import numpy as np
import matplotlib.pyplot as plt

col = {0.5: 'orange', 1: 'purple', 1.5: 'lightblue'}

X = np.arange(0, 5, 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 L in 0.5,1,1.5:
    P = L*np.exp(-L*X)
    a = plt.plot(X, P, '-', color=col[L], lw=2.5)
    A.append(a)

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

bx = plt.legend(A, (r"$\lambda=0.5$", r"$\lambda=1$", r"$\lambda=1.5$"),\
                numpoints=1, handletextpad=0.5, loc="upper right")
bx.draw_frame(False)
plt.xlim(0,5)

plt.savefig("exponential_pdf.pdf")
plt.savefig("exponential_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 L in 0.5,1,1.5:
    P = 1- np.exp(-L*X)
    a = plt.plot(X, P, '-', color=col[L], lw=2.5)
    A.append(a)

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

bx = plt.legend(A, (r"$\lambda=0.5$", r"$\lambda=1$", r"$\lambda=1.5$"),\
                numpoints=1, handletextpad=0.5, loc="lower right")
bx.draw_frame(False)
plt.xlim(0,5)

plt.savefig("exponential_cdf.pdf")
plt.savefig("exponential_cdf.svg")