# Show the max/min value xmin = 0 xmax = xvalues ymin = 0 ymax = delta * len(data) # The overall dimensions xdim = 3.0 xscale = xdim / (2 * xvalues) ydim = 1.5 yscale = ydim / ymax # Get the header info s = tkz.get_header() s += tkz.get_begin_tikz(xdim=3.5, ydim=2.0, xunit='in', yunit='in') # Create the plot background for y in yticks: s += tkz.get_2d_plot([xmin, xmax], [y, y], xscale=xscale, yscale=yscale, color='gray', line_dim='thin', xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax) for y in np.linspace(0, delta * (len(data) - 1), len(data)):
# Set legend parameters length = 0.15 xlegend = 2.0 # Set the positions of the tick locations yticks = [0, 0.25, 0.5, 0.75, 1.0] xticks = [ 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5 ] ylabel_offset = 0.15 length = 0.15 ylegend = 0.025 + len(heuristics)*0.06 xlegend = 3.5 # Get the header info s = tikz.get_header() s += tikz.get_begin_tikz(xdim=2, ydim=2, xunit='in', yunit='in') # Plot the axes s += tikz.get_2d_axes(xmin, xmax, ymin, ymax, tick_frac=tick_frac, ylabel_offset=ylabel_offset, xscale=xscale, yscale=yscale, xticks=xticks, yticks=yticks, xlabel='$\\alpha$', ylabel='Fraction of problems') for k in xrange(len(heuristics)): tau, rho = get_performance_profile(r[:, k], 1.5*xmax) s += tikz.get_2d_plot(tau, rho, xscale=xscale, yscale=yscale, color=colors[k], line_dim='ultra thick', xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, symbol=None)