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 self.nc_var[item].view('complex')
nc = netCDF4.Dataset('my.nc')
cplx_data = WrapComplex(nc.groups['mygroup'].variables['cplx_stuff'])
Now you can index cplx_data (e.g., cplx_data[:10]) and only the values you
need will be read from disk and converted on the fly.
Hope this helps!
Cheers,
Stephan
On Sat, Mar 29, 2014 at 6:13 PM, G Jones <glenn.calt...@gmail.com> wrote:
> 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['mygroup'].variables['cplx_stuff'][:].view('complex')
>
> which directly gives a nice complex numpy array. This is OK for small
> arrays, but is wasteful if I only need some chunks of the array because it
> reads all the data in, reducing the utility of the mmap feature of netCDF.
>
> I'm wondering if there is a better way to directly make a numpy array view
> that uses the netcdf variable's memory mapped buffer directly. Looking at
> the Variable class, there is no access to this buffer directly which could
> then be passed to np.ndarray(buffer=...).
>
> Any ideas of simple solutions to this problem?
>
> Thanks,
> Glenn
>
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