def afni_copy(filename):
''' creates a ``+orig`` copy of the given dataset and returns the filename as a string '''
if nl.pkg_available('afni',True):
afni_filename = "%s+orig" % nl.prefix(filename)
if not os.path.exists(afni_filename + ".HEAD"):
nl.calc(filename,'a',prefix=nl.prefix(filename))
return afni_filename
def afni_copy(filename):
''' creates a ``+orig`` copy of the given dataset and returns the filename as a string '''
if nl.pkg_available('afni', True):
afni_filename = "%s+orig" % nl.prefix(filename)
if not os.path.exists(afni_filename + ".HEAD"):
nl.calc(filename, 'a', prefix=nl.prefix(filename))
return afni_filename
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 affine_align(dset_from,dset_to,skull_strip=True,mask=None,suffix='_aff',prefix=None,cost=None,epi=False,resample='wsinc5',grid_size=None,opts=[]):
''' interface to 3dAllineate to align anatomies and EPIs '''
dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1]
def dset_source(dset):
if skull_strip==True or skull_strip==dset:
return dset_ss(dset)
else:
return dset
dset_affine = prefix
if dset_affine==None:
dset_affine = os.path.split(nl.suffix(dset_from,suffix))[1]
dset_affine_mat_1D = nl.prefix(dset_affine) + '_matrix.1D'
dset_affine_par_1D = nl.prefix(dset_affine) + '_params.1D'
if os.path.exists(dset_affine):
# final product already exists
return
for dset in [dset_from,dset_to]:
if skull_strip==True or skull_strip==dset:
nl.skull_strip(dset,'_ns')
mask_use = mask
if mask:
# the mask was probably made in the space of the original dset_to anatomy,
# which has now been cropped from the skull stripping. So the lesion mask
# needs to be resampled to match the corresponding mask
if skull_strip==True or skull_strip==dset_to:
nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam'))
mask_use = nl.suffix(mask,'_resam')
all_cmd = [
'3dAllineate',
'-prefix', dset_affine,
'-base', dset_source(dset_to),
'-source', dset_source(dset_from),
'-source_automask',
'-1Dmatrix_save', dset_affine_mat_1D,
'-1Dparam_save',dset_affine_par_1D,
'-autoweight',
'-final',resample,
'-cmass'
] + opts
if grid_size:
all_cmd += ['-newgrid',grid_size]
if cost:
all_cmd += ['-cost',cost]
if epi:
all_cmd += ['-EPI']
if mask:
all_cmd += ['-emask', mask_use]
nl.run(all_cmd,products=dset_affine)
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 mgz_to_nifti(filename,prefix=None,gzip=True):
'''Convert ``filename`` to a NIFTI file using ``mri_convert``'''
setup_freesurfer()
if prefix==None:
prefix = nl.prefix(filename) + '.nii'
if gzip and not prefix.endswith('.gz'):
prefix += '.gz'
nl.run([os.path.join(freesurfer_home,'bin','mri_convert'),filename,prefix],products=prefix)
def mgz_to_nifti(filename, prefix=None, gzip=True):
'''Convert ``filename`` to a NIFTI file using ``mri_convert``'''
setup_freesurfer()
if prefix == None:
prefix = nl.prefix(filename) + '.nii'
if gzip and not prefix.endswith('.gz'):
prefix += '.gz'
nl.run([
os.path.join(freesurfer_home, 'bin', 'mri_convert'), filename, prefix
],
products=prefix)
def volreg(dset,suffix='_volreg',base=3,tshift=3,dfile_suffix='_volreg.1D'):
'''simple interface to 3dvolreg
:suffix: suffix to add to ``dset`` for volreg'ed file
:base: either a number or ``dset[#]`` of the base image to register to
:tshift: if a number, then tshift ignoring that many images, if ``None``
then don't tshift
:dfile_suffix: suffix to add to ``dset`` to save the motion parameters to
'''
cmd = ['3dvolreg','-prefix',nl.suffix(dset,suffix),'-base',base,'-dfile',nl.prefix(dset)+dfile_suffix]
if tshift:
cmd += ['-tshift',tshift]
cmd += [dset]
nl.run(cmd,products=nl.suffix(dset,suffix))
def qwarp_epi(dset, align_subbrick=5, suffix='_qwal', prefix=None):
'''aligns an EPI time-series using 3dQwarp
Very expensive and not efficient at all, but it can produce pretty impressive alignment for EPI time-series with significant
distortions due to motion'''
info = nl.dset_info(dset)
if info == None:
nl.notify('Error reading dataset "%s"' % (dset), level=nl.level.error)
return False
if prefix == None:
prefix = nl.suffix(dset, suffix)
dset_sub = lambda x: '_tmp_qwarp_epi-%s_%d.nii.gz' % (nl.prefix(dset), x)
try:
align_dset = nl.suffix(dset_sub(align_subbrick), '_warp')
nl.calc('%s[%d]' % (dset, align_subbrick),
expr='a',
prefix=align_dset,
datum='float')
for i in xrange(info.reps):
if i != align_subbrick:
nl.calc('%s[%d]' % (dset, i),
expr='a',
prefix=dset_sub(i),
datum='float')
nl.run([
'3dQwarp', '-nowarp', '-workhard', '-superhard',
'-minpatch', '9', '-blur', '0', '-pear', '-nopenalty',
'-base', align_dset, '-source',
dset_sub(i), '-prefix',
nl.suffix(dset_sub(i), '_warp')
],
quiet=True)
cmd = ['3dTcat', '-prefix', prefix]
if info.TR:
cmd += ['-tr', info.TR]
if info.slice_timing:
cmd += ['-tpattern', info.slice_timing]
cmd += [nl.suffix(dset_sub(i), '_warp') for i in xrange(info.reps)]
nl.run(cmd, quiet=True)
except Exception as e:
raise e
finally:
for i in xrange(info.reps):
for suffix in ['', 'warp']:
try:
os.remove(nl.suffix(dset_sub(i), suffix))
except:
pass
def create_censor_file(input_dset,out_prefix=None,fraction=0.1,clip_to=0.1,max_exclude=0.3,motion_file=None,motion_exclude=1.0):
'''create a binary censor file using 3dToutcount
:input_dset: the input dataset
:prefix: output 1D file (default: ``prefix(input_dset)`` + ``.1D``)
:fraction: censor a timepoint if proportional of outliers in this
time point is greater than given value
:clip_to: keep the number of time points censored under this proportion
of total reps. If more time points would be censored,
it will only pick the top ``clip_to*reps`` points
:max_exclude: if more time points than the given proportion of reps are excluded for the
entire run, throw an exception -- something is probably wrong
:motion_file: optional filename of a "motion" file with multiple columns and rows corresponding to reps.
It doesn't really matter what the values are, as long as they are appropriate relative to ``motion_exclude``
:motion_exclude: Will exclude any reps that have a value greater than this in any column of ``motion_file``
'''
(outcount,perc_outliers) = nl.qc.outcount(input_dset,fraction)
info = nl.dset_info(input_dset)
binarize = lambda o,f: [oo<f for oo in o]
perc_outliers = lambda o: 1.-(sum(o)/float(info.reps))
if motion_file:
with open(motion_file,'Ur') as f:
motion = [max([float(y) for y in x.strip().split()]) for x in f.read().split('\n') if len(x.strip())>0 and x.strip()[0]!='#']
motion_1D = [x for x in binarize(motion,motion_exclude)]
if perc_outliers(motion_1D) > max_exclude:
nl.notify('Error: Too many points excluded because of motion (%.2f) in dset %s' % (perc_outliers(motion_1D),input_dset),level=nl.level.error)
return False
outcount = [outcount[i] if motion_1D[i] else 1. for i in range(len(outcount))]
binary_outcount = binarize(outcount,fraction)
if max_exclude and perc_outliers(binary_outcount) > max_exclude:
nl.notify('Error: Found %.1f%% outliers in dset %s' % (100*perc_outliers(outcount),input_dset),level=nl.level.error)
return False
if clip_to:
while perc_outliers(binary_outcount) > clip_to:
best_outlier = min([(outcount[i],i) for i in range(len(outcount)) if not binary_outcount[i]])
binary_outcount[best_outlier[1]] = True
if not out_prefix:
out_prefix = nl.prefix(input_dset) + '.1D'
with open(out_prefix,'w') as f:
f.write('\n'.join([str(int(x)) for x in binary_outcount]))
return True
def qwarp_epi(dset,align_subbrick=5,suffix='_qwal',prefix=None):
'''aligns an EPI time-series using 3dQwarp
Very expensive and not efficient at all, but it can produce pretty impressive alignment for EPI time-series with significant
distortions due to motion'''
info = nl.dset_info(dset)
if info==None:
nl.notify('Error reading dataset "%s"' % (dset),level=nl.level.error)
return False
if prefix==None:
prefix = nl.suffix(dset,suffix)
dset_sub = lambda x: '_tmp_qwarp_epi-%s_%d.nii.gz' % (nl.prefix(dset),x)
try:
align_dset = nl.suffix(dset_sub(align_subbrick),'_warp')
nl.calc('%s[%d]' % (dset,align_subbrick),expr='a',prefix=align_dset,datum='float')
for i in xrange(info.reps):
if i != align_subbrick:
nl.calc('%s[%d]' % (dset,i),expr='a',prefix=dset_sub(i),datum='float')
nl.run([
'3dQwarp', '-nowarp',
'-workhard', '-superhard', '-minpatch', '9', '-blur', '0',
'-pear', '-nopenalty',
'-base', align_dset,
'-source', dset_sub(i),
'-prefix', nl.suffix(dset_sub(i),'_warp')
],quiet=True)
cmd = ['3dTcat','-prefix',prefix]
if info.TR:
cmd += ['-tr',info.TR]
if info.slice_timing:
cmd += ['-tpattern',info.slice_timing]
cmd += [nl.suffix(dset_sub(i),'_warp') for i in xrange(info.reps)]
nl.run(cmd,quiet=True)
except Exception as e:
raise e
finally:
for i in xrange(info.reps):
for suffix in ['','warp']:
try:
os.remove(nl.suffix(dset_sub(i),suffix))
except:
pass
def volreg(dset,
suffix='_volreg',
base=3,
tshift=3,
dfile_suffix='_volreg.1D'):
'''simple interface to 3dvolreg
:suffix: suffix to add to ``dset`` for volreg'ed file
:base: either a number or ``dset[#]`` of the base image to register to
:tshift: if a number, then tshift ignoring that many images, if ``None``
then don't tshift
:dfile_suffix: suffix to add to ``dset`` to save the motion parameters to
'''
cmd = [
'3dvolreg', '-prefix',
nl.suffix(dset, suffix), '-base', base, '-dfile',
nl.prefix(dset) + dfile_suffix
]
if tshift:
cmd += ['-tshift', tshift]
cmd += [dset]
nl.run(cmd, products=nl.suffix(dset, suffix))
def _create_dset_dicom(directory,slice_order='alt+z',sort_order=None,force_slices=None):
tags = {
'num_rows': (0x0028,0x0010),
'num_reps': (0x0020,0x0105),
'num_frames': (0x0028,0x0008),
'acq_time': (0x0008,0x0032),
'siemens_slices': (0x0019, 0x100a),
'TR': (0x0018,0x0080)
}
with nl.notify('Trying to create datasets from %s' % directory):
directory = os.path.abspath(directory)
if not os.path.exists(directory):
nl.notify('Error: could not find %s' % directory,level=nl.level.error)
return False
out_file = '%s.nii.gz' % nl.prefix(os.path.basename(directory))
if os.path.exists(out_file):
nl.notify('Error: file "%s" already exists!' % out_file,level=nl.level.error)
return False
cwd = os.getcwd()
sorted_dir = tempfile.mkdtemp()
try:
with nl.run_in(sorted_dir):
file_list = glob.glob(directory + '/*')
num_reps = None
new_file_list = []
for f in file_list:
try:
if len(info_for_tags(f,tags['num_rows']))>0:
# Only include DICOMs that actually have image information
new_file_list.append(f)
except:
pass
file_list = new_file_list
if len(file_list)==0:
nl.notify('Error: Couldn\'t find any valid DICOM images',level=nl.level.error)
return False
with open('file_list.txt','w') as f:
f.write('\n'.join(file_list))
try:
subprocess.check_output([
'Dimon',
'-infile_list','file_list.txt',
'-dicom_org',
'-save_details','details',
'-max_images','100000',
'-fast','-no_wait',
'-quit'],stderr=subprocess.STDOUT)
except subprocess.CalledProcessError, e:
nl.notify('Warning: Dimon returned an error while sorting images',level=nl.level.warning)
else:
if os.path.exists('details.2.final_list.txt'):
with open('details.2.final_list.txt') as f:
details = [x.strip().split() for x in f.readlines() if x[0]!='#']
file_list = [x[0] for x in details]
else:
nl.notify('Warning: Dimon didn\'t return expected output, unable to sort images',level=nl.level.warning)
def affine_align(dset_from,
dset_to,
skull_strip=True,
mask=None,
suffix='_aff',
prefix=None,
cost=None,
epi=False,
resample='wsinc5',
grid_size=None,
opts=[]):
''' interface to 3dAllineate to align anatomies and EPIs '''
dset_ss = lambda dset: os.path.split(nl.suffix(dset, '_ns'))[1]
def dset_source(dset):
if skull_strip == True or skull_strip == dset:
return dset_ss(dset)
else:
return dset
dset_affine = prefix
if dset_affine == None:
dset_affine = os.path.split(nl.suffix(dset_from, suffix))[1]
dset_affine_mat_1D = nl.prefix(dset_affine) + '_matrix.1D'
dset_affine_par_1D = nl.prefix(dset_affine) + '_params.1D'
if os.path.exists(dset_affine):
# final product already exists
return
for dset in [dset_from, dset_to]:
if skull_strip == True or skull_strip == dset:
nl.skull_strip(dset, '_ns')
mask_use = mask
if mask:
# the mask was probably made in the space of the original dset_to anatomy,
# which has now been cropped from the skull stripping. So the lesion mask
# needs to be resampled to match the corresponding mask
if skull_strip == True or skull_strip == dset_to:
nl.run([
'3dresample', '-master',
dset_u(dset_ss(dset)), '-inset', mask, '-prefix',
nl.suffix(mask, '_resam')
],
products=nl.suffix(mask, '_resam'))
mask_use = nl.suffix(mask, '_resam')
all_cmd = [
'3dAllineate', '-prefix', dset_affine, '-base',
dset_source(dset_to), '-source',
dset_source(dset_from), '-1Dmatrix_save', dset_affine_mat_1D,
'-1Dparam_save', dset_affine_par_1D, '-autoweight', '-final', resample,
'-cmass'
] + opts
if grid_size:
all_cmd += ['-newgrid', grid_size]
if cost:
all_cmd += ['-cost', cost]
if epi:
all_cmd += ['-EPI']
if mask:
all_cmd += ['-emask', mask_use]
nl.run(all_cmd, products=dset_affine)
def qwarp_align(dset_from,dset_to,skull_strip=True,mask=None,affine_suffix='_aff',suffix='_qwarp',prefix=None):
'''aligns ``dset_from`` to ``dset_to`` using 3dQwarp
Will run ``3dSkullStrip`` (unless ``skull_strip`` is ``False``), ``3dUnifize``,
``3dAllineate``, and then ``3dQwarp``. This method will add suffixes to the input
dataset for the intermediate files (e.g., ``_ss``, ``_u``). If those files already
exist, it will assume they were intelligently named, and use them as is
:skull_strip: If True/False, turns skull-stripping of both datasets on/off.
If a string matching ``dset_from`` or ``dset_to``, will only
skull-strip the given dataset
:mask: Applies the given mask to the alignment. Because of the nature
of the alignment algorithms, the mask is **always** applied to
the ``dset_to``. If this isn't what you want, you need to reverse
the transform and re-apply it (e.g., using :meth:`qwarp_invert`
and :meth:`qwarp_apply`). If the ``dset_to`` dataset is skull-stripped,
the mask will also be resampled to match the ``dset_to`` grid.
:affine_suffix: Suffix applied to ``dset_from`` to name the new dataset, as well as
the ``.1D`` file.
:suffix: Suffix applied to the final ``dset_from`` dataset. An additional file
with the additional suffix ``_WARP`` will be created containing the parameters
(e.g., with the default ``_qwarp`` suffix, the parameters will be in a file with
the suffix ``_qwarp_WARP``)
:prefix: Alternatively to ``suffix``, explicitly give the full output filename
The output affine dataset and 1D, as well as the output of qwarp are named by adding
the given suffixes (``affine_suffix`` and ``qwarp_suffix``) to the ``dset_from`` file
If ``skull_strip`` is a string instead of ``True``/``False``, it will only skull strip the given
dataset instead of both of them
# TODO: currently does not work with +tlrc datasets because the filenames get mangled
'''
dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1]
dset_u = lambda dset: os.path.split(nl.suffix(dset,'_u'))[1]
def dset_source(dset):
if skull_strip==True or skull_strip==dset:
return dset_ss(dset)
else:
return dset
dset_affine = os.path.split(nl.suffix(dset_from,affine_suffix))[1]
dset_affine_1D = nl.prefix(dset_affine) + '.1D'
dset_qwarp = prefix
if dset_qwarp==None:
dset_qwarp = os.path.split(nl.suffix(dset_from,suffix))[1]
if os.path.exists(dset_qwarp):
# final product already exists
return
affine_align(dset_from,dset_to,skull_strip,mask,affine_suffix)
for dset in [dset_from,dset_to]:
nl.run([
'3dUnifize',
'-prefix', dset_u(dset_source(dset)),
'-input', dset_source(dset)
],products=[dset_u(dset_source(dset))])
mask_use = mask
if mask:
# the mask was probably made in the space of the original dset_to anatomy,
# which has now been cropped from the skull stripping. So the lesion mask
# needs to be resampled to match the corresponding mask
if skull_strip==True or skull_strip==dset_to:
nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam'))
mask_use = nl.suffix(mask,'_resam')
warp_cmd = [
'3dQwarp',
'-prefix', dset_qwarp,
'-duplo', '-useweight', '-blur', '0', '3',
'-iwarp',
'-base', dset_u(dset_source(dset_to)),
'-source', dset_affine
]
if mask:
warp_cmd += ['-emask', mask_use]
nl.run(warp_cmd,products=dset_qwarp)
def qwarp_align(dset_from,
dset_to,
skull_strip=True,
mask=None,
affine_suffix='_aff',
suffix='_qwarp',
prefix=None):
'''aligns ``dset_from`` to ``dset_to`` using 3dQwarp
Will run ``3dSkullStrip`` (unless ``skull_strip`` is ``False``), ``3dUnifize``,
``3dAllineate``, and then ``3dQwarp``. This method will add suffixes to the input
dataset for the intermediate files (e.g., ``_ss``, ``_u``). If those files already
exist, it will assume they were intelligently named, and use them as is
:skull_strip: If True/False, turns skull-stripping of both datasets on/off.
If a string matching ``dset_from`` or ``dset_to``, will only
skull-strip the given dataset
:mask: Applies the given mask to the alignment. Because of the nature
of the alignment algorithms, the mask is **always** applied to
the ``dset_to``. If this isn't what you want, you need to reverse
the transform and re-apply it (e.g., using :meth:`qwarp_invert`
and :meth:`qwarp_apply`). If the ``dset_to`` dataset is skull-stripped,
the mask will also be resampled to match the ``dset_to`` grid.
:affine_suffix: Suffix applied to ``dset_from`` to name the new dataset, as well as
the ``.1D`` file.
:suffix: Suffix applied to the final ``dset_from`` dataset. An additional file
with the additional suffix ``_WARP`` will be created containing the parameters
(e.g., with the default ``_qwarp`` suffix, the parameters will be in a file with
the suffix ``_qwarp_WARP``)
:prefix: Alternatively to ``suffix``, explicitly give the full output filename
The output affine dataset and 1D, as well as the output of qwarp are named by adding
the given suffixes (``affine_suffix`` and ``qwarp_suffix``) to the ``dset_from`` file
If ``skull_strip`` is a string instead of ``True``/``False``, it will only skull strip the given
dataset instead of both of them
# TODO: currently does not work with +tlrc datasets because the filenames get mangled
'''
dset_ss = lambda dset: os.path.split(nl.suffix(dset, '_ns'))[1]
dset_u = lambda dset: os.path.split(nl.suffix(dset, '_u'))[1]
def dset_source(dset):
if skull_strip == True or skull_strip == dset:
return dset_ss(dset)
else:
return dset
dset_affine = os.path.split(nl.suffix(dset_from, affine_suffix))[1]
dset_affine_1D = nl.prefix(dset_affine) + '.1D'
dset_qwarp = prefix
if dset_qwarp == None:
dset_qwarp = os.path.split(nl.suffix(dset_from, suffix))[1]
if os.path.exists(dset_qwarp):
# final product already exists
return
affine_align(dset_from, dset_to, skull_strip, mask, affine_suffix)
for dset in [dset_from, dset_to]:
nl.run([
'3dUnifize', '-prefix',
dset_u(dset_source(dset)), '-input',
dset_source(dset)
],
products=[dset_u(dset_source(dset))])
mask_use = mask
if mask:
# the mask was probably made in the space of the original dset_to anatomy,
# which has now been cropped from the skull stripping. So the lesion mask
# needs to be resampled to match the corresponding mask
if skull_strip == True or skull_strip == dset_to:
nl.run([
'3dresample', '-master',
dset_u(dset_ss(dset)), '-inset', mask, '-prefix',
nl.suffix(mask, '_resam')
],
products=nl.suffix(mask, '_resam'))
mask_use = nl.suffix(mask, '_resam')
warp_cmd = [
'3dQwarp', '-prefix', dset_qwarp, '-duplo', '-useweight', '-blur', '0',
'3', '-iwarp', '-base',
dset_u(dset_source(dset_to)), '-source', dset_affine
]
if mask:
warp_cmd += ['-emask', mask_use]
nl.run(warp_cmd, products=dset_qwarp)
def _create_dset_dicom(directory,
slice_order='alt+z',
sort_order=None,
force_slices=None):
tags = {
'num_rows': (0x0028, 0x0010),
'num_reps': (0x0020, 0x0105),
'num_frames': (0x0028, 0x0008),
'acq_time': (0x0008, 0x0032),
'siemens_slices': (0x0019, 0x100a),
'TR': (0x0018, 0x0080)
}
with nl.notify('Trying to create datasets from %s' % directory):
directory = os.path.abspath(directory)
if not os.path.exists(directory):
nl.notify('Error: could not find %s' % directory,
level=nl.level.error)
return False
out_file = '%s.nii.gz' % nl.prefix(os.path.basename(directory))
if os.path.exists(out_file):
nl.notify('Error: file "%s" already exists!' % out_file,
level=nl.level.error)
return False
cwd = os.getcwd()
sorted_dir = tempfile.mkdtemp()
try:
with nl.run_in(sorted_dir):
file_list = glob.glob(directory + '/*')
num_reps = None
new_file_list = []
for f in file_list:
try:
if len(info_for_tags(f, tags['num_rows'])) > 0:
# Only include DICOMs that actually have image information
new_file_list.append(f)
except:
pass
file_list = new_file_list
if len(file_list) == 0:
nl.notify('Error: Couldn\'t find any valid DICOM images',
level=nl.level.error)
return False
with open('file_list.txt', 'w') as f:
f.write('\n'.join(file_list))
try:
subprocess.check_output([
'Dimon', '-infile_list', 'file_list.txt', '-dicom_org',
'-save_details', 'details', '-max_images', '100000',
'-fast', '-no_wait', '-quit'
],
stderr=subprocess.STDOUT)
except subprocess.CalledProcessError, e:
nl.notify(
'Warning: Dimon returned an error while sorting images',
level=nl.level.warning)
else:
if os.path.exists('details.2.final_list.txt'):
with open('details.2.final_list.txt') as f:
details = [
x.strip().split() for x in f.readlines()
if x[0] != '#'
]
file_list = [x[0] for x in details]
else:
nl.notify(
'Warning: Dimon didn\'t return expected output, unable to sort images',
level=nl.level.warning)
def create_censor_file(input_dset,
out_prefix=None,
fraction=0.1,
clip_to=0.1,
max_exclude=0.3,
motion_file=None,
motion_exclude=1.0):
'''create a binary censor file using 3dToutcount
:input_dset: the input dataset
:prefix: output 1D file (default: ``prefix(input_dset)`` + ``.1D``)
:fraction: censor a timepoint if proportional of outliers in this
time point is greater than given value
:clip_to: keep the number of time points censored under this proportion
of total reps. If more time points would be censored,
it will only pick the top ``clip_to*reps`` points
:max_exclude: if more time points than the given proportion of reps are excluded for the
entire run, throw an exception -- something is probably wrong
:motion_file: optional filename of a "motion" file with multiple columns and rows corresponding to reps.
It doesn't really matter what the values are, as long as they are appropriate relative to ``motion_exclude``
:motion_exclude: Will exclude any reps that have a value greater than this in any column of ``motion_file``
'''
(outcount, perc_outliers) = nl.qc.outcount(input_dset, fraction)
info = nl.dset_info(input_dset)
binarize = lambda o, f: [oo < f for oo in o]
perc_outliers = lambda o: 1. - (sum(o) / float(info.reps))
if motion_file:
with open(motion_file, 'Ur') as f:
motion = [
max([float(y) for y in x.strip().split()])
for x in f.read().split('\n')
if len(x.strip()) > 0 and x.strip()[0] != '#'
]
motion_1D = [x for x in binarize(motion, motion_exclude)]
if perc_outliers(motion_1D) > max_exclude:
nl.notify(
'Error: Too many points excluded because of motion (%.2f) in dset %s'
% (perc_outliers(motion_1D), input_dset),
level=nl.level.error)
return False
outcount = [
outcount[i] if motion_1D[i] else 1.
for i in range(len(outcount))
]
binary_outcount = binarize(outcount, fraction)
if max_exclude and perc_outliers(binary_outcount) > max_exclude:
nl.notify('Error: Found %.1f%% outliers in dset %s' %
(100 * perc_outliers(outcount), input_dset),
level=nl.level.error)
return False
if clip_to:
while perc_outliers(binary_outcount) > clip_to:
best_outlier = min([(outcount[i], i) for i in range(len(outcount))
if not binary_outcount[i]])
binary_outcount[best_outlier[1]] = True
if not out_prefix:
out_prefix = nl.prefix(input_dset) + '.1D'
with open(out_prefix, 'w') as f:
f.write('\n'.join([str(int(x)) for x in binary_outcount]))
return True