for j in range(MAX_ROBOTS):
samples[i].append([])
first = True
print 'Reading from: ' + sys.argv[2]
with open(sys.argv[2], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(float(line[i + num_methods*j]))
fig, ax, rects, means= \
graph.draw_3d_bar_chart(samples[2:], [' ' + str(0.1 * i) for i in range(0, 11, 2)],
range(1,6),
ylabel='maxRobots',
xlabel=r'$\lambda$',
zlabel='Distance Traveled')
# fig, ax, rects, means= \
# graph.draw_line_graph(samples[2:], [r'$\lambda$=' + str(0.1 * i) for i in range(11)],
# range(1,6),
# xlabel='maxRobots',
# ylabel='Normalized Distance')
# sigs = None
# if num_methods == 2:
# sigs = []
# for robots in range(MAX_ROBOTS):
# sig = graph.is_significant(samples[0][robots], samples[1][robots])
# if sig:
for j in range(MAX_ROBOTS):
samples[i].append([])
first = True
print 'Reading from: ' + sys.argv[2]
with open(sys.argv[2], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(float(line[i + num_methods*j]))
fig, ax, rects, means= \
graph.draw_3d_bar_chart(samples[2:], [str(0.1 * i) for i in range(0, 11, 2)],
range(1,6),
ylabel='maxRobots',
xlabel=r'$\lambda$',
zlabel='Normalized Time Taken')
ax.view_init(15,-120)
# fig, ax, rects, means= \
# graph.draw_line_graph(samples[2:], [r'$\lambda$=' + str(0.1 * i) for i in range(11)],
# range(1,6),
# xlabel='maxRobots',
# ylabel='Normalized Distance')
# sigs = None
# if num_methods == 2:
# sigs = []
# for robots in range(MAX_ROBOTS):
# sig = graph.is_significant(samples[0][robots], samples[1][robots])
print 'Reading reward from: ' + sys.argv[1]
with open(sys.argv[1], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(-float(line[i + num_methods*j]))
top_level_names = ['10','20','30','40','50']
second_level_names = ['1','2','3','4','5']
third_level_names = ['0.0','-0.5','-1','-1.5','-2','-2.5', '-3', '-3.5', '-4']
fig, ax, rects, means= \
graph.draw_3d_bar_chart(samples, top_level_names=top_level_names,
second_level_names=second_level_names,
third_level_names=third_level_names,
xlabel='visRange (meters)',
ylabel='adjDepth',
zlabel='Normalized Reward (negated)',
flip_y=False)
ax.view_init(15,-135)
fig = plt.gcf()
fig.set_size_inches(5,5)
plt.savefig('out.png',bbox_inches='tight',pad_inches=0.0,dpi=300)
plt.show()
# pylab.figure()
# for method in range(num_methods):
# pylab.hist(samples[method][4], [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 20.0, 50.0, 100.0], histtype='step', label=method_names[method])
for i in range(num_methods):
samples.append([])
for j in range(MAX_ROBOTS):
samples[i].append([])
first = True
print "Reading from: " + sys.argv[2]
with open(sys.argv[2], "rb") as csvfile:
content_reader = csv.reader(csvfile, delimiter=",")
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(float(line[i + num_methods * j]))
fig, ax, rects, means = graph.draw_3d_bar_chart(
samples[2:], method_names[2:], range(1, 6), ylabel="maxRobots", zlabel="Normalized Time Taken", xtickrotation=45
)
ax.view_init(15, -60)
# fig, ax, rects, means= \
# graph.draw_line_graph(samples[2:], [r'$\lambda$=' + str(0.1 * i) for i in range(11)],
# range(1,6),
# xlabel='maxRobots',
# ylabel='Normalized Distance')
# sigs = None
# if num_methods == 2:
# sigs = []
# for robots in range(MAX_ROBOTS):
# sig = graph.is_significant(samples[0][robots], samples[1][robots])