  Travis-CI: Using histograms to plot a cumulative distribution¶

This shows how to plot a cumulative, normalized histogram as a step function in order to visualize the empirical cumulative distribution function (CDF) of a sample. We also use the `mlab` module to show the theoretical CDF.

A couple of other options to the `hist` function are demonstrated. Namely, we use the `normed` parameter to normalize the histogram and a couple of different options to the `cumulative` parameter. The `normed` parameter takes a boolean value. When `True`, the bin heights are scaled such that the total area of the histogram is 1. The `cumulative` kwarg is a little more nuanced. Like `normed`, you can pass it True or False, but you can also pass it -1 to reverse the distribution.

Since we’re showing a normalized and cumulative histogram, these curves are effectively the cumulative distribution functions (CDFs) of the samples. In engineering, empirical CDFs are sometimes called “non-exceedance” curves. In other words, you can look at the y-value for a given-x-value to get the probability of and observation from the sample not exceeding that x-value. For example, the value of 225 on the x-axis corresponds to about 0.85 on the y-axis, so there’s an 85% chance that an observation in the sample does not exceed 225. Conversely, setting, `cumulative` to -1 as is done in the last series for this example, creates a “exceedance” curve.

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 numpy as np
import matplotlib.pyplot as plt
from matplotlib import mlab

np.random.seed(19680801)

mu = 200
sigma = 25
n_bins = 50
x = np.random.normal(mu, sigma, size=100)

fig, ax = plt.subplots(figsize=(8, 4))

# plot the cumulative histogram
n, bins, patches = ax.hist(x, n_bins, normed=1, histtype='step',
cumulative=True, label='Empirical')

# Add a line showing the expected distribution.
y = mlab.normpdf(bins, mu, sigma).cumsum()
y /= y[-1]

ax.plot(bins, y, 'k--', linewidth=1.5, label='Theoretical')

# Overlay a reversed cumulative histogram.
ax.hist(x, bins=bins, normed=1, histtype='step', cumulative=-1,
label='Reversed emp.')

# tidy up the figure
ax.grid(True)
ax.legend(loc='right')
ax.set_title('Cumulative step histograms')
ax.set_xlabel('Annual rainfall (mm)')
ax.set_ylabel('Likelihood of occurrence')

plt.show()
```

Total running time of the script: ( 0 minutes 0.021 seconds)

Gallery generated by Sphinx-Gallery