from scipy.stats import invweibull
from scipy.stats import burr
from random import randint
secondsInHour = 60.0*60.0
c = 5.8
x = np.linspace(invweibull.ppf(0.01, c),invweibull.ppf(0.99,c),720)
arr = invweibull.pdf(x, c)
q = arr*10
q = np.round(q)
q = q.astype(int)
c = 5.9
d = 1.4
x = np.linspace(burr.ppf(0.01, c,d),burr.ppf(0.99,c,d),900)
arr = burr.pdf(x, c,d)
b = arr*10
b = np.round(b)
b = b.astype(int)
edges = ['D1 D4', 'D1 D3', 'D7 D5', 'D7 D2', 'D8 D5', 'D8 D2', 'D6 D3', 'D6 D4', 'D7 D4', 'D8 D3', 'D6 D5', 'D1 D2']
with open('rl.invw.rou.xml', 'w') as routes:
routes.write("""<?xml version="1.0"?>""" + '\n' + '\n')
routes.write("""<routes xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://sumo.dlr.de/xsd/routes_file.xsd">""" + '\n')
#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) ax.legend(loc='best', frameon=False) plt.show()