docs/changes/latest.inc

@@ -75,6 +75,8 @@ Enhancements
 
 - Change documentation template to furo. Fix references and standardize documentation (:gh:`114` by `Fede Raimondo`_).
 
+- Add :class:`junifer.preprocess.fMRIPrepConfoundRemover` (:gh:`111` by `Fede Raimondo`_ and `Leonard Sasse`_).
+
 Bugs
 ~~~~
 

docs/links.inc

@@ -19,6 +19,8 @@
 .. _`Python`: https://python.org
 .. _`seaborn`: https://seaborn.pydata.org
 .. _`matplotlib`: https://matplotlib.org
+.. _`fMRIPrep`: https://fmriprep.org
+.. _`nilearn`: https://nilearn.github.io
 
 .. _`venv`: https://docs.python.org/3/tutorial/venv.html
 .. _`conda env`: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

docs/understanding/preprocess.rst

@@ -6,10 +6,111 @@ Preprocess
 ==========
 
 Description
-^^^^^^^^^^^
+-----------
 
 The ``Preprocess`` step of the pipeline is meant for pre-processing before or after :ref:`Marker <marker>` step
 depending on the use-case. For example, you might want to perform confound removal on ``BOLD`` data before
 feature extraction.
 
 This step is still under development and is an optional one for the pipeline to work.
+
+.. _preprocess_confounds:
+
+Confound Removal
+----------------
+
+The *Confound Removal* step is meant to remove *confounds* from the ``BOLD`` data. The confounds are
+extracted from the ``BOLD_confounds`` data (must be provided by the :ref:`DataGrabber <datagrabber>`).
+The confounds are then regressed out from the ``BOLD`` data using :func:`nilearn.image.clean_img`.
+
+Currently, junifer supports only one confound removal class: 
+:class:`junifer.preprocess.fMRIPrepConfoundRemover`. This class is meant to remove confounds as described
+before, using the output of `fMRIPrep`_ as reference.
+
+Strategy
+~~~~~~~~
+
+This confound remover uses the `nilearn`_ API from :func:`nilearn.interfaces.fmriprep.load_confounds`. That is,
+define a *strategy* to extract the confounds from the ``BOLD_confounds`` data. The *strategy* is defined
+by choosing the *noise components* to be used and the *confounds* to be extracted from each noise components.
+The *noise components* currently supported are:
+
+* ``motion``
+* ``wm_csf``
+* ``global_signal``
+
+The confounds options for each *noise component* are:
+
+* ``basic``: the basic confounds for each *noise component*. For example, for ``motion``, the basic confounds
+  are the 6 motion parameters (3 translations and 3 rotations). For ``wm_csf``, the basic
+  confounds are the mean signal of the white matter and CSF regions. For ``global_signal``, the basic confound
+  is the mean signal of the whole brain.
+* ``power2``: the basic confounds plus the square of each basic confound.
+* ``derivatives``: the basic confounds plus the derivative of each basic confound.
+* ``full``: the basic confounds, the derivative of each basic confound, the square of each basic confound and the
+  square of each derivative of each basic confound.
+
+The *strategy* is defined as a dictionary, with the *noise components* as keys and the *confounds* as values.
+
+Example in python format:
+
+.. code-block:: 
+
+    strategy = {
+        "motion": "basic",
+        "wm_csf": "full",
+        "global_signal": "derivatives"
+    }
+
+or in YAML format:
+
+.. code-block:: 
+    
+    strategy:
+        motion: basic
+        wm_csf: full
+        global_signal: derivatives
+
+The default value is to use all the *noise components* with the ``full`` *confounds*:
+
+.. code-block:: 
+
+    strategy = {
+        "motion": "full",
+        "wm_csf": "full",
+        "global_signal": "full"
+    }
+
+Other parameters
+~~~~~~~~~~~~~~~~
+
+Additionaly, the :class:`junifer.preprocess.fMRIPrepConfoundRemover` supports the following parameters:
+
+.. list-table::
+   :widths: 10, 30, 5
+   :header-rows: 1
+
+   * - Parameter
+     - Description
+     - Default
+   * - ``spike``
+     - Add a spike regressor in the timepoints when the framewise displacement exceeds this threshold.
+     - deactivated
+   * - ``detrend``
+     - Apply detrending on timeseries, before confound removal.
+     - activated
+   * - ``standardize``
+     - Scale signals to unit variance.
+     - activated
+   * - ``low_pass``
+     - Low cutoff frequencies, in Hertz.
+     - deactivated
+   * - ``high_pass``
+     - High cutoff frequencies, in Hertz.
+     - deactivated
+   * - ``t_r``
+     - Repetition time, in second (sampling period).
+     - from nifti header
+   * - ``mask``
+     - If provided, signal is only cleaned from voxels inside the mask. If not, a mask is computed using :func:`nilearn.masking.compute_brain_mask`.
+     - compute

junifer/api/decorators.py

@@ -32,6 +32,30 @@ def register_datagrabber(klass: type) -> type:
     return klass
 
 
+def register_preprocessor(klass: type) -> type:
+    """Preprocessor registration decorator.
+
+    Registers the preprocessor so it can be used by name.
+
+    Parameters
+    ----------
+    klass: class
+        The class of the preprocessor to register.
+
+    Returns
+    -------
+    klass: class
+        The unmodified input class.
+
+    """
+    register(
+        step="preprocessing",
+        name=klass.__name__,
+        klass=klass,
+    )
+    return klass
+
+
 def register_marker(klass: type) -> type:
     """Marker registration decorator.
 

junifer/preprocess/__init__.py

@@ -4,4 +4,5 @@
 #          Leonard Sasse <l.sasse@fz-juelich.de>
 # License: AGPL
 
-from .confounds import BaseConfoundRemover
+from .confounds import fMRIPrepConfoundRemover
+from .base import BasePreprocessor

junifer/preprocess/base.py

@@ -0,0 +1,175 @@
+"""Provide abstract base class for preprocessor."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Union, Tuple, Any
+
+
+from ..pipeline import PipelineStepMixin
+from ..utils import logger, raise_error
+
+
+class BasePreprocessor(ABC, PipelineStepMixin):
+    """Provide abstract base class for all preprocessors.
+
+    Parameters
+    ----------
+    on : str or list of str, optional
+        The kind of data to apply the preprocessor to. If None,
+        will work on all available data (default None).
+    """
+
+    def __init__(
+        self,
+        on: Optional[Union[List[str], str]] = None,
+    ) -> None:
+        if on is None:
+            on = self.get_valid_inputs()
+        if not isinstance(on, list):
+            on = [on]
+
+        if any(x not in self.get_valid_inputs() for x in on):
+            name = self.__class__.__name__
+            wrong_on = [x for x in on if x not in self.get_valid_inputs()]
+            raise ValueError(f"{name} cannot be computed on {wrong_on}")
+        self._on = on
+
+    def validate_input(self, input: List[str]) -> None:
+        """Validate input.
+
+        Parameters
+        ----------
+        input : list of str
+            The input to the pipeline step. The list must contain the
+            available Junifer Data dictionary keys.
+
+        Raises
+        ------
+        ValueError
+            If the input does not have the required data.
+        """
+        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}"
+            )
+
+    @abstractmethod
+    def get_output_kind(self, input: List[str]) -> List[str]:
+        """Get output kind.
+
+        Parameters
+        ----------
+        input : list of str
+            The input to the preprocessor. The list must contain the
+            available Junifer Data dictionary keys.
+
+        Returns
+        -------
+        list of str
+            The updated list of available Junifer Data object keys after
+            the pipeline step.
+
+        """
+        raise_error(
+            msg="Concrete classes need to implement get_output_kind().",
+            klass=NotImplementedError,
+        )
+
+    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
+            preprocessor.
+        """
+        raise_error(
+            msg="Concrete classes need to implement get_valid_inputs().",
+            klass=NotImplementedError,
+        )
+
+    def get_meta(self, kind: str) -> Dict:
+        """Get metadata.
+
+        Parameters
+        ----------
+        kind : str
+            The kind of pipeline step.
+
+        Returns
+        -------
+        dict
+            The metadata as a dictionary with the only key 'preprocess'.
+        """
+        s_meta = super().get_meta()
+        return {"preprocess": s_meta}
+
+    def fit_transform(
+        self,
+        input: Dict[str, Dict],
+    ) -> Dict:
+        """Fit and transform.
+
+        Parameters
+        ----------
+        input : dict
+            The Junifer Data object.
+
+        Returns
+        -------
+        dict
+            The processed output as a dictionary.
+
+        """
+        out = input
+        for kind in self._on:
+            if kind in input.keys():
+                logger.info(f"Computing {kind}")
+                t_input = input[kind]
+                extra_input = input.copy()
+                extra_input.pop(kind)
+                t_meta = t_input.get("meta", {})  # input kind meta
+                t_meta.update(self.get_meta(kind))
+                key, t_out = self.preprocess(
+                    input=t_input, extra_input=extra_input)
+                t_out.update(meta=t_meta)
+                out[key] = t_out
+        return out
+
+    @abstractmethod
+    def preprocess(
+        self,
+        input: Dict[str, Any],
+        extra_input: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Preprocess.
+
+        Parameters
+        ----------
+        input : dict[str, dict]
+            A single input from the Junifer Data object to preprocess.
+        extra_input : dict, optional
+            The other fields in the Junifer Data object. Useful for accessing
+            other data kind that needs to be used in the computation. For
+            example, the confound removers can make use of the
+            confounds if available (default None).
+
+        Returns
+        -------
+        key : str
+            The key to store the output in the Junifer Data object.
+        object : dict
+            The computed result as dictionary. This will be stored in the
+            Junifer Data object under the key 'key'.
+
+        """
+        raise_error(
+            msg="Concrete classes need to implement preprocess().",
+            klass=NotImplementedError,
+        )

junifer/preprocess/confounds.py

@@ -1,487 +0,0 @@
-"""Provide base class for confound removal."""
-
-# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
-#          Leonard Sasse <l.sasse@fz-juelich.de>
-#          Synchon Mandal <s.mandal@fz-juelich.de>
-# License: AGPL
-
-from typing import TYPE_CHECKING, Dict, List, Optional, Union
-
-import numpy as np
-import pandas as pd
-from nilearn._utils.niimg_conversions import check_niimg_4d
-from nilearn.image import clean_img
-from nilearn.masking import compute_brain_mask
-
-from ..pipeline import PipelineStepMixin
-from ..utils import logger, raise_error
-
-
-if TYPE_CHECKING:
-    from nibabel import MGHImage, Nifti1Image, Nifti2Image
-
-
-class BaseConfoundRemover(PipelineStepMixin):
-    """Base class for confound removal.
-
-    Read confound files and select columns according to
-    a pre-defined strategy.
-
-    Confound removal is based on `nilearn.image.clean_img`.
-
-    Parameters
-    ----------
-    strategy : dict, optional
-        The keys of the dictionary should correspond to names of noise
-        components to include:
-        - 'motion'
-        - 'wm_csf'
-        - 'global_signal'
-        The values of dictionary should correspond to types of confounds
-        extracted from each signal:
-        - 'basic': only the confounding time series
-        - 'power2': signal + quadratic term
-        - 'derivatives': signal + derivatives
-        - 'full': signal + deriv. + quadratic terms + power2 deriv.
-        (default None).
-    spike : float, optional
-        If None, no spike regressor is added. If spike is a float, it will
-        add a spike regressor for every point at which FD exceeds the
-        specified float (default None).
-    detrend : bool, Optional
-        If True, detrending will be applied on timeseries
-        (before confound removal) (default True).
-    standardize : bool, optional
-        If True, returned signals are set to unit variance (default True).
-    low_pass : float, optional
-        Low cutoff frequencies, in Hertz. If None, no filtering is applied
-        (default None).
-    high_pass : float, optional
-        High cutoff frequencies, in Hertz. If None, no filtering is
-        applied (default None).
-    t_r : float, optional
-        Repetition time, in second (sampling period).
-        If None, it will use t_r from nifti header (default None).
-    mask_img: Niimg-like object, optional
-        If provided, signal is only cleaned from voxels inside the mask.
-        If mask is provided, it should have same shape and affine as imgs.
-        If not provided, a mask is computed using
-        `nilearn.masking.compute_brain_mask` (default None).
-
-    """
-
-    # lower priority
-    # TODO: implement more strategies from
-    # nilearn.interfaces.fmriprep.load_confounds for Felix's confound files,
-    # in particular scrubbing
-    # TODO: Implement read_confounds for fmriprep data
-
-    def __init__(
-        self,
-        strategy: Optional[Dict[str, str]] = None,
-        spike: Optional[float] = None,
-        detrend: bool = True,
-        standardize: bool = True,
-        low_pass: Optional[float] = None,
-        high_pass: Optional[float] = None,
-        t_r: Optional[float] = None,
-        mask_img: Optional["Nifti1Image"] = None,
-    ) -> None:
-        """Initialise the class."""
-        if strategy is None:
-            strategy = {
-                "motion": "full",
-                "wm_csf": "full",
-                "global_signal": "full",
-            }
-        self.strategy = strategy
-        self.spike = spike
-        self.detrend = detrend
-        self.standardize = standardize
-        self.low_pass = low_pass
-        self.high_pass = high_pass
-        self.t_r = t_r
-        self.mask_img = mask_img
-
-        self._valid_components = ["motion", "wm_csf", "global_signal"]
-        self._valid_confounds = ["basic", "power2", "derivatives", "full"]
-
-        if any(not isinstance(k, str) for k in strategy.keys()):
-            raise_error("Strategy keys must be strings", ValueError)
-
-        if any(not isinstance(v, str) for v in strategy.values()):
-            raise_error("Strategy values must be strings", ValueError)
-
-        if any(x not in self._valid_components for x in strategy.keys()):
-            raise_error(
-                msg=f"Invalid component names {list(strategy.keys())}. "
-                f"Valid components are {self._valid_components}.\n"
-                f"If any of them is a valid parameter in "
-                "nilearn.interfaces.fmriprep.load_confounds we may "
-                "include it in the future",
-                klass=ValueError,
-            )
-
-        if any(x not in self._valid_confounds for x in strategy.values()):
-            raise_error(
-                msg=f"Invalid component names {list(strategy.values())}. "
-                f"Valid confound types are {self._valid_confounds}.\n"
-                f"If any of them is a valid parameter in "
-                "nilearn.interfaces.fmriprep.load_confounds we may "
-                "include it in the future",
-                klass=ValueError,
-            )
-
-    def validate_input(self, input: List[str]) -> None:
-        """Validate the input to the pipeline step.
-
-        Parameters
-        ----------
-        input : list of str
-            The input to the pipeline step. The list must contain the
-            available Junifer Data dictionary keys.
-
-        Raises
-        ------
-        ValueError
-            If the input does not have the required data.
-
-        """
-        _required_inputs = ["BOLD", "confounds"]
-        if any(x not in input for x in _required_inputs):
-            raise_error(
-                msg="Input does not have the required data. \n"
-                f"Input: {input} \n"
-                f"Required (all off): {_required_inputs} \n",
-                klass=ValueError,
-            )
-
-    def get_output_kind(self, input: List[str]) -> List[str]:
-        """Get the kind of the pipeline step.
-
-        Parameters
-        ----------
-        input : list of str
-            The input to the pipeline step. The list must contain the
-            available Junifer Data dictionary keys.
-
-        Returns
-        -------
-        list of str
-            The updated list of available Junifer Data dictionary keys after
-            the pipeline step.
-
-        """
-        # Does not add any new keys
-        return input
-
-    # TODO: complete type annotations
-    def _pick_confounds(self, input):
-        """Select relevant confounds from the specified file."""
-        to_select = []
-        confounds_df = input["data"]
-        confounds_spec = input["names"]["spec"]
-        # for every confound there is a derivative
-        # and for every confound + derivative there should be squares
-        derivatives_to_compute = input["names"].get("derivatives", {})
-        squares_to_compute = input["names"].get("squares", {})
-        spike_name = input["names"]["spike"]
-
-        # Get all the column names according to the strategy
-        for comp, param in self.strategy.items():
-            to_select.extend(confounds_spec[comp][param])
-
-        # Add derivatives if needed
-        to_compute = [x in derivatives_to_compute.keys() for x in to_select]
-        out_df = confounds_df.copy()
-        if any(to_compute):
-            for t_dst, t_src in derivatives_to_compute.items():
-                out_df[t_dst] = np.append(  # type: ignore
-                    np.diff(out_df[t_src]), 0
-                )  # type: ignore
-
-        # Add squares (of base confounds and derivatives) if needed
-        to_compute = [x in squares_to_compute.keys() for x in to_select]
-        if any(to_compute):
-            for t_dst, t_src in squares_to_compute.items():
-                out_df[t_dst] = out_df[t_src] ** 2
-        out_df = out_df[to_select]
-
-        # add binary spike regressor if needed at given threshold
-        if self.spike is not None:
-            fd = confounds_df[spike_name].copy()
-            fd.loc[fd > self.spike] = 1
-            fd.loc[fd != 1] = 0
-            out_df["spike"] = fd
-
-        return out_df
-
-    def _remove_confounds(
-        self, bold_img: "Nifti1Image", confounds_df: pd.DataFrame
-    ) -> Union["Nifti1Image", "Nifti2Image", "MGHImage", List]:
-        """Remove confounds from the BOLD data."""
-        """Remove confounds from the BOLD image.
-
-        Parameters
-        ----------
-        bold_img : Niimg-like object
-            4D image. The signals in the last dimension are filtered
-            (see http://nilearn.github.io/manipulating_images/input_output.html
-            for a detailed description of the valid input types).
-        confounds_df : pd.DataFrame
-            Dataframe containing confounds to remove. Number of rows should
-            correspond to number of volumes in the BOLD image.
-
-        Returns
-        --------
-        Niimg-like object
-            Input image with confounds removed.
-
-        """
-        confounds_array = confounds_df.values
-
-        t_r = self.t_r
-        if t_r is None:
-            logger.info("No `t_r` specified, using t_r from nifti header")
-            zooms = bold_img.header.get_zooms()  # type: ignore
-            t_r = zooms[3]
-            logger.info(
-                f"Read t_r from nifti header: {t_r}",
-            )
-
-        mask_img = self.mask_img
-        if mask_img is None:
-            logger.info("Computing brain mask from image")
-            mask_img = compute_brain_mask(bold_img)
-
-        clean_bold = clean_img(
-            imgs=bold_img,
-            detrend=self.detrend,
-            standardize=self.standardize,
-            confounds=confounds_array,
-            low_pass=self.low_pass,
-            high_pass=self.high_pass,
-            t_r=t_r,
-            mask_img=mask_img,
-        )
-
-        return clean_bold
-
-    # TODO: complete type annotations
-    def _validate_data(self, input):
-        """Validate input data."""
-        # Bold must be 4D niimg
-        check_niimg_4d(input["BOLD"]["data"])
-
-        # Confounds must be a dataframe
-        if not isinstance(input["confounds"]["data"], pd.DataFrame):
-            raise_error(
-                "confounds data must be a pandas dataframe", ValueError
-            )
-
-        confound_df = input["confounds"]["data"]
-        bold_img = input["BOLD"]["data"]
-        if bold_img.get_fdata().shape[3] != len(confound_df):
-            raise_error(
-                "Image time series and confounds have different length!\n"
-                f"\tImage time series: { bold_img.get_fdata().shape[3]}\n"
-                f"\tConfounds: {len(confound_df)}"
-            )
-
-        # Check the column names of the dataframe and the spec
-        # spec must be a dictionary:
-        # {
-        # 'motion': {
-        #     'basic': [(list of columns)]
-        #     'power2', [(list of columns)]
-        #     'derivatives', [(list of columns)]
-        #     'full', [(list of columns)]},
-        # 'wm_csf': {
-        #     'basic': [(list of columns)]
-        #     'power2', [(list of columns)]
-        #     'derivatives', [(list of columns)]
-        #     'full', [(list of columns)]}
-        # 'global_signal': {
-        #     'basic': [(list of columns)]
-        #     'power2', [(list of columns)]
-        #     'derivatives', [(list of columns)]
-        #     'full', [(list of columns)]}
-        # }
-
-        # Check the columns in the dataframe
-        conf_spec = input["confounds"]["names"]["spec"]
-        if any(x not in conf_spec.keys() for x in self._valid_components):
-            raise_error(
-                "All of the component types must be in the confounds data "
-                "object `spec`. Please check your datagrabber.",
-                ValueError,
-            )
-
-        if any(
-            x not in v.keys()
-            for x in self._valid_confounds
-            for v in conf_spec.values()
-        ):
-            raise_error(
-                "All of the confound types must be in the confounds data "
-                "object `spec`. Please check your datagrabber.",
-                ValueError,
-            )
-
-        spike_name = input["confounds"]["names"]["spike"]
-
-        derivatives_to_compute = input["confounds"]["names"].get(
-            "derivatives", {}
-        )
-        if not (isinstance(derivatives_to_compute, dict)):
-            raise_error(
-                'input["confounds"]["names"]["derivatives"] '
-                "must be a dictionary. Please check your datagrabber",
-                ValueError,
-            )
-
-        if any(
-            not (isinstance(k, str) or isinstance(v, str))
-            for k, v in derivatives_to_compute.items()
-        ):
-            raise_error(
-                'input["confounds"]["names"]["derivatives"] '
-                "must be a dictionary with string keys and values. "
-                "Please check your datagrabber",
-                ValueError,
-            )
-
-        missing_derivatives = [
-            x
-            for x in derivatives_to_compute.values()
-            if x not in confound_df.columns
-        ]
-        if len(missing_derivatives) > 0:
-            raise_error(
-                "Some of the derivatives to calculate are not in the confounds"
-                f" dataframe: {missing_derivatives}."
-                "Please check your data "
-                f'({input["confounds"]["path"].as_posix()}) '
-                "and the datagrabber.",
-                ValueError,
-            )
-
-        t_conf_spec = {
-            k: input["confounds"]["names"]["spec"][k][v]
-            for k, v in self.strategy.items()
-        }
-
-        column_names = set([x for y in t_conf_spec.values() for x in y])
-        column_names.add(spike_name)
-
-        missing_columns = [
-            x
-            for x in column_names
-            if x not in confound_df.columns
-            and x not in derivatives_to_compute.keys()
-        ]
-
-        if len(missing_columns) > 0:
-            raise_error(
-                "Some of the columns in the confound spec are not in the "
-                f"confounds dataframe: {missing_columns}. "
-                "Please check your data "
-                f'({input["confounds"]["path"].as_posix()}) '
-                "and the datagrabber.",
-                ValueError,
-            )
-
-    # TODO: complete type annotations
-    def fit_transform(self, input):
-        """Fit and transform."""
-        self._validate_data(input)
-        bold_img = input["BOLD"]["data"]
-        confounds_df = self._pick_confounds(input["confounds"])
-        input["BOLD"]["data"] = self._remove_confounds(bold_img, confounds_df)
-
-        # TODO: Update meta
-        return input
-
-
-# class FelixConfoundRemover(BaseConfoundRemover):
-#     """ A class to read confounds from confound files generated by Felix's
-#     pipeline using CAT and some other custom scripts. It is meant to emulate
-#     the new nilearn.interfaces.fmriprep.load_confounds as closely as
-#     possible.
-
-#     """
-
-#     def read_confounds(self, confound_dataframe):
-
-#         confounds_to_select = []
-#         # for some confounds we need to manually calculate derivatives using
-#         # numpy and add them to the output
-#         derivatives_to_compute = []
-
-#         for comp, param in self.strategy.items():
-#             if comp == 'motion':
-#                 confounds = []
-#                 # there should be six rigid body parameters
-#                 for i in range(1, 7):
-#                     # select basic
-#                     confounds_to_select.append(f'RP.{i}')
-
-#                     # select squares
-#                     if param in ['power2', 'full']:
-#                         confounds_to_select.append(f'RP^2.{i}')
-
-#                     # select derivatives
-#                     if param in ['derivatives', 'full']:
-#                         confounds_to_select.append(f'DRP.{i}')
-
-#                     # if 'full' we should not forget the derivative
-#                     # of the squares
-#                     if param in ['full']:
-#                         confounds_to_select.append(f'DRP^2.{i}')
-
-#             elif comp == 'wm_csf':
-#                 confounds = ['WM', 'CSF']
-#             elif comp == 'global_signal':
-#                 confounds = ['GS']
-
-#             for conf in confounds:
-
-#                 confounds_to_select.append(conf)
-
-#                 # select squares
-#                 if param in ['power2', 'full']:
-#                     confounds_to_select.append(f'{conf}^2')
-
-#                 # we have to calculate derivatives (not included in felix'
-#                 # confound files)
-#                 if param in ['derivatives', 'full']:
-#                     derivatives_to_compute.append(conf)
-
-#                 if param in ['full']:
-#                     derivatives_to_compute.append(f'{conf}^2')
-
-#         confounds_to_remove = confound_dataframe[confounds_to_select]
-
-#         # calc additional derivatives
-#         for conf in derivatives_to_compute:
-#             confounds_to_remove[f'D{conf}'] = np.append(
-#                 np.diff(confound_dataframe[conf]), 0
-#             )
-
-#         # add binary spike regressor if needed at given threshold
-#         if self.spike is not None:
-#             fd = confound_dataframe["FD"].copy()
-#             fd.loc[fd > self.spike] = 1
-#             fd.loc[fd != 1] = 0
-#             confounds_to_remove['spike'] = fd
-
-#         return confounds_to_remove
-
-
-# class FmriprepConfoundRemover(BaseConfoundRemover):
-#     """ A ConfoundRemover class for fmriprep output utilising
-#     nilearn's nilearn.interfaces.fmriprep.load_confounds
-#     """
-
-#     def read_confounds(self):
-#         raise NotImplementedError('read_confounds not implemented')

junifer/preprocess/confounds/__init__.py

@@ -0,0 +1,7 @@
+"""Provide imports for confounds sub-package."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+#          Leonard Sasse <l.sasse@fz-juelich.de>
+# License: AGPL
+
+from .fmriprep_confound_remover import fMRIPrepConfoundRemover

junifer/preprocess/confounds/fmriprep_confound_remover.py

@@ -0,0 +1,604 @@
+"""Provide class for confound removal using fMRIPrep format."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+#          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Union, Tuple, Any
+
+import numpy as np
+import pandas as pd
+from nilearn._utils.niimg_conversions import check_niimg_4d
+from nilearn.image import clean_img
+from nilearn.masking import compute_brain_mask
+
+from ..base import BasePreprocessor
+from ...api.decorators import register_preprocessor
+from ...utils import logger, raise_error
+
+
+if TYPE_CHECKING:
+    from nibabel import MGHImage, Nifti1Image, Nifti2Image
+
+
+FMRIPREP_BASICS = {
+    "motion": [
+        "trans_x",
+        "trans_y",
+        "trans_z",
+        "rot_x",
+        "rot_y",
+        "rot_z",
+    ],
+    "wm_csf": ["csf", "white_matter"],
+    "global_signal": ["global_signal"],
+}
+
+FMRIPREP_POWER2 = {
+    "motion": [
+        "trans_x_power2",
+        "trans_y_power2",
+        "trans_z_power2",
+        "rot_x_power2",
+        "rot_y_power2",
+        "rot_z_power2",
+    ],
+    "wm_csf": ["csf_power2", "white_matter_power2"],
+    "global_signal": ["global_signal_power2"],
+}
+
+FMRIPREP_DERIVATIVES = {
+    "motion": [
+        "trans_x_derivative1",
+        "trans_y_derivative1",
+        "trans_z_derivative1",
+        "rot_x_derivative1",
+        "rot_y_derivative1",
+        "rot_z_derivative1",
+    ],
+    "wm_csf": ["csf_derivative1", "white_matter_derivative1"],
+    "global_signal": ["global_signal_derivative1"],
+}
+
+FMRIPREP_DERIVATIVES_POWER2 = {
+    "motion": [
+        "trans_x_derivative1_power2",
+        "trans_y_derivative1_power2",
+        "trans_z_derivative1_power2",
+        "rot_x_derivative1_power2",
+        "rot_y_derivative1_power2",
+        "rot_z_derivative1_power2",
+    ],
+    "wm_csf": ["csf_derivative1_power2", "white_matter_derivative1_power2"],
+    "global_signal": ["global_signal_derivative1_power2"],
+}
+
+FMRIPREP_VALID_NAMES = [
+    elem
+    for x in [
+        FMRIPREP_BASICS,
+        FMRIPREP_POWER2,
+        FMRIPREP_DERIVATIVES,
+        FMRIPREP_DERIVATIVES_POWER2,
+    ]
+    for t_list in x.values()
+    for elem in t_list
+]
+
+
+@register_preprocessor
+class fMRIPrepConfoundRemover(BasePreprocessor):
+    """Class for confound removal using fMRIPrep confounds format.
+
+    Read confound files and select columns according to
+    a pre-defined strategy.
+
+    Confound removal is based on `nilearn.image.clean_img`.
+
+    Parameters
+    ----------
+    strategy : dict, optional
+        The strategy to use for each component. If None, will use the *full*
+        strategy for all components (default None).
+        The keys of the dictionary should correspond to names of noise
+        components to include:
+
+        * 'motion'
+        * 'wm_csf'
+        * 'global_signal'
+
+        The values of dictionary should correspond to types of confounds
+        extracted from each signal:
+
+        * 'basic': only the confounding time series
+        * 'power2': signal + quadratic term
+        * 'derivatives': signal + derivatives
+        * 'full': signal + deriv. + quadratic terms + power2 deriv.
+
+    spike : float, optional
+        If None, no spike regressor is added. If spike is a float, it will
+        add a spike regressor for every point at which framewise displacement
+        exceeds the specified float (default None).
+    detrend : bool, optional
+        If True, detrending will be applied on timeseries, before confound
+        removal (default True).
+    standardize : bool, optional
+        If True, returned signals are set to unit variance (default True).
+    low_pass : float, optional
+        Low cutoff frequencies, in Hertz. If None, no filtering is applied
+        (default None).
+    high_pass : float, optional
+        High cutoff frequencies, in Hertz. If None, no filtering is
+        applied (default None).
+    t_r : float, optional
+        Repetition time, in second (sampling period).
+        If None, it will use t_r from nifti header (default None).
+    mask_img: Niimg-like object, optional
+        If provided, signal is only cleaned from voxels inside the mask.
+        If mask is provided, it should have same shape and affine as imgs.
+        If not provided, a mask is computed using
+        :func:`nilearn.masking.compute_brain_mask` (default None).
+
+    """
+
+    def __init__(
+        self,
+        strategy: Optional[Dict[str, str]] = None,
+        spike: Optional[float] = None,
+        detrend: bool = True,
+        standardize: bool = True,
+        low_pass: Optional[float] = None,
+        high_pass: Optional[float] = None,
+        t_r: Optional[float] = None,
+        mask_img: Optional["Nifti1Image"] = None,
+    ) -> None:
+        """Initialise the class."""
+        if strategy is None:
+            strategy = {
+                "motion": "full",
+                "wm_csf": "full",
+                "global_signal": "full",
+            }
+        self.strategy = strategy
+        self.spike = spike
+        self.detrend = detrend
+        self.standardize = standardize
+        self.low_pass = low_pass
+        self.high_pass = high_pass
+        self.t_r = t_r
+        self.mask_img = mask_img
+
+        self._valid_components = ["motion", "wm_csf", "global_signal"]
+        self._valid_confounds = ["basic", "power2", "derivatives", "full"]
+
+        if any(not isinstance(k, str) for k in strategy.keys()):
+            raise_error("Strategy keys must be strings", ValueError)
+
+        if any(not isinstance(v, str) for v in strategy.values()):
+            raise_error("Strategy values must be strings", ValueError)
+
+        if any(x not in self._valid_components for x in strategy.keys()):
+            raise_error(
+                msg=f"Invalid component names {list(strategy.keys())}. "
+                f"Valid components are {self._valid_components}.\n"
+                f"If any of them is a valid parameter in "
+                "nilearn.interfaces.fmriprep.load_confounds we may "
+                "include it in the future",
+                klass=ValueError,
+            )
+
+        if any(x not in self._valid_confounds for x in strategy.values()):
+            raise_error(
+                msg=f"Invalid confound types {list(strategy.values())}. "
+                f"Valid confound types are {self._valid_confounds}.\n"
+                f"If any of them is a valid parameter in "
+                "nilearn.interfaces.fmriprep.load_confounds we may "
+                "include it in the future",
+                klass=ValueError,
+            )
+        super().__init__()
+
+    def validate_input(self, input: List[str]) -> None:
+        """Validate the input to the pipeline step.
+
+        Parameters
+        ----------
+        input : list of str
+            The input to the pipeline step. The list must contain the
+            available Junifer Data object keys.
+
+        Raises
+        ------
+        ValueError
+            If the input does not have the required data.
+
+        """
+        _required_inputs = ["BOLD", "BOLD_confounds"]
+        if any(x not in input for x in _required_inputs):
+            raise_error(
+                msg="Input does not have the required data. \n"
+                f"Input: {input} \n"
+                f"Required (all off): {_required_inputs} \n",
+                klass=ValueError,
+            )
+
+    def get_output_kind(self, input: List[str]) -> List[str]:
+        """Get the kind of the pipeline step.
+
+        Parameters
+        ----------
+        input : list of str
+            The input to the pipeline step. The list must contain the
+            available Junifer Data object keys.
+
+        Returns
+        -------
+        list of str
+            The updated list of available Junifer Data object keys after
+            the pipeline step.
+
+        """
+        # Does not add any new keys
+        return input
+
+    def get_valid_inputs(self) -> List[str]:
+        """Get the valid inputs for the pipeline step.
+
+        Returns
+        -------
+        list of str
+            The valid inputs for the pipeline step.
+
+        """
+        return ["BOLD"]
+
+    def _map_adhoc_to_fmriprep(self, input: Dict[str, Any]) -> None:
+        """Map the adhoc format to the fmpriprep format spec.
+
+        Based on the spec, map the column names to match the fmriprep format.
+
+        It assumes that the data is valid, meaning that all the mapping
+        keys are in the confounds dataframe columns and all the mapped
+        values are valid fmriprep column names.
+
+        This method modifies the dataframe in place.
+
+        Parameters
+        ----------
+        input : dict
+            Dictionary containing the following keys:
+            - data: the confounds file loaded in memory (dataframe)
+            - format: format of the confounds file (must be "adhoc")
+            - mappings: dictionary containing the mappings from the adhoc
+            format to the fmriprep format as a dictionary with key "fmriprep"
+
+        """
+
+        confounds_df = input["data"]
+        confounds_mapping = input["mappings"]["fmriprep"]
+
+        # Rename the columns
+        confounds_df.rename(columns=confounds_mapping, inplace=True)
+
+    def _process_fmriprep_spec(
+        self, input: Dict[str, Any]
+    ) -> Tuple[List[str], Dict[str, str], Dict[str, str], str]:
+        """Process the fmpriprep format spec from the specified file.
+
+        Based on the strategy, find the relevant column names in the dataframe,
+        as well as the required squared and derivatives to compute.
+
+        Parameters
+        ----------
+        input : dict
+            Dictionary containing the following keys:
+
+            * path: path to the confounds file
+            * data: the confounds file loaded in memory (dataframe)
+            * format: format of the confounds file (must be "fmriprep")
+
+        Returns
+        -------
+        to_select : list of str
+            List of confounds to select from the confounds file, based on the
+            strategy
+        squares_to_compute : dict
+            Dictionary containing the missing power2 confounds to compute
+            (keys) and the corresponding confounds to square (values)
+        derivatives_to_compute : dict
+            Dictionary containing the missing derivatives confounds to compute
+            (keys) and the corresponding confounds to compute the derivative
+            from (values)
+        spike_name : str
+            Name of the confound to use for spike detection
+
+        """
+        confounds_df = input["data"]
+        available_vars = confounds_df.columns
+
+        # This function should build this 3 variables
+        to_select = []  # the list of confounds to select
+        squares_to_compute = {}  # the dictionary of missing squares
+        derivatives_to_compute = {}  # the dictionary of missing derivatives
+
+        for t_kind, t_strategy in self.strategy.items():
+            t_basics = FMRIPREP_BASICS[t_kind]
+
+            if any(x not in available_vars for x in t_basics):
+                missing = [x for x in t_basics if x not in available_vars]
+                raise ValueError(
+                    "Invalid confounds file. Missing basic confounds: "
+                    f"{missing}. "
+                    "Check if this file is really an fmriprep confounds file. "
+                    "You can also modify the confound removal strategy."
+                )
+
+            to_select.extend(t_basics)
+
+            if t_strategy in ["power2", "full"]:
+                for x in t_basics:
+                    x_2 = f"{x}_power2"
+                    to_select.append(x_2)
+                    if x_2 not in available_vars:
+                        squares_to_compute[x_2] = x
+
+            if t_strategy in ["derivatives", "full"]:
+                for x in t_basics:
+                    x_derivative = f"{x}_derivative1"
+                    to_select.append(x_derivative)
+                    if x_derivative not in available_vars:
+                        derivatives_to_compute[x_derivative] = x
+                    if t_strategy == "full":
+                        x_derivative_2 = f"{x_derivative}_power2"
+                        to_select.append(x_derivative_2)
+                        if x_derivative_2 not in available_vars:
+                            squares_to_compute[x_derivative_2] = x_derivative
+        spike_name = "framewise_displacement"
+        if self.spike is not None:
+            if spike_name not in available_vars:
+                raise ValueError(
+                    "Invalid confounds file. Missing framewise_displacement "
+                    "(spike) confound. "
+                    "Check if this file is really an fmriprep confounds file. "
+                    "You can also deactivate spike (set spike = None)."
+                )
+        out = to_select, squares_to_compute, derivatives_to_compute, spike_name
+        return out
+
+    def _pick_confounds(self, input: Dict[str, Any]) -> pd.DataFrame:
+        """Select relevant confounds from the specified file.
+
+        Parameters
+        ----------
+        input : dict
+            Dictionary containing the "BOLD_confounds" value from the
+            Junifer Data object.
+
+        Returns
+        -------
+        confounds_df : pandas.DataFrame
+            Dataframe containing the relevant confounds.
+
+        """
+
+        confounds_format = input["format"]
+        if confounds_format == "adhoc":
+            self._map_adhoc_to_fmriprep(input)
+
+        processed_spec = self._process_fmriprep_spec(input)
+
+        (
+            to_select,
+            squares_to_compute,
+            derivatives_to_compute,
+            spike_name,
+        ) = processed_spec
+        # Copy the confounds
+        out_df = input["data"].copy()
+
+        # Add derivatives if needed
+        for t_dst, t_src in derivatives_to_compute.items():
+            out_df[t_dst] = np.insert(np.diff(out_df[t_src]), 0, np.nan)
+
+        # Add squares (of base confounds and derivatives) if needed
+        for t_dst, t_src in squares_to_compute.items():
+            out_df[t_dst] = out_df[t_src] ** 2
+
+        # add binary spike regressor if needed at given threshold
+        if self.spike is not None:
+            fd = out_df[spike_name].copy()
+            fd.loc[fd > self.spike] = 1
+            fd.loc[fd != 1] = 0
+            out_df["spike"] = fd
+            to_select.append("spike")
+
+        # Now pick all the relevant confounds
+        out_df = out_df[to_select]
+
+        # Derivatives have NaN on the first row
+        # Replace them by estimates at second time point,
+        # otherwise nilearn will crash.
+        mask_nan = np.isnan(out_df.values[0, :])
+        out_df.iloc[0, mask_nan] = out_df.iloc[1, mask_nan]
+
+        return out_df
+
+    def _validate_data(
+        self,
+        input: Dict[str, Any],
+        extra_input: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Validate input data.
+
+        Parameters
+        ----------
+        input : dict
+            Dictionary containing the "BOLD" value from the
+            Junifer Data object.
+        extra_input : dict, optional
+            Dictionary containing the rest of the Junifer Data object. Must
+            include the "BOLD_confounds" key.
+
+        """
+
+        # Bold must be 4D niimg
+        check_niimg_4d(input["data"])
+
+        if extra_input is None:
+            raise_error("No extra input provided", ValueError)
+        if "BOLD_confounds" not in extra_input:
+            raise_error("No BOLD_confounds provided", ValueError)
+        if "data" not in extra_input["BOLD_confounds"]:
+            raise_error("No BOLD_confounds data provided", ValueError)
+        # Confounds must be a dataframe
+        if not isinstance(extra_input["BOLD_confounds"]["data"], pd.DataFrame):
+            raise_error(
+                "Confounds data must be a pandas dataframe", ValueError
+            )
+
+        confound_df = extra_input["BOLD_confounds"]["data"]
+        bold_img = input["data"]
+        if bold_img.get_fdata().shape[3] != len(confound_df):
+            raise_error(
+                "Image time series and confounds have different length!\n"
+                f"\tImage time series: { bold_img.get_fdata().shape[3]}\n"
+                f"\tConfounds: {len(confound_df)}"
+            )
+
+        if "format" not in extra_input["BOLD_confounds"]:
+            raise_error(
+                "Confounds format must be specified in "
+                'input["BOLD_confounds"]'
+            )
+
+        t_format = extra_input["BOLD_confounds"]["format"]
+
+        if t_format == "adhoc":
+            if "mappings" not in extra_input["BOLD_confounds"]:
+                raise_error(
+                    "When using adhoc format, you must specify "
+                    "the variables names mappings in "
+                    'input["BOLD_confounds"]["mappings"]'
+                )
+            if "fmriprep" not in extra_input["BOLD_confounds"]["mappings"]:
+                raise_error(
+                    "When using adhoc format, you must specify "
+                    "the variables names mappings to fmriprep format in "
+                    'input["BOLD_confounds"]["mappings"]["fmriprep"]'
+                )
+            fmriprep_mappings = extra_input["BOLD_confounds"]["mappings"][
+                "fmriprep"
+            ]
+            wrong_names = [
+                x
+                for x in fmriprep_mappings.values()
+                if x not in FMRIPREP_VALID_NAMES
+            ]
+            if len(wrong_names) > 0:
+                raise_error(
+                    "The following mapping values are not valid fmriprep "
+                    f"names: {wrong_names}"
+                )
+            # Check that all the required columns are present
+            missing = [
+                x
+                for x in fmriprep_mappings.keys()
+                if x not in confound_df.columns
+            ]
+
+            if len(missing) > 0:
+                raise ValueError(
+                    "Invalid confounds file. Missing columns: "
+                    f"{missing}. "
+                    "Check if this file matches the adhoc specification for "
+                    "this dataset."
+                )
+        elif t_format != "fmriprep":
+            raise ValueError(f"Invalid confounds format {t_format}")
+
+    def _remove_confounds(
+        self, bold_img: "Nifti1Image", confounds_df: pd.DataFrame
+    ) -> Union["Nifti1Image", "Nifti2Image", "MGHImage", List]:
+        """Remove confounds from the BOLD image.
+
+        Parameters
+        ----------
+        bold_img : Niimg-like object
+            4D image. The signals in the last dimension are filtered
+            (see http://nilearn.github.io/manipulating_images/input_output.html
+            for a detailed description of the valid input types).
+        confounds_df : pd.DataFrame
+            Dataframe containing confounds to remove. Number of rows should
+            correspond to number of volumes in the BOLD image.
+
+        Returns
+        --------
+        Niimg-like object
+            Input image with confounds removed.
+
+        """
+        confounds_array = confounds_df.values
+
+        t_r = self.t_r
+        if t_r is None:
+            logger.info("No `t_r` specified, using t_r from nifti header")
+            zooms = bold_img.header.get_zooms()  # type: ignore
+            t_r = zooms[3]
+            logger.info(
+                f"Read t_r from nifti header: {t_r}",
+            )
+
+        mask_img = self.mask_img
+        if mask_img is None:
+            logger.info("Computing brain mask from image")
+            mask_img = compute_brain_mask(bold_img)
+
+        logger.info("Cleaning image")
+        logger.debug(f"\tdetrend: {self.detrend}")
+        logger.debug(f"\tstandardize: {self.standardize}")
+        logger.debug(f"\tlow_pass: {self.low_pass}")
+        logger.debug(f"\thigh_pass: {self.high_pass}")
+        clean_bold = clean_img(
+            imgs=bold_img,
+            detrend=self.detrend,
+            standardize=self.standardize,
+            confounds=confounds_array,
+            low_pass=self.low_pass,
+            high_pass=self.high_pass,
+            t_r=t_r,
+            mask_img=mask_img,
+        )
+
+        return clean_bold
+
+    def preprocess(
+        self,
+        input: Dict[str, Any],
+        extra_input: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Preprocess.
+
+        Parameters
+        ----------
+        input : dict
+            A single input from the Junifer Data object in which to preprocess.
+        extra_input : dict, optional
+            The other fields in the Junifer Data object. Must include the
+            "BOLD_confounds" key.
+
+        Returns
+        -------
+        key : str
+            The key to store the output in the Junifer Data object.
+        object : dict
+            The computed result as dictionary. This will be stored in the
+            Junifer Data object under the key 'key'.
+
+        """
+        self._validate_data(input, extra_input)
+        assert extra_input is not None
+        bold_img = input["data"]
+        confounds_df = self._pick_confounds(extra_input["BOLD_confounds"])
+        input["data"] = self._remove_confounds(bold_img, confounds_df)
+        return "BOLD", input

junifer/preprocess/confounds/tests/test_fmriprep_confound_remover.py

@@ -0,0 +1,539 @@
+"""Provide tests for fMRIPrepConfoundRemover."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+#          Leonard Sasse <l.sasse@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import typing
+from numpy.testing import assert_array_equal, assert_raises
+import pytest
+import pandas as pd
+from pandas.testing import assert_frame_equal
+import numpy as np
+
+import nibabel as nib
+from nilearn._utils.exceptions import DimensionError
+
+from junifer.preprocess.confounds import fMRIPrepConfoundRemover
+from junifer.testing.datagrabbers import (
+    OasisVBMTestingDatagrabber,
+    PartlyCloudyTestingDataGrabber,
+)
+from junifer.testing import get_testing_data
+from junifer.datareader import DefaultDataReader
+
+
+def test_fMRIPrepConfoundRemover_init() -> None:
+    """Test fMRIPrepConfoundRemover init."""
+
+    with pytest.raises(ValueError, match="keys must be strings"):
+        fMRIPrepConfoundRemover(strategy={1: "full"})  # type: ignore
+
+    with pytest.raises(ValueError, match="values must be strings"):
+        fMRIPrepConfoundRemover(strategy={"motion": 1})  # type: ignore
+
+    with pytest.raises(ValueError, match="component names"):
+        fMRIPrepConfoundRemover(strategy={"wrong": "full"})
+
+    with pytest.raises(ValueError, match="confound types"):
+        fMRIPrepConfoundRemover(strategy={"motion": "wrong"})
+
+
+def test_fMRIPrepConfoundRemover_validate_input() -> None:
+    """Test fMRIPrepConfoundRemover validate_input."""
+    confound_remover = fMRIPrepConfoundRemover()
+
+    # Input is valid when both BOLD and BOLD_confounds are present
+
+    input = ["T1w"]
+    with pytest.raises(ValueError, match="not have the required data"):
+        confound_remover.validate_input(input)
+
+    input = ["BOLD"]
+    with pytest.raises(ValueError, match="not have the required data"):
+        confound_remover.validate_input(input)
+
+    input = ["BOLD", "T1w"]
+    with pytest.raises(ValueError, match="not have the required data"):
+        confound_remover.validate_input(input)
+
+    input = ["BOLD", "T1w", "BOLD_confounds"]
+    confound_remover.validate_input(input)
+
+
+def test_fMRIPrepConfoundRemover_get_output_kind() -> None:
+    """Test fMRIPrepConfoundRemover validate_input."""
+    confound_remover = fMRIPrepConfoundRemover()
+    inputs = [
+        ["BOLD", "T1w", "BOLD_confounds"],
+        ["BOLD", "VBM_GM", "BOLD_confounds"],
+        ["BOLD", "BOLD_confounds"],
+    ]
+    # Confound remover works in place
+    for input in inputs:
+        assert confound_remover.get_output_kind(input) == input
+
+
+def test_fMRIPrepConfoundRemover__map_adhoc_to_fmriprep() -> None:
+    """Test fMRIPrepConfoundRemover adhoc to fmriprep spec mapping."""
+    confound_remover = fMRIPrepConfoundRemover()
+    # Use non fmriprep variable names
+    adhoc_names = [f"var{i}" for i in range(6)]
+    adhoc_df = pd.DataFrame(np.random.randn(10, 6), columns=adhoc_names)
+
+    # map them to valid variable names
+    fmriprep_names = [
+        "trans_x",
+        "trans_y",
+        "trans_z",
+        "rot_x",
+        "rot_y",
+        "rot_z",
+    ]
+
+    # Build mappings dictionary
+    mappings = {x: y for x, y in zip(adhoc_names, fmriprep_names)}
+    input = {
+        "mappings": {"fmriprep": mappings},
+        "data": adhoc_df,
+    }
+
+    confound_remover._map_adhoc_to_fmriprep(input)
+    # This should work in-place
+    assert adhoc_df.columns.tolist() == fmriprep_names
+
+
+def test_fMRIPrepConfoundRemover__process_fmriprep_spec() -> None:
+    """Test fMRIPrepConfoundRemover fmriprep spec processing."""
+
+    # Test one strategy, full, no spike
+    confound_remover = fMRIPrepConfoundRemover(strategy={"wm_csf": "full"})
+
+    var_names = [
+        "csf",
+        "white_matter",
+        "csf_power2",
+        "white_matter_power2",
+        "csf_derivative1",
+        "white_matter_derivative1",
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names)), columns=var_names
+    )
+
+    out = confound_remover._process_fmriprep_spec({"data": confounds_df})
+    to_select, sq_to_compute, der_to_compute, spike_name = out
+    assert set(to_select) == set(var_names)
+    assert len(sq_to_compute) == 0
+    assert len(der_to_compute) == 0
+    assert spike_name == "framewise_displacement"
+
+    # Same strategy, but derivatives are not present
+    var_names = ["csf", "white_matter", "csf_power2", "white_matter_power2"]
+    missing_der_names = ["csf_derivative1", "white_matter_derivative1"]
+    missing_sq_names = [
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    all_names = var_names + missing_der_names + missing_sq_names
+
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names)), columns=var_names
+    )
+    out = confound_remover._process_fmriprep_spec({"data": confounds_df})
+    to_select, sq_to_compute, der_to_compute, spike_name = out
+    assert set(to_select) == set(all_names)
+    assert set(sq_to_compute) == set(missing_sq_names)
+    assert set(der_to_compute) == set(missing_der_names)
+    assert spike_name == "framewise_displacement"
+
+    # Same strategy, with spike, only basics are present
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "full"}, spike=0.2
+    )
+
+    var_names = ["csf", "white_matter"]
+    missing_der_names = ["csf_derivative1", "white_matter_derivative1"]
+    missing_sq_names = [
+        "csf_power2",
+        "white_matter_power2",
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    all_names = var_names + missing_der_names + missing_sq_names
+
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names) + 1),
+        columns=var_names + ["framewise_displacement"],
+    )
+    out = confound_remover._process_fmriprep_spec({"data": confounds_df})
+    to_select, sq_to_compute, der_to_compute, spike_name = out
+    assert set(to_select) == set(all_names)
+    assert set(sq_to_compute) == set(missing_sq_names)
+    assert set(der_to_compute) == set(missing_der_names)
+    assert spike_name == "framewise_displacement"
+
+    # Two component strategy, mixed confounds, no spike
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "power2", "global_signal": "derivatives"}
+    )
+
+    var_names = ["csf", "white_matter", "global_signal"]
+    missing_der_names = ["global_signal_derivative1"]
+    missing_sq_names = [
+        "csf_power2",
+        "white_matter_power2",
+    ]
+
+    all_names = var_names + missing_der_names + missing_sq_names
+
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names)), columns=var_names
+    )
+    out = confound_remover._process_fmriprep_spec({"data": confounds_df})
+    to_select, sq_to_compute, der_to_compute, spike_name = out
+    assert set(to_select) == set(all_names)
+    assert set(sq_to_compute) == set(missing_sq_names)
+    assert set(der_to_compute) == set(missing_der_names)
+    assert spike_name == "framewise_displacement"
+
+    # Test for wrong columns/strategy pairs
+
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "full"}, spike=0.2
+    )
+    var_names = ["csf"]
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names)), columns=var_names
+    )
+
+    msg = r"Missing basic confounds: \['white_matter'\]"
+    with pytest.raises(ValueError, match=msg):
+        confound_remover._process_fmriprep_spec({"data": confounds_df})
+
+    var_names = ["csf", "white_matter"]
+    confounds_df = pd.DataFrame(
+        np.random.randn(7, len(var_names)), columns=var_names
+    )
+
+    msg = r"Missing framewise_displacement"
+    with pytest.raises(ValueError, match=msg):
+        confound_remover._process_fmriprep_spec({"data": confounds_df})
+
+
+def test_fMRIPrepConfoundRemover__pick_confounds_adhoc() -> None:
+    """Test fMRIPrepConfoundRemover pick confounds on adhoc confounds."""
+    confound_remover = fMRIPrepConfoundRemover(strategy={"wm_csf": "full"})
+    # Use non fmriprep variable names
+    adhoc_names = [f"var{i}" for i in range(2)]
+    adhoc_df = pd.DataFrame(np.random.randn(10, 2), columns=adhoc_names)
+
+    # map them to valid variable names
+    fmriprep_names = ["csf", "white_matter"]
+    fmriprep_all_vars = [
+        "csf",
+        "white_matter",
+        "csf_power2",
+        "white_matter_power2",
+        "csf_derivative1",
+        "white_matter_derivative1",
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    # Build mappings dictionary
+    mappings = {x: y for x, y in zip(adhoc_names, fmriprep_names)}
+    input = {
+        "mappings": {"fmriprep": mappings},
+        "data": adhoc_df,
+        "format": "adhoc",
+    }
+
+    out = confound_remover._pick_confounds(input)
+    assert set(out.columns) == set(fmriprep_all_vars)
+
+
+def test_FMRIPRepConfoundRemover__pick_confounds_fmriprep() -> None:
+    """Test fMRIPrepConfoundRemover pick confounds on fmriprep confounds."""
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "full"}, spike=0.2
+    )
+    fmriprep_all_vars = [
+        "csf",
+        "white_matter",
+        "csf_power2",
+        "white_matter_power2",
+        "csf_derivative1",
+        "white_matter_derivative1",
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    reader = DefaultDataReader()
+    out1, out2 = None, None
+    with PartlyCloudyTestingDataGrabber() as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        out1 = confound_remover._pick_confounds(input["BOLD_confounds"])
+        assert set(out1.columns) == set(fmriprep_all_vars + ["spike"])
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        out2 = confound_remover._pick_confounds(input["BOLD_confounds"])
+        assert set(out2.columns) == set(fmriprep_all_vars + ["spike"])
+
+    assert_frame_equal(out1, out2)
+
+
+def test_FMRIPRepConfoundRemover__pick_confounds_fmriprep_compute() -> None:
+    """Test if fmriprep returns the same derivatives/power2 as we compute."""
+
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "full"}
+    )
+    fmriprep_all_vars = [
+        "csf",
+        "white_matter",
+        "csf_power2",
+        "white_matter_power2",
+        "csf_derivative1",
+        "white_matter_derivative1",
+        "csf_derivative1_power2",
+        "white_matter_derivative1_power2",
+    ]
+
+    t_fname = get_testing_data(
+        "sub-0001_task-anticipation_acq-seq_desc-confounds_regressors.tsv"
+    )
+    t_full_confounds = pd.read_csv(t_fname, sep="\t")[fmriprep_all_vars]
+    t_basic_confounds = t_full_confounds[["csf", "white_matter"]]
+
+    input_basic = {"data": t_basic_confounds, "format": "fmriprep"}
+    out_junifer = confound_remover._pick_confounds(input_basic)
+
+    input_full = {"data": t_full_confounds, "format": "fmriprep"}
+    out_fmriprep = confound_remover._pick_confounds(input_full)
+
+    assert np.all(out_fmriprep.values != np.nan)
+    assert_frame_equal(out_junifer, out_fmriprep)
+
+
+def test_fMRIPrepConfoundRemover__validate_data() -> None:
+    """Test fMRIPrepConfoundRemover validate data."""
+    confound_remover = fMRIPrepConfoundRemover(strategy={"wm_csf": "full"})
+    reader = DefaultDataReader()
+    with OasisVBMTestingDatagrabber() as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        new_input = input["VBM_GM"]
+        with pytest.raises(
+            DimensionError, match="incompatible dimensionality"
+        ):
+            confound_remover._validate_data(new_input, None)
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        new_input = input["BOLD"]
+
+        with pytest.raises(ValueError, match="No extra input"):
+            confound_remover._validate_data(new_input, None)
+        with pytest.raises(ValueError, match="No BOLD_confounds provided"):
+            confound_remover._validate_data(new_input, {})
+        with pytest.raises(
+            ValueError, match="No BOLD_confounds data provided"
+        ):
+            confound_remover._validate_data(new_input, {"BOLD_confounds": {}})
+
+        extra_input = {
+            "BOLD_confounds": {"data": "wrong"},
+        }
+        msg = "must be a pandas dataframe"
+        with pytest.raises(ValueError, match=msg):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {"BOLD_confounds": {"data": pd.DataFrame()}}
+        with pytest.raises(ValueError, match="Image time series and"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {"data": input["BOLD_confounds"]["data"]}
+        }
+        with pytest.raises(ValueError, match="format must be specified"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "wrong",
+            }
+        }
+        with pytest.raises(ValueError, match="Invalid confounds format wrong"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "adhoc",
+            }
+        }
+        with pytest.raises(ValueError, match="variables names mappings"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "adhoc",
+                "mappings": {},
+            }
+        }
+        with pytest.raises(ValueError, match="mappings to fmriprep"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "adhoc",
+                "mappings": {
+                    "fmriprep": {
+                        "rot_x": "wrong",
+                        "rot_y": "rot_z",
+                        "rot_z": "rot_y",
+                    }
+                },
+            }
+        }
+        with pytest.raises(ValueError, match=r"names: \['wrong'\]"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "adhoc",
+                "mappings": {
+                    "fmriprep": {
+                        "wrong": "rot_x",
+                        "rot_y": "rot_z",
+                        "rot_z": "rot_y",
+                    }
+                },
+            }
+        }
+        with pytest.raises(ValueError, match=r"Missing columns: \['wrong'\]"):
+            confound_remover._validate_data(new_input, extra_input)
+
+        extra_input = {
+            "BOLD_confounds": {
+                "data": input["BOLD_confounds"]["data"],
+                "format": "adhoc",
+                "mappings": {
+                    "fmriprep": {
+                        "rot_x": "rot_x",
+                        "rot_y": "rot_z",
+                        "rot_z": "rot_y",
+                    }
+                },
+            }
+        }
+        confound_remover._validate_data(new_input, extra_input)
+
+
+def test_fMRIPrepConfoundRemover__remove_confounds() -> None:
+    """Test fMRIPrepConfoundRemover remove confounds."""
+    confound_remover = fMRIPrepConfoundRemover(
+        strategy={"wm_csf": "full"}, spike=0.2
+    )
+    reader = DefaultDataReader()
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        confounds = confound_remover._pick_confounds(input["BOLD_confounds"])
+        raw_bold = input["BOLD"]["data"]
+        clean_bold = confound_remover._remove_confounds(
+            bold_img=raw_bold, confounds_df=confounds
+        )
+        clean_bold = typing.cast(nib.Nifti1Image, clean_bold)
+        # TODO: Find a better way to test functionality here
+        assert (
+            clean_bold.header.get_zooms()  # type: ignore
+            == raw_bold.header.get_zooms()
+        )
+        assert clean_bold.get_fdata().shape == raw_bold.get_fdata().shape
+    # TODO: Test confound remover with mask, needs #79 to be implemented
+
+
+def test_fMRIPrepConfoundRemover_preprocess() -> None:
+    """Test fMRIPrepConfoundRemover with all confounds present."""
+
+    # need reader for the data
+    reader = DefaultDataReader()
+    # All strategies full, no spike
+    confound_remover = fMRIPrepConfoundRemover()
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        orig_bold = input["BOLD"]["data"].get_fdata().copy()
+        pre_input = input["BOLD"]
+        pre_extra_input = {"BOLD_confounds": input["BOLD_confounds"]}
+        key, output = confound_remover.preprocess(pre_input, pre_extra_input)
+        trans_bold = output["data"].get_fdata()
+        # Transformation is in place
+        assert_array_equal(trans_bold, input["BOLD"]["data"].get_fdata())
+
+        # Data should have the same shape
+        assert orig_bold.shape == trans_bold.shape
+
+        # but be different
+        assert_raises(
+            AssertionError, assert_array_equal, orig_bold, trans_bold
+        )
+        assert key == "BOLD"
+
+
+def test_fMRIPrepConfoundRemover_fit_transform() -> None:
+    """Test fMRIPrepConfoundRemover with all confounds present."""
+
+    # need reader for the data
+    reader = DefaultDataReader()
+    # All strategies full, no spike
+    confound_remover = fMRIPrepConfoundRemover()
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        orig_bold = input["BOLD"]["data"].get_fdata().copy()
+        output = confound_remover.fit_transform(input)
+        trans_bold = output["BOLD"]["data"].get_fdata()
+        # Transformation is in place
+        assert_array_equal(trans_bold, input["BOLD"]["data"].get_fdata())
+
+        # Data should have the same shape
+        assert orig_bold.shape == trans_bold.shape
+
+        # but be different
+        assert_raises(
+            AssertionError, assert_array_equal, orig_bold, trans_bold
+        )
+
+        assert output["meta"] == input["meta"]  # general meta does not change
+        assert "meta" in output["BOLD"]
+        assert "preprocess" in output["BOLD"]["meta"]
+        t_meta = output["BOLD"]["meta"]["preprocess"]
+        assert t_meta["class"] == "fMRIPrepConfoundRemover"
+        # It should have all the default parameters
+        assert t_meta["strategy"] == confound_remover.strategy
+        assert t_meta["spike"] is None
+        assert t_meta["detrend"] is True
+        assert t_meta["standardize"] is True
+        assert t_meta["low_pass"] is None
+        assert t_meta["high_pass"] is None
+        assert t_meta["t_r"] is None
+        assert t_meta["mask_img"] is None

junifer/preprocess/tests/test_confounds.py

@@ -1,347 +0,0 @@
-"""Provide tests for confound removal."""
-
-# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
-#          Leonard Sasse <l.sasse@fz-juelich.de>
-#          Synchon Mandal <s.mandal@fz-juelich.de>
-# License: AGPL
-
-import random
-import string
-from pathlib import Path
-from typing import Tuple
-
-import numpy as np
-import pandas as pd
-from nibabel import Nifti1Image
-from nilearn._utils.niimg_conversions import check_niimg_4d
-
-from junifer.preprocess.confounds import BaseConfoundRemover
-
-
-# Set RNG seed for reproducibility
-np.random.seed(1234567)
-
-
-def generate_conf_name(
-    size: int = 6, chars: str = string.ascii_uppercase + string.digits
-) -> str:
-    """Generate configuration name."""
-    return "".join(random.choice(chars) for _ in range(size))
-
-
-def _simu_img() -> Tuple[Nifti1Image, Nifti1Image]:
-    # Random 4D volume with 100 time points
-    vol = 100 + 10 * np.random.randn(5, 5, 2, 100)
-    img = Nifti1Image(vol, np.eye(4))
-    # Create an nifti image with the data, and corresponding mask
-    mask = Nifti1Image(np.ones([5, 5, 2]), np.eye(4))
-    return img, mask
-
-
-# TODO: split the tests
-def test_baseconfoundremover() -> None:
-    """Test BaseConfoundRemover."""
-    # Generate a simulated BOLD img
-    siimg, simsk = _simu_img()
-
-    # generate random confound dataframe with Felix's column naming
-
-    motion_basic = [f"RP.{i}" for i in range(1, 7)]
-    motion_power2 = [f"RP^2.{i}" for i in range(1, 7)]
-    motion_derivatives = [f"DRP.{i}" for i in range(1, 7)]
-    motion_full = [f"DRP^2.{i}" for i in range(1, 7)]
-
-    wm_csf_basic = ["WM", "CSF"]
-    wm_csf_power2 = ["WM^2", "CSF^2"]
-    wm_csf_derivatives = ["DWM", "DCSF"]
-    wm_csf_full = ["DWM^2", "DCSF^2"]
-
-    gs_basic = ["GS"]
-    gs_power2 = ["GS^2"]
-    gs_derivatives = ["DGS"]
-    gs_full = ["DGS^2"]
-
-    confound_column_names = []
-
-    confound_column_names.append("FD")  # spike
-
-    confound_column_names.extend(motion_basic)
-    confound_column_names.extend(motion_power2)
-    confound_column_names.extend(motion_derivatives)
-    confound_column_names.extend(motion_full)
-
-    confound_column_names.extend(wm_csf_basic)
-    confound_column_names.extend(wm_csf_power2)
-    confound_column_names.extend(wm_csf_derivatives)
-    confound_column_names.extend(wm_csf_full)
-
-    confound_column_names.extend(gs_basic)
-    confound_column_names.extend(gs_power2)
-    confound_column_names.extend(gs_derivatives)
-    confound_column_names.extend(gs_full)
-
-    # add some random irrelevant confounds
-    for _ in range(10):
-        confound_column_names.append(generate_conf_name())
-
-    np.random.shuffle(confound_column_names)
-    n_cols = len(confound_column_names)
-    confounds_df = pd.DataFrame(
-        np.random.randint(0, 100, size=(100, n_cols)),
-        columns=confound_column_names,
-    )
-
-    # Generate spec from Felix's column naming
-    spec = {
-        "motion": {
-            "basic": motion_basic,
-            "power2": motion_basic + motion_power2,
-            "derivatives": motion_basic + motion_derivatives,
-            "full": motion_basic
-            + motion_derivatives
-            + motion_power2
-            + motion_full,
-        },
-        "wm_csf": {
-            "basic": wm_csf_basic,
-            "power2": wm_csf_basic + wm_csf_power2,
-            "derivatives": wm_csf_basic + wm_csf_derivatives,
-            "full": wm_csf_basic
-            + wm_csf_derivatives
-            + wm_csf_power2
-            + wm_csf_full,
-        },
-        "global_signal": {
-            "basic": gs_basic,
-            "power2": gs_basic + gs_power2,
-            "derivatives": gs_basic + gs_derivatives,
-            "full": gs_basic + gs_derivatives + gs_power2 + gs_full,
-        },
-    }
-
-    # generate a junifer pipeline data object dictionary
-    input_data_obj = {}
-    input_data_obj["meta"] = {}
-    input_data_obj["BOLD"] = {}
-    input_data_obj["BOLD"]["data"] = siimg
-    input_data_obj["confounds"] = {}
-    input_data_obj["confounds"]["path"] = Path("/test.df")
-    input_data_obj["confounds"]["data"] = confounds_df
-    input_data_obj["confounds"]["names"] = {}
-    input_data_obj["confounds"]["names"]["spec"] = spec
-    input_data_obj["confounds"]["names"]["spike"] = "FD"
-
-    # generate confound removal strategies with varying numbers of parameters
-
-    # Test #1: 36 params, no derivatives to compute, no spike
-    # 36 params
-    strat1 = {"motion": "full", "wm_csf": "full", "global_signal": "full"}
-
-    cr = BaseConfoundRemover(
-        strategy=strat1, spike=None, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 36
-    assert all(x in t_df.columns for x in motion_basic)
-    assert all(x in t_df.columns for x in motion_power2)
-    assert all(x in t_df.columns for x in motion_derivatives)
-    assert all(x in t_df.columns for x in motion_full)
-    assert all(x in t_df.columns for x in wm_csf_basic)
-    assert all(x in t_df.columns for x in wm_csf_power2)
-    assert all(x in t_df.columns for x in wm_csf_derivatives)
-    assert all(x in t_df.columns for x in wm_csf_full)
-    assert all(x in t_df.columns for x in gs_basic)
-    assert all(x in t_df.columns for x in gs_power2)
-    assert all(x in t_df.columns for x in gs_derivatives)
-    assert all(x in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" not in t_df.columns
-
-    # Test #2: 24 params, no derivatives to compute, no spike
-    # 24 params
-    strat2 = {
-        "motion": "full",
-    }
-
-    cr = BaseConfoundRemover(
-        strategy=strat2, spike=None, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 24
-    assert all(x in t_df.columns for x in motion_basic)
-    assert all(x in t_df.columns for x in motion_power2)
-    assert all(x in t_df.columns for x in motion_derivatives)
-    assert all(x in t_df.columns for x in motion_full)
-    assert all(x not in t_df.columns for x in wm_csf_basic)
-    assert all(x not in t_df.columns for x in wm_csf_power2)
-    assert all(x not in t_df.columns for x in wm_csf_derivatives)
-    assert all(x not in t_df.columns for x in wm_csf_full)
-    assert all(x not in t_df.columns for x in gs_basic)
-    assert all(x not in t_df.columns for x in gs_power2)
-    assert all(x not in t_df.columns for x in gs_derivatives)
-    assert all(x not in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" not in t_df.columns
-
-    # Test #3: 9 params, no derivatives to compute, no spike
-    strat3 = {"motion": "basic", "wm_csf": "basic", "global_signal": "basic"}
-
-    cr = BaseConfoundRemover(
-        strategy=strat3, spike=None, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 9
-    assert all(x in t_df.columns for x in motion_basic)
-    assert all(x not in t_df.columns for x in motion_power2)
-    assert all(x not in t_df.columns for x in motion_derivatives)
-    assert all(x not in t_df.columns for x in motion_full)
-    assert all(x in t_df.columns for x in wm_csf_basic)
-    assert all(x not in t_df.columns for x in wm_csf_power2)
-    assert all(x not in t_df.columns for x in wm_csf_derivatives)
-    assert all(x not in t_df.columns for x in wm_csf_full)
-    assert all(x in t_df.columns for x in gs_basic)
-    assert all(x not in t_df.columns for x in gs_power2)
-    assert all(x not in t_df.columns for x in gs_derivatives)
-    assert all(x not in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" not in t_df.columns
-
-    # Test #4: 6 params, no derivatives to compute, no spike
-    strat4 = {
-        "motion": "basic",
-    }
-    cr = BaseConfoundRemover(
-        strategy=strat4, spike=None, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 6
-    assert all(x in t_df.columns for x in motion_basic)
-    assert all(x not in t_df.columns for x in motion_power2)
-    assert all(x not in t_df.columns for x in motion_derivatives)
-    assert all(x not in t_df.columns for x in motion_full)
-    assert all(x not in t_df.columns for x in wm_csf_basic)
-    assert all(x not in t_df.columns for x in wm_csf_power2)
-    assert all(x not in t_df.columns for x in wm_csf_derivatives)
-    assert all(x not in t_df.columns for x in wm_csf_full)
-    assert all(x not in t_df.columns for x in gs_basic)
-    assert all(x not in t_df.columns for x in gs_power2)
-    assert all(x not in t_df.columns for x in gs_derivatives)
-    assert all(x not in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" not in t_df.columns
-
-    # Test #5: 2 params, no derivatives to compute, no spike
-    strat5 = {
-        "wm_csf": "basic",
-    }
-    cr = BaseConfoundRemover(
-        strategy=strat5, spike=None, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 2
-    assert all(x not in t_df.columns for x in motion_basic)
-    assert all(x not in t_df.columns for x in motion_power2)
-    assert all(x not in t_df.columns for x in motion_derivatives)
-    assert all(x not in t_df.columns for x in motion_full)
-    assert all(x in t_df.columns for x in wm_csf_basic)
-    assert all(x not in t_df.columns for x in wm_csf_power2)
-    assert all(x not in t_df.columns for x in wm_csf_derivatives)
-    assert all(x not in t_df.columns for x in wm_csf_full)
-    assert all(x not in t_df.columns for x in gs_basic)
-    assert all(x not in t_df.columns for x in gs_power2)
-    assert all(x not in t_df.columns for x in gs_derivatives)
-    assert all(x not in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" not in t_df.columns
-
-    out = cr.fit_transform(input_data_obj)
-    check_niimg_4d(out["BOLD"]["data"])
-    # TODO: check meta
-
-    # Test #6: 12 params, derivatives to compute, spike
-    to_select = [
-        x for x in confounds_df.columns if x not in motion_derivatives
-    ]
-    no_d_df = confounds_df[to_select]
-    input_data_obj["confounds"]["data"] = no_d_df
-
-    derivatives = {f"D{x}": x for x in motion_basic}
-
-    input_data_obj["confounds"]["names"]["derivatives"] = derivatives
-
-    strat6 = {
-        "motion": "derivatives",
-    }
-    cr = BaseConfoundRemover(
-        strategy=strat6, spike=0.75, mask_img=simsk, t_r=0.75
-    )
-    cr.validate_input(list(input_data_obj.keys()))
-    out_type = cr.get_output_kind(list(input_data_obj.keys()))
-
-    assert "BOLD" in out_type
-
-    # Check if the input data is valid
-    cr._validate_data(input_data_obj)
-
-    # Check that the confounds are picked correctly:
-    t_df = cr._pick_confounds(input_data_obj["confounds"])
-    assert len(t_df.columns) == 13
-    assert all(x in t_df.columns for x in motion_basic)
-    assert all(x not in t_df.columns for x in motion_power2)
-    assert all(x in t_df.columns for x in motion_derivatives)
-    assert all(x not in t_df.columns for x in motion_full)
-    assert all(x not in t_df.columns for x in wm_csf_basic)
-    assert all(x not in t_df.columns for x in wm_csf_power2)
-    assert all(x not in t_df.columns for x in wm_csf_derivatives)
-    assert all(x not in t_df.columns for x in wm_csf_full)
-    assert all(x not in t_df.columns for x in gs_basic)
-    assert all(x not in t_df.columns for x in gs_power2)
-    assert all(x not in t_df.columns for x in gs_derivatives)
-    assert all(x not in t_df.columns for x in gs_full)
-    assert "FD" not in t_df.columns
-    assert "spike" in t_df.columns

junifer/preprocess/tests/test_preprocess_base.py

@@ -0,0 +1,65 @@
+"""Provide tests for BasePreprocessor."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+#          Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import pytest
+
+from junifer.preprocess.base import BasePreprocessor
+
+
+def test_base_preprocessor_abstractness() -> None:
+    """Test BasePreprocessor is abstract base class."""
+    with pytest.raises(TypeError, match=r"abstract"):
+        BasePreprocessor(on=["BOLD"])  # type: ignore
+
+
+def test_base_preprocessor_subclassing() -> None:
+    """Test proper subclassing of BasePreprocessor."""
+    # Create concrete class
+    class MyBasePreprocessor(BasePreprocessor):
+        def get_valid_inputs(self):
+            return ["BOLD", "T1w"]
+
+        def get_output_kind(self, input):
+            return ["timeseries"]
+
+        def preprocess(self, input, extra_input):
+            input["data"] = f"mofidied_{input['data']}"
+            return "BOLD", input
+
+    with pytest.raises(ValueError, match=r"cannot be computed on \['T2w'\]"):
+        MyBasePreprocessor(on=["BOLD", "T2w"])
+
+    with pytest.raises(ValueError, match=r"cannot be computed on \['T2w'\]"):
+        MyBasePreprocessor(on="T2w")
+
+    # Create input for marker
+    input_ = {
+        "meta": {"datagrabber": "dg", "element": "elem", "datareader": "dr"},
+        "BOLD": {
+            "path": ".",
+            "data": "data",
+        },
+        "T1w": {
+            "path": ".",
+            "data": "data",
+        },
+    }
+    prep = MyBasePreprocessor(on=["BOLD"])
+
+    with pytest.raises(ValueError, match="not have the required data"):
+        prep.validate_input(["T1w"])
+
+    output = prep.fit_transform(input=input_)  # process
+    # Check output
+    assert "BOLD" in output
+    assert "data" in output["BOLD"]
+    assert output["BOLD"]["data"] == "mofidied_data"
+    assert "path" in output["BOLD"]
+    assert "meta" in output["BOLD"]
+
+    assert "T1w" in output
+    assert "data" in output["T1w"]
+    assert output["T1w"]["data"] == "data"

junifer/testing/__init__.py

@@ -5,3 +5,4 @@
 # License: AGPL
 
 from . import datagrabbers
+from .utils import get_testing_data

junifer/testing/data/sub-0001_task-anticipation_acq-seq_desc-confounds_regressors.tsv

@@ -0,0 +1,201 @@
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junifer/testing/datagrabbers.py

@@ -5,6 +5,7 @@
 # License: AGPL
 
 import tempfile
+from pathlib import Path
 from typing import Dict, List
 
 import nibabel as nib
@@ -39,7 +40,7 @@ class OasisVBMTestingDatagrabber(BaseDataGrabber):
         """
         out = super().__getitem__(element)
         i_sub = int(element.split("-")[1]) - 1
-        out["VBM_GM"] = {"path": self._dataset.gray_matter_maps[i_sub]}
+        out["VBM_GM"] = {"path": Path(self._dataset.gray_matter_maps[i_sub])}
         # Set the element accordingly
         out["meta"]["element"] = {"subject": element}
         return out
@@ -120,3 +121,93 @@ class SPMAuditoryTestingDatagrabber(BaseDataGrabber):
         # Set the element accordingly
         out["meta"]["element"] = {"subject": element}
         return out
+
+
+class PartlyCloudyTestingDataGrabber(BaseDataGrabber):
+    """DataGrabber for Partly Cloudy dataset.
+
+    Wrapper for nilearn.datasets.fetch_partly_cloudy
+
+
+    Parameters
+    ----------
+    reduce_confounds : bool, optional
+        If True, the returned confounds only include 6 motion parameters,
+        mean framewise displacement, signal from white matter, csf, and
+        6 anatomical compcor parameters. This selection only serves the
+        purpose of having realistic examples. Depending on your research
+        question, other confounds might be more appropriate.
+        If False, returns all :term:`fMRIPrep` confounds (default True).
+
+    age_group : {"adults", "child", "both"}, optional
+       Which age group to fetch (default "both"):
+
+        * 'adults' = fetch adults only (n=33, ages 18-39)
+        * 'child' = fetch children only (n=122, ages 3-12)
+        * 'both' = fetch full sample (n=155)
+    """
+
+    def __init__(
+        self, reduce_confounds: bool = True, age_group: str = "both"
+    ) -> None:
+        """Initialize the class."""
+        datadir = tempfile.mkdtemp()
+        # Define types
+        types = ["BOLD", "BOLD_confounds"]
+        self.reduce_confounds = reduce_confounds
+        self.age_group = age_group
+        super().__init__(types=types, datadir=datadir)
+
+    def __enter__(self) -> "PartlyCloudyTestingDataGrabber":
+        """Implement context entry.
+
+        Returns
+        -------
+        PartlyCloudyTestingDataGrabber
+
+        """
+        self._dataset = datasets.fetch_development_fmri(
+            n_subjects=10,
+            reduce_confounds=self.reduce_confounds,
+            age_group=self.age_group,
+        )
+        return self
+
+    def get_elements(self) -> List[str]:
+        """Get elements.
+
+        Returns
+        -------
+        list of str
+            List of elements that can be grabbed.
+
+        """
+        return [f"sub-{x:02d}" for x in list(range(1, 11))]
+
+    def __getitem__(self, element: str) -> Dict:
+        """Implement indexing support.
+
+        Parameters
+        ----------
+        element : str
+            The element to retrieve.
+
+        Returns
+        -------
+        dict
+            The data along with the metadata.
+
+        """
+        out = super().__getitem__(element)
+        i_sub = int(element.split("-")[1]) - 1
+        out["BOLD"] = {"path": Path(self._dataset["func"][i_sub])}
+        conf_format = "fmriprep"
+
+        out["BOLD_confounds"] = {
+            "path": Path(self._dataset["confounds"][i_sub]),
+            "format": conf_format,
+        }
+
+        # Set the element accordingly
+        out["meta"]["element"] = {"subject": element}
+        return out

junifer/testing/tests/test_oasisvmbtesting_datagrabber.py

@@ -0,0 +1,29 @@
+"""Provide tests for Oasis VBM Testing datagrabber."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+# License: AGPL
+
+from junifer.testing.datagrabbers import OasisVBMTestingDatagrabber
+
+
+def test_OasisVBMTestingDatagrabber() -> None:
+    """Test Oasis VBM Testing datagrabber."""
+    expected_elements = [
+        "sub-01",
+        "sub-02",
+        "sub-03",
+        "sub-04",
+        "sub-05",
+        "sub-06",
+        "sub-07",
+        "sub-08",
+        "sub-09",
+        "sub-10",
+    ]
+    with OasisVBMTestingDatagrabber() as dg:
+        all_elements = dg.get_elements()
+        assert set(all_elements) == set(expected_elements)
+        out = dg["sub-01"]
+        assert "VBM_GM" in out
+        assert out["VBM_GM"]["path"].exists()
+        assert out["VBM_GM"]["path"].is_file()

junifer/testing/tests/test_partlycloudytesting_datagrabber.py

@@ -0,0 +1,40 @@
+"""Provide tests for PartlyCloudy datagrabber."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+# License: AGPL
+
+from junifer.testing.datagrabbers import PartlyCloudyTestingDataGrabber
+
+
+def test_PartlyCloudyTestingDataGrabber() -> None:
+    """Test PartlyCloudy datagrabber."""
+    expected_elements = [
+        "sub-01",
+        "sub-02",
+        "sub-03",
+        "sub-04",
+        "sub-05",
+        "sub-06",
+        "sub-07",
+        "sub-08",
+        "sub-09",
+        "sub-10",
+    ]
+    with PartlyCloudyTestingDataGrabber() as dg:
+        all_elements = dg.get_elements()
+        assert set(all_elements) == set(expected_elements)
+        out = dg["sub-01"]
+        assert "BOLD" in out
+        assert out["BOLD"]["path"].exists()
+        assert out["BOLD"]["path"].is_file()
+
+        assert "BOLD_confounds" in out
+        assert out["BOLD_confounds"]["path"].exists()
+        assert out["BOLD_confounds"]["path"].is_file()
+        assert "format" in out["BOLD_confounds"]
+        assert "fmriprep" == out["BOLD_confounds"]["format"]
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        out = dg["sub-01"]
+        assert "format" in out["BOLD_confounds"]
+        assert "fmriprep" == out["BOLD_confounds"]["format"]

junifer/testing/utils.py

@@ -0,0 +1,25 @@
+"""Testing utils."""
+
+# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
+# License: AGPL
+
+from pathlib import Path
+
+
+def get_testing_data(fname : str) -> Path:
+    """Get the path to a testing data file.
+
+    Parameters
+    ----------
+    fname : str
+        The name of the file.
+
+    Returns
+    -------
+    pathlib.Path
+        The absolute path to the file.
+    """
+    t_path = Path(__file__).parent / "data" / fname
+    if not t_path.exists():
+        raise FileNotFoundError(f"File {fname} not found in testing data")
+    return t_path.resolve()