Re: [Matplotlib-users] Slow imshow when zooming or panning with several synced subplots

[Guillaume G. <gui...@mi...>]

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
>
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