def testFinalizeAndQuery(self):
""" Test the finalization and query functions. """
tsd = TimeStepDistribution()
# Set the member data.
histogram = numpy.array(numpy.random.rand(12) * 34, dtype=int)
normalized_histogram = histogram / float(numpy.sum(histogram))
binsize = 2.13
tsd._TimeStepDistribution__binsize = binsize
tsd._TimeStepDistribution__histogram = histogram
# Call the finalize function.
tsd.finalize()
# We now have the normalized histogram and timesteps on the class.
ret_histogram = tsd.histogram()
ret_norm_histogram = tsd.normalizedHistogram()
ret_time_steps = tsd.timeSteps()
# Check the histograms.
self.assertAlmostEqual(numpy.linalg.norm(ret_histogram - histogram),
0.0, 12)
self.assertAlmostEqual(
numpy.linalg.norm(ret_norm_histogram - normalized_histogram), 0.0,
12)
# Check the time steps.
time_steps = (numpy.arange(12) + 1) * binsize - binsize / 2.0
self.assertAlmostEqual(numpy.linalg.norm(ret_time_steps - time_steps),
0.0, 12)
def testFinalizeAndQuery(self):
""" Test the finalization and query functions. """
tsd = TimeStepDistribution()
# Set the member data.
histogram = numpy.array(numpy.random.rand(12)*34, dtype=int)
normalized_histogram = histogram / float(numpy.sum(histogram))
binsize = 2.13
tsd._TimeStepDistribution__binsize = binsize
tsd._TimeStepDistribution__histogram = histogram
# Call the finalize function.
tsd.finalize()
# We now have the normalized histogram and timesteps on the class.
ret_histogram = tsd.histogram()
ret_norm_histogram = tsd.normalizedHistogram()
ret_time_steps = tsd.timeSteps()
# Check the histograms.
self.assertAlmostEqual(numpy.linalg.norm(ret_histogram - histogram), 0.0, 12)
self.assertAlmostEqual(numpy.linalg.norm(ret_norm_histogram - normalized_histogram), 0.0, 12)
# Check the time steps.
time_steps = (numpy.arange(12)+1)*binsize - binsize/2.0
self.assertAlmostEqual(numpy.linalg.norm(ret_time_steps - time_steps), 0.0, 12)
def testTimeStepDistribution(self):
""" Test the distribution for a system with only one moving particle. """
# Setup a system, a periodic 10x10x10 atoms long 3D block.
unit_cell = KMCUnitCell(cell_vectors=numpy.array([[1.0,0.0,0.0],
[0.0,1.0,0.0],
[0.0,0.0,1.0]]),
basis_points=[[0.0,0.0,0.0]])
# And a lattice.
lattice = KMCLattice(unit_cell=unit_cell,
repetitions=(10,10,10),
periodic=(True,True,True))
# Setup an initial configuration with one B in a sea of A:s.
types = ["A"]*10*10*10
types[5] = "B"
config = KMCConfiguration(lattice=lattice,
types=types,
possible_types=["A","B"])
# Setup a all diffusion processes.
coordinates_p0 = [[0.0, 0.0, 0.0],[-1.0, 0.0, 0.0]]
p0 = KMCProcess(coordinates=coordinates_p0,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.1)
coordinates_p1 = [[0.0, 0.0, 0.0],[1.0, 0.0, 0.0]]
p1 = KMCProcess(coordinates=coordinates_p1,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.2)
coordinates_p2 = [[0.0, 0.0, 0.0],[0.0,-1.0, 0.0]]
p2 = KMCProcess(coordinates=coordinates_p2,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.3)
coordinates_p3 = [[0.0, 0.0, 0.0],[0.0, 1.0, 0.0]]
p3 = KMCProcess(coordinates=coordinates_p3,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.4)
coordinates_p4 = [[0.0, 0.0, 0.0],[0.0, 0.0,-1.0]]
p4 = KMCProcess(coordinates=coordinates_p4,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.5)
coordinates_p5 = [[0.0, 0.0, 0.0],[0.0, 0.0, 1.0]]
p5 = KMCProcess(coordinates=coordinates_p5,
elements_before=["B","A"],
elements_after=["A","B"],
move_vectors=None,
basis_sites=[0],
rate_constant=0.6)
interactions = KMCInteractions(processes=[p0, p1, p2, p3, p4, p5],
implicit_wildcards=True)
model = KMCLatticeModel(configuration=config,
interactions=interactions)
# Setup the analysis.
tsd = TimeStepDistribution(binsize=0.001)
# Setup the control parameters.
control_parameters = KMCControlParameters(number_of_steps=1000000,
dump_interval=10000,
analysis_interval=1)
# Run the model.
model.run(control_parameters=control_parameters,
analysis=[tsd])
# Define the analytical reference function.
# The exponent is the total rate and the coefficient is
# chosen to normalize to one.
total_rate = 2.1
def ref(dt):
return total_rate*0.001*numpy.exp(-total_rate * dt)
hst = tsd.normalizedHistogram()
time = tsd.timeSteps()
sum_diff = 0.0
for t, h in zip(time, hst):
sum_diff += numpy.abs(ref(t) - h)
# Make sure the relative error is less than five percent.
self.assertTrue( sum_diff*100.0 < 5.0 )
