5

I am new to using Python and also new to NetCDF, so apologies if I'm unclear. I have an nc file that has several variables and I need to extract data from those nc files in a new order.

My nc file has 8 variables (longitude, latitude, time, u10, v10, swh, mwd, mwp) and the logic I'm trying is "If I input longitude and latitude, my program outputs other variables (u10, v10, swh, mwd, mwp) ordered by time." Then I would put the extracted data in another database.

I tested my nc file as below:

import netCDF4
from netCDF4 import Dataset

jan = Dataset('2016_01.nc')
print jan.variables.keys()

lon = jan.variables['longitude']
lat = jan.variables['latitude']
time = jan.variables['time']

for d in jan.dimensions.items():
    print d

lon_array = lon[:]
lat_array = lat[:]
time_array = time[:]

print lon_array
print lat_array
print time_array

and some of the result is below

[u'longitude', u'latitude', u'time', u'u10', u'v10', u'swh', u'mwd', u'mwp']

(u'longitude', <type 'netCDF4._netCDF4.Dimension'>: name = 'longitude', size = 1440)

(u'latitude', <type 'netCDF4._netCDF4.Dimension'>: name = 'latitude', size = 721)

(u'time', <type 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 186)

Any advice would be appreciated. Thank you.

ClimateUnboxed
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박성우
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3 Answers3

8

2022 edit: this is now much easier with xarray, as shown in Adrian's answer: https://stackoverflow.com/a/74599597/3581217

You first need to know the order of the dimensions in the time/space varying variables like e.g. u10, which you can obtain with:

u10 = jan.variables['u10']
print(u10.dimensions)

Next it is a matter of slicing/indexing the array correctly. If you want data for lets say latitude=30, longitude = 10, the corresponding (closest) indexes can be found with (after importing Numpy as import numpy as np):

i = np.abs(lon_array - 10).argmin()
j = np.abs(lat_array - 30).argmin()

Assuming that the dimensions of u10 are ordered as {time, lat, lon}, you can read the data as:

u10_time = u10[:,j,i]

Which gives you all (time varying) u10 values for your requested location.

Bart
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1

This kind of task is straightforward using xarray, for example

import xarray as xr
lon=30
lat=10

# open the file, select the location and write to new netcdf 
da=xr.open_dataset('2016_01.nc') 
ts=da.sel(x=lon, y=lat, method="nearest")
ts.to_netcdf('timeseries.nc')
ClimateUnboxed
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-1

I used this on netCDF files that are generated with the WRF model.

import numpy as np
from netCDF4 import Dataset  # http://code.google.com/p/netcdf4-python/
import pandas as pd
import os

os.chdir('.../netcdf') # Select your dir
f = Dataset('wrfout_d01_2007-01-01_10_00_00', 'r') #Charge your file

latbounds = [ 4.691417 ]# Latitud
lonbounds = [ -74.209 ]# Longitud

cor_lat = pd.DataFrame(f.variables['XLAT'][0][:])
cor_lat2 = pd.DataFrame({'a':cor_lat.iloc[:,0], 'b':abs(cor_lat.iloc[:,0] - latbounds)})

a = cor_lat2[cor_lat2.b == min(cor_lat2.b)].index.get_values()[0]

cor_lon = pd.DataFrame(f.variables['XLONG'][0][:])
cor_lon2 = pd.DataFrame({'a':cor_lon.iloc[0,:], 'b':abs(cor_lon.iloc[0,:] - lonbounds)})

b = cor_lon2[cor_lon2.b == min(cor_lon2.b)].index.get_values()[0]

vlr = (f.variables['T2'][ : , a , b ] - 273.15)[0] #This change from kelvin to celsius
vlr
ClimateUnboxed
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Edwin Torres
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