On Tue, May 29, 2012 at 12:06 PM, Charles R Harris
wrote:
> I'd like to see these functions is scipy somewhere. The function names
> aren't very descriptive and the one line summaries don't give a very good
> idea of what they do, so I think those bits could use improvement. Mention
> of the Hough
On Tue, 29 May 2012 10:03:04 -0700
Stéfan van der Walt wrote:
> On Mon, May 28, 2012 at 11:53 AM, Travis Oliphant wrote:
> > I could see these functions going into scipy.ndimage but again because they
> > are not necessarily just image processing functions, and the fact that they
> > are so simp
On Mon, May 28, 2012 at 9:18 PM, wrote:
>
> https://github.com/numpy/numpy/commit/74b9f5eef8fac643bf9012dbb2ac6b4b19f46892
> broke return_inverse for structured arrays, because of the use of mergesort
>
> I'm using structured dtypes to get uniques and return_inverse by rows
>
> >>> groups = np.ra
On Tue, May 29, 2012 at 11:40 AM, Robert Jördens wrote:
> On Tue, May 29, 2012 at 11:03 AM, Stéfan van der Walt
> wrote:
> > On Mon, May 28, 2012 at 11:53 AM, Travis Oliphant
> wrote:
> >> I could see these functions going into scipy.ndimage but again because
> they
> >> are not necessarily jus
On Tue, May 29, 2012 at 11:03 AM, Stéfan van der Walt wrote:
> On Mon, May 28, 2012 at 11:53 AM, Travis Oliphant wrote:
>> I could see these functions going into scipy.ndimage but again because they
>> are not necessarily just image processing functions, and the fact that they
>> are so simple, p
On Mon, May 28, 2012 at 11:53 AM, Travis Oliphant wrote:
> I could see these functions going into scipy.ndimage but again because they
> are not necessarily just image processing functions, and the fact that they
> are so simple, perhaps they are best put into NumPy itself.
I'm wondering about th
Another possible solution is to sort the numbers and add them in a
binary tree. It reduces the truncation error but makes the problem n-
log-n and therefore not worth the trouble.
Massimo
On May 29, 2012, at 9:45 AM, Pauli Virtanen wrote:
> Val Kalatsky gmail.com> writes:
>> You'll need some
Val Kalatsky gmail.com> writes:
> You'll need some patience to get non-zeros, especially for k=1e-5
>
> In [84]: np.sum(np.random.gamma(1e-5,size=100)!=0.0)
> Out[84]: 7259
> that's less than 1%. For k=1e-4 it's ~7%
To clarify: the distribution is peaked at numbers
that are too small to be r
On 29 May 2012, at 15:42, Nathaniel Smith wrote:
>> I note the fine distinction between np.isscalar( ('hello') ) and
>> np.isscalar( ('hello'), )...
>
> NB you mean np.isscalar( ('hello',) ), which creates a single-element
> tuple. A trailing comma attached to a value in Python normally creates
On Tue, May 29, 2012 at 2:31 PM, Derek Homeier
wrote:
> On 29 May 2012, at 15:00, Mark Bakker wrote:
>
>> Why does isscalar('hello') return True?
>>
>> I thought it would check for a number?
>
> No, it checks for something that is of 'scalar type', which probably can be
> translated as 'not equiva
On 29 May 2012, at 15:00, Mark Bakker wrote:
> Why does isscalar('hello') return True?
>
> I thought it would check for a number?
No, it checks for something that is of 'scalar type', which probably can be
translated as 'not equivalent to an array'. Since strings can form numpy
arrays,
I gues
On Tue, May 29, 2012 at 2:00 PM, Mark Bakker wrote:
> Why does isscalar('hello') return True?
>
> I thought it would check for a number?
>
> Numpy 1.6.1
>
> Silly question?
Nope, but you're thinking of a different sense of "scalar" :-).
In numpy, "scalar" means something like "anything that you
Why does isscalar('hello') return True?
I thought it would check for a number?
Numpy 1.6.1
Silly question?
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