def test_make_adaptive_mask():
# load data make masks
data = io.load_data(fnames, n_echos=len(tes))[0]
minmask = utils.make_adaptive_mask(data)
mask, masksum = utils.make_adaptive_mask(data, minimum=False, getsum=True)
# minimum mask different than adaptive mask
assert not np.allclose(minmask, mask)
# getsum doesn't change mask values
assert np.allclose(mask, utils.make_adaptive_mask(data, minimum=False))
# shapes are all the same
assert mask.shape == masksum.shape == (64350, )
assert np.allclose(mask, masksum.astype(bool))
# mask has correct # of entries
assert mask.sum() == 50786
# masksum has correct values
vals, counts = np.unique(masksum, return_counts=True)
assert np.allclose(vals, np.array([0, 1, 2, 3]))
assert np.allclose(counts, np.array([13564, 3977, 5060, 41749]))
# test user-defined mask
# TODO: Add mask file with no bad voxels to test against
mask, masksum = utils.make_adaptive_mask(data,
mask=pjoin(
datadir, 'mask.nii.gz'),
minimum=False,
getsum=True)
assert np.allclose(mask, masksum.astype(bool))
def test_make_min_mask():
# load data make mask
data = io.load_data(fnames, n_echos=len(tes))[0]
minmask = utils.make_min_mask(data)
assert minmask.shape == (64350, )
assert minmask.sum() == 58378
def test_smoke_load_data():
"""
Ensures that data is loaded when given a random neuroimage
"""
data = os.path.join(data_dir, 'mask.nii.gz')
n_echos = 1
fdata, ref_img = me.load_data(data, n_echos)
assert fdata is not None
assert ref_img is not None
def testdata1():
tes = np.array([14.5, 38.5, 62.5])
in_files = [op.join(get_test_data_path(), 'echo{0}.nii.gz'.format(i+1))
for i in range(3)]
data, _ = io.load_data(in_files, n_echos=len(tes))
mask, mask_sum = utils.make_adaptive_mask(data, getsum=True)
data_dict = {'data': data,
'tes': tes,
'mask': mask,
'mask_sum': mask_sum,
}
return data_dict
def test_load_data():
fimg = [nib.load(f) for f in fnames]
exp_shape = (64350, 3, 5)
# list of filepath to images
d, ref = me.load_data(fnames, n_echos=len(tes))
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert np.allclose(ref.get_data(), nib.load(fnames[0]).get_data())
# list of img_like
d, ref = me.load_data(fimg, n_echos=len(tes))
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert ref == fimg[0]
# imagine z-cat img
d, ref = me.load_data(fnames[0], n_echos=3)
assert d.shape == (21450, 3, 5)
assert isinstance(ref, nib.Nifti1Image)
assert ref.shape == (39, 50, 11)
with pytest.raises(ValueError):
me.load_data(fnames[0])
def testdata1():
tes = np.array([14.5, 38.5, 62.5])
in_files = [
op.join(get_test_data_path(), "echo{0}.nii.gz".format(i + 1))
for i in range(3)
]
data, _ = io.load_data(in_files, n_echos=len(tes))
mask, adaptive_mask = utils.make_adaptive_mask(data, getsum=True)
fittype = "loglin"
data_dict = {
"data": data,
"tes": tes,
"mask": mask,
"adaptive_mask": adaptive_mask,
"fittype": fittype,
}
return data_dict
def testdata1():
"""Data used for tests of the metrics module."""
tes = np.array([14.5, 38.5, 62.5])
in_files = [
op.join(get_test_data_path(), "echo{0}.nii.gz".format(i + 1))
for i in range(3)
]
data_cat, ref_img = io.load_data(in_files, n_echos=len(tes))
_, adaptive_mask = utils.make_adaptive_mask(data_cat, getsum=True)
data_optcom = np.mean(data_cat, axis=1)
mixing = np.random.random((data_optcom.shape[1], 50))
io_generator = io.OutputGenerator(ref_img)
data_dict = {
"data_cat": data_cat,
"tes": tes,
"data_optcom": data_optcom,
"adaptive_mask": adaptive_mask,
"generator": io_generator,
"mixing": mixing,
}
return data_dict
def t2smap_workflow(data,
tes,
out_dir='.',
mask=None,
fittype='loglin',
fitmode='all',
combmode='t2s',
debug=False,
quiet=False):
"""
Estimate T2 and S0, and optimally combine data across TEs.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
out_dir : :obj:`str`, optional
Output directory.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially
aligned with `data`.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method.
'loglin' means to use the the default linear fit to the log of
the data.
'curvefit' means to use a monoexponential fit to the raw data,
which is slightly slower but may be more accurate.
fitmode : {'all', 'ts'}, optional
Monoexponential model fitting scheme.
'all' means that the model is fit, per voxel, across all timepoints.
'ts' means that the model is fit, per voxel and per timepoint.
Default is 'all'.
combmode : {'t2s', 'paid'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'paid' (Poser).
Other Parameters
----------------
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppress logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files, which are described below:
========================== =================================================
Filename Content
========================== =================================================
T2starmap.nii.gz Limited estimated T2* 3D map or 4D timeseries.
Will be a 3D map if ``fitmode`` is 'all' and a
4D timeseries if it is 'ts'.
S0map.nii.gz Limited S0 3D map or 4D timeseries.
desc-full_T2starmap.nii.gz Full T2* map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the single echo's
value while the limited map has a NaN.
desc-full_S0map.nii.gz Full S0 map/timeseries.
desc-optcom_bold.nii.gz Optimally combined timeseries.
========================== =================================================
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
if debug and not quiet:
logging.basicConfig(level=logging.DEBUG)
elif quiet:
logging.basicConfig(level=logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if mask is None:
LGR.info('Computing adaptive mask')
else:
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
getsum=True,
threshold=1)
LGR.info('Computing adaptive T2* map')
if fitmode == 'all':
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay(catd, tes, mask, masksum, fittype)
else:
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay_ts(catd, tes, mask, masksum, fittype)
# set a hard cap for the T2* map/timeseries
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(),
99.5,
interpolation_method='lower')
cap_t2s_sec = utils.millisec2sec(cap_t2s * 10.)
LGR.debug('Setting cap on T2* map at {:.5f}s'.format(cap_t2s_sec))
t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
LGR.info('Computing optimal combination')
# optimally combine data
OCcatd = combine.make_optcom(catd,
tes,
masksum,
t2s=t2s_full,
combmode=combmode)
# clean up numerical errors
for arr in (OCcatd, s0_limited, t2s_limited):
np.nan_to_num(arr, copy=False)
s0_limited[s0_limited < 0] = 0
t2s_limited[t2s_limited < 0] = 0
io.filewrite(utils.millisec2sec(t2s_limited),
op.join(out_dir, 'T2starmap.nii.gz'), ref_img)
io.filewrite(s0_limited, op.join(out_dir, 'S0map.nii.gz'), ref_img)
io.filewrite(utils.millisec2sec(t2s_full),
op.join(out_dir, 'desc-full_T2starmap.nii.gz'), ref_img)
io.filewrite(s0_full, op.join(out_dir, 'desc-full_S0map.nii.gz'), ref_img)
io.filewrite(OCcatd, op.join(out_dir, 'desc-optcom_bold.nii.gz'), ref_img)
def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
tedort=False, gscontrol=None, tedpca='mle',
source_tes=-1, combmode='t2s', verbose=False, stabilize=False,
out_dir='.', fixed_seed=42, maxit=500, maxrestart=10,
debug=False, quiet=False, png=False, png_cmap='coolwarm'):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be
spatially aligned with `data`. If an explicit mask is not provided,
then Nilearn's compute_epi_mask function will be used to derive a mask
from the first echo's data.
mixm : :obj:`str`, optional
File containing mixing matrix. If not provided, ME-PCA and ME-ICA are
done.
ctab : :obj:`str`, optional
File containing component table from which to extract pre-computed
classifications.
manacc : :obj:`list`, :obj:`str`, or None, optional
List of manually accepted components. Can be a list of the components,
a comma-separated string with component numbers, or None. Default is
None.
tedort : :obj:`bool`, optional
Orthogonalize rejected components w.r.t. accepted ones prior to
denoising. Default is False.
gscontrol : {None, 't1c', 'gsr'} or :obj:`list`, optional
Perform additional denoising to remove spatially diffuse noise. Default
is None.
tedpca : {'mle', 'kundu', 'kundu-stabilize'}, optional
Method with which to select components in TEDPCA. Default is 'mle'.
source_tes : :obj:`int`, optional
Source TEs for models. 0 for all, -1 for optimal combination.
Default is -1.
combmode : {'t2s'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default).
verbose : :obj:`bool`, optional
Generate intermediate and additional files. Default is False.
png : obj:'bool', optional
Generate simple plots and figures. Default is false.
png_cmap : obj:'str', optional
Name of a matplotlib colormap to be used when generating figures.
--png must still be used to request figures. Default is 'coolwarm'
out_dir : :obj:`str`, optional
Output directory.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
maxit : :obj:`int`, optional
Maximum number of iterations for ICA. Default is 500.
maxrestart : :obj:`int`, optional
Maximum number of attempts for ICA. If ICA fails to converge, the
fixed seed will be updated and ICA will be run again. If convergence
is achieved before maxrestart attempts, ICA will finish early.
Default is 10.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
if debug and not quiet:
# ensure old logs aren't over-written
basename = 'tedana_run'
extension = 'txt'
logname = op.join(out_dir, (basename + '.' + extension))
logex = op.join(out_dir, (basename + '*'))
previouslogs = glob.glob(logex)
previouslogs.sort(reverse=True)
for f in previouslogs:
previousparts = op.splitext(f)
newname = previousparts[0] + '_old' + previousparts[1]
os.rename(f, newname)
# set logging format
formatter = logging.Formatter(
'%(asctime)s\t%(name)-12s\t%(levelname)-8s\t%(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
# set up logging file and open it for writing
fh = logging.FileHandler(logname)
fh.setFormatter(formatter)
logging.basicConfig(level=logging.DEBUG,
handlers=[fh, logging.StreamHandler()])
elif quiet:
logging.basicConfig(level=logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# Coerce gscontrol to list
if not isinstance(gscontrol, list):
gscontrol = [gscontrol]
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if mixm is not None and op.isfile(mixm):
mixm = op.abspath(mixm)
# Allow users to re-run on same folder
if mixm != op.join(out_dir, 'meica_mix.1D'):
shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D'))
shutil.copyfile(mixm, op.join(out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
ctab = op.abspath(ctab)
# Allow users to re-run on same folder
if ctab != op.join(out_dir, 'comp_table_ica.txt'):
shutil.copyfile(ctab, op.join(out_dir, 'comp_table_ica.txt'))
shutil.copyfile(ctab, op.join(out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
if isinstance(manacc, str):
manacc = [int(comp) for comp in manacc.split(',')]
if ctab and not mixm:
LGR.warning('Argument "ctab" requires argument "mixm".')
ctab = None
elif ctab and (manacc is None):
LGR.warning('Argument "ctab" requires argument "manacc".')
ctab = None
elif manacc is not None and not mixm:
LGR.warning('Argument "manacc" requires argument "mixm".')
manacc = None
if mask is None:
LGR.info('Computing EPI mask from first echo')
first_echo_img = io.new_nii_like(ref_img, catd[:, 0, :])
mask = compute_epi_mask(first_echo_img)
else:
# TODO: add affine check
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd, mask=mask, getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
if verbose:
io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)
os.chdir(out_dir)
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = decay.fit_decay(catd, tes, mask, masksum)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s.flatten(), 99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s[t2s > cap_t2s * 10] = cap_t2s
io.filewrite(t2s, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0, op.join(out_dir, 's0v.nii'), ref_img)
if verbose:
io.filewrite(t2ss, op.join(out_dir, 't2ss.nii'), ref_img)
io.filewrite(s0s, op.join(out_dir, 's0vs.nii'), ref_img)
io.filewrite(t2sG, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0G, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
data_oc = combine.make_optcom(catd, tes, mask, t2s=t2sG, combmode=combmode)
# regress out global signal unless explicitly not desired
if 'gsr' in gscontrol:
catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, ref_img)
if mixm is None:
# Identify and remove thermal noise from data
dd, n_components = decomposition.tedpca(catd, data_oc, combmode, mask,
t2s, t2sG, ref_img,
tes=tes, algorithm=tedpca,
source_tes=source_tes,
kdaw=10., rdaw=1.,
out_dir=out_dir, verbose=verbose)
mmix_orig = decomposition.tedica(dd, n_components, fixed_seed,
maxit, maxrestart)
if verbose:
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
if source_tes == -1:
io.filewrite(utils.unmask(dd, mask),
op.join(out_dir, 'ts_OC_whitened.nii'), ref_img)
LGR.info('Making second component selection guess from ICA results')
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
comptable, metric_maps, betas, mmix = model.dependence_metrics(
catd, data_oc, mmix_orig, mask, t2s, tes,
ref_img, reindex=True, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
comptable = model.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D'))
comptable, metric_maps, betas, mmix = model.dependence_metrics(
catd, data_oc, mmix_orig, mask, t2s, tes,
ref_img, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
if ctab is None:
comptable = model.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
else:
comptable = pd.read_csv(ctab, sep='\t', index_col='component')
comptable = selection.manual_selection(comptable, acc=manacc)
comptable.to_csv(op.join(out_dir, 'comp_table_ica.txt'), sep='\t',
index=True, index_label='component', float_format='%.6f')
if comptable[comptable.classification == 'accepted'].shape[0] == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
mmix_orig = mmix.copy()
if tedort:
acc_idx = comptable.loc[
~comptable.classification.str.contains('rejected')].index.values
rej_idx = comptable.loc[
comptable.classification.str.contains('rejected')].index.values
acc_ts = mmix[:, acc_idx]
rej_ts = mmix[:, rej_idx]
betas = np.linalg.lstsq(acc_ts, rej_ts, rcond=None)[0]
pred_rej_ts = np.dot(acc_ts, betas)
resid = rej_ts - pred_rej_ts
mmix[:, rej_idx] = resid
np.savetxt(op.join(out_dir, 'meica_mix_orth.1D'), mmix)
io.writeresults(data_oc, mask=mask, comptable=comptable, mmix=mmix,
n_vols=n_vols, ref_img=ref_img)
if 't1c' in gscontrol:
LGR.info('Performing T1c global signal regression to remove spatially '
'diffuse noise')
gsc.gscontrol_mmix(data_oc, mmix, mask, comptable, ref_img)
if verbose:
io.writeresults_echoes(catd, mmix, mask, comptable, ref_img)
if png:
LGR.info('Making figures folder with static component maps and '
'timecourse plots.')
# make figure folder first
if not op.isdir(op.join(out_dir, 'figures')):
os.mkdir(op.join(out_dir, 'figures'))
viz.write_comp_figs(data_oc, mask=mask, comptable=comptable,
mmix=mmix_orig, ref_img=ref_img,
out_dir=op.join(out_dir, 'figures'),
png_cmap=png_cmap)
LGR.info('Making Kappa vs Rho scatter plot')
viz.write_kappa_scatter(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Making overall summary figure')
viz.write_summary_fig(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Workflow completed')
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
def t2smap_workflow(data, tes, mask=None, fitmode='all', combmode='t2s',
label=None, debug=False, fittype='loglin', quiet=False):
"""
Estimate T2 and S0, and optimally combine data across TEs.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially
aligned with `data`.
fitmode : {'all', 'ts'}, optional
Monoexponential model fitting scheme.
'all' means that the model is fit, per voxel, across all timepoints.
'ts' means that the model is fit, per voxel and per timepoint.
Default is 'all'.
combmode : {'t2s', 'paid'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'paid' (Poser).
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method.
'loglin' means to use the the default linear fit to the log of
the data.
'curvefit' means to use a monoexponential fit to the raw data,
which is slightly slower but may be more accurate.
Other Parameters
----------------
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files, which are written out to a folder
named TED.[ref_label].[label] if ``label`` is provided and TED.[ref_label]
if not. ``ref_label`` is determined based on the name of the first ``data``
file.
Files are listed below:
====================== =================================================
Filename Content
====================== =================================================
t2sv.nii Limited estimated T2* 3D map or 4D timeseries.
Will be a 3D map if ``fitmode`` is 'all' and a
4D timeseries if it is 'ts'.
s0v.nii Limited S0 3D map or 4D timeseries.
t2svG.nii Full T2* map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the single echo's
value while the limited map has a NaN.
s0vG.nii Full S0 map/timeseries.
ts_OC.nii Optimally combined timeseries.
====================== =================================================
"""
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
try:
ref_label = op.basename(ref_img).split('.')[0]
except (TypeError, AttributeError):
ref_label = op.basename(str(data[0])).split('.')[0]
if label is not None:
out_dir = 'TED.{0}.{1}'.format(ref_label, label)
else:
out_dir = 'TED.{0}'.format(ref_label)
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
LGR.info('Creating output directory: {}'.format(out_dir))
os.mkdir(out_dir)
else:
LGR.info('Using output directory: {}'.format(out_dir))
if mask is None:
LGR.info('Computing adaptive mask')
else:
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd, getsum=True)
LGR.info('Computing adaptive T2* map')
if fitmode == 'all':
(t2s_limited, s0_limited,
t2ss, s0s,
t2s_full, s0_full) = decay.fit_decay(catd, tes, mask, masksum,
fittype)
else:
(t2s_limited, s0_limited,
t2s_full, s0_full) = decay.fit_decay_ts(catd, tes, mask, masksum,
fittype)
# set a hard cap for the T2* map/timeseries
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(), 99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
LGR.info('Computing optimal combination')
# optimally combine data
OCcatd = combine.make_optcom(catd, tes, mask, t2s=t2s_full,
combmode=combmode)
# clean up numerical errors
for arr in (OCcatd, s0_limited, t2s_limited):
np.nan_to_num(arr, copy=False)
s0_limited[s0_limited < 0] = 0
t2s_limited[t2s_limited < 0] = 0
io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)
io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)
io.filewrite(OCcatd, op.join(out_dir, 'ts_OC.nii'), ref_img)
def test_load_data():
fimg = [nib.load(f) for f in fnames]
exp_shape = (64350, 3, 5)
# list of filepath to images
d, ref = me.load_data(fnames, n_echos=len(tes))
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert np.allclose(ref.get_fdata(), nib.load(fnames[0]).get_fdata())
# list of filepath to images *without n_echos*
d, ref = me.load_data(fnames)
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert np.allclose(ref.get_fdata(), nib.load(fnames[0]).get_fdata())
# list of img_like
d, ref = me.load_data(fimg, n_echos=len(tes))
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert ref == fimg[0]
# list of img_like *without n_echos*
d, ref = me.load_data(fimg)
assert d.shape == exp_shape
assert isinstance(ref, nib.Nifti1Image)
assert ref == fimg[0]
# bad entry
fimg_with_bad_item = fimg[:]
fimg_with_bad_item[-1] = 5
with pytest.raises(TypeError):
d, ref = me.load_data(fimg_with_bad_item)
# unsupported tuple of img_like
fimg_tuple = tuple(fimg)
with pytest.raises(TypeError):
d, ref = me.load_data(fimg_tuple, n_echos=len(tes))
# tuple of img_like *without n_echos*
with pytest.raises(TypeError):
d, ref = me.load_data(fimg_tuple)
# two echos should raise value error
with pytest.raises(ValueError):
me.load_data(fnames[:2])
# imagine z-cat img
d, ref = me.load_data(fnames[0], n_echos=3)
assert d.shape == (21450, 3, 5)
assert isinstance(ref, nib.Nifti1Image)
assert ref.shape == (39, 50, 11, 1)
# z-cat without n_echos should raise an error
with pytest.raises(ValueError):
me.load_data(fnames[0])
# imagine z-cat img in list
d, ref = me.load_data(fnames[:1], n_echos=3)
assert d.shape == (21450, 3, 5)
assert isinstance(ref, nib.Nifti1Image)
assert ref.shape == (39, 50, 11, 1)
# z-cat in list without n_echos should raise an error
with pytest.raises(ValueError):
me.load_data(fnames[:1])
def t2smap_workflow(data,
tes,
out_dir='.',
mask=None,
prefix='',
convention='bids',
fittype='loglin',
fitmode='all',
combmode='t2s',
debug=False,
quiet=False):
"""
Estimate T2 and S0, and optimally combine data across TEs.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
out_dir : :obj:`str`, optional
Output directory.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially
aligned with `data`.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method.
'loglin' means to use the the default linear fit to the log of
the data.
'curvefit' means to use a monoexponential fit to the raw data,
which is slightly slower but may be more accurate.
fitmode : {'all', 'ts'}, optional
Monoexponential model fitting scheme.
'all' means that the model is fit, per voxel, across all timepoints.
'ts' means that the model is fit, per voxel and per timepoint.
Default is 'all'.
combmode : {'t2s', 'paid'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'paid' (Poser).
Other Parameters
----------------
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppress logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files, which are described below:
============================= =================================================
Filename Content
============================= =================================================
T2starmap.nii.gz Estimated T2* 3D map or 4D timeseries.
Will be a 3D map if ``fitmode`` is 'all' and a
4D timeseries if it is 'ts'.
S0map.nii.gz S0 3D map or 4D timeseries.
desc-limited_T2starmap.nii.gz Limited T2* map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the T2* estimate
from the first two echos, while the limited map
will have a NaN.
desc-limited_S0map.nii.gz Limited S0 map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the S0 estimate
from the first two echos, while the limited map
will have a NaN.
desc-optcom_bold.nii.gz Optimally combined timeseries.
============================= =================================================
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
utils.setup_loggers(quiet=quiet, debug=debug)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
io_generator = io.OutputGenerator(
ref_img,
convention=convention,
out_dir=out_dir,
prefix=prefix,
config="auto",
make_figures=False,
)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if mask is None:
LGR.info('Computing adaptive mask')
else:
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
getsum=True,
threshold=1)
LGR.info('Computing adaptive T2* map')
if fitmode == 'all':
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay(catd, tes, mask, masksum, fittype)
else:
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay_ts(catd, tes, mask, masksum, fittype)
# set a hard cap for the T2* map/timeseries
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_full.flatten(),
99.5,
interpolation_method='lower')
cap_t2s_sec = utils.millisec2sec(cap_t2s * 10.)
LGR.debug('Setting cap on T2* map at {:.5f}s'.format(cap_t2s_sec))
t2s_full[t2s_full > cap_t2s * 10] = cap_t2s
LGR.info('Computing optimal combination')
# optimally combine data
OCcatd = combine.make_optcom(catd,
tes,
masksum,
t2s=t2s_full,
combmode=combmode)
# clean up numerical errors
for arr in (OCcatd, s0_full, t2s_full):
np.nan_to_num(arr, copy=False)
s0_full[s0_full < 0] = 0
t2s_full[t2s_full < 0] = 0
io_generator.save_file(
utils.millisec2sec(t2s_full),
't2star img',
)
io_generator.save_file(s0_full, 's0 img')
io_generator.save_file(
utils.millisec2sec(t2s_limited),
'limited t2star img',
)
io_generator.save_file(
s0_limited,
'limited s0 img',
)
io_generator.save_file(OCcatd, 'combined img')
# Write out BIDS-compatible description file
derivative_metadata = {
"Name":
"t2smap Outputs",
"BIDSVersion":
"1.5.0",
"DatasetType":
"derivative",
"GeneratedBy": [{
"Name":
"tedana",
"Version":
__version__,
"Description":
("A pipeline estimating T2* from multi-echo fMRI data and "
"combining data across echoes."),
"CodeURL":
"https://github.com/ME-ICA/tedana"
}]
}
io_generator.save_file(derivative_metadata, 'data description json')
LGR.info("Workflow completed")
utils.teardown_loggers()
def t2smap_workflow(
data,
tes,
out_dir=".",
mask=None,
prefix="",
convention="bids",
fittype="loglin",
fitmode="all",
combmode="t2s",
debug=False,
quiet=False,
):
"""
Estimate T2 and S0, and optimally combine data across TEs.
Please remember to cite [1]_.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
out_dir : :obj:`str`, optional
Output directory.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially
aligned with `data`.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method.
'loglin' means to use the the default linear fit to the log of
the data.
'curvefit' means to use a monoexponential fit to the raw data,
which is slightly slower but may be more accurate.
fitmode : {'all', 'ts'}, optional
Monoexponential model fitting scheme.
'all' means that the model is fit, per voxel, across all timepoints.
'ts' means that the model is fit, per voxel and per timepoint.
Default is 'all'.
combmode : {'t2s', 'paid'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'paid' (Poser).
Other Parameters
----------------
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppress logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files, which are described below:
============================= =================================================
Filename Content
============================= =================================================
T2starmap.nii.gz Estimated T2* 3D map or 4D timeseries.
Will be a 3D map if ``fitmode`` is 'all' and a
4D timeseries if it is 'ts'.
S0map.nii.gz S0 3D map or 4D timeseries.
desc-limited_T2starmap.nii.gz Limited T2* map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the T2* estimate
from the first two echos, while the limited map
will have a NaN.
desc-limited_S0map.nii.gz Limited S0 map/timeseries. The difference between
the limited and full maps is that, for voxels
affected by dropout where only one echo contains
good data, the full map uses the S0 estimate
from the first two echos, while the limited map
will have a NaN.
desc-optcom_bold.nii.gz Optimally combined timeseries.
============================= =================================================
References
----------
.. [1] DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L.,
Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S.,
Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S.,
Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M.,
Whitaker, K., & Handwerker, D. A. (2021).
TE-dependent analysis of multi-echo fMRI with tedana.
Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
utils.setup_loggers(quiet=quiet, debug=debug)
LGR.info("Using output directory: {}".format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info("Loading input data: {}".format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
io_generator = io.OutputGenerator(
ref_img,
convention=convention,
out_dir=out_dir,
prefix=prefix,
config="auto",
make_figures=False,
)
n_samp, n_echos, n_vols = catd.shape
LGR.debug("Resulting data shape: {}".format(catd.shape))
if mask is None:
LGR.info("Computing adaptive mask")
else:
LGR.info("Using user-defined mask")
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
getsum=True,
threshold=1)
LGR.info("Computing adaptive T2* map")
if fitmode == "all":
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay(catd, tes, mask, masksum, fittype)
else:
(t2s_limited, s0_limited, t2s_full,
s0_full) = decay.fit_decay_ts(catd, tes, mask, masksum, fittype)
# set a hard cap for the T2* map/timeseries
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_full.flatten(),
99.5,
interpolation_method="lower")
cap_t2s_sec = utils.millisec2sec(cap_t2s * 10.0)
LGR.debug("Setting cap on T2* map at {:.5f}s".format(cap_t2s_sec))
t2s_full[t2s_full > cap_t2s * 10] = cap_t2s
LGR.info("Computing optimal combination")
# optimally combine data
OCcatd = combine.make_optcom(catd,
tes,
masksum,
t2s=t2s_full,
combmode=combmode)
# clean up numerical errors
for arr in (OCcatd, s0_full, t2s_full):
np.nan_to_num(arr, copy=False)
s0_full[s0_full < 0] = 0
t2s_full[t2s_full < 0] = 0
io_generator.save_file(
utils.millisec2sec(t2s_full),
"t2star img",
)
io_generator.save_file(s0_full, "s0 img")
io_generator.save_file(
utils.millisec2sec(t2s_limited),
"limited t2star img",
)
io_generator.save_file(
s0_limited,
"limited s0 img",
)
io_generator.save_file(OCcatd, "combined img")
# Write out BIDS-compatible description file
derivative_metadata = {
"Name":
"t2smap Outputs",
"BIDSVersion":
"1.5.0",
"DatasetType":
"derivative",
"GeneratedBy": [{
"Name":
"tedana",
"Version":
__version__,
"Description":
("A pipeline estimating T2* from multi-echo fMRI data and "
"combining data across echoes."),
"CodeURL":
"https://github.com/ME-ICA/tedana",
}],
}
io_generator.save_file(derivative_metadata, "data description json")
LGR.info("Workflow completed")
utils.teardown_loggers()
def test_new_nii_like():
data, ref = me.load_data(fnames, n_echos=len(tes))
nimg = me.new_nii_like(ref, data)
assert isinstance(nimg, nib.Nifti1Image)
assert nimg.shape == (39, 50, 33, 3, 5)
def tedana_workflow(data,
tes,
mask=None,
mixm=None,
ctab=None,
manacc=None,
tedort=False,
gscontrol=None,
tedpca='mle',
ste=-1,
combmode='t2s',
verbose=False,
stabilize=False,
wvpca=False,
out_dir='.',
fixed_seed=42,
debug=False,
quiet=False):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be
spatially aligned with `data`.
mixm : :obj:`str`, optional
File containing mixing matrix. If not provided, ME-PCA and ME-ICA are
done.
ctab : :obj:`str`, optional
File containing component table from which to extract pre-computed
classifications.
manacc : :obj:`str`, optional
Comma separated list of manually accepted components in string form.
Default is None.
tedort : :obj:`bool`, optional
Orthogonalize rejected components w.r.t. accepted ones prior to
denoising. Default is False.
gscontrol : {None, 't1c', 'gsr'} or :obj:`list`, optional
Perform additional denoising to remove spatially diffuse noise. Default
is None.
tedpca : {'mle', 'kundu', 'kundu-stabilize'}, optional
Method with which to select components in TEDPCA. Default is 'mle'.
ste : :obj:`int`, optional
Source TEs for models. 0 for all, -1 for optimal combination.
Default is -1.
combmode : {'t2s', 'ste'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'ste' (Poser).
verbose : :obj:`bool`, optional
Generate intermediate and additional files. Default is False.
wvpca : :obj:`bool`, optional
Whether or not to perform PCA on wavelet-transformed data.
Default is False.
out_dir : :obj:`str`, optional
Output directory.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
if debug and not quiet:
formatter = logging.Formatter(
'%(asctime)s\t%(name)-12s\t%(levelname)-8s\t%(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
fh = logging.FileHandler(
op.join(
out_dir,
'runlog-{0}.tsv'.format(datetime.now().isoformat().replace(
':', '.'))))
fh.setFormatter(formatter)
logging.basicConfig(level=logging.DEBUG,
handlers=[fh, logging.StreamHandler()])
elif quiet:
logging.basicConfig(level=logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# Coerce gscontrol to list
if not isinstance(gscontrol, list):
gscontrol = [gscontrol]
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if mixm is not None and op.isfile(mixm):
shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D'))
shutil.copyfile(mixm, op.join(out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
shutil.copyfile(ctab, op.join(out_dir, 'comp_table_ica.txt'))
shutil.copyfile(ctab, op.join(out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
if mask is None:
LGR.info('Computing adaptive mask')
else:
# TODO: add affine check
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
minimum=False,
getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
if verbose:
io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)
os.chdir(out_dir)
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = decay.fit_decay(catd, tes, mask, masksum)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s.flatten(),
99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s[t2s > cap_t2s * 10] = cap_t2s
io.filewrite(t2s, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0, op.join(out_dir, 's0v.nii'), ref_img)
if verbose:
io.filewrite(t2ss, op.join(out_dir, 't2ss.nii'), ref_img)
io.filewrite(s0s, op.join(out_dir, 's0vs.nii'), ref_img)
io.filewrite(t2sG, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0G, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
data_oc = combine.make_optcom(catd, tes, mask, t2s=t2sG, combmode=combmode)
# regress out global signal unless explicitly not desired
if 'gsr' in gscontrol:
catd, data_oc = model.gscontrol_raw(catd, data_oc, n_echos, ref_img)
if mixm is None:
# Identify and remove thermal noise from data
n_components, dd = decomposition.tedpca(catd,
data_oc,
combmode,
mask,
t2s,
t2sG,
ref_img,
tes=tes,
method=tedpca,
ste=ste,
kdaw=10.,
rdaw=1.,
wvpca=wvpca,
verbose=verbose)
mmix_orig, fixed_seed = decomposition.tedica(n_components, dd,
fixed_seed)
if verbose:
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
if ste == -1:
io.filewrite(utils.unmask(dd, mask),
op.join(out_dir, 'ts_OC_whitened.nii'), ref_img)
LGR.info('Making second component selection guess from ICA results')
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
seldict, comptable, betas, mmix = model.fitmodels_direct(
catd,
mmix_orig,
mask,
t2s,
t2sG,
tes,
combmode,
ref_img,
reindex=True,
label='meica_',
out_dir=out_dir,
verbose=verbose)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
comptable = selection.selcomps(seldict, comptable, mmix, manacc,
n_echos)
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D'))
seldict, comptable, betas, mmix = model.fitmodels_direct(
catd,
mmix_orig,
mask,
t2s,
t2sG,
tes,
combmode,
ref_img,
label='meica_',
out_dir=out_dir,
verbose=verbose)
if ctab is None:
comptable = selection.selcomps(seldict, comptable, mmix, manacc,
n_echos)
else:
comptable = pd.read_csv(ctab, sep='\t', index_col='component')
comptable.to_csv(op.join(out_dir, 'comp_table_ica.txt'),
sep='\t',
index=True,
index_label='component',
float_format='%.6f')
if 'component' not in comptable.columns:
comptable['component'] = comptable.index
acc = comptable.loc[comptable['classification'] == 'accepted', 'component']
rej = comptable.loc[comptable['classification'] == 'rejected', 'component']
midk = comptable.loc[comptable['classification'] == 'midk', 'component']
ign = comptable.loc[comptable['classification'] == 'ignored', 'component']
if len(acc) == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
if tedort:
acc_idx = comptable.loc[
~comptable['classification'].str.contains('rejected'), 'component']
rej_idx = comptable.loc[
comptable['classification'].str.contains('rejected'), 'component']
acc_ts = mmix[:, acc_idx]
rej_ts = mmix[:, rej_idx]
betas = np.linalg.lstsq(acc_ts, rej_ts, rcond=None)[0]
pred_rej_ts = np.dot(acc_ts, betas)
resid = rej_ts - pred_rej_ts
mmix[:, rej_idx] = resid
np.savetxt(op.join(out_dir, 'meica_mix_orth.1D'), mmix)
io.writeresults(data_oc,
mask=mask,
comptable=comptable,
mmix=mmix,
n_vols=n_vols,
fixed_seed=fixed_seed,
acc=acc,
rej=rej,
midk=midk,
empty=ign,
ref_img=ref_img)
if 't1c' in gscontrol:
LGR.info('Performing T1c global signal regression to remove spatially '
'diffuse noise')
io.gscontrol_mmix(data_oc, mmix, mask, comptable, ref_img)
if verbose:
io.writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img)
LGR.info('Workflow completed')
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
def tedana_workflow(data,
tes,
out_dir='.',
mask=None,
convention='bids',
prefix='',
fittype='loglin',
combmode='t2s',
tedpca='mdl',
fixed_seed=42,
maxit=500,
maxrestart=10,
tedort=False,
gscontrol=None,
no_reports=False,
png_cmap='coolwarm',
verbose=False,
low_mem=False,
debug=False,
quiet=False,
t2smap=None,
mixm=None,
ctab=None,
manacc=None):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
out_dir : :obj:`str`, optional
Output directory.
mask : :obj:`str` or None, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be
spatially aligned with `data`. If an explicit mask is not provided,
then Nilearn's compute_epi_mask function will be used to derive a mask
from the first echo's data.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method. 'loglin' uses the the default linear
fit to the log of the data. 'curvefit' uses a monoexponential fit to
the raw data, which is slightly slower but may be more accurate.
Default is 'loglin'.
combmode : {'t2s'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default).
tedpca : {'mdl', 'aic', 'kic', 'kundu', 'kundu-stabilize', float}, optional
Method with which to select components in TEDPCA.
If a float is provided, then it is assumed to represent percentage of variance
explained (0-1) to retain from PCA.
Default is 'mdl'.
tedort : :obj:`bool`, optional
Orthogonalize rejected components w.r.t. accepted ones prior to
denoising. Default is False.
gscontrol : {None, 'mir', 'gsr'} or :obj:`list`, optional
Perform additional denoising to remove spatially diffuse noise. Default
is None.
verbose : :obj:`bool`, optional
Generate intermediate and additional files. Default is False.
no_reports : obj:'bool', optional
Do not generate .html reports and .png plots. Default is false such
that reports are generated.
png_cmap : obj:'str', optional
Name of a matplotlib colormap to be used when generating figures.
Cannot be used with --no-png. Default is 'coolwarm'.
t2smap : :obj:`str`, optional
Precalculated T2* map in the same space as the input data. Values in
the map must be in seconds.
mixm : :obj:`str` or None, optional
File containing mixing matrix, to be used when re-running the workflow.
If not provided, ME-PCA and ME-ICA are done. Default is None.
ctab : :obj:`str` or None, optional
File containing component table from which to extract pre-computed
classifications, to be used with 'mixm' when re-running the workflow.
Default is None.
manacc : :obj:`list` of :obj:`int` or None, optional
List of manually accepted components. Can be a list of the components
numbers or None.
If provided, this parameter requires ``mixm`` and ``ctab`` to be provided as well.
Default is None.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
maxit : :obj:`int`, optional
Maximum number of iterations for ICA. Default is 500.
maxrestart : :obj:`int`, optional
Maximum number of attempts for ICA. If ICA fails to converge, the
fixed seed will be updated and ICA will be run again. If convergence
is achieved before maxrestart attempts, ICA will finish early.
Default is 10.
low_mem : :obj:`bool`, optional
Enables low-memory processing, including the use of IncrementalPCA.
May increase workflow duration. Default is False.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
# boilerplate
basename = 'report'
extension = 'txt'
repname = op.join(out_dir, (basename + '.' + extension))
repex = op.join(out_dir, (basename + '*'))
previousreps = glob(repex)
previousreps.sort(reverse=True)
for f in previousreps:
previousparts = op.splitext(f)
newname = previousparts[0] + '_old' + previousparts[1]
os.rename(f, newname)
refname = op.join(out_dir, '_references.txt')
# create logfile name
basename = 'tedana_'
extension = 'tsv'
start_time = datetime.datetime.now().strftime('%Y-%m-%dT%H%M%S')
logname = op.join(out_dir, (basename + start_time + '.' + extension))
utils.setup_loggers(logname, repname, refname, quiet=quiet, debug=debug)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# Coerce gscontrol to list
if not isinstance(gscontrol, list):
gscontrol = [gscontrol]
# Check value of tedpca *if* it is a float
tedpca = check_tedpca_value(tedpca, is_parser=False)
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
io_generator = io.OutputGenerator(
ref_img,
convention=convention,
out_dir=out_dir,
prefix=prefix,
config="auto",
verbose=verbose,
)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
# check if TR is 0
img_t_r = io_generator.reference_img.header.get_zooms()[-1]
if img_t_r == 0:
raise IOError(
'Dataset has a TR of 0. This indicates incorrect'
' header information. To correct this, we recommend'
' using this snippet:'
'\n'
'https://gist.github.com/jbteves/032c87aeb080dd8de8861cb151bff5d6'
'\n'
'to correct your TR to the value it should be.')
if mixm is not None and op.isfile(mixm):
mixm = op.abspath(mixm)
# Allow users to re-run on same folder
mixing_name = io_generator.get_name("ICA mixing tsv")
if mixm != mixing_name:
shutil.copyfile(mixm, mixing_name)
shutil.copyfile(mixm,
op.join(io_generator.out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
ctab = op.abspath(ctab)
# Allow users to re-run on same folder
metrics_name = io_generator.get_name("ICA metrics tsv")
if ctab != metrics_name:
shutil.copyfile(ctab, metrics_name)
shutil.copyfile(ctab,
op.join(io_generator.out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
if ctab and not mixm:
LGR.warning('Argument "ctab" requires argument "mixm".')
ctab = None
elif manacc is not None and (not mixm or not ctab):
LGR.warning('Argument "manacc" requires arguments "mixm" and "ctab".')
manacc = None
elif manacc is not None:
# coerce to list of integers
manacc = [int(m) for m in manacc]
if t2smap is not None and op.isfile(t2smap):
t2smap_file = io_generator.get_name('t2star img')
t2smap = op.abspath(t2smap)
# Allow users to re-run on same folder
if t2smap != t2smap_file:
shutil.copyfile(t2smap, t2smap_file)
elif t2smap is not None:
raise IOError('Argument "t2smap" must be an existing file.')
RepLGR.info("TE-dependence analysis was performed on input data.")
if mask and not t2smap:
# TODO: add affine check
LGR.info('Using user-defined mask')
RepLGR.info("A user-defined mask was applied to the data.")
elif t2smap and not mask:
LGR.info('Using user-defined T2* map to generate mask')
t2s_limited_sec = utils.load_image(t2smap)
t2s_limited = utils.sec2millisec(t2s_limited_sec)
t2s_full = t2s_limited.copy()
mask = (t2s_limited != 0).astype(int)
elif t2smap and mask:
LGR.info('Combining user-defined mask and T2* map to generate mask')
t2s_limited_sec = utils.load_image(t2smap)
t2s_limited = utils.sec2millisec(t2s_limited_sec)
t2s_full = t2s_limited.copy()
mask = utils.load_image(mask)
mask[t2s_limited == 0] = 0 # reduce mask based on T2* map
else:
LGR.info('Computing EPI mask from first echo')
first_echo_img = io.new_nii_like(io_generator.reference_img,
catd[:, 0, :])
mask = compute_epi_mask(first_echo_img)
RepLGR.info("An initial mask was generated from the first echo using "
"nilearn's compute_epi_mask function.")
# Create an adaptive mask with at least 1 good echo, for denoising
mask_denoise, masksum_denoise = utils.make_adaptive_mask(
catd,
mask=mask,
getsum=True,
threshold=1,
)
LGR.debug('Retaining {}/{} samples for denoising'.format(
mask_denoise.sum(), n_samp))
io_generator.save_file(masksum_denoise, "adaptive mask img")
# Create an adaptive mask with at least 3 good echoes, for classification
masksum_clf = masksum_denoise.copy()
masksum_clf[masksum_clf < 3] = 0
mask_clf = masksum_clf.astype(bool)
RepLGR.info(
"A two-stage masking procedure was applied, in which a liberal mask "
"(including voxels with good data in at least the first echo) was used for "
"optimal combination, T2*/S0 estimation, and denoising, while a more conservative mask "
"(restricted to voxels with good data in at least the first three echoes) was used for "
"the component classification procedure.")
LGR.debug('Retaining {}/{} samples for classification'.format(
mask_clf.sum(), n_samp))
if t2smap is None:
LGR.info('Computing T2* map')
t2s_limited, s0_limited, t2s_full, s0_full = decay.fit_decay(
catd, tes, mask_denoise, masksum_denoise, fittype)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_full.flatten(),
99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}s'.format(
utils.millisec2sec(cap_t2s)))
t2s_full[t2s_full > cap_t2s * 10] = cap_t2s
io_generator.save_file(utils.millisec2sec(t2s_full), 't2star img')
io_generator.save_file(s0_full, 's0 img')
if verbose:
io_generator.save_file(utils.millisec2sec(t2s_limited),
'limited t2star img')
io_generator.save_file(s0_limited, 'limited s0 img')
# optimally combine data
data_oc = combine.make_optcom(catd,
tes,
masksum_denoise,
t2s=t2s_full,
combmode=combmode)
# regress out global signal unless explicitly not desired
if 'gsr' in gscontrol:
catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, io_generator)
fout = io_generator.save_file(data_oc, 'combined img')
LGR.info('Writing optimally combined data set: {}'.format(fout))
if mixm is None:
# Identify and remove thermal noise from data
dd, n_components = decomposition.tedpca(catd,
data_oc,
combmode,
mask_clf,
masksum_clf,
t2s_full,
io_generator,
tes=tes,
algorithm=tedpca,
kdaw=10.,
rdaw=1.,
verbose=verbose,
low_mem=low_mem)
if verbose:
io_generator.save_file(utils.unmask(dd, mask_clf), 'whitened img')
# Perform ICA, calculate metrics, and apply decision tree
# Restart when ICA fails to converge or too few BOLD components found
keep_restarting = True
n_restarts = 0
seed = fixed_seed
while keep_restarting:
mmix, seed = decomposition.tedica(dd,
n_components,
seed,
maxit,
maxrestart=(maxrestart -
n_restarts))
seed += 1
n_restarts = seed - fixed_seed
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
LGR.info(
'Making second component selection guess from ICA results')
required_metrics = [
'kappa', 'rho', 'countnoise', 'countsigFT2', 'countsigFS0',
'dice_FT2', 'dice_FS0', 'signal-noise_t', 'variance explained',
'normalized variance explained', 'd_table_score'
]
comptable = metrics.collect.generate_metrics(
catd,
data_oc,
mmix,
masksum_clf,
tes,
io_generator,
'ICA',
metrics=required_metrics,
)
comptable, metric_metadata = selection.kundu_selection_v2(
comptable, n_echos, n_vols)
n_bold_comps = comptable[comptable.classification ==
'accepted'].shape[0]
if (n_restarts < maxrestart) and (n_bold_comps == 0):
LGR.warning("No BOLD components found. Re-attempting ICA.")
elif (n_bold_comps == 0):
LGR.warning(
"No BOLD components found, but maximum number of restarts reached."
)
keep_restarting = False
else:
keep_restarting = False
RepLGR.disabled = True # Disable the report to avoid duplicate text
RepLGR.disabled = False # Re-enable the report after the while loop is escaped
else:
LGR.info('Using supplied mixing matrix from ICA')
mixing_file = io_generator.get_name("ICA mixing tsv")
mmix = pd.read_table(mixing_file).values
if ctab is None:
required_metrics = [
'kappa', 'rho', 'countnoise', 'countsigFT2', 'countsigFS0',
'dice_FT2', 'dice_FS0', 'signal-noise_t', 'variance explained',
'normalized variance explained', 'd_table_score'
]
comptable = metrics.collect.generate_metrics(
catd,
data_oc,
mmix,
masksum_clf,
tes,
io_generator,
'ICA',
metrics=required_metrics,
)
comptable, metric_metadata = selection.kundu_selection_v2(
comptable, n_echos, n_vols)
else:
comptable = pd.read_table(ctab)
if manacc is not None:
comptable, metric_metadata = selection.manual_selection(
comptable, acc=manacc)
# Write out ICA files.
comp_names = comptable["Component"].values
mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
io_generator.save_file(mixing_df, "ICA mixing tsv")
betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask_denoise),
mask_denoise)
io_generator.save_file(betas_oc, 'z-scored ICA components img')
# Save component table and associated json
io_generator.save_file(comptable, "ICA metrics tsv")
metric_metadata = metrics.collect.get_metadata(comptable)
io_generator.save_file(metric_metadata, "ICA metrics json")
decomp_metadata = {
"Method": ("Independent components analysis with FastICA "
"algorithm implemented by sklearn. "),
}
for comp_name in comp_names:
decomp_metadata[comp_name] = {
"Description":
"ICA fit to dimensionally-reduced optimally combined data.",
"Method": "tedana",
}
with open(io_generator.get_name("ICA decomposition json"), "w") as fo:
json.dump(decomp_metadata, fo, sort_keys=True, indent=4)
if comptable[comptable.classification == 'accepted'].shape[0] == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
mmix_orig = mmix.copy()
if tedort:
acc_idx = comptable.loc[~comptable.classification.str.
contains('rejected')].index.values
rej_idx = comptable.loc[comptable.classification.str.contains(
'rejected')].index.values
acc_ts = mmix[:, acc_idx]
rej_ts = mmix[:, rej_idx]
betas = np.linalg.lstsq(acc_ts, rej_ts, rcond=None)[0]
pred_rej_ts = np.dot(acc_ts, betas)
resid = rej_ts - pred_rej_ts
mmix[:, rej_idx] = resid
comp_names = [
io.add_decomp_prefix(comp,
prefix='ica',
max_value=comptable.index.max())
for comp in comptable.index.values
]
mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
io_generator.save_file(mixing_df, "ICA orthogonalized mixing tsv")
RepLGR.info("Rejected components' time series were then "
"orthogonalized with respect to accepted components' time "
"series.")
io.writeresults(data_oc,
mask=mask_denoise,
comptable=comptable,
mmix=mmix,
n_vols=n_vols,
io_generator=io_generator)
if 'mir' in gscontrol:
gsc.minimum_image_regression(data_oc, mmix, mask_denoise, comptable,
io_generator)
if verbose:
io.writeresults_echoes(catd, mmix, mask_denoise, comptable,
io_generator)
# Write out BIDS-compatible description file
derivative_metadata = {
"Name":
"tedana Outputs",
"BIDSVersion":
"1.5.0",
"DatasetType":
"derivative",
"GeneratedBy": [{
"Name":
"tedana",
"Version":
__version__,
"Description":
("A denoising pipeline for the identification and removal "
"of non-BOLD noise from multi-echo fMRI data."),
"CodeURL":
"https://github.com/ME-ICA/tedana"
}]
}
with open(io_generator.get_name("data description json"), "w") as fo:
json.dump(derivative_metadata, fo, sort_keys=True, indent=4)
RepLGR.info("This workflow used numpy (Van Der Walt, Colbert, & "
"Varoquaux, 2011), scipy (Jones et al., 2001), pandas "
"(McKinney, 2010), scikit-learn (Pedregosa et al., 2011), "
"nilearn, and nibabel (Brett et al., 2019).")
RefLGR.info(
"Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The "
"NumPy array: a structure for efficient numerical computation. "
"Computing in Science & Engineering, 13(2), 22.")
RefLGR.info("Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source "
"Scientific Tools for Python, 2001-, http://www.scipy.org/")
RefLGR.info("McKinney, W. (2010, June). Data structures for statistical "
"computing in python. In Proceedings of the 9th Python in "
"Science Conference (Vol. 445, pp. 51-56).")
RefLGR.info("Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., "
"Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). "
"Scikit-learn: Machine learning in Python. Journal of machine "
"learning research, 12(Oct), 2825-2830.")
RefLGR.info("Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., "
"Cipollini, B., McCarthy, P., … freec84. (2019, May 28). "
"nipy/nibabel. Zenodo. http://doi.org/10.5281/zenodo.3233118")
RepLGR.info("This workflow also used the Dice similarity index "
"(Dice, 1945; Sørensen, 1948).")
RefLGR.info("Dice, L. R. (1945). Measures of the amount of ecologic "
"association between species. Ecology, 26(3), 297-302.")
RefLGR.info(
"Sørensen, T. J. (1948). A method of establishing groups of "
"equal amplitude in plant sociology based on similarity of "
"species content and its application to analyses of the "
"vegetation on Danish commons. I kommission hos E. Munksgaard.")
with open(repname, 'r') as fo:
report = [line.rstrip() for line in fo.readlines()]
report = ' '.join(report)
with open(refname, 'r') as fo:
reference_list = sorted(list(set(fo.readlines())))
references = '\n'.join(reference_list)
report += '\n\nReferences:\n\n' + references
with open(repname, 'w') as fo:
fo.write(report)
if not no_reports:
LGR.info(
'Making figures folder with static component maps and timecourse plots.'
)
dn_ts, hikts, lowkts = io.denoise_ts(data_oc, mmix, mask_denoise,
comptable)
reporting.static_figures.carpet_plot(
optcom_ts=data_oc,
denoised_ts=dn_ts,
hikts=hikts,
lowkts=lowkts,
mask=mask_denoise,
io_generator=io_generator,
gscontrol=gscontrol,
)
reporting.static_figures.comp_figures(
data_oc,
mask=mask_denoise,
comptable=comptable,
mmix=mmix_orig,
io_generator=io_generator,
png_cmap=png_cmap,
)
if sys.version_info.major == 3 and sys.version_info.minor < 6:
warn_msg = ("Reports requested but Python version is less than "
"3.6.0. Dynamic reports will not be generated.")
LGR.warn(warn_msg)
else:
LGR.info('Generating dynamic report')
reporting.generate_report(io_generator, tr=img_t_r)
LGR.info('Workflow completed')
utils.teardown_loggers()
os.remove(refname)
def tedana_workflow(data,
tes,
mask=None,
mixm=None,
ctab=None,
manacc=None,
strict=False,
gscontrol=True,
kdaw=10.,
rdaw=1.,
conv=2.5e-5,
ste=-1,
combmode='t2s',
dne=False,
cost='logcosh',
stabilize=False,
filecsdata=False,
wvpca=False,
label=None,
fixed_seed=42,
debug=False,
quiet=False):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially
aligned with `data`.
mixm : :obj:`str`, optional
File containing mixing matrix. If not provided, ME-PCA and ME-ICA are
done.
ctab : :obj:`str`, optional
File containing component table from which to extract pre-computed
classifications.
manacc : :obj:`str`, optional
Comma separated list of manually accepted components in string form.
Default is None.
strict : :obj:`bool`, optional
Ignore low-variance ambiguous components. Default is False.
gscontrol : :obj:`bool`, optional
Control global signal using spatial approach. Default is True.
kdaw : :obj:`float`, optional
Dimensionality augmentation weight (Kappa). Default is 10.
-1 for low-dimensional ICA.
rdaw : :obj:`float`, optional
Dimensionality augmentation weight (Rho). Default is 1.
-1 for low-dimensional ICA.
conv : :obj:`float`, optional
Convergence limit. Default is 2.5e-5.
ste : :obj:`int`, optional
Source TEs for models. 0 for all, -1 for optimal combination.
Default is -1.
combmode : {'t2s', 'ste'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'ste' (Poser).
dne : :obj:`bool`, optional
Denoise each TE dataset separately. Default is False.
cost : {'logcosh', 'exp', 'cube'} str, optional
Cost function for ICA
stabilize : :obj:`bool`, optional
Stabilize convergence by reducing dimensionality, for low quality data.
Default is False.
filecsdata : :obj:`bool`, optional
Save component selection data to file. Default is False.
wvpca : :obj:`bool`, optional
Whether or not to perform PCA on wavelet-transformed data.
Default is False.
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files, which are written out to a folder
named TED.[ref_label].[label] if ``label`` is provided and TED.[ref_label]
if not. ``ref_label`` is determined based on the name of the first ``data``
file. For a complete list of the files generated by this workflow, please
visit https://tedana.readthedocs.io/en/latest/outputs.html
"""
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
kdaw, rdaw = float(kdaw), float(rdaw)
try:
ref_label = op.basename(ref_img).split('.')[0]
except (TypeError, AttributeError):
ref_label = op.basename(str(data[0])).split('.')[0]
if label is not None:
out_dir = 'TED.{0}.{1}'.format(ref_label, label)
else:
out_dir = 'TED.{0}'.format(ref_label)
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
LGR.info('Creating output directory: {}'.format(out_dir))
os.mkdir(out_dir)
else:
LGR.info('Using output directory: {}'.format(out_dir))
if mixm is not None and op.isfile(mixm):
shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D'))
shutil.copyfile(mixm, op.join(out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
shutil.copyfile(ctab, op.join(out_dir, 'comp_table_ica.txt'))
shutil.copyfile(ctab, op.join(out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
os.chdir(out_dir)
if mask is None:
LGR.info('Computing adaptive mask')
else:
# TODO: add affine check
LGR.info('Using user-defined mask')
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
minimum=False,
getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = decay.fit_decay(catd, tes, mask, masksum)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s.flatten(),
99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s[t2s > cap_t2s * 10] = cap_t2s
io.filewrite(t2s, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0, op.join(out_dir, 's0v.nii'), ref_img)
io.filewrite(t2ss, op.join(out_dir, 't2ss.nii'), ref_img)
io.filewrite(s0s, op.join(out_dir, 's0vs.nii'), ref_img)
io.filewrite(t2sG, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0G, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
data_oc = combine.make_optcom(catd, tes, mask, t2s=t2sG, combmode=combmode)
# regress out global signal unless explicitly not desired
if gscontrol:
catd, data_oc = model.gscontrol_raw(catd, data_oc, n_echos, ref_img)
if mixm is None:
# Identify and remove thermal noise from data
n_components, dd = decomposition.tedpca(catd,
data_oc,
combmode,
mask,
t2s,
t2sG,
stabilize,
ref_img,
tes=tes,
kdaw=kdaw,
rdaw=rdaw,
ste=ste,
wvpca=wvpca)
mmix_orig, fixed_seed = decomposition.tedica(n_components,
dd,
conv,
fixed_seed,
cost=cost)
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
LGR.info('Making second component selection guess from ICA results')
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
seldict, comptable, betas, mmix = model.fitmodels_direct(catd,
mmix_orig,
mask,
t2s,
t2sG,
tes,
combmode,
ref_img,
reindex=True)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
comptable = selection.selcomps(seldict, comptable, mmix, manacc,
n_echos)
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D'))
seldict, comptable, betas, mmix = model.fitmodels_direct(
catd, mmix_orig, mask, t2s, t2sG, tes, combmode, ref_img)
if ctab is None:
comptable = selection.selcomps(seldict, comptable, mmix, manacc,
n_echos)
else:
comptable = pd.read_csv(ctab, sep='\t', index_col='component')
comptable.to_csv(op.join(out_dir, 'comp_table_ica.txt'),
sep='\t',
index=True,
index_label='component',
float_format='%.6f')
if 'component' not in comptable.columns:
comptable['component'] = comptable.index
acc = comptable.loc[comptable['classification'] == 'accepted', 'component']
rej = comptable.loc[comptable['classification'] == 'rejected', 'component']
midk = comptable.loc[comptable['classification'] == 'midk', 'component']
ign = comptable.loc[comptable['classification'] == 'ignored', 'component']
if len(acc) == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
io.writeresults(data_oc,
mask=mask,
comptable=comptable,
mmix=mmix,
n_vols=n_vols,
fixed_seed=fixed_seed,
acc=acc,
rej=rej,
midk=midk,
empty=ign,
ref_img=ref_img)
io.gscontrol_mmix(data_oc, mmix, mask, comptable, ref_img)
if dne:
io.writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img)
def combine_tedana(tes,
data,
combmodes=('t2s', 'ste'),
mask=None,
overwrite=True):
""" Function based on tedana main workflow """
from tedana import utils, model, io, decay, combine
from scipy import stats
import numpy as np
import os
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
mask, masksum = utils.make_adaptive_mask(catd,
mask=mask,
minimum=False,
getsum=True)
# check if the t2s-map is already created first
base_name = data[0].replace('_echo-1',
'').replace('desc-preproc-hp',
'desc-preproc-hp-%s').replace(
'.nii', '').replace('.gz', '')
if not os.path.exists(base_name % 't2sv' + '.nii.gz') or overwrite:
t2s, s0, t2ss, s0s, t2sG, s0G = decay.fit_decay(
catd, tes, mask, masksum)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s.flatten(),
99.5,
interpolation_method='lower')
t2s[t2s > cap_t2s * 10] = cap_t2s
# save
io.filewrite(t2s, base_name % 't2sv' + '.nii', ref_img, gzip=True)
io.filewrite(s0, base_name % 's0v' + '.nii', ref_img, gzip=True)
io.filewrite(t2ss, base_name % 't2ss' + '.nii', ref_img, gzip=True)
io.filewrite(s0s, base_name % 's0vs' + '.nii', ref_img, gzip=True)
io.filewrite(t2sG, base_name % 't2svG' + '.nii', ref_img, gzip=True)
io.filewrite(s0G, base_name % 's0vG' + '.nii', ref_img, gzip=True)
else:
t2sG = utils.load_image(base_name % 't2svG' + '.nii.gz')
t2s = utils.load_image(base_name % 't2sv' + '.nii.gz')
# optimally combine data
if 't2s' in combmodes:
print('Combining echos using optcomb...', end='')
ext = 'optcomb'
data_oc = combine.make_optcom(catd,
tes,
mask,
t2s=t2sG,
combmode='t2s')
# make sure to set all nan-values/inf to 0
data_oc[np.isinf(data_oc)] = 0
data_oc[np.isnan(data_oc)] = 0
print('Done, writing results...')
io.filewrite(data_oc, base_name % ext + '.nii', ref_img, gzip=True)
if 'ste' in combmodes:
print('Combining echos using optcomb...', end='')
ext = 'PAID'
data_oc = combine.make_optcom(catd,
tes,
mask,
t2s=t2sG,
combmode='ste')
# make sure to set all nan-values/inf to 0
data_oc[np.isinf(data_oc)] = 0
data_oc[np.isnan(data_oc)] = 0
io.filewrite(data_oc, base_name % ext + '.nii', ref_img, gzip=True)
print('Done, writing results...')
return 0
def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
tedort=False, gscontrol=None, tedpca='mle',
source_tes=-1, combmode='t2s', verbose=False, stabilize=False,
out_dir='.', fixed_seed=42, maxit=500, maxrestart=10,
debug=False, quiet=False, png=False, png_cmap='coolwarm',
low_mem=False):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
mask : :obj:`str`, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be
spatially aligned with `data`. If an explicit mask is not provided,
then Nilearn's compute_epi_mask function will be used to derive a mask
from the first echo's data.
mixm : :obj:`str`, optional
File containing mixing matrix. If not provided, ME-PCA and ME-ICA are
done.
ctab : :obj:`str`, optional
File containing component table from which to extract pre-computed
classifications.
manacc : :obj:`list`, :obj:`str`, or None, optional
List of manually accepted components. Can be a list of the components,
a comma-separated string with component numbers, or None. Default is
None.
tedort : :obj:`bool`, optional
Orthogonalize rejected components w.r.t. accepted ones prior to
denoising. Default is False.
gscontrol : {None, 't1c', 'gsr'} or :obj:`list`, optional
Perform additional denoising to remove spatially diffuse noise. Default
is None.
tedpca : {'mle', 'kundu', 'kundu-stabilize'}, optional
Method with which to select components in TEDPCA. Default is 'mle'.
source_tes : :obj:`int`, optional
Source TEs for models. 0 for all, -1 for optimal combination.
Default is -1.
combmode : {'t2s'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default).
verbose : :obj:`bool`, optional
Generate intermediate and additional files. Default is False.
png : obj:'bool', optional
Generate simple plots and figures. Default is false.
png_cmap : obj:'str', optional
Name of a matplotlib colormap to be used when generating figures.
--png must still be used to request figures. Default is 'coolwarm'
out_dir : :obj:`str`, optional
Output directory.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
maxit : :obj:`int`, optional
Maximum number of iterations for ICA. Default is 500.
maxrestart : :obj:`int`, optional
Maximum number of attempts for ICA. If ICA fails to converge, the
fixed seed will be updated and ICA will be run again. If convergence
is achieved before maxrestart attempts, ICA will finish early.
Default is 10.
low_mem : :obj:`bool`, optional
Enables low-memory processing, including the use of IncrementalPCA.
May increase workflow duration. Default is False.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
# boilerplate
refs = []
basename = 'report'
extension = 'txt'
repname = op.join(out_dir, (basename + '.' + extension))
repex = op.join(out_dir, (basename + '*'))
previousreps = glob(repex)
previousreps.sort(reverse=True)
for f in previousreps:
previousparts = op.splitext(f)
newname = previousparts[0] + '_old' + previousparts[1]
os.rename(f, newname)
if debug and not quiet:
# ensure old logs aren't over-written
basename = 'tedana_run'
extension = 'txt'
logname = op.join(out_dir, (basename + '.' + extension))
logex = op.join(out_dir, (basename + '*'))
previouslogs = glob(logex)
previouslogs.sort(reverse=True)
for f in previouslogs:
previousparts = op.splitext(f)
newname = previousparts[0] + '_old' + previousparts[1]
os.rename(f, newname)
# set logging format
formatter = logging.Formatter(
'%(asctime)s\t%(name)-12s\t%(levelname)-8s\t%(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
# set up logging file and open it for writing
fh = logging.FileHandler(logname)
fh.setFormatter(formatter)
logging.basicConfig(level=logging.DEBUG,
handlers=[fh, logging.StreamHandler()])
elif quiet:
logging.basicConfig(level=logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# Coerce gscontrol to list
if not isinstance(gscontrol, list):
gscontrol = [gscontrol]
# coerce data to samples x echos x time array
if isinstance(data, str):
data = [data]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
# check if TR is 0
img_t_r = ref_img.header.get_zooms()[-1]
if img_t_r == 0 and png:
raise IOError('Dataset has a TR of 0. This indicates incorrect'
' header information. To correct this, we recommend'
' using this snippet:'
'\n'
'https://gist.github.com/jbteves/032c87aeb080dd8de8861cb151bff5d6'
'\n'
'to correct your TR to the value it should be.')
if mixm is not None and op.isfile(mixm):
mixm = op.abspath(mixm)
# Allow users to re-run on same folder
if mixm != op.join(out_dir, 'meica_mix.1D'):
shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D'))
shutil.copyfile(mixm, op.join(out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
ctab = op.abspath(ctab)
# Allow users to re-run on same folder
if ctab != op.join(out_dir, 'comp_table_ica.txt'):
shutil.copyfile(ctab, op.join(out_dir, 'comp_table_ica.txt'))
shutil.copyfile(ctab, op.join(out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
if isinstance(manacc, str):
manacc = [int(comp) for comp in manacc.split(',')]
if ctab and not mixm:
LGR.warning('Argument "ctab" requires argument "mixm".')
ctab = None
elif ctab and (manacc is None):
LGR.warning('Argument "ctab" requires argument "manacc".')
ctab = None
elif manacc is not None and not mixm:
LGR.warning('Argument "manacc" requires argument "mixm".')
manacc = None
bp_str = ("TE-dependence analysis was performed on input data.")
if mask is None:
LGR.info('Computing EPI mask from first echo')
first_echo_img = io.new_nii_like(ref_img, catd[:, 0, :])
mask = compute_epi_mask(first_echo_img)
bp_str += (" An initial mask was generated from the first echo using "
"nilearn's compute_epi_mask function.")
else:
# TODO: add affine check
LGR.info('Using user-defined mask')
bp_str += (" A user-defined mask was applied to the data.")
mask, masksum = utils.make_adaptive_mask(catd, mask=mask, getsum=True)
bp_str += (" An adaptive mask was then generated, in which each voxel's "
"value reflects the number of echoes with 'good' data.")
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
if verbose:
io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)
os.chdir(out_dir)
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = decay.fit_decay(catd, tes, mask, masksum)
bp_str += (" A monoexponential model was fit to the data at each voxel "
"using log-linear regression in order to estimate T2* and S0 "
"maps. For each voxel, the value from the adaptive mask was "
"used to determine which echoes would be used to estimate T2* "
"and S0.")
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s.flatten(), 99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s[t2s > cap_t2s * 10] = cap_t2s
io.filewrite(t2s, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0, op.join(out_dir, 's0v.nii'), ref_img)
if verbose:
io.filewrite(t2ss, op.join(out_dir, 't2ss.nii'), ref_img)
io.filewrite(s0s, op.join(out_dir, 's0vs.nii'), ref_img)
io.filewrite(t2sG, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0G, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
data_oc = combine.make_optcom(catd, tes, mask, t2s=t2sG, combmode=combmode)
if combmode == 't2s':
cm_str = "'t2s' (Posse et al., 1999)"
refs += ["Posse, S., Wiese, S., Gembris, D., Mathiak, K., Kessler, "
"C., Grosse‐Ruyken, M. L., ... & Kiselev, V. G. (1999). "
"Enhancement of BOLD‐contrast sensitivity by single‐shot "
"multi‐echo functional MR imaging. Magnetic Resonance in "
"Medicine: An Official Journal of the International Society "
"for Magnetic Resonance in Medicine, 42(1), 87-97."]
bp_str += (" Multi-echo data were then optimally combined using the {0} "
"combination method.").format(cm_str)
# regress out global signal unless explicitly not desired
if 'gsr' in gscontrol:
catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, ref_img)
bp_str += (" Global signal regression was applied to the multi-echo "
"and optimally combined datasets.")
if mixm is None:
# Identify and remove thermal noise from data
dd, n_components = decomposition.tedpca(catd, data_oc, combmode, mask,
t2s, t2sG, ref_img,
tes=tes, algorithm=tedpca,
source_tes=source_tes,
kdaw=10., rdaw=1.,
out_dir=out_dir,
verbose=verbose,
low_mem=low_mem)
if tedpca == 'mle':
alg_str = "using MLE dimensionality estimation (Minka, 2001)"
refs += ["Minka, T. P. (2001). Automatic choice of dimensionality "
"for PCA. In Advances in neural information processing "
"systems (pp. 598-604)."]
elif tedpca == 'kundu':
alg_str = ("followed by the Kundu component selection decision "
"tree (Kundu et al., 2013)")
refs += ["Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., "
"Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. "
"(2013). Integrated strategy for improving functional "
"connectivity mapping using multiecho fMRI. Proceedings "
"of the National Academy of Sciences, 110(40), "
"16187-16192."]
elif tedpca == 'kundu-stabilize':
alg_str = ("followed by the 'stabilized' Kundu component "
"selection decision tree (Kundu et al., 2013)")
refs += ["Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., "
"Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. "
"(2013). Integrated strategy for improving functional "
"connectivity mapping using multiecho fMRI. Proceedings "
"of the National Academy of Sciences, 110(40), "
"16187-16192."]
if source_tes == -1:
dat_str = "the optimally combined data"
elif source_tes == 0:
dat_str = "the z-concatenated multi-echo data"
else:
dat_str = "a z-concatenated subset of echoes from the input data"
bp_str += (" Principal component analysis {0} was applied to "
"{1} for dimensionality reduction.").format(alg_str,
dat_str)
mmix_orig = decomposition.tedica(dd, n_components, fixed_seed,
maxit, maxrestart)
bp_str += (" Independent component analysis was then used to "
"decompose the dimensionally reduced dataset.")
if verbose:
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
if source_tes == -1:
io.filewrite(utils.unmask(dd, mask),
op.join(out_dir, 'ts_OC_whitened.nii'), ref_img)
LGR.info('Making second component selection guess from ICA results')
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd, data_oc, mmix_orig, t2s, tes,
ref_img, reindex=True, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
bp_str += (" A series of TE-dependence metrics were calculated for "
"each ICA component, including Kappa, Rho, and variance "
"explained.")
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
bp_str += (" Next, component selection was performed to identify "
"BOLD (TE-dependent), non-BOLD (TE-independent), and "
"uncertain (low-variance) components using the Kundu "
"decision tree (v2.5; Kundu et al., 2013).")
refs += ["Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., "
"Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. "
"(2013). Integrated strategy for improving functional "
"connectivity mapping using multiecho fMRI. Proceedings "
"of the National Academy of Sciences, 110(40), "
"16187-16192."]
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D'))
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd, data_oc, mmix_orig, t2s, tes,
ref_img, label='meica_', out_dir=out_dir,
algorithm='kundu_v2', verbose=verbose)
if ctab is None:
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
bp_str += (" Next, component selection was performed to identify "
"BOLD (TE-dependent), non-BOLD (TE-independent), and "
"uncertain (low-variance) components using the Kundu "
"decision tree (v2.5; Kundu et al., 2013).")
refs += ["Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., "
"Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. "
"(2013). Integrated strategy for improving functional "
"connectivity mapping using multiecho fMRI. Proceedings "
"of the National Academy of Sciences, 110(40), "
"16187-16192."]
else:
comptable = pd.read_csv(ctab, sep='\t', index_col='component')
comptable = selection.manual_selection(comptable, acc=manacc)
bp_str += (" Next, components were manually classified as "
"BOLD (TE-dependent), non-BOLD (TE-independent), or "
"uncertain (low-variance).")
comptable.to_csv(op.join(out_dir, 'comp_table_ica.txt'), sep='\t',
index=True, index_label='component', float_format='%.6f')
if comptable[comptable.classification == 'accepted'].shape[0] == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
mmix_orig = mmix.copy()
if tedort:
acc_idx = comptable.loc[
~comptable.classification.str.contains('rejected')].index.values
rej_idx = comptable.loc[
comptable.classification.str.contains('rejected')].index.values
acc_ts = mmix[:, acc_idx]
rej_ts = mmix[:, rej_idx]
betas = np.linalg.lstsq(acc_ts, rej_ts, rcond=None)[0]
pred_rej_ts = np.dot(acc_ts, betas)
resid = rej_ts - pred_rej_ts
mmix[:, rej_idx] = resid
np.savetxt(op.join(out_dir, 'meica_mix_orth.1D'), mmix)
bp_str += (" Rejected components' time series were then "
"orthogonalized with respect to accepted components' time "
"series.")
io.writeresults(data_oc, mask=mask, comptable=comptable, mmix=mmix,
n_vols=n_vols, ref_img=ref_img)
if 't1c' in gscontrol:
LGR.info('Performing T1c global signal regression to remove spatially '
'diffuse noise')
gsc.gscontrol_mmix(data_oc, mmix, mask, comptable, ref_img)
bp_str += (" T1c global signal regression was then applied to the "
"data in order to remove spatially diffuse noise.")
if verbose:
io.writeresults_echoes(catd, mmix, mask, comptable, ref_img)
if png:
LGR.info('Making figures folder with static component maps and '
'timecourse plots.')
# make figure folder first
if not op.isdir(op.join(out_dir, 'figures')):
os.mkdir(op.join(out_dir, 'figures'))
viz.write_comp_figs(data_oc, mask=mask, comptable=comptable,
mmix=mmix_orig, ref_img=ref_img,
out_dir=op.join(out_dir, 'figures'),
png_cmap=png_cmap)
LGR.info('Making Kappa vs Rho scatter plot')
viz.write_kappa_scatter(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Making overall summary figure')
viz.write_summary_fig(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Workflow completed')
bp_str += ("\n\nThis workflow used numpy (Van Der Walt, Colbert, & "
"Varoquaux, 2011), scipy (Jones et al., 2001), pandas "
"(McKinney, 2010), scikit-learn (Pedregosa et al., 2011), "
"nilearn, and nibabel (Brett et al., 2019).")
refs += ["Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The "
"NumPy array: a structure for efficient numerical computation. "
"Computing in Science & Engineering, 13(2), 22.",
"Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source "
"Scientific Tools for Python, 2001-, http://www.scipy.org/",
"McKinney, W. (2010, June). Data structures for statistical "
"computing in python. In Proceedings of the 9th Python in "
"Science Conference (Vol. 445, pp. 51-56).",
"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., "
"Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). "
"Scikit-learn: Machine learning in Python. Journal of machine "
"learning research, 12(Oct), 2825-2830.",
"Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., "
"Cipollini, B., McCarthy, P., … freec84. (2019, May 28). "
"nipy/nibabel. Zenodo. http://doi.org/10.5281/zenodo.3233118"]
bp_str += ("\n\nThis workflow also used the Dice similarity index "
"(Dice, 1945; Sørensen, 1948).")
refs += ["Dice, L. R. (1945). Measures of the amount of ecologic "
"association between species. Ecology, 26(3), 297-302.",
"Sørensen, T. J. (1948). A method of establishing groups of "
"equal amplitude in plant sociology based on similarity of "
"species content and its application to analyses of the "
"vegetation on Danish commons. I kommission hos E. Munksgaard."]
bp_str += '\n\nReferences\n\n'
refs = sorted(list(set(refs)))
bp_str += '\n\n'.join(refs)
with open(repname, 'w') as fo:
fo.write(bp_str)
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
def tedana_workflow(data,
tes,
out_dir='.',
mask=None,
fittype='loglin',
combmode='t2s',
tedpca='mdl',
fixed_seed=42,
maxit=500,
maxrestart=10,
tedort=False,
gscontrol=None,
no_png=False,
png_cmap='coolwarm',
verbose=False,
low_mem=False,
debug=False,
quiet=False,
t2smap=None,
mixm=None,
ctab=None,
manacc=None):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list or str) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
out_dir : :obj:`str`, optional
Output directory.
mask : :obj:`str` or None, optional
Binary mask of voxels to include in TE Dependent ANAlysis. Must be
spatially aligned with `data`. If an explicit mask is not provided,
then Nilearn's compute_epi_mask function will be used to derive a mask
from the first echo's data.
fittype : {'loglin', 'curvefit'}, optional
Monoexponential fitting method. 'loglin' uses the the default linear
fit to the log of the data. 'curvefit' uses a monoexponential fit to
the raw data, which is slightly slower but may be more accurate.
Default is 'loglin'.
combmode : {'t2s'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default).
tedpca : {'kundu', 'kundu-stabilize', 'mdl', 'aic', 'kic'}, optional
Method with which to select components in TEDPCA. Default is 'mdl'.
tedort : :obj:`bool`, optional
Orthogonalize rejected components w.r.t. accepted ones prior to
denoising. Default is False.
gscontrol : {None, 't1c', 'gsr'} or :obj:`list`, optional
Perform additional denoising to remove spatially diffuse noise. Default
is None.
verbose : :obj:`bool`, optional
Generate intermediate and additional files. Default is False.
no_png : obj:'bool', optional
Do not generate .png plots and figures. Default is false.
png_cmap : obj:'str', optional
Name of a matplotlib colormap to be used when generating figures.
Cannot be used with --no-png. Default is 'coolwarm'.
t2smap : :obj:`str`, optional
Precalculated T2* map in the same space as the input data.
mixm : :obj:`str` or None, optional
File containing mixing matrix, to be used when re-running the workflow.
If not provided, ME-PCA and ME-ICA are done. Default is None.
ctab : :obj:`str` or None, optional
File containing component table from which to extract pre-computed
classifications, to be used with 'mixm' when re-running the workflow.
Default is None.
manacc : :obj:`list`, :obj:`str`, or None, optional
List of manually accepted components. Can be a list of the components,
a comma-separated string with component numbers, or None. Default is
None.
Other Parameters
----------------
fixed_seed : :obj:`int`, optional
Value passed to ``mdp.numx_rand.seed()``.
Set to a positive integer value for reproducible ICA results;
otherwise, set to -1 for varying results across calls.
maxit : :obj:`int`, optional
Maximum number of iterations for ICA. Default is 500.
maxrestart : :obj:`int`, optional
Maximum number of attempts for ICA. If ICA fails to converge, the
fixed seed will be updated and ICA will be run again. If convergence
is achieved before maxrestart attempts, ICA will finish early.
Default is 10.
low_mem : :obj:`bool`, optional
Enables low-memory processing, including the use of IncrementalPCA.
May increase workflow duration. Default is False.
debug : :obj:`bool`, optional
Whether to run in debugging mode or not. Default is False.
quiet : :obj:`bool`, optional
If True, suppresses logging/printing of messages. Default is False.
Notes
-----
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
# boilerplate
basename = 'report'
extension = 'txt'
repname = op.join(out_dir, (basename + '.' + extension))
repex = op.join(out_dir, (basename + '*'))
previousreps = glob(repex)
previousreps.sort(reverse=True)
for f in previousreps:
previousparts = op.splitext(f)
newname = previousparts[0] + '_old' + previousparts[1]
os.rename(f, newname)
refname = op.join(out_dir, '_references.txt')
# create logfile name
basename = 'tedana_'
extension = 'tsv'
start_time = datetime.datetime.now().strftime('%Y-%m-%dT%H%M%S')
logname = op.join(out_dir, (basename + start_time + '.' + extension))
# set logging format
log_formatter = logging.Formatter(
'%(asctime)s\t%(name)-12s\t%(levelname)-8s\t%(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
text_formatter = logging.Formatter('%(message)s')
# set up logging file and open it for writing
log_handler = logging.FileHandler(logname)
log_handler.setFormatter(log_formatter)
# Removing handlers after basicConfig doesn't work, so we use filters
# for the relevant handlers themselves.
log_handler.addFilter(ContextFilter())
sh = logging.StreamHandler()
sh.addFilter(ContextFilter())
if quiet:
logging.basicConfig(level=logging.WARNING, handlers=[log_handler, sh])
elif debug:
logging.basicConfig(level=logging.DEBUG, handlers=[log_handler, sh])
else:
logging.basicConfig(level=logging.INFO, handlers=[log_handler, sh])
# Loggers for report and references
rep_handler = logging.FileHandler(repname)
rep_handler.setFormatter(text_formatter)
ref_handler = logging.FileHandler(refname)
ref_handler.setFormatter(text_formatter)
RepLGR.setLevel(logging.INFO)
RepLGR.addHandler(rep_handler)
RepLGR.setLevel(logging.INFO)
RefLGR.addHandler(ref_handler)
LGR.info('Using output directory: {}'.format(out_dir))
# ensure tes are in appropriate format
tes = [float(te) for te in tes]
n_echos = len(tes)
# Coerce gscontrol to list
if not isinstance(gscontrol, list):
gscontrol = [gscontrol]
LGR.info('Loading input data: {}'.format([f for f in data]))
catd, ref_img = io.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if no_png and (png_cmap != 'coolwarm'):
LGR.warning('Overriding --no-png since --png-cmap provided.')
no_png = False
# check if TR is 0
img_t_r = ref_img.header.get_zooms()[-1]
if img_t_r == 0 and not no_png:
raise IOError(
'Dataset has a TR of 0. This indicates incorrect'
' header information. To correct this, we recommend'
' using this snippet:'
'\n'
'https://gist.github.com/jbteves/032c87aeb080dd8de8861cb151bff5d6'
'\n'
'to correct your TR to the value it should be.')
if mixm is not None and op.isfile(mixm):
mixm = op.abspath(mixm)
# Allow users to re-run on same folder
if mixm != op.join(out_dir, 'ica_mixing.tsv'):
shutil.copyfile(mixm, op.join(out_dir, 'ica_mixing.tsv'))
shutil.copyfile(mixm, op.join(out_dir, op.basename(mixm)))
elif mixm is not None:
raise IOError('Argument "mixm" must be an existing file.')
if ctab is not None and op.isfile(ctab):
ctab = op.abspath(ctab)
# Allow users to re-run on same folder
if ctab != op.join(out_dir, 'ica_decomposition.json'):
shutil.copyfile(ctab, op.join(out_dir, 'ica_decomposition.json'))
shutil.copyfile(ctab, op.join(out_dir, op.basename(ctab)))
elif ctab is not None:
raise IOError('Argument "ctab" must be an existing file.')
if isinstance(manacc, str):
manacc = [int(comp) for comp in manacc.split(',')]
if ctab and not mixm:
LGR.warning('Argument "ctab" requires argument "mixm".')
ctab = None
elif manacc is not None and not mixm:
LGR.warning('Argument "manacc" requires argument "mixm".')
manacc = None
if t2smap is not None and op.isfile(t2smap):
t2smap = op.abspath(t2smap)
# Allow users to re-run on same folder
if t2smap != op.join(out_dir, 't2sv.nii.gz'):
shutil.copyfile(t2smap, op.join(out_dir, 't2sv.nii.gz'))
shutil.copyfile(t2smap, op.join(out_dir, op.basename(t2smap)))
elif t2smap is not None:
raise IOError('Argument "t2smap" must be an existing file.')
RepLGR.info("TE-dependence analysis was performed on input data.")
if mask and not t2smap:
# TODO: add affine check
LGR.info('Using user-defined mask')
RepLGR.info("A user-defined mask was applied to the data.")
elif t2smap and not mask:
LGR.info('Using user-defined T2* map to generate mask')
t2s_limited = utils.load_image(t2smap)
t2s_full = t2s_limited.copy()
mask = (t2s_limited != 0).astype(int)
elif t2smap and mask:
LGR.info('Combining user-defined mask and T2* map to generate mask')
t2s_limited = utils.load_image(t2smap)
t2s_full = t2s_limited.copy()
mask = utils.load_image(mask)
mask[t2s_limited == 0] = 0 # reduce mask based on T2* map
else:
LGR.info('Computing EPI mask from first echo')
first_echo_img = io.new_nii_like(ref_img, catd[:, 0, :])
mask = compute_epi_mask(first_echo_img)
RepLGR.info("An initial mask was generated from the first echo using "
"nilearn's compute_epi_mask function.")
mask, masksum = utils.make_adaptive_mask(catd, mask=mask, getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)
if t2smap is None:
LGR.info('Computing T2* map')
t2s_limited, s0_limited, t2s_full, s0_full = decay.fit_decay(
catd, tes, mask, masksum, fittype)
# set a hard cap for the T2* map
# anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(),
99.5,
interpolation_method='lower')
LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)
if verbose:
io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
data_oc = combine.make_optcom(catd,
tes,
mask,
t2s=t2s_full,
combmode=combmode)
# regress out global signal unless explicitly not desired
if 'gsr' in gscontrol:
catd, data_oc = gsc.gscontrol_raw(catd,
data_oc,
n_echos,
ref_img,
out_dir=out_dir)
if mixm is None:
# Identify and remove thermal noise from data
dd, n_components = decomposition.tedpca(catd,
data_oc,
combmode,
mask,
t2s_limited,
t2s_full,
ref_img,
tes=tes,
algorithm=tedpca,
kdaw=10.,
rdaw=1.,
out_dir=out_dir,
verbose=verbose,
low_mem=low_mem)
mmix_orig = decomposition.tedica(dd, n_components, fixed_seed, maxit,
maxrestart)
if verbose:
io.filewrite(utils.unmask(dd, mask),
op.join(out_dir, 'ts_OC_whitened.nii.gz'), ref_img)
LGR.info('Making second component selection guess from ICA results')
# Estimate betas and compute selection metrics for mixing matrix
# generated from dimensionally reduced data using full data (i.e., data
# with thermal noise)
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd,
data_oc,
mmix_orig,
t2s_limited,
tes,
ref_img,
reindex=True,
label='meica_',
out_dir=out_dir,
algorithm='kundu_v2',
verbose=verbose)
comp_names = [
io.add_decomp_prefix(comp,
prefix='ica',
max_value=comptable.index.max())
for comp in comptable.index.values
]
mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
mixing_df.to_csv(op.join(out_dir, 'ica_mixing.tsv'),
sep='\t',
index=False)
betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask), mask)
io.filewrite(betas_oc, op.join(out_dir, 'ica_components.nii.gz'),
ref_img)
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
else:
LGR.info('Using supplied mixing matrix from ICA')
mmix_orig = pd.read_table(op.join(out_dir, 'ica_mixing.tsv')).values
if ctab is None:
comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
catd,
data_oc,
mmix_orig,
t2s_limited,
tes,
ref_img,
label='meica_',
out_dir=out_dir,
algorithm='kundu_v2',
verbose=verbose)
comptable = metrics.kundu_metrics(comptable, metric_maps)
comptable = selection.kundu_selection_v2(comptable, n_echos,
n_vols)
else:
mmix = mmix_orig.copy()
comptable = io.load_comptable(ctab)
if manacc is not None:
comptable = selection.manual_selection(comptable, acc=manacc)
betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask), mask)
io.filewrite(betas_oc, op.join(out_dir, 'ica_components.nii.gz'),
ref_img)
# Save decomposition
comptable[
'Description'] = 'ICA fit to dimensionally-reduced optimally combined data.'
mmix_dict = {}
mmix_dict['Method'] = ('Independent components analysis with FastICA '
'algorithm implemented by sklearn. Components '
'are sorted by Kappa in descending order. '
'Component signs are flipped to best match the '
'data.')
io.save_comptable(comptable,
op.join(out_dir, 'ica_decomposition.json'),
label='ica',
metadata=mmix_dict)
if comptable[comptable.classification == 'accepted'].shape[0] == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
mmix_orig = mmix.copy()
if tedort:
acc_idx = comptable.loc[~comptable.classification.str.
contains('rejected')].index.values
rej_idx = comptable.loc[comptable.classification.str.contains(
'rejected')].index.values
acc_ts = mmix[:, acc_idx]
rej_ts = mmix[:, rej_idx]
betas = np.linalg.lstsq(acc_ts, rej_ts, rcond=None)[0]
pred_rej_ts = np.dot(acc_ts, betas)
resid = rej_ts - pred_rej_ts
mmix[:, rej_idx] = resid
comp_names = [
io.add_decomp_prefix(comp,
prefix='ica',
max_value=comptable.index.max())
for comp in comptable.index.values
]
mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
mixing_df.to_csv(op.join(out_dir, 'ica_orth_mixing.tsv'),
sep='\t',
index=False)
RepLGR.info("Rejected components' time series were then "
"orthogonalized with respect to accepted components' time "
"series.")
io.writeresults(data_oc,
mask=mask,
comptable=comptable,
mmix=mmix,
n_vols=n_vols,
ref_img=ref_img,
out_dir=out_dir)
if 't1c' in gscontrol:
gsc.gscontrol_mmix(data_oc,
mmix,
mask,
comptable,
ref_img,
out_dir=out_dir)
if verbose:
io.writeresults_echoes(catd,
mmix,
mask,
comptable,
ref_img,
out_dir=out_dir)
if not no_png:
LGR.info('Making figures folder with static component maps and '
'timecourse plots.')
# make figure folder first
if not op.isdir(op.join(out_dir, 'figures')):
os.mkdir(op.join(out_dir, 'figures'))
viz.write_comp_figs(data_oc,
mask=mask,
comptable=comptable,
mmix=mmix_orig,
ref_img=ref_img,
out_dir=op.join(out_dir, 'figures'),
png_cmap=png_cmap)
LGR.info('Making Kappa vs Rho scatter plot')
viz.write_kappa_scatter(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Making Kappa/Rho scree plot')
viz.write_kappa_scree(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Making overall summary figure')
viz.write_summary_fig(comptable=comptable,
out_dir=op.join(out_dir, 'figures'))
LGR.info('Workflow completed')
RepLGR.info("This workflow used numpy (Van Der Walt, Colbert, & "
"Varoquaux, 2011), scipy (Jones et al., 2001), pandas "
"(McKinney, 2010), scikit-learn (Pedregosa et al., 2011), "
"nilearn, and nibabel (Brett et al., 2019).")
RefLGR.info(
"Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The "
"NumPy array: a structure for efficient numerical computation. "
"Computing in Science & Engineering, 13(2), 22.")
RefLGR.info("Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source "
"Scientific Tools for Python, 2001-, http://www.scipy.org/")
RefLGR.info("McKinney, W. (2010, June). Data structures for statistical "
"computing in python. In Proceedings of the 9th Python in "
"Science Conference (Vol. 445, pp. 51-56).")
RefLGR.info("Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., "
"Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). "
"Scikit-learn: Machine learning in Python. Journal of machine "
"learning research, 12(Oct), 2825-2830.")
RefLGR.info("Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., "
"Cipollini, B., McCarthy, P., … freec84. (2019, May 28). "
"nipy/nibabel. Zenodo. http://doi.org/10.5281/zenodo.3233118")
RepLGR.info("This workflow also used the Dice similarity index "
"(Dice, 1945; Sørensen, 1948).")
RefLGR.info("Dice, L. R. (1945). Measures of the amount of ecologic "
"association between species. Ecology, 26(3), 297-302.")
RefLGR.info(
"Sørensen, T. J. (1948). A method of establishing groups of "
"equal amplitude in plant sociology based on similarity of "
"species content and its application to analyses of the "
"vegetation on Danish commons. I kommission hos E. Munksgaard.")
with open(repname, 'r') as fo:
report = [line.rstrip() for line in fo.readlines()]
report = ' '.join(report)
with open(refname, 'r') as fo:
reference_list = sorted(list(set(fo.readlines())))
references = '\n'.join(reference_list)
report += '\n\nReferences\n' + references
with open(repname, 'w') as fo:
fo.write(report)
os.remove(refname)
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)