multimatch_gaze.tests package¶
Submodules¶
multimatch_gaze.tests.test_MultiMatch module¶
multimatch_gaze.tests.utils module¶
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multimatch_gaze.tests.utils.
create_chunks
(onsets, fixations, dur)[source]¶ Chunk eyetracking data into scanpaths.
Use onset data to obtain indices of full eyetracking data for chunking.
Param: onsets: array-like, onset times of movie shots Param: fixations: record array, nx4 fixation vector (onset, x, y, duration), output of preprocess() function Param: dur: float, desired duration of segment length Returns: startidx, endix: array, start and end ids of eyemovement data to chunk into segments
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multimatch_gaze.tests.utils.
create_offsetchunks
(offsets, fixations, dur)[source]¶ Chunk eyetracking data into scanpaths.
Use offset data to obtain indices of full eyetracking data for chunking.
Param: offsets: array-like, offset times of movie shots Param: fixations: record array, nx4 fixation vector (onset, x, y, duration), output of preprocess() Param: dur: float, desired duration of segment length Returns: startidx, endix: array start and end ids of eyemovement data to chunk into segments
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multimatch_gaze.tests.utils.
create_offsets
(data, dur)[source]¶ Create shot offsets from studyforrests location annotation.
Create offset times of all shots of at least ‘dur’ seconds of length
Param: data: dataframe, location annotation from studyforrest Param: dur: float, time in seconds a shot should at least be long Returns: onsets: array-like, list of shot offset times
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multimatch_gaze.tests.utils.
create_onsets
(data, dur)[source]¶ Create shot onsets from studyforrests location annotation.
Create onset times of all shots of at least ‘dur’ seconds of length.
Param: data: dataframe location annotation from studyforrest Param: dur: float time in seconds a shot should at least be long Returns: onsets: array-like, list of shot onset times
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multimatch_gaze.tests.utils.
docomparison_forrest
(shots, data1, data2, screensize=[1280, 720], dur=4.92, ldur=0, offset=False, TDur=0, TDir=0, TAmp=0, grouping=False)[source]¶ Compare two scanpaths on five similarity dimensions.
Param: data1, data2: recarray, eyemovement information of forrest gump studyforrest dataset Param: screensize: list, screen dimensions in px. Param: ldur: float, duration in seconds. An attempt is made to group short shots together to form shots of ldur length Param: grouping: boolean, if True, simplification is performed based on thresholds TAmp, TDir, and TDur Param: TDir: float, Direction threshold, angle in degrees. Param: TDur: float, Duration threshold, duration in seconds. Param: TAmp: float, Amplitude threshold, length in px. Returns: scanpathcomparisons: array array of 5 scanpath similarity measures Returns: durations: array-like durations of extracted scanpaths. Vector (Shape), Direction (Angle), Length, Position, and Duration. 1 = absolute similarity, 0 = lowest similarity possible. Returns: onsets: array-like onset times of the scanpaths
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multimatch_gaze.tests.utils.
fixations_chunks
(fixations, startid, endid)[source]¶ Chunk eyemovement data into scanpaths.
Param: fixations: record array, nx4 fixation vector (onset, x, y, duration), output of preprocess() Param: startid, endid: array, start- and end-ids of the scanpaths, output from either create_chunks() or create_offsetchunks() Returns: fixation_vector: array-like, a nx3 fixation vector (x, y, duration)
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multimatch_gaze.tests.utils.
longshot
(shots, group_shots, ldur=4.92)[source]¶ Group movie shots without a cut together to obtain longer segments.
Note: This way, fewer but longer scanpaths are obtained. Example: use median shotlength of 4.92s.
Param: shots: dataframe, contains movie location annotation Param: group_shots: boolean, if True, grouping of movie shots is performed Param: dur: float, length in seconds for movie shot. An attempt is made to group short shots without a cut together to form longer shots of ldur length Returns: aggregated, dataframe of aggregated movie shots
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multimatch_gaze.tests.utils.
mk_angles
()[source]¶ creates vectors with predefined angular relations. angles1 and angles2 contain the following properties: 1. same 0, 2. 60 diff, 3. 90 diff, 4.120 diff,4. 180 diff (max. dissimilar). They are in sectors (0,1) and (0, -1). Angles3 and angles4 contain the same properties reversed and lie in sectors (-1, 0) and (-1, -1)
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multimatch_gaze.tests.utils.
mk_fix_vector
(length=5)[source]¶ creates a random length x 3 fixation vector in form of a record array
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multimatch_gaze.tests.utils.
mk_strucarray
(length=5)[source]¶ create a random scanpath in the data format generateScanpathStructureArray would output
Preprocess record array of eye-events.
A record array from REMoDNaV data is preprocessed in the following way: Subset to only get fixation data, disregard out-of-frame gazes, subset to only keep x, y coordinates, duration.
Param: data: recordarray, REMoDNaV output of eye events from movie data Param: screensize: list of float, screen measurements in px Returns: fixations: array-like nx3 fixation vectors (onset, x, y, duration)
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multimatch_gaze.tests.utils.
pursuits_to_fixations
(remodnav_data)[source]¶ Transform start and endpoints of pursuits to fixations.
Uses the output of a record array created by the remodnav algorithm for eye-movement classification to transform pursuit data into fixations. The start and end point of a pursuit are relabeled as a fixation. This is useful for example if the underlying stimulus material is a moving image - visual intake of a moving object would then resemble a pursuit.
Param: npdata: recordarray, remodnav output of eyemovement data Returns: newdata: recordarray
Helper to read input data produced by the REMoDNaV algorithm. Further information on the REMoDNaV algorithm can be found here: https://github.com/psychoinformatics-de/remodnav
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multimatch_gaze.tests.utils.
same_sample
(run=1, subj=1)[source]¶ duplicate dataset to force exactly similar scanpaths. Choose the run (integer between 1-8) and whether you want a lab (1) or mri (2) subject
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multimatch_gaze.tests.utils.
short_shots
(run=3)[source]¶ create a shortened shots location annotation to test longshots()