def test_spread_rand_rand(self):
# random input to rand
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = npr.random((N, 1))
output_target = normalize_vec(npr.random((N, 1)))
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='spread')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
def test_updown_1_1(self):
# uniform to uniform
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = self.one
output_target = self.one
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='up_down')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
def test_spread_top_top(self):
# top input to top
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = self.top
output_target = self.top
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='spread')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
def test_spread_1_mid(self):
# uniform to middle
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = self.one
output_target = self.mid
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='spread')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
def test_updown_rand_top(self):
# random input to top
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = npr.random((N, 1))
output_target = self.top
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='up_down')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
def test_updown_top_mid(self):
# top input to middle
N = self.N
mesh = Mesh(N, mesh_type='triangular', initialization='random', M=None)
input_values = self.top
output_target = self.mid
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
TO.optimize(algorithm='up_down')
# mesh.plot_powers(); plt.show()
self.check_power(mesh, output_target)
input_values = npr.random((N, 1))
f, (ax1, ax2) = plt.subplots(2, constrained_layout=True, figsize=(5, 5))
# couple light in and look at powers throughout mesh
mesh.input_couple(input_values)
im1 = plot_powers(mesh, ax=ax1)
colorbar(im1)
ax1.set_title('power distribution before optimizing')
# target output complex amplitude
output_target = np.ones((N, 1))
# define an optimizer over the triangular mesh
TO = TriangleOptimizer(mesh,
input_values=input_values,
output_target=output_target)
# optimize the mesh by pushing power to top port and redistributing
TO.optimize(algorithm='up_down')
# can also try the 'spread' algorithm, which distributes power as much as possible
TO.optimize(algorithm='spread')
# look at powers after optimizing
im2 = plot_powers(mesh, ax=ax2)
colorbar(im2)
ax2.set_title('power distribution after optimizing')
""" NEXT OPTIMIZE A CLEMENTS MESH """
# create a clements mesh
