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"""
==============================================
Some features of the histogram (hist) function
==============================================
In addition to the basic histogram, this demo shows a few optional features:
* Setting the number of data bins.
* The *density* parameter, which normalizes bin heights so that the integral of
the histogram is 1. The resulting histogram is an approximation of the
probability density function.
* Setting the face color of the bars.
* Setting the opacity (alpha value).
Selecting different bin counts and sizes can significantly affect the shape
of a histogram. The Astropy docs have a great section_ on how to select these
parameters.
.. _section: http://docs.astropy.org/en/stable/visualization/histogram.html
"""
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(19680801)
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(437)
num_bins = 50
fig, ax = plt.subplots()
# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, density=True)
# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
plt.show()
#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions and methods is shown in this example:
matplotlib.axes.Axes.hist
matplotlib.axes.Axes.set_title
matplotlib.axes.Axes.set_xlabel
matplotlib.axes.Axes.set_ylabel
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