Contains functions useful to the Gaussian chapter.

We do not want to expose all of the Python code used to generate the book
because it just makes things unreadable.

gaussian_internal.py

@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu May  8 23:16:31 2014
+
+@author: rlabbe
+"""
+
+import math
+import matplotlib.pylab as pylab
+import matplotlib.pyplot as plt
+import numpy as np
+import gaussian as g
+
+def plot_gaussian (mu, variance,
+                   mu_line=False,
+                   xlim=None,
+                   xlabel=None,
+                   ylabel=None):
+    xs = np.arange(mu-variance*2,mu+variance*2,0.1)
+    ys = [g.gaussian (x, mu, variance)*100 for x in xs]
+    plt.plot (xs, ys)
+    if mu_line:
+        plt.axvline(mu)
+    if xlim:
+        plt.xlim(xlim)
+    if xlabel:
+       plt.xlabel(xlabel)
+    if ylabel:
+       plt.ylabel(ylabel)
+    plt.show()
+
+def display_stddev_plot():
+    figsize = pylab.rcParams['figure.figsize']
+    pylab.rcParams['figure.figsize'] = 12,6
+    xs = np.arange(10,30,0.1)
+    var = 8; stddev = math.sqrt(var)
+    p2, = plt.plot (xs,[g.gaussian(x, 20, var) for x in xs])
+    x = 20+stddev
+    y = g.gaussian(x, 20, var)
+    plt.plot ([x,x], [0,y],'g')
+    plt.plot ([20-stddev, 20-stddev], [0,y], 'g')
+    y = g.gaussian(20,20,var)
+    plt.plot ([20,20],[0,y],'b')
+    ax = plt.axes()
+    ax.annotate('68%', xy=(20.3, 0.045))
+    ax.annotate('', xy=(20-stddev,0.04), xytext=(x,0.04),
+                arrowprops=dict(arrowstyle="<->",
+                                ec="r",
+                                shrinkA=2, shrinkB=2))
+    ax.xaxis.set_ticks ([20-stddev, 20, 20+stddev])
+    ax.xaxis.set_ticklabels(['$-\sigma$','$\mu$','$\sigma$'])
+    ax.yaxis.set_ticks([])
+    plt.show()
+    pylab.rcParams['figure.figsize'] = figsize
+
+if __name__ == '__main__':
+    display_stddev_plot()