On Fri, Sep 3, 2010 at 3:50 PM, Benjamin Root wrote:
> Here is a fun one...
>
> import numpy as np
>
> a_2d = np.random.random((3, 5))
> b_1d = np.random.random(5)
> b_2d = np.vstack((b_1d, b_1d, b_1d))
>
> a_ma_2d = np.ma.masked_array(a_2d, mask=(numpy.random.random((3, 5)) <
> 0.25))
> b_ma_1d
On Tue, Sep 7, 2010 at 15:12, Friedrich Romstedt
wrote:
> Ah, no need to answer, I do this myself:
>
> Friedrich, would you please use numpy.inf and -numpy.inf.
But if you have an integer array, you will run into the same problem.
The result will be upcast to float. I think we would accept a patc
Ondrej,
I was confused an unclear in my original question. I subsequently posted a
followup, titled "Simplified question on tensordot' where I explained myself
a lot better, and got some really good help. So, thank you very much for
looking into this issue, but I believe that this has been resolve
Ah, no need to answer, I do this myself:
Friedrich, would you please use numpy.inf and -numpy.inf.
Thanks, and sorry for the noise,
Friedrich
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On Tue, Sep 7, 2010 at 12:23 PM, Ondrej Certik wrote:
> Hi Rick!
>
> On Fri, Sep 3, 2010 at 4:02 AM, Rick Muller wrote:
>> Can someone help me replace a slow expression with a faster one based on
>> tensordot? I've read the documentation and I'm still confused.
>>
>> I have two matrices b and d.
I just came across a problem with the intention to specify unset
boundaries given to numpy.clip() or array.clip():
a.clip(1e-10, None)
a.clip(None, -1e-10)
When doing this, the returned array is dtype=numpy.object, seemingly
None gets converted to a numpy.asarray(None, dtype=numpy.object), and
th
> indices = argsort(a1)
> ranks = zeros_like(indices)
> ranks[indices] = arange(len(indices))
Doesn't answer your original question directly, but I only recently
learned from this list that the following does the same as the above:
ranks = a1.argsort().argsort()
Will wonders never cease...
So d
Calculating ranks by inverting the results of an argsort is
straightforward and fast for 1D arrays:
indices = argsort(a1)
ranks = zeros_like(indices)
ranks[indices] = arange(len(indices))
I was wondering if there was an equally pithy way to do this for
multiple data samples stored column-wise in
Hi Rick!
On Fri, Sep 3, 2010 at 4:02 AM, Rick Muller wrote:
> Can someone help me replace a slow expression with a faster one based on
> tensordot? I've read the documentation and I'm still confused.
>
> I have two matrices b and d. b is n x m and d is m x m. I want to replace
> the expression
>
Hi,
I'm using distutils to build extensions written in C.
I noticed that lately (it seems to be python 2.7 related) whenever I
touch 1 C file, ALL the C files are rebuilt.
Since I have a lot of C code, it takes a lot of time for nothing.
Any idea why this is happening?
Do I need to set somethi
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Mon, 06 Sep 2010 15:03:50 -0400, Andreas Kloeckner wrote:
[clip]
> I gather that "pt" in the symbol name means that this is a
> pthreads-accelerated version of dsyrk. How do I convey to numpy that I
> don't have (nor want) Pthreads-accelerated ATLAS bits? Failing that, how
> do I tell it to just no
It works only if I can find a value belonging to the dtype of the
array that is meaningless in the context of my data. For example, if it
is only composed of positive integers, then I can fill the missing
values with -1. But in my case, I would like to write a string (empty
string, actually)
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