def test_bids_eventsfile():
"""Check if correct BIDS events file can be sourced."""
from labbookdb.report.tracking import bids_eventsfile
import pandas as pd
df = bids_eventsfile(DB_PATH, 'chr_longSOA')
bids_eventsfile = path.join(DATA_DIR, 'bids_eventsfile.csv')
df_ = pd.read_csv(bids_eventsfile, index_col=0)
assert df[['onset', 'duration']].equals(df_[['onset', 'duration']])
def write_bids_events_file(scan_dir,
db_path="~/syncdata/meta.db",
metadata_file='',
out_file="events.tsv",
prefer_labbookdb=False,
timecourse_file='',
task='',
forced_dummy_scans=0.,
):
"""Adjust a BIDS event file to reflect delays introduced after the trigger and before the scan onset.
Parameters
----------
scan_dir : str
ParaVision scan directory path.
db_path : str, optional
LabbookDB database file path from which to source the evets profile for the identifier assigned to the `task` parameter.
metadata_file : str, optional
Path to a BIDS metadata file.
out_file : str, optional
Path to which to write the adjusted events file
prefer_labbookdb : bool, optional
Whether to query the events file in the LabbookDB database file first (rather than look for the events file in the scan directory).
timecourse_file : str, optional
Path to a NIfTI file.
task : str, optional
Task identifier from a LabbookDB database.
Returns
-------
str : Path to which the adjusted events file was saved.
"""
import csv
import sys
import json
import os
import pandas as pd
import nibabel as nib
import numpy as np
from datetime import datetime
out_file = os.path.abspath(os.path.expanduser(out_file))
scan_dir = os.path.abspath(os.path.expanduser(scan_dir))
db_path = os.path.abspath(os.path.expanduser(db_path))
if not prefer_labbookdb:
try:
scan_dir_contents = os.listdir(scan_dir)
sequence_files = [i for i in scan_dir_contents if ("sequence" in i and "tsv" in i)]
if sequence_files:
sequence_file = os.path.join(scan_dir, sequence_files[0])
else:
timecourse_dir = os.path.dirname(timecourse_file)
timecourse_name = os.path.basename(timecourse_file)
stripped_name = timecourse_name.split('.', 1)[0].rsplit('_', 1)[0]
sequence_file = os.path.join(timecourse_dir,stripped_name+'_events.tsv')
mydf = pd.read_csv(sequence_file, sep="\s", engine='python')
except:
if os.path.isfile(db_path):
from labbookdb.report.tracking import bids_eventsfile
mydf = bids_eventsfile(db_path, task)
else:
return '/dev/null'
else:
try:
if os.path.isfile(db_path):
from labbookdb.report.tracking import bids_eventsfile
mydf = bids_eventsfile(db_path, task)
else:
return '/dev/null'
except ImportError:
scan_dir_contents = os.listdir(scan_dir)
sequence_files = [i for i in scan_dir_contents if ("sequence" in i and "tsv" in i)]
if sequence_files:
sequence_file = os.path.join(scan_dir, sequence_files[0])
else:
timecourse_dir = os.path.dirname(timecourse_file)
timecourse_name = os.path.basename(timecourse_file)
stripped_name = timecourse_name.split('.', 1)[0].rsplit('_', 1)[0]
sequence_file = os.path.join(timecourse_dir,stripped_name+'_events.tsv')
mydf = pd.read_csv(sequence_file, sep="\s", engine='python')
timecourse_file = os.path.abspath(os.path.expanduser(timecourse_file))
timecourse = nib.load(timecourse_file)
zooms = timecourse.header.get_zooms()
tr = float(zooms[-1])
delay = 0.
if forced_dummy_scans:
delay = forced_dummy_scans * tr
elif metadata_file:
metadata_file = os.path.abspath(os.path.expanduser(metadata_file))
with open(metadata_file) as metadata:
metadata = json.load(metadata)
try:
delay += metadata['NumberOfVolumesDiscardedByScanner'] * tr
except:
pass
try:
delay += metadata['DelayAfterTrigger']
except:
pass
mydf['onset'] = mydf['onset'] - delay
mydf.to_csv(out_file, sep=str('\t'), index=False)
return out_file
def write_bids_events_file(
scan_dir,
db_path="~/syncdata/meta.db",
metadata_file='',
out_file="events.tsv",
prefer_labbookdb=False,
timecourse_file='',
task='',
forced_dummy_scans=0.,
):
"""Adjust a BIDS event file to reflect delays introduced after the trigger and before the scan onset.
Parameters
----------
scan_dir : str
ParaVision scan directory path.
db_path : str, optional
LabbookDB database file path from which to source the evets profile for the identifier assigned to the `task` parameter.
metadata_file : str, optional
Path to a BIDS metadata file.
out_file : str, optional
Path to which to write the adjusted events file
prefer_labbookdb : bool, optional
Whether to query the events file in the LabbookDB database file first (rather than look for the events file in the scan directory).
timecourse_file : str, optional
Path to a NIfTI file.
task : str, optional
Task identifier from a LabbookDB database.
Returns
-------
str : Path to which the adjusted events file was saved.
"""
import csv
import sys
import json
import os
import pandas as pd
import nibabel as nib
import numpy as np
from datetime import datetime
out_file = os.path.abspath(os.path.expanduser(out_file))
scan_dir = os.path.abspath(os.path.expanduser(scan_dir))
db_path = os.path.abspath(os.path.expanduser(db_path))
if not prefer_labbookdb:
try:
scan_dir_contents = os.listdir(scan_dir)
sequence_files = [
i for i in scan_dir_contents
if ("sequence" in i and "tsv" in i)
]
if sequence_files:
sequence_file = os.path.join(scan_dir, sequence_files[0])
else:
timecourse_dir = os.path.dirname(timecourse_file)
timecourse_name = os.path.basename(timecourse_file)
stripped_name = timecourse_name.split('.',
1)[0].rsplit('_', 1)[0]
sequence_file = os.path.join(timecourse_dir,
stripped_name + '_events.tsv')
mydf = pd.read_csv(sequence_file, sep="\s", engine='python')
except:
if os.path.isfile(db_path):
from labbookdb.report.tracking import bids_eventsfile
mydf = bids_eventsfile(db_path, task)
else:
return '/dev/null'
else:
try:
if os.path.isfile(db_path):
from labbookdb.report.tracking import bids_eventsfile
mydf = bids_eventsfile(db_path, task)
else:
return '/dev/null'
except ImportError:
scan_dir_contents = os.listdir(scan_dir)
sequence_files = [
i for i in scan_dir_contents
if ("sequence" in i and "tsv" in i)
]
if sequence_files:
sequence_file = os.path.join(scan_dir, sequence_files[0])
else:
timecourse_dir = os.path.dirname(timecourse_file)
timecourse_name = os.path.basename(timecourse_file)
stripped_name = timecourse_name.split('.',
1)[0].rsplit('_', 1)[0]
sequence_file = os.path.join(timecourse_dir,
stripped_name + '_events.tsv')
mydf = pd.read_csv(sequence_file, sep="\s", engine='python')
timecourse_file = os.path.abspath(os.path.expanduser(timecourse_file))
timecourse = nib.load(timecourse_file)
zooms = timecourse.header.get_zooms()
tr = float(zooms[-1])
delay = 0.
if forced_dummy_scans:
delay = forced_dummy_scans * tr
elif metadata_file:
metadata_file = os.path.abspath(os.path.expanduser(metadata_file))
with open(metadata_file) as metadata:
metadata = json.load(metadata)
try:
delay += metadata['NumberOfVolumesDiscardedByScanner'] * tr
except:
pass
try:
delay += metadata['DelayAfterTrigger']
except:
pass
mydf['onset'] = mydf['onset'] - delay
mydf.to_csv(out_file, sep=str('\t'), index=False)
return out_file