Hi, here is an example script which places minor ticks with 2 per major tick (minor tick spacing is "fractional" of major tick spacing with relative interval of 1/2):
from pylab import * fig=figure() ax=subplot(111) ax.autoscale(tight=True) plot([1,2,4],[1,2,3]) x_ticks_maj_spacing = float(abs(ax.xaxis.get_ticklocs()[0]-ax.xaxis.get_ticklocs()[1])) x_ticks_min_spacing = x_ticks_maj_spacing/2 ax.xaxis.set_minor_locator(MultipleLocator(x_ticks_min_spacing)) y_ticks_maj_spacing = float(abs(ax.yaxis.get_ticklocs()[0]-ax.yaxis.get_ticklocs()[1])) y_ticks_min_spacing = y_ticks_maj_spacing/2 ax.yaxis.set_minor_locator(MultipleLocator(y_ticks_min_spacing)) show() This works fine. However, if one changes the axes limits then the major ticks get automatically adjusted to a different interval but the minor ticks remain at the positions they were already at. To see this, either use the zoom tools or do the following after running the above: xlim(1,2.5) fig.canvas.draw() The question is, what is the best way to maintain the fractional minor tick spacing? I suppose one could set up a way to update the set_minor_locator and redraw the figure each time the figure axes limits are adjusted, but is there a better way? Best, Chris -- View this message in context: http://old.nabble.com/how-to-maintain-fractional-minor-tick-spacing-tp33480612p33480612.html Sent from the matplotlib - users mailing list archive at Nabble.com. ------------------------------------------------------------------------------ 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
