def t2smap(data, tes, combmode='t2s', label=None):
"""
Estimate T2 and S0, and optimally combine data across TEs.
Parameters
----------
data : :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
combmode : {'t2s', 'ste'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'ste' (Poser).
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
"""
if label is not None:
suf = '_%s' % str(label)
else:
suf = ''
tes, data, combmode = tes, data, combmode
tes = [float(te) for te in tes]
n_echos = len(tes)
catd = utils.load_data(data, n_echos=n_echos)
_, n_echos, _ = catd.shape
ref_img = data[0] if isinstance(data, list) else data
LGR.info('Computing adaptive mask')
mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True)
utils.filewrite(masksum, 'masksum%s' % suf, ref_img, copy_header=False)
LGR.info('Computing adaptive T2* map')
t2s, s0, t2ss, s0vs, _, _ = model.t2sadmap(catd, tes, mask, masksum, 2)
utils.filewrite(t2ss, 't2ss%s' % suf, ref_img, copy_header=False)
utils.filewrite(s0vs, 's0vs%s' % suf, ref_img, copy_header=False)
LGR.info('Computing optimal combination')
tsoc = np.array(model.make_optcom(catd, t2s, tes, mask, combmode),
dtype=float)
# Clean up numerical errors
t2sm = t2s.copy()
for n in (tsoc, s0, t2s, t2sm):
np.nan_to_num(n, copy=False)
s0[s0 < 0] = 0
t2s[t2s < 0] = 0
t2sm[t2sm < 0] = 0
utils.filewrite(tsoc, 'ocv%s' % suf, ref_img, copy_header=False)
utils.filewrite(s0, 's0v%s' % suf, ref_img, copy_header=False)
utils.filewrite(t2s, 't2sv%s' % suf, ref_img, copy_header=False)
utils.filewrite(t2sm, 't2svm%s' % suf, ref_img, copy_header=False)
def fitmodels_direct(catd,
mmix,
mask,
t2s,
t2sG,
tes,
combmode,
ref_img,
fout=None,
reindex=False,
mmixN=None,
full_sel=True):
"""
Fit models directly.
Parameters
----------
catd : (S x E x T) array_like
Input data, where `S` is samples, `E` is echos, and `T` is time
mmix : (T x C) array_like
Mixing matrix for converting input data to component space, where `C`
is components and `T` is the same as in `catd`
mask : (S,) array_like
Boolean mask array
t2s : (S,) array_like
t2sG : (S,) array_like
tes : list
List of echo times associated with `catd`, in milliseconds
combmode : {'t2s', 'ste'} str
How optimal combination of echos should be made, where 't2s' indicates
using the method of Posse 1999 and 'ste' indicates using the method of
Poser 2006
ref_img : str or img_like
Reference image to dictate how outputs are saved to disk
fout : bool
Whether to output per-component TE-dependence maps. Default: None
reindex : bool, optional
Default: False
mmixN : array_like, optional
Default: None
full_sel : bool, optional
Whether to perform selection of components based on Rho/Kappa scores.
Default: True
Returns
-------
seldict : dict
comptab : (N x 5) :obj:`numpy.ndarray`
Array with columns denoting (1) index of component, (2) Kappa score of
component, (3) Rho score of component, (4) variance explained by
component, and (5) normalized variance explained bycomponent
betas : :obj:`numpy.ndarray`
mmix_new : :obj:`numpy.ndarray`
"""
# compute optimal combination of raw data
tsoc = model.make_optcom(catd, t2sG, tes, mask, combmode,
verbose=False).astype(float)[mask]
# demean optimal combination
tsoc_dm = tsoc - tsoc.mean(axis=-1, keepdims=True)
# compute un-normalized weight dataset (features)
if mmixN is None:
mmixN = mmix
WTS = computefeats2(utils.unmask(tsoc, mask), mmixN, mask, normalize=False)
# compute PSC dataset - shouldn't have to refit data
tsoc_B = get_coeffs(utils.unmask(tsoc_dm, mask), mask, mmix)[mask]
tsoc_Babs = np.abs(tsoc_B)
PSC = tsoc_B / tsoc.mean(axis=-1, keepdims=True) * 100
# compute skews to determine signs based on unnormalized weights,
# correct mmix & WTS signs based on spatial distribution tails
signs = stats.skew(WTS, axis=0)
signs /= np.abs(signs)
mmix = mmix.copy()
mmix *= signs
WTS *= signs
PSC *= signs
totvar = (tsoc_B**2).sum()
totvar_norm = (WTS**2).sum()
# compute Betas and means over TEs for TE-dependence analysis
betas = get_coeffs(catd, np.repeat(mask[:, np.newaxis], len(tes), axis=1),
mmix)
n_samp, n_echos, n_components = betas.shape
n_voxels = mask.sum()
n_data_voxels = (t2s != 0).sum()
mu = catd.mean(axis=-1, dtype=float)
tes = np.reshape(tes, (n_echos, 1))
fmin, fmid, fmax = utils.getfbounds(n_echos)
# mask arrays
mumask = mu[t2s != 0]
t2smask = t2s[t2s != 0]
betamask = betas[t2s != 0]
# set up Xmats
X1 = mumask.T # Model 1
X2 = np.tile(tes, (1, n_data_voxels)) * mumask.T / t2smask.T # Model 2
# tables for component selection
Kappas = np.zeros([n_components])
Rhos = np.zeros([n_components])
varex = np.zeros([n_components])
varex_norm = np.zeros([n_components])
Z_maps = np.zeros([n_voxels, n_components])
F_R2_maps = np.zeros([n_data_voxels, n_components])
F_S0_maps = np.zeros([n_data_voxels, n_components])
Z_clmaps = np.zeros([n_voxels, n_components])
F_R2_clmaps = np.zeros([n_data_voxels, n_components])
F_S0_clmaps = np.zeros([n_data_voxels, n_components])
Br_clmaps_R2 = np.zeros([n_voxels, n_components])
Br_clmaps_S0 = np.zeros([n_voxels, n_components])
LGR.info('Fitting TE- and S0-dependent models to components')
for i in range(n_components):
# size of B is (n_components, nx*ny*nz)
B = np.atleast_3d(betamask)[:, :, i].T
alpha = (np.abs(B)**2).sum(axis=0)
varex[i] = (tsoc_B[:, i]**2).sum() / totvar * 100.
varex_norm[i] = (utils.unmask(WTS, mask)[t2s != 0][:, i]**
2).sum() / totvar_norm * 100.
# S0 Model
coeffs_S0 = (B * X1).sum(axis=0) / (X1**2).sum(axis=0)
SSE_S0 = (B - X1 * np.tile(coeffs_S0, (n_echos, 1)))**2
SSE_S0 = SSE_S0.sum(axis=0)
F_S0 = (alpha - SSE_S0) * 2 / (SSE_S0)
F_S0_maps[:, i] = F_S0
# R2 Model
coeffs_R2 = (B * X2).sum(axis=0) / (X2**2).sum(axis=0)
SSE_R2 = (B - X2 * np.tile(coeffs_R2, (n_echos, 1)))**2
SSE_R2 = SSE_R2.sum(axis=0)
F_R2 = (alpha - SSE_R2) * 2 / (SSE_R2)
F_R2_maps[:, i] = F_R2
# compute weights as Z-values
wtsZ = (WTS[:, i] - WTS[:, i].mean()) / WTS[:, i].std()
wtsZ[np.abs(wtsZ) > Z_MAX] = (
Z_MAX * (np.abs(wtsZ) / wtsZ))[np.abs(wtsZ) > Z_MAX]
Z_maps[:, i] = wtsZ
# compute Kappa and Rho
F_S0[F_S0 > F_MAX] = F_MAX
F_R2[F_R2 > F_MAX] = F_MAX
norm_weights = np.abs(
np.squeeze(utils.unmask(wtsZ, mask)[t2s != 0]**2.))
Kappas[i] = np.average(F_R2, weights=norm_weights)
Rhos[i] = np.average(F_S0, weights=norm_weights)
# tabulate component values
comptab_pre = np.vstack(
[np.arange(n_components), Kappas, Rhos, varex, varex_norm]).T
if reindex:
# re-index all components in Kappa order
comptab = comptab_pre[comptab_pre[:, 1].argsort()[::-1], :]
Kappas = comptab[:, 1]
Rhos = comptab[:, 2]
varex = comptab[:, 3]
varex_norm = comptab[:, 4]
nnc = np.array(comptab[:, 0], dtype=np.int)
mmix_new = mmix[:, nnc]
F_S0_maps = F_S0_maps[:, nnc]
F_R2_maps = F_R2_maps[:, nnc]
Z_maps = Z_maps[:, nnc]
WTS = WTS[:, nnc]
PSC = PSC[:, nnc]
tsoc_B = tsoc_B[:, nnc]
tsoc_Babs = tsoc_Babs[:, nnc]
comptab[:, 0] = np.arange(comptab.shape[0])
else:
comptab = comptab_pre
mmix_new = mmix
# full selection including clustering criteria
seldict = None
if full_sel:
LGR.info('Performing spatial clustering of components')
csize = np.max([int(n_voxels * 0.0005) + 5, 20])
LGR.debug('Using minimum cluster size: {}'.format(csize))
for i in range(n_components):
# save out files
out = np.zeros((n_samp, 4))
out[:, 0] = np.squeeze(utils.unmask(PSC[:, i], mask))
out[:, 1] = np.squeeze(utils.unmask(F_R2_maps[:, i], t2s != 0))
out[:, 2] = np.squeeze(utils.unmask(F_S0_maps[:, i], t2s != 0))
out[:, 3] = np.squeeze(utils.unmask(Z_maps[:, i], mask))
if utils.get_dtype(ref_img) == 'GIFTI':
continue # TODO: pass through GIFTI file data as below
ccimg = utils.new_nii_like(ref_img, out)
# Do simple clustering on F
sel = spatclust(ccimg,
min_cluster_size=csize,
threshold=int(fmin),
index=[1, 2],
mask=(t2s != 0))
F_R2_clmaps[:, i] = sel[:, 0]
F_S0_clmaps[:, i] = sel[:, 1]
countsigFR2 = F_R2_clmaps[:, i].sum()
countsigFS0 = F_S0_clmaps[:, i].sum()
# Do simple clustering on Z at p<0.05
sel = spatclust(ccimg,
min_cluster_size=csize,
threshold=1.95,
index=3,
mask=mask)
Z_clmaps[:, i] = sel
# Do simple clustering on ranked signal-change map
spclust_input = utils.unmask(stats.rankdata(tsoc_Babs[:, i]), mask)
spclust_input = utils.new_nii_like(ref_img, spclust_input)
Br_clmaps_R2[:, i] = spatclust(spclust_input,
min_cluster_size=csize,
threshold=max(tsoc_Babs.shape) -
countsigFR2,
mask=mask)
Br_clmaps_S0[:, i] = spatclust(spclust_input,
min_cluster_size=csize,
threshold=max(tsoc_Babs.shape) -
countsigFS0,
mask=mask)
seldict = {}
selvars = [
'Kappas', 'Rhos', 'WTS', 'varex', 'Z_maps', 'F_R2_maps',
'F_S0_maps', 'Z_clmaps', 'F_R2_clmaps', 'F_S0_clmaps', 'tsoc_B',
'Br_clmaps_R2', 'Br_clmaps_S0', 'PSC'
]
for vv in selvars:
seldict[vv] = eval(vv)
return seldict, comptab, betas, mmix_new
def tedana(data,
tes,
mixm=None,
ctab=None,
manacc=None,
strict=False,
gscontrol=True,
kdaw=10.,
rdaw=1.,
conv=2.5e-5,
ste=-1,
combmode='t2s',
dne=False,
initcost='tanh',
finalcost='tanh',
stabilize=False,
fout=False,
filecsdata=False,
label=None,
fixed_seed=42,
debug=False,
quiet=False):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
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.
initcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Initial cost function for ICA. Default is 'tanh'.
finalcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Final cost function. Default is 'tanh'.
stabilize : :obj:`bool`, optional
Stabilize convergence by reducing dimensionality, for low quality data.
Default is False.
fout : :obj:`bool`, optional
Save output TE-dependence Kappa/Rho SPMs. Default is False.
filecsdata : :obj:`bool`, optional
Save component selection data to file. Default is False.
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
fixed_seed : :obj:`int`, optional
Seeded value for ICA, for reproducibility.
"""
# 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
LGR.info('Loading input data: {}'.format([op.abspath(f) for f in data]))
catd, ref_img = utils.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if fout:
fout = ref_img
else:
fout = None
kdaw, rdaw = float(kdaw), float(rdaw)
if label is not None:
out_dir = 'TED.{0}'.format(label)
else:
out_dir = 'TED'
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.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)
LGR.info('Computing adapative mask')
mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = model.fit_decay(catd,
tes,
mask,
masksum,
start_echo=1)
# 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
utils.filewrite(t2s, op.join(out_dir, 't2sv'), ref_img)
utils.filewrite(s0, op.join(out_dir, 's0v'), ref_img)
utils.filewrite(t2ss, op.join(out_dir, 't2ss'), ref_img)
utils.filewrite(s0s, op.join(out_dir, 's0vs'), ref_img)
utils.filewrite(t2sG, op.join(out_dir, 't2svG'), ref_img)
utils.filewrite(s0G, op.join(out_dir, 's0vG'), ref_img)
# optimally combine data
OCcatd = model.make_optcom(catd, t2sG, tes, mask, combmode)
# regress out global signal unless explicitly not desired
if gscontrol:
catd, OCcatd = model.gscontrol_raw(catd, OCcatd, n_echos, ref_img)
if mixm is None:
n_components, dd = decomposition.tedpca(catd,
OCcatd,
combmode,
mask,
t2s,
t2sG,
stabilize,
ref_img,
tes=tes,
kdaw=kdaw,
rdaw=rdaw,
ste=ste)
mmix_orig = decomposition.tedica(n_components,
dd,
conv,
fixed_seed,
cost=initcost,
final_cost=finalcost,
verbose=debug)
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
LGR.info('Making second component selection guess from ICA results')
seldict, comptable, betas, mmix = model.fitmodels_direct(catd,
mmix_orig,
mask,
t2s,
t2sG,
tes,
combmode,
ref_img,
fout=fout,
reindex=True)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
acc, rej, midk, empty = selection.selcomps(seldict,
mmix,
mask,
ref_img,
manacc,
n_echos,
t2s,
s0,
strict_mode=strict,
filecsdata=filecsdata)
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,
fout=fout)
if ctab is None:
acc, rej, midk, empty = selection.selcomps(seldict,
mmix,
mask,
ref_img,
manacc,
n_echos,
t2s,
s0,
filecsdata=filecsdata,
strict_mode=strict)
else:
acc, rej, midk, empty = utils.ctabsel(ctab)
if len(acc) == 0:
LGR.warning(
'No BOLD components detected! Please check data and results!')
utils.writeresults(OCcatd, mask, comptable, mmix, n_vols, acc, rej, midk,
empty, ref_img)
utils.gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img)
if dne:
utils.writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img)
def t2smap_workflow(data, tes, fitmode='all', combmode='t2s', label=None):
"""
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.
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', 'ste'}, optional
Combination scheme for TEs: 't2s' (Posse 1999, default), 'ste' (Poser).
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
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 = utils.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 = os.path.basename(ref_img).split('.')[0]
except TypeError:
ref_label = os.path.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))
LGR.info('Computing adaptive mask')
mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True)
LGR.info('Computing adaptive T2* map')
if fitmode == 'all':
(t2s_limited, s0_limited,
t2ss, s0s,
t2s_full, s0_full) = model.fit_decay(catd, tes, mask,
masksum, start_echo=1)
else:
(t2s_limited, s0_limited,
t2s_full, s0_full) = model.fit_decay_ts(catd, tes, mask, masksum,
start_echo=1)
# 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 = model.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
utils.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
utils.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)
utils.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
utils.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)
utils.filewrite(OCcatd, op.join(out_dir, 'ts_OC.nii'), ref_img)
def tedana(data, tes, mixm=None, ctab=None, manacc=None, strict=False,
gscontrol=True, kdaw=10., rdaw=1., conv=2.5e-5, ste=-1,
combmode='t2s', dne=False, initcost='tanh', finalcost='tanh',
stabilize=False, fout=False, filecsdata=False, label=None,
fixed_seed=42, debug=False, quiet=False):
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
Parameters
----------
data : :obj:`list` of :obj:`str`
Either a single z-concatenated file (single-entry list) or a
list of echo-specific files, in ascending order.
tes : :obj:`list`
List of echo times associated with data in milliseconds.
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.
initcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Initial cost function for ICA. Default is 'tanh'.
finalcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Final cost function. Default is 'tanh'.
stabilize : :obj:`bool`, optional
Stabilize convergence by reducing dimensionality, for low quality data.
Default is False.
fout : :obj:`bool`, optional
Save output TE-dependence Kappa/Rho SPMs. Default is False.
filecsdata : :obj:`bool`, optional
Save component selection data to file. Default is False.
label : :obj:`str` or :obj:`None`, optional
Label for output directory. Default is None.
fixed_seed : :obj:`int`, optional
Seeded value for ICA, for reproducibility.
"""
# 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
LGR.info('Loading input data: {}'.format([op.abspath(f) for f in data]))
catd, ref_img = utils.load_data(data, n_echos=n_echos)
n_samp, n_echos, n_vols = catd.shape
LGR.debug('Resulting data shape: {}'.format(catd.shape))
if fout:
fout = ref_img
else:
fout = None
kdaw, rdaw = float(kdaw), float(rdaw)
if label is not None:
out_dir = 'TED.{0}'.format(label)
else:
out_dir = 'TED'
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.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)
LGR.info('Computing adapative mask')
mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True)
LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
LGR.info('Computing T2* map')
t2s, s0, t2ss, s0s, t2sG, s0G = model.fit_decay(catd, tes, mask, masksum,
start_echo=1)
# 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
utils.filewrite(t2s, op.join(out_dir, 't2sv'), ref_img)
utils.filewrite(s0, op.join(out_dir, 's0v'), ref_img)
utils.filewrite(t2ss, op.join(out_dir, 't2ss'), ref_img)
utils.filewrite(s0s, op.join(out_dir, 's0vs'), ref_img)
utils.filewrite(t2sG, op.join(out_dir, 't2svG'), ref_img)
utils.filewrite(s0G, op.join(out_dir, 's0vG'), ref_img)
# optimally combine data
OCcatd = model.make_optcom(catd, t2sG, tes, mask, combmode)
# regress out global signal unless explicitly not desired
if gscontrol:
catd, OCcatd = model.gscontrol_raw(catd, OCcatd, n_echos, ref_img)
if mixm is None:
n_components, dd = decomposition.tedpca(catd, OCcatd, combmode, mask, t2s, t2sG,
stabilize, ref_img,
tes=tes, kdaw=kdaw, rdaw=rdaw, ste=ste)
mmix_orig = decomposition.tedica(n_components, dd, conv, fixed_seed, cost=initcost,
final_cost=finalcost, verbose=debug)
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
LGR.info('Making second component selection guess from ICA results')
seldict, comptable, betas, mmix = model.fitmodels_direct(catd, mmix_orig,
mask, t2s, t2sG,
tes, combmode,
ref_img,
fout=fout,
reindex=True)
np.savetxt(op.join(out_dir, 'meica_mix.1D'), mmix)
acc, rej, midk, empty = selection.selcomps(seldict, mmix, mask, ref_img, manacc,
n_echos, t2s, s0, strict_mode=strict,
filecsdata=filecsdata)
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,
fout=fout)
if ctab is None:
acc, rej, midk, empty = selection.selcomps(seldict, mmix, mask,
ref_img, manacc,
n_echos, t2s, s0,
filecsdata=filecsdata,
strict_mode=strict)
else:
acc, rej, midk, empty = utils.ctabsel(ctab)
if len(acc) == 0:
LGR.warning('No BOLD components detected! Please check data and results!')
utils.writeresults(OCcatd, mask, comptable, mmix, n_vols, acc, rej, midk, empty, ref_img)
utils.gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img)
if dne:
utils.writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img)
def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img,
fout=None, reindex=False, mmixN=None, full_sel=True):
"""
Fit models directly.
Parameters
----------
catd : (S x E x T) array_like
Input data, where `S` is samples, `E` is echos, and `T` is time
mmix : (T x C) array_like
Mixing matrix for converting input data to component space, where `C`
is components and `T` is the same as in `catd`
mask : (S,) array_like
Boolean mask array
t2s : (S,) array_like
t2sG : (S,) array_like
tes : list
List of echo times associated with `catd`, in milliseconds
combmode : {'t2s', 'ste'} str
How optimal combination of echos should be made, where 't2s' indicates
using the method of Posse 1999 and 'ste' indicates using the method of
Poser 2006
ref_img : str or img_like
Reference image to dictate how outputs are saved to disk
fout : bool
Whether to output per-component TE-dependence maps. Default: None
reindex : bool, optional
Default: False
mmixN : array_like, optional
Default: None
full_sel : bool, optional
Whether to perform selection of components based on Rho/Kappa scores.
Default: True
Returns
-------
seldict : dict
comptab : (N x 5) :obj:`numpy.ndarray`
Array with columns denoting (1) index of component, (2) Kappa score of
component, (3) Rho score of component, (4) variance explained by
component, and (5) normalized variance explained bycomponent
betas : :obj:`numpy.ndarray`
mmix_new : :obj:`numpy.ndarray`
"""
# compute optimal combination of raw data
tsoc = model.make_optcom(catd, t2sG, tes, mask, combmode, verbose=False).astype(float)[mask]
# demean optimal combination
tsoc_dm = tsoc - tsoc.mean(axis=-1, keepdims=True)
# compute un-normalized weight dataset (features)
if mmixN is None:
mmixN = mmix
WTS = computefeats2(utils.unmask(tsoc, mask), mmixN, mask, normalize=False)
# compute PSC dataset - shouldn't have to refit data
tsoc_B = get_coeffs(utils.unmask(tsoc_dm, mask), mask, mmix)[mask]
tsoc_Babs = np.abs(tsoc_B)
PSC = tsoc_B / tsoc.mean(axis=-1, keepdims=True) * 100
# compute skews to determine signs based on unnormalized weights,
# correct mmix & WTS signs based on spatial distribution tails
signs = stats.skew(WTS, axis=0)
signs /= np.abs(signs)
mmix = mmix.copy()
mmix *= signs
WTS *= signs
PSC *= signs
totvar = (tsoc_B**2).sum()
totvar_norm = (WTS**2).sum()
# compute Betas and means over TEs for TE-dependence analysis
betas = get_coeffs(catd, np.repeat(mask[:, np.newaxis], len(tes), axis=1), mmix)
n_samp, n_echos, n_components = betas.shape
n_voxels = mask.sum()
n_data_voxels = (t2s != 0).sum()
mu = catd.mean(axis=-1, dtype=float)
tes = np.reshape(tes, (n_echos, 1))
fmin, fmid, fmax = utils.getfbounds(n_echos)
# mask arrays
mumask = mu[t2s != 0]
t2smask = t2s[t2s != 0]
betamask = betas[t2s != 0]
# set up Xmats
X1 = mumask.T # Model 1
X2 = np.tile(tes, (1, n_data_voxels)) * mumask.T / t2smask.T # Model 2
# tables for component selection
Kappas = np.zeros([n_components])
Rhos = np.zeros([n_components])
varex = np.zeros([n_components])
varex_norm = np.zeros([n_components])
Z_maps = np.zeros([n_voxels, n_components])
F_R2_maps = np.zeros([n_data_voxels, n_components])
F_S0_maps = np.zeros([n_data_voxels, n_components])
Z_clmaps = np.zeros([n_voxels, n_components])
F_R2_clmaps = np.zeros([n_data_voxels, n_components])
F_S0_clmaps = np.zeros([n_data_voxels, n_components])
Br_clmaps_R2 = np.zeros([n_voxels, n_components])
Br_clmaps_S0 = np.zeros([n_voxels, n_components])
LGR.info('Fitting TE- and S0-dependent models to components')
for i in range(n_components):
# size of B is (n_components, nx*ny*nz)
B = np.atleast_3d(betamask)[:, :, i].T
alpha = (np.abs(B)**2).sum(axis=0)
varex[i] = (tsoc_B[:, i]**2).sum() / totvar * 100.
varex_norm[i] = (utils.unmask(WTS, mask)[t2s != 0][:, i]**2).sum() / totvar_norm * 100.
# S0 Model
coeffs_S0 = (B * X1).sum(axis=0) / (X1**2).sum(axis=0)
SSE_S0 = (B - X1 * np.tile(coeffs_S0, (n_echos, 1)))**2
SSE_S0 = SSE_S0.sum(axis=0)
F_S0 = (alpha - SSE_S0) * 2 / (SSE_S0)
F_S0_maps[:, i] = F_S0
# R2 Model
coeffs_R2 = (B * X2).sum(axis=0) / (X2**2).sum(axis=0)
SSE_R2 = (B - X2 * np.tile(coeffs_R2, (n_echos, 1)))**2
SSE_R2 = SSE_R2.sum(axis=0)
F_R2 = (alpha - SSE_R2) * 2 / (SSE_R2)
F_R2_maps[:, i] = F_R2
# compute weights as Z-values
wtsZ = (WTS[:, i] - WTS[:, i].mean()) / WTS[:, i].std()
wtsZ[np.abs(wtsZ) > Z_MAX] = (Z_MAX * (np.abs(wtsZ) / wtsZ))[np.abs(wtsZ) > Z_MAX]
Z_maps[:, i] = wtsZ
# compute Kappa and Rho
F_S0[F_S0 > F_MAX] = F_MAX
F_R2[F_R2 > F_MAX] = F_MAX
norm_weights = np.abs(np.squeeze(utils.unmask(wtsZ, mask)[t2s != 0]**2.))
Kappas[i] = np.average(F_R2, weights=norm_weights)
Rhos[i] = np.average(F_S0, weights=norm_weights)
# tabulate component values
comptab_pre = np.vstack([np.arange(n_components), Kappas, Rhos, varex, varex_norm]).T
if reindex:
# re-index all components in Kappa order
comptab = comptab_pre[comptab_pre[:, 1].argsort()[::-1], :]
Kappas = comptab[:, 1]
Rhos = comptab[:, 2]
varex = comptab[:, 3]
varex_norm = comptab[:, 4]
nnc = np.array(comptab[:, 0], dtype=np.int)
mmix_new = mmix[:, nnc]
F_S0_maps = F_S0_maps[:, nnc]
F_R2_maps = F_R2_maps[:, nnc]
Z_maps = Z_maps[:, nnc]
WTS = WTS[:, nnc]
PSC = PSC[:, nnc]
tsoc_B = tsoc_B[:, nnc]
tsoc_Babs = tsoc_Babs[:, nnc]
comptab[:, 0] = np.arange(comptab.shape[0])
else:
comptab = comptab_pre
mmix_new = mmix
# full selection including clustering criteria
seldict = None
if full_sel:
LGR.info('Performing spatial clustering of components')
csize = np.max([int(n_voxels * 0.0005) + 5, 20])
LGR.debug('Using minimum cluster size: {}'.format(csize))
for i in range(n_components):
# save out files
out = np.zeros((n_samp, 4))
out[:, 0] = np.squeeze(utils.unmask(PSC[:, i], mask))
out[:, 1] = np.squeeze(utils.unmask(F_R2_maps[:, i], t2s != 0))
out[:, 2] = np.squeeze(utils.unmask(F_S0_maps[:, i], t2s != 0))
out[:, 3] = np.squeeze(utils.unmask(Z_maps[:, i], mask))
if utils.get_dtype(ref_img) == 'GIFTI':
continue # TODO: pass through GIFTI file data as below
ccimg = utils.new_nii_like(ref_img, out)
# Do simple clustering on F
sel = spatclust(ccimg, min_cluster_size=csize,
threshold=int(fmin), index=[1, 2], mask=(t2s != 0))
F_R2_clmaps[:, i] = sel[:, 0]
F_S0_clmaps[:, i] = sel[:, 1]
countsigFR2 = F_R2_clmaps[:, i].sum()
countsigFS0 = F_S0_clmaps[:, i].sum()
# Do simple clustering on Z at p<0.05
sel = spatclust(ccimg, min_cluster_size=csize,
threshold=1.95, index=3, mask=mask)
Z_clmaps[:, i] = sel
# Do simple clustering on ranked signal-change map
spclust_input = utils.unmask(stats.rankdata(tsoc_Babs[:, i]), mask)
spclust_input = utils.new_nii_like(ref_img, spclust_input)
Br_clmaps_R2[:, i] = spatclust(spclust_input,
min_cluster_size=csize,
threshold=max(tsoc_Babs.shape)-countsigFR2,
mask=mask)
Br_clmaps_S0[:, i] = spatclust(spclust_input,
min_cluster_size=csize,
threshold=max(tsoc_Babs.shape)-countsigFS0,
mask=mask)
seldict = {}
selvars = ['Kappas', 'Rhos', 'WTS', 'varex', 'Z_maps', 'F_R2_maps',
'F_S0_maps', 'Z_clmaps', 'F_R2_clmaps', 'F_S0_clmaps',
'tsoc_B', 'Br_clmaps_R2', 'Br_clmaps_S0', 'PSC']
for vv in selvars:
seldict[vv] = eval(vv)
return seldict, comptab, betas, mmix_new
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,
initcost='tanh', finalcost='tanh',
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.
initcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Initial cost function for ICA. Default is 'tanh'.
finalcost : {'tanh', 'pow3', 'gaus', 'skew'}, optional
Final cost function. Default is 'tanh'.
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
-----
PROCEDURE 2 : Computes ME-PCA and ME-ICA
- Computes T2* map
- Computes PCA of concatenated ME data, then computes TE-dependence of PCs
- Computes ICA of TE-dependence PCs
- Identifies TE-dependent ICs, outputs high-\kappa (BOLD) component
and denoised time series
or computes TE-dependence of each component of a general linear model
specified by input (includes MELODIC FastICA mixing matrix)
PROCEDURE 2a: Model fitting and component selection routines
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.
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.
s0v.nii Limited S0 3D map.
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.
t2ss.nii ???
s0vs.nii ???
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.
__meica_mix.1D A mixing matrix
meica_mix.1D Another mixing matrix
ts_OC.nii Optimally combined timeseries.
betas_OC.nii Full ICA coefficient feature set.
betas_hik_OC.nii Denoised ICA coefficient feature set
feats_OC2.nii Z-normalized spatial component maps
comp_table.txt Component table
sphis_hik.nii T1-like effect
hik_ts_OC_T1c.nii T1 corrected time series by regression
dn_ts_OC_T1c.nii ME-DN version of T1 corrected time series
betas_hik_OC_T1c.nii T1-GS corrected components
meica_mix_T1c.1D T1-GS corrected mixing matrix
====================== =================================================
If ``dne`` is set to True:
====================== =================================================
Filename Content
====================== =================================================
hik_ts_e[echo].nii High-Kappa timeseries for echo number ``echo``
midk_ts_e[echo].nii Mid-Kappa timeseries for echo number ``echo``
lowk_ts_e[echo].nii Low-Kappa timeseries for echo number ``echo``
dn_ts_e[echo].nii Denoised timeseries for echo number ``echo``
====================== =================================================
"""
# 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 = utils.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:
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.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 = model.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
utils.filewrite(t2s, op.join(out_dir, 't2sv.nii'), ref_img)
utils.filewrite(s0, op.join(out_dir, 's0v.nii'), ref_img)
utils.filewrite(t2ss, op.join(out_dir, 't2ss.nii'), ref_img)
utils.filewrite(s0s, op.join(out_dir, 's0vs.nii'), ref_img)
utils.filewrite(t2sG, op.join(out_dir, 't2svG.nii'), ref_img)
utils.filewrite(s0G, op.join(out_dir, 's0vG.nii'), ref_img)
# optimally combine data
OCcatd = model.make_optcom(catd, tes, mask, t2s=t2sG, combmode=combmode)
# regress out global signal unless explicitly not desired
if gscontrol:
catd, OCcatd = model.gscontrol_raw(catd, OCcatd, n_echos, ref_img)
if mixm is None:
n_components, dd = decomposition.tedpca(catd, OCcatd, 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=initcost, final_cost=finalcost,
verbose=debug)
np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig)
LGR.info('Making second component selection guess from ICA results')
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)
acc, rej, midk, empty = selection.selcomps(seldict, mmix, mask, ref_img, manacc,
n_echos, t2s, s0, strict_mode=strict,
filecsdata=filecsdata)
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:
acc, rej, midk, empty = selection.selcomps(seldict, mmix, mask,
ref_img, manacc,
n_echos, t2s, s0,
filecsdata=filecsdata,
strict_mode=strict)
else:
acc, rej, midk, empty = utils.ctabsel(ctab)
if len(acc) == 0:
LGR.warning('No BOLD components detected! Please check data and '
'results!')
utils.writeresults(OCcatd, mask, comptable, mmix, fixed_seed, n_vols,
acc, rej, midk, empty, ref_img)
utils.gscontrol_mmix(OCcatd, mmix, mask, acc, ref_img)
if dne:
utils.writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img)