I'm finding it difficult to tell which methods/operations respect the
mask and which do not, in masked arrays.
mydata.filled returns a copy of the data (in a numpy array) with all
masked elements set to the fill_value. So, masked respected, but data
returned as a new data-type when what I wanted was to set all masked
values in the array to the same value.
mydata.fill however modifies the data array in-place, modifies all
values regardless of the mask, and leaves the mask unchanged.
Assignment (mydata[:] = 10) sets all values in the slice and updates the mask.
Basic methods respect the mask, like mydata.mean(), but np.asarray
ignores the mask.
Example
------------
In [32]: mydata = ma.array([0,1,2,3,4,5], mask=[1,0,1,0,1,0])
In [34]: mydata
Out[34]:
masked_array(data = [-- 1 -- 3 -- 5],
mask = [ True False True False True False],
fill_value=999999)
In [35]: mydata.filled(np.nan)
Out[35]: array([0, 1, 0, 3, 0, 5])
In [36]: mydata.fill(np.nan)
In [37]: mydata
Out[37]:
masked_array(data = [-- 0 -- 0 -- 0],
mask = [ True False True False True False],
fill_value=999999)
In [38]: mydata.data
Out[38]: array([0, 0, 0, 0, 0, 0])
In [48]: mydata[:] = 456
In [49]: mydata
Out[49]:
masked_array(data = [456 456 456 456 456 456],
mask = [False False False False False False],
fill_value=999999)
In [53]: mydata = ma.array([0,1,2,3,4,5], mask=[1,0,1,0,1,0])
In [54]: mydata.mean()
Out[54]: 3.0
In [55]: np.asarray(mydata)
Out[55]: array([0, 1, 2, 3, 4, 5])
In summary, is there a tutorial that would show how to use masked
arrays? Because at this point I'm confused and don't know how to use
them.
Google yields this out of data doc:
http://numpy.scipy.org/numpydoc/numpy-22.html
Thanks!
--
Christopher Burns
Computational Infrastructure for Research Labs
10 Giannini Hall, UC Berkeley
phone: 510.643.4014
http://cirl.berkeley.edu/
_______________________________________________
Numpy-discussion mailing list
[email protected]
http://projects.scipy.org/mailman/listinfo/numpy-discussion
