python: Fixed comparing stored and current statistical results.

The old comparison incorrectly assumed that the indices of the data frames are the same.
The new comparison uses an outer merge on the two data frames.

python/inet/test/statistical.py

@@ -11,6 +11,7 @@
 import difflib
 import glob
 import logging
+import math
 import pandas
 import re
 import shutil
@@ -25,9 +26,8 @@
 
 def _read_scalar_result_file(file_name):
     df = read_result_files(file_name, include_fields_as_scalars=True)
-    df = get_scalars(df)
-    if "runID" in df:
-        df.drop("runID", axis=1, inplace=True)
+    df = get_scalars(df, include_runattrs=True)
+    df = df[["experiment", "measurement", "replication", "module", "name", "value"]]
     return df
 
 def _write_diff_file(a_file_name, b_file_name, diff_file_name):
@@ -71,16 +71,19 @@ def check_simulation_task_result(self, simulation_task_result, result_name_filte
                 elif stored_df.empty:
                     return self.task_result_class(task=self, simulation_task_result=simulation_task_result, result="FAIL", reason="Stored statistical results are empty")
                 else:
-                    df = pandas.DataFrame()
-                    df["module"] = stored_df["module"]
-                    df["name"] = stored_df["name"]
-                    df["stored_value"] = stored_df["value"]
-                    df["current_value"] = current_df["value"]
-                    df["absolute_error"] = df.apply(lambda row: abs(row["current_value"] - row["stored_value"]), axis=1)
-                    df["relative_error"] = df.apply(lambda row: row["absolute_error"] / abs(row["stored_value"]) if row["stored_value"] != 0 else (float("inf") if row["current_value"] != 0 else 0), axis=1)
+                    merged = stored_df.merge(current_df, on=['experiment', 'measurement', 'replication', 'module', 'name'], how='outer', suffixes=('_stored', '_current'))
+                    df = merged[
+                        (merged['value_stored'].isna() & merged['value_current'].notna()) |
+                        (merged['value_stored'].notna() & merged['value_current'].isna()) |
+                        (merged['value_stored'] != merged['value_current'])].dropna(subset=['value_stored', 'value_current'], how='all').copy()
+                    df["absolute_error"] = df.apply(lambda row: abs(row["value_current"] - row["value_stored"]), axis=1)
+                    df["relative_error"] = df.apply(lambda row: row["absolute_error"] / abs(row["value_stored"]) if row["value_stored"] != 0 else (float("inf") if row["value_current"] != 0 else 0), axis=1)
                     df = df[df.apply(lambda row: matches_filter(row["name"], result_name_filter, exclude_result_name_filter, full_match) and \
                                                  matches_filter(row["module"], result_module_filter, exclude_result_module_filter, full_match), axis=1)]
-                    reason = df.loc[df["relative_error"].idxmax()].to_string()
+                    id = df["relative_error"].idxmax()
+                    if math.isnan(id):
+                        id = next(iter(df.index), None)
+                    reason = df.loc[id].to_string()
                     reason = re.sub(" +", " = ", reason)
                     reason = re.sub("\\n", ", ", reason)
                     return self.task_result_class(task=self, simulation_task_result=simulation_task_result, result="FAIL", reason=reason)