# 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)
