def create_report_drone1_mass_cost(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'num_missions', 'endurance')
figure_discrete_choices2(r, data, cs, 'num_missions', 'endurance')
f = r.figure()
with f.plot('total_cost', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('num_missions')
y = cs.get_functionality('endurance')
z = cs.get_min_resource('total_cost')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('total_cost', color=color_resources, y=1.08)
with f.plot('total_mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('num_missions')
y = cs.get_functionality('endurance')
z = cs.get_min_resource('total_mass')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('total_mass', color=color_resources, y=1.08)
return r
def create_report_min_joint(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'missions', 'capacity')
figure_discrete_choices2(r, data, cs, 'missions', 'capacity')
return r
def create_report_drone1_mass_cost(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'num_missions', 'endurance')
figure_discrete_choices2(r, data, cs, 'num_missions', 'endurance')
f = r.figure()
with f.plot('total_cost', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('num_missions')
y = cs.get_functionality('endurance')
z = cs.get_min_resource('total_cost')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('total_cost', color=color_resources, y=1.08)
with f.plot('total_mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('num_missions')
y = cs.get_functionality('endurance')
z = cs.get_min_resource('total_mass')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('total_mass', color=color_resources, y=1.08)
return r
def create_report_drone1_cost(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'num_missions', 'endurance')
figure_discrete_choices2(r, data, cs, 'num_missions', 'endurance')
return r
def create_report_min_mass(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'missions', 'capacity')
figure_discrete_choices2(r, data, cs, 'missions', 'capacity')
f = r.figure()
with f.plot('mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('missions')
y = cs.get_functionality('capacity')
z = cs.get_min_resource('mass')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('mass', color=color_resources, y=1.08)
do_axes(pylab)
return r
def create_report_min_mass(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
figure_num_implementations2(r, data, cs, 'missions', 'capacity')
figure_discrete_choices2(r, data, cs, 'missions', 'capacity')
f = r.figure()
with f.plot('mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality('missions')
y = cs.get_functionality('capacity')
z = cs.get_min_resource('mass')
plot_field(pylab, x, y, z, cmap=colormap)
pylab.title('mass', color=color_resources, y=1.08)
do_axes(pylab)
return r
def report_plane2(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
what_to_plot_res = dict(total_mass="kg", total_cost="USD")
what_to_plot_fun = dict(endurance="Wh", extra_payload="g")
plot_all_directions(r, queries=data['queries'], results=data['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
fig1 = dict(figsize=(3, 3))
fig2 = dict(figsize=(4, 4))
fnames = ('endurance', 'extra_payload')
rnames = ('total_cost', 'total_mass')
axis = (108, 145, 0.05, 0.8)
axis2 = (105, 111.5, 0.05, 0.27)
fs, rs = cs.iterate(fnames, rnames)
colors = get_colors(len(fs))
f = r.figure()
with f.plot('resources1', **fig1) as pylab:
ieee_spines_zoom3(pylab)
for i, ((f1, f2), resources) in enumerate(zip(fs, rs)):
color = colors[i]
if resources:
marker = 'k.'
else:
marker = 'x'
pylab.plot(f1, f2, marker, markerfacecolor=color, clip_on=False)
pylab.xlabel('endurance [min]')
pylab.ylabel('extra_payload [g]')
# pylab.xticks([0, 30, 60, 90, 120])
set_axis_colors(pylab, color_functions, color_functions)
params0 = dict(color_shadow=[1.0, 0.8, 0.8], markers='k.',
markers_params={})
color_shadow = params0['color_shadow']
markers = params0['markers']
P = parse_poset('dimensionless x dimensionless')
with f.plot('resources2', **fig2) as pylab:
ieee_spines_zoom3(pylab)
for i, resources in enumerate(rs):
v = P.Us(resources)
color = colors[i]
plot_upset_R2(pylab, v, axis, extra_space_shadow=0,
color_shadow=color, markers=markers,
marker_params=dict(markerfacecolor=color))
pylab.ylabel('total mass [kg]')
pylab.xlabel('total cost [USD]')
pylab.xticks([110, 120, 130, 140, 150])
# pylab.yticks([0.2, 0.25, 0.3, 0.35])
set_axis_colors(pylab, color_resources, color_resources)
pylab.axis(axis)
rs_subset = rs[:3]
with f.plot('resources3', **fig2) as pylab:
ieee_spines_zoom3(pylab)
for i, resources in enumerate(rs_subset):
v = P.Us(resources)
color = colors[i]
plot_upset_R2(pylab, v, axis2, extra_space_shadow=0,
color_shadow=color, markers=markers,
marker_params=dict(markerfacecolor=color))
pylab.ylabel('total mass [kg]')
pylab.xlabel('total cost [USD]')
pylab.xticks([110, 110.5, 111,111.5,])
set_axis_colors(pylab, color_resources, color_resources)
return r
def report_plane2(data):
matplotlib_settings()
cs = CommonStats(data)
r = Report()
what_to_plot_res = dict(total_mass="kg", total_cost="USD")
what_to_plot_fun = dict(endurance="Wh", extra_payload="g")
plot_all_directions(r,
queries=data['queries'],
results=data['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
fig1 = dict(figsize=(3, 3))
fig2 = dict(figsize=(4, 4))
fnames = ('endurance', 'extra_payload')
rnames = ('total_cost', 'total_mass')
axis = (108, 145, 0.05, 0.8)
axis2 = (105, 111.5, 0.05, 0.27)
fs, rs = cs.iterate(fnames, rnames)
colors = get_colors(len(fs))
f = r.figure()
with f.plot('resources1', **fig1) as pylab:
ieee_spines_zoom3(pylab)
for i, ((f1, f2), resources) in enumerate(zip(fs, rs)):
color = colors[i]
if resources:
marker = 'k.'
else:
marker = 'x'
pylab.plot(f1, f2, marker, markerfacecolor=color, clip_on=False)
pylab.xlabel('endurance [min]')
pylab.ylabel('extra_payload [g]')
# pylab.xticks([0, 30, 60, 90, 120])
set_axis_colors(pylab, color_functions, color_functions)
params0 = dict(color_shadow=[1.0, 0.8, 0.8],
markers='k.',
markers_params={})
color_shadow = params0['color_shadow']
markers = params0['markers']
P = parse_poset('dimensionless x dimensionless')
with f.plot('resources2', **fig2) as pylab:
ieee_spines_zoom3(pylab)
for i, resources in enumerate(rs):
v = P.Us(resources)
color = colors[i]
plot_upset_R2(pylab,
v,
axis,
extra_space_shadow=0,
color_shadow=color,
markers=markers,
marker_params=dict(markerfacecolor=color))
pylab.ylabel('total mass [kg]')
pylab.xlabel('total cost [USD]')
pylab.xticks([110, 120, 130, 140, 150])
# pylab.yticks([0.2, 0.25, 0.3, 0.35])
set_axis_colors(pylab, color_resources, color_resources)
pylab.axis(axis)
rs_subset = rs[:3]
with f.plot('resources3', **fig2) as pylab:
ieee_spines_zoom3(pylab)
for i, resources in enumerate(rs_subset):
v = P.Us(resources)
color = colors[i]
plot_upset_R2(pylab,
v,
axis2,
extra_space_shadow=0,
color_shadow=color,
markers=markers,
marker_params=dict(markerfacecolor=color))
pylab.ylabel('total mass [kg]')
pylab.xlabel('total cost [USD]')
pylab.xticks([
110,
110.5,
111,
111.5,
])
set_axis_colors(pylab, color_resources, color_resources)
return r
