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, 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 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)