how to apply a mask from one array to another array?

Question

I've read the masked array documentation several times now, searched everywhere and feel thoroughly stupid. I can't figure out for the life in me how to apply a mask from one array to another.

Example:

import numpy as np  y = np.array([2,1,5,2])          # y axis x = np.array([1,2,3,4])          # x axis m = np.ma.masked_where(y>2, y)   # filter out values larger than 5 print m [2 1 -- 2] print np.ma.compressed(m) [2 1 2] 

So this works fine.... but to plot this y axis, I need a matching x axis. How do I apply the mask from the y array to the x array? Something like this would make sense, but produces rubbish:

new_x = x[m.mask].copy() new_x array([5]) 

So, how on earth is that done (note the new x array needs to be a new array).

Edit:

Well, it seems one way to do this works like this:

>>> import numpy as np >>> x = np.array([1,2,3,4]) >>> y = np.array([2,1,5,2]) >>> m = np.ma.masked_where(y>2, y) >>> new_x = np.ma.masked_array(x, m.mask) >>> print np.ma.compressed(new_x) [1 2 4] 

But that's incredibly messy! I'm trying to find a solution as elegant as IDL...

Answer 1

I had a similar issue, but involving loads more masking commands and more arrays to apply them. My solution is that I do all the masking on one array and then use the finally masked array as the condition in the mask_where command.

For example:

y = np.array([2,1,5,2])                         # y axis x = np.array([1,2,3,4])                         # x axis m = np.ma.masked_where(y>5, y)                  # filter out values larger than 5 new_x = np.ma.masked_where(np.ma.getmask(m), x) # applies the mask of m on x 

The nice thing is you can now apply this mask to many more arrays without going through the masking process for each of them.

Answer 2

Why not simply

import numpy as np  y = np.array([2,1,5,2])          # y axis x = np.array([1,2,3,4])          # x axis m = np.ma.masked_where(y>2, y)   # filter out values larger than 5 print list(m) print np.ma.compressed(m)  # mask x the same way m_ = np.ma.masked_where(y>2, x)   # filter out values larger than 5 # print here the list print list(m_)  print np.ma.compressed(m_) 

code is for Python 2.x

Also, as proposed by joris, this do the work new_x = x[~m.mask].copy() giving an array

>>> new_x array([1, 2, 4])