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"""
==========
Histograms
==========
How to plot histograms with Matplotlib.
"""
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
# Create a random number generator with a fixed seed for reproducibility
rng = np.random.default_rng(19680801)
# %%
# Generate data and plot a simple histogram
# -----------------------------------------
#
# To generate a 1D histogram we only need a single vector of numbers. For a 2D
# histogram we'll need a second vector. We'll generate both below, and show
# the histogram for each vector.
N_points = 100000
n_bins = 20
# Generate two normal distributions
dist1 = rng.standard_normal(N_points)
dist2 = 0.4 * rng.standard_normal(N_points) + 5
fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
# We can set the number of bins with the *bins* keyword argument.
axs[0].hist(dist1, bins=n_bins)
axs[1].hist(dist2, bins=n_bins)
plt.show()
# %%
# Updating histogram colors
# -------------------------
#
# The histogram method returns (among other things) a ``patches`` object. This
# gives us access to the properties of the objects drawn. Using this, we can
# edit the histogram to our liking. Let's change the color of each bar
# based on its y value.
fig, axs = plt.subplots(1, 2, tight_layout=True)
# N is the count in each bin, bins is the lower-limit of the bin
N, bins, patches = axs[0].hist(dist1, bins=n_bins)
# We'll color code by height, but you could use any scalar
fracs = N / N.max()
# we need to normalize the data to 0..1 for the full range of the colormap
norm = colors.Normalize(fracs.min(), fracs.max())
# Now, we'll loop through our objects and set the color of each accordingly
for thisfrac, thispatch in zip(fracs, patches):
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
# We can also normalize our inputs by the total number of counts
axs[1].hist(dist1, bins=n_bins, density=True)
# Now we format the y-axis to display percentage
axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
# %%
# Plot a 2D histogram
# -------------------
#
# To plot a 2D histogram, one only needs two vectors of the same length,
# corresponding to each axis of the histogram.
fig, ax = plt.subplots(tight_layout=True)
hist = ax.hist2d(dist1, dist2)
# %%
# Customizing your histogram
# --------------------------
#
# Customizing a 2D histogram is similar to the 1D case, you can control
# visual components such as the bin size or color normalization.
fig, axs = plt.subplots(3, 1, figsize=(5, 15), sharex=True, sharey=True,
tight_layout=True)
# We can increase the number of bins on each axis
axs[0].hist2d(dist1, dist2, bins=40)
# As well as define normalization of the colors
axs[1].hist2d(dist1, dist2, bins=40, norm=colors.LogNorm())
# We can also define custom numbers of bins for each axis
axs[2].hist2d(dist1, dist2, bins=(80, 10), norm=colors.LogNorm())
# %%
#
# .. tags::
#
# plot-type: histogram,
# plot-type: histogram2d
# domain: statistics
# styling: color,
# component: normalization
# component: patch
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.axes.Axes.hist` / `matplotlib.pyplot.hist`
# - `matplotlib.pyplot.hist2d`
# - `matplotlib.ticker.PercentFormatter`
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