| """ | |
| ========== | |
| 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) | |
| # %% | |
| # 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()) | |
| plt.show() | |
| # %% | |
| # | |
| # .. 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` | |