Re: [Matplotlib-users] Problems with rasterizing multiple elements

[Jae-Joon Lee]

I recommend you to use LineCollection as it is rasterized as a single image.

For example,

from matplotlib.collections import LineCollection
d = [np.array([ts[0], ys1]).T for ys1 in ys]

lc = LineCollection(d, color='r', lw=0.5, alpha=0.5,
rasterized=True)
ax.add_collection(lc)


ax.set_xlim(0, 7)
ax.set_ylim(-1, 1)


If you need to stick to plot command somehow, you need to create your
own artist.
There is a similar example,

http://matplotlib.sourceforge.net/examples/pylab_examples/demo_agg_filter.html

Take a look at the FilteredArtistList class. Your draw method should
be something like

def draw(self, renderer):
renderer.start_rasterizing()
for a in self._artist_list:
a.draw(renderer)
renderer.stop_rasterizing()


IHTH,

-JJ


On Wed, Aug 22, 2012 at 2:45 AM, Peter St. John peterc.stj...@gmail.com wrote:
 Hi Everyone,

 I'm having problems when rasterizing many lines in a plot using the
 rasterized=True keyword using the pdf output.
 Some version info:
 matplotlib version 1.1.1rc
 ubuntu 12.04
 python 2.7.3


 Here's a basic example that demonstrates my problem:
 # Import matplotlib to create a pdf document
 import matplotlib
 matplotlib.use('Agg')
 from matplotlib.backends.backend_pdf import PdfPages
 pdf = PdfPages('rasterized_test.pdf')

 import matplotlib.pylab as plt

 # some test data
 import numpy as np
 ts = np.linspace(0,2*np.pi,100) * np.ones((200,100))
 ts += (np.linspace(0, np.pi, 200)[np.newaxis] * np.ones((100,200))).T
 ys = np.sin(ts)

 fig = plt.figure()
 ax = fig.add_subplot(111)
 ax.plot(ts[0], ys.T, color='r', lw=0.5, alpha=0.5, rasterized=True)
 pdf.savefig()

 pdf.close()



 Essentially, I have a lot (200 in this case) of closely overlapping lines
 which makes the resulting figure (not rasterized) overly difficult to load.
 I would like to rasterize these lines, such that the axis labels (and other
 elements of the plot, not shown) remain vectors while the solution
 trajectories are flattened to a single raster background. However, using the
 code above, the image still takes a long time to load since each trajectory
 is independently rasterized, resulting in multiple layers. (If I open the
 resulting pdf with a program like inkscape, I can manipulate each trajectory
 independently.)

 Is it possible to flatten all of the rasterized elements into a single
 layer, so the pdf size would be greatly reduced?

 Thanks,
 --Peter


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[Matplotlib-users] Problems with rasterizing multiple elements

[Peter St. John]

Hi Everyone,

I'm having problems when rasterizing many lines in a plot using the
rasterized=True keyword using the pdf output.
Some version info:
matplotlib version 1.1.1rc
ubuntu 12.04
python 2.7.3


Here's a basic example that demonstrates my problem:
# Import matplotlib to create a pdf document
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages('rasterized_test.pdf')

import matplotlib.pylab as plt

# some test data
import numpy as np
ts = np.linspace(0,2*np.pi,100) * np.ones((200,100))
ts += (np.linspace(0, np.pi, 200)[np.newaxis] * np.ones((100,200))).T
ys = np.sin(ts)

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(ts[0], ys.T, color='r', lw=0.5, alpha=0.5, rasterized=True)
pdf.savefig()

pdf.close()



Essentially, I have a lot (200 in this case) of closely overlapping lines
which makes the resulting figure (not rasterized) overly difficult to load.
I would like to rasterize these lines, such that the axis labels (and other
elements of the plot, not shown) remain vectors while the solution
trajectories are flattened to a single raster background. However, using
the code above, the image still takes a long time to load since each
trajectory is independently rasterized, resulting in multiple layers. (If I
open the resulting pdf with a program like inkscape, I can manipulate each
trajectory independently.)

Is it possible to flatten all of the rasterized elements into a single
layer, so the pdf size would be greatly reduced?

Thanks,
--Peter
--
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/___
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