Use consistent imports

Author: tonysyu

examples/pylab_examples/histogram_demo_extended.py

@@ -1,5 +1,6 @@
 import numpy as np
-import pylab as P
+import matplotlib.pyplot as plt
+from matplotlib.mlab import normpdf
 
 #
 # The hist() function now has a lot more options
@@ -9,98 +10,98 @@
 # first create a single histogram
 #
 mu, sigma = 200, 25
-x = mu + sigma*P.randn(10000)
+x = mu + sigma*np.random.randn(10000)
 
 # the histogram of the data with histtype='step'
-n, bins, patches = P.hist(x, 50, normed=1, histtype='stepfilled')
-P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
+n, bins, patches = plt.hist(x, 50, normed=1, histtype='stepfilled')
+plt.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
 
 # add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma)
-l = P.plot(bins, y, 'k--', linewidth=1.5)
+y = normpdf( bins, mu, sigma)
+l = plt.plot(bins, y, 'k--', linewidth=1.5)
 
 
 #
 # create a histogram by providing the bin edges (unequally spaced)
 #
-P.figure()
+plt.figure()
 
 bins = [100,125,150,160,170,180,190,200,210,220,230,240,250,275,300]
 # the histogram of the data with histtype='step'
-n, bins, patches = P.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
+n, bins, patches = plt.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
 
 #
 # now we create a cumulative histogram of the data
 #
-P.figure()
+plt.figure()
 
-n, bins, patches = P.hist(x, 50, normed=1, histtype='step', cumulative=True)
+n, bins, patches = plt.hist(x, 50, normed=1, histtype='step', cumulative=True)
 
 # add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma).cumsum()
+y = normpdf( bins, mu, sigma).cumsum()
 y /= y[-1]
-l = P.plot(bins, y, 'k--', linewidth=1.5)
+l = plt.plot(bins, y, 'k--', linewidth=1.5)
 
 # create a second data-set with a smaller standard deviation
 sigma2 = 15.
-x = mu + sigma2*P.randn(10000)
+x = mu + sigma2*np.random.randn(10000)
 
-n, bins, patches = P.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
+n, bins, patches = plt.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
 
 # add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma2).cumsum()
+y = normpdf( bins, mu, sigma2).cumsum()
 y /= y[-1]
-l = P.plot(bins, y, 'r--', linewidth=1.5)
+l = plt.plot(bins, y, 'r--', linewidth=1.5)
 
 # finally overplot a reverted cumulative histogram
-n, bins, patches = P.hist(x, bins=bins, normed=1,
+n, bins, patches = plt.hist(x, bins=bins, normed=1,
     histtype='step', cumulative=-1)
 
 
-P.grid(True)
-P.ylim(0, 1.05)
+plt.grid(True)
+plt.ylim(0, 1.05)
 
 
 #
 # histogram has the ability to plot multiple data in parallel ...
 # Note the new color kwarg, used to override the default, which
 # uses the line color cycle.
 #
-P.figure()
+plt.figure()
 
 # create a new data-set
-x = mu + sigma*P.randn(1000,3)
+x = mu + sigma*np.random.randn(1000,3)
 
-n, bins, patches = P.hist(x, 10, normed=1, histtype='bar',
+n, bins, patches = plt.hist(x, 10, normed=1, histtype='bar',
                             color=['crimson', 'burlywood', 'chartreuse'],
                             label=['Crimson', 'Burlywood', 'Chartreuse'])
-P.legend()
+plt.legend()
 
 #
 # ... or we can stack the data
 #
-P.figure()
+plt.figure()
 
-n, bins, patches = P.hist(x, 10, normed=1, histtype='bar', stacked=True)
+n, bins, patches = plt.hist(x, 10, normed=1, histtype='bar', stacked=True)
 
-P.show()
+plt.show()
 
 #
 # we can also stack using the step histtype
 #
 
-P.figure()
+plt.figure()
 
-n, bins, patches = P.hist(x, 10, histtype='step', stacked=True, fill=True)
+n, bins, patches = plt.hist(x, 10, histtype='step', stacked=True, fill=True)
 
-P.show()
+plt.show()
 
 #
 # finally: make a multiple-histogram of data-sets with different length
 #
-x0 = mu + sigma*P.randn(10000)
-x1 = mu + sigma*P.randn(7000)
-x2 = mu + sigma*P.randn(3000)
+x0 = mu + sigma*np.random.randn(10000)
+x1 = mu + sigma*np.random.randn(7000)
+x2 = mu + sigma*np.random.randn(3000)
 
 # and exercise the weights option by arbitrarily giving the first half
 # of each series only half the weight of the others:
@@ -114,8 +115,8 @@
 
 
 
-P.figure()
+plt.figure()
 
-n, bins, patches = P.hist( [x0,x1,x2], 10, weights=[w0, w1, w2], histtype='bar')
+n, bins, patches = plt.hist( [x0,x1,x2], 10, weights=[w0, w1, w2], histtype='bar')
 
-P.show()
+plt.show()