U f{jY@sddlZddlmZddlmZmZmZmZddl m Z m Z m Z m Z mZddlmZddlmZddlmZmZmZmZmZedd d ZdddZddZdddZedddddZdS)N)BaseRaw) Annotations_annotations_starts_stopsannotations_from_events_adjust_onset_meas_date) quat_to_rot_average_quats_angle_between_quats apply_trans_quat_to_affine) filter_data) Transform)_mask_to_onsets_offsetsloggerverbose_validate_type_pl皙?ncCsddlm}ddlm}|} |dkrp| } d| kr>d}n$d| krLd}nd| krZd}ntdtd ||d kr| j |d d nd d |di}| j f|| j |d|ddd|d| j d|d| j dd} t | d} | jd} || dd| fdd}|jddt |jd}t|| dd}t | jd}||| <||k}t j|| <|dkrldn|}|| }||dk\}}td|dD].}t ||kd}t||krd||<qt| j|d|jdd}t||||fS)aCreate annotations for segments that likely contain muscle artifacts. Detects data segments containing activity in the frequency range given by ``filter_freq`` whose envelope magnitude exceeds the specified z-score threshold, when summed across channels and divided by ``sqrt(n_channels)``. False-positive transient peaks are prevented by low-pass filtering the resulting z-score time series at 4 Hz. Only operates on a single channel type, if ``ch_type`` is ``None`` it will select the first type in the list ``mag``, ``grad``, ``eeg``. See :footcite:`Muthukumaraswamy2013` for background on choosing ``filter_freq`` and ``threshold``. Parameters ---------- raw : instance of Raw Data to estimate segments with muscle artifacts. threshold : float The threshold in z-scores for marking segments as containing muscle activity artifacts. ch_type : 'mag' | 'grad' | 'eeg' | None The type of sensors to use. If ``None`` it will take the first type in ``mag``, ``grad``, ``eeg``. min_length_good : float | None The shortest allowed duration of "good data" (in seconds) between adjacent annotations; shorter segments will be incorporated into the surrounding annotations.``None`` is equivalent to ``0``. Default is ``0.1``. filter_freq : array-like, shape (2,) The lower and upper frequencies of the band-pass filter. Default is ``(110, 140)``. %(n_jobs)s %(verbose)s Returns ------- annot : mne.Annotations Periods with muscle artifacts annotated as BAD_muscle. scores_muscle : array Z-score values averaged across channels for each sample. References ---------- .. footbibliography:: r)zscore)labelNmaggradZeegzSNo M/EEG channel types found, please specify a ch_type or provide M/EEG sensor dataz.Using %s sensors for muscle artifact detection)rrF)megZref_megrTZfirwinZreflect_limited)Z fir_designpadn_jobs)enveloper NaN)Zreject_by_annotationsfreqZaxisrZ BAD_muscle meas_date orig_time)Z scipy.statsr scipy.ndimagercopyZget_channel_types ValueErrorrinfoZ pick_typesfilterZ apply_hilbertget_datanpisnansumsqrtshaper zerosnanrangeZnonzerolen_annotations_from_masktimesr)raw thresholdZch_typeZmin_length_goodZ filter_freqr rrrZraw_copyZ raw_ch_typedataZnan_maskr#Z art_scoresZ scores_muscleZart_maskZ min_sampscompsZ num_compscomZl_idxannotr?c/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/preprocessing/artifact_detection.pyannotate_muscle_zscores`/         rAaveragec sv|jd}|dddf|j}t|}t||dd|gg}|jd} tggg| d} |jd} |dk r.|dkstt |dddd f|dddd f} | |} | t |k} t | \}}||||}}d || | }t d |t||t| f| t|| d | d7} |dk r|dksFttjjtj|ddd d fdddd}||}||k} t | \}}||||}}d || | }t d|t|||f| t|| d| d7} g}|dk rd|dkst|}|dkr$tdd|f|dkr:t||}n|dkrN|jd}tdd|jdddDdddtddDtfdd|dddd fD}t|}|t||jdddf}t|d j d d}tj||kdd} t | \}}||||}}d || | }t d!|t|||f| t|| d"| d7} t| || |fS)#a,Detect segments with movement. Detects segments periods further from rotation_velocity_limit, translation_velocity_limit and mean_distance_limit. It returns an annotation with the bad segments. Parameters ---------- raw : instance of Raw Data to compute head position. pos : array, shape (N, 10) The position and quaternion parameters from cHPI fitting. Obtained with `mne.chpi` functions. rotation_velocity_limit : float Head rotation velocity limit in radians per second. translation_velocity_limit : float Head translation velocity limit in radians per second. mean_distance_limit : float Head position limit from mean recording in meters. use_dev_head_trans : 'average' (default) | 'info' Identify the device to head transform used to define the fixed HPI locations for computing moving distances. If ``average`` the average device to head transform is computed using ``compute_average_dev_head_t``. If ``info``, ``raw.info['dev_head_t']`` is used. Returns ------- annot : mne.Annotations Periods with head motion. hpi_disp : array Head position over time with respect to the mean head pos. See Also -------- compute_average_dev_head_t r#Nr?r%r&rrduAOmitting %5.1f%% (%3d segments): ω >= %5.1f°/s (max: %0.1f°/s)ZBAD_mov_rotat_velr$z>Omitting %5.1f%% (%3d segments): v >= %5.4fm/s (max: %5.4fm/s)ZBAD_mov_trans_vel)rBr+z!use_dev_head_trans must be eitherz 'average' or 'info': got '%s'rBr+ dev_head_tcSsg|]}|qSr?r?.0dr?r?r@ sz%annotate_movement..Z hpi_resultsZ dig_pointscSs|dS)Nidentr?)xr?r?r@z#annotate_movement..)keycSsg|] }|dqS)rr?rHr?r?r@rKscsg|]}tt|qSr?)r r )rIZquatZchpi_posr?r@rKsrz=Omitting %5.1f%% (%3d segments): disp >= %5.4fm (max: %5.4fm)Z BAD_mov_dist)r+r) first_timer.diff concatenaterr8AssertionErrorr Zdeg2radrr0rr6Zrad2degmaxr7linalgnormlowerr*compute_average_dev_head_tsortedarrayr Ztiler2r1anyr)r9posZrotation_velocity_limitZtranslation_velocity_limitZmean_distance_limitZuse_dev_head_transr#hp_tsdtr'r>Zt_totrQZbad_maskonsetsoffsetsZbad_pctvZdispZfixed_dev_head_tZ chpi_pos_movZ chpi_pos_fixZhpi_dispr?rRr@annotate_movement~s(      *    *           recCs|jd}tt|j}td}|}|dddf|j}|ddkrjd|d<|dd|ksjt||jdk}| st dt ||jdf||}~~t |dddf|j d|gf}t|dk st||j|8}|j|d d } ~| ddkr.d| d<| ddks.t| dk s@t| dt|ksVtt| dk sntt|d \} } t| | D]\} } d|| | <qttt dg|g| }|dk st||}~~ |t||ddd d f7}|dddd f}t||d}td }t||ddddf<|| |dddf<tj|dddfdksxttdd|}|S)aGet new device to head transform based on good segments. Segments starting with "BAD" annotations are not included for calculating the mean head position. Parameters ---------- raw : instance of Raw Data to compute head position. pos : array, shape (N, 10) The position and quaternion parameters from cHPI fitting. Returns ------- dev_head_t : array New trans matrix using the averaged good head positions. r#NrrrDrCz2 Removing %d samples > raw.times[-1] (%s)T)Z use_roundingbadrrF)weightsrhead)r+r.Zonesr6r8r3r)Z _first_timerVallrr0rUZ last_samprTZ first_sampZ time_as_indexrzipZcumsumdotr eyerrXrYr)r9r_r#Zseg_goodZ trans_poshpr`masktsidxrbendsonsetendraZrot_qsZbest_qZtransrGr?r?r@r[sX   &    " r[cCs ddlm}ddlm}||}ttd|tdf}||dd}tt|d||ddddd} ||| t d} | ||  t } d| | dk<t|t|krt|d| | t|k<nt|| | t|k<|| } || } | | }|gt|}t | |||dS)z4Construct annotations from boolean mask of the data.r)distance_transform_edt) find_peaksrNrCr&) r(ruZ scipy.signalrvr.rUr3flipr6Zastypeintr)r8roZ annot_namer'rurvZmask_tfZ ins_mask_tfZleft_midpt_indexZright_midpt_indexZ onsets_indexZ ends_indexrbrrZ durationsdescr?r?r@r7Hs.    r7.@@rgedge)rcst|dtddt|ddtjfd|||dkrN|||}td||dk rh|jdkrhtd |dk st|s|t}nt|}|D]}t|d d d q|dkr|j} |rt d d |nt ||j d|j dd} | stdtdd|D}dgt | } t| jD]2\} tfdd|Drd| | <qddt| j| | j| D} | dg} | D]V}| dd}|\}}||krq|n,||kr||kr|| dd<n | |q|t| } | |j8} g}g}d| ddkrD| dd|krDd}| dd|}||}||||t| ddddfddD]d\} }| | df}| | ddf}|||krq`||}||}||}||||q`|jd| ddkr0|jd| dd|kr0| dd|}|jd}||}||||t||dg|j dd}t |}ddt|j|jD}d |}t|j}||jd}t d!|d"t|d#|d$|d%td&|dd'd(t|dd'd) t|||S)*a Create `~mne.Annotations` for breaks in an ongoing recording. This function first searches for segments in the data that are not annotated or do not contain any events and are at least ``min_break_duration`` seconds long, and then proceeds to creating annotations for those break periods. Parameters ---------- raw : instance of Raw The continuous data to analyze. events : None | array, shape (n_events, 3) If ``None`` (default), operate based solely on the annotations present in ``raw``. If an events array, ignore any annotations in the raw data, and operate based on these events only. min_break_duration : float The minimum time span in seconds between the offset of one and the onset of the subsequent annotation (if ``events`` is ``None``) or between two consecutive events (if ``events`` is an array) to consider this period a "break". Defaults to 15 seconds. .. note:: This value defines the minimum duration of a break period in the data, **not** the minimum duration of the generated annotations! See also ``t_start_after_previous`` and ``t_stop_before_next`` for details. t_start_after_previous, t_stop_before_next : float Specifies how far the to-be-created "break" annotation extends towards the two annotations or events spanning the break. This can be used to ensure e.g. that the break annotation doesn't start and end immediately with a stimulation event. If, for example, your data contains a break of 30 seconds between two stimuli, and ``t_start_after_previous`` is set to ``5`` and ``t_stop_before_next`` is set to ``3``, the break annotation will start 5 seconds after the first stimulus, and end 3 seconds before the second stimulus, yielding an annotated break of ``30 - 5 - 3 = 22`` seconds. Both default to 5 seconds. .. note:: The beginning and the end of the recording will be annotated as breaks, too, if the period from recording start until the first annotation or event (or from last annotation or event until recording end) is at least ``min_break_duration`` seconds long. ignore : iterable of str Annotation descriptions starting with these strings will be ignored by the break-finding algorithm. The string comparison is case-insensitive, i.e., ``('bad',)`` and ``('BAD',)`` are equivalent. By default, all annotation descriptions starting with "bad" and annotations indicating "edges" (produced by data concatenation) will be ignored. Pass an empty list or tuple to take all existing annotations into account. If ``events`` is passed, this parameter has no effect. %(verbose)s Returns ------- break_annotations : instance of Annotations The break annotations, each with the description ``'BAD_break'``. If no breaks could be found given the provided function parameters, an empty `~mne.Annotations` object will be returned. Notes ----- .. versionadded:: 0.24 r9ZRaw)item item_nametypes type_nameeventsN)r~rrrzrThe result of min_break_duration - t_start_after_previous - t_stop_before_next must be greater than 0, but it is: z#The events array must not be empty.strzAll elements of "ignore")r~rrz6Ignoring annotations with descriptions starting with: z, r#r%)rr#r'z:Could not find (or generate) any annotations in your data.css|]}|VqdSN)rZrIir?r?r@ sz!annotate_break..Tc3s|]}|VqdSr) startswithr descriptionr?r@rsFcSsg|]\}}|||gqSr?r?)rIrsdurationr?r?r@rKsz"annotate_break..rCr)startZ BAD_break)rsrrr'cSs0g|](\}}|dd||dd|ddqS).1fu – z sec [z sec]r?)rIorJr?r?r@rK,sz z Detected z break periodz of >= z sec duration: z In total, rErz% of the data (z# sec) have been marked as a break. )rrr.Zndarrayr*sizetuple annotationsr)rr+joinrr6 enumeraterrZr^rkrsrappendr]rSr8rr0rroundr)r9rZmin_break_durationZt_start_after_previousZt_stop_before_nextignorerZ annot_durr~rZ keep_maskrqZannotated_intervalsZmerged_intervalsintervalZmerged_interval_stopZinterval_startZ interval_stopZ break_onsetsZbreak_durationsrsoffsetr_Z this_startZ previous_stopZbreak_annotationsZn_breaksZ break_timesZtotal_break_durZfraction_breaksr?rr@annotate_breakisH           $       H r)rNrrNN)NNNrB)N)Nrzr{r{r|)numpyr.Zio.baserrrrrrZ transformsrr r r r r,r rutilsrrrrrrArer[r7rr?r?r?r@s:    j J !