Francesco Montesano, on 2011-02-04 17:01, wrote: > Dear all again, > > I've tried to play with it again, but I couldn't find a > solution for the problem. For clarity I report an example of > what each of the subplots looks like:
Hi Francesco, thanks for the clarification, here are two ways to get the look you want. I added some comments to help you understand what was going on before. (The resulting figure is attached, just in case). import numpy as np import matplotlib.pyplot as plt mean=np.array([-0.9206394, -0.90127456, -0.91983625, -0.97765539, -1.02991184, -1.02267017, -0.97730167, -0.93715172, -0.94324653, -0.92884379]) stddev= np.array([0.16351397,0.15075966,0.13413909,0.15404823,0.13559582, 0.13109754,0.12128598,0.11589682,0.11921571,0.10866761]) ax = plt.figure().add_axes([0.1,0.1,0.8,0.8]) ax.errorbar(np.arange(10,20)/100., mean, yerr=stddev) ax.set_xlim([0.095, 0.195]) lab = ax.get_ymajorticklabels() plt.draw() # ticks only get text assigned during a call to draw print lab for i in lab: print i # note that \u2212 is a unicode minus sign # this work for the first draw - relies on l.get_text() returning # nothing for labels which aren't used/drawn - which isn't the # case in general after panning and zooming interactively shown_lab = [l for l in lab if l.get_text()] shown_lab[0].set_visible(False) shown_lab[-1].set_visible(False) ## alternative solution without extra draw(). more robust, can be ## used even after initial draw. #ymin,ymax = ax.get_ylim() #tl = ax.yaxis.get_majorticklocs() #lab[(tl<ymin).sum()].set_visible(False) #lab[-(tl>ymax).sum()-1].set_visible(False) ## hybrid of the two. #ymin,ymax = ax.get_ylim() #tl = ax.yaxis.get_majorticklocs() #shown_lab = [l for l,t in zip(lab,tl) if t>ymin and t<ymax) #shown_lab[0].set_visible(False) #shown_lab[-1].set_visible(False) plt.show() best, -- Paul Ivanov 314 address only used for lists, off-list direct email at: http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7
<<attachment: hide-ticklabels.png>>
signature.asc
Description: Digital signature
------------------------------------------------------------------------------ The modern datacenter depends on network connectivity to access resources and provide services. The best practices for maximizing a physical server's connectivity to a physical network are well understood - see how these rules translate into the virtual world? http://p.sf.net/sfu/oracle-sfdevnlfb
_______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users