sol1 = bst1.sample_at(l_vals)
print("values of l: %s" % l_vals)
print("values of A: [%s] %s" %
(" ".join("% .1f" % n for n in sol1("A").magnitude), sol1("A").units))
cost_estimate = sol1["cost"]
cost_lb, cost_ub = sol1.cost_lb(), sol1.cost_ub()
print("cost lower bound:\n%s\n" % cost_lb)
print("cost estimate:\n%s\n" % cost_estimate)
print("cost upper bound:\n%s\n" % cost_ub)
# you can evaluate arbitrary posynomials
np.testing.assert_allclose(mag(2*sol1(A)), mag(sol1(2*A)))
assert (sol1["cost"] == sol1(A**2)).all()
# the cost estimate is the logspace mean of its upper and lower bounds
np.testing.assert_allclose((np.log(mag(cost_lb)) + np.log(mag(cost_ub)))/2,
np.log(mag(cost_estimate)))
# save autosweep to a file and retrieve it
bst1.save("autosweep.pkl")
bst1_loaded = pickle.load(open("autosweep.pkl", "rb"))
# this problem is two intersecting lines in logspace
m2 = Model(A**2, [A >= (l/3)**2, A >= (l/3)**0.5 * units.m**1.5])
tol2 = {"mosek": 1e-12, "cvxopt": 1e-7,
"mosek_cli": 1e-6,
'mosek_conif': 1e-6}[gpkit.settings["default_solver"]]
# test Model method
sol2 = m2.autosweep({l: [1, 10]}, tol2, verbosity=0)
bst2 = sol2.bst
print("Solved after %2i passes, cost logtol +/-%.3g" % (bst2.nsols, bst2.tol))
print("Table of solutions used in the autosweep:")
print(bst2.solarray.table())
"Demonstrates manual and auto sweeping and plotting"
import matplotlib as mpl
mpl.use('Agg')
# comment out the imports above and `show()` to show figures in a window
import numpy as np
from gpkit import Model, Variable, units
x = Variable("x", "m", "Swept Variable")
y = Variable("y", "m^2", "Cost")
m = Model(y, [y >= (x/2)**-0.5 * units.m**2.5 + 1*units.m**2, y >= (x/2)**2])
# arguments are: model, swept: values, posnomial for y-axis
sol = m.sweep({x: np.linspace(1, 3, 20)}, verbosity=0)
f, ax = sol.plot(y)
ax.set_title("Manually swept (20 points)")
# f.show()
f.savefig("plot_sweep1d.png")
# arguments are: model, swept: (min, max, optional logtol), posnomial for y-axis
sol = m.autosweep({x: (1, 3)}, tol=0.001, verbosity=0)
f, ax = sol.plot(y)
ax.set_title("Autoswept (7 points)\nGuaranteed to be in blue region")
# f.show()
f.savefig("plot_autosweep1d.png")
"Demonstrates manual and auto sweeping and plotting"
import matplotlib as mpl
mpl.use('Agg')
# comment out the lines above to show figures in a window
import numpy as np
from gpkit import Model, Variable, units
x = Variable("x", "m", "Swept Variable")
y = Variable("y", "m^2", "Cost")
m = Model(
y, [y >= (x / 2)**-0.5 * units.m**2.5 + 1 * units.m**2, y >= (x / 2)**2])
# arguments are: model, swept: values, posnomial for y-axis
sol = m.sweep({x: np.linspace(1, 3, 20)}, verbosity=0)
f, ax = sol.plot(y)
ax.set_title("Manually swept (20 points)")
f.show()
f.savefig("plot_sweep1d.png")
# arguments are: model, swept: (min, max, optional logtol), posnomial for y-axis
sol = m.autosweep({x: (1, 3)}, tol=0.001, verbosity=0)
f, ax = sol.plot(y)
ax.set_title("Autoswept (7 points)\nGuaranteed to be in blue region")
f.show()
f.savefig("plot_autosweep1d.png")
