get_fitted: also provide information on static (sub)models

2 files changed, 12 insertions, 2 deletions

diff --git a/lib/functions.py b/lib/functions.py
index 067514f..0f0bf47 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -157,6 +157,13 @@ class ModelInfo:
         pass
 
 
+class StaticInfo:
+    def __init__(self, data):
+        self.mean = np.mean(data)
+        self.median = np.median(data)
+        self.std = np.std(data)
+
+
 class AnalyticInfo(ModelInfo):
     def __init__(self, fit_result, function):
         self.fit_result = fit_result
diff --git a/lib/model.py b/lib/model.py
index cddfe27..5ee9e4f 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -543,7 +543,10 @@ class ModelAttribute:
 
     def set_data_from_paramfit_this(self, paramfit, prefix):
         fit_result = paramfit.get_result((self.name, self.attr) + prefix)
-        param_model = (df.StaticFunction(np.median(self.data)), None)
+        param_model = (
+            df.StaticFunction(np.median(self.data)),
+            df.StaticInfo(self.data),
+        )
         if self.function_override is not None:
             function_str = self.function_override
             x = df.AnalyticFunction(function_str, self.param_names, self.arg_count)
@@ -811,7 +814,7 @@ class AnalyticModel:
         def model_getter(name, key, **kwargs):
             param_function, param_info = self.attr_by_name[name][key].get_fitted()
 
-            if param_info is None:
+            if type(param_info) is df.StaticInfo:
                 return static_model[name][key]
 
             if "arg" in kwargs and "param" in kwargs: