On 03/11/2011 02:54 PM, onet wrote:
> Hi,
>
> Using matplotlib I try to plot satellite observations, which consists of
> roughly one million patches that are not gridded regularly.
> I first collect the vertices (corner points of the observations) and
> colors and then use PolyCollection and ax.add_collection to add these
> patches to the figure.
>
> On my 64bit Linux machine:
> # 518400 patches will use>2Gb of memory
> # 1M patches will use> 4Gb of memory
> On a 32bit machine the memory is roughly half compared to 64bit.
>
> My question: how can I plot more efficiently and use less memory?
If your data are on a quadrilateral mesh, as in your example, (or can be
approximately mapped onto such a mesh) then pcolormesh should be very
much more efficient both in time and in memory than making a PolyCollection.
Eric
>
> An example script with random data is below.
>
> System:
> Debian Testing: kernel 2.6.32-5-amd64 x86_64 GNU/Linux
> Python 2.6.6 (r266:84292, Dec 26 2010, 22:31:48)
> matplotlib.__version__ = '1.0.0'
>
> Best regards,
>
> Onet.
>
>
>
> #!/usr/bin/env python
> #
> # Purpose : Show large use of memory when plotting
> # large numbers of patches
> #
>
> import random
>
> import matplotlib
> matplotlib.use('AGG') # produce AGG graphics (o.a. PNG) by default
> import matplotlib.pyplot as plt
> import matplotlib.colors as colors
> from matplotlib.collections import PolyCollection
> from mpl_toolkits.basemap import Basemap
>
>
> def test_polycollection (NLats, NLons):
> """ Test poly collections """
>
> fig = plt.figure()
> ax = fig.add_axes([0.1,0.1,0.8,0.8])
> figmap = Basemap (llcrnrlon=-180., llcrnrlat=-90,
> urcrnrlon=180., urcrnrlat=90.,\
> resolution='c', area_thresh=10000.,
> projection='cyl')
>
> # Color map and min/max bounds
> cmap = plt.cm.jet
> vmin = 0
> vmax = 10
>
> # Arrays for the vertices and the colors
> Poly_Vertices = []
> Poly_Colors = []
>
> # add pixel to array of vertices and set a random color
> for LatNr in range (0, NLats):
> for LonNr in range (0, NLons, 2):
>
> # shift lon 1 point if odd for staggered grid
> if (LatNr % 2 == 0):
> # even
> ShiftLon = 0
> else:
> # odd
> ShiftLon = 1
>
> # calc coordinates for vertex storage
> x1,y1 = (359.*(ShiftLon + LonNr + 1)/(NLons+1) - 179.9,
> 179.*(LatNr )/(NLats+1) - 89.9)
> x2,y2 = (359.*(ShiftLon + LonNr + 2)/(NLons+1) - 179.9,
> 179.*(LatNr + 1)/(NLats+1) - 89.9)
> x3,y3 = (359.*(ShiftLon + LonNr + 1)/(NLons+1) - 179.9,
> 179.*(LatNr + 2)/(NLats+1) - 89.9)
> x4,y4 = (359.*(ShiftLon + LonNr )/(NLons+1) - 179.9,
> 179.*(LatNr + 1)/(NLats+1) - 89.9)
>
> # get RGB colors, cut off alpha.
> RandomValue = random.random() * vmax
> colorsgen = cmap ((RandomValue - vmin) / (vmax - vmin))[:3]
>
> # add the polygon vertices and the color to the array
> Poly_Vertices.append ([(x1,y1), (x2,y2), (x3,y3), (x4,y4)])
> Poly_Colors.append (colorsgen)
>
>
> # Create PolyCollection and add it to the axes
> print 'PolyCollection: number of elements: ', len (Poly_Colors)
> Data_PatchCollection = PolyCollection (Poly_Vertices,
> facecolor=Poly_Colors,
> edgecolor='black',
> linewidth=0)
>
> print 'add_collection'
> ax.add_collection (Data_PatchCollection)
>
> print 'add_collection done'
>
> # finish the plot by drawing coastlines
> figmap.drawcoastlines()
>
> plt.title ('PolyCollection on a map')
> fig.savefig ('polycol.png', dpi=300)
> plt.close()
>
> return
> #
> # End test_polycollection
> #
>
>
> if __name__ == "__main__":
> """ Test the memory size of matplotlib using poly
> collections.
>
> On a 64 bit linux machine the memory use is
> enormous when plotting large numbers of patches
> via matplotlib / PolyCollection.
>
> For 518400 patches matplotlib will use ~2Gb of
> memory. On a 32 bit Linux machine, the memory
> usage is roughly half.
>
> Can this be done more efficient?
>
> Debian Testing: Linux host 2.6.32-5-amd64 #1 SMP
> Wed Jan 12 03:40:32 UTC 2011 x86_64 GNU/Linux
> Python 2.6.6 (r266:84292, Dec 26 2010, 22:31:48)
> matplotlib.__version__ = '1.0.0'
> """
>
> # 129600 patches will use ~630Mb of memory (on 64bit Linux)
> #NLats=360
> #NLons=720
>
> # 259200 patches will use ~1Gb of memory (on 64bit Linux)
> #NLats=360
> #NLons=1440
>
> # 518400 patches will use ~2Gb of memory (on 64bit Linux)
> NLats=720
> NLons=1440
>
> #
> # test the memory size of using poly collections.
> test_polycollection (NLats, NLons)
>
>
>
>
>
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