8000 Add test for added parameters · matplotlib/matplotlib@34df06a · GitHub
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Add test for added parameters
Specify extensions for test Added modified baseline images Modified test for histogram with single parameters Fixed test Add modified baseline images Made changes concise according to suggestion Made a more detailed gallery example Fix Docs Added whats new note, documentation for vectorization, doc fix Added new test, and reverted changes in old test Added baseline images Modified test to pass codecov, added plot in whats new entry Fix test Added baseline images Altered whats new entry, docs and gallery example Resolved edgecolor and facecolor setting Minor fix Fix docs Modified files to include facecolor and added test Removed figsize from test Add multiple baseline image names Fixed test? Fixed test? Removed parametrize usage Add baseline images Add baseline image Fix docs Fix docs Deleted baseline images, changed test Fix test Fix test Handled None array passing to color Handled passing None list Modified nested patch condition Minor Fix Grammar nits Modified test, edited None handling in sequence so that it errors out
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‎doc/users/next_whats_new/histogram_vectorized_parameters.rst

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Vectorize ``hatch``, ``edgecolor``, ``facecolor``, ``linewidth`` and ``linestyle`` in *hist* methods
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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 unique parameters for each histogram that is generated
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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: ax1 uses different linewidths, ax2 uses different hatches, ax3 uses different edgecolors, and ax4 uses different facecolors. Each histogram in ax1 and ax3 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, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
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colors = ['red', 'tan', 'lime']
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ax0.hist(x, n_bins, density=True, histtype='bar', color=colors,
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label=colors, hatch=['o', '*', '.'],
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edgecolor=['black', 'red', 'blue'], linewidth=[1, 2, 3])
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ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)
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ax0.legend(prop={'size': 10})
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ax0.set_title('bars with legend')
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ax1.hist(
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x, n_bins, density=True, histtype="bar", stacked=True,
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edgecolor=["black", "yellow", "blue"])
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ax1.hist(x, n_bins, density=True, histtype='bar', stacked=True)
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ax1.set_title('stacked bar')
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ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False,
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linewidth=[1, 2, 3])
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ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
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ax2.set_title('stack step (unfilled)')
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# Make a multiple-histogram of data-sets with different length.
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x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
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ax3.hist(x_multi, n_bins, histtype='bar', hatch=['\\', '/', '-'])
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ax3.hist(x_multi, n_bins, histtype='bar')
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ax3.set_title('different sample sizes')
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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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# Plotting bar charts with datasets differentiated using:
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#
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# * edgecolors
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# * facecolors
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# * hatches
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# * linewidths
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# * linestyles
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#
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#
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# Histograms with Edge-Colors
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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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# Face colors
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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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# Hatches
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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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# Linewidths
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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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# LineStyles
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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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@@ -6937,7 +6937,10 @@ 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*, *linewidths*, *linestyles*, *hatches*.
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See Also
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--------
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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 'hatch' in kwargs:
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if not isinstance(kwargs['hatch'], str):
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hatches = itertools.cycle(kwargs['hatch'])
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else:
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hatches = itertools.cycle([kwargs['hatch']])
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if 'edgecolor' in kwargs:
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if not isinstance(kwargs['edgecolor'], str):
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edgecolors = itertools.cycle(kwargs['edgecolor'])
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else:
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edgecolors = itertools.cycle([kwargs['edgecolor']])
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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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if 'linewidth' in kwargs:
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if isinstance(kwargs['linewidth'], list or tuple):
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linewidths = itertools.cycle(kwargs['linewidth'])
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else:
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linewidths = itertools.cycle([kwargs['linewidth']])
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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 'hatch' in kwargs:
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kwargs['hatch'] = next(hatches)
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if 'edgecolor' in kwargs:
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kwargs['edgecolor'] = next(edgecolors)
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if 'linewidth' in kwargs:
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kwargs['linewidth'] = next(linewidths)
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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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@@ -4603,6 +4603,39 @@ def test_hist_stacked_bar():
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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("histtype", ["step", "stepfilled"])
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@pytest.mark.parametrize("color", [["blue", "green", "brown"], None])
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@pytest.mark.parametrize("edgecolor", [["red", "black", "blue"], [None]*3])
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@pytest.mark.parametrize("facecolor", [["blue", "green", "brown"], [None]*3])
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@check_figures_equal(extensions=["png"])
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def test_hist_vectorized_params(fig_test, fig_ref, histtype, color, edgecolor,
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facecolor):
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np.random.seed(19680801)
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x = [np.random.randn(n) for n in [2000, 5000, 10000]]
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linewidth = [1, 1.5, 2]
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hatch = ["/", "\\", "."]
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linestyle = ["-", "--", ":"]
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facecolor = facecolor if facecolor[0] is not None else color
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if histtype == "step":
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edgecolor = edgecolor if edgecolor[0] is not None else color
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_, bins, _ = fig_test.subplots().hist(x, bins=10, histtype=histtype, color=color,
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edgecolor=edgecolor, facecolor=facecolor,
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linewidth=linewidth, hatch=hatch,
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linestyle=linestyle)
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ref_ax = fig_ref.subplots()
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color = [None]*3 if color is None else color
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edgecolor = [None]*3 if edgecolor is None else edgecolor
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facecolor = [None]*3 if facecolor is None else facecolor
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for i in range(2, -1, -1):
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ref_ax.hist(x[i], bins=bins, histtype=histtype, color=color[i],
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edgecolor=edgecolor[i], facecolor=facecolor[i],
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linewidth=linewidth[i], hatch=hatch[i],
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linestyle=linestyle[i])
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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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