ENH: SDC integration

.circleci/config.yml

@@ -1,14 +1,49 @@
+# reusable anchors
+_machine_defaults: &machine_defaults
+  environment:
+    TZ: "/usr/share/zoneinfo/America/Los_Angeles"
+  machine:
+    image: ubuntu-2204:current
+    docker_layer_caching: true
+
+_docker_auth: &docker_auth
+  name: Docker authentication
+  command: |
+    if [[ -n $DOCKER_PAT ]]; then
+      echo "$DOCKER_PAT" | docker login -u $DOCKER_USER --password-stdin
+    fi
+
+_setup_docker_registry: &setup_docker_registry
+  name: Set up Docker registry
+  command: |
+    if [[ -f /tmp/images/registry.tar.gz ]]; then
+      echo "Loading saved registry image"
+      docker load < /tmp/images/registry.tar.gz
+    else
+      echo "Pulling registry image from DockerHub"
+      docker pull registry:2
+    fi
+    docker run -d -p 5000:5000 --restart=always --name=registry \
+        -v /tmp/docker:/var/lib/registry registry:2
+
+_pull_from_registry: &pull_from_registry
+  name: Pull and tag image from local registry
+  command: |
+    docker pull localhost:5000/fmriprep-rodents
+    docker tag localhost:5000/fmriprep-rodents nipreps/fmriprep-rodents:latest
+
+
 version: 2.1
 orbs:
   docker: circleci/docker@2.1.4
 
 jobs:
   build:
-    environment:
-      TZ: "/usr/share/zoneinfo/America/Los_Angeles"
-    docker:
-      - image: cimg/python:3.9
+    <<: *machine_defaults
+    resource_class: large
     working_directory: /tmp/src/fmriprep-rodents
+    environment:
+      DOCKER_BUILDKIT: 1
     steps:
       - checkout
       - run:
@@ -21,39 +56,26 @@ jobs:
               echo "Only docs build"
               circleci step halt
             fi
-
+      - restore_cache:
+          keys:
+            - build-v0-{{ .Branch }}-{{ .Revision }}
+            - build-v0--{{ .Revision }}
+            - build-v0-{{ .Branch }}-
+            - build-v0-master-
+            - build-v0-
+          paths:
+            - /tmp/docker
+            - /tmp/images
       - docker/install-docker-credential-helper
-      - run:
-          name: Docker authentication
-          command: |
-            if [[ -n $DOCKER_PAT ]]; then
-              echo "$DOCKER_PAT" | docker login -u $DOCKER_USER --password-stdin
-            fi
-
-      - setup_remote_docker:
-          docker_layer_caching: true
-
-      - run:
-          name: Set up Docker registry
-          command: |
-            if [[ -f /tmp/images/registry.tar.gz ]]; then
-              echo "Loading saved registry image"
-              docker load < /tmp/images/registry.tar.gz
-            else
-              echo "Pulling registry image from DockerHub"
-              docker pull registry:2
-            fi
-            docker run -d -p 5000:5000 --restart=always --name=registry \
-                -v /tmp/docker:/var/lib/registry registry:2
-
+      - run: *docker_auth
+      - run: *setup_docker_registry
       - run:
           name: Save docker registry
           command: |
             if [[ ! -f /tmp/images/registry.tar.gz ]]; then
               mkdir -p /tmp/images
               docker save registry:2 | gzip > /tmp/images/registry.tar.gz
             fi
-
       - run:
           name: Pull Ubuntu/jammy image
           command: |
@@ -77,7 +99,6 @@ jobs:
                 docker tag ubuntu:jammy localhost:5000/ubuntu
                 docker push localhost:5000/ubuntu
             fi
-
       - run:
           name: Pull a base image if not cached
           no_output_timeout: 60m
@@ -94,7 +115,6 @@ jobs:
                 echo "Pulling from Docker Hub"
                 docker pull nipreps/fmriprep-rodents:latest
             fi
-
       - run:
           name: Build Docker image
           no_output_timeout: 60m
@@ -116,7 +136,6 @@ jobs:
 
             # Build docker image
             make docker-build version="${CIRCLE_TAG:-$THISVERSION}"
-
       - run:
           name: Check Docker image
           command: |
@@ -218,7 +237,6 @@ jobs:
           key: data-se-v0-{{ .Branch }}-{{ .Revision }}-{{ epoch }}
           paths:
             - /tmp/data/nirodents-bold-se
-
       - run:
           name: Store FreeSurfer license file
           command: |
@@ -240,10 +258,8 @@ jobs:
             - config/nipype.cfg
 
   smoke_test:
-    machine:
-      image: ubuntu-2204:current
-      docker_layer_caching: true
-      resource_class: xlarge
+    <<: *machine_defaults
+    resource_class: xlarge
     working_directory: /tmp/rodent-se
     environment:
       - FS_LICENSE: /tmp/fslicense/license.txt
@@ -269,6 +285,9 @@ jobs:
       - restore_cache:
           keys:
             - build-v0-{{ .Branch }}-{{ .Revision }}
+          paths:
+            - /tmp/docker
+            - /tmp/images
       - restore_cache:
           keys:
             - data-se-v0-{{ .Branch }}-{{ .Revision }}-{{ epoch }}
@@ -281,6 +300,10 @@ jobs:
             - nirodents-bold-se-anat-v01-{{ .Branch }}
             - nirodents-bold-se-anat-v01-master
             - nirodents-bold-se-anat-v01-
+      - docker/install-docker-credential-helper
+      - run: *docker_auth
+      - run: *setup_docker_registry
+      - run: *pull_from_registry
       - run:
           name: Run anatomical workflow on rodents
           no_output_timeout: 2h

fprodents/data/translation_rigid.json

@@ -2,23 +2,21 @@
     "dimension": 3,
     "float": true,
     "winsorize_lower_quantile": 0.005,
-    "winsorize_upper_quantile": 0.998,
+    "winsorize_upper_quantile": 0.995,
     "collapse_output_transforms": true,
     "write_composite_transform": true,
-    "use_histogram_matching": [ true, true ],
-    "use_estimate_learning_rate_once": [ true, true ],
-    "transforms": [ "Translation", "Rigid" ],
-    "number_of_iterations": [ [ 500 ], [ 200 ] ],
-    "transform_parameters": [ [ 0.05 ], [ 0.01 ] ],
-    "convergence_threshold": [ 1e-07, 1e-08 ],
-    "convergence_window_size": [ 200, 100 ],
-    "metric": [ "Mattes", "Mattes" ],
-    "sampling_percentage": [ 0.5, 0.5 ],
-    "sampling_strategy": [ "Random", "Random" ],
-    "smoothing_sigmas": [ [ 8.0 ], [ 2.0 ] ],
-    "sigma_units": [ "mm", "mm" ],
-    "metric_weight": [ 1.0, 1.0 ],
-    "shrink_factors": [ [ 2 ], [ 1 ] ],
-    "radius_or_number_of_bins": [ 64, 64 ],
+    "use_histogram_matching": [ true ],
+    "use_estimate_learning_rate_once": [ true ],
+    "transforms": [ "Rigid" ],
+    "number_of_iterations": [ [ 500, 250, 100 ] ],
+    "transform_parameters": [ [ 0.1 ] ],
+    "metric": [ "Mattes" ],
+    "sampling_percentage": [ 0.3 ],
+    "sampling_strategy": [ "Regular" ],
+    "smoothing_sigmas": [ [ 2.0, 1.0, 0.0 ] ],
+    "sigma_units": [ "vox" ],
+    "metric_weight": [ 1.0 ],
+    "shrink_factors": [ [ 4, 2, 1 ] ],
+    "radius_or_number_of_bins": [ 32 ],
     "interpolation": "LanczosWindowedSinc"
 }

fprodents/interfaces/confounds.py

@@ -12,6 +12,7 @@
 import os
 import re
 import shutil
+import nibabel as nb
 import numpy as np
 import pandas as pd
 from nipype import logging
@@ -25,6 +26,8 @@
     isdefined,
     SimpleInterface,
 )
+from niworkflows.utils.timeseries import _nifti_timeseries
+from niworkflows.viz.plots import fMRIPlot
 
 LOGGER = logging.getLogger("nipype.interface")
 
@@ -315,28 +318,20 @@ def _get_ica_confounds(ica_out_dir, skip_vols, newpath=None):
 
 
 class FMRISummaryInputSpec(BaseInterfaceInputSpec):
-    in_func = File(
-        exists=True,
-        mandatory=True,
-        desc="input BOLD time-series (4D file) or dense timeseries CIFTI",
-    )
-    in_mask = File(exists=True, desc="3D brain mask")
-    in_segm = File(exists=True, desc="resampled segmentation")
+    in_nifti = File(exists=True, mandatory=True, desc="input BOLD (4D NIfTI file)")
+    in_segm = File(exists=True, desc="volumetric segmentation corresponding to in_nifti")
     confounds_file = File(exists=True, desc="BIDS' _confounds.tsv file")
 
     str_or_tuple = traits.Either(
         traits.Str,
         traits.Tuple(traits.Str, traits.Either(None, traits.Str)),
-        traits.Tuple(
-            traits.Str, traits.Either(None, traits.Str), traits.Either(None, traits.Str)
-        ),
+        traits.Tuple(traits.Str, traits.Either(None, traits.Str), traits.Either(None, traits.Str)),
     )
     confounds_list = traits.List(
-        str_or_tuple,
-        minlen=1,
-        desc="list of headers to extract from the confounds_file",
+        str_or_tuple, minlen=1, desc='list of headers to extract from the confounds_file'
     )
     tr = traits.Either(None, traits.Float, usedefault=True, desc="the repetition time")
+    drop_trs = traits.Int(0, usedefault=True, desc="dummy scans")
 
 
 class FMRISummaryOutputSpec(TraitedSpec):
@@ -352,22 +347,26 @@ class FMRISummary(SimpleInterface):
     output_spec = FMRISummaryOutputSpec
 
     def _run_interface(self, runtime):
-        from niworkflows.viz.plots import fMRIPlot
+        self._results['out_file'] = fname_presuffix(
+            self.inputs.in_nifti, suffix='_fmriplot.svg', use_ext=False, newpath=runtime.cwd
+        )
 
-        self._results["out_file"] = fname_presuffix(
-            self.inputs.in_func,
-            suffix="_fmriplot.svg",
-            use_ext=False,
-            newpath=runtime.cwd,
+        # Read input object and create timeseries + segments object
+        seg_file = self.inputs.in_segm if isdefined(self.inputs.in_segm) else None
+        dataset, segments = _nifti_timeseries(
+            nb.load(self.inputs.in_nifti),
+            nb.load(seg_file),
+            remap_rois=False,
+            labels=(("GM", "WM", "CSF", "other")),
         )
 
         dataframe = pd.read_csv(
             self.inputs.confounds_file,
             sep="\t",
             index_col=None,
-            dtype="float32",
+            dtype='float32',
             na_filter=True,
-            na_values="n/a",
+            na_values='n/a',
         )
 
         headers = []
@@ -391,20 +390,16 @@ def _run_interface(self, runtime):
         else:
             data = dataframe[headers]
 
-        colnames = data.columns.ravel().tolist()
-
-        for name, newname in list(names.items()):
-            colnames[colnames.index(name)] = newname
-
-        data.columns = colnames
+        data = data.rename(columns=names)
 
         fig = fMRIPlot(
-            self.inputs.in_func,
-            mask_file=self.inputs.in_mask if isdefined(self.inputs.in_mask) else None,
-            seg_file=(self.inputs.in_segm if isdefined(self.inputs.in_segm) else None),
+            dataset,
+            segments=segments,
             tr=self.inputs.tr,
-            data=data,
+            confounds=data,
             units=units,
+            nskip=self.inputs.drop_trs,
+            paired_carpet=False,
         ).plot()
         fig.savefig(self._results["out_file"], bbox_inches="tight")
         return runtime

fprodents/patch/workflows/anatomical.py

@@ -287,7 +287,10 @@ def init_anat_preproc_wf(
 
     # 1. Anatomical reference generation - average input T1w images.
     anat_template_wf = init_anat_template_wf(
-        longitudinal=longitudinal, omp_nthreads=omp_nthreads, num_t1w=num_t2w
+        longitudinal=longitudinal,
+        omp_nthreads=omp_nthreads,
+        num_files=num_t2w,
+        contrast="T2w"
     )
 
     anat_validate = pe.Node(
@@ -338,15 +341,15 @@ def _check_img(img):
     # fmt:off
     workflow.connect([
         # Step 1.
-        (inputnode, anat_template_wf, [('t2w', 'inputnode.t1w')]),
+        (inputnode, anat_template_wf, [('t2w', 'inputnode.anat_files')]),
         (anat_template_wf, anat_validate, [
-            ('outputnode.t1w_ref', 'in_file')]),
+            ('outputnode.anat_ref', 'in_file')]),
         (anat_validate, brain_extraction_wf, [
             ('out_file', 'inputnode.in_files')]),
         (brain_extraction_wf, outputnode, [
             (('outputnode.out_corrected', _pop), 't2w_preproc')]),
         (anat_template_wf, outputnode, [
-            ('outputnode.t1w_realign_xfm', 't2w_ref_xfms')]),
+            ('outputnode.anat_realign_xfm', 't2w_ref_xfms')]),
         (buffernode, outputnode, [('t2w_brain', 't2w_brain'),
                                   ('t2w_mask', 't2w_mask')]),
         # Steps 2 and 3
@@ -389,7 +392,7 @@ def _check_img(img):
     workflow.connect([
         # Connect derivatives
         (anat_template_wf, anat_derivatives_wf, [
-            ('outputnode.t1w_valid_list', 'inputnode.source_files')]),
+            ('outputnode.anat_valid_list', 'inputnode.source_files')]),
         (anat_norm_wf, anat_derivatives_wf, [
             ('outputnode.template', 'inputnode.template'),
             ('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),