Refactor gscontrol module (ME-ICA#1086)

* Refactor the gscontrol function.

* Update docstring.

* Fix things.

* Update ica_reclassify.py

* Update fiu_four_echo_outputs.txt

* Update.

* Incorporate classification tags into MIR.

* Update gscontrol.py

* Clean up gscontrol_raw.

* Fix.

* Update reclassify_debug_out.txt

* Update gscontrol.py

docs/approach.rst

@@ -376,7 +376,7 @@ presented at MRITogether 2022 for a hands-on tutorial.
 Removal of spatially diffuse noise (optional)
 *********************************************
 
-:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.gscontrol_mmix`
+:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.minimum_image_regression`
 
 Due to the constraints of ICA, TEDICA is able to identify and remove spatially
 localized noise components, but it cannot identify components that are spread

tedana/metrics/dependence.py

@@ -60,20 +60,20 @@ def calculate_betas(
     betas : (M [x E] x C) array_like
         Unstandardized parameter estimates
     """
-    if len(data.shape) == 2:
+    if data.ndim == 2:
         data_optcom = data
         assert data_optcom.shape[1] == mixing.shape[0]
         # mean-center optimally-combined data
         data_optcom_dm = data_optcom - data_optcom.mean(axis=-1, keepdims=True)
-        # betas are the result of a normal OLS fit of the mixing matrix
-        # against the mean-center data
+        # betas are from a normal OLS fit of the mixing matrix against the mean-centered data
         betas = get_coeffs(data_optcom_dm, mixing)
-        return betas
+
     else:
         betas = np.zeros([data.shape[0], data.shape[1], mixing.shape[1]])
         for n_echo in range(data.shape[1]):
             betas[:, n_echo, :] = get_coeffs(data[:, n_echo, :], mixing)
-        return betas
+
+    return betas
 
 
 def calculate_psc(

tedana/reporting/static_figures.py

@@ -164,23 +164,24 @@ def carpet_plot(
             )
         )
 
-        mir_denoised_img = io_generator.get_name("ICA accepted mir denoised img")
-        fig, ax = plt.subplots(figsize=(14, 7))
-        plotting.plot_carpet(
-            mir_denoised_img,
-            mask_img,
-            figure=fig,
-            axes=ax,
-            title="High-Kappa Data (Post-MIR)",
-        )
-        fig.tight_layout()
-        fig.savefig(
-            os.path.join(
-                io_generator.out_dir,
-                "figures",
-                f"{io_generator.prefix}carpet_accepted_mir.svg",
+        if io_generator.verbose:
+            mir_denoised_img = io_generator.get_name("ICA accepted mir denoised img")
+            fig, ax = plt.subplots(figsize=(14, 7))
+            plotting.plot_carpet(
+                mir_denoised_img,
+                mask_img,
+                figure=fig,
+                axes=ax,
+                title="High-Kappa Data (Post-MIR)",
+            )
+            fig.tight_layout()
+            fig.savefig(
+                os.path.join(
+                    io_generator.out_dir,
+                    "figures",
+                    f"{io_generator.prefix}carpet_accepted_mir.svg",
+                )
             )
-        )
 
 
 def plot_component(

tedana/tests/data/reclassify_debug_out.txt

@@ -4,12 +4,10 @@ sub-testymctestface_references.bib
 sub-testymctestface_report.txt
 sub-testymctestface_betas_OC.nii.gz
 sub-testymctestface_betas_hik_OC.nii.gz
-sub-testymctestface_betas_hik_OC_MIR.nii.gz
 sub-testymctestface_dataset_description.json
 sub-testymctestface_dn_ts_OC.nii.gz
 sub-testymctestface_dn_ts_OC_MIR.nii.gz
 sub-testymctestface_feats_OC2.nii.gz
-sub-testymctestface_hik_ts_OC_MIR.nii.gz
 sub-testymctestface_ica_components.nii.gz
 sub-testymctestface_ica_cross_component_metrics.json
 sub-testymctestface_ica_decision_tree.json
@@ -22,3 +20,4 @@ sub-testymctestface_ica_orth_mixing.tsv
 sub-testymctestface_ica_status_table.tsv
 sub-testymctestface_registry.json
 sub-testymctestface_sphis_hik.nii.gz
+sub-testymctestface_confounds.tsv

tedana/tests/test_gscontrol.py

@@ -14,11 +14,7 @@
 
 
 def test_break_gscontrol_raw():
-    """
-    Ensure that gscontrol_raw fails when input data do not have the right.
-
-    shapes.
-    """
+    """Ensure that gscontrol_raw fails when input data do not have the right shapes."""
     n_samples, n_echos, n_vols = 10000, 4, 100
     catd = np.empty((n_samples, n_echos, n_vols))
     optcom = np.empty((n_samples, n_vols))
@@ -27,28 +23,41 @@ def test_break_gscontrol_raw():
     catd = np.empty((n_samples + 1, n_echos, n_vols))
     with pytest.raises(ValueError) as e_info:
         gsc.gscontrol_raw(
-            catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4
+            data_cat=catd,
+            data_optcom=optcom,
+            n_echos=n_echos,
+            io_generator=io_generator,
+            dtrank=4,
         )
     assert str(e_info.value) == (
-        f"First dimensions of catd ({catd.shape[0]}) and optcom ({optcom.shape[0]}) do not match"
+        f"First dimensions of data_cat ({catd.shape[0]}) and data_optcom ({optcom.shape[0]}) "
+        "do not match"
     )
 
     catd = np.empty((n_samples, n_echos + 1, n_vols))
     with pytest.raises(ValueError) as e_info:
         gsc.gscontrol_raw(
-            catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4
+            data_cat=catd,
+            data_optcom=optcom,
+            n_echos=n_echos,
+            io_generator=io_generator,
+            dtrank=4,
         )
     assert str(e_info.value) == (
-        f"Second dimension of catd ({catd.shape[1]}) does not match n_echos ({n_echos})"
+        f"Second dimension of data_cat ({catd.shape[1]}) does not match n_echos ({n_echos})"
     )
 
     catd = np.empty((n_samples, n_echos, n_vols))
     optcom = np.empty((n_samples, n_vols + 1))
     with pytest.raises(ValueError) as e_info:
         gsc.gscontrol_raw(
-            catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4
+            data_cat=catd,
+            data_optcom=optcom,
+            n_echos=n_echos,
+            io_generator=io_generator,
+            dtrank=4,
         )
     assert str(e_info.value) == (
-        f"Third dimension of catd ({catd.shape[2]}) does not match "
-        f"second dimension of optcom ({optcom.shape[1]})"
+        f"Third dimension of data_cat ({catd.shape[2]}) does not match "
+        f"second dimension of data_optcom ({optcom.shape[1]})"
     )

tedana/workflows/ica_reclassify.py

@@ -489,7 +489,14 @@ def ica_reclassify_workflow(
 
     if mir:
         io_generator.overwrite = True
-        gsc.minimum_image_regression(data_oc, mmix, mask_denoise, comptable, io_generator)
+        gsc.minimum_image_regression(
+            data_optcom=data_oc,
+            mixing=mmix,
+            mask=mask_denoise,
+            comptable=comptable,
+            classification_tags=selector.classification_tags,
+            io_generator=io_generator,
+        )
         io_generator.overwrite = False
 
     # Write out BIDS-compatible description file

tedana/workflows/tedana.py

@@ -697,7 +697,12 @@ def tedana_workflow(
 
     if "gsr" in gscontrol:
         # regress out global signal
-        catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, io_generator)
+        catd, data_oc = gsc.gscontrol_raw(
+            data_cat=catd,
+            data_optcom=data_oc,
+            n_echos=n_echos,
+            io_generator=io_generator,
+        )
 
     fout = io_generator.save_file(data_oc, "combined img")
     LGR.info(f"Writing optimally combined data set: {fout}")
@@ -886,7 +891,14 @@ def tedana_workflow(
     )
 
     if "mir" in gscontrol:
-        gsc.minimum_image_regression(data_oc, mmix, mask_denoise, comptable, io_generator)
+        gsc.minimum_image_regression(
+            data_optcom=data_oc,
+            mixing=mmix,
+            mask=mask_denoise,
+            comptable=comptable,
+            classification_tags=selector.classification_tags,
+            io_generator=io_generator,
+        )
 
     if verbose:
         io.writeresults_echoes(catd, mmix, mask_denoise, comptable, io_generator)