On 01/24/2011 02:53 PM, John wrote: > Hello, > > I'm trying to cycle over some vectors (lat,lon,emissions) of > irregularly spaced lat/lon spots, and values. I need to sum the values > each contributing to grid on a regular lat lon grid. > > This is what I have presently, but it is too slow. Is there a more > efficient way to do this? I would prefer not to create an external > module (f2py, cython) unless there is really no way to make this more > efficient... it's the looping through the grid I guess that takes so > long.
Use np.histogram2d with weights=emissions, and lat and lon as your x and y to histogram. Choose the bins to match your grid, and it will effectively sum the emission values for each grid cell. Vincent. > > Thanks, > john > > > > def grid_emissions(lon,lat,emissions,grid.dx, grid.dy, > grid.outlat0, grid.outlon0, grid.nxmax, grid.nymax): > """ sample the emissions into a grid to fold into model output > """ > > dx = grid.dxout > dy = grid.dyout > > # Generate a regular grid to fill with the sum of emissions > xi = np.linspace(grid.outlon0, > grid.outlon0+(grid.nxmax*grid.d), grid.nxmax) > yi = np.linspace(grid.outlat0, > grid.outlat0+(grid.nymax*grid.dy), grid.nymax) > > X, Y = np.meshgrid(yi, xi) > Z = np.zeros(X.shape) > > for i,x in enumerate(xi): > for j,y in enumerate(yi): > Z[i,j] = np.sum( emissions[\ > np.where(((lat>y-dy)& (lat<y+dy))& > ((lon>x-dx)& (lon<x+dx)))[0]]) > > return Z _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
