Python: Normal and exponential distributions

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Here is the Python code [#1]

import matplotlib.pyplot as plt
import rsPlot
import numpy as np
import scipy.stats as stats
import math
plot1 = rsPlot.plot("dist11-1",1,0,72,640,480)
mean1 = 0.0
variance1 = 1.0
sigma1 = math.sqrt(variance1)
colorList1 = [
	"#CCCCFF",
	"#9999FF",
	"#6666FF",
	]
for factor0 in range(0,3):
	print("{0:d}".format(factor0))
	factor1 = float(3-factor0)
	xmin1 = mean1 - factor1*sigma1
	xmax1 = mean1 + factor1*sigma1
	points1 = 100
	xData1 = np.linspace(xmin1, xmax1, points1)
	yData1 = stats.norm.pdf(xData1, mean1, sigma1)
	plt.plot(xData1, yData1, color="#0000FF")
	color1 = colorList1[factor0]
	plt.fill_between(xData1,yData1,color=color1)
plot1.save()

Here is the output of the Python code.

(module rsPlot imported)
0
1
2
	./dist11-1-01.png : 571 x 414 , 23,688 bytes

Here is the Python code [#2]

import matplotlib.pyplot as plt
import rsPlot
import numpy as np
from scipy.stats import expon
plot1 = rsPlot.plot("dist11-2",1,0,72,640,480)
points1 = 100
xData1 = np.linspace(expon.ppf(0.01),expon.ppf(0.99), points1)
yData1 = expon.pdf(xData1)
plt.plot(xData1, yData1, color="#0000FF")
plt.fill_between(xData1,yData1,color="#9999FF")
plot1.save()

Here is the output of the Python code.

(module rsPlot imported)
	./dist11-2-01.png : 557 x 414 , 15,275 bytes