On Fri, Feb 8, 2013 at 6:44 PM, Charles R Harris
charlesr.har...@gmail.comwrote:
My money is on 'five year old bug'.
A bug indeed it seems to be. I have cloned the source code, and in
item_selection.c, in function PyArray_TakeFrom, when 'out' is an argument
in the call, the code is actually
On Tue, Feb 12, 2013 at 9:53 AM, Nicolas Rougier
nicolas.roug...@inria.frwrote:
Did I do something wrong or is it expected behavior ?
Try:
print (Z.view('f4'))[:50].base.base is Z # True
print Z[:50].view('f4').base.base is Z # True
This weird behaviour is fixed in the just-released numpy
On Wed, Feb 27, 2013 at 5:41 AM, Jorge Scandaliaris
jorgesmbox...@yahoo.eswrote:
Jorge Scandaliaris jorgesmbox-ml at yahoo.es writes:
I have an ndarray A of shape (M,2,2) representing M 2 x 2 matrices.
Now I want to apply a transform T of shape (2,2) to each of matrix.
np.einsum makes a
A couple of days back, answering a question in StackExchange (
http://stackoverflow.com/a/15196628/110026), I found myself using Lagrange
multipliers to fit a polynomial with least squares to data, making sure it
went through some fixed points. This time it was relatively easy, because
some 5
of the Lagrange
multipliers calculated during the fit.
Jaime
A
On Mon, Mar 4, 2013 at 7:23 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
A couple of days back, answering a question in StackExchange (
http://stackoverflow.com/a/15196628/110026), I found myself using
Lagrange
On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris
charlesr.har...@gmail.comwrote:
There are actually seven versions of polynomial fit, two for the usual
polynomial basis, and one each for Legendre, Chebyshev, Hermite, Hermite_e,
and Laguerre ;)
Correct me if I am wrong, but the fitted
On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris
charlesr.har...@gmail.comwrote:
On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
There are actually seven versions
The other day I found myself finding trailing edges in binary images doing
something like this:
arr = np.random.randint(2, size=1000).astype(np.int8)
pattern = np.array([1, 1, 1, 1, 0, 0])
arr_match = 2*arr - 1
pat_match = 2*pattern - 1
from numpy.lib.stride_tricks import as_strided
arr_win =
On Sat, Mar 30, 2013 at 11:01 AM, Ivan Oseledets
ivan.oseled...@gmail.comwrote:
I am using numpy 1.6.1,
and encountered a wierd fancy indexing bug:
import numpy as np
c = np.random.randn(10,200,10);
In [29]: print c[[0,1],:200,:2].shape
(2, 200, 2)
In [30]: print
I noticed today that the documentation for np.transpose states, for the
return value, that A view is returned whenever possible.
Is there really any situation where swapping axes around could trigger the
need to copy data, or will a view always be returned no matter what?
I can't think of any
On Wed, Jun 12, 2013 at 6:48 AM, Nathaniel Smith n...@pobox.com wrote:
Sounds like a doc bug. (Probably someone being over-careful -- the
default for many operations in numpy is that it's undefined whether
they return a view or not, so if it makes a difference to you you need
to take an
Hi!
I have spent the last couple of weeks playing around with GUFUNCS, and am
literally blown away by the power that a C compiler and NumPy put at the
tip of my fingers! I still have many questions, but the following ones are
the most pressing in my current state of amazed ignorance:
1. **The
Hi,
I think I have found an undocumented feature of the gufuncs machinery. I
have filed a bug report:
https://github.com/numpy/numpy/issues/3582
Some more background on what i am seeing...
I have coded a gufunc with signature '(r,c,p),(g,g,g,q)-(r,c,q)'. It is a
color map, i.e. a
I haven't tried to compile skimage, but the easiest way to get set up for
compilation with Windows and MSVC is to follow the instructions in the
Cython wiki. It is routinely spammed, so here's a link to the last
non-corrupt version as of right now:
Hi all,
I am seeing some very weird behavior on a gufunc I coded.
It has a pretty complicated signature:
'(r,c,p),(i,j,k,n),(u,v),(d),(n,q)-(q,r,c)'
And a single registered loop function, for types:
uint8, uint16, uint16, uintp, uint8-uint8.
In general it performs beautifully well, returning
On Fri, Sep 27, 2013 at 5:27 AM, Sebastian Berg
sebast...@sipsolutions.netwrote:
And most importantly, is there any behaviour thing in the index
machinery that is bugging you, which I may have forgotten until now?
I find this behavior of boolean indexing a little bit annoying:
a =
On Thu, Oct 3, 2013 at 4:05 PM, Moroney, Catherine M (398D)
catherine.m.moro...@jpl.nasa.gov wrote:
I know I have a lot yet to learn about array striding tricks, so please
pardon the triviality of this question.
Here is the problem both in words and dumb python:
I have a large NxM array
On Tue, Oct 8, 2013 at 4:38 AM, Ke Sun sunk...@gmail.com wrote:
On Tue, Oct 08, 2013 at 01:49:14AM -0700, Matthew Brett wrote:
Hi,
On Tue, Oct 8, 2013 at 1:06 AM, Ke Sun sunk...@gmail.com wrote:
Dear all,
I have written the following function to compute the square distances
of a
I recently came up with a way of vectorizing some recursive sequence
calculations. While it works, I am afraid it is relying on implementation
details potentially subject to change. The basic idea is illustrated by
this function, calculating the first n items of the Fibonacci sequence:
def
On Tue, Nov 5, 2013 at 10:10 AM, Sergey Petrov qweqwe...@yahoo.com wrote:
Rather stupid question here, but I can't figure out by myself:
Why does the following c program segfaults? And how can I avoid it?
You need to call import_array before using the C-API, see here:
Hi,
Inspired by the great rewrite of numpy.linalg in 1.8, I've spent the last
couple of days coding a couple of the functions in numpy.lib as gufuncs,
namely np.interp and np.bincount. I want to do something along the same
lines to np.digitize, but haven't started on it just yet. I'm currently
On Thu, Nov 14, 2013 at 9:37 AM, David Cournapeau courn...@gmail.comwrote:
You can for example compare np.fft.fft(a) for 2**16 and 2**16+1 (and
2**16-1 that while bad is not prime, so only 1 order of magnitude slower).
I actually did...
Each step of a FFT basically splits a DFT of size N =
With the new np.partition functionality, there is a more efficient, but
also less obvious, way of extracting the n largest (or smallest) elements
from an array, i.e.:
def smallest_n(a, n):
return np.sort(np.partition(a, n)[:n])
def argsmallest_n(a, n):
ret = np.argpartition(a, n)[:n]
On Wed, Jan 8, 2014 at 11:12 AM, Neal Becker ndbeck...@gmail.com wrote:
I have a 1d vector d. I want compute the means of subsets of this vector.
The subsets are selected by looking at another vector s or same shape as d.
This can be done as:
[np.mean (d[s == i]) for i in range (size)]
Hi,
I have just sent a PR, adding a `return_counts` keyword argument to
`np.unique` that does exactly what the name suggests: counting the number
of times each unique time comes up in the array. It reuses the `flag` array
that is constructed whenever any optional index is requested, extracts the
Hi,
I have just added a new PR: https://github.com/numpy/numpy/pull/4244
From the commit message:
This PR replaces the generic binary search functions used by `searchsorted`
with type specific ones for numeric types. This results in a speed-up of
calls to `searchsorted` which is highly
Cannot test right now, but np.unique(b, return_inverse=True)[1].reshape(2,
-1) should do what you are after, I think.
On Feb 2, 2014 11:58 AM, Mads Ipsen mads.ip...@gmail.com wrote:
Hi,
I have run into a potential 'for loop' bottleneck. Let me outline:
The following array describes bonds
Perhaps you could reuse np.dot, by giving its second argument a default
None value, and passing a tuple as first argument, i.e. np.dot((a, b, c))
would compute a.dot(b).dot(c), possibly not in that order.
As is suggested in the matlab thread linked by Josef, if you do implement
an optimal
On Tue, Feb 18, 2014 at 9:03 AM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Tue, Feb 18, 2014 at 9:40 AM, Nathaniel Smith n...@pobox.com wrote:
On 18 Feb 2014 11:05, Charles R Harris charlesr.har...@gmail.com
wrote:
Hi All,
There is an old ticket, #1499, that suggest
I have just submitted a PR (https://github.com/numpy/numpy/pull/4330)
adding an axis argument to bincount. It lets you do things that would have
been hard before, but the UI when broadcasting arrays together and having
an axis argument can get tricky, and there is no obvious example already in
On Feb 22, 2014 2:03 PM, Nathaniel Smith n...@pobox.com wrote:
Hi all,
Currently numpy's 'dot' acts a bit weird for ndim2 or ndim1. In
practice this doesn't usually matter much, because these are very
rarely used. But, I would like to nail down the behaviour so we can
say something precise
On Thu, Feb 27, 2014 at 6:11 PM, Alan G Isaac alan.is...@gmail.com wrote:
I have a bincount array `cts`.
I'd like to produce any one array `a` such that `cts==np.bincounts(a)`.
Easy to do in a loop, but does NumPy offer a better (i.e., faster) way?
cts = np.bincount([1,1,2,3,4,4,6])
On Fri, Mar 14, 2014 at 9:32 PM, Nathaniel Smith n...@pobox.com wrote:
Here are the interesting use cases for @@ that I can think of:
- 'vector @@ 2' gives the squared Euclidean length (because it's the
same as vector @ vector). Kind of handy.
- 'matrix @@ n' of course gives the matrix
On Fri, Mar 14, 2014 at 9:15 PM, Chris Laumann chris.laum...@gmail.comwrote:
Hi all,
Let me preface my two cents by saying that I think the best part of @
being accepted is the potential for deprecating the matrix class -- the
syntactic beauty of infix for matrix multiply is a nice side
On Mar 17, 2014 5:54 PM, Nathaniel Smith n...@pobox.com wrote:
On Sat, Mar 15, 2014 at 6:28 PM, Nathaniel Smith n...@pobox.com wrote:
Mathematica: instead of having an associativity, a @ b @ c gets
converted into mdot([a, b, c])
So, I've been thinking about this (thanks to @rfateman for
I submitted a PR that makes some improvements to the numpy functions
dealing with triangular arrays. Aside from a general speed-up of about 2x
for most functions, there are some minor changes to the public API. In case
anyone is concerned about them, here's a list:
* 'np.tri' now accepts a
On Wed, Mar 26, 2014 at 1:28 PM, Slaunger slaun...@gmail.com wrote:
See if you can make sense of the following. It is a little cryptic, but it
works:
f_change = np.array([2, 3, 39, 41, 58, 59, 65, 66, 93, 102, 145])
g_change = np.array([2, 94, 101, 146, 149])
N = 150
if len(f_change) % 2 :
On Wed, Mar 26, 2014 at 2:23 PM, Slaunger slaun...@gmail.com wrote:
Jaime Fernández del Río wrote
You saved my evening! Actually, my head has been spinning about this
problem
the last three evenings without having been able to nail it down.
I had to quit Project Euler about 5 years ago
Hi,
I have submitted a PR (https://github.com/numpy/numpy/pull/4568) that
speeds up `np.vander` by using accumulated multiplication instead of
exponentiation to compute the Vandermonde matrix. For largish matrices the
speed-ups can be quite dramatic, over an order of magnitude.
Julian has raised
On Sat, Mar 29, 2014 at 8:55 AM, josef.p...@gmail.com wrote:
On Sat, Mar 29, 2014 at 7:31 AM, josef.p...@gmail.com wrote:
On Sat, Mar 29, 2014 at 12:12 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
Hi,
I have submitted a PR (https://github.com/numpy/numpy/pull/4568
On Sun, May 4, 2014 at 9:34 PM, srean srean.l...@gmail.com wrote:
Hi all,
is there an efficient way to do the following without allocating A where
A = np.repeat(x, [4, 2, 1, 3], axis=0)
c = A.dot(b)# b.shape
If x is a 2D array you can call repeat **after** dot, not before, which
On Tue, May 27, 2014 at 12:27 PM, Nicolas Rougier
nicolas.roug...@inria.frwrote:
Any other tricky stride_trick tricks ? I promised to put them in the
master section.
It doesn't use stride_tricks, and seberg doesn't quite like it, but this
made the rounds in StackOverflow a couple of years
On Fri, May 30, 2014 at 8:48 AM, Bob Dowling rjd4+nu...@cam.ac.uk wrote:
Is there a clean way to create a view on an existing ND-array with its
axes in a different order.
There's an epidemic of axes reordering, the exact same thing was asked
yesterday in StackOverflow:
On Sun, Jun 8, 2014 at 1:43 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Sun, Jun 8, 2014 at 2:34 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
Hello,
I'm happy to announce the fist beta release of Numpy 1.9.0.
1.9.0 will be a new feature release supporting Python
On Tue, Jul 15, 2014 at 2:22 AM, Neil Hodgson hodgson.n...@yahoo.co.uk
wrote:
Hi,
We came across this bug while using np.cross on 3D arrays of 2D vectors.
What version of numpy are you using? This should already be solved in numpy
master, and be part of the 1.9 release. Here's the relevant
On Thu, Jul 24, 2014 at 4:56 AM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
In practice one of the better methods is pairwise summation that is
pretty much as fast as a naive summation but has an accuracy of
O(logN) ulp.
This is the method numpy 1.9 will use this method by default (+
On Wed, Aug 6, 2014 at 5:31 AM, Nathaniel Smith n...@pobox.com wrote:
I think the other obvious strategy to consider, is defining a 'dot'
gufunc, with semantics identical to @. (This would be useful for
backcompat as well: adding/dropping compatibility with older python
versions would be as
On Wed, Aug 20, 2014 at 6:26 AM, Pierre-Andre Noel
noel.pierre.an...@gmail.com wrote:
Thanks all for the feedback!
So there appears to be interest for this feature, and I think that I can
implement it. However, it may take a while before I do so: I have other
priorities right now.
In
I can confirm, the issue seems to be in sorting:
np.sort(V_)
array([([0.5, 0.0, 1.0],), ([0.5, 0.0, -1.0],), ([0.5, -0.0, 1.0],),
([0.5, -0.0, -1.0],)],
dtype=[('v', 'f4', (3,))])
These I think are handled by the generic sort functions, and it looks like
the comparison function
it over the weekend.
Jaime
On Fri, Aug 22, 2014 at 7:54 AM, Eelco Hoogendoorn
hoogendoorn.ee...@gmail.com wrote:
Oh yeah this could be. Floating point equality and bitwise equality are
not the same thing.
--
From: Jaime Fernández del Río jaime.f...@gmail.com
Sent
You can always write your own gufunc with signature '(),(),()-(a, a)', and
write a Python wrapper that always call it with an `out=` parameter of
shape (..., 3, 3), something along the lines of:
def my_wrapper(a, b, c, out=None):
if out is None:
out =
After reading this stackoverflow question:
http://stackoverflow.com/questions/25530223/append-a-list-at-the-end-of-each-row-of-2d-array
I was reminded that the `np.concatenate` family of functions do not
broadcast the shapes of their inputs:
import numpy as np
a = np.arange(6).reshape(3, 2)
A request was open in github to add a `merge` function to numpy that would
merge two sorted 1d arrays into a single sorted 1d array. I have been
playing around with that idea for a while, and have a branch in my numpy
fork that adds a `mergesorted` function to `numpy.lib`:
On Wed, Aug 27, 2014 at 10:01 AM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Aug 27, 2014 at 5:44 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
After reading this stackoverflow question:
http://stackoverflow.com/questions/25530223/append-a-list-at-the-end-of-each-row
in this direction, and I
feel this draft could really use a bit of back and forth. If we are going
to completely rewrite arraysetops, we might as well do it right.
On Wed, Aug 27, 2014 at 7:02 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
A request was open in github to add
. If it isn't, it is more efficient to perform a reduction by means of
splitting the array by its groups first, and then map the iterable of
groups over some reduction operation (as noted in the docstring of
GroupBy.reduce).
On Wed, Aug 27, 2014 at 8:29 PM, Jaime Fernández del Río
jaime.f
Hi,
I have just sent a PR (https://github.com/numpy/numpy/pull/5015), adding
the possibility of having frozen dimensions in gufunc signatures. As a
proof of concept, I have added a `cross1d` gufunc to
`numpy.core.umath_tests`:
In [1]: import numpy as np
In [2]: from numpy.core.umath_tests import
On Thu, Aug 28, 2014 at 5:40 PM, Nathaniel Smith n...@pobox.com wrote:
Some thoughts:
But, for your computed dimension idea I'm wondering if what we should
do instead is just let a gufunc provide a C callback that looks at the
input array dimensions and explicitly says somehow which
On Tue, Sep 2, 2014 at 5:40 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
What do you think about the suggestion of timsort? One would need to
concatenate the arrays before sorting, but it should be fairly efficient.
Timsort is very cool, and it would definitely be fun to implement
On Wed, Sep 3, 2014 at 6:41 AM, Eelco Hoogendoorn
hoogendoorn.ee...@gmail.com wrote:
Not sure about the hashing. Indeed one can also build an index of a set
by means of a hash table, but its questionable if this leads to improved
performance over performing an argsort. Hashing may have
On Wed, Sep 3, 2014 at 9:33 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Wed, Sep 3, 2014 at 6:41 AM, Eelco Hoogendoorn
hoogendoorn.ee...@gmail.com wrote:
Not sure about the hashing. Indeed one can also build an index of a set
by means of a hash table, but its questionable
On Wed, Sep 3, 2014 at 5:26 AM, cjw c...@ncf.ca wrote:
These are good issues, that need to be discussed and resolved. Python has
the benefit of having a BDFL. Numpy has no similar arrangement.
In the post-numarray period, Travis Oliphant took that role and advanced
the package in many ways.
On Wed, Sep 3, 2014 at 5:47 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
I think the ayes will have it.
As I told Chuck (because I now get to call Charles Chuck, right? :-)), I am
not sure I am fully qualified for the job: looking at the names on that
list is a humbling experience.
On Thu, Sep 4, 2014 at 10:39 AM, Eelco Hoogendoorn
hoogendoorn.ee...@gmail.com wrote:
On Thu, Sep 4, 2014 at 10:31 AM, Eelco Hoogendoorn
hoogendoorn.ee...@gmail.com wrote:
On Wed, Sep 3, 2014 at 6:46 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Wed, Sep 3, 2014 at 9:33
On Mon, Sep 8, 2014 at 7:41 AM, Sturla Molden sturla.mol...@gmail.com
wrote:
Stefan Otte stefan.o...@gmail.com wrote:
stack([[a, b], [c, d]])
In my case `stack` replaced `hstack` and `vstack` almost completely.
If you're interested in including it in numpy I created a pull request
On Tue, Sep 16, 2014 at 12:27 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
It turns out that gufuncs will broadcast the last dimension if it is one.
For instance, inner1d has signature `(n), (n) - ()`, yet
In [27]: inner1d([1,1,1], [1])
Out[27]: 3
In [28]:
On Tue, Sep 16, 2014 at 3:26 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Tue, Sep 16, 2014 at 2:51 PM, Nathaniel Smith n...@pobox.com wrote:
On Tue, Sep 16, 2014 at 4:31 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
If it is a bug, it is an extended one, because
On Tue, Sep 16, 2014 at 4:32 PM, Nathaniel Smith n...@pobox.com wrote:
On Tue, Sep 16, 2014 at 6:56 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Tue, Sep 16, 2014 at 3:26 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Sep 16, 2014 at 2:51 PM, Nathaniel Smith
On Tue, Sep 16, 2014 at 4:32 PM, Nathaniel Smith n...@pobox.com wrote:
On Tue, Sep 16, 2014 at 6:56 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Tue, Sep 16, 2014 at 3:26 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Sep 16, 2014 at 2:51 PM, Nathaniel Smith
On Wed, Sep 17, 2014 at 1:27 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Wed, Sep 17, 2014 at 6:57 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Sep 17, 2014 at 6:48 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Mi, 2014-09-17 at 06:33 -0600,
On Thu, Oct 2, 2014 at 4:29 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Thu, Oct 2, 2014 at 5:02 PM, T J tjhn...@gmail.com wrote:
Hi, I'm using NumPy 1.8.2:
In [1]: np.array(0) / np.array(0)
Out[1]: 0
In [2]: np.array(0) / np.array(0.0)
Out[2]: nan
In [3]: np.array(0.0)
On Sun, Oct 12, 2014 at 9:29 AM, Warren Weckesser
warren.weckes...@gmail.com wrote:
On Sun, Oct 12, 2014 at 12:14 PM, Warren Weckesser
warren.weckes...@gmail.com wrote:
On Sat, Oct 11, 2014 at 6:51 PM, Warren Weckesser
warren.weckes...@gmail.com wrote:
I created an issue on github
On Thu, Oct 16, 2014 at 8:39 AM, Warren Weckesser
warren.weckes...@gmail.com wrote:
On Sun, Oct 12, 2014 at 9:13 PM, Nathaniel Smith n...@pobox.com wrote:
On Sun, Oct 12, 2014 at 5:14 PM, Sebastian se...@sebix.at wrote:
On 2014-10-12 16:54, Warren Weckesser wrote:
On Sun, Oct 12,
There is an oldish feature request in github
(https://github.com/numpy/numpy/issues/4752), complaining about it not
being possible to pass multiple output arguments to a ufunc using
keyword arguments.
You can pass them all as positional arguments:
out1 = np.empty(1)
out2 = np.empty(1)
On Wed, Nov 12, 2014 at 11:10 PM, Sebastian se...@sebix.at wrote:
On 2014-11-04 19:44, Charles R Harris wrote:
On Tue, Nov 4, 2014 at 11:19 AM, Sebastian se...@sebix.at wrote:
On 2014-11-04 15:06, Todd wrote:
On Tue, Nov 4, 2014 at 2:50 PM, Sebastian Wagner se...@sebix.at
On Wed, Dec 3, 2014 at 2:21 AM, Emanuele Olivetti emanu...@relativita.com
wrote:
On 12/03/2014 04:32 AM, Ryan Nelson wrote:
Emanuele,
This doesn't address your question directly. However, I wonder if you
could approach this problem from a different way to get what you want.
First of
On Wed, Dec 3, 2014 at 8:44 AM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I just noticed this using Christophe Gohlke's MKL builds of numpy:
import numpy as np
val = 2**63 + 2**62
np.float64(val)
1.3835058055282164e+19
np.float64(val).astype(np.uint64)
9223372036854775808
I
On Wed, Dec 3, 2014 at 4:02 PM, Stefan van der Walt ste...@sun.ac.za
wrote:
Hi Catherine
On 2014-12-04 01:12:30, Moroney, Catherine M (398E)
catherine.m.moro...@jpl.nasa.gov wrote:
I have an array A of shape (NX, NY, NZ), and then I have a second
array B of shape (NX, NY)
that ranges
On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer sho...@gmail.com wrote:
On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
One option
would also be to have something like:
np.common_shape(*arrays)
np.broadcast_to(array, shape)
# (though I would like many
On Fri, Dec 12, 2014 at 5:57 AM, Sebastian Berg sebast...@sipsolutions.net
wrote:
On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río wrote:
On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer sho...@gmail.com
wrote:
On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg
On Fri, Dec 12, 2014 at 11:28 AM, Stephan Hoyer sho...@gmail.com wrote:
On Fri, Dec 12, 2014 at 5:48 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
np.broadcast is the Python object of the old iterator. It may be a better
idea to write all of these functions using the new one
On Fri, Jan 2, 2015 at 3:06 AM, Simen Langseth simlan...@gmail.com wrote:
import numpy as np
from scipy import signal
y = np.array([[2, 1, 2, 3, 2, 0, 1, 0],
[2, 1, 2, 3, 2, 0, 1, 0]])
maximas = signal.argrelmax(y, axis=1)
print maximas
(array([0, 0, 1, 1], dtype=int64),
On Tue, Feb 3, 2015 at 12:58 PM, Warren Weckesser
warren.weckes...@gmail.com wrote:
I have an implementation of the Heaviside function as numpy ufunc. Is
there any interest in adding this to numpy? The function is simply:
0if x 0
heaviside(x) = 0.5 if x == 0
On Tue, Feb 3, 2015 at 1:28 AM, Sebastian Berg sebast...@sipsolutions.net
wrote:
On Mo, 2015-02-02 at 06:25 -0800, Jaime Fernández del Río wrote:
On Sat, Jan 31, 2015 at 1:17 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Fr, 2015-01-30 at 19:52 -0800, Jaime Fernández del
On Tue, Feb 3, 2015 at 1:52 PM, Ian Henriksen
insertinterestingnameh...@gmail.com wrote:
On Tue Feb 03 2015 at 1:47:34 PM Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Tue, Feb 3, 2015 at 8:59 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Di, 2015-02-03 at 07:18 -0800
On Thu, Feb 5, 2015 at 11:10 AM, Benjamin Root ben.r...@ou.edu wrote:
+1! I could never keep straight which stack function I needed anyway.
Wasn't there a proposal a while back for a more generic stacker, like
tetrix or something that allowed one to piece together tiles of different
sizes?
On Tue, Feb 3, 2015 at 8:59 AM, Sebastian Berg sebast...@sipsolutions.net
wrote:
On Di, 2015-02-03 at 07:18 -0800, Jaime Fernández del Río wrote:
snip
Do you have a concrete example of what a non (1, 1) array that fails
with relaxed strides would look like?
If we used
Hi all,
I have been taking a deep look at the sorting functionality in numpy, and I
think it could use a face lift in the form of a big code refactor, to get
rid of some of the ugliness in the code and make it easier to maintain.
What I have in mind basically amounts to:
1. Refactor
On Fri, Jan 16, 2015 at 4:19 AM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Fri, Jan 16, 2015 at 5:24 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
Hi all,
I have been taking a deep look at the sorting functionality in numpy,
and I
think it could use a face lift
On Fri, Jan 16, 2015 at 8:15 AM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Fri, Jan 16, 2015 at 7:11 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
On Fri, Jan 16, 2015 at 3:33 AM, Lars Buitinck larsm...@gmail.com
wrote:
2015-01-16 11:55 GMT+01:00 numpy-discussion
On Mon, Jan 26, 2015 at 10:28 PM, Jens Jørgen Mortensen je...@fysik.dtu.dk
wrote:
On 01/26/2015 11:02 AM, Jaime Fernández del Río wrote:
On Mon, Jan 26, 2015 at 1:41 AM, Sebastian Berg
sebast...@sipsolutions.net mailto:sebast...@sipsolutions.net wrote:
On Mo, 2015-01-26 at 09:24
HI all,
There has been some recent discussion going on on the limitations that
numpy imposes to taking views of an array with a different dtype.
As of right now, you can basically only take a view of an array if it has
no Python objects and neither the old nor the new dtype are structured.
On Thu, Jan 29, 2015 at 8:57 AM, Nathaniel Smith n...@pobox.com wrote:
On Thu, Jan 29, 2015 at 12:56 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
[...]
With all these in mind, my proposal for the new behavior is that taking a
view of an array with a different dtype would require
While working on something else, I realized that linspace is not handling
requests for returning the sampling spacing consistently:
np.linspace(0, 1, 3, retstep=True)
(array([ 0. , 0.5, 1. ]), 0.5)
np.linspace(0, 1, 1, retstep=True)
array([ 0.])
np.linspace(0, 1, 0, retstep=True)
array([],
On Sat, Feb 14, 2015 at 5:21 PM, josef.p...@gmail.com wrote:
On Sat, Feb 14, 2015 at 4:27 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sat, Feb 14, 2015 at 12:36 PM, josef.p...@gmail.com wrote:
On Sat, Feb 14, 2015 at 12:05 PM, cjw c...@ncf.ca wrote:
On 14-Feb-15
On Sun, Mar 15, 2015 at 11:06 PM, Robert McGibbon rmcgi...@gmail.com
wrote:
It might make sense to dispatch to difference c implements if the bins are
equally spaced (as created by using an integer for the np.histogram bins
argument), vs. non-equally-spaced bins.
Dispatching to a different
On Mon, Mar 16, 2015 at 9:28 AM, Jerome Kieffer jerome.kief...@esrf.fr
wrote:
On Mon, 16 Mar 2015 06:56:58 -0700
Jaime Fernández del Río jaime.f...@gmail.com wrote:
Dispatching to a different method seems like a no brainer indeed. The
question is whether we really need to do this in C.
I
On Sun, Mar 15, 2015 at 9:32 PM, Robert McGibbon rmcgi...@gmail.com wrote:
Hi,
Numpy.histogram is implemented in python, and is a little sluggish. This
has been discussed previously on the mailing list, [1, 2]. It came up in a
project that I maintain, where a new feature is bottlenecked by
On Sat, Mar 7, 2015 at 1:52 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Sat, Mar 7, 2015 at 2:45 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sat, Mar 7, 2015 at 2:02 PM, Dinesh Vadhia dineshbvad...@hotmail.com
wrote:
This was originally posted on SO (
On Thu, Mar 12, 2015 at 10:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sun, Mar 8, 2015 at 3:43 PM, Ralf Gommers ralf.gomm...@gmail.com
wrote:
On Sat, Mar 7, 2015 at 12:40 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
Time to start thinking about
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