From 1090de8c20fa10428844a8aff7eae8a17afd31c2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Mon, 23 Oct 2023 09:46:02 -0600
Subject: [PATCH 01/13] initial thoughts

---
 wecopttool/core.py | 115 +++++++++++++++++++--------------------------
 1 file changed, 49 insertions(+), 66 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 0ac3e025f..80df37b4f 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -16,8 +16,8 @@
 
 __all__ = [
     "WEC",
-    "ncomponents",
-    "frequency",
+    "ncomponents", #
+    "frequency", #
     "time",
     "time_mat",
     "derivative_mat",
@@ -48,8 +48,8 @@
     "subset_close",
     "scale_dofs",
     "decompose_state",
-    "frequency_parameters",
-    "time_results",
+    "frequency_parameters", #
+    "time_results", #
 ]
 
 
@@ -390,8 +390,8 @@ def from_bem(
             inertia_matrix = hydro_data['inertia_matrix'].values
 
         # frequency array
-        f1, nfreq = frequency_parameters(
-            hydro_data.omega.values/(2*np.pi), False)
+        f1, nfreq, nfreq_start = frequency_parameters(
+            hydro_data.omega.values/(2*np.pi))
 
         # check real part of damping diagonal > 0
         if min_damping is not None:
@@ -566,7 +566,7 @@ def from_impedance(
             If :python:`impedance` does not have the correct size:
             :python:`(ndof, ndof, nfreq)`.
         """
-        f1, nfreq = frequency_parameters(freqs, False)
+        f1, nfreq, nfreq_start = frequency_parameters(freqs)
 
         # impedance matrix shape
         shape = impedance.shape
@@ -1299,43 +1299,45 @@ def td_to_fd(
 
 def ncomponents(
     nfreq : int,
-    zero_freq: Optional[bool] = True,
+    nfreq_start: Optional[int] = 0,
 ) -> int:
-    """Number of Fourier components (:python:`2*nfreq`) for each
-    DOF. The sine component of the highest frequency (the 2-point wave)
-    is excluded as it will always evaluate to zero.
+    """Number of Fourier components for each DOF.
 
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`X0` is excluded, and the number of components is reduced by 1.
+    For most frequencies this is two compenents per frequency.
+    However, the sine component of the highest frequency
+    (the 2-point wave) is excluded as it will always evaluate to zero,
+    i.e., :python:`2*nfreq-1`.
+    If the first frequency is :python:`0`, an additional (DC) compoenent
+    is added, i.e., :python:`2*nfreq`.
 
     Parameters
     ----------
     nfreq
         Number of frequencies.
-    zero_freq
-        Whether to include the zero-frequency.
+    nfreq_start
+        Frequency index at which to start.
     """
-    ncomp = 2*nfreq - 1
-    if zero_freq:
-        ncomp = ncomp + 1
-    return ncomp
+    return 2*nfreq if (nfreq_start==0) else 2*nfreq-1
 
 
 def frequency(
     f1: float,
     nfreq: int,
-    zero_freq: Optional[bool] = True,
+    nfreq_start: Optional[int] = 0,
 ) -> ndarray:
     """Construct equally spaced frequency array.
 
-    The array includes :python:`0` and has length of :python:`nfreq+1`.
-    :python:`f1` is fundamental frequency (1st harmonic).
-
-    Returns the frequency array, e.g.,
-    :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`.
-
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`0` is excluded, and the vector length is reduced by 1.
+    Returns the frequency array
+    :python:`[f1*n_start, f1*(nfreq_start+1), ..., f1*(nfreq_start+nfreq-1)]`.
+    :python:`f1` is fundamental frequency (1st harmonic) and
+    :python:`n_start` allows the vector starting at a frequency other
+    than :python:`0`.
+    In particular, :python:`nfreq_start=0` includes the mean (DC)
+    component, while :python:`nfreq_start=1` excludes it (zero-mean).
+    Larger values of :python:`nfreq_start` can be useful in reducing the
+    computational cost when there is little or no energy in the lower
+    frequency components, specially when using a very fine
+    discretization (small f1).
 
     Parameters
     ----------
@@ -1343,12 +1345,10 @@ def frequency(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    zero_freq
-        Whether to include the zero-frequency.
+    nfreq_start
+        Frequency index at which to start.
     """
-    freq = np.arange(0, nfreq+1)*f1
-    freq = freq[1:] if not zero_freq else freq
-    return freq
+    return np.arange(nfreq_start, nfreq_start+nfreq)*f1
 
 
 def time(
@@ -2491,61 +2491,44 @@ def decompose_state(
 
 
 def frequency_parameters(
-    freqs: ArrayLike,
-    zero_freq: bool = True,
+    freqs: ArrayLike
 ) -> tuple[float, int]:
-    """Return the fundamental frequency and the number of frequencies
-    in a frequency array.
+    """Return the fundamental frequency, the number of frequencies, and
+    first frequency in a frequency array.
 
     This function can be used as a check for inputs to other functions
     since it raises an error if the frequency vector does not have
-    the correct format :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`
-    (or :python:`freqs = [f1, 2*f1, ..., nfreq*f1]` if
-    :python:`zero_freq = False`).
+    the correct format (equally spaced).
 
     Parameters
     ----------
     freqs
         The frequency array, starting at zero and having equal spacing.
-    zero_freq
-        Whether the first frequency should be zero.
 
     Returns
     -------
     f1
-        Fundamental frequency :python:`f1` [:math:`Hz`]
+        Fundamental frequency :python:`f1` [:math:`Hz`].
+        This is also the spacing.
     nfreq
         Number of frequencies (not including zero frequency),
         i.e., :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`.
+    nfreq_start
+        Frequency at which to start vector.
 
     Raises
     ------
     ValueError
         If the frequency vector is not evenly spaced.
-    ValueError
-        If the zero-frequency was expected but not included or not
-        expected but included.
     """
-    if np.isclose(freqs[0], 0.0):
-        if zero_freq:
-            freqs0 = freqs[:]
-        else:
-            raise ValueError('Zero frequency was included.')
-    else:
-        if zero_freq:
-            raise ValueError(
-                'Frequency array must start with the zero frequency.')
-        else:
-            freqs0 = np.concatenate([[0.0,], freqs])
-
-    f1 = freqs0[1]
-    nfreq = len(freqs0) - 1
-    f_check = np.arange(0, f1*(nfreq+0.5), f1)
-    if not np.allclose(f_check, freqs0):
-        raise ValueError("Frequency array 'omega' must be evenly spaced by" +
-                         "the fundamental frequency " +
-                         "(i.e., 'omega = [0, f1, 2*f1, ..., nfreq*f1]')")
-    return f1, nfreq
+    f1 = freqs[1] - freqs[0]
+    nfreq_start = int(freqs[0]/f1)
+    nfreq = len(freqs)
+    f_check = np.arange(nfreq_start, nfreq_start+nfreq, f1)
+    if not np.allclose(f_check, freqs):
+        raise ValueError("Frequency array must be evenly spaced by" +
+                         "the fundamental frequency ")
+    return f1, nfreq, nfreq_start
 
 
 def time_results(fd: DataArray, time: DataArray) -> ndarray:

From c660d7e1c198c83744327b3b2bf32e6c49a83bea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 09:07:23 -0600
Subject: [PATCH 02/13] frequency, ncomponents

---
 wecopttool/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 83ecb8927..10ba430d3 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -1283,7 +1283,7 @@ def ncomponents(
     nfreq_start
         Frequency index at which to start.
     """
-    return 2*nfreq if (nfreq_start==0) else 2*nfreq-1
+    return 2*nfreq-2 if (nfreq_start==0) else 2*nfreq-1
 
 
 def frequency(

From f6d770233223401daf3a3938e147fcc7a3866ff7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 09:25:56 -0600
Subject: [PATCH 03/13] fix ncomponents

---
 wecopttool/core.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 10ba430d3..6de9dec85 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -1325,6 +1325,7 @@ def frequency(
 def time(
     f1: float,
     nfreq: int,
+    nfreq_start: Optional[int] = 0,
     nsubsteps: Optional[int] = 1,
 ) -> ndarray:
     """Assemble the time vector with :python:`nsubsteps` subdivisions.
@@ -1341,6 +1342,8 @@ def time(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
+    nfreq_start
+        Frequency index at which to start.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.

From 38368a7caf64c4095bc43885f9e01da05877e3b7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 09:54:35 -0600
Subject: [PATCH 04/13] time, time_mat

---
 wecopttool/core.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 6de9dec85..66a704ade 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -1266,6 +1266,8 @@ def td_to_fd(
 def ncomponents(
     nfreq : int,
     nfreq_start: Optional[int] = 0,
+    total: Optional[bool] = False,
+    zero_freq_total: Optional[bool] = True,
 ) -> int:
     """Number of Fourier components for each DOF.
 
@@ -1282,8 +1284,18 @@ def ncomponents(
         Number of frequencies.
     nfreq_start
         Frequency index at which to start.
+    total
+        Whether to return the total number of frequencies as if it
+        started at f=0.
+    zero_freq_total
+        Whether to include the zero (DC) component when `total=True`.
     """
-    return 2*nfreq-2 if (nfreq_start==0) else 2*nfreq-1
+    if total:
+        nfreq = nfreq + nfreq_start
+        ncomp = 2*nfreq-2 if zero_freq_total else 2*nfreq-3
+    else:
+        ncomp = 2*nfreq-2 if (nfreq_start==0) else 2*nfreq-1
+    return ncomp
 
 
 def frequency(
@@ -1350,13 +1362,15 @@ def time(
     """
     if nsubsteps < 1:
         raise ValueError("'nsubsteps' must be 1 or greater")
-    nsteps = nsubsteps * ncomponents(nfreq)
+    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq_total=True)
+    nsteps = nsubsteps * ncomp
     return np.linspace(0, 1/f1, nsteps, endpoint=False)
 
 
 def time_mat(
     f1: float,
     nfreq: int,
+    nfreq_start: Optional[int] = 0,
     nsubsteps: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
 ) -> ndarray:
@@ -1381,23 +1395,25 @@ def time_mat(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
+    nfreq_start
+        Frequency index at which to start.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
     zero_freq
         Whether the first frequency should be zero.
     """
-    t = time(f1, nfreq, nsubsteps)
-    omega = frequency(f1, nfreq) * 2*np.pi
+    t = time(f1, nfreq, nfreq_start, nsubsteps)
+    omega = frequency(f1, nfreq+nfreq_start, nfreq_start=0) * 2*np.pi
     wt = np.outer(t, omega[1:])
-    ncomp = ncomponents(nfreq)
+    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq_total=True)
     time_mat = np.empty((nsubsteps*ncomp, ncomp))
     time_mat[:, 0] = 1.0
     time_mat[:, 1::2] = np.cos(wt)
     time_mat[:, 2::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
-    if not zero_freq:
-        time_mat = time_mat[:, 1:]
-    return time_mat
+    # if not zero_freq:
+        # time_mat = time_mat[:, 1:]
+    return time_mat[:, nfreq_start:]
 
 
 def derivative_mat(

From a7f89b0ea49d9994a5649141d1294d910487e0ae Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 11:14:38 -0600
Subject: [PATCH 05/13] derivative_mat, derivative2_mat

---
 wecopttool/core.py | 83 +++++++++++++++++++++-------------------------
 1 file changed, 37 insertions(+), 46 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 66a704ade..736036113 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -18,10 +18,10 @@
     "WEC",
     "ncomponents", #
     "frequency", #
-    "time",
-    "time_mat",
-    "derivative_mat",
-    "derivative2_mat",
+    "time", #
+    "time_mat", #
+    "derivative_mat", #
+    "derivative2_mat", #
     "mimo_transfer_mat",
     "vec_to_dofmat",
     "dofmat_to_vec",
@@ -50,7 +50,7 @@
     "decompose_state",
     "frequency_parameters",
     "time_results",
-    "set_fb_centers",
+    "set_fb_centers", #
 ]
 
 
@@ -1263,6 +1263,11 @@ def td_to_fd(
         return td_to_fd(td, fft, True)
 
 
+# TODO: directly create the submatrix needed.
+#       Currently creating the full matrix and returning a submatrix.
+#       functions: time_mat,
+
+
 def ncomponents(
     nfreq : int,
     nfreq_start: Optional[int] = 0,
@@ -1372,7 +1377,6 @@ def time_mat(
     nfreq: int,
     nfreq_start: Optional[int] = 0,
     nsubsteps: Optional[int] = 1,
-    zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Assemble the time matrix that converts the state to a
     time-series.
@@ -1384,11 +1388,6 @@ def time_mat(
     the response vector in the time-domain (:math:`x(t)`) is given as
     :math:`Mx`, where :math:`M` is the time matrix.
 
-    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
-
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
-
     Parameters
     ---------
     f1
@@ -1400,8 +1399,6 @@ def time_mat(
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
-    zero_freq
-        Whether the first frequency should be zero.
     """
     t = time(f1, nfreq, nfreq_start, nsubsteps)
     omega = frequency(f1, nfreq+nfreq_start, nfreq_start=0) * 2*np.pi
@@ -1411,15 +1408,13 @@ def time_mat(
     time_mat[:, 0] = 1.0
     time_mat[:, 1::2] = np.cos(wt)
     time_mat[:, 2::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
-    # if not zero_freq:
-        # time_mat = time_mat[:, 1:]
     return time_mat[:, nfreq_start:]
 
 
 def derivative_mat(
     f1: float,
     nfreq: int,
-    zero_freq: Optional[bool] = True,
+    nfreq_start: Optional[int] = 0,
 ) -> ndarray:
     """Assemble the derivative matrix that converts the state vector of
     a response to the state vector of its derivative.
@@ -1431,47 +1426,38 @@ def derivative_mat(
     the state of its derivative is given as :math:`Dx`, where
     :math:`D` is the derivative matrix.
 
-    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
-
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
-
     Parameters
     ---------
     f1
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    zero_freq
-        Whether the first frequency should be zero.
+    nfreq_start
+        Frequency index at which to start.
     """
     def block(n): return np.array([[0, -1], [1, 0]]) * n*f1 * 2*np.pi
-    blocks = [block(n+1) for n in range(nfreq)]
-    if zero_freq:
-        blocks = [0.0] + blocks
-    deriv_mat = block_diag(*blocks)
-    return deriv_mat[:-1, :-1] # remove 2pt wave sine component
+    if nfreq_start==0:
+        blocks = [0.0] + [block(n) for n in range(1, nfreq)]
+    else:
+        blocks = [block(n) for n in range(nfreq_start, nfreq+nfreq_start)]
+    deriv_mat = block_diag(*blocks)[:-1, :-1] # remove 2pt wave sine component
+    return deriv_mat
 
 
 def derivative2_mat(
     f1: float,
     nfreq: int,
-    zero_freq: Optional[bool] = True,
+    nfreq_start: Optional[int] = 0,
 ) -> ndarray:
-    """Assemble the second derivative matrix that converts the state vector of
-    a response to the state vector of its second derivative.
+    """Assemble the second derivative matrix that converts the state
+    vector of a response to the state vector of its second derivative.
 
     For a state :math:`x` consisting of the mean (DC) component
     followed by the real and imaginary components of the Fourier
-    coefficients (excluding the imaginary component of the 2-point wave) as
-    :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
-    the state of its second derivative is given as :math:`(DD)x`, where
-    :math:`DD` is the second derivative matrix.
-
-    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
-
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
+    coefficients (excluding the imaginary component of the 2-point wave)
+    as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
+    second derivative is given as :math:`(DD)x`, where :math:`DD` is the
+    second derivative matrix.
 
     Parameters
     ---------
@@ -1479,13 +1465,18 @@ def derivative2_mat(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    zero_freq
-        Whether the first frequency should be zero.
+    nfreq_start
+        Frequency index at which to start.
     """
-    vals = [((n+1)*f1 * 2*np.pi)**2 for n in range(nfreq)]
-    diagonal = np.repeat(-np.ones(nfreq) * vals, 2)[:-1] # remove 2pt wave sine
-    if zero_freq:
-        diagonal = np.concatenate(([0.0], diagonal))
+    if nfreq_start==0:
+        vals = [((n)*f1 * 2*np.pi)**2 for n in range(1, nfreq)]
+        diagonal = np.concatenate((
+            [0.0],
+            np.squeeze(np.repeat(-np.ones(nfreq-1) * vals, 2))[:-1], # remove 2pt wave sine
+        ))
+    else:
+        vals = [((n)*f1 * 2*np.pi)**2 for n in range(nfreq_start, nfreq+nfreq_start)]
+        diagonal = np.repeat(-np.ones(nfreq) * vals, 2)[:-1] # remove 2pt wave sine
     return np.diag(diagonal)
 
 

From 477e420ac1419670f4f7ca9acaa276358820b917 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 11:47:18 -0600
Subject: [PATCH 06/13] mimo_transfer_mat

---
 wecopttool/core.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 736036113..2e17dd93c 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -22,9 +22,9 @@
     "time_mat", #
     "derivative_mat", #
     "derivative2_mat", #
-    "mimo_transfer_mat",
-    "vec_to_dofmat",
-    "dofmat_to_vec",
+    "mimo_transfer_mat", #
+    "vec_to_dofmat", #
+    "dofmat_to_vec", #
     "real_to_complex",
     "complex_to_real",
     "fd_to_td",
@@ -1494,23 +1494,21 @@ def mimo_transfer_mat(
     :python`(ndof, ndof, nfreq)`.
 
     Returns the 2D real matrix that transform the state representation
-    of the input variable variable to the state representation of the
-    output variable.
+    of the input variables to the state representation of the output
+    variables.
     Here, a state representation :python:`x` consists of the mean (DC)
-    component followed by the real and imaginary components of the
-    Fourier coefficients (excluding the imaginary component of the
-    2-point wave) as
+    component (if included) followed by the real and imaginary c
+    omponents of the Fourier coefficients (excluding the imaginary
+    component of the 2-point wave) as
     :python:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`.
 
-    If :python:`zero_freq = False` (not default), the mean (DC) component
-    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
-
     Parameters
     ----------
     transfer_mat
         Complex transfer matrix.
     zero_freq
-        Whether the first frequency should be zero.
+        Whether the first elements :python:`tranfser_mat[:,:,0]`
+        correspond to frequency zero.
     """
     ndof = transfer_mat.shape[0]
     assert transfer_mat.shape[1] == ndof
@@ -1520,16 +1518,18 @@ def block(re, im): return np.array([[re, -im], [im, re]])
         for jdof in range(ndof):
             if zero_freq:
                 Zp0 = transfer_mat[idof, jdof, 0]
-                assert np.all(np.isreal(Zp0))
+                if not np.all(np.isreal(Zp0)):
+                    raise ValueError("Zero frequency component must be real.")
                 Zp0 = np.real(Zp0)
                 Zp = transfer_mat[idof, jdof, 1:]
             else:
-                Zp0 = [0.0]
                 Zp = transfer_mat[idof, jdof, :]
             re = np.real(Zp)
             im = np.imag(Zp)
             blocks = [block(ire, iim) for (ire, iim) in zip(re[:-1], im[:-1])]
-            blocks = [Zp0] + blocks + [re[-1]]
+            blocks = blocks + [re[-1]]
+            if zero_freq:
+                blocks = [Zp0] + blocks
             elem[idof][jdof] = block_diag(*blocks)
     return np.block(elem)
 

From 9aff59b396613e26420734079fe17b2a41356cb9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 12:11:16 -0600
Subject: [PATCH 07/13] clean docstrings so far

---
 wecopttool/core.py | 39 ++++++++++++++++++++++++++-------------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 2e17dd93c..ab26b3be2 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -1280,8 +1280,9 @@ def ncomponents(
     However, the sine component of the highest frequency
     (the 2-point wave) is excluded as it will always evaluate to zero,
     i.e., :python:`2*nfreq-1`.
-    If the first frequency is :python:`0`, an additional (DC) compoenent
-    is added, i.e., :python:`2*nfreq`.
+    If the first frequency is :python:`nfreq_start=0`, that frequency
+    only has one component (DC) and the total number of components is
+    :python:`2*nfreq`.
 
     Parameters
     ----------
@@ -1321,7 +1322,7 @@ def frequency(
     Larger values of :python:`nfreq_start` can be useful in reducing the
     computational cost when there is little or no energy in the lower
     frequency components, specially when using a very fine
-    discretization (small f1).
+    discretization (small :python:`f1`).
 
     Parameters
     ----------
@@ -1349,9 +1350,9 @@ def time(
 
     Returns the 1D time vector, in seconds, starting at time
     :python:`0`, and not containing the end time :python:`tf=1/f1`.
-    The time vector has length :python:`(2*nfreq)*nsubsteps`.
+    The time vector has length :python:`2*(nfreq+nfreq_start)*nsubsteps`.
     The timestep length is :python:`dt = dt_default * 1/nsubsteps`,
-    where :python:`dt_default=tf/(2*nfreq)`.
+    where :python:`dt_default=tf/(2*(nfreq+nfreq_start))`.
 
     Parameters
     ----------
@@ -1382,11 +1383,15 @@ def time_mat(
     time-series.
 
     For a state :math:`x` consisting of the mean (DC) component
-    followed by the real and imaginary components of the Fourier
-    coefficients (excluding the imaginary component of the 2-point wave) as
+    (if :python:`nfreq_start=0`) followed by the real and imaginary
+    components of the Fourier coefficients (excluding the imaginary
+    component of the 2-point wave) as
     :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
     the response vector in the time-domain (:math:`x(t)`) is given as
     :math:`Mx`, where :math:`M` is the time matrix.
+    If :python:`nfreq_start>0` the state vector does not include the
+    DC component and starts at components for frequency
+    :python:`nfreq_start*f1`.
 
     Parameters
     ---------
@@ -1421,10 +1426,13 @@ def derivative_mat(
 
     For a state :math:`x` consisting of the mean (DC) component
     followed by the real and imaginary components of the Fourier
-    coefficients (excluding the imaginary component of the 2-point wave) as
-    :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
-    the state of its derivative is given as :math:`Dx`, where
-    :math:`D` is the derivative matrix.
+    coefficients (excluding the imaginary component of the 2-point wave)
+    as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
+    derivative is given as :math:`Dx`, where :math:`D` is the derivative
+    matrix.
+    If :python:`nfreq_start>0` the state vector does not include the
+    DC component and starts at components for frequency
+    :python:`nfreq_start*f1`.
 
     Parameters
     ---------
@@ -1458,6 +1466,9 @@ def derivative2_mat(
     as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
     second derivative is given as :math:`(DD)x`, where :math:`DD` is the
     second derivative matrix.
+    If :python:`nfreq_start>0` the state vector does not include the
+    DC component and starts at components for frequency
+    :python:`nfreq_start*f1`.
 
     Parameters
     ---------
@@ -1497,10 +1508,12 @@ def mimo_transfer_mat(
     of the input variables to the state representation of the output
     variables.
     Here, a state representation :python:`x` consists of the mean (DC)
-    component (if included) followed by the real and imaginary c
-    omponents of the Fourier coefficients (excluding the imaginary
+    component (if included) followed by the real and imaginary
+    components of the Fourier coefficients (excluding the imaginary
     component of the 2-point wave) as
     :python:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`.
+    For multiple degrees of freedom, the state vector is stacked, as
+    :python:`[[x1], [x2], ...]`.
 
     Parameters
     ----------

From b41be92f006c06b5c180f01deeef046dcd56bb61 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 12:27:33 -0600
Subject: [PATCH 08/13] cleanup time_mat

---
 wecopttool/core.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index ab26b3be2..a802cc39a 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -1263,11 +1263,6 @@ def td_to_fd(
         return td_to_fd(td, fft, True)
 
 
-# TODO: directly create the submatrix needed.
-#       Currently creating the full matrix and returning a submatrix.
-#       functions: time_mat,
-
-
 def ncomponents(
     nfreq : int,
     nfreq_start: Optional[int] = 0,
@@ -1406,14 +1401,19 @@ def time_mat(
         A value of :python:`1` corresponds to the default step length.
     """
     t = time(f1, nfreq, nfreq_start, nsubsteps)
-    omega = frequency(f1, nfreq+nfreq_start, nfreq_start=0) * 2*np.pi
-    wt = np.outer(t, omega[1:])
-    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq_total=True)
-    time_mat = np.empty((nsubsteps*ncomp, ncomp))
-    time_mat[:, 0] = 1.0
-    time_mat[:, 1::2] = np.cos(wt)
-    time_mat[:, 2::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
-    return time_mat[:, nfreq_start:]
+    omega = frequency(f1, nfreq, nfreq_start) * 2*np.pi
+    ncomp = ncomponents(nfreq, nfreq_start)
+    time_mat = np.empty((nsubsteps*len(t), ncomp))
+    if nfreq_start==0:
+        wt = np.outer(t, omega[1:])
+        time_mat[:, 0] = 1.0
+        c0, s0 = 1,2
+    else:
+        wt = np.outer(t, omega)
+        c0,s0 = 0,1
+    time_mat[:, c0::2] = np.cos(wt)
+    time_mat[:, s0::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
+    return time_mat
 
 
 def derivative_mat(

From 370c7f9d215b6173571acac6592028f820c2460d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 1 Nov 2023 12:45:06 -0600
Subject: [PATCH 09/13] frequency_parameters, decompose_state

---
 wecopttool/core.py | 32 +++++++++++++++++++++-----------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index a802cc39a..1c5e7d4f1 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -48,8 +48,8 @@
     "subset_close",
     "scale_dofs",
     "decompose_state",
-    "frequency_parameters",
-    "time_results",
+    "frequency_parameters", #
+    "time_results", #
     "set_fb_centers", #
 ]
 
@@ -2427,6 +2427,7 @@ def decompose_state(
     state: ndarray,
     ndof: int,
     nfreq: int,
+    nfreq_start: Optional[int] = 0,
 ) -> tuple[ndarray, ndarray]:
     """Split the state vector into the WEC dynamics state and the
     optimization (control) state.
@@ -2444,6 +2445,8 @@ def decompose_state(
         Number of degrees of freedom for the WEC dynamics.
     nfreq
         Number of frequencies.
+    nfreq_start
+        Frequency index at which frequency vector starts.
 
     Returns
     -------
@@ -2452,12 +2455,13 @@ def decompose_state(
     state_opt
         Optimization (control) state.
     """
-    nstate_wec = ndof * ncomponents(nfreq)
+    nstate_wec = ndof * ncomponents(nfreq, nfreq_start)
     return state[:nstate_wec], state[nstate_wec:]
 
 
 def frequency_parameters(
-    freqs: ArrayLike
+    freqs: ArrayLike,
+    precision: Optional[int] = 10,
 ) -> tuple[float, int]:
     """Return the fundamental frequency, the number of frequencies, and
     first frequency in a frequency array.
@@ -2469,7 +2473,9 @@ def frequency_parameters(
     Parameters
     ----------
     freqs
-        The frequency array, starting at zero and having equal spacing.
+        The frequency array with equal spacing.
+    precision
+        Controls rounding of fundamental frequency.
 
     Returns
     -------
@@ -2477,10 +2483,9 @@ def frequency_parameters(
         Fundamental frequency :python:`f1` [:math:`Hz`].
         This is also the spacing.
     nfreq
-        Number of frequencies (not including zero frequency),
-        i.e., :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`.
+        Number of frequencies.
     nfreq_start
-        Frequency at which to start vector.
+        Frequency (index) at which vector starts.
 
     Raises
     ------
@@ -2488,11 +2493,16 @@ def frequency_parameters(
         If the frequency vector is not evenly spaced.
     """
     f1 = freqs[1] - freqs[0]
-    nfreq_start = int(freqs[0]/f1)
+    if precision is not None:
+        f1 = np.floor(f1*10**precision) / 10**precision
+    nfreq_start = round(freqs[0]/f1)
+    assert np.isclose(nfreq_start, freqs[0]/f1)
     nfreq = len(freqs)
-    f_check = np.arange(nfreq_start, nfreq_start+nfreq, f1)
+    f_check = np.arange(nfreq_start, nfreq_start+nfreq)*f1
     if not np.allclose(f_check, freqs):
-        raise ValueError("Frequency array must be evenly spaced by" +
+        print(f_check)
+        print(freqs)
+        raise ValueError("Frequency array must be evenly spaced by " +
                          "the fundamental frequency ")
     return f1, nfreq, nfreq_start
 

From 00b1c518a2fa174a25a3785f3644ee88e8e1ba5a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Thu, 2 Nov 2023 12:04:09 -0600
Subject: [PATCH 10/13] working on keeping DC component

---
 wecopttool/core.py | 158 +++++++++++++++++++++++----------------------
 1 file changed, 80 insertions(+), 78 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 1c5e7d4f1..2d8724b12 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -16,15 +16,15 @@
 
 __all__ = [
     "WEC",
-    "ncomponents", #
-    "frequency", #
-    "time", #
-    "time_mat", #
-    "derivative_mat", #
-    "derivative2_mat", #
-    "mimo_transfer_mat", #
-    "vec_to_dofmat", #
-    "dofmat_to_vec", #
+    "ncomponents",
+    "frequency",
+    "time",
+    "time_mat",
+    "derivative_mat",
+    "derivative2_mat",
+    "mimo_transfer_mat",
+    "vec_to_dofmat",
+    "dofmat_to_vec", ##
     "real_to_complex",
     "complex_to_real",
     "fd_to_td",
@@ -43,13 +43,13 @@
     "add_linear_friction",
     "wave_excitation",
     "hydrodynamic_impedance",
-    "atleast_2d",
+    "atleast_2d", ##
     "degrees_to_radians",
     "subset_close",
     "scale_dofs",
     "decompose_state",
-    "frequency_parameters", #
-    "time_results", #
+    "frequency_parameters",
+    "time_results",
     "set_fb_centers", #
 ]
 
@@ -1265,55 +1265,57 @@ def td_to_fd(
 
 def ncomponents(
     nfreq : int,
-    nfreq_start: Optional[int] = 0,
+    nfreq_start: Optional[int] = 1,
+    zero_freq: Optional[bool] = True,
     total: Optional[bool] = False,
-    zero_freq_total: Optional[bool] = True,
 ) -> int:
     """Number of Fourier components for each DOF.
 
     For most frequencies this is two compenents per frequency.
     However, the sine component of the highest frequency
-    (the 2-point wave) is excluded as it will always evaluate to zero,
-    i.e., :python:`2*nfreq-1`.
-    If the first frequency is :python:`nfreq_start=0`, that frequency
-    only has one component (DC) and the total number of components is
-    :python:`2*nfreq`.
+    (the 2-point wave) is excluded as it will always evaluate to zero.
+    Similarly, the zero-frequency (DC) component, if included via
+    :python:`zero_freq=True` (default) , only has a real part.
+
+    With the DC component included the  total number of components is
+    :python:`2*nfreq`, corresponding to :math:`x = [X0, Re(X1), Im(x1), ..., Re(Xn)]`
+    were :math:`X0` corresponds to frequency :python:`0`,
+    :math:`X1` corresponds to frequency :python:`nfreq_start*f1` and
+    :math:`Xn` corresponds to frequency :python:`(nfreq_start+nfreq-1)*f1`.
+
+    Without the DC component the totral number of components is one
+    less, or :python:`2*nfreq-1`
 
     Parameters
     ----------
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
+        Frequency index at which frequency vector starts.
     total
         Whether to return the total number of frequencies as if it
-        started at f=0.
-    zero_freq_total
-        Whether to include the zero (DC) component when `total=True`.
+        started at :python:`nfreq_start=1`.
+    zero_freq
+        Whether to include the zero (DC) component.
     """
-    if total:
-        nfreq = nfreq + nfreq_start
-        ncomp = 2*nfreq-2 if zero_freq_total else 2*nfreq-3
-    else:
-        ncomp = 2*nfreq-2 if (nfreq_start==0) else 2*nfreq-1
+    nfreq = nfreq if (not total) else (nfreq+nfreq_start-1)
+    ncomp = 2*nfreq if zero_freq else 2*nfreq-1
     return ncomp
 
 
 def frequency(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
-    precision: Optional[int] = 10,
+    nfreq_start: Optional[int] = 1,
+    zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Construct equally spaced frequency array.
 
     Returns the frequency array
-    :python:`[f1*n_start, f1*(nfreq_start+1), ..., f1*(nfreq_start+nfreq-1)]`.
+    :python:`[0, f1*nfreq_start, f1*(nfreq_start+1), ..., f1*(nfreq_start+nfreq-1)]`.
     :python:`f1` is fundamental frequency (1st harmonic) and
     :python:`n_start` allows the vector starting at a frequency other
-    than :python:`0`.
-    In particular, :python:`nfreq_start=0` includes the mean (DC)
-    component, while :python:`nfreq_start=1` excludes it (zero-mean).
+    than :python:`f1`.
     Larger values of :python:`nfreq_start` can be useful in reducing the
     computational cost when there is little or no energy in the lower
     frequency components, specially when using a very fine
@@ -1326,28 +1328,28 @@ def frequency(
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
-    precision
-        Controls rounding of fundamental frequency.
+        Frequency index at which frequency vector starts.
+    zero_freq
+        Whether to include the zero (DC) component.
     """
-    if precision is not None:
-        f1 = np.floor(f1*10**precision) / 10**precision
-    return np.arange(nfreq_start, nfreq_start+nfreq)*f1
+    freq = np.arange(nfreq_start, nfreq_start+nfreq)*f1
+    freq = freq if (not zero_freq) else np.concatenate(([0], freq))
+    return freq
 
 
 def time(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
+    nfreq_start: Optional[int] = 1,
     nsubsteps: Optional[int] = 1,
 ) -> ndarray:
     """Assemble the time vector with :python:`nsubsteps` subdivisions.
 
     Returns the 1D time vector, in seconds, starting at time
     :python:`0`, and not containing the end time :python:`tf=1/f1`.
-    The time vector has length :python:`2*(nfreq+nfreq_start)*nsubsteps`.
+    The time vector has length :python:`2*(nfreq+nfreq_start-1)*nsubsteps`.
     The timestep length is :python:`dt = dt_default * 1/nsubsteps`,
-    where :python:`dt_default=tf/(2*(nfreq+nfreq_start))`.
+    where :python:`dt_default=tf/(2*(nfreq+nfreq_start-1))`.
 
     Parameters
     ----------
@@ -1356,14 +1358,12 @@ def time(
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
+        Frequency index at which frequency vector starts.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
     """
-    if nsubsteps < 1:
-        raise ValueError("'nsubsteps' must be 1 or greater")
-    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq_total=True)
+    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq=True)
     nsteps = nsubsteps * ncomp
     return np.linspace(0, 1/f1, nsteps, endpoint=False)
 
@@ -1371,8 +1371,9 @@ def time(
 def time_mat(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
+    nfreq_start: Optional[int] = 1,
     nsubsteps: Optional[int] = 1,
+    zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Assemble the time matrix that converts the state to a
     time-series.
@@ -1384,9 +1385,10 @@ def time_mat(
     :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
     the response vector in the time-domain (:math:`x(t)`) is given as
     :math:`Mx`, where :math:`M` is the time matrix.
-    If :python:`nfreq_start>0` the state vector does not include the
-    DC component and starts at components for frequency
-    :python:`nfreq_start*f1`.
+    If :python:`nfreq_start>0` the state vector starts at components for
+    frequency :python:`nfreq_start*f1` instead of :python:`f1`.
+    If :python:`zero_freq=False` the DC component is assumed missing
+    from the state vector.
 
     Parameters
     ---------
@@ -1395,21 +1397,22 @@ def time_mat(
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
+        Frequency index at which the frequency vector starts.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
+    zero_freq
+        Whether the state vector includes the zero (DC) component.
     """
     t = time(f1, nfreq, nfreq_start, nsubsteps)
-    omega = frequency(f1, nfreq, nfreq_start) * 2*np.pi
-    ncomp = ncomponents(nfreq, nfreq_start)
+    omega = frequency(f1, nfreq, nfreq_start, zero_freq=False) * 2*np.pi
+    ncomp = ncomponents(nfreq, nfreq_start, zero_freq)
     time_mat = np.empty((nsubsteps*len(t), ncomp))
-    if nfreq_start==0:
-        wt = np.outer(t, omega[1:])
+    wt = np.outer(t, omega)
+    if zero_freq:
         time_mat[:, 0] = 1.0
         c0, s0 = 1,2
     else:
-        wt = np.outer(t, omega)
         c0,s0 = 0,1
     time_mat[:, c0::2] = np.cos(wt)
     time_mat[:, s0::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
@@ -1419,7 +1422,8 @@ def time_mat(
 def derivative_mat(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
+    nfreq_start: Optional[int] = 1,
+    zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Assemble the derivative matrix that converts the state vector of
     a response to the state vector of its derivative.
@@ -1430,7 +1434,7 @@ def derivative_mat(
     as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
     derivative is given as :math:`Dx`, where :math:`D` is the derivative
     matrix.
-    If :python:`nfreq_start>0` the state vector does not include the
+    If :python:`zero_freq=False` the state vector does not include the
     DC component and starts at components for frequency
     :python:`nfreq_start*f1`.
 
@@ -1441,13 +1445,14 @@ def derivative_mat(
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
+        Frequency index at which frequency vector starts.
+    zero_freq
+        Whether the state vector includes the zero (DC) component.
     """
     def block(n): return np.array([[0, -1], [1, 0]]) * n*f1 * 2*np.pi
-    if nfreq_start==0:
-        blocks = [0.0] + [block(n) for n in range(1, nfreq)]
-    else:
-        blocks = [block(n) for n in range(nfreq_start, nfreq+nfreq_start)]
+    blocks = [block(n) for n in range(nfreq_start, nfreq+nfreq_start)]
+    if zero_freq:
+        blocks = [0.0] + blocks
     deriv_mat = block_diag(*blocks)[:-1, :-1] # remove 2pt wave sine component
     return deriv_mat
 
@@ -1456,6 +1461,7 @@ def derivative2_mat(
     f1: float,
     nfreq: int,
     nfreq_start: Optional[int] = 0,
+    zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Assemble the second derivative matrix that converts the state
     vector of a response to the state vector of its second derivative.
@@ -1466,7 +1472,7 @@ def derivative2_mat(
     as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
     second derivative is given as :math:`(DD)x`, where :math:`DD` is the
     second derivative matrix.
-    If :python:`nfreq_start>0` the state vector does not include the
+    If :python:`zero_freq=False` the state vector does not include the
     DC component and starts at components for frequency
     :python:`nfreq_start*f1`.
 
@@ -1477,17 +1483,14 @@ def derivative2_mat(
     nfreq
         Number of frequencies.
     nfreq_start
-        Frequency index at which to start.
+        Frequency index at which frequency vector starts.
+    zero_freq
+        Whether the state vector includes the zero (DC) component.
     """
-    if nfreq_start==0:
-        vals = [((n)*f1 * 2*np.pi)**2 for n in range(1, nfreq)]
-        diagonal = np.concatenate((
-            [0.0],
-            np.squeeze(np.repeat(-np.ones(nfreq-1) * vals, 2))[:-1], # remove 2pt wave sine
-        ))
-    else:
-        vals = [((n)*f1 * 2*np.pi)**2 for n in range(nfreq_start, nfreq+nfreq_start)]
-        diagonal = np.repeat(-np.ones(nfreq) * vals, 2)[:-1] # remove 2pt wave sine
+    vals = [((n)*f1 * 2*np.pi)**2 for n in range(nfreq_start, nfreq+nfreq_start)]
+    diagonal = np.squeeze(np.repeat(-np.ones(nfreq-1) * vals, 2))[:-1] # remove 2pt wave sine
+    if zero_freq:
+        diagonal = np.concatenate(([0.0], diagonal))
     return np.diag(diagonal)
 
 
@@ -2493,9 +2496,8 @@ def frequency_parameters(
         If the frequency vector is not evenly spaced.
     """
     f1 = freqs[1] - freqs[0]
-    if precision is not None:
-        f1 = np.floor(f1*10**precision) / 10**precision
-    nfreq_start = round(freqs[0]/f1)
+    f1 = f1 if precision is None else round(f1, precision)
+    nfreq_start = round(f   reqs[0]/f1)
     assert np.isclose(nfreq_start, freqs[0]/f1)
     nfreq = len(freqs)
     f_check = np.arange(nfreq_start, nfreq_start+nfreq)*f1

From 5bf698d1f4aec01c4dfacd4d25814dc2c0770dc9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 22 Nov 2023 11:05:29 -0700
Subject: [PATCH 11/13] reset

---
 wecopttool/core.py | 261 +++++++++++++++++++++------------------------
 1 file changed, 119 insertions(+), 142 deletions(-)

diff --git a/wecopttool/core.py b/wecopttool/core.py
index 863023146..55b49616a 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -24,7 +24,7 @@
     "derivative2_mat",
     "mimo_transfer_mat",
     "vec_to_dofmat",
-    "dofmat_to_vec", ##
+    "dofmat_to_vec",
     "real_to_complex",
     "complex_to_real",
     "fd_to_td",
@@ -43,14 +43,14 @@
     "add_linear_friction",
     "wave_excitation",
     "hydrodynamic_impedance",
-    "atleast_2d", ##
+    "atleast_2d",
     "degrees_to_radians",
     "subset_close",
     "scale_dofs",
     "decompose_state",
     "frequency_parameters",
     "time_results",
-    "set_fb_centers", #
+    "set_fb_centers",
 ]
 
 
@@ -363,8 +363,8 @@ def from_bem(
         inertia_matrix = hydro_data['inertia_matrix'].values
 
         # frequency array
-        f1, nfreq, nfreq_start = frequency_parameters(
-            hydro_data.omega.values/(2*np.pi))
+        f1, nfreq = frequency_parameters(
+            hydro_data.omega.values/(2*np.pi), False)
 
         # check real part of damping diagonal > 0
         if min_damping is not None:
@@ -532,7 +532,7 @@ def from_impedance(
             If :python:`impedance` does not have the correct size:
             :python:`(ndof, ndof, nfreq)`.
         """
-        f1, nfreq, nfreq_start = frequency_parameters(freqs)
+        f1, nfreq = frequency_parameters(freqs, False)
 
         # impedance matrix shape
         shape = impedance.shape
@@ -1269,61 +1269,43 @@ def td_to_fd(
 
 def ncomponents(
     nfreq : int,
-    nfreq_start: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
-    total: Optional[bool] = False,
 ) -> int:
-    """Number of Fourier components for each DOF.
+    """Number of Fourier components (:python:`2*nfreq`) for each
+    DOF. The sine component of the highest frequency (the 2-point wave)
+    is excluded as it will always evaluate to zero.
 
-    For most frequencies this is two compenents per frequency.
-    However, the sine component of the highest frequency
-    (the 2-point wave) is excluded as it will always evaluate to zero.
-    Similarly, the zero-frequency (DC) component, if included via
-    :python:`zero_freq=True` (default) , only has a real part.
-
-    With the DC component included the  total number of components is
-    :python:`2*nfreq`, corresponding to :math:`x = [X0, Re(X1), Im(x1), ..., Re(Xn)]`
-    were :math:`X0` corresponds to frequency :python:`0`,
-    :math:`X1` corresponds to frequency :python:`nfreq_start*f1` and
-    :math:`Xn` corresponds to frequency :python:`(nfreq_start+nfreq-1)*f1`.
-
-    Without the DC component the totral number of components is one
-    less, or :python:`2*nfreq-1`
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`X0` is excluded, and the number of components is reduced by 1.
 
     Parameters
     ----------
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
-    total
-        Whether to return the total number of frequencies as if it
-        started at :python:`nfreq_start=1`.
     zero_freq
-        Whether to include the zero (DC) component.
+        Whether to include the zero-frequency.
     """
-    nfreq = nfreq if (not total) else (nfreq+nfreq_start-1)
-    ncomp = 2*nfreq if zero_freq else 2*nfreq-1
+    ncomp = 2*nfreq - 1
+    if zero_freq:
+        ncomp = ncomp + 1
     return ncomp
 
 
 def frequency(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Construct equally spaced frequency array.
 
-    Returns the frequency array
-    :python:`[0, f1*nfreq_start, f1*(nfreq_start+1), ..., f1*(nfreq_start+nfreq-1)]`.
-    :python:`f1` is fundamental frequency (1st harmonic) and
-    :python:`n_start` allows the vector starting at a frequency other
-    than :python:`f1`.
-    Larger values of :python:`nfreq_start` can be useful in reducing the
-    computational cost when there is little or no energy in the lower
-    frequency components, specially when using a very fine
-    discretization (small :python:`f1`).
+    The array includes :python:`0` and has length of :python:`nfreq+1`.
+    :python:`f1` is fundamental frequency (1st harmonic).
+
+    Returns the frequency array, e.g.,
+    :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`.
+
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`0` is excluded, and the vector length is reduced by 1.
 
     Parameters
     ----------
@@ -1331,8 +1313,6 @@ def frequency(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
     zero_freq
         Whether to include the zero-frequency.
     """
@@ -1344,16 +1324,15 @@ def frequency(
 def time(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 1,
     nsubsteps: Optional[int] = 1,
 ) -> ndarray:
     """Assemble the time vector with :python:`nsubsteps` subdivisions.
 
     Returns the 1D time vector, in seconds, starting at time
     :python:`0`, and not containing the end time :python:`tf=1/f1`.
-    The time vector has length :python:`2*(nfreq+nfreq_start-1)*nsubsteps`.
+    The time vector has length :python:`(2*nfreq)*nsubsteps`.
     The timestep length is :python:`dt = dt_default * 1/nsubsteps`,
-    where :python:`dt_default=tf/(2*(nfreq+nfreq_start-1))`.
+    where :python:`dt_default=tf/(2*nfreq)`.
 
     Parameters
     ----------
@@ -1361,21 +1340,19 @@ def time(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
     """
-    ncomp = ncomponents(nfreq, nfreq_start, total=True, zero_freq=True)
-    nsteps = nsubsteps * ncomp
+    if nsubsteps < 1:
+        raise ValueError("'nsubsteps' must be 1 or greater")
+    nsteps = nsubsteps * ncomponents(nfreq)
     return np.linspace(0, 1/f1, nsteps, endpoint=False)
 
 
 def time_mat(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 1,
     nsubsteps: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
 ) -> ndarray:
@@ -1383,16 +1360,16 @@ def time_mat(
     time-series.
 
     For a state :math:`x` consisting of the mean (DC) component
-    (if :python:`nfreq_start=0`) followed by the real and imaginary
-    components of the Fourier coefficients (excluding the imaginary
-    component of the 2-point wave) as
+    followed by the real and imaginary components of the Fourier
+    coefficients (excluding the imaginary component of the 2-point wave) as
     :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
     the response vector in the time-domain (:math:`x(t)`) is given as
     :math:`Mx`, where :math:`M` is the time matrix.
-    If :python:`nfreq_start>0` the state vector starts at components for
-    frequency :python:`nfreq_start*f1` instead of :python:`f1`.
-    If :python:`zero_freq=False` the DC component is assumed missing
-    from the state vector.
+
+    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
+
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
 
     Parameters
     ---------
@@ -1400,33 +1377,28 @@ def time_mat(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which the frequency vector starts.
     nsubsteps
         Number of steps between the default (implied) time steps.
         A value of :python:`1` corresponds to the default step length.
     zero_freq
-        Whether the state vector includes the zero (DC) component.
+        Whether the first frequency should be zero.
     """
-    t = time(f1, nfreq, nfreq_start, nsubsteps)
-    omega = frequency(f1, nfreq, nfreq_start, zero_freq=False) * 2*np.pi
-    ncomp = ncomponents(nfreq, nfreq_start, zero_freq)
-    time_mat = np.empty((nsubsteps*len(t), ncomp))
-    wt = np.outer(t, omega)
-    if zero_freq:
-        time_mat[:, 0] = 1.0
-        c0, s0 = 1,2
-    else:
-        c0,s0 = 0,1
-    time_mat[:, c0::2] = np.cos(wt)
-    time_mat[:, s0::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
+    t = time(f1, nfreq, nsubsteps)
+    omega = frequency(f1, nfreq) * 2*np.pi
+    wt = np.outer(t, omega[1:])
+    ncomp = ncomponents(nfreq)
+    time_mat = np.empty((nsubsteps*ncomp, ncomp))
+    time_mat[:, 0] = 1.0
+    time_mat[:, 1::2] = np.cos(wt)
+    time_mat[:, 2::2] = -np.sin(wt[:, :-1]) # remove 2pt wave sine component
+    if not zero_freq:
+        time_mat = time_mat[:, 1:]
     return time_mat
 
 
 def derivative_mat(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
 ) -> ndarray:
     """Assemble the derivative matrix that converts the state vector of
@@ -1434,13 +1406,15 @@ def derivative_mat(
 
     For a state :math:`x` consisting of the mean (DC) component
     followed by the real and imaginary components of the Fourier
-    coefficients (excluding the imaginary component of the 2-point wave)
-    as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
-    derivative is given as :math:`Dx`, where :math:`D` is the derivative
-    matrix.
-    If :python:`zero_freq=False` the state vector does not include the
-    DC component and starts at components for frequency
-    :python:`nfreq_start*f1`.
+    coefficients (excluding the imaginary component of the 2-point wave) as
+    :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
+    the state of its derivative is given as :math:`Dx`, where
+    :math:`D` is the derivative matrix.
+
+    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
+
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
 
     Parameters
     ---------
@@ -1448,37 +1422,36 @@ def derivative_mat(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
     zero_freq
-        Whether the state vector includes the zero (DC) component.
+        Whether the first frequency should be zero.
     """
     def block(n): return np.array([[0, -1], [1, 0]]) * n*f1 * 2*np.pi
-    blocks = [block(n) for n in range(nfreq_start, nfreq+nfreq_start)]
+    blocks = [block(n+1) for n in range(nfreq)]
     if zero_freq:
         blocks = [0.0] + blocks
-    deriv_mat = block_diag(*blocks)[:-1, :-1] # remove 2pt wave sine component
-    return deriv_mat
+    deriv_mat = block_diag(*blocks)
+    return deriv_mat[:-1, :-1] # remove 2pt wave sine component
 
 
 def derivative2_mat(
     f1: float,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
     zero_freq: Optional[bool] = True,
 ) -> ndarray:
-    """Assemble the second derivative matrix that converts the state
-    vector of a response to the state vector of its second derivative.
+    """Assemble the second derivative matrix that converts the state vector of
+    a response to the state vector of its second derivative.
 
     For a state :math:`x` consisting of the mean (DC) component
     followed by the real and imaginary components of the Fourier
-    coefficients (excluding the imaginary component of the 2-point wave)
-    as :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`, the state of its
-    second derivative is given as :math:`(DD)x`, where :math:`DD` is the
-    second derivative matrix.
-    If :python:`zero_freq=False` the state vector does not include the
-    DC component and starts at components for frequency
-    :python:`nfreq_start*f1`.
+    coefficients (excluding the imaginary component of the 2-point wave) as
+    :math:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`,
+    the state of its second derivative is given as :math:`(DD)x`, where
+    :math:`DD` is the second derivative matrix.
+
+    The time matrix has size :python:`(nfreq*2, nfreq*2)`.
+
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
 
     Parameters
     ---------
@@ -1486,13 +1459,11 @@ def derivative2_mat(
         Fundamental frequency :python:`f1` [:math:`Hz`].
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
     zero_freq
-        Whether the state vector includes the zero (DC) component.
+        Whether the first frequency should be zero.
     """
-    vals = [((n)*f1 * 2*np.pi)**2 for n in range(nfreq_start, nfreq+nfreq_start)]
-    diagonal = np.squeeze(np.repeat(-np.ones(nfreq-1) * vals, 2))[:-1] # remove 2pt wave sine
+    vals = [((n+1)*f1 * 2*np.pi)**2 for n in range(nfreq)]
+    diagonal = np.repeat(-np.ones(nfreq) * vals, 2)[:-1] # remove 2pt wave sine
     if zero_freq:
         diagonal = np.concatenate(([0.0], diagonal))
     return np.diag(diagonal)
@@ -1512,23 +1483,23 @@ def mimo_transfer_mat(
     :python`(nfreq, ndof, ndof)`.
 
     Returns the 2D real matrix that transform the state representation
-    of the input variables to the state representation of the output
-    variables.
+    of the input variable variable to the state representation of the
+    output variable.
     Here, a state representation :python:`x` consists of the mean (DC)
-    component (if included) followed by the real and imaginary
-    components of the Fourier coefficients (excluding the imaginary
-    component of the 2-point wave) as
+    component followed by the real and imaginary components of the
+    Fourier coefficients (excluding the imaginary component of the
+    2-point wave) as
     :python:`x=[X0, Re(X1), Im(X1), ..., Re(Xn)]`.
-    For multiple degrees of freedom, the state vector is stacked, as
-    :python:`[[x1], [x2], ...]`.
+
+    If :python:`zero_freq = False` (not default), the mean (DC) component
+    :python:`X0` is excluded, and the matrix/vector length is reduced by 1.
 
     Parameters
     ----------
     transfer_mat
         Complex transfer matrix.
     zero_freq
-        Whether the first elements :python:`tranfser_mat[:,:,0]`
-        correspond to frequency zero.
+        Whether the first frequency should be zero.
     """
     ndof = transfer_mat.shape[1]
     assert transfer_mat.shape[2] == ndof
@@ -1547,9 +1518,7 @@ def block(re, im): return np.array([[re, -im], [im, re]])
             re = np.real(Zp)
             im = np.imag(Zp)
             blocks = [block(ire, iim) for (ire, iim) in zip(re[:-1], im[:-1])]
-            blocks = blocks + [re[-1]]
-            if zero_freq:
-                blocks = [Zp0] + blocks
+            blocks = [Zp0] + blocks + [re[-1]]
             elem[idof][jdof] = block_diag(*blocks)
     return np.block(elem)
 
@@ -2431,7 +2400,6 @@ def decompose_state(
     state: ndarray,
     ndof: int,
     nfreq: int,
-    nfreq_start: Optional[int] = 0,
 ) -> tuple[ndarray, ndarray]:
     """Split the state vector into the WEC dynamics state and the
     optimization (control) state.
@@ -2449,8 +2417,6 @@ def decompose_state(
         Number of degrees of freedom for the WEC dynamics.
     nfreq
         Number of frequencies.
-    nfreq_start
-        Frequency index at which frequency vector starts.
 
     Returns
     -------
@@ -2459,55 +2425,66 @@ def decompose_state(
     state_opt
         Optimization (control) state.
     """
-    nstate_wec = ndof * ncomponents(nfreq, nfreq_start)
+    nstate_wec = ndof * ncomponents(nfreq)
     return state[:nstate_wec], state[nstate_wec:]
 
 
 def frequency_parameters(
     freqs: ArrayLike,
-    precision: Optional[int] = 10,
+    zero_freq: bool = True,
 ) -> tuple[float, int]:
-    """Return the fundamental frequency, the number of frequencies, and
-    first frequency in a frequency array.
+    """Return the fundamental frequency and the number of frequencies
+    in a frequency array.
 
     This function can be used as a check for inputs to other functions
     since it raises an error if the frequency vector does not have
-    the correct format (equally spaced).
+    the correct format :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`
+    (or :python:`freqs = [f1, 2*f1, ..., nfreq*f1]` if
+    :python:`zero_freq = False`).
 
     Parameters
     ----------
     freqs
-        The frequency array with equal spacing.
-    precision
-        Controls rounding of fundamental frequency.
+        The frequency array, starting at zero and having equal spacing.
+    zero_freq
+        Whether the first frequency should be zero.
 
     Returns
     -------
     f1
-        Fundamental frequency :python:`f1` [:math:`Hz`].
-        This is also the spacing.
+        Fundamental frequency :python:`f1` [:math:`Hz`]
     nfreq
-        Number of frequencies.
-    nfreq_start
-        Frequency (index) at which vector starts.
+        Number of frequencies (not including zero frequency),
+        i.e., :python:`freqs = [0, f1, 2*f1, ..., nfreq*f1]`.
 
     Raises
     ------
     ValueError
         If the frequency vector is not evenly spaced.
+    ValueError
+        If the zero-frequency was expected but not included or not
+        expected but included.
     """
-    f1 = freqs[1] - freqs[0]
-    f1 = f1 if precision is None else round(f1, precision)
-    nfreq_start = round(f   reqs[0]/f1)
-    assert np.isclose(nfreq_start, freqs[0]/f1)
-    nfreq = len(freqs)
-    f_check = np.arange(nfreq_start, nfreq_start+nfreq)*f1
-    if not np.allclose(f_check, freqs):
-        print(f_check)
-        print(freqs)
-        raise ValueError("Frequency array must be evenly spaced by " +
-                         "the fundamental frequency ")
-    return f1, nfreq, nfreq_start
+    if np.isclose(freqs[0], 0.0):
+        if zero_freq:
+            freqs0 = freqs[:]
+        else:
+            raise ValueError('Zero frequency was included.')
+    else:
+        if zero_freq:
+            raise ValueError(
+                'Frequency array must start with the zero frequency.')
+        else:
+            freqs0 = np.concatenate([[0.0,], freqs])
+
+    f1 = freqs0[1]
+    nfreq = len(freqs0) - 1
+    f_check = np.arange(0, f1*(nfreq+0.5), f1)
+    if not np.allclose(f_check, freqs0):
+        raise ValueError("Frequency array 'omega' must be evenly spaced by" +
+                         "the fundamental frequency " +
+                         "(i.e., 'omega = [0, f1, 2*f1, ..., nfreq*f1]')")
+    return f1, nfreq
 
 
 def time_results(fd: DataArray, time: DataArray) -> ndarray:

From 897ba812ee2aa8c68c37bfcbd15244b0b0ebb01a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Wed, 6 Dec 2023 11:40:47 -0700
Subject: [PATCH 12/13] there's some bug

---
 examples/tutorial_1_WaveBot.ipynb | 1407 ++++++++++++++++++++++++-----
 wecopttool/core.py                |  232 +++--
 wecopttool/pto.py                 |    7 +-
 wecopttool/waves.py               |   14 +-
 4 files changed, 1374 insertions(+), 286 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index 2f82207e6..55b701154 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
     "import matplotlib.pyplot as plt\n",
     "from scipy.optimize import brute\n",
     "\n",
-    "import wecopttool as wot"
+    "import wecopttool as wot\n"
    ]
   },
   {
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
     "mesh = wb.mesh(mesh_size_factor)\n",
     "fb = cpy.FloatingBody.from_meshio(mesh, name=\"WaveBot\")\n",
     "fb.add_translation_dof(name=\"Heave\")\n",
-    "ndof = fb.nb_dofs"
+    "ndof = fb.nb_dofs\n"
    ]
   },
   {
@@ -107,12 +107,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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AQJUqKLJrwYYDuuftOB09l6Mwf2+Nv6GF3CwWSRdD0At3d1Sof51qr40eIQAAUGUOn8nSlKUJ2nkiXZI0NCpMTw/pKP86Hvrz9S109GyOmjfycUoIkghCAACgChiGocVbk/X82kRdKCiSn7e7nr8rUnd0DnO0CfWv47QA9BuCEAAAqFSpmbmavnyXvk46I0m6rlVDzR/e2emhpyQEIQAAUGm+2GvTzM92Ky07X57uVs0Y2E5jezeX1WpxdmklIggBAICrlpVXqKdX79Un209IkjqE+il2VJTaBNdzcmWXRxACAABX5aejaZqyLEHH0y7IYpEm9m2pKf3byNO95k9OJwgBAIAKyS+0a+HGA3pr8yHZDalxQB0tGBmlHi0aOLu0MiMIAQCAcjuYmqnJSxO0JyVDkjTs2iaae2cH1fP2cHJl5UMQAgAAZWa3G/ow7qhi1u1XXqFd9X089MJdkRoUGers0iqEIAQAAMrElp6rJ5fv1Hf/vWt83zaBevmeTgry83ZyZRVHEAIAAFe0dtcpzVqxW+kXCuTtYdWswe11f89mslhq5rT4siIIAQCAUmXkFmjOqr1a8XOKJKlTE3+9OiJKrYJ8nVxZ5ShTEJo6dWq5Nzx79mw1aOA6o8YBAEBxPx4+p2nLdirl/AVZLdIjN7XSY/1ay8Ot5k+LLyuLYRjGlRpZrVb16tVLnp6eZdro999/r6SkJEVERFx1gb9JS0vTo48+qs8//1xWq1XDhg3TwoUL5etbciJNS0vTnDlz9OWXXyo5OVmBgYEaOnSonn32Wfn7+5d5vxkZGfL391d6err8/Pwq6+0AAFBj5RUW6dUvD2jRd4dlGFKzhj56dUSUujar7+zSyqys399lPjW2YsUKBQUFlaltvXqVfxXJMWPG6NSpU9qwYYMKCgr04IMPasKECfroo49KbH/y5EmdPHlS8+fPV4cOHXTs2DFNnDhRJ0+e1PLlyyu9PgAAaoP9tgxNXpKg/bZMSdKo7uF66vYOqutVO0fTlKlH6IMPPtCoUaPk5eVVpo1+9NFHGjJkiOrWrXvVBUpSYmKiOnTooG3btqlbt26SpPXr12vw4ME6ceKEwsLCrrCFiz755BPdd999ys7Olrt72Q4oPUIAADOw2w29t+WIXlqfpPwiuxrW9dS8YZ10S4dgZ5dWIZXaI/TAAw+Ua+ejR48uV/sriYuLU0BAgCMESVL//v1ltVq1detW3XXXXWXazm//GZcLQXl5ecrLy3M8z8jIqHjhAAC4gJPnL2jasp2KO3xOktSvXZDmDeukwHpl6wBxZVfVz5WVlSW73V5sWVX0mthstktOy7m7u6tBgway2Wxl2sbZs2f17LPPasKECZdtFxMTo6effrrCtQIA4EpWJaRo9so9yswtVB0PNz11ewfd2yPc5afFl1W5h30fOXJEt912m+rWrSt/f3/Vr19f9evXV0BAgOrXL98gqhkzZshisVz2sX///vKWeImMjAzddttt6tChg+bOnXvZtjNnzlR6errjcfz48avePwAANU16ToEe/fhnPb4kQZm5hYoKD9C6x2/Q6OimpglBUgV6hO677z4ZhqH33ntPwcHBV/WfNW3aNI0dO/aybSIiIhQSEqLU1NRiywsLC5WWlqaQkJDLrp+ZmamBAweqXr16WrFihTw8Ln8PFC8vrzKPhQIAwBVtOXhW05btlC0jV25Wix67ubUeuaml3GvRtPiyKncQ2rlzp7Zv3662bdte9c4DAwMVGBh4xXa9evXS+fPntX37dnXt2lWStGnTJtntdkVHR5e6XkZGhgYMGCAvLy+tXr1a3t6uewlwAACuVm5BkV5an6T3thyRJLVoVFcLRkYpKjzAuYU5UbmjX/fu3av9dFH79u01cOBAjR8/XvHx8dqyZYsmTZqkUaNGOWaMpaSkqF27doqPj5d0MQTdeuutys7O1rvvvquMjAzZbDbZbDYVFRVVa/0AADjb3pPpuuP17x0h6L6eTbX2setNHYKkCvQIvfPOO5o4caJSUlLUsWPHS041derUqdKK+73Fixdr0qRJ6tevn+OCiq+99prj9YKCAiUlJSknJ0eStGPHDm3dulWS1KpVq2LbOnLkiJo3b14ldQIAUJMU2Q0t+vawXt2QpIIiQ418vfTyPZ10U7uyXRuwtivTdYR+78cff9To0aN19OjR/78Ri0WGYchisdS63hauIwQAcFXH03I0bdlOxR9NkyTd2iFYMXdHqqFv7R8LW+lXlv7Nn//8Z3Xp0kUff/zxVQ+WBgAAlc8wDH26I0VzV+9VVl6h6nq6ac6d12h41yZ8b/9BuYPQsWPHtHr16ktONwEAAOf7NTtfs1bs1ro9F6+z161Zfb06IkpNG/o4ubKaqdxB6Oabb9bOnTsJQgAA1DCbk1L1t+W7lJqZJ3erRVNuaaOJfVvKzUovUGnKHYTuuOMOTZkyRbt371ZkZOQlg6XvvPPOSisOAABc2YX8IsWsS9SHccckSa2CfBU7MkodG/s7ubKar9yDpa3W0mfcM1gaAIDqtftEuh5f+rMOn8mWJI3t3VwzBrWTt4ebkytzriobLP3He4sBAIDqV1hk11ubD2nhxl9UaDcU7Oel+cM764bWV75QMf6/q7rpKgAAqH7HzmVrytIE7Ug+L0m6LTJUz9/VUQE+ns4tzAWV6crSr732mnJzc8u80bfffluZmZkVLgoAAFzKMAwtiU/WoIXfaUfyedXzcteCkZ31xuguhKAKKtMYITc3N9lstjLdF0yS/Pz8lJCQoIiIiKsu0NkYIwQAqAnOZuVp5me7tWHfaUlSdIsGemVEZzWpz7T4klTqGCHDMNSvXz+5u5ftTNqFCxfKViUAALiijYmnNf3TXTqblS9PN6ueGNBG466PYFp8JShTspkzZ065NjpkyBA1aNCgQgUBAICLcvIL9eyaRH0cnyxJahtcT7GjotQ+lDMUlaXc0+fNhlNjAABn+Dn5V01ZmqCj5y7eTPyh61voiQFtTT8tvqyqbPo8AACoOgVFdr2x6aDe+PqgiuyGwvy9NX94Z/Vu1cjZpdVKBCEAAGqIw2eyNGVpgnaeSJckDY0K09NDOsq/jscV1kRFEYQAAHAywzC0eGuynlu7T7kFdvl5u+v5uyJ1R+cwZ5dW6xGEAABwotTMXE1fvktfJ52RJF3XqqHmD++sUP86Tq7MHMp0QcXfe+aZZ5STk3PJ8gsXLuiZZ56plKIAADCDL/baNDD2O32ddEae7lb94/YO+r8/RxOCqlG5Z425ubnp1KlTCgoKKrb83LlzCgoK4qarAABcQVZeoZ5evVefbD8hSeoQ6qfYUVFqE1zPyZXVHlU2a8wwDFksl17AaefOnVw7CACAK/jpaJqmLEvQ8bQLslikh/u01NRb2sjTvdwnaVAJyhyE6tevL4vFIovFojZt2hQLQ0VFRcrKytLEiROrpEgAAFxdfqFdCzce0FubD8luSI0D6mjByCj1aEEngjOVOQjFxsbKMAz9+c9/1tNPPy1/f3/Ha56enmrevLl69epVJUUCAODKDqZmavLSBO1JyZAkDbu2iebe2UH1vJkW72xlDkIPPPCAJKlFixbq3bu3PDw4eAAAXI7dbujDuKOKWbdfeYV2Bfh4KOauSA2KDHV2afivco8R6tu3r+x2uw4cOKDU1FTZ7fZir/fp06fSigMAwFXZ0nP15PKd+u6Xs5Kkvm0C9fI9nRTk5+3kyvB75Q5CP/74o0aPHq1jx47pjxPOLBZLrZs1BgBAea3ddUqzVuxW+oUCeXtYNWtwe93fs1mJk43gXOUOQhMnTlS3bt20du1ahYaGclABAPivjNwCzVm1Vyt+TpEkRTb214KRUWoV5OvkylCacgehX375RcuXL1erVq2qoh4AAFzSj4fPadqynUo5f0FWi/TITa30WL/W8nBjWnxNVu4gFB0drYMHDxKEAACQlFdYpFe/PKBF3x2WYUjNGvro1RFR6tqsvrNLQxmUKQjt2rXL8e9HH31U06ZNk81mU2Rk5CWzxzp16lS5FQIAUEPtt2Vo8pIE7bdlSpJGdQ/XU7d3UF0vbuXpKsp0iw2r1SqLxXLJ4GjHRv77Wm0cLM0tNgAAf2S3G3pvyxG9tD5J+UV2NazrqXnDOumWDsHOLg3/Vam32Dhy5EilFQYAgCs7ef6Cpi3bqbjD5yRJ/doFad6wTgqs5+XkylARZQpCzZo1q+o6AACo8VYlpGj2yj3KzC1UHQ83PXV7B93bI5wZ1C6s3CcxV69eXeJyi8Uib29vtWrVSi1atLjqwgAAqCnScwo0e9Uefb7zpCQpKjxAC0ZGqUWjuk6uDFer3EFo6NChJY4X+v04oeuvv14rV65U/fqMmAcAuLYtB89q2rKdsmXkys1q0WM3t9YjN7WUO9Pia4VyH8UNGzaoe/fu2rBhg9LT05Wenq4NGzYoOjpaa9as0bfffqtz587piSeeqIp6AQCoFrkFRXrm830a885W2TJy1aJRXX36l956vH9rQlAtUu4eoccff1yLFi1S7969Hcv69esnb29vTZgwQXv37lVsbKz+/Oc/V2qhAABUl70n0zV5SYJ+Sc2SJN3Xs6lmDW4vH0+mxdc25T6ihw4dKnEamp+fnw4fPixJat26tc6ePXv11QEAUI2K7IYWfXtYr25IUkGRoUa+Xnr5nk66qV2Qs0tDFSl3317Xrl315JNP6syZM45lZ86c0d/+9jd1795d0sXbcISHh1delQAAVLHjaTm6d9GPenH9fhUUGbq1Q7C+mHwDIaiWK3eP0LvvvqshQ4aoSZMmjrBz/PhxRUREaNWqVZKkrKwszZ49u3IrBQCgChiGoU93pGju6r3KyitUXU83zbnzGg3v2oRp8SZQpitL/5HdbteXX36pAwcOSJLatm2rW265RVZr7Rs8xpWlAaD2+jU7X7NW7Na6PTZJUrdm9fXqiCg1bejj5Mpwtcr6/V2hIGQmBCEAqJ02J6Xqb8t3KTUzT+5Wi6bc0kYT+7aUm5VeoNqgUm+x8dprr2nChAny9vbWa6+9dtm2jz32WPkqLaO0tDQ9+uij+vzzz2W1WjVs2DAtXLhQvr6+pa7z8MMP66uvvtLJkyfl6+ur3r1768UXX1S7du2qpEYAQM13Ib9IMesS9WHcMUlSqyBfxY6MUsfG/k6uDM5Qph6hFi1a6KefflLDhg0ve9Voi8XimDlW2QYNGqRTp07pf/7nf1RQUKAHH3xQ3bt310cffVTqOosWLVK7du3UtGlTpaWlae7cuUpISNCRI0fk5uZWpv3SIwQAtcfuE+l6fOnPOnwmW5I0tndzzRjUTt4eZftOgOuoVafGEhMT1aFDB23btk3dunWTJK1fv16DBw/WiRMnFBYWVqbt7Nq1S507d9bBgwfVsmXLMq1DEAIA11dYZNdbmw9p4cZfVGg3FOznpZfv6aw+bQKdXRqqSFm/vys8ujk/P19JSUkqLCys6CbKLC4uTgEBAY4QJEn9+/eX1WrV1q1by7SN7Oxsvf/++2rRosVlp/bn5eUpIyOj2AMA4LqOncvWiP+J0ysbDqjQbui2yFB9MbkPIQiSKhCEcnJyNG7cOPn4+Oiaa65RcnKyJOnRRx/VvHnzKr1ASbLZbAoKKn4dB3d3dzVo0EA2m+2y6/7zn/+Ur6+vfH19tW7dOm3YsEGenp6lto+JiZG/v7/jwfWQAMA1GYahJfHJGrTwO+1IPq96Xu5aMLKz3hjdRQE+pX8PwFzKHYRmzpypnTt3avPmzfL29nYs79+/v5YuXVqubc2YMUMWi+Wyj/3795e3xGLGjBmjn3/+Wd98843atGmjESNGKDc3t9T2M2fOdNxDLT09XcePH7+q/QMAqt/ZrDxN+L/tmvHZbuXkFym6RQOtm3yD7urCtYFQXLkvqLhy5UotXbpUPXv2LPbDdM011+jQoUPl2ta0adM0duzYy7aJiIhQSEiIUlNTiy0vLCxUWlqaQkJCLrv+bz07rVu3Vs+ePVW/fn2tWLFC9957b4ntvby85OXlVa73AQCoOTYmntb0T3fpbFa+PN2semJAG427PoJp8ShRuYPQmTNnLjlNJV0cg1PelB0YGKjAwCufo+3Vq5fOnz+v7du3q2vXrpKkTZs2yW63Kzo6usz7MwxDhmEoLy+vXHUCAGq+7LxCPbc2UR/HXxyy0Ta4nmJHRal9KBNdULpynxrr1q2b1q5d63j+W/h555131KtXr8qr7Hfat2+vgQMHavz48YqPj9eWLVs0adIkjRo1yjFjLCUlRe3atVN8fLwk6fDhw4qJidH27duVnJysH374QcOHD1edOnU0ePDgKqkTAOAcPyf/qtte+84Rgh66voVWTbqOEIQrKneP0AsvvKBBgwZp3759Kiws1MKFC7Vv3z798MMP+uabb6qiRknS4sWLNWnSJPXr189xQcXfX9yxoKBASUlJysnJkSR5e3vru+++U2xsrH799VcFBwerT58++uGHH0rs0QIAuJ6CIrve2HRQb3x9UEV2Q2H+3po/vLN6t2rk7NLgIip0HaFDhw5p3rx52rlzp7KysnTttddq+vTpioyMrIoanYrrCAFAzXT4TJamLE3QzhPpkqShUWF6ekhH+dfxcHJlqAlq1QUVnYkgBAA1i2EYWrw1Wc+t3afcArv8vN31/F2RuqNz2S6uC3Oo1HuN/bbBsiAsAACqSmpmrqYv36Wvk85Ikq5r1VDzh3dWqH8dJ1cGV1XmIBQQEHDZWWGGYchisaioqKhSCgMA4Pe+2GvTzM92Ky07X57uVs0Y2E5jezeXlWnxuAplDkJff/2149+GYWjw4MF655131Lhx4yopDAAAScrKK9TTq/fqk+0nJEkdQv0UOypKbYLrObky1AZlDkJ9+/Yt9tzNzU09e/ZUREREpRcFAIAk/XQ0TVOWJeh42gVZLNLDfVpqyi2t5eXO3eJROco9fR4AgKqWX2jXwo0H9NbmQ7IbUuOAOlowMko9WjRwdmmoZQhCAIAa5WBqpiYvTdCelIuTdIZd20Rz7+yget5Mi0flu6ogxI3rAACVxW439GHcUcWs26+8QrsCfDwUc1ekBkWGOrs01GJlDkJ33313see5ubmaOHGi6tatW2z5Z599VjmVAQBMw5aeqyeX79R3v5yVJPVtE6iX7+mkID9vJ1eG2q7MQcjf37/Y8/vuu6/SiwEAmM/aXac0a8VupV8okLeHVbMGt9f9PZtx1gHVosxB6P3336/KOgAAJpORW6A5q/Zqxc8pkqTIxv5aMDJKrYJ8nVwZzITB0gCAavfj4XOatmynUs5fkNUiPXJTKz3Wr7U83KzOLg0mQxACAFSbvMIivfrlAS367rAMQ2rW0EevjohS12b1nV0aTIogBACoFvttGZq8JEH7bZmSpFHdw/XU7R1U14uvIjgPP30AgCpltxt6b8sRvbQ+SflFdjWs66mYuyN16zUhzi4NIAgBAKrOyfMXNG3ZTsUdPidJ6tcuSPOGdVJgPS8nVwZcRBACAFSJVQkpmr1yjzJzC1XHw01P3d5B9/YIZ1o8ahSCEACgUqXnFGj2qj36fOdJSVJUeIAWjIxSi0Z1r7AmUP0IQgCASrPl4FlNW7ZTtoxcuVkteuzm1nrkppZyZ1o8aiiCEADgquUWFOml9Ul6b8sRSVKLRnW1YGSUosIDnFsYcAUEIQDAVdl7Ml2TlyTol9QsSdJ9PZtq1uD28vHkKwY1Hz+lAIAKKbIbWvTtYb26IUkFRYYa+Xrp5Xs66aZ2Qc4uDSgzghAAoNyOp+Vo2rKdij+aJkm6tUOwYu6OVENfpsXDtRCEAABlZhiGPt2Rormr9yorr1B1Pd00585rNLxrE6bFwyURhAAAZfJrdr5mrditdXtskqRuzerr1RFRatrQx8mVARVHEAIAXNHmpFT9bfkupWbmyd1q0ZRb2mhi35Zys9ILBNdGEAIAlOpCfpFi1iXqw7hjkqRWQb6KHRmljo39nVwZUDkIQgCAEu06cV6Tlybo8JlsSdLY3s01Y1A7eXu4ObkyoPIQhAAAxRQW2fXW5kNauPEXFdoNBft56eV7OqtPm0BnlwZUOoIQAMDh2LlsTVmaoB3J5yVJt0WG6vm7OirAx9O5hQFVhCAEAJBhGFq67bieWbNPOflFquflrmeGXqOhUY2ZFo9ajSAEACZ3NitPMz7dra8ST0uSols00CsjOqtJfabFo/YjCAGAiW1MPK3pn+7S2ax8ebpZ9cSANhp3fQTT4mEaBCEAMKHsvEI9tzZRH8cnS5LaBtdT7KgotQ/1c3JlQPUiCAGAyfyc/KumLE3Q0XM5kqSHrm+hJwa0ZVo8TIkgBAAmUVBk1xubDuqNrw+qyG4ozN9b84d3Vu9WjZxdGuA0BCEAMIHDZ7I0ZWmCdp5IlyQNiQrTM0M6yr+Oh5MrA5yLIAQAtZhhGPr31mQ9v3afcgvs8vN213N3RerOzmHOLg2oEQhCAFBLpWbmavryXfo66Ywk6bpWDTV/eGeF+tdxcmVAzUEQAoBa6Iu9Ns38bLfSsvPl6W7V9IHt9GDv5rIyLR4oxursAsoqLS1NY8aMkZ+fnwICAjRu3DhlZWWVaV3DMDRo0CBZLBatXLmyagsFACfKyivUk5/s1MP/t11p2flqH+qnzyddr3HXtyAEASVwmR6hMWPG6NSpU9qwYYMKCgr04IMPasKECfroo4+uuG5sbCyXiAdQ6/10NE1TliXoeNoFWSzSw31aasotreXlzrR4oDQuEYQSExO1fv16bdu2Td26dZMkvf766xo8eLDmz5+vsLDSB/0lJCTolVde0U8//aTQ0NDqKhkAqk1+oV0LNx7QW5sPyW5IjQPqaMHIKPVo0cDZpQE1nkucGouLi1NAQIAjBElS//79ZbVatXXr1lLXy8nJ0ejRo/Xmm28qJCSkOkoFgGp1MDVTd7+1RW9+fTEEDbu2idZPvoEQBJSRS/QI2Ww2BQUFFVvm7u6uBg0ayGazlbrelClT1Lt3bw0ZMqTM+8rLy1NeXp7jeUZGRvkLBoAqZrcb+iDuqOat26+8QrsCfDwUc1ekBkXS8w2Uh1N7hGbMmCGLxXLZx/79+yu07dWrV2vTpk2KjY0t13oxMTHy9/d3PMLDwyu0fwCoKrb0XD3wfrye/nyf8grt6tsmUF9O7kMIAirAqT1C06ZN09ixYy/bJiIiQiEhIUpNTS22vLCwUGlpaaWe8tq0aZMOHTqkgICAYsuHDRumG264QZs3by5xvZkzZ2rq1KmO5xkZGYQhADXG2l2nNGvFbqVfKJCXu1V/v6297u/ZjAkhQAU5NQgFBgYqMDDwiu169eql8+fPa/v27eratauki0HHbrcrOjq6xHVmzJihhx56qNiyyMhILViwQHfccUep+/Ly8pKXl1c53gUAVL2M3ALNWbVXK35OkSRFNvbXgpFRahXk6+TKANfmEmOE2rdvr4EDB2r8+PF6++23VVBQoEmTJmnUqFGOGWMpKSnq16+fPvzwQ/Xo0UMhISEl9hY1bdpULVq0qO63AAAV9uPhc5q2bKdSzl+Q1SI9clMrPdavtTzcXGK+C1CjuUQQkqTFixdr0qRJ6tevn6xWq4YNG6bXXnvN8XpBQYGSkpKUk5PjxCoBoPLkFRbp1S8PaNF3h2UYUtMGPlowsrO6NmNGGFBZLIZhGM4uoibLyMiQv7+/0tPT5efn5+xyAJjEfluGJi9J0H5bpiRpVPdwzb69g3y9XObvV8Cpyvr9zW8UANQgdruh97Yc0Uvrk5RfZFfDup6KuTtSt17DtdCAqkAQAoAa4uT5C5q2bKfiDp+TJPVrF6R5wzopsB4TOICqQhACgBpgVUKKZq/co8zcQtXxcNNTt3fQvT3CmRYPVDGCEAA4UXpOgWav2qPPd56UJEWFB2jByCi1aFTXyZUB5kAQAgAn2XLwrKYt2ylbRq7crBY9dnNrPXJTS7kzLR6oNgQhAKhmuQVFeml9kt7bckSS1KJRXS0YGaWo8ADnFgaYEEEIAKrR3pPpmrwkQb+kZkmS7uvZVLMGt5ePJx/HgDPwmwcA1aDIbmjRt4f16oYkFRQZauTrpZfv6aSb2gU5uzTA1AhCAFDFjqflaNqynYo/miZJurVDsGLujlRDX6bFA85GEAKAKmIYhj7dkaK5q/cqK69QdT3dNOfOazS8axOmxQM1BEEIAKpAWna+/r5it9btsUmSujWrr1dHRKlpQx8nVwbg9whCAFDJNiel6snlu3QmM0/uVoum3NJGE/u2lJuVXiCgpiEIAUAluZBfpJh1ifow7pgkqVWQr2JHRqljY38nVwagNAQhAKgEu06c1+SlCTp8JluSNLZ3c80Y1E7eHm5OrgzA5RCEAOAqFBbZ9dbmQ1q48RcV2g0F+3np5Xs6q0+bQGeXBqAMCEIAUEHHzmVrytIE7Ug+L0m6LTJUz9/VUQE+ns4tDECZEYQAoJwMw9DSbcf1zJp9yskvUj0vdz095Brd1aUx0+IBF0MQAoByOJuVpxmf7tZXiaclSdEtGuiVEZ3VpD7T4gFXRBACgDLamHha0z/dpbNZ+fJws+iJW9vqoRsimBYPuDCCEABcQXZeoZ5bm6iP45MlSW2D62nByCh1CPNzcmUArhZBCAAuY0fyr5q6NEFHz+VIkh66voWeGNCWafFALUEQAoASFBTZ9fqmg3rz64MqshsK9ffWK8M7q3erRs4uDUAlIggBwB8cPpOlKUsTtPNEuiRpSFSYnrmzo/x9PJxcGYDKRhACgP8yDEP/3pqs59fuU26BXX7e7nrurkjd2TnM2aUBqCIEIQCQlJqZq+nLd+nrpDOSpOtaNdT84Z0V6l/HyZUBqEoEIQCm98Vem2Z+tltp2fnydLdq+sB2erB3c1mZFg/UegQhAKaVlVeop1fv1SfbT0iS2of6KXZklNqG1HNyZQCqC0EIgCltO5qmqcsSdDztgiwW6eE+LTXlltbycmdaPGAmBCEAppJfaFfsVwf09jeHZDekxgF19OqIzoqOaOjs0gA4AUEIgGkcTM3U5KUJ2pOSIUkadm0Tzbmzg/y8mRYPmBVBCECtZ7cb+iDuqOat26+8QrsCfDwUc1ekBkWGOrs0AE5GEAJQq9nSc/Xk8p367pezkqQ+bQL18j2dFOzn7eTKANQEBCEAtdbaXac0a8VupV8okJe7VX+/rb3u79lMFgvT4gFcRBACUOtk5BZozqq9WvFziiQpsrG/FoyMUqsgXydXBqCmIQgBqFV+PHxO05btVMr5C7JapEduaqXH+rWWh5vV2aUBqIEIQgBqhbzCIr365QEt+u6wDENq2sBHC0Z2VtdmDZxdGoAajCAEwOXtt2Vo8pIE7bdlSpJGdQ/X7Ns7yNeLjzgAl8enBACXZbcbem/LEb20Pkn5RXY1rOupmLsjdes1Ic4uDYCLIAgBcEknz1/QtGU7FXf4nCTp5nZBenFYJwXW83JyZQBcCUEIgMtZlZCi2Sv3KDO3UHU83PTU7R10b49wpsUDKDeXmUaRlpamMWPGyM/PTwEBARo3bpyysrIuu86NN94oi8VS7DFx4sRqqhhAZUvPKdCjH/+sx5ckKDO3UFHhAfrP4zdodHRTQhCACnGZHqExY8bo1KlT2rBhgwoKCvTggw9qwoQJ+uijjy673vjx4/XMM884nvv4+FR1qQCqwJaDZzVt2U7ZMnLlZrXo0ZtbadJNreTOtHgAV8ElglBiYqLWr1+vbdu2qVu3bpKk119/XYMHD9b8+fMVFhZW6ro+Pj4KCWHgJOCqcguK9NL6JL235YgkqUWjulowMkpR4QHOLQxAreASf0rFxcUpICDAEYIkqX///rJardq6detl1128eLEaNWqkjh07aubMmcrJyanqcgFUkr0n03XH6987QtB9PZtq7WPXE4IAVBqX6BGy2WwKCgoqtszd3V0NGjSQzWYrdb3Ro0erWbNmCgsL065duzR9+nQlJSXps88+K3WdvLw85eXlOZ5nZGRc/RsAUC5FdkOLvj2sVzckqaDIUCNfL718Tyfd1C7oyisDQDk4NQjNmDFDL7744mXbJCYmVnj7EyZMcPw7MjJSoaGh6tevnw4dOqSWLVuWuE5MTIyefvrpCu8TwNU5npajact2Kv5omiTp1g7Birk7Ug19mRYPoPI5NQhNmzZNY8eOvWybiIgIhYSEKDU1tdjywsJCpaWllWv8T3R0tCTp4MGDpQahmTNnaurUqY7nGRkZCg8PL/M+AFSMYRj6dEeK5q7eq6y8QtX1dNOcO6/R8K5NmBEGoMo4NQgFBgYqMDDwiu169eql8+fPa/v27erataskadOmTbLb7Y5wUxYJCQmSpNDQ0FLbeHl5ycuLvzyB6pSWna+/r9itdXsunuru1qy+Xh0RpaYNmeUJoGq5xGDp9u3ba+DAgRo/frzi4+O1ZcsWTZo0SaNGjXLMGEtJSVG7du0UHx8vSTp06JCeffZZbd++XUePHtXq1av1pz/9SX369FGnTp2c+XYA/M7mpFQNiP1W6/bY5G616MkBbbX04V6EIADVwiUGS0sXZ39NmjRJ/fr1k9Vq1bBhw/Taa685Xi8oKFBSUpJjVpinp6e++uorxcbGKjs7W+Hh4Ro2bJhmz57trLcA4Hcu5BcpZl2iPow7JklqFeSr2JFR6tjY38mVATATi2EYhrOLqMkyMjLk7++v9PR0+fn5ObscoFbYdeK8Ji9N0OEz2ZKksb2ba8agdvL2cHNyZQBqi7J+f7tMjxAA11dYZNdbmw9p4cZfVGg3FFTPS/OHd1afNlceKwgAVYEgBKBaHDuXrSlLE7Qj+bwk6bbIUD03tKPq1/V0bmEATI0gBKBKGYahpduO65k1+5STX6R6Xu56esg1uqtLY6bFA3A6ghCAKnM2K08zPt2trxJPS5KiWzTQKyM6q0l9ZoQBqBkIQgCqxMbE05r+6S6dzcqXh5tFT9zaVg/dECE3K71AAGoOghCASpWdV6jn1ibq4/hkSVLb4HpaMDJKHcKYdQmg5iEIOcmp9As6cjZbLRrVVah/HWeXA1yV336es/IK9cLaRB09d/F6Xg9d30JPDGjLtHgANRZByAmWbkvWzM92y25IVosUc3ekRnZv6uyygAr5/c/zb0L9vfXK8M7q3aqR8woDgDIgCFWzU+kXin1p2A1p+qe79X9xx+Tp7hJ3PAEc8gvt2nMyo9gyi6QPHuyhNiH1nFMUAJQDQaiaHTmbXewv59/88csEcFWGpHPZ+c4uAwDKhCBUzVo0qiurRcXCkNUivXBXJBeWg8v5NTtfMz/brd9nezeLRc0bMT0egGsgCFWzUP86irk7UrM+26Miw5CbxaIX7u7IGCG4LItFl/w8MwEAgKvgpqtXUFU3XT2VfkFHz+aoeSMfvjTg8vh5BlDTcNPVGi7Uvw5fGKg1+HkG4KqYpgQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEzLZYJQWlqaxowZIz8/PwUEBGjcuHHKysq64npxcXG6+eabVbduXfn5+alPnz66cOFCNVQMAABqOpcJQmPGjNHevXu1YcMGrVmzRt9++60mTJhw2XXi4uI0cOBA3XrrrYqPj9e2bds0adIkWa0u87YBAEAVshiGYTi7iCtJTExUhw4dtG3bNnXr1k2StH79eg0ePFgnTpxQWFhYiev17NlTt9xyi5599tkK7zsjI0P+/v5KT0+Xn59fhbcDAACqT1m/v12iayQuLk4BAQGOECRJ/fv3l9Vq1datW0tcJzU1VVu3blVQUJB69+6t4OBg9e3bV99///1l95WXl6eMjIxiDwAAUDu5RBCy2WwKCgoqtszd3V0NGjSQzWYrcZ3Dhw9LkubOnavx48dr/fr1uvbaa9WvXz/98ssvpe4rJiZG/v7+jkd4eHjlvREAAFCjODUIzZgxQxaL5bKP/fv3V2jbdrtdkvTwww/rwQcfVJcuXbRgwQK1bdtW7733XqnrzZw5U+np6Y7H8ePHK7R/AABQ87k7c+fTpk3T2LFjL9smIiJCISEhSk1NLba8sLBQaWlpCgkJKXG90NBQSVKHDh2KLW/fvr2Sk5NL3Z+Xl5e8vLzKUD0AAHB1Tg1CgYGBCgwMvGK7Xr166fz589q+fbu6du0qSdq0aZPsdruio6NLXKd58+YKCwtTUlJSseUHDhzQoEGDrr54AADg8lxijFD79u01cOBAjR8/XvHx8dqyZYsmTZqkUaNGOWaMpaSkqF27doqPj5ckWSwWPfnkk3rttde0fPlyHTx4UE899ZT279+vcePGOfPtAACAGsKpPULlsXjxYk2aNEn9+vWT1WrVsGHD9NprrzleLygoUFJSknJychzLJk+erNzcXE2ZMkVpaWnq3LmzNmzYoJYtWzrjLQAAgBrGJa4j5ExcRwgAANdTq64jBAAAUBUIQgAAwLQIQgAAwLRcZrC0s/w2hIpbbQAA4Dp++96+0lBogtAVZGZmShK32gAAwAVlZmbK39+/1NeZNXYFdrtdJ0+eVL169WSxWCptuxkZGQoPD9fx48eZjVbDcGxqJo5LzcWxqZnMflwMw1BmZqbCwsJktZY+EogeoSuwWq1q0qRJlW3fz8/PlD+groBjUzNxXGoujk3NZObjcrmeoN8wWBoAAJgWQQgAAJgWQchJvLy8NGfOHO50XwNxbGomjkvNxbGpmTguZcNgaQAAYFr0CAEAANMiCAEAANMiCAEAANMiCFWhN998U82bN5e3t7eio6MVHx9/2faffPKJ2rVrJ29vb0VGRuo///lPNVVqPuU5Nv/61790ww03qH79+qpfv7769+9/xWOJiinv78xvlixZIovFoqFDh1ZtgSZW3mNz/vx5PfLIIwoNDZWXl5fatGnDZ1oVKO9xiY2NVdu2bVWnTh2Fh4drypQpys3NraZqaygDVWLJkiWGp6en8d577xl79+41xo8fbwQEBBinT58usf2WLVsMNzc346WXXjL27dtnzJ492/Dw8DB2795dzZXXfuU9NqNHjzbefPNN4+effzYSExONsWPHGv7+/saJEyequfLarbzH5TdHjhwxGjdubNxwww3GkCFDqqdYkynvscnLyzO6detmDB482Pj++++NI0eOGJs3bzYSEhKqufLarbzHZfHixYaXl5exePFi48iRI8YXX3xhhIaGGlOmTKnmymsWglAV6dGjh/HII484nhcVFRlhYWFGTExMie1HjBhh3HbbbcWWRUdHGw8//HCV1mlG5T02f1RYWGjUq1fP+OCDD6qqRFOqyHEpLCw0evfubbzzzjvGAw88QBCqIuU9Nm+99ZYRERFh5OfnV1eJplTe4/LII48YN998c7FlU6dONa677roqrbOm49RYFcjPz9f27dvVv39/xzKr1ar+/fsrLi6uxHXi4uKKtZekAQMGlNoeFVORY/NHOTk5KigoUIMGDaqqTNOp6HF55plnFBQUpHHjxlVHmaZUkWOzevVq9erVS4888oiCg4PVsWNHvfDCCyoqKqqusmu9ihyX3r17a/v27Y7TZ4cPH9Z//vMfDR48uFpqrqm411gVOHv2rIqKihQcHFxseXBwsPbv31/iOjabrcT2Nputyuo0o4ocmz+aPn26wsLCLgmuqLiKHJfvv/9e7777rhISEqqhQvOqyLE5fPiwNm3apDFjxug///mPDh48qL/+9a8qKCjQnDlzqqPsWq8ix2X06NE6e/asrr/+ehmGocLCQk2cOFGzZs2qjpJrLHqEgHKYN2+elixZohUrVsjb29vZ5ZhWZmam7r//fv3rX/9So0aNnF0O/sButysoKEiLFi1S165dNXLkSP3973/X22+/7ezSTG3z5s164YUX9M9//lM7duzQZ599prVr1+rZZ591dmlORY9QFWjUqJHc3Nx0+vTpYstPnz6tkJCQEtcJCQkpV3tUTEWOzW/mz5+vefPm6auvvlKnTp2qskzTKe9xOXTokI4ePao77rjDscxut0uS3N3dlZSUpJYtW1Zt0SZRkd+Z0NBQeXh4yM3NzbGsffv2stlsys/Pl6enZ5XWbAYVOS5PPfWU7r//fj300EOSpMjISGVnZ2vChAn6+9//LqvVnH0j5nzXVczT01Ndu3bVxo0bHcvsdrs2btyoXr16lbhOr169irWXpA0bNpTaHhVTkWMjSS+99JKeffZZrV+/Xt26dauOUk2lvMelXbt22r17txISEhyPO++8UzfddJMSEhIUHh5eneXXahX5nbnuuut08OBBRziVpAMHDig0NJQQVEkqclxycnIuCTu/hVXDzHfbcvZo7dpqyZIlhpeXl/G///u/xr59+4wJEyYYAQEBhs1mMwzDMO6//35jxowZjvZbtmwx3N3djfnz5xuJiYnGnDlzmD5fRcp7bObNm2d4enoay5cvN06dOuV4ZGZmOust1ErlPS5/xKyxqlPeY5OcnGzUq1fPmDRpkpGUlGSsWbPGCAoKMp577jlnvYVaqbzHZc6cOUa9evWMjz/+2Dh8+LDx5ZdfGi1btjRGjBjhrLdQIxCEqtDrr79uNG3a1PD09DR69Ohh/Pjjj47X+vbtazzwwAPF2i9btsxo06aN4enpaVxzzTXG2rVrq7li8yjPsWnWrJkh6ZLHnDlzqr/wWq68vzO/RxCqWuU9Nj/88IMRHR1teHl5GREREcbzzz9vFBYWVnPVtV95jktBQYExd+5co2XLloa3t7cRHh5u/PWvfzV+/fXX6i+8BuHu8wAAwLQYIwQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIAQAAEyLIASgVrjxxhs1efJkx/PmzZsrNja2SvdnsVhksViUkJBwVdsaO3asY1srV66slPoAlA1BCEC1+f0XvoeHh1q0aKG//e1vys3NrfR9bdu2TRMmTKj07f7e+PHjderUKXXs2PGqtrNw4UKdOnWqkqoCUB7uzi4AgLkMHDhQ77//vgoKCrR9+3Y98MADslgsevHFFyt1P4GBgZW6vZL4+PgoJCTkqrfj7+8vf3//SqgIQHnRIwSgWnl5eSkkJETh4eEaOnSo+vfvrw0bNjheP3funO699141btxYPj4+ioyM1Mcff1xsG9nZ2frTn/4kX19fhYaG6pVXXrlkP78/NXb06NFLTmGdP39eFotFmzdvliT9+uuvGjNmjAIDA1WnTh21bt1a77//frne2+bNm2WxWPTFF1+oS5cuqlOnjm6++WalpqZq3bp1at++vfz8/DR69Gjl5OSUa9sAqgY9QgCcZs+ePfrhhx/UrFkzx7Lc3Fx17dpV06dPl5+fn9auXav7779fLVu2VI8ePSRJTz75pL755hutWrVKQUFBmjVrlnbs2KGoqKgK1/LUU09p3759WrdunRo1aqSDBw/qwoULFdrW3Llz9cYbb8jHx0cjRozQiBEj5OXlpY8++khZWVm666679Prrr2v69OkVrhdA5SAIAahWa9aska+vrwoLC5WXlyer1ao33njD8Xrjxo31xBNPOJ4/+uij+uKLL7Rs2TL16NFDWVlZevfdd/Xvf/9b/fr1kyR98MEHatKkyVXVlZycrC5duqhbt26SLvYoVdRzzz2n6667TpI0btw4zZw5U4cOHVJERIQk6Z577tHXX39NEAJqAIIQgGp100036a233lJ2drYWLFggd3d3DRs2zPF6UVGRXnjhBS1btkwpKSnKz89XXl6efHx8JEmHDh1Sfn6+oqOjHes0aNBAbdu2vaq6/vKXv2jYsGHasWOHbr31Vg0dOlS9e/eu0LY6derk+HdwcLB8fHwcIei3ZfHx8VdVL4DKwRghANWqbt26atWqlTp37qz33ntPW7du1bvvvut4/eWXX9bChQs1ffp0ff3110pISNCAAQOUn59f4X1arRc/6gzDcCwrKCgo1mbQoEE6duyYpkyZopMnT6pfv37FeqbKw8PDw/Hv32bI/Z7FYpHdbq/QtgFULoIQAKexWq2aNWuWZs+e7RiPs2XLFg0ZMkT33XefOnfurIiICB04cMCxTsuWLeXh4aGtW7c6lv3666/F2vzRbzPIfj9FvaRr/wQGBuqBBx7Qv//9b8XGxmrRokVX+xYB1HAEIQBONXz4cLm5uenNN9+UJLVu3VobNmzQDz/8oMTERD388MM6ffq0o72vr6/GjRunJ598Ups2bdKePXs0duxYR69PSerUqaOePXtq3rx5SkxM1DfffKPZs2cXa/OPf/xDq1at0sGDB7V3716tWbNG7du3r5o3DaDGIAgBcCp3d3dNmjRJL730krKzszV79mxde+21GjBggG688UaFhIRo6NChxdZ5+eWXdcMNN+iOO+5Q//79df3116tr166X3c97772nwsJCde3aVZMnT9Zzzz1X7HVPT0/NnDlTnTp1Up8+feTm5qYlS5ZU9tsFUMNYjN+fNAcAlMmNN96oqKioSr2Nh8Vi0YoVKy4JfgCqDj1CAFBB//znP+Xr66vdu3df1XYmTpwoX1/fSqoKQHnQIwQAFZCSkuIY4N20aVN5enpWeFupqanKyMiQJIWGhqpu3bqVUiOAKyMIAQAA0+LUGAAAMC2CEAAAMC2CEAAAMC2CEAAAMC2CEAAAMC2CEAAAMC2CEAAAMC2CEAAAMC2CEAAAMK3/B2XrYwAnL8FfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fb.show_matplotlib()\n",
-    "_ = wb.plot_cross_section(show=True)  # specific to WaveBot"
+    "_ = wb.plot_cross_section(show=True)  # specific to WaveBot\n"
    ]
   },
   {
@@ -127,22 +148,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Minimum wavelength in frequency spectrum (0.24980959867703897) is smaller than the minimum computable wavelength (0.8056649972775113).\n"
+     ]
+    }
+   ],
    "source": [
     "f1 = 0.05\n",
-    "nfreq = 50\n",
-    "freq = wot.frequency(f1, nfreq, False) # False -> no zero frequency\n",
+    "ifreq_start = 3\n",
+    "ifreq_end = 50\n",
+    "freq = wot.frequency(f1, ifreq_end)[ifreq_start:]\n",
     "\n",
     "min_computable_wavelength = fb.minimal_computable_wavelength\n",
     "g = 9.81\n",
-    "min_period = 1/(f1*nfreq)\n",
+    "min_period = 1/(f1*ifreq_end)\n",
     "min_wavelength = (g*(min_period)**2)/(2*np.pi)\n",
     "\n",
     "if min_wavelength < min_computable_wavelength:\n",
     "    print(f'Warning: Minimum wavelength in frequency spectrum ({min_wavelength}) is smaller'\n",
-    "         f' than the minimum computable wavelength ({min_computable_wavelength}).')"
+    "         f' than the minimum computable wavelength ({min_computable_wavelength}).')\n"
    ]
   },
   {
@@ -157,11 +187,158 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:wecopttool.core:Using the geometric centroid as the center of gravity (COG).\n",
+      "WARNING:wecopttool.core:Using the center of gravity (COG) as the rotation center for hydrostatics.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=8.796, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.111, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.425, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.739, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.053, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.367, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.681, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.996, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.310, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.624, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.938, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.252, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.566, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.881, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.195, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.509, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.823, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.137, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.451, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.765, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.080, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.394, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.708, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=8.796, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.111, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.425, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.739, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.053, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.367, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.681, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.996, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.310, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.624, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.938, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.252, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.566, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.881, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.195, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.509, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.823, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.137, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.451, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.765, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.080, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.394, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
+      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.708, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
+      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
+      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
+     ]
+    }
+   ],
    "source": [
-    "bem_data = wot.run_bem(fb, freq)"
+    "bem_data = wot.run_bem(fb, freq)\n"
    ]
   },
   {
@@ -181,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +367,7 @@
     "controller = None\n",
     "loss = None\n",
     "pto_impedance = None\n",
-    "pto = wot.pto.PTO(ndof, kinematics, controller, pto_impedance, loss, name)"
+    "pto = wot.pto.PTO(ndof, kinematics, controller, pto_impedance, loss, name)\n"
    ]
   },
   {
@@ -202,25 +379,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# PTO dynamics forcing function\n",
     "f_add = {'PTO': pto.force_on_wec}\n",
     "\n",
-    "# Constraint\n",
-    "f_max = 2000.0\n",
-    "nsubsteps = 4\n",
+    "# # Constraint\n",
+    "# f_max = 2000.0\n",
+    "# nsubsteps = 4\n",
     "\n",
-    "def const_f_pto(wec, x_wec, x_opt, waves): # Format for scipy.optimize.minimize\n",
-    "    f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
-    "    return f_max - np.abs(f.flatten())\n",
+    "# def const_f_pto(wec, x_wec, x_opt, waves): # Format for scipy.optimize.minimize\n",
+    "#     f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
+    "#     return f_max - np.abs(f.flatten())\n",
     "\n",
-    "ineq_cons = {'type': 'ineq',\n",
-    "             'fun': const_f_pto,\n",
-    "             }\n",
-    "constraints = [ineq_cons]"
+    "# ineq_cons = {'type': 'ineq',\n",
+    "#              'fun': const_f_pto,\n",
+    "#              }\n",
+    "# constraints = [ineq_cons]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DEBUG\n",
+    "constraints = []\n"
    ]
   },
   {
@@ -233,16 +420,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 37.36584622341133 N/(m/s).\n"
+     ]
+    }
+   ],
    "source": [
     "wec = wot.WEC.from_bem(\n",
     "    bem_data,\n",
     "    constraints=constraints,\n",
     "    friction=None,\n",
     "    f_add=f_add,\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -252,6 +447,66 @@
     "Note: We might receive a warning regarding negative linear damping values. Per default, WecOptTool ensures that the BEM data does not contain non-negative damping values. If you would like to correct the BEM solution manually to a minimum damping value you can specify `min_damping`. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 , 0.65,\n",
+       "       0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15, 1.2 ,\n",
+       "       1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 , 1.75,\n",
+       "       1.8 , 1.85, 1.9 , 1.95, 2.  , 2.05, 2.1 , 2.15, 2.2 , 2.25, 2.3 ,\n",
+       "       2.35, 2.4 , 2.45, 2.5 ])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-2.886579864025407e-16, -4.329869796038111e-17)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# DEBUG\n",
+    "x_wec = np.zeros(wec.ncomponents); x_wec[3] = 1\n",
+    "x_opt = np.zeros(wec.ncomponents); x_opt[3] = 1\n",
+    "# plt.plot(pto.force_on_wec(wec, x_wec, x_opt, None))\n",
+    "plt.plot(pto.mechanical_power(wec, x_wec, x_opt, None))\n",
+    "\n",
+    "pto.energy(wec, x_wec, x_opt, None), pto.mechanical_average_power(wec, x_wec, x_opt, None)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -267,15 +522,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "amplitude = 0.0625  \n",
+    "amplitude = 0.0625\n",
     "wavefreq = 0.3\n",
     "phase = 30\n",
     "wavedir = 0\n",
-    "waves = wot.waves.regular_wave(f1, nfreq, wavefreq, amplitude, phase, wavedir)"
+    "waves = wot.waves.regular_wave(f1, ifreq_end, wavefreq, amplitude, phase, wavedir, ifreq_start)\n"
    ]
   },
   {
@@ -290,12 +545,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "obj_fun = pto.mechanical_average_power\n",
-    "nstate_opt = 2*nfreq"
+    "nstate_opt = wec.ncomponents\n"
    ]
   },
   {
@@ -315,9 +570,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 1814.8949648460753\n",
+      "            Iterations: 26\n",
+      "            Function evaluations: 81\n",
+      "            Gradient evaluations: 26\n",
+      "Optimal average mechanical power: 181489.49648460752 W\n"
+     ]
+    }
+   ],
    "source": [
     "options = {'maxiter': 200}\n",
     "scale_x_wec = 1e1\n",
@@ -325,17 +593,17 @@
     "scale_obj = 1e-2\n",
     "\n",
     "results = wec.solve(\n",
-    "    waves, \n",
-    "    obj_fun, \n",
+    "    waves,\n",
+    "    obj_fun,\n",
     "    nstate_opt,\n",
-    "    optim_options=options, \n",
+    "    optim_options=options,\n",
     "    scale_x_wec=scale_x_wec,\n",
     "    scale_x_opt=scale_x_opt,\n",
     "    scale_obj=scale_obj,\n",
     "    )\n",
     "\n",
     "opt_mechanical_average_power = results.fun\n",
-    "print(f'Optimal average mechanical power: {opt_mechanical_average_power} W')"
+    "print(f'Optimal average mechanical power: {opt_mechanical_average_power} W')\n"
    ]
   },
   {
@@ -348,13 +616,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "nsubsteps = 5\n",
     "pto_fdom, pto_tdom = pto.post_process(wec, results, waves, nsubsteps=nsubsteps)\n",
-    "wec_fdom, wec_tdom = wec.post_process(results, waves, nsubsteps=nsubsteps)"
+    "wec_fdom, wec_tdom = wec.post_process(results, waves, nsubsteps=nsubsteps)\n"
    ]
   },
   {
@@ -366,12 +634,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12a573e20>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
-    "pto_tdom['power'].loc['mech',:,:].plot()"
+    "pto_tdom['power'].loc['mech',:,:].plot()\n"
    ]
   },
   {
@@ -383,22 +672,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12a54a470>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
-    "wec_tdom['pos'].plot()"
+    "wec_tdom['pos'].plot()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12a67f070>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
-    "pto_tdom['force'].plot()"
+    "pto_tdom['force'].plot()\n"
    ]
   },
   {
@@ -410,11 +741,623 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       " */\n",
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+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
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+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
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+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (time: 500, dof: 1, type: 2)\n",
+       "Coordinates:\n",
+       "  * time     (time) float64 0.0 0.01333 0.02667 0.04 ... 6.613 6.627 6.64 6.653\n",
+       "  * dof      (dof) &lt;U9 &#x27;PTO_Heave&#x27;\n",
+       "  * type     (type) &lt;U4 &#x27;mech&#x27; &#x27;elec&#x27;\n",
+       "Data variables:\n",
+       "    pos      (time, dof) float64 4.697 3.707 2.201 0.6375 ... 3.079 4.235 4.875\n",
+       "    vel      (time, dof) float64 -15.28 -33.16 -40.84 ... 31.74 24.67 6.664\n",
+       "    acc      (time, dof) float64 -522.7 -332.5 -34.93 ... -4.326 -334.8 -527.7\n",
+       "    force    (time, dof) float64 -7.828e+05 -4.828e+05 ... -4.702e+05 -7.859e+05\n",
+       "    power    (type, time, dof) float64 1.196e+07 1.601e+07 ... -5.237e+06\n",
+       "Attributes:\n",
+       "    time_created_utc:  2023-12-06 17:12:35.128070</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-844fa7a0-e63d-4bb5-8280-1b6f2357f269' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-844fa7a0-e63d-4bb5-8280-1b6f2357f269' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 500</li><li><span class='xr-has-index'>dof</span>: 1</li><li><span class='xr-has-index'>type</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-caa57cd5-9641-400a-ac6f-b19eb14cec09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-caa57cd5-9641-400a-ac6f-b19eb14cec09' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.01333 0.02667 ... 6.64 6.653</div><input id='attrs-a251a644-d60c-40a7-bf8a-72355c7498e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a251a644-d60c-40a7-bf8a-72355c7498e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bce732-0d7d-47c2-a74a-4be13774eb58' class='xr-var-data-in' type='checkbox'><label for='data-20bce732-0d7d-47c2-a74a-4be13774eb58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.013333, 0.026667, ..., 6.626667, 6.64    , 6.653333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dof</span></div><div class='xr-var-dims'>(dof)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;PTO_Heave&#x27;</div><input id='attrs-eec1658b-7b32-4a41-afbf-60dcf658aff9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eec1658b-7b32-4a41-afbf-60dcf658aff9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e114f36f-2c06-4dcd-99a6-bb22aa21970c' class='xr-var-data-in' type='checkbox'><label for='data-e114f36f-2c06-4dcd-99a6-bb22aa21970c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PTO degree of freedom</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;PTO_Heave&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>type</span></div><div class='xr-var-dims'>(type)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;mech&#x27; &#x27;elec&#x27;</div><input id='attrs-5f628936-8f86-495a-aded-93d84ddf1451' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f628936-8f86-495a-aded-93d84ddf1451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67429ae4-eebb-40c6-a07d-30c4cf5b5168' class='xr-var-data-in' type='checkbox'><label for='data-67429ae4-eebb-40c6-a07d-30c4cf5b5168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Power</dd><dt><span>units :</span></dt><dd>W</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;mech&#x27;, &#x27;elec&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fe4bdac-871a-4a0b-827a-2b25940dbf6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fe4bdac-871a-4a0b-827a-2b25940dbf6f' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pos</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.697 3.707 2.201 ... 4.235 4.875</div><input id='attrs-48ab93a9-b3d9-4460-a508-01d471c450ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48ab93a9-b3d9-4460-a508-01d471c450ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5221580d-ddde-4e16-80d5-414af458a9ab' class='xr-var-data-in' type='checkbox'><label for='data-5221580d-ddde-4e16-80d5-414af458a9ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Position</dd><dt><span>units :</span></dt><dd>m or rad</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.69676101],\n",
+       "       [ 3.7074971 ],\n",
+       "       [ 2.20073865],\n",
+       "       [ 0.63749863],\n",
+       "       [-0.52640229],\n",
+       "       [-1.00021876],\n",
+       "       [-0.74828766],\n",
+       "       [ 0.01879649],\n",
+       "       [ 0.94291635],\n",
+       "       [ 1.66499679],\n",
+       "       [ 1.96107046],\n",
+       "       [ 1.81581005],\n",
+       "       [ 1.40743938],\n",
+       "       [ 1.01331073],\n",
+       "       [ 0.87660207],\n",
+       "       [ 1.08963975],\n",
+       "       [ 1.54258167],\n",
+       "       [ 1.96022168],\n",
+       "       [ 2.01445413],\n",
+       "       [ 1.46901245],\n",
+       "...\n",
+       "       [ 7.82641539],\n",
+       "       [ 4.52633003],\n",
+       "       [ 1.54485773],\n",
+       "       [-0.35521161],\n",
+       "       [-0.71992371],\n",
+       "       [ 0.45325814],\n",
+       "       [ 2.71797626],\n",
+       "       [ 5.32541483],\n",
+       "       [ 7.46506041],\n",
+       "       [ 8.51903991],\n",
+       "       [ 8.24597146],\n",
+       "       [ 6.83561799],\n",
+       "       [ 4.82052347],\n",
+       "       [ 2.87943204],\n",
+       "       [ 1.60279318],\n",
+       "       [ 1.30106385],\n",
+       "       [ 1.91884778],\n",
+       "       [ 3.07905715],\n",
+       "       [ 4.23539637],\n",
+       "       [ 4.87481735]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.28 -33.16 ... 24.67 6.664</div><input id='attrs-54f4cddb-96f2-4564-b7d8-90663b5ba0b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f4cddb-96f2-4564-b7d8-90663b5ba0b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3767671-4b9a-43f6-8275-92ca06a8a99c' class='xr-var-data-in' type='checkbox'><label for='data-d3767671-4b9a-43f6-8275-92ca06a8a99c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Velocity</dd><dt><span>units :</span></dt><dd>m/s or rad/s</dd></dl></div><div class='xr-var-data'><pre>array([[-1.52806344e+01],\n",
+       "       [-3.31567578e+01],\n",
+       "       [-4.08417830e+01],\n",
+       "       [-3.61634368e+01],\n",
+       "       [-2.14383445e+01],\n",
+       "       [-2.35928477e+00],\n",
+       "       [ 1.42856810e+01],\n",
+       "       [ 2.31690918e+01],\n",
+       "       [ 2.22934716e+01],\n",
+       "       [ 1.35025635e+01],\n",
+       "       [ 1.49528466e+00],\n",
+       "       [-8.22492655e+00],\n",
+       "       [-1.16363949e+01],\n",
+       "       [-7.81779195e+00],\n",
+       "       [ 7.32385549e-01],\n",
+       "       [ 9.19455739e+00],\n",
+       "       [ 1.25073730e+01],\n",
+       "       [ 7.60102652e+00],\n",
+       "       [-5.11393412e+00],\n",
+       "       [-2.16258397e+01],\n",
+       "...\n",
+       "       [-7.93620968e+01],\n",
+       "       [-8.23154999e+01],\n",
+       "       [-6.41322779e+01],\n",
+       "       [-2.97300052e+01],\n",
+       "       [ 1.08463400e+01],\n",
+       "       [ 4.56376358e+01],\n",
+       "       [ 6.46239293e+01],\n",
+       "       [ 6.29170801e+01],\n",
+       "       [ 4.22231386e+01],\n",
+       "       [ 1.01039468e+01],\n",
+       "       [-2.26765177e+01],\n",
+       "       [-4.57870062e+01],\n",
+       "       [-5.26943130e+01],\n",
+       "       [-4.26289187e+01],\n",
+       "       [-2.05296669e+01],\n",
+       "       [ 4.94144815e+00],\n",
+       "       [ 2.45604522e+01],\n",
+       "       [ 3.17422943e+01],\n",
+       "       [ 2.46711013e+01],\n",
+       "       [ 6.66400217e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acc</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-522.7 -332.5 ... -334.8 -527.7</div><input id='attrs-fe2ca592-8c50-4978-9eea-34606ce94bfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2ca592-8c50-4978-9eea-34606ce94bfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fa0187-ca92-4527-8390-b38ff8c29d8d' class='xr-var-data-in' type='checkbox'><label for='data-46fa0187-ca92-4527-8390-b38ff8c29d8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Acceleration</dd><dt><span>units :</span></dt><dd>m/s^2 or rad/s^2</dd></dl></div><div class='xr-var-data'><pre>array([[ -522.71767197],\n",
+       "       [ -332.53075254],\n",
+       "       [  -34.93363082],\n",
+       "       [  258.7038327 ],\n",
+       "       [  445.31996557],\n",
+       "       [  467.08212545],\n",
+       "       [  330.00563115],\n",
+       "       [   98.11664356],\n",
+       "       [ -133.77691263],\n",
+       "       [ -277.77871519],\n",
+       "       [ -286.50284459],\n",
+       "       [ -169.8948715 ],\n",
+       "       [   10.80474551],\n",
+       "       [  169.22935029],\n",
+       "       [  229.77523998],\n",
+       "       [  159.11535384],\n",
+       "       [  -19.06681614],\n",
+       "       [ -232.10061355],\n",
+       "       [ -386.3399652 ],\n",
+       "       [ -404.62109514],\n",
+       "...\n",
+       "       [ -330.96836164],\n",
+       "       [  202.84573168],\n",
+       "       [  692.6768524 ],\n",
+       "       [  984.17550625],\n",
+       "       [  985.62058904],\n",
+       "       [  698.65525255],\n",
+       "       [  217.51186935],\n",
+       "       [ -302.92372133],\n",
+       "       [ -699.10086917],\n",
+       "       [ -852.91145713],\n",
+       "       [ -729.4499908 ],\n",
+       "       [ -385.85328176],\n",
+       "       [   51.16900931],\n",
+       "       [  430.64715776],\n",
+       "       [  629.33092414],\n",
+       "       [  592.24636587],\n",
+       "       [  348.19333706],\n",
+       "       [   -4.32558065],\n",
+       "       [ -334.76997373],\n",
+       "       [ -527.74364974]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>force</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.828e+05 ... -7.859e+05</div><input id='attrs-24e4bf0b-1ca2-4d10-85ca-5157e644fe8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24e4bf0b-1ca2-4d10-85ca-5157e644fe8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26119098-cf25-41be-b9fb-f47f90699c1c' class='xr-var-data-in' type='checkbox'><label for='data-26119098-cf25-41be-b9fb-f47f90699c1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Force or moment on WEC</dd><dt><span>units :</span></dt><dd>N or Nm</dd></dl></div><div class='xr-var-data'><pre>array([[ -782832.96347105],\n",
+       "       [ -482796.38939677],\n",
+       "       [  -11380.13267833],\n",
+       "       [  452353.68920421],\n",
+       "       [  743361.25896803],\n",
+       "       [  769834.35986832],\n",
+       "       [  542856.66645464],\n",
+       "       [  166671.25527764],\n",
+       "       [ -205438.40057645],\n",
+       "       [ -432013.32057917],\n",
+       "       [ -437566.22179696],\n",
+       "       [ -239586.2128614 ],\n",
+       "       [   61378.86863422],\n",
+       "       [  324153.21154303],\n",
+       "       [  425078.96313546],\n",
+       "       [  309130.64409823],\n",
+       "       [   13958.72036409],\n",
+       "       [ -342837.18944828],\n",
+       "       [ -608813.19243686],\n",
+       "       [ -657224.43683103],\n",
+       "...\n",
+       "       [ -385879.92826719],\n",
+       "       [  439989.28946113],\n",
+       "       [ 1200633.88176686],\n",
+       "       [ 1651365.16313436],\n",
+       "       [ 1646479.19871744],\n",
+       "       [ 1188352.7042935 ],\n",
+       "       [  426329.58649498],\n",
+       "       [ -394079.89902149],\n",
+       "       [-1013972.08023278],\n",
+       "       [-1247166.25310344],\n",
+       "       [-1040186.60635186],\n",
+       "       [ -486056.16973034],\n",
+       "       [  211865.44570966],\n",
+       "       [  812418.59443909],\n",
+       "       [ 1119661.11301784],\n",
+       "       [ 1047446.12289875],\n",
+       "       [  643799.88232169],\n",
+       "       [   67972.60875276],\n",
+       "       [ -470177.36488641],\n",
+       "       [ -785853.82099391]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power</span></div><div class='xr-var-dims'>(type, time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.196e+07 1.601e+07 ... -5.237e+06</div><input id='attrs-6c68710a-d677-47b4-91dc-8284ab86a8cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c68710a-d677-47b4-91dc-8284ab86a8cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7364bda-0741-41e1-ae75-42b59c73af2b' class='xr-var-data-in' type='checkbox'><label for='data-d7364bda-0741-41e1-ae75-42b59c73af2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Power</dd><dt><span>units :</span></dt><dd>W</dd></dl></div><div class='xr-var-data'><pre>array([[[ 1.19621843e+07],\n",
+       "        [ 1.60079629e+07],\n",
+       "        [ 4.64784909e+05],\n",
+       "        [-1.63586640e+07],\n",
+       "        [-1.59364348e+07],\n",
+       "        [-1.81625848e+06],\n",
+       "        [ 7.75507716e+06],\n",
+       "        [ 3.86162162e+06],\n",
+       "        [-4.57993514e+06],\n",
+       "        [-5.83328727e+06],\n",
+       "        [-6.54286059e+05],\n",
+       "        [ 1.97057900e+06],\n",
+       "        [-7.14228751e+05],\n",
+       "        [-2.53416237e+06],\n",
+       "        [ 3.11321690e+05],\n",
+       "        [ 2.84231945e+06],\n",
+       "        [ 1.74586922e+05],\n",
+       "        [-2.60591457e+06],\n",
+       "        [ 3.11343056e+06],\n",
+       "        [ 1.42130303e+07],\n",
+       "...\n",
+       "        [ 3.06242402e+07],\n",
+       "        [-3.62179383e+07],\n",
+       "        [-7.69993857e+07],\n",
+       "        [-4.90950949e+07],\n",
+       "        [ 1.78582732e+07],\n",
+       "        [ 5.42336079e+07],\n",
+       "        [ 2.75510930e+07],\n",
+       "        [-2.47943566e+07],\n",
+       "        [-4.28130837e+07],\n",
+       "        [-1.26013014e+07],\n",
+       "        [ 2.35878100e+07],\n",
+       "        [ 2.22550569e+07],\n",
+       "        [-1.11641041e+07],\n",
+       "        [-3.46325262e+07],\n",
+       "        [-2.29862697e+07],\n",
+       "        [ 5.17590070e+06],\n",
+       "        [ 1.58120162e+07],\n",
+       "        [ 2.15760655e+06],\n",
+       "        [-1.15997934e+07],\n",
+       "        [-5.23693157e+06]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dab467ac-d622-4824-97f5-8b6670cac65e' class='xr-section-summary-in' type='checkbox'  ><label for='section-dab467ac-d622-4824-97f5-8b6670cac65e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64423ac0-f2c1-4f7e-9876-390ba86c9d2b' class='xr-index-data-in' type='checkbox'/><label for='index-64423ac0-f2c1-4f7e-9876-390ba86c9d2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.013333333333333332, 0.026666666666666665,\n",
+       "       0.039999999999999994,  0.05333333333333333,  0.06666666666666667,\n",
+       "        0.07999999999999999,  0.09333333333333332,  0.10666666666666666,\n",
+       "                       0.12,\n",
+       "       ...\n",
+       "          6.533333333333333,    6.546666666666666,                 6.56,\n",
+       "          6.573333333333333,    6.586666666666666,                  6.6,\n",
+       "          6.613333333333333,    6.626666666666666,                 6.64,\n",
+       "          6.653333333333333],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dof</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9257f9c-464b-4316-8e41-553171194775' class='xr-index-data-in' type='checkbox'/><label for='index-f9257f9c-464b-4316-8e41-553171194775' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;PTO_Heave&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dof&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>type</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6169e33a-90fb-4c3f-9318-712fb914471b' class='xr-index-data-in' type='checkbox'/><label for='index-6169e33a-90fb-4c3f-9318-712fb914471b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;mech&#x27;, &#x27;elec&#x27;], dtype=&#x27;object&#x27;, name=&#x27;type&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d17ff5c1-19a3-4c02-9f10-9bc79501fc54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d17ff5c1-19a3-4c02-9f10-9bc79501fc54' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>time_created_utc :</span></dt><dd>2023-12-06 17:12:35.128070</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (time: 500, dof: 1, type: 2)\n",
+       "Coordinates:\n",
+       "  * time     (time) float64 0.0 0.01333 0.02667 0.04 ... 6.613 6.627 6.64 6.653\n",
+       "  * dof      (dof) <U9 'PTO_Heave'\n",
+       "  * type     (type) <U4 'mech' 'elec'\n",
+       "Data variables:\n",
+       "    pos      (time, dof) float64 4.697 3.707 2.201 0.6375 ... 3.079 4.235 4.875\n",
+       "    vel      (time, dof) float64 -15.28 -33.16 -40.84 ... 31.74 24.67 6.664\n",
+       "    acc      (time, dof) float64 -522.7 -332.5 -34.93 ... -4.326 -334.8 -527.7\n",
+       "    force    (time, dof) float64 -7.828e+05 -4.828e+05 ... -4.702e+05 -7.859e+05\n",
+       "    power    (type, time, dof) float64 1.196e+07 1.601e+07 ... -5.237e+06\n",
+       "Attributes:\n",
+       "    time_created_utc:  2023-12-06 17:12:35.128070"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pto_tdom"
+    "pto_tdom\n"
    ]
   },
   {
@@ -440,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,20 +1397,20 @@
     "drivetrain_friction = 1.0\n",
     "drivetrain_stiffness = 0.0\n",
     "\n",
-    "drivetrain_impedance = (1j*omega*drivetrain_inertia + \n",
-    "                        drivetrain_friction + \n",
-    "                        1/(1j*omega)*drivetrain_stiffness) \n",
+    "drivetrain_impedance = (1j*omega*drivetrain_inertia +\n",
+    "                        drivetrain_friction +\n",
+    "                        1/(1j*omega)*drivetrain_stiffness)\n",
     "\n",
     "winding_impedance = winding_resistance + 1j*omega*winding_inductance\n",
     "\n",
     "\n",
     "pto_impedance_11 = -1* gear_ratio**2 * drivetrain_impedance\n",
     "off_diag = np.sqrt(3.0/2.0) * torque_constant * gear_ratio\n",
-    "pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape) \n",
+    "pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
     "pto_impedance_21 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
     "pto_impedance_22 = winding_impedance\n",
     "pto_impedance_2 = np.array([[pto_impedance_11, pto_impedance_12],\n",
-    "                            [pto_impedance_21, pto_impedance_22]])"
+    "                            [pto_impedance_21, pto_impedance_22]])\n"
    ]
   },
   {
@@ -479,7 +1422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,7 +1437,7 @@
     "    return f_max_2 - np.abs(f.flatten())\n",
     "ineq_cons_2 = {'type': 'ineq', 'fun': const_f_pto_2}\n",
     "constraints_2 = [ineq_cons_2]\n",
-    "f_add_2 = {'PTO': pto_2.force_on_wec}"
+    "f_add_2 = {'PTO': pto_2.force_on_wec}\n"
    ]
   },
   {
@@ -506,9 +1449,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 37.36584622341133 N/(m/s).\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "shapes (192,192) and (200,) not aligned: 192 (dim 1) != 200 (dim 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb Celda 41\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m scale_x_opt \u001b[39m=\u001b[39m \u001b[39m1e-3\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m scale_obj \u001b[39m=\u001b[39m \u001b[39m1e-2\u001b[39m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m results_2 \u001b[39m=\u001b[39m wec_2\u001b[39m.\u001b[39;49msolve(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     waves,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     obj_fun_2,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     nstate_opt,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m     scale_x_wec\u001b[39m=\u001b[39;49mscale_x_wec,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m     scale_x_opt\u001b[39m=\u001b[39;49mscale_x_opt,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m     scale_obj\u001b[39m=\u001b[39;49mscale_obj,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m opt_average_power \u001b[39m=\u001b[39m results_2\u001b[39m.\u001b[39mfun\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mOptimal average electrical power: \u001b[39m\u001b[39m{\u001b[39;00mopt_average_power\u001b[39m}\u001b[39;00m\u001b[39m W\u001b[39m\u001b[39m'\u001b[39m)\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/core.py:852\u001b[0m, in \u001b[0;36mWEC.solve\u001b[0;34m(self, waves, obj_fun, nstate_opt, x_wec_0, x_opt_0, scale_x_wec, scale_x_opt, scale_obj, optim_options, use_grad, maximize, bounds_wec, bounds_opt, callback)\u001b[0m\n\u001b[1;32m    849\u001b[0m     problem[\u001b[39m'\u001b[39m\u001b[39mjac\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m grad(obj_fun_scaled)\n\u001b[1;32m    851\u001b[0m \u001b[39m# minimize\u001b[39;00m\n\u001b[0;32m--> 852\u001b[0m optim_res \u001b[39m=\u001b[39m minimize(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mproblem)\n\u001b[1;32m    854\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00moptim_res\u001b[39m.\u001b[39mmessage\u001b[39m}\u001b[39;00m\u001b[39m    (Exit mode \u001b[39m\u001b[39m{\u001b[39;00moptim_res\u001b[39m.\u001b[39mstatus\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    855\u001b[0m \u001b[39mif\u001b[39;00m optim_res\u001b[39m.\u001b[39mstatus \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_minimize.py:719\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    716\u001b[0m     res \u001b[39m=\u001b[39m _minimize_cobyla(fun, x0, args, constraints, callback\u001b[39m=\u001b[39mcallback,\n\u001b[1;32m    717\u001b[0m                            bounds\u001b[39m=\u001b[39mbounds, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[1;32m    718\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mslsqp\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> 719\u001b[0m     res \u001b[39m=\u001b[39m _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m    720\u001b[0m                           constraints, callback\u001b[39m=\u001b[39;49mcallback, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions)\n\u001b[1;32m    721\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtrust-constr\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m    722\u001b[0m     res \u001b[39m=\u001b[39m _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,\n\u001b[1;32m    723\u001b[0m                                        bounds, constraints,\n\u001b[1;32m    724\u001b[0m                                        callback\u001b[39m=\u001b[39mcallback, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_slsqp_py.py:374\u001b[0m, in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m    371\u001b[0m     xu[infbnd[:, \u001b[39m1\u001b[39m]] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan\n\u001b[1;32m    373\u001b[0m \u001b[39m# ScalarFunction provides function and gradient evaluation\u001b[39;00m\n\u001b[0;32m--> 374\u001b[0m sf \u001b[39m=\u001b[39m _prepare_scalar_function(func, x, jac\u001b[39m=\u001b[39;49mjac, args\u001b[39m=\u001b[39;49margs, epsilon\u001b[39m=\u001b[39;49meps,\n\u001b[1;32m    375\u001b[0m                               finite_diff_rel_step\u001b[39m=\u001b[39;49mfinite_diff_rel_step,\n\u001b[1;32m    376\u001b[0m                               bounds\u001b[39m=\u001b[39;49mnew_bounds)\n\u001b[1;32m    377\u001b[0m \u001b[39m# gh11403 SLSQP sometimes exceeds bounds by 1 or 2 ULP, make sure this\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[39m# doesn't get sent to the func/grad evaluator.\u001b[39;00m\n\u001b[1;32m    379\u001b[0m wrapped_fun \u001b[39m=\u001b[39m _clip_x_for_func(sf\u001b[39m.\u001b[39mfun, new_bounds)\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_optimize.py:383\u001b[0m, in \u001b[0;36m_prepare_scalar_function\u001b[0;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)\u001b[0m\n\u001b[1;32m    379\u001b[0m     bounds \u001b[39m=\u001b[39m (\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39minf, np\u001b[39m.\u001b[39minf)\n\u001b[1;32m    381\u001b[0m \u001b[39m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[39;00m\n\u001b[1;32m    382\u001b[0m \u001b[39m# calculation reduces overall function evaluations.\u001b[39;00m\n\u001b[0;32m--> 383\u001b[0m sf \u001b[39m=\u001b[39m ScalarFunction(fun, x0, args, grad, hess,\n\u001b[1;32m    384\u001b[0m                     finite_diff_rel_step, bounds, epsilon\u001b[39m=\u001b[39;49mepsilon)\n\u001b[1;32m    386\u001b[0m \u001b[39mreturn\u001b[39;00m sf\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:158\u001b[0m, in \u001b[0;36mScalarFunction.__init__\u001b[0;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf \u001b[39m=\u001b[39m fun_wrapped(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mx)\n\u001b[1;32m    157\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_fun_impl \u001b[39m=\u001b[39m update_fun\n\u001b[0;32m--> 158\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_fun()\n\u001b[1;32m    160\u001b[0m \u001b[39m# Gradient evaluation\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcallable\u001b[39m(grad):\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:251\u001b[0m, in \u001b[0;36mScalarFunction._update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_update_fun\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    250\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf_updated:\n\u001b[0;32m--> 251\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_fun_impl()\n\u001b[1;32m    252\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf_updated \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:155\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.update_fun\u001b[0;34m()\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate_fun\u001b[39m():\n\u001b[0;32m--> 155\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf \u001b[39m=\u001b[39m fun_wrapped(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mx)\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:137\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.fun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnfev \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    134\u001b[0m \u001b[39m# Send a copy because the user may overwrite it.\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[39m# Overwriting results in undefined behaviour because\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[39m# fun(self.x) will change self.x, with the two no longer linked.\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m fx \u001b[39m=\u001b[39m fun(np\u001b[39m.\u001b[39;49mcopy(x), \u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    138\u001b[0m \u001b[39m# Make sure the function returns a true scalar\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m np\u001b[39m.\u001b[39misscalar(fx):\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/core.py:770\u001b[0m, in \u001b[0;36mWEC.solve.<locals>.obj_fun_scaled\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    768\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mobj_fun_scaled\u001b[39m(x):\n\u001b[1;32m    769\u001b[0m     x_wec, x_opt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdecompose_state(x\u001b[39m/\u001b[39mscale)\n\u001b[0;32m--> 770\u001b[0m     \u001b[39mreturn\u001b[39;00m obj_fun(\u001b[39mself\u001b[39;49m, x_wec, x_opt, waves)\u001b[39m*\u001b[39mscale_obj\u001b[39m*\u001b[39msign\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:573\u001b[0m, in \u001b[0;36mPTO.average_power\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    547\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maverage_power\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    548\u001b[0m     wec: TWEC,\n\u001b[1;32m    549\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    552\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    553\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mfloat\u001b[39m:\n\u001b[1;32m    554\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the average power in each PTO DOF for a given\u001b[39;00m\n\u001b[1;32m    555\u001b[0m \u001b[39m    system state.\u001b[39;00m\n\u001b[1;32m    556\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    572\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 573\u001b[0m     energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menergy(wec, x_wec, x_opt, waves, nsubsteps)\n\u001b[1;32m    574\u001b[0m     \u001b[39mreturn\u001b[39;00m energy \u001b[39m/\u001b[39m wec\u001b[39m.\u001b[39mtf\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:544\u001b[0m, in \u001b[0;36mPTO.energy\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39menergy\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    519\u001b[0m     wec: TWEC,\n\u001b[1;32m    520\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    523\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    524\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mfloat\u001b[39m:\n\u001b[1;32m    525\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the energy in each PTO DOF for a given system\u001b[39;00m\n\u001b[1;32m    526\u001b[0m \u001b[39m    state.\u001b[39;00m\n\u001b[1;32m    527\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    543\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m     power_td \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpower(wec, x_wec, x_opt, waves, nsubsteps)\n\u001b[1;32m    545\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39msum(power_td) \u001b[39m*\u001b[39m wec\u001b[39m.\u001b[39mdt\u001b[39m/\u001b[39mnsubsteps\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:510\u001b[0m, in \u001b[0;36mPTO.power\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpower\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    485\u001b[0m     wec: TWEC,\n\u001b[1;32m    486\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    489\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    490\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m ndarray:\n\u001b[1;32m    491\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the power time-series in each PTO DOF for a given\u001b[39;00m\n\u001b[1;32m    492\u001b[0m \u001b[39m    system state.\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    509\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 510\u001b[0m     q2_td, e2_td \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpower_variables(wec, x_wec,\n\u001b[1;32m    511\u001b[0m                                         x_opt, waves, nsubsteps)\n\u001b[1;32m    512\u001b[0m     \u001b[39m# power\u001b[39;00m\n\u001b[1;32m    513\u001b[0m     power_out \u001b[39m=\u001b[39m q2_td \u001b[39m*\u001b[39m e2_td\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:472\u001b[0m, in \u001b[0;36mPTO.power_variables\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    470\u001b[0m vars_1 \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mhstack([q1, e1])\n\u001b[1;32m    471\u001b[0m vars_1_flat \u001b[39m=\u001b[39m dofmat_to_vec(vars_1)\n\u001b[0;32m--> 472\u001b[0m vars_2_flat \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mdot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransfer_mat, vars_1_flat)\n\u001b[1;32m    473\u001b[0m vars_2 \u001b[39m=\u001b[39m vec_to_dofmat(vars_2_flat, \u001b[39m2\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndof)\n\u001b[1;32m    474\u001b[0m q2 \u001b[39m=\u001b[39m vars_2[:, :\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndof]\n",
+      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[39mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m     \u001b[39mreturn\u001b[39;00m f_raw(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "\u001b[0;31mValueError\u001b[0m: shapes (192,192) and (200,) not aligned: 192 (dim 1) != 200 (dim 0)"
+     ]
+    }
+   ],
    "source": [
     "# Update WEC\n",
     "\n",
@@ -522,14 +1498,14 @@
     "obj_fun_2 = pto_2.average_power\n",
     "\n",
     "# Solve\n",
-    "scale_x_wec = 1e1  \n",
-    "scale_x_opt = 1e-3  \n",
-    "scale_obj = 1e-2  \n",
+    "scale_x_wec = 1e1\n",
+    "scale_x_opt = 1e-3\n",
+    "scale_obj = 1e-2\n",
     "\n",
     "results_2 = wec_2.solve(\n",
-    "    waves, \n",
-    "    obj_fun_2, \n",
-    "    nstate_opt, \n",
+    "    waves,\n",
+    "    obj_fun_2,\n",
+    "    nstate_opt,\n",
     "    scale_x_wec=scale_x_wec,\n",
     "    scale_x_opt=scale_x_opt,\n",
     "    scale_obj=scale_obj,\n",
@@ -539,7 +1515,7 @@
     "\n",
     "# Post-process\n",
     "wec_fdom_2, wec_tdom_2 = wec_2.post_process(results_2, waves, nsubsteps)\n",
-    "pto_fdom_2, pto_tdom_2 = pto_2.post_process(wec_2, results_2, waves, nsubsteps)"
+    "pto_fdom_2, pto_tdom_2 = pto_2.post_process(wec_2, results_2, waves, nsubsteps)\n"
    ]
   },
   {
@@ -563,7 +1539,7 @@
     "\n",
     "results_2_nocon = wec_2_nocon.solve(\n",
     "    waves,\n",
-    "    obj_fun_2, \n",
+    "    obj_fun_2,\n",
     "    nstate_opt,\n",
     "    scale_x_wec=scale_x_wec,\n",
     "    scale_x_opt=scale_x_opt,\n",
@@ -574,7 +1550,7 @@
     "wec_fdom_2_nocon, wec_tdom_2_nocon = wec_2_nocon.post_process(\n",
     "    results_2_nocon, waves, nsubsteps)\n",
     "pto_fdom_2_nocon, pto_tdom_2_nocon = pto_2.post_process(\n",
-    "    wec_2_nocon, results_2_nocon, waves, nsubsteps)"
+    "    wec_2_nocon, results_2_nocon, waves, nsubsteps)\n"
    ]
   },
   {
@@ -652,29 +1628,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "r1 = 0.88\n",
-    "r2_0 = 0.35\n",
-    "h2_0 = 0.37\n",
-    "V0 = 1/3*np.pi*h2_0*(r1**2+r2_0**2+(r1*r2_0))\n",
+    "# r1 = 0.88\n",
+    "# r2_0 = 0.35\n",
+    "# h2_0 = 0.37\n",
+    "# V0 = 1/3*np.pi*h2_0*(r1**2+r2_0**2+(r1*r2_0))\n",
     "\n",
-    "r2_vals = np.linspace(0.05, 0.88*0.999, 8, endpoint=True)\n",
+    "# r2_vals = np.linspace(0.05, 0.88*0.999, 8, endpoint=True)\n",
     "\n",
     "\n",
-    "def h2_from_r2(r2, V=V0, r1=r1):\n",
-    "    h2 = V/(1/3*np.pi*(r1**2+r2**2+(r1*r2)))\n",
-    "    return h2\n",
+    "# def h2_from_r2(r2, V=V0, r1=r1):\n",
+    "#     h2 = V/(1/3*np.pi*(r1**2+r2**2+(r1*r2)))\n",
+    "#     return h2\n",
     "\n",
     "\n",
-    "# plot\n",
-    "mapres = map(h2_from_r2, r2_vals)\n",
-    "h2_vals = list(mapres)\n",
+    "# # plot\n",
+    "# mapres = map(h2_from_r2, r2_vals)\n",
+    "# h2_vals = list(mapres)\n",
     "\n",
-    "fig1, ax1 = plt.subplots(figsize=(8,5))\n",
-    "for r2, h2 in zip(r2_vals.tolist(), h2_vals):\n",
-    "    _ = wot.geom.WaveBot(r2=r2, h2=h2, freeboard=0.2).plot_cross_section(\n",
-    "        ax=ax1, label=f\"r2={r2:.2f}, h2={h2:.2f}\")\n",
-    "ax1.legend(loc='best', fontsize='small',ncol=2)\n",
-    "_ = ax1.set_title('WaveBot hull cross-sections')\n"
+    "# fig1, ax1 = plt.subplots(figsize=(8,5))\n",
+    "# for r2, h2 in zip(r2_vals.tolist(), h2_vals):\n",
+    "#     _ = wot.geom.WaveBot(r2=r2, h2=h2, freeboard=0.2).plot_cross_section(\n",
+    "#         ax=ax1, label=f\"r2={r2:.2f}, h2={h2:.2f}\")\n",
+    "# ax1.legend(loc='best', fontsize='small',ncol=2)\n",
+    "# _ = ax1.set_title('WaveBot hull cross-sections')\n"
    ]
   },
   {
@@ -690,101 +1666,101 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def design_obj_fun(x):\n",
-    "\n",
-    "    # Unpack geometry variables\n",
-    "    r2 = x[0]\n",
-    "    h2 = h2_from_r2(r2)\n",
-    "    print(f\"\\nr2 = {r2:.2f}:\")\n",
-    "\n",
-    "    # Set up Capytaine floating body\n",
-    "    wb = wot.geom.WaveBot(r2=r2, h2=h2)\n",
-    "    mesh = wb.mesh(mesh_size_factor=0.5)\n",
-    "    fb = cpy.FloatingBody.from_meshio(mesh, name=\"WaveBot\")\n",
-    "    fb.add_translation_dof(name=\"Heave\")\n",
-    "    ndof = fb.nb_dofs\n",
-    "\n",
-    "    # Run BEM\n",
-    "    f1 = 0.05\n",
-    "    nfreq = 50\n",
-    "    bem_data = wot.run_bem(fb, freq)\n",
-    "\n",
-    "    # Impedance definition\n",
-    "    omega = bem_data.omega.values\n",
-    "    gear_ratio = 12.0\n",
-    "    torque_constant = 6.7\n",
-    "    winding_resistance = 0.5\n",
-    "    winding_inductance = 0.0\n",
-    "    drivetrain_inertia = 2.0\n",
-    "    drivetrain_friction = 1.0\n",
-    "    drivetrain_stiffness = 0.0\n",
-    "\n",
-    "    drivetrain_impedance = (1j*omega*drivetrain_inertia + \n",
-    "                            drivetrain_friction + \n",
-    "                            1/(1j*omega)*drivetrain_stiffness) \n",
-    "\n",
-    "    winding_impedance = winding_resistance + 1j*omega*winding_inductance\n",
-    "\n",
-    "\n",
-    "    pto_impedance_11 = -1* gear_ratio**2 * drivetrain_impedance\n",
-    "    off_diag = np.sqrt(3.0/2.0) * torque_constant * gear_ratio\n",
-    "    pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape) \n",
-    "    pto_impedance_21 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
-    "    pto_impedance_22 = winding_impedance\n",
-    "    pto_impedance_3 = np.array([[pto_impedance_11, pto_impedance_12],\n",
-    "                            [pto_impedance_21, pto_impedance_22]])\n",
-    "\n",
-    "    # Set PTO object\n",
-    "    name = [\"PTO_Heave\",]\n",
-    "    kinematics = np.eye(ndof)\n",
-    "    efficiency = None\n",
-    "    controller = None\n",
-    "    pto = wot.pto.PTO(ndof, kinematics, controller, pto_impedance_3, efficiency, name)\n",
-    "    \n",
-    "\n",
-    "    # Set PTO constraint and additional dynamic force\n",
-    "    nsubsteps = 4\n",
-    "    f_max = 600.0\n",
-    "\n",
-    "    def const_f_pto(wec, x_wec, x_opt, waves):\n",
-    "        f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
-    "        return f_max - np.abs(f.flatten())\n",
-    "\n",
-    "    ineq_cons = {'type': 'ineq', 'fun': const_f_pto}\n",
-    "    constraints = [ineq_cons]\n",
-    "\n",
-    "    f_add = {'PTO': pto.force_on_wec}\n",
-    "\n",
-    "    # Create WEC\n",
-    "    wec = wot.WEC.from_bem(bem_data,\n",
-    "            constraints=constraints,\n",
-    "            friction=None, \n",
-    "            f_add=f_add,\n",
-    "            )\n",
-    "\n",
-    "    # Waves\n",
-    "    wfreq = 0.3\n",
-    "    amplitude = 0.0625\n",
-    "    phase = -40\n",
-    "    waves_3 = wot.waves.regular_wave(f1, nfreq, wfreq, amplitude, phase)\n",
-    "\n",
-    "    # Objective function\n",
-    "    obj_fun = pto.average_power\n",
-    "    nstate_opt = 2*nfreq\n",
-    "    \n",
-    "    # Solve\n",
-    "    scale_x_wec = 1e1  \n",
-    "    scale_x_opt = 1e-3  \n",
-    "    scale_obj = 1e-2  \n",
-    "    res = wec.solve(\n",
-    "        waves, \n",
-    "        obj_fun, \n",
-    "        nstate_opt, \n",
-    "        scale_x_wec=scale_x_wec,\n",
-    "        scale_x_opt=scale_x_opt,\n",
-    "        scale_obj=scale_obj)\n",
-    "\n",
-    "    return res.fun"
+    "# def design_obj_fun(x):\n",
+    "\n",
+    "#     # Unpack geometry variables\n",
+    "#     r2 = x[0]\n",
+    "#     h2 = h2_from_r2(r2)\n",
+    "#     print(f\"\\nr2 = {r2:.2f}:\")\n",
+    "\n",
+    "#     # Set up Capytaine floating body\n",
+    "#     wb = wot.geom.WaveBot(r2=r2, h2=h2)\n",
+    "#     mesh = wb.mesh(mesh_size_factor=0.5)\n",
+    "#     fb = cpy.FloatingBody.from_meshio(mesh, name=\"WaveBot\")\n",
+    "#     fb.add_translation_dof(name=\"Heave\")\n",
+    "#     ndof = fb.nb_dofs\n",
+    "\n",
+    "#     # Run BEM\n",
+    "#     f1 = 0.05\n",
+    "#     ifreq_end = 50\n",
+    "#     bem_data = wot.run_bem(fb, freq)\n",
+    "\n",
+    "#     # Impedance definition\n",
+    "#     omega = bem_data.omega.values\n",
+    "#     gear_ratio = 12.0\n",
+    "#     torque_constant = 6.7\n",
+    "#     winding_resistance = 0.5\n",
+    "#     winding_inductance = 0.0\n",
+    "#     drivetrain_inertia = 2.0\n",
+    "#     drivetrain_friction = 1.0\n",
+    "#     drivetrain_stiffness = 0.0\n",
+    "\n",
+    "#     drivetrain_impedance = (1j*omega*drivetrain_inertia +\n",
+    "#                             drivetrain_friction +\n",
+    "#                             1/(1j*omega)*drivetrain_stiffness)\n",
+    "\n",
+    "#     winding_impedance = winding_resistance + 1j*omega*winding_inductance\n",
+    "\n",
+    "\n",
+    "#     pto_impedance_11 = -1* gear_ratio**2 * drivetrain_impedance\n",
+    "#     off_diag = np.sqrt(3.0/2.0) * torque_constant * gear_ratio\n",
+    "#     pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
+    "#     pto_impedance_21 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
+    "#     pto_impedance_22 = winding_impedance\n",
+    "#     pto_impedance_3 = np.array([[pto_impedance_11, pto_impedance_12],\n",
+    "#                             [pto_impedance_21, pto_impedance_22]])\n",
+    "\n",
+    "#     # Set PTO object\n",
+    "#     name = [\"PTO_Heave\",]\n",
+    "#     kinematics = np.eye(ndof)\n",
+    "#     efficiency = None\n",
+    "#     controller = None\n",
+    "#     pto = wot.pto.PTO(ndof, kinematics, controller, pto_impedance_3, efficiency, name)\n",
+    "\n",
+    "\n",
+    "#     # Set PTO constraint and additional dynamic force\n",
+    "#     nsubsteps = 4\n",
+    "#     f_max = 600.0\n",
+    "\n",
+    "#     def const_f_pto(wec, x_wec, x_opt, waves):\n",
+    "#         f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
+    "#         return f_max - np.abs(f.flatten())\n",
+    "\n",
+    "#     ineq_cons = {'type': 'ineq', 'fun': const_f_pto}\n",
+    "#     constraints = [ineq_cons]\n",
+    "\n",
+    "#     f_add = {'PTO': pto.force_on_wec}\n",
+    "\n",
+    "#     # Create WEC\n",
+    "#     wec = wot.WEC.from_bem(bem_data,\n",
+    "#             constraints=constraints,\n",
+    "#             friction=None,\n",
+    "#             f_add=f_add,\n",
+    "#             )\n",
+    "\n",
+    "#     # Waves\n",
+    "#     wfreq = 0.3\n",
+    "#     amplitude = 0.0625\n",
+    "#     phase = -40\n",
+    "#     waves_3 = wot.waves.regular_wave(f1, ifreq_end, wfreq, amplitude, phase)\n",
+    "\n",
+    "#     # Objective function\n",
+    "#     obj_fun = pto.average_power\n",
+    "#     nstate_opt = 2*ifreq_end\n",
+    "\n",
+    "#     # Solve\n",
+    "#     scale_x_wec = 1e1\n",
+    "#     scale_x_opt = 1e-3\n",
+    "#     scale_obj = 1e-2\n",
+    "#     res = wec.solve(\n",
+    "#         waves,\n",
+    "#         obj_fun,\n",
+    "#         nstate_opt,\n",
+    "#         scale_x_wec=scale_x_wec,\n",
+    "#         scale_x_opt=scale_x_opt,\n",
+    "#         scale_obj=scale_obj)\n",
+    "\n",
+    "#     return res.fun\n"
    ]
   },
   {
@@ -806,13 +1782,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wot.set_loglevel(\"error\")  # Suppress warnings\n",
+    "# wot.set_loglevel(\"error\")  # Suppress warnings\n",
     "\n",
-    "# range over which to search\n",
-    "ranges = (slice(r2_vals[0], r2_vals[-1]+np.diff(r2_vals)[0], np.diff(r2_vals)[0]),)\n",
+    "# # range over which to search\n",
+    "# ranges = (slice(r2_vals[0], r2_vals[-1]+np.diff(r2_vals)[0], np.diff(r2_vals)[0]),)\n",
     "\n",
-    "# solve\n",
-    "opt_x0, opt_fval, x0s, fvals = brute(func=design_obj_fun, ranges=ranges, full_output=True,  finish=None)"
+    "# # solve\n",
+    "# opt_x0, opt_fval, x0s, fvals = brute(func=design_obj_fun, ranges=ranges, full_output=True,  finish=None)\n"
    ]
   },
   {
@@ -830,15 +1806,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:len(x0s)]\n",
-    "ax.plot(x0s, fvals, 'k', zorder=0)\n",
-    "ax.scatter(x0s, fvals, c=colors, zorder=1)\n",
-    "\n",
-    "ax.set_xlabel('Keel radius, $r_2$ [m]')\n",
-    "ax.set_ylabel('Average Power [W]')\n",
-    "ax.set_title('Design optimization results')\n",
-    "fig.tight_layout()"
+    "# fig, ax = plt.subplots()\n",
+    "# colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:len(x0s)]\n",
+    "# ax.plot(x0s, fvals, 'k', zorder=0)\n",
+    "# ax.scatter(x0s, fvals, c=colors, zorder=1)\n",
+    "\n",
+    "# ax.set_xlabel('Keel radius, $r_2$ [m]')\n",
+    "# ax.set_ylabel('Average Power [W]')\n",
+    "# ax.set_title('Design optimization results')\n",
+    "# fig.tight_layout()\n"
    ]
   },
   {
@@ -847,6 +1823,31 @@
    "source": [
     "Note that in this case the magnitude of average power between the different keel radii is rather small, this is because the PTO force constraint is active most of the time, therefore all considered geometries perform similarily. If you remove the PTO constraint and re-run the co-optimization study you will see that the impact of radius on average electrical power is significantly higher."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DEBUG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(results.x), len(wec.decompose_state(results.x)[0]), len(wec.decompose_state(results.x)[1])\n",
+    "# 200, 100, 100 (ifreq_start = 1)\n",
+    "# (ifreq_start = 3, all for PTO)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -865,7 +1866,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.13"
   },
   "vscode": {
    "interpreter": {
diff --git a/wecopttool/core.py b/wecopttool/core.py
index 55b49616a..e4bd7f7f0 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -36,7 +36,7 @@
     "force_from_rao_transfer_function",
     "force_from_impedance",
     "force_from_waves",
-    "inertia",
+    # "inertia",
     "standard_forces",
     "run_bem",
     "change_bem_convention",
@@ -51,7 +51,7 @@
     "frequency_parameters",
     "time_results",
     "set_fb_centers",
-]
+    ]
 
 
 import logging
@@ -115,8 +115,9 @@ class WEC:
     def __init__(
         self,
         f1: float,
-        nfreq: int,
+        ifreq_end: int,
         forces: TIForceDict,
+        ifreq_start: int = 1,
         constraints: Optional[Iterable[Mapping]] = None,
         inertia_matrix: Optional[ndarray] = None,
         ndof: Optional[int] = None,
@@ -206,11 +207,14 @@ def __init__(
             Initialize a :py:class:`wecopttool.WEC` object from an
             intrinsic impedance array and excitation coefficients.
         """
-        self._freq = frequency(f1, nfreq)
-        self._time = time(f1, nfreq)
-        self._time_mat = time_mat(f1, nfreq)
-        self._derivative_mat = derivative_mat(f1, nfreq)
-        self._derivative2_mat = derivative2_mat(f1, nfreq)
+        self._ifreq_start = ifreq_start
+        self._ifreq_end = ifreq_end
+        idx_1, idx_2 = self._idxs(ifreq_start, ifreq_end)
+        self._freq = frequency(f1, ifreq_end)[idx_1]
+        self._time = time(f1, ifreq_end)
+        self._time_mat = time_mat(f1, ifreq_end)[:, idx_2]
+        self._derivative_mat = derivative_mat(f1, ifreq_end)[idx_2][:, idx_2]
+        self._derivative2_mat = derivative2_mat(f1, ifreq_end)[idx_2][:, idx_2]
         self._forces = forces
         constraints = list(constraints) if (constraints is not None) else []
         self._constraints = constraints
@@ -258,7 +262,7 @@ def _ignored(var_name, condition):
         if inertia_in_forces:
             _inertia = None
         else:
-            _inertia = inertia(f1, nfreq, inertia_matrix)
+            _inertia = WEC._get_inertia(f1, ifreq_start, ifreq_end, inertia_matrix)
         self._inertia = _inertia
 
         # names
@@ -271,7 +275,7 @@ def _ignored(var_name, condition):
     def __str__(self) -> str:
         str = (f'{self.__class__.__name__}: ' +
                f'DOFs ({self.ndof})={self.dof_names}, ' +
-               f'f=[0, {self.f1}, ..., {self.nfreq}({self.f1})] Hz.')
+               f'f=[0, {self.f1}, ..., {self.ifreq_end}({self.f1})] Hz.')
         return str
 
     def __repr__(self) -> str:
@@ -281,6 +285,37 @@ def __repr__(self) -> str:
         repr_org = f"<{module}.{qualname} object at {hex(id(self))}>"
         return repr_org + " :: " + self.__str__()
 
+    @staticmethod
+    def _idxs(ifreq_start, ifreq_end):
+        idx_1 = [True if i==0 or i>=ifreq_start else False for i in range(ifreq_end+1)]
+        idx_2 = [True if i==0 or i>=2*ifreq_start-1 else False for i in range(2*ifreq_end)]
+        return idx_1, idx_2
+
+    @staticmethod
+    def _get_inertia(
+        f1: float,
+        ifreq_start: int,
+        ifreq_end: int,
+        inertia_matrix: ArrayLike,
+        ) -> TStateFunction:
+        """Create the inertia "force" from the inertia matrix.
+
+        Parameters
+        ----------
+        f1
+            Fundamental frequency :python:`f1` [:math:`Hz`].
+        nfreq
+            Number of frequencies.
+        inertia_matrix
+            Inertia matrix.
+        """
+        omega = np.expand_dims(frequency(f1, ifreq_end)[ifreq_start:]*2*np.pi, [1,2])
+        inertia_matrix = np.expand_dims(inertia_matrix, 0)
+        rao_transfer_function = -1*omega**2*inertia_matrix + 0j
+        inertia_fun = force_from_rao_transfer_function(
+            rao_transfer_function, False)
+        return inertia_fun
+
     # other initialization methods
     @staticmethod
     def from_bem(
@@ -363,8 +398,8 @@ def from_bem(
         inertia_matrix = hydro_data['inertia_matrix'].values
 
         # frequency array
-        f1, nfreq = frequency_parameters(
-            hydro_data.omega.values/(2*np.pi), False)
+        f1, ifreq_start, ifreq_end = WEC.frequency_parameters(
+            hydro_data.omega.values/(2*np.pi))
 
         # check real part of damping diagonal > 0
         if min_damping is not None:
@@ -377,13 +412,17 @@ def from_bem(
         forces = linear_force_functions | f_add
         # constraints
         constraints = constraints if (constraints is not None) else []
-        return WEC(f1, nfreq, forces, constraints, inertia_matrix, dof_names=dof_names)
+        wec = WEC(
+            f1, ifreq_end, forces, ifreq_start, constraints,
+            inertia_matrix, dof_names=dof_names)
+        return wec
 
     @staticmethod
     def from_floating_body(
         fb: cpy.FloatingBody,
         f1: float,
-        nfreq: int,
+        ifreq_end: int,
+        ifreq_start: int = 1,
         friction: Optional[ndarray] = None,
         f_add: Optional[TIForceDict] = None,
         constraints: Optional[Iterable[Mapping]] = None,
@@ -392,7 +431,7 @@ def from_floating_body(
         rho: Optional[float] = _default_parameters['rho'],
         g: Optional[float] = _default_parameters['g'],
         depth: Optional[float] = _default_parameters['depth'],
-    ) -> TWEC:
+        ) -> TWEC:
         """Create a WEC object from a Capytaine :python:`FloatingBody`
         (:py:class:capytaine.bodies.bodies.FloatingBody).
 
@@ -464,9 +503,11 @@ def from_floating_body(
         """
 
         # RUN BEM
-        _log.info(f"Running Capytaine (BEM): {nfreq+1} frequencies x " +
+        nfreq = ifreq_end - ifreq_start + 1
+        _log.info(f"Running Capytaine (BEM): {nfreq} frequencies x " +
                  f"{len(wave_directions)} wave directions.")
-        freq = frequency(f1, nfreq)[1:]
+        idx_1, _ = self._idxs(ifreq_start, ifreq_end)
+        freq = frequency(f1, ifreq_end)[idx_1][1:]
         bem_data = run_bem(
             fb, freq, wave_directions, rho=rho, g=g, depth=depth)
         wec = WEC.from_bem(
@@ -483,7 +524,7 @@ def from_impedance(
         f_add: Optional[TIForceDict] = None,
         constraints: Optional[Iterable[Mapping]] = None,
         min_damping: Optional[float] = _default_min_damping,
-    ) -> TWEC:
+        ) -> TWEC:
         """Create a WEC object from the intrinsic impedance and
         excitation coefficients.
 
@@ -496,7 +537,6 @@ def from_impedance(
         The impedance can also be obtained experimentally.
         Note that the impedance is not defined at :math:`ω=0`.
 
-
         Parameters
         ----------
         freqs
@@ -533,6 +573,8 @@ def from_impedance(
             :python:`(ndof, ndof, nfreq)`.
         """
         f1, nfreq = frequency_parameters(freqs, False)
+        f1, ifreq_start, ifreq_end = self.frequency_parameters(freqs)
+        nfreq = ifreq_end - ifreq_start + 1
 
         # impedance matrix shape
         shape = impedance.shape
@@ -564,10 +606,11 @@ def from_impedance(
         forces = forces | f_add
 
         # wec
-        wec = WEC(f1, nfreq, forces, constraints,
-                  inertia_in_forces=True, ndof=shape[1])
+        wec = WEC(f1, ifreq_end, forces, ifreq_start, constraints,
+            inertia_in_forces=True, ndof=shape[1])
         return wec
 
+    # solve
     def residual(self, x_wec: ndarray, x_opt: ndarray, waves: Dataset,
         ) -> float:
         """
@@ -586,13 +629,12 @@ def residual(self, x_wec: ndarray, x_opt: ndarray, waves: Dataset,
         if not self.inertia_in_forces:
             ri = self.inertia(self, x_wec, x_opt, waves)
         else:
-            ri = np.zeros([self.ncomponents, self.ndof])
+            ri = np.zeros([self.nt, self.ndof])
         # forces, -Σf
         for f in self.forces.values():
             ri = ri - f(self, x_wec, x_opt, waves)
         return self.dofmat_to_vec(ri)
 
-    # solve
     def solve(self,
         waves: Dataset,
         obj_fun: TStateFunction,
@@ -828,7 +870,7 @@ def post_process(self,
         res: OptimizeResult,
         waves: Dataset,
         nsubsteps: Optional[int] = 1,
-    ) -> tuple[Dataset, Dataset]:
+        ) -> tuple[Dataset, Dataset]:
         """Post-process the results from
         :py:meth:`wecopttool.WEC.solve`.
 
@@ -1011,10 +1053,20 @@ def f1(self) -> float:
         """Fundamental frequency :python:`f1` [:math:`Hz`]."""
         return self._freq[1]
 
+    @property
+    def ifreq_end(self) -> int:
+        """Number of frequencies, not including the zero-frequency."""
+        return self._ifreq_end
+
+    @property
+    def ifreq_start(self) -> int:
+        """Number of frequencies, not including the zero-frequency."""
+        return self._ifreq_start
+
     @property
     def nfreq(self) -> int:
         """Number of frequencies, not including the zero-frequency."""
-        return len(self._freq)-1
+        return self.ifreq_end - self.ifreq_start + 1
 
     @property
     def omega(self) -> ndarray:
@@ -1090,7 +1142,7 @@ def tf(self) -> float:
     @property
     def nt(self) -> int:
         """Number of timesteps."""
-        return self.ncomponents
+        return 2*self.ifreq_end
 
     @property
     def ncomponents(self) -> int:
@@ -1111,7 +1163,7 @@ def nstate_wec(self) -> int:
     # other methods
     def decompose_state(self,
         state: ndarray
-    ) -> tuple[ndarray, ndarray]:
+        ) -> tuple[ndarray, ndarray]:
         """Split the state vector into the WEC dynamics state and the
         optimization (control) state.
 
@@ -1155,7 +1207,7 @@ def time_nsubsteps(self, nsubsteps: int) -> ndarray:
         --------
         time, WEC.time
         """
-        return time(self.f1, self.nfreq, nsubsteps)
+        return time(self.f1, self.ifreq_end, nsubsteps)
 
     def time_mat_nsubsteps(self, nsubsteps: int) -> ndarray:
         """Create a time matrix similar to
@@ -1174,7 +1226,8 @@ def time_mat_nsubsteps(self, nsubsteps: int) -> ndarray:
         --------
         time_mat, WEC.time_mat, WEC.time_nsubsteps
         """
-        return time_mat(self.f1, self.nfreq, nsubsteps)
+        _, idx_2 = self._idxs(self.ifreq_start, self.ifreq_end)
+        return time_mat(self.f1, self.ifreq_end, nsubsteps)[:, idx_2]
 
     def vec_to_dofmat(self, vec: ndarray) -> ndarray:
         """Convert a vector to a matrix with one column per degree of
@@ -1238,7 +1291,7 @@ def fd_to_td(self, fd: ndarray) -> ndarray:
         --------
         fd_to_td, WEC.td_to_fd
         """
-        return fd_to_td(fd, self.f1, self.nfreq, True)
+        return self.time_mat @ complex_to_real(fd)
 
     def td_to_fd(
         self,
@@ -1264,13 +1317,62 @@ def td_to_fd(
         --------
         td_to_fd, WEC.fd_to_td
         """
-        return td_to_fd(td, fft, True)
+        idx_1, _ = self._idxs(self.ifreq_start, self.ifreq_end)
+        return td_to_fd(td, fft, True)[idx_1, :]
+
+    @staticmethod
+    def frequency_parameters(
+        freqs: ArrayLike,
+        precision: Optional[int] = 10,
+        ) -> tuple[float, int]:
+        """Return the fundamental frequency, the number of frequencies,
+        and first frequency in a frequency array.
+
+        This function can be used as a check for inputs to other
+        functions since it raises an error if the frequency vector does
+        not have the correct format (equally spaced).
+
+        Parameters
+        ----------
+        freqs
+            The frequency array with equal spacing.
+        precision
+            Controls rounding of fundamental frequency.
+
+        Returns
+        -------
+        f1
+            Fundamental frequency :python:`f1` [:math:`Hz`].
+            This is also the spacing.
+        ifreq_end
+            Last frequency (index).
+        ifreq_start
+            Frequency (index) at which vector starts.
+
+        Raises
+        ------
+        ValueError
+            If the frequency vector is not evenly spaced.
+        """
+        f1 = freqs[1] - freqs[0]
+        f1 = f1 if precision is None else round(f1, precision)
+        ifreq_start = round(freqs[0]/f1)
+        assert np.isclose(ifreq_start, freqs[0]/f1)
+        ifreq_end = ifreq_start + len(freqs) - 1
+        f_check = np.arange(ifreq_start, ifreq_end+1)*f1
+        if not np.allclose(f_check, freqs):
+            print(f_check)
+            print(freqs)
+            raise ValueError(
+                "Frequency array must be evenly spaced by " +
+                "the fundamental frequency ")
+        return f1, ifreq_start, ifreq_end
 
 
 def ncomponents(
     nfreq : int,
     zero_freq: Optional[bool] = True,
-) -> int:
+    ) -> int:
     """Number of Fourier components (:python:`2*nfreq`) for each
     DOF. The sine component of the highest frequency (the 2-point wave)
     is excluded as it will always evaluate to zero.
@@ -1295,7 +1397,7 @@ def frequency(
     f1: float,
     nfreq: int,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Construct equally spaced frequency array.
 
     The array includes :python:`0` and has length of :python:`nfreq+1`.
@@ -1325,7 +1427,7 @@ def time(
     f1: float,
     nfreq: int,
     nsubsteps: Optional[int] = 1,
-) -> ndarray:
+    ) -> ndarray:
     """Assemble the time vector with :python:`nsubsteps` subdivisions.
 
     Returns the 1D time vector, in seconds, starting at time
@@ -1355,7 +1457,7 @@ def time_mat(
     nfreq: int,
     nsubsteps: Optional[int] = 1,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Assemble the time matrix that converts the state to a
     time-series.
 
@@ -1400,7 +1502,7 @@ def derivative_mat(
     f1: float,
     nfreq: int,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Assemble the derivative matrix that converts the state vector of
     a response to the state vector of its derivative.
 
@@ -1437,7 +1539,7 @@ def derivative2_mat(
     f1: float,
     nfreq: int,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Assemble the second derivative matrix that converts the state vector of
     a response to the state vector of its second derivative.
 
@@ -1472,7 +1574,7 @@ def derivative2_mat(
 def mimo_transfer_mat(
     transfer_mat: DataArray,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Create a block matrix of the MIMO transfer function.
 
     The input is a complex transfer matrix that relates the complex
@@ -1568,7 +1670,7 @@ def dofmat_to_vec(mat: ArrayLike) -> ndarray:
 def real_to_complex(
     fd: ArrayLike,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Convert from two real amplitudes to one complex amplitude per
     frequency.
 
@@ -1617,7 +1719,7 @@ def real_to_complex(
 def complex_to_real(
     fd: ArrayLike,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Convert from one complex amplitude to two real amplitudes per
     frequency.
 
@@ -1679,7 +1781,7 @@ def fd_to_td(
     f1: Optional[float] = None,
     nfreq: Optional[int] = None,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Convert a complex array of Fourier coefficients to a real array
     of time-domain responses.
 
@@ -1748,7 +1850,7 @@ def td_to_fd(
     td: ArrayLike,
     fft: Optional[bool] = True,
     zero_freq: Optional[bool] = True,
-) -> ndarray:
+    ) -> ndarray:
     """Convert a real array of time-domain responses to a complex array
     of Fourier coefficients.
 
@@ -1828,7 +1930,7 @@ def check_linear_damping(
     hydro_data: Dataset,
     min_damping: Optional[float] = 1e-6,
     uniform_shift: Optional[bool] = True,
-) -> Dataset:
+    ) -> Dataset:
     """Ensure that the linear hydrodynamics (friction + radiation
     damping) have positive damping.
 
@@ -1885,7 +1987,7 @@ def check_linear_damping(
 def check_impedance(
     Zi: DataArray,
     min_damping: Optional[float] = 1e-6,
-) -> DataArray:
+    ) -> DataArray:
     """Ensure that the real part of the impedance (resistive) is positive.
 
     Adds to real part of the impedance.
@@ -1916,7 +2018,7 @@ def check_impedance(
 def force_from_rao_transfer_function(
     rao_transfer_mat: DataArray,
     zero_freq: Optional[bool] = True,
-) -> TStateFunction:
+    ) -> TStateFunction:
     """Create a force function from its position transfer matrix.
 
     This is the position equivalent to the velocity-based
@@ -1947,7 +2049,7 @@ def force(wec, x_wec, x_opt, waves):
 def force_from_impedance(
     omega: ArrayLike,
     impedance: DataArray,
-) -> TStateFunction:
+    ) -> TStateFunction:
     """Create a force function from its impedance.
 
     Parameters
@@ -1980,30 +2082,6 @@ def force(wec, x_wec, x_opt, waves):
     return force
 
 
-def inertia(
-    f1: float,
-    nfreq: int,
-    inertia_matrix: ArrayLike,
-) -> TStateFunction:
-    """Create the inertia "force" from the inertia matrix.
-
-    Parameters
-    ----------
-    f1
-        Fundamental frequency :python:`f1` [:math:`Hz`].
-    nfreq
-        Number of frequencies.
-    inertia_matrix
-        Inertia matrix.
-    """
-    omega = np.expand_dims(frequency(f1, nfreq, False)*2*np.pi, [1,2])
-    inertia_matrix = np.expand_dims(inertia_matrix, 0)
-    rao_transfer_function = -1*omega**2*inertia_matrix + 0j
-    inertia_fun = force_from_rao_transfer_function(
-        rao_transfer_function, False)
-    return inertia_fun
-
-
 def standard_forces(hydro_data: Dataset) -> TForceDict:
     """Create functions for linear hydrodynamic forces.
 
@@ -2064,7 +2142,7 @@ def run_bem(
     depth: float = _default_parameters['depth'],
     write_info: Optional[Mapping[str, bool]] = None,
     njobs: int = 1,
-) -> Dataset:
+    ) -> Dataset:
     """Run Capytaine for a range of frequencies and wave directions.
 
     This simplifies running *Capytaine* and ensures the output are in
@@ -2160,7 +2238,7 @@ def change_bem_convention(bem_data: Dataset) -> Dataset:
 def add_linear_friction(
     bem_data: Dataset,
     friction: Optional[ArrayLike] = None
-) -> Dataset:
+    ) -> Dataset:
     """Add linear friction to BEM data.
 
     Returns the Dataset with the additional information added.
@@ -2286,7 +2364,7 @@ def atleast_2d(array: ArrayLike) -> ndarray:
 def degrees_to_radians(
     degrees: FloatOrArray,
     sort: Optional[bool] = True,
-) -> Union[float, ndarray]:
+    ) -> Union[float, ndarray]:
     """Convert a 1D array of angles in degrees to radians in the range
     :math:`[-π, π)` and optionally sort them.
 
@@ -2313,7 +2391,7 @@ def subset_close(
     rtol: float = 1.e-5,
     atol: float = 1.e-8,
     equal_nan: bool = False,
-) -> tuple[bool, list]:
+    ) -> tuple[bool, list]:
     """Check if the first set :python:`set_a` is contained, to some
     tolerance, in the second set :python:`set_b`.
 
@@ -2400,7 +2478,7 @@ def decompose_state(
     state: ndarray,
     ndof: int,
     nfreq: int,
-) -> tuple[ndarray, ndarray]:
+    ) -> tuple[ndarray, ndarray]:
     """Split the state vector into the WEC dynamics state and the
     optimization (control) state.
 
@@ -2432,7 +2510,7 @@ def decompose_state(
 def frequency_parameters(
     freqs: ArrayLike,
     zero_freq: bool = True,
-) -> tuple[float, int]:
+    ) -> tuple[float, int]:
     """Return the fundamental frequency and the number of frequencies
     in a frequency array.
 
@@ -2508,7 +2586,7 @@ def time_results(fd: DataArray, time: DataArray) -> ndarray:
 def set_fb_centers(
     fb: FloatingBody,
     rho: float = _default_parameters["rho"],
-) -> FloatingBody:
+    ) -> FloatingBody:
     """Sets default properties if not provided by the user:
         - `center_of_mass` is set to the geometric centroid
         - `rotation_center` is set to the center of mass
diff --git a/wecopttool/pto.py b/wecopttool/pto.py
index e34b3b1e0..a178560be 100644
--- a/wecopttool/pto.py
+++ b/wecopttool/pto.py
@@ -1080,7 +1080,11 @@ def controller_p(
 
 
 # utilities
-def nstate_unstructured(nfreq: int, ndof: int) -> int:
+def nstate_unstructured(
+        ndof: int,
+        ifreq_end: int,
+        ifreq_start=1
+    ) -> int:
     """
     Number of states needed to represent an unstructured controller.
 
@@ -1091,6 +1095,7 @@ def nstate_unstructured(nfreq: int, ndof: int) -> int:
     ndof
         Number of degrees of freedom.
     """
+    nfreq = ifreq_end - ifreq_start + 1
     return 2*nfreq*ndof
 
 
diff --git a/wecopttool/waves.py b/wecopttool/waves.py
index 7603f9b75..184048ab3 100644
--- a/wecopttool/waves.py
+++ b/wecopttool/waves.py
@@ -52,8 +52,9 @@
 
 def elevation_fd(
     f1: float,
-    nfreq: int,
+    ifreq_end: int,
     directions: Union[float, ArrayLike],
+    ifreq_start: int = 1,
     amplitudes: Optional[ArrayLike] = None,
     phases: Optional[ArrayLike] = None,
     attr: Optional[Mapping] = None,
@@ -88,7 +89,7 @@ def elevation_fd(
     """
     directions = np.atleast_1d(degrees_to_radians(directions, sort=False))
     ndirections = len(directions)
-    freq = frequency(f1, nfreq, False)
+    freq = frequency(f1, ifreq_end)[ifreq_start:]
     omega = freq*2*np.pi
 
     dims = ('omega', 'wave_direction')
@@ -99,6 +100,7 @@ def elevation_fd(
               'freq': (dims[0], freq, freq_attr),
               'wave_direction': (dims[1], directions, dir_attr)}
 
+    nfreq = (ifreq_end - ifreq_start + 1)
     if amplitudes is None:
         amplitudes = np.zeros([nfreq, ndirections])
 
@@ -120,11 +122,12 @@ def elevation_fd(
 
 def regular_wave(
     f1: float,
-    nfreq: int,
+    ifreq_end: int,
     freq: float,
     amplitude: float,
     phase: Optional[float] = None,
     direction: float = 0.0,
+    ifreq_start: int = 1,
 ) -> DataArray:
     """Create the dataset for a regular wave.
 
@@ -149,7 +152,8 @@ def regular_wave(
 
     # attributes & index
     omega = freq*2*np.pi
-    tmp_waves = elevation_fd(f1, nfreq, direction)
+    nfreq = (ifreq_end - ifreq_start + 1)
+    tmp_waves = elevation_fd(f1, ifreq_end, direction, ifreq_start)
     iomega = tmp_waves.sel(omega=omega, method='nearest').omega.values
     ifreq = iomega/(2*np.pi)
 
@@ -176,7 +180,7 @@ def regular_wave(
 
     # wave elevation
     tmp = np.zeros([nfreq, 1])
-    waves = elevation_fd(f1, nfreq, direction, tmp, tmp, attrs)
+    waves = elevation_fd(f1, ifreq_end, direction, ifreq_start, tmp, tmp, attrs)
     waves.loc[{'omega': iomega}] = amplitude  * np.exp(1j*rphase)
 
     return waves

From 137911804727687bebe3e7ff0d651a5d2717e9c3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carlos=20A=2E=20Michel=C3=A9n=20Str=C3=B6fer?=
 <c.michelen.strofer@gmail.com>
Date: Fri, 22 Dec 2023 11:15:05 -0700
Subject: [PATCH 13/13] debugging

---
 examples/tutorial_1_WaveBot.ipynb | 1487 +++--------------------------
 wecopttool/core.py                |  166 ++--
 2 files changed, 237 insertions(+), 1416 deletions(-)

diff --git a/examples/tutorial_1_WaveBot.ipynb b/examples/tutorial_1_WaveBot.ipynb
index 55b701154..a3131a840 100644
--- a/examples/tutorial_1_WaveBot.ipynb
+++ b/examples/tutorial_1_WaveBot.ipynb
@@ -109,31 +109,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fb.show_matplotlib()\n",
-    "_ = wb.plot_cross_section(show=True)  # specific to WaveBot\n"
+    "# fb.show_matplotlib()\n",
+    "# _ = wb.plot_cross_section(show=True)  # specific to WaveBot\n"
    ]
   },
   {
@@ -155,19 +134,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Warning: Minimum wavelength in frequency spectrum (0.24980959867703897) is smaller than the minimum computable wavelength (0.8056649972775113).\n"
+      "Warning: Minimum wavelength in frequency spectrum (0.6939155518806638) is smaller than the minimum computable wavelength (0.8056649972775113).\n"
      ]
     }
    ],
    "source": [
-    "f1 = 0.05\n",
-    "ifreq_start = 3\n",
-    "ifreq_end = 50\n",
-    "freq = wot.frequency(f1, ifreq_end)[ifreq_start:]\n",
+    "df = 0.15\n",
+    "f1 = df\n",
+    "ifreq_start = 2\n",
+    "ifreq_end = 10 # 50\n",
+    "\n",
+    "freq = wot.frequency(df, ifreq_end)[ifreq_start:]\n",
     "\n",
     "min_computable_wavelength = fb.minimal_computable_wavelength\n",
     "g = 9.81\n",
-    "min_period = 1/(f1*ifreq_end)\n",
+    "min_period = 1/(df*ifreq_end)\n",
     "min_wavelength = (g*(min_period)**2)/(2*np.pi)\n",
     "\n",
     "if min_wavelength < min_computable_wavelength:\n",
@@ -196,143 +177,11 @@
      "text": [
       "WARNING:wecopttool.core:Using the geometric centroid as the center of gravity (COG).\n",
       "WARNING:wecopttool.core:Using the center of gravity (COG) as the rotation center for hydrostatics.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=8.796, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.111, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
       "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.425, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
       "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
       "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=9.739, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.053, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.367, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.681, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=10.996, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.310, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.624, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=11.938, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.252, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.566, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=12.881, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.195, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.509, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=13.823, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.137, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.451, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=14.765, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.080, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.394, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for DiffractionProblem(body=WaveBot_immersed, omega=15.708, water_depth=inf, wave_direction=0.000, rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=8.796, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.97e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.111, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=7.43e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
       "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.425, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
       "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.94e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=9.739, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.50e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.053, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=6.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.367, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.73e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.681, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.40e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=10.996, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=5.10e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.310, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.82e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.624, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.56e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=11.938, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.32e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.252, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=4.11e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.566, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.90e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=12.881, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.72e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.195, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.54e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.509, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.38e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=13.823, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.23e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.137, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=3.08e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.451, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.95e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=14.765, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.83e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.080, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.71e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.394, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.60e-01.\n",
-      "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n",
-      "WARNING:capytaine.bem.problems_and_results:Mesh resolution for RadiationProblem(body=WaveBot_immersed, omega=15.708, water_depth=inf, radiating_dof='Heave', rho=1025.0):\n",
-      "The resolution of the mesh 'WaveBot' of the body 'WaveBot_immersed' might be insufficient for the wavelength λ=2.50e-01.\n",
       "This warning appears because the largest panel of this mesh has radius 1.01e-01 > λ/8.\n"
      ]
     }
@@ -377,29 +226,6 @@
     "Now let's define the PTO forcing on the WEC and the PTO constraints. For our optimization problem, the constraints must be in the correct format for `scipy.optimize.minimize()`. We will enforce the constraint at 4 times more points than the dynamics (see Theory for why this is helpful for the pseudo-spectral problem)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# PTO dynamics forcing function\n",
-    "f_add = {'PTO': pto.force_on_wec}\n",
-    "\n",
-    "# # Constraint\n",
-    "# f_max = 2000.0\n",
-    "# nsubsteps = 4\n",
-    "\n",
-    "# def const_f_pto(wec, x_wec, x_opt, waves): # Format for scipy.optimize.minimize\n",
-    "#     f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
-    "#     return f_max - np.abs(f.flatten())\n",
-    "\n",
-    "# ineq_cons = {'type': 'ineq',\n",
-    "#              'fun': const_f_pto,\n",
-    "#              }\n",
-    "# constraints = [ineq_cons]\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -422,15 +248,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 37.36584622341133 N/(m/s).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "wec = wot.WEC.from_bem(\n",
     "    bem_data,\n",
@@ -447,66 +265,6 @@
     "Note: We might receive a warning regarding negative linear damping values. Per default, WecOptTool ensures that the BEM data does not contain non-negative damping values. If you would like to correct the BEM solution manually to a minimum damping value you can specify `min_damping`. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 , 0.65,\n",
-       "       0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15, 1.2 ,\n",
-       "       1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 , 1.75,\n",
-       "       1.8 , 1.85, 1.9 , 1.95, 2.  , 2.05, 2.1 , 2.15, 2.2 , 2.25, 2.3 ,\n",
-       "       2.35, 2.4 , 2.45, 2.5 ])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "freq\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-2.886579864025407e-16, -4.329869796038111e-17)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# DEBUG\n",
-    "x_wec = np.zeros(wec.ncomponents); x_wec[3] = 1\n",
-    "x_opt = np.zeros(wec.ncomponents); x_opt[3] = 1\n",
-    "# plt.plot(pto.force_on_wec(wec, x_wec, x_opt, None))\n",
-    "plt.plot(pto.mechanical_power(wec, x_wec, x_opt, None))\n",
-    "\n",
-    "pto.energy(wec, x_wec, x_opt, None), pto.mechanical_average_power(wec, x_wec, x_opt, None)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -522,11 +280,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "amplitude = 0.0625\n",
+    "f1 = df\n",
+    "amplitude = 0 # 0.0625\n",
     "wavefreq = 0.3\n",
     "phase = 30\n",
     "wavedir = 0\n",
@@ -545,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,815 +329,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 1814.8949648460753\n",
-      "            Iterations: 26\n",
-      "            Function evaluations: 81\n",
-      "            Gradient evaluations: 26\n",
-      "Optimal average mechanical power: 181489.49648460752 W\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "options = {'maxiter': 200}\n",
-    "scale_x_wec = 1e1\n",
-    "scale_x_opt = 1e-3\n",
-    "scale_obj = 1e-2\n",
+    "options = {'maxiter': 100}\n",
+    "# scale_x_wec = 1.0 # 1e1\n",
+    "# scale_x_opt = 1.0 # 1e-3\n",
+    "# scale_obj = 1.0 # 1e-2\n",
+    "\n",
     "\n",
-    "results = wec.solve(\n",
+    "scale_x_wec = 1.0\n",
+    "scale_x_opt = 1.0\n",
+    "scale_obj = 1.0\n",
+    "\n",
+    "# results  = wec.solve(\n",
+    "problem  = wec.solve(\n",
     "    waves,\n",
     "    obj_fun,\n",
     "    nstate_opt,\n",
+    "    # x_wec_0 = x_wec*1.1,\n",
+    "    # x_opt_0 = x_opt*0.9,\n",
     "    optim_options=options,\n",
     "    scale_x_wec=scale_x_wec,\n",
     "    scale_x_opt=scale_x_opt,\n",
     "    scale_obj=scale_obj,\n",
+    "    # use_grad = False,\n",
     "    )\n",
     "\n",
-    "opt_mechanical_average_power = results.fun\n",
-    "print(f'Optimal average mechanical power: {opt_mechanical_average_power} W')\n"
+    "# opt_mechanical_average_power = results.fun\n",
+    "# print(f'Optimal average mechanical power: {opt_mechanical_average_power} W')\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Analyzing results\n",
-    "We will use two post-processing functions to obtain frequency- and time-domain results for the WEC and PTO responses. The pseudospectral method gives continuous in time results. To get smoother looking plots, we specify the number of subpoints betweeen co-location points. In this case we will use 5. "
+    "## DEBUG"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nsubsteps = 5\n",
-    "pto_fdom, pto_tdom = pto.post_process(wec, results, waves, nsubsteps=nsubsteps)\n",
-    "wec_fdom, wec_tdom = wec.post_process(results, waves, nsubsteps=nsubsteps)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 13,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.11.4\n",
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 0.0004998016765365283\n",
+      "            Iterations: 8\n",
+      "            Function evaluations: 8\n",
+      "            Gradient evaluations: 8\n"
+     ]
+    }
+   ],
    "source": [
-    "The `pto.post_process` function returns `xarray.Dataset`s, which have built-in integration with PyPlot for smart plotting that automagically sets titles and formatting. We will plot the mechanical power (`mech_power`), position (`pos`), and the PTO force (`force`)."
+    "## SOLVE!!!! ##\n",
+    "from scipy.optimize import minimize\n",
+    "import scipy; print(scipy.__version__)\n",
+    "\n",
+    "optim_res = minimize(**problem)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12a573e20>]"
+       "dict_keys(['fun', 'x0', 'method', 'constraints', 'options', 'bounds', 'callback', 'jac'])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure()\n",
-    "pto_tdom['power'].loc['mech',:,:].plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We could similarly plot any time- or frequency-domain repsonse of the WEC or PTO. For instance, here is the PTO heave motion."
+    "problem.keys()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12a54a470>]"
+       "'SLSQP'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure()\n",
-    "wec_tdom['pos'].plot()\n"
+    "problem['method']\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12a67f070>]"
+       "{'maxiter': 100, 'disp': True}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure()\n",
-    "pto_tdom['force'].plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that there are other dynamic responses available in the post-processed WEC and PTO variables (`wec_tdom`, `pto_tdom`, `wec_fdom`, `pto_fdom`). For example, the time domain PTO variable contains the following response:"
+    "problem['options']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
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-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:  (time: 500, dof: 1, type: 2)\n",
-       "Coordinates:\n",
-       "  * time     (time) float64 0.0 0.01333 0.02667 0.04 ... 6.613 6.627 6.64 6.653\n",
-       "  * dof      (dof) &lt;U9 &#x27;PTO_Heave&#x27;\n",
-       "  * type     (type) &lt;U4 &#x27;mech&#x27; &#x27;elec&#x27;\n",
-       "Data variables:\n",
-       "    pos      (time, dof) float64 4.697 3.707 2.201 0.6375 ... 3.079 4.235 4.875\n",
-       "    vel      (time, dof) float64 -15.28 -33.16 -40.84 ... 31.74 24.67 6.664\n",
-       "    acc      (time, dof) float64 -522.7 -332.5 -34.93 ... -4.326 -334.8 -527.7\n",
-       "    force    (time, dof) float64 -7.828e+05 -4.828e+05 ... -4.702e+05 -7.859e+05\n",
-       "    power    (type, time, dof) float64 1.196e+07 1.601e+07 ... -5.237e+06\n",
-       "Attributes:\n",
-       "    time_created_utc:  2023-12-06 17:12:35.128070</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-844fa7a0-e63d-4bb5-8280-1b6f2357f269' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-844fa7a0-e63d-4bb5-8280-1b6f2357f269' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 500</li><li><span class='xr-has-index'>dof</span>: 1</li><li><span class='xr-has-index'>type</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-caa57cd5-9641-400a-ac6f-b19eb14cec09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-caa57cd5-9641-400a-ac6f-b19eb14cec09' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.01333 0.02667 ... 6.64 6.653</div><input id='attrs-a251a644-d60c-40a7-bf8a-72355c7498e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a251a644-d60c-40a7-bf8a-72355c7498e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bce732-0d7d-47c2-a74a-4be13774eb58' class='xr-var-data-in' type='checkbox'><label for='data-20bce732-0d7d-47c2-a74a-4be13774eb58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.013333, 0.026667, ..., 6.626667, 6.64    , 6.653333])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dof</span></div><div class='xr-var-dims'>(dof)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;PTO_Heave&#x27;</div><input id='attrs-eec1658b-7b32-4a41-afbf-60dcf658aff9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eec1658b-7b32-4a41-afbf-60dcf658aff9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e114f36f-2c06-4dcd-99a6-bb22aa21970c' class='xr-var-data-in' type='checkbox'><label for='data-e114f36f-2c06-4dcd-99a6-bb22aa21970c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PTO degree of freedom</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;PTO_Heave&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>type</span></div><div class='xr-var-dims'>(type)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;mech&#x27; &#x27;elec&#x27;</div><input id='attrs-5f628936-8f86-495a-aded-93d84ddf1451' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f628936-8f86-495a-aded-93d84ddf1451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67429ae4-eebb-40c6-a07d-30c4cf5b5168' class='xr-var-data-in' type='checkbox'><label for='data-67429ae4-eebb-40c6-a07d-30c4cf5b5168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Power</dd><dt><span>units :</span></dt><dd>W</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;mech&#x27;, &#x27;elec&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fe4bdac-871a-4a0b-827a-2b25940dbf6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fe4bdac-871a-4a0b-827a-2b25940dbf6f' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pos</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.697 3.707 2.201 ... 4.235 4.875</div><input id='attrs-48ab93a9-b3d9-4460-a508-01d471c450ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48ab93a9-b3d9-4460-a508-01d471c450ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5221580d-ddde-4e16-80d5-414af458a9ab' class='xr-var-data-in' type='checkbox'><label for='data-5221580d-ddde-4e16-80d5-414af458a9ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Position</dd><dt><span>units :</span></dt><dd>m or rad</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.69676101],\n",
-       "       [ 3.7074971 ],\n",
-       "       [ 2.20073865],\n",
-       "       [ 0.63749863],\n",
-       "       [-0.52640229],\n",
-       "       [-1.00021876],\n",
-       "       [-0.74828766],\n",
-       "       [ 0.01879649],\n",
-       "       [ 0.94291635],\n",
-       "       [ 1.66499679],\n",
-       "       [ 1.96107046],\n",
-       "       [ 1.81581005],\n",
-       "       [ 1.40743938],\n",
-       "       [ 1.01331073],\n",
-       "       [ 0.87660207],\n",
-       "       [ 1.08963975],\n",
-       "       [ 1.54258167],\n",
-       "       [ 1.96022168],\n",
-       "       [ 2.01445413],\n",
-       "       [ 1.46901245],\n",
-       "...\n",
-       "       [ 7.82641539],\n",
-       "       [ 4.52633003],\n",
-       "       [ 1.54485773],\n",
-       "       [-0.35521161],\n",
-       "       [-0.71992371],\n",
-       "       [ 0.45325814],\n",
-       "       [ 2.71797626],\n",
-       "       [ 5.32541483],\n",
-       "       [ 7.46506041],\n",
-       "       [ 8.51903991],\n",
-       "       [ 8.24597146],\n",
-       "       [ 6.83561799],\n",
-       "       [ 4.82052347],\n",
-       "       [ 2.87943204],\n",
-       "       [ 1.60279318],\n",
-       "       [ 1.30106385],\n",
-       "       [ 1.91884778],\n",
-       "       [ 3.07905715],\n",
-       "       [ 4.23539637],\n",
-       "       [ 4.87481735]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.28 -33.16 ... 24.67 6.664</div><input id='attrs-54f4cddb-96f2-4564-b7d8-90663b5ba0b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f4cddb-96f2-4564-b7d8-90663b5ba0b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3767671-4b9a-43f6-8275-92ca06a8a99c' class='xr-var-data-in' type='checkbox'><label for='data-d3767671-4b9a-43f6-8275-92ca06a8a99c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Velocity</dd><dt><span>units :</span></dt><dd>m/s or rad/s</dd></dl></div><div class='xr-var-data'><pre>array([[-1.52806344e+01],\n",
-       "       [-3.31567578e+01],\n",
-       "       [-4.08417830e+01],\n",
-       "       [-3.61634368e+01],\n",
-       "       [-2.14383445e+01],\n",
-       "       [-2.35928477e+00],\n",
-       "       [ 1.42856810e+01],\n",
-       "       [ 2.31690918e+01],\n",
-       "       [ 2.22934716e+01],\n",
-       "       [ 1.35025635e+01],\n",
-       "       [ 1.49528466e+00],\n",
-       "       [-8.22492655e+00],\n",
-       "       [-1.16363949e+01],\n",
-       "       [-7.81779195e+00],\n",
-       "       [ 7.32385549e-01],\n",
-       "       [ 9.19455739e+00],\n",
-       "       [ 1.25073730e+01],\n",
-       "       [ 7.60102652e+00],\n",
-       "       [-5.11393412e+00],\n",
-       "       [-2.16258397e+01],\n",
-       "...\n",
-       "       [-7.93620968e+01],\n",
-       "       [-8.23154999e+01],\n",
-       "       [-6.41322779e+01],\n",
-       "       [-2.97300052e+01],\n",
-       "       [ 1.08463400e+01],\n",
-       "       [ 4.56376358e+01],\n",
-       "       [ 6.46239293e+01],\n",
-       "       [ 6.29170801e+01],\n",
-       "       [ 4.22231386e+01],\n",
-       "       [ 1.01039468e+01],\n",
-       "       [-2.26765177e+01],\n",
-       "       [-4.57870062e+01],\n",
-       "       [-5.26943130e+01],\n",
-       "       [-4.26289187e+01],\n",
-       "       [-2.05296669e+01],\n",
-       "       [ 4.94144815e+00],\n",
-       "       [ 2.45604522e+01],\n",
-       "       [ 3.17422943e+01],\n",
-       "       [ 2.46711013e+01],\n",
-       "       [ 6.66400217e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acc</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-522.7 -332.5 ... -334.8 -527.7</div><input id='attrs-fe2ca592-8c50-4978-9eea-34606ce94bfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2ca592-8c50-4978-9eea-34606ce94bfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fa0187-ca92-4527-8390-b38ff8c29d8d' class='xr-var-data-in' type='checkbox'><label for='data-46fa0187-ca92-4527-8390-b38ff8c29d8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Acceleration</dd><dt><span>units :</span></dt><dd>m/s^2 or rad/s^2</dd></dl></div><div class='xr-var-data'><pre>array([[ -522.71767197],\n",
-       "       [ -332.53075254],\n",
-       "       [  -34.93363082],\n",
-       "       [  258.7038327 ],\n",
-       "       [  445.31996557],\n",
-       "       [  467.08212545],\n",
-       "       [  330.00563115],\n",
-       "       [   98.11664356],\n",
-       "       [ -133.77691263],\n",
-       "       [ -277.77871519],\n",
-       "       [ -286.50284459],\n",
-       "       [ -169.8948715 ],\n",
-       "       [   10.80474551],\n",
-       "       [  169.22935029],\n",
-       "       [  229.77523998],\n",
-       "       [  159.11535384],\n",
-       "       [  -19.06681614],\n",
-       "       [ -232.10061355],\n",
-       "       [ -386.3399652 ],\n",
-       "       [ -404.62109514],\n",
-       "...\n",
-       "       [ -330.96836164],\n",
-       "       [  202.84573168],\n",
-       "       [  692.6768524 ],\n",
-       "       [  984.17550625],\n",
-       "       [  985.62058904],\n",
-       "       [  698.65525255],\n",
-       "       [  217.51186935],\n",
-       "       [ -302.92372133],\n",
-       "       [ -699.10086917],\n",
-       "       [ -852.91145713],\n",
-       "       [ -729.4499908 ],\n",
-       "       [ -385.85328176],\n",
-       "       [   51.16900931],\n",
-       "       [  430.64715776],\n",
-       "       [  629.33092414],\n",
-       "       [  592.24636587],\n",
-       "       [  348.19333706],\n",
-       "       [   -4.32558065],\n",
-       "       [ -334.76997373],\n",
-       "       [ -527.74364974]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>force</span></div><div class='xr-var-dims'>(time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.828e+05 ... -7.859e+05</div><input id='attrs-24e4bf0b-1ca2-4d10-85ca-5157e644fe8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24e4bf0b-1ca2-4d10-85ca-5157e644fe8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26119098-cf25-41be-b9fb-f47f90699c1c' class='xr-var-data-in' type='checkbox'><label for='data-26119098-cf25-41be-b9fb-f47f90699c1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Force or moment on WEC</dd><dt><span>units :</span></dt><dd>N or Nm</dd></dl></div><div class='xr-var-data'><pre>array([[ -782832.96347105],\n",
-       "       [ -482796.38939677],\n",
-       "       [  -11380.13267833],\n",
-       "       [  452353.68920421],\n",
-       "       [  743361.25896803],\n",
-       "       [  769834.35986832],\n",
-       "       [  542856.66645464],\n",
-       "       [  166671.25527764],\n",
-       "       [ -205438.40057645],\n",
-       "       [ -432013.32057917],\n",
-       "       [ -437566.22179696],\n",
-       "       [ -239586.2128614 ],\n",
-       "       [   61378.86863422],\n",
-       "       [  324153.21154303],\n",
-       "       [  425078.96313546],\n",
-       "       [  309130.64409823],\n",
-       "       [   13958.72036409],\n",
-       "       [ -342837.18944828],\n",
-       "       [ -608813.19243686],\n",
-       "       [ -657224.43683103],\n",
-       "...\n",
-       "       [ -385879.92826719],\n",
-       "       [  439989.28946113],\n",
-       "       [ 1200633.88176686],\n",
-       "       [ 1651365.16313436],\n",
-       "       [ 1646479.19871744],\n",
-       "       [ 1188352.7042935 ],\n",
-       "       [  426329.58649498],\n",
-       "       [ -394079.89902149],\n",
-       "       [-1013972.08023278],\n",
-       "       [-1247166.25310344],\n",
-       "       [-1040186.60635186],\n",
-       "       [ -486056.16973034],\n",
-       "       [  211865.44570966],\n",
-       "       [  812418.59443909],\n",
-       "       [ 1119661.11301784],\n",
-       "       [ 1047446.12289875],\n",
-       "       [  643799.88232169],\n",
-       "       [   67972.60875276],\n",
-       "       [ -470177.36488641],\n",
-       "       [ -785853.82099391]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power</span></div><div class='xr-var-dims'>(type, time, dof)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.196e+07 1.601e+07 ... -5.237e+06</div><input id='attrs-6c68710a-d677-47b4-91dc-8284ab86a8cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c68710a-d677-47b4-91dc-8284ab86a8cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7364bda-0741-41e1-ae75-42b59c73af2b' class='xr-var-data-in' type='checkbox'><label for='data-d7364bda-0741-41e1-ae75-42b59c73af2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Power</dd><dt><span>units :</span></dt><dd>W</dd></dl></div><div class='xr-var-data'><pre>array([[[ 1.19621843e+07],\n",
-       "        [ 1.60079629e+07],\n",
-       "        [ 4.64784909e+05],\n",
-       "        [-1.63586640e+07],\n",
-       "        [-1.59364348e+07],\n",
-       "        [-1.81625848e+06],\n",
-       "        [ 7.75507716e+06],\n",
-       "        [ 3.86162162e+06],\n",
-       "        [-4.57993514e+06],\n",
-       "        [-5.83328727e+06],\n",
-       "        [-6.54286059e+05],\n",
-       "        [ 1.97057900e+06],\n",
-       "        [-7.14228751e+05],\n",
-       "        [-2.53416237e+06],\n",
-       "        [ 3.11321690e+05],\n",
-       "        [ 2.84231945e+06],\n",
-       "        [ 1.74586922e+05],\n",
-       "        [-2.60591457e+06],\n",
-       "        [ 3.11343056e+06],\n",
-       "        [ 1.42130303e+07],\n",
-       "...\n",
-       "        [ 3.06242402e+07],\n",
-       "        [-3.62179383e+07],\n",
-       "        [-7.69993857e+07],\n",
-       "        [-4.90950949e+07],\n",
-       "        [ 1.78582732e+07],\n",
-       "        [ 5.42336079e+07],\n",
-       "        [ 2.75510930e+07],\n",
-       "        [-2.47943566e+07],\n",
-       "        [-4.28130837e+07],\n",
-       "        [-1.26013014e+07],\n",
-       "        [ 2.35878100e+07],\n",
-       "        [ 2.22550569e+07],\n",
-       "        [-1.11641041e+07],\n",
-       "        [-3.46325262e+07],\n",
-       "        [-2.29862697e+07],\n",
-       "        [ 5.17590070e+06],\n",
-       "        [ 1.58120162e+07],\n",
-       "        [ 2.15760655e+06],\n",
-       "        [-1.15997934e+07],\n",
-       "        [-5.23693157e+06]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dab467ac-d622-4824-97f5-8b6670cac65e' class='xr-section-summary-in' type='checkbox'  ><label for='section-dab467ac-d622-4824-97f5-8b6670cac65e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64423ac0-f2c1-4f7e-9876-390ba86c9d2b' class='xr-index-data-in' type='checkbox'/><label for='index-64423ac0-f2c1-4f7e-9876-390ba86c9d2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                 0.0, 0.013333333333333332, 0.026666666666666665,\n",
-       "       0.039999999999999994,  0.05333333333333333,  0.06666666666666667,\n",
-       "        0.07999999999999999,  0.09333333333333332,  0.10666666666666666,\n",
-       "                       0.12,\n",
-       "       ...\n",
-       "          6.533333333333333,    6.546666666666666,                 6.56,\n",
-       "          6.573333333333333,    6.586666666666666,                  6.6,\n",
-       "          6.613333333333333,    6.626666666666666,                 6.64,\n",
-       "          6.653333333333333],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dof</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9257f9c-464b-4316-8e41-553171194775' class='xr-index-data-in' type='checkbox'/><label for='index-f9257f9c-464b-4316-8e41-553171194775' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;PTO_Heave&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dof&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>type</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6169e33a-90fb-4c3f-9318-712fb914471b' class='xr-index-data-in' type='checkbox'/><label for='index-6169e33a-90fb-4c3f-9318-712fb914471b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;mech&#x27;, &#x27;elec&#x27;], dtype=&#x27;object&#x27;, name=&#x27;type&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d17ff5c1-19a3-4c02-9f10-9bc79501fc54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d17ff5c1-19a3-4c02-9f10-9bc79501fc54' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>time_created_utc :</span></dt><dd>2023-12-06 17:12:35.128070</dd></dl></div></li></ul></div></div>"
-      ],
       "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:  (time: 500, dof: 1, type: 2)\n",
-       "Coordinates:\n",
-       "  * time     (time) float64 0.0 0.01333 0.02667 0.04 ... 6.613 6.627 6.64 6.653\n",
-       "  * dof      (dof) <U9 'PTO_Heave'\n",
-       "  * type     (type) <U4 'mech' 'elec'\n",
-       "Data variables:\n",
-       "    pos      (time, dof) float64 4.697 3.707 2.201 0.6375 ... 3.079 4.235 4.875\n",
-       "    vel      (time, dof) float64 -15.28 -33.16 -40.84 ... 31.74 24.67 6.664\n",
-       "    acc      (time, dof) float64 -522.7 -332.5 -34.93 ... -4.326 -334.8 -527.7\n",
-       "    force    (time, dof) float64 -7.828e+05 -4.828e+05 ... -4.702e+05 -7.859e+05\n",
-       "    power    (type, time, dof) float64 1.196e+07 1.601e+07 ... -5.237e+06\n",
-       "Attributes:\n",
-       "    time_created_utc:  2023-12-06 17:12:35.128070"
+       "True"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pto_tdom\n"
+    "problem['bounds'] == None"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## 2. Optimal control for maximum electrical power\n",
-    "\n",
-    "The rest of this tutorial will focus on optimizing for electrical power (new objective function) rather than mechanical, as this is a form of power that is usable and transportable.\n",
-    "\n",
-    "Since we're still dealing with the same WaveBot as in part 1, we can reuse the BEM and wave data from before. Look back at part 1 if you need a refresher on how to create these data.\n",
+    "# SETUP\n",
+    "# idx = 3\n",
+    "# x_wec = np.zeros(wec.ncomponents); x_wec[idx*2] = 1; x_wec[idx*2+1] = 1; x_wec[0] = 1\n",
+    "# x_opt = np.zeros(wec.ncomponents); x_opt[idx*2] = 1; x_opt[idx*2+1] = 1; x_opt[0] = 1\n",
     "\n",
-    "<div>\n",
-    "<img src=\"https://live.staticflickr.com/65535/52435033525_b8efc28d16_k.jpg\" width=\"1000\">\n",
-    "</div>\n",
-    "The WEC-PTO kinematics remain the same as well (unity). The major difference now is that we consider the dynamics of PTO, since they impact the electrical power and we shall not assume a lossless PTO.\n",
-    "\n",
-    "We will express the PTO's dynamics in form of a 2-port impedance model, to incoporate the dynamics of the drive-train and the dynamics of the generator.\n",
-    "The additional mechanical energy storage through the drive-train is modelled using Newton's second law and we assume a linear generator using a power-invariant park transform.\n",
+    "# x = np.concatenate([x_wec, x_opt])\n",
     "\n",
-    "The PTO impedance matrix components are then obtained under open-circuit  conditions, i.e.  no  load  current  or  no  WEC  velocity,  respectively."
+    "# t = wec.time"
    ]
   },
   {
@@ -1387,37 +496,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "## PTO impedance definition\n",
-    "omega = bem_data.omega.values\n",
-    "gear_ratio = 12.0\n",
-    "torque_constant = 6.7\n",
-    "winding_resistance = 0.5\n",
-    "winding_inductance = 0.0\n",
-    "drivetrain_inertia = 2.0\n",
-    "drivetrain_friction = 1.0\n",
-    "drivetrain_stiffness = 0.0\n",
-    "\n",
-    "drivetrain_impedance = (1j*omega*drivetrain_inertia +\n",
-    "                        drivetrain_friction +\n",
-    "                        1/(1j*omega)*drivetrain_stiffness)\n",
-    "\n",
-    "winding_impedance = winding_resistance + 1j*omega*winding_inductance\n",
-    "\n",
-    "\n",
-    "pto_impedance_11 = -1* gear_ratio**2 * drivetrain_impedance\n",
-    "off_diag = np.sqrt(3.0/2.0) * torque_constant * gear_ratio\n",
-    "pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
-    "pto_impedance_21 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
-    "pto_impedance_22 = winding_impedance\n",
-    "pto_impedance_2 = np.array([[pto_impedance_11, pto_impedance_12],\n",
-    "                            [pto_impedance_21, pto_impedance_22]])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will create a new `PTO` object with this impedance matrix. We will also update the definitions of our PTO constraint and additional dynamic forcing function to use the new object. We will set our PTO constraint to $600 N$ for this example, since the dynamics for optimal electrical power will be different."
+    "# Optimal\n",
+    "x_wec = np.array([\n",
+    "         -5.34590357e-05,\n",
+    "        # -1.44175178e-04, -1.69577759e-04,\n",
+    "        # -1.93732274e-05,  2.95446742e-04,\n",
+    "         9.23568964e-04,  -3.34575436e-04,\n",
+    "        -6.84667669e-05, -4.50653781e-04,\n",
+    "         9.28453882e-04,   7.75556889e-05,\n",
+    "         1.65461770e-01,  -2.25669377e-01,\n",
+    "         5.34106551e-04,   1.36036042e-04,\n",
+    "        -1.36430591e-04,  4.02670813e-04,\n",
+    "        -3.80444249e-05,  1.63276367e-05,\n",
+    "        -1.57437036e-04,\n",
+    "        ]);\n",
+    "\n",
+    "x_opt = np.array([\n",
+    "        -1.30402770e+00,\n",
+    "        # -3.48316585e+00, -4.09789281e+00,\n",
+    "        # -4.67649366e-01,  6.92938769e+00,\n",
+    "         2.06482440e+01, -7.26660182e+00,\n",
+    "        -1.16610808e+00, -9.37008614e+00,\n",
+    "         1.74020260e+01,  2.51384525e+00,\n",
+    "         2.35013292e+03, -4.10306916e+03,\n",
+    "         7.42264452e+00,  3.45914541e+00,\n",
+    "        -3.14415012e+00,  4.46682294e+00,\n",
+    "        -4.50072846e-01, -6.55878884e-03,\n",
+    "        -1.16075834e+00,\n",
+    "        ]);"
    ]
   },
   {
@@ -1426,25 +532,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "## Update PTO\n",
-    "name_2 = ['PTO_Heave_Ex2']\n",
-    "pto_2 = wot.pto.PTO(ndof, kinematics, controller, pto_impedance_2, loss, name_2)\n",
+    "x = np.concatenate([x_wec, x_opt])\n",
     "\n",
-    "## Update PTO constraints and forcing\n",
-    "f_max_2 = 600.0\n",
-    "def const_f_pto_2(wec, x_wec, x_opt, waves):\n",
-    "    f = pto_2.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
-    "    return f_max_2 - np.abs(f.flatten())\n",
-    "ineq_cons_2 = {'type': 'ineq', 'fun': const_f_pto_2}\n",
-    "constraints_2 = [ineq_cons_2]\n",
-    "f_add_2 = {'PTO': pto_2.force_on_wec}\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, let's update our `WEC` object with the new PTO constraint, then run our optimization problem. Note we're now using `average_power` instead of `mechanical_average_power` as our objective function."
+    "t = wec.time"
    ]
   },
   {
@@ -1452,77 +542,24 @@
    "execution_count": 21,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:wecopttool.core:Linear damping for DOF \"Heave\" has negative or close to zero terms. Shifting up via linear friction of 37.36584622341133 N/(m/s).\n"
-     ]
-    },
     {
      "ename": "ValueError",
-     "evalue": "shapes (192,192) and (200,) not aligned: 192 (dim 1) != 200 (dim 0)",
+     "evalue": "operands could not be broadcast together with shapes (32,) (36,) ",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb Celda 41\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m scale_x_opt \u001b[39m=\u001b[39m \u001b[39m1e-3\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m scale_obj \u001b[39m=\u001b[39m \u001b[39m1e-2\u001b[39m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m results_2 \u001b[39m=\u001b[39m wec_2\u001b[39m.\u001b[39;49msolve(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     waves,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     obj_fun_2,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     nstate_opt,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m     scale_x_wec\u001b[39m=\u001b[39;49mscale_x_wec,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m     scale_x_opt\u001b[39m=\u001b[39;49mscale_x_opt,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m     scale_obj\u001b[39m=\u001b[39;49mscale_obj,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m opt_average_power \u001b[39m=\u001b[39m results_2\u001b[39m.\u001b[39mfun\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cmichel/repos/WecOptTool/examples/tutorial_1_WaveBot.ipynb#X53sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mOptimal average electrical power: \u001b[39m\u001b[39m{\u001b[39;00mopt_average_power\u001b[39m}\u001b[39;00m\u001b[39m W\u001b[39m\u001b[39m'\u001b[39m)\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/core.py:852\u001b[0m, in \u001b[0;36mWEC.solve\u001b[0;34m(self, waves, obj_fun, nstate_opt, x_wec_0, x_opt_0, scale_x_wec, scale_x_opt, scale_obj, optim_options, use_grad, maximize, bounds_wec, bounds_opt, callback)\u001b[0m\n\u001b[1;32m    849\u001b[0m     problem[\u001b[39m'\u001b[39m\u001b[39mjac\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m grad(obj_fun_scaled)\n\u001b[1;32m    851\u001b[0m \u001b[39m# minimize\u001b[39;00m\n\u001b[0;32m--> 852\u001b[0m optim_res \u001b[39m=\u001b[39m minimize(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mproblem)\n\u001b[1;32m    854\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00moptim_res\u001b[39m.\u001b[39mmessage\u001b[39m}\u001b[39;00m\u001b[39m    (Exit mode \u001b[39m\u001b[39m{\u001b[39;00moptim_res\u001b[39m.\u001b[39mstatus\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    855\u001b[0m \u001b[39mif\u001b[39;00m optim_res\u001b[39m.\u001b[39mstatus \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_minimize.py:719\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    716\u001b[0m     res \u001b[39m=\u001b[39m _minimize_cobyla(fun, x0, args, constraints, callback\u001b[39m=\u001b[39mcallback,\n\u001b[1;32m    717\u001b[0m                            bounds\u001b[39m=\u001b[39mbounds, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[1;32m    718\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mslsqp\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> 719\u001b[0m     res \u001b[39m=\u001b[39m _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m    720\u001b[0m                           constraints, callback\u001b[39m=\u001b[39;49mcallback, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions)\n\u001b[1;32m    721\u001b[0m \u001b[39melif\u001b[39;00m meth \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtrust-constr\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m    722\u001b[0m     res \u001b[39m=\u001b[39m _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,\n\u001b[1;32m    723\u001b[0m                                        bounds, constraints,\n\u001b[1;32m    724\u001b[0m                                        callback\u001b[39m=\u001b[39mcallback, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_slsqp_py.py:374\u001b[0m, in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m    371\u001b[0m     xu[infbnd[:, \u001b[39m1\u001b[39m]] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan\n\u001b[1;32m    373\u001b[0m \u001b[39m# ScalarFunction provides function and gradient evaluation\u001b[39;00m\n\u001b[0;32m--> 374\u001b[0m sf \u001b[39m=\u001b[39m _prepare_scalar_function(func, x, jac\u001b[39m=\u001b[39;49mjac, args\u001b[39m=\u001b[39;49margs, epsilon\u001b[39m=\u001b[39;49meps,\n\u001b[1;32m    375\u001b[0m                               finite_diff_rel_step\u001b[39m=\u001b[39;49mfinite_diff_rel_step,\n\u001b[1;32m    376\u001b[0m                               bounds\u001b[39m=\u001b[39;49mnew_bounds)\n\u001b[1;32m    377\u001b[0m \u001b[39m# gh11403 SLSQP sometimes exceeds bounds by 1 or 2 ULP, make sure this\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[39m# doesn't get sent to the func/grad evaluator.\u001b[39;00m\n\u001b[1;32m    379\u001b[0m wrapped_fun \u001b[39m=\u001b[39m _clip_x_for_func(sf\u001b[39m.\u001b[39mfun, new_bounds)\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_optimize.py:383\u001b[0m, in \u001b[0;36m_prepare_scalar_function\u001b[0;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)\u001b[0m\n\u001b[1;32m    379\u001b[0m     bounds \u001b[39m=\u001b[39m (\u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39minf, np\u001b[39m.\u001b[39minf)\n\u001b[1;32m    381\u001b[0m \u001b[39m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[39;00m\n\u001b[1;32m    382\u001b[0m \u001b[39m# calculation reduces overall function evaluations.\u001b[39;00m\n\u001b[0;32m--> 383\u001b[0m sf \u001b[39m=\u001b[39m ScalarFunction(fun, x0, args, grad, hess,\n\u001b[1;32m    384\u001b[0m                     finite_diff_rel_step, bounds, epsilon\u001b[39m=\u001b[39;49mepsilon)\n\u001b[1;32m    386\u001b[0m \u001b[39mreturn\u001b[39;00m sf\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:158\u001b[0m, in \u001b[0;36mScalarFunction.__init__\u001b[0;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf \u001b[39m=\u001b[39m fun_wrapped(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mx)\n\u001b[1;32m    157\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_fun_impl \u001b[39m=\u001b[39m update_fun\n\u001b[0;32m--> 158\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_fun()\n\u001b[1;32m    160\u001b[0m \u001b[39m# Gradient evaluation\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcallable\u001b[39m(grad):\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:251\u001b[0m, in \u001b[0;36mScalarFunction._update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_update_fun\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    250\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf_updated:\n\u001b[0;32m--> 251\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_fun_impl()\n\u001b[1;32m    252\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf_updated \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:155\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.update_fun\u001b[0;34m()\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate_fun\u001b[39m():\n\u001b[0;32m--> 155\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf \u001b[39m=\u001b[39m fun_wrapped(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mx)\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py:137\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.fun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnfev \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    134\u001b[0m \u001b[39m# Send a copy because the user may overwrite it.\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[39m# Overwriting results in undefined behaviour because\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[39m# fun(self.x) will change self.x, with the two no longer linked.\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m fx \u001b[39m=\u001b[39m fun(np\u001b[39m.\u001b[39;49mcopy(x), \u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    138\u001b[0m \u001b[39m# Make sure the function returns a true scalar\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m np\u001b[39m.\u001b[39misscalar(fx):\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/core.py:770\u001b[0m, in \u001b[0;36mWEC.solve.<locals>.obj_fun_scaled\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    768\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mobj_fun_scaled\u001b[39m(x):\n\u001b[1;32m    769\u001b[0m     x_wec, x_opt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdecompose_state(x\u001b[39m/\u001b[39mscale)\n\u001b[0;32m--> 770\u001b[0m     \u001b[39mreturn\u001b[39;00m obj_fun(\u001b[39mself\u001b[39;49m, x_wec, x_opt, waves)\u001b[39m*\u001b[39mscale_obj\u001b[39m*\u001b[39msign\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:573\u001b[0m, in \u001b[0;36mPTO.average_power\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    547\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maverage_power\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    548\u001b[0m     wec: TWEC,\n\u001b[1;32m    549\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    552\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    553\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mfloat\u001b[39m:\n\u001b[1;32m    554\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the average power in each PTO DOF for a given\u001b[39;00m\n\u001b[1;32m    555\u001b[0m \u001b[39m    system state.\u001b[39;00m\n\u001b[1;32m    556\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    572\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 573\u001b[0m     energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menergy(wec, x_wec, x_opt, waves, nsubsteps)\n\u001b[1;32m    574\u001b[0m     \u001b[39mreturn\u001b[39;00m energy \u001b[39m/\u001b[39m wec\u001b[39m.\u001b[39mtf\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:544\u001b[0m, in \u001b[0;36mPTO.energy\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39menergy\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    519\u001b[0m     wec: TWEC,\n\u001b[1;32m    520\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    523\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    524\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mfloat\u001b[39m:\n\u001b[1;32m    525\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the energy in each PTO DOF for a given system\u001b[39;00m\n\u001b[1;32m    526\u001b[0m \u001b[39m    state.\u001b[39;00m\n\u001b[1;32m    527\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    543\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m     power_td \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpower(wec, x_wec, x_opt, waves, nsubsteps)\n\u001b[1;32m    545\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39msum(power_td) \u001b[39m*\u001b[39m wec\u001b[39m.\u001b[39mdt\u001b[39m/\u001b[39mnsubsteps\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:510\u001b[0m, in \u001b[0;36mPTO.power\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpower\u001b[39m(\u001b[39mself\u001b[39m,\n\u001b[1;32m    485\u001b[0m     wec: TWEC,\n\u001b[1;32m    486\u001b[0m     x_wec: ndarray,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    489\u001b[0m     nsubsteps: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m    490\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m ndarray:\n\u001b[1;32m    491\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Calculate the power time-series in each PTO DOF for a given\u001b[39;00m\n\u001b[1;32m    492\u001b[0m \u001b[39m    system state.\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[39m        length.\u001b[39;00m\n\u001b[1;32m    509\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 510\u001b[0m     q2_td, e2_td \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpower_variables(wec, x_wec,\n\u001b[1;32m    511\u001b[0m                                         x_opt, waves, nsubsteps)\n\u001b[1;32m    512\u001b[0m     \u001b[39m# power\u001b[39;00m\n\u001b[1;32m    513\u001b[0m     power_out \u001b[39m=\u001b[39m q2_td \u001b[39m*\u001b[39m e2_td\n",
-      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/pto.py:472\u001b[0m, in \u001b[0;36mPTO.power_variables\u001b[0;34m(self, wec, x_wec, x_opt, waves, nsubsteps)\u001b[0m\n\u001b[1;32m    470\u001b[0m vars_1 \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mhstack([q1, e1])\n\u001b[1;32m    471\u001b[0m vars_1_flat \u001b[39m=\u001b[39m dofmat_to_vec(vars_1)\n\u001b[0;32m--> 472\u001b[0m vars_2_flat \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mdot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransfer_mat, vars_1_flat)\n\u001b[1;32m    473\u001b[0m vars_2 \u001b[39m=\u001b[39m vec_to_dofmat(vars_2_flat, \u001b[39m2\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndof)\n\u001b[1;32m    474\u001b[0m q2 \u001b[39m=\u001b[39m vars_2[:, :\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndof]\n",
-      "File \u001b[0;32m~/miniforge3/envs/wot-dev/lib/python3.10/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[39mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m     \u001b[39mreturn\u001b[39;00m f_raw(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "\u001b[0;31mValueError\u001b[0m: shapes (192,192) and (200,) not aligned: 192 (dim 1) != 200 (dim 0)"
+      "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# objetive function\u001b[39;00m\n\u001b[1;32m      2\u001b[0m f \u001b[38;5;241m=\u001b[39m problem[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m y\n",
+      "File \u001b[0;32m~/repos/WecOptTool/wecopttool/core.py:816\u001b[0m, in \u001b[0;36mWEC.solve.<locals>.obj_fun_scaled\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    815\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mobj_fun_scaled\u001b[39m(x):\n\u001b[0;32m--> 816\u001b[0m     x_wec, x_opt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecompose_state(\u001b[43mx\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mscale\u001b[49m)\n\u001b[1;32m    817\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj_fun(\u001b[38;5;28mself\u001b[39m, x_wec, x_opt, waves)\u001b[38;5;241m*\u001b[39mscale_obj\u001b[38;5;241m*\u001b[39msign\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (32,) (36,) "
      ]
     }
    ],
    "source": [
-    "# Update WEC\n",
-    "\n",
-    "wec_2 = wot.WEC.from_bem(bem_data,\n",
-    "                         constraints=constraints_2,\n",
-    "                         friction=None,\n",
-    "                         f_add=f_add_2\n",
-    ")\n",
-    "\n",
-    "# Update objective function\n",
-    "obj_fun_2 = pto_2.average_power\n",
-    "\n",
-    "# Solve\n",
-    "scale_x_wec = 1e1\n",
-    "scale_x_opt = 1e-3\n",
-    "scale_obj = 1e-2\n",
-    "\n",
-    "results_2 = wec_2.solve(\n",
-    "    waves,\n",
-    "    obj_fun_2,\n",
-    "    nstate_opt,\n",
-    "    scale_x_wec=scale_x_wec,\n",
-    "    scale_x_opt=scale_x_opt,\n",
-    "    scale_obj=scale_obj,\n",
-    ")\n",
-    "opt_average_power = results_2.fun\n",
-    "print(f'Optimal average electrical power: {opt_average_power} W')\n",
-    "\n",
-    "# Post-process\n",
-    "wec_fdom_2, wec_tdom_2 = wec_2.post_process(results_2, waves, nsubsteps)\n",
-    "pto_fdom_2, pto_tdom_2 = pto_2.post_process(wec_2, results_2, waves, nsubsteps)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will compare our optimal results to the unconstrained case to gain some insight into the effect of the constraint on the optimal PTO force. Let's do the same process as before, but unset the `constraints` parameter in a new `WEC` object."
+    "# objetive function\n",
+    "f = problem['fun']\n",
+    "y = f(x)\n",
+    "y"
    ]
   },
   {
@@ -1531,34 +568,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wec_2_nocon = wot.WEC.from_bem(\n",
-    "    bem_data,\n",
-    "    constraints=None,\n",
-    "    friction=None,\n",
-    "    f_add=f_add_2)\n",
-    "\n",
-    "results_2_nocon = wec_2_nocon.solve(\n",
-    "    waves,\n",
-    "    obj_fun_2,\n",
-    "    nstate_opt,\n",
-    "    scale_x_wec=scale_x_wec,\n",
-    "    scale_x_opt=scale_x_opt,\n",
-    "    scale_obj=scale_obj,\n",
-    ")\n",
-    "opt_average_power = results_2_nocon.fun\n",
-    "print(f'Optimal average electrical power: {opt_average_power} W')\n",
-    "wec_fdom_2_nocon, wec_tdom_2_nocon = wec_2_nocon.post_process(\n",
-    "    results_2_nocon, waves, nsubsteps)\n",
-    "pto_fdom_2_nocon, pto_tdom_2_nocon = pto_2.post_process(\n",
-    "    wec_2_nocon, results_2_nocon, waves, nsubsteps)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the optimal constrained PTO force follows the optimal unconstrained solution (sinusoidal) whenever the unconstrained solution is within the constraint. \n",
-    "When the constraint is active the optimal PTO force is the maximum PTO force of $600 N$."
+    "# jacobian\n",
+    "f = problem['jac']\n",
+    "y = f(x)\n",
+    "plt.plot(y, 'o')"
    ]
   },
   {
@@ -1567,59 +580,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure()\n",
-    "wec_tdom_2['pos'].plot(label='constrained')\n",
-    "wec_tdom_2_nocon['pos'].plot(label='unconstrained')\n",
-    "plt.legend(loc='lower right')\n",
-    "\n",
-    "plt.figure()\n",
-    "pto_tdom_2['force'].plot(label='constrained')\n",
-    "pto_tdom_2_nocon['force'].plot(label='unconstrained')\n",
-    "plt.legend(loc='lower right')\n",
-    "\n",
-    "plt.figure()\n",
-    "pto_tdom_2['power'].loc['elec',:,:].plot(label='constrained')\n",
-    "pto_tdom_2_nocon['power'].loc['elec',:,:].plot(label='unconstrained')\n",
-    "plt.legend(loc='lower right')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The attentive user might have noticed that the amplitude of the position, force and power signals is about half the magnitude of the signals we plotted in the first part of the tutorial. We can see that optimizing for electrical power requires optimal state trajectories with smaller amplitudes. For most WECs the electrical power is the usable form of power, thus the WEC should be designed for electrical power and we can avoid over-designing, which would results from expecting the forces associated with the optimal trajectories for mechanical power maximisation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Control co-design of the WEC geometry for maximum electrical power\n",
-    "The first two examples only used the inner optimization loop in WecOptTool to optimize PTO power. Here in Part 3 we bring it all together and show how to use both the inner and outer optimization loops in WecOptTool to do control co-optimization of a hull design in conjunction with an optimal controller for electrical power.\n",
-    "Again, we use the WaveBot WEC in one degree of freedom in regular waves. \n",
-    "The goal is to **find the optimal keel radius** (`r2`) that maximizes the average produced electrical power, while maintaining a constant hull volume. \n",
-    "A constant volume is achieved by setting the height of the conical section (`h2`) in conjunction with the keel radius (`r2`).\n",
-    "\n",
-    "This example demonstrates a complete case of the types of optimization studies WecOptTool is meant for. \n",
-    "The main optimization (outer optimization loop) is to find the optimal geometry (radius `r2`), and for each geometry considered the optimal PTO force (inner optimization loop) will be found.\n",
-    "The inner loop was showcased in Example 2 and uses a gradient-based optimization method, with the gradients obtained with automatic differentiation. \n",
-    "The outer loop optimization is for the user to setup. \n",
-    "In this example, we will do a simple *brute force* optimization using `scipy.optimize.brute`.  \n",
-    "\n",
-    "![Device Diagram](https://live.staticflickr.com/65535/51751577441_515afec334_z.jpg) \n",
-    "<div>\n",
-    "<img src=\"https://live.staticflickr.com/65535/52434071157_187eb4334c_k.jpg\" width=\"1000\">\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Problem setup\n",
-    "First, we define a function for `h2` based on `r1` that maintains a constant volume. \n",
-    "We see that, as expected, smaller values of `r2` require larger values of `h2` in order to maintain a constant hull volume."
+    "# constraint (dynamics in residual form)\n",
+    "problem['constraints']"
    ]
   },
   {
@@ -1628,36 +590,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# r1 = 0.88\n",
-    "# r2_0 = 0.35\n",
-    "# h2_0 = 0.37\n",
-    "# V0 = 1/3*np.pi*h2_0*(r1**2+r2_0**2+(r1*r2_0))\n",
-    "\n",
-    "# r2_vals = np.linspace(0.05, 0.88*0.999, 8, endpoint=True)\n",
-    "\n",
-    "\n",
-    "# def h2_from_r2(r2, V=V0, r1=r1):\n",
-    "#     h2 = V/(1/3*np.pi*(r1**2+r2**2+(r1*r2)))\n",
-    "#     return h2\n",
-    "\n",
-    "\n",
-    "# # plot\n",
-    "# mapres = map(h2_from_r2, r2_vals)\n",
-    "# h2_vals = list(mapres)\n",
-    "\n",
-    "# fig1, ax1 = plt.subplots(figsize=(8,5))\n",
-    "# for r2, h2 in zip(r2_vals.tolist(), h2_vals):\n",
-    "#     _ = wot.geom.WaveBot(r2=r2, h2=h2, freeboard=0.2).plot_cross_section(\n",
-    "#         ax=ax1, label=f\"r2={r2:.2f}, h2={h2:.2f}\")\n",
-    "# ax1.legend(loc='best', fontsize='small',ncol=2)\n",
-    "# _ = ax1.set_title('WaveBot hull cross-sections')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we will define an objective function for our design optimization problem. We use the same workflow illustrated in Part 2 to set up a WaveBot device and solve for the optimal solution, but wrap this in a function definition which can set `r2` and (indirectly) `h2`."
+    "f = problem['constraints'][0]['fun']\n",
+    "y = f(x)\n",
+    "plt.plot(t, y)"
    ]
   },
   {
@@ -1666,114 +601,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# def design_obj_fun(x):\n",
-    "\n",
-    "#     # Unpack geometry variables\n",
-    "#     r2 = x[0]\n",
-    "#     h2 = h2_from_r2(r2)\n",
-    "#     print(f\"\\nr2 = {r2:.2f}:\")\n",
-    "\n",
-    "#     # Set up Capytaine floating body\n",
-    "#     wb = wot.geom.WaveBot(r2=r2, h2=h2)\n",
-    "#     mesh = wb.mesh(mesh_size_factor=0.5)\n",
-    "#     fb = cpy.FloatingBody.from_meshio(mesh, name=\"WaveBot\")\n",
-    "#     fb.add_translation_dof(name=\"Heave\")\n",
-    "#     ndof = fb.nb_dofs\n",
-    "\n",
-    "#     # Run BEM\n",
-    "#     f1 = 0.05\n",
-    "#     ifreq_end = 50\n",
-    "#     bem_data = wot.run_bem(fb, freq)\n",
-    "\n",
-    "#     # Impedance definition\n",
-    "#     omega = bem_data.omega.values\n",
-    "#     gear_ratio = 12.0\n",
-    "#     torque_constant = 6.7\n",
-    "#     winding_resistance = 0.5\n",
-    "#     winding_inductance = 0.0\n",
-    "#     drivetrain_inertia = 2.0\n",
-    "#     drivetrain_friction = 1.0\n",
-    "#     drivetrain_stiffness = 0.0\n",
-    "\n",
-    "#     drivetrain_impedance = (1j*omega*drivetrain_inertia +\n",
-    "#                             drivetrain_friction +\n",
-    "#                             1/(1j*omega)*drivetrain_stiffness)\n",
-    "\n",
-    "#     winding_impedance = winding_resistance + 1j*omega*winding_inductance\n",
-    "\n",
-    "\n",
-    "#     pto_impedance_11 = -1* gear_ratio**2 * drivetrain_impedance\n",
-    "#     off_diag = np.sqrt(3.0/2.0) * torque_constant * gear_ratio\n",
-    "#     pto_impedance_12 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
-    "#     pto_impedance_21 = -1*(off_diag+0j) * np.ones(omega.shape)\n",
-    "#     pto_impedance_22 = winding_impedance\n",
-    "#     pto_impedance_3 = np.array([[pto_impedance_11, pto_impedance_12],\n",
-    "#                             [pto_impedance_21, pto_impedance_22]])\n",
-    "\n",
-    "#     # Set PTO object\n",
-    "#     name = [\"PTO_Heave\",]\n",
-    "#     kinematics = np.eye(ndof)\n",
-    "#     efficiency = None\n",
-    "#     controller = None\n",
-    "#     pto = wot.pto.PTO(ndof, kinematics, controller, pto_impedance_3, efficiency, name)\n",
-    "\n",
-    "\n",
-    "#     # Set PTO constraint and additional dynamic force\n",
-    "#     nsubsteps = 4\n",
-    "#     f_max = 600.0\n",
-    "\n",
-    "#     def const_f_pto(wec, x_wec, x_opt, waves):\n",
-    "#         f = pto.force_on_wec(wec, x_wec, x_opt, waves, nsubsteps)\n",
-    "#         return f_max - np.abs(f.flatten())\n",
-    "\n",
-    "#     ineq_cons = {'type': 'ineq', 'fun': const_f_pto}\n",
-    "#     constraints = [ineq_cons]\n",
-    "\n",
-    "#     f_add = {'PTO': pto.force_on_wec}\n",
-    "\n",
-    "#     # Create WEC\n",
-    "#     wec = wot.WEC.from_bem(bem_data,\n",
-    "#             constraints=constraints,\n",
-    "#             friction=None,\n",
-    "#             f_add=f_add,\n",
-    "#             )\n",
-    "\n",
-    "#     # Waves\n",
-    "#     wfreq = 0.3\n",
-    "#     amplitude = 0.0625\n",
-    "#     phase = -40\n",
-    "#     waves_3 = wot.waves.regular_wave(f1, ifreq_end, wfreq, amplitude, phase)\n",
-    "\n",
-    "#     # Objective function\n",
-    "#     obj_fun = pto.average_power\n",
-    "#     nstate_opt = 2*ifreq_end\n",
-    "\n",
-    "#     # Solve\n",
-    "#     scale_x_wec = 1e1\n",
-    "#     scale_x_opt = 1e-3\n",
-    "#     scale_obj = 1e-2\n",
-    "#     res = wec.solve(\n",
-    "#         waves,\n",
-    "#         obj_fun,\n",
-    "#         nstate_opt,\n",
-    "#         scale_x_wec=scale_x_wec,\n",
-    "#         scale_x_opt=scale_x_opt,\n",
-    "#         scale_obj=scale_obj)\n",
-    "\n",
-    "#     return res.fun\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solve\n",
-    "Finally, we may call this objective function with an optimization algorithm. \n",
-    "Here, a simple *brute force* optimization approach is used for illustrative purposes, but any variety of options could be applied. \n",
-    "The optimization algorithm will call our objective function, which in turn will create a new WaveBot hull, run the necessary BEM calculations for the hull, and find the PTO force that provides the most electric power for that hull. \n",
-    "This process will be conducted for the range of `r2` values that we specify.\n",
-    "\n",
-    "_(note: the cell below will likely take 5+ minutes to run on a standard personal computer)_"
+    "f = problem['constraints'][0]['jac']\n",
+    "y = f(x)\n",
+    "plt.imshow(y, cmap=\"seismic\")\n",
+    "plt.colorbar()\n",
+    "print(y.shape)"
    ]
   },
   {
@@ -1782,22 +614,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# wot.set_loglevel(\"error\")  # Suppress warnings\n",
-    "\n",
-    "# # range over which to search\n",
-    "# ranges = (slice(r2_vals[0], r2_vals[-1]+np.diff(r2_vals)[0], np.diff(r2_vals)[0]),)\n",
-    "\n",
-    "# # solve\n",
-    "# opt_x0, opt_fval, x0s, fvals = brute(func=design_obj_fun, ranges=ranges, full_output=True,  finish=None)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Results\n",
-    "From a quick plot of the results, we see that the power absorption (where negative power is power absorbed by the device) generally improves for smaller values of `r2`.\n",
-    "It is also clear that when the WEC is cylindrical (where `r2=0.88`), power absorption is reduced."
+    "f = problem['constraints'][0]['jac']\n",
+    "y = f(x) * x\n",
+    "plt.imshow(y, cmap=\"seismic\")\n",
+    "plt.colorbar()"
    ]
   },
   {
@@ -1806,41 +626,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# fig, ax = plt.subplots()\n",
-    "# colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:len(x0s)]\n",
-    "# ax.plot(x0s, fvals, 'k', zorder=0)\n",
-    "# ax.scatter(x0s, fvals, c=colors, zorder=1)\n",
-    "\n",
-    "# ax.set_xlabel('Keel radius, $r_2$ [m]')\n",
-    "# ax.set_ylabel('Average Power [W]')\n",
-    "# ax.set_title('Design optimization results')\n",
-    "# fig.tight_layout()\n"
+    "# callback\n",
+    "f = problem['callback']\n",
+    "y = f(x)\n",
+    "y == None"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Note that in this case the magnitude of average power between the different keel radii is rather small, this is because the PTO force constraint is active most of the time, therefore all considered geometries perform similarily. If you remove the PTO constraint and re-run the co-optimization study you will see that the impact of radius on average electrical power is significantly higher."
+    "# x0 initial guess\n",
+    "plt.plot(problem['x0'], 'o')\n",
+    "plt.plot([16, 16], [-3, 3], 'k--')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "# DEBUG"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "len(results.x), len(wec.decompose_state(results.x)[0]), len(wec.decompose_state(results.x)[1])\n",
-    "# 200, 100, 100 (ifreq_start = 1)\n",
-    "# (ifreq_start = 3, all for PTO)\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/wecopttool/core.py b/wecopttool/core.py
index e4bd7f7f0..6077382a0 100644
--- a/wecopttool/core.py
+++ b/wecopttool/core.py
@@ -114,7 +114,7 @@ class WEC:
     """
     def __init__(
         self,
-        f1: float,
+        df: float,
         ifreq_end: int,
         forces: TIForceDict,
         ifreq_start: int = 1,
@@ -207,14 +207,15 @@ def __init__(
             Initialize a :py:class:`wecopttool.WEC` object from an
             intrinsic impedance array and excitation coefficients.
         """
+        self._df = df
         self._ifreq_start = ifreq_start
         self._ifreq_end = ifreq_end
         idx_1, idx_2 = self._idxs(ifreq_start, ifreq_end)
-        self._freq = frequency(f1, ifreq_end)[idx_1]
-        self._time = time(f1, ifreq_end)
-        self._time_mat = time_mat(f1, ifreq_end)[:, idx_2]
-        self._derivative_mat = derivative_mat(f1, ifreq_end)[idx_2][:, idx_2]
-        self._derivative2_mat = derivative2_mat(f1, ifreq_end)[idx_2][:, idx_2]
+        self._freq = frequency(df, ifreq_end)[idx_1]
+        self._time = time(df, ifreq_end)
+        self._time_mat = time_mat(df, ifreq_end)[:, idx_2]
+        self._derivative_mat = derivative_mat(df, ifreq_end)[idx_2][:, idx_2]
+        self._derivative2_mat = derivative2_mat(df, ifreq_end)[idx_2][:, idx_2]
         self._forces = forces
         constraints = list(constraints) if (constraints is not None) else []
         self._constraints = constraints
@@ -262,7 +263,7 @@ def _ignored(var_name, condition):
         if inertia_in_forces:
             _inertia = None
         else:
-            _inertia = WEC._get_inertia(f1, ifreq_start, ifreq_end, inertia_matrix)
+            _inertia = WEC._get_inertia(df, ifreq_start, ifreq_end, inertia_matrix)
         self._inertia = _inertia
 
         # names
@@ -275,7 +276,7 @@ def _ignored(var_name, condition):
     def __str__(self) -> str:
         str = (f'{self.__class__.__name__}: ' +
                f'DOFs ({self.ndof})={self.dof_names}, ' +
-               f'f=[0, {self.f1}, ..., {self.ifreq_end}({self.f1})] Hz.')
+               f'f=[0, {self.df}, ..., {self.ifreq_end}({self.df})] Hz.')
         return str
 
     def __repr__(self) -> str:
@@ -293,7 +294,7 @@ def _idxs(ifreq_start, ifreq_end):
 
     @staticmethod
     def _get_inertia(
-        f1: float,
+        df: float,
         ifreq_start: int,
         ifreq_end: int,
         inertia_matrix: ArrayLike,
@@ -302,20 +303,67 @@ def _get_inertia(
 
         Parameters
         ----------
-        f1
-            Fundamental frequency :python:`f1` [:math:`Hz`].
+        df
+            Frequency spacing [:math:`Hz`].
         nfreq
             Number of frequencies.
         inertia_matrix
             Inertia matrix.
         """
-        omega = np.expand_dims(frequency(f1, ifreq_end)[ifreq_start:]*2*np.pi, [1,2])
+        omega = np.expand_dims(frequency(df, ifreq_end)[ifreq_start:]*2*np.pi, [1,2])
         inertia_matrix = np.expand_dims(inertia_matrix, 0)
         rao_transfer_function = -1*omega**2*inertia_matrix + 0j
         inertia_fun = force_from_rao_transfer_function(
             rao_transfer_function, False)
         return inertia_fun
 
+    @staticmethod
+    def frequency_parameters(
+        freqs: ArrayLike,
+        precision: Optional[int] = 10,
+        ) -> tuple[float, int]:
+        """Return the fundamental frequency, the number of frequencies,
+        and first frequency in a frequency array.
+
+        This function can be used as a check for inputs to other
+        functions since it raises an error if the frequency vector does
+        not have the correct format (equally spaced).
+
+        Parameters
+        ----------
+        freqs
+            The frequency array with equal spacing.
+        precision
+            Controls rounding of fundamental frequency.
+
+        Returns
+        -------
+        df
+            Frequency spacing [:math:`Hz`].
+        ifreq_end
+            Last frequency (index).
+        ifreq_start
+            Frequency (index) at which vector starts.
+
+        Raises
+        ------
+        ValueError
+            If the frequency vector is not evenly spaced.
+        """
+        df = freqs[1] - freqs[0]
+        df = df if precision is None else round(df, precision)
+        ifreq_start = round(freqs[0]/df)
+        assert np.isclose(ifreq_start, freqs[0]/df)
+        ifreq_end = ifreq_start + len(freqs) - 1
+        f_check = np.arange(ifreq_start, ifreq_end+1)*df
+        if not np.allclose(f_check, freqs):
+            print(f_check)
+            print(freqs)
+            raise ValueError(
+                "Frequency array must be evenly spaced by " +
+                "the fundamental frequency ")
+        return df, ifreq_start, ifreq_end
+
     # other initialization methods
     @staticmethod
     def from_bem(
@@ -398,7 +446,7 @@ def from_bem(
         inertia_matrix = hydro_data['inertia_matrix'].values
 
         # frequency array
-        f1, ifreq_start, ifreq_end = WEC.frequency_parameters(
+        df, ifreq_start, ifreq_end = WEC.frequency_parameters(
             hydro_data.omega.values/(2*np.pi))
 
         # check real part of damping diagonal > 0
@@ -413,14 +461,14 @@ def from_bem(
         # constraints
         constraints = constraints if (constraints is not None) else []
         wec = WEC(
-            f1, ifreq_end, forces, ifreq_start, constraints,
+            df, ifreq_end, forces, ifreq_start, constraints,
             inertia_matrix, dof_names=dof_names)
         return wec
 
     @staticmethod
     def from_floating_body(
         fb: cpy.FloatingBody,
-        f1: float,
+        df: float,
         ifreq_end: int,
         ifreq_start: int = 1,
         friction: Optional[ndarray] = None,
@@ -507,7 +555,7 @@ def from_floating_body(
         _log.info(f"Running Capytaine (BEM): {nfreq} frequencies x " +
                  f"{len(wave_directions)} wave directions.")
         idx_1, _ = self._idxs(ifreq_start, ifreq_end)
-        freq = frequency(f1, ifreq_end)[idx_1][1:]
+        freq = frequency(df, ifreq_end)[idx_1][1:]
         bem_data = run_bem(
             fb, freq, wave_directions, rho=rho, g=g, depth=depth)
         wec = WEC.from_bem(
@@ -572,8 +620,7 @@ def from_impedance(
             If :python:`impedance` does not have the correct size:
             :python:`(ndof, ndof, nfreq)`.
         """
-        f1, nfreq = frequency_parameters(freqs, False)
-        f1, ifreq_start, ifreq_end = self.frequency_parameters(freqs)
+        df, ifreq_start, ifreq_end = self.frequency_parameters(freqs)
         nfreq = ifreq_end - ifreq_start + 1
 
         # impedance matrix shape
@@ -606,7 +653,7 @@ def from_impedance(
         forces = forces | f_add
 
         # wec
-        wec = WEC(f1, ifreq_end, forces, ifreq_start, constraints,
+        wec = WEC(df, ifreq_end, forces, ifreq_start, constraints,
             inertia_in_forces=True, ndof=shape[1])
         return wec
 
@@ -837,17 +884,19 @@ def callback_scipy(x):
 
         # optimization problem
         optim_options['disp'] = optim_options.get('disp', True)
-        problem = {'fun': obj_fun_scaled,
-                    'x0': x0,
-                    'method': 'SLSQP',
-                    'constraints': constraints,
-                    'options': optim_options,
-                    'bounds': bounds,
-                    'callback': callback_scipy,
-                    }
+        problem = {
+            'fun': obj_fun_scaled,
+            'x0': x0,
+            'method': 'SLSQP',
+            'constraints': constraints,
+            'options': optim_options,
+            'bounds': bounds,
+            'callback': callback_scipy,
+        }
         if use_grad:
             problem['jac'] = grad(obj_fun_scaled)
 
+        return problem ## DEBUG
         # minimize
         optim_res = minimize(**problem)
 
@@ -1048,10 +1097,15 @@ def frequency(self) -> ndarray:
         """Frequency vector [:math:`Hz`]."""
         return self._freq
 
+    @property
+    def df(self) -> float:
+        """Frequency spacing [:math:`Hz`]."""
+        return self._df
+
     @property
     def f1(self) -> float:
         """Fundamental frequency :python:`f1` [:math:`Hz`]."""
-        return self._freq[1]
+        return self.df
 
     @property
     def ifreq_end(self) -> int:
@@ -1137,7 +1191,7 @@ def tf(self) -> float:
         """Final time (repeat period) [s]. Not included in
         :python:`time` vector.
         """
-        return 1/self.f1
+        return 1/self.df
 
     @property
     def nt(self) -> int:
@@ -1207,7 +1261,7 @@ def time_nsubsteps(self, nsubsteps: int) -> ndarray:
         --------
         time, WEC.time
         """
-        return time(self.f1, self.ifreq_end, nsubsteps)
+        return time(self.df, self.ifreq_end, nsubsteps)
 
     def time_mat_nsubsteps(self, nsubsteps: int) -> ndarray:
         """Create a time matrix similar to
@@ -1227,7 +1281,7 @@ def time_mat_nsubsteps(self, nsubsteps: int) -> ndarray:
         time_mat, WEC.time_mat, WEC.time_nsubsteps
         """
         _, idx_2 = self._idxs(self.ifreq_start, self.ifreq_end)
-        return time_mat(self.f1, self.ifreq_end, nsubsteps)[:, idx_2]
+        return time_mat(self.df, self.ifreq_end, nsubsteps)[:, idx_2]
 
     def vec_to_dofmat(self, vec: ndarray) -> ndarray:
         """Convert a vector to a matrix with one column per degree of
@@ -1291,7 +1345,7 @@ def fd_to_td(self, fd: ndarray) -> ndarray:
         --------
         fd_to_td, WEC.td_to_fd
         """
-        return self.time_mat @ complex_to_real(fd)
+        return np.dot(self.time_mat, complex_to_real(fd))
 
     def td_to_fd(
         self,
@@ -1320,54 +1374,6 @@ def td_to_fd(
         idx_1, _ = self._idxs(self.ifreq_start, self.ifreq_end)
         return td_to_fd(td, fft, True)[idx_1, :]
 
-    @staticmethod
-    def frequency_parameters(
-        freqs: ArrayLike,
-        precision: Optional[int] = 10,
-        ) -> tuple[float, int]:
-        """Return the fundamental frequency, the number of frequencies,
-        and first frequency in a frequency array.
-
-        This function can be used as a check for inputs to other
-        functions since it raises an error if the frequency vector does
-        not have the correct format (equally spaced).
-
-        Parameters
-        ----------
-        freqs
-            The frequency array with equal spacing.
-        precision
-            Controls rounding of fundamental frequency.
-
-        Returns
-        -------
-        f1
-            Fundamental frequency :python:`f1` [:math:`Hz`].
-            This is also the spacing.
-        ifreq_end
-            Last frequency (index).
-        ifreq_start
-            Frequency (index) at which vector starts.
-
-        Raises
-        ------
-        ValueError
-            If the frequency vector is not evenly spaced.
-        """
-        f1 = freqs[1] - freqs[0]
-        f1 = f1 if precision is None else round(f1, precision)
-        ifreq_start = round(freqs[0]/f1)
-        assert np.isclose(ifreq_start, freqs[0]/f1)
-        ifreq_end = ifreq_start + len(freqs) - 1
-        f_check = np.arange(ifreq_start, ifreq_end+1)*f1
-        if not np.allclose(f_check, freqs):
-            print(f_check)
-            print(freqs)
-            raise ValueError(
-                "Frequency array must be evenly spaced by " +
-                "the fundamental frequency ")
-        return f1, ifreq_start, ifreq_end
-
 
 def ncomponents(
     nfreq : int,