get_fitted: also provide information on static (sub)models

3 files changed, 23 insertions, 8 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 872025e..78f5d79 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -43,7 +43,7 @@ import random
 import sys
 from dfatool import plotter
 from dfatool.loader import RawData, pta_trace_to_aggregate
-from dfatool.functions import gplearn_to_function, SplitInfo, AnalyticInfo
+from dfatool.functions import gplearn_to_function, SplitInfo, AnalyticInfo, StaticInfo
 from dfatool.model import PTAModel
 from dfatool.validation import CrossValidator
 from dfatool.utils import filter_aggregate_by_param, detect_outliers_in_aggregate
@@ -89,12 +89,15 @@ def model_quality_table(header, result_lists, info_list):
                 buf += "  |||  "
                 if (
                     info is None
-                    or info(state_or_tran, key)
+                    or (
+                        key != "energy_Pt"
+                        and type(info(state_or_tran, key)) is not StaticInfo
+                    )
                     or (
                         key == "energy_Pt"
                         and (
-                            info(state_or_tran, "power")
-                            or info(state_or_tran, "duration")
+                            type(info(state_or_tran, "power")) is not StaticInfo
+                            or type(info(state_or_tran, "duration")) is not StaticInfo
                         )
                     )
                 ):
@@ -380,9 +383,11 @@ def print_splitinfo(param_names, info, prefix=""):
                 param_name = f"arg{info.param_index - len(param_names)}"
             print_splitinfo(param_names, v, f"{prefix} {param_name}={k}")
     elif type(info) is AnalyticInfo:
-        print(f"{prefix} = analytic")
+        print_analyticinfo(prefix, info)
+    elif type(info) is StaticInfo:
+        print(f"{prefix}: {info.median}")
     else:
-        print(f"{prefix} = static")
+        print(f"{prefix}: UNKNOWN")
 
 
 if __name__ == "__main__":
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: