Zachary Pincus wrote:
Hi,
intersect1d and setmember1d doesn't give expected results in case
there are duplicate values in either array becuase it works by
sorting data and substracting previous value. Is there an
alternative in numpy to get indices of intersected values.
From the
Hi all,
Is it possible to modify the behaviour of float wrt
the following situation
permas_M[0,2]
'1.569809265137D+01'
float(permas_M[0,2])
Traceback (most recent call last):
File stdin, line 1, in module
ValueError: invalid literal for float():
1.569809265137D+01
The following
On Fri, Feb 27, 2009 at 8:20 AM, Nils Wagner
nwag...@iam.uni-stuttgart.dewrote:
Hi all,
Is it possible to modify the behaviour of float wrt
the following situation
permas_M[0,2]
'1.569809265137D+01'
float(permas_M[0,2])
Traceback (most recent call last):
File stdin, line 1, in
Hi,
This a little wiered problem. I am having a black and white image. (black
background)
Entire image is filled with noisy white patterns of different size and shape. I
need to fill the
white patches if there area is more then given one. Logically this could
possible to use a quickfill
Hi Prashant
2009/2/27 Prashant Saxena animator...@yahoo.com:
This a little wiered problem. I am having a black and white image. (black
background)
Entire image is filled with noisy white patterns of different size and
shape. I need to fill the
white patches if there area is more then given
Hi,
That's a call for testing for 64 bits windows users out there:
please try the following binary with the test suite:
http://www.ar.media.kyoto-u.ac.jp/members/david/archives/numpy/numpy-1.3.0.dev6517.win-amd64-py2.6.exe
python -c import numpy; numpy.test()
Report any crash. I am
On Fri, Feb 27, 2009 at 12:31 PM, David Cournapeau
da...@ar.media.kyoto-u.ac.jp wrote:
Hi,
That's a call for testing for 64 bits windows users out there:
please try the following binary with the test suite:
On Sat, Feb 28, 2009 at 4:15 AM, Nathan Bell wnb...@gmail.com wrote:
On Fri, Feb 27, 2009 at 12:31 PM, David Cournapeau
da...@ar.media.kyoto-u.ac.jp wrote:
Hi,
That's a call for testing for 64 bits windows users out there:
please try the following binary with the test suite:
Well,
I came up with a slightly different approach.
Get the first row on the image.
It would be something like this:
[1,1,1,0,0,0,1,1,0,0,1]
1 = white, 0 = black.
Run the floodfill on [0],[6] and [10] pixel
if floodfill area is smaller then given area then paint that area black
if floodfill area
Hey Jon,
On 26-Feb-09, at 10:00 PM, Jonathan Taylor wrote:
Am I right to assume that there is no way elegant way to interact with
slices. i.e. Is there anyway to get
a[ix_([2,3,6],:,[3,2])]
to work? So that the dimension is completely specified? Or perhaps
the only way to do this is
On Thu, Feb 26, 2009 at 21:00, Jonathan Taylor
jonathan.tay...@utoronto.ca wrote:
Am I right to assume that there is no way elegant way to interact with
slices. i.e. Is there anyway to get
a[ix_([2,3,6],:,[3,2])]
to work? So that the dimension is completely specified? Or perhaps
the only
On Fri, Feb 27, 2009 at 14:41, Delbert Franz d...@iqdotdt.com wrote:
Message: 2
Date: Thu, 26 Feb 2009 23:32:35 -0600
From: Robert Kern robert.k...@gmail.com
Subject: Re: [Numpy-discussion] Speedup creation of a 3-color array
from a 2-d color-index array a color lut
To: Discussion of
Hi all,
I just grabbed the latest bilateral filter from Stéfan's repository,
but I can't get it to work! I'm using a recent numpy SVN and the
latest release of cython...
In [10]: bl = bilateral.bilateral(image, 2, 150)
Message: 2
Date: Thu, 26 Feb 2009 23:32:35 -0600
From: Robert Kern robert.k...@gmail.com
Subject: Re: [Numpy-discussion] Speedup creation of a 3-color array
from a 2-d color-index array a color lut
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID:
On Fri, Feb 27, 2009 at 2:33 PM, David Cournapeau courn...@gmail.com wrote:
Great, thanks. Do you have VS installed ? Did you install python for
all users (I would guess so, but I am not yet clear on all the details
on that matter).
I do not have VS installed. I just downloaded the official
On Fri, Feb 27, 2009 at 4:08 PM, Nathan Bell wnb...@gmail.com wrote:
On Fri, Feb 27, 2009 at 2:33 PM, David Cournapeau courn...@gmail.com
wrote:
Great, thanks. Do you have VS installed ? Did you install python for
all users (I would guess so, but I am not yet clear on all the details
on
Nathan Bell wrote:
On Fri, Feb 27, 2009 at 2:33 PM, David Cournapeau courn...@gmail.com wrote:
Great, thanks. Do you have VS installed ? Did you install python for
all users (I would guess so, but I am not yet clear on all the details
on that matter).
I do not have VS installed.
Hi-
I'm quite new to numpy and to python in general, so I apologize if I'm
missing something obvious, but I've come across some seemingly nasty
behavior when trying to assign values to the fields of an indexed
subarray of a numpy record array. Perhaps an example would explain
it best.
On Fri, Feb 27, 2009 at 18:26, Brian Gerke bge...@slac.stanford.edu wrote:
Hi-
I'm quite new to numpy and to python in general, so I apologize if I'm
missing something obvious, but I've come across some seemingly nasty
behavior when trying to assign values to the fields of an indexed
As a follow-up to Robert's answer:
r[r.field1 == 1].field2 = 1
doesn't work, but
r.field2[r.field1==1] = 1
does.
So far, so good.
Now I want to change the value of field2 for those same elements:
In [128]: r[where(r.field1 == 1.)].field2 = 1
Ok, so now the values of field
On Feb 27, 2009, at 4:30 PM, Robert Kern wrote:
r[where(r.field1 == 1.)] make a copy. There is no way for us to
construct a view onto the original memory for this circumstance given
numpy's memory model.
Many thanks for the quick reply. I assume that this is true only for
record arrays,
On 27-Feb-09, at 3:35 PM, David Warde-Farley wrote:
a[[2,3,6],:,:][:,:,[3,2]] should do what you want.
Slightly more elegantly (I always forget about this syntax):
a[[2,3,6], ...][..., [3,2]]
David
___
Numpy-discussion mailing list
On Fri, Feb 27, 2009 at 19:06, Brian Gerke bge...@slac.stanford.edu wrote:
On Feb 27, 2009, at 4:30 PM, Robert Kern wrote:
r[where(r.field1 == 1.)] make a copy. There is no way for us to
construct a view onto the original memory for this circumstance given
numpy's memory model.
Many thanks
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