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Regulating regular data netcdf

I have a netcdf file containing global sea surface temperatures. Using matplotlib and Basemap, I was able to map this data with the following code:

from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap

filename = '/Users/Nick/Desktop/SST/SST.nc'
fh = Dataset(filename, mode='r')

lons = fh.variables['LON'][:]
lats = fh.variables['LAT'][:]
sst = fh.variables['SST'][:].squeeze()

fig = plt.figure()

m = Basemap(projection='merc', llcrnrlon=80.,llcrnrlat=-25.,urcrnrlon=150.,urcrnrlat=25.,lon_0=115., lat_0=0., resolution='l')

lon, lat = np.meshgrid(lons, lats)
xi, yi = m(lon, lat)

cs = m.pcolormesh(xi,yi,sst, vmin=18, vmax=32)

m.drawmapboundary(fill_color='0.3')
m.fillcontinents(color='0.3', lake_color='0.3')
cbar = m.colorbar(cs, location='bottom', pad="10%", ticks=[18., 20., 22., 24., 26., 28., 30., 32.])
cbar.set_label('January SST (' + u'\u00b0' + 'C)')
plt.savefig('SST.png', dpi=300)

The problem is that the data has a very high resolution (9 km grid), which makes the resulting image quite noisy. I would like to put the data in a grid with a lower resolution (like 1 degree), but I'm struggling to figure out how to do this. I implemented the developed solution to try to use the matplotlib griddata function by inserting the code below into my example above, but this led to "ValueError: condition must be the 1st array".

xi, yi = np.meshgrid(lons, lats)

X = np.arange(min(x), max(x), 1)
Y = np.arange(min(y), max(y), 1)

Xi, Yi = np.meshgrid(X, Y)

Z = griddata(xi, yi, z, Xi, Yi)

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from mpl_toolkits import basemap
from netCDF4 import Dataset

filename = '/Users/Nick/Desktop/SST/SST.nc'
with Dataset(filename, mode='r') as fh:
   lons = fh.variables['LON'][:]
   lats = fh.variables['LAT'][:]
   sst = fh.variables['SST'][:].squeeze()

lons_sub, lats_sub = np.meshgrid(lons[::4], lats[::4])

sst_coarse = basemap.interp(sst, lons, lats, lons_sub, lats_sub, order=1)

(order=1) SST ( ). . , , ,

sst_smooth = basemap.interp(sst_coarse, lons_sub[0,:], lats_sub[:,0], *np.meshgrid(lons, lats), order=1)
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# Laplace filter for 2D field with/without mask
# M = 1 on - cells used
# M = 0 off - grid cells not used
# Default is without masking

import numpy as np
def laplace_X(F,M):
    jmax, imax = F.shape
    # Add strips of land
    F2 = np.zeros((jmax, imax+2), dtype=F.dtype)
    F2[:, 1:-1] = F
    M2 = np.zeros((jmax, imax+2), dtype=M.dtype)
    M2[:, 1:-1] = M

    MS = M2[:, 2:] + M2[:, :-2]
    FS = F2[:, 2:]*M2[:, 2:] + F2[:, :-2]*M2[:, :-2]

    return np.where(M > 0.5, (1-0.25*MS)*F + 0.25*FS, F)

def laplace_Y(F,M):
    jmax, imax = F.shape

    # Add strips of land
    F2 = np.zeros((jmax+2, imax), dtype=F.dtype)
    F2[1:-1, :] = F
    M2 = np.zeros((jmax+2, imax), dtype=M.dtype)
    M2[1:-1, :] = M

    MS = M2[2:, :] + M2[:-2, :]
    FS = F2[2:, :]*M2[2:, :] + F2[:-2, :]*M2[:-2, :]

    return np.where(M > 0.5, (1-0.25*MS)*F + 0.25*FS, F)


# The mask may cause laplace_X and laplace_Y to not commute
# Take average of both directions

def laplace_filter(F, M=None):
    if M == None:
        M = np.ones_like(F)
    return 0.5*(laplace_X(laplace_Y(F, M), M) +
                laplace_Y(laplace_X(F, M), M))
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To answer your original question regarding scipy.interpolate.griddataalso:

Take a close look at the parameter specifications for this function (for example, in the SciPy Documentation ) and make sure your input arrays are right . Maybe you need to do something like

import numpy as np
points = np.vstack([a.flat for a in np.meshgrid(lons,lats)]).T # (n,D)
values = sst.ravel() # (n)

and etc.

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