U f{j@snddlmZddlZddlmZmZddlmZddl m Z ddl m Z m Z dd Ze dd d Zd dZdS))partialN)_COLA_Storer) _picks_to_idx)_normalize_vectors)loggerverbosecCsxtj|\}}t|jf}|ttj|dk}tj||d}d||||<||9}t |j ||}|||fS)z2Use a covariance matrix to compute the SVD faster.)outg?) npZlinalgZeighZonessizeZfinfofloatepssqrtdotT)covdatasuZnormmaskvrT/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/preprocessing/otp.py_svd_covsr$@c Cstdt|j|dd}tt}}t|D]2\}}|j||jdkrX||q0||q0t|t }t|t }t t t ||jd}|t |dkrt d||jd|t |df|d} |jd d } ttt||d t| j|d t |j|| |jd} ttd t |j|t |jg} t| dd| ddD]D\} }tdt|j| |dg| ||| |fd qP| S)aDenoise MEG channels using leave-one-out temporal projection. Parameters ---------- raw : instance of Raw Raw data to denoise. duration : float | str The window duration (in seconds; default 10.) to use. Can also be "min" to use as short a window as possible. %(picks_all_data)s %(verbose)s Returns ------- raw_clean : instance of Raw The cleaned data. Notes ----- This algorithm is computationally expensive, and can be several times slower than realtime for conventional M/EEG datasets. It uses a leave-one-out procedure with parallel temporal projection to remove individual sensor noise under the assumption that sampled fields (e.g., MEG and EEG) are oversampled by the sensor array :footcite:`LarsonTaulu2018`. OTP can improve sensor noise levels (especially under visual inspection) and repair some bad channels. This noise reduction is known to interact with :func:`tSSS ` such that increasing the ``st_correlation`` value will likely be necessary. Channels marked as bad will not be used to reconstruct good channels, but good channels will be used to process the bad channels. Depending on the type of noise present in the bad channels, this might make them usable again. Use of this algorithm is covered by a provisional patent. .. versionadded:: 0.16 References ---------- .. footbibliography:: z9Processing MEG data using oversampled temporal projectionr)excludeZbadsZsfreqzTduration (%s) yielded %s samples, which is fewer than the number of channels -1 (%s)rF)r ) picks_good picks_bad)picksrNr z! Denoising % 8.2f - % 8.2f sec)rinforlist enumerateZch_namesappendr arrayintroundrlen ValueErrorcopyZ load_datarr_otpr_datatimesrangeziptuplefeed)rawdurationr!r rr iipiZ n_samplesZ n_overlapZraw_otpZotpZ read_limsstartstoprrroversampled_temporal_projectionsB/        ""r9c Cs4t|stdtj|ddd}||8}||}t|}t||j}t|dkrft ||d}t |D]\}} t t |t t |dt|} t |t | | || d} tt||| j| } | ||9} | || 7} | || <qn|D]6} tt|| |j||| <|| || 7<q|gS)z#Perform OTP on one segment of data.z2non-finite data (inf or nan) found in raw instancer T)ZaxisZkeepdimsrrr)r isfiniteall RuntimeErrorZmeanrrrr)rr$r#r/Zix_) rrr Z data_meansZ data_goodZnormsrZ full_basismipickidxZt_basisxrrrr,ms( "   r,)rNN) functoolsrnumpyr Z_olarrZio.pickrZsurfacerutilsrr rr9r,rrrrs    M