Add band over censored volumes in executive summary carpet plots (Pen…

…nLINC#1077)

.circleci/config.yml

@@ -155,7 +155,7 @@ jobs:
           paths:
             - .coverage.fmriprep_without_freesurfer
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_fmriprep_without_freesurfer/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_fmriprep_without_freesurfer/
 
   ds001419_nifti:
     <<: *dockersetup
@@ -186,7 +186,7 @@ jobs:
           paths:
             - .coverage.ds001419_nifti
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_ds001419_nifti/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_ds001419_nifti/
 
   ds001419_cifti:
     <<: *dockersetup
@@ -217,7 +217,7 @@ jobs:
           paths:
             - .coverage.ds001419_cifti
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_ds001419_cifti/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_ds001419_cifti/
 
   ukbiobank:
     <<: *dockersetup
@@ -235,7 +235,7 @@ jobs:
           key: ukbiobank-08
       - run: *runinstall
       - run:
-          name: Run full xcp_d on nifti with freesurfer
+          name: Run full xcp_d on UK Biobank data
           no_output_timeout: 1h
           command: |
             pytest -rP -o log_cli=true -m "ukbiobank" --cov-append --cov-report term-missing --cov=xcp_d --data_dir=/src/xcp_d/.circleci/data --output_dir=/src/xcp_d/.circleci/out --working_dir=/src/xcp_d/.circleci/work xcp_d
@@ -248,7 +248,7 @@ jobs:
           paths:
             - .coverage.ukbiobank
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_ukbiobank/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_ukbiobank/
 
   nibabies:
     <<: *dockersetup
@@ -279,7 +279,7 @@ jobs:
           paths:
             - .coverage.nibabies
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_nibabies/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_nibabies/
 
   pnc_cifti:
     <<: *dockersetup
@@ -310,7 +310,7 @@ jobs:
           paths:
             - .coverage.pnc_cifti
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_pnc_cifti/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_pnc_cifti/
 
   pnc_cifti_t2wonly:
     <<: *dockersetup
@@ -341,7 +341,7 @@ jobs:
           paths:
             - .coverage.pnc_cifti_t2wonly
       - store_artifacts:
-          path: /src/xcp_d/.circleci/out/test_pnc_cifti_t2wonly/xcp_d/
+          path: /src/xcp_d/.circleci/out/test_pnc_cifti_t2wonly/
 
   pytests:
     <<: *dockersetup

xcp_d/cli/run.py

@@ -893,7 +893,7 @@ def build_workflow(opts, retval):
     ``multiprocessing.Process`` that allows fmriprep to enforce
     a hard-limited memory-scope.
     """
-    from bids import BIDSLayout
+    from bids.layout import BIDSLayout
     from nipype import config as ncfg
     from nipype import logging as nlogging
 

xcp_d/interfaces/plotting.py

@@ -84,17 +84,18 @@ def _run_interface(self, runtime):
         # The number of colors in the palette depends on whether there are random censors or not
         palette = sns.color_palette("colorblind", 4 + censoring_df.shape[1])
 
-        fig, ax = plt.subplots(figsize=(16, 8))
-
         time_array = np.arange(preproc_fd_timeseries.size) * self.inputs.TR
 
-        ax.plot(
-            time_array,
-            preproc_fd_timeseries,
-            label="Raw Framewise Displacement",
-            color=palette[0],
-        )
-        ax.axhline(self.inputs.fd_thresh, label="Outlier Threshold", color="gray", alpha=0.5)
+        with sns.axes_style("whitegrid"):
+            fig, ax = plt.subplots(figsize=(8, 4))
+
+            ax.plot(
+                time_array,
+                preproc_fd_timeseries,
+                label="Raw Framewise Displacement",
+                color=palette[0],
+            )
+            ax.axhline(self.inputs.fd_thresh, label="Outlier Threshold", color="salmon", alpha=0.5)
 
         dummy_scans = self.inputs.dummy_scans
         # This check is necessary, because init_prepare_confounds_wf connects dummy_scans from the
@@ -182,9 +183,9 @@ def _run_interface(self, runtime):
                 alpha=0.5,
             )
 
-        ax.set_xlabel("Time (seconds)", fontsize=20)
-        ax.set_ylabel("Movement (millimeters)", fontsize=20)
-        ax.legend(fontsize=20)
+        ax.set_xlabel("Time (seconds)", fontsize=10)
+        ax.set_ylabel("Movement (millimeters)", fontsize=10)
+        ax.legend(fontsize=10)
         fig.tight_layout()
 
         self._results["out_file"] = fname_presuffix(
@@ -563,14 +564,19 @@ class _QCPlotsESInputSpec(BaseInterfaceInputSpec):
         mandatory=True,
         desc="TSV file with filtered motion parameters.",
     )
+    temporal_mask = File(
+        exists=True,
+        mandatory=True,
+        desc="TSV file with temporal mask.",
+    )
     TR = traits.Float(default_value=1, desc="Repetition time")
     standardize = traits.Bool(
         mandatory=True,
         desc=(
             "Whether to standardize the data or not. "
             "If False, then the preferred DCAN version of the plot will be generated, "
-            "where the BOLD data are not rescaled, and the carpet plot has color limits of -600 "
-            "and 600. "
+            "where the BOLD data are not rescaled, and the carpet plot has color limits from "
+            "the 2.5th percentile to the 97.5th percentile. "
             "If True, then the BOLD data will be z-scored and the color limits will be -2 and 2."
         ),
     )
@@ -610,14 +616,14 @@ class QCPlotsES(SimpleInterface):
     output_spec = _QCPlotsESOutputSpec
 
     def _run_interface(self, runtime):
-        preprocessed_bold_figure = fname_presuffix(
+        preprocessed_figure = fname_presuffix(
             "carpetplot_before_",
             suffix="file.svg",
             newpath=runtime.cwd,
             use_ext=False,
         )
 
-        denoised_bold_figure = fname_presuffix(
+        denoised_figure = fname_presuffix(
             "carpetplot_after_",
             suffix="file.svg",
             newpath=runtime.cwd,
@@ -635,12 +641,12 @@ def _run_interface(self, runtime):
 
         self._results["before_process"], self._results["after_process"] = plot_fmri_es(
             preprocessed_bold=self.inputs.preprocessed_bold,
-            denoised_censored_bold=self.inputs.denoised_interpolated_bold,
             denoised_interpolated_bold=self.inputs.denoised_interpolated_bold,
             TR=self.inputs.TR,
             filtered_motion=self.inputs.filtered_motion,
-            preprocessed_bold_figure=preprocessed_bold_figure,
-            denoised_bold_figure=denoised_bold_figure,
+            temporal_mask=self.inputs.temporal_mask,
+            preprocessed_figure=preprocessed_figure,
+            denoised_figure=denoised_figure,
             standardize=self.inputs.standardize,
             temporary_file_dir=runtime.cwd,
             mask=mask_file,

xcp_d/tests/test_utils_plotting.py

@@ -2,6 +2,9 @@
 
 import os
 
+import numpy as np
+import pandas as pd
+
 from xcp_d.utils import plotting
 
 
@@ -10,25 +13,32 @@ def test_plot_fmri_es(ds001419_data, tmp_path_factory):
     tmpdir = tmp_path_factory.mktemp("test_plot_fmri_es")
 
     preprocessed_bold = ds001419_data["cifti_file"]
-    denoised_censored_bold = ds001419_data["cifti_file"]
     denoised_interpolated_bold = ds001419_data["cifti_file"]
 
     # Using unfiltered FD instead of calculating filtered version.
     filtered_motion = ds001419_data["confounds_file"]
-    preprocessed_bold_figure = os.path.join(tmpdir, "unprocessed.svg")
-    denoised_bold_figure = os.path.join(tmpdir, "processed.svg")
+    preprocessed_figure = os.path.join(tmpdir, "unprocessed.svg")
+    denoised_figure = os.path.join(tmpdir, "processed.svg")
     t_r = 2
+    n_volumes = pd.read_table(filtered_motion).shape[0]
+    tmask_arr = np.zeros(n_volumes, dtype=bool)
+    tmask_arr[:10] = True  # flag first 10 volumes as bad
+    tmask_arr = tmask_arr.astype(int)
+    temporal_mask = os.path.join(tmpdir, "temporal_mask.tsv")
+    pd.DataFrame(columns=["framewise_displacement"], data=tmask_arr).to_csv(
+        temporal_mask, sep="\t", index=False
+    )
 
     out_file1, out_file2 = plotting.plot_fmri_es(
         preprocessed_bold=preprocessed_bold,
-        denoised_censored_bold=denoised_censored_bold,
         denoised_interpolated_bold=denoised_interpolated_bold,
         filtered_motion=filtered_motion,
-        preprocessed_bold_figure=preprocessed_bold_figure,
-        denoised_bold_figure=denoised_bold_figure,
+        preprocessed_figure=preprocessed_figure,
+        denoised_figure=denoised_figure,
         TR=t_r,
         standardize=False,
         temporary_file_dir=tmpdir,
+        temporal_mask=temporal_mask,
     )
     assert os.path.isfile(out_file1)
     assert os.path.isfile(out_file2)
@@ -38,14 +48,14 @@ def test_plot_fmri_es(ds001419_data, tmp_path_factory):
 
     out_file1, out_file2 = plotting.plot_fmri_es(
         preprocessed_bold=preprocessed_bold,
-        denoised_censored_bold=denoised_censored_bold,
         denoised_interpolated_bold=denoised_interpolated_bold,
         filtered_motion=filtered_motion,
-        preprocessed_bold_figure=preprocessed_bold_figure,
-        denoised_bold_figure=denoised_bold_figure,
+        preprocessed_figure=preprocessed_figure,
+        denoised_figure=denoised_figure,
         TR=t_r,
         standardize=True,
         temporary_file_dir=tmpdir,
+        temporal_mask=temporal_mask,
     )
     assert os.path.isfile(out_file1)
     assert os.path.isfile(out_file2)

xcp_d/utils/bids.py

@@ -11,7 +11,7 @@
 
 import nibabel as nb
 import yaml
-from bids import BIDSLayout
+from bids.layout import BIDSLayout
 from nipype import logging
 from packaging.version import Version