"""
Example of a Gamma distribution
-------------------------------
Figure 3.18.

This shows an example of a gamma distribution with various parameters.
We'll generate the distribution using::

    dist = scipy.stats.gamma(...)

Where ... should be filled in with the desired distribution parameters
Once we have defined the distribution parameters in this way, these
distribution objects have many useful methods; for example:

* ``dist.pmf(x)`` computes the Probability Mass Function at values ``x``
  in the case of discrete distributions

* ``dist.pdf(x)`` computes the Probability Density Function at values ``x``
  in the case of continuous distributions

* ``dist.rvs(N)`` computes ``N`` random variables distributed according
  to the given distribution

Many further options exist; refer to the documentation of ``scipy.stats``
for more details.
"""
# Author: Jake VanderPlas
# License: BSD
#   The figure produced by this code is published in the textbook
#   "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
#   For more information, see http://astroML.github.com
#   To report a bug or issue, use the following forum:
#    https://groups.google.com/forum/#!forum/astroml-general
import numpy as np
from scipy.stats import gamma
from matplotlib import pyplot as plt

#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX.  This may
# result in an error if LaTeX is not installed on your system.  In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)

#------------------------------------------------------------
# plot the distributions
k_values = [1, 2, 3, 5]
theta_values = [2, 1, 1, 0.5]
linestyles = ['-', '--', ':', '-.']
x = np.linspace(1E-6, 10, 1000)

#------------------------------------------------------------
# plot the distributions
fig, ax = plt.subplots(figsize=(5, 3.75))

for k, t, ls in zip(k_values, theta_values, linestyles):
    dist = gamma(k, 0, t)
    plt.plot(x, dist.pdf(x), ls=ls, c='black',
             label=r'$k=%.1f,\ \theta=%.1f$' % (k, t))

plt.xlim(0, 10)
plt.ylim(0, 0.45)

plt.xlabel('$x$')
plt.ylabel(r'$p(x|k,\theta)$')
plt.title('Gamma Distribution')

plt.legend(loc=0)
plt.show()
