/usr/share/doc/python3-astroml/examples/book_figures/chapter3/fig_weibull_distribution.py is in python3-astroml 0.3-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Example of a Weibull distribution
----------------------------------
Figure 3.19.
This shows an example of a weibull distribution with various parameters.
We'll generate the distribution using::
dist = scipy.stats.dweibull(...)
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 dweibull
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)
#------------------------------------------------------------
# Define the distribution parameters to be plotted
k_values = [0.5, 1, 2, 2]
lam_values = [1, 1, 1, 2]
linestyles = ['-', '--', ':', '-.', '--']
mu = 0
x = np.linspace(-10, 10, 1000)
#------------------------------------------------------------
# plot the distributions
fig, ax = plt.subplots(figsize=(5, 3.75))
for (k, lam, ls) in zip(k_values, lam_values, linestyles):
dist = dweibull(k, mu, lam)
plt.plot(x, dist.pdf(x), ls=ls, c='black',
label=r'$k=%.1f,\ \lambda=%i$' % (k, lam))
plt.xlim(0, 5)
plt.ylim(0, 0.6)
plt.xlabel('$x$')
plt.ylabel(r'$p(x|k,\lambda)$')
plt.title('Weibull Distribution')
plt.legend()
plt.show()
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