合并大量的netCDF文件 [英] Combining a large amount of netCDF files

问题描述

我有一个很大的netCDF(.nc)文件文件夹，每个文件都有相似的名称.数据文件包含时间，经度，纬度和月降水量的变量.目标是获得每个月X年内的平均月降水量.因此，最后我将获得12个值，分别代表每个纬度和经度X年的平均月降水量.多年以来，每个文件都在同一位置. 每个文件都以相同的名称开头，并以"date.sub.nc"结尾，例如:

I have a large folder of netCDF (.nc) files each one with a similar name. The data files contain variables of time, longitude, latitude, and monthly precipitation. The goal is to get the average monthly precipitation over X amount of years for each month. So in the end I would have 12 values representing the average monthly precipitation over X amount of years for each lat and long. Each file is the same location over many years. Each file starts with the same name and ends in a "date.sub.nc" for example:

'data1.somthing.somthing1.avg_2d_Ind_Nx.200109.SUB.nc'
'data1.somthing.somthing1.avg_2d_Ind_Nx.200509.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201104.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201004.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201003.SUB.nc'
'data2.somthing.somthing1.avg_2d_Ind_Nx.201103.SUB.nc'
'data1.somthing.somthing1.avg_2d_Ind_Nx.201203.SUB.nc'

结尾是YearMonth.SUB.nc 到目前为止，我有:

The ending is YearMonth.SUB.nc What I have so far is:

array=[]
f = nc.MFDataset('data*.nc')
precp = f.variables['prectot']
time = f.variables['time']
array = f.variables['time','longitude','latitude','prectot'] 

我收到一个KeyError :(时间"，经度"，纬度"，"prectot").有没有一种方法可以合并所有这些数据，以便我可以操纵它?

I get a KeyError: ('time', 'longitude', 'latitude', 'prectot'). Is there a way to combine all this data so I am able to manipulate it?

推荐答案

正如@CharlieZender所述，ncra是此处的解决方法，我将提供有关将该函数集成到Python脚本中的更多详细信息. (PS-您可以使用Homebrew轻松安装NCO，例如 http://alejandrosoto.net/blog/2014/01/22/setting-up-my-mac-for-scientific-research/)

As @CharlieZender mentioned, ncra is the way to go here and I'll provide some more details on integrating that function into a Python script. (PS - you can install NCO easily with Homebrew, e.g. http://alejandrosoto.net/blog/2014/01/22/setting-up-my-mac-for-scientific-research/)

import subprocess
import netCDF4
import glob
import numpy as np

for month in range(1,13):
    # Gather all the files for this month
    month_files = glob.glob('/path/to/files/*{0:0>2d}.SUB.nc'.format(month))


    # Using NCO functions ---------------
    avg_file = './precip_avg_{0:0>2d}.nc'.format(month)

    # Concatenate the files using ncrcat
    subprocess.call(['ncrcat'] + month_files + ['-O', avg_file])

    # Take the time (record) average using ncra 
    subprocess.call(['ncra', avg_file, '-O', avg_file])

    # Read in the monthly precip climatology file and do whatever now
    ncfile = netCDF4.Dataset(avg_file, 'r')
    pr = ncfile.variables['prectot'][:,:,:]
    ....

    # Using only Python -------------
    # Initialize an array to store monthly-mean precip for all years
    # let's presume we know the lat and lon dimensions (nlat, nlon)
    nyears = len(month_files)
    pr_arr = np.zeros([nyears,nlat,nlon], dtype='f4')

    # Populate pr_arr with each file's monthly-mean precip
    for idx, filename in enumerate(month_files):
        ncfile = netCDF4.Dataset(filename, 'r')
        pr = ncfile.variable['prectot'][:,:,:]  
        pr_arr[idx,:,:] = np.mean(pr, axis=0)
        ncfile.close()

    # Take the average along all years for a monthly climatology
    pr_clim = np.mean(pr_arr, axis=0)  # 2D now [lat,lon]

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