def _generate_DROI_tables_call(tup):
import os, neuropythy as ny
# set our niceness!
os.nice(10)
flnm = os.path.join(tup[3], '%s.csv' % (tup[0], ))
if os.path.isfile(flnm): return flnm
f = VisualPerformanceFieldsDataset._generate_subject_DROI_table
subjects = ny.data['visual_performance_fields'].subjects
tbl = f(subjects,
tup[0],
angles=tup[1],
eccens=tup[2],
min_variance_explained=tup[4],
method=tup[5])
ny.save(flnm, tbl)
return flnm
if not os.path.isabs(maskfile): maskfile = os.path.join(tdir, maskfile)
if not os.path.isfile(stimfile):
warn('Could not find mask file (%s) in test dir %s!' %
(maskfile, tdir))
else:
mask = ny.load(maskfile, to='image')
# Okay, now convert into something more standard:
params['frame_rate'] = frate
params['pixels_per_degree'] = d2p
params['TR_length'] = trlen
stim = np.asarray(stim.dataobj)
params['stimulus'] = np.reshape(stim, [stim.shape[0], stim.shape[1], -1])
params['data'] = np.asarray(data.dataobj)
if mask is None:
params['mask'] = np.ones(params['data'].shape[:-1], dtype='bool')
else:
params['mask'] = np.asarray(mask.dataobj)
# Okay, now we can call the solver:
note(' Solving...')
try:
results = solver(params)
# Now, write out each of the results
for (k, v) in six.iteritems(results):
flnm = os.path.join(tdir, 'out_' + k + '.nii.gz')
ny.save(flnm, v, 'nifti')
except Exception:
warn('Exception raised while solving test dir: %s' % (tdir, ))
raise
sys.exit(0)
n = nl + nr
pial = np.vstack([lpial.coordinates.T, rpial.coordinates.T])
try:
sh = space.cKDTree(pial)
except Exception:
sh = space.KDTree(pial)
(d, ii) = sh.query(coords)
lpts = (ii < nl)
rpts = ~lpts
(lpts, rpts) = [np.where(pts)[0] for pts in (lpts, rpts)]
fsa = ny.freesurfer_subject('fsaverage')
res = np.full(coords.shape, np.nan)
for (h, pts) in zip(['lh', 'rh'], [lpts, rpts]):
if len(pts) == 0: continue
idcs = ii[pts]
if h == 'rh': idcs = idcs - nl
fshem = fsa.hemis[h]
sbhem = sub.hemis[h]
fsmsh = fshem.registrations['fsaverage']
addrs = fsmsh.address(sbhem.registrations['fsaverage'].coordinates[:,
idcs].T)
res[pts, :] = fshem.pial_surface.unaddress(addrs).T
df = ny.to_dataframe({k: v for (k, v) in zip(['x', 'y', 'z'], res.T)})
ny.save(sys.argv[3], df)
note('Success!')
sys.exit(0)
def main(args):
# Parse the arguments...
parser = argparse.ArgumentParser()
parser.add_argument('sub', metavar='subject', nargs=1,
help=('The FreeSurfer subject ID or directory.'))
parser.add_argument('outdir', metavar='directory', type=str, nargs=1,
help='The directory containing the output nifti files.')
parser.add_argument('-x', '--no-surf', required=False, default=True,
dest='surface', action='store_false',
help=('If provided, instructs the script not to produce files of the '
'pRF parameters resampled onto the cortical surface.'))
parser.add_argument('-m', '--method', required=False, default='linear', dest='method',
help=('The method to use for volume-to-surface interpolation; this may'
' be nearest or linear; the default is linear.'))
parser.add_argument('-l', '--layer', required=False, default='midgray', dest='layer',
help=('Specifies the cortical layer to user in interpolation from volume'
' to surface. By default, uses midgray. May be set to a value'
' between 0 (white) and 1 (pial) to specify an intermediate surface'
' or may be simply white, pial, or midgray.'))
parser.add_argument('-d', '--subjects-dir', required=False, default=None, dest='sdir',
help=('Specifies the subjects directory to use; by default uses the'
' environment variable SUBJECTS_DIR.'))
parser.add_argument('-y', '--no-invert-y', required=False, default=True, dest='invert_y',
action='store_false',
help=('If provided, does not invert the y-coordinate exported from'
' VistaSoft; by default this inversion is performed.'))
parser.add_argument('-v', '--verbose', required=False, default=False, action='store_true',
dest='verbose', help='Print verbose output')
if args[0].startswith('python'): args = args[2:]
else: args = args[1:]
args = parser.parse_args(args)
# Check some of the arguments...
sub = ny.freesurfer_subject(args.sub[0])
dosurf = args.surface
outdir = args.outdir[0]
if not os.path.isdir(outdir):
raise RuntimeError('Directory %s does not exist' % outdir)
else:
os.chdir(outdir)
if args.verbose:
def note(*args):
six.print_(*args, flush=True)
return True
else:
def note(*args):
return False
try: args.layer = float(args.layer)
except: pass
# figure out what datasets are here...
dsets = [fl[:-13] for fl in os.listdir('.') if fl.endswith('-xcrds.nii.gz')]
# loop over the given EPIs
for ds in dsets:
note('Processing Dataset %s...' % ds)
# import the files...
note(' - Importing parameters...')
(x,y,s,v) = [ny.load('%s-%s.nii.gz' % (ds, suff), to='image')
for suff in ['xcrds','ycrds','sigma','vexpl']]
# fix polar angle/eccen
note(' - Creating polar angle/eccentricity images...')
ang = np.arctan2(-y.get_data() if args.invert_y else y.get_data(), x.get_data())
ang = np.mod((90.0 - 180.0/np.pi * ang) + 180, 360) - 180
a = nib.Nifti1Image(ang, x.affine, x.header)
e = nib.Nifti1Image(np.sqrt(y.get_data()**2 + x.get_data()**2), x.affine, x.header)
a.to_filename('%s-angle.nii.gz' % ds)
e.to_filename('%s-eccen.nii.gz' % ds)
# surfaces...
if not dosurf: continue
note(' - Projecting to surface...')
to_output = {'xcrds':x, 'ycrds':y, 'sigma': s, 'vexpl': v}
xy = ([0,0],[0,0])
for (dname,im) in six.iteritems(to_output):
(ldat, rdat) = sub.image_to_cortex(im, args.layer,
method=args.method, dtype=np.float32,
weights=v)
if dname == 'xcrds':
xy[0][0] = ldat
xy[1][0] = rdat
elif dname == 'ycrds':
xy[0][1] = ldat
xy[1][1] = rdat
# we need to fix the dimensions...
for (d,h) in zip([ldat,rdat], ['lh','rh']):
ny.save('%s.%s-%s.mgz' % (h, ds, dname), d)
# Special handling for the angle and eccen (avoids need for circular averaging)
xy = (np.asarray(xy[0]), np.asarray(xy[1]))
(lecc,recc) = [np.sqrt(np.sum(u**2, axis=0)) for u in xy]
(lang,rang) = [np.arctan2(-u[1] if args.invert_y else u[1], u[0])
for u in (xy[0] * ny.util.zinv(lecc), xy[1] * ny.util.zinv(recc))]
(lang,rang) = [np.mod((90 - 180/np.pi * aa) + 180, 360) - 180 for aa in (lang,rang)]
# export these...
for (h,nm,dat) in [('lh','eccen',lecc), ('lh','angle',lang),
('rh','eccen',recc), ('rh','angle',rang)]:
ny.save('%s.%s-%s.mgz' % (h, ds, nm), dat)
# That's it!
return 0
(anat,sid,h,meta) = msg.split(':')[0].split(' path ')[-1].split('/')
sid = int(sid)
tag = 'mesh'
elif 'Incomplete set of lines for' in msg:
(anat, sid, h) = msg.split(' lines for ')[-1].split(' / ')
sid = int(sid)
tag = 'anatomist'
else: raise ValueError('Unrecognized warning in file %s: %s' % (flnm,ln))
if anat != 'mean': errs.append((anat,sid,h))
dat = errnotes[sid]
if anat not in dat[h]: dat[h][anat] = []
lst = dat[h][anat]
entry = {'tag': tag, 'message': msg}
if meta is not None: entry['metadata'] = meta
lst.append(entry)
if bh:
h = ('lh' if h == 'rh' else 'rh')
if anat not in dat[h]: dat[h][anat] = []
lst = dat[h][anat]
lst.append(entry)
errs = list(map(list, sorted(set(errs))))
# save the json exclusions file
ny.save(os.path.join(log_path, 'exclusions.json'), errs, 'JSON')
# and the json errors file
errnotes = {str(k):v for (k,v) in six.iteritems(errnotes)}
ny.save(os.path.join(log_path, 'errors.json'), errnotes, 'JSON')
sys.exit(0)