AnalyticModel, PTAModel: add distinct_param_values_by_name

2 files changed, 24 insertions, 2 deletions

diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py
index 4a71617..ffe7648 100755
--- a/bin/analyze-kconfig.py
+++ b/bin/analyze-kconfig.py
@@ -134,7 +134,12 @@ def main():
         raise NotImplementedError()
 
     if args.info:
-        print("TODO")
+        for name in model.names:
+            print(f"{name}:")
+            print(f"""    Number of Measurements: {len(by_name[name]["param"])}""")
+            for i, param in enumerate(model.parameters):
+                param_values = model.distinct_param_values_by_name[name][i]
+                print(f"    Parameter {param} ∈ {param_values}")
 
     if args.export_tree:
         with open("nfpkeys.json", "r") as f:
diff --git a/lib/model.py b/lib/model.py
index 3f784cc..590c6f0 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -5,7 +5,12 @@ import numpy as np
 import os
 from .automata import PTA, ModelAttribute
 from .functions import StaticFunction, SubstateFunction, SplitFunction
-from .parameters import ParallelParamStats, ParamStats, codependent_param_dict
+from .parameters import (
+    ParallelParamStats,
+    ParamStats,
+    codependent_param_dict,
+    distinct_param_values,
+)
 from .paramfit import ParamFit
 from .utils import soft_cast_int, by_name_to_by_param, regression_measures
 
@@ -118,6 +123,12 @@ class AnalyticModel:
         if self._num_args is None:
             self._num_args = _num_args_from_by_name(by_name)
 
+        self.distinct_param_values_by_name = dict()
+        for name in self.names:
+            self.distinct_param_values_by_name[name] = distinct_param_values(
+                by_name[name]["param"]
+            )
+
         self.fit_done = False
 
         if compute_stats:
@@ -535,6 +546,12 @@ class PTAModel(AnalyticModel):
         self.pta = pta
         self.ignore_trace_indexes = ignore_trace_indexes
 
+        self.distinct_param_values_by_name = dict()
+        for name in self.names:
+            self.distinct_param_values_by_name[name] = distinct_param_values(
+                by_name[name]["param"]
+            )
+
         self.fit_done = False
 
         if traces is not None and pelt is not None: