Changed megacomplex scale application and definition.

Megacomplex scales are now a property of the dataset_descriptor. They
are defined per dataset per megacomplex.

Fixes glotaran#606

glotaran/builtin/io/yml/test/test_model_spec_kinetic.yml

@@ -56,7 +56,7 @@ megacomplex:
       k_matrix: [km1] # A megacomplex has one or more k-matrices
   cmplx2:
       k_matrix: [km2]
-  cmplx3: [[km3], None]
+  cmplx3: [[km3]]
 
 spectral_constraints:
   - type: zero

glotaran/builtin/models/kinetic_image/initial_concentration.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 
-from glotaran.model import DatasetDescriptor
 from glotaran.model import model_attribute
 from glotaran.parameter import Parameter
 
@@ -22,16 +21,8 @@ class InitialConcentration:
     """An initial concentration describes the population of the compartments at
     the beginning of an experiment."""
 
-    def normalized(self, dataset: DatasetDescriptor) -> InitialConcentration:
-        parameters = self.parameters
-        for megacomplex in dataset.megacomplex:
-            scale = [
-                megacomplex.scale
-                if c in megacomplex.involved_compartments and megacomplex.scale
-                else 1
-                for c in self.compartments
-            ]
-            parameters = np.multiply(parameters, scale)
+    def normalized(self) -> InitialConcentration:
+        parameters = np.array(self.parameters)
         idx = [c not in self.exclude_from_normalize for c in self.compartments]
         parameters[idx] /= np.sum(parameters[idx])
         new = copy.deepcopy(self)

glotaran/builtin/models/kinetic_image/kinetic_image_dataset_descriptor.py

@@ -12,11 +12,38 @@
     }
 )
 class KineticImageDatasetDescriptor(DatasetDescriptor):
-    def get_k_matrices(self):
-        return [mat for mat in [cmplx.full_k_matrix() for cmplx in self.megacomplex] if mat]
+    def get_megacomplex_k_matrices(self):
+        scales = (
+            [
+                self.megacomplex_scale[i]
+                for i in [
+                    i
+                    for i, megacomplex in enumerate(self.megacomplex)
+                    if megacomplex.has_k_matrix()
+                ]
+            ]
+            if self.megacomplex_scale is not None
+            else None
+        )
+
+        matrices = [
+            matrix
+            for matrix in [
+                megacomplex.full_k_matrix()
+                for megacomplex in self.megacomplex
+                if megacomplex.has_k_matrix()
+            ]
+        ]
+
+        return scales, matrices
+
+    def has_k_matrix(self):
+        _, matrices = self.get_megacomplex_k_matrices()
+        return len(matrices) != 0
 
     def compartments(self):
         compartments = []
-        for k in self.get_k_matrices():
+        _, k_matrices = self.get_megacomplex_k_matrices()
+        for k in k_matrices:
             compartments += k.involved_compartments()
         return list(set(compartments))

glotaran/builtin/models/kinetic_image/kinetic_image_matrix.py

@@ -20,7 +20,7 @@ def kinetic_matrix(
 
     compartments = None
     matrix = None
-    k_matrices = dataset_descriptor.get_k_matrices()
+    megacomplex_scales, k_matrices = dataset_descriptor.get_megacomplex_k_matrices()
 
     if len(k_matrices) == 0:
         return (None, None)
@@ -29,9 +29,9 @@ def kinetic_matrix(
         raise Exception(
             f'No initial concentration specified in dataset "{dataset_descriptor.label}"'
         )
-    initial_concentration = dataset_descriptor.initial_concentration.normalized(dataset_descriptor)
+    initial_concentration = dataset_descriptor.initial_concentration.normalized()
 
-    for k_matrix in k_matrices:
+    for i, k_matrix in enumerate(k_matrices):
 
         if k_matrix is None:
             continue
@@ -46,6 +46,9 @@ def kinetic_matrix(
             matrix_implementation,
         )
 
+        if megacomplex_scales is not None:
+            this_matrix *= megacomplex_scales[i]
+
         if matrix is None:
             compartments = this_compartments
             matrix = this_matrix

glotaran/builtin/models/kinetic_image/kinetic_image_megacomplex.py

@@ -3,18 +3,19 @@
 from typing import List
 
 from glotaran.model import model_attribute
-from glotaran.parameter import Parameter
 
 
 @model_attribute(
     properties={
         "k_matrix": {"type": List[str], "default": []},
-        "scale": {"type": Parameter, "allow_none": True},
     }
 )
 class KineticImageMegacomplex:
     """A Megacomplex with one or more K-Matrices."""
 
+    def has_k_matrix(self) -> bool:
+        return len(self.k_matrix) != 0
+
     def full_k_matrix(self, model=None):
         full_k_matrix = None
         for k_matrix in self.k_matrix:

glotaran/builtin/models/kinetic_image/kinetic_image_result.py

@@ -12,7 +12,8 @@ def finalize_kinetic_image_result(model, problem: Problem, data: dict[str, xr.Da
     for label, dataset in data.items():
 
         dataset_descriptor = problem.filled_dataset_descriptors[label]
-        if not dataset_descriptor.get_k_matrices():
+
+        if not dataset_descriptor.has_k_matrix():
             continue
 
         retrieve_species_assocatiated_data(problem.model, dataset, dataset_descriptor, "images")

glotaran/builtin/models/kinetic_spectrum/kinetic_spectrum_result.py

@@ -17,7 +17,7 @@ def finalize_kinetic_spectrum_result(model, problem: Problem, data: dict[str, xr
     for label, dataset in data.items():
 
         dataset_descriptor = problem.filled_dataset_descriptors[label]
-        if not dataset_descriptor.get_k_matrices():
+        if not dataset_descriptor.has_k_matrix():
             continue
 
         retrieve_species_assocatiated_data(problem.model, dataset, dataset_descriptor, "spectra")

glotaran/model/dataset_descriptor.py

@@ -10,6 +10,7 @@
 @model_attribute(
     properties={
         "megacomplex": List[str],
+        "megacomplex_scale": {"type": List[Parameter], "default": None, "allow_none": True},
         "scale": {"type": Parameter, "default": None, "allow_none": True},
     }
 )

glotaran/model/test/test_model.py

@@ -75,7 +75,7 @@ def model():
                 "megacomplex": ["m1", "m2"],
                 "scale": "scale_1",
             },
-            "dataset2": [["m2"], "scale_2"],
+            "dataset2": [["m2"], ["bar"], "scale_2"],
         },
     }
     return MockModel.from_dict(d)
@@ -98,7 +98,7 @@ def model_error():
                 "megacomplex": ["N1", "N2"],
                 "scale": "scale_1",
             },
-            "dataset2": [["mrX"], "scale_3"],
+            "dataset2": [["mrX"], ["bar"], "scale_3"],
         },
     }
     return MockModel.from_dict(d)