def plot_opening(csv_data):
keys = ['distance', 'type', 'repetition']
llists = expand(extract_keys(csv_data, keys), keys)
dists = np.array([float(f) for f in llists[0]])/100.
types = llists[1]
types_arr = np.array(types)
drawer_dists = dists[np.where(types_arr == 'R')[0]]
other_dists = dists[np.where(types_arr != 'R')[0]]
print 'Opening distances types', set(types)
bin_width = 0.02
bins = np.arange(-bin_width/2., np.max(dists)+2*bin_width, bin_width)
dists_list = [dists]#, drawer_dists, other_dists]
titles = ['Opening Distances of Drawers']#, 'drawer', 'other']
# print 'Total number of drawers:', len(dists)
mpu.set_figure_size(5.,4.)
for idx, d in enumerate(dists_list):
f = pb.figure()
f.set_facecolor('w')
hist, bin_edges = np.histogram(d, bins)
#import pdb; pdb.set_trace()
mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=0.8*bin_width, xlabel='Opening Distance (meters)',
ylabel='\# of Mechanisms',
plot_title=titles[idx], color='#3366FF')
def handle_height_histogram_advait(mean_height_list,
plot_title='',
color='#3366FF',
max_height=2.2,
bin_width=.1,
ymax=35,
new_figure=True,
label='__no_legend__'):
if new_figure:
mpu.set_figure_size(5., 4.)
# f = mpu.figure()
# f.subplots_adjust(bottom=.25, top=.99, right=0.99, left=0.12)
bins = np.arange(0. - bin_width / 2., max_height + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(np.array(mean_height_list), bins)
h = mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=bin_width * 0.8,
plot_title=plot_title,
xlabel='Height (meters)',
ylabel='\# of mechanisms',
color=color,
label=label)
pb.xlim(0, max_height)
pb.ylim(0, ymax)
return h
def plot_opening(csv_data):
keys = ['distance', 'type', 'repetition']
llists = expand(extract_keys(csv_data, keys), keys)
dists = np.array([float(f) for f in llists[0]]) / 100.
types = llists[1]
types_arr = np.array(types)
drawer_dists = dists[np.where(types_arr == 'R')[0]]
other_dists = dists[np.where(types_arr != 'R')[0]]
print 'Opening distances types', set(types)
bin_width = 0.02
bins = np.arange(-bin_width / 2., np.max(dists) + 2 * bin_width, bin_width)
dists_list = [dists] #, drawer_dists, other_dists]
titles = ['Opening Distances of Drawers'] #, 'drawer', 'other']
# print 'Total number of drawers:', len(dists)
mpu.set_figure_size(5., 4.)
for idx, d in enumerate(dists_list):
f = pb.figure()
f.set_facecolor('w')
hist, bin_edges = np.histogram(d, bins)
#import pdb; pdb.set_trace()
mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=0.8 * bin_width,
xlabel='Opening Distance (meters)',
ylabel='\# of Mechanisms',
plot_title=titles[idx],
color='#3366FF')
def handle_height_histogram_advait(mean_height_list, plot_title='',
color='#3366FF', max_height=2.2, bin_width=.1, ymax=35,
new_figure = True, label = '__no_legend__'):
if new_figure:
mpu.set_figure_size(5.,4.)
# f = mpu.figure()
# f.subplots_adjust(bottom=.25, top=.99, right=0.99, left=0.12)
bins = np.arange(0.-bin_width/2., max_height+2*bin_width, bin_width)
hist, bin_edges = np.histogram(np.array(mean_height_list), bins)
h = mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=bin_width*0.8, plot_title=plot_title,
xlabel='Height (meters)', ylabel='\# of mechanisms', color=color, label = label)
pb.xlim(0, max_height)
pb.ylim(0, ymax)
return h
print 'rad, cx, cy:', rad, cx, cy
c_ts = np.matrix([cx, cy, 0.]).T
start_angle = tr.angle_within_mod180(
math.atan2(pts_2d[1, 0] - cy, pts_2d[0, 0] - cx) - math.pi / 2)
end_angle = tr.angle_within_mod180(
math.atan2(pts_2d[1, -1] - cy, pts_2d[0, -1] - cx) - math.pi / 2)
mpu.plot_circle(cx,
cy,
rad,
start_angle,
end_angle,
label='Actual\_opt',
color='r')
if opt.icra_presentation_plot:
mpu.set_figure_size(30, 20)
rad = 1.0
x_guess = pts_2d[0, 0]
y_guess = pts_2d[1, 0] - rad
rad_guess = rad
rad, cx, cy = fit_circle(rad_guess,
x_guess,
y_guess,
pts_2d,
method='fmin_bfgs',
verbose=False)
print 'Estimated rad, cx, cy:', rad, cx, cy
start_angle = tr.angle_within_mod180(
math.atan2(pts_2d[1, 0] - cy, pts_2d[0, 0] - cx) - math.pi / 2)
def plot_radii(csv_data, color='#3366FF'):
keys = ['radius', 'type', 'name', 'repetition']
llists = expand(extract_keys(csv_data, keys), keys)
rad_list = np.array([float(r) for r in llists[0]]) / 100.0
types = llists[1]
names = np.array(llists[2])
all_types = set(types)
print 'Radii types', all_types
types_arr = np.array(types)
# np.where(types_arr == 'C')
cabinet_rad_list = rad_list[np.where(types_arr == 'C')[0]]
others_rad_list = rad_list[np.where(types_arr != 'C')[0]]
other_names = names[np.where(types_arr != 'C')[0]]
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>'
print 'radii other names'
for i, n in enumerate(other_names):
print n, others_rad_list[i]
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>'
rad_lists = [rad_list, cabinet_rad_list, others_rad_list]
titles = ['Radii of Rotary Mechanisms', 'Radii of Cabinets', 'Radii of Other Mechanisms']
bin_width = 0.05
max_radius = np.max(rad_list)
print 'MIN RADIUS', np.min(rad_list)
print 'MAX RADIUS', max_radius
mpu.set_figure_size(5.,5.)
for idx, radii in enumerate(rad_lists):
f = pb.figure()
f.set_facecolor('w')
bins = np.arange(0.-bin_width/2., max_radius+2*bin_width, bin_width)
hist, bin_edges = np.histogram(radii, bins)
h = mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=0.8*bin_width, xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title=titles[idx],
color=color, label='All')
pb.xlim(.1, 1.)
pb.ylim(0, 55)
mpu.legend(display_mode = 'less_space', handlelength=1.)
#-- different classes in different colors in the same histogram.
f = pb.figure()
f.set_facecolor('w')
bins = np.arange(0.-bin_width/2., max_radius+2*bin_width, bin_width)
hist, bin_edges = np.histogram(rad_lists[0], bins)
h = mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=0.8*bin_width, xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title=titles[1],
color='g', label='Cabinets')
hist, bin_edges = np.histogram(rad_lists[2], bins)
h = mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=0.8*bin_width, xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title='Cabinets and Other Mechanisms',
color='y', label='Other')
pb.xlim(.1, 1.)
pb.ylim(0, 55)
mpu.legend(display_mode = 'less_space', handlelength=1.)
color_list = ['g', 'b', 'r']
marker_list = ['s', '^', 'v']
label_list = ['All', 'Cabinets', 'Other']
scatter_size_list = [8, 5, 5]
mpu.set_figure_size(5.,5.)
mpu.figure()
for idx, radii in enumerate(rad_lists):
bins = np.arange(0.-bin_width/2., max_radius+2*bin_width, bin_width)
hist, bin_edges = np.histogram(radii, bins)
bin_midpoints = np.arange(0., max_radius+bin_width, bin_width)
mpu.plot_yx(hist, bin_midpoints, color = color_list[idx],
alpha = 0.6, marker = marker_list[idx],
scatter_size = scatter_size_list[idx], xlabel='Radius (meters)',
ylabel='\# of mechanisms', label = label_list[idx])
mpu.legend(display_mode = 'less_space')
def plot_radii(csv_data, color='#3366FF'):
keys = ['radius', 'type', 'name', 'repetition']
llists = expand(extract_keys(csv_data, keys), keys)
rad_list = np.array([float(r) for r in llists[0]]) / 100.0
types = llists[1]
names = np.array(llists[2])
all_types = set(types)
print 'Radii types', all_types
types_arr = np.array(types)
# np.where(types_arr == 'C')
cabinet_rad_list = rad_list[np.where(types_arr == 'C')[0]]
others_rad_list = rad_list[np.where(types_arr != 'C')[0]]
other_names = names[np.where(types_arr != 'C')[0]]
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>'
print 'radii other names'
for i, n in enumerate(other_names):
print n, others_rad_list[i]
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>'
rad_lists = [rad_list, cabinet_rad_list, others_rad_list]
titles = [
'Radii of Rotary Mechanisms', 'Radii of Cabinets',
'Radii of Other Mechanisms'
]
bin_width = 0.05
max_radius = np.max(rad_list)
print 'MIN RADIUS', np.min(rad_list)
print 'MAX RADIUS', max_radius
mpu.set_figure_size(5., 5.)
for idx, radii in enumerate(rad_lists):
f = pb.figure()
f.set_facecolor('w')
bins = np.arange(0. - bin_width / 2., max_radius + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(radii, bins)
h = mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=0.8 * bin_width,
xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title=titles[idx],
color=color,
label='All')
pb.xlim(.1, 1.)
pb.ylim(0, 55)
mpu.legend(display_mode='less_space', handlelength=1.)
#-- different classes in different colors in the same histogram.
f = pb.figure()
f.set_facecolor('w')
bins = np.arange(0. - bin_width / 2., max_radius + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(rad_lists[0], bins)
h = mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=0.8 * bin_width,
xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title=titles[1],
color='g',
label='Cabinets')
hist, bin_edges = np.histogram(rad_lists[2], bins)
h = mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=0.8 * bin_width,
xlabel='Radius (meters)',
ylabel='\# of mechanisms',
plot_title='Cabinets and Other Mechanisms',
color='y',
label='Other')
pb.xlim(.1, 1.)
pb.ylim(0, 55)
mpu.legend(display_mode='less_space', handlelength=1.)
color_list = ['g', 'b', 'r']
marker_list = ['s', '^', 'v']
label_list = ['All', 'Cabinets', 'Other']
scatter_size_list = [8, 5, 5]
mpu.set_figure_size(5., 5.)
mpu.figure()
for idx, radii in enumerate(rad_lists):
bins = np.arange(0. - bin_width / 2., max_radius + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(radii, bins)
bin_midpoints = np.arange(0., max_radius + bin_width, bin_width)
mpu.plot_yx(hist,
bin_midpoints,
color=color_list[idx],
alpha=0.6,
marker=marker_list[idx],
scatter_size=scatter_size_list[idx],
xlabel='Radius (meters)',
ylabel='\# of mechanisms',
label=label_list[idx])
mpu.legend(display_mode='less_space')
rad_guess = rad
rad, cx, cy = fit_circle(rad_guess,x_guess,y_guess,pts_2d,
method='fmin_bfgs',verbose=False)
print 'rad, cx, cy:', rad, cx, cy
c_ts = np.matrix([cx, cy, 0.]).T
start_angle = tr.angle_within_mod180(math.atan2(pts_2d[1,0]-cy,
pts_2d[0,0]-cx) - math.pi/2)
end_angle = tr.angle_within_mod180(math.atan2(pts_2d[1,-1]-cy,
pts_2d[0,-1]-cx) - math.pi/2)
mpu.plot_circle(cx, cy, rad, start_angle, end_angle,
label='Actual\_opt', color='r')
if opt.icra_presentation_plot:
mpu.set_figure_size(30,20)
rad = 1.0
x_guess = pts_2d[0,0]
y_guess = pts_2d[1,0] - rad
rad_guess = rad
rad, cx, cy = fit_circle(rad_guess,x_guess,y_guess,pts_2d,
method='fmin_bfgs',verbose=False)
print 'Estimated rad, cx, cy:', rad, cx, cy
start_angle = tr.angle_within_mod180(math.atan2(pts_2d[1,0]-cy,
pts_2d[0,0]-cx) - math.pi/2)
end_angle = tr.angle_within_mod180(math.atan2(pts_2d[1,-1]-cy,
pts_2d[0,-1]-cx) - math.pi/2)
subsample_ratio = 1
