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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=True</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>An image attached to an Axes.</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>ax</strong><span class="classifier"><a class="reference internal" href="axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a></span></dt><dd><p>The axes the image will belong to.</p>
</dd>
<dt><strong>cmap</strong><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>, default: <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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'viridis'</span></code>)</span></dt><dd><p>The Colormap instance or registered colormap name used to map scalar
data to colors.</p>
</dd>
<dt><strong>norm</strong><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><p>Maps luminance to 0-1.</p>
</dd>
<dt><strong>interpolation</strong><span class="classifier">str, default: <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html?highlight=image.interpolation#a-sample-matplotlibrc-file"><span class="pre">rcParams["image.interpolation"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'antialiased'</span></code>)</span></dt><dd><p>Supported values are 'none', 'antialiased', 'nearest', 'bilinear',
'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',
'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',
'sinc', 'lanczos'.</p>
</dd>
<dt><strong>origin</strong><span class="classifier">{'upper', 'lower'}, default: <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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'upper'</span></code>)</span></dt><dd><p>Place the [0, 0] index of the array in the upper left or lower left
corner of the axes. The convention 'upper' is typically used for
matrices and images.</p>
</dd>
<dt><strong>extent</strong><span class="classifier">tuple, optional</span></dt><dd><p>The 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>
</dd>
<dt><strong>filternorm</strong><span class="classifier">bool, default: True</span></dt><dd><p>A parameter for the antigrain image resize filter
(see the antigrain documentation).
If filternorm is set, the filter normalizes integer values and corrects
the rounding errors. It doesn't do anything with the source floating
point values, it corrects only integers according to the rule of 1.0
which means that any sum of pixel weights must be equal to 1.0. So,
the filter function must produce a graph of the proper shape.</p>
</dd>
<dt><strong>filterrad</strong><span class="classifier">float > 0, default: 4</span></dt><dd><p>The filter radius for filters that have a radius parameter, i.e. when
interpolation is one of: 'sinc', 'lanczos' or 'blackman'.</p>
</dd>
<dt><strong>resample</strong><span class="classifier">bool, default: False</span></dt><dd><p>When True, use a full resampling method. When False, only resample when
the output image is larger than the input image.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier"><a class="reference internal" href="artist_api.html#matplotlib.artist.Artist" title="matplotlib.artist.Artist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Artist</span></code></a> properties</span></dt><dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
<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"><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">matplotlib.colors.Normalize</span></code></a> (or subclass thereof)</span></dt><dd><p>The normalizing object which scales data, typically into the
interval <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1]</span></code>.
If <em>None</em>, <em>norm</em> defaults to a <em>colors.Normalize</em> object which
initializes its scaling based on the first data processed.</p>
</dd>
<dt><strong>cmap</strong><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></span></dt><dd><p>The colormap used to map normalized data values to RGBA colors.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<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>Return the image extent as tuple (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">(M, N, 4) uint8 array</span></dt><dd><p>The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">float</span></dt><dd><p>The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong><span class="classifier">Affine2D</span></dt><dd><p>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>Set the image extent.</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>extent</strong><span class="classifier">4-tuple of float</span></dt><dd><p>The position and size of the image as tuple
<code class="docutils literal notranslate"><span class="pre">(left,</span> <span class="pre">right,</span> <span class="pre">bottom,</span> <span class="pre">top)</span></code> in data coordinates.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>This updates <code class="docutils literal notranslate"><span class="pre">ax.dataLim</span></code>, and, if autoscaling, sets <code class="docutils literal notranslate"><span class="pre">ax.viewLim</span></code>
to tightly fit the image, regardless of <code class="docutils literal notranslate"><span class="pre">dataLim</span></code>. Autoscaling
state is not changed, so following this with <code class="docutils literal notranslate"><span class="pre">ax.autoscale_view()</span></code>
will redo the autoscaling in accord with <code class="docutils literal notranslate"><span class="pre">dataLim</span></code>.</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=True</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#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="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">(M, N, 4) uint8 array</span></dt><dd><p>The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">float</span></dt><dd><p>The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong><span class="classifier">Affine2D</span></dt><dd><p>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>An image attached to a figure.</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>Return the image extent as tuple (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">(M, N, 4) uint8 array</span></dt><dd><p>The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">float</span></dt><dd><p>The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong><span class="classifier">Affine2D</span></dt><dd><p>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>
<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>interpolation</strong><span class="classifier">{'nearest', 'bilinear'}, default: 'nearest'</span></dt><dd></dd>
<dt><strong>**kwargs</strong></dt><dd><p>All other keyword arguments are identical to those of <a class="reference internal" href="#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AxesImage</span></code></a>.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<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>Return the image extent as tuple (left, right, bottom, top).</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.is_grayscale">
<em class="property">property </em><code class="descname">is_grayscale</code><a class="headerlink" href="#matplotlib.image.NonUniformImage.is_grayscale" title="Permalink to this definition">¶</a></dt>
<dd></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">(M, N, 4) uint8 array</span></dt><dd><p>The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">float</span></dt><dd><p>The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong><span class="classifier">Affine2D</span></dt><dd><p>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">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"><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> or str or None</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>
<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>x, y</strong><span class="classifier">1D array-like</span></dt><dd><p>Monotonic arrays of shapes (N,) and (M,), respectively, specifying
pixel centers.</p>
</dd>
<dt><strong>A</strong><span class="classifier">array-like</span></dt><dd><p>(M, N) ndarray or masked array of values to be colormapped, or
(M, N, 3) RGB array, or (M, N, 4) RGBA array.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</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">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">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">{'nearest', 'bilinear'} or None</span></dt><dd><p>If None, use <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html?highlight=image.interpolation#a-sample-matplotlibrc-file"><span class="pre">rcParams["image.interpolation"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'antialiased'</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"><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> or None</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>
<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>ax</strong><span class="classifier"><a class="reference internal" href="axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a></span></dt><dd><p>The axes the image will belong to.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">1D array-like, optional</span></dt><dd><p>Monotonic arrays of length N+1 and M+1, respectively, specifying
rectangle boundaries. If not given, will default to
<code class="docutils literal notranslate"><span class="pre">range(N</span> <span class="pre">+</span> <span class="pre">1)</span></code> and <code class="docutils literal notranslate"><span class="pre">range(M</span> <span class="pre">+</span> <span class="pre">1)</span></code>, respectively.</p>
</dd>
<dt><strong>A</strong><span class="classifier">array-like</span></dt><dd><p>The data to be color-coded. The interpretation depends on the
shape:</p>
<ul class="simple">
<li>(M, N) ndarray or masked array: values to be colormapped</li>
<li>(M, N, 3): RGB array</li>
<li>(M, N, 4): RGBA array</li>
</ul>
</dd>
<dt><strong>cmap</strong><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>, default: <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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'viridis'</span></code>)</span></dt><dd><p>The Colormap instance or registered colormap name used to map
scalar data to colors.</p>
</dd>
<dt><strong>norm</strong><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><p>Maps luminance to 0-1.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier"><a class="reference internal" href="artist_api.html#matplotlib.artist.Artist" title="matplotlib.artist.Artist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Artist</span></code></a> properties</span></dt><dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
<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.is_grayscale">
<em class="property">property </em><code class="descname">is_grayscale</code><a class="headerlink" href="#matplotlib.image.PcolorImage.is_grayscale" title="Permalink to this definition">¶</a></dt>
<dd></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">(M, N, 4) uint8 array</span></dt><dd><p>The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong><span class="classifier">float</span></dt><dd><p>The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong><span class="classifier">Affine2D</span></dt><dd><p>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">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>
<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>x, y</strong><span class="classifier">1D array-like, optional</span></dt><dd><p>Monotonic arrays of length N+1 and M+1, respectively, specifying
rectangle boundaries. If not given, will default to
<code class="docutils literal notranslate"><span class="pre">range(N</span> <span class="pre">+</span> <span class="pre">1)</span></code> and <code class="docutils literal notranslate"><span class="pre">range(M</span> <span class="pre">+</span> <span class="pre">1)</span></code>, respectively.</p>
</dd>
<dt><strong>A</strong><span class="classifier">array-like</span></dt><dd><p>The data to be color-coded. The interpretation depends on the
shape:</p>
<ul class="simple">
<li>(M, N) ndarray or masked array: values to be colormapped</li>
<li>(M, N, 3): RGB array</li>
<li>(M, N, 4): RGBA array</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</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 <em>images</em> 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">list of Images</span></dt><dd><p>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"><a class="reference internal" href="backend_bases_api.html#matplotlib.backend_bases.RendererBase" title="matplotlib.backend_bases.RendererBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RendererBase</span></code></a></span></dt><dd></dd>
<dt><strong>magnification</strong><span class="classifier">float, default: 1</span></dt><dd><p>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>image</strong><span class="classifier">uint8 3d array</span></dt><dd><p>The composited RGBA image.</p>
</dd>
<dt><strong>offset_x, offset_y</strong><span class="classifier">float</span></dt><dd><p>The (left, bottom) offset where the composited image should be placed
in the output figure.</p>
</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">str or file-like</span></dt><dd><p>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">str, optional</span></dt><dd><p>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><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="(in NumPy v1.19)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.array</span></code></a></dt><dd><p>The image data. The returned array has shape</p>
<ul class="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.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>, <em>*</em>, <em>metadata=None</em>, <em>pil_kwargs=None</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">str or path-like or file-like</span></dt><dd><p>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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'png'</span></code>) otherwise.
If <em>format</em> is set, it determines the output format.</p>
</dd>
<dt><strong>arr</strong><span class="classifier">array-like</span></dt><dd><p>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">float, optional</span></dt><dd><p><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">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>, default: <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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'viridis'</span></code>)</span></dt><dd><p>A Colormap instance or registered colormap name. The colormap
maps scalar data to colors. It is ignored for RGB(A) data.</p>
</dd>
<dt><strong>format</strong><span class="classifier">str, optional</span></dt><dd><p>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">{'upper', 'lower'}, default: <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></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'upper'</span></code>)</span></dt><dd><p>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.</p>
</dd>
<dt><strong>dpi</strong><span class="classifier">float</span></dt><dd><p>The DPI to store in the metadata of the file. This does not affect the
resolution of the output image. Depending on file format, this may be
rounded to the nearest integer.</p>
</dd>
<dt><strong>metadata</strong><span class="classifier">dict, optional</span></dt><dd><p>Metadata in the image file. The supported keys depend on the output
format, see the documentation of the respective backends for more
information.</p>
</dd>
<dt><strong>pil_kwargs</strong><span class="classifier">dict, optional</span></dt><dd><p>Keyword arguments passed to <a class="reference external" href="https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.save" title="(in Pillow (PIL Fork) v7.2.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PIL.Image.Image.save</span></code></a>. If the 'pnginfo'
key is present, it completely overrides <em>metadata</em>, including the
default 'Software' key.</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 int 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>The array shape depends on the image type:</p>
<ul class="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">str or file-like</span></dt><dd><p>The image file. Matplotlib relies on <a class="reference external" href="https://python-pillow.org/">Pillow</a> for image reading, and
thus supports a wide range of file formats, including PNG, JPG, TIFF
and others.</p>
</dd>
<dt><strong>thumbfile</strong><span class="classifier">str or file-like</span></dt><dd><p>The thumbnail filename.</p>
</dd>
<dt><strong>scale</strong><span class="classifier">float, default: 0.1</span></dt><dd><p>The scale factor for the thumbnail.</p>
</dd>
<dt><strong>interpolation</strong><span class="classifier">str, default: 'bilinear'</span></dt><dd><p>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">bool, default: False</span></dt><dd><p>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 <a class="reference internal" href="backend_bases_api.html#matplotlib.backend_bases.FigureCanvasBase" title="matplotlib.backend_bases.FigureCanvasBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FigureCanvasBase</span></code></a> and the drawing backend is selected
as <a class="reference internal" href="_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.savefig" title="matplotlib.figure.Figure.savefig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure.savefig</span></code></a> 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><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></dt><dd><p>The figure instance containing the thumbnail.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
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