On Wed, May 23, 2012 at 11:00 AM, Guillaume Gay
<guilla...@mitotic-machine.org> wrote:
> Hello
>
>
> What is the size of a single image file? If they are very big, it is
> better to do everything from processing to ploting at once for each file.
As stated below, each image is single-channel, of 4600x3840 pixels. As
you can see on the code, there is not much processing, just loading
the images and plotting them. What it's slow is not the execution of
the code, is the interactive zooming and panning once the plots "are
in the screen".
>> It's 15 images, single-channel, of 4600x3840 pixels each.
> This is a lot of data. 8bit or 16bit ?
They are floating point values (for example, from 0 to 45.xxx). If I
understood correctly, setting the vmin and vmax, matplotlib should
normalize the values to an appropriate number of bits.
>> for f in filelist:
> everything should happen in this loop
>
>> dataset = ncdf.Dataset(os.path.join(sys.argv[1],f), 'r')
>> data.append(dataset.variables[variable][:])
> instead of creating this big list, use a temporary array (which will be
> overwritten)
>> dataset.close()
>> dates.append((f.split('_')[2][:-3],f.split('_')[1]))
Why? It's true that this way at the beginning it eats a lot of RAM,
but then it is released after each pop() (and calculating the maximum
of all the data without plotting is needed to use the same
normalization level on all the plots). Anyway, the slowness ocurrs
during the interaction of the plot, not during the execution of the
code.
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