Flow Processor. Misc.

cfatools/processor/flow.py

@@ -257,14 +257,19 @@ def get_datetime_out(datetime_in: pd.Series, mlmin_df: pd.DataFrame, tube_volume
     -------
     pd.Series
         Date/time when the fluid of interest exists the tube.
-    """    
+    """
+    # Convert Series to single-column Dataframe (it is easier to merge and apply)
+    datetime_in_df = pd.DataFrame(datetime_in)
+    data_colname = datetime_in_df.columns[0]
+    datetime_in_df = datetime_in_df.set_index(data_colname, drop=False)
+
     # Add events' datetime to pump's mlmin change datetime
-    mlmin_df = pd.merge(mlmin_df, datetime_in, left_index=True, right_index=True, how="outer")
+    mlmin_df = pd.merge(mlmin_df, datetime_in_df, left_index=True, right_index=True, how="outer")
     mlmin_df = mlmin_df[["mlmin"]].ffill()
     mlmin_df["ml_cumul"] = pump.add_cumulated_volume(mlmin_df["mlmin"])
 
     # Make sure the earliest datetime_in is after the first available ml_cumul
-    datetime_in = datetime_in[datetime_in.index >= mlmin_df["ml_cumul"].dropna().index[0]]
+    datetime_in = datetime_in[datetime_in >= mlmin_df["ml_cumul"].dropna().index[0]]
 
     if parallel:
         pandarallel.initialize()

cfatools/tests/test_processor/test_flow.py

@@ -56,9 +56,6 @@ class TestFlow(TestCase):
                                       "2021-01-01 00:11:00"])
         icecore_df = pd.DataFrame({'icecore_id': icecore_id, 'datetime_in': datetime_in})
 
-        icecore_df.set_index("datetime_in", inplace=True)
-        icecore_df["datetime_in"] = icecore_df.index
-
         icecore_df["datetime_out"] = flow.get_datetime_out(icecore_df["datetime_in"], mlmin_df, tube_volume_ml,
                                                            parallel=False)