U f{j: @sdZddlZddlmZddlmZddlmZm Z ddl m Z ddl m Z mZmZdd lmZmZdd lmZmZmZmZmZd d lmZmZmZmZmZmZed ddZddZ eddddZ!edddddZ"ed!ddddZ#ed"ddZ$dS)#z@Compute Linearly constrained minimum variance (LCMV) beamformer.N) compute_rank)_simplify_info)pick_channels_cov pick_info)_subject_from_forward) combine_xyz_check_reference _check_depth) _make_stc _get_src_type)loggerverbose_check_channels_spatial_filter_check_one_ch_type_check_info_inv)_prepare_beamformer_input_compute_power_compute_beamformer_check_src_type Beamformer_proj_whiten_data皙?infounit-noise-gain-invariantFmatrixc $Cst|}td||||\}} }t||||}t||}t|||d}t|||d}|D]>}||ksr||||krV|sVtd|||||dfqV~|}td|f~t | d} | dkrd| d <t ||||f||dd | \}}}}}}}}t |d }t ||d }| }d |kr&|d =t|}~|r>dnd}t||||||| || |||d \}}t|d|} t|}!|dkr|nd}t|d}"td|||||||||"|||d| |d|!||| d}#|#S)aCompute LCMV spatial filter. Parameters ---------- %(info_not_none)s Specifies the channels to include. Bad channels (in ``info['bads']``) are not used. forward : instance of Forward Forward operator. data_cov : instance of Covariance The data covariance. reg : float The regularization for the whitened data covariance. noise_cov : instance of Covariance The noise covariance. If provided, whitening will be done. Providing a noise covariance is mandatory if you mix sensor types, e.g. gradiometers with magnetometers or EEG with MEG. label : instance of Label Restricts the LCMV solution to a given label. %(pick_ori_bf)s - ``'vector'`` Keeps the currents for each direction separate %(rank_info)s %(weight_norm)s Defaults to ``'unit-noise-gain-invariant'``. %(reduce_rank)s %(depth)s .. versionadded:: 0.18 %(inversion_bf)s .. versionadded:: 0.21 %(verbose)s Returns ------- filters : instance of Beamformer Dictionary containing filter weights from LCMV beamformer. Contains the following keys: 'kind' : str The type of beamformer, in this case 'LCMV'. 'weights' : array The filter weights of the beamformer. 'data_cov' : instance of Covariance The data covariance matrix used to compute the beamformer. 'noise_cov' : instance of Covariance | None The noise covariance matrix used to compute the beamformer. 'whitener' : None | ndarray, shape (n_channels, n_channels) Whitening matrix, provided if whitening was applied to the covariance matrix and leadfield during computation of the beamformer weights. 'weight_norm' : str | None Type of weight normalization used to compute the filter weights. 'pick-ori' : None | 'max-power' | 'normal' | 'vector' The orientation in which the beamformer filters were computed. 'ch_names' : list of str Channels used to compute the beamformer. 'proj' : array Projections used to compute the beamformer. 'is_ssp' : bool If True, projections were applied prior to filter computation. 'vertices' : list Vertices for which the filter weights were computed. 'is_free_ori' : bool If True, the filter was computed with free source orientation. 'n_sources' : int Number of source location for which the filter weight were computed. 'src_type' : str Type of source space. 'source_nn' : ndarray, shape (n_sources, 3) For each source location, the surface normal. 'proj' : ndarray, shape (n_channels, n_channels) Projections used to compute the beamformer. 'subject' : str The subject ID. 'rank' : int The rank of the data covariance matrix used to compute the beamformer weights. 'max-power-ori' : ndarray, shape (n_sources, 3) | None When pick_ori='max-power', this fields contains the estimated direction of maximum power at each source location. 'inversion' : 'single' | 'matrix' Whether the spatial filters were computed for each dipole separately or jointly for all dipoles at each vertex using a matrix inversion. Notes ----- The original reference is :footcite:`VanVeenEtAl1997`. To obtain the Sekihara unit-noise-gain vector beamformer, you should use ``weight_norm='unit-noise-gain', pick_ori='vector'`` followed by :meth:`vec_stc.project('pca', src) `. .. versionchanged:: 0.21 The computations were extensively reworked, and the default for ``weight_norm`` was set to ``'unit-noise-gain-invariant'``. References ---------- .. footbibliography:: Zlcmv)rankrz3%s data rank (%s) did not match the noise rank (%s)Nz#Making LCMV beamformer with rank %sZ depth_sparseZsingleFr) noise_covrZpcach_names)includeZ estimatorr) inversionnn orient_stdwhitenersrc)NvectorprojsZLCMVZnsource source_nn)kindweightsdata_covrr% weight_normpick_orirprojis_sspvertices is_free_ori n_sourcessrc_typer)subjectr max_power_orir")rrrrr ValueErrorgetr rr rlistrZ _get_squaresumvaluesrr rboolrcopy)$rforwardr,regrlabelr.rr-Z reduce_rankdepthr"r_Zallow_mismatchZpicksZ data_rankZ noise_rankkeyr2r/ZvertnoGr%r#r$rZCmZrank_intn_orientWr6r4Z subject_fromr0filtersrHS/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/beamformer/_lcmv.py make_lcmvsq            rJc csJt|tjr"|jdkr"|g}d}nd}|d}t|D]\}}t|t|dkr\td|srtd|dt ||d |}d}|d rt ||} |d d krd}qtd t | } n6|j d|j dkr|d dkr||f} n t ||} d|d} t |\}} t| |d|| |d||d|d| d Vq6tddS)zPsz%apply_lcmv_epochs..)r rr[rrget_datarY) ZepochsrGr`rrrLr]rVZstcsrHrHrIapply_lcmv_epochs(s  rec CsNt||j}t|j|}||||f\}}|d} t|||| d} t| S)aApply Linearly Constrained Minimum Variance (LCMV) beamformer weights. Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights on raw data. Parameters ---------- raw : mne.io.Raw Raw data to invert. filters : instance of Beamformer LCMV spatial filter (beamformer weights). Filter weights returned from :func:`make_lcmv`. start : int Index of first time sample (index not time is seconds). stop : int Index of first time sample not to include (index not time is seconds). %(verbose)s Returns ------- stc : SourceEstimate | VolSourceEstimate Source time courses. See Also -------- make_lcmv, apply_lcmv_epochs, apply_lcmv, apply_lcmv_cov rrZ)r rrrrYr\) rawrGstartstoprrr]rVr[rLr^rHrHrIapply_lcmv_rawUs ric stj|}fdd|D}t||djd|d}tdjd|}t|jd|}t||d|}t|\}}t||d|d d d |d |d |dS)aApply Linearly Constrained Minimum Variance (LCMV) beamformer weights. Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights to a data covariance matrix to estimate source power. Parameters ---------- data_cov : instance of Covariance Data covariance matrix. filters : instance of Beamformer LCMV spatial filter (beamformer weights). Filter weights returned from :func:`make_lcmv`. %(verbose)s Returns ------- stc : SourceEstimate | VolSourceEstimate Source power. See Also -------- make_lcmv, apply_lcmv, apply_lcmv_epochs, apply_lcmv_raw csg|]}j|qSrH)r)raiir,rHrIrcsz"apply_lcmv_cov..r+rr3rVr(r1r4grKr5r))r1r4rLrMr5r)rN) rrrrUrTrrr ) r,rGrr]Z sel_namesrErVZ source_powerrNrHrkrIapply_lcmv_covs"    rm) rNNNrrFNrN)NN)N)%__doc__numpyrPrrZ io.meas_inforZio.pickrrr>rZminimum_norm.inverserr r Zsource_estimater r utilsr rrrrrrrrrrrJrYr_rerirmrHrHrHrIs>     41+,)