There has been some discussion on FFTPACK lately. Problems with FFTPACK
seems to be:
- Written in old Fortran 77.
- Unprecise for single precision.
- Can sometimes be very slow, depending on input size.
- Can only handle a few small prime factors {2,3,4,5} efficiently.
- How to control integer si
2010/7/12 Jochen Schröder
> On 13/07/10 08:47, David Goldsmith wrote:
> > In light of my various questions and the responses thereto, here's what
> > I've done (but not yet committed) to numpy.fft.
> >
> > There are many ways to define the DFT, varying in the sign of the
> > exponent, normalizati
2010/7/12 Jochen Schröder
> On 13/07/10 08:04, Eric Firing wrote:
> > On 07/12/2010 11:43 AM, David Goldsmith wrote:
> >> > From the docstring:
> >>
> >> "A[0] contains the zero-frequency term (the mean of the signal)"
> >>
> >> And yet, consistent w/ the definition given in the docstring (and
> match(v1, v2) => returns a boolean array of length len(v1) indicating
> whether element i in v1 is in v2.
You want numpy.in1d (and friends, probably, like numpy.unique and the
others that are all collected in numpy.lib.arraysetops...)
Definition: numpy.in1d(ar1, ar2, assume_unique=Fals
I have two vectors of integers of not necessarily the same length.
Consider the hypothetical function match (or if you are familiar to R
then consider that function).
match(v1, v2) => returns a boolean array of length len(v1) indicating
whether element i in v1 is in v2.
I cannot find this funct
On 13/07/10 08:47, David Goldsmith wrote:
> In light of my various questions and the responses thereto, here's what
> I've done (but not yet committed) to numpy.fft.
>
> There are many ways to define the DFT, varying in the sign of the
> exponent, normalization, etc. In this implementation, the DF
On Mon, Jul 12, 2010 at 6:33 PM, Travis Oliphant wrote:
>
> On Jul 12, 2010, at 5:47 PM, David Goldsmith wrote:
>
> > In light of my various questions and the responses thereto, here's what
> I've done (but not yet committed) to numpy.fft.
> >
> > There are many ways to define the DFT, varying in
On 13/07/10 08:04, Eric Firing wrote:
> On 07/12/2010 11:43 AM, David Goldsmith wrote:
>> > From the docstring:
>>
>> "A[0] contains the zero-frequency term (the mean of the signal)"
>>
>> And yet, consistent w/ the definition given in the docstring (and
>> included w/ an earlier email), the code
On Jul 12, 2010, at 5:47 PM, David Goldsmith wrote:
> In light of my various questions and the responses thereto, here's what I've
> done (but not yet committed) to numpy.fft.
>
> There are many ways to define the DFT, varying in the sign of the
> exponent, normalization, etc. In this impleme
On Mon, Jul 12, 2010 at 17:30, Rob Speer wrote:
> rec['305'] extracts a single value from a single record.
No, in Neil's example `rec` was a structured array. You can index
structured arrays using the names of the record members, not just
scalars.
> arr.named[:,305] extracts an *entire column* f
In light of my various questions and the responses thereto, here's what I've
done (but not yet committed) to numpy.fft.
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
.. math::
A_k = \sum_{m=0}^{n-
Wes McKinney writes:
> Did you mean to post a different link? That's the ticket I just created :)
How silly of me! I meant http://projects.scipy.org/numpy/ticket/1427
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rec['305'] extracts a single value from a single record.
arr.named[:,305] extracts an *entire column* from a 2-D datarray,
returning you a 1-D datarray.
Once again, 1-D record arrays and 2-D labeled arrays look similar when
you print them, but the data structures are so unrelated that there is
rea
On Mon, Jul 12, 2010 at 3:04 PM, Eric Firing wrote:
> On 07/12/2010 11:43 AM, David Goldsmith wrote:
> > >From the docstring:
> >
> > "A[0] contains the zero-frequency term (the mean of the signal)"
> >
> > And yet, consistent w/ the definition given in the docstring (and
> > included w/ an earl
On 07/12/2010 11:43 AM, David Goldsmith wrote:
> >From the docstring:
>
> "A[0] contains the zero-frequency term (the mean of the signal)"
>
> And yet, consistent w/ the definition given in the docstring (and
> included w/ an earlier email), the code gives, e.g.:
>
> >>> import numpy as np
> >>>
Rob Speer MIT.EDU> writes:
> It's not just about the rows: a 2-D datarray can also index by
> columns, an operation that has no equivalent in a 1-D array of records
> like your example.
rec['305'] effectively indexes by column. This is one the main attractions of
structured/record arrays.
_
>From the docstring:
"A[0] contains the zero-frequency term (the mean of the signal)"
And yet, consistent w/ the definition given in the docstring (and included
w/ an earlier email), the code gives, e.g.:
>>> import numpy as np
>>> x = np.ones((16,)); x
array([ 1., 1., 1., 1., 1., 1., 1.,
On Mon, Jul 12, 2010 at 3:39 PM, Nathaniel Peterson
wrote:
> This memory leak may be related: http://projects.scipy.org/numpy/ticket/1542
> It shows what appears to be a memory leak when calling astype('float')
> on an array of dtype 'object'.
> ___
> Nu
This memory leak may be related: http://projects.scipy.org/numpy/ticket/1542
It shows what appears to be a memory leak when calling astype('float')
on an array of dtype 'object'.
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On Mon, Jul 12, 2010 at 2:22 PM, Wes McKinney wrote:
> This one was quite a bear to track down, starting from the of course
> very high level observation of "why is my application leaking memory".
> I've reproduced it on Windows XP using NumPy 1.3.0 on Python 2.5 and
> 1.4.1 on Python 2.6 (EPD). B
On 12 July 2010 13:24, K.-Michael Aye wrote:
> Dear numpy hackers,
>
> I can't find the syntax for unpacking the 3 dimensions of a rgb array.
> so i have a MxNx3 image array 'img' and would like to do:
>
> red, green, blue = img[magical_slicing]
>
> Which slicing magic do I need to apply?
Not sli
Dear numpy hackers,
I can't find the syntax for unpacking the 3 dimensions of a rgb array.
so i have a MxNx3 image array 'img' and would like to do:
red, green, blue = img[magical_slicing]
Which slicing magic do I need to apply?
Thanks for your help!
BR,
Michael
_
This one was quite a bear to track down, starting from the of course
very high level observation of "why is my application leaking memory".
I've reproduced it on Windows XP using NumPy 1.3.0 on Python 2.5 and
1.4.1 on Python 2.6 (EPD). Basically it seems that calling
.astype(bool) on an ndarray sli
It's not just about the rows: a 2-D datarray can also index by
columns, an operation that has no equivalent in a 1-D array of records
like your example.
In the movie example, arr.col_named(305) (or, in datarray syntax,
arr.named[:,305], or arr.user.named[305]) contains the movie ratings
for the us
Thanks, both.
On Mon, Jul 12, 2010 at 5:39 AM, Fabrice Silva wrote:
> Le lundi 12 juillet 2010 à 18:14 +1000, Jochen Schröder a écrit :
> > On 07/12/2010 12:36 PM, David Goldsmith wrote:
> > > On Sun, Jul 11, 2010 at 6:18 PM, David Goldsmith
> > > mailto:d.l.goldsm...@gmail.com>> wrote:
> > >
> >
On Mon, Jul 12, 2010 at 01:04:55PM +, Neil Crighton wrote:
> Thanks, that's a really nice description. Instead of
> data.ax_day.mean(axis=0)
> I think it would be clearer to do something like
> data.mean(axis='day')
Yes, that's even better. The problem is that it does not extend to
operati
On Mon, Jul 12, 2010 at 8:04 AM, Neil Crighton wrote:
> Gael Varoquaux normalesup.org> writes:
>> I do such manipulation all the time, and keeping track of which axis is
>> what is fairly tedious and error prone. It would be much nicer to be able
>> to write:
>>
>> data.ax_day.mean(axis=0)
>>
Le lundi 12 juillet 2010 à 18:14 +1000, Jochen Schröder a écrit :
> On 07/12/2010 12:36 PM, David Goldsmith wrote:
> > On Sun, Jul 11, 2010 at 6:18 PM, David Goldsmith
> > mailto:d.l.goldsm...@gmail.com>> wrote:
> >
> > In numpy.fft we find the following:
> >
> > "Then A[1:n/2] contains the
Gael Varoquaux normalesup.org> writes:
> Let say that you have a dataset that is in a 3D array, where axis 0
> corresponds to days, axis 1 to hours of the day, and axis 2 to
> temperature, you might want to have the mean of the temperature in each
> day, which would be in current numpy:
>
>
>On 12 July 2010 11:45, allan oware wrote:
> Hi All!
>
> def height_diffs():
> h = []
> i = 0
> while True:
> x = raw_input("Enter height difference ")
> if x == 'q':
> break
> else:
> h.append(x)
> i = i + 1
>
> m = asarr
On Sun, Jul 11, 2010 at 11:59:30AM +, Neil Crighton wrote:
> What is a use case for the new array type that can't be solved by
> structured/record arrays? Sounds like it was decided at the Sciy
> BOF they were a good idea, several people have implemented a
> version of them and Fernando and Ga
Hi All!
def height_diffs():
h = []
i = 0
while True:
x = raw_input("Enter height difference ")
if x == 'q':
break
else:
h.append(x)
i = i + 1
m = asarray(h,dtype=float)
return m
why does return statement return not
On 07/12/2010 12:36 PM, David Goldsmith wrote:
> On Sun, Jul 11, 2010 at 6:18 PM, David Goldsmith
> mailto:d.l.goldsm...@gmail.com>> wrote:
>
> In numpy.fft we find the following:
>
> "Then A[1:n/2] contains the positive-frequency terms, and A[n/2+1:]
> contains the negative-frequency t
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