[MAINT] Update poetry lock (aramis-lab#1531)

* Update poetry lock

* Nilearn:from Mesh to InMemoryMesh

* Restrict pydra temporarily

* Add antspy tests for instantiation and regression

* Fix client test

* Actually fix test client

* Fix anat linear with antspynet

* Fix client merge_tsv

Author: AliceJoubert

clinica/iotools/merge_tsv.py

@@ -57,11 +57,11 @@ def merge_tsv(
 
     ignore_session_scan_files : bool, optional
     """
-    from clinica.utils.inputs import check_bids_folder
+    from clinica.dataset import check_bids_dataset
 
     from .data_handling import create_merge_file
 
-    check_bids_folder(bids_directory)
+    check_bids_dataset(bids_directory)
 
     atlas_selection = []
     if volume_atlas_selection is not None:

clinica/pipelines/statistics_surface/surfstat/_utils.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 import pandas as pd
-from nilearn.surface import Mesh
+from nilearn.surface import InMemoryMesh
 
 TSV_FIRST_COLUMN = "participant_id"
 TSV_SECOND_COLUMN = "session_id"
@@ -116,15 +116,15 @@ def build_thickness_array(
     return thickness
 
 
-def get_average_surface(fsaverage_path: Path) -> Tuple[Dict, Mesh]:
+def get_average_surface(fsaverage_path: Path) -> Tuple[Dict, InMemoryMesh]:
     """This function extracts the average surface and the average mesh
     from the path to the fsaverage templates.
 
     .. note::
 
         Note that the average surface is returned as a dictionary
         with 'coord' and 'tri' as keys, while the average mesh is
-        returned as a Nilearn Mesh object (basically a NamedTuple
+        returned as a Nilearn SurfaceMesh object (basically a NamedTuple
         with 'coordinates' and 'faces' attributes). The surface
         isn't returned as a Nilearn Surface object for compatibility
         with BrainStats.
@@ -145,12 +145,12 @@ def get_average_surface(fsaverage_path: Path) -> Tuple[Dict, Mesh]:
     average_surface : dict
         Average surface as a dictionary for BrainStat compatibility.
 
-    average_mesh : nilearn.surface.Mesh
-        Average mesh as a Nilearn Mesh object.
+    average_mesh : nilearn.surface.SurfaceMesh
+        Average mesh as a Nilearn SurfaceMesh object.
     """
     import copy
 
-    from nilearn.surface import Mesh, load_surf_mesh
+    from nilearn.surface import InMemoryMesh, load_surf_mesh
 
     meshes = [
         load_surf_mesh(str(fsaverage_path / f"{hemi}.pial")) for hemi in ("lh", "rh")
@@ -159,7 +159,7 @@ def get_average_surface(fsaverage_path: Path) -> Tuple[Dict, Mesh]:
     faces = np.vstack(
         [meshes[0].faces, meshes[1].faces + meshes[0].coordinates.shape[0]]
     )
-    average_mesh = Mesh(
+    average_mesh = InMemoryMesh(
         coordinates=coordinates,
         faces=copy.deepcopy(faces),
     )

clinica/pipelines/statistics_surface/surfstat/models/_base.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from brainstat.stats.SLM import SLM
 from brainstat.stats.terms import FixedEffect
-from nilearn.surface import Mesh
+from nilearn.surface import InMemoryMesh
 
 from clinica.utils.stream import cprint
 
@@ -205,7 +205,7 @@ def plot_results(
         self,
         output_dir: Path,
         method: Union[str, List[str]],
-        mesh: Mesh,
+        mesh: InMemoryMesh,
     ) -> None:
         """Plot results to the provided directory.
 
@@ -217,7 +217,7 @@ def plot_results(
         method : str or List[str]
             The method(s) to make the plots.
 
-        mesh : nilearn.surface.Mesh
+        mesh : nilearn.surface.SurfaceMesh
             The mesh on which to plot the result data.
         """
         if not self._is_fitted():

clinica/pipelines/statistics_surface/surfstat/models/results/_plot.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 from typing import Callable
 
-from nilearn.surface import Mesh
+from nilearn.surface import InMemoryMesh
 
 from ._statistics import StatisticsResults
 
@@ -16,11 +16,11 @@ class StatisticsResultsPlotter:
     output_file : PathLike
         Path to the output file.
 
-    mesh : nilearn.surface.Mesh
+    mesh : nilearn.surface.SurfaceMesh
         The mesh to be used for plotting results.
     """
 
-    def __init__(self, output_file: Path, mesh: Mesh):
+    def __init__(self, output_file: Path, mesh: InMemoryMesh):
         self.output_file = output_file
         self.mesh = mesh
         self.plotting_extension = ".png"

clinica/pipelines/t1_linear/anat_linear_utils.py

@@ -153,7 +153,7 @@ def _call_n4_bias_field_correction(
     verbose: bool = False,
 ) -> Path:
     import ants
-    from ants.utils.bias_correction import n4_bias_field_correction
+    from ants.ops.bias_correction import n4_bias_field_correction
 
     return n4_bias_field_correction(
         ants.image_read(str(input_image)),

poetry.lock

@@ -54,7 +54,7 @@ dependencies = [
     "openpyxl",
     "fsspec",
     "pydra-nipype1",
-    "pydra (>=0.22,<1.0)",
+    "pydra (>=0.22,<0.23)",
     "pybids (>=0.16,<1.0)",
     "joblib (>=1.2.0,<2.0)",
     "attrs (>=20.1.0)",

pyproject.toml

@@ -24,7 +24,7 @@ def test_first_lv(generate_cli_first_lv):
     runner = CliRunner()
     cli_input = generate_cli_first_lv
     print(f"Testing input cli {cli_input}")
-    result = runner.invoke(cli, cli_input)
+    result = runner.invoke(cli, cli_input + " -h")
     assert result.exit_code == 0
 
 

test/instantiation/test_cli.py

@@ -468,7 +468,7 @@ def test_instantiate_t1_freesurfer_longitudinal_correction(cmdopt, tmp_path):
     ).build()
 
 
-def test_instantiate_t1_linear(cmdopt, tmp_path):
+def test_instantiate_t1_linear_ants(cmdopt, tmp_path):
     from clinica.pipelines.t1_linear.anat_linear_pipeline import AnatLinear
 
     base_dir = Path(cmdopt["input"])
@@ -484,6 +484,22 @@ def test_instantiate_t1_linear(cmdopt, tmp_path):
     ).build()
 
 
+def test_instantiate_t1_linear_antspy(cmdopt, tmp_path):
+    from clinica.pipelines.t1_linear.anat_linear_pipeline import AnatLinear
+
+    base_dir = Path(cmdopt["input"])
+    working_dir = Path(cmdopt["wd"])
+    input_dir, tmp_dir, ref_dir = configure_paths(base_dir, tmp_path, "T1Linear")
+    AnatLinear(
+        bids_directory=fspath(input_dir / "bids"),
+        caps_directory=fspath(tmp_dir / "caps"),
+        tsv_file=fspath(input_dir / "subjects.tsv"),
+        base_dir=fspath(working_dir),
+        name="t1-linear",
+        parameters={"uncropped_image": False, "use_antspy": True},
+    ).build()
+
+
 def test_instantiate_statistics_volume(cmdopt, tmp_path):
     from clinica.pipelines.statistics_volume.statistics_volume_pipeline import (
         StatisticsVolume,

test/instantiation/test_instantiate_all_pipelines.py

@@ -10,11 +10,12 @@
 
 
 @pytest.mark.fast
-def test_t1_linear(cmdopt, tmp_path):
+@pytest.mark.parametrize("antspy", (False, True))
+def test_t1_linear(cmdopt, tmp_path, antspy):
     base_dir = Path(cmdopt["input"])
     working_dir = Path(cmdopt["wd"])
     input_dir, tmp_dir, ref_dir = configure_paths(base_dir, tmp_path, "T1Linear")
-    run_t1_linear(input_dir, tmp_dir, ref_dir, working_dir)
+    run_t1_linear(input_dir, tmp_dir, ref_dir, working_dir, antspy)
 
 
 @pytest.mark.fast
@@ -26,7 +27,11 @@ def test_flair_linear(cmdopt, tmp_path):
 
 
 def run_t1_linear(
-    input_dir: Path, output_dir: Path, ref_dir: Path, working_dir: Path
+    input_dir: Path,
+    output_dir: Path,
+    ref_dir: Path,
+    working_dir: Path,
+    antspy: bool = False,
 ) -> None:
     from clinica.pipelines.t1_linear.anat_linear_pipeline import AnatLinear
 
@@ -38,7 +43,7 @@ def run_t1_linear(
         caps_directory=fspath(out_caps),
         tsv_file=fspath(input_dir / "subjects.tsv"),
         base_dir=fspath(working_dir),
-        parameters={"uncropped_image": False},
+        parameters={"uncropped_image": False, "use_antspy": antspy},
         name="t1-linear",
     )
     pipeline.run(plugin="MultiProc", plugin_args={"n_procs": 4}, bypass_check=True)