def make_3D_NetCDF(ncfilename, wd, debug=False):
""" Create NetCDF of 3D arrays for all variables in 2D NetCDF file
Takes a table form NetCDF and build 3D arrays from lat and lon
in the given file. """
# --- Read existing & setup new NetCDF file
ncfile2D = Dataset(ncfilename, 'r', format='NETCDF4')
# Setup dimensions
vars = ncfile2D.variables
if debug:
print[i for i in vars]
lats, lons, Epoch = [ncfile2D[i] for i in 'LAT', 'LON', 'Epoch']
if debug:
print[len(i) for i in lats, lons, Epoch]
lats, lons, Epoch = [np.array(i) for i in lats, lons, Epoch]
lats, lons = [list(sorted(set(i))) for i in lats, lons]
# remove fill value. ( 9.969209968386869e+36 ) <= Improve this approach...
[i.pop(-1) for i in lons, lats]
# setup 3D NetCDF file
ncfilename = ncfilename.split('.nc')[0]+'_3D.nc'
ncfile = Dataset(ncfilename, 'w', format='NETCDF4')
ncfile.createDimension('lat', len(lats))
ncfile.createDimension('lon', len(lons))
ncfile.createDimension('time', None)
# Define the coordinate variables. They will hold the coordinate
# information, that is, the latitudes and longitudes.
time = ncfile.createVariable('time', 'f4', ('time',))
lat = ncfile.createVariable('lat', 'f4', ('lat',))
lon = ncfile.createVariable('lon', 'f4', ('lon',))
# --- Add meta data
# Assign units attributes to coordinate var data. This attaches a
# text attribute to each of the coordinate variables, containing the
# units.
lat.units = 'degrees_north'
lat.long_name = 'Latitude'
lat.standard_name = 'Latitude'
lat.axis = "Y"
lon.units = 'degrees_east'
lon.long_name = 'Longitude'
lon.standard_name = 'Longitude'
lon.axis = "X"
time.units = 'seconds since 1970-01-01 00:00:00'
time.calendar = "standard"
time.standard_name = 'Time'
time.axis = "T"
# set global varibles
ncfile.Description = 'planeflight output from '.format(wd)
ncfile.Contact = 'Tomas Sherwen ([email protected])'
# ncfile.History = 'Created {}'.format( time.ctime(time.time()) )
ncfile.Grid = 'lat: {}-{}, lon: {}-{}'.format(lats[0], lats[-1],
lons[0], lons[-1])
# ncfile.Temp_Res = "Hourly"
# ncfile.SpatialCoverage='Global'
# write data to coordinate vars.
lon[:] = lons
lat[:] = lats
# Get unique timesteps
timesteps = sorted(set(Epoch))
# masked 1st value ( headers? )
timesteps.pop(0)
# set time dimension to timestep values
time[:] = timesteps
# select only 3D vars
vars3D = get_3D_vars(vars)
# --- Loop 3D species and create variables (with set dimensions)
for var in vars3D:
ncfile.createVariable(var, var2type(var), ('time', 'lat', 'lon'), )
# close NetCDF
ncfile.close()
# --- Loop through timesteps (epoch) and add to NetCDF
# Loop over timesteps
for t in timesteps:
# open NetCDF in append mode
ncfile = Dataset(ncfilename, 'a', format='NETCDF4')
# get 1st and last indices for time stamp
start, end = [(i.min(), i.max())
for i in np.where(ncfile2D.variables['Epoch'] == t)][0]
# Extract Data for timestep & species
for var in vars3D:
data_ = ncfile2D.variables[var][start:end]
lons_ = ncfile2D.variables['LON'][start:end]
lats_ = ncfile2D.variables['LAT'][start:end]
if debug:
print t, var, [i.shape for i in data_, lats_, lons_]
# stack data by LAT/LON to 3D array ( using pandas )
df = DataFrame(data_, index=[lats_, lons_]).unstack()
# add data to array
ncfile.variables[var][timesteps.index(t)] = df.values
# remove from memory
del df, data_, lats_, lons_
# Save out final NetCDF file
ncfile.close()
