def xgauss(x, k, loc, scale, amp):
from scipy.stats import exponnorm
return amp * exponnorm.cdf(x, k, loc, scale)
x = np.linspace(exponnorm.ppf(0.01, K),
exponnorm.ppf(0.99, K), 100)
ax.plot(x, exponnorm.pdf(x, K),
'r-', lw=5, alpha=0.6, label='exponnorm 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 = exponnorm(K)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = exponnorm.ppf([0.001, 0.5, 0.999], K)
np.allclose([0.001, 0.5, 0.999], exponnorm.cdf(vals, K))
# True
# Generate random numbers:
r = exponnorm.rvs(K, size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
def xGaussCDF(x, k, loc, scale, amp):
from scipy.stats import exponnorm
return exponnorm.cdf(x, k, loc, scale)
def main():
""" trying different distributions for the m2s238 efficiency functions """
# asymmetric cdf of xgauss
# dt = 1
# ts = np.arange(0,1000,dt)
# mu, sig, tau = 500, 50, -100
# wf = wl.evalXGaus(ts,mu,sig,tau)
# dw = [0]
# for i in range(1,len(ts)):
# dw.append((wf[i]-wf[i-1])/dt)
# iw = [wf[0]]
# for i in range(1,len(ts)):
# iw.append(wf[i]+iw[i-1])
# plt.plot(ts, wf/np.sum(wf))
# plt.plot(ts, dw/np.sum(dw))
# plt.plot(ts, iw)
# plt.show()
# from scipy.stats import skewnorm
# a=3
# x = np.linspace(skewnorm.ppf(0.01, a), skewnorm.ppf(0.99, a), 100)
# plt.plot(x, skewnorm.cdf(x, a),'r-', label='skewnorm cdf')
# plt.show()
# compare logistic to genlogistic (fits a little better)
# from scipy.stats import logistic
# yL = amp * logistic.cdf(xE, mu, sig) # ok this is identical to the manual one
# plt.plot(xE, yL, "-k", lw=5, label='regular')
# from scipy.stats import genlogistic
# skews = np.arange(0.9,2.0,0.1)
# cmap = plt.cm.get_cmap('hsv',len(skews)+1)
# for i,skew in enumerate(skews):
# yG = amp * genlogistic.cdf(xE, skew, mu, sig)
# plt.plot(xE, yG, "-", c=cmap(i), label='skew=%.1f' % skew)
# plt.xlabel("Energy")
# plt.ylim(0,1)
# plt.legend(loc=4)
# best-fit to all det's with logistic
xE = np.arange(0, 30, 0.1)
mu, sig, amp = 0.6, 5.54, 0.91
from scipy.stats import logistic
yE = amp * logistic.cdf(xE, mu, sig)
plt.plot(xE, yE, "-b", lw=5, label='regular')
# weibull is pretty good
# from scipy.stats import weibull_min as wb
# c is like sharpness (sig)
# loc is the last zero (not mu)
# c, loc, scale = 1, 1, 5
# plt.plot(xE, 0.9 * wb.cdf(xE,c,loc,scale),'-',c='r')
# plt.legend(loc=4)
# xgauss cdf
from scipy.stats import exponnorm as xg
k, loc, scale, amp = 10, -1, 0.5, 1
yG = amp * xg.cdf(xE, k, loc, scale)
plt.plot(xE, yG, "-", c='r')
plt.axhline(0.9, c='g', alpha=0.5)
plt.ylim(0, 1)
plt.show()