more changes

Author: Aserhisham

src/sed/loader/cfel/buffer_handler.py

@@ -274,7 +274,8 @@ def _save_buffer_file(self, file_set, is_first_file=True, base_timestamp=None, i
         # Save electron resolved dataframe
         electron_channels = get_channels(self._config, "per_electron")
         dtypes = get_dtypes(self._config, df.columns.values)
-        electron_df = df.dropna(subset=electron_channels).astype(dtypes).reset_index()
+        # This maintains cumulative event counts across multiple files
+        electron_df = df.dropna(subset=electron_channels).astype(dtypes)
         logger.debug(f"Saving electron buffer with shape: {electron_df.shape}")
         electron_df.to_parquet(paths["electron"])
 

src/sed/loader/cfel/dataframe.py

@@ -136,7 +136,7 @@ def df_electron(self) -> pd.DataFrame:
     @property
     def df_train(self) -> pd.DataFrame:
         """
-        Returns a pandas DataFrame for given channel names of type [per train].
+        Returns a pandas DataFrame for given channel names of type [per_train].
 
         Returns:
             pd.DataFrame: The pandas DataFrame for the 'per_train' channel's data.

src/sed/loader/cfel/loader.py

@@ -568,7 +568,9 @@ def get_count_rate(
                 - time       : array of global times in seconds since scan start
         """
         mode = kwds.pop("mode", "point")
-        return self.get_count_rate_ms(fids=fids, mode=mode, runs=runs, **kwds)
+        # Resolve runs to fids before calling get_count_rate_ms
+        fids_resolved = self._resolve_fids(fids=fids, runs=runs)
+        return self.get_count_rate_ms(fids=fids_resolved, mode=mode, **kwds)
 
     # -------------------------------
     # Time-resolved count rate (binned)
@@ -685,18 +687,33 @@ def get_elapsed_time(
         )
 
         elapsed_per_file: list[float] = []
+        prev_max_ts_s = None  # Track previous file's max timestamp in seconds
 
-        for fid in fids_resolved:
+        for i, fid in enumerate(fids_resolved):
             try:
                 ts_info = file_statistics[str(fid)]["columns"][ts_alias]
-                print(f"ts_info: {ts_info}")
-                dt = ts_info["max"] - ts_info["min"]
-    
-                # normalize to seconds
-                if hasattr(dt, "total_seconds"):
-                    dt_s = dt.total_seconds()
+                
+                max_ts = ts_info["max"]
+                min_ts = ts_info["min"]
+                
+                # Normalize to float seconds
+                if hasattr(max_ts, "total_seconds"):
+                    max_ts_s = max_ts.total_seconds()
+                else:
+                    max_ts_s = float(max_ts)
+                    
+                if hasattr(min_ts, "total_seconds"):
+                    min_ts_s = min_ts.total_seconds()
+                else:
+                    min_ts_s = float(min_ts)
+                
+                # Calculate elapsed time correctly for multi-file runs
+                if i == 0:
+                    dt_s = max_ts_s - min_ts_s
                 else:
-                    dt_s = float(dt)
+                    dt_s = max_ts_s - prev_max_ts_s
+                
+                prev_max_ts_s = max_ts_s
 
                 if dt_s < 0:
                     raise ValueError(
@@ -717,10 +734,8 @@ def get_elapsed_time(
             elapsed_per_file.append(dt_s)
 
         if aggregate:
-            print("aggregate is True")
             return sum(elapsed_per_file)
 
-        print(f"Elapsed time: {elapsed_per_file}")
         return elapsed_per_file
 
     def read_dataframe(