U f{j'@sxddlZddlmZddlmZmZddlmZm Z m Z ddl m Z m Z mZmZmZmZe dd d Ze dddZdS)N)is_fixed_orient)_check_reference _log_exp_var)loggerverbosewarn) _check_ori_make_sparse_stc _prepare_gain_reapply_source_weighting_compute_residual_make_dipoles_sparse'ư>c (Csddlm} |}|}|dkr:tj|jdtjd}ttj } |jd} |j\} } tjj t ||j dd}|t |}||}tjj |tjd}||}| |dkrtd| }t| }|}|d krtj }nd d }d }t|D]}d |t|<t|| k}||}||}|t|krH|j}|dd|f}t ||tjddf|j }|jdd| d|7<| j|dd\}}}|tjddf}~t ||| |j }t ||}t |j |}|dkr|d tj||jdd}|tj||dd}nH|d kr^|t tj||jdd}tj||dd}ntd|dkr|dkr|}n|t||tdj }nXtj|d |dd} |dkr| }!n"tj|d |dd}"| ||"}!t|!||}tj | tjd}#||#|<tt|#|tt|}$|#}|$|kpF|dk}%t||ks\|%r|t!"d|t||$ft|}|%rqq||dkrt!"dnt#dt ||}&|&|dddf|}'|'|fS)aHierarchical Bayes (Gamma-MAP). Parameters ---------- M : array, shape=(n_sensors, n_times) Observation. G : array, shape=(n_sensors, n_sources) Forward operator. alpha : float Regularization parameter (noise variance). maxit : int Maximum number of iterations. tol : float Tolerance parameter for convergence. group_size : int Number of consecutive sources which use the same gamma. update_mode : int Update mode, 1: MacKay update (default), 3: Modified MacKay update. gammas : array, shape=(n_sources,) Initial values for posterior variances (gammas). If None, a variance of 1.0 is used. %(verbose)s Returns ------- X : array, shape=(n_active, n_times) Estimated source time courses. active_set : array, shape=(n_active,) Indices of active sources. r)linalgNr dtypeZfro)ordz>Number of sources has to be evenly dividable by the group sizercSs|S)N)xrr\/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/inverse_sparse/_gamma_map.py denom_funPsz!_gamma_map_opt..denom_fungF)Z full_matrices)ZaxiszInvalid value for update_modefloatz6Iteration: %d active set size: %d convergence: %0.3ez Convergence reached ! z Convergence NOT reached ! )$ZscipyrcopynpZonesshapeZfloat64ZfinforepsZnormdotTsqrtinf ValueErrorarangerangeisnanabslensizenewaxisZflatZsvdZmeanZconjrealsummaximumZreshaperepeatzerosrinfor)(MGalphamaxittol update_mode group_sizegammasrrr n_sourcesZ n_sensorsZn_timesZM_normalize_constantZG_normalize_constantZn_active active_setZgammas_full_oldrZ last_sizeZitnoZgidxZCMUS_ZCMinvZCMinvGAZnumerdenomZ numer_combZ gammas_combZ denom_combZ gammas_fullerrZbreakingZn_constZx_activerrr_gamma_map_opts!           "        rBauto皙?TFc% sDtt|j|| |||\}}}}}}t||t|s@|sDdnd}fdd|dD}j|}tdt||}t |||||| | ||d \}}t |dkrt d |d d |f|}t |||}| rt ||||}|dkrt|st|d\}}t|}t |dt |krtjt |d|jdf|jd } t||}|| ||d d f<t |d|jdf| _| }|d d tjfdtd}d||dk<|d d |f||}!~jd}"d jd }#| rt||||"|#||!dd}$nt||||"|#d||d}$t||ddtd| r@|$|f}$|$S)asHierarchical Bayes (Gamma-MAP) sparse source localization method. Models each source time course using a zero-mean Gaussian prior with an unknown variance (gamma) parameter. During estimation, most gammas are driven to zero, resulting in a sparse source estimate, as in :footcite:`WipfEtAl2007` and :footcite:`WipfNagarajan2009`. For fixed-orientation forward operators, a separate gamma is used for each source time course, while for free-orientation forward operators, the same gamma is used for the three source time courses at each source space point (separate gammas can be used in this case by using xyz_same_gamma=False). Parameters ---------- evoked : instance of Evoked Evoked data to invert. forward : dict Forward operator. noise_cov : instance of Covariance Noise covariance to compute whitener. alpha : float Regularization parameter (noise variance). %(loose)s %(depth)s xyz_same_gamma : bool Use same gamma for xyz current components at each source space point. Recommended for free-orientation forward solutions. maxit : int Maximum number of iterations. tol : float Tolerance parameter for convergence. update_mode : int Update mode, 1: MacKay update (default), 2: Modified MacKay update. gammas : array, shape=(n_sources,) Initial values for posterior variances (gammas). If None, a variance of 1.0 is used. pca : bool If True the rank of the data is reduced to the true dimension. return_residual : bool If True, the residual is returned as an Evoked instance. return_as_dipoles : bool If True, the sources are returned as a list of Dipole instances. %(rank_none)s .. versionadded:: 0.18 %(pick_ori)s %(verbose)s Returns ------- stc : instance of SourceEstimate Source time courses. residual : instance of Evoked The residual a.k.a. data not explained by the sources. Only returned if return_residual is True. References ---------- .. footbibliography:: r csg|]}j|qSr)ch_namesindex).0nameevokedrr szgamma_map..rFzWhitening data matrix.)r5r6r7r9r8rrz*No active dipoles found. alpha is too big.Nrg?ZsfreqT) active_is_idx)rMpick_orir)prefixz[done])rr r1r rdatarrr rBr) Exceptionr rdivmoduniquer0rrZ searchsortedr+r%Zraveltimesrr r)%rKforwardZ noise_covr4loosedepthZxyz_same_gammar5r6r7r9ZpcaZreturn_residualZreturn_as_dipolesZrankrNrZgainZ gain_infoZwhitenerZsource_weightingmaskr8selr2Xr;Z M_estimateZresidualidxoffsetZ active_srcZX_xyzZ gain_activeZtminZtstepoutrrJr gamma_mapsxA            $    r_)rrr r NN) rCrDTrrr NTFFNNN)numpyrrVrZminimum_norm.inverserrutilsrrrZ mxne_inverser r r r rrrBr_rrrrs2