docs/api/onthefly.rst

@@ -5,5 +5,5 @@ On-the-fly
    :members:
    :imported-members:
 
-.. automodule:: junifer.onthefly.brainprint
+.. automodule:: junifer.onthefly._brainprint
    :members:

docs/builtin.rst

@@ -158,16 +158,16 @@ Available
        | (subject-native or other template spaces)
      - Done
      - 0.0.4
-   * - ``Smoothing``
+   * - :class:`.Smoothing`
      - | Apply smoothing to data, particularly useful when dealing with
        | ``fMRIPrep``-ed data
      - In Progress
      - :gh:`161`
-   * - ``TemporalSlicer``
+   * - :class:`.TemporalSlicer`
      - Slice ``BOLD`` data temporally
      - | Done
      - :gh:`443`
-   * - ``TemporalFilter``
+   * - :class:`.TemporalFilter`
      - Filter (clean) ``BOLD`` data temporally
      - | Done
      - :gh:`432`

docs/changes/newsfragments/364.doc

@@ -0,0 +1 @@
+Add documentation on adding confounds format by `Synchon Mandal`_

docs/changes/newsfragments/364.enh

@@ -0,0 +1 @@
+Adopt Pydantic for user-facing core objects to perform automatic validation using typing annotations by `Synchon Mandal`_

docs/changes/newsfragments/364.feature

@@ -0,0 +1 @@
+Enable confounds format to be added by introducing :func:`.register_confounds_format` by `Synchon Mandal`_

docs/conf.py

@@ -65,6 +65,8 @@ extensions = [
     "sphinx_copybutton",  # copy button for code blocks
     "sphinxcontrib.mermaid",  # mermaid support
     "sphinxcontrib.towncrier.ext",  # towncrier fragment support
+    "sphinxcontrib.autodoc_pydantic",  # autodoc support for pydantic models
+    "enum_tools.autoenum",  # enum support
 ]
 
 if use_multiversion:
@@ -97,6 +99,15 @@ nitpick_ignore_regex = [
     ("py:class", "pipeline.Pipeline"),  # nilearn
     ("py:obj", "neurokit2.*"),  # ignore neurokit2
     ("py:obj", "datalad.*"),  # ignore datalad
+    ("py:obj", "junifer.*"),  # ignore junifer internal
+    ("py:class", "annotated_types.*"),  # ignore pydantic annotated types
+    ("py:obj", "variants"),  # ignore variants
+    ("py:obj", "them"),  # ignore them
+    ("py:class", "junifer.utils.helpers.ensure_list"),  # ignore ensure_list
+    ("py:class", "junifer.utils.helpers.ensure_list_or_none"),  # ignore ensure_list_or_none
+    ("py:class", "PydanticUndefined"),  # ignore PydanticUndefined
+    ("py:class", "FieldInfo"),  # ignore FieldInfo
+    ("py:class", "NoneType"),  # ignore NoneType
 ]
 
 # -- Options for HTML output -------------------------------------------------
@@ -154,6 +165,7 @@ intersphinx_mapping = {
     "pandas": ("https://pandas.pydata.org/pandas-docs/dev", None),
     # "sqlalchemy": ("https://docs.sqlalchemy.org/en/20/", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/", None),
+    "pydantic": ("https://docs.pydantic.dev/latest/", None),
 }
 
 # -- sphinx.ext.extlinks configuration ---------------------------------------

docs/extending/confounds_format.rst

@@ -0,0 +1,22 @@
+.. include:: ../links.inc
+
+.. _adding_confounds_format:
+
+Adding Confounds Format
+=======================
+
+#. Check :ref:`extending junifer <extending_extension>` on how to create a
+   *junifer extension* if you have not done so.
+#. Register the confounds format before defining / using a DataGrabber like so:
+
+   .. code-block:: python
+
+      from junifer.datagrabber import register_confounds_format
+      ...
+
+
+      # registers the confounds format as "confounds" and accessible as
+      # ``ConfoundsFormat.Confounds``
+      register_confounds_format(name="Confounds", alias="confounds")
+
+      ...

docs/extending/datagrabber.rst

@@ -140,29 +140,24 @@ With the variables defined above, we can create our DataGrabber and name it
 
     from pathlib import Path
 
-    from junifer.datagrabber import PatternDataGrabber
+    from junifer.datagrabber import PatternDataGrabber, DataType
+    from junifer.typing import DataGrabberPatterns
 
 
     class ExampleBIDSDataGrabber(PatternDataGrabber):
-        def __init__(self, datadir: str | Path) -> None:
-            types = ["T1w", "BOLD"]
-            patterns = {
-                "T1w": {
-                    "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
-                    "space": "native",
-                },
-                "BOLD": {
-                    "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
-                    "space": "MNI152NLin6Asym",
-                },
-            }
-            replacements = ["subject", "session"]
-            super().__init__(
-                datadir=datadir,
-                types=types,
-                patterns=patterns,
-                replacements=replacements,
-            )
+
+        types: list[DataType] = [DataType.T1w, DataType.BOLD]
+        patterns: DataGrabberPatterns = {
+            "T1w": {
+                "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
+                "space": "native",
+            },
+            "BOLD": {
+                "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+        }
+        replacements: list[str] = ["subject", "session"]
 
 Our DataGrabber is ready to be used by ``junifer``. However, it is still unknown
 to the library. We need to register it in the library. To do so, we need to
@@ -175,29 +170,24 @@ use the :func:`.register_datagrabber` decorator.
 
     from junifer.api.decorators import register_datagrabber
     from junifer.datagrabber import PatternDataGrabber
+    from junifer.typing import DataGrabberPatterns
 
 
     @register_datagrabber
     class ExampleBIDSDataGrabber(PatternDataGrabber):
-        def __init__(self, datadir: str | Path) -> None:
-            types = ["T1w", "BOLD"]
-            patterns = {
-                "T1w": {
-                    "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
-                    "space": "native",
-                },
-                "BOLD": {
-                    "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
-                    "space": "MNI152NLin6Asym",
-                },
-            }
-            replacements = ["subject", "session"]
-            super().__init__(
-                datadir=datadir,
-                types=types,
-                patterns=patterns,
-                replacements=replacements,
-            )
+
+        types: list[DataType] = [DataType.T1w, DataType.BOLD]
+        patterns: DataGrabberPatterns = {
+            "T1w": {
+                "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
+                "space": "native",
+            },
+            "BOLD": {
+                "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+        }
+        replacements: list[str] = ["subject", "session"]
 
 
 Now, we can use our DataGrabber in ``junifer``, by setting the ``datagrabber``
@@ -259,35 +249,30 @@ And we can create our DataGrabber:
 
 .. code-block:: python
 
+    from pathlib import Path
+
     from junifer.api.decorators import register_datagrabber
     from junifer.datagrabber import PatternDataladDataGrabber
+    from pydantic import AnyUrl
 
 
     @register_datagrabber
     class ExampleBIDSDataGrabber(PatternDataladDataGrabber):
-        def __init__(self) -> None:
-            types = ["T1w", "BOLD"]
-            patterns = {
-                "T1w": {
-                    "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
-                    "space": "native",
-                },
-                "BOLD": {
-                    "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
-                    "space": "MNI152NLin6Asym",
-                },
-            }
-            replacements = ["subject", "session"]
-            uri = "https://gin.g-node.org/juaml/datalad-example-bids"
-            rootdir = "example_bids_ses"
-            super().__init__(
-                datadir=None,
-                uri=uri,
-                rootdir=rootdir,
-                types=types,
-                patterns=patterns,
-                replacements=replacements,
-            )
+
+        uri: AnyUrl = "https://gin.g-node.org/juaml/datalad-example-bids"
+        types: list[DataType] = ["T1w", "BOLD"]
+        patterns: DataGrabberPatterns = {
+            "T1w": {
+                "pattern": "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz",
+                "space": "native",
+            },
+            "BOLD": {
+                "pattern": "{subject}/{session}/func/{subject}_{session}_task-rest_bold.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+        }
+        replacements: list[str] = ["subject", "session"]
+        rootdir: Path = "example_bids_ses"
 
 This approach can be used directly from the YAML, like so:
 
@@ -376,8 +361,8 @@ need to implement the following methods:
 
 .. note::
 
-   The ``__init__`` method could also be implemented, but it is not mandatory.
-   This is required if the DataGrabber requires any extra parameter.
+   If the DataGrabber requires any extra parameter, they could be defined as
+   class attributes.
 
 We will now implement our BIDS example with this method.
 
@@ -494,8 +479,8 @@ more information about the format of the confounds file. Thus, the
 ``BOLD.confounds`` element is a dictionary with the following keys:
 
 - ``path``: the path to the confounds file.
-- ``format``: the format of the confounds file. Currently, this can be either
-  ``fmriprep`` or ``adhoc``.
+- ``format``: the format of the confounds file. Check :enum:`.ConfoundsFormat`
+  for options.
 
 The ``fmriprep`` format corresponds to the format of the confounds files
 generated by `fMRIPrep`_. The ``adhoc`` format corresponds to a format that is

docs/extending/dependencies.rst

@@ -46,7 +46,7 @@ by having a class attribute like so:
 
     _EXT_DEPENDENCIES: ClassVar[ExternalDependencies] = [
         {
-            "name": "afni",
+            "name": ExtDep.AFNI,
             "commands": ["3dReHo", "3dAFNItoNIFTI"],
         },
     ]
@@ -55,7 +55,7 @@ The above example is taken from the class which computes regional homogeneity
 (ReHo) using AFNI. The general pattern is that you need to have the value of
 ``_EXT_DEPENDENCIES`` as a list of dictionary with two keys:
 
-* ``name`` (str) : lowercased name of the toolbox
+* ``name`` (:enum:`.ExtDep`) : name of the toolbox
 * ``commands`` (list of str) : actual names of the commands you need to use
 
 This is simple but powerful as we will see in the following sub-sections.
@@ -81,11 +81,11 @@ that it shows the problem a bit better and how we solve it:
         _CONDITIONAL_DEPENDENCIES: ClassVar[ConditionalDependencies] = [
             {
                 "using": "fsl",
-                "depends_on": FSLWarper,
+                "depends_on": [FSLWarper],
             },
             {
                 "using": "ants",
-                "depends_on": ANTsWarper,
+                "depends_on": [ANTSWarper],
             },
             {
                 "using": "auto",
@@ -93,18 +93,16 @@ that it shows the problem a bit better and how we solve it:
             },
         ]
 
-        def __init__(
-            self, using: str, reference: str, on: Union[List[str], str]
-        ) -> None:
-            # validation and setting up
-            ...
+        using: str
+        reference: str
+        on: List[DataType]
 
 
 Here, you see a new class attribute ``_CONDITIONAL_DEPENDENCIES`` which is a
 list of dictionaries with two keys:
 
 * ``using`` (str) : lowercased name of the toolbox
-* ``depends_on`` (object or list of objects) : a class or list of classes which \
+* ``depends_on`` (list of objects) : list of classes which \
   implements the particular tool's use
 
 It is mandatory to have the ``using`` positional argument in the constructor in
@@ -128,7 +126,7 @@ similar. ``FSLWarper`` looks like this (only the relevant part is shown here):
 
         _EXT_DEPENDENCIES: ClassVar[ExternalDependencies] = [
             {
-                "name": "fsl",
+                "name": ExtDep.FSL,
                 "commands": ["flirt", "applywarp"],
             },
         ]

docs/extending/index.rst

@@ -34,4 +34,5 @@ DataGrabbers, Preprocessors, Markers, etc.,  following the *junifer* way.
    plugins
    data_registries
    data_types
+   confounds_format
    data_dump_asset

docs/extending/marker.rst

@@ -14,7 +14,7 @@ Most of the functionality of a ``junifer`` Marker has been taken care by the
 :class:`.BaseMarker` class. Thus, only a few methods and class attributes are
 required:
 
-#. ``__init__``: The initialisation method, where the Marker is configured.
+#. (optional) ``validate_marker_params``: The method to perform logical validation of parameters (if required).
 #. ``compute``: The method that given the data, computes the Marker.
 
 As an example, we will develop a ``ParcelMean`` Marker, a Marker that first
@@ -29,8 +29,8 @@ Step 1: Configure input and output
 This step is quite simple: we need to define the input and output of the Marker.
 Based on the current :ref:`data types <data_types>`, we can have ``BOLD``,
 ``VBM_WM`` and ``VBM_GM`` as valid inputs. The output of the Marker depends on
-the input. For ``BOLD``, it will be ``timeseries``, while for the rest of the
-inputs, it will be ``vector``. Thus, we have a class attribute like so:
+the input. For ``BOLD``, it will be ``Timeseries``, while for the rest of the
+inputs, it will be ``Vector``. Thus, we have a class attribute like so:
 
 .. code-block:: python
 
@@ -38,14 +38,14 @@ inputs, it will be ``vector``. Thus, we have a class attribute like so:
     # You can have multiple features for one data type,
     # each feature having same or different storage type
     _MARKER_INOUT_MAPPINGS = {
-        "BOLD": {
-            "parcel_mean": "timeseries",
+        DataType.BOLD: {
+            "parcel_mean": StorageType.Timeseries,
         },
-        "VBM_WM": {
-            "parcel_mean": "vector",
+        DataType.VBM_WM: {
+            "parcel_mean": StorageType.Vector,
         },
-        "VBM_GM": {
-            "parcel_mean": "vector",
+        DataType.VBM_GM: {
+            "parcel_mean": StorageType.Vector,
         },
     }
 
@@ -57,13 +57,11 @@ Step 2: Initialise the Marker
 In this step we need to define the parameters of the Marker the user can provide
 to configure how the Marker will behave.
 
-The parameters of the Marker are defined in the ``__init__`` method. The
-:class:`.BaseMarker` class requires two optional parameters:
+The parameters of the Marker are defined as class attributes. The
+:class:`.BaseMarker` class defines two optional parameters:
 
-1. ``name``: the name of the Marker. This is used to identify the Marker in the
-   configuration file.
-2. ``on``: a list or string with the data types that the Marker will be applied
-   to.
+1. ``name``: the name of the Marker. This is used to identify the Marker in the configuration file.
+2. ``on``: a list of :enum:`.DataType` with the data types that the Marker will be applied to.
 
 .. attention::
 
@@ -72,18 +70,11 @@ The parameters of the Marker are defined in the ``__init__`` method. The
    JSON format, and JSON only supports these types.
 
 In this example, only parameter required for the computation is the name of the
-parcellation to use. Thus, we can define the ``__init__`` method as follows:
+parcellation to use. Thus, we can define as follows:
 
 .. code-block:: python
 
-    def __init__(
-        self,
-        parcellation: str,
-        on: str | list[str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        self.parcellation = parcellation
-        super().__init__(on=on, name=name)
+    parcellation: str
 
 .. caution::
 
@@ -121,14 +112,14 @@ and the values would be a dictionary of storage type specific key-value pairs.
 
    To simplify the ``store`` method, define keys of the dictionary based on the
    corresponding store functions in the :ref:`storage types <storage_types>`.
-   For example, if the output is a ``vector``, the keys of the dictionary should
+   For example, if the output is a ``Vector``, the keys of the dictionary should
    be ``data`` and ``col_names``.
 
 .. code-block:: python
 
     from typing import Any
 
-    from junifer.data import get_parcellation
+    from junifer.data import get_data
     from nilearn.maskers import NiftiLabelsMasker
 
 
@@ -141,8 +132,9 @@ and the values would be a dictionary of storage type specific key-value pairs.
         data = input["data"]
 
         # Get the parcellation tailored for the target
-        t_parcellation, t_labels, _ = get_parcellation(
-            name=self.parcellation_name,
+        t_parcellation, t_labels, _ = get_data(
+            kind="parcellation",
+            name=[self.parcellation],
             target_data=input,
             extra_input=extra_input,
         )
@@ -194,8 +186,10 @@ Finally, we need to register the Marker using the ``@register_marker`` decorator
     from typing import Any, ClassVar
 
     from junifer.api.decorators import register_marker
-    from junifer.data import get_parcellation
+    from junifer.data import get_data
+    from junifer.datagrabber import DataType
     from junifer.markers import BaseMarker
+    from junifer.storage import StorageType
     from junifer.typing import Dependencies, MarkerInOutMappings
     from nilearn.maskers import NiftiLabelsMasker
 
@@ -206,25 +200,18 @@ Finally, we need to register the Marker using the ``@register_marker`` decorator
         _DEPENDENCIES: ClassVar[Dependencies] = {"nilearn", "numpy"}
 
         _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings] = {
-            "BOLD": {
-                "parcel_mean": "timeseries",
+            DataType.BOLD: {
+                "parcel_mean": StorageType.Timeseries,
             },
-            "VBM_WM": {
-                "parcel_mean": "vector",
+            DataType.VBM_WM: {
+                "parcel_mean": StorageType.Vector,
             },
-            "VBM_GM": {
-                "parcel_mean": "vector",
+            DataType.VBM_GM: {
+                "parcel_mean": StorageType.Vector,
             },
         }
 
-        def __init__(
-            self,
-            parcellation: str,
-            on: str | list[str] | None = None,
-            name: str | None = None,
-        ) -> None:
-            self.parcellation = parcellation
-            super().__init__(on=on, name=name)
+        parcellation: str
 
         def compute(
             self,
@@ -235,8 +222,9 @@ Finally, we need to register the Marker using the ``@register_marker`` decorator
             data = input["data"]
 
             # Get the parcellation tailored for the target
-            t_parcellation, t_labels, _ = get_parcellation(
-                name=self.parcellation_name,
+            t_parcellation, t_labels, _ = get_data(
+                kind="parcellation",
+                name=[self.parcellation],
                 target_data=input,
                 extra_input=extra_input,
             )
@@ -280,9 +268,13 @@ Template for a custom Marker
         # TODO: add the input-output mappings
         _MARKER_INOUT_MAPPINGS = {}
 
-        def __init__(self, on=None, name=None):
-            # TODO: add marker-specific parameters
-            super().__init__(on=on, name=name)
+        # TODO: define marker-specific parameters
+
+        # optional
+        def validate_marker_params(self):
+            # TODO: add validation logic for marker parameters
+            pass
 
         def compute(self, input, extra_input):
             # TODO: compute the marker and create the output dictionary
+            return {}

docs/extending/parcellations.rst

@@ -124,7 +124,7 @@ parcellation when registering it. For example, we can add a
   markers:
     - name: CustomParcellation_mean
       kind: ParcelAggregation
-      parcellation: my_custom_parcellation
+      parcellation: <my_custom_parcellation>
       method: mean
 
 Now, you can simply use this YAML file to run your pipeline.

docs/extending/preprocessor.rst

@@ -16,8 +16,7 @@ own Preprocessor.
 While implementing your own Preprocessor, you need to always inherit from
 :class:`.BasePreprocessor` and implement a few methods and class attributes:
 
-#. ``__init__``: The initialisation method, where the Preprocessor is
-   configured.
+#. (optional) ``validate_preprocessor_params``: The method to perform logical validation of parameters (if required).
 #. ``preprocess``: The method that given the data, preprocesses the data.
 
 As an example, we will develop a ``NilearnSmoothing`` Preprocessor, which
@@ -43,8 +42,8 @@ For input we can accept ``T1w``, ``T2w`` and ``BOLD``
 Step 2: Initialise the Preprocessor
 -----------------------------------
 
-Now we need to define our Preprocessor class' constructor which is also how
-you configure it. Our class will have the following arguments:
+Now we need to define our Preprocessor class' parameters as class attributes.
+Our class will have the following:
 
 1. ``fwhm``: The smoothing strength as a full-width at half maximum
    (in millimetres). Since we depend on :func:`nilearn.image.smooth_img`, we
@@ -59,6 +58,8 @@ you configure it. Our class will have the following arguments:
    are allowed as parameters. This is because the parameters are stored in
    JSON format, and JSON only supports these types.
 
+As :class:`.BasePreprocessor` already defines ``on``, we can define the other:
+
 .. code-block:: python
 
     from typing import Literal
@@ -68,15 +69,7 @@ you configure it. Our class will have the following arguments:
 
     ...
 
-
-    def __init__(
-        self,
-        fwhm: int | float | ArrayLike | Literal["fast"] | None,
-        on: str | list[str] | None = None,
-    ) -> None:
-        self.fwhm = fwhm
-        super().__init__(on=on)
-
+    fwhm: int | float | ArrayLike | Literal["fast"] | None
 
     ...
 
@@ -165,15 +158,9 @@ decorator and our final code should look like this:
 
         _DEPENDENCIES = {"nilearn"}
 
-        _VALID_DATA_TYPES: ClassVar[Sequence[str]] = ["T1w", "T2w", "BOLD"]
+        _VALID_DATA_TYPES: ClassVar[Sequence[DataType]] = ["T1w", "T2w", "BOLD"]
 
-        def __init__(
-            self,
-            fwhm: int | float | ArrayLike | Literal["fast"] | None,
-            on: str | list[str] | None = None,
-        ) -> None:
-            self.fwhm = fwhm
-            super().__init__(on=on)
+        fwhm: int | float | ArrayLike | Literal["fast"] | None
 
         def preprocess(
             self,
@@ -191,7 +178,11 @@ Template for a custom Preprocessor
 
 .. code-block:: python
 
+    from collections.abc import Sequence
+    from typing import ClassVar
+
     from junifer.api.decorators import register_preprocessor
+    from junifer.datagrabber import DataType
     from junifer.preprocess import BasePreprocessor
 
 
@@ -202,11 +193,14 @@ Template for a custom Preprocessor
         _DEPENDENCIES = {}
 
         # TODO: add the inputs
-        _VALID_DATA_TYPES = []
+        _VALID_DATA_TYPES: ClassVar[Sequence[DataType]] = []
 
-        def __init__(self, on=None):
-            # TODO: add preprocessor-specific parameters
-            super().__init__(on=on)
+        # TODO: define preprocessor-specific parameters
+
+        # optional
+        def validate_preprocessor_params(self):
+            # TODO: add validation logic for preprocessor parameters
+            pass
 
         def preprocess(self, input, extra_input):
             # TODO: add the preprocessor logic

docs/whats_new.rst

@@ -265,7 +265,7 @@ Features
 ^^^^^^^^
 
 - Introduce :func:`.normalize` and :func:`.reweight` functions for downstream
-  BrainPrint analysis in :mod:`.onthefly.brainprint` by `Synchon Mandal`_
+  BrainPrint analysis in :mod:`.onthefly._brainprint` by `Synchon Mandal`_
   (:gh:`354`)
 - Introduce :class:`junifer.pipeline.PipelineComponentRegistry` to centralise
   pipeline component management by `Synchon Mandal`_ (:gh:`362`)

examples/run_compute_parcel_mean.py

@@ -15,8 +15,10 @@ from junifer.testing.datagrabbers import (
     OasisVBMTestingDataGrabber,
     SPMAuditoryTestingDataGrabber,
 )
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers import ParcelAggregation
+from junifer.stats import AggFunc
 from junifer.utils import configure_logging
 
 
@@ -32,7 +34,10 @@ with OasisVBMTestingDataGrabber() as dg:
     # Read the element
     element_data = DefaultDataReader().fit_transform(dg[element])
     # Initialize marker
-    marker = ParcelAggregation(parcellation="Schaefer100x7", method="mean")
+    marker = ParcelAggregation(
+        parcellation="Schaefer100x7",
+        method=AggFunc.Mean,
+    )
     # Compute feature
     feature = marker.fit_transform(element_data)
     # Print the output
@@ -48,7 +53,9 @@ with SPMAuditoryTestingDataGrabber() as dg:
     element_data = DefaultDataReader().fit_transform(dg[element])
     # Initialize marker
     marker = ParcelAggregation(
-        parcellation="Schaefer100x7", method="mean", on="BOLD"
+        parcellation="Schaefer100x7",
+        method=AggFunc.Mean,
+        on=[DataType.BOLD],
     )
     # Compute feature
     feature = marker.fit_transform(element_data)

examples/run_datagrabber_bids_datalad.py

@@ -10,7 +10,7 @@ Authors: Federico Raimondo
 License: BSD 3 clause
 """
 
-from junifer.datagrabber import PatternDataladDataGrabber
+from junifer.datagrabber import DataType, PatternDataladDataGrabber
 from junifer.utils import configure_logging
 
 
@@ -23,7 +23,7 @@ configure_logging(level="INFO")
 # The BIDS DataGrabber requires three parameters: the types of data we want,
 # the specific pattern that matches each type, and the variables that will be
 # replaced in the patterns.
-types = ["T1w", "BOLD"]
+types = [DataType.T1w, DataType.BOLD]
 patterns = {
     "T1w": {
         "pattern": "{subject}/anat/{subject}_T1w.nii.gz",

examples/run_junifer_julearn.py

@@ -20,7 +20,7 @@ from julearn import run_cross_validation, PipelineCreator
 
 import junifer.testing.registry  # noqa: F401
 from junifer.api import collect, run
-from junifer.storage.sqlite import SQLiteFeatureStorage
+from junifer.storage import SQLiteFeatureStorage
 from junifer.utils import configure_logging
 
 

junifer/api/functions.py

@@ -5,6 +5,7 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+import atexit
 import os
 import shutil
 from pathlib import Path
@@ -182,6 +183,7 @@ def run(
                 "elements will be processed"
             )
     WorkDirManager(**workdir)
+    atexit.register(WorkDirManager()._cleanup)
 
     # Get datagrabber to use
     datagrabber_object = _get_datagrabber(datagrabber.copy())

junifer/api/queue_context/__init__.pyi

@@ -1,5 +1,13 @@
-__all__ = ["QueueContextAdapter", "HTCondorAdapter", "GnuParallelLocalAdapter"]
+__all__ = [
+    "QueueContextAdapter",
+    "EnvKind",
+    "EnvShell",
+    "QueueContextEnv",
+    "HTCondorAdapter",
+    "HTCondorCollect",
+    "GnuParallelLocalAdapter",
+]
 
-from .queue_context_adapter import QueueContextAdapter
-from .htcondor_adapter import HTCondorAdapter
+from .queue_context_adapter import QueueContextAdapter, EnvKind, EnvShell, QueueContextEnv
+from .htcondor_adapter import HTCondorAdapter, HTCondorCollect
 from .gnu_parallel_local_adapter import GnuParallelLocalAdapter

junifer/api/queue_context/gnu_parallel_local_adapter.py

@@ -6,10 +6,16 @@
 import shutil
 import textwrap
 from pathlib import Path
+from typing import Any
 
 from ...typing import Elements
 from ...utils import logger, make_executable, raise_error, run_ext_cmd
-from .queue_context_adapter import QueueContextAdapter
+from .queue_context_adapter import (
+    EnvKind,
+    EnvShell,
+    QueueContextAdapter,
+    QueueContextEnv,
+)
 
 
 __all__ = ["GnuParallelLocalAdapter"]
@@ -26,14 +32,14 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
         The path to the job directory.
     yaml_config_path : pathlib.Path
         The path to the YAML config file.
-    elements : list of str or tuple
+    elements : ``Elements``
         Element(s) to process. Will be used to index the DataGrabber.
-    pre_run : str or None, optional
+    pre_run_cmds : str or None, optional
         Extra shell commands to source before the run (default None).
-    pre_collect : str or None, optional
-        Extra bash commands to source before the collect (default None).
-    env : dict, optional
-        The Python environment configuration. If None, will run without a
+    pre_collect_cmds : str or None, optional
+        Extra shell commands to source before the collect (default None).
+    env : :class:`.QueueContextEnv` or None, optional
+        The environment configuration. If None, will run without a
         virtual environment of any kind (default None).
     verbose : str, optional
         The level of verbosity (default "info").
@@ -43,12 +49,6 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
     submit : bool, optional
         Whether to submit the jobs (default False).
 
-    Raises
-    ------
-    ValueError
-        If ``env.kind`` is invalid or
-        if ``env.shell`` is invalid.
-
     See Also
     --------
     QueueContextAdapter :
@@ -58,87 +58,44 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
 
     """
 
-    def __init__(
-        self,
-        job_name: str,
-        job_dir: Path,
-        yaml_config_path: Path,
-        elements: Elements,
-        pre_run: str | None = None,
-        pre_collect: str | None = None,
-        env: dict[str, str] | None = None,
-        verbose: str = "info",
-        verbose_datalad: str | None = None,
-        submit: bool = False,
-    ) -> None:
-        """Initialize the class."""
-        self._job_name = job_name
-        self._job_dir = job_dir
-        self._yaml_config_path = yaml_config_path
-        self._elements = elements
-        self._pre_run = pre_run
-        self._pre_collect = pre_collect
-        self._check_env(env)
-        self._verbose = verbose
-        self._verbose_datalad = verbose_datalad
-        self._submit = submit
+    job_name: str
+    job_dir: Path
+    yaml_config_path: Path
+    elements: Elements
+    pre_run_cmds: str | None = None
+    pre_collect_cmds: str | None = None
+    env: QueueContextEnv | None = None
+    verbose: str = "info"
+    verbose_datalad: str | None = None
+    submit: bool = False
 
-        self._log_dir = self._job_dir / "logs"
-        self._pre_run_path = self._job_dir / "pre_run.sh"
-        self._pre_collect_path = self._job_dir / "pre_collect.sh"
-        self._run_path = self._job_dir / f"run_{self._job_name}.sh"
-        self._collect_path = self._job_dir / f"collect_{self._job_name}.sh"
-        self._run_joblog_path = self._job_dir / f"run_{self._job_name}_joblog"
-        self._elements_file_path = self._job_dir / "elements"
-
-    def _check_env(self, env: dict[str, str] | None) -> None:
-        """Check value of env parameter on init.
-
-        Parameters
-        ----------
-        env : dict or None
-            The value of env parameter.
-
-        Raises
-        ------
-        ValueError
-            If ``env.kind`` is invalid.
-
-        """
-        # Set env related variables
-        if env is None:
-            env = {"kind": "local"}
-        # Check env kind
-        valid_env_kinds = ["conda", "venv", "local"]
-        if env["kind"] not in valid_env_kinds:
-            raise_error(
-                f"Invalid value for `env.kind`: {env['kind']}, "
-                f"must be one of {valid_env_kinds}"
+    def model_post_init(self, context: Any):  # noqa: D102
+        if self.env is None:
+            self.env = QueueContextEnv(
+                kind=EnvKind.Local, shell=EnvShell.Bash, name=""
             )
+        if self.env["kind"] == EnvKind.Local:
+            # No virtual environment
+            self._executable = "junifer"
+            self._arguments = ""
         else:
-            # Check shell
-            shell = env.get("shell", "bash")
-            valid_shells = ["bash", "zsh"]
-            if shell not in valid_shells:
-                raise_error(
-                    f"Invalid value for `env.shell`: {shell}, "
-                    f"must be one of {valid_shells}"
-                )
-            self._shell = shell
-            # Set variables
-            if env["kind"] == "local":
-                # No virtual environment
-                self._executable = "junifer"
-                self._arguments = ""
-            else:
-                self._executable = f"run_{env['kind']}.{self._shell}"
-                self._arguments = f"{env['name']} junifer"
-                self._exec_path = self._job_dir / self._executable
+            if self.env["name"] is None:
+                raise_error("`env.name` is required")
+            self._executable = f"run_{self.env['kind']}.{self.env['shell']}"
+            self._arguments = f"{self.env['name']} junifer"
+            self._exec_path = self.job_dir / self._executable
+        self._log_dir = self.job_dir / "logs"
+        self._pre_run_path = self.job_dir / "pre_run.sh"
+        self._pre_collect_path = self.job_dir / "pre_collect.sh"
+        self._run_path = self.job_dir / f"run_{self.job_name}.sh"
+        self._collect_path = self.job_dir / f"collect_{self.job_name}.sh"
+        self._run_joblog_path = self.job_dir / f"run_{self.job_name}_joblog"
+        self._elements_file_path = self.job_dir / "elements"
 
-    def elements(self) -> str:
+    def elements_to_run(self) -> str:
         """Return elements to run."""
         elements_to_run = []
-        for element in self._elements:
+        for element in self.elements:
             # Stringify elements if tuple for operation
             str_element = (
                 ",".join(element) if isinstance(element, tuple) else element
@@ -150,23 +107,23 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
     def pre_run(self) -> str:
         """Return pre-run commands."""
         fixed = (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n\n"
             "# Force datalad to run in non-interactive mode\n"
             "DATALAD_UI_INTERACTIVE=false\n"
         )
-        var = self._pre_run or ""
+        var = self.pre_run_cmds or ""
         return fixed + "\n" + var
 
     def run(self) -> str:
         """Return run commands."""
-        verbose_args = f"--verbose {self._verbose}"
-        if self._verbose_datalad:
+        verbose_args = f"--verbose {self.verbose}"
+        if self.verbose_datalad:
             verbose_args = (
-                f"{verbose_args} --verbose-datalad {self._verbose_datalad}"
+                f"{verbose_args} --verbose-datalad {self.verbose_datalad}"
             )
         return (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n\n"
             "# Run pre_run.sh\n"
             f"sh {self._pre_run_path.resolve()!s}\n\n"
@@ -176,9 +133,9 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
             "--delay 60 "  # wait 1 min before next job is spawned
             f"--results {self._log_dir} "
             f"--arg-file {self._elements_file_path.resolve()!s} "
-            f"{self._job_dir.resolve()!s}/{self._executable} "
+            f"{self.job_dir.resolve()!s}/{self._executable} "
             f"{self._arguments} run "
-            f"{self._yaml_config_path.resolve()!s} "
+            f"{self.yaml_config_path.resolve()!s} "
             f"{verbose_args} "
             f"--element"
         )
@@ -186,28 +143,28 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
     def pre_collect(self) -> str:
         """Return pre-collect commands."""
         fixed = (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n"
         )
-        var = self._pre_collect or ""
+        var = self.pre_collect_cmds or ""
         return fixed + "\n" + var
 
     def collect(self) -> str:
         """Return collect commands."""
-        verbose_args = f"--verbose {self._verbose}"
-        if self._verbose_datalad:
+        verbose_args = f"--verbose {self.verbose}"
+        if self.verbose_datalad:
             verbose_args = (
-                f"{verbose_args} --verbose-datalad {self._verbose_datalad}"
+                f"{verbose_args} --verbose-datalad {self.verbose_datalad}"
             )
         return (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n\n"
             "# Run pre_collect.sh\n"
             f"sh {self._pre_collect_path.resolve()!s}\n\n"
             "# Run `junifer collect`\n"
-            f"{self._job_dir.resolve()!s}/{self._executable} "
+            f"{self.job_dir.resolve()!s}/{self._executable} "
             f"{self._arguments} collect "
-            f"{self._yaml_config_path.resolve()!s} "
+            f"{self.yaml_config_path.resolve()!s} "
             f"{verbose_args}"
         )
 
@@ -230,17 +187,19 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
             f"{self._elements_file_path.resolve()!s}"
         )
         self._elements_file_path.touch()
-        self._elements_file_path.write_text(textwrap.dedent(self.elements()))
+        self._elements_file_path.write_text(
+            textwrap.dedent(self.elements_to_run())
+        )
         # Create pre run
         logger.info(
-            f"Writing {self._pre_run_path.name} to {self._job_dir.resolve()!s}"
+            f"Writing {self._pre_run_path.name} to {self.job_dir.resolve()!s}"
         )
         self._pre_run_path.touch()
         self._pre_run_path.write_text(textwrap.dedent(self.pre_run()))
         make_executable(self._pre_run_path)
         # Create run
         logger.info(
-            f"Writing {self._run_path.name} to {self._job_dir.resolve()!s}"
+            f"Writing {self._run_path.name} to {self.job_dir.resolve()!s}"
         )
         self._run_path.touch()
         self._run_path.write_text(textwrap.dedent(self.run()))
@@ -248,14 +207,14 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
         # Create pre collect
         logger.info(
             f"Writing {self._pre_collect_path.name} to "
-            f"{self._job_dir.resolve()!s}"
+            f"{self.job_dir.resolve()!s}"
         )
         self._pre_collect_path.touch()
         self._pre_collect_path.write_text(textwrap.dedent(self.pre_collect()))
         make_executable(self._pre_collect_path)
         # Create collect
         logger.info(
-            f"Writing {self._collect_path.name} to {self._job_dir.resolve()!s}"
+            f"Writing {self._collect_path.name} to {self.job_dir.resolve()!s}"
         )
         self._collect_path.touch()
         self._collect_path.write_text(textwrap.dedent(self.collect()))
@@ -263,7 +222,7 @@ class GnuParallelLocalAdapter(QueueContextAdapter):
         # Submit if required
         run_cmd = f"sh {self._run_path.resolve()!s}"
         collect_cmd = f"sh {self._collect_path.resolve()!s}"
-        if self._submit:
+        if self.submit:
             logger.info(
                 "Shell scripts created, the following will be run:\n"
                 f"{run_cmd}\n"

junifer/api/queue_context/htcondor_adapter.py

@@ -5,14 +5,37 @@
 
 import shutil
 import textwrap
+from enum import Enum
 from pathlib import Path
+from typing import Any
 
 from ...typing import Elements
 from ...utils import logger, make_executable, raise_error, run_ext_cmd
-from .queue_context_adapter import QueueContextAdapter
+from .queue_context_adapter import (
+    EnvKind,
+    EnvShell,
+    QueueContextAdapter,
+    QueueContextEnv,
+)
 
 
-__all__ = ["HTCondorAdapter"]
+__all__ = ["HTCondorAdapter", "HTCondorCollect"]
+
+
+class HTCondorCollect(str, Enum):
+    """Accepted HTCondor collect commands.
+
+    * ``"yes"``: Submit "collect" task and run even if some of the jobs
+        fail.
+    * ``"on_success_only"``: Submit "collect" task and run only if all jobs
+        succeed.
+    * ``"no"``: Do not submit "collect" task.
+
+    """
+
+    Yes = "yes"
+    No = "no"
+    OnSuccessOnly = "on_success_only"
 
 
 class HTCondorAdapter(QueueContextAdapter):
@@ -26,14 +49,14 @@ class HTCondorAdapter(QueueContextAdapter):
         The path to the job directory.
     yaml_config_path : pathlib.Path
         The path to the YAML config file.
-    elements : list of str or tuple
+    elements : ``Elements``
         Element(s) to process. Will be used to index the DataGrabber.
-    pre_run : str or None, optional
-        Extra bash commands to source before the run (default None).
-    pre_collect : str or None, optional
-        Extra bash commands to source before the collect (default None).
-    env : dict, optional
-        The Python environment configuration. If None, will run without a
+    pre_run_cmds : str or None, optional
+        Extra shell commands to source before the run (default None).
+    pre_collect_cmds : str or None, optional
+        Extra shell commands to source before the collect (default None).
+    env : :class:`.QueueContextEnv` or None, optional
+        The environment configuration. If None, will run without a
         virtual environment of any kind (default None).
     verbose : str, optional
         The level of verbosity (default "info").
@@ -48,25 +71,12 @@ class HTCondorAdapter(QueueContextAdapter):
         The size of disk (HDD or SSD) to use (default "1G").
     extra_preamble : str or None, optional
         Extra commands to pass to HTCondor (default None).
-    collect : {"yes", "on_success_only", "no"}, optional
+    collect_task : :class:`.HTCondorCollect`, optional
         Whether to submit "collect" task for junifer (default "yes").
-        Valid options are:
-
-            * "yes": Submit "collect" task and run even if some of the jobs
-                fail.
-            * "on_success_only": Submit "collect" task and run only if all jobs
-                succeed.
-            * "no": Do not submit "collect" task.
-
     submit : bool, optional
         Whether to submit the jobs. In any case, .dag files will be created
         for submission (default False).
 
-    Raises
-    ------
-    ValueError
-        If ``collect`` is invalid or if ``env`` is invalid.
-
     See Also
     --------
     QueueContextAdapter :
@@ -76,144 +86,67 @@ class HTCondorAdapter(QueueContextAdapter):
 
     """
 
-    def __init__(
-        self,
-        job_name: str,
-        job_dir: Path,
-        yaml_config_path: Path,
-        elements: Elements,
-        pre_run: str | None = None,
-        pre_collect: str | None = None,
-        env: dict[str, str] | None = None,
-        verbose: str = "info",
-        verbose_datalad: str | None = None,
-        cpus: int = 1,
-        mem: str = "8G",
-        disk: str = "1G",
-        extra_preamble: str | None = None,
-        collect: str = "yes",
-        submit: bool = False,
-    ) -> None:
-        """Initialize the class."""
-        self._job_name = job_name
-        self._job_dir = job_dir
-        self._yaml_config_path = yaml_config_path
-        self._elements = elements
-        self._pre_run = pre_run
-        self._pre_collect = pre_collect
-        self._check_env(env)
-        self._verbose = verbose
-        self._verbose_datalad = verbose_datalad
-        self._cpus = cpus
-        self._mem = mem
-        self._disk = disk
-        self._extra_preamble = extra_preamble
-        self._collect = self._check_collect(collect)
-        self._submit = submit
+    job_name: str
+    job_dir: Path
+    yaml_config_path: Path
+    elements: Elements
+    pre_run_cmds: str | None = None
+    pre_collect_cmds: str | None = None
+    env: QueueContextEnv | None = None
+    verbose: str = "info"
+    verbose_datalad: str | None = None
+    cpus: int = 1
+    mem: str = "8G"
+    disk: str = "1G"
+    extra_preamble: str | None = None
+    collect_task: HTCondorCollect = HTCondorCollect.Yes
+    submit: bool = False
 
-        self._log_dir = self._job_dir / "logs"
-        self._pre_run_path = self._job_dir / "pre_run.sh"
-        self._pre_collect_path = self._job_dir / "pre_collect.sh"
-        self._submit_run_path = self._job_dir / f"run_{self._job_name}.submit"
+    def model_post_init(self, context: Any):  # noqa: D102
+        if self.env is None:
+            self.env = QueueContextEnv(
+                kind=EnvKind.Local, shell=EnvShell.Bash, name=""
+            )
+        if self.env["kind"] == EnvKind.Local:
+            # No virtual environment
+            self._executable = "junifer"
+            self._arguments = ""
+        else:
+            if self.env["name"] is None:
+                raise_error("`env.name` is required")
+            self._executable = f"run_{self.env['kind']}.{self.env['shell']}"
+            self._arguments = f"{self.env['name']} junifer"
+            self._exec_path = self.job_dir / self._executable
+        self._log_dir = self.job_dir / "logs"
+        self._pre_run_path = self.job_dir / "pre_run.sh"
+        self._pre_collect_path = self.job_dir / "pre_collect.sh"
+        self._submit_run_path = self.job_dir / f"run_{self.job_name}.submit"
         self._submit_collect_path = (
-            self._job_dir / f"collect_{self._job_name}.submit"
+            self.job_dir / f"collect_{self.job_name}.submit"
         )
-        self._dag_path = self._job_dir / f"{self._job_name}.dag"
-
-    def _check_env(self, env: dict[str, str] | None) -> None:
-        """Check value of env parameter on init.
-
-        Parameters
-        ----------
-        env : dict or None
-            The value of env parameter.
-
-        Raises
-        ------
-        ValueError
-            If ``env.kind`` is invalid or
-            if ``env.shell`` is invalid.
-
-        """
-        # Set env related variables
-        if env is None:
-            env = {"kind": "local"}
-        # Check env kind
-        valid_env_kinds = ["conda", "venv", "local"]
-        if env["kind"] not in valid_env_kinds:
-            raise_error(
-                f"Invalid value for `env.kind`: {env['kind']}, "
-                f"must be one of {valid_env_kinds}"
-            )
-        else:
-            # Check shell
-            shell = env.get("shell", "bash")
-            valid_shells = ["bash", "zsh"]
-            if shell not in valid_shells:
-                raise_error(
-                    f"Invalid value for `env.shell`: {shell}, "
-                    f"must be one of {valid_shells}"
-                )
-            self._shell = shell
-            # Set variables
-            if env["kind"] == "local":
-                # No virtual environment
-                self._executable = "junifer"
-                self._arguments = ""
-            else:
-                self._executable = f"run_{env['kind']}.{self._shell}"
-                self._arguments = f"{env['name']} junifer"
-                self._exec_path = self._job_dir / self._executable
-
-    def _check_collect(self, collect: str) -> str:
-        """Check value of collect parameter on init.
-
-        Parameters
-        ----------
-        collect : str
-            The value of collect parameter.
-
-        Returns
-        -------
-        str
-            The checked value of collect parameter.
-
-        Raises
-        ------
-        ValueError
-            If ``collect`` is invalid.
-
-        """
-        valid_options = ["yes", "no", "on_success_only"]
-        if collect not in valid_options:
-            raise_error(
-                f"Invalid value for `collect`: {collect}, "
-                f"must be one of {valid_options}"
-            )
-        else:
-            return collect
+        self._dag_path = self.job_dir / f"{self.job_name}.dag"
 
     def pre_run(self) -> str:
         """Return pre-run commands."""
         fixed = (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n\n"
             "# Force datalad to run in non-interactive mode\n"
             "DATALAD_UI_INTERACTIVE=false\n"
         )
-        var = self._pre_run or ""
+        var = self.pre_run_cmds or ""
         return fixed + "\n" + var
 
     def run(self) -> str:
         """Return run commands."""
-        verbose_args = f"--verbose {self._verbose} "
-        if self._verbose_datalad is not None:
+        verbose_args = f"--verbose {self.verbose} "
+        if self.verbose_datalad is not None:
             verbose_args = (
-                f"{verbose_args} --verbose-datalad {self._verbose_datalad} "
+                f"{verbose_args} --verbose-datalad {self.verbose_datalad} "
             )
         junifer_run_args = (
             "run "
-            f"{self._yaml_config_path.resolve()!s} "
+            f"{self.yaml_config_path.resolve()!s} "
             f"{verbose_args}"
             "--element $(element)"
         )
@@ -226,11 +159,11 @@ class HTCondorAdapter(QueueContextAdapter):
             "universe = vanilla\n"
             "getenv = True\n\n"
             "# Resources\n"
-            f"request_cpus = {self._cpus}\n"
-            f"request_memory = {self._mem}\n"
-            f"request_disk = {self._disk}\n\n"
+            f"request_cpus = {self.cpus}\n"
+            f"request_memory = {self.mem}\n"
+            f"request_disk = {self.disk}\n\n"
             "# Executable\n"
-            f"initial_dir = {self._job_dir.resolve()!s}\n"
+            f"initial_dir = {self.job_dir.resolve()!s}\n"
             f"executable = $(initial_dir)/{self._executable}\n"
             f"transfer_executable = False\n\n"
             f"arguments = {self._arguments} {junifer_run_args}\n\n"
@@ -239,31 +172,31 @@ class HTCondorAdapter(QueueContextAdapter):
             f"output = {log_dir_prefix}.out\n"
             f"error = {log_dir_prefix}.err\n"
         )
-        var = self._extra_preamble or ""
+        var = self.extra_preamble or ""
         return fixed + "\n" + var + "\n" + "queue"
 
     def pre_collect(self) -> str:
         """Return pre-collect commands."""
         fixed = (
-            f"#!/usr/bin/env {self._shell}\n\n"
+            f"#!/usr/bin/env {self.env['shell']}\n\n"
             "# This script is auto-generated by junifer.\n"
         )
-        var = self._pre_collect or ""
+        var = self.pre_collect_cmds or ""
         # Add commands if collect="yes"
-        if self._collect == "yes":
+        if self.collect_task == "yes":
             var += 'if [ "${1}" == "4" ]; then\n    exit 1\nfi\n'
         return fixed + "\n" + var
 
     def collect(self) -> str:
         """Return collect commands."""
-        verbose_args = f"--verbose {self._verbose} "
-        if self._verbose_datalad is not None:
+        verbose_args = f"--verbose {self.verbose} "
+        if self.verbose_datalad is not None:
             verbose_args = (
-                f"{verbose_args} --verbose-datalad {self._verbose_datalad} "
+                f"{verbose_args} --verbose-datalad {self.verbose_datalad} "
             )
 
         junifer_collect_args = (
-            f"collect {self._yaml_config_path.resolve()!s} {verbose_args}"
+            f"collect {self.yaml_config_path.resolve()!s} {verbose_args}"
         )
         log_dir_prefix = f"{self._log_dir.resolve()!s}/junifer_collect"
         fixed = (
@@ -272,11 +205,11 @@ class HTCondorAdapter(QueueContextAdapter):
             "universe = vanilla\n"
             "getenv = True\n\n"
             "# Resources\n"
-            f"request_cpus = {self._cpus}\n"
-            f"request_memory = {self._mem}\n"
-            f"request_disk = {self._disk}\n\n"
+            f"request_cpus = {self.cpus}\n"
+            f"request_memory = {self.mem}\n"
+            f"request_disk = {self.disk}\n\n"
             "# Executable\n"
-            f"initial_dir = {self._job_dir.resolve()!s}\n"
+            f"initial_dir = {self.job_dir.resolve()!s}\n"
             f"executable = $(initial_dir)/{self._executable}\n"
             "transfer_executable = False\n\n"
             f"arguments = {self._arguments} {junifer_collect_args}\n\n"
@@ -285,13 +218,13 @@ class HTCondorAdapter(QueueContextAdapter):
             f"output = {log_dir_prefix}.out\n"
             f"error = {log_dir_prefix}.err\n"
         )
-        var = self._extra_preamble or ""
+        var = self.extra_preamble or ""
         return fixed + "\n" + var + "\n" + "queue"
 
     def dag(self) -> str:
         """Return HTCondor DAG commands."""
         fixed = ""
-        for idx, element in enumerate(self._elements):
+        for idx, element in enumerate(self.elements):
             # Stringify elements if tuple for operation
             str_element = (
                 ",".join(element) if isinstance(element, tuple) else element
@@ -306,15 +239,15 @@ class HTCondorAdapter(QueueContextAdapter):
                 f'log_element="{log_element}"\n\n'  # double quoted
             )
         var = ""
-        if self._collect == "yes":
+        if self.collect_task == "yes":
             var += (
                 f"FINAL collect {self._submit_collect_path}\n"
                 f"SCRIPT PRE collect {self._pre_collect_path.as_posix()} "
                 "$DAG_STATUS\n"
             )
-        elif self._collect == "on_success_only":
+        elif self.collect_task == "on_success_only":
             var += f"JOB collect {self._submit_collect_path}\nPARENT "
-            for idx, _ in enumerate(self._elements):
+            for idx, _ in enumerate(self.elements):
                 var += f"run{idx} "
             var += "CHILD collect\n"
 
@@ -325,7 +258,7 @@ class HTCondorAdapter(QueueContextAdapter):
         logger.info("Creating HTCondor job")
         # Create logs
         logger.info(
-            f"Creating logs directory under {self._job_dir.resolve()!s}"
+            f"Creating logs directory under {self.job_dir.resolve()!s}"
         )
         self._log_dir.mkdir(exist_ok=True, parents=True)
         # Copy executable if not local
@@ -340,7 +273,7 @@ class HTCondorAdapter(QueueContextAdapter):
             make_executable(self._exec_path)
         # Create pre run
         logger.info(
-            f"Writing {self._pre_run_path.name} to {self._job_dir.resolve()!s}"
+            f"Writing {self._pre_run_path.name} to {self.job_dir.resolve()!s}"
         )
         self._pre_run_path.touch()
         self._pre_run_path.write_text(textwrap.dedent(self.pre_run()))
@@ -348,14 +281,14 @@ class HTCondorAdapter(QueueContextAdapter):
         # Create run
         logger.debug(
             f"Writing {self._submit_run_path.name} to "
-            f"{self._job_dir.resolve()!s}"
+            f"{self.job_dir.resolve()!s}"
         )
         self._submit_run_path.touch()
         self._submit_run_path.write_text(textwrap.dedent(self.run()))
         # Create pre collect
         logger.info(
             f"Writing {self._pre_collect_path.name} to "
-            f"{self._job_dir.resolve()!s}"
+            f"{self.job_dir.resolve()!s}"
         )
         self._pre_collect_path.touch()
         self._pre_collect_path.write_text(textwrap.dedent(self.pre_collect()))
@@ -363,13 +296,13 @@ class HTCondorAdapter(QueueContextAdapter):
         # Create collect
         logger.debug(
             f"Writing {self._submit_collect_path.name} to "
-            f"{self._job_dir.resolve()!s}"
+            f"{self.job_dir.resolve()!s}"
         )
         self._submit_collect_path.touch()
         self._submit_collect_path.write_text(textwrap.dedent(self.collect()))
         # Create DAG
         logger.debug(
-            f"Writing {self._dag_path.name} to {self._job_dir.resolve()!s}"
+            f"Writing {self._dag_path.name} to {self.job_dir.resolve()!s}"
         )
         self._dag_path.touch()
         self._dag_path.write_text(textwrap.dedent(self.dag()))
@@ -379,7 +312,7 @@ class HTCondorAdapter(QueueContextAdapter):
             "-include_env HOME",
             f"{self._dag_path.resolve()!s}",
         ]
-        if self._submit:
+        if self.submit:
             run_ext_cmd(name="condor_submit_dag", cmd=condor_submit_dag_cmd)
         else:
             logger.info(

junifer/api/queue_context/queue_context_adapter.py

@@ -3,22 +3,66 @@
 # Authors: Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+import sys
+
+
+if sys.version_info < (3, 12):  # pragma: no cover
+    from typing_extensions import TypedDict
+else:
+    from typing import TypedDict
+
+if sys.version_info < (3, 11):  # pragma: no cover
+    from typing_extensions import Required
+else:
+    from typing import Required
+
 from abc import ABC, abstractmethod
+from enum import Enum
+
+from pydantic import BaseModel, ConfigDict
 
 from ...utils import raise_error
 
 
-__all__ = ["QueueContextAdapter"]
+__all__ = ["EnvKind", "EnvShell", "QueueContextAdapter", "QueueContextEnv"]
 
 
-class QueueContextAdapter(ABC):
+class EnvKind(str, Enum):
+    """Accepted Python environment kind."""
+
+    Venv = "venv"
+    Conda = "conda"
+    Local = "local"
+
+
+class EnvShell(str, Enum):
+    """Accepted environment shell."""
+
+    Bash = "bash"
+    Zsh = "zsh"
+
+
+class QueueContextEnv(TypedDict, total=False):
+    """Accepted environment configuration for queue context."""
+
+    kind: Required[EnvKind]
+    name: str
+    shell: Required[EnvShell]
+
+
+class QueueContextAdapter(BaseModel, ABC):
     """Abstract base class for queue context adapter.
 
-    For every interface that is required, one needs to provide a concrete
+    For every queue context, one needs to provide a concrete
     implementation of this abstract class.
 
     """
 
+    model_config = ConfigDict(
+        use_enum_values=True,
+        extra="allow",
+    )
+
     @abstractmethod
     def pre_run(self) -> str:
         """Return pre-run commands."""

junifer/api/queue_context/tests/test_gnu_parallel_local_adapter.py

@@ -11,30 +11,6 @@ import pytest
 from junifer.api.queue_context import GnuParallelLocalAdapter
 
 
-def test_GnuParallelLocalAdapter_env_kind_error() -> None:
-    """Test error for invalid env kind."""
-    with pytest.raises(ValueError, match=r"Invalid value for `env.kind`"):
-        GnuParallelLocalAdapter(
-            job_name="check_env_kind",
-            job_dir=Path("."),
-            yaml_config_path=Path("."),
-            elements=["sub01"],
-            env={"kind": "jambalaya"},
-        )
-
-
-def test_GnuParallelLocalAdapter_env_shell_error() -> None:
-    """Test error for invalid env shell."""
-    with pytest.raises(ValueError, match=r"Invalid value for `env.shell`"):
-        GnuParallelLocalAdapter(
-            job_name="check_env_shell",
-            job_dir=Path("."),
-            yaml_config_path=Path("."),
-            elements=["sub01"],
-            env={"kind": "conda", "shell": "fish"},
-        )
-
-
 @pytest.mark.parametrize(
     "elements, expected_text",
     [
@@ -62,7 +38,7 @@ def test_GnuParallelLocalAdapter_elements(
         yaml_config_path=Path("."),
         elements=elements,
     )
-    assert expected_text in adapter.elements()
+    assert expected_text in adapter.elements_to_run()
 
 
 @pytest.mark.parametrize(
@@ -97,7 +73,7 @@ def test_GnuParallelLocalAdapter_pre_run(
         yaml_config_path=Path("."),
         elements=["sub01"],
         env={"kind": "conda", "name": "junifer", "shell": shell},
-        pre_run=pre_run,
+        pre_run_cmds=pre_run,
     )
     assert shell in adapter.pre_run()
     assert expected_text in adapter.pre_run()
@@ -135,7 +111,7 @@ def test_GnuParallelLocalAdapter_pre_collect(
         yaml_config_path=Path("."),
         elements=["sub01"],
         env={"kind": "venv", "name": "junifer", "shell": shell},
-        pre_collect=pre_collect,
+        pre_collect_cmds=pre_collect,
     )
     assert shell in adapter.pre_collect()
     assert expected_text in adapter.pre_collect()

junifer/api/queue_context/tests/test_htcondor_adapter.py

@@ -11,42 +11,6 @@ import pytest
 from junifer.api.queue_context import HTCondorAdapter
 
 
-def test_HTCondorAdapter_env_kind_error() -> None:
-    """Test error for invalid env kind."""
-    with pytest.raises(ValueError, match=r"Invalid value for `env.kind`"):
-        HTCondorAdapter(
-            job_name="check_env_kind",
-            job_dir=Path("."),
-            yaml_config_path=Path("."),
-            elements=["sub01"],
-            env={"kind": "jambalaya"},
-        )
-
-
-def test_HTCondorAdapter_env_shell_error() -> None:
-    """Test error for invalid env shell."""
-    with pytest.raises(ValueError, match=r"Invalid value for `env.shell`"):
-        HTCondorAdapter(
-            job_name="check_env_shell",
-            job_dir=Path("."),
-            yaml_config_path=Path("."),
-            elements=["sub01"],
-            env={"kind": "conda", "shell": "fish"},
-        )
-
-
-def test_HTCondorAdapter_collect_error() -> None:
-    """Test error for invalid collect option."""
-    with pytest.raises(ValueError, match=r"Invalid value for `collect`"):
-        HTCondorAdapter(
-            job_name="check_collect",
-            job_dir=Path("."),
-            yaml_config_path=Path("."),
-            elements=["sub01"],
-            collect="off",
-        )
-
-
 @pytest.mark.parametrize(
     "pre_run, expected_text, shell",
     [
@@ -79,7 +43,7 @@ def test_HTCondorAdapter_pre_run(
         yaml_config_path=Path("."),
         elements=["sub01"],
         env={"kind": "conda", "name": "junifer", "shell": shell},
-        pre_run=pre_run,
+        pre_run_cmds=pre_run,
     )
     assert shell in adapter.pre_run()
     assert expected_text in adapter.pre_run()
@@ -124,8 +88,8 @@ def test_HTCondorAdapter_pre_collect(
         yaml_config_path=Path("."),
         elements=["sub01"],
         env={"kind": "venv", "name": "junifer", "shell": shell},
-        pre_collect=pre_collect,
-        collect=collect,
+        pre_collect_cmds=pre_collect,
+        collect_task=collect,
     )
     assert shell in adapter.pre_collect()
     assert expected_text in adapter.pre_collect()
@@ -199,7 +163,7 @@ def test_HTCondor_dag(
         job_dir=Path("."),
         yaml_config_path=Path("."),
         elements=elements,
-        collect=collect,
+        collect_task=collect,
     )
     assert expected_text in adapter.dag()
 

junifer/api/tests/test_functions.py

@@ -62,7 +62,7 @@ def datagrabber() -> dict[str, str]:
 
 
 @pytest.fixture
-def markers() -> list[dict[str, str]]:
+def markers() -> list[dict[str, list[str] | str]]:
     """Return markers as a list of dictionary."""
     return [
         {

junifer/cli/tests/data/gmd_mean_htcondor.yaml

@@ -17,4 +17,5 @@ queue:
   env:
     kind: conda
     name: junifer
+    shell: bash
   mem: 8G

junifer/cli/tests/test_cli_utils.py

@@ -34,6 +34,7 @@ def test_get_dependency_information_short() -> None:
     """Test short version of _get_dependency_information()."""
     dependency_information = _get_dependency_information(long_=False)
     dependency_list = [
+        "aenum",
         "click",
         "numpy",
         "scipy",
@@ -50,6 +51,8 @@ def test_get_dependency_information_short() -> None:
         "looseversion",
         "junifer_data",
         "structlog",
+        "pydantic",
+        "typing_extensions",
     ]
 
     if sys.version_info < (3, 11):

junifer/configs/__init__.py

@@ -2,3 +2,8 @@
 
 # Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
 # License: AGPL
+
+import lazy_loader as lazy
+
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

junifer/configs/juseless/datagrabbers/__init__.pyi

@@ -2,12 +2,14 @@ __all__ = [
     "JuselessDataladAOMICID1000VBM",
     "JuselessDataladCamCANVBM",
     "JuselessDataladIXIVBM",
+    "IXISite",
     "JuselessUCLA",
+    "UCLATask",
     "JuselessDataladUKBVBM",
 ]
 
 from .aomic_id1000_vbm import JuselessDataladAOMICID1000VBM
 from .camcan_vbm import JuselessDataladCamCANVBM
-from .ixi_vbm import JuselessDataladIXIVBM
-from .ucla import JuselessUCLA
+from .ixi_vbm import JuselessDataladIXIVBM, IXISite
+from .ucla import JuselessUCLA, UCLATask
 from .ukb_vbm import JuselessDataladUKBVBM

junifer/configs/juseless/datagrabbers/aomic_id1000_vbm.py

@@ -4,10 +4,13 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from pathlib import Path
+from typing import Literal
+
+from pydantic import AnyUrl
 
 from ....api.decorators import register_datagrabber
-from ....datagrabber import PatternDataladDataGrabber
+from ....datagrabber import DataType, PatternDataladDataGrabber
+from ....typing import DataGrabberPatterns
 
 
 __all__ = ["JuselessDataladAOMICID1000VBM"]
@@ -21,27 +24,19 @@ class JuselessDataladAOMICID1000VBM(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or pathlib.Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
 
     """
 
-    def __init__(self, datadir: str | Path | None = None) -> None:
-        uri = "https://gin.g-node.org/felixh/ds003097_ReproVBM"
-        types = ["VBM_GM"]
-        replacements = ["subject"]
-        patterns = {
-            "VBM_GM": {
-                "pattern": ("{subject}/mri/mwp1{subject}_run-2_T1w.nii.gz"),
-                "space": "IXI549Space",
-            },
-        }
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            replacements=replacements,
-            patterns=patterns,
-        )
+    uri: AnyUrl = AnyUrl("https://gin.g-node.org/felixh/ds003097_ReproVBM")
+    types: list[Literal[DataType.VBM_GM]] = [DataType.VBM_GM]  # noqa: RUF012
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "VBM_GM": {
+            "pattern": ("{subject}/mri/mwp1{subject}_run-2_T1w.nii.gz"),
+            "space": "IXI549Space",
+        },
+    }
+    replacements: list[str] = ["subject"]  # noqa: RUF012

junifer/configs/juseless/datagrabbers/camcan_vbm.py

@@ -5,10 +5,13 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from pathlib import Path
+from typing import Literal
+
+from pydantic import AnyUrl
 
 from ....api.decorators import register_datagrabber
-from ....datagrabber import PatternDataladDataGrabber
+from ....datagrabber import DataType, PatternDataladDataGrabber
+from ....typing import DataGrabberPatterns
 
 
 __all__ = ["JuselessDataladCamCANVBM"]
@@ -22,30 +25,21 @@ class JuselessDataladCamCANVBM(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or pathlib.Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
 
     """
 
-    def __init__(self, datadir: str | Path | None = None) -> None:
-        uri = (
-            "ria+http://cat_12.5.ds.inm7.de"
-            "#a139b26a-8406-11ea-8f94-a0369f287950"
-        )
-        types = ["VBM_GM"]
-        replacements = ["subject"]
-        patterns = {
-            "VBM_GM": {
-                "pattern": "{subject}/mri/m0wp1{subject}.nii.gz",
-                "space": "IXI549Space",
-            },
-        }
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            replacements=replacements,
-            patterns=patterns,
-        )
+    uri: AnyUrl = AnyUrl(
+        "ria+http://cat_12.5.ds.inm7.de#a139b26a-8406-11ea-8f94-a0369f287950"
+    )
+    types: list[Literal[DataType.VBM_GM]] = [DataType.VBM_GM]  # noqa: RUF012
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "VBM_GM": {
+            "pattern": "{subject}/mri/m0wp1{subject}.nii.gz",
+            "space": "IXI549Space",
+        },
+    }
+    replacements: list[str] = ["subject"]  # noqa: RUF012

junifer/configs/juseless/datagrabbers/ixi_vbm.py

@@ -5,14 +5,29 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from pathlib import Path
+from enum import Enum
+from typing import Annotated, ClassVar, Literal
+
+from pydantic import AnyUrl, BeforeValidator
 
 from ....api.decorators import register_datagrabber
-from ....datagrabber import PatternDataladDataGrabber
-from ....utils import raise_error
+from ....datagrabber import DataType, PatternDataladDataGrabber
+from ....typing import DataGrabberPatterns
+from ....utils import ensure_list
 
 
-__all__ = ["JuselessDataladIXIVBM"]
+__all__ = ["IXISite", "JuselessDataladIXIVBM"]
+
+
+class IXISite(str, Enum):
+    """Accepted IXI sites."""
+
+    Guys = "Guys"
+    HH = "HH"
+    IOP = "IOP"
+
+
+_sites = Literal[IXISite.Guys, IXISite.HH, IXISite.IOP]
 
 
 @register_datagrabber
@@ -23,53 +38,31 @@ class JuselessDataladIXIVBM(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or pathlib.Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    sites : {"Guys", "HH", "IOP"} or list of the options or None, optional
-        Which sites to access data from. If None, all available sites are
-        selected (default None).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
+    sites : {"Guys", "HH", "IOP"} or list of the options, optional
+        IXI sites.
+        By default, all available sites are selected.
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        sites: str | list[str] | None = None,
-    ) -> None:
-        uri = (
-            "ria+http://cat_12.5.ds.inm7.de"
-            "#b7107c52-8408-11ea-89c6-a0369f287950"
-        )
-        types = ["VBM_GM"]
-        replacements = ["site", "subject"]
-        patterns = {
-            "VBM_GM": {
-                "pattern": ("{site}/{subject}/mri/m0wp1{subject}.nii.gz"),
-                "space": "IXI549Space",
-            },
-        }
-
-        # validate and/or transform 'site' input
-        all_sites = ["HH", "Guys", "IOP"]
-        if sites is None:
-            sites = all_sites
-
-        if isinstance(sites, str):
-            sites = [sites]
-
-        for s in sites:
-            if s not in all_sites:
-                raise_error(
-                    f"{s} not a valid site in IXI VBM dataset!"
-                    f"Available sites are {all_sites}"
-                )
-        self.sites = sites
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            replacements=replacements,
-            patterns=patterns,
-        )
+    uri: AnyUrl = AnyUrl(
+        "ria+http://cat_12.5.ds.inm7.de#b7107c52-8408-11ea-89c6-a0369f287950"
+    )
+    types: list[Literal[DataType.VBM_GM]] = [DataType.VBM_GM]  # noqa: RUF012
+    sites: ClassVar[
+        Annotated[IXISite | list[IXISite], BeforeValidator(ensure_list)]
+    ] = [
+        IXISite.Guys,
+        IXISite.HH,
+        IXISite.IOP,
+    ]
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "VBM_GM": {
+            "pattern": ("{site}/{subject}/mri/m0wp1{subject}.nii.gz"),
+            "space": "IXI549Space",
+        },
+    }
+    replacements: list[str] = ["site", "subject"]  # noqa: RUF012

junifer/configs/juseless/datagrabbers/tests/test_ixi_vbm.py

@@ -31,10 +31,3 @@ def test_JuselessDataladIXIVBM() -> None:
             out["VBM_GM"]["path"].name == f"m0wp1sub-{test_element[1]}.nii.gz"
         )
         assert out["VBM_GM"]["path"].exists()
-
-
-def test_JuselessDataladIXIVBM_invalid_site() -> None:
-    """Test JuselessDataladIXIVBM with invalid site."""
-    with pytest.raises(ValueError, match="notavalidsite not a valid site"):
-        with JuselessDataladIXIVBM(sites="notavalidsite"):
-            pass

junifer/configs/juseless/datagrabbers/tests/test_ucla.py

@@ -79,14 +79,6 @@ def test_JuselessUCLA_partial_data_access(
             assert types in out
 
 
-def test_JuselessUCLA_incorrect_data_type() -> None:
-    """Test JuselessUCLA DataGrabber incorrect data type."""
-    with pytest.raises(
-        ValueError, match="`patterns` must contain all `types`"
-    ):
-        _ = JuselessUCLA(types="Eunomia")
-
-
 @pytest.mark.parametrize(
     "tasks",
     [None, "rest", ["rest", "stopsignal"]],
@@ -124,11 +116,3 @@ def test_JuselessUCLA_task_params(tasks: str | None) -> None:
         else:
             for el in all_elements:
                 assert el[1] in ["rest", "stopsignal"]
-
-
-def test_JuselessUCLA_invalid_tasks() -> None:
-    """Test JuselessUCLA with invalid task parameters."""
-    with pytest.raises(
-        ValueError, match="invalid is not a valid task in the UCLA"
-    ):
-        JuselessUCLA(tasks="invalid")

junifer/configs/juseless/datagrabbers/ucla.py

@@ -4,14 +4,52 @@
 #          Leonard Sasse <l.sasse@fz-juelich.de>
 # License: AGPL
 
+from enum import Enum
 from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import BeforeValidator
 
 from ....api.decorators import register_datagrabber
-from ....datagrabber import PatternDataGrabber
-from ....utils import raise_error
+from ....datagrabber import ConfoundsFormat, DataType, PatternDataGrabber
+from ....typing import DataGrabberPatterns
+from ....utils import ensure_list
 
 
-__all__ = ["JuselessUCLA"]
+__all__ = ["JuselessUCLA", "UCLATask"]
+
+
+class UCLATask(str, Enum):
+    """Accepted UCLA tasks."""
+
+    REST = "rest"
+    BART = "bart"
+    BHT = "bht"
+    PAMENC = "pamenc"
+    PAMRET = "pamret"
+    SCAP = "scap"
+    TASKSWITCH = "taskswitch"
+    STOPSIGNAL = "stopsignal"
+
+
+_types = Literal[
+    DataType.BOLD,
+    DataType.T1w,
+    DataType.VBM_CSF,
+    DataType.VBM_GM,
+    DataType.VBM_WM,
+]
+
+_tasks = Literal[
+    UCLATask.REST,
+    UCLATask.BART,
+    UCLATask.BHT,
+    UCLATask.PAMENC,
+    UCLATask.PAMRET,
+    UCLATask.SCAP,
+    UCLATask.TASKSWITCH,
+    UCLATask.STOPSIGNAL,
+]
 
 
 @register_datagrabber
@@ -22,116 +60,88 @@ class JuselessUCLA(PatternDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path, optional
+    datadir : Path, optional
         The directory where the dataset is stored.
         (default "/data/project/psychosis_thalamus/data/fmriprep").
     types: {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM"} or \
            list of the options, optional
-        UCLA data types. If None, all available data types are selected.
-        (default None).
+        The data type(s) to grab.
     tasks : {"rest", "bart", "bht", "pamenc", "pamret", \
             "scap", "taskswitch", "stopsignal"} or \
-            list of the options or None, optional
-        UCLA task sessions. If None, all available task sessions are
-        selected (default None).
+            list of the options, optional
+        UCLA task sessions.
+        By default, all available task are selected.
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path = "/data/project/psychosis_thalamus/data/fmriprep",
-        types: str | list[str] | None = None,
-        tasks: str | list[str] | None = None,
-    ) -> None:
-        # Declare all tasks
-        all_tasks = [
-            "rest",
-            "bart",
-            "bht",
-            "pamenc",
-            "pamret",
-            "scap",
-            "taskswitch",
-            "stopsignal",
-        ]
-        # Set default tasks
-        if tasks is None:
-            tasks = all_tasks
-        else:
-            # Convert single task into list
-            if isinstance(tasks, str):
-                tasks = [tasks]
-            # Verify valid tasks
-            for t in tasks:
-                if t not in all_tasks:
-                    raise_error(
-                        f"{t} is not a valid task in the UCLA dataset!"
-                    )
-        self.tasks = tasks
-        # The patterns
-        patterns = {
-            "BOLD": {
+    # the commented out uri leads to new open neuro dataset which does
+    # NOT have preprocessed data
+    # uri = "https://github.com/OpenNeuroDatasets/ds000030.git"
+    datadir: Path = Path("/data/project/psychosis_thalamus/data/fmriprep")
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.VBM_CSF,
+        DataType.VBM_GM,
+        DataType.VBM_WM,
+    ]
+    tasks: Annotated[
+        UCLATask | list[UCLATask], BeforeValidator(ensure_list)
+    ] = [  # noqa: RUF012
+        UCLATask.REST,
+        UCLATask.BART,
+        UCLATask.BHT,
+        UCLATask.PAMENC,
+        UCLATask.PAMRET,
+        UCLATask.SCAP,
+        UCLATask.TASKSWITCH,
+        UCLATask.STOPSIGNAL,
+    ]
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "{subject}/func/{subject}_task-{task}_bold_space-"
+                "MNI152NLin2009cAsym_preproc.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+            "confounds": {
                 "pattern": (
-                    "{subject}/func/{subject}_task-{task}_bold_space-"
-                    "MNI152NLin2009cAsym_preproc.nii.gz"
+                    "{subject}/func/{subject}_task-{task}_bold_confounds.tsv"
                 ),
-                "space": "MNI152NLin2009cAsym",
-                "confounds": {
-                    "pattern": (
-                        "{subject}/func/{subject}_"
-                        "task-{task}_bold_confounds.tsv"
-                    ),
-                    "space": "fmriprep",
-                },
+                "space": "fmriprep",
             },
-            "T1w": {
-                "pattern": (
-                    "{subject}/anat/{subject}_"
-                    "T1w_space-MNI152NLin2009cAsym_preproc.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-            "VBM_CSF": {
-                "pattern": (
-                    "{subject}/anat/{subject}_T1w_space-"
-                    "MNI152NLin2009cAsym_class-CSF_probtissue.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-            "VBM_GM": {
-                "pattern": (
-                    "{subject}/anat/{subject}_T1w_space-"
-                    "MNI152NLin2009cAsym_class-GM_probtissue.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-            "VBM_WM": {
-                "pattern": (
-                    "{subject}/anat/{subject}_T1w_space"
-                    "-MNI152NLin2009cAsym_class-WM_probtissue.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-        }
-        # Set default types
-        if types is None:
-            types = list(patterns.keys())
-        # Convert single type into list
-        else:
-            if not isinstance(types, list):
-                types = [types]
-        # The replacements
-        replacements = ["subject", "task"]
-        # the commented out uri leads to new open neuro dataset which does
-        # NOT have preprocessed data
-        # uri = "https://github.com/OpenNeuroDatasets/ds000030.git"
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            patterns=patterns,
-            replacements=replacements,
-            confounds_format="fmriprep",
-        )
+        },
+        "T1w": {
+            "pattern": (
+                "{subject}/anat/{subject}_"
+                "T1w_space-MNI152NLin2009cAsym_preproc.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+        "VBM_CSF": {
+            "pattern": (
+                "{subject}/anat/{subject}_T1w_space-"
+                "MNI152NLin2009cAsym_class-CSF_probtissue.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+        "VBM_GM": {
+            "pattern": (
+                "{subject}/anat/{subject}_T1w_space-"
+                "MNI152NLin2009cAsym_class-GM_probtissue.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+        "VBM_WM": {
+            "pattern": (
+                "{subject}/anat/{subject}_T1w_space"
+                "-MNI152NLin2009cAsym_class-WM_probtissue.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+    }
+    replacements: list[str] = ["subject", "task"]  # noqa: RUF012
+    confounds_format: ConfoundsFormat = ConfoundsFormat.FMRIPrep
 
     def get_elements(self) -> list:
         """Implement fetching list of elements in the dataset.

junifer/configs/juseless/datagrabbers/ukb_vbm.py

@@ -6,9 +6,13 @@
 # License: AGPL
 
 from pathlib import Path
+from typing import Literal
+
+from pydantic import AnyUrl
 
 from ....api.decorators import register_datagrabber
-from ....datagrabber import PatternDataladDataGrabber
+from ....datagrabber import DataType, PatternDataladDataGrabber
+from ....typing import DataGrabberPatterns
 
 
 __all__ = ["JuselessDataladUKBVBM"]
@@ -22,29 +26,20 @@ class JuselessDataladUKBVBM(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or pathlib.Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
 
     """
 
-    def __init__(self, datadir: str | Path | None = None) -> None:
-        uri = "ria+http://ukb.ds.inm7.de#~cat_m0wp1"
-        rootdir = "m0wp1"
-        types = ["VBM_GM"]
-        replacements = ["subject", "session"]
-        patterns = {
-            "VBM_GM": {
-                "pattern": "m0wp1{subject}_ses-{session}_T1w.nii.gz",
-                "space": "IXI549Space",
-            },
-        }
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            rootdir=rootdir,
-            replacements=replacements,
-            patterns=patterns,
-        )
+    uri: AnyUrl = AnyUrl("ria+http://ukb.ds.inm7.de#~cat_m0wp1")
+    rootdir: Path = Path("m0wp1")
+    types: list[Literal[DataType.VBM_GM]] = [DataType.VBM_GM]  # noqa: RUF012
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "VBM_GM": {
+            "pattern": "m0wp1{subject}_ses-{session}_T1w.nii.gz",
+            "space": "IXI549Space",
+        },
+    }
+    replacements: list[str] = ["subject", "session"]  # noqa: RUF012

junifer/conftest.py

@@ -7,8 +7,9 @@
 from pathlib import Path
 
 import pytest
+from pydantic import AnyUrl
 
-from junifer.datagrabber import PatternDataladDataGrabber
+from junifer.datagrabber import DataType, PatternDataladDataGrabber
 from junifer.utils.singleton import Singleton
 
 
@@ -40,8 +41,8 @@ def maps_datagrabber(tmp_path: Path) -> PatternDataladDataGrabber:
 
     """
     dg = PatternDataladDataGrabber(
-        uri="https://github.com/OpenNeuroDatasets/ds005226.git",
-        types=["BOLD"],
+        uri=AnyUrl("https://github.com/OpenNeuroDatasets/ds005226.git"),
+        types=DataType.BOLD,
         patterns={
             "BOLD": {
                 "pattern": (

junifer/data/_dispatch.py

@@ -94,7 +94,7 @@ class DataDispatcher(MutableMapping):
         # Update global
         self._registries[key] = value
 
-    def popitem():
+    def popitem(self):
         """Not implemented."""
         pass
 

junifer/data/masks/tests/test_masks.py

@@ -98,11 +98,11 @@ def test_compute_brain_mask_for_native(mask_type: str) -> None:
 
     """
     with DMCC13Benchmark(
-        types=["BOLD"],
-        sessions=["ses-wave1bas"],
-        tasks=["Rest"],
-        phase_encodings=["AP"],
-        runs=["1"],
+        types="BOLD",
+        sessions="ses-wave1bas",
+        tasks="Rest",
+        phase_encodings="AP",
+        runs="1",
         native_t1w=True,
     ) as dg:
         element_data = DefaultDataReader().fit_transform(
@@ -177,7 +177,7 @@ def test_register_already_registered() -> None:
 )
 def test_register(
     name: str,
-    mask_path: str,
+    mask_path: str | Path,
     space: str,
     overwrite: bool,
 ) -> None:

junifer/datagrabber/__init__.pyi

@@ -3,30 +3,62 @@ __all__ = [
     "DataladDataGrabber",
     "PatternDataGrabber",
     "PatternDataladDataGrabber",
+    "AOMICSpace",
+    "AOMICTask",
     "DataladAOMICID1000",
     "DataladAOMICPIOP1",
     "DataladAOMICPIOP2",
     "HCP1200",
+    "HCP1200Task",
+    "HCP1200PhaseEncoding",
     "DataladHCP1200",
     "MultipleDataGrabber",
     "DMCC13Benchmark",
+    "DMCCSession",
+    "DMCCTask",
+    "DMCCPhaseEncoding",
+    "DMCCRun",
     "DataTypeManager",
     "DataTypeSchema",
     "OptionalTypeSchema",
     "PatternValidationMixin",
     "register_data_type",
+    "DataType",
+    "ConfoundsFormat",
+    "register_confounds_format",
 ]
 
 # These 4 need to be in this order, otherwise it is a circular import
-from .base import BaseDataGrabber
+from .base import BaseDataGrabber, DataType
 from .datalad_base import DataladDataGrabber
-from .pattern import PatternDataGrabber
+from .pattern import (
+    PatternDataGrabber,
+    ConfoundsFormat,
+    register_confounds_format,
+)
 from .pattern_datalad import PatternDataladDataGrabber
 
-from .aomic import DataladAOMICID1000, DataladAOMICPIOP1, DataladAOMICPIOP2
-from .hcp1200 import HCP1200, DataladHCP1200
+from .aomic import (
+    AOMICSpace,
+    AOMICTask,
+    DataladAOMICID1000,
+    DataladAOMICPIOP1,
+    DataladAOMICPIOP2,
+)
+from .hcp1200 import (
+    HCP1200,
+    HCP1200Task,
+    HCP1200PhaseEncoding,
+    DataladHCP1200,
+)
 from .multiple import MultipleDataGrabber
-from .dmcc13_benchmark import DMCC13Benchmark
+from .dmcc13_benchmark import (
+    DMCC13Benchmark,
+    DMCCSession,
+    DMCCTask,
+    DMCCPhaseEncoding,
+    DMCCRun,
+)
 
 from .pattern_validation_mixin import (
     DataTypeManager,

junifer/datagrabber/aomic/__init__.pyi

@@ -1,5 +1,12 @@
-__all__ = ["DataladAOMICID1000", "DataladAOMICPIOP1", "DataladAOMICPIOP2"]
+__all__ = [
+    "AOMICSpace",
+    "AOMICTask",
+    "DataladAOMICID1000",
+    "DataladAOMICPIOP1",
+    "DataladAOMICPIOP2",
+]
 
+from ._types import AOMICSpace, AOMICTask
 from .id1000 import DataladAOMICID1000
 from .piop1 import DataladAOMICPIOP1
 from .piop2 import DataladAOMICPIOP2

junifer/datagrabber/aomic/_types.py

@@ -0,0 +1,22 @@
+"""Provide common types for AOMIC DataGrabbers."""
+
+from enum import Enum
+
+
+class AOMICSpace(str, Enum):
+    """Accepted spaces for AOMIC."""
+
+    Native = "native"
+    MNI152NLin2009cAsym = "MNI152NLin2009cAsym"
+
+
+class AOMICTask(str, Enum):
+    """Accepted tasks for AOMIC."""
+
+    RestingState = "restingstate"
+    Anticipation = "anticipation"
+    EmoMatching = "emomatching"
+    Faces = "faces"
+    Gstroop = "gstroop"
+    WorkingMemory = "workingmemory"
+    StopSignal = "stopsignal"

junifer/datagrabber/aomic/id1000.py

@@ -7,15 +7,32 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import AnyUrl, BeforeValidator
 
 from ...api.decorators import register_datagrabber
-from ...utils import raise_error
+from ...typing import DataGrabberPatterns
+from ...utils import ensure_list
+from ..base import DataType
+from ..pattern import ConfoundsFormat
 from ..pattern_datalad import PatternDataladDataGrabber
+from ._types import AOMICSpace
 
 
 __all__ = ["DataladAOMICID1000"]
 
+_types = Literal[
+    DataType.BOLD,
+    DataType.T1w,
+    DataType.VBM_CSF,
+    DataType.VBM_GM,
+    DataType.VBM_WM,
+    DataType.DWI,
+    DataType.FreeSurfer,
+    DataType.Warp,
+]
+
 
 @register_datagrabber
 class DataladAOMICID1000(PatternDataladDataGrabber):
@@ -23,212 +40,206 @@ class DataladAOMICID1000(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    types: {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
-           "FreeSurfer"} or list of the options, optional
-        AOMIC data types. If None, all available data types are selected.
-        (default None).
+    types : {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
+            "FreeSurfer", "Warp"} or list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
     space : {"native", "MNI152NLin2009cAsym"}, optional
-        The space to use for the data (default "MNI152NLin2009cAsym").
-
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for:
-         * ``space``
+        AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        types: str | list[str] | None = None,
-        space: str = "MNI152NLin2009cAsym",
-    ) -> None:
-        valid_spaces = ["native", "MNI152NLin2009cAsym"]
-        if space not in ["native", "MNI152NLin2009cAsym"]:
-            raise_error(
-                f"Invalid space {space}. Must be one of {valid_spaces}"
-            )
-
-        # Descriptor for space in `anat`
-        sp_anat_desc = (
-            "" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # Descriptor for space in `func`
-        sp_func_desc = (
-            "space-T1w_" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # The patterns
-        patterns = {
-            "BOLD": {
+    uri: AnyUrl = AnyUrl("https://github.com/OpenNeuroDatasets/ds003097.git")
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.VBM_CSF,
+        DataType.VBM_GM,
+        DataType.VBM_WM,
+        DataType.DWI,
+        DataType.FreeSurfer,
+        DataType.Warp,
+    ]
+    space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/func/"
+                "{subject}_task-moviewatching_"
+                "{sp_func_desc}"
+                "desc-preproc_bold.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/func/"
                     "{subject}_task-moviewatching_"
-                    f"{sp_func_desc}"
-                    "desc-preproc_bold.nii.gz"
+                    "{sp_func_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-moviewatching_"
-                        f"{sp_func_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
-                "confounds": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-moviewatching_"
-                        "desc-confounds_regressors.tsv"
-                    ),
-                    "format": "fmriprep",
-                },
-                "reference": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-moviewatching_"
-                        f"{sp_func_desc}"
-                        "boldref.nii.gz"
-                    ),
-                },
             },
-            "T1w": {
+            "confounds": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-moviewatching_"
+                    "desc-confounds_regressors.tsv"
+                ),
+                "format": "fmriprep",
+            },
+            "reference": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-moviewatching_"
+                    "{sp_func_desc}"
+                    "boldref.nii.gz"
+                ),
+            },
+        },
+        "T1w": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "desc-preproc_T1w.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
                     "{subject}_"
-                    f"{sp_anat_desc}"
-                    "desc-preproc_T1w.nii.gz"
+                    "{sp_anat_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_"
-                        f"{sp_anat_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
             },
-            "VBM_CSF": {
+        },
+        "VBM_CSF": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-CSF_probseg.nii.gz"
+            ),
+        },
+        "VBM_GM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-GM_probseg.nii.gz"
+            ),
+        },
+        "VBM_WM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-WM_probseg.nii.gz"
+            ),
+        },
+        "DWI": {
+            "pattern": (
+                "derivatives/dwipreproc/{subject}/dwi/"
+                "{subject}_desc-preproc_dwi.nii.gz"
+            ),
+        },
+        "FreeSurfer": {
+            "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
+            "aseg": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
+                )
+            },
+            "norm": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
+                )
+            },
+            "lh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
+                )
+            },
+            "rh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
+                )
+            },
+            "lh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
+                )
+            },
+            "rh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
+                )
+            },
+        },
+        "Warp": [
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-CSF_probseg.nii.gz"
+                    "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "MNI152NLin2009cAsym",
+                "dst": "native",
+                "warper": "ants",
             },
-            "VBM_GM": {
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-GM_probseg.nii.gz"
+                    "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "native",
+                "dst": "MNI152NLin2009cAsym",
+                "warper": "ants",
             },
-            "VBM_WM": {
-                "pattern": (
-                    "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-WM_probseg.nii.gz"
-                ),
-                "space": space,
-            },
-            "DWI": {
-                "pattern": (
-                    "derivatives/dwipreproc/{subject}/dwi/"
-                    "{subject}_desc-preproc_dwi.nii.gz"
-                ),
-            },
-            "FreeSurfer": {
-                "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
-                "aseg": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
-                    )
-                },
-                "norm": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
-                    )
-                },
-                "lh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
-                    )
-                },
-                "rh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
-                    )
-                },
-                "lh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
-                    )
-                },
-                "rh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
-                    )
-                },
-            },
-            "Warp": [
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "MNI152NLin2009cAsym",
-                    "dst": "native",
-                    "warper": "ants",
-                },
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "native",
-                    "dst": "MNI152NLin2009cAsym",
-                    "warper": "ants",
-                },
-            ],
-        }
-        if space == "native":
-            patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
+        ],
+    }
+    replacements: list[str] = ["subject"]  # noqa: RUF012
+    confounds_format: ConfoundsFormat = ConfoundsFormat.FMRIPrep
 
-        else:
-            patterns["BOLD"]["prewarp_space"] = "native"
-
-        # Use native T1w assets
-        self.space = space
-
-        # Set default types
-        if types is None:
-            types = list(patterns.keys())
-        # Convert single type into list
-        else:
-            if not isinstance(types, list):
-                types = [types]
-        # The replacements
-        replacements = ["subject"]
-        uri = "https://github.com/OpenNeuroDatasets/ds003097.git"
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            patterns=patterns,
-            replacements=replacements,
-            confounds_format="fmriprep",
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        # Descriptor for space in `anat`
+        sp_anat_desc = (
+            "" if self.space == "native" else "space-MNI152NLin2009cAsym_"
         )
+        # Descriptor for space in `func`
+        sp_func_desc = (
+            "space-T1w_"
+            if self.space == "native"
+            else "space-MNI152NLin2009cAsym_"
+        )
+        self.patterns["BOLD"]["pattern"] = self.patterns["BOLD"][
+            "pattern"
+        ].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["mask"]["pattern"] = self.patterns["BOLD"][
+            "mask"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["reference"]["pattern"] = self.patterns["BOLD"][
+            "reference"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["T1w"]["pattern"] = self.patterns["T1w"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        self.patterns["T1w"]["mask"]["pattern"] = self.patterns["T1w"]["mask"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["pattern"] = self.patterns[t]["pattern"].replace(
+                "{sp_anat_desc}", sp_anat_desc
+            )
+        for t in ["BOLD", "T1w"]:
+            self.patterns[t]["space"] = self.space
+            self.patterns[t]["mask"]["space"] = self.space
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["space"] = self.space
+        if self.space == "native":
+            self.patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
+        else:
+            self.patterns["BOLD"]["prewarp_space"] = "native"
+        super().validate_datagrabber_params()

junifer/datagrabber/aomic/piop1.py

@@ -8,15 +8,41 @@
 # License: AGPL
 
 from itertools import product
-from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import AnyUrl, BeforeValidator
 
 from ...api.decorators import register_datagrabber
-from ...utils import raise_error
+from ...typing import DataGrabberPatterns
+from ...utils import ensure_list
+from ..base import DataType
+from ..pattern import ConfoundsFormat
 from ..pattern_datalad import PatternDataladDataGrabber
+from ._types import AOMICSpace, AOMICTask
 
 
 __all__ = ["DataladAOMICPIOP1"]
 
+_types = Literal[
+    DataType.BOLD,
+    DataType.T1w,
+    DataType.VBM_CSF,
+    DataType.VBM_GM,
+    DataType.VBM_WM,
+    DataType.DWI,
+    DataType.FreeSurfer,
+    DataType.Warp,
+]
+
+_tasks = Literal[
+    AOMICTask.RestingState,
+    AOMICTask.Anticipation,
+    AOMICTask.EmoMatching,
+    AOMICTask.Faces,
+    AOMICTask.Gstroop,
+    AOMICTask.WorkingMemory,
+]
+
 
 @register_datagrabber
 class DataladAOMICPIOP1(PatternDataladDataGrabber):
@@ -24,246 +50,224 @@ class DataladAOMICPIOP1(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    types: {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
-           "FreeSurfer"} or list of the options, optional
-        AOMIC data types. If None, all available data types are selected.
-        (default None).
+    types : {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
+            "FreeSurfer", "Warp"} or list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
     tasks : {"restingstate", "anticipation", "emomatching", "faces", \
             "gstroop", "workingmemory"} or list of the options, optional
-        AOMIC PIOP1 task sessions. If None, all available task sessions are
-        selected (default None).
+        AOMIC PIOP1 task sessions.
+        By default, all available task sessions are selected.
     space : {"native", "MNI152NLin2009cAsym"}, optional
-        The space to use for the data (default "MNI152NLin2009cAsym").
-
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for:
-         * ``tasks``
-         * ``space``
+        AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        types: str | list[str] | None = None,
-        tasks: str | list[str] | None = None,
-        space: str = "MNI152NLin2009cAsym",
-    ) -> None:
-        valid_spaces = ["native", "MNI152NLin2009cAsym"]
-        if space not in ["native", "MNI152NLin2009cAsym"]:
-            raise_error(
-                f"Invalid space {space}. Must be one of {valid_spaces}"
-            )
-        # Declare all tasks
-        all_tasks = [
-            "restingstate",
-            "anticipation",
-            "emomatching",
-            "faces",
-            "gstroop",
-            "workingmemory",
-        ]
-        # Set default tasks
-        if tasks is None:
-            tasks = all_tasks
-        else:
-            # Convert single task into list
-            if isinstance(tasks, str):
-                tasks = [tasks]
-            # Verify valid tasks
-            for t in tasks:
-                if t not in all_tasks:
-                    raise_error(
-                        f"{t} is not a valid task in the AOMIC PIOP1 dataset!"
-                    )
-        self.tasks = tasks
-        # Descriptor for space in `anat`
-        sp_anat_desc = (
-            "" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # Descriptor for space in `func`
-        sp_func_desc = (
-            "space-T1w_" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # The patterns
-        patterns = {
-            "BOLD": {
+    uri: AnyUrl = AnyUrl("https://github.com/OpenNeuroDatasets/ds002785")
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.VBM_CSF,
+        DataType.VBM_GM,
+        DataType.VBM_WM,
+        DataType.DWI,
+        DataType.FreeSurfer,
+        DataType.Warp,
+    ]
+    tasks: Annotated[_tasks | list[_tasks], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        AOMICTask.RestingState,
+        AOMICTask.Anticipation,
+        AOMICTask.EmoMatching,
+        AOMICTask.Faces,
+        AOMICTask.Gstroop,
+        AOMICTask.WorkingMemory,
+    ]
+    space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/func/"
+                "{subject}_task-{task}_"
+                "{sp_func_desc}"
+                "desc-preproc_bold.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/func/"
                     "{subject}_task-{task}_"
-                    f"{sp_func_desc}"
-                    "desc-preproc_bold.nii.gz"
+                    "{sp_func_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        f"{sp_func_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
-                "confounds": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        "desc-confounds_regressors.tsv"
-                    ),
-                    "format": "fmriprep",
-                },
-                "reference": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        f"{sp_func_desc}"
-                        "boldref.nii.gz"
-                    ),
-                },
             },
-            "T1w": {
+            "confounds": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-{task}_"
+                    "desc-confounds_regressors.tsv"
+                ),
+                "format": "fmriprep",
+            },
+            "reference": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-{task}_"
+                    "{sp_func_desc}"
+                    "boldref.nii.gz"
+                ),
+            },
+        },
+        "T1w": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "desc-preproc_T1w.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
                     "{subject}_"
-                    f"{sp_anat_desc}"
-                    "desc-preproc_T1w.nii.gz"
+                    "{sp_anat_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_"
-                        f"{sp_anat_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
             },
-            "VBM_CSF": {
+        },
+        "VBM_CSF": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-CSF_probseg.nii.gz"
+            ),
+        },
+        "VBM_GM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-GM_probseg.nii.gz"
+            ),
+        },
+        "VBM_WM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-WM_probseg.nii.gz"
+            ),
+        },
+        "DWI": {
+            "pattern": (
+                "derivatives/dwipreproc/{subject}/dwi/"
+                "{subject}_desc-preproc_dwi.nii.gz"
+            ),
+        },
+        "FreeSurfer": {
+            "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
+            "aseg": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
+                )
+            },
+            "norm": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
+                )
+            },
+            "lh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
+                )
+            },
+            "rh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
+                )
+            },
+            "lh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
+                )
+            },
+            "rh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
+                )
+            },
+        },
+        "Warp": [
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-CSF_probseg.nii.gz"
+                    "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "MNI152NLin2009cAsym",
+                "dst": "native",
+                "warper": "ants",
             },
-            "VBM_GM": {
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-GM_probseg.nii.gz"
+                    "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "native",
+                "dst": "MNI152NLin2009cAsym",
+                "warper": "ants",
             },
-            "VBM_WM": {
-                "pattern": (
-                    "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-WM_probseg.nii.gz"
-                ),
-                "space": space,
-            },
-            "DWI": {
-                "pattern": (
-                    "derivatives/dwipreproc/{subject}/dwi/"
-                    "{subject}_desc-preproc_dwi.nii.gz"
-                ),
-            },
-            "FreeSurfer": {
-                "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
-                "aseg": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
-                    )
-                },
-                "norm": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
-                    )
-                },
-                "lh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
-                    )
-                },
-                "rh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
-                    )
-                },
-                "lh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
-                    )
-                },
-                "rh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
-                    )
-                },
-            },
-            "Warp": [
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "MNI152NLin2009cAsym",
-                    "dst": "native",
-                    "warper": "ants",
-                },
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "native",
-                    "dst": "MNI152NLin2009cAsym",
-                    "warper": "ants",
-                },
-            ],
-        }
+        ],
+    }
+    replacements: list[str] = ["subject", "task"]  # noqa: RUF012
+    confounds_format: ConfoundsFormat = ConfoundsFormat.FMRIPrep
 
-        if space == "native":
-            patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
-        else:
-            patterns["BOLD"]["prewarp_space"] = "native"
-
-        # Use native T1w assets
-        self.space = space
-
-        # Set default types
-        if types is None:
-            types = list(patterns.keys())
-        # Convert single type into list
-        else:
-            if not isinstance(types, list):
-                types = [types]
-        # The replacements
-        replacements = ["subject", "task"]
-        uri = "https://github.com/OpenNeuroDatasets/ds002785"
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            patterns=patterns,
-            replacements=replacements,
-            confounds_format="fmriprep",
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        # Descriptor for space in `anat`
+        sp_anat_desc = (
+            "" if self.space == "native" else "space-MNI152NLin2009cAsym_"
         )
+        # Descriptor for space in `func`
+        sp_func_desc = (
+            "space-T1w_"
+            if self.space == "native"
+            else "space-MNI152NLin2009cAsym_"
+        )
+        self.patterns["BOLD"]["pattern"] = self.patterns["BOLD"][
+            "pattern"
+        ].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["mask"]["pattern"] = self.patterns["BOLD"][
+            "mask"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["reference"]["pattern"] = self.patterns["BOLD"][
+            "reference"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["T1w"]["pattern"] = self.patterns["T1w"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        self.patterns["T1w"]["mask"]["pattern"] = self.patterns["T1w"]["mask"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["pattern"] = self.patterns[t]["pattern"].replace(
+                "{sp_anat_desc}", sp_anat_desc
+            )
+        for t in ["BOLD", "T1w"]:
+            self.patterns[t]["space"] = self.space
+            self.patterns[t]["mask"]["space"] = self.space
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["space"] = self.space
+        if self.space == "native":
+            self.patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
+        else:
+            self.patterns["BOLD"]["prewarp_space"] = "native"
+        super().validate_datagrabber_params()
 
     def get_item(self, subject: str, task: str) -> dict:
-        """Index one element in the dataset.
+        """Get the specified item from the dataset.
 
         Parameters
         ----------

junifer/datagrabber/aomic/piop2.py

@@ -8,15 +8,39 @@
 # License: AGPL
 
 from itertools import product
-from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import AnyUrl, BeforeValidator
 
 from ...api.decorators import register_datagrabber
-from ...utils import raise_error
+from ...typing import DataGrabberPatterns
+from ...utils import ensure_list
+from ..base import DataType
+from ..pattern import ConfoundsFormat
 from ..pattern_datalad import PatternDataladDataGrabber
+from ._types import AOMICSpace, AOMICTask
 
 
 __all__ = ["DataladAOMICPIOP2"]
 
+_types = Literal[
+    DataType.BOLD,
+    DataType.T1w,
+    DataType.VBM_CSF,
+    DataType.VBM_GM,
+    DataType.VBM_WM,
+    DataType.DWI,
+    DataType.FreeSurfer,
+    DataType.Warp,
+]
+
+_tasks = Literal[
+    AOMICTask.RestingState,
+    AOMICTask.StopSignal,
+    AOMICTask.EmoMatching,
+    AOMICTask.WorkingMemory,
+]
+
 
 @register_datagrabber
 class DataladAOMICPIOP2(PatternDataladDataGrabber):
@@ -24,241 +48,219 @@ class DataladAOMICPIOP2(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    types: {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
-           "FreeSurfer"} or list of the options, optional
-        AOMIC data types. If None, all available data types are selected.
-        (default None).
+    types : {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", \
+            "FreeSurfer", "Warp"} or list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
     tasks : {"restingstate", "stopsignal", "workingmemory", "emomatching"} or \
             list of the options, optional
-        AOMIC PIOP2 task sessions. If None, all available task sessions are
-        selected (default None).
+        AOMIC PIOP2 task sessions.
+        By default, all available task sessions are selected.
     space : {"native", "MNI152NLin2009cAsym"}, optional
-        The space to use for the data (default "MNI152NLin2009cAsym").
-
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for:
-         * ``tasks``
-         * ``space``
+        AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        types: str | list[str] | None = None,
-        tasks: str | list[str] | None = None,
-        space: str = "MNI152NLin2009cAsym",
-    ) -> None:
-        valid_spaces = ["native", "MNI152NLin2009cAsym"]
-        if space not in ["native", "MNI152NLin2009cAsym"]:
-            raise_error(
-                f"Invalid space {space}. Must be one of {valid_spaces}"
-            )
-        # Declare all tasks
-        all_tasks = [
-            "restingstate",
-            "stopsignal",
-            "workingmemory",
-            "emomatching",
-        ]
-        # Set default tasks
-        if tasks is None:
-            tasks = all_tasks
-        else:
-            # Convert single task into list
-            if isinstance(tasks, str):
-                tasks = [tasks]
-            # Verify valid tasks
-            for t in tasks:
-                if t not in all_tasks:
-                    raise_error(
-                        f"{t} is not a valid task in the AOMIC PIOP2 dataset!"
-                    )
-        self.tasks = tasks
-        # Descriptor for space in `anat`
-        sp_anat_desc = (
-            "" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # Descriptor for space in `func`
-        sp_func_desc = (
-            "space-T1w_" if space == "native" else "space-MNI152NLin2009cAsym_"
-        )
-        # The patterns
-        patterns = {
-            "BOLD": {
+    uri: AnyUrl = AnyUrl("https://github.com/OpenNeuroDatasets/ds002790")
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.VBM_CSF,
+        DataType.VBM_GM,
+        DataType.VBM_WM,
+        DataType.DWI,
+        DataType.FreeSurfer,
+        DataType.Warp,
+    ]
+    tasks: Annotated[_tasks | list[_tasks], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        AOMICTask.RestingState,
+        AOMICTask.StopSignal,
+        AOMICTask.EmoMatching,
+        AOMICTask.WorkingMemory,
+    ]
+    space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/func/"
+                "{subject}_task-{task}_"
+                "{sp_func_desc}"
+                "desc-preproc_bold.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/func/"
                     "{subject}_task-{task}_"
-                    f"{sp_func_desc}"
-                    "desc-preproc_bold.nii.gz"
+                    "{sp_func_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        f"{sp_func_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
-                "confounds": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        "desc-confounds_regressors.tsv"
-                    ),
-                    "format": "fmriprep",
-                },
-                "reference": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/func/"
-                        "{subject}_task-{task}_"
-                        f"{sp_func_desc}"
-                        "boldref.nii.gz"
-                    ),
-                },
             },
-            "T1w": {
+            "confounds": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-{task}_"
+                    "desc-confounds_regressors.tsv"
+                ),
+                "format": "fmriprep",
+            },
+            "reference": {
+                "pattern": (
+                    "derivatives/fmriprep/{subject}/func/"
+                    "{subject}_task-{task}_"
+                    "{sp_func_desc}"
+                    "boldref.nii.gz"
+                ),
+            },
+        },
+        "T1w": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "desc-preproc_T1w.nii.gz"
+            ),
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
                     "{subject}_"
-                    f"{sp_anat_desc}"
-                    "desc-preproc_T1w.nii.gz"
+                    "{sp_anat_desc}"
+                    "desc-brain_mask.nii.gz"
                 ),
-                "space": space,
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_"
-                        f"{sp_anat_desc}"
-                        "desc-brain_mask.nii.gz"
-                    ),
-                    "space": space,
-                },
             },
-            "VBM_CSF": {
+        },
+        "VBM_CSF": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-CSF_probseg.nii.gz"
+            ),
+        },
+        "VBM_GM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-GM_probseg.nii.gz"
+            ),
+        },
+        "VBM_WM": {
+            "pattern": (
+                "derivatives/fmriprep/{subject}/anat/"
+                "{subject}_"
+                "{sp_anat_desc}"
+                "label-WM_probseg.nii.gz"
+            ),
+        },
+        "DWI": {
+            "pattern": (
+                "derivatives/dwipreproc/{subject}/dwi/"
+                "{subject}_desc-preproc_dwi.nii.gz"
+            ),
+        },
+        "FreeSurfer": {
+            "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
+            "aseg": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
+                )
+            },
+            "norm": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
+                )
+            },
+            "lh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
+                )
+            },
+            "rh_white": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
+                )
+            },
+            "lh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
+                )
+            },
+            "rh_pial": {
+                "pattern": (
+                    "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
+                )
+            },
+        },
+        "Warp": [
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-CSF_probseg.nii.gz"
+                    "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "MNI152NLin2009cAsym",
+                "dst": "native",
+                "warper": "ants",
             },
-            "VBM_GM": {
+            {
                 "pattern": (
                     "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-GM_probseg.nii.gz"
+                    "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
+                    "mode-image_xfm.h5"
                 ),
-                "space": space,
+                "src": "native",
+                "dst": "MNI152NLin2009cAsym",
+                "warper": "ants",
             },
-            "VBM_WM": {
-                "pattern": (
-                    "derivatives/fmriprep/{subject}/anat/"
-                    "{subject}_"
-                    f"{sp_anat_desc}"
-                    "label-WM_probseg.nii.gz"
-                ),
-                "space": space,
-            },
-            "DWI": {
-                "pattern": (
-                    "derivatives/dwipreproc/{subject}/dwi/"
-                    "{subject}_desc-preproc_dwi.nii.gz"
-                ),
-            },
-            "FreeSurfer": {
-                "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
-                "aseg": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]"
-                    )
-                },
-                "norm": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]"
-                    )
-                },
-                "lh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]"
-                    )
-                },
-                "rh_white": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]"
-                    )
-                },
-                "lh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]"
-                    )
-                },
-                "rh_pial": {
-                    "pattern": (
-                        "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]"
-                    )
-                },
-            },
-            "Warp": [
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-MNI152NLin2009cAsym_to-T1w_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "MNI152NLin2009cAsym",
-                    "dst": "native",
-                    "warper": "ants",
-                },
-                {
-                    "pattern": (
-                        "derivatives/fmriprep/{subject}/anat/"
-                        "{subject}_from-T1w_to-MNI152NLin2009cAsym_"
-                        "mode-image_xfm.h5"
-                    ),
-                    "src": "native",
-                    "dst": "MNI152NLin2009cAsym",
-                    "warper": "ants",
-                },
-            ],
-        }
+        ],
+    }
+    replacements: list[str] = ["subject", "task"]  # noqa: RUF012
+    confounds_format: ConfoundsFormat = ConfoundsFormat.FMRIPrep
 
-        if space == "native":
-            patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
-        else:
-            patterns["BOLD"]["prewarp_space"] = "native"
-
-        # Use native T1w assets
-        self.space = space
-
-        # Set default types
-        if types is None:
-            types = list(patterns.keys())
-        # Convert single type into list
-        else:
-            if not isinstance(types, list):
-                types = [types]
-        # The replacements
-        replacements = ["subject", "task"]
-        uri = "https://github.com/OpenNeuroDatasets/ds002790"
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            patterns=patterns,
-            replacements=replacements,
-            confounds_format="fmriprep",
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        # Descriptor for space in `anat`
+        sp_anat_desc = (
+            "" if self.space == "native" else "space-MNI152NLin2009cAsym_"
         )
+        # Descriptor for space in `func`
+        sp_func_desc = (
+            "space-T1w_"
+            if self.space == "native"
+            else "space-MNI152NLin2009cAsym_"
+        )
+        self.patterns["BOLD"]["pattern"] = self.patterns["BOLD"][
+            "pattern"
+        ].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["mask"]["pattern"] = self.patterns["BOLD"][
+            "mask"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["BOLD"]["reference"]["pattern"] = self.patterns["BOLD"][
+            "reference"
+        ]["pattern"].replace("{sp_func_desc}", sp_func_desc)
+        self.patterns["T1w"]["pattern"] = self.patterns["T1w"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        self.patterns["T1w"]["mask"]["pattern"] = self.patterns["T1w"]["mask"][
+            "pattern"
+        ].replace("{sp_anat_desc}", sp_anat_desc)
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["pattern"] = self.patterns[t]["pattern"].replace(
+                "{sp_anat_desc}", sp_anat_desc
+            )
+        for t in ["BOLD", "T1w"]:
+            self.patterns[t]["space"] = self.space
+            self.patterns[t]["mask"]["space"] = self.space
+        for t in ["VBM_CSF", "VBM_GM", "VBM_WM"]:
+            self.patterns[t]["space"] = self.space
+        if self.space == "native":
+            self.patterns["BOLD"]["prewarp_space"] = "MNI152NLin2009cAsym"
+        else:
+            self.patterns["BOLD"]["prewarp_space"] = "native"
+        super().validate_datagrabber_params()
 
     def get_elements(self) -> list:
         """Implement fetching list of elements in the dataset.
@@ -277,7 +279,7 @@ class DataladAOMICPIOP2(PatternDataladDataGrabber):
         return elems
 
     def get_item(self, subject: str, task: str) -> dict:
-        """Index one element in the dataset.
+        """Get the specified item from the dataset.
 
         Parameters
         ----------

junifer/datagrabber/aomic/tests/test_id1000.py

@@ -8,31 +8,32 @@
 # License: AGPL
 
 import pytest
+from pydantic import AnyUrl
 
 from junifer.datagrabber.aomic.id1000 import DataladAOMICID1000
 
 
-URI = "https://gin.g-node.org/juaml/datalad-example-aomic1000"
+URI = AnyUrl("https://gin.g-node.org/juaml/datalad-example-aomic1000")
 
 
 @pytest.mark.parametrize(
     "type_, nested_types, space",
     [
         ("BOLD", ["confounds", "mask", "reference"], "MNI152NLin2009cAsym"),
-        ("BOLD", ["confounds", "mask", "reference"], "native"),
+        (["BOLD"], ["confounds", "mask", "reference"], "native"),
         ("T1w", ["mask"], "MNI152NLin2009cAsym"),
-        ("T1w", ["mask"], "native"),
+        (["T1w"], ["mask"], "native"),
         ("VBM_CSF", None, "MNI152NLin2009cAsym"),
-        ("VBM_CSF", None, "native"),
+        (["VBM_CSF"], None, "native"),
         ("VBM_GM", None, "MNI152NLin2009cAsym"),
-        ("VBM_GM", None, "native"),
+        (["VBM_GM"], None, "native"),
         ("VBM_WM", None, "MNI152NLin2009cAsym"),
-        ("DWI", None, "MNI152NLin2009cAsym"),
-        ("FreeSurfer", None, "MNI152NLin2009cAsym"),
+        (["DWI"], None, "MNI152NLin2009cAsym"),
+        (["FreeSurfer"], None, "MNI152NLin2009cAsym"),
     ],
 )
 def test_DataladAOMICID1000(
-    type_: str,
+    type_: str | list[str],
     nested_types: list[str] | None,
     space: str,
 ) -> None:
@@ -40,7 +41,7 @@ def test_DataladAOMICID1000(
 
     Parameters
     ----------
-    type_ : str
+    type_ : str or list of str
         The parametrized type.
     nested_types : list of str or None
         The parametrized nested types.
@@ -48,32 +49,29 @@ def test_DataladAOMICID1000(
         The parametrized space.
 
     """
-    dg = DataladAOMICID1000(types=type_, space=space)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICID1000(uri=URI, types=type_, space=space)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        assert type_ in out
-        assert out[type_]["path"].exists()
-        assert out[type_]["path"].is_file()
-        # Asserts data type metadata
-        assert "meta" in out[type_]
-        meta = out[type_]["meta"]
-        assert "element" in meta
-        assert "subject" in meta["element"]
-        assert test_element == meta["element"]["subject"]
-        # Assert nested data type if not None
-        if nested_types is not None:
-            for nested_type in nested_types:
-                assert out[type_][nested_type]["path"].exists()
-                assert out[type_][nested_type]["path"].is_file()
+        if isinstance(type_, str):
+            type_ = [type_]
+        for t in type_:
+            assert t in out
+            assert out[t]["path"].exists()
+            assert out[t]["path"].is_file()
+            # Asserts data type metadata
+            assert "meta" in out[t]
+            meta = out[t]["meta"]
+            assert "element" in meta
+            assert "subject" in meta["element"]
+            assert test_element == meta["element"]["subject"]
+            # Assert nested data type if not None
+            if nested_types is not None:
+                for nested_type in nested_types:
+                    assert out[t][nested_type]["path"].exists()
+                    assert out[t][nested_type]["path"].is_file()
 
 
 @pytest.mark.parametrize(
@@ -102,28 +100,13 @@ def test_DataladAOMICID1000_partial_data_access(
         The parametrized types.
 
     """
-    dg = DataladAOMICID1000(types=types)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICID1000(uri=URI, types=types)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        if isinstance(types, list):
-            for type_ in types:
-                assert type_ in out
-        else:
-            assert types in out
-
-
-def test_DataladAOMICID1000_incorrect_data_type() -> None:
-    """Test DataladAOMICID1000 DataGrabber incorrect data type."""
-    with pytest.raises(
-        ValueError, match="`patterns` must contain all `types`"
-    ):
-        _ = DataladAOMICID1000(types="Scooby-Doo")
+        if isinstance(types, str):
+            types = [types]
+        for t in types:
+            assert t in out

junifer/datagrabber/aomic/tests/test_piop1.py

@@ -8,11 +8,12 @@
 # License: AGPL
 
 import pytest
+from pydantic import AnyUrl
 
 from junifer.datagrabber import DataladAOMICPIOP1
 
 
-URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop1"
+URI = AnyUrl("https://gin.g-node.org/juaml/datalad-example-aomicpiop1")
 
 
 @pytest.mark.parametrize(
@@ -21,18 +22,11 @@ URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop1"
         (
             "BOLD",
             ["confounds", "mask", "reference"],
-            None,
-            "MNI152NLin2009cAsym",
-        ),
-        ("BOLD", ["confounds", "mask", "reference"], None, "native"),
-        (
-            "BOLD",
-            ["confounds", "mask", "reference"],
-            ["anticipation"],
+            "anticipation",
             "MNI152NLin2009cAsym",
         ),
         (
-            "BOLD",
+            ["BOLD"],
             ["confounds", "mask", "reference"],
             ["emomatching", "faces"],
             "MNI152NLin2009cAsym",
@@ -40,97 +34,88 @@ URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop1"
         (
             "BOLD",
             ["confounds", "mask", "reference"],
-            ["restingstate"],
+            "restingstate",
             "MNI152NLin2009cAsym",
         ),
         (
-            "BOLD",
+            ["BOLD"],
             ["confounds", "mask", "reference"],
             ["workingmemory", "gstroop"],
             "MNI152NLin2009cAsym",
         ),
         (
-            "BOLD",
+            ["BOLD"],
             ["confounds", "mask", "reference"],
             ["anticipation", "faces", "restingstate"],
             "MNI152NLin2009cAsym",
         ),
-        ("T1w", ["mask"], None, "MNI152NLin2009cAsym"),
-        ("T1w", ["mask"], None, "native"),
-        ("VBM_CSF", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_CSF", None, None, "native"),
-        ("VBM_GM", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_GM", None, None, "native"),
-        ("VBM_WM", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_WM", None, None, "native"),
-        ("DWI", None, None, "MNI152NLin2009cAsym"),
-        ("FreeSurfer", None, None, "MNI152NLin2009cAsym"),
+        (["T1w"], ["mask"], "restingstate", "MNI152NLin2009cAsym"),
+        ("T1w", ["mask"], ["restingstate"], "native"),
+        (["VBM_CSF"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_CSF", None, ["restingstate"], "native"),
+        (["VBM_GM"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_GM", None, ["restingstate"], "native"),
+        (["VBM_WM"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_WM", None, ["restingstate"], "native"),
+        (["DWI"], None, "restingstate", "MNI152NLin2009cAsym"),
+        (["FreeSurfer"], None, ["restingstate"], "MNI152NLin2009cAsym"),
     ],
 )
 def test_DataladAOMICPIOP1(
-    type_: str,
+    type_: str | list[str],
     nested_types: list[str] | None,
-    tasks: list[str] | None,
+    tasks: str | list[str],
     space: str,
 ) -> None:
     """Test DataladAOMICPIOP1 DataGrabber.
 
     Parameters
     ----------
-    type_ : str
+    type_ : str or list of str
         The parametrized type.
     nested_types : list of str or None
         The parametrized nested types.
-    tasks : list of str or None
+    tasks : str or list of str
         The parametrized task values.
     space: str
         The parametrized space.
 
     """
-    dg = DataladAOMICPIOP1(types=type_, tasks=tasks, space=space)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICPIOP1(uri=URI, types=type_, tasks=tasks, space=space)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
-        out = dg[test_element]
-        # Get all elements
-        all_elements = dg.get_elements()
-        # Get test element
-        test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        assert type_ in out
-        # Check task name if BOLD
-        if type_ == "BOLD" and tasks is not None:
-            # Depending on task 'acquisition is different'
-            task_acqs = {
-                "anticipation": "seq",
-                "emomatching": "seq",
-                "faces": "mb3",
-                "gstroop": "seq",
-                "restingstate": "mb3",
-                "workingmemory": "seq",
-            }
-            assert task_acqs[test_element[1]] in out[type_]["path"].name
-        assert out[type_]["path"].exists()
-        assert out[type_]["path"].is_file()
-        # Asserts data type metadata
-        assert "meta" in out[type_]
-        meta = out[type_]["meta"]
-        assert "element" in meta
-        assert "subject" in meta["element"]
-        assert test_element[0] == meta["element"]["subject"]
-        # Assert nested data type if not None
-        if nested_types is not None:
-            for nested_type in nested_types:
-                assert out[type_][nested_type]["path"].exists()
-                assert out[type_][nested_type]["path"].is_file()
+        if isinstance(type_, str):
+            type_ = [type_]
+        for t in type_:
+            assert t in out
+            # Check task name if BOLD
+            if t == "BOLD":
+                # Depending on task 'acquisition is different'
+                task_acqs = {
+                    "anticipation": "seq",
+                    "emomatching": "seq",
+                    "faces": "mb3",
+                    "gstroop": "seq",
+                    "restingstate": "mb3",
+                    "workingmemory": "seq",
+                }
+                assert task_acqs[test_element[1]] in out[t]["path"].name
+            assert out[t]["path"].exists()
+            assert out[t]["path"].is_file()
+            # Asserts data type metadata
+            assert "meta" in out[t]
+            meta = out[t]["meta"]
+            assert "element" in meta
+            assert "subject" in meta["element"]
+            assert test_element[0] == meta["element"]["subject"]
+            # Assert nested data type if not None
+            if nested_types is not None:
+                for nested_type in nested_types:
+                    assert out[t][nested_type]["path"].exists()
+                    assert out[t][nested_type]["path"].is_file()
 
 
 @pytest.mark.parametrize(
@@ -159,40 +144,13 @@ def test_DataladAOMICPIOP1_partial_data_access(
         The parametrized types.
 
     """
-    dg = DataladAOMICPIOP1(types=types)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICPIOP1(uri=URI, types=types)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        if isinstance(types, list):
-            for type_ in types:
-                assert type_ in out
-        else:
-            assert types in out
-
-
-def test_DataladAOMICPIOP1_incorrect_data_type() -> None:
-    """Test DataladAOMICPIOP1 DataGrabber incorrect data type."""
-    with pytest.raises(
-        ValueError, match="`patterns` must contain all `types`"
-    ):
-        _ = DataladAOMICPIOP1(types="Ceres")
-
-
-def test_DataladAOMICPIOP1_invalid_tasks():
-    """Test DataladAOMICIDPIOP1 DataGrabber invalid tasks."""
-    with pytest.raises(
-        ValueError,
-        match=(
-            "thisisnotarealtask is not a valid task in "
-            "the AOMIC PIOP1 dataset!"
-        ),
-    ):
-        DataladAOMICPIOP1(tasks="thisisnotarealtask")
+        if isinstance(types, str):
+            types = [types]
+        for t in types:
+            assert t in out

junifer/datagrabber/aomic/tests/test_piop2.py

@@ -8,11 +8,12 @@
 # License: AGPL
 
 import pytest
+from pydantic import AnyUrl
 
 from junifer.datagrabber import DataladAOMICPIOP2
 
 
-URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop2"
+URI = AnyUrl("https://gin.g-node.org/juaml/datalad-example-aomicpiop2")
 
 
 @pytest.mark.parametrize(
@@ -21,18 +22,11 @@ URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop2"
         (
             "BOLD",
             ["confounds", "mask", "reference"],
-            None,
-            "MNI152NLin2009cAsym",
-        ),
-        ("BOLD", ["confounds", "mask", "reference"], None, "native"),
-        (
-            "BOLD",
-            ["confounds", "mask", "reference"],
-            ["restingstate"],
+            "restingstate",
             "MNI152NLin2009cAsym",
         ),
         (
-            "BOLD",
+            ["BOLD"],
             ["confounds", "mask", "reference"],
             ["restingstate", "stopsignal"],
             "MNI152NLin2009cAsym",
@@ -44,72 +38,69 @@ URI = "https://gin.g-node.org/juaml/datalad-example-aomicpiop2"
             "MNI152NLin2009cAsym",
         ),
         (
-            "BOLD",
+            ["BOLD"],
             ["confounds", "mask", "reference"],
-            ["workingmemory"],
+            "workingmemory",
             "MNI152NLin2009cAsym",
         ),
-        ("T1w", ["mask"], None, "MNI152NLin2009cAsym"),
-        ("T1w", ["mask"], None, "native"),
-        ("VBM_CSF", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_CSF", None, None, "native"),
-        ("VBM_GM", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_GM", None, None, "native"),
-        ("VBM_WM", None, None, "MNI152NLin2009cAsym"),
-        ("VBM_WM", None, None, "native"),
-        ("DWI", None, None, "MNI152NLin2009cAsym"),
-        ("FreeSurfer", None, None, "MNI152NLin2009cAsym"),
+        (["T1w"], ["mask"], "restingstate", "MNI152NLin2009cAsym"),
+        ("T1w", ["mask"], ["restingstate"], "native"),
+        (["VBM_CSF"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_CSF", None, ["restingstate"], "native"),
+        (["VBM_GM"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_GM", None, ["restingstate"], "native"),
+        (["VBM_WM"], None, "restingstate", "MNI152NLin2009cAsym"),
+        ("VBM_WM", None, ["restingstate"], "native"),
+        (["DWI"], None, "restingstate", "MNI152NLin2009cAsym"),
+        (["FreeSurfer"], None, ["restingstate"], "MNI152NLin2009cAsym"),
     ],
 )
 def test_DataladAOMICPIOP2(
-    type_: str,
+    type_: str | list[str],
     nested_types: list[str] | None,
-    tasks: list[str] | None,
+    tasks: str | list[str],
     space: str,
 ) -> None:
     """Test DataladAOMICPIOP2 DataGrabber.
 
     Parameters
     ----------
-    type_ : str
+    type_ : str or list of str
         The parametrized type.
     nested_types : list of str or None
         The parametrized nested types.
-    tasks : list of str or None
+    tasks : str or list of str
         The parametrized task values.
     space: str
         The parametrized space.
 
     """
-    dg = DataladAOMICPIOP2(types=type_, tasks=tasks, space=space)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICPIOP2(uri=URI, types=type_, tasks=tasks, space=space)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        assert type_ in out
-        # Check task name if BOLD
-        if type_ == "BOLD" and tasks is not None:
-            assert test_element[1] in out[type_]["path"].name
-        assert out[type_]["path"].exists()
-        assert out[type_]["path"].is_file()
-        # Asserts data type metadata
-        assert "meta" in out[type_]
-        meta = out[type_]["meta"]
-        assert "element" in meta
-        assert "subject" in meta["element"]
-        assert test_element[0] == meta["element"]["subject"]
-        # Assert nested data type if not None
-        if nested_types is not None:
-            for nested_type in nested_types:
-                assert out[type_][nested_type]["path"].exists()
-                assert out[type_][nested_type]["path"].is_file()
+        if isinstance(type_, str):
+            type_ = [type_]
+        for t in type_:
+            assert t in out
+            # Check task name if BOLD
+            if t == "BOLD":
+                assert test_element[1] in out[t]["path"].name
+            assert out[t]["path"].exists()
+            assert out[t]["path"].is_file()
+            # Asserts data type metadata
+            assert "meta" in out[t]
+            meta = out[t]["meta"]
+            assert "element" in meta
+            assert "subject" in meta["element"]
+            assert test_element[0] == meta["element"]["subject"]
+            # Assert nested data type if not None
+            if nested_types is not None:
+                for nested_type in nested_types:
+                    assert out[t][nested_type]["path"].exists()
+                    assert out[t][nested_type]["path"].is_file()
 
 
 @pytest.mark.parametrize(
@@ -138,40 +129,13 @@ def test_DataladAOMICPIOP2_partial_data_access(
         The parametrized types.
 
     """
-    dg = DataladAOMICPIOP2(types=types)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DataladAOMICPIOP2(uri=URI, types=types)
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element data
         out = dg[test_element]
         # Assert data type
-        if isinstance(types, list):
-            for type_ in types:
-                assert type_ in out
-        else:
-            assert types in out
-
-
-def test_DataladAOMICPIOP2_incorrect_data_type() -> None:
-    """Test DataladAOMICPIOP2 DataGrabber incorrect data type."""
-    with pytest.raises(
-        ValueError, match="`patterns` must contain all `types`"
-    ):
-        _ = DataladAOMICPIOP2(types="Vesta")
-
-
-def test_DataladAOMICPIOP2_invalid_tasks():
-    """Test DataladAOMICIDPIOP2 DataGrabber invalid tasks."""
-    with pytest.raises(
-        ValueError,
-        match=(
-            "thisisnotarealtask is not a valid task in "
-            "the AOMIC PIOP2 dataset!"
-        ),
-    ):
-        DataladAOMICPIOP2(tasks="thisisnotarealtask")
+        if isinstance(types, str):
+            types = [types]
+        for t_ in types:
+            assert t_ in out

junifer/datagrabber/base.py

@@ -7,56 +7,77 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import Iterator
+from enum import Enum
 from pathlib import Path
+from typing import Annotated, Any
+
+from aenum import Enum as AEnum
+from pydantic import BaseModel, BeforeValidator, ConfigDict, Field
 
 from ..pipeline import UpdateMetaMixin
 from ..typing import Element, Elements
-from ..utils import logger, raise_error
+from ..utils import ensure_list, logger, raise_error
 
 
-__all__ = ["BaseDataGrabber"]
+__all__ = ["BaseDataGrabber", "DataType"]
 
 
-class BaseDataGrabber(ABC, UpdateMetaMixin):
-    """Abstract base class for DataGrabber.
+class DataType(str, AEnum):
+    """Accepted data type."""
 
-    For every interface that is required, one needs to provide a concrete
+    T1w = "T1w"
+    T2w = "T2w"
+    BOLD = "BOLD"
+    Warp = "Warp"
+    VBM_GM = "VBM_GM"
+    VBM_WM = "VBM_WM"
+    VBM_CSF = "VBM_CSF"
+    FALFF = "fALFF"
+    GCOR = "GCOR"
+    LCOR = "LCOR"
+    DWI = "DWI"
+    FreeSurfer = "FreeSurfer"
+
+
+class BaseDataGrabber(BaseModel, ABC, UpdateMetaMixin):
+    """Abstract base class for data fetcher.
+
+    For every datagrabber, one needs to provide a concrete
     implementation of this abstract class.
 
     Parameters
     ----------
-    types : list of str
-        The types of data to be grabbed.
-    datadir : str or pathlib.Path
-        The directory where the data is / will be stored.
-
-    Raises
-    ------
-    TypeError
-        If ``types`` is not a list or if the values are not string.
+    types : :enum:`.DataType` or list of variants
+        The data type(s) to grab.
+    datadir : pathlib.Path
+        The path where the data is or will be stored.
 
     """
 
-    def __init__(self, types: list[str], datadir: str | Path) -> None:
-        # Validate types
-        if not isinstance(types, list):
-            raise_error(msg="`types` must be a list", klass=TypeError)
-        if any(not isinstance(x, str) for x in types):
-            raise_error(
-                msg="`types` must be a list of strings", klass=TypeError
-            )
-        self.types = types
+    model_config = ConfigDict(use_enum_values=True)
 
-        # Convert str to Path
-        if not isinstance(datadir, Path):
-            datadir = Path(datadir)
-        self._datadir = datadir
+    types: Annotated[
+        DataType | list[DataType],
+        Field(frozen=True),
+        BeforeValidator(ensure_list),
+    ]
+    datadir: Path
 
+    def model_post_init(self, context: Any):  # noqa: D102
         logger.debug("Initializing BaseDataGrabber")
-        logger.debug(f"\t_datadir = {datadir}")
-        logger.debug(f"\ttypes = {types}")
+        logger.debug(f"\tdatadir = {self.datadir}")
+        logger.debug(f"\ttypes = {self.types}")
+        # Run extra validation for datagrabbers and fail early if needed
+        self.validate_datagrabber_params()
 
-    def __iter__(self) -> Iterator:
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber.
+
+        Subclasses can override to provide validation.
+        """
+        pass
+
+    def __iter__(self) -> Iterator[Elements]:
         """Enable iterable support.
 
         Yields
@@ -72,7 +93,7 @@ class BaseDataGrabber(ABC, UpdateMetaMixin):
 
         Parameters
         ----------
-        element : str or tuple of str
+        element : `Element`
             The element to be indexed.
 
         Returns
@@ -82,10 +103,14 @@ class BaseDataGrabber(ABC, UpdateMetaMixin):
             specified element.
 
         """
+        # Convert element to tuple if not already and extract enum values if
+        # present
+        element = (
+            (element,)
+            if not isinstance(element, tuple)
+            else tuple(i.value if isinstance(i, Enum) else i for i in element)
+        )
         logger.info(f"Getting element {element}")
-        # Convert element to tuple if not already
-        if not isinstance(element, tuple):
-            element = (element,)
         # Zip through element keys and actual values to construct element
         # access dictionary
         named_element: dict = dict(
@@ -120,29 +145,30 @@ class BaseDataGrabber(ABC, UpdateMetaMixin):
         Returns
         -------
         list of str
-            The types of data to be grabbed.
+            The data type(s) to grab.
 
         """
-        return self.types.copy()
+        return [x.value if isinstance(x, Enum) else x for x in self.types]
 
     @property
-    def datadir(self) -> Path:
-        """Get data directory path.
+    def fulldir(self) -> Path:
+        """Get complete data directory path.
 
         Returns
         -------
         pathlib.Path
-            Path to the data directory. Can be overridden by subclasses.
+            Complete path to the data directory.
+            Can be overridden by subclasses.
 
         """
-        return self._datadir
+        return self.datadir
 
     def filter(self, selection: Elements) -> Iterator:
         """Filter elements to be grabbed.
 
         Parameters
         ----------
-        selection : list
+        selection : ``Elements``
             The list of partial or complete element selectors to filter using.
 
         Yields
@@ -157,7 +183,7 @@ class BaseDataGrabber(ABC, UpdateMetaMixin):
 
             Parameters
             ----------
-            element : str or tuple of str
+            element : ``Elements``
                 The element to be filtered.
 
             Returns

junifer/datagrabber/datalad_base.py

@@ -9,12 +9,15 @@ import atexit
 import os
 import tempfile
 from pathlib import Path
+from typing import Any, NoReturn
 
 import datalad
 import datalad.api as dl
 from datalad.support.exceptions import IncompleteResultsError
 from datalad.support.gitrepo import GitRepo
+from pydantic import AnyUrl, Field, field_validator
 
+from ..api.decorators import register_datagrabber
 from ..pipeline import WorkDirManager
 from ..typing import Element
 from ..utils import config, logger, raise_error, warn_with_log
@@ -24,6 +27,26 @@ from .base import BaseDataGrabber
 __all__ = ["DataladDataGrabber"]
 
 
+def _create_datadir() -> Path:
+    """Create a temporary directory for datalad dataset."""
+    datadir = WorkDirManager().get_tempdir(
+        prefix="datalad", suffix="juniferauto"
+    )
+    logger.info(
+        "Created a temporary directory for datalad dataset at: "
+        f"{datadir.resolve()!s}"
+    )
+    return datadir
+
+
+def _remove_datadir(datadir: Path) -> None:
+    """Remove temporary directory if it exists."""
+    if datadir.exists():
+        logger.debug(f"Removing temporary directory at: {datadir.resolve()!s}")
+        WorkDirManager().delete_tempdir(datadir)
+
+
+@register_datagrabber
 class DataladDataGrabber(BaseDataGrabber):
     """Abstract base class for datalad-based data fetching.
 
@@ -31,17 +54,15 @@ class DataladDataGrabber(BaseDataGrabber):
 
     Parameters
     ----------
-    rootdir : str or pathlib.Path, optional
+    uri : pydantic.AnyUrl
+        URI of the datalad sibling.
+    rootdir : pathlib.Path, optional
         The path within the datalad dataset to the root directory
-        (default ".").
-    datadir : str or pathlib.Path or None, optional
-        That directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    uri : str or None, optional
-        URI of the datalad sibling (default None).
-    **kwargs
-        Keyword arguments passed to superclass.
+        (default Path(".")).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
 
     Methods
     -------
@@ -66,24 +87,62 @@ class DataladDataGrabber(BaseDataGrabber):
     This class is intended to be used as a superclass of a subclass
     with multiple inheritance.
 
+    If the ``datadir`` is specified and has the stem prefix as ``"datalad"``
+    and the stem suffix as ``"juniferauto"``, it will be automatically
+    deleted after use.
+
     """
 
-    def __init__(
-        self,
-        rootdir: str | Path = ".",
-        datadir: str | Path | None = None,
-        uri: str | None = None,
-        **kwargs,
-    ):
-        if datadir is None:
-            logger.info("`datadir` is None, creating a temporary directory")
-            # Create temporary directory
-            tmpdir = WorkDirManager().get_tempdir(prefix="datalad")
-            self._tmpdir = tmpdir
-            datadir = tmpdir / "datadir"
-            datadir.mkdir(parents=True, exist_ok=False)
-            logger.info(f"`datadir` set to {datadir}")
-            cache_dir = tmpdir / ".datalad_cache"
+    uri: AnyUrl = Field(frozen=True)
+    rootdir: Path = Field(frozen=True, default=Path("."))
+    datadir: Path = Field(default_factory=lambda: _create_datadir())
+    _repodir: Path = Path(".")
+    # Flag to indicate if the dataset was cloned before and it might be
+    # dirty
+    datalad_dirty: bool = False
+    datalad_commit_id: str | None = None
+    datalad_id: str | None = None
+    _dataset: dl.Dataset | None = None
+    _got_files: list[str] = []  # noqa: RUF012
+    _was_cloned: bool = False
+
+    @field_validator("datadir", mode="after")
+    @classmethod
+    def warn_existing_datalad_autodir(cls, value: Path) -> Path:
+        """Warn if existing datalad autodir exists."""
+        if value.stem.startswith("datalad") and value.stem.endswith(
+            "juniferauto"
+        ):
+            warn_with_log(
+                f"{value.resolve()!s} already exists and will reuse assets "
+                "from previous run."
+            )
+        return value
+
+    @field_validator("datalad_dirty", mode="before")
+    @classmethod
+    def disable_tag(cls, value: Any) -> NoReturn:
+        """Disable setting datalad_dirty directly."""
+        raise_error(
+            msg="datalad_dirty cannot be set directly",
+            klass=ValueError,
+        )
+
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        logger.debug("Initializing DataladDataGrabber")
+        logger.debug(f"\turi = {self.uri}")
+        logger.debug(f"\trootdir = {self.rootdir}")
+        if self.datadir.stem.startswith(
+            "datalad"
+        ) and self.datadir.stem.endswith("juniferauto"):
+            self._repodir = self.datadir / "dataset"
+            self._repodir.mkdir(parents=True, exist_ok=False)
+            logger.info(
+                "Datalad dataset installation path set to: "
+                f"{self._repodir.resolve()!s}"
+            )
+            cache_dir = self.datadir / ".datalad_cache"
             sockets_dir = cache_dir / "sockets"
             locks_dir = cache_dir / "locks"
             sockets_dir.mkdir(parents=True, exist_ok=False)
@@ -105,43 +164,29 @@ class DataladDataGrabber(BaseDataGrabber):
                 "Datalad locks set to "
                 f"{datalad.cfg.get('datalad.locations.locks')}"
             )
-            atexit.register(self._rmtmpdir)
-        # TODO: uri can be converted to a positional argument
-        if uri is None:
-            raise_error("`uri` must be provided")
-
-        super().__init__(datadir=datadir, **kwargs)
-        logger.debug("Initializing DataladDataGrabber")
-        logger.debug(f"\turi = {uri}")
-        logger.debug(f"\t_rootdir = {rootdir}")
-        self.uri = uri
-        self._rootdir = rootdir
-        # Flag to indicate if the dataset was cloned before and it might be
-        # dirty
-        self.datalad_dirty = False
+            atexit.register(_remove_datadir, self.datadir)
+        else:
+            self._repodir = self.datadir
+        super().validate_datagrabber_params()
 
     def __del__(self) -> None:
         """Destructor."""
-        if hasattr(self, "_tmpdir"):
-            self._rmtmpdir()
-
-    def _rmtmpdir(self) -> None:
-        """Remove temporary directory if it exists."""
-        if self._tmpdir.exists():
-            logger.debug("Removing temporary directory")
-            WorkDirManager().delete_tempdir(self._tmpdir)
+        if self.datadir.stem.startswith(
+            "datalad"
+        ) and self.datadir.stem.endswith("juniferauto"):
+            _remove_datadir(self.datadir)
 
     @property
-    def datadir(self) -> Path:
-        """Get data directory path.
+    def fulldir(self) -> Path:
+        """Get complete data directory path.
 
         Returns
         -------
         pathlib.Path
-            Path to the data directory.
+            Complete path to the data directory.
 
         """
-        return super().datadir / self._rootdir
+        return self._repodir / self.rootdir
 
     def _get_dataset_id_remote(self) -> tuple[str, bool]:
         """Get the dataset ID from the remote.
@@ -164,8 +209,10 @@ class DataladDataGrabber(BaseDataGrabber):
         with tempfile.TemporaryDirectory() as tmpdir:
             if not config.get("datagrabber.skipidcheck", False):
                 logger.debug(f"Querying {self.uri} for dataset ID")
-                repo = GitRepo.clone(
-                    self.uri, path=tmpdir, clone_options=["-n", "--depth=1"]
+                repo: GitRepo = GitRepo.clone(
+                    str(self.uri),
+                    path=tmpdir,
+                    clone_options=["-n", "--depth=1"],
                 )
                 repo.checkout(name=".datalad/config", options=["HEAD"])
                 remote_id = repo.config.get("datalad.dataset.id", None)
@@ -178,10 +225,11 @@ class DataladDataGrabber(BaseDataGrabber):
                     is_dirty = False
             else:
                 logger.debug("Skipping dataset ID check")
+                # Should be already set to the dataset
                 remote_id = self._dataset.id
                 is_dirty = False
             logger.debug(
-                f"Remote dataset is {'' if is_dirty else 'not'} dirty"
+                f"Remote dataset is {'dirty' if is_dirty else 'not dirty'}"
             )
         if remote_id is None:
             raise_error("Could not get dataset ID from remote")
@@ -251,7 +299,7 @@ class DataladDataGrabber(BaseDataGrabber):
         return out
 
     def install(self) -> None:
-        """Install the datalad dataset into the ``datadir``.
+        """Installs the datalad dataset.
 
         Raises
         ------
@@ -261,12 +309,10 @@ class DataladDataGrabber(BaseDataGrabber):
             If there is a datalad-related problem while cloning dataset.
 
         """
-        isinstalled = dl.Dataset(self._datadir).is_installed()
-        if isinstalled:
+        is_installed = dl.Dataset(self._repodir).is_installed()
+        if is_installed:
             logger.debug("Dataset already installed")
-            self._got_files = []
-            self._dataset: dl.Dataset = dl.Dataset(self._datadir)
-
+            self._dataset = dl.Dataset(self._repodir)
             # Check if dataset is already installed with a different ID
             remote_id, is_dirty = self._get_dataset_id_remote()
             if remote_id != self._dataset.id:
@@ -274,7 +320,6 @@ class DataladDataGrabber(BaseDataGrabber):
                     "Dataset already installed but with a different "
                     f"ID: {self._dataset.id} (local) != {remote_id} (remote)"
                 )
-
             # Conditional reporting on dataset dirtiness
             self.datalad_dirty = is_dirty
             if self.datalad_dirty:
@@ -286,18 +331,18 @@ class DataladDataGrabber(BaseDataGrabber):
                 logger.debug(f"Dataset (id: {self._dataset.id}) is clean")
 
         else:
-            logger.debug(f"Installing dataset {self.uri} to {self._datadir}")
+            logger.debug(f"Installing dataset {self.uri} to {self._repodir}")
             try:
-                self._dataset: dl.Dataset = dl.clone(  # type: ignore
-                    self.uri, self._datadir, result_renderer="disabled"
+                self._dataset = dl.clone(
+                    self.uri, self._repodir, result_renderer="disabled"
                 )
             except IncompleteResultsError as e:
                 raise_error(f"Failed to clone dataset: {e.failed}")
             logger.debug("Dataset installed")
-        self._was_cloned = not isinstalled
-
-        self.datalad_commit_id = self._dataset.repo.get_hexsha(  # type: ignore
-            self._dataset.repo.get_corresponding_branch()  # type: ignore
+        self._was_cloned = not is_installed
+        # Dataset should be set already
+        self.datalad_commit_id = self._dataset.repo.get_hexsha(
+            self._dataset.repo.get_corresponding_branch()
         )
         self.datalad_id = self._dataset.id
 
@@ -320,7 +365,7 @@ class DataladDataGrabber(BaseDataGrabber):
 
         Parameters
         ----------
-        element : str or tuple of str
+        element : `Element`
             The element to be indexed. If one string is provided, it is
             assumed to be a tuple with only one item. If a tuple is provided,
             each item in the tuple is the value for the replacement string

junifer/datagrabber/dmcc13_benchmark.py

@@ -3,15 +3,93 @@
 # Authors: Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from enum import Enum
 from itertools import product
-from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import AnyUrl, BeforeValidator
 
 from ..api.decorators import register_datagrabber
-from ..utils import raise_error
+from ..typing import DataGrabberPatterns
+from ..utils import ensure_list
+from .base import DataType
+from .pattern import ConfoundsFormat
 from .pattern_datalad import PatternDataladDataGrabber
 
 
-__all__ = ["DMCC13Benchmark"]
+__all__ = [
+    "DMCC13Benchmark",
+    "DMCCPhaseEncoding",
+    "DMCCRun",
+    "DMCCSession",
+    "DMCCTask",
+]
+
+
+class DMCCSession(str, Enum):
+    """Accepted DMCC sessions."""
+
+    Wave1Bas = "ses-wave1bas"
+    Wave1Pro = "ses-wave1pro"
+    Wave1Rea = "ses-wave1rea"
+
+
+class DMCCTask(str, Enum):
+    """Accepted DMCC tasks."""
+
+    Rest = "Rest"
+    Axcpt = "Axcpt"
+    Cuedts = "Cuedts"
+    Stern = "Stern"
+    Stroop = "Stroop"
+
+
+class DMCCPhaseEncoding(str, Enum):
+    """Accepted DMCC phase encoding directions."""
+
+    AP = "AP"
+    PA = "PA"
+
+
+class DMCCRun(str, Enum):
+    """Accepted DMCC runs."""
+
+    One = "1"
+    Two = "2"
+
+
+_types = Literal[
+    DataType.BOLD,
+    DataType.T1w,
+    DataType.VBM_CSF,
+    DataType.VBM_GM,
+    DataType.VBM_WM,
+    DataType.Warp,
+]
+
+_sessions = Literal[
+    DMCCSession.Wave1Bas,
+    DMCCSession.Wave1Pro,
+    DMCCSession.Wave1Rea,
+]
+
+_tasks = Literal[
+    DMCCTask.Rest,
+    DMCCTask.Axcpt,
+    DMCCTask.Cuedts,
+    DMCCTask.Stern,
+    DMCCTask.Stroop,
+]
+
+_phase_encodings = Literal[
+    DMCCPhaseEncoding.AP,
+    DMCCPhaseEncoding.PA,
+]
+
+_runs = Literal[
+    DMCCRun.One,
+    DMCCRun.Two,
+]
 
 
 @register_datagrabber
@@ -20,191 +98,142 @@ class DMCC13Benchmark(PatternDataladDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    types: {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM"} or \
-           list of the options, optional
-        DMCC data types. If None, all available data types are selected.
-        (default None).
-    sessions: {"ses-wave1bas", "ses-wave1pro", "ses-wave1rea"} or \
-              list of the options, optional
-        DMCC sessions. If None, all available sessions are selected
-        (default None).
-    tasks: {"Rest", "Axcpt", "Cuedts", "Stern", "Stroop"} or \
-           list of the options, optional
-        DMCC task sessions. If None, all available task sessions are selected
-        (default None).
+    types : {"BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "Warp"} or \
+            list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
+    sessions : {"ses-wave1bas", "ses-wave1pro", "ses-wave1rea"} or \
+               list of the options, optional
+        DMCC sessions.
+        By default, all available sessions are selected.
+    tasks : {"Rest", "Axcpt", "Cuedts", "Stern", "Stroop"} or \
+            list of the options, optional
+        DMCC tasks.
+        By default, all available tasks are selected.
     phase_encodings : {"AP", "PA"} or list of the options, optional
-        DMCC phase encoding directions. If None, all available phase encodings
-        are selected (default None).
+        DMCC phase encoding directions.
+        By default, all available phase encodings are selected.
     runs : {"1", "2"} or list of the options, optional
-        DMCC runs. If None, all available runs are selected (default None).
+        DMCC runs.
+        By default, all available runs are selected.
     native_t1w : bool, optional
         Whether to use T1w in native space (default False).
 
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for:
-         * ``sessions``
-         * ``tasks``
-         * ``phase_encodings``
-         * ``runs``
-
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        types: str | list[str] | None = None,
-        sessions: str | list[str] | None = None,
-        tasks: str | list[str] | None = None,
-        phase_encodings: str | list[str] | None = None,
-        runs: str | list[str] | None = None,
-        native_t1w: bool = False,
-    ) -> None:
-        # Declare all sessions
-        all_sessions = [
-            "ses-wave1bas",
-            "ses-wave1pro",
-            "ses-wave1rea",
-        ]
-        # Set default sessions
-        if sessions is None:
-            sessions = all_sessions
-        else:
-            # Convert single session into list
-            if isinstance(sessions, str):
-                sessions = [sessions]
-            # Verify valid sessions
-            for s in sessions:
-                if s not in all_sessions:
-                    raise_error(
-                        f"{s} is not a valid session in the DMCC dataset"
-                    )
-        self.sessions = sessions
-        # Declare all tasks
-        all_tasks = [
-            "Rest",
-            "Axcpt",
-            "Cuedts",
-            "Stern",
-            "Stroop",
-        ]
-        # Set default tasks
-        if tasks is None:
-            tasks = all_tasks
-        else:
-            # Convert single task into list
-            if isinstance(tasks, str):
-                tasks = [tasks]
-            # Verify valid tasks
-            for t in tasks:
-                if t not in all_tasks:
-                    raise_error(f"{t} is not a valid task in the DMCC dataset")
-        self.tasks = tasks
-        # Declare all phase encodings
-        all_phase_encodings = ["AP", "PA"]
-        # Set default phase encodings
-        if phase_encodings is None:
-            phase_encodings = all_phase_encodings
-        else:
-            # Convert single phase encoding into list
-            if isinstance(phase_encodings, str):
-                phase_encodings = [phase_encodings]
-            # Verify valid phase encodings
-            for p in phase_encodings:
-                if p not in all_phase_encodings:
-                    raise_error(
-                        f"{p} is not a valid phase encoding in the DMCC "
-                        "dataset"
-                    )
-        self.phase_encodings = phase_encodings
-        # Declare all runs
-        all_runs = ["1", "2"]
-        # Set default runs
-        if runs is None:
-            runs = all_runs
-        else:
-            # Convert single run into list
-            if isinstance(runs, str):
-                runs = [runs]
-            # Verify valid runs
-            for r in runs:
-                if r not in all_runs:
-                    raise_error(f"{r} is not a valid run in the DMCC dataset")
-        self.runs = runs
-        # The patterns
-        patterns = {
-            "BOLD": {
+    uri: AnyUrl = AnyUrl("https://github.com/OpenNeuroDatasets/ds003452.git")
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.VBM_CSF,
+        DataType.VBM_GM,
+        DataType.VBM_WM,
+    ]
+    sessions: Annotated[
+        _sessions | list[_sessions], BeforeValidator(ensure_list)
+    ] = [  # noqa: RUF012
+        DMCCSession.Wave1Bas,
+        DMCCSession.Wave1Pro,
+        DMCCSession.Wave1Rea,
+    ]
+    tasks: Annotated[_tasks | list[_tasks], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DMCCTask.Rest,
+        DMCCTask.Axcpt,
+        DMCCTask.Cuedts,
+        DMCCTask.Stern,
+        DMCCTask.Stroop,
+    ]
+    phase_encodings: Annotated[
+        _phase_encodings | list[_phase_encodings],
+        BeforeValidator(ensure_list),
+    ] = [  # noqa: RUF012
+        DMCCPhaseEncoding.AP,
+        DMCCPhaseEncoding.PA,
+    ]
+    runs: Annotated[_runs | list[_runs], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DMCCRun.One,
+        DMCCRun.Two,
+    ]
+    native_t1w: bool = False
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "derivatives/fmriprep-1.3.2/{subject}/{session}/"
+                "func/{subject}_{session}_task-{task}_acq-mb4"
+                "{phase_encoding}_run-{run}_"
+                "space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep-1.3.2/{subject}/{session}/"
                     "func/{subject}_{session}_task-{task}_acq-mb4"
                     "{phase_encoding}_run-{run}_"
-                    "space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
+                    "space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz"
                 ),
                 "space": "MNI152NLin2009cAsym",
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep-1.3.2/{subject}/{session}/"
-                        "func/{subject}_{session}_task-{task}_acq-mb4"
-                        "{phase_encoding}_run-{run}_"
-                        "space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz"
-                    ),
-                    "space": "MNI152NLin2009cAsym",
-                },
-                "confounds": {
-                    "pattern": (
-                        "derivatives/fmriprep-1.3.2/{subject}/{session}/"
-                        "func/{subject}_{session}_task-{task}_acq-mb4"
-                        "{phase_encoding}_run-{run}_desc-confounds_regressors.tsv"
-                    ),
-                    "format": "fmriprep",
-                },
             },
-            "T1w": {
+            "confounds": {
+                "pattern": (
+                    "derivatives/fmriprep-1.3.2/{subject}/{session}/"
+                    "func/{subject}_{session}_task-{task}_acq-mb4"
+                    "{phase_encoding}_run-{run}_desc-confounds_regressors.tsv"
+                ),
+                "format": "fmriprep",
+            },
+        },
+        "T1w": {
+            "pattern": (
+                "derivatives/fmriprep-1.3.2/{subject}/anat/"
+                "{subject}_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+            "mask": {
                 "pattern": (
                     "derivatives/fmriprep-1.3.2/{subject}/anat/"
-                    "{subject}_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-                "mask": {
-                    "pattern": (
-                        "derivatives/fmriprep-1.3.2/{subject}/anat/"
-                        "{subject}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz"
-                    ),
-                    "space": "MNI152NLin2009cAsym",
-                },
-            },
-            "VBM_CSF": {
-                "pattern": (
-                    "derivatives/fmriprep-1.3.2/{subject}/anat/"
-                    "{subject}_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz"
+                    "{subject}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz"
                 ),
                 "space": "MNI152NLin2009cAsym",
             },
-            "VBM_GM": {
-                "pattern": (
-                    "derivatives/fmriprep-1.3.2/{subject}/anat/"
-                    "{subject}_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-            "VBM_WM": {
-                "pattern": (
-                    "derivatives/fmriprep-1.3.2/{subject}/anat/"
-                    "{subject}_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz"
-                ),
-                "space": "MNI152NLin2009cAsym",
-            },
-        }
-        # Use native T1w assets
-        self.native_t1w = False
-        if native_t1w:
-            self.native_t1w = True
-            patterns.update(
+        },
+        "VBM_CSF": {
+            "pattern": (
+                "derivatives/fmriprep-1.3.2/{subject}/anat/"
+                "{subject}_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+        "VBM_GM": {
+            "pattern": (
+                "derivatives/fmriprep-1.3.2/{subject}/anat/"
+                "{subject}_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+        "VBM_WM": {
+            "pattern": (
+                "derivatives/fmriprep-1.3.2/{subject}/anat/"
+                "{subject}_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz"
+            ),
+            "space": "MNI152NLin2009cAsym",
+        },
+    }
+    replacements: list[str] = [  # noqa: RUF012
+        "subject",
+        "session",
+        "task",
+        "phase_encoding",
+        "run",
+    ]
+    confounds_format: ConfoundsFormat = ConfoundsFormat.FMRIPrep
+
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        if self.native_t1w:
+            self.patterns.update(
                 {
                     "T1w": {
                         "pattern": (
@@ -244,24 +273,8 @@ class DMCC13Benchmark(PatternDataladDataGrabber):
                     ],
                 }
             )
-        # Set default types
-        if types is None:
-            types = list(patterns.keys())
-        # Convert single type into list
-        else:
-            if not isinstance(types, list):
-                types = [types]
-        # The replacements
-        replacements = ["subject", "session", "task", "phase_encoding", "run"]
-        uri = "https://github.com/OpenNeuroDatasets/ds003452.git"
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            uri=uri,
-            patterns=patterns,
-            replacements=replacements,
-            confounds_format="fmriprep",
-        )
+            self.types.append(DataType.Warp)
+        super().validate_datagrabber_params()
 
     def get_item(
         self,
@@ -271,7 +284,7 @@ class DMCC13Benchmark(PatternDataladDataGrabber):
         phase_encoding: str,
         run: str,
     ) -> dict:
-        """Index one element in the dataset.
+        """Get the specified item from the dataset.
 
         Parameters
         ----------

junifer/datagrabber/hcp1200/__init__.pyi

@@ -1,4 +1,9 @@
-__all__ = ["HCP1200", "DataladHCP1200"]
+__all__ = [
+    "HCP1200",
+    "HCP1200Task",
+    "HCP1200PhaseEncoding",
+    "DataladHCP1200",
+]
 
-from .hcp1200 import HCP1200
+from .hcp1200 import HCP1200, HCP1200Task, HCP1200PhaseEncoding
 from .datalad_hcp1200 import DataladHCP1200

junifer/datagrabber/hcp1200/datalad_hcp1200.py

@@ -6,66 +6,60 @@
 # License: AGPL
 
 from pathlib import Path
+from typing import Annotated, Literal
 
-from junifer.datagrabber.datalad_base import DataladDataGrabber
+from pydantic import AnyUrl, BeforeValidator
 
 from ...api.decorators import register_datagrabber
+from ...utils import ensure_list
+from ..base import DataType
+from ..datalad_base import DataladDataGrabber
 from .hcp1200 import HCP1200
 
 
 __all__ = ["DataladHCP1200"]
 
 
+_types = Literal[DataType.BOLD, DataType.T1w, DataType.Warp]
+
+
 @register_datagrabber
 class DataladHCP1200(DataladDataGrabber, HCP1200):
     """Concrete implementation for datalad-based data fetching of HCP1200.
 
     Parameters
     ----------
-    datadir : str or Path or None, optional
-        The directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
+    types : {"BOLD", "T1w", "Warp"} or list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
     tasks : {"REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", \
-            "LANGUAGE", "GAMBLING", "MOTOR"} or list of the options or None \
-            , optional
-        HCP task sessions. If None, all available task sessions are selected
-        (default None).
-    phase_encodings : {"LR", "RL"} or list of the options or None, optional
-        HCP phase encoding directions. If None, both will be used
-        (default None).
+            "LANGUAGE", "GAMBLING", "MOTOR"} or list of the options, optional
+        HCP task sessions.
+        By default, all available task sessions are selected.
+    phase_encodings : {"LR", "RL"} or list of the options, optional
+        HCP phase encoding directions.
+        By default, all are used.
     ica_fix : bool, optional
         Whether to retrieve data that was processed with ICA+FIX.
-        Only "REST1" and "REST2" tasks are available with ICA+FIX (default
-        False).
-
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for ``tasks`` or ``phase_encodings``.
+        Only ``HCP1200Task.REST1`` and ``HCP1200Task.REST2`` tasks
+        are available with ICA+FIX
+        (default False).
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path | None = None,
-        tasks: str | list[str] | None = None,
-        phase_encodings: str | list[str] | None = None,
-        ica_fix: bool = False,
-    ) -> None:
-        uri = (
-            "https://github.com/datalad-datasets/"
-            "human-connectome-project-openaccess.git"
-        )
-        rootdir = "HCP1200"
-        super().__init__(
-            datadir=datadir,
-            tasks=tasks,
-            phase_encodings=phase_encodings,
-            uri=uri,
-            rootdir=rootdir,
-            ica_fix=ica_fix,
-        )
+    uri: AnyUrl = AnyUrl(
+        "https://github.com/datalad-datasets/"
+        "human-connectome-project-openaccess.git"
+    )
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.Warp,
+    ]
+    rootdir: Path = Path("HCP1200")
 
     # Needed here as HCP1200's subjects are sub-datasets, so will not be
     # found when elements are checked.

junifer/datagrabber/hcp1200/hcp1200.py

@@ -5,15 +5,61 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from enum import Enum
 from itertools import product
-from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import BeforeValidator
 
 from ...api.decorators import register_datagrabber
-from ...utils import raise_error
+from ...typing import DataGrabberPatterns
+from ...utils import ensure_list, raise_error
+from ..base import DataType
 from ..pattern import PatternDataGrabber
 
 
-__all__ = ["HCP1200"]
+__all__ = ["HCP1200", "HCP1200PhaseEncoding", "HCP1200Task"]
+
+
+class HCP1200Task(str, Enum):
+    """Accepted HCP1200 tasks."""
+
+    REST1 = "REST1"
+    REST2 = "REST2"
+    SOCIAL = "SOCIAL"
+    WM = "WM"
+    RELATIONAL = "RELATIONAL"
+    EMOTION = "EMOTION"
+    LANGUAGE = "LANGUAGE"
+    GAMBLING = "GAMBLING"
+    MOTOR = "MOTOR"
+
+
+class HCP1200PhaseEncoding(str, Enum):
+    """Accepted HCP1200 phase encoding directions."""
+
+    LR = "LR"
+    RL = "RL"
+
+
+_types = Literal[DataType.BOLD, DataType.T1w, DataType.Warp]
+
+_tasks = Literal[
+    HCP1200Task.REST1,
+    HCP1200Task.REST2,
+    HCP1200Task.SOCIAL,
+    HCP1200Task.WM,
+    HCP1200Task.RELATIONAL,
+    HCP1200Task.EMOTION,
+    HCP1200Task.LANGUAGE,
+    HCP1200Task.GAMBLING,
+    HCP1200Task.MOTOR,
+]
+
+_phase_encodings = Literal[
+    HCP1200PhaseEncoding.RL,
+    HCP1200PhaseEncoding.LR,
+]
 
 
 @register_datagrabber
@@ -22,135 +68,99 @@ class HCP1200(PatternDataGrabber):
 
     Parameters
     ----------
-    datadir : str or Path, optional
-        The directory where the data is / will be stored.
+    types : {"BOLD", "T1w", "Warp"} or list of the options, optional
+        The data type(s) to grab.
+    datadir : pathlib.Path
+        The path where the data is stored.
     tasks : {"REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", \
-            "LANGUAGE", "GAMBLING", "MOTOR"} or list of the options or None \
-            , optional
-        HCP task sessions. If None, all available task sessions are selected
-        (default None).
-    phase_encodings : {"LR", "RL"} or list of the options or None, optional
-        HCP phase encoding directions. If None, both will be used
-        (default None).
+            "LANGUAGE", "GAMBLING", "MOTOR"} or list of the options, optional
+        HCP task sessions.
+        By default, all available task sessions are selected.
+    phase_encodings : {"LR", "RL"} or list of the options, optional
+        HCP phase encoding directions.
+        By default, all are used.
     ica_fix : bool, optional
         Whether to retrieve data that was processed with ICA+FIX.
-        Only "REST1" and "REST2" tasks are available with ICA+FIX (default
-        False).
-
-    Raises
-    ------
-    ValueError
-        If invalid value is passed for ``tasks`` or ``phase_encodings``.
+        Only ``HCP1200Task.REST1`` and ``HCP1200Task.REST2`` tasks
+        are available with ICA+FIX
+        (default False).
 
     """
 
-    def __init__(
-        self,
-        datadir: str | Path,
-        tasks: str | list[str] | None = None,
-        phase_encodings: str | list[str] | None = None,
-        ica_fix: bool = False,
-    ) -> None:
-        # All tasks
-        all_tasks = [
-            "REST1",
-            "REST2",
-            "SOCIAL",
-            "WM",
-            "RELATIONAL",
-            "EMOTION",
-            "LANGUAGE",
-            "GAMBLING",
-            "MOTOR",
-        ]
-        # Set default tasks
-        if tasks is None:
-            self.tasks: list[str] = all_tasks
-        # Convert single task into list
-        else:
-            if not isinstance(tasks, list):
-                tasks = [tasks]
-            # Check for invalid task(s)
-            for task in tasks:
-                if task not in all_tasks:
-                    raise_error(
-                        f"'{task}' is not a valid HCP-YA fMRI task input. "
-                        f"Valid task values can be any or all of {all_tasks}."
-                    )
-            self.tasks: list[str] = tasks
+    types: Annotated[_types | list[_types], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        DataType.BOLD,
+        DataType.T1w,
+        DataType.Warp,
+    ]
+    tasks: Annotated[_tasks | list[_tasks], BeforeValidator(ensure_list)] = [  # noqa: RUF012
+        HCP1200Task.REST1,
+        HCP1200Task.REST2,
+        HCP1200Task.SOCIAL,
+        HCP1200Task.WM,
+        HCP1200Task.RELATIONAL,
+        HCP1200Task.EMOTION,
+        HCP1200Task.LANGUAGE,
+        HCP1200Task.GAMBLING,
+        HCP1200Task.MOTOR,
+    ]
+    phase_encodings: Annotated[
+        _phase_encodings | list[_phase_encodings],
+        BeforeValidator(ensure_list),
+    ] = [  # noqa: RUF012
+        HCP1200PhaseEncoding.RL,
+        HCP1200PhaseEncoding.LR,
+    ]
+    ica_fix: bool = False
+    patterns: DataGrabberPatterns = {  # noqa: RUF012
+        "BOLD": {
+            "pattern": (
+                "{subject}/MNINonLinear/Results/"
+                "{task}_{phase_encoding}/"
+                "{task}_{phase_encoding}"
+                "{suffix}.nii.gz"
+            ),
+            "space": "MNI152NLin6Asym",
+        },
+        "T1w": {
+            "pattern": "{subject}/T1w/T1w_acpc_dc_restore.nii.gz",
+            "space": "native",
+        },
+        "Warp": [
+            {
+                "pattern": (
+                    "{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz"
+                ),
+                "src": "MNI152NLin6Asym",
+                "dst": "native",
+                "warper": "fsl",
+            },
+            {
+                "pattern": (
+                    "{subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz"
+                ),
+                "src": "native",
+                "dst": "MNI152NLin6Asym",
+                "warper": "fsl",
+            },
+        ],
+    }
+    replacements: list[str] = ["subject", "task", "phase_encoding"]  # noqa: RUF012
 
-        # All phase encodings
-        all_phase_encodings = ["LR", "RL"]
-        # Set phase encodings
-        if phase_encodings is None:
-            phase_encodings = all_phase_encodings
-        # Convert single phase encoding into list
-        if isinstance(phase_encodings, str):
-            phase_encodings = [phase_encodings]
-        # Check for invalid phase encoding(s)
-        for pe in phase_encodings:
-            if pe not in all_phase_encodings:
-                raise_error(
-                    f"'{pe}' is not a valid HCP-YA phase encoding. "
-                    "Valid phase encoding can be any or all of "
-                    f"{all_phase_encodings}."
-                )
-        self.phase_encodings = phase_encodings
-
-        if ica_fix:
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        if self.ica_fix:
             if not all(task in ["REST1", "REST2"] for task in self.tasks):
                 raise_error(
                     "ICA+FIX is only available for 'REST1' and 'REST2' tasks."
                 )
-        suffix = "_hp2000_clean" if ica_fix else ""
-
-        # The types of data
-        types = ["BOLD", "T1w", "Warp"]
-        # The patterns
-        patterns = {
-            "BOLD": {
-                "pattern": (
-                    "{subject}/MNINonLinear/Results/"
-                    "{task}_{phase_encoding}/"
-                    "{task}_{phase_encoding}"
-                    f"{suffix}.nii.gz"
-                ),
-                "space": "MNI152NLin6Asym",
-            },
-            "T1w": {
-                "pattern": "{subject}/T1w/T1w_acpc_dc_restore.nii.gz",
-                "space": "native",
-            },
-            "Warp": [
-                {
-                    "pattern": (
-                        "{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz"
-                    ),
-                    "src": "MNI152NLin6Asym",
-                    "dst": "native",
-                    "warper": "fsl",
-                },
-                {
-                    "pattern": (
-                        "{subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz"
-                    ),
-                    "src": "native",
-                    "dst": "MNI152NLin6Asym",
-                    "warper": "fsl",
-                },
-            ],
-        }
-        # The replacements
-        replacements = ["subject", "task", "phase_encoding"]
-        super().__init__(
-            types=types,
-            datadir=datadir,
-            patterns=patterns,
-            replacements=replacements,
-        )
+        suffix = "_hp2000_clean" if self.ica_fix else ""
+        self.patterns["BOLD"]["pattern"] = self.patterns["BOLD"][
+            "pattern"
+        ].replace("{suffix}", suffix)
+        super().validate_datagrabber_params()
 
     def get_item(self, subject: str, task: str, phase_encoding: str) -> dict:
-        """Implement single element indexing in the database.
+        """Get the specified item from the dataset.
 
         Parameters
         ----------

junifer/datagrabber/hcp1200/tests/test_hcp1200.py

@@ -9,62 +9,61 @@ from collections.abc import Iterable
 from pathlib import Path
 
 import pytest
+from pydantic import AnyUrl
 
 from junifer.datagrabber import HCP1200, DataladHCP1200
-from junifer.utils import configure_logging
-
-
-URI = "https://gin.g-node.org/juaml/datalad-example-hcp1200"
+from junifer.utils import config, configure_logging
 
 
 @pytest.fixture(scope="module")
 def hcpdg() -> Iterable[DataladHCP1200]:
     """Return a HCP1200 DataGrabber."""
     tmpdir = Path(tempfile.gettempdir())
-    dg = DataladHCP1200(datadir=tmpdir / "datadir")
-    # Set URI to Gin
-    dg.uri = URI
-    # Set correct root directory
-    dg._rootdir = "."
+    config.set(key="datagrabber.skipidcheck", val=True)
+    dg = DataladHCP1200(
+        uri=AnyUrl("https://gin.g-node.org/juaml/datalad-example-hcp1200"),
+        datadir=tmpdir / "hcp1200_test",
+        rootdir=Path("."),
+    )
     with dg:
         for t_elem in dg.get_elements():
             dg[t_elem]
         yield dg
-    shutil.rmtree(tmpdir / "datadir", ignore_errors=True)
+    config.set(key="datagrabber.skipidcheck", val=False)
+    shutil.rmtree(tmpdir / "hcp1200_test", ignore_errors=True)
 
 
 @pytest.mark.parametrize(
     "tasks, phase_encodings, ica_fix, expected_path_name",
     [
-        (None, None, False, "rfMRI_REST1_LR.nii.gz"),
         ("REST1", "LR", False, "rfMRI_REST1_LR.nii.gz"),
-        ("REST1", "RL", False, "rfMRI_REST1_RL.nii.gz"),
+        (["REST1"], ["RL"], False, "rfMRI_REST1_RL.nii.gz"),
         ("REST2", "LR", False, "rfMRI_REST2_LR.nii.gz"),
-        ("REST2", "RL", False, "rfMRI_REST2_RL.nii.gz"),
+        (["REST2"], ["RL"], False, "rfMRI_REST2_RL.nii.gz"),
         ("SOCIAL", "LR", False, "tfMRI_SOCIAL_LR.nii.gz"),
-        ("SOCIAL", "RL", False, "tfMRI_SOCIAL_RL.nii.gz"),
+        (["SOCIAL"], ["RL"], False, "tfMRI_SOCIAL_RL.nii.gz"),
         ("WM", "LR", False, "tfMRI_WM_LR.nii.gz"),
-        ("WM", "RL", False, "tfMRI_WM_RL.nii.gz"),
+        (["WM"], ["RL"], False, "tfMRI_WM_RL.nii.gz"),
         ("RELATIONAL", "LR", False, "tfMRI_RELATIONAL_LR.nii.gz"),
-        ("RELATIONAL", "RL", False, "tfMRI_RELATIONAL_RL.nii.gz"),
+        (["RELATIONAL"], ["RL"], False, "tfMRI_RELATIONAL_RL.nii.gz"),
         ("EMOTION", "LR", False, "tfMRI_EMOTION_LR.nii.gz"),
-        ("EMOTION", "RL", False, "tfMRI_EMOTION_RL.nii.gz"),
+        (["EMOTION"], ["RL"], False, "tfMRI_EMOTION_RL.nii.gz"),
         ("LANGUAGE", "LR", False, "tfMRI_LANGUAGE_LR.nii.gz"),
-        ("LANGUAGE", "RL", False, "tfMRI_LANGUAGE_RL.nii.gz"),
+        (["LANGUAGE"], ["RL"], False, "tfMRI_LANGUAGE_RL.nii.gz"),
         ("GAMBLING", "LR", False, "tfMRI_GAMBLING_LR.nii.gz"),
-        ("GAMBLING", "RL", False, "tfMRI_GAMBLING_RL.nii.gz"),
+        (["GAMBLING"], ["RL"], False, "tfMRI_GAMBLING_RL.nii.gz"),
         ("MOTOR", "LR", False, "tfMRI_MOTOR_LR.nii.gz"),
-        ("MOTOR", "RL", False, "tfMRI_MOTOR_RL.nii.gz"),
+        (["MOTOR"], ["RL"], False, "tfMRI_MOTOR_RL.nii.gz"),
         ("REST1", "LR", True, "rfMRI_REST1_LR_hp2000_clean.nii.gz"),
-        ("REST1", "RL", True, "rfMRI_REST1_RL_hp2000_clean.nii.gz"),
+        (["REST1"], ["RL"], True, "rfMRI_REST1_RL_hp2000_clean.nii.gz"),
         ("REST2", "LR", True, "rfMRI_REST2_LR_hp2000_clean.nii.gz"),
-        ("REST2", "RL", True, "rfMRI_REST2_RL_hp2000_clean.nii.gz"),
+        (["REST2"], ["RL"], True, "rfMRI_REST2_RL_hp2000_clean.nii.gz"),
     ],
 )
 def test_HCP1200(
     hcpdg: DataladHCP1200,
-    tasks: str | None,
-    phase_encodings: str | None,
+    tasks: str | list[str],
+    phase_encodings: str | list[str],
     ica_fix: bool,
     expected_path_name: str,
 ) -> None:
@@ -75,9 +74,9 @@ def test_HCP1200(
     hcpdg : DataladHCP1200
         The Datalad version of the DataGrabber with the first subject
         already cloned.
-    tasks : str
+    tasks : str or list of str
         The parametrized tasks.
-    phase_encodings : str
+    phase_encodings : str or list of str
         The parametrized phase encodings.
     ica_fix : bool
         The parametrized ICA-FIX flag.
@@ -118,29 +117,29 @@ def test_HCP1200(
     "tasks, phase_encodings",
     [
         ("REST1", "LR"),
-        ("REST1", "RL"),
+        (["REST1"], ["RL"]),
         ("REST2", "LR"),
-        ("REST2", "RL"),
+        (["REST2"], ["RL"]),
         ("SOCIAL", "LR"),
-        ("SOCIAL", "RL"),
+        (["SOCIAL"], ["RL"]),
         ("WM", "LR"),
-        ("WM", "RL"),
+        (["WM"], ["RL"]),
         ("RELATIONAL", "LR"),
-        ("RELATIONAL", "RL"),
+        (["RELATIONAL"], ["RL"]),
         ("EMOTION", "LR"),
-        ("EMOTION", "RL"),
+        (["EMOTION"], ["RL"]),
         ("LANGUAGE", "LR"),
-        ("LANGUAGE", "RL"),
+        (["LANGUAGE"], ["RL"]),
         ("GAMBLING", "LR"),
-        ("GAMBLING", "RL"),
+        (["GAMBLING"], ["RL"]),
         ("MOTOR", "LR"),
-        ("MOTOR", "RL"),
+        (["MOTOR"], ["RL"]),
     ],
 )
 def test_HCP1200_single_access(
     hcpdg: DataladHCP1200,
-    tasks: str | None,
-    phase_encodings: str | None,
+    tasks: str | list[str],
+    phase_encodings: str | list[str],
 ) -> None:
     """Test HCP1200 DataGrabber single access.
 
@@ -149,9 +148,9 @@ def test_HCP1200_single_access(
     hcpdg : DataladHCP1200
         The Datalad version of the DataGrabber with the first subject
         already cloned.
-    tasks : str
+    tasks : str or list of str
         The parametrized tasks.
-    phase_encodings : str
+    phase_encodings : str or list of str
         The parametrized phase encodings.
 
     """
@@ -166,21 +165,16 @@ def test_HCP1200_single_access(
         all_elements = dg.get_elements()
         # Check only specified task and phase encoding are found
         for element in all_elements:
-            assert element[1] == tasks
-            assert element[2] == phase_encodings
+            assert element[1] == tasks if isinstance(tasks, str) else tasks[0]
+            assert (
+                element[2] == phase_encodings
+                if isinstance(phase_encodings, str)
+                else phase_encodings[0]
+            )
 
 
-@pytest.mark.parametrize(
-    "tasks, phase_encodings",
-    [
-        (["REST1", "REST2"], ["LR", "RL"]),
-        (["REST1", "REST2"], None),
-    ],
-)
 def test_HCP1200_multi_access(
     hcpdg: DataladHCP1200,
-    tasks: str | None,
-    phase_encodings: str | None,
 ) -> None:
     """Test HCP1200 DataGrabber multiple access.
 
@@ -189,17 +183,13 @@ def test_HCP1200_multi_access(
     hcpdg : DataladHCP1200
         The Datalad version of the DataGrabber with the first subject
         already cloned.
-    tasks : str
-        The parametrized tasks.
-    phase_encodings : str
-        The parametrized phase encodings.
 
     """
     configure_logging(level="DEBUG")
     dg = HCP1200(
         datadir=hcpdg.datadir,
-        tasks=tasks,
-        phase_encodings=phase_encodings,
+        tasks=["REST1", "REST2"],
+        phase_encodings=["LR", "RL"],
     )
     with dg:
         # Get all elements
@@ -264,70 +254,6 @@ def test_HCP1200_multi_access_phase_simple(
             assert element[2] == "LR"
 
 
-@pytest.mark.parametrize(
-    "tasks, phase_encodings",
-    [
-        ("FOO", ["LR", "RL"]),
-        ("FOO", "RL"),
-        (["FOO", "BAR"], ["LR", "RL"]),
-        (["FOO", "BAR"], "LR"),
-    ],
-)
-def test_HCP1200_incorrect_access_task(
-    tasks: str | None,
-    phase_encodings: str | None,
-) -> None:
-    """Test HCP1200 DataGrabber incorrect access for task.
-
-    Parameters
-    ----------
-    tasks : str
-        The parametrized tasks.
-    phase_encodings : str
-        The parametrized phase encodings.
-
-    """
-    configure_logging(level="DEBUG")
-    with pytest.raises(ValueError, match="not a valid HCP-YA fMRI task input"):
-        _ = HCP1200(
-            datadir=".",
-            tasks=tasks,
-            phase_encodings=phase_encodings,
-        )
-
-
-@pytest.mark.parametrize(
-    "tasks, phase_encodings",
-    [
-        ("REST1", ["FOO", "BAR"]),
-        ("REST1", "FOO"),
-        (["REST1", "REST2"], ["FOO", "BAR"]),
-        (["REST1", "REST2"], "BAR"),
-    ],
-)
-def test_HCP1200_incorrect_access_phase(
-    tasks: str | None,
-    phase_encodings: str | None,
-) -> None:
-    """Test HCP1200 DataGrabber incorrect access for phase.
-
-    Parameters
-    ----------
-    tasks : str
-        The parametrized tasks.
-    phase_encodings : str
-        The parametrized phase encodings.
-
-    """
-    configure_logging(level="DEBUG")
-    with pytest.raises(ValueError, match="not a valid HCP-YA phase encoding"):
-        _ = HCP1200(
-            datadir=".",
-            tasks=tasks,
-            phase_encodings=phase_encodings,
-        )
-
-
 def test_HCP1200_elements(
     hcpdg: DataladHCP1200,
 ) -> None:
@@ -363,23 +289,24 @@ def test_HCP1200_elements(
 @pytest.mark.parametrize(
     "tasks, ica_fix",
     [
-        ("SOCIAL", True),
+        (["SOCIAL"], True),
         ("WM", True),
-        ("RELATIONAL", True),
+        (["RELATIONAL"], True),
         ("EMOTION", True),
-        ("LANGUAGE", True),
+        (["LANGUAGE"], True),
         ("GAMBLING", True),
-        ("MOTOR", True),
+        (["MOTOR"], True),
     ],
 )
 def test_HCP1200_incorrect_access_icafix(
-    tasks: str | None, ica_fix: bool
+    tasks: str | list[str],
+    ica_fix: bool,
 ) -> None:
     """Test HCP1200 DataGrabber incorrect access for icafix.
 
     Parameters
     ----------
-    tasks : str
+    tasks : str or list of str
         The parametrized tasks.
     ica_fix : bool
         The parametrized ICA-FIX flag.
@@ -388,7 +315,7 @@ def test_HCP1200_incorrect_access_icafix(
     configure_logging(level="DEBUG")
     with pytest.raises(ValueError, match="is only available for"):
         _ = HCP1200(
-            datadir=".",
+            datadir=Path("."),
             tasks=tasks,
             ica_fix=ica_fix,
         )

junifer/datagrabber/multiple.py

@@ -5,10 +5,17 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from pathlib import Path
+from typing import Annotated
+
+from pydantic import BeforeValidator, ConfigDict
+
 from ..api.decorators import register_datagrabber
-from ..typing import DataGrabberLike
-from ..utils import deep_update, raise_error
-from .base import BaseDataGrabber
+from ..typing import DataGrabberLike, Element
+from ..utils import deep_update, ensure_list, raise_error
+from .base import BaseDataGrabber, DataType
+from .pattern import PatternDataGrabber
+from .pattern_datalad import PatternDataladDataGrabber
 
 
 __all__ = ["MultipleDataGrabber"]
@@ -36,22 +43,33 @@ class MultipleDataGrabber(BaseDataGrabber):
 
     """
 
-    def __init__(self, datagrabbers: list[DataGrabberLike], **kwargs) -> None:
+    model_config = ConfigDict(extra="allow")
+
+    datagrabbers: list[
+        DataGrabberLike | PatternDataGrabber | PatternDataladDataGrabber
+    ]
+    types: Annotated[
+        DataType | list[DataType], BeforeValidator(ensure_list)
+    ] = []  # noqa: RUF012
+    datadir: Path = Path(".")
+
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
         # Check datagrabbers consistency
         # Check for same element keys
-        first_keys = datagrabbers[0].get_element_keys()
-        for dg in datagrabbers[1:]:
+        first_keys = self.datagrabbers[0].get_element_keys()
+        for dg in self.datagrabbers[1:]:
             if dg.get_element_keys() != first_keys:
                 raise_error(
                     msg="DataGrabbers have different element keys",
                     klass=RuntimeError,
                 )
         # Check for no overlapping types (and nested data types)
-        types = [x for dg in datagrabbers for x in dg.get_types()]
+        types = [x for dg in self.datagrabbers for x in dg.get_types()]
         if len(types) != len(set(types)):
-            if all(hasattr(dg, "patterns") for dg in datagrabbers):
-                first_patterns = datagrabbers[0].patterns
-                for dg in datagrabbers[1:]:
+            if all(hasattr(dg, "patterns") for dg in self.datagrabbers):
+                first_patterns = self.datagrabbers[0].patterns
+                for dg in self.datagrabbers[1:]:
                     for data_type in set(types):
                         dtype_pattern = dg.patterns.get(data_type)
                         if dtype_pattern is None:
@@ -75,14 +93,13 @@ class MultipleDataGrabber(BaseDataGrabber):
                     msg="DataGrabbers have overlapping types",
                     klass=RuntimeError,
                 )
-        self._datagrabbers = datagrabbers
 
-    def __getitem__(self, element: str | tuple) -> dict:
+    def __getitem__(self, element: Element) -> dict:
         """Implement indexing.
 
         Parameters
         ----------
-        element : str or tuple
+        element : `Element`
             The element to be indexed. If one string is provided, it is
             assumed to be a tuple with only one item. If a tuple is provided,
             each item in the tuple is the value for the replacement string
@@ -98,7 +115,7 @@ class MultipleDataGrabber(BaseDataGrabber):
 
         out = {}
         metas = []
-        for dg in self._datagrabbers:
+        for dg in self.datagrabbers:
             t_out = dg[element]
             deep_update(out, t_out)
             # Now get the meta for this datagrabber
@@ -119,16 +136,15 @@ class MultipleDataGrabber(BaseDataGrabber):
 
     def __enter__(self) -> "MultipleDataGrabber":
         """Implement context entry."""
-        for dg in self._datagrabbers:
+        for dg in self.datagrabbers:
             dg.__enter__()
         return self
 
     def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
         """Implement context exit."""
-        for dg in self._datagrabbers:
+        for dg in self.datagrabbers:
             dg.__exit__(exc_type, exc_value, exc_traceback)
 
-    # TODO: return type should be List[List[str]], but base type is List[str]
     def get_types(self) -> list[str]:
         """Get types.
 
@@ -138,7 +154,7 @@ class MultipleDataGrabber(BaseDataGrabber):
             The types of data to be grabbed.
 
         """
-        types = [x for dg in self._datagrabbers for x in dg.get_types()]
+        types = [x for dg in self.datagrabbers for x in dg.get_types()]
         return types
 
     def get_element_keys(self) -> list[str]:
@@ -153,7 +169,7 @@ class MultipleDataGrabber(BaseDataGrabber):
             The element keys.
 
         """
-        return self._datagrabbers[0].get_element_keys()
+        return self.datagrabbers[0].get_element_keys()
 
     def get_elements(self) -> list:
         """Get elements.
@@ -167,7 +183,7 @@ class MultipleDataGrabber(BaseDataGrabber):
             related DataGrabbers.
 
         """
-        all_elements = [dg.get_elements() for dg in self._datagrabbers]
+        all_elements = [dg.get_elements() for dg in self.datagrabbers]
         elements = set(all_elements[0])
         for s in all_elements[1:]:
             elements.intersection_update(s)

junifer/datagrabber/pattern.py

@@ -10,6 +10,9 @@ from copy import deepcopy
 from pathlib import Path
 
 import numpy as np
+from aenum import Enum as AEnum
+from aenum import extend_enum
+from pydantic import Field
 
 from ..api.decorators import register_datagrabber
 from ..typing import DataGrabberPatterns, Elements
@@ -18,11 +21,32 @@ from .base import BaseDataGrabber
 from .pattern_validation_mixin import PatternValidationMixin
 
 
-__all__ = ["PatternDataGrabber"]
+__all__ = [
+    "ConfoundsFormat",
+    "PatternDataGrabber",
+    "register_confounds_format",
+]
 
 
-# Accepted formats for confounds specification
-_CONFOUNDS_FORMATS = ("fmriprep", "adhoc")
+class ConfoundsFormat(str, AEnum):
+    """Accepted confounds format."""
+
+    FMRIPrep = "fmriprep"
+    AdHoc = "adhoc"
+
+
+def register_confounds_format(name: str, alias: str) -> None:
+    """Register custom confounds format.
+
+    Parameters
+    ----------
+    name : str
+        The confounds format name to be referred.
+    alias : str
+        The confounds format alias for string representation.
+
+    """
+    extend_enum(ConfoundsFormat, name, alias)
 
 
 @register_datagrabber
@@ -33,125 +57,16 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
 
     Parameters
     ----------
-    types : list of str
-        The types of data to be grabbed.
-    patterns : dict
-        Data type patterns as a dictionary. It has the following schema:
-
-        * ``"T1w"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"T2w"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"BOLD"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-                  "confounds": {
-                      "mandatory": ["pattern", "format"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"Warp"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "src", "dst", "warper"],
-              "optional": []
-            }
-
-        * ``"VBM_GM"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": []
-            }
-
-        * ``"VBM_WM"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": []
-            }
-
-        Basically, for each data type, one needs to provide ``mandatory`` keys
-        and can choose to also provide ``optional`` keys. The value for each
-        key is a string. So, one needs to provide necessary data types as a
-        dictionary, for example:
-
-        .. code-block:: none
-
-          {
-              "BOLD": {
-                "pattern": "...",
-                "space": "...",
-              },
-              "T1w": {
-                "pattern": "...",
-                "space": "...",
-              },
-          }
-
-        except ``Warp``, which needs to be a list of dictionaries as there can
-        be multiple spaces to warp (for example, with fMRIPrep):
-
-        .. code-block:: none
-
-          {
-              "Warp": [
-                {
-                  "pattern": "...",
-                  "src": "...",
-                  "dst": "...",
-                  "warper": "...",
-                },
-              ],
-          }
-
-        taken from :class:`.HCP1200`.
-    replacements : str or list of str
-        Replacements in the ``pattern`` key of each data type. The value needs
-        to be a list of all possible replacements.
-    datadir : str or pathlib.Path
-        The directory where the data is / will be stored.
-    confounds_format : {"fmriprep", "adhoc"} or None, optional
+    types : :enum:`.DataType` or list of variants
+        The data type(s) to grab.
+    datadir : pathlib.Path
+        The path where the data is stored.
+    patterns : ``DataGrabberPatterns``
+        The datagrabber patterns. Check :class:`.DataTypeSchema` for the \
+        schema.
+    replacements : list of str
+        All possible replacements in ``patterns.<data_type>.pattern``.
+    confounds_format : :enum:`.ConfoundsFormat` or None, optional
         The format of the confounds for the dataset (default None).
     partial_pattern_ok : bool, optional
         Whether to raise error if partial pattern for a data type is found.
@@ -161,52 +76,30 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
         powerful when used with :class:`.MultipleDataGrabber`
         (default True).
 
-    Raises
-    ------
-    ValueError
-        If ``confounds_format`` is invalid.
+    Attributes
+    ----------
+    skip_file_check
 
     """
 
-    def __init__(
-        self,
-        types: list[str],
-        patterns: DataGrabberPatterns,
-        replacements: list[str] | str,
-        datadir: str | Path,
-        confounds_format: str | None = None,
-        partial_pattern_ok: bool = False,
-    ) -> None:
-        # Convert replacements to list if not already
-        if not isinstance(replacements, list):
-            replacements = [replacements]
+    patterns: DataGrabberPatterns = Field(frozen=True)
+    replacements: list[str] = Field(frozen=True)
+    confounds_format: ConfoundsFormat | None = Field(None, frozen=True)
+    partial_pattern_ok: bool = Field(False, frozen=True)
+
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
         # Validate patterns
         self.validate_patterns(
-            types=types,
-            replacements=replacements,
-            patterns=patterns,
-            partial_pattern_ok=partial_pattern_ok,
+            types=self.types,
+            replacements=self.replacements,
+            patterns=self.patterns,
+            partial_pattern_ok=self.partial_pattern_ok,
         )
-        self.replacements = replacements
-        self.patterns = patterns
-        self.partial_pattern_ok = partial_pattern_ok
-
-        # Validate confounds format
-        if (
-            confounds_format is not None
-            and confounds_format not in _CONFOUNDS_FORMATS
-        ):
-            raise_error(
-                "Invalid value for `confounds_format`, should be one of "
-                f"{_CONFOUNDS_FORMATS}."
-            )
-        self.confounds_format = confounds_format
-
-        super().__init__(types=types, datadir=datadir)
         logger.debug("Initializing PatternDataGrabber")
-        logger.debug(f"\tpatterns = {patterns}")
-        logger.debug(f"\treplacements = {replacements}")
-        logger.debug(f"\tconfounds_format = {confounds_format}")
+        logger.debug(f"\tpatterns = {self.patterns}")
+        logger.debug(f"\treplacements = {self.replacements}")
+        logger.debug(f"\tconfounds_format = {self.confounds_format}")
 
     @property
     def skip_file_check(self) -> bool:
@@ -324,7 +217,7 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
         resolved_pattern = self._replace_patterns_glob(element, pattern)
         # Resolve path for wildcard
         if "*" in resolved_pattern:
-            t_matches = list(self.datadir.absolute().glob(resolved_pattern))
+            t_matches = list(self.fulldir.absolute().glob(resolved_pattern))
             # Multiple matches
             if len(t_matches) > 1:
                 raise_error(
@@ -340,7 +233,7 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
                 )
             path = t_matches[0]
         else:
-            path = self.datadir / resolved_pattern
+            path = self.fulldir / resolved_pattern
             if not self.skip_file_check:
                 if not path.exists() and not path.is_symlink():
                     raise_error(
@@ -367,7 +260,7 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
         return self.replacements
 
     def get_item(self, **element: dict) -> dict[str, dict]:
-        """Implement single element indexing for the datagrabber.
+        """Get the specified item from the dataset.
 
         This method constructs a real path to the requested item's data, by
         replacing the ``patterns`` with actual values passed via ``**element``.
@@ -514,8 +407,8 @@ class PatternDataGrabber(BaseDataGrabber, PatternValidationMixin):
                     glob_pattern,
                     t_replacements,
                 ) = self._replace_patterns_regex(pattern)
-                for fname in self.datadir.glob(glob_pattern):
-                    suffix = fname.relative_to(self.datadir).as_posix()
+                for fname in self.fulldir.glob(glob_pattern):
+                    suffix = fname.relative_to(self.fulldir).as_posix()
                     m = re.match(re_pattern, suffix)
                     if m is not None:
                         # Find the groups of replacements present in the

junifer/datagrabber/pattern_datalad.py

@@ -5,6 +5,8 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from pydantic import ConfigDict
+
 from ..api.decorators import register_datagrabber
 from ..utils import logger
 from .datalad_base import DataladDataGrabber
@@ -23,122 +25,24 @@ class PatternDataladDataGrabber(DataladDataGrabber, PatternDataGrabber):
 
     Parameters
     ----------
-    types : list of str
-        The types of data to be grabbed.
-    patterns : dict
-        Data type patterns as a dictionary. It has the following schema:
-
-        * ``"T1w"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"T2w"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"BOLD"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": {
-                  "mask": {
-                      "mandatory": ["pattern", "space"],
-                      "optional": []
-                  }
-                  "confounds": {
-                      "mandatory": ["pattern", "format"],
-                      "optional": []
-                  }
-              }
-            }
-
-        * ``"Warp"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "src", "dst"],
-              "optional": []
-            }
-
-        * ``"VBM_GM"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": []
-            }
-
-        * ``"VBM_WM"`` :
-
-          .. code-block:: none
-
-            {
-              "mandatory": ["pattern", "space"],
-              "optional": []
-            }
-
-        Basically, for each data type, one needs to provide ``mandatory`` keys
-        and can choose to also provide ``optional`` keys. The value for each
-        key is a string. So, one needs to provide necessary data types as a
-        dictionary, for example:
-
-        .. code-block:: none
-
-          {
-              "BOLD": {
-                "pattern": "...",
-                "space": "...",
-              },
-              "T1w": {
-                "pattern": "...",
-                "space": "...",
-              },
-              "Warp": {
-                "pattern": "...",
-                "src": "...",
-                "dst": "...",
-              }
-          }
-
-        taken from :class:`.HCP1200`.
-    replacements : str or list of str
-        Replacements in the ``pattern`` key of each data type. The value needs
-        to be a list of all possible replacements.
-    confounds_format : {"fmriprep", "adhoc"} or None, optional
-        The format of the confounds for the dataset (default None).
-    datadir : str or pathlib.Path or None, optional
-        That directory where the datalad dataset will be cloned. If None,
-        the datalad dataset will be cloned into a temporary directory
-        (default None).
-    rootdir : str or pathlib.Path, optional
+    uri : pydantic.AnyUrl
+        URI of the datalad sibling.
+    types : enum:`.DataType` or list of variants
+        The data type(s) to grab.
+    patterns : ``DataGrabberPatterns``
+        The datagrabber patterns. Check :class:`DataTypeSchema` for the schema.
+    replacements : list of str
+        All possible replacements in ``patterns.<data_type>.pattern``.
+    rootdir : pathlib.Path, optional
         The path within the datalad dataset to the root directory
-        (default ".").
-    uri : str or None, optional
-        URI of the datalad sibling (default None).
+        (default Path(".")).
+    confounds_format : :enum:`.ConfoundsFormat` or None, optional
+        The format of the confounds for the dataset (default None).
+    datadir : pathlib.Path, optional
+        That path where the datalad dataset will be cloned.
+        If not specified, the datalad dataset will be cloned into a temporary
+        directory.
+
 
     See Also
     --------
@@ -149,15 +53,11 @@ class PatternDataladDataGrabber(DataladDataGrabber, PatternDataGrabber):
 
     """
 
-    def __init__(
-        self,
-        **kwargs,
-    ) -> None:
-        # TODO(synchon): needs to be reworked, DataladDataGrabber needs to be
-        # a mixin to avoid multiple inheritance wherever possible.
+    model_config = ConfigDict(extra="allow")
 
+    def validate_datagrabber_params(self) -> None:
+        """Run extra logical validation for datagrabber."""
+        super().validate_datagrabber_params()
         logger.debug("Initializing PatternDataladDataGrabber")
-        for key, val in kwargs.items():
+        for key, val in self.__pydantic_extra__.items():
             logger.debug(f"\t{key} = {val}")
-
-        super().__init__(**kwargs)

junifer/datagrabber/pattern_validation_mixin.py

@@ -3,9 +3,20 @@
 # Authors: Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from collections.abc import Iterator, MutableMapping
-from typing import TypedDict
+import sys
 
+
+if sys.version_info < (3, 12):  # pragma: no cover
+    from typing_extensions import TypedDict
+else:
+    from typing import TypedDict
+
+
+from collections.abc import Iterator, MutableMapping
+
+from aenum import extend_enum
+
+from ..datagrabber import DataType
 from ..typing import DataGrabberPatterns
 from ..utils import logger, raise_error, warn_with_log
 
@@ -191,37 +202,17 @@ def register_data_type(name: str, schema: DataTypeSchema) -> None:
     ----------
     name : str
         The data type name.
-    schema : DataTypeSchema
+    schema : ``DataTypeSchema``
         The data type schema.
 
     """
     DataTypeManager()[name] = schema
+    extend_enum(DataType, name, name)
 
 
 class PatternValidationMixin:
     """Mixin class for pattern validation."""
 
-    def _validate_types(self, types: list[str]) -> None:
-        """Validate the types.
-
-        Parameters
-        ----------
-        types : list of str
-            The data types to validate.
-
-        Raises
-        ------
-        TypeError
-            If ``types`` is not a list or if the values are not string.
-
-        """
-        if not isinstance(types, list):
-            raise_error(msg="`types` must be a list", klass=TypeError)
-        if any(not isinstance(x, str) for x in types):
-            raise_error(
-                msg="`types` must be a list of strings", klass=TypeError
-            )
-
     def _validate_replacements(
         self,
         replacements: list[str],
@@ -234,15 +225,13 @@ class PatternValidationMixin:
         ----------
         replacements : list of str
             The replacements to validate.
-        patterns : dict
-            The patterns to validate replacements against.
+        patterns : ``DataGrabberPatterns``
+            The patterns to validate ``replacements`` against.
         partial_pattern_ok : bool
             Whether to raise error if partial pattern for a data type is found.
 
         Raises
         ------
-        TypeError
-            If ``replacements`` is not a list or if the values are not string.
         ValueError
             If a value in ``replacements`` is not part of a data type pattern
             and ``partial_pattern_ok=False`` or
@@ -256,15 +245,6 @@ class PatternValidationMixin:
             and ``partial_pattern_ok=True``.
 
         """
-        if not isinstance(replacements, list):
-            raise_error(msg="`replacements` must be a list.", klass=TypeError)
-
-        if any(not isinstance(x, str) for x in replacements):
-            raise_error(
-                msg="`replacements` must be a list of strings",
-                klass=TypeError,
-            )
-
         # Make a list of all patterns recursively
         all_patterns = []
         for dtype_val in patterns.values():
@@ -390,7 +370,7 @@ class PatternValidationMixin:
 
     def validate_patterns(
         self,
-        types: list[str],
+        types: list[DataType],
         replacements: list[str],
         patterns: DataGrabberPatterns,
         partial_pattern_ok: bool = False,
@@ -399,11 +379,11 @@ class PatternValidationMixin:
 
         Parameters
         ----------
-        types : list of str
-            The data types to check patterns of.
+        types : list of :enum:`.DataType`
+            The data type(s) to check patterns of.
         replacements : list of str
-            The replacements to be replaced in the patterns.
-        patterns : dict
+            The replacements to be replaced in the ``patterns``.
+        patterns : ``DataGrabberPatterns``
             The patterns to validate.
         partial_pattern_ok : bool, optional
             Whether to raise error if partial pattern for a data type is found.
@@ -412,8 +392,6 @@ class PatternValidationMixin:
 
         Raises
         ------
-        TypeError
-            If ``patterns`` is not a dictionary.
         ValueError
             If length of ``types`` and ``patterns`` are different or
             if ``patterns`` is missing entries from ``types`` or
@@ -421,12 +399,6 @@ class PatternValidationMixin:
             if data type pattern key contains '*' as value.
 
         """
-        # Validate types
-        self._validate_types(types=types)
-
-        # Validate patterns
-        if not isinstance(patterns, dict):
-            raise_error(msg="`patterns` must be a dict", klass=TypeError)
         # Unequal length of objects
         if len(types) > len(patterns):
             raise_error(

junifer/datagrabber/tests/test_base.py

@@ -26,7 +26,7 @@ def test_BaseDataGrabber() -> None:
         def get_element_keys(self):
             return ["subject"]
 
-    dg = MyDataGrabber(datadir="/tmp", types=["BOLD"])
+    dg = MyDataGrabber(datadir="/tmp", types="BOLD")
     elem = dg["sub01"]
     assert "BOLD" in elem
     assert "meta" in elem["BOLD"]
@@ -55,7 +55,7 @@ def test_BaseDataGrabber() -> None:
         def get_element_keys(self):
             return super().get_element_keys()
 
-    dg = MyDataGrabber2(datadir="/tmp", types=["BOLD"])
+    dg = MyDataGrabber2(datadir="/tmp", types="BOLD")
     with pytest.raises(NotImplementedError):
         dg.get_element_keys()
 
@@ -77,7 +77,7 @@ def test_BaseDataGrabber_filter_single() -> None:
         def get_element_keys(self):
             return ["subject"]
 
-    dg = FilterDataGrabber(datadir="/tmp", types=["BOLD"])
+    dg = FilterDataGrabber(datadir="/tmp", types="BOLD")
     with dg:
         assert "sub01" in list(dg.filter(["sub01"]))
         assert "sub02" not in list(dg.filter(["sub01"]))
@@ -104,7 +104,7 @@ def test_BaseDataGrabber_filter_multi() -> None:
         def get_element_keys(self):
             return ["subject", "task"]
 
-    dg = FilterDataGrabber(datadir="/tmp", types=["BOLD"])
+    dg = FilterDataGrabber(datadir="/tmp", types="BOLD")
     with dg:
         assert ("sub01", "rest") in list(
             dg.filter([("sub01", "rest")])  # type: ignore

junifer/datagrabber/tests/test_datalad_base.py

@@ -9,7 +9,7 @@ from pathlib import Path
 import datalad.api as dl
 import pytest
 
-from junifer.datagrabber import DataladDataGrabber
+from junifer.datagrabber import DataladDataGrabber, DataType
 from junifer.utils import config
 
 
@@ -38,23 +38,18 @@ def concrete_datagrabber() -> type[DataladDataGrabber]:
 
     """
 
-    class MyDataGrabber(DataladDataGrabber):  # type: ignore
-        def __init__(self, datadir, uri):
-            super().__init__(
-                datadir=datadir,
-                rootdir="example_bids",
-                uri=uri,
-                types=["T1w", "BOLD"],
-            )
+    class MyDataGrabber(DataladDataGrabber):
+        types: list[DataType] = [DataType.T1w, DataType.BOLD]  # noqa: RUF012
+        rootdir: Path = Path("example_bids")
 
         def get_item(self, subject):
             out = {
                 "T1w": {
-                    "path": self.datadir
+                    "path": self.fulldir
                     / f"{subject}/anat/{subject}_T1w.nii.gz"
                 },
                 "BOLD": {
-                    "path": self.datadir
+                    "path": self.fulldir
                     / f"{subject}/func/{subject}_task-rest_bold.nii.gz"
                 },
             }
@@ -91,7 +86,7 @@ def test_DataladDataGrabber_install_errors(
     # Files are not there
     assert datadir.exists() is False
     # Clone dataset
-    dl.clone(uri, datadir)  # type: ignore
+    dl.clone(uri, datadir)
     dg = concrete_datagrabber(datadir=datadir, uri=uri2)
     with pytest.raises(ValueError, match=r"different ID"):
         with dg:
@@ -160,7 +155,6 @@ def test_DataladDataGrabber_clone_cleanup(
         assert "datagrabber" in meta
         assert "datalad_dirty" in meta["datagrabber"]
         assert meta["datagrabber"]["datalad_dirty"] is False
-        assert hasattr(dg, "_got_files") is False
         assert datadir.exists() is True
         assert elem1_bold.is_file() is True
         assert elem1_bold.is_symlink() is True
@@ -185,8 +179,8 @@ def test_DataladDataGrabber_clone_create_cleanup(
 
     # Clone whole dataset
     uri = _testing_dataset["example_bids"]["uri"]
-    with concrete_datagrabber(datadir=None, uri=uri) as dg:
-        datadir = dg._tmpdir / "datadir"
+    with concrete_datagrabber(uri=uri) as dg:
+        datadir = dg._repodir
         elem1_bold = (
             datadir / "example_bids/sub-01/func/sub-01_task-rest_bold.nii.gz"
         )
@@ -206,7 +200,6 @@ def test_DataladDataGrabber_clone_create_cleanup(
         assert "datagrabber" in meta
         assert "datalad_dirty" in meta["datagrabber"]
         assert meta["datagrabber"]["datalad_dirty"] is False
-        assert hasattr(dg, "_got_files") is False
         assert datadir.exists() is True
         assert elem1_bold.is_file() is True
         assert elem1_bold.is_symlink() is True
@@ -246,7 +239,7 @@ def test_DataladDataGrabber_previously_cloned(
     assert elem1_t1w.exists() is False
 
     # Clone dataset
-    dl.clone(uri, datadir, result_renderer="disabled")  # type: ignore
+    dl.clone(uri, datadir, result_renderer="disabled")
 
     # Files are there, but are empty symbolic links
     assert datadir.exists() is True
@@ -316,7 +309,7 @@ def test_DataladDataGrabber_previously_cloned_and_get(
     assert elem1_t1w.exists() is False
 
     # Clone dataset
-    dl.clone(uri, datadir, result_renderer="disabled")  # type: ignore
+    dl.clone(uri, datadir, result_renderer="disabled")
 
     # Files are there, but are empty symbolic links
     assert datadir.exists() is True
@@ -325,9 +318,7 @@ def test_DataladDataGrabber_previously_cloned_and_get(
     assert elem1_t1w.is_symlink() is True
     assert elem1_t1w.is_file() is False
 
-    dl.get(  # type: ignore
-        elem1_t1w, dataset=datadir, result_renderer="disabled"
-    )
+    dl.get(elem1_t1w, dataset=datadir, result_renderer="disabled")
 
     assert elem1_bold.is_symlink() is True
     assert elem1_bold.is_file() is False
@@ -399,7 +390,7 @@ def test_DataladDataGrabber_previously_cloned_and_get_dirty(
     assert elem1_t1w.exists() is False
 
     # Clone dataset
-    dl.clone(uri, datadir, result_renderer="disabled")  # type: ignore
+    dl.clone(uri, datadir, result_renderer="disabled")
 
     # Files are there, but are empty symbolic links
     assert datadir.exists() is True
@@ -408,9 +399,7 @@ def test_DataladDataGrabber_previously_cloned_and_get_dirty(
     assert elem1_t1w.is_symlink() is True
     assert elem1_t1w.is_file() is False
 
-    dl.get(  # type: ignore
-        elem1_t1w, dataset=datadir, result_renderer="disabled"
-    )
+    dl.get(elem1_t1w, dataset=datadir, result_renderer="disabled")
 
     assert elem1_bold.is_symlink() is True
     assert elem1_bold.is_file() is False

junifer/datagrabber/tests/test_dmcc13_benchmark.py

@@ -4,101 +4,95 @@
 # License: AGPL
 
 import pytest
+from pydantic import AnyUrl
 
-from junifer.datagrabber import DMCC13Benchmark
+from junifer.datagrabber import DataType, DMCC13Benchmark
 
 
-URI = "https://gin.g-node.org/synchon/datalad-example-dmcc13-benchmark"
+URI = AnyUrl("https://gin.g-node.org/synchon/datalad-example-dmcc13-benchmark")
 
 
 @pytest.mark.parametrize(
     "sessions, tasks, phase_encodings, runs, native_t1w",
     [
-        (None, None, None, None, False),
-        ("ses-wave1bas", "Rest", "AP", "1", False),
-        ("ses-wave1bas", "Axcpt", "AP", "1", False),
-        ("ses-wave1bas", "Cuedts", "AP", "1", False),
-        ("ses-wave1bas", "Stern", "AP", "1", False),
-        ("ses-wave1bas", "Stroop", "AP", "1", False),
-        ("ses-wave1bas", "Rest", "PA", "2", False),
-        ("ses-wave1bas", "Axcpt", "PA", "2", False),
-        ("ses-wave1bas", "Cuedts", "PA", "2", False),
-        ("ses-wave1bas", "Stern", "PA", "2", False),
-        ("ses-wave1bas", "Stroop", "PA", "2", False),
-        ("ses-wave1bas", "Rest", "AP", "1", True),
-        ("ses-wave1bas", "Axcpt", "AP", "1", True),
-        ("ses-wave1bas", "Cuedts", "AP", "1", True),
-        ("ses-wave1bas", "Stern", "AP", "1", True),
-        ("ses-wave1bas", "Stroop", "AP", "1", True),
-        ("ses-wave1bas", "Rest", "PA", "2", True),
-        ("ses-wave1bas", "Axcpt", "PA", "2", True),
-        ("ses-wave1bas", "Cuedts", "PA", "2", True),
-        ("ses-wave1bas", "Stern", "PA", "2", True),
-        ("ses-wave1bas", "Stroop", "PA", "2", True),
-        ("ses-wave1pro", "Rest", "AP", "1", False),
-        ("ses-wave1pro", "Rest", "PA", "2", False),
-        ("ses-wave1pro", "Rest", "AP", "1", True),
-        ("ses-wave1pro", "Rest", "PA", "2", True),
-        ("ses-wave1rea", "Rest", "AP", "1", False),
-        ("ses-wave1rea", "Rest", "PA", "2", False),
-        ("ses-wave1rea", "Rest", "AP", "1", True),
-        ("ses-wave1rea", "Rest", "PA", "2", True),
+        (["ses-wave1bas"], ["Rest"], ["AP"], ["1"], False),
+        (["ses-wave1bas"], ["Axcpt"], ["AP"], ["1"], False),
+        (["ses-wave1bas"], ["Cuedts"], ["AP"], ["1"], False),
+        (["ses-wave1bas"], ["Stern"], ["AP"], ["1"], False),
+        (["ses-wave1bas"], ["Stroop"], ["AP"], ["1"], False),
+        (["ses-wave1bas"], ["Rest"], ["PA"], ["2"], False),
+        (["ses-wave1bas"], ["Axcpt"], ["PA"], ["2"], False),
+        (["ses-wave1bas"], ["Cuedts"], ["PA"], ["2"], False),
+        (["ses-wave1bas"], ["Stern"], ["PA"], ["2"], False),
+        (["ses-wave1bas"], ["Stroop"], ["PA"], ["2"], False),
+        (["ses-wave1bas"], ["Rest"], ["AP"], ["1"], True),
+        (["ses-wave1bas"], ["Axcpt"], ["AP"], ["1"], True),
+        (["ses-wave1bas"], ["Cuedts"], ["AP"], ["1"], True),
+        (["ses-wave1bas"], ["Stern"], ["AP"], ["1"], True),
+        (["ses-wave1bas"], ["Stroop"], ["AP"], ["1"], True),
+        (["ses-wave1bas"], ["Rest"], ["PA"], ["2"], True),
+        (["ses-wave1bas"], ["Axcpt"], ["PA"], ["2"], True),
+        (["ses-wave1bas"], ["Cuedts"], ["PA"], ["2"], True),
+        (["ses-wave1bas"], ["Stern"], ["PA"], ["2"], True),
+        (["ses-wave1bas"], ["Stroop"], ["PA"], ["2"], True),
+        (["ses-wave1pro"], ["Rest"], ["AP"], ["1"], False),
+        (["ses-wave1pro"], ["Rest"], ["PA"], ["2"], False),
+        (["ses-wave1pro"], ["Rest"], ["AP"], ["1"], True),
+        (["ses-wave1pro"], ["Rest"], ["PA"], ["2"], True),
+        (["ses-wave1rea"], ["Rest"], ["AP"], ["1"], False),
+        (["ses-wave1rea"], ["Rest"], ["PA"], ["2"], False),
+        (["ses-wave1rea"], ["Rest"], ["AP"], ["1"], True),
+        (["ses-wave1rea"], ["Rest"], ["PA"], ["2"], True),
     ],
 )
 def test_DMCC13Benchmark(
-    sessions: str | None,
-    tasks: str | None,
-    phase_encodings: str | None,
-    runs: str | None,
+    sessions: list[str],
+    tasks: list[str],
+    phase_encodings: list[str],
+    runs: list[str],
     native_t1w: bool,
 ) -> None:
     """Test DMCC13Benchmark DataGrabber.
 
     Parameters
     ----------
-    sessions : str or None
+    sessions : list of str
         The parametrized session values.
-    tasks : str or None
+    tasks : list of str
         The parametrized task values.
-    phase_encodings : str or None
+    phase_encodings : list of str
         The parametrized phase encoding values.
-    runs : str or None
+    runs : list of str
         The parametrized run values.
     native_t1w : bool
         The parametrized values for fetching native T1w.
 
     """
     dg = DMCC13Benchmark(
+        uri=URI,
         sessions=sessions,
         tasks=tasks,
         phase_encodings=phase_encodings,
         runs=runs,
         native_t1w=native_t1w,
     )
-    # Set URI to Gin
-    dg.uri = URI
-
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element's access values
         _, ses, task, phase, run = test_element
-        # Access data
         out = dg[("sub-01", ses, task, phase, run)]
 
         # Available data types
         data_types = [
-            "BOLD",
-            "VBM_CSF",
-            "VBM_GM",
-            "VBM_WM",
-            "T1w",
+            DataType.BOLD,
+            DataType.VBM_CSF,
+            DataType.VBM_GM,
+            DataType.VBM_WM,
+            DataType.T1w,
         ]
         # Add Warp if native T1w is accessed
         if native_t1w:
-            data_types.append("Warp")
+            data_types.append(DataType.Warp)
 
         # Data type file name formats
         data_file_names = [
@@ -131,9 +125,9 @@ def test_DMCC13Benchmark(
             data_types, data_file_names, strict=False
         ):
             # Assert data type
-            assert data_type in out
+            assert data_type in out.keys()
             # Conditional for Warp
-            if data_type == "Warp":
+            if data_type is DataType.Warp:
                 for idx, fname in enumerate(data_file_name):
                     # Assert data file path exists
                     assert out[data_type][idx]["path"].exists()
@@ -200,15 +194,15 @@ def test_DMCC13Benchmark(
 @pytest.mark.parametrize(
     "types, native_t1w",
     [
-        ("BOLD", True),
+        (["BOLD"], True),
         ("BOLD", False),
-        ("T1w", True),
+        (["T1w"], True),
         ("T1w", False),
-        ("VBM_CSF", True),
+        (["VBM_CSF"], True),
         ("VBM_CSF", False),
-        ("VBM_GM", True),
+        (["VBM_GM"], True),
         ("VBM_GM", False),
-        ("VBM_WM", True),
+        (["VBM_WM"], True),
         ("VBM_WM", False),
         (["BOLD", "VBM_CSF"], True),
         (["BOLD", "VBM_CSF"], False),
@@ -232,66 +226,18 @@ def test_DMCC13Benchmark_partial_data_access(
         The parametrized values for fetching native T1w.
 
     """
-    dg = DMCC13Benchmark(types=types, native_t1w=native_t1w)
-    # Set URI to Gin
-    dg.uri = URI
-
+    dg = DMCC13Benchmark(
+        uri=URI,
+        types=types,
+        native_t1w=native_t1w,
+    )
     with dg:
-        # Get all elements
         all_elements = dg.get_elements()
-        # Get test element
         test_element = all_elements[0]
-        # Get test element's access values
         _, ses, task, phase, run = test_element
-        # Access data
         out = dg[("sub-01", ses, task, phase, run)]
         # Assert data type
-        if isinstance(types, list):
-            for type_ in types:
-                assert type_ in out
-        else:
-            assert types in out
-
-
-def test_DMCC13Benchmark_incorrect_data_type() -> None:
-    """Test DMCC13Benchmark DataGrabber incorrect data type."""
-    with pytest.raises(
-        ValueError, match="`patterns` must contain all `types`"
-    ):
-        _ = DMCC13Benchmark(types="Orcus")
-
-
-def test_DMCC13Benchmark_invalid_sessions():
-    """Test DMCC13Benchmark DataGrabber invalid sessions."""
-    with pytest.raises(
-        ValueError,
-        match=("phonyses is not a valid session in the DMCC dataset"),
-    ):
-        DMCC13Benchmark(sessions="phonyses")
-
-
-def test_DMCC13Benchmark_invalid_tasks():
-    """Test DMCC13Benchmark DataGrabber invalid tasks."""
-    with pytest.raises(
-        ValueError,
-        match=("thisisnotarealtask is not a valid task in the DMCC dataset"),
-    ):
-        DMCC13Benchmark(tasks="thisisnotarealtask")
-
-
-def test_DMCC13Benchmark_phase_encodings():
-    """Test DMCC13Benchmark DataGrabber invalid phase encodings."""
-    with pytest.raises(
-        ValueError,
-        match=("moonphase is not a valid phase encoding in the DMCC dataset"),
-    ):
-        DMCC13Benchmark(phase_encodings="moonphase")
-
-
-def test_DMCC13Benchmark_runs():
-    """Test DMCC13Benchmark DataGrabber invalid runs."""
-    with pytest.raises(
-        ValueError,
-        match=("cerebralrun is not a valid run in the DMCC dataset"),
-    ):
-        DMCC13Benchmark(runs="cerebralrun")
+        if isinstance(types, str):
+            types = [types]
+        for type_ in types:
+            assert type_ in out

junifer/datagrabber/tests/test_multiple.py

@@ -3,7 +3,10 @@
 # Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
 # License: AGPL
 
+from pathlib import Path
+
 import pytest
+from pydantic import AnyUrl
 
 from junifer.datagrabber import MultipleDataGrabber, PatternDataladDataGrabber
 
@@ -22,8 +25,8 @@ _testing_dataset = {
 
 def test_MultipleDataGrabber() -> None:
     """Test MultipleDataGrabber."""
-    repo_uri = _testing_dataset["example_bids_ses"]["uri"]
-    rootdir = "example_bids_ses"
+    repo_uri = AnyUrl(_testing_dataset["example_bids_ses"]["uri"])
+    rootdir = Path("example_bids_ses")
     replacements = ["subject", "session"]
 
     dg1 = PatternDataladDataGrabber(
@@ -73,7 +76,7 @@ def test_MultipleDataGrabber() -> None:
     dg2 = PatternDataladDataGrabber(
         rootdir=rootdir,
         uri=repo_uri,
-        types=["BOLD"],
+        types="BOLD",
         patterns={
             "BOLD": {
                 "pattern": (
@@ -93,7 +96,7 @@ def test_MultipleDataGrabber() -> None:
         replacements=replacements,
     )
 
-    dg = MultipleDataGrabber([dg1, dg2])
+    dg = MultipleDataGrabber(datagrabbers=[dg1, dg2])
 
     types = dg.get_types()
     assert "T1w" in types
@@ -129,12 +132,12 @@ def test_MultipleDataGrabber() -> None:
 
 def test_MultipleDataGrabber_no_intersection() -> None:
     """Test MultipleDataGrabber without intersection (0 elements)."""
-    rootdir = "example_bids_ses"
+    rootdir = Path("example_bids_ses")
     replacements = ["subject", "session"]
 
     dg1 = PatternDataladDataGrabber(
         rootdir=rootdir,
-        uri=_testing_dataset["example_bids"]["uri"],
+        uri=AnyUrl(_testing_dataset["example_bids"]["uri"]),
         types=["T1w", "Warp"],
         patterns={
             "T1w": {
@@ -171,8 +174,8 @@ def test_MultipleDataGrabber_no_intersection() -> None:
 
     dg2 = PatternDataladDataGrabber(
         rootdir=rootdir,
-        uri=_testing_dataset["example_bids_ses"]["uri"],
-        types=["BOLD"],
+        uri=AnyUrl(_testing_dataset["example_bids_ses"]["uri"]),
+        types="BOLD",
         patterns={
             "BOLD": {
                 "pattern": (
@@ -185,7 +188,7 @@ def test_MultipleDataGrabber_no_intersection() -> None:
         replacements=replacements,
     )
 
-    dg = MultipleDataGrabber([dg1, dg2])
+    dg = MultipleDataGrabber(datagrabbers=[dg1, dg2])
     expected_subs = set()
     with dg:
         subs = list(dg)
@@ -195,9 +198,9 @@ def test_MultipleDataGrabber_no_intersection() -> None:
 def test_MultipleDataGrabber_get_item() -> None:
     """Test MultipleDataGrabber get_item() error."""
     dg1 = PatternDataladDataGrabber(
-        rootdir="example_bids_ses",
-        uri=_testing_dataset["example_bids"]["uri"],
-        types=["T1w"],
+        rootdir=Path("example_bids_ses"),
+        uri=AnyUrl(_testing_dataset["example_bids"]["uri"]),
+        types="T1w",
         patterns={
             "T1w": {
                 "pattern": (
@@ -209,19 +212,19 @@ def test_MultipleDataGrabber_get_item() -> None:
         replacements=["subject", "session"],
     )
 
-    dg = MultipleDataGrabber([dg1])
+    dg = MultipleDataGrabber(datagrabbers=[dg1])
     with pytest.raises(NotImplementedError):
-        dg.get_item(subject="sub-01")  # type: ignore
+        dg.get_item(subject="sub-01")
 
 
 def test_MultipleDataGrabber_validation() -> None:
     """Test MultipleDataGrabber init validation."""
-    rootdir = "example_bids_ses"
+    rootdir = Path("example_bids_ses")
 
     dg1 = PatternDataladDataGrabber(
         rootdir=rootdir,
-        uri=_testing_dataset["example_bids"]["uri"],
-        types=["T1w"],
+        uri=AnyUrl(_testing_dataset["example_bids"]["uri"]),
+        types="T1w",
         patterns={
             "T1w": {
                 "pattern": (
@@ -235,8 +238,8 @@ def test_MultipleDataGrabber_validation() -> None:
 
     dg2 = PatternDataladDataGrabber(
         rootdir=rootdir,
-        uri=_testing_dataset["example_bids_ses"]["uri"],
-        types=["BOLD"],
+        uri=AnyUrl(_testing_dataset["example_bids_ses"]["uri"]),
+        types="BOLD",
         patterns={
             "BOLD": {
                 "pattern": "{subject}/func/{subject}_task-rest_bold.nii.gz",
@@ -247,22 +250,22 @@ def test_MultipleDataGrabber_validation() -> None:
     )
 
     with pytest.raises(RuntimeError, match="have different element keys"):
-        MultipleDataGrabber([dg1, dg2])
+        MultipleDataGrabber(datagrabbers=[dg1, dg2])
 
     with pytest.raises(RuntimeError, match="have overlapping mandatory"):
-        MultipleDataGrabber([dg1, dg1])
+        MultipleDataGrabber(datagrabbers=[dg1, dg1])
 
 
 def test_MultipleDataGrabber_partial_pattern() -> None:
     """Test MultipleDataGrabber partial pattern."""
-    repo_uri = _testing_dataset["example_bids_ses"]["uri"]
-    rootdir = "example_bids_ses"
+    repo_uri = AnyUrl(_testing_dataset["example_bids_ses"]["uri"])
+    rootdir = Path("example_bids_ses")
     replacements = ["subject", "session"]
 
     dg1 = PatternDataladDataGrabber(
         rootdir=rootdir,
         uri=repo_uri,
-        types=["BOLD"],
+        types="BOLD",
         patterns={
             "BOLD": {
                 "pattern": (
@@ -278,7 +281,7 @@ def test_MultipleDataGrabber_partial_pattern() -> None:
     dg2 = PatternDataladDataGrabber(
         rootdir=rootdir,
         uri=repo_uri,
-        types=["BOLD"],
+        types="BOLD",
         patterns={
             "BOLD": {
                 "confounds": {
@@ -295,7 +298,7 @@ def test_MultipleDataGrabber_partial_pattern() -> None:
         partial_pattern_ok=True,
     )
 
-    dg = MultipleDataGrabber([dg1, dg2])
+    dg = MultipleDataGrabber(datagrabbers=[dg1, dg2])
 
     types = dg.get_types()
     assert "BOLD" in types

junifer/datagrabber/tests/test_pattern.py

@@ -10,7 +10,21 @@ from pathlib import Path
 
 import pytest
 
-from junifer.datagrabber import PatternDataGrabber
+from junifer.datagrabber import (
+    ConfoundsFormat,
+    PatternDataGrabber,
+    register_confounds_format,
+)
+
+
+def test_register_confounds_format() -> None:
+    """Test confounds format registration."""
+
+    register_confounds_format(
+        name="Confounds",
+        alias="confounds",
+    )
+    assert "confounds" in list(ConfoundsFormat)
 
 
 def test_PatternDataGrabber_errors(tmp_path: Path) -> None:
@@ -117,7 +131,7 @@ def test_PatternDataGrabber(tmp_path: Path) -> None:
     """
 
     datagrabber_first = PatternDataGrabber(
-        datadir="/tmp/data",
+        datadir=Path("/tmp/data"),
         types=["BOLD", "T1w"],
         patterns={
             "BOLD": {
@@ -129,7 +143,7 @@ def test_PatternDataGrabber(tmp_path: Path) -> None:
                 "space": "native",
             },
         },
-        replacements="subject",
+        replacements=["subject"],
     )
     assert datagrabber_first.datadir == Path("/tmp/data")
     assert set(datagrabber_first.types) == {"T1w", "BOLD"}
@@ -181,7 +195,7 @@ def test_PatternDataGrabber(tmp_path: Path) -> None:
 
     datagrabber_third = PatternDataGrabber(
         datadir=tmpdir,
-        types=["T1w"],
+        types="T1w",
         patterns={
             "T1w": {
                 "pattern": "anat/{subject}_{session}.nii",
@@ -258,7 +272,7 @@ def test_PatternDataGrabber_unix_path_expansion(tmp_path: Path) -> None:
     # Create datagrabber
     dg = PatternDataGrabber(
         datadir=tmp_path,
-        types=["FreeSurfer"],
+        types="FreeSurfer",
         patterns={
             "FreeSurfer": {
                 "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]",
@@ -279,22 +293,3 @@ def test_PatternDataGrabber_unix_path_expansion(tmp_path: Path) -> None:
     # Check paths are found
     assert set(out["FreeSurfer"].keys()) == {"path", "aseg", "meta"}
     assert list(out["FreeSurfer"]["aseg"].keys()) == ["path"]
-
-
-def test_PatternDataGrabber_confounds_format_error_on_init() -> None:
-    """Test PatterDataGrabber confounds format error on initialisation."""
-    with pytest.raises(
-        ValueError, match="Invalid value for `confounds_format`"
-    ):
-        PatternDataGrabber(
-            types=["BOLD"],
-            patterns={
-                "BOLD": {
-                    "pattern": "func/{subject}.nii",
-                    "space": "MNI152NLin6Asym",
-                },
-            },
-            replacements=["subject"],
-            datadir="/tmp",
-            confounds_format="foobar",
-        )

junifer/datagrabber/tests/test_pattern_datalad.py

@@ -7,9 +7,9 @@
 
 from pathlib import Path
 
-import pytest
+from pydantic import AnyUrl
 
-from junifer.datagrabber import PatternDataladDataGrabber
+from junifer.datagrabber import DataType, PatternDataladDataGrabber
 
 
 _testing_dataset = {
@@ -26,45 +26,25 @@ _testing_dataset = {
 }
 
 
-def test_bids_PatternDataladDataGrabber_missing_uri() -> None:
-    """Test check of missing URI in PatternDataladDataGrabber."""
-    with pytest.raises(ValueError, match=r"`uri` must be provided"):
-        PatternDataladDataGrabber(
-            datadir=None,
-            types=[],
-            patterns={},
-            replacements=[],
-        )
-
-
 def test_bids_PatternDataladDataGrabber() -> None:
     """Test subject-based BIDS PatternDataladDataGrabber."""
-    # Define types
-    types = ["T1w", "BOLD"]
-    # Define patterns
-    patterns = {
-        "T1w": {
-            "pattern": "{subject}/anat/{subject}_T1w.nii.gz",
-            "space": "MNI152NLin6Asym",
-        },
-        "BOLD": {
-            "pattern": "{subject}/func/{subject}_task-rest_bold.nii.gz",
-            "space": "MNI152NLin6Asym",
-        },
-    }
-    # Define replacements
-    replacements = ["subject"]
-
-    repo_uri = _testing_dataset["example_bids"]["uri"]
-    rootdir = "example_bids"
     repo_commit = _testing_dataset["example_bids"]["commit"]
-
+    repo_uri = _testing_dataset["example_bids"]["uri"]
     with PatternDataladDataGrabber(
-        rootdir=rootdir,
-        uri=repo_uri,
-        types=types,
-        patterns=patterns,
-        replacements=replacements,
+        uri=AnyUrl(repo_uri),
+        types=[DataType.T1w, DataType.BOLD],
+        patterns={
+            "T1w": {
+                "pattern": "{subject}/anat/{subject}_T1w.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+            "BOLD": {
+                "pattern": "{subject}/func/{subject}_task-rest_bold.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+        },
+        replacements=["subject"],
+        rootdir=Path("example_bids"),
     ) as dg:
         subs = list(dg)
         expected_subs = [f"sub-{i:02d}" for i in range(1, 10)]
@@ -74,11 +54,11 @@ def test_bids_PatternDataladDataGrabber() -> None:
             t_sub = dg[elem]
             assert "path" in t_sub["T1w"]
             assert t_sub["T1w"]["path"] == (
-                dg.datadir / f"{elem}/anat/{elem}_T1w.nii.gz"
+                dg.fulldir / f"{elem}/anat/{elem}_T1w.nii.gz"
             )
             assert "path" in t_sub["BOLD"]
             assert t_sub["BOLD"]["path"] == (
-                dg.datadir / f"{elem}/func/{elem}_task-rest_bold.nii.gz"
+                dg.fulldir / f"{elem}/func/{elem}_task-rest_bold.nii.gz"
             )
 
             assert "meta" in t_sub["BOLD"]
@@ -88,7 +68,7 @@ def test_bids_PatternDataladDataGrabber() -> None:
             assert "class" in dg_meta
             assert dg_meta["class"] == "PatternDataladDataGrabber"
             assert "uri" in dg_meta
-            assert dg_meta["uri"] == repo_uri
+            assert str(dg_meta["uri"]) == repo_uri
             assert "datalad_commit_id" in dg_meta
             assert dg_meta["datalad_commit_id"] == repo_commit
 
@@ -98,80 +78,64 @@ def test_bids_PatternDataladDataGrabber() -> None:
 
 def test_bids_PatternDataladDataGrabber_datadir() -> None:
     """Test PatternDataladDataGrabber with a datadir set to a relative path."""
-    # Define patterns
-    patterns = {
-        "T1w": {
-            "pattern": "{subject}/anat/{subject}_T*w.nii.gz",
-            "space": "MNI152NLin6Asym",
-        },
-        "BOLD": {
-            "pattern": "{subject}/func/{subject}_task-rest_*.nii.gz",
-            "space": "MNI152NLin6Asym",
-        },
-    }
-    # Define datadir
-    datadir = "dataset"  # use string and not absolute path
+    datadir = Path("dataset")  # use string and not absolute path
     with PatternDataladDataGrabber(
-        uri=_testing_dataset["example_bids"]["uri"],
-        types=["T1w", "BOLD"],
-        patterns=patterns,
-        datadir=datadir,
-        rootdir="example_bids",
+        uri=AnyUrl(_testing_dataset["example_bids"]["uri"]),
+        types=[DataType.T1w, DataType.BOLD],
+        patterns={
+            "T1w": {
+                "pattern": "{subject}/anat/{subject}_T*w.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+            "BOLD": {
+                "pattern": "{subject}/func/{subject}_task-rest_*.nii.gz",
+                "space": "MNI152NLin6Asym",
+            },
+        },
         replacements=["subject"],
+        datadir=datadir,
+        rootdir=Path("example_bids"),
     ) as dg:
-        assert dg.datadir == Path(datadir) / "example_bids"
+        assert dg.fulldir == Path(datadir) / "example_bids"
         for elem in dg:
             t_sub = dg[elem]
             assert "path" in t_sub["T1w"]
             assert t_sub["T1w"]["path"] == (
-                dg.datadir / f"{elem}/anat/{elem}_T1w.nii.gz"
+                dg.fulldir / f"{elem}/anat/{elem}_T1w.nii.gz"
             )
             assert "path" in t_sub["BOLD"]
             assert t_sub["BOLD"]["path"] == (
-                dg.datadir / f"{elem}/func/{elem}_task-rest_bold.nii.gz"
+                dg.fulldir / f"{elem}/func/{elem}_task-rest_bold.nii.gz"
             )
 
 
 def test_bids_PatternDataladDataGrabber_session():
     """Test a subject and session-based BIDS PatternDataladDataGrabber."""
-    types = ["T1w", "BOLD"]
-    patterns = {
-        "T1w": {
-            "pattern": (
-                "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz"
-            ),
-            "space": "MNI152NLin6Asym",
-        },
-        "BOLD": {
-            "pattern": (
-                "{subject}/{session}/func/"
-                "{subject}_{session}_task-rest_bold.nii.gz"
-            ),
-            "space": "MNI152NLin6Asym",
-        },
-    }
-    replacements = ["subject", "session"]
-
-    # Check error
-    with pytest.raises(ValueError, match=r"`uri` must be provided"):
-        PatternDataladDataGrabber(
-            datadir=None,
-            types=types,
-            patterns=patterns,
-            replacements=replacements,
-        )
-
     # Set parameters
-    repo_uri = _testing_dataset["example_bids_ses"]["uri"]
-    rootdir = "example_bids_ses"
-
+    repo_uri = AnyUrl(_testing_dataset["example_bids_ses"]["uri"])
+    rootdir = Path("example_bids_ses")
+    replacements = ["subject", "session"]
     # With T1W and bold, only 2 sessions are available
     with PatternDataladDataGrabber(
-        rootdir=rootdir,
         uri=repo_uri,
-        types=types,
-        patterns=patterns,
+        types=[DataType.T1w, DataType.BOLD],
+        patterns={
+            "T1w": {
+                "pattern": (
+                    "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz"
+                ),
+                "space": "MNI152NLin6Asym",
+            },
+            "BOLD": {
+                "pattern": (
+                    "{subject}/{session}/func/"
+                    "{subject}_{session}_task-rest_bold.nii.gz"
+                ),
+                "space": "MNI152NLin6Asym",
+            },
+        },
         replacements=replacements,
+        rootdir=rootdir,
     ) as dg:
         subs = list(dg.get_elements())
         expected_subs = [
@@ -182,21 +146,19 @@ def test_bids_PatternDataladDataGrabber_session():
         assert set(subs) == set(expected_subs)
 
     # Test with a different T1w only, it should have 3 sessions
-    types = ["T1w"]
-    patterns = {
-        "T1w": {
-            "pattern": (
-                "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz"
-            ),
-            "space": "MNI152NLin6Asym",
-        },
-    }
     with PatternDataladDataGrabber(
-        rootdir=rootdir,
         uri=repo_uri,
-        types=types,
-        patterns=patterns,
+        types=DataType.T1w,
+        patterns={
+            "T1w": {
+                "pattern": (
+                    "{subject}/{session}/anat/{subject}_{session}_T1w.nii.gz"
+                ),
+                "space": "MNI152NLin6Asym",
+            },
+        },
         replacements=replacements,
+        rootdir=rootdir,
     ) as dg:
         subs = list(dg)
         expected_subs = [

junifer/datagrabber/tests/test_pattern_validation_mixin.py

@@ -8,7 +8,8 @@ from contextlib import AbstractContextManager, nullcontext
 
 import pytest
 
-from junifer.datagrabber.pattern_validation_mixin import (
+from junifer.datagrabber import (
+    DataType,
     DataTypeManager,
     DataTypeSchema,
     PatternValidationMixin,
@@ -79,7 +80,7 @@ def test_dtype_mgr(dtype: DataTypeSchema) -> None:
 
     Parameters
     ----------
-    dtype : DataTypeSchema
+    dtype : ``DataTypeSchema``
         The parametrized schema.
 
     """
@@ -110,33 +111,15 @@ def test_register_data_type() -> None:
     )
 
     assert "dtype" in DataTypeManager()
+    assert "dtype" in list(DataType)
     _ = DataTypeManager().pop("dtype")
-    assert "dumb" not in DataTypeManager()
+    assert "dtype" not in DataTypeManager()
+    assert "dtype" in list(DataType)
 
 
 @pytest.mark.parametrize(
     "types, replacements, patterns, expect",
     [
-        (
-            "wrong",
-            [],
-            {},
-            pytest.raises(TypeError, match="`types` must be a list"),
-        ),
-        (
-            [1],
-            [],
-            {},
-            pytest.raises(
-                TypeError, match="`types` must be a list of strings"
-            ),
-        ),
-        (
-            ["BOLD"],
-            [],
-            "wrong",
-            pytest.raises(TypeError, match="`patterns` must be a dict"),
-        ),
         (
             ["T1w", "BOLD"],
             "",
@@ -204,30 +187,6 @@ def test_register_data_type() -> None:
             },
             pytest.raises(ValueError, match="following a replacement"),
         ),
-        (
-            ["T1w"],
-            "wrong",
-            {
-                "T1w": {
-                    "pattern": "{subject}/anat/{subject}_T1w.nii",
-                    "space": "native",
-                },
-            },
-            pytest.raises(TypeError, match="`replacements` must be a list"),
-        ),
-        (
-            ["T1w"],
-            [1],
-            {
-                "T1w": {
-                    "pattern": "{subject}/anat/{subject}_T1w.nii",
-                    "space": "native",
-                },
-            },
-            pytest.raises(
-                TypeError, match="`replacements` must be a list of strings"
-            ),
-        ),
         (
             ["T1w", "BOLD"],
             ["subject", "session"],

junifer/datareader/default.py

@@ -8,6 +8,7 @@ from pathlib import Path
 
 import nibabel as nib
 import pandas as pd
+from pydantic import BaseModel
 
 from ..api.decorators import register_datareader
 from ..pipeline import PipelineStepMixin, UpdateMetaMixin
@@ -33,7 +34,7 @@ _readers["TSV"] = {"func": pd.read_csv, "params": {"sep": "\t"}}
 
 
 @register_datareader
-class DefaultDataReader(PipelineStepMixin, UpdateMetaMixin):
+class DefaultDataReader(BaseModel, PipelineStepMixin, UpdateMetaMixin):
     """Concrete implementation for common data reading."""
 
     def validate_input(self, input: list[str]) -> list[str]:

junifer/datareader/tests/test_default_reader.py

@@ -30,7 +30,7 @@ def test_DefaultDataReader_validation(type_) -> None:
     """
     reader = DefaultDataReader()
     assert reader.validate_input(type_) == type_
-    assert reader.validate(type_) == type_
+    assert reader.validate_component(type_) == type_
 
 
 def test_DefaultDataReader_meta() -> None:

junifer/external/__init__.py

@@ -2,3 +2,8 @@
 
 # Authors: Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
+
+import lazy_loader as lazy
+
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

junifer/markers/__init__.pyi

@@ -11,9 +11,11 @@ __all__ = [
     "EdgeCentricFCMaps",
     "EdgeCentricFCParcels",
     "EdgeCentricFCSpheres",
+    "ReHoImpl",
     "ReHoMaps",
     "ReHoParcels",
     "ReHoSpheres",
+    "ALFFImpl",
     "ALFFMaps",
     "ALFFParcels",
     "ALFFSpheres",
@@ -37,8 +39,8 @@ from .functional_connectivity import (
     EdgeCentricFCParcels,
     EdgeCentricFCSpheres,
 )
-from .reho import ReHoMaps, ReHoParcels, ReHoSpheres
-from .falff import ALFFMaps, ALFFParcels, ALFFSpheres
+from .reho import ReHoImpl, ReHoMaps, ReHoParcels, ReHoSpheres
+from .falff import ALFFImpl, ALFFMaps, ALFFParcels, ALFFSpheres
 from .temporal_snr import (
     TemporalSNRMaps,
     TemporalSNRParcels,

junifer/markers/base.py

@@ -8,7 +8,11 @@ from abc import ABC, abstractmethod
 from copy import deepcopy
 from typing import Any, ClassVar
 
+from pydantic import BaseModel, ConfigDict
+
+from ..datagrabber import DataType
 from ..pipeline import PipelineStepMixin, UpdateMetaMixin
+from ..storage import StorageType
 from ..typing import MarkerInOutMappings, StorageLike
 from ..utils import logger, raise_error
 
@@ -16,7 +20,7 @@ from ..utils import logger, raise_error
 __all__ = ["BaseMarker"]
 
 
-class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
+class BaseMarker(BaseModel, ABC, PipelineStepMixin, UpdateMetaMixin):
     """Abstract base class for marker.
 
     For every marker, one needs to provide a concrete
@@ -24,12 +28,17 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
 
     Parameters
     ----------
-    on : str or list of str or None, optional
-        The data type to apply the marker on. If None,
-        will work on all available data types (default None).
-    name : str, optional
-        The name of the marker. If None, will use the class name as the
-        name of the marker (default None).
+    on : :enum:`.DataType` or list of variants or None, optional
+        The data type(s) to apply the marker on.
+        If None, will work on all available data types.
+        Check :enum:`.DataType` for valid values (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
+
+    Attributes
+    ----------
+    valid_inputs
 
     Raises
     ------
@@ -42,11 +51,12 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
 
     _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings]
 
-    def __init__(
-        self,
-        on: list[str] | str | None = None,
-        name: str | None = None,
-    ) -> None:
+    model_config = ConfigDict(use_enum_values=True)
+
+    on: list[DataType] | None = None
+    name: str | None = None
+
+    def model_post_init(self, context: Any):  # noqa: D102
         # Check for missing mapping attribute
         if not hasattr(self, "_MARKER_INOUT_MAPPINGS"):
             raise_error(
@@ -54,18 +64,48 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
                 klass=AttributeError,
             )
         # Use all data types if not provided
-        if on is None:
-            on = self.get_valid_inputs()
-        # Convert data types to list
-        if not isinstance(on, list):
-            on = [on]
+        if self.on is None:
+            self.on = self.valid_inputs
+        else:
+            # Convert to correct data type
+            self.on = [
+                DataType(t) if isinstance(t, str) else t for t in self.on
+            ]
+            # Check if required input data types are provided
+            if any(x not in self.valid_inputs for x in self.on):
+                wrong_on = [
+                    x.value for x in self.on if x not in self.valid_inputs
+                ]
+                raise_error(
+                    f"{self.__class__.__name__} cannot be computed on "
+                    f"{wrong_on}"
+                )
+        # Run extra validation for markers and fail early if needed
+        self.validate_marker_params()
         # Set default name if not provided
-        self.name = self.__class__.__name__ if name is None else name
-        # Check if required inputs are found
-        if any(x not in self.get_valid_inputs() for x in on):
-            wrong_on = [x for x in on if x not in self.get_valid_inputs()]
-            raise_error(f"{self.name} cannot be computed on {wrong_on}")
-        self._on = on
+        self.name = self.__class__.__name__ if self.name is None else self.name
+
+    @property
+    def valid_inputs(self) -> list[DataType]:
+        """Valid data types to operate on.
+
+        Returns
+        -------
+        list of :enum:`.DataType`
+            The list of data types that can be used as input for this marker.
+
+        """
+        return [
+            DataType(x) if isinstance(x, str) else x
+            for x in self._MARKER_INOUT_MAPPINGS.keys()
+        ]
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker.
+
+        Subclasses can override to provide validation.
+        """
+        pass
 
     def validate_input(self, input: list[str]) -> list[str]:
         """Validate input.
@@ -88,38 +128,29 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
             If the input does not have the required data.
 
         """
-        if not any(x in input for x in self._on):
+        if not any(x in input for x in self.on):
             raise_error(
-                "Input does not have the required data."
-                f"\t Input: {input}"
-                f"\t Required (any of): {self._on}"
+                "Input does not have the required data.\n"
+                f"\t Input: {input}\n"
+                f"\t Required (any of): {[t.value for t in self.on]}"
             )
-        return [x for x in self._on if x in input]
+        return [x.value for x in self.on if x in input]
 
-    def get_valid_inputs(self) -> list[str]:
-        """Get valid data types for input.
-
-        Returns
-        -------
-        list of str
-            The list of data types that can be used as input for this marker.
-
-        """
-        return list(self._MARKER_INOUT_MAPPINGS.keys())
-
-    def storage_type(self, input_type: str, output_feature: str) -> str:
-        """Get storage type for a feature.
+    def storage_type(
+        self, input_type: DataType, output_feature: str
+    ) -> StorageType:
+        """Get :enum:`.StorageType` for a feature.
 
         Parameters
         ----------
-        input_type : str
+        input_type : :enum:`.DataType`
             The data type input to the marker.
         output_feature : str
             The feature output of the marker.
 
         Returns
         -------
-        str
+        :enum:`.StorageType`
             The storage type output of the marker.
 
         """
@@ -155,28 +186,28 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
 
     def store(
         self,
-        type_: str,
+        data_type: DataType,
         feature: str,
-        out: dict[str, Any],
+        output: dict[str, Any],
         storage: StorageLike,
     ) -> None:
         """Store.
 
         Parameters
         ----------
-        type_ : str
+        data_type : :enum:`.DataType`
             The data type to store.
         feature : str
             The feature to store.
-        out : dict
+        output : dict
             The computed result as a dictionary to store.
         storage : storage-like
             The storage class, for example, SQLiteFeatureStorage.
 
         """
-        output_type_ = self.storage_type(type_, feature)
-        logger.debug(f"Storing {output_type_} in {storage}")
-        storage.store(kind=output_type_, **out)
+        s_type = self.storage_type(data_type, feature)
+        logger.debug(f"Storing {s_type} in {storage}")
+        storage.store(kind=s_type, **output)
 
     def _fit_transform(
         self,
@@ -195,58 +226,56 @@ class BaseMarker(ABC, PipelineStepMixin, UpdateMetaMixin):
         Returns
         -------
         dict
-            The processed output as a dictionary. If `storage` is provided,
+            The processed output as a dictionary. If ``storage`` is provided,
             empty dictionary is returned.
 
         """
         out = {}
-        for type_ in self._on:
-            if type_ in input.keys():
-                logger.info(f"Computing {type_}")
+        for t in self.on:
+            if t in input.keys():
+                logger.info(f"Computing {t}")
                 # Get data dict for data type
-                t_input = input[type_]
+                t_input = input[t]
                 # Pass the other data types as extra input, removing
                 # the current type
                 extra_input = input.copy()
-                extra_input.pop(type_)
+                extra_input.pop(t)
                 logger.debug(
                     f"Extra data type for feature extraction: "
                     f"{extra_input.keys()}"
                 )
                 # Copy metadata
                 t_meta = t_input["meta"].copy()
-                t_meta["type"] = type_
+                t_meta["type"] = t.value
                 # Compute marker
                 t_out = self.compute(input=t_input, extra_input=extra_input)
                 # Initialize empty dictionary if no storage object is provided
                 if storage is None:
-                    out[type_] = {}
+                    out[t] = {}
                 # Store individual features
-                for feature_name, feature_data in t_out.items():
+                for f_name, f_data in t_out.items():
                     # Make deep copy of the feature data for manipulation
-                    feature_data_copy = deepcopy(feature_data)
+                    f_data_copy = deepcopy(f_data)
                     # Make deep copy of metadata and add to feature data
-                    feature_data_copy["meta"] = deepcopy(t_meta)
+                    f_data_copy["meta"] = deepcopy(t_meta)
                     # Update metadata for the feature,
                     # feature data is not manipulated, only meta
-                    self.update_meta(feature_data_copy, "marker")
+                    self.update_meta(f_data_copy, "marker")
                     # Update marker feature's metadata name
-                    feature_data_copy["meta"]["marker"]["name"] += (
-                        f"_{feature_name}"
-                    )
+                    f_data_copy["meta"]["marker"]["name"] += f"_{f_name}"
 
                     if storage is not None:
                         logger.info(f"Storing in {storage}")
                         self.store(
-                            type_=type_,
-                            feature=feature_name,
-                            out=feature_data_copy,
+                            data_type=t,
+                            feature=f_name,
+                            output=f_data_copy,
                             storage=storage,
                         )
                     else:
                         logger.info(
                             "No storage specified, returning dictionary"
                         )
-                        out[type_][feature_name] = feature_data_copy
+                        out[t][f_name] = f_data_copy
 
         return out

junifer/markers/brainprint.py

@@ -12,14 +12,17 @@ from typing import (
 
 import numpy as np
 import numpy.typing as npt
+from pydantic import PositiveInt
 
 from ..api.decorators import register_marker
+from ..datagrabber import DataType
 from ..external.BrainPrint.brainprint.brainprint import (
     compute_asymmetry,
     compute_brainprint,
 )
 from ..external.BrainPrint.brainprint.surfaces import surf_to_vtk
-from ..pipeline import WorkDirManager
+from ..pipeline import ExtDep, WorkDirManager
+from ..storage import StorageType
 from ..typing import Dependencies, ExternalDependencies, MarkerInOutMappings
 from ..utils import logger, run_ext_cmd
 from .base import BaseMarker
@@ -58,15 +61,15 @@ class BrainPrint(BaseMarker):
         execution speed. Requires the ``scikit-sparse`` library. If it cannot
         be found, an error will be thrown. If False, will use slower LU
         decomposition (default False).
-    name : str, optional
-        The name of the marker. If None, will use the class name (default
-        None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     """
 
     _EXT_DEPENDENCIES: ClassVar[ExternalDependencies] = [
         {
-            "name": "freesurfer",
+            "name": ExtDep.FreeSurfer,
             "commands": [
                 "mri_binarize",
                 "mri_pretess",
@@ -79,35 +82,25 @@ class BrainPrint(BaseMarker):
     _DEPENDENCIES: ClassVar[Dependencies] = {"lapy", "numpy"}
 
     _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings] = {
-        "FreeSurfer": {
-            "eigenvalues": "scalar_table",
-            "areas": "vector",
-            "volumes": "vector",
-            "distances": "vector",
+        DataType.FreeSurfer: {
+            "eigenvalues": StorageType.ScalarTable,
+            "areas": StorageType.Vector,
+            "volumes": StorageType.Vector,
+            "distances": StorageType.Vector,
         }
     }
 
-    def __init__(
-        self,
-        num: int = 50,
-        skip_cortex=False,
-        keep_eigenvectors: bool = False,
-        norm: str = "none",
-        reweight: bool = False,
-        asymmetry: bool = False,
-        asymmetry_distance: str = "euc",
-        use_cholmod: bool = False,
-        name: str | None = None,
-    ) -> None:
-        self.num = num
-        self.skip_cortex = skip_cortex
-        self.keep_eigenvectors = keep_eigenvectors
-        self.norm = norm
-        self.reweight = reweight
-        self.asymmetry = asymmetry
-        self.asymmetry_distance = asymmetry_distance
-        self.use_cholmod = use_cholmod
-        super().__init__(name=name, on="FreeSurfer")
+    num: PositiveInt = 50
+    skip_cortex: bool = False
+    keep_eigenvectors: bool = False
+    norm: str = "none"
+    reweight: bool = False
+    asymmetry: bool = False
+    asymmetry_distance: str = "euc"
+    use_cholmod: bool = False
+
+    _tempdir = Path()
+    _element_tempdir = Path()
 
     def _create_aseg_surface(
         self,
@@ -351,7 +344,7 @@ class BrainPrint(BaseMarker):
                 - ``col_names`` : surface labels as list of str
                 - ``row_names`` : eigenvalue count labels as list of str
                 - ``row_header_col_name`` : "eigenvalue"
-                                ()
+
             * ``areas`` : dictionary with the following keys:
 
                 - ``data`` : areas as ``np.ndarray``
@@ -362,7 +355,7 @@ class BrainPrint(BaseMarker):
                 - ``data`` : volumes as ``np.ndarray``
                 - ``col_names`` : surface labels as list of str
 
-            * ``distances`` : dictionary with the following keys
+            * ``distances`` : dictionary with the following keys \
                               if ``asymmetry = True``:
 
                 - ``data`` : distances as ``np.ndarray``

junifer/markers/complexity/complexity_base.py

@@ -1,17 +1,23 @@
 """Provide base class for complexity."""
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 from abc import abstractmethod
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
     ClassVar,
 )
 
+from pydantic import BeforeValidator
+
+from ...datagrabber import DataType
+from ...storage import StorageType
 from ...typing import Dependencies, MarkerInOutMappings
-from ...utils import raise_error
+from ...utils import ensure_list, ensure_list_or_none, raise_error
 from ..base import BaseMarker
 from ..parcel_aggregation import ParcelAggregation
 
@@ -24,50 +30,45 @@ __all__ = ["ComplexityBase"]
 
 
 class ComplexityBase(BaseMarker):
-    """Base class for complexity computation.
+    """Abstract base class for complexity computation.
 
     Parameters
     ----------
     parcellation : str or list of str
-        The name(s) of the parcellation(s). Check valid options by calling
-        :func:`junifer.data.parcellations.list_parcellations`.
+        The name(s) of the parcellation(s) to use.
+        See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
-        (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     """
 
     _DEPENDENCIES: ClassVar[Dependencies] = {"nilearn", "neurokit2"}
 
     _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings] = {
-        "BOLD": {
-            "complexity": "vector",
+        DataType.BOLD: {
+            "complexity": StorageType.Vector,
         },
     }
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        self.parcellation = parcellation
-        self.agg_method = agg_method
-        self.agg_method_params = agg_method_params
-        self.masks = masks
-        super().__init__(on="BOLD", name=name)
+    parcellation: Annotated[str | list[str], BeforeValidator(ensure_list)]
+    agg_method: str = "mean"
+    agg_method_params: dict | None = None
+    masks: Annotated[
+        dict | str | list[dict | str] | None,
+        BeforeValidator(ensure_list_or_none),
+    ] = None
 
     @abstractmethod
     def compute_complexity(
@@ -115,7 +116,7 @@ class ComplexityBase(BaseMarker):
             method=self.agg_method,
             method_params=self.agg_method_params,
             masks=self.masks,
-            on="BOLD",
+            on=DataType.BOLD,
         ).compute(input=input, extra_input=extra_input)
         # Compute complexity measure
         return {

junifer/markers/complexity/hurst_exponent.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,22 +26,23 @@ class HurstExponent(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the Hurst exponent calculation function. For more
-        information, check out ``junifer.markers.utils._hurst_exponent``.
-        If None, value is set to {"method": "dfa"} (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
-        (default None).
+    params : dict or None, optional
+        The parameters to pass to the Hurst exponent calculation function.
+        See ``junifer.markers.utils._hurst_exponent`` for more information.
+        If None, value is set to ``{"method": "dfa"}`` (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -51,26 +53,12 @@ class HurstExponent(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"method": "dfa"}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/multiscale_entropy_auc.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,23 +26,25 @@ class MultiscaleEntropyAUC(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the AUC of multiscale entropy calculation
-        function. For more information, check out
-        ``junifer.markers.utils._multiscale_entropy_auc``. If None, value
-        is set to {"m": 2, "tol": 0.5, "scale": 10} (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
+    params : dict or None, optional
+        The parameters to pass to the AUC of multiscale entropy calculation
+        function. See
+        ``junifer.markers.utils._multiscale_entropy_auc`` for more information.
+        If None, value is set to ``{"m": 2, "tol": 0.5, "scale": 10}``
         (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -52,26 +55,12 @@ class MultiscaleEntropyAUC(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 2, "tol": 0.5, "scale": 10}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/perm_entropy.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,23 +26,23 @@ class PermEntropy(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the permutation entropy calculation function.
-        For more information, check out
-        ``junifer.markers.utils._perm_entropy``. If None, value is set to
-        {"m": 2, "delay": 1} (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
-        (default None).
+    params : dict or None, optional
+        The parameters to pass to the permutation entropy calculation function.
+        See ``junifer.markers.utils._perm_entropy`` for more information.
+        If None, value is set to ``{"m": 2, "delay": 1}`` (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -52,26 +53,12 @@ class PermEntropy(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 4, "delay": 1}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/range_entropy.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,23 +26,24 @@ class RangeEntropy(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the range entropy calculation function. For more
-        information, check out ``junifer.markers.utils._range_entropy``.
-        If None, value is set to {"m": 2, "tol": 0.5, "delay": 1}
-        (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
+    params : dict or None, optional
+        The parameters to pass to the range entropy calculation function.
+        See ``junifer.markers.utils._range_entropy`` for more information.
+        If None, value is set to ``{"m": 2, "tol": 0.5, "delay": 1}``
         (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -52,26 +54,12 @@ class RangeEntropy(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 2, "tol": 0.5, "delay": 1}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/range_entropy_auc.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,23 +26,24 @@ class RangeEntropyAUC(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the range entropy calculation function. For more
-        information, check out ``junifer.markers.utils._range_entropy``.
-        If None, value is set to {"m": 2, "delay": 1, "n_r": 10}
-        (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
+    params : dict or None, optional
+        The parameters to pass to the range entropy calculation function.
+        See ``junifer.markers.utils._range_entropy`` for more information.
+        If None, value is set to ``{"m": 2, "delay": 1, "n_r": 10}``
         (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -52,26 +54,12 @@ class RangeEntropyAUC(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 2, "delay": 1, "n_r": 10}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/sample_entropy.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,24 +26,24 @@ class SampleEntropy(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the sample entropy calculation function.
-        For more information, check out
-        ``junifer.markers.utils._sample_entropy``.
-        If None, value is set to
-        {"m": 2, "delay": 1, "tol": 0.5} (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
+    params : dict or None, optional
+        The parameters to pass to the sample entropy calculation function.
+        See ``junifer.markers.utils._sample_entropy`` for more information.
+        If None, value is set to ``{"m": 2, "delay": 1, "tol": 0.5}``
         (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -53,26 +54,12 @@ class SampleEntropy(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 4, "delay": 1, "tol": 0.5}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/complexity/tests/test_hurst_exponent.py

@@ -12,6 +12,7 @@ import pytest
 pytest.importorskip("neurokit2")
 
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import HurstExponent
 from junifer.pipeline.utils import _check_ants
@@ -46,7 +47,7 @@ def test_compute() -> None:
 def test_storage_type() -> None:
     """Test HurstExponent storage_type."""
     assert "vector" == HurstExponent(parcellation=PARCELLATION).storage_type(
-        input_type="BOLD", output_feature="complexity"
+        input_type=DataType.BOLD, output_feature="complexity"
     )
 
 

junifer/markers/complexity/tests/test_multiscale_entropy_auc.py

@@ -11,6 +11,7 @@ import pytest
 
 pytest.importorskip("neurokit2")
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import MultiscaleEntropyAUC
 from junifer.pipeline.utils import _check_ants
@@ -46,7 +47,7 @@ def test_storage_type() -> None:
     """Test MultiscaleEntropyAUC storage_type."""
     assert "vector" == MultiscaleEntropyAUC(
         parcellation=PARCELLATION
-    ).storage_type(input_type="BOLD", output_feature="complexity")
+    ).storage_type(input_type=DataType.BOLD, output_feature="complexity")
 
 
 @pytest.mark.skipif(

junifer/markers/complexity/tests/test_perm_entropy.py

@@ -11,6 +11,7 @@ import pytest
 
 pytest.importorskip("neurokit2")
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import PermEntropy
 from junifer.pipeline.utils import _check_ants
@@ -45,7 +46,7 @@ def test_compute() -> None:
 def test_storage_type() -> None:
     """Test PermEntropy storage_type."""
     assert "vector" == PermEntropy(parcellation=PARCELLATION).storage_type(
-        input_type="BOLD", output_feature="complexity"
+        input_type=DataType.BOLD, output_feature="complexity"
     )
 
 

junifer/markers/complexity/tests/test_range_entropy.py

@@ -12,6 +12,7 @@ import pytest
 pytest.importorskip("neurokit2")
 
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import RangeEntropy
 from junifer.pipeline.utils import _check_ants
@@ -46,7 +47,7 @@ def test_compute() -> None:
 def test_storage_type() -> None:
     """Test RangeEntropy storage_type."""
     assert "vector" == RangeEntropy(parcellation=PARCELLATION).storage_type(
-        input_type="BOLD", output_feature="complexity"
+        input_type=DataType.BOLD, output_feature="complexity"
     )
 
 

junifer/markers/complexity/tests/test_range_entropy_auc.py

@@ -12,6 +12,7 @@ import pytest
 pytest.importorskip("neurokit2")
 
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import RangeEntropyAUC
 from junifer.pipeline.utils import _check_ants
@@ -46,7 +47,7 @@ def test_compute() -> None:
 def test_storage_type() -> None:
     """Test RangeEntropyAUC storage_type."""
     assert "vector" == RangeEntropyAUC(parcellation=PARCELLATION).storage_type(
-        input_type="BOLD", output_feature="complexity"
+        input_type=DataType.BOLD, output_feature="complexity"
     )
 
 

junifer/markers/complexity/tests/test_sample_entropy.py

@@ -11,6 +11,7 @@ import pytest
 
 pytest.importorskip("neurokit2")
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import SampleEntropy
 from junifer.pipeline.utils import _check_ants
@@ -45,7 +46,7 @@ def test_compute() -> None:
 def test_storage_type() -> None:
     """Test SampleEntropy storage_type."""
     assert "vector" == SampleEntropy(parcellation=PARCELLATION).storage_type(
-        input_type="BOLD", output_feature="complexity"
+        input_type=DataType.BOLD, output_feature="complexity"
     )
 
 

junifer/markers/complexity/tests/test_weighted_perm_entropy.py

@@ -11,6 +11,7 @@ import pytest
 
 pytest.importorskip("neurokit2")
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
 from junifer.markers.complexity import WeightedPermEntropy
 from junifer.pipeline.utils import _check_ants
@@ -46,7 +47,7 @@ def test_storage_type() -> None:
     """Test WeightedPermEntropy storage_type."""
     assert "vector" == WeightedPermEntropy(
         parcellation=PARCELLATION
-    ).storage_type(input_type="BOLD", output_feature="complexity")
+    ).storage_type(input_type=DataType.BOLD, output_feature="complexity")
 
 
 @pytest.mark.skipif(

junifer/markers/complexity/weighted_perm_entropy.py

@@ -2,6 +2,7 @@
 
 # Authors: Amir Omidvarnia <a.omidvarnia@fz-juelich.de>
 #          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
 import neurokit2 as nk
@@ -25,24 +26,24 @@ class WeightedPermEntropy(ComplexityBase):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    params : dict, optional
-        Parameters to pass to the weighted permutation entropy calculation
-        function.
-        For more information, check out
-        ``junifer.markers.utils._weighted_perm_entropy``. If None, value
-        is set to {"m": 2, "delay": 1} (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
+    params : dict or None, optional
+        The parameters to pass to the weighted permutation entropy calculation
+        function. See ``junifer.markers.utils._weighted_perm_entropy`` for more
+        information. If None, value is set to ``{"m": 2, "delay": 1}``
         (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Warnings
     --------
@@ -53,26 +54,12 @@ class WeightedPermEntropy(ComplexityBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        params: dict | None = None,
-        name: str | None = None,
-    ) -> None:
-        super().__init__(
-            parcellation=parcellation,
-            agg_method=agg_method,
-            agg_method_params=agg_method_params,
-            masks=masks,
-            name=name,
-        )
-        if params is None:
+    params: dict | None = None
+
+    def validate_marker_params(self) -> None:
+        """Run extra logical validation for marker."""
+        if self.params is None:
             self.params = {"m": 4, "delay": 1}
-        else:
-            self.params = params
 
     def compute_complexity(
         self,

junifer/markers/ets_rss.py

@@ -6,13 +6,16 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from typing import Any, ClassVar
+from typing import Annotated, Any, ClassVar
 
 import numpy as np
+from pydantic import BeforeValidator
 
 from ..api.decorators import register_marker
+from ..datagrabber import DataType
+from ..storage import StorageType
 from ..typing import Dependencies, MarkerInOutMappings
-from ..utils import logger
+from ..utils import ensure_list, ensure_list_or_none, logger
 from .base import BaseMarker
 from .parcel_aggregation import ParcelAggregation
 from .utils import _ets
@@ -31,42 +34,37 @@ class RSSETSMarker(BaseMarker):
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    name : str, optional
-        The name of the marker. If None, will use the class name (default
-        None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     """
 
     _DEPENDENCIES: ClassVar[Dependencies] = {"nilearn"}
 
     _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings] = {
-        "BOLD": {
-            "rss_ets": "timeseries",
+        DataType.BOLD: {
+            "rss_ets": StorageType.Timeseries,
         },
     }
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        self.parcellation = parcellation
-        self.agg_method = agg_method
-        self.agg_method_params = agg_method_params
-        self.masks = masks
-        super().__init__(name=name)
+    parcellation: Annotated[str | list[str], BeforeValidator(ensure_list)]
+    agg_method: str = "mean"
+    agg_method_params: dict | None = None
+    masks: Annotated[
+        dict | str | list[dict | str] | None,
+        BeforeValidator(ensure_list_or_none),
+    ] = None
 
     def compute(
         self,
@@ -110,6 +108,7 @@ class RSSETSMarker(BaseMarker):
             parcellation=self.parcellation,
             method=self.agg_method,
             method_params=self.agg_method_params,
+            on=DataType.BOLD,
             masks=self.masks,
         ).compute(input=input, extra_input=extra_input)
         # Compute edgewise timeseries

junifer/markers/falff/__init__.pyi

@@ -1,5 +1,11 @@
-__all__ = ["ALFFMaps", "ALFFParcels", "ALFFSpheres"]
+__all__ = [
+    "ALFFImpl",
+    "ALFFMaps",
+    "ALFFParcels",
+    "ALFFSpheres",
+]
 
+from .falff_base import ALFFImpl
 from .falff_maps import ALFFMaps
 from .falff_parcels import ALFFParcels
 from .falff_spheres import ALFFSpheres

junifer/markers/falff/_afni_falff.py

@@ -3,6 +3,7 @@
 # Authors: Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+import atexit
 from functools import lru_cache
 from pathlib import Path
 from typing import (
@@ -12,7 +13,7 @@ from typing import (
 
 import nibabel as nib
 
-from ...pipeline import WorkDirManager
+from ...pipeline import ExtDep, WorkDirManager
 from ...typing import ExternalDependencies
 from ...utils import logger, run_ext_cmd
 from ...utils.singleton import Singleton
@@ -35,13 +36,15 @@ class AFNIALFF(metaclass=Singleton):
 
     _EXT_DEPENDENCIES: ClassVar[ExternalDependencies] = [
         {
-            "name": "afni",
+            "name": ExtDep.AFNI,
             "commands": ["3dRSFC", "3dAFNItoNIFTI"],
         },
     ]
 
-    def __del__(self) -> None:
-        """Terminate the class."""
+    def __init__(self) -> None:
+        atexit.register(self._del)
+
+    def _del(self) -> None:
         # Clear the computation cache
         logger.debug("Clearing cache for ALFF computation via AFNI")
         self.compute.cache_clear()

junifer/markers/falff/falff_base.py

@@ -6,15 +6,21 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from enum import Enum
 from pathlib import Path
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
     ClassVar,
 )
 
+from pydantic import BeforeValidator, PositiveFloat
+
+from ...datagrabber import DataType
+from ...storage import StorageType
 from ...typing import ConditionalDependencies, MarkerInOutMappings
-from ...utils.logging import logger, raise_error
+from ...utils import ensure_list_or_none, logger
 from ..base import BaseMarker
 from ._afni_falff import AFNIALFF
 from ._junifer_falff import JuniferALFF
@@ -24,7 +30,19 @@ if TYPE_CHECKING:
     from nibabel.nifti1 import Nifti1Image
 
 
-__all__ = ["ALFFBase"]
+__all__ = ["ALFFBase", "ALFFImpl"]
+
+
+class ALFFImpl(str, Enum):
+    """Accepted ALFF implementations.
+
+    * ``junifer`` : ``junifer``'s ALFF
+    * ``afni`` : AFNI's ``3dRSFC``
+
+    """
+
+    junifer = "junifer"
+    afni = "afni"
 
 
 class ALFFBase(BaseMarker):
@@ -32,22 +50,28 @@ class ALFFBase(BaseMarker):
 
     Parameters
     ----------
-    highpass : positive float
-        Highpass cutoff frequency.
-    lowpass : positive float
-        Lowpass cutoff frequency.
-    using : {"junifer", "afni"}
-        Implementation to use for computing ALFF:
-
-        * "junifer" : Use ``junifer``'s own ALFF implementation
-        * "afni" : Use AFNI's ``3dRSFC``
-
+    using : :enum:`.ALFFImpl`
+    highpass : positive float, optional
+        Highpass cutoff frequency (default 0.01).
+    lowpass : positive float, optional
+        Lowpass cutoff frequency (default 0.1).
     tr : positive float, optional
-        The Repetition Time of the BOLD data. If None, will extract
-        the TR from NIfTI header (default None).
-    name : str, optional
-        The name of the marker. If None, it will use the class name
-        (default None).
+        The repetition time of the BOLD data.
+        If None, will extract the TR from NIfTI header (default None).
+    agg_method : str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options
+        (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for options (default None).
+    masks : str, dict, list of them or None, optional
+        The specification of the masks to apply to regions before extracting
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
+        If None, will not apply any mask (default None).
+    name : str or None, optional
+        The name of the marker.
+        If None, it will use the class name (default None).
 
     Notes
     -----
@@ -57,59 +81,36 @@ class ALFFBase(BaseMarker):
     reported that some preprocessed data might not have the correct ``tr`` in
     the NIfTI header.
 
-    Raises
-    ------
-    ValueError
-        If ``highpass`` is not positive or zero or
-        if ``lowpass`` is not positive or
-        if ``highpass`` is higher than ``lowpass`` or
-        if ``using`` is invalid.
-
     """
 
     _CONDITIONAL_DEPENDENCIES: ClassVar[ConditionalDependencies] = [
         {
-            "using": "afni",
-            "depends_on": AFNIALFF,
+            "using": ALFFImpl.afni,
+            "depends_on": [AFNIALFF],
         },
         {
-            "using": "junifer",
-            "depends_on": JuniferALFF,
+            "using": ALFFImpl.junifer,
+            "depends_on": [JuniferALFF],
         },
     ]
 
     _MARKER_INOUT_MAPPINGS: ClassVar[MarkerInOutMappings] = {
-        "BOLD": {
-            "alff": "vector",
-            "falff": "vector",
+        DataType.BOLD: {
+            "alff": StorageType.Vector,
+            "falff": StorageType.Vector,
         },
     }
 
-    def __init__(
-        self,
-        highpass: float,
-        lowpass: float,
-        using: str,
-        tr: float | None = None,
-        name: str | None = None,
-    ) -> None:
-        if highpass < 0:
-            raise_error("Highpass must be positive or 0")
-        if lowpass <= 0:
-            raise_error("Lowpass must be positive")
-        if highpass >= lowpass:
-            raise_error("Highpass must be lower than lowpass")
-        self.highpass = highpass
-        self.lowpass = lowpass
-        # Validate `using` parameter
-        valid_using = [dep["using"] for dep in self._CONDITIONAL_DEPENDENCIES]
-        if using not in valid_using:
-            raise_error(
-                f"Invalid value for `using`, should be one of: {valid_using}"
-            )
-        self.using = using
-        self.tr = tr
-        super().__init__(on="BOLD", name=name)
+    using: ALFFImpl
+    highpass: PositiveFloat = 0.01
+    lowpass: PositiveFloat = 0.1
+    tr: PositiveFloat | None = None
+    agg_method: str = "mean"
+    agg_method_params: dict | None = None
+    masks: Annotated[
+        dict | str | list[dict | str] | None,
+        BeforeValidator(ensure_list_or_none),
+    ] = None
 
     def _compute(
         self,

junifer/markers/falff/falff_maps.py

@@ -6,6 +6,7 @@
 from typing import Any
 
 from ...api.decorators import register_marker
+from ...datagrabber import DataType
 from ...utils import logger
 from ..maps_aggregation import MapsAggregation
 from .falff_base import ALFFBase
@@ -23,27 +24,21 @@ class ALFFMaps(ALFFBase):
     maps : str
         The name of the map(s) to use.
         See :func:`.list_data` for options.
-    using : {"junifer", "afni"}
-        Implementation to use for computing ALFF:
-
-        * "junifer" : Use ``junifer``'s own ALFF implementation
-        * "afni" : Use AFNI's ``3dRSFC``
-
+    using : :enum:`.ALFFImpl`
     highpass : positive float, optional
-        The highpass cutoff frequency for the bandpass filter. If 0,
-        it will not apply a highpass filter (default 0.01).
+        Highpass cutoff frequency (default 0.01).
     lowpass : positive float, optional
-        The lowpass cutoff frequency for the bandpass filter (default 0.1).
+        Lowpass cutoff frequency (default 0.1).
     tr : positive float, optional
-        The Repetition Time of the BOLD data. If None, will extract
-        the TR from NIfTI header (default None).
-    masks : str, dict or list of dict or str, optional
+        The repetition time of the BOLD data.
+        If None, will extract the TR from NIfTI header (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    name : str, optional
-        The name of the marker. If None, will use the class name (default
-        None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Notes
     -----
@@ -59,26 +54,7 @@ class ALFFMaps(ALFFBase):
 
     """
 
-    def __init__(
-        self,
-        maps: str,
-        using: str,
-        highpass: float = 0.01,
-        lowpass: float = 0.1,
-        tr: float | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        # Superclass init first to validate `using` parameter
-        super().__init__(
-            highpass=highpass,
-            lowpass=lowpass,
-            using=using,
-            tr=tr,
-            name=name,
-        )
-        self.maps = maps
-        self.masks = masks
+    maps: str
 
     def compute(
         self,
@@ -132,7 +108,7 @@ class ALFFMaps(ALFFBase):
                 **MapsAggregation(
                     maps=self.maps,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_alff_input,
                     extra_input=extra_input,
@@ -142,7 +118,7 @@ class ALFFMaps(ALFFBase):
                 **MapsAggregation(
                     maps=self.maps,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_falff_input,
                     extra_input=extra_input,

junifer/markers/falff/falff_parcels.py

@@ -6,10 +6,13 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from typing import Any
+from typing import Annotated, Any
+
+from pydantic import BeforeValidator
 
 from ...api.decorators import register_marker
-from ...utils import logger
+from ...datagrabber import DataType
+from ...utils import ensure_list, logger
 from ..parcel_aggregation import ParcelAggregation
 from .falff_base import ALFFBase
 
@@ -26,33 +29,27 @@ class ALFFParcels(ALFFBase):
     parcellation : str or list of str
         The name(s) of the parcellation(s) to use.
         See :func:`.list_data` for options.
-    using : {"junifer", "afni"}
-        Implementation to use for computing ALFF:
-
-        * "junifer" : Use ``junifer``'s own ALFF implementation
-        * "afni" : Use AFNI's ``3dRSFC``
-
-    highpass : positive float, optional
-        The highpass cutoff frequency for the bandpass filter. If 0,
-        it will not apply a highpass filter (default 0.01).
-    lowpass : positive float, optional
-        The lowpass cutoff frequency for the bandpass filter (default 0.1).
-    tr : positive float, optional
-        The Repetition Time of the BOLD data. If None, will extract
-        the TR from NIfTI header (default None).
+    using : :enum:`.ALFFImpl`
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`.get_aggfunc_by_name` (default None).
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for valid options (default None).
+    highpass : positive float, optional
+        Highpass cutoff frequency (default 0.01).
+    lowpass : positive float, optional
+        Lowpass cutoff frequency (default 0.1).
+    tr : positive float, optional
+        The repetition time of the BOLD data.
+        If None, will extract the TR from NIfTI header (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    name : str, optional
-        The name of the marker. If None, will use the class name (default
-        None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Notes
     -----
@@ -68,30 +65,7 @@ class ALFFParcels(ALFFBase):
 
     """
 
-    def __init__(
-        self,
-        parcellation: str | list[str],
-        using: str,
-        highpass: float = 0.01,
-        lowpass: float = 0.1,
-        tr: float | None = None,
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        # Superclass init first to validate `using` parameter
-        super().__init__(
-            highpass=highpass,
-            lowpass=lowpass,
-            using=using,
-            tr=tr,
-            name=name,
-        )
-        self.parcellation = parcellation
-        self.agg_method = agg_method
-        self.agg_method_params = agg_method_params
-        self.masks = masks
+    parcellation: Annotated[str | list[str], BeforeValidator(ensure_list)]
 
     def compute(
         self,
@@ -147,7 +121,7 @@ class ALFFParcels(ALFFBase):
                     method=self.agg_method,
                     method_params=self.agg_method_params,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_alff_input,
                     extra_input=extra_input,
@@ -159,7 +133,7 @@ class ALFFParcels(ALFFBase):
                     method=self.agg_method,
                     method_params=self.agg_method_params,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_falff_input,
                     extra_input=extra_input,

junifer/markers/falff/falff_spheres.py

@@ -6,9 +6,12 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from typing import Any
+from typing import Any, Literal
+
+from pydantic import PositiveFloat
 
 from ...api.decorators import register_marker
+from ...datagrabber import DataType
 from ...utils import logger
 from ..sphere_aggregation import SphereAggregation
 from .falff_base import ALFFBase
@@ -26,40 +29,35 @@ class ALFFSpheres(ALFFBase):
     coords : str
         The name of the coordinates list to use.
         See :func:`.list_data` for options.
-    using : {"junifer", "afni"}
-        Implementation to use for computing ALFF:
-
-        * "junifer" : Use ``junifer``'s own ALFF implementation
-        * "afni" : Use AFNI's ``3dRSFC``
-
-    radius : float, optional
-        The radius of the sphere in mm. If None, the signal will be extracted
-        from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
-        for more information (default None).
+    using : :enum:`.ALFFImpl`
+    radius : ``zero`` or positive float or None, optional
+        The radius of the sphere in millimetres.
+        If None, the signal will be extracted from a single voxel.
+        See :class:`.JuniferNiftiSpheresMasker` for more information
+        (default None).
     allow_overlap : bool, optional
-        Whether to allow overlapping spheres. If False, an error is raised if
-        the spheres overlap (default is False).
-    highpass : positive float, optional
-        The highpass cutoff frequency for the bandpass filter. If 0,
-        it will not apply a highpass filter (default 0.01).
-    lowpass : positive float, optional
-        The lowpass cutoff frequency for the bandpass filter (default 0.1).
-    tr : positive float, optional
-        The Repetition Time of the BOLD data. If None, will extract
-        the TR from NIfTI header (default None).
+        Whether to allow overlapping spheres.
+        If False, an error is raised if the spheres overlap (default False).
     agg_method : str, optional
-        The method to perform aggregation using. Check valid options in
-        :func:`.get_aggfunc_by_name` (default "mean").
-    agg_method_params : dict, optional
-        Parameters to pass to the aggregation function. Check valid options in
-        :func:`.get_aggfunc_by_name`.
-    masks : str, dict or list of dict or str, optional
+        The aggregation function to use.
+        See :func:`.get_aggfunc_by_name` for options (default "mean").
+    agg_method_params : dict or None, optional
+        The parameters to pass to the aggregation function.
+        See :func:`.get_aggfunc_by_name` for valid options (default None).
+    highpass : positive float, optional
+        Highpass cutoff frequency (default 0.01).
+    lowpass : positive float, optional
+        Lowpass cutoff frequency (default 0.1).
+    tr : positive float, optional
+        The repetition time of the BOLD data.
+        If None, will extract the TR from NIfTI header (default None).
+    masks : str, dict, list of them or None, optional
         The specification of the masks to apply to regions before extracting
         signals. Check :ref:`Using Masks <using_masks>` for more details.
         If None, will not apply any mask (default None).
-    name : str, optional
-        The name of the marker. If None, will use the class name (default
-        None).
+    name : str or None, optional
+        The name of the marker.
+        If None, will use the class name (default None).
 
     Notes
     -----
@@ -75,34 +73,9 @@ class ALFFSpheres(ALFFBase):
 
     """
 
-    def __init__(
-        self,
-        coords: str,
-        using: str,
-        radius: float | None = None,
-        allow_overlap: bool = False,
-        highpass: float = 0.01,
-        lowpass: float = 0.1,
-        tr: float | None = None,
-        agg_method: str = "mean",
-        agg_method_params: dict | None = None,
-        masks: str | dict | list[dict | str] | None = None,
-        name: str | None = None,
-    ) -> None:
-        # Superclass init first to validate `using` parameter
-        super().__init__(
-            highpass=highpass,
-            lowpass=lowpass,
-            using=using,
-            tr=tr,
-            name=name,
-        )
-        self.coords = coords
-        self.radius = radius
-        self.allow_overlap = allow_overlap
-        self.agg_method = agg_method
-        self.agg_method_params = agg_method_params
-        self.masks = masks
+    coords: str
+    radius: Literal[0] | PositiveFloat | None = None
+    allow_overlap: bool = False
 
     def compute(
         self,
@@ -160,7 +133,7 @@ class ALFFSpheres(ALFFBase):
                     method=self.agg_method,
                     method_params=self.agg_method_params,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_alff_input,
                     extra_input=extra_input,
@@ -174,7 +147,7 @@ class ALFFSpheres(ALFFBase):
                     method=self.agg_method,
                     method_params=self.agg_method_params,
                     masks=self.masks,
-                    on="BOLD",
+                    on=DataType.BOLD,
                 ).compute(
                     input=aggregation_falff_input,
                     extra_input=extra_input,

junifer/markers/falff/tests/test_falff_maps.py

@@ -7,11 +7,11 @@ import logging
 from pathlib import Path
 
 import pytest
-import scipy as sp
+import scipy.stats as sps
 
-from junifer.datagrabber import PatternDataladDataGrabber
+from junifer.datagrabber import DataType, PatternDataladDataGrabber
 from junifer.datareader import DefaultDataReader
-from junifer.markers import ALFFMaps
+from junifer.markers import ALFFImpl, ALFFMaps
 from junifer.pipeline import WorkDirManager
 from junifer.pipeline.utils import _check_afni
 from junifer.storage import HDF5FeatureStorage
@@ -38,8 +38,8 @@ def test_ALFFMaps_storage_type(feature: str) -> None:
     """
     assert "vector" == ALFFMaps(
         maps=MAPS,
-        using="junifer",
-    ).storage_type(input_type="BOLD", output_feature=feature)
+        using=ALFFImpl.junifer,
+    ).storage_type(input_type=DataType.BOLD, output_feature=feature)
 
 
 def test_ALFFMaps(
@@ -70,12 +70,12 @@ def test_ALFFMaps(
             # Initialize marker
             marker = ALFFMaps(
                 maps=MAPS,
-                using="junifer",
+                using=ALFFImpl.junifer,
             )
             # Check correct output
             for name in ["alff", "falff"]:
                 assert "vector" == marker.storage_type(
-                    input_type="BOLD", output_feature=name
+                    input_type=DataType.BOLD, output_feature=name
                 )
 
             # Fit transform marker on data
@@ -99,7 +99,7 @@ def test_ALFFMaps(
             # Reset log capture
             caplog.clear()
             # Initialize storage
-            storage = HDF5FeatureStorage(tmp_path / "falff_maps.hdf5")
+            storage = HDF5FeatureStorage(uri=tmp_path / "falff_maps.hdf5")
             # Fit transform marker on data with storage
             marker.fit_transform(
                 input=element_data,
@@ -141,7 +141,7 @@ def test_ALFFMaps_comparison(
         # Initialize marker
         junifer_marker = ALFFMaps(
             maps=MAPS,
-            using="junifer",
+            using=ALFFImpl.junifer,
         )
         # Fit transform marker on data
         junifer_output = junifer_marker.fit_transform(element_data)
@@ -151,7 +151,7 @@ def test_ALFFMaps_comparison(
         # Initialize marker
         afni_marker = ALFFMaps(
             maps=MAPS,
-            using="afni",
+            using=ALFFImpl.afni,
         )
         # Fit transform marker on data
         afni_output = afni_marker.fit_transform(element_data)
@@ -160,7 +160,7 @@ def test_ALFFMaps_comparison(
 
         for feature in afni_output_bold.keys():
             # Check for Pearson correlation coefficient
-            r, _ = sp.stats.pearsonr(
+            r, _ = sps.pearsonr(
                 junifer_output_bold[feature]["data"][0],
                 afni_output_bold[feature]["data"][0],
             )

junifer/markers/falff/tests/test_falff_parcels.py

@@ -8,10 +8,11 @@ import logging
 from pathlib import Path
 
 import pytest
-import scipy as sp
+import scipy.stats as sps
 
+from junifer.datagrabber import DataType
 from junifer.datareader import DefaultDataReader
-from junifer.markers.falff import ALFFParcels
+from junifer.markers import ALFFImpl, ALFFParcels
 from junifer.pipeline import WorkDirManager
 from junifer.pipeline.utils import _check_afni
 from junifer.storage import SQLiteFeatureStorage
@@ -39,8 +40,8 @@ def test_ALFFParcels_storage_type(feature: str) -> None:
     """
     assert "vector" == ALFFParcels(
         parcellation=PARCELLATION,
-        using="junifer",
-    ).storage_type(input_type="BOLD", output_feature=feature)
+        using=ALFFImpl.junifer,
+    ).storage_type(input_type=DataType.BOLD, output_feature=feature)
 
 
 def test_ALFFParcels(caplog: pytest.LogCaptureFixture, tmp_path: Path) -> None:
@@ -63,7 +64,7 @@ def test_ALFFParcels(caplog: pytest.LogCaptureFixture, tmp_path: Path) -> None:
             # Initialize marker
             marker = ALFFParcels(
                 parcellation=PARCELLATION,
-                using="junifer",
+                using=ALFFImpl.junifer,
             )
             # Fit transform marker on data
             output = marker.fit_transform(element_data)
@@ -86,7 +87,9 @@ def test_ALFFParcels(caplog: pytest.LogCaptureFixture, tmp_path: Path) -> None:
             # Reset log capture
             caplog.clear()
             # Initialize storage
-            storage = SQLiteFeatureStorage(tmp_path / "falff_parcels.sqlite")
+            storage = SQLiteFeatureStorage(
+                uri=tmp_path / "falff_parcels.sqlite"
+            )
             # Fit transform marker on data with storage
             marker.fit_transform(
                 input=element_data,
@@ -116,7 +119,7 @@ def test_ALFFParcels_comparison(tmp_path: Path) -> None:
         # Initialize marker
         junifer_marker = ALFFParcels(
             parcellation=PARCELLATION,
-            using="junifer",
+            using=ALFFImpl.junifer,
         )
         # Fit transform marker on data
         junifer_output = junifer_marker.fit_transform(element_data)
@@ -126,7 +129,7 @@ def test_ALFFParcels_comparison(tmp_path: Path) -> None:
         # Initialize marker
         afni_marker = ALFFParcels(
             parcellation=PARCELLATION,
-            using="afni",
+            using=ALFFImpl.afni,
         )
         # Fit transform marker on data
         afni_output = afni_marker.fit_transform(element_data)
@@ -135,7 +138,7 @@ def test_ALFFParcels_comparison(tmp_path: Path) -> None:
 
         for feature in afni_output_bold.keys():
             # Check for Pearson correlation coefficient
-            r, _ = sp.stats.pearsonr(
+            r, _ = sps.pearsonr(
                 junifer_output_bold[feature]["data"][0],
                 afni_output_bold[feature]["data"][0],
             )