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== Example: ARGO floats == = Example data from Argo system =
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http://en.wikipedia.org/wiki/Argo_%28oceanography%29 [[http://en.wikipedia.org/wiki/Argo_%28oceanography%29|Argo observation system]]
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First we have to download the data. Here is a script that can be used to download one month of data in a temporary directory called {{{tmp_dir}}}: First we have to download the data. Here is a [[/download script]] that can be used to retrieve one month of Argo data.
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{{{#!python
#!/usr/bin/env python
import os,os.path
from ftplib import FTP		 We extract only the surface temperature to reduce the large amount of data. This [[/extract script]] generates an overview of the data and writes the surface temperature in a file [[attachment:lat_lon_T.tab]] which contains latitude, longitude and surface temperature. We can use this file in the following.
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tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/'
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ftp_adr='ftp.ifremer.fr'
ftp_dir='/ifremer/argo/geo/atlantic_ocean/2011/01/'		 {{attachment:argo_position.png}}
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ftp = FTP(ftp_adr) # connect to host, default port
ftp.login() # user anonymous, passwd anonymous@
ftp.cwd(ftp_dir)		 Argo positions of measurements
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file_list=ftp.nlst() {{attachment:Argo_plot.png}}
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for f in file_list:
    urlfile='ftp://'+ftp_adr+ftp_dir+f
    localfile=tmp_dir+f
   
    if not(os.path.exists(localfile)):
        print 'Getting file '+f
         #We use curl instead of ftp.retrbinary for download
        os.system("curl -s -k -o "+localfile+" "+urlfile)
    else:
        print 'file '+f+' exists'		 Measured temperature profiles (unfiltered data).
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ftp.close() = Tools for 2-dimensional interpolation and gridding =

== Python ==

== GMT ==

[[http://gmt.soest.hawaii.edu/|GMT]]


Nearest neighbor gridding with 5 degree grid cell size, output is written to file {{grid.nc}} (GMT-netcdf):
{{{nearneighbor lat_lon_T.tab -Rd -I300m -S300m -N1 -Ggrid.nc}}}

Generate color table:
{{{
makecpt -Crainbow -T-2/30/1 > g.cpt
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The next script generates an overview of the data and writes the surface temperature in a file called {{{lat_lon_T.tab}}} which contains latitude, longitude and surface temperature. [[attachment:lat_lon_T.tab]]
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{{attachment:argo_position.png}}
{{attachment:Argo_plot.png}}		 {{{
grdimage grid.nc -Rg -JG0/0/4i -Cg.cpt -K > out.ps
}}}
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{{{#!python

import scipy.io as io
import glob
from pylab import *
from mpl_toolkits.basemap import Basemap

tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/'


file_liste=glob.glob(tmp_dir+'*.nc')
D={}# Empty dictionary to store selected profiles
for f in file_liste:# Loop over all data


    # Open netcdf data file
    fid=io.netcdf_file(f,'r')

    # Read content into variables
    lat=fid.variables['LATITUDE'][:].copy()
    lon=fid.variables['LONGITUDE'][:].copy()
    T=fid.variables['TEMP'][:].copy()
    P=fid.variables['PRES'][:].copy()
    
    T[T>=99999]=nan # Set 99999.0 to "Not a Number"
    P[P>=99999]=nan

    (nr_profs,Z)=T.shape # Get dimension

    for i in range(nr_profs):
        D[(lon[i],lat[i])]=(T[i,:],P[i,:])

    # Close data file
    fid.close()

fid=open('lat_lon_T.tab','w')
for k in D.keys():# write position (lat,lon), surface temperature to file
    fid.write(str(k[0])+'\t'+str(k[1])+'\t'+str(D[k][0][0])+'\n')
fid.close()
stop


# Draw map of positions
m = Basemap(projection='ortho',lon_0=-45,lat_0=0,resolution='l')
m.bluemarble()
for lon,lat in D.keys():
    x,y=m(lon,lat) # Coordinate transfer
    m.plot(x,y,'r.')
    
savefig('argo_position.png',dpi=150)

# Plot profiles
figure()
for k in D.keys():
# print k
    plot(D[k][0][:],D[k][1][:])
axis([-2,30,2000,0])
xlabel('T')
ylabel('P')
show()
savefig('Argo_plot.png',dpi=75)		 {{{
pscoast -Rd -JG0/0/4i -B15g15 -Dc -Ggrey -P -O >> out.ps

Contents

  1. 2-dimensional interpolation and gridding
  2. Example data from Argo system
  3. Tools for 2-dimensional interpolation and gridding
    1. Python
    2. GMT

2-dimensional interpolation and gridding

2-dimensional interpolation and gridding is a common problem for the representation of measurements on a map. Usually measurements are taken at irregular sample points and not in a regular grid. There are various approaches for the problem and the best solution depends on the data.

In the following we will look at oceanographic parameters that have been measured with the Argo system.

Example data from Argo system

Argo observation system

Data are provided at, i.e. ftp://ftp.ifremer.fr//ifremer/argo/

First we have to download the data. Here is a /download script that can be used to retrieve one month of Argo data.

We extract only the surface temperature to reduce the large amount of data. This /extract script generates an overview of the data and writes the surface temperature in a file lat_lon_T.tab which contains latitude, longitude and surface temperature. We can use this file in the following.

argo_position.png

Argo positions of measurements

Argo_plot.png

Measured temperature profiles (unfiltered data).

Tools for 2-dimensional interpolation and gridding

Python

GMT

GMT

Nearest neighbor gridding with 5 degree grid cell size, output is written to file grid.nc (GMT-netcdf): nearneighbor lat_lon_T.tab -Rd -I300m -S300m -N1 -Ggrid.nc

Generate color table: 

makecpt -Crainbow -T-2/30/1 > g.cpt

grdimage grid.nc -Rg -JG0/0/4i -Cg.cpt -K  > out.ps

pscoast -Rd -JG0/0/4i -B15g15 -Dc  -Ggrey -P -O >> out.ps

LehreWiki: OpenSource2010/Lesson12 (last edited 2011-01-17 13:34:14 by anonymous)