def test_index_associated_false(testvalidator):
testvalidator = BIDSValidator(index_associated=False)
target_list = [
"/code/", "/derivatives/", "/sourcedata/", "/stimuli/", "/.git/"
]
for item in target_list:
result = testvalidator.is_associated_data(item)
assert not result
def drp_seed_fc():
import numpy as np
from os import path
#from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
from samri.utilities import N_PROCS
N_PROCS = max(N_PROCS - 8, 2)
from bids.grabbids import BIDSLayout
from bids.grabbids import BIDSValidator
import os
base = '~/ni_data/ofM.dr/bids/preprocessing/generic/'
base = os.path.abspath(os.path.expanduser(base))
validate = BIDSValidator()
for x in os.walk(base):
print(x[0])
print(validate.is_bids(x[0]))
layout = BIDSLayout(base)
df = layout.as_data_frame()
df = df[df.type.isin(['cbv'])]
print(df)
#substitutions = bids_substitution_iterator(
# list(df['session'].unique()),
# all_subjects,
# ["CogB",],
# "~/ni_data/ofM.dr/",
# "composite",
# acquisitions=["EPI",],
# check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
#substitutions = df.T.to_dict().values()[:2]
substitutions = df.T.to_dict().values()
print(substitutions)
fc_results = fc.seed_based(
substitutions,
"~/ni_data/templates/roi/DSURQEc_drp.nii.gz",
"/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template=
'~/ni_data/ofM.dr/bids/preprocessing/generic/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results=
"~/ni_data/ofM.dr/bids/fc/DSURQEc_drp/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
n_procs=N_PROCS,
cachedir='/mnt/data/joblib')
def drp_seed_fc():
import numpy as np
from os import path
#from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
from samri.utilities import N_PROCS
N_PROCS=max(N_PROCS-8, 2)
from bids.grabbids import BIDSLayout
from bids.grabbids import BIDSValidator
import os
base = '~/ni_data/ofM.dr/bids/preprocessing/generic/'
base = os.path.abspath(os.path.expanduser(base))
validate = BIDSValidator()
for x in os.walk(base):
print(x[0])
print(validate.is_bids(x[0]))
layout = BIDSLayout(base)
df = layout.as_data_frame()
df = df[df.type.isin(['cbv'])]
print(df)
#substitutions = bids_substitution_iterator(
# list(df['session'].unique()),
# all_subjects,
# ["CogB",],
# "~/ni_data/ofM.dr/",
# "composite",
# acquisitions=["EPI",],
# check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
#substitutions = df.T.to_dict().values()[:2]
substitutions = df.T.to_dict().values()
print(substitutions)
fc_results = fc.seed_based(substitutions, "~/ni_data/templates/roi/DSURQEc_drp.nii.gz", "/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template='~/ni_data/ofM.dr/bids/preprocessing/generic/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results="~/ni_data/ofM.dr/bids/fc/DSURQEc_drp/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
n_procs=N_PROCS,
cachedir='/mnt/data/joblib')
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
df.loc[df.modality == 'func',
'task'] = df.path.str.partition('task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if (subjects):
df = filter_data(df, 'subject', subjects)
if (sessions):
df = filter_data(df, 'session', sessions)
return df
def bids_autograb(bids_dir):
bids_dir = path.abspath(path.expanduser(bids_dir))
validate = BIDSValidator()
layout = BIDSLayout(bids_dir)
df = layout.as_data_frame()
return df
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# Not crashing if the run field is not present
try:
# Run is for some reason recorded as float
df.loc[df['run'].notna(), 'run'] = df.loc[df['run'].notnull(),
'run'].apply(int).apply(str)
except KeyError:
pass
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition(
'task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
return df
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
"""
Creates a Pandas Dataframe descriptor from a BIDS datapath, optionally filtering out conditions.
Parameters
----------
base : str
path specifying the root directory of the BIDS data
structural_match : dict or bool
Dictionary specifying a whitelist of BIDS field identifiers.
False if no whitelist is specified.
functional_match : dict or bool
Dictionary specifying a whitelist of BIDS field identifiers.
False if no whitelist is specified.
subjects: list or bool
A list of subjects which may be present in the 'subjects' column of the created Pandas DataFrame, 'df'.
False if user does not want to filter DataFrame by subjects.
sessions: list or bool
A list of session names which may be present in the 'sessions' column of the created Pandas DataFrame, 'df'.
False if user does not want to filter DataFrame by sessions.
Returns
-------
df : pandas.DataFrame
A Pandas DataFrame with information corresponding to the whitelisted BIDS identifiers and optionally filtered by subjects and/or sessions.
"""
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
#layout = BIDSLayout(base, validate=False)
layout = BIDSLayout(base, validate=False, derivatives=True)
try:
df = layout.as_data_frame()
except AttributeError:
df = layout.to_df()
# Not crashing if the run field is not present
try:
# Run is for some reason recorded as float
df.loc[df['run'].notna(), 'run'] = df.loc[df['run'].notnull(),
'run'].apply(int).apply(str)
except KeyError:
pass
if verbose:
print(df)
print(df.columns)
# drop event files
# PyBIDS 0.6.5 and 0.10.2 compatibility
try:
df = df[df.type != 'events']
except AttributeError:
df = df[df.suffix != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition(
'task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
# Can be removed after Pybids 0.10.2 migration
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
return df
def bids_data_selection(base, structural_match, functional_match, subjects, sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# Run is for some reason recorded as float
df.loc[df['run'].notna(),'run'] = df.loc[df['run'].notnull(),'run'].apply(int).apply(str)
#df['run'] = df['run'].astype(int)
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition('task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func', 'scan_type'] = 'task-' + df['task'] + '_acq-'+ df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq'] +'_' + df['type']
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
return df
def testvalidator():
return BIDSValidator()