"""
=========================================================
Demo of the histogram (hist) function with a few features
=========================================================

In addition to the basic histogram, this demo shows a few optional
features:

    * Setting the number of data bins
    * The ``normed`` flag, 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:
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=1)

# 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
