: ${{ matrix.manylinux_image }} CIBW_MANYLINUX_AARCH64_IMAGE: ${{ matrix.manylinux_image }} CIBW_ARCHS: all + CIBW_ARCHS_MACOS: universal2 CIBW_ENVIRONMENT_PASS_LINUX: DEVELOP TIMESTAMP # Set the minimum required MacOS version for the wheels. diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index b040dd8ea8..a3ab2cc96f 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -60,41 +60,23 @@ jobs: platform_id: manylinux_aarch64 manylinux_image: manylinux2014 - # MacOS x86_64 - - os: macos-15-intel - python_version: "3.10" - cibw_python_version: 310 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.11" - cibw_python_version: 311 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.12" - cibw_python_version: 312 - platform_id: macosx_x86_64 - - os: macos-15-intel - python_version: "3.13" - cibw_python_version: 313 - platform_id: macosx_x86_64 - # MacOS arm64 - os: macos-latest python_version: "3.10" cibw_python_version: 310 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.11" cibw_python_version: 311 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.12" cibw_python_version: 312 - platform_id: macosx_arm64 + platform_id: macosx_universal2 - os: macos-latest python_version: "3.13" cibw_python_version: 313 - platform_id: macosx_arm64 + platform_id: macosx_universal2 steps: - name: Checkout @@ -145,6 +127,7 @@ jobs: CIBW_MANYLINUX_X86_64_IMAGE: ${{ matrix.manylinux_image }} CIBW_MANYLINUX_AARCH64_IMAGE: ${{ matrix.manylinux_image }} CIBW_ARCHS: all + CIBW_ARCHS_MACOS: universal2 # Set the minimum required MacOS version for the wheels. MACOSX_DEPLOYMENT_TARGET: 10.15 From a7bdc4be8331a353ecfa359f3141f86134416ecd Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Fri, 13 Feb 2026 02:26:58 -0500 Subject: [PATCH 210/266] Silence annoying Doxygen messages --- doc/Doxyfile.in | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in index 3461127ae1..bba6056e11 100644 --- a/doc/Doxyfile.in +++ b/doc/Doxyfile.in @@ -814,7 +814,7 @@ CITE_BIB_FILES = # messages are off. # The default value is: NO. -QUIET = NO +QUIET = YES # The WARNINGS tag can be used to turn on/off the warning messages that are # generated to standard error (stderr) by doxygen. If WARNINGS is set to YES From 66fa39d601e91a510b9e1794057ffd016bb2e71d Mon Sep 17 00:00:00 2001 From: Gold856 <117957790+Gold856@users.noreply.github.com> Date: Fri, 13 Feb 2026 13:34:54 -0500 Subject: [PATCH 211/266] Install and test built wheel --- .github/workflows/build-cibw.yml | 39 ++++++++++++++++++++------------ .github/workflows/prod-cibw.yml | 38 +++++++++++++++++++------------ 2 files changed, 49 insertions(+), 28 deletions(-) diff --git a/.github/workflows/build-cibw.yml b/.github/workflows/build-cibw.yml index bf4795149d..dd24d395f1 100644 --- a/.github/workflows/build-cibw.yml +++ b/.github/workflows/build-cibw.yml @@ -32,11 +32,6 @@ jobs: matrix: include: # Linux x86_64 - - os: ubuntu-latest - python_version: "3.10" - cibw_python_version: 310 - platform_id: manylinux_x86_64 - manylinux_image: manylinux2014 - os: ubuntu-latest python_version: "3.11" cibw_python_version: 311 @@ -52,13 +47,13 @@ jobs: cibw_python_version: 313 platform_id: manylinux_x86_64 manylinux_image: manylinux2014 + - os: ubuntu-latest + python_version: "3.14" + cibw_python_version: 314 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 # Linux aarch64 - - os: ubuntu-24.04-arm - python_version: "3.10" - cibw_python_version: 310 - platform_id: manylinux_aarch64 - manylinux_image: manylinux2014 - os: ubuntu-24.04-arm python_version: "3.11" cibw_python_version: 311 @@ -74,12 +69,13 @@ jobs: cibw_python_version: 313 platform_id: manylinux_aarch64 manylinux_image: manylinux2014 + - os: ubuntu-24.04-arm + python_version: "3.14" + cibw_python_version: 314 + platform_id: manylinux_aarch64 + manylinux_image: manylinux2014 # MacOS arm64 - - os: macos-latest - python_version: "3.10" - cibw_python_version: 310 - platform_id: macosx_universal2 - os: macos-latest python_version: "3.11" cibw_python_version: 311 @@ -92,6 +88,10 @@ jobs: python_version: "3.13" cibw_python_version: 313 platform_id: macosx_universal2 + - os: macos-latest + python_version: "3.14" + cibw_python_version: 314 + platform_id: macosx_universal2 steps: - name: Checkout @@ -165,6 +165,17 @@ jobs: run: bash .github/scripts/python_wheels/build_wheels.sh + - name: Test wheel + run: | + python3 -m pip install wheelhouse/*.whl + python3 -c "import platform; print(platform.machine()); import gtsam" + if [ "$RUNNER_OS" == "macOS" ]; then + # Uninstall numpy and reinstall wheel to get x86 numpy for smoke test + arch -x86_64 python3 -m pip uninstall -y numpy + arch -x86_64 python3 -m pip install wheelhouse/*.whl + arch -x86_64 python3 -c "import platform; print(platform.machine()); import gtsam;" + fi + - name: Store artifacts uses: actions/upload-artifact@v4 with: diff --git a/.github/workflows/prod-cibw.yml b/.github/workflows/prod-cibw.yml index a3ab2cc96f..1bfc99b747 100644 --- a/.github/workflows/prod-cibw.yml +++ b/.github/workflows/prod-cibw.yml @@ -17,11 +17,6 @@ jobs: matrix: include: # Linux x86_64 - - os: ubuntu-latest - python_version: "3.10" - cibw_python_version: 310 - platform_id: manylinux_x86_64 - manylinux_image: manylinux2014 - os: ubuntu-latest python_version: "3.11" cibw_python_version: 311 @@ -37,13 +32,13 @@ jobs: cibw_python_version: 313 platform_id: manylinux_x86_64 manylinux_image: manylinux2014 + - os: ubuntu-latest + python_version: "3.14" + cibw_python_version: 314 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 # Linux aarch64 - - os: ubuntu-24.04-arm - python_version: "3.10" - cibw_python_version: 310 - platform_id: manylinux_aarch64 - manylinux_image: manylinux2014 - os: ubuntu-24.04-arm python_version: "3.11" cibw_python_version: 311 @@ -59,12 +54,13 @@ jobs: cibw_python_version: 313 platform_id: manylinux_aarch64 manylinux_image: manylinux2014 + - os: ubuntu-24.04-arm + python_version: "3.14" + cibw_python_version: 314 + platform_id: manylinux_aarch64 + manylinux_image: manylinux2014 # MacOS arm64 - - os: macos-latest - python_version: "3.10" - cibw_python_version: 310 - platform_id: macosx_universal2 - os: macos-latest python_version: "3.11" cibw_python_version: 311 @@ -77,6 +73,10 @@ jobs: python_version: "3.13" cibw_python_version: 313 platform_id: macosx_universal2 + - os: macos-latest + python_version: "3.14" + cibw_python_version: 314 + platform_id: macosx_universal2 steps: - name: Checkout @@ -145,6 +145,16 @@ jobs: run: bash .github/scripts/python_wheels/build_wheels.sh + - name: Test wheel + run: | + python3 -m pip install wheelhouse/*.whl + python3 -c "import platform; print(platform.machine()); import gtsam" + if [ "$RUNNER_OS" == "macOS" ]; then + arch -x86_64 python3 -m pip uninstall -y numpy + arch -x86_64 python3 -m pip install wheelhouse/*.whl + arch -x86_64 python3 -c "import platform; print(platform.machine()); import gtsam;" + fi + - name: Store artifacts uses: actions/upload-artifact@v4 with: From a7ba2136dd3cd92797131b3747b8ba5d9bc6a2c9 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 15 Feb 2026 21:45:10 -0500 Subject: [PATCH 212/266] Allowing Non Noise Model Factors --- gtsam/nonlinear/GncOptimizer.h | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 01dcef3c3d..241b465915 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -65,6 +65,12 @@ class GncOptimizer { for (size_t i = 0; i < graph.size(); i++) { if (graph[i]) { NoiseModelFactor::shared_ptr factor = graph.at(i); + if (!factor) { + // Non-NoiseModelFactor (e.g. KarcherMeanFactorPose3). + nfg_[i] = graph[i]; + params_.knownInliers.push_back(i); + continue; + } auto robust = std::dynamic_pointer_cast(factor->noiseModel()); // if the factor has a robust loss, we remove the robust loss @@ -72,6 +78,12 @@ class GncOptimizer { } } + // Ensure known inliers are sorted/unique before set operations. + std::sort(params_.knownInliers.begin(), params_.knownInliers.end()); + params_.knownInliers.erase( + std::unique(params_.knownInliers.begin(), params_.knownInliers.end()), + params_.knownInliers.end()); + // check that known inliers and outliers make sense: std::vector inconsistentlySpecifiedWeights; // measurements the user has incorrectly specified // to be BOTH known inliers and known outliers @@ -131,6 +143,12 @@ class GncOptimizer { barcSq_ = Vector::Ones(nfg_.size()); // initialize for (size_t k = 0; k < nfg_.size(); k++) { if (nfg_[k]) { + auto nf = std::dynamic_pointer_cast(nfg_[k]); + if (!nf) { + // Non-NoiseModelFactor: exclude from GNC thresholding. + barcSq_[k] = 0.0; + continue; + } barcSq_[k] = 0.5 * Chi2inv(alpha, nfg_[k]->dim()); // 0.5 derives from the error definition in gtsam } } @@ -280,6 +298,10 @@ class GncOptimizer { */ for (size_t k = 0; k < nfg_.size(); k++) { if (nfg_[k]) { + auto nf = std::dynamic_pointer_cast(nfg_[k]); + if (!nf) { + continue; + } mu_init = std::max(mu_init, 2 * nfg_[k]->error(state_) / barcSq_[k]); } } @@ -294,6 +316,10 @@ class GncOptimizer { mu_init = std::numeric_limits::infinity(); for (size_t k = 0; k < nfg_.size(); k++) { if (nfg_[k]) { + auto nf = std::dynamic_pointer_cast(nfg_[k]); + if (!nf) { + continue; + } double rk = nfg_[k]->error(state_); mu_init = (2 * rk - barcSq_[k]) > 0 ? // if positive, update mu, otherwise keep same std::min(mu_init, barcSq_[k] / (2 * rk - barcSq_[k]) ) : mu_init; @@ -400,6 +426,11 @@ class GncOptimizer { for (size_t i = 0; i < nfg_.size(); i++) { if (nfg_[i]) { auto factor = nfg_.at(i); + if (!factor) { + // Keep non-NoiseModel factors as-is. + newGraph[i] = nfg_[i]; + continue; + } auto noiseModel = std::dynamic_pointer_cast( factor->noiseModel()); if (noiseModel) { From 9e48dcf825c0b0044a48614270471f032d44d81d Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Mon, 16 Feb 2026 12:52:59 -0500 Subject: [PATCH 213/266] Adding unit tests for updated GncOptimizer --- tests/testGncOptimizer.cpp | 50 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp index 4e0ebf516c..4e643ed4fc 100644 --- a/tests/testGncOptimizer.cpp +++ b/tests/testGncOptimizer.cpp @@ -30,7 +30,10 @@ #include #include #include +#include #include +#include +#include #include #include @@ -683,6 +686,53 @@ TEST(GncOptimizer, barcsq_heterogeneousFactors) { // std::cout << "fg[3]->dim() " << fg[3]->dim() << std::endl; // this segfaults? } +/* ************************************************************************* */ +TEST(GncOptimizer, nonNoiseFactorBehavior) { + NonlinearFactorGraph nfg; + SharedNoiseModel pose_noise = noiseModel::Isotropic::Sigma(6, 0.5); + nfg.add(PriorFactor(X(0), Pose3(), pose_noise)); + + KeyVector keys; + keys.push_back(X(0)); + keys.push_back(X(1)); + nfg.emplace_shared>(keys, 6, 1000.0); + + Values initial; + initial.insert(X(0), Pose3(Rot3(), Point3(7.0, 0.0, 0.0))); + initial.insert(X(1), Pose3()); + + GncParams gncParams; + gncParams.setLossType(GncLossType::GM); + auto gnc = GncOptimizer>(nfg, initial, + gncParams); + + // checks if non noise model factors as set as knownInliers + const auto& known = gnc.getParams().knownInliers; + CHECK(known.size() == 1); + CHECK(known[0] == 1); + + // check if the weight is carried correctly and non noise model factor is unchanged + Vector weights = Vector::Ones(nfg.size()); + weights[1] = 0.0; + NonlinearFactorGraph weighted = gnc.makeWeightedGraph(weights); + CHECK(!weighted.at(1)); + CHECK(weighted.at(0)); + CHECK(weighted.at(1).get() == nfg.at(1).get()); + + // checks if knownInliers (our non noise model factor) is not reweighted + double mu = 1.5; + double expected_weight = 1.0; + Vector w = gnc.calculateWeights(initial, mu); + DOUBLES_EQUAL(expected_weight, w[1], tol); + CHECK(w[0] < 1.0); + + // checks if non noise model factors are ignored is calculating mu + double err0 = gnc.getFactors().at(0)->error(initial); + Vector barcSq = gnc.getInlierCostThresholds(); + double expected_mu = 2.0 * err0 / barcSq[0]; + EXPECT_DOUBLES_EQUAL(expected_mu, gnc.initializeMu(), 1e-6); +} + /* ************************************************************************* */ TEST(GncOptimizer, setInlierCostThresholds) { auto fg = example::sharedNonRobustFactorGraphWithOutliers(); From f2ede7a3703abf455e1fc3a3ecfd06791157e8b4 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Mon, 16 Feb 2026 18:15:45 -0500 Subject: [PATCH 214/266] Adding allow_non_noisemodel_factors Gnc parameter --- gtsam/nonlinear/GncOptimizer.h | 28 ++++++++++++++-------------- gtsam/nonlinear/GncParams.h | 9 ++++++++- tests/testGncOptimizer.cpp | 6 +----- 3 files changed, 23 insertions(+), 20 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 241b465915..c76f1805e1 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -29,6 +29,7 @@ #include #include #include +#include namespace gtsam { /* @@ -52,6 +53,7 @@ class GncOptimizer { GncParameters params_; ///< GNC parameters. Vector weights_; ///< Weights associated to each factor in GNC (this could be a local variable in optimize, but it is useful to make it accessible from outside). Vector barcSq_; ///< Inlier thresholds. A factor is considered an inlier if factor.error() < barcSq_[i] (where i is the position of the factor in the factor graph. Note that factor.error() whitens by the covariance. + std::set knownInliers_; ///< Set of known Inliers for the Gnc Optimization process public: /// Constructor. @@ -61,14 +63,18 @@ class GncOptimizer { params_(params) { // make sure all noiseModels are Gaussian or convert to Gaussian + knownInliers_.insert(params.knownInliers.begin(), params.knownInliers.end()); nfg_.resize(graph.size()); for (size_t i = 0; i < graph.size(); i++) { if (graph[i]) { NoiseModelFactor::shared_ptr factor = graph.at(i); if (!factor) { - // Non-NoiseModelFactor (e.g. KarcherMeanFactorPose3). + if (!params.allow_non_noisemodel_factors) { + throw std::runtime_error("GncOptimizer::constructor: the user must set allow_non_noisemodel_factors as " + " true if the factor graph contains factors without noise model."); + } nfg_[i] = graph[i]; - params_.knownInliers.push_back(i); + knownInliers_.insert(i); continue; } auto robust = @@ -78,16 +84,10 @@ class GncOptimizer { } } - // Ensure known inliers are sorted/unique before set operations. - std::sort(params_.knownInliers.begin(), params_.knownInliers.end()); - params_.knownInliers.erase( - std::unique(params_.knownInliers.begin(), params_.knownInliers.end()), - params_.knownInliers.end()); - // check that known inliers and outliers make sense: std::vector inconsistentlySpecifiedWeights; // measurements the user has incorrectly specified // to be BOTH known inliers and known outliers - std::set_intersection(params.knownInliers.begin(),params.knownInliers.end(), + std::set_intersection(knownInliers_.begin(), knownInliers_.end(), params.knownOutliers.begin(),params.knownOutliers.end(), std::inserter(inconsistentlySpecifiedWeights, inconsistentlySpecifiedWeights.begin())); if(inconsistentlySpecifiedWeights.size() > 0){ // if we have inconsistently specified weights, we throw an exception @@ -96,8 +96,8 @@ class GncOptimizer { " to be BOTH a known inlier and a known outlier."); } // check that known inliers are in the graph - for (size_t i = 0; i < params.knownInliers.size(); i++){ - if( params.knownInliers[i] > nfg_.size()-1 ){ // outside graph + for (size_t i: knownInliers_){ + if( i > nfg_.size()-1 ){ // outside graph throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" "that are not in the factor graph to be known inliers."); } @@ -211,7 +211,7 @@ class GncOptimizer { // maximum residual errors at initialization // For GM: if residual error is small, mu -> 0 // For TLS: if residual error is small, mu -> -1 - int nrUnknownInOrOut = nfg_.size() - ( params_.knownInliers.size() + params_.knownOutliers.size() ); + int nrUnknownInOrOut = nfg_.size() - ( knownInliers_.size() + params_.knownOutliers.size() ); // ^^ number of measurements that are not known to be inliers or outliers (GNC will need to figure them out) if (mu <= 0 || nrUnknownInOrOut == 0) { // no need to even call GNC in this case if (mu <= 0 && params_.verbosity >= GncParameters::Verbosity::SUMMARY) { @@ -427,7 +427,7 @@ class GncOptimizer { if (nfg_[i]) { auto factor = nfg_.at(i); if (!factor) { - // Keep non-NoiseModel factors as-is. + // Keep non-NoiseModel factors same. newGraph[i] = nfg_[i]; continue; } @@ -456,7 +456,7 @@ class GncOptimizer { allWeights.push_back(k); } std::vector knownWeights; - std::set_union(params_.knownInliers.begin(), params_.knownInliers.end(), + std::set_union(knownInliers_.begin(), knownInliers_.end(), params_.knownOutliers.begin(), params_.knownOutliers.end(), std::inserter(knownWeights, knownWeights.begin())); diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index b1237b7901..552d2223b2 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -72,6 +72,7 @@ class GncParams { double relativeCostTol = 1e-5; ///< If relative cost change is below this threshold, stop iterating double weightsTol = 1e-4; ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS) Verbosity verbosity = SILENT; ///< Verbosity level + bool allow_non_noisemodel_factors = false; ///< If true, factors without noise model are not reweighted and not not included in mu calculation /// Use IndexVector for inliers and outliers since it is fast using IndexVector = FastVector; @@ -137,13 +138,18 @@ class GncParams { std::sort(knownOutliers.begin(), knownOutliers.end()); } + void setAllowNonNoiseModelFactors(bool allow) { + allow_non_noisemodel_factors = allow; + } + /// Equals. bool equals(const GncParams& other, double tol = 1e-9) const { return baseOptimizerParams.equals(other.baseOptimizerParams) && lossType == other.lossType && maxIterations == other.maxIterations && std::fabs(muStep - other.muStep) <= tol && verbosity == other.verbosity && knownInliers == other.knownInliers - && knownOutliers == other.knownOutliers; + && knownOutliers == other.knownOutliers + && allow_non_noisemodel_factors == other.allow_non_noisemodel_factors; } /// Print. @@ -168,6 +174,7 @@ class GncParams { std::cout << "knownInliers: " << knownInliers[i] << "\n"; for (size_t i = 0; i < knownOutliers.size(); i++) std::cout << "knownOutliers: " << knownOutliers[i] << "\n"; + std::cout << "allow_non_noisemodel_factors: " << allow_non_noisemodel_factors << "\n"; baseOptimizerParams.print("Base optimizer params: "); } }; diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp index 4e643ed4fc..b800c2e4ce 100644 --- a/tests/testGncOptimizer.cpp +++ b/tests/testGncOptimizer.cpp @@ -703,14 +703,10 @@ TEST(GncOptimizer, nonNoiseFactorBehavior) { GncParams gncParams; gncParams.setLossType(GncLossType::GM); + gncParams.setAllowNonNoiseModelFactors(true); auto gnc = GncOptimizer>(nfg, initial, gncParams); - // checks if non noise model factors as set as knownInliers - const auto& known = gnc.getParams().knownInliers; - CHECK(known.size() == 1); - CHECK(known[0] == 1); - // check if the weight is carried correctly and non noise model factor is unchanged Vector weights = Vector::Ones(nfg.size()); weights[1] = 0.0; From 908d1d464a45df0c0b26a1b4dee15b50425c1f9a Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Mon, 16 Feb 2026 23:31:06 -0500 Subject: [PATCH 215/266] Resolving gnc comments --- gtsam/nonlinear/GncOptimizer.h | 8 ++------ tests/testGncOptimizer.cpp | 6 ++++++ 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index c76f1805e1..d2cbf99c21 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -299,9 +299,7 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (nfg_[k]) { auto nf = std::dynamic_pointer_cast(nfg_[k]); - if (!nf) { - continue; - } + if (!nf) continue; mu_init = std::max(mu_init, 2 * nfg_[k]->error(state_) / barcSq_[k]); } } @@ -317,9 +315,7 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (nfg_[k]) { auto nf = std::dynamic_pointer_cast(nfg_[k]); - if (!nf) { - continue; - } + if (!nf) continue; double rk = nfg_[k]->error(state_); mu_init = (2 * rk - barcSq_[k]) > 0 ? // if positive, update mu, otherwise keep same std::min(mu_init, barcSq_[k] / (2 * rk - barcSq_[k]) ) : mu_init; diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp index b800c2e4ce..c116cf6bad 100644 --- a/tests/testGncOptimizer.cpp +++ b/tests/testGncOptimizer.cpp @@ -727,6 +727,12 @@ TEST(GncOptimizer, nonNoiseFactorBehavior) { Vector barcSq = gnc.getInlierCostThresholds(); double expected_mu = 2.0 * err0 / barcSq[0]; EXPECT_DOUBLES_EQUAL(expected_mu, gnc.initializeMu(), 1e-6); + + // checks if gnc optimization runs and keeps the non noise model factor weight fixed at 1 + Values result = gnc.optimize(); + CHECK(result.exists(X(0))); + Vector finalWeights = gnc.getWeights(); + DOUBLES_EQUAL(1.0, finalWeights[1], tol); } /* ************************************************************************* */ From ce24300ba30ff9bf7df4646f2777ffb532ad3bb8 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Sat, 31 Jan 2026 16:56:00 +0200 Subject: [PATCH 216/266] Refactor rowCounts out of Multifrontal SymbolicJunctionTree. --- gtsam/linear/MultifrontalClique.h | 4 ++-- gtsam/linear/MultifrontalSolver.cpp | 16 ++++++++-------- gtsam/linear/MultifrontalSolver.h | 1 + gtsam/nonlinear/NonlinearMultifrontalSolver.cpp | 12 ++++++++---- 4 files changed, 19 insertions(+), 14 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index dc4f6f7963..012c2dc67e 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -51,8 +51,8 @@ class IndexedSymbolicFactor : public SymbolicFactor { public: size_t index_; size_t rows_; - IndexedSymbolicFactor(const KeyVector& keys, size_t index, size_t rows) - : SymbolicFactor(), index_(index), rows_(rows) { + IndexedSymbolicFactor(const KeyVector& keys, size_t index) + : SymbolicFactor(), index_(index) { keys_ = keys; } }; diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index a273e134c3..6a3c48ac61 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -134,8 +134,7 @@ PrecomputeScratch precomputeFromGraph(const GaussianFactorGraph& graph) { // Build SymbolicFactorGraph from GaussianFactorGraph SymbolicFactorGraph buildSymbolicGraph( const GaussianFactorGraph& graph, - const std::unordered_set& fixedKeys, - const std::vector& rowCounts) { + const std::unordered_set& fixedKeys) { SymbolicFactorGraph symbolicGraph; symbolicGraph.reserve(graph.size()); for (size_t i = 0; i < graph.size(); ++i) { @@ -149,8 +148,7 @@ SymbolicFactorGraph buildSymbolicGraph( } // Skip factors that are fully constrained away. if (keys.empty()) continue; - symbolicGraph.emplace_shared( - keys, i, rowCounts.at(i)); + symbolicGraph.emplace_shared(keys, i); } return symbolicGraph; } @@ -455,6 +453,7 @@ struct CliqueBuilder { std::vector* cliques; const std::unordered_set* fixedKeys; const MultifrontalParameters* params; + const std::vector* rowCounts; SymbolicJunctionTree::Cluster::KeySetMap separatorCache = {}; BuiltClique build(const SymbolicJunctionTree::sharedNode& cluster, @@ -472,7 +471,7 @@ struct CliqueBuilder { auto indexed = std::static_pointer_cast(factor); factorIndices.push_back(indexed->index_); - vbmRows += indexed->rows_; + vbmRows += rowCounts->at(indexed->index_); } // Create the clique node and cache static structure. @@ -557,7 +556,8 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, } // Build the actual MultifrontalClique structure. - CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_}; + CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_, + &data.rowCounts}; for (const auto& rootCluster : data.junctionTree.roots()) { if (rootCluster) { roots_.push_back( @@ -583,13 +583,13 @@ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( } SymbolicFactorGraph symbolicGraph = - buildSymbolicGraph(graph, scratch.fixedKeys, scratch.rowCounts); + buildSymbolicGraph(graph, scratch.fixedKeys); SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); SymbolicJunctionTree junctionTree(eliminationTree); return MultifrontalSolver::PrecomputedData{ std::move(scratch.dims), std::move(scratch.fixedKeys), - std::move(junctionTree)}; + std::move(junctionTree), std::move(scratch.rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 955a5a6e66..77591d459a 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -86,6 +86,7 @@ class GTSAM_EXPORT MultifrontalSolver std::map dims; ///< Map from variable key to dimension. std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. SymbolicJunctionTree junctionTree; ///< Precomputed symbolic junction tree. + std::vector rowCounts; ///< Row counts indexed by factor index. }; /// Shared pointer to a MultifrontalClique. diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index 493e991607..930fb52b25 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -65,9 +65,7 @@ SymbolicFactorGraph buildSymbolicGraph( } } if (keys.empty()) continue; - const size_t rows = graph[i]->dim(); - symbolicGraph.emplace_shared(keys, i, - rows); + symbolicGraph.emplace_shared(keys, i); } return symbolicGraph; } @@ -102,9 +100,15 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); SymbolicJunctionTree junctionTree(eliminationTree); + std::vector rowCounts; + rowCounts.reserve(graph.size()); + for (const auto& factor : graph) { + size_t dim = factor ? factor->dim() : 0; + rowCounts.push_back(dim); + } return MultifrontalSolver::PrecomputedData{ - std::move(dims), std::move(fixedKeys), std::move(junctionTree)}; + std::move(dims), std::move(fixedKeys), std::move(junctionTree), std::move(rowCounts)}; } /* ************************************************************************* */ From 8633b7bee811641b55f016cdddf1c0718e122c9c Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Sun, 1 Feb 2026 23:08:20 +0200 Subject: [PATCH 217/266] Optimize multifrontal elimination by caching JunctionTree. Refactor IndexedSymbolicFactor into shared IndexedJunctionTreeBuilder.h, enabling efficient reuse of junction tree structure across multiple nonlinear optimization iterations. Key changes: - Add IndexedJunctionTreeBuilder.h with buildIndexedJunctionTree() template - Add GaussianFactorGraph::optimize() overload using cached junction tree - Cache SymbolicJunctionTree in NonlinearOptimizer for multifrontal solve - Refactor MultifrontalSolver and NonlinearMultifrontalSolver to use shared builder functions Performance improvement: reduces runtime from ~120s to ~90s (~25% faster) in specific test cases by avoiding repeated elimination tree construction. --- .../inference/EliminateableFactorGraph-inst.h | 110 ++++++++++++++++++ gtsam/inference/EliminateableFactorGraph.h | 36 +++++- gtsam/linear/MultifrontalClique.h | 11 -- gtsam/linear/MultifrontalSolver.cpp | 45 ++----- gtsam/linear/MultifrontalSolver.h | 25 ++-- .../linear/tests/testGaussianFactorGraph.cpp | 17 +++ .../nonlinear/NonlinearMultifrontalSolver.cpp | 30 +---- gtsam/nonlinear/NonlinearOptimizer.cpp | 14 ++- gtsam/nonlinear/NonlinearOptimizer.h | 9 ++ gtsam/symbolic/IndexedJunctionTree.h | 106 +++++++++++++++++ .../symbolic/tests/testSymbolicBayesTree.cpp | 13 ++- 11 files changed, 331 insertions(+), 85 deletions(-) create mode 100644 gtsam/symbolic/IndexedJunctionTree.h diff --git a/gtsam/inference/EliminateableFactorGraph-inst.h b/gtsam/inference/EliminateableFactorGraph-inst.h index f6b11f785c..2ce7d03887 100644 --- a/gtsam/inference/EliminateableFactorGraph-inst.h +++ b/gtsam/inference/EliminateableFactorGraph-inst.h @@ -20,6 +20,10 @@ #include #include +#include +#include + +#include namespace gtsam { @@ -145,6 +149,112 @@ namespace gtsam { } } + /* ************************************************************************* */ + template + IndexedJunctionTree + EliminateableFactorGraph::buildIndexedJunctionTree( + const Ordering& ordering, + const std::unordered_set& fixedKeys) const { + return IndexedJunctionTree(asDerived(), ordering, fixedKeys); + } + + /* ************************************************************************* */ + template + std::shared_ptr::BayesTreeType> + EliminateableFactorGraph::eliminateMultifrontal( + const IndexedJunctionTree& indexedJunctionTree, + const Eliminate& function) const { + gttic(eliminateMultifrontal); + + using BayesTreeNode = typename BayesTreeType::Node; + using SharedFactor = typename FactorGraphType::sharedFactor; + + // Helper struct for cluster elimination traversal. + struct ClusterEliminationData { + ClusterEliminationData* const parentData; + size_t myIndexInParent; + FastVector childFactors; + std::shared_ptr bayesTreeNode; + + explicit ClusterEliminationData(ClusterEliminationData* p) + : parentData(p), bayesTreeNode(std::make_shared()) { + if (parentData) { + myIndexInParent = parentData->childFactors.size(); + parentData->childFactors.push_back(SharedFactor()); + if (parentData->parentData) + bayesTreeNode->parent_ = parentData->bayesTreeNode; + parentData->bayesTreeNode->children.push_back(bayesTreeNode); + } else { + myIndexInParent = 0; + } + } + }; + + // Post-order visitor functor for elimination. + class EliminationPostOrderVisitor { + const FactorGraphType& graph_; + const Eliminate& function_; + + public: + EliminationPostOrderVisitor(const FactorGraphType& graph, + const Eliminate& function) + : graph_(graph), function_(function) {} + + void operator()(const SymbolicJunctionTree::sharedNode& cluster, + ClusterEliminationData& myData) { + FactorGraphType gatheredFactors; + gatheredFactors.reserve(cluster->factors.size() + myData.childFactors.size()); + + // Gather factors from IndexedSymbolicFactor. + for (const auto& factor : cluster->factors) { + auto indexed = + std::static_pointer_cast(factor); + gatheredFactors.push_back(graph_.at(indexed->index_)); + } + + for (const auto& childFactor : myData.childFactors) + if (childFactor) gatheredFactors.push_back(childFactor); + + auto result = function_(gatheredFactors, cluster->orderedFrontalKeys); + myData.bayesTreeNode->setEliminationResult(result); + + if (myData.parentData && !result.second->empty()) + myData.parentData->childFactors[myData.myIndexInParent] = result.second; + } + }; + + // Run depth-first traversal. + ClusterEliminationData rootsContainer(nullptr); + EliminationPostOrderVisitor visitorPost(asDerived(), function); + + auto visitorPre = [](const SymbolicJunctionTree::sharedNode& node, + ClusterEliminationData& parentData) { + ClusterEliminationData myData(&parentData); + myData.bayesTreeNode->problemSize_ = node->problemSize_; + return myData; + }; + + treeTraversal::DepthFirstForest(indexedJunctionTree, rootsContainer, visitorPre, + visitorPost); + + // If any factors are remaining, the ordering was incomplete. + KeySet remainingKeys; + for (const auto& factor : rootsContainer.childFactors) { + if (!factor || factor->empty()) continue; + remainingKeys.insert(factor->begin(), factor->end()); + } + if (!remainingKeys.empty()) { + throw InconsistentEliminationRequested(remainingKeys); + } + + // Create BayesTree from roots stored in the dummy BayesTree node. + auto bayesTree = std::make_shared(); + for (const auto& rootClique : rootsContainer.bayesTreeNode->children) + bayesTree->insertRoot(rootClique); + + return bayesTree; + } + /* ************************************************************************* */ template std::pair::BayesNetType>, std::shared_ptr > diff --git a/gtsam/inference/EliminateableFactorGraph.h b/gtsam/inference/EliminateableFactorGraph.h index 52e1479a9c..4d3a19e882 100644 --- a/gtsam/inference/EliminateableFactorGraph.h +++ b/gtsam/inference/EliminateableFactorGraph.h @@ -19,14 +19,17 @@ #pragma once #include -#include #include #include +#include #include #include namespace gtsam { + // Forward declaration + class IndexedJunctionTree; + /// Traits class for eliminateable factor graphs, specifies the types that result from /// elimination, etc. This must be defined for each factor graph that inherits from /// EliminateableFactorGraph. @@ -94,6 +97,20 @@ namespace gtsam { /// Typedef for an optional ordering type typedef std::optional OptionalOrderingType; + /** + * Build an `IndexedJunctionTree` for this factor graph and a fixed ordering. + * + * This structure can be cached and reused for repeated eliminations when the + * factor graph structure and ordering are unchanged. + * + * @param ordering The elimination ordering + * @param fixedKeys Optional set of keys to filter out (e.g., from hard constraints) + * @return An IndexedJunctionTree that can be reused for elimination + */ + IndexedJunctionTree buildIndexedJunctionTree( + const Ordering& ordering, + const std::unordered_set& fixedKeys = {}) const; + /** Do sequential elimination of all variables to produce a Bayes net. If an ordering is not * provided, the ordering provided by COLAMD will be used. * @@ -173,6 +190,23 @@ namespace gtsam { const Eliminate& function = EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex = {}) const; + /** + * Do multifrontal elimination using a pre-built `IndexedJunctionTree`. + * + * This eliminates the factor graph following the cluster structure encoded in + * the indexed junction tree and calls the provided dense elimination function + * on each cluster. The indexed junction tree must have been built from a + * factor graph with the same factor ordering/indices and the same variable + * ordering. + * + * @param indexedJunctionTree Pre-built indexed junction tree + * @param function The elimination function to use for each cluster + * @return A Bayes tree containing the elimination results + */ + std::shared_ptr eliminateMultifrontal( + const IndexedJunctionTree& indexedJunctionTree, + const Eliminate& function = EliminationTraitsType::DefaultEliminate) const; + /** Do sequential elimination of some variables, in \c ordering provided, to produce a Bayes net * and a remaining factor graph. This computes the factorization \f$ p(X) = p(A|B) p(B) \f$, * where \f$ A = \f$ \c variables, \f$ X \f$ is all the variables in the factor graph, and \f$ diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 012c2dc67e..42afe3484a 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -46,17 +46,6 @@ using KeyDimMap = std::map; namespace internal { -/// Helper class to track original factor indices and row counts. -class IndexedSymbolicFactor : public SymbolicFactor { - public: - size_t index_; - size_t rows_; - IndexedSymbolicFactor(const KeyVector& keys, size_t index) - : SymbolicFactor(), index_(index) { - keys_ = keys; - } -}; - /// Sum variable dimensions for a key range, skipping unknown keys. template inline size_t sumDims(const KeyDimMap& dims, const KeyRange& keys) { diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 6a3c48ac61..c327ea80cc 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -26,8 +26,7 @@ #include #include #include -#include -#include +#include #include #include @@ -131,28 +130,6 @@ PrecomputeScratch precomputeFromGraph(const GaussianFactorGraph& graph) { return out; } -// Build SymbolicFactorGraph from GaussianFactorGraph -SymbolicFactorGraph buildSymbolicGraph( - const GaussianFactorGraph& graph, - const std::unordered_set& fixedKeys) { - SymbolicFactorGraph symbolicGraph; - symbolicGraph.reserve(graph.size()); - for (size_t i = 0; i < graph.size(); ++i) { - if (!graph[i]) continue; - KeyVector keys; - keys.reserve(graph[i]->size()); - for (Key key : graph[i]->keys()) { - if (!fixedKeys.count(key)) { - keys.push_back(key); - } - } - // Skip factors that are fully constrained away. - if (keys.empty()) continue; - symbolicGraph.emplace_shared(keys, i); - } - return symbolicGraph; -} - // Sum the dimensions of frontal variables in a symbolic cluster. size_t frontalDimForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster, @@ -534,31 +511,31 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, } // Report the symbolic structure before any merge. - reportStructure(data.junctionTree, dims_, "Symbolic cluster structure", + reportStructure(data.indexedJunctionTree, dims_, "Symbolic cluster structure", params_.reportStream); // If applicable, merge leaf children by a separate cap first. if (params_.leafMergeDimCap > 0) { - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { mergeLeafChildren(rootCluster, dims_, params_.leafMergeDimCap); } - reportStructure(data.junctionTree, dims_, + reportStructure(data.indexedJunctionTree, dims_, "Clique structure after leaf merge", params_.reportStream); } // If applicable, merge small child cliques bottom-up. if (params_.mergeDimCap > 0) { - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { mergeSmallClusters(rootCluster, dims_, params_.mergeDimCap); } - reportStructure(data.junctionTree, dims_, "Clique structure after merge", + reportStructure(data.indexedJunctionTree, dims_, "Clique structure after merge", params_.reportStream); } // Build the actual MultifrontalClique structure. CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_, &data.rowCounts}; - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { if (rootCluster) { roots_.push_back( builder.build(rootCluster, std::weak_ptr()) @@ -582,14 +559,12 @@ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( } } - SymbolicFactorGraph symbolicGraph = - buildSymbolicGraph(graph, scratch.fixedKeys); - SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); - SymbolicJunctionTree junctionTree(eliminationTree); + IndexedJunctionTree indexedJunctionTree = + graph.buildIndexedJunctionTree(reducedOrdering, scratch.fixedKeys); return MultifrontalSolver::PrecomputedData{ std::move(scratch.dims), std::move(scratch.fixedKeys), - std::move(junctionTree), std::move(scratch.rowCounts)}; + std::move(indexedJunctionTree), std::move(scratch.rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 77591d459a..a00bb7c17d 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include #include @@ -82,11 +82,12 @@ class GTSAM_EXPORT MultifrontalSolver /// Tuning parameters for traversal and reporting. using Parameters = MultifrontalParameters; + /// Precomputed symbolic and sizing data for multifrontal solver construction. struct PrecomputedData { - std::map dims; ///< Map from variable key to dimension. - std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. - SymbolicJunctionTree junctionTree; ///< Precomputed symbolic junction tree. - std::vector rowCounts; ///< Row counts indexed by factor index. + std::map dims; ///< Map from variable key to dimension. + std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. + IndexedJunctionTree indexedJunctionTree; ///< Precomputed indexed junction tree. + std::vector rowCounts; ///< Row counts indexed by factor index. }; /// Shared pointer to a MultifrontalClique. @@ -110,7 +111,7 @@ class GTSAM_EXPORT MultifrontalSolver public: /** * Construct the solver from a factor graph and an ordering. - * This builds the symbolic junction tree and pre-allocates all matrices. + * This builds the indexed junction tree and pre-allocates all matrices. * Call load() before eliminating to populate numerical values. * @param graph The factor graph to solve. * Must contain only JacobianFactor instances. @@ -130,8 +131,16 @@ class GTSAM_EXPORT MultifrontalSolver MultifrontalSolver(PrecomputedData data, const Ordering& ordering, const Parameters& params = Parameters{}); - /// Precompute symbolic structure and sizing data from a factor graph. - /// Only JacobianFactor inputs are supported. + /** + * Precompute symbolic structure and sizing data from a factor graph. + * This builds an IndexedJunctionTree that can be reused across multiple + * solver instances when the graph structure and ordering remain unchanged. + * Only JacobianFactor inputs are supported. + * + * @param graph The factor graph (must contain only JacobianFactor instances) + * @param ordering The variable elimination ordering + * @return PrecomputedData containing the indexed junction tree and sizing info + */ static PrecomputedData Precompute(const GaussianFactorGraph& graph, const Ordering& ordering); diff --git a/gtsam/linear/tests/testGaussianFactorGraph.cpp b/gtsam/linear/tests/testGaussianFactorGraph.cpp index b6b804b6c2..da43d97944 100644 --- a/gtsam/linear/tests/testGaussianFactorGraph.cpp +++ b/gtsam/linear/tests/testGaussianFactorGraph.cpp @@ -21,9 +21,12 @@ #include #include #include +#include #include +#include #include #include +#include #include #include @@ -457,6 +460,20 @@ TEST(GaussianFactorGraph, DenseSolve) { EXPECT(assert_equal(expected, actual)); } +/* ************************************************************************* */ +TEST(GaussianFactorGraph, optimizeWithIndexedJunctionTree) { + GaussianFactorGraph fg = createSimpleGaussianFactorGraph(); + Ordering ordering{0, 1, 2}; // x2=0, l1=1, x1=2 + + IndexedJunctionTree indexedJunctionTree = fg.buildIndexedJunctionTree(ordering); + + VectorValues expected = fg.optimize(ordering, EliminateQR); + VectorValues actual = + fg.eliminateMultifrontal(indexedJunctionTree, EliminateQR)->optimize(); + + EXPECT(assert_equal(expected, actual, 1e-9)); +} + /* ************************************************************************* */ TEST(GaussianFactorGraph, ProbPrime) { GaussianFactorGraph gfg; diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index 930fb52b25..9063bafa84 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -21,8 +21,7 @@ #include #include #include -#include -#include +#include #include namespace gtsam { @@ -50,26 +49,6 @@ std::unordered_set collectFixedKeys(const NonlinearFactorGraph& graph) { return fixedKeys; } -SymbolicFactorGraph buildSymbolicGraph( - const NonlinearFactorGraph& graph, - const std::unordered_set& fixedKeys) { - SymbolicFactorGraph symbolicGraph; - symbolicGraph.reserve(graph.size()); - for (size_t i = 0; i < graph.size(); ++i) { - if (!graph[i]) continue; - KeyVector keys; - keys.reserve(graph[i]->size()); - for (Key key : graph[i]->keys()) { - if (!fixedKeys.count(key)) { - keys.push_back(key); - } - } - if (keys.empty()) continue; - symbolicGraph.emplace_shared(keys, i); - } - return symbolicGraph; -} - } // namespace /* ************************************************************************* */ @@ -97,9 +76,8 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( } } - SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); - SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); - SymbolicJunctionTree junctionTree(eliminationTree); + IndexedJunctionTree indexedJunctionTree(graph, reducedOrdering, fixedKeys); + std::vector rowCounts; rowCounts.reserve(graph.size()); for (const auto& factor : graph) { @@ -108,7 +86,7 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( } return MultifrontalSolver::PrecomputedData{ - std::move(dims), std::move(fixedKeys), std::move(junctionTree), std::move(rowCounts)}; + std::move(dims), std::move(fixedKeys), std::move(indexedJunctionTree), std::move(rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/nonlinear/NonlinearOptimizer.cpp b/gtsam/nonlinear/NonlinearOptimizer.cpp index 816789d223..b3f7160c4d 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.cpp +++ b/gtsam/nonlinear/NonlinearOptimizer.cpp @@ -25,10 +25,11 @@ #include #include #include +#include #include +#include -#include #include #include #include @@ -139,8 +140,15 @@ VectorValues NonlinearOptimizer::solve(const GaussianFactorGraph& gfg, // Check which solver we are using if (params.isMultifrontal()) { // Multifrontal QR or Cholesky (decided by params.getEliminationFunction()) - if (params.ordering) - delta = gfg.optimize(*params.ordering, params.getEliminationFunction()); + if (params.ordering) { + if (!indexedJunctionTreeCache_.has_value()) { + indexedJunctionTreeCache_ = gfg.buildIndexedJunctionTree(*params.ordering); + } + + delta = gfg.eliminateMultifrontal(*indexedJunctionTreeCache_, + params.getEliminationFunction()) + ->optimize(); + } else delta = gfg.optimize(params.getEliminationFunction()); } else if (params.isSequential()) { diff --git a/gtsam/nonlinear/NonlinearOptimizer.h b/gtsam/nonlinear/NonlinearOptimizer.h index 2d6f8afe02..0dc2bd4f2d 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.h +++ b/gtsam/nonlinear/NonlinearOptimizer.h @@ -20,6 +20,10 @@ #include #include +#include + +#include +#include namespace gtsam { @@ -86,6 +90,11 @@ class GTSAM_EXPORT NonlinearOptimizer { mutable std::unique_ptr nonlinearMultifrontalSolver_; + private: + /// Cached indexed junction tree used to avoid rebuilding the symbolic structure + /// across iterations when the ordering remains constant. + mutable std::optional indexedJunctionTreeCache_; + public: /** A shared pointer to this class */ using shared_ptr = std::shared_ptr; diff --git a/gtsam/symbolic/IndexedJunctionTree.h b/gtsam/symbolic/IndexedJunctionTree.h new file mode 100644 index 0000000000..201b26f7f3 --- /dev/null +++ b/gtsam/symbolic/IndexedJunctionTree.h @@ -0,0 +1,106 @@ +/* ---------------------------------------------------------------------------- + * + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * + * See LICENSE for the license information + * + * -------------------------------------------------------------------------- */ + +/** + * @file IndexedJunctionTree.h + * @brief Build a symbolic junction tree that stores original factor indices. + * @author Tzvi Strauss + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace gtsam { + +namespace internal { +/// A SymbolicFactor that stores the index of the originating factor. +class IndexedSymbolicFactor : public SymbolicFactor { + public: + size_t index_; + IndexedSymbolicFactor(const KeyVector& keys, size_t index) + : SymbolicFactor(), index_(index) { + keys_ = keys; + } +}; +} // namespace internal + +/** + * A symbolic junction tree whose factors record the original factor indices from + * a corresponding (non-symbolic) factor graph. This allows the junction tree + * structure to be cached and reused for repeated eliminations when the factor + * graph structure and ordering remain unchanged, avoiding the cost of rebuilding + * the symbolic structure. + * + * The underlying implementation uses a `SymbolicJunctionTree` where each factor + * is an `IndexedSymbolicFactor` that stores the original factor index. + * During elimination, these indices are used to retrieve the actual numerical + * factors from the original factor graph. + * + */ +class GTSAM_EXPORT IndexedJunctionTree : public SymbolicJunctionTree { + public: + using SymbolicJunctionTree::SymbolicJunctionTree; + + /** + * Construct an IndexedJunctionTree from any factor graph, ordering, and + * optional set of fixed keys to filter out. + * + * @tparam GRAPH Factor graph type (e.g. GaussianFactorGraph, NonlinearFactorGraph) + * @param graph The input factor graph + * @param ordering The elimination ordering + * @param fixedKeys Keys to filter out (e.g., from hard constraints) + */ + template + IndexedJunctionTree(const GRAPH& graph, const Ordering& ordering, + const std::unordered_set& fixedKeys = {}) + : SymbolicJunctionTree(makeIndexedEliminationTree(graph, ordering, fixedKeys)) {} + + private: + template + static SymbolicFactorGraph buildIndexedSymbolicFactorGraph( + const GRAPH& graph, const std::unordered_set& fixedKeys) { + SymbolicFactorGraph symbolicGraph; + symbolicGraph.reserve(graph.size()); + for (size_t i = 0; i < graph.size(); ++i) { + if (!graph.at(i)) continue; + KeyVector keys; + keys.reserve(graph[i]->size()); + for (Key key : graph[i]->keys()) { + if (!fixedKeys.count(key)) keys.push_back(key); + } + // Skip factors that are fully constrained away. + if (keys.empty()) continue; + symbolicGraph.emplace_shared(keys, i); + } + return symbolicGraph; + } + + template + static SymbolicEliminationTree makeIndexedEliminationTree( + const GRAPH& graph, const Ordering& ordering, + const std::unordered_set& fixedKeys) { + SymbolicFactorGraph symbolicGraph = + buildIndexedSymbolicFactorGraph(graph, fixedKeys); + return SymbolicEliminationTree(std::move(symbolicGraph), ordering); + } +}; +} // namespace gtsam + diff --git a/gtsam/symbolic/tests/testSymbolicBayesTree.cpp b/gtsam/symbolic/tests/testSymbolicBayesTree.cpp index 04fe0434be..b5df33ae42 100644 --- a/gtsam/symbolic/tests/testSymbolicBayesTree.cpp +++ b/gtsam/symbolic/tests/testSymbolicBayesTree.cpp @@ -20,11 +20,11 @@ #include #include #include +#include #include #include #include -#include #include using namespace std; @@ -96,6 +96,17 @@ TEST(SymbolicBayesTree, clique_structure) { SymbolicBayesTree actual = *graph.eliminateMultifrontal(order); EXPECT(assert_equal(expected, actual)); + + // Reuse an indexed junction tree built from the same graph and ordering. + IndexedJunctionTree indexedJunctionTree = graph.buildIndexedJunctionTree(order); + + SymbolicBayesTree actualReuse1 = + *graph.eliminateMultifrontal(indexedJunctionTree); + SymbolicBayesTree actualReuse2 = + *graph.eliminateMultifrontal(indexedJunctionTree); + + EXPECT(assert_equal(expected, actualReuse1)); + EXPECT(assert_equal(expected, actualReuse2)); } /* ************************************************************************* * From 737461f374c18700eb9b78ea4d6e7058e0604ee0 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Tue, 17 Feb 2026 21:28:26 -0500 Subject: [PATCH 218/266] Adding a vector of enums with Types for each factor --- gtsam/nonlinear/GncOptimizer.h | 154 ++++++++++++++++++--------------- gtsam/nonlinear/GncParams.h | 8 +- 2 files changed, 88 insertions(+), 74 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index d2cbf99c21..e5124080c5 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -29,7 +29,6 @@ #include #include #include -#include namespace gtsam { /* @@ -40,6 +39,35 @@ static double Chi2inv(const double alpha, const size_t dofs) { return internal::chiSquaredQuantile(dofs, alpha); } +/** + * @enum Type + * @brief Enum to classify factor types in GNC optimization. + */ +enum class Type { + Normal, ///< Normal case. + Inlier, ///< Factor is a known inlier. + Outlier, ///< Factor is a known outlier. + NonNoiseModel, ///< Factor does not have a noise model + NullPointer, ///< Factor pointer is null. + InlierNonNoiseModel ///< Factor is an inlier and does not have a noise model +}; + +bool isNullType(Type type) { + return type == Type::NullPointer; +} + +bool isNonNoiseModelType(Type type) { + return type == Type::NonNoiseModel; +} + +bool needsWeightUpdate(Type type) { + return type == Type::Normal; +} + +bool hasNoise(Type type) { + return type == Type::Normal || type == Type::Inlier || type == Type::Outlier; +} + /* ************************************************************************* */ template class GncOptimizer { @@ -53,7 +81,7 @@ class GncOptimizer { GncParameters params_; ///< GNC parameters. Vector weights_; ///< Weights associated to each factor in GNC (this could be a local variable in optimize, but it is useful to make it accessible from outside). Vector barcSq_; ///< Inlier thresholds. A factor is considered an inlier if factor.error() < barcSq_[i] (where i is the position of the factor in the factor graph. Note that factor.error() whitens by the covariance. - std::set knownInliers_; ///< Set of known Inliers for the Gnc Optimization process + std::vector factorTypes_; ///< Cached factor types for GNC. public: /// Constructor. @@ -63,52 +91,57 @@ class GncOptimizer { params_(params) { // make sure all noiseModels are Gaussian or convert to Gaussian - knownInliers_.insert(params.knownInliers.begin(), params.knownInliers.end()); nfg_.resize(graph.size()); + factorTypes_.assign(graph.size(), Type::NullPointer); for (size_t i = 0; i < graph.size(); i++) { - if (graph[i]) { - NoiseModelFactor::shared_ptr factor = graph.at(i); - if (!factor) { - if (!params.allow_non_noisemodel_factors) { - throw std::runtime_error("GncOptimizer::constructor: the user must set allow_non_noisemodel_factors as " - " true if the factor graph contains factors without noise model."); - } - nfg_[i] = graph[i]; - knownInliers_.insert(i); - continue; + if (!graph[i]) { + factorTypes_[i] = Type::NullPointer; + continue; + } + NoiseModelFactor::shared_ptr factor = graph.at(i); + if (!factor) { + if (!params.allowNonNoiseModelFactors) { + throw std::runtime_error("GncOptimizer::constructor: the user must set allowNonNoiseModelFactors as" + " true if the factor graph contains factors without noise model."); } - auto robust = - std::dynamic_pointer_cast(factor->noiseModel()); - // if the factor has a robust loss, we remove the robust loss - nfg_[i] = robust ? factor-> cloneWithNewNoiseModel(robust->noise()) : factor; + nfg_[i] = graph[i]; + factorTypes_[i] = Type::NonNoiseModel; + continue; } + auto robust = + std::dynamic_pointer_cast(factor->noiseModel()); + // if the factor has a robust loss, we remove the robust loss + nfg_[i] = robust ? factor-> cloneWithNewNoiseModel(robust->noise()) : factor; + factorTypes_[i] = Type::Normal; } - // check that known inliers and outliers make sense: - std::vector inconsistentlySpecifiedWeights; // measurements the user has incorrectly specified - // to be BOTH known inliers and known outliers - std::set_intersection(knownInliers_.begin(), knownInliers_.end(), - params.knownOutliers.begin(),params.knownOutliers.end(), - std::inserter(inconsistentlySpecifiedWeights, inconsistentlySpecifiedWeights.begin())); - if(inconsistentlySpecifiedWeights.size() > 0){ // if we have inconsistently specified weights, we throw an exception - params.print("params\n"); - throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" - " to be BOTH a known inlier and a known outlier."); - } // check that known inliers are in the graph - for (size_t i: knownInliers_){ - if( i > nfg_.size()-1 ){ // outside graph + for (size_t i = 0; i < params.knownInliers.size(); i++){ + if( params.knownInliers[i] > nfg_.size()-1 || isNullType(factorTypes_[params.knownInliers[i]])) { // outside graph throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" "that are not in the factor graph to be known inliers."); } + if (isNonNoiseModelType(factorTypes_[params.knownInliers[i]])) { + factorTypes_[params.knownInliers[i]] = Type::InlierNonNoiseModel; + } + else { + factorTypes_[params.knownInliers[i]] = Type::Inlier; + } } // check that known outliers are in the graph for (size_t i = 0; i < params.knownOutliers.size(); i++){ - if( params.knownOutliers[i] > nfg_.size()-1 ){ // outside graph + if( params.knownOutliers[i] > nfg_.size()-1 || isNullType(factorTypes_[params.knownOutliers[i]])) { // outside graph throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" "that are not in the factor graph to be known outliers."); } + if (!needsWeightUpdate(factorTypes_[params.knownOutliers[i]])) { + // it can only be normal, InlierNonNoiseModel, Inlier or NonNoiseModel here so this statement works + throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" + " to be an outlier that is either an inlier or a non noise model factor."); + } + factorTypes_[params.knownOutliers[i]] = Type::Outlier; } + // initialize weights (if we don't have prior knowledge of inliers/outliers // the weights are all initialized to 1. weights_ = initializeWeightsFromKnownInliersAndOutliers(); @@ -142,20 +175,14 @@ class GncOptimizer { void setInlierCostThresholdsAtProbability(const double alpha) { barcSq_ = Vector::Ones(nfg_.size()); // initialize for (size_t k = 0; k < nfg_.size(); k++) { - if (nfg_[k]) { - auto nf = std::dynamic_pointer_cast(nfg_[k]); - if (!nf) { - // Non-NoiseModelFactor: exclude from GNC thresholding. - barcSq_[k] = 0.0; - continue; - } + if (hasNoise(factorTypes_[k])) { barcSq_[k] = 0.5 * Chi2inv(alpha, nfg_[k]->dim()); // 0.5 derives from the error definition in gtsam } } } /** Set weights for each factor. This is typically not needed, but - * provides an extra interface for the user to initialize the weightst + * provides an extra interface for the user to initialize the weights * */ void setWeights(const Vector w) { if (size_t(w.size()) != nfg_.size()) { @@ -191,6 +218,7 @@ class GncOptimizer { Vector initializeWeightsFromKnownInliersAndOutliers() const{ Vector weights = Vector::Ones(nfg_.size()); + // we do not loop through the factorTypes_ vector because in general params_.knownOutliers will always be smaller for (size_t i = 0; i < params_.knownOutliers.size(); i++){ weights[ params_.knownOutliers[i] ] = 0.0; // known to be outliers } @@ -211,7 +239,12 @@ class GncOptimizer { // maximum residual errors at initialization // For GM: if residual error is small, mu -> 0 // For TLS: if residual error is small, mu -> -1 - int nrUnknownInOrOut = nfg_.size() - ( knownInliers_.size() + params_.knownOutliers.size() ); + int nrUnknownInOrOut = 0; + for (Type t : factorTypes_) { + if (needsWeightUpdate(t)) { + nrUnknownInOrOut++; + } + } // ^^ number of measurements that are not known to be inliers or outliers (GNC will need to figure them out) if (mu <= 0 || nrUnknownInOrOut == 0) { // no need to even call GNC in this case if (mu <= 0 && params_.verbosity >= GncParameters::Verbosity::SUMMARY) { @@ -297,9 +330,7 @@ class GncOptimizer { Since barcSq_ can be different for each factor, we compute the max of the quantity in remark 5 in GNC paper */ for (size_t k = 0; k < nfg_.size(); k++) { - if (nfg_[k]) { - auto nf = std::dynamic_pointer_cast(nfg_[k]); - if (!nf) continue; + if (hasNoise(factorTypes_[k])) { mu_init = std::max(mu_init, 2 * nfg_[k]->error(state_) / barcSq_[k]); } } @@ -313,9 +344,7 @@ class GncOptimizer { */ mu_init = std::numeric_limits::infinity(); for (size_t k = 0; k < nfg_.size(); k++) { - if (nfg_[k]) { - auto nf = std::dynamic_pointer_cast(nfg_[k]); - if (!nf) continue; + if (hasNoise(factorTypes_[k])) { double rk = nfg_[k]->error(state_); mu_init = (2 * rk - barcSq_[k]) > 0 ? // if positive, update mu, otherwise keep same std::min(mu_init, barcSq_[k] / (2 * rk - barcSq_[k]) ) : mu_init; @@ -420,13 +449,13 @@ class GncOptimizer { NonlinearFactorGraph newGraph; newGraph.resize(nfg_.size()); for (size_t i = 0; i < nfg_.size(); i++) { - if (nfg_[i]) { - auto factor = nfg_.at(i); - if (!factor) { - // Keep non-NoiseModel factors same. + if (!isNullType(factorTypes_[i])) { + if (!hasNoise(factorTypes_[i])) { + // Keep non NoiseModel factors same. newGraph[i] = nfg_[i]; continue; } + auto factor = std::static_pointer_cast(nfg_[i]); auto noiseModel = std::dynamic_pointer_cast( factor->noiseModel()); if (noiseModel) { @@ -446,26 +475,11 @@ class GncOptimizer { Vector calculateWeights(const Values& currentEstimate, const double mu) { Vector weights = initializeWeightsFromKnownInliersAndOutliers(); - // do not update the weights that the user has decided are known inliers - std::vector allWeights; - for (size_t k = 0; k < nfg_.size(); k++) { - allWeights.push_back(k); - } - std::vector knownWeights; - std::set_union(knownInliers_.begin(), knownInliers_.end(), - params_.knownOutliers.begin(), params_.knownOutliers.end(), - std::inserter(knownWeights, knownWeights.begin())); - - std::vector unknownWeights; - std::set_difference(allWeights.begin(), allWeights.end(), - knownWeights.begin(), knownWeights.end(), - std::inserter(unknownWeights, unknownWeights.begin())); - - // update weights of known inlier/outlier measurements + // update weights of unknown measurements switch (params_.lossType) { case GncLossType::GM: { // use eq (12) in GNC paper - for (size_t k : unknownWeights) { - if (nfg_[k]) { + for (size_t k = 0; k < nfg_.size(); k++) { + if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual weights[k] = std::pow( (mu * barcSq_[k]) / (u2_k + mu * barcSq_[k]), 2); @@ -474,8 +488,8 @@ class GncOptimizer { return weights; } case GncLossType::TLS: { // use eq (14) in GNC paper - for (size_t k : unknownWeights) { - if (nfg_[k]) { + for (size_t k = 0; k < nfg_.size(); k++) { + if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual double upperbound = (mu + 1) / mu * barcSq_[k]; double lowerbound = mu / (mu + 1) * barcSq_[k]; diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index 552d2223b2..bd90de7fc6 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -72,7 +72,7 @@ class GncParams { double relativeCostTol = 1e-5; ///< If relative cost change is below this threshold, stop iterating double weightsTol = 1e-4; ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS) Verbosity verbosity = SILENT; ///< Verbosity level - bool allow_non_noisemodel_factors = false; ///< If true, factors without noise model are not reweighted and not not included in mu calculation + bool allowNonNoiseModelFactors = false; ///< If true, factors without noise model are not reweighted and not not included in mu calculation /// Use IndexVector for inliers and outliers since it is fast using IndexVector = FastVector; @@ -139,7 +139,7 @@ class GncParams { } void setAllowNonNoiseModelFactors(bool allow) { - allow_non_noisemodel_factors = allow; + allowNonNoiseModelFactors = allow; } /// Equals. @@ -149,7 +149,7 @@ class GncParams { && std::fabs(muStep - other.muStep) <= tol && verbosity == other.verbosity && knownInliers == other.knownInliers && knownOutliers == other.knownOutliers - && allow_non_noisemodel_factors == other.allow_non_noisemodel_factors; + && allowNonNoiseModelFactors == other.allowNonNoiseModelFactors; } /// Print. @@ -174,7 +174,7 @@ class GncParams { std::cout << "knownInliers: " << knownInliers[i] << "\n"; for (size_t i = 0; i < knownOutliers.size(); i++) std::cout << "knownOutliers: " << knownOutliers[i] << "\n"; - std::cout << "allow_non_noisemodel_factors: " << allow_non_noisemodel_factors << "\n"; + std::cout << "allowNonNoiseModelFactors: " << allowNonNoiseModelFactors << "\n"; baseOptimizerParams.print("Base optimizer params: "); } }; From 1048686416083678ecc3ded39d33089653f1e7d1 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 17 Feb 2026 23:04:34 -0500 Subject: [PATCH 219/266] Remove redundant type --- gtsam/nonlinear/GncOptimizer.h | 34 +++++++++++++++++++++------------- tests/testGncOptimizer.cpp | 19 +++++++++++++------ 2 files changed, 34 insertions(+), 19 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index e5124080c5..4572751ca0 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -48,8 +48,7 @@ enum class Type { Inlier, ///< Factor is a known inlier. Outlier, ///< Factor is a known outlier. NonNoiseModel, ///< Factor does not have a noise model - NullPointer, ///< Factor pointer is null. - InlierNonNoiseModel ///< Factor is an inlier and does not have a noise model + NullPointer ///< Factor pointer is null. }; bool isNullType(Type type) { @@ -76,12 +75,24 @@ class GncOptimizer { typedef typename GncParameters::OptimizerType BaseOptimizer; private: - NonlinearFactorGraph nfg_; ///< Original factor graph to be solved by GNC. - Values state_; ///< Initial values to be used at each iteration by GNC. - GncParameters params_; ///< GNC parameters. - Vector weights_; ///< Weights associated to each factor in GNC (this could be a local variable in optimize, but it is useful to make it accessible from outside). - Vector barcSq_; ///< Inlier thresholds. A factor is considered an inlier if factor.error() < barcSq_[i] (where i is the position of the factor in the factor graph. Note that factor.error() whitens by the covariance. - std::vector factorTypes_; ///< Cached factor types for GNC. + /// Original factor graph to be solved by GNC. + NonlinearFactorGraph nfg_; + + /// Initial values to be used at each iteration by GNC. + Values state_; + + /// GNC parameters. + GncParameters params_; + + /// Weights associated to each factor in GNC (accessible from outside). + Vector weights_; + + /// Inlier thresholds. A factor is considered an inlier if factor.error() < + /// barcSq_[i]. Note: factor.error() whitens by the covariance. + Vector barcSq_; + + /// Cached factor types for GNC. + std::vector factorTypes_; public: /// Constructor. @@ -121,10 +132,7 @@ class GncOptimizer { throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" "that are not in the factor graph to be known inliers."); } - if (isNonNoiseModelType(factorTypes_[params.knownInliers[i]])) { - factorTypes_[params.knownInliers[i]] = Type::InlierNonNoiseModel; - } - else { + if (!isNonNoiseModelType(factorTypes_[params.knownInliers[i]])) { factorTypes_[params.knownInliers[i]] = Type::Inlier; } } @@ -135,7 +143,7 @@ class GncOptimizer { "that are not in the factor graph to be known outliers."); } if (!needsWeightUpdate(factorTypes_[params.knownOutliers[i]])) { - // it can only be normal, InlierNonNoiseModel, Inlier or NonNoiseModel here so this statement works + // it can only be Normal, Inlier, or NonNoiseModel here so this works throw std::runtime_error("GncOptimizer::constructor: the user has selected one or more measurements" " to be an outlier that is either an inlier or a non noise model factor."); } diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp index c116cf6bad..df4cbc236b 100644 --- a/tests/testGncOptimizer.cpp +++ b/tests/testGncOptimizer.cpp @@ -704,10 +704,16 @@ TEST(GncOptimizer, nonNoiseFactorBehavior) { GncParams gncParams; gncParams.setLossType(GncLossType::GM); gncParams.setAllowNonNoiseModelFactors(true); + GncParams::IndexVector knownInliers; + knownInliers.push_back(1); + knownInliers.push_back( + 1); // duplicate should still keep factor 1 as non-noise + gncParams.setKnownInliers(knownInliers); auto gnc = GncOptimizer>(nfg, initial, gncParams); - // check if the weight is carried correctly and non noise model factor is unchanged + // check if the weight is carried correctly and non noise model factor is + // unchanged Vector weights = Vector::Ones(nfg.size()); weights[1] = 0.0; NonlinearFactorGraph weighted = gnc.makeWeightedGraph(weights); @@ -717,18 +723,19 @@ TEST(GncOptimizer, nonNoiseFactorBehavior) { // checks if knownInliers (our non noise model factor) is not reweighted double mu = 1.5; - double expected_weight = 1.0; + double expectedWeight = 1.0; Vector w = gnc.calculateWeights(initial, mu); - DOUBLES_EQUAL(expected_weight, w[1], tol); + DOUBLES_EQUAL(expectedWeight, w[1], tol); CHECK(w[0] < 1.0); // checks if non noise model factors are ignored is calculating mu double err0 = gnc.getFactors().at(0)->error(initial); Vector barcSq = gnc.getInlierCostThresholds(); - double expected_mu = 2.0 * err0 / barcSq[0]; - EXPECT_DOUBLES_EQUAL(expected_mu, gnc.initializeMu(), 1e-6); + double expectedMu = 2.0 * err0 / barcSq[0]; + EXPECT_DOUBLES_EQUAL(expectedMu, gnc.initializeMu(), 1e-6); - // checks if gnc optimization runs and keeps the non noise model factor weight fixed at 1 + // checks if gnc optimization runs and keeps the non noise model factor weight + // fixed at 1 Values result = gnc.optimize(); CHECK(result.exists(X(0))); Vector finalWeights = gnc.getWeights(); From 1cc5dec0e580a5a2b54ed9d7d4f3c97fe83dee07 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Wed, 18 Feb 2026 13:48:21 +0200 Subject: [PATCH 220/266] Add parallel elimination in EliminateableFactorGraph. Now aligned with ClusterTree imp. --- .../inference/EliminateableFactorGraph-inst.h | 121 ++++++++++++------ 1 file changed, 80 insertions(+), 41 deletions(-) diff --git a/gtsam/inference/EliminateableFactorGraph-inst.h b/gtsam/inference/EliminateableFactorGraph-inst.h index 2ce7d03887..6605392925 100644 --- a/gtsam/inference/EliminateableFactorGraph-inst.h +++ b/gtsam/inference/EliminateableFactorGraph-inst.h @@ -23,6 +23,9 @@ #include #include +#ifdef GTSAM_USE_TBB +#include +#endif #include namespace gtsam { @@ -169,73 +172,114 @@ namespace gtsam { using BayesTreeNode = typename BayesTreeType::Node; using SharedFactor = typename FactorGraphType::sharedFactor; - // Helper struct for cluster elimination traversal. + // Elimination traversal data - stores a pointer to the parent data and collects + // the factors resulting from elimination of the children. Also sets up BayesTree + // cliques with parent and child pointers. struct ClusterEliminationData { ClusterEliminationData* const parentData; size_t myIndexInParent; FastVector childFactors; std::shared_ptr bayesTreeNode; - - explicit ClusterEliminationData(ClusterEliminationData* p) - : parentData(p), bayesTreeNode(std::make_shared()) { +#ifdef GTSAM_USE_TBB + std::shared_ptr writeLock; +#endif + + ClusterEliminationData(ClusterEliminationData* _parentData, size_t nChildren) + : parentData(_parentData), bayesTreeNode(std::make_shared()) +#ifdef GTSAM_USE_TBB + , writeLock(std::make_shared()) +#endif + { if (parentData) { +#ifdef GTSAM_USE_TBB + parentData->writeLock->lock(); +#endif myIndexInParent = parentData->childFactors.size(); parentData->childFactors.push_back(SharedFactor()); +#ifdef GTSAM_USE_TBB + parentData->writeLock->unlock(); +#endif + } else { + myIndexInParent = 0; + } + if (parentData) { if (parentData->parentData) bayesTreeNode->parent_ = parentData->bayesTreeNode; parentData->bayesTreeNode->children.push_back(bayesTreeNode); - } else { - myIndexInParent = 0; } } + + static ClusterEliminationData EliminationPreOrderVisitor( + const SymbolicJunctionTree::sharedNode& node, + ClusterEliminationData& parentData) { + assert(node); + ClusterEliminationData myData(&parentData, node->nrChildren()); + myData.bayesTreeNode->problemSize_ = node->problemSize(); + return myData; + } }; - // Post-order visitor functor for elimination. + // Elimination post-order visitor - gather factors, eliminate, store results. class EliminationPostOrderVisitor { const FactorGraphType& graph_; - const Eliminate& function_; + const Eliminate& eliminationFunction_; public: - EliminationPostOrderVisitor(const FactorGraphType& graph, - const Eliminate& function) - : graph_(graph), function_(function) {} + EliminationPostOrderVisitor( + const FactorGraphType& graph, + const Eliminate& eliminationFunction) + : graph_(graph), eliminationFunction_(eliminationFunction) {} - void operator()(const SymbolicJunctionTree::sharedNode& cluster, + void operator()(const SymbolicJunctionTree::sharedNode& node, ClusterEliminationData& myData) { + assert(node); + FactorGraphType gatheredFactors; - gatheredFactors.reserve(cluster->factors.size() + myData.childFactors.size()); + gatheredFactors.reserve(node->factors.size() + node->nrChildren()); - // Gather factors from IndexedSymbolicFactor. - for (const auto& factor : cluster->factors) { + for (const auto& factor : node->factors) { auto indexed = std::static_pointer_cast(factor); gatheredFactors.push_back(graph_.at(indexed->index_)); } - - for (const auto& childFactor : myData.childFactors) - if (childFactor) gatheredFactors.push_back(childFactor); - - auto result = function_(gatheredFactors, cluster->orderedFrontalKeys); - myData.bayesTreeNode->setEliminationResult(result); - - if (myData.parentData && !result.second->empty()) - myData.parentData->childFactors[myData.myIndexInParent] = result.second; + gatheredFactors.push_back(myData.childFactors); + + auto eliminationResult = + eliminationFunction_(gatheredFactors, node->orderedFrontalKeys); + + myData.bayesTreeNode->setEliminationResult(eliminationResult); + + if (!eliminationResult.second->empty()) { +#ifdef GTSAM_USE_TBB + myData.parentData->writeLock->lock(); +#endif + myData.parentData->childFactors[myData.myIndexInParent] = + eliminationResult.second; +#ifdef GTSAM_USE_TBB + myData.parentData->writeLock->unlock(); +#endif + } } }; - // Run depth-first traversal. - ClusterEliminationData rootsContainer(nullptr); - EliminationPostOrderVisitor visitorPost(asDerived(), function); + // Do elimination (depth-first traversal). The rootsContainer stores a 'dummy' + // BayesTree node that contains all of the roots as its children. rootsContainer + // also stores the remaining un-eliminated factors passed up from the roots. + std::shared_ptr result = std::make_shared(); - auto visitorPre = [](const SymbolicJunctionTree::sharedNode& node, - ClusterEliminationData& parentData) { - ClusterEliminationData myData(&parentData); - myData.bayesTreeNode->problemSize_ = node->problemSize_; - return myData; - }; + ClusterEliminationData rootsContainer(0, indexedJunctionTree.nrRoots()); + + EliminationPostOrderVisitor visitorPost(asDerived(), function); + { + TbbOpenMPMixedScope threadLimiter; + treeTraversal::DepthFirstForestParallel( + indexedJunctionTree, rootsContainer, + ClusterEliminationData::EliminationPreOrderVisitor, visitorPost, 10); + } - treeTraversal::DepthFirstForest(indexedJunctionTree, rootsContainer, visitorPre, - visitorPost); + // Create BayesTree from roots stored in the dummy BayesTree node. + for (const auto& rootClique : rootsContainer.bayesTreeNode->children) + result->insertRoot(rootClique); // If any factors are remaining, the ordering was incomplete. KeySet remainingKeys; @@ -247,12 +291,7 @@ namespace gtsam { throw InconsistentEliminationRequested(remainingKeys); } - // Create BayesTree from roots stored in the dummy BayesTree node. - auto bayesTree = std::make_shared(); - for (const auto& rootClique : rootsContainer.bayesTreeNode->children) - bayesTree->insertRoot(rootClique); - - return bayesTree; + return result; } /* ************************************************************************* */ From e6686a6c0e85154865083e7bb502eaf3ec58bec4 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Wed, 18 Feb 2026 18:30:52 -0500 Subject: [PATCH 221/266] [skip ci] overloading SfmTrack --- gtsam/sfm/sfm.i | 1 + 1 file changed, 1 insertion(+) diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index a53e53f893..1084b10d5e 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -22,6 +22,7 @@ class SfmTrack2d { virtual class SfmTrack : gtsam::SfmTrack2d { SfmTrack(); SfmTrack(const gtsam::Point3& pt); + SfmTrack(const gtsam::Point3& pt, float r, float g, float b); const Point3& point3() const; Point3 p; From 4b652bf911e4791078bd5af24065bdb6be2b9d20 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Wed, 18 Feb 2026 18:32:14 -0500 Subject: [PATCH 222/266] Making Gnc NonNoiseModel Factor updates available in python wrapper --- gtsam/nonlinear/nonlinear.i | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i index 9189d2d974..c5802dbe12 100644 --- a/gtsam/nonlinear/nonlinear.i +++ b/gtsam/nonlinear/nonlinear.i @@ -313,6 +313,7 @@ virtual class GncParams { gtsam::This::Verbosity verbosity; gtsam::This::IndexVector knownInliers; gtsam::This::IndexVector knownOutliers; + bool allowNonNoiseModelFactors; void setLossType(const gtsam::GncLossType type); void setMaxIterations(const size_t maxIter); @@ -322,6 +323,7 @@ virtual class GncParams { void setVerbosityGNC(const gtsam::This::Verbosity value); void setKnownInliers(const gtsam::This::IndexVector& knownIn); void setKnownOutliers(const gtsam::This::IndexVector& knownOut); + void setAllowNonNoiseModelFactors(bool allow); void print(const string& str = "GncParams: ") const; enum Verbosity { From aabb068d306c14fcb81a5234cb92ac0686a9c0d1 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sat, 21 Feb 2026 16:56:55 -0500 Subject: [PATCH 223/266] Weight update in Gnc GM through static function is LossFunctions --- gtsam/linear/LossFunctions.cpp | 7 +++++++ gtsam/linear/LossFunctions.h | 1 + gtsam/nonlinear/GncOptimizer.h | 6 ++++-- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/gtsam/linear/LossFunctions.cpp b/gtsam/linear/LossFunctions.cpp index 682783e7c0..6a23eeda4d 100644 --- a/gtsam/linear/LossFunctions.cpp +++ b/gtsam/linear/LossFunctions.cpp @@ -324,6 +324,13 @@ double GemanMcClure::weight(double distance) const { return c4/(c2error*c2error); } +double GemanMcClure::Weight(double distance, double c) { + const double c2 = c*c; + const double c4 = c2*c2; + const double c2error = c2 + distance*distance; + return c4/(c2error*c2error); +} + double GemanMcClure::loss(double distance) const { const double c2 = c_*c_; const double error2 = distance*distance; diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index 989557d87b..47fc720cbc 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -373,6 +373,7 @@ class GTSAM_EXPORT GemanMcClure : public Base { double loss(double distance) const override; void print(const std::string &s) const override; bool equals(const Base &expected, double tol = 1e-8) const override; + static double Weight(double distance, double c); static shared_ptr Create(double k, const ReweightScheme reweight = Block); double modelParameter() const { return c_; } diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 4572751ca0..3081e97ac3 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -26,6 +26,7 @@ #pragma once +#include #include #include #include @@ -489,8 +490,9 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual - weights[k] = std::pow( - (mu * barcSq_[k]) / (u2_k + mu * barcSq_[k]), 2); + const double distance = std::sqrt(2.0 * u2_k); + const double c = std::sqrt(2.0 * mu * barcSq_[k]); + weights[k] = noiseModel::mEstimator::GemanMcClure::Weight(distance, c); } } return weights; From 0574a6894cc22bbe31061ec4bdbf45835714922f Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sat, 21 Feb 2026 18:00:00 -0500 Subject: [PATCH 224/266] Removing redundant code for GM Weight calculation --- gtsam/linear/LossFunctions.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/gtsam/linear/LossFunctions.cpp b/gtsam/linear/LossFunctions.cpp index 6a23eeda4d..76d62f702d 100644 --- a/gtsam/linear/LossFunctions.cpp +++ b/gtsam/linear/LossFunctions.cpp @@ -318,10 +318,7 @@ GemanMcClure::GemanMcClure(double c, const ReweightScheme reweight) } double GemanMcClure::weight(double distance) const { - const double c2 = c_*c_; - const double c4 = c2*c2; - const double c2error = c2 + distance*distance; - return c4/(c2error*c2error); + return Weight(distance, c_); } double GemanMcClure::Weight(double distance, double c) { From f0d8bc71d096ecf359ac219fcce9cf72e5eb16df Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 10:27:09 -0500 Subject: [PATCH 225/266] ExtendedPose3 class --- gtsam/geometry/ExtendedPose3.h | 494 +++++++++++++++++++++ gtsam/geometry/doc/ExtendedPose3.ipynb | 90 ++++ gtsam/geometry/tests/testExtendedPose3.cpp | 222 +++++++++ 3 files changed, 806 insertions(+) create mode 100644 gtsam/geometry/ExtendedPose3.h create mode 100644 gtsam/geometry/doc/ExtendedPose3.ipynb create mode 100644 gtsam/geometry/tests/testExtendedPose3.cpp diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h new file mode 100644 index 0000000000..f35cf54a7a --- /dev/null +++ b/gtsam/geometry/ExtendedPose3.h @@ -0,0 +1,494 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file ExtendedPose3.h + * @brief Extended pose Lie group SE_k(3), with static or dynamic k. + * @author Frank Dellaert, et al. + */ + +#pragma once + +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#if GTSAM_ENABLE_BOOST_SERIALIZATION +#include +#endif + +namespace gtsam { + +namespace internal { + +/// Manifold dimensionality for SE_k(3): 3 + 3*k. +constexpr int DimensionExtendedPose3(int k) { + return (k == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * k; +} + +/// Homogeneous matrix size for SE_k(3): (3 + k) x (3 + k). +constexpr int MatrixDimExtendedPose3(int k) { + return (k == Eigen::Dynamic) ? Eigen::Dynamic : 3 + k; +} + +} // namespace internal + +/** + * Lie group SE_k(3): semidirect product of SO(3) with k copies of R^3. + * Tangent ordering is [omega, x1, x2, ..., xk], each xi in R^3. + * + * Template parameter K can be fixed (K >= 1) or Eigen::Dynamic. + */ +template +class GTSAM_EXPORT ExtendedPose3 + : public MatrixLieGroup, internal::DimensionExtendedPose3(K), + internal::MatrixDimExtendedPose3(K)> { + public: + inline constexpr static auto dimension = internal::DimensionExtendedPose3(K); + inline constexpr static auto matrix_dim = internal::MatrixDimExtendedPose3(K); + + using Base = MatrixLieGroup, dimension, matrix_dim>; + using TangentVector = typename Base::TangentVector; + using Jacobian = typename Base::Jacobian; + using ChartJacobian = typename Base::ChartJacobian; + using ComponentJacobian = + std::conditional_t, + OptionalJacobian<3, dimension>>; + using LieAlgebra = Eigen::Matrix; + using Matrix3K = Eigen::Matrix; + + static_assert(K == Eigen::Dynamic || K >= 1, + "ExtendedPose3: K should be >= 1 or Eigen::Dynamic."); + + protected: + Rot3 R_; ///< Rotation component. + Matrix3K x_; ///< K translation-like columns in world frame. + + template + using IsDynamic = typename std::enable_if::type; + template + using IsFixed = typename std::enable_if= 1, void>::type; + + static size_t RuntimeK(const TangentVector& xi) { + if constexpr (K == Eigen::Dynamic) { + if (xi.size() < 3 || (xi.size() - 3) % 3 != 0) { + throw std::invalid_argument( + "ExtendedPose3: tangent vector size must be 3 + 3*k."); + } + return static_cast((xi.size() - 3) / 3); + } else { + return static_cast(K); + } + } + + static void ZeroJacobian(ChartJacobian H, size_t d) { + if (!H) return; + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(d, d); + } else { + (void)d; + H->setZero(); + } + } + + public: + /// @name Constructors + /// @{ + + /// Construct fixed-size identity. + template > + ExtendedPose3() : R_(Rot3::Identity()), x_(Matrix3K::Zero()) {} + + /// Construct dynamic identity, optionally with runtime k. + template > + explicit ExtendedPose3(size_t k = 0) : R_(Rot3::Identity()), x_(3, k) { + x_.setZero(); + } + + ExtendedPose3(const ExtendedPose3&) = default; + ExtendedPose3& operator=(const ExtendedPose3&) = default; + + /// Construct from rotation and 3xK block. + ExtendedPose3(const Rot3& R, const Matrix3K& x) : R_(R), x_(x) {} + + /// Construct from homogeneous matrix representation. + explicit ExtendedPose3(const LieAlgebra& T) { + if constexpr (K == Eigen::Dynamic) { + const auto n = T.rows(); + if (T.cols() != n || n < 3) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + const auto k = n - 3; + x_.resize(3, k); + x_.setZero(); + } + + const auto n = T.rows(); + if constexpr (K != Eigen::Dynamic) { + if (n != matrix_dim || T.cols() != matrix_dim) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + } else { + if (T.cols() != n || n < 3) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + } + + R_ = Rot3(T.template block<3, 3>(0, 0)); + x_ = T.block(0, 3, 3, n - 3); + } + + /// @} + /// @name Access + /// @{ + + static size_t Dimension(size_t k) { return 3 + 3 * k; } + + /// Number of R^3 columns. + size_t k() const { return static_cast(x_.cols()); } + + /// Runtime manifold dimension. + template > + size_t dim() const { + return Dimension(k()); + } + + /// Rotation component. + const Rot3& rotation(ComponentJacobian H = {}) const { + if (H) { + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(3, dim()); + } else { + H->setZero(); + } + H->block(0, 0, 3, 3) = I_3x3; + } + return R_; + } + + /// i-th R^3 component, returned by value. + Point3 x(size_t i, ComponentJacobian H = {}) const { + if (i >= k()) throw std::out_of_range("ExtendedPose3: x(i) out of range."); + if (H) { + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(3, dim()); + } else { + H->setZero(); + } + H->block(0, 3 + 3 * i, 3, 3) = R_.matrix(); + } + return x_.col(static_cast(i)); + } + + const Matrix3K& xMatrix() const { return x_; } + Matrix3K& xMatrix() { return x_; } + + /// @} + /// @name Testable + /// @{ + + void print(const std::string& s = "") const { + std::cout << (s.empty() ? s : s + " ") << *this << std::endl; + } + + bool equals(const ExtendedPose3& other, double tol = 1e-9) const { + return R_.equals(other.R_, tol) && equal_with_abs_tol(x_, other.x_, tol); + } + + /// @} + /// @name Group + /// @{ + + template > + static ExtendedPose3 Identity() { + return ExtendedPose3(); + } + + template > + static ExtendedPose3 Identity(size_t k = 0) { + return ExtendedPose3(k); + } + + ExtendedPose3 inverse() const { + const Rot3 Rt = R_.inverse(); + const Matrix3K x = -(Rt.matrix() * x_); + return ExtendedPose3(Rt, x); + } + + ExtendedPose3 operator*(const ExtendedPose3& other) const { + if (k() != other.k()) { + throw std::invalid_argument( + "ExtendedPose3: compose requires matching k."); + } + Matrix3K x = x_ + R_.matrix() * other.x_; + return ExtendedPose3(R_ * other.R_, x); + } + + /// @} + /// @name Lie Group + /// @{ + + static ExtendedPose3 Expmap(const TangentVector& xi, ChartJacobian Hxi = {}) { + const size_t k = RuntimeK(xi); + + const Vector3 w = xi.template head<3>(); + const so3::DexpFunctor local(w); + const Rot3 R(local.expmap()); + const Matrix3 Rt = R.transpose(); + + Matrix3K x; + if constexpr (K == Eigen::Dynamic) x.resize(3, static_cast(k)); + + if (Hxi) { + const size_t d = 3 + 3 * k; + ZeroJacobian(Hxi, d); + const Matrix3 Jr = local.Jacobian().right(); + Hxi->block(0, 0, 3, 3) = Jr; + for (size_t i = 0; i < k; ++i) { + Matrix3 H_xi_w; + const Vector3 rho = xi.template segment<3>(3 + 3 * i); + x.col(static_cast(i)) = + local.Jacobian().applyLeft(rho, &H_xi_w); + Hxi->block(3 + 3 * i, 0, 3, 3) = Rt * H_xi_w; + Hxi->block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jr; + } + } else { + for (size_t i = 0; i < k; ++i) { + const Vector3 rho = xi.template segment<3>(3 + 3 * i); + x.col(static_cast(i)) = local.Jacobian().applyLeft(rho); + } + } + + return ExtendedPose3(R, x); + } + + static TangentVector Logmap(const ExtendedPose3& pose, ChartJacobian Hpose = {}) { + const Vector3 w = Rot3::Logmap(pose.R_); + const so3::DexpFunctor local(w); + + TangentVector xi; + if constexpr (K == Eigen::Dynamic) xi.resize(static_cast(pose.dim())); + xi.template head<3>() = w; + for (size_t i = 0; i < pose.k(); ++i) { + xi.template segment<3>(3 + 3 * i) = + local.InvJacobian().applyLeft(pose.x_.col(static_cast(i))); + } + + if (Hpose) *Hpose = LogmapDerivative(xi); + return xi; + } + + Jacobian AdjointMap() const { + const Matrix3 R = R_.matrix(); + Jacobian adj; + if constexpr (dimension == Eigen::Dynamic) { + adj.setZero(dim(), dim()); + } else { + adj.setZero(); + } + + adj.block(0, 0, 3, 3) = R; + for (size_t i = 0; i < k(); ++i) { + adj.block(3 + 3 * i, 0, 3, 3) = + skewSymmetric(x_.col(static_cast(i))) * R; + adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = R; + } + return adj; + } + + TangentVector Adjoint(const TangentVector& xi_b, + ChartJacobian H_this = {}, + ChartJacobian H_xib = {}) const { + const Jacobian Ad = AdjointMap(); + if (H_this) *H_this = -Ad * adjointMap(xi_b); + if (H_xib) *H_xib = Ad; + return Ad * xi_b; + } + + static Jacobian adjointMap(const TangentVector& xi) { + const size_t k = RuntimeK(xi); + const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); + + Jacobian adj; + if constexpr (dimension == Eigen::Dynamic) { + adj.setZero(3 + 3 * k, 3 + 3 * k); + } else { + adj.setZero(); + } + + adj.block(0, 0, 3, 3) = w_hat; + for (size_t i = 0; i < k; ++i) { + adj.block(3 + 3 * i, 0, 3, 3) = + skewSymmetric(xi(3 + 3 * i + 0), xi(3 + 3 * i + 1), xi(3 + 3 * i + 2)); + adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = w_hat; + } + return adj; + } + + static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, + ChartJacobian Hxi = {}, ChartJacobian H_y = {}) { + const Jacobian ad_xi = adjointMap(xi); + if (Hxi) { + if constexpr (dimension == Eigen::Dynamic) { + Hxi->setZero(xi.size(), xi.size()); + } else { + Hxi->setZero(); + } + for (Eigen::Index i = 0; i < xi.size(); ++i) { + TangentVector dxi; + if constexpr (dimension == Eigen::Dynamic) { + dxi = TangentVector::Zero(xi.size()); + } else { + dxi = TangentVector::Zero(); + } + dxi(i) = 1.0; + Hxi->col(i) = adjointMap(dxi) * y; + } + } + if (H_y) *H_y = ad_xi; + return ad_xi * y; + } + + static Jacobian ExpmapDerivative(const TangentVector& xi) { + Jacobian J; + Expmap(xi, J); + return J; + } + + static Jacobian LogmapDerivative(const TangentVector& xi) { + const size_t k = RuntimeK(xi); + const Vector3 w = xi.template head<3>(); + const so3::DexpFunctor local(w); + const Matrix3 Rt = local.expmap().transpose(); + const Matrix3 Jw = Rot3::LogmapDerivative(w); + + Jacobian J; + if constexpr (dimension == Eigen::Dynamic) { + J.setZero(3 + 3 * k, 3 + 3 * k); + } else { + J.setZero(); + } + + J.block(0, 0, 3, 3) = Jw; + for (size_t i = 0; i < k; ++i) { + Matrix3 H_xi_w; + local.Jacobian().applyLeft(xi.template segment<3>(3 + 3 * i), H_xi_w); + const Matrix3 Q = Rt * H_xi_w; + J.block(3 + 3 * i, 0, 3, 3) = -Jw * Q * Jw; + J.block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jw; + } + return J; + } + + static Jacobian LogmapDerivative(const ExtendedPose3& pose) { + return LogmapDerivative(Logmap(pose)); + } + + struct ChartAtOrigin { + static ExtendedPose3 Retract(const TangentVector& xi, ChartJacobian Hxi = {}) { + return Expmap(xi, Hxi); + } + + static TangentVector Local(const ExtendedPose3& pose, ChartJacobian Hpose = {}) { + return Logmap(pose, Hpose); + } + }; + + using LieGroup, dimension>::inverse; + + /// @} + /// @name Matrix Lie Group + /// @{ + + LieAlgebra matrix() const { + LieAlgebra M; + if constexpr (matrix_dim == Eigen::Dynamic) { + const Eigen::Index n = 3 + static_cast(k()); + M = LieAlgebra::Identity(n, n); + } else { + M = LieAlgebra::Identity(); + } + M.template block<3, 3>(0, 0) = R_.matrix(); + M.block(0, 3, 3, static_cast(k())) = x_; + return M; + } + + static LieAlgebra Hat(const TangentVector& xi) { + const size_t k = RuntimeK(xi); + LieAlgebra X; + if constexpr (matrix_dim == Eigen::Dynamic) { + X.setZero(3 + k, 3 + k); + } else { + X.setZero(); + } + X.block(0, 0, 3, 3) = skewSymmetric(xi(0), xi(1), xi(2)); + for (size_t i = 0; i < k; ++i) { + X.block(0, 3 + i, 3, 1) = xi.template segment<3>(3 + 3 * i); + } + return X; + } + + static TangentVector Vee(const LieAlgebra& X) { + const Eigen::Index k = X.cols() - 3; + TangentVector xi; + if constexpr (dimension == Eigen::Dynamic) { + xi.resize(3 + 3 * k); + } + xi(0) = X(2, 1); + xi(1) = X(0, 2); + xi(2) = X(1, 0); + for (Eigen::Index i = 0; i < k; ++i) { + xi.template segment<3>(3 + 3 * i) = X.template block<3, 1>(0, 3 + i); + } + return xi; + } + + /// @} + + friend std::ostream& operator<<(std::ostream& os, const ExtendedPose3& p) { + os << "R: " << p.R_ << "\n"; + os << "x: " << p.x_; + return os; + } + + private: +#if GTSAM_ENABLE_BOOST_SERIALIZATION + friend class boost::serialization::access; + template + void serialize(Archive& ar, const unsigned int /*version*/) { + ar& BOOST_SERIALIZATION_NVP(R_); + ar& BOOST_SERIALIZATION_NVP(x_); + } +#endif +}; + +/// Convenience typedef for dynamic k. +using ExtendedPose3Dynamic = ExtendedPose3; + +template +struct traits> + : public internal::MatrixLieGroup, + internal::MatrixDimExtendedPose3(K)> {}; + +template +struct traits> + : public internal::MatrixLieGroup, + internal::MatrixDimExtendedPose3(K)> {}; + +} // namespace gtsam diff --git a/gtsam/geometry/doc/ExtendedPose3.ipynb b/gtsam/geometry/doc/ExtendedPose3.ipynb new file mode 100644 index 0000000000..3016d4a6a7 --- /dev/null +++ b/gtsam/geometry/doc/ExtendedPose3.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ExtendedPose3\n", + "\n", + "`ExtendedPose3` is the generic Lie group $SE_K(3)$: the semi-direct product of `SO(3)` with `K` copies of $\\mathbb{R}^3$.\n", + "\n", + "It is a matrix Lie group with homogeneous representation size $(3+K)\\times(3+K)$ and manifold dimension $3 + 3K$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## State and Tangent Layout\n", + "\n", + "An element is parameterized as:\n", + "\n", + "$$\n", + "X = (R, x_1, \\ldots, x_K), \\qquad R \\in SO(3),\\; x_i \\in \\mathbb{R}^3\n", + "$$\n", + "\n", + "with tangent vector ordering:\n", + "\n", + "$$\n", + "\\xi = [\\omega, \\rho_1, \\ldots, \\rho_K] \\in \\mathbb{R}^{3+3K}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matrix Representation\n", + "\n", + "$$\n", + "X = \\begin{bmatrix}\n", + "R & x_1 & \\cdots & x_K \\\\\n", + "0 & 1 & & 0 \\\\\n", + "\\vdots & & \\ddots & \\\\\n", + "0 & 0 & & 1\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "The group operation is:\n", + "\n", + "$$\n", + "(R,x_i)\\,(S,y_i) = (RS,\\; x_i + R y_i).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## C++ Usage Notes\n", + "\n", + "- Fixed-size: `ExtendedPose3<3>` gives a 12D manifold.\n", + "- Dynamic-size: `ExtendedPose3` takes runtime `k` in constructor.\n", + "- Default dynamic construction (`k=0`) is supported for serialization and placeholder use.\n", + "- The API provides the standard `Manifold`, `LieGroup`, and `MatrixLieGroup` surface: `retract/localCoordinates`, `Expmap/Logmap`, `AdjointMap`, `Hat/Vee`, and `matrix/vec`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Related Guides\n", + "\n", + "- Pose manifold background: [Pose3](./Pose3.ipynb)\n", + "- Navigation usage and `NavState`: [ImuFactor / NavState docs](../../navigation/doc/ImuFactor.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtsam/geometry/tests/testExtendedPose3.cpp b/gtsam/geometry/tests/testExtendedPose3.cpp new file mode 100644 index 0000000000..e9b5157d88 --- /dev/null +++ b/gtsam/geometry/tests/testExtendedPose3.cpp @@ -0,0 +1,222 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file testExtendedPose3.cpp + * @brief Unit tests for ExtendedPose3 + */ + +#include + +#include +#include +#include +#include + +#include + +using namespace gtsam; + +using ExtendedPose33 = ExtendedPose3<3>; +using ExtendedPose3d = ExtendedPose3; + +GTSAM_CONCEPT_TESTABLE_INST(ExtendedPose33) +GTSAM_CONCEPT_MATRIX_LIE_GROUP_INST(ExtendedPose33) +GTSAM_CONCEPT_TESTABLE_INST(ExtendedPose3d) +GTSAM_CONCEPT_MATRIX_LIE_GROUP_INST(ExtendedPose3d) + +namespace { + +const Rot3 kR1 = Rot3::RzRyRx(0.1, -0.2, 0.3); +const Rot3 kR2 = Rot3::RzRyRx(-0.3, 0.4, -0.2); +const Matrix33 kX1 = (Matrix33() << 1.0, 4.0, -1.0, 2.0, 5.0, 0.5, 3.0, 6.0, + 2.0) + .finished(); +const Matrix33 kX2 = (Matrix33() << -2.0, 1.0, 0.1, 0.3, -1.5, 2.2, 0.7, -0.9, + 1.6) + .finished(); +const Vector12 kXi = + (Vector12() << 0.11, -0.07, 0.05, 0.3, -0.4, 0.1, -0.2, 0.6, -0.5, 0.7, + -0.1, 0.2) + .finished(); + +ExtendedPose3d MakeDynamic(const Rot3& R, const Matrix33& X) { + Matrix Xd = X; + return ExtendedPose3d(R, Xd); +} + +} // namespace + +//****************************************************************************** +TEST(ExtendedPose3, Concept) { + GTSAM_CONCEPT_ASSERT(IsGroup); + GTSAM_CONCEPT_ASSERT(IsManifold); + GTSAM_CONCEPT_ASSERT(IsMatrixLieGroup); + + GTSAM_CONCEPT_ASSERT(IsGroup); + GTSAM_CONCEPT_ASSERT(IsManifold); + GTSAM_CONCEPT_ASSERT(IsMatrixLieGroup); +} + +//****************************************************************************** +TEST(ExtendedPose3, Dimensions) { + const ExtendedPose33 fixed; + EXPECT_LONGS_EQUAL(12, traits::GetDimension(fixed)); + EXPECT_LONGS_EQUAL(3, fixed.k()); + + const ExtendedPose3d placeholder; + EXPECT_LONGS_EQUAL(0, placeholder.k()); + EXPECT_LONGS_EQUAL(3, placeholder.dim()); + + const ExtendedPose3d dynamic(3); + EXPECT_LONGS_EQUAL(3, dynamic.k()); + EXPECT_LONGS_EQUAL(12, dynamic.dim()); +} + +//****************************************************************************** +TEST(ExtendedPose3, ConstructorsAndAccess) { + const ExtendedPose33 fixed(kR1, kX1); + const ExtendedPose3d dynamic = MakeDynamic(kR1, kX1); + + EXPECT(assert_equal(kR1, fixed.rotation())); + EXPECT(assert_equal(kR1, dynamic.rotation())); + + for (size_t i = 0; i < 3; ++i) { + EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), fixed.x(i))); + EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), dynamic.x(i))); + } + + EXPECT(assert_equal(fixed.matrix(), dynamic.matrix())); +} + +//****************************************************************************** +TEST(ExtendedPose3, GroupOperationsMatch) { + const ExtendedPose33 f1(kR1, kX1), f2(kR2, kX2); + const ExtendedPose3d d1 = MakeDynamic(kR1, kX1), d2 = MakeDynamic(kR2, kX2); + + const auto f12 = f1 * f2; + const auto d12 = d1 * d2; + EXPECT(assert_equal(f12.matrix(), d12.matrix())); + + const auto f_id = f1 * f1.inverse(); + const auto d_id = d1 * d1.inverse(); + EXPECT(assert_equal(ExtendedPose33::Identity().matrix(), f_id.matrix(), 1e-9)); + EXPECT(assert_equal(ExtendedPose3d::Identity(3).matrix(), d_id.matrix(), 1e-9)); + + const auto f_between = f1.between(f2); + const auto d_between = d1.between(d2); + EXPECT(assert_equal(f_between.matrix(), d_between.matrix(), 1e-9)); +} + +//****************************************************************************** +TEST(ExtendedPose3, HatVee) { + const Vector xi = kXi; + const auto Xf = ExtendedPose33::Hat(kXi); + const auto Xd = ExtendedPose3d::Hat(xi); + + EXPECT(assert_equal(xi, Vector(ExtendedPose33::Vee(Xf)))); + EXPECT(assert_equal(xi, ExtendedPose3d::Vee(Xd))); + EXPECT(assert_equal(Xf, Xd)); +} + +//****************************************************************************** +TEST(ExtendedPose3, ExpmapLogmapRoundTrip) { + const Vector xi = kXi; + const ExtendedPose33 f = ExtendedPose33::Expmap(kXi); + const ExtendedPose3d d = ExtendedPose3d::Expmap(xi); + + EXPECT(assert_equal(f.matrix(), d.matrix(), 1e-9)); + EXPECT(assert_equal(xi, Vector(ExtendedPose33::Logmap(f)), 1e-9)); + EXPECT(assert_equal(xi, ExtendedPose3d::Logmap(d), 1e-9)); +} + +//****************************************************************************** +TEST(ExtendedPose3, AdjointConsistency) { + const ExtendedPose33 f(kR1, kX1); + const ExtendedPose3d d = MakeDynamic(kR1, kX1); + const Vector xi = kXi; + + const auto f_conj = f * ExtendedPose33::Expmap(kXi) * f.inverse(); + const auto d_conj = d * ExtendedPose3d::Expmap(xi) * d.inverse(); + + const auto f_adj = ExtendedPose33::Expmap(f.Adjoint(kXi)); + const auto d_adj = ExtendedPose3d::Expmap(d.Adjoint(xi)); + EXPECT(assert_equal(f_conj.matrix(), f_adj.matrix(), 1e-9)); + EXPECT(assert_equal(d_conj.matrix(), d_adj.matrix(), 1e-9)); + + const ExtendedPose33::Jacobian f_generic = + static_cast*>(&f)->AdjointMap(); + const Matrix d_generic = static_cast< + const MatrixLieGroup*>(&d) + ->AdjointMap(); + EXPECT(assert_equal(Matrix(f_generic), Matrix(f.AdjointMap()), 1e-9)); + EXPECT(assert_equal(d_generic, d.AdjointMap(), 1e-9)); +} + +//****************************************************************************** +TEST(ExtendedPose3, Derivatives) { + const ExtendedPose33 f(kR1, kX1); + const ExtendedPose3d d = MakeDynamic(kR1, kX1); + const Vector xi = kXi; + + Matrix Hf; + ExtendedPose33::Expmap(kXi, Hf); + const auto f_exp = [](const Vector12& v) { return ExtendedPose33::Expmap(v); }; + const Matrix Hf_num = numericalDerivative11(f_exp, kXi); + EXPECT(assert_equal(Hf_num, Hf, 1e-6)); + + Matrix Hd; + ExtendedPose3d::Expmap(xi, Hd); + const auto d_exp = [](const Vector& v) { return ExtendedPose3d::Expmap(v); }; + const Matrix Hd_num = numericalDerivative11(d_exp, xi); + EXPECT(assert_equal(Hd_num, Hd, 1e-6)); + + Matrix Lf; + ExtendedPose33::Logmap(f, Lf); + const auto f_log = [](const ExtendedPose33& g) { return Vector12(ExtendedPose33::Logmap(g)); }; + const Matrix Lf_num = + numericalDerivative11(f_log, f); + EXPECT(assert_equal(Lf_num, Lf, 1e-6)); + + Matrix Ld; + ExtendedPose3d::Logmap(d, Ld); + const auto d_log = [](const ExtendedPose3d& g) { return ExtendedPose3d::Logmap(g); }; + const Matrix Ld_num = + numericalDerivative11(d_log, d); + EXPECT(assert_equal(Ld_num, Ld, 1e-6)); +} + +//****************************************************************************** +TEST(ExtendedPose3, VecJacobian) { + const ExtendedPose33 f(kR1, kX1); + const ExtendedPose3d d = MakeDynamic(kR1, kX1); + + Matrix Hf; + const Vector vf = f.vec(Hf); + const auto fv = [](const ExtendedPose33& g) { return Vector(g.vec()); }; + const Matrix Hf_num = numericalDerivative11(fv, f); + EXPECT(assert_equal(Hf_num, Hf, 1e-6)); + EXPECT_LONGS_EQUAL(36, vf.size()); + + Matrix Hd; + const Vector vd = d.vec(Hd); + const auto dv = [](const ExtendedPose3d& g) { return Vector(g.vec()); }; + const Matrix Hd_num = numericalDerivative11(dv, d); + EXPECT(assert_equal(Hd_num, Hd, 1e-6)); + EXPECT_LONGS_EQUAL(36, vd.size()); +} + +//****************************************************************************** +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} +//****************************************************************************** From 81f8bf71221a5a32fc917e491f6275009d208b52 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 10:27:34 -0500 Subject: [PATCH 226/266] Pose3 now SE_1(3) --- gtsam/geometry/Pose3.cpp | 351 ++++++++++++--------------------------- gtsam/geometry/Pose3.h | 121 +++++--------- 2 files changed, 148 insertions(+), 324 deletions(-) diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 163c3c0a7f..58681da5a5 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -31,10 +31,9 @@ namespace gtsam { GTSAM_CONCEPT_POSE_INST(Pose3) /* ************************************************************************* */ -Pose3::Pose3(const Pose2& pose2) : - R_(Rot3::Rodrigues(0, 0, pose2.theta())), t_( - Point3(pose2.x(), pose2.y(), 0)) { -} +Pose3::Pose3(const Pose2& pose2) + : Base(Rot3::Rodrigues(0, 0, pose2.theta()), + Vector3(pose2.x(), pose2.y(), 0.0)) {} /* ************************************************************************* */ Pose3 Pose3::Create(const Rot3& R, const Point3& t, OptionalJacobian<6, 3> HR, @@ -59,38 +58,101 @@ Pose3 Pose3::FromPose2(const Pose2& p, OptionalJacobian<6, 3> H) { } /* ************************************************************************* */ -Pose3 Pose3::inverse() const { - Rot3 Rt = R_.inverse(); - return Pose3(Rt, Rt * (-t_)); +Pose3 Pose3::inverse() const { return Pose3(Base::inverse()); } + +/* ************************************************************************* */ +Pose3 Pose3::operator*(const Pose3& T) const { + return Pose3(Base::operator*(T)); } /* ************************************************************************* */ -// Calculate Adjoint map -// Ad_pose is 6*6 matrix that when applied to twist xi, returns Ad_pose(xi) -Matrix6 Pose3::AdjointMap() const { - const Matrix3 R = R_.matrix(); - Matrix3 A = skewSymmetric(t_.x(), t_.y(), t_.z()) * R; - Matrix6 adj; - adj << R, Z_3x3, A, R; // Gives [R 0; A R] - return adj; +Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi) { + return Pose3(Base::Expmap(xi, Hxi)); } /* ************************************************************************* */ -// Calculate AdjointMap applied to xi_b, with Jacobians -Vector6 Pose3::Adjoint(const Vector6& xi_b, OptionalJacobian<6, 6> H_pose, +Vector6 Pose3::Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose) { + return Base::Logmap(pose, Hpose); +} + +/* ************************************************************************* */ +Matrix6 Pose3::AdjointMap() const { return Base::AdjointMap(); } + +/* ************************************************************************* */ +Vector6 Pose3::Adjoint(const Vector6& xi_b, OptionalJacobian<6, 6> H_this, OptionalJacobian<6, 6> H_xib) const { - const Matrix6 Ad = AdjointMap(); + return Base::Adjoint(xi_b, H_this, H_xib); +} - // Jacobians - // D1 Ad_T(xi_b) = D1 Ad_T Ad_I(xi_b) = Ad_T * D1 Ad_I(xi_b) = Ad_T * ad_xi_b - // D2 Ad_T(xi_b) = Ad_T - // See docs/math.pdf for more details. - // In D1 calculation, we could be more efficient by writing it out, but do not - // for readability - if (H_pose) *H_pose = -Ad * adjointMap(xi_b); - if (H_xib) *H_xib = Ad; +/* ************************************************************************* */ +Matrix6 Pose3::adjointMap(const Vector6& xi) { return Base::adjointMap(xi); } - return Ad * xi_b; +/* ************************************************************************* */ +Vector6 Pose3::adjoint(const Vector6& xi, const Vector6& y, + OptionalJacobian<6, 6> Hxi, + OptionalJacobian<6, 6> H_y) { + return Base::adjoint(xi, y, Hxi, H_y); +} + +/* ************************************************************************* */ +Matrix6 Pose3::ExpmapDerivative(const Vector6& xi) { + return Base::ExpmapDerivative(xi); +} + +/* ************************************************************************* */ +Matrix6 Pose3::LogmapDerivative(const Vector6& xi) { + return Base::LogmapDerivative(xi); +} + +/* ************************************************************************* */ +Matrix6 Pose3::LogmapDerivative(const Pose3& pose) { + return Base::LogmapDerivative(pose); +} + +/* ************************************************************************* */ +Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { +#ifdef GTSAM_POSE3_EXPMAP + return Expmap(xi, Hxi); +#else + Matrix3 DR; + const Rot3 R = Rot3::Retract(xi.head<3>(), Hxi ? &DR : nullptr); + if (Hxi) { + Hxi->setIdentity(); + Hxi->block<3, 3>(0, 0) = DR; + } + return Pose3(R, xi.tail<3>()); +#endif +} + +/* ************************************************************************* */ +Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { +#ifdef GTSAM_POSE3_EXPMAP + return Logmap(pose, Hpose); +#else + Matrix3 DR; + Vector6 xi; + xi.head<3>() = Rot3::LocalCoordinates(pose.rotation(), Hpose ? &DR : nullptr); + xi.tail<3>() = pose.translation(); + if (Hpose) { + Hpose->setIdentity(); + Hpose->block<3, 3>(0, 0) = DR; + } + return xi; +#endif +} + +/* ************************************************************************* */ +Matrix4 Pose3::Hat(const Vector6& xi) { return Base::Hat(xi); } + +/* ************************************************************************* */ +Vector6 Pose3::Vee(const Matrix4& X) { return Base::Vee(X); } + +/* ************************************************************************* */ +Matrix4 Pose3::matrix() const { return Base::matrix(); } + +/* ************************************************************************* */ +Pose3::Vector16 Pose3::vec(OptionalJacobian<16, 6> H) const { + return Base::vec(H); } /* ************************************************************************* */ @@ -115,34 +177,6 @@ Vector6 Pose3::AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_pose, return AdTx; } -/* ************************************************************************* */ -Matrix6 Pose3::adjointMap(const Vector6& xi) { - Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); - Matrix3 v_hat = skewSymmetric(xi(3), xi(4), xi(5)); - Matrix6 adj; - adj << w_hat, Z_3x3, v_hat, w_hat; - - return adj; -} - -/* ************************************************************************* */ -Vector6 Pose3::adjoint(const Vector6& xi, const Vector6& y, - OptionalJacobian<6, 6> Hxi, OptionalJacobian<6, 6> H_y) { - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 6; ++i) { - Vector6 dxi; - dxi.setZero(); - dxi(i) = 1.0; - Matrix6 Gi = adjointMap(dxi); - Hxi->col(i) = Gi * y; - } - } - const Matrix6& ad_xi = adjointMap(xi); - if (H_y) *H_y = ad_xi; - return ad_xi * y; -} - /* ************************************************************************* */ Vector6 Pose3::adjointTranspose(const Vector6& xi, const Vector6& y, OptionalJacobian<6, 6> Hxi, OptionalJacobian<6, 6> H_y) { @@ -161,21 +195,6 @@ Vector6 Pose3::adjointTranspose(const Vector6& xi, const Vector6& y, return adT_xi * y; } -/* ************************************************************************* */ -Matrix4 Pose3::Hat(const Vector6& xi) { - Matrix4 X; - const double wx = xi(0), wy = xi(1), wz = xi(2), vx = xi(3), vy = xi(4), vz = xi(5); - X << 0., -wz, wy, vx, wz, 0., -wx, vy, -wy, wx, 0., vz, 0., 0., 0., 0.; - return X; -} - -/* ************************************************************************* */ -Vector6 Pose3::Vee(const Matrix4& Xi) { - Vector6 xi; - xi << Xi(2, 1), Xi(0, 2), Xi(1, 0), Xi(0, 3), Xi(1, 3), Xi(2, 3); - return xi; -} - /* ************************************************************************* */ void Pose3::print(const std::string& s) const { std::cout << (s.empty() ? s : s + " ") << *this << std::endl; @@ -183,7 +202,8 @@ void Pose3::print(const std::string& s) const { /* ************************************************************************* */ bool Pose3::equals(const Pose3& pose, double tol) const { - return R_.equals(pose.R_, tol) && traits::Equals(t_, pose.t_, tol); + return R_.equals(pose.R_, tol) && + traits::Equals(x_.col(0), pose.x_.col(0), tol); } /* ************************************************************************* */ @@ -198,190 +218,31 @@ Pose3 Pose3::interpolateRt(const Pose3& T, double t, typename MakeJacobian::type HtPoint; Rot3 Rint = interpolate(R_, T.R_, t, HselfRot, HargRot, HtRot); - Point3 Pint = interpolate(t_, T.t_, t, HselfPoint, HargPoint, HtPoint); + Point3 Pint = interpolate(x_.col(0), T.x_.col(0), t, HselfPoint, + HargPoint, HtPoint); Pose3 result = Pose3(Rint, Pint); if(Hself) *Hself << HselfRot, Z_3x3, Z_3x3, Rint.transpose() * R_.matrix() * HselfPoint; - if(Harg) *Harg << HargRot, Z_3x3, Z_3x3, Rint.transpose() * T.R_.matrix() * HargPoint; + if(Harg) *Harg << HargRot, Z_3x3, Z_3x3, + Rint.transpose() * T.R_.matrix() * HargPoint; if(Ht) *Ht << HtRot, Rint.transpose() * HtPoint; return result; } return Pose3(interpolate(R_, T.R_, t), - interpolate(t_, T.t_, t)); - -} + interpolate(x_.col(0), T.x_.col(0), t)); -/* ************************************************************************* */ -// Expmap is implemented in so3::ExpmapFunctor::expmap, based on Ethan Eade's -// elegant Lie group document, at https://www.ethaneade.org/lie.pdf. -// See also [this document](doc/Jacobians.md) -Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi) { - // Get angular velocity omega and translational velocity v from twist xi - const Vector3 w = xi.head<3>(), v = xi.tail<3>(); - - // Instantiate functor for Dexp-related operations: - const so3::DexpFunctor local(w); - - // Compute rotation using Expmap -#ifdef GTSAM_USE_QUATERNIONS - const Rot3 R = traits::Expmap(w); -#else - const Rot3 R(local.expmap()); -#endif - - // The translation t = local.Jacobian().left() * v. - // Here we call local.Jacobian().applyLeft, which is faster if you don't need - // Jacobians, and returns Jacobian of t with respect to w if asked. - // NOTE(Frank): this does the same as the intuitive formulas: - // t_parallel = w * w.dot(v); // translation parallel to axis - // w_cross_v = w.cross(v); // translation orthogonal to axis - // t = (w_cross_v - Rot3::Expmap(w) * w_cross_v + t_parallel) / theta2; - // but Local does not need R, deals automatically with the case where theta2 - // is near zero, and also gives us the machinery for the Jacobians. - Matrix3 H; - const Vector3 t = local.Jacobian().applyLeft(v, Hxi ? &H : nullptr); - - if (Hxi) { - // The Jacobian of expmap is given by the right Jacobian of SO(3): - const Matrix3 Jr = local.Jacobian().right(); - // We are creating a Pose3, so we still need to chain H with R^T, the - // Jacobian of Pose3::Create with respect to t. - const Matrix3 Rt = R.transpose(); - *Hxi << Jr, Z_3x3, // Jr here *is* the Jacobian of expmap - Rt * H, Jr; // Here Jr = R^T * Jl, with Jl the Jacobian of t in v. - } - - return Pose3(R, t); -} - -/* ************************************************************************* */ -Vector6 Pose3::Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose) { - const Vector3 w = Rot3::Logmap(pose.rotation()); - - // Instantiate functor for Dexp-related operations: - const so3::DexpFunctor local(w); - - const Vector3 t = pose.translation(); - const Vector3 u = local.InvJacobian().applyLeft(t); - Vector6 xi; - xi << w, u; - if (Hpose) *Hpose = LogmapDerivative(xi); - return xi; -} - -/* ************************************************************************* */ -Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { -#ifdef GTSAM_POSE3_EXPMAP - return Expmap(xi, Hxi); -#else - Matrix3 DR; - Rot3 R = Rot3::Retract(xi.head<3>(), Hxi ? &DR : 0); - if (Hxi) { - *Hxi = I_6x6; - Hxi->topLeftCorner<3, 3>() = DR; - } - return Pose3(R, Point3(xi.tail<3>())); -#endif -} - -/* ************************************************************************* */ -Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { -#ifdef GTSAM_POSE3_EXPMAP - return Logmap(pose, Hpose); -#else - Matrix3 DR; - Vector3 omega = Rot3::LocalCoordinates(pose.rotation(), Hpose ? &DR : 0); - if (Hpose) { - *Hpose = I_6x6; - Hpose->topLeftCorner<3, 3>() = DR; - } - Vector6 xi; - xi << omega, pose.translation(); - return xi; -#endif -} - -/* ************************************************************************* */ -Matrix6 Pose3::ExpmapDerivative(const Vector6& xi) { - Matrix6 J; - Expmap(xi, J); - return J; -} - -/* ************************************************************************* */ -Matrix6 Pose3::LogmapDerivative(const Vector6& xi) { - const Vector3 w = xi.head<3>(); - Vector3 v = xi.segment<3>(3); - - // Instantiate functor for Dexp-related operations: - const so3::DexpFunctor local(w); - - // Call applyLeftJacobian to get its Jacobians - Matrix3 H_t_w; - local.Jacobian().applyLeft(v, H_t_w); - - // Multiply with R^T to account for NavState::Create Jacobian. - const Matrix3 R = local.expmap(); - const Matrix3 Qt = R.transpose() * H_t_w; - - // Now compute the blocks of the LogmapDerivative Jacobian - const Matrix3 Jw = Rot3::LogmapDerivative(w); - const Matrix3 Qt2 = -Jw * Qt * Jw; - - Matrix6 J; - J << Jw, Z_3x3, Qt2, Jw; - return J; -} - -/* ************************************************************************* */ -Matrix6 Pose3::LogmapDerivative(const Pose3& pose) { - const Vector6 xi = Logmap(pose); - return LogmapDerivative(xi); } /* ************************************************************************* */ const Point3& Pose3::translation(OptionalJacobian<3, 6> Hself) const { - if (Hself) *Hself << Z_3x3, rotation().matrix(); - return t_; + t_cache_ = Base::x(0, Hself); + return t_cache_; } /* ************************************************************************* */ const Rot3& Pose3::rotation(OptionalJacobian<3, 6> Hself) const { - if (Hself) { - *Hself << I_3x3, Z_3x3; - } - return R_; -} - -/* ************************************************************************* */ -Matrix4 Pose3::matrix() const { - static const auto A14 = Eigen::RowVector4d(0,0,0,1); - Matrix4 mat; - mat << R_.matrix(), t_, A14; - return mat; -} - -/* ************************************************************************* */ -Pose3::Vector16 Pose3::vec(OptionalJacobian<16, 6> H) const { - // Vectorize - const Matrix4 M = matrix(); - const Vector16 v = Eigen::Map(M.data()); - - // If requested, calculate H - if (H) { - H->setZero(); - auto R = M.block<3, 3>(0, 0); - H->block<3, 1>(0, 1) = -R.col(2); - H->block<3, 1>(0, 2) = R.col(1); - H->block<3, 1>(4, 0) = R.col(2); - H->block<3, 1>(4, 2) = -R.col(0); - H->block<3, 1>(8, 0) = -R.col(1); - H->block<3, 1>(8, 1) = R.col(0); - H->block<3,3>(12,3) = R; - } - - return v; + return Base::rotation(Hself); } /* ************************************************************************* */ @@ -413,7 +274,7 @@ Point3 Pose3::transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself, if (Hpoint) { *Hpoint = R; } - return R_ * point + t_; + return R_ * point + x_.col(0); } Matrix Pose3::transformFrom(const Matrix& points) const { @@ -421,7 +282,7 @@ Matrix Pose3::transformFrom(const Matrix& points) const { throw std::invalid_argument("Pose3:transformFrom expects 3*N matrix."); } const Matrix3 R = R_.matrix(); - return (R * points).colwise() + t_; // Eigen broadcasting! + return (R * points).colwise() + x_.col(0); // Eigen broadcasting! } /* ************************************************************************* */ @@ -430,7 +291,7 @@ Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself, // Only get transpose once, to avoid multiple allocations, // as well as multiple conversions in the Quaternion case const Matrix3 Rt = R_.transpose(); - const Point3 q(Rt*(point - t_)); + const Point3 q(Rt * (point - x_.col(0))); if (Hself) { const double wx = q.x(), wy = q.y(), wz = q.z(); (*Hself) << @@ -449,13 +310,13 @@ Matrix Pose3::transformTo(const Matrix& points) const { throw std::invalid_argument("Pose3:transformTo expects 3*N matrix."); } const Matrix3 Rt = R_.transpose(); - return Rt * (points.colwise() - t_); // Eigen broadcasting! + return Rt * (points.colwise() - x_.col(0)); // Eigen broadcasting! } /* ************************************************************************* */ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Vector3 delta = point - t_; + const Vector3 delta = point - x_.col(0); if (!Hself && !Hpoint) return delta.norm(); Matrix13 D_r_point; @@ -474,7 +335,7 @@ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, /* ************************************************************************* */ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 6> Hpose) const { - const Vector3 delta = pose.t_ - t_; + const Vector3 delta = pose.x_.col(0) - x_.col(0); if (!Hself && !Hpose) return delta.norm(); Matrix13 D_r_point; @@ -500,7 +361,7 @@ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> Hself, OptionalJacobian<2, 3> Hpoint) const { const Matrix3 Rt = R_.transpose(); - const Point3 local(Rt * (point - t_)); + const Point3 local(Rt * (point - x_.col(0))); if (!Hself && !Hpoint) return Unit3(local); diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index a6b9c40b94..85eae9f8c2 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -20,10 +20,15 @@ #include #include +#include #include #include #include +#if GTSAM_ENABLE_BOOST_SERIALIZATION +#include +#endif + namespace gtsam { class Pose2; @@ -34,17 +39,15 @@ class Pose2; * @ingroup geometry * \nosubgrouping */ -class GTSAM_EXPORT Pose3: public MatrixLieGroup { +class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPose3<1> { public: + using Base = ExtendedPose3<1>; + using LieGroupBase = MatrixLieGroup; /** Pose Concept requirements */ typedef Rot3 Rotation; typedef Point3 Translation; - -private: - - Rot3 R_; ///< Rotation gRp, between global and pose frame - Point3 t_; ///< Translation gPp, from global origin to pose frame origin + inline constexpr static auto dimension = 6; public: using Vector16 = Eigen::Matrix; @@ -53,26 +56,23 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { /// @{ /** Default constructor is origin */ - Pose3() : R_(traits::Identity()), t_(traits::Identity()) {} + Pose3() : Base() {} /** Copy constructor */ Pose3(const Pose3& pose) = default; Pose3& operator=(const Pose3& other) = default; + Pose3(const Base& other) : Base(other) {} + /** Construct from R,t */ - Pose3(const Rot3& R, const Point3& t) : - R_(R), t_(t) { - } + Pose3(const Rot3& R, const Point3& t) : Base(R, Vector3(t.x(), t.y(), t.z())) {} /** Construct from Pose2 */ explicit Pose3(const Pose2& pose2); /** Constructor from 4*4 matrix */ - Pose3(const Matrix &T) : - R_(T(0, 0), T(0, 1), T(0, 2), T(1, 0), T(1, 1), T(1, 2), T(2, 0), T(2, 1), - T(2, 2)), t_(T(0, 3), T(1, 3), T(2, 3)) { - } + Pose3(const Matrix &T) : Base(Matrix4(T)) {} /// Named constructor with derivatives static Pose3 Create(const Rot3& R, const Point3& t, @@ -106,18 +106,14 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { /// @name Group /// @{ - /// identity for group operation static Pose3 Identity() { - return Pose3(); + return Pose3(Base::Identity()); } - /// inverse transformation Pose3 inverse() const; /// compose syntactic sugar - Pose3 operator*(const Pose3& T) const { - return Pose3(R_ * T.R_, t_ + R_ * T.t_); - } + Pose3 operator*(const Pose3& T) const; /** * Interpolate between two poses via individual rotation and translation @@ -144,56 +140,26 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { using LieAlgebra = Matrix4; - /// Exponential map at identity - create a rotation from canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = {}); - /// Log map at identity - return the canonical coordinates \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$ of this rotation static Vector6 Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose = {}); - /** - * Calculate Adjoint map, transforming a twist in this pose's (i.e, body) frame to the world spatial frame - * Ad_pose is 6*6 matrix that when applied to twist xi \f$ [R_x,R_y,R_z,T_x,T_y,T_z] \f$, returns Ad_pose(xi) - */ Matrix6 AdjointMap() const; - /** - * Apply this pose's AdjointMap Ad_g to a twist \f$ \xi_b \f$, i.e. a - * body-fixed velocity, transforming it to the spatial frame - * \f$ \xi^s = g*\xi^b*g^{-1} = Ad_g * \xi^b \f$ - * Note that H_xib = AdjointMap() - */ Vector6 Adjoint(const Vector6& xi_b, OptionalJacobian<6, 6> H_this = {}, OptionalJacobian<6, 6> H_xib = {}) const; - - /// The dual version of Adjoint - Vector6 AdjointTranspose(const Vector6& x, - OptionalJacobian<6, 6> H_this = {}, - OptionalJacobian<6, 6> H_x = {}) const; - /** - * Compute the [ad(w,v)] operator as defined in [Kobilarov09siggraph], pg 11 - * [ad(w,v)] = [w^, zero3; v^, w^] - * Note that this is the matrix representation of the adjoint operator for se3 Lie algebra, - * aka the Lie bracket, and also the derivative of Adjoint map for the Lie group SE3. - * - * Let \f$ \hat{\xi}_i \f$ be the se3 Lie algebra, and \f$ \hat{\xi}_i^\vee = \xi_i = [\omega_i,v_i] \in \mathbb{R}^6\f$ be its - * vector representation. - * We have the following relationship: - * \f$ [\hat{\xi}_1,\hat{\xi}_2]^\vee = ad_{\xi_1}(\xi_2) = [ad_{(\omega_1,v_1)}]*\xi_2 \f$ - * - * We use this to compute the discrete version of the inverse right-trivialized tangent map, - * and its inverse transpose in the discrete Euler Poincare' (DEP) operator. - * - */ static Matrix6 adjointMap(const Vector6& xi); - /** - * Action of the adjointMap on a Lie-algebra vector y, with optional derivatives - */ static Vector6 adjoint(const Vector6& xi, const Vector6& y, OptionalJacobian<6, 6> Hxi = {}, OptionalJacobian<6, 6> H_y = {}); + + /// The dual version of Adjoint + Vector6 AdjointTranspose(const Vector6& x, + OptionalJacobian<6, 6> H_this = {}, + OptionalJacobian<6, 6> H_x = {}) const; // temporary fix for wrappers until case issue is resolved static Matrix6 adjointMap_(const Vector6 &xi) { return adjointMap(xi);} @@ -206,35 +172,37 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { OptionalJacobian<6, 6> Hxi = {}, OptionalJacobian<6, 6> H_y = {}); - /// Derivative of Expmap static Matrix6 ExpmapDerivative(const Vector6& xi); - /// Derivative of Logmap static Matrix6 LogmapDerivative(const Vector6& xi); - /// Derivative of Logmap, Pose3 version. TODO(Frank): deprecate? static Matrix6 LogmapDerivative(const Pose3& pose); - // Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP struct GTSAM_EXPORT ChartAtOrigin { static Pose3 Retract(const Vector6& xi, ChartJacobian Hxi = {}); static Vector6 Local(const Pose3& pose, ChartJacobian Hpose = {}); }; - using LieGroup::inverse; // version with derivative + using LieGroupBase::between; + using LieGroupBase::compose; + using LieGroupBase::expmap; + using LieGroupBase::LocalCoordinates; + using LieGroupBase::logmap; + using LieGroupBase::Retract; + using LieGroup::inverse; + using LieGroupBase::localCoordinates; + using LieGroupBase::retract; - /** - * Hat for Pose3: - * @param xi 6-dim twist (omega,v) where - * omega = (wx,wy,wz) 3D angular velocity - * v (vx,vy,vz) = 3D velocity - * @return xihat, 4*4 element of Lie algebra that can be exponentiated - */ static Matrix4 Hat(const Vector6& xi); - /// Vee maps from Lie algebra to tangent vector static Vector6 Vee(const Matrix4& X); + /** convert to 4*4 matrix */ + Matrix4 matrix() const; + + /// Return vectorized SE(3) matrix in column order. + Vector16 vec(OptionalJacobian<16, 6> H = {}) const; + /// @} /// @name Group Action on Point3 /// @{ @@ -290,25 +258,19 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { /// get x double x() const { - return t_.x(); + return translation().x(); } /// get y double y() const { - return t_.y(); + return translation().y(); } /// get z double z() const { - return t_.z(); + return translation().z(); } - /** convert to 4*4 matrix */ - Matrix4 matrix() const; - - /// Return vectorized SE(3) matrix in column order. - Vector16 vec(OptionalJacobian<16, 6> H = {}) const; - /** * Assuming self == wTa, takes a pose aTb in local coordinates * and transforms it to world coordinates wTb = wTa * aTb. @@ -405,13 +367,14 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup { /// @} private: + mutable Point3 t_cache_ = Point3::Zero(); + #if GTSAM_ENABLE_BOOST_SERIALIZATION /** Serialization function */ friend class boost::serialization::access; template void serialize(Archive & ar, const unsigned int /*version*/) { - ar & BOOST_SERIALIZATION_NVP(R_); - ar & BOOST_SERIALIZATION_NVP(t_); + ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); } #endif /// @} From 81cc49874ca6b27ab5a5d4f2df9f00330d5e10ec Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 10:27:53 -0500 Subject: [PATCH 227/266] NavState now SE_2(3) --- gtsam/navigation/NavState.cpp | 334 +++++++++------------------------- gtsam/navigation/NavState.h | 134 +++++--------- 2 files changed, 128 insertions(+), 340 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 4abe029503..58cc9c35d2 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -23,6 +23,75 @@ namespace gtsam { +//------------------------------------------------------------------------------ +NavState NavState::inverse() const { return NavState(Base::inverse()); } + +//------------------------------------------------------------------------------ +NavState NavState::operator*(const NavState& other) const { + return NavState(Base::operator*(other)); +} + +//------------------------------------------------------------------------------ +NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { + return NavState(Base::Expmap(xi, Hxi)); +} + +//------------------------------------------------------------------------------ +Vector9 NavState::Logmap(const NavState& pose, OptionalJacobian<9, 9> Hpose) { + return Base::Logmap(pose, Hpose); +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::AdjointMap() const { return Base::AdjointMap(); } + +//------------------------------------------------------------------------------ +Vector9 NavState::Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_this, + OptionalJacobian<9, 9> H_xib) const { + return Base::Adjoint(xi_b, H_this, H_xib); +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::adjointMap(const Vector9& xi) { return Base::adjointMap(xi); } + +//------------------------------------------------------------------------------ +Vector9 NavState::adjoint(const Vector9& xi, const Vector9& y, + OptionalJacobian<9, 9> Hxi, + OptionalJacobian<9, 9> H_y) { + return Base::adjoint(xi, y, Hxi, H_y); +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { + return Base::ExpmapDerivative(xi); +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::LogmapDerivative(const Vector9& xi) { + return Base::LogmapDerivative(xi); +} + +//------------------------------------------------------------------------------ +Matrix9 NavState::LogmapDerivative(const NavState& state) { + return Base::LogmapDerivative(state); +} + +//------------------------------------------------------------------------------ +NavState NavState::ChartAtOrigin::Retract(const Vector9& xi, ChartJacobian Hxi) { + return NavState(Base::ChartAtOrigin::Retract(xi, Hxi)); +} + +//------------------------------------------------------------------------------ +Vector9 NavState::ChartAtOrigin::Local(const NavState& state, + ChartJacobian Hstate) { + return Base::ChartAtOrigin::Local(state, Hstate); +} + +//------------------------------------------------------------------------------ +Matrix5 NavState::Hat(const Vector9& xi) { return Base::Hat(xi); } + +//------------------------------------------------------------------------------ +Vector9 NavState::Vee(const Matrix5& X) { return Base::Vee(X); } + //------------------------------------------------------------------------------ NavState NavState::Create(const Rot3& R, const Point3& t, const Velocity3& v, OptionalJacobian<9, 3> H1, OptionalJacobian<9, 3> H2, @@ -46,29 +115,25 @@ NavState NavState::FromPoseVelocity(const Pose3& pose, const Vector3& vel, //------------------------------------------------------------------------------ const Rot3& NavState::attitude(OptionalJacobian<3, 9> H) const { - if (H) - *H << I_3x3, Z_3x3, Z_3x3; - return R_; + return Base::rotation(H); } //------------------------------------------------------------------------------ const Point3& NavState::position(OptionalJacobian<3, 9> H) const { - if (H) - *H << Z_3x3, R(), Z_3x3; - return t_; + p_cache_ = Base::x(0, H); + return p_cache_; } //------------------------------------------------------------------------------ const Vector3& NavState::velocity(OptionalJacobian<3, 9> H) const { - if (H) - *H << Z_3x3, Z_3x3, R(); - return v_; + v_cache_ = Base::x(1, H); + return v_cache_; } //------------------------------------------------------------------------------ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { const Rot3& nRb = R_; - const Vector3& n_v = v_; + const Vector3 n_v = x_.col(1); Matrix3 D_bv_nRb; Vector3 b_v = nRb.unrotate(n_v, H ? &D_bv_nRb : 0); if (H) @@ -79,7 +144,7 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { //------------------------------------------------------------------------------ double NavState::range(const Point3& point, OptionalJacobian<1, 9> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Vector3 delta = point - t_; + const Vector3 delta = point - x_.col(0); const double r = delta.norm(); if (!Hself && !Hpoint) return r; @@ -109,35 +174,6 @@ Unit3 NavState::bearing(const Point3& point, OptionalJacobian<2, 9> Hself, return b; } -//------------------------------------------------------------------------------ -Matrix5 NavState::matrix() const { - Matrix3 R = this->R(); - - Matrix5 T = Matrix5::Identity(); - T.block<3, 3>(0, 0) = R; - T.block<3, 1>(0, 3) = t_; - T.block<3, 1>(0, 4) = v_; - return T; -} - -//------------------------------------------------------------------------------ -NavState::Vector25 NavState::vec(OptionalJacobian<25, 9> H) const { - const Matrix5 T = this->matrix(); - if (H) { - H->setZero(); - auto R = T.block<3, 3>(0, 0); - H->block<3, 1>(0, 1) = -R.col(2); - H->block<3, 1>(0, 2) = R.col(1); - H->block<3, 1>(5, 0) = R.col(2); - H->block<3, 1>(5, 2) = -R.col(0); - H->block<3, 1>(10, 0) = -R.col(1); - H->block<3, 1>(10, 1) = R.col(0); - H->block<3, 3>(15, 3) = R; - H->block<3, 3>(20, 6) = R; - } - return Eigen::Map(T.data()); -} - //------------------------------------------------------------------------------ std::ostream& operator<<(std::ostream& os, const NavState& state) { os << "R: " << state.attitude() << "\n"; @@ -153,218 +189,16 @@ void NavState::print(const std::string& s) const { //------------------------------------------------------------------------------ bool NavState::equals(const NavState& other, double tol) const { - return R_.equals(other.R_, tol) && traits::Equals(t_, other.t_, tol) - && equal_with_abs_tol(v_, other.v_, tol); -} - -//------------------------------------------------------------------------------ -NavState NavState::inverse() const { - Rot3 Rt = R_.inverse(); - return NavState(Rt, Rt * (-t_), Rt * -(v_)); -} - -//------------------------------------------------------------------------------ -// See [this document](doc/Jacobians.md) for details. -NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { - // Get angular velocity w and components rho (for t) and nu (for v) from xi - Vector3 w = xi.head<3>(), rho = xi.segment<3>(3), nu = xi.tail<3>(); - - // Instantiate functor for Dexp-related operations: - const so3::DexpFunctor local(w); - - // Compute rotation using Expmap -#ifdef GTSAM_USE_QUATERNIONS - const Rot3 R = traits::Expmap(w); -#else - const Rot3 R(local.expmap()); -#endif - - // Compute translation and velocity. See Pose3::Expmap - Matrix3 H_t_w, H_v_w; - const Vector3 t = local.Jacobian().applyLeft(rho, Hxi ? &H_t_w : nullptr); - const Vector3 v = local.Jacobian().applyLeft(nu, Hxi ? &H_v_w : nullptr); - - if (Hxi) { - const Matrix3 Jr = local.Jacobian().right(); - // We are creating a NavState, so we still need to chain H_t_w and H_v_w - // with R^T, the Jacobian of Navstate::Create with respect to both t and v. - const Matrix3 Rt = R.transpose(); - *Hxi << Jr, Z_3x3, Z_3x3, // Jr here *is* the Jacobian of expmap - Rt * H_t_w, Jr, Z_3x3, // - Rt * H_v_w, Z_3x3, Jr; - // In the last two rows, Jr = R^T * Jl, see Barfoot eq. (8.83). - // Jl is the left Jacobian of SO(3) at w. - } - - return NavState(R, t, v); -} - -//------------------------------------------------------------------------------ -Vector9 NavState::Logmap(const NavState& state, OptionalJacobian<9, 9> Hstate) { - if (Hstate) *Hstate = LogmapDerivative(state); - - const Vector3 phi = Rot3::Logmap(state.rotation()); - const Vector3& p = state.position(); - const Vector3& v = state.velocity(); - const double t = phi.norm(); - if (t < 1e-8) { - Vector9 log; - log << phi, p, v; - return log; - - } else { - const Matrix3 W = skewSymmetric(phi / t); - - const double Tan = tan(0.5 * t); - const Vector3 Wp = W * p; - const Vector3 Wv = W * v; - const Vector3 rho = p - (0.5 * t) * Wp + (1 - t / (2. * Tan)) * (W * Wp); - const Vector3 nu = v - (0.5 * t) * Wv + (1 - t / (2. * Tan)) * (W * Wv); - Vector9 log; - // Order is ω, p, v - log << phi, rho, nu; - return log; - } -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::AdjointMap() const { - const Matrix3 R = R_.matrix(); - Matrix3 A = skewSymmetric(t_) * R; - Matrix3 B = skewSymmetric(v_) * R; - // Eqn 2 in Barrau20icra - Matrix9 adj; - adj << R, Z_3x3, Z_3x3, A, R, Z_3x3, B, Z_3x3, R; - return adj; -} - -//------------------------------------------------------------------------------ -Vector9 NavState::Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_state, - OptionalJacobian<9, 9> H_xib) const { - const Matrix9 Ad = AdjointMap(); - - // Jacobians - if (H_state) *H_state = -Ad * adjointMap(xi_b); - if (H_xib) *H_xib = Ad; - - return Ad * xi_b; -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::adjointMap(const Vector9& xi) { - Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); - Matrix3 v_hat = skewSymmetric(xi(3), xi(4), xi(5)); - Matrix3 a_hat = skewSymmetric(xi(6), xi(7), xi(8)); - Matrix9 adj; - adj << w_hat, Z_3x3, Z_3x3, v_hat, w_hat, Z_3x3, a_hat, Z_3x3, w_hat; - return adj; -} - -//------------------------------------------------------------------------------ -Vector9 NavState::adjoint(const Vector9& xi, const Vector9& y, - OptionalJacobian<9, 9> Hxi, - OptionalJacobian<9, 9> H_y) { - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 9; ++i) { - Vector9 dxi; - dxi.setZero(); - dxi(i) = 1.0; - Matrix9 Gi = adjointMap(dxi); - Hxi->col(i) = Gi * y; - } - } - - const Matrix9& ad_xi = adjointMap(xi); - if (H_y) *H_y = ad_xi; - - return ad_xi * y; -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { - Matrix9 J; - Expmap(xi, J); - return J; -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::LogmapDerivative(const Vector9& xi) { - const Vector3 w = xi.head<3>(); - Vector3 rho = xi.segment<3>(3); - Vector3 nu = xi.tail<3>(); - - // Instantiate functor for Dexp-related operations: - const so3::DexpFunctor local(w); - - // Call Jacobian().applyLeft to get its Jacobians - Matrix3 H_t_w, H_v_w; - local.Jacobian().applyLeft(rho, H_t_w); - local.Jacobian().applyLeft(nu, H_v_w); - - // Multiply with R^T to account for NavState::Create Jacobian. - const Matrix3 Rt = local.expmap().transpose(); - const Matrix3 Qt = Rt * H_t_w; - const Matrix3 Qv = Rt * H_v_w; - - // Now compute the blocks of the LogmapDerivative Jacobian - const Matrix3 Jw = Rot3::LogmapDerivative(w); - const Matrix3 Qt2 = -Jw * Qt * Jw; - const Matrix3 Qv2 = -Jw * Qv * Jw; - - Matrix9 J; - J << Jw, Z_3x3, Z_3x3, - Qt2, Jw, Z_3x3, - Qv2, Z_3x3, Jw; - return J; -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::LogmapDerivative(const NavState& state) { - const Vector9 xi = Logmap(state); - return LogmapDerivative(xi); -} - -//------------------------------------------------------------------------------ -Matrix5 NavState::Hat(const Vector9& xi) { - Matrix5 X; - const double wx = xi(0), wy = xi(1), wz = xi(2); - const double px = xi(3), py = xi(4), pz = xi(5); - const double vx = xi(6), vy = xi(7), vz = xi(8); - X << 0., -wz, wy, px, vx, - wz, 0., -wx, py, vy, - -wy, wx, 0., pz, vz, - 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0.; - return X; -} - -//------------------------------------------------------------------------------ -Vector9 NavState::Vee(const Matrix5& Xi) { - Vector9 xi; - xi << Xi(2, 1), Xi(0, 2), Xi(1, 0), - Xi(0, 3), Xi(1, 3), Xi(2, 3), - Xi(0, 4), Xi(1, 4), Xi(2, 4); - return xi; -} - -//------------------------------------------------------------------------------ -NavState NavState::ChartAtOrigin::Retract(const Vector9& xi, - ChartJacobian Hxi) { - return Expmap(xi, Hxi); -} - -//------------------------------------------------------------------------------ -Vector9 NavState::ChartAtOrigin::Local(const NavState& state, - ChartJacobian Hstate) { - return Logmap(state, Hstate); + return R_.equals(other.R_, tol) && + traits::Equals(x_.col(0), other.x_.col(0), tol) && + equal_with_abs_tol(x_.col(1), other.x_.col(1), tol); } //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { Rot3 nRb = R_; - Point3 n_t = t_, n_v = v_; + Point3 n_t = x_.col(0), n_v = x_.col(1); Matrix3 D_bRc_xi, D_R_nRb, D_t_nRb, D_v_nRb; const Rot3 bRc = Rot3::Expmap(dR(xi), H2 ? &D_bRc_xi : 0); const Rot3 nRc = nRb.compose(bRc, H1 ? &D_R_nRb : 0); @@ -392,8 +226,8 @@ Vector9 NavState::localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { Matrix3 D_dR_R, D_dt_R, D_dv_R; const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); - const Point3 dP = R_.unrotate(g.t_ - t_, H1 ? &D_dt_R : 0); - const Vector dV = R_.unrotate(g.v_ - v_, H1 ? &D_dv_R : 0); + const Point3 dP = R_.unrotate(g.x_.col(0) - x_.col(0), H1 ? &D_dt_R : 0); + const Vector dV = R_.unrotate(g.x_.col(1) - x_.col(1), H1 ? &D_dv_R : 0); Vector9 xi; Matrix3 D_xi_R; @@ -479,7 +313,7 @@ NavState NavState::update(const Vector3& b_acceleration, const Vector3& b_omega, Vector9 NavState::coriolis(double dt, const Vector3& omega, bool secondOrder, OptionalJacobian<9, 9> H) const { Rot3 nRb = R_; - Point3 n_t = t_, n_v = v_; + Point3 n_t = x_.col(0), n_v = x_.col(1); const double dt2 = dt * dt; const Vector3 omega_cross_vel = omega.cross(n_v); @@ -531,7 +365,7 @@ Vector9 NavState::correctPIM(const Vector9& pim, double dt, bool use2ndOrderCoriolis, OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { const Rot3& nRb = R_; - const Velocity3& n_v = v_; // derivative is Ri ! + const Velocity3 n_v = x_.col(1); // derivative is Ri ! const double dt22 = 0.5 * dt * dt; Vector9 xi; diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 8b2a2d9141..5489e17d44 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -19,10 +19,15 @@ #pragma once #include +#include #include #include #include +#if GTSAM_ENABLE_BOOST_SERIALIZATION +#include +#endif + namespace gtsam { /// Velocity is currently typedef'd to Vector3 @@ -36,46 +41,39 @@ using Velocity3 = Vector3; * NOTE: While Barrau20icra follow a R,v,t order, * we use a R,t,v order to maintain backwards compatibility. */ -class GTSAM_EXPORT NavState : public MatrixLieGroup { - private: - - // TODO(frank): - // - should we rename t_ to p_? if not, we should rename dP do dT - Rot3 R_; ///< Rotation nRb, rotates points/velocities in body to points/velocities in nav - Point3 t_; ///< position n_t, in nav frame - Velocity3 v_; ///< velocity n_v in nav frame - +class GTSAM_EXPORT NavState : public MatrixLieGroup, + public ExtendedPose3<2> { public: + using Base = ExtendedPose3<2>; + using LieGroupBase = MatrixLieGroup; using LieAlgebra = Matrix5; using Vector25 = Eigen::Matrix; + inline constexpr static auto dimension = 9; /// @name Constructors /// @{ /// Default constructor - NavState() : - t_(0, 0, 0), v_(Vector3::Zero()) { - } + NavState() : Base() {} + + NavState(const Base& other) : Base(other) {} /// Construct from attitude, position, velocity - NavState(const Rot3& R, const Point3& t, const Velocity3& v) : - R_(R), t_(t), v_(v) { - } + NavState(const Rot3& R, const Point3& t, const Velocity3& v) + : Base(R, (Eigen::Matrix() << t.x(), v.x(), t.y(), v.y(), + t.z(), v.z()) + .finished()) {} /// Construct from pose and velocity - NavState(const Pose3& pose, const Velocity3& v) : - R_(pose.rotation()), t_(pose.translation()), v_(v) { - } + NavState(const Pose3& pose, const Velocity3& v) + : NavState(pose.rotation(), pose.translation(), v) {} /// Construct from SO(3) and R^6 - NavState(const Matrix3& R, const Vector6& tv) : - R_(R), t_(tv.head<3>()), v_(tv.tail<3>()) { - } + NavState(const Matrix3& R, const Vector6& tv) + : NavState(Rot3(R), tv.head<3>(), tv.tail<3>()) {} /// Construct from Matrix5 - NavState(const Matrix5& T) : - R_(T.block<3, 3>(0, 0)), t_(T.block<3, 1>(0, 3)), v_(T.block<3, 1>(0, 4)) { - } + NavState(const Matrix5& T) : Base(T) {} /// Named constructor with derivatives static NavState Create(const Rot3& R, const Point3& t, const Velocity3& v, @@ -130,22 +128,15 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup { } /// Return position as Vector3 Vector3 t() const { - return t_; + return x_.col(0); } /// Return velocity as Vector3. Computation-free. const Vector3& v() const { - return v_; + return velocity(); } // Return velocity in body frame Velocity3 bodyVelocity(OptionalJacobian<3, 9> H = {}) const; - /// Return matrix group representation, in MATLAB notation: - /// nTb = [nRb n_t n_v; 0_1x3 1 0; 0_1x3 0 1] - Matrix5 matrix() const; - - /// Vectorize 5x5 matrix into a 25-dim vector. - Vector25 vec(OptionalJacobian<25, 9> H = {}) const; - /// @} /// @name Testable /// @{ @@ -164,20 +155,9 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup { /// @name Group /// @{ - /// identity for group operation - static NavState Identity() { - return NavState(); - } - - /// inverse transformation with derivatives + static NavState Identity() { return NavState(Base::Identity()); } NavState inverse() const; - - using LieGroup::inverse; // version with derivative - - /// compose syntactic sugar - NavState operator*(const NavState& T) const { - return NavState(R_ * T.R_, t_ + R_ * T.t_, v_ + R_ * T.v_); - } + NavState operator*(const NavState& other) const; /// Syntactic sugar const Rot3& rotation() const { return attitude(); }; @@ -217,68 +197,41 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup { /// @name Lie Group /// @{ - /** - * Exponential map at identity - create a NavState from canonical coordinates - * \f$ [R_x,R_y,R_z,T_x,T_y,T_z,V_x,V_y,V_z] \f$ - */ static NavState Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi = {}); - - /** - * Log map at identity - return the canonical coordinates \f$ - * [R_x,R_y,R_z,T_x,T_y,T_z,V_x,V_y,V_z] \f$ of this NavState - */ static Vector9 Logmap(const NavState& pose, OptionalJacobian<9, 9> Hpose = {}); - /** - * Calculate Adjoint map, transforming a twist in this pose's (i.e, body) - * frame to the world spatial frame. - */ Matrix9 AdjointMap() const; - - /** - * Apply this NavState's AdjointMap Ad_g to a twist \f$ \xi_b \f$, i.e. a - * body-fixed velocity, transforming it to the spatial frame - * \f$ \xi^s = g*\xi^b*g^{-1} = Ad_g * \xi^b \f$ - * Note that H_xib = AdjointMap() - */ - Vector9 Adjoint(const Vector9& xi_b, - OptionalJacobian<9, 9> H_this = {}, + Vector9 Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_this = {}, OptionalJacobian<9, 9> H_xib = {}) const; - - /** - * Compute the [ad(w,v)] operator as defined in [Kobilarov09siggraph], pg 11 - * but for the NavState [ad(w,v)] = [w^, zero3; v^, w^] - */ - static Matrix9 adjointMap(const Vector9& xi); - /** - * Action of the adjointMap on a Lie-algebra vector y, with optional derivatives - */ + static Matrix9 adjointMap(const Vector9& xi); static Vector9 adjoint(const Vector9& xi, const Vector9& y, OptionalJacobian<9, 9> Hxi = {}, OptionalJacobian<9, 9> H_y = {}); - /// Derivative of Expmap static Matrix9 ExpmapDerivative(const Vector9& xi); - - /// Derivative of Logmap static Matrix9 LogmapDerivative(const Vector9& xi); + static Matrix9 LogmapDerivative(const NavState& state); - /// Derivative of Logmap, NavState version - static Matrix9 LogmapDerivative(const NavState& xi); - - // Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP struct GTSAM_EXPORT ChartAtOrigin { static NavState Retract(const Vector9& xi, ChartJacobian Hxi = {}); static Vector9 Local(const NavState& state, ChartJacobian Hstate = {}); }; - /// Hat maps from tangent vector to Lie algebra - static Matrix5 Hat(const Vector9& xi); + using LieGroupBase::between; + using LieGroupBase::compose; + using LieGroupBase::expmap; + using LieGroupBase::LocalCoordinates; + using LieGroupBase::logmap; + using LieGroupBase::Retract; + using LieGroup::inverse; - /// Vee maps from Lie algebra to tangent vector + static Matrix5 Hat(const Vector9& xi); static Vector9 Vee(const Matrix5& X); + Matrix5 matrix() const { return Base::matrix(); } + Vector25 vec(OptionalJacobian<25, 9> H = {}) const { return Base::vec(H); } + /// @} /// @name Dynamics /// @{ @@ -322,15 +275,16 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup { /// @} private: + mutable Point3 p_cache_ = Point3::Zero(); + mutable Velocity3 v_cache_ = Velocity3::Zero(); + /// @{ /// serialization #if GTSAM_ENABLE_BOOST_SERIALIZATION /// friend class boost::serialization::access; template void serialize(ARCHIVE & ar, const unsigned int /*version*/) { - ar & BOOST_SERIALIZATION_NVP(R_); - ar & BOOST_SERIALIZATION_NVP(t_); - ar & BOOST_SERIALIZATION_NVP(v_); + ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); } #endif /// @} From 7b2e055f34e6550c1734ab570326ebee831d5696 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 10:38:07 -0500 Subject: [PATCH 228/266] Move definitions --- gtsam/geometry/ExtendedPose3-inl.h | 409 +++++++++++++++++++++++++++++ gtsam/geometry/ExtendedPose3.cpp | 27 ++ gtsam/geometry/ExtendedPose3.h | 378 +++++--------------------- 3 files changed, 499 insertions(+), 315 deletions(-) create mode 100644 gtsam/geometry/ExtendedPose3-inl.h create mode 100644 gtsam/geometry/ExtendedPose3.cpp diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h new file mode 100644 index 0000000000..6fb909c3a8 --- /dev/null +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -0,0 +1,409 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file ExtendedPose3-inl.h + * @brief Template implementations for ExtendedPose3 + * @author Frank Dellaert, et al. + */ + +#pragma once + +namespace gtsam { + +template +size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { + if constexpr (K == Eigen::Dynamic) { + if (xi.size() < 3 || (xi.size() - 3) % 3 != 0) { + throw std::invalid_argument( + "ExtendedPose3: tangent vector size must be 3 + 3*k."); + } + return static_cast((xi.size() - 3) / 3); + } else { + return static_cast(K); + } +} + +template +void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { + if (!H) return; + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(d, d); + } else { + (void)d; + H->setZero(); + } +} + +template +template +ExtendedPose3::ExtendedPose3() : R_(Rot3::Identity()), x_(Matrix3K::Zero()) { +} + +template +template +ExtendedPose3::ExtendedPose3(size_t k) : R_(Rot3::Identity()), x_(3, k) { + x_.setZero(); +} + +template +ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) : R_(R), x_(x) {} + +template +ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { + if constexpr (K == Eigen::Dynamic) { + const auto n = T.rows(); + if (T.cols() != n || n < 3) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + const auto k = n - 3; + x_.resize(3, k); + x_.setZero(); + } + + const auto n = T.rows(); + if constexpr (K != Eigen::Dynamic) { + if (n != matrix_dim || T.cols() != matrix_dim) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + } else { + if (T.cols() != n || n < 3) { + throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); + } + } + + R_ = Rot3(T.template block<3, 3>(0, 0)); + x_ = T.block(0, 3, 3, n - 3); +} + +template +size_t ExtendedPose3::Dimension(size_t k) { + return 3 + 3 * k; +} + +template +size_t ExtendedPose3::k() const { + return static_cast(x_.cols()); +} + +template +template +size_t ExtendedPose3::dim() const { + return Dimension(k()); +} + +template +const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { + if (H) { + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(3, dim()); + } else { + H->setZero(); + } + H->block(0, 0, 3, 3) = I_3x3; + } + return R_; +} + +template +Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { + if (i >= k()) throw std::out_of_range("ExtendedPose3: x(i) out of range."); + if (H) { + if constexpr (dimension == Eigen::Dynamic) { + H->setZero(3, dim()); + } else { + H->setZero(); + } + H->block(0, 3 + 3 * i, 3, 3) = R_.matrix(); + } + return x_.col(static_cast(i)); +} + +template +const typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() const { + return x_; +} + +template +typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() { + return x_; +} + +template +void ExtendedPose3::print(const std::string& s) const { + std::cout << (s.empty() ? s : s + " ") << *this << std::endl; +} + +template +bool ExtendedPose3::equals(const ExtendedPose3& other, double tol) const { + return R_.equals(other.R_, tol) && equal_with_abs_tol(x_, other.x_, tol); +} + +template +template +ExtendedPose3 ExtendedPose3::Identity() { + return ExtendedPose3(); +} + +template +template +ExtendedPose3 ExtendedPose3::Identity(size_t k) { + return ExtendedPose3(k); +} + +template +ExtendedPose3 ExtendedPose3::inverse() const { + const Rot3 Rt = R_.inverse(); + const Matrix3K x = -(Rt.matrix() * x_); + return ExtendedPose3(Rt, x); +} + +template +ExtendedPose3 ExtendedPose3::operator*(const ExtendedPose3& other) const { + if (k() != other.k()) { + throw std::invalid_argument("ExtendedPose3: compose requires matching k."); + } + Matrix3K x = x_ + R_.matrix() * other.x_; + return ExtendedPose3(R_ * other.R_, x); +} + +template +ExtendedPose3 ExtendedPose3::Expmap(const TangentVector& xi, + ChartJacobian Hxi) { + const size_t k = RuntimeK(xi); + + const Vector3 w = xi.template head<3>(); + const so3::DexpFunctor local(w); + const Rot3 R(local.expmap()); + const Matrix3 Rt = R.transpose(); + + Matrix3K x; + if constexpr (K == Eigen::Dynamic) x.resize(3, static_cast(k)); + + if (Hxi) { + const size_t d = 3 + 3 * k; + ZeroJacobian(Hxi, d); + const Matrix3 Jr = local.Jacobian().right(); + Hxi->block(0, 0, 3, 3) = Jr; + for (size_t i = 0; i < k; ++i) { + Matrix3 H_xi_w; + const Vector3 rho = xi.template segment<3>(3 + 3 * i); + x.col(static_cast(i)) = + local.Jacobian().applyLeft(rho, &H_xi_w); + Hxi->block(3 + 3 * i, 0, 3, 3) = Rt * H_xi_w; + Hxi->block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jr; + } + } else { + for (size_t i = 0; i < k; ++i) { + const Vector3 rho = xi.template segment<3>(3 + 3 * i); + x.col(static_cast(i)) = local.Jacobian().applyLeft(rho); + } + } + + return ExtendedPose3(R, x); +} + +template +typename ExtendedPose3::TangentVector ExtendedPose3::Logmap( + const ExtendedPose3& pose, ChartJacobian Hpose) { + const Vector3 w = Rot3::Logmap(pose.R_); + const so3::DexpFunctor local(w); + + TangentVector xi; + if constexpr (K == Eigen::Dynamic) xi.resize(static_cast(pose.dim())); + xi.template head<3>() = w; + for (size_t i = 0; i < pose.k(); ++i) { + xi.template segment<3>(3 + 3 * i) = + local.InvJacobian().applyLeft(pose.x_.col(static_cast(i))); + } + + if (Hpose) *Hpose = LogmapDerivative(xi); + return xi; +} + +template +typename ExtendedPose3::Jacobian ExtendedPose3::AdjointMap() const { + const Matrix3 R = R_.matrix(); + Jacobian adj; + if constexpr (dimension == Eigen::Dynamic) { + adj.setZero(dim(), dim()); + } else { + adj.setZero(); + } + + adj.block(0, 0, 3, 3) = R; + for (size_t i = 0; i < k(); ++i) { + adj.block(3 + 3 * i, 0, 3, 3) = + skewSymmetric(x_.col(static_cast(i))) * R; + adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = R; + } + return adj; +} + +template +typename ExtendedPose3::TangentVector ExtendedPose3::Adjoint( + const TangentVector& xi_b, ChartJacobian H_this, ChartJacobian H_xib) const { + const Jacobian Ad = AdjointMap(); + if (H_this) *H_this = -Ad * adjointMap(xi_b); + if (H_xib) *H_xib = Ad; + return Ad * xi_b; +} + +template +typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap( + const TangentVector& xi) { + const size_t k = RuntimeK(xi); + const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); + + Jacobian adj; + if constexpr (dimension == Eigen::Dynamic) { + adj.setZero(3 + 3 * k, 3 + 3 * k); + } else { + adj.setZero(); + } + + adj.block(0, 0, 3, 3) = w_hat; + for (size_t i = 0; i < k; ++i) { + adj.block(3 + 3 * i, 0, 3, 3) = skewSymmetric( + xi(3 + 3 * i + 0), xi(3 + 3 * i + 1), xi(3 + 3 * i + 2)); + adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = w_hat; + } + return adj; +} + +template +typename ExtendedPose3::TangentVector ExtendedPose3::adjoint( + const TangentVector& xi, const TangentVector& y, ChartJacobian Hxi, + ChartJacobian H_y) { + const Jacobian ad_xi = adjointMap(xi); + if (Hxi) { + if constexpr (dimension == Eigen::Dynamic) { + Hxi->setZero(xi.size(), xi.size()); + } else { + Hxi->setZero(); + } + for (Eigen::Index i = 0; i < xi.size(); ++i) { + TangentVector dxi; + if constexpr (dimension == Eigen::Dynamic) { + dxi = TangentVector::Zero(xi.size()); + } else { + dxi = TangentVector::Zero(); + } + dxi(i) = 1.0; + Hxi->col(i) = adjointMap(dxi) * y; + } + } + if (H_y) *H_y = ad_xi; + return ad_xi * y; +} + +template +typename ExtendedPose3::Jacobian ExtendedPose3::ExpmapDerivative( + const TangentVector& xi) { + Jacobian J; + Expmap(xi, J); + return J; +} + +template +typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative( + const TangentVector& xi) { + const size_t k = RuntimeK(xi); + const Vector3 w = xi.template head<3>(); + const so3::DexpFunctor local(w); + const Matrix3 Rt = local.expmap().transpose(); + const Matrix3 Jw = Rot3::LogmapDerivative(w); + + Jacobian J; + if constexpr (dimension == Eigen::Dynamic) { + J.setZero(3 + 3 * k, 3 + 3 * k); + } else { + J.setZero(); + } + + J.block(0, 0, 3, 3) = Jw; + for (size_t i = 0; i < k; ++i) { + Matrix3 H_xi_w; + local.Jacobian().applyLeft(xi.template segment<3>(3 + 3 * i), H_xi_w); + const Matrix3 Q = Rt * H_xi_w; + J.block(3 + 3 * i, 0, 3, 3) = -Jw * Q * Jw; + J.block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jw; + } + return J; +} + +template +typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative( + const ExtendedPose3& pose) { + return LogmapDerivative(Logmap(pose)); +} + +template +ExtendedPose3 ExtendedPose3::ChartAtOrigin::Retract( + const TangentVector& xi, ChartJacobian Hxi) { + return Expmap(xi, Hxi); +} + +template +typename ExtendedPose3::TangentVector ExtendedPose3::ChartAtOrigin::Local( + const ExtendedPose3& pose, ChartJacobian Hpose) { + return Logmap(pose, Hpose); +} + +template +typename ExtendedPose3::LieAlgebra ExtendedPose3::matrix() const { + LieAlgebra M; + if constexpr (matrix_dim == Eigen::Dynamic) { + const Eigen::Index n = 3 + static_cast(k()); + M = LieAlgebra::Identity(n, n); + } else { + M = LieAlgebra::Identity(); + } + M.template block<3, 3>(0, 0) = R_.matrix(); + M.block(0, 3, 3, static_cast(k())) = x_; + return M; +} + +template +typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( + const TangentVector& xi) { + const size_t k = RuntimeK(xi); + LieAlgebra X; + if constexpr (matrix_dim == Eigen::Dynamic) { + X.setZero(3 + k, 3 + k); + } else { + X.setZero(); + } + X.block(0, 0, 3, 3) = skewSymmetric(xi(0), xi(1), xi(2)); + for (size_t i = 0; i < k; ++i) { + X.block(0, 3 + i, 3, 1) = xi.template segment<3>(3 + 3 * i); + } + return X; +} + +template +typename ExtendedPose3::TangentVector ExtendedPose3::Vee( + const LieAlgebra& X) { + const Eigen::Index k = X.cols() - 3; + TangentVector xi; + if constexpr (dimension == Eigen::Dynamic) { + xi.resize(3 + 3 * k); + } + xi(0) = X(2, 1); + xi(1) = X(0, 2); + xi(2) = X(1, 0); + for (Eigen::Index i = 0; i < k; ++i) { + xi.template segment<3>(3 + 3 * i) = X.template block<3, 1>(0, 3 + i); + } + return xi; +} + +} // namespace gtsam diff --git a/gtsam/geometry/ExtendedPose3.cpp b/gtsam/geometry/ExtendedPose3.cpp new file mode 100644 index 0000000000..88c59ca072 --- /dev/null +++ b/gtsam/geometry/ExtendedPose3.cpp @@ -0,0 +1,27 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file ExtendedPose3.cpp + * @brief Explicit instantiations for ExtendedPose3 + * @author Frank Dellaert, et al. + */ + +#include + +namespace gtsam { + +template class ExtendedPose3<1>; +template class ExtendedPose3<2>; +template class ExtendedPose3<3>; +template class ExtendedPose3; + +} // namespace gtsam diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index f35cf54a7a..fb05a91bc0 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -36,15 +36,19 @@ namespace gtsam { namespace internal { -/// Manifold dimensionality for SE_k(3): 3 + 3*k. -constexpr int DimensionExtendedPose3(int k) { - return (k == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * k; -} +/// Compile-time traits for SE_k(3) dimensions. +template +struct ExtendedPose3Traits { + /// Manifold dimensionality for SE_k(3): 3 + 3*k. + static constexpr int Dimension() { + return (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K; + } -/// Homogeneous matrix size for SE_k(3): (3 + k) x (3 + k). -constexpr int MatrixDimExtendedPose3(int k) { - return (k == Eigen::Dynamic) ? Eigen::Dynamic : 3 + k; -} + /// Homogeneous matrix size for SE_k(3): (3 + k) x (3 + k). + static constexpr int MatrixDim() { + return (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + K; + } +}; } // namespace internal @@ -56,11 +60,14 @@ constexpr int MatrixDimExtendedPose3(int k) { */ template class GTSAM_EXPORT ExtendedPose3 - : public MatrixLieGroup, internal::DimensionExtendedPose3(K), - internal::MatrixDimExtendedPose3(K)> { + : public MatrixLieGroup, + internal::ExtendedPose3Traits::Dimension(), + internal::ExtendedPose3Traits::MatrixDim()> { public: - inline constexpr static auto dimension = internal::DimensionExtendedPose3(K); - inline constexpr static auto matrix_dim = internal::MatrixDimExtendedPose3(K); + inline constexpr static auto dimension = + internal::ExtendedPose3Traits::Dimension(); + inline constexpr static auto matrix_dim = + internal::ExtendedPose3Traits::MatrixDim(); using Base = MatrixLieGroup, dimension, matrix_dim>; using TangentVector = typename Base::TangentVector; @@ -85,27 +92,8 @@ class GTSAM_EXPORT ExtendedPose3 template using IsFixed = typename std::enable_if= 1, void>::type; - static size_t RuntimeK(const TangentVector& xi) { - if constexpr (K == Eigen::Dynamic) { - if (xi.size() < 3 || (xi.size() - 3) % 3 != 0) { - throw std::invalid_argument( - "ExtendedPose3: tangent vector size must be 3 + 3*k."); - } - return static_cast((xi.size() - 3) / 3); - } else { - return static_cast(K); - } - } - - static void ZeroJacobian(ChartJacobian H, size_t d) { - if (!H) return; - if constexpr (dimension == Eigen::Dynamic) { - H->setZero(d, d); - } else { - (void)d; - H->setZero(); - } - } + static size_t RuntimeK(const TangentVector& xi); + static void ZeroJacobian(ChartJacobian H, size_t d); public: /// @name Constructors @@ -113,301 +101,96 @@ class GTSAM_EXPORT ExtendedPose3 /// Construct fixed-size identity. template > - ExtendedPose3() : R_(Rot3::Identity()), x_(Matrix3K::Zero()) {} + ExtendedPose3(); /// Construct dynamic identity, optionally with runtime k. template > - explicit ExtendedPose3(size_t k = 0) : R_(Rot3::Identity()), x_(3, k) { - x_.setZero(); - } + explicit ExtendedPose3(size_t k = 0); ExtendedPose3(const ExtendedPose3&) = default; ExtendedPose3& operator=(const ExtendedPose3&) = default; /// Construct from rotation and 3xK block. - ExtendedPose3(const Rot3& R, const Matrix3K& x) : R_(R), x_(x) {} + ExtendedPose3(const Rot3& R, const Matrix3K& x); /// Construct from homogeneous matrix representation. - explicit ExtendedPose3(const LieAlgebra& T) { - if constexpr (K == Eigen::Dynamic) { - const auto n = T.rows(); - if (T.cols() != n || n < 3) { - throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); - } - const auto k = n - 3; - x_.resize(3, k); - x_.setZero(); - } - - const auto n = T.rows(); - if constexpr (K != Eigen::Dynamic) { - if (n != matrix_dim || T.cols() != matrix_dim) { - throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); - } - } else { - if (T.cols() != n || n < 3) { - throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); - } - } - - R_ = Rot3(T.template block<3, 3>(0, 0)); - x_ = T.block(0, 3, 3, n - 3); - } + explicit ExtendedPose3(const LieAlgebra& T); /// @} /// @name Access /// @{ - static size_t Dimension(size_t k) { return 3 + 3 * k; } + static size_t Dimension(size_t k); /// Number of R^3 columns. - size_t k() const { return static_cast(x_.cols()); } + size_t k() const; /// Runtime manifold dimension. template > - size_t dim() const { - return Dimension(k()); - } + size_t dim() const; /// Rotation component. - const Rot3& rotation(ComponentJacobian H = {}) const { - if (H) { - if constexpr (dimension == Eigen::Dynamic) { - H->setZero(3, dim()); - } else { - H->setZero(); - } - H->block(0, 0, 3, 3) = I_3x3; - } - return R_; - } + const Rot3& rotation(ComponentJacobian H = {}) const; /// i-th R^3 component, returned by value. - Point3 x(size_t i, ComponentJacobian H = {}) const { - if (i >= k()) throw std::out_of_range("ExtendedPose3: x(i) out of range."); - if (H) { - if constexpr (dimension == Eigen::Dynamic) { - H->setZero(3, dim()); - } else { - H->setZero(); - } - H->block(0, 3 + 3 * i, 3, 3) = R_.matrix(); - } - return x_.col(static_cast(i)); - } + Point3 x(size_t i, ComponentJacobian H = {}) const; - const Matrix3K& xMatrix() const { return x_; } - Matrix3K& xMatrix() { return x_; } + const Matrix3K& xMatrix() const; + Matrix3K& xMatrix(); /// @} /// @name Testable /// @{ - void print(const std::string& s = "") const { - std::cout << (s.empty() ? s : s + " ") << *this << std::endl; - } + void print(const std::string& s = "") const; - bool equals(const ExtendedPose3& other, double tol = 1e-9) const { - return R_.equals(other.R_, tol) && equal_with_abs_tol(x_, other.x_, tol); - } + bool equals(const ExtendedPose3& other, double tol = 1e-9) const; /// @} /// @name Group /// @{ template > - static ExtendedPose3 Identity() { - return ExtendedPose3(); - } + static ExtendedPose3 Identity(); template > - static ExtendedPose3 Identity(size_t k = 0) { - return ExtendedPose3(k); - } + static ExtendedPose3 Identity(size_t k = 0); - ExtendedPose3 inverse() const { - const Rot3 Rt = R_.inverse(); - const Matrix3K x = -(Rt.matrix() * x_); - return ExtendedPose3(Rt, x); - } + ExtendedPose3 inverse() const; - ExtendedPose3 operator*(const ExtendedPose3& other) const { - if (k() != other.k()) { - throw std::invalid_argument( - "ExtendedPose3: compose requires matching k."); - } - Matrix3K x = x_ + R_.matrix() * other.x_; - return ExtendedPose3(R_ * other.R_, x); - } + ExtendedPose3 operator*(const ExtendedPose3& other) const; /// @} /// @name Lie Group /// @{ - static ExtendedPose3 Expmap(const TangentVector& xi, ChartJacobian Hxi = {}) { - const size_t k = RuntimeK(xi); - - const Vector3 w = xi.template head<3>(); - const so3::DexpFunctor local(w); - const Rot3 R(local.expmap()); - const Matrix3 Rt = R.transpose(); - - Matrix3K x; - if constexpr (K == Eigen::Dynamic) x.resize(3, static_cast(k)); - - if (Hxi) { - const size_t d = 3 + 3 * k; - ZeroJacobian(Hxi, d); - const Matrix3 Jr = local.Jacobian().right(); - Hxi->block(0, 0, 3, 3) = Jr; - for (size_t i = 0; i < k; ++i) { - Matrix3 H_xi_w; - const Vector3 rho = xi.template segment<3>(3 + 3 * i); - x.col(static_cast(i)) = - local.Jacobian().applyLeft(rho, &H_xi_w); - Hxi->block(3 + 3 * i, 0, 3, 3) = Rt * H_xi_w; - Hxi->block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jr; - } - } else { - for (size_t i = 0; i < k; ++i) { - const Vector3 rho = xi.template segment<3>(3 + 3 * i); - x.col(static_cast(i)) = local.Jacobian().applyLeft(rho); - } - } - - return ExtendedPose3(R, x); - } + static ExtendedPose3 Expmap(const TangentVector& xi, ChartJacobian Hxi = {}); - static TangentVector Logmap(const ExtendedPose3& pose, ChartJacobian Hpose = {}) { - const Vector3 w = Rot3::Logmap(pose.R_); - const so3::DexpFunctor local(w); + static TangentVector Logmap(const ExtendedPose3& pose, + ChartJacobian Hpose = {}); - TangentVector xi; - if constexpr (K == Eigen::Dynamic) xi.resize(static_cast(pose.dim())); - xi.template head<3>() = w; - for (size_t i = 0; i < pose.k(); ++i) { - xi.template segment<3>(3 + 3 * i) = - local.InvJacobian().applyLeft(pose.x_.col(static_cast(i))); - } + Jacobian AdjointMap() const; - if (Hpose) *Hpose = LogmapDerivative(xi); - return xi; - } + TangentVector Adjoint(const TangentVector& xi_b, ChartJacobian H_this = {}, + ChartJacobian H_xib = {}) const; - Jacobian AdjointMap() const { - const Matrix3 R = R_.matrix(); - Jacobian adj; - if constexpr (dimension == Eigen::Dynamic) { - adj.setZero(dim(), dim()); - } else { - adj.setZero(); - } - - adj.block(0, 0, 3, 3) = R; - for (size_t i = 0; i < k(); ++i) { - adj.block(3 + 3 * i, 0, 3, 3) = - skewSymmetric(x_.col(static_cast(i))) * R; - adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = R; - } - return adj; - } - - TangentVector Adjoint(const TangentVector& xi_b, - ChartJacobian H_this = {}, - ChartJacobian H_xib = {}) const { - const Jacobian Ad = AdjointMap(); - if (H_this) *H_this = -Ad * adjointMap(xi_b); - if (H_xib) *H_xib = Ad; - return Ad * xi_b; - } - - static Jacobian adjointMap(const TangentVector& xi) { - const size_t k = RuntimeK(xi); - const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); - - Jacobian adj; - if constexpr (dimension == Eigen::Dynamic) { - adj.setZero(3 + 3 * k, 3 + 3 * k); - } else { - adj.setZero(); - } - - adj.block(0, 0, 3, 3) = w_hat; - for (size_t i = 0; i < k; ++i) { - adj.block(3 + 3 * i, 0, 3, 3) = - skewSymmetric(xi(3 + 3 * i + 0), xi(3 + 3 * i + 1), xi(3 + 3 * i + 2)); - adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = w_hat; - } - return adj; - } + static Jacobian adjointMap(const TangentVector& xi); static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, - ChartJacobian Hxi = {}, ChartJacobian H_y = {}) { - const Jacobian ad_xi = adjointMap(xi); - if (Hxi) { - if constexpr (dimension == Eigen::Dynamic) { - Hxi->setZero(xi.size(), xi.size()); - } else { - Hxi->setZero(); - } - for (Eigen::Index i = 0; i < xi.size(); ++i) { - TangentVector dxi; - if constexpr (dimension == Eigen::Dynamic) { - dxi = TangentVector::Zero(xi.size()); - } else { - dxi = TangentVector::Zero(); - } - dxi(i) = 1.0; - Hxi->col(i) = adjointMap(dxi) * y; - } - } - if (H_y) *H_y = ad_xi; - return ad_xi * y; - } + ChartJacobian Hxi = {}, + ChartJacobian H_y = {}); - static Jacobian ExpmapDerivative(const TangentVector& xi) { - Jacobian J; - Expmap(xi, J); - return J; - } + static Jacobian ExpmapDerivative(const TangentVector& xi); - static Jacobian LogmapDerivative(const TangentVector& xi) { - const size_t k = RuntimeK(xi); - const Vector3 w = xi.template head<3>(); - const so3::DexpFunctor local(w); - const Matrix3 Rt = local.expmap().transpose(); - const Matrix3 Jw = Rot3::LogmapDerivative(w); - - Jacobian J; - if constexpr (dimension == Eigen::Dynamic) { - J.setZero(3 + 3 * k, 3 + 3 * k); - } else { - J.setZero(); - } - - J.block(0, 0, 3, 3) = Jw; - for (size_t i = 0; i < k; ++i) { - Matrix3 H_xi_w; - local.Jacobian().applyLeft(xi.template segment<3>(3 + 3 * i), H_xi_w); - const Matrix3 Q = Rt * H_xi_w; - J.block(3 + 3 * i, 0, 3, 3) = -Jw * Q * Jw; - J.block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jw; - } - return J; - } + static Jacobian LogmapDerivative(const TangentVector& xi); - static Jacobian LogmapDerivative(const ExtendedPose3& pose) { - return LogmapDerivative(Logmap(pose)); - } + static Jacobian LogmapDerivative(const ExtendedPose3& pose); struct ChartAtOrigin { - static ExtendedPose3 Retract(const TangentVector& xi, ChartJacobian Hxi = {}) { - return Expmap(xi, Hxi); - } - - static TangentVector Local(const ExtendedPose3& pose, ChartJacobian Hpose = {}) { - return Logmap(pose, Hpose); - } + static ExtendedPose3 Retract(const TangentVector& xi, + ChartJacobian Hxi = {}); + static TangentVector Local(const ExtendedPose3& pose, + ChartJacobian Hpose = {}); }; using LieGroup, dimension>::inverse; @@ -416,48 +199,11 @@ class GTSAM_EXPORT ExtendedPose3 /// @name Matrix Lie Group /// @{ - LieAlgebra matrix() const { - LieAlgebra M; - if constexpr (matrix_dim == Eigen::Dynamic) { - const Eigen::Index n = 3 + static_cast(k()); - M = LieAlgebra::Identity(n, n); - } else { - M = LieAlgebra::Identity(); - } - M.template block<3, 3>(0, 0) = R_.matrix(); - M.block(0, 3, 3, static_cast(k())) = x_; - return M; - } + LieAlgebra matrix() const; - static LieAlgebra Hat(const TangentVector& xi) { - const size_t k = RuntimeK(xi); - LieAlgebra X; - if constexpr (matrix_dim == Eigen::Dynamic) { - X.setZero(3 + k, 3 + k); - } else { - X.setZero(); - } - X.block(0, 0, 3, 3) = skewSymmetric(xi(0), xi(1), xi(2)); - for (size_t i = 0; i < k; ++i) { - X.block(0, 3 + i, 3, 1) = xi.template segment<3>(3 + 3 * i); - } - return X; - } + static LieAlgebra Hat(const TangentVector& xi); - static TangentVector Vee(const LieAlgebra& X) { - const Eigen::Index k = X.cols() - 3; - TangentVector xi; - if constexpr (dimension == Eigen::Dynamic) { - xi.resize(3 + 3 * k); - } - xi(0) = X(2, 1); - xi(1) = X(0, 2); - xi(2) = X(1, 0); - for (Eigen::Index i = 0; i < k; ++i) { - xi.template segment<3>(3 + 3 * i) = X.template block<3, 1>(0, 3 + i); - } - return xi; - } + static TangentVector Vee(const LieAlgebra& X); /// @} @@ -483,12 +229,14 @@ using ExtendedPose3Dynamic = ExtendedPose3; template struct traits> - : public internal::MatrixLieGroup, - internal::MatrixDimExtendedPose3(K)> {}; + : public internal::MatrixLieGroup< + ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; template struct traits> - : public internal::MatrixLieGroup, - internal::MatrixDimExtendedPose3(K)> {}; + : public internal::MatrixLieGroup< + ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; } // namespace gtsam + +#include "ExtendedPose3-inl.h" From 7c3f7ef41e59a9716b42f6b9019e4d333d20c537 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 11:02:34 -0500 Subject: [PATCH 229/266] CRTP --- gtsam/geometry/ExtendedPose3-inl.h | 191 +++++++++++++----------- gtsam/geometry/ExtendedPose3.h | 68 ++++++--- gtsam/geometry/Pose3.cpp | 66 -------- gtsam/geometry/Pose3.h | 57 +------ gtsam/geometry/tests/testPose3.cpp | 15 -- gtsam/navigation/NavState.cpp | 69 --------- gtsam/navigation/NavState.h | 45 +----- gtsam/navigation/tests/testNavState.cpp | 15 -- 8 files changed, 157 insertions(+), 369 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 6fb909c3a8..e45e8a05d3 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -11,7 +11,7 @@ /** * @file ExtendedPose3-inl.h - * @brief Template implementations for ExtendedPose3 + * @brief Template implementations for ExtendedPose3 * @author Frank Dellaert, et al. */ @@ -19,8 +19,8 @@ namespace gtsam { -template -size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { +template +size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { if constexpr (K == Eigen::Dynamic) { if (xi.size() < 3 || (xi.size() - 3) % 3 != 0) { throw std::invalid_argument( @@ -32,8 +32,8 @@ size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { } } -template -void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { +template +void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { if (!H) return; if constexpr (dimension == Eigen::Dynamic) { H->setZero(d, d); @@ -43,22 +43,24 @@ void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { } } -template +template template -ExtendedPose3::ExtendedPose3() : R_(Rot3::Identity()), x_(Matrix3K::Zero()) { -} +ExtendedPose3::ExtendedPose3() + : R_(Rot3::Identity()), x_(Matrix3K::Zero()) {} -template +template template -ExtendedPose3::ExtendedPose3(size_t k) : R_(Rot3::Identity()), x_(3, k) { +ExtendedPose3::ExtendedPose3(size_t k) + : R_(Rot3::Identity()), x_(3, k) { x_.setZero(); } -template -ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) : R_(R), x_(x) {} +template +ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) + : R_(R), x_(x) {} -template -ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { +template +ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { if constexpr (K == Eigen::Dynamic) { const auto n = T.rows(); if (T.cols() != n || n < 3) { @@ -84,24 +86,24 @@ ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { x_ = T.block(0, 3, 3, n - 3); } -template -size_t ExtendedPose3::Dimension(size_t k) { +template +size_t ExtendedPose3::Dimension(size_t k) { return 3 + 3 * k; } -template -size_t ExtendedPose3::k() const { +template +size_t ExtendedPose3::k() const { return static_cast(x_.cols()); } -template +template template -size_t ExtendedPose3::dim() const { +size_t ExtendedPose3::dim() const { return Dimension(k()); } -template -const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { +template +const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { if (H) { if constexpr (dimension == Eigen::Dynamic) { H->setZero(3, dim()); @@ -113,8 +115,8 @@ const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { return R_; } -template -Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { +template +Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { if (i >= k()) throw std::out_of_range("ExtendedPose3: x(i) out of range."); if (H) { if constexpr (dimension == Eigen::Dynamic) { @@ -127,57 +129,64 @@ Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { return x_.col(static_cast(i)); } -template -const typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() const { +template +const typename ExtendedPose3::Matrix3K& +ExtendedPose3::xMatrix() const { return x_; } -template -typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() { +template +typename ExtendedPose3::Matrix3K& +ExtendedPose3::xMatrix() { return x_; } -template -void ExtendedPose3::print(const std::string& s) const { +template +void ExtendedPose3::print(const std::string& s) const { std::cout << (s.empty() ? s : s + " ") << *this << std::endl; } -template -bool ExtendedPose3::equals(const ExtendedPose3& other, double tol) const { +template +bool ExtendedPose3::equals(const ExtendedPose3& other, + double tol) const { return R_.equals(other.R_, tol) && equal_with_abs_tol(x_, other.x_, tol); } -template +template template -ExtendedPose3 ExtendedPose3::Identity() { - return ExtendedPose3(); +typename ExtendedPose3::This ExtendedPose3::Identity() { + return MakeReturn(ExtendedPose3()); } -template +template template -ExtendedPose3 ExtendedPose3::Identity(size_t k) { - return ExtendedPose3(k); +typename ExtendedPose3::This +ExtendedPose3::Identity(size_t k) { + return MakeReturn(ExtendedPose3(k)); } -template -ExtendedPose3 ExtendedPose3::inverse() const { +template +typename ExtendedPose3::This +ExtendedPose3::inverse() const { const Rot3 Rt = R_.inverse(); const Matrix3K x = -(Rt.matrix() * x_); - return ExtendedPose3(Rt, x); + return MakeReturn(ExtendedPose3(Rt, x)); } -template -ExtendedPose3 ExtendedPose3::operator*(const ExtendedPose3& other) const { - if (k() != other.k()) { +template +typename ExtendedPose3::This +ExtendedPose3::operator*(const This& other) const { + const ExtendedPose3& otherBase = AsBase(other); + if (k() != otherBase.k()) { throw std::invalid_argument("ExtendedPose3: compose requires matching k."); } - Matrix3K x = x_ + R_.matrix() * other.x_; - return ExtendedPose3(R_ * other.R_, x); + Matrix3K x = x_ + R_.matrix() * otherBase.x_; + return MakeReturn(ExtendedPose3(R_ * otherBase.R_, x)); } -template -ExtendedPose3 ExtendedPose3::Expmap(const TangentVector& xi, - ChartJacobian Hxi) { +template +typename ExtendedPose3::This ExtendedPose3::Expmap( + const TangentVector& xi, ChartJacobian Hxi) { const size_t k = RuntimeK(xi); const Vector3 w = xi.template head<3>(); @@ -208,29 +217,32 @@ ExtendedPose3 ExtendedPose3::Expmap(const TangentVector& xi, } } - return ExtendedPose3(R, x); + return MakeReturn(ExtendedPose3(R, x)); } -template -typename ExtendedPose3::TangentVector ExtendedPose3::Logmap( - const ExtendedPose3& pose, ChartJacobian Hpose) { - const Vector3 w = Rot3::Logmap(pose.R_); +template +typename ExtendedPose3::TangentVector ExtendedPose3::Logmap( + const This& pose, ChartJacobian Hpose) { + const ExtendedPose3& poseBase = AsBase(pose); + const Vector3 w = Rot3::Logmap(poseBase.R_); const so3::DexpFunctor local(w); TangentVector xi; - if constexpr (K == Eigen::Dynamic) xi.resize(static_cast(pose.dim())); + if constexpr (K == Eigen::Dynamic) + xi.resize(static_cast(poseBase.dim())); xi.template head<3>() = w; - for (size_t i = 0; i < pose.k(); ++i) { + for (size_t i = 0; i < poseBase.k(); ++i) { xi.template segment<3>(3 + 3 * i) = - local.InvJacobian().applyLeft(pose.x_.col(static_cast(i))); + local.InvJacobian().applyLeft(poseBase.x_.col(static_cast(i))); } if (Hpose) *Hpose = LogmapDerivative(xi); return xi; } -template -typename ExtendedPose3::Jacobian ExtendedPose3::AdjointMap() const { +template +typename ExtendedPose3::Jacobian +ExtendedPose3::AdjointMap() const { const Matrix3 R = R_.matrix(); Jacobian adj; if constexpr (dimension == Eigen::Dynamic) { @@ -248,8 +260,8 @@ typename ExtendedPose3::Jacobian ExtendedPose3::AdjointMap() const { return adj; } -template -typename ExtendedPose3::TangentVector ExtendedPose3::Adjoint( +template +typename ExtendedPose3::TangentVector ExtendedPose3::Adjoint( const TangentVector& xi_b, ChartJacobian H_this, ChartJacobian H_xib) const { const Jacobian Ad = AdjointMap(); if (H_this) *H_this = -Ad * adjointMap(xi_b); @@ -257,8 +269,8 @@ typename ExtendedPose3::TangentVector ExtendedPose3::Adjoint( return Ad * xi_b; } -template -typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap( +template +typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap( const TangentVector& xi) { const size_t k = RuntimeK(xi); const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); @@ -279,8 +291,8 @@ typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap( return adj; } -template -typename ExtendedPose3::TangentVector ExtendedPose3::adjoint( +template +typename ExtendedPose3::TangentVector ExtendedPose3::adjoint( const TangentVector& xi, const TangentVector& y, ChartJacobian Hxi, ChartJacobian H_y) { const Jacobian ad_xi = adjointMap(xi); @@ -305,17 +317,17 @@ typename ExtendedPose3::TangentVector ExtendedPose3::adjoint( return ad_xi * y; } -template -typename ExtendedPose3::Jacobian ExtendedPose3::ExpmapDerivative( - const TangentVector& xi) { +template +typename ExtendedPose3::Jacobian +ExtendedPose3::ExpmapDerivative(const TangentVector& xi) { Jacobian J; Expmap(xi, J); return J; } -template -typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative( - const TangentVector& xi) { +template +typename ExtendedPose3::Jacobian +ExtendedPose3::LogmapDerivative(const TangentVector& xi) { const size_t k = RuntimeK(xi); const Vector3 w = xi.template head<3>(); const so3::DexpFunctor local(w); @@ -340,26 +352,29 @@ typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative( return J; } -template -typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative( - const ExtendedPose3& pose) { +template +typename ExtendedPose3::Jacobian +ExtendedPose3::LogmapDerivative(const This& pose) { return LogmapDerivative(Logmap(pose)); } -template -ExtendedPose3 ExtendedPose3::ChartAtOrigin::Retract( - const TangentVector& xi, ChartJacobian Hxi) { - return Expmap(xi, Hxi); +template +typename ExtendedPose3::This +ExtendedPose3::ChartAtOrigin::Retract(const TangentVector& xi, + ChartJacobian Hxi) { + return ExtendedPose3::Expmap(xi, Hxi); } -template -typename ExtendedPose3::TangentVector ExtendedPose3::ChartAtOrigin::Local( - const ExtendedPose3& pose, ChartJacobian Hpose) { - return Logmap(pose, Hpose); +template +typename ExtendedPose3::TangentVector +ExtendedPose3::ChartAtOrigin::Local(const This& pose, + ChartJacobian Hpose) { + return ExtendedPose3::Logmap(pose, Hpose); } -template -typename ExtendedPose3::LieAlgebra ExtendedPose3::matrix() const { +template +typename ExtendedPose3::LieAlgebra +ExtendedPose3::matrix() const { LieAlgebra M; if constexpr (matrix_dim == Eigen::Dynamic) { const Eigen::Index n = 3 + static_cast(k()); @@ -372,8 +387,8 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::matrix() const { return M; } -template -typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( +template +typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( const TangentVector& xi) { const size_t k = RuntimeK(xi); LieAlgebra X; @@ -389,8 +404,8 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( return X; } -template -typename ExtendedPose3::TangentVector ExtendedPose3::Vee( +template +typename ExtendedPose3::TangentVector ExtendedPose3::Vee( const LieAlgebra& X) { const Eigen::Index k = X.cols() - 3; TangentVector xi; diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index fb05a91bc0..7990700c03 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -34,6 +34,9 @@ namespace gtsam { +template +class ExtendedPose3; + namespace internal { /// Compile-time traits for SE_k(3) dimensions. @@ -50,6 +53,16 @@ struct ExtendedPose3Traits { } }; +template +struct ExtendedPose3Class { + using type = Derived; +}; + +template +struct ExtendedPose3Class { + using type = ExtendedPose3; +}; + } // namespace internal /** @@ -58,18 +71,19 @@ struct ExtendedPose3Traits { * * Template parameter K can be fixed (K >= 1) or Eigen::Dynamic. */ -template +template class GTSAM_EXPORT ExtendedPose3 - : public MatrixLieGroup, + : public MatrixLieGroup::type, internal::ExtendedPose3Traits::Dimension(), internal::ExtendedPose3Traits::MatrixDim()> { public: + using This = typename internal::ExtendedPose3Class::type; inline constexpr static auto dimension = internal::ExtendedPose3Traits::Dimension(); inline constexpr static auto matrix_dim = internal::ExtendedPose3Traits::MatrixDim(); - using Base = MatrixLieGroup, dimension, matrix_dim>; + using Base = MatrixLieGroup; using TangentVector = typename Base::TangentVector; using Jacobian = typename Base::Jacobian; using ChartJacobian = typename Base::ChartJacobian; @@ -92,6 +106,22 @@ class GTSAM_EXPORT ExtendedPose3 template using IsFixed = typename std::enable_if= 1, void>::type; + static This MakeReturn(const ExtendedPose3& value) { + if constexpr (std::is_void_v) { + return value; + } else { + return This(value); + } + } + + static const ExtendedPose3& AsBase(const This& value) { + if constexpr (std::is_void_v) { + return value; + } else { + return static_cast(value); + } + } + static size_t RuntimeK(const TangentVector& xi); static void ZeroJacobian(ChartJacobian H, size_t d); @@ -151,22 +181,22 @@ class GTSAM_EXPORT ExtendedPose3 /// @{ template > - static ExtendedPose3 Identity(); + static This Identity(); template > - static ExtendedPose3 Identity(size_t k = 0); + static This Identity(size_t k = 0); - ExtendedPose3 inverse() const; + This inverse() const; - ExtendedPose3 operator*(const ExtendedPose3& other) const; + This operator*(const This& other) const; /// @} /// @name Lie Group /// @{ - static ExtendedPose3 Expmap(const TangentVector& xi, ChartJacobian Hxi = {}); + static This Expmap(const TangentVector& xi, ChartJacobian Hxi = {}); - static TangentVector Logmap(const ExtendedPose3& pose, + static TangentVector Logmap(const This& pose, ChartJacobian Hpose = {}); Jacobian AdjointMap() const; @@ -184,16 +214,16 @@ class GTSAM_EXPORT ExtendedPose3 static Jacobian LogmapDerivative(const TangentVector& xi); - static Jacobian LogmapDerivative(const ExtendedPose3& pose); + static Jacobian LogmapDerivative(const This& pose); struct ChartAtOrigin { - static ExtendedPose3 Retract(const TangentVector& xi, + static This Retract(const TangentVector& xi, ChartJacobian Hxi = {}); - static TangentVector Local(const ExtendedPose3& pose, + static TangentVector Local(const This& pose, ChartJacobian Hpose = {}); }; - using LieGroup, dimension>::inverse; + using LieGroup::inverse; /// @} /// @name Matrix Lie Group @@ -227,15 +257,15 @@ class GTSAM_EXPORT ExtendedPose3 /// Convenience typedef for dynamic k. using ExtendedPose3Dynamic = ExtendedPose3; -template -struct traits> +template +struct traits> : public internal::MatrixLieGroup< - ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; + ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; -template -struct traits> +template +struct traits> : public internal::MatrixLieGroup< - ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; + ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; } // namespace gtsam diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 58681da5a5..7eea6a2cee 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -57,58 +57,6 @@ Pose3 Pose3::FromPose2(const Pose2& p, OptionalJacobian<6, 3> H) { return Pose3(p); } -/* ************************************************************************* */ -Pose3 Pose3::inverse() const { return Pose3(Base::inverse()); } - -/* ************************************************************************* */ -Pose3 Pose3::operator*(const Pose3& T) const { - return Pose3(Base::operator*(T)); -} - -/* ************************************************************************* */ -Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi) { - return Pose3(Base::Expmap(xi, Hxi)); -} - -/* ************************************************************************* */ -Vector6 Pose3::Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose) { - return Base::Logmap(pose, Hpose); -} - -/* ************************************************************************* */ -Matrix6 Pose3::AdjointMap() const { return Base::AdjointMap(); } - -/* ************************************************************************* */ -Vector6 Pose3::Adjoint(const Vector6& xi_b, OptionalJacobian<6, 6> H_this, - OptionalJacobian<6, 6> H_xib) const { - return Base::Adjoint(xi_b, H_this, H_xib); -} - -/* ************************************************************************* */ -Matrix6 Pose3::adjointMap(const Vector6& xi) { return Base::adjointMap(xi); } - -/* ************************************************************************* */ -Vector6 Pose3::adjoint(const Vector6& xi, const Vector6& y, - OptionalJacobian<6, 6> Hxi, - OptionalJacobian<6, 6> H_y) { - return Base::adjoint(xi, y, Hxi, H_y); -} - -/* ************************************************************************* */ -Matrix6 Pose3::ExpmapDerivative(const Vector6& xi) { - return Base::ExpmapDerivative(xi); -} - -/* ************************************************************************* */ -Matrix6 Pose3::LogmapDerivative(const Vector6& xi) { - return Base::LogmapDerivative(xi); -} - -/* ************************************************************************* */ -Matrix6 Pose3::LogmapDerivative(const Pose3& pose) { - return Base::LogmapDerivative(pose); -} - /* ************************************************************************* */ Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { #ifdef GTSAM_POSE3_EXPMAP @@ -141,20 +89,6 @@ Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { #endif } -/* ************************************************************************* */ -Matrix4 Pose3::Hat(const Vector6& xi) { return Base::Hat(xi); } - -/* ************************************************************************* */ -Vector6 Pose3::Vee(const Matrix4& X) { return Base::Vee(X); } - -/* ************************************************************************* */ -Matrix4 Pose3::matrix() const { return Base::matrix(); } - -/* ************************************************************************* */ -Pose3::Vector16 Pose3::vec(OptionalJacobian<16, 6> H) const { - return Base::vec(H); -} - /* ************************************************************************* */ /// The dual version of Adjoint Vector6 Pose3::AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_pose, diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 85eae9f8c2..00353deec9 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -39,10 +39,9 @@ class Pose2; * @ingroup geometry * \nosubgrouping */ -class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPose3<1> { +class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { public: - using Base = ExtendedPose3<1>; - using LieGroupBase = MatrixLieGroup; + using Base = ExtendedPose3<1, Pose3>; /** Pose Concept requirements */ typedef Rot3 Rotation; @@ -51,6 +50,7 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPos public: using Vector16 = Eigen::Matrix; + using Base::operator*; /// @name Standard Constructors /// @{ @@ -106,15 +106,6 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPos /// @name Group /// @{ - static Pose3 Identity() { - return Pose3(Base::Identity()); - } - - Pose3 inverse() const; - - /// compose syntactic sugar - Pose3 operator*(const Pose3& T) const; - /** * Interpolate between two poses via individual rotation and translation * interpolation. @@ -140,22 +131,6 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPos using LieAlgebra = Matrix4; - static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = {}); - - static Vector6 Logmap(const Pose3& pose, OptionalJacobian<6, 6> Hpose = {}); - - Matrix6 AdjointMap() const; - - Vector6 Adjoint(const Vector6& xi_b, - OptionalJacobian<6, 6> H_this = {}, - OptionalJacobian<6, 6> H_xib = {}) const; - - static Matrix6 adjointMap(const Vector6& xi); - - static Vector6 adjoint(const Vector6& xi, const Vector6& y, - OptionalJacobian<6, 6> Hxi = {}, - OptionalJacobian<6, 6> H_y = {}); - /// The dual version of Adjoint Vector6 AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_this = {}, @@ -172,37 +147,11 @@ class GTSAM_EXPORT Pose3: public MatrixLieGroup, public ExtendedPos OptionalJacobian<6, 6> Hxi = {}, OptionalJacobian<6, 6> H_y = {}); - static Matrix6 ExpmapDerivative(const Vector6& xi); - - static Matrix6 LogmapDerivative(const Vector6& xi); - - static Matrix6 LogmapDerivative(const Pose3& pose); - struct GTSAM_EXPORT ChartAtOrigin { static Pose3 Retract(const Vector6& xi, ChartJacobian Hxi = {}); static Vector6 Local(const Pose3& pose, ChartJacobian Hpose = {}); }; - using LieGroupBase::between; - using LieGroupBase::compose; - using LieGroupBase::expmap; - using LieGroupBase::LocalCoordinates; - using LieGroupBase::logmap; - using LieGroupBase::Retract; - using LieGroup::inverse; - using LieGroupBase::localCoordinates; - using LieGroupBase::retract; - - static Matrix4 Hat(const Vector6& xi); - - static Vector6 Vee(const Matrix4& X); - - /** convert to 4*4 matrix */ - Matrix4 matrix() const; - - /// Return vectorized SE(3) matrix in column order. - Vector16 vec(OptionalJacobian<16, 6> H = {}) const; - /// @} /// @name Group Action on Point3 /// @{ diff --git a/gtsam/geometry/tests/testPose3.cpp b/gtsam/geometry/tests/testPose3.cpp index 3797e77619..fc3a82f4dc 100644 --- a/gtsam/geometry/tests/testPose3.cpp +++ b/gtsam/geometry/tests/testPose3.cpp @@ -1469,21 +1469,6 @@ TEST(Pose3, Vec) { EXPECT(assert_equal(numericalH, actualH, 1e-9)); } -/* ************************************************************************* */ -TEST(Pose3, AdjointMap) { - // Create a non-trivial Pose3 object - const Pose3 pose(Rot3::Rodrigues(0.1, 0.2, 0.3), Point3(1.0, 2.0, 3.0)); - - // Call the specialized AdjointMap - Matrix6 specialized_Adj = pose.AdjointMap(); - - // Call the generic AdjointMap from the base class - Matrix6 generic_Adj = static_cast*>(&pose)->AdjointMap(); - - // Assert that they are equal - EXPECT(assert_equal(specialized_Adj, generic_Adj, 1e-9)); -} - /* ************************************************************************* */ int main() { TestResult tr; diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 58cc9c35d2..caffd492dc 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -23,75 +23,6 @@ namespace gtsam { -//------------------------------------------------------------------------------ -NavState NavState::inverse() const { return NavState(Base::inverse()); } - -//------------------------------------------------------------------------------ -NavState NavState::operator*(const NavState& other) const { - return NavState(Base::operator*(other)); -} - -//------------------------------------------------------------------------------ -NavState NavState::Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi) { - return NavState(Base::Expmap(xi, Hxi)); -} - -//------------------------------------------------------------------------------ -Vector9 NavState::Logmap(const NavState& pose, OptionalJacobian<9, 9> Hpose) { - return Base::Logmap(pose, Hpose); -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::AdjointMap() const { return Base::AdjointMap(); } - -//------------------------------------------------------------------------------ -Vector9 NavState::Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_this, - OptionalJacobian<9, 9> H_xib) const { - return Base::Adjoint(xi_b, H_this, H_xib); -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::adjointMap(const Vector9& xi) { return Base::adjointMap(xi); } - -//------------------------------------------------------------------------------ -Vector9 NavState::adjoint(const Vector9& xi, const Vector9& y, - OptionalJacobian<9, 9> Hxi, - OptionalJacobian<9, 9> H_y) { - return Base::adjoint(xi, y, Hxi, H_y); -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::ExpmapDerivative(const Vector9& xi) { - return Base::ExpmapDerivative(xi); -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::LogmapDerivative(const Vector9& xi) { - return Base::LogmapDerivative(xi); -} - -//------------------------------------------------------------------------------ -Matrix9 NavState::LogmapDerivative(const NavState& state) { - return Base::LogmapDerivative(state); -} - -//------------------------------------------------------------------------------ -NavState NavState::ChartAtOrigin::Retract(const Vector9& xi, ChartJacobian Hxi) { - return NavState(Base::ChartAtOrigin::Retract(xi, Hxi)); -} - -//------------------------------------------------------------------------------ -Vector9 NavState::ChartAtOrigin::Local(const NavState& state, - ChartJacobian Hstate) { - return Base::ChartAtOrigin::Local(state, Hstate); -} - -//------------------------------------------------------------------------------ -Matrix5 NavState::Hat(const Vector9& xi) { return Base::Hat(xi); } - -//------------------------------------------------------------------------------ -Vector9 NavState::Vee(const Matrix5& X) { return Base::Vee(X); } - //------------------------------------------------------------------------------ NavState NavState::Create(const Rot3& R, const Point3& t, const Velocity3& v, OptionalJacobian<9, 3> H1, OptionalJacobian<9, 3> H2, diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 5489e17d44..ea9a621c03 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -41,11 +41,9 @@ using Velocity3 = Vector3; * NOTE: While Barrau20icra follow a R,v,t order, * we use a R,t,v order to maintain backwards compatibility. */ -class GTSAM_EXPORT NavState : public MatrixLieGroup, - public ExtendedPose3<2> { +class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { public: - using Base = ExtendedPose3<2>; - using LieGroupBase = MatrixLieGroup; + using Base = ExtendedPose3<2, NavState>; using LieAlgebra = Matrix5; using Vector25 = Eigen::Matrix; inline constexpr static auto dimension = 9; @@ -155,10 +153,6 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup, /// @name Group /// @{ - static NavState Identity() { return NavState(Base::Identity()); } - NavState inverse() const; - NavState operator*(const NavState& other) const; - /// Syntactic sugar const Rot3& rotation() const { return attitude(); }; @@ -197,41 +191,6 @@ class GTSAM_EXPORT NavState : public MatrixLieGroup, /// @name Lie Group /// @{ - static NavState Expmap(const Vector9& xi, OptionalJacobian<9, 9> Hxi = {}); - static Vector9 Logmap(const NavState& pose, OptionalJacobian<9, 9> Hpose = {}); - - Matrix9 AdjointMap() const; - Vector9 Adjoint(const Vector9& xi_b, OptionalJacobian<9, 9> H_this = {}, - OptionalJacobian<9, 9> H_xib = {}) const; - - static Matrix9 adjointMap(const Vector9& xi); - static Vector9 adjoint(const Vector9& xi, const Vector9& y, - OptionalJacobian<9, 9> Hxi = {}, - OptionalJacobian<9, 9> H_y = {}); - - static Matrix9 ExpmapDerivative(const Vector9& xi); - static Matrix9 LogmapDerivative(const Vector9& xi); - static Matrix9 LogmapDerivative(const NavState& state); - - struct GTSAM_EXPORT ChartAtOrigin { - static NavState Retract(const Vector9& xi, ChartJacobian Hxi = {}); - static Vector9 Local(const NavState& state, ChartJacobian Hstate = {}); - }; - - using LieGroupBase::between; - using LieGroupBase::compose; - using LieGroupBase::expmap; - using LieGroupBase::LocalCoordinates; - using LieGroupBase::logmap; - using LieGroupBase::Retract; - using LieGroup::inverse; - - static Matrix5 Hat(const Vector9& xi); - static Vector9 Vee(const Matrix5& X); - - Matrix5 matrix() const { return Base::matrix(); } - Vector25 vec(OptionalJacobian<25, 9> H = {}) const { return Base::vec(H); } - /// @} /// @name Dynamics /// @{ diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 0e19d83eff..9b9308c58a 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -803,21 +803,6 @@ TEST(NavState, Vec) { EXPECT(assert_equal(numericalH, actualH, 1e-9)); } -/* ************************************************************************* */ -TEST(NavState, AdjointMap_GenericVsSpecialized) { - // Create a non-trivial NavState object - const NavState navState(Rot3::Rodrigues(0.1, 0.2, 0.3), Point3(1.0, 2.0, 3.0), Velocity3(0.4, 0.5, 0.6)); - - // Call the specialized AdjointMap - Matrix9 specialized_Adj = navState.AdjointMap(); - - // Call the generic AdjointMap from the base class - Matrix9 generic_Adj = static_cast*>(&navState)->AdjointMap(); - - // Assert that they are equal - EXPECT(assert_equal(specialized_Adj, generic_Adj, 1e-9)); -} - /* ************************************************************************* */ TEST(NavState, AutonomousFlow) { const double dt = 0.1; From 243c76e5f8b40ccfad41939730ab67639cfd9aeb Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 11:39:48 -0500 Subject: [PATCH 230/266] Python wrapper --- .github/copilot-instructions.md | 7 + gtsam/geometry/ExtendedPose3-inl.h | 1 - gtsam/geometry/ExtendedPose3.cpp | 2 + gtsam/geometry/ExtendedPose3.h | 1 - gtsam/geometry/doc/ExtendedPose3.ipynb | 464 +++++++++++++++++++---- gtsam/geometry/geometry.i | 55 +++ python/gtsam/tests/test_ExtendedPose3.py | 110 ++++++ 7 files changed, 558 insertions(+), 82 deletions(-) create mode 100644 python/gtsam/tests/test_ExtendedPose3.py diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index b2179615fd..493c4540bf 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -8,4 +8,11 @@ For reviewing PRs: * Classes are Uppercase, methods and functions lowerMixedCase. * Public fields in structs keep plain names (no trailing underscore). * Apart from those naming conventions, we adopt Google C++ style. +* Notebooks in `*/doc/*.ipynb` and `*/examples/*.ipynb` should follow the standard preamble: + 1) title/introduction markdown cell, + 2) copyright markdown cell tagged `remove-cell`, + 3) Colab badge markdown cell, + 4) Colab install code cell tagged `remove-cell`, + 5) imports/setup code cell. + Use the same `remove-cell` tagging convention as existing notebooks so docs build and Colab behavior stay consistent. * After any code change, always run relevant tests via `make -j6 testXXX.run` in the build folder $WORKSPACE/build. If in VS code, ask for escalated permissions if needed. diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index e45e8a05d3..111de538ee 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -97,7 +97,6 @@ size_t ExtendedPose3::k() const { } template -template size_t ExtendedPose3::dim() const { return Dimension(k()); } diff --git a/gtsam/geometry/ExtendedPose3.cpp b/gtsam/geometry/ExtendedPose3.cpp index 88c59ca072..67e7754fe4 100644 --- a/gtsam/geometry/ExtendedPose3.cpp +++ b/gtsam/geometry/ExtendedPose3.cpp @@ -22,6 +22,8 @@ namespace gtsam { template class ExtendedPose3<1>; template class ExtendedPose3<2>; template class ExtendedPose3<3>; +template class ExtendedPose3<4>; +template class ExtendedPose3<6>; template class ExtendedPose3; } // namespace gtsam diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index 7990700c03..63eb5d9f7f 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -156,7 +156,6 @@ class GTSAM_EXPORT ExtendedPose3 size_t k() const; /// Runtime manifold dimension. - template > size_t dim() const; /// Rotation component. diff --git a/gtsam/geometry/doc/ExtendedPose3.ipynb b/gtsam/geometry/doc/ExtendedPose3.ipynb index 3016d4a6a7..e13a30b04a 100644 --- a/gtsam/geometry/doc/ExtendedPose3.ipynb +++ b/gtsam/geometry/doc/ExtendedPose3.ipynb @@ -1,90 +1,394 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "090d20ac", + "metadata": {}, + "source": [ + "# ExtendedPose3\n", + "\n", + "The `ExtendedPose3` class is the generic matrix Lie group $SE_K(3)$: the semi-direct product of `SO(3)` with `K` copies of $\\mathbb{R}^3$.\n", + "\n", + "This notebook focuses on Python usage of `ExtendedPose36` (the `K=6` instantiation), including construction, group operations, and manifold/Lie operations.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e5b31ed7", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n", + "Atlanta, Georgia 30332-0415\n", + "All Rights Reserved\n", + "\n", + "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n", + "\n", + "See LICENSE for the license information\n" + ] + }, + { + "cell_type": "markdown", + "id": "b45cbfc5", + "metadata": {}, + "source": [ + "\"Open\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "acccf586", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# Install GTSAM from pip if running in Google Colab\n", + "try:\n", + " import google.colab\n", + " %pip install --quiet gtsam-develop\n", + "except ImportError:\n", + " pass # Not in Colab\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c70f339f", + "metadata": {}, + "outputs": [], + "source": [ + "import gtsam\n", + "import numpy as np\n", + "from gtsam import Rot3\n", + "\n", + "ExtendedPose36 = gtsam.ExtendedPose36\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa791c1f", + "metadata": {}, + "source": [ + "## Constructing `ExtendedPose36`\n", + "\n", + "`ExtendedPose36` stores one `Rot3` and six 3-vectors $x_1,\\dots,x_6$.\n", + "The manifold dimension is `3 + 3*K = 21`, and the homogeneous matrix is `(3+K) x (3+K) = 9 x 9`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "207ee70e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ExtendedPose3\n", - "\n", - "`ExtendedPose3` is the generic Lie group $SE_K(3)$: the semi-direct product of `SO(3)` with `K` copies of $\\mathbb{R}^3$.\n", - "\n", - "It is a matrix Lie group with homogeneous representation size $(3+K)\\times(3+K)$ and manifold dimension $3 + 3K$." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 6, dim = 21, Dim = 21\n", + "matrix shape: (9, 9)\n", + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "X_identity = ExtendedPose36()\n", + "print(f\"k = {X_identity.k()}, dim = {X_identity.dim()}, Dim = {ExtendedPose36.Dim()}\")\n", + "print(\"matrix shape:\", X_identity.matrix().shape)\n", + "print(X_identity.matrix())\n" + ] + }, + { + "cell_type": "markdown", + "id": "44ec8306", + "metadata": {}, + "source": [ + "### Construct from rotation + 3xK block\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3c6f232d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x(0): [1. 2. 3.]\n", + "xMatrix: [[ 1. 4. -1. 0.5 2.1 -0.3]\n", + " [ 2. 5. 0.5 -1.2 3.3 0.8]\n", + " [ 3. 6. 2. 1.4 -2.7 0.6]]\n" + ] + } + ], + "source": [ + "R = Rot3.Yaw(np.deg2rad(20.0))\n", + "X_block = np.array([\n", + " [1.0, 4.0, -1.0, 0.5, 2.1, -0.3],\n", + " [2.0, 5.0, 0.5, -1.2, 3.3, 0.8],\n", + " [3.0, 6.0, 2.0, 1.4, -2.7, 0.6],\n", + "])\n", + "\n", + "X1 = ExtendedPose36(R, X_block)\n", + "print(\"x(0):\", np.array(X1.x(0)))\n", + "print(\"xMatrix:\", X1.xMatrix())\n" + ] + }, + { + "cell_type": "markdown", + "id": "2c4a45a8", + "metadata": {}, + "source": [ + "### Construct from homogeneous matrix\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3408e416", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "equals: True\n" + ] + } + ], + "source": [ + "T = X1.matrix()\n", + "X1_from_T = ExtendedPose36(T)\n", + "print(\"equals:\", X1.equals(X1_from_T, 1e-9))" + ] + }, + { + "cell_type": "markdown", + "id": "caa03a92", + "metadata": {}, + "source": [ + "## Group operations\n", + "\n", + "Composition, inverse, and between follow the same `Pose3`-style API.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "51421896", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State and Tangent Layout\n", - "\n", - "An element is parameterized as:\n", - "\n", - "$$\n", - "X = (R, x_1, \\ldots, x_K), \\qquad R \\in SO(3),\\; x_i \\in \\mathbb{R}^3\n", - "$$\n", - "\n", - "with tangent vector ordering:\n", - "\n", - "$$\n", - "\\xi = [\\omega, \\rho_1, \\ldots, \\rho_K] \\in \\mathbb{R}^{3+3K}.\n", - "$$" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "compose == matrix multiply: True\n", + "between == inverse-compose: True\n" + ] + } + ], + "source": [ + "xi2 = np.array([\n", + " 0.02, -0.01, 0.03,\n", + " 0.1, 0.2, -0.1,\n", + " -0.2, 0.3, 0.4,\n", + " 0.5, -0.6, 0.2,\n", + " -0.3, 0.1, 0.2,\n", + " 0.4, 0.2, -0.5,\n", + " -0.1, 0.7, 0.2,\n", + "])\n", + "X2 = ExtendedPose36.Expmap(xi2)\n", + "\n", + "X12 = X1.compose(X2)\n", + "print(\"compose == matrix multiply:\", np.allclose(X12.matrix(), X1.matrix() @ X2.matrix(), atol=1e-9))\n", + "\n", + "X_between = X1.between(X2)\n", + "X_between_expected = X1.inverse().compose(X2)\n", + "print(\"between == inverse-compose:\", X_between.equals(X_between_expected, 1e-9))\n" + ] + }, + { + "cell_type": "markdown", + "id": "45698ac4", + "metadata": {}, + "source": [ + "## Expmap / Logmap and tangent vectors\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "25a46216", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matrix Representation\n", - "\n", - "$$\n", - "X = \\begin{bmatrix}\n", - "R & x_1 & \\cdots & x_K \\\\\n", - "0 & 1 & & 0 \\\\\n", - "\\vdots & & \\ddots & \\\\\n", - "0 & 0 & & 1\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "The group operation is:\n", - "\n", - "$$\n", - "(R,x_i)\\,(S,y_i) = (RS,\\; x_i + R y_i).\n", - "$$" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "log(exp(xi)) ~= xi: True\n" + ] + } + ], + "source": [ + "xi = np.array([\n", + " 0.11, -0.07, 0.05,\n", + " 0.30, -0.40, 0.10,\n", + " -0.20, 0.60, -0.50,\n", + " 0.70, -0.10, 0.20,\n", + " -0.40, 0.30, 0.80,\n", + " 0.50, -0.20, -0.30,\n", + " 0.10, 0.20, -0.60,\n", + "])\n", + "\n", + "X = ExtendedPose36.Expmap(xi)\n", + "xi_round_trip = ExtendedPose36.Logmap(X)\n", + "print(\"log(exp(xi)) ~= xi:\", np.allclose(xi_round_trip, xi, atol=1e-9))" + ] + }, + { + "cell_type": "markdown", + "id": "1a2eb160", + "metadata": {}, + "source": [ + "### Hat / Vee\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "576cb79d", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## C++ Usage Notes\n", - "\n", - "- Fixed-size: `ExtendedPose3<3>` gives a 12D manifold.\n", - "- Dynamic-size: `ExtendedPose3` takes runtime `k` in constructor.\n", - "- Default dynamic construction (`k=0`) is supported for serialization and placeholder use.\n", - "- The API provides the standard `Manifold`, `LieGroup`, and `MatrixLieGroup` surface: `retract/localCoordinates`, `Expmap/Logmap`, `AdjointMap`, `Hat/Vee`, and `matrix/vec`." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "vee(hat(xi)) ~= xi: True\n" + ] + } + ], + "source": [ + "X_hat = ExtendedPose36.Hat(xi)\n", + "xi_from_hat = ExtendedPose36.Vee(X_hat)\n", + "print(\"vee(hat(xi)) ~= xi:\", np.allclose(xi_from_hat, xi, atol=1e-9))" + ] + }, + { + "cell_type": "markdown", + "id": "494d04cc", + "metadata": {}, + "source": [ + "## Manifold operations\n", + "\n", + "As with `Pose3`, optimization-facing operations are `retract` and `localCoordinates`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "20419fb4", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Related Guides\n", - "\n", - "- Pose manifold background: [Pose3](./Pose3.ipynb)\n", - "- Navigation usage and `NavState`: [ImuFactor / NavState docs](../../navigation/doc/ImuFactor.ipynb)\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "localCoordinates(retract(delta)) ~= delta: True\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" + ], + "source": [ + "delta = 1e-2 * np.ones(21)\n", + "X_perturbed = X1.retract(delta)\n", + "delta_back = X1.localCoordinates(X_perturbed)\n", + "print(\"localCoordinates(retract(delta)) ~= delta:\", np.allclose(delta_back, delta, atol=1e-7))" + ] + }, + { + "cell_type": "markdown", + "id": "cde834ce", + "metadata": {}, + "source": [ + "## Adjoint\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "46a15f60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AdjointMap shape: (21, 21)\n", + "Adjoint(eta) == AdjointMap @ eta: True\n" + ] } + ], + "source": [ + "eta = np.linspace(-0.3, 0.3, 21)\n", + "Ad = X1.AdjointMap()\n", + "eta_adjoint = X1.Adjoint(eta)\n", + "print(\"AdjointMap shape:\", Ad.shape)\n", + "print(\"Adjoint(eta) == AdjointMap @ eta:\", np.allclose(eta_adjoint, Ad @ eta, atol=1e-9))" + ] + }, + { + "cell_type": "markdown", + "id": "6a2a253f", + "metadata": {}, + "source": [ + "## Notes and references\n", + "\n", + "`ExtendedPose3` is meaning-agnostic: it only defines the group structure. Semantic interpretation of each $x_i$ (e.g., position, velocity, bias, contact points) is left to derived/application-level code.\n", + "\n", + "For an invariant-filter application context (including walking-robot style state design ideas), see:\n", + "- [InvariantEKF notebook](../../navigation/doc/InvariantEKF.ipynb)\n", + "- [NavState notebook](../../navigation/doc/NavState.ipynb)\n", + "\n", + "For `Pose3`-specific behavior and conventions, see:\n", + "- [Pose3 notebook](./Pose3.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/gtsam/geometry/geometry.i b/gtsam/geometry/geometry.i index 05b637e075..28ab12079f 100644 --- a/gtsam/geometry/geometry.i +++ b/gtsam/geometry/geometry.i @@ -686,6 +686,61 @@ class Pose3 { void serialize() const; }; +#include +template +class ExtendedPose3 { + // Standard Constructors + ExtendedPose3(); + ExtendedPose3(const This& other); + ExtendedPose3(const gtsam::Rot3& R, const gtsam::Matrix& x); + ExtendedPose3(const gtsam::Matrix& T); + + // Testable + void print(string s = "") const; + bool equals(const This& other, double tol = 1e-9) const; + + // Access + size_t k() const; + gtsam::Rot3 rotation() const; + gtsam::Point3 x(size_t i) const; + gtsam::Matrix xMatrix() const; + + // Group + static This Identity(); + This inverse() const; + This compose(const This& g) const; + This between(const This& g) const; + + // Operator Overloads + This operator*(const This& other) const; + + // Manifold + static size_t Dim(); + size_t dim() const; + This retract(gtsam::Vector v) const; + gtsam::Vector localCoordinates(const This& g) const; + + // Lie Group + static This Expmap(gtsam::Vector xi); + static gtsam::Vector Logmap(const This& pose); + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi_b) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Matrix ExpmapDerivative(gtsam::Vector xi); + static gtsam::Matrix LogmapDerivative(gtsam::Vector xi); + static gtsam::Matrix LogmapDerivative(const This& pose); + + // Matrix Lie Group + gtsam::Vector vec() const; + gtsam::Matrix matrix() const; + static gtsam::Matrix Hat(const gtsam::Vector& xi); + static gtsam::Vector Vee(const gtsam::Matrix& X); + + // enabling serialization functionality + void serialize() const; +}; + #include class SL4 { // Standard constructors diff --git a/python/gtsam/tests/test_ExtendedPose3.py b/python/gtsam/tests/test_ExtendedPose3.py new file mode 100644 index 0000000000..a1f8523a44 --- /dev/null +++ b/python/gtsam/tests/test_ExtendedPose3.py @@ -0,0 +1,110 @@ +""" +GTSAM Copyright 2010-2019, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved + +See LICENSE for the license information + +ExtendedPose3 wrapper unit tests. +""" + +import unittest + +import numpy as np + +import gtsam +from gtsam import Rot3 +from gtsam.utils.test_case import GtsamTestCase + + +class TestExtendedPose3(GtsamTestCase): + """Test wrapped ExtendedPose3 static instantiations.""" + + @staticmethod + def class_for_k(k: int): + return getattr(gtsam, f"ExtendedPose3{k}") + + def test_constructors_static_k(self): + """All requested static-K classes are wrapped and default-constructible.""" + for k in (2, 3, 4, 6): + cls = self.class_for_k(k) + pose = cls() + self.assertEqual(pose.k(), k) + self.gtsamAssertEquals(pose, cls.Identity(), 1e-12) + + def test_k6_lie_group_and_matrix_lie_group(self): + """Test selected MatrixLieGroup operations on K=6.""" + cls = self.class_for_k(6) + + xi = np.array([ + 0.11, -0.07, 0.05, + 0.30, -0.40, 0.10, + -0.20, 0.60, -0.50, + 0.70, -0.10, 0.20, + -0.40, 0.30, 0.80, + 0.50, -0.20, -0.30, + 0.10, 0.20, -0.60, + ]) + yi = np.array([ + -0.06, 0.04, 0.02, + 0.10, -0.15, 0.05, + -0.12, 0.18, 0.07, + 0.22, -0.09, 0.03, + -0.14, 0.11, 0.08, + 0.06, 0.13, -0.17, + -0.05, 0.09, 0.04, + ]) + + hat = cls.Hat(xi) + np.testing.assert_allclose(cls.Vee(hat), xi, atol=1e-9) + + p = cls.Expmap(xi) + q = cls.Expmap(yi) + np.testing.assert_allclose(cls.Logmap(p), xi, atol=1e-9) + + composed = p * q + np.testing.assert_allclose(composed.matrix(), p.matrix() @ q.matrix(), atol=1e-9) + self.gtsamAssertEquals(composed, p.compose(q), 1e-9) + self.gtsamAssertEquals(p.between(q), p.inverse().compose(q), 1e-9) + self.gtsamAssertEquals(p.inverse() * p, cls.Identity(), 1e-9) + + self.assertEqual(cls.Dim(), 21) + self.assertEqual(p.dim(), 21) + self.assertEqual(p.k(), 6) + self.assertEqual(p.vec().shape[0], 81) + + def test_k6_constructor_with_components(self): + """Construct K=6 from (Rot3, x) and access x(i)/xMatrix().""" + cls = self.class_for_k(6) + x = np.array([ + [1.0, 4.0, -1.0, 0.5, 2.1, -0.3], + [2.0, 5.0, 0.5, -1.2, 3.3, 0.8], + [3.0, 6.0, 2.0, 1.4, -2.7, 0.6], + ]) + pose = cls(Rot3(), x) + + np.testing.assert_allclose(pose.xMatrix(), x, atol=1e-12) + for i in range(6): + np.testing.assert_allclose(pose.x(i), x[:, i], atol=1e-12) + + @unittest.skipUnless(hasattr(gtsam.ExtendedPose36, "serialize"), "Serialization not enabled") + def test_serialization_k6(self): + """Serialization works when boost serialization is enabled.""" + cls = self.class_for_k(6) + expected = cls.Expmap(np.array([ + 0.01, -0.02, 0.03, + 0.04, -0.05, 0.06, + 0.07, -0.08, 0.09, + -0.10, 0.11, -0.12, + 0.13, -0.14, 0.15, + -0.16, 0.17, -0.18, + 0.19, -0.20, 0.21, + ])) + actual = cls() + serialized = expected.serialize() + actual.deserialize(serialized) + self.gtsamAssertEquals(expected, actual, 1e-10) + + +if __name__ == "__main__": + unittest.main() From 5b234daea11d28c5c3e63cbb8788184774dc0234 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 11:58:05 -0500 Subject: [PATCH 231/266] Add math --- gtsam/geometry/doc/ExtendedPose3.ipynb | 47 ++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/gtsam/geometry/doc/ExtendedPose3.ipynb b/gtsam/geometry/doc/ExtendedPose3.ipynb index e13a30b04a..b2be902286 100644 --- a/gtsam/geometry/doc/ExtendedPose3.ipynb +++ b/gtsam/geometry/doc/ExtendedPose3.ipynb @@ -71,6 +71,53 @@ "ExtendedPose36 = gtsam.ExtendedPose36\n" ] }, + { + "cell_type": "markdown", + "id": "9b4dc088", + "metadata": {}, + "source": [ + "## Lie Group Math Overview\n", + "\n", + "An element of $SE_K(3)$ is $X=(R, x_1, \\ldots, x_K)$ with $R \\in SO(3)$ and $x_i \\in \\mathbb{R}^3$.\n", + "\n", + "Group composition is:\n", + "\n", + "\\begin{equation}\n", + "(R, x_i) \\cdot (S, y_i) = (RS, x_i + R y_i).\n", + "\\end{equation}\n", + "\n", + "Group inverse is:\n", + "\n", + "\\begin{equation}\n", + "(R, x_i)^{-1} = (R^T, -R^T x_i).\n", + "\\end{equation}\n", + "\n", + "The tangent vector is ordered as $\\xi=[\\omega, \\rho_1, \\ldots, \\rho_K] \\in \\mathbb{R}^{3+3K}$.\n", + "The exponential and logarithm maps are:\n", + "\n", + "\\begin{equation}\n", + "X = \\operatorname{Exp}(\\xi), \\quad \\xi = \\operatorname{Log}(X).\n", + "\\end{equation}\n", + "\n", + "For the matrix-Lie-group view, $\\operatorname{Hat}(\\xi)$ maps tangent vectors to Lie algebra matrices and $\\operatorname{Vee}(\\cdot)$ is the inverse map:\n", + "\n", + "\\begin{equation}\n", + "\\operatorname{Vee}(\\operatorname{Hat}(\\xi)) = \\xi.\n", + "\\end{equation}\n", + "\n", + "The adjoint map transports perturbations between frames:\n", + "\n", + "\\begin{equation}\n", + "\\operatorname{Ad}_X \\eta = \\operatorname{AdjointMap}(X) \\; \\eta.\n", + "\\end{equation}\n", + "\n", + "As a manifold, local updates use retraction and local coordinates:\n", + "\n", + "\\begin{equation}\n", + "X' = X \\oplus \\delta = \\operatorname{Retract}_X(\\delta), \\quad \\delta = \\operatorname{Local}_X(X').\n", + "\\end{equation}\n" + ] + }, { "cell_type": "markdown", "id": "fa791c1f", From 52c0ae18869dde99ff47da803d31c58e8b3d98b3 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 12:48:04 -0500 Subject: [PATCH 232/266] Review --- gtsam/geometry/ExtendedPose3-inl.h | 132 +++++---- gtsam/geometry/ExtendedPose3.h | 320 +++++++++++++++------ gtsam/geometry/Pose3.cpp | 94 +++--- gtsam/geometry/Pose3.h | 9 +- gtsam/geometry/tests/testExtendedPose3.cpp | 83 +++--- 5 files changed, 407 insertions(+), 231 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 111de538ee..208eddcaf8 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -86,21 +86,6 @@ ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { x_ = T.block(0, 3, 3, n - 3); } -template -size_t ExtendedPose3::Dimension(size_t k) { - return 3 + 3 * k; -} - -template -size_t ExtendedPose3::k() const { - return static_cast(x_.cols()); -} - -template -size_t ExtendedPose3::dim() const { - return Dimension(k()); -} - template const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { if (H) { @@ -123,7 +108,8 @@ Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { } else { H->setZero(); } - H->block(0, 3 + 3 * i, 3, 3) = R_.matrix(); + const Eigen::Index idx = 3 + 3 * static_cast(i); + H->block(0, idx, 3, 3) = R_.matrix(); } return x_.col(static_cast(i)); } @@ -159,22 +145,22 @@ typename ExtendedPose3::This ExtendedPose3::Identity() { template template -typename ExtendedPose3::This -ExtendedPose3::Identity(size_t k) { +typename ExtendedPose3::This ExtendedPose3::Identity( + size_t k) { return MakeReturn(ExtendedPose3(k)); } template -typename ExtendedPose3::This -ExtendedPose3::inverse() const { +typename ExtendedPose3::This ExtendedPose3::inverse() + const { const Rot3 Rt = R_.inverse(); const Matrix3K x = -(Rt.matrix() * x_); return MakeReturn(ExtendedPose3(Rt, x)); } template -typename ExtendedPose3::This -ExtendedPose3::operator*(const This& other) const { +typename ExtendedPose3::This ExtendedPose3::operator*( + const This& other) const { const ExtendedPose3& otherBase = AsBase(other); if (k() != otherBase.k()) { throw std::invalid_argument("ExtendedPose3: compose requires matching k."); @@ -183,15 +169,36 @@ ExtendedPose3::operator*(const This& other) const { return MakeReturn(ExtendedPose3(R_ * otherBase.R_, x)); } +// Expmap is implemented in so3::ExpmapFunctor::expmap, based on Ethan Eade's +// elegant Lie group document, at https://www.ethaneade.org/lie.pdf. +// See also [this document](doc/Jacobians.md) template typename ExtendedPose3::This ExtendedPose3::Expmap( const TangentVector& xi, ChartJacobian Hxi) { const size_t k = RuntimeK(xi); + // Get angular velocity omega const Vector3 w = xi.template head<3>(); + + // Instantiate functor for Dexp-related operations: const so3::DexpFunctor local(w); + + // Compute rotation using Expmap +#ifdef GTSAM_USE_QUATERNIONS + const Rot3 R = traits::Expmap(w); +#else const Rot3 R(local.expmap()); - const Matrix3 Rt = R.transpose(); +#endif + + // The translation t = local.Jacobian().left() * v. + // Below we call local.Jacobian().applyLeft, which is faster if you don't need + // Jacobians, and returns Jacobian of t with respect to w if asked. + // NOTE(Frank): this does the same as the intuitive formulas: + // t_parallel = w * w.dot(v); // translation parallel to axis + // w_cross_v = w.cross(v); // translation orthogonal to axis + // t = (w_cross_v - Rot3::Expmap(w) * w_cross_v + t_parallel) / theta2; + // but Local does not need R, deals automatically with the case where theta2 + // is near zero, and also gives us the machinery for the Jacobians. Matrix3K x; if constexpr (K == Eigen::Dynamic) x.resize(3, static_cast(k)); @@ -200,18 +207,23 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( const size_t d = 3 + 3 * k; ZeroJacobian(Hxi, d); const Matrix3 Jr = local.Jacobian().right(); - Hxi->block(0, 0, 3, 3) = Jr; + Hxi->block(0, 0, 3, 3) = Jr; // Jr here *is* the Jacobian of expmap + const Matrix3 Rt = R.transpose(); for (size_t i = 0; i < k; ++i) { Matrix3 H_xi_w; - const Vector3 rho = xi.template segment<3>(3 + 3 * i); + const Eigen::Index idx = 3 + 3 * static_cast(i); + const Vector3 rho = xi.template segment<3>(idx); x.col(static_cast(i)) = local.Jacobian().applyLeft(rho, &H_xi_w); - Hxi->block(3 + 3 * i, 0, 3, 3) = Rt * H_xi_w; - Hxi->block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jr; + Hxi->block(idx, 0, 3, 3) = Rt * H_xi_w; + Hxi->block(idx, idx, 3, 3) = Jr; + // In the last row, Jr = R^T * Jl, see Barfoot eq. (8.83). + // Jl is the left Jacobian of SO(3) at w. } } else { for (size_t i = 0; i < k; ++i) { - const Vector3 rho = xi.template segment<3>(3 + 3 * i); + const Eigen::Index idx = 3 + 3 * static_cast(i); + const Vector3 rho = xi.template segment<3>(idx); x.col(static_cast(i)) = local.Jacobian().applyLeft(rho); } } @@ -220,8 +232,8 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( } template -typename ExtendedPose3::TangentVector ExtendedPose3::Logmap( - const This& pose, ChartJacobian Hpose) { +typename ExtendedPose3::TangentVector +ExtendedPose3::Logmap(const This& pose, ChartJacobian H) { const ExtendedPose3& poseBase = AsBase(pose); const Vector3 w = Rot3::Logmap(poseBase.R_); const so3::DexpFunctor local(w); @@ -231,11 +243,12 @@ typename ExtendedPose3::TangentVector ExtendedPose3::Log xi.resize(static_cast(poseBase.dim())); xi.template head<3>() = w; for (size_t i = 0; i < poseBase.k(); ++i) { - xi.template segment<3>(3 + 3 * i) = - local.InvJacobian().applyLeft(poseBase.x_.col(static_cast(i))); + const Eigen::Index idx = 3 + 3 * static_cast(i); + xi.template segment<3>(idx) = local.InvJacobian().applyLeft( + poseBase.x_.col(static_cast(i))); } - if (Hpose) *Hpose = LogmapDerivative(xi); + if (H) *H = LogmapDerivative(xi); return xi; } @@ -252,16 +265,19 @@ ExtendedPose3::AdjointMap() const { adj.block(0, 0, 3, 3) = R; for (size_t i = 0; i < k(); ++i) { - adj.block(3 + 3 * i, 0, 3, 3) = + const Eigen::Index idx = 3 + 3 * static_cast(i); + adj.block(idx, 0, 3, 3) = skewSymmetric(x_.col(static_cast(i))) * R; - adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = R; + adj.block(idx, idx, 3, 3) = R; } return adj; } template -typename ExtendedPose3::TangentVector ExtendedPose3::Adjoint( - const TangentVector& xi_b, ChartJacobian H_this, ChartJacobian H_xib) const { +typename ExtendedPose3::TangentVector +ExtendedPose3::Adjoint(const TangentVector& xi_b, + ChartJacobian H_this, + ChartJacobian H_xib) const { const Jacobian Ad = AdjointMap(); if (H_this) *H_this = -Ad * adjointMap(xi_b); if (H_xib) *H_xib = Ad; @@ -269,8 +285,8 @@ typename ExtendedPose3::TangentVector ExtendedPose3::Adj } template -typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap( - const TangentVector& xi) { +typename ExtendedPose3::Jacobian +ExtendedPose3::adjointMap(const TangentVector& xi) { const size_t k = RuntimeK(xi); const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); @@ -283,17 +299,19 @@ typename ExtendedPose3::Jacobian ExtendedPose3::adjointM adj.block(0, 0, 3, 3) = w_hat; for (size_t i = 0; i < k; ++i) { - adj.block(3 + 3 * i, 0, 3, 3) = skewSymmetric( - xi(3 + 3 * i + 0), xi(3 + 3 * i + 1), xi(3 + 3 * i + 2)); - adj.block(3 + 3 * i, 3 + 3 * i, 3, 3) = w_hat; + const Eigen::Index idx = 3 + 3 * static_cast(i); + adj.block(idx, 0, 3, 3) = + skewSymmetric(xi(idx + 0), xi(idx + 1), xi(idx + 2)); + adj.block(idx, idx, 3, 3) = w_hat; } return adj; } template -typename ExtendedPose3::TangentVector ExtendedPose3::adjoint( - const TangentVector& xi, const TangentVector& y, ChartJacobian Hxi, - ChartJacobian H_y) { +typename ExtendedPose3::TangentVector +ExtendedPose3::adjoint(const TangentVector& xi, + const TangentVector& y, ChartJacobian Hxi, + ChartJacobian H_y) { const Jacobian ad_xi = adjointMap(xi); if (Hxi) { if constexpr (dimension == Eigen::Dynamic) { @@ -329,7 +347,10 @@ typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative(const TangentVector& xi) { const size_t k = RuntimeK(xi); const Vector3 w = xi.template head<3>(); + + // Instantiate functor for Dexp-related operations: const so3::DexpFunctor local(w); + const Matrix3 Rt = local.expmap().transpose(); const Matrix3 Jw = Rot3::LogmapDerivative(w); @@ -343,10 +364,11 @@ ExtendedPose3::LogmapDerivative(const TangentVector& xi) { J.block(0, 0, 3, 3) = Jw; for (size_t i = 0; i < k; ++i) { Matrix3 H_xi_w; - local.Jacobian().applyLeft(xi.template segment<3>(3 + 3 * i), H_xi_w); + const Eigen::Index idx = 3 + 3 * static_cast(i); + local.Jacobian().applyLeft(xi.template segment<3>(idx), H_xi_w); const Matrix3 Q = Rt * H_xi_w; - J.block(3 + 3 * i, 0, 3, 3) = -Jw * Q * Jw; - J.block(3 + 3 * i, 3 + 3 * i, 3, 3) = Jw; + J.block(idx, 0, 3, 3) = -Jw * Q * Jw; + J.block(idx, idx, 3, 3) = Jw; } return J; } @@ -367,8 +389,8 @@ ExtendedPose3::ChartAtOrigin::Retract(const TangentVector& xi, template typename ExtendedPose3::TangentVector ExtendedPose3::ChartAtOrigin::Local(const This& pose, - ChartJacobian Hpose) { - return ExtendedPose3::Logmap(pose, Hpose); + ChartJacobian H) { + return ExtendedPose3::Logmap(pose, H); } template @@ -398,14 +420,15 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( } X.block(0, 0, 3, 3) = skewSymmetric(xi(0), xi(1), xi(2)); for (size_t i = 0; i < k; ++i) { - X.block(0, 3 + i, 3, 1) = xi.template segment<3>(3 + 3 * i); + const Eigen::Index idx = 3 + 3 * static_cast(i); + X.block(0, 3 + i, 3, 1) = xi.template segment<3>(idx); } return X; } template -typename ExtendedPose3::TangentVector ExtendedPose3::Vee( - const LieAlgebra& X) { +typename ExtendedPose3::TangentVector +ExtendedPose3::Vee(const LieAlgebra& X) { const Eigen::Index k = X.cols() - 3; TangentVector xi; if constexpr (dimension == Eigen::Dynamic) { @@ -415,7 +438,8 @@ typename ExtendedPose3::TangentVector ExtendedPose3::Vee xi(1) = X(0, 2); xi(2) = X(1, 0); for (Eigen::Index i = 0; i < k; ++i) { - xi.template segment<3>(3 + 3 * i) = X.template block<3, 1>(0, 3 + i); + const Eigen::Index idx = 3 + 3 * i; + xi.template segment<3>(idx) = X.template block<3, 1>(0, idx); } return xi; } diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index 63eb5d9f7f..2ab491625f 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -37,51 +37,27 @@ namespace gtsam { template class ExtendedPose3; -namespace internal { - -/// Compile-time traits for SE_k(3) dimensions. -template -struct ExtendedPose3Traits { - /// Manifold dimensionality for SE_k(3): 3 + 3*k. - static constexpr int Dimension() { - return (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K; - } - - /// Homogeneous matrix size for SE_k(3): (3 + k) x (3 + k). - static constexpr int MatrixDim() { - return (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + K; - } -}; - -template -struct ExtendedPose3Class { - using type = Derived; -}; - -template -struct ExtendedPose3Class { - using type = ExtendedPose3; -}; - -} // namespace internal - /** * Lie group SE_k(3): semidirect product of SO(3) with k copies of R^3. - * Tangent ordering is [omega, x1, x2, ..., xk], each xi in R^3. + * State ordering is (R, x_1, ..., x_k) with R in SO(3) and x_i in R^3. + * Tangent ordering is [omega, rho_1, ..., rho_k], each block in R^3. * + * The manifold dimension is 3+3k and the homogeneous matrix size is 3+k. * Template parameter K can be fixed (K >= 1) or Eigen::Dynamic. */ template class GTSAM_EXPORT ExtendedPose3 - : public MatrixLieGroup::type, - internal::ExtendedPose3Traits::Dimension(), - internal::ExtendedPose3Traits::MatrixDim()> { + : public MatrixLieGroup, + ExtendedPose3, Derived>, + (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K, + (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + K> { public: - using This = typename internal::ExtendedPose3Class::type; - inline constexpr static auto dimension = - internal::ExtendedPose3Traits::Dimension(); - inline constexpr static auto matrix_dim = - internal::ExtendedPose3Traits::MatrixDim(); + using This = std::conditional_t, + ExtendedPose3, Derived>; + inline constexpr static int dimension = + (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K; + inline constexpr static int matrix_dim = + (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + K; using Base = MatrixLieGroup; using TangentVector = typename Base::TangentVector; @@ -106,120 +82,259 @@ class GTSAM_EXPORT ExtendedPose3 template using IsFixed = typename std::enable_if= 1, void>::type; - static This MakeReturn(const ExtendedPose3& value) { - if constexpr (std::is_void_v) { - return value; - } else { - return This(value); - } - } - - static const ExtendedPose3& AsBase(const This& value) { - if constexpr (std::is_void_v) { - return value; - } else { - return static_cast(value); - } - } - - static size_t RuntimeK(const TangentVector& xi); - static void ZeroJacobian(ChartJacobian H, size_t d); - public: /// @name Constructors /// @{ - /// Construct fixed-size identity. + /** + * Construct a fixed-size identity element. + * + * For fixed K, this creates R=I and x_i=0 for i=1..k. + * The manifold dimension is 3+3k and matrix size is (3+k)x(3+k). + */ template > ExtendedPose3(); - /// Construct dynamic identity, optionally with runtime k. + /** + * Construct a dynamic-size identity element. + * + * @param k Number of R^3 blocks. + * Creates R=I and x_i=0 for i=1..k. + * The manifold dimension is 3+3k and matrix size is (3+k)x(3+k). + */ template > explicit ExtendedPose3(size_t k = 0); + /** Copy constructor. */ ExtendedPose3(const ExtendedPose3&) = default; + + /** Copy assignment. */ ExtendedPose3& operator=(const ExtendedPose3&) = default; - /// Construct from rotation and 3xK block. + /** + * Construct from rotation and 3xk block. + * + * @param R Rotation in SO(3). + * @param x Matrix in R^(3xk), where column i stores x_i. + */ ExtendedPose3(const Rot3& R, const Matrix3K& x); - /// Construct from homogeneous matrix representation. + /** + * Construct from homogeneous matrix representation. + * + * @param T Homogeneous matrix in R^((3+k)x(3+k)). + * Top-left 3x3 is R, top-right 3xk stores x_1..x_k. + */ explicit ExtendedPose3(const LieAlgebra& T); /// @} /// @name Access /// @{ - static size_t Dimension(size_t k); - - /// Number of R^3 columns. - size_t k() const; - - /// Runtime manifold dimension. - size_t dim() const; - - /// Rotation component. + /** + * Runtime manifold dimension helper. + * + * @param k Number of R^3 blocks. + * @return 3+3k. + */ + static size_t Dimension(size_t k) { return 3 + 3 * k; } + + /** @return Number of R^3 blocks, k. */ + size_t k() const { return static_cast(x_.cols()); } + + /** @return Runtime manifold dimension, 3+3k. */ + size_t dim() const { return Dimension(k()); } + + /** + * Rotation component. + * + * @param H Optional Jacobian in R^(3xdim) for local rotation coordinates. + * @return Rotation R. + */ const Rot3& rotation(ComponentJacobian H = {}) const; - /// i-th R^3 component, returned by value. + /** + * i-th R^3 component, returned by value. + * + * @param i Zero-based block index in [0, k). + * @param H Optional Jacobian in R^(3xdim). + * @return x_i in R^3. + */ Point3 x(size_t i, ComponentJacobian H = {}) const; + /** + * Access all x_i blocks. + * + * @return Matrix in R^(3xk) with columns x_1..x_k. + */ const Matrix3K& xMatrix() const; + + /** + * Mutable access to all x_i blocks. + * + * @return Matrix in R^(3xk) with columns x_1..x_k. + */ Matrix3K& xMatrix(); /// @} /// @name Testable /// @{ + /** + * Print this state. + * + * @param s Optional prefix string. + */ void print(const std::string& s = "") const; + /** + * Equality check with tolerance. + * + * @param other Other state. + * @param tol Absolute tolerance. + * @return True if rotation and all x_i blocks are equal within tol. + */ bool equals(const ExtendedPose3& other, double tol = 1e-9) const; /// @} /// @name Group /// @{ + /** + * Identity element for fixed-size K. + * + * @return Identity with manifold dimension 3+3k and matrix size 3+k. + */ template > static This Identity(); + /** + * Identity element for dynamic-size K. + * + * @param k Number of R^3 blocks. + * @return Identity with manifold dimension 3+3k and matrix size 3+k. + */ template > static This Identity(size_t k = 0); + /** + * Group inverse. + * + * @return X^{-1}. + */ This inverse() const; + /** + * Group composition. + * + * @param other Right-hand operand with the same k. + * @return this * other. + */ This operator*(const This& other) const; /// @} /// @name Lie Group /// @{ + /** + * Exponential map from tangent to group. + * + * @param xi Tangent vector in R^dim. + * @param Hxi Optional Jacobian in R^(dimxdim). + * @return Group element in SE_k(3). + */ static This Expmap(const TangentVector& xi, ChartJacobian Hxi = {}); - static TangentVector Logmap(const This& pose, - ChartJacobian Hpose = {}); - + /** + * Logarithm map from group to tangent. + * + * @param pose Group element in SE_k(3). + * @param Hpose Optional Jacobian in R^(dimxdim). + * @return Tangent vector in R^dim. + */ + static TangentVector Logmap(const This& pose, ChartJacobian Hpose = {}); + + /** + * Adjoint map. + * + * @return Matrix in R^(dimxdim). + */ Jacobian AdjointMap() const; + /** + * Adjoint action on a tangent vector. + * + * @param xi_b Tangent vector in R^dim. + * @param H_this Optional Jacobian in R^(dimxdim). + * @param H_xib Optional Jacobian in R^(dimxdim). + * @return Ad_X xi_b in R^dim. + */ TangentVector Adjoint(const TangentVector& xi_b, ChartJacobian H_this = {}, ChartJacobian H_xib = {}) const; + /** + * Lie algebra adjoint map. + * + * @param xi Tangent vector in R^dim. + * @return ad_xi matrix in R^(dimxdim). + */ static Jacobian adjointMap(const TangentVector& xi); + /** + * Lie bracket action ad_xi(y). + * + * @param xi Tangent vector in R^dim. + * @param y Tangent vector in R^dim. + * @param Hxi Optional Jacobian in R^(dimxdim). + * @param H_y Optional Jacobian in R^(dimxdim). + * @return ad_xi(y) in R^dim. + */ static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, - ChartJacobian Hxi = {}, - ChartJacobian H_y = {}); - + ChartJacobian Hxi = {}, ChartJacobian H_y = {}); + + /** + * Jacobian of Expmap. + * + * @param xi Tangent vector in R^dim. + * @return Matrix in R^(dimxdim). + */ static Jacobian ExpmapDerivative(const TangentVector& xi); + /** + * Jacobian of Logmap evaluated from tangent coordinates. + * + * @param xi Tangent vector in R^dim. + * @return Matrix in R^(dimxdim). + */ static Jacobian LogmapDerivative(const TangentVector& xi); + /** + * Jacobian of Logmap evaluated at a group element. + * + * @param pose Group element in SE_k(3). + * @return Matrix in R^(dimxdim). + */ static Jacobian LogmapDerivative(const This& pose); + /** Chart operations at identity for LieGroup/Manifold compatibility. */ struct ChartAtOrigin { - static This Retract(const TangentVector& xi, - ChartJacobian Hxi = {}); - static TangentVector Local(const This& pose, - ChartJacobian Hpose = {}); + /** + * Retract at identity. + * + * @param xi Tangent vector in R^dim. + * @param Hxi Optional Jacobian in R^(dimxdim). + * @return Expmap(xi). + */ + static This Retract(const TangentVector& xi, ChartJacobian Hxi = {}); + + /** + * Local coordinates at identity. + * + * @param pose Group element in SE_k(3). + * @param Hpose Optional Jacobian in R^(dimxdim). + * @return Logmap(pose) in R^dim. + */ + static TangentVector Local(const This& pose, ChartJacobian Hpose = {}); }; using LieGroup::inverse; @@ -228,10 +343,27 @@ class GTSAM_EXPORT ExtendedPose3 /// @name Matrix Lie Group /// @{ + /** + * Homogeneous matrix representation. + * + * @return Matrix in R^((3+k)x(3+k)). + */ LieAlgebra matrix() const; + /** + * Hat operator from tangent to Lie algebra. + * + * @param xi Tangent vector in R^dim. + * @return Matrix in R^((3+k)x(3+k)). + */ static LieAlgebra Hat(const TangentVector& xi); + /** + * Vee operator from Lie algebra to tangent. + * + * @param X Matrix in R^((3+k)x(3+k)). + * @return Tangent vector in R^dim. + */ static TangentVector Vee(const LieAlgebra& X); /// @} @@ -242,6 +374,26 @@ class GTSAM_EXPORT ExtendedPose3 return os; } + protected: + static This MakeReturn(const ExtendedPose3& value) { + if constexpr (std::is_void_v) { + return value; + } else { + return This(value); + } + } + + static const ExtendedPose3& AsBase(const This& value) { + if constexpr (std::is_void_v) { + return value; + } else { + return static_cast(value); + } + } + + static size_t RuntimeK(const TangentVector& xi); + static void ZeroJacobian(ChartJacobian H, size_t d); + private: #if GTSAM_ENABLE_BOOST_SERIALIZATION friend class boost::serialization::access; @@ -258,13 +410,13 @@ using ExtendedPose3Dynamic = ExtendedPose3; template struct traits> - : public internal::MatrixLieGroup< - ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; + : public internal::MatrixLieGroup, + ExtendedPose3::matrix_dim> {}; template struct traits> - : public internal::MatrixLieGroup< - ExtendedPose3, internal::ExtendedPose3Traits::MatrixDim()> {}; + : public internal::MatrixLieGroup, + ExtendedPose3::matrix_dim> {}; } // namespace gtsam diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 7eea6a2cee..1f9053c59b 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -57,38 +57,6 @@ Pose3 Pose3::FromPose2(const Pose2& p, OptionalJacobian<6, 3> H) { return Pose3(p); } -/* ************************************************************************* */ -Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { -#ifdef GTSAM_POSE3_EXPMAP - return Expmap(xi, Hxi); -#else - Matrix3 DR; - const Rot3 R = Rot3::Retract(xi.head<3>(), Hxi ? &DR : nullptr); - if (Hxi) { - Hxi->setIdentity(); - Hxi->block<3, 3>(0, 0) = DR; - } - return Pose3(R, xi.tail<3>()); -#endif -} - -/* ************************************************************************* */ -Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { -#ifdef GTSAM_POSE3_EXPMAP - return Logmap(pose, Hpose); -#else - Matrix3 DR; - Vector6 xi; - xi.head<3>() = Rot3::LocalCoordinates(pose.rotation(), Hpose ? &DR : nullptr); - xi.tail<3>() = pose.translation(); - if (Hpose) { - Hpose->setIdentity(); - Hpose->block<3, 3>(0, 0) = DR; - } - return xi; -#endif -} - /* ************************************************************************* */ /// The dual version of Adjoint Vector6 Pose3::AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_pose, @@ -136,8 +104,7 @@ void Pose3::print(const std::string& s) const { /* ************************************************************************* */ bool Pose3::equals(const Pose3& pose, double tol) const { - return R_.equals(pose.R_, tol) && - traits::Equals(x_.col(0), pose.x_.col(0), tol); + return R_.equals(pose.R_, tol) && traits::Equals(x_, pose.x_, tol); } /* ************************************************************************* */ @@ -152,8 +119,8 @@ Pose3 Pose3::interpolateRt(const Pose3& T, double t, typename MakeJacobian::type HtPoint; Rot3 Rint = interpolate(R_, T.R_, t, HselfRot, HargRot, HtRot); - Point3 Pint = interpolate(x_.col(0), T.x_.col(0), t, HselfPoint, - HargPoint, HtPoint); + Point3 Pint = + interpolate(x_, T.x_, t, HselfPoint, HargPoint, HtPoint); Pose3 result = Pose3(Rint, Pint); if(Hself) *Hself << HselfRot, Z_3x3, Z_3x3, Rint.transpose() * R_.matrix() * HselfPoint; @@ -164,19 +131,48 @@ Pose3 Pose3::interpolateRt(const Pose3& T, double t, return result; } return Pose3(interpolate(R_, T.R_, t), - interpolate(x_.col(0), T.x_.col(0), t)); + interpolate(x_, T.x_, t)); +} +/* ************************************************************************* */ +Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { +#ifdef GTSAM_POSE3_EXPMAP + return Expmap(xi, Hxi); +#else + Matrix3 DR; + const Rot3 R = Rot3::Retract(xi.head<3>(), Hxi ? &DR : nullptr); + if (Hxi) { + Hxi->setIdentity(); + Hxi->block<3, 3>(0, 0) = DR; + } + return Pose3(R, xi.tail<3>()); +#endif } /* ************************************************************************* */ -const Point3& Pose3::translation(OptionalJacobian<3, 6> Hself) const { - t_cache_ = Base::x(0, Hself); - return t_cache_; +Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { +#ifdef GTSAM_POSE3_EXPMAP + return Logmap(pose, Hpose); +#else + Matrix3 DR; + Vector6 xi; + xi.head<3>() = Rot3::LocalCoordinates(pose.rotation(), Hpose ? &DR : nullptr); + xi.tail<3>() = pose.translation(); + if (Hpose) { + Hpose->setIdentity(); + Hpose->block<3, 3>(0, 0) = DR; + } + return xi; +#endif } /* ************************************************************************* */ -const Rot3& Pose3::rotation(OptionalJacobian<3, 6> Hself) const { - return Base::rotation(Hself); +const Point3& Pose3::translation(OptionalJacobian<3, 6> Hself) const { + if (Hself) { + Hself->setZero(); + Hself->block<3, 3>(0, 3) = R_.matrix(); + } + return x_; } /* ************************************************************************* */ @@ -208,7 +204,7 @@ Point3 Pose3::transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself, if (Hpoint) { *Hpoint = R; } - return R_ * point + x_.col(0); + return R_ * point + x_; } Matrix Pose3::transformFrom(const Matrix& points) const { @@ -216,7 +212,7 @@ Matrix Pose3::transformFrom(const Matrix& points) const { throw std::invalid_argument("Pose3:transformFrom expects 3*N matrix."); } const Matrix3 R = R_.matrix(); - return (R * points).colwise() + x_.col(0); // Eigen broadcasting! + return (R * points).colwise() + x_; // Eigen broadcasting! } /* ************************************************************************* */ @@ -225,7 +221,7 @@ Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself, // Only get transpose once, to avoid multiple allocations, // as well as multiple conversions in the Quaternion case const Matrix3 Rt = R_.transpose(); - const Point3 q(Rt * (point - x_.col(0))); + const Point3 q(Rt * (point - x_)); if (Hself) { const double wx = q.x(), wy = q.y(), wz = q.z(); (*Hself) << @@ -244,13 +240,13 @@ Matrix Pose3::transformTo(const Matrix& points) const { throw std::invalid_argument("Pose3:transformTo expects 3*N matrix."); } const Matrix3 Rt = R_.transpose(); - return Rt * (points.colwise() - x_.col(0)); // Eigen broadcasting! + return Rt * (points.colwise() - x_); // Eigen broadcasting! } /* ************************************************************************* */ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Vector3 delta = point - x_.col(0); + const Vector3 delta = point - x_; if (!Hself && !Hpoint) return delta.norm(); Matrix13 D_r_point; @@ -269,7 +265,7 @@ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, /* ************************************************************************* */ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 6> Hpose) const { - const Vector3 delta = pose.x_.col(0) - x_.col(0); + const Vector3 delta = pose.x_ - x_; if (!Hself && !Hpose) return delta.norm(); Matrix13 D_r_point; @@ -295,7 +291,7 @@ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> Hself, OptionalJacobian<2, 3> Hpoint) const { const Matrix3 Rt = R_.transpose(); - const Point3 local(Rt * (point - x_.col(0))); + const Point3 local(Rt * (point - x_)); if (!Hself && !Hpoint) return Unit3(local); diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 00353deec9..7b1544c443 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -66,7 +66,8 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { Pose3(const Base& other) : Base(other) {} /** Construct from R,t */ - Pose3(const Rot3& R, const Point3& t) : Base(R, Vector3(t.x(), t.y(), t.z())) {} + Pose3(const Rot3& R, const Point3& t) + : Base(R, Vector3(t.x(), t.y(), t.z())) {} /** Construct from Pose2 */ explicit Pose3(const Pose2& pose2); @@ -147,6 +148,7 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { OptionalJacobian<6, 6> Hxi = {}, OptionalJacobian<6, 6> H_y = {}); + // Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP struct GTSAM_EXPORT ChartAtOrigin { static Pose3 Retract(const Vector6& xi, ChartJacobian Hxi = {}); static Vector6 Local(const Pose3& pose, ChartJacobian Hpose = {}); @@ -199,9 +201,6 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { /// @name Standard Interface /// @{ - /// get rotation - const Rot3& rotation(OptionalJacobian<3, 6> Hself = {}) const; - /// get translation const Point3& translation(OptionalJacobian<3, 6> Hself = {}) const; @@ -316,8 +315,6 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { /// @} private: - mutable Point3 t_cache_ = Point3::Zero(); - #if GTSAM_ENABLE_BOOST_SERIALIZATION /** Serialization function */ friend class boost::serialization::access; diff --git a/gtsam/geometry/tests/testExtendedPose3.cpp b/gtsam/geometry/tests/testExtendedPose3.cpp index e9b5157d88..75272bf141 100644 --- a/gtsam/geometry/tests/testExtendedPose3.cpp +++ b/gtsam/geometry/tests/testExtendedPose3.cpp @@ -14,14 +14,12 @@ * @brief Unit tests for ExtendedPose3 */ -#include - +#include #include #include #include #include - -#include +#include using namespace gtsam; @@ -37,21 +35,13 @@ namespace { const Rot3 kR1 = Rot3::RzRyRx(0.1, -0.2, 0.3); const Rot3 kR2 = Rot3::RzRyRx(-0.3, 0.4, -0.2); -const Matrix33 kX1 = (Matrix33() << 1.0, 4.0, -1.0, 2.0, 5.0, 0.5, 3.0, 6.0, - 2.0) +const Matrix3 kX1 = + (Matrix3() << 1.0, 4.0, -1.0, 2.0, 5.0, 0.5, 3.0, 6.0, 2.0).finished(); +const Matrix3 kX2 = + (Matrix3() << -2.0, 1.0, 0.1, 0.3, -1.5, 2.2, 0.7, -0.9, 1.6).finished(); +const Vector12 kXi = (Vector12() << 0.11, -0.07, 0.05, 0.3, -0.4, 0.1, -0.2, + 0.6, -0.5, 0.7, -0.1, 0.2) .finished(); -const Matrix33 kX2 = (Matrix33() << -2.0, 1.0, 0.1, 0.3, -1.5, 2.2, 0.7, -0.9, - 1.6) - .finished(); -const Vector12 kXi = - (Vector12() << 0.11, -0.07, 0.05, 0.3, -0.4, 0.1, -0.2, 0.6, -0.5, 0.7, - -0.1, 0.2) - .finished(); - -ExtendedPose3d MakeDynamic(const Rot3& R, const Matrix33& X) { - Matrix Xd = X; - return ExtendedPose3d(R, Xd); -} } // namespace @@ -84,14 +74,16 @@ TEST(ExtendedPose3, Dimensions) { //****************************************************************************** TEST(ExtendedPose3, ConstructorsAndAccess) { const ExtendedPose33 fixed(kR1, kX1); - const ExtendedPose3d dynamic = MakeDynamic(kR1, kX1); + const ExtendedPose3d dynamic(kR1, ExtendedPose3d::Matrix3K(kX1)); EXPECT(assert_equal(kR1, fixed.rotation())); EXPECT(assert_equal(kR1, dynamic.rotation())); for (size_t i = 0; i < 3; ++i) { - EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), fixed.x(i))); - EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), dynamic.x(i))); + EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), + fixed.x(i))); + EXPECT(assert_equal(Point3(kX1.col(static_cast(i))), + dynamic.x(i))); } EXPECT(assert_equal(fixed.matrix(), dynamic.matrix())); @@ -100,7 +92,8 @@ TEST(ExtendedPose3, ConstructorsAndAccess) { //****************************************************************************** TEST(ExtendedPose3, GroupOperationsMatch) { const ExtendedPose33 f1(kR1, kX1), f2(kR2, kX2); - const ExtendedPose3d d1 = MakeDynamic(kR1, kX1), d2 = MakeDynamic(kR2, kX2); + const ExtendedPose3d d1(kR1, ExtendedPose3d::Matrix3K(kX1)); + const ExtendedPose3d d2(kR2, ExtendedPose3d::Matrix3K(kX2)); const auto f12 = f1 * f2; const auto d12 = d1 * d2; @@ -108,8 +101,10 @@ TEST(ExtendedPose3, GroupOperationsMatch) { const auto f_id = f1 * f1.inverse(); const auto d_id = d1 * d1.inverse(); - EXPECT(assert_equal(ExtendedPose33::Identity().matrix(), f_id.matrix(), 1e-9)); - EXPECT(assert_equal(ExtendedPose3d::Identity(3).matrix(), d_id.matrix(), 1e-9)); + EXPECT( + assert_equal(ExtendedPose33::Identity().matrix(), f_id.matrix(), 1e-9)); + EXPECT( + assert_equal(ExtendedPose3d::Identity(3).matrix(), d_id.matrix(), 1e-9)); const auto f_between = f1.between(f2); const auto d_between = d1.between(d2); @@ -141,7 +136,7 @@ TEST(ExtendedPose3, ExpmapLogmapRoundTrip) { //****************************************************************************** TEST(ExtendedPose3, AdjointConsistency) { const ExtendedPose33 f(kR1, kX1); - const ExtendedPose3d d = MakeDynamic(kR1, kX1); + const ExtendedPose3d d(kR1, ExtendedPose3d::Matrix3K(kX1)); const Vector xi = kXi; const auto f_conj = f * ExtendedPose33::Expmap(kXi) * f.inverse(); @@ -153,10 +148,12 @@ TEST(ExtendedPose3, AdjointConsistency) { EXPECT(assert_equal(d_conj.matrix(), d_adj.matrix(), 1e-9)); const ExtendedPose33::Jacobian f_generic = - static_cast*>(&f)->AdjointMap(); - const Matrix d_generic = static_cast< - const MatrixLieGroup*>(&d) - ->AdjointMap(); + static_cast*>(&f) + ->AdjointMap(); + const Matrix d_generic = + static_cast*>(&d) + ->AdjointMap(); EXPECT(assert_equal(Matrix(f_generic), Matrix(f.AdjointMap()), 1e-9)); EXPECT(assert_equal(d_generic, d.AdjointMap(), 1e-9)); } @@ -164,31 +161,39 @@ TEST(ExtendedPose3, AdjointConsistency) { //****************************************************************************** TEST(ExtendedPose3, Derivatives) { const ExtendedPose33 f(kR1, kX1); - const ExtendedPose3d d = MakeDynamic(kR1, kX1); + const ExtendedPose3d d(kR1, ExtendedPose3d::Matrix3K(kX1)); const Vector xi = kXi; Matrix Hf; ExtendedPose33::Expmap(kXi, Hf); - const auto f_exp = [](const Vector12& v) { return ExtendedPose33::Expmap(v); }; - const Matrix Hf_num = numericalDerivative11(f_exp, kXi); + const auto f_exp = [](const Vector12& v) { + return ExtendedPose33::Expmap(v); + }; + const Matrix Hf_num = + numericalDerivative11(f_exp, kXi); EXPECT(assert_equal(Hf_num, Hf, 1e-6)); Matrix Hd; ExtendedPose3d::Expmap(xi, Hd); const auto d_exp = [](const Vector& v) { return ExtendedPose3d::Expmap(v); }; - const Matrix Hd_num = numericalDerivative11(d_exp, xi); + const Matrix Hd_num = + numericalDerivative11(d_exp, xi); EXPECT(assert_equal(Hd_num, Hd, 1e-6)); Matrix Lf; ExtendedPose33::Logmap(f, Lf); - const auto f_log = [](const ExtendedPose33& g) { return Vector12(ExtendedPose33::Logmap(g)); }; + const auto f_log = [](const ExtendedPose33& g) { + return Vector12(ExtendedPose33::Logmap(g)); + }; const Matrix Lf_num = numericalDerivative11(f_log, f); EXPECT(assert_equal(Lf_num, Lf, 1e-6)); Matrix Ld; ExtendedPose3d::Logmap(d, Ld); - const auto d_log = [](const ExtendedPose3d& g) { return ExtendedPose3d::Logmap(g); }; + const auto d_log = [](const ExtendedPose3d& g) { + return ExtendedPose3d::Logmap(g); + }; const Matrix Ld_num = numericalDerivative11(d_log, d); EXPECT(assert_equal(Ld_num, Ld, 1e-6)); @@ -197,19 +202,21 @@ TEST(ExtendedPose3, Derivatives) { //****************************************************************************** TEST(ExtendedPose3, VecJacobian) { const ExtendedPose33 f(kR1, kX1); - const ExtendedPose3d d = MakeDynamic(kR1, kX1); + const ExtendedPose3d d(kR1, ExtendedPose3d::Matrix3K(kX1)); Matrix Hf; const Vector vf = f.vec(Hf); const auto fv = [](const ExtendedPose33& g) { return Vector(g.vec()); }; - const Matrix Hf_num = numericalDerivative11(fv, f); + const Matrix Hf_num = + numericalDerivative11(fv, f); EXPECT(assert_equal(Hf_num, Hf, 1e-6)); EXPECT_LONGS_EQUAL(36, vf.size()); Matrix Hd; const Vector vd = d.vec(Hd); const auto dv = [](const ExtendedPose3d& g) { return Vector(g.vec()); }; - const Matrix Hd_num = numericalDerivative11(dv, d); + const Matrix Hd_num = + numericalDerivative11(dv, d); EXPECT(assert_equal(Hd_num, Hd, 1e-6)); EXPECT_LONGS_EQUAL(36, vd.size()); } From 8627df9038154e6330dd48451fc872316dec3f85 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 13:17:22 -0500 Subject: [PATCH 233/266] Harden Vee --- gtsam/geometry/ExtendedPose3-inl.h | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 208eddcaf8..2354c4955c 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -429,17 +429,34 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( template typename ExtendedPose3::TangentVector ExtendedPose3::Vee(const LieAlgebra& X) { - const Eigen::Index k = X.cols() - 3; + if (X.rows() != X.cols() || X.rows() < 3) { + throw std::invalid_argument("ExtendedPose3::Vee: invalid matrix shape."); + } + + const Eigen::Index k = [&]() -> Eigen::Index { + if constexpr (K == Eigen::Dynamic) { + return X.cols() - 3; + } else { + if (X.rows() != matrix_dim) { + throw std::invalid_argument("ExtendedPose3::Vee: invalid matrix shape."); + } + return static_cast(K); + } + }(); + TangentVector xi; if constexpr (dimension == Eigen::Dynamic) { xi.resize(3 + 3 * k); + xi.setZero(); + } else { + xi.setZero(); } xi(0) = X(2, 1); xi(1) = X(0, 2); xi(2) = X(1, 0); for (Eigen::Index i = 0; i < k; ++i) { const Eigen::Index idx = 3 + 3 * i; - xi.template segment<3>(idx) = X.template block<3, 1>(0, idx); + xi.template segment<3>(idx) = X.template block<3, 1>(0, 3 + i); } return xi; } From 502f867a183991145e4661962209ec9565dad280 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 13:32:17 -0500 Subject: [PATCH 234/266] Rename to t_ --- gtsam/geometry/ExtendedPose3-inl.h | 38 ++++++++++++++++-------------- gtsam/geometry/ExtendedPose3.h | 8 +++---- gtsam/geometry/Pose3.cpp | 31 ++++++++++-------------- gtsam/geometry/Pose3.h | 3 ++- gtsam/navigation/NavState.cpp | 18 +++++++------- gtsam/navigation/NavState.h | 2 +- 6 files changed, 49 insertions(+), 51 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 2354c4955c..51825b2816 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -46,18 +46,18 @@ void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { template template ExtendedPose3::ExtendedPose3() - : R_(Rot3::Identity()), x_(Matrix3K::Zero()) {} + : R_(Rot3::Identity()), t_(Matrix3K::Zero()) {} template template ExtendedPose3::ExtendedPose3(size_t k) - : R_(Rot3::Identity()), x_(3, k) { - x_.setZero(); + : R_(Rot3::Identity()), t_(3, k) { + t_.setZero(); } template ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) - : R_(R), x_(x) {} + : R_(R), t_(x) {} template ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { @@ -67,8 +67,8 @@ ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); } const auto k = n - 3; - x_.resize(3, k); - x_.setZero(); + t_.resize(3, k); + t_.setZero(); } const auto n = T.rows(); @@ -83,7 +83,7 @@ ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { } R_ = Rot3(T.template block<3, 3>(0, 0)); - x_ = T.block(0, 3, 3, n - 3); + t_ = T.block(0, 3, 3, n - 3); } template @@ -111,19 +111,19 @@ Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { const Eigen::Index idx = 3 + 3 * static_cast(i); H->block(0, idx, 3, 3) = R_.matrix(); } - return x_.col(static_cast(i)); + return t_.col(static_cast(i)); } template const typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() const { - return x_; + return t_; } template typename ExtendedPose3::Matrix3K& ExtendedPose3::xMatrix() { - return x_; + return t_; } template @@ -134,7 +134,7 @@ void ExtendedPose3::print(const std::string& s) const { template bool ExtendedPose3::equals(const ExtendedPose3& other, double tol) const { - return R_.equals(other.R_, tol) && equal_with_abs_tol(x_, other.x_, tol); + return R_.equals(other.R_, tol) && equal_with_abs_tol(t_, other.t_, tol); } template @@ -154,7 +154,7 @@ template typename ExtendedPose3::This ExtendedPose3::inverse() const { const Rot3 Rt = R_.inverse(); - const Matrix3K x = -(Rt.matrix() * x_); + const Matrix3K x = -(Rt.matrix() * t_); return MakeReturn(ExtendedPose3(Rt, x)); } @@ -165,7 +165,7 @@ typename ExtendedPose3::This ExtendedPose3::operator*( if (k() != otherBase.k()) { throw std::invalid_argument("ExtendedPose3: compose requires matching k."); } - Matrix3K x = x_ + R_.matrix() * otherBase.x_; + Matrix3K x = t_ + R_.matrix() * otherBase.t_; return MakeReturn(ExtendedPose3(R_ * otherBase.R_, x)); } @@ -185,7 +185,8 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( // Compute rotation using Expmap #ifdef GTSAM_USE_QUATERNIONS - const Rot3 R = traits::Expmap(w); + // Reuse any quaternion-specific validation inside Rot3::Expmap. + const Rot3 R = Rot3::Expmap(w); #else const Rot3 R(local.expmap()); #endif @@ -245,7 +246,7 @@ ExtendedPose3::Logmap(const This& pose, ChartJacobian H) { for (size_t i = 0; i < poseBase.k(); ++i) { const Eigen::Index idx = 3 + 3 * static_cast(i); xi.template segment<3>(idx) = local.InvJacobian().applyLeft( - poseBase.x_.col(static_cast(i))); + poseBase.t_.col(static_cast(i))); } if (H) *H = LogmapDerivative(xi); @@ -267,7 +268,7 @@ ExtendedPose3::AdjointMap() const { for (size_t i = 0; i < k(); ++i) { const Eigen::Index idx = 3 + 3 * static_cast(i); adj.block(idx, 0, 3, 3) = - skewSymmetric(x_.col(static_cast(i))) * R; + skewSymmetric(t_.col(static_cast(i))) * R; adj.block(idx, idx, 3, 3) = R; } return adj; @@ -404,7 +405,7 @@ ExtendedPose3::matrix() const { M = LieAlgebra::Identity(); } M.template block<3, 3>(0, 0) = R_.matrix(); - M.block(0, 3, 3, static_cast(k())) = x_; + M.block(0, 3, 3, static_cast(k())) = t_; return M; } @@ -438,7 +439,8 @@ ExtendedPose3::Vee(const LieAlgebra& X) { return X.cols() - 3; } else { if (X.rows() != matrix_dim) { - throw std::invalid_argument("ExtendedPose3::Vee: invalid matrix shape."); + throw std::invalid_argument( + "ExtendedPose3::Vee: invalid matrix shape."); } return static_cast(K); } diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index 2ab491625f..6a110dded9 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -75,7 +75,7 @@ class GTSAM_EXPORT ExtendedPose3 protected: Rot3 R_; ///< Rotation component. - Matrix3K x_; ///< K translation-like columns in world frame. + Matrix3K t_; ///< K translation-like columns in world frame. template using IsDynamic = typename std::enable_if::type; @@ -140,7 +140,7 @@ class GTSAM_EXPORT ExtendedPose3 static size_t Dimension(size_t k) { return 3 + 3 * k; } /** @return Number of R^3 blocks, k. */ - size_t k() const { return static_cast(x_.cols()); } + size_t k() const { return static_cast(t_.cols()); } /** @return Runtime manifold dimension, 3+3k. */ size_t dim() const { return Dimension(k()); } @@ -370,7 +370,7 @@ class GTSAM_EXPORT ExtendedPose3 friend std::ostream& operator<<(std::ostream& os, const ExtendedPose3& p) { os << "R: " << p.R_ << "\n"; - os << "x: " << p.x_; + os << "x: " << p.t_; return os; } @@ -400,7 +400,7 @@ class GTSAM_EXPORT ExtendedPose3 template void serialize(Archive& ar, const unsigned int /*version*/) { ar& BOOST_SERIALIZATION_NVP(R_); - ar& BOOST_SERIALIZATION_NVP(x_); + ar& BOOST_SERIALIZATION_NVP(t_); } #endif }; diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 1f9053c59b..db22342729 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -104,7 +104,7 @@ void Pose3::print(const std::string& s) const { /* ************************************************************************* */ bool Pose3::equals(const Pose3& pose, double tol) const { - return R_.equals(pose.R_, tol) && traits::Equals(x_, pose.x_, tol); + return R_.equals(pose.R_, tol) && traits::Equals(t_, pose.t_, tol); } /* ************************************************************************* */ @@ -119,19 +119,17 @@ Pose3 Pose3::interpolateRt(const Pose3& T, double t, typename MakeJacobian::type HtPoint; Rot3 Rint = interpolate(R_, T.R_, t, HselfRot, HargRot, HtRot); - Point3 Pint = - interpolate(x_, T.x_, t, HselfPoint, HargPoint, HtPoint); + Point3 Pint = interpolate(t_, T.t_, t, HselfPoint, HargPoint, HtPoint); Pose3 result = Pose3(Rint, Pint); if(Hself) *Hself << HselfRot, Z_3x3, Z_3x3, Rint.transpose() * R_.matrix() * HselfPoint; - if(Harg) *Harg << HargRot, Z_3x3, Z_3x3, - Rint.transpose() * T.R_.matrix() * HargPoint; + if(Harg) *Harg << HargRot, Z_3x3, Z_3x3, Rint.transpose() * T.R_.matrix() * HargPoint; if(Ht) *Ht << HtRot, Rint.transpose() * HtPoint; return result; } return Pose3(interpolate(R_, T.R_, t), - interpolate(x_, T.x_, t)); + interpolate(t_, T.t_, t)); } /* ************************************************************************* */ @@ -168,11 +166,8 @@ Vector6 Pose3::ChartAtOrigin::Local(const Pose3& pose, ChartJacobian Hpose) { /* ************************************************************************* */ const Point3& Pose3::translation(OptionalJacobian<3, 6> Hself) const { - if (Hself) { - Hself->setZero(); - Hself->block<3, 3>(0, 3) = R_.matrix(); - } - return x_; + if (Hself) *Hself << Z_3x3, rotation().matrix(); + return t_; } /* ************************************************************************* */ @@ -204,7 +199,7 @@ Point3 Pose3::transformFrom(const Point3& point, OptionalJacobian<3, 6> Hself, if (Hpoint) { *Hpoint = R; } - return R_ * point + x_; + return R_ * point + t_; } Matrix Pose3::transformFrom(const Matrix& points) const { @@ -212,7 +207,7 @@ Matrix Pose3::transformFrom(const Matrix& points) const { throw std::invalid_argument("Pose3:transformFrom expects 3*N matrix."); } const Matrix3 R = R_.matrix(); - return (R * points).colwise() + x_; // Eigen broadcasting! + return (R * points).colwise() + t_; // Eigen broadcasting! } /* ************************************************************************* */ @@ -221,7 +216,7 @@ Point3 Pose3::transformTo(const Point3& point, OptionalJacobian<3, 6> Hself, // Only get transpose once, to avoid multiple allocations, // as well as multiple conversions in the Quaternion case const Matrix3 Rt = R_.transpose(); - const Point3 q(Rt * (point - x_)); + const Point3 q(Rt*(point - t_)); if (Hself) { const double wx = q.x(), wy = q.y(), wz = q.z(); (*Hself) << @@ -240,13 +235,13 @@ Matrix Pose3::transformTo(const Matrix& points) const { throw std::invalid_argument("Pose3:transformTo expects 3*N matrix."); } const Matrix3 Rt = R_.transpose(); - return Rt * (points.colwise() - x_); // Eigen broadcasting! + return Rt * (points.colwise() - t_); // Eigen broadcasting! } /* ************************************************************************* */ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Vector3 delta = point - x_; + const Vector3 delta = point - t_; if (!Hself && !Hpoint) return delta.norm(); Matrix13 D_r_point; @@ -265,7 +260,7 @@ double Pose3::range(const Point3& point, OptionalJacobian<1, 6> Hself, /* ************************************************************************* */ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, OptionalJacobian<1, 6> Hpose) const { - const Vector3 delta = pose.x_ - x_; + const Vector3 delta = pose.t_ - t_; if (!Hself && !Hpose) return delta.norm(); Matrix13 D_r_point; @@ -291,7 +286,7 @@ double Pose3::range(const Pose3& pose, OptionalJacobian<1, 6> Hself, Unit3 Pose3::bearing(const Point3& point, OptionalJacobian<2, 6> Hself, OptionalJacobian<2, 3> Hpoint) const { const Matrix3 Rt = R_.transpose(); - const Point3 local(Rt * (point - x_)); + const Point3 local(Rt * (point - t_)); if (!Hself && !Hpoint) return Unit3(local); diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 7b1544c443..02238ea4c2 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -320,7 +320,8 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { friend class boost::serialization::access; template void serialize(Archive & ar, const unsigned int /*version*/) { - ar& BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); + ar & BOOST_SERIALIZATION_NVP(R_); + ar & BOOST_SERIALIZATION_NVP(t_); } #endif /// @} diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index caffd492dc..165b0f022e 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -64,7 +64,7 @@ const Vector3& NavState::velocity(OptionalJacobian<3, 9> H) const { //------------------------------------------------------------------------------ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { const Rot3& nRb = R_; - const Vector3 n_v = x_.col(1); + const Vector3 n_v = t_.col(1); Matrix3 D_bv_nRb; Vector3 b_v = nRb.unrotate(n_v, H ? &D_bv_nRb : 0); if (H) @@ -75,7 +75,7 @@ Vector3 NavState::bodyVelocity(OptionalJacobian<3, 9> H) const { //------------------------------------------------------------------------------ double NavState::range(const Point3& point, OptionalJacobian<1, 9> Hself, OptionalJacobian<1, 3> Hpoint) const { - const Vector3 delta = point - x_.col(0); + const Vector3 delta = point - t_.col(0); const double r = delta.norm(); if (!Hself && !Hpoint) return r; @@ -121,15 +121,15 @@ void NavState::print(const std::string& s) const { //------------------------------------------------------------------------------ bool NavState::equals(const NavState& other, double tol) const { return R_.equals(other.R_, tol) && - traits::Equals(x_.col(0), other.x_.col(0), tol) && - equal_with_abs_tol(x_.col(1), other.x_.col(1), tol); + traits::Equals(t_.col(0), other.t_.col(0), tol) && + equal_with_abs_tol(t_.col(1), other.t_.col(1), tol); } //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { Rot3 nRb = R_; - Point3 n_t = x_.col(0), n_v = x_.col(1); + Point3 n_t = t_.col(0), n_v = t_.col(1); Matrix3 D_bRc_xi, D_R_nRb, D_t_nRb, D_v_nRb; const Rot3 bRc = Rot3::Expmap(dR(xi), H2 ? &D_bRc_xi : 0); const Rot3 nRc = nRb.compose(bRc, H1 ? &D_R_nRb : 0); @@ -157,8 +157,8 @@ Vector9 NavState::localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { Matrix3 D_dR_R, D_dt_R, D_dv_R; const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); - const Point3 dP = R_.unrotate(g.x_.col(0) - x_.col(0), H1 ? &D_dt_R : 0); - const Vector dV = R_.unrotate(g.x_.col(1) - x_.col(1), H1 ? &D_dv_R : 0); + const Point3 dP = R_.unrotate(g.t_.col(0) - t_.col(0), H1 ? &D_dt_R : 0); + const Vector dV = R_.unrotate(g.t_.col(1) - t_.col(1), H1 ? &D_dv_R : 0); Vector9 xi; Matrix3 D_xi_R; @@ -244,7 +244,7 @@ NavState NavState::update(const Vector3& b_acceleration, const Vector3& b_omega, Vector9 NavState::coriolis(double dt, const Vector3& omega, bool secondOrder, OptionalJacobian<9, 9> H) const { Rot3 nRb = R_; - Point3 n_t = x_.col(0), n_v = x_.col(1); + Point3 n_t = t_.col(0), n_v = t_.col(1); const double dt2 = dt * dt; const Vector3 omega_cross_vel = omega.cross(n_v); @@ -296,7 +296,7 @@ Vector9 NavState::correctPIM(const Vector9& pim, double dt, bool use2ndOrderCoriolis, OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { const Rot3& nRb = R_; - const Velocity3 n_v = x_.col(1); // derivative is Ri ! + const Velocity3 n_v = t_.col(1); // derivative is Ri ! const double dt22 = 0.5 * dt * dt; Vector9 xi; diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index ea9a621c03..5824e6e486 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -126,7 +126,7 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { } /// Return position as Vector3 Vector3 t() const { - return x_.col(0); + return t_.col(0); } /// Return velocity as Vector3. Computation-free. const Vector3& v() const { From 605067d8c41954d3427991d6964a94a636a0325d Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 13:46:08 -0500 Subject: [PATCH 235/266] Remove cache hack --- gtsam/navigation/NavState.cpp | 10 ++++------ gtsam/navigation/NavState.h | 11 ++++------- gtsam/navigation/navigation.i | 9 +++++---- 3 files changed, 13 insertions(+), 17 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 165b0f022e..1832a920f6 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -50,15 +50,13 @@ const Rot3& NavState::attitude(OptionalJacobian<3, 9> H) const { } //------------------------------------------------------------------------------ -const Point3& NavState::position(OptionalJacobian<3, 9> H) const { - p_cache_ = Base::x(0, H); - return p_cache_; +Point3 NavState::position(OptionalJacobian<3, 9> H) const { + return Base::x(0, H); } //------------------------------------------------------------------------------ -const Vector3& NavState::velocity(OptionalJacobian<3, 9> H) const { - v_cache_ = Base::x(1, H); - return v_cache_; +Vector3 NavState::velocity(OptionalJacobian<3, 9> H) const { + return Base::x(1, H); } //------------------------------------------------------------------------------ diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 5824e6e486..3129964f94 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -89,8 +89,8 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { /// @{ const Rot3& attitude(OptionalJacobian<3, 9> H = {}) const; - const Point3& position(OptionalJacobian<3, 9> H = {}) const; - const Velocity3& velocity(OptionalJacobian<3, 9> H = {}) const; + Point3 position(OptionalJacobian<3, 9> H = {}) const; + Velocity3 velocity(OptionalJacobian<3, 9> H = {}) const; const Pose3 pose() const { return Pose3(attitude(), position()); @@ -128,8 +128,8 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { Vector3 t() const { return t_.col(0); } - /// Return velocity as Vector3. Computation-free. - const Vector3& v() const { + /// Return velocity as Vector3. + Vector3 v() const { return velocity(); } // Return velocity in body frame @@ -234,9 +234,6 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { /// @} private: - mutable Point3 p_cache_ = Point3::Zero(); - mutable Velocity3 v_cache_ = Velocity3::Zero(); - /// @{ /// serialization #if GTSAM_ENABLE_BOOST_SERIALIZATION /// diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index 665abb0842..edbe8bc822 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -45,8 +45,9 @@ class ConstantBias { class NavState { // Constructors NavState(); - NavState(const gtsam::Rot3& R, const gtsam::Point3& t, gtsam::Vector v); - NavState(const gtsam::Pose3& pose, gtsam::Vector v); + NavState(const gtsam::Rot3& R, const gtsam::Point3& t, + const gtsam::Vector3& v); + NavState(const gtsam::Pose3& pose, const gtsam::Vector3& v); // Testable void print(string s = "") const; @@ -55,8 +56,8 @@ class NavState { // Access gtsam::Rot3 attitude() const; gtsam::Point3 position() const; - gtsam::Vector velocity() const; - gtsam::Vector bodyVelocity() const; + gtsam::Vector3 velocity() const; + gtsam::Vector3 bodyVelocity() const; gtsam::Pose3 pose() const; // Standard Interface From 787f8e99b188ae75fde6f460a8970b54fcc731b6 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Sat, 31 Jan 2026 16:56:00 +0200 Subject: [PATCH 236/266] Refactor rowCounts out of Multifrontal SymbolicJunctionTree. --- gtsam/linear/MultifrontalClique.h | 4 ++-- gtsam/linear/MultifrontalSolver.cpp | 16 ++++++++-------- gtsam/linear/MultifrontalSolver.h | 1 + gtsam/nonlinear/NonlinearMultifrontalSolver.cpp | 12 ++++++++---- 4 files changed, 19 insertions(+), 14 deletions(-) diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index dc4f6f7963..012c2dc67e 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -51,8 +51,8 @@ class IndexedSymbolicFactor : public SymbolicFactor { public: size_t index_; size_t rows_; - IndexedSymbolicFactor(const KeyVector& keys, size_t index, size_t rows) - : SymbolicFactor(), index_(index), rows_(rows) { + IndexedSymbolicFactor(const KeyVector& keys, size_t index) + : SymbolicFactor(), index_(index) { keys_ = keys; } }; diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index a273e134c3..6a3c48ac61 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -134,8 +134,7 @@ PrecomputeScratch precomputeFromGraph(const GaussianFactorGraph& graph) { // Build SymbolicFactorGraph from GaussianFactorGraph SymbolicFactorGraph buildSymbolicGraph( const GaussianFactorGraph& graph, - const std::unordered_set& fixedKeys, - const std::vector& rowCounts) { + const std::unordered_set& fixedKeys) { SymbolicFactorGraph symbolicGraph; symbolicGraph.reserve(graph.size()); for (size_t i = 0; i < graph.size(); ++i) { @@ -149,8 +148,7 @@ SymbolicFactorGraph buildSymbolicGraph( } // Skip factors that are fully constrained away. if (keys.empty()) continue; - symbolicGraph.emplace_shared( - keys, i, rowCounts.at(i)); + symbolicGraph.emplace_shared(keys, i); } return symbolicGraph; } @@ -455,6 +453,7 @@ struct CliqueBuilder { std::vector* cliques; const std::unordered_set* fixedKeys; const MultifrontalParameters* params; + const std::vector* rowCounts; SymbolicJunctionTree::Cluster::KeySetMap separatorCache = {}; BuiltClique build(const SymbolicJunctionTree::sharedNode& cluster, @@ -472,7 +471,7 @@ struct CliqueBuilder { auto indexed = std::static_pointer_cast(factor); factorIndices.push_back(indexed->index_); - vbmRows += indexed->rows_; + vbmRows += rowCounts->at(indexed->index_); } // Create the clique node and cache static structure. @@ -557,7 +556,8 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, } // Build the actual MultifrontalClique structure. - CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_}; + CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_, + &data.rowCounts}; for (const auto& rootCluster : data.junctionTree.roots()) { if (rootCluster) { roots_.push_back( @@ -583,13 +583,13 @@ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( } SymbolicFactorGraph symbolicGraph = - buildSymbolicGraph(graph, scratch.fixedKeys, scratch.rowCounts); + buildSymbolicGraph(graph, scratch.fixedKeys); SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); SymbolicJunctionTree junctionTree(eliminationTree); return MultifrontalSolver::PrecomputedData{ std::move(scratch.dims), std::move(scratch.fixedKeys), - std::move(junctionTree)}; + std::move(junctionTree), std::move(scratch.rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 955a5a6e66..77591d459a 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -86,6 +86,7 @@ class GTSAM_EXPORT MultifrontalSolver std::map dims; ///< Map from variable key to dimension. std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. SymbolicJunctionTree junctionTree; ///< Precomputed symbolic junction tree. + std::vector rowCounts; ///< Row counts indexed by factor index. }; /// Shared pointer to a MultifrontalClique. diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index 493e991607..930fb52b25 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -65,9 +65,7 @@ SymbolicFactorGraph buildSymbolicGraph( } } if (keys.empty()) continue; - const size_t rows = graph[i]->dim(); - symbolicGraph.emplace_shared(keys, i, - rows); + symbolicGraph.emplace_shared(keys, i); } return symbolicGraph; } @@ -102,9 +100,15 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); SymbolicJunctionTree junctionTree(eliminationTree); + std::vector rowCounts; + rowCounts.reserve(graph.size()); + for (const auto& factor : graph) { + size_t dim = factor ? factor->dim() : 0; + rowCounts.push_back(dim); + } return MultifrontalSolver::PrecomputedData{ - std::move(dims), std::move(fixedKeys), std::move(junctionTree)}; + std::move(dims), std::move(fixedKeys), std::move(junctionTree), std::move(rowCounts)}; } /* ************************************************************************* */ From ef72cbbc5db904169a567d1b8bc4ee176bfb285c Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Sun, 1 Feb 2026 23:08:20 +0200 Subject: [PATCH 237/266] Optimize multifrontal elimination by caching JunctionTree. Refactor IndexedSymbolicFactor into shared IndexedJunctionTreeBuilder.h, enabling efficient reuse of junction tree structure across multiple nonlinear optimization iterations. Key changes: - Add IndexedJunctionTreeBuilder.h with buildIndexedJunctionTree() template - Add GaussianFactorGraph::optimize() overload using cached junction tree - Cache SymbolicJunctionTree in NonlinearOptimizer for multifrontal solve - Refactor MultifrontalSolver and NonlinearMultifrontalSolver to use shared builder functions Performance improvement: reduces runtime from ~120s to ~90s (~25% faster) in specific test cases by avoiding repeated elimination tree construction. --- .../inference/EliminateableFactorGraph-inst.h | 110 ++++++++++++++++++ gtsam/inference/EliminateableFactorGraph.h | 36 +++++- gtsam/linear/MultifrontalClique.h | 11 -- gtsam/linear/MultifrontalSolver.cpp | 45 ++----- gtsam/linear/MultifrontalSolver.h | 25 ++-- .../linear/tests/testGaussianFactorGraph.cpp | 17 +++ .../nonlinear/NonlinearMultifrontalSolver.cpp | 30 +---- gtsam/nonlinear/NonlinearOptimizer.cpp | 14 ++- gtsam/nonlinear/NonlinearOptimizer.h | 9 ++ gtsam/symbolic/IndexedJunctionTree.h | 106 +++++++++++++++++ .../symbolic/tests/testSymbolicBayesTree.cpp | 13 ++- 11 files changed, 331 insertions(+), 85 deletions(-) create mode 100644 gtsam/symbolic/IndexedJunctionTree.h diff --git a/gtsam/inference/EliminateableFactorGraph-inst.h b/gtsam/inference/EliminateableFactorGraph-inst.h index f6b11f785c..2ce7d03887 100644 --- a/gtsam/inference/EliminateableFactorGraph-inst.h +++ b/gtsam/inference/EliminateableFactorGraph-inst.h @@ -20,6 +20,10 @@ #include #include +#include +#include + +#include namespace gtsam { @@ -145,6 +149,112 @@ namespace gtsam { } } + /* ************************************************************************* */ + template + IndexedJunctionTree + EliminateableFactorGraph::buildIndexedJunctionTree( + const Ordering& ordering, + const std::unordered_set& fixedKeys) const { + return IndexedJunctionTree(asDerived(), ordering, fixedKeys); + } + + /* ************************************************************************* */ + template + std::shared_ptr::BayesTreeType> + EliminateableFactorGraph::eliminateMultifrontal( + const IndexedJunctionTree& indexedJunctionTree, + const Eliminate& function) const { + gttic(eliminateMultifrontal); + + using BayesTreeNode = typename BayesTreeType::Node; + using SharedFactor = typename FactorGraphType::sharedFactor; + + // Helper struct for cluster elimination traversal. + struct ClusterEliminationData { + ClusterEliminationData* const parentData; + size_t myIndexInParent; + FastVector childFactors; + std::shared_ptr bayesTreeNode; + + explicit ClusterEliminationData(ClusterEliminationData* p) + : parentData(p), bayesTreeNode(std::make_shared()) { + if (parentData) { + myIndexInParent = parentData->childFactors.size(); + parentData->childFactors.push_back(SharedFactor()); + if (parentData->parentData) + bayesTreeNode->parent_ = parentData->bayesTreeNode; + parentData->bayesTreeNode->children.push_back(bayesTreeNode); + } else { + myIndexInParent = 0; + } + } + }; + + // Post-order visitor functor for elimination. + class EliminationPostOrderVisitor { + const FactorGraphType& graph_; + const Eliminate& function_; + + public: + EliminationPostOrderVisitor(const FactorGraphType& graph, + const Eliminate& function) + : graph_(graph), function_(function) {} + + void operator()(const SymbolicJunctionTree::sharedNode& cluster, + ClusterEliminationData& myData) { + FactorGraphType gatheredFactors; + gatheredFactors.reserve(cluster->factors.size() + myData.childFactors.size()); + + // Gather factors from IndexedSymbolicFactor. + for (const auto& factor : cluster->factors) { + auto indexed = + std::static_pointer_cast(factor); + gatheredFactors.push_back(graph_.at(indexed->index_)); + } + + for (const auto& childFactor : myData.childFactors) + if (childFactor) gatheredFactors.push_back(childFactor); + + auto result = function_(gatheredFactors, cluster->orderedFrontalKeys); + myData.bayesTreeNode->setEliminationResult(result); + + if (myData.parentData && !result.second->empty()) + myData.parentData->childFactors[myData.myIndexInParent] = result.second; + } + }; + + // Run depth-first traversal. + ClusterEliminationData rootsContainer(nullptr); + EliminationPostOrderVisitor visitorPost(asDerived(), function); + + auto visitorPre = [](const SymbolicJunctionTree::sharedNode& node, + ClusterEliminationData& parentData) { + ClusterEliminationData myData(&parentData); + myData.bayesTreeNode->problemSize_ = node->problemSize_; + return myData; + }; + + treeTraversal::DepthFirstForest(indexedJunctionTree, rootsContainer, visitorPre, + visitorPost); + + // If any factors are remaining, the ordering was incomplete. + KeySet remainingKeys; + for (const auto& factor : rootsContainer.childFactors) { + if (!factor || factor->empty()) continue; + remainingKeys.insert(factor->begin(), factor->end()); + } + if (!remainingKeys.empty()) { + throw InconsistentEliminationRequested(remainingKeys); + } + + // Create BayesTree from roots stored in the dummy BayesTree node. + auto bayesTree = std::make_shared(); + for (const auto& rootClique : rootsContainer.bayesTreeNode->children) + bayesTree->insertRoot(rootClique); + + return bayesTree; + } + /* ************************************************************************* */ template std::pair::BayesNetType>, std::shared_ptr > diff --git a/gtsam/inference/EliminateableFactorGraph.h b/gtsam/inference/EliminateableFactorGraph.h index 52e1479a9c..4d3a19e882 100644 --- a/gtsam/inference/EliminateableFactorGraph.h +++ b/gtsam/inference/EliminateableFactorGraph.h @@ -19,14 +19,17 @@ #pragma once #include -#include #include #include +#include #include #include namespace gtsam { + // Forward declaration + class IndexedJunctionTree; + /// Traits class for eliminateable factor graphs, specifies the types that result from /// elimination, etc. This must be defined for each factor graph that inherits from /// EliminateableFactorGraph. @@ -94,6 +97,20 @@ namespace gtsam { /// Typedef for an optional ordering type typedef std::optional OptionalOrderingType; + /** + * Build an `IndexedJunctionTree` for this factor graph and a fixed ordering. + * + * This structure can be cached and reused for repeated eliminations when the + * factor graph structure and ordering are unchanged. + * + * @param ordering The elimination ordering + * @param fixedKeys Optional set of keys to filter out (e.g., from hard constraints) + * @return An IndexedJunctionTree that can be reused for elimination + */ + IndexedJunctionTree buildIndexedJunctionTree( + const Ordering& ordering, + const std::unordered_set& fixedKeys = {}) const; + /** Do sequential elimination of all variables to produce a Bayes net. If an ordering is not * provided, the ordering provided by COLAMD will be used. * @@ -173,6 +190,23 @@ namespace gtsam { const Eliminate& function = EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex = {}) const; + /** + * Do multifrontal elimination using a pre-built `IndexedJunctionTree`. + * + * This eliminates the factor graph following the cluster structure encoded in + * the indexed junction tree and calls the provided dense elimination function + * on each cluster. The indexed junction tree must have been built from a + * factor graph with the same factor ordering/indices and the same variable + * ordering. + * + * @param indexedJunctionTree Pre-built indexed junction tree + * @param function The elimination function to use for each cluster + * @return A Bayes tree containing the elimination results + */ + std::shared_ptr eliminateMultifrontal( + const IndexedJunctionTree& indexedJunctionTree, + const Eliminate& function = EliminationTraitsType::DefaultEliminate) const; + /** Do sequential elimination of some variables, in \c ordering provided, to produce a Bayes net * and a remaining factor graph. This computes the factorization \f$ p(X) = p(A|B) p(B) \f$, * where \f$ A = \f$ \c variables, \f$ X \f$ is all the variables in the factor graph, and \f$ diff --git a/gtsam/linear/MultifrontalClique.h b/gtsam/linear/MultifrontalClique.h index 012c2dc67e..42afe3484a 100644 --- a/gtsam/linear/MultifrontalClique.h +++ b/gtsam/linear/MultifrontalClique.h @@ -46,17 +46,6 @@ using KeyDimMap = std::map; namespace internal { -/// Helper class to track original factor indices and row counts. -class IndexedSymbolicFactor : public SymbolicFactor { - public: - size_t index_; - size_t rows_; - IndexedSymbolicFactor(const KeyVector& keys, size_t index) - : SymbolicFactor(), index_(index) { - keys_ = keys; - } -}; - /// Sum variable dimensions for a key range, skipping unknown keys. template inline size_t sumDims(const KeyDimMap& dims, const KeyRange& keys) { diff --git a/gtsam/linear/MultifrontalSolver.cpp b/gtsam/linear/MultifrontalSolver.cpp index 6a3c48ac61..c327ea80cc 100644 --- a/gtsam/linear/MultifrontalSolver.cpp +++ b/gtsam/linear/MultifrontalSolver.cpp @@ -26,8 +26,7 @@ #include #include #include -#include -#include +#include #include #include @@ -131,28 +130,6 @@ PrecomputeScratch precomputeFromGraph(const GaussianFactorGraph& graph) { return out; } -// Build SymbolicFactorGraph from GaussianFactorGraph -SymbolicFactorGraph buildSymbolicGraph( - const GaussianFactorGraph& graph, - const std::unordered_set& fixedKeys) { - SymbolicFactorGraph symbolicGraph; - symbolicGraph.reserve(graph.size()); - for (size_t i = 0; i < graph.size(); ++i) { - if (!graph[i]) continue; - KeyVector keys; - keys.reserve(graph[i]->size()); - for (Key key : graph[i]->keys()) { - if (!fixedKeys.count(key)) { - keys.push_back(key); - } - } - // Skip factors that are fully constrained away. - if (keys.empty()) continue; - symbolicGraph.emplace_shared(keys, i); - } - return symbolicGraph; -} - // Sum the dimensions of frontal variables in a symbolic cluster. size_t frontalDimForSymbolicCluster( const SymbolicJunctionTree::sharedNode& cluster, @@ -534,31 +511,31 @@ MultifrontalSolver::MultifrontalSolver(PrecomputedData data, } // Report the symbolic structure before any merge. - reportStructure(data.junctionTree, dims_, "Symbolic cluster structure", + reportStructure(data.indexedJunctionTree, dims_, "Symbolic cluster structure", params_.reportStream); // If applicable, merge leaf children by a separate cap first. if (params_.leafMergeDimCap > 0) { - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { mergeLeafChildren(rootCluster, dims_, params_.leafMergeDimCap); } - reportStructure(data.junctionTree, dims_, + reportStructure(data.indexedJunctionTree, dims_, "Clique structure after leaf merge", params_.reportStream); } // If applicable, merge small child cliques bottom-up. if (params_.mergeDimCap > 0) { - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { mergeSmallClusters(rootCluster, dims_, params_.mergeDimCap); } - reportStructure(data.junctionTree, dims_, "Clique structure after merge", + reportStructure(data.indexedJunctionTree, dims_, "Clique structure after merge", params_.reportStream); } // Build the actual MultifrontalClique structure. CliqueBuilder builder{dims_, &solution_, &cliques_, &fixedKeys_, ¶ms_, &data.rowCounts}; - for (const auto& rootCluster : data.junctionTree.roots()) { + for (const auto& rootCluster : data.indexedJunctionTree.roots()) { if (rootCluster) { roots_.push_back( builder.build(rootCluster, std::weak_ptr()) @@ -582,14 +559,12 @@ MultifrontalSolver::PrecomputedData MultifrontalSolver::Precompute( } } - SymbolicFactorGraph symbolicGraph = - buildSymbolicGraph(graph, scratch.fixedKeys); - SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); - SymbolicJunctionTree junctionTree(eliminationTree); + IndexedJunctionTree indexedJunctionTree = + graph.buildIndexedJunctionTree(reducedOrdering, scratch.fixedKeys); return MultifrontalSolver::PrecomputedData{ std::move(scratch.dims), std::move(scratch.fixedKeys), - std::move(junctionTree), std::move(scratch.rowCounts)}; + std::move(indexedJunctionTree), std::move(scratch.rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/linear/MultifrontalSolver.h b/gtsam/linear/MultifrontalSolver.h index 77591d459a..a00bb7c17d 100644 --- a/gtsam/linear/MultifrontalSolver.h +++ b/gtsam/linear/MultifrontalSolver.h @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include #include @@ -82,11 +82,12 @@ class GTSAM_EXPORT MultifrontalSolver /// Tuning parameters for traversal and reporting. using Parameters = MultifrontalParameters; + /// Precomputed symbolic and sizing data for multifrontal solver construction. struct PrecomputedData { - std::map dims; ///< Map from variable key to dimension. - std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. - SymbolicJunctionTree junctionTree; ///< Precomputed symbolic junction tree. - std::vector rowCounts; ///< Row counts indexed by factor index. + std::map dims; ///< Map from variable key to dimension. + std::unordered_set fixedKeys; ///< Keys fixed by constrained factors. + IndexedJunctionTree indexedJunctionTree; ///< Precomputed indexed junction tree. + std::vector rowCounts; ///< Row counts indexed by factor index. }; /// Shared pointer to a MultifrontalClique. @@ -110,7 +111,7 @@ class GTSAM_EXPORT MultifrontalSolver public: /** * Construct the solver from a factor graph and an ordering. - * This builds the symbolic junction tree and pre-allocates all matrices. + * This builds the indexed junction tree and pre-allocates all matrices. * Call load() before eliminating to populate numerical values. * @param graph The factor graph to solve. * Must contain only JacobianFactor instances. @@ -130,8 +131,16 @@ class GTSAM_EXPORT MultifrontalSolver MultifrontalSolver(PrecomputedData data, const Ordering& ordering, const Parameters& params = Parameters{}); - /// Precompute symbolic structure and sizing data from a factor graph. - /// Only JacobianFactor inputs are supported. + /** + * Precompute symbolic structure and sizing data from a factor graph. + * This builds an IndexedJunctionTree that can be reused across multiple + * solver instances when the graph structure and ordering remain unchanged. + * Only JacobianFactor inputs are supported. + * + * @param graph The factor graph (must contain only JacobianFactor instances) + * @param ordering The variable elimination ordering + * @return PrecomputedData containing the indexed junction tree and sizing info + */ static PrecomputedData Precompute(const GaussianFactorGraph& graph, const Ordering& ordering); diff --git a/gtsam/linear/tests/testGaussianFactorGraph.cpp b/gtsam/linear/tests/testGaussianFactorGraph.cpp index b6b804b6c2..da43d97944 100644 --- a/gtsam/linear/tests/testGaussianFactorGraph.cpp +++ b/gtsam/linear/tests/testGaussianFactorGraph.cpp @@ -21,9 +21,12 @@ #include #include #include +#include #include +#include #include #include +#include #include #include @@ -457,6 +460,20 @@ TEST(GaussianFactorGraph, DenseSolve) { EXPECT(assert_equal(expected, actual)); } +/* ************************************************************************* */ +TEST(GaussianFactorGraph, optimizeWithIndexedJunctionTree) { + GaussianFactorGraph fg = createSimpleGaussianFactorGraph(); + Ordering ordering{0, 1, 2}; // x2=0, l1=1, x1=2 + + IndexedJunctionTree indexedJunctionTree = fg.buildIndexedJunctionTree(ordering); + + VectorValues expected = fg.optimize(ordering, EliminateQR); + VectorValues actual = + fg.eliminateMultifrontal(indexedJunctionTree, EliminateQR)->optimize(); + + EXPECT(assert_equal(expected, actual, 1e-9)); +} + /* ************************************************************************* */ TEST(GaussianFactorGraph, ProbPrime) { GaussianFactorGraph gfg; diff --git a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp index 930fb52b25..9063bafa84 100644 --- a/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp +++ b/gtsam/nonlinear/NonlinearMultifrontalSolver.cpp @@ -21,8 +21,7 @@ #include #include #include -#include -#include +#include #include namespace gtsam { @@ -50,26 +49,6 @@ std::unordered_set collectFixedKeys(const NonlinearFactorGraph& graph) { return fixedKeys; } -SymbolicFactorGraph buildSymbolicGraph( - const NonlinearFactorGraph& graph, - const std::unordered_set& fixedKeys) { - SymbolicFactorGraph symbolicGraph; - symbolicGraph.reserve(graph.size()); - for (size_t i = 0; i < graph.size(); ++i) { - if (!graph[i]) continue; - KeyVector keys; - keys.reserve(graph[i]->size()); - for (Key key : graph[i]->keys()) { - if (!fixedKeys.count(key)) { - keys.push_back(key); - } - } - if (keys.empty()) continue; - symbolicGraph.emplace_shared(keys, i); - } - return symbolicGraph; -} - } // namespace /* ************************************************************************* */ @@ -97,9 +76,8 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( } } - SymbolicFactorGraph symbolicGraph = buildSymbolicGraph(graph, fixedKeys); - SymbolicEliminationTree eliminationTree(symbolicGraph, reducedOrdering); - SymbolicJunctionTree junctionTree(eliminationTree); + IndexedJunctionTree indexedJunctionTree(graph, reducedOrdering, fixedKeys); + std::vector rowCounts; rowCounts.reserve(graph.size()); for (const auto& factor : graph) { @@ -108,7 +86,7 @@ MultifrontalSolver::PrecomputedData NonlinearMultifrontalSolver::Precompute( } return MultifrontalSolver::PrecomputedData{ - std::move(dims), std::move(fixedKeys), std::move(junctionTree), std::move(rowCounts)}; + std::move(dims), std::move(fixedKeys), std::move(indexedJunctionTree), std::move(rowCounts)}; } /* ************************************************************************* */ diff --git a/gtsam/nonlinear/NonlinearOptimizer.cpp b/gtsam/nonlinear/NonlinearOptimizer.cpp index 816789d223..b3f7160c4d 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.cpp +++ b/gtsam/nonlinear/NonlinearOptimizer.cpp @@ -25,10 +25,11 @@ #include #include #include +#include #include +#include -#include #include #include #include @@ -139,8 +140,15 @@ VectorValues NonlinearOptimizer::solve(const GaussianFactorGraph& gfg, // Check which solver we are using if (params.isMultifrontal()) { // Multifrontal QR or Cholesky (decided by params.getEliminationFunction()) - if (params.ordering) - delta = gfg.optimize(*params.ordering, params.getEliminationFunction()); + if (params.ordering) { + if (!indexedJunctionTreeCache_.has_value()) { + indexedJunctionTreeCache_ = gfg.buildIndexedJunctionTree(*params.ordering); + } + + delta = gfg.eliminateMultifrontal(*indexedJunctionTreeCache_, + params.getEliminationFunction()) + ->optimize(); + } else delta = gfg.optimize(params.getEliminationFunction()); } else if (params.isSequential()) { diff --git a/gtsam/nonlinear/NonlinearOptimizer.h b/gtsam/nonlinear/NonlinearOptimizer.h index 2d6f8afe02..0dc2bd4f2d 100644 --- a/gtsam/nonlinear/NonlinearOptimizer.h +++ b/gtsam/nonlinear/NonlinearOptimizer.h @@ -20,6 +20,10 @@ #include #include +#include + +#include +#include namespace gtsam { @@ -86,6 +90,11 @@ class GTSAM_EXPORT NonlinearOptimizer { mutable std::unique_ptr nonlinearMultifrontalSolver_; + private: + /// Cached indexed junction tree used to avoid rebuilding the symbolic structure + /// across iterations when the ordering remains constant. + mutable std::optional indexedJunctionTreeCache_; + public: /** A shared pointer to this class */ using shared_ptr = std::shared_ptr; diff --git a/gtsam/symbolic/IndexedJunctionTree.h b/gtsam/symbolic/IndexedJunctionTree.h new file mode 100644 index 0000000000..201b26f7f3 --- /dev/null +++ b/gtsam/symbolic/IndexedJunctionTree.h @@ -0,0 +1,106 @@ +/* ---------------------------------------------------------------------------- + * + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + * + * See LICENSE for the license information + * + * -------------------------------------------------------------------------- */ + +/** + * @file IndexedJunctionTree.h + * @brief Build a symbolic junction tree that stores original factor indices. + * @author Tzvi Strauss + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace gtsam { + +namespace internal { +/// A SymbolicFactor that stores the index of the originating factor. +class IndexedSymbolicFactor : public SymbolicFactor { + public: + size_t index_; + IndexedSymbolicFactor(const KeyVector& keys, size_t index) + : SymbolicFactor(), index_(index) { + keys_ = keys; + } +}; +} // namespace internal + +/** + * A symbolic junction tree whose factors record the original factor indices from + * a corresponding (non-symbolic) factor graph. This allows the junction tree + * structure to be cached and reused for repeated eliminations when the factor + * graph structure and ordering remain unchanged, avoiding the cost of rebuilding + * the symbolic structure. + * + * The underlying implementation uses a `SymbolicJunctionTree` where each factor + * is an `IndexedSymbolicFactor` that stores the original factor index. + * During elimination, these indices are used to retrieve the actual numerical + * factors from the original factor graph. + * + */ +class GTSAM_EXPORT IndexedJunctionTree : public SymbolicJunctionTree { + public: + using SymbolicJunctionTree::SymbolicJunctionTree; + + /** + * Construct an IndexedJunctionTree from any factor graph, ordering, and + * optional set of fixed keys to filter out. + * + * @tparam GRAPH Factor graph type (e.g. GaussianFactorGraph, NonlinearFactorGraph) + * @param graph The input factor graph + * @param ordering The elimination ordering + * @param fixedKeys Keys to filter out (e.g., from hard constraints) + */ + template + IndexedJunctionTree(const GRAPH& graph, const Ordering& ordering, + const std::unordered_set& fixedKeys = {}) + : SymbolicJunctionTree(makeIndexedEliminationTree(graph, ordering, fixedKeys)) {} + + private: + template + static SymbolicFactorGraph buildIndexedSymbolicFactorGraph( + const GRAPH& graph, const std::unordered_set& fixedKeys) { + SymbolicFactorGraph symbolicGraph; + symbolicGraph.reserve(graph.size()); + for (size_t i = 0; i < graph.size(); ++i) { + if (!graph.at(i)) continue; + KeyVector keys; + keys.reserve(graph[i]->size()); + for (Key key : graph[i]->keys()) { + if (!fixedKeys.count(key)) keys.push_back(key); + } + // Skip factors that are fully constrained away. + if (keys.empty()) continue; + symbolicGraph.emplace_shared(keys, i); + } + return symbolicGraph; + } + + template + static SymbolicEliminationTree makeIndexedEliminationTree( + const GRAPH& graph, const Ordering& ordering, + const std::unordered_set& fixedKeys) { + SymbolicFactorGraph symbolicGraph = + buildIndexedSymbolicFactorGraph(graph, fixedKeys); + return SymbolicEliminationTree(std::move(symbolicGraph), ordering); + } +}; +} // namespace gtsam + diff --git a/gtsam/symbolic/tests/testSymbolicBayesTree.cpp b/gtsam/symbolic/tests/testSymbolicBayesTree.cpp index 04fe0434be..b5df33ae42 100644 --- a/gtsam/symbolic/tests/testSymbolicBayesTree.cpp +++ b/gtsam/symbolic/tests/testSymbolicBayesTree.cpp @@ -20,11 +20,11 @@ #include #include #include +#include #include #include #include -#include #include using namespace std; @@ -96,6 +96,17 @@ TEST(SymbolicBayesTree, clique_structure) { SymbolicBayesTree actual = *graph.eliminateMultifrontal(order); EXPECT(assert_equal(expected, actual)); + + // Reuse an indexed junction tree built from the same graph and ordering. + IndexedJunctionTree indexedJunctionTree = graph.buildIndexedJunctionTree(order); + + SymbolicBayesTree actualReuse1 = + *graph.eliminateMultifrontal(indexedJunctionTree); + SymbolicBayesTree actualReuse2 = + *graph.eliminateMultifrontal(indexedJunctionTree); + + EXPECT(assert_equal(expected, actualReuse1)); + EXPECT(assert_equal(expected, actualReuse2)); } /* ************************************************************************* * From 4c28bb7a51170081c6aa2b2df8c796d04bd7cc33 Mon Sep 17 00:00:00 2001 From: Tzvi Strauss Date: Wed, 18 Feb 2026 13:48:21 +0200 Subject: [PATCH 238/266] Add parallel elimination in EliminateableFactorGraph. Now aligned with ClusterTree imp. --- .../inference/EliminateableFactorGraph-inst.h | 121 ++++++++++++------ 1 file changed, 80 insertions(+), 41 deletions(-) diff --git a/gtsam/inference/EliminateableFactorGraph-inst.h b/gtsam/inference/EliminateableFactorGraph-inst.h index 2ce7d03887..6605392925 100644 --- a/gtsam/inference/EliminateableFactorGraph-inst.h +++ b/gtsam/inference/EliminateableFactorGraph-inst.h @@ -23,6 +23,9 @@ #include #include +#ifdef GTSAM_USE_TBB +#include +#endif #include namespace gtsam { @@ -169,73 +172,114 @@ namespace gtsam { using BayesTreeNode = typename BayesTreeType::Node; using SharedFactor = typename FactorGraphType::sharedFactor; - // Helper struct for cluster elimination traversal. + // Elimination traversal data - stores a pointer to the parent data and collects + // the factors resulting from elimination of the children. Also sets up BayesTree + // cliques with parent and child pointers. struct ClusterEliminationData { ClusterEliminationData* const parentData; size_t myIndexInParent; FastVector childFactors; std::shared_ptr bayesTreeNode; - - explicit ClusterEliminationData(ClusterEliminationData* p) - : parentData(p), bayesTreeNode(std::make_shared()) { +#ifdef GTSAM_USE_TBB + std::shared_ptr writeLock; +#endif + + ClusterEliminationData(ClusterEliminationData* _parentData, size_t nChildren) + : parentData(_parentData), bayesTreeNode(std::make_shared()) +#ifdef GTSAM_USE_TBB + , writeLock(std::make_shared()) +#endif + { if (parentData) { +#ifdef GTSAM_USE_TBB + parentData->writeLock->lock(); +#endif myIndexInParent = parentData->childFactors.size(); parentData->childFactors.push_back(SharedFactor()); +#ifdef GTSAM_USE_TBB + parentData->writeLock->unlock(); +#endif + } else { + myIndexInParent = 0; + } + if (parentData) { if (parentData->parentData) bayesTreeNode->parent_ = parentData->bayesTreeNode; parentData->bayesTreeNode->children.push_back(bayesTreeNode); - } else { - myIndexInParent = 0; } } + + static ClusterEliminationData EliminationPreOrderVisitor( + const SymbolicJunctionTree::sharedNode& node, + ClusterEliminationData& parentData) { + assert(node); + ClusterEliminationData myData(&parentData, node->nrChildren()); + myData.bayesTreeNode->problemSize_ = node->problemSize(); + return myData; + } }; - // Post-order visitor functor for elimination. + // Elimination post-order visitor - gather factors, eliminate, store results. class EliminationPostOrderVisitor { const FactorGraphType& graph_; - const Eliminate& function_; + const Eliminate& eliminationFunction_; public: - EliminationPostOrderVisitor(const FactorGraphType& graph, - const Eliminate& function) - : graph_(graph), function_(function) {} + EliminationPostOrderVisitor( + const FactorGraphType& graph, + const Eliminate& eliminationFunction) + : graph_(graph), eliminationFunction_(eliminationFunction) {} - void operator()(const SymbolicJunctionTree::sharedNode& cluster, + void operator()(const SymbolicJunctionTree::sharedNode& node, ClusterEliminationData& myData) { + assert(node); + FactorGraphType gatheredFactors; - gatheredFactors.reserve(cluster->factors.size() + myData.childFactors.size()); + gatheredFactors.reserve(node->factors.size() + node->nrChildren()); - // Gather factors from IndexedSymbolicFactor. - for (const auto& factor : cluster->factors) { + for (const auto& factor : node->factors) { auto indexed = std::static_pointer_cast(factor); gatheredFactors.push_back(graph_.at(indexed->index_)); } - - for (const auto& childFactor : myData.childFactors) - if (childFactor) gatheredFactors.push_back(childFactor); - - auto result = function_(gatheredFactors, cluster->orderedFrontalKeys); - myData.bayesTreeNode->setEliminationResult(result); - - if (myData.parentData && !result.second->empty()) - myData.parentData->childFactors[myData.myIndexInParent] = result.second; + gatheredFactors.push_back(myData.childFactors); + + auto eliminationResult = + eliminationFunction_(gatheredFactors, node->orderedFrontalKeys); + + myData.bayesTreeNode->setEliminationResult(eliminationResult); + + if (!eliminationResult.second->empty()) { +#ifdef GTSAM_USE_TBB + myData.parentData->writeLock->lock(); +#endif + myData.parentData->childFactors[myData.myIndexInParent] = + eliminationResult.second; +#ifdef GTSAM_USE_TBB + myData.parentData->writeLock->unlock(); +#endif + } } }; - // Run depth-first traversal. - ClusterEliminationData rootsContainer(nullptr); - EliminationPostOrderVisitor visitorPost(asDerived(), function); + // Do elimination (depth-first traversal). The rootsContainer stores a 'dummy' + // BayesTree node that contains all of the roots as its children. rootsContainer + // also stores the remaining un-eliminated factors passed up from the roots. + std::shared_ptr result = std::make_shared(); - auto visitorPre = [](const SymbolicJunctionTree::sharedNode& node, - ClusterEliminationData& parentData) { - ClusterEliminationData myData(&parentData); - myData.bayesTreeNode->problemSize_ = node->problemSize_; - return myData; - }; + ClusterEliminationData rootsContainer(0, indexedJunctionTree.nrRoots()); + + EliminationPostOrderVisitor visitorPost(asDerived(), function); + { + TbbOpenMPMixedScope threadLimiter; + treeTraversal::DepthFirstForestParallel( + indexedJunctionTree, rootsContainer, + ClusterEliminationData::EliminationPreOrderVisitor, visitorPost, 10); + } - treeTraversal::DepthFirstForest(indexedJunctionTree, rootsContainer, visitorPre, - visitorPost); + // Create BayesTree from roots stored in the dummy BayesTree node. + for (const auto& rootClique : rootsContainer.bayesTreeNode->children) + result->insertRoot(rootClique); // If any factors are remaining, the ordering was incomplete. KeySet remainingKeys; @@ -247,12 +291,7 @@ namespace gtsam { throw InconsistentEliminationRequested(remainingKeys); } - // Create BayesTree from roots stored in the dummy BayesTree node. - auto bayesTree = std::make_shared(); - for (const auto& rootClique : rootsContainer.bayesTreeNode->children) - bayesTree->insertRoot(rootClique); - - return bayesTree; + return result; } /* ************************************************************************* */ From ed1366161da5becce77c5c3494f1db2f65985d6f Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 15:57:58 -0500 Subject: [PATCH 239/266] Export --- .github/copilot-instructions.md | 1 + gtsam/geometry/ExtendedPose3.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 493c4540bf..5998d60cf0 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -4,6 +4,7 @@ For reviewing PRs: * Use meaningful variable names, e.g. `measurement` not `msm`, avoid abbreviations. * Flag overly complex or long/functions: break up in smaller functions * On Windows it is necessary to explicitly export all functions from the library which should be externally accessible. To do this, include the macro `GTSAM_EXPORT` in your class or function definition. +* When adding or modifying public classes/methods, always review and follow `Using-GTSAM-EXPORT.md` before finalizing changes, including template specialization/export rules. * If we add a C++ function to a `.i` file to expose it to the wrapper, we must ensure that the parameter names match exactly between the declaration in the header file and the declaration in the `.i`. Similarly, if we change any parameter names in a wrapped function in a header file, or change any parameter names in a `.i` file, we must change the corresponding function in the other file to reflect those changes. * Classes are Uppercase, methods and functions lowerMixedCase. * Public fields in structs keep plain names (no trailing underscore). diff --git a/gtsam/geometry/ExtendedPose3.cpp b/gtsam/geometry/ExtendedPose3.cpp index 67e7754fe4..535aec8177 100644 --- a/gtsam/geometry/ExtendedPose3.cpp +++ b/gtsam/geometry/ExtendedPose3.cpp @@ -19,11 +19,11 @@ namespace gtsam { -template class ExtendedPose3<1>; -template class ExtendedPose3<2>; -template class ExtendedPose3<3>; -template class ExtendedPose3<4>; -template class ExtendedPose3<6>; -template class ExtendedPose3; +template class GTSAM_EXPORT ExtendedPose3<1>; +template class GTSAM_EXPORT ExtendedPose3<2>; +template class GTSAM_EXPORT ExtendedPose3<3>; +template class GTSAM_EXPORT ExtendedPose3<4>; +template class GTSAM_EXPORT ExtendedPose3<6>; +template class GTSAM_EXPORT ExtendedPose3; } // namespace gtsam From 231f797edc15f537836f782dee8b56ee96793a89 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 16:14:52 -0500 Subject: [PATCH 240/266] Review comments --- gtsam/geometry/ExtendedPose3-inl.h | 83 +++++++++++++----------------- gtsam/geometry/ExtendedPose3.h | 4 +- gtsam/navigation/NavState.cpp | 4 +- 3 files changed, 40 insertions(+), 51 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 51825b2816..113a246a41 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -33,7 +33,7 @@ size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { } template -void ExtendedPose3::ZeroJacobian(ChartJacobian H, size_t d) { +void ExtendedPose3::ZeroJacobian(ChartJacobian H, Eigen::Index d) { if (!H) return; if constexpr (dimension == Eigen::Dynamic) { H->setZero(d, d); @@ -51,7 +51,7 @@ ExtendedPose3::ExtendedPose3() template template ExtendedPose3::ExtendedPose3(size_t k) - : R_(Rot3::Identity()), t_(3, k) { + : R_(Rot3::Identity()), t_(3, static_cast(k)) { t_.setZero(); } @@ -61,23 +61,14 @@ ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) template ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { + const Eigen::Index n = T.rows(); if constexpr (K == Eigen::Dynamic) { - const auto n = T.rows(); if (T.cols() != n || n < 3) { throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); } - const auto k = n - 3; - t_.resize(3, k); - t_.setZero(); - } - - const auto n = T.rows(); - if constexpr (K != Eigen::Dynamic) { - if (n != matrix_dim || T.cols() != matrix_dim) { - throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); - } + t_.resize(3, n - 3); } else { - if (T.cols() != n || n < 3) { + if (n != matrix_dim || T.cols() != matrix_dim) { throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); } } @@ -90,7 +81,7 @@ template const Rot3& ExtendedPose3::rotation(ComponentJacobian H) const { if (H) { if constexpr (dimension == Eigen::Dynamic) { - H->setZero(3, dim()); + H->setZero(3, static_cast(dim())); } else { H->setZero(); } @@ -104,7 +95,7 @@ Point3 ExtendedPose3::x(size_t i, ComponentJacobian H) const { if (i >= k()) throw std::out_of_range("ExtendedPose3: x(i) out of range."); if (H) { if constexpr (dimension == Eigen::Dynamic) { - H->setZero(3, dim()); + H->setZero(3, static_cast(dim())); } else { H->setZero(); } @@ -175,7 +166,7 @@ typename ExtendedPose3::This ExtendedPose3::operator*( template typename ExtendedPose3::This ExtendedPose3::Expmap( const TangentVector& xi, ChartJacobian Hxi) { - const size_t k = RuntimeK(xi); + const Eigen::Index k = static_cast(RuntimeK(xi)); // Get angular velocity omega const Vector3 w = xi.template head<3>(); @@ -202,30 +193,28 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( // is near zero, and also gives us the machinery for the Jacobians. Matrix3K x; - if constexpr (K == Eigen::Dynamic) x.resize(3, static_cast(k)); + if constexpr (K == Eigen::Dynamic) x.resize(3, k); if (Hxi) { - const size_t d = 3 + 3 * k; - ZeroJacobian(Hxi, d); + ZeroJacobian(Hxi, 3 + 3 * k); const Matrix3 Jr = local.Jacobian().right(); Hxi->block(0, 0, 3, 3) = Jr; // Jr here *is* the Jacobian of expmap const Matrix3 Rt = R.transpose(); - for (size_t i = 0; i < k; ++i) { + for (Eigen::Index i = 0; i < k; ++i) { Matrix3 H_xi_w; - const Eigen::Index idx = 3 + 3 * static_cast(i); + const Eigen::Index idx = 3 + 3 * i; const Vector3 rho = xi.template segment<3>(idx); - x.col(static_cast(i)) = - local.Jacobian().applyLeft(rho, &H_xi_w); + x.col(i) = local.Jacobian().applyLeft(rho, &H_xi_w); Hxi->block(idx, 0, 3, 3) = Rt * H_xi_w; Hxi->block(idx, idx, 3, 3) = Jr; // In the last row, Jr = R^T * Jl, see Barfoot eq. (8.83). // Jl is the left Jacobian of SO(3) at w. } } else { - for (size_t i = 0; i < k; ++i) { - const Eigen::Index idx = 3 + 3 * static_cast(i); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; const Vector3 rho = xi.template segment<3>(idx); - x.col(static_cast(i)) = local.Jacobian().applyLeft(rho); + x.col(i) = local.Jacobian().applyLeft(rho); } } @@ -243,10 +232,11 @@ ExtendedPose3::Logmap(const This& pose, ChartJacobian H) { if constexpr (K == Eigen::Dynamic) xi.resize(static_cast(poseBase.dim())); xi.template head<3>() = w; - for (size_t i = 0; i < poseBase.k(); ++i) { - const Eigen::Index idx = 3 + 3 * static_cast(i); - xi.template segment<3>(idx) = local.InvJacobian().applyLeft( - poseBase.t_.col(static_cast(i))); + const Eigen::Index k = static_cast(poseBase.k()); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; + xi.template segment<3>(idx) = + local.InvJacobian().applyLeft(poseBase.t_.col(i)); } if (H) *H = LogmapDerivative(xi); @@ -265,10 +255,10 @@ ExtendedPose3::AdjointMap() const { } adj.block(0, 0, 3, 3) = R; - for (size_t i = 0; i < k(); ++i) { - const Eigen::Index idx = 3 + 3 * static_cast(i); - adj.block(idx, 0, 3, 3) = - skewSymmetric(t_.col(static_cast(i))) * R; + const Eigen::Index k = static_cast(this->k()); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; + adj.block(idx, 0, 3, 3) = skewSymmetric(t_.col(i)) * R; adj.block(idx, idx, 3, 3) = R; } return adj; @@ -288,7 +278,7 @@ ExtendedPose3::Adjoint(const TangentVector& xi_b, template typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap(const TangentVector& xi) { - const size_t k = RuntimeK(xi); + const Eigen::Index k = static_cast(RuntimeK(xi)); const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); Jacobian adj; @@ -299,8 +289,8 @@ ExtendedPose3::adjointMap(const TangentVector& xi) { } adj.block(0, 0, 3, 3) = w_hat; - for (size_t i = 0; i < k; ++i) { - const Eigen::Index idx = 3 + 3 * static_cast(i); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; adj.block(idx, 0, 3, 3) = skewSymmetric(xi(idx + 0), xi(idx + 1), xi(idx + 2)); adj.block(idx, idx, 3, 3) = w_hat; @@ -346,7 +336,7 @@ ExtendedPose3::ExpmapDerivative(const TangentVector& xi) { template typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative(const TangentVector& xi) { - const size_t k = RuntimeK(xi); + const Eigen::Index k = static_cast(RuntimeK(xi)); const Vector3 w = xi.template head<3>(); // Instantiate functor for Dexp-related operations: @@ -363,9 +353,9 @@ ExtendedPose3::LogmapDerivative(const TangentVector& xi) { } J.block(0, 0, 3, 3) = Jw; - for (size_t i = 0; i < k; ++i) { + for (Eigen::Index i = 0; i < k; ++i) { Matrix3 H_xi_w; - const Eigen::Index idx = 3 + 3 * static_cast(i); + const Eigen::Index idx = 3 + 3 * i; local.Jacobian().applyLeft(xi.template segment<3>(idx), H_xi_w); const Matrix3 Q = Rt * H_xi_w; J.block(idx, 0, 3, 3) = -Jw * Q * Jw; @@ -399,20 +389,21 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::matrix() const { LieAlgebra M; if constexpr (matrix_dim == Eigen::Dynamic) { - const Eigen::Index n = 3 + static_cast(k()); + const Eigen::Index k = static_cast(this->k()); + const Eigen::Index n = 3 + k; M = LieAlgebra::Identity(n, n); } else { M = LieAlgebra::Identity(); } M.template block<3, 3>(0, 0) = R_.matrix(); - M.block(0, 3, 3, static_cast(k())) = t_; + M.block(0, 3, 3, static_cast(this->k())) = t_; return M; } template typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( const TangentVector& xi) { - const size_t k = RuntimeK(xi); + const Eigen::Index k = static_cast(RuntimeK(xi)); LieAlgebra X; if constexpr (matrix_dim == Eigen::Dynamic) { X.setZero(3 + k, 3 + k); @@ -420,8 +411,8 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( X.setZero(); } X.block(0, 0, 3, 3) = skewSymmetric(xi(0), xi(1), xi(2)); - for (size_t i = 0; i < k; ++i) { - const Eigen::Index idx = 3 + 3 * static_cast(i); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; X.block(0, 3 + i, 3, 1) = xi.template segment<3>(idx); } return X; diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index 6a110dded9..d2d0103a9f 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -46,7 +46,7 @@ class ExtendedPose3; * Template parameter K can be fixed (K >= 1) or Eigen::Dynamic. */ template -class GTSAM_EXPORT ExtendedPose3 +class ExtendedPose3 : public MatrixLieGroup, ExtendedPose3, Derived>, (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K, @@ -392,7 +392,7 @@ class GTSAM_EXPORT ExtendedPose3 } static size_t RuntimeK(const TangentVector& xi); - static void ZeroJacobian(ChartJacobian H, size_t d); + static void ZeroJacobian(ChartJacobian H, Eigen::Index d); private: #if GTSAM_ENABLE_BOOST_SERIALIZATION diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 1832a920f6..384a171b12 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -118,9 +118,7 @@ void NavState::print(const std::string& s) const { //------------------------------------------------------------------------------ bool NavState::equals(const NavState& other, double tol) const { - return R_.equals(other.R_, tol) && - traits::Equals(t_.col(0), other.t_.col(0), tol) && - equal_with_abs_tol(t_.col(1), other.t_.col(1), tol); + return Base::equals(other, tol); } //------------------------------------------------------------------------------ From e7a7b52abfc33151fa6f11336402266cb91c0823 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 16:22:57 -0500 Subject: [PATCH 241/266] Try windows fix --- gtsam/geometry/ExtendedPose3.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/gtsam/geometry/ExtendedPose3.cpp b/gtsam/geometry/ExtendedPose3.cpp index 535aec8177..128233cd18 100644 --- a/gtsam/geometry/ExtendedPose3.cpp +++ b/gtsam/geometry/ExtendedPose3.cpp @@ -24,6 +24,5 @@ template class GTSAM_EXPORT ExtendedPose3<2>; template class GTSAM_EXPORT ExtendedPose3<3>; template class GTSAM_EXPORT ExtendedPose3<4>; template class GTSAM_EXPORT ExtendedPose3<6>; -template class GTSAM_EXPORT ExtendedPose3; } // namespace gtsam From a8dc187f3039825b52e9665ca6d8294cae1f513f Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 22 Feb 2026 18:32:39 -0500 Subject: [PATCH 242/266] improving design choices --- gtsam/linear/LossFunctions.cpp | 12 +++++------- gtsam/linear/LossFunctions.h | 4 +++- gtsam/nonlinear/GncOptimizer.h | 6 +++--- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/gtsam/linear/LossFunctions.cpp b/gtsam/linear/LossFunctions.cpp index 76d62f702d..9e23592507 100644 --- a/gtsam/linear/LossFunctions.cpp +++ b/gtsam/linear/LossFunctions.cpp @@ -314,24 +314,22 @@ Welsch::shared_ptr Welsch::Create(double c, const ReweightScheme reweight) { // GemanMcClure /* ************************************************************************* */ GemanMcClure::GemanMcClure(double c, const ReweightScheme reweight) - : Base(reweight), c_(c) { + : Base(reweight), c_(c), csquared_(c * c) { } double GemanMcClure::weight(double distance) const { - return Weight(distance, c_); + return Weight(distance*distance, csquared_); } -double GemanMcClure::Weight(double distance, double c) { - const double c2 = c*c; +double GemanMcClure::Weight(double distance2, double c2) { const double c4 = c2*c2; - const double c2error = c2 + distance*distance; + const double c2error = c2 + distance2; return c4/(c2error*c2error); } double GemanMcClure::loss(double distance) const { - const double c2 = c_*c_; const double error2 = distance*distance; - return 0.5 * (c2 * error2) / (c2 + error2); + return 0.5 * (csquared_ * error2) / (csquared_ + error2); } void GemanMcClure::print(const std::string &s="") const { diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index 47fc720cbc..6e0eb051d9 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -373,12 +373,13 @@ class GTSAM_EXPORT GemanMcClure : public Base { double loss(double distance) const override; void print(const std::string &s) const override; bool equals(const Base &expected, double tol = 1e-8) const override; - static double Weight(double distance, double c); + static double Weight(double distance2, double c2); static shared_ptr Create(double k, const ReweightScheme reweight = Block); double modelParameter() const { return c_; } protected: double c_; + double csquared_; private: #if GTSAM_ENABLE_BOOST_SERIALIZATION @@ -388,6 +389,7 @@ class GTSAM_EXPORT GemanMcClure : public Base { void serialize(ARCHIVE &ar, const unsigned int /*version*/) { ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); ar &BOOST_SERIALIZATION_NVP(c_); + ar &BOOST_SERIALIZATION_NVP(csquared_); } #endif }; diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 3081e97ac3..656c7d32a5 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -490,9 +490,9 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual - const double distance = std::sqrt(2.0 * u2_k); - const double c = std::sqrt(2.0 * mu * barcSq_[k]); - weights[k] = noiseModel::mEstimator::GemanMcClure::Weight(distance, c); + const double distance2_k = 2.0 * u2_k; + const double c2_k = 2.0 * mu * barcSq_[k]; + weights[k] = noiseModel::mEstimator::GemanMcClure::Weight(distance2_k, c2_k); } } return weights; From 37c58b7d7482ad64a773847457641024c80d6cf1 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 22 Feb 2026 19:01:54 -0500 Subject: [PATCH 243/266] removed redundant scalar factor --- gtsam/nonlinear/GncOptimizer.h | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 656c7d32a5..ba19155a8c 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -490,9 +490,7 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual - const double distance2_k = 2.0 * u2_k; - const double c2_k = 2.0 * mu * barcSq_[k]; - weights[k] = noiseModel::mEstimator::GemanMcClure::Weight(distance2_k, c2_k); + weights[k] = noiseModel::mEstimator::GemanMcClure::Weight(u2_k, mu * barcSq_[k]); } } return weights; From ddadc0472504fdbee64dc86bb00c16a3e4cd6350 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 22 Feb 2026 19:27:53 -0500 Subject: [PATCH 244/266] Added doxygen block --- gtsam/linear/LossFunctions.h | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index 6e0eb051d9..d7fc955c82 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -373,9 +373,21 @@ class GTSAM_EXPORT GemanMcClure : public Base { double loss(double distance) const override; void print(const std::string &s) const override; bool equals(const Base &expected, double tol = 1e-8) const override; - static double Weight(double distance2, double c2); static shared_ptr Create(double k, const ReweightScheme reweight = Block); double modelParameter() const { return c_; } + /** @brief A static helper function to compute the Geman-McClure robust weight. + * The static function takes the squared value of the residual and the scale parameter. + * The weight member function now calls the this function, while the member function takes the residual as input, + * it passes r² and c² to the static helper. + * + *w(d², c²) = \phi(d)/d = c⁴/(c²+d²)² + * + * + * @param distance2 Squared residual magnitude. + * @param c2 Squared scale parameter. + * @return Weight w(x) in (0, 1] + */ + static double Weight(double distance2, double c2); protected: double c_; From 4250bbfe924d701d519f5f73b7f5e5f3e20ad0b3 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 22 Feb 2026 19:36:47 -0500 Subject: [PATCH 245/266] fixing convention in doxygen block --- gtsam/linear/LossFunctions.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index d7fc955c82..ee0a02b28f 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -377,10 +377,10 @@ class GTSAM_EXPORT GemanMcClure : public Base { double modelParameter() const { return c_; } /** @brief A static helper function to compute the Geman-McClure robust weight. * The static function takes the squared value of the residual and the scale parameter. - * The weight member function now calls the this function, while the member function takes the residual as input, - * it passes r² and c² to the static helper. + * The weight member function now calls this function. While the member function takes the residual as input, + * it passes x² and c² to the static helper. * - *w(d², c²) = \phi(d)/d = c⁴/(c²+d²)² + * w(x², c²) = \phi(x)/x = c⁴/(c²+x²)² * * * @param distance2 Squared residual magnitude. From 5aea9987c2cd7928aed5d09e0b50275cf5884ad8 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 19:58:08 -0500 Subject: [PATCH 246/266] Fix tests --- gtsam/navigation/tests/testNavState.cpp | 101 +++++++----------------- 1 file changed, 28 insertions(+), 73 deletions(-) diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index 51b8956f29..c232a74707 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -353,49 +353,6 @@ TEST(NavState, interpolate) { EXPECT(assert_equal(T3, interpolate(T2, T3, 1.0))); } -/* ************************************************************************* */ -TEST(NavState, Lie) { - NavState nav_state_a(Rot3::Identity(), {0.0, 1.0, 2.0}, {1.0, -1.0, 1.0}); - NavState nav_state_b(Rot3::Rx(M_PI_4), {0.0, 1.0, 3.0}, {1.0, -1.0, 2.0}); - NavState nav_state_c(Rot3::Ry(M_PI / 180.0), {1.0, 1.0, 2.0}, - {3.0, -1.0, 1.0}); - - // TODO(Varun) - // logmap - // Matrix9 H1, H2; - // auto logmap_b = NavState::Create(Rot3::Identity(), - // Vector3::Zero(), Vector3::Zero()) - // .localCoordinates(nav_state_b, H1, H2); - - // Matrix6 J1, J2; - // auto logmap_pose_b = Pose3::Create(Rot3(), Vector3::Zero()) - // .localCoordinates(nav_state_b.pose(), J1, J2); - - // // Check retraction - // auto retraction_b = NavState().retract(logmap_b); - // CHECK(assert_equal(retraction_b, nav_state_b)); - - // // Test if the sum of the logmap is the same as the logmap of the product - // auto logmap_c = NavState::Create(Rot3::Identity(), - // Vector3::Zero(), Vector3::Zero()) - // .localCoordinates(nav_state_c); - - // auto logmap_bc = NavState::Create( - // gtsam::Rot3::Identity(), Eigen::Vector3d::Zero(), - // Eigen::Vector3d::Zero(), {}, {}, {}) - // .localCoordinates(nav_state_b * nav_state_c); - // Vector9 logmap_bc2 = NavState::Logmap(nav_state_b * nav_state_c); - - // Vector9 logmap_bc_sum = logmap_b + logmap_c; - // std::cout << "logmap_bc = " << logmap_bc.transpose() << std::endl; - // std::cout << "logmap_bc2 = " << logmap_bc2.transpose() << std::endl; - - // // std::cout << "logmap_bc + logmap_c = " << logmap_bc_sum.transpose() << std::endl; - // // std::cout << "logmap_b + logmap_c = " << (NavState::Logmap(nav_state_b) + NavState::Logmap(nav_state_c)).transpose() << std::endl; - // // std::cout << "logmap_bc = " << logmap_bc.transpose() << std::endl; - // // CHECK(assert_equal(logmap_bc_sum, logmap_bc)); -} - /* ************************************************************************* */ static const double dt = 2.0; std::function coriolis = @@ -428,7 +385,7 @@ TEST(NavState, Coriolis2) { TEST(NavState, Coriolis3) { /** Consider a massless planet with an attached nav frame at - * n_omega = [0 0 1]', and a body at position n_t = [1 0 0]', travelling with + * n_omega = [0 0 1]', and a body at position n_t = [1 0 0]', traveling with * velocity n_v = [0 1 0]'. Orient the body so that it is not instantaneously * aligned with the nav frame (i.e., nRb != I_3x3). Test that first and * second order Coriolis corrections are as expected. @@ -599,11 +556,10 @@ NavState expected(expectedR, expectedV, expectedP); /* ************************************************************************* */ // Checks correct exponential map (Expmap) with brute force matrix exponential TEST(NavState, Expmap_c_full) { - //TODO(Varun) - // EXPECT(assert_equal(screwNavState::expected, - // expm(screwNavState::xi), 1e-6)); - // EXPECT(assert_equal(screwNavState::expected, - // NavState::Expmap(screwNavState::xi), 1e-6)); + EXPECT(assert_equal(screwNavState::expected, + expm(screwNavState::xi), 1e-6)); + EXPECT(assert_equal(screwNavState::expected, + NavState::Expmap(screwNavState::xi), 1e-6)); } /* ************************************************************************* */ @@ -638,30 +594,29 @@ TEST(NavState, Adjoint_compose_full) { /* ************************************************************************* */ TEST(NavState, expmaps_galore_full) { Vector xi; - //TODO(Varun) - // NavState actual; - // xi = (Vector(9) << 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9).finished(); - // actual = NavState::Expmap(xi); - // EXPECT(assert_equal(expm(xi), actual, 1e-6)); - // EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-6)); - - // xi = (Vector(9) << 0.1, -0.2, 0.3, -0.4, 0.5, -0.6, -0.7, -0.8, -0.9) - // .finished(); - // for (double theta = 1.0; 0.3 * theta <= M_PI; theta *= 2) { - // Vector txi = xi * theta; - // actual = NavState::Expmap(txi); - // EXPECT(assert_equal(expm(txi, 30), actual, 1e-6)); - // Vector log = NavState::Logmap(actual); - // EXPECT(assert_equal(actual, NavState::Expmap(log), 1e-6)); - // EXPECT(assert_equal(txi, log, 1e-6)); // not true once wraps - // } - - // // Works with large v as well, but expm needs 10 iterations! - // xi = - // (Vector(9) << 0.2, 0.3, -0.8, 100.0, 120.0, -60.0, 12, 14, 45).finished(); - // actual = NavState::Expmap(xi); - // EXPECT(assert_equal(expm(xi, 10), actual, 1e-5)); - // EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-9)); + NavState actual; + xi = (Vector(9) << 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9).finished(); + actual = NavState::Expmap(xi); + EXPECT(assert_equal(expm(xi), actual, 1e-6)); + EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-6)); + + xi = (Vector(9) << 0.1, -0.2, 0.3, -0.4, 0.5, -0.6, -0.7, -0.8, -0.9) + .finished(); + for (double theta = 1.0; 0.3 * theta <= M_PI; theta *= 2) { + Vector txi = xi * theta; + actual = NavState::Expmap(txi); + EXPECT(assert_equal(expm(txi, 30), actual, 1e-6)); + Vector log = NavState::Logmap(actual); + EXPECT(assert_equal(actual, NavState::Expmap(log), 1e-6)); + EXPECT(assert_equal(txi, log, 1e-6)); // not true once wraps + } + + // Works with large v as well, but expm needs 10 iterations! + xi = + (Vector(9) << 0.2, 0.3, -0.8, 100.0, 120.0, -60.0, 12, 14, 45).finished(); + actual = NavState::Expmap(xi); + EXPECT(assert_equal(expm(xi, 10), actual, 1e-5)); + EXPECT(assert_equal(xi, NavState::Logmap(actual), 1e-9)); } /* ************************************************************************* */ From 49905821910895fdfe424b57233227d63db042df Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 20:17:43 -0500 Subject: [PATCH 247/266] Comment retract [slip ci] --- gtsam/navigation/NavState.cpp | 27 +++------------------------ gtsam/navigation/NavState.h | 13 +++++++++++-- 2 files changed, 14 insertions(+), 26 deletions(-) diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index 026bcc94a8..e38fde63fb 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -168,6 +168,8 @@ Vector9 NavState::adjointTranspose(const Vector9& xi, const Vector9& y, //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { + // NOTE: This is an intentional custom chart for NavState manifold updates. + // It differs from the default LieGroup chart based on full Expmap/Logmap. Rot3 nRb = R_; Point3 n_t = t_.col(0), n_v = t_.col(1); Matrix3 D_bRc_xi, D_R_nRb, D_t_nRb, D_v_nRb; @@ -195,30 +197,7 @@ NavState NavState::retract(const Vector9& xi, // //------------------------------------------------------------------------------ Vector9 NavState::localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { - // return LieGroup::localCoordinates(g, H1, H2); - - //TODO(Varun) Fix so that test on L680 passes - - // Matrix3 D_dR_R, D_dt_R, D_dv_R; - // const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); - // const Point3 dP = R_.unrotate(g.t_ - t_, H1 ? &D_dt_R : 0); - // const Vector dV = R_.unrotate(g.v_ - v_, H1 ? &D_dv_R : 0); - - // Vector9 xi; - // Matrix3 D_xi_R; - // xi << Rot3::Logmap(dR, (H1 || H2) ? &D_xi_R : 0), dP, dV; - // if (H1) { - // *H1 << D_xi_R * D_dR_R, Z_3x3, Z_3x3, // - // D_dt_R, -I_3x3, Z_3x3, // - // D_dv_R, Z_3x3, -I_3x3; - // } - // if (H2) { - // *H2 << D_xi_R, Z_3x3, Z_3x3, // - // Z_3x3, dR.matrix(), Z_3x3, // - // Z_3x3, Z_3x3, dR.matrix(); - // } - // return xi; - + // Inverse of the custom component-wise chart used in retract(). Matrix3 D_dR_R, D_dt_R, D_dv_R; const Rot3 dR = R_.between(g.R_, H1 ? &D_dR_R : 0); const Point3 dP = R_.unrotate(g.t_.col(0) - t_.col(0), H1 ? &D_dt_R : 0); diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 49963444bf..7401401745 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -177,12 +177,21 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { return v.segment<3>(6); } - /// retract with optional derivatives + /** + * Manifold retract used by optimization. + * This intentionally uses a component-wise chart (R via Expmap, and p/v via + * world-frame rotation of the tangent increments), not the default LieGroup + * chart based on full Expmap/Logmap. + */ NavState retract(const Vector9& v, // OptionalJacobian<9, 9> H1 = {}, OptionalJacobian<9, 9> H2 = {}) const; - /// localCoordinates with optional derivatives + /** + * Inverse of the custom manifold chart used by retract. + * Kept consistent with retract for optimization; Lie expmap/logmap remain + * available separately for group operations. + */ Vector9 localCoordinates(const NavState& g, // OptionalJacobian<9, 9> H1 = {}, OptionalJacobian<9, 9> H2 = {}) const; From 45488d344d03800e8beba1532de5876f38c5d537 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Sun, 22 Feb 2026 22:16:02 -0500 Subject: [PATCH 248/266] Tiny perf issues [skip ci] --- gtsam/geometry/ExtendedPose3-inl.h | 11 +++++------ gtsam/geometry/Pose3.h | 5 +++++ 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 113a246a41..9f7b3d1d46 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -22,10 +22,7 @@ namespace gtsam { template size_t ExtendedPose3::RuntimeK(const TangentVector& xi) { if constexpr (K == Eigen::Dynamic) { - if (xi.size() < 3 || (xi.size() - 3) % 3 != 0) { - throw std::invalid_argument( - "ExtendedPose3: tangent vector size must be 3 + 3*k."); - } + assert(xi.size() >= 3 && (xi.size() - 3) % 3 == 0); return static_cast((xi.size() - 3) / 3); } else { return static_cast(K); @@ -153,8 +150,10 @@ template typename ExtendedPose3::This ExtendedPose3::operator*( const This& other) const { const ExtendedPose3& otherBase = AsBase(other); - if (k() != otherBase.k()) { - throw std::invalid_argument("ExtendedPose3: compose requires matching k."); + if constexpr (K == Eigen::Dynamic) { + if (k() != otherBase.k()) { + throw std::invalid_argument("ExtendedPose3: compose requires matching k."); + } } Matrix3K x = t_ + R_.matrix() * otherBase.t_; return MakeReturn(ExtendedPose3(R_ * otherBase.R_, x)); diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 02238ea4c2..fdd7160e0b 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -126,6 +126,11 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { OptionalJacobian<6, 6> Harg = {}, OptionalJacobian<6, 1> Ht = {}) const; + /// Compose syntactic sugar. + Pose3 operator*(const Pose3& T) const { + return Pose3(R_ * T.R_, t_ + R_ * T.t_); + } + /// @} /// @name Lie Group /// @{ From c37d510a287599e985f89cca6bb91926e52a103a Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 23 Feb 2026 08:44:21 -0500 Subject: [PATCH 249/266] Expmap fast path [skip ci] --- gtsam/geometry/ExtendedPose3-inl.h | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 9f7b3d1d46..786473d4e1 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -165,8 +165,6 @@ typename ExtendedPose3::This ExtendedPose3::operator*( template typename ExtendedPose3::This ExtendedPose3::Expmap( const TangentVector& xi, ChartJacobian Hxi) { - const Eigen::Index k = static_cast(RuntimeK(xi)); - // Get angular velocity omega const Vector3 w = xi.template head<3>(); @@ -181,6 +179,22 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( const Rot3 R(local.expmap()); #endif + if constexpr (K == 1) { + Matrix3 H; + const Vector3 v = xi.template tail<3>(); + const Vector3 t = local.Jacobian().applyLeft(v, Hxi ? &H : nullptr); + if (Hxi) { + const Matrix3 Jr = local.Jacobian().right(); + const Matrix3 Rt = R.transpose(); + *Hxi << Jr, Z_3x3, Rt * H, Jr; + } + Matrix3K x; + x.col(0) = t; + return MakeReturn(ExtendedPose3(R, x)); + } + + const Eigen::Index k = static_cast(RuntimeK(xi)); + // The translation t = local.Jacobian().left() * v. // Below we call local.Jacobian().applyLeft, which is faster if you don't need // Jacobians, and returns Jacobian of t with respect to w if asked. @@ -246,6 +260,7 @@ template typename ExtendedPose3::Jacobian ExtendedPose3::AdjointMap() const { const Matrix3 R = R_.matrix(); + Jacobian adj; if constexpr (dimension == Eigen::Dynamic) { adj.setZero(dim(), dim()); @@ -277,9 +292,10 @@ ExtendedPose3::Adjoint(const TangentVector& xi_b, template typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap(const TangentVector& xi) { - const Eigen::Index k = static_cast(RuntimeK(xi)); const Matrix3 w_hat = skewSymmetric(xi(0), xi(1), xi(2)); + const Eigen::Index k = static_cast(RuntimeK(xi)); + Jacobian adj; if constexpr (dimension == Eigen::Dynamic) { adj.setZero(3 + 3 * k, 3 + 3 * k); @@ -335,12 +351,12 @@ ExtendedPose3::ExpmapDerivative(const TangentVector& xi) { template typename ExtendedPose3::Jacobian ExtendedPose3::LogmapDerivative(const TangentVector& xi) { - const Eigen::Index k = static_cast(RuntimeK(xi)); const Vector3 w = xi.template head<3>(); // Instantiate functor for Dexp-related operations: const so3::DexpFunctor local(w); + const Eigen::Index k = static_cast(RuntimeK(xi)); const Matrix3 Rt = local.expmap().transpose(); const Matrix3 Jw = Rot3::LogmapDerivative(w); From fc993a2d4ef04534da449ffa132dd5591d9c42b7 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 23 Feb 2026 10:41:28 -0500 Subject: [PATCH 250/266] Custom Pose3::Expmap [skip ci] --- gtsam/geometry/ExtendedPose3-inl.h | 14 -------------- gtsam/geometry/Pose3.cpp | 31 ++++++++++++++++++++++++++++++ gtsam/geometry/Pose3.h | 3 +++ 3 files changed, 34 insertions(+), 14 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 786473d4e1..f910a7d32a 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -179,20 +179,6 @@ typename ExtendedPose3::This ExtendedPose3::Expmap( const Rot3 R(local.expmap()); #endif - if constexpr (K == 1) { - Matrix3 H; - const Vector3 v = xi.template tail<3>(); - const Vector3 t = local.Jacobian().applyLeft(v, Hxi ? &H : nullptr); - if (Hxi) { - const Matrix3 Jr = local.Jacobian().right(); - const Matrix3 Rt = R.transpose(); - *Hxi << Jr, Z_3x3, Rt * H, Jr; - } - Matrix3K x; - x.col(0) = t; - return MakeReturn(ExtendedPose3(R, x)); - } - const Eigen::Index k = static_cast(RuntimeK(xi)); // The translation t = local.Jacobian().left() * v. diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index db22342729..44288ea79f 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -132,6 +132,37 @@ Pose3 Pose3::interpolateRt(const Pose3& T, double t, interpolate(t_, T.t_, t)); } +/* ************************************************************************* */ +Pose3 Pose3::Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi) { + // Get angular velocity omega and translational velocity v from twist xi + const Vector3 w = xi.head<3>(), v = xi.tail<3>(); + + // Instantiate functor for Dexp-related operations: + const so3::DexpFunctor local(w); + + // Compute rotation using Expmap +#ifdef GTSAM_USE_QUATERNIONS + const Rot3 R = traits::Expmap(w); +#else + const Rot3 R(local.expmap()); +#endif + + // The translation t = local.Jacobian().left() * v. + Matrix3 H; + const Vector3 t = local.Jacobian().applyLeft(v, Hxi ? &H : nullptr); + + if (Hxi) { + // The Jacobian of expmap is given by the right Jacobian of SO(3): + const Matrix3 Jr = local.Jacobian().right(); + // Chain H with R^T, the Jacobian of Pose3::Create with respect to t. + const Matrix3 Rt = R.transpose(); + *Hxi << Jr, Z_3x3, // Jr here *is* the Jacobian of expmap + Rt * H, Jr; // Jr = R^T * Jl, with Jl Jacobian of t in v. + } + + return Pose3(R, t); +} + /* ************************************************************************* */ Pose3 Pose3::ChartAtOrigin::Retract(const Vector6& xi, ChartJacobian Hxi) { #ifdef GTSAM_POSE3_EXPMAP diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index fdd7160e0b..8b31ddbaf1 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -137,6 +137,9 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { using LieAlgebra = Matrix4; + /// Exponential map at identity. + static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = {}); + /// The dual version of Adjoint Vector6 AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_this = {}, From 3611155e9b3d7d707a3d6da1a058eb478250481c Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Sun, 22 Feb 2026 23:17:58 -0500 Subject: [PATCH 251/266] Added TLS loss in LossFunction --- gtsam/linear/LossFunctions.cpp | 46 +++++++++++++++++++++++ gtsam/linear/LossFunctions.h | 53 +++++++++++++++++++++++++++ gtsam/linear/tests/testNoiseModel.cpp | 39 ++++++++++++++++++++ gtsam/nonlinear/GncOptimizer.h | 8 +--- 4 files changed, 140 insertions(+), 6 deletions(-) diff --git a/gtsam/linear/LossFunctions.cpp b/gtsam/linear/LossFunctions.cpp index 9e23592507..a92cc303f8 100644 --- a/gtsam/linear/LossFunctions.cpp +++ b/gtsam/linear/LossFunctions.cpp @@ -346,6 +346,52 @@ GemanMcClure::shared_ptr GemanMcClure::Create(double c, const ReweightScheme rew return shared_ptr(new GemanMcClure(c, reweight)); } +/* ************************************************************************* */ +// TruncatedLeastSquares +/* ************************************************************************* */ + +TruncatedLeastSquares::TruncatedLeastSquares(double c, const ReweightScheme reweight) + : Base(reweight), c_(c), csquared_(c * c) { + if (c_ <= 0) { + throw runtime_error("mEstimator TruncatedLeastSquares takes only positive double in constructor."); + } +} + +double TruncatedLeastSquares::weight(double distance) const { + return Weight(distance * distance, csquared_, csquared_, 0.0); +} + +double TruncatedLeastSquares::Weight(double distance2, double lowerbound, double upperbound, double transition_weight) { + if (distance2 <= lowerbound) return 1.0; + if (distance2 >= upperbound) return 0.0; + + // Clamping + if (transition_weight < 0.0) return 0.0; + if (transition_weight > 1.0) return 1.0; + return transition_weight; +} + +double TruncatedLeastSquares::loss(double distance) const { + if (std::abs(distance) <= c_) { + return 0.5 * distance * distance; + } + return 0.5 * csquared_; +} + +void TruncatedLeastSquares::print(const std::string &s="") const { + std::cout << s << ": TLS (" << c_ << ")" << std::endl; +} + +bool TruncatedLeastSquares::equals(const Base &expected, double tol) const { + const TruncatedLeastSquares* p = dynamic_cast(&expected); + if (p == nullptr) return false; + return std::abs(c_ - p->c_) < tol; +} + +TruncatedLeastSquares::shared_ptr TruncatedLeastSquares::Create(double c, const ReweightScheme reweight) { + return shared_ptr(new TruncatedLeastSquares(c, reweight)); +} + /* ************************************************************************* */ // DCS /* ************************************************************************* */ diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index ee0a02b28f..be5e8388d9 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -406,6 +406,59 @@ class GTSAM_EXPORT GemanMcClure : public Base { #endif }; +/** Truncated Least Squares (TLS) robust error model. + * + * This model has a scalar parameter "c" (threshold). + * + * - Loss \rho(x) = 0.5 x^2 if |x|<=c, 0.5 c^2 otherwise + * - Derivative \phi(x) = x if |x|<=c, 0 otherwise + * - Weight w(x) = \phi(x)/x = 1 if |x|<=c, 0 otherwise + */ +class GTSAM_EXPORT TruncatedLeastSquares : public Base { + public: + typedef std::shared_ptr shared_ptr; + + TruncatedLeastSquares(double c = 1.0, const ReweightScheme reweight = Block); + double weight(double distance) const override; + double loss(double distance) const override; + void print(const std::string &s) const override; + bool equals(const Base &expected, double tol = 1e-8) const override; + static shared_ptr Create(double c, const ReweightScheme reweight = Block); + double modelParameter() const { return c_; } + /** @brief A static helper function to compute the TLS robust weight. + * The static function takes the squared value of the residual, the squared lower bound, the squared upper bound, + * and the transition weight (used in GNC). + * This helper is mainly useful for GNC, where the weight is not strictly binary (0 or 1) + * when the residual is within the transition region between inliers and outliers. + * The weight member function now calls the this function. + * While the member function takes the residual as input, it passes x², c², c² and 0 to the static helper. + * + * + * @param distance2 Squared residual magnitude. + * @param lowerbound Squared lower bound. + * @param upperbound Squared upper bound. + * @param transition_weight weight when residual within the transition region. + * @return Weight w(x) in [0, 1] + */ + static double Weight(double distance2, double lowerbound, double upperbound, double transition_weight); + + protected: + double c_; + double csquared_; + + private: +#if GTSAM_ENABLE_BOOST_SERIALIZATION + /** Serialization function */ + friend class boost::serialization::access; + template + void serialize(ARCHIVE &ar, const unsigned int /*version*/) { + ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); + ar &BOOST_SERIALIZATION_NVP(c_); + ar &BOOST_SERIALIZATION_NVP(csquared_); + } +#endif +}; + /** DCS implements the Dynamic Covariance Scaling robust error model * from the paper Robust Map Optimization (Agarwal13icra). * diff --git a/gtsam/linear/tests/testNoiseModel.cpp b/gtsam/linear/tests/testNoiseModel.cpp index 686caca0f9..f31f6ec323 100644 --- a/gtsam/linear/tests/testNoiseModel.cpp +++ b/gtsam/linear/tests/testNoiseModel.cpp @@ -687,6 +687,21 @@ TEST(NoiseModel, robustFunctionGemanMcClure) DOUBLES_EQUAL(0.2500, gmc->loss(error4), 1e-8); } +TEST(NoiseModel, robustFunctionTLS) +{ + const double k = 4.0, error1 = 0.5, error2 = 10.0, error3 = -10.0, error4 = -0.5; + const mEstimator::TruncatedLeastSquares::shared_ptr tls = mEstimator::TruncatedLeastSquares::Create(k); + DOUBLES_EQUAL(1.0, tls->weight(error1), 1e-8); + DOUBLES_EQUAL(0.0, tls->weight(error2), 1e-8); + DOUBLES_EQUAL(0.0, tls->weight(error3), 1e-8); + DOUBLES_EQUAL(1.0, tls->weight(error4), 1e-8); + + DOUBLES_EQUAL(0.1250, tls->loss(error1), 1e-8); + DOUBLES_EQUAL(8.0, tls->loss(error2), 1e-8); + DOUBLES_EQUAL(8.0, tls->loss(error3), 1e-8); + DOUBLES_EQUAL(0.1250, tls->loss(error4), 1e-8); +} + TEST(NoiseModel, robustFunctionWelsch) { const double k = 5.0, error1 = 1.0, error2 = 10.0, error3 = -10.0, error4 = -1.0; @@ -816,6 +831,30 @@ TEST(NoiseModel, robustNoiseGemanMcClure) DOUBLES_EQUAL(sqrt_weight_error2*a11, A(1,1), 1e-8); } +TEST(NoiseModel, robustNoiseTLS) +{ + const double k = 1.0, error1 = 1.0, error2 = 100.0; + const double a00 = 1.0, a01 = 10.0, a10 = 100.0, a11 = 1000.0; + Matrix A = (Matrix(2, 2) << a00, a01, a10, a11).finished(); + Vector b = Vector2(error1, error2); + const Robust::shared_ptr robust = Robust::Create( + mEstimator::TruncatedLeastSquares::Create(k, mEstimator::TruncatedLeastSquares::Scalar), + Unit::Create(2)); + + robust->WhitenSystem(A, b); + + const double sqrt_weight_error1 = 1.0; + const double sqrt_weight_error2 = 0.0; + + DOUBLES_EQUAL(sqrt_weight_error1*error1, b(0), 1e-8); + DOUBLES_EQUAL(sqrt_weight_error2*error2, b(1), 1e-8); + + DOUBLES_EQUAL(sqrt_weight_error1*a00, A(0,0), 1e-8); + DOUBLES_EQUAL(sqrt_weight_error1*a01, A(0,1), 1e-8); + DOUBLES_EQUAL(sqrt_weight_error2*a10, A(1,0), 1e-8); + DOUBLES_EQUAL(sqrt_weight_error2*a11, A(1,1), 1e-8); +} + TEST(NoiseModel, robustNoiseDCS) { const double k = 1.0, error1 = 1.0, error2 = 100.0; diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index ba19155a8c..6161eaeb0b 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -501,12 +501,8 @@ class GncOptimizer { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual double upperbound = (mu + 1) / mu * barcSq_[k]; double lowerbound = mu / (mu + 1) * barcSq_[k]; - weights[k] = std::sqrt(barcSq_[k] * mu * (mu + 1) / u2_k) - mu; - if (u2_k >= upperbound || weights[k] < 0) { - weights[k] = 0; - } else if (u2_k <= lowerbound || weights[k] > 1) { - weights[k] = 1; - } + const double transition_weight = std::sqrt(barcSq_[k] * mu * (mu + 1) / u2_k) - mu; + weights[k] = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound, transition_weight); } } return weights; From 64fad34fb3222137502a5113065c81c5a617be89 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Mon, 23 Feb 2026 12:26:03 -0500 Subject: [PATCH 252/266] Added SuperLinear Schedule for TLS --- gtsam/linear/LossFunctions.cpp | 12 ++-- gtsam/linear/LossFunctions.h | 16 ++--- gtsam/nonlinear/GncOptimizer.h | 66 ++++++++++++++++-- gtsam/nonlinear/GncParams.h | 25 +++++++ tests/testGncOptimizer.cpp | 121 +++++++++++++++++++++++++++++++++ 5 files changed, 218 insertions(+), 22 deletions(-) diff --git a/gtsam/linear/LossFunctions.cpp b/gtsam/linear/LossFunctions.cpp index a92cc303f8..616d8a696d 100644 --- a/gtsam/linear/LossFunctions.cpp +++ b/gtsam/linear/LossFunctions.cpp @@ -19,6 +19,7 @@ #include #include +#include #include #include @@ -358,17 +359,14 @@ TruncatedLeastSquares::TruncatedLeastSquares(double c, const ReweightScheme rewe } double TruncatedLeastSquares::weight(double distance) const { - return Weight(distance * distance, csquared_, csquared_, 0.0); + const auto w = Weight(distance * distance, csquared_, csquared_); + return w.value(); } -double TruncatedLeastSquares::Weight(double distance2, double lowerbound, double upperbound, double transition_weight) { +std::optional TruncatedLeastSquares::Weight(double distance2, double lowerbound, double upperbound) { if (distance2 <= lowerbound) return 1.0; if (distance2 >= upperbound) return 0.0; - - // Clamping - if (transition_weight < 0.0) return 0.0; - if (transition_weight > 1.0) return 1.0; - return transition_weight; + return std::nullopt; } double TruncatedLeastSquares::loss(double distance) const { diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index be5e8388d9..703209bad9 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -20,6 +20,7 @@ #pragma once +#include #include #include #include @@ -426,21 +427,18 @@ class GTSAM_EXPORT TruncatedLeastSquares : public Base { static shared_ptr Create(double c, const ReweightScheme reweight = Block); double modelParameter() const { return c_; } /** @brief A static helper function to compute the TLS robust weight. - * The static function takes the squared value of the residual, the squared lower bound, the squared upper bound, - * and the transition weight (used in GNC). - * This helper is mainly useful for GNC, where the weight is not strictly binary (0 or 1) - * when the residual is within the transition region between inliers and outliers. + * The static function takes the squared value of the residual, the squared lower bound, the squared upper bound. + * This helper returns a optional because it is also used for GNC, and we encounter transition weight cases, + * where the weight is not strictly binary (0 or 1) when the residual is within the transition region between inliers and outliers. * The weight member function now calls the this function. - * While the member function takes the residual as input, it passes x², c², c² and 0 to the static helper. - * + * While the member function takes the residual as input, it passes x², c² and c²to the static helper. * * @param distance2 Squared residual magnitude. * @param lowerbound Squared lower bound. * @param upperbound Squared upper bound. - * @param transition_weight weight when residual within the transition region. - * @return Weight w(x) in [0, 1] + * @return Weight w(x) is {0, 1} or None if the residual is between lowerbound and upperbound. */ - static double Weight(double distance2, double lowerbound, double upperbound, double transition_weight); + static std::optional Weight(double distance2, double lowerbound, double upperbound); protected: double c_; diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index 6161eaeb0b..aafee3254c 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -26,6 +26,8 @@ #pragma once +#include + #include #include #include @@ -236,6 +238,7 @@ class GncOptimizer { /// Compute optimal solution using graduated non-convexity. Values optimize() { + validateLossSchedulerCombination(); NonlinearFactorGraph graph_initial = this->makeWeightedGraph(weights_); BaseOptimizer baseOptimizer( graph_initial, state_, params_.baseOptimizerParams); @@ -328,6 +331,18 @@ class GncOptimizer { return result; } + void validateLossSchedulerCombination() const { + if (params_.lossType == GncLossType::GM && + params_.scheduler != GncScheduler::Linear) { + throw std::runtime_error( + "GncOptimizer::optimize: scheduler must be Linear for GM."); + } + if (params_.lossType == GncLossType::TLS) { + // Linear and SuperLinear are both valid for TLS. + return; + } + } + /// Initialize the gnc parameter mu such that loss is approximately convex (remark 5 in GNC paper). double initializeMu() const { @@ -381,7 +396,17 @@ class GncOptimizer { return std::max(1.0, mu / params_.muStep); case GncLossType::TLS: // increases mu at each iteration (original cost is recovered for mu -> inf) - return mu * params_.muStep; + switch (params_.scheduler) { + case GncScheduler::SuperLinear: { + if (mu < 1) return std::min(std::sqrt(mu) * params_.muStep, 1e16); + return std::min(mu * params_.muStep, 1e16); + } + case GncScheduler::Linear: { + return mu * params_.muStep; + } + default: + throw std::runtime_error("GncOptimizer::updateMu: unknown scheduler type."); + } default: throw std::runtime_error( "GncOptimizer::updateMu: called with unknown loss type."); @@ -495,14 +520,43 @@ class GncOptimizer { } return weights; } - case GncLossType::TLS: { // use eq (14) in GNC paper + case GncLossType::TLS: { for (size_t k = 0; k < nfg_.size(); k++) { if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual - double upperbound = (mu + 1) / mu * barcSq_[k]; - double lowerbound = mu / (mu + 1) * barcSq_[k]; - const double transition_weight = std::sqrt(barcSq_[k] * mu * (mu + 1) / u2_k) - mu; - weights[k] = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound, transition_weight); + double lowerbound = 0.0; + double upperbound = 0.0; + double transition_weight = 0.0; + switch (params_.scheduler) { + case GncScheduler::SuperLinear: { + lowerbound = barcSq_[k]; + upperbound = ((mu + 1.0) * (mu + 1.0) / (mu * mu)) * barcSq_[k]; + auto w = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound); + if (w) { + weights[k] = *w; + } + else { + transition_weight = std::sqrt(barcSq_[k] / u2_k) * (mu + 1.0) - mu; + weights[k] = std::clamp(transition_weight, 0.0, 1.0); + } + break; + } + case GncScheduler::Linear: { // use eq (14) in GNC paper + upperbound = (mu + 1.0) / mu * barcSq_[k]; + lowerbound = mu / (mu + 1.0) * barcSq_[k]; + auto w = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound); + if (w) { + weights[k] = *w; + } + else { + transition_weight = std::sqrt(barcSq_[k] * mu * (mu + 1.0) / u2_k) - mu; + weights[k] = std::clamp(transition_weight, 0.0, 1.0); + } + break; + } + default: + throw std::runtime_error("GncOptimizer::calculateWeights: unknown scheduler type."); + } } } return weights; diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index bd90de7fc6..3d0fc1a785 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -28,6 +28,7 @@ #include #include +#include namespace gtsam { @@ -38,6 +39,13 @@ enum GncLossType { TLS /*Truncated least squares*/ }; +/// Choice of GNC scheduling strategy. +/// SuperLinear reference https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_On_the_Convergence_of_IRLS_and_Its_Variants_in_Outlier-Robust_CVPR_2023_paper.pdf +enum class GncScheduler { + Linear, + SuperLinear +}; + template class GncParams { public: @@ -67,6 +75,7 @@ class GncParams { BaseOptimizerParameters baseOptimizerParams; ///< Optimization parameters used to solve the weighted least squares problem at each GNC iteration /// any other specific GNC parameters: GncLossType lossType = TLS; ///< Default loss + GncScheduler scheduler = GncScheduler::Linear; ///< Default scheduler size_t maxIterations = 100; ///< Maximum number of iterations double muStep = 1.4; ///< Multiplicative factor to reduce/increase the mu in gnc double relativeCostTol = 1e-5; ///< If relative cost change is below this threshold, stop iterating @@ -86,6 +95,11 @@ class GncParams { lossType = type; } + /// Set the scheduler type. + void setScheduler(const GncScheduler s) { + scheduler = s; + } + /// Set the maximum number of iterations in GNC (changing the max nr of iters might lead to less accurate solutions and is not recommended). void setMaxIterations(const size_t maxIter) { std::cout @@ -147,6 +161,7 @@ class GncParams { return baseOptimizerParams.equals(other.baseOptimizerParams) && lossType == other.lossType && maxIterations == other.maxIterations && std::fabs(muStep - other.muStep) <= tol + && scheduler == other.scheduler && verbosity == other.verbosity && knownInliers == other.knownInliers && knownOutliers == other.knownOutliers && allowNonNoiseModelFactors == other.allowNonNoiseModelFactors; @@ -165,6 +180,16 @@ class GncParams { default: throw std::runtime_error("GncParams::print: unknown loss type."); } + switch (scheduler) { + case GncScheduler::Linear: + std::cout << "scheduler: Linear" << "\n"; + break; + case GncScheduler::SuperLinear: + std::cout << "scheduler: SuperLinear" << "\n"; + break; + default: + throw std::runtime_error("GncParams::print: unknown scheduler type."); + } std::cout << "maxIterations: " << maxIterations << "\n"; std::cout << "muStep: " << muStep << "\n"; std::cout << "relativeCostTol: " << relativeCostTol << "\n"; diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp index df4cbc236b..34a95dc69c 100644 --- a/tests/testGncOptimizer.cpp +++ b/tests/testGncOptimizer.cpp @@ -215,6 +215,29 @@ TEST(GncOptimizer, updateMuTLS) { EXPECT_DOUBLES_EQUAL(gnc.updateMu(mu), mu * 1.4, tol); } +/* ************************************************************************* */ +TEST(GncOptimizer, updateMuTLSSuperLinear) { + // has to have Gaussian noise models ! + auto fg = example::createReallyNonlinearFactorGraph(); + + Point2 p0(3, 3); + Values initial; + initial.insert(X(1), p0); + + GncParams gncParams; + gncParams.setMuStep(4.0); + gncParams.setLossType(GncLossType::TLS); + gncParams.setScheduler(GncScheduler::SuperLinear); + auto gnc = GncOptimizer>(fg, initial, + gncParams); + + double mu = 0.25; + EXPECT_DOUBLES_EQUAL(gnc.updateMu(mu), 2.0, tol); + + mu = 5.0; + EXPECT_DOUBLES_EQUAL(gnc.updateMu(mu), 20.0, tol); +} + /* ************************************************************************* */ TEST(GncOptimizer, checkMuConvergence) { // has to have Gaussian noise models ! @@ -411,6 +434,31 @@ TEST(GncOptimizer, calculateWeightsTLS) { CHECK(assert_equal(weights_expected, weights_actual, tol)); } +/* ************************************************************************* */ +TEST(GncOptimizer, calculateWeightsTLSSuperLinear) { + auto fg = example::sharedNonRobustFactorGraphWithOutliers(); + + Point2 p0(0, 0); + Values initial; + initial.insert(X(1), p0); + + // we have 4 factors, 3 with zero errors (inliers), 1 with error + Vector weights_expected = Vector::Zero(4); + weights_expected[0] = 1.0; // zero error + weights_expected[1] = 1.0; // zero error + weights_expected[2] = 1.0; // zero error + weights_expected[3] = 0; // outliers + + GaussNewtonParams gnParams; + GncParams gncParams(gnParams); + gncParams.setLossType(GncLossType::TLS); + gncParams.setScheduler(GncScheduler::SuperLinear); + auto gnc = GncOptimizer>(fg, initial, gncParams); + double mu = 1.0; + Vector weights_actual = gnc.calculateWeights(initial, mu); + CHECK(assert_equal(weights_expected, weights_actual, tol)); +} + /* ************************************************************************* */ TEST(GncOptimizer, calculateWeightsTLS2) { @@ -480,6 +528,79 @@ TEST(GncOptimizer, calculateWeightsTLS2) { } } +/* ************************************************************************* */ +TEST(GncOptimizer, calculateWeightsTLSSuperLinear2) { + + // create values + Point2 x_val(0.0, 0.0); + Point2 x_prior(1.0, 0.0); + Values initial; + initial.insert(X(1), x_val); + + // create very simple factor graph with a single factor 0.5 * 1/sigma^2 * || x - [1;0] ||^2 + double sigma = 1; + SharedDiagonal noise = noiseModel::Diagonal::Sigmas(Vector2(sigma, sigma)); + NonlinearFactorGraph nfg; + nfg.add(PriorFactor(X(1), x_prior, noise)); + + // cost of the factor: + DOUBLES_EQUAL(0.5 * 1 / (sigma * sigma), nfg.error(initial), tol); + + // check the TLS weights are correct: CASE 1: residual below barcsq + { + // expected: + Vector weights_expected = Vector::Zero(1); + weights_expected[0] = 1.0; // inlier + // actual: + GaussNewtonParams gnParams; + GncParams gncParams(gnParams); + gncParams.setLossType(GncLossType::TLS); + gncParams.setScheduler(GncScheduler::SuperLinear); + auto gnc = GncOptimizer>(nfg, initial, + gncParams); + gnc.setInlierCostThresholds(0.51); // if inlier threshold is slightly larger than 0.5, then measurement is inlier + + double mu = 1e6; + Vector weights_actual = gnc.calculateWeights(initial, mu); + CHECK(assert_equal(weights_expected, weights_actual, tol)); + } + // check the TLS weights are correct: CASE 2: residual above barcsq + { + // expected: + Vector weights_expected = Vector::Zero(1); + weights_expected[0] = 0.0; // outlier + // actual: + GaussNewtonParams gnParams; + GncParams gncParams(gnParams); + gncParams.setLossType(GncLossType::TLS); + gncParams.setScheduler(GncScheduler::SuperLinear); + auto gnc = GncOptimizer>(nfg, initial, + gncParams); + gnc.setInlierCostThresholds(0.49); // if inlier threshold is slightly below 0.5, then measurement is outlier + double mu = 1e6; + Vector weights_actual = gnc.calculateWeights(initial, mu); + CHECK(assert_equal(weights_expected, weights_actual, tol)); + } + // check the TLS weights are correct: CASE 3: residual in transition region + { + // expected: + Vector weights_expected = Vector::Zero(1); + double barcSq = 0.4; + double mu = 1.0; + weights_expected[0] = std::sqrt(barcSq / 0.5) * (mu + 1.0) - mu; + // actual: + GaussNewtonParams gnParams; + GncParams gncParams(gnParams); + gncParams.setLossType(GncLossType::TLS); + gncParams.setScheduler(GncScheduler::SuperLinear); + auto gnc = GncOptimizer>(nfg, initial, + gncParams); + gnc.setInlierCostThresholds(barcSq); + Vector weights_actual = gnc.calculateWeights(initial, mu); + CHECK(assert_equal(weights_expected, weights_actual, tol)); + } +} + /* ************************************************************************* */ TEST(GncOptimizer, makeWeightedGraph) { // create original factor From de538af6b61274b6cb783028b4cc9177ea7af5bb Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Mon, 23 Feb 2026 12:27:53 -0500 Subject: [PATCH 253/266] Added SuperLinear Schedule for TLS --- gtsam/nonlinear/GncParams.h | 1 - 1 file changed, 1 deletion(-) diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index 3d0fc1a785..4b6a5b8279 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -28,7 +28,6 @@ #include #include -#include namespace gtsam { From 9f9ba857ca42a2657c5e3c7f6184e80fbfe0254e Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 23 Feb 2026 18:28:16 -0500 Subject: [PATCH 254/266] Address review comments --- gtsam/geometry/ExtendedPose3-inl.h | 43 ++++++------------------- gtsam/geometry/ExtendedPose3.h | 32 +++++++++++------- gtsam/navigation/tests/testNavState.cpp | 2 +- 3 files changed, 31 insertions(+), 46 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index f910a7d32a..7551da2556 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -40,24 +40,12 @@ void ExtendedPose3::ZeroJacobian(ChartJacobian H, Eigen::Index d) { } } -template -template -ExtendedPose3::ExtendedPose3() - : R_(Rot3::Identity()), t_(Matrix3K::Zero()) {} - -template -template -ExtendedPose3::ExtendedPose3(size_t k) - : R_(Rot3::Identity()), t_(3, static_cast(k)) { - t_.setZero(); -} - template ExtendedPose3::ExtendedPose3(const Rot3& R, const Matrix3K& x) : R_(R), t_(x) {} template -ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { +ExtendedPose3::ExtendedPose3(const MatrixRep& T) { const Eigen::Index n = T.rows(); if constexpr (K == Eigen::Dynamic) { if (T.cols() != n || n < 3) { @@ -65,7 +53,7 @@ ExtendedPose3::ExtendedPose3(const LieAlgebra& T) { } t_.resize(3, n - 3); } else { - if (n != matrix_dim || T.cols() != matrix_dim) { + if (n != matrixDim || T.cols() != matrixDim) { throw std::invalid_argument("ExtendedPose3: invalid matrix shape."); } } @@ -125,19 +113,6 @@ bool ExtendedPose3::equals(const ExtendedPose3& other, return R_.equals(other.R_, tol) && equal_with_abs_tol(t_, other.t_, tol); } -template -template -typename ExtendedPose3::This ExtendedPose3::Identity() { - return MakeReturn(ExtendedPose3()); -} - -template -template -typename ExtendedPose3::This ExtendedPose3::Identity( - size_t k) { - return MakeReturn(ExtendedPose3(k)); -} - template typename ExtendedPose3::This ExtendedPose3::inverse() const { @@ -386,15 +361,15 @@ ExtendedPose3::ChartAtOrigin::Local(const This& pose, } template -typename ExtendedPose3::LieAlgebra +typename ExtendedPose3::MatrixRep ExtendedPose3::matrix() const { - LieAlgebra M; - if constexpr (matrix_dim == Eigen::Dynamic) { + MatrixRep M; + if constexpr (matrixDim == Eigen::Dynamic) { const Eigen::Index k = static_cast(this->k()); const Eigen::Index n = 3 + k; - M = LieAlgebra::Identity(n, n); + M = MatrixRep::Identity(n, n); } else { - M = LieAlgebra::Identity(); + M = MatrixRep::Identity(); } M.template block<3, 3>(0, 0) = R_.matrix(); M.block(0, 3, 3, static_cast(this->k())) = t_; @@ -406,7 +381,7 @@ typename ExtendedPose3::LieAlgebra ExtendedPose3::Hat( const TangentVector& xi) { const Eigen::Index k = static_cast(RuntimeK(xi)); LieAlgebra X; - if constexpr (matrix_dim == Eigen::Dynamic) { + if constexpr (matrixDim == Eigen::Dynamic) { X.setZero(3 + k, 3 + k); } else { X.setZero(); @@ -430,7 +405,7 @@ ExtendedPose3::Vee(const LieAlgebra& X) { if constexpr (K == Eigen::Dynamic) { return X.cols() - 3; } else { - if (X.rows() != matrix_dim) { + if (X.rows() != matrixDim) { throw std::invalid_argument( "ExtendedPose3::Vee: invalid matrix shape."); } diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index d2d0103a9f..63df5a8556 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -56,10 +56,10 @@ class ExtendedPose3 ExtendedPose3, Derived>; inline constexpr static int dimension = (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + 3 * K; - inline constexpr static int matrix_dim = + inline constexpr static int matrixDim = (K == Eigen::Dynamic) ? Eigen::Dynamic : 3 + K; - using Base = MatrixLieGroup; + using Base = MatrixLieGroup; using TangentVector = typename Base::TangentVector; using Jacobian = typename Base::Jacobian; using ChartJacobian = typename Base::ChartJacobian; @@ -67,7 +67,10 @@ class ExtendedPose3 std::conditional_t, OptionalJacobian<3, dimension>>; - using LieAlgebra = Eigen::Matrix; + /// Homogeneous matrix representation in the group. + using MatrixRep = Eigen::Matrix; + /// Lie algebra matrix type used by Hat/Vee. + using LieAlgebra = Eigen::Matrix; using Matrix3K = Eigen::Matrix; static_assert(K == Eigen::Dynamic || K >= 1, @@ -93,7 +96,7 @@ class ExtendedPose3 * The manifold dimension is 3+3k and matrix size is (3+k)x(3+k). */ template > - ExtendedPose3(); + ExtendedPose3() : R_(Rot3::Identity()), t_(Matrix3K::Zero()) {} /** * Construct a dynamic-size identity element. @@ -103,7 +106,10 @@ class ExtendedPose3 * The manifold dimension is 3+3k and matrix size is (3+k)x(3+k). */ template > - explicit ExtendedPose3(size_t k = 0); + explicit ExtendedPose3(size_t k = 0) + : R_(Rot3::Identity()), t_(3, static_cast(k)) { + t_.setZero(); + } /** Copy constructor. */ ExtendedPose3(const ExtendedPose3&) = default; @@ -125,7 +131,7 @@ class ExtendedPose3 * @param T Homogeneous matrix in R^((3+k)x(3+k)). * Top-left 3x3 is R, top-right 3xk stores x_1..x_k. */ - explicit ExtendedPose3(const LieAlgebra& T); + explicit ExtendedPose3(const MatrixRep& T); /// @} /// @name Access @@ -206,7 +212,9 @@ class ExtendedPose3 * @return Identity with manifold dimension 3+3k and matrix size 3+k. */ template > - static This Identity(); + static This Identity() { + return MakeReturn(ExtendedPose3()); + } /** * Identity element for dynamic-size K. @@ -215,7 +223,9 @@ class ExtendedPose3 * @return Identity with manifold dimension 3+3k and matrix size 3+k. */ template > - static This Identity(size_t k = 0); + static This Identity(size_t k = 0) { + return MakeReturn(ExtendedPose3(k)); + } /** * Group inverse. @@ -348,7 +358,7 @@ class ExtendedPose3 * * @return Matrix in R^((3+k)x(3+k)). */ - LieAlgebra matrix() const; + MatrixRep matrix() const; /** * Hat operator from tangent to Lie algebra. @@ -411,12 +421,12 @@ using ExtendedPose3Dynamic = ExtendedPose3; template struct traits> : public internal::MatrixLieGroup, - ExtendedPose3::matrix_dim> {}; + ExtendedPose3::matrixDim> {}; template struct traits> : public internal::MatrixLieGroup, - ExtendedPose3::matrix_dim> {}; + ExtendedPose3::matrixDim> {}; } // namespace gtsam diff --git a/gtsam/navigation/tests/testNavState.cpp b/gtsam/navigation/tests/testNavState.cpp index c232a74707..3a6b06134e 100644 --- a/gtsam/navigation/tests/testNavState.cpp +++ b/gtsam/navigation/tests/testNavState.cpp @@ -592,7 +592,7 @@ TEST(NavState, Adjoint_compose_full) { } /* ************************************************************************* */ -TEST(NavState, expmaps_galore_full) { +TEST(NavState, ExpmapsGaloreFull) { Vector xi; NavState actual; xi = (Vector(9) << 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9).finished(); From 13cc819ac26b7a6f4dfb12c19f6539b600ed2517 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Mon, 23 Feb 2026 18:48:26 -0500 Subject: [PATCH 255/266] transposes --- gtsam/geometry/ExtendedPose3-inl.h | 57 ++++++++++++++ gtsam/geometry/ExtendedPose3.h | 26 +++++++ gtsam/geometry/Pose3.cpp | 40 ---------- gtsam/geometry/Pose3.h | 12 --- gtsam/geometry/tests/testExtendedPose3.cpp | 89 ++++++++++++++++++++++ gtsam/navigation/NavState.cpp | 44 ----------- gtsam/navigation/NavState.h | 14 +--- 7 files changed, 173 insertions(+), 109 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index 7551da2556..e4b2973879 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -250,6 +250,35 @@ ExtendedPose3::Adjoint(const TangentVector& xi_b, return Ad * xi_b; } +template +typename ExtendedPose3::TangentVector +ExtendedPose3::AdjointTranspose(const TangentVector& x, + ChartJacobian H_this, + ChartJacobian H_x) const { + const Jacobian Ad = AdjointMap(); + const TangentVector AdTx = Ad.transpose() * x; + + if (H_this) { + if constexpr (dimension == Eigen::Dynamic) { + H_this->setZero(x.size(), x.size()); + } else { + H_this->setZero(); + } + + H_this->block(0, 0, 3, 3) = skewSymmetric(AdTx.template head<3>()); + const Eigen::Index k = static_cast(this->k()); + for (Eigen::Index i = 0; i < k; ++i) { + const Eigen::Index idx = 3 + 3 * i; + const Matrix3 x_i_hat = skewSymmetric(AdTx.template segment<3>(idx)); + H_this->block(0, idx, 3, 3) = x_i_hat; + H_this->block(idx, 0, 3, 3) = x_i_hat; + } + } + + if (H_x) *H_x = Ad.transpose(); + return AdTx; +} + template typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap(const TangentVector& xi) { @@ -301,6 +330,34 @@ ExtendedPose3::adjoint(const TangentVector& xi, return ad_xi * y; } +template +typename ExtendedPose3::TangentVector +ExtendedPose3::adjointTranspose(const TangentVector& xi, + const TangentVector& y, + ChartJacobian Hxi, + ChartJacobian H_y) { + if (Hxi) { + if constexpr (dimension == Eigen::Dynamic) { + Hxi->setZero(xi.size(), xi.size()); + } else { + Hxi->setZero(); + } + for (Eigen::Index i = 0; i < xi.size(); ++i) { + TangentVector dxi; + if constexpr (dimension == Eigen::Dynamic) { + dxi = TangentVector::Zero(xi.size()); + } else { + dxi = TangentVector::Zero(); + } + dxi(i) = 1.0; + Hxi->col(i) = adjointMap(dxi).transpose() * y; + } + } + const Jacobian adT_xi = adjointMap(xi).transpose(); + if (H_y) *H_y = adT_xi; + return adT_xi * y; +} + template typename ExtendedPose3::Jacobian ExtendedPose3::ExpmapDerivative(const TangentVector& xi) { diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index 63df5a8556..c2f6ca43e0 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -282,6 +282,18 @@ class ExtendedPose3 TangentVector Adjoint(const TangentVector& xi_b, ChartJacobian H_this = {}, ChartJacobian H_xib = {}) const; + /** + * Dual adjoint action on a tangent covector. + * + * @param x Tangent vector in R^dim. + * @param H_this Optional Jacobian in R^(dimxdim). + * @param H_x Optional Jacobian in R^(dimxdim). + * @return Ad_X^T x in R^dim. + */ + TangentVector AdjointTranspose(const TangentVector& x, + ChartJacobian H_this = {}, + ChartJacobian H_x = {}) const; + /** * Lie algebra adjoint map. * @@ -302,6 +314,20 @@ class ExtendedPose3 static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, ChartJacobian Hxi = {}, ChartJacobian H_y = {}); + /** + * Dual Lie bracket action ad_xi^T(y). + * + * @param xi Tangent vector in R^dim. + * @param y Tangent vector in R^dim. + * @param Hxi Optional Jacobian in R^(dimxdim). + * @param H_y Optional Jacobian in R^(dimxdim). + * @return ad_xi^T(y) in R^dim. + */ + static TangentVector adjointTranspose(const TangentVector& xi, + const TangentVector& y, + ChartJacobian Hxi = {}, + ChartJacobian H_y = {}); + /** * Jacobian of Expmap. * diff --git a/gtsam/geometry/Pose3.cpp b/gtsam/geometry/Pose3.cpp index 44288ea79f..846be08064 100644 --- a/gtsam/geometry/Pose3.cpp +++ b/gtsam/geometry/Pose3.cpp @@ -57,46 +57,6 @@ Pose3 Pose3::FromPose2(const Pose2& p, OptionalJacobian<6, 3> H) { return Pose3(p); } -/* ************************************************************************* */ -/// The dual version of Adjoint -Vector6 Pose3::AdjointTranspose(const Vector6& x, OptionalJacobian<6, 6> H_pose, - OptionalJacobian<6, 6> H_x) const { - const Matrix6 Ad = AdjointMap(); - const Vector6 AdTx = Ad.transpose() * x; - - // Jacobians - // See docs/math.pdf for more details. - if (H_pose) { - const auto w_T_hat = skewSymmetric(AdTx.head<3>()), - v_T_hat = skewSymmetric(AdTx.tail<3>()); - *H_pose << w_T_hat, v_T_hat, // - /* */ v_T_hat, Z_3x3; - } - if (H_x) { - *H_x = Ad.transpose(); - } - - return AdTx; -} - -/* ************************************************************************* */ -Vector6 Pose3::adjointTranspose(const Vector6& xi, const Vector6& y, - OptionalJacobian<6, 6> Hxi, OptionalJacobian<6, 6> H_y) { - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 6; ++i) { - Vector6 dxi; - dxi.setZero(); - dxi(i) = 1.0; - Matrix6 GTi = adjointMap(dxi).transpose(); - Hxi->col(i) = GTi * y; - } - } - const Matrix6& adT_xi = adjointMap(xi).transpose(); - if (H_y) *H_y = adT_xi; - return adT_xi * y; -} - /* ************************************************************************* */ void Pose3::print(const std::string& s) const { std::cout << (s.empty() ? s : s + " ") << *this << std::endl; diff --git a/gtsam/geometry/Pose3.h b/gtsam/geometry/Pose3.h index 8b31ddbaf1..6d4bf0c3bc 100644 --- a/gtsam/geometry/Pose3.h +++ b/gtsam/geometry/Pose3.h @@ -140,22 +140,10 @@ class GTSAM_EXPORT Pose3: public ExtendedPose3<1, Pose3> { /// Exponential map at identity. static Pose3 Expmap(const Vector6& xi, OptionalJacobian<6, 6> Hxi = {}); - /// The dual version of Adjoint - Vector6 AdjointTranspose(const Vector6& x, - OptionalJacobian<6, 6> H_this = {}, - OptionalJacobian<6, 6> H_x = {}) const; - // temporary fix for wrappers until case issue is resolved static Matrix6 adjointMap_(const Vector6 &xi) { return adjointMap(xi);} static Vector6 adjoint_(const Vector6 &xi, const Vector6 &y) { return adjoint(xi, y);} - /** - * The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space. - */ - static Vector6 adjointTranspose(const Vector6& xi, const Vector6& y, - OptionalJacobian<6, 6> Hxi = {}, - OptionalJacobian<6, 6> H_y = {}); - // Chart at origin, depends on compile-time flag GTSAM_POSE3_EXPMAP struct GTSAM_EXPORT ChartAtOrigin { static Pose3 Retract(const Vector6& xi, ChartJacobian Hxi = {}); diff --git a/gtsam/geometry/tests/testExtendedPose3.cpp b/gtsam/geometry/tests/testExtendedPose3.cpp index 75272bf141..32bef7936e 100644 --- a/gtsam/geometry/tests/testExtendedPose3.cpp +++ b/gtsam/geometry/tests/testExtendedPose3.cpp @@ -21,6 +21,8 @@ #include #include +#include + using namespace gtsam; using ExtendedPose33 = ExtendedPose3<3>; @@ -158,6 +160,93 @@ TEST(ExtendedPose3, AdjointConsistency) { EXPECT(assert_equal(d_generic, d.AdjointMap(), 1e-9)); } +//****************************************************************************** +TEST(ExtendedPose3, AdjointTranspose) { + const ExtendedPose33 f(kR1, kX1); + const ExtendedPose3d d(kR1, ExtendedPose3d::Matrix3K(kX1)); + const Vector12 x = kXi; + const Vector x_dynamic = x; + + EXPECT(assert_equal(Vector(f.AdjointMap().transpose() * x), + Vector(f.AdjointTranspose(x)))); + EXPECT(assert_equal(d.AdjointMap().transpose() * x_dynamic, + d.AdjointTranspose(x_dynamic))); + + std::function + f_adjoint_transpose = [](const ExtendedPose33& g, const Vector12& v) { + return Vector12(g.AdjointTranspose(v)); + }; + Matrix Hf_state, Hf_x; + f.AdjointTranspose(x, Hf_state, Hf_x); + EXPECT(assert_equal(numericalDerivative21(f_adjoint_transpose, f, x), Hf_state, + 1e-8)); + EXPECT( + assert_equal(numericalDerivative22(f_adjoint_transpose, f, x), Hf_x)); + + std::function + d_adjoint_transpose = [](const ExtendedPose3d& g, const Vector& v) { + return g.AdjointTranspose(v); + }; + Matrix Hd_state, Hd_x; + d.AdjointTranspose(x_dynamic, Hd_state, Hd_x); + EXPECT(assert_equal( + numericalDerivative21( + d_adjoint_transpose, d, x_dynamic), + Hd_state, 1e-8)); + EXPECT(assert_equal( + numericalDerivative22( + d_adjoint_transpose, d, x_dynamic), + Hd_x)); +} + +//****************************************************************************** +TEST(ExtendedPose3, adjointTranspose) { + const Vector12 xi = kXi; + const Vector12 y = + (Vector12() << 0.03, -0.07, 0.02, 0.4, 0.1, -0.2, -0.3, 0.5, 0.9, 0.2, + -0.8, 0.6) + .finished(); + const Vector xi_dynamic = xi; + const Vector y_dynamic = y; + + EXPECT(assert_equal(Vector(ExtendedPose33::adjointMap(xi).transpose() * y), + Vector(ExtendedPose33::adjointTranspose(xi, y)))); + EXPECT(assert_equal(ExtendedPose3d::adjointMap(xi_dynamic).transpose() * + y_dynamic, + ExtendedPose3d::adjointTranspose(xi_dynamic, y_dynamic))); + + std::function + f_adjoint_transpose = [](const Vector12& x, const Vector12& v) { + return Vector12(ExtendedPose33::adjointTranspose(x, v)); + }; + Matrix Hf_xi, Hf_y; + EXPECT(assert_equal(Vector(ExtendedPose33::adjointTranspose(xi, y, Hf_xi, Hf_y)), + Vector(f_adjoint_transpose(xi, y)))); + EXPECT( + assert_equal(numericalDerivative21(f_adjoint_transpose, xi, y, 1e-5), Hf_xi, + 1e-5)); + EXPECT( + assert_equal(numericalDerivative22(f_adjoint_transpose, xi, y, 1e-5), Hf_y, + 1e-5)); + + std::function d_adjoint_transpose = + [](const Vector& x, const Vector& v) { + return ExtendedPose3d::adjointTranspose(x, v); + }; + Matrix Hd_xi, Hd_y; + EXPECT(assert_equal(ExtendedPose3d::adjointTranspose(xi_dynamic, y_dynamic, + Hd_xi, Hd_y), + d_adjoint_transpose(xi_dynamic, y_dynamic))); + EXPECT(assert_equal( + numericalDerivative21( + d_adjoint_transpose, xi_dynamic, y_dynamic, 1e-5), + Hd_xi, 1e-5)); + EXPECT(assert_equal( + numericalDerivative22( + d_adjoint_transpose, xi_dynamic, y_dynamic, 1e-5), + Hd_y, 1e-5)); +} + //****************************************************************************** TEST(ExtendedPose3, Derivatives) { const ExtendedPose33 f(kR1, kX1); diff --git a/gtsam/navigation/NavState.cpp b/gtsam/navigation/NavState.cpp index e38fde63fb..a93bfead45 100644 --- a/gtsam/navigation/NavState.cpp +++ b/gtsam/navigation/NavState.cpp @@ -121,50 +121,6 @@ bool NavState::equals(const NavState& other, double tol) const { return Base::equals(other, tol); } -//------------------------------------------------------------------------------ -/// The dual version of Adjoint -Vector9 NavState::AdjointTranspose(const Vector9& x, OptionalJacobian<9, 9> H_state, - OptionalJacobian<9, 9> H_x) const { - const Matrix9 Ad = AdjointMap(); - const Vector9 AdTx = Ad.transpose() * x; - - // Jacobians - if (H_state) { - //TODO(Varun) - // const auto w_T_hat = skewSymmetric(AdTx.head<3>()), - // v_T_hat = skewSymmetric(AdTx.segment<3>(3)), - // a_T_hat = skewSymmetric(AdTx.tail<3>()); - // *H_state << w_T_hat, v_T_hat, // - // /* */ v_T_hat, Z_3x3; - throw std::runtime_error( - "AdjointTranpose H_state Jacobian not implemented."); - } - if (H_x) { - *H_x = Ad.transpose(); - } - - return AdTx; -} - -//------------------------------------------------------------------------------ -Vector9 NavState::adjointTranspose(const Vector9& xi, const Vector9& y, - OptionalJacobian<9, 9> Hxi, - OptionalJacobian<9, 9> H_y) { - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 9; ++i) { - Vector9 dxi; - dxi.setZero(); - dxi(i) = 1.0; - Matrix9 GTi = adjointMap(dxi).transpose(); - Hxi->col(i) = GTi * y; - } - } - const Matrix9& adT_xi = adjointMap(xi).transpose(); - if (H_y) *H_y = adT_xi; - return adT_xi * y; -} - //------------------------------------------------------------------------------ NavState NavState::retract(const Vector9& xi, // OptionalJacobian<9, 9> H1, OptionalJacobian<9, 9> H2) const { diff --git a/gtsam/navigation/NavState.h b/gtsam/navigation/NavState.h index 7401401745..03f0eee3a1 100644 --- a/gtsam/navigation/NavState.h +++ b/gtsam/navigation/NavState.h @@ -197,21 +197,9 @@ class GTSAM_EXPORT NavState : public ExtendedPose3<2, NavState> { {}) const; /// @} - /// @name Lie Group + /// @name Lie Group (all Lie group operations are implemented in ExtendedPose3) /// @{ - /// The dual version of Adjoint - Vector9 AdjointTranspose(const Vector9& x, - OptionalJacobian<9, 9> H_this = {}, - OptionalJacobian<9, 9> H_x = {}) const; - - /** - * The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space. - */ - static Vector9 adjointTranspose(const Vector9& xi, const Vector9& y, - OptionalJacobian<9, 9> Hxi = {}, - OptionalJacobian<9, 9> H_y = {}); - /// @} /// @name Dynamics /// @{ From 3ea927fb4d181a616840ad040a0689e42e6736ee Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 24 Feb 2026 08:58:13 -0500 Subject: [PATCH 256/266] Generic adjointTranspose --- gtsam/base/MatrixLieGroup.h | 71 ++++++++++++++++++++++++ gtsam/base/doc/MatrixLieGroup.md | 1 + gtsam/geometry/Pose2.h | 17 +++++- gtsam/geometry/tests/testPose2.cpp | 34 ++++++++++++ gtsam/geometry/tests/testSimilarity2.cpp | 37 ++++++++++++ gtsam/geometry/tests/testSimilarity3.cpp | 37 +++++++++++- 6 files changed, 193 insertions(+), 4 deletions(-) diff --git a/gtsam/base/MatrixLieGroup.h b/gtsam/base/MatrixLieGroup.h index 7653d28f1c..ce1e736f38 100644 --- a/gtsam/base/MatrixLieGroup.h +++ b/gtsam/base/MatrixLieGroup.h @@ -130,7 +130,78 @@ namespace gtsam { return adj; } + /** + * Dual Adjoint action on a tangent covector. + * + * Returns Ad_g^T * x with optional Jacobians with respect to g and x. + */ + TangentVector AdjointTranspose(const TangentVector& x, + ChartJacobian H_this = {}, + ChartJacobian H_x = {}) const { + const auto& m = static_cast(*this); + const Jacobian Ad = m.AdjointMap(); + const TangentVector AdTx = Ad.transpose() * x; + + if (H_this) { + const Eigen::Index d = + (D == Eigen::Dynamic) ? static_cast(m.dim()) : D; + if constexpr (D == Eigen::Dynamic) { + H_this->setZero(d, d); + } else { + H_this->setZero(); + } + for (Eigen::Index i = 0; i < d; ++i) { + H_this->col(i) = Class::adjointTranspose(TangentVector::Unit(d, i), AdTx); + } + } + + if (H_x) *H_x = Ad.transpose(); + return AdTx; + } + + /** + * Dual Lie algebra action ad_xi^T(y), with optional Jacobians. + */ + static TangentVector adjointTranspose(const TangentVector& xi, + const TangentVector& y, + ChartJacobian Hxi = {}, + ChartJacobian H_y = {}) { + const Jacobian adT_xi = AdjointMapFromHatVee(xi).transpose(); + if (Hxi) { + const Eigen::Index d = + (D == Eigen::Dynamic) ? static_cast(xi.size()) : D; + if constexpr (D == Eigen::Dynamic) { + Hxi->setZero(d, d); + } else { + Hxi->setZero(); + } + for (Eigen::Index i = 0; i < d; ++i) { + Hxi->col(i) = AdjointMapFromHatVee(TangentVector::Unit(d, i)).transpose() * y; + } + } + if (H_y) *H_y = adT_xi; + return adT_xi * y; + } + private: + /// Generic Lie algebra adjoint map from Hat/Vee via matrix commutator. + static Jacobian AdjointMapFromHatVee(const TangentVector& xi) { + const Eigen::Index d = + (D == Eigen::Dynamic) ? static_cast(xi.size()) : D; + Jacobian ad; + if constexpr (D == Eigen::Dynamic) { + ad.setZero(d, d); + } else { + ad.setZero(); + } + const auto Xi = Class::Hat(xi); + for (Eigen::Index i = 0; i < d; ++i) { + const auto Ei = Class::Hat(TangentVector::Unit(d, i)); + ad.col(i) = Class::Vee(Xi * Ei - Ei * Xi); + } + return ad; + } + /// Pre-compute and store vectorized generators for fixed-size groups. inline static const Eigen::Matrix& VectorizedGenerators() { diff --git a/gtsam/base/doc/MatrixLieGroup.md b/gtsam/base/doc/MatrixLieGroup.md index f2228fb0db..8699281a35 100644 --- a/gtsam/base/doc/MatrixLieGroup.md +++ b/gtsam/base/doc/MatrixLieGroup.md @@ -30,6 +30,7 @@ You must implement everything from the `LieGroup.md` guide, plus the following: By inheriting from `gtsam::MatrixLieGroup`, you get: * **A default `AdjointMap()` implementation**: This generic version works by repeatedly calling your `Hat` and `Vee` methods. While it is correct, it is often slow. For performance-critical applications, you should still provide a faster, closed-form `AdjointMap()` implementation in your class, which will override the default. +* **Default dual-adjoint helpers**: `AdjointTranspose(x)` and `adjointTranspose(xi, y)` are provided generically, including Jacobians. * **A `vec()` method**: This vectorizes the `N x N` matrix representation of your group element into an `(N*N) x 1` vector. ### 4. Traits and Concept Checking diff --git a/gtsam/geometry/Pose2.h b/gtsam/geometry/Pose2.h index 2fd5d34dc5..074436bcab 100644 --- a/gtsam/geometry/Pose2.h +++ b/gtsam/geometry/Pose2.h @@ -178,8 +178,20 @@ class GTSAM_EXPORT Pose2: public MatrixLieGroup { /** * The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space. */ - static Vector3 adjointTranspose(const Vector3& xi, const Vector3& y) { - return adjointMap(xi).transpose() * y; + static Vector3 adjointTranspose(const Vector3& xi, const Vector3& y, + OptionalJacobian<3, 3> Hxi = {}, + OptionalJacobian<3, 3> H_y = {}) { + const Matrix3 adT = adjointMap(xi).transpose(); + if (Hxi) { + Hxi->setZero(); + for (int i = 0; i < 3; ++i) { + Vector3 dxi = Vector3::Zero(); + dxi(i) = 1.0; + Hxi->col(i) = adjointMap(dxi).transpose() * y; + } + } + if (H_y) *H_y = adT; + return adT * y; } // temporary fix for wrappers until case issue is resolved @@ -393,4 +405,3 @@ template struct Range : HasRange {}; } // namespace gtsam - diff --git a/gtsam/geometry/tests/testPose2.cpp b/gtsam/geometry/tests/testPose2.cpp index 00a0ef78ee..7282522527 100644 --- a/gtsam/geometry/tests/testPose2.cpp +++ b/gtsam/geometry/tests/testPose2.cpp @@ -1005,6 +1005,40 @@ TEST(Pose2, AdjointMap) { EXPECT(assert_equal(specialized_Adj, generic_Adj, 1e-9)); } +/* ************************************************************************* */ +TEST(Pose2, AdjointTranspose) { + const Pose2 pose(Rot2::fromAngle(0.5), Point2(1.0, 2.0)); + const Vector3 xi(0.2, -0.4, 0.7); + + EXPECT(assert_equal(Vector(pose.AdjointMap().transpose() * xi), + Vector(pose.AdjointTranspose(xi)))); + + Matrix33 actualH1, actualH2; + std::function proxy = + [](const Pose2& g, const Vector3& x) { + return Vector3(g.AdjointTranspose(x)); + }; + pose.AdjointTranspose(xi, actualH1, actualH2); + EXPECT(assert_equal(numericalDerivative21(proxy, pose, xi), actualH1, 1e-8)); + EXPECT(assert_equal(numericalDerivative22(proxy, pose, xi), actualH2)); +} + +/* ************************************************************************* */ +TEST(Pose2, adjointTranspose) { + const Vector3 xi(0.2, -0.4, 0.7); + const Vector3 y(-0.3, 0.5, 0.9); + + Matrix33 Hxi, Hy; + const Vector3 actual = Pose2::adjointTranspose(xi, y, Hxi, Hy); + std::function f = + [](const Vector3& x, const Vector3& v) { + return Pose2::adjointTranspose(x, v); + }; + EXPECT(assert_equal(f(xi, y), actual)); + EXPECT(assert_equal(numericalDerivative21(f, xi, y, 1e-5), Hxi, 1e-5)); + EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); +} + /* ************************************************************************* */ int main() { TestResult tr; diff --git a/gtsam/geometry/tests/testSimilarity2.cpp b/gtsam/geometry/tests/testSimilarity2.cpp index 3ad23cac2d..ef3867f081 100644 --- a/gtsam/geometry/tests/testSimilarity2.cpp +++ b/gtsam/geometry/tests/testSimilarity2.cpp @@ -240,6 +240,43 @@ TEST(Similarity2, AdjointMap) { EXPECT(assert_equal(specialized_Adj, generic_Adj, 1e-9)); } +//****************************************************************************** +TEST(Similarity2, AdjointTranspose) { + const Similarity2 sim(Rot2::fromAngle(-0.7), Point2(1.5, -2.3), 0.5); + const Vector4 xi(0.2, -0.4, 0.7, -0.1); + + EXPECT(assert_equal(Vector(sim.AdjointMap().transpose() * xi), + Vector(sim.AdjointTranspose(xi)))); + + Matrix44 actualH1, actualH2; + std::function + adjointTransposeProxy = [](const Similarity2& g, const Vector4& x) { + return Vector4(g.AdjointTranspose(x)); + }; + sim.AdjointTranspose(xi, actualH1, actualH2); + EXPECT(assert_equal(numericalDerivative21(adjointTransposeProxy, sim, xi), + actualH1, 1e-8)); + EXPECT(assert_equal(numericalDerivative22(adjointTransposeProxy, sim, xi), + actualH2)); +} + +//****************************************************************************** +TEST(Similarity2, adjointTranspose) { + const Vector4 xi(0.2, -0.4, 0.7, -0.1); + const Vector4 y(-0.3, 0.5, 0.9, -0.2); + + std::function f = + [](const Vector4& x, const Vector4& v) { + return Vector4(Similarity2::adjointTranspose(x, v)); + }; + + Matrix44 Hxi, Hy; + const Vector4 actual = Similarity2::adjointTranspose(xi, y, Hxi, Hy); + EXPECT(assert_equal(f(xi, y), actual)); + EXPECT(assert_equal(numericalDerivative21(f, xi, y, 1e-5), Hxi, 1e-5)); + EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); +} + //****************************************************************************** int main() { TestResult tr; diff --git a/gtsam/geometry/tests/testSimilarity3.cpp b/gtsam/geometry/tests/testSimilarity3.cpp index 42998a86b6..233fba0832 100644 --- a/gtsam/geometry/tests/testSimilarity3.cpp +++ b/gtsam/geometry/tests/testSimilarity3.cpp @@ -653,10 +653,45 @@ TEST(Similarity3, AdjointMap) { EXPECT(assert_equal(specialized_Adj, generic_Adj, 1e-9)); } +//****************************************************************************** +TEST(Similarity3, AdjointTranspose) { + const Similarity3 sim(Rot3::Rodrigues(0.3, 0.2, 0.1), Point3(3.5, -8.2, 4.2), + 0.8); + const Vector7 xi(0.2, -0.4, 0.7, -0.1, 0.3, -0.6, 0.5); + + EXPECT(assert_equal(Vector(sim.AdjointMap().transpose() * xi), + Vector(sim.AdjointTranspose(xi)))); + + Matrix77 actualH1, actualH2; + std::function proxy = + [](const Similarity3& g, const Vector7& x) { + return Vector7(g.AdjointTranspose(x)); + }; + sim.AdjointTranspose(xi, actualH1, actualH2); + EXPECT(assert_equal(numericalDerivative21(proxy, sim, xi), actualH1, 1e-8)); + EXPECT(assert_equal(numericalDerivative22(proxy, sim, xi), actualH2)); +} + +//****************************************************************************** +TEST(Similarity3, adjointTranspose) { + const Vector7 xi(0.2, -0.4, 0.7, -0.1, 0.3, -0.6, 0.5); + const Vector7 y(-0.3, 0.5, 0.9, -0.2, 0.4, -0.8, 0.1); + + std::function f = + [](const Vector7& x, const Vector7& v) { + return Vector7(Similarity3::adjointTranspose(x, v)); + }; + + Matrix77 Hxi, Hy; + const Vector7 actual = Similarity3::adjointTranspose(xi, y, Hxi, Hy); + EXPECT(assert_equal(f(xi, y), actual)); + EXPECT(assert_equal(numericalDerivative21(f, xi, y, 1e-5), Hxi, 1e-5)); + EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); +} + //****************************************************************************** int main() { TestResult tr; return TestRegistry::runAllTests(tr); } //****************************************************************************** - From c3abd6b01ec8862520d8d42457f1a2f2125b4d45 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 24 Feb 2026 10:13:47 -0500 Subject: [PATCH 257/266] Clean up entire Ad/ad API --- gtsam/base/Lie.h | 1 + gtsam/base/MatrixLieGroup.h | 164 +++++++++++++++++++---- gtsam/base/doc/MatrixLieGroup.md | 4 +- gtsam/geometry/Gal3.cpp | 33 ----- gtsam/geometry/Gal3.h | 11 -- gtsam/geometry/Pose2.h | 31 ----- gtsam/geometry/tests/testSimilarity2.cpp | 17 +++ gtsam/geometry/tests/testSimilarity3.cpp | 17 +++ 8 files changed, 173 insertions(+), 105 deletions(-) diff --git a/gtsam/base/Lie.h b/gtsam/base/Lie.h index 1a6023ee95..96e0efc161 100644 --- a/gtsam/base/Lie.h +++ b/gtsam/base/Lie.h @@ -209,6 +209,7 @@ struct LieGroupTraits : public GetDimensionImpl { // GetDimensionImpl handles resolving this to a static value or providing GetDimension(obj). inline constexpr static auto dimension = Class::dimension; using TangentVector = Eigen::Matrix; + using Jacobian = Eigen::Matrix; using ChartJacobian = OptionalJacobian; static TangentVector Local(const Class& origin, const Class& other, diff --git a/gtsam/base/MatrixLieGroup.h b/gtsam/base/MatrixLieGroup.h index ce1e736f38..89f6732ff9 100644 --- a/gtsam/base/MatrixLieGroup.h +++ b/gtsam/base/MatrixLieGroup.h @@ -19,6 +19,7 @@ #pragma once #include +#include #include namespace gtsam { @@ -56,6 +57,9 @@ namespace gtsam { using Jacobian = typename Base::Jacobian; using TangentVector = typename Base::TangentVector; + /// @name Matrix Lie Group + /// @{ + /** * Vectorize the matrix representation of a Lie group element. * The derivative `H` is the `(N*N) x D` Jacobian of this vectorization map. @@ -130,6 +134,21 @@ namespace gtsam { return adj; } + /** + * Adjoint action on a tangent vector. + * + * Returns Ad_g * xi with optional Jacobians with respect to g and xi. + */ + TangentVector Adjoint(const TangentVector& xi, + ChartJacobian H_this = {}, + ChartJacobian H_xi = {}) const { + const auto& m = static_cast(*this); + const Jacobian Ad = m.AdjointMap(); + if (H_this) *H_this = -Ad * Class::adjointMap(xi); + if (H_xi) *H_xi = Ad; + return Ad * xi; + } + /** * Dual Adjoint action on a tangent covector. * @@ -143,15 +162,18 @@ namespace gtsam { const TangentVector AdTx = Ad.transpose() * x; if (H_this) { - const Eigen::Index d = - (D == Eigen::Dynamic) ? static_cast(m.dim()) : D; + const Eigen::Index d = tangentDim(&m, nullptr); + setZeroJacobian(H_this, d); if constexpr (D == Eigen::Dynamic) { - H_this->setZero(d, d); + for (Eigen::Index i = 0; i < d; ++i) { + H_this->col(i) = + Class::adjointMap(TangentVector::Unit(d, i)).transpose() * AdTx; + } } else { - H_this->setZero(); - } - for (Eigen::Index i = 0; i < d; ++i) { - H_this->col(i) = Class::adjointTranspose(TangentVector::Unit(d, i), AdTx); + const auto& basis = adjointBasis(); + for (Eigen::Index i = 0; i < d; ++i) { + H_this->col(i) = basis[static_cast(i)].transpose() * AdTx; + } } } @@ -159,6 +181,38 @@ namespace gtsam { return AdTx; } + /** + * Lie algebra adjoint map ad_xi, with optional specialization in derived + * classes. + */ + static Jacobian adjointMap(const TangentVector& xi) { + const Eigen::Index d = tangentDim(nullptr, &xi); + Jacobian ad; + if constexpr (D == Eigen::Dynamic) { + ad.setZero(d, d); + } else { + ad.setZero(); + } + const auto Xi = Class::Hat(xi); + for (Eigen::Index i = 0; i < d; ++i) { + const auto Ei = Class::Hat(TangentVector::Unit(d, i)); + ad.col(i) = Class::Vee(Xi * Ei - Ei * Xi); + } + return ad; + } + + /** + * Lie algebra action ad_xi(y), with optional Jacobians. + */ + static TangentVector adjoint(const TangentVector& xi, + const TangentVector& y, ChartJacobian Hxi = {}, + ChartJacobian H_y = {}) { + const Jacobian ad_xi = Class::adjointMap(xi); + if (Hxi) *Hxi = -Class::adjointMap(y); + if (H_y) *H_y = ad_xi; + return ad_xi * y; + } + /** * Dual Lie algebra action ad_xi^T(y), with optional Jacobians. */ @@ -166,40 +220,61 @@ namespace gtsam { const TangentVector& y, ChartJacobian Hxi = {}, ChartJacobian H_y = {}) { - const Jacobian adT_xi = AdjointMapFromHatVee(xi).transpose(); + const Jacobian adT_xi = Class::adjointMap(xi).transpose(); if (Hxi) { - const Eigen::Index d = - (D == Eigen::Dynamic) ? static_cast(xi.size()) : D; + const Eigen::Index d = tangentDim(nullptr, &xi); + setZeroJacobian(Hxi, d); if constexpr (D == Eigen::Dynamic) { - Hxi->setZero(d, d); + for (Eigen::Index i = 0; i < d; ++i) { + Hxi->col(i) = + Class::adjointMap(TangentVector::Unit(d, i)).transpose() * y; + } } else { - Hxi->setZero(); - } - for (Eigen::Index i = 0; i < d; ++i) { - Hxi->col(i) = AdjointMapFromHatVee(TangentVector::Unit(d, i)).transpose() * y; + const auto& basis = adjointBasis(); + for (Eigen::Index i = 0; i < d; ++i) { + Hxi->col(i) = basis[static_cast(i)].transpose() * y; + } } } if (H_y) *H_y = adT_xi; return adT_xi * y; } + /// @} + private: - /// Generic Lie algebra adjoint map from Hat/Vee via matrix commutator. - static Jacobian AdjointMapFromHatVee(const TangentVector& xi) { - const Eigen::Index d = - (D == Eigen::Dynamic) ? static_cast(xi.size()) : D; - Jacobian ad; + static Eigen::Index tangentDim(const Class* m, const TangentVector* xi) { if constexpr (D == Eigen::Dynamic) { - ad.setZero(d, d); + return m ? static_cast(m->dim()) + : static_cast(xi->size()); } else { - ad.setZero(); + (void)m; + (void)xi; + return D; } - const auto Xi = Class::Hat(xi); - for (Eigen::Index i = 0; i < d; ++i) { - const auto Ei = Class::Hat(TangentVector::Unit(d, i)); - ad.col(i) = Class::Vee(Xi * Ei - Ei * Xi); + } + + static void setZeroJacobian(ChartJacobian H, Eigen::Index d) { + if constexpr (D == Eigen::Dynamic) { + H->setZero(d, d); + } else { + (void)d; + H->setZero(); } - return ad; + } + + /// Basis maps ad_{e_i}, cached for fixed-size groups. + template = 0> + static const std::array& adjointBasis() { + static const std::array basis = []() { + std::array B{}; + for (int i = 0; i < DD; ++i) { + B[static_cast(i)] = + Class::adjointMap(TangentVector::Unit(DD, i)); + } + return B; + }(); + return basis; } /// Pre-compute and store vectorized generators for fixed-size groups. @@ -213,10 +288,12 @@ namespace gtsam { namespace internal { - /// Adds LieAlgebra, Hat, Vee, and Vec to LieGroupTraits + /// Adds MatrixLieGroup methods to LieGroupTraits template struct MatrixLieGroupTraits : LieGroupTraits { using LieAlgebra = typename Class::LieAlgebra; using TangentVector = typename LieGroupTraits::TangentVector; + using Jacobian = typename LieGroupTraits::Jacobian; + using ChartJacobian = typename LieGroupTraits::ChartJacobian; static LieAlgebra Hat(const TangentVector& v) { return Class::Hat(v); @@ -233,6 +310,37 @@ namespace gtsam { LieGroupTraits::dimension> H = {}) { return m.vec(H); } + + static TangentVector AdjointTranspose(const Class& m, + const TangentVector& x, + ChartJacobian Hm = {}, + ChartJacobian Hx = {}) { + return m.AdjointTranspose(x, Hm, Hx); + } + + static TangentVector Adjoint(const Class& m, const TangentVector& x, + ChartJacobian Hm = {}, + ChartJacobian Hx = {}) { + return m.Adjoint(x, Hm, Hx); + } + + static Jacobian adjointMap(const TangentVector& xi) { + return Class::adjointMap(xi); + } + + static TangentVector adjoint(const TangentVector& xi, + const TangentVector& y, + ChartJacobian Hxi = {}, + ChartJacobian H_y = {}) { + return Class::adjoint(xi, y, Hxi, H_y); + } + + static TangentVector adjointTranspose(const TangentVector& xi, + const TangentVector& y, + ChartJacobian Hxi = {}, + ChartJacobian H_y = {}) { + return Class::adjointTranspose(xi, y, Hxi, H_y); + } }; /// Both LieGroupTraits and Testable diff --git a/gtsam/base/doc/MatrixLieGroup.md b/gtsam/base/doc/MatrixLieGroup.md index 8699281a35..0ae188ef1f 100644 --- a/gtsam/base/doc/MatrixLieGroup.md +++ b/gtsam/base/doc/MatrixLieGroup.md @@ -29,8 +29,8 @@ You must implement everything from the `LieGroup.md` guide, plus the following: By inheriting from `gtsam::MatrixLieGroup`, you get: -* **A default `AdjointMap()` implementation**: This generic version works by repeatedly calling your `Hat` and `Vee` methods. While it is correct, it is often slow. For performance-critical applications, you should still provide a faster, closed-form `AdjointMap()` implementation in your class, which will override the default. -* **Default dual-adjoint helpers**: `AdjointTranspose(x)` and `adjointTranspose(xi, y)` are provided generically, including Jacobians. +* **Default group adjoint methods**: `AdjointMap()`, `Adjoint(xi)`, and `AdjointTranspose(x)` are provided generically, including Jacobians for the latter two. +* **Default Lie algebra methods**: `adjointMap(xi)`, `adjoint(xi, y)`, and `adjointTranspose(xi, y)` are provided generically, including Jacobians for the latter two. * **A `vec()` method**: This vectorizes the `N x N` matrix representation of your group element into an `(N*N) x 1` vector. ### 4. Traits and Concept Checking diff --git a/gtsam/geometry/Gal3.cpp b/gtsam/geometry/Gal3.cpp index 6e5be3b7c6..abcc05eeb4 100644 --- a/gtsam/geometry/Gal3.cpp +++ b/gtsam/geometry/Gal3.cpp @@ -389,29 +389,6 @@ Gal3::Jacobian Gal3::AdjointMap() const { return Ad; } -//------------------------------------------------------------------------------ -Gal3::TangentVector Gal3::Adjoint(const TangentVector& xi, OptionalJacobian<10, 10> H_g, OptionalJacobian<10, 10> H_xi) const { - Jacobian Ad = AdjointMap(); - TangentVector y = Ad * xi; - - if (H_xi) { - *H_xi = Ad; - } - - if (H_g) { - // NOTE: Using numerical derivative for the Jacobian with respect to - // the group element instead of deriving the analytical expression. - // Future work to use analytical instead. - std::function adjoint_action_wrt_g = - [&](const Gal3& g_in, const TangentVector& xi_in) { - return g_in.Adjoint(xi_in); - }; - *H_g = numericalDerivative21(adjoint_action_wrt_g, *this, xi, 1e-7); - } - return y; -} - -//------------------------------------------------------------------------------ Gal3::Jacobian Gal3::adjointMap(const TangentVector& xi) { // Implements adjoint representation as in Equation 28, Page 10 const Matrix3 Omega = skewSymmetric(xi_w(xi)); @@ -428,16 +405,6 @@ Gal3::Jacobian Gal3::adjointMap(const TangentVector& xi) { return ad; } -//------------------------------------------------------------------------------ -Gal3::TangentVector Gal3::adjoint(const TangentVector& xi, const TangentVector& y, OptionalJacobian<10, 10> Hxi, OptionalJacobian<10, 10> Hy) { - Jacobian ad_xi = adjointMap(xi); - if (Hy) *Hy = ad_xi; - if (Hxi) { - *Hxi = -adjointMap(y); - } - return ad_xi * y; -} - //------------------------------------------------------------------------------ Gal3::Jacobian Gal3::ExpmapDerivative(const TangentVector& xi) { Gal3::Jacobian J; diff --git a/gtsam/geometry/Gal3.h b/gtsam/geometry/Gal3.h index a03689ba34..f824898007 100644 --- a/gtsam/geometry/Gal3.h +++ b/gtsam/geometry/Gal3.h @@ -188,17 +188,6 @@ class GTSAM_EXPORT Gal3 : public MatrixLieGroup { /// Calculate Adjoint map Ad_g Jacobian AdjointMap() const; - /// Apply this element's AdjointMap Ad_g to a tangent vector xi_base at - /// identity - TangentVector Adjoint(const TangentVector& xi_base, - OptionalJacobian<10, 10> H_g = {}, - OptionalJacobian<10, 10> H_xi = {}) const; - - /// The adjoint action `ad(xi, y)` = `adjointMap(xi) * y` - static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, - OptionalJacobian<10, 10> Hxi = {}, - OptionalJacobian<10, 10> Hy = {}); - /// Compute the adjoint map `ad(xi)` associated with tangent vector xi static Jacobian adjointMap(const TangentVector& xi); diff --git a/gtsam/geometry/Pose2.h b/gtsam/geometry/Pose2.h index 074436bcab..5da5c24ab4 100644 --- a/gtsam/geometry/Pose2.h +++ b/gtsam/geometry/Pose2.h @@ -158,42 +158,11 @@ class GTSAM_EXPORT Pose2: public MatrixLieGroup { */ Matrix3 AdjointMap() const; - /// Apply AdjointMap to twist xi - inline Vector3 Adjoint(const Vector3& xi) const { - return AdjointMap()*xi; - } - /** * Compute the [ad(w,v)] operator for SE2 as in [Kobilarov09siggraph], pg 19 */ static Matrix3 adjointMap(const Vector3& v); - /** - * Action of the adjointMap on a Lie-algebra vector y, with optional derivatives - */ - static Vector3 adjoint(const Vector3& xi, const Vector3& y) { - return adjointMap(xi) * y; - } - - /** - * The dual version of adjoint action, acting on the dual space of the Lie-algebra vector space. - */ - static Vector3 adjointTranspose(const Vector3& xi, const Vector3& y, - OptionalJacobian<3, 3> Hxi = {}, - OptionalJacobian<3, 3> H_y = {}) { - const Matrix3 adT = adjointMap(xi).transpose(); - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 3; ++i) { - Vector3 dxi = Vector3::Zero(); - dxi(i) = 1.0; - Hxi->col(i) = adjointMap(dxi).transpose() * y; - } - } - if (H_y) *H_y = adT; - return adT * y; - } - // temporary fix for wrappers until case issue is resolved static Matrix3 adjointMap_(const Vector3 &xi) { return adjointMap(xi);} static Vector3 adjoint_(const Vector3 &xi, const Vector3 &y) { return adjoint(xi, y);} diff --git a/gtsam/geometry/tests/testSimilarity2.cpp b/gtsam/geometry/tests/testSimilarity2.cpp index ef3867f081..483f64a8ad 100644 --- a/gtsam/geometry/tests/testSimilarity2.cpp +++ b/gtsam/geometry/tests/testSimilarity2.cpp @@ -277,6 +277,23 @@ TEST(Similarity2, adjointTranspose) { EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); } +//****************************************************************************** +TEST(Similarity2, adjoint) { + const Vector4 xi(0.2, -0.4, 0.7, -0.1); + const Vector4 y(-0.3, 0.5, 0.9, -0.2); + + std::function f = + [](const Vector4& x, const Vector4& v) { + return Vector4(Similarity2::adjoint(x, v)); + }; + + Matrix44 Hxi, Hy; + const Vector4 actual = Similarity2::adjoint(xi, y, Hxi, Hy); + EXPECT(assert_equal(f(xi, y), actual)); + EXPECT(assert_equal(numericalDerivative21(f, xi, y, 1e-5), Hxi, 1e-5)); + EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); +} + //****************************************************************************** int main() { TestResult tr; diff --git a/gtsam/geometry/tests/testSimilarity3.cpp b/gtsam/geometry/tests/testSimilarity3.cpp index 233fba0832..9ab493ce49 100644 --- a/gtsam/geometry/tests/testSimilarity3.cpp +++ b/gtsam/geometry/tests/testSimilarity3.cpp @@ -689,6 +689,23 @@ TEST(Similarity3, adjointTranspose) { EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); } +//****************************************************************************** +TEST(Similarity3, adjoint) { + const Vector7 xi(0.2, -0.4, 0.7, -0.1, 0.3, -0.6, 0.5); + const Vector7 y(-0.3, 0.5, 0.9, -0.2, 0.4, -0.8, 0.1); + + std::function f = + [](const Vector7& x, const Vector7& v) { + return Vector7(Similarity3::adjoint(x, v)); + }; + + Matrix77 Hxi, Hy; + const Vector7 actual = Similarity3::adjoint(xi, y, Hxi, Hy); + EXPECT(assert_equal(f(xi, y), actual)); + EXPECT(assert_equal(numericalDerivative21(f, xi, y, 1e-5), Hxi, 1e-5)); + EXPECT(assert_equal(numericalDerivative22(f, xi, y, 1e-5), Hy, 1e-5)); +} + //****************************************************************************** int main() { TestResult tr; From 303c38a0b4f78eec7043129340389fb8ace1cb68 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 24 Feb 2026 12:29:25 -0500 Subject: [PATCH 258/266] Wrapper --- gtsam/geometry/Rot3.h | 39 +++++++++++++++ gtsam/geometry/geometry.i | 92 +++++++++++++++++++++++++++++------ gtsam/navigation/navigation.i | 8 ++- 3 files changed, 121 insertions(+), 18 deletions(-) diff --git a/gtsam/geometry/Rot3.h b/gtsam/geometry/Rot3.h index 31947c6491..c9f6d0d8d1 100644 --- a/gtsam/geometry/Rot3.h +++ b/gtsam/geometry/Rot3.h @@ -396,6 +396,30 @@ class GTSAM_EXPORT Rot3 : public LieGroup { /** Calculate Adjoint map */ Matrix3 AdjointMap() const { return matrix(); } + /// Apply this element's AdjointMap to a tangent vector. + Vector3 Adjoint(const Vector3& xi, OptionalJacobian<3, 3> H_this = {}, + OptionalJacobian<3, 3> H_xi = {}) const { + const Matrix3 Ad = AdjointMap(); + if (H_this) *H_this = -Ad * adjointMap(xi); + if (H_xi) *H_xi = Ad; + return Ad * xi; + } + + /// Apply the dual adjoint action to a tangent covector. + Vector3 AdjointTranspose(const Vector3& x, + OptionalJacobian<3, 3> H_this = {}, + OptionalJacobian<3, 3> H_x = {}) const { + const Matrix3 AdT = AdjointMap().transpose(); + if (H_this) { + H_this->setZero(); + for (int i = 0; i < 3; ++i) { + H_this->col(i) = adjointMap(Vector3::Unit(i)).transpose() * (AdT * x); + } + } + if (H_x) *H_x = AdT; + return AdT * x; + } + /// Matrix representation of the Lie-algebra adjoint operator ad_xi on so(3). static Matrix3 adjointMap(const Vector3& xi); @@ -404,6 +428,21 @@ class GTSAM_EXPORT Rot3 : public LieGroup { OptionalJacobian<3, 3> Hxi = {}, OptionalJacobian<3, 3> Hy = {}); + /// Apply the dual Lie-algebra adjoint map to y with optional derivatives. + static Vector3 adjointTranspose(const Vector3& xi, const Vector3& y, + OptionalJacobian<3, 3> Hxi = {}, + OptionalJacobian<3, 3> H_y = {}) { + const Matrix3 adT = adjointMap(xi).transpose(); + if (Hxi) { + Hxi->setZero(); + for (int i = 0; i < 3; ++i) { + Hxi->col(i) = adjointMap(Vector3::Unit(i)).transpose() * y; + } + } + if (H_y) *H_y = adT; + return adT * y; + } + // Chart at origin, depends on compile-time flag ROT3_DEFAULT_COORDINATES_MODE struct GTSAM_EXPORT ChartAtOrigin { static Rot3 Retract(const Vector3& v, ChartJacobian H = {}); diff --git a/gtsam/geometry/geometry.i b/gtsam/geometry/geometry.i index 28ab12079f..d559ae54a7 100644 --- a/gtsam/geometry/geometry.i +++ b/gtsam/geometry/geometry.i @@ -177,6 +177,12 @@ class Rot2 { gtsam::Vector logmap(const gtsam::Rot2& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -293,6 +299,12 @@ class SO3 { gtsam::Vector3 logmap(const gtsam::SO3& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -332,6 +344,12 @@ class SO4 { gtsam::Vector logmap(const gtsam::SO4& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -373,6 +391,12 @@ class SOn { gtsam::Vector logmap(const gtsam::SOn& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -450,6 +474,12 @@ class Rot3 { gtsam::Vector logmap(const gtsam::Rot3& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -530,13 +560,16 @@ class Pose2 { gtsam::Vector logmap(const gtsam::Pose2& g, Eigen::Ref H1, Eigen::Ref H2); static gtsam::Matrix ExpmapDerivative(gtsam::Vector v); static gtsam::Matrix LogmapDerivative(const gtsam::Pose2& v); + + // Matrix Lie Group gtsam::Matrix AdjointMap() const; gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); static gtsam::Matrix adjointMap_(gtsam::Vector xi); static gtsam::Vector adjoint_(gtsam::Vector xi, gtsam::Vector y); - static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); - - // Matrix Lie Group gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -624,20 +657,15 @@ class Pose3 { gtsam::Vector logmap(const gtsam::Pose3& g); gtsam::Vector logmap(const gtsam::Pose3& g, Eigen::Ref H1, Eigen::Ref H2); + // Matrix Lie Group gtsam::Matrix AdjointMap() const; gtsam::Vector Adjoint(gtsam::Vector xi_b) const; - gtsam::Vector Adjoint(gtsam::Vector xi_b, Eigen::Ref H_this, - Eigen::Ref H_xib) const; gtsam::Vector AdjointTranspose(gtsam::Vector x) const; - gtsam::Vector AdjointTranspose(gtsam::Vector x, Eigen::Ref H_this, - Eigen::Ref H_x) const; static gtsam::Matrix adjointMap(gtsam::Vector xi); static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); static gtsam::Matrix adjointMap_(gtsam::Vector xi); static gtsam::Vector adjoint_(gtsam::Vector xi, gtsam::Vector y); - static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); - - // Matrix Lie Group gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -723,15 +751,17 @@ class ExtendedPose3 { // Lie Group static This Expmap(gtsam::Vector xi); static gtsam::Vector Logmap(const This& pose); - gtsam::Matrix AdjointMap() const; - gtsam::Vector Adjoint(gtsam::Vector xi_b) const; - static gtsam::Matrix adjointMap(gtsam::Vector xi); - static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); static gtsam::Matrix ExpmapDerivative(gtsam::Vector xi); static gtsam::Matrix LogmapDerivative(gtsam::Vector xi); static gtsam::Matrix LogmapDerivative(const This& pose); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi_b) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -782,6 +812,12 @@ class SL4 { gtsam::Vector logmap(const gtsam::SL4& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); @@ -1467,13 +1503,22 @@ class Similarity2 { static gtsam::Vector Logmap(const gtsam::Similarity2& S); gtsam::Similarity2 expmap(const gtsam::Vector& v); gtsam::Vector logmap(const gtsam::Similarity2& g); + + // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); + gtsam::Vector vec() const; + gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); static gtsam::Vector Vee(const gtsam::Matrix& X); // Standard Interface bool equals(const gtsam::Similarity2& sim, double tol) const; void print(string s = "") const; - gtsam::Matrix matrix() const; gtsam::Rot2& rotation(); gtsam::Point2& translation(); double scale() const; @@ -1514,13 +1559,22 @@ class Similarity3 { static gtsam::Vector Logmap(const gtsam::Similarity3& s); gtsam::Similarity3 expmap(const gtsam::Vector& v); gtsam::Vector logmap(const gtsam::Similarity3& g); + + // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); + gtsam::Vector vec() const; + gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); static gtsam::Vector Vee(const gtsam::Matrix& X); // Standard Interface bool equals(const gtsam::Similarity3& sim, double tol) const; void print(string s = "") const; - gtsam::Matrix matrix() const; gtsam::Rot3& rotation(); gtsam::Point3& translation(); double scale() const; @@ -1584,6 +1638,12 @@ class Gal3 { gtsam::Vector10 logmap(const gtsam::Gal3& g); // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); diff --git a/gtsam/navigation/navigation.i b/gtsam/navigation/navigation.i index edbe8bc822..3530d0e62c 100644 --- a/gtsam/navigation/navigation.i +++ b/gtsam/navigation/navigation.i @@ -90,10 +90,14 @@ class NavState { gtsam::NavState expmap(gtsam::Vector v, Eigen::Ref H1, Eigen::Ref H2); gtsam::Vector logmap(const gtsam::NavState& p); gtsam::Vector logmap(const gtsam::NavState& p, Eigen::Ref H1, Eigen::Ref H2); - gtsam::Matrix AdjointMap() const; - gtsam::Vector Adjoint(gtsam::Vector xi_b) const; // Matrix Lie Group + gtsam::Matrix AdjointMap() const; + gtsam::Vector Adjoint(gtsam::Vector xi_b) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + static gtsam::Matrix adjointMap(gtsam::Vector xi); + static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); + static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); gtsam::Vector vec() const; gtsam::Matrix matrix() const; static gtsam::Matrix Hat(const gtsam::Vector& xi); From fd665716cb1f1da98074b2262517eaf54e17ca08 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 24 Feb 2026 14:44:56 -0500 Subject: [PATCH 259/266] Rot3 -> MatrixLieGroup --- gtsam/geometry/Rot3.cpp | 13 ----------- gtsam/geometry/Rot3.h | 48 ++--------------------------------------- 2 files changed, 2 insertions(+), 59 deletions(-) diff --git a/gtsam/geometry/Rot3.cpp b/gtsam/geometry/Rot3.cpp index c02c74c067..0d82e5bdb2 100644 --- a/gtsam/geometry/Rot3.cpp +++ b/gtsam/geometry/Rot3.cpp @@ -245,19 +245,6 @@ Matrix3 Rot3::LogmapDerivative(const Vector3& x) { return SO3::LogmapDerivative(x); } -/* ************************************************************************* */ -Matrix3 Rot3::adjointMap(const Vector3& xi) { return Hat(xi); } - -/* ************************************************************************* */ -Vector3 Rot3::adjoint(const Vector3& xi, const Vector3& y, - OptionalJacobian<3, 3> Hxi, - OptionalJacobian<3, 3> Hy) { - const Matrix3 ad_xi = adjointMap(xi); - if (Hxi) *Hxi = -Hat(y); - if (Hy) *Hy = ad_xi; - return ad_xi * y; -} - /* ************************************************************************* */ pair RQ(const Matrix3& A, OptionalJacobian<3, 9> H) { const double x = -atan2(-A(2, 1), A(2, 2)); diff --git a/gtsam/geometry/Rot3.h b/gtsam/geometry/Rot3.h index c9f6d0d8d1..5c5b0496cd 100644 --- a/gtsam/geometry/Rot3.h +++ b/gtsam/geometry/Rot3.h @@ -55,7 +55,7 @@ namespace gtsam { * if it is defined. * @ingroup geometry */ -class GTSAM_EXPORT Rot3 : public LieGroup { +class GTSAM_EXPORT Rot3 : public MatrixLieGroup { public: static constexpr size_t MatrixM = 3; private: @@ -396,52 +396,8 @@ class GTSAM_EXPORT Rot3 : public LieGroup { /** Calculate Adjoint map */ Matrix3 AdjointMap() const { return matrix(); } - /// Apply this element's AdjointMap to a tangent vector. - Vector3 Adjoint(const Vector3& xi, OptionalJacobian<3, 3> H_this = {}, - OptionalJacobian<3, 3> H_xi = {}) const { - const Matrix3 Ad = AdjointMap(); - if (H_this) *H_this = -Ad * adjointMap(xi); - if (H_xi) *H_xi = Ad; - return Ad * xi; - } - - /// Apply the dual adjoint action to a tangent covector. - Vector3 AdjointTranspose(const Vector3& x, - OptionalJacobian<3, 3> H_this = {}, - OptionalJacobian<3, 3> H_x = {}) const { - const Matrix3 AdT = AdjointMap().transpose(); - if (H_this) { - H_this->setZero(); - for (int i = 0; i < 3; ++i) { - H_this->col(i) = adjointMap(Vector3::Unit(i)).transpose() * (AdT * x); - } - } - if (H_x) *H_x = AdT; - return AdT * x; - } - /// Matrix representation of the Lie-algebra adjoint operator ad_xi on so(3). - static Matrix3 adjointMap(const Vector3& xi); - - /// Apply the Lie-algebra adjoint map to y with optional derivatives. - static Vector3 adjoint(const Vector3& xi, const Vector3& y, - OptionalJacobian<3, 3> Hxi = {}, - OptionalJacobian<3, 3> Hy = {}); - - /// Apply the dual Lie-algebra adjoint map to y with optional derivatives. - static Vector3 adjointTranspose(const Vector3& xi, const Vector3& y, - OptionalJacobian<3, 3> Hxi = {}, - OptionalJacobian<3, 3> H_y = {}) { - const Matrix3 adT = adjointMap(xi).transpose(); - if (Hxi) { - Hxi->setZero(); - for (int i = 0; i < 3; ++i) { - Hxi->col(i) = adjointMap(Vector3::Unit(i)).transpose() * y; - } - } - if (H_y) *H_y = adT; - return adT * y; - } + static Matrix3 adjointMap(const Vector3& xi) { return Hat(xi); } // Chart at origin, depends on compile-time flag ROT3_DEFAULT_COORDINATES_MODE struct GTSAM_EXPORT ChartAtOrigin { From 152d65d3788b9d91918a1a1c3d7c7b9186dd86ad Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Tue, 24 Feb 2026 14:45:27 -0500 Subject: [PATCH 260/266] Remove generics --- gtsam/geometry/ExtendedPose3-inl.h | 95 ------------------------------ gtsam/geometry/ExtendedPose3.h | 49 --------------- 2 files changed, 144 deletions(-) diff --git a/gtsam/geometry/ExtendedPose3-inl.h b/gtsam/geometry/ExtendedPose3-inl.h index e4b2973879..8cadbccb52 100644 --- a/gtsam/geometry/ExtendedPose3-inl.h +++ b/gtsam/geometry/ExtendedPose3-inl.h @@ -239,46 +239,6 @@ ExtendedPose3::AdjointMap() const { return adj; } -template -typename ExtendedPose3::TangentVector -ExtendedPose3::Adjoint(const TangentVector& xi_b, - ChartJacobian H_this, - ChartJacobian H_xib) const { - const Jacobian Ad = AdjointMap(); - if (H_this) *H_this = -Ad * adjointMap(xi_b); - if (H_xib) *H_xib = Ad; - return Ad * xi_b; -} - -template -typename ExtendedPose3::TangentVector -ExtendedPose3::AdjointTranspose(const TangentVector& x, - ChartJacobian H_this, - ChartJacobian H_x) const { - const Jacobian Ad = AdjointMap(); - const TangentVector AdTx = Ad.transpose() * x; - - if (H_this) { - if constexpr (dimension == Eigen::Dynamic) { - H_this->setZero(x.size(), x.size()); - } else { - H_this->setZero(); - } - - H_this->block(0, 0, 3, 3) = skewSymmetric(AdTx.template head<3>()); - const Eigen::Index k = static_cast(this->k()); - for (Eigen::Index i = 0; i < k; ++i) { - const Eigen::Index idx = 3 + 3 * i; - const Matrix3 x_i_hat = skewSymmetric(AdTx.template segment<3>(idx)); - H_this->block(0, idx, 3, 3) = x_i_hat; - H_this->block(idx, 0, 3, 3) = x_i_hat; - } - } - - if (H_x) *H_x = Ad.transpose(); - return AdTx; -} - template typename ExtendedPose3::Jacobian ExtendedPose3::adjointMap(const TangentVector& xi) { @@ -303,61 +263,6 @@ ExtendedPose3::adjointMap(const TangentVector& xi) { return adj; } -template -typename ExtendedPose3::TangentVector -ExtendedPose3::adjoint(const TangentVector& xi, - const TangentVector& y, ChartJacobian Hxi, - ChartJacobian H_y) { - const Jacobian ad_xi = adjointMap(xi); - if (Hxi) { - if constexpr (dimension == Eigen::Dynamic) { - Hxi->setZero(xi.size(), xi.size()); - } else { - Hxi->setZero(); - } - for (Eigen::Index i = 0; i < xi.size(); ++i) { - TangentVector dxi; - if constexpr (dimension == Eigen::Dynamic) { - dxi = TangentVector::Zero(xi.size()); - } else { - dxi = TangentVector::Zero(); - } - dxi(i) = 1.0; - Hxi->col(i) = adjointMap(dxi) * y; - } - } - if (H_y) *H_y = ad_xi; - return ad_xi * y; -} - -template -typename ExtendedPose3::TangentVector -ExtendedPose3::adjointTranspose(const TangentVector& xi, - const TangentVector& y, - ChartJacobian Hxi, - ChartJacobian H_y) { - if (Hxi) { - if constexpr (dimension == Eigen::Dynamic) { - Hxi->setZero(xi.size(), xi.size()); - } else { - Hxi->setZero(); - } - for (Eigen::Index i = 0; i < xi.size(); ++i) { - TangentVector dxi; - if constexpr (dimension == Eigen::Dynamic) { - dxi = TangentVector::Zero(xi.size()); - } else { - dxi = TangentVector::Zero(); - } - dxi(i) = 1.0; - Hxi->col(i) = adjointMap(dxi).transpose() * y; - } - } - const Jacobian adT_xi = adjointMap(xi).transpose(); - if (H_y) *H_y = adT_xi; - return adT_xi * y; -} - template typename ExtendedPose3::Jacobian ExtendedPose3::ExpmapDerivative(const TangentVector& xi) { diff --git a/gtsam/geometry/ExtendedPose3.h b/gtsam/geometry/ExtendedPose3.h index c2f6ca43e0..0d121a94b1 100644 --- a/gtsam/geometry/ExtendedPose3.h +++ b/gtsam/geometry/ExtendedPose3.h @@ -271,29 +271,6 @@ class ExtendedPose3 */ Jacobian AdjointMap() const; - /** - * Adjoint action on a tangent vector. - * - * @param xi_b Tangent vector in R^dim. - * @param H_this Optional Jacobian in R^(dimxdim). - * @param H_xib Optional Jacobian in R^(dimxdim). - * @return Ad_X xi_b in R^dim. - */ - TangentVector Adjoint(const TangentVector& xi_b, ChartJacobian H_this = {}, - ChartJacobian H_xib = {}) const; - - /** - * Dual adjoint action on a tangent covector. - * - * @param x Tangent vector in R^dim. - * @param H_this Optional Jacobian in R^(dimxdim). - * @param H_x Optional Jacobian in R^(dimxdim). - * @return Ad_X^T x in R^dim. - */ - TangentVector AdjointTranspose(const TangentVector& x, - ChartJacobian H_this = {}, - ChartJacobian H_x = {}) const; - /** * Lie algebra adjoint map. * @@ -302,32 +279,6 @@ class ExtendedPose3 */ static Jacobian adjointMap(const TangentVector& xi); - /** - * Lie bracket action ad_xi(y). - * - * @param xi Tangent vector in R^dim. - * @param y Tangent vector in R^dim. - * @param Hxi Optional Jacobian in R^(dimxdim). - * @param H_y Optional Jacobian in R^(dimxdim). - * @return ad_xi(y) in R^dim. - */ - static TangentVector adjoint(const TangentVector& xi, const TangentVector& y, - ChartJacobian Hxi = {}, ChartJacobian H_y = {}); - - /** - * Dual Lie bracket action ad_xi^T(y). - * - * @param xi Tangent vector in R^dim. - * @param y Tangent vector in R^dim. - * @param Hxi Optional Jacobian in R^(dimxdim). - * @param H_y Optional Jacobian in R^(dimxdim). - * @return ad_xi^T(y) in R^dim. - */ - static TangentVector adjointTranspose(const TangentVector& xi, - const TangentVector& y, - ChartJacobian Hxi = {}, - ChartJacobian H_y = {}); - /** * Jacobian of Expmap. * From c12a51fef10b7296bda5f2c1870a21fe7756c6e3 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 25 Feb 2026 00:04:42 +0000 Subject: [PATCH 261/266] Initial plan From 5515f78a457f3c5c9372caf62a7d4ae61da99ca9 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Wed, 25 Feb 2026 00:07:52 +0000 Subject: [PATCH 262/266] Apply review feedback: restore Jacobian overloads, use trait-based dim, add perf note Co-authored-by: dellaert <10515273+dellaert@users.noreply.github.com> --- gtsam/base/MatrixLieGroup.h | 2 +- gtsam/base/doc/MatrixLieGroup.md | 2 ++ gtsam/geometry/geometry.i | 4 ++++ 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/gtsam/base/MatrixLieGroup.h b/gtsam/base/MatrixLieGroup.h index 89f6732ff9..6987d36406 100644 --- a/gtsam/base/MatrixLieGroup.h +++ b/gtsam/base/MatrixLieGroup.h @@ -245,7 +245,7 @@ namespace gtsam { private: static Eigen::Index tangentDim(const Class* m, const TangentVector* xi) { if constexpr (D == Eigen::Dynamic) { - return m ? static_cast(m->dim()) + return m ? static_cast(traits::GetDimension(*m)) : static_cast(xi->size()); } else { (void)m; diff --git a/gtsam/base/doc/MatrixLieGroup.md b/gtsam/base/doc/MatrixLieGroup.md index 0ae188ef1f..0788b1da5e 100644 --- a/gtsam/base/doc/MatrixLieGroup.md +++ b/gtsam/base/doc/MatrixLieGroup.md @@ -33,6 +33,8 @@ By inheriting from `gtsam::MatrixLieGroup`, you get: * **Default Lie algebra methods**: `adjointMap(xi)`, `adjoint(xi, y)`, and `adjointTranspose(xi, y)` are provided generically, including Jacobians for the latter two. * **A `vec()` method**: This vectorizes the `N x N` matrix representation of your group element into an `(N*N) x 1` vector. +**Performance Note:** The generic implementation of `AdjointMap()` is correct but may be slower because it is derived via the `Hat`/`Vee` mappings. If a closed-form expression for `AdjointMap()` is available for your group, consider overriding the default implementation for better performance. + ### 4. Traits and Concept Checking Finally, the traits specialization in your header file must be updated to reflect that your class is now a `MatrixLieGroup`. diff --git a/gtsam/geometry/geometry.i b/gtsam/geometry/geometry.i index d559ae54a7..f282d53c73 100644 --- a/gtsam/geometry/geometry.i +++ b/gtsam/geometry/geometry.i @@ -660,7 +660,11 @@ class Pose3 { // Matrix Lie Group gtsam::Matrix AdjointMap() const; gtsam::Vector Adjoint(gtsam::Vector xi_b) const; + gtsam::Vector Adjoint(gtsam::Vector xi_b, Eigen::Ref H_this, + Eigen::Ref H_xib) const; gtsam::Vector AdjointTranspose(gtsam::Vector x) const; + gtsam::Vector AdjointTranspose(gtsam::Vector x, Eigen::Ref H_this, + Eigen::Ref H_x) const; static gtsam::Matrix adjointMap(gtsam::Vector xi); static gtsam::Vector adjoint(gtsam::Vector xi, gtsam::Vector y); static gtsam::Vector adjointTranspose(gtsam::Vector xi, gtsam::Vector y); From 64ac1747a71ebe99787f46affbbddbd96f7ec925 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Wed, 25 Feb 2026 15:07:01 -0500 Subject: [PATCH 263/266] Resolving TLS PR comments --- gtsam/linear/LossFunctions.h | 2 +- gtsam/nonlinear/GncOptimizer.h | 15 ++++++--------- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h index 703209bad9..3976466d48 100644 --- a/gtsam/linear/LossFunctions.h +++ b/gtsam/linear/LossFunctions.h @@ -431,7 +431,7 @@ class GTSAM_EXPORT TruncatedLeastSquares : public Base { * This helper returns a optional because it is also used for GNC, and we encounter transition weight cases, * where the weight is not strictly binary (0 or 1) when the residual is within the transition region between inliers and outliers. * The weight member function now calls the this function. - * While the member function takes the residual as input, it passes x², c² and c²to the static helper. + * While the member function takes the residual as input, it passes x², c² and c² to the static helper. * * @param distance2 Squared residual magnitude. * @param lowerbound Squared lower bound. diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index aafee3254c..bfc0b6be6e 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -524,32 +524,29 @@ class GncOptimizer { for (size_t k = 0; k < nfg_.size(); k++) { if (needsWeightUpdate(factorTypes_[k])) { double u2_k = nfg_[k]->error(currentEstimate); // squared (and whitened) residual - double lowerbound = 0.0; - double upperbound = 0.0; - double transition_weight = 0.0; switch (params_.scheduler) { case GncScheduler::SuperLinear: { - lowerbound = barcSq_[k]; - upperbound = ((mu + 1.0) * (mu + 1.0) / (mu * mu)) * barcSq_[k]; + double lowerbound = barcSq_[k]; + double upperbound = ((mu + 1.0) * (mu + 1.0) / (mu * mu)) * barcSq_[k]; auto w = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound); if (w) { weights[k] = *w; } else { - transition_weight = std::sqrt(barcSq_[k] / u2_k) * (mu + 1.0) - mu; + double transition_weight = std::sqrt(barcSq_[k] / u2_k) * (mu + 1.0) - mu; weights[k] = std::clamp(transition_weight, 0.0, 1.0); } break; } case GncScheduler::Linear: { // use eq (14) in GNC paper - upperbound = (mu + 1.0) / mu * barcSq_[k]; - lowerbound = mu / (mu + 1.0) * barcSq_[k]; + double upperbound = ((mu + 1.0) / mu) * barcSq_[k]; + double lowerbound = (mu / (mu + 1.0)) * barcSq_[k]; auto w = noiseModel::mEstimator::TruncatedLeastSquares::Weight(u2_k, lowerbound, upperbound); if (w) { weights[k] = *w; } else { - transition_weight = std::sqrt(barcSq_[k] * mu * (mu + 1.0) / u2_k) - mu; + double transition_weight = std::sqrt(barcSq_[k] * mu * (mu + 1.0) / u2_k) - mu; weights[k] = std::clamp(transition_weight, 0.0, 1.0); } break; From 5c38f3e2879f20a6e9c7f2195a4e551ee2338146 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Wed, 25 Feb 2026 20:03:59 -0500 Subject: [PATCH 264/266] Adding a muMax param for TLS --- gtsam/nonlinear/GncOptimizer.h | 4 ++-- gtsam/nonlinear/GncParams.h | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h index bfc0b6be6e..3f4f769f59 100644 --- a/gtsam/nonlinear/GncOptimizer.h +++ b/gtsam/nonlinear/GncOptimizer.h @@ -398,8 +398,8 @@ class GncOptimizer { // increases mu at each iteration (original cost is recovered for mu -> inf) switch (params_.scheduler) { case GncScheduler::SuperLinear: { - if (mu < 1) return std::min(std::sqrt(mu) * params_.muStep, 1e16); - return std::min(mu * params_.muStep, 1e16); + if (mu < 1) return std::min(std::sqrt(mu) * params_.muStep, params_.muMax); + return std::min(mu * params_.muStep, params_.muMax); } case GncScheduler::Linear: { return mu * params_.muStep; diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index 4b6a5b8279..436f5d0821 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -79,6 +79,7 @@ class GncParams { double muStep = 1.4; ///< Multiplicative factor to reduce/increase the mu in gnc double relativeCostTol = 1e-5; ///< If relative cost change is below this threshold, stop iterating double weightsTol = 1e-4; ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS) + double muMax = 1e16; ///< Maximum value of mu in GNC, acts as a cap (only for TLS) Verbosity verbosity = SILENT; ///< Verbosity level bool allowNonNoiseModelFactors = false; ///< If true, factors without noise model are not reweighted and not not included in mu calculation From 57aada7405106821e30e32d68ea6a5f1293e38dc Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Thu, 26 Feb 2026 20:43:26 -0500 Subject: [PATCH 265/266] Making TLS Scheduler available in Python wrapper --- gtsam/nonlinear/nonlinear.i | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i index c5802dbe12..0838523e0a 100644 --- a/gtsam/nonlinear/nonlinear.i +++ b/gtsam/nonlinear/nonlinear.i @@ -300,6 +300,11 @@ enum GncLossType { TLS /*Truncated least squares*/ }; +enum GncScheduler { + Linear, + SuperLinear +}; + template virtual class GncParams { GncParams(const PARAMS& baseOptimizerParams); @@ -314,6 +319,7 @@ virtual class GncParams { gtsam::This::IndexVector knownInliers; gtsam::This::IndexVector knownOutliers; bool allowNonNoiseModelFactors; + gtsam::GncScheduler scheduler; void setLossType(const gtsam::GncLossType type); void setMaxIterations(const size_t maxIter); @@ -324,6 +330,7 @@ virtual class GncParams { void setKnownInliers(const gtsam::This::IndexVector& knownIn); void setKnownOutliers(const gtsam::This::IndexVector& knownOut); void setAllowNonNoiseModelFactors(bool allow); + void setScheduler(const gtsam::GncScheduler scheduler); void print(const string& str = "GncParams: ") const; enum Verbosity { From 9d72d1e3c171e6b16dcda3ddcf3475944da0ad52 Mon Sep 17 00:00:00 2001 From: Harneet Singh Khanuja Date: Fri, 27 Feb 2026 16:56:25 -0500 Subject: [PATCH 266/266] Making TLS loss available in python wrapper --- gtsam/linear/linear.i | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/gtsam/linear/linear.i b/gtsam/linear/linear.i index 7c5b721fb5..1b7ce58a98 100644 --- a/gtsam/linear/linear.i +++ b/gtsam/linear/linear.i @@ -176,6 +176,21 @@ virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base { double loss(double error) const; }; +virtual class TruncatedLeastSquares: gtsam::noiseModel::mEstimator::Base { + TruncatedLeastSquares(double c); + TruncatedLeastSquares(double c, gtsam::noiseModel::mEstimator::Base::ReweightScheme reweight); + static gtsam::noiseModel::mEstimator::TruncatedLeastSquares* Create(double c); + static gtsam::noiseModel::mEstimator::TruncatedLeastSquares* Create( + double c, gtsam::noiseModel::mEstimator::Base::ReweightScheme reweight); + + // enabling serialization functionality + void serializable() const; + + double weight(double error) const; + double loss(double error) const; +}; + + virtual class DCS: gtsam::noiseModel::mEstimator::Base { DCS(double c); DCS(double c, gtsam::noiseModel::mEstimator::Base::ReweightScheme reweight);