✨ Use xarray internally and move relations/constraints/penaltys to gl…

…otaran.model (#734)

* Changed calculate_matrix function to return xarrays

* Added relation and constraint to basemodel

* Added functions to apply relations and constraints

* Removed LabelAndMatrix class

* Added relations and constraints tests to base model

* Added penalties to base model

* Adapted models to changes

* Address xarray deprecation warnings

Fixed DeprecationWarning: Using a DataArray object to construct a variable is ambiguous, please extract the data using the .data property. This will raise a TypeError in 0.19.0.

Co-authored-by: Jörn Weißenborn <[email protected]>
Co-authored-by: Joris Snellenburg <[email protected]>

glotaran/analysis/problem.py

@@ -92,9 +92,7 @@ def __init__(self, scheme: Scheme):
 
         # all of the above are always not None
 
-        self._clp_labels = None
         self._matrices = None
-        self._reduced_clp_labels = None
         self._reduced_matrices = None
         self._reduced_clps = None
         self._clps = None
@@ -166,14 +164,6 @@ def groups(self) -> dict[str, list[str]]:
             self.init_bag()
         return self._groups
 
-    @property
-    def clp_labels(
-        self,
-    ) -> dict[str, list[str] | list[list[str]]]:
-        if self._clp_labels is None:
-            self.calculate_matrices()
-        return self._clp_labels
-
     @property
     def matrices(
         self,
@@ -182,14 +172,6 @@ def matrices(
             self.calculate_matrices()
         return self._matrices
 
-    @property
-    def reduced_clp_labels(
-        self,
-    ) -> dict[str, list[str] | list[list[str]]]:
-        if self._reduced_clp_labels is None:
-            self.calculate_matrices()
-        return self._reduced_clp_labels
-
     @property
     def reduced_matrices(
         self,
@@ -235,23 +217,12 @@ def additional_penalty(
         self,
     ) -> dict[str, list[float]]:
         if self._additional_penalty is None:
-            self.calculate_additional_penalty()
+            self.calculate_residual()
         return self._additional_penalty
 
     @property
     def full_penalty(self) -> np.ndarray:
-        if self._full_penalty is None:
-            residuals = self.weighted_residuals
-            additional_penalty = self.additional_penalty
-            if not self.grouped:
-                residuals = [np.concatenate(residuals[label]) for label in residuals.keys()]
-
-            self._full_penalty = (
-                np.concatenate((np.concatenate(residuals), additional_penalty))
-                if additional_penalty is not None
-                else np.concatenate(residuals)
-            )
-        return self._full_penalty
+        raise NotImplementedError
 
     @property
     def cost(self) -> float:
@@ -272,9 +243,7 @@ def reset(self):
         self._reset_results()
 
     def _reset_results(self):
-        self._clp_labels = None
         self._matrices = None
-        self._reduced_clp_labels = None
         self._reduced_matrices = None
         self._reduced_clps = None
         self._clps = None
@@ -372,24 +341,6 @@ def calculate_matrices(self):
     def calculate_residual(self):
         raise NotImplementedError
 
-    def calculate_additional_penalty(self) -> np.ndarray | dict[str, np.ndarray]:
-        """Calculates additional penalties by calling the model.additional_penalty function."""
-        if (
-            callable(self.model.has_additional_penalty_function)
-            and self.model.has_additional_penalty_function()
-        ):
-            self._additional_penalty = self.model.additional_penalty_function(
-                self.parameters,
-                self.clp_labels,
-                self.clps,
-                self.matrices,
-                self.data,
-                self._scheme.group_tolerance,
-            )
-        else:
-            self._additional_penalty = None
-        return self._additional_penalty
-
     def create_result_data(
         self, copy: bool = True, history_index: int | None = None
     ) -> dict[str, xr.Dataset]: