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loadData.py
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343 lines (232 loc) · 12.7 KB
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import sys
sys.path.append("Modules/")
# sys.path.append("../")
import os
import pandas as pd
import numpy as np
import logging
import pickle
import json
#fmt = 'logging.Formatter(''%(levelname)s_%(name)s-%(funcName)s(): - %(message)s'
fmt = '%(levelname)s_%(name)s-%(funcName)s(): - %(message)s'
logging.basicConfig(level=logging.INFO, format=fmt)
logger = logging.getLogger(__name__)
# In[1]:
wd = os.getcwd()
os.chdir(wd)
################################################################################
#### f(trialData, trIdx)
def convertIndexToMultiIndexUsingUnderscore(labelIn):
s = labelIn.split('_')
if len(s) is 2:
# otherwise, its already a scalar
(first,second) = s
return (first,second)
elif len(s) == 3 :
(first,desc,second)= s
return (first,second)
else:
return (labelIn,'')
def convertIndexToMultiIndexIgnoringUnderscore(labelIn):
if( labelIn.split('_')[-1] == 'x' or labelIn.split('_')[-1] == 'y' or labelIn.split('_')[-1] == 'z' ):
top = '-'.join(labelIn.split('_')[:-1])
bottom = labelIn.split('_')[-1]
return (top,bottom)
else:
return (labelIn,'')
def processTrial(dataFolder, trialResults, gazeConfidenceThreshold, numTrials = False):
if(numTrials):
logger.info('Processing subject: ' + trialResults['ppid'] + ' t = ' + str(trialResults['trial_num']) + ' of ' + str(numTrials) )
else:
logger.info('Processing subject: ' + trialResults['ppid'] + ' t = ' + str(trialResults['trial_num']))
## Import ball data and rename some columns
dataFileName = trialResults['ball_movement_filename']
ballData = pd.read_csv( dataFolder + dataFileName)
ballData = ballData.rename(columns={"time": "frameTime"})
ballData.rename(columns={"pos_x": "ballPos_x", "pos_y": "ballPos_y","pos_z": "ballPos_z"},inplace=True)
ballData.rename(columns={"rot_x": "ballRot_x", "rot_y": "ballRot_y","rot_z": "ballRot_z"},inplace=True)
## Import paddle data and rename some columns
dataFileName = trialResults['paddle_movement_filename']
paddleData = pd.read_csv( dataFolder + dataFileName)
paddleData = paddleData.rename(columns={"time": "frameTime"})
## Import view data and rename some columns
dataFileName = trialResults['camera_movement_filename']
viewData = pd.read_csv( dataFolder + dataFileName)
viewData = viewData.rename(columns={"time": "frameTime"})
## Merge view and ball data into rawTrialData
if( len(ballData) == 0 ):
rawTrialData = viewData.reindex(viewData.columns.union(ballData.columns), axis=1)
else:
rawTrialData = pd.merge(viewData, ballData, on ='frameTime',validate= 'one_to_many')
## Merge rawTrialData and paddle data
if( len(paddleData) == 0 ):
rawTrialData = rawTrialData.reindex(viewData.columns.union(paddleData.columns), axis=1)
else:
rawTrialData = pd.merge(rawTrialData, paddleData, on ='frameTime',validate= 'one_to_many')
## Import and merge pupil timestamp data (recorded within Unity)
dataFileName = trialResults['pupil_pupilTimeStamp_filename']
pupilTimestampData = pd.read_csv( dataFolder + dataFileName)
pupilTimestampData = pupilTimestampData.rename(columns={"time": "frameTime"})
rawTrialData = pd.merge( rawTrialData, pupilTimestampData, on ='frameTime',validate= 'one_to_many')
rawTrialData['trialNumber'] = trialResults['trial_num']
rawTrialData['blockNumber'] = trialResults['block_num']
## Import gaze data
gazeDataFolderList = []
[gazeDataFolderList.append(name) for name in os.listdir(dataFolder + 'PupilData') if name[0] is not '.']
pupilSessionFolder = '/' + gazeDataFolderList[0]
gazeDataFolder = dataFolder + 'PupilData' + pupilSessionFolder
try:
pupilExportsFolder = []
[pupilExportsFolder.append(name) for name in os.listdir(gazeDataFolder + '/Exports') if name[0] is not '.']
# Defaults to the most recent pupil export folder (highest number)
gazePositionsDF = pd.read_csv( gazeDataFolder + '/Exports/' + pupilExportsFolder[-1] + '/gaze_positions.csv' )
gazePositionsDF.head()
except:
logger.exception('No gaze_positions.csv. Process and export data in Pupil Player.')
gazePositionsDF = gazePositionsDF.rename(columns={"gaze_timestamp": "pupilTimestamp"})
# Sort, because the left/right eye data is written asynchronously, and this means timestamps may not be monotonically increasing.
gazePositionsDF.sort_values(by='pupilTimestamp',inplace=True)
## Merge gaze data
gbBlTr = rawTrialData.groupby(['blockNumber','trialNumber'])
tr = gbBlTr.get_group( list(gbBlTr.groups.keys())[0])
## Gaze data is for the whole experiment while trial data is for the trial only.
## Find the slice of gaze data that maps onto the trial timestamps
firstTS = tr.head(1)['pupilTimestamp']
lastTS = tr.tail(1)['pupilTimestamp']
firstIdx = list(map(lambda i: i> float(firstTS), gazePositionsDF['pupilTimestamp'])).index(True)
lastIdx = list(map(lambda i: i> float(lastTS), gazePositionsDF['pupilTimestamp'])).index(True)
rawGazeData = gazePositionsDF.loc[firstIdx:lastIdx]
# Drop data below the confidence level
filteredGazeData = rawGazeData.reset_index().drop(np.where(rawGazeData['confidence'] < gazeConfidenceThreshold )[0])
# Merge gaze and trial data
interpDF = pd.merge( rawTrialData, filteredGazeData, on ='pupilTimestamp',how='outer',sort=True)
# Upsample trial data to the resolution of gaze data
interpDF = interpDF.interpolate(method='linear',downcast='infer')
# Convert to multiindex
newColList = [convertIndexToMultiIndexUsingUnderscore(c) for c in interpDF.columns[:len(rawTrialData.columns)]]
newGazeColList = [ convertIndexToMultiIndexIgnoringUnderscore(c) for c in interpDF.columns[(len(rawTrialData.columns)):] ]
newColList.extend(newGazeColList)
interpDF.columns = pd.MultiIndex.from_tuples(newColList)
### Some checks ###
if( len(interpDF) > ( len(rawTrialData) + len(filteredGazeData)) ):
logger.warning('len(interpDF) > ( len(rawTrialData) + len(filteredGazeData))')
dictOut = {"rawUnityData": rawTrialData, "rawGazeData": rawGazeData, "interpolatedData": interpDF}
return dictOut
################################################################################
################################################################################
def unpackSession(subNum, gazeDataConfidenceThreshold = 0.6, doNotLoad = False):
# Get folder/filenames
dataFolderList = []
[dataFolderList.append(name) for name in os.listdir("Data/") if name[0] is not '.']
for i, name in enumerate(dataFolderList):
if i == subNum:
print('***> ' + str(i) + ': ' + name )
else:
print(str(i) + ': ' + name )
dataParentFolder = "Data/" + dataFolderList[subNum]
dataSubFolderList = []
[dataSubFolderList.append(name) for name in os.listdir(dataParentFolder) if name[0] is not '.']
dataFolder = dataParentFolder + '/' + dataSubFolderList[0] + '/'
logger.info('Processing session: ' + dataParentFolder)
# Try to load pickle if doNotLoad == False
picklePath = dataFolder + dataSubFolderList[0] + '.pickle'
from os import path
if( doNotLoad == False and path.exists(picklePath)):
file = open(picklePath, 'rb')
sessionData = pickle.load(file)
file.close()
logger.info('Importing session dict from pickle.')
return sessionData
logger.info('Compiling session dict from *.csv.')
# If not loading from pickle, create and populate dataframes
rawExpUnityDataDf = pd.DataFrame()
rawExpGazeDataDf = pd.DataFrame()
processedExpDataDf = pd.DataFrame()
rawCalibUnityDataDf = pd.DataFrame()
rawCalibGazeDataDf = pd.DataFrame()
processedCalibDataDf = pd.DataFrame()
trialData = pd.read_csv( dataFolder + 'trial_results.csv')
for trIdx, trialResults in trialData.iterrows():
trialDict = processTrial(dataFolder, trialResults, gazeDataConfidenceThreshold,len(trialData))
def addToDF(targetDF,dfIn):
if( targetDF.empty ):
targetDF = dfIn
else:
targetDF = targetDF.append(dfIn)
return targetDF
if (trialResults['trialType'] == 'interception'):
processedExpDataDf = addToDF(processedExpDataDf,trialDict['interpolatedData'])
rawExpUnityDataDf = addToDF(rawExpUnityDataDf,trialDict['rawUnityData'])
rawExpGazeDataDf = addToDF(rawExpGazeDataDf,trialDict['rawGazeData'])
elif(trialResults['trialType'] == 'CalibrationAssessment'):
processedCalibDataDf = addToDF(processedCalibDataDf,trialDict['interpolatedData'])
rawCalibUnityDataDf = addToDF(rawCalibUnityDataDf,trialDict['rawUnityData'])
rawCalibGazeDataDf = addToDF(rawCalibGazeDataDf,trialDict['rawGazeData'])
# Rename trialdata columns and convert to multiindex
trialData.rename(columns={"session_num":"sessionNumber","trial_num":"trialNumber",
"block_num":"blockNumber","trial_num_in_block":"trialNumberInBlock",
"start_time":"startTime","end_time":"endTime"},inplace=True)
trDataFiles = [i for i in trialData.columns.to_list() if '_filename' in i]
newColList = [convertIndexToMultiIndexUsingUnderscore(c) for c in trialData.columns[:-len(trDataFiles)]]
newColList.extend([convertIndexToMultiIndexIgnoringUnderscore(c) for c in trialData.columns[-len(trDataFiles):]])
trialData.columns = pd.MultiIndex.from_tuples(newColList)
expDict = json.load( open(dataFolder + 'settings.json'))
processedExpDataDf = processedExpDataDf.reset_index(drop=True)
processedCalibDataDf = processedCalibDataDf.reset_index(drop=True)
analysisParameters = json.load( open('analysisParameters.json'))
analysisParameters['gazeDataConfidenceThreshold'] = gazeDataConfidenceThreshold
subID = json.load( open(dataFolder + 'participant_details.json'))['ppid']
dictOut = {"subID": subID, "trialInfo": trialData.sort_index(axis=1),"expConfig": expDict,
"rawExpUnity": rawExpUnityDataDf.sort_index(axis=1), "rawExpGaze": rawExpGazeDataDf.sort_index(axis=1), "processedExp": processedExpDataDf.sort_index(axis=1),
"rawCalibUnity": rawExpUnityDataDf.sort_index(axis=1), "rawCalibGaze": rawExpGazeDataDf.sort_index(axis=1), "processedCalib": processedCalibDataDf.sort_index(axis=1),
"analysisParameters":analysisParameters}
with open(picklePath, 'wb') as handle:
pickle.dump(dictOut, handle, protocol=pickle.HIGHEST_PROTOCOL)
return dictOut
# def compileSubData():
# rawDataDF = False
# calibDF = False
# # ### Load dataframes
# # Remember to set loadParsedData, loadProcessedData.
# dataFolderList = []
# [dataFolderList.append(name) for name in os.listdir("Data/") if name[0] is not '.']
# allSessionData = []
# for subNum, subString in enumerate(dataFolderList):
# dataParentFolder = "Data/" + subString
# dataSubFolderList = [];
# [dataSubFolderList.append(name) for name in os.listdir("Data/" + dataParentFolder) if name[0] is not '.'];
# dataFolder = dataParentFolder + '/' + dataSubFolderList[0] + '/'
# trialData = pd.read_csv( dataFolder + 'trial_results.csv')
# for trIdx, trialResults in trialData.iterrows():
# trialDF = processTrial(dataFolder, trialResults)
# if (trialResults['trialType'] == 'interception'):
# if( rawDataDF is False):
# rawDataDF = trialDF
# else:
# rawDataDF = rawDataDF.append(trialDF)
# elif(trialResults['trialType'] == 'CalibrationAssessment'):
# if( calibDF is False):
# calibDF = trialDF
# else:
# calibDF = calibDF.append(trialDF)
# sessionDict = {"expInfo": trialData,"rawData": rawDataDF, "calibData": calibDF}
# allSessionData.append(sessionDict)
# with open('../allSessionData.pickle', 'wb') as handle:
# pickle.dump(allSessionData, handle, protocol=pickle.HIGHEST_PROTOCOL)
# %%
if __name__ == "__main__":
import os
subNum = 0
rawDataDF = False
calibDF = False
# ### Load dataframes
# Remember to set loadParsedData, loadProcessedData.
dataFolderList = []
[dataFolderList.append(name) for name in os.listdir("Data/") if name[0] is not '.']
for i, name in enumerate(dataFolderList):
print(str(i) + ': ' + name )
sessionDict = unpackSession(subNum, gazeDataConfidenceThreshold = 0.6, doNotLoad = True)
# %%