On Wed, Jul 16, 2014 at 1:47 PM, Ralf Gommers ralf.gomm...@gmail.com
wrote:
On Wed, Jul 16, 2014 at 6:37 AM, Tony Yu tsy...@gmail.com wrote:
Is there any reason why the defaults for `allclose` and `assert_allclose`
differ? This makes debugging a broken test much more difficult. More
Is there any reason why the defaults for `allclose` and `assert_allclose`
differ? This makes debugging a broken test much more difficult. More
importantly, using an absolute tolerance of 0 causes failures for some
common cases. For example, if two values are very close to zero, a test
will fail:
On Tue, Feb 18, 2014 at 11:11 AM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
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
Announcement: scikits-image 0.7.0
=
We're happy to announce the 7th version of scikits-image!
Scikits-image is an image processing toolbox for SciPy that includes
algorithms
for segmentation, geometric transformations, color space manipulation,
analysis,
On Fri, Jul 27, 2012 at 11:39 AM, Derek Homeier
de...@astro.physik.uni-goettingen.de wrote:
On 27.07.2012, at 3:27PM, Benjamin Root wrote:
I would prefer not to use: from xxx import *,
because of the name pollution.
The name convention that I copied above facilitates avoiding
On Mon, Jun 18, 2012 at 11:55 AM, bob tnur bobtnu...@gmail.com wrote:
Hi,
how I can convert (by adding zero) of any non-square numpy matrix in to
square matrix using numpy? then how to find the minimum number in each row
except the zeros added(for making square matrix)? ;)
On Sun, May 20, 2012 at 3:47 AM, eat e.antero.ta...@gmail.com wrote:
Hi,
On Sun, May 20, 2012 at 10:21 AM, Chao YUE chaoyue...@gmail.com wrote:
Dear all,
could anybody give one sentence about this? why in the loop I didn't get
zerodivision error by when I explicitly do this, I get a
On Thu, May 3, 2012 at 9:57 AM, Robert Kern robert.k...@gmail.com wrote:
On Thu, May 3, 2012 at 2:50 PM, Robert Elsner ml...@re-factory.de wrote:
Am 03.05.2012 15:45, schrieb Robert Kern:
On Thu, May 3, 2012 at 2:24 PM, Robert Elsner ml...@re-factory.de
wrote:
Hello Everybody,
is
On Fri, Apr 20, 2012 at 2:15 PM, Andre Martel soucoupevola...@yahoo.comwrote:
What would be the best way to remove the maximum from a cube and
collapse the remaining elements along the z-axis ?
For example, I want to reduce Cube to NewCube:
Cube
array([[[ 13, 2, 3, 42],
[
On Mon, Apr 16, 2012 at 5:27 PM, Skipper Seabold jsseab...@gmail.comwrote:
Hi,
I have a pull request here [1] to add a cut function similar to R's
[2]. It seems there are often requests for similar functionality. It's
something I'm making use of for my own work and would like to use in
On Mon, Apr 16, 2012 at 6:01 PM, Skipper Seabold jsseab...@gmail.comwrote:
On Mon, Apr 16, 2012 at 5:51 PM, Tony Yu tsy...@gmail.com wrote:
On Mon, Apr 16, 2012 at 5:27 PM, Skipper Seabold jsseab...@gmail.com
wrote:
Hi,
I have a pull request here [1] to add a cut function similar
On Mon, Apr 9, 2012 at 12:22 PM, Benjamin Root ben.r...@ou.edu wrote:
On Mon, Apr 9, 2012 at 12:14 PM, Jonathan T. Niehof jnie...@lanl.govwrote:
On 04/06/2012 06:54 AM, Benjamin Root wrote:
Take a peek at how np.gradient() does it. It creates a list of None with
a length equal to the
On Fri, Apr 6, 2012 at 8:54 AM, Benjamin Root ben.r...@ou.edu wrote:
On Friday, April 6, 2012, Val Kalatsky wrote:
The only slicing short-cut I can think of is the Ellipsis object, but
it's not going to help you much here.
The alternatives that come to my mind are (1) manipulation of
Is there a way to slice an nd-array along a specified axis? It's easy to
slice along a fixed axis, e.g.:
axis = 0:
array[start:end]
axis = 1:
array[:, start:end]
...
But I need to do this inside of a function that accepts arrays of any
dimension, and the user can operate on any axis of the
On Fri, Jan 27, 2012 at 9:28 AM, Paul Anton Letnes
paul.anton.let...@gmail.com wrote:
On 27. jan. 2012, at 14:52, Chao YUE wrote:
Dear all,
suppose I have a ndarray a:
In [66]: a
Out[66]: array([0, 1, 2, 3, 4])
how can use it as 5X1 array without doing a=a.reshape(5,1)?
On Sun, Jan 15, 2012 at 10:45 AM, a...@pdauf.de wrote:
Counting the Colors of RGB-Image,
nameit im0 with im0.shape = 2500,3500,3
with this code:
tab0 = zeros( (256,256,256) , dtype=int)
tt = im0.view()
tt.shape = -1,3
for r,g,b in tt:
tab0[r,g,b] += 1
Question:
Is there a faster
On Tue, Dec 6, 2011 at 2:51 AM, Xavier Barthelemy xab...@gmail.com wrote:
ok let me be more precise
I have an Z array which is the elevation
from this I extract a discrete array of Zero Crossing, and another
discrete array of Crests.
len(crest) is different than len(Xzeros). I have a
On Wed, Nov 30, 2011 at 1:49 PM, Neal Becker ndbeck...@gmail.com wrote:
My suggestion is: don't.
It's easier to script runs if you read parameters from the command line.
I recommend argparse.
I think setting parameters in a config file and setting them on the
command line both have their
Hi,
I noticed a type-checking inconsistency between assignments using slicing
and fancy-indexing. The first will happily cast on assignment (regardless of
type), while the second will throw a type error if there's reason to believe
the casting will be unsafe. I'm not sure which would be the
On Sun, Oct 16, 2011 at 12:39 PM, Tony Yu tsy...@gmail.com wrote:
Hi,
I noticed a type-checking inconsistency between assignments using slicing
and fancy-indexing. The first will happily cast on assignment (regardless of
type), while the second will throw a type error if there's reason
On Sun, Oct 16, 2011 at 12:49 PM, Pauli Virtanen p...@iki.fi wrote:
(16.10.2011 18:39), Tony Yu wrote:
import numpy as np
a = np.arange(10)
b = np.ones(10, dtype=np.uint8)
# this runs without error
b[:5] = a[:5]
mask = a 5
b[mask] = b[mask]
TypeError: array
On Thu, Sep 1, 2011 at 5:33 PM, Jonas Wallin jonas.walli...@gmail.comwrote:
Hello,
I implemented the following line of code:
Gami[~index0].shape (100,)
sigma.shape (1,1)
Gami[~index0] = Gam[~index0] - sigma**2
I get the error message:
*** ValueError: array is not
I'm building documentation using Sphinx, and it seems that numpydoc is
raising
a lot of warnings. Specifically, the warnings look like failed to import
method_name, toctree
references unknown document u'method_name', toctree contains reference
to nonexisting document 'method_name'---for each
On Sun, Jul 17, 2011 at 3:35 PM, Ralf Gommers
ralf.gomm...@googlemail.comwrote:
On Sun, Jul 17, 2011 at 7:15 PM, Tony Yu tsy...@gmail.com wrote:
Am I doing something wrong here?
You're not, it's a Sphinx bug that Pauli already has a fix for. See
http://projects.scipy.org/numpy/ticket/1772
Date: Thu, 16 Jul 2009 23:37:58 -0400
From: Ralf Gommers ralf.gomm...@googlemail.com
It seems to me that there are quite a few other functions that will
give
errors with 0-D arrays (apply_along/over_axis are two that come to
mind).
There is nothing to interpolate so I'm not surprised.
Date: Fri, 17 Jul 2009 13:27:25 -0400
From: Ralf Gommers ralf.gomm...@googlemail.com
Subject: Re: [Numpy-discussion] Using interpolate with zero-rank array
raises error
[snip]
If it works with scalars it should work with 0-D arrays I think. So
you
should probably open a ticket and
Sorry, I don't know if its proper mailing-list-etiquette to bump my
own post...
Are there any comments on whether this interp error is expected
behavior?
Thanks,
-Tony
Date: Mon, 13 Jul 2009 13:50:50 -0400
From: Tony Yu tsy...@gmail.com
Subject: [Numpy-discussion] Using interpolate
(Sorry if this is a duplicate; I think sent this from the wrong email
the first time)
When using interpolate with a zero-rank array, I get ValueError:
object of too small depth for desired array. The following code
reproduces this issue
import numpy as np
x0 = np.array(0.1)
x =
Ok, so you guys shot down my last attempt at finding a bug :). Here's
another attempt.
array + masked_array
outputs a masked array
array += masked_array
outputs an array.
I'm actually not sure if this is a bug (works the same for both the
old and new masked arrays), but I
for your help.
-Tony Yu
Example:
In [1]: import numpy
In [2]: masked = numpy.ma.masked_array([[1, 2, 3, 4, 5]], mask=False)
In [3]: masked[:] = numpy.fliplr(masked.copy())
In [4]: print masked
[[5 4 3 2 1]]
In [5]: masked[:] = numpy.fliplr(masked)
In [6]: print masked
[[1 2 3 2 1
. Copying
seems inefficient.
-Tony
Matthieu
2008/5/31 Tony Yu [EMAIL PROTECTED]:
Great job getting numpy 1.1.0 out and thanks for including the old API
of masked arrays.
I've been playing around with some software using numpy 1.0.4 and took
a crack at upgrading it to numpy 1.1.0, but I ran
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