Hello,
I'm attempting to implement a subclass of ndarray, and becoming confused
about the way __array_wrap__ and __array_finalize__ operate. I boiled it
down to a short subclass, which is the example on the website at
http://docs.scipy.org/doc/numpy-1.6.0/user/basics.subclassing.html, with one
add
You can use scipy.stats.truncnorm, can't you? Unless I misread, you want
to sample a normal distribution but with generated values only being
within a specified range? However you also say you want to do this with
triangular and log normal and for these I presume the easiest way is to
sample an
Thanks very much Lou for the information. I tried delving into the C
code and found a line in the dlasd4_ routine which reads:
for (niter = iter; niter <= MAXITERLOOPS; ++niter) {
This is apparently the main loop for this subroutine and the value of
MAXITERLOOPS = 100. All I did was increase th
Never mind, I've been digging through too much stuff and got confused...
I think trying to read Matlab code can do that to you. 8-)
>I'm translating some code from Matlab to numpy, and struggling a bit
>since I have very little knowledge of Matlab.
>
>My question is this - the arg function in Mat
Le dimanche 14 août 2011 à 12:43 -0500, a...@ajackson.org a écrit :
> I'm translating some code from Matlab to numpy, and struggling a bit
> since I have very little knowledge of Matlab.
>
> My question is this - the arg function in Matlab (which seems to be
> deprecated,
> they don't show it in
I'm translating some code from Matlab to numpy, and struggling a bit
since I have very little knowledge of Matlab.
My question is this - the arg function in Matlab (which seems to be deprecated,
they don't show it in their current documentation) is exactly equivalent to
what in Numpy? I know it is
On Sat, Aug 13, 2011 at 8:06 PM, Mark Wiebe wrote:
> Looks like this is the second-oldest open bug in the bug tracker.
> http://projects.scipy.org/numpy/ticket/236
> For what it's worth, I'm in favour of changing this behavior to be more
> consistent as proposed in that ticket.
> -Mark
>
> On Thu,
Try the fromiter function, that will allow you to pass an iterator
which can read the file line by line and not preload the whole file.
file_iterator = iter(open('filename.txt')
line_parser = lambda x: map(float,x.split('\t'))
a=np.fromiter(itertools.imap(line_parser,file_iterator),dtype=float)
Y
Chuck wrote:
Fails here also, fedora 15 64 bits AMD 940. There should be a maximum
iterations argument somewhere...
Chuck
---
*** Here's the "FIX":
Chuck is right. There is a max iterations. Here is a re
I had a quick look at the code
(https://github.com/numpy/numpy/blob/master/numpy/linalg/linalg.py) and
the numpy.linalg.svd function calls lapack_lite.dgesdd (for real
matrices) so I guess the non-convergence occurs in this function. As I
understood lapack_lite is used by default unless numpy i
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