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AZC_Feature_Paper/script_AZC_datasetPreProcessing.py
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script_AZC_datasetPreProcessing.py
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#!~/anaconda3/bin python
__authors__ = "Una Pale and Renato Zanetti"
__license__ = "LGPL"
__version__ = "1.0"
__email__ = "{una.pale, renato.zaneti} at epfl.ch"
'''script for:
- converts edf files to csv files without any changes in data (for CHB-MIT) adding the labels
- calculated features for each input file and saves them
- reorders feature data on a rolling basis (TSCV approach) and stores the a indexes vector to future train/test division
- plots labels in time (before/after TSCV reordering)
'''
from parametersSetup import *
from ZeroCrossLibrary import *
###################################
# Initial setup
###################################
Dataset='01_SWEC' #'01_SWEC', '02_CHB' #select the dataset to be processed
Server = 1 #0 - local execution, 1- server execution
parallelize=1 #chose whether to parallelize feature extraction per file per core
perc_cores= 0.5 #percetage of cores target to be charged with data processing
datasetPreparationType='Filt_1to20Hz' # 'Raw', 'Filt_1to20Hz'
# DEFINING INPUT/OUTPUT FOLDERS
if Dataset=='01_SWEC':
if Server:
folderInData = '/shares/eslfiler1/databases/medical/swec-ethz/ieeg/long-term-dataset/'
os.nice(20) #set low priority level for process running longer
else:
folderInData = '../iEEG/'
patients =['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18']
if Dataset=='02_CHB':
if Server:
folderInData = '/shares/eslfiler1/databases/medical/chb-mit/edf/' #expect all subject's files in the same directory
os.nice(20) #set low priority level for process running longer
samedir=1 #1- in case all chb files are in the same directory
else:
folderInData = '../chbmit/'
samedir=0 #1- in case all chb files are in the same directory
patients =['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24']
#folder that store partial results
if Server:
folderResults = '/shares/eslfiler1/scratch/ZeroCrossFeatEpilepsy/'
else:
folderResults = '../AZC_results/'
createFolderIfNotExists(folderResults)
createFolderIfNotExists( folderResults+Dataset)
#folder for feature files
folderOutFeatures= folderResults+Dataset+'/02_Features_' +datasetPreparationType + '/'
createFolderIfNotExists(folderOutFeatures)
###################################
# Parameters update regarding definition in 'parametersSetup.py'
GeneralParams.patients = patients
# ############################################################################################
#CHB-MIT edf files are converted to csv, adding extra column for labels
if Dataset=='02_CHB':
folderOutCSV= folderResults+Dataset+'/01_DataCSV_raw/'
createFolderIfNotExists(folderOutCSV)
# next function considers that all CHB-MIT edf files are in the same directory
extractEDFdataToCSV_originalData(parallelize, perc_cores, samedir, folderInData, folderOutCSV, SigInfoParams, patients)
#########################################################################
# CALCULATE FEATURES FOR EACH DATA FILE
calculateCLFFeat = 1 #enable the calculation of each group of features
calculateAZCFeat = 1
if Dataset=='01_SWEC':
calculateFeaturesPerEachFile_CLFAndAZCFeatures_SWEC(parallelize, perc_cores, ZeroCrossFeatureParams.EPS_thresh_arr,folderInData, folderOutFeatures, GeneralParams.patients, SigInfoParams, ZeroCrossFeatureParams, calculateCLFFeat, calculateAZCFeat)
if Dataset=='02_CHB':
calculateFeaturesPerEachFile_CLFAndAZCFeatures_CHB(parallelize, perc_cores, ZeroCrossFeatureParams.EPS_thresh_arr,folderOutCSV, folderOutFeatures, GeneralParams.patients, SigInfoParams, ZeroCrossFeatureParams, calculateCLFFeat, calculateAZCFeat)
# # Plotting data labels before reorganizing files for TSCV approach
plotRawDataLabels(Dataset, folderOutFeatures, GeneralParams,SegSymbParams)
###########################################################################################
# Reorganizing feature files for TSCV (first file containing min 6h of data and a seizure)
windowSize=60*60 #target for files length in data
folderOutTSCV=folderResults+Dataset+'/03_Features_' +datasetPreparationType+'_TSCVprep_'+str(windowSize) +'s_DivBig/'
createFolderIfNotExists(folderOutTSCV)
#considering that CHB-MIT presents big time gaps in between some files for a subject and that some files have more than 1hour of data, we generate indexes
#for TSCV classification considering a first file with a minimum of 5hours and a seizure, trying to keep data grouped be origin file and only subdividing
#data of a file in case it's 1.5x bigger than the target 'windowSize'. Test data is always originated from a specific file, thus no data is merged from
# multiple files.
generateTSCVindexesFromAllFiles_storeTSCVLabels(Dataset, GeneralParams, ZeroCrossFeatureParams, SegSymbParams, folderOutFeatures, folderOutTSCV, windowSize)
plotRearangedDataLabelsInTime(Dataset, folderOutTSCV, GeneralParams,SegSymbParams)
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