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= NetCDF = * http://www.unidata.ucar.edu/software/netcdf/docs/netcdf.html You can use the {{{scipy.io}}} subroutines to read NetCDF data: * Open a file with {{{fid=scipy.io.netcdf_file(filename,'r')}}} * Print variables with {{{fid.variables.keys()}}} * Read (copy) a variable to an array {{{a=fid.variables['NAME'][:].copy() * Close the file {{{fid.close()}}} == NetCDF Data Examples == * ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-concentration/data/ Sea ice * http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml NCEP/NCAR Atmopsheric Reanalysis * http://www.argo.ucsd.edu/index.html Argo |
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{{attachment:Argo_plot.png}} |
Contents
NetCDF
You can use the scipy.io subroutines to read NetCDF data:
Open a file with fid=scipy.io.netcdf_file(filename,'r')
Print variables with fid.variables.keys()
- Read (copy) a variable to an array {{{a=fid.variables['NAME'][:].copy()
Close the file fid.close()
NetCDF Data Examples
ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-concentration/data/ Sea ice
http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml NCEP/NCAR Atmopsheric Reanalysis
Excercise
Write scripts
to download all Argo data in the Atlantic for the last month, and
- to plot the pressure against the temperature for only those profiles with a surface temperature below -1 deg C.
1 #download_argo.py
2 import os,os.path
3 from ftplib import FTP
4
5 tmp_dir='/home/lars/data/tmp/'
6 ftp_adr='ftp.ifremer.fr'
7 ftp_dir='/ifremer/argo/geo/atlantic_ocean/2009/11/'
8
9 ftp = FTP(ftp_adr) # connect to host, default port
10 ftp.login() # user anonymous, passwd anonymous@
11 ftp.cwd(ftp_dir)
12
13 file_list=ftp.nlst()
14
15 for f in file_list:
16 urlfile='ftp://'+ftp_adr+ftp_dir+f
17 localfile=tmp_dir+f
18
19 if not(os.path.exists(localfile)):
20 print 'Getting file '+f
21 #We use curl instead of ftp.retrbinary for download
22 os.system("curl -s -k -o "+localfile+" "+urlfile)
23 else:
24 print 'file '+f+' exists'
25
26 ftp.close()
1 #Plot Argo profiles with surface temperature below 1 deg C
2 import scipy.io as io
3 import glob
4 from pylab import *
5
6 tmp_dir='/home/lars/data/tmp/'
7 file_liste=glob.glob(tmp_dir+'*.nc')
8
9 D={}# Empty dictionary to store selected profiles
10 for f in file_liste:
11 print f
12
13 # Open netcdf data file
14 fid=io.netcdf_file(f,'r')
15
16 #print fid.variables.keys()
17
18 # Make a copy of the content
19 lat=fid.variables['LATITUDE'][:].copy()
20 lon=fid.variables['LONGITUDE'][:].copy()
21 T=fid.variables['TEMP'][:].copy()
22 P=fid.variables['PRES'][:].copy()
23
24 T[T>=99999]=nan # Set 99999.0 to "Not a Number"
25 P[P>=99999]=nan
26
27 (nr_profs,Z)=T.shape # Get dimension
28
29 for i in range(nr_profs):
30 if T[i,0]<1.0: # Select only those profiles with surface temperature below 1 deg C
31 # Store profile in dictionary with the position as a key
32 D[(lat[i],lon[i])]=(T[i,:],P[i,:])
33 # Close data file
34 fid.close()
35
36
37 # Plot data
38 figure()
39 for k in D.keys():
40 print k
41 plot(D[k][0][:],D[k][1][:])
42 axis([-2,3,2000,0])
43 xlabel('T')
44 ylabel('P')
45 show()
