Split demo that tried to do too much

Author: tonysyu

examples/pylab_examples/histogram_demo_cumulative.py

@@ -0,0 +1,33 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import mlab
+
+mu, sigma = 200, 25
+x = mu + sigma*np.random.randn(10000)
+n, bins, patches = plt.hist(x, 50, normed=1, histtype='step', cumulative=True)
+
+# Add a line showing the expected distribution.
+y = mlab.normpdf( bins, mu, sigma).cumsum()
+y /= y[-1]
+plt.plot(bins, y, 'k--', linewidth=1.5)
+
+# Create a second data-set with a smaller standard deviation.
+sigma2 = 15.
+x = mu + sigma2*np.random.randn(10000)
+
+n, bins, patches = plt.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
+
+# Add a line showing the expected distribution.
+y = mlab.normpdf( bins, mu, sigma2).cumsum()
+y /= y[-1]
+plt.plot(bins, y, 'r--', linewidth=1.5)
+
+# Overlay a reverted cumulative histogram.
+n, bins, patches = plt.hist(x, bins=bins, normed=1,
+    histtype='step', cumulative=-1)
+
+plt.grid(True)
+plt.ylim(0, 1.05)
+plt.title('cumulative step')
+
+plt.show()

examples/pylab_examples/histogram_demo_histtypes.py

@@ -1,19 +1,18 @@
-
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.mlab import normpdf
+from matplotlib import mlab
 
 
 mu, sigma = 200, 25
 x = mu + sigma*np.random.randn(10000)
 
-fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 3))
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 4))
 
 n, bins, patches = ax0.hist(x, 50, normed=1, histtype='stepfilled',
                             facecolor='g', alpha=0.75)
 
 # Add a line showing the expected distribution.
-y = normpdf( bins, mu, sigma)
+y = mlab.normpdf( bins, mu, sigma)
 ax0.plot(bins, y, 'k--', linewidth=1.5)
 
 ax0.set_title('stepfilled')
@@ -23,31 +22,5 @@
 n, bins, patches = ax1.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
 ax1.set_title('unequal bins')
 
-n, bins, patches = ax2.hist(x, 50, normed=1, histtype='step', cumulative=True)
-
-# Add a line showing the expected distribution.
-y = normpdf( bins, mu, sigma).cumsum()
-y /= y[-1]
-ax2.plot(bins, y, 'k--', linewidth=1.5)
-
-# Create a second data-set with a smaller standard deviation.
-sigma2 = 15.
-x = mu + sigma2*np.random.randn(10000)
-
-n, bins, patches = ax2.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
-
-# Add a line showing the expected distribution.
-y = normpdf( bins, mu, sigma2).cumsum()
-y /= y[-1]
-ax2.plot(bins, y, 'r--', linewidth=1.5)
-
-# Overlay a reverted cumulative histogram.
-n, bins, patches = ax2.hist(x, bins=bins, normed=1,
-    histtype='step', cumulative=-1)
-
-ax2.grid(True)
-ax2.set_ylim(0, 1.05)
-ax2.set_title('cumulative step')
-
 plt.tight_layout()
 plt.show()