I've been reading through the masked array documentation and I'm confused - what is different about MaskedArray than just maintaining an array of values and a boolean mask? Can someone give me an e...

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 ma...

I am trying to calculate the moving average in a large numpy array that contains NaNs. Currently I am using: import numpy as np def moving_average(a,n=5): ret = np.cumsum(a,dtype=float) ret[n:]...

Is there an idiomatic way to mask elements of an array in vanilla Python 3? For example: a = [True, False, True, False] b = [2, 3, 5, 7] b[a] I was hoping b[a] would return [2, 5], but I get an er...

I have an image that I found contours on with skimage.measure.find_contours() but now I want to create a mask for the pixels fully outside the largest closed contour. Any idea how to do this? Mod...

>> masks = [[1,1],[0,0]] >> [np.ma.masked_array(data=np.array([1.0,2.0]), mask=m, fill_value=np.nan).mean() for m in masks] [masked, 1.5] I'd like to replace the masked result with ...

I need to mask an array a by a condition fulfilled by another array b. For example values in a should only be preserved if the values in the same position of b equal 0, otherwise return as None. ...

In my code, at some point I try to modify a value of a masked array, yet python seems to ignore this. I'm thinking this has to do with the way memory is stored in arrays, as if I were modifying a c...

I'm working with masked arrays thanks to some of the help I've gotten on stackoverflow, but I'm running into a problem with the np.where evaluation of a masked array. My masked array is: m_pt0 = ...

Currently, my code heavily uses structured masked arrays with multidimensional dtypes, with dozens of fields and item sizes of many kilobytes. It appears that xarray could be a great alternative, b...

I understand how to create a masked array, and I would like to use masking in a record array so that I can access this data using named attributes. The masking seems to be "lost" when I create a re...

I have a numpy structured array with a dtype such as: A = numpy.empty(10, dtype=([('segment', '<i8'), ('material', '<i8'), ('rxN', '<i8')])) I know I can create a mask such as: A[A['se...