Coverage for docs/source/examples/autosweep.py: 100%

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1"Show autosweep_1d functionality" 

2import pickle 

3import numpy as np 

4import gpkit 

5from gpkit import units, Variable, Model 

6from gpkit.tools.autosweep import autosweep_1d 

7from gpkit.small_scripts import mag 

8 

9A = Variable("A", "m**2") 

10l = Variable("l", "m") 

11 

12m1 = Model(A**2, [A >= l**2 + units.m**2]) 

13tol1 = 1e-3 

14bst1 = autosweep_1d(m1, tol1, l, [1, 10], verbosity=0) 

15print("Solved after %2i passes, cost logtol +/-%.3g" % (bst1.nsols, bst1.tol)) 

16# autosweep solution accessing 

17l_vals = np.linspace(1, 10, 10) 

18sol1 = bst1.sample_at(l_vals) 

19print("values of l: %s" % l_vals) 

20print("values of A: [%s] %s" % 

21 (" ".join("% .1f" % n for n in sol1("A").magnitude), sol1("A").units)) 

22cost_estimate = sol1["cost"] 

23cost_lb, cost_ub = sol1.cost_lb(), sol1.cost_ub() 

24print("cost lower bound:\n%s\n" % cost_lb) 

25print("cost estimate:\n%s\n" % cost_estimate) 

26print("cost upper bound:\n%s\n" % cost_ub) 

27# you can evaluate arbitrary posynomials 

28np.testing.assert_allclose(mag(2*sol1(A)), mag(sol1(2*A))) 

29assert (sol1["cost"] == sol1(A**2)).all() 

30# the cost estimate is the logspace mean of its upper and lower bounds 

31np.testing.assert_allclose((np.log(mag(cost_lb)) + np.log(mag(cost_ub)))/2, 

32 np.log(mag(cost_estimate))) 

33# save autosweep to a file and retrieve it 

34bst1.save("autosweep.pkl") 

35bst1_loaded = pickle.load(open("autosweep.pkl", "rb")) 

36 

37# this problem is two intersecting lines in logspace 

38m2 = Model(A**2, [A >= (l/3)**2, A >= (l/3)**0.5 * units.m**1.5]) 

39tol2 = {"mosek_cli": 1e-6, "mosek_conif": 1e-6, 

40 "cvxopt": 1e-7}[gpkit.settings["default_solver"]] 

41# test Model method 

42sol2 = m2.autosweep({l: [1, 10]}, tol2, verbosity=0) 

43bst2 = sol2.bst 

44print("Solved after %2i passes, cost logtol +/-%.3g" % (bst2.nsols, bst2.tol)) 

45print("Table of solutions used in the autosweep:") 

46print(bst2.solarray.table())