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avalanche_analyses.py
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avalanche_analyses.py
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from avalanchetoolbox import avalanches
from avalanchetoolbox import database as db
import h5py
import os
#import BCNI_database as db
#cluster=False
from Helix_database import Session, database_url
session = Session()
cluster=True
analyses_directory = '/home/alstottj/biowulf/analyses/'
swarms_directory = '/home/alstottj/biowulf/swarms/'
python_location= '/usr/local/Python/2.7.2/bin/python'
time_scales = [1, 2, 3, 4, 5, 6, 7, 8, 16, 32]
threshold_mode = 'SD'
threshold_levels = [3]
threshold_directions = ['both']
#bins = [1]
#percentiles =[99]
given_xmin_xmax = [(None, None), (1, None), (1, 'channels'), (1,102)]
event_signals = ['amplitude', 'displacement']
event_detections = ['local_extrema', 'local', 'excursion_extrema']
cascade_methods = ['grid']
spatial_samples = [('all', 'all')]
temporal_samples = [('all', 'all')]
visits = [2, 3]
tasks = ['rest']
eyes = ['shut', 'open']
sensors = ['gradiometer']
remicas = ['raw']
sampling_rate = 250.0
#data_path = '/data/alstottj/MRC/'
data_path = '/scratch/alstottj/MRC/For_Analysis/'
filter_type = 'FIR'
taps = 25
window = 'blackmanharris'
transd = True
mains = 50
dirList=os.listdir(data_path)
for fname in dirList:
file = data_path+fname
f = h5py.File(file)
group_name = f.attrs['group_name']
number_in_group = f.attrs['number_in_group']
species = f.attrs['species']
location = f.attrs['location']
subject = session.query(db.Subject).\
filter_by(species=species, group_name=group_name, number_in_group=number_in_group).first()
if not subject:
subject = db.Subject(species=species, group_name=group_name, number_in_group=number_in_group)
session.add(subject)
session.commit()
print file
conditions = [(v,t,e,s,rem) for v in visits for t in tasks for e in eyes for s in sensors for rem in remicas]
for visit, task_type, eye, sensor_type, rem in conditions:
base = str(visit)+'/'+task_type+'/'+eye+'/'+sensor_type+'/'+rem
base_filtered = base+'/filter_'+filter_type+'_'+str(taps)+'_'+window
#If this particular set of conditions doesn't exist for this subject, just continue to the next set of conditions
try:
f[base_filtered]
except KeyError:
continue
print base
if group_name=='GSK1':
drug = 'none'
rest = 'rested'
elif group_name=='GSK2' and number_in_group in [21, 25, 30, 35, 37, 44, 48, 137, 148]:
drug = 'placebo'
if visit==2:
rest = 'sleep_deprived'
if visit==3:
rest = 'rested'
elif group_name=='GSK2' and number_in_group in [22, 28, 32, 34, 39, 42, 45, 49, 50]:
drug = 'placebo'
if visit==2:
rest = 'rested'
if visit==3:
rest = 'sleep_deprived'
elif group_name=='GSK2' and number_in_group in [23, 27,33, 38,46,143]:
drug = 'donepezil'
if visit==2:
rest = 'sleep_deprived'
if visit==3:
rest = 'rested'
elif group_name=='GSK2' and number_in_group in [24, 26, 31, 36, 40, 41, 231, 149]:
drug = 'donepezil'
if visit==2:
rest = 'rested'
if visit==3:
rest = 'sleep_deprived'
else:
print("Couldn't find this subject!")
drug = 'unknown'
rest = 'unknown'
print drug
print rest
duration = f[base+'/raw/displacement'].shape[1]
task = session.query(db.Task).\
filter_by(type=task_type, eyes=eye).first()
if not task:
print('Task not found!')
break
sensor = session.query(db.Sensor).\
filter_by(location=location, sensor_type=sensor_type).first()
if not sensor:
print('Sensor not found!')
break
experiment = session.query(db.Experiment).\
filter_by(location=location, subject_id=subject.id, visit_number=visit, mains=mains, drug=drug,\
rest=rest, task_id=task.id).first()
if not experiment:
experiment = db.Experiment(location=location, subject_id=subject.id, visit_number=visit, mains=mains, drug=drug,\
rest=rest, task_id=task.id)
session.add(experiment)
session.commit()
if rem=='remica':
rem=True
elif rem=='raw':
rem=False
else:
raise KeyError("Don't know this kind of remica processing!")
recording = session.query(db.Recording).\
filter_by(experiment_id=experiment.id, sensor_id=sensor.id, duration=duration, \
subject_id = subject.id, task_id=task.id,\
sampling_rate=sampling_rate, eye_movement_removed=rem, transd=transd).first()
if not recording:
recording = db.Recording(experiment_id=experiment.id, sensor_id=sensor.id, duration=duration,\
subject_id = subject.id, task_id=task.id,\
sampling_rate=sampling_rate, eye_movement_removed=rem, transd=transd)
session.add(recording)
session.commit()
for band in list(f[base_filtered]):
print band
data = f[base_filtered+'/'+band]
band_range = data.attrs['frequency_range']
if band_range.shape[0]==1:
band_min=0.
band_max=band_range[0]
else:
band_min=band_range[0]
band_max=band_range[1]
filter = session.query(db.Filter).\
filter_by(recording_id=recording.id, filter_type=filter_type, poles=taps-1, window=window,\
band_name=band, band_min=band_min, band_max=band_max, duration=data['displacement'].shape[1],\
notch=False,phase_shuffled=False).first()
if not filter:
filter = db.Filter(\
recording_id=recording.id, filter_type=filter_type, poles=taps-1, window=window,\
band_name=band, band_min=band_min, band_max=band_max, duration=data['displacement'].shape[1],\
notch=False,phase_shuffled=False,\
subject_id = subject.id, task_id=task.id, experiment_id=experiment.id, sensor_id=sensor.id)
session.add(filter)
session.commit()
avalanches.avalanche_analyses(f.file.filename, HDF5_group=base_filtered+'/'+band,\
threshold_mode=threshold_mode, threshold_levels=threshold_levels, threshold_directions=threshold_directions,\
event_signals=event_signals, event_detections=event_detections,\
time_scales=time_scales, cascade_methods=cascade_methods,\
given_xmin_xmax=given_xmin_xmax,\
spatial_samples=spatial_samples, temporal_samples=temporal_samples,\
session=session, database_url=database_url,\
subject_id=subject.id, task_id=task.id, experiment_id=experiment.id,\
sensor_id=sensor.id, recording_id=recording.id, filter_id=filter.id,\
cluster=cluster, swarms_directory=swarms_directory, analyses_directory=analyses_directory,\
python_location=python_location,\
verbose=True)
break
session.close()
session.bind.dispose()