Collect associated files at beginning of workflow (#246)

* Use m0scan as available.

* Fix.

* Update bids.py

* Release strict.

* Remove "bold" from collected data.

* Fix collect_run_data.

* Fix.

* Whoops.

* Fix the BIDSDataGrabber.

* Don't find aslcontext in ExtractCBForDeltaM.

* Update base.py

aslprep/interfaces/bids.py

@@ -3,10 +3,19 @@
 from pathlib import Path
 
 from bids.layout import Config
-from nipype import logging
+from nipype.interfaces.base import (
+    BaseInterfaceInputSpec,
+    OutputMultiObject,
+    SimpleInterface,
+    Str,
+    TraitedSpec,
+    traits,
+)
 from niworkflows.interfaces.bids import DerivativesDataSink as BaseDerivativesDataSink
 from pkg_resources import resource_filename as pkgrf
 
+from aslprep import config
+
 # NOTE: Modified for aslprep's purposes
 aslprep_spec = loads(Path(pkgrf("aslprep", "data/aslprep_bids_config.json")).read_text())
 bids_config = Config.load("bids")
@@ -19,7 +28,62 @@
 merged_entities = [{"name": k, "pattern": v} for k, v in merged_entities.items()]
 config_entities = frozenset({e["name"] for e in merged_entities})
 
-LOGGER = logging.getLogger("nipype.interface")
+
+class _BIDSDataGrabberInputSpec(BaseInterfaceInputSpec):
+    subject_data = traits.Dict(Str, traits.Any)
+    subject_id = Str()
+
+
+class _BIDSDataGrabberOutputSpec(TraitedSpec):
+    out_dict = traits.Dict(desc="output data structure")
+    fmap = OutputMultiObject(desc="output fieldmaps")
+    bold = OutputMultiObject(desc="output functional images")
+    sbref = OutputMultiObject(desc="output sbrefs")
+    t1w = OutputMultiObject(desc="output T1w images")
+    roi = OutputMultiObject(desc="output ROI images")
+    t2w = OutputMultiObject(desc="output T2w images")
+    flair = OutputMultiObject(desc="output FLAIR images")
+    asl = OutputMultiObject(desc="output ASL images")
+
+
+class BIDSDataGrabber(SimpleInterface):
+    """Collect files from a BIDS directory structure."""
+
+    input_spec = _BIDSDataGrabberInputSpec
+    output_spec = _BIDSDataGrabberOutputSpec
+    _require_funcs = True
+
+    def __init__(self, *args, **kwargs):
+        anat_only = kwargs.pop("anat_only")
+        super(BIDSDataGrabber, self).__init__(*args, **kwargs)
+        if anat_only is not None:
+            self._require_funcs = not anat_only
+
+    def _run_interface(self, runtime):
+        bids_dict = self.inputs.subject_data
+
+        self._results["out_dict"] = bids_dict
+        self._results.update(bids_dict)
+
+        if not bids_dict["t1w"]:
+            raise FileNotFoundError(
+                f"No T1w images found for subject sub-{self.inputs.subject_id}"
+            )
+
+        if self._require_funcs and not bids_dict["asl"]:
+            raise FileNotFoundError(
+                f"No ASL images found for subject sub-{self.inputs.subject_id}"
+            )
+
+        for imtype in ["t2w", "flair", "fmap", "sbref", "roi", "asl"]:
+            if not bids_dict[imtype]:
+                config.loggers.interface.info(
+                    'No "%s" images found for sub-%s',
+                    imtype,
+                    self.inputs.subject_id,
+                )
+
+        return runtime
 
 
 class DerivativesDataSink(BaseDerivativesDataSink):

aslprep/interfaces/cbf_computation.py

@@ -21,13 +21,7 @@
 from nipype.utils.filemanip import fname_presuffix
 
 from aslprep import config
-from aslprep.utils.misc import (
-    _getcbfscore,
-    _scrubcbf,
-    parcellate_cbf,
-    pcasl_or_pasl,
-    readjson,
-)
+from aslprep.utils.misc import _getcbfscore, _scrubcbf, parcellate_cbf, pcasl_or_pasl
 from aslprep.utils.qc import (
     cbf_qei,
     coverage,
@@ -82,14 +76,28 @@ def _run_interface(self, runtime):
 class _ExtractCBFInputSpec(BaseInterfaceInputSpec):
     name_source = File(exists=True, mandatory=True, desc="raw asl file")
     asl_file = File(exists=True, mandatory=True, desc="preprocessed asl file")
+    metadata = traits.Dict(mandatory=True, desc="metadata for ASL file")
     aslcontext = File(exists=True, mandatory=True, desc="aslcontext TSV file for run.")
+    m0scan = traits.Either(
+        File(exists=True),
+        None,
+        mandatory=True,
+        desc="m0scan file associated with the ASL file. Only defined if M0Type is 'Separate'.",
+    )
+    m0scan_metadata = traits.Either(
+        traits.Dict,
+        None,
+        mandatory=True,
+        desc="metadata for M0 scan. Only defined if M0Type is 'Separate'.",
+    )
     in_mask = File(exists=True, mandatory=True, desc="mask")
     dummy_vols = traits.Int(
-        default_value=0, exit=False, mandatory=False, desc="remove first n volumes"
+        default_value=0,
+        use_default=True,
+        mandatory=False,
+        desc="remove first n volumes",
     )
-    in_metadata = traits.Dict(exists=True, mandatory=True, desc="metadata for asl or deltam ")
-    bids_dir = traits.Str(exits=True, mandatory=True, desc=" bids directory")
-    fwhm = traits.Float(default_value=5, exists=True, mandatory=False, desc="fwhm")
+    fwhm = traits.Float(default_value=5, use_default=True, mandatory=False, desc="fwhm")
 
 
 class _ExtractCBFOutputSpec(TraitedSpec):
@@ -125,7 +133,7 @@ class ExtractCBF(SimpleInterface):
 
     def _run_interface(self, runtime):
         aslcontext = pd.read_table(self.inputs.aslcontext)
-        metadata = self.inputs.in_metadata.copy()
+        metadata = self.inputs.metadata.copy()
 
         mask_data = nb.load(self.inputs.in_mask).get_fdata()
 
@@ -143,17 +151,12 @@ def _run_interface(self, runtime):
 
         # extract m0 file and register it to ASL if separate
         if metadata["M0Type"] == "Separate":
-            m0file = self.inputs.in_file.replace("asl.nii.gz", "m0scan.nii.gz")
-            m0file_metadata = readjson(m0file.replace("nii.gz", "json"))
-            aslfile_linkedM0 = os.path.abspath(
-                os.path.join(self.inputs.bids_dir, m0file_metadata["IntendedFor"])
-            )
-            if self.inputs.in_file not in aslfile_linkedM0:
-                raise RuntimeError("there is no separate m0scan for the asl data")
+            m0file = self.inputs.m0scan
+            m0file_metadata = self.inputs.m0scan_metadata
 
-            newm0 = fname_presuffix(self.inputs.asl_file, suffix="_m0file")
-            newm0 = regmotoasl(asl=self.inputs.asl_file, m0file=m0file, m02asl=newm0)
-            m0data_smooth = smooth_image(nb.load(newm0), fwhm=self.inputs.fwhm).get_fdata()
+            m0_in_asl = fname_presuffix(self.inputs.asl_file, suffix="_m0file")
+            m0_in_asl = regmotoasl(asl=self.inputs.asl_file, m0file=m0file, m02asl=m0_in_asl)
+            m0data_smooth = smooth_image(nb.load(m0_in_asl), fwhm=self.inputs.fwhm).get_fdata()
             if len(m0data_smooth.shape) > 3:
                 m0data = mask_data * np.mean(m0data_smooth, axis=3)
             else:
@@ -205,10 +208,6 @@ def _run_interface(self, runtime):
         else:
             raise RuntimeError("no pathway to m0scan")
 
-        if asl_data.ndim == 5:
-            # XXX: Why specifically check for 5D data? NIFTIs can go up to 8, I think.
-            raise RuntimeError("Input image (%s) is 5D.", self.inputs.asl_file)
-
         pld = np.array(metadata["PostLabelingDelay"])
         multi_pld = pld.size > 1
         if multi_pld and pld.size != asl_data.shape[3]:
@@ -1174,7 +1173,8 @@ def _run_interface(self, runtime):
 
 
 class _ExtractCBForDeltaMInputSpec(BaseInterfaceInputSpec):
-    in_asl = File(exists=True, mandatory=True, desc="raw asl file")
+    asl_file = File(exists=True, mandatory=True, desc="raw asl file")
+    aslcontext = File(exists=True, mandatory=True, desc="aslcontext TSV file for run.")
     in_aslmask = File(exists=True, mandatory=True, desct="asl mask")
     file_type = traits.Str(desc="file type, c for cbf, d for deltam", mandatory=True)
 
@@ -1195,11 +1195,10 @@ def _run_interface(self, runtime):
             suffix="_cbfdeltam",
             newpath=runtime.cwd,
         )
-        asl_img = nb.load(self.inputs.in_asl)
+        asl_img = nb.load(self.inputs.asl_file)
         asl_data = asl_img.get_fdata()
 
-        # XXX: Not a good way to find the aslcontext file.
-        aslcontext = pd.read_csv(self.inputs.in_asl.replace("_asl.nii.gz", "_aslcontext.tsv"))
+        aslcontext = pd.read_table(self.inputs.aslcontext)
         vol_types = aslcontext["volume_type"].tolist()
         control_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "control"]
         label_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "label"]