Use docstring interpolation instead. · matplotlib/matplotlib@c6772c0 · GitHub
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Use docstring interpolation instead.
1 parent 7b34dc1 commit c6772c0

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Lines changed: 106 additions & 75 deletions

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‎lib/matplotlib/axes/_axes.py‎

Lines changed: 43 additions & 51 deletions

‎lib/matplotlib/cm.py‎

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@@ -633,3 +633,35 @@ def changed(self):
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"""
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self.callbacks.process('changed', self)
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self.stale = True
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# The docstrings here must be generic enough to apply to all relevant methods.
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mpl._docstring.interpd.update(
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cmap_doc="""\
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cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
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The Colormap instance or registered colormap name used to map scalar data
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to colors. This parameter is ignored for RGB(A) data.""",
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norm_doc="""\
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norm : str or `~matplotlib.colors.Normalize`, optional
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The normalization method used to scale scalar data to the [0, 1] range
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before mapping to colors using *cmap*. By default, a linear scaling is
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used, mapping the lowest value to 0 and the highest to 1. This parameter is
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ignored for RGB(A) data.
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If given, this can be one of the following:
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- An instance of `.Normalize` or one of its subclasses
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(see :doc:`/tutorials/colors/colormapnorms`).
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- A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a
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list of available scales, call `matplotlib.scale.get_scale_names()`.
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In that case, a suitable `.Normalize` subclass is dynamically generated
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and instantiated.""",
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vmin_vmax_doc="""\
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vmin, vmax : float, optional
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When using scalar data and no explicit *norm*, *vmin* and *vmax* define
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the data range that the colormap covers. By default, the colormap covers
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the complete value range of the supplied data. It is an error to use
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*vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm*
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name together with *vmin*/*vmax* is acceptable). When using RGB(A) data,
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parameters *vmin*/*vmax* are ignored.""",
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)

‎lib/matplotlib/contour.py‎

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@@ -1592,7 +1592,8 @@ def _initialize_x_y(self, z):
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``X = range(N)``, ``Y = range(M)``.
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Z : (M, N) array-like
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The height values over which the contour is drawn.
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The height values over which the contour is drawn. Color-mapping is
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controlled by *cmap*, *norm*, *vmin*, and *vmax*.
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levels : int or array-like, optional
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Determines the number and positions of the contour lines / regions.
@@ -1635,11 +1636,11 @@ def _initialize_x_y(self, z):
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alpha : float, default: 1
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The alpha blending value, between 0 (transparent) and 1 (opaque).
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cmap, norm, vmin, vmax
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Data normalization and colormapping parameters for *Z*. See `~.Axes.imshow`
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for a detailed description.
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%(cmap_doc)s
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*cmap* cannot be given together with *colors*.
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%(norm_doc)s
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%(vmin_vmax_doc)s
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If *vmin* or *vmax* are not given, the default color scaling is based on
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*levels*.
@@ -1798,4 +1799,4 @@ def _initialize_x_y(self, z):
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<https://en.wikipedia.org/wiki/Marching_squares>`_ algorithm to
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compute contour locations. More information can be found in
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`ContourPy documentation <https://contourpy.readthedocs.io>`_.
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""")
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""" % _docstring.interpd.params)

‎lib/matplotlib/figure.py‎

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@@ -2724,6 +2724,7 @@ def set_canvas(self, canvas):
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"""
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self.canvas = canvas
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@_docstring.interpd
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def figimage(self, X, xo=0, yo=0, alpha=None, norm=None, cmap=None,
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vmin=None, vmax=None, origin=None, resize=False, **kwargs):
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"""
@@ -2737,19 +2738,23 @@ def figimage(self, X, xo=0, yo=0, alpha=None, norm=None, cmap=None,
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X
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The image data. This is an array of one of the following shapes:
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- MxN: luminance (grayscale) values
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- MxNx3: RGB values
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- MxNx4: RGBA values
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- (M, N): an image with scalar data. Color-mapping is controlled
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by *cmap*, *norm*, *vmin*, and *vmax*.
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- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
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- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
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i.e. including transparency.
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xo, yo : int
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The *x*/*y* image offset in pixels.
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alpha : None or float
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The alpha blending value.
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cmap, norm, vmin, vmax
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Data normalization and colormapping parameters for *X*. See
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`~.Axes.imshow` for a detailed description.
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%(cmap_doc)s
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%(norm_doc)s
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%(vmin_vmax_doc)s
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origin : {'upper', 'lower'}, default: :rc:`image.origin`
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Indicates where the [0, 0] index of the array is in the upper left

‎lib/matplotlib/tri/tricontour.py‎

Lines changed: 13 additions & 12 deletions

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