demo

Author: bgamari

examples/api/power_norm_demo.py

@@ -1,27 +1,27 @@
 #!/usr/bin/python
 
-from scipy.stats import norm
-from matplotlib import pyplot as pl
+from matplotlib import pyplot as plt
 import matplotlib.colors as mcolors
 import numpy as np
 from numpy.random import multivariate_normal
 
-samples = np.vstack([ multivariate_normal([10,10], [[3,5],[4,2]], size=100000),
-                      multivariate_normal([30,20], [[2,3],[1,3]], size=1000)
+samples = np.vstack([multivariate_normal([10, 10], [[3, 5],[4, 2]], size=100000),
+                     multivariate_normal([30, 20], [[2, 3],[1, 3]], size=1000)
                     ])
 
 gammas = [0.8, 0.5, 0.3]
-xgrid = np.floor((len(gammas)+1.)/2)
-ygrid = np.ceil((len(gammas)+1.)/2)
+xgrid = np.floor((len(gammas) + 1.) / 2)
+ygrid = np.ceil((len(gammas) + 1.) / 2)
 
-pl.subplot(xgrid, ygrid, 1)
-pl.title('Linear normalization')
-pl.hist2d(samples[:,0], samples[:,1], bins=100)
+plt.subplot(xgrid, ygrid, 1)
+plt.title('Linear normalization')
+plt.hist2d(samples[:,0], samples[:,1], bins=100)
 
-for i,gamma in enumerate(gammas):
-    pl.subplot(xgrid, ygrid, i+2)
-    pl.title('Power law normalization\n$(\gamma=%1.1f)$' % gamma)
-    pl.hist2d(samples[:,0], samples[:,1], bins=100, norm=mcolors.PowerNorm(gamma))
+for i, gamma in enumerate(gammas):
+    plt.subplot(xgrid, ygrid, i + 2)
+    plt.title('Power law normalization\n$(\gamma=%1.1f)$' % gamma)
+    plt.hist2d(samples[:, 0], samples[:, 1],
+               bins=100, norm=mcolors.PowerNorm(gamma))
 
-pl.subplots_adjust(hspace=0.39)
-pl.show()
+plt.subplots_adjust(hspace=0.39)
+plt.show()