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_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_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 figure_discrete_choices2(r, data, cs, fname1, fname2):
num_solutions = cs.all_num_implementations
all_discrete_choices = compute_discrete_choices(data)
possible = [
"",
"LCO",
"LFP",
"NiCad",
"NiMH",
"LiPo",
"LCO-LiPo-NiMH",
"LCO-LiPo-NiCad",
"LCO-LFP-LiPo-NiCad",
"LCO-LFP-LiPo",
"LCO-LFP-LiPo-NiMH",
"LCO-LiPo-NiCad-NiMH",
"LCO-LFP-LiPo-NiCad-NiMH",
"LCO-LiPo",
"LCO-LFP",
"LCO-LFP-NiMH",
"LCO-NiMH",
"LCO-NiCad",
"LCO-NiCad-NiMH",
"LCO-LFP-NiCad",
"LCO-LFP-NiCad-NiMH",
"LCO-NiCad-NiMH",
]
possible_this = sorted(np.unique(all_discrete_choices))
for p in possible_this:
if not p in possible:
msg = "Did not anticipate %r as an option." % p
raise ValueError(msg)
choice_as_int = np.zeros(dtype="int", shape=len(all_discrete_choices))
for i, x in enumerate(all_discrete_choices):
choice_as_int[i] = possible.index(x)
def do_axes(pylab):
from mcdp_ipython_utils.plotting import set_axis_colors
set_axis_colors(pylab, color_functions, color_functions)
with r.plot("choice_as_int", **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality(fname1)
y = cs.get_functionality(fname2)
z = choice_as_int
plot_field(pylab, x, y, z, cmap="viridis", vmin=0, vmax=len(possible))
pylab.title("choice_as_int", y=1.08)
do_axes(pylab)
possible_indiv = set()
for p in possible:
for x in p.split("-"):
possible_indiv.add(x)
f = r.figure()
for p in sorted(possible_indiv):
if not p:
continue
feasible = np.array([p in _ for _ in all_discrete_choices])
with f.plot("where_%s" % p, **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality(fname1)
y = cs.get_functionality(fname2)
AND = np.logical_and
is_best = AND(feasible, num_solutions == 1)
is_one_of_two = AND(feasible, num_solutions == 2)
is_one_of_three = AND(feasible, num_solutions == 3)
is_one_of_four_or_more = AND(feasible, num_solutions >= 4)
grey = "#bbbbbb"
color_infeasible = "red"
pylab.plot(
x[cs.is_feasible],
y[cs.is_feasible],
".",
markersize=2,
markerfacecolor=grey,
markeredgecolor="none",
color="none",
clip_on=False,
)
pylab.plot(
x[cs.is_not_feasible],
y[cs.is_not_feasible],
".",
markersize=2,
markerfacecolor=color_infeasible,
markeredgecolor="none",
color="none",
clip_on=False,
)
def plot(where, marker, color):
markersize = 3.5
pylab.plot(
x[where],
y[where],
marker,
markersize=markersize,
markeredgecolor="none",
markerfacecolor=color,
color="none",
clip_on=False,
)
plot(is_best, "o", "black")
plot(is_one_of_two, "d", "#000088")
plot(is_one_of_three, ">", "#880000")
plot(is_one_of_four_or_more, "s", "#880088")
pylab.title("%s" % p, y=1.08)
do_axes(pylab)
r.text("possible", possible)
r.text("text_choice_as_int", choice_as_int)
r.text("choices", all_discrete_choices)
def figure_num_implementations2(r, data, cs, fname1, fname2):
assert isinstance(cs, CommonStats)
f1 = cs.get_functionality(fname1)
f2 = cs.get_functionality(fname2)
def do_axes(pylab):
pylab.xlabel(fname1)
pylab.ylabel(fname2)
set_axis_colors(pylab, color_functions, color_functions)
with r.plot('feasibility', **fig) as pylab:
ieee_spines_zoom3(pylab)
pylab.plot(f1[cs.one_solution],
f2[cs.one_solution],
'k.', markersize=4, **popt)
pylab.plot(f1[cs.multiple_solutions],
f2[cs.multiple_solutions],
'ms', markersize=4, **popt)
pylab.plot(f1[cs.is_not_feasible],
f2[cs.is_not_feasible], 'r.', markersize=0.5, **popt)
do_axes(pylab)
with r.plot('implementations', **fig) as pylab:
ieee_spines_zoom3(pylab)
pylab.plot(f1[cs.one_implementation],
f2[cs.one_implementation],
'k.', markersize=4, **popt)
pylab.plot(f1[cs.multiple_implementations],
f2[cs.multiple_implementations],
'ms', markersize=4, **popt)
pylab.plot(f1[cs.is_not_feasible],
f2[cs.is_not_feasible], 'r.', markersize=0.5, **popt)
do_axes(pylab)
with r.plot('num_implementations', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = f1
y = f2
z = cs.all_num_implementations
plot_field(pylab, x, y, z, cmap='winter', vmin=0, vmax=5)
pylab.title('num implementations', color=color_resources, y=1.08)
do_axes(pylab)
with r.plot('num_solutions', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = f1
y = f2
z = cs.all_num_solutions
plot_field(pylab, x, y, z, cmap='winter', vmin=0, vmax=5)
pylab.title('num solutions', color=color_resources, y=1.08)
do_axes(pylab)
misc = 'num solutions: %s\n num implementations: %s' % (cs.all_num_solutions, cs.all_num_implementations)
# r.text('misc', misc)
def figure_discrete_choices2(r, data, cs, fname1, fname2):
num_solutions = cs.all_num_implementations
all_discrete_choices = compute_discrete_choices(data)
possible = ['', 'LCO',
'LFP',
'NiCad',
'NiMH',
'LiPo',
'LCO-LiPo-NiMH',
'LCO-LiPo-NiCad',
'LCO-LFP-LiPo-NiCad',
'LCO-LFP-LiPo',
'LCO-LFP-LiPo-NiMH',
'LCO-LiPo-NiCad-NiMH',
'LCO-LFP-LiPo-NiCad-NiMH',
'LCO-LiPo',
'LCO-LFP',
'LCO-LFP-NiMH',
'LCO-NiMH',
'LCO-NiCad',
'LCO-NiCad-NiMH',
'LCO-LFP-NiCad',
'LCO-LFP-NiCad-NiMH',
'LCO-NiCad-NiMH']
possible_this = sorted(np.unique(all_discrete_choices))
for p in possible_this:
if not p in possible:
msg = 'Did not anticipate %r as an option.' % p
raise ValueError(msg)
choice_as_int = np.zeros(dtype='int', shape=len(all_discrete_choices))
for i, x in enumerate(all_discrete_choices):
choice_as_int[i] = possible.index(x)
def do_axes(pylab):
from mcdp_ipython_utils.plotting import set_axis_colors
set_axis_colors(pylab, color_functions, color_functions)
with r.plot('choice_as_int', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality(fname1)
y = cs.get_functionality(fname2)
z = choice_as_int
plot_field(pylab, x, y, z, cmap='viridis', vmin=0, vmax=len(possible))
pylab.title('choice_as_int', y=1.08)
do_axes(pylab)
possible_indiv = set()
for p in possible:
for x in p.split('-'):
possible_indiv.add(x)
f = r.figure()
for p in sorted(possible_indiv):
if not p: continue
feasible = np.array([p in _ for _ in all_discrete_choices])
with f.plot('where_%s' % p, **fig) as pylab:
ieee_spines_zoom3(pylab)
x = cs.get_functionality(fname1)
y = cs.get_functionality(fname2)
AND = np.logical_and
is_best = AND(feasible, num_solutions == 1)
is_one_of_two = AND(feasible, num_solutions == 2)
is_one_of_three = AND(feasible, num_solutions == 3)
is_one_of_four_or_more = AND(feasible, num_solutions >= 4)
grey = '#bbbbbb'
color_infeasible = 'red'
pylab.plot(x[cs.is_feasible],
y[cs.is_feasible], '.', markersize=2,
markerfacecolor=grey,
markeredgecolor='none', color='none',
clip_on=False)
pylab.plot(x[cs.is_not_feasible],
y[cs.is_not_feasible], '.', markersize=2,
markerfacecolor=color_infeasible,
markeredgecolor='none', color='none',
clip_on=False)
def plot(where, marker, color):
markersize = 3.5
pylab.plot(x[where], y[where], marker, markersize=markersize,
markeredgecolor='none',
markerfacecolor=color,
color='none', clip_on=False)
plot(is_best, 'o', 'black')
plot(is_one_of_two, 'd', '#000088')
plot(is_one_of_three, '>','#880000')
plot(is_one_of_four_or_more,'s', '#880088')
pylab.title('%s' % p, y=1.08)
do_axes(pylab)
r.text('possible', possible)
r.text('text_choice_as_int', choice_as_int)
r.text('choices', all_discrete_choices)
def figure_num_implementations2(r, data, cs, fname1, fname2):
assert isinstance(cs, CommonStats)
f1 = cs.get_functionality(fname1)
f2 = cs.get_functionality(fname2)
def do_axes(pylab):
pylab.xlabel(fname1)
pylab.ylabel(fname2)
set_axis_colors(pylab, color_functions, color_functions)
with r.plot('feasibility', **fig) as pylab:
ieee_spines_zoom3(pylab)
pylab.plot(f1[cs.one_solution],
f2[cs.one_solution],
'k.',
markersize=4,
**popt)
pylab.plot(f1[cs.multiple_solutions],
f2[cs.multiple_solutions],
'ms',
markersize=4,
**popt)
pylab.plot(f1[cs.is_not_feasible],
f2[cs.is_not_feasible],
'r.',
markersize=0.5,
**popt)
do_axes(pylab)
with r.plot('implementations', **fig) as pylab:
ieee_spines_zoom3(pylab)
pylab.plot(f1[cs.one_implementation],
f2[cs.one_implementation],
'k.',
markersize=4,
**popt)
pylab.plot(f1[cs.multiple_implementations],
f2[cs.multiple_implementations],
'ms',
markersize=4,
**popt)
pylab.plot(f1[cs.is_not_feasible],
f2[cs.is_not_feasible],
'r.',
markersize=0.5,
**popt)
do_axes(pylab)
with r.plot('num_implementations', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = f1
y = f2
z = cs.all_num_implementations
plot_field(pylab, x, y, z, cmap='winter', vmin=0, vmax=5)
pylab.title('num implementations', color=color_resources, y=1.08)
do_axes(pylab)
with r.plot('num_solutions', **fig) as pylab:
ieee_spines_zoom3(pylab)
x = f1
y = f2
z = cs.all_num_solutions
plot_field(pylab, x, y, z, cmap='winter', vmin=0, vmax=5)
pylab.title('num solutions', color=color_resources, y=1.08)
do_axes(pylab)
misc = 'num solutions: %s\n num implementations: %s' % (
cs.all_num_solutions, cs.all_num_implementations)