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John Hunter wrote: >>>>>>"Géza" == Géza Groma <gr...@nu...> writes: > > Géza> Is there any other solution better then modifying two > Géza> modules of separate packages? > > Probably not -- numpy should probably remove this -- you might want to > email their list. Building extensions for 2.4 with Mingw is not 100% reliable no matter how it's done. Mingw has been written to use MSVCRT.dll as its C runtime. Python 2.4 uses MSVCR71.dll as its C runtime. If you leave out the 'msvcr71' library and use an out-of-box Mingw installation, then you will get two different C runtimes. If a FILE pointer passes between them (or any other structure provided by the C runtime), you will crash the interpreter. However, the headers for Mingw have been written against MSVCRT.dll and reference things not present in MSVCR71.dll (in this case _ctype, a jump table used by isalpha() and friends IIRC). There is currently no general solution that will work for all extensions. The only general solution is to modify Mingw to use MSVCR71.dll as its runtime. But no one seems to be working on that. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco
>>>>> "Eric" == Eric Emsellem <ems...@ob...> writes: Eric> great! a quick one then: how would you then do to have a Eric> marker symbols where some part of the polygon is not Eric> attached to the rest (a vertical line plus an ellipse for Eric> example). I could do that using two different markers and Eric> plotting them one after the other, but is there a simpler Eric> way? thanks Eric Just use the brute force approach for now, and when I get some time I'll look into generalizing this for general paths. JDH
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content="text/html;charset=ISO-8859-1" http-equiv="Content-Type"> <title></title> </head> <body bgcolor="#ffffff" text="#000000"> great!<br> <br> a quick one then: how would you then do to have a marker symbols where some part of the polygon is not attached to the rest (a vertical line plus an ellipse for example). I could do that using two different markers and plotting them one after the other, but is there a simpler way?<br> <br> thanks<br> Eric<br> <br> John Hunter wrote: <blockquote cite="mid...@pe..." type="cite"> <blockquote type="cite"> <blockquote type="cite"> <blockquote type="cite"> <blockquote type="cite"> <blockquote type="cite"> <pre wrap="">"Eric" == Eric Emsellem <a class="moz-txt-link-rfc2396E" href="mailto:ems...@ob..."><ems...@ob...></a> writes: </pre> </blockquote> </blockquote> </blockquote> </blockquote> </blockquote> <pre wrap=""><!----> Eric> Hi again, I am trying to see if I could produce a scatter Eric> plot (using "scatter" or "plot") but using NEW symbols (so Eric> not already available in the list provided). I would like to Eric> design new symbols (in some way which is to be defined) so Eric> that scatter and/or plot would be able to use them. Eric> Examples: horizontal or vertical or even rotated ellipses, Eric> icon sketched galaxy-looking symbols, ... Eric> Any hint on how to do this? Eric> thanks in advance for any help here! Eric> Eric P.S.: I already posted a similar question a few days Eric> ago, so forgive me for this repetition... persistence wins the day! I added an option to scatter to support custom markers. You pass in a list if x,y vertices for the polygon you want to use as the marker. I haven't quite worked out the scaling yet, so for now use scale arguments that look right and understand that this may change when we get the scaling issue cleaned up. I added an example to svn revision 2402 examples/scatter_custom_symbol.py. Here it is, using an ellipse from pylab import figure, nx, show # unit area ellipse rx, ry = 3., 1. area = rx * ry * nx.pi theta = nx.arange(0, 2*nx.pi+0.01, 0.1) verts = zip(rx/area*nx.cos(theta), ry/area*nx.sin(theta)) x,y,s,c = nx.rand(4, 30) s*= 10**2. fig = figure() ax = fig.add_subplot(111) ax.scatter(x,y,s,c,marker=None,verts =verts) show() One thing we can do to make this more user friendly is to add new symbols to the scatter symbol table, eg 'wellipse' : a wide ellipse 'tellipse' : a tall ellipse and map names to sequences of vertices. So as you create the custom symbols you want to use, send them to me and I'll add them to the defaults, where appropriate. Cheers, JDH </pre> </blockquote> <br> <pre class="moz-signature" cols="72">-- ==================================================================== Eric Emsellem <a class="moz-txt-link-abbreviated" href="mailto:ems...@ob...">ems...@ob...</a> Centre de Recherche Astrophysique de Lyon 9 av. Charles-Andre tel: +33 (0)4 78 86 83 84 69561 Saint-Genis Laval Cedex fax: +33 (0)4 78 86 83 86 France <a class="moz-txt-link-freetext" href="http://www-obs.univ-lyon1.fr/eric.emsellem">http://www-obs.univ-lyon1.fr/eric.emsellem</a> ==================================================================== </pre> </body> </html>
>>>>> "Eric" == Eric Emsellem <ems...@ob...> writes: Eric> Hi again, I am trying to see if I could produce a scatter Eric> plot (using "scatter" or "plot") but using NEW symbols (so Eric> not already available in the list provided). I would like to Eric> design new symbols (in some way which is to be defined) so Eric> that scatter and/or plot would be able to use them. Eric> Examples: horizontal or vertical or even rotated ellipses, Eric> icon sketched galaxy-looking symbols, ... Eric> Any hint on how to do this? Eric> thanks in advance for any help here! Eric> Eric P.S.: I already posted a similar question a few days Eric> ago, so forgive me for this repetition... persistence wins the day! I added an option to scatter to support custom markers. You pass in a list if x,y vertices for the polygon you want to use as the marker. I haven't quite worked out the scaling yet, so for now use scale arguments that look right and understand that this may change when we get the scaling issue cleaned up. I added an example to svn revision 2402 examples/scatter_custom_symbol.py. Here it is, using an ellipse from pylab import figure, nx, show # unit area ellipse rx, ry = 3., 1. area = rx * ry * nx.pi theta = nx.arange(0, 2*nx.pi+0.01, 0.1) verts = zip(rx/area*nx.cos(theta), ry/area*nx.sin(theta)) x,y,s,c = nx.rand(4, 30) s*= 10**2. fig = figure() ax = fig.add_subplot(111) ax.scatter(x,y,s,c,marker=None,verts =verts) show() One thing we can do to make this more user friendly is to add new symbols to the scatter symbol table, eg 'wellipse' : a wide ellipse 'tellipse' : a tall ellipse and map names to sequences of vertices. So as you create the custom symbols you want to use, send them to me and I'll add them to the defaults, where appropriate. Cheers, JDH
>>>>> "G=E9za" =3D=3D G=E9za Groma <gr...@nu...> write= s: G=E9za> Is there any other solution better then modifying two G=E9za> modules of separate packages? Probably not -- numpy should probably remove this -- you might want to email their list. JDH
I tried to install matplotlib from svn (2398) on a system of WindowsXP, python-2.4.2, numpy-0.9.6r1, MinGW-5.0.2. Even applying the hack described in http://mail.python.org/pipermail/python-list/2004-December/254826.html, running from pylab import * caused the error message the procedure entry point _ctype could not be located in the dynamic link libary msvcr71.dll I realized that matplotib relies on the distutils package of numpy which in turn has its own mingw32ccompiler.py module and found that the problem can be cured by commenting out the following lines in Mingw32CCompiler.link if sys.version[:3] > '2.3': if libraries: libraries.append('msvcr71') else: libraries =3D ['msvcr71'] Is there any other solution better then modifying two modules of separate packages? Regargs, G=E9za Groma Institute of Biophysics Biological Research Center of the Hungarian Academy of Sciences Temesv=E1ri krt. 62. 6701 Szeged, Hungary phone: +36 62 599 620 fax: +36 62 433 133 cellular: +36 20 5648 303
Hi again, I am trying to see if I could produce a scatter plot (using "scatter" or "plot") but using NEW symbols (so not already available in the list provided). I would like to design new symbols (in some way which is to be defined) so that scatter and/or plot would be able to use them. Examples: horizontal or vertical or even rotated ellipses, icon sketched galaxy-looking symbols, ... Any hint on how to do this? thanks in advance for any help here! Eric P.S.: I already posted a similar question a few days ago, so forgive me for this repetition... -- ==================================================================== Eric Emsellem ems...@ob... Centre de Recherche Astrophysique de Lyon 9 av. Charles-Andre tel: +33 (0)4 78 86 83 84 69561 Saint-Genis Laval Cedex fax: +33 (0)4 78 86 83 86 France http://www-obs.univ-lyon1.fr/eric.emsellem ====================================================================
I have added examples/multi_image.py to svn in response to Marcel Oliver's question. In the process, I found and fixed a bug in the new colorbar (also only in svn); now the ticks are labelled with the order of magnitude when the colors are mapped to very small or very large numbers, in the same way as they are for any other axes object. Eric
Hi, I think I have a Mac OS X specific problem using 'ipython -pylab' I have some pylab plotting that updates for every event on ubuntu with the wxpython backend. However, the same code on OS X does not update until the final pylab.show(). However, when I enter a series of plot commands from the ipython prompt, they update fine. I'm presuming it's some kind of threading problem, so my question is; is there a way to explicitly give app.mainloop() or whatever some oxygen? Here's the example code which updates each plot separately as expected/desired under ubuntu +python2.4 + wxPython2.6 + ipython 0.6.x, matplotlib-0.86.2 Under Mac OS 10.4 + python 2.4.1 + wxpython2.6 + ipython 0.7.1.fix1 (didn't work for ipython 0.6.x either) + matplotlib-0.86.2cvs-py2.4- macosx-10.4-ppc.egg, the update does not occur until the pylab.show() statement is reached (and yes, I tried replacing the draw with show, no difference). It was a lot faster when I removed the pylab.draw() statement, so the rendering would seem to be taking place, it just doesn't make it to the physical screen. Have any other Mac OS X users experienced this? Any suggestions? regards, Graeme % ipython -pylab In [1]:import interactive as i In [2]:i.main() isinteractive() = True here is interactive.py #!/usr/bin/env python # # ipython -pylab # In [1]: import interactive as i # In [2]: i.main() # import pylab, numpy class data : def __init__(self, N) : self.n = 0 self.N = N self.t = numpy.arange(0.0, 2*numpy.pi, numpy.pi / 64) #end def read(self) : self.n += 1 self.y = numpy.sin(self.t) + numpy.randn(len(self.t))/10.0 if self.n >= self.N : return False else : return True #end #end def main(N = 100) : print 'isinteractive() = ', pylab.interactive() d = data(N) f = pylab.figure() a = f.add_subplot(1,1,1) cols = 'rgbcmyk' n = 0 while d.read() : # a.hold(False) a.plot(d.t, d.y, cols[n % len(cols)]) a.grid(True) pylab.draw() n += 1 #end pylab.show() #end if __name__ == '__main__' : main(10) #end
>>>>> "Joseph" == <jo...@3t...> writes: Joseph> Hi, Joseph> I'm plotting y values which exceed 10000. I would like the Joseph> y axis labels to be formatted as whole numbers such as Joseph> 10000, not in scientific notation such as 1.0e4, with the Joseph> exponent appearing up at the top left corner. This occurs Joseph> if I plot something like: Joseph> from pylab import * y = (1000,9000,14000) plot(y) show() Joseph> I've looked around the docs and source a bit, but haven't Joseph> found the love yet. I'm running version 0.86.2. \ See the chapter in the user's guide on tick labeling and formatting. The code below is freestyle and untested, so may contain minor bugs, but here is the idea from matplotlib.ticker import ScalarFormatter ax = subplot(111) ax.plot(something) ax.yaxis.set_major_formatter(ScalarFormatter()) Darren: some pylab functionality with a dead-simple interface would be nice here, something that let us say something like yticks(formatter='scalar') and so on to select the formatter or the locator.... JDH
Hi, I'm plotting y values which exceed 10000. I would like the y axis labels to be formatted as whole numbers such as 10000, not in scientific notation such as 1.0e4, with the exponent appearing up at the top left corner. This occurs if I plot something like: from pylab import * y = (1000,9000,14000) plot(y) show() I've looked around the docs and source a bit, but haven't found the love yet. I'm running version 0.86.2. Any help would be appreciated. Thanks! Joseph Sheedy Technical Specialist, 3TIER Environmental Forecast Group jo...@3t... | (206)325-1573 x116
>>>>> "Frederic" == Frederic Back <fre...@gm...> writes: Frederic> On Thu, 2006年05月18日 at 15:57 -0500, John Hunter wrote: >> You can comment out the code in backends/backend_gtk.py Frederic> Well, this seems like a bug to me, then. Matplotlib Frederic> offers the possibility to draw to a file instead of Frederic> opening a window. In this case, the default window icon Frederic> should *not* get overridden. FYI, if all you are using matplotlib for is to write to a file, you can just use the Agg backend (or PS or SVG or whatever) and GTK will never be imported and the icon won't be touched. As an aside, if you are writing a GTK plugin you probably shouldn't be using pylab at all, but rather the OO classes directly, as in examples/embedding_in_gtk*.py. Frederic> The best way to patch this would be to set the default Frederic> window icon only when the gtk main loop is entered. Frederic> I attached a patch against the current SVN. It simply Frederic> moves the window icon declaration just before the line Frederic> where gtk.main() gets called. I don't think this is the right place to put the patch -- "show" is for pylab only, and it may be that application developers (me for one) who won't be using pylab or calling show (they'll start the mainloop themselves) and who like the icon for their mpl figures. I agree that always overriding the icon when import backend_gtk is a bit heavy handed, though. I hate to suggest another rc param, but we could have gtk.icon : matplotlib.svg # use None to suppress the default # minimization icon or an svg file to # provide your own any better ideas? JDH
On Thu, 2006年05月18日 at 15:57 -0500, John Hunter wrote: > You can comment out the code in backends/backend_gtk.py Well, this seems like a bug to me, then. Matplotlib offers the possibility to draw to a file instead of opening a window. In this case, the default window icon should *not* get overridden. The best way to patch this would be to set the default window icon only when the gtk main loop is entered. I attached a patch against the current SVN. It simply moves the window icon declaration just before the line where gtk.main() gets called. Should I file a bug somewhere? Or does the patch reach the devs here? Thanks in advance, Fred
>>>>> "Frederic" == Frederic Back <fre...@gm...> writes:
Frederic> Hello, I'm developing a plugin in pygtk, which
Frederic> indirectly (via networkx) uses matplotlib to display
Frederic> graphs.
Frederic> Now, I have the following problem: When I import pylab,
Frederic> my window icon (the small icon in the window decoration)
Frederic> gets replaced by the matplotlib icon. I pasted a sample
Frederic> script below to illustrate.
Frederic> This is amazingly annoying for me since I am merely
Frederic> writing a plugin for an existing application and cannot
Frederic> simply replace the original window icon.
Frederic> Is there any way to supress this behaviour?
You can comment out the code in backends/backend_gtk.py
try:
gtk.window_set_default_icon_from_file (
os.path.join (matplotlib.rcParams['datapath'], 'matplotlib.svg'))
except:
verbose.report('Could not load matplotlib icon: %s' % sys.exc_info()[1])
or physically remove the icon in which case you'll just get a report
that it is missing.
You can also set the icon yourself after import matplotlib/backend_gtk
using the same code.
Hello,
I'm developing a plugin in pygtk, which indirectly (via networkx) uses
matplotlib to display graphs.
Now, I have the following problem: When I import pylab, my window icon
(the small icon in the window decoration) gets replaced by the
matplotlib icon. I pasted a sample script below to illustrate.
This is amazingly annoying for me since I am merely writing a plugin for
an existing application and cannot simply replace the original window
icon.
Is there any way to supress this behaviour?
Please help me, and thanks in advance,
Fred
# ------------------------------------- sample code below
#!/usr/bin/python
import gtk
import pylab
w = gtk.Window()
b = gtk.Button("click me")
w.add(b)
w.show_all()
gtk.main()
>>>>> "Ryan" == Ryan Krauss <rya...@gm...> writes: Ryan> There is a serious flaw to my approach. It seems that if Ryan> plot is called with an explicit linetype like 'b-', then Ryan> ax._get_lines.count is not automatically incremented. You should never use an underscore variable since the leading underscore basically indicates this is an internal variable and it could be removed or changes at any point w/o documentation. If something changes in the "public" API, it will at least be documented in the API_CHANGES file. I think your best approach is to write some helper classes rather than try and set the matplotlib defaults from mycyclers import colorcycler, linecycler for data in mydata: ax.plot(data, linestyle=linecycler(), color=colorcycler()) where linecycler and colorcycler are generators of some sort. Rhen you can alter your linecycler and colorcycler to do whatever you want, make different defaults for color or grayscale, etc... I'm not opposed to making the default linestyles and colorcycles configurable, it has come up a few times, but it may be easier and quicker for you just to code up some custom classes or functions that have just the behavior you want. JDH
However, calling plot with color and linestyle keyword args instead of 'b-' in args leads to incrementing the line count, and things seem to be working. (I had actually written a little line count incrementing function and then had to take it out when I wanted to specify rgb values and switched to the kwargs). Ryan On 5/18/06, Ryan Krauss <rya...@gm...> wrote: > There is a serious flaw to my approach. It seems that if plot is > called with an explicit linetype like 'b-', then ax._get_lines.count > is not automatically incremented. > > Ryan > > On 5/18/06, Ryan Krauss <rya...@gm...> wrote: > > Thanks Jouni. > > > > I can modify the color order using gca() and _getlines.colors, as you > > mentioned. But if I can't specify the line type in a similar fashion, > > then this approach isn't going to work for me. > > > > The trick with the other approach (with a global counter for how many > > lines are on the plot), is how to reset the counter for each new plot. > > gca()._get_lines.count seems to handle this problem by counting the > > lines already on the axis. (I wouldn't have known to poke around > > there if you had got me started.) > > > > So, unless a cleaner approach is suggested by someone else, I am going > > to follow an approach similar to Jouni's suggestion, only using > > gca()._get_lines.count+1 as the index to my global colors and line > > types list so that I am always calling plot (or actually semilogx) > > with explicit linetype specifications (like 'y-','b--',...) > > > > Any better ideas? > > > > Ryan > > > > On 5/18/06, Jouni K Seppanen <jk...@ik...> wrote: > > > "Ryan Krauss" <rya...@gm...> writes: > > > > > > > How do I change the default color order > > > > > > The colors are hardwired in the pylab interface, but you can hack > > > around it: > > > > > > gca()._get_lines.colors = ['#101050', '#105010', '#501010'] > > > gca()._get_lines.Ncolors = 3 > > > gca()._get_lines.firstColor = '#101050' > > > > > > Support for this might be a useful addition to the pylab interface. > > > Does anyone know how to do this in Matlab? > > > > > > > and how do I set up a similar default linetype order, so that the > > > > first call to plot generates a solid line and the second a dashed > > > > one (for example). > > > > > > I don't think there is support for this in pylab. > > > > > > Of course, if all your plot calls just draw a single line, you can > > > cycle both the color and the line style easily by defining your own > > > function: > > > > > > my_colors = ['b','g','r']; my_styles = ['-', ':', '--'] > > > my_c = 0; my_s = 0 > > > def plot(x, y): > > > global my_colors, my_styles, my_c, my_s > > > pylab.plot(x, y, my_colors[my_c % len(my_colors)] > > > + my_styles[my_s % len(my_styles)]) > > > my_c += 1; my_s += 1 > > > > > > But if you want the full pylab.plot argument parsing functionality, > > > the easiest thing would probably be to implement this in > > > matplotlib.axis. > > > > > > -- > > > Jouni > > > > > > > > > > > > ------------------------------------------------------- > > > Using Tomcat but need to do more? Need to support web services, security? > > > Get stuff done quickly with pre-integrated technology to make your job easier > > > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > > > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > > > _______________________________________________ > > > Matplotlib-users mailing list > > > Mat...@li... > > > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > > > > >
There is a serious flaw to my approach. It seems that if plot is called with an explicit linetype like 'b-', then ax._get_lines.count is not automatically incremented. Ryan On 5/18/06, Ryan Krauss <rya...@gm...> wrote: > Thanks Jouni. > > I can modify the color order using gca() and _getlines.colors, as you > mentioned. But if I can't specify the line type in a similar fashion, > then this approach isn't going to work for me. > > The trick with the other approach (with a global counter for how many > lines are on the plot), is how to reset the counter for each new plot. > gca()._get_lines.count seems to handle this problem by counting the > lines already on the axis. (I wouldn't have known to poke around > there if you had got me started.) > > So, unless a cleaner approach is suggested by someone else, I am going > to follow an approach similar to Jouni's suggestion, only using > gca()._get_lines.count+1 as the index to my global colors and line > types list so that I am always calling plot (or actually semilogx) > with explicit linetype specifications (like 'y-','b--',...) > > Any better ideas? > > Ryan > > On 5/18/06, Jouni K Seppanen <jk...@ik...> wrote: > > "Ryan Krauss" <rya...@gm...> writes: > > > > > How do I change the default color order > > > > The colors are hardwired in the pylab interface, but you can hack > > around it: > > > > gca()._get_lines.colors = ['#101050', '#105010', '#501010'] > > gca()._get_lines.Ncolors = 3 > > gca()._get_lines.firstColor = '#101050' > > > > Support for this might be a useful addition to the pylab interface. > > Does anyone know how to do this in Matlab? > > > > > and how do I set up a similar default linetype order, so that the > > > first call to plot generates a solid line and the second a dashed > > > one (for example). > > > > I don't think there is support for this in pylab. > > > > Of course, if all your plot calls just draw a single line, you can > > cycle both the color and the line style easily by defining your own > > function: > > > > my_colors = ['b','g','r']; my_styles = ['-', ':', '--'] > > my_c = 0; my_s = 0 > > def plot(x, y): > > global my_colors, my_styles, my_c, my_s > > pylab.plot(x, y, my_colors[my_c % len(my_colors)] > > + my_styles[my_s % len(my_styles)]) > > my_c += 1; my_s += 1 > > > > But if you want the full pylab.plot argument parsing functionality, > > the easiest thing would probably be to implement this in > > matplotlib.axis. > > > > -- > > Jouni > > > > > > > > ------------------------------------------------------- > > Using Tomcat but need to do more? Need to support web services, security? > > Get stuff done quickly with pre-integrated technology to make your job easier > > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > > _______________________________________________ > > Matplotlib-users mailing list > > Mat...@li... > > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > >
Thanks Jouni. I can modify the color order using gca() and _getlines.colors, as you mentioned. But if I can't specify the line type in a similar fashion, then this approach isn't going to work for me. The trick with the other approach (with a global counter for how many lines are on the plot), is how to reset the counter for each new plot. gca()._get_lines.count seems to handle this problem by counting the lines already on the axis. (I wouldn't have known to poke around there if you had got me started.) So, unless a cleaner approach is suggested by someone else, I am going to follow an approach similar to Jouni's suggestion, only using gca()._get_lines.count+1 as the index to my global colors and line types list so that I am always calling plot (or actually semilogx) with explicit linetype specifications (like 'y-','b--',...) Any better ideas? Ryan On 5/18/06, Jouni K Seppanen <jk...@ik...> wrote: > "Ryan Krauss" <rya...@gm...> writes: > > > How do I change the default color order > > The colors are hardwired in the pylab interface, but you can hack > around it: > > gca()._get_lines.colors = ['#101050', '#105010', '#501010'] > gca()._get_lines.Ncolors = 3 > gca()._get_lines.firstColor = '#101050' > > Support for this might be a useful addition to the pylab interface. > Does anyone know how to do this in Matlab? > > > and how do I set up a similar default linetype order, so that the > > first call to plot generates a solid line and the second a dashed > > one (for example). > > I don't think there is support for this in pylab. > > Of course, if all your plot calls just draw a single line, you can > cycle both the color and the line style easily by defining your own > function: > > my_colors = ['b','g','r']; my_styles = ['-', ':', '--'] > my_c = 0; my_s = 0 > def plot(x, y): > global my_colors, my_styles, my_c, my_s > pylab.plot(x, y, my_colors[my_c % len(my_colors)] > + my_styles[my_s % len(my_styles)]) > my_c += 1; my_s += 1 > > But if you want the full pylab.plot argument parsing functionality, > the easiest thing would probably be to implement this in > matplotlib.axis. > > -- > Jouni > > > > ------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
"Ryan Krauss" <rya...@gm...> writes: > How do I change the default color order The colors are hardwired in the pylab interface, but you can hack around it: gca()._get_lines.colors = ['#101050', '#105010', '#501010'] gca()._get_lines.Ncolors = 3 gca()._get_lines.firstColor = '#101050' Support for this might be a useful addition to the pylab interface. Does anyone know how to do this in Matlab? > and how do I set up a similar default linetype order, so that the > first call to plot generates a solid line and the second a dashed > one (for example). I don't think there is support for this in pylab. Of course, if all your plot calls just draw a single line, you can cycle both the color and the line style easily by defining your own function: my_colors = ['b','g','r']; my_styles = ['-', ':', '--'] my_c = 0; my_s = 0 def plot(x, y): global my_colors, my_styles, my_c, my_s pylab.plot(x, y, my_colors[my_c % len(my_colors)] + my_styles[my_s % len(my_styles)]) my_c += 1; my_s += 1 But if you want the full pylab.plot argument parsing functionality, the easiest thing would probably be to implement this in matplotlib.axis. -- Jouni
Marcel, It sounds like you want something like the attached example. It works on my system based on svn and I expect it will work on a recent official release, but I haven't tried it. Marcel Oliver wrote: > Hi, I have a plot which is divided into 3x3 subplots, each of > which is generated by imshow. I now want to do the following > and have difficulties finding the appropriate documentation: > > 1. Display a single title for the entire plot. title() will either > have no effect or attach the title to a separate subplot. > > 2. Have the same color scale on each subplot. Is it possible to > use automatic scaling? > > 3. Attach a single colorbar to the entire plot, rather than > 9 identical scales to each subplot. > > 4. Have the labeling of the colorbar use the scientific > number format. I sometimes have rather small values, > and colorbar() will display 0.0 throughout the scale. This part depends on whether you are using svn, which as of this week has a new colorbar, or an earlier mpl release, with the original colorbar. With either you can set the format string: for example, use tickfmt='%.2g' for the old colorbar or format='%.2g' for the new one. The new one should automatically choose a suitable format, but I see there is a problem with that. I have to look into it. Eric
Hi, I have a plot which is divided into 3x3 subplots, each of which is generated by imshow. I now want to do the following and have difficulties finding the appropriate documentation: 1. Display a single title for the entire plot. title() will either have no effect or attach the title to a separate subplot. 2. Have the same color scale on each subplot. Is it possible to use automatic scaling? 3. Attach a single colorbar to the entire plot, rather than 9 identical scales to each subplot. 4. Have the labeling of the colorbar use the scientific number format. I sometimes have rather small values, and colorbar() will display 0.0 throughout the scale. I'd be grateful for any hints, Marcel
I am trying to create plots that look good in color or grayscale (I had asked about this before and was trying to write code with a switch - now I just want decent looking plots without having to switch - and I never really finished the with switching stuff either). I use the pylab interface and rely a lot on the default behavior for incrementing my colors when I overlay plots - i.e. I call plot different times with different data and it automatically makes the first one blue and the second green, ... How do I change the default color order and how do I set up a similar default linetype order, so that the first call to plot generates a solid line and the second a dashed one (for example). Thanks, Ryan
Albert Swart wrote: > > Matlab 's contour function returns the contour data as x- and y- > coordinates in a contour matrix C: > > [C,H] = CONTOUR(...) You are using an old version of Matlab... > > pylab.contour(...) returns a ContourSet object that only seems to > contain contour heights. How do I get the actual contour data? I need > the (x,y) coordinates as given by matlab. In fact even the binary > contour image that is displayed by contour() will be usefull. > > albert The ContourSet object includes the attribute "collections", a list of LineCollections or PolyCollections (for contour and contourf, respectively), with one collection per line or color band. For each collection in the list, you can access the vertices using the get_verts() method. Eric
>
>
>>>>"clovis" == clovis <cl...@pe...> writes:
>>>>
>>>>
>
> clovis> All, I followed up the 'memory leak' discussion in the
> clovis> sourceforge list and I know the Matplotlib-FAQ entry about
> clovis> this subject. I've also seen John Hunter's post about the
> clovis> need of matching figure/close pairs. Anyway, I still feel
> clovis> that there are problems in this subject, which can be
> clovis> exposed by the following script (for Windows, but can
> clovis> easily be adapted to Unix).
>
> clovis> As can be seen by the results (also given below), there is
> clovis> a steady increase in memory usage which is not recovered!
>
>If I recall correctly, there is a known leak in tkagg when you create
>multiple canvases, and this is in Tk and not matplotlib proper. Todd
>may have something to add here.
>
>JDH
>
>
>
>
Yesterday you mentioned that that 'memory leak' would probably be caused
by Tkagg
and not by matplotlib. You also mentioned that it would be good to hear
Todd Miller
about this subject (I agree).
Following your idea I tested the memory usage under different backends.
The results are given below:
##############
#TKAgg results
##############
Date/time of test = Wed May 17 10:08:39 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = TkAgg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 15740 21564 5824
Memory usage before/after figure[ 1] = 21564 25944 4380
Memory usage before/after figure[ 2] = 25944 30316 4372
Memory usage before/after figure[ 3] = 30316 34668 4352
Memory usage before/after figure[ 4] = 34668 39020 4352
Memory usage before/after figure[ 5] = 39020 43376 4356
Memory usage before/after figure[ 6] = 43376 47740 4364
Memory usage before/after figure[ 7] = 47740 52096 4356
Memory usage before/after figure[ 8] = 52096 56472 4376
Memory usage before/after figure[ 9] = 56472 60836 4364
############
#Agg results
############
Date/time of test = Wed May 17 09:24:26 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = Agg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 14160 17008 2848
Memory usage before/after figure[ 1] = 17008 17504 496
Memory usage before/after figure[ 2] = 17504 17604 100
Memory usage before/after figure[ 3] = 17604 17604 0
Memory usage before/after figure[ 4] = 17604 17588 -16
Memory usage before/after figure[ 5] = 17588 17608 20
Memory usage before/after figure[ 6] = 17608 17600 -8
Memory usage before/after figure[ 7] = 17600 17596 -4
Memory usage before/after figure[ 8] = 17596 17584 -12
Memory usage before/after figure[ 9] = 17584 17604 20
##############
#Cairo results
##############
Date/time of test = Wed May 17 10:29:09 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = Cairo
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 14024 15436 1412
Memory usage before/after figure[ 1] = 15436 15944 508
Memory usage before/after figure[ 2] = 15944 16252 308
Memory usage before/after figure[ 3] = 16252 16460 208
Memory usage before/after figure[ 4] = 16460 16468 8
Memory usage before/after figure[ 5] = 16468 18448 1980
Memory usage before/after figure[ 6] = 18448 18464 16
Memory usage before/after figure[ 7] = 18464 16744 -1720
Memory usage before/after figure[ 8] = 16744 18144 1400
Memory usage before/after figure[ 9] = 18144 18488 344
#################
#GTKCairo results
#################
Date/time of test = Wed May 17 10:28:45 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = GTKCairo
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 20888 26384 5496
Memory usage before/after figure[ 1] = 26384 26972 588
Memory usage before/after figure[ 2] = 26972 28224 1252
Memory usage before/after figure[ 3] = 28224 27848 -376
Memory usage before/after figure[ 4] = 27848 27440 -408
Memory usage before/after figure[ 5] = 27440 29444 2004
Memory usage before/after figure[ 6] = 29444 29668 224
Memory usage before/after figure[ 7] = 29668 28840 -828
Memory usage before/after figure[ 8] = 28840 28804 -36
Memory usage before/after figure[ 9] = 28804 29884 1080
############
#GTK results
############
Date/time of test = Wed May 17 09:20:02 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = GTK
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 22128 28044 5916
Memory usage before/after figure[ 1] = 28048 28624 576
Memory usage before/after figure[ 2] = 28624 28804 180
Memory usage before/after figure[ 3] = 28804 28872 68
Memory usage before/after figure[ 4] = 28872 28948 76
Memory usage before/after figure[ 5] = 28948 29020 72
Memory usage before/after figure[ 6] = 29020 29080 60
Memory usage before/after figure[ 7] = 29080 29144 64
Memory usage before/after figure[ 8] = 29144 29240 96
Memory usage before/after figure[ 9] = 29240 29292 52
###############
#GTKAgg results
###############
Date/time of test = Wed May 17 09:20:24 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = GTKAgg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 23880 31124 7244
Memory usage before/after figure[ 1] = 31124 31708 584
Memory usage before/after figure[ 2] = 31708 31904 196
Memory usage before/after figure[ 3] = 31904 32308 404
Memory usage before/after figure[ 4] = 32308 32388 80
Memory usage before/after figure[ 5] = 32388 32144 -244
Memory usage before/after figure[ 6] = 32144 32560 416
Memory usage before/after figure[ 7] = 32560 32276 -284
Memory usage before/after figure[ 8] = 32276 32380 104
Memory usage before/after figure[ 9] = 32380 32444 64
############
#EMF results
############
Date/time of test = Wed May 17 09:18:19 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = EMF
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=True SAVEFIG=True
Memory usage before/after figure[ 0] = 12352 13268 916
Memory usage before/after figure[ 1] = 13268 13732 464
Memory usage before/after figure[ 2] = 13732 13896 164
Memory usage before/after figure[ 3] = 13896 13900 4
Memory usage before/after figure[ 4] = 13900 13804 -96
Memory usage before/after figure[ 5] = 13804 13904 100
Memory usage before/after figure[ 6] = 13904 13904 0
Memory usage before/after figure[ 7] = 13904 13912 8
Memory usage before/after figure[ 8] = 13912 13836 -76
Memory usage before/after figure[ 9] = 13836 13920 84
###########
#PS results
###########
Date/time of test = Wed May 17 09:21:15 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = PS
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 12912 14032 1120
Memory usage before/after figure[ 1] = 14032 14508 476
Memory usage before/after figure[ 2] = 14508 16344 1836
Memory usage before/after figure[ 3] = 16344 16548 204
Memory usage before/after figure[ 4] = 16548 17032 484
Memory usage before/after figure[ 5] = 17032 16996 -36
Memory usage before/after figure[ 6] = 16996 16648 -348
Memory usage before/after figure[ 7] = 16648 14876 -1772
Memory usage before/after figure[ 8] = 14876 16928 2052
Memory usage before/after figure[ 9] = 16928 16804 -124
############
#SVG results
############
Date/time of test = Wed May 17 09:21:34 2006
OS version = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32
bit (Intel)]
OS platform = win32
Matplotlib version = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend = SVG
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after figure
Column #3 = (after-before) memory
Configuration SHOWFIG=False SAVEFIG=True
Memory usage before/after figure[ 0] = 12844 13648 804
Memory usage before/after figure[ 1] = 13648 14128 480
Memory usage before/after figure[ 2] = 14128 14224 96
Memory usage before/after figure[ 3] = 14224 14224 0
Memory usage before/after figure[ 4] = 14224 14224 0
Memory usage before/after figure[ 5] = 14224 14228 4
Memory usage before/after figure[ 6] = 14228 14228 0
Memory usage before/after figure[ 7] = 14228 14228 0
Memory usage before/after figure[ 8] = 14228 14228 0
Memory usage before/after figure[ 9] = 14228 14228 0
As can be seen by the previously show results, it seems that the TKAgg
still has some problems.
I'm not worried the the 'DC level' of the backends or with the results
concerning figure[0].
The problem I'm pointing is the continuous increase in memory usage in
the TKAgg backend.
The script used for these tests is:
import pylab
import os
import time
N = 10 # number of loops to execute
SAVEFIG = True # SAVEFIG execution flag
SHOWFIG = False # SHOWFIG execution flag
report_filename = 'memory_report_%s.txt' % pylab.matplotlib.get_backend()
fid = file(report_filename,'wt')
fid.write('Date/time of test = %s\n' % time.asctime())
fid.write('OS version = %s\n' % os.sys.version)
fid.write('OS platform = %s\n' % os.sys.platform)
fid.write('Matplotlib version = %s\n' % pylab.matplotlib.__version__)
fid.write('Matplotlib revision = %s\n' % pylab.matplotlib.__revision__)
fid.write('Matplotlib backend = %s\n' % pylab.matplotlib.get_backend())
fid.write('Column #0 = figure index\n')
fid.write('Column #1 = memory usage before figure\n')
fid.write('Column #2 = memory usage after figure\n')
fid.write('Column #3 = (after-before) memory\n')
pylab.ion()
a=pylab.arange(0,10)
def report_memory():
### Attention: the path to the pslist utility should be adjusted
according to installation!
if os.sys.platform == 'win32':
ps_exe_filename = os.path.join(os.getcwd(),'pslist.exe')
#Build ps filename
a = os.popen('%s -m python' % ps_exe_filename).readlines()
#Build and execute command
b = a[8]
c = b.split()
return int(c[3])
else:
print 'Sorry, you have to adapt the command for your OS!'
return 0
def figureloop(N):
for i in range(0,N):
memory_usage_before = report_memory()
fid.write('Memory usage before/after figure[%2d] = %8d' % (i,
memory_usage_before))
pylab.figure(i)
pylab.plot(a,2*a)
figurename = 'fig%02d' % i
if SAVEFIG:
pylab.savefig(figurename)
pylab.savefig(figurename+'.eps')
if SHOWFIG:
pylab.show()
pylab.close(i)
time.sleep(1.0) # wait 1.0 second before
inspecting memory usage
if os.path.isfile(figurename): # remove figure ...
#os.remove(figurename)
pass
memory_usage_after = report_memory()
delta_memory = memory_usage_after - memory_usage_before
fid.write(' %8d %8d\n' % (memory_usage_after, delta_memory))
print '%2d %6d %6d %6d' % (i, memory_usage_before,
memory_usage_after, delta_memory)
print 'Column #0 = figure index'
print 'Column #1 = memory usage before figure'
print 'Column #2 = memory usage after figure'
print 'Column #3 = (after-before) memory'
print 'There is a sleep time of 1s between each figure!'
print 'Close Figure[0] to continue execution!'
print('\nConfiguration SHOWFIG=%s SAVEFIG=%s' % (SHOWFIG, SAVEFIG))
fid.write('\nConfiguration SHOWFIG=%s SAVEFIG=%s\n' % (SHOWFIG, SAVEFIG))
figureloop(N)
This script should be executed with the backend parameters, such as:
python memory_test.py -dTKAgg
python memory_test.py -dAgg
etc
for all the desired backends.
Thanks for your support and congratulations for your great work
(matplotlib).
clovis