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Description
Problem
Plotting a 2-dim histogram using thehist2d function can leave a lot of excess space in the plot when using the argumentcmin to exclude bins with insufficient numbers of data-points. (There's presumably a similar problem when using the argumentcmax).
%matplotlib inlineimport numpy as npimport matplotlib.pyplot as plt# Generate random x/y values.rng = np.random.default_rng(1234)size = 100000x = rng.normal(scale=10, size=size)y = rng.normal(size=size)# This plot has properly adjusted axis-limits.plt.hist2d(x, y, bins=100, cmin=1);
# This plot has a lot of excess space.plt.hist2d(x, y, bins=100, cmin=20);
Proposed solution
The following is a quick solution that seems to work. But it may be possible to make a better solution when integrating it into thehist2d function. It should be made as an optional argument such ashist2d(adapt_lim=True, ...) to adapt both x and y-axis, or specify a given axis such ashist2d(adapt_lim='x', ...)
I don't really have time to add this to Matplotlib's code-base myself, and make sure everything is made according to your standards etc. So hopefully someone else can do it, if you like the feature. Thanks!
# Plot 2-dim histogram and get bins and edges.h, xedges, yedges, _ = plt.hist2d(x, y, bins=100, cmin=20);# Boolean mask whether a bin is actually used in the 2-dim grid.mask = ~np.isnan(h)# Flattened boolean masks whether a bin is actually used for each axis.mask_flat_x = np.any(mask, axis=1)mask_flat_y = np.any(mask, axis=0)# Get x-axis min/max for bins that are actually used.x_min = xedges[:-1][mask_flat_x].min()x_max = xedges[1:][mask_flat_x].max()# Get y-axis min/max for bins that are actually used.y_min = yedges[:-1][mask_flat_y].min()y_max = yedges[1:][mask_flat_y].max()# Adjust x-axis limits to have a little padding.x_pad = 0.01 * (x_max - x_min)plt.xlim(x_min - x_pad, x_max + x_pad)# Adjust y-axis limits to have a little padding.y_pad = 0.01 * (y_max - y_min)plt.ylim(y_min - y_pad, y_max + y_pad)plt.show();