Update test case with parallel model

examples/01-fluorescence/scheme.yaml

@@ -7,6 +7,14 @@ library:
       (s4, s3): rates_global.3
       (s4, s4): rates_global.4
 
+  parallel_model:  # TJust a name, could be MyDescriptiveModelName
+    type: kinetic
+    rates:
+      (s1, s1): rates_global.1
+      (s2, s2): rates_global.2
+      (s3, s3): rates_global.3
+      (s4, s4): rates_global.4
+
 experiments:
   experiment1:  # Just a name, could be MyFirstExperiment
     datasets:
@@ -20,3 +28,16 @@ experiments:
             width: irf.width
             backsweep: 13200.0 # backsweep.period
     residual_function: non_negative_least_squares
+
+  experiment2:  # Just a name, could be MyFirstExperiment
+    datasets:
+      dataset2: # Should be possible to uses dataset1 here as well
+        elements: [parallel_model]
+        activation:
+          - type: gaussian
+            compartments:
+              s1: 1.0  # input.1
+            center: irf.center
+            width: irf.width
+            backsweep: 13200.0 # backsweep.period
+    residual_function: non_negative_least_squares

tests/examples/test_01_fluorescence.py

@@ -1,21 +1,34 @@
-from glotaran.io import load_dataset, load_parameters, load_scheme
+from __future__ import annotations
+
 from pathlib import Path
-from pytest import approx
+
 from pyreporoot import project_root
+from pytest import approx
+
+from glotaran.io import load_dataset
+from glotaran.io import load_parameters
+from glotaran.io import load_scheme
 
 repo_root = Path(project_root(__file__, root_files=[".git", "pyproject.toml"]))
 example_folder = repo_root / "examples/01-fluorescence"
 
 
 def test_01_fluorescence():
-    fit_scheme = load_scheme(example_folder/"scheme.yaml", format_name="yml")
-    parameters = load_parameters(example_folder/"parameters.yaml")
-    data_path = example_folder/"data/data.ascii"
-    fit_scheme.load_data({"dataset1": load_dataset(data_path)})
+    fit_scheme = load_scheme(example_folder / "scheme.yaml", format_name="yml")
+    parameters = load_parameters(example_folder / "parameters.yaml")
+    data_path = example_folder / "data/data.ascii"
+    fit_scheme.load_data(
+        {"dataset1": load_dataset(data_path), "dataset2": load_dataset(data_path)}
+    )
     result = fit_scheme.optimize(parameters=parameters)
     assert result is not None
-    assert result.data['dataset1'].root_mean_square_error == approx(43.98496093)
+    assert result.data["dataset1"].root_mean_square_error == approx(43.98496093)
+
+    # Compare the 'SAS' (EAS / DAS) for the both cases:
+    # result.data['dataset1'].species_associated_estimation
+    # result.data['dataset2'].species_associated_estimation
+    # Currently seems incorrect.
+
 
 if __name__ == "__main__":
     test_01_fluorescence()
-