On Wed, Sep 25, 2013 at 11:05 PM, wrote:
> On Wed, Sep 25, 2013 at 8:26 PM, Faraz Mirzaei wrote:
>> Hi everyone,
>>
>> I'm using np.ma.corrcoef to compute the correlation coefficients among rows
>> of a masked matrix, where the masked elements are the missing data. I've
>> observed that in some
On Wed, Sep 25, 2013 at 8:26 PM, Faraz Mirzaei wrote:
> Hi everyone,
>
> I'm using np.ma.corrcoef to compute the correlation coefficients among rows
> of a masked matrix, where the masked elements are the missing data. I've
> observed that in some cases, the np.ma.corrcoef gives invalid coefficien
Hi everyone,
I'm using np.ma.corrcoef to compute the correlation coefficients among rows
of a masked matrix, where the masked elements are the missing data. I've
observed that in some cases, the np.ma.corrcoef gives invalid coefficients
that are greater than 1 or less than -1.
Here's an example:
On Wed, Sep 25, 2013 at 1:41 PM, David Goldsmith wrote:
> Thanks, guys. Yeah, I realized the problem w/ the
> uniform-increment-variable-direction approach this morning: physically, it
> ignores the fact that the particles hitting the particle being tracked are
> going to have a distribution of m
Thanks, guys. Yeah, I realized the problem w/ the
uniform-increment-variable-direction approach this morning: physically, it
ignores the fact that the particles hitting the particle being tracked are
going to have a distribution of momentum, not all the same, just varying in
direction. But I don'
On Wed, Sep 25, 2013 at 12:51 PM, Warren Weckesser <
warren.weckes...@gmail.com> wrote:
>
> On Wed, Sep 25, 2013 at 9:36 AM, Neal Becker wrote:
>
>> David Goldsmith wrote:
>>
>> > Is this a valid algorithm for generating a 3D Wiener process? (When I
>> > graph the results, they certainly look li
On Wed, Sep 25, 2013 at 9:36 AM, Neal Becker wrote:
> David Goldsmith wrote:
>
> > Is this a valid algorithm for generating a 3D Wiener process? (When I
> > graph the results, they certainly look like potential Brownian motion
> > tracks.)
> >
> > def Wiener3D(incr, N):
> > r = incr*(R.randi
Hi all,
Possibly of interest to some folks here, it looks like in Python 3.4
there'll be a generic system for hooking and tracking memory
allocations:
http://www.python.org/dev/peps/pep-0445/
I'm not planning to do anything with this information myself, but if
anyone's been thinking about numpy
David Goldsmith wrote:
> Is this a valid algorithm for generating a 3D Wiener process? (When I
> graph the results, they certainly look like potential Brownian motion
> tracks.)
>
> def Wiener3D(incr, N):
> r = incr*(R.randint(3, size=(N,))-1)
> r[0] = 0
> r = r.cumsum()
> t = 2*
On 9/25/2013 3:06 AM, Edmondo Porcu wrote:
> advice on how to create a matrix with certain characteristics :
> - Every entry should be minimum 0 maximum 1 with a step of 0.1 (legal
> values are 0,0.1,0.2,0.3 etc)
> - The number of columns of the matrix is a parameter of this matrix creation
> a
On Wed, Sep 25, 2013 at 1:12 PM, Edmondo Porcu
wrote:
>
> That's what I was looking for, except that I want to be sure to generate
all the possible combinations, and to have no repeated values.
Okay, then you need to find all of the integer partitions of 10 with
`ncols` elements (padding with 0s
That's what I was looking for, except that I want to be sure to generate
all the possible combinations, and to have no repeated values.
Thanks
Edmondo
2013/9/25 Robert Kern
> On Wed, Sep 25, 2013 at 8:06 AM, Edmondo Porcu
> wrote:
> >
> > Dear all,
> >
> > I am a Newbie with Numpy and I woul
On Wed, Sep 25, 2013 at 8:06 AM, Edmondo Porcu
wrote:
>
> Dear all,
>
> I am a Newbie with Numpy and I would need some advice on how to create a
matrix with certain characteristics :
>
> - Every entry should be minimum 0 maximum 1 with a step of 0.1 (legal
values are 0,0.1,0.2,0.3 etc)
>
> - The
On Wed, Sep 25, 2013 at 9:06 AM, Edmondo Porcu wrote:
> I am a Newbie with Numpy and I would need some advice on how to create a
> matrix with certain characteristics :
>
> - Every entry should be minimum 0 maximum 1 with a step of 0.1 (legal
> values are 0,0.1,0.2,0.3 etc)
You can generate rand
Dear all,
I am a Newbie with Numpy and I would need some advice on how to create a
matrix with certain characteristics :
- Every entry should be minimum 0 maximum 1 with a step of 0.1 (legal
values are 0,0.1,0.2,0.3 etc)
- The number of columns of the matrix is a parameter of this matrix
creati
Is this a valid algorithm for generating a 3D Wiener process? (When I
graph the results, they certainly look like potential Brownian motion
tracks.)
def Wiener3D(incr, N):
r = incr*(R.randint(3, size=(N,))-1)
r[0] = 0
r = r.cumsum()
t = 2*np.pi*incr*(R.randint(3, size=(N,))-1)
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