[ENH]: Move data downloading/handling to junifer-data package.

docs/changes/newsfragments/418.enh

@@ -0,0 +1 @@
+Adapt usage of ``junifer-data`` DataLad dataset to fetch parcellations, masks, coordinates and xfms by `Synchon Mandal`_

docs/changes/newsfragments/418.misc

@@ -0,0 +1 @@
+Remove ``httpx`` as a dependency by `Synchon Mandal`_

junifer/cli/tests/test_cli_utils.py

@@ -43,7 +43,6 @@ def test_get_dependency_information_short() -> None:
         "nilearn",
         "sqlalchemy",
         "ruamel.yaml",
-        "httpx",
         "tqdm",
         "templateflow",
         "lapy",
@@ -73,7 +72,6 @@ def test_get_dependency_information_long() -> None:
         "nilearn",
         "sqlalchemy",
         "ruamel.yaml",
-        "httpx",
         "tqdm",
         "templateflow",
         "lapy",

junifer/data/coordinates/_coordinates.py

@@ -9,12 +9,13 @@
 
 import numpy as np
 import pandas as pd
+from junifer_data import get
 from numpy.typing import ArrayLike
 
 from ...utils import logger, raise_error
 from ...utils.singleton import Singleton
 from ..pipeline_data_registry_base import BasePipelineDataRegistry
-from ..utils import get_native_warper
+from ..utils import JUNIFER_DATA_VERSION, get_dataset_path, get_native_warper
 from ._ants_coordinates_warper import ANTsCoordinatesWarper
 from ._fsl_coordinates_warper import FSLCoordinatesWarper
 
@@ -32,104 +33,104 @@ class CoordinatesRegistry(BasePipelineDataRegistry, metaclass=Singleton):
 
     def __init__(self) -> None:
         """Initialize the class."""
+        super().__init__()
         # Each entry in registry is a dictionary that must contain at least
         # the following keys:
         # * 'space': the coordinates' space (e.g., 'MNI')
-        # The built-in coordinates are files that are shipped with the package
-        # in the data/VOIs directory. The user can also register their own
+        # The built-in coordinates are files that are shipped with the
+        # junifer-data dataset. The user can also register their own
         # coordinates, which will be stored as numpy arrays in the dictionary.
         # Make built-in and external dictionaries for validation later
         self._builtin = {}
         self._external = {}
 
-        # Path to the metadata of the VOIs
-        _vois_meta_path = Path(__file__).parent / "VOIs" / "meta"
-
-        self._builtin = {
-            "CogAC": {
-                "path": _vois_meta_path / "CogAC_VOIs.txt",
-                "space": "MNI",
-            },
-            "CogAR": {
-                "path": _vois_meta_path / "CogAR_VOIs.txt",
-                "space": "MNI",
-            },
-            "DMNBuckner": {
-                "path": _vois_meta_path / "DMNBuckner_VOIs.txt",
-                "space": "MNI",
-            },
-            "eMDN": {
-                "path": _vois_meta_path / "eMDN_VOIs.txt",
-                "space": "MNI",
-            },
-            "Empathy": {
-                "path": _vois_meta_path / "Empathy_VOIs.txt",
-                "space": "MNI",
-            },
-            "eSAD": {
-                "path": _vois_meta_path / "eSAD_VOIs.txt",
-                "space": "MNI",
-            },
-            "extDMN": {
-                "path": _vois_meta_path / "extDMN_VOIs.txt",
-                "space": "MNI",
-            },
-            "Motor": {
-                "path": _vois_meta_path / "Motor_VOIs.txt",
-                "space": "MNI",
-            },
-            "MultiTask": {
-                "path": _vois_meta_path / "MultiTask_VOIs.txt",
-                "space": "MNI",
-            },
-            "PhysioStress": {
-                "path": _vois_meta_path / "PhysioStress_VOIs.txt",
-                "space": "MNI",
-            },
-            "Rew": {
-                "path": _vois_meta_path / "Rew_VOIs.txt",
-                "space": "MNI",
-            },
-            "Somatosensory": {
-                "path": _vois_meta_path / "Somatosensory_VOIs.txt",
-                "space": "MNI",
-            },
-            "ToM": {
-                "path": _vois_meta_path / "ToM_VOIs.txt",
-                "space": "MNI",
-            },
-            "VigAtt": {
-                "path": _vois_meta_path / "VigAtt_VOIs.txt",
-                "space": "MNI",
-            },
-            "WM": {
-                "path": _vois_meta_path / "WM_VOIs.txt",
-                "space": "MNI",
-            },
-            "Power": {
-                "path": _vois_meta_path / "Power2011_MNI_VOIs.txt",
-                "space": "MNI",
-            },
-            "Power2011": {
-                "path": _vois_meta_path / "Power2011_MNI_VOIs.txt",
-                "space": "MNI",
-            },
-            "Dosenbach": {
-                "path": _vois_meta_path / "Dosenbach2010_MNI_VOIs.txt",
-                "space": "MNI",
-            },
-            "Power2013": {
-                "path": _vois_meta_path / "Power2013_MNI_VOIs.tsv",
-                "space": "MNI",
-            },
-            "AutobiographicalMemory": {
-                "path": _vois_meta_path / "AutobiographicalMemory_VOIs.txt",
-                "space": "MNI",
-            },
-        }
-
-        # Set built-in to registry
-        self._registry = self._builtin
+        self._builtin.update(
+            {
+                "CogAC": {
+                    "file_path_suffix": "CogAC_VOIs.txt",
+                    "space": "MNI",
+                },
+                "CogAR": {
+                    "file_path_suffix": "CogAR_VOIs.txt",
+                    "space": "MNI",
+                },
+                "DMNBuckner": {
+                    "file_path_suffix": "DMNBuckner_VOIs.txt",
+                    "space": "MNI",
+                },
+                "eMDN": {
+                    "file_path_suffix": "eMDN_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Empathy": {
+                    "file_path_suffix": "Empathy_VOIs.txt",
+                    "space": "MNI",
+                },
+                "eSAD": {
+                    "file_path_suffix": "eSAD_VOIs.txt",
+                    "space": "MNI",
+                },
+                "extDMN": {
+                    "file_path_suffix": "extDMN_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Motor": {
+                    "file_path_suffix": "Motor_VOIs.txt",
+                    "space": "MNI",
+                },
+                "MultiTask": {
+                    "file_path_suffix": "MultiTask_VOIs.txt",
+                    "space": "MNI",
+                },
+                "PhysioStress": {
+                    "file_path_suffix": "PhysioStress_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Rew": {
+                    "file_path_suffix": "Rew_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Somatosensory": {
+                    "file_path_suffix": "Somatosensory_VOIs.txt",
+                    "space": "MNI",
+                },
+                "ToM": {
+                    "file_path_suffix": "ToM_VOIs.txt",
+                    "space": "MNI",
+                },
+                "VigAtt": {
+                    "file_path_suffix": "VigAtt_VOIs.txt",
+                    "space": "MNI",
+                },
+                "WM": {
+                    "file_path_suffix": "WM_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Power": {
+                    "file_path_suffix": "Power2011_MNI_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Power2011": {
+                    "file_path_suffix": "Power2011_MNI_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Dosenbach": {
+                    "file_path_suffix": "Dosenbach2010_MNI_VOIs.txt",
+                    "space": "MNI",
+                },
+                "Power2013": {
+                    "file_path_suffix": "Power2013_MNI_VOIs.tsv",
+                    "space": "MNI",
+                },
+                "AutobiographicalMemory": {
+                    "file_path_suffix": "AutobiographicalMemory_VOIs.txt",
+                    "space": "MNI",
+                },
+            }
+        )
+
+        # Update registry with built-in ones
+        self._registry.update(self._builtin)
 
     def register(
         self,
@@ -161,9 +162,9 @@ def register(
         Raises
         ------
         ValueError
-            If the coordinates ``name`` is already registered and
+            If the coordinates ``name`` is a built-in coordinates or
+            if the coordinates ``name`` is already registered and
             ``overwrite=False`` or
-            if the coordinates ``name`` is a built-in coordinates or
             if the ``coordinates`` is not a 2D array or
             if coordinate value does not have 3 components or
             if the ``voi_names`` shape does not match the
@@ -174,19 +175,20 @@ def register(
         """
         # Check for attempt of overwriting built-in coordinates
         if name in self._builtin:
-            if isinstance(self._registry[name].get("path"), Path):
-                raise_error(
-                    f"Coordinates: {name} already registered as built-in "
-                    "coordinates."
-                )
+            raise_error(
+                f"Coordinates: {name} already registered as built-in "
+                "coordinates."
+            )
+        # Check for attempt of overwriting external coordinates
+        if name in self._external:
             if overwrite:
                 logger.info(f"Overwriting coordinates: {name}")
             else:
                 raise_error(
                     f"Coordinates: {name} already registered. "
                     "Set `overwrite=True` to update its value."
                 )
-
+        # Further checks
         if not isinstance(coordinates, np.ndarray):
             raise_error(
                 "Coordinates must be a `numpy.ndarray`, "
@@ -207,6 +209,7 @@ def register(
                 f"Length of `voi_names` ({len(voi_names)}) does not match the "
                 f"number of `coordinates` ({coordinates.shape[0]})."
             )
+        # Registration
         logger.info(f"Registering coordinates: {name}")
         # Add coordinates info
         self._external[name] = {
@@ -257,6 +260,8 @@ def load(self, name: str) -> tuple[ArrayLike, list[str], str]:
         ------
         ValueError
             If ``name`` is invalid.
+        RuntimeError
+            If there is a problem fetching the coordinates file.
 
         """
         # Check for valid coordinates name
@@ -265,17 +270,31 @@ def load(self, name: str) -> tuple[ArrayLike, list[str], str]:
                 f"Coordinates: {name} not found. "
                 f"Valid options are: {self.list}"
             )
-        # Load coordinates
+        # Load coordinates info
         t_coord = self._registry[name]
-        # Load data
-        if isinstance(t_coord.get("path"), Path):
-            logger.debug(f"Loading coordinates {t_coord['path'].absolute()!s}")
+
+        # Load data for in-built ones
+        if t_coord.get("file_path_suffix") is not None:
+            # Set file path to retrieve
+            coords_file_path = Path(
+                f"coordinates/{name}/{t_coord['file_path_suffix']}"
+            )
+            logger.debug(f"Loading coordinates: `{name}`")
             # Load via pandas
-            df_coords = pd.read_csv(t_coord["path"], sep="\t", header=None)
+            df_coords = pd.read_csv(
+                get(
+                    file_path=coords_file_path,
+                    dataset_path=get_dataset_path(),
+                    tag=JUNIFER_DATA_VERSION,
+                ),
+                sep="\t",
+                header=None,
+            )
             # Convert dataframe to numpy ndarray
             coords = df_coords.iloc[:, [0, 1, 2]].to_numpy()
             # Get label names
             names = list(df_coords.iloc[:, [3]].values[:, 0])
+        # Load data for external ones
         else:
             coords = t_coord["coords"]
             names = t_coord["voi_names"]

junifer/data/coordinates/tests/test_coordinates.py

@@ -21,7 +21,6 @@ def test_register_built_in_check() -> None:
             coordinates=np.zeros(2),
             voi_names=["1", "2"],
             space="MNI",
-            overwrite=True,
         )
 
 
@@ -32,14 +31,14 @@ def test_register_overwrite() -> None:
         coordinates=np.zeros((2, 3)),
         voi_names=["roi1", "roi2"],
         space="MNI",
-        overwrite=True,
     )
     with pytest.raises(ValueError, match=r"already registered"):
         CoordinatesRegistry().register(
             name="MyList",
             coordinates=np.ones((2, 3)),
             voi_names=["roi2", "roi3"],
             space="MNI",
+            overwrite=False,
         )
 
     CoordinatesRegistry().register(