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Merge branch 'master' of https://github.com/matplotlib/matplotlib into issue_10267
2 parents f30e583 + 482bde0 commit c0712ca

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‎doc/api/api_changes/2018-01-29-AL.rst‎

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Deprecation of backend_tkagg.AxisMenu
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`````````````````````````````````````
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The above-mentioned class is deprecated, as it has become unused since the
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removal of "classic" toolbars.

‎doc/conf.py‎

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'sphinx.ext.inheritance_diagram', 'sphinx.ext.intersphinx',
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'sphinx_gallery.gen_gallery',
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'matplotlib.sphinxext.plot_directive',
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'sphinxext.github',
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'sphinxext.github', 'sphinxext.custom_roles',
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'numpydoc']
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exclude_patterns = ['api/api_changes/*', 'users/whats_new/*']

‎doc/devel/MEP/MEP23.rst‎

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desirable to be able to group these under the same window
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[see](https://github.com/matplotlib/matplotlib/issues/2194).
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The proposed solution modifies `FigureManagerBase` to contain and
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manage more than one `canvas`. The settings parameter
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`rcParams['backend.multifigure']` control when the **MultiFigure**
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behaviour is desired.
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The proposed solution modifies `FigureManagerBase` to contain and manage more
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than one `canvas`. The settings parameter :rc:`backend.multifigure` control
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when the **MultiFigure** behaviour is desired.
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**Note**
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‎doc/devel/documenting_mpl.rst‎

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-------
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lines : `~matplotlib.collections.LineCollection`
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* rcParams can be referenced with the custom ``:rc:`` role:
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:literal:`:rc:\`foo\`` yields ``rcParams["foo"]``.
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Deprecated formatting conventions
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---------------------------------
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* Formerly, we have used square brackets for explicit parameter lists
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``['solid' | 'dashed' | 'dotted']``. With numpydoc we have switched to their
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standard using curly braces ``{'solid', 'dashed', 'dotted'}``.
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Linking to other code
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---------------------
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To link to other methods, classes, or modules in Matplotlib you can encase

‎doc/sphinxext/custom_roles.py‎

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from docutils import nodes
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def rcparam_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
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rendered = nodes.Text('rcParams["{}"]'.format(text))
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return [nodes.literal(rawtext, rendered)], []
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def setup(app):
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app.add_role("rc", rcparam_role)
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return {"parallel_read_safe": True, "parallel_write_safe": True}

‎examples/api/mathtext_asarray.py‎

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r'some other string', color='red', fontsize=20, dpi=200)
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fig = plt.figure()
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fig.figimage(rgba1.astype(float)/255., 100, 100)
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fig.figimage(rgba2.astype(float)/255., 100, 300)
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fig.figimage(rgba1, 100, 100)
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fig.figimage(rgba2, 100, 300)
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plt.show()

‎examples/color/color_cycle_default.py‎

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colors = prop_cycle.by_key()['color']
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lwbase = plt.rcParams['lines.linewidth']
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thin = float('%.1f' % (lwbase / 2))
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thin = lwbase / 2
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thick = lwbase * 3
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fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
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axs[1, icol].set_facecolor('k')
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axs[1, icol].xaxis.set_ticks(np.arange(0, 10, 2))
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axs[0, icol].set_title('line widths (pts): %.1f, %.1f' % (lwx, lwy),
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axs[0, icol].set_title('line widths (pts): %g, %g' % (lwx, lwy),
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fontsize='medium')
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for irow in range(2):

‎examples/images_contours_and_fields/quadmesh_demo.py‎

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This demo illustrates a bug in quadmesh with masked data.
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"""
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import copy
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from matplotlib import cm, colors, pyplot as plt
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import numpy as np
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from matplotlib.pyplot import figure, show, savefig
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from matplotlib import cm, colors
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from numpy import ma
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n = 12
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x = np.linspace(-1.5, 1.5, n)
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y = np.linspace(-1.5, 1.5, n * 2)
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X, Y = np.meshgrid(x, y)
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Qx = np.cos(Y) - np.cos(X)
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Qz = np.sin(Y) + np.sin(X)
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Qx = (Qx + 1.1)
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Z = np.sqrt(X**2 + Y**2) / 5
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Z = (Z - Z.min()) / (Z.max() - Z.min())
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# The color array can include masked values:
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Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
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# The color array can include masked values.
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Zm = np.ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
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fig, axs = plt.subplots(1, 3)
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axs[0].pcolormesh(Qx, Qz, Z, shading='gouraud')
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axs[0].set_title('Without masked values')
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fig = figure()
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ax = fig.add_subplot(121)
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ax.pcolormesh(Qx, Qz, Z, shading='gouraud')
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ax.set_title('Without masked values')
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# You can control the color of the masked region. We copy the default colormap
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# before modifying it.
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cmap = copy.copy(cm.get_cmap(plt.rcParams['image.cmap']))
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cmap.set_bad('y', 1.0)
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axs[1].pcolormesh(Qx, Qz, Zm, shading='gouraud', cmap=cmap)
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axs[1].set_title('With masked values')
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ax = fig.add_subplot(122)
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# You can control the color of the masked region:
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# cmap = cm.RdBu
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# cmap.set_bad('y', 1.0)
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# ax.pcolormesh(Qx, Qz, Zm, cmap=cmap)
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# Or use the default, which is transparent:
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col = ax.pcolormesh(Qx, Qz, Zm, shading='gouraud')
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ax.set_title('With masked values')
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# Or use the default, which is transparent.
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axs[2].pcolormesh(Qx, Qz, Zm, shading='gouraud')
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axs[2].set_title('With masked values')
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fig.tight_layout()
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show()
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plt.show()

‎examples/lines_bars_and_markers/stackplot_demo.py‎

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y = 2 * np.random.random() - .5
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z = 10 / (.1 + np.random.random())
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for i in range(m):
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w = (i / float(m) - y) * z
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w = (i / m - y) * z
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a[i] += x * np.exp(-w * w)
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a = np.zeros((m, n))
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for i in range(n):

‎examples/statistics/hist.py‎

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N, bins, patches = axs[0].hist(x, bins=n_bins)
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# We'll color code by height, but you could use any scalar
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fracs = N.astype(float) / N.max()
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fracs = N / N.max()
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# we need to normalize the data to 0..1 for the full range of the colormap
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norm = colors.Normalize(fracs.min(), fracs.max())

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