Create reformatted BrainSwipes figures for HBCD QC (PennLINC#1091)

Co-authored-by: Barry Tikalsky <[email protected]>

.circleci/config.yml

@@ -376,7 +376,7 @@ jobs:
           paths:
             - .coverage.pytests
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/
+          path: /src/xcp_d/.circleci/out
 
   merge_coverage:
     <<: *dockersetup

xcp_d/interfaces/execsummary.py

@@ -10,8 +10,18 @@
 from bs4 import BeautifulSoup
 from jinja2 import Environment, FileSystemLoader
 from markupsafe import Markup
+from nipype.interfaces.base import (
+    BaseInterfaceInputSpec,
+    File,
+    InputMultiPath,
+    SimpleInterface,
+    TraitedSpec,
+)
+from PIL import Image
 from pkg_resources import resource_filename as pkgrf
 
+from xcp_d.utils.filemanip import fname_presuffix
+
 
 class ExecutiveSummary(object):
     """A class to build an executive summary.
@@ -327,3 +337,68 @@ def include_file(name):
         )
 
         self.write_html(html, out_file)
+
+
+class _FormatForBrainSwipesInputSpec(BaseInterfaceInputSpec):
+    in_files = InputMultiPath(
+        File(exists=True),
+        desc=(
+            "Figure files. Must be the derivative's filename, "
+            "not the file from the working directory."
+        ),
+    )
+
+
+class _FormatForBrainSwipesOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="Reformatted png file.")
+
+
+class FormatForBrainSwipes(SimpleInterface):
+    """Reformat figure for Brain Swipes.
+
+    From https://github.com/DCAN-Labs/BrainSwipes/blob/cb2ce964bcae93c9a234e4421c07b88bcadf2908/\
+    tools/images/ingest_brainswipes_data.py#L113
+    Credit to @BarryTik.
+    """
+
+    input_spec = _FormatForBrainSwipesInputSpec
+    output_spec = _FormatForBrainSwipesOutputSpec
+
+    def _run_interface(self, runtime):
+        input_files = self.inputs.in_files
+        assert len(input_files) == 9, "There must be 9 input files."
+        idx = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+        widths, rows = [], []
+        for i_row in range(3):
+            row_idx = idx[i_row]
+            row_files = [input_files[j_col] for j_col in row_idx]
+            row = [np.asarray(Image.open(row_file)) for row_file in row_files]
+            row = np.concatenate(row, axis=1)
+            widths.append(row.shape[1])
+            rows.append(row)
+
+        max_width = np.max(widths)
+
+        for i_row, row in enumerate(rows):
+            width = widths[i_row]
+            if width < max_width:
+                pad = max_width - width
+                prepad = pad // 2
+                postpad = pad - prepad
+                rows[i_row] = np.pad(row, ((0, 0), (prepad, postpad), (0, 0)), mode="constant")
+
+        x = np.concatenate(rows, axis=0)
+        new_x = ((x - x.min()) * (1 / (x.max() - x.min()) * 255)).astype("uint8")
+        new_im = Image.fromarray(np.uint8(new_x))
+
+        output_file = fname_presuffix(
+            input_files[0],
+            newpath=runtime.cwd,
+            suffix="_reformatted.png",
+            use_ext=False,
+        )
+        # all images should have the a .png extension
+        new_im.save(output_file)
+        self._results["out_file"] = output_file
+
+        return runtime

xcp_d/interfaces/plotting.py

@@ -730,7 +730,8 @@ class _SlicesDirInputSpec(FSLCommandInputSpec):
 
 class _SlicesDirOutputSpec(TraitedSpec):
     out_dir = Directory(exists=True, desc="Output directory.")
-    out_files = OutputMultiPath(File(exists=True), desc="List of generated PNG files.")
+    out_files = OutputMultiPath(File(exists=True), desc="Concatenated PNG files.")
+    slicewise_files = OutputMultiPath(File(exists=True), desc="List of generated PNG files.")
 
 
 class SlicesDir(FSLCommand):
@@ -775,6 +776,18 @@ def _list_outputs(self):
             )
             for f in self.inputs.in_files
         ]
+        temp_files = [
+            "grota.png",
+            "grotb.png",
+            "grotc.png",
+            "grotd.png",
+            "grote.png",
+            "grotf.png",
+            "grotg.png",
+            "groth.png",
+            "groti.png",
+        ]
+        outputs["slicewise_files"] = [os.path.join(out_dir, f) for f in temp_files]
         return outputs
 
     def _gen_filename(self, name):

xcp_d/tests/test_cli.py

@@ -479,3 +479,4 @@ def _run_and_generate(
     check_generated_files(out_dir, output_list_file)
 
     check_affines(data_dir, out_dir, input_type=input_type)
+    LOGGER.warning(f"Test passed in {out_dir}")

xcp_d/workflows/execsummary.py

@@ -12,6 +12,7 @@
 from pkg_resources import resource_filename as pkgrf
 
 from xcp_d.interfaces.bids import DerivativesDataSink
+from xcp_d.interfaces.execsummary import FormatForBrainSwipes
 from xcp_d.interfaces.nilearn import BinaryMath, ResampleToImage
 from xcp_d.interfaces.plotting import AnatomicalPlot, PNGAppend
 from xcp_d.interfaces.workbench import ShowScene
@@ -761,14 +762,12 @@ def init_plot_overlay_wf(
         name="plot_overlay_figure",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, plot_overlay_figure, [
             ("underlay_file", "in_files"),
             ("overlay_file", "outline_image"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     ds_overlay_figure = pe.Node(
         DerivativesDataSink(
@@ -782,11 +781,31 @@ def init_plot_overlay_wf(
         run_without_submitting=True,
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, ds_overlay_figure, [("name_source", "source_file")]),
         (plot_overlay_figure, ds_overlay_figure, [("out_files", "in_file")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
+
+    reformat_for_brain_swipes = pe.Node(FormatForBrainSwipes(), name="reformat_for_brain_swipes")
+    workflow.connect([
+        (plot_overlay_figure, reformat_for_brain_swipes, [("slicewise_files", "in_files")]),
+    ])  # fmt:skip
+
+    ds_reformatted_figure = pe.Node(
+        DerivativesDataSink(
+            base_directory=output_dir,
+            dismiss_entities=["den"],
+            datatype="figures",
+            desc=f"{desc}BrainSwipes",
+            extension=".png",
+        ),
+        name="ds_reformatted_figure",
+        run_without_submitting=True,
+    )
+
+    workflow.connect([
+        (inputnode, ds_reformatted_figure, [("name_source", "source_file")]),
+        (reformat_for_brain_swipes, ds_reformatted_figure, [("out_file", "in_file")]),
+    ])  # fmt:skip
 
     return workflow