Flow. Get conductivity by melting time.

cfatools/processor/flow.py

@@ -433,6 +433,54 @@ def add_flask_info(arrival_df: pd.DataFrame, collector_df: pd.DataFrame) -> pd.D
     return arrival_df
 
 
+def get_conduct_by_melt_time(arrival_df: pd.DataFrame, conduct_df: pd.DataFrame,
+                             probes: list = ["C1", "C2", "C3", "C4", "C5"],
+                             reference_datetime: str = None,
+                             export_in_dataset: DatasetReader = None) -> pd.DataFrame:
+    """Get the conductivity values as a function of the sample melting time.
+
+    Parameters
+    ----------
+    arrival_df: pd.DataFrame
+        Output of :func:`get_arrival_df` function.
+    conduct_df: pd.DataFrame
+        Conductivity's timestamped data, as obtained with :func:`get_datasets_data` function.
+    probes: list, optional
+        List of probes (e.g. `["C1", "C2", "C3"]`) which should be computed.
+    reference_datetime: str, optional
+        If set, all the values of a probe will be subtracted by the value at the reference point. This is used in order
+        to "gather" all probes in the same area of the plot, thus minimizing the gap induced by the values of the
+        non-calibrated probes. It eases the conductivity peak's comparison over several probes.
+    export_in_dataset: DatasetReader, optional
+        If set, the resulting dataset will be exported as .log file in the regular "Dataset" place and format, making it
+        readable by cfa-analysis software.
+
+    Returns
+    -------
+    pd.DataFrame
+        Index is the datetime when the ice section was melted. Each column give the conductivity value of this ice
+        section.
+    """
+    conduct_arrival_df = arrival_df[probes]
+    conduct_rescaled_df = pd.DataFrame(columns=probes)
+
+    for probe in probes:
+        df = pd.merge_asof(conduct_arrival_df[probe], conduct_df[probe], left_on=probe, right_index=True)
+        df = df[probe + "_y"].rename(probe)
+        if reference_datetime is not None:
+            idx_ref = df.index.searchsorted(reference_datetime)
+            df = df - df.iloc[idx_ref]
+        conduct_rescaled_df[probe] = df
+
+    if export_in_dataset is not None:
+        conduct_rescaled_path = os.path.join(export_in_dataset.dataset_path,
+                                             export_in_dataset.dataset_name + "_CONDUCTI-R_periodic.log")
+        conduct_rescaled_df.index.name = 'datetime'
+        conduct_rescaled_df.to_csv(conduct_rescaled_path, date_format="%Y-%m-%dT%H:%M:%SZ", sep="\t")
+
+    return conduct_rescaled_df
+
+
 def __compute_mm__(df: pd.DataFrame) -> pd.DataFrame:
     df["datetime"] = df.index
     df["mm_step"] = df["speed"].abs() * df["datetime"].diff().dt.total_seconds().fillna(0)