I am either getting a nasty bug when indexing structured arrays, or I
don't really understand how they work. I have imported some data using
genfromtxt and an associated list of dtypes:
ndtype=[('include', int), ('year', int), ('month', int), ('day', int),
('deg_day_north', int), ('deg_day_south',
On Thu, Oct 7, 2010 at 8:59 AM, Pierre GM wrote:
>
> Not needed. The unpack argument is used as the very end of the function
> anyway.
> Anyhow, could you open a ticket to that effect (else I'm quite likely to
> forget about it).
>
I can open this one.
__
On Thu, Oct 7, 2010 at 4:00 AM, Pierre GM wrote:
>
> On Oct 7, 2010, at 4:48 AM, Chris Fonnesbeck wrote:
>
>> The documentation for loadtxt and genfromtxt state that the unpack
>> argument functions as follows:
>>
>> If True, the returned array is transposed, so
The documentation for loadtxt and genfromtxt state that the unpack
argument functions as follows:
If True, the returned array is transposed, so that arguments may be
unpacked using x, y, z = loadtxt(...).
In practice, this does not always occur. I have a csv file of mixed
data types, and try impo
I'm trying to generate negative binomial random numbers where the n parameter
is non integer (<1 in fact). This should be possible, as n only needs to be
real. However, I get the following message:
ValueError: n <= 0
I assume this is because some rounding is going on behind the scenes.
The reas
Chris Fonnesbeck mac.com> writes:
> It would be great to be able to do the following:
>
> arange(0, 100, 0.1)
As it turns out, I am an idiot.
Apologies.
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Is there any prospect for easily getting ranges of floats in numpy, rather than
just integers? In R, for example, you can specify a decimal value for the step
size. It would be great to be able to do the following:
arange(0, 100, 0.1)
for a sequence from 0 to 100 in steps of 0.1. It seems rather
I have narrowed a memory leak in PyMC down to the vectorize() function
in numpy. I have a simple inverse logit transformation function:
invlogit = lambda x: 1.0 / (1.0 + exp(-1.0 * x))
which runs without leaking when used iteratively during simulations.
However, when I try to vectorize it, the
