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<div class="section" id="module-matplotlib.image">
<span id="matplotlib-image"></span><h1><code class="docutils literal notranslate"><span class="pre">matplotlib.image</span></code><a class="headerlink" href="#module-matplotlib.image" title="Permalink to this headline">¶</a></h1>
<p>The image module supports basic image loading, rescaling and display
operations.</p>
<dl class="class">
<dt id="matplotlib.image.AxesImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">AxesImage</code><span class="sig-paren">(</span><em>ax</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>interpolation=None</em>, <em>origin=None</em>, <em>extent=None</em>, <em>filternorm=1</em>, <em>filterrad=4.0</em>, <em>resample=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>interpolation and cmap default to their rc settings</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.</p>
<p>Additional kwargs are matplotlib.artist properties</p>
<dl class="method">
<dt id="matplotlib.image.AxesImage.format_cursor_data">
<code class="descname">format_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>data</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.format_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.format_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a string representation of <em>data</em>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This method is intended to be overridden by artist subclasses.
As an end-user of Matplotlib you will most likely not call this
method yourself.</p>
</div>
<p>The default implementation converts ints and floats and arrays of ints
and floats into a comma-separated string enclosed in square brackets.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.image.AxesImage.get_cursor_data" title="matplotlib.image.AxesImage.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_cursor_data">
<code class="descname">get_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>event</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the image value at the event position or <em>None</em> if the event is
outside the image.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="_as_gen/matplotlib.artist.Artist.get_cursor_data.html#matplotlib.artist.Artist.get_cursor_data" title="matplotlib.artist.Artist.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.artist.Artist.get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_window_extent">
<code class="descname">get_window_extent</code><span class="sig-paren">(</span><em>self</em>, <em>renderer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_window_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_window_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the axes bounding box in display space.</p>
<p>The bounding box' width and height are nonnegative.</p>
<p>Subclasses should override for inclusion in the bounding box
"tight" calculation. Default is to return an empty bounding
box at 0, 0.</p>
<p>Be careful when using this function, the results will not update
if the artist window extent of the artist changes. The extent
can change due to any changes in the transform stack, such as
changing the axes limits, the figure size, or the canvas used
(as is done when saving a figure). This can lead to unexpected
behavior where interactive figures will look fine on the screen,
but will save incorrectly.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.set_extent">
<code class="descname">set_extent</code><span class="sig-paren">(</span><em>self</em>, <em>extent</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.set_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.set_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>extent is data axes (left, right, bottom, top) for making image plots</p>
<p>This updates ax.dataLim, and, if autoscaling, sets viewLim
to tightly fit the image, regardless of dataLim. Autoscaling
state is not changed, so following this with ax.autoscale_view
will redo the autoscaling in accord with dataLim.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.BboxImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">BboxImage</code><span class="sig-paren">(</span><em>bbox</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>interpolation=None</em>, <em>origin=None</em>, <em>filternorm=1</em>, <em>filterrad=4.0</em>, <em>resample=False</em>, <em>interp_at_native=<deprecated parameter></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>The Image class whose size is determined by the given bbox.</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>kwargs are an optional list of Artist keyword args</p>
<dl class="method">
<dt id="matplotlib.image.BboxImage.contains">
<code class="descname">contains</code><span class="sig-paren">(</span><em>self</em>, <em>mouseevent</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.contains"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.contains" title="Permalink to this definition">¶</a></dt>
<dd><p>Test whether the mouse event occurred within the image.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.get_transform">
<code class="descname">get_transform</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.get_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.get_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the <a class="reference internal" href="transformations.html#matplotlib.transforms.Transform" title="matplotlib.transforms.Transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transform</span></code></a> instance used by this artist.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.get_window_extent">
<code class="descname">get_window_extent</code><span class="sig-paren">(</span><em>self</em>, <em>renderer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.get_window_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.get_window_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the axes bounding box in display space.</p>
<p>The bounding box' width and height are nonnegative.</p>
<p>Subclasses should override for inclusion in the bounding box
"tight" calculation. Default is to return an empty bounding
box at 0, 0.</p>
<p>Be careful when using this function, the results will not update
if the artist window extent of the artist changes. The extent
can change due to any changes in the transform stack, such as
changing the axes limits, the figure size, or the canvas used
(as is done when saving a figure). This can lead to unexpected
behavior where interactive figures will look fine on the screen,
but will save incorrectly.</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.image.BboxImage.interp_at_native">
<code class="descname">interp_at_native</code><a class="headerlink" href="#matplotlib.image.BboxImage.interp_at_native" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.FigureImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">FigureImage</code><span class="sig-paren">(</span><em>fig</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>offsetx=0</em>, <em>offsety=0</em>, <em>origin=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>kwargs are an optional list of Artist keyword args</p>
<dl class="method">
<dt id="matplotlib.image.FigureImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.FigureImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.FigureImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the image array.</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.image.FigureImage.zorder">
<code class="descname">zorder</code><em class="property"> = 0</em><a class="headerlink" href="#matplotlib.image.FigureImage.zorder" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.NonUniformImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">NonUniformImage</code><span class="sig-paren">(</span><em>ax</em>, <em>*</em>, <em>interpolation='nearest'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image.AxesImage</span></code></a></p>
<p>kwargs are identical to those for AxesImage, except
that 'nearest' and 'bilinear' are the only supported 'interpolation'
options.</p>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>self</em>, <em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Retained for backwards compatibility - use set_data instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>A</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_cmap">
<code class="descname">set_cmap</code><span class="sig-paren">(</span><em>self</em>, <em>cmap</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_cmap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>set the colormap for luminance data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>cmap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">colormap or registered colormap name</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the grid for the pixel centers, and the pixel values.</p>
<blockquote>
<div><dl class="docutils">
<dt><em>x</em> and <em>y</em> are monotonic 1-D ndarrays of lengths N and M,</dt>
<dd>respectively, specifying pixel centers</dd>
<dt><em>A</em> is an (M,N) ndarray or masked array of values to be</dt>
<dd>colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.</dd>
</dl>
</div></blockquote>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_filternorm">
<code class="descname">set_filternorm</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_filternorm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_filternorm" title="Permalink to this definition">¶</a></dt>
<dd><p>Set whether the resize filter normalizes the weights.</p>
<p>See help for <a class="reference internal" href="_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow" title="matplotlib.axes.Axes.imshow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imshow</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>filternorm</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_filterrad">
<code class="descname">set_filterrad</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_filterrad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_filterrad" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the resize filter radius only applicable to some
interpolation schemes -- see help for imshow</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>filterrad</strong> <span class="classifier-delimiter">:</span> <span class="classifier">positive float</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_interpolation">
<code class="descname">set_interpolation</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_interpolation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_interpolation" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>s</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, None</span></dt>
<dd><p class="first last">Either 'nearest', 'bilinear', or <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_norm">
<code class="descname">set_norm</code><span class="sig-paren">(</span><em>self</em>, <em>norm</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_norm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the normalization instance.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>norm</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><a class="reference internal" href="_as_gen/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize" title="matplotlib.colors.Normalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Normalize</span></code></a></span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>If there are any colorbars using the mappable for this norm, setting
the norm of the mappable will reset the norm, locator, and formatters
on the colorbar to default.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.PcolorImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">PcolorImage</code><span class="sig-paren">(</span><em>ax</em>, <em>x=None</em>, <em>y=None</em>, <em>A=None</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image.AxesImage</span></code></a></p>
<p>Make a pcolor-style plot with an irregular rectangular grid.</p>
<p>This uses a variation of the original irregular image code,
and it is used by pcolorfast for the corresponding grid type.</p>
<p>cmap defaults to its rc setting</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>Additional kwargs are matplotlib.artist properties</p>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.get_cursor_data">
<code class="descname">get_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>event</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.get_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the image value at the event position or <em>None</em> if the event is
outside the image.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="_as_gen/matplotlib.artist.Artist.get_cursor_data.html#matplotlib.artist.Artist.get_cursor_data" title="matplotlib.artist.Artist.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.artist.Artist.get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>self</em>, <em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.set_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.set_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Retained for backwards compatibility - use set_data instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>A</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the grid for the rectangle boundaries, and the data values.</p>
<blockquote>
<div><dl class="docutils">
<dt><em>x</em> and <em>y</em> are monotonic 1-D ndarrays of lengths N+1 and M+1,</dt>
<dd>respectively, specifying rectangle boundaries. If None,
they will be created as uniform arrays from 0 through N
and 0 through M, respectively.</dd>
<dt><em>A</em> is an (M,N) ndarray or masked array of values to be</dt>
<dd>colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.</dd>
</dl>
</div></blockquote>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.composite_images">
<code class="descclassname">matplotlib.image.</code><code class="descname">composite_images</code><span class="sig-paren">(</span><em>images</em>, <em>renderer</em>, <em>magnification=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#composite_images"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.composite_images" title="Permalink to this definition">¶</a></dt>
<dd><p>Composite a number of RGBA images into one. The images are
composited in the order in which they appear in the <code class="xref py py-obj docutils literal notranslate"><span class="pre">images</span></code> list.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>images</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list of Images</span></dt>
<dd><p class="first last">Each must have a <code class="xref py py-obj docutils literal notranslate"><span class="pre">make_image</span></code> method. For each image,
<code class="xref py py-obj docutils literal notranslate"><span class="pre">can_composite</span></code> should return <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">True</span></code></a>, though this is not
enforced by this function. Each image must have a purely
affine transformation with no shear.</p>
</dd>
<dt><strong>renderer</strong> <span class="classifier-delimiter">:</span> <span class="classifier">RendererBase instance</span></dt>
<dd></dd>
<dt><strong>magnification</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The additional magnification to apply for the renderer in use.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>tuple</strong> <span class="classifier-delimiter">:</span> <span class="classifier">image, offset_x, offset_y</span></dt>
<dd><p class="first">Returns the tuple:</p>
<ul class="last simple">
<li>image: A numpy array of the same type as the input images.</li>
<li>offset_x, offset_y: The offset of the image (left, bottom)
in the output figure.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.imread">
<code class="descclassname">matplotlib.image.</code><code class="descname">imread</code><span class="sig-paren">(</span><em>fname</em>, <em>format=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#imread"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.imread" title="Permalink to this definition">¶</a></dt>
<dd><p>Read an image from a file into an array.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>fname</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The image file to read: a filename, a URL or a file-like object opened
in read-binary mode.</p>
</dd>
<dt><strong>format</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The image file format assumed for reading the data. If not
given, the format is deduced from the filename. If nothing can
be deduced, PNG is tried.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>imagedata</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.array</span></code></span></dt>
<dd><p class="first">The image data. The returned array has shape</p>
<ul class="last simple">
<li>(M, N) for grayscale images.</li>
<li>(M, N, 3) for RGB images.</li>
<li>(M, N, 4) for RGBA images.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>Matplotlib can only read PNGs natively. Further image formats are
supported via the optional dependency on Pillow. Note, URL strings
are not compatible with Pillow. Check the <a class="reference external" href="http://pillow.readthedocs.io/en/latest/">Pillow documentation</a>
for more information.</p>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.imsave">
<code class="descclassname">matplotlib.image.</code><code class="descname">imsave</code><span class="sig-paren">(</span><em>fname</em>, <em>arr</em>, <em>vmin=None</em>, <em>vmax=None</em>, <em>cmap=None</em>, <em>format=None</em>, <em>origin=None</em>, <em>dpi=100</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#imsave"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.imsave" title="Permalink to this definition">¶</a></dt>
<dd><p>Save an array as an image file.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>fname</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or PathLike or file-like</span></dt>
<dd><p class="first last">A path or a file-like object to store the image in.
If <em>format</em> is not set, then the output format is inferred from the
extension of <em>fname</em>, if any, and from <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html?highlight=savefig.format#a-sample-matplotlibrc-file"><span class="pre">rcParams["savefig.format"]</span> <span class="pre">=</span> <span class="pre">'png'</span></a></code> otherwise.
If <em>format</em> is set, it determines the output format.</p>
</dd>
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd><p class="first last">The image data. The shape can be one of
MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA).</p>
</dd>
<dt><strong>vmin, vmax</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar, optional</span></dt>
<dd><p class="first last"><em>vmin</em> and <em>vmax</em> set the color scaling for the image by fixing the
values that map to the colormap color limits. If either <em>vmin</em>
or <em>vmax</em> is None, that limit is determined from the <em>arr</em>
min/max value.</p>
</dd>
<dt><strong>cmap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or <a class="reference internal" href="_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap" title="matplotlib.colors.Colormap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Colormap</span></code></a>, optional</span></dt>
<dd><p class="first last">A Colormap instance or registered colormap name. The colormap
maps scalar data to colors. It is ignored for RGB(A) data.
Defaults to <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html?highlight=image.cmap#a-sample-matplotlibrc-file"><span class="pre">rcParams["image.cmap"]</span> <span class="pre">=</span> <span class="pre">'viridis'</span></a></code> ('viridis').</p>
</dd>
<dt><strong>format</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The file format, e.g. 'png', 'pdf', 'svg', ... The behavior when this
is unset is documented under <em>fname</em>.</p>
</dd>
<dt><strong>origin</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'upper', 'lower'}, optional</span></dt>
<dd><p class="first last">Indicates whether the <code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">0)</span></code> index of the array is in the upper
left or lower left corner of the axes. Defaults to <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html?highlight=image.origin#a-sample-matplotlibrc-file"><span class="pre">rcParams["image.origin"]</span> <span class="pre">=</span> <span class="pre">'upper'</span></a></code>
('upper').</p>
</dd>
<dt><strong>dpi</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.pil_to_array">
<code class="descclassname">matplotlib.image.</code><code class="descname">pil_to_array</code><span class="sig-paren">(</span><em>pilImage</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#pil_to_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.pil_to_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a <a class="reference external" href="https://pillow.readthedocs.io/en/latest/reference/Image.html">PIL image</a> and return it as a numpy array.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt>numpy.array</dt>
<dd><p class="first">The array shape depends on the image type:</p>
<ul class="last simple">
<li>(M, N) for grayscale images.</li>
<li>(M, N, 3) for RGB images.</li>
<li>(M, N, 4) for RGBA images.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.thumbnail">
<code class="descclassname">matplotlib.image.</code><code class="descname">thumbnail</code><span class="sig-paren">(</span><em>infile</em>, <em>thumbfile</em>, <em>scale=0.1</em>, <em>interpolation='bilinear'</em>, <em>preview=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#thumbnail"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.thumbnail" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a thumbnail of image in <em>infile</em> with output filename <em>thumbfile</em>.</p>
<p>See <a class="reference internal" href="../gallery/misc/image_thumbnail_sgskip.html"><span class="doc">Image Thumbnail</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>infile</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The image file -- must be PNG, or Pillow-readable if you have <a class="reference external" href="http://python-pillow.org/">Pillow</a>
installed.</p>
</dd>
<dt><strong>thumbfile</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The thumbnail filename.</p>
</dd>
<dt><strong>scale</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float, optional</span></dt>
<dd><p class="first last">The scale factor for the thumbnail.</p>
</dd>
<dt><strong>interpolation</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The interpolation scheme used in the resampling. See the
<em>interpolation</em> parameter of <a class="reference internal" href="_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow" title="matplotlib.axes.Axes.imshow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imshow</span></code></a> for possible values.</p>
</dd>
<dt><strong>preview</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool, optional</span></dt>
<dd><p class="first last">If True, the default backend (presumably a user interface
backend) will be used which will cause a figure to be raised if
<a class="reference internal" href="_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show</span></code></a> is called. If it is False, the figure is
created using <code class="xref py py-obj docutils literal notranslate"><span class="pre">FigureCanvasBase</span></code> and the drawing backend is selected
as <code class="xref py py-obj docutils literal notranslate"><span class="pre">savefig</span></code> would normally do.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>figure</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><a class="reference internal" href="_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure</span></code></a></span></dt>
<dd><p class="first last">The figure instance containing the thumbnail.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
</div>
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