On Wed, Jun 5, 2013 at 12:00 PM, Charles R Harris wrote:
>
>
> On Wed, Jun 5, 2013 at 11:59 AM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>>
>>
>> On Wed, Jun 5, 2013 at 11:48 AM, Nathaniel Smith wrote:
>>
>>> On Wed, Jun 5, 2013 at 6:08 PM, Julian Taylor
>>> wrote:
>>> > On 05.0
On Wed, Jun 5, 2013 at 11:59 AM, Charles R Harris wrote:
>
>
> On Wed, Jun 5, 2013 at 11:48 AM, Nathaniel Smith wrote:
>
>> On Wed, Jun 5, 2013 at 6:08 PM, Julian Taylor
>> wrote:
>> > On 05.06.2013 16:33, Nathaniel Smith wrote:
>> >> The slow down people are worried about is, suppose that 'xp'
On Wed, Jun 5, 2013 at 11:48 AM, Nathaniel Smith wrote:
> On Wed, Jun 5, 2013 at 6:08 PM, Julian Taylor
> wrote:
> > On 05.06.2013 16:33, Nathaniel Smith wrote:
> >> The slow down people are worried about is, suppose that 'xp' has
> >> 1,000,000 entries, and the user wants to interpolate 1 point
On Wed, Jun 5, 2013 at 6:08 PM, Julian Taylor
wrote:
> On 05.06.2013 16:33, Nathaniel Smith wrote:
>> The slow down people are worried about is, suppose that 'xp' has
>> 1,000,000 entries, and the user wants to interpolate 1 point. If we
>> can assume the array is sorted, then we can find which bi
On 05.06.2013 16:33, Nathaniel Smith wrote:
> On Wed, Jun 5, 2013 at 3:16 PM, Slavin, Jonathan
> wrote:
>> The simplest monotonicity test that I've seen is:
>>
>> dx = np.diff(x)
>> monotonic = np.all(dx < 0.) or np.all(dx > 0.)
>>
>> I expect that this is pretty fast, though I haven't tested it y
On Wed, Jun 5, 2013 at 3:16 PM, Slavin, Jonathan
wrote:
> The simplest monotonicity test that I've seen is:
>
> dx = np.diff(x)
> monotonic = np.all(dx < 0.) or np.all(dx > 0.)
>
> I expect that this is pretty fast, though I haven't tested it yet. If we
> want to make checking optional, then I th
o skip the check.
Jon
On Tue, Jun 4, 2013 at 9:03 PM, wrote:
> From: Eric Firing
> To: numpy-discussion@scipy.org
> Cc:
> Date: Tue, 04 Jun 2013 15:08:29 -1000
> Subject: Re: [Numpy-discussion] suggested change of behavior for interp
> On 2013/06/04 2:05 PM, Charles R Harr
On 5 Jun 2013 03:21, "Eric Firing" wrote:
>
> On 2013/06/04 4:15 PM, Benjamin Root wrote:
> > Could non-monotonicity be detected as part of the interp process?
> > Perhaps a sign switch in the deltas?
>
> There are two code paths, depending on the number of points to be
> interpolated. When it is
On 2013/06/04 4:15 PM, Benjamin Root wrote:
> Could non-monotonicity be detected as part of the interp process?
> Perhaps a sign switch in the deltas?
There are two code paths, depending on the number of points to be
interpolated. When it is greater than the size of the table, the deltas
are pr
Could non-monotonicity be detected as part of the interp process? Perhaps a
sign switch in the deltas?
I have been bitten by this problem too.
Cheers!
Ben Root
On Jun 4, 2013 9:08 PM, "Eric Firing" wrote:
>
> On 2013/06/04 2:05 PM, Charles R Harris wrote:
> >
> >
> > On Tue, Jun 4, 2013 at 12:0
On 2013/06/04 2:05 PM, Charles R Harris wrote:
>
>
> On Tue, Jun 4, 2013 at 12:07 PM, Slavin, Jonathan
> mailto:jsla...@cfa.harvard.edu>> wrote:
>
> Hi,
>
> I would like to suggest that the behavior of numpy.interp be changed
> regarding treatment of situations in which the x-coordinate
On Tue, Jun 4, 2013 at 12:07 PM, Slavin, Jonathan
wrote:
> Hi,
>
> I would like to suggest that the behavior of numpy.interp be changed
> regarding treatment of situations in which the x-coordinates are not
> monotonically increasing. Specifically, it seems to me that interp should
> work correct
Hi,
I would like to suggest that the behavior of numpy.interp be changed
regarding treatment of situations in which the x-coordinates are not
monotonically increasing. Specifically, it seems to me that interp should
work correctly when the x-coordinate is decreasing monotonically. Clearly
it can
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