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<li><a class="reference internal" href="#">mne.viz.plot_tfr_topomap</a></li>
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  <div class="section" id="mne-viz-plot-tfr-topomap">
<h1>mne.viz.plot_tfr_topomap<a class="headerlink" href="#mne-viz-plot-tfr-topomap" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="mne.viz.plot_tfr_topomap">
<code class="descclassname">mne.viz.</code><code class="descname">plot_tfr_topomap</code><span class="sig-paren">(</span><em>tfr</em>, <em>tmin=None</em>, <em>tmax=None</em>, <em>fmin=None</em>, <em>fmax=None</em>, <em>ch_type=None</em>, <em>baseline=None</em>, <em>mode='mean'</em>, <em>layout=None</em>, <em>vmin=None</em>, <em>vmax=None</em>, <em>cmap=None</em>, <em>sensors=True</em>, <em>colorbar=True</em>, <em>unit=None</em>, <em>res=64</em>, <em>size=2</em>, <em>cbar_fmt='%1.1e'</em>, <em>show_names=False</em>, <em>title=None</em>, <em>axes=None</em>, <em>show=True</em>, <em>outlines='head'</em>, <em>head_pos=None</em>, <em>contours=6</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/mne-tools/mne-python/blob/maint/0.16/mne/viz/topomap.py#L1050-L1259"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mne.viz.plot_tfr_topomap" title="Permalink to this definition">¶</a></dt>
<dd><p>Plot topographic maps of specific time-frequency intervals of TFR 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 docutils">
<dt><strong>tfr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">AverageTFR</span></dt>
<dd><p class="first last">The AverageTFR object.</p>
</dd>
<dt><strong>tmin</strong> <span class="classifier-delimiter">:</span> <span class="classifier">None | float</span></dt>
<dd><p class="first last">The first time instant to display. If None the first time point
available is used.</p>
</dd>
<dt><strong>tmax</strong> <span class="classifier-delimiter">:</span> <span class="classifier">None | float</span></dt>
<dd><p class="first last">The last time instant to display. If None the last time point available
is used.</p>
</dd>
<dt><strong>fmin</strong> <span class="classifier-delimiter">:</span> <span class="classifier">None | float</span></dt>
<dd><p class="first last">The first frequency to display. If None the first frequency available
is used.</p>
</dd>
<dt><strong>fmax</strong> <span class="classifier-delimiter">:</span> <span class="classifier">None | float</span></dt>
<dd><p class="first last">The last frequency to display. If None the last frequency available is
used.</p>
</dd>
<dt><strong>ch_type</strong> <span class="classifier-delimiter">:</span> <span class="classifier">‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None</span></dt>
<dd><p class="first last">The channel type to plot. For ‘grad’, the gradiometers are collected in
pairs and the RMS for each pair is plotted. If None, then channels are
chosen in the order given above.</p>
</dd>
<dt><strong>baseline</strong> <span class="classifier-delimiter">:</span> <span class="classifier">tuple or list of length 2</span></dt>
<dd><p class="first last">The time interval to apply rescaling / baseline correction. If None do
not apply it. If baseline is (a, b) the interval is between “a (s)” and
“b (s)”. If a is None the beginning of the data is used and if b is
None then b is set to the end of the interval. If baseline is equal to
(None, None) the whole time interval is used.</p>
</dd>
<dt><strong>mode</strong> <span class="classifier-delimiter">:</span> <span class="classifier">‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’ | None</span></dt>
<dd><p class="first">Perform baseline correction by</p>
<blockquote>
<div><ul class="simple">
<li>subtracting the mean baseline power (‘mean’)</li>
<li>dividing by the mean baseline power (‘ratio’)</li>
<li>dividing by the mean baseline power and taking the log (‘logratio’)</li>
<li>subtracting the mean baseline power followed by dividing by the
mean baseline power (‘percent’)</li>
<li>subtracting the mean baseline power and dividing by the standard
deviation of the baseline power (‘zscore’)</li>
<li>dividing by the mean baseline power, taking the log, and dividing
by the standard deviation of the baseline power (‘zlogratio’)</li>
</ul>
</div></blockquote>
<p class="last">If None no baseline correction is applied.</p>
</dd>
<dt><strong>layout</strong> <span class="classifier-delimiter">:</span> <span class="classifier">None | Layout</span></dt>
<dd><p class="first last">Layout instance specifying sensor positions (does not need to be
specified for Neuromag data). If possible, the correct layout file is
inferred from the data; if no appropriate layout file was found, the
layout is automatically generated from the sensor locations.</p>
</dd>
<dt><strong>vmin</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float | callable | None</span></dt>
<dd><p class="first last">The value specifying the lower bound of the color range.
If None, and vmax is None, -vmax is used. Else np.min(data) or in case
data contains only positive values 0. If callable, the output equals
vmin(data). Defaults to None.</p>
</dd>
<dt><strong>vmax</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float | callable | None</span></dt>
<dd><p class="first last">The value specifying the upper bound of the color range. If None, the
maximum value is used. If callable, the output equals vmax(data).
Defaults to None.</p>
</dd>
<dt><strong>cmap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">matplotlib colormap | (colormap, bool) | ‘interactive’ | None</span></dt>
<dd><p class="first last">Colormap to use. If tuple, the first value indicates the colormap to
use and the second value is a boolean defining interactivity. In
interactive mode the colors are adjustable by clicking and dragging the
colorbar with left and right mouse button. Left mouse button moves the
scale up and down and right mouse button adjusts the range. Hitting
space bar resets the range. Up and down arrows can be used to change
the colormap. If None (default), ‘Reds’ is used for all positive data,
otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to
(None, True).</p>
</dd>
<dt><strong>sensors</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool | str</span></dt>
<dd><p class="first last">Add markers for sensor locations to the plot. Accepts matplotlib plot
format string (e.g., ‘r+’). If True (default), circles will be used.</p>
</dd>
<dt><strong>colorbar</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool</span></dt>
<dd><p class="first last">Plot a colorbar.</p>
</dd>
<dt><strong>unit</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str | None</span></dt>
<dd><p class="first last">The unit of the channel type used for colorbar labels.</p>
</dd>
<dt><strong>res</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The resolution of the topomap image (n pixels along each side).</p>
</dd>
<dt><strong>size</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Side length per topomap in inches (only applies when plotting multiple
topomaps at a time).</p>
</dd>
<dt><strong>cbar_fmt</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
<dd><p class="first last">String format for colorbar values.</p>
</dd>
<dt><strong>show_names</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool | callable</span></dt>
<dd><p class="first last">If True, show channel names on top of the map. If a callable is passed,
channel names will be formatted using the callable; e.g., to delete the
prefix ‘MEG ‘ from all channel names, pass the function
<code class="docutils literal notranslate"><span class="pre">lambda</span> <span class="pre">x:</span> <span class="pre">x.replace('MEG</span> <span class="pre">',</span> <span class="pre">'')</span></code>. If <cite>mask</cite> is not None, only
significant sensors will be shown.</p>
</dd>
<dt><strong>title</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str | None</span></dt>
<dd><p class="first last">Plot title. If None (default), no title is displayed.</p>
</dd>
<dt><strong>axes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">instance of Axis | None</span></dt>
<dd><p class="first last">The axes to plot to. If None the axes is defined automatically.</p>
</dd>
<dt><strong>show</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool</span></dt>
<dd><p class="first last">Show figure if True.</p>
</dd>
<dt><strong>outlines</strong> <span class="classifier-delimiter">:</span> <span class="classifier">‘head’ | ‘skirt’ | dict | None</span></dt>
<dd><p class="first last">The outlines to be drawn. If ‘head’, the default head scheme will be
drawn. If ‘skirt’ the head scheme will be drawn, but sensors are
allowed to be plotted outside of the head circle. If dict, each key
refers to a tuple of x and y positions, the values in ‘mask_pos’ will
serve as image mask, and the ‘autoshrink’ (bool) field will trigger
automated shrinking of the positions due to points outside the outline.
Alternatively, a matplotlib patch object can be passed for advanced
masking options, either directly or as a function that returns patches
(required for multi-axis plots). If None, nothing will be drawn.
Defaults to ‘head’.</p>
</dd>
<dt><strong>head_pos</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict | None</span></dt>
<dd><p class="first last">If None (default), the sensors are positioned such that they span the
head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’
(tuple) for what the center and scale of the head should be relative to
the electrode locations.</p>
</dd>
<dt><strong>contours</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int | array of float</span></dt>
<dd><p class="first last">The number of contour lines to draw. If 0, no contours will be drawn.
When an integer, matplotlib ticker locator is used to find suitable
values for the contour thresholds (may sometimes be inaccurate, use
array for accuracy). If an array, the values represent the levels for
the contours. If colorbar=True, the ticks in colorbar correspond to the
contour levels. Defaults to 6.</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>fig</strong> <span class="classifier-delimiter">:</span> <span class="classifier">matplotlib.figure.Figure</span></dt>
<dd><p class="first last">The figure containing the topography.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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