[Numpy-discussion] question/request with Numeric compress and putmask
vineet at eswap.com
Mon Feb 2 21:51:01 CST 2004
Thanks for the response. The format of the extract is actually extract(arry,
condition) and not extract(condition, arry). It worked after making that
How is the compress in Numeric different than the extract?
Are the scipy.base classes implemented in c?
Will scipy.base support numarray module since I'll be upgrading to it in a
From: numpy-discussion-admin at lists.sourceforge.net
[mailto:numpy-discussion-admin at lists.sourceforge.net]On Behalf Of Travis
Sent: Sunday, February 01, 2004 11:32 PM
To: vinj at alumni.rice.edu; numpy-discussion at lists.sourceforge.net
Subject: Re: [Numpy-discussion] question/request with Numeric compress
Vineet Jain wrote:
> I'm using the Numeric arrays for financial data elements. I'm interfacing
> with an external c library which does not support invalid elements. To get
> around this I maintain a separate mask array in my python class which
> denotes which elements are valid. I then use the compress function with
> mask array to get an array with valid elements which I pass to the c
> What I would like to do is:
> putmask(full_return_value, my_mask, return_value)
> where return_value is treated like a list so that every 1 that is found in
> my_mask the next element in return_value is used. Is their anything that
> matches this?
There are functions in SciPy to handle exactly this situation.
>>> from scipy import *
insert(arr, mask, vals)
Similar to putmask arr[mask] = vals but 1d array vals has the
same number of elements as the non-zero values of mask. Inverse of extract.
Elements of ravel(condition) where ravel(condition) is true (1-d)
Equivalent of compress(ravel(condition), ravel(arr))
Thus, for your problem I would do:
financial_data = [10, 11, 22, 33, INVALID, INVALID, 44, 55]
my_mask = [1, 1, 1, 1, 0, 0, 1, 1]
compressed_data = extract(my_mask, financial_data)
return_value = some_c_function(compressed_data)
insert(financial_data, my_mask, return_value)
These functions are in scipy_base and so you only need to install
scipy_base to get them.
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