I'm using Lambert projection which means if longitude is monotonic then latitude is non-monotonic.

It's difficult to using binned_statistic_2d like this:

from scipy import stats

x = [-118,-117.8,-117.7,-80,-70]

y = [18.1,18,18,17.8,18]

lon = [-118.09100342, -117.987854, -117.88466644, -117.78141785, -117.67811584, -72.32189178, -72.21858215, -72.11533356, -72.012146, -71.90899658]

lat = [17.94124222, 17.96657181, 17.99184036, 18.01698875, 18.04202652,

18.04202652, 18.01698875, 17.99184036, 17.96657181, 17.94124222]

binx = lon

biny = lat

ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx,biny])

Error:

ValueError: bins must be monotonically increasing or decreasing

How to deal with this issue?

My solution

1. Use zip to get boundaries of every grid;


2. 
   

   Add condition to make sure the order is correct

   
   
   

   for row in np.arange(lon_b.shape[0]-1):

   
   
   

   k = 0
   
   for lon_1,lat_1,lon_2,lat_2 in zip(*[iter(lon_b[row,:]),iter(lat_b[row,:])]*2):
   
       # Because binned_statistic_2d need bin_* increase monotonically
   
       if lat_1 < lat_2:
   
           CG_bin[row,k] = stats.binned_statistic_2d(lon_CG, lat_CG, None, 
   
                   'count', bins=[[lat_1,lat_2],[lon_1,lon_2]]).statistic
   
           IC_bin[row,k] = stats.binned_statistic_2d(lon_IC, lat_IC, None, 
   
                   'count', bins=[[lat_1,lat_2],[lon_1,lon_2]]).statistic
   
       else:
   
           CG_bin[row,k] = stats.binned_statistic_2d(lon_CG, lat_CG, None, 
   
                   'count', bins=[[lat_2,lat_1],[lon_1,lon_2]]).statistic
   
           IC_bin[row,k] = stats.binned_statistic_2d(lon_IC, lat_IC, None, 
   
                   'count', bins=[[lat_2,lat_1],[lon_1,lon_2]]).statistic
   
   
   
           k += 2
   
   
   
   k = 1
   
   for lon_1,lat_1,lon_2,lat_2 in zip(*[iter(lon_b[row,1:]),iter(lat_b[row,1:])]*2):
   
       if lat_1 < lat_2:
   
           CG_bin[row,k] = stats.binned_statistic_2d(lon_CG, lat_CG, None, 
   
                   'count', bins=[[lat_1,lat_2],[lon_1,lon_2]]).statistic
   
           IC_bin[row,k] = stats.binned_statistic_2d(lon_IC, lat_IC, None, 
   
                   'count', bins=[[lat_1,lat_2],[lon_1,lon_2]]).statistic
   
       else:
   
           CG_bin[row,k] = stats.binned_statistic_2d(lon_CG, lat_CG, None, 
   
                   'count', bins=[[lat_2,lat_1],[lon_1,lon_2]]).statistic
   
           IC_bin[row,k] = stats.binned_statistic_2d(lon_IC, lat_IC, None, 
   
                   'count', bins=[[lat_2,lat_1],[lon_1,lon_2]]).statistic                    
   
       k += 2