U f{jw@sJddlZddlmZmZddlmZddlmZddl m Z ddl m Z ddl mZmZdd lmZmZmZmZdd lmZmZd d lmZmZmZmZmZmZmZdd l m!Z!ddl"m#Z#m$Z$m%Z%m&Z&d1ddZ'e$d2ddZ(ddZ)e$d3ddZ*ddZ+e$d4d d!Z,e$d5d"d#Z-e$d6dd)d*d+Z.d7d-d.Z/e$d8d/d0Z0dS)9N)Epochsmake_fixed_length_events) EvokedArray)FIFF) pick_info) _make_stc)cwtmorlet) _psd_from_mt_compute_mt_params_psd_from_mt_adaptive _mt_spectra)rescale _log_rescale) combine_xyz_check_or_prepare_assemble_kernel_pick_channels_inverse_operatorINVERSE_METHODS _check_ori_subject_from_inverse) parallel_func)loggerverbose ProgressBar _check_optionqq?dSPMTnormalFc Csddlm} t||||| | }t|j|}tdt|tdt||||| d\}}}}|r| j |dd\}}}t |d|dk}|d ||d d d |f}|d |}td t||fnd }|d t j k}||||||fS) z@Prepare inverse operator and params for spectral / TFR analysis.r)linalgz Picked %d channels from the datazComputing inverse...)use_cpsF)Z full_matricesg:0yE>NzReducing data rank %d -> %d source_ori)Zscipyr!rrZch_namesrinfolenrZsvdnpsumrFIFFV_MNE_FREE_ORI)instinverse_operatorlabellambda2methodnavedecimpcapick_oriprepared method_paramsr"rr!invselK noise_normvertno_UsVhZrank is_free_orir>^/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/minimum_norm/time_frequency.py_prepare_source_paramss0      r@logratioc s&td|ttfdd|D}t||||||||| | | |d|||d\}}}|jd}t}t|}t | | |D]\}fddt |D}tj |dd|ddfd d }t ||j dd| | | ddd }|j d }t| |}t||||||d jd}|||<tdq|S)a Compute source space induced power in given frequency bands. Parameters ---------- epochs : instance of Epochs The epochs. inverse_operator : instance of InverseOperator The inverse operator. bands : dict Example : bands = dict(alpha=[8, 9]). label : Label Restricts the source estimates to a given label. lambda2 : float The regularization parameter of the minimum norm. method : "MNE" | "dSPM" | "sLORETA" | "eLORETA" Use minimum norm, dSPM (default), sLORETA, or eLORETA. nave : int The number of averages used to scale the noise covariance matrix. n_cycles : float | array of float Number of cycles. Fixed number or one per frequency. df : float Delta frequency within bands. use_fft : bool Do convolutions in time or frequency domain with FFT. decim : int Temporal decimation factor. baseline : None (default) or tuple, shape (2,) The time interval to apply 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. baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio' Perform baseline correction by - subtracting the mean of baseline values ('mean') - dividing by the mean of baseline values ('ratio') - dividing by the mean of baseline values and taking the log ('logratio') - subtracting the mean of baseline values followed by dividing by the mean of baseline values ('percent') - subtracting the mean of baseline values and dividing by the standard deviation of baseline values ('zscore') - dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values ('zlogratio') pca : bool If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64). %(n_jobs)s prepared : bool If True, do not call :func:`prepare_inverse_operator`. method_params : dict | None Additional options for eLORETA. See Notes of :func:`apply_inverse`. .. versionadded:: 0.16 %(use_cps_restricted)s .. versionadded:: 0.20 %(verbose)s Returns ------- stcs : dict of SourceEstimate (or VolSourceEstimate) The estimated source space induced power estimates. r-cs.g|]&\}}t|d|ddqS)rrg@)r&Zarange).0r9band)dfr>r? sz-source_band_induced_power..F) r+r,r-r.n_cyclesr/use_fftr0n_jobswith_plvr2r3r"sfreqcs4g|],\}}d|kr(dkrnq|qS)rrr>)rCkf)rDr>r?rFs  Nr)Zaxis)copyrrsrc)verticestmintstepsubjectsrc_typez[done])rrr& concatenateitems_source_induced_powerr$dictrr enumeratemeanrtimesfloatrkindr) epochsr*Zbandsr+r,r-r.rGrErHr/baseline baseline_moder0rIr2r3r"rfreqsZpowersr9r8FsstcsrSnameidxpowerrQrRstcr>)rDrEr?source_band_induced_power?sTK         rhc Csh|dddddd|fjd}t|}|jd}d} |tjkrV|dkrVd} |d}|||f} | | fS)z Prepare TFR source localization.NrrFT)shaper%rr() datar/r1Wsr6r#n_timesZn_freqs n_sourcesr=rjr>r>r? _prepare_tfrs"  roc  Cst|| | |||\} } tj| tjd}|r:tj| tjdnd}|D]R}||}|dk rbt||}t|| |||| | ||d \}}||7}|rB||7}qB||fS)z'Aux function for induced power and PLV.dtypeN) rkr=r6rlrHr/rjrJ with_power)ror&zerosfloat64 complex128dot_single_epoch_tfr)rkr6r5rlr#rHr<rrrJr1r/rrjr=rfplvepochZpower_eplv_er>r>r?_compute_pow_plvs,   r{c Cstj|tjd} |r$tj|tjdnd} |\} } } t|D]J\}}t||g||d}t|t|d}|rtj| | ftjd}tj| | ftjd}tt |t |gD]\}}t ||}|}|r|ddd}|r|dkr||7}n |d|7}|rt d t||d }n|r(||9}||7}~q| dd|ddf|7<~|r:|t|}| dd|ddf|7<~q:| | fS) z8Compute single trial TFRs, either ITC, power or raw TFR.rpN)rHr/rriry?z#combining the current components...Zsquare)r&rsrtrurYr ZasfortranarrayZreshaper%realimagrvrdebugrabs)rkr=r6rlrHr/rjrJrrZtfr_erzrnr9rmrMwZtfr_Zplv_fZtfr_frLtZsolZsol_pick_normalr>r>r?rws@      rwcs(|}t||||||| ||||d \}}}|tt| t|d\}} |jd}tdt||||d| f ddt || D}t dd|D}|t|} rt d d|D}t |}|t|}nd }|d k r||d d d d tj fd 9}|||fS) z&Aux function for source induced power. r)r*r+r,r-r.r0r1r2r3r"r)Zmax_jobsrKzComputing source power ...)rG zero_meanc3s0|](}|d dd VqdS)r#T) rkr6r5rlr#rHr<rJrrr1r/Nr>)rCrk r6r<rlr/r4Zmy_compute_source_tfrsr1r5rHrJr>r? s z(_source_induced_power..css|]}|dVqdS)rNr>rCor>r>r?r&scss|]}|dVqdS)rNr>rr>r>r?r*sNr) get_datar@rr{r%r$rr r& array_splitr'rnewaxis)r^r*rar+r,r-r.rGr/rHr0r1rIrJrr2r3r"rZ epochs_datar8r=r7parallelrboutrfrxr>rr?rWsJ        rWcCsvtd|tt| |d|dt|||||||||| | | |||||d\}}}t||jdd|| | dd}||fS)a Compute induced power and phase lock. Computation can optionally be restricted in a label. Parameters ---------- epochs : instance of Epochs The epochs. inverse_operator : instance of InverseOperator The inverse operator. freqs : array Array of frequencies of interest. label : Label Restricts the source estimates to a given label. lambda2 : float The regularization parameter of the minimum norm. method : "MNE" | "dSPM" | "sLORETA" | "eLORETA" Use minimum norm, dSPM (default), sLORETA, or eLORETA. nave : int The number of averages used to scale the noise covariance matrix. n_cycles : float | array of float Number of cycles. Fixed number or one per frequency. decim : int Temporal decimation factor. use_fft : bool Do convolutions in time or frequency domain with FFT. pick_ori : None | "normal" If "normal", rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations. baseline : None (default) or tuple of length 2 The time interval to apply 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. baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio' Perform baseline correction by - subtracting the mean of baseline values ('mean') - dividing by the mean of baseline values ('ratio') - dividing by the mean of baseline values and taking the log ('logratio') - subtracting the mean of baseline values followed by dividing by the mean of baseline values ('percent') - subtracting the mean of baseline values and dividing by the standard deviation of baseline values ('zscore') - dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values ('zlogratio') pca : bool If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64). %(n_jobs)s zero_mean : bool Make sure the wavelets are zero mean. prepared : bool If True, do not call :func:`prepare_inverse_operator`. method_params : dict | None Additional options for eLORETA. See Notes of :func:`apply_inverse`. %(use_cps_restricted)s .. versionadded:: 0.20 %(verbose)s Returns ------- power : array The induced power. r-r#rO)r+r,r-r.rGr/rHr1r0rIr3rr2r"NF)rN)rrrrWrr[)r^r*rar+r,r-r.rGr/rHr1r_r`r0rIrr2r3r"rrfrxr8r>r>r?source_induced_power6s6Q   ri@?hannrc!Csl|dkr dnt|}t| } d| kr0dks@ntd| ft|}d| ||jd}t|d|||}t||dd|d|jddd}t||||||| | | | ||||d |||d d }d}d}d}|D]$\}} ||j7}|| j7}|d7}q|dkst||}||}|rDt j ||d |d 9}t j ||d |d 9}|| _|| _ ||_|}|rh|| f}|S) a Compute source power spectral density (PSD). Parameters ---------- raw : instance of Raw The raw data. inverse_operator : instance of InverseOperator The inverse operator. lambda2 : float The regularization parameter. method : "MNE" | "dSPM" | "sLORETA" Use minimum norm, dSPM (default), sLORETA, or eLORETA. tmin : float The beginning of the time interval of interest (in seconds). Use 0. for the beginning of the file. tmax : float | None The end of the time interval of interest (in seconds). If None stop at the end of the file. fmin : float The lower frequency of interest. fmax : float The upper frequency of interest. n_fft : int Window size for the FFT. Should be a power of 2. overlap : float The overlap fraction between windows. Should be between 0 and 1. 0 means no overlap. pick_ori : None | "normal" If "normal", rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations. label : Label Restricts the source estimates to a given label. nave : int The number of averages used to scale the noise covariance matrix. pca : bool If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64). prepared : bool If True, do not call :func:`prepare_inverse_operator`. method_params : dict | None Additional options for eLORETA. See Notes of :func:`apply_inverse`. .. versionadded:: 0.16 inv_split : int or None Split inverse operator into inv_split parts in order to save memory. .. versionadded:: 0.17 bandwidth : float | str The bandwidth of the multi taper windowing function in Hz. Can also be a string (e.g., 'hann') to use a single window. For backward compatibility, the default is 'hann'. .. versionadded:: 0.17 adaptive : bool Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation). .. versionadded:: 0.17 low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. .. versionadded:: 0.17 %(n_jobs)s It is only used if adaptive=True. .. versionadded:: 0.17 return_sensor : bool If True, return the sensor PSDs as an EvokedArray. .. versionadded:: 0.17 dB : bool If True (default False), return output it decibels. .. versionadded:: 0.17 %(verbose)s Returns ------- stc_psd : instance of SourceEstimate | VolSourceEstimate The PSD of each of the sources. sensor_psd : instance of EvokedArray The PSD of each sensor. Only returned if ``return_sensor`` is True. See Also -------- compute_source_psd_epochs Notes ----- Each window is multiplied by a window before processing, so using a non-zero overlap is recommended. This function is different from :func:`compute_source_psd_epochs` in that: 1. ``bandwidth='hann'`` by default, skipping multitaper estimation 2. For convenience it wraps :func:`mne.make_fixed_length_events` and :class:`mne.Epochs`. Otherwise the two should produce identical results. Nrrrz2Overlap must be at least 0 and less than 1, got %s?rK)r_T) return_sensor)rg$@) r\ ValueErrorintr$rrcompute_source_psd_epochsrkAssertionErrorr&log10r.)!rawr*r,r-rQZtmaxfminfmaxZn_fftZoverlapr1r+r.r0r2r3 inv_split bandwidthadaptivelow_biasrIrZdBrdurationeventsr^rZ source_dataZ sensor_datacountrgevokedr>r>r?compute_source_psdsnp    r@c5#sttd||ft||||||| ||||td \}}}}}}|dkrVt|jdn|}| dk rpt|| }n|g}t|j }|jd}t ||| | | dd\}} t|}z t|}Wn(t k rt|j }d|f}Yn Xd |f}t | trd | f} n d || f} td | |f| r:tt|\} }nttjddtjf}!t|}"t||d }#t|j|dd}$t|#D]\}%}&t||&|}'t|'||\}(})|%dkr|)|k|)|k@tt|)}*|$d|*|$d<W5QRX|)})tjt||(jd|(jdf|(jd}+t|D]8},t|j|(dd|,ddf|+dd|,ddf<q,| r| fddt|+t |t|+D}-t!|-}.n |+ddddf}+t"|+|!}.t|jdt#f}/d}0|D]}1tj|1jd|(jd|(jdf|(jd}2t|D]6},t|1|(dd|,ddf|2dd|,ddf<q| r| fddt|2t |t|2D}-t!|-}3n |2ddddf}2t"|2|!}3|3|/|0|0|1jdddf<|0|1jd7}0q|r|dkrt$|/dd}/|dk r|/|d9}/t%|/|)d|*||"|dj&d}-|r\d|%f}4|-t'|.|$(|)d|4|f}-|-Vq|#)|dS)z#Generate compute_source_psd_epochs.z$Considering frequencies %g ... %g HzrNrrKFrzon at most %d epochsz on %d epochsz %s windowingz!%d tapers with bandwidth %0.1f Hzz Using %s %s)Z max_valuerrrrpc3s|]}|VqdSNr>rCxZeigvalsZ freq_maskZmy_psd_from_mt_adaptiver>r?rxsz-_compute_source_psd_epochs..c3s|]}|VqdSrr>rrr>r?rsr}rO)rQrRrPrSrTz Epoch %d PSD)*rr$r@rr&eyerjrr%r[r RuntimeErrorr isinstancestrrr sqrtrrrrrYrvrrZdiffZ_unlockemptyrqrangeTminrUr r'rrr]rrNupdate)5r^r*r,r-rrr1r+r.r0rrrrrIr2r3rr"r6r5r<r8r=r7ZK_splitrmrKZdpssZn_tapersZn_epochsextrarweightsrSZ iter_epochsZ evoked_inforLerkZx_mtraZfstepZ x_mt_sensorirZ sensor_psdZpsdposZK_partZx_mt_srcZthis_psdcommentr>rr?_compute_source_psd_epochs.s              6   4       rcCsXt|||||||||| | | | | |||||d}|r6|St}|D]}||q@|SdS)a2 Compute source power spectral density (PSD) from Epochs. This uses the multi-taper method to compute the PSD for each epoch. Parameters ---------- epochs : instance of Epochs The raw data. inverse_operator : instance of InverseOperator The inverse operator. lambda2 : float The regularization parameter. method : "MNE" | "dSPM" | "sLORETA" | "eLORETA" Use minimum norm, dSPM (default), sLORETA, or eLORETA. fmin : float The lower frequency of interest. fmax : float The upper frequency of interest. pick_ori : None | "normal" If "normal", rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations. label : Label Restricts the source estimates to a given label. nave : int The number of averages used to scale the noise covariance matrix. pca : bool If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64). inv_split : int or None Split inverse operator into inv_split parts in order to save memory. bandwidth : float | str The bandwidth of the multi taper windowing function in Hz. Can also be a string (e.g., 'hann') to use a single window. adaptive : bool Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation). low_bias : bool Only use tapers with more than 90%% spectral concentration within bandwidth. return_generator : bool Return a generator object instead of a list. This allows iterating over the stcs without having to keep them all in memory. %(n_jobs)s It is only used if adaptive=True. prepared : bool If True, do not call :func:`prepare_inverse_operator`. method_params : dict | None Additional options for eLORETA. See Notes of :func:`apply_inverse`. .. versionadded:: 0.16 return_sensor : bool If True, also return the sensor PSD for each epoch as an EvokedArray. .. versionadded:: 0.17 %(use_cps_restricted)s .. versionadded:: 0.20 %(verbose)s Returns ------- out : list (or generator object) A list (or generator) for the source space PSD (and optionally the sensor PSD) for each epoch. See Also -------- compute_source_psd )r,r-rrr1r+r.r0rrrrrIr2r3rr"N)rlistappend)r^r*r,r-rrr1r+r.r0rrrrZreturn_generatorrIr2r3rr"rZstcs_genrcrgr>r>r?rs6Q r) NrrrrTr FNTN)NrrrrArFrNrBTNFNTN)N)NrrrrArFTr NTFFNTN)NrrrrArFNNrBTNFFNTN)rrrNrrrrNNrTFNNrFFNFF)rrrrNNrTNrFTNFNFT)rrrrNNrTNrFTFNFNFTN)1numpyr&r^rrrrZ io.constantsrZio.pickrZsource_estimaterZtime_frequency.tfrr r Ztime_frequency.multitaperr r r rr_rrZinverserrrrrrrrrutilsrrrrr@rhror{rwrWrrrrr>r>r>r?s(    $  ' o 1 - a