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
In [3]:
On Sun, Nov 1, 2009 at 7:26 PM, josef.p...@gmail.com wrote:
On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith d.l.goldsm...@gmail.com
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
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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
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
2009/11/1 Bill Blinn bbl...@gmail.com:
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
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
___
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
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
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
___
NumPy-Discussion mailing
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
On Sun, Nov 1, 2009 at 21:09, Benjamin Deschamps bdesc...@gmail.com 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
On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith d.l.goldsm...@gmail.com 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?
On Sun, Nov 1, 2009 at 10:26 PM, josef.p...@gmail.com wrote:
On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith d.l.goldsm...@gmail.com
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
On Sun, Nov 1, 2009 at 21:27, josef.p...@gmail.com wrote:
On Sun, Nov 1, 2009 at 10:26 PM, josef.p...@gmail.com wrote:
On Sun, Nov 1, 2009 at 9:58 PM, David Goldsmith d.l.goldsm...@gmail.com
wrote:
I Googled scipy brownian and the top hit was the doc for numpy.random.wald,
but said doc has
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])
This fails
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
On Sun, Nov 1, 2009 at 10:20 PM, David Cournapeau
da...@ar.media.kyoto-u.ac.jp 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,
On Sun, Nov 1, 2009 at 20:57, Thomas Robitaille
thomas.robitai...@gmail.com 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
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 not
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
On Sun, Nov 1, 2009 at 22:17, Sturla Molden stu...@molden.no 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
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, even 32-bit
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 confuses mtrand.
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]:
On Sun, Nov 1, 2009 at 10:55 PM, David Cournapeau
da...@ar.media.kyoto-u.ac.jp 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
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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NumPy-Discussion@scipy.org
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 randint
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_integers
On Sun, Nov 1, 2009 at 23:14, Sturla Molden stu...@molden.no 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.
--
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 a
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.maxint-1, as
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