Convert a full lat-lon NetCDF dataset to a "list" representation

txt2nc_cells.py

from __future__ import division
from netCDF4 import Dataset
import numpy as np
import os
import math

PATH = '.'
LL = np.loadtxt('%s/global_soils_default.txt' % PATH);
LL = LL[:,2:4]
OBS = np.loadtxt('%s/VIC_GRDC_Monthly_Climatology.txt' % PATH, delimiter=',', skiprows=1)

old_root= Dataset('vic_LHS_climatology.nc', 'r', format='NETCDF4')
old_vic_runoff = old_root.variables['vic_runoff']
old_obs_runoff = old_root.variables['obs_runoff']

# New file
root_grp = Dataset('vic_LHS_climatology_cells.nc', 'w', format='NETCDF4')
root_grp.description = '(Cells only) Results from VIC 10K Latin Hypercube ensemble, 60-year simulation on Blue Waters'

# dimensions
root_grp.createDimension('ncells', 15836)
root_grp.createDimension('month', 12)
ensemble = root_grp.createDimension('ensemble', 10000)

# variables
latitudes = root_grp.createVariable('latitude', 'f4', ('ncells',))
longitudes = root_grp.createVariable('longitude', 'f4', ('ncells',))
vic_runoff = root_grp.createVariable('vic_runoff', 'f4', ('ncells', 'ensemble', 'month',), fill_value=-9999.0)
obs_runoff = root_grp.createVariable('obs_runoff', 'f4', ('ncells', 'month'), fill_value=-9999.0)

vic_runoff.units = 'mm/month'
obs_runoff.units = 'mm/month'

cellsdone = 0
latiter = np.arange(-90.5, 89.5, 1.0)
loniter = np.arange(0.5, 360.5, 1.0)

for lati, lat in enumerate(latiter):
  for loni, lon in enumerate(loniter):

    # grab the index of the 0-15836 list of grid cells
    i = np.where((np.floor(LL[:,0]) == np.floor(lat)) & (np.floor(LL[:,1]) == np.floor(lon)))

    # if this is one of our land surface grid cells...
    if(np.size(i) > 0):
      i = i[0]
      print i
      latitudes[i] = LL[i,0]
      longitudes[i] = LL[i,1]
      obs_runoff[i,:] = old_obs_runoff[lati,loni,:]
      vic_runoff[i,:,:] = old_vic_runoff[lati,loni,:,:]

root_grp.close()

Dear Sir

I have had a difficult time trying to find an example script to write a netcdf file from real data(.csv) rather than random number generators.
I have tried Rnetcdf, r ncdf , ncdf4 and xarray but the example from your blog lools like the best. I would like to test it out with the data you used but can't find them.

Thanks
Dave Casciotti