On Sun, Nov 1, 2009 at 7:26 PM, wrote:
> On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith
> wrote:
> > I Googled scipy brownian and the top hit was the doc for
> numpy.random.wald,
> > but said doc has a "tone" that suggests there are more "sophisticated"
> ways
> > to generate a random Brownian
On Sun, Nov 1, 2009 at 8:37 PM, Thomas Robitaille <
thomas.robitai...@gmail.com> wrote:
> Hello,
>
> I have a question concerning uint64 numbers - let's say I want to
> format a uint64 number that is > 2**31, at the moment it's necessary
> to wrap the numpy number inside long before formatting
>
>
Sturla Molden wrote:
> Sturla Molden skrev:
>
>> Robert Kern skrev:
>>
>>
>>> Then let me clarify: it was written to support integer ranges up to
>>> sys.maxint. Absolutely, it would be desirable to extend it.
>>>
>>>
>>>
>>>
>> Actually it only supports integers up to sys
josef.p...@gmail.com wrote:
>
> No, it wouldn't be a proper distribution. However in Bayesian analysis
> it is used as an improper (diffuse) prior
Ah, right - I wonder how this is handled rigorously, though. I know some
basics of Bayesian statistics, but I don't much about Bayesian
statistics from
On Sun, Nov 1, 2009 at 23:14, Sturla Molden wrote:
> I'll call this a bug.
Yes.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
Sturla Molden skrev:
> Robert Kern skrev:
>
>> Then let me clarify: it was written to support integer ranges up to
>> sys.maxint. Absolutely, it would be desirable to extend it.
>>
>>
>>
> Actually it only supports integers up to sys.maxint-1, as
> random_integers call randint. random_i
Robert Kern skrev:
> Then let me clarify: it was written to support integer ranges up to
> sys.maxint. Absolutely, it would be desirable to extend it.
>
>
Actually it only supports integers up to sys.maxint-1, as
random_integers call randint. random_integers includes the upper range,
but randi
Seems like this was a rookie mistake with code later in the function.
Thanks for suggesting the use of numpy.where, that is a much better
function for the purpose.
Benjamin
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On Sun, Nov 1, 2009 at 10:55 PM, David Cournapeau
wrote:
> josef.p...@gmail.com wrote:
>>
>> array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf])
>>
>> might actually be the right answer if you want a uniform distribution
>> on the real line.
>
> Does it make sense to define a unifo
Hello,
I have a question concerning uint64 numbers - let's say I want to
format a uint64 number that is > 2**31, at the moment it's necessary
to wrap the numpy number inside long before formatting
In [3]: "%40i" % np.uint64(2**64-1)
Out[3]: ' -1'
In [4]:
Robert Kern skrev:
> Then let me clarify: it was written to support integer ranges up to
> sys.maxint. Absolutely, it would be desirable to extend it.
>
>
I know, but look at this:
>>> import sys
>>> sys.maxint
2147483647
>>> 2**31-1
2147483647L
sys.maxint becomes a long, which is what conf
Sturla Molden wrote:
> Robert Kern skrev:
>
>> 64-bit and larger integers could be done, but it requires
>> modification. The integer distributions were written to support C
>> longs, not anything larger. You could also use .bytes() and
>> np.fromstring().
>>
>>
> But as of Python 2.6.4,
On Sun, Nov 1, 2009 at 22:17, Sturla Molden wrote:
> Robert Kern skrev:
>> 64-bit and larger integers could be done, but it requires
>> modification. The integer distributions were written to support C
>> longs, not anything larger. You could also use .bytes() and
>> np.fromstring().
>>
> But as o
Robert Kern skrev:
> 64-bit and larger integers could be done, but it requires
> modification. The integer distributions were written to support C
> longs, not anything larger. You could also use .bytes() and
> np.fromstring().
>
But as of Python 2.6.4, even 32-bit integers fail, at least on Win
josef.p...@gmail.com wrote:
>
> array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf])
>
> might actually be the right answer if you want a uniform distribution
> on the real line.
Does it make sense to define a uniform random variable whose range is
the extended real line ? It would
On Sun, Nov 1, 2009 at 20:57, Thomas Robitaille
wrote:
> Hi,
>
> I'm trying to generate random 64-bit integer values for integers and
> floats using Numpy, within the entire range of valid values for that
> type.
64-bit and larger integers could be done, but it requires
modification. The integer
On Sun, Nov 1, 2009 at 10:20 PM, David Cournapeau
wrote:
> Thomas Robitaille wrote:
>> Hi,
>>
>> I'm trying to generate random 64-bit integer values for integers and
>> floats using Numpy, within the entire range of valid values for that
>> type. To generate random 32-bit floats, I can use:
>>
>>
Thomas Robitaille wrote:
> Hi,
>
> I'm trying to generate random 64-bit integer values for integers and
> floats using Numpy, within the entire range of valid values for that
> type. To generate random 32-bit floats, I can use:
>
> np.random.uniform(low=np.finfo(np.float32).min,high=np.finfo
>
Thomas Robitaille skrev:
> np.random.random_integers(np.iinfo(np.int32).min,high=np.iinfo
> (np.int32).max,size=10)
>
> which gives
>
> array([-1506183689, 662982379, -1616890435, -1519456789, 1489753527,
> -604311122, 2034533014, 449680073, -444302414,
> -1924170329])
>
>
Th
On Sun, Nov 1, 2009 at 21:27, wrote:
> On Sun, Nov 1, 2009 at 10:26 PM, wrote:
>> On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith
>> wrote:
>>> I Googled scipy brownian and the top hit was the doc for numpy.random.wald,
>>> but said doc has a "tone" that suggests there are more "sophisticated
On Sun, Nov 1, 2009 at 10:26 PM, wrote:
> On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith
> wrote:
>> I Googled scipy brownian and the top hit was the doc for numpy.random.wald,
>> but said doc has a "tone" that suggests there are more "sophisticated" ways
>> to generate a random Brownian signa
On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith wrote:
> I Googled scipy brownian and the top hit was the doc for numpy.random.wald,
> but said doc has a "tone" that suggests there are more "sophisticated" ways
> to generate a random Brownian signal? Or is wald indeed SotA? Thanks!
>
> DG
Do you
On Sun, Nov 1, 2009 at 21:09, Benjamin Deschamps wrote:
> I am getting strange behaviour with the following code:
> Pd = ((numpy.sign(C_02) == 1) * Pd_pos) + ((numpy.sign(C_02) == -1) *
> Pd_neg)
> Ps = ((numpy.sign(C_02) == 1) * Ps_pos) + ((numpy.sign(C_02) == -1) *
> Ps_neg)
> where Pd, Ps, C_02
I am getting strange behaviour with the following code:
Pd = ((numpy.sign(C_02) == 1) * Pd_pos) + ((numpy.sign(C_02) == -1) *
Pd_neg)
Ps = ((numpy.sign(C_02) == 1) * Ps_pos) + ((numpy.sign(C_02) == -1) *
Ps_neg)
where Pd, Ps, C_02, Pd_pos, Pd_neg, Ps_pos and Ps_neg are all Float32
numpy a
I Googled scipy brownian and the top hit was the doc for numpy.random.wald,
but said doc has a "tone" that suggests there are more "sophisticated" ways
to generate a random Brownian signal? Or is wald indeed SotA? Thanks!
DG
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Hi,
I'm trying to generate random 64-bit integer values for integers and
floats using Numpy, within the entire range of valid values for that
type. To generate random 32-bit floats, I can use:
np.random.uniform(low=np.finfo(np.float32).min,high=np.finfo
(np.float32).max,size=10)
which gives
Bill Blinn skrev:
> v = multiview((3, 4))
> #the idea of the following lines is that the 0th row of v is
> #a view on the first row of a. the same would hold true for
> #the 1st and 2nd row of v and the 0th rows of b and c, respectively
> v[0] = a[0]
This would not even work, becuase a[0] does not
Anne Archibald skrev:
> The short answer is, you can't.
Not really true. It is possible create an array (sub)class that stores
memory addresses (pointers) instead of values. It is doable, but I am
not wasting my time implementing it.
Sturla
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2009/11/1 Bill Blinn :
> What is the best way to create a view that is composed of sections of many
> different arrays?
The short answer is, you can't. Numpy arrays must be located
contiguous blocks of memory, and the elements along any dimension must
be equally spaced. A view is simply another ar
What is the best way to create a view that is composed of sections of many
different arrays?
For example, imagine I had
a = np.array(range(0, 12)).reshape(3, 4)
b = np.array(range(12, 24)).reshape(3, 4)
c = np.array(range(24, 36)).reshape(3, 4)
v = multiview((3, 4))
#the idea of the following lin
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Friday, November 6:
How do I... use Envisage for GUIs?
Dear Leah,
Envisage is a Python-based framework for building extensible
applications. The Envisage Core and corresponding Envisage Plugins are
components of the Enthought Tool Suite. We've f
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