def setUp(self):
super(ContextTests, self).setUp()
patcher = patch('niprov.context.niprov')
self.niprov = patcher.start()
self.addCleanup(patcher.stop)
from niprov import ProvenanceContext
self.context = ProvenanceContext()
self.context.deps = self.dependencies
def setUp(self):
self.dbpath = os.path.expanduser(os.path.join('~','provenance_test.json'))
if os.path.exists(self.dbpath):
os.remove(self.dbpath)
os.mkdir('temp')
from niprov import ProvenanceContext
self.provenance = ProvenanceContext()
self.provenance.config.database_type = 'file'
self.provenance.config.database_url = self.dbpath
def setUp(self):
self.dbpath = os.path.expanduser(
os.path.join('~', 'provenance_test.json'))
if os.path.exists(self.dbpath):
os.remove(self.dbpath)
os.mkdir('temp')
from niprov import ProvenanceContext
self.provenance = ProvenanceContext()
self.provenance.config.database_type = 'file'
self.provenance.config.database_url = self.dbpath
self.provenance.config.verbose = False
self.oldstdout = sys.stdout
sys.stdout = self.stdout = StringIO()
import os
from niprov import ProvenanceContext
provenance = ProvenanceContext()
img = provenance.add('testdata/nifti/qt1.nii.gz')
provenance.view(img)
img.viewSnapshot()
def handler(text, func, out, params, dependencies=Dependencies()):
"""mnefun on_process handler
Responds to the following mnefun steps:
fetch_raw_files: Runs discover on the subjects' raw data dirs.
fetch_sss_files,
apply_preprocessing_combined,
save_epochs: For these steps, runs log for the new files.
"""
listener = dependencies.getListener()
libs = dependencies.getLibraries()
provenance = ProvenanceContext()
funcname = func.func_name
listener.mnefunEventReceived(funcname)
paramdict = {}
for paramname in dir(params):
objectvalue = getattr(params, paramname)
blacklist = ['get_projs_from', 'inv_runs']
if hasattr(objectvalue, '__call__'):
continue
if paramname[0] == '_':
continue
if 'array' in str(type(objectvalue)):
continue
if paramname in blacklist:
continue
paramdict[paramname] = objectvalue
subjects = [params.subjects[i] for i in params.subject_indices]
for subj in subjects:
customprov = {'mnefun': paramdict}
if funcname in ['fetch_raw_files', 'score_fun', 'fetch_sss_files']:
rawfiles = libs.mnefun.get_raw_fnames(
params,
subj,
'raw',
add_splits=True,
run_indices=params.subject_run_indices)
if funcname == 'fetch_raw_files':
for f in rawfiles:
provenance.add(f, provenance=customprov)
elif funcname == 'score_fun':
eventfiles = libs.mnefun._paths.get_event_fnames(
params, subj, params.subject_run_indices)
for rawfile, eventfile in zip(rawfiles, eventfiles):
provenance.log(eventfile,
'scoring',
rawfile,
provenance=customprov)
elif funcname == 'fetch_sss_files':
trans = 'Signal Space Separation'
sssfiles = libs.mnefun.get_raw_fnames(params, subj, 'sss')
for rawfile, sssfile in zip(rawfiles, sssfiles):
provenance.log(sssfile, trans, rawfile, provenance=customprov)
elif funcname == 'apply_preprocessing_combined':
trans = 'Signal Space Projection'
sssfiles = libs.mnefun.get_raw_fnames(params, subj, 'sss')
pcafiles = libs.mnefun.get_raw_fnames(params, subj, 'pca')
for sssfile, pcafile in zip(sssfiles, pcafiles):
provenance.log(pcafile, trans, sssfile, provenance=customprov)
elif funcname == 'save_epochs':
pcafiles = libs.mnefun.get_raw_fnames(params, subj, 'pca')
evtfiles = libs.mnefun._paths.get_epochs_evokeds_fnames(
params, subj, params.analyses)
for evtfile in evtfiles:
if os.path.isfile(evtfile[0]):
provenance.log(evtfile[0],
'Epoching',
pcafiles,
provenance=customprov)
def setUp(self):
from niprov import ProvenanceContext
self.provenance = ProvenanceContext()
self.fullURL = URL.format(PASSWORD)
self.provenance.config.database_type = 'MongoDB'
self.provenance.config.database_url = self.fullURL
