apply to pipelines using groups

clinica/pipelines/machine_learning_spatial_svm/spatial_svm_cli.py

@@ -79,7 +79,6 @@ def cli(
 
     parameters = {
         # Clinica compulsory arguments
-        "group_label": group_label,
         "orig_input_data_ml": orig_input_data_ml,
         # Optional arguments for inputs from pet-volume pipeline
         "acq_label": acq_label,
@@ -95,6 +94,7 @@ def cli(
         base_dir=working_directory,
         parameters=parameters,
         name=pipeline_name,
+        group_label=group_label,
     )
 
     exec_pipeline = (

clinica/pipelines/machine_learning_spatial_svm/spatial_svm_pipeline.py

@@ -1,9 +1,9 @@
 from typing import List
 
-from clinica.pipelines.engine import Pipeline
+from clinica.pipelines.engine import GroupPipeline
 
 
-class SpatialSVM(Pipeline):
+class SpatialSVM(GroupPipeline):
     """SpatialSVM - Prepare input data for SVM with spatial and anatomical regularization.
 
     Returns:
@@ -12,10 +12,6 @@ class SpatialSVM(Pipeline):
 
     def _check_pipeline_parameters(self) -> None:
         """Check pipeline parameters."""
-        from clinica.utils.group import check_group_label
-
-        self.parameters.setdefault("group_label", None)
-        check_group_label(self.parameters["group_label"])
         if "orig_input_data_ml" not in self.parameters.keys():
             raise KeyError(
                 "Missing compulsory orig_input_data key in pipeline parameter."
@@ -70,12 +66,10 @@ def _build_input_node(self):
         )
         from clinica.utils.ux import print_groups_in_caps_directory
 
-        if not (
-            self.caps_directory / "groups" / f"group-{self.parameters['group_label']}"
-        ).exists():
+        if not self.group_directory.exists():
             print_groups_in_caps_directory(self.caps_directory)
             raise ClinicaException(
-                f"Group {self.parameters['group_label']} does not exist. "
+                f"Group {self.group_label} does not exist. "
                 "Did you run pet-volume, t1-volume or t1-volume-create-dartel pipeline?"
             )
 
@@ -93,7 +87,7 @@ def _build_input_node(self):
                     "t1",
                     "spm",
                     "dartel",
-                    "group-" + self.parameters["group_label"],
+                    str(self.group_id),
                     "*_T1w_segm-graymatter_space-Ixi549Space_modulated-on_probability.nii.gz",
                 ),
                 "description": "graymatter tissue segmented in T1w MRI in Ixi549 space",
@@ -136,7 +130,7 @@ def _build_input_node(self):
         try:
             dartel_input = clinica_group_reader(
                 self.caps_directory,
-                t1_volume_final_group_template(self.parameters["group_label"]),
+                t1_volume_final_group_template(self.group_label),
             )
         except ClinicaException as e:
             all_errors.append(e)
@@ -216,24 +210,17 @@ def _build_core_nodes(self):
             datasink.inputs.regexp_substitutions = [
                 (
                     r"(.*)/regularized_image/.*/(.*(sub-(.*)_ses-(.*))_T1w(.*)_probability(.*))$",
-                    r"\1/subjects/sub-\4/ses-\5/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/subjects/sub-\4/ses-\5/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"/\3_T1w\6_spatialregularization\7",
                 ),
                 (
                     r"(.*)json_file/(output_data.json)$",
-                    r"\1/groups/group-"
-                    + self.parameters["group_label"]
-                    + r"/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/groups/{self.group_id}/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"_space-Ixi549Space_parameters.json",
                 ),
                 (
                     r"(.*)fisher_tensor_path/(output_fisher_tensor.npy)$",
-                    r"\1/groups/group-"
-                    + self.parameters["group_label"]
-                    + r"/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/groups/{self.group_id}/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"_space-Ixi549Space_gram.npy",
                 ),
             ]
@@ -242,24 +229,17 @@ def _build_core_nodes(self):
             datasink.inputs.regexp_substitutions = [
                 (
                     r"(.*)/regularized_image/.*/(.*(sub-(.*)_ses-(.*))_(task.*)_pet(.*))$",
-                    r"\1/subjects/sub-\4/ses-\5/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/subjects/sub-\4/ses-\5/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"/\3_\6_spatialregularization\7",
                 ),
                 (
                     r"(.*)json_file/(output_data.json)$",
-                    r"\1/groups/group-"
-                    + self.parameters["group_label"]
-                    + r"/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/groups/{self.group_id}/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"_space-Ixi549Space_parameters.json",
                 ),
                 (
                     r"(.*)fisher_tensor_path/(output_fisher_tensor.npy)$",
-                    r"\1/groups/group-"
-                    + self.parameters["group_label"]
-                    + r"/machine_learning/input_spatial_svm/group-"
-                    + self.parameters["group_label"]
+                    rf"\1/groups/{self.group_id}/machine_learning/input_spatial_svm/{self.group_id}"
                     + r"_space-Ixi549Space_gram.npy",
                 ),
             ]

clinica/pipelines/pet/volume/cli.py

@@ -98,7 +98,6 @@ def cli(
     from .pipeline import PETVolume
 
     parameters = {
-        "group_label": group_label,
         "acq_label": acq_label,
         "suvr_reference_region": suvr_reference_region,
         "reconstruction_method": reconstruction_method,
@@ -117,6 +116,7 @@ def cli(
         base_dir=working_directory,
         parameters=parameters,
         name=pipeline_name,
+        group_label=group_label,
     )
 
     exec_pipeline = (

clinica/pipelines/pet/volume/pipeline.py

@@ -1,7 +1,8 @@
-from typing import List
+from typing import List, Optional
 
 from nipype import config
 
+from clinica.pipelines.engine import GroupPipeline
 from clinica.pipelines.pet.engine import PETPipeline
 
 # Use hash instead of parameters for iterables folder names
@@ -10,7 +11,7 @@
 config.update_config(cfg)
 
 
-class PETVolume(PETPipeline):
+class PETVolume(GroupPipeline, PETPipeline):
     """PETVolume - Volume-based processing of PET images using SPM.
 
     Returns:
@@ -20,11 +21,8 @@ class PETVolume(PETPipeline):
     def _check_pipeline_parameters(self) -> None:
         """Check pipeline parameters."""
         from clinica.utils.atlas import T1AndPetVolumeAtlasName
-        from clinica.utils.group import check_group_label
 
         super()._check_pipeline_parameters()
-        self.parameters.setdefault("group_label", None)
-        check_group_label(self.parameters["group_label"])
         self.parameters.setdefault("pvc_psf_tsv", None)
         self.parameters.setdefault("mask_tissues", [1, 2, 3])
         self.parameters.setdefault("mask_threshold", 0.3)
@@ -110,12 +108,10 @@ def _build_input_node(self):
         )
 
         # Check that group already exists
-        if not (
-            self.caps_directory / "groups" / f"group-{self.parameters['group_label']}"
-        ).exists():
+        if not self.group_directory.exists():
             print_groups_in_caps_directory(self.caps_directory)
             raise ClinicaException(
-                f"Group {self.parameters['group_label']} does not exist. "
+                f"Group {self.group_label} does not exist. "
                 "Did you run t1-volume or t1-volume-create-dartel pipeline?"
             )
 
@@ -170,21 +166,21 @@ def _build_input_node(self):
             self.subjects,
             self.sessions,
             self.caps_directory,
-            t1_volume_deformation_to_template(self.parameters["group_label"]),
+            t1_volume_deformation_to_template(self.group_label),
         )
         if flowfields_errors:
             all_errors.append(
                 format_clinica_file_reader_errors(
                     flowfields_errors,
-                    t1_volume_deformation_to_template(self.parameters["group_label"]),
+                    t1_volume_deformation_to_template(self.group_label),
                 )
             )
 
         # Dartel Template
         try:
             final_template = clinica_group_reader(
                 self.caps_directory,
-                t1_volume_final_group_template(self.parameters["group_label"]),
+                t1_volume_final_group_template(self.group_label),
             )
         except ClinicaException as e:
             all_errors.append(e)
@@ -411,7 +407,7 @@ def _build_output_node(self):
                                 fix_join,
                                 "pet",
                                 "preprocessing",
-                                f"group-{self.parameters['group_label']}",
+                                str(self.group_id),
                             ),
                             "container",
                         )
@@ -450,7 +446,7 @@ def _build_output_node(self):
                                 fix_join,
                                 "pet",
                                 "preprocessing",
-                                f"group-{self.parameters['group_label']}",
+                                str(self.group_id),
                             ),
                             "container",
                         )

clinica/pipelines/statistics_surface/_utils.py

@@ -1,6 +1,8 @@
 from pathlib import Path
 from typing import Optional
 
+from clinica.utils.group import GroupID, GroupLabel
+
 __all__ = [
     "init_input_node",
     "run_clinica_surfstat",
@@ -39,7 +41,7 @@ def get_pet_surface_custom_file(acq_label: str, suvr_reference_region: str) -> s
     )
 
 
-def init_input_node(parameters: dict, base_dir, subjects_visits_tsv):
+def init_input_node(parameters: dict, group_id: GroupID, base_dir, subjects_visits_tsv):
     """Initialize the pipeline.
 
     This function will:
@@ -53,6 +55,9 @@ def init_input_node(parameters: dict, base_dir, subjects_visits_tsv):
     parameters : dict
         The pipeline's parameters.
 
+    group_id: GroupID
+        The group ID.
+
     base_dir : Path
         The path to the pipeline's base directory.
         This is a pathlib Path. No type hints because of Nipype.
@@ -63,9 +68,6 @@ def init_input_node(parameters: dict, base_dir, subjects_visits_tsv):
 
     Returns
     -------
-    group_label : str
-        The group label.
-
     surfstat_results_dir : Path
         The folder which will contain the results for SurfStat.
     """
@@ -75,8 +77,7 @@ def init_input_node(parameters: dict, base_dir, subjects_visits_tsv):
     from clinica.pipelines.statistics_surface._utils import create_glm_info_dictionary
     from clinica.utils.ux import print_begin_image
 
-    group_id = "group-" + parameters["group_label"]
-    surfstat_results_dir = base_dir / group_id
+    surfstat_results_dir = base_dir / str(group_id)
     surfstat_results_dir.mkdir(parents=True, exist_ok=True)
 
     # Save pipeline parameters in JSON file
@@ -97,10 +98,9 @@ def init_input_node(parameters: dict, base_dir, subjects_visits_tsv):
         str(parameters["full_width_at_half_maximum"]),
         str(parameters["cluster_threshold"]),
     ]
-    group_id = "group-" + parameters["group_label"]
     print_begin_image(group_id, list_keys, list_values)
 
-    return parameters["group_label"], surfstat_results_dir
+    return surfstat_results_dir
 
 
 def _get_string_format_from_tsv(tsv_file: Path) -> str:
@@ -189,6 +189,7 @@ def run_clinica_surfstat(
     output_dir,  # Path. no type hint because of Nipype self-contained requirement
     subjects_visits_tsv,  # Path. no type hint because of Nipype self-contained requirement
     pipeline_parameters: dict,
+    group_label: GroupLabel,
 ):
     """Call clinica_surfstat function.
 
@@ -206,6 +207,9 @@ def run_clinica_surfstat(
     pipeline_parameters : dict
         Parameters of StatisticsSurface pipeline.
 
+    group_label: GroupLabel
+        The group label.
+
     Returns
     -------
     output_dir : Path
@@ -224,7 +228,7 @@ def run_clinica_surfstat(
         ),
         pipeline_parameters["contrast"],
         pipeline_parameters["glm_type"],
-        pipeline_parameters["group_label"],
+        str(group_label),
         get_freesurfer_home(),
         pipeline_parameters["measure_label"],
         surface_file=pipeline_parameters["custom_file"],
@@ -234,7 +238,9 @@ def run_clinica_surfstat(
     return output_dir
 
 
-def create_glm_info_dictionary(tsv_file: Path, pipeline_parameters: dict) -> dict:
+def create_glm_info_dictionary(
+    tsv_file: Path, group_label: GroupLabel, pipeline_parameters: dict
+) -> dict:
     """Create dictionary containing the GLM information that will be stored in a JSON file."""
     glm_info = {
         # Clinica compulsory arguments
@@ -245,7 +251,7 @@ def create_glm_info_dictionary(tsv_file: Path, pipeline_parameters: dict) -> dic
         ),
         "StringFormatTSV": _get_string_format_from_tsv(tsv_file),
         "Contrast": pipeline_parameters["contrast"],
-        "GroupLabel": pipeline_parameters["group_label"],
+        "GroupLabel": str(group_label),
         # Optional arguments
         "Covariates": pipeline_parameters["covariates"],
         "FWHM": pipeline_parameters["full_width_at_half_maximum"],
@@ -272,7 +278,7 @@ def save_to_caps(
     source_dir,  # Path. no type hint because of Nipype self-contained requirement
     caps_dir,  # Path. no type hint because of Nipype self-contained requirement
     overwrite_caps: bool,
-    group_label: str,
+    group_id: GroupID,
     glm_type: str,
 ) -> None:
     """Save `source_dir`/ to CAPS folder.
@@ -307,12 +313,12 @@ def save_to_caps(
     destination_dir = (
         caps_dir.expanduser()
         / "groups"
-        / f"group-{group_label}"
+        / str(group_id)
         / "statistics"
         / surfstat_folder
     )
 
     if overwrite_caps:
         raise NotImplementedError("save_to_caps(overwrite_caps=True) not implemented")
     shutil.copytree(source_dir, destination_dir, symlinks=True)
-    print_end_image(f"group-{group_label}")
+    print_end_image(str(group_id))

clinica/pipelines/statistics_surface/cli.py

@@ -132,7 +132,6 @@ def cli(
 
     parameters = {
         # Clinica compulsory arguments
-        "group_label": group_label,
         "orig_input_data": orig_input_data,
         "glm_type": glm_type,
         "contrast": contrast,
@@ -154,6 +153,7 @@ def cli(
         tsv_file=subject_visits_with_covariates_tsv,
         base_dir=working_directory,
         parameters=parameters,
+        group_label=group_label,
         name=pipeline_name,
     )