def go_(model_name, queries, result_like, what_to_plot_res, what_to_plot_fun, fn):
lib = MCDPLibrary()
lib.add_search_dir('.')
ndp = lib.load_ndp(model_name)
data = solve_queries(ndp, queries, result_like)
r = Report()
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)
print('writing to %r' % fn)
r.to_html(fn)
def go_(model_name, queries, result_like, what_to_plot_res, what_to_plot_fun,
fn):
lib = MCDPLibrary()
lib.add_search_dir('.')
ndp = lib.load_ndp(model_name)
data = solve_queries(ndp, queries, result_like)
r = Report()
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)
print('writing to %r' % fn)
r.to_html(fn)
def go(lib):
combinations = {
"capacity": (np.linspace(50, 3000, 10), "Wh"),
"missions": ( 1000, "[]"),
}
result_like = dict(maintenance="dimensionless", cost="USD", mass='kg')
what_to_plot_res = result_like
what_to_plot_fun = dict(capacity="Wh", missions="[]")
ndp = lib.load_ndp('batteries')
data = solve_combinations(ndp, combinations, result_like)
r = Report()
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)
r.to_html('out/batteries-c1.html')
def go2(lib):
model_name = 'batteries_squash'
combinations = {
"capacity": (np.linspace(50, 3000, 10), "Wh"),
"missions": (1000, "[]"),
}
result_like = dict(cost="USD", mass='kg')
what_to_plot_res = result_like
what_to_plot_fun = dict(capacity="Wh", missions="[]")
ndp = lib.load_ndp(model_name)
data = solve_combinations(ndp, combinations, result_like)
r = Report()
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)
r.to_html('out/batteries_squash-c2.html')
def go():
model_name = 'droneC'
queries = []
def add(q):
queries.append(q)
n = 10
endurance = np.linspace(1, 20, n)
payload = np.linspace(5, 50, n)
for endurance, payload in zip(endurance, payload):
q = {
"num_missions": (1000, "[]"),
"extra_power": (5, "W"),
"extra_payload": (payload, "g"),
"endurance": (endurance, "minutes"),
}
add(q)
result_like = dict(total_cost="CHF", total_mass='kg')
what_to_plot_res = result_like
what_to_plot_fun = dict(extra_payload="g", endurance="minutes")
librarian = Librarian()
librarian.find_libraries('..')
lib = librarian.load_library('droneC_cost_v1')
ndp = lib.load_ndp(model_name)
data = solve_queries(ndp, queries, result_like)
r = Report()
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)
fn = 'out/droneC_c1.html'
print('writing to %r' % fn)
r.to_html(fn)
def go():
fn = 'out/actuation_c1.html'
model_name = 'actuation'
queries = []
def add(q):
queries.append(q)
n = 10
lifts = np.linspace(0, 10.0, n)
for lift, in zip(lifts):
q = {
"lift": (lift, "N"),
}
add(q)
result_like = dict(power="W", cost='$')
what_to_plot_res = result_like
what_to_plot_fun = dict(lift="N")
lib = MCDPLibrary()
lib.add_search_dir('.')
ndp = lib.load_ndp(model_name)
data = solve_queries(ndp, queries, result_like)
r = Report()
plot_all_directions(r,
queries=data['queries'],
results=data['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
print('writing to %r' % fn)
r.to_html(fn)
def go():
fn = 'out/actuation_c1.html'
model_name = 'actuation'
queries = []
def add(q):
queries.append(q)
n = 10
lifts = np.linspace(0, 10.0, n)
for lift, in zip(lifts):
q = {
"lift": (lift, "N"),
}
add(q)
result_like = dict(power="W", cost='$')
what_to_plot_res = result_like
what_to_plot_fun = dict(lift="N")
lib = MCDPLibrary()
lib.add_search_dir('.')
ndp = lib.load_ndp(model_name)
data = solve_queries(ndp, queries, result_like)
r = Report()
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)
print('writing to %r' % fn)
r.to_html(fn)
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(res):
r = Report()
dataL = res['dataL']
dataU = res['dataU']
what_to_plot_res = dict(total_cost="USD", total_mass='kg')
what_to_plot_fun = dict(endurance="hour", extra_payload="g")
queries = dataL['queries']
endurance = [q['endurance'] for q in queries]
def get_value(data, field):
for res in data['results']:
a = to_numpy_array({field: 'kg'}, res)
if len(a):
a = min(a[field])
else:
a = None
yield a
from matplotlib import pylab
ieee_fonts_zoom3(pylab)
markers = dict(markeredgecolor='none', markerfacecolor='black', markersize=6,
marker='o')
LOWER2 = dict(color='orange', linewidth=4, linestyle='-', clip_on=False)
UPPER2 = dict(color='purple', linewidth=4, linestyle='-', clip_on=False)
LOWER2.update(markers)
UPPER2.update(markers)
color_resources = '#700000'
color_functions = '#007000'
fig = dict(figsize=(4.5, 4))
with r.plot('total_mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
total_massL = np.array(list(get_value(dataL, 'total_mass')))
total_massU = np.array(list(get_value(dataU, 'total_mass')))
print endurance
print total_massL, total_massU
pylab.plot(endurance, total_massL, **LOWER2)
pylab.plot(endurance, total_massU, **UPPER2)
set_axis_colors(pylab, color_functions, color_resources)
pylab.xlabel('endurance [hours]')
pylab.ylabel('total_mass [kg]')
return r
print('Plotting lower')
with r.subsection('lower') as rL:
plot_all_directions(rL,
queries=dataL['queries'],
results=dataL['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
print('Plotting upper')
with r.subsection('upper') as rU:
plot_all_directions(rU,
queries=dataU['queries'],
results=dataU['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
return r
def report(res):
r = Report()
dataL = res['dataL']
dataU = res['dataU']
what_to_plot_res = dict(total_cost="USD", total_mass='kg')
what_to_plot_fun = dict(endurance="hour", extra_payload="g")
queries = dataL['queries']
endurance = [q['endurance'] for q in queries]
def get_value(data, field):
for res in data['results']:
a = to_numpy_array({field: 'kg'}, res)
if len(a):
a = min(a[field])
else:
a = None
yield a
from matplotlib import pylab
ieee_fonts_zoom3(pylab)
markers = dict(markeredgecolor='none',
markerfacecolor='black',
markersize=6,
marker='o')
LOWER2 = dict(color='orange', linewidth=4, linestyle='-', clip_on=False)
UPPER2 = dict(color='purple', linewidth=4, linestyle='-', clip_on=False)
LOWER2.update(markers)
UPPER2.update(markers)
color_resources = '#700000'
color_functions = '#007000'
fig = dict(figsize=(4.5, 4))
with r.plot('total_mass', **fig) as pylab:
ieee_spines_zoom3(pylab)
total_massL = np.array(list(get_value(dataL, 'total_mass')))
total_massU = np.array(list(get_value(dataU, 'total_mass')))
print endurance
print total_massL, total_massU
pylab.plot(endurance, total_massL, **LOWER2)
pylab.plot(endurance, total_massU, **UPPER2)
set_axis_colors(pylab, color_functions, color_resources)
pylab.xlabel('endurance [hours]')
pylab.ylabel('total_mass [kg]')
return r
print('Plotting lower')
with r.subsection('lower') as rL:
plot_all_directions(rL,
queries=dataL['queries'],
results=dataL['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
print('Plotting upper')
with r.subsection('upper') as rU:
plot_all_directions(rU,
queries=dataU['queries'],
results=dataU['results'],
what_to_plot_res=what_to_plot_res,
what_to_plot_fun=what_to_plot_fun)
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
