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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...
619
To combine two masks with the logical_or operator, use the mask_or() method in Python Numpy. If copy parameter is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False. The shrink parameter suggests whether to shrink the output to nomask if all its values are False.
To create a boolean mask from an array, use the ma. make_mask() method in Python Numpy. The function can accept any sequence that is convertible to integers, or nomask. Does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True.
Definition. Matrix Masking refers to a class of statistical disclosure limitation (SDL) methods used to protect confidentiality of statistical data, transforming an n × p (cases by variables) data matrix Z through pre- and post-multiplication and the possible addition of noise.
A boolean array can be created manually by using dtype=bool when creating the array. Values other than 0 , None , False or empty strings are considered True. Alternatively, numpy automatically creates a boolean array when comparisons are made between arrays and scalars or between arrays of the same shape.
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.
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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
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