How to properly mask a numpy 2D array?

Question

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?

Answer 1

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) 

Answer 2

Your x is 3x2:

In [379]: x Out[379]:  array([[1, 2],        [2, 3],        [3, 4]]) 

Make a 3 element boolean mask:

In [380]: rowmask=np.array([False,False,True]) 

That can be used to select the rows where it is True, or where it is False. In both cases the result is 2d:

In [381]: x[rowmask,:] Out[381]: array([[3, 4]])  In [382]: x[~rowmask,:] Out[382]:  array([[1, 2],        [2, 3]]) 

This is without using the MaskedArray subclass. To make such array, we need a mask that matches x in shape. There isn't provision for masking just one dimension.

In [393]: xmask=np.stack((rowmask,rowmask),-1)  # column stack  In [394]: xmask Out[394]:  array([[False, False],        [False, False],        [ True,  True]], dtype=bool)  In [395]: np.ma.MaskedArray(x,xmask) Out[395]:  masked_array(data =  [[1 2]  [2 3]  [-- --]],              mask =  [[False False]  [False False]  [ True  True]],        fill_value = 999999) 

Applying compressed to that produces a raveled array: array([1, 2, 2, 3])

Since masking is element by element, it could mask one element in row 1, 2 in row 2 etc. So in general compressing, removing the masked elements, will not yield a 2d array. The flattened form is the only general choice.

np.ma makes most sense when there's a scattering of masked values. It isn't of much value if you want want to select, or deselect, whole rows or columns.

===============

Here are more typical masked arrays:

In [403]: np.ma.masked_inside(x,2,3) Out[403]:  masked_array(data =  [[1 --]  [-- --]  [-- 4]],              mask =  [[False  True]  [ True  True]  [ True False]],        fill_value = 999999)  In [404]: np.ma.masked_equal(x,2) Out[404]:  masked_array(data =  [[1 --]  [-- 3]  [3 4]],              mask =  [[False  True]  [ True False]  [False False]],        fill_value = 2)  In [406]: np.ma.masked_outside(x,2,3) Out[406]:  masked_array(data =  [[-- 2]  [2 3]  [3 --]],              mask =  [[ True False]  [False False]  [False  True]],        fill_value = 999999)