117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
# -*- coding: utf-8 -*-
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import argparse
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import os
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from osgeo import gdal, osr
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import numpy as np
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import netCDF4 as nc
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from datetime import datetime, timezone
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import time
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def nc2ENVIBin(nc_file_path,output_envi_path,utctime):
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# Input parameters (replace these with your actual values)
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time_index = -1 # Example: 0 for the first time step; adjust if needed
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# Step 1: Read the NetCDF file using netCDF4
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ds=None
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filename = "数据/测试文件.nc"
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with open(nc_file_path, 'rb') as f:
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ds = nc.Dataset('in_memory', mode='r', memory=f.read())
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print(f"Available variables: {list(ds.variables.keys())}") # Optional: Print variables for verification
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valid_time_list=ds.variables["valid_time"][:]
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for timeID in range(0,len(valid_time_list)):
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# print(valid_time_list[timeID],utctime)
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if valid_time_list[timeID] == utctime:
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time_index = timeID
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break
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if time_index == -1:
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return -1
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else:
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print(f"time_index={time_index}")
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# Extract the variable data (assuming 3D: time, lat, lon)
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data_U10 = ds.variables["u10"][time_index, :, :].filled(np.nan) # Handle masked values as NaN
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data_V10 = ds.variables["v10"][time_index, :, :].filled(np.nan) # Handle masked values as NaN
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# Extract lat and lon (adjust variable names if your file uses different ones, e.g., 'latitude', 'longitude')
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lat = ds.variables['latitude'][:]
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lon = ds.variables['longitude'][:]
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# Calculate geotransform parameters
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x_res = (lon[-1] - lon[0]) / (len(lon) - 1)
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y_res = (lat[0] - lat[-1]) / (len(lat) - 1) # Note: lat often decreases north to south
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top_left_x = lon[0] - x_res / 2
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top_left_y = lat[0] + y_res / 2
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geotransform = (top_left_x, x_res, 0, top_left_y, 0, -y_res) # Negative y_res for north-up
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# Step 2: Create ENVI file using GDAL
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driver = gdal.GetDriverByName('ENVI')
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envi_ds = driver.Create(
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output_envi_path,
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data_U10.shape[1], # Width (lon)
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data_U10.shape[0], # Height (lat)
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2, # Single band
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gdal.GDT_Float32 # Assuming float data; adjust to gdal.GDT_Float64 if needed
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)
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# Set geotransform and projection (EPSG:4326 for WGS84 lat/lon)
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envi_ds.SetGeoTransform(geotransform)
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srs = osr.SpatialReference()
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srs.ImportFromEPSG(4326)
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envi_ds.SetProjection(srs.ExportToWkt())
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# Write the data to the band
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envi_band = envi_ds.GetRasterBand(1)
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envi_band.SetNoDataValue(np.nan) # Set NaN as no-data value (optional)
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envi_band.WriteArray(data_U10)
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envi_band = envi_ds.GetRasterBand(2)
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envi_band.SetNoDataValue(np.nan) # Set NaN as no-data value (optional)
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envi_band.WriteArray(data_V10)
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# Add ENVI-specific metadata to the header (optional, but recommended)
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envi_ds.SetMetadataItem('wavelength units', 'None', 'ENVI') # Example; customize as needed
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envi_ds.SetMetadataItem('data type', '4', 'ENVI') # 4 = float32; matches GDT_Float32
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# Clean up
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envi_ds.FlushCache()
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envi_ds = None
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ds.close()
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print(f"Conversion complete: {output_envi_path} (with .hdr)")
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def getParams():
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parser = argparse.ArgumentParser()
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parser.add_argument('-i','--infile',default=r'D:\风场\20250807_SAR_EC_2024年\ERA5风场\total_precipitation202410.nc', help='输入风场文件路径')
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parser.add_argument('-o', '--outfile',default=r'D:\风场\20250807_SAR_EC_2024年\ERA5风场\total_precipitation202410.bin', help='输出Bin文件路径')
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parser.add_argument('-t', '--time',type=int, default=2024100108,help='yyyymmddhh')
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args = parser.parse_args()
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return args
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def timestr2utc(timeint):
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timestr = str(timeint)
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# 解析字符串为datetime对象（默认无时区信息）
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dt = datetime.strptime(timestr, "%Y%m%d%H")
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# 显式标记为UTC时间（两种等效方式）
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dt_utc = dt.replace(tzinfo=timezone.utc) # 方式1：使用标准库
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# 或使用pytz库：dt_utc = pytz.utc.localize(dt)
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timestamp = time.mktime(dt_utc.timetuple())
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utc_timestamp = int(timestamp) # 当前UTC时间戳（秒级）
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print("UTC时间:", dt_utc)
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print("ISO格式:", dt_utc.isoformat()) # 输出：2024-08-01T02:00:00+00:00
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print("UTC时间戳:", utc_timestamp) # 输出：2024-08-01T02:00:00+00:00
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return utc_timestamp
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if __name__ == '__main__':
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parser = getParams()
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inNcFilePath=parser.infile
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outbinpath=parser.outfile
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searchTime=parser.time
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print('infile=',inNcFilePath)
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print('outfile=',outbinpath)
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time_utc=timestr2utc(searchTime)
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nc2ENVIBin(inNcFilePath,outbinpath,time_utc) |