def test_diagonal_rectangulation_15rect(self):
w = 320
h = 180
k = 1.5
# 0.1: proportion is predominant
# 0.05: seems good ballanced
c = 0.05
B, dim = r.do_diagonal_rectangulation(
[7, 12, 6, 4, 10, 1, 13, 5, 14, 8, 9, 2, 0, 3, 11])
Bc = np.array([[0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 11, 11, 11, 11],
[1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 11, 11, 11, 11],
[1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 11, 11, 11, 11],
[1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 11, 11, 11, 11],
[4, 4, 4, 4, 4, 5, 5, 5, 8, 9, 9, 11, 11, 11, 11],
[4, 4, 4, 4, 4, 5, 5, 5, 8, 9, 9, 11, 11, 11, 11],
[6, 6, 6, 6, 6, 6, 6, 6, 8, 9, 9, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 9, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 9, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 9, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 10, 10, 10, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 10, 10, 10, 11, 11, 11, 11],
[7, 7, 7, 7, 7, 7, 7, 7, 12, 12, 12, 12, 12, 13, 14],
[7, 7, 7, 7, 7, 7, 7, 7, 12, 12, 12, 12, 12, 13, 14],
[7, 7, 7, 7, 7, 7, 7, 7, 12, 12, 12, 12, 12, 13, 14]],
dtype=int)
self.assertTrue((B == Bc).all())
r.draw_resized_rectangles(B, dim, 1, 1)
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
mat_sol = r.minimize_rectangulation(E, dim, w, h, k, c)
r.draw_resized_rectangles(B, mat_sol, w, h)
def test_scipy_minimize(self):
w = 320
h = 180
k = 1.5
c = 0.05
Bc = [
np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]], dtype=int),
np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]], dtype=int),
np.array([[0, 0, 2], [1, 1, 2], [1, 1, 2]], dtype=int),
np.array([[0, 1, 1, 3, 3], [0, 1, 1, 3, 3], [2, 2, 2, 3, 3],
[2, 2, 2, 3, 3], [2, 2, 2, 4, 4]],
dtype=int)
]
Ec = [[[1., 1., 1., 0., 0., 0.], [0., 0., 0., 1., 0., 0.],
[0., 0., 0., 1., -1., 0.], [0., 0., 0., 0., 1., -1.]],
[[1., 0., 0., 0., 0., 0.], [0., 0., 0., 1., 1., 1.],
[1., -1., 0., 0., 0., 0.], [0., 1., -1., 0., 0., 0.]],
[[1., 0., 1., 0., 0., 0.], [0., 0., 0., 1., 1., 0.],
[1., -1., 0., 0., 0., 0.], [0., 0., 0., 1., 1., -1.]],
[[1., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 1., 0., 0.],
[1., 1., -1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., -1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., -1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 1., -1., -1.]]]
sol_c = [
np.array([[106.66666667, 106.66666667, 106.66666667],
[180., 180., 180.]]),
np.array([[320., 320., 320.], [60., 60., 60.]]),
np.array([[203.63636358, 203.63636358, 116.36363642],
[90.00000004, 89.99999996, 180.]]),
np.array([[
94.76075857, 94.76075848, 189.52151705, 130.47848295,
130.47848295
],
[
109.87696965, 109.87696965, 70.12303035, 89.99999997,
90.00000003
]])
]
diagonals = [[0, 1, 2], [2, 1, 0], [1, 0, 2], [2, 0, 4, 1, 3]]
for i, diag in enumerate(diagonals):
B, dim = r.do_diagonal_rectangulation(diag)
self.assertTrue((B == Bc[i]).all())
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
self.assertTrue((E == Ec[i]).all())
sol = r.minimize_rectangulation(E, dim, w, h, k, c)
self.assertLess(np.sum(np.abs(sol_c[i] - sol)), 0.01)
r.draw_resized_rectangles(B, dim, w, h)
def test_fit_sides(self):
w = 320
h = 180
k = 1.5
diagonals = [[0, 1, 2], [2, 1, 0], [1, 0, 2], [2, 0, 4, 1, 3]]
for diag in diagonals:
N = len(diag)
B, dim = r.do_diagonal_rectangulation(diag)
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
sol = rfs.solve_fit_rectangles(E, B, w, h, k)
mat_sol = self.get_matrix_from_finiteset(N, sol)
r.draw_resized_rectangles(B, mat_sol, w, h)
def test_diagonal_rectangulation_random(self):
w = 320
h = 180
k = 1.5
c = 0.05
rect = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
shuffle(rect)
print(rect)
B, dim = r.do_diagonal_rectangulation(rect)
r.draw_resized_rectangles(B, dim, 1, 1)
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
mat_sol = r.minimize_rectangulation(E, dim, w, h, k, c)
r.draw_resized_rectangles(B, mat_sol, w, h)
def test_lagrange_method_analitycal(self):
w = 320
h = 180
k = 1.5
# [2, 0, 4, 1, 3] can be handled, but takes a little bit
diagonals = [[0, 1, 2], [2, 1, 0], [1, 0, 2]]
for diag in diagonals:
N = len(diag)
B, dim = r.do_diagonal_rectangulation(diag)
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
sol = rl.solve_rectangle_eqs(E, w, h, k)
print(sol)
mat_sol = self.get_matrix_from_finiteset(N, sol)
r.draw_resized_rectangles(B, mat_sol, w, h)
def test_lagrange_method_numerical(self):
w = 320
h = 180
k = 1.5
diagonals = [[0, 1, 2], [2, 1, 0], [1, 0, 2]]
for diag in diagonals:
N = len(diag)
B, dim = r.do_diagonal_rectangulation(diag)
dim[0, :] *= w
dim[1, :] *= h
E = r.build_rectangulation_equations(B)
# Number of lambda values will be same as number of eqs (N+1)
initial_est = np.ones(dim.size + N + 1)
initial_est[0:dim.size] = dim.reshape(dim.size)
def dfunc(X):
return rl.get_derivative_from_eqs(X, E, w, h, k)
X = fsolve(dfunc, initial_est)
self.assertAlmostEqual(0.,
rl.get_optimization_f_val(X, E, w, h, k))
def get_best_rect_for_window_old(N, k, w, h):
# Check all permutations
# TODO filter duplicates
sol_best = np.zeros(0)
B_best = None
dev_from_k = 0.15
dev_from_k = 0.5
seq_first = [r for r in range(0, N)]
# XXX
# seq_first = [1, 4, 2, 0, 3]
for seq in itertools.permutations(seq_first):
B, dim = rr.do_diagonal_rectangulation(seq)
E = rr.build_rectangulation_equations(B)
print(seq)
print(B)
sol_sympy = solve_rectangle_eqs(E, w, h, k)
sol = np.zeros((2, N))
# Only one solution
print(sol_sympy)
full_sol = True
for i in range(N):
if not isinstance(sol_sympy.args[0][i], sp.Float) or \
not isinstance(sol_sympy.args[0][N + i], sp.Float):
print('Not fully determined:', sol_sympy)
full_sol = False
break
sol[0, i] = sol_sympy.args[0][i]
sol[1, i] = sol_sympy.args[0][N + i]
# sol = get_matrix_from_solution(N, sol_sympy)
if full_sol and check_proportions_in_range(k, sol, dev_from_k):
if sol_best.size == 0 or \
compare_rectangulations_size(sol, sol_best, w, h):
sol_best = sol
B_best = B
rr.draw_resized_rectangles(B, sol, w, h)
return B_best, sol_best