adapted data registry to accomodate data schema updates

CERN · Mar 4, 2024 · c56c68c · c56c68c
1 parent 50627c6
commit c56c68c
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Showing 3 changed files with 24 additions and 22 deletions.
diff --git a/caimira/apps/calculator/report_generator.py b/caimira/apps/calculator/report_generator.py
@@ -164,7 +164,9 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel) -> t
     else:
         uncertainties_plot_src = None
         conditional_probability_data = None
-        vl_dist = list(np.log10(model.concentration_model.virus.viral_load_in_sputum))
+        vl = model.concentration_model.virus.viral_load_in_sputum
+        if isinstance(vl, np.ndarray): vl_dist = list(np.log10(model.concentration_model.virus.viral_load_in_sputum))
+        else: vl_dist = np.log10(model.concentration_model.virus.viral_load_in_sputum)
 
     return {
         "model_repr": repr(model),

diff --git a/caimira/monte_carlo/data.py b/caimira/monte_carlo/data.py
@@ -20,7 +20,7 @@ def evaluate_vl(root: typing.Dict, value: str, data_registry: DataRegistry):
     elif root[value] == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
         return symptomatic_vl_frequencies
     elif root[value] == "Custom":
-        return param_evaluation(root, 'Viral load custom distribution')
+        return param_evaluation(root, 'Viral load custom')
     else:
         raise ValueError(f"Invalid ViralLoads value {value}")
 
@@ -29,7 +29,7 @@ def evaluate_infectd(root: typing.Dict, value: str, data_registry: DataRegistry)
     if root[value] == InfectiousDoses.DISTRIBUTION.value:
         return infectious_dose_distribution(data_registry)
     elif root[value] == "Custom":
-        return param_evaluation(root, 'Infectious dose custom distribution')
+        return param_evaluation(root, 'Infectious dose custom')
     else:
         raise ValueError(f"Invalid InfectiousDoses value {value}")
 
@@ -38,7 +38,7 @@ def evaluate_vtrr(root: typing.Dict, value: str, data_registry: DataRegistry):
     if root[value] == ViableToRNARatios.DISTRIBUTION.value:
         return viable_to_RNA_ratio_distribution(data_registry)
     elif root[value] == "Custom":
-        return param_evaluation(root, 'Viable to RNA ratio custom distribution')
+        return param_evaluation(root, 'Viable to RNA ratio custom')
     else:
         raise ValueError(f"Invalid ViableToRNARatios value {value}")
 
@@ -61,7 +61,7 @@ def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
     try:
         for key, value in dict.items():
             if (key_part in key):
-                return value['associated_distribution']
+                return value['associated_value']
     except KeyError:
         return f"Key '{key_part}' not found."
 
@@ -81,8 +81,8 @@ def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
         ValueError: If the distribution type is not recognized.
 
     """
-    if dist == 'Linear Space':
-        return np.linspace(params['start'], params['stop'], params['num'])
+    if dist == 'Constant':
+        return params
     elif dist == 'Normal':
         return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
     elif dist == 'Log-normal':
@@ -111,7 +111,7 @@ def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) ->
     value = root.get(param)
 
     if isinstance(value, dict):
-        dist: str = root[param]['associated_distribution']
+        dist: str = root[param]['associated_value']
         params: typing.Dict = root[param]['parameters']
         return evaluate_custom_distribution(dist, params)
 

diff --git a/caimira/store/data_registry.py b/caimira/store/data_registry.py
@@ -26,14 +26,14 @@ class DataRegistry:
     activity_distributions = {
         "Seated": {
             "inhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": -0.6872121723362303,
                     "lognormal_standard_deviation_gaussian": 0.10498338229297108,
                 },
             },
             "exhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": -0.6872121723362303,
                     "lognormal_standard_deviation_gaussian": 0.10498338229297108,
@@ -42,14 +42,14 @@ class DataRegistry:
         },
         "Standing": {
             "inhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": -0.5742377578494785,
                     "lognormal_standard_deviation_gaussian": 0.09373162411398223,
                 },
             },
             "exhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": -0.5742377578494785,
                     "lognormal_standard_deviation_gaussian": 0.09373162411398223,
@@ -58,14 +58,14 @@ class DataRegistry:
         },
         "Light activity": {
             "inhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 0.21380242785625422,
                     "lognormal_standard_deviation_gaussian": 0.09435378091059601,
                 },
             },
             "exhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 0.21380242785625422,
                     "lognormal_standard_deviation_gaussian": 0.09435378091059601,
@@ -74,14 +74,14 @@ class DataRegistry:
         },
         "Moderate activity": {
             "inhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 0.551771330362601,
                     "lognormal_standard_deviation_gaussian": 0.1894616357138137,
                 },
             },
             "exhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 0.551771330362601,
                     "lognormal_standard_deviation_gaussian": 0.1894616357138137,
@@ -90,14 +90,14 @@ class DataRegistry:
         },
         "Heavy exercise": {
             "inhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 1.1644665696723049,
                     "lognormal_standard_deviation_gaussian": 0.21744554768657565,
                 },
             },
             "exhalation_rate": {
-                "associated_distribution": "Log-normal",
+                "associated_value": "Log-normal",
                 "parameters": {
                     "lognormal_mean_gaussian": 1.1644665696723049,
                     "lognormal_standard_deviation_gaussian": 0.21744554768657565,
@@ -255,7 +255,7 @@ class DataRegistry:
     mask_distributions = {
         "Type I": {
             "η_inhale": {
-                "associated_distribution": "Uniform",
+                "associated_value": "Uniform",
                 "parameters": {
                     "low": 0.25,
                     "high": 0.80,
@@ -266,7 +266,7 @@ class DataRegistry:
         },
         "FFP2": {
             "η_inhale": {
-                "associated_distribution": "Uniform",
+                "associated_value": "Uniform",
                 "parameters": {
                     "low": 0.83,
                     "high": 0.91,
@@ -277,15 +277,15 @@ class DataRegistry:
         },
         "Cloth": {
             "η_inhale": {
-                "associated_distribution": "Uniform",
+                "associated_value": "Uniform",
                 "parameters": {
                     "low": 0.05,
                     "high": 0.40,
                 },
             },
             "Known filtration efficiency of masks when exhaling?": "Yes",
             "η_exhale": {
-                "associated_distribution": "Uniform",
+                "associated_value": "Uniform",
                 "parameters": {
                     "low": 0.20,
                     "high": 0.50,