U f{j@s:dZddlmZddlmZddlZddlmZm Z ddl m Z m Z m Z mZmZddlmZmZmZdd lmZd d l mZmZmZmZmZmZmZmZmZedddZ"ddZ#ddZ$ddZ%d?d$d%Z&d@d'd(Z'dAd)d*Z(dBd+d,Z)dCd-d.Z*dDd/d0Z+dEd4d5Z,d6d7Z-dFd:d;Z.dS)Gz|r>d}qq>|r|d|d7}|d ddf\}} ||||| gS) z&Add a legend axis to the bottom right.rNrFrTg333333?)matplotlib.pyplotpyplotmaxminaxes) pospltleftbottomoverlaps_posZ h_overlapZ v_overlapZwidZheir%r%r& _legend_axisOs *((r5c  #sddlm} m} |dkr | }ddd}ddd}|||dkrNt|}|dk rptt|d}|j d ||j }|r|dddd f|9<t |d fd d t |jD}|r| dkr| ddddg}|dn| }t|||jd|_|jddgddgdt}|D]\}}|}|s| ||}|j||jD]}||q\| s|jggd||D]}|dq||_||_||_t||d|_||fVn$t|||t|||d}| |q |s| rt!|}|dfV|r||_"t#$|dddd f|dddd f|ddd dfddg|dddd f|ddd df|dddd f|ddd dfddggddd g}| s|%| j&|||dd|D]}||jfVqdS)a'Iterate over topography. Has the same parameters as iter_topography, plus: unified : bool If False (default), multiple matplotlib axes will be used. If True, a single axis will be constructed. The former is useful for custom plotting, the latter for speed. r)r+ collectionsNcSst||g|ddddf}t|dkjddd}t|dkr|tj||ddfddd}||ddf||ddfk}nd}|rd||SdS) 1Update status bar with channel name under cursor.Nrrr)axisF%s (click to magnify)zNo channel here)nparraywherealllensumargmin)xyr/ch_namesZpdistZpindZclosestZin_boxr%r%r&format_coord_unifiedss" &&z._iter_topography..format_coord_unifiedcSsd|S)r7r9r%)rArBch_namer%r%r&format_coord_multiaxissz0_iter_topography..format_coord_multiaxis) show_funcZbutton_press_eventrrCcs g|]\}}|kr||fqSr%r%).0rArBrCr%r& sz$_iter_topography..roff)r/rC)xlimylim)Z xticklabels yticklabelsF)rE)axr/ data_lines _mne_ch_name _mne_ch_idx_mne_ax_face_colorr)?)Z facecolorZ edgecolor linewidth)NN)N)' matplotlibr+r6figure set_facecolorrr_plot_topo_onpickcanvasZ mpl_connectr/copyr enumeratenamesr.r8 format_coordsetlistindexpatchspinesvalues set_colorZget_xticklinesZget_yticklinesZ set_visiblerQrRrSr appendr5_mne_axsr:Z transposeZadd_collectionZPolyCollection)rrrr r!r"r#r$unifiedimgr.rr0r6rDrFcallbackr/Ziter_chZunder_axZaxsidxnamech_idxrOZspineZtickZvertsr%rIr&ras              .&.rnoneRdBu_rwc! sddlm}|jdkrt|}|jddddf|jddddfd8<|jddddf|jddddfd<|d|d}}|dkr|n|}t||||||||d}|dkr|}| ddd d g}| | n|j}|r\|j j | | ||d }|t|||j||dd d dd}||jj d}|j||d|dt||||| | | | |||d }|D]b\}}|jdkr|dk rdddt||fdd|D} n|} |||||||| dq|dk r |jdd ||ddd|S)zPlot on sensor layout.rNZcustomrr))tmintmaxvminvmaxrMx_labely_labelgQ?g?g ףp= ?ffffff?)cmapZnormg333333??)r)ry)rOpadZfractionshrinkanchorrNcolorrK) rrr r$r#r"r!rhrir.zVectorview-allrmaggradcs g|]}t|r|n|qSr%) _check_vlimrHvZ this_typer%r&rJsz_plot_topo..)rqrrrsrtrMgQ?top)r~Zfontsizeva)r*r+kindrr/r-r,rrWr.rXcmZScalarMappableZ NormalizeZ set_arrayr:linspacecolorbarZgetprOZsetpr8rrZfigtext)!rtimesrG click_funcrrsrtrMrborderr"r!rxr$titlerurv font_colorrhrir.r0rqrrrr smcbZ cb_yticksZ my_topo_plotrOrmylim_r%rr& _plot_toposb  66       rc Cs|j}ddlm}zt|dr|j|j}}|jD]X}||jdkr2||jdkr2||jd|jdkr2||jd|jdkr2|}qq2WdSnt|dsWdS|j}|j }| d\} }| |j | ||||t| dWn.tk r} zt| W5d} ~ XYnXdS) z._format_coord..cs$g|]\}}|dkr|qS)dr%)rHrZtdiff)rrrqr%r&rJs()rsz %6.3f %s: z%s Nothing hereNcs"g|]}|||fqSr%r%)rHn)datalabelsrr%r&rJs cs g|]}dtt|qS) )r>)rHlabel)yunitr%r&rJsrg@z%7.2f %s z%s..%sz [%s]  ) r\Z get_xlabelfindr>Z get_ylabelr?rWZget_size_inchesZdpir:rr@)rArBrrOZtdiffsZnearbyZxlabelZxunitZtimestrZ nearby_dataZylabelslen bar_widthZ trunc_labelsrdata_rrrk)rmrrrrrq)rrArr& _format_coord{sH  z'_plot_timeseries.._format_coordcs||dS)N)rrOr%)rArB)rrOrr%r&z"_plot_timeseries..cSsF|j}|sdS|jdk r"|j|j|j|jd|_|jjdS)zDraw cursor (vertical line).Nr}) r _cursorlineremoveaxvliner _cursorcolorrWrZdrawrrOr%r%r& _cursor_vlines  z'_plot_timeseries.._cursor_vlinecSs0|j}|jdk r |jd|_|jjdSN)rrrrWrZrrr%r%r& _rm_cursors   z$_plot_timeseries.._rm_cursori+iKrwhiteblackZmotion_notify_eventZaxes_leave_event)rrprr2r1rAr)r8colorswhichrBrT--r~rUZ linestyler)r~rUZzorder)r*r+zipplotZset_pickradiusr^rr Z get_facecolorr:dotrconnectZget_ylimrrWrXrcreZ tick_paramsrZxaxisrZyaxisrrr`rrZaxhliner)rOrmrqrrrsrtrMrr~rrrurvrr hvline_colorrr0Z picker_flagrcolor_times_linerrZfacecolZface_brightnessZyminZymaxr%)rrOrmrrrrrrqr&_plot_timeseriesksT               rc Csddlm}|r"tdd|DrFtdd|Dtdd|Dg}t|||f||j}|j}|j}t ||| D]F\}}}| |j |j |j ||j|j||d|d|d dqt| rt| |j |j } |j| |d |d |d |dd d |rsz+_plot_timeseries_unified..css|]}t|VqdSr)r:r-rHdr%r%r&rscss|]}t|VqdSr)r:r,rr%r%r&rsryT)rUr~rrrr(rrr)r~rUg@centerr)ZhorizontalalignmentverticalalignmentrightmiddleZ)ZhorizontalignmentrZrotation)r*r+anyr-r,rr/rPrOrrfrrrrrr:r;ZvlinesZhlinestextrr`r)rrmrqrrrsrtrMrr~rrrurvrrrr0r/rPrOrrrr%r%r&_plot_timeseries_unifiedsV $  rc Csddlm}ddlm}|dd|ddf}|dk rB||\}}t| rV| |j|} | dk rf|| }| dkr~|j|| dd}|j|||dt|gdd||d|d d }| }| dk r| | |dk r| ||r|j |d dS) z#Plot erfimage on sensor topography.rndimageNsigmar8rrTnearestrrrrsrtrrx interpolation)r) scipyrr*r+callablergaussian_filter1drr>Zgcarrr)rOrmrqrrrsrtrMrepochsr orderscalingsrrurvrrx vlim_arrayrr0 this_datarir%r%r&_erfimage_imshows6      rc Csddlm}t|||fdt|jf|j}|j}|j|j||j|j||j |j |j t|jf}|dd|ddf}|dkrdn||\}}t | r| |j |} | dk r|| }| dkr|j || dd}||j||dd||d |d d dS) z-Plot erfimage topography using a single axis.rrN)NNrrrrTr r )r rrr>eventsrOrPrrrrrrrrfr)rrmrqrrrsrtrMrrr rrrrurvrrxrrrOrPrrr%r%r&_erfimage_imshow_unifieds.   r= ףp=?rbadsc6sddlm}ddlmtttfkr.gtdjdk r\fddDnddDdjdj t d|}t fd d Dst d t |rtd d }t}|dddD]2}d|}|dddd|d<||qֈjddd|d<td<W5QRXt}D]J}t|j|dg\}}dkrp||d9}||_|tt|q@|}dg}dkrt ddnd}d|} dkrtd|sfddD}!fdd|!D}tt|}dd|D}tdd|Ddk}"tdd|Ddk}#|"rpt|ddd}fdd|D}nT|#rt|ddd}fd d|D}n&d!d"|D}$tfd d#|$g}t|trt|t|kstdkr(D]2}t||D] \}}%|j|||%9<qq|dkrVt d$d DrVd%dDn|d&krtD]}t|qdt} tt|!D]6}&dkrt dt|&}nd}| d'|q|dkrtfd(d|D}'tfd)d|D}(|(|'fn~t|trpt d*|fd+d|Dtd,krBdn,d-dD|"s\|#rtt ttnt!d.t|d/dD}d0dD})d1dD}*t"t#|||*| | |d2}+t"t$|||*| | ||)d3},t%d4d|*D}-t&d5d|*D}.t'|-|.g|+|,d d||| || |d6| d|d7}/t(|/| |d k r|dkrNdn|}0d8dD}1|j)|1|0d9d:id;}|*+| |,}2t|2|D]\}3}4|3-|4q|d&krD]}t|qt|*t.djd<|/d=}5t/d|5t0||/S)>adPlot 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 objects | color object | 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 ylim 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. 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 floats | None The values at which to show a vertical line. hline : list of floats | None The values at which to show a horizontal line. fig_facecolor : color The figure face color. Defaults to black. fig_background : None | array A background image for the figure. This must be a valid input to `matplotlib.pyplot.imshow`. Defaults to None. axis_facecolor : color The face color to be used for each sensor plot. Defaults to black. font_color : color The color of text in the colorbar and title. Defaults to white. merge_channels : bool Whether to use RMS value of gradiometer pairs. Only works for Neuromag data. Defaults to False. legend : bool | int | string | 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. noise_cov : instance of Covariance | str | None Noise covariance used to whiten the data while plotting. Whitened data channels names are shown in italic. Can be a string to load a covariance from disk. exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. By default, exclude is set to 'bads'. show : bool Show figure if True. .. versionadded:: 0.16.0 Returns ------- fig : instance of matplotlib.figure.Figure Images of evoked responses at sensor locations rNr) whiten_evokedcsg|]}|qSr%r%rHe) noise_covrr%r&rJsz%_plot_evoked_topo..cSsg|] }|qSr%r[rr%r%r&rJsrc3s|]}|jkVqdSrrIrrIr%r&rsz$_plot_evoked_topo..z!All evoked.picks must be the sameF)Ztopomap_coordsexcludechsrEr)XT)Zupdate_redundantZ check_afterrrZunitsZNAzRMS amplitude (%s))r csg|]}|jkr|qSr%)r]rHrE)rr%r&rJs csg|]}t|qSr%)rra)rHch)rCrr%r&rJscSsg|]}|dkr|qS)ref_megr%)rH types_usedr%r%r&rJscSsg|]}|dkr|qS)rr%rHrAr%r%r&rJscSsg|]}|dkr|qS))ZhboZhbrZfnirs_cw_amplitudeZfnirs_odr%r'r%r%r&rJs csg|]}t|ddqS)F)megr%r rrHkkr rr%r&rJscsg|]}t|ddqS)F)Zfnirsr%r r)r*r,r%r&rJscSsi|] }|dqS)Tr%rHtr%r%r& sz%_plot_evoked_topo..)r(r css|]}|jdk VqdS)TN)projrr%r%r&rscSsg|] }|qSr%) apply_projrr%r%r&rJs interactivezAmplitude (%s)cs"g|]tfddDqS)c3s|]}|jVqdSr)rr,rr.r%r&rs/_plot_evoked_topo...)r,rHevokedr3r&rJscs"g|]tfddDqS)c3s|]}|jVqdSr)rr-rr3r%r&rsr4)r-r5r6r3r&rJsrMcsg|] }|qSr%r%r*)rr%r&rJsrcSsg|]}t|qSr%)r:r;)rHZylr%r%r&rJsz$ylim must be None or a dict. Got %s.cSsg|] }|jqSr%)rrr%r%r&rJscSsg|] }|jqSr%commentrr%r%r&rJscSsg|] }|jqSr%)rrr%r%r&rJs)rr~rrrr)rr~rrrrrcSsg|] }|dqS)rr%r-r%r%r&rJ scSsg|] }|dqS)r)r%r-r%r%r&rJsTime (s))rrrGrrrrMrxr$rr!rr"rrurvrhr.cSsg|]}|jr|jndqS)Unknownr8rr%r%r&rJssizer)locpropprojs)evokedsrZplot_update_proj_callbackr?r )1r*r+Zcovrtyperr`rrrCrr= ValueErrorrr rfZ_unlockr rranger>rdictfromkeysrrAssertionErrorrrrr:r;map TypeErrorrrrr-r,rrrZ get_framerXZ get_textsre_plot_update_evoked_topo_projrr)6r7rr$r~rrMrrr0rrr!fig_backgroundr"rZmerge_channelsrr.r showrr0Zpicksr!pickr$Z new_picksrr_r&unitrvZ chs_in_layoutZis_megZis_nirsZtypes_used_kwargsch_typermZymaxesZyminscommentsrrGrZtime_minZtime_maxr Z legend_locrZtxtstxtcolparamsr%)rCr7r rrrrrr&_plot_evoked_topo;s0W              "                  rTc Csdd|dD}|d}ddt|d|D}||d<|D]}|j|dd |q>|jd jD]8}t|j|D]&\}}||j|j|j |j qvqf|j d S) zUpdate topo sensor plots.cSsg|] }|qSr%rrr%r%r&rJ2sz1_plot_update_evoked_topo_proj..r@r cSsg|]\}}|r|qSr%r%)rHr0br%r%r&rJ4sr?Z proj_boolsT)Zremove_existingrN) rZadd_projr1r.rgrPZ set_ydatarrrrRrZr) rSZboolsr@r r?rrOrr7r%r%r&rI0s "rIrrwcstd|dkr(tj}t|jtj@}fdd|D}| }fdd|D}j t |ddt j f9_ }tt|D]4}|ddt t ||kf}t||||<qt fdd|D}|dkrtt|dk}|r8|dkr8|dkr8|d\}}tt||||||d }tt||||||d d }tjj||||||||| | || d d d d d}t|| t||S)u Plot Event Related Potential / Fields image on topographies. Parameters ---------- epochs : instance of :class:`~mne.Epochs` The epochs. layout : instance of Layout System specific sensor positions. sigma : float The standard deviation of the Gaussian smoothing to apply along the epoch axis to apply in the image. If 0., no smoothing is applied. vmin : float The min value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. vmax : float The max value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. colorbar : bool | None Whether to display a colorbar or not. If ``None`` a colorbar will be shown only if all channels are of the same type. Defaults to ``None``. order : None | array of int | callable If not None, order is used to reorder the epochs on the y-axis of the image. If it's an array of int it should be of length the number of good epochs. If it's a callable the arguments passed are the times vector and the data as 2d array (data.shape[1] == len(times)). cmap : colormap Colors to be mapped to the values. layout_scale : float Scaling factor for adjusting the relative size of the layout on the canvas. title : str Title of the figure. 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)``. border : str Matplotlib borders style to be used for each sensor plot. fig_facecolor : color The figure face color. Defaults to black. fig_background : None | array A background image for the figure. This must be a valid input to :func:`matplotlib.pyplot.imshow`. Defaults to ``None``. font_color : color The color of tick labels in the colorbar. Defaults to white. show : bool Whether to show the figure. Defaults to ``True``. Returns ------- fig : instance of :class:`matplotlib.figure.Figure` Figure distributing one image per channel across sensor topography. Notes ----- In an interactive Python session, this plot will be interactive; clicking on a channel image will pop open a larger view of the image; this image will always have a colorbar even when the topo plot does not (because it shows multiple sensor types). rNcsg|]}j|qSr%)rCrar#)rr%r&rJsz*plot_topo_image_epochs..csg|]}|dqS)r)getrHrO)rr%r&rJscsg|] }|qSr%r%rW) vlim_dictr%r&rJsrr)rrrrr rxrT)rrrrr rxrrr:ZEpoch)rrrrGrrrsrtrxr$rr!rrrurvrhri)rr[Z load_datarrr_r]rCrLZget_channel_types_datar:r;Znewaxisget_datarDr<rr>rrrrrrr)rrr rsrtrrrxr$rrrr!rJrrKrCZidxsZch_typesZ scale_coeffsrrOrrrGZ erf_imshowr r%)rrrXr&plot_topo_image_epochsBsrA    "      r[)NNNrrrNF)rrrNFFNF)NNNNNNrnrrroNNNNrpFFN)NNNNNFrrNrrrFN) NNNNNNFTroNN)NNNFNrpN)NNNFNrp) NNNNNNNNNFroN) NNNNNNNNNFroN)NrNrnNNNFrrrNrrpFTNrTN)NrNNNNrorwNNrnrNrpT)/__doc__r[r functoolsrnumpyr:Zio.pickrrutilsrrr r r Zchannels.layoutr r rdefaultsrrrrrrrrrrr'r5rrrYrrrrrrrrrTrIr[r%r%r%r&s,   , 5 g :'  $  l & !  v