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- create new raw class for et data recorded with SR research's eyelink system - can read eyelink .asc files (using djangrew's ParseEyelinkAscFiles function - https://github.com/djangraw/ParseEyeLinkAscFiles/blob/master/ParseEyeLinkAsc.py) - tbd: adding annotations, create info from file-header work on parsed data integrity - fill missing data in epoched recording with nan - work on parsing errors exclude rows that have negative time values init testing framework adapt higher level files to new class: - add to mne.io.__init__.py - add eyetrack as option to create_info() docstring assert integrity of parsed data (not finished) init annotation handling for asc reader (WIP) lines too long in asc parser module add meas_date from asc header pep style fix setting annotation times handle sample intervals for different sample frequencies linearly interpolate missing data, for plot functions etc to work. add option to annotate missing data move nan interpolation function to preprocessing.interpolation fiff constants for new channel types point test_constants to my fork of fiff-constants set correct channel_type, loc, and coil_types fix git target update used fiff-constants make plots work for ch_type='eyetrack' adjust defaults for plotting let test use mne-testing-data optional reading of blinks/saccades from eyelink data file add eyetrack also to the instance.pick_types() method add eyetracker units to fif-constants function to find eyeblinks via nan values in eyetrack channels handled error handling for file without blinks
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| # ParseEyeLinkAsc.py | ||
| # - Reads in .asc data files from EyeLink and produces pandas dataframes for | ||
| # further analysis | ||
| # | ||
| # Created 7/31/18-8/15/18 by DJ. | ||
| # Updated 7/4/19 by DJ - detects and handles monocular sample data. | ||
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| def ParseEyeLinkAsc_(elFilename): | ||
| # dfRec,dfMsg,dfFix,dfSacc,dfBlink,dfSamples = ParseEyeLinkAsc(elFilename) | ||
| # -Reads in data files from EyeLink .asc file and produces readable | ||
| # dataframes for further analysis. | ||
| # | ||
| # INPUTS: | ||
| # -elFilename is a string indicating an EyeLink data file from an AX-CPT | ||
| # task in the current path. | ||
| # | ||
| # OUTPUTS: | ||
| # -dfRec contains information about recording periods (often trials) | ||
| # -dfMsg contains information about messages (usually sent from stimulus | ||
| # software) | ||
| # -dfFix contains information about fixations | ||
| # -dfSacc contains information about saccades | ||
| # -dfBlink contains information about blinks | ||
| # -dfSamples contains information about individual samples | ||
| # | ||
| # Created 7/31/18-8/15/18 by DJ. | ||
| # Updated 11/12/18 by DJ - switched from "trials" to "recording periods" | ||
| # for experiments with continuous recording | ||
| # Updated 9/??/19 by Dominik Welke - fixed read-in of data | ||
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| # Import packages | ||
| import numpy as np | ||
| import pandas as pd | ||
| import time | ||
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| # ===== READ IN FILES ===== # | ||
| # Read in EyeLink file | ||
| print('Reading in EyeLink file %s...' % elFilename) | ||
| t = time.time() | ||
| with open(elFilename, "r+") as f: | ||
| fileTxt0 = (line.rstrip() for line in f) | ||
| # fileTxt0 = [line for line in fileTxt0 if line] # Non-blank lines in | ||
| # a list | ||
| fileTxt0 = [line for line in fileTxt0] # lines in a list | ||
| fileTxt0 = np.array(fileTxt0) | ||
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| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # Separate lines into samples and messages | ||
| print('Sorting lines...') | ||
| nLines = len(fileTxt0) | ||
| lineType = np.array(['OTHER'] * nLines, dtype='object') | ||
| # iStartRec = None | ||
| iStartRec = [] # DW: make a list of all rec-starts | ||
| t = time.time() | ||
| for iLine in range(nLines): | ||
| if len(fileTxt0[iLine]) < 3: | ||
| lineType[iLine] = 'EMPTY' | ||
| elif ( | ||
| fileTxt0[iLine].startswith('*') or | ||
| fileTxt0[iLine].startswith('>>>>>')): | ||
| lineType[iLine] = 'COMMENT' | ||
| elif ( | ||
| fileTxt0[iLine].split()[0][0].isdigit() or | ||
| fileTxt0[iLine].split()[0].startswith('-')): | ||
| lineType[iLine] = 'SAMPLE' | ||
| else: | ||
| lineType[iLine] = fileTxt0[iLine].split()[0] | ||
| # TODO: Find more general way of determining if recording has started | ||
| # if '!CAL' in fileTxt0[iLine]: | ||
| # iStartRec = iLine + 1 | ||
| if 'START' in fileTxt0[iLine]: | ||
| # DW: more general way of determining if recording has started.. | ||
| iStartRec.append(iLine + 1) | ||
| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # ===== PARSE EYELINK FILE ===== # | ||
| t = time.time() | ||
| # Trials | ||
| print('Parsing recording markers...') | ||
| iNotStart = np.nonzero(lineType != 'START')[0] | ||
| dfRecStart = pd.read_csv(elFilename, skiprows=iNotStart, header=None, | ||
| delim_whitespace=True, usecols=[1]) | ||
| dfRecStart.columns = ['tStart'] | ||
| iNotEnd = np.nonzero(lineType != 'END')[0] | ||
| dfRecEnd = pd.read_csv(elFilename, skiprows=iNotEnd, header=None, | ||
| delim_whitespace=True, usecols=[1, 5, 6]) | ||
| dfRecEnd.columns = ['tEnd', 'xRes', 'yRes'] | ||
| # combine trial info | ||
| dfRec = pd.concat([dfRecStart, dfRecEnd], axis=1) | ||
| nRec = dfRec.shape[0] | ||
| print('%d recording periods found.' % nRec) | ||
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| # Import Messages | ||
| print('Parsing stimulus messages...') | ||
| t = time.time() | ||
| iMsg = np.nonzero(lineType == 'MSG')[0] | ||
| # set up | ||
| tMsg = [] | ||
| txtMsg = [] | ||
| t = time.time() | ||
| for i in range(len(iMsg)): | ||
| # separate MSG prefix and timestamp from rest of message | ||
| info = fileTxt0[iMsg[i]].split() | ||
| # extract info | ||
| tMsg.append(int(info[1])) | ||
| txtMsg.append(' '.join(info[2:])) | ||
| # Convert dict to dataframe | ||
| dfMsg = pd.DataFrame({'time': tMsg, 'text': txtMsg}) | ||
| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # Import Fixations | ||
| print('Parsing fixations...') | ||
| t = time.time() | ||
| iNotEfix = np.nonzero(lineType != 'EFIX')[0] | ||
| dfFix = pd.read_csv(elFilename, skiprows=iNotEfix, header=None, | ||
| delim_whitespace=True, usecols=range(1, 8)) | ||
| dfFix.columns = ['eye', 'tStart', 'tEnd', 'duration', | ||
| 'xAvg', 'yAvg', 'pupilAvg'] | ||
| nFix = dfFix.shape[0] | ||
| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # Saccades | ||
| print('Parsing saccades...') | ||
| t = time.time() | ||
| iNotEsacc = np.nonzero(lineType != 'ESACC')[0] | ||
| dfSacc = pd.read_csv(elFilename, skiprows=iNotEsacc, header=None, | ||
| delim_whitespace=True, usecols=range(1, 11)) | ||
| dfSacc.columns = ['eye', 'tStart', 'tEnd', 'duration', 'xStart', | ||
| 'yStart', 'xEnd', 'yEnd', 'ampDeg', 'vPeak'] | ||
| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # Blinks | ||
| print('Parsing blinks...') | ||
| iNotEblink = np.nonzero(lineType != 'EBLINK')[0] | ||
| try: | ||
| dfBlink = pd.read_csv(elFilename, skiprows=iNotEblink, header=None, | ||
| delim_whitespace=True, usecols=range(1, 5)) | ||
| except ValueError as this_error: | ||
| if str(this_error) == 'No columns to parse from file': | ||
| print('No blinks in file') | ||
| dfBlink = pd.DataFrame(columns=['eye','tStart','tEnd','duration']) | ||
| else: | ||
| raise | ||
| dfBlink.columns = ['eye', 'tStart', 'tEnd', 'duration'] | ||
| print('Done! Took %f seconds.' % (time.time() - t)) | ||
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| # determine sample columns based on eyes recorded in file | ||
| eyesInFile = np.unique(dfFix.eye) | ||
| if eyesInFile.size == 2: | ||
| print('binocular data detected.') | ||
| cols = ['tSample', 'LX', 'LY', 'LPupil', 'RX', 'RY', 'RPupil'] | ||
| else: | ||
| eye = eyesInFile[0] | ||
| print('monocular data detected (%c eye).' % eye) | ||
| cols = ['tSample', '%cX' % eye, '%cY' % eye, '%cPupil' % eye] | ||
| # Import samples | ||
| print('Parsing samples...') | ||
| t = time.time() | ||
| # iNotSample = np.nonzero(np.logical_or( | ||
| # lineType != 'SAMPLE', np.arange(nLines) < iStartRec))[0] | ||
| iNotSample = np.nonzero( # DW: try this, to get ALL data | ||
| np.logical_or( | ||
| lineType != 'SAMPLE', np.arange(nLines) < iStartRec[0]))[0] | ||
| dfSamples = pd.read_csv(elFilename, skiprows=iNotSample, header=None, | ||
| delim_whitespace=True, usecols=range(0, len(cols))) | ||
| dfSamples.columns = cols | ||
| # Convert values to numbers | ||
| for eye in ['L', 'R']: | ||
| if eye in eyesInFile: | ||
| dfSamples['%cX' % eye] = pd.to_numeric(dfSamples['%cX' % eye], | ||
| errors='coerce') | ||
| dfSamples['%cY' % eye] = pd.to_numeric(dfSamples['%cY' % eye], | ||
| errors='coerce') | ||
| dfSamples['%cPupil' % eye] = pd.to_numeric( | ||
| dfSamples['%cPupil' % eye], errors='coerce') | ||
| else: | ||
| dfSamples['%cX' % eye] = np.nan | ||
| dfSamples['%cY' % eye] = np.nan | ||
| dfSamples['%cPupil' % eye] = np.nan | ||
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| print('Done! Took %.1f seconds.' % (time.time() - t)) | ||
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| # Return new compilation dataframe | ||
| return dfRec, dfMsg, dfFix, dfSacc, dfBlink, dfSamples |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| """Module for loading Eye-Tracker data""" | ||
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| # Author: Dominik Welke <[email protected]> | ||
| # | ||
| # License: BSD-3-Clause | ||
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| from .eyetrack import read_raw_eyelink | ||
| from .ParseEyeLinkAscFiles_ import ParseEyeLinkAsc_ |
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