[Numpy-discussion] Extract Indices of Numpy Array Based on Given Bit Information

[Artur Bercik]

Dear Julian Taylor

Thank you very much, I really appreciated your codes.


On Sat, Oct 18, 2014 at 9:28 PM, Julian Taylor <
jtaylor.debian at googlemail.com> wrote:

> On 18.10.2014 14:14, Artur Bercik wrote:
> >
> >
> > On Sat, Oct 18, 2014 at 9:00 PM, Artur Bercik <vbubbly21 at gmail.com
> > <mailto:vbubbly21 at gmail.com>> wrote:
> >
> >
> >
> >     On Sat, Oct 18, 2014 at 8:28 PM, Julian Taylor
> >     <jtaylor.debian at googlemail.com
> >     <mailto:jtaylor.debian at googlemail.com>> wrote:
> >
> >         On 18.10.2014 07:58, Artur Bercik wrote:
> >         > Dear Python and Numpy Users:
> >         >
> >         > My data are in the form of '32-bit unsigned integer' as
> follows:
> >         >
> >         > myData = np.array([1073741824, 1073741877, 1073742657,
> 1073742709,
> >         > 1073742723, 1073755137, 1073755189,1073755969],dtype=np.int32)
> >         >
> >         > I want to get the index of my data where the following occurs:
> >         >
> >         > Bit No. 0–1
> >         > Bit Combination: 00
> >         >
> >         > How can I do it? I heard this type of problem first time,
> please help me.
> >         >
> >         > Artur
> >         >
> >
> >         not sure I understand the problem, maybe this?
> >
> >         np.where((myData & 0x3) == 0)
> >
> >
> >     yes, it works greatly for the following case:
> >
> >     myData = np.array([1073741824, 1073741877, 1073742657, 1073742709,
> >     1073742723, 1073755137, 1073755189,1073755969],dtype=np.uint32)
> >     Bit No. 0–1
> >     Bit Combination: 00
> >
> >     Can you make such automation for the following case as well?
> >
> >     Bit No. 2–5
> >     Bit Combination: 1101
> >
>
> sure, you can do any of these with the right masks:
> np.where((myData & 0x3c) == 0x34)
>
> you can use bin(number) to check if your numbers are correct.
>
>
> >
> > Also wondering why np.where((myData & 0x3) == 0) instead of
> > just np.where((myData & 3) == 0)
> >
>
> its the same, 0x means the number is in hexadecimal representation, for
> 3 they happen to be equal (as 3 < 10)
> It is often easier to work in the hexadecimal representation when
> dealing with binary data as its base is a power of two. So two digits in
> hexadecimal represent one byte.
> In the case above: 0x3c
> c is 12 -> 1100
> 3 is 3  -> 11
> together you get 111100, mask for bits 2-5
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20141018/53e88405/attachment.html>