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On 28/10/2013 12:44, Pierre Haessig wrote:
Hi,
Le 27/10/2013 19:28, Freddie Witherden a écrit :
I wish to sort these points into a canonical order in a fashion
which is robust against small perturbations. In other words
changing any component
Hi Freddie,
Le 29/10/2013 10:21, Freddie Witherden a écrit :
The order itself does not need to satisfy any specific properties.
I can't agree with you : if there is no specific property, then keeping
the list *unchanged* would be a fine solution (and very fast and very
very robust) ;-)
what
Le 29/10/2013 11:37, Pierre Haessig a écrit :
def compare(point, other):
delta = point - other
argmax = np.abs(delta).argmax()
delta_max = delta[argmax]
if delta_max 0:
return 1
elif delta_max 0:
return -1
else:
return 0
This function
Is there a way to extract the size of array that would be created by
doing 1j*array?
The problem I'm having is in creating an empty array to fill with
complex values without knowing a priori what the input data type is.
For example, I have a real or int array `a`.
I want to create an array
On 29/10/13 16:47, Henry Gomersall wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
Of course, I mean dtype of the array.
Henry
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Hi,
I've been using np.random.uniform and mpi4py.
I found that the random number each processor (or rank) generated are the
same, so I was wondering how random.uniform chose its seeds. Theoretically,
those ranks shouldn't have anything to do with others. The only possibility
that I can think of
On Tue, Oct 29, 2013 at 4:47 PM, Henry Gomersall h...@cantab.net wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
The problem I'm having is in creating an empty array to fill with
complex values without knowing a priori what the input data type is.
On Tue, Oct 29, 2013 at 5:02 PM, Ao Liu frankli...@gmail.com wrote:
Hi,
I've been using np.random.uniform and mpi4py.
I found that the random number each processor (or rank) generated are the
same, so I was wondering how random.uniform chose its seeds. Theoretically,
those ranks shouldn't
On 29/10/13 17:02, Robert Kern wrote:
Quick and dirty:
# Get a tiny array from `a` to test the dtype of its output when
multiplied
# by a complex float. It must be an array rather than a scalar since the
# casting rules are different for array*scalar and scalar*scalar.
dt = (a.flat[:2] *
On Tue, 2013-10-29 at 16:47 +, Henry Gomersall wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
There is np.result_type. It does the handling of scalars as normal,
dtypes will be handled like arrays (scalars are allowed to lose
precision).
-
Hi All,
I'm going to tag 1.7.2 soon. That is, unless someone else would like the
experience of making a release. Any volunteers?
Chuck
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On Tue, Oct 29, 2013 at 1:57 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
Hi All,
I'm going to tag 1.7.2 soon. That is, unless someone else would like the
experience of making a release. Any volunteers?
Make that 1.7.2rc1.
Chuck
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On 29.10.2013 21:00, Charles R Harris wrote:
On Tue, Oct 29, 2013 at 1:57 PM, Charles R Harris
charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote:
Hi All,
I'm going to tag 1.7.2 soon. That is, unless someone else would like
the experience of making a
On Tue, Oct 29, 2013 at 4:55 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
On 29.10.2013 21:00, Charles R Harris wrote:
On Tue, Oct 29, 2013 at 1:57 PM, Charles R Harris
charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote:
Hi All,
I'm going to tag
We really ought to have a special page for all of Robert's little gems!
DG
On Tue, Oct 29, 2013 at 10:00 AM, numpy-discussion-requ...@scipy.orgwrote:
-Message: 5
Date: Tue, 29 Oct 2013 17:02:33 +
From: Robert Kern robert.k...@gmail.com
Subject: Re:
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