On Fri, Aug 7, 2009 at 3:46 AM, Zachary Pincuszachary.pin...@yale.edu wrote:
We have a need to to generate half-size version of RGB images as
quickly
as possible.
How good do these need to look? You could just throw away every other
pixel... image[::2, ::2].
Failing that, you could also
Sturla Molden a écrit :
Thus, here is my plan:
1. a special context-manager class
2. immutable arrays inside with statement
3. lazy evaluation: expressions build up a parse tree
4. dynamic code generation
5. evaluation on exit
There seems to be some similarity with what we want to do to
But if it were an unsigned int64, it should be able to hold 2**64 or at
least 2**64-1.
Am I correct?
On Fri, Aug 7, 2009 at 1:03 AM, David Warde-Farley d...@cs.toronto.eduwrote:
On 6-Aug-09, at 7:29 PM, Robert Kern wrote:
For that value, yes, but not for long objects in general. We don't
Hi,
I receive the following test errors after building numpy rev7229 from svn:
==
FAIL: test_simple_circular (test_multiarray.TestStackedNeighborhoodIter)
--
Christopher Hanley wrote:
Hi,
I receive the following test errors after building numpy rev7229 from svn:
Yep, a bug slipped in the last commit, I am fixing it right now,
David
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Zachary Pincus wrote:
We have a need to to generate half-size version of RGB images as
quickly
as possible.
How good do these need to look? You could just throw away every other
pixel... image[::2, ::2].
I do the as good quality as I can get. throwing away pixels gets a bit ugly.
I was wondering why vectorize doesn't make the ufunc available at the
topmost level
def a(x,y): return x + y
b = vectorize(a)
b.reduce
Instead, the ufunc is stored at b.ufunc.
Also, b.ufunc.reduce() doesn't seem to exist until I *use* the
vectorized function at least once. Can this be
Documenting my way through the statistics modules in numpy, I ran into
the Power Distribution.
Anyone know what that is? I Googled for it, and found a lot of stuff on
electricity, but no reference for a statistical distribution of that name. Does
it have a common alias?
--
You might get better results for 'power-law distribution'
http://en.wikipedia.org/wiki/Power_law
Andrew
-Original Message-
From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
boun...@scipy.org] On Behalf Of a...@ajackson.org
Sent: 7 Aug 2009 11:45 AM
To: Discussion of
If this appears twice, forgive me. I sent it previously (7:13 am PDT)
via a browser interface to JPL's Office Outlook. I have doubts about
this system. This time, from Iceweasel through our SMTP server.
There are two things I'd like to do using memmap. I suspect that they
are impossible
The reduce function of ufunc of a vectorized function doesn't seem to
respect the dtype.
def a(x,y): return x+y
b = vectorize(a)
c = array([1,2])
b(c, c) # use once to populate b.ufunc
d = b.ufunc.reduce(c)
c.dtype, type(d)
dtype('int32'), type 'int'
c = array([[1,2,3],[4,5,6]])
The short answer is that it was easier this way.
The ufunc is created on the fly and it needs to know several things
that are easy to get once the function is called.
Sent from my iPhone
On Aug 7, 2009, at 11:42 AM, T J tjhn...@gmail.com wrote:
I was wondering why vectorize doesn't make
I don't think that is it, since the one in numpy has a range restricted
to the interval 0-1.
Try out hist(np.random.power(5, 100), bins=100)
You might get better results for 'power-law distribution'
http://en.wikipedia.org/wiki/Power_law
Andrew
-Original Message-
From:
Hi, the documentation for dot says that a value error is raised if:
If the last dimension of a is not the same size as the
second-to-last dimension of b.
(http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.htm)
This doesn't appear to be the case:
a = array([[1,2],[3,4]])
b =
Oh. b.shape = (2,). So I suppose the second to last dimension is, in
fact, the last dimension...and 2 == 2.
nvm
On Fri, Aug 7, 2009 at 2:19 PM, T Jtjhn...@gmail.com wrote:
Hi, the documentation for dot says that a value error is raised if:
If the last dimension of a is not the same size
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I see no good reason why the domain should be limited to [0,1].
def test(a):
nums =
plt.hist(np.random.power(a,10),bins=100,ec='none',fc='#dd')
x = np.linspace(0,1,200)
On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawrylukhawr...@novachem.com wrote:
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I see no good reason why the domain should be limited to [0,1].
def test(a):
nums =
On Fri, Aug 7, 2009 at 5:42 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawrylukhawr...@novachem.com wrote:
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I see no good reason why the domain should be limited to [0,1].
On Fri, Aug 7, 2009 at 3:24 PM, T J tjhn...@gmail.com wrote:
Oh. b.shape = (2,). So I suppose the second to last dimension is, in
fact, the last dimension...and 2 == 2.
nvm
On Fri, Aug 7, 2009 at 2:19 PM, T Jtjhn...@gmail.com wrote:
Hi, the documentation for dot says that a value error
On Fri, Aug 7, 2009 at 6:13 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:42 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawrylukhawr...@novachem.com wrote:
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I
To finish off the thread for posterity:
Robert Bradshaw wrote:
Robert's Cython code clipped.
Robert's version operated on a 2-d array, so only one band at a time if
you have RGB. So I edited it a bit:
import cython
import numpy as np
cimport numpy as np
@cython.boundscheck(False)
def
On Fri, Aug 7, 2009 at 6:57 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:13 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:42 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawrylukhawr...@novachem.com wrote:
Hmm ... good point.
It appears to give
On Fri, Aug 7, 2009 at 8:54 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:57 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:13 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:42 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:25 PM, Andrew
Thanks! That helps a lot.
On Fri, Aug 7, 2009 at 8:54 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:57 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:13 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:42 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 5:25
Does it make any (statistical) sense to have numpy.random.pareto
produce random numbers that start at zero?
Can we change it to start at 1 which is the usual default?
Notation from
http://docs.scipy.org/numpy/docs/numpy.random.mtrand.RandomState.pareto/
The probability density for the
I ask again,
Datetime is getting really stale and hasn't been touched recently. Do the
datetime folks want it merged or not, because it's getting to be a bit of
work.
Chuck
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On Fri, Aug 7, 2009 at 10:17 PM, a...@ajackson.org wrote:
Thanks! That helps a lot.
Thanks for improving the docs.
On Fri, Aug 7, 2009 at 8:54 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:57 PM, josef.p...@gmail.com wrote:
On Fri, Aug 7, 2009 at 6:13 PM, josef.p...@gmail.com
I'd like to be able to make a slice of a 3-dimensional array, doing something
like the following:
Y= X[A, B, C]
where A, B, and C are lists of indices. This works, but has an unexpected
side-effect. When A, B, or C is a length-1 list, Y has fewer dimensions than
X. Is there a way to do the
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