Re: [Numpy-discussion] Manipulate neighboring points in 2D array
> I have 2D array, let's say: `np.random.random((100,100))` and I want to do
> simple manipulation on each point neighbors, like divide their values by 3.
>
> So for each array value, x, and it neighbors n:
>
> n n nn/3 n/3 n/3
> n x n -> n/3 x n/3
> n n nn/3 n/3 n/3
>
> I searched a bit, and found about scipy ndimage filters, but if I'm not
> wrong, there is no such function. Of course me being wrong is quite possible,
> as I did not comprehend whole ndimage module, but I tried generic filter for
> example and browser other functions.
>
> Is there better way to make above manipulation, instead using for loop over
> every array element?
I am not sure I understand the above manipulation... typically neighborhood
operators take an array element and the its neighborhood and then give a single
output that becomes the value of the new array at that point. That is, a 3x3
neighborhood filter would act as a function F(R^{3x3}) -> R. It appears that
what you're talking about above is a function F(R^{3x3}) -> R^{3x3}. But how is
this output to map onto the original array positions? Is the function to be
applied to non-overlapping neighborhoods? Is it to be applied to all
neighborhoods and then summed at each position to give the output array?
If you can describe the problem in a bit more detail, with perhaps some sample
input and output for what you desire (and/or with some pseudocode describing
how it would work in a looping-over-each-element approach), I'm sure folks can
figure out how best to do this in numpy.
Zach
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.testing.asserts and masked array
Thanks. I tried again, it works. On Thu, Dec 27, 2012 at 10:35 PM, Ralf Gommers wrote: > from numpy.ma import testutils > -- *** Chao YUE Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) UMR 1572 CEA-CNRS-UVSQ Batiment 712 - Pe 119 91191 GIF Sur YVETTE Cedex Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.testing.asserts and masked array
On Thu, Dec 27, 2012 at 12:23 AM, Chao YUE wrote: > Dear all, > > I found here > http://mail.scipy.org/pipermail/numpy-discussion/2009-January/039681.html > that to use* numpy.ma.testutils.assert_almost_equal* for masked array > assertion, but I cannot find the np.ma.testutils module? > Am I getting somewhere wrong? my numpy version is 1.6.2 thanks! "from numpy.ma import testutils" works for me. Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Manipulate neighboring points in 2D array
Hi, I have 2D array, let's say: `np.random.random((100,100))` and I want to do simple manipulation on each point neighbors, like divide their values by 3. So for each array value, x, and it neighbors n: n n nn/3 n/3 n/3 n x n -> n/3 x n/3 n n nn/3 n/3 n/3 I searched a bit, and found about scipy ndimage filters, but if I'm not wrong, there is no such function. Of course me being wrong is quite possible, as I did not comprehend whole ndimage module, but I tried generic filter for example and browser other functions. Is there better way to make above manipulation, instead using for loop over every array element? TIA___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Pre-allocate array
On Thu, Dec 27, 2012 at 8:44 AM, Nikolaus Rath wrote: > I have an array that I know will need to grow to X elements. However, I > will need to work with it before it's completely filled. what sort of "work with it" do you mean? -- resize() is dangerous if there are any other views on the data block... > bigarray = np.empty(X) > current_size = 0 > for i in something: > buf = produce_data(i) > bigarray[current_size:current_size+len(buf)] = buf > current_size += len(buf) > # Do things with bigarray[:current_size] > > This avoids having to allocate new buffers and copying data around, but > I have to separately manage the current array size. yup -- but not a bad option, really. > Alternatively, I > could do > > bigarray = np.empty(0) > current_size = 0 > for i in something: > buf = produce_data(i) > bigarray.resize(len(bigarray)+len(buf)) > bigarray[-len(buf):] = buf > # Do things with bigarray > > this is much more elegant, but the resize() calls may have to copy data > around. Yes, they will -- but whether that's a problem or not depends on your use-case. If you are adding elements one-by-one, the re-allocatiing and copying of memory could be a big overhead. But if buf is not that "small", then the overhead gets lost in teh wash. Yopu'd have to profile to be sure, but I found that if, in this case, "buf" is on order of larger than 1/16 of the size of bigarray, you'll not see it (vague memory...) > Is there any way to tell numpy to allocate all the required memory while > using only a part of it for the array? Something like: > > bigarray = np.empty(50, will_grow_to=X) > bigarray.resize(X) # Guaranteed to work without copying stuff around no -- though you could probably fudge it by messing with the strides -- though you'd need to either keep track of how much memory was originally allocated, or how much is currently used yourself, like you did above. NOTE: I've written a couple of "growable array" classes for just this problem. One in pure Python, and one in Cython that isn't quite finished. I've enclosed the pure python one, let me know if your interested in the Cython version (it may need some work to b fully functional). -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov accumulator.py Description: Binary data test_accumulator.py Description: Binary data ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Pre-allocate array
Hello, I have an array that I know will need to grow to X elements. However, I will need to work with it before it's completely filled. I see two ways of doing this: bigarray = np.empty(X) current_size = 0 for i in something: buf = produce_data(i) bigarray[current_size:current_size+len(buf)] = buf current_size += len(buf) # Do things with bigarray[:current_size] This avoids having to allocate new buffers and copying data around, but I have to separately manage the current array size. Alternatively, I could do bigarray = np.empty(0) current_size = 0 for i in something: buf = produce_data(i) bigarray.resize(len(bigarray)+len(buf)) bigarray[-len(buf):] = buf # Do things with bigarray this is much more elegant, but the resize() calls may have to copy data around. Is there any way to tell numpy to allocate all the required memory while using only a part of it for the array? Something like: bigarray = np.empty(50, will_grow_to=X) bigarray.resize(X) # Guaranteed to work without copying stuff around Thanks, -Nikolaus ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] dtype "reduction"
Yep, I'm trying to construct dtype2 programmaticaly and was hoping for some
function giving me a "canonical" expression of the dtype. I've started playing
with fields but it's just a bit harder than I though (lot of different cases
and recursion).
Thanks for the answer.
Nicolas
On Dec 27, 2012, at 1:32 , Nathaniel Smith wrote:
> On Wed, Dec 26, 2012 at 8:09 PM, Nicolas Rougier
> wrote:
>>
>>
>> Hi all,
>>
>>
>> I'm looking for a way to "reduce" dtype1 into dtype2 (when it is possible of
>> course).
>> Is there some easy way to do that by any chance ?
>>
>>
>> dtype1 = np.dtype( [ ('vertex', [('x', 'f4'),
>> ('y', 'f4'),
>> ('z', 'f4')]),
>>('normal', [('x', 'f4'),
>> ('y', 'f4'),
>> ('z', 'f4')]),
>>('color', [('r', 'f4'),
>> ('g', 'f4'),
>> ('b', 'f4'),
>> ('a', 'f4')]) ] )
>>
>> dtype2 = np.dtype( [ ('vertex', 'f4', 3),
>> ('normal', 'f4', 3),
>> ('color', 'f4', 4)] )
>>
>
> If you have an array whose dtype is dtype1, and you want to convert it
> into an array with dtype2, then you just do
> my_dtype2_array = my_dtype1_array.view(dtype2)
>
> If you have dtype1 and you want to programmaticaly construct dtype2,
> then that's a little more fiddly and depends on what exactly you're
> trying to do, but start by poking around with dtype1.names and
> dtype1.fields, which contain information on how dtype1 is put together
> in the form of regular python structures.
>
> -n
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
