[Numpy-discussion] Numpy-discussion Digest, Vol 22, Issue 32
Wed Jul 9 12:11:03 CDT 2008
On Jul 9, 2008, at 10:00 AM, email@example.com wrote:
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> 1. Re: element-wise logical operations on numpy arrays
> (Anne Archibald)
> From: "Anne Archibald" <firstname.lastname@example.org>
> Date: July 9, 2008 9:35:20 AM PDT
> To: "Discussion of Numerical Python" <email@example.com>
> Subject: Re: [Numpy-discussion] element-wise logical operations on
> numpy arrays
> Reply-To: Discussion of Numerical Python <firstname.lastname@example.org>
> 2008/7/9 Catherine Moroney <Catherine.M.Moroney@jpl.nasa.gov>:
>> I have a question about performing element-wise logical operations
>> on numpy arrays.
>> If "a", "b" and "c" are numpy arrays of the same size, does the
>> following syntax work?
>> mask = (a > 1.0) & ((b > 3.0) | (c > 10.0))
>> It seems to be performing correctly, but the documentation that I've
>> read indicates that "&" and "|" are for bitwise operations, not
>> element operations in arrays.
>> I'm trying to avoid using "logical_and" and "logical_or" because they
>> make the code more cumbersome and difficult to read. Are "&" and "|"
>> acceptable substitutes for numpy arrays?
> Yes. Unfortunately it is impossible to make python's usual logical
> operators, "and", "or", etcetera, behave correctly on numpy arrays. So
> the decision was made to use the bitwise operators to express logical
> operations on boolean arrays. If you like, you can think of boolean
> arrays as containing single bits, so that the bitwise operators *are*
> the logical operators.
> Confusing, but I'm afraid there really isn't anything the numpy
> developers can do about it, besides write good documentation.
Do "&" and "|" work on all types of numpy arrays (i.e. floats and
16 and 32-bit integers), or only on arrays of booleans? The short
tests I've done seem to indicate that it does, but I'd like to have
> Good luck,
> Numpy-discussion mailing list
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