Identifying the nearest grid point

Question

I have three arrays

lat=[15,15.25,15.75,16,....30]
long=[91,91.25,91.75,92....102]

data=
array([[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],
       [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],
       [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],
       ..., 
       [-99.9, -99.9, -99.9, ...,   0. ,   0. ,   0. ],
       [-99.9, -99.9, -99.9, ...,   0. ,   0. ,   0. ],
       [-99.9, -99.9, -99.9, ...,   0. ,   0. ,   0. ]])

It is of [44 cols and 60 rows] same as long x lat

If I input any point (16.3,101.6), I need to find out the nearest grid and extract the data from that grid from third array. How can I do it using numpy in python ? Here I am giving example of one point, but in real problem, I have several points.

I have tried with this function,

def getclosest_ij(lats,lons,latpt,lonpt):
    dis_sq1=(lats-latpt)
    dis_sq2=(lons-lonpt)
    minidex_lat=dis_sq1.argmin()
    minidex_lon=dis_sq2.argmin()
    return minidex_lon,minidex_lat

Answer 1

The algorithm you are looking for is a nearest-neighbour interpolation on a regular grid. For instance, you could use,

from scipy.interpolate import RegularGridInterpolator

itp = RegularGridInterpolator( (lat, lon), data, method='nearest') 
res = itp(some_new_point)

As a bonus, this function can also perform more precise linear interpolations if you set method='linear'.