matplotlib-users Mailing List for matplotlib

HI,
My initial thought was that you need to use the "zorder" keyword
argument and set the zorder to a large value. However, the more I
thought about it, I'm not really sure how you are plotting the
satellite data. Can you provide a code snippet?
PTM
---
Patrick Marsh
Ph.D. Student / Liaison to the HWT
School of Meteorology / University of Oklahoma
Cooperative Institute for Mesoscale Meteorological Studies
National Severe Storms Laboratory
http://www.patricktmarsh.com
On Fri, Dec 9, 2011 at 6:58 AM, Laat de, Jos (KNMI) <jos...@kn...> wrote:
> I am working with (geostationary) satellite data, and one of the things I
> want to do is plot a map (coastlines) on top of the satellite image. As an
> IDL user I know how to do this in IDL (although in IDL it is a bit of a
> hassle), but I don’t seem to be able to figure out how this could be done in
> python.
>
>
>
> The satellite data consists of a rectangular field of N by N pixels. The
> data is already converted to bitmap RGB values. I have figured out how this
> can be written to a bitmap image like PNG or JPG.
>
>
>
> I further figured out how to do the satellite projection in Basemap, and how
> to plot Basemap coastlines. However, the Basemap appears to have a
> non-transparent background, which overplots all bitmap data if I plot the
> Bitmap data first. I had hoped that there would be some transparency setting
> in Basemap, but alas.
>
>
>
> (ps. Keep in mind that I do not want to use some contour filling routine for
> plotting the satellite data. I want to retain the original N x N pixels and
> image size)
>
>
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I have been working on a program that uses Matplotlib to plot data
consisting of around one million points. Sometimes the plots succeed but
often I get an exception: OverFlowError: Agg rendering complexity exceeded.
I can make this message go away by plotting the data in "chunks" as
illustrated in the demo code below. However, the extra code is a chore
which I don't think should be necessary - I hope the developers will
be able to fix this issue sometime soon. I know that the development
version has some modifications to addressing this issue. I wonder if it is
expected to make the problem go away?
By the way, this plot takes about 30 seconds to render on my I7 2600k.
The main program reaches the show() statement quickly and prints
"Done plotting?". Then I see that the program reaches 100% usage
on one CPU core (4 real, 8 virtual on the 2600k) until the plot is
displayed. I wonder if there is any way to persuade Matplotlib to run
some of the chunks in parallel so as to use more CPU cores?
Plotting something other than random data, the plots run faster and
the maximum chunk size is smaller. The maximum chunk size
also depends on the plot size - it is smaller for larger plots. I am
wondering if I could use this to plot course and fine versions of the
plots. The course plot is zoomed in version of the small-sized raster.
That would be better than decimation as all the points would at least
be there.
Thanks in advance,
David
--------------------------- start code ---------------------------------
## Demo program shows how to "chunk" plots to avoid the exception:
##
##  OverflowError: Agg rendering complexity exceeded.
##  Consider downsampling or decimating your data.
##
## David Smith December 2011.
from pylab import *
import numpy as np
nPts=600100
x = np.random.rand(nPts)
y = np.random.rand(nPts)
## This seems to always succeed if Npts <= 20000, but fails
## for Npts > 30000. For points between, it sometimes succeeds
## and sometimes fails.
figure(1)
plot (x, y)
## Chunking the plot alway succeeds.
figure(2)
chunk_size=20000
iStarts=range(x.size/chunk_size)
for iStart in iStarts:
  print "Plotting chunk starting at %d\n" % iStart
  plot(x[iStart:iStart+chunk_size], y[iStart:iStart+chunk_size], '-b')
left_overs = nPts % chunk_size
if left_overs > 0:
  print "Leftovers %d points\n" % left_overs
  plot(x[-left_overs-1:], y[-left_overs-1:], '-r')
print "done plotting?"
show()
---------------------------------- end code ------------------------
Please don't reply to this post "It is rediculous to plot 1 million points on
screen". I am routinely capturing million-point traces from oscilloscopes and
other test equipment and to I need to be able to spot features in the
data (glitches if you will) that may not show up plotting decimated data.
I can then zoom the plot to inspect these features.

Hello,
I'm trying to plot the stresses in colour of a strained isoparametric
element.
I have a six noded triangle with vertice coordinates
[(xa1,ya1),(xa2,ya2),(xa3,ya3)] = pos_a
This triangle deforms and the new coordinate positions are
[(xb1,yb1),(xa2,yb2),(xb3,yb3)] = pos_b
The remaining nodes are mid nodes also with rest and deformed coordinate
positions.
To plot the edges of the triangle I use a Jacobian transformation function
so that the coordinates of the triangle are in Jacobian coordinates xi1 and
xi2 (with xi3 = 1 - xi1 - xi2). This is required as the elements are
quadratic with mid-nodes.
Each interval is hard coded so that:
xi1 = [1.0,0.9,0.8,0.7,0.6,0.5, etc..]
xi2 = [0.0,0.1,0.2,0.3,0.4,0.5, etc..]
I would like to plot the strains in colour so that the interior of the
triangle is filled but I don't want to hard code the Jacobian intervals as
this seems an awkward way of doing it.
With strain as a function of xi1 and xi2, How can matplotlib provide a
continuous interior strain plot of the triangle for all the xi1 and xi2
values from 0 to 1?
Regards
Alex Naysmith
My finite element program can be downloaded from here:
http://www.pynw.org.uk/Talks?action=AttachFile&do=view&target=2D_FEA.zip

Hello,
I am trying to make a figure with a text that includes H$\beta$ but for
some reason matplotlib would freeze. I cannot understand what's wrong with
$\beta$. I tried other Greek letters and some of them worked but not all.
Any suggestions?
Cheers,
Aycha