Hi Ben, glad you found the answer. Once again, does F.get_size_inches() have to return to the user the numpy array? Why not a list or tuple? I don't mind matplotlib internal stuff. ;-)
In an answer to your proposed workaround > DefaultSize = tuple(F.get_size_inches()) let me comment that (I think) I tried also DefaultSize = F.get_size_inches()[:] but that also did not work for me. And was similarly think of the copy module haven't bothered to try that. ;-) Yes, please document this at least if you really cannot return a simple list or tuple. Thanks, Martin Benjamin Root wrote: > > > On Thu, Feb 16, 2012 at 3:09 PM, Martin Mokrejs <mmokr...@fold.natur.cuni.cz > <mailto:mmokr...@fold.natur.cuni.cz>> wrote: > > Hi Benjamin, > thank you for you explanation. My comment is below in the text: > > Benjamin Root wrote: > > > > > > On Tue, Feb 14, 2012 at 4:43 PM, Martin Mokrejs > <mmokr...@fold.natur.cuni.cz <mailto:mmokr...@fold.natur.cuni.cz> > <mailto:mmokr...@fold.natur.cuni.cz <mailto:mmokr...@fold.natur.cuni.cz>>> > wrote: > > > > Ah, this seems to be the issue that my figsize was growing all the > time so it > > went over the maximum limits. > > > > I thought this is valid: > > DefaultSize = F.get_size_inches() > > print str(DefaultSize) > > blah > > F.set_size_inches(DefaultSize) > > > > See http://matplotlib.sourceforge.net/api/figure_api.html > > > > <quote> > > set_size_inches(*args, **kwargs) > > > > set_size_inches(w,h, forward=False) > > > > Set the figure size in inches > > > > Usage: > > > > fig.set_size_inches(w,h) # OR > > fig.set_size_inches((w,h) ) > > > > optional kwarg forward=True will cause the canvas size to be > automatically updated; eg you can resize the figure window from the shell > > > > ACCEPTS: a w,h tuple with w,h in inches > > </quote> > > > > Nope, it does not work. The print call gives me: [ 8. 6.]. So, > this is not a tuple? > > Or python-2.7 issue how is it printed ... I fear? ;-) > > Anyway, doing > > > > F.set_size_inches(11.2, 15) > > > > works for me. > > > > Martin > > > > > > I am a little bit confused by your code example. You get the figure > size and print it, and *then* you set it with the exact same values, and you > are surprised that it came out as [8. 6.]? Note that the figure size is > stored internally as a numpy array, so when you do "print str(DefaultSize)", > you will get the string representation of the numpy array. You can still > pass in a tuple, list, or two separate elements. Try this code: > > No, in my experience it did NOT work. I suspect F.set_size_inches() > either did not like the input tuple or something else. Now. after reading > your clarification, are you sure it can input the numpy array as well? What I > also tried was to re-set the figsize to original values. > > > Yes, it can. I found the source of the problem, see further down. > > > Ouch, I use pylab not matplotlib directly. :( > > > Doesn't matter. > > > $ python > Python 2.7.2 (default, Feb 7 2012, 19:33:08) > [GCC 4.5.3] on linux2 > Type "help", "copyright", "credits" or "license" for more information. > >>> import pylab > >>> F = pylab.gcf() > >>> print F.get_size_inches() > [ 8. 6.] > >>> DefaultSize = F.get_size_inches() > >>> print DefaultSize > [ 8. 6.] > >>> F.set_size_inches(10, 10) > >>> print F.get_size_inches() > [ 10. 10.] > >>> F.set_size_inches(DefaultSize[0], DefaultSize[1]) > >>> print F.get_size_inches() > [ 10. 10.] > >>> > > > The bug here is assuming that DefaultSize still contained the values you > printed earlier. This is subtle (and I missed it before), but what you are > getting back from F.get_size_inches() is a view of the internal numpy array. > When you set the new size, the internal array was updated, not replaced. > This is much in the same vein as Python mutables, but taken a bit further > than you are probably used to. Because the internal array was updated, the > view (stored in DefaultSize) showed the new data as well. So, when you tried > to set (what you thought was still) the original size, it was merely setting > the current values back to itself. Therefore, no change. > > So, to force DefaultSize to be immutable, just cast it as a tuple: > >>>> DefaultSize = tuple(F.get_size_inches()) > > > > Why in the above example I cannot return back to figsize [ 8. 6.] ? > > > > > import matplotlib.pyplot as plt > > fig = plt.figure() > > print fig.get_size_inches() > > fig.set_size_inches(11.2, 15.0) > > print fig.get_size_inches() > > fig.set_size_inches((4.0, 7.2)) > > print fig.get_size_inches() > > fig.set_size_inches([9.3, 11.1]) > > print fig.get_size_inches() > > > > > > You should see: > > > > [ 8. 6.] > > [ 11.2 15. ] > > [ 4. 7.2] > > [ 9.2 11.1] > > Yes, this works. > > > > > Everything works as expected. There is nothing special about python > 2.7 in this regard. Let us know if you are still having problems updating > your figures and include a stand-alone example showing how the figure size is > not being updated. > > What does the internal numpy array representation bring good to the > figsize? ;-) > Why don't you use a simple list/tuple? I am sure you know what you're > doing, > am just curious. Especially if slicing behaves differently compared to > list/tuple > and the .__str__() also gives in my eyes weird output. Sure, matter of > taste. ;) > > > We use numpy arrays internally for several reasons. Primarially is that we > have to do mathematical operations with this information. list/tuple do not > lend itself to that (which is why numpy exists in the first place). numpy > arrays also enforce type checking (so you can't put a string for a size > value, or anything else that doesn't make sense). Another reason is that the > slicing is significantly more advanced than for list/tuples. Internally, > there is a 2x2 array. I can slice in either dimension very easily, while a > list of lists would not be easy. Lastly, we need a combination of the > immutability of tuples (don't want people changing the array size), but the > mutability of lists (we want to be able to change the individual values). > > I will admit that this issue is not immediately intuitive, and some > documentation should probably be added to help prevent users like yourself > from falling into this trap. > > I hope that helps! > Ben Root > ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
