extract_gebco_ll.py

"""
  extract depths from gebco bathymetric data obtained from
  British Oceanographic Data Centre http://www.bodc.ac.uk
  from where the data from a selected lat/long bounding box can be downloaded with either 1 minute or 30 second resolution
  needs netCDF4  http://code.google.com/p/netcdf4-python/
  
  this script extracts a bounding box from a larger downloaded source file.  For example the file may contain the whole of the Mediterranean 
  and the script extracts smaller areas from the whole
 
  parameters
     top left lat, long
     botom right lat,lomg
     source file except for .nc suffix)
     target file (except for .dat suffix)
     
  Chris Wallace
  kitwallace.co.uk
"""

def latlong_to_array(lat,long) :
   return [ int((long - xrangemin  ) /scale) , int((yrangemax - lat) / scale)]
   
import netCDF4
from numpy import * 
import sys

lat_max = float(sys.argv[1])
long_min = float(sys.argv[2])
lat_min = float(sys.argv[3])
long_max = float(sys.argv[4])
source= sys.argv[5]
target= sys.argv[6]

d= netCDF4.Dataset(source+'.nc', 'r')

print "dimensions" , d.variables['dimension'][:]
print "spacing ", d.variables['spacing'][:]
scale = d.variables['spacing'][0]
xrangemin = d.variables['x_range'][0]
yrangemax = d.variables['y_range'][1]
print 'data br', xrangemin, yrangemax

tl = latlong_to_array(lat_max,long_min)
br = latlong_to_array(lat_min,long_max)

print "tl ", tl
print "br ", br
print 'source', source
print 'target', target
print 'long samples',  br[0] - tl[0] 
print 'lat samples',  br[1] - tl[1]

depths = d.variables['z'][:]
depths = depths.reshape(d.variables['dimension'][1], d.variables['dimension'][0])
depths = depths[ tl[1]: br[1],tl[0]: br[0]]
print depths.shape
depths = flipud(depths)
savetxt(target+'.dat',depths,"%d")

d.close()