U f{j,@sddlZddlmZddlmZmZmZmZm Z ddl m Z ddl m Z ddlmZmZddlmZdd lmZdd lmZmZd d Zd dZedddddZedddZdS)N) pick_types)_validate_type _ensure_int_check_preloadverboselogger)BaseRaw)FIFF) BaseEpochsmake_fixed_length_epochs)Evoked)fit_sphere_to_headshape)_calc_g_calc_hcCsJ|jddt|d|7<tj|}|d}t|}|||fS)Nr)flatlennplinalginvsum)Glambda2GiTCsgirU/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/preprocessing/_csd.py _prepare_Gs     rcCs|jd}t|}|d}||}|\}}} t||} tj| dd| } | t|ddtjf| tjddf} t| j|j|d} | S)zCompute the CSD.raxisNr)shapereyeZmeandotrnewaxisT) G_precomputedHradiusZ n_channelsdatamuZrrrZCp2Zc02ZC2Xrrr _compute_csd's     ,r.autoh㈵>2T)rc sXttttfdtdjdtjkr4tdt|t d|rL nt jddgdt fd d jd Drtd t d krtdt|ddd |krdksntd|t|dd|d krtd|t|d}|dkr td|t|trJ|dkrJtj\}}} || \} } } | | | |f}zt|t}|\} } } }Wn$tk rtd|YnXt| ddt| ddt| ddt|dd|d krtd|tfdd D} t| rt| dd rtd | | | | f8} | tjj| ddd!} tt| | jd"d}~ t|||d#}t|||d#}t ||}t!|||d$}ttsj"gnj"}|D]}t|||<qj#tjjd<W5QRXD]"}jd%|j$tj%tj&d&qttrTj'r8d'j'kr8j'd'=j(rTd'j(krTj(d'=S)(aGet the current source density (CSD) transformation. Transformation based on spherical spline surface Laplacian :footcite:`PerrinEtAl1987,PerrinEtAl1989,Cohen2014,KayserTenke2015`. This function can be used to re-reference the signal using a Laplacian (LAP) "reference-free" transformation. Parameters ---------- inst : instance of Raw, Epochs or Evoked The data to be transformed. sphere : array-like, shape (4,) | str The sphere, head-model of the form (x, y, z, r) where x, y, z is the center of the sphere and r is the radius in meters. Can also be "auto" to use a digitization-based fit. lambda2 : float Regularization parameter, produces smoothness. Defaults to 1e-5. stiffness : float Stiffness of the spline. n_legendre_terms : int Number of Legendre terms to evaluate. copy : bool Whether to overwrite instance data or create a copy. %(verbose)s Returns ------- inst_csd : instance of Raw, Epochs or Evoked The transformed data. Output type will match input type. Notes ----- .. versionadded:: 0.20 References ---------- .. footbibliography:: instz Computing CSDZcustom_ref_appliedz,CSD already applied, should not be reappliedcopyFT)Zmegeegexcludecs g|]}|tjkqSr)rarraych_names).0chr3picksrr msz2compute_current_source_density..ZbadszCSD cannot be computed with bad EEG channels. Either drop (inst.drop_channels(inst.info['bads']) or interpolate (`inst.interpolate_bads()`) bad EEG channels.rzNo EEG channels found.numericrrz'lambda2 must be between 0 and 1, got %s stiffnessz%stiffness must be non-negative got %sn_legendre_termsz/n_legendre_terms must be greater than 0, got %sr/z9sphere must be "auto" or array-like with shape (4,), got xyzr)z,sphere radius must be greater than 0, got %scs&g|]}jd|dddqS)chslocN)info)r9pick)r3rrr=sgz&Zero or infinite position found in chs)r!Zkeepdims)r?r@)r'r(r)rD)Z coil_typeunitr5))rr r r rrGr ZFIFFV_MNE_CUSTOM_REF_CSD ValueErrorboolr4ranyrr isinstancestrrrr7float ExceptionisfinitealliscloserZnormZclipr$r&rrrr._dataZ_unlockupdateZFIFFV_COIL_EEG_CSDZFIFF_UNIT_V_M2Zrejectr)r3Zsphererr?r@r4rr)Z origin_headZ origin_devicerArBrCposZcos_distrr(r'Z trans_csdepochsZeporHrr;rcompute_current_source_density7s+                 (    rY?c sJddlm}ddlm} t|d|}t|jdd} t| dkrLt d|j ||| dd t |t rxt ||ddd }|j| d } t |tr| tjd f} | jd} t| | j| jftj} t| jD]R}t|d | jD]<}tj| dd|f| dd|fd d| dd||f<qq| d9} t}| t| }||kj| |kr\|| fS|||ktjddt| fddj}W5QRXtd|dt| jD]}t|d | jD]p}t| dd||f|k}|| |krtd|j| |d|j| || | || |fq̐q|| fS)a|Compute bridged EEG electrodes using the intrinsic Hjorth algorithm. First an electrical distance matrix is computed by taking the pairwise variance. Then, a local maximum near 0 :math:`{\mu}V^2` and a a local minimum below 5 :math:`{\mu}V^2` are found, the presence of which is indicative of bridging. Finally, electrode distances below the local minimum are marked as bridged as long as they happen on more than the ``epoch_threshold`` proportion of epochs. Based on :footcite:`TenkeKayser2001,GreischarEtAl2004,DelormeMakeig2004` and the `EEGLAB implementation `_. Parameters ---------- inst : instance of Raw, Epochs or Evoked The data to compute electrode bridging on. lm_cutoff : float The distance in :math:`{\mu}V^2` cutoff below which to search for a local minimum (lm) indicative of bridging. Defaults to 16 :math:`{\mu}V^2` to be conservative based on the distributions in :footcite:`GreischarEtAl2004`. epoch_threshold : float The proportion of epochs with electrical distance less than ``ed_threshold`` in order to consider the channel bridged. The default is 0.5. l_freq : float The low cutoff frequency to use. Default is 0.5 Hz. h_freq : float The high cutoff frequency to use. Default is 30 Hz. epoch_duration : float The time in seconds to divide the raw into fixed-length epochs to check for consistent bridging. Only used if ``inst`` is :class:`mne.io.BaseRaw`. The default is 2 seconds. bw_method : None ``bw_method`` to pass to :class:`scipy.stats.gaussian_kde`. %(verbose)s Returns ------- bridged_idx : list of tuple The indices of channels marked as bridged with each bridged pair stored as a tuple. ed_matrix : ndarray of float, shape (n_epochs, n_channels, n_channels) The electrical distance matrix for each pair of EEG electrodes. Notes ----- .. versionadded:: 1.1 References ---------- .. footbibliography:: r) gaussian_kde)minimize_scalarzComputing bridged electrodesT)r5z8No EEG channels found, cannot compute electrode bridgingF)l_freqh_freqr<r)durationZpreloadr)r<.rNr gmBignore)invalidcs|kr|dkr|StjS)Nr)rinf)rAZkde lm_cutoffrr$z,compute_bridged_electrodes..zLocal minimum z foundzBridge detected between z and )!Z scipy.statsr]Zscipy.optimizer^rr4rrGr RuntimeErrorfilterrNr r get_datar rr%r"ZzerossizenanrangevarlistisnanZerrstaterPrArrr8append)r3rfZepoch_thresholdr_r`Zepoch_durationZ bw_methodrr]r^r<r*Zn_epochsZ ed_matrixijZ bridged_idxZed_flatZ local_minimumZ bridged_countrrercompute_bridged_electrodessN:        < *ru)r/r0r1r2T)rZr[r[r\rNN)numpyrrutilsrrrrrior Z io.constantsr rXr r Zevokedr ZbemrZchannels.interpolationrrrr.rYrurrrrs6