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Csddlm}|dk st|dkrB|dk r>t|dkr8dnd}nd}t|}||}||}|dkrjd}d }|dk r||}| dk r| |} ||}| dks|| krt|}| }n | |\}}t|t |||| d|d|||||d \}}| d krdn| } | dk r| | | rL|j ||d }|j ||d rLt|||_|rVdnd}| |dt|t|f|}|j|d|d|dt||tt|}|dkrtt|t|}|rt||nt|} |j|| |ddS)z Plot images.rNautorTFrTr()ZyvalsrrrrrrrrFrZChannelszChannel (index)z (%d channel%srr)ylabelrr)yticks yticklabels)rrrFrXrrVrrr2r(rVrrGrrZCBr/rrXZarange intersect1dround get_yticksZastypeintr)!rrGrrrrrrrrrrrrrrrrrrrQrr]r\rrrZt_endcbarrktrlrmrCrCrHr]sh            r)rr0cCs6t|||||||||| | | | d| ||||||||dS)uPlot evoked data using butterfly plots. Left click to a line shows the channel name. Selecting an area by clicking and holding left mouse button plots a topographic map of the painted area. .. note:: If bad channels are not excluded they are shown in red. Parameters ---------- evoked : instance of Evoked The evoked data. %(picks_all)s exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. unit : bool Scale plot with channel (SI) unit. show : bool Show figure if True. ylim : dict | None Y limits for plots (after scaling has been applied). e.g. ylim = dict(eeg=[-20, 20]) Valid keys are eeg, mag, grad, misc. If None, the ylim parameter for each channel equals the pyplot default. xlim : 'tight' | tuple | None X limits for plots. %(proj_plot)s hline : list of float | None The values at which to show an horizontal line. units : dict | None The units of the channel types used for axes labels. If None, defaults to ``dict(eeg='µV', grad='fT/cm', mag='fT')``. scalings : dict | None The scalings of the channel types to be applied for plotting. If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. titles : dict | None The titles associated with the channels. If None, defaults to ``dict(eeg='EEG', grad='Gradiometers', mag='Magnetometers')``. axes : instance of Axes | list | None The axes to plot to. If list, the list must be a list of Axes of the same length as the number of channel types. If instance of Axes, there must be only one channel type plotted. gfp : bool | 'only' Plot the global field power (GFP) or the root mean square (RMS) of the data. For MEG data, this will plot the RMS. For EEG, it plots GFP, i.e. the standard deviation of the signal across channels. The GFP is equivalent to the RMS of an average-referenced signal. - ``True`` Plot GFP or RMS (for EEG and MEG, respectively) and traces for all channels. - ``'only'`` Plot GFP or RMS (for EEG and MEG, respectively), and omit the traces for individual channels. The color of the GFP/RMS trace will be green if ``spatial_colors=False``, and black otherwise. .. versionchanged:: 0.23 Plot GFP for EEG instead of RMS. Label RMS traces correctly as such. window_title : str | None The title to put at the top of the figure. spatial_colors : bool If True, the lines are color coded by mapping physical sensor coordinates into color values. Spatially similar channels will have similar colors. Bad channels will be dotted. If False, the good channels are plotted black and bad channels red. Defaults to False. zorder : str | callable Which channels to put in the front or back. Only matters if ``spatial_colors`` is used. If str, must be ``std`` or ``unsorted`` (defaults to ``unsorted``). If ``std``, data with the lowest standard deviation (weakest effects) will be put in front so that they are not obscured by those with stronger effects. If ``unsorted``, channels are z-sorted as in the evoked instance. If callable, must take one argument: a numpy array of the same dimensionality as the evoked raw data; and return a list of unique integers corresponding to the number of channels. .. versionadded:: 0.13.0 selectable : bool Whether to use interactive features. If True (default), it is possible to paint an area to draw topomaps. When False, the interactive features are disabled. Disabling interactive features reduces memory consumption and is useful when using ``axes`` parameter to draw multiaxes figures. .. versionadded:: 0.13.0 noise_cov : instance of Covariance | str | None Noise covariance used to whiten the data while plotting. Whitened data channel names are shown in italic. Can be a string to load a covariance from disk. See also :meth:`mne.Evoked.plot_white` for additional inspection of noise covariance properties when whitening evoked data. For data processed with SSS, the effective dependence between magnetometers and gradiometers may introduce differences in scaling, consider using :meth:`mne.Evoked.plot_white`. .. versionadded:: 0.16.0 %(time_unit)s .. versionadded:: 0.16 %(sphere_topomap_auto)s highlight : array-like of float, shape(2,) | array-like of float, shape (n, 2) | None Segments of the data to highlight by means of a light-yellow background color. Can be used to put visual emphasis on certain time periods. The time periods must be specified as ``array-like`` objects in the form of ``(t_start, t_end)`` in the unit given by the ``time_unit`` parameter. Multiple time periods can be specified by passing an ``array-like`` object of individual time periods (e.g., for 3 time periods, the shape of the passed object would be ``(3, 2)``. If ``None``, no highlighting is applied. .. versionadded:: 1.1 %(verbose)s Returns ------- fig : instance of matplotlib.figure.Figure Figure containing the butterfly plots. See Also -------- mne.viz.plot_evoked_white r)rrrrrrrrrrrrrJrrrrrrprrrxrr)rrrrrrrrrrrrrJrrrrprrrrxrr0rCrCrH plot_evokeds4rug= ףp=?rrdrcCs t|ttfkr|g}t|dd}t|dk}|rB|}|}d}n |}|}d}t|ttfrvt|t|krtdnf|dkr|rdgt }nt }|t|t|d}|dt|}nt|t std t |g}t |||||||||| || ||| | | |||d S) ae Plot 2D topography of evoked responses. Clicking on the plot of an individual sensor opens a new figure showing the evoked response for the selected sensor. Parameters ---------- evoked : list of Evoked | Evoked The evoked response to plot. layout : instance of Layout | None Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout is inferred from the data. layout_scale : float Scaling factor for adjusting the relative size of the layout on the canvas. color : list of color | color | None Everything matplotlib accepts to specify colors. If not list-like, the color specified will be repeated. If None, colors are automatically drawn. border : str Matplotlib borders style to be used for each sensor plot. ylim : dict | None Y limits for plots (after scaling has been applied). The value determines the upper and lower subplot limits. e.g. ylim = dict(eeg=[-20, 20]). Valid keys are eeg, mag, grad, misc. If None, the ylim parameter for each channel type is determined by the minimum and maximum peak. scalings : dict | None The scalings of the channel types to be applied for plotting. If None,` defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. title : str Title of the figure. proj : bool | 'interactive' If true SSP projections are applied before display. If 'interactive', a check box for reversible selection of SSP projection vectors will be shown. vline : list of float | None The values at which to show a vertical line. fig_background : None | ndarray A background image for the figure. This must work with a call to plt.imshow. Defaults to None. merge_grads : bool Whether to use RMS value of gradiometer pairs. Only works for Neuromag data. Defaults to False. legend : bool | int | str | tuple If True, create a legend based on evoked.comment. If False, disable the legend. Otherwise, the legend is created and the parameter value is passed as the location parameter to the matplotlib legend call. It can be an integer (e.g. 0 corresponds to upper right corner of the plot), a string (e.g. 'upper right'), or a tuple (x, y coordinates of the lower left corner of the legend in the axes coordinate system). See matplotlib documentation for more details. axes : instance of matplotlib Axes | None Axes to plot into. If None, axes will be created. background_color : color Background color. Typically 'k' (black) or 'w' (white; default). .. versionadded:: 0.15.0 noise_cov : instance of Covariance | str | None Noise covariance used to whiten the data while plotting. Whitened data channel names are shown in italic. Can be a string to load a covariance from disk. .. versionadded:: 0.16.0 exclude : list of str | 'bads' Channels names to exclude from the plot. If 'bads', the bad channels are excluded. By default, exclude is set to 'bads'. show : bool Show figure if True. Returns ------- fig : instance of matplotlib.figure.Figure Images of evoked responses at sensor locations. background_color)namer0rr-z.)rrcs&g|]\}}|tdkr|qSrrf)rDrbr)boolsrCrHrI3srZ proj_boolsrJrrcs$g|]}d|kr|qS)rrCr*)r^rrsrCrHrI<sNrrrr)rr rZadd_proj apply_projrrJrXrrPZ set_ydatarset_datarerf) r^rrrZ new_evokedrGr\rgr^rMZdirC)rr^rrsrHr0s     rc4 svddlmm}m} ddlmt|j\}} t|t t |dft |t t fsV|g}t |D]&\} } | || d| ddd || <q^ d d t jd D} | ddd D]}|qjfdddttj||dd\}}}}|rtdfdd t||D}dPdd}t|dkr@d}d}nd}d}||}t|dkrf||fn|f}t|t t tjdfd|dkrj||dddd|fd\}}n t|}t |jD]\}}t|jd|fq|j|krtd|d|j|jdj}|dkr8d|ddd }| |t!|dkoJ|dk|dkoZ|dkfr||d|}|d d}n>|dkr|dkr|d|df}|dddf}nt"d!t#d"} |rd#| d$<fd%d t$dd&t|D}!t#d'd}"t||||!}#|s,|dj%d|d(d)gd|d*n|t||D]p\\}$}%}|j%| |dj&|%j'd+d&d,d-D]}&|j(|&d.d/dd0qd|j)d1| |$t|%t*t|%fd2q6|#D]0\}'}}(})d}*|D]\}+},|(|+}-d3+|dkr| |+n|+|-|dkrd4nd5}.|dd }/||*}|,|dkr(|.nd6|/|'j&|,}0||0|-d7}1|+d$krVd8n|+}2|j%| |1|dkrp|/n|.|dkr|)n|"|2d&d9|j)d:|d;d<| d| d gd=d>|j(dd.d/d?d0|dkr|*d7}*qq|d}|dkr |j-d@dAt.dBdCdDnV|j-d@t.dEdCdFt.dGdHdIg|ddJdKdLg|ddM}3|rTdN|3dO<|j/f|3|j01t2||S)QuPlot whitened evoked response. Plots the whitened evoked response and the whitened GFP as described in :footcite:`EngemannGramfort2015`. This function is especially useful for investigating noise covariance properties to determine if data are properly whitened (e.g., achieving expected values in line with model assumptions, see Notes below). Parameters ---------- evoked : instance of mne.Evoked The evoked response. noise_cov : list | instance of Covariance | path-like The noise covariance. Can be a string to load a covariance from disk. show : bool Show figure if True. %(rank_none)s time_unit : str The units for the time axis, can be "ms" or "s" (default). .. versionadded:: 0.16 %(sphere_topomap_auto)s axes : list | None List of axes to plot into. .. versionadded:: 0.21.0 %(verbose)s Returns ------- fig : instance of matplotlib.figure.Figure The figure object containing the plot. See Also -------- mne.Evoked.plot Notes ----- If baseline signals match the assumption of Gaussian white noise, values should be centered at 0, and be within 2 standard deviations (±1.96) for 95%% of the time points. For the global field power (GFP), we expect it to fluctuate around a value of 1. If one single covariance object is passed, the GFP panel (bottom) will depict different sensor types. If multiple covariance objects are passed as a list, the left column will display the whitened evoked responses for each channel based on the whitener from the noise covariance that has the highest log-likelihood. The left column will depict the whitened GFPs based on each estimator separately for each sensor type. Instead of numbers of channels the GFP display shows the estimated rank. Note. The rank estimation will be printed by the logger (if ``verbose=True``) for each noise covariance estimator that is passed. References ---------- .. [1] Engemann D. and Gramfort A. (2015) Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, vol. 108, 328-342, NeuroImage. r) whiten_evoked Covariance _ensure_covrNz path-likez noise_cov[]F)r0cSsg|]\}}|ds|qS)ZactiverC)rDrgrrCrCrHrIsz%plot_evoked_white..rr1r)Zref_megr)rzESSS has been applied to data. Showing mag and grad whitening jointly.cs g|]\}}|d|dqS)N)rrankrC)rDcovr.)rrrCrHrIscSs&tj|ddd|dkr t|n|S)zWhitened Global Field Power. The MNE inverse solver assumes zero mean whitened data as input. Therefore, a chi^2 statistic will be best to detect model violations. rrr}N)rVsumrX)r`rrCrCrH whitened_gfpsz'plot_evoked_white..whitened_gfprrJTg!@g@)ZsharexZshareyrvz axes.flat[%d]zaxes must have shape z, got zfWhitened evoked (left, best estimator = "%s") and global field power (right, comparison of estimators)r|Z empiricalzWrong axes inputsrzMEG (combined)Zmegcsg|]}j|qSrC)cmZSet1r*rrCrHrIsr0rq\(\\(\?)rrJrrrr-)rqlw)rrror4)rqr<rr9r:z {0} ({2}{1})zrank rzWhitened GFP, method = "%s")rrm)rarqrrrzGFP ($\chi^2$))r )rrkrrr upper right)g\(\?? r)rbbox_to_anchorpropr)rrgGz?g= ףp=?gףp= ?g)\(?gp= ף?g ףp= ?)rrg\(\?r)N)3rrrrrrr'rr1rrIrrr rZdel_projZ pick_typesrrr,rrXrVrrrZflatrr rrgetrrr rlinspacerOrrrYrr/rMrrrr rerfr)4rrrrrrxrJr0rrrcirdZ passive_idxrgZ n_ch_usedZ rank_listZ picks_listZhas_sssZ evokeds_whiter n_columnsZ n_extra_rowZn_rowsZ want_shaperrrGrrZ axes_evokedZax_gfpZtitles_rZ ch_colorsZiter_gfprrrZ evoked_whiteZrank_rqr+rZ sub_picksZ this_rankrrarrZcolor_chr^rC)rrrrHplot_evoked_whiteFs?                          rcCsHddlm}ddlm}|||\}}t|d|jf|dkrP|dd\} } n|} ~| j} t |j ddgd| gg} | j ddddd | j ddddd t} d }| | j|j |d d d gdd| | j|j |dddd gdd| j| dd| ddddd|jdk r,| |j| j| |ddt|| S)a%Plot a data SNR estimate. Parameters ---------- evoked : instance of Evoked The evoked instance. This should probably be baseline-corrected. inv : instance of InverseOperator The minimum-norm inverse operator. show : bool Show figure if True. axes : instance of Axes | None The axes to plot into. .. versionadded:: 0.21.0 %(verbose)s Returns ------- fig : instance of matplotlib.figure.Figure The figure object containing the plot. Notes ----- The bluish green line is the SNR determined by the GFP of the whitened evoked data. The orange line is the SNR estimated based on the mismatch between the data and the data re-estimated from the regularized inverse. .. versionadded:: 0.9.0 rNr) estimate_snrrr1r-:)rqlsr)ZInversez Whitened GFPrdg333333?r0)rqrg?ZSNRzTime (s))rrrkrzEstimation methodr:)rrZ minimum_normrr1rrrrV concatenaterrrrYrrrOrcommentrrr)rinvrrJr0rrZsnrZsnr_estrrGrZlimshslabelsrCrCrHplot_snr_estimatesH     rpeaksrc%s\ddlm}|dk r0t|ts0tdt|f|dkr>tn|}t|dd|j \|d<} |dkrptn|}d} ddd h} ||fD]2t fd d | Drt d d t| qd|ksd|kr>d|krd|kst dt|dgd|dkr d} nt|trtt|d} tt|d| d} |dd} |dd}|| krvt d| d|dtd| d|}| r|| dkr|d|d<|d<t|||dd}|jtddd}t|dkrt| rtdt}|D]j|fddtd D}tt|jdd!dkrNtd"|t|||||t|d#q|St||dd$}~t|d|\} }| st t|d%d&\}}n6|d|d=|ddd'}|dd'}|d=|j!tdddd(ddddddddd)ddd*}|"|t#|fdd+gd,|$}%d-|dk r&dd.d/}|d-krn|}|j'd0d0||j(d1d1d2|)d3|d4d5}|*}+\}}|d6k}|rdn|}t,|j-|||\}}t|tt.j/fst0|||\}}nd}|d7krtd8d9nt}|"||d:d;|d:<||d4<|j1f||ddd<||d=drdd>l2m3} |4d|j5|dj6d|d?}!t|tt.j/fr|!7|n|dkr| j8d@dA}||!_9|!:| s|j;dBdCdD|dk rdEndFdGfdHdt<||D}"|"D]}#j=|#q|D]}$j>|$dIdJdKdLddMq2t?|S)Na} Plot evoked data as butterfly plot and add topomaps for time points. .. note:: Axes to plot in can be passed by the user through ``ts_args`` or ``topomap_args``. In that case both ``ts_args`` and ``topomap_args`` axes have to be used. Be aware that when the axes are provided, their position may be slightly modified. Parameters ---------- evoked : instance of Evoked The evoked instance. times : float | array of float | "auto" | "peaks" The time point(s) to plot. If ``"auto"``, 5 evenly spaced topographies between the first and last time instant will be shown. If ``"peaks"``, finds time points automatically by checking for 3 local maxima in Global Field Power. Defaults to ``"peaks"``. title : str | None The title. If ``None``, suppress printing channel type title. If an empty string, a default title is created. Defaults to ''. If custom axes are passed make sure to set ``title=None``, otherwise some of your axes may be removed during placement of the title axis. %(picks_all)s exclude : None | list of str | 'bads' Channels names to exclude from being shown. If ``'bads'``, the bad channels are excluded. Defaults to ``None``. show : bool Show figure if ``True``. Defaults to ``True``. ts_args : None | dict A dict of ``kwargs`` that are forwarded to :meth:`mne.Evoked.plot` to style the butterfly plot. If they are not in this dict, the following defaults are passed: ``spatial_colors=True``, ``zorder='std'``. ``show`` and ``exclude`` are illegal. If ``None``, no customizable arguments will be passed. Defaults to ``None``. topomap_args : None | dict A dict of ``kwargs`` that are forwarded to :meth:`mne.Evoked.plot_topomap` to style the topomaps. If it is not in this dict, ``outlines='skirt'`` will be passed. ``show``, ``times``, ``colorbar`` are illegal. If ``None``, no customizable arguments will be passed. Defaults to ``None``. Returns ------- fig : instance of matplotlib.figure.Figure | list The figure object containing the plot. If ``evoked`` has multiple channel types, a list of figures, one for each channel type, is returned. Notes ----- .. versionadded:: 0.12.0 rNz)ts_args must be dict or None, got type %srriFrrrc3s|]}|kVqdSrrC)rDr`)argsrCrHrsz$plot_evoked_joint..zDon't pass any of {} as *_args., rJzLIf one of `ts_args` and `topomap_args` contains 'axes', the other must, too.r)Nrr&Trz(topomap_args["proj"] (default True, got z0) must match ts_args["proj"] (default True, got rztopomap_args["proj"])TFrr)r )uniqueZ only_data_chszoCurrently, passing axes manually (via `ts_args` or `topomap_args`) is not supported for multiple channel types.cs&g|]}t|krd|qSr)r r)rrrCrHrIsz%plot_evoked_joint..Znchan)rzsPossibly infinite loop due to channel selection problem. This should never happen! 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Nz - u − ru−r)replace startswith)rirCrCrH_ascii_minus_to_unicodes   r/cgs ddlmddlm}m}t||r,|g}t|ttfr|}t }dd|D}t t ||D]F\}\}}t |d}|r| |dkr|}n|d|}|||<qd~t|t std t|jt|}|D]H\}}t|d d t||r|g||<||D]}t||d d qqt|g} || ~ t|}!||!dd}"|"j}#|"j}$t|$t||"j\}}#|#d|#d}%}&| dkrt } |dkr|%dkr|&krnndgng}t|ttfddt|}'t|$|dd\}}(t|"j|})ddt |$|ddD}*t!|$|tfdd|*Dg}+t"||+}| dkrjt#|dk} t|ddt fddt#|dkr|dk rt$dt#||t|t o|dk},|dkrt#|dkr|,sd}t%|}-t&||(|'|)|d}|,r,d} t#|d krt'd!dgt#|*}nB|dkrJfd"d#|*D}n$t|j(rn|g}t)|t#|*d$t#|*dkrt'd%t}.t |*|D]x\}/}0|/}1t|"j|1}2|(r|/n|1}1t&||(|1|2dd}3|.*t+||1|||||||| | | | |0|3||d&q|.St,|||||!\}4}5}6}7}8}9t#|*dksBt-|*d}/t.d'|/}:t.d(|/};|}j3d-d.}?||||||||| | | | | |||ffd/d0 }@t4|$}Ad1}Bd2}C|Aj5d3}D|Aj5d4}E|Aj5dddf6}F|Aj5dddf7|D}G|Aj5dddf6}H|Aj5dddf7|E}Id5d|B|G|F}Jd6d|C|I|H}K|Aj5dddf|F|J|Bd7|Aj5dddf<|Aj5dddf|H|K|Cd7|Aj5dddf<|Aj5dddf}L|Aj5dddf}M|L7}N|M6}Ot8t9t9|L|Nk|N|L|Dkt9|M|Ok|O|M|Ek}P|N|D}G|G|L6|D}Q|Q|Qd}R|Pr|Aj5ddddgf|R9<t|>|$|A|@|?d8d8d9ddd: }t|}~$|,rdn|-}St}Tt}Ut ||D]\}1\}0}t }Vt }W|!D]p}||}Xt:||,d;}Yt;|sTt#|XdkrP|Ynd}Yt<|X||S|1|;|Yd<}Z|Zd|V|<|Ydk r|Zd|W|<q|T=|V|U=|Wq~t}[|TD]*}\t|\D]}]|[=|]tj>qq|UD]}\|[*t|\qt?|[}[t@|[dk}^| A|/ddg\}_}`tB|[|_|`|^\}a}b~[|,rt|T=d=d>|VD|U=d?d>|WD|==d@t ||T|UD]t\\}0}}c}d|,r|=|}|dkrgn|#}etC|0|c|!|e|d|4||^|, |,o|dk}ftD|0|a|b|| | ||%|&|:|f| q| rHt| tjEtFt tdfdAdBtG|"j|` or :class:`floats ` indicating steps or percentiles (respectively) along the colormap. If ``cmap`` is ``None``, list elements or dict values of ``colors`` must be :class:`ints ` or valid :doc:`matplotlib colors `; lists are cycled through sequentially, while dicts must have keys matching the keys or conditions of an ``evokeds`` dict (see Notes for details). If ``None``, the current :doc:`matplotlib color cycle ` is used. Defaults to ``None``. linestyles : list | dict | None Styles to use when plotting the ERP/F lines. If a :class:`list` or :class:`dict`, elements must be valid :doc:`matplotlib linestyles `. Lists are cycled through sequentially; dictionaries must have keys matching the keys or conditions of an ``evokeds`` dict (see Notes for details). If ``None``, all lines will be solid. Defaults to ``None``. styles : dict | None Dictionary of styles to use when plotting ERP/F lines. Keys must match keys or conditions of ``evokeds``, and values must be a :class:`dict` of legal inputs to :func:`matplotlib.pyplot.plot`. Those values will be passed as parameters to the line plot call of the corresponding condition, overriding defaults (e.g., ``styles={"Aud/L": {"linewidth": 3}}`` will set the linewidth for "Aud/L" to 3). As with ``colors`` and ``linestyles``, keys matching conditions in ``/``-separated ``evokeds`` keys are supported (see Notes for details). cmap : None | str | tuple | instance of matplotlib.colors.Colormap Colormap from which to draw color values when plotting the ERP/F lines and confidence bands. If not ``None``, ints or floats in the ``colors`` parameter are mapped to steps or percentiles (respectively) along the colormap. If ``cmap`` is a :class:`str`, it will be passed to :func:`matplotlib.cm.get_cmap`; if ``cmap`` is a tuple, its first element will be used as a string to label the colorbar, and its second element will be passed to :func:`matplotlib.cm.get_cmap` (unless it is already an instance of :class:`~matplotlib.colors.Colormap`). .. versionchanged:: 0.19 Support for passing :class:`~matplotlib.colors.Colormap` instances. vlines : "auto" | list of float A list in seconds at which to plot dashed vertical lines. If "auto" and the supplied data includes 0, it is set to [0.] and a vertical bar is plotted at time 0. If an empty list is passed, no vertical lines are plotted. ci : float | bool | callable | None Confidence band around each ERP/F time series. If ``False`` or ``None`` no confidence band is drawn. If :class:`float`, ``ci`` must be between 0 and 1, and will set the threshold for a bootstrap (single plot)/parametric (when ``axes=='topo'``) estimation of the confidence band; ``True`` is equivalent to setting a threshold of 0.95 (i.e., the 95%% confidence band is drawn). If a callable, it must take a single array (n_observations × n_times) as input and return upper and lower confidence margins (2 × n_times). Defaults to ``True``. truncate_yaxis : bool | 'auto' Whether to shorten the y-axis spine. If 'auto', the spine is truncated at the minimum and maximum ticks. If ``True``, it is truncated at the multiple of 0.25 nearest to half the maximum absolute value of the data. If ``truncate_xaxis=False``, only the far bound of the y-axis will be truncated. Defaults to 'auto'. truncate_xaxis : bool Whether to shorten the x-axis spine. If ``True``, the spine is truncated at the minimum and maximum ticks. If ``truncate_yaxis=False``, only the far bound of the x-axis will be truncated. Defaults to ``True``. ylim : dict | None Y-axis limits for plots (after scaling has been applied). :class:`dict` keys should match channel types; valid keys are eeg, mag, grad, misc (example: ``ylim=dict(eeg=[-20, 20])``). If ``None``, the y-axis limits will be set automatically by matplotlib. Defaults to ``None``. invert_y : bool Whether to plot negative values upward (as is sometimes done for ERPs out of tradition). Defaults to ``False``. show_sensors : bool | int | str | None Whether to display an inset showing sensor locations on a head outline. If :class:`int` or :class:`str`, indicates position of the inset (see :func:`mpl_toolkits.axes_grid1.inset_locator.inset_axes`). If ``None``, treated as ``True`` if there is only one channel in ``picks``. If ``True``, location is upper or lower right corner, depending on data values. Defaults to ``None``. legend : bool | int | str Whether to show a legend for the colors/linestyles of the conditions plotted. If :class:`int` or :class:`str`, indicates position of the legend (see :func:`mpl_toolkits.axes_grid1.inset_locator.inset_axes`). If ``True``, equivalent to ``'upper left'``. Defaults to ``True``. split_legend : bool | None Whether to separate color and linestyle in the legend. If ``None``, a separate linestyle legend will still be shown if ``cmap`` is specified. Defaults to ``None``. axes : None | Axes instance | list of Axes | 'topo' :class:`~matplotlib.axes.Axes` object to plot into. If plotting multiple channel types (or multiple channels when ``combine=None``), ``axes`` should be a list of appropriate length containing :class:`~matplotlib.axes.Axes` objects. If ``'topo'``, a new :class:`~matplotlib.figure.Figure` is created with one axis for each channel, in a topographical layout. If ``None``, a new :class:`~matplotlib.figure.Figure` is created for each channel type. Defaults to ``None``. title : str | None Title printed above the plot. If ``None``, a title will be automatically generated based on channel name(s) or type(s) and the value of the ``combine`` parameter. Defaults to ``None``. show : bool Whether to show the figure. Defaults to ``True``. %(combine)s If callable, the callable must accept one positional input (data of shape ``(n_evokeds, n_channels, n_times)``) and return an :class:`array ` of shape ``(n_epochs, n_times)``. For example:: combine = lambda data: np.median(data, axis=1) If ``combine`` is ``None``, channels are combined by computing GFP, unless ``picks`` is a single channel (not channel type) or ``axes='topo'``, in which cases no combining is performed. Defaults to ``None``. %(sphere_topomap_auto)s %(time_unit)s .. versionadded:: 1.1 Returns ------- fig : list of Figure instances A list of the figure(s) generated. Notes ----- If the parameters ``styles``, ``colors``, or ``linestyles`` are passed as :class:`dicts `, then ``evokeds`` must also be a :class:`python:dict`, and the keys of the plot-style parameters must either match the keys of ``evokeds``, or match a ``/``-separated partial key ("condition") of ``evokeds``. For example, if evokeds has keys "Aud/L", "Aud/R", "Vis/L", and "Vis/R", then ``linestyles=dict(L='--', R='-')`` will plot both Aud/L and Vis/L conditions with dashed lines and both Aud/R and Vis/R conditions with solid lines. Similarly, ``colors=dict(Aud='r', Vis='b')`` will plot Aud/L and Aud/R conditions red and Vis/L and Vis/R conditions blue. Color specification depends on whether a colormap has been provided in the ``cmap`` parameter. The following table summarizes how the ``colors`` parameter is interpreted: .. cssclass:: table-bordered .. rst-class:: midvalign +-------------+----------------+------------------------------------------+ | ``cmap`` | ``colors`` | result | +=============+================+==========================================+ | | None | matplotlib default color cycle; unique | | | | color for each condition | | +----------------+------------------------------------------+ | | | matplotlib default color cycle; lowest | | | list or dict | integer mapped to first cycle color; | | | of integers | conditions with same integer get same | | None | | color; unspecified conditions are "gray" | | +----------------+------------------------------------------+ | | list or dict | ``ValueError`` | | | of floats | | | +----------------+------------------------------------------+ | | list or dict | the specified hex colors; unspecified | | | of hexadecimal | conditions are "gray" | | | color strings | | +-------------+----------------+------------------------------------------+ | | None | equally spaced colors on the colormap; | | | | unique color for each condition | | +----------------+------------------------------------------+ | | | equally spaced colors on the colormap; | | | list or dict | lowest integer mapped to first cycle | | string or | of integers | color; conditions with same integer | | instance of | | get same color | | matplotlib +----------------+------------------------------------------+ | Colormap | list or dict | floats mapped to corresponding colormap | | | of floats | values | | +----------------+------------------------------------------+ | | list or dict | | | | of hexadecimal | ``TypeError`` | | | color strings | | +-------------+----------------+------------------------------------------+ rNr)Evoked_check_evokeds_ch_names_timescSsg|]}tt|ddqS)rN)r/getattr)rDZ_evkrCrCrHrIsz(plot_compare_evokeds..rz: zA"evokeds" must be a dict, list, or instance of mne.Evoked; got {}rZ ConditionszAll evokeds entries r0r1rirdrz list or tupleT)Z return_kindcSsg|]}|tdkr|qS))misc)r )rDrsrCrCrHrI s )rrcsg|] }|qSrCrC)rDr) picks_by_typerCrHrI srKr)rZ item_namez:Only {} channel in "picks"; cannot combine by method "{}".r)r)rrQrFFzYou are plotting to a topographical layout with >70 sensors. 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