如何获取numpy掩码数组的有效值的索引

Question

I have a netCDF4 dataset representing multiple matrices of the same dimension (551, 146), one matrix ( M1 ) contains longitude values, another matrix ( M2 ) contains latitude values. Each matrix is a numpy masked array.

Given a lon/lat tuple, (A, B) , I want to get the matrix indices ( lon, lat ) where value A matches in M1 and value B matches in M2 .

I thought I could represent the indices with:

lon_idx, lat_idx = np.mgrid[:lon.shape[0], :lon.shape[1]]

and two matrices of same shape where one have all values set to A and the other have all values set to B .

Then I am hoping to somehow combine these matrices and end up with an array of lon , lat indices where the values matched.

What is the idiomatic way to do this in numpy?

Answer 1

Thanks to SnoopJeDi on #python @freenode who found the solution for me:

In [58]: np.argwhere((lon == lon[250][145]) & (lat == lat[250][145]))
Out[58]: array([[250, 145]])