Track Sources with BIDSURI interface (PennLINC#1283)

xcp_d/config.py

@@ -524,6 +524,9 @@ def _process_value(value):
             "preprocessed": cls.fmri_dir,
             "templateflow": Path(TF_LAYOUT.root),
         }
+        if cls.atlases:
+            dataset_links["atlas"] = cls.output_dir / "atlases"
+
         for dset_name, dset_path in cls.datasets.items():
             dataset_links[dset_name] = dset_path
         cls.dataset_links = dataset_links

xcp_d/interfaces/ants.py

@@ -25,7 +25,7 @@
 
 
 class _ConvertTransformFileInputSpec(CommandLineInputSpec):
-    dimension = traits.Enum((3, 2), default=3, usedefault=True, argstr="%d", position=0)
+    dimension = traits.Enum(3, 2, usedefault=True, argstr="%d", position=0)
     in_transform = traits.File(exists=True, argstr="%s", mandatory=True, position=1)
     out_transform = traits.File(
         argstr="%s",

xcp_d/interfaces/bids.py

@@ -11,15 +11,17 @@
 from nipype.interfaces.base import (
     BaseInterfaceInputSpec,
     Directory,
+    DynamicTraitedSpec,
     File,
     SimpleInterface,
     TraitedSpec,
     traits,
 )
+from nipype.interfaces.io import add_traits
 from niworkflows.interfaces.bids import DerivativesDataSink as BaseDerivativesDataSink
 
 from xcp_d.data import load as load_data
-from xcp_d.utils.bids import get_entity
+from xcp_d.utils.bids import _get_bidsuris, get_entity
 
 # NOTE: Modified for xcpd's purposes
 xcp_d_spec = loads(load_data("xcp_d_bids_config.json").read_text())
@@ -193,6 +195,11 @@ class _CopyAtlasInputSpec(BaseInterfaceInputSpec):
         desc="The atlas name.",
         mandatory=True,
     )
+    Sources = traits.List(
+        traits.Str,
+        desc="List of sources for the atlas.",
+        mandatory=False,
+    )
 
 
 class _CopyAtlasOutputSpec(TraitedSpec):
@@ -243,6 +250,7 @@ def _run_interface(self, runtime):
         meta_dict = self.inputs.meta_dict
         name_source = self.inputs.name_source
         atlas = self.inputs.atlas
+        Sources = self.inputs.Sources
 
         atlas_out_dir = os.path.join(output_dir, f"atlases/atlas-{atlas}")
 
@@ -284,11 +292,69 @@ def _run_interface(self, runtime):
             shutil.copyfile(in_file, out_file)
 
         # Only write out a sidecar if metadata are provided
-        if meta_dict:
+        if meta_dict or Sources:
             meta_file = os.path.join(atlas_out_dir, f"{out_basename}.json")
+            meta_dict = meta_dict or {}
+            meta_dict = meta_dict.copy()
+            if Sources:
+                meta_dict["Sources"] = meta_dict.get("Sources", []) + Sources
+
             with open(meta_file, "w") as fo:
                 dump(meta_dict, fo, sort_keys=True, indent=4)
 
         self._results["out_file"] = out_file
 
         return runtime
+
+
+class _BIDSURIInputSpec(DynamicTraitedSpec):
+    dataset_links = traits.Dict(mandatory=True, desc="Dataset links")
+    out_dir = traits.Str(mandatory=True, desc="Output directory")
+    metadata = traits.Dict(desc="Metadata dictionary")
+    field = traits.Str(
+        "Sources",
+        usedefault=True,
+        desc="Field to use for BIDS URIs in metadata dict",
+    )
+
+
+class _BIDSURIOutputSpec(TraitedSpec):
+    out = traits.List(
+        traits.Str,
+        desc="BIDS URI(s) for file",
+    )
+    metadata = traits.Dict(
+        desc="Dictionary with 'Sources' field.",
+    )
+
+
+class BIDSURI(SimpleInterface):
+    """Convert input filenames to BIDS URIs, based on links in the dataset.
+
+    This interface can combine multiple lists of inputs.
+    """
+
+    input_spec = _BIDSURIInputSpec
+    output_spec = _BIDSURIOutputSpec
+
+    def __init__(self, numinputs=0, **inputs):
+        super().__init__(**inputs)
+        self._numinputs = numinputs
+        if numinputs >= 1:
+            input_names = [f"in{i + 1}" for i in range(numinputs)]
+        else:
+            input_names = []
+        add_traits(self.inputs, input_names)
+
+    def _run_interface(self, runtime):
+        inputs = [getattr(self.inputs, f"in{i + 1}") for i in range(self._numinputs)]
+        uris = _get_bidsuris(inputs, self.inputs.dataset_links, self.inputs.out_dir)
+        self._results["out"] = uris
+
+        # Add the URIs to the metadata dictionary.
+        metadata = self.inputs.metadata or {}
+        metadata = metadata.copy()
+        metadata[self.inputs.field] = metadata.get(self.inputs.field, []) + uris
+        self._results["metadata"] = metadata
+
+        return runtime

xcp_d/interfaces/censoring.py

@@ -215,7 +215,7 @@ class _CensorInputSpec(BaseInterfaceInputSpec):
         ),
     )
     column = traits.Str(
-        default="framewise_displacement",
+        "framewise_displacement",
         usedefault=True,
         mandatory=False,
         desc="Column name in the temporal mask to use for censoring.",
@@ -404,11 +404,12 @@ def _run_interface(self, runtime):
 class _ProcessMotionInputSpec(BaseInterfaceInputSpec):
     TR = traits.Float(mandatory=True, desc="Repetition time in seconds")
     fd_thresh = traits.Float(
+        0.3,
         mandatory=False,
-        default_value=0.3,
+        usedefault=True,
         desc="Framewise displacement threshold. All values above this will be dropped.",
     )
-    head_radius = traits.Float(mandatory=False, default_value=50, desc="Head radius in mm ")
+    head_radius = traits.Float(50, mandatory=False, usedefault=True, desc="Head radius in mm")
     motion_file = File(
         exists=True,
         mandatory=True,
@@ -494,6 +495,11 @@ def _run_interface(self, runtime):
             filtered=False,
         )
         fd_timeseries = motion_df["framewise_displacement"].to_numpy()
+        motion_metadata["framewise_displacement"] = {
+            "Description": "Framewise displacement calculated according to Power et al. (2012).",
+            "HeadRadius": self.inputs.head_radius,
+            "Units": "mm",
+        }
         if self.inputs.motion_filter_type:
             motion_df["framewise_displacement_filtered"] = compute_fd(
                 confound=motion_df,
@@ -504,18 +510,14 @@ def _run_interface(self, runtime):
 
         # Compile motion metadata from confounds metadata, adding in filtering info
         # First drop any columns that are not motion parameters
-        orig_motion_df = pd.read_table(self.inputs.motion_file)
-        orig_motion_cols = orig_motion_df.columns.tolist()
-        cols_to_drop = sorted(list(set(orig_motion_cols) - set(motion_df.columns.tolist())))
+        orig_cols = list(motion_metadata.keys())
+        orig_cols = [c for c in orig_cols if c[0] == c[0].lower()]
+        cols_to_drop = sorted(list(set(orig_cols) - set(motion_df.columns.tolist())))
         motion_metadata = {k: v for k, v in motion_metadata.items() if k not in cols_to_drop}
         for col in motion_df.columns.tolist():
             col_metadata = motion_metadata.get(col, {})
-            if col.startswith("framewise_displacement"):
-                col_metadata["Description"] = (
-                    "Framewise displacement calculated according to Power et al. (2012)."
-                )
-                col_metadata["Units"] = "mm"
-                col_metadata["HeadRadius"] = self.inputs.head_radius
+            if col.endswith("_filtered") and col[:-9] in motion_metadata:
+                col_metadata = motion_metadata[col[:-9]]
 
             if self.inputs.motion_filter_type == "lp" and col.endswith("_filtered"):
                 filters = col_metadata.get("SoftwareFilters", {})
@@ -694,7 +696,7 @@ def _run_interface(self, runtime):
         import nibabel as nb
         import pandas as pd
 
-        from xcp_d.utils.bids import make_bids_uri
+        from xcp_d.utils.bids import _get_bidsuris
         from xcp_d.utils.confounds import filter_motion, volterra
 
         in_img = nb.load(self.inputs.in_file)
@@ -752,7 +754,7 @@ def _run_interface(self, runtime):
                             confounds_metadata[found_column] = confounds_metadata.get(
                                 found_column, {}
                             )
-                            confounds_metadata[found_column]["Sources"] = make_bids_uri(
+                            confounds_metadata[found_column]["Sources"] = _get_bidsuris(
                                 in_files=[confound_file],
                                 dataset_links=self.inputs.dataset_links,
                                 out_dir=self.inputs.out_dir,
@@ -772,19 +774,12 @@ def _run_interface(self, runtime):
                         new_confound_df.fillna({column: 0}, inplace=True)
 
                         confounds_metadata[column] = confounds_metadata.get(column, {})
-                        confounds_metadata[column]["Sources"] = make_bids_uri(
+                        confounds_metadata[column]["Sources"] = _get_bidsuris(
                             in_files=[confound_file],
                             dataset_links=self.inputs.dataset_links,
                             out_dir=self.inputs.out_dir,
                         )
 
-                # Collect column metadata
-                for column in new_confound_df.columns:
-                    if column in confound_metadata:
-                        confounds_metadata[column] = confound_metadata[column]
-                    else:
-                        confounds_metadata[column] = {}
-
             else:  # Voxelwise confounds
                 confound_img = nb.load(confound_file)
                 if confound_img.ndim == 2:  # CIFTI
@@ -804,7 +799,7 @@ def _run_interface(self, runtime):
                 # Collect image metadata
                 new_confound_df.loc[:, confound_name] = np.nan  # fill with NaNs as a placeholder
                 confounds_metadata[confound_name] = confound_metadata
-                confounds_metadata[confound_name]["Sources"] = make_bids_uri(
+                confounds_metadata[confound_name]["Sources"] = _get_bidsuris(
                     in_files=[confound_file],
                     dataset_links=self.inputs.dataset_links,
                     out_dir=self.inputs.out_dir,
@@ -815,7 +810,7 @@ def _run_interface(self, runtime):
                 )
 
         # This actually gets overwritten in init_postproc_derivatives_wf.
-        confounds_metadata["Sources"] = make_bids_uri(
+        confounds_metadata["Sources"] = _get_bidsuris(
             in_files=confound_files,
             dataset_links=self.inputs.dataset_links,
             out_dir=self.inputs.out_dir,

xcp_d/interfaces/connectivity.py

@@ -31,7 +31,7 @@ class _NiftiParcellateInputSpec(BaseInterfaceInputSpec):
     atlas = File(exists=True, mandatory=True, desc="atlas file")
     atlas_labels = File(exists=True, mandatory=True, desc="atlas labels file")
     min_coverage = traits.Float(
-        default=0.5,
+        0.5,
         usedefault=True,
         desc=(
             "Coverage threshold to apply to parcels. "

xcp_d/interfaces/nilearn.py

@@ -261,9 +261,9 @@ class _DenoiseImageInputSpec(BaseInterfaceInputSpec):
     )
     TR = traits.Float(mandatory=True, desc="Repetition time")
     bandpass_filter = traits.Bool(mandatory=True, desc="To apply bandpass or not")
-    low_pass = traits.Float(mandatory=True, default_value=0.10, desc="Lowpass filter in Hz")
-    high_pass = traits.Float(mandatory=True, default_value=0.01, desc="Highpass filter in Hz")
-    filter_order = traits.Int(mandatory=True, default_value=2, desc="Filter order")
+    low_pass = traits.Float(mandatory=True, desc="Lowpass filter in Hz")
+    high_pass = traits.Float(mandatory=True, desc="Highpass filter in Hz")
+    filter_order = traits.Int(mandatory=True, desc="Filter order")
 
 
 class _DenoiseImageOutputSpec(TraitedSpec):

xcp_d/interfaces/plotting.py

@@ -368,7 +368,7 @@ class _QCPlotsESInputSpec(BaseInterfaceInputSpec):
         Undefined,
         desc="TSV file with temporal mask.",
     )
-    TR = traits.Float(default_value=1, desc="Repetition time")
+    TR = traits.Float(1, usedefault=True, desc="Repetition time")
     standardize = traits.Bool(
         mandatory=True,
         desc=(
@@ -529,7 +529,6 @@ class _SlicesDirInputSpec(FSLCommandInputSpec):
 
     out_extension = traits.Enum(
         (".gif", ".png", ".svg"),
-        default=".gif",
         usedefault=True,
         desc="Convenience parameter to let xcp_d select the extension.",
     )
@@ -670,27 +669,27 @@ class _PlotCiftiParcellationInputSpec(BaseInterfaceInputSpec):
         desc="Labels for the CIFTI files.",
     )
     out_file = File(
+        "plot.svg",
         exists=False,
         mandatory=False,
         desc="Output file.",
-        default="plot.svg",
         usedefault=True,
     )
     vmin = traits.Float(
+        0,
         mandatory=False,
-        default_value=0,
         usedefault=True,
         desc="Minimum value for the colormap.",
     )
     vmax = traits.Float(
+        0,
         mandatory=False,
-        default_value=0,
         usedefault=True,
         desc="Maximum value for the colormap.",
     )
     base_desc = traits.Str(
+        "",
         mandatory=False,
-        default_value="",
         usedefault=True,
         desc="Base description for the output file.",
     )
@@ -910,8 +909,8 @@ class _PlotDenseCiftiInputSpec(BaseInterfaceInputSpec):
         desc="CIFTI file to plot.",
     )
     base_desc = traits.Str(
+        "",
         mandatory=False,
-        default_value="",
         usedefault=True,
         desc="Base description for the output file.",
     )

xcp_d/interfaces/workbench.py

@@ -622,7 +622,7 @@ class _CiftiParcellateWorkbenchInputSpec(_WBCommandInputSpec):
         "MODE",
         "COUNT_NONZERO",
         position=12,
-        default="MEAN",
+        usedefault=True,
         argstr="-method %s",
         desc="Specify method of parcellation (default MEAN, or MODE if label data)",
     )
@@ -709,7 +709,7 @@ class _CiftiSurfaceResampleInputSpec(_WBCommandInputSpec):
             "The BARYCENTRIC method is generally recommended for anatomical surfaces, "
             "in order to minimize smoothing."
         ),
-        default="BARYCENTRIC",
+        usedefault=True,
     )
     out_file = File(
         name_source=["in_file"],

xcp_d/tests/data/test_fmriprep_without_freesurfer_outputs.txt

@@ -5,6 +5,7 @@ atlases/atlas-4S156Parcels/atlas-4S156Parcels_space-MNI152NLin2009cAsym_dseg.jso
 atlases/atlas-4S156Parcels/atlas-4S156Parcels_space-MNI152NLin2009cAsym_dseg.nii.gz
 atlases/atlas-Schaefer100
 atlases/atlas-Schaefer100/atlas-Schaefer100_dseg.tsv
+atlases/atlas-Schaefer100/atlas-Schaefer100_space-MNI152NLin2009cAsym_dseg.json
 atlases/atlas-Schaefer100/atlas-Schaefer100_space-MNI152NLin2009cAsym_dseg.nii.gz
 atlases/dataset_description.json
 dataset_description.json