Hi Dan,
I have recently created an AMI for running python processes.
I recommend using the ubuntu server ami's provided by http://alestic.com/.
Alestic is a well known provider of public AMI images. I think this is
exactly the place you want to start from; anything you need is an apt-get or
easy_
Hi all:
I'm gearing up to build an Amazon Machine Instance (AMI) for use in doing
Numpy/Scipy computations on the Amazon EC2 cloud.
I'm writing to ask if anyone has any advice for which (if any) publicly
available AMI I should start with.
If any one has any specific AMI's that they think are goo
ke, 2009-10-28 kello 14:21 +0100, Ole Streicher kirjoitti:
> Is there something wrong with scipy.special.hermite? The following
> code produces glibc errors:
It's probably this issue:
http://projects.scipy.org/numpy/ticket/1211
The most likely cause is that the linear algebra libraries
(
that code works fine for me:
ubuntu 9.04 x64
python 2.6.2
scipy 0.7.1
numpy 1.3.0
ipython 0.9.1
On Wed, Oct 28, 2009 at 2:21 PM, Ole Streicher wrote:
> Hi,
>
> Is there something wrong with scipy.special.hermite? The following code
> produces glibc errors:
>
> 8<-
Robert Kern wrote:
>>f=gzip.open( "myfile.gz", "r" )
>> xyz=npy.fromfile(f,dtype="float32",count=400)
> Read in reasonably-sized chunks of bytes at a time, and use
> np.fromstring() to create arrays from them.
Something like:
count = 400
xyz = np.fromstring(f.read(count*4), dtype=np.floa
On Wed, Oct 28, 2009 at 14:31, Peter Schmidtke wrote:
> Dear Numpy Mailing List Readers,
>
> I have a quite simple problem, for what I did not find a solution for now.
> I have a gzipped file lying around that has some numbers stored in it and I
> want to read them into a numpy array as fast as po
Dear Numpy Mailing List Readers,
I have a quite simple problem, for what I did not find a solution for now.
I have a gzipped file lying around that has some numbers stored in it and I
want to read them into a numpy array as fast as possible but only a bunch
of data at a time.
So I would like to
On Wed, Oct 28, 2009 at 9:52 AM, Gökhan Sever wrote:
>
>
> On Tue, Oct 27, 2009 at 12:23 PM, Pierre GM wrote:
>>
>> On Oct 27, 2009, at 7:56 AM, Gökhan Sever wrote:
>> >
>> >
>> > Unfortunately, matplotlib.mlab's prctile cannot handle this division:
>>
>> Actually, the division's OK, it's mlab.pr
On Tue, Oct 27, 2009 at 12:23 PM, Pierre GM wrote:
>
> On Oct 27, 2009, at 7:56 AM, Gökhan Sever wrote:
> >
> >
> > Unfortunately, matplotlib.mlab's prctile cannot handle this division:
>
> Actually, the division's OK, it's mlab.prctile which is borked. It
> uses the length of the input array ins
On Tue, Oct 27, 2009 at 8:25 AM, wrote:
> This should not be the correct results if you use
> scipy.stats.scoreatpercentile,
> it doesn't have correct missing value handling, it treats nans or
> mask/fill values as regular numbers sorted to the end.
>
> stats.mstats.scoreatpercentile is the corr
Hi,
Is there something wrong with scipy.special.hermite? The following code
produces glibc errors:
8<---
import scipy.special
h = []
for i in xrange(15):
print i
h.append(scipy.special.hermite(i+1))
8<---
results in
...
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