示例#1
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def regrid(lat1,lon1,lats,lons,var,years):
    """
    Interpolated on a 180x180 grid (latxlon) from CryoSat-2 using EASE2.0
    100 km grid
    """
    
    varn_re = np.reshape(var,(var.shape[0],(448*304)))       
    varn = np.empty((var.shape[0],lats.shape[0],lons.shape[1]))
    
    print 'Completed: Start regridding process:'
    
    for i in xrange(varn.shape[0]):
        z = g((np.ravel(lat1),np.ravel(lon1)),varn_re[i,:],
              (lats,lons),method='linear')
        varn[i,:,:] = z
        print 'Completed: Year %s Regridding---' % (years[i])
    print 'Completed: Done regridding process!'
    return varn
示例#2
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def regrid(lat1,lon1,lat2,lon2,var,years):
    """
    Interpolated on selected grid. Reads PIOMAS in as 4d with 
    [year,month,lat,lon]
    """
    
    varn_re = np.reshape(var,(var.shape[0],var.shape[1],(120*360)))   
    
    varn = np.empty((var.shape[0],var.shape[1],lat2.shape[0],lon2.shape[1]))
    
    print 'Completed: Start regridding process:'
    
    for i in xrange(varn.shape[0]):
        for j in xrange(varn.shape[1]):
            z = g((np.ravel(lat1),np.ravel(lon1)),varn_re[i,j,:],(lat2,lon2),method='linear')
            varn[i,j,:,:] = z
        print 'Completed: Year %s Regridding---' % (years[i])
    return varn
示例#3
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def regrid(lat1, lon1, lats, lons, var, years):
    """
    Interpolated on a 180x180 grid (latxlon) from CryoSat-2 using EASE2.0
    100 km grid
    """

    varn_re = np.reshape(var, (var.shape[0], (448 * 304)))
    varn = np.empty((var.shape[0], lats.shape[0], lons.shape[1]))

    print 'Completed: Start regridding process:'

    for i in xrange(varn.shape[0]):
        z = g((np.ravel(lat1), np.ravel(lon1)),
              varn_re[i, :], (lats, lons),
              method='linear')
        varn[i, :, :] = z
        print 'Completed: Year %s Regridding---' % (years[i])
    print 'Completed: Done regridding process!'
    return varn
lat1,lon1,snc1 = piomasReader(directorydata1,'snow',years)

### Select month for data analysis (April)
sitpa = sit1[:,3,:,:]
sncpa = snc1[:,3,:,:]

### Select years for data analysis 
yearq = np.where(yearsp >= 2011)[0]
sitpa = sitpa[yearq,:,:]
sncpa = sncpa[yearq,:,:]

#### Find PIOMAS values of CS2 lat/lons    
interpsit = []
interpsnc = []
for i in xrange(years.shape[0]):    
    psit = g((lon1.flatten(),lat1.flatten()),
             sitpa[i,:,:].flatten(),(lonvals[i],latvals[i]))  
    psnc = g((lon1.flatten(),lat1.flatten()),
             sncpa[i,:,:].flatten(),(lonvals[i],latvals[i]))
             
    interpsit.append(psit)
    interpsnc.append(psnc)
    
    print '\nCompleted: Interpolated year %s!' % years[i]

###########################################################################
###########################################################################
###########################################################################
### Save interpolated PIOMAS data
directorytext = '/home/zlabe/Documents/Projects/CAAthickness/Data/'

for i in xrange(years.shape[0]):
示例#5
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for i in range(days.shape[0]):
    filename = directorydata2 + 'jan_2018_%s.nc' % days[i]
    data = Dataset(filename, 'r')
    sic19[i, :, :] = data.variables['ice_conc'][:]
    latold2 = data.variables['lat'][:]
    lonold2 = data.variables['lon'][:]
    data.close()

sic19[np.where(sic19 == -999)] = np.nan

### Calculate monthly average
sicmean = np.nanmean(sic19, axis=0)

#### Regrid data
ak = g((np.ravel(latold2), np.ravel(lonold2)),
       sicmean.ravel(), (lat2, lon2),
       method='linear')


def netcdfAlaska(lats, lons, var, directory):
    print('\n>>> Using netcdfAlaska function!')

    name = 'Alaska_SIC_Jan_2018.nc'
    filename = directory + name
    ncfile = Dataset(filename, 'w', format='NETCDF4')
    ncfile.description = 'January 2018 SIC from OSISAF ' \
                        'interpolated on grid from' \
                        'Alaska Sea Ice Atlas'

    ### Dimensions
    ncfile.createDimension('lat', var.shape[0])