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Merge pull request #28073 from Impaler343/histogram
Add support for multiple hatches, edgecolors and linewidths in histograms
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‎doc/users/next_whats_new/histogram_vectorized_parameters.rst

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Vectorized ``hist`` style parameters
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------------------------------------
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The parameters *hatch*, *edgecolor*, *facecolor*, *linewidth* and *linestyle*
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of the `~matplotlib.axes.Axes.hist` method are now vectorized.
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This means that you can pass in individual parameters for each histogram
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when the input *x* has multiple datasets.
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.. plot::
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:include-source: true
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:alt: Four charts, each displaying stacked histograms of three Poisson distributions. Each chart differentiates the histograms using various parameters: top left uses different linewidths, top right uses different hatches, bottom left uses different edgecolors, and bottom right uses different facecolors. Each histogram on the left side also has a different edgecolor.
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import matplotlib.pyplot as plt
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import numpy as np
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np.random.seed(19680801)
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(9, 9))
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data1 = np.random.poisson(5, 1000)
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data2 = np.random.poisson(7, 1000)
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data3 = np.random.poisson(10, 1000)
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labels = ["Data 1", "Data 2", "Data 3"]
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ax1.hist([data1, data2, data3], bins=range(17), histtype="step", stacked=True,
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edgecolor=["red", "green", "blue"], linewidth=[1, 2, 3])
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ax1.set_title("Different linewidths")
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ax1.legend(labels)
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ax2.hist([data1, data2, data3], bins=range(17), histtype="barstacked",
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hatch=["/", ".", "*"])
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ax2.set_title("Different hatch patterns")
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ax2.legend(labels)
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ax3.hist([data1, data2, data3], bins=range(17), histtype="bar", fill=False,
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edgecolor=["red", "green", "blue"], linestyle=["--", "-.", ":"])
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ax3.set_title("Different linestyles")
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ax3.legend(labels)
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ax4.hist([data1, data2, data3], bins=range(17), histtype="barstacked",
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facecolor=["red", "green", "blue"])
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ax4.set_title("Different facecolors")
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ax4.legend(labels)
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plt.show()

‎galleries/examples/statistics/histogram_multihist.py

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select these parameters:
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http://docs.astropy.org/en/stable/visualization/histogram.html
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"""
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# %%
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import matplotlib.pyplot as plt
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import numpy as np
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fig.tight_layout()
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plt.show()
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# %%
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# -----------------------------------
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# Setting properties for each dataset
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# -----------------------------------
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#
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# You can style the histograms individually by passing a list of values to the
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# following parameters:
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#
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# * edgecolor
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# * facecolor
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# * hatch
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# * linewidth
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# * linestyle
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#
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#
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# edgecolor
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# .........
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fig, ax = plt.subplots()
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edgecolors = ['green', 'red', 'blue']
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ax.hist(x, n_bins, fill=False, histtype="step", stacked=True,
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edgecolor=edgecolors, label=edgecolors)
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ax.legend()
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ax.set_title('Stacked Steps with Edgecolors')
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plt.show()
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# %%
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# facecolor
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# .........
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fig, ax = plt.subplots()
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facecolors = ['green', 'red', 'blue']
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ax.hist(x, n_bins, histtype="barstacked", facecolor=facecolors, label=facecolors)
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ax.legend()
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ax.set_title("Bars with different Facecolors")
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plt.show()
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# %%
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# hatch
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# .....
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fig, ax = plt.subplots()
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hatches = [".", "o", "x"]
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ax.hist(x, n_bins, histtype="barstacked", hatch=hatches, label=hatches)
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ax.legend()
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ax.set_title("Hatches on Stacked Bars")
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plt.show()
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# %%
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# linewidth
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# .........
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fig, ax = plt.subplots()
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linewidths = [1, 2, 3]
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edgecolors = ["green", "red", "blue"]
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ax.hist(x, n_bins, fill=False, histtype="bar", linewidth=linewidths,
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edgecolor=edgecolors, label=linewidths)
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ax.legend()
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ax.set_title("Bars with Linewidths")
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plt.show()
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# %%
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# linestyle
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# .........
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fig, ax = plt.subplots()
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linestyles = ['-', ':', '--']
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ax.hist(x, n_bins, fill=False, histtype='bar', linestyle=linestyles,
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edgecolor=edgecolors, label=linestyles)
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ax.legend()
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ax.set_title('Bars with Linestyles')
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plt.show()
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# %%
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#
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# .. admonition:: References

‎lib/matplotlib/axes/_axes.py

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@@ -6946,7 +6946,13 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
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DATA_PARAMETER_PLACEHOLDER
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**kwargs
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`~matplotlib.patches.Patch` properties
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`~matplotlib.patches.Patch` properties. The following properties
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additionally accept a sequence of values corresponding to the
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datasets in *x*:
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*edgecolors*, *facecolors*, *lines*, *linestyles*, *hatches*.
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.. versionadded:: 3.10
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Allowing sequences of values in above listed Patch properties.
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See Also
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--------
@@ -7219,15 +7225,35 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
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# If None, make all labels None (via zip_longest below); otherwise,
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# cast each element to str, but keep a single str as it.
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labels = [] if label is None else np.atleast_1d(np.asarray(label, str))
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if histtype == "step":
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edgecolors = itertools.cycle(np.atleast_1d(kwargs.get('edgecolor',
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colors)))
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else:
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edgecolors = itertools.cycle(np.atleast_1d(kwargs.get("edgecolor", None)))
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facecolors = itertools.cycle(np.atleast_1d(kwargs.get('facecolor', colors)))
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hatches = itertools.cycle(np.atleast_1d(kwargs.get('hatch', None)))
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linewidths = itertools.cycle(np.atleast_1d(kwargs.get('linewidth', None)))
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linestyles = itertools.cycle(np.atleast_1d(kwargs.get('linestyle', None)))
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for patch, lbl in itertools.zip_longest(patches, labels):
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if patch:
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p = patch[0]
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if not patch:
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continue
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p = patch[0]
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kwargs.update({
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'hatch': next(hatches),
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'linewidth': next(linewidths),
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'linestyle': next(linestyles),
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'edgecolor': next(edgecolors),
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'facecolor': next(facecolors),
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})
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p._internal_update(kwargs)
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if lbl is not None:
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p.set_label(lbl)
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for p in patch[1:]:
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p._internal_update(kwargs)
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if lbl is not None:
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p.set_label(lbl)
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for p in patch[1:]:
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p._internal_update(kwargs)
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p.set_label('_nolegend_')
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p.set_label('_nolegend_')
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if nx == 1:
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return tops[0], bins, patches[0]

‎lib/matplotlib/tests/test_axes.py

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ax.legend(loc='upper right', bbox_to_anchor=(1.0, 1.0), ncols=1)
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@pytest.mark.parametrize('kwargs', ({'facecolor': ["b", "g", "r"]},
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{'edgecolor': ["b", "g", "r"]},
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{'hatch': ["/", "\\", "."]},
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{'linestyle': ["-", "--", ":"]},
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{'linewidth': [1, 1.5, 2]},
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{'color': ["b", "g", "r"]}))
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@check_figures_equal(extensions=["png"])
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def test_hist_vectorized_params(fig_test, fig_ref, kwargs):
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np.random.seed(19680801)
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xs = [np.random.randn(n) for n in [20, 50, 100]]
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(axt1, axt2) = fig_test.subplots(2)
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(axr1, axr2) = fig_ref.subplots(2)
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for histtype, axt, axr in [("stepfilled", axt1, axr1), ("step", axt2, axr2)]:
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_, bins, _ = axt.hist(xs, bins=10, histtype=histtype, **kwargs)
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kw, values = next(iter(kwargs.items()))
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for i, (x, value) in enumerate(zip(xs, values)):
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axr.hist(x, bins=bins, histtype=histtype, **{kw: value},
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zorder=(len(xs)-i)/2)
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@pytest.mark.parametrize('kwargs, patch_face, patch_edge',
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# 'C0'(blue) stands for the first color of the
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# default color cycle as well as the patch.facecolor rcParam
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# When the expected edgecolor is 'k'(black),
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# it corresponds to the patch.edgecolor rcParam
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[({'histtype': 'stepfilled', 'color': 'r',
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'facecolor': 'y', 'edgecolor': 'g'}, 'y', 'g'),
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({'histtype': 'step', 'color': 'r',
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'facecolor': 'y', 'edgecolor': 'g'}, ('y', 0), 'g'),
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({'histtype': 'stepfilled', 'color': 'r',
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'edgecolor': 'g'}, 'r', 'g'),
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({'histtype': 'step', 'color': 'r',
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'edgecolor': 'g'}, ('r', 0), 'g'),
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({'histtype': 'stepfilled', 'color': 'r',
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'facecolor': 'y'}, 'y', 'k'),
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({'histtype': 'step', 'color': 'r',
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'facecolor': 'y'}, ('y', 0), 'r'),
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({'histtype': 'stepfilled',
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'facecolor': 'y', 'edgecolor': 'g'}, 'y', 'g'),
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({'histtype': 'step', 'facecolor': 'y',
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'edgecolor': 'g'}, ('y', 0), 'g'),
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({'histtype': 'stepfilled', 'color': 'r'}, 'r', 'k'),
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({'histtype': 'step', 'color': 'r'}, ('r', 0), 'r'),
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({'histtype': 'stepfilled', 'facecolor': 'y'}, 'y', 'k'),
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({'histtype': 'step', 'facecolor': 'y'}, ('y', 0), 'C0'),
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({'histtype': 'stepfilled', 'edgecolor': 'g'}, 'C0', 'g'),
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({'histtype': 'step', 'edgecolor': 'g'}, ('C0', 0), 'g'),
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({'histtype': 'stepfilled'}, 'C0', 'k'),
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({'histtype': 'step'}, ('C0', 0), 'C0')])
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def test_hist_color_semantics(kwargs, patch_face, patch_edge):
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_, _, patches = plt.figure().subplots().hist([1, 2, 3], **kwargs)
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assert all(mcolors.same_color([p.get_facecolor(), p.get_edgecolor()],
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[patch_face, patch_edge]) for p in patches)
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def test_hist_barstacked_bottom_unchanged():
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b = np.array([10, 20])
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plt.hist([[0, 1], [0, 1]], 2, histtype="barstacked", bottom=b)
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ax.hist([[], range(10), range(10)], histtype="step")
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def test_hist_unused_labels():
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# When a list with one dataset and N elements is provided and N labels, ensure
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# that the first label is used for the dataset and all other labels are ignored
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fig, ax = plt.subplots()
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ax.hist([[1, 2, 3]], label=["values", "unused", "also unused"])
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_, labels = ax.get_legend_handles_labels()
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assert labels == ["values"]
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def test_hist_labels():
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# test singleton labels OK
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fig, ax = plt.subplots()

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