energy_0 = adjoint_energy_grid[index] cs_0 = adjoint_brem_cs[index] energy_1 = adjoint_energy_grid[index+1] cs_1 = adjoint_brem_cs[index+1] print "energy = ", energy cs = cs_0 + (cs_1 - cs_0)*( energy - energy_0 )/( energy_1 - energy_0 ) print '\tcs = ','%.16e' % cs energy = 20.0 print "energy = ", energy print '\tcs = ','%.16e' % adjoint_brem_cs[adjoint_brem_cs.size -1] brem_dist = Collision.createLogLogLogUnitBaseCorrelatedBremsstrahlungDistribution(adjoint_data, 1e-7) E_in = [1e-6, 1e-5, 1.1e-5, 20.0, 21.0] E_out = [2.0e-5, 20.2, 1.0, 20.000000201, 22.0] print "\nEvaluate[ E_in, E_out]" for i in range(0,len(E_in)): pdf = brem_dist.evaluate( E_in[i], E_out[i] ) print "\teval[",E_in[i],",",E_out[i],"] =\t",'%.16e' % pdf print "\nEvaluate PDF[ E_in, E_out]" for i in range(0,len(E_in)): pdf = brem_dist.evaluatePDF( E_in[i], E_out[i] ) print "\teval[",E_in[i],",",E_out[i],"] =\t",'%.16e' % pdf E_in = [1e-6, 1e-5, 1.1e-5, 20.0, 21.0] E_out = [2.0e-5, 10.1000050505, 1.0, 20.1000000505, 22.0]
angle_random_number = numpy.zeros((len(random_numbers)*2))
angle_random_number[0::2] = random_numbers
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
