Explo. Iceblocks. Which one is melting.

src/dataprovider/exploprovider.py

@@ -70,7 +70,7 @@ class Dataset:
     def explore_dataset(self) -> None:
         for filename in os.listdir(self.full_directory_name):
             try:
-                inst_and_type = re.search(self.directory_name + '_(.+?).log', filename).group(1)
+                inst_and_type = re.search("^" + self.directory_name + '_(.+?).log$', filename).group(1)
             except AttributeError:
                 # The found file does not match normal instrument's log file pattern
                 print("File [" + filename + "] does not appear to be a valid CFA log file")
@@ -81,7 +81,10 @@ class Dataset:
             if len(inst_and_type.split("_")) == 2:
                 log_type = inst_and_type.split("_")[1]
                 if log_type == "instant":
-                    instrument_log = InstrumentInstantLog(self.full_directory_name, filename, instrument_name)
+                    if instrument_name == "ICBKCTRL":
+                        instrument_log = IceblockInstantLog(self.full_directory_name, filename, instrument_name)
+                    else:
+                        instrument_log = InstrumentInstantLog(self.full_directory_name, filename, instrument_name)
                 elif log_type == "periodic":
                     instrument_log = InstrumentPeriodicLog(self.full_directory_name, filename, instrument_name)
                     self.last_data_datetime = max(self.last_data_datetime,
@@ -135,6 +138,51 @@ class InstrumentInstantLog(InstrumentLog):
         return timeseries_df
 
 
+class IceblockInstantLog(InstrumentLog):
+
+    def __init__(self, full_directory_name: str, filename: str, instrument_name: str):
+        InstrumentLog.__init__(self, full_directory_name, filename, instrument_name)
+
+    def __get_df__(self) -> pd.DataFrame:
+        df = pd.read_csv(self.full_file_name, sep=",", parse_dates=["datetime"])
+        df["datetime"] = df["datetime"].dt.tz_localize('UTC')
+        return df
+
+    def get_variables(self):
+        return ["melting"]
+
+    def get_timeseries(self, variable: str) -> pd.DataFrame:
+        if variable == "melting":
+            timeseries_df = self.__get_melting_timeseries__()
+        else:
+            raise ValueError("Variable name [" + variable + "] not yet managed.")
+
+        return timeseries_df
+
+    def __get_melting_timeseries__(self) -> pd.DataFrame:
+        # Get the mapping between iceblock id and iceblock name (assuming that the last name's modification is the
+        # good one.
+        mapping_df = self.df[["datetime", "id", "name"]].copy()
+        mapping_df = mapping_df.groupby("id")["id", "name"].tail(1)
+        mapping_df = mapping_df.append({"id": 0, "name": "None"}, ignore_index=True)
+        mapping_df = mapping_df.set_index("id")
+        mapping_dict = mapping_df["name"].to_dict()
+
+        # Get the datetime of the beginning of each iceblock's melting
+        melting_df = self.df[["datetime", "id", "status"]].copy()
+        start_df = melting_df[melting_df["status"] == "Melting"].groupby("id")["datetime", "id"].head(1)
+
+        # Get the end of the last iceblock's melting, and set that after that the current melting block is 0/None.
+        end_df = melting_df[melting_df["status"] == "Done"].groupby("id").head(1)
+        melting_df = start_df.append({"datetime": end_df.iloc[-1]["datetime"], "id": 0},
+                                     ignore_index=True)
+
+        # Get the value (iceblocks name) and value_int (coded value, iceblock id in this case).
+        melting_df.rename(columns={"id": 'value_int'}, inplace=True)
+        melting_df["value"] = melting_df["value_int"].map(mapping_dict)
+
+        return melting_df
+
 class InstrumentPeriodicLog(InstrumentLog):
 
     def __init__(self, full_directory_name: str, filename: str, instrument_name: str):