识别最近的网格点

我有三个数组

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. ]])

与长 x 纬度相同的 [44 列和 60 行]

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

如果我输入任何点 (16.3,101.6)，我需要找出最近的网格并从第三个数组的网格中提取数据.我如何在 python 中使用 numpy 来做到这一点?在这里，我举了一个例子，但在实际问题中，我有几点.

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.

我试过这个功能，

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