Hi all, I was a little dissappointed when I discover after accomplished to install the ETS spkg to work, in my Ubuntu 9.04 athlon x86_64 computer, that although it has mayavi2 and chaco working in all is beautifullness, it has not the render for pylab (matplotlib). Because all my 2-D graphs are of this kind I try to see how to do to get a render for it. I do not know "traits" and it take me too much time to convince myself to look for another possibility, until I found an example (attached) of Ken McIvor using wx and a library developed by him: "wxmpl" to render at full capacity matplotlib graphs. My first trial was with the 1.2.9 wxmpl version whithout success, asking him for help he adviced me to try with this last one: wxmpl-1.3.1, then I install it and it works very well, in such a way that it can be called also running the example inside the notebook.
CHeers, Jorge _________________________________________________________________ Actualiza y gana con Windows Live http://www.actualizatuperfil.com.mx/ --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URLs: http://www.sagemath.org -~----------~----~----~----~------~----~------~--~---
#!/usr/bin/env python # Purpose: Demonstrates different plots from the matplotlib examples collection # Author: Ken McIvor <mci...@iit.edu>, deriving from the matplotlib examples # collection # # Copyright 2002-2004 John D. Hunter, 2005 Illinois Institute of Technology # # Distributed under the license agreement for matplotlib 0.72. # # For information on the usage and redistribution of this file, and for a # DISCLAIMER OF ALL WARRANTIES, see the file "LICENSE" that ships with the # matplotlib 0.72 or http://matplotlib.sourceforge.net/license.html __version__ = '1.0' import wx import wxmpl import matplotlib import matplotlib.cm as cm from pylab import array, arange, sin, cos, exp, pi, randn, normpdf, meshgrid, \ convolve def plot_simple(fig): t = arange(0.0, 2.0, 0.01) s = sin(2*pi*t) c = cos(2*pi*t) axes = fig.gca() axes.plot(t, s, linewidth=1.0) axes.plot(t, c, linewidth=1.0) axes.set_xlabel('time (s)') axes.set_ylabel('voltage (mV)') axes.set_title('About as simple as it gets, folks') axes.grid(True) def plot_subplot(fig): def f(t): return cos(2*pi*t) * exp(-t) t1 = arange(0.0, 5.0, 0.10) t2 = arange(0.0, 5.0, 0.02) a1 = fig.add_subplot(2, 1, 1) a1.plot(t1, f(t1), 'bo') a1.plot(t2, f(t2), 'k') a1.grid(True) a1.set_title('A Tale of 2 Subplots') a1.set_ylabel('Damped oscillation') a2 = fig.add_subplot(2, 1, 2) a2.plot(t2, cos(2*pi*t2), 'r>') a2.grid(True) a2.set_xlabel('time (s)') a2.set_ylabel('Undamped') def plot_subplot_sharex(fig): def f(t): return cos(2*pi*t) * exp(-t) t1 = arange(0.0, 5.0, 0.10) t2 = arange(0.0, 5.0, 0.02) a1 = fig.add_subplot(2, 1, 1) a1.plot(t1, f(t1), 'bo') a1.plot(t2, f(t2), 'k') a1.grid(True) a1.set_title('Two Subplots Sharing an Axis') a1.set_ylabel('Damped oscillation') for ticklabel in a1.get_xticklabels(): ticklabel.set_visible(False) a2 = fig.add_subplot(2, 1, 2, sharex=a1) a2.plot(t2, cos(2*pi*t2), 'r>') a2.grid(True) a2.set_xlabel('time (s)') a2.set_ylabel('Undamped') def plot_histogram(fig): mu, sigma = 100, 15 x = mu + sigma*randn(10000) axes = fig.gca() # the histogram of the data n, bins, patches = axes.hist(x, 100, normed=1) # add a 'best fit' line y = normpdf( bins, mu, sigma) l = axes.plot(bins, y, 'r--', linewidth=2) axes.set_xlim((40, 160)) axes.set_xlabel('Smarts') axes.set_ylabel('P') axes.set_title('IQ: mu=100, sigma=15') # axes.set_title(r'$\rm{IQ:}\/ \mu=100,\/ \sigma=15$') def plot_fill(fig): t = arange(0.0, 1.01, 0.01) s = sin(2*2*pi*t) axes = fig.gca() axes.fill(t, s*exp(-5*t), 'r') axes.grid(True) def plot_log(fig): dt = 0.01 t = arange(dt, 20.0, dt) a1 = fig.add_subplot(2, 1, 1) a1.semilogx(t, sin(2*pi*t)) a1.set_ylabel('semilogx') a1.grid(True) a2 = fig.add_subplot(2, 1, 2) a2.loglog(t, 20*exp(-t/10.0), basey=4) a2.xaxis.grid(True, which='minor') # minor grid on too a2.set_xlabel('time (s)') a2.set_ylabel('loglog') a2.grid(True) def plot_polar(fig): import pylab r = arange(0,1,0.001) theta = 2*2*pi*r # radar green, solid grid lines matplotlib.rc('grid', color='#316931', linewidth=1, linestyle='-') ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True, axisbg='#d5de9c') ax.plot(theta, r, color='#ee8d18', lw=3) ax.set_title("And there was much rejoicing!", fontsize=14) matplotlib.rcdefaults() def plot_polar_subplot(fig): # # Polar demo # import pylab r = arange(0,1,0.001) theta = 2*2*pi*r # radar green, solid grid lines matplotlib.rc('grid', color='#316931', linewidth=1, linestyle='-') ax = fig.add_subplot(1, 2, 1, polar=True, axisbg='#d5de9c') ax.plot(theta, r, color='#ee8d18', lw=3) ax.set_title("And there was much rejoicing!", fontsize=14) matplotlib.rcdefaults() # # First part of the subplot demo # def f(t): return cos(2*pi*t) * exp(-t) t1 = arange(0.0, 5.0, 0.10) t2 = arange(0.0, 5.0, 0.02) A1 = fig.add_subplot(1, 2, 2) A1.plot(t1, f(t1), 'bo') A1.plot(t2, f(t2), 'k') A1.grid(True) A1.set_title('A tale of one subplot') A1.set_ylabel('Damped oscillation', fontsize=10) A1.set_xlabel('time (s)', fontsize=10) def plot_legend(fig): a = arange(0,3,.02) b = arange(0,3,.02) c = exp(a) d = c.tolist() d.reverse() d = array(d) axes = fig.gca() axes.plot(a,c,'k--',a,d,'k:',a,c+d,'k') axes.legend(('Model length', 'Data length', 'Total message length'), 'upper center', shadow=True) axes.set_ylim([-1,20]) axes.grid(False) axes.set_xlabel('Model complexity --->') axes.set_ylabel('Message length --->') axes.set_title('Minimum Message Length') axes.set_xticklabels([]) axes.set_yticklabels([]) def plot_image(fig): def func3(x,y): return (1- x/2 + x**5 + y**3)*exp(-x**2-y**2) dx, dy = 0.025, 0.025 x = arange(-3.0, 3.0, dx) y = arange(-3.0, 3.0, dy) X,Y = meshgrid(x, y) Z = func3(X, Y) axes = fig.gca() im = axes.imshow(Z, cmap=cm.jet, extent=(-3, 3, -3, 3)) def plot_layered_images(fig): def func3(x,y): return (1- x/2 + x**5 + y**3)*exp(-x**2-y**2) # make these smaller to increase the resolution dx, dy = 0.05, 0.05 x = arange(-3.0, 3.0, dx) y = arange(-3.0, 3.0, dy) X,Y = meshgrid(x, y) # when layering multiple images, the images need to have the same # extent. This does not mean they need to have the same shape, but # they both need to render to the same coordinate system determined by # xmin, xmax, ymin, ymax xmin, xmax, ymin, ymax = min(x), max(x), min(y), max(y) extent = xmin, xmax, ymin, ymax Z1 = array(([0,1]*4 + [1,0]*4)*4); Z1.shape = 8,8 # chessboard Z2 = func3(X, Y) axes = fig.gca() axes.imshow(Z1, cmap=cm.gray, interpolation='nearest', extent=extent) axes.hold(True) axes.imshow(Z2, cmap=cm.jet, alpha=.9, interpolation='bilinear', extent=extent) def plot_axes(fig): # create some data to use for the plot dt = 0.001 t = arange(0.0, 10.0, dt) r = exp(-t[:1000]/0.05) # impulse response x = randn(len(t)) s = convolve(x,r,mode=2)[:len(x)]*dt # colored noise # the main axes is subplot(111) by default axes = fig.gca() axes.plot(t, s) axes.set_xlim((0, 1)) axes.set_ylim((1.1*min(s), 2*max(s))) axes.set_xlabel('time (s)') axes.set_ylabel('current (nA)') axes.set_title('Gaussian colored noise') # this is an inset axes over the main axes a = fig.add_axes([.65, .6, .2, .2], axisbg='y') n, bins, patches = a.hist(s, 400, normed=1) a.set_title('Probability') a.set_xticks([]) a.set_yticks([]) # this is another inset axes over the main axes a = fig.add_axes([.2, .6, .2, .2], axisbg='y') a.plot(t[:len(r)], r) a.set_title('Impulse response') a.set_xlim((0, 0.2)) a.set_xticks([]) a.set_yticks([]) # # Demo Infrastructure # class Demo: def __init__(self, title, plotFunction, size=(6.0, 3.7), dpi=96): self.title = title self.plotFunction = plotFunction self.size = size self.dpi = dpi def run(self): frame = wxmpl.PlotFrame(None, -1, self.title, size=self.size, dpi=self.dpi) self.plotFunction(frame.get_figure()) frame.draw() frame.Show() def makeButton(self, parent): btn = wx.Button(parent, -1, self.title) wx.EVT_BUTTON(btn, btn.GetId(), self.OnButton) return btn def OnButton(self, evt): self.run() DEMONSTRATIONS = [ Demo('Simple Plot', plot_simple), Demo('Subplots', plot_subplot), Demo('Shared X Axes', plot_subplot_sharex), Demo('Histogram Plot', plot_histogram), Demo('Filled Polygons', plot_fill), Demo('Logarithmic Scaling', plot_log), Demo('Polar Plot', plot_polar, (6.0, 6.0)), Demo('Polar and Linear Subplots', plot_polar_subplot, (8.0,4.0)), Demo('Linear Plot with a Legend', plot_legend), Demo('Pseudocolor Image', plot_image), Demo('Layered Images', plot_layered_images), Demo('Overlapping Axes', plot_axes) ] class TestFrame(wx.Frame): def __init__(self, parent, id, title, **kwds): wx.Frame.__init__(self, parent, id, title, **kwds) buttons = [demo.makeButton(self) for demo in DEMONSTRATIONS] sizer = wx.BoxSizer(wx.VERTICAL) for btn in buttons: sizer.Add(btn, 0, wx.EXPAND|wx.ALL, 5) self.SetSizer(sizer) self.Fit() wx.EVT_WINDOW_DESTROY(self, self.OnWindowDestroy) def OnWindowDestroy(self, evt): wx.GetApp().ExitMainLoop() def main(): app = wx.PySimpleApp() frame = TestFrame(None, -1, 'WxMpl Demos') frame.Show(True) app.MainLoop() if __name__ == '__main__': main()
