Sep 25, 2020 · Sep 25, 2020
diff --git a/doc/api/prev_api_changes/api_changes_2.0.1.rst b/doc/api/prev_api_changes/api_changes_2.0.1.rst
 There is no way to restore the old behavior.


 Deprecate 'Vega'color maps
 ---------------------------
 Deprecate 'Vega'colormaps
 --------------------------

 The "Vega" colormaps are deprecated in Matplotlib 2.0.1 and will be
 removed in Matplotlib 2.2. Use the "tab" colormaps instead: "tab10",
diff --git a/doc/api/prev_api_changes/api_changes_2.2.0.rst b/doc/api/prev_api_changes/api_changes_2.2.0.rst
 The unused ``FONT_SCALE`` and ``fontd`` attributes of the `.RendererSVG`
 class have been removed.

 color maps
 ~~~~~~~~~~
 colormaps
 ~~~~~~~~~

 The ``spectral`` colormap has been removed.  The ``Vega*`` colormaps, which
 were aliases for the ``tab*`` colormaps, have been removed.
diff --git a/doc/devel/color_changes.rst b/doc/devel/color_changes.rst
 *********************

 As discussed at length elsewhere [insert links], ``jet`` is an
 empirically badcolor mapand should not be the defaultcolor map.
 empirically badcolormapand should not be the defaultcolormap.
 Due to the position that changing the appearance of the plot breaks
 backward compatibility, this change has been put off for far longer
 than it should have been.  In addition to changing the default color
 map we plan to take the chance to change the default color-cycle on
 plots and to adopt a differentcolor map for filled plots (``imshow``,
 plots and to adopt a differentcolormap for filled plots (``imshow``,
 ``pcolor``, ``contourf``, etc) and for scatter like plots.


 Default Heat Map Colormap
 -------------------------

 The choice of a newcolor map is fertile ground to bike-shedding ("No,
 The choice of a newcolormap is fertile ground to bike-shedding ("No,
 it should be _this_ color") so we have a proposed set criteria (via
 Nathaniel Smith) to evaluate proposedcolor maps.
 Nathaniel Smith) to evaluate proposedcolormaps.

 - it should be a sequential colormap, because diverging colormaps are
  really misleading unless you know where the "center" of the data is,
 ------------------------

 For heat-map like applications it can be desirable to cover as much of
 the luminance scale as possible, however whencolor mapping markers,
 the luminance scale as possible, however whencolormapping markers,
 having markers too close to white can be a problem.  For that reason
 we propose using a different (but maybe related)color map to the
 we propose using a different (but maybe related)colormap to the
 heat map for marker-based.  The design parameters are the same as
 above, only with a more limited luminance variation.

 Proposed Colormaps
 ++++++++++++++++++

 Color Cycle / Qualitativecolor map
 Color Cycle / Qualitativecolormap
 -----------------------------------

 When plotting lines it is frequently desirable to plot multiple lines
diff --git a/doc/thirdpartypackages/index.rst b/doc/thirdpartypackages/index.rst

 - Scatter, line, density, histogram, and box plot types
 - Settings for the marker size, line width, number of bins of histogram,
 color map (from cmocean)
 colormap (from cmocean)
 - Save figure as editable PDF
 - Code of the plotted graph is available so that it can be reused and modified
  outside of sviewgui
diff --git a/doc/users/dflt_style_changes.rst b/doc/users/dflt_style_changes.rst



 Colors, color cycles, andcolor maps
 ====================================
 Colors, color cycles, andcolormaps
 ===================================

 Colors in default property cycle
 --------------------------------
 Colormap
 --------

 The new defaultcolor map used by `matplotlib.cm.ScalarMappable` instances is
 The new defaultcolormap used by `matplotlib.cm.ScalarMappable` instances is
 'viridis' (aka `option D <https://bids.github.io/colormap/>`__).

 .. plot::
 watch Nathaniel Smith and Stéfan van der Walt's talk from SciPy2015.
 See `here for many more details <https://bids.github.io/colormap/>`__
 about the other alternatives and the tools used to create the color
 map.  For details on all of thecolor maps available in matplotlib see
 map.  For details on all of thecolormaps available in matplotlib see
 :doc:`/tutorials/colors/colormaps`.

 .. raw:: html

 The default interpolation method for `~matplotlib.axes.Axes.imshow` is
 now ``'nearest'`` and by default it resamples the data (both up and down
 sampling) beforecolor mapping.
 sampling) beforecolormapping.


 .. plot::
 Colormapping pipeline
 ---------------------

 Previously, the input data was normalized, thencolor mapped, and then
 Previously, the input data was normalized, thencolormapped, and then
 resampled to the resolution required for the screen.  This meant that
 the final resampling was being done in color space.  Because the color
 maps are not generally linear in RGB space, colors not in thecolor map
 maps are not generally linear in RGB space, colors not in thecolormap
 may appear in the final image.  This bug was addressed by an almost
 complete overhaul of the image handling code.

 The input data is now normalized, then resampled to the correct
 resolution (in normalized dataspace), and thencolor mapped to
 RGB space.  This ensures that only colors from thecolor map appear
 resolution (in normalized dataspace), and thencolormapped to
 RGB space.  This ensures that only colors from thecolormap appear
 in the final image. (If your viewer subsequently resamples the image,
 the artifact may reappear.)

diff --git a/doc/users/next_whats_new/colormap_repr.rst b/doc/users/next_whats_new/colormap_repr.rst
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 The `matplotlib.colors.Colormap` object now has image representations for
 IPython / Jupyter backends. Cells returning acolor map on the last line will
 display an image of thecolor map.
 IPython / Jupyter backends. Cells returning acolormap on the last line will
 display an image of thecolormap.
diff --git a/doc/users/prev_whats_new/changelog.rst b/doc/users/prev_whats_new/changelog.rst

 2008-04-20 Fix double-zoom bug. - MM

 2008-04-15 Speed upcolor mapping. - EF
 2008-04-15 Speed upcolormapping. - EF

 2008-04-12 Speed up zooming and panning of dense images. - EF

           a sequence of (x,y) tuples for specifying paths in
           collections, quiver, contour, pcolor, transforms.
           Fixed contour bug involving setting limits for
 color mapping.  Added numpy-style all() to numerix. - EF
 colormapping.  Added numpy-style all() to numerix. - EF

 2006-06-20 Added custom FigureClass hook to pylab interface - see
           examples/custom_figure_class.py
diff --git a/doc/users/prev_whats_new/whats_new_2.0.0.rst b/doc/users/prev_whats_new/whats_new_2.0.0.rst
 ----------------------

 Prior to version 2.0, matplotlib resampled images by first applying
 thecolor map and then resizing the result.  Since the resampling was
 thecolormap and then resizing the result.  Since the resampling was
 performed on the colored image, this introduced colors in the output
 image that didn't actually exist in thecolor map.  Now, images are
 image that didn't actually exist in thecolormap.  Now, images are
 resampled first (and entirely in floating-point, if the input image is
 floating-point), and then thecolor map is applied.
 floating-point), and then thecolormap is applied.

 In order to make this important change, the image handling code was
 almost entirely rewritten.  As a side effect, image resampling uses
diff --git a/examples/images_contours_and_fields/image_masked.py b/examples/images_contours_and_fields/image_masked.py
 # set up the Axes objects
 fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))

 # plot using 'continuous'color map
 # plot using 'continuous'colormap
 im = ax1.imshow(Zm, interpolation='bilinear',
                cmap=palette,
                norm=colors.Normalize(vmin=-1.0, vmax=1.0),
diff --git a/examples/mplot3d/surface3d.py b/examples/mplot3d/surface3d.py
 """
 ======================
 3D surface (color map)
 ======================
 =====================
 3D surface (colormap)
 =====================

 Demonstrates plotting a 3D surface colored with the coolwarmcolor map.
 Demonstrates plotting a 3D surface colored with the coolwarmcolormap.
 The surface is made opaque by using antialiased=False.

 Also demonstrates using the LinearLocator and custom formatting for the
diff --git a/examples/mplot3d/surface3d_radial.py b/examples/mplot3d/surface3d_radial.py
 =================================

 Demonstrates plotting a surface defined in polar coordinates.
 Uses the reversed version of the YlGnBucolor map.
 Uses the reversed version of the YlGnBucolormap.
 Also demonstrates writing axis labels with latex math mode.

 Example contributed by Armin Moser.
diff --git a/lib/matplotlib/_cm.py b/lib/matplotlib/_cm.py
 _prism_data = {'red': _prism_red, 'green': _prism_green, 'blue': _prism_blue}

 def _ch_helper(gamma, s, r, h, p0, p1, x):
    """Helper function for generating picklable cubehelixcolor maps."""
    """Helper function for generating picklable cubehelixcolormaps."""
    # Apply gamma factor to emphasise low or high intensity values
    xg = x ** gamma
    # Calculate amplitude and angle of deviation from the black to white
 def _gist_yarg(x): return 1 - x
 _gist_yarg_data = {'red': _gist_yarg, 'green': _gist_yarg, 'blue': _gist_yarg}

 # This bipolarcolor map was generated from CoolWarmFloat33.csv of
 # This bipolarcolormap was generated from CoolWarmFloat33.csv of
 # "Diverging Color Maps for Scientific Visualization" by Kenneth Moreland.
 # <http://www.kennethmoreland.com/color-maps/>
 _coolwarm_data = {
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py

            - If *None*, no binning is applied; the color of each hexagon
              directly corresponds to its count value.
            - If 'log', use a logarithmic scale for thecolor map.
            - If 'log', use a logarithmic scale for thecolormap.
              Internally, :math:`log_{10}(i+1)` is used to determine the
              hexagon color. This is equivalent to ``norm=LogNorm()``.
            - If an integer, divide the counts in the specified number

        The input may either be actual RGB(A) data, or 2D scalar data, which
        will be rendered as a pseudocolor image. For displaying a grayscale
        image set up thecolor mapping using the parameters
        image set up thecolormapping using the parameters
        ``cmap='gray', vmin=0, vmax=255``.

        The number of pixels used to render an image is set by the axes size
diff --git a/lib/matplotlib/collections.py b/lib/matplotlib/collections.py
    """
    A generic collection of patches.

    This makes it easier to assign acolor map to a heterogeneous
    This makes it easier to assign acolormap to a heterogeneous
    collection of patches.

    This also may improve plotting speed, since PatchCollection will
diff --git a/lib/matplotlib/colorbar.py b/lib/matplotlib/colorbar.py
    *values*       None or a sequence which must be of length 1 less
                   than the sequence of *boundaries*. For each region
                   delimited by adjacent entries in *boundaries*, the
 color mapped to the corresponding value in values
 colormapped to the corresponding value in values
                   will be used.
    ============   ===================================================

diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
        raise NotImplementedError("Abstract class only")

    def is_gray(self):
        """Return whether thecolor map is grayscale."""
        """Return whether thecolormap is grayscale."""
        if not self._isinit:
            self._init()
        return (np.all(self._lut[:, 0] == self._lut[:, 1]) and
                np.all(self._lut[:, 0] == self._lut[:, 2]))

    def _resample(self, lutsize):
        """Return a newcolor map with *lutsize* entries."""
        """Return a newcolormap with *lutsize* entries."""
        raise NotImplementedError()

    def reversed(self, name=None):
                    (_REPR_PNG_SIZE[1], 1))
        pixels = self(X, bytes=True)
        png_bytes = io.BytesIO()
        title = self.name + 'color map'
        title = self.name + 'colormap'
        author = f'Matplotlib v{mpl.__version__}, https://matplotlib.org'
        pnginfo = PngInfo()
        pnginfo.add_text('Title', title)
                f'<strong>{self.name}</strong> '
                '</div>'
                '<div class="cmap"><img '
                f'alt="{self.name}color map" '
                f'alt="{self.name}colormap" '
                f'title="{self.name}" '
                'style="border: 1px solid #555;" '
                f'src="data:image/png;base64,{png_base64}"></div>'

    def __init__(self, name, segmentdata, N=256, gamma=1.0):
        """
        Createcolor map from linear mapping segments
        Createcolormap from linear mapping segments

        segmentdata argument is a dictionary with a red, green and blue
        entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
        self._set_extremes()

    def set_gamma(self, gamma):
        """Set a new gamma value and regeneratecolor map."""
        """Set a new gamma value and regeneratecolormap."""
        self._gamma = gamma
        self._init()

        return LinearSegmentedColormap(name, cdict, N, gamma)

    def _resample(self, lutsize):
        """Return a newcolor map with *lutsize* entries."""
        """Return a newcolormap with *lutsize* entries."""
        new_cmap = LinearSegmentedColormap(self.name, self._segmentdata,
                                           lutsize)
        new_cmap._rgba_over = self._rgba_over
        self._set_extremes()

    def _resample(self, lutsize):
        """Return a newcolor map with *lutsize* entries."""
        """Return a newcolormap with *lutsize* entries."""
        colors = self(np.linspace(0, 1, lutsize))
        new_cmap = ListedColormap(colors, name=self.name)
        # Keep the over/under values too
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
        """
        Color argument processing for contouring.

        Note that we base thecolor mapping on the contour levels
        Note that we base thecolormapping on the contour levels
        and layers, not on the actual range of the Z values.  This
        means we don't have to worry about bad values in Z, and we
        always have the full dynamic range available for the selected
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
                # would not full eliminate it and breaks a number of
                # tests (due to the slightly different error bouncing
                # some pixels across a boundary in the (very
                # quantized)color mapping step).
                # quantized)colormapping step).
                offset = .1
                frac = .8
                # we need to run the vmin/vmax through the same rescaling
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
      for nominal data that has no inherent ordering, where color is used
      only to distinguish categories

    Matplotlib ships with 4 perceptually uniformcolor maps which are
    the recommendedcolor maps for sequential data:
    Matplotlib ships with 4 perceptually uniformcolormaps which are
    the recommendedcolormaps for sequential data:

      =========   ===================================================
      Colormap    Description
      Colormap    Description
      =========   =======================================================
      autumn      sequential linearly-increasing shades of red-orange-yellow
      bone        sequential increasing black-whitecolor map with
      bone        sequential increasing black-whitecolormap with
                  a tinge of blue, to emulate X-ray film
      cool        linearly-decreasing shades of cyan-magenta
      copper      sequential increasing shades of black-copper
                    Language software
      ============  =======================================================

    A set of cycliccolor maps:
    A set of cycliccolormaps:

      ================  =================================================
      Colormap          Description
Original file line number	Diff line number	Diff line change
Expand Up		@@ -55,8 +55,8 @@ line width the final patterns would not change.
		There is no way to restore the old behavior.


		Deprecate 'Vega'color maps
		---------------------------
		Deprecate 'Vega'colormaps
		--------------------------

		The "Vega" colormaps are deprecated in Matplotlib 2.0.1 and will be
		removed in Matplotlib 2.2. Use the "tab" colormaps instead: "tab10",
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -110,8 +110,8 @@ The ``Axes.get_axis_bgcolor``, ``Axes.set_axis_bgcolor``,
		The unused ``FONT_SCALE`` and ``fontd`` attributes of the `.RendererSVG`
		class have been removed.

		color maps
		~~~~~~~~~~
		colormaps
		~~~~~~~~~

		The ``spectral`` colormap has been removed. The ``Vega*`` colormaps, which
		were aliases for the ``tab*`` colormaps, have been removed.
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -5,21 +5,21 @@ Default Color changes
		*********************

		As discussed at length elsewhere [insert links], ``jet`` is an
		empirically badcolor mapand should not be the defaultcolor map.
		empirically badcolormapand should not be the defaultcolormap.
		Due to the position that changing the appearance of the plot breaks
		backward compatibility, this change has been put off for far longer
		than it should have been. In addition to changing the default color
		map we plan to take the chance to change the default color-cycle on
		plots and to adopt a differentcolor map for filled plots (``imshow``,
		plots and to adopt a differentcolormap for filled plots (``imshow``,
		``pcolor``, ``contourf``, etc) and for scatter like plots.


		Default Heat Map Colormap
		-------------------------

		The choice of a newcolor map is fertile ground to bike-shedding ("No,
		The choice of a newcolormap is fertile ground to bike-shedding ("No,
		it should be _this_ color") so we have a proposed set criteria (via
		Nathaniel Smith) to evaluate proposedcolor maps.
		Nathaniel Smith) to evaluate proposedcolormaps.

		- it should be a sequential colormap, because diverging colormaps are
		really misleading unless you know where the "center" of the data is,
Expand DownExpand Up		@@ -64,9 +64,9 @@ Default Scatter Colormap
		------------------------

		For heat-map like applications it can be desirable to cover as much of
		the luminance scale as possible, however whencolor mapping markers,
		the luminance scale as possible, however whencolormapping markers,
		having markers too close to white can be a problem. For that reason
		we propose using a different (but maybe related)color map to the
		we propose using a different (but maybe related)colormap to the
		heat map for marker-based. The design parameters are the same as
		above, only with a more limited luminance variation.

Expand DownExpand Up		@@ -102,7 +102,7 @@ Example script
		Proposed Colormaps
		++++++++++++++++++

		Color Cycle / Qualitativecolor map
		Color Cycle / Qualitativecolormap
		-----------------------------------

		When plotting lines it is frequently desirable to plot multiple lines
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -331,7 +331,7 @@ visualisation of data from csv files or `pandas.DataFrame`\s. Main features:

		- Scatter, line, density, histogram, and box plot types
		- Settings for the marker size, line width, number of bins of histogram,
		color map (from cmocean)
		colormap (from cmocean)
		- Save figure as editable PDF
		- Code of the plotted graph is available so that it can be reused and modified
		outside of sviewgui
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -28,8 +28,8 @@ persistently and selectively revert many of these changes.



		Colors, color cycles, andcolor maps
		====================================
		Colors, color cycles, andcolormaps
		===================================

		Colors in default property cycle
		--------------------------------
Expand DownExpand Up		@@ -117,7 +117,7 @@ in your :file:`matplotlibrc` file.
		Colormap
		--------

		The new defaultcolor map used by `matplotlib.cm.ScalarMappable` instances is
		The new defaultcolormap used by `matplotlib.cm.ScalarMappable` instances is
		'viridis' (aka `option D <https://bids.github.io/colormap/>`__).

		.. plot::
Expand All		@@ -144,7 +144,7 @@ For an introduction to color theory and how 'viridis' was generated
		watch Nathaniel Smith and Stéfan van der Walt's talk from SciPy2015.
		See `here for many more details <https://bids.github.io/colormap/>`__
		about the other alternatives and the tools used to create the color
		map. For details on all of thecolor maps available in matplotlib see
		map. For details on all of thecolormaps available in matplotlib see
		:doc:`/tutorials/colors/colormaps`.

		.. raw:: html
Expand DownExpand Up		@@ -846,7 +846,7 @@ Interpolation

		The default interpolation method for `~matplotlib.axes.Axes.imshow` is
		now ``'nearest'`` and by default it resamples the data (both up and down
		sampling) beforecolor mapping.
		sampling) beforecolormapping.


		.. plot::
Expand DownExpand Up		@@ -888,16 +888,16 @@ in your :file:`matplotlibrc` file.
		Colormapping pipeline
		---------------------

		Previously, the input data was normalized, thencolor mapped, and then
		Previously, the input data was normalized, thencolormapped, and then
		resampled to the resolution required for the screen. This meant that
		the final resampling was being done in color space. Because the color
		maps are not generally linear in RGB space, colors not in thecolor map
		maps are not generally linear in RGB space, colors not in thecolormap
		may appear in the final image. This bug was addressed by an almost
		complete overhaul of the image handling code.

		The input data is now normalized, then resampled to the correct
		resolution (in normalized dataspace), and thencolor mapped to
		RGB space. This ensures that only colors from thecolor map appear
		resolution (in normalized dataspace), and thencolormapped to
		RGB space. This ensures that only colors from thecolormap appear
		in the final image. (If your viewer subsequently resamples the image,
		the artifact may reappear.)

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2,5 +2,5 @@ IPython representations for Colormap objects
		~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

		The `matplotlib.colors.Colormap` object now has image representations for
		IPython / Jupyter backends. Cells returning acolor map on the last line will
		display an image of thecolor map.
		IPython / Jupyter backends. Cells returning acolormap on the last line will
		display an image of thecolormap.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1980,7 +1980,7 @@ the `API changes <../../api/api_changes.html>`_.

		2008-04-20 Fix double-zoom bug. - MM

		2008-04-15 Speed upcolor mapping. - EF
		2008-04-15 Speed upcolormapping. - EF

		2008-04-12 Speed up zooming and panning of dense images. - EF

Expand DownExpand Up		@@ -3197,7 +3197,7 @@ the `API changes <../../api/api_changes.html>`_.
		a sequence of (x,y) tuples for specifying paths in
		collections, quiver, contour, pcolor, transforms.
		Fixed contour bug involving setting limits for
		color mapping. Added numpy-style all() to numerix. - EF
		colormapping. Added numpy-style all() to numerix. - EF

		2006-06-20 Added custom FigureClass hook to pylab interface - see
		examples/custom_figure_class.py
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -187,11 +187,11 @@ Improved image support
		----------------------

		Prior to version 2.0, matplotlib resampled images by first applying
		thecolor map and then resizing the result. Since the resampling was
		thecolormap and then resizing the result. Since the resampling was
		performed on the colored image, this introduced colors in the output
		image that didn't actually exist in thecolor map. Now, images are
		image that didn't actually exist in thecolormap. Now, images are
		resampled first (and entirely in floating-point, if the input image is
		floating-point), and then thecolor map is applied.
		floating-point), and then thecolormap is applied.

		In order to make this important change, the image handling code was
		almost entirely rewritten. As a side effect, image resampling uses
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -45,7 +45,7 @@
		# set up the Axes objects
		fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))

		# plot using 'continuous'color map
		# plot using 'continuous'colormap
		im = ax1.imshow(Zm, interpolation='bilinear',
		cmap=palette,
		norm=colors.Normalize(vmin=-1.0, vmax=1.0),
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,9 +1,9 @@
		"""
		======================
		3D surface (color map)
		======================
		=====================
		3D surface (colormap)
		=====================

		Demonstrates plotting a 3D surface colored with the coolwarmcolor map.
		Demonstrates plotting a 3D surface colored with the coolwarmcolormap.
		The surface is made opaque by using antialiased=False.

		Also demonstrates using the LinearLocator and custom formatting for the
Expand Down