[ENH] BIDS Derivatives-compatible outputs

tedana/decomposition/pca.py

@@ -140,13 +140,13 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
 
     This function writes out several files:
 
-    ======================    =================================================
-    Filename                  Content
-    ======================    =================================================
-    pca_decomposition.json    PCA component table.
-    pca_mixing.tsv            PCA mixing matrix.
-    pca_components.nii.gz     Component weight maps.
-    ======================    =================================================
+    ===========================    =============================================
+    Filename                       Content
+    ===========================    =============================================
+    desc-PCA_decomposition.json    PCA component table
+    desc-PCA_mixing.tsv            PCA mixing matrix
+    desc-PCA_components.nii.gz     Component weight maps
+    ===========================    =============================================
     """
     if algorithm == 'kundu':
         alg_str = ("followed by the Kundu component selection decision "
@@ -209,7 +209,7 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
     comptable, _, _, _ = metrics.dependence_metrics(
                 data_cat, data_oc, comp_ts, adaptive_mask, tes, ref_img,
                 reindex=False, mmixN=vTmixN, algorithm=None,
-                label='mepca_', out_dir=out_dir, verbose=verbose)
+                label='PCA', out_dir=out_dir, verbose=verbose)
 
     # varex_norm from PCA overrides varex_norm from dependence_metrics,
     # but we retain the original
@@ -218,9 +218,8 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
     comptable['normalized variance explained'] = varex_norm
 
     # write component maps to 4D image
-    comp_ts_z = stats.zscore(comp_ts, axis=0)
-    comp_maps = utils.unmask(computefeats2(data_oc, comp_ts_z, mask), mask)
-    io.filewrite(comp_maps, op.join(out_dir, 'pca_components.nii.gz'), ref_img)
+    comp_maps = utils.unmask(computefeats2(data_oc, comp_ts, mask), mask)
+    io.filewrite(comp_maps, op.join(out_dir, 'desc-PCA_components.nii.gz'), ref_img)
 
     # Select components using decision tree
     if algorithm == 'kundu':
@@ -238,7 +237,7 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
                   for comp in comptable.index.values]
 
     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)
+    mixing_df.to_csv(op.join(out_dir, 'desc-PCA_mixing.tsv'), sep='\t', index=False)
 
     comptable['Description'] = 'PCA fit to optimally combined data.'
     mmix_dict = {}
@@ -247,7 +246,7 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
                            'explained in descending order. '
                            'Component signs are flipped to best match the '
                            'data.')
-    io.save_comptable(comptable, op.join(out_dir, 'pca_decomposition.json'),
+    io.save_comptable(comptable, op.join(out_dir, 'desc-PCA_decomposition.json'),
                       label='pca', metadata=mmix_dict)
 
     acc = comptable[comptable.classification == 'accepted'].index.values

tedana/gscontrol.py

@@ -6,6 +6,7 @@
 
 import numpy as np
 from numpy.linalg import lstsq
+import pandas as pd
 from scipy import stats
 from scipy.special import lpmv
 
@@ -78,23 +79,38 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, out_dir='.', dtrank=4):
     detr = dat - np.dot(sol.T, Lmix.T)[0]
     sphis = (detr).min(axis=1)
     sphis -= sphis.mean()
-    io.filewrite(utils.unmask(sphis, Gmask), op.join(out_dir, 'T1gs'), ref_img)
+    io.filewrite(
+        utils.unmask(sphis, Gmask),
+        op.join(out_dir, 'T1gs.nii.gz'),
+        ref_img
+    )
 
     # find time course ofc the spatial global signal
     # make basis with the Legendre basis
     glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None)[0]
     glsig = stats.zscore(glsig, axis=None)
-    np.savetxt(op.join(out_dir, 'glsig.1D'), glsig)
+
+    glsig_df = pd.DataFrame(data=glsig.T, columns=['global_signal'])
+    glsig_df.to_csv(op.join(out_dir, 'desc-globalSignal_regressors.tsv'),
+                    sep='\t', index=False)
     glbase = np.hstack([Lmix, glsig.T])
 
     # Project global signal out of optimally combined data
     sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0]
     tsoc_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T,
                              np.atleast_2d(glbase.T[dtrank])) + Gmu[Gmask][:, np.newaxis]
 
-    io.filewrite(optcom, op.join(out_dir, 'tsoc_orig'), ref_img)
+    io.filewrite(
+        optcom,
+        op.join(out_dir, 'desc-optcomWithGlobalSignal_bold.nii.gz'),
+        ref_img
+    )
     dm_optcom = utils.unmask(tsoc_nogs, Gmask)
-    io.filewrite(dm_optcom, op.join(out_dir, 'tsoc_nogs'), ref_img)
+    io.filewrite(
+        dm_optcom,
+        op.join(out_dir, 'desc-optcomNoGlobalSignal_bold.nii.gz'),
+        ref_img
+    )
 
     # Project glbase out of each echo
     dm_catd = catd.copy()  # don't overwrite catd
@@ -170,7 +186,11 @@ def gscontrol_mmix(optcom_ts, mmix, mask, comptable, ref_img, out_dir='.'):
     bold_ts = np.dot(cbetas[:, acc], mmix[:, acc].T)
     t1_map = bold_ts.min(axis=-1)
     t1_map -= t1_map.mean()
-    io.filewrite(utils.unmask(t1_map, mask), op.join(out_dir, 'sphis_hik'), ref_img)
+    io.filewrite(
+        utils.unmask(t1_map, mask),
+        op.join(out_dir, 'desc-optcomAccepted_min.nii.gz'),
+        ref_img
+    )
     t1_map = t1_map[:, np.newaxis]
 
     """
@@ -184,14 +204,21 @@ def gscontrol_mmix(optcom_ts, mmix, mask, comptable, ref_img, out_dir='.'):
     bold_noT1gs = bold_ts - np.dot(lstsq(glob_sig.T, bold_ts.T,
                                          rcond=None)[0].T, glob_sig)
     hik_ts = bold_noT1gs * optcom_std
-    io.filewrite(utils.unmask(hik_ts, mask), op.join(out_dir, 'hik_ts_OC_T1c'),
-                 ref_img)
+    io.filewrite(
+        utils.unmask(hik_ts, mask),
+        op.join(out_dir, 'desc-optcomAcceptedT1cDenoised_bold.nii.gz'),
+        ref_img
+    )
 
     """
     Make denoised version of T1-corrected time series
     """
     medn_ts = optcom_mu + ((bold_noT1gs + resid) * optcom_std)
-    io.filewrite(utils.unmask(medn_ts, mask), op.join(out_dir, 'dn_ts_OC_T1c'), ref_img)
+    io.filewrite(
+        utils.unmask(medn_ts, mask),
+        op.join(out_dir, 'desc-optcomT1cDenoised_bold.nii.gz'),
+        ref_img
+    )
 
     """
     Orthogonalize mixing matrix w.r.t. T1-GS
@@ -208,6 +235,14 @@ def gscontrol_mmix(optcom_ts, mmix, mask, comptable, ref_img, out_dir='.'):
     Write T1-GS corrected components and mixing matrix
     """
     cbetas_norm = lstsq(mmixnogs_norm.T, data_norm.T, rcond=None)[0].T
-    io.filewrite(utils.unmask(cbetas_norm[:, 2:], mask),
-                 op.join(out_dir, 'betas_hik_OC_T1c'), ref_img)
-    np.savetxt(op.join(out_dir, 'meica_mix_T1c.1D'), mmixnogs)
+    io.filewrite(
+        utils.unmask(cbetas_norm[:, 2:], mask),
+        op.join(out_dir, 'desc-TEDICAAcceptedT1cDenoised_components.nii.gz'),
+        ref_img
+    )
+    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=mmixnogs.T, columns=comp_names)
+    mixing_df.to_csv(op.join(out_dir, 'desc-TEDICAT1cDenoised_mixing.tsv'),
+                     sep='\t', index=False)