The Axes class of the axisartist toolkit uses very different way to handle ticks and tick labels. Thus most of the tick-related commands of original matplotlib do not work!
Unfortunately, there is no easy way to support a logarithmic scale. To utilize the full functionality, you need to create a transform object and provide an appropriate tick locator. The first part is easy but the second part is not. You can use origial mapltolib's tick locator, but it has a limitation that minor ticks are not well supported. The easiest way is to convert your data to log scale and use a fixed locator with a custom formatter. An example script is attached. The better approach is to create your own locator and formatter. Regards, -JJ On Fri, Apr 22, 2011 at 1:49 AM, Junghun Shin <jhs...@ef.eie.eng.osaka-u.ac.jp> wrote: > Hi, everyone. > > Let me explain what I wanted to do: First, I wanted to make a polar > plot with angles from 0 to 90. I could do it by adopting the > curvelinear grid demo ( http://goo.gl/kruXf ). And then I wanted to > present the radius in log10 scale. But setting the plot command to > semilogy or trying to set 'set_rscale('log')' all failed. Below I > pasted the code that works for radius in linear scale. > > [Code] > #!/usr/bin/env python > import matplotlib.pyplot as plt > import numpy as np > > #Modified from > http://matplotlib.sourceforge.net/plot_directive/mpl_toolkits/axes_grid/examples/demo_floating_axis.py > > #def curvelinear_test2(fig, lp_r, lp_t): > def curvelinear_test2(fig, lp_t, lp_r, rLower, rUpper): > rmin = np.min(lp_r) > rmax = np.max(lp_r) > print 'rm: ', rmin, 'rM: ', rmax,'rL: ', rLower, 'rU: ', rUpper > """ > polar projection, but in a rectangular box. > """ > global ax1 > import mpl_toolkits.axisartist.angle_helper as angle_helper > from matplotlib.projections import PolarAxes > from matplotlib.transforms import Affine2D > > from mpl_toolkits.axisartist import SubplotHost, ParasiteAxesAuxTrans > > from mpl_toolkits.axisartist import GridHelperCurveLinear > > from mpl_toolkits.axisartist.grid_helper_curvelinear import > GridHelperCurveLinear > > > # see demo_curvelinear_grid.py for details > tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform() > > extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, > lon_cycle = 360, > lat_cycle = None, > lon_minmax = (0, 360), > lat_minmax = > (-np.inf, np.inf), > #lat_minmax = > (rmin, np.inf), > ) > > grid_locator1 = angle_helper.LocatorDMS(12) > > tick_formatter1 = angle_helper.FormatterDMS() > > grid_helper = GridHelperCurveLinear(tr, > extreme_finder=extreme_finder, > grid_locator1=grid_locator1, > tick_formatter1=tick_formatter1 > ) > > > ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) > > fig.add_subplot(ax1) > > # make ticklabels of all axis visible. > ax1.axis[:].major_ticklabels.set_visible(True) > #ax1.axis["top"].major_ticklabels.set_visible(True) > #ax1.axis["left"].major_ticklabels.set_visible(True) > #ax1.axis["bottom"].major_ticklabels.set_visible(True) > > # show angle (0) at right and top > ax1.axis["right"].get_helper().nth_coord_ticks=0 > ax1.axis["top"].get_helper().nth_coord_ticks=0 > > # show radius (1) at left and bottom > ax1.axis["left"].get_helper().nth_coord_ticks=1 > ax1.axis["bottom"].get_helper().nth_coord_ticks=1 > > # set labels > ax1.axis["left"].label.set_text(r'ylabel') > ax1.axis["bottom"].label.set_text(r'xlabel') > ax1.axis["bottom"].major_ticklabels.set_rotation(-30) > > ax1.set_aspect(1.) > ax1.set_xlim(rLower, rUpper) > ax1.set_ylim(rLower, rUpper) > #ax1.rscale('log') > > # A parasite axes with given transform > ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal") > # note that ax2.transData == tr + ax1.transData > # Anthing you draw in ax2 will match the ticks and grids of ax1. > ax1.parasites.append(ax2) > > ax2.plot(lp_t, lp_r, 'o-') > #ax2.semilogy(lp_t, lp_r, 'o-') > #ax2.set_rscale('log') > #ax1.set_xscale('log') > #ax1.set_yscale('log') > > ax1.grid(True) > > if __name__ == "__main__": > > fig = plt.figure(1, figsize=(5, 5)) > fig.clf() > > rmin = 1e-1 > rmax = 1e2 > lp_t = np.linspace(0., 90., 5) > lp_r = np.linspace(rmin, rmax/10, 5)*5 > > print "lp_t: ", lp_t > print "lp_r: ", lp_r > print "log10(lp_r): ", np.log10(lp_r) > > curvelinear_test2(fig, lp_t, lp_r, rmin, rmax) > #curvelinear_test2(fig, lp_t, np.log10(lp_r), np.log10(rmin), np.log(rmax)) > > plt.show() > > [/Code] > > I'm using Enthought Python Distribution Version: 7.0-2 (32-bit) Python > 2.7.1 on win32 > and matplotlib.__version__ is '1.0.1'. > > Thanks, > Junghun Shin > -- > Division of Electrical, Electronic and Information Engineering, > Graduate School of Engineering, Osaka University, > 2-6 Yamada-oka, Suita-shi, Osaka, 565-0871, JAPAN > Tel / Fax: +81-6-6879-8755 > E-mail: jhs...@ef.eie.eng.osaka-u.ac.jp > Group Homepage: http://www.eie.eng.osaka-u.ac.jp/ef/ > > ------------------------------------------------------------------------------ > Benefiting from Server Virtualization: Beyond Initial Workload > Consolidation -- Increasing the use of server virtualization is a top > priority.Virtualization can reduce costs, simplify management, and improve > application availability and disaster protection. Learn more about boosting > the value of server virtualization. http://p.sf.net/sfu/vmware-sfdev2dev > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
testet0.py
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