def prep_energy_prices_contract(self, plot):
#Bratford distribution parameters
c = 1
loc = self.cotract_price_range[0]
scale = self.cotract_price_range[1] - self.cotract_price_range[0]
price_vec = bradford.rvs(c, loc=loc, scale=scale, size=1000)
if plot:
fig, ax = plt.subplots(1, 1)
ax.hist(price_vec, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
return price_vec
#Calculate a few first moments: c = 0.299 mean, var, skew, kurt = bradford.stats(c, moments='mvsk') #Display the probability density function (pdf): x = np.linspace(bradford.ppf(0.01, c), bradford.ppf(0.99, c), 100) ax.plot(x, bradford.pdf(x, c), 'r-', lw=5, alpha=0.6, label='bradford 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 = bradford(c) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = bradford.ppf([0.001, 0.5, 0.999], c) np.allclose([0.001, 0.5, 0.999], bradford.cdf(vals, c)) True #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):