Here are my values for your comparison:
test.nc file is about 715 MB. The details are below:
In [21]: netCDF4.__version__
Out[21]: '0.9.4'
In [22]: np.__version__
Out[22]: '2.0.0.dev-b233716'
In [23]: from netCDF4 import Dataset
In [24]: f = Dataset("test.nc")
In [25]: f.variables['reflectivity'].shape
Out[25]: (6, 18909, 506)
In [26]: f.variables['reflectivity'].size
Out[26]: 57407724
In [27]: f.variables['reflectivity'][:].dtype
Out[27]: dtype('float32')
In [28]: timeit z = f.variables['reflectivity'][:]
1 loops, best of 3: 731 ms per loop
How long it takes in your side to read that big array?
On Wed, Aug 3, 2011 at 10:30 AM, Kiko <kikocorre...@gmail.com> wrote:
> Hi.
>
> I'm trying to read a big netcdf file (445 Mb) using netcdf4-python.
>
> The data are described as:
> *The GEBCO gridded data set is stored in NetCDF as a one dimensional array
> of 2-byte signed integers that represent integer elevations in metres.
> The complete data set gives global coverage. It consists of 21601 x 10801
> data values, one for each one minute of latitude and longitude for 233312401
> points.
> The data start at position 90°N, 180°W and are arranged in bands of 360
> degrees x 60 points/degree + 1 = 21601 values. The data range eastward from
> 180°W longitude to 180°E longitude, i.e. the 180° value is repeated.*
>
> The problem is that it is very slow (or I am quite newbie).
>
> Anyone has a suggestion to get these data in a numpy array in a faster way?
>
> Thanks in advance.
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
--
Gökhan
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
