docs/changes/latest.inc

@@ -57,6 +57,8 @@ Bugs
 - Fix :class:`junifer.markers.AmplitudeLowFrequencyFluctuationParcels`, :class:`junifer.markers.AmplitudeLowFrequencyFluctuationSpheres`
   :class:`junifer.markers.ReHoSpheres` and :class:`junifer.markers.ReHoParcels` pass the ``extra_input`` parameter (:gh:`187` by `Fede Raimondo`_).
 
+- Fix several markers that did not properly handle the ``extra_input`` parameter (:gh:`189` by `Fede Raimondo`_).
+
 API changes
 ~~~~~~~~~~~
 

junifer/markers/functional_connectivity/crossparcellation_functional_connectivity.py

@@ -130,12 +130,12 @@ class CrossParcellationFC(BaseMarker):
             parcellation=self.parcellation_one,
             method=self.aggregation_method,
             masks=self.masks,
-        ).compute(input)
+        ).compute(input, extra_input=extra_input)
         parcellation_two_dict = ParcelAggregation(
             parcellation=self.parcellation_two,
             method=self.aggregation_method,
             masks=self.masks,
-        ).compute(input)
+        ).compute(input, extra_input=extra_input)
 
         parcellated_ts_one = parcellation_one_dict["data"]
         parcellated_ts_two = parcellation_two_dict["data"]

junifer/markers/functional_connectivity/edge_functional_connectivity_parcels.py

@@ -72,8 +72,32 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
             name=name,
         )
 
-    def aggregate(self, input: Dict[str, Any]) -> Dict:
-        """Perform parcel aggregation."""
+    def aggregate(
+        self, input: Dict[str, Any], extra_input: Optional[Dict] = None
+    ) -> Dict:
+        """Perform parcel aggregation and ETS computation.
+
+        Parameters
+        ----------
+        input : dict
+            A single input from the pipeline data object in which to compute
+            the marker.
+        extra_input : dict, optional
+            The other fields in the pipeline data object. Useful for accessing
+            other data kind that needs to be used in the computation. For
+            example, the functional connectivity markers can make use of the
+            confounds if available (default None).
+
+        Returns
+        -------
+        dict
+            The computed result as dictionary. This will be either returned
+            to the user or stored in the storage by calling the store method
+            with this as a parameter. The dictionary has the following keys:
+
+            * ``data`` : the actual computed values as a numpy.ndarray
+            * ``col_names`` : the column labels for the computed values as list
+        """
         parcel_aggregation = ParcelAggregation(
             parcellation=self.parcellation,
             method=self.agg_method,
@@ -82,7 +106,9 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
             on="BOLD",
         )
 
-        bold_aggregated = parcel_aggregation.compute(input)
+        bold_aggregated = parcel_aggregation.compute(
+            input, extra_input=extra_input
+        )
         ets, edge_names = _ets(
             bold_aggregated["data"], bold_aggregated["col_names"]
         )

junifer/markers/functional_connectivity/edge_functional_connectivity_spheres.py

@@ -79,8 +79,33 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
             name=name,
         )
 
-    def aggregate(self, input: Dict[str, Any]) -> Dict:
-        """Perform sphere aggregation."""
+    def aggregate(
+        self, input: Dict[str, Any], extra_input: Optional[Dict] = None
+    ) -> Dict:
+        """Perform sphere aggregation and ETS computation.
+
+        Parameters
+        ----------
+        input : dict
+            A single input from the pipeline data object in which to compute
+            the marker.
+        extra_input : dict, optional
+            The other fields in the pipeline data object. Useful for accessing
+            other data kind that needs to be used in the computation. For
+            example, the functional connectivity markers can make use of the
+            confounds if available (default None).
+
+        Returns
+        -------
+        dict
+            The computed result as dictionary. This will be either returned
+            to the user or stored in the storage by calling the store method
+            with this as a parameter. The dictionary has the following keys:
+
+            * ``data`` : the actual computed values as a numpy.ndarray
+            * ``col_names`` : the column labels for the computed values as list
+
+        """
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
@@ -89,7 +114,9 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
             masks=self.masks,
             on="BOLD",
         )
-        bold_aggregated = sphere_aggregation.compute(input)
+        bold_aggregated = sphere_aggregation.compute(
+            input, extra_input=extra_input
+        )
         ets, edge_names = _ets(
             bold_aggregated["data"], bold_aggregated["col_names"]
         )

junifer/markers/functional_connectivity/functional_connectivity_parcels.py

@@ -64,8 +64,33 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
             name=name,
         )
 
-    def aggregate(self, input: Dict[str, Any]) -> Dict:
-        """Perform parcel aggregation."""
+    def aggregate(
+        self, input: Dict[str, Any], extra_input: Optional[Dict] = None
+    ) -> Dict:
+        """Perform parcel aggregation.
+
+        Parameters
+        ----------
+        input : dict
+            A single input from the pipeline data object in which to compute
+            the marker.
+        extra_input : dict, optional
+            The other fields in the pipeline data object. Useful for accessing
+            other data kind that needs to be used in the computation. For
+            example, the functional connectivity markers can make use of the
+            confounds if available (default None).
+
+        Returns
+        -------
+        dict
+            The computed result as dictionary. This will be either returned
+            to the user or stored in the storage by calling the store method
+            with this as a parameter. The dictionary has the following keys:
+
+            * ``data`` : the actual computed values as a numpy.ndarray
+            * ``col_names`` : the column labels for the computed values as list
+
+        """
         parcel_aggregation = ParcelAggregation(
             parcellation=self.parcellation,
             method=self.agg_method,
@@ -74,4 +99,4 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
             on="BOLD",
         )
         # Return the 2D timeseries after parcel aggregation
-        return parcel_aggregation.compute(input)
+        return parcel_aggregation.compute(input, extra_input=extra_input)

junifer/markers/functional_connectivity/functional_connectivity_spheres.py

@@ -73,8 +73,33 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
             name=name,
         )
 
-    def aggregate(self, input: Dict[str, Any]) -> Dict:
-        """Perform sphere aggregation."""
+    def aggregate(
+        self, input: Dict[str, Any], extra_input: Optional[Dict] = None
+    ) -> Dict:
+        """Perform sphere aggregation.
+
+        Parameters
+        ----------
+        input : dict
+            A single input from the pipeline data object in which to compute
+            the marker.
+        extra_input : dict, optional
+            The other fields in the pipeline data object. Useful for accessing
+            other data kind that needs to be used in the computation. For
+            example, the functional connectivity markers can make use of the
+            confounds if available (default None).
+
+        Returns
+        -------
+        dict
+            The computed result as dictionary. This will be either returned
+            to the user or stored in the storage by calling the store method
+            with this as a parameter. The dictionary has the following keys:
+
+            * ``data`` : the actual computed values as a numpy.ndarray
+            * ``col_names`` : the column labels for the computed values as list
+
+        """
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
@@ -84,4 +109,4 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
             on="BOLD",
         )
         # Return the 2D timeseries after sphere aggregation
-        return sphere_aggregation.compute(input)
+        return sphere_aggregation.compute(input, extra_input=extra_input)