

In [52]:

    
import netCDF4
from pylab import *



In [63]:

    
#aggregation
url='http://opendap.co-ops.nos.noaa.gov/thredds/dodsC/SJROFS/fmrc/Aggregated_7_day_SJROFS_Fields_Forecast_best.ncd'
#non aggregation
url='http://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/SJROFS/MODELS/201405/nos.sjrofs.fields.forecast.20140506.t17z.nc'
nc = netCDF4.Dataset(url)
ncv = nc.variables

check out the time bounds:



In [64]:

    
t=ncv['time'][:]
plot(t,'o-')









    Out[64]:





[<matplotlib.lines.Line2D at 0x37bce10>]



In [65]:

    
plot(diff(t),'o-')









    Out[65]:





[<matplotlib.lines.Line2D at 0x43d0ed0>]



In [66]:

    
dt= diff(t)



In [73]:

    
dt[:10]*3600*24  # delta t in seconds









    Out[73]:





array([ 3601.7578125 ,  3599.12109375,  3599.12182617,  3601.7578125 ,
        3599.12109375,  3599.11962891,  3601.7578125 ,  3599.12109375,
        3599.12109375,  3601.7578125 ], dtype=float32)

Check out the lon/lat bounds:



In [68]:

    
lon = ncv['lon'][:]
lat = ncv['lat'][:]



In [69]:

    
lon.min()









    Out[69]:





-81.761909



In [70]:

    
lon.max()









    Out[70]:





0.1



In [71]:

    
lat.min()









    Out[71]:





-85.488747



In [72]:

    
lat.max()









    Out[72]:





30.497652



In [76]:

    
shape(lon)









    Out[76]:





(105, 188)



In [77]:

    
lon[0:2,0:2
]









    Out[77]:





array([[ 0.,  0.],
       [ 0.,  0.]], dtype=float32)



In [89]:

    
lon=ma.masked_where(lon>=0,lon)
lat=ma.masked_where(lat<=0,lat)



In [90]:

    
lon.min()









    Out[90]:





-81.761909



In [91]:

    
lon.max()









    Out[91]:





-81.307274



In [92]:

    
lat.min()









    Out[92]:





29.56171



In [93]:

    
lat.max()









    Out[93]:





30.497652



In [96]:

    
plot(lon,lat,lon.T,lat.T);



In [ ]: