python二进制文件转nc（以PIOMAS海冰厚度数据为例）
一、数据下载
数据网址Polar Science Center » PIOMAS Variables on Model Grid (uw.edu)
以其中的海冰厚度数据为例进行转化
点击下载需要的年份：
首先要明白，二进制文件是4个字节按顺序依次存储所有数据，因此heff.H1979是没有记录对应的地理信息的。数据的地理信息会在另一个文件中，也就是下图中的grid.dat。这是第二个要下载的文件。
（二进制文件存储的数据和.dat文件存储的地理信息是一一对应的，及二进制第一个数据所在的位置就是.dat中第一个经纬度）
二、python进行转化
路径如图所示：girds放下载的grid.bat；sit放下载的二进制海冰厚度数据；output是输出结果文件的地方。
processor.py代码如下:
import numpy as np
import pandas as pd
import struct
import xarray as xr
import matplotlib.pyplot as plt
from cartoplot import cartoplot
grids = {}
dims = (120,360)
for i in ['lon','lat']:
grid = np.array(pd.read_csv(f'grids/{i}grid.dat',
header=None,
delim_whitespace=True))
flat_grid = grid.ravel()
# if i == 'lon':
shaped_grid = flat_grid.reshape(dims)
# else:
# shaped_grid = flat_grid.reshape((360,120))
grids[i] = shaped_grid
def process_piomas(year):
binary_dir = f'sit/heff.H{year}'
############################################################
# Read File
with open(binary_dir, mode='rb') as file:
fileContent = file.read()
data = struct.unpack("f" * (len(fileContent)// 4), fileContent)
############################################################
# Put it in a 3D array
native_data = np.full((12,dims[0],dims[1]),np.nan)
for month in range(1,13):
start = (month-1)*(dims[0]*dims[1])
end = month*(dims[0]*dims[1])
thickness_list = np.array(data[start:end])
gridded = thickness_list.reshape(dims[0],dims[1])
native_data[month-1,:,:] = gridded
# cartoplot(grids['lon'],grids['lat'],gridded)
plt.imshow(gridded)
pass
############################################################
# Output to NetCDF4
ds = xr.Dataset( data_vars={'thickness':(['t','x','y'],native_data)},
coords = {'longitude':(['x','y'],grids['lon']),
'latitude':(['x','y'],grids['lat']),
'month':(['t'],np.array(range(1,13)))})
ds.attrs['data_name'] = 'Monthly mean Piomas sea ice thickness data'
ds.attrs['description'] = """Sea ice thickness in meters on the native 360x120 grid,
data produced by University of Washington Polar Science Center"""
ds.attrs['year'] = f"""These data are for the year {year}"""
ds.attrs['citation'] = """When using this data please use the citation:
Zhang, Jinlun and D.A. Rothrock: Modeling global sea
ice with a thickness and enthalpy distribution model
in generalized curvilinear coordinates,
Mon. Wea. Rev. 131(5), 681-697, 2003."""
ds.attrs['code to read'] = """ # Example code to read a month of this data
def read_month_of_piomas(year,month):
data_dir = 'output/'
with xr.open_dataset(f'{data_dir}{year}.nc') as data:
ds_month = data.where(int(month) == data.month, drop =True)
return(ds_month)"""
ds.attrs['python author'] = """Robbie Mallett wrote this python code. If there's a problem with it,
email him at [email protected]"""
output_dir = f'output/'
ds.to_netcdf(f'{output_dir}{year}.nc','w')
return native_data
for year in range(1993,1994):
x = process_piomas(year)