matplotlib-users Mailing List for matplotlib

Thanks Jeff,
I'll study your code and will see if I can adapt it for my use.
Great tool also,
Roger
--
On Fri, Dec 12, 2008 at 3:20 PM, Jeff Whitaker <js...@fa...> wrote:
> Roger André wrote:
>
>> Hi All,
>>
>> I'm very new to Matplotlib and am having some trouble getting a colorbar
>> to be positioned and sized I want it to. A big part of the problem is that
>> I have adapted several examples from the Cookbook and Gallery, just to see
>> if I could roughly approximate what I want to see, and now am having trouble
>> integrating the different pieces. Specifically, I can't seem to resolve
>> when to use add_subplot vs. add_axes. Below are 2 examples of code. The
>> first one shows correct layout of a data figure and a separate colorbar
>> below it. The colorbar is the correct size, and is located in the right
>> spot. The second example has the correct data mapped in it using the
>> basemap module, but I cannot get the colorbar to move up closer to the
>> figure, or to shrink it. Could someone advise me on this? I've looked at
>> the "Artist tutorial", and although it is very well written, I'm still not
>> sure how to get this done .
>>
>> Thanks in advance,
>>
>> Roger
>>
>
> Roger: I usually explicity two separate axes instances, one for the plot
> and one for the colorbar. The location of the axes always takes a bit of
> tweaking to get right. Here's an example:
>
> from mpl_toolkits.basemap import Basemap
> import matplotlib.pyplot as plt
> import numpy as np
> # create figure instance.
> fig = plt.figure(figsize=(8,8))
> # create an axes instance, leaving room for colorbars on right and bottom.
> ax = fig.add_axes([0.05,0.15,0.8,0.8]) # color on bottom
> # set up orthographic map projection with
> # perspective of satellite looking down at 50N, 100W.
> # use low resolution coastlines.
> map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='c')
> # draw coastlines, country boundaries, fill continents.
> map.drawcoastlines()
> # draw the edge of the map projection region (the projection limb)
> map.drawmapboundary()
> # draw lat/lon grid lines every 30 degrees.
> map.drawmeridians(np.arange(0,360,30))
> map.drawparallels(np.arange(-90,90,30))
> # make up some data on a regular lat/lon grid.
> nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
> lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
> lons = (delta*np.indices((nlats,nlons))[1,:,:])
> wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
> mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
> # compute native map projection coordinates of lat/lon grid.
> x, y = map(lons*180./np.pi, lats*180./np.pi)
> # contour data over the map.
> cs = map.contourf(x,y,wave+mean,15)
> # get axes bounds.
> pos = ax.get_position()
> l, b, w, h = pos.bounds
> # create axes instance for colorbar on right.
> cax = plt.axes([l+w+0.03, b, 0.04, h])
> # draw colorbar on right.
> plt.colorbar(cax=cax,orientation='vertical')
> # create axes instance for colorbar on bottom.
> cax = plt.axes([l, b-0.07, w, 0.04])
> # draw colorbar on bottom.
> plt.colorbar(cax=cax,orientation='horizontal')
> plt.show()
>
> HTH,
>
> -Jeff
>
>> -------------
>>
>> Example 1:
>>
>> #! /usr/bin/python
>>
>> import matplotlib.pyplot as plt
>> import numpy as np
>> from numpy.random import randn
>> from matplotlib import mpl
>>
>> # Make plot with horizontal colorbar
>>
>> fig = plt.figure(figsize=(7,8))
>> ax = fig.add_subplot(111)
>>
>> # 'add_axes' for color bar
>> ax1 = fig.add_axes([0.25, .07, 0.5, 0.03]) # [x_loc, y_loc, x_size,
>> y_size]
>>
>> data = np.clip(randn(250, 250), -1, 1) # DATA FOR SQUARE FIG
>> ax.imshow(data, interpolation='nearest') # DRAW DATA IN SQUARE FIG
>>
>> ax.set_title('Monthly PCP percentiles for 9-2008')
>>
>> ########### Colorbar Settings ########
>> cmap = mpl.cm.cool
>> norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)
>> cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm,
>> orientation='horizontal')
>> cb1.set_label('percentile')
>> ################################
>>
>> plt.show()
>> fig.savefig('test.png')
>>
>>
>> --------------------------------------------------------------------------------------------
>> Example 2:
>>
>> #! /usr/bin/python
>>
>> """taken from geos_demo_2.py"""
>>
>> from PIL import Image
>> from mpl_toolkits.basemap import Basemap
>> import numpy as np
>> import matplotlib
>> from matplotlib import mpl
>> import matplotlib.pyplot as plt
>> from matplotlib.image import pil_to_array
>>
>> plot_name = 'hydro_demo.png'
>> overlay_color = 'black'
>>
>> # read in jpeg image to rgb array
>> pilImage = Image.open('wms_mapser.png')
>>
>> #data = asarray(pilImage)
>> data = pil_to_array(pilImage)
>> data = data[:, :, :] # get data from RGB channels of image
>>
>> # define data region and projection parameters
>> ll_lon = -125
>> ll_lat = 39
>> ur_lon = -108
>> ur_lat = 54
>> lon_0 = 0
>>
>>
>> fig = plt.figure(figsize=(7,8))
>> #ax = fig.add_axes((0.1,0.1,0.8,0.8))
>> ax = fig.add_axes((.1,0.1,0.8,0.8))
>>
>> # create Basemap instance for cylindrical equidistant projection, htdro
>> image domain
>> m = Basemap(projection='cyl', lon_0=lon_0, llcrnrlon=ll_lon,
>> llcrnrlat=ll_lat, urcrnrlon=ur_lon,
>> urcrnrlat=ur_lat, suppress_ticks=False)
>>
>> # add data
>> cmap = mpl.cm.cool
>> m.imshow(data, cmap, interpolation=None)
>> plt.clim(0, 1)
>>
>> # add a colobar
>> plt.colorbar(orientation='horizontal')
>>
>> # add timestamp and save
>> fig = plt.gcf()
>>
>> # ADD FIGURE TEXT
>> fig.text(x=0.5, y=0.1,
>> s='percentile',
>> fontsize=10,
>> )
>>
>> fig.set_size_inches((7,7))
>>
>> # ADD A FIGURE TITLE
>> plt.title('Monthly PCP percentiles for 9-2008',y=1.05,fontsize=12)
>>
>> plt.show()
>> #fig.savefig(plot_name)
>> #print 'Plot saved to %s' % (plot_name)
>> ------------------------------------------------------------------------
>>
>>
>> ------------------------------------------------------------------------------
>> SF.Net email is Sponsored by MIX09, March 18-20, 2009 in Las Vegas,
>> Nevada.
>> The future of the web can't happen without you. Join us at MIX09 to help
>> pave the way to the Next Web now. Learn more and register at
>>
>> http://ad.doubleclick.net/clk;208669438;13503038;i?http://2009.visitmix.com/
>> ------------------------------------------------------------------------
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
> --
> Jeffrey S. Whitaker Phone : (303)497-6313
> Meteorologist FAX : (303)497-6449
> NOAA/OAR/PSD R/PSD1 Email : Jef...@no...
> 325 Broadway Office : Skaggs Research Cntr 1D-113
> Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg
>
>

Roger André wrote:
> Hi All,
>
> I'm very new to Matplotlib and am having some trouble getting a 
> colorbar to be positioned and sized I want it to. A big part of the 
> problem is that I have adapted several examples from the Cookbook and 
> Gallery, just to see if I could roughly approximate what I want to 
> see, and now am having trouble integrating the different pieces. 
> Specifically, I can't seem to resolve when to use add_subplot vs. 
> add_axes. Below are 2 examples of code. The first one shows correct 
> layout of a data figure and a separate colorbar below it. The 
> colorbar is the correct size, and is located in the right spot. The 
> second example has the correct data mapped in it using the basemap 
> module, but I cannot get the colorbar to move up closer to the figure, 
> or to shrink it. Could someone advise me on this? I've looked at the 
> "Artist tutorial", and although it is very well written, I'm still not 
> sure how to get this done .
>
> Thanks in advance,
>
> Roger
Roger: I usually explicity two separate axes instances, one for the 
plot and one for the colorbar. The location of the axes always takes a 
bit of tweaking to get right. Here's an example:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# create figure instance.
fig = plt.figure(figsize=(8,8))
# create an axes instance, leaving room for colorbars on right and bottom.
ax = fig.add_axes([0.05,0.15,0.8,0.8]) # color on bottom
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='c')
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines()
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary()
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30))
map.drawparallels(np.arange(-90,90,30))
# make up some data on a regular lat/lon grid.
nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
lons = (delta*np.indices((nlats,nlons))[1,:,:])
wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons*180./np.pi, lats*180./np.pi)
# contour data over the map.
cs = map.contourf(x,y,wave+mean,15)
# get axes bounds.
pos = ax.get_position()
l, b, w, h = pos.bounds
# create axes instance for colorbar on right.
cax = plt.axes([l+w+0.03, b, 0.04, h])
# draw colorbar on right.
plt.colorbar(cax=cax,orientation='vertical')
# create axes instance for colorbar on bottom.
cax = plt.axes([l, b-0.07, w, 0.04])
# draw colorbar on bottom.
plt.colorbar(cax=cax,orientation='horizontal')
plt.show()
HTH,
-Jeff
> -------------
>
> Example 1:
>
> #! /usr/bin/python
>
> import matplotlib.pyplot as plt
> import numpy as np
> from numpy.random import randn
> from matplotlib import mpl
>
> # Make plot with horizontal colorbar
>
> fig = plt.figure(figsize=(7,8))
> ax = fig.add_subplot(111)
>
> # 'add_axes' for color bar
> ax1 = fig.add_axes([0.25, .07, 0.5, 0.03]) # [x_loc, y_loc, x_size, 
> y_size]
>
> data = np.clip(randn(250, 250), -1, 1) # DATA FOR SQUARE FIG
> ax.imshow(data, interpolation='nearest') # DRAW DATA IN SQUARE FIG
>
> ax.set_title('Monthly PCP percentiles for 9-2008')
>
> ########### Colorbar Settings ########
> cmap = mpl.cm.cool
> norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)
> cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, 
> orientation='horizontal')
> cb1.set_label('percentile')
> ################################
>
> plt.show()
> fig.savefig('test.png')
>
> --------------------------------------------------------------------------------------------
> Example 2:
>
> #! /usr/bin/python
>
> """taken from geos_demo_2.py"""
>
> from PIL import Image
> from mpl_toolkits.basemap import Basemap
> import numpy as np
> import matplotlib
> from matplotlib import mpl
> import matplotlib.pyplot as plt
> from matplotlib.image import pil_to_array
>
> plot_name = 'hydro_demo.png'
> overlay_color = 'black'
>
> # read in jpeg image to rgb array
> pilImage = Image.open('wms_mapser.png')
>
> #data = asarray(pilImage)
> data = pil_to_array(pilImage)
> data = data[:, :, :] # get data from RGB channels of image
>
> # define data region and projection parameters
> ll_lon = -125
> ll_lat = 39
> ur_lon = -108
> ur_lat = 54
> lon_0 = 0
>
>
> fig = plt.figure(figsize=(7,8))
> #ax = fig.add_axes((0.1,0.1,0.8,0.8))
> ax = fig.add_axes((.1,0.1,0.8,0.8))
>
> # create Basemap instance for cylindrical equidistant projection, 
> htdro image domain
> m = Basemap(projection='cyl', lon_0=lon_0, llcrnrlon=ll_lon, 
> llcrnrlat=ll_lat, urcrnrlon=ur_lon,
> urcrnrlat=ur_lat, suppress_ticks=False)
>
> # add data
> cmap = mpl.cm.cool
> m.imshow(data, cmap, interpolation=None)
> plt.clim(0, 1)
>
> # add a colobar
> plt.colorbar(orientation='horizontal')
>
> # add timestamp and save
> fig = plt.gcf()
>
> # ADD FIGURE TEXT
> fig.text(x=0.5, y=0.1,
> s='percentile',
> fontsize=10,
> )
>
> fig.set_size_inches((7,7))
>
> # ADD A FIGURE TITLE
> plt.title('Monthly PCP percentiles for 9-2008',y=1.05,fontsize=12)
>
> plt.show()
> #fig.savefig(plot_name)
> #print 'Plot saved to %s' % (plot_name)
> ------------------------------------------------------------------------
>
> ------------------------------------------------------------------------------
> SF.Net email is Sponsored by MIX09, March 18-20, 2009 in Las Vegas, Nevada.
> The future of the web can't happen without you. Join us at MIX09 to help
> pave the way to the Next Web now. Learn more and register at
> http://ad.doubleclick.net/clk;208669438;13503038;i?http://2009.visitmix.com/
> ------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jef...@no...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

Hi All,
I'm very new to Matplotlib and am having some trouble getting a colorbar to
be positioned and sized I want it to. A big part of the problem is that I
have adapted several examples from the Cookbook and Gallery, just to see if
I could roughly approximate what I want to see, and now am having trouble
integrating the different pieces. Specifically, I can't seem to resolve
when to use add_subplot vs. add_axes. Below are 2 examples of code. The
first one shows correct layout of a data figure and a separate colorbar
below it. The colorbar is the correct size, and is located in the right
spot. The second example has the correct data mapped in it using the
basemap module, but I cannot get the colorbar to move up closer to the
figure, or to shrink it. Could someone advise me on this? I've looked at
the "Artist tutorial", and although it is very well written, I'm still not
sure how to get this done.
Thanks in advance,
Roger
-------------
Example 1:
#! /usr/bin/python
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import randn
from matplotlib import mpl
# Make plot with horizontal colorbar
fig = plt.figure(figsize=(7,8))
ax = fig.add_subplot(111)
# 'add_axes' for color bar
ax1 = fig.add_axes([0.25, .07, 0.5, 0.03]) # [x_loc, y_loc, x_size, y_size]
data = np.clip(randn(250, 250), -1, 1) # DATA FOR SQUARE FIG
ax.imshow(data, interpolation='nearest') # DRAW DATA IN SQUARE FIG
ax.set_title('Monthly PCP percentiles for 9-2008')
########### Colorbar Settings ########
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm,
orientation='horizontal')
cb1.set_label('percentile')
################################
plt.show()
fig.savefig('test.png')
--------------------------------------------------------------------------------------------
Example 2:
#! /usr/bin/python
"""taken from geos_demo_2.py"""
from PIL import Image
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib
from matplotlib import mpl
import matplotlib.pyplot as plt
from matplotlib.image import pil_to_array
plot_name = 'hydro_demo.png'
overlay_color = 'black'
# read in jpeg image to rgb array
pilImage = Image.open('wms_mapser.png')
#data = asarray(pilImage)
data = pil_to_array(pilImage)
data = data[:, :, :] # get data from RGB channels of image
# define data region and projection parameters
ll_lon = -125
ll_lat = 39
ur_lon = -108
ur_lat = 54
lon_0 = 0
fig = plt.figure(figsize=(7,8))
#ax = fig.add_axes((0.1,0.1,0.8,0.8))
ax = fig.add_axes((.1,0.1,0.8,0.8))
# create Basemap instance for cylindrical equidistant projection, htdro
image domain
m = Basemap(projection='cyl', lon_0=lon_0, llcrnrlon=ll_lon,
llcrnrlat=ll_lat, urcrnrlon=ur_lon,
 urcrnrlat=ur_lat, suppress_ticks=False)
# add data
cmap = mpl.cm.cool
m.imshow(data, cmap, interpolation=None)
plt.clim(0, 1)
# add a colobar
plt.colorbar(orientation='horizontal')
# add timestamp and save
fig = plt.gcf()
# ADD FIGURE TEXT
fig.text(x=0.5, y=0.1,
 s='percentile',
 fontsize=10,
 )
fig.set_size_inches((7,7))
# ADD A FIGURE TITLE
plt.title('Monthly PCP percentiles for 9-2008',y=1.05,fontsize=12)
plt.show()
#fig.savefig(plot_name)
#print 'Plot saved to %s' % (plot_name)

Hi Everyone,
I am running into a problem when trying to install 0.98.5 egg on a Mac OS X 
machine. Any help much appreciated. Relevant error messages follow:
Thanks
-Kaushik
sudo easy_install 
matplotlib-0.98.5-py2.5-macosx-10.3.egg**********************************************************************
============================================================================
BUILDING MATPLOTLIB
 matplotlib: 0.98.5
 python: 2.5.2 (r252:60911, Feb 22 2008, 07:57:53) [GCC
 4.0.1 (Apple Computer, Inc. build 5363)]
 platform: darwin
REQUIRED DEPENDENCIES
 numpy: 1.2.1
 freetype2: found, but unknown version (no pkg-config)
OPTIONAL BACKEND DEPENDENCIES
 libpng: found, but unknown version (no pkg-config)
 Tkinter: Tkinter: 50704, Tk: 8.4, Tcl: 8.4
 wxPython: no
 * wxPython not found
 Gtk+: no
 * Building for Gtk+ requires pygtk; you must be able
 * to "import gtk" in your build/install environment
 Mac OS X native: yes
 Qt: no
 Qt4: no
 Cairo: no
OPTIONAL DATE/TIMEZONE DEPENDENCIES
 datetime: present, version unknown
 dateutil: matplotlib will provide
 pytz: 2008c
OPTIONAL USETEX DEPENDENCIES
 dvipng: no
 ghostscript: /bin/sh: gs: command not found
 latex: no
EXPERIMENTAL CONFIG PACKAGE DEPENDENCIES
 configobj: matplotlib will provide
 enthought.traits: no
[Edit setup.cfg to suppress the above messages]
============================================================================
error: lib/matplotlib/mpl-data/matplotlib.conf.template: No such file or directory
Exception exceptions.OSError: (2, 'No such file or directory', 'src/image.cpp') 
in <bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 
0xf61120>> ignored
Exception exceptions.OSError: (2, 'No such file or directory', 'src/path.cpp') 
in <bound method CleanUpFile.__del__ of <setupext.CleanUpFile instance at 
0xf48968>> ignored
Exception exceptions.OSError: (2, 'No such file or directory', 
'src/backend_agg.cpp') in <bound method CleanUpFile.__del__ of 
<setupext.CleanUpFile instance at 0xf48c38>> ignored
 From within 
ipython:***************************************************************************************************
In [1]: import pylab
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/Users/kghose/<ipython console> in <module>()
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/pylab.py 
in <module>()
----> 1 from matplotlib.pylab import *
 2 import matplotlib.pylab
 3 __doc__ = matplotlib.pylab.__doc__
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/pylab.py 
in <module>()
 204 from numpy import ma
 205
--> 206 from matplotlib import mpl # pulls in most modules
 207
 208 from matplotlib.dates import date2num, num2date,\
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/mpl.py 
in <module>()
 2 from matplotlib import axis
----> 3 from matplotlib import axes
 4 from matplotlib import cbook
 5 from matplotlib import collections
 6 from matplotlib import colors
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/axes.py 
in <module>()
 16 import matplotlib.dates as mdates
 17 import matplotlib.font_manager as font_manager
---> 18 import matplotlib.image as mimage
 19 import matplotlib.legend as mlegend
 20 import matplotlib.lines as mlines
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/image.py 
in <module>()
 17 # For clarity, names from _image are given explicitly in this module:
 18 from matplotlib import _image
---> 19 from matplotlib import _png
 20
 21 # For user convenience, the names from _image are also imported into
ImportError: 
dlopen(/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/_png.so, 
2): Library not loaded: /usr/local/lib/libpng12.0.dylib
 Referenced from: 
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib-0.98.5-py2.5-macosx-10.3.egg/matplotlib/_png.so
 Reason: image not found
In [2]:

Jouni K. Seppänen skrev:
> Michael Droettboom <md...@st...> writes:
> 
>> Jörgen Stenarson wrote:
>>> I tried to use usetex to generate my pdf figures but I got a crash 
>>> when saving the figure, log attached. I traced the crash to 
>>> find_tex_file(), apparently ' can not be used to quote filenames in 
>>> the windows shell it has to be ". In my patch I just changed it to 
>>> always use " I don't know if that works on other systems.
>> Someone who knows about usetex should review and apply this patch.
> 
> In Unix shells ' is the better quoting character because all sorts of
> things have special meaning within " characters... but I changed it to
> use subprocess.Popen instead, so we shouldn't need to worry about shell
> quoting at all.
> 
> Jörgen: Thanks for your report, which I think is the first one from a
> Windows user using usetex with the pdf backend. Can you check that the
> latest version on the trunk works (either by updating from svn or by
> applying the attached patch)? Have you run into any other problems? What
> TeX distribution are you using?
> 
It works from current svn. I'm running miktex2.7. I have seen some font 
mismatch problems but I'm not sure they are bugs. But I will get back to 
you on those.
/Jörgen

Dear Jeff,
2008年12月12日 Jeff Whitaker <js...@fa...>:
> Mauro: The image flipping looks to be a Wx backend bug - it doesn't happen
> if you use WxAgg instead.
Thank you very much. It works quite well with WxAgg. Incidentally, the
WXAgg backend also provides for a slightly better and faster rendering
(not of the Blue Marble itself, but of usual maps).
> P.S. It's not recommended to use tabs in python code. Especially don't mix
> tabs and spaces (http://www.python.org/dev/peps/pep-0008/).
I do agree that tabs and spaces have *never* to be mixed. The mixing
of tabs and spaces may be a misconfiguration of the IDE I use
(DrPython). Have to check.
With best regards,
-- 
Dr. Mauro J. Cavalcanti
Ecoinformatics Studio
P.O. Box 46521, CEP 20551-970
Rio de Janeiro, RJ, BRASIL
E-mail: mau...@gm...
Web: http://studio.infobio.net
Linux Registered User #473524 * Ubuntu User #22717
"Life is complex. It consists of real and imaginary parts."

We have just released a new version of matplotlib, available for download at
 
https://sourceforge.net/project/showfiles.php?group_id=80706&package_id=278194&release_id=646644
It is a simple bugfix release to fix a number of critical bugs found in 
0.98.4.
These "what's new" release notes, with graphs and links, are available 
in html at
 http://matplotlib.sourceforge.net/users/whats_new.html
Thanks to Charlie Moad for testing and preparing the source release,
including binaries for OS X and Windows for python 2.4 and 2.5 (2.6
and 3.0 will not be available until numpy is available on those
releases). Thanks to the many developers who contributed to this
release, with contributions from Jae-Joon Lee, Michael Droettboom,
Ryan May, Eric Firing, Manuel Metz, Jouni K. Seppaenen, Jeff Whitaker,
Darren Dale, David Kaplan, Michiel de Hoon and many others who
submitted patches
What's new in 0.98.5
==============================
It's only been a matter of days since 0.98.4, but there were a number of 
critical bugs that warranted a new release.
2008年12月11日 Use subprocess.Popen instead of os.popen in dviread
 (Windows problem reported by Jorgen Stenarson) - JKS
2008年12月10日 Added Michael's font_manager fix and Jae-Joon's
 figure/subplot fix. Bumped version number to 0.98.5 - JDH
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

On 12/12/2008 7:48 AM Jeff Whitaker apparently wrote:
> It's not recommended to use tabs in python code.
That is project dependent. And a religious war.
(Note that tabs have many advantages.
E.g., explicit is better than implicit,
and one tab is explicitly one level on indent.)
> Especially don't mix tabs and spaces (http://www.python.org/dev/peps/pep-0008/).
True enough. (For indenting.)
Maybe PEP 666 should have been accepted.
http://www.python.org/dev/peps/pep-0666/
Alan Isaac

It's very nice to have a native macosx backend. Many thanks to Michiel.
It does seem to work with ipython fine.
However, at present, it's not always faster than WXAgg.
With matplotlib svn r6575, python 2.5.2, everything compiled with
gcc4.0, 10.5.5, macbook pro 2.33GHz, 2GB memory
(i) interactive zooming of the plot window seems very slow
(ii) in scripts, pcolormesh and pcolor seem much slower than WXAgg,
though ax.pcolorfast is faster..
on a 1000x200 array:
pcolormesh:
[242-132:~/Python] agn% time python testspeed.py -dmacosx
32.717u 1.479s 0:43.76 78.1%	0+0k 0+23io 0pf+0w
242-132:~/Python] agn% time python testspeed.py -dWXAgg
1.226u 0.444s 0:05.80 28.6%	0+0k 0+25io 0pf+0w
pcolor:
[242-132:~/Python] agn% time python testspeed3.py -dmacosx
53.652u 2.099s 2:09.58 43.0%	0+0k 0+154io 0pf+0w
[242-132:~/Python] agn% time python testspeed3.py -dWXAgg
26.775u 1.542s 0:32.25 87.7%	0+0k 0+35io 0pf+0w
ax.pcolorfast:
[242-132:~/Python] agn% time python testspeed2.py -dmacosx
0.580u 0.331s 0:04.07 22.3%	0+0k 0+2io 0pf+0w
[242-132:~/Python] agn% time python testspeed2.py -dWXAgg
0.988u 0.423s 0:04.70 29.7%	0+0k 0+11io 0pf+0w
(iii) explicit creation of figure and axes instances seems to slow it
down radically in ipython:
e.g
In [3]: a= numpy.random.rand(100,200)
.. a few creations of plots and theitr removal, to get started..
With WxAgg backend:
In [8]: time pcolormesh(a)
CPU times: user 0.22 s, sys: 0.03 s, total: 0.25 s
Wall time: 0.25 s
Out[9]: <matplotlib.collections.QuadMesh object at 0x19661330>
With macosx backend:
In [39]: time pcolormesh(a)
CPU times: user 0.05 s, sys: 0.01 s, total: 0.06 s
Wall time: 0.08 s
so, 3x as fast.
However, explicitly creating the figure is much slower on macosx:
In [26]: time fig=figure();ax=fig.add_subplot(1,1,1);ax.pcolormesh(a);draw()
CPU times: user 3.33 s, sys: 0.03 s, total: 3.36 s
Wall time: 3.41 s
In [37]: time fig=figure();ax=fig.add_subplot(1,1,1);pcolormesh(a);draw()
CPU times: user 3.39 s, sys: 0.03 s, total: 3.42 s
Wall time: 3.46 s
while with wxagg there is little change in speed:
In [16]: time fig=figure();ax=fig.add_subplot(1,1,1);ax.pcolormesh(a);draw()
CPU times: user 0.21 s, sys: 0.03 s, total: 0.25 s
Wall time: 0.25 s
--George Nurser.

Mauro Cavalcanti wrote:
> Dear Jeff & ALL,
>
> Attached is the latest version of my Basemap embedded in wxPython
> sample application. I have added a check menu option that allows one
> to toggle the overlay of the Blue Marble image on and off the Basemap
> figure. Everything works well -- except that the Blue Marble image is
> plotted upside down! I could not figure out the cause of this, say,
> rather bizarre behaviour. Any hints?
>
> Thanks in advance for any assistance you can provide.
>
> Best regards,
>
> PS This version has another known bug, to be eventually fixed -- if
> the user has plotted a point coordinate file, the points are erased if
> the Blue Marble overlay is requested because the PlotMap() routine
> calls ax.cla() at the start.
>
> 
Mauro: The image flipping looks to be a Wx backend bug - it doesn't happen if you use WxAgg instead.
-Jeff
P.S. It's not recommended to use tabs in python code. Especially don't mix tabs and spaces (http://www.python.org/dev/peps/pep-0008/).
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
NOAA/OAR/CDC R/PSD1 FAX : (303)497-6449
325 Broadway Boulder, CO, USA 80305-3328

+1 for including Brian's changes in the shipping example.
Brian, You might also be interested in an alternate, polygon-based
lasso I developed a while back. Though it meets my needs, beware of
backend-specific problems with idle events that I never resolved.
-Eric
http://www.nabble.com/Alternate-lasso:-click-to-form-polygon-td18724261.html
On Sun, Dec 7, 2008 at 10:24 AM, B Clowers <clo...@ya...> wrote:
> When running the initial lasso demo I found that only one lasso event was
> allowed and other canvas interaction events (e.g. zooming) would not work
> properly after the lasso event. As such I came up with my own solution to
> the problem by modifying the example source. The primary addition was a
> button release event call and a boolean to keep track whether the lasso was
> the last event to lock the widget from interaction. Anyway, I hope it can
> help someone else out.
>
> Cheers,
>
> Brian
>
> ####################################
> """
> Show how to use a lasso to select a set of points and get the indices
> of the selected points. A callback is used to change the color of the
> selected points
>
> This is currently a proof-of-concept implementation (though it is
> usable as is). There will be some refinement of the API and the
> inside polygon detection routine.
> """
> from matplotlib.widgets import Lasso
> import matplotlib.mlab
> from matplotlib.nxutils import points_inside_poly
> from matplotlib.colors import colorConverter
> from matplotlib.collections import RegularPolyCollection
>
> from matplotlib.pyplot import figure, show
> from numpy import nonzero
> from numpy.random import rand
>
> class Datum:
> colorin = colorConverter.to_rgba('red')
> colorout = colorConverter.to_rgba('green')
> def __init__(self, x, y, include=False):
> self.x = x
> self.y = y
> if include: self.color = self.colorin
> else: self.color = self.colorout
>
>
> class LassoManager:
> def __init__(self, ax, data):
> self.axes = ax
> self.canvas = ax.figure.canvas
> self.data = data
> #the lasso lock boolean is used to tell whether another
> #widget event has priority
> self.lassoLock = False
>
> self.Nxy = len(data)
>
> facecolors = [d.color for d in data]
> self.xys = [(d.x, d.y) for d in data]
> fig = ax.figure
> self.collection = RegularPolyCollection(
> fig.dpi, 6, sizes=(100,),
> facecolors=facecolors,
> offsets = self.xys,
> transOffset = ax.transData)
>
> ax.add_collection(self.collection)
>
> self.cid = self.canvas.mpl_connect('button_press_event',
> self.onpress)
> self.cidRelease = self.canvas.mpl_connect('button_release_event',
> self.onrelease)
>
> self.ind = None
>
> def callback(self, verts):
> facecolors = self.collection.get_facecolors()
> ind = nonzero(points_inside_poly(self.xys, verts))[0]
> for i in range(self.Nxy):
> if i in ind:
> facecolors[i] = Datum.colorin
> else:
> facecolors[i] = Datum.colorout
>
> self.canvas.draw_idle()
> self.canvas.widgetlock.release(self.lasso)
> #del self.lasso
> self.ind = ind
>
> def onpress(self, event):
> if self.canvas.widgetlock.locked(): return
> if event.inaxes is None: return
> self.lasso = Lasso(event.inaxes, (event.xdata, event.ydata),
> self.callback)
> # acquire a lock on the widget drawing
> self.canvas.widgetlock(self.lasso)
> # establish boolean that can be used to release the widgetlock
> self.lassoLock = True
>
> def onrelease(self, event):
> 'on release we reset the press data'
> # test whether the widgetlock was initiated by the lasso
> if self.lassoLock:
> self.canvas.widgetlock.release(self.lasso)
> self.lassoLock = False
> print self.ind
>
>
> if __name__ == '__main__':
>
> data = [Datum(*xy) for xy in rand(100, 2)]
>
> fig = figure()
> ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
> lman = LassoManager(ax, data)
>
> show()
>
>
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>

Good Day! 
In the course of testing two ode solvers (stiff and non-stiff) I noticed
that the figures that were saved (either eps or pdf) were fuzzy. The figures
produced by the command pylab.plot(xdata, ydata) followed by pylab.show()
were essentially smooth. When I zoomed in all lines were straight and I
could distinguish where the ode solver was making steps. But the saved
figures are completely different. Sure, the curve follows the same
trajectory as the original plot it was produced from, but they line itself
is fuzzy. I've attached a sample: 
http://www.nabble.com/file/p20970084/testode.rk45.a0.99.eps1e-07.pdf
testode.rk45.a0.99.eps1e-07.pdf . This comes from a completely deterministic
ode. But is looks like I've added a tiny amount of noise.
On a technical note, I'm running Ubuntu 8.04, python2.5.1, matplotlib0.91.2
(with GTKAgg backend). 
(Hopefully I didn't miss a similar question--and solution--elsewhere in the
forum.)
Thanks for your help,
-Jesse
-- 
View this message in context: http://www.nabble.com/Fuzzy-plots-of-ode-solutions-tp20970084p20970084.html
Sent from the matplotlib - users mailing list archive at Nabble.com.