In [75]:

    

import netCDF4


    

In [3]:

    

url='http://comt.sura.org/thredds/dodsC/comt_1_archive_full/estuarine_hypoxia/VIMS_EFDC/2004_DO3d/Output/EFDC_ele2d_20040101to_20041225.nc'


    

In [4]:

    

nc = netCDF4.Dataset(url)


    

In [5]:

    

ncv = nc.variables


    

In [6]:

    

ncv.keys()


    

    
Out[6]:





[u'Water_Level', u'Time', u'X', u'Y', u'Latitude', u'Longtitude', u'LIJ']

In [7]:

    

lij=ncv['LIJ'][:,:]


    

In [8]:

    

shape(lij)


    

    
Out[8]:





(3, 8932)

In [9]:

    

lij[:,0]


    

    
Out[9]:





array([ 1, 88,  2], dtype=int32)

In [10]:

    

lij[:,-1]


    

    
Out[10]:





array([8932,   87,  240], dtype=int32)

In [11]:

    

lon = ncv['Longtitude'][:,:]


    

In [12]:

    

lat = ncv['Latitude'][:,:]


    

In [13]:

    

lon = ma.masked_where(lon==-999.,lon)
lat = ma.masked_where(lat==-999.,lat)


    

In [14]:

    

zt= ncv['Water_Level'][:,0]


    

In [15]:

    

zeta = zeros_like(lon)
zeta = ma.masked_where(lon==-999.,zeta)
zeta[lij[2,:]-1,lij[1,:]-1]=zt


    

In [16]:

    

pcolor(lon,lat,zeta)


    

    
Out[16]:





<matplotlib.collections.PolyCollection at 0x3813110>






    

In [43]:

    

nco = netCDF4.Dataset('efdc.nc','r+')


    

In [44]:

    

ncov = nco.variables


    

In [45]:

    

ncov['lon'][:,:]=lon
ncov['lat'][:,:]=lat


    

In [46]:

    

ncov['zeta'][0,:,:]=zeta
r = shape(ncov['do'][0,:,:,:])


    

In [47]:

    

nco.close()


    

In [48]:

    

nc2=netCDF4.Dataset('efdc.nc')
nc2v = nc2.variables
lon = nc2v['lon'][:,:]
lat = nc2v['lat'][:,:]
zeta = nc2v['zeta'][0,:,:]


    

In [49]:

    

pcolor(lon,lat,zeta)


    

    
Out[49]:





<matplotlib.collections.PolyCollection at 0x56e46d0>






    

In [76]:

    

url='http://comt.sura.org/thredds/dodsC/comt_1_archive_full/estuarine_hypoxia/VIMS_EFDC/2004_DO3d/Output/EFDC_DO3d_20040101_to_20040131.nc'


    

In [77]:

    

nc = netCDF4.Dataset(url)
ncv = nc.variables


    

In [78]:

    

nc.variables.keys()


    

    
Out[78]:





[u'DO', u'Time', u'X', u'Y', u'Latitude', u'Longtitude', u'LIJ']

In [79]:

    

len(ncv['Time'])


    

    
Out[79]:





6

In [71]:

    

dox = zeros(r)


    

In [72]:

    

doxt = ncv['DO'][:,:,0]
print shape(doxt)


    

    




(20, 8932)

In [73]:

    

dox[:,lij[2,:]-1,lij[1,:]-1] = doxt


    

In [74]:

    

pcolor(lon,lat,dox[0,:])


    

    
Out[74]:





<matplotlib.collections.PolyCollection at 0x583a3d0>






    

In [ ]: