put back plot_topomap

Author: agramfort

mne/time_frequency/tfr.py

@@ -1258,6 +1258,135 @@ def plot_topo(self, picks=None, baseline=None, mode='mean', tmin=None,
         plt_show(show)
         return fig
 
+    def plot_topomap(self, tmin=None, tmax=None, fmin=None, fmax=None,
+                     ch_type=None, baseline=None, mode='mean',
+                     layout=None, vmin=None, vmax=None, cmap=None,
+                     sensors=True, colorbar=True, unit=None, res=64, size=2,
+                     cbar_fmt='%1.1e', show_names=False, title=None,
+                     axes=None, show=True, outlines='head', head_pos=None):
+        """Plot topographic maps of time-frequency intervals of TFR data
+
+        Parameters
+        ----------
+        tmin : None | float
+            The first time instant to display. If None the first time point
+            available is used.
+        tmax : None | float
+            The last time instant to display. If None the last time point
+            available is used.
+        fmin : None | float
+            The first frequency to display. If None the first frequency
+            available is used.
+        fmax : None | float
+            The last frequency to display. If None the last frequency
+            available is used.
+        ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
+            The channel type to plot. For 'grad', the gradiometers are
+            collected in pairs and the RMS for each pair is plotted.
+            If None, then first available channel type from order given
+            above is used. Defaults to None.
+        baseline : tuple or list of length 2
+            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) all the time
+            interval is used.
+        mode : None | 'ratio' | 'zscore' | 'mean' | 'percent' | 'logratio' | 'zlogratio'
+            Do baseline correction with ratio (power is divided by mean
+            power during baseline) or zscore (power is divided by standard
+            deviation of power during baseline after subtracting the mean,
+            power = [power - mean(power_baseline)] / std(power_baseline)),
+            mean simply subtracts the mean power, percent is the same as
+            applying ratio then mean, logratio is the same as mean but then
+            rendered in log-scale, zlogratio is the same as zscore but data
+            is rendered in log-scale first.
+            If None no baseline correction is applied.
+        layout : None | Layout
+            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.
+        vmin : float | callable | None
+            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.
+        vmax : float | callable | None
+            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.
+        cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None
+            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).
+        sensors : bool | str
+            Add markers for sensor locations to the plot. Accepts matplotlib
+            plot format string (e.g., 'r+' for red plusses). If True, a circle
+            will be used (via .add_artist). Defaults to True.
+        colorbar : bool
+            Plot a colorbar.
+        unit : dict | str | None
+            The unit of the channel type used for colorbar label. If
+            scale is None the unit is automatically determined.
+        res : int
+            The resolution of the topomap image (n pixels along each side).
+        size : float
+            Side length per topomap in inches.
+        cbar_fmt : str
+            String format for colorbar values.
+        show_names : bool | callable
+            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 lambda x: x.replace('MEG ', ''). If `mask` is not None,
+            only significant sensors will be shown.
+        title : str | None
+            Title. If None (default), no title is displayed.
+        axes : instance of Axes | None
+            The axes to plot to. If None the axes is defined automatically.
+        show : bool
+            Call pyplot.show() at the end.
+        outlines : 'head' | 'skirt' | dict | None
+            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'.
+        head_pos : dict | None
+            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.
+
+        Returns
+        -------
+        fig : matplotlib.figure.Figure
+            The figure containing the topography.
+        """  # noqa
+        from ..viz import plot_tfr_topomap
+        return plot_tfr_topomap(self, tmin=tmin, tmax=tmax, fmin=fmin,
+                                fmax=fmax, ch_type=ch_type, baseline=baseline,
+                                mode=mode, layout=layout, vmin=vmin, vmax=vmax,
+                                cmap=cmap, sensors=sensors, colorbar=colorbar,
+                                unit=unit, res=res, size=size,
+                                cbar_fmt=cbar_fmt, show_names=show_names,
+                                title=title, axes=axes, show=show,
+                                outlines=outlines, head_pos=head_pos)
+
     def _check_compat(self, tfr):
         """checks that self and tfr have the same time-frequency ranges"""
         assert np.all(tfr.times == self.times)