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 slice
On Fri, Aug 7, 2009 at 10:17 PM, wrote:
> Thanks! That helps a lot.
Thanks for improving the docs.
>
>>On Fri, Aug 7, 2009 at 8:54 PM, wrote:
>>> On Fri, Aug 7, 2009 at 6:57 PM, wrote:
On Fri, Aug 7, 2009 at 6:13 PM, wrote:
> On Fri, Aug 7, 2009 at 5:42 PM, wrote:
>> On Fri, Aug
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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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 Par
Thanks! That helps a lot.
>On Fri, Aug 7, 2009 at 8:54 PM, wrote:
>> On Fri, Aug 7, 2009 at 6:57 PM, wrote:
>>> On Fri, Aug 7, 2009 at 6:13 PM, wrote:
On Fri, Aug 7, 2009 at 5:42 PM, wrote:
> On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawryluk
> wrote:
>> Hmm ... good point.
On Fri, Aug 7, 2009 at 8:54 PM, wrote:
> On Fri, Aug 7, 2009 at 6:57 PM, wrote:
>> On Fri, Aug 7, 2009 at 6:13 PM, wrote:
>>> On Fri, Aug 7, 2009 at 5:42 PM, wrote:
On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawryluk
wrote:
> Hmm ... good point.
> It appears to give a probability
On Fri, Aug 7, 2009 at 6:57 PM, wrote:
> On Fri, Aug 7, 2009 at 6:13 PM, wrote:
>> On Fri, Aug 7, 2009 at 5:42 PM, wrote:
>>> On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawryluk wrote:
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I se
To finish off the thread for posterity:
Robert Bradshaw wrote:
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 halfsize(np.ndarray[np.uint8_t, ndim=3
Hi all,
I just noticed that np.concatenate does not necessarily produce
contiguous arrays.
I has figured that it was making a copy, so would produce a C-contiguous
array, but not so:
In [88]: a = np.arange(60).reshape((4,5,3))
In [89]: b = np.concatenate((a, a[:, -1:, :]), axis=1)
In [90]: b
On Fri, Aug 7, 2009 at 6:13 PM, wrote:
> On Fri, Aug 7, 2009 at 5:42 PM, wrote:
>> On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawryluk 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 limit
On Fri, Aug 7, 2009 at 3:24 PM, T J 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 J wrote:
> > Hi, the documentation for dot says that a value error is raised if:
> >
> >I
On Fri, Aug 7, 2009 at 5:42 PM, wrote:
> On Fri, Aug 7, 2009 at 5:25 PM, Andrew Hawryluk 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 5:25 PM, Andrew Hawryluk 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 =
> plt.hist(np.random.power(a,10),bins=100,ec=
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)
plt.plot(x,nu
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 J 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 as the
> secon
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]])
>>>
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: nu
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 wrote:
> I was wondering why vectorize doesn't make the ufunc available
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'),
>>> c = array([[1,2,3],[4,5,6]]
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 bu
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: Discussio
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?
--
--
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
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 ugl
On Fri, Aug 7, 2009 at 11:51 PM, David
Cournapeau wrote:
> 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,
Hm, the fix does not look so obvious, so I just rever
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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Hi,
I receive the following test errors after building numpy rev7229 from svn:
==
FAIL: test_simple_circular (test_multiarray.TestStackedNeighborhoodIter)
--
Tra
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 wrote:
> 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
> > look at the val
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 t
On Aug 7, 2009, at 12:23 AM, Sebastian Haase wrote:
> On Fri, Aug 7, 2009 at 3:46 AM, 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...
On Fri, Aug 7, 2009 at 3:46 AM, 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].
>
> Failing that, you could also try using ndima
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