def make_detectornoniondist(alphaType, incEnergy):
incEnergy = incEnergy / 1000.0 # parameters fitted for an energy in keV
# distribution was modeled for only energies around the peaks, so need to know
# which peak to take parameters from
c = 0
d = 0
loc = 0
scale = 0
if alphaType == 'gd':
c = 2.602
d = 1.289
loc = 0.042 * incEnergy + 3093.0
scale = 0.224 * incEnergy + 2678.0
elif alphaType == 'cm1' or alphaType == 'cm2':
c = 2.560
d = 1.450
loc = 0.146 * incEnergy + 2762.0
scale = 0.116 * incEnergy + 2959.0
lossbins = np.linspace(100000, 0, 100001)
# parameters output energy loss in eV
losses = burr.pdf(lossbins, c, d, loc, scale)
mean = float(burr.stats(c, d, loc=loc, scale=scale, moments='m'))
# return the loss and some additional information
return (losses, mean)
#Generate random numbers: r = bradford.rvs(c, size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() #burr Continuous distributions¶ from scipy.stats import burr import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots(1, 1) #Calculate a few first moments: c, d = 10.5, 4.3 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') #Display the probability density function (pdf): x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) ax.plot(x, burr.pdf(x, c, d), 'r-', lw=5, alpha=0.6, label='burr pdf') #Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. #Freeze the distribution and display the frozen pdf: rv = burr(c, d) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = burr.ppf([0.001, 0.5, 0.999], c, d) np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d)) True #Generate random numbers: r = burr.rvs(c, d, size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)