float128 are 16 bytes wide but have the structure of x87 80-bits + extra 6
bytes for alignment:
>From "http://lwn.net/2001/features/OLS/pdf/pdf/x86-64.pdf":
"... The x87 stack with 80-bit precision is only used for long double."
And:
>>> e47 = float128(1e-47)
>>> e30 = float128(1e-30)
>>> e50 =
On Wed, Dec 10, 2008 at 01:49, Charles R Harris
<[EMAIL PROTECTED]> wrote:
> Hi All,
>
> I bumped into this while searching for something else:
> http://www.ohloh.net/p/numpy/analyses/latest
-14 lines of Javascript?
--
Robert Kern
"I have come to believe that the whole world is an enigma, a har
Hi All,
I bumped into this while searching for something else:
http://www.ohloh.net/p/numpy/analyses/latest
Chuck
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On Tue, Dec 09, 2008 at 01:34:29AM -0800, Jarrod Millman wrote:
> It was decided last year that numpy io should provide simple, generic,
> core io functionality. While scipy io would provide more domain- or
> application-specific io code (e.g., Matlab IO, WAV IO, etc.) My
> vision for scipy io, w
Peter Norton wrote:
> I've got a few issues that I hope won't be overwhelming on one message:
>
> (1) Because of some issues in the past in building numpy with
> numscons, the numpy.core.umath_tests don't get built with
> numpy+numscons (at least not as of svn version 6128).
>
> $ python -c 'import
> 2008/12/10 Robert Kern <[EMAIL PROTECTED]>:
> On Mon, Dec 8, 2008 at 19:15, frank wang <[EMAIL PROTECTED]> wrote:
>> Hi,
>>
>> I have a program with some variables consume a lot of memory. The first time
>> I run it, it is fine. The second time I run it, I will get MemoryError. If I
>> close the
On Tue, Dec 9, 2008 at 8:10 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Tue, Dec 9, 2008 at 21:01, Charles R Harris
> <[EMAIL PROTECTED]> wrote:
> >
> >
> > On Tue, Dec 9, 2008 at 1:40 PM, Robert Kern <[EMAIL PROTECTED]>
> wrote:
> >>
> >> On Tue, Dec 9, 2008 at 09:51, Nadav Horesh <[EMAIL PRO
On Tue, Dec 9, 2008 at 21:01, Charles R Harris
<[EMAIL PROTECTED]> wrote:
>
>
> On Tue, Dec 9, 2008 at 1:40 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>>
>> On Tue, Dec 9, 2008 at 09:51, Nadav Horesh <[EMAIL PROTECTED]> wrote:
>> > As much as I know float128 are in fact 80 bits (64 mantissa + 16
>>
On Wed, Dec 10, 2008 at 11:04, Robert Kern <[EMAIL PROTECTED]> wrote:
> v / numpy.linalg.norm(v)
>
Thanks a lot ~;)
--
Cheers,
Grissiom
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On Tue, Dec 9, 2008 at 20:56, Grissiom <[EMAIL PROTECTED]> wrote:
> On Wed, Dec 10, 2008 at 10:36, Robert Kern <[EMAIL PROTECTED]> wrote:
>>
>> On Tue, Dec 9, 2008 at 20:24, Grissiom <[EMAIL PROTECTED]> wrote:
>> > Hi all,
>> >
>> > Nice to neet you all. I am a newbie in numpy. Is there any functio
On Mon, Dec 8, 2008 at 19:15, frank wang <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I have a program with some variables consume a lot of memory. The first time
> I run it, it is fine. The second time I run it, I will get MemoryError. If I
> close the ipython and reopen it again, then I can run the progr
On Tue, Dec 9, 2008 at 1:40 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Tue, Dec 9, 2008 at 09:51, Nadav Horesh <[EMAIL PROTECTED]> wrote:
> > As much as I know float128 are in fact 80 bits (64 mantissa + 16
> exponent) so the precision is 18-19 digits (not 34)
>
> float128 should be 128 bits
On Wed, Dec 10, 2008 at 10:36, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Tue, Dec 9, 2008 at 20:24, Grissiom <[EMAIL PROTECTED]> wrote:
> > Hi all,
> >
> > Nice to neet you all. I am a newbie in numpy. Is there any function that
> > could unitize a array?
>
> If you mean like the Mathematica fun
On Tue, Dec 9, 2008 at 4:50 PM, Peter Norton <
[EMAIL PROTECTED]> wrote:
> I've got a few issues that I hope won't be overwhelming on one message:
>
> (1) Because of some issues in the past in building numpy with
> numscons, the numpy.core.umath_tests don't get built with
> numpy+numscons (at leas
On Tue, Dec 9, 2008 at 20:40, Vagabond_Aero <[EMAIL PROTECTED]> wrote:
> I have the same problem. I tried the del command below, but foundon that it
> removes the names of the ndarrays from memory, but does not free up the
> memory on my XP system (python 2.5.2, numpy 1.2.1). Regular python objec
I have the same problem. I tried the del command below, but foundon that it
removes the names of the ndarrays from memory, but does not free up the
memory on my XP system (python 2.5.2, numpy 1.2.1). Regular python objects
release their memory when I use the del command, but it looks like the
nda
On Tue, Dec 9, 2008 at 20:24, Grissiom <[EMAIL PROTECTED]> wrote:
> Hi all,
>
> Nice to neet you all. I am a newbie in numpy. Is there any function that
> could unitize a array?
If you mean like the Mathematica function Unitize[] defined here:
http://reference.wolfram.com/mathematica/ref/Unitiz
On Tue, Dec 9, 2008 at 20:24, Grissiom <[EMAIL PROTECTED]> wrote:
> Hi all,
>
> Nice to neet you all. I am a newbie in numpy. Is there any function that
> could unitize a array?
What do you mean by "unitize"?
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
e
Hi all,
Nice to neet you all. I am a newbie in numpy. Is there any function that
could unitize a array?
Thanks in advance.
--
Cheers,
Grissiom
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I've got a few issues that I hope won't be overwhelming on one message:
(1) Because of some issues in the past in building numpy with
numscons, the numpy.core.umath_tests don't get built with
numpy+numscons (at least not as of svn version 6128).
$ python -c 'import numpy; print numpy.__version__;
Hi,
Can you be more specific? Do you need sparse matrices to represent
observation vectors because they are sparse? Or do you need sparse
matrices to represent distance matrices because most vectors you are
clustering are similar while a few are dissimilar?
The clustering code is written mostly i
On Tue, Dec 9, 2008 at 12:25 PM, Bab Tei <[EMAIL PROTECTED]> wrote:
> I can exclude a list of items by using negative index in R (R-project) ie
> myarray[-excludeindex]. As negative indexing in numpy (And python) behave
> differently ,how can I exclude a list of item in numpy?
Here's a painful
You can make a mask array in numpy to prune out items from an array
that you don't want, denoting indices you want to keep with 1's and
those you don't want to keep with 0's. For instance,
a = np.array([1,3,45,67,123])
mask = np.array([0,1,1,0,1],dtype=np.bool)
anew = a[mask]
will set anew equal
On Tue, Dec 9, 2008 at 09:51, Nadav Horesh <[EMAIL PROTECTED]> wrote:
> As much as I know float128 are in fact 80 bits (64 mantissa + 16 exponent) so
> the precision is 18-19 digits (not 34)
float128 should be 128 bits wide. If it's not on your platform, please
let us know as that is a bug in you
Hi
Does the distance function in spatial package support sparse matrix?
regards
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Hi
I can exclude a list of items by using negative index in R (R-project) ie
myarray[-excludeindex]. As negative indexing in numpy (And python) behave
differently ,how can I exclude a list of item in numpy?
Regards, Teimourpour
___
Numpy-discus
On Dec 9, 2008, at 12:59 PM, Christopher Barker wrote:
> Jarrod Millman wrote:
>
>>> From the user's perspective, I would like all the NumPy IO code to
>>> be
>> in the same place in NumPy; and all the SciPy IO code to be in the
>> same place in SciPy.
>
> +1
So, no problem w/ importing numpy.
Jarrod Millman wrote:
>>From the user's perspective, I would like all the NumPy IO code to be
> in the same place in NumPy; and all the SciPy IO code to be in the
> same place in SciPy.
+1
> So I
> wonder if it would make sense to incorporate AstroAsciiData?
Doesn't it overlap a lot with genlo
Gong, Shawn (Contractor) wrote:
> hi list,
Hi Shawn,
> I tried to build numpy 1.2.1 on Solaris 9 with gcc 3.4.6
>
> when I typed “python setup.py build”, I got error from hashlib.py
>
> File "/home/sgong/dev181/dist/lib/python2.5/hashlib.py", line 133, in
>
>
> md5 = __get_builtin_cons
You should ask on a general Python list, as it's a Python problem, not
a numpy one ;)
Matthieu
PS: look at the log when you built Python, there must be a mention of
the not building of the md5 module.
2008/12/9 Gong, Shawn (Contractor) <[EMAIL PROTECTED]>:
> hi Matthieu,
>
> import md5 doesn't w
hi Matthieu,
import md5 doesn't work. I got:
>>> import md5
Traceback (most recent call last):
File "", line 1, in
File "/home/sgong/dev181/dist.org/lib/python2.5/md5.py", line 6, in
from hashlib import md5
File "/home/sgong/dev181/dist.org/lib/python2.5/hashlib.py", line 133,
in
I found one solution that's pretty simple for easy read and write to/from a
file of a numpy array (see my original message below). Just use the method
tolist().
e.g. a complex 2 x 2 array
arr=array([[1.0,3.0-7j],[55.2+4.0j,-95.34]])
ls=arr.tolist()
Then use the repr - eval pairings to write a
On Wed, Dec 10, 2008 at 1:00 AM, Gong, Shawn (Contractor)
<[EMAIL PROTECTED]> wrote:
> hi list,
>
> Do I need to do the same on Solaris?
This has nothing to do with ATLAS. You did not build correctly python,
or the python you are using is not built correctly. _md5 is a module
from python, not from
Hi,
Does:
>>> import md5
work? If it doesn't, it's a packaging problem. md5 must be available.
Matthieu
2008/12/9 Gong, Shawn (Contractor) <[EMAIL PROTECTED]>:
> hi list,
>
> I tried to build numpy 1.2.1 on Solaris 9 with gcc 3.4.6
>
> when I typed "python setup.py build", I got error from has
hi list,
I tried to build numpy 1.2.1 on Solaris 9 with gcc 3.4.6
when I typed "python setup.py build", I got error from hashlib.py
File "/home/sgong/dev181/dist/lib/python2.5/hashlib.py", line 133, in
md5 = __get_builtin_constructor('md5')
File "/home/sgong/dev181/dist/lib/python2.5/has
As much as I know float128 are in fact 80 bits (64 mantissa + 16 exponent) so
the precision is 18-19 digits (not 34)
Nadav.
-הודעה מקורית-
מאת: [EMAIL PROTECTED] בשם Bruce Southey
נשלח: ג 09-דצמבר-08 17:46
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] Importance of o
Hanni Ali wrote:
> Hi Bruce,
>
> Ahh, but I would have thought the precision for the array operation
> would be the same no matter which values I wish to sum? The array is
> in float64 in all cases.
>
> I would not have thought altering the type of the integer values would
> make any difference
Hi Bruce,
Ahh, but I would have thought the precision for the array operation would be
the same no matter which values I wish to sum? The array is in float64 in
all cases.
I would not have thought altering the type of the integer values would make
any difference as these indices are all below 5 m
Nadav Horesh wrote:
> The highest accuracy is obtained when you sum an acceding ordered series, and
> the lowest accuracy with descending ordered. In between you might get a
> variety of rounding errors.
>
> Nadav.
>
> -הודעה מקורית-
> מאת: [EMAIL PROTECTED] בשם Hanni Ali
> נשלח: ג 09-
Thank you Nadav.
2008/12/9 Nadav Horesh <[EMAIL PROTECTED]>
> The highest accuracy is obtained when you sum an acceding ordered series,
> and the lowest accuracy with descending ordered. In between you might get a
> variety of rounding errors.
>
> Nadav.
>
> -הודעה מקורית-
> מאת: [EMAIL
On 12/8/2008 3:32 PM James apparently wrote:
> I have a very simple plot, and the lines join point to point, however i
> would like to add a line of best fit now onto the chart, i am really new
> to python etc, and didnt really understand those links!
See the `slope_intercept` method of the OLS
The highest accuracy is obtained when you sum an acceding ordered series, and
the lowest accuracy with descending ordered. In between you might get a variety
of rounding errors.
Nadav.
-הודעה מקורית-
מאת: [EMAIL PROTECTED] בשם Hanni Ali
נשלח: ג 09-דצמבר-08 16:07
אל: Discussion of Num
Hi All,
I have encountered a puzzling issue and I am not certain if this is a
mistake of my own doing or not. Would someone kindly just look over this
issue to make sure I'm not doing something very silly.
So, why would the sum of an array have a different value depending on the
order I select th
James wrote:
> Hi,
>
> Thanks for all your help so far!
>
> Right i think it would be easier to just show you the chart i have so far;
>
> --
> import numpy as np
> import matplotlib.pyplot as plt
>
> plt.plot([4,8,12,16,20,24], [0.008,0.016,0.021,0.038,0.062,0.116], 'bo')
>
Hi,
Thanks for all your help so far!
Right i think it would be easier to just show you the chart i have so far;
--
import numpy as np
import matplotlib.pyplot as plt
plt.plot([4,8,12,16,20,24], [0.008,0.016,0.021,0.038,0.062,0.116], 'bo')
plt.xlabel("F (Number of washer
We are almost ready for SciPy 0.7.0rc1 (we just need to sort out the
Numerical Recipes issues and I haven't had time to look though them
yet). So I wanted to ask once more for help with preparing the
release notes:
http://projects.scipy.org/scipy/scipy/browser/trunk/doc/release/0.7.0-notes.rst
Th
On Fri, Dec 5, 2008 at 3:59 PM, Pierre GM <[EMAIL PROTECTED]> wrote:
> All,
> Here's the latest version of genloadtxt, with some recent corrections. With
> just a couple of tweaking, we end up with some decent speed: it's still
> slower than np.loadtxt, but only 15% so according to the test at the
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