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Description of changes: - track particles with default-constructed type 0 (fixes #4588) - restore the original particle velocity when a Monte Carlo displacement move is rejected (fixes #4587)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| # | ||
| # Copyright (C) 2022 The ESPResSo project | ||
| # | ||
| # This file is part of ESPResSo. | ||
| # | ||
| # ESPResSo is free software: you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation, either version 3 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # ESPResSo is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
| # | ||
|
|
||
| import espressomd | ||
| import espressomd.constraints | ||
| import espressomd.observables | ||
| import espressomd.accumulators | ||
| import espressomd.reaction_methods | ||
|
|
||
| import numpy as np | ||
| import scipy.optimize | ||
| import unittest as ut | ||
| import unittest_decorators as utx | ||
|
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|
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| class Test(ut.TestCase): | ||
|
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| system = espressomd.System(box_l=[1., 1., 1.]) | ||
| system.cell_system.skin = 0.4 | ||
| system.setup_type_map(type_list=[0]) | ||
|
|
||
| def tearDown(self): | ||
| self.system.part.clear() | ||
| self.system.constraints.clear() | ||
|
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| @utx.skipIfMissingFeatures("ELECTROSTATICS") | ||
| def test_linear_potential(self): | ||
| """ | ||
| Set a particle in a box with a linear potential along the x-axis. | ||
| The particle distribution resulting from accepted Monte Carlo moves | ||
| should follow a Maxwell-Boltzmann distribution. | ||
| """ | ||
|
|
||
| method = espressomd.reaction_methods.ReactionEnsemble( | ||
| kT=0.2, seed=42, exclusion_range=0., search_algorithm="order_n") | ||
| method.set_non_interacting_type(type=1) | ||
|
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| p = self.system.part.add(pos=[0., 0., 0.], q=1, type=0) | ||
| obs_pos = espressomd.observables.ParticlePositions(ids=(p.id,)) | ||
| obs_vel = espressomd.observables.ParticleVelocities(ids=(p.id,)) | ||
| acc_pos = espressomd.accumulators.TimeSeries(obs=obs_pos) | ||
| acc_vel = espressomd.accumulators.TimeSeries(obs=obs_vel) | ||
|
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| E = np.array([-1., 0., 0.]) | ||
| field = espressomd.constraints.LinearElectricPotential(E=E, phi0=0.) | ||
| self.system.constraints.add(field) | ||
|
|
||
| for _ in range(5000): | ||
| accepted = method.displacement_mc_move_for_particles_of_type( | ||
| type_mc=0, particle_number_to_be_changed=1) | ||
| if accepted: | ||
| acc_pos.update() | ||
| acc_vel.update() | ||
| p.pos = [0., 0., 0.] | ||
| p.v = [0., 0., 0.] | ||
| else: | ||
| self.assertAlmostEqual(np.linalg.norm(p.v), 0., delta=1e-12) | ||
|
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| # the x-position should follow an exponential distribution | ||
| # -> mean = kT, median = kT x ln(2), variance = kT^2 | ||
| series = acc_pos.time_series()[:, p.id, 0] | ||
| ydata, xbins = np.histogram(series, bins=15, range=[0., 1.]) | ||
| xdata = (xbins[1:] + xbins[:-1]) / 2. | ||
| ydata = ydata / float(ydata[0]) | ||
| (a, b, c), _ = scipy.optimize.curve_fit( | ||
| lambda x, a, b, c: a * np.exp(-b * x) + c, xdata, ydata) | ||
| # check histogram profile is roughly exponential | ||
| self.assertAlmostEqual(a, 1., delta=0.2) | ||
| self.assertAlmostEqual(b, 1. / method.kT, delta=0.3) | ||
| self.assertAlmostEqual(c, 0., delta=0.01) | ||
| # check distribution parameters with high accuracy | ||
| ln2 = np.log(2) | ||
| self.assertAlmostEqual(np.mean(series), method.kT, delta=0.02) | ||
| self.assertAlmostEqual(np.median(series) / ln2, method.kT, delta=0.02) | ||
| self.assertAlmostEqual(np.sqrt(np.var(series)), method.kT, delta=0.02) | ||
|
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||
| # the y- and z-position should follow a uniform distribution | ||
| for axis in (1, 2): | ||
| series = acc_pos.time_series()[:, p.id, axis] | ||
| ydata, _ = np.histogram(series, bins=10, range=[0., 1.]) | ||
| ydata = ydata / np.mean(ydata) | ||
| np.testing.assert_allclose(ydata, 1., atol=0.25) | ||
|
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| # the velocity vector should follow a normal distribution | ||
| # -> mean = 0, median = 0, variance = kT | ||
| for axis in (0, 1, 2): | ||
| series = acc_vel.time_series()[:, p.id, axis] | ||
| ydata, xbins = np.histogram(series, bins=25, range=[-1.5, 1.5]) | ||
| xdata = (xbins[1:] + xbins[:-1]) / 2. | ||
| ydata = ydata / len(series) | ||
| (_, b, c), _ = scipy.optimize.curve_fit( | ||
| lambda x, a, b, c: a * np.exp(-b * x**2) + c, xdata, ydata) | ||
| # check histogram profile is roughly gaussian | ||
| self.assertAlmostEqual(b, 0.5 / method.kT, delta=0.45) | ||
| self.assertAlmostEqual(c, 0., delta=0.002) | ||
| # check distribution parameters with high accuracy | ||
| self.assertAlmostEqual(np.mean(series), 0., delta=0.05) | ||
| self.assertAlmostEqual(np.median(series), 0., delta=0.025) | ||
| self.assertAlmostEqual(np.var(series), method.kT, delta=0.025) | ||
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| if __name__ == "__main__": | ||
| ut.main() |
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