status: in development for own use on the test case cylinder; hosted at test.pypi
Markov chain Monte Carlo simulation on a physics system. Mixed complex and real parameter space and adaptive. Works together with some external function that takes a list of (real and complex) parameters and returns an energy.
INSTALL:
pip install --index-url https://test.pypi.org/simple metropolisengine
IMPORT:
import metropolisengine as me
USE:
see demo/, cylinder/run.py;
MINIMAL EXAMPLE:
simulation of one real variable x subject to the potential E = x^2. Set up the "physics system", here just
def energy_function(x):
return x**2
initialize MetropolisEngine object. You must give at least an energy function ( [real params] [complex params] -> float ) and at least a list of one value as either initial_real_params or initial_complex_params. Also set a non-zero temperature so that something interesting will happen in our simulation.
import metropolisengine as me
my_engine = me.MetropolisEngine(lambda real_params, complex_params: energy_function(*real_params), initial_real_params=[0.0], temp=.01)
run the simulation by looping over step_all(), and record data at every step with measure()
n_steps = 1000
for i in range(n_steps):
my_engine.step_all()
my_engine.measure()
look at measured quatities such as mean, (co-)variances, other default measured quantities.
print(my_engine.real_mean)
print(my_engine.covariance_matrix_real)
print(list(zip(my_engine.observables_names, my_engine.observables_mean)))
finally prompt the finalization of time series data into a table and look at it
my_engine.save_time_series()
print(my_engine.df)