Rename data variables in histogram example.

Author: dstansby

examples/statistics/hist.py

@@ -3,17 +3,16 @@
 Histograms
 ==========
 
-Demonstrates how to plot histograms with matplotlib.
+How to plot histograms with Matplotlib.
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib import colors
 from matplotlib.ticker import PercentFormatter
 
-# Fixing random state for reproducibility
-np.random.seed(19680801)
-
+# Create a random number generator with a fixed seed for reproducibility
+rng = np.random.default_rng(19680801)
 
 ###############################################################################
 # Generate data and plot a simple histogram
@@ -26,15 +25,15 @@
 N_points = 100000
 n_bins = 20
 
-# Generate a normal distribution, center at x=0 and y=5
-x = np.random.randn(N_points)
-y = .4 * x + np.random.randn(100000) + 5
+# Generate two normal distributions
+dist1 = rng.standard_normal(N_points)
+dist2 = 0.4 * rng.standard_normal(N_points) + 5
 
 fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
 
 # We can set the number of bins with the *bins* keyword argument.
-axs[0].hist(x, bins=n_bins)
-axs[1].hist(y, bins=n_bins)
+axs[0].hist(dist1, bins=n_bins)
+axs[1].hist(dist2, bins=n_bins)
 
 
 ###############################################################################
@@ -49,7 +48,7 @@
 fig, axs = plt.subplots(1, 2, tight_layout=True)
 
 # N is the count in each bin, bins is the lower-limit of the bin
-N, bins, patches = axs[0].hist(x, bins=n_bins)
+N, bins, patches = axs[0].hist(dist1, bins=n_bins)
 
 # We'll color code by height, but you could use any scalar
 fracs = N / N.max()
@@ -63,7 +62,7 @@
     thispatch.set_facecolor(color)
 
 # We can also normalize our inputs by the total number of counts
-axs[1].hist(x, bins=n_bins, density=True)
+axs[1].hist(dist1, bins=n_bins, density=True)
 
 # Now we format the y-axis to display percentage
 axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
@@ -77,7 +76,7 @@
 # corresponding to each axis of the histogram.
 
 fig, ax = plt.subplots(tight_layout=True)
-hist = ax.hist2d(x, y)
+hist = ax.hist2d(dist1, dist2)
 
 
 ###############################################################################
@@ -91,13 +90,13 @@
                         tight_layout=True)
 
 # We can increase the number of bins on each axis
-axs[0].hist2d(x, y, bins=40)
+axs[0].hist2d(dist1, dist2, bins=40)
 
 # As well as define normalization of the colors
-axs[1].hist2d(x, y, bins=40, norm=colors.LogNorm())
+axs[1].hist2d(dist1, dist2, bins=40, norm=colors.LogNorm())
 
 # We can also define custom numbers of bins for each axis
-axs[2].hist2d(x, y, bins=(80, 10), norm=colors.LogNorm())
+axs[2].hist2d(dist1, dist2, bins=(80, 10), norm=colors.LogNorm())
 
 plt.show()