Hi,
The following example demonstrates a rather unexpected result:
>>> import numpy
>>> x = numpy.array( complex( 1.0 , 1.0 ) , numpy.object )
>>> print x.real
(1+1j)
>>> print x.imag
0
Shouldn't real and imag return an error in such a situation?
Thanks,
Mike
___
On Wed, Sep 8, 2010 at 5:35 PM, Michael Gilbert wrote:
> On Wed, 8 Sep 2010 15:44:02 -0400, Michael Gilbert wrote:
>> On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
>> >
>> >
>> > On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
>> > wrote:
On Wed, 8 Sep 2010 15:44:02 -0400, Michael Gilbert wrote:
> On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
> >
> >
> > On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> > wrote:
> >>
> >> On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris
On Wed, 8 Sep 2010 22:20:30 +0200, Sandro Tosi wrote:
> On Wed, Sep 8, 2010 at 22:10, Michael Gilbert
> wrote:
> > Here is an example:
> >
> > >>> 0.3/3.0 - 0.1
> > -1.3877787807814457e-17
> >
> > >>> mpmath.mpf(
On Wed, 8 Sep 2010 15:04:17 -0500, Robert Kern wrote:
> On Wed, Sep 8, 2010 at 14:44, Michael Gilbert
> wrote:
> > On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
> >>
> >>
> >> On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> >> wrot
On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
>
>
> On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> wrote:
>>
>> On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris wrote:
>> > On Wed, Sep 8, 2010 at 9:26 AM, Michael Gilbert
>> > > >
On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris wrote:
> On Wed, Sep 8, 2010 at 9:26 AM, Michael Gilbert > wrote:
>
> > Hi,
> >
> > Are there any plans to add support for decimal floating point
> > arithmetic, as defined in the 2008 revision of the IE
Hi,
Are there any plans to add support for decimal floating point
arithmetic, as defined in the 2008 revision of the IEEE 754 standard
[0], in numpy?
Thanks for any info.
Best wishes,
Mike
[0] http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4610935&tag=1
___
On Wed, 1 Sep 2010 21:15:22 + (UTC), Pauli Virtanen wrote:
> Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
> > I've been using numpy's float96 class lately, and I've run into some
> > strange precision errors.
> [clip]
> > >>> x
Hi,
I've been using numpy's float96 class lately, and I've run into some
strange precision errors. See example below:
>>> import numpy
>>> numpy.version.version
'1.5.0'
>>> sys.version
'3.1.2 (release31-maint, Jul 8 2010, 01:16:48) \n[GCC 4.4.4]'
>>> x = numpy.array( [0.01] , numpy.
On Thu, 8 Apr 2010 09:06:16 +0200, ioannis syntychakis wrote:
> thanks for all your answers.
> Now i can get al the values above the 150, but i would also like to have
> their positions in de matrix.
>
> excample:
>
> [[1. 4. 5. 6. 7. 1
> 2. 5. 7. 8. 9. 3
> 3. 5. 7. 1. 3. 7]]
>
> so, if i no
On Wed, 7 Apr 2010 16:40:24 +0200, ioannis syntychakis wrote:
> Hallo Everybody,
>
> I am new in this mail list and python. But I am working at something and I
> need your help.
>
> I have a very big matrix. What I want is to search in that matrix for values
> above the (for example:) 150. If the
Hi,
I am applying Monte Carlo for a problem involving mixed deterministic
and random values. In order to avoid a lot of special handling and
corner cases, I am using using numpy arrays full of a single value to
represent the deterministic quantities.
Anyway, I found that the standard deviation t
On Tue, 15 Sep 2009 13:31:21 -0600, Charles R Harris wrote:
> On Tue, Sep 15, 2009 at 1:28 PM, Michael Gilbert <
> michael.s.gilb...@gmail.com> wrote:
>
> > On Tue, 15 Sep 2009 13:17:43 -0600, Charles R Harris wrote:
> > > On Tue, Sep 15, 2009 at 12:57 PM, Michael
On Tue, 15 Sep 2009 13:17:43 -0600, Charles R Harris wrote:
> On Tue, Sep 15, 2009 at 12:57 PM, Michael Gilbert <
> michael.s.gilb...@gmail.com> wrote:
>
> > On Tue, 15 Sep 2009 13:26:23 -0500, Robert Kern wrote:
> > > On Tue, Sep 15, 2009 at 12:50
On Tue, 15 Sep 2009 13:26:23 -0500, Robert Kern wrote:
> On Tue, Sep 15, 2009 at 12:50, Charles R
> Harris wrote:
> >
> >
> > On Tue, Sep 15, 2009 at 11:38 AM, Michael Gilbert
> > wrote:
> >>
> >> hi,
> >>
> >> when using numpy.ran
hi,
when using numpy.random.multivariate_normal, would it make sense to warn
the user that they have entered a non-physical covariance matrix? i was
recently working on a problem and getting very strange results until i
finally realized that i had actually entered a bogus covariance matrix.
its e
On Thu, 26 Mar 2009 16:56:13 -0700 Lutz Maibaum wrote:
> Hello,
>
> I just started to use python and numpy for some numerical analysis. I
> have a question about the definition of the inverse Fourier transform.
> The user gives the formula (p.180)
>
> x[m] = Sum_k X[k] exp(j 2pi k m / n)
>
>
On Mon, 9 Mar 2009 18:21:45 -0400 "Michael S. Gilbert" wrote:
> On Mon, 9 Mar 2009 21:45:42 +0100, Mark Bakker wrote:
>
> > Hello -
> >
> > I tried to figure this out from the list, but haven't succeeded yet.
> >
> > I have a simple FORTRAN binary file.
> > It contains:
> > 1 integer
> > 1 floa
On Sun, 1 Mar 2009 14:29:54 -0500 Michael Gilbert wrote:
> i have rewritten loadtxt to be smarter about allocating memory, but
> it is slower overall and doesn't support all of the original
> arguments/options (yet).
i had meant to say that my version is slower for smaller data
On Sun, 1 Mar 2009 16:12:14 -0500 Gideon Simpson wrote:
> So I have some data sets of about 16 floating point numbers stored
> in text files. I find that loadtxt is rather slow. Is this to be
> expected? Would it be faster if it were loading binary data?
i have run into this as well.
According to wikipedia [1], some common Mersenne twister algorithms
use a linear congruential gradient (LCG) to generate seeds. LCGs have
been known to produce poor random numbers. Does numpy's Mersenne
twister do this? And if so, is this potentially a problem?
http://en.wikipedia.org/wiki/Line
> Exactly, change task_helper.py to
>
>
> import numpy as np
>
> def task(x):
> import os
> print "Hi, I'm", os.getpid()
> return np.random.random(x)
>
>
> and note the output
>
>
> Hi, I'm 16197
> Hi, I'm 16198
> Hi, I'm 16199
> Hi, I'm 16199
> [ 0.58175647 0.16293922
> Bruce Carneal did some tests of robustness and speed for various normal
> generators. I don't know what his final tests showed for Box-Muller. IIRC,
> it had some failures but nothing spectacular. The tests were pretty
> stringent and based on using the erf to turn the normal distribution into a
Hello,
I have been reading that there may be potential issues with the
Box-Muller transform, which is used by the numpy.random.normal()
function. Supposedly, since f*x1 and f*x2 are not independent variables, then
the individual elements (corresponding to f*x1 and f*x2 ) of the
distribution
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