Menggabungkan file NC

import xarray as xr

ds = xr.open_mfdataset("lokasi file/*.nc", combine='by_coords')

ds.to_netcdf("output.nc")

import netCDF4

import numpy as np

import pandas as pd

date_range = pd.date_range(start='2022-01-31', end='2022-05-06', freq='D')

for single_date in date_range:

    date_str = single_date.strftime('%Y%m%d')

    date_str1 = single_date.strftime('%d')

    date_str2 = single_date.strftime('%m')

    file_pattern = f'D:/GSMAP NC 2022/{date_str2}/{date_str1}/{date_str}.nc'

    output_file = f'D:/GSMAP NC 2022/{date_str2}/{date_str1}/StametBiak{date_str}.csv'

    latitude = -1.15    # specific latitude

    longitude = 136.15  # specific longitude

    # Open the netCDF4 file and extract the 'rainrate' variable

    with netCDF4.Dataset(file_pattern) as f:

        rain = f.variables['rainrate']

        # Get dimensions assuming 3D: time, latitude, longitude

        time_dim, lat_dim, lon_dim = rain.get_dims()

        time_var = f.variables[time_dim.name]

        times = netCDF4.num2date(time_var[:], time_var.units)

        # Find the indices of the specific latitude and longitude

        lat_indices = np.where(f.variables[lat_dim.name][:] == latitude)[0]

        lon_indices = np.where(f.variables[lon_dim.name][:] == longitude)[0]

        # Check if the latitude and longitude exist in the netCDF4 file

        if len(lat_indices) == 0:

            print(f"The specific latitude {latitude} does not exist in the file.")

            exit()

        if len(lon_indices) == 0:

            print(f"The specific longitude {longitude} does not exist in the file.")

            exit()

        # Extract the 'rainrate' values for the specific latitude and longitude

        specific_rain_values = rain[:, lat_indices, lon_indices]

        # Extract the corresponding time values

        time_indices = np.tile(np.arange(len(times)), (len(lat_indices), len(lon_indices)))

        specific_times = times[time_indices.flatten()]

    # Create a DataFrame with the specific latitude, longitude, time, and rainrate values

    df = pd.DataFrame({

        'time': specific_times,

        'latitude': np.repeat(latitude, len(specific_rain_values.flatten())),

        'longitude': np.repeat(longitude, len(specific_rain_values.flatten())),

        'rain': specific_rain_values.flatten()})

    # Write the DataFrame to a CSV file

        print(f'Writing data in tabular form to {output_file} (this may take some time)...')

    df.to_csv(output_file, index=False)

    print('Done')

import xarray as xr

import glob

# Definisikan rentang latitude dan longitude yang diinginkan (hapus pagarnya bila digunakan)

# lat_min, lat_max = 0, 10              

# lon_min, lon_max = 100, 120

base_dir = "base"

output_file = "output.nc"

files = sorted(glob.glob(f"{base_dir}/*.nc"))

# Temukan dan filter file netCDF berdasarkan rentang latitude dan longitude (hapus pagarnya bila digunakan)

#filtered_files = [f for f in files if (f"{lat_min}:{lat_max}" in f) and (f"{lon_min}:{lon_max}" in f)]

print(f'Menggabungkan file (ini mungkin memakan waktu)...')

try:

    ds = xr.open_mfdataset(filtered_files, combine='by_coords')

except:

    ds = xr.open_mfdataset(files, combine='by_coords')

ds.to_netcdf(output_file)

print('Selesai')