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<div class="section" id="image">
<h1>image<a class="headerlink" href="#image" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-matplotlib.image">
<span id="matplotlib-image"></span><h2><a class="reference internal" href="#module-matplotlib.image" title="matplotlib.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.image</span></code></a><a class="headerlink" href="#module-matplotlib.image" title="Permalink to this headline">¶</a></h2>
<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="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.get_cursor_data">
<code class="descname">get_cursor_data</code><span class="sig-paren">(</span><em>event</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.AxesImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the cursor data for a given event</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><span class="sig-paren">)</span><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>renderer=None</em><span class="sig-paren">)</span><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.
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>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.AxesImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.set_extent">
<code class="descname">set_extent</code><span class="sig-paren">(</span><em>extent</em><span class="sig-paren">)</span><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=True</em>, <em>**kwargs</em><span class="sig-paren">)</span><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>interp_at_native is a flag that determines whether or not
interpolation should still be applied when the image is
displayed at its native resolution. A common use case for this
is when displaying an image for annotational purposes; it is
treated similarly to Photoshop (interpolation is only used when
displaying the image at non-native resolutions).</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>mouseevent</em><span class="sig-paren">)</span><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><span class="sig-paren">)</span><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-class 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>renderer=None</em><span class="sig-paren">)</span><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.
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>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.BboxImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd></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="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><span class="sig-paren">)</span><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>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.FigureImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.FigureImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>A</em><span class="sig-paren">)</span><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>**kwargs</em><span class="sig-paren">)</span><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><span class="sig-paren">)</span><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>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.NonUniformImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><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>
<p>ACCEPTS: numpy array A or PIL Image</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_cmap">
<code class="descname">set_cmap</code><span class="sig-paren">(</span><em>cmap</em><span class="sig-paren">)</span><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>
<p>ACCEPTS: a colormap or registered colormap name</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><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>s</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_filternorm" title="Permalink to this definition">¶</a></dt>
<dd><p>Set whether the resize filter norms the weights – see
help for imshow</p>
<p>ACCEPTS: 0 or 1</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_filterrad">
<code class="descname">set_filterrad</code><span class="sig-paren">(</span><em>s</em><span class="sig-paren">)</span><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>
<p>ACCEPTS: positive float</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_interpolation">
<code class="descname">set_interpolation</code><span class="sig-paren">(</span><em>s</em><span class="sig-paren">)</span><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"><p class="first"><strong>s</strong> : str, None</p>
<blockquote class="last">
<div><p>Either ‘nearest’, ‘bilinear’, or <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</div></blockquote>
</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>norm</em><span class="sig-paren">)</span><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"><strong>norm</strong> : <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></td>
</tr>
</tbody>
</table>
</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="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>event</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.PcolorImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the cursor data for a given event</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.image.PcolorImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><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>
<p>ACCEPTS: numpy array A or PIL Image</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><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="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"><p class="first"><strong>images</strong> : list of Images</p>
<blockquote>
<div><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 v3.6)"><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>
</div></blockquote>
<p><strong>renderer</strong> : RendererBase instance</p>
<p><strong>magnification</strong> : float</p>
<blockquote>
<div><p>The additional magnification to apply for the renderer in use.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>tuple</strong> : image, offset_x, offset_y</p>
<blockquote class="last">
<div><p>Returns the tuple:</p>
<ul class="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>
</div></blockquote>
</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="headerlink" href="#matplotlib.image.imread" title="Permalink to this definition">¶</a></dt>
<dd><p>Read an image from a file into an array.</p>
<p><em>fname</em> may be a string path, a valid URL, or a Python
file-like object. If using a file object, it must be opened in binary
mode.</p>
<p>If <em>format</em> is provided, will try to read file of that type,
otherwise the format is deduced from the filename. If nothing can
be deduced, PNG is tried.</p>
<p>Return value is a <code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.array</span></code>. For grayscale images, the
return array is MxN. For RGB images, the return value is MxNx3.
For RGBA images the return value is MxNx4.</p>
<p>matplotlib can only read PNGs natively, but if <a class="reference external" href="http://www.pythonware.com/products/pil/">PIL</a> is installed, it will
use it to load the image and return an array (if possible) which
can be used with <a class="reference internal" href="_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><code class="xref py py-func docutils literal notranslate"><span class="pre">imshow()</span></code></a>. Note, URL strings
may not be compatible with PIL. Check the PIL documentation 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="headerlink" href="#matplotlib.image.imsave" title="Permalink to this definition">¶</a></dt>
<dd><p>Save an array as in image file.</p>
<p>The output formats available depend on the backend being used.</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"><p class="first"><strong>fname</strong> : str or file-like</p>
<blockquote>
<div><p>Path string to a filename, or a Python file-like object.
If <em>format</em> is <em>None</em> and <em>fname</em> is a string, the output
format is deduced from the extension of the filename.</p>
</div></blockquote>
<p><strong>arr</strong> : array-like</p>
<blockquote>
<div><p>An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.</p>
</div></blockquote>
<p><strong>vmin, vmax: [ None | scalar ]</strong></p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>cmap</strong> : matplotlib.colors.Colormap, optional</p>
<blockquote>
<div><p>For example, <code class="docutils literal notranslate"><span class="pre">cm.viridis</span></code>. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the
<code class="docutils literal notranslate"><span class="pre">image.cmap</span></code> rcParam.</p>
</div></blockquote>
<p><strong>format</strong> : str</p>
<blockquote>
<div><p>One of the file extensions supported by the active backend. Most
backends support png, pdf, ps, eps and svg.</p>
</div></blockquote>
<p><strong>origin</strong> : [ ‘upper’ | ‘lower’ ]</p>
<blockquote>
<div><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. Defaults to the
<code class="docutils literal notranslate"><span class="pre">image.origin</span></code> rcParam.</p>
</div></blockquote>
<p><strong>dpi</strong> : int</p>
<blockquote class="last">
<div><p>The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.</p>
</div></blockquote>
</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="headerlink" href="#matplotlib.image.pil_to_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a PIL image and return it as a numpy array.</p>
<p>Grayscale images are returned as <code class="docutils literal notranslate"><span class="pre">(M,</span> <span class="pre">N)</span></code> arrays. RGB images are
returned as <code class="docutils literal notranslate"><span class="pre">(M,</span> <span class="pre">N,</span> <span class="pre">3)</span></code> arrays. RGBA images are returned as <code class="docutils literal notranslate"><span class="pre">(M,</span> <span class="pre">N,</span>
<span class="pre">4)</span></code> arrays.</p>
</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="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>
<blockquote>
<div><dl class="docutils">
<dt><em>infile</em> the image file – must be PNG or Pillow-readable if you</dt>
<dd>have <a class="reference external" href="http://python-pillow.org/">Pillow</a> installed</dd>
<dt><em>thumbfile</em></dt>
<dd>the thumbnail filename</dd>
<dt><em>scale</em></dt>
<dd>the scale factor for the thumbnail</dd>
<dt><em>interpolation</em></dt>
<dd>the interpolation scheme used in the resampling</dd>
<dt><em>preview</em></dt>
<dd>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-func docutils literal notranslate"><span class="pre">show()</span></code></a> is called. If it is False,
a pure image backend will be used depending on the extension,
‘png’->FigureCanvasAgg, ‘pdf’->FigureCanvasPdf,
‘svg’->FigureCanvasSVG</dd>
</dl>
</div></blockquote>
<p>See examples/misc/image_thumbnail.py.</p>
<a class="reference internal" href="../gallery/misc/image_thumbnail_sgskip.html#sphx-glr-gallery-misc-image-thumbnail-sgskip-py"><span class="std std-ref">Image Thumbnail</span></a><p>Return value is the figure instance containing the thumbnail</p>
</dd></dl>
</div>
</div>
</div>
</div>
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