How to convert the original Lana DMS climatology (in the CSV format) into the netcdf format

I used this script (http://stackoverflow.com/q/22933855) as a reference to create my Python script to convert the CSV formatted Lana DMS climatology (http://www.bodc.ac.uk/solas_integration/implementation_products/group1/dms/) into the netCDF format. Here is my script:

import netCDF4
from pylab import *
from pandas import read_csv

ncout = netCDF4.Dataset('test.nc','w')

data=array(read_csv('../dmsclimatology/DMSclim_APR.csv',header=None))
dim0 = size(data,0)
dim1 = size(data,1)
lats_out = 89.5 - 1.0*arange(dim0,dtype='float32')
lons_out = -179.5 + 1.0*arange(dim1,dtype='float32')

ncout.createDimension('latitude',dim0)
ncout.createDimension('longitude',dim1)

lats = ncout.createVariable('latitude',dtype('float32').char,('latitude',))
lons = ncout.createVariable('longitude',dtype('float32').char,('longitude',))

lats.units = 'degrees_north'
lons.units = 'degrees_east'

lats[:] = lats_out
lons[:] = lons_out

dms = ncout.createVariable('DMS',dtype('float32').char,('latitude','longitude'))

dms.units =  'nmol L-1'

dms[:] = data
ncout.close()

If you want to use this script, you would need to make sure you have those three modules and change the file name associated with the read_csv function to your own.

The output looks like this (on the right) and you can confirm that this is consistent with the published figure (on the left). NOTE: the color bar scales are different, so they don't look exactly the same.