import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import chi2

col = {1: 'black', 2: 'blue', 3: 'green', 4: 'red', 5: 'purple'}

X = np.arange(0, 8, 0.01)

##                                                                              
## PDF                                                                          
##                                                                              

plt.clf()
plt.figure(figsize=(4,3.2))
plt.axes([0.15,0.13,0.79,0.8])
plt.hold(True)

A = []
for k in col.keys():
    plt.plot(X, chi2.pdf(X, k), '-', color=col[k], label="k="+str(k))

plt.xlabel("x")
plt.ylabel("P(x)")

bx = plt.legend(numpoints=1, handletextpad=0.5, loc="upper right")
bx.draw_frame(False)
plt.xlim(0,8)
plt.ylim(0,1)

plt.savefig("chi-square_pdf.svg")

##                                                                              
## CDF                                                                          
##                                                                              

plt.clf()
plt.figure(figsize=(4,3.2))
plt.axes([0.15,0.13,0.79,0.8])
plt.hold(True)

A = []
for k in col.keys():
    a = plt.plot(X, chi2.cdf(X, k), '-', color=col[k], label="k="+str(k))
    A.append(a)

plt.xlabel("x")
plt.ylabel(u"P(X\N{Less-THAN OR EQUAL TO}x)")

bx = plt.legend(numpoints=1, handletextpad=0.5, loc="lower right")
bx.draw_frame(False)
plt.xlim(0,8)
plt.ylim(0,1)

plt.savefig("chi-square_cdf.svg")