from tqdm import tqdm import requests import os import xarray as xr import pandas as pd from glob import glob import cfgrib # ### GLOSEA5 ### # path = "raw/glosea.grib" # print("Processing Glosea5...") # # Open the GRIB file as an xarray dataset # ds_1 = cfgrib.open_datasets("raw/glosea_1.grib") # ds_2 = cfgrib.open_datasets("raw/glosea_2.grib") # ds_3 = cfgrib.open_datasets("raw/glosea_3.grib") # glosea_xarray_1, glosea_xarray_2, glosea_xarray_3 = ds_1[0], ds_2[0], ds_3[0] # # Convert to pandas DataFrame # df_1 = glosea_xarray_1.to_dataframe().reset_index() # df_2 = glosea_xarray_2.to_dataframe().reset_index() # df_3 = glosea_xarray_3.to_dataframe().reset_index() # # Concatenate the two DataFrames # glosea_df = pd.concat([df_1, df_2, df_3], ignore_index=True) # # Convert tprate NaN to 0.0 # glosea_df['tprate'] = glosea_df['tprate'].fillna(0.0) # # Aggregate across steps # glosea_df = glosea_df.groupby(['valid_time', 'latitude', 'longitude'])['tprate'].mean().reset_index() # # Keep between 1981 and 2019 # glosea_df = glosea_df[glosea_df['valid_time'].between('1981-01-01', '2018-12-31')] # # Rename columns # glosea_df.rename(columns={'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon', 'valid_time': 'time'}, inplace=True) # glosea_df.reset_index(inplace=True, drop=True) # print(f'Saving Glosea5 data to parquet (length of dataframe = {len(glosea_df)})...') # glosea_df.to_parquet('processed/glosea5.parquet') # # Delete any files ending in .idx from the raw folder # for file in os.listdir("raw"): # if file.endswith(".idx"): # os.remove(os.path.join("raw", file)) # ### ECMWF ### # path = "raw/ecmwf.grib" # print("Processing ECMWF...") # # Open the GRIB file as an xarray dataset # ds_1 = cfgrib.open_datasets("raw/ecmwf_1.grib") # ds_2 = cfgrib.open_datasets("raw/ecmwf_2.grib") # ds_3 = cfgrib.open_datasets("raw/ecmwf_3.grib") # ecmwf_xarray_1, ecmwf_xarray_2, ecmwf_xarray_3 = ds_1[0], ds_2[0], ds_3[0] # # Convert to pandas DataFrame # df_1 = ecmwf_xarray_1.to_dataframe().reset_index() # df_2 = ecmwf_xarray_2.to_dataframe().reset_index() # df_3 = ecmwf_xarray_3.to_dataframe().reset_index() # # Concatenate the two DataFrames # ecmwf_df = pd.concat([df_1, df_2, df_3], ignore_index=True) # # Convert tprate NaN to 0.0 # ecmwf_df['tprate'] = ecmwf_df['tprate'].fillna(0.0) # # Aggregate across steps # ecmwf_df = ecmwf_df.groupby(['valid_time', 'latitude', 'longitude'])['tprate'].mean().reset_index() # # Keep between 1981 and 2019 # ecmwf_df = ecmwf_df[ecmwf_df['valid_time'].between('1981-01-01', '2018-12-31')] # # Rename columns # ecmwf_df.rename(columns={'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon', 'valid_time': 'time'}, inplace=True) # ecmwf_df.reset_index(inplace=True, drop=True) # print(f'Saving ECMWF data to parquet (length of dataframe = {len(ecmwf_df)})...') # ecmwf_df.to_parquet('processed/ecmwf.parquet') # # Delete any files ending in .idx from the raw folder # for file in os.listdir("raw"): # if file.endswith(".idx"): # os.remove(os.path.join("raw", file)) # ### ACCESS-S2 ### # # Define the output file path # output_file = 'processed/access.parquet' # # Define the path and file pattern # path = "/g/data/ux62/access-s2/hindcast/calibrated/atmos/pr/daily/e09/" # # Check if the output file already exists and read it if it does # if os.path.exists(output_file): # master_df = pd.read_parquet(output_file) # # Extract already processed years # processed_years = master_df['time'].dt.year.unique() # else: # # Initialise an empty DataFrame if the file does not exist # master_df = pd.DataFrame() # processed_years = [] # # Generate file patterns for each year from 1983 to 2018 and get matching files # files = [] # for year in range(1983, 2018): # if year not in processed_years: # pattern = f"*pr_{year}*.nc" # files.extend(glob(os.path.join(path, pattern))) # print(f"Processing data for years: {set(range(1983, 2018)) - set(processed_years)}") # # Loop through the list of files and load each one # for file in tqdm(files): # # Load the xarray dataset # ds = xr.open_dataset(file) # # Slice the dataset for three specific lat/lon grids # ds_sliced1 = ds.sel(lon=slice(142, 145), lat=slice(-25, -22)) # ds_sliced2 = ds.sel(lon=slice(150, 153), lat=slice(-29, -26)) # ds_sliced3 = ds.sel(lon=slice(143, 146), lat=slice(-20, -17)) # # Flatten the sliced data for 'pr' variable for both slices # df1 = ds_sliced1['pr'].to_dataframe().reset_index() # df2 = ds_sliced2['pr'].to_dataframe().reset_index() # df3 = ds_sliced3['pr'].to_dataframe().reset_index() # # Concatenate the two DataFrames # combined_df = pd.concat([df1, df2, df3], ignore_index=True) # # Filter rows where latitude and longitude are integers # combined_df = combined_df[combined_df['lat'].apply(lambda x: x.is_integer())] # combined_df = combined_df[combined_df['lon'].apply(lambda x: x.is_integer())] # # Append the DataFrame to the master DataFrame # master_df = pd.concat([master_df, combined_df], ignore_index=True) # # Close the xarray dataset # ds.close() # # Save the updated master_df to the Parquet file # os.makedirs(os.path.dirname(output_file), exist_ok=True) # master_df.to_parquet(output_file) # # Print the processed year for tracking # processed_year = pd.to_datetime(master_df['time'].max()).year # print(f"Year {processed_year} processed and saved.") # # After the loop, perform any final processing needed on master_df # if os.path.exists(output_file): # master_df = pd.read_parquet(output_file) # master_df['time'] = pd.to_datetime(master_df['time']) # # Group by time, lat, and lon, then sum the pr values # deduped_df = master_df.groupby(['time', 'lat', 'lon']).agg({'pr': 'sum'}).reset_index() # # Save the final processed DataFrame to a Parquet file # deduped_df.to_parquet(output_file) # print(f"Final file saved to {output_file}") # else: # print(f"No data processed. {output_file} does not exist.") # def create_master_parquet(): # files = ['access', 'ecmwf', 'glosea5', 'silo'] # frames = [] # for file in files: # df = pd.read_parquet(f'processed/{file}.parquet') # df['model'] = file # frames.append(df) # access = frames[0] # access.reset_index(inplace=True, drop=True) # columns = access.columns # # Convert time to string # access['time'] = access['time'].astype(str) # ecmwf = frames[1] # ecmwf.rename(columns={'date': 'time', 'precip': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True) # ecmwf = ecmwf[columns] # ecmwf.reset_index(inplace=True, drop=True) # # Convert time to string # ecmwf['time'] = ecmwf['time'].astype(str) # glosea = frames[2] # glosea.rename(columns={'date': 'time', 'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, inplace=True) # glosea = glosea[columns] # glosea.reset_index(inplace=True, drop=True) # # Convert time to string # glosea['time'] = glosea['time'].astype(str) # silo = frames[3] # silo.rename(columns={'daily_rain': 'pr'}, inplace=True) # silo = silo[columns] # # Convert lat and lon to float32 # silo['lat'] = silo['lat'].astype('float32') # silo['lon'] = silo['lon'].astype('float32') # silo.reset_index(inplace=True, drop=True) # # Convert time to string # silo['time'] = silo['time'].astype(str) # dfs = [access, ecmwf, glosea, silo] # master_df = pd.concat(dfs) # master_df.reset_index(inplace=True, drop=True) # master_df.to_parquet('processed/master.parquet') # print(f"Final file saved to processed/master.parquet") import pandas as pd def standardize_df(df, rename_dict, default_columns): """Standardize the DataFrame structure.""" df = df.rename(columns=rename_dict) df = df[default_columns] df.reset_index(inplace=True, drop=True) df['time'] = df['time'].astype(str) return df def create_master_parquet(): files = ['access', 'ecmwf', 'glosea5', 'silo'] rename_dicts = [ {}, {'date': 'time', 'precip': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, {'date': 'time', 'tprate': 'pr', 'latitude': 'lat', 'longitude': 'lon'}, {'daily_rain': 'pr'} ] # Read and append the 'model' column to each DataFrame frames = [] for file, rename_dict in zip(files, rename_dicts): df = pd.read_parquet(f'processed/{file}.parquet') df['model'] = file df = standardize_df(df, rename_dict, default_columns=['time', 'lat', 'lon', 'pr', 'model']) frames.append(df) # Use the first DataFrame (access) as a template for column names columns = frames[0].columns # Standardize each DataFrame for i in range(1, len(frames)): frames[i] = standardize_df(frames[i], rename_dicts[i], columns) master_df = pd.concat(frames) master_df.reset_index(inplace=True, drop=True) master_df.to_parquet('processed/master.parquet') print("Final file saved to processed/master.parquet") create_master_parquet()