Flow. Get picarro by melt time

cfatools/processor/flow.py

@@ -488,11 +488,45 @@ def get_conduct_by_melt_time(arrival_df: pd.DataFrame, conduct_df: pd.DataFrame,
         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")
+        conduct_rescaled_df.to_csv(conduct_rescaled_path, date_format="%Y-%m-%dT%H:%M:%S.fZ", sep="\t")
 
     return conduct_rescaled_df
 
 
+def get_picarro_by_melt_time(arrival_df: pd.DataFrame,
+                             picarro_df: pd.DataFrame,
+                             export_in_dataset: DatasetReader = None) -> pd.DataFrame:
+    """Get the Picarro values as a function of the sample melting time.
+
+    Parameters
+    ----------
+    arrival_df: pd.DataFrame
+        Output of :func:`get_arrival_df` function.
+    picarro_df: pd.DataFrame
+        Picarro's timestamped data, as obtained with :func:`get_datasets_data` function.
+    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 Picarro (H2O & isotope) value of
+        this ice section.
+    """
+    arrival_picarro_df = arrival_df[["picarro"]]
+    picarro_rescaled_df = pd.merge_asof(arrival_picarro_df["picarro"], picarro_df, left_on="picarro", right_index=True)
+    picarro_rescaled_df = picarro_rescaled_df.drop(columns=["picarro"])
+
+    if export_in_dataset is not None:
+        picarro_rescaled_path = os.path.join(export_in_dataset.dataset_path,
+                                             export_in_dataset.dataset_name + "_PICARRO-R_periodic.log")
+        picarro_rescaled_df.index.name = 'datetime'
+        picarro_rescaled_df.to_csv(picarro_rescaled_path, date_format="%Y-%m-%dT%H:%M:%S.%fZ", sep="\t")
+
+    return picarro_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)