U f{j @sFdZddlZddlZddlmZddlmZddlmZddl Z ddl m Z ddl Z ddlmZddlmZmZmZmZdd lmZdd lmZdd lmZdd lmZdd lmZmZm Z m!Z!ddl"m#Z#m$Z$m%Z%ddl&m'Z'm(Z(ddl)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2ddlm3Z3ddl4m5Z5ddl)m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;ddZdddddddd d!d"Z?e2dNd$d%Z@d&d'ZAe+dOd)d*ZBd+d,ZCdPd-d.ZDdQd0d1ZEd2d3ZFdRd4d5ZGd6d7ZHd8ZIdddd9dd:eIddd;df dd<d=d>ZJdd?dd:eId@dAdBdf dCdDZKdEdFZLe2dSdHdIZMdTdJdKZNdS)Uz/Functions to make simple plots with M/EEG data.N)glob)cycle) defaultdict)DEFAULTS)_reorient_image_read_mri_info _check_mri_mri_orientation) compute_rank) read_surface)FIFF)make_projector)_DATA_CH_TYPES_SPLIT pick_types pick_info pick_channels)read_source_spaces SourceSpaces _ensure_src) apply_trans _frame_to_str) loggerverbosewarn _check_optionget_subjects_dir_mask_to_onsets_offsets_pl _on_missingfill_doc)_picks_by_type)estimate_ringing_samples) tight_layout_get_color_list_prepare_trellisplt_show _figure_agg_validate_typec s|dkrd}ttdd|~tdddd}t|}fddjDfd dD}dtt|D] \}}fd d|D|<qfd dtD}j |dd|f} | |fS) NZbadsch_namesnamesF)Z meg_combinedref_megexcludecsg|]}|dkr|qSr*r,.0ninfor,K/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/viz/misc.py 5s z#_index_info_cov..csg|]}j|qSr,r*indexr0)covr,r5r66scs&g|]}|kr|qSr,r8r1c)r* info_ch_namesr,r5r6;s csJg|]B}t|dkr|dtd|td|td||fqS)rz %s covariancetitlesunitsscalings)lenr)r1key) idx_by_typer,r5r6=s  ) rrr!dictr*rlistitemsrdata) r4r9r.Z picks_listZ picks_by_typeZch_idxch_typesel idx_namesCr,)r*r9rCr4r=r5_index_info_cov*s0   rLr,TFc& sddlm}ddlm} ddlm} ddlm} t||\} } }~~g}|rt d}|D] }d|d <qbt || \}}}|dkrt d |t|t| |j} n t d t| rt| | j} |jd t|d dt|dfd\}}t|D]\}\}}}}}tt| |dd|f}|d|fj| |dd|fd| | |ddd}|d|fj|d|rddlm}||d|f}|jdddd}|d |j ||ddq|!dddd d!d"t"|d#d}|r|jd t|d dt|dfd\}}t|D]Z\}\}}}} }!| |dd|f}"| j#|"d d$}#| |"fd%d&|Dggd}"t$|}$|$%g|$d<W5QRXt&|"|$d'}%d(|#|#dk'|#|#dk<t|#| }#|d|fj(|#d)d*d+|%|!}%|d|fj)|%d d,d-d.d/d d0|d|fj*|%d |d|f+d d1|%fdd2d-d.d/d3|d|fjd4|d5d6|dt|#d gd7q$t"|d#t,|||fS)8a8Plot Covariance data. Parameters ---------- cov : instance of Covariance The covariance matrix. %(info_not_none)s exclude : list of str | str List of channels to exclude. If empty do not exclude any channel. If 'bads', exclude info['bads']. colorbar : bool Show colorbar or not. proj : bool Apply projections or not. show_svd : bool Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations. show : bool Show figure if True. %(verbose)s Returns ------- fig_cov : instance of matplotlib.figure.Figure The covariance plot. fig_svd : instance of matplotlib.figure.Figure | None The SVD spectra plot of the covariance. See Also -------- mne.compute_rank Notes ----- For each channel type, the rank is estimated using :func:`mne.compute_rank`. .. versionchanged:: 0.19 Approximate ranks for each channel type are shown with red dashed lines. rN) Normalize)linalgr) CovarianceprojsTZactivez5 Created an SSP operator (subspace dimension = %d)z: The projection vectors do not apply to these channels.r#Fgffffff@g @Zsqueezefigsizenearest)vminvmaxZRdBu_r) interpolationZnormcmaptitle)make_axes_locatablerightz5.5%皙?)sizepadz%.0e)caxformatg{Gz?g\(\?gGz?g?gp= ף?fig)Z compute_uvcsg|]}d|qSr/r,)r1iir3r,r5r6szplot_cov..r3g|=k)colorzorder--r?)lsrgalpharhclip_onu rank ≈ %dtop)havargrnrhu Noise σ (%s)logzEigenvalue index)ylabelyscalexlabelrYxlim)-matplotlib.pyplotpyplotZmatplotlib.colorsrMZscipyrNr9rOrLcopydeepcopyrrr4npdotTZ iscomplexobjsqrtconjrealsubplotsrA enumeratemaxabsimshowsetZmpl_toolkits.axes_grid1rZZ append_axesgridcolorbarsubplots_adjustr$ZsvdrZ_unlockr minplotZaxvlinetextZget_ylimr')&r9r4r.rZprojZshow_svdshowrpltrMrNrOrKr*rJrPpPZncomp_Zfig_covaxesreidxnameZvlimimrZdividerr_Zfig_svdunitscalingrBZthis_CsZ this_infoZ this_rankr,r3r5plot_covHs+                  $  rcCsddlm}t|dkr td|d}|dk rF||jdkrFtd|dk rj||jd|jkrjtd|dkr||jd}|dkr|jd|j}t|||j|j} tt t |} t | } g} |D]*}| }| |||j| |jqt| } |dkr$t| | jd}g} tt|dD]D}||d}||dd}||kr8| || ||fq8t| | \}}|jdd }|j||| dd|ddfd d |}|j d d dddd| D}dt| d}tt| D]}||dkrd||<q|| |||| d| d|d|| |dd| D| | d| d|j!ddd|r|"t#|d| D]2\}}|j$|| d| d||| dddqt%||S)a-Plot source power in time-freqency grid. Parameters ---------- stcs : list of SourceEstimate Source power for consecutive time windows, one SourceEstimate object should be provided for each frequency bin. freq_bins : list of tuples of float Start and end points of frequency bins of interest. tmin : float Minimum time instant to show. tmax : float Maximum time instant to show. source_index : int | None Index of source for which the spectrogram will be plotted. If None, the source with the largest activation will be selected. colorbar : bool If true, a colorbar will be added to the plot. show : bool Show figure if True. Returns ------- fig : instance of Figure The figure. rNz)cannot plot spectrogram if len(stcs) == 0zAtmin cannot be smaller than the first time point provided in stcszgtmax cannot be larger than the sum of the last time point and the time step, which are provided in stcsr#) )rRZReds)rWz Source powerTime (s)Frequency (Hz))rYrvrtcSsg|]}tt|dqSr)strr|roundr1tr,r,r5r6sz+plot_source_spectrogram.. rscSsg|]}t|dqSr)r|r)r1freqr,r,r5r6#sT-)rmrbz#666666rg)&rxryrA ValueErrortimesZtstepr|arangesortedrZravelrzr{cropappendrGarrayZ unravel_indexZargmaxshaperangeremoveZmeshgridfigureZpcolorgca set_xticksZset_xticklabelsrwru set_yticksset_yticklabelsylimrrr$Zbarhr')ZstcsZ freq_binsZtminZtmaxZ source_indexrrrZstcZ time_boundsZ freq_boundsZ freq_ticksZ source_powerZ gap_boundsi lower_bound upper_boundZ time_gridZ freq_gridrcaxZtime_tick_labelsZn_skipr,r,r5plot_source_spectrograms                   rcoronali) orientationslicesr show_indicesshow_orientationwidthslices_as_subplotsc 7 Csddlm} ddlm} t|ttfdt|trBtd|dddtd|d t |d d d \} } } } } } t | \}}t j |}t|\}}}|j|}|dkrt t jd|d ddt}|d d}t |}||dk|7<t t ||rD|jd ksD|jd ksD|ddksD|d|ksD|jjdkrXtd|d |ft}|D]D\}}t}t|\|d<|d<t ||d|d<|!||fqbt}|dk r6t"|dd|ddt#j$krtdt%|dd|D]0}|d|dt}|!t ||dqt j&|dd}| rrd}t't(||\}}} } |)d|*}t(|} n0d }} d}||}!|!|!|j||j|f}"t &t j+ g|ddt ,|dt j+gg}#t-dgd}$td d!d"d#}%|%d$||%d$|}&}'| j.ddd%d&g}(g})t/t0||#dd|#d dD]f\}*\}+},}-| r^||*}.n&t1|"|dd'}|j2ddd d gd(dd)}.|+|$|<|t3|$j4}/|.j5|/| j6j7d*d+|.8d(|.9d,|.:d-|D]|\}}t;j|ddd|f|ddd|f|d|ddd|f|+g|d0d d1W5QRXqt(|r|dd|f|,k|dd|f|-k@}0|.j?||0|f||0|fd2d3d d4d5|r|.j@|/jd d6d7d7t|+d8d9d:d;d<td=d>|(d?d(d@dAdB}1|dCk}2|r|*|dks|2r8|.j@d|/jdd|&dfdDd;dE|1|*||d ks^|*| d ks^|2r|.j@|/jd d |/jdd|&d fdDdFdE|1|*| |ks|2r|.j@|/jd dd|'dfdDd:dG|1|*|ks|d ks|2r|.j@|/jd d|/jdd |'d fdDdHdG|1| s>tABP}3|jC|3dIdJd|dK|jDE\}4}5| F||3Gdt jH|3It jJdL}6W5QRX|6Kt|5t|4df}|)!|q>| r|jLdMdMd0d0dMdMdNtM||dO|S|)SdS)PaPlot BEM contours on anatomical MRI slices. Parameters ---------- slices_as_subplots : bool Whether to add all slices as subplots to a single figure, or to create a new figure for each slice. If ``False``, return NumPy arrays instead of Matplotlib figures. Returns ------- matplotlib.figure.Figure | list of array The plotted slices. rN) patheffectsr)alwayszwhen str)extrar)rZaxialZsagittalmmT)r?Z return_imgr#)startstopnumriuz{slices must be a sorted 1D array of int with unique elements, at least one element, and no elements greater than %d, got %sZrrZtrisz or NoneZ coord_framez-Source space must be in MRI coordinates, got Zinuseg@@)axisrlre`@rfZLRZPAZIS)RASZRASg?) linewidth foregroundrn)rRdpi facecolorF)Zframe_onrlower)rWoriginoffequalrecordignore?)ZlevelscolorsZ linewidthsrh.z#FF00FFr)markerrgrrhrkwzx-smallbottomleft)rgfontsizerrrqz#66CCEEZmediumZ monospacebold)rgr path_effectsfamilyrorhweightrcenter)rrrqr[)rqrrrprawZtight)r`Z bbox_inchesZ pad_inchesr)dtypera)rrr[rpZwspacehspacerb)NrxryZ matplotlibrr)boolr isinstancerrrr|rNinvr rrlinspaceZastypeintZ atleast_1drzZ array_equalsortndimr]rkindrrErDr rrrr ZFIFFV_COORD_MRIrZ concatenater&rAZ set_facecolorZget_dpiinfdiffsliceZ withStrokerzipr(Zadd_axestupler~rcmgrayZset_autoscale_onrZ set_aspectwarningscatch_warnings simplefilterZ tricontourZscatterrioBytesIOZsavefigcanvasZget_width_heightcloseseekZ frombuffergetvalueZuint8Zreshaperr')7 mri_fnamesurfacessrcrrrrrrrrrrnimrGZ rasvox_mri_tZ mri_rasvox_trxyZn_slicesZsurfs file_namergsurfsourcessrc_ZpointsZn_colrcZaxsrZn_axesrrRZboundsZslicerZ ori_labelsZxlabelsZylabelsrfigsZaislrupperrZdatZin_slicekwargsrZbuffZw_Zh_Z fig_arrayr,r,r5_plot_mri_contours4sj                         ( (&$$    rT1.mgzc  CsVt|dd}t|||} t||d} t| s@td| dt| } |dk rt|tr`|f}|D]L} dD]B}t||d|d | }t |r| |d fqltd |qlqdt|trt |st||d|}t |r|}n td |t |}n&|dk r"t|t s"t d t|t| dkr8tdt| | |||||| dd }|S)a Plot BEM contours on anatomical MRI slices. Parameters ---------- %(subject)s %(subjects_dir)s orientation : str 'coronal' or 'axial' or 'sagittal'. slices : list of int | None The indices of the MRI slices to plot. If ``None``, automatically pick 12 equally-spaced slices. brain_surfaces : None | str | list of str One or more brain surface to plot (optional). Entries should correspond to files in the subject's ``surf`` directory (e.g. ``"white"``). src : None | SourceSpaces | str SourceSpaces instance or path to a source space to plot individual sources as scatter-plot. Sources will be shown on exactly one slice (whichever slice is closest to each source in the given orientation plane). Path can be absolute or relative to the subject's ``bem`` folder. .. versionchanged:: 0.20 All sources are shown on the nearest slice rather than some being omitted. show : bool Show figure if True. show_indices : bool Show slice indices if True. .. versionadded:: 0.20 mri : str The name of the MRI to use. Can be a standard FreeSurfer MRI such as ``'T1.mgz'``, or a full path to a custom MRI file. .. versionadded:: 0.21 show_orientation : bool | str Show the orientation (L/R, P/A, I/S) of the data slices. True (default) will only show it on the outside most edges of the figure, False will never show labels, and "always" will label each plot. .. versionadded:: 0.21 .. versionchanged:: 0.24 Added support for "always". Returns ------- fig : instance of matplotlib.figure.Figure The figure. See Also -------- mne.viz.plot_alignment Notes ----- Images are plotted in MRI voxel coordinates. If ``src`` is not None, for a given slice index, all source points are shown that are halfway between the previous slice and the given slice, and halfway between the given slice and the next slice. For large slice decimations, this can make some source points appear outside the BEM contour, which is shown for the given slice index. For example, in the case where the single midpoint slice is used ``slices=[128]``, all source points will be shown on top of the midpoint MRI slice with the BEM boundary drawn for that slice. T)Z raise_errorZbemzSubject bem directory "z" does not existN)ZlhZrhrrz#00DD00zSurface %s does not exist.z%s does not existz:src needs to be None, str or SourceSpaces instance, not %srziNo surface files found. Surface files must end with inner_skull.surf, outer_skull.surf or outer_skin.surf) r r r rrrrrr)rr opjoinisdirIOError_get_bem_plotting_surfacesrrexistsrrr TypeErrorreprrAr)subjectZ subjects_dirrrZbrain_surfacesr rrZmrirr bem_pathr  surf_nameZhemi surf_fnamerrcr,r,r5plot_bemsTH            r'cCsZg}dD]L\}}tt||d}t|dkr|d}td||||fq|S)N))z *inner_skullz#FF0000)z *outer_skullz#FFFF00)z *outer_skinz#FFAA80z.surfrzUsing surface: %s)rrrrArr4r)r$r r%rgr&r,r,r5rds  rraisec $Cs|dkrd}d} nd} t|}t|dkr4tdt|dddf} |dk r*dd |D} tt|d d d \} }tt|t }t |D],\}}|| kr|d }t ||d||<qddt| |D} ddt||D}t|dkrt d| D]}||kr t d|q n| }t|| |}ddlm}d}|dkr\|}|rf|n|}t|}t|}t|}|t|dddf|df||}tt}}t |D]\}}|dddf|k}|}|dkrqt||r|dn||dfd}|dk rBd| ||f} n d|f} || i}!||krp|||!d<||j||df|||dfddi|!dq|r|d|jd|dt|j| |n||d|dg|j!| dd|gd|"d|dk r$|n|#}|ddd|ddd}}|$}"|dk r`dnd}#|%|"j&|"j'|"j(|#|"j)g|j*||d d!d"d#|j+,t-||S)$aPlot :term:`events` to get a visual display of the paradigm. Parameters ---------- %(events)s sfreq : float | None The sample frequency. If None, data will be displayed in samples (not seconds). first_samp : int The index of the first sample. Recordings made on Neuromag systems number samples relative to the system start (not relative to the beginning of the recording). In such cases the ``raw.first_samp`` attribute can be passed here. Default is 0. color : dict | None Dictionary of event_id integers as keys and colors as values. If None, colors are automatically drawn from a default list (cycled through if number of events longer than list of default colors). Color can be any valid :doc:`matplotlib color `. event_id : dict | None Dictionary of event labels (e.g. 'aud_l') as keys and their associated event_id values. Labels are used to plot a legend. If None, no legend is drawn. axes : instance of Axes The subplot handle. equal_spacing : bool Use equal spacing between events in y-axis. show : bool Show figure if True. %(on_missing_events)s %(verbose)s Returns ------- fig : matplotlib.figure.Figure The figure object containing the plot. Notes ----- .. versionadded:: 0.9.0 NrZSamplesrrz'No events in events array, cannot plot.rcSsi|]\}}||qSr,r,)r1revr,r,r5 szplot_events..cSs|dS)Nr#r,)rr,r,r5zplot_events..)rBz( from event_id is not present in events.FcSsg|]\}}|r|qSr,r,)r1Zcondrer,r,r5r6szplot_events..cSsg|]\}}|r|qSr,r,)r1Zid_rer,r,r5r6szNo usable event IDs foundz.event %s missing from event_id will be ignoredr#z%s (%s)zN=%drgrrozEvent id)rvrtrwTr皙?g?z center left)r#rkZsmall)locZbbox_to_anchorr).r|ZasarrayrAruniquerFrronesrrr RuntimeErrorr_handle_event_colorsrxryrrrrrZin1drEsumfullrrZset_ylimr]rrrrrZgcfZ get_positionZ set_positionZx0Zy0rheightlegendrZdrawr')$eventssfreqZ first_samprgevent_idrZ equal_spacingrZ on_missingrrv unique_eventsZ event_id_rev conditionsZunique_events_idZkeeprdZ this_eventmsgrrcrZ min_eventZ max_eventZmax_xZhandleslabelsrZevZev_maskcountrZ event_labelrboxfactorr,r,r5 plot_eventsqs,                " &         rAcCsP|jjjd}d}|D]$\}}|}|jjdkr:q@qd}q|dk sLt|S)zGet our press callback.Zbutton_press_eventNpartial)r callbacksrF __class____name__AssertionError)rcrCfuncrBvalr,r,r5 _get_pressers  rIc Csddlm}|dkrtt}|dd\}}tjtj g}t||D]R\}}|j|j |j d|ddt |d|j d|d<t |d|j d|d<qF|j |dd d |r|jd d |S) aPlot the amplitude traces of a set of dipoles. Parameters ---------- dipoles : list of instance of Dipole The dipoles whose amplitudes should be shown. colors : list of color | None Color to plot with each dipole. If None default colors are used. show : bool Show figure if True. Returns ------- fig : matplotlib.figure.Figure The figure object containing the plot. Notes ----- .. versionadded:: 0.9.0 rNr#geAg?)rgrrrzAmplitude (nAm))rwrvrtF)r)rxryrr%rr|rrrrZ amplituderrrr) ZdipolesrrrrcrrwZdiprgr,r,r5plot_dipole_amplitudes s   rJrpr[cCsTt|tttjfs|gn|}|D].}|r4|jdd|D]}|j|dq8q dS)zAdjust some properties of axes. Parameters ---------- axes : list List of axes to process. remove_spines : list of str Which axis spines to remove. grid : bool Turn grid on (True) or off (False). r)rhFN)rrErr|ndarrayrZspinesZ set_visible)rZ remove_spinesrrrBr,r,r5 adjust_axes.s  rMc Cs|dkr dSt|}t}|dd|dkrBtdddg}n tdd}tttt|dttt|ddD]}||d |7}qt|}|||dk||dk@}d d |D}||fS) z/Create approximately spaced ticks between lims.linear)NNr#rrrl rcSs g|]}|dkrdnd|qS)r#z%gz%dr,rr,r,r5r6Qsz!_filter_ticks..) r|rrErrrfloorlog10tolist)ZlimsfscaleZticksbaseexpZ ticklabelsr,r,r5 _filter_ticksBs   rWcCs|dkrn|dkr*|dkrdnd|dg}nD|dkr>|dg}n"|ddkrR|dn d|dg}||d g7}|dkr|ddkrtd |dn|ddkrtd |d|S) z Get reasonable frequency limits.Nrs皙?rarrNrr#rz flim[0] must be positive, got %sz$flim[0] must be non-negative, got %s)r)flimrTrr8r,r,r5 _get_flimUs "  rZcCs$t|tr|dkr td|fdS)zCheck for valid fscale.)rsrNz(fscale must be "log" or "linear", got %sN)rrr)rTr,r,r5 _check_fscalehsr[)irz#1f77b4rs)time magnitudedelay)dlimc , Csddlm}m}m}m}m}m}ddlm}t |}t d|ddgt | t rT| g} t | D]\}}t d||dq\t||||}|dkrtt|dt|d d }nt|d|d d }t||\}}||d tj}t |tr*d |krtt|tj}tt|}|d D]}||dd |d d|d }||9}| rd||9}nHtjdd6td|||dd |d df|d 7}W5QRXq$t|d }t|}d |d<| rt||d dfgd}|} |t|d d 7}n|} | |d |}n||d|d|d }| rD||9}tjddTtd||d|df|d }| r|||d|d|d 7}W5QRXt|d|df}t|}d |d<| rt||d dfgd}|} n|} | |d|d|}|dkrd}| r|d7}nf|||dd }tjdd$td||dgf|d }W5QRX|dkr~dn|}| r|d7}d}!| dkr| t| d \}!} t | |j!r| g} nt | tj"rt#| } |!dkr| d$}!t| t| krt%dt| t| ft&t|}"| dkrRt'|"(d} | | g} | rt|d d }#|"|#8}"|"d|"d kst)||#8}|"|}"||}||d tj}$t*|dkrdnd dd}%dtt+||j,d}&d| krDt-d d!| Ddd}'| |'j.|"||d"| |'j/|"ddgd#d$|d%d&| krt-d'd!| Ddd}(| |(j.|$|%|&|%|d d(d)|dk r|dk rt0||| |(|d*d+| |(j/d,d-|d.|dk r| |(j/|d/| |(j/|d0| |(j/||d1d2d3d4| krt-d5d!| Ddd})| |)j.|$|%||%|d d(d)t1t2|&d6kD].\}*}+| |)j3|$|*|$|+d d7d8d9d:q^| |)j/|d;d1|d<|dk r| |)j/|d/| |)j/|d0| |)j/|| d1d=d3t4| t5t6| |!S)>a Plot properties of a filter. Parameters ---------- h : dict or ndarray An IIR dict or 1D ndarray of coefficients (for FIR filter). sfreq : float Sample rate of the data (Hz). freq : array-like or None The ideal response frequencies to plot (must be in ascending order). If None (default), do not plot the ideal response. gain : array-like or None The ideal response gains to plot. If None (default), do not plot the ideal response. title : str | None The title to use. If None (default), determine the title based on the type of the system. color : color object The color to use (default '#1f77b4'). flim : tuple or None If not None, the x-axis frequency limits (Hz) to use. If None, freq will be used. If None (default) and freq is None, ``(0.1, sfreq / 2.)`` will be used. fscale : str Frequency scaling to use, can be "log" (default) or "linear". alim : tuple The y-axis amplitude limits (dB) to use (default: (-60, 10)). show : bool Show figure if True (default). compensate : bool If True, compensate for the filter delay (phase will not be shown). - For linear-phase FIR filters, this visualizes the filter coefficients assuming that the output will be shifted by ``N // 2``. - For IIR filters, this changes the filter coefficient display by filtering backward and forward, and the frequency response by squaring it. .. versionadded:: 0.18 plot : list | tuple | str A list of the requested plots from ``time``, ``magnitude`` and ``delay``. Default is to plot all three filter properties ('time', 'magnitude', 'delay'). .. versionadded:: 0.21.0 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 requested plot types. If instance of Axes, there must be only one filter property plotted. Defaults to ``None``. .. versionadded:: 0.21.0 dlim : None | tuple The y-axis delay limits (sec) to use (default: ``(-tmax / 2., tmax / 2.)``). .. versionadded:: 1.1.0 Returns ------- fig : matplotlib.figure.Figure The figure containing the plots. See Also -------- mne.filter.create_filter plot_ideal_filter Notes ----- .. versionadded:: 0.14 r)freqz group_delaylfilterfiltfiltsosfilt sosfiltfiltNrTrsrNzplot[%d])r]r^r\r#irsosrfTrrZconstantbazSOS (IIR) filterz (forward-backward))ZworNrz FIR filterz (delay-compensated)zRLength of axes (%d) must be the same as number of requested filter properties (%d)rrrg#B ;r\cSsg|] }|dkqS)r\r,r1rr,r,r5r6&szplot_filter..rrZ Amplitude)rwrvrtrYr]cSsg|] }|dkqS)r]r,rir,r,r5r6,srl)rgrrhF)rTrzMagnitude (dB)r)rtrvxscalexticks xticklabelsrAmplitude (dB))rwrrvrtr^cSsg|] }|dkqS)r^r,rir,r,r5r6:sg33333Crer\r)rrnrhzGroup delay (s))rwrtrvrjz Delay (s))7Z scipy.signalr`rarbrcrdrerxryfloatrrrrrZr|ZlogspacerRrrWpirDr0rAZ complex128Zzerosrrrrr"r^rZAxesrLrE get_figurerrrrrFrmaximumrwhererrplot_ideal_filterrraxvspanrMr$r'),hr8rgainrYrgrYrTalimrZ compensaterrr_r`rarbrcrdrerxiromegarlrnHgdsectionZthis_Hr2deltarGrcrZn_shiftfrmagZ ax_time_idxZ ax_mag_idxZ ax_delay_idxrrr,r,r5 plot_filterrsL   $     4    .                  rrrjrkric  Cs@ddlm} tt} } |ddkr8td|dft|}td|ddg|dkr|dkrnd|dnt|d|d|d<t|||}t}t t |D]}|t |dkr6||||dkr6|||||dgg7}| tj ||||dd d d  7} | tj ||||dd d d  7} q| ||| ||qd tt| d |dd } |dkr| dd}|D]}|j||ddq|j| | || ddddt||\}}|j|dd|d|dk r|j|d|j|d|j|d|r$|j|dt|tt| |jS)a{Plot an ideal filter response. Parameters ---------- freq : array-like The ideal response frequencies to plot (must be in ascending order). gain : array-like or None The ideal response gains to plot. axes : instance of Axes | None The subplot handle. With None (default), axes are created. title : str The title to use, (default: ''). flim : tuple or None If not None, the x-axis frequency limits (Hz) to use. If None (default), freq used. fscale : str Frequency scaling to use, can be "log" (default) or "linear". alim : tuple If not None (default), the y-axis limits (dB) to use. color : color object The color to use (default: 'r'). alpha : float The alpha to use (default: 0.5). linestyle : str The line style to use (default: '--'). show : bool Show figure if True (default). Returns ------- fig : instance of matplotlib.figure.Figure The figure. See Also -------- plot_filter Notes ----- .. versionadded:: 0.14 Examples -------- Plot a simple ideal band-pass filter:: >>> from mne.viz import plot_ideal_filter >>> freq = [0, 1, 40, 50] >>> gain = [0, 1, 1, 0] >>> plot_ideal_filter(freq, gain, flim=(0.1, 100)) #doctest: +SKIP <...Figure...> rNzHfreq should start with DC (zero) and end with Nyquist, but got %s for DCrTrsrNrXr#rOF)Zendpointrg$@)rgrnrkrlrf)rg linestylernrrhrro)rrvrtrjrkrm)rwrX)rxryrErr|rrrrZrrArrSrrRrsrrvrrWrrMr$r'r)rrxrrYrYrTryrgrnrrrZmy_freqZmy_gainZ transitionsrdZ transitionrlrnr,r,r5ruQs\6   * ( "       ruc Csttt|tt}|dkrsz'_handle_event_colors..zCColor was not assigned for event%s %s. Default colors will be used.) rDrrrr%rArrFrrrrupdate) Z color_dictr:r9Zdefault_colorsZ custom_colorsrBrgZ unassignedZunassigned_strr,r,r5r2s*    r2csdc% sddlm}|dkrtd|dk r|dt|dddgd}t|d ddgd} t|d ddgd} fd d |D} fd d | D} fdd | D} | | | g}dddg}|dkrtdddd}tdddd}n@ttjg}|dkrdg}t}t}n|dkrdg}tj }|dkr@t t t |}t t |t |}g}t||dd d gD]\}}}t|dkrql|j||dd|dd |fd!\}}g}ttj D]}j|d"|dd|f}|dkrt|||d}nH|dkrLt|j}t|d|tjddf|ddtjf}||qt|}ttj |D]\}\}} |||||}!|!j| d#|d|d$}"|!g|!gjr|!d%t|t|fn|!d&|qt| ||j!d'd(|rr|j"|"d)d |Dd*}#|dkr^d+}$||krR|$d,||7}$|##|$n|dkrr|##d||qlt$||S)-aPlot CSD matrices. A sub-plot is created for each frequency. If an info object is passed to the function, different channel types are plotted in different figures. Parameters ---------- csd : instance of CrossSpectralDensity The CSD matrix to plot. %(info)s Used to split the figure by channel-type, if provided. By default, the CSD matrix is plotted as a whole. mode : 'csd' | 'coh' Whether to plot the cross-spectral density ('csd', the default), or the coherence ('coh') between the channels. colorbar : bool Whether to show a colorbar. Defaults to ``True``. cmap : str | None The matplotlib colormap to use. Defaults to None, which means the colormap will default to matplotlib's default. n_cols : int | None CSD matrices are plotted in a grid. This parameter controls how many matrix to plot side by side before starting a new row. By default, a number will be chosen to make the grid as square as possible. show : bool Whether to show the figure. Defaults to ``True``. Returns ------- fig : list of Figure The figures created by this function. rN)rcohz'"mode" should be either "csd" or "coh".r*FT)Zmegeegr-r.rgradcs*g|]"}|jkrj|qSr,r7r;rr=r,r5r6szplot_csd..cs*g|]"}|jkrj|qSr,r7r;rr,r5r6scs*g|]"}|jkrj|qSr,r7r;rr,r5r6sZEEGZ MagnetometersZ GradiometersruµV²u fT²/cm²ufT²)rrrgmBg52TEgꌠ9Y>)FzCross-spectral densityrZ Coherencerrr#g@rQr:rS)rVrWrTrUz %.1f-%.1f Hz.z%.1f Hz.r-)rpcSsg|]}|D]}|q qSr,r,)r1Zax_rhr,r,r5r6Bs)rZCSDz (%s))%rxryrrrDr|rrAr*Z frequenciesrceilrrprrrget_datargetZdiagrZnewaxisrrrrrrZ_is_sum set_titlerZsuptitlerr set_labelr')%rr4moderrWZn_colsrrZsel_eegZsel_magZsel_gradZidx_eegZidx_magZidx_gradindicesr>r?r@Zn_freqsZn_rowsrindrYrHrcrZcsd_matsrrZpsdrUrZmatrrcblabelr,rr5plot_csds$                  2            rcCs ddlm}t|dd}tdddd}tdd d }|dk}|r\|jt|d d d \}}n |d}t|t|krtdt|dt|d|d|ddd}t ||D]\} } | d\} } | dkrd n t d| } | dkrtddnt}| j |d|| | df|}|sRt ||dD]\}}| |dq2d }t d| }d|krtd|d n|}t|| d!|| | dkrd"n|d#d$}|s|jd%d&| jf|q|r| jd%d&||jd'd(d)d*d+d,|jd-d.d/|S)0u9Plot time-varying SNR estimates of the HPI coils. Parameters ---------- snr_dict : dict The dictionary returned by `~mne.chpi.compute_chpi_snr`. Must have keys ``times``, ``freqs``, ``TYPE_snr``, ``TYPE_power``, and ``TYPE_resid`` (where ``TYPE`` can be ``mag`` or ``grad`` or both). axes : None | list of matplotlib.axes.Axes Figure axes in which to draw the SNR, power, and residual plots. The number of axes should be 3× the number of MEG sensor types present in ``snr_dict``. If ``None`` (the default), a new `~matplotlib.figure.Figure` is created with the required number of axes. Returns ------- fig : instance of matplotlib.figure.Figure A figure with subplots for SNR, power, and residual variance, separately for magnetometers and/or gradiometers (depending on what is present in ``snr_dict``). Notes ----- If you supply a list of existing `~matplotlib.axes.Axes`, then the figure legend will not be drawn automatically. If you still want it, running ``fig.legend(loc='right', title='cHPI frequencies')`` will recreate it, though you may also need to manually adjust the margin to make room for it (e.g., using ``fig.subplots_adjust(right=0.8)``). .. versionadded:: 0.24 rNrZSNRz cHPI powerzResidual variance)snrpowerresidZ magnetometersZ gradiometers)rrr#T)Zsharexzaxes must be a list of z axes, got length z (z).rFrrr@rrerrZfreqsz Hzr?/()rZdB²)rYrtr)rvrXgffffff?g333333?gffffff?gffffff?)rr[rrprr[zcHPI frequencies)r.rY)rxryrErDrrArrrZset_size_inchesrsplitrrrrrZ align_ylabelsrr6)Zsnr_dictrrZ valid_keysr>Z full_namesZ axes_was_nonercZlegend_labels_existrBrrHrrZ plot_kwargslineslinerrZ set_kwargsr,r,r5 plot_chpi_snrQsN!   $     r)r,TFTTN)NNNFT) NrNNNTTrT) NrNNNTTr(N)NT)rKT)N)NrTNNT)N)O__doc__rzrr itertoolsros.pathpathrr collectionsrnumpyr|defaultsrZ _freesurferrrr r Zrankr Zsurfacer Z io.constantsr Zio.projrZio.pickrrrrZ source_spacerrrZ transformsrrutilsrrrrrrrrr r!filterr"r$r%r&r'r(r)rLrrrr'rrArIrJrMrWrZr[Z _DEFAULT_ALIMrrur2rrr,r,r,r5s          ,    z q 9 w    $   ` b