max_ionization = total_ionization / cs
max_brem = (total_ionization + brem_cs) / cs
max_excitation = (total_ionization + brem_cs + excitation_cs) / cs
max_elastic = (total_ionization + brem_cs + excitation_cs +
elastic_cs) / cs
print '\tAdjoint_weight_factor = ', '%.16e' % weight_factor
print '\t---------------------------------------'
print '\tmax ionization random number = ', '%.16e' % max_ionization
print '\tmax brem random number = ', '%.16e' % max_brem
print '\tactual min excitation random number = .28545'
print '\tactual max excitation random number = ', excitation_cs / cs + .28545
print '\tmax excitation random number = ', max_excitation
print '\tmax elastic random number = ', '%.16e' % max_elastic
brem_dist = Collision.createLogLogLogCorrelatedBremsstrahlungDistribution(
adjoint_data, 1e-12)
atomic_dist = Collision.createAtomicExcitationDistribution(adjoint_data)
random_numbers = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Prng.RandomNumberGenerator.setFakeStream(random_numbers)
incoming_energy = 1.55
outgoing_energy, scattering_angle = brem_dist.sample(incoming_energy)
print "\noutgoing_energy = ", '%.16e' % outgoing_energy
print "scattering_angle = ", '%.16e' % scattering_angle
outgoing_energy, scattering_angle = atomic_dist.sample(incoming_energy)
print "\noutgoing_energy = ", '%.16e' % outgoing_energy
print "scattering_angle = ", '%.16e' % scattering_angle
pdfs = numpy.zeros(shape=(len(schemes), length))
cdfs = numpy.zeros(shape=(len(schemes), length))
samples = numpy.zeros(shape=(len(schemes), length))
labels = []
Num = len(schemes)
for n in range(0, len(schemes)):
if interpolation == "LogLogLog":
labels.append(schemes[n] + " - log")
if schemes[n] == "Unit-base":
dist = Collision.createLogLogLogUnitBaseBremsstrahlungDistribution(native_data, tol)
if schemes[n] == "Unit-base Correlated":
dist = Collision.createLogLogLogUnitBaseCorrelatedBremsstrahlungDistribution(native_data, tol)
if schemes[n] == "Correlated":
dist = Collision.createLogLogLogCorrelatedBremsstrahlungDistribution(native_data, tol)
elif interpolation == "LinLinLin":
labels.append(schemes[n] + " - lin")
if schemes[n] == "Unit-base":
dist = Collision.createLinLinLinUnitBaseBremsstrahlungDistribution(native_data, tol)
elif schemes[n] == "Unit-base Correlated":
dist = Collision.createLinLinLinUnitBaseCorrelatedBremsstrahlungDistribution(native_data, tol)
elif schemes[n] == "Correlated":
dist = Collision.createLinLinLinCorrelatedBremsstrahlungDistribution(native_data, tol)
if schemes[n] == "Unit-base":
upper_samples = numpy.zeros(len(random_numbers))
Num = n
# Sample both upper and lower distrbutions
lower_random_numbers = numpy.zeros((len(random_numbers)*3))
