Flow Processor. Compute arrival times for conductivity probes

cfatools/processor/flow.py

@@ -282,6 +282,7 @@ def get_arrival_df(encoder_df: pd.DataFrame,
                    pump_df: pd.DataFrame,
                    volumes_dict: dict,
                    keep_intermediate: bool = False,
+                   with_conduct: bool = True,
                    parallel: bool = True,
                    force_update: bool = False,
                    dataset: DatasetReader = None) -> pd.DataFrame:
@@ -297,6 +298,8 @@ def get_arrival_df(encoder_df: pd.DataFrame,
         Tubing volumes, as obtained with :func:`get_tubing_volume_dict` function.
     keep_intermediate: bool, default: False
         If `True` keep arrival datetime at intermediate position: debubbler, T1 and T2.
+    with_conduct: bool, default: True
+        If `True`, return also arrival datetimes at conductivity probes.
     parallel: bool, default: True
         If `True` compute arrival datetime in parallel. This speeds up the process but can mess up the debugger.
     force_update: bool, default: False
@@ -325,17 +328,37 @@ def get_arrival_df(encoder_df: pd.DataFrame,
         arrival_df = arrival_df.drop(columns="speed")
         arrival_df["melt"] = arrival_df.index
 
-        # Melting base -> to T1
+        # Melting base -> T1
         arrival_df["debubbler"] = get_datetime_out(arrival_df["melt"], pump_df["com"],
                                                    volumes_dict["melt_debub"], parallel=parallel)
         arrival_df["T1"] = get_datetime_out(arrival_df["debubbler"], pump_df["colpic"],
                                             volumes_dict["debub_T1"], parallel=parallel)
 
-        # To Collector
+        # Melting base -> C1
+        arrival_df["C1"] = get_datetime_out(arrival_df["melt"], pump_df["com"],
+                                            volumes_dict["melt_C1"], parallel=parallel)
+
+        # T1 -> C2
+        arrival_df["C2"] = get_datetime_out(arrival_df["T1"], pump_df["col"], volumes_dict["T1_C2"],
+                                            parallel=parallel)
+
+        # C2 -> C4
+        arrival_df["C4"] = get_datetime_out(arrival_df["C2"], pump_df["col"], volumes_dict["C2_C4"],
+                                            parallel=parallel)
+
+        # T1 -> Collector
         arrival_df["needle"] = get_datetime_out(arrival_df["T1"], pump_df["col"], volumes_dict["T1_needle"],
                                                 parallel=parallel)
 
-        # To Picarro
+        # T1 -> C3
+        arrival_df["C3"] = get_datetime_out(arrival_df["T1"], pump_df["pic"], volumes_dict["T1_C3"],
+                                            parallel=parallel)
+
+        # C3 -> C5
+        arrival_df["C5"] = get_datetime_out(arrival_df["C3"], pump_df["pic"], volumes_dict["C3_C5"],
+                                            parallel=parallel)
+
+        # T1 -> Picarro
         arrival_df["T2"] = get_datetime_out(arrival_df["T1"], pump_df["pic"], volumes_dict["T1_T2"],
                                             parallel=parallel)
         arrival_df["picarro"] = arrival_df["T2"] + pd.Timedelta(seconds=volumes_dict["T2_pic_sec"])
@@ -348,6 +371,8 @@ def get_arrival_df(encoder_df: pd.DataFrame,
 
     if not keep_intermediate:
         arrival_df = arrival_df.drop(columns=["debubbler", "T1", "T2"])
+    if not with_conduct:
+        arrival_df = arrival_df.drop(columns=["C1", "C2", "C3", "C4", "C5"])
 
     return arrival_df
 

cfatools/tests/test_processor/tubing_volumes.csv

-datetime,melt_debub,debub_T1,T1_needle,T1_T2,T2_pic_sec
-2019-12-01,1,2,3,4,100
-2021-04-01,3.5,0.43,1.8,0.35,50
-2020-12-01,5,6,7,8,200
\ No newline at end of file
+datetime,melt_debub,melt_C1,debub_T1,T1_needle,T1_T2,T2_pic_sec,T1_C2,C2_C4,T1_C3,C3_C5
+2019-12-01,1,2,3,4,5,6,7,8,9,10
+2020-12-01,3.5,3.457,0.43,1.8,0.35,50,0.17,0.7,0.05,0.154
+2020-12-01,11,12,13,14,15,16,17,18,18,19
\ No newline at end of file