U f{jB @sdZddlZddlmZddlmZddlmZm Z m Z m Z ddl m Z ddlmZmZmZmZmZmZmZmZmZmZmZmZmZed#d dZd$ddZd%ddZed&ddZed'ddZ ed(ddZ!ddZ"ddZ#ed)d!d"Z$dS)*z+Some utility functions for rank estimation.N)_handle_default)_simplify_info)_picks_by_type pick_infopick_channels_cov _picks_to_idx)make_projector) logger_compute_row_norms_pl_validate_type_apply_scaling_cov_undo_scaling_cov _scaled_arraywarn _check_rank _on_missingverbose_check_on_missingfill_docautoFTabsolutec Csdddlm}|r6|}t|}||ddtjf}||}t|||} |dkr\| |fS| SdS)a9Estimate the rank of data. This function will normalize the rows of the data (typically channels or vertices) such that non-zero singular values should be close to one. Parameters ---------- data : array Data to estimate the rank of (should be 2-dimensional). %(tol_rank)s return_singular : bool If True, also return the singular values that were used to determine the rank. norm : bool If True, data will be scaled by their estimated row-wise norm. Else data are assumed to be scaled. Defaults to True. %(tol_kind_rank)s Returns ------- rank : int Estimated rank of the data. s : array If return_singular is True, the singular values that were thresholded to determine the rank are also returned. r)linalgNT)Zscipyrcopyr npZnewaxisZsvdvals_estimate_rank_from_s) datatolreturn_singularnormtol_kindrrZnormssrankr$G/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/rank.py estimate_ranks   r&cCst|t}tj|dd}t|tr|dkr:td|f|dkrRttjj }nttj j }|j d||}|j dkrt d||t||fn0t|tjr|jjdkst|}|d kr||}tj||kdd}|S) a=Estimate the rank of a matrix from its singular values. Parameters ---------- s : ndarray, shape (..., ndim) The singular values of the matrix. tol : float | 'auto' Tolerance for singular values to consider non-zero in calculating the rank. Can be 'auto' to use the same thresholding as ``scipy.linalg.orth`` (assuming np.float64 datatype) adjusted by a factor of 2. tol_kind : str Can be "absolute" or "relative". Returns ------- rank : ndarray, shape (...) The estimated rank. Zaxis)rfloat32z#tol must be "auto" or float, got %rr)rzJ Using tolerance %0.2g (%0.2g eps * %d dim * %0.2g max singular value)frelative)rarrayfloatZamax isinstancestr ValueErrorZfinfor)epsZfloat64shapendimr infolenZndarrayZdtypekindsum)r"rr!Zmax_sr1r#r$r$r%r?s&   r-C6?r cCs:|dkrt|j||d}t||dt|j|||d|S)z/Aid the transition away from raw.estimate_rank.N) with_ref_megrF)rr4_estimate_rank_meeg_signalsr)rawpicksrscalingsr9r!r$r$r%_estimate_rank_rawns r>c Cst|}|jd|jdkr$tdt|||t||d||d}W5QRXt|tr`|dn|}dtt |d} t d| |f|S)aEstimate rank for M/EEG data. Parameters ---------- data : np.ndarray of float, shape(n_channels, n_samples) The M/EEG signals. %(info_not_none)s scalings : dict | 'norm' | np.ndarray | None The rescaling method to be applied. If dict, it will override the following default dict: dict(mag=1e15, grad=1e13, eeg=1e6) If 'norm' data will be scaled by channel-wise norms. If array, pre-specified norms will be used. If None, no scaling will be applied. tol : float | str Tolerance. See ``estimate_rank``. return_singular : bool If True, also return the singular values that were used to determine the rank. tol_kind : str Tolerance kind. See ``estimate_rank``. Returns ------- rank : int Estimated rank of the data. s : array If return_singular is True, the singular values that were thresholded to determine the rank are also returned. rrOYou've got fewer samples than channels, your rank estimate might be inaccurate.F)rr rr! +  Estimated rank (%s): %d) rr2r0rr&r.tuplejoinlistzipr r4) rr4r=rrr! picks_listoutr#ch_typer$r$r%r:ys"r:c Cst|gd}td|}t||||jd|jdkr>tdt||d|d}t|tr`|dn|}dt t |d} t d | |ft ||||S) aEstimate rank of M/EEG covariance data, given the covariance. Parameters ---------- data : np.ndarray of float, shape (n_channels, n_channels) The M/EEG covariance. %(info_not_none)s scalings : dict | 'norm' | np.ndarray | None The rescaling method to be applied. If dict, it will override the following default dict: dict(mag=1e12, grad=1e11, eeg=1e5) If 'norm' data will be scaled by channel-wise norms. If array, pre-specified norms will be used. If None, no scaling will be applied. tol : float | str Tolerance. See ``estimate_rank``. return_singular : bool If True, also return the singular values that were used to determine the rank. Returns ------- rank : int Estimated rank of the data. s : array If return_singular is True, the singular values that were thresholded to determine the rank are also returned. excludescalings_cov_rankrrr?F)rr rr@rA)rrrr2r0r&r.rBrCrDrEr r4r) rr4r=rrrrFrGr#rHr$r$r%_estimate_rank_meeg_covs    rL/You should use data-based rank estimate insteadc Csddlm}t||r|n|j}~|dg}t|dkrDtdt|dkspd|dkspd|dddkr|td ||d}|d}|dd}|dd d}|t|dd d ||dd d |8}|S) aaLook up rank from SSS data. .. note:: Throws an error if SSS has not been applied. Parameters ---------- inst : instance of Raw, Epochs or Evoked, or Info Any MNE object with an .info attribute Returns ------- rank : int The numerical rank as predicted by the number of SSS components. r)InfoZ proc_historyz,Found multiple SSS records. Using the first.rmax_infoZin_orderZsss_infoz@Could not find Maxfilter information in info["proc_history"]. %s componentsN) io.meas_inforNr.r4getr5r r0r7) instmsgrrNr4Z proc_inforOZinsideZnfreer$r$r% _get_rank_ssss(     rVcCs8|dkr0|dkr0z t|WStk r.YnXt|S)N)megmagZgradfull)rVr0r5)r4rHr<r#r$r$r% _info_ranks  rZcOstt|f||S)z#Wrap compute_rank but yield an int.)r7 compute_rankvalues)rTargskwargsr$r$r%_compute_rank_int sr_ignorec  sZddlm} ddlm} ddlm} t|}td|}t|dt || rd} dkr^t d  gd <t |t |d t d @gd }d |d krtfdd|d Dn |jd} td| |ft|ttdfdt |trd} |}t}nd} |dkrt}t}tddd d}|D]\}}d}||kr||t|krt dt|d||dt|dd}|dkr8| dkr8|dkr8t || r8|drƐq8fdd|D}t|}|rtd |\}}}nd!\}}| dkrpt|||}|d"krV||8}td#|||t||t|fntd$||fn| dks~tt || | frt || r|j|d%d&}n(|dd|ddf}tj|dd'}|rt ||}t!|t||||d|}nt || st|dr2|d|d(k"|}n|d|dd|f}|rft t |||j#}t$|t||||d|d)\}}||kr||d|||d}|d*kr8d+t|d||d,|d-t%|d.d/ }t&||dq8t||d}td0|||t||t|f||kr@t'd1||f||kr8|||<q8|S)2akCompute the rank of data or noise covariance. This function will normalize the rows of the data (typically channels or vertices) such that non-zero singular values should be close to one. Parameters ---------- inst : instance of Raw, Epochs, or Covariance Raw measurements to compute the rank from or the covariance. %(rank_none)s scalings : dict | None (default None) Defaults to ``dict(mag=1e15, grad=1e13, eeg=1e6)``. These defaults will scale different channel types to comparable values. %(info)s Only necessary if ``inst`` is a :class:`mne.Covariance` object (since this does not provide ``inst.info``). %(tol_rank)s proj : bool If True, all projs in ``inst`` and ``info`` will be applied or considered when ``rank=None`` or ``rank='info'``. %(tol_kind_rank)s %(on_rank_mismatch)s %(verbose)s Returns ------- rank : dict Estimated rank of the data for each channel type. To get the total rank, you can use ``sum(rank.values())``. Notes ----- .. versionadded:: 0.18 r)BaseRaw) BaseEpochs) CovariancerKon_rank_mismatchZ covarianceNz,info cannot be None if inst is a Covariance.Zbadsnamesch_namesrIcsg|]}d|qSrf)index).0namer4r$r% Ksz compute_rank..rz#Computing rank from %s with rank=%rr#r4Z estimatedTF)Z meg_combinedZref_megrJzrank[z]=z! exceeds the number of channels ()r`rWZdiagcsg|]}d|qSrgr$)ripickrkr$r%rlpsZprojs)NrrYz< %s: rank %d after %d projector%s applied to %d channel%sz %s: rank %d from infoZomit)Zreject_by_annotationr(r)rrdzThe passed rank[z5 exceeds the estimated rank of the noise covariance (zK) leading to a potential increase in noise during whitening by a factor of z0.1gzZ. Ensure that the rank correctly corresponds to that of the given noise covariance matrix.zC %s: rank %d computed from %d data channel%s with %d projector%szSomething went wrong in the data-driven estimation of the data rank as it exceeds the theoretical rank from the info (%d > %d). Consider setting rank to "auto" or setting it explicitly as an integer.)(Zio.baseraZepochsrbrcrrrr.r0rrsetrr4r r r/dictrrr5reprr rZupperr AssertionErrorget_datarZ concatenatedotr:r7TrLsqrtrr)rTr#r=r4rZprojr!rdrrarbrcZ inst_typeZ rank_typeZ info_typeZ simple_inforFrHr<Z est_verboserfZn_chanZproj_opZn_proj_Z this_rankrZsingratiorUZthis_info_rankr$rkr%r[s'          "          ,    r[)rFTrN)rr)Nr8r Fr)rFr)rFN)rMN)NNNrTrr`N)%__doc__numpyrdefaultsrrRrZio.pickrrrrZio.projr utilsr r r r rrrrrrrrrr&rr>r:rLrVrZr_r[r$r$r$r%sN   < * /  / . (