Say I have a two dimensional array of coordinates that looks something like
x = array([[1,2],[2,3],[3,4]])
Previously in my work so far, I generated a mask that ends up looking something like
mask = [False,False,True]
When I try to use this mask on the 2D coordinate vector, I get an error
newX = np.ma.compressed(np.ma.masked_array(x,mask))
>>>numpy.ma.core.MaskError: Mask and data not compatible: data size
is 6, mask size is 3.`
which makes sense, I suppose. So I tried to simply use the following mask instead:
mask2 = np.column_stack((mask,mask))
newX = np.ma.compressed(np.ma.masked_array(x,mask2))
And what I get is close:
>>>array([1,2,2,3])
to what I would expect (and want):
>>>array([[1,2],[2,3]])
There must be an easier way to do this?
Is this what you are looking for?
import numpy as np
x[~np.array(mask)]
# array([[1, 2],
# [2, 3]])
Or from numpy masked array:
newX = np.ma.array(x, mask = np.column_stack((mask, mask)))
newX
# masked_array(data =
# [[1 2]
# [2 3]
# [-- --]],
# mask =
# [[False False]
# [False False]
# [ True True]],
# fill_value = 999999)