wiski export

src/scripts/bsh_extractor_v2.py

@@ -107,13 +107,14 @@ def read_dat_file(zip_file:Path,dat_file:str,tsVorh:str) -> pd.DataFrame:
             df = df[["timestamp","forecast","member","pegel"]]
             return df
 
-def writeZrxp(target_file:Path,df:pd.DataFrame):
+def writeZrxp(target_file:Path,df:pd.DataFrame,only_two=False):
     """Writes the DataFrame to a .zrx file with the correct header.
     Overwrites the file if it already exists.
 
     Args:
         target_file (Path): Where the file should be saved
         df (pd.DataFrame): DataFrame with zrx data
+        only_two (bool, optional): If True, only the cols for wiski are written.
     """
     if target_file.exists():
         print(f"Overwriting existing file {target_file}")
@@ -122,6 +123,10 @@ def writeZrxp(target_file:Path,df:pd.DataFrame):
     with open(target_file , 'w', encoding='utf-8') as file:
         file.write('#REXCHANGE9530010.W.BSH_VH|*|\n')
         file.write('#RINVAL-777|*|\n')
+        if only_two:
+            file.write('#LAYOUT(timestamp,value)|*|\n')
+            df = df[["forecast","pegel"]]
+        else:
             file.write('#LAYOUT(timestamp,forecast,member,value)|*|\n')
 
     df.to_csv(path_or_buf =target_file ,
@@ -158,7 +163,7 @@ def main():
     target_file = make_target_file_name(base_dir / '4WISKI',tsVorh)
 
     df = read_dat_file(mos_file_name,dat_file,tsVorh)
-    writeZrxp(target_file,df)
+    writeZrxp(target_file,df,only_two=True)
 
     #Resample and cut data to fit the 3h interval, hourly sample rate used by ICON
     df2 = df[["forecast","pegel"]].resample("1h",on="forecast").first()