RF: Move resampling to "full" section

Author: effigies

fmriprep/workflows/bold/base.py

@@ -292,6 +292,7 @@ def init_bold_wf(
     ])  # fmt:skip
 
     boldref_out = bool(nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')))
+    boldref_out |= config.workflow.level == 'full'
     echos_out = multiecho and config.execution.me_output_echos
 
     if boldref_out or echos_out:
@@ -316,30 +317,6 @@ def init_bold_wf(
             ]),
         ])  # fmt:skip
 
-    if nonstd_spaces.intersection(('anat', 'T1w')):
-        ds_bold_t1_wf = init_ds_volumes_wf(
-            bids_root=str(config.execution.bids_dir),
-            output_dir=fmriprep_dir,
-            multiecho=multiecho,
-            metadata=all_metadata[0],
-            name='ds_bold_t1_wf',
-        )
-        ds_bold_t1_wf.inputs.inputnode.source_files = bold_series
-        ds_bold_t1_wf.inputs.inputnode.space = 'T1w'
-
-        workflow.connect([
-            (inputnode, ds_bold_t1_wf, [
-                ('t1w_preproc', 'inputnode.ref_file'),
-            ]),
-            (bold_fit_wf, ds_bold_t1_wf, [
-                ('outputnode.bold_mask', 'inputnode.bold_mask'),
-                ('outputnode.coreg_boldref', 'inputnode.bold_ref'),
-                ('outputnode.boldref2anat_xfm', 'inputnode.boldref2anat_xfm'),
-            ]),
-            (bold_native_wf, ds_bold_t1_wf, [('outputnode.t2star_map', 'inputnode.t2star')]),
-            (bold_anat_wf, ds_bold_t1_wf, [('outputnode.bold_file', 'inputnode.bold')]),
-        ])  # fmt:skip
-
     if multiecho:
         t2s_reporting_wf = init_t2s_reporting_wf()
 
@@ -379,6 +356,31 @@ def init_bold_wf(
     if config.workflow.level == "resampling":
         return workflow
 
+    # Full derivatives, including resampled BOLD series
+    if nonstd_spaces.intersection(('anat', 'T1w')):
+        ds_bold_t1_wf = init_ds_volumes_wf(
+            bids_root=str(config.execution.bids_dir),
+            output_dir=fmriprep_dir,
+            multiecho=multiecho,
+            metadata=all_metadata[0],
+            name='ds_bold_t1_wf',
+        )
+        ds_bold_t1_wf.inputs.inputnode.source_files = bold_series
+        ds_bold_t1_wf.inputs.inputnode.space = 'T1w'
+
+        workflow.connect([
+            (inputnode, ds_bold_t1_wf, [
+                ('t1w_preproc', 'inputnode.ref_file'),
+            ]),
+            (bold_fit_wf, ds_bold_t1_wf, [
+                ('outputnode.bold_mask', 'inputnode.bold_mask'),
+                ('outputnode.coreg_boldref', 'inputnode.bold_ref'),
+                ('outputnode.boldref2anat_xfm', 'inputnode.boldref2anat_xfm'),
+            ]),
+            (bold_native_wf, ds_bold_t1_wf, [('outputnode.t2star_map', 'inputnode.t2star')]),
+            (bold_anat_wf, ds_bold_t1_wf, [('outputnode.bold_file', 'inputnode.bold')]),
+        ])  # fmt:skip
+
     # Fill-in datasinks of reportlets seen so far
     for node in workflow.list_node_names():
         if node.split(".")[-1].startswith("ds_report"):

fmriprep/workflows/bold/outputs.py

@@ -587,6 +587,27 @@ def init_ds_bold_native_wf(
     raw_sources.inputs.bids_root = bids_root
     workflow.connect(inputnode, 'source_files', raw_sources, 'in_files')
 
+    # Masks should be output if any other derivatives are output
+    ds_bold_mask = pe.Node(
+        DerivativesDataSink(
+            base_directory=output_dir,
+            desc='brain',
+            suffix='mask',
+            compress=True,
+            dismiss_entities=("echo",),
+        ),
+        name='ds_bold_mask',
+        run_without_submitting=True,
+        mem_gb=DEFAULT_MEMORY_MIN_GB,
+    )
+    workflow.connect([
+        (inputnode, ds_bold_mask, [
+            ('source_files', 'source_file'),
+            ('bold_mask', 'in_file'),
+        ]),
+        (raw_sources, ds_bold_mask, [('out', 'RawSources')]),
+    ])  # fmt:skip
+
     if bold_output:
         ds_bold = pe.Node(
             DerivativesDataSink(
@@ -609,27 +630,6 @@ def init_ds_bold_native_wf(
             ]),
         ])  # fmt:skip
 
-    if bold_output or echo_output:
-        ds_bold_mask = pe.Node(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                desc='brain',
-                suffix='mask',
-                compress=True,
-                dismiss_entities=("echo",),
-            ),
-            name='ds_bold_mask',
-            run_without_submitting=True,
-            mem_gb=DEFAULT_MEMORY_MIN_GB,
-        )
-        workflow.connect([
-            (inputnode, ds_bold_mask, [
-                ('source_files', 'source_file'),
-                ('bold_mask', 'in_file'),
-            ]),
-            (raw_sources, ds_bold_mask, [('out', 'RawSources')]),
-        ])  # fmt:skip
-
     if bold_output and multiecho:
         t2star_meta = {
             'Units': 's',