Unfortunately, axes_grid toolkit (in most cases) creates an axes using its own Axes class by default. Here is some more details.
http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#axisline To use mpl's original Axes class, append axes_class parameter. import matplotlib.axes as maxes cax = divider.new_horizontal(size="5%", pad=0.05, axes_class=maxes.Axes) Then your code should work. Just in case, with the axes_grid's own Axes class, instead of looping over major_ticks, you do cax.axis["left"].toggle(all=False) cax.axis["right"].toggle(all=True) Regards, -JJ On Fri, Mar 5, 2010 at 9:38 AM, Thomas Robitaille <thomas.robitai...@gmail.com> wrote: > Hi Jae-Joon, > > I am encountering another issue, when using the method you suggest in > combination with the parasite_axes from the matplotlib toolkit: > > --- > > import matplotlib.pyplot as mpl > import numpy as np > from mpl_toolkits.axes_grid import make_axes_locatable > import mpl_toolkits.axes_grid.parasite_axes as mpltk > > fig = mpl.figure() > > ax = mpltk.SubplotHost(fig, 1, 1, 1) > fig.add_axes(ax) > > divider = make_axes_locatable(ax) > > cax = divider.new_horizontal(size="5%", pad=0.05) > fig.add_axes(cax) > > image = ax.imshow(np.random.random((100,100))) > > cb = fig.colorbar(image, cax=cax) > > --- > > In the above case, the labels end up on the wrong side of the plot, and the > usual method for changing the label position, e.g.: > > for tick in cax.xaxis.get_major_ticks(): > tick.tick1On = True > tick.tick2On = True > tick.label1On = False > tick.label2On = True > > does not work. Do you have any idea why this might be? > > Thanks for any help, > > Thomas > > > > On Mar 4, 2010, at 10:28 PM, Jae-Joon Lee wrote: > >> see >> >> http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg15919.html >> >> axes_grid toolkit provides some helper function that utilizes >> axes_locator (take a look at demo_locatable_axes_easy function in the >> example below) >> >> http://matplotlib.sourceforge.net/examples/axes_grid/demo_axes_divider.html >> >> -JJ >> >> >> >> >> On Thu, Mar 4, 2010 at 9:05 PM, Thomas Robitaille >> <thomas.robitai...@gmail.com> wrote: >>> Hi, >>> >>> I am trying to set up a colorbar that automatically resizes if I zoom in to >>> an image (which changes the aspect ratio of the axes, so I want the >>> colorbar to get resized too). Let's say I have two Axes instances, say ax >>> (for the main image) and cax (for the colorbar). I can set up a callback if >>> the view limits in one axes change, for example >>> >>> ax.callbacks.connect('xlim_changed', update_colorbar) >>> ax.callbacks.connect('ylim_changed', update_colorbar) >>> >>> Now I can store a reference to cax inside ax: >>> >>> ax._cax = cax >>> >>> And I can now define update_colorbar so that it basically changes the >>> position of cax: >>> >>> def update_colorbar(ax): >>> >>> # Get current position >>> xmin = ax..get_position().xmin >>> ... >>> >>> # Compute new colorbar position >>> ... >>> >>> # Set new position >>> ax._cax.set_position(...) >>> >>> # Return axes instance >>> return ax >>> >>> Now the issue is that if I select a region of the image to zoom into, then >>> as soon as I've selected the region, update_colorbar gets called, but by >>> then, the aspect ratio of ax hasn't changed, and so the position I find >>> when I do xmin = ax..get_position().xmin in update_colorbar is the *old* >>> position of ax, not the new one. So the colorbar position is always one >>> step behind compared to the main image axes. >>> >>> Can anyone think of any way that would avoid this issue, and to be able to >>> use the *new* position of ax inside update_colorbar? >>> >>> Thanks in advance for any help, >>> >>> Thomas >>> ------------------------------------------------------------------------------ >>> Download Intel® Parallel Studio Eval >>> Try the new software tools for yourself. Speed compiling, find bugs >>> proactively, and fine-tune applications for parallel performance. >>> See why Intel Parallel Studio got high marks during beta. >>> http://p.sf.net/sfu/intel-sw-dev >>> _______________________________________________ >>> Matplotlib-users mailing list >>> Matplotlib-users@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >>> > > ------------------------------------------------------------------------------ Download Intel® Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
