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.
Le 23/05/2012 10:11, Sergi Pons Freixes a écrit :
> I'm plotting several images at once, sharing axes, because I use it
> for exploratory purposes. Each image is the same satellite image at
> different dates. I'm experimenting a slow response from matplotlib
> when zooming and panning, and I would like to ask for any tips that
> could speed up the process.
>
> What I am doing now is:
> - Load data from several netcdf files.
> - Calculate maximum value of all the data, for normalization.
> - Create a grid of subplots using ImageGrid. As each subplot is
> generated, I delete the array to free some memory (each array is
> stored in a list, the "deletion" is just a list.pop()). See the code
> below.
>
> It's 15 images, single-channel, of 4600x3840 pixels each.
This is a lot of data. 8bit or 16bit ?
> I've noticed
> that the bottleneck is not the RAM (I have 8 GB), but the processor.
> Python spikes to 100% usage on one of the cores when zooming or
> panning (it's an Intel(R) Core(TM) i5-2500 CPU @ 3.30GHz, 4 cores, 64
> bit).
>
> The code is:
> -------------------------------------------
> import os
> import sys
>
> import numpy as np
> import netCDF4 as ncdf
> import matplotlib.pyplot as plt
> from mpl_toolkits.axes_grid1 import ImageGrid
> from matplotlib.colors import LogNorm
>
> MIN = 0.001 # Hardcoded minimum data value used in normalization
>
> variable = 'conc_chl'
> units = r'$mg/m^3$'
> data = []
> dates = []
>
> # Get a list of only netCDF files
> filelist = os.listdir(sys.argv[1])
> filelist = [f for f in filelist if os.path.splitext(f)[1] == '.nc']
> filelist.sort()
> filelist.reverse()
>
> # Load data and extract dates from filenames
> 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]))
>
> # Get the maximum value of all data. Will be used for normalization
> maxc = np.array(data).max()
>
> # Plot the grid of images + dates
> fig = plt.figure()
> grid = ImageGrid(fig, 111,\
> nrows_ncols = (3, 5),\
> axes_pad = 0.0,\
> share_all=True,\
> aspect = False,\
> cbar_location = "right",\
> cbar_mode = "single",\
> cbar_size = '2.5%',\
> )
> for g in grid:
> v = data.pop()
> d = dates.pop()
> im = g.imshow(v, interpolation='none', norm=LogNorm(), vmin=MIN,
> vmax=maxc)
> g.text(0.01, 0.01, '-'.join(d), transform = g.transAxes) # Date on a
> corner
> cticks = np.logspace(np.log10(MIN), np.log10(maxc), 5)
> cbar = grid.cbar_axes[0].colorbar(im)
> cbar.ax.set_yticks(cticks)
> cbar.ax.set_yticklabels([str(np.round(t, 2)) for t in cticks])
> cbar.set_label_text(units)
>
> # Fine-tune figure; make subplots close to each other and hide x ticks for
> # all
> fig.subplots_adjust(left=0.02, bottom=0.02, right=0.95, top=0.98,
> hspace=0, wspace=0)
> grid.axes_llc.set_yticklabels([], visible=False)
> grid.axes_llc.set_xticklabels([], visible=False)
>
> plt.show()
> -------------------------------------------
>
> Any clue about what could be improved to make it more responsive?
>
> PD: This question has been posted previously on Stackoverflow, but it
> hasn't got any answer:
> http://stackoverflow.com/questions/10635901/slow-imshow-when-zooming-or-panning-with-several-synced-subplots
>
> ------------------------------------------------------------------------------
> Live Security Virtual Conference
> Exclusive live event will cover all the ways today's security and
> threat landscape has changed and how IT managers can respond. Discussions
> will include endpoint security, mobile security and the latest in malware
> threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
------------------------------------------------------------------------------
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond. Discussions
will include endpoint security, mobile security and the latest in malware
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
