Keith Goodman wrote:
> I'd rather not pull in a scipy dependency for one function if there is
> a numpy alternative. I think it is funny that you picked up on my
> brief mention of scipy and called it unreasonable.
>
(I didn't follow this exact discussion, arguing from general principles
here a
On Tue, Jun 10, 2008 at 12:56 AM, Anne Archibald
<[EMAIL PROTECTED]> wrote:
> 2008/6/9 Keith Goodman <[EMAIL PROTECTED]>:
>> Does anyone have a function that converts ranks into a Gaussian?
>>
>> I have an array x:
>>
import numpy as np
x = np.random.rand(5)
>>
>> I rank it:
>>
x = x
2008/6/9 Keith Goodman <[EMAIL PROTECTED]>:
> Does anyone have a function that converts ranks into a Gaussian?
>
> I have an array x:
>
>>> import numpy as np
>>> x = np.random.rand(5)
>
> I rank it:
>
>>> x = x.argsort().argsort()
>>> x_ranked = x.argsort().argsort()
>>> x_ranked
> array([3, 1,
On Mon, Jun 9, 2008 at 7:35 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Mon, Jun 9, 2008 at 21:06, Keith Goodman <[EMAIL PROTECTED]> wrote:
>> On Mon, Jun 9, 2008 at 4:45 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>>> On Mon, Jun 9, 2008 at 18:34, Keith Goodman <[EMAIL PROTECTED]> wrote:
D
On Monday 09 June 2008 22:30:09 Keith Goodman wrote:
> On Mon, Jun 9, 2008 at 7:02 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> > There's a scipy.stats.mstats.rankdata() that take care of both ties and
> > missing data. Missing data are allocated a rank of either 0 or the
> > average rank, depending
On Mon, Jun 9, 2008 at 21:06, Keith Goodman <[EMAIL PROTECTED]> wrote:
> On Mon, Jun 9, 2008 at 4:45 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>> On Mon, Jun 9, 2008 at 18:34, Keith Goodman <[EMAIL PROTECTED]> wrote:
>>> Does anyone have a function that converts ranks into a Gaussian?
>>>
>>> I ha
On Mon, Jun 9, 2008 at 7:02 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> On Monday 09 June 2008 22:06:24 Keith Goodman wrote:
>> On Mon, Jun 9, 2008 at 4:45 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>
>> > There are subtleties in computing ranks when ties are involved. Take a
>> > look at the implem
On Monday 09 June 2008 22:06:24 Keith Goodman wrote:
> On Mon, Jun 9, 2008 at 4:45 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> > There are subtleties in computing ranks when ties are involved. Take a
> > look at the implementation of scipy.stats.rankdata().
>
> Good point. I had to deal with ties
On Mon, Jun 9, 2008 at 4:45 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Mon, Jun 9, 2008 at 18:34, Keith Goodman <[EMAIL PROTECTED]> wrote:
>> Does anyone have a function that converts ranks into a Gaussian?
>>
>> I have an array x:
>>
import numpy as np
x = np.random.rand(5)
>>
>> I
On Mon, Jun 9, 2008 at 18:34, Keith Goodman <[EMAIL PROTECTED]> wrote:
> Does anyone have a function that converts ranks into a Gaussian?
>
> I have an array x:
>
>>> import numpy as np
>>> x = np.random.rand(5)
>
> I rank it:
>
>>> x = x.argsort().argsort()
>>> x_ranked = x.argsort().argsort()
>>>
Does anyone have a function that converts ranks into a Gaussian?
I have an array x:
>> import numpy as np
>> x = np.random.rand(5)
I rank it:
>> x = x.argsort().argsort()
>> x_ranked = x.argsort().argsort()
>> x_ranked
array([3, 1, 4, 2, 0])
I would like to convert the ranks to a Gaussian w
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