[REF] Remove the sourceTEs option (#493)

tedana/decomposition/pca.py

@@ -88,7 +88,7 @@ def low_mem_pca(data):
 
 
 def tedpca(data_cat, data_oc, combmode, mask, t2s, t2sG,
-           ref_img, tes, algorithm='mdl', source_tes=-1, kdaw=10., rdaw=1.,
+           ref_img, tes, algorithm='mdl', kdaw=10., rdaw=1.,
            out_dir='.', verbose=False, low_mem=False):
     """
     Use principal components analysis (PCA) to identify and remove thermal
@@ -119,12 +119,6 @@ def tedpca(data_cat, data_oc, combmode, mask, t2s, t2sG,
         decomposition with the mdl, kic and aic options are based on a Moving Average
         (stationary Gaussian) process and are ordered from most to least aggresive.
         See (Li et al., 2007).
-    source_tes : :obj:`int` or :obj:`list` of :obj:`int`, optional
-        Which echos to use in PCA. Values -1 and 0 are special, where a value
-        of -1 will indicate using the optimal combination of the echos
-        and 0  will indicate using all the echos. A list can be provided
-        to indicate a subset of echos.
-        Default: -1
     kdaw : :obj:`float`, optional
         Dimensionality augmentation weight for Kappa calculations. Must be a
         non-negative float, or -1 (a special value). Default is 10.
@@ -232,28 +226,14 @@ def tedpca(data_cat, data_oc, combmode, mask, t2s, t2sG,
                     "functional magnetic resonance imaging data. "
                     "Human brain mapping, 28(11), pp.1251-1266.")
 
-    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"
-
     RepLGR.info("Principal component analysis {0} was applied to "
-                "{1} for dimensionality reduction.".format(alg_str, dat_str))
+                "the optimally combined data for dimensionality "
+                "reduction.".format(alg_str))
 
     n_samp, n_echos, n_vols = data_cat.shape
-    source_tes = np.array([int(ee) for ee in str(source_tes).split(',')])
-
-    if len(source_tes) == 1 and source_tes[0] == -1:
-        LGR.info('Computing PCA of optimally combined multi-echo data')
-        data = data_oc[mask, :][:, np.newaxis, :]
-    elif len(source_tes) == 1 and source_tes[0] == 0:
-        LGR.info('Computing PCA of spatially concatenated multi-echo data')
-        data = data_cat[mask, ...]
-    else:
-        LGR.info('Computing PCA of echo #{0}'.format(','.join([str(ee) for ee in source_tes])))
-        data = np.stack([data_cat[mask, ee, :] for ee in source_tes - 1], axis=1)
+
+    LGR.info('Computing PCA of optimally combined multi-echo data')
+    data = data_oc[mask, :][:, np.newaxis, :]
 
     eim = np.squeeze(_utils.eimask(data))
     data = np.squeeze(data[eim])
@@ -340,8 +320,7 @@ def tedpca(data_cat, data_oc, combmode, mask, t2s, t2sG,
     mixing_df = pd.DataFrame(data=comp_ts, columns=comp_names)
     mixing_df.to_csv(op.join(out_dir, 'pca_mixing.tsv'), sep='\t', index=False)
 
-    data_type = 'optimally combined data' if source_tes == -1 else 'z-concatenated data'
-    comptable['Description'] = 'PCA fit to {0}.'.format(data_type)
+    comptable['Description'] = 'PCA fit to optimally combined data.'
     mmix_dict = {}
     mmix_dict['Method'] = ('Principal components analysis implemented by '
                            'sklearn. Components are sorted by variance '

tedana/workflows/tedana.py

@@ -95,13 +95,6 @@ def _get_parser():
                           help=('Comma separated list of manually '
                                 'accepted components'),
                           default=None)
-    optional.add_argument('--sourceTEs',
-                          dest='source_tes',
-                          type=str,
-                          help=('Source TEs for models. E.g., 0 for all, '
-                                '-1 for opt. com., and 1,2 for just TEs 1 and '
-                                '2. Default=-1.'),
-                          default=-1)
     optional.add_argument('--combmode',
                           dest='combmode',
                           action='store',
@@ -216,7 +209,7 @@ def _get_parser():
 
 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,
+                    combmode='t2s', verbose=False, stabilize=False,
                     out_dir='.', fixed_seed=42, maxit=500, maxrestart=10,
                     debug=False, quiet=False, no_png=False,
                     png_cmap='coolwarm',
@@ -254,9 +247,6 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
         is None.
     tedpca : {'mle', 'kundu', 'kundu-stabilize', 'mdl', 'aic', 'kic'}, optional
         Method with which to select components in TEDPCA. Default is 'mdl'.
-    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).
     fittype : {'loglin', 'curvefit'}, optional
@@ -474,15 +464,14 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
         dd, n_components = decomposition.tedpca(catd, data_oc, combmode, mask,
                                                 t2s_limited, t2s_full, ref_img,
                                                 tes=tes, algorithm=tedpca,
-                                                source_tes=source_tes,
                                                 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 and (source_tes == -1):
+        if verbose:
             io.filewrite(utils.unmask(dd, mask),
                          op.join(out_dir, 'ts_OC_whitened.nii'), ref_img)
 
@@ -525,8 +514,7 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
             comptable = selection.manual_selection(comptable, acc=manacc)
 
     # Save decomposition
-    data_type = 'optimally combined data' if source_tes == -1 else 'z-concatenated data'
-    comptable['Description'] = 'ICA fit to dimensionally reduced {0}.'.format(data_type)
+    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 '