import sys
import os
from tools import Logger
log_dir = ''
log_dir += sys.argv[1]
if log_dir is '':
print('[ERROR] Please provide the log directory')
exit()
log = Logger(log_dir, '*log_of_logs.log', printLog=True, timestampOn=False)
log.emit('NEW STATUS REPORT', newRun=True)
filenames = os.listdir(log_dir)
filenames.sort()
sleepsightLogs = []
for filename in filenames:
if 'sleepsight' in filename:
ssLog = Logger(log_dir, filename)
log.emit('{}\t{}'.format(filename, ssLog.getLastMessage()))
path = args[2]
plot_path = args[3]
log_path = args[4]
p = Participant(id=participantID, path=path)
p.activeSensingFilenameSelector = 'diary'
p.metaDataFileName = 'meta_patients.json'
p.sleepSummaryFileName = 'FB_sleep_summary.csv'
p.load()
p.pipelineStatus['GP model sim.'] = False
#p.saveSnapshot(p.path)
print(p)
log = Logger(log_path, 'sleepsight' + p.id + '.log', printLog=True)
log.emit('BEGIN ANALYSIS PIPELINE', newRun=True)
# Task: 'trim data' to Study Duration
if not p.isPipelineTaskCompleted('trim data'):
log.emit('Continuing with TRIM DATA...')
p.trimData(p.info['startDate'], duration=56)
p.updatePipelineStatusForTask('trim data', log=log)
p.saveSnapshot(path, log=log)
else:
log.emit('Skipping TRIM DATA - already completed.')
# Task: 'missingness' (Decision tree: No missingness vs not worn vs not charged)
if not p.isPipelineTaskCompleted('missingness'):
log.emit('Continuing with MISSINGNESS computation...')
mdt = MissingnessDT(passiveData=p.passiveData,
activeDataSymptom=p.activeDataSymptom,
options = {'periodicity': False,
'participant-info': False,
'compliance': False,
'stationarity': False,
'symptom-score-discretisation': False,
'feature-delay': False,
'feature-selection': False,
'non-parametric-svm': False,
'non-parametric-gp': True
}
log = Logger(log_path, 'thesis_outputs.log', printLog=True)
# Load Participants
log.emit('Loading participants...', newRun=True)
aggr = T.Aggregates('.pkl', path, plot_path)
# Export Periodicity tables
if options['periodicity']:
log.emit('Generating PERIODCITY table...')
pt = T.PeriodictyTable(aggr, log)
pt.run()
pt.exportLatexTable(summary=False)
pt.exportLatexTable(summary=True)
# Export Participant Info
if options['participant-info']:
log.emit('Generating PARTICIPANTS-INFO table...')
