Hi Glenn,
Here is a full example of how we wrap a netCDF4.Variable object,
implementing all of its ndarray-like methods:
https://github.com/akleeman/xray/blob/0c1a963be0542b7303dc875278f3b163a15429c5/src/xray/conventions.py#L91
The __array__ method would be the most relevant one for you: it means
Hi Stephan,
Thanks for the reply. I was thinking of something along these lines but was
hesitant because while this provides clean access to chunks of the data,
you still have to remember to do cplx_data[:].mean() for example in the
case that you want cplx_data.mean().
I was hoping to basically ha
Hi Glenn,
My usual strategy for this sort of thing is to make a light-weight wrapper
class which reads and converts values when you access them. For example:
class WrapComplex(object):
def __init__(self, nc_var):
self.nc_var = nc_var
def __getitem__(self, item):
return se
Hi,
I am using netCDF4 to store complex data using the recommended strategy of
creating a compound data type with the real and imaginary parts. This all
works well, but reading the data into a numpy array is a bit clumsy.
Typically I do:
nc = netCDF4.Dataset('my.nc')
cplx_data = nc.groups['mygrou
On Sat, Mar 29, 2014 at 8:55 AM, wrote:
> On Sat, Mar 29, 2014 at 7:31 AM, wrote:
> > On Sat, Mar 29, 2014 at 12:12 AM, Jaime Fernández del Río
> > wrote:
> >> Hi,
> >>
> >> I have submitted a PR (https://github.com/numpy/numpy/pull/4568) that
> speeds
> >> up `np.vander` by using accumulated
On 29 Mar 2014 20:57, "Chris Barker" wrote:
> I think this is somewhat open for discussion -- yes, it's odd, but in the
spirit of practicality beats purity, it seems OK. We could allow any TZ
specifier for that matter -- that's kind of how "naive" or "local" timezone
(non) handling works -- it's u
On Sat, Mar 29, 2014 at 1:04 PM, Nathaniel Smith wrote:
> > 1- You give as an example of "naive" datetime handling:
> >
> np.datetime64('2005-02-25T03:00Z')
> > np.datetime64('2005-02-25T03:00')
> >
> > This IIUC is incorrect. The Z modifier is a timezone offset, and for
> normal
> > "naive"
On Fri, Mar 28, 2014 at 9:30 PM, Sankarshan Mudkavi
wrote:
>
> Hi Nathaniel,
>
> 1- You give as an example of "naive" datetime handling:
>
np.datetime64('2005-02-25T03:00Z')
> np.datetime64('2005-02-25T03:00')
>
> This IIUC is incorrect. The Z modifier is a timezone offset, and for normal
> "
On Sat, Mar 29, 2014 at 7:31 AM, wrote:
> On Sat, Mar 29, 2014 at 12:12 AM, Jaime Fernández del Río
> wrote:
>> Hi,
>>
>> I have submitted a PR (https://github.com/numpy/numpy/pull/4568) that speeds
>> up `np.vander` by using accumulated multiplication instead of exponentiation
>> to compute the
On Sat, Mar 29, 2014 at 12:12 AM, Jaime Fernández del Río
wrote:
> Hi,
>
> I have submitted a PR (https://github.com/numpy/numpy/pull/4568) that speeds
> up `np.vander` by using accumulated multiplication instead of exponentiation
> to compute the Vandermonde matrix. For largish matrices the speed
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