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_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 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