def align_epi_anat(anatomy, epi_dsets, skull_strip_anat=True):
''' aligns epis to anatomy using ``align_epi_anat.py`` script
:epi_dsets: can be either a string or list of strings of the epi child datasets
:skull_strip_anat: if ``True``, ``anatomy`` will be skull-stripped using the default method
The default output suffix is "_al"
'''
if isinstance(epi_dsets, basestring):
epi_dsets = [epi_dsets]
if len(epi_dsets) == 0:
nl.notify('Warning: no epi alignment datasets given for anatomy %s!' %
anatomy,
level=nl.level.warning)
return
if all(os.path.exists(nl.suffix(x, '_al')) for x in epi_dsets):
return
anatomy_use = anatomy
if skull_strip_anat:
nl.skull_strip(anatomy, '_ns')
anatomy_use = nl.suffix(anatomy, '_ns')
inputs = [anatomy_use] + epi_dsets
dset_products = lambda dset: [
nl.suffix(dset, '_al'),
nl.prefix(dset) + '_al_mat.aff12.1D',
nl.prefix(dset) + '_tsh_vr_motion.1D'
]
products = nl.flatten([dset_products(dset) for dset in epi_dsets])
with nl.run_in_tmp(inputs, products):
if nl.is_nifti(anatomy_use):
anatomy_use = nl.afni_copy(anatomy_use)
epi_dsets_use = []
for dset in epi_dsets:
if nl.is_nifti(dset):
epi_dsets_use.append(nl.afni_copy(dset))
else:
epi_dsets_use.append(dset)
cmd = [
"align_epi_anat.py", "-epi2anat", "-anat_has_skull", "no",
"-epi_strip", "3dAutomask", "-anat", anatomy_use, "-epi_base", "5",
"-epi", epi_dsets_use[0]
]
if len(epi_dsets_use) > 1:
cmd += ['-child_epi'] + epi_dsets_use[1:]
out = nl.run(cmd)
for dset in epi_dsets:
if nl.is_nifti(dset):
dset_nifti = nl.nifti_copy(nl.prefix(dset) + '_al+orig')
if dset_nifti and os.path.exists(
dset_nifti) and dset_nifti.endswith(
'.nii') and dset.endswith('.gz'):
nl.run(['gzip', dset_nifti])
def align_epi_anat(anatomy,epi_dsets,skull_strip_anat=True):
''' aligns epis to anatomy using ``align_epi_anat.py`` script
:epi_dsets: can be either a string or list of strings of the epi child datasets
:skull_strip_anat: if ``True``, ``anatomy`` will be skull-stripped using the default method
The default output suffix is "_al"
'''
if isinstance(epi_dsets,basestring):
epi_dsets = [epi_dsets]
if len(epi_dsets)==0:
nl.notify('Warning: no epi alignment datasets given for anatomy %s!' % anatomy,level=nl.level.warning)
return
if all(os.path.exists(nl.suffix(x,'_al')) for x in epi_dsets):
return
anatomy_use = anatomy
if skull_strip_anat:
nl.skull_strip(anatomy,'_ns')
anatomy_use = nl.suffix(anatomy,'_ns')
inputs = [anatomy_use] + epi_dsets
dset_products = lambda dset: [nl.suffix(dset,'_al'), nl.prefix(dset)+'_al_mat.aff12.1D', nl.prefix(dset)+'_tsh_vr_motion.1D']
products = nl.flatten([dset_products(dset) for dset in epi_dsets])
with nl.run_in_tmp(inputs,products):
if nl.is_nifti(anatomy_use):
anatomy_use = nl.afni_copy(anatomy_use)
epi_dsets_use = []
for dset in epi_dsets:
if nl.is_nifti(dset):
epi_dsets_use.append(nl.afni_copy(dset))
else:
epi_dsets_use.append(dset)
cmd = ["align_epi_anat.py", "-epi2anat", "-anat_has_skull", "no", "-epi_strip", "3dAutomask","-anat", anatomy_use, "-epi_base", "5", "-epi", epi_dsets_use[0]]
if len(epi_dsets_use)>1:
cmd += ['-child_epi'] + epi_dsets_use[1:]
out = nl.run(cmd)
for dset in epi_dsets:
if nl.is_nifti(dset):
dset_nifti = nl.nifti_copy(nl.prefix(dset)+'_al+orig')
if dset_nifti and os.path.exists(dset_nifti) and dset_nifti.endswith('.nii') and dset.endswith('.gz'):
nl.run(['gzip',dset_nifti])
def subjects(experiment=None,label=None,tags=None,only_included=True):
'''returns a list of subject objects for all subjects with valid JSON files
:experiment: only return subjects that have a scan for the given experiment
:label: only return subjects who have datasets with that label
:tags: only return subjects who have a session with given tag
:only_included: if True (the default), will exclude any subjects with ``subject.include``
set to False
Using this list, it is easy to filter again to find a specific subset of subjects.
For example, for all the subjects for the experiment ``Exp1``::
subjects = padre.subjects('Exp1')
To find all subjects who contain a run called ``rest``::
subjects = padre.subjects(label='rest')
To get a list of all subjects who have had a left ATL surgery::
subjects = [s for s in padre.subjects() if s.meta['clinical']['surgery_type']=='left atl']
'''
if not _indexed_and_loaded_all_subjects:
_index_all_subjects(True)
if experiment==None and p._global_experiment:
experiment = p._global_experiment
all_subjs = _all_subjects.values()
if label:
all_subjs = [x for x in all_subjs if len(x.dsets(label))]
if experiment:
all_subjs = [x for x in all_subjs if experiment in [x._sessions[y]['experiment'] for y in x._sessions if 'experiment' in x._sessions[y]]]
if tags:
if isinstance(tags,basestring):
tags = [tags]
all_subjs = [x for x in all_subjs if all([tag in nl.flatten([x._sessions[y]['tags'] for y in x._sessions if 'tags' in x._sessions[y]]) for tag in tags])]
if only_included and not p._include_all:
all_subjs = [x for x in all_subjs if x.include]
return all_subjs
def partial(self,start=0,end=None,run=0):
'''chops the stimulus by only including time points ``start`` through ``end`` (in reps, inclusive; ``None``=until the end)
if using stim_times-style simulus, will change the ``run``'th run. If a column, will just chop the column'''
self.read_file()
decon_stim = copy.copy(self)
if start<0:
start = 0
if self.type()=="column":
decon_stim.column_file = None
if end>=len(decon_stim.column):
end = None
if end==None:
decon_stim.column = decon_stim.column[start:]
else:
decon_stim.column = decon_stim.column[start:end+1]
if len(decon_stim.column)==0:
return None
if self.type()=="times":
if self.TR==None:
nl.notify('Error: cannot get partial segment of a stim_times stimulus without a TR',level=nl.level.error)
return None
def time_in(a):
first_number = r'^(\d+(\.\d+)?)'
if isinstance(a,basestring):
m = re.match(first_number,a)
if m:
a = m.group(1)
else:
nl.notify('Warning: cannot intepret a number from the stim_time: "%s"' % a,level=nl.level.warning)
return False
a = float(a)/self.TR
if a>=start and (end==None or a<=end):
return True
return False
decon_stim.times_file = None
if len(decon_stim.times)==0 or '__iter__' not in dir(decon_stim.times[0]):
decon_stim.times = [decon_stim.times]
decon_stim.times[run] = [x for x in decon_stim.times[run] if time_in(x)]
if len(nl.flatten(decon_stim.times))==0:
return None
return decon_stim
def partial(self,start=0,end=None,run=0):
'''chops the stimulus by only including time points ``start`` through ``end`` (in reps, inclusive; ``None``=until the end)
if using stim_times-style simulus, will change the ``run``'th run. If a column, will just chop the column'''
self.read_file()
decon_stim = copy.copy(self)
if start<0:
start = 0
if self.type()=="column":
decon_stim.column_file = None
if end>=len(decon_stim.column):
end = None
if end==None:
decon_stim.column = decon_stim.column[start:]
else:
decon_stim.column = decon_stim.column[start:end+1]
if len(decon_stim.column)==0:
return None
if self.type()=="times":
if self.TR==None:
nl.notify('Error: cannot get partial segment of a stim_times stimulus without a TR',level=nl.level.error)
return None
def time_in(a):
first_number = r'^(\d+(\.\d+)?)'
if isinstance(a,basestring):
m = re.match(first_number,a)
if m:
a = m.group(1)
else:
nl.notify('Warning: cannot intepret a number from the stim_time: "%s"' % a,level=nl.level.warning)
return False
a = float(a)/self.TR
if a>=start and (end==None or a<=end):
return True
return False
decon_stim.times_file = None
if len(decon_stim.times)==0 or '__iter__' not in dir(decon_stim.times[0]):
decon_stim.times = [decon_stim.times]
decon_stim.times[run] = [x for x in decon_stim.times[run] if time_in(x)]
if len(nl.flatten(decon_stim.times))==0:
return None
return decon_stim
def stack_decon_stims(stim_list):
'''take a ``list`` (in order of runs) of ``dict``s of stim_name:DeconStim and stack them together. returns
a single ``dict`` of stim_name:decon_stim
As in, takes:
[
# Run 1
{ "stim1": decon_stim1a, "stim2": decon_stim2a },
# Run 2
{ "stim1": decon_stim1b, "stim2": decon_stim2b, "stim3": decon_stim3 }
]
And makes:
{ "stim1": decon_stim1, "stim2": decon_stim2, "stim3": decon_stim3 }
If a stimulus is not present in a run, it will fill that run with an empty stimulus
'''
stim_names = list(set(nl.flatten([stims.keys() for stims in stim_list])))
stim_dict = {}
for stim_name in stim_names:
types = list(set([stims[stim_name].type() for stims in stim_list if stim_name in stims]))
if len(types)>1:
nl.notify('Error: Trying to stack stimuli of different types! (%s)' % stim_name,level=nl.level.error)
return None
type = types[0]
stim_stack = []
for i in xrange(len(stim_list)):
if stim_name in stim_list[i]:
stim_stack.append(stim_list[i][stim_name])
else:
stim_stack.append(stim_list[i].values()[0].blank_stim(type=type))
stim_dict[stim_name] = copy.copy(stim_stack[0])
for stim in stim_stack[1:]:
stim_dict[stim_name] = stim_dict[stim_name].concat_stim(stim)
return stim_dict.values()
def stack_decon_stims(stim_list):
'''take a ``list`` (in order of runs) of ``dict``s of stim_name:DeconStim and stack them together. returns
a single ``dict`` of stim_name:decon_stim
As in, takes:
[
# Run 1
{ "stim1": decon_stim1a, "stim2": decon_stim2a },
# Run 2
{ "stim1": decon_stim1b, "stim2": decon_stim2b, "stim3": decon_stim3 }
]
And makes:
{ "stim1": decon_stim1, "stim2": decon_stim2, "stim3": decon_stim3 }
If a stimulus is not present in a run, it will fill that run with an empty stimulus
'''
stim_names = list(set(nl.flatten([stims.keys() for stims in stim_list])))
stim_dict = {}
for stim_name in stim_names:
types = list(set([stims[stim_name].type() for stims in stim_list if stim_name in stims]))
if len(types)>1:
nl.notify('Error: Trying to stack stimuli of different types! (%s)' % stim_name,level=nl.level.error)
return None
type = types[0]
stim_stack = []
for i in xrange(len(stim_list)):
if stim_name in stim_list[i]:
stim_stack.append(stim_list[i][stim_name])
else:
stim_stack.append(stim_list[i].values()[0].blank_stim(type=type))
stim_dict[stim_name] = copy.copy(stim_stack[0])
for stim in stim_stack[1:]:
stim_dict[stim_name] = stim_dict[stim_name].concat_stim(stim)
return stim_dict.values()
def dset_in_dict(fname, l):
return len(
[x for x in nl.flatten(l.values()) if fname == x['filename']]) > 1
def dset_in_dict(fname,l):
return len([x for x in nl.flatten(l.values()) if fname==x['filename']])>1
def smooth_decon_to_fwhm(decon,fwhm,cache=True):
'''takes an input :class:`Decon` object and uses ``3dBlurToFWHM`` to make the output as close as possible to ``fwhm``
returns the final measured fwhm. If ``cache`` is ``True``, will save the blurred input file (and use it again in the future)'''
if os.path.exists(decon.prefix):
return
blur_dset = lambda dset: nl.suffix(dset,'_smooth_to_%.2f' % fwhm)
with nl.notify('Running smooth_decon_to_fwhm analysis (with %.2fmm blur)' % fwhm):
tmpdir = tempfile.mkdtemp()
try:
cwd = os.getcwd()
random_files = [re.sub(r'\[\d+\]$','',str(x)) for x in nl.flatten([x for x in decon.__dict__.values() if isinstance(x,basestring) or isinstance(x,list)]+[x.values() for x in decon.__dict__.values() if isinstance(x,dict)])]
files_to_copy = [x for x in random_files if os.path.exists(x) and x[0]!='/']
files_to_copy += [blur_dset(dset) for dset in decon.input_dsets if os.path.exists(blur_dset(dset))]
# copy crap
for file in files_to_copy:
try:
shutil.copytree(file,tmpdir)
except OSError as e:
shutil.copy(file,tmpdir)
shutil.copy(file,tmpdir)
copyback_files = [decon.prefix,decon.errts]
with nl.run_in(tmpdir):
if os.path.exists(decon.prefix):
os.remove(decon.prefix)
# Create the blurred inputs (or load from cache)
if cache and all([os.path.exists(os.path.join(cwd,blur_dset(dset))) for dset in decon.input_dsets]):
# Everything is already cached...
nl.notify('Using cache\'d blurred datasets')
else:
# Need to make them from scratch
with nl.notify('Creating blurred datasets'):
old_errts = decon.errts
decon.errts = 'residual.nii.gz'
decon.prefix = os.path.basename(decon.prefix)
# Run once in place to get the residual dataset
decon.run()
running_reps = 0
for dset in decon.input_dsets:
info = nl.dset_info(dset)
residual_dset = nl.suffix(dset,'_residual')
nl.run(['3dbucket','-prefix',residual_dset,'%s[%d..%d]'%(decon.errts,running_reps,running_reps+info.reps-1)],products=residual_dset)
cmd = ['3dBlurToFWHM','-quiet','-input',dset,'-blurmaster',residual_dset,'-prefix',blur_dset(dset),'-FWHM',fwhm]
if decon.mask:
if decon.mask=='auto':
cmd += ['-automask']
else:
cmd += ['-mask',decon.mask]
nl.run(cmd,products=blur_dset(dset))
running_reps += info.reps
if cache:
copyback_files.append(blur_dset(dset))
decon.errts = old_errts
decon.input_dsets = [blur_dset(dset) for dset in decon.input_dsets]
for d in [decon.prefix,decon.errts]:
if os.path.exists(d):
try:
os.remove(d)
except:
pass
decon.run()
for copyfile in copyback_files:
if os.path.exists(copyfile):
shutil.copy(copyfile,cwd)
else:
nl.notify('Warning: deconvolve did not produce expected file %s' % decon.prefix,level=nl.level.warning)
except:
raise
finally:
shutil.rmtree(tmpdir,True)
def smooth_decon_to_fwhm(decon,fwhm,cache=True):
'''takes an input :class:`Decon` object and uses ``3dBlurToFWHM`` to make the output as close as possible to ``fwhm``
returns the final measured fwhm. If ``cache`` is ``True``, will save the blurred input file (and use it again in the future)'''
if os.path.exists(decon.prefix):
return
blur_dset = lambda dset: nl.suffix(dset,'_smooth_to_%.2f' % fwhm)
with nl.notify('Running smooth_decon_to_fwhm analysis (with %.2fmm blur)' % fwhm):
tmpdir = tempfile.mkdtemp()
try:
cwd = os.getcwd()
random_files = [re.sub(r'\[\d+\]$','',str(x)) for x in nl.flatten([x for x in decon.__dict__.values() if isinstance(x,basestring) or isinstance(x,list)]+[x.values() for x in decon.__dict__.values() if isinstance(x,dict)])]
files_to_copy = [x for x in random_files if os.path.exists(x) and x[0]!='/']
files_to_copy += [blur_dset(dset) for dset in decon.input_dsets if os.path.exists(blur_dset(dset))]
# copy crap
for file in files_to_copy:
try:
shutil.copytree(file,tmpdir)
except OSError as e:
shutil.copy(file,tmpdir)
shutil.copy(file,tmpdir)
copyback_files = [decon.prefix,decon.errts]
with nl.run_in(tmpdir):
if os.path.exists(decon.prefix):
os.remove(decon.prefix)
# Create the blurred inputs (or load from cache)
if cache and all([os.path.exists(os.path.join(cwd,blur_dset(dset))) for dset in decon.input_dsets]):
# Everything is already cached...
nl.notify('Using cache\'d blurred datasets')
else:
# Need to make them from scratch
with nl.notify('Creating blurred datasets'):
old_errts = decon.errts
decon.errts = 'residual.nii.gz'
decon.prefix = os.path.basename(decon.prefix)
# Run once in place to get the residual dataset
decon.run()
running_reps = 0
for dset in decon.input_dsets:
info = nl.dset_info(dset)
residual_dset = nl.suffix(dset,'_residual')
nl.run(['3dbucket','-prefix',residual_dset,'%s[%d..%d]'%(decon.errts,running_reps,running_reps+info.reps-1)],products=residual_dset)
cmd = ['3dBlurToFWHM','-quiet','-input',dset,'-blurmaster',residual_dset,'-prefix',blur_dset(dset),'-FWHM',fwhm]
if decon.mask:
if decon.mask=='auto':
cmd += ['-automask']
else:
cmd += ['-mask',decon.mask]
nl.run(cmd,products=blur_dset(dset))
running_reps += info.reps
if cache:
copyback_files.append(blur_dset(dset))
decon.errts = old_errts
decon.input_dsets = [blur_dset(dset) for dset in decon.input_dsets]
for d in [decon.prefix,decon.errts]:
if os.path.exists(d):
try:
os.remove(d)
except:
pass
decon.run()
for copyfile in copyback_files:
if os.path.exists(copyfile):
shutil.copy(copyfile,cwd)
else:
nl.notify('Warning: deconvolve did not produce expected file %s' % decon.prefix,level=nl.level.warning)
except:
raise
finally:
shutil.rmtree(tmpdir,True)