Adds error message for undefined source

Author: DrPaulSharp

RATapi/inputs.py

@@ -154,6 +154,23 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
     hydrate_id = {"bulk in": 1, "bulk out": 2}
     prior_id = {"uniform": 1, "gaussian": 2, "jeffreys": 3}
 
+    # Ensure backgrounds and resolutions have a source defined
+    for contrast in project.contrasts:
+        background = project.backgrounds[contrast.background]
+        resolution = project.resolutions[contrast.resolution]
+        if background.source == "":
+            raise ValueError(
+                f"All backgrounds must have a source defined. For a {background.type} type background, "
+                f"the source must be defined in "
+                f'"{RATapi.project.values_defined_in[f"backgrounds.{background.type}.source"]}"'
+            )
+        if resolution.source == "" and resolution.type != TypeOptions.Data:
+            raise ValueError(
+                f"All constant and function resolutions must have a source defined. For a "
+                f"{resolution.type} type resolution, the source must be defined in "
+                f'"{RATapi.project.values_defined_in[f"resolutions.{resolution.type}.source"]}"'
+            )
+
     # Set contrast parameters according to model type
     if project.model == LayerModels.StandardLayers:
         if project.calculation == Calculations.Domains:
@@ -194,9 +211,9 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
     contrast_resolution_params = []
     contrast_resolution_types = []
 
-    # set data, background and resolution for each contrast
+    # Set data, background and resolution for each contrast
     for contrast in project.contrasts:
-        # set data
+        # Set data
         data_index = project.data.index(contrast.data)
         data = project.data[data_index].data
         data_range = project.data[data_index].data_range
@@ -212,7 +229,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
         else:
             simulation_limits.append([0.0, 0.0])
 
-        # set background parameters
+        # Set background parameters
         background = project.backgrounds[contrast.background]
         contrast_background_types.append(background.type)
         contrast_background_param = []
@@ -221,7 +238,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
             contrast_background_param.append(project.data.index(background.source, True))
             if background.value_1 != "":
                 contrast_background_param.append(project.background_parameters.index(background.value_1))
-            # if we are using a data background, we add the background data to the contrast data
+            # If we are using a data background, we add the background data to the contrast data
             data = append_data_background(data, project.data[background.source].data)
 
         elif background.type == TypeOptions.Function:
@@ -245,7 +262,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
 
         contrast_background_params.append(contrast_background_param)
 
-        # set resolution parameters
+        # Set resolution parameters
         resolution = project.resolutions[contrast.resolution]
         contrast_resolution_types.append(resolution.type)
         contrast_resolution_param = []
@@ -270,7 +287,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
 
         contrast_resolution_params.append(contrast_resolution_param)
 
-        # contrast data has exactly six columns to include background data if relevant
+        # Contrast data has exactly six columns to include background data if relevant
         all_data.append(np.column_stack((data, np.zeros((data.shape[0], 6 - data.shape[1])))))
 
     problem = ProblemDefinition()

RATapi/models.py

@@ -340,13 +340,13 @@ class Data(RATModel, arbitrary_types_allowed=True):
     """
 
     name: str = Field(default_factory=lambda: f"New Data {next(data_number)}", min_length=1)
-    data: np.ndarray[np.float64] = np.empty([0, 3])
+    data: np.ndarray = np.empty([0, 3])
     data_range: list[float] = Field(default=[], min_length=2, max_length=2)
     simulation_range: list[float] = Field(default=[], min_length=2, max_length=2)
 
     @field_validator("data")
     @classmethod
-    def check_data_dimension(cls, data: np.ndarray[float]) -> np.ndarray[float]:
+    def check_data_dimension(cls, data: np.ndarray) -> np.ndarray:
         """Ensure the data is be a two-dimensional array containing at least three columns."""
         try:
             data.shape[1]