On Sun, Apr 11, 2010 at 1:00 AM, Lane Brooks wrote:
>
> On Apr 10, 2010, at 5:17 AM, josef.p...@gmail.com wrote:
>
>> On Sat, Apr 10, 2010 at 3:49 AM, Lane Brooks wrote:
>>> I am trying out masked arrays for the first time and having some
>>> problems. I have a 2-D image as dtype=numpy.int16
>>>
On Apr 10, 2010, at 5:17 AM, josef.p...@gmail.com wrote:
> On Sat, Apr 10, 2010 at 3:49 AM, Lane Brooks wrote:
>> I am trying out masked arrays for the first time and having some
>> problems. I have a 2-D image as dtype=numpy.int16
>>
>> I create a mask of all False to not mask out any pixels.
>
On Sat, Apr 10, 2010 at 9:53 AM, Dag Sverre Seljebotn
wrote:
> Kurt Smith wrote:
>> On Fri, Apr 9, 2010 at 2:25 AM, David wrote:
>>> On 04/07/2010 11:52 AM, Kurt Smith wrote:
>> Fortran's .mod files are essentially compiler-generated header files;
>> fwrap needs to use these 'headers' to get typ
On Sat, Apr 10, 2010 at 7:31 PM, Charles R Harris wrote:
>
>
> On Sat, Apr 10, 2010 at 6:17 PM, Gökhan Sever wrote:
>
>> Hello,
>>
>> Is there a simpler way to get "c" from "a"
>>
>> I[1]: a = np.arange(10)
>>
>> I[2]: b = a[3:]
>>
>> I[3]: b
>> O[3]: array([3, 4, 5, 6, 7, 8, 9])
>>
>> I[4]: c =
On Sat, Apr 10, 2010 at 6:17 PM, Gökhan Sever wrote:
> Hello,
>
> Is there a simpler way to get "c" from "a"
>
> I[1]: a = np.arange(10)
>
> I[2]: b = a[3:]
>
> I[3]: b
> O[3]: array([3, 4, 5, 6, 7, 8, 9])
>
> I[4]: c = np.insert(b, [7]*3, 0)
> O[5]: array([3, 4, 5, 6, 7, 8, 9, 0, 0, 0])
>
> a an
On Sat, Apr 10, 2010 at 5:17 PM, Gökhan Sever wrote:
> Hello,
>
> Is there a simpler way to get "c" from "a"
>
> I[1]: a = np.arange(10)
>
> I[2]: b = a[3:]
>
> I[3]: b
> O[3]: array([3, 4, 5, 6, 7, 8, 9])
>
> I[4]: c = np.insert(b, [7]*3, 0)
> O[5]: array([3, 4, 5, 6, 7, 8, 9, 0, 0, 0])
>
> a and
Hello,
Is there a simpler way to get "c" from "a"
I[1]: a = np.arange(10)
I[2]: b = a[3:]
I[3]: b
O[3]: array([3, 4, 5, 6, 7, 8, 9])
I[4]: c = np.insert(b, [7]*3, 0)
O[5]: array([3, 4, 5, 6, 7, 8, 9, 0, 0, 0])
a and c have to be same in length and the left shift must be balanced with
equal nu
On Sat, Apr 10, 2010 at 12:45, Pauli Virtanen wrote:
> la, 2010-04-10 kello 12:23 -0500, Travis Oliphant kirjoitti:
> [clip]
>> Here are my suggested additions to NumPy:
>> ufunc methods:
> [clip]
>> * reducein (array, indices, axis=0)
>> similar to reduce-at, but the indices
There is too much out there which is making me confuse, I want to install
Numpy and Scipy on cygwinCan some body give me steps...There is
different version
of Numpy ...which one i need to download.and how to check it after
installing..
I already have cygwin full version on my pc...
Ak
On 10 April 2010 19:45, Pauli Virtanen wrote:
> Another addition to ufuncs that should be though about is specifying the
> Python-side interface to generalized ufuncs.
This is an interesting idea; what do you have in mind?
Regards
Stéfan
___
NumPy-Disc
On Sat, Apr 10, 2010 at 1:23 PM, Travis Oliphant wrote:
>
> Hi,
>
> I've been mulling over a couple of ideas for new ufunc methods plus a
> couple of numpy functions that I think will help implement group-by
> operations with NumPy arrays.
>
> I wanted to discuss them on this list before putting f
la, 2010-04-10 kello 12:23 -0500, Travis Oliphant kirjoitti:
[clip]
> Here are my suggested additions to NumPy:
> ufunc methods:
[clip]
> * reducein (array, indices, axis=0)
>similar to reduce-at, but the indices provide both the
> start and end points (rather than being fen
Hi,
I've been mulling over a couple of ideas for new ufunc methods plus a
couple of numpy functions that I think will help implement group-by
operations with NumPy arrays.
I wanted to discuss them on this list before putting forward an actual
proposal or patch to get input from others.
Th
Kurt Smith wrote:
> On Fri, Apr 9, 2010 at 2:25 AM, David wrote:
>> On 04/07/2010 11:52 AM, Kurt Smith wrote:
>>> Briefly, I'm encountering difficulties getting things working in numpy
>>> distutils for fwrap's build system.
>>>
>>> Here are the steps I want the build system to accomplish:
>>>
>>>
On Sat, Apr 10, 2010 at 3:49 AM, Lane Brooks wrote:
> I am trying out masked arrays for the first time and having some
> problems. I have a 2-D image as dtype=numpy.int16
>
> I create a mask of all False to not mask out any pixels.
>
> I calculate the mean of the image original image and it comes
I am trying out masked arrays for the first time and having some
problems. I have a 2-D image as dtype=numpy.int16
I create a mask of all False to not mask out any pixels.
I calculate the mean of the image original image and it comes out ~597.
I calculate the mean of the masked array and it co
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