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add complex support #5

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39 changes: 37 additions & 2 deletions stringgen/string_emulator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,14 @@
from pkg_resources import resource_filename


def complex_to_real(x):
return np.array([x.real, x.imag]).ravel()


def real_to_complex(x):
return x[: len(x) // 2] + 1j * x[len(x) // 2 :]


class CosmicStringEmulator:
def __init__(
self,
Expand All @@ -26,6 +34,7 @@ def __init__(
self.emulation_shape = emulation_shape
self.norm = norm # Normalization
self.pbc = pbc # Periodic boundary conditions
self.cplx = cplx # Complex inputs
if device is None:
self.device = 0 if torch.cuda.is_available() else "cpu"
else:
Expand Down Expand Up @@ -196,13 +205,34 @@ def emulate(self, features, n_emulations=1, max_iterations=100):
del tensor_norms

# initialise random starting image with mean=0, std=1
x0 = np.random.normal(0, 1, self.emulation_shape)
if self.cplx:
x0 = np.random.normal(
0,
np.sqrt(0.5),
self.emulation_shape,
) + 1j * np.random.normal(
0,
np.sqrt(0.5),
self.emulation_shape,
)
x0 = complex_to_real(x0)
else:
x0 = np.random.normal(
0,
1,
self.emulation_shape,
)
x0 = x0.ravel()

coeffs = torch.from_numpy(wph_coeffs).to(self.device).contiguous()

# synthesis
def objective(x):
start_time = time.time()
# Reshape x
if self.cplx:
x = real_to_complex(x)

x_curr = x.reshape((self.emulation_shape))
# Compute the loss (squared 2-norm)
loss_tot = torch.zeros(1)
Expand All @@ -228,12 +258,14 @@ def objective(x):
print(
f"Loss: {loss_tot.item()} (computed in {time.time() - start_time}s)"
)
if self.cplx:
return loss_tot.item(), complex_to_real(x_grad)
return loss_tot.item(), x_grad.ravel()

total_start_time = time.time()
result = opt.minimize(
objective,
x0.ravel(),
x0,
method="L-BFGS-B",
jac=True,
tol=None,
Expand All @@ -250,6 +282,9 @@ def objective(x):
)
print(f"Synthesis time: {time.time() - total_start_time}s")

if self.cplx:
x_final = real_to_complex(x_final)

x_final = x_final.reshape(self.emulation_shape).astype(np.float32)
x_final = x_final * std + mean
emulations.append(x_final)
Expand Down