Using Eric's latest speed-testing, here's David's results:
[EMAIL PROTECTED]:~/code_snippets/histogram$ python histogram_speed.py
type: uint8
millions of elements: 100.0
sec (C indexing based): 8.44 1
sec (numpy iteration based): 8.91 1
sec (rick's pure python): 6.4 1
sec (
Brian Granger wrote:
> Can we please change how Numpy handles the version string of fortran
> compilers?
Yes, please. I'll be happy to apply any patch you might provide for this.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible
Hi,
I have been doing quite a bit of numpy evangelism here at my work and
slowly people are starting to use it. One of the main things people
are interested in is f2py. But, I am finding that there is one
persistent problem that keeps coming up when people try to install
numpy on various systems
Hi,
I spent some time a while ago on an histogram function for numpy. It uses
digitize and bincount instead of sorting the data. If I remember right, it
was significantly faster than numpy's histogram, but I don't know how it
will behave with very large data sets.
I attached the file if you want
El dj 14 de 12 del 2006 a les 11:21 -0700, en/na Tim Hochberg va
escriure:
>
> I was just going to try pyrex out with numpy to see how it compares with
> weave (which is cool but quirky). My first attempt ended in failure: I
> tried to compile the demo in in numpy/doc/pyrex and got this error:
>
I just noticed a bug in this code. "PyArray_ITER_NEXT(iter);" should be moved
out of the if statement.
eric
eric jones wrote:
>
>
> Rick White wrote:
>> Just so we don't get too smug about the speed, if I do this in IDL
>> on the same machine it is 10 times faster (0.28 seconds instead of
>>
Rick White wrote:
Just so we don't get too smug about the speed, if I do this in IDL on
the same machine it is 10 times faster (0.28 seconds instead of 4
seconds). I'm sure the IDL version uses the much faster approach of
just sweeping through the array once, incrementing counts in the
I was just going to try pyrex out with numpy to see how it compares with
weave (which is cool but quirky). My first attempt ended in failure: I
tried to compile the demo in in numpy/doc/pyrex and got this error:
c_numpy.pxd:99:22: Array element cannot be a Python object
Does anyone who us
This same idea could be used to parallelize the histogram computation.
Then you could really get into large (many Gb/TB/PB) data sets. I
might try to find time to do this with ipython1, but someone else
could do this as well.
Brian
On 12/13/06, Rick White <[EMAIL PROTECTED]> wrote:
> On Dec 12,
Sven Schreiber wrote:
>> In the old file I created a matrix on the fly. I know that Numpy and
>> python cannot do that so I found a workaround
numpy can create matrices on the fly, in fact, you are doing that with
this code! The only thing it doesn't do is have a lateral that joins
matrices
[you probably should have started a new thread instead of replying to
another one...]
Giorgio Luciano schrieb:
> In the old file I created a matrix on the fly. I know that Numpy and
> python cannot do that so I found a workaround
I'm not sure what you mean what numpy cannot do, but...
> here
I was converting a matlab file to my new favority scientific language
Numpy :)
In the old file I created a matrix on the fly. I know that Numpy and
python cannot do that so I found a workaround
here's the code
lev2=empty((1,h))
ir=1
for j in arange(1,nstep+2):
#a=gr[[arange(ir-1,ir+nstep)],:
On Dec 14, 2006, at 2:56 AM, Cameron Walsh wrote:
> At some point I might try and test
> different cache sizes for different data-set sizes and see what the
> effect is. For now, 65536 seems a good number and I would be happy to
> see this replace the current numpy.histogram.
I experimented a li
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