diff --git a/QA/1D_grating_in_2D_pattern.py b/QA/1D_grating_in_2D_pattern.py deleted file mode 100644 index 4df7d8b..0000000 --- a/QA/1D_grating_in_2D_pattern.py +++ /dev/null @@ -1,58 +0,0 @@ -import numpy as np - -from meent.main import call_mee - - -def test(): - backend = 0 - pol = 1 # 0: TE, 1: TM - - n_top = 1 # n_incidence - n_bot = 1 # n_transmission - - theta = 1E-10 # angle of incidence in radian - phi = 0 # azimuth angle in radian - - wavelength = 300 # wavelength - thickness = [460, 22] - period = [700, 700] - fto = [10, 0] - - # 1D - ucell = np.array([ - [ - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - ], - [ - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - ], - ]) - - AA = call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, - fto=fto, wavelength=wavelength, period=period, ucell=ucell, thickness=thickness) - de_ri, de_ti = AA.conv_solve() - print('1D', de_ri.sum(), de_ti.sum()) - - # 2D case - - ucell = np.array([ - [ - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - ], - [ - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], - ], - ]) - - AA = call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, - fto=fto, wavelength=wavelength, period=period, ucell=ucell, thickness=thickness) - de_ri, de_ti = AA.conv_solve() - print('2D', de_ri.sum(), de_ti.sum()) - - -if __name__ == '__main__': - test() diff --git a/QA/1d_pattern_in_1dc_and_2d.py b/QA/1d_pattern_in_1dc_and_2d.py new file mode 100644 index 0000000..ddc5ac4 --- /dev/null +++ b/QA/1d_pattern_in_1dc_and_2d.py @@ -0,0 +1,87 @@ +# This demo shows a case with 1D grating and TM polarization. +# If phi is set to 'None', this will use 1D TETM formulation (without azimuthal rotation, phi == 0) +# But if phi is set to '0', then the simulation will be taken for 1D conical or 2D case which is general but slower. + +import numpy as np +from time import time + +from meent import call_mee + + +def compare(): + backend = 0 + pol = 1 # 0: TE, 1: TM + + n_top = 1 # n_incidence + n_bot = 1 # n_transmission + + theta = 1E-10 # angle of incidence in radian + + wavelength = 300 # wavelength + thickness = [460, 22] + period = [700, 700] + fto = [100, 0] + + ucell_1d = np.array([ + [ + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + ], + [ + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + ], + ]) + ucell_2d = np.array([ + [ + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + ], + [ + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + [1, 1, 1, 3.48, 3.48, 3.48, 1, 1, 1, 1], + ], + ]) + + mee = call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, fto=fto, + wavelength=wavelength, period=period, thickness=thickness) + + # 1D + mee.phi = None # which is default + mee.ucell = ucell_1d + + t0_1d = time() + res = mee.conv_solve().res + t1_1d = time() + de_ri1, de_ti1 = res.de_ri, res.de_ti + print('1D (de_ri, de_ti): ', de_ri1, de_ti1) + + # 1D conical + mee.phi = 0 + t0_1dc = time() + res = mee.conv_solve().res + t1_1dc = time() + de_ri1c, de_ti1c = res.de_ri, res.de_ti + print('1Dc (de_ri, de_ti): ', de_ri1c, de_ti1c) + + # 2D + mee.phi = 0 + t0_2d = time() + mee.ucell = ucell_2d + res = mee.conv_solve().res + t1_2d = time() + de_ri2, de_ti2 = res.de_ri, res.de_ti + print('2D (de_ri, de_ti): ', de_ri2, de_ti2) + + print('time for 1D formulation: ', t1_1d-t0_1d, 's') + print('time for 1Dc formulation: ', t1_1dc-t0_1dc, 's') + print('time for 2D formulation: ', t1_2d-t0_2d, 's') + print('Simulation Difference between 1D and 1Dc formulation: ', + np.linalg.norm(de_ri1 - de_ri1c), np.linalg.norm(de_ti1 - de_ti1c)) + print('Simulation Difference between 1D and 2D formulation: ', + np.linalg.norm(de_ri1 - de_ri2), np.linalg.norm(de_ti1 - de_ti2)) + + print('Simulation Difference between 1Dc and 2D formulation: ', + np.linalg.norm(de_ri1c - de_ri2), np.linalg.norm(de_ti1c - de_ti2)) + + +if __name__ == '__main__': + compare() diff --git a/QA/autograd_complex_ucell.py b/QA/autodiff_raster1.py similarity index 88% rename from QA/autograd_complex_ucell.py rename to QA/autodiff_raster1.py index e7b97ed..0054364 100644 --- a/QA/autograd_complex_ucell.py +++ b/QA/autodiff_raster1.py @@ -8,7 +8,7 @@ import torch import meent -from meent.on_torch.optimizer.loss import LossDeflector + type_complex = 0 device = 0 @@ -48,7 +48,19 @@ pois = ['ucell', 'thickness'] # Parameter Of Interests forward = jmee.conv_solve -loss_fn = LossDeflector(x_order=0, y_order=0) + + +class Loss: + def __call__(self, meent_result, *args, **kwargs): + res_psi, res_te, res_ti = meent_result.res, meent_result.res_te_inc, meent_result.res_tm_inc + de_ti = res_psi.de_ti + center = [a // 2 for a in de_ti.shape] + res = de_ti[center[0], center[1]+1] + + return res + + +loss_fn = Loss() # case 1: Gradient grad_j = jmee.grad(pois, forward, loss_fn) @@ -58,7 +70,7 @@ print('thickness gradient:') print(grad_j['thickness']) -optimizer = optax.sgd(learning_rate=1e-2) +optimizer = optax.sgd(learning_rate=1E2) t0 = time.time() res_j = jmee.fit(pois, forward, loss_fn, optimizer, iteration=iteration) print('Time JAX', time.time() - t0) @@ -74,7 +86,6 @@ thickness=thickness, type_complex=type_complex, device=device) forward = tmee.conv_solve -loss_fn = LossDeflector(x_order=0) # predefined in meent grad_t = tmee.grad(pois, forward, loss_fn) print('ucell gradient:') @@ -83,7 +94,7 @@ print(grad_t['thickness']) opt_torch = torch.optim.SGD -opt_options = {'lr': 1E-2} +opt_options = {'lr': 1E2} t0 = time.time() res_t = tmee.fit(pois, forward, loss_fn, opt_torch, opt_options, iteration=iteration) @@ -102,6 +113,6 @@ print('End') -# Note that the gradient in JAX is conjugated. +# Note that the gradient in JAX is conjugation of PyTorch's. # https://github.com/google/jax/issues/4891 # https://pytorch.org/docs/stable/notes/autograd.html#autograd-for-complex-numbers diff --git a/QA/autodiff_raster2.py b/QA/autodiff_raster2.py new file mode 100644 index 0000000..d65aefc --- /dev/null +++ b/QA/autodiff_raster2.py @@ -0,0 +1,172 @@ +import jax +import torch + +import jax.numpy as jnp +import numpy as np + +from time import time + +from meent import call_mee + + +def load_setting(): + pol = 1 # 0: TE, 1: TM + + n_top = 1 # n_incidence + n_bot = 1 # n_transmission + + theta = 0 * np.pi / 180 + phi = 0 * np.pi / 180 + + wavelength = 900 + + fto = [5, 5] + + period = [1000, 1000] + thickness = [1120] + + ucell = np.array([[[2.58941352 + 0.47745679j, 4.17771602 + 0.88991205j, + 2.04255624 + 2.23670125j, 2.50478974 + 2.05242759j, + 3.32747593 + 2.3854387j], + [2.80118605 + 0.53053715j, 4.46498861 + 0.10812571j, + 3.99377545 + 1.0441131j, 3.10728537 + 0.6637353j, + 4.74697849 + 0.62841253j], + [3.80944424 + 2.25899274j, 3.70371553 + 1.32586402j, + 3.8011133 + 1.49939415j, 3.14797238 + 2.91158289j, + 4.3085404 + 2.44344691j], + [2.22510179 + 2.86017146j, 2.36613053 + 2.82270351j, + 4.5087168 + 0.2035904j, 3.15559949 + 2.55311298j, + 4.29394604 + 0.98362617j], + [3.31324163 + 2.77590131j, 2.11744834 + 1.65894674j, + 3.59347907 + 1.28895345j, 3.85713467 + 1.90714056j, + 2.93805426 + 2.63385392j]]]) + ucell = ucell.real + + type_complex = 0 + device = 0 + + setting = {'pol': pol, 'n_top': n_top, 'n_bot': n_bot, 'theta': theta, 'phi': phi, 'fto': fto, + 'wavelength': wavelength, 'period': period, 'ucell': ucell, 'thickness': thickness, 'device': device, + 'type_complex': type_complex} + + return setting + + +def optimize_jax(setting): + ucell = setting['ucell'] + + mee = call_mee(backend=1, **setting) + + @jax.jit + def grad_loss(ucell): + mee.ucell = ucell + res = mee.conv_solve().res + de_ri, de_ti = res.de_ri, res.de_ti + + loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2] + + return loss + + def grad_numerical(ucell, delta): + grad_arr = jnp.zeros(ucell.shape, dtype=ucell.dtype) + + @jax.jit + def compute(ucell): + mee.ucell = ucell + result = mee.conv_solve() + de_ti = result.res.de_ti + loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2] + + return loss + + for layer in range(ucell.shape[0]): + for r in range(ucell.shape[1]): + for c in range(ucell.shape[2]): + ucell_delta_m = ucell.copy() + ucell_delta_m[layer, r, c] -= delta + mee.ucell = ucell_delta_m + de_ti_delta_m = compute(ucell_delta_m, ) + + ucell_delta_p = ucell.copy() + ucell_delta_p[layer, r, c] += delta + mee.ucell = ucell_delta_p + de_ti_delta_p = compute(ucell_delta_p, ) + + grad_numeric = (de_ti_delta_p - de_ti_delta_m) / (2 * delta) + grad_arr = grad_arr.at[layer, r, c].set(grad_numeric) + + return grad_arr + + jax.grad(grad_loss)(ucell) # Dry run for jit compilation. This is to make time comparison fair. + t0 = time() + grad_ad = jax.grad(grad_loss)(ucell) + t_ad = time() - t0 + print('JAX grad_ad:\n', grad_ad) + t0 = time() + grad_nume = grad_numerical(ucell, 1E-6) + t_nume = time() - t0 + print('JAX grad_numeric:\n', grad_nume) + print('JAX norm of difference: ', jnp.linalg.norm(grad_nume - grad_ad) / grad_nume.size) + return t_ad, t_nume + + +def optimize_torch(setting): + mee = call_mee(backend=2, **setting) + + mee.ucell.requires_grad = True + + t0 = time() + res = mee.conv_solve().res + de_ri, de_ti = res.de_ri, res.de_ti + + loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2] + + loss.backward() + grad_ad = mee.ucell.grad + t_ad = time() - t0 + + def grad_numerical(ucell, delta): + ucell.requires_grad = False + grad_arr = torch.zeros(ucell.shape, dtype=ucell.dtype) + + for layer in range(ucell.shape[0]): + for r in range(ucell.shape[1]): + for c in range(ucell.shape[2]): + ucell_delta_m = ucell.clone().detach() + ucell_delta_m[layer, r, c] -= delta + mee.ucell = ucell_delta_m + res = mee.conv_solve().res + de_ri_delta_m, de_ti_delta_m = res.de_ri, res.de_ti + + ucell_delta_p = ucell.clone().detach() + ucell_delta_p[layer, r, c] += delta + mee.ucell = ucell_delta_p + res = mee.conv_solve().res + de_ri_delta_p, de_ti_delta_p = res.de_ri, res.de_ti + + cy, cx = np.array(de_ti_delta_p.shape) // 2 + grad_numeric = (de_ti_delta_p[cy, cx] - de_ti_delta_m[cy, cx]) / (2 * delta) + grad_arr[layer, r, c] = grad_numeric + + return grad_arr + + t0 = time() + grad_nume = grad_numerical(mee.ucell, 1E-6) + t_nume = time() - t0 + + print('Torch grad_ad:\n', grad_ad) + print('Torch grad_numeric:\n', grad_nume) + print('torch.norm: ', torch.linalg.norm(grad_nume - grad_ad) / grad_nume.numel()) + return t_ad, t_nume + + +if __name__ == '__main__': + setting = load_setting() + + print('JaxMeent') + j_t_ad, j_t_nume = optimize_jax(setting) + print('TorchMeent') + t_t_ad, t_t_nume = optimize_torch(setting) + + print(f'Time for Backprop, JAX, AD: {j_t_ad} s, Numerical: {j_t_nume} s') + print(f'Time for Backprop, Torch, AD: {t_t_ad} s, Numerical: {t_t_nume} s') diff --git a/QA/autodiff_vector.py b/QA/autodiff_vector.py new file mode 100644 index 0000000..b9a9447 --- /dev/null +++ b/QA/autodiff_vector.py @@ -0,0 +1,165 @@ +import meent + + +def run_jax(): + print('RUN JAXMeent') + import jax + import optax + import jax.numpy as jnp + + backend = 1 + + period = [1000., 1000.] + thickness = ([300.]) + wavelength = 900 + + input_length1 = jnp.array([160], dtype=jnp.float64) + input_length2 = jnp.array([100], dtype=jnp.float64) + input_length3 = jnp.array([30], dtype=jnp.float64) + input_length4 = jnp.array([20], dtype=jnp.float64) + + fto = [5, 5] + + mee = meent.call_mee(backend=backend, fto=fto, wavelength=wavelength, thickness=thickness, period=period, + device=0, type_complex=0) + + opt = optax.sgd(learning_rate=1E5, momentum=0) + + def forward(param_list): + [length1, length2, length3, length4] = param_list + ucell = [ + [3 - 1j, [ + ['rectangle', 0 + 1000, 410 + 1000, length1, 80, 4, 0, 0, 0], # obj 1 + ['ellipse', 0 + 1000, -10 + 1000, length2, 80, 4, 1, 20, 20], # obj 2 + ['rectangle', 120 + 1000, 500 + 1000, length3, 160, 4 + 0.3j, 1.1, 5, 5], # obj 3 + ['ellipse', -400 + 1000, -700 + 1000, length4, 160, 4, 0.4, 20, 20], # obj 4 + ], ], + ] + mee.ucell = ucell + + res = mee.conv_solve().res + de_ti = res.de_ti + + cy, cx = de_ti.shape[0] // 2, de_ti.shape[1] // 2 + loss = -de_ti[cy, cx + 1] + + return loss + + pois = [input_length1, input_length2, input_length3, input_length4] + opt_state = opt.init(pois) + + for i in range(10): + print('Parameters: ', [p.item() for p in pois]) + + input_length1, input_length2, input_length3, input_length4 = pois + + dx = 1E-5 + loss_a = forward([input_length1 + dx, input_length2, input_length3, input_length4]) + loss_b = forward([input_length1 - dx, input_length2, input_length3, input_length4]) + grad1 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward([input_length1, input_length2 + dx, input_length3, input_length4]) + loss_b = forward([input_length1, input_length2 - dx, input_length3, input_length4]) + grad2 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward([input_length1, input_length2, input_length3 + dx, input_length4]) + loss_b = forward([input_length1, input_length2, input_length3 - dx, input_length4]) + grad3 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward([input_length1, input_length2, input_length3, input_length4 + dx]) + loss_b = forward([input_length1, input_length2, input_length3, input_length4 - dx]) + grad4 = (loss_a - loss_b) / (2 * dx) + + print('grad_nume: ', grad1.item(), grad2.item(), grad3.item(), grad4.item()) + + # grad = jax.grad(forward)(pois) + loss, grad = jax.value_and_grad(forward)(pois) + updates, opt_state = opt.update(grad, opt_state, pois) + + pois = optax.apply_updates(pois, updates) + print('grad_auto: ', *[g.item() for g in grad]) + print('Loss:', loss) + + +def run_torch(): + print('RUN TorchMeent') + import torch + backend = 2 + + period = [1000., 1000.] + thickness = torch.tensor([300.]) + wavelength = 900 + + input_length1 = 160 + input_length2 = 100 + input_length3 = 30 + input_length4 = 20 + + fto = [5, 5] + + # layer_base = torch.tensor(n_index_base) + input_length1 = torch.tensor([input_length1], dtype=torch.float64, requires_grad=True) + input_length2 = torch.tensor([input_length2], dtype=torch.float64, requires_grad=True) + input_length3 = torch.tensor([input_length3], dtype=torch.float64, requires_grad=True) + input_length4 = torch.tensor([input_length4], dtype=torch.float64, requires_grad=True) + + mee = meent.call_mee(backend=backend, fto=fto, wavelength=wavelength, thickness=thickness, period=period, + device=0, type_complex=0) + + opt = torch.optim.SGD([input_length1, input_length2, input_length3, input_length4], lr=1E5, momentum=0) + + def forward(length1, length2, length3, length4): + + ucell = [ + [3 - 1j, [ + ['rectangle', 0+1000, 410+1000, length1, 80, 4, 0, 0, 0], # obj 1 + ['ellipse', 0+1000, -10+1000, length2, 80, 4, 1, 20, 20], # obj 2 + ['rectangle', 120+1000, 500+1000, length3, 160, 4+0.3j, 1.1, 5, 5], # obj 3 + ['ellipse', -400+1000, -700+1000, length4, 160, 4, 0.4, 20, 20], # obj 4 + ], ], + ] + mee.ucell = ucell + + res = mee.conv_solve().res + de_ti = res.de_ti + + cy, cx = de_ti.shape[0] // 2, de_ti.shape[1] // 2 + loss = -de_ti[cy, cx + 1] + + return loss + + for i in range(10): + print('Parameters: ', input_length1.detach().numpy(), input_length2.detach().numpy(), + input_length3.detach().numpy(), input_length4.detach().numpy()) + dx = 1E-5 + loss_a = forward(input_length1 + dx, input_length2, input_length3, input_length4) + loss_b = forward(input_length1 - dx, input_length2, input_length3, input_length4) + grad1 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward(input_length1, input_length2 + dx, input_length3, input_length4) + loss_b = forward(input_length1, input_length2 - dx, input_length3, input_length4) + grad2 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward(input_length1, input_length2, input_length3 + dx, input_length4) + loss_b = forward(input_length1, input_length2, input_length3 - dx, input_length4) + grad3 = (loss_a - loss_b) / (2 * dx) + + loss_a = forward(input_length1, input_length2, input_length3, input_length4 + dx) + loss_b = forward(input_length1, input_length2, input_length3, input_length4 - dx) + grad4 = (loss_a - loss_b) / (2 * dx) + + print('grad_nume: ', grad1.item(), grad2.item(), grad3.item(), grad4.item()) + + loss = forward(input_length1, input_length2, input_length3, input_length4) + loss.backward() + print('grad_auto: ', input_length1.grad.numpy()[0], input_length2.grad.numpy()[0], input_length3.grad.numpy()[0], + input_length4.grad.numpy()[0]) + + opt.step() + opt.zero_grad() + print('Loss:', loss) + + +if __name__ == '__main__': + run_jax() + run_torch() diff --git a/QA/autograd_raster.py b/QA/autograd_raster.py deleted file mode 100644 index 1794911..0000000 --- a/QA/autograd_raster.py +++ /dev/null @@ -1,191 +0,0 @@ -import warnings -import jax -import jax.numpy as jnp -import torch - -import numpy as np - -from copy import deepcopy - -from meent import call_mee - - -def load_setting(): - pol = 1 # 0: TE, 1: TM - - n_top = 1 # n_incidence - n_bot = 1 # n_transmission - - theta = 0 * np.pi / 180 - phi = 0 * np.pi / 180 - psi = 0 * np.pi / 180 if pol else 90 * np.pi / 180 - - wavelength = 900 - - fto = [2, 2] - - # case 1 - period = [1000, 1000] - thickness = [1120., 400, 300] - - ucell = np.array( - [ - [ - [3.1, 1.1, 1.2, 1.6, 3.1], - [3.5, 1.4, 1.1, 1.2, 3.6], - ], - [ - [3.5, 1.2, 1.5, 1.2, 3.3], - [3.1, 1.5, 1.5, 1.4, 3.1], - ], - [ - [3.5, 1.2, 1.5, 1.2, 3.3], - [3.1, 1.5, 1.5, 1.4, 3.1], - ], - ] - ) - - # Case 4 - thickness = [1120] - - ucell = np.array([[[2.58941352 + 0.47745679j, 4.17771602 + 0.88991205j, - 2.04255624 + 2.23670125j, 2.50478974 + 2.05242759j, - 3.32747593 + 2.3854387j], - [2.80118605 + 0.53053715j, 4.46498861 + 0.10812571j, - 3.99377545 + 1.0441131j, 3.10728537 + 0.6637353j, - 4.74697849 + 0.62841253j], - [3.80944424 + 2.25899274j, 3.70371553 + 1.32586402j, - 3.8011133 + 1.49939415j, 3.14797238 + 2.91158289j, - 4.3085404 + 2.44344691j], - [2.22510179 + 2.86017146j, 2.36613053 + 2.82270351j, - 4.5087168 + 0.2035904j, 3.15559949 + 2.55311298j, - 4.29394604 + 0.98362617j], - [3.31324163 + 2.77590131j, 2.11744834 + 1.65894674j, - 3.59347907 + 1.28895345j, 3.85713467 + 1.90714056j, - 2.93805426 + 2.63385392j]]]) - ucell = ucell.real - - type_complex = 0 - device = 0 - return pol, n_top, n_bot, theta, phi, psi, wavelength, thickness, period, fto, type_complex, device, ucell - - -def optimize_jax(setting): - pol, n_top, n_bot, theta, phi, psi, wavelength, thickness, period, fto, \ - type_complex, device, ucell = setting - - mee = call_mee(backend=1, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, - fto=fto, wavelength=wavelength, period=period, ucell=ucell, - thickness=thickness, device=device, - type_complex=type_complex) - ucell = mee.ucell - - @jax.grad - def grad_loss(ucell): - mee.ucell = ucell - # de_ri, de_ti, _, _, _ = mee._conv_solve() - de_ri, de_ti = mee.conv_solve() - try: - loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2] - except: - loss = de_ti[de_ti.shape[0] // 2] - return loss - - def grad_numerical(ucell, delta): - grad_arr = jnp.zeros(ucell.shape, dtype=ucell.dtype) - for layer in range(ucell.shape[0]): - for r in range(ucell.shape[1]): - for c in range(ucell.shape[2]): - ucell_delta_m = ucell.at[layer, r, c].set(ucell[layer, r, c] - delta) - mee.ucell = ucell_delta_m - # de_ri_delta_m, de_ti_delta_m, _, _, _ = mee._conv_solve() - de_ri_delta_m, de_ti_delta_m = mee.conv_solve() - ucell_delta_p = ucell.at[layer, r, c].set(ucell[layer, r, c] + delta) - mee.ucell = ucell_delta_p - # de_ri_delta_p, de_ti_delta_p, _, _, _ = mee._conv_solve() - de_ri_delta_p, de_ti_delta_p = mee.conv_solve() - try: - grad_numeric = \ - (de_ti_delta_p[de_ti_delta_p.shape[0] // 2, de_ti_delta_p.shape[1] // 2] - - de_ti_delta_m[de_ti_delta_p.shape[0] // 2, de_ti_delta_p.shape[1] // 2]) / (2 * delta) - except: - grad_numeric = \ - (de_ti_delta_p[de_ti_delta_p.shape[0] // 2] - - de_ti_delta_m[de_ti_delta_p.shape[0] // 2]) / (2 * delta) - grad_arr = grad_arr.at[layer, r, c].set(grad_numeric) - - return grad_arr - - grad_ad = grad_loss(ucell) - print('JAX grad_ad:\n', grad_ad) - grad_nume = grad_numerical(ucell, 1E-6) - print('JAX grad_numeric:\n', grad_nume) - print('JAX norm: ', jnp.linalg.norm(grad_nume - grad_ad) / grad_nume.size) - - -def optimize_torch(setting): - """ - out of date. - Will be updated. - """ - - pol, n_top, n_bot, theta, phi, psi, wavelength, thickness, period, fto, \ - type_complex, device, ucell = setting - - tmee = call_mee(backend=2, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, - fto=fto, wavelength=wavelength, period=period, ucell=ucell, - thickness=thickness, device=device, - type_complex=type_complex, ) - tmee.ucell.requires_grad = True - de_ri, de_ti = tmee.conv_solve() - - try: - loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2] - except: - loss = de_ti[de_ti.shape[0] // 2] - - loss.backward() - grad_ad = tmee.ucell.grad - - def grad_numerical(ucell, delta): - ucell.requires_grad = False - grad_arr = torch.zeros(ucell.shape, dtype=ucell.dtype) - - for layer in range(ucell.shape[0]): - for r in range(ucell.shape[1]): - for c in range(ucell.shape[2]): - ucell_delta_m = deepcopy(ucell) - ucell_delta_m[layer, r, c] -= delta - tmee.ucell = ucell_delta_m - de_ri_delta_m, de_ti_delta_m = tmee.conv_solve() - - ucell_delta_p = deepcopy(ucell) - ucell_delta_p[layer, r, c] += delta - tmee.ucell = ucell_delta_p - de_ri_delta_p, de_ti_delta_p = tmee.conv_solve() - try: - grad_numeric = \ - (de_ti_delta_p[de_ti_delta_p.shape[0] // 2, de_ti_delta_p.shape[1] // 2] - - de_ti_delta_m[de_ti_delta_p.shape[0] // 2, de_ti_delta_p.shape[1] // 2]) / (2 * delta) - except: - grad_numeric = \ - (de_ti_delta_p[de_ti_delta_p.shape[0] // 2] - - de_ti_delta_m[de_ti_delta_p.shape[0] // 2]) / (2 * delta) - grad_arr[layer, r, c] = grad_numeric - - return grad_arr - - grad_nume = grad_numerical(tmee.ucell, 1E-6) - print('Torch grad_ad:\n', grad_ad) - print('Torch grad_numeric:\n', grad_nume) - print('torch.norm: ', torch.linalg.norm(grad_nume - grad_ad) / grad_nume.numel()) - - -if __name__ == '__main__': - setting = load_setting() - - print('JaxMeent') - optimize_jax(setting) - - print('TorchMeent') - optimize_torch(setting) diff --git a/QA/autograd_vector.py b/QA/autograd_vector.py deleted file mode 100644 index 285ea27..0000000 --- a/QA/autograd_vector.py +++ /dev/null @@ -1,84 +0,0 @@ -import torch - -import meent - - -backend = 2 - -period = [1000., 1000.] -thickness = torch.tensor([300.]) -wavelength = 900 - -input_length1 = 160 -input_length2 = 100 -input_length3 = 30 -input_length4 = 20 - -fto = [5, 5] - -# layer_base = torch.tensor(n_index_base) -input_length1 = torch.tensor([input_length1], dtype=torch.float64, requires_grad=True) -input_length2 = torch.tensor([input_length2], dtype=torch.float64, requires_grad=True) -input_length3 = torch.tensor([input_length3], dtype=torch.float64, requires_grad=True) -input_length4 = torch.tensor([input_length4], dtype=torch.float64, requires_grad=True) - -mee = meent.call_mee(backend=backend, fto=fto, wavelength=wavelength, thickness=thickness, period=period, - device=0, type_complex=0) - -opt = torch.optim.SGD([input_length1, input_length2, input_length3, input_length4], lr=1E6, momentum=0.9) - - -def forward(length1, length2, length3, length4): - - ucell = [ - [3 - 1j, [ - ['rectangle', 0+1000, 410+1000, length1, 80, 4, 0, 0, 0], # obj 1 - ['ellipse', 0+1000, -10+1000, length2, 80, 4, 1, 20, 20], # obj 2 - ['rectangle', 120+1000, 500+1000, length3, 160, 4+0.3j, 1.1, 5, 5], # obj 3 - ['ellipse', -400+1000, -700+1000, length4, 160, 4, 0.4, 20, 20], # obj 4 - ], ], - ] - mee.ucell = ucell - - de_ri, de_ti = mee.conv_solve() - - center = de_ti.shape[0] // 2 - loss = -de_ti[center + 0, center + 0] - - return loss - - -for i in range(50): - print('Parameters: ', input_length1.detach().numpy(), input_length2.detach().numpy(), - input_length3.detach().numpy(), input_length4.detach().numpy()) - dx = 1E-5 - loss_a = forward(input_length1 + dx, input_length2, input_length3, input_length4) - loss_b = forward(input_length1 - dx, input_length2, input_length3, input_length4) - grad1 = (loss_a - loss_b) / (2 * dx) - - loss_a = forward(input_length1, input_length2 + dx, input_length3, input_length4) - loss_b = forward(input_length1, input_length2 - dx, input_length3, input_length4) - grad2 = (loss_a - loss_b) / (2 * dx) - - loss_a = forward(input_length1, input_length2, input_length3 + dx, input_length4) - loss_b = forward(input_length1, input_length2, input_length3 - dx, input_length4) - grad3 = (loss_a - loss_b) / (2 * dx) - - loss_a = forward(input_length1, input_length2, input_length3, input_length4 + dx) - loss_b = forward(input_length1, input_length2, input_length3, input_length4 - dx) - grad4 = (loss_a - loss_b) / (2 * dx) - - print('grad_nume: ', grad1.item(), grad2.item(), grad3.item(), grad4.item()) - - loss = forward(input_length1, input_length2, input_length3, input_length4) - loss.backward() - print('grad_auto: ', input_length1.grad.numpy()[0], input_length2.grad.numpy()[0], input_length3.grad.numpy()[0], - input_length4.grad.numpy()[0]) - - opt.step() - opt.zero_grad() - print('Loss:', loss) - - print() - -print(input_length1, input_length2, input_length3, input_length4) diff --git a/QA/fourier_analysis_methods.py b/QA/fourier_analysis_methods.py index 850289d..0e7268e 100644 --- a/QA/fourier_analysis_methods.py +++ b/QA/fourier_analysis_methods.py @@ -28,13 +28,16 @@ def compare_conv_mat_method(backend, type_complex, device): mee.fourier_type = 0 mee.enhanced_dfs = False - de_ri_dfs, de_ti_dfs = mee.conv_solve() + res_dfs = mee.conv_solve().res + de_ri_dfs, de_ti_dfs = res_dfs.de_ri, res_dfs.de_ti mee.enhanced_dfs = True - de_ri_efs, de_ti_efs = mee.conv_solve() + res_efs = mee.conv_solve().res + de_ri_efs, de_ti_efs = res_efs.de_ri, res_efs.de_ti mee.fourier_type = 1 - de_ri_cfs, de_ti_cfs = mee.conv_solve() + res_cfs = mee.conv_solve().res + de_ri_cfs, de_ti_cfs = res_cfs.de_ri, res_cfs.de_ti a = np.linalg.norm(de_ri_dfs - de_ri_efs) b = np.linalg.norm(de_ti_dfs - de_ti_efs) @@ -43,9 +46,9 @@ def compare_conv_mat_method(backend, type_complex, device): e = np.linalg.norm(de_ri_efs - de_ri_cfs) f = np.linalg.norm(de_ti_efs - de_ti_cfs) - print('DFS-EFS ', a, b) - print('DFS-CFS ', c, d) - print('EFS-CFS ', e, f) + print('Norm of DFS-EFS: ', a, b) + print('Norm of DFS-CFS: ', c, d) + print('Norm of EFS-CFS: ', e, f) if __name__ == '__main__': diff --git a/QA/rcwa_backend_consistency.py b/QA/rcwa_backend_consistency.py index fd9db4b..cc23e6f 100644 --- a/QA/rcwa_backend_consistency.py +++ b/QA/rcwa_backend_consistency.py @@ -5,71 +5,70 @@ def consistency_check(option): - mee = meent.call_mee(backend=0, perturbation=1E-30, **option) # NumPy - de_ri_numpy, de_ti_numpy = mee.conv_solve() - field_cell_numpy = mee.calculate_field(res_z=50, res_x=50) - - mee = meent.call_mee(backend=1, perturbation=1E-30, **option) # JAX - de_ri_jax, de_ti_jax = mee.conv_solve() - field_cell_jax = mee.calculate_field(res_z=50, res_x=50) - - mee = meent.call_mee(backend=2, perturbation=1E-30, **option) # PyTorch - de_ri_torch, de_ti_torch = mee.conv_solve() - field_cell_torch = mee.calculate_field(res_z=50, res_x=50) - de_ri_torch, de_ti_torch = de_ri_torch.numpy(), de_ti_torch.numpy() - field_cell_torch = field_cell_torch.numpy() - - digit = 20 - - res1 = [(np.linalg.norm(de_ri_numpy - de_ri_jax) / de_ri_numpy.size).round(digit), - (np.linalg.norm(de_ri_jax - de_ri_torch) / de_ri_numpy.size).round(digit), - (np.linalg.norm(de_ri_torch - de_ri_numpy) / de_ri_numpy.size).round(digit),] - res2 = [(np.linalg.norm(de_ti_numpy - de_ti_jax) / de_ti_numpy.size).round(digit), - (np.linalg.norm(de_ti_jax - de_ti_torch) / de_ti_numpy.size).round(digit), - (np.linalg.norm(de_ti_torch - de_ti_numpy) / de_ti_numpy.size).round(digit),] - res3 = [(np.linalg.norm(field_cell_numpy - field_cell_jax) / field_cell_numpy.size).round(digit), - (np.linalg.norm(field_cell_jax - field_cell_torch) / field_cell_numpy.size).round(digit), - (np.linalg.norm(field_cell_torch - field_cell_numpy) / field_cell_numpy.size).round(digit),] + mee = meent.call_mee(backend=0, **option) # NumPy + res_nupmy = mee.conv_solve() + res_numpy_psi = res_nupmy.res + res_numpy_te = res_nupmy.res_te_inc + res_numpy_tm = res_nupmy.res_tm_inc - print('Refle', res1) - print('Trans', res2) - print('Field', res3) - - -def consistency_check_vector(option, instructions): - - mee = meent.call_mee(backend=0, perturbation=1E-30, **option) # NumPy - mee.modeling_vector_instruction(instructions) - - de_ri_numpy, de_ti_numpy = mee.conv_solve() field_cell_numpy = mee.calculate_field(res_z=50, res_x=50) - mee = meent.call_mee(backend=1, perturbation=1E-30, **option) # JAX - mee.modeling_vector_instruction(instructions) - de_ri_jax, de_ti_jax = mee.conv_solve() + mee = meent.call_mee(backend=1, **option) # JAX + res_jax = mee.conv_solve() + res_jax_psi = res_jax.res + res_jax_te = res_jax.res_te_inc + res_jax_tm = res_jax.res_tm_inc + field_cell_jax = mee.calculate_field(res_z=50, res_x=50) - mee = meent.call_mee(backend=2, perturbation=1E-30, **option) # PyTorch - mee.modeling_vector_instruction(instructions) - de_ri_torch, de_ti_torch = mee.conv_solve() + mee = meent.call_mee(backend=2, **option) # PyTorch + res_torch = mee.conv_solve() + res_torch_psi = res_torch.res + res_torch_te = res_torch.res_te_inc + res_torch_tm = res_torch.res_tm_inc + field_cell_torch = mee.calculate_field(res_z=50, res_x=50) - de_ri_torch, de_ti_torch = de_ri_torch.numpy(), de_ti_torch.numpy() field_cell_torch = field_cell_torch.numpy() - digit = 20 - - res1 = [(np.linalg.norm(de_ri_numpy - de_ri_jax) / de_ri_numpy.size).round(digit), - (np.linalg.norm(de_ri_jax - de_ri_torch) / de_ri_numpy.size).round(digit), - (np.linalg.norm(de_ri_torch - de_ri_numpy) / de_ri_numpy.size).round(digit),] - res2 = [(np.linalg.norm(de_ti_numpy - de_ti_jax) / de_ti_numpy.size).round(digit), - (np.linalg.norm(de_ti_jax - de_ti_torch) / de_ti_numpy.size).round(digit), - (np.linalg.norm(de_ti_torch - de_ti_numpy) / de_ti_numpy.size).round(digit),] - res3 = [(np.linalg.norm(field_cell_numpy - field_cell_jax) / field_cell_numpy.size).round(digit), - (np.linalg.norm(field_cell_jax - field_cell_torch) / field_cell_numpy.size).round(digit), - (np.linalg.norm(field_cell_torch - field_cell_numpy) / field_cell_numpy.size).round(digit),] + check_attr = ['R_s', 'R_p', 'T_s', 'T_p', 'de_ri_s', 'de_ri_p', 'de_ri', 'de_ti_s', 'de_ti_p', 'de_ti'] + + print('res_psi') + for attr in check_attr: + a = getattr(res_numpy_psi, attr) + b = getattr(res_jax_psi, attr) + c = getattr(res_torch_psi, attr).numpy() + + res1 = [float(np.linalg.norm(a - b) / a.size), + float(np.linalg.norm(b - c) / a.size), + float(np.linalg.norm(c - a) / a.size),] + print(attr, res1) + + print('res_te') + for attr in check_attr: + a = getattr(res_numpy_te, attr) + b = getattr(res_jax_te, attr) + c = getattr(res_torch_te, attr).numpy() + + res1 = [float(np.linalg.norm(a - b) / a.size), + float(np.linalg.norm(b - c) / a.size), + float(np.linalg.norm(c - a) / a.size),] + print(attr, res1) + + print('res_tm') + for attr in check_attr: + a = getattr(res_numpy_tm, attr) + b = getattr(res_jax_tm, attr) + c = getattr(res_torch_tm, attr).numpy() + + res1 = [float(np.linalg.norm(a - b) / a.size), + float(np.linalg.norm(b - c) / a.size), + float(np.linalg.norm(c - a) / a.size),] + print(attr, res1) + + res3 = [float(np.linalg.norm(field_cell_numpy - field_cell_jax) / field_cell_numpy.size), + float(np.linalg.norm(field_cell_jax - field_cell_torch) / field_cell_numpy.size), + float(np.linalg.norm(field_cell_torch - field_cell_numpy) / field_cell_numpy.size),] - print('Refle', res1) - print('Trans', res2) print('Field', res3) @@ -83,11 +82,11 @@ def consistency_check_vector(option, instructions): 'ucell': np.array([[[3, 3, 3.3, 3, 3, 4, 1, 1, 1, 1.2, 1.1, 3, 2, 1.1]], ])} option3 = {'psi': 40/180*np.pi, 'n_top': 1, 'n_bot': 1, 'theta': 0 * np.pi / 180, 'phi': 12 * np.pi / 180, - 'fto': 1, + 'fto': 80, 'period': [200], 'wavelength': 1000, 'thickness': [100], 'fourier_type': 0, 'enhanced_dfs': False, 'ucell': np.array([[[3, 3, 3.3, 3, 3, 4, 1, 1, 1, 1.2, 1.1, 3, 2, 1.1]], ])} - option4 = {'psi': 10/180*np.pi, 'n_top': 1, 'n_bot': 1, 'theta': 0 * np.pi / 180, 'phi': 12 * np.pi / 180, + option4 = {'psi': 10/180*np.pi, 'n_top': 1, 'n_bot': 1.5, 'theta': 30 * np.pi / 180, 'phi': 0 * np.pi / 180, 'fto': [10, 10], 'period': [200, 600], 'wavelength': 1000, 'thickness': [100, 111, 222, 102, 44], 'fourier_type': 0, 'enhanced_dfs': True, @@ -96,41 +95,40 @@ def consistency_check_vector(option, instructions): ucell5 = [ # layer 1 [1,[ - ['rectangle', 0+240, 120+240, 160, 80, 4, 0, 0, 0], # obj 1 + ['rectangle', 0+240, 120+240, 160, 80, 4, 1, 20, 20], # obj 1 ['rectangle', 0+240, -120+240, 160, 80, 4, 0, 0, 0], # obj 2 ['rectangle', 120+240, 0+240, 80, 160, 4, 0, 0, 0], # obj 3 ['rectangle', -120+240, 0+240, 80, 160, 4, 0, 0, 0], # obj 4 ], ], ] - option5 = {'pol': 0, 'n_top': 2, 'n_bot': 1, 'theta': 12 * np.pi / 180, 'phi': 0 * np.pi / 180, 'fto': 0, + option5 = {'pol': 0, 'n_top': 2, 'n_bot': 1, 'theta': 12 * np.pi / 180, 'phi': 0 * np.pi / 180, 'fto': [5,5], 'period': [770], 'wavelength': 777, 'thickness': [100], 'fourier_type': 0, 'ucell': ucell5} ucell6 = [ # layer 1 - [3 - 1j, [ + [3, [ ['rectangle', 0+1000, 410+1000, 160, 80, 4, 0, 0, 0], # obj 1 ['ellipse', 0+1000, -10+1000, 160, 80, 4, 1, 20, 20], # obj 2 - ['rectangle', 120+1000, 500+1000, 80, 160, 4+0.3j, 1.1, 5, 5], # obj 3 + ['rectangle', 120+1000, 500+1000, 80, 160, 4, 1, 5, 5], # obj 3 ['ellipse', -400+1000, -700+1000, 80, 160, 4, 0.4, 20, 20], # obj 4 ], ], # layer 2 - [3.1, [ - ['rectangle', 0+240, 120+240, 160, 80, 4, 0.4, 5, 5], # obj 1 - ['ellipse', 0+240, -120+240, 160, 80, 4, 0.1, 20, 20], # obj 2 - ['ellipse', 120+240, 0+240, 80, 160, 4, 1, 20, 20], # obj 3 - ['rectangle', -120+240, 0+240, 80, 160, 4, 2, 5, 5], # obj 4 + [3.1 - 1j, [ + ['rectangle', 0+240, 120+240, 160, 80, 4, 0, 10, 10], # obj 1 + ['ellipse', 0+240, -120+240, 160, 80, 4, 0, 20, 20], # obj 2 + ['ellipse', 120+240, 0+240, 80, 160, 4, 0.4, 20, 20], # obj 3 + ['rectangle', -120+240, 0+240, 80, 160, 4+3j, 1, 10, 10], # obj 4 ], ], ] - option6 = {'pol': 0, 'n_top': 2, 'n_bot': 1, 'theta': 12 * np.pi / 180, 'phi': 0 * np.pi / 180, 'fto': [5,5], - 'period': [770], 'wavelength': 777, 'thickness': [100, 333], 'fourier_type': 0, + option6 = {'pol': 0, 'n_top': 2, 'n_bot': 2, 'theta': 2 * np.pi / 180, 'phi': 10 * np.pi / 180, 'fto': [5, 5], + 'period': [770], 'wavelength': 777, 'thickness': [100, 90], 'fourier_type': 0, 'ucell': ucell6} - # consistency_check(option1) - # consistency_check(option2) - # consistency_check(option3) - # consistency_check(option4) + cands = [option1, option2, option3, option4, option5, option6] + # cands = [option6] + for i, case in enumerate(cands): - consistency_check(option5) - consistency_check(option6) + print(f'case {i+1}') + consistency_check(case) diff --git a/benchmarks/interface/Reticolo.py b/benchmarks/interface/Reticolo.py index 19ecee1..11e507f 100644 --- a/benchmarks/interface/Reticolo.py +++ b/benchmarks/interface/Reticolo.py @@ -97,7 +97,7 @@ def run_res3(self, grating_type, period, fto, ucell, thickness, theta, phi, pol, matlab_plot_field=0, res3_npts=0, *args, **kwargs): # theta *= (180 / np.pi) - phi *= (180 / np.pi) + # phi *= (180 / np.pi) if grating_type in (0, 1): period = period[0] @@ -148,22 +148,32 @@ def run_res3(self, grating_type, period, fto, ucell, thickness, theta, phi, pol, profile = np.array([[0, *thickness, 0], range(1, len(thickness) + 3)]) - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell = \ + (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, + bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, field_cell) = \ self._run(pol, theta, phi, period, n_top, fto, textures, profile, wavelength, grating_type, cal_field=True, matlab_plot_field=matlab_plot_field, res3_npts=res3_npts) - return top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell + return (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, + bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, field_cell) def _run(self, pol, theta, phi, period, n_top, fto, textures, profile, wavelength, grating_type, cal_field=False, matlab_plot_field=0, res3_npts=0): + if phi is None: + phi = 0 + else: + phi = phi * (180 / np.pi) + if cal_field: - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell = \ + # top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell = \ + (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, + bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, field_cell) = \ self.eng.reticolo_res3(pol, theta, phi, period, n_top, fto, textures, profile, wavelength, grating_type, matlab_plot_field, res3_npts, - nout=5) - res = (top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell) + nout=9) + + res = (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, + bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, field_cell) else: top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info = \ self.eng.reticolo_res2(pol, theta, phi, period, n_top, fto, diff --git a/benchmarks/interface/reti_2d.m b/benchmarks/interface/reti_2d.m index 5b53e8b..182b935 100644 --- a/benchmarks/interface/reti_2d.m +++ b/benchmarks/interface/reti_2d.m @@ -1,26 +1,28 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d(ex_case); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d(ex_case); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); warning('off', 'findstr is obsolete; use strfind instead'); if ex_case == 1 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_1(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_1(); elseif ex_case == 2 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_2(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_2(); elseif ex_case == 3 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_3(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_3(); elseif ex_case == 4 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_4(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_4(); elseif ex_case == 5 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_5(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_5(); elseif ex_case == 6 - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_6(); + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_6(); + elseif ex_case == 7 + [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_7(); end end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_1(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_1(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -71,10 +73,10 @@ aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); res = res2(aa, profile); - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(-period(1)/2, period(1)/2, 11); + y = linspace(period(2)/2, -period(2)/2, 11); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -93,20 +95,31 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; + end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_2(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_2(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -152,11 +165,11 @@ aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); res = res2(aa, profile); - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(-period(1)/2, period(1)/2, 11); + y = linspace(-period(2)/2, period(2)/2, 11); + y = linspace(period(2)/2, -period(2)/2, 11); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -165,7 +178,7 @@ % y = [50:-1:1] .* period(2) / 50 - period(2)/2 % y = [49:-1:0] .* period(2) / 50 - period(2)/2 - parm.res3.trace=1; %trace automatique % automatic trace + parm.res3.trace=0; %trace automatique % automatic trace if pol == 1 einc = [0, 1]; @@ -176,21 +189,31 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_3(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_3(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -219,7 +242,7 @@ parm = res0; parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; + parm.res1.trace = 0; k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. @@ -233,11 +256,11 @@ aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); res = res2(aa, profile); - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(-period(1)/2, period(1)/2, 11); + y = linspace(-period(2)/2, period(2)/2, 11); + y = linspace(period(2)/2, -period(2)/2, 11); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -246,7 +269,7 @@ % y = [50:-1:1] .* period(2) / 50 - period(2)/2 % y = [49:-1:0] .* period(2) / 50 - period(2)/2 - parm.res3.trace=1; %trace automatique % automatic trace + parm.res3.trace=0; %trace automatique % automatic trace if pol == 1 einc = [0, 1]; @@ -257,21 +280,31 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_4(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_4(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -302,7 +335,7 @@ parm = res0; parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; + parm.res1.trace = 0; k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. @@ -335,8 +368,8 @@ % x = linspace(-period(1)/2, period(1)/2, 50); % y = linspace(-period(2)/2, period(2)/2, 50); % y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -345,7 +378,7 @@ % y = [50:-1:1] .* period(2) / 50 - period(2)/2 % y = [49:-1:0] .* period(2) / 50 - period(2)/2 - parm.res3.trace=1; %trace automatique % automatic trace + parm.res3.trace=0; %trace automatique % automatic trace % parm.res3.npts = res3_npts; @@ -358,20 +391,30 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_5(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_5(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -418,7 +461,7 @@ parm = res0; parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; + parm.res1.trace = 0; k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. @@ -448,13 +491,13 @@ %parm.res3.sens=1; %##parm.res3.gauss_x = 100 - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); + x = linspace(-period(1)/2, period(1)/2, 11); + y = linspace(-period(2)/2, period(2)/2, 11); + y = linspace(period(2)/2, -period(2)/2, 11); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -463,7 +506,7 @@ % y = [50:-1:1] .* period(2) / 50 - period(2)/2 % y = [49:-1:0] .* period(2) / 50 - period(2)/2 - parm.res3.trace=1; %trace automatique % automatic trace + parm.res3.trace=0; %trace automatique % automatic trace % parm.res3.npts = res3_npts; @@ -476,20 +519,30 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; end -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reti_2d_6(); +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_6(); warning('off', 'Octave:possible-matlab-short-circuit-operator'); warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); @@ -520,7 +573,7 @@ parm = res0; parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; + parm.res1.trace = 0; k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. @@ -531,7 +584,7 @@ % parm.res3.npts=[0,1,0]; parm.res3.npts=[11,11,11]; - %parm.res1.trace = 1; % show the texture + %parm.res1.trace = 0; % show the texture % %textures = cell(1, size(_textures, 2)); %for i = 1:length(_textures) @@ -553,8 +606,8 @@ % x = linspace(-period(1)/2, period(1)/2, 50); % y = linspace(-period(2)/2, period(2)/2, 50); % y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); % x = [0:1:49] * period(1) / 50 - period(1)/2; % x = [1:1:50] * period(1) / 50 - period(1)/2; @@ -563,7 +616,7 @@ % y = [50:-1:1] .* period(2) / 50 - period(2)/2 % y = [49:-1:0] .* period(2) / 50 - period(2)/2 - parm.res3.trace=1; %trace automatique % automatic trace + parm.res3.trace=0; %trace automatique % automatic trace % parm.res3.npts = res3_npts; @@ -576,17 +629,137 @@ end [e,z,o]=res3(x,y,aa,profile,einc, parm); - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; +end + +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reti_2d_7(); + + warning('off', 'Octave:possible-matlab-short-circuit-operator'); + warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); + warning('off', 'findstr is obsolete; use strfind instead'); + + factor = 1; + pol = 1; + n_top = 1; + n_bot = 1; + theta = 10; + phi = 20; + nn = [2,2]; + period = [480/factor, 480/factor]; + wavelength = 550/factor; + thickness = 220/factor; + + + a = [0+240, 120+240, 160, 80, 4-1i, 1]; + b = [0+240, -120+240, 160, 80, 4, 1]; + c = [120+240, 0+240, 80, 160, 4+5i, 1]; + d = [-120+240, 0+240, 80, 160, 4, 1]; + + textures = cell(1,3); + textures{1} = n_top; + textures{2} = {1,a, b, c, d}; + textures{3} = n_bot; + + + parm = res0; + parm.res1.champ = 1; % calculate precisely + parm.res1.trace = 0; + + k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. + + parm = res0; + + parm.not_io = 1; % no write data on hard disk + parm.res1.champ = 1; % the electromagnetic field is calculated accurately +% parm.res3.npts=[0,1,0]; + parm.res3.npts=[11,11,11]; + + %parm.res1.trace = 0; % show the texture + % + %textures = cell(1, size(_textures, 2)); + %for i = 1:length(_textures) + % textures(i) = _textures(i); + %end + % + %profile = cell(1, size(_profile, 1)); + %profile(1) = _profile(1, :); + %profile(2) = _profile(2, :); + + profile = {[0, thickness, 0], [1, 2, 3]}; + aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); + res = res2(aa, profile); + + %res3(aa) + %parm.res3.sens=1; + %##parm.res3.gauss_x = 100 + +% x = linspace(-period(1)/2, period(1)/2, 50); +% y = linspace(-period(2)/2, period(2)/2, 50); +% y = linspace(period(2)/2, -period(2)/2, 50); + x = linspace(0, period(1), 11); + y = linspace(period(2), 0, 11); + +% x = [0:1:49] * period(1) / 50 - period(1)/2; +% x = [1:1:50] * period(1) / 50 - period(1)/2; +% y = [0:1:49] .* period(2) / 50 - period(2)/2 +% y = [1:1:50] .* period(2) / 50 - period(2)/2 +% y = [50:-1:1] .* period(2) / 50 - period(2)/2 +% y = [49:-1:0] .* period(2) / 50 - period(2)/2 + + parm.res3.trace=0; %trace automatique % automatic trace + +% parm.res3.npts = res3_npts; + + if pol == 1 + einc = [0, 1]; + elseif pol == -1 + einc = [1, 0]; + else + disp('only TE or TM is allowed.'); end + [e,z,o]=res3(x,y,aa,profile,einc, parm); + +% if pol == 1 % TE +% top_refl_info = res.TEinc_top_reflected; +% top_tran_info = res.TEinc_top_transmitted; +% bottom_refl_info = res.TEinc_bottom_reflected; +% bottom_tran_info = res.TEinc_bottom_transmitted; +% else % TM +% top_refl_info = res.TMinc_top_reflected; +% top_tran_info = res.TMinc_top_transmitted; +% bottom_refl_info = res.TMinc_bottom_reflected; +% bottom_tran_info = res.TMinc_bottom_transmitted; +% end + + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; end % Divides the given geometry into rectangles to be used in Reticolo diff --git a/benchmarks/interface/reticolo_res3.m b/benchmarks/interface/reticolo_res3.m index e1d8675..5262dbf 100644 --- a/benchmarks/interface/reticolo_res3.m +++ b/benchmarks/interface/reticolo_res3.m @@ -1,4 +1,6 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reticolo_res3(_pol, +%function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = reticolo_res3(_pol, +function [top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, e] = reticolo_res3(_pol, +%function [res] = reticolo_res3(_pol, theta, phi, period, n_inc, nn, _textures, _profile, wavelength, grating_type, matlab_plot_field, res3_npts); %UNTITLED4 Summary of this function goes here % Detailed explanation goes here @@ -41,7 +43,7 @@ res = res2(aa, profile); end -x = linspace(0, period(1), 50); +x = linspace(0, period(1), 11); %parm.res3.sens=1; %##parm.res3.gauss_x = 100 @@ -58,10 +60,11 @@ if grating_type == 0 [e,z,o]=res3(x,aa,profile,1,parm); else +% y = linspace(period(1), 0, 50); if grating_type == 1 y=0; else - y = linspace(period(1), 0, 50); + y = linspace(period(1), 0, 11); end if pol == 1 @@ -75,20 +78,30 @@ end if grating_type == 0 - top_refl_info = res.inc_top_reflected; - top_tran_info = res.inc_top_transmitted; - bottom_refl_info = res.inc_bottom_reflected; - bottom_tran_info = res.inc_bottom_transmitted; +% top_refl_info = res.inc_top_reflected; +% top_tran_info = res.inc_top_transmitted; +% bottom_refl_info = res.inc_bottom_reflected; +% bottom_tran_info = res.inc_bottom_transmitted; + + top_refl_info_te = res.inc_top_reflected; + top_tran_info_te = res.inc_top_transmitted; + top_refl_info_tm = res.inc_top_reflected; + top_tran_info_tm = res.inc_top_transmitted; + + bottom_refl_info_te = res.inc_bottom_reflected; + bottom_tran_info_te = res.inc_bottom_transmitted; + bottom_refl_info_tm = res.inc_bottom_reflected; + bottom_tran_info_tm = res.inc_bottom_transmitted; + else - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end + top_refl_info_te = res.TEinc_top_reflected; + top_tran_info_te = res.TEinc_top_transmitted; + top_refl_info_tm = res.TMinc_top_reflected; + top_tran_info_tm = res.TMinc_top_transmitted; + + bottom_refl_info_te = res.TEinc_bottom_reflected; + bottom_tran_info_te = res.TEinc_bottom_transmitted; + bottom_refl_info_tm = res.TMinc_bottom_reflected; + bottom_tran_info_tm = res.TMinc_bottom_transmitted; + end diff --git a/benchmarks/interface/trashcan/reti_meent_1D.py b/benchmarks/interface/trashcan/reti_meent_1D.py deleted file mode 100644 index 61362c5..0000000 --- a/benchmarks/interface/trashcan/reti_meent_1D.py +++ /dev/null @@ -1,291 +0,0 @@ -import os -import numpy as np -import matplotlib.pyplot as plt - -import meent - -# os.environ['OCTAVE_EXECUTABLE'] = '/opt/homebrew/bin/octave-cli' - - -class Reticolo: - - def __init__(self, engine_type='octave', *args, **kwargs): - - if engine_type == 'octave': - try: - from oct2py import octave - except Exception as e: - raise e - self.eng = octave - - elif engine_type == 'matlab': - try: - import matlab.engine - except Exception as e: - raise e - self.eng = matlab.engine.start_matlab() - else: - raise ValueError - - # path that has file to run in octave - m_path = os.path.dirname(__file__) - self.eng.addpath(self.eng.genpath(m_path)) - - def run_res2(self, grating_type, period, fto, ucell, thickness, theta, phi, pol, wavelength, n_I, n_II, - *args, **kwargs): - theta *= (180 / np.pi) - phi *= (180 / np.pi) - - if grating_type in (0, 1): - period = period[0] - - fto = fto - Nx = ucell.shape[2] - period_x = period - grid_x = np.linspace(0, period, Nx + 1)[1:] - grid_x -= period_x / 2 - - # grid = np.linspace(0, period, Nx) - - ucell_new = [] - for z in range(ucell.shape[0]): - ucell_layer = [grid_x, ucell[z, 0]] - ucell_new.append(ucell_layer) - - textures = [n_I, *ucell_new, n_II] - - else: - - Nx = ucell.shape[2] - Ny = ucell.shape[1] - period_x = period[0] - period_y = period[1] - - unit_x = period_x / Nx - unit_y = period_y / Ny - - grid_x = np.linspace(0, period[0], Nx + 1)[1:] - grid_y = np.linspace(0, period[1], Ny + 1)[1:] - - grid_x -= period_x / 2 - grid_y -= period_y / 2 - - ucell_new = [] - for z in range(ucell.shape[0]): - ucell_layer = [10] - for y, yval in enumerate(grid_y): - for x, xval in enumerate(grid_x): - obj = [xval, yval, unit_x, unit_y, ucell[z, y, x], 1] - ucell_layer.append(obj) - ucell_new.append(ucell_layer) - textures = [n_I, *ucell_new, n_II] - - profile = np.array([[0, *thickness, 0], range(1, len(thickness) + 3)]) - - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info = \ - self._run(pol, theta, phi, period, n_I, fto, textures, profile, wavelength, grating_type, - cal_field=False) - - return top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, bottom_tran_info.efficiency - - def run_res3(self, grating_type, period, fto, ucell, thickness, theta, phi, pol, wavelength, n_top, n_bot, - matlab_plot_field=0, res3_npts=0, *args, **kwargs): - - # theta *= (180 / np.pi) - phi *= (180 / np.pi) - - if grating_type in (0, 1): - period = period[0] - - fto = fto - Nx = ucell.shape[2] - period_x = period - grid_x = np.linspace(0, period, Nx + 1)[1:] - grid_x -= period_x / 2 - - # grid = np.linspace(0, period, Nx) - - ucell_new = [] - for z in range(ucell.shape[0]): - ucell_layer = [grid_x, ucell[z, 0]] - ucell_new.append(ucell_layer) - - textures = [n_top, *ucell_new, n_bot] - - else: - - Nx = ucell.shape[2] - Ny = ucell.shape[1] - period_x = period[0] - period_y = period[1] - - unit_x = period_x / Nx - unit_y = period_y / Ny - - grid_x = np.linspace(0, period[0], Nx + 1)[1:] - grid_y = np.linspace(0, period[1], Ny + 1)[1:] - - grid_x -= period_x / 2 - grid_y -= period_y / 2 - - ucell_new = [] - for z in range(ucell.shape[0]): - ucell_layer = [10] - for y, yval in enumerate(grid_y): - for x, xval in enumerate(grid_x): - obj = [xval, yval, unit_x, unit_y, ucell[z, y, x], 1] - ucell_layer.append(obj) - ucell_new.append(ucell_layer) - textures = [n_top, *ucell_new, n_bot] - - profile = np.array([[0, *thickness, 0], range(1, len(thickness) + 3)]) - - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell = \ - self._run(pol, theta, phi, period, n_top, fto, textures, profile, wavelength, grating_type, - cal_field=True, matlab_plot_field=matlab_plot_field, res3_npts=res3_npts) - - return top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell - - def _run(self, pol, theta, phi, period, n_top, fto, - textures, profile, wavelength, grating_type, cal_field=False, matlab_plot_field=0, res3_npts=0): - - if cal_field: - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell = \ - self.eng.reticolo_res3(pol, theta, phi, period, n_top, fto, - textures, profile, wavelength, grating_type, matlab_plot_field, res3_npts, - nout=5) - res = (top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell) - else: - top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info = \ - self.eng.reticolo_res2(pol, theta, phi, period, n_top, fto, - textures, profile, wavelength, grating_type, nout=4) - res = (top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info) - return res - - -if __name__ == '__main__': - - factor = 100 - option = {} - option['grating_type'] = 0 # 0 : just 1D grating, 1 : 1D rotating grating, 2 : 2D grating - option['pol'] = 0 # 0: TE, 1: TM - option['n_top'] = 2.2 # n_incidence - option['n_bot'] = 1 # n_transmission - option['theta'] = 60 * np.pi / 180 - option['phi'] = 0 * np.pi / 180 - option['fto'] = 1 - option['period'] = [770/factor] - option['wavelength'] = 777/factor - option['thickness'] = [100/factor, 100/factor, 100/factor, 100/factor, 100/factor, 100/factor] # final term is for h_substrate - # option['thickness'] = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1] # final term is for h_substrate - option['fourier_type'] = 2 - - ucell = np.array( - [ - [[3, 3, 3, 3, 3, 1, 1, 1, 1,]], - ]) - - option['ucell'] = ucell - option['thickness'] = [100/factor,] # final term is for h_substrate - - res3_npts = 20 - reti = Reticolo() - reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, matlab_plot_field=0, res3_npts=res3_npts) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - # Numpy - backend = 0 - nmee = meent.call_mee(backend=backend, perturbation=1E-30, **option) - n_de_ri, n_de_ti = nmee.conv_solve() - n_field_cell = nmee.calculate_field(res_z=20, res_x=ucell.shape[-1]) - - # n_field_cell = np.roll(n_field_cell, -1, 2) - - print('nmeent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('nmeent de_ti', n_de_ti[n_de_ti > 1E-5]) - - - if option['pol'] == 0: # TE - title = ['1D Ey', '1D Hx', '1D Hz', ] - else: # TM - title = ['1D Hy', '1D Ex', '1D Ez', ] - - for i in range(3): - a0 = np.flipud(r_field_cell[res3_npts:-res3_npts, :, i]) - b0 = n_field_cell[:, 0, :, i] - - res = [] - res.append(np.linalg.norm(a0.conj() - b0).round(3)) - res.append(np.linalg.norm(abs(a0.conj())**2 - abs(b0)**2).round(3)) - res.append(np.linalg.norm(a0.conj().real - b0.real).round(3)) - res.append(np.linalg.norm(a0.conj().imag - b0.imag).round(3)) - print(f'{title[i]}, {res}') - - aa = np.angle(a0.conj()) - bb = np.angle(b0) - - # print(aa[0][1:] - aa[0][:-1]) - # print(bb[0][1:] - bb[0][:-1]) - - print(aa[0] - bb[0]) - print(1) - - # - # print('Ey, val diff', np.linalg.norm(a0.conj() - b0)) - # print('Ey, abs2 diff', np.linalg.norm(abs(a0.conj())**2 - abs(b0)**2)) - # print('Ey, real diff', np.linalg.norm(a0.conj().real - b0.real)) - # print('Ey, imag diff', np.linalg.norm(a0.conj().imag - b0.imag)) - # - # a1 = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 1]) - # b1 = n_field_cell[:, 0, :, 1] - # print(np.linalg.norm(a1.conj() - b1)) - # print('Hx, val diff', np.linalg.norm(a1.conj() - b1)) - # print('Ey, abs2 diff', np.linalg.norm(abs(a1.conj())**2 - abs(b1)**2)) - # print('Ey, real diff', np.linalg.norm(a1.conj().real - b1.real)) - # print('Ey, imag diff', np.linalg.norm(a1.conj().imag - b1.imag)) - # - # a2 = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 2]) - # b2 = n_field_cell[:, 0, :, 2] - # print(np.linalg.norm(a2.conj() - b2)) - # print('Hz, val diff', np.linalg.norm(a2.conj() - b2)) - # print('Ey, abs2 diff', np.linalg.norm(abs(a2.conj())**2 - abs(b2)**2)) - # print('Ey, real diff', np.linalg.norm(a2.conj().real - b2.real)) - # print('Ey, imag diff', np.linalg.norm(a2.conj().imag - b2.imag)) - - fig, axes = plt.subplots(3, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, ix]).conj() - - im = axes[ix, 0].imshow(abs(r_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix=0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - 1 diff --git a/benchmarks/interface/trashcan/test2d_1.m b/benchmarks/interface/trashcan/test2d_1.m deleted file mode 100644 index 0c8068b..0000000 --- a/benchmarks/interface/trashcan/test2d_1.m +++ /dev/null @@ -1,86 +0,0 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = test2d_1(); - - warning('off', 'Octave:possible-matlab-short-circuit-operator'); - warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); - warning('off', 'findstr is obsolete; use strfind instead'); - - factor = 1; - pol = -1; - n_top = 1; - n_bot = 1; - theta = 20; - phi = 33; - nn = [11,11]; - period = [770/factor, 770/factor]; - wavelength = 777/factor; - thickness = 100/factor; - - b = [-period(1)/4, -period(2)/4, period(1)/2, period(2)*5/10, 3, 1]; - c = [-period(1)/10*4, period(2)/10*4, period(1)*2/10, period(2)*2/10, 4, 1]; - d = [-period(1)/10*2, period(2)/10*4, period(1)*2/10, period(2)*2/10, 6, 1]; - - b = [-period(1)/4+period(1)/2, -period(2)/4+period(2)/2, period(1)/2, period(2)*5/10, 3, 1]; - c = [-period(1)/10*4+period(1)/2, period(2)/10*4+period(2)/2, period(1)*2/10, period(2)*2/10, 4, 1]; - d = [-period(1)/10*2+period(1)/2, period(2)/10*4+period(2)/2, period(1)*2/10, period(2)*2/10, 6, 1]; - - tt = {1, b, c, d}; - - retio; - textures = cell(1,3); - textures{1} = n_top; - textures{2} = tt; - textures{3} = n_bot; - - parm = res0; - parm.res1.champ = 1; % calculate precisely -% parm.res1.trace = 1; -% parm.res3.trace = 1; % trace automatique % automatic trace - - k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. - - parm = res0; - - parm.not_io = 1; % no write data on hard disk - parm.res1.champ = 1; % the electromagnetic field is calculated accurately -% parm.res3.npts=[0,1,0]; - parm.res3.npts=[11,11,11]; - - profile = {[0, thickness, 0], [1, 2, 3]}; - aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); - res = res2(aa, profile); - - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); - -% x = [0:1:49] * period(1) / 50 - period(1)/2; -% x = [1:1:50] * period(1) / 50 - period(1)/2; -% y = [0:1:49] .* period(2) / 50 - period(2)/2 -% y = [1:1:50] .* period(2) / 50 - period(2)/2 -% y = [50:-1:1] .* period(2) / 50 - period(2)/2 -% y = [49:-1:0] .* period(2) / 50 - period(2)/2 - - - if pol == 1 - einc = [0, 1]; - elseif pol == -1 - einc = [1, 0]; - else - disp('only TE or TM is allowed.'); - end - [e,z,o]=res3(x,y,aa,profile,einc, parm); - - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end - disp(1) -end diff --git a/benchmarks/interface/trashcan/test2d_2.m b/benchmarks/interface/trashcan/test2d_2.m deleted file mode 100644 index c80777c..0000000 --- a/benchmarks/interface/trashcan/test2d_2.m +++ /dev/null @@ -1,83 +0,0 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = test2d_2(); - - warning('off', 'Octave:possible-matlab-short-circuit-operator'); - warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); - warning('off', 'findstr is obsolete; use strfind instead'); - - factor = 1; - pol = -1; - n_top = 1; - n_bot = 1; - theta = 20; - phi = 33; - nn = [11,11]; - period = [770/factor, 770/factor]; - wavelength = 777/factor; - thickness = 100/factor; - - b = [-period(1)/4, -period(2)/4, period(1)/2, period(2)*5/10, 3, 1]; - b = [period(1)/4, period(2)/4, period(1)/2, period(2)*5/10, 3, 1]; - - tt = {1, b}; - - retio; - textures = cell(1,3); - textures{1} = n_top; - textures{2} = tt; - textures{3} = n_bot; - - parm = res0; - parm.res1.champ = 1; % calculate precisely -% parm.res1.trace = 1; -% parm.res3.trace = 1; % trace automatique % automatic trace - - k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. - - parm = res0; - - parm.not_io = 1; % no write data on hard disk - parm.res1.champ = 1; % the electromagnetic field is calculated accurately -% parm.res3.npts=[0,1,0]; - parm.res3.npts=[11,11,11]; - - profile = {[0, thickness, 0], [1, 2, 3]}; - aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); - res = res2(aa, profile); - - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); - -% x = [0:1:49] * period(1) / 50 - period(1)/2; -% x = [1:1:50] * period(1) / 50 - period(1)/2; -% y = [0:1:49] .* period(2) / 50 - period(2)/2 -% y = [1:1:50] .* period(2) / 50 - period(2)/2 -% y = [50:-1:1] .* period(2) / 50 - period(2)/2 -% y = [49:-1:0] .* period(2) / 50 - period(2)/2 - - parm.res3.trace=1; %trace automatique % automatic trace - - if pol == 1 - einc = [0, 1]; - elseif pol == -1 - einc = [1, 0]; - else - disp('only TE or TM is allowed.'); - end - [e,z,o]=res3(x,y,aa,profile,einc, parm); - - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end - disp(1) -end diff --git a/benchmarks/interface/trashcan/test2d_3.m b/benchmarks/interface/trashcan/test2d_3.m deleted file mode 100644 index 201a1cc..0000000 --- a/benchmarks/interface/trashcan/test2d_3.m +++ /dev/null @@ -1,80 +0,0 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = test2d_3(); - - warning('off', 'Octave:possible-matlab-short-circuit-operator'); - warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); - warning('off', 'findstr is obsolete; use strfind instead'); - - factor = 1; - pol = -1; - n_top = 1; - n_bot = 1; - theta = 20; - phi = 33; - nn = [11,11]; - period = [770/factor, 770/factor]; - wavelength = 777/factor; - thickness = 100/factor; - - - a = [period(1)/4, period(2)/2, period(1)/2, period(2), 4, 1]; - tt = {1, a}; - - retio; - textures = cell(1,3); - textures{1} = n_top; - textures{2} = tt; - textures{3} = n_bot; - - parm = res0; - parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; - - k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. - - parm = res0; - - parm.not_io = 1; % no write data on hard disk - parm.res1.champ = 1; % the electromagnetic field is calculated accurately - parm.res3.npts=[11,11,11]; - - profile = {[0, thickness, 0], [1, 2, 3]}; - aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); - res = res2(aa, profile); - - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); - -% x = [0:1:49] * period(1) / 50 - period(1)/2; -% x = [1:1:50] * period(1) / 50 - period(1)/2; -% y = [0:1:49] .* period(2) / 50 - period(2)/2 -% y = [1:1:50] .* period(2) / 50 - period(2)/2 -% y = [50:-1:1] .* period(2) / 50 - period(2)/2 -% y = [49:-1:0] .* period(2) / 50 - period(2)/2 - - parm.res3.trace=1; %trace automatique % automatic trace - - if pol == 1 - einc = [0, 1]; - elseif pol == -1 - einc = [1, 0]; - else - disp('only TE or TM is allowed.'); - end - [e,z,o]=res3(x,y,aa,profile,einc, parm); - - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end - disp(1) -end diff --git a/benchmarks/interface/trashcan/test2d_4.m b/benchmarks/interface/trashcan/test2d_4.m deleted file mode 100644 index 103c323..0000000 --- a/benchmarks/interface/trashcan/test2d_4.m +++ /dev/null @@ -1,123 +0,0 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = test2d_4(); - - warning('off', 'Octave:possible-matlab-short-circuit-operator'); - warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); - warning('off', 'findstr is obsolete; use strfind instead'); - - factor = 1; - pol = 1; - n_top = 1; - n_bot = 1; - theta = 0; - phi = 0; - nn = [11,11]; - period = [480/factor, 480/factor]; - wavelength = 550/factor; - thickness = 220/factor; - - - a = [0+240, 120+240, 160, 80, 4, 1]; - b = [0+240, -120+240, 160, 80, 4, 1]; - c = [120+240, 0+240, 80, 160, 4, 1]; - d = [-120+240, 0+240, 80, 160, 4, 1]; - - textures = cell(1,3); - textures{1} = n_top; - textures{2} = {1,a, b, c, d}; - textures{3} = n_bot; - - - parm = res0; - parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; - - k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. - - parm = res0; - - parm.not_io = 1; % no write data on hard disk - parm.res1.champ = 1; % the electromagnetic field is calculated accurately -% parm.res3.npts=[0,1,0]; - parm.res3.npts=[11,11,11]; - - %parm.res1.trace = 1; % show the texture - % - %textures = cell(1, size(_textures, 2)); - %for i = 1:length(_textures) - % textures(i) = _textures(i); - %end - % - %profile = cell(1, size(_profile, 1)); - %profile(1) = _profile(1, :); - %profile(2) = _profile(2, :); - - profile = {[0, thickness, 0], [1, 2, 3]}; - aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); - res = res2(aa, profile); - - %res3(aa) - %parm.res3.sens=1; - %##parm.res3.gauss_x = 100 - - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); - -% x = [0:1:49] * period(1) / 50 - period(1)/2; -% x = [1:1:50] * period(1) / 50 - period(1)/2; -% y = [0:1:49] .* period(2) / 50 - period(2)/2 -% y = [1:1:50] .* period(2) / 50 - period(2)/2 -% y = [50:-1:1] .* period(2) / 50 - period(2)/2 -% y = [49:-1:0] .* period(2) / 50 - period(2)/2 - - parm.res3.trace=1; %trace automatique % automatic trace - -% parm.res3.npts = res3_npts; - - if pol == 1 - einc = [0, 1]; - elseif pol == -1 - einc = [1, 0]; - else - disp('only TE or TM is allowed.'); - end - [e,z,o]=res3(x,y,aa,profile,einc, parm); - - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end - disp(1) -end - -% Divides the given geometry into rectangles to be used in Reticolo -function GeometryOut = FractureGeom(PatternIn,nLow,nHigh,XGrid,YGrid) - - % Acceptable refractive index tolerance in fracturing - - % Extract grid parameters - dX = abs(XGrid(2)-XGrid(1)); - dY = abs(YGrid(2)-YGrid(1)); - [Nx, Ny] = size(PatternIn) - - Geometry = {nLow}; %Define background index - - % Fracture non binarized pixels - for i = 1:Nx % Defining texture for patterned layer. Probably could have vectorized this. - for j = 1:Ny - if PatternIn(i,j) == 1 - Geometry = [Geometry,{[XGrid(i),YGrid(j),dX,dY,nHigh,1]}]; - end - end - end - GeometryOut = Geometry; -end \ No newline at end of file diff --git a/benchmarks/interface/trashcan/test2d_5.m b/benchmarks/interface/trashcan/test2d_5.m deleted file mode 100644 index a4cb0e5..0000000 --- a/benchmarks/interface/trashcan/test2d_5.m +++ /dev/null @@ -1,141 +0,0 @@ -function [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, e] = test2d_5(); - - warning('off', 'Octave:possible-matlab-short-circuit-operator'); - warning('off', 'Invalid UTF-8 byte sequences have been replaced.'); - warning('off', 'findstr is obsolete; use strfind instead'); - - factor = 1; - pol = 1; - n_top = 1; - n_bot = 1; - theta = 0; - phi = 0; - nn = [11,11]; - period = [480/factor, 480/factor]; - wavelength = 550/factor; - thickness = 220/factor; - - PatternIn = [0 0 0 0 0 0; 0 0 1 1 0 0; 0 1 0 0 1 0; 0 1 0 0 1 0; 0 0 1 1 0 0; 0 0 0 0 0 0]; -% PatternIn = [3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; 3, 3, 3, 3, 3, 1, 1, 1, 1, 1; ] -% PatternIn = [3, 3, 3, 3, 3, 1, 1, 1, 1, 1] -% PatternIn = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;1, 1, 1, 1, 1, 0, 0, 0, 0, 0;] -% PatternIn = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0] - - XGrid = [3.5:1:8.5]/6 * period(1); - YGrid = [3.5:1:8.5]/6 * period(1); - - XGrid = linspace(-period(1)/2 + period(1)/12, period(1)/2 - period(1)/12, 6) + period(1)/2; - YGrid = linspace(-period(2)/2 + period(2)/12, period(2)/2 - period(2)/12, 6) + period(2)/2; - YGrid = -linspace(-period(2)/2 + period(2)/12, period(2)/2 - period(2)/12, 6) - period(2)/2; - -% XGrid = linspace(-period(1)/2 + period(1)/12, period(1)/2 - period(1)/12, 6); -% YGrid = -linspace(-period(2)/2 + period(2)/12, period(2)/2 - period(2)/12, 6); - -% XGrid = [0.5:1:9.5] * period(1) / 10 -% YGrid = [0.5:1:9.5] * period(2) / 10 - - % RCWA - - retio; - textures = cell(1,3); - textures{1} = {n_top}; - textures{2} = FractureGeom(PatternIn,1,4,XGrid,YGrid); - textures{3} = {n_bot}; - profile = {[0, thickness, 0], [1, 2, 3]}; - - parm = res0; - parm.res1.champ = 1; % calculate precisely - parm.res1.trace = 1; - - k_parallel = n_top*sind(theta); % n_air, or whatever the refractive index of the medium where light is coming in. - - parm = res0; - - parm.not_io = 1; % no write data on hard disk - parm.res1.champ = 1; % the electromagnetic field is calculated accurately -% parm.res3.npts=[0,1,0]; - parm.res3.npts=[11,11,11]; - - %parm.res1.trace = 1; % show the texture - % - %textures = cell(1, size(_textures, 2)); - %for i = 1:length(_textures) - % textures(i) = _textures(i); - %end - % - %profile = cell(1, size(_profile, 1)); - %profile(1) = _profile(1, :); - %profile(2) = _profile(2, :); - - profile = {[0, thickness, 0], [1, 2, 3]}; - aa = res1(wavelength,period,textures,nn,k_parallel, phi, parm); - res = res2(aa, profile); - - %res3(aa) - %parm.res3.sens=1; - %##parm.res3.gauss_x = 100 - - x = linspace(-period(1)/2, period(1)/2, 50); - y = linspace(-period(2)/2, period(2)/2, 50); - y = linspace(period(2)/2, -period(2)/2, 50); - - - x = linspace(0, period(1), 50); - y = linspace(period(2), 0, 50); - -% x = [0:1:49] * period(1) / 50 - period(1)/2; -% x = [1:1:50] * period(1) / 50 - period(1)/2; -% y = [0:1:49] .* period(2) / 50 - period(2)/2 -% y = [1:1:50] .* period(2) / 50 - period(2)/2 -% y = [50:-1:1] .* period(2) / 50 - period(2)/2 -% y = [49:-1:0] .* period(2) / 50 - period(2)/2 - - parm.res3.trace=1; %trace automatique % automatic trace - -% parm.res3.npts = res3_npts; - - if pol == 1 - einc = [0, 1]; - elseif pol == -1 - einc = [1, 0]; - else - disp('only TE or TM is allowed.'); - end - [e,z,o]=res3(x,y,aa,profile,einc, parm); - - if pol == 1 % TE - top_refl_info = res.TEinc_top_reflected; - top_tran_info = res.TEinc_top_transmitted; - bottom_refl_info = res.TEinc_bottom_reflected; - bottom_tran_info = res.TEinc_bottom_transmitted; - else % TM - top_refl_info = res.TMinc_top_reflected; - top_tran_info = res.TMinc_top_transmitted; - bottom_refl_info = res.TMinc_bottom_reflected; - bottom_tran_info = res.TMinc_bottom_transmitted; - end - disp(1) -end - -% Divides the given geometry into rectangles to be used in Reticolo -function GeometryOut = FractureGeom(PatternIn,nLow,nHigh,XGrid,YGrid) - - % Acceptable refractive index tolerance in fracturing - - % Extract grid parameters - dX = abs(XGrid(2)-XGrid(1)); - dY = abs(YGrid(2)-YGrid(1)); - [Nx, Ny] = size(PatternIn); - - Geometry = {nLow}; %Define background index - - % Fracture non binarized pixels - for i = 1:Nx % Defining texture for patterned layer. Probably could have vectorized this. - for j = 1:Ny - if PatternIn(i,j) == 1 - Geometry = [Geometry,{[XGrid(i),YGrid(j),dX,dY,nHigh,1]}]; - end - end - end - GeometryOut = Geometry; -end \ No newline at end of file diff --git a/benchmarks/reti_meent_1D.py b/benchmarks/reti_meent_1D.py index 6ee0c7f..844fb52 100644 --- a/benchmarks/reti_meent_1D.py +++ b/benchmarks/reti_meent_1D.py @@ -14,15 +14,126 @@ from Reticolo import Reticolo -def test1d_1(plot_figure=False): +def run_1d(option, plot_figure=False): + res_z = 11 + res_y = 1 + res_x = 11 + reti = Reticolo() + (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, + bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, reti_field_cell) \ + = reti.run_res3(**option, grating_type=0, matlab_plot_field=0, res3_npts=res_z) + # print('reti de_ri', np.array(reti_de_ri).flatten()) + # print('reti de_ti', np.array(reti_de_ti).flatten()) + + reti_field_cell = reti_field_cell[:, None, :, :] + reti_field_cell = reti_field_cell[res_z:-res_z] + reti_field_cell = np.flip(reti_field_cell, 0) + reti_field_cell = reti_field_cell.conj() + + # Numpy + mee = meent.call_mee(backend=0, **option) + res_numpy = mee.conv_solve() + field_cell_numpy = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # JAX + mee = meent.call_mee(backend=1, **option) # JAX + res_jax = mee.conv_solve() + field_cell_jax = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # Torch + mee = meent.call_mee(backend=2, **option) # PyTorch + res_torch = mee.conv_solve() + field_cell_torch = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x).numpy() + + bds = ['Numpy', 'JAX', 'Torch'] + fields = [field_cell_numpy, field_cell_jax, field_cell_torch] + + print('Norm of (meent - reti) per backend') + for i, res_t in enumerate([res_numpy, res_jax, res_torch]): + reti_de_ri_te, reti_de_ti_te = np.array(top_refl_info_te.efficiency).T, np.array(top_tran_info_te.efficiency).T + reti_de_ri_tm, reti_de_ti_tm = np.array(top_refl_info_tm.efficiency).T, np.array(top_tran_info_tm.efficiency).T + + # de_ri_te, de_ti_te = np.array(res_t.res_te_inc.de_ri).T, np.array(res_t.res_te_inc.de_ti).T + # de_ri_tm, de_ti_tm = np.array(res_t.res_tm_inc.de_ri).T, np.array(res_t.res_tm_inc.de_ti).T + # + # de_ri_te = de_ri_te[de_ri_te > 1E-5] + # de_ti_te = de_ti_te[de_ti_te > 1E-5] + # de_ri_tm = de_ri_tm[de_ri_tm > 1E-5] + # de_ti_tm = de_ti_tm[de_ti_tm > 1E-5] + + de_ri, de_ti = np.array(res_t.res.de_ri).T, np.array(res_t.res.de_ti).T + + de_ri = de_ri[de_ri > 1E-5] + de_ti = de_ti[de_ti > 1E-5] + + # reti_R_s_te = top_refl_info_te.amplitude_TE + # reti_T_s_te = top_tran_info_te.amplitude_TE + # reti_R_p_tm = top_refl_info_tm.amplitude_TM + # reti_T_p_tm = top_tran_info_tm.amplitude_TM + # + # R_s_te = res_t.res_te_inc.R_s + # T_s_te = res_t.res_te_inc.T_s + # R_p_tm = res_t.res_tm_inc.R_p + # T_p_tm = res_t.res_tm_inc.T_p + + print(bds[i]) + print('de_ri', np.linalg.norm(de_ri - reti_de_ri_te), + 'de_ti', np.linalg.norm(de_ti - reti_de_ti_te), + ) + + for i_field in range(reti_field_cell.shape[-1]): + res_temp = np.linalg.norm(fields[i][i_field] - reti_field_cell[i_field]) + print(f'field, {i_field+1}th: {res_temp}') + + if plot_figure: + if option['pol'] == 0: # TE + title = ['1D Ey', '1D Hx', '1D Hz', ] + else: # TM + title = ['1D Hy', '1D Ex', '1D Ez', ] + + fig, axes = plt.subplots(3, 6, figsize=(10, 5)) + + for ix in range(len(title)): + r_data = reti_field_cell[:, res_y//2, :, ix] + + im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 0], shrink=1) + im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 2], shrink=1) + im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 4], shrink=1) + + n_data = fields[i][:, res_y//2, :, ix] + + im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 1], shrink=1) + + im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 3], shrink=1) + + im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 5], shrink=1) + + ix = 0 + axes[ix, 0].title.set_text('abs**2 reti') + axes[ix, 2].title.set_text('Re, reti') + axes[ix, 4].title.set_text('Im, reti') + axes[ix, 1].title.set_text('abs**2 meen') + axes[ix, 3].title.set_text('Re, meen') + axes[ix, 5].title.set_text('Im, meen') + + plt.show() + + +def case_1d_1(plot_figure=False): factor = 1000 option = {} option['pol'] = 0 # 0: TE, 1: TM option['n_top'] = 2 # n_incidence option['n_bot'] = 1 # n_transmission - option['theta'] = 12 * np.pi / 180 - option['phi'] = 0 * np.pi / 180 + option['theta'] = 0 * np.pi / 180 + option['phi'] = None option['fto'] = 1 option['period'] = [770/factor] option['wavelength'] = 777/factor @@ -36,71 +147,10 @@ def test1d_1(plot_figure=False): option['ucell'] = ucell - res_z = 11 - reti = Reticolo() - reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, grating_type=0, matlab_plot_field=0, res3_npts=res_z) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - # Numpy - backend = 0 - nmee = meent.call_mee(backend=backend, perturbation=1E-30, **option) - n_de_ri, n_de_ti = nmee.conv_solve() - n_field_cell = nmee.calculate_field(res_z=res_z, res_x=50) - - print('nmeent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('nmeent de_ti', n_de_ti[n_de_ti > 1E-5]) - - # r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[:, None, :, :] - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(r_field_cell.shape[-1]): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - - if option['pol'] == 0: # TE - title = ['1D Ey', '1D Hx', '1D Hz', ] - else: # TM - title = ['1D Hy', '1D Ex', '1D Ez', ] - - fig, axes = plt.subplots(3, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = r_field_cell[:, 0, :, ix] - - im = axes[ix, 0].imshow(abs(r_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) + run_1d(option, plot_figure) - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') - - plt.show() - - -def test1d_2(plot_figure=False): +def case_1d_2(plot_figure=False): factor = 1 option = {} @@ -108,7 +158,7 @@ def test1d_2(plot_figure=False): option['n_top'] = 1 # n_incidence option['n_bot'] = 2.2 # n_transmission option['theta'] = 0 * np.pi / 180 - option['phi'] = 0 * np.pi / 180 + option['phi'] = None option['fto'] = 80 option['period'] = [770/factor] option['wavelength'] = 777/factor @@ -122,70 +172,9 @@ def test1d_2(plot_figure=False): option['ucell'] = ucell - res_z = 11 - reti = Reticolo() - reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, grating_type=0, matlab_plot_field=0, res3_npts=res_z) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - # Numpy - backend = 0 - nmee = meent.call_mee(backend=backend, perturbation=1E-30, **option) - n_de_ri, n_de_ti = nmee.conv_solve() - n_field_cell = nmee.calculate_field(res_z=res_z, res_x=50) - - print('nmeent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('nmeent de_ti', n_de_ti[n_de_ti > 1E-5]) - - # r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[:, None, :, :] - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(r_field_cell.shape[-1]): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - - if option['pol'] == 0: # TE - title = ['1D Ey', '1D Hx', '1D Hz', ] - else: # TM - title = ['1D Hy', '1D Ex', '1D Ez', ] - - fig, axes = plt.subplots(3, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = r_field_cell[:, 0, :, ix] - - im = axes[ix, 0].imshow(abs(r_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data)**2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') - - plt.show() + run_1d(option, plot_figure) if __name__ == '__main__': - test1d_1(False) - test1d_2(False) + case_1d_1(False) + case_1d_2(False) diff --git a/benchmarks/reti_meent_1Dc.py b/benchmarks/reti_meent_1Dc.py index 11acdf9..a9d6d58 100644 --- a/benchmarks/reti_meent_1Dc.py +++ b/benchmarks/reti_meent_1Dc.py @@ -14,99 +14,123 @@ from Reticolo import Reticolo -def test1dc_1(plot_figure=False): - factor = 100 - option = {} - option['pol'] = 0 # 0: TE, 1: TM - option['n_top'] = 2.2 # n_incidence - option['n_bot'] = 2 # n_transmission - option['theta'] = 40 * np.pi / 180 - option['phi'] = 20 * np.pi / 180 - option['fto'] = [40, 1] - option['period'] = [770 / factor] - option['wavelength'] = 777 / factor - option['thickness'] = [100 / factor, ] - option['fourier_type'] = 1 - - ucell = np.array( - [ - [[3, 3, 3, 3, 3, 1, 1, 1, 1, 1]], - ]) - - option['ucell'] = ucell - +def run_1dc(option, plot_figure=False): res_z = 11 + res_y = 11 + res_x = 11 reti = Reticolo() - reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, grating_type=1, matlab_plot_field=0, res3_npts=res_z) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) + (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, + bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, reti_field_cell) \ + = reti.run_res3(**option, grating_type=1, matlab_plot_field=0, res3_npts=res_z) + # print('reti de_ri', np.array(reti_de_ri).flatten()) + # print('reti de_ti', np.array(reti_de_ti).flatten()) + + reti_field_cell = reti_field_cell[res_z:-res_z].swapaxes(1, 2) + reti_field_cell = np.flip(reti_field_cell, 0) + reti_field_cell = reti_field_cell.conj() # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, perturbation=1E-30, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=1, res_x=50) + mee = meent.call_mee(backend=0, **option) + res_numpy = mee.conv_solve() + field_cell_numpy = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # JAX + mee = meent.call_mee(backend=1, **option) # JAX + res_jax = mee.conv_solve() + field_cell_jax = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # Torch + mee = meent.call_mee(backend=2, **option) # PyTorch + res_torch = mee.conv_solve() + field_cell_torch = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x).numpy() + + bds = ['Numpy', 'JAX', 'Torch'] + fields = [field_cell_numpy, field_cell_jax, field_cell_torch] + + print('Norm of (meent - reti) per backend') + for i, res_t in enumerate([res_numpy, res_jax, res_torch]): + reti_de_ri_te, reti_de_ti_te = np.array(top_refl_info_te.efficiency).T, np.array(top_tran_info_te.efficiency).T + reti_de_ri_tm, reti_de_ti_tm = np.array(top_refl_info_tm.efficiency).T, np.array(top_tran_info_tm.efficiency).T + + de_ri_te, de_ti_te = np.array(res_t.res_te_inc.de_ri).T, np.array(res_t.res_te_inc.de_ti).T + de_ri_tm, de_ti_tm = np.array(res_t.res_tm_inc.de_ri).T, np.array(res_t.res_tm_inc.de_ti).T - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) + de_ri_te = de_ri_te[de_ri_te > 1E-5] + de_ti_te = de_ti_te[de_ti_te > 1E-5] + de_ri_tm = de_ri_tm[de_ri_tm > 1E-5] + de_ti_tm = de_ti_tm[de_ti_tm > 1E-5] - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() + # reti_R_s_te = top_refl_info_te.amplitude_TE + # reti_T_s_te = top_tran_info_te.amplitude_TE + # reti_R_p_tm = top_refl_info_tm.amplitude_TM + # reti_T_p_tm = top_tran_info_tm.amplitude_TM + # + # R_s_te = res_t.res_te_inc.R_s + # T_s_te = res_t.res_te_inc.T_s + # R_p_tm = res_t.res_tm_inc.R_p + # T_p_tm = res_t.res_tm_inc.T_p - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) + print(bds[i]) + print('de_ri_te', np.linalg.norm(de_ri_te - reti_de_ri_te), + 'de_ti_te', np.linalg.norm(de_ti_te - reti_de_ti_te), + 'de_ri_tm', np.linalg.norm(de_ri_tm - reti_de_ri_tm), + 'de_ti_tm', np.linalg.norm(de_ti_tm - reti_de_ti_tm), + ) - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] + for i_field in range(reti_field_cell.shape[-1]): + res_temp = np.linalg.norm(fields[i][i_field] - reti_field_cell[i_field]) + print(f'field, {i_field+1}th: {res_temp}') - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) + if plot_figure: + title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - for ix in range(len(title)): - r_data = r_field_cell[:, 0, :, ix] + fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) + for ix in range(len(title)): + r_data = reti_field_cell[:, res_y//2, :, ix] - n_data = n_field_cell[:, 0, :, ix] + im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 0], shrink=1) + im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 2], shrink=1) + im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 4], shrink=1) - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) + n_data = fields[i][:, res_y//2, :, ix] - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) + im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 1], shrink=1) - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) + im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 3], shrink=1) - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') + im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 5], shrink=1) - plt.show() + ix = 0 + axes[ix, 0].title.set_text('abs**2 reti') + axes[ix, 2].title.set_text('Re, reti') + axes[ix, 4].title.set_text('Im, reti') + axes[ix, 1].title.set_text('abs**2 meen') + axes[ix, 3].title.set_text('Re, meen') + axes[ix, 5].title.set_text('Im, meen') + plt.show() -def test1dc_2(plot_figure=False): - factor = 10 + +def case_1dc_1(plot_figure=False): + + factor = 1000 option = {} - option['pol'] = 1 # 0: TE, 1: TM - option['n_top'] = 1 # n_incidence - option['n_bot'] = 2 # n_transmission + option['pol'] = 0 # 0: TE, 1: TM + option['n_top'] = 2 # n_incidence + option['n_bot'] = 1 # n_transmission option['theta'] = 0 * np.pi / 180 - option['phi'] = 90 * np.pi / 180 - option['fto'] = [10, 0] - option['period'] = [3000 / factor] - option['wavelength'] = 100 / factor - option['thickness'] = [400 / factor, ] # final term is for h_substrate + option['phi'] = 0 / 180 * np.pi + option['fto'] = 1 + option['period'] = [770/factor] + option['wavelength'] = 777/factor + option['thickness'] = [100/factor,] option['fourier_type'] = 1 ucell = np.array( @@ -116,67 +140,34 @@ def test1dc_2(plot_figure=False): option['ucell'] = ucell - res_z = 11 - reti = Reticolo() - reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, grating_type=1, matlab_plot_field=0, res3_npts=res_z) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, perturbation=1E-30, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=1, res_x=50) - - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = r_field_cell[:, 0, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) + run_1dc(option, plot_figure) - n_data = n_field_cell[:, 0, :, ix] - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) +def case_1dc_2(plot_figure=False): - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) + factor = 1 + option = {} + option['pol'] = 1 # 0: TE, 1: TM + option['n_top'] = 1 # n_incidence + option['n_bot'] = 2.2 # n_transmission + option['theta'] = 0 * np.pi / 180 + option['phi'] = 30 * np.pi / 180 + option['fto'] = 80 + option['period'] = [770/factor] + option['wavelength'] = 777/factor + option['thickness'] = [100/factor,] + option['fourier_type'] = 1 - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) + ucell = np.array( + [ + [[3, 3, 3, 3, 3, 1, 1, 1, 1, 1]], + ]) - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') + option['ucell'] = ucell - plt.show() + run_1dc(option, plot_figure) if __name__ == '__main__': - test1dc_1() - test1dc_2() - + case_1dc_1(False) + case_1dc_2(False) diff --git a/benchmarks/reti_meent_2D.py b/benchmarks/reti_meent_2D.py index c83069b..863c542 100644 --- a/benchmarks/reti_meent_2D.py +++ b/benchmarks/reti_meent_2D.py @@ -17,13 +17,115 @@ # oct2py.octave.addpath(octave.genpath('E:/funcs/software/octave_calls')) -def test2d_1(plot_figure=False): +def run_2d(option, case, plot_figure=False): + res_z = 11 + res_y = 11 + res_x = 11 reti = Reticolo() - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(1, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell + (top_refl_info_te, top_tran_info_te, top_refl_info_tm, top_tran_info_tm, + bottom_refl_info_te, bottom_tran_info_te, bottom_refl_info_tm, bottom_tran_info_tm, reti_field_cell)\ + = reti.eng.reti_2d(case, nout=9) + + reti_field_cell = reti_field_cell[res_z:-res_z].swapaxes(1, 2) + reti_field_cell = np.flip(reti_field_cell, 0) + reti_field_cell = reti_field_cell.conj() + # Numpy + mee = meent.call_mee(backend=0, **option) + res_numpy = mee.conv_solve() + field_cell_numpy = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # JAX + mee = meent.call_mee(backend=1, **option) # JAX + res_jax = mee.conv_solve() + field_cell_jax = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x) + + # Torch + mee = meent.call_mee(backend=2, **option) # PyTorch + res_torch = mee.conv_solve() + field_cell_torch = mee.calculate_field(res_z=res_z, res_y=res_y, res_x=res_x).numpy() + + bds = ['Numpy', 'JAX', 'Torch'] + fields = [field_cell_numpy, field_cell_jax, field_cell_torch] + + print('Norm of (meent - reti) per backend') + for i, res_t in enumerate([res_numpy, res_jax, res_torch]): + reti_de_ri_te, reti_de_ti_te = np.array(top_refl_info_te.efficiency).T, np.array(top_tran_info_te.efficiency).T + reti_de_ri_tm, reti_de_ti_tm = np.array(top_refl_info_tm.efficiency).T, np.array(top_tran_info_tm.efficiency).T + + de_ri_te, de_ti_te = np.array(res_t.res_te_inc.de_ri).T, np.array(res_t.res_te_inc.de_ti).T + de_ri_tm, de_ti_tm = np.array(res_t.res_tm_inc.de_ri).T, np.array(res_t.res_tm_inc.de_ti).T + + reti_de_ri_te = reti_de_ri_te[reti_de_ri_te > 1E-5] + reti_de_ti_te = reti_de_ti_te[reti_de_ti_te > 1E-5] + reti_de_ri_tm = reti_de_ri_tm[reti_de_ri_tm > 1E-5] + reti_de_ti_tm = reti_de_ti_tm[reti_de_ti_tm > 1E-5] + + de_ri_te = de_ri_te[de_ri_te > 1E-5] + de_ti_te = de_ti_te[de_ti_te > 1E-5] + de_ri_tm = de_ri_tm[de_ri_tm > 1E-5] + de_ti_tm = de_ti_tm[de_ti_tm > 1E-5] + + # reti_R_s_te = top_refl_info_te.amplitude_TE + # reti_T_s_te = top_tran_info_te.amplitude_TE + # reti_R_p_tm = top_refl_info_tm.amplitude_TM + # reti_T_p_tm = top_tran_info_tm.amplitude_TM + # + # R_s_te = res_t.res_te_inc.R_s + # T_s_te = res_t.res_te_inc.T_s + # R_p_tm = res_t.res_tm_inc.R_p + # T_p_tm = res_t.res_tm_inc.T_p + + print(bds[i]) + print('de_ri_te', np.linalg.norm(de_ri_te - reti_de_ri_te), + 'de_ti_te', np.linalg.norm(de_ti_te - reti_de_ti_te), + 'de_ri_tm', np.linalg.norm(de_ri_tm - reti_de_ri_tm), + 'de_ti_tm', np.linalg.norm(de_ti_tm - reti_de_ti_tm), + ) + + for i_field in range(reti_field_cell.shape[-1]): + res_temp = np.linalg.norm(fields[i][i_field] - reti_field_cell[i_field]) + print(f'field, {i_field+1}th: {res_temp}') + + if plot_figure: + title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] + + fig, axes = plt.subplots(6, 6, figsize=(10, 5)) + + for ix in range(len(title)): + r_data = reti_field_cell[:, res_y//2, :, ix] + + im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 0], shrink=1) + im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 2], shrink=1) + im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 4], shrink=1) + + n_data = fields[i][:, res_y//2, :, ix] + + im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 1], shrink=1) + + im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 3], shrink=1) + + im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') + fig.colorbar(im, ax=axes[ix, 5], shrink=1) + + ix = 0 + axes[ix, 0].title.set_text('abs**2 reti') + axes[ix, 2].title.set_text('Re, reti') + axes[ix, 4].title.set_text('Im, reti') + axes[ix, 1].title.set_text('abs**2 meen') + axes[ix, 3].title.set_text('Re, meen') + axes[ix, 5].title.set_text('Im, meen') + + plt.show() + + +def case_2d_1(plot_figure=False): factor = 1 option = {} option['pol'] = 1 # 0: TE, 1: TM @@ -53,108 +155,10 @@ def test2d_1(plot_figure=False): option['ucell'] = ucell - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - res_z = 11 - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 0, ix]).conj() - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.transpose(r_field_cell[2*res3_npts, :, :, ix]).conj() - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) + run_2d(option, 1, plot_figure) - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - return - - -def test2d_2(plot_figure=False): - reti = Reticolo() - - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(2, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell +def case_2d_2(plot_figure=False): factor = 1 option = {} option['pol'] = 1 # 0: TE, 1: TM @@ -184,110 +188,10 @@ def test2d_2(plot_figure=False): option['ucell'] = ucell - # reti = Reticolo() - # reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, res3_npts=res3_npts) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - res_z = 11 - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 0, ix]).conj() - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.transpose(r_field_cell[2*res3_npts, :, :, ix]).conj() - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) + run_2d(option, 2, plot_figure) - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - return - - -def test2d_3(plot_figure=False): - reti = Reticolo() - - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(3, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell +def case_2d_3(plot_figure=False): factor = 1 option = {} option['pol'] = 1 # 0: TE, 1: TM @@ -316,111 +220,10 @@ def test2d_3(plot_figure=False): ]]) option['ucell'] = ucell + run_2d(option, 3, plot_figure) - # reti = Reticolo() - # reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, res3_npts=res3_npts) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - res_z = 11 - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 0, ix]).conj() - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.transpose(r_field_cell[2*res3_npts, :, :, ix]).conj() - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - return - - -def test2d_4(plot_figure=False): - reti = Reticolo() - - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(4, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell +def case_2d_4(plot_figure=False): factor = 1 option = {} option['pol'] = 0 # 0: TE, 1: TM @@ -445,113 +248,10 @@ def test2d_4(plot_figure=False): ) * 3 + 1 option['ucell'] = ucell + run_2d(option, 4, plot_figure) - # reti = Reticolo() - # reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, res3_npts=res3_npts) - # print('reti de_ri', np.array(reti_de_ri)) - # print('reti de_ti', np.array(reti_de_ti)) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - res_z = 11 - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 0, ix]).conj() - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.transpose(r_field_cell[2*res3_npts, :, :, ix]).conj() - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - ix = 0 - axes[ix, 0].title.set_text('abs**2 reti') - axes[ix, 2].title.set_text('Re, reti') - axes[ix, 4].title.set_text('Im, reti') - axes[ix, 1].title.set_text('abs**2 meen') - axes[ix, 3].title.set_text('Re, meen') - axes[ix, 5].title.set_text('Im, meen') - - plt.show() - - return - - -def test2d_5(plot_figure=False): - reti = Reticolo() - - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(5, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell +def case_2d_5(plot_figure=False): factor = 1 option = {} option['pol'] = 0 # 0: TE, 1: TM @@ -576,123 +276,10 @@ def test2d_5(plot_figure=False): ) * 3 + 1 option['ucell'] = ucell + run_2d(option, 5, plot_figure) - # reti = Reticolo() - # reti_de_ri, reti_de_ti, c, d, r_field_cell = reti.run_res3(**option, res3_npts=res3_npts) - # print('reti de_ri', np.array(reti_de_ri)) - # print('reti de_ti', np.array(reti_de_ti)) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - res_z = 11 - - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.flipud(r_field_cell[res3_npts:-res3_npts, :, 0, ix]).conj() - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - # r_data = np.transpose(r_field_cell[2*res3_npts, :, :, ix]).conj() - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') - - plt.show() - - return - - -def test2d_6(plot_figure=False): - - res_z = 11 - - reti = Reticolo() - - [top_refl_info, top_tran_info, bottom_refl_info, bottom_tran_info, field_cell] = reti.eng.reti_2d(6, nout=5) - reti_de_ri, reti_de_ti, c, d, r_field_cell = top_refl_info.efficiency, top_tran_info.efficiency, bottom_refl_info.efficiency, \ - bottom_tran_info.efficiency, field_cell - # print('reti de_ri', np.array(reti_de_ri)) - # print('reti de_ti', np.array(reti_de_ti)) - print('reti de_ri', np.array(reti_de_ri).flatten()) - print('reti de_ti', np.array(reti_de_ti).flatten()) - - r_field_cell = np.moveaxis(r_field_cell, 2, 1) - r_field_cell = r_field_cell[res_z:-res_z] - r_field_cell = np.flip(r_field_cell, 0) - r_field_cell = r_field_cell.conj() +def case_2d_6(plot_figure=False): factor = 1 option = {} option['pol'] = 0 # 0: TE, 1: TM @@ -723,93 +310,49 @@ def test2d_6(plot_figure=False): ] option['ucell'] = ucell + run_2d(option, 6, plot_figure) - # Numpy - backend = 0 - mee = meent.call_mee(backend=backend, **option) - n_de_ri, n_de_ti = mee.conv_solve() - n_field_cell = mee.calculate_field(res_z=res_z, res_y=50, res_x=50) - # print('meent de_ri', n_de_ri) - # print('meent de_ti', n_de_ti) - print('meent de_ri', n_de_ri[n_de_ri > 1E-5]) - print('meent de_ti', n_de_ti[n_de_ti > 1E-5]) - - for i in range(6): - print(i, np.linalg.norm(r_field_cell[:, :, :, i] - n_field_cell[:, :, :, i])) - - if plot_figure: - title = ['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'] - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = r_field_cell[:, 0, :, ix] - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[:, 0, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') - - plt.show() - - fig, axes = plt.subplots(6, 6, figsize=(10, 5)) - - for ix in range(len(title)): - r_data = r_field_cell[5, :, :, ix] - - im = axes[ix, 0].imshow(abs(r_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 0], shrink=1) - im = axes[ix, 2].imshow(r_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 2], shrink=1) - im = axes[ix, 4].imshow(r_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 4], shrink=1) - - n_data = n_field_cell[5, :, :, ix] - - im = axes[ix, 1].imshow(abs(n_data) ** 2, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 1], shrink=1) - - im = axes[ix, 3].imshow(n_data.real, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 3], shrink=1) - - im = axes[ix, 5].imshow(n_data.imag, cmap='jet', aspect='auto') - fig.colorbar(im, ax=axes[ix, 5], shrink=1) - - axes[0, 0].title.set_text('abs**2 reti') - axes[0, 2].title.set_text('Re, reti') - axes[0, 4].title.set_text('Im, reti') - axes[0, 1].title.set_text('abs**2 meen') - axes[0, 3].title.set_text('Re, meen') - axes[0, 5].title.set_text('Im, meen') +def case_2d_7(plot_figure=False): + factor = 1 + option = {} + option['pol'] = 0 # 0: TE, 1: TM + option['n_top'] = 1 # n_incidence + option['n_bot'] = 1 # n_transmission + option['theta'] = 10 * np.pi / 180 + option['phi'] = 20 * np.pi / 180 + option['fto'] = [2, 2] + option['period'] = [480 / factor, 480 / factor] + option['wavelength'] = 550 / factor + option['thickness'] = [220 / factor, ] # final term is for h_substrate + option['fourier_type'] = 1 - plt.show() + ucell = [ + # layer 1 + [1, + [ + # obj 1 + ['rectangle', 0+240, 120+240, 160, 80, 4+1j, 0, 0, 0], + # obj 2 + ['rectangle', 0+240, -120+240, 160, 80, 4, 0, 0, 0], + # obj 3 + ['rectangle', 120+240, 0+240, 80, 160, 4-5j, 0, 0, 0], + # obj 4 + ['rectangle', -120+240, 0+240, 80, 160, 4, 0, 0, 0], + ], + ], + ] - return + option['ucell'] = ucell + run_2d(option, 7, plot_figure) if __name__ == '__main__': - test2d_1() - test2d_2() - test2d_3() - test2d_4() - test2d_5() - test2d_6() + case_2d_1() + case_2d_2() + case_2d_3() + case_2d_4() + case_2d_5() + case_2d_6() + case_2d_7() diff --git a/examples/electric-field-fno/data.py b/examples/electric-field-fno/data.py index ca9d614..864691e 100644 --- a/examples/electric-field-fno/data.py +++ b/examples/electric-field-fno/data.py @@ -66,7 +66,7 @@ def get_field( ucell=ucell_new ) # Calculate field distribution: OLD - de_ri, de_ti, field_cell = mee.conv_solve_field( + result, field_cell = mee.conv_solve_field( res_x=field_res[0], res_y=field_res[1], res_z=field_res[2], ) diff --git a/examples/ocd/arxiv_ocd_optimize.py b/examples/ocd/arxiv_ocd_optimize.py index 63db6a3..19383e4 100644 --- a/examples/ocd/arxiv_ocd_optimize.py +++ b/examples/ocd/arxiv_ocd_optimize.py @@ -119,60 +119,16 @@ def modelling_ref_index(wavelength, rcwa_options, modeling_options, params_name, ucell.append([a, obj_list_per_layer]) mee.ucell = ucell - # mee.draw(layer_info_list) return mee, ucell -def modelling_ref_index_old(wavelength, rcwa_options, modeling_options, params_name, params_value, instructions): - - mee = meent.call_mee(wavelength=wavelength, **rcwa_options) - - t = mee.thickness - - for i in range(len(t)): - if f'l{i+1}_thickness' in params_name: - t[i] = params_value[params_name[f'l{i+1}_thickness']].reshape((1, 1)) - mee.thickness = t - - mat_table = read_material_table() - - layer_info_list = [] - for i, layer in enumerate(instructions): - obj_list_per_layer = [] - for j, _ in enumerate(layer): - instructions_new = [] - instructions_target = instructions[i][j] - for k, inst in enumerate(instructions_target): - if k == 0: - func = getattr(mee, inst) - elif inst in params_name: - instructions_new.append(params_value[params_name[inst]]) - elif inst in modeling_options: - if inst[-7:] == 'n_index' and type(modeling_options[inst]) is str: - a = find_nk_index(modeling_options[inst], mat_table, wavelength).conj() - else: - a = modeling_options[inst] - instructions_new.append(a) - else: - raise ValueError - obj_list_per_layer += func(*instructions_new) - - a = modeling_options[f'l{i+1}_n_base'] - if type(a) is str: - a = find_nk_index(a, mat_table, wavelength).conj() - - layer_info_list.append([a, obj_list_per_layer]) - - mee.draw(layer_info_list) - - return mee, layer_info_list - - def reflectance_mode_00(mee, wavelength): mee.wavelength = wavelength - de_ri, de_ti = mee.conv_solve() + # de_ri, de_ti = mee.conv_solve() + result = mee.conv_solve() + de_ri, de_ti = result.de_ri, result.de_ti x_c, y_c = np.array(de_ti.shape) // 2 reflectance = de_ri[x_c, y_c] diff --git a/examples/vector_1d.py b/examples/vector_1d.py index c2f4040..1710c09 100644 --- a/examples/vector_1d.py +++ b/examples/vector_1d.py @@ -4,28 +4,20 @@ def run(): - rcwa_options = dict(backend=2, grating_type=2, thickness=[205, 305, 100000], period=[300, 300], - fourier_order=[3, 3], - n_I=1, n_II=1, + rcwa_options = dict(backend=2, thickness=[205, 100000], period=[300, 300], + fto=[3, 0], + n_top=1, n_bot=1, wavelength=900, - fft_type=2, + pol=0.5, ) - si = 3.638751670074983-0.007498295841854125j + # si = 3.638751670074983-0.007498295841854125j + si = 3.638751670074983 sio2 = 1.4518-0j si3n4 = 2.0056-0j - instructions = [ + ucell = [ # layer 1 - [sio2, - [ - # obj 1 - ['ellipse', 75, 225, 101.5, 81.5, si, 20 * torch.pi / 180, 40, 40], - # obj 2 - ['rectangle', 225, 75, 98.5, 81.5, si, 0, 0, 0], - ], - ], - # layer 2 [si3n4, [ # obj 1 @@ -34,23 +26,34 @@ def run(): ['rectangle', 200, 150, 49.5, 300, si, 0, 0, 0], ], ], - # layer 3 + # layer 2 [si, [] ], ] mee = meent.call_mee(**rcwa_options) - mee.modeling_vector_instruction(instructions) + mee.ucell = ucell + + result = mee.conv_solve() + + result_given_pol = result.res + result_te_incidence = result.res_te_inc + result_tm_incidence = result.res_tm_inc + + de_ri, de_ti = result_given_pol.de_ri, result_given_pol.de_ti + de_ri1, de_ti1 = result_te_incidence.de_ri, result_te_incidence.de_ti + de_ri2, de_ti2 = result_tm_incidence.de_ri, result_tm_incidence.de_ti - de_ri, de_ti = mee.conv_solve() - print(de_ri) + print(de_ri.sum(), de_ti.sum()) + print(de_ri1.sum(), de_ti1.sum()) + print(de_ri2.sum(), de_ti2.sum()) return if __name__ == '__main__': - res = run() + run() print(0) diff --git a/examples/vector_1d_verification.py b/examples/vector_1d_verification.py index b9a79be..c0637d0 100644 --- a/examples/vector_1d_verification.py +++ b/examples/vector_1d_verification.py @@ -8,52 +8,53 @@ def run_vector(rcwa_options, backend): rcwa_options['backend'] = backend mee = meent.call_mee(**rcwa_options) - mee.modeling_vector_instruction(instructions) + mee.ucell = ucell_vector - de_ri, de_ti = mee.conv_solve() + res = mee.conv_solve() - return de_ri, de_ti + return res.de_ri, res.de_ti -def run_raster(rcwa_options, backend, fft_type): +def run_raster(rcwa_options, backend, fourier_type): - # ucell = ucell.numpy() + # ucell_raster = ucell_raster.numpy() rcwa_options['backend'] = backend - rcwa_options['fourier_type'] = fft_type + rcwa_options['fourier_type'] = fourier_type # 0: Discrete Fourier series; 1 is for Continuous FS which is used in vector modeling. - if backend == 0: - ucell = np.asarray(rcwa_options['ucell']) + ucell_raster_1 = np.asarray(ucell_raster) elif backend == 1: - ucell = np.asarray(rcwa_options['ucell']) + ucell_raster_1 = np.asarray(ucell_raster) elif backend == 2: - ucell = torch.as_tensor(rcwa_options['ucell']) + ucell_raster_1 = torch.as_tensor(ucell_raster) else: raise ValueError - rcwa_options['ucell'] = ucell + rcwa_options['ucell'] = ucell_raster_1 mee = meent.call_mee(**rcwa_options) - de_ri, de_ti = mee.conv_solve() + res = mee.conv_solve() + de_ri, de_ti = res.de_ri, res.de_ti + return de_ri, de_ti if __name__ == '__main__': - rcwa_options = dict(backend=0, grating_type=2, thickness=[205, 100000], period=[300, 300], - fourier_order=[3, 0], - n_I=1, n_II=1, + rcwa_options = dict(backend=0, thickness=[205, 100000], period=[300, 300], + fto=[3, 0], + n_top=1, n_bot=1, wavelength=900, - fft_type=2, ) - si = 3.638751670074983-0.007498295841854125j + # si = 3.638751670074983-0.007498295841854125j + si = 3.638751670074983 sio2 = 1.4518 si3n4 = 2.0056 - instructions = [ + ucell_vector = [ # layer 1 [si3n4, [ @@ -73,7 +74,7 @@ def run_raster(rcwa_options, backend, fft_type): b = si c = si - ucell = [ + ucell_raster = [ [ [c,c,c,c,c,c,c,c,c,c] + [a,a,a,a,a,a,a,a,c,c] + [c,c,a,a,a,a,a,a,a,a], [c,c,c,c,c,c,c,c,c,c] + [a,a,a,a,a,a,a,a,c,c] + [c,c,a,a,a,a,a,a,a,a], @@ -139,7 +140,6 @@ def run_raster(rcwa_options, backend, fft_type): [b,b,b,b,b,b,b,b,b,b] + [b,b,b,b,b,b,b,b,b,b] + [b,b,b,b,b,b,b,b,b,b], ], ] - # ucell = np.array(ucell) de_ri_v_0, de_ti_v_0 = run_vector(rcwa_options, 0) # NumPy de_ri_v_1, de_ti_v_1 = run_vector(rcwa_options, 1) # JAX @@ -159,7 +159,6 @@ def run_raster(rcwa_options, backend, fft_type): print(f'Norm of difference JAX and Torch; R: {np.linalg.norm(de_ri_v_1-de_ri_v_2)}, T: {np.linalg.norm(de_ti_v_1-de_ti_v_2)}') print(f'Norm of difference Torch and NumPy; R: {np.linalg.norm(de_ri_v_1-de_ri_v_2)}, T: {np.linalg.norm(de_ti_v_1-de_ti_v_2)}') - rcwa_options['ucell'] = ucell de_ri_r_0_dfs, de_ti_r_0_dfs = run_raster(rcwa_options, 0, 0) # NumPy de_ri_r_0_cfs, de_ti_r_0_cfs = run_raster(rcwa_options, 0, 1) # NumPy de_ri_r_1_dfs, de_ti_r_1_dfs = run_raster(rcwa_options, 1, 0) # JAX diff --git a/examples/vector_2d.py b/examples/vector_2d.py index 5a11568..c2b7ef8 100644 --- a/examples/vector_2d.py +++ b/examples/vector_2d.py @@ -4,18 +4,19 @@ def run(): - rcwa_options = dict(backend=1, grating_type=2, thickness=[205, 305, 100000], period=[300, 300], - fourier_order=[3, 3], - n_I=1, n_II=1, + rcwa_options = dict(backend=1, thickness=[205, 305, 100000], period=[300, 300], + fto=[3, 3], + n_top=1, n_bot=1, wavelength=900, - fft_type=2, + pol=0.5, ) - si = 3.638751670074983-0.007498295841854125j + # si = 3.638751670074983-0.007498295841854125j + si = 3.638751670074983 sio2 = 1.4518-0j si3n4 = 2.0056-0j - instructions = [ + ucell = [ # layer 1 [sio2, [ @@ -41,16 +42,27 @@ def run(): ] mee = meent.call_mee(**rcwa_options) - mee.modeling_vector_instruction(instructions) + mee.ucell = ucell - de_ri, de_ti = mee.conv_solve() - print(de_ri) + result = mee.conv_solve() + + result_given_pol = result.res + result_te_incidence = result.res_te_inc + result_tm_incidence = result.res_tm_inc + + de_ri, de_ti = result_given_pol.de_ri, result_given_pol.de_ti + de_ri1, de_ti1 = result_te_incidence.de_ri, result_te_incidence.de_ti + de_ri2, de_ti2 = result_tm_incidence.de_ri, result_tm_incidence.de_ti + + print(de_ri.sum(), de_ti.sum()) + print(de_ri1.sum(), de_ti1.sum()) + print(de_ri2.sum(), de_ti2.sum()) return if __name__ == '__main__': - res = run() + run() print(0) diff --git a/examples/vector_2d_verification.py b/examples/vector_2d_verification.py index ecc425b..8d565d7 100644 --- a/examples/vector_2d_verification.py +++ b/examples/vector_2d_verification.py @@ -8,52 +8,53 @@ def run_vector(rcwa_options, backend): rcwa_options['backend'] = backend mee = meent.call_mee(**rcwa_options) - mee.modeling_vector_instruction(instructions) + mee.ucell = ucell_vector - de_ri, de_ti = mee.conv_solve() + res = mee.conv_solve() - return de_ri, de_ti + return res.de_ri, res.de_ti -def run_raster(rcwa_options, backend, fft_type): +def run_raster(rcwa_options, backend, fourier_type): - # ucell = ucell.numpy() + # ucell_raster = ucell_raster.numpy() rcwa_options['backend'] = backend - rcwa_options['fourier_type'] = fft_type + rcwa_options['fourier_type'] = fourier_type # 0: Discrete Fourier series; 1 is for Continuous FS which is used in vector modeling. - if backend == 0: - ucell = np.asarray(rcwa_options['ucell']) + ucell_raster_1 = np.asarray(ucell_raster) elif backend == 1: - ucell = np.asarray(rcwa_options['ucell']) + ucell_raster_1 = np.asarray(ucell_raster) elif backend == 2: - ucell = torch.as_tensor(rcwa_options['ucell']) + ucell_raster_1 = torch.as_tensor(ucell_raster) else: raise ValueError - rcwa_options['ucell'] = ucell + rcwa_options['ucell'] = ucell_raster_1 mee = meent.call_mee(**rcwa_options) - de_ri, de_ti = mee.conv_solve() + res = mee.conv_solve() + de_ri, de_ti = res.de_ri, res.de_ti + return de_ri, de_ti if __name__ == '__main__': - rcwa_options = dict(backend=0, grating_type=2, thickness=[205, 100000], period=[300, 300], - fourier_order=[3, 3], - n_I=1, n_II=1, + rcwa_options = dict(backend=0, thickness=[205, 100000], period=[300, 300], + fto=[3, 3], + n_top=1, n_bot=1, wavelength=900, - fft_type=2, ) - si = 3.638751670074983-0.007498295841854125j + # si = 3.638751670074983-0.007498295841854125j + si = 3.638751670074983 sio2 = 1.4518 si3n4 = 2.0056 - instructions = [ + ucell_vector = [ # layer 1 [si3n4, [ @@ -73,7 +74,7 @@ def run_raster(rcwa_options, backend, fft_type): b = si c = si - ucell = [ + ucell_raster = [ [ [c,c,c,c,c,c,c,c,c,c] + [a,a,a,a,a,a,a,a,a,a] + [a,a,a,a,a,a,a,a,a,a], [c,c,c,c,c,c,c,c,c,c] + [a,a,a,a,a,a,a,a,a,a] + [a,a,a,a,a,a,a,a,a,a], @@ -142,7 +143,6 @@ def run_raster(rcwa_options, backend, fft_type): [b,b,b,b,b,b,b,b,b,b] + [b,b,b,b,b,b,b,b,b,b] + [b,b,b,b,b,b,b,b,b,b], ], ] - # ucell = np.array(ucell) de_ri_v_0, de_ti_v_0 = run_vector(rcwa_options, 0) # NumPy de_ri_v_1, de_ti_v_1 = run_vector(rcwa_options, 1) # JAX @@ -162,7 +162,6 @@ def run_raster(rcwa_options, backend, fft_type): print(f'Norm of difference JAX and Torch; R: {np.linalg.norm(de_ri_v_1-de_ri_v_2)}, T: {np.linalg.norm(de_ti_v_1-de_ti_v_2)}') print(f'Norm of difference Torch and NumPy; R: {np.linalg.norm(de_ri_v_1-de_ri_v_2)}, T: {np.linalg.norm(de_ti_v_1-de_ti_v_2)}') - rcwa_options['ucell'] = ucell de_ri_r_0_dfs, de_ti_r_0_dfs = run_raster(rcwa_options, 0, 0) # NumPy de_ri_r_0_cfs, de_ti_r_0_cfs = run_raster(rcwa_options, 0, 1) # NumPy de_ri_r_1_dfs, de_ti_r_1_dfs = run_raster(rcwa_options, 1, 0) # JAX diff --git a/meent/on_jax/emsolver/_base.py b/meent/on_jax/emsolver/_base.py index 4f1c7cd..ac7c77f 100644 --- a/meent/on_jax/emsolver/_base.py +++ b/meent/on_jax/emsolver/_base.py @@ -6,7 +6,8 @@ from .scattering_method import (scattering_1d_1, scattering_1d_2, scattering_1d_3, scattering_2d_1, scattering_2d_wv, scattering_2d_2, scattering_2d_3) -from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, +from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, transfer_1d_conical_1, + transfer_1d_conical_2, transfer_1d_conical_3, transfer_1d_conical_4, transfer_2d_1, transfer_2d_2, transfer_2d_3, transfer_2d_4) @@ -23,10 +24,10 @@ def wrap(*args, **kwargs): class _BaseRCWA: - def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=(2, 0), - period=(100., 100.), wavelength=1., + def __init__(self, n_top=1., n_bot=1., theta=0., phi=None, psi=None, pol=0., fto=(0, 0), + period=(1., 1.), wavelength=1., thickness=(0.,), connecting_algo='TMM', perturbation=1E-20, - device=0, type_complex=jnp.complex128): + device=0, type_complex=jnp.complex128, use_pinv=False): self.device = device @@ -51,16 +52,16 @@ def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=( self.phi = phi self.pol = pol self.psi = psi - # self._psi = jnp.array((jnp.pi / 2 * (1 - pol)), dtype=self.type_float) self.fto = fto self.period = period self.wavelength = wavelength self.thickness = thickness self.connecting_algo = connecting_algo + self.use_pinv = use_pinv + self.layer_info_list = [] self.T1 = None - # self.kx = None # only kx, not ky, because kx is always used while ky is 2D only. @property def device(self): @@ -108,33 +109,17 @@ def type_float(self): def type_int(self): return self._type_int - @property - def pol(self): - return self._pol - - @pol.setter - def pol(self, pol): - room = 1E-6 - if 1 < pol < 1 + room: - pol = 1 - elif 0 - room < pol < 0: - pol = 0 - - if not 0 <= pol <= 1: - raise ValueError - - self._pol = pol - psi = jnp.pi / 2 * (1 - self.pol) - self._psi = jnp.array(psi, dtype=self.type_float) - @property def theta(self): return self._theta @theta.setter def theta(self, theta): - self._theta = jnp.array(theta, dtype=self.type_float) - self._theta = jnp.where(self._theta == 0, self.perturbation, self._theta) # perturbation + if theta is None: + self._theta = None + else: + self._theta = jnp.array(theta, dtype=self.type_complex) + self._theta = jnp.where(self._theta == 0, self.perturbation, self._theta) # perturbation @property def phi(self): @@ -142,7 +127,10 @@ def phi(self): @phi.setter def phi(self, phi): - self._phi = jnp.array(phi, dtype=self.type_float) + if phi is None: + self._phi = None + else: + self._phi = jnp.array(phi, dtype=self.type_complex) @property def psi(self): @@ -151,10 +139,35 @@ def psi(self): @psi.setter def psi(self, psi): if psi is not None: - self._psi = jnp.array(psi, dtype=self.type_float) + self._psi = jnp.array(psi, dtype=self.type_complex) pol = -(2 * psi / jnp.pi - 1) self._pol = pol + @property + def pol(self): + """ + portion of TM. 0: full TE, 1: full TM + + Returns: polarization ratio + + """ + return self._pol + + @pol.setter + def pol(self, pol): + room = 1E-6 + if 1 < pol < 1 + room: + pol = 1 + elif 0 - room < pol < 0: + pol = 0 + + if not 0 <= pol <= 1: + raise ValueError + + self._pol = pol + psi = jnp.array(jnp.pi / 2 * (1 - self.pol), dtype=self.type_complex) + self._psi = psi + @property def fto(self): return self._fto @@ -241,15 +254,28 @@ def get_kx_ky_vector(self, wavelength): fto_x_range = jnp.arange(-self.fto[0], self.fto[0] + 1) fto_y_range = jnp.arange(-self.fto[1], self.fto[1] + 1) - kx_vector = (self.n_top * jnp.sin(self.theta) * jnp.cos(self.phi) + fto_x_range * ( - wavelength / self.period[0])).astype(self.type_complex) + def adjust_theta(): + # https://github.com/numpy/numpy/issues/27306 + check = self.theta.real >= jnp.float32(jnp.pi / 2) + sin_theta_true_case = jnp.sin( + jnp.nextafter(jnp.float32(jnp.pi / 2), jnp.float32(0)) + self.theta.imag * jnp.complex64(1j)) + sin_theta_false_case = jnp.sin(self.theta) + return jnp.where(check, sin_theta_true_case, sin_theta_false_case) + + sin_theta = adjust_theta() + + phi = 0 if self.phi is None else self.phi # phi is None -> 1D TE TM case + + kx = (self.n_top * sin_theta * jnp.cos(phi) + fto_x_range * ( + wavelength / self.period[0])).astype(self.type_complex).conj() - ky_vector = (self.n_top * jnp.sin(self.theta) * jnp.sin(self.phi) + fto_y_range * ( - wavelength / self.period[1])).astype(self.type_complex) + ky = (self.n_top * sin_theta * jnp.sin(phi) + fto_y_range * ( + wavelength / self.period[1])).astype(self.type_complex).conj() - return kx_vector, ky_vector + return kx, ky @jax_device_set + # @jax.jit # TODO: make optional def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): self.layer_info_list = [] self.T1 = None @@ -261,11 +287,13 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): if self.connecting_algo == 'TMM': kz_top, kz_bot, F, G, T \ - = transfer_1d_1(self.pol, ff_x, kx, self.n_top, self.n_bot, type_complex=self.type_complex) + = transfer_1d_1(self.pol, kx, self.n_top, self.n_bot, type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, self.period, - self.pol, wl=wavelength) + raise ValueError + + # Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ + # = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, self.period, + # self.pol, wl=wavelength) else: raise ValueError @@ -279,98 +307,108 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, self.type_complex) + W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, self.type_complex, + self.perturbation, use_pinv=self.use_pinv) - X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=self.type_complex) + X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=self.type_complex, + use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, A_i, B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) + raise ValueError + # A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) + else: + raise ValueError + + if self.connecting_algo == 'TMM': + result, T1 = transfer_1d_4(self.pol, ff_x, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, + type_complex=self.type_complex, use_pinv=self.use_pinv) + self.T1 = T1 # Hurdle for jitting. This is not saved. + + elif self.connecting_algo == 'SMM': + raise ValueError + # de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, ff, Wr, self.fto, Kzr, Kzt, + # self.n_top, self.n_bot, self.theta, self.pol) + else: + raise ValueError + + # return de_ri, de_ti, self.layer_info_list, self.T1 + return result + + @jax_device_set + def solve_1d_conical(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + + self.layer_info_list = [] + self.T1 = None + + ff_x = self.fto[0] * 2 + 1 + ff_y = 1 + + k0 = 2 * jnp.pi / wavelength + kx, ky = self.get_kx_ky_vector(wavelength) + + if self.connecting_algo == 'TMM': + # Kx, ky, k_I_z, k_II_z, varphi, Y_I, Y_II, Z_I, Z_II, big_F, big_G, big_T \ + # = transfer_1d_conical_1(ff, k0, self.n_top, self.n_bot, self.kx, self.theta, self.phi, + # type_complex=self.type_complex) + kz_top, kz_bot, varphi, big_F, big_G, big_T \ + = transfer_1d_conical_1(kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) + + elif self.connecting_algo == 'SMM': + print('SMM for 1D conical is not implemented') + return jnp.nan, jnp.nan + else: + raise ValueError + + for layer_index in range(len(self.thickness))[::-1]: + + epx_conv = epx_conv_all[layer_index] + epy_conv = epy_conv_all[layer_index] + epz_conv_i = epz_conv_i_all[layer_index] + + d = self.thickness[layer_index] + + if self.connecting_algo == 'TMM': + # big_X, big_F, big_G, big_T, big_A_i, big_B, W_1, W_2, V_11, V_12, V_21, V_22, q_1, q_2 \ + # = transfer_1d_conical_2(k0, Kx, ky, E_conv, E_conv_i, o_E_conv_i, ff, d, + # varphi, big_F, big_G, big_T, + # type_complex=self.type_complex, device=self.device) + W, V, q = transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=self.type_complex, + perturbation=self.perturbation, device=self.device, + use_pinv=self.use_pinv) + + big_X, big_F, big_G, big_T, big_A_i, big_B, \ + = transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex, + use_pinv=self.use_pinv) + + layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] + self.layer_info_list.append(layer_info) + + elif self.connecting_algo == 'SMM': + raise ValueError else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, T1 = transfer_1d_4(self.pol, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, - type_complex=self.type_complex) - self.T1 = T1 + # de_ri, de_ti, big_T1 = transfer_1d_conical_3(big_F, big_G, big_T, Z_I, Y_I, self.psi, self.theta, ff, + # delta_i0, k_I_z, k0, self.n_top, self.n_bot, k_II_z, + # type_complex=self.type_complex) + result, big_T1 = transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, + self.theta, self.n_top, self.n_bot, type_complex=self.type_complex, + use_pinv=self.use_pinv) + self.T1 = big_T1 elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, ff, Wr, self.fto, Kzr, Kzt, - self.n_top, self.n_bot, self.theta, self.pol) + raise ValueError else: raise ValueError - return de_ri, de_ti, self.layer_info_list, self.T1 - # @jax_device_set - # def solve_1d_conical(self, wavelength, E_conv_all, o_E_conv_all): - # - # self.layer_info_list = [] - # self.T1 = None - # - # # fourier_indices = jnp.arange(-self.fto, self.fto + 1) - # ff = self.fto[0] * 2 + 1 - # - # delta_i0 = jnp.zeros(ff, dtype=self.type_complex) - # delta_i0 = delta_i0.at[self.fto[0]].set(1) - # - # k0 = 2 * jnp.pi / wavelength - # - # if self.connecting_algo == 'TMM': - # Kx, ky, k_I_z, k_II_z, varphi, Y_I, Y_II, Z_I, Z_II, big_F, big_G, big_T \ - # = transfer_1d_conical_1(ff, k0, self.n_top, self.n_bot, self.kx, self.theta, self.phi, - # type_complex=self.type_complex) - # elif self.connecting_algo == 'SMM': - # print('SMM for 1D conical is not implemented') - # return jnp.nan, jnp.nan - # else: - # raise ValueError - # - # # for E_conv, o_E_conv, d in zip(E_conv_all[::-1], o_E_conv_all[::-1], self.thickness[::-1]): - # count = min(len(E_conv_all), len(o_E_conv_all), len(self.thickness)) - # - # # From the last layer - # for layer_index in range(count)[::-1]: - # - # E_conv = E_conv_all[layer_index] - # # o_E_conv = o_E_conv_all[layer_index] - # o_E_conv = None - # - # d = self.thickness[layer_index] - # - # E_conv_i = jnp.linalg.inv(E_conv) - # # o_E_conv_i = jnp.linalg.inv(o_E_conv) - # o_E_conv_i = None - # - # if self.connecting_algo == 'TMM': - # big_X, big_F, big_G, big_T, big_A_i, big_B, W_1, W_2, V_11, V_12, V_21, V_22, q_1, q_2 \ - # = transfer_1d_conical_2(k0, Kx, ky, E_conv, E_conv_i, o_E_conv_i, ff, d, - # varphi, big_F, big_G, big_T, - # type_complex=self.type_complex, device=self.device) - # - # layer_info = [E_conv_i, q_1, q_2, W_1, W_2, V_11, V_12, V_21, V_22, big_X, big_A_i, big_B, d] - # self.layer_info_list.append(layer_info) - # - # elif self.connecting_algo == 'SMM': - # raise ValueError - # else: - # raise ValueError - # - # if self.connecting_algo == 'TMM': - # de_ri, de_ti, big_T1 = transfer_1d_conical_3(big_F, big_G, big_T, Z_I, Y_I, self.psi, self.theta, ff, - # delta_i0, k_I_z, k0, self.n_top, self.n_bot, k_II_z, - # type_complex=self.type_complex) - # self.T1 = big_T1 - # - # elif self.connecting_algo == 'SMM': - # raise ValueError - # else: - # raise ValueError - # - # return de_ri, de_ti, self.layer_info_list, self.T1 + return result @jax_device_set + # @jax.jit def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): self.layer_info_list = [] @@ -384,11 +422,12 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): if self.connecting_algo == 'TMM': kz_top, kz_bot, varphi, big_F, big_G, big_T \ - = transfer_2d_1(ff_x, ff_y, kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) + = transfer_2d_1(kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto) + raise ValueError + # Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ + # = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto) else: raise ValueError @@ -402,32 +441,37 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=self.type_complex) + W, V, q = transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, self.type_complex, self.perturbation, + use_pinv=self.use_pinv) big_X, big_F, big_G, big_T, big_A_i, big_B, \ - = transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex) + = transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex, + use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - W, V, q = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) - A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, q) + raise ValueError + # W, V, q = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) + # A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, q) else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, big_T1 = transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, - self.n_top, self.n_bot, type_complex=self.type_complex) + result, big_T1 = transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, + self.n_top, self.n_bot, type_complex=self.type_complex, + use_pinv=self.use_pinv) self.T1 = big_T1 elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_2d_3(ff_xy, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, - self.pol, self.theta, self.phi, self.fto) + raise ValueError + # de_ri, de_ti = scattering_2d_3(ff_xy, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, + # self.pol, self.theta, self.phi, self.fto) else: raise ValueError - de_ri = de_ri.reshape((ff_y, ff_x)).T - de_ti = de_ti.reshape((ff_y, ff_x)).T - - return de_ri, de_ti, self.layer_info_list, self.T1 + # de_ri = de_ri.reshape((ff_y, ff_x)).T + # de_ti = de_ti.reshape((ff_y, ff_x)).T + # return de_ri, de_ti, self.layer_info_list, self.T1 + return result diff --git a/meent/on_jax/emsolver/convolution_matrix.py b/meent/on_jax/emsolver/convolution_matrix.py index ad93838..e83bfb8 100644 --- a/meent/on_jax/emsolver/convolution_matrix.py +++ b/meent/on_jax/emsolver/convolution_matrix.py @@ -4,6 +4,7 @@ from functools import partial from .fourier_analysis import dfs2d, cfs2d +from .primitives import meeinv def cell_compression(cell, type_complex=jnp.complex128): @@ -47,60 +48,7 @@ def cell_compression(cell, type_complex=jnp.complex128): return cell_comp, x, y - -# @partial(jax.jit, static_argnums=(1,2 )) -# def fft_piecewise_constant(cell, x, y, fto_x, fto_y, type_complex=jnp.complex128): -# -# period_x, period_y = x[-1], y[-1] -# -# # X axis -# cell_next_x = jnp.roll(cell, -1, axis=1) -# cell_diff_x = cell_next_x - cell -# -# modes_x = jnp.arange(-2 * fto_x, 2 * fto_x + 1, 1) -# -# f_coeffs_x = cell_diff_x @ jnp.exp(-1j * 2 * jnp.pi * x @ modes_x[None, :] / period_x).astype(type_complex) -# c = f_coeffs_x.shape[1] // 2 -# -# x_next = jnp.vstack((jnp.roll(x, -1, axis=0)[:-1], period_x)) - x -# -# assign_index = (jnp.arange(len(f_coeffs_x)), jnp.array([c])) -# assign_value = (cell @ jnp.vstack((x[0], x_next[:-1])) / period_x).flatten().astype(type_complex) -# f_coeffs_x = f_coeffs_x.at[assign_index].set(assign_value) -# -# mask = jnp.hstack([jnp.arange(c), jnp.arange(c+1, f_coeffs_x.shape[1])]) -# assign_index = mask -# assign_value = f_coeffs_x[:, mask] / (1j * 2 * jnp.pi * modes_x[mask]) -# f_coeffs_x = f_coeffs_x.at[:, assign_index].set(assign_value) -# -# # Y axis -# f_coeffs_x_next_y = jnp.roll(f_coeffs_x, -1, axis=0) -# f_coeffs_x_diff_y = f_coeffs_x_next_y - f_coeffs_x -# -# modes_y = jnp.arange(-2 * fto_y, 2 * fto_y + 1, 1) -# -# f_coeffs_xy = f_coeffs_x_diff_y.T @ jnp.exp(-1j * 2 * jnp.pi * y @ modes_y[None, :] / period_y).astype(type_complex) -# c = f_coeffs_xy.shape[1] // 2 -# -# y_next = jnp.vstack((jnp.roll(y, -1, axis=0)[:-1], period_y)) - y -# -# assign_index = [c] -# assign_value = (f_coeffs_x.T @ jnp.vstack((y[0], y_next[:-1])) / period_y).astype(type_complex) -# f_coeffs_xy = f_coeffs_xy.at[:, assign_index].set(assign_value) -# -# if c: -# mask = jnp.hstack([jnp.arange(c), jnp.arange(c + 1, f_coeffs_x.shape[1])]) -# -# assign_index = mask -# assign_value = f_coeffs_xy[:, mask] / (1j * 2 * jnp.pi * modes_y[mask]) -# -# f_coeffs_xy = f_coeffs_xy.at[:, assign_index].set(assign_value) -# -# return f_coeffs_xy.T - - -def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, - type_complex=jnp.complex128): +def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, type_complex=jnp.complex128, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -116,39 +64,14 @@ def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, epy_conv = cfs2d(eps_matrix, x_list, y_list, 1, 0, fto_x, fto_y, type_complex) epx_conv = cfs2d(eps_matrix, x_list, y_list, 0, 1, fto_x, fto_y, type_complex) - # epx_conv_all[i] = epx_conv - # epy_conv_all[i] = epy_conv - # epz_conv_i_all[i] = jnp.linalg.inv(epz_conv) - epx_conv_all = epx_conv_all.at[i].set(epx_conv) epy_conv_all = epy_conv_all.at[i].set(epy_conv) - epz_conv_i_all = epz_conv_i_all.at[i].set(jnp.linalg.inv(epz_conv)) - - # f_coeffs = fft_piecewise_constant(ucell_layer, x_list, y_list, - # fto_x, fto_y, type_complex=type_complex) - # o_f_coeffs = fft_piecewise_constant(1/ucell_layer, x_list, y_list, - # fto_x, fto_y, type_complex=type_complex) - # center = jnp.array(f_coeffs.shape) // 2 - # - # conv_idx_y = jnp.arange(-ff_y + 1, ff_y, 1) - # conv_idx_y = circulant(conv_idx_y) - # conv_i = jnp.repeat(conv_idx_y, ff_x, axis=1) - # conv_i = jnp.repeat(conv_i, jnp.array([ff_x] * ff_y), axis=0, total_repeat_length=ff_x * ff_y) - # - # conv_idx_x = jnp.arange(-ff_x + 1, ff_x, 1) - # conv_idx_x = circulant(conv_idx_x) - # conv_j = jnp.tile(conv_idx_x, (ff_y, ff_y)) - # - # e_conv = f_coeffs[center[0] + conv_i, center[1] + conv_j] - # o_e_conv = o_f_coeffs[center[0] + conv_i, center[1] + conv_j] - # - # e_conv_all = e_conv_all.at[i].set(e_conv) - # o_e_conv_all = o_e_conv_all.at[i].set(o_e_conv) + epz_conv_i_all = epz_conv_i_all.at[i].set(meeinv(epz_conv, use_pinv)) return epx_conv_all, epy_conv_all, epz_conv_i_all -def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex=jnp.complex128): +def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex=jnp.complex128, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -164,20 +87,17 @@ def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex epy_conv = cfs2d(eps_matrix, x_list, y_list, 1, 0, fto_x, fto_y, type_complex) epx_conv = cfs2d(eps_matrix, x_list, y_list, 0, 1, fto_x, fto_y, type_complex) - # epx_conv_all[i] = epx_conv - # epy_conv_all[i] = epy_conv - # epz_conv_i_all[i] = jnp.linalg.inv(epz_conv) - epx_conv_all = epx_conv_all.at[i].set(epx_conv) epy_conv_all = epy_conv_all.at[i].set(epy_conv) - epz_conv_i_all = epz_conv_i_all.at[i].set(jnp.linalg.inv(epz_conv)) + epz_conv_i_all = epz_conv_i_all.at[i].set(meeinv(epz_conv, use_pinv)) return epx_conv_all, epy_conv_all, epz_conv_i_all # @partial(jax.jit, static_argnums=(1, 2, 3, 4, 5)) -def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=jnp.complex128, - enhanced_dfs=True): +def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=jnp.complex128, enhanced_dfs=True, + use_pinv=False): + ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) epx_conv_all = jnp.zeros((ucell.shape[0], ff_xy, ff_xy)).astype(type_complex) @@ -206,13 +126,9 @@ def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=j epy_conv = dfs2d(eps_matrix, 1, 0, fto_x, fto_y, type_complex) epx_conv = dfs2d(eps_matrix, 0, 1, fto_x, fto_y, type_complex) - # epx_conv_all[i] = epx_conv - # epy_conv_all[i] = epy_conv - # epz_conv_i_all[i] = jnp.linalg.inv(epz_conv) - epx_conv_all = epx_conv_all.at[i].set(epx_conv) epy_conv_all = epy_conv_all.at[i].set(epy_conv) - epz_conv_i_all = epz_conv_i_all.at[i].set(jnp.linalg.inv(epz_conv)) + epz_conv_i_all = epz_conv_i_all.at[i].set(meeinv(epz_conv, use_pinv=False)) return epx_conv_all, epy_conv_all, epz_conv_i_all diff --git a/meent/on_jax/emsolver/field_distribution.py b/meent/on_jax/emsolver/field_distribution.py index 72f1ff6..fa27598 100644 --- a/meent/on_jax/emsolver/field_distribution.py +++ b/meent/on_jax/emsolver/field_distribution.py @@ -25,8 +25,8 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, pol, res_x=20, re # z_1d = jnp.arange(res_z, dtype=type_float).reshape((-1, 1, 1)) / res_z * d z_1d = jnp.linspace(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d - My = W @ (diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) - Mx = V @ (-diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) + My = W @ (d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) + Mx = V @ (-d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) if pol == 0: Mz = -1j * Kx @ My @@ -64,9 +64,100 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, pol, res_x=20, re return field_cell +# @partial(jax.jit, static_argnums=(5, 6, 10, 11, 12, 13)) +def field_dist_1d_conical(wavelength, kx, ky, T1, layer_info_list, period, + res_x=20, res_y=20, res_z=20, type_complex=jnp.complex128): + + k0 = 2 * jnp.pi / wavelength + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + Kx = jnp.diag(jnp.tile(kx, ff_y).flatten()) + Ky = jnp.diag(jnp.tile(ky.reshape((-1, 1)), ff_x).flatten()) + + field_cell = jnp.zeros((res_z * len(layer_info_list), res_y, res_x, 6), dtype=type_complex) + + T_layer = T1 + + big_I = jnp.eye((len(T1))).astype(type_complex) + O = jnp.zeros((ff_xy, ff_xy), dtype=type_complex) + + # From the first layer + for idx_layer, (epz_conv_i, W, V, q, d, big_A_i, big_B) in enumerate(layer_info_list[::-1]): + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] + + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] + + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + + X_1 = jnp.diag(jnp.exp(-k0 * q_1 * d)) + X_2 = jnp.diag(jnp.exp(-k0 * q_2 * d)) + + big_X = jnp.block([[X_1, O], [O, X_2]]) + + c = jnp.block([[big_I], [big_B @ big_A_i @ big_X]]) @ T_layer + # z_1d = np.arange(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d + z_1d = jnp.linspace(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d + + c1_plus = c[0 * ff_xy:1 * ff_xy] + c2_plus = c[1 * ff_xy:2 * ff_xy] + c1_minus = c[2 * ff_xy:3 * ff_xy] + c2_minus = c[3 * ff_xy:4 * ff_xy] + + big_Q1 = jnp.diag(q_1) + big_Q2 = jnp.diag(q_2) + + Sx = W_2 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = V_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = W_1 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) + Uz = -1j * (Kx @ Sy - Ky @ Sx) + + # x_1d = jnp.arange(res_x).reshape((1, -1, 1)) * period[0] / res_x + x_1d = jnp.linspace(0, period[0], res_x).reshape((1, -1, 1)) + x_2d = jnp.tile(x_1d, (res_y, 1, 1)) + x_2d = x_2d * kx * k0 + x_2d = x_2d.reshape((res_y, res_x, 1, len(kx))) + + # y_1d = jnp.arange(res_y-1, -1, -1).reshape((-1, 1, 1)) * period[1] / res_y + y_1d = jnp.linspace(0, period[1], res_y)[::-1].reshape((-1, 1, 1)) + y_2d = jnp.tile(y_1d, (1, res_x, 1)) + y_2d = y_2d * ky * k0 + y_2d = y_2d.reshape((res_y, res_x, len(ky), 1)) + + inv_fourier = jnp.exp(-1j * x_2d) * jnp.exp(-1j * y_2d) + inv_fourier = inv_fourier.reshape((res_y, res_x, -1)) + + Ex = inv_fourier[:, :, None, :] @ Sx[:, None, None, :, :] + Ey = inv_fourier[:, :, None, :] @ Sy[:, None, None, :, :] + Ez = inv_fourier[:, :, None, :] @ Sz[:, None, None, :, :] + Hx = 1j * inv_fourier[:, :, None, :] @ Ux[:, None, None, :, :] + Hy = 1j * inv_fourier[:, :, None, :] @ Uy[:, None, None, :, :] + Hz = 1j * inv_fourier[:, :, None, :] @ Uz[:, None, None, :, :] + + val = jnp.concatenate( + (Ex.squeeze(-1), Ey.squeeze(-1), Ez.squeeze(-1), Hx.squeeze(-1), Hy.squeeze(-1), Hz.squeeze(-1)), -1) + + field_cell = field_cell.at[res_z * idx_layer:res_z * (idx_layer + 1)].set(val) + + T_layer = big_A_i @ big_X @ T_layer + + return field_cell + + # @partial(jax.jit, static_argnums=(5, 6, 10, 11, 12, 13)) def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, - res_x=20, res_y=20, res_z=20, type_complex=jnp.complex128, type_float=jnp.float64): + res_x=20, res_y=20, res_z=20, type_complex=jnp.complex128): k0 = 2 * jnp.pi / wavelength @@ -112,25 +203,14 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, big_Q1 = jnp.diag(q1) big_Q2 = jnp.diag(q2) - Sx = W_11 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_12 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - - Sy = W_21 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_22 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - - # Ux = -V_11 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - # - V_12 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - # Uy = -V_21 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - # - V_22 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - - Ux = V_11 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch( - k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_12 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch( - k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Uy = V_21 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch( - k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_22 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch( - k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sx = W_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = W_21 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_22 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = V_11 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) Uz = -1j * (Kx @ Sy - Ky @ Sx) @@ -207,11 +287,7 @@ def field_plot(field_cell, pol=0, plot_indices=(1, 1, 1, 1, 1, 1), y_slice=0, z_ plt.show() -def diag_exp(x): - return jnp.diag(jnp.exp(jnp.diag(x))) - - -def diag_exp_batch(x): +def d_exp(x): res = jnp.zeros(x.shape, dtype=x.dtype) ix = jnp.diag_indices_from(x[0]) res = res.at[:, ix[0], ix[1]].set(jnp.exp(x[:, ix[0], ix[1]])) diff --git a/meent/on_jax/emsolver/primitives.py b/meent/on_jax/emsolver/primitives.py index 8a46c45..22b3fe4 100644 --- a/meent/on_jax/emsolver/primitives.py +++ b/meent/on_jax/emsolver/primitives.py @@ -10,8 +10,8 @@ def conj(arr): @partial(jax.custom_vjp, nondiff_argnums=(1, 2, 3)) -def eig(x, type_complex=jnp.complex128, perturbation=1E-10, device='cpu'): - +def eig(x, type_complex=jnp.complex128, perturbation=1E-20, device='cpu'): + # TODO: check perturbation in backprop _eig = jax.jit(jnp.linalg.eig, device=jax.devices('cpu')[0]) eigenvalues_shape = jax.ShapeDtypeStruct(x.shape[:-1], type_complex) @@ -68,3 +68,12 @@ def eig_bwd(type_complex, perturbation, device, res, g): eig.defvjp(eig_fwd, eig_bwd) + + +def meeinv(x, use_pinv=False): + if use_pinv: + res = jnp.linalg.pinv(x) + else: + res = jnp.linalg.inv(x) + + return res diff --git a/meent/on_jax/emsolver/rcwa.py b/meent/on_jax/emsolver/rcwa.py index d3d2711..8188d60 100644 --- a/meent/on_jax/emsolver/rcwa.py +++ b/meent/on_jax/emsolver/rcwa.py @@ -7,7 +7,44 @@ from ._base import _BaseRCWA, jax_device_set from .convolution_matrix import to_conv_mat_raster_discrete, to_conv_mat_raster_continuous, to_conv_mat_vector -from .field_distribution import field_dist_1d, field_dist_2d, field_plot +from .field_distribution import field_dist_1d, field_dist_1d_conical, field_dist_2d, field_plot + + +class ResultJax: + def __init__(self, res=None, res_te_inc=None, res_tm_inc=None): + + self.res = res + self.res_te_inc = res_te_inc + self.res_tm_inc = res_tm_inc + + @property + def de_ri(self): + if self.res is not None: + return self.res.de_ri + else: + return None + + @property + def de_ti(self): + if self.res is not None: + return self.res.de_ti + else: + return None + + +class ResultSubJax: + def __init__(self, R_s, R_p, T_s, T_p, de_ri, de_ri_s, de_ri_p, de_ti, de_ti_s, de_ti_p): + self.R_s = R_s + self.R_p = R_p + self.T_s = T_s + self.T_p = T_p + self.de_ri = de_ri + self.de_ri_s = de_ri_s + self.de_ri_p = de_ri_p + + self.de_ti = de_ti + self.de_ti_s = de_ti_s + self.de_ti_p = de_ti_p class RCWAJax(_BaseRCWA): @@ -15,29 +52,32 @@ def __init__(self, n_top=1., n_bot=1., theta=0., - phi=0., + phi=None, psi=None, - period=(100., 100.), - wavelength=900., + period=(1., 1.), + wavelength=1., ucell=None, thickness=(0., ), - backend=0, + backend=1, pol=0., fto=(0, 0), ucell_materials=None, connecting_algo='TMM', perturbation=1E-20, device='cpu', - type_complex=np.complex128, + type_complex=jnp.complex128, fourier_type=0, # 0 DFS, 1 CFS enhanced_dfs=True, - # **kwargs, + use_pinv=False, ): super().__init__(n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, psi=psi, pol=pol, fto=fto, period=period, wavelength=wavelength, thickness=thickness, connecting_algo=connecting_algo, perturbation=perturbation, - device=device, type_complex=type_complex) + device=device, type_complex=type_complex, use_pinv=use_pinv) + + self._modeling_type_assigned = None + self._grating_type_assigned = None self.ucell = ucell self.ucell_materials = ucell_materials @@ -45,8 +85,7 @@ def __init__(self, self.backend = backend self.fourier_type = fourier_type self.enhanced_dfs = enhanced_dfs - self._modeling_type_assigned = None - self._grating_type_assigned = None + self.use_pinv = use_pinv @property def ucell(self): @@ -56,6 +95,7 @@ def ucell(self): def ucell(self, ucell): if isinstance(ucell, jnp.ndarray): # Raster + self._modeling_type_assigned = 0 if ucell.dtype in (jnp.float64, jnp.float32, jnp.int64, jnp.int32): dtype = self.type_float self._ucell = ucell.astype(dtype) @@ -64,6 +104,7 @@ def ucell(self, ucell): self._ucell = ucell.astype(dtype) elif isinstance(ucell, np.ndarray): # Raster + self._modeling_type_assigned = 0 if ucell.dtype in (np.int64, np.float64, np.int32, np.float32): dtype = self.type_float self._ucell = jnp.array(ucell, dtype=dtype) @@ -72,6 +113,7 @@ def ucell(self, ucell): self._ucell = jnp.array(ucell, dtype=dtype) elif type(ucell) is list: # Vector + self._modeling_type_assigned = 1 self._ucell = ucell elif ucell is None: self._ucell = ucell @@ -82,30 +124,45 @@ def ucell(self, ucell): def modeling_type_assigned(self): return self._modeling_type_assigned - @modeling_type_assigned.setter - def modeling_type_assigned(self, modeling_type_assigned): - self._modeling_type_assigned = modeling_type_assigned + # @modeling_type_assigned.setter + # def modeling_type_assigned(self, modeling_type_assigned): + # self._modeling_type_assigned = modeling_type_assigned + + def _assign_grating_type(self): + """ + Select the grating type for RCWA simulation. This decides the efficient formulation for given case. - def _assign_modeling_type(self): - if isinstance(self.ucell, (np.ndarray, jnp.ndarray)): # Raster - self.modeling_type_assigned = 0 - if (self.ucell.shape[1] == 1) and (self.pol in (0, 1)): + `_grating_type_assigned` == 0(1D TETM) is for 1D grating, no rotation (phi or azimuth), and either TE or TM. + `_grating_type_assigned` == 1(1D conical) is for 1D grating with generality. + `_grating_type_assigned` == 2(2D) is for 2D grating with generality. - def false_fun(): return 0 # 1D TE and TM only - def true_fun(): return 1 + Note that no rotation means 'phi' is `None`. If phi is given as '0', then it takes 1D conical form + even though when the case itself is 1D TETM. - gear = jax.lax.cond(self.phi % (2 * np.pi) + self.fto[1], true_fun, false_fun) + 1D conical is under implementation. - self._grating_type_assigned = gear + Returns: - # if (self.ucell.shape[1] == 1) and (self.pol in (0, 1)) and (self.phi % (2 * np.pi) == 0): - # self._grating_type_assigned = 0 # 1D TE and TM only + """ + if self.modeling_type_assigned == 0: # Raster + if self.ucell.shape[1] == 1: + if (self.pol in (0, 1)) and (self.phi is None) and (self.fto[1] == 0): + self._grating_type_assigned = 0 + else: + self._grating_type_assigned = 1 + + # TODO: jit + # def false_fun(): return 0 # 1D TE and TM only + # def true_fun(): return 1 + # + # gear = jax.lax.cond(self.phi % (2 * np.pi) + self.fto[1], true_fun, false_fun) + # + # self._grating_type_assigned = gear else: - self._grating_type_assigned = 1 # else + self._grating_type_assigned = 2 - elif isinstance(self.ucell, list): # Vector - self.modeling_type_assigned = 1 - self.grating_type_assigned = 1 + elif self.modeling_type_assigned == 1: # Vector + self.grating_type_assigned = 2 @property def grating_type_assigned(self): @@ -115,7 +172,8 @@ def grating_type_assigned(self): def grating_type_assigned(self, grating_type_assigned): self._grating_type_assigned = grating_type_assigned - def _solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + @jax_device_set + def solve_for_conv(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): # def false_fun(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): # de_ri, de_ti, layer_info_list, T1 = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) @@ -127,82 +185,91 @@ def _solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): # # de_ri, de_ti, layer_info_list, T1 = jax.lax.cond(self._grating_type_assigned, true_fun, false_fun, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - # if self._grating_type_assigned == 0: - # de_ri, de_ti, layer_info_list, T1 = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - # else: - # de_ri, de_ti, layer_info_list, T1 = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + self._assign_grating_type() - # In JAXMeent, 1D TE TM are turned off for jit compilation. - de_ri, de_ti, layer_info_list, T1 = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + if self._grating_type_assigned == 0: + result_dict = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + elif self._grating_type_assigned == 1: + result_dict = self.solve_1d_conical(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + else: + result_dict = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - return de_ri, de_ti, layer_info_list, T1 + # TODO: In JAXMeent, 1D TE TM are turned off for jit compilation. - @jax_device_set - def solve(self, wavelength, e_conv_all, o_e_conv_all): - de_ri, de_ti, layer_info_list, T1, kx_vector = jax.jit(self._solve)(wavelength, e_conv_all, o_e_conv_all) + res_psi = ResultSubJax(**result_dict['res']) if 'res' in result_dict else None + res_te_inc = ResultSubJax(**result_dict['res_te_inc']) if 'res_te_inc' in result_dict else None + res_tm_inc = ResultSubJax(**result_dict['res_tm_inc']) if 'res_tm_inc' in result_dict else None - self.layer_info_list = layer_info_list - self.T1 = T1 + result = ResultJax(res_psi, res_te_inc, res_tm_inc) - return de_ri, de_ti + return result + + # @jax_device_set + # def solve(self, wavelength, e_conv_all, o_e_conv_all): + # de_ri, de_ti, layer_info_list, T1, kx_vector = jax.jit(self._solve)(wavelength, e_conv_all, o_e_conv_all) + # + # self.layer_info_list = layer_info_list + # self.T1 = T1 + # + # return de_ri, de_ti - def _conv_solve(self, **kwargs): - self._assign_modeling_type() + @jax_device_set + def conv_solve(self, **kwargs): + [setattr(self, k, v) for k, v in kwargs.items()] # needed for optimization if self._modeling_type_assigned == 0: # Raster if self.fourier_type == 0: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_discrete( self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex, - enhanced_dfs=self.enhanced_dfs) + enhanced_dfs=self.enhanced_dfs, use_pinv=self.use_pinv) elif self.fourier_type == 1: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_continuous( - self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex) + self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex, use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") elif self._modeling_type_assigned == 1: # Vector ucell_vector = self.modeling_vector_instruction(self.ucell) epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_vector( - ucell_vector, self.fto[0], self.fto[1], type_complex=self.type_complex) + ucell_vector, self.fto[0], self.fto[1], type_complex=self.type_complex, use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") - de_ri, de_ti, layer_info_list, T1 = self._solve(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + result = self.solve_for_conv(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - self.layer_info_list = layer_info_list - self.T1 = T1 + return result - return de_ri, de_ti, layer_info_list, T1 + # @jax.jit + # def _conv_solve_jit(self): + # return self._conv_solve() - @jax.jit - def _conv_solve_jit(self): - return self._conv_solve() - - @jax_device_set - def conv_solve(self, **kwargs): - [setattr(self, k, v) for k, v in kwargs.items()] # needed for optimization - if self.fourier_type == 1: - # print('CFT (fourier_type=1) is not supported for jit-compilation. Using non-jit-compiled method.') - de_ri, de_ti, layer_info_list, T1 = self._conv_solve() - - else: - de_ri, de_ti, layer_info_list, T1 = self._conv_solve() - # de_ri, de_ti, layer_info_list, T1 = self._conv_solve_jit() - - return de_ri, de_ti + # @jax_device_set + # def conv_solve(self, **kwargs): + # [setattr(self, k, v) for k, v in kwargs.items()] # needed for optimization + # if self.fourier_type == 1: + # # print('CFT (fourier_type=1) is not supported for jit-compilation. Using non-jit-compiled method.') + # de_ri, de_ti, layer_info_list, T1 = self._conv_solve() + # + # else: + # de_ri, de_ti, layer_info_list, T1 = self._conv_solve() + # # de_ri, de_ti, layer_info_list, T1 = self._conv_solve_jit() + # + # return de_ri, de_ti @jax_device_set def calculate_field(self, res_x=20, res_y=20, res_z=20): - kx, ky = self.get_kx_ky_vector(wavelength=self.wavelength) if self._grating_type_assigned == 0: res_y = 1 field_cell = field_dist_1d(self.wavelength, kx, self.T1, self.layer_info_list, self.period, self.pol, res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) + elif self._grating_type_assigned == 1: + field_cell = field_dist_1d_conical(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, + res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) else: field_cell = field_dist_2d(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) diff --git a/meent/on_jax/emsolver/transfer_method.py b/meent/on_jax/emsolver/transfer_method.py index 41710ef..ca1d9f7 100644 --- a/meent/on_jax/emsolver/transfer_method.py +++ b/meent/on_jax/emsolver/transfer_method.py @@ -1,12 +1,11 @@ import jax import jax.numpy as jnp -from .primitives import eig, conj +from .primitives import eig, conj, meeinv -def transfer_1d_1(pol, ff_x, kx, n_top, n_bot, type_complex=jnp.complex128): - - ff_xy = ff_x * 1 +def transfer_1d_1(pol, kx, n_top, n_bot, type_complex=jnp.complex128): + ff_x = len(kx) kz_top = (n_top ** 2 - kx ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2) ** 0.5 @@ -14,7 +13,7 @@ def transfer_1d_1(pol, ff_x, kx, n_top, n_bot, type_complex=jnp.complex128): kz_top = kz_top.conjugate() kz_bot = kz_bot.conjugate() - F = jnp.eye(ff_xy, dtype=type_complex) + F = jnp.eye(ff_x, dtype=type_complex) def false_fun(kz_bot): Kz_bot = jnp.diag(kz_bot) @@ -26,9 +25,9 @@ def true_fun(kz_bot): G = 1j * Kz_bot return Kz_bot, G - Kz_bot, G = jax.lax.cond(pol, true_fun, false_fun, kz_bot) + Kz_bot, G = jax.lax.cond(pol.real, true_fun, false_fun, kz_bot) - T = jnp.eye(ff_xy, dtype=type_complex) + T = jnp.eye(ff_x, dtype=type_complex) return kz_top, kz_bot, F, G, T @@ -50,13 +49,13 @@ def true_fun(kz_bot): # return kz_top, kz_bot, F, G, T -def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.complex128): - +def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.complex128, + perturbation=1E-20, use_pinv=False): Kx = jnp.diag(kx) def false_fun(Kx, epy_conv): # TE A = Kx ** 2 - epy_conv - eigenvalues, W = eig(A) + eigenvalues, W = eig(A, type_complex, perturbation) eigenvalues += 0j # to get positive square root q = eigenvalues ** 0.5 Q = jnp.diag(q) @@ -66,16 +65,17 @@ def false_fun(Kx, epy_conv): # TE def true_fun(Kx, epy_conv): # TM B = Kx @ epz_conv_i @ Kx - jnp.eye(epy_conv.shape[0], dtype=type_complex) - eigenvalues, W = eig(epx_conv @ B) + eigenvalues, W = eig(epx_conv @ B, type_complex, perturbation) eigenvalues += 0j # to get positive square root q = eigenvalues ** 0.5 Q = jnp.diag(q) - V = jnp.linalg.inv(epx_conv) @ W @ Q + # V = jnp.linalg.inv(epx_conv) @ W @ Q + V = meeinv(epx_conv, use_pinv) @ W @ Q return W, V, q - W, V, q = jax.lax.cond(pol, true_fun, false_fun, Kx, epy_conv) + W, V, q = jax.lax.cond(pol.real, true_fun, false_fun, Kx, epy_conv) return W, V, q # if pol == 0: @@ -103,21 +103,23 @@ def true_fun(Kx, epy_conv): # TM # return W, V, q -def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=jnp.complex128): - +def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=jnp.complex128, use_pinv=False): ff_x = len(q) I = jnp.eye(ff_x, dtype=type_complex) X = jnp.diag(jnp.exp(-k0 * q * d)) - W_i = jnp.linalg.inv(W) - V_i = jnp.linalg.inv(V) + # W_i = jnp.linalg.inv(W) + # V_i = jnp.linalg.inv(V) + W_i = meeinv(W, use_pinv) + V_i = meeinv(V, use_pinv) A = 0.5 * (W_i @ F + V_i @ G) B = 0.5 * (W_i @ F - V_i @ G) - A_i = jnp.linalg.inv(A) + # A_i = jnp.linalg.inv(A) + A_i = meeinv(A, use_pinv) F = W @ (I + X @ B @ A_i @ X) G = V @ (I - X @ B @ A_i @ X) @@ -126,65 +128,359 @@ def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=jnp.complex128): return X, F, G, T, A_i, B -def transfer_1d_4(pol, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, type_complex=jnp.complex128): +def transfer_1d_4(pol, ff_x, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, type_complex=jnp.complex128, use_pinv=False): + Kz_top = jnp.diag(kz_top) + kz_top = kz_top.reshape((1, ff_x)) + kz_bot = kz_bot.reshape((1, ff_x)) - ff_xy = len(kz_top) + delta_i0 = jnp.zeros(ff_x, dtype=type_complex) + delta_i0 = delta_i0.at[ff_x // 2].set(1) - Kz_top = jnp.diag(kz_top) + def false_fun(): # TE + inc_term = 1j * n_top * jnp.cos(theta) * delta_i0 + T1 = meeinv(G + 1j * Kz_top @ F, use_pinv) @ (1j * Kz_top @ delta_i0 + inc_term) - delta_i0 = jnp.zeros(ff_xy, dtype=type_complex) - delta_i0 = delta_i0.at[ff_xy // 2].set(1) + R = (F @ T1 - delta_i0).reshape((1, ff_x)) + _T = (T @ T1).reshape((1, ff_x)) - # if pol == 0: # TE - # inc_term = 1j * n_top * jnp.cos(theta) * delta_i0 - # T1 = jnp.linalg.inv(G + 1j * Kz_top @ F) @ (1j * Kz_top @ delta_i0 + inc_term) - # - # elif pol == 1: # TM - # inc_term = 1j * delta_i0 * jnp.cos(theta) / n_top - # T1 = jnp.linalg.inv(G + 1j * Kz_top/(n_top ** 2) @ F) @ (1j * Kz_top/(n_top ** 2) @ delta_i0 + inc_term) + de_ri = (R * R.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ti = (_T * _T.conj() * (kz_bot / (n_top * jnp.cos(theta))).real).real - def false_fun(n_top, theta, delta_i0, G, Kz_top, T): # TE - inc_term = 1j * n_top * jnp.cos(theta) * delta_i0 - T1 = jnp.linalg.inv(G + 1j * Kz_top @ F) @ (1j * Kz_top @ delta_i0 + inc_term) - R = F @ T1 - delta_i0 - T = T @ T1 + R_s = R + R_p = jnp.zeros(R.shape, dtype=R.dtype) + T_s = _T + T_p = jnp.zeros(_T.shape, dtype=_T.dtype) + de_ri_s = de_ri + de_ri_p = jnp.zeros(de_ri.shape, dtype=de_ri.dtype) + de_ti_s = de_ti + de_ti_p = jnp.zeros(de_ri.shape, dtype=de_ti.dtype) + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri': de_ri, 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, + 'de_ti': de_ti, 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, + } - de_ri = jnp.real(R * jnp.conj(R) * kz_top / (n_top * jnp.cos(theta))) - de_ti = T * jnp.conj(T) * jnp.real(kz_bot / (n_top * jnp.cos(theta))) + _result = {'res': res} - return de_ri, de_ti, T1 + return _result, T1 - def true_fun(n_top, theta, delta_i0, G, Kz_top, T): # TM + def true_fun(): # TM inc_term = 1j * delta_i0 * jnp.cos(theta) / n_top - T1 = jnp.linalg.inv(G + 1j * Kz_top / (n_top ** 2) @ F) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + T1 = meeinv(G + 1j * Kz_top / (n_top ** 2) @ F, use_pinv) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + + R = (F @ T1 - delta_i0).reshape((1, ff_x)) + _T = (T @ T1).reshape((1, ff_x)) + + de_ri = (R * R.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ti = (_T * _T.conj() * (kz_bot / n_bot ** 2 / (jnp.cos(theta) / n_top)).real).real + + R_s = jnp.zeros(R.shape, dtype=R.dtype) + R_p = R + T_s = jnp.zeros(_T.shape, dtype=_T.dtype) + T_p = _T + de_ri_s = jnp.zeros(de_ri.shape, dtype=de_ri.dtype) + de_ri_p = de_ri + de_ti_s = jnp.zeros(de_ri.shape, dtype=de_ti.dtype) + de_ti_p = de_ti + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri': de_ri, 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, + 'de_ti': de_ti, 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, + } + + _result = {'res': res} + + return _result, T1 + + result, T1 = jax.lax.cond(pol.real, true_fun, false_fun) + + return result, T1 + + +def transfer_1d_conical_1(kx, ky, n_top, n_bot, type_complex=jnp.complex128): + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y - R = F @ T1 - delta_i0 - T = T @ T1 + I = jnp.eye(ff_xy).astype(type_complex) + O = jnp.zeros((ff_xy, ff_xy)).astype(type_complex) - de_ri = jnp.real(R * jnp.conj(R) * kz_top / (n_top * jnp.cos(theta))) - de_ti = T * jnp.conj(T) * jnp.real(kz_bot / n_bot ** 2) / (jnp.cos(theta) / n_top) + kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 - return de_ri, de_ti, T1 + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() - de_ri, de_ti, T1 = jax.lax.cond(pol, true_fun, false_fun, n_top, theta, delta_i0, G, Kz_top, T) + varphi = jnp.arctan(ky.reshape((-1, 1)) / kx).flatten() + Kz_bot = jnp.diag(kz_bot) + + big_F = jnp.block([[I, O], [O, 1j * Kz_bot / (n_bot ** 2)]]) + big_G = jnp.block([[1j * Kz_bot, O], [O, I]]) + big_T = jnp.eye(2 * ff_xy, dtype=type_complex) + + return kz_top, kz_bot, varphi, big_F, big_G, big_T - # R = F @ T1 - delta_i0 - # T = T @ T1 # - # de_ri = jnp.real(R * jnp.conj(R) * kz_top / (n_top * jnp.cos(theta))) + # ky = k0 * n_I * jnp.sin(theta) * jnp.sin(phi) # - # if pol == 0: - # de_ti = T * jnp.conj(T) * jnp.real(kz_bot / (n_top * jnp.cos(theta))) - # elif pol == 1: - # de_ti = T * jnp.conj(T) * jnp.real(kz_bot / n_bot ** 2) / (jnp.cos(theta) / n_top) - # else: - # raise ValueError + # k_I_z = (k0 ** 2 * n_I ** 2 - kx_vector ** 2 - ky ** 2) ** 0.5 + # k_II_z = (k0 ** 2 * n_II ** 2 - kx_vector ** 2 - ky ** 2) ** 0.5 + # + # # conj() is not allowed with grad x jit + # # k_I_z = k_I_z.conjugate() + # # k_II_z = k_II_z.conjugate() + # + # k_I_z = conj(k_I_z) # manual conjugate + # k_II_z = conj(k_II_z) # manual conjugate + # + # Kx = jnp.diag(kx_vector / k0) + # varphi = jnp.arctan(ky / kx_vector) + # + # Y_I = jnp.diag(k_I_z / k0) + # Y_II = jnp.diag(k_II_z / k0) + # + # Z_I = jnp.diag(k_I_z / (k0 * n_I ** 2)) + # Z_II = jnp.diag(k_II_z / (k0 * n_II ** 2)) + # + # big_F = jnp.block([[I, O], [O, 1j * Z_II]]) + # big_G = jnp.block([[1j * Y_II, O], [O, I]]) + # + # big_T = jnp.eye(2 * ff).astype(type_complex) + # + # return Kx, ky, k_I_z, k_II_z, varphi, Y_I, Y_II, Z_I, Z_II, big_F, big_G, big_T - return de_ri.real, de_ti.real, T1 + +# def transfer_1d_conical_2(k0, Kx, ky, E_conv, E_conv_i, o_E_conv_i, ff, d, varphi, big_F, big_G, big_T, +# type_complex=jnp.complex128, perturbation=1E-10, device='cpu'): +def transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.complex128, perturbation=1E-20, + device='cpu', use_pinv=False): + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + I = jnp.eye(ff_xy).astype(type_complex) + + Kx = jnp.diag(jnp.tile(kx, ff_y).flatten()) + Ky = jnp.diag(jnp.tile(ky.reshape((-1, 1)), ff_x).flatten()) + + A = Kx ** 2 - epy_conv + B = Kx @ epz_conv_i @ Kx - I + + Omega2_RL = Ky ** 2 + A + Omega2_LR = Ky ** 2 + B @ epx_conv + + eigenvalues_1, W_1 = eig(Omega2_RL, type_complex=type_complex, perturbation=perturbation, device=device) + eigenvalues_2, W_2 = eig(Omega2_LR, type_complex=type_complex, perturbation=perturbation, device=device) + + eigenvalues_1 += 0j # to get positive square root + eigenvalues_2 += 0j # to get positive square root + + q_1 = eigenvalues_1 ** 0.5 + q_2 = eigenvalues_2 ** 0.5 + + Q_1 = jnp.diag(q_1) + Q_2 = jnp.diag(q_2) + + A_i = meeinv(A, use_pinv) + B_i = meeinv(B, use_pinv) + + V_11 = A_i @ W_1 @ Q_1 + V_12 = Ky @ A_i @ Kx @ W_2 + V_21 = Ky @ B_i @ Kx @ epz_conv_i @ W_1 + V_22 = B_i @ W_2 @ Q_2 + + W = jnp.block([W_1, W_2]) + V = jnp.block([[V_11, V_12], + [V_21, V_22]]) + q = jnp.hstack([q_1, q_2]) + + return W, V, q + + +def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp.complex128, use_pinv=False): + ff_xy = len(q) // 2 + I = jnp.eye(ff_xy, dtype=type_complex) + O = jnp.zeros((ff_xy, ff_xy), dtype=type_complex) + + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] + + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] + + X_1 = jnp.diag(jnp.exp(-k0 * q_1 * d)) + X_2 = jnp.diag(jnp.exp(-k0 * q_2 * d)) + + F_c = jnp.diag(jnp.cos(varphi)) + F_s = jnp.diag(jnp.sin(varphi)) + + V_ss = F_c @ V_11 + V_sp = F_c @ V_12 - F_s @ W_2 + W_ss = F_c @ W_1 + F_s @ V_21 + W_sp = F_s @ V_22 + W_ps = F_s @ V_11 + W_pp = F_c @ W_2 + F_s @ V_12 + V_ps = F_c @ V_21 - F_s @ W_1 + V_pp = F_c @ V_22 + + big_I = jnp.eye(2 * (len(I)), dtype=type_complex) + big_X = jnp.block([[X_1, O], [O, X_2]]) + big_W = jnp.block([[V_ss, V_sp], [W_ps, W_pp]]) + big_V = jnp.block([[W_ss, W_sp], [V_ps, V_pp]]) + + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) + + big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) + big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) + + big_A_i = meeinv(big_A, use_pinv) + + big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) + big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) + + big_T = big_T @ big_A_i @ big_X + + return big_X, big_F, big_G, big_T, big_A_i, big_B -def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=jnp.complex128): +# def transfer_1d_conical_4(big_F, big_G, big_T, Z_I, Y_I, psi, theta, ff, delta_i0, k_I_z, k0, n_I, n_II, k_II_z, +# type_complex=jnp.complex128): +def transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, + type_complex=jnp.complex128, use_pinv=False): + + ff_xy = ff_x * ff_y + + Kz_top = jnp.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) + + I = jnp.eye(ff_xy, dtype=type_complex) + O = jnp.zeros((ff_xy, ff_xy), dtype=type_complex) + + + big_F_11 = big_F[:ff_xy, :ff_xy] + big_F_12 = big_F[:ff_xy, ff_xy:] + big_F_21 = big_F[ff_xy:, :ff_xy] + big_F_22 = big_F[ff_xy:, ff_xy:] + + big_G_11 = big_G[:ff_xy, :ff_xy] + big_G_12 = big_G[:ff_xy, ff_xy:] + big_G_21 = big_G[ff_xy:, :ff_xy] + big_G_22 = big_G[ff_xy:, ff_xy:] + + delta_i0 = jnp.zeros((ff_xy, 1), dtype=type_complex) + delta_i0 = delta_i0.at[ff_xy // 2, 0].set(1) + + # Final Equation in form of AX=B + final_A = jnp.block( + [ + [I, O, -big_F_11, -big_F_12], + [O, -1j * Kz_top / (n_top ** 2), -big_F_21, -big_F_22], + [-1j * Kz_top, O, -big_G_11, -big_G_12], + [O, I, -big_G_21, -big_G_22], + ] + ) + final_B = jnp.block( + [ + [-jnp.sin(psi) * delta_i0], + [jnp.cos(psi) * jnp.cos(theta) * delta_i0], + [-1j * jnp.sin(psi) * n_top * jnp.cos(theta) * delta_i0], + [-1j * n_top * jnp.cos(psi) * delta_i0] + ] + ) + + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B + + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) + + big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.copy() + big_T = big_T @ big_T1 + + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) + + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * jnp.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} + + # TE TM incidence + psi_tm = jnp.array(0, dtype=type_complex) + final_B_tm = jnp.block( + [ + [-jnp.sin(psi_tm) * delta_i0], + [jnp.cos(psi_tm) * jnp.cos(theta) * delta_i0], + [-1j * jnp.sin(psi_tm) * n_top * jnp.cos(theta) * delta_i0], + [-1j * n_top * jnp.cos(psi_tm) * delta_i0] + ] + ) + + psi_te = jnp.array(jnp.pi / 2, dtype=type_complex) + final_B_te = jnp.block( + [ + [-jnp.sin(psi_te) * delta_i0], + [jnp.cos(psi_te) * jnp.cos(theta) * delta_i0], + [-1j * jnp.sin(psi_te) * n_top * jnp.cos(theta) * delta_i0], + [-1j * n_top * jnp.cos(psi_te) * delta_i0] + ] + ) + + final_B_tetm = jnp.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * jnp.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} + + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} + + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} + + return result, big_T1 + + +def transfer_2d_1(kx, ky, n_top, n_bot, type_complex=jnp.complex128): + ff_x = len(kx) + ff_y = len(ky) ff_xy = ff_x * ff_y I = jnp.eye(ff_xy, dtype=type_complex) @@ -193,11 +489,10 @@ def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=jnp.complex128) kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 - kz_top = kz_top.flatten().conjugate() - kz_bot = kz_bot.flatten().conjugate() + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() varphi = jnp.arctan(ky.reshape((-1, 1)) / kx).flatten() - Kz_bot = jnp.diag(kz_bot) big_F = jnp.block([[I, O], [O, 1j * Kz_bot / (n_bot ** 2)]]) @@ -207,8 +502,8 @@ def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=jnp.complex128) return kz_top, kz_bot, varphi, big_F, big_G, big_T -def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.complex128): - +def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.complex128, + perturbation=1E-20, use_pinv=False): ff_x = len(kx) ff_y = len(ky) @@ -227,12 +522,12 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.compl ]) # eigenvalues, W = jnp.linalg.eig(Omega2_LR) - eigenvalues, W = eig(Omega2_LR) + eigenvalues, W = eig(Omega2_LR, type_complex, perturbation) eigenvalues += 0j # to get positive square root q = eigenvalues ** 0.5 Q = jnp.diag(q) - Q_i = jnp.linalg.inv(Q) + Q_i = meeinv(Q, use_pinv) Omega_R = jnp.block( [ @@ -246,15 +541,14 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=jnp.compl return W, V, q -def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp.complex128): - +def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp.complex128, use_pinv=False): ff_xy = len(q)//2 I = jnp.eye(ff_xy, dtype=type_complex) O = jnp.zeros((ff_xy, ff_xy), dtype=type_complex) - q1 = q[:ff_xy] - q2 = q[ff_xy:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] W_11 = W[:ff_xy, :ff_xy] W_12 = W[:ff_xy, ff_xy:] @@ -266,8 +560,8 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp. V_21 = V[ff_xy:, :ff_xy] V_22 = V[ff_xy:, ff_xy:] - X_1 = jnp.diag(jnp.exp(-k0 * q1 * d)) - X_2 = jnp.diag(jnp.exp(-k0 * q2 * d)) + X_1 = jnp.diag(jnp.exp(-k0 * q_1 * d)) + X_2 = jnp.diag(jnp.exp(-k0 * q_2 * d)) F_c = jnp.diag(jnp.cos(varphi)) F_s = jnp.diag(jnp.sin(varphi)) @@ -287,13 +581,13 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp. big_W = jnp.block([[W_ss, W_sp], [W_ps, W_pp]]) big_V = jnp.block([[V_ss, V_sp], [V_ps, V_pp]]) - big_W_i = jnp.linalg.inv(big_W) - big_V_i = jnp.linalg.inv(big_V) + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) - big_A_i = jnp.linalg.inv(big_A) + big_A_i = meeinv(big_A, use_pinv) big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) @@ -303,12 +597,15 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=jnp. return big_X, big_F, big_G, big_T, big_A_i, big_B -def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, type_complex=jnp.complex128): - - ff_xy = len(big_F) // 2 +def transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, + type_complex=jnp.complex128, use_pinv=False): + ff_xy = ff_x * ff_y Kz_top = jnp.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) + I = jnp.eye(ff_xy, dtype=type_complex) O = jnp.zeros((ff_xy, ff_xy), dtype=type_complex) @@ -344,22 +641,83 @@ def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, ] ) - final_RT = jnp.linalg.inv(final_A) @ final_B + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B - R_s = final_RT[:ff_xy, :].flatten() - R_p = final_RT[ff_xy: 2 * ff_xy, :].flatten() + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.copy() big_T = big_T @ big_T1 - T_s = big_T[:ff_xy, :].flatten() - T_p = big_T[ff_xy:, :].flatten() + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) + + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * jnp.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} + + # TE TM incidence + psi_tm = jnp.array(0, dtype=type_complex) + final_B_tm = jnp.block( + [ + [-jnp.sin(psi_tm) * delta_i0], + [jnp.cos(psi_tm) * jnp.cos(theta) * delta_i0], + [-1j * jnp.sin(psi_tm) * n_top * jnp.cos(theta) * delta_i0], + [-1j * n_top * jnp.cos(psi_tm) * delta_i0] + ] + ) + psi_te = jnp.array(jnp.pi/2, dtype=type_complex) + final_B_te = jnp.block( + [ + [-jnp.sin(psi_te) * delta_i0], + [jnp.cos(psi_te) * jnp.cos(theta) * delta_i0], + [-1j * jnp.sin(psi_te) * n_top * jnp.cos(theta) * delta_i0], + [-1j * n_top * jnp.cos(psi_te) * delta_i0] + ] + ) + + final_B_tetm = jnp.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * jnp.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * jnp.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * jnp.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} + + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} - de_ri = R_s * jnp.conj(R_s) * jnp.real(kz_top / (n_top * jnp.cos(theta))) \ - + R_p * jnp.conj(R_p) * jnp.real(kz_top / n_top ** 2 / (n_top * jnp.cos(theta))) + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} - de_ti = T_s * jnp.conj(T_s) * jnp.real(kz_bot / (n_top * jnp.cos(theta))) \ - + T_p * jnp.conj(T_p) * jnp.real(kz_bot / n_bot ** 2 / (n_top * jnp.cos(theta))) + return result, big_T1 - return de_ri.real, de_ti.real, big_T1 diff --git a/meent/on_jax/optimizer/loss.py b/meent/on_jax/optimizer/loss.py deleted file mode 100644 index e24d125..0000000 --- a/meent/on_jax/optimizer/loss.py +++ /dev/null @@ -1,31 +0,0 @@ -import jax.numpy as jnp - - -class LossDeflector: - def __init__(self, x_order=0, y_order=0): - self.x_order = x_order - self.y_order = y_order - - def __call__(self, value, *args, **kwargs): - de_ri, de_ti = value - - if len(de_ti.shape) == 1: - c_x = de_ti.shape[0] // 2 - res = de_ti[c_x + self.x_order] - elif len(de_ti.shape) == 2: - c_x = de_ti.shape[0] // 2 - c_y = de_ti.shape[1] // 2 - res = de_ti[c_x + self.x_order, c_y + self.y_order] - else: - raise ValueError - - return res - - -class LossSpectrumL2: - def __init__(self): - pass - - def __call__(self, pred, target, *args, **kwargs): - gap = jnp.linalg.norm(pred, target) - return gap diff --git a/meent/on_numpy/emsolver/_base.py b/meent/on_numpy/emsolver/_base.py index afa8a63..230a482 100644 --- a/meent/on_numpy/emsolver/_base.py +++ b/meent/on_numpy/emsolver/_base.py @@ -2,15 +2,16 @@ from .scattering_method import scattering_1d_1, scattering_1d_2, scattering_1d_3, scattering_2d_1, scattering_2d_wv, \ scattering_2d_2, scattering_2d_3 -from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, +from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, transfer_1d_conical_1, + transfer_1d_conical_2, transfer_1d_conical_3, transfer_1d_conical_4, transfer_2d_1, transfer_2d_2, transfer_2d_3, transfer_2d_4) class _BaseRCWA: - def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=(0, 0), - period=(100., 100.), wavelength=1., - thickness=(0., ), connecting_algo='TMM', perturbation=1E-20, - type_complex=np.complex128, *args, **kwargs): # TODO: delete args and kwargs? + def __init__(self, n_top=1., n_bot=1., theta=0., phi=None, psi=None, pol=0., fto=(0, 0), + period=(1., 1.), wavelength=1., + thickness=(0.,), connecting_algo='TMM', perturbation=1E-20, + device=0, type_complex=np.complex128, use_pinv=False): self._device = 0 @@ -40,6 +41,8 @@ def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=( self.wavelength = wavelength self.thickness = thickness self.connecting_algo = connecting_algo + self.use_pinv = use_pinv + self.layer_info_list = [] self.T1 = None @@ -83,33 +86,17 @@ def type_float(self): def type_int(self): return self._type_int - @property - def pol(self): - return self._pol - - @pol.setter - def pol(self, pol): - room = 1E-6 - if 1 < pol < 1 + room: - pol = 1 - elif 0 - room < pol < 0: - pol = 0 - - if not 0 <= pol <= 1: - raise ValueError - - self._pol = pol - psi = np.pi / 2 * (1 - self.pol) - self._psi = np.array(psi, dtype=self.type_float) - @property def theta(self): return self._theta @theta.setter def theta(self, theta): - self._theta = np.array(theta, dtype=self.type_float) - self._theta = np.where(self._theta == 0, self.perturbation, self._theta) # perturbation + if theta is None: + self._theta = None + else: + self._theta = np.array(theta, dtype=self.type_complex) + self._theta = np.where(self._theta == 0, self.perturbation, self._theta) # perturbation @property def phi(self): @@ -117,7 +104,11 @@ def phi(self): @phi.setter def phi(self, phi): - self._phi = np.array(phi, dtype=self.type_float) + if phi is None: + self._phi = None + else: + self._phi = np.array(phi, dtype=self.type_complex) + # self._phi = np.array(phi, dtype=self.type_complex) if phi is not None else None @property def psi(self): @@ -126,10 +117,29 @@ def psi(self): @psi.setter def psi(self, psi): if psi is not None: - self._psi = np.array(psi, dtype=self.type_float) + self._psi = np.array(psi, dtype=self.type_complex) # TODO: complex, QA pol = -(2 * psi / np.pi - 1) self._pol = pol + @property + def pol(self): + """ + portion of TM. 0: full TE, 1: full TM + + Returns: polarization ratio + + """ + return self._pol + + @pol.setter + def pol(self, pol): + if not 0 <= pol <= 1: + raise ValueError + + self._pol = pol + psi = np.array(np.pi / 2 * (1 - self.pol), dtype=self.type_complex) + self._psi = psi + @property def fto(self): return self._fto @@ -195,11 +205,19 @@ def get_kx_ky_vector(self, wavelength): fto_x_range = np.arange(-self.fto[0], self.fto[0] + 1) fto_y_range = np.arange(-self.fto[1], self.fto[1] + 1) - kx = (self.n_top * np.sin(self.theta) * np.cos(self.phi) + fto_x_range * ( - wavelength / self.period[0])).astype(self.type_complex) + if self.theta.real >= np.float32(np.pi / 2): + # https://github.com/numpy/numpy/issues/27306 + sin_theta = np.sin(np.nextafter(np.float32(np.pi / 2), np.float32(0)) + self.theta.imag * np.complex64(1j)) + else: + sin_theta = np.sin(self.theta) - ky = (self.n_top * np.sin(self.theta) * np.sin(self.phi) + fto_y_range * ( - wavelength / self.period[1])).astype(self.type_complex) + phi = 0 if self.phi is None else self.phi # phi is None -> 1D TE TM case + + kx = (self.n_top * sin_theta * np.cos(phi) + fto_x_range * ( + wavelength / self.period[0])).astype(self.type_complex).conj() + + ky = (self.n_top * sin_theta * np.sin(phi) + fto_y_range * ( + wavelength / self.period[1])).astype(self.type_complex).conj() return kx, ky @@ -214,11 +232,13 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): if self.connecting_algo == 'TMM': kz_top, kz_bot, F, G, T \ - = transfer_1d_1(self.pol, ff_x, kx, self.n_top, self.n_bot, type_complex=self.type_complex) + = transfer_1d_1(self.pol, kx, self.n_top, self.n_bot, type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, self.period, - self.pol, wl=wavelength) + raise ValueError + + # Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ + # = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, self.period, + # self.pol, wl=wavelength) else: raise ValueError @@ -232,33 +252,95 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, self.type_complex) + W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, self.type_complex, + use_pinv=self.use_pinv) - X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=self.type_complex) + X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=self.type_complex, + use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, A_i, B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) + raise ValueError + + # A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, T1 = transfer_1d_4(self.pol, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, - type_complex=self.type_complex) + result, T1 = transfer_1d_4(self.pol, ff_x, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, + type_complex=self.type_complex, use_pinv=self.use_pinv) self.T1 = T1 elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, ff, Wr, self.fto, Kzr, Kzt, - self.n_top, self.n_bot, self.theta, self.pol) + raise ValueError + + # de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, ff, Wr, self.fto, Kzr, Kzt, + # self.n_top, self.n_bot, self.theta, self.pol) else: raise ValueError - return de_ri, de_ti, self.layer_info_list, self.T1 + return result - def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + def solve_1d_conical(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + self.layer_info_list = [] + self.T1 = None + + ff_x = self.fto[0] * 2 + 1 + ff_y = 1 + + k0 = 2 * np.pi / wavelength + kx, ky = self.get_kx_ky_vector(wavelength) + if self.connecting_algo == 'TMM': + kz_top, kz_bot, varphi, big_F, big_G, big_T \ + = transfer_1d_conical_1(kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) + + elif self.connecting_algo == 'SMM': + print('SMM for 1D conical is not implemented') + return np.nan, np.nan + else: + raise ValueError + + for layer_index in range(len(self.thickness))[::-1]: + + epx_conv = epx_conv_all[layer_index] + epy_conv = epy_conv_all[layer_index] + epz_conv_i = epz_conv_i_all[layer_index] + + d = self.thickness[layer_index] + + if self.connecting_algo == 'TMM': + W, V, q = transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=self.type_complex, + use_pinv=self.use_pinv) + + big_X, big_F, big_G, big_T, big_A_i, big_B, \ + = transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex, + use_pinv=self.use_pinv) + + layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] + self.layer_info_list.append(layer_info) + + elif self.connecting_algo == 'SMM': + raise ValueError + else: + raise ValueError + + if self.connecting_algo == 'TMM': + result, big_T1 = transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, + self.theta, self.n_top, self.n_bot, type_complex=self.type_complex, + use_pinv=self.use_pinv) + self.T1 = big_T1 + + elif self.connecting_algo == 'SMM': + raise ValueError + else: + raise ValueError + + return result + + def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): self.layer_info_list = [] self.T1 = None @@ -270,11 +352,13 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): if self.connecting_algo == 'TMM': kz_top, kz_bot, varphi, big_F, big_G, big_T \ - = transfer_2d_1(ff_x, ff_y, kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) + = transfer_2d_1(kx, ky, self.n_top, self.n_bot, type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto) + raise ValueError + + # Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg, kz_top, kz_bot \ + # = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto, kx, ky) else: raise ValueError @@ -288,32 +372,47 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=self.type_complex) + W, V, q = transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=self.type_complex, + use_pinv=self.use_pinv) big_X, big_F, big_G, big_T, big_A_i, big_B, \ - = transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex) + = transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=self.type_complex, + use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - W, V, q = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) - A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, q) + raise ValueError + + # W, V, q = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) + # A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, q) + + # W, V, q = scattering_2d_wv(Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) + # W, V, q = scattering_2d_wv(Kx, Ky, epx_conv, epy_conv, epz_conv_i) + # A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, q) else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, big_T1 = transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, - self.n_top, self.n_bot, type_complex=self.type_complex) + result, big_T1 = transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, + self.n_top, self.n_bot, type_complex=self.type_complex, + use_pinv=self.use_pinv) self.T1 = big_T1 elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_2d_3(ff_xy, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, - self.pol, self.theta, self.phi, self.fto) + raise ValueError + + # de_ri, de_ti = scattering_2d_3(ff_xy, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, + # self.pol, self.theta, self.phi, self.fto) + + # de_ri_s, de_ri_p, de_ti_s, de_ti_p, R_s, R_p, T_s, T_p =\ + # scattering_2d_3(Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_top, kz_bot, self.n_top, self.n_bot, + # self.pol, self.theta, self.phi, self.fto) else: raise ValueError - de_ri = de_ri.reshape((ff_y, ff_x)).T - de_ti = de_ti.reshape((ff_y, ff_x)).T - return de_ri, de_ti, self.layer_info_list, self.T1 + # de_ri = de_ri.reshape((ff_y, ff_x)).T # TODO: check benchmarks codes + # de_ti = de_ti.reshape((ff_y, ff_x)).T + return result diff --git a/meent/on_numpy/emsolver/convolution_matrix.py b/meent/on_numpy/emsolver/convolution_matrix.py index ba44100..9d8426c 100644 --- a/meent/on_numpy/emsolver/convolution_matrix.py +++ b/meent/on_numpy/emsolver/convolution_matrix.py @@ -1,5 +1,6 @@ import numpy as np from .fourier_analysis import dfs2d, cfs2d +from .primitives import meeinv def cell_compression(cell, type_complex=np.complex128): @@ -43,7 +44,7 @@ def cell_compression(cell, type_complex=np.complex128): return cell_comp, x, y -def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, type_complex=np.complex128): +def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, type_complex=np.complex128, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -61,12 +62,12 @@ def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=None, type_complex= epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = np.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all -def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex=np.complex128): +def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex=np.complex128, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -84,13 +85,13 @@ def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=None, type_complex epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = np.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=np.complex128, - enhanced_dfs=True): + enhanced_dfs=True, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -123,7 +124,7 @@ def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=n epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = np.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all diff --git a/meent/on_numpy/emsolver/field_distribution.py b/meent/on_numpy/emsolver/field_distribution.py index a0ad4c7..aa37549 100644 --- a/meent/on_numpy/emsolver/field_distribution.py +++ b/meent/on_numpy/emsolver/field_distribution.py @@ -3,7 +3,6 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, pol, res_x=20, res_y=1, res_z=20, type_complex=np.complex128): - k0 = 2 * np.pi / wavelength Kx = np.diag(kx) @@ -21,8 +20,8 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, z_1d = np.linspace(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d - My = W @ (diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) - Mx = V @ (-diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) + My = W @ (d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) + Mx = V @ (-d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) if pol == 0: Mz = -1j * Kx @ My @@ -59,9 +58,108 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, return field_cell +def field_dist_1d_conical(wavelength, kx, ky, T1, layer_info_list, period, + res_x=20, res_y=20, res_z=20, type_complex=np.complex128): + k0 = 2 * np.pi / wavelength + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + Kx = np.diag(np.tile(kx, ff_y).flatten()) + Ky = np.diag(np.tile(ky.reshape((-1, 1)), ff_x).flatten()) + + field_cell = np.zeros((res_z * len(layer_info_list), res_y, res_x, 6), dtype=type_complex) + + T_layer = T1 + + big_I = np.eye((len(T1))).astype(type_complex) + O = np.zeros((ff_xy, ff_xy), dtype=type_complex) + + # From the first layer + for idx_layer, (epz_conv_i, W, V, q, d, big_A_i, big_B) in enumerate(layer_info_list[::-1]): + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] + + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] + + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + + X_1 = np.diag(np.exp(-k0 * q_1 * d)) + X_2 = np.diag(np.exp(-k0 * q_2 * d)) + + big_X = np.block([[X_1, O], [O, X_2]]) + + c = np.block([[big_I], [big_B @ big_A_i @ big_X]]) @ T_layer + # z_1d = np.arange(res_z).reshape((-1, 1, 1)) / res_z * d + z_1d = np.linspace(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d + + c1_plus = c[0 * ff_xy:1 * ff_xy] + c2_plus = c[1 * ff_xy:2 * ff_xy] + c1_minus = c[2 * ff_xy:3 * ff_xy] + c2_minus = c[3 * ff_xy:4 * ff_xy] + + big_Q1 = np.diag(q_1) + big_Q2 = np.diag(q_2) + + Sx = W_2 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = V_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = W_1 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) + Uz = -1j * (Kx @ Sy - Ky @ Sx) + + # x_1d = np.arange(res_x).reshape((1, -1, 1)) + # x_1d = -1j * x_1d * period[0] / res_x + x_1d = np.linspace(0, period[0], res_x).reshape((1, -1, 1)) + x_2d = np.tile(x_1d, (res_y, 1, 1)) + x_2d = x_2d * kx * k0 + x_2d = x_2d.reshape((res_y, res_x, 1, len(kx))) + + y_1d = np.linspace(0, period[1], res_y)[::-1].reshape((-1, 1, 1)) + y_2d = np.tile(y_1d, (1, res_x, 1)) + y_2d = y_2d * ky * k0 + y_2d = y_2d.reshape((res_y, res_x, len(ky), 1)) + + # exp_K = np.exp(x_2d) + # exp_K = exp_K.reshape((res_y, res_x, -1)) + + inv_fourier = np.exp(-1j * x_2d) * np.exp(-1j * y_2d) + inv_fourier = inv_fourier.reshape((res_y, res_x, -1)) + + # Ex = exp_K[:, :, None, :] @ Sx[:, None, None, :, :] + # Ey = exp_K[:, :, None, :] @ Sy[:, None, None, :, :] + # Ez = exp_K[:, :, None, :] @ Sz[:, None, None, :, :] + # + # Hx = -1j * exp_K[:, :, None, :] @ Ux[:, None, None, :, :] + # Hy = -1j * exp_K[:, :, None, :] @ Uy[:, None, None, :, :] + # Hz = -1j * exp_K[:, :, None, :] @ Uz[:, None, None, :, :] + + Ex = inv_fourier[:, :, None, :] @ Sx[:, None, None, :, :] + Ey = inv_fourier[:, :, None, :] @ Sy[:, None, None, :, :] + Ez = inv_fourier[:, :, None, :] @ Sz[:, None, None, :, :] + Hx = 1j * inv_fourier[:, :, None, :] @ Ux[:, None, None, :, :] + Hy = 1j * inv_fourier[:, :, None, :] @ Uy[:, None, None, :, :] + Hz = 1j * inv_fourier[:, :, None, :] @ Uz[:, None, None, :, :] + + val = np.concatenate( + (Ex.squeeze(-1), Ey.squeeze(-1), Ez.squeeze(-1), Hx.squeeze(-1), Hy.squeeze(-1), Hz.squeeze(-1)), -1) + + field_cell[res_z * idx_layer:res_z * (idx_layer + 1)] = val + + T_layer = big_A_i @ big_X @ T_layer + + return field_cell + + def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, res_x=20, res_y=20, res_z=20, type_complex=np.complex128): - k0 = 2 * np.pi / wavelength ff_x = len(kx) @@ -79,7 +177,6 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, # From the first layer for idx_layer, (epz_conv_i, W, V, q, d, big_A_i, big_B) in enumerate(layer_info_list[::-1]): - W_11 = W[:ff_xy, :ff_xy] W_12 = W[:ff_xy, ff_xy:] W_21 = W[ff_xy:, :ff_xy] @@ -90,6 +187,9 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, V_21 = V[ff_xy:, :ff_xy] V_22 = V[ff_xy:, ff_xy:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + big_X = np.diag(np.exp(-k0 * q * d)) c = np.block([[big_I], [big_B @ big_A_i @ big_X]]) @ T_layer @@ -101,25 +201,17 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, c1_minus = c[2 * ff_xy:3 * ff_xy] c2_minus = c[3 * ff_xy:4 * ff_xy] - q1 = q[:len(q) // 2] - q2 = q[len(q) // 2:] - big_Q1 = np.diag(q1) - big_Q2 = np.diag(q2) + big_Q1 = np.diag(q_1) + big_Q2 = np.diag(q_2) - Sx = W_11 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_12 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Sy = W_21 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_22 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - - # Ux = -V_11 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - # - V_12 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - # Uy = -V_21 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - # - V_22 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - - Ux = V_11 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_12 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Uy = V_21 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_22 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sx = W_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = W_21 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_22 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = V_11 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) Uz = -1j * (Kx @ Sy - Ky @ Sx) @@ -196,11 +288,7 @@ def field_plot(field_cell, pol=0, plot_indices=(1, 1, 1, 1, 1, 1), y_slice=0, z_ plt.show() -def diag_exp(x): - return np.diag(np.exp(np.diag(x))) - - -def diag_exp_batch(x): +def d_exp(x): res = np.zeros(x.shape).astype(x.dtype) ix = np.arange(x.shape[-1]) res[:, ix, ix] = np.exp(x[:, ix, ix]) diff --git a/meent/on_numpy/emsolver/fourier_analysis.py b/meent/on_numpy/emsolver/fourier_analysis.py index ab89b9e..47e3156 100644 --- a/meent/on_numpy/emsolver/fourier_analysis.py +++ b/meent/on_numpy/emsolver/fourier_analysis.py @@ -28,7 +28,7 @@ def cfs2d(cell, x, y, conti_x, conti_y, fto_x, fto_y, type_complex=np.complex128 ff_x = 2 * fto_x + 1 ff_y = 2 * fto_y + 1 - period_x, period_y = x[-1], y[-1] # TODO: needed? for vector modeling? + period_x, period_y = x[-1], y[-1] # needed for vector modeling? cell = cell.T diff --git a/meent/on_numpy/emsolver/primitives.py b/meent/on_numpy/emsolver/primitives.py new file mode 100644 index 0000000..14976a3 --- /dev/null +++ b/meent/on_numpy/emsolver/primitives.py @@ -0,0 +1,10 @@ +import numpy as np + + +def meeinv(x, use_pinv=False): + if use_pinv: + res = np.linalg.pinv(x) + else: + res = np.linalg.inv(x) + + return res diff --git a/meent/on_numpy/emsolver/rcwa.py b/meent/on_numpy/emsolver/rcwa.py index 8cf6c27..5447780 100644 --- a/meent/on_numpy/emsolver/rcwa.py +++ b/meent/on_numpy/emsolver/rcwa.py @@ -2,7 +2,43 @@ from ._base import _BaseRCWA from .convolution_matrix import to_conv_mat_raster_continuous, to_conv_mat_raster_discrete, to_conv_mat_vector -from .field_distribution import field_plot, field_dist_1d, field_dist_2d +from .field_distribution import field_plot, field_dist_1d, field_dist_1d_conical, field_dist_2d + + +class ResultNumpy: + def __init__(self, res=None, res_te_inc=None, res_tm_inc=None): + self.res = res + self.res_te_inc = res_te_inc + self.res_tm_inc = res_tm_inc + + @property + def de_ri(self): + if self.res is not None: + return self.res.de_ri + else: + return None + + @property + def de_ti(self): + if self.res is not None: + return self.res.de_ti + else: + return None + + +class ResultSubNumpy: + def __init__(self, R_s, R_p, T_s, T_p, de_ri, de_ri_s, de_ri_p, de_ti, de_ti_s, de_ti_p): + self.R_s = R_s + self.R_p = R_p + self.T_s = T_s + self.T_p = T_p + self.de_ri = de_ri + self.de_ri_s = de_ri_s + self.de_ri_p = de_ri_p + + self.de_ti = de_ti + self.de_ti_s = de_ti_s + self.de_ti_p = de_ti_p class RCWANumpy(_BaseRCWA): @@ -10,12 +46,12 @@ def __init__(self, n_top=1., n_bot=1., theta=0., - phi=0., + phi=None, psi=None, - period=(100., 100.), - wavelength=900., + period=(1., 1.), + wavelength=1., ucell=None, - thickness=(0., ), + thickness=(0.,), backend=0, pol=0., fto=(0, 0), @@ -26,13 +62,16 @@ def __init__(self, type_complex=np.complex128, fourier_type=0, # 0 DFS, 1 CFS enhanced_dfs=True, - # **kwargs, + use_pinv=False, ): super().__init__(n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, psi=psi, pol=pol, fto=fto, period=period, wavelength=wavelength, thickness=thickness, connecting_algo=connecting_algo, perturbation=perturbation, - device=device, type_complex=type_complex, ) + device=device, type_complex=type_complex, use_pinv=use_pinv) + + self._modeling_type_assigned = None + self._grating_type_assigned = None self.ucell = ucell self.ucell_materials = ucell_materials @@ -40,8 +79,7 @@ def __init__(self, self.backend = backend self.fourier_type = fourier_type self.enhanced_dfs = enhanced_dfs - self.modeling_type_assigned = None - self.grating_type_assigned = None + self.use_pinv = use_pinv @property def ucell(self): @@ -51,16 +89,18 @@ def ucell(self): def ucell(self, ucell): if isinstance(ucell, np.ndarray): # Raster + self._modeling_type_assigned = 0 if ucell.dtype in (np.int64, np.float64, np.int32, np.float32): dtype = self.type_float elif ucell.dtype in (np.complex128, np.complex64): dtype = self.type_complex else: raise ValueError - self._ucell = np.array(ucell, dtype=dtype) + # self._ucell = np.array(ucell, dtype=dtype) self._ucell = ucell.astype(dtype) elif type(ucell) is list: # Vector + self._modeling_type_assigned = 1 self._ucell = ucell elif ucell is None: self._ucell = ucell @@ -71,22 +111,38 @@ def ucell(self, ucell): def modeling_type_assigned(self): return self._modeling_type_assigned - @modeling_type_assigned.setter - def modeling_type_assigned(self, modeling_type_assigned): - self._modeling_type_assigned = modeling_type_assigned + # @modeling_type_assigned.setter + # def modeling_type_assigned(self, modeling_type_assigned): + # self._modeling_type_assigned = modeling_type_assigned + + def _assign_grating_type(self): + """ + Select the grating type for RCWA simulation. This decides the efficient formulation for given case. + + `_grating_type_assigned` == 0(1D TETM) is for 1D grating, no rotation (phi or azimuth), and either TE or TM. + `_grating_type_assigned` == 1(1D conical) is for 1D grating with generality. + `_grating_type_assigned` == 2(2D) is for 2D grating with generality. + + Note that no rotation means 'phi' is `None`. If phi is given as '0', then it takes 1D conical form + even though when the case itself is 1D TETM. - def _assign_modeling_type(self): + 1D conical is under implementation. - if isinstance(self.ucell, np.ndarray): # Raster - self.modeling_type_assigned = 0 - if (self.ucell.shape[1] == 1) and (self.pol in (0, 1)) and (self.phi % (2 * np.pi) == 0) and (self.fto[1] == 0): - self._grating_type_assigned = 0 # 1D TE and TM only + Returns: + + """ + + if self.modeling_type_assigned == 0: # Raster + if self.ucell.shape[1] == 1: + if (self.pol in (0, 1)) and (self.phi is None) and (self.fto[1] == 0): + self._grating_type_assigned = 0 # 1D TE and TM only + else: + self._grating_type_assigned = 1 # 1D conical else: - self._grating_type_assigned = 1 # else + self._grating_type_assigned = 2 # else - elif isinstance(self.ucell, list): # Vector - self.modeling_type_assigned = 1 - self.grating_type_assigned = 1 + elif self.modeling_type_assigned == 1: # Vector + self.grating_type_assigned = 2 # TODO: 1d conical case @property def grating_type_assigned(self): @@ -96,55 +152,51 @@ def grating_type_assigned(self): def grating_type_assigned(self, grating_type_assigned): self._grating_type_assigned = grating_type_assigned - def _solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + def solve_for_conv(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + self._assign_grating_type() - if self._grating_type_assigned == 0: - de_ri, de_ti, layer_info_list, T1 = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + if self.grating_type_assigned == 0: + result_dict = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + elif self._grating_type_assigned == 1: + result_dict = self.solve_1d_conical(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) else: - de_ri, de_ti, layer_info_list, T1 = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - - return de_ri, de_ti, layer_info_list, T1 - - def solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + result_dict = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - de_ri, de_ti, layer_info_list, T1 = self._solve(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + res_psi = ResultSubNumpy(**result_dict['res']) if 'res' in result_dict else None + res_te_inc = ResultSubNumpy(**result_dict['res_te_inc']) if 'res_te_inc' in result_dict else None + res_tm_inc = ResultSubNumpy(**result_dict['res_tm_inc']) if 'res_tm_inc' in result_dict else None - self.layer_info_list = layer_info_list - self.T1 = T1 + result = ResultNumpy(res_psi, res_te_inc, res_tm_inc) - return de_ri, de_ti + return result def conv_solve(self, **kwargs): # [setattr(self, k, v) for k, v in kwargs.items()] # no need in npmeent - self._assign_modeling_type() - if self._modeling_type_assigned == 0: # Raster + if self.modeling_type_assigned == 0: # Raster if self.fourier_type == 0: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_discrete( self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex, - enhanced_dfs=self.enhanced_dfs) + enhanced_dfs=self.enhanced_dfs, use_pinv=self.use_pinv) elif self.fourier_type == 1: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_continuous( - self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex) + self.ucell, self.fto[0], self.fto[1], type_complex=self.type_complex, use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") - elif self._modeling_type_assigned == 1: # Vector + elif self.modeling_type_assigned == 1: # Vector ucell_vector = self.modeling_vector_instruction(self.ucell) epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_vector( - ucell_vector, self.fto[0], self.fto[1], type_complex=self.type_complex) + ucell_vector, self.fto[0], self.fto[1], type_complex=self.type_complex, use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") - # print(epz_conv_i_all) - de_ri, de_ti, layer_info_list, T1 = self._solve(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - self.layer_info_list = layer_info_list - self.T1 = T1 + result = self.solve_for_conv(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - return de_ri, de_ti + return result def calculate_field(self, res_x=20, res_y=20, res_z=20): # TODO: change res_ to accept array of points. @@ -154,6 +206,10 @@ def calculate_field(self, res_x=20, res_y=20, res_z=20): res_y = 1 field_cell = field_dist_1d(self.wavelength, kx, self.T1, self.layer_info_list, self.period, self.pol, res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) + elif self._grating_type_assigned == 1: + # TODO other bds + field_cell = field_dist_1d_conical(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, + res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) else: field_cell = field_dist_2d(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, res_x=res_x, res_y=res_y, res_z=res_z, type_complex=self.type_complex) @@ -161,9 +217,9 @@ def calculate_field(self, res_x=20, res_y=20, res_z=20): return field_cell def conv_solve_field(self, res_x=20, res_y=20, res_z=20): - de_ri, de_ti = self.conv_solve() + res = self.conv_solve() field_cell = self.calculate_field(res_x, res_y, res_z) - return de_ri, de_ti, field_cell + return res, field_cell def field_plot(self, field_cell): field_plot(field_cell, self.pol) diff --git a/meent/on_numpy/emsolver/scattering_method.py b/meent/on_numpy/emsolver/scattering_method.py index 9726a5b..6f3459b 100644 --- a/meent/on_numpy/emsolver/scattering_method.py +++ b/meent/on_numpy/emsolver/scattering_method.py @@ -73,13 +73,13 @@ def scattering_1d_3(Wt, Wg, Vt, Vg, Sg, ff, Wr, fourier_order, Kzr, Kzt, n_I, n_ return de_ri.flatten(), de_ti.flatten() -def scattering_2d_1(n_I, n_II, theta, phi, k0, period, fourier_order): +def scattering_2d_1(n_I, n_II, theta, phi, k0, period, fourier_order, kx, ky): kx_inc = n_I * np.sin(theta) * np.cos(phi) ky_inc = n_I * np.sin(theta) * np.sin(phi) kz_inc = np.sqrt(n_I ** 2 * 1 - kx_inc ** 2 - ky_inc ** 2) Kx, Ky = K_matrix_cubic_2D(kx_inc, ky_inc, k0, period[0], period[1], fourier_order[0], fourier_order[1]) - + print(Kx.shape, Ky.shape) # specify gap media (this is an LHI so no eigenvalue problem should be solved e_h = 1 Wg, Vg, Kzg = homogeneous_module(Kx, Ky, e_h) @@ -99,7 +99,24 @@ def scattering_2d_1(n_I, n_II, theta, phi, k0, period, fourier_order): _, Sr_dict = S_RT(Ar, Br, ref_mode=True) # scatter matrix for the reflection region Sg = Sr_dict - return Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg + ff_x, ff_y = fourier_order + ff_xy = ff_x * ff_y + + # I = np.eye(ff_xy, dtype=type_complex) + # O = np.zeros((ff_xy, ff_xy), dtype=type_complex) + I = np.eye(ff_xy) + O = np.zeros((ff_xy, ff_xy)) + + # kz_top = (n_I ** 2 - Kx.diagonal() ** 2 - Ky.diagonal().reshape((-1, 1)) ** 2) ** 0.5 + # kz_bot = (n_II ** 2 - Kx.diagonal() ** 2 - Ky.diagonal().reshape((-1, 1)) ** 2) ** 0.5 + + kz_top = (n_I ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + kz_bot = (n_II ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + + kz_top = kz_top.flatten().conjugate() + kz_bot = kz_bot.flatten().conjugate() + + return Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg, kz_top, kz_bot def scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, LAMBDA): @@ -111,7 +128,7 @@ def scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, LAMBDA): return A, B, Sl_dict, Sg_matrix, Sg -def scattering_2d_3(ff, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, n_I, pol, theta, +def scattering_2d_3(Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_top, kz_bot, n_top, n_bot, pol, theta, phi, fourier_order): normal_vector = np.array([0, 0, 1]) # positive z points down; # amplitude of the te vs tm modes (which are decoupled) @@ -126,8 +143,9 @@ def scattering_2d_3(ff, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, n_I, p raise ValueError M, N = fourier_order - NM = ff ** 2 - NM = ff + # NM = ff ** 2 + NM = ((2*M+1)*(2*N+1)) + # get At, Bt # since transmission is the same as gap, order does not matter At, Bt = A_B_matrices_half_space(Vt, Vg) @@ -138,7 +156,7 @@ def scattering_2d_3(ff, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, n_I, p # finally CONVERT THE GLOBAL SCATTERING MATRIX BACK TO A MATRIX - K_inc_vector = n_I * np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]) + K_inc_vector = n_top * np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]) _, e_src, _ = initial_conditions(K_inc_vector, theta, normal_vector, pte, ptm, N, M) @@ -147,32 +165,49 @@ def scattering_2d_3(ff, Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, n_I, p reflected = Wr @ Sg['S11'] @ c_inc transmitted = Wt @ Sg['S21'] @ c_inc - rx = reflected[0:NM, :] # rx is the Ex component. - ry = reflected[NM:, :] - tx = transmitted[0:NM, :] - ty = transmitted[NM:, :] + R_s = np.array(reflected[0:NM, :]).flatten() # rx is the Ex component. + R_p = np.array(reflected[NM:, :]).flatten() + T_s = np.array(transmitted[0:NM, :]).flatten() + T_p = np.array(transmitted[NM:, :]).flatten() + # R_s = reflected[0:NM, 0] # rx is the Ex component. + # R_p = reflected[NM:, 0] + # T_s = transmitted[0:NM, 0] + # T_p = transmitted[NM:, 0] + + # rz = np.linalg.inv(Kzr) @ (Kx @ R_s + Ky @ R_p) + # tz = np.linalg.inv(Kzt) @ (Kx @ T_s + Ky @ T_p) + # + # rsq = np.square(np.abs(R_s)) + np.square(np.abs(R_p)) + np.square(np.abs(rz)) + # tsq = np.square(np.abs(T_s)) + np.square(np.abs(T_p)) + np.square(np.abs(tz)) + # + # de_ri = np.real(Kzr)@rsq/np.real(K_inc_vector[2]) # real because we only want propagating components + # de_ti = np.real(Kzt)@tsq/np.real(K_inc_vector[2]) - rz = np.linalg.inv(Kzr) @ (Kx @ rx + Ky @ ry) - tz = np.linalg.inv(Kzt) @ (Kx @ tx + Ky @ ty) + # return de_ri, de_ti - rsq = np.square(np.abs(rx)) + np.square(np.abs(ry)) + np.square(np.abs(rz)) - tsq = np.square(np.abs(tx)) + np.square(np.abs(ty)) + np.square(np.abs(tz)) + print(R_s.shape, kz_top.shape) + de_ri_s = R_s * np.conj(R_s) * np.real(kz_top / (n_top * np.cos(theta))) + de_ri_p = R_p * np.conj(R_p) * np.real(kz_top / n_top ** 2 / (n_top * np.cos(theta))) - de_ri = np.real(Kzr)@rsq/np.real(K_inc_vector[2]) # real because we only want propagating components - de_ti = np.real(Kzt)@tsq/np.real(K_inc_vector[2]) + de_ti_s = T_s * np.conj(T_s) * np.real(kz_bot / (n_top * np.cos(theta))) + de_ti_p = T_p * np.conj(T_p) * np.real(kz_bot / n_bot ** 2 / (n_top * np.cos(theta))) - return de_ri, de_ti + # return de_ri.real, de_ti.real, big_T1 + return de_ri_s.real, de_ri_p.real, de_ti_s.real, de_ti_p.real, R_s, R_p, T_s, T_p -def scattering_2d_wv(ff, Kx, Ky, E_conv, oneover_E_conv, oneover_E_conv_i, E_i, mu_conv=None): +def scattering_2d_wv(Kx, Ky, epx_conv, epy_conv, epz_conv_i, mu_conv=None): # ------------------------- # W and V from SMM method. - NM = ff ** 2 - NM = ff + # NM = ff ** 2 + # M, N = fourier_order + # print(N,M) + # NM = ((2*M+1)*(2*N+1)) + NM = len(Kx) if mu_conv is None: mu_conv = np.identity(NM) - P, Q, _ = P_Q_kz(Kx, Ky, E_conv, mu_conv, oneover_E_conv, oneover_E_conv_i, E_i) + P, Q, _ = P_Q_kz(Kx, Ky, epx_conv, epy_conv, epz_conv_i, mu_conv) GAMMA = P @ Q Lambda, W = np.linalg.eig(GAMMA) # LAMBDa is effectively refractive index diff --git a/meent/on_numpy/emsolver/smm_util.py b/meent/on_numpy/emsolver/smm_util.py index c754de9..9d06d1e 100644 --- a/meent/on_numpy/emsolver/smm_util.py +++ b/meent/on_numpy/emsolver/smm_util.py @@ -202,7 +202,7 @@ def K_matrix_cubic_2D(beta_x, beta_y, k0, a_x, a_y, N_p, N_q): return Kx, Ky -def P_Q_kz(Kx, Ky, e_conv, mu_conv, oneover_E_conv, oneover_E_conv_i, E_i): +def P_Q_kz(Kx, Ky, epx_conv, epy_conv, epz_conv_i, mu_conv): ''' r is for relative so do not put epsilon_0 or mu_0 here :param Kx: NM x NM matrix @@ -211,20 +211,20 @@ def P_Q_kz(Kx, Ky, e_conv, mu_conv, oneover_E_conv, oneover_E_conv_i, E_i): :param mu_r: :return: ''' - argument = e_conv - Kx ** 2 - Ky ** 2 + argument = np.linalg.inv(epz_conv_i) - Kx ** 2 - Ky ** 2 Kz = np.conj(np.sqrt(argument.astype('complex'))) # Kz = np.sqrt(argument.astype('complex')) # CHECK: conjugate? # CHECK: confirm whether oneonver_E_conv is indeed not used # CHECK: Check sign of P and Q P = np.block([ - [Kx @ E_i @ Ky, -Kx @ E_i @ Kx + mu_conv], - [Ky @ E_i @ Ky - mu_conv, -Ky @ E_i @ Kx] + [Kx @ epz_conv_i @ Ky, -Kx @ epz_conv_i @ Kx + mu_conv], + [Ky @ epz_conv_i @ Ky - mu_conv, -Ky @ epz_conv_i @ Kx] ]) Q = np.block([ - [Kx @ inv(mu_conv) @ Ky, -Kx @ inv(mu_conv) @ Kx + e_conv], - [-oneover_E_conv_i + Ky @ inv(mu_conv) @ Ky, -Ky @ inv(mu_conv) @ Kx] + [Kx @ inv(mu_conv) @ Ky, -Kx @ inv(mu_conv) @ Kx + epy_conv], + [-epx_conv + Ky @ inv(mu_conv) @ Ky, -Ky @ inv(mu_conv) @ Kx] ]) return P, Q, Kz diff --git a/meent/on_numpy/emsolver/transfer_method.py b/meent/on_numpy/emsolver/transfer_method.py index e50c68f..6b616c7 100644 --- a/meent/on_numpy/emsolver/transfer_method.py +++ b/meent/on_numpy/emsolver/transfer_method.py @@ -1,38 +1,34 @@ import numpy as np +from .primitives import meeinv -def transfer_1d_1(pol, ff_x, kx, n_top, n_bot, type_complex=np.complex128): - ff_xy = ff_x * 1 +def transfer_1d_1(pol, kx, n_top, n_bot, type_complex=np.complex128): + ff_x = len(kx) kz_top = (n_top ** 2 - kx ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2) ** 0.5 - kz_top = kz_top.conjugate() - kz_bot = kz_bot.conjugate() + kz_top = kz_top.conj() + kz_bot = kz_bot.conj() - F = np.eye(ff_xy, dtype=type_complex) + F = np.eye(ff_x, dtype=type_complex) if pol == 0: # TE Kz_bot = np.diag(kz_bot) - G = 1j * Kz_bot - elif pol == 1: # TM Kz_bot = np.diag(kz_bot / (n_bot ** 2)) - G = 1j * Kz_bot - else: raise ValueError - T = np.eye(ff_xy, dtype=type_complex) + T = np.eye(ff_x, dtype=type_complex) return kz_top, kz_bot, F, G, T -def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128): - +def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128, use_pinv=False): Kx = np.diag(kx) if pol == 0: @@ -52,7 +48,7 @@ def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=np.compl q = eigenvalues ** 0.5 Q = np.diag(q) - V = np.linalg.inv(epx_conv) @ W @ Q + V = meeinv(epx_conv, use_pinv) @ W @ Q else: raise ValueError @@ -60,21 +56,20 @@ def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, type_complex=np.compl return W, V, q -def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=np.complex128): - +def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=np.complex128, use_pinv=False): ff_x = len(q) I = np.eye(ff_x, dtype=type_complex) X = np.diag(np.exp(-k0 * q * d)) - W_i = np.linalg.inv(W) - V_i = np.linalg.inv(V) + W_i = meeinv(W, use_pinv) + V_i = meeinv(V, use_pinv) A = 0.5 * (W_i @ F + V_i @ G) B = 0.5 * (W_i @ F - V_i @ G) - A_i = np.linalg.inv(A) + A_i = meeinv(A, use_pinv) F = W @ (I + X @ B @ A_i @ X) G = V @ (I - X @ B @ A_i @ X) @@ -83,54 +78,78 @@ def transfer_1d_3(k0, W, V, q, d, F, G, T, type_complex=np.complex128): return X, F, G, T, A_i, B -def transfer_1d_4(pol, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, type_complex=np.complex128): - - ff_xy = len(kz_top) - +def transfer_1d_4(pol, ff_x, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, type_complex=np.complex128, use_pinv=False): Kz_top = np.diag(kz_top) + kz_top = kz_top.reshape((1, ff_x)) + kz_bot = kz_bot.reshape((1, ff_x)) - delta_i0 = np.zeros(ff_xy, dtype=type_complex) - delta_i0[ff_xy // 2] = 1 + delta_i0 = np.zeros(ff_x, dtype=type_complex) + delta_i0[ff_x // 2] = 1 if pol == 0: # TE inc_term = 1j * n_top * np.cos(theta) * delta_i0 - T1 = np.linalg.inv(G + 1j * Kz_top @ F) @ (1j * Kz_top @ delta_i0 + inc_term) + T1 = meeinv(G + 1j * Kz_top @ F, use_pinv) @ (1j * Kz_top @ delta_i0 + inc_term) elif pol == 1: # TM inc_term = 1j * delta_i0 * np.cos(theta) / n_top - T1 = np.linalg.inv(G + 1j * Kz_top / (n_top ** 2) @ F) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + T1 = meeinv(G + 1j * Kz_top / (n_top ** 2) @ F, use_pinv) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + else: + raise ValueError - # T1 = np.linalg.inv(G + 1j * YZ_I @ F) @ (1j * YZ_I @ delta_i0 + inc_term) - R = F @ T1 - delta_i0 - T = T @ T1 + R = (F @ T1 - delta_i0).reshape((1, ff_x)) + T = (T @ T1).reshape((1, ff_x)) - de_ri = np.real(R * np.conj(R) * kz_top / (n_top * np.cos(theta))) + de_ri = (R * R.conj() * (kz_top / (n_top * np.cos(theta))).real).real if pol == 0: - de_ti = T * np.conj(T) * np.real(kz_bot / (n_top * np.cos(theta))) + de_ti = (T * T.conj() * (kz_bot / (n_top * np.cos(theta))).real).real + R_s = R + R_p = np.zeros(R.shape) + T_s = T + T_p = np.zeros(T.shape) + de_ri_s = de_ri + de_ri_p = np.zeros(de_ri.shape) + de_ti_s = de_ti + de_ti_p = np.zeros(de_ri.shape) + elif pol == 1: - de_ti = T * np.conj(T) * np.real(kz_bot / n_bot ** 2) / (np.cos(theta) / n_top) + de_ti = (T * T.conj() * (kz_bot / n_bot ** 2 / (np.cos(theta) / n_top)).real).real + R_s = np.zeros(R.shape) + R_p = R + T_s = np.zeros(T.shape) + T_p = T + de_ri_s = np.zeros(de_ri.shape) + de_ri_p = de_ri + de_ti_s = np.zeros(de_ri.shape) + de_ti_p = de_ti else: raise ValueError - return de_ri.real, de_ti.real, T1 + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri': de_ri, 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, + 'de_ti': de_ti, 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, + } + result = {'res': res} -def transfer_1d_conical_1(ff_x, ff_y, kx_vector, ky_vector, n_top, n_bot, type_complex=np.complex128): + return result, T1 + +def transfer_1d_conical_1(kx, ky, n_top, n_bot, type_complex=np.complex128): + ff_x = len(kx) + ff_y = len(ky) ff_xy = ff_x * ff_y I = np.eye(ff_xy, dtype=type_complex) O = np.zeros((ff_xy, ff_xy), dtype=type_complex) - kz_top = (n_top ** 2 - kx_vector ** 2 - ky_vector ** 2) ** 0.5 - kz_bot = (n_bot ** 2 - kx_vector ** 2 - ky_vector ** 2) ** 0.5 - - kz_top = kz_top.conjugate() - kz_bot = kz_bot.conjugate() + kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 - varphi = np.arctan(ky_vector / kx_vector) + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() + varphi = np.arctan(ky.reshape((-1, 1)) / kx).flatten() Kz_bot = np.diag(kz_bot) big_F = np.block([[I, O], [O, 1j * Kz_bot / (n_bot ** 2)]]) @@ -140,26 +159,25 @@ def transfer_1d_conical_1(ff_x, ff_y, kx_vector, ky_vector, n_top, n_bot, type_c return kz_top, kz_bot, varphi, big_F, big_G, big_T -def transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128): - +def transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128, use_pinv=False): ff_x = len(kx) ff_y = len(ky) + ff_xy = ff_x * ff_y - I = np.eye(ff_y * ff_x, dtype=type_complex) + I = np.eye(ff_xy, dtype=type_complex) Kx = np.diag(np.tile(kx, ff_y).flatten()) Ky = np.diag(np.tile(ky.reshape((-1, 1)), ff_x).flatten()) A = Kx ** 2 - epy_conv - # A = Kx ** 2 - np.linalg.inv(epz_conv_i) B = Kx @ epz_conv_i @ Kx - I Omega2_RL = Ky ** 2 + A Omega2_LR = Ky ** 2 + B @ epx_conv - # Omega2_LR = Ky ** 2 + B @ np.linalg.inv(epz_conv_i) eigenvalues_1, W_1 = np.linalg.eig(Omega2_RL) eigenvalues_2, W_2 = np.linalg.eig(Omega2_LR) + eigenvalues_1 += 0j # to get positive square root eigenvalues_2 += 0j # to get positive square root @@ -169,40 +187,37 @@ def transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=n Q_1 = np.diag(q_1) Q_2 = np.diag(q_2) - A_i = np.linalg.inv(A) - B_i = np.linalg.inv(B) + A_i = meeinv(A, use_pinv) + B_i = meeinv(B, use_pinv) V_11 = A_i @ W_1 @ Q_1 - V_12 = Ky * A_i @ Kx @ W_2 - V_21 = Ky * B_i @ Kx @ epz_conv_i @ W_1 - # V_21 = Ky * B_i @ Kx @ np.linalg.inv(epy_conv) @ W_1 + V_12 = Ky @ A_i @ Kx @ W_2 + V_21 = Ky @ B_i @ Kx @ epz_conv_i @ W_1 V_22 = B_i @ W_2 @ Q_2 W = np.block([W_1, W_2]) V = np.block([[V_11, V_12], [V_21, V_22]]) - q = np.block([q_1, q_2]) + q = np.hstack([q_1, q_2]) return W, V, q -def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.complex128): - - ff_x = len(W) - - I = np.eye(ff_x, dtype=type_complex) - O = np.zeros((ff_x, ff_x), dtype=type_complex) +def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.complex128, use_pinv=False): + ff_xy = len(q) // 2 + I = np.eye(ff_xy, dtype=type_complex) + O = np.zeros((ff_xy, ff_xy), dtype=type_complex) - W_1 = W[:, :ff_x] - W_2 = W[:, ff_x:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] - V_11 = V[:ff_x, :ff_x] - V_12 = V[:ff_x, ff_x:] - V_21 = V[ff_x:, :ff_x] - V_22 = V[ff_x:, ff_x:] + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] - q_1 = q[:ff_x] - q_2 = q[ff_x:] + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] X_1 = np.diag(np.exp(-k0 * q_1 * d)) X_2 = np.diag(np.exp(-k0 * q_2 * d)) @@ -224,13 +239,13 @@ def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_comp big_W = np.block([[V_ss, V_sp], [W_ps, W_pp]]) big_V = np.block([[W_ss, W_sp], [V_ps, V_pp]]) - big_W_i = np.linalg.inv(big_W) - big_V_i = np.linalg.inv(big_V) + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) - big_A_i = np.linalg.inv(big_A) + big_A_i = meeinv(big_A, use_pinv) big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) @@ -240,11 +255,14 @@ def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_comp return big_X, big_F, big_G, big_T, big_A_i, big_B -def transfer_1d_conical_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, type_complex=np.complex128): +def transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, + type_complex=np.complex128, use_pinv=False): - ff_xy = len(big_F) // 2 + ff_xy = ff_x * ff_y Kz_top = np.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) I = np.eye(ff_xy, dtype=type_complex) O = np.zeros((ff_xy, ff_xy), dtype=type_complex) @@ -259,9 +277,6 @@ def transfer_1d_conical_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top big_G_21 = big_G[ff_xy:, :ff_xy] big_G_22 = big_G[ff_xy:, ff_xy:] - # delta_i0 = np.zeros(ff_xy, dtype=type_complex) - # delta_i0[ff_xy // 2] = 1 - delta_i0 = np.zeros((ff_xy, 1), dtype=type_complex) delta_i0[ff_xy // 2, 0] = 1 @@ -274,36 +289,99 @@ def transfer_1d_conical_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top [O, I, -big_G_21, -big_G_22], ] ) + final_B = np.block( + [ + [-np.sin(psi) * delta_i0], + [np.cos(psi) * np.cos(theta) * delta_i0], + [-1j * np.sin(psi) * n_top * np.cos(theta) * delta_i0], + [-1j * n_top * np.cos(psi) * delta_i0] + ] + ) - final_B = np.block([ - [-np.sin(psi) * delta_i0], - [-np.cos(psi) * np.cos(theta) * delta_i0], - [-1j * np.sin(psi) * n_top * np.cos(theta) * delta_i0], - [1j * n_top * np.cos(psi) * delta_i0] - ]) - - final_RT = np.linalg.inv(final_A) @ final_B + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B - R_s = final_RT[:ff_xy, :].flatten() - R_p = final_RT[ff_xy:2 * ff_xy, :].flatten() + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.copy() big_T = big_T @ big_T1 - T_s = big_T[:ff_xy, :].flatten() - T_p = big_T[ff_xy:, :].flatten() + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) - de_ri = R_s * np.conj(R_s) * np.real(kz_top / (n_top * np.cos(theta))) \ - + R_p * np.conj(R_p) * np.real(kz_top / n_top ** 2 / (n_top * np.cos(theta))) + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * np.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * np.cos(theta))).real).real - de_ti = T_s * np.conj(T_s) * np.real(kz_bot / (n_top * np.cos(theta))) \ - + T_p * np.conj(T_p) * np.real(kz_bot / n_bot ** 2 / (n_top * np.cos(theta))) + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * np.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * np.cos(theta))).real).real - return de_ri.real, de_ti.real, big_T1 + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} -def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=np.complex128): + # TE TM incidence + psi_tm = np.array(0, dtype=type_complex) + final_B_tm = np.block( + [ + [-np.sin(psi_tm) * delta_i0], + [np.cos(psi_tm) * np.cos(theta) * delta_i0], + [-1j * np.sin(psi_tm) * n_top * np.cos(theta) * delta_i0], + [-1j * n_top * np.cos(psi_tm) * delta_i0] + ] + ) + psi_te = np.array(np.pi / 2, dtype=type_complex) + final_B_te = np.block( + [ + [-np.sin(psi_te) * delta_i0], + [np.cos(psi_te) * np.cos(theta) * delta_i0], + [-1j * np.sin(psi_te) * n_top * np.cos(theta) * delta_i0], + [-1j * n_top * np.cos(psi_te) * delta_i0] + ] + ) + + final_B_tetm = np.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * np.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * np.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * np.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * np.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} + + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} + + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} + + return result, big_T1 + + +def transfer_2d_1(kx, ky, n_top, n_bot, type_complex=np.complex128): + ff_x = len(kx) + ff_y = len(ky) ff_xy = ff_x * ff_y I = np.eye(ff_xy, dtype=type_complex) @@ -312,11 +390,10 @@ def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=np.complex128): kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 - kz_top = kz_top.flatten().conjugate() - kz_bot = kz_bot.flatten().conjugate() + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() varphi = np.arctan(ky.reshape((-1, 1)) / kx).flatten() - Kz_bot = np.diag(kz_bot) big_F = np.block([[I, O], [O, 1j * Kz_bot / (n_bot ** 2)]]) @@ -326,8 +403,7 @@ def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, type_complex=np.complex128): return kz_top, kz_bot, varphi, big_F, big_G, big_T -def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128): - +def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.complex128, use_pinv=False): ff_x = len(kx) ff_y = len(ky) ff_xy = ff_x * ff_y @@ -347,11 +423,12 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.comple ]) eigenvalues, W = np.linalg.eig(Omega2_LR) + eigenvalues += 0j # to get positive square root q = eigenvalues ** 0.5 Q = np.diag(q) - Q_i = np.linalg.inv(Q) + Q_i = meeinv(Q, use_pinv) Omega_R = np.block( [ @@ -365,15 +442,14 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, type_complex=np.comple return W, V, q -def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.complex128): - - ff_xy = len(q)//2 +def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.complex128, use_pinv=False): + ff_xy = len(q) // 2 I = np.eye(ff_xy, dtype=type_complex) O = np.zeros((ff_xy, ff_xy), dtype=type_complex) - q1 = q[:ff_xy] - q2 = q[ff_xy:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] W_11 = W[:ff_xy, :ff_xy] W_12 = W[:ff_xy, ff_xy:] @@ -385,8 +461,8 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.c V_21 = V[ff_xy:, :ff_xy] V_22 = V[ff_xy:, ff_xy:] - X_1 = np.diag(np.exp(-k0 * q1 * d)) - X_2 = np.diag(np.exp(-k0 * q2 * d)) + X_1 = np.diag(np.exp(-k0 * q_1 * d)) + X_2 = np.diag(np.exp(-k0 * q_2 * d)) F_c = np.diag(np.cos(varphi)) F_s = np.diag(np.sin(varphi)) @@ -406,13 +482,13 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.c big_W = np.block([[W_ss, W_sp], [W_ps, W_pp]]) big_V = np.block([[V_ss, V_sp], [V_ps, V_pp]]) - big_W_i = np.linalg.inv(big_W) - big_V_i = np.linalg.inv(big_V) + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) - big_A_i = np.linalg.inv(big_A) + big_A_i = meeinv(big_A, use_pinv) big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) @@ -422,11 +498,14 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, type_complex=np.c return big_X, big_F, big_G, big_T, big_A_i, big_B -def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, type_complex=np.complex128): +def transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, + type_complex=np.complex128, use_pinv=False): - ff_xy = len(big_F) // 2 + ff_xy = ff_x * ff_y Kz_top = np.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) I = np.eye(ff_xy, dtype=type_complex) O = np.zeros((ff_xy, ff_xy), dtype=type_complex) @@ -463,21 +542,82 @@ def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, ] ) - final_RT = np.linalg.inv(final_A) @ final_B + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B - R_s = final_RT[:ff_xy, :].flatten() - R_p = final_RT[ff_xy: 2 * ff_xy, :].flatten() + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.copy() big_T = big_T @ big_T1 - T_s = big_T[:ff_xy, :].flatten() - T_p = big_T[ff_xy:, :].flatten() + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) + + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * np.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * np.cos(theta))).real).real + + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * np.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * np.cos(theta))).real).real + + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} + + # TE TM incidence + psi_tm = np.array(0, dtype=type_complex) + final_B_tm = np.block( + [ + [-np.sin(psi_tm) * delta_i0], + [np.cos(psi_tm) * np.cos(theta) * delta_i0], + [-1j * np.sin(psi_tm) * n_top * np.cos(theta) * delta_i0], + [-1j * n_top * np.cos(psi_tm) * delta_i0] + ] + ) + + psi_te = np.array(np.pi/2, dtype=type_complex) + final_B_te = np.block( + [ + [-np.sin(psi_te) * delta_i0], + [np.cos(psi_te) * np.cos(theta) * delta_i0], + [-1j * np.sin(psi_te) * n_top * np.cos(theta) * delta_i0], + [-1j * n_top * np.cos(psi_te) * delta_i0] + ] + ) + + final_B_tetm = np.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * np.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * np.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * np.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * np.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} - de_ri = R_s * np.conj(R_s) * np.real(kz_top / (n_top * np.cos(theta))) \ - + R_p * np.conj(R_p) * np.real(kz_top / n_top ** 2 / (n_top * np.cos(theta))) + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} - de_ti = T_s * np.conj(T_s) * np.real(kz_bot / (n_top * np.cos(theta))) \ - + T_p * np.conj(T_p) * np.real(kz_bot / n_bot ** 2 / (n_top * np.cos(theta))) + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} - return de_ri.real, de_ti.real, big_T1 + return result, big_T1 diff --git a/meent/on_numpy/mee.py b/meent/on_numpy/mee.py index 1a8f148..410e751 100644 --- a/meent/on_numpy/mee.py +++ b/meent/on_numpy/mee.py @@ -10,17 +10,17 @@ def __init__(self, *args, **kwargs): ModelingNumpy.__init__(self, *args, **kwargs) RCWANumpy.__init__(self, *args, **kwargs) - def spectrum(self, wavelength_list): - if self.grating_type in (0, 1): - de_ri_list = np.zeros((len(wavelength_list), self.fourier_order)) - de_ti_list = np.zeros((len(wavelength_list), self.fourier_order)) - else: - de_ri_list = np.zeros((len(wavelength_list), self.ff, self.ff)) - de_ti_list = np.zeros((len(wavelength_list), self.ff, self.ff)) - - for i, wavelength in enumerate(wavelength_list): - de_ri, de_ti = self.conv_solve(wavelength=wavelength) - de_ri_list[i] = de_ri - de_ti_list[i] = de_ti - - return de_ri_list, de_ti_list + # def spectrum(self, wavelength_list): + # if self.grating_type in (0, 1): + # de_ri_list = np.zeros((len(wavelength_list), self.fourier_order)) + # de_ti_list = np.zeros((len(wavelength_list), self.fourier_order)) + # else: + # de_ri_list = np.zeros((len(wavelength_list), self.ff, self.ff)) + # de_ti_list = np.zeros((len(wavelength_list), self.ff, self.ff)) + # + # for i, wavelength in enumerate(wavelength_list): + # de_ri, de_ti = self.conv_solve(wavelength=wavelength) + # de_ri_list[i] = de_ri + # de_ti_list[i] = de_ti + # + # return de_ri_list, de_ti_list diff --git a/meent/on_numpy/modeler/modeling.py b/meent/on_numpy/modeler/modeling.py index 97c536d..fb6f4f9 100644 --- a/meent/on_numpy/modeler/modeling.py +++ b/meent/on_numpy/modeler/modeling.py @@ -160,34 +160,6 @@ def rectangle(self, cx, cy, lx, ly, n_index, angle=0, n_split_triangle=2, n_spli length = length_top12 / np.sin(angle_inside) top3_cp = [top3[0] - length, top3[1]] - # for i in range(n_split_triangle + 1): - # x = top1[0] - (top1[0] - top2[0]) / n_split_triangle * i - # y = top1[1] - (top1[1] - top2[1]) / n_split_parallelogram * i - # xxx.append(x) - # yyy.append(y) - # - # xxx_cp.append(x + length / n_split_triangle * i) - # yyy_cp.append(y) - # - # for i in range(n_split_parallelogram + 1): - # - # x = top2[0] + (top3_cp[0] - top2[0]) / n_split_triangle * i - # y = top2[1] - (top2[1] - top3_cp[1]) / n_split_parallelogram * i - # xxx.append(x) - # yyy.append(y) - # - # xxx_cp.append(x + length) - # yyy_cp.append(y) - # - # for i in range(n_split_triangle + 1): - # x = top3_cp[0] + (top4[0] - top3_cp[0]) / n_split_triangle * i - # y = top3_cp[1] - (top3_cp[1] - top4[1]) / n_split_parallelogram * i - # xxx.append(x) - # yyy.append(y) - # - # xxx_cp.append(x + length / n_split_triangle * (n_split_triangle - i)) - # yyy_cp.append(y) - # 1: Upper triangle xxx1 = top1[0] - (top1[0] - top2[0]) / n_split_triangle * np.arange(n_split_triangle+1).reshape((-1, 1)) yyy1 = top1[1] - (top1[1] - top2[1]) / n_split_parallelogram * np.arange(n_split_triangle+1).reshape((-1, 1)) @@ -501,8 +473,11 @@ def ellipse(self, cx, cy, lx, ly, n_index, angle=0, n_split_w=2, n_split_h=2, an ax, ay = arr[:, i] bx, by = arr_roll[:, i] + # LL = [min(ay.real, by.real), min(ax.real, bx.real)] + # UR = [max(ay.real, by.real), max(ax.real, bx.real)] LL = [min(ay.real, by.real)+0j, min(ax.real, bx.real)+0j] UR = [max(ay.real, by.real)+0j, max(ax.real, bx.real)+0j] + # What is 0j for? res.append([LL, UR, n_index]) diff --git a/meent/on_torch/emsolver/_base.py b/meent/on_torch/emsolver/_base.py index ca72c35..1443db2 100644 --- a/meent/on_torch/emsolver/_base.py +++ b/meent/on_torch/emsolver/_base.py @@ -5,15 +5,16 @@ from .scattering_method import scattering_1d_1, scattering_1d_2, scattering_1d_3, scattering_2d_1, scattering_2d_wv, \ scattering_2d_2, scattering_2d_3 -from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, +from .transfer_method import (transfer_1d_1, transfer_1d_2, transfer_1d_3, transfer_1d_4, transfer_1d_conical_1, + transfer_1d_conical_2, transfer_1d_conical_3, transfer_1d_conical_4, transfer_2d_1, transfer_2d_2, transfer_2d_3, transfer_2d_4) class _BaseRCWA: - def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=(0, 0), - period=(100., 100.), wavelength=1., + def __init__(self, n_top=1., n_bot=1., theta=0., phi=None, psi=None, pol=0., fto=(0, 0), + period=(1., 1.), wavelength=1., thickness=(0.,), connecting_algo='TMM', perturbation=1E-20, - device='cpu', type_complex=torch.complex128): + device='cpu', type_complex=torch.complex128, use_pinv=False): # device if device in (0, 'cpu'): @@ -43,7 +44,6 @@ def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=( self.theta = theta self.phi = phi self.pol = pol - # self._psi = torch.tensor((torch.pi / 2 * (1 - pol)), device=self.device, dtype=self.type_float) self.psi = psi self.fto = fto @@ -51,12 +51,11 @@ def __init__(self, n_top=1., n_bot=1., theta=0., phi=0., psi=None, pol=0., fto=( self.wavelength = wavelength self.thickness = thickness self.connecting_algo = connecting_algo + self.use_pinv = use_pinv + self.layer_info_list = [] self.T1 = None - self.rayleigh_R = None # TODO: other bds - self.rayleigh_T = None - @property def device(self): return self._device @@ -85,6 +84,15 @@ def type_complex(self, type_complex): else: raise ValueError('type_complex') + self._type_float = torch.float64 if self.type_complex is not torch.complex64 else torch.float32 + self._type_int = torch.int64 if self.type_complex is not torch.complex64 else torch.int32 + self.theta = self.theta + self.phi = self.phi + self._psi = self.psi + + self.fto = self.fto + self.thickness = self.thickness + @property def type_float(self): return self._type_float @@ -93,33 +101,17 @@ def type_float(self): def type_int(self): return self._type_int - @property - def pol(self): - return self._pol - - @pol.setter - def pol(self, pol): - room = 1E-6 - if 1 < pol < 1 + room: - pol = 1 - elif 0 - room < pol < 0: - pol = 0 - - if not 0 <= pol <= 1: - raise ValueError - - self._pol = pol - psi = torch.pi / 2 * (1 - self.pol) - self._psi = torch.tensor(psi, device=self.device, dtype=self.type_float) - @property def theta(self): return self._theta @theta.setter def theta(self, theta): - self._theta = torch.tensor(theta, device=self.device, dtype=self.type_float) - self._theta = torch.where(self._theta == 0, self.perturbation, self._theta) # perturbation + if theta is None: + self._theta = None + else: + self._theta = torch.tensor(theta, device=self.device, dtype=self.type_complex) + self._theta = torch.where(self._theta == 0, self.perturbation, self._theta) # perturbation @property def phi(self): @@ -127,7 +119,10 @@ def phi(self): @phi.setter def phi(self, phi): - self._phi = torch.tensor(phi, device=self.device, dtype=self.type_float) + if phi is None: + self._phi = None + else: + self._phi = torch.tensor(phi, device=self.device, dtype=self.type_complex) @property def psi(self): @@ -136,10 +131,29 @@ def psi(self): @psi.setter def psi(self, psi): if psi is not None: - self._psi = torch.tensor(psi, dtype=self.type_float) + self._psi = torch.tensor(psi, dtype=self.type_complex) pol = -(2 * psi / torch.pi - 1) self._pol = pol + @property + def pol(self): + """ + portion of TM. 0: full TE, 1: full TM + + Returns: polarization ratio + + """ + return self._pol + + @pol.setter + def pol(self, pol): + if not 0 <= pol <= 1: + raise ValueError + + self._pol = pol + psi = torch.tensor(torch.pi / 2 * (1 - self.pol), device=self.device, dtype=self.type_complex) + self._psi = psi + @property def fto(self): return self._fto @@ -172,13 +186,6 @@ def fto(self, fto): else: raise ValueError('Torch fto') - # if type(fto) in (int, float): - # self._fto = torch.tensor([int(fto), 0], device=self.device) - # elif len(fto) == 1: - # self._fto = torch.tensor([int(fto[0]), 0], device=self.device) - # else: - # self._fto = torch.tensor([int(v) for v in fto], device=self.device) - @property def period(self): return self._period @@ -216,19 +223,29 @@ def get_kx_ky_vector(self, wavelength): fto_y_range = torch.arange(-self.fto[1], self.fto[1] + 1, device=self.device, dtype=self.type_float) - kx = (self.n_top * torch.sin(self.theta) * torch.cos(self.phi) + fto_x_range * ( - wavelength / self.period[0])).type(self.type_complex) + if self.theta.real >= torch.tensor(torch.pi/2, dtype=torch.float32): + # https://github.com/numpy/numpy/issues/27306 + sin_theta = torch.sin( + torch.nextafter(torch.float32(torch.pi / 2), torch.float32(0)) + self.theta.imag * np.complex64(1j)) + else: + sin_theta = np.sin(self.theta) + + if self.phi is None: + phi = torch.tensor(0, device=self.device, dtype=self.type_complex) + else: + phi = self.phi + + kx = (self.n_top * sin_theta * torch.cos(phi) + fto_x_range * ( + wavelength / self.period[0])).type(self.type_complex).conj() - ky = (self.n_top * torch.sin(self.theta) * torch.sin(self.phi) + fto_y_range * ( - wavelength / self.period[1])).type(self.type_complex) + ky = (self.n_top * sin_theta * torch.sin(phi) + fto_y_range * ( + wavelength / self.period[1])).type(self.type_complex).conj() return kx, ky def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): - self.layer_info_list = [] self.T1 = None - self.rayleigh_R, self.rayleigh_T = [], [] ff_x = self.fto[0] * 2 + 1 @@ -237,12 +254,14 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): if self.connecting_algo == 'TMM': kz_top, kz_bot, F, G, T \ - = transfer_1d_1(self.pol, ff_x, kx, self.n_top, self.n_bot, device=self.device, type_complex=self.type_complex) + = transfer_1d_1(self.pol, kx, self.n_top, self.n_bot, device=self.device, + type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, fourier_indices, self.period, - self.pol, wl=wavelength) + raise ValueError + # Kx, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ + # = scattering_1d_1(k0, self.n_top, self.n_bot, self.theta, self.phi, fourier_indices, self.period, + # self.pol, wl=wavelength) else: raise ValueError @@ -256,38 +275,101 @@ def solve_1d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, device=self.device, type_complex=self.type_complex) + W, V, q = transfer_1d_2(self.pol, kx, epx_conv, epy_conv, epz_conv_i, device=self.device, + type_complex=self.type_complex, perturbation=self.perturbation, + use_pinv=self.use_pinv) - X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, device=self.device, type_complex=self.type_complex) + X, F, G, T, A_i, B = transfer_1d_3(k0, W, V, q, d, F, G, T, device=self.device, + type_complex=self.type_complex, use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, A_i, B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) + raise ValueError + # A, B, S_dict, Sg = scattering_1d_2(W, Wg, V, Vg, d, k0, Q, Sg) else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, T1, [R], [T] = transfer_1d_4(self.pol, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, - device=self.device, type_complex=self.type_complex) + result, T1 = transfer_1d_4(self.pol, ff_x, F, G, T, kz_top, kz_bot, self.theta, self.n_top, self.n_bot, + device=self.device, type_complex=self.type_complex, use_pinv=self.use_pinv) self.T1 = T1 - self.rayleigh_R = [R] - self.rayleigh_T = [T] elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, self.ff, Wr, self.fto, Kzr, Kzt, - self.n_top, self.n_bot, self.theta, self.pol) + raise ValueError + # de_ri, de_ti = scattering_1d_3(Wt, Wg, Vt, Vg, Sg, self.ff, Wr, self.fto, Kzr, Kzt, + # self.n_top, self.n_bot, self.theta, self.pol) else: raise ValueError - return de_ri, de_ti, self.rayleigh_R, self.rayleigh_T, self.layer_info_list, self.T1 + # return de_ri, de_ti, self.rayleigh_R, self.rayleigh_T, self.layer_info_list, self.T1 + return result + + def solve_1d_conical(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + self.layer_info_list = [] + self.T1 = None + + ff_x = self.fto[0] * 2 + 1 + ff_y = 1 + + k0 = 2 * torch.pi / wavelength + kx, ky = self.get_kx_ky_vector(wavelength) + + if self.connecting_algo == 'TMM': + kz_top, kz_bot, varphi, big_F, big_G, big_T \ + = transfer_1d_conical_1(kx, ky, self.n_top, self.n_bot, device=self.device, + type_complex=self.type_complex) + + elif self.connecting_algo == 'SMM': + print('SMM for 1D conical is not implemented') + return np.nan, np.nan + else: + raise ValueError + + for layer_index in range(len(self.thickness))[::-1]: + + epx_conv = epx_conv_all[layer_index] + epy_conv = epy_conv_all[layer_index] + epz_conv_i = epz_conv_i_all[layer_index] + + d = self.thickness[layer_index] + + if self.connecting_algo == 'TMM': + W, V, q = transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device=self.device, + type_complex=self.type_complex, perturbation=self.perturbation, + use_pinv=self.use_pinv) + + big_X, big_F, big_G, big_T, big_A_i, big_B, \ + = transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=self.device, + type_complex=self.type_complex, use_pinv=self.use_pinv) + + layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] + self.layer_info_list.append(layer_info) + + elif self.connecting_algo == 'SMM': + raise ValueError + else: + raise ValueError + + if self.connecting_algo == 'TMM': + result, big_T1 = transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, + self.theta, self.n_top, self.n_bot, device=self.device, + type_complex=self.type_complex, + use_pinv=self.use_pinv) + self.T1 = big_T1 + + elif self.connecting_algo == 'SMM': + raise ValueError + else: + raise ValueError + + return result def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): self.layer_info_list = [] self.T1 = None - self.rayleigh_R, self.rayleigh_T = [], [] ff_x = self.fto[0] * 2 + 1 ff_y = self.fto[1] * 2 + 1 @@ -296,16 +378,13 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): kx, ky = self.get_kx_ky_vector(wavelength) if self.connecting_algo == 'TMM': - # kx, ky, Kx, Ky, k_I_z, k_II_z, varphi, Y_I, Y_II, Z_I, Z_II, big_F, big_G, big_T \ - # = transfer_2d_1(ff_x, ff_y, ff_xy, k0, self.n_top, self.n_bot, self.kx, self.period, fourier_indices_y, - # self.theta, self.phi, wavelength, device=self.device, type_complex=self.type_complex) kz_top, kz_bot, varphi, big_F, big_G, big_T \ - = transfer_2d_1(ff_x, ff_y, kx, ky, self.n_top, self.n_bot, device=self.device, - type_complex=self.type_complex) + = transfer_2d_1(kx, ky, self.n_top, self.n_bot, device=self.device, type_complex=self.type_complex) elif self.connecting_algo == 'SMM': - Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ - = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto) + raise ValueError + # Kx, Ky, kz_inc, Wg, Vg, Kzg, Wr, Vr, Kzr, Wt, Vt, Kzt, Ar, Br, Sg \ + # = scattering_2d_1(self.n_top, self.n_bot, self.theta, self.phi, k0, self.period, self.fto) else: raise ValueError @@ -319,38 +398,51 @@ def solve_2d(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): d = self.thickness[layer_index] if self.connecting_algo == 'TMM': - W, V, q = transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device=self.device, - type_complex=self.type_complex) + type_complex=self.type_complex, perturbation=self.perturbation, + use_pinv=self.use_pinv) big_X, big_F, big_G, big_T, big_A_i, big_B, \ = transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=self.device, - type_complex=self.type_complex) + type_complex=self.type_complex, use_pinv=self.use_pinv) layer_info = [epz_conv_i, W, V, q, d, big_A_i, big_B] self.layer_info_list.append(layer_info) elif self.connecting_algo == 'SMM': - W, V, LAMBDA = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) - A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, LAMBDA) + raise ValueError + # W, V, LAMBDA = scattering_2d_wv(ff_xy, Kx, Ky, E_conv, o_E_conv, o_E_conv_i, E_conv_i) + # A, B, Sl_dict, Sg_matrix, Sg = scattering_2d_2(W, Wg, V, Vg, d, k0, Sg, LAMBDA) else: raise ValueError if self.connecting_algo == 'TMM': - de_ri, de_ti, big_T1, [R_s, R_p], [T_s, T_p], = transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, - self.n_top, self.n_bot, device=self.device, - type_complex=self.type_complex) + # de_ri, de_ti, big_T1, [R_s, R_p], [T_s, T_p], = transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, + # self.n_top, self.n_bot, device=self.device, + # type_complex=self.type_complex) + # self.T1 = big_T1 + # self.rayleigh_R = [R_s, R_p] + # self.rayleigh_T = [T_s, T_p] + + # de_ri_s, de_ri_p, de_ti_s, de_ti_p, big_T1, R_s, R_p, T_s, T_p = transfer_2d_4(big_F, big_G, big_T, kz_top, + # kz_bot, self.psi, self.theta, + # self.n_top, self.n_bot, + # type_complex=self.type_complex) + result, big_T1 = transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, self.psi, self.theta, + self.n_top, self.n_bot, device=self.device, type_complex=self.type_complex, + use_pinv=self.use_pinv) self.T1 = big_T1 - self.rayleigh_R = [R_s, R_p] - self.rayleigh_T = [T_s, T_p] elif self.connecting_algo == 'SMM': - de_ri, de_ti = scattering_2d_3(Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, - self.pol, self.theta, self.phi, self.fto) + raise ValueError + # de_ri, de_ti = scattering_2d_3(Wt, Wg, Vt, Vg, Sg, Wr, Kx, Ky, Kzr, Kzt, kz_inc, self.n_top, + # self.pol, self.theta, self.phi, self.fto) else: raise ValueError - de_ri = de_ri.reshape((ff_y, ff_x)).T - de_ti = de_ti.reshape((ff_y, ff_x)).T - - return de_ri, de_ti, self.rayleigh_R, self.rayleigh_T, self.layer_info_list, self.T1 + # de_ri = de_ri.reshape((ff_y, ff_x)).T + # de_ti = de_ti.reshape((ff_y, ff_x)).T + # + # return de_ri, de_ti, self.rayleigh_R, self.rayleigh_T, self.layer_info_list, self.T1 + # return de_ri_s, de_ri_p, de_ti_s, de_ti_p, self.layer_info_list, self.T1, R_s, R_p, T_s, T_p + return result diff --git a/meent/on_torch/emsolver/convolution_matrix.py b/meent/on_torch/emsolver/convolution_matrix.py index c5a74e8..a3ffad4 100644 --- a/meent/on_torch/emsolver/convolution_matrix.py +++ b/meent/on_torch/emsolver/convolution_matrix.py @@ -1,5 +1,6 @@ import torch from .fourier_analysis import dfs2d, cfs2d +from .primitives import meeinv def cell_compression(cell, device=torch.device('cpu'), type_complex=torch.complex128): @@ -43,6 +44,7 @@ def cell_compression(cell, device=torch.device('cpu'), type_complex=torch.comple return cell_comp, x, y +# TODO: delete def fft_piecewise_constant(cell, x, y, fourier_order_x, fourier_order_y, device=torch.device('cpu'), type_complex=torch.complex128): @@ -88,8 +90,8 @@ def fft_piecewise_constant(cell, x, y, fourier_order_x, fourier_order_y, device= return f_coeffs_xy.T -def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=torch.device('cpu'), - type_complex=torch.complex128): +def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=torch.device('cpu'), type_complex=torch.complex128, + use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -107,13 +109,13 @@ def to_conv_mat_vector(ucell_info_list, fto_x, fto_y, device=torch.device('cpu') epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = torch.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all -def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=torch.device('cpu'), - type_complex=torch.complex128): +def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=torch.device('cpu'), type_complex=torch.complex128, + use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) epx_conv_all = torch.zeros((ucell.shape[0], ff_xy, ff_xy), device=device, dtype=type_complex) @@ -130,13 +132,13 @@ def to_conv_mat_raster_continuous(ucell, fto_x, fto_y, device=torch.device('cpu' epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = torch.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all -def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=torch.complex128, - enhanced_dfs=True): +def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=torch.device('cpu'), type_complex=torch.complex128, + enhanced_dfs=True, use_pinv=False): ff_xy = (2 * fto_x + 1) * (2 * fto_y + 1) @@ -170,6 +172,6 @@ def to_conv_mat_raster_discrete(ucell, fto_x, fto_y, device=None, type_complex=t epx_conv_all[i] = epx_conv epy_conv_all[i] = epy_conv - epz_conv_i_all[i] = torch.linalg.inv(epz_conv) + epz_conv_i_all[i] = meeinv(epz_conv, use_pinv=use_pinv) return epx_conv_all, epy_conv_all, epz_conv_i_all diff --git a/meent/on_torch/emsolver/field_distribution.py b/meent/on_torch/emsolver/field_distribution.py index b4bf02c..46ba0d3 100644 --- a/meent/on_torch/emsolver/field_distribution.py +++ b/meent/on_torch/emsolver/field_distribution.py @@ -22,8 +22,8 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, z_1d = torch.linspace(0, res_z, res_z, device=device, dtype=type_complex).reshape((-1, 1, 1)) / res_z * d - My = W @ (diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) - Mx = V @ (-diag_exp_batch(-k0 * Q * z_1d) @ c1 + diag_exp_batch(k0 * Q * (z_1d - d)) @ c2) + My = W @ (d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) + Mx = V @ (-d_exp(-k0 * Q * z_1d) @ c1 + d_exp(k0 * Q * (z_1d - d)) @ c2) if pol == 0: Mz = -1j * Kx @ My else: @@ -61,7 +61,7 @@ def field_dist_1d(wavelength, kx, T1, layer_info_list, period, return field_cell -def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, +def field_dist_1d_conical(wavelength, kx, ky, T1, layer_info_list, period, res_x=20, res_y=20, res_z=20, device='cpu', type_complex=torch.complex128, type_float=torch.float64): k0 = 2 * torch.pi / wavelength @@ -77,6 +77,100 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, T_layer = T1 + big_I = torch.eye((len(T1)), device=device, dtype=type_complex) + O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) + + # From the first layer + for idx_layer, (epz_conv_i, W, V, q, d, big_A_i, big_B) in enumerate(layer_info_list[::-1]): + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] + + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] + + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + + X_1 = torch.diag(torch.exp(-k0 * q_1 * d)) + X_2 = torch.diag(torch.exp(-k0 * q_2 * d)) + + big_X = torch.cat([ + torch.cat([X_1, O], dim=1), + torch.cat([O, X_2], dim=1)]) + + c = torch.cat([big_I, big_B @ big_A_i @ big_X]) @ T_layer + + # z_1d = np.arange(0, res_z, res_z).reshape((-1, 1, 1)) / res_z * d + z_1d = torch.linspace(0, res_z, res_z, device=device, dtype=type_complex).reshape((-1, 1, 1)) / res_z * d + + c1_plus = c[0 * ff_xy:1 * ff_xy] + c2_plus = c[1 * ff_xy:2 * ff_xy] + c1_minus = c[2 * ff_xy:3 * ff_xy] + c2_minus = c[3 * ff_xy:4 * ff_xy] + + big_Q1 = torch.diag(q_1) + big_Q2 = torch.diag(q_2) + + Sx = W_2 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = V_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = W_1 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) + Uz = -1j * (Kx @ Sy - Ky @ Sx) + + # x_1d = np.arange(res_x).reshape((1, -1, 1)) * period[0] / res_x + x_1d = torch.linspace(0, period[0], res_x, device=device, dtype=type_complex).reshape((1, -1, 1)) + x_2d = torch.tile(x_1d, (res_y, 1, 1)) + x_2d = x_2d * kx * k0 + x_2d = x_2d.reshape((res_y, res_x, 1, len(kx))) + + # y_1d = np.arange(res_y-1, -1, -1).reshape((-1, 1, 1)) * period[1] / res_y + # y_1d = torch.linspace(0, period[1], res_y, device=device, dtype=type_complex)[::-1].reshape((-1, 1, 1)) + y_1d = torch.flip(torch.linspace(0, period[1], res_y, device=device, dtype=type_complex), dims=(0,)).reshape((-1, 1, 1)) + y_2d = torch.tile(y_1d, (1, res_x, 1)) + y_2d = y_2d * ky * k0 + y_2d = y_2d.reshape((res_y, res_x, len(ky), 1)) + + inv_fourier = torch.exp(-1j * x_2d) * torch.exp(-1j * y_2d) + inv_fourier = inv_fourier.reshape((res_y, res_x, -1)) + + Ex = inv_fourier[:, :, None, :] @ Sx[:, None, None, :, :] + Ey = inv_fourier[:, :, None, :] @ Sy[:, None, None, :, :] + Ez = inv_fourier[:, :, None, :] @ Sz[:, None, None, :, :] + Hx = 1j * inv_fourier[:, :, None, :] @ Ux[:, None, None, :, :] + Hy = 1j * inv_fourier[:, :, None, :] @ Uy[:, None, None, :, :] + Hz = 1j * inv_fourier[:, :, None, :] @ Uz[:, None, None, :, :] + + val = torch.cat( + (Ex.squeeze(-1), Ey.squeeze(-1), Ez.squeeze(-1), Hx.squeeze(-1), Hy.squeeze(-1), Hz.squeeze(-1)), -1) + + field_cell[res_z * idx_layer:res_z * (idx_layer + 1)] = val + + T_layer = big_A_i @ big_X @ T_layer + + return field_cell + + +def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, + res_x=20, res_y=20, res_z=20, device='cpu', type_complex=torch.complex128): + + k0 = 2 * torch.pi / wavelength + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + Kx = torch.diag(torch.tile(kx, (ff_y, )).flatten()) + Ky = torch.diag(torch.tile(ky.reshape((-1, 1)), (ff_x, )).flatten()) + + field_cell = torch.zeros((res_z * len(layer_info_list), res_y, res_x, 6), device=device, dtype=type_complex) + + T_layer = T1 + big_I = torch.eye((len(T1)), device=device, dtype=type_complex) # From the first layer @@ -92,6 +186,9 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, V_21 = V[ff_xy:, :ff_xy] V_22 = V[ff_xy:, ff_xy:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + big_X = torch.diag(torch.exp(-k0 * q * d)) c = torch.cat([big_I, big_B @ big_A_i @ big_X]) @ T_layer @@ -104,20 +201,18 @@ def field_dist_2d(wavelength, kx, ky, T1, layer_info_list, period, c1_minus = c[2 * ff_xy:3 * ff_xy] c2_minus = c[3 * ff_xy:4 * ff_xy] - q1 = q[:len(q) // 2] - q2 = q[len(q) // 2:] - big_Q1 = torch.diag(q1) - big_Q2 = torch.diag(q2) + big_Q1 = torch.diag(q_1) + big_Q2 = torch.diag(q_2) - Sx = W_11 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_12 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Sy = W_21 @ (diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + W_22 @ (diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sx = W_11 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_12 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Sy = W_21 @ (d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + W_22 @ (d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Ux = V_11 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_12 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) - Uy = V_21 @ (-diag_exp_batch(-k0 * big_Q1 * z_1d) @ c1_plus + diag_exp_batch(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ - + V_22 @ (-diag_exp_batch(-k0 * big_Q2 * z_1d) @ c2_plus + diag_exp_batch(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Ux = V_11 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_12 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) + Uy = V_21 @ (-d_exp(-k0 * big_Q1 * z_1d) @ c1_plus + d_exp(k0 * big_Q1 * (z_1d - d)) @ c1_minus) \ + + V_22 @ (-d_exp(-k0 * big_Q2 * z_1d) @ c2_plus + d_exp(k0 * big_Q2 * (z_1d - d)) @ c2_minus) Sz = -1j * epz_conv_i @ (Kx @ Uy - Ky @ Ux) Uz = -1j * (Kx @ Sy - Ky @ Sx) @@ -195,11 +290,7 @@ def field_plot(field_cell, pol=0, plot_indices=(1, 1, 1, 1, 1, 1), y_slice=0, z_ plt.show() -def diag_exp(x): - return torch.diag(torch.exp(torch.diag(x))) - - -def diag_exp_batch(x): +def d_exp(x): res = torch.zeros(x.shape, device=x.device, dtype=x.dtype) ix = torch.arange(x.shape[-1], device=x.device) res[:, ix, ix] = torch.exp(x[:, ix, ix]) diff --git a/meent/on_torch/emsolver/primitives.py b/meent/on_torch/emsolver/primitives.py index 73dae28..d8089b9 100644 --- a/meent/on_torch/emsolver/primitives.py +++ b/meent/on_torch/emsolver/primitives.py @@ -44,3 +44,12 @@ def backward(ctx, grad_eigval, grad_eigvec): grad = grad.real return grad + + +def meeinv(x, use_pinv=False): + if use_pinv: + res = torch.linalg.pinv(x) + else: + res = torch.linalg.inv(x) + + return res diff --git a/meent/on_torch/emsolver/rcwa.py b/meent/on_torch/emsolver/rcwa.py index 7e84c25..ea01ed6 100644 --- a/meent/on_torch/emsolver/rcwa.py +++ b/meent/on_torch/emsolver/rcwa.py @@ -1,11 +1,47 @@ -import time import torch import numpy as np from ._base import _BaseRCWA from .convolution_matrix import to_conv_mat_raster_discrete, to_conv_mat_raster_continuous, to_conv_mat_vector -from .field_distribution import field_dist_1d, field_dist_2d, field_plot +from .field_distribution import field_dist_1d, field_dist_1d_conical, field_dist_2d, field_plot + + +class ResultTorch: + def __init__(self, res=None, res_te_inc=None, res_tm_inc=None): + + self.res = res + self.res_te_inc = res_te_inc + self.res_tm_inc = res_tm_inc + + @property + def de_ri(self): + if self.res is not None: + return self.res.de_ri + else: + return None + + @property + def de_ti(self): + if self.res is not None: + return self.res.de_ti + else: + return None + + +class ResultSubTorch: + def __init__(self, R_s, R_p, T_s, T_p, de_ri, de_ri_s, de_ri_p, de_ti, de_ti_s, de_ti_p): + self.R_s = R_s + self.R_p = R_p + self.T_s = T_s + self.T_p = T_p + self.de_ri = de_ri + self.de_ri_s = de_ri_s + self.de_ri_p = de_ri_p + + self.de_ti = de_ti + self.de_ti_s = de_ti_s + self.de_ti_p = de_ti_p class RCWATorch(_BaseRCWA): @@ -13,10 +49,10 @@ def __init__(self, n_top=1., n_bot=1., theta=0., - phi=0., + phi=None, psi=None, - period=(100., 100.), - wavelength=900., + period=(1., 1.), + wavelength=1., ucell=None, thickness=(0., ), backend=2, @@ -29,13 +65,16 @@ def __init__(self, type_complex=torch.complex128, fourier_type=0, enhanced_dfs=True, - # **kwargs, + use_pinv=False, ): super().__init__(n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, psi=psi, pol=pol, fto=fto, period=period, wavelength=wavelength, thickness=thickness, connecting_algo=connecting_algo, perturbation=perturbation, - device=device, type_complex=type_complex) + device=device, type_complex=type_complex, use_pinv=use_pinv) + + self._modeling_type_assigned = None + self._grating_type_assigned = None self.ucell = ucell self.ucell_materials = ucell_materials @@ -43,8 +82,7 @@ def __init__(self, self.backend = backend self.fourier_type = fourier_type self.enhanced_dfs = enhanced_dfs - self._modeling_type_assigned = None - self._grating_type_assigned = None + self.use_pinv = use_pinv @property def ucell(self): @@ -54,6 +92,7 @@ def ucell(self): def ucell(self, ucell): if isinstance(ucell, (torch.Tensor, np.ndarray)): # Raster + self._modeling_type_assigned = 0 if ucell.dtype in (torch.complex128, torch.complex64): dtype = self.type_complex self._ucell = ucell.to(device=self.device, dtype=dtype) @@ -70,6 +109,7 @@ def ucell(self, ucell): raise ValueError elif type(ucell) is list: # Vector + self._modeling_type_assigned = 1 self._ucell = ucell elif ucell is None: self._ucell = ucell @@ -80,22 +120,25 @@ def ucell(self, ucell): def modeling_type_assigned(self): return self._modeling_type_assigned - @modeling_type_assigned.setter - def modeling_type_assigned(self, modeling_type_assigned): - self._modeling_type_assigned = modeling_type_assigned + # @modeling_type_assigned.setter + # def modeling_type_assigned(self, modeling_type_assigned): + # self._modeling_type_assigned = modeling_type_assigned - def _assign_modeling_type(self): + def _assign_grating_type(self): + # self.modeling_type_assigned = 0 + # self._grating_type_assigned = 1 # else - if isinstance(self.ucell, torch.Tensor): # Raster - self.modeling_type_assigned = 0 - if (self.ucell.shape[1] == 1) and (self.pol in (0, 1)) and (self.phi % (2 * np.pi) == 0) and (self.fto[1] == 0): - self._grating_type_assigned = 0 # 1D TE and TM only + if self.modeling_type_assigned == 0: + if self.ucell.shape[1] == 1: + if (self.pol in (0, 1)) and (self.phi is None) and (self.fto[1] == 0): + self._grating_type_assigned = 0 # 1D TE and TM only + else: + self._grating_type_assigned = 1 # 1D conical else: - self._grating_type_assigned = 1 # else + self._grating_type_assigned = 2 # else - elif isinstance(self.ucell, list): # Vector - self.modeling_type_assigned = 1 - self.grating_type_assigned = 1 + elif self.modeling_type_assigned == 1: + self.grating_type_assigned = 2 @property def grating_type_assigned(self): @@ -105,57 +148,53 @@ def grating_type_assigned(self): def grating_type_assigned(self, grating_type_assigned): self._grating_type_assigned = grating_type_assigned - def _solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + def solve_for_conv(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + self._assign_grating_type() if self._grating_type_assigned == 0: - de_ri, de_ti, rayleigh_R, rayleigh_T, layer_info_list, T1 = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + result_dict = self.solve_1d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + elif self._grating_type_assigned == 1: + result_dict = self.solve_1d_conical(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) else: - de_ri, de_ti, rayleigh_R, rayleigh_T, layer_info_list, T1 = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + result_dict = self.solve_2d(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - return de_ri, de_ti, rayleigh_R, rayleigh_T, layer_info_list, T1 + res_psi = ResultSubTorch(**result_dict['res']) if 'res' in result_dict else None + res_te_inc = ResultSubTorch(**result_dict['res_te_inc']) if 'res_te_inc' in result_dict else None + res_tm_inc = ResultSubTorch(**result_dict['res_tm_inc']) if 'res_tm_inc' in result_dict else None - def solve(self, wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all): + result = ResultTorch(res_psi, res_te_inc, res_tm_inc) - de_ri, de_ti, rayleigh_R, rayleigh_T, layer_info_list, T1 = self._solve(wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - - self.rayleigh_R = rayleigh_R - self.rayleigh_T = rayleigh_T - self.layer_info_list = layer_info_list - self.T1 = T1 - - return de_ri, de_ti + return result def conv_solve(self, **kwargs): [setattr(self, k, v) for k, v in kwargs.items()] # needed for optimization - self._assign_modeling_type() - if self._modeling_type_assigned == 0: # Raster + if self.modeling_type_assigned == 0: # Raster if self.fourier_type == 0: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_discrete( self.ucell, self.fto[0], self.fto[1], device=self.device, type_complex=self.type_complex, - enhanced_dfs=self.enhanced_dfs) + enhanced_dfs=self.enhanced_dfs, use_pinv=self.use_pinv) elif self.fourier_type == 1: epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_raster_continuous( - self.ucell, self.fto[0], self.fto[1], device=self.device, type_complex=self.type_complex) + self.ucell, self.fto[0], self.fto[1], device=self.device, type_complex=self.type_complex, + use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") - elif self._modeling_type_assigned == 1: # Vector + elif self.modeling_type_assigned == 1: # Vector ucell_vector = self.modeling_vector_instruction(self.ucell) epx_conv_all, epy_conv_all, epz_conv_i_all = to_conv_mat_vector( - ucell_vector, self.fto[0], self.fto[1], device=self.device, type_complex=self.type_complex) + ucell_vector, self.fto[0], self.fto[1], device=self.device, type_complex=self.type_complex, + use_pinv=self.use_pinv) else: raise ValueError("Check 'modeling_type' and 'fourier_type' in 'conv_solve'.") - de_ri, de_ti, rayleigh_r, rayleigh_t, layer_info_list, T1 = self._solve(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) + result = self.solve_for_conv(self.wavelength, epx_conv_all, epy_conv_all, epz_conv_i_all) - self.layer_info_list = layer_info_list - self.T1 = T1 - - return de_ri, de_ti + return result def calculate_field(self, res_x=20, res_y=20, res_z=20): kx, ky = self.get_kx_ky_vector(wavelength=self.wavelength) @@ -164,6 +203,9 @@ def calculate_field(self, res_x=20, res_y=20, res_z=20): res_y = 1 field_cell = field_dist_1d(self.wavelength, kx, self.T1, self.layer_info_list, self.period, self.pol, res_x=res_x, res_y=res_y, res_z=res_z, device=self.device, type_complex=self.type_complex) + elif self._grating_type_assigned == 1: + field_cell = field_dist_1d_conical(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, + res_x=res_x, res_y=res_y, res_z=res_z, device=self.device, type_complex=self.type_complex) else: field_cell = field_dist_2d(self.wavelength, kx, ky, self.T1, self.layer_info_list, self.period, res_x=res_x, res_y=res_y, res_z=res_z, device=self.device, type_complex=self.type_complex) diff --git a/meent/on_torch/emsolver/transfer_method.py b/meent/on_torch/emsolver/transfer_method.py index 982bb48..f03ff38 100644 --- a/meent/on_torch/emsolver/transfer_method.py +++ b/meent/on_torch/emsolver/transfer_method.py @@ -1,18 +1,19 @@ import torch -from .primitives import Eig +from .primitives import Eig, meeinv -def transfer_1d_1(pol, ff_x, kx, n_top, n_bot, device=torch.device('cpu'), type_complex=torch.complex128): - - ff_xy = ff_x * 1 +def transfer_1d_1(pol, kx, n_top, n_bot, device=torch.device('cpu'), type_complex=torch.complex128): + ff_x = len(kx) kz_top = (n_top ** 2 - kx ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2) ** 0.5 - kz_top = torch.conj(kz_top) - kz_bot = torch.conj(kz_bot) + # kz_top = torch.conj(kz_top) + # kz_bot = torch.conj(kz_bot) + kz_top = kz_top.conj() + kz_bot = kz_bot.conj() - F = torch.eye(ff_xy, device=device, dtype=type_complex) + F = torch.eye(ff_x, device=device, dtype=type_complex) if pol == 0: # TE Kz_bot = torch.diag(kz_bot) @@ -23,12 +24,13 @@ def transfer_1d_1(pol, ff_x, kx, n_top, n_bot, device=torch.device('cpu'), type_ else: raise ValueError - T = torch.eye(ff_xy, device=device, dtype=type_complex) + T = torch.eye(ff_x, device=device, dtype=type_complex) return kz_top, kz_bot, F, G, T -def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, device=torch.device('cpu'), type_complex=torch.complex128, perturbation=1E-10): +def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, device=torch.device('cpu'), type_complex=torch.complex128, + perturbation=1E-20, use_pinv=False): Kx = torch.diag(kx) @@ -53,7 +55,7 @@ def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, device=torch.device(' q = eigenvalues ** 0.5 Q = torch.diag(q) - V = torch.linalg.inv(epx_conv) @ W @ Q + V = meeinv(epx_conv, use_pinv) @ W @ Q else: raise ValueError @@ -61,40 +63,20 @@ def transfer_1d_2(pol, kx, epx_conv, epy_conv, epz_conv_i, device=torch.device(' return W, V, q -def transfer_1d_2_(k0, q, d, W, V, f, g, fourier_order, T, device=torch.device('cpu'), type_complex=torch.complex128): - - X = torch.diag(torch.exp(-k0 * q * d)) - - W_i = torch.linalg.inv(W) - V_i = torch.linalg.inv(V) - - a = 0.5 * (W_i @ f + V_i @ g) - b = 0.5 * (W_i @ f - V_i @ g) - - a_i = torch.linalg.inv(a) - - f = W @ (torch.eye(2 * fourier_order[0] + 1, device=device, dtype=type_complex) + X @ b @ a_i @ X) - g = V @ (torch.eye(2 * fourier_order[0] + 1, device=device, dtype=type_complex) - X @ b @ a_i @ X) - T = T @ a_i @ X - - return X, f, g, T, a_i, b - - -def transfer_1d_3(k0, W, V, q, d, F, G, T, device=torch.device('cpu'), type_complex=torch.complex128): - +def transfer_1d_3(k0, W, V, q, d, F, G, T, device=torch.device('cpu'), type_complex=torch.complex128, use_pinv=False): ff_x = len(q) I = torch.eye(ff_x, device=device, dtype=type_complex) X = torch.diag(torch.exp(-k0 * q * d)) - W_i = torch.linalg.inv(W) - V_i = torch.linalg.inv(V) + W_i = meeinv(W, use_pinv) + V_i = meeinv(V, use_pinv) A = 0.5 * (W_i @ F + V_i @ G) B = 0.5 * (W_i @ F - V_i @ G) - A_i = torch.linalg.inv(A) + A_i = meeinv(A, use_pinv) F = W @ (I + X @ B @ A_i @ X) G = V @ (I - X @ B @ A_i @ X) @@ -103,54 +85,387 @@ def transfer_1d_3(k0, W, V, q, d, F, G, T, device=torch.device('cpu'), type_comp return X, F, G, T, A_i, B -def transfer_1d_4(pol, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, device=torch.device('cpu'), type_complex=torch.complex128): - - ff_xy = len(kz_top) +def transfer_1d_4(pol, ff_x, F, G, T, kz_top, kz_bot, theta, n_top, n_bot, device=torch.device('cpu'), + type_complex=torch.complex128, use_pinv=False): Kz_top = torch.diag(kz_top) + kz_top = kz_top.reshape((1, ff_x)) + kz_bot = kz_bot.reshape((1, ff_x)) - delta_i0 = torch.zeros(ff_xy, device=device, dtype=type_complex) - delta_i0[ff_xy // 2] = 1 + delta_i0 = torch.zeros(ff_x, device=device, dtype=type_complex) + delta_i0[ff_x // 2] = 1 if pol == 0: # TE inc_term = 1j * n_top * torch.cos(theta) * delta_i0 - T1 = torch.linalg.inv(G + 1j * Kz_top @ F) @ (1j * Kz_top @ delta_i0 + inc_term) + T1 = meeinv(G + 1j * Kz_top @ F, use_pinv) @ (1j * Kz_top @ delta_i0 + inc_term) elif pol == 1: # TM inc_term = 1j * delta_i0 * torch.cos(theta) / n_top - T1 = torch.linalg.inv(G + 1j * Kz_top / (n_top ** 2) @ F) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + T1 = meeinv(G + 1j * Kz_top / (n_top ** 2) @ F, use_pinv) @ (1j * Kz_top / (n_top ** 2) @ delta_i0 + inc_term) + else: + raise ValueError - # T1 = np.linalg.inv(G + 1j * YZ_I @ F) @ (1j * YZ_I @ delta_i0 + inc_term) - R = F @ T1 - delta_i0 - T = T @ T1 + # T1 = np.linalg.pinv(G + 1j * YZ_I @ F) @ (1j * YZ_I @ delta_i0 + inc_term) + R = (F @ T1 - delta_i0).reshape((1, ff_x)) + T = (T @ T1).reshape((1, ff_x)) - de_ri = torch.real(R * torch.conj(R) * kz_top / (n_top * torch.cos(theta))) + # de_ri = np.real(np.real(R * np.conj(R) * kz_top / (n_top * np.cos(theta)))) + # de_ri = np.real(R * np.conj(R) * np.real(kz_top / (n_top * np.cos(theta)))) + de_ri = (R * R.conj() * (kz_top / (n_top * torch.cos(theta))).real).real if pol == 0: - de_ti = T * torch.conj(T) * torch.real(kz_bot / (n_top * torch.cos(theta))) + # de_ti = np.real(T * np.conj(T) * np.real(kz_bot / (n_top * np.cos(theta)))) + # de_ti = np.real(T * np.conj(T) * np.real(kz_bot / (n_top * np.cos(theta)))) + de_ti = (T * T.conj() * (kz_bot / (n_top * torch.cos(theta))).real).real + R_s = R + R_p = torch.zeros(R.shape) + T_s = T + T_p = torch.zeros(T.shape) + de_ri_s = de_ri + de_ri_p = torch.zeros(de_ri.shape) + de_ti_s = de_ti + de_ti_p = torch.zeros(de_ri.shape) + elif pol == 1: - de_ti = T * torch.conj(T) * torch.real(kz_bot / n_bot ** 2) / (torch.cos(theta) / n_top) + # de_ti = np.real(T * np.conj(T) * np.real(kz_bot / n_bot ** 2) / (np.cos(theta) / n_top)) + # de_ti = np.real(T * np.conj(T) * np.real(kz_bot / n_bot ** 2 / (np.cos(theta) / n_top))) + de_ti = (T * T.conj() * (kz_bot / n_bot ** 2 / (torch.cos(theta) / n_top)).real).real + R_s = torch.zeros(R.shape) + R_p = R + T_s = torch.zeros(T.shape) + T_p = T + de_ri_s = torch.zeros(de_ri.shape) + de_ri_p = de_ri + de_ti_s = torch.zeros(de_ri.shape) + de_ti_p = de_ti else: raise ValueError - return de_ri.real, de_ti.real, T1, [R], [T] + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri': de_ri, 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, + 'de_ti': de_ti, 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, + } + result = {'res': res} -def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, device=torch.device('cpu'), type_complex=torch.complex128): + return result, T1 + +def transfer_1d_conical_1(kx, ky, n_top, n_bot, device='cpu', type_complex=torch.complex128): + ff_x = len(kx) + ff_y = len(ky) ff_xy = ff_x * ff_y I = torch.eye(ff_xy, device=device, dtype=type_complex) O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) + # TODO: cleaning + # ky = k0 * n_I * torch.sin(theta) * torch.sin(phi) + # + # k_I_z = (k0 ** 2 * n_I ** 2 - kx_vector ** 2 - ky ** 2) ** 0.5 + # k_II_z = (k0 ** 2 * n_II ** 2 - kx_vector ** 2 - ky ** 2) ** 0.5 + # + # k_I_z = torch.conj(k_I_z.flatten()) + # k_II_z = torch.conj(k_II_z.flatten()) + # + # Kx = torch.diag(kx_vector / k0) + + kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 - kz_top = torch.conj(kz_top).flatten() - kz_bot = torch.conj(kz_bot).flatten() + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() + + + # varphi = torch.arctan(ky / kx_vector) varphi = torch.arctan(ky.reshape((-1, 1)) / kx).flatten() + Kz_bot = torch.diag(kz_bot) + + + # Y_I = torch.diag(k_I_z / k0) + # Y_II = torch.diag(k_II_z / k0) + # + # Z_I = torch.diag(k_I_z / (k0 * n_I ** 2)) + # Z_II = torch.diag(k_II_z / (k0 * n_II ** 2)) + + big_F = torch.cat( + [ + torch.cat([I, O], dim=1), + torch.cat([O, 1j * Kz_bot / (n_bot ** 2)], dim=1), + ] + ) + + big_G = torch.cat( + [ + torch.cat([1j * Kz_bot, O], dim=1), + torch.cat([O, I], dim=1), + ] + ) + + big_T = torch.eye(2*ff_xy, device=device, dtype=type_complex) + return kz_top, kz_bot, varphi, big_F, big_G, big_T + + # return Kx, ky, k_I_z, k_II_z, varphi, Y_I, Y_II, Z_I, Z_II, big_F, big_G, big_T + + +# def transfer_1d_conical_2(k0, Kx, ky, E_conv, E_i, o_E_conv_i, ff, d, varphi, big_F, big_G, big_T, +# device='cpu', type_complex=torch.complex128, perturbation=1E-10): +def transfer_1d_conical_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device='cpu', type_complex=torch.complex128, + perturbation=1E-20, use_pinv=False): + + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + I = torch.eye(ff_xy, device=device, dtype=type_complex) + + Kx = torch.diag(kx.tile(ff_y).flatten()) + Ky = torch.diag(ky.reshape((-1, 1)).tile(ff_x).flatten()) + + A = Kx ** 2 - epy_conv + B = Kx @ epz_conv_i @ Kx - I + + Omega2_RL = Ky ** 2 + A + Omega2_LR = Ky ** 2 + B @ epx_conv + + Eig.perturbation = perturbation + eigenvalues_1, W_1 = Eig.apply(Omega2_RL) + eigenvalues_2, W_2 = Eig.apply(Omega2_LR) + + q_1 = eigenvalues_1 ** 0.5 + q_2 = eigenvalues_2 ** 0.5 + + Q_1 = torch.diag(q_1) + Q_2 = torch.diag(q_2) + + A_i = meeinv(A, use_pinv) + B_i = meeinv(B, use_pinv) + + V_11 = A_i @ W_1 @ Q_1 + V_12 = Ky @ A_i @ Kx @ W_2 + V_21 = Ky @ B_i @ Kx @ epz_conv_i @ W_1 + V_22 = B_i @ W_2 @ Q_2 + + W = torch.cat([W_1, W_2], dim=1) + V = torch.cat( + [ + torch.cat([V_11, V_12], dim=1), + torch.cat([V_21, V_22], dim=1), + ]) + + q = torch.hstack([q_1, q_2]) + + return W, V, q + + +# def transfer_1d_conical_3(big_F, big_G, big_T, Z_I, Y_I, psi, theta, ff, delta_i0, k_I_z, k0, n_I, n_II, k_II_z, +# device='cpu', type_complex=torch.complex128): +def transfer_1d_conical_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device='cpu', type_complex=torch.complex128, + use_pinv=False): + + ff_xy = len(q) // 2 + I = torch.eye(ff_xy, device=device, dtype=type_complex) + O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) + + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] + + W_1 = W[:, :ff_xy] + W_2 = W[:, ff_xy:] + + V_11 = V[:ff_xy, :ff_xy] + V_12 = V[:ff_xy, ff_xy:] + V_21 = V[ff_xy:, :ff_xy] + V_22 = V[ff_xy:, ff_xy:] + + + X_1 = torch.diag(torch.exp(-k0 * q_1 * d)) + X_2 = torch.diag(torch.exp(-k0 * q_2 * d)) + + F_c = torch.diag(torch.cos(varphi)) + F_s = torch.diag(torch.sin(varphi)) + + V_ss = F_c @ V_11 + V_sp = F_c @ V_12 - F_s @ W_2 + W_ss = F_c @ W_1 + F_s @ V_21 + W_sp = F_s @ V_22 + W_ps = F_s @ V_11 + W_pp = F_c @ W_2 + F_s @ V_12 + V_ps = F_c @ V_21 - F_s @ W_1 + V_pp = F_c @ V_22 + + big_I = torch.eye(2 * (len(I)), device=device, dtype=type_complex) + + big_X = torch.cat([ + torch.cat([X_1, O], dim=1), + torch.cat([O, X_2], dim=1)]) + + big_W = torch.cat([ + torch.cat([V_ss, V_sp], dim=1), + torch.cat([W_ps, W_pp], dim=1)]) + + big_V = torch.cat([ + torch.cat([W_ss, W_sp], dim=1), + torch.cat([V_ps, V_pp], dim=1)]) + + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) + + big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) + big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) + + big_A_i = meeinv(big_A, use_pinv) + + big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) + big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) + + big_T = big_T @ big_A_i @ big_X + + return big_X, big_F, big_G, big_T, big_A_i, big_B + + +def transfer_1d_conical_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, device='cpu', + type_complex=torch.complex128, use_pinv=False): + + ff_xy = ff_x * ff_y + + Kz_top = torch.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) + + I = torch.eye(ff_xy, device=device, dtype=type_complex) + O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) + + big_F_11 = big_F[:ff_xy, :ff_xy] + big_F_12 = big_F[:ff_xy, ff_xy:] + big_F_21 = big_F[ff_xy:, :ff_xy] + big_F_22 = big_F[ff_xy:, ff_xy:] + + big_G_11 = big_G[:ff_xy, :ff_xy] + big_G_12 = big_G[:ff_xy, ff_xy:] + big_G_21 = big_G[ff_xy:, :ff_xy] + big_G_22 = big_G[ff_xy:, ff_xy:] + + delta_i0 = torch.zeros((ff_xy, 1), device=device, dtype=type_complex) + delta_i0[ff_xy // 2, 0] = 1 + + # Final Equation in form of AX=B + final_A = torch.cat( + [ + torch.cat([I, O, -big_F_11, -big_F_12], dim=1), + torch.cat([O, -1j * Kz_top / (n_top ** 2), -big_F_21, -big_F_22], dim=1), + torch.cat([-1j * Kz_top, O, -big_G_11, -big_G_12], dim=1), + torch.cat([O, I, -big_G_21, -big_G_22], dim=1), + ] + ) + + final_B = torch.cat( + [ + torch.cat([-torch.sin(psi) * delta_i0], dim=1), + torch.cat([torch.cos(psi) * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * torch.sin(psi) * n_top * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * n_top * torch.cos(psi) * delta_i0], dim=1), + ] + ) + + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B + + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) + + big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.clone().detach() + big_T = big_T @ big_T1 + + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) + + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * torch.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * torch.cos(theta))).real).real + + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * torch.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * torch.cos(theta))).real).real + + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} + # TE TM incidence + psi_tm = torch.tensor(0, dtype=type_complex) + final_B_tm = torch.cat( + [ + torch.cat([-torch.sin(psi_tm) * delta_i0], dim=1), + torch.cat([torch.cos(psi_tm) * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * torch.sin(psi_tm) * n_top * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * n_top * torch.cos(psi_tm) * delta_i0], dim=1), + ] + ) + + psi_te = torch.tensor(torch.pi / 2, dtype=type_complex) + final_B_te = torch.cat( + [ + torch.cat([-torch.sin(psi_te) * delta_i0], dim=1), + torch.cat([torch.cos(psi_te) * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * torch.sin(psi_te) * n_top * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * n_top * torch.cos(psi_te) * delta_i0], dim=1), + ] + ) + + final_B_tetm = torch.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * torch.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * torch.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * torch.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * torch.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} + + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} + + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} + + return result, big_T1 + + +def transfer_2d_1(kx, ky, n_top, n_bot, device=torch.device('cpu'), type_complex=torch.complex128): + ff_x = len(kx) + ff_y = len(ky) + ff_xy = ff_x * ff_y + + I = torch.eye(ff_xy, device=device, dtype=type_complex) + O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) + + kz_top = (n_top ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + kz_bot = (n_bot ** 2 - kx ** 2 - ky.reshape((-1, 1)) ** 2) ** 0.5 + + kz_top = kz_top.flatten().conj() + kz_bot = kz_bot.flatten().conj() + + varphi = torch.arctan(ky.reshape((-1, 1)) / kx).flatten() Kz_bot = torch.diag(kz_bot) big_F = torch.cat( @@ -173,18 +488,14 @@ def transfer_2d_1(ff_x, ff_y, kx, ky, n_top, n_bot, device=torch.device('cpu'), def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device=torch.device('cpu'), type_complex=torch.complex128, - perturbation=1E-10): + perturbation=1E-20, use_pinv=False): ff_x = len(kx) ff_y = len(ky) ff_xy = ff_x * ff_y - # I = np.eye(ff_y * ff_x, dtype=type_complex) I = torch.eye(ff_xy, device=device, dtype=type_complex) - # Kx = torch.diag(torch.tile(kx, ff_y).flatten()) - # Ky = torch.diag(torch.tile(ky.reshape((-1, 1)), ff_x).flatten()) - Kx = torch.diag(kx.tile(ff_y).flatten()) Ky = torch.diag(ky.reshape((-1, 1)).tile(ff_x).flatten()) @@ -202,7 +513,7 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device=torch.device('c q = eigenvalues ** 0.5 Q = torch.diag(q) - Q_i = torch.linalg.inv(Q) + Q_i = meeinv(Q, use_pinv) Omega_R = torch.cat( [ torch.cat([-Kx @ Ky, Kx ** 2 - epy_conv], dim=1), @@ -214,14 +525,15 @@ def transfer_2d_2(kx, ky, epx_conv, epy_conv, epz_conv_i, device=torch.device('c return W, V, q -def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=torch.device('cpu'), type_complex=torch.complex128): +def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=torch.device('cpu'), + type_complex=torch.complex128, use_pinv=False): ff_xy = len(q)//2 I = torch.eye(ff_xy, device=device, dtype=type_complex) O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) - q1 = q[:ff_xy] - q2 = q[ff_xy:] + q_1 = q[:ff_xy] + q_2 = q[ff_xy:] W_11 = W[:ff_xy, :ff_xy] W_12 = W[:ff_xy, ff_xy:] @@ -233,8 +545,8 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=torch.devi V_21 = V[ff_xy:, :ff_xy] V_22 = V[ff_xy:, ff_xy:] - X_1 = torch.diag(torch.exp(-k0 * q1 * d)) - X_2 = torch.diag(torch.exp(-k0 * q2 * d)) + X_1 = torch.diag(torch.exp(-k0 * q_1 * d)) + X_2 = torch.diag(torch.exp(-k0 * q_2 * d)) F_c = torch.diag(torch.cos(varphi)) F_s = torch.diag(torch.sin(varphi)) @@ -263,13 +575,13 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=torch.devi torch.cat([V_ss, V_sp], dim=1), torch.cat([V_ps, V_pp], dim=1)]) - big_W_i = torch.linalg.inv(big_W) - big_V_i = torch.linalg.inv(big_V) + big_W_i = meeinv(big_W, use_pinv) + big_V_i = meeinv(big_V, use_pinv) big_A = 0.5 * (big_W_i @ big_F + big_V_i @ big_G) big_B = 0.5 * (big_W_i @ big_F - big_V_i @ big_G) - big_A_i = torch.linalg.inv(big_A) + big_A_i = meeinv(big_A, use_pinv) big_F = big_W @ (big_I + big_X @ big_B @ big_A_i @ big_X) big_G = big_V @ (big_I - big_X @ big_B @ big_A_i @ big_X) @@ -279,12 +591,14 @@ def transfer_2d_3(k0, W, V, q, d, varphi, big_F, big_G, big_T, device=torch.devi return big_X, big_F, big_G, big_T, big_A_i, big_B -def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, - device=torch.device('cpu'), type_complex=torch.complex128): +def transfer_2d_4(ff_x, ff_y, big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, + device=torch.device('cpu'), type_complex=torch.complex128, use_pinv=False): - ff_xy = len(big_F) // 2 + ff_xy = ff_x * ff_y Kz_top = torch.diag(kz_top) + kz_top = kz_top.reshape((ff_y, ff_x)) + kz_bot = kz_bot.reshape((ff_y, ff_x)) I = torch.eye(ff_xy, device=device, dtype=type_complex) O = torch.zeros((ff_xy, ff_xy), device=device, dtype=type_complex) @@ -321,21 +635,82 @@ def transfer_2d_4(big_F, big_G, big_T, kz_top, kz_bot, psi, theta, n_top, n_bot, ] ) - final_RT = torch.linalg.inv(final_A) @ final_B + final_A_inv = meeinv(final_A, use_pinv) + final_RT = final_A_inv @ final_B - R_s = final_RT[:ff_xy, :].flatten() # TODO: why flatten? - R_p = final_RT[ff_xy:2 * ff_xy, :].flatten() + R_s = final_RT[:ff_xy, :].reshape((ff_y, ff_x)) + R_p = final_RT[ff_xy: 2 * ff_xy, :].reshape((ff_y, ff_x)) big_T1 = final_RT[2 * ff_xy:, :] + big_T_tetm = big_T.clone().detach() big_T = big_T @ big_T1 - T_s = big_T[:ff_xy, :].flatten() - T_p = big_T[ff_xy:, :].flatten() + T_s = big_T[:ff_xy, :].reshape((ff_y, ff_x)) + T_p = big_T[ff_xy:, :].reshape((ff_y, ff_x)) + + de_ri_s = (R_s * R_s.conj() * (kz_top / (n_top * torch.cos(theta))).real).real + de_ri_p = (R_p * R_p.conj() * (kz_top / n_top ** 2 / (n_top * torch.cos(theta))).real).real + + de_ti_s = (T_s * T_s.conj() * (kz_bot / (n_top * torch.cos(theta))).real).real + de_ti_p = (T_p * T_p.conj() * (kz_bot / n_bot ** 2 / (n_top * torch.cos(theta))).real).real + + de_ri = de_ri_s + de_ri_p + de_ti = de_ti_s + de_ti_p + + res = {'R_s': R_s, 'R_p': R_p, 'T_s': T_s, 'T_p': T_p, + 'de_ri_s': de_ri_s, 'de_ri_p': de_ri_p, 'de_ri': de_ri, + 'de_ti_s': de_ti_s, 'de_ti_p': de_ti_p, 'de_ti': de_ti} + + # TE TM incidence + psi_tm = torch.tensor(0, dtype=type_complex) + final_B_tm = torch.cat( + [ + torch.cat([-torch.sin(psi_tm) * delta_i0], dim=1), + torch.cat([torch.cos(psi_tm) * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * torch.sin(psi_tm) * n_top * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * n_top * torch.cos(psi_tm) * delta_i0], dim=1), + ] + ) + + psi_te = torch.tensor(torch.pi/2, dtype=type_complex) + final_B_te = torch.cat( + [ + torch.cat([-torch.sin(psi_te) * delta_i0], dim=1), + torch.cat([torch.cos(psi_te) * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * torch.sin(psi_te) * n_top * torch.cos(theta) * delta_i0], dim=1), + torch.cat([-1j * n_top * torch.cos(psi_te) * delta_i0], dim=1), + ] + ) + + final_B_tetm = torch.hstack([final_B_te, final_B_tm]) + final_RT_tetm = final_A_inv @ final_B_tetm + + R_s_tetm = final_RT_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + R_p_tetm = final_RT_tetm[ff_xy: 2 * ff_xy, :].T.reshape((2, ff_y, ff_x)) + + big_T1_tetm = final_RT_tetm[2 * ff_xy:, :] + big_T_tetm = big_T_tetm @ big_T1_tetm + + T_s_tetm = big_T_tetm[:ff_xy, :].T.reshape((2, ff_y, ff_x)) + T_p_tetm = big_T_tetm[ff_xy:, :].T.reshape((2, ff_y, ff_x)) + + de_ri_s_tetm = (R_s_tetm * R_s_tetm.conj() * (kz_top / (n_top * torch.cos(theta))).real).real + de_ri_p_tetm = (R_p_tetm * R_p_tetm.conj() * (kz_top / n_top ** 2 / (n_top * torch.cos(theta))).real).real + + de_ti_s_tetm = (T_s_tetm * T_s_tetm.conj() * (kz_bot / (n_top * torch.cos(theta))).real).real + de_ti_p_tetm = (T_p_tetm * T_p_tetm.conj() * (kz_bot / n_bot ** 2 / (n_top * torch.cos(theta))).real).real + + de_ri_tetm = de_ri_s_tetm + de_ri_p_tetm + de_ti_tetm = de_ti_s_tetm + de_ti_p_tetm + + res_te_inc = {'R_s': R_s_tetm[0], 'R_p': R_p_tetm[0], 'T_s': T_s_tetm[0], 'T_p': T_p_tetm[0], + 'de_ri_s': de_ri_s_tetm[0], 'de_ri_p': de_ri_p_tetm[0], 'de_ri': de_ri_tetm[0], + 'de_ti_s': de_ti_s_tetm[0], 'de_ti_p': de_ti_p_tetm[0], 'de_ti': de_ti_tetm[0]} - de_ri = R_s * torch.conj(R_s) * torch.real(kz_top / (n_top * torch.cos(theta))) \ - + R_p * torch.conj(R_p) * torch.real((kz_top / n_top ** 2) / (n_top * torch.cos(theta))) + res_tm_inc = {'R_s': R_s_tetm[1], 'R_p': R_p_tetm[1], 'T_s': T_s_tetm[1], 'T_p': T_p_tetm[1], + 'de_ri_s': de_ri_s_tetm[1], 'de_ri_p': de_ri_p_tetm[1], 'de_ri': de_ri_tetm[1], + 'de_ti_s': de_ti_s_tetm[1], 'de_ti_p': de_ti_p_tetm[1], 'de_ti': de_ti_tetm[1]} - de_ti = T_s * torch.conj(T_s) * torch.real(kz_bot / (n_top * torch.cos(theta))) \ - + T_p * torch.conj(T_p) * torch.real((kz_bot / n_bot ** 2) / (n_top * torch.cos(theta))) + result = {'res': res, 'res_tm_inc': res_tm_inc, 'res_te_inc': res_te_inc} - return de_ri.real, de_ti.real, big_T1, [R_s, R_p], [T_s, T_p] + return result, big_T1 diff --git a/meent/on_torch/mee.py b/meent/on_torch/mee.py index 44d9b87..1a24f1b 100644 --- a/meent/on_torch/mee.py +++ b/meent/on_torch/mee.py @@ -19,7 +19,7 @@ def __init__(self, device=0, type_complex=0, *args, **kwargs): self._device = device else: raise ValueError('device') - + # # type_complex if type_complex in (0, torch.complex128, np.complex128): self._type_complex = torch.complex128 @@ -27,13 +27,13 @@ def __init__(self, device=0, type_complex=0, *args, **kwargs): self._type_complex = torch.complex64 else: raise ValueError('Torch type_complex') - + # self._type_float = torch.float64 if self._type_complex is not torch.complex64 else torch.float32 self._type_int = torch.int64 if self._type_complex is not torch.complex64 else torch.int32 # self.perturbation = perturbation - + # self.device = device - self.type_complex = type_complex + # self.type_complex = type_complex ModelingTorch.__init__(self, device=device, type_complex=type_complex, *args, **kwargs) RCWATorch.__init__(self, device=device, type_complex=type_complex, *args, **kwargs) diff --git a/meent/on_torch/modeler/modeling.py b/meent/on_torch/modeler/modeling.py index d3d2ca0..978abe8 100644 --- a/meent/on_torch/modeler/modeling.py +++ b/meent/on_torch/modeler/modeling.py @@ -38,7 +38,7 @@ def __init__(self, period=None, *args, **kwargs): self.mat_table = None self.ucell_info_list = None self.period = period - self.type_complex = torch.complex128 + # self.type_complex = torch.complex128 # self.type_float = torch.float64 self.film_layer = None diff --git a/meent/on_torch/optimizer/loss.py b/meent/on_torch/optimizer/loss.py deleted file mode 100644 index d1a4ca2..0000000 --- a/meent/on_torch/optimizer/loss.py +++ /dev/null @@ -1,31 +0,0 @@ -import torch - - -class LossDeflector: - def __init__(self, x_order=0, y_order=0): - self.x_order = x_order - self.y_order = y_order - - def __call__(self, value, *args, **kwargs): - de_ri, de_ti = value - - if len(de_ti.shape) == 1: - c_x = de_ti.shape[0] // 2 - res = de_ti[c_x + self.x_order] - elif len(de_ti.shape) == 2: - c_x = de_ti.shape[0] // 2 - c_y = de_ti.shape[1] // 2 - res = de_ti[c_x + self.x_order, c_y + self.y_order] - else: - raise ValueError - - return res - - -class LossSpectrumL2: - def __init__(self): - pass - - def __call__(self, pred, target, *args, **kwargs): - gap = torch.linalg.norm(pred, target) - return gap diff --git a/setup.py b/setup.py index 35ca149..6c4c752 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,5 @@ +import os + from setuptools import setup, find_packages extras = { @@ -10,9 +12,14 @@ 'tqdm>=4.64.1', ], } +# Read in README.md for our long_description +cwd = os.path.dirname(os.path.abspath(__file__)) +with open(os.path.join(cwd, "README.md"), encoding="utf-8") as f: + long_description = f.read() + setup( name='meent', - version='0.10.0', + version='0.11.0', url='https://github.com/kc-ml2/meent', author='KC ML2', author_email='yongha@kc-ml2.com', @@ -23,6 +30,10 @@ ], extras_require=extras, python_requires='>=3.8', + description=( + "Electromagnetic simulation (RCWA) & optimization package in Python" + ), + long_description=long_description, long_description_content_type="text/markdown", package_data={ 'meent': ['nk_data/filmetrics/*.txt', 'nk_data/matlab/*.mat'], diff --git a/tutorials/01-modeling-and-emsolver.ipynb b/tutorials/01-modeling-and-emsolver.ipynb index 043b7f6..250efce 100644 --- a/tutorials/01-modeling-and-emsolver.ipynb +++ b/tutorials/01-modeling-and-emsolver.ipynb @@ -296,11 +296,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness, type_complex=type_complex)" + "mee = meent.call_mee(backend=0, \n", + " pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, fto=fto, \n", + " wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness,\n", + " type_complex=type_complex)" ] }, { @@ -321,49 +324,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Diffraction Efficiency" + "### Case 1: 1D TE/TM\n", + "For 1D TE or TM case, fast calculation can be achieved.\n", + "Setting phi as `None` (which is default). \n", + "\n", + "This returns result of either TE or TM while the general case does both." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 8, "metadata": {}, + "outputs": [], "source": [ - "#### Diffraction" + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, fto=fto, \n", + " wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness,\n", + " type_complex=type_complex)\n", + "mee.fto = [200]\n", + "mee.pol = 0 # 0 or 1. Other values will be operated in General form.\n", + "mee.phi = None # None is by default. This explicit assign is for demo.\n", + "\n", + "result = mee.conv_solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - " Incidence Backward Diffraction \n", - " (Reflected)\n", - " || \n", - " || -1th 0th +1th\n", - " || order order order\n", - " || \\ | /\n", - " || ... \\ | / ...\n", - " || \\ | / \n", - " || \\ | / n_top:refractive index of superstrate\n", - " ____________________________________\n", - " | Layer 1 |\n", - " |____________________________________|\n", - " . z-axis \n", - " . |\n", - " . |\n", - " ____________________________________ |_____ x-axis \n", - " | Layer N |\n", - " |____________________________________|\n", - " n_bot:refractive index of substrate\n", - " / | \\ \n", - " / | \\\n", - " ... / | \\ ...\n", - " / | \\\n", - " -2nd -1th 0th +1th +2th\n", - " order order order order order\n", - " \n", - " Forward Diffraction \n", - " (Transmitted) " + "For 1D TE/TM, the result is in `res` in `result`." ] }, { @@ -375,82 +363,294 @@ "name": "stdout", "output_type": "stream", "text": [ - "time: 0.11336421966552734\n" + "Diffraction efficiency: 0.8028173479774741 0.19718265202260613\n" ] } ], "source": [ - "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", - "print(f'time: ', time.time() - t0)" + "res = result.res\n", + "de_ri = res.de_ri\n", + "de_ti = res.de_ti\n", + "print('Diffraction efficiency: ', de_ri.sum(), de_ti.sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case 2: General\n", + "General form includes 1D grating with non TE or TM input, 1D conical and 2D gratings.\n", + "\n", + "This returns 3 results: result from given polarization(or psi), result from TE incidence, and result from TM incidence." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, + "outputs": [], + "source": [ + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, \n", + " wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness,\n", + " type_complex=type_complex)\n", + "mee.fto = [200]\n", + "mee.pol = 0.5 \n", + "mee.phi = 30\n", + "\n", + "result = mee.conv_solve()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The return `result` has 3 sub-result classes each of which contains: result for given polarization (or psi), for TE incidence and for TM incidence." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Diffraction Efficiency of Reflection: [[0. ]\n", - " [0.287]\n", - " [0. ]]\n", - "Diffraction Efficiency of Transmission: [[0. ]\n", - " [0.713]\n", - " [0. ]]\n" + "Diffraction efficiency: 0.6154849015525773 0.3845150984544151\n", + "Diffraction efficiency: 0.13853479697001853 0.8614652030395099\n", + "Diffraction efficiency: 0.7254065902537097 0.2745934097456641\n" ] } ], "source": [ - "center = de_ri.shape[0] // 2\n", + "res = result.res\n", + "res_te_inc = result.res_te_inc\n", + "res_tm_inc = result.res_tm_inc\n", + "\n", + "de_ri, de_ti = res.de_ri, res.de_ti\n", + "de_ri_te, de_ti_te = res_te_inc.de_ri, res_te_inc.de_ti\n", + "de_ri_tm, de_ti_tm = res_tm_inc.de_ri, res_tm_inc.de_ti\n", "\n", - "print('Diffraction Efficiency of Reflection:', np.round(de_ri[center-1:center+2], 3))\n", - "print('Diffraction Efficiency of Transmission:', np.round(de_ti[center-1:center+2], 3))" + "print('Diffraction efficiency: ', de_ri.sum(), de_ti.sum())\n", + "print('Diffraction efficiency: ', de_ri_te.sum(), de_ti_te.sum())\n", + "print('Diffraction efficiency: ', de_ri_tm.sum(), de_ti_tm.sum())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Field Distribution" + "call meent operator by `meent.call_mee`.\n", + "Here, backend can be selected with keyword `backend`\n", + "\n", + "```python\n", + "backend = 0 # Numpy backend\n", + "backend = 1 # JAX backend\n", + "backend = 2 # PyTorch backend\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 RCWA Result" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "fto = [4, 2]\n", + "thickness = [100, 200, 400, 245]\n", + "period = [1000, 2000]\n", + "\n", + "ucell_2d_m = np.array([\n", + " [\n", + " [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, ],\n", + " [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ],\n", + " ],\n", + " [\n", + " [1, 1, 0, 1, 1, 0, 1, 0, 2, 1, ],\n", + " [0, 1, 1, 1, 2, 4, 1, 0, 1, 1, ],\n", + " ],\n", + " [\n", + " [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, ],\n", + " [1, 1, 1, 2, 0, 1, 2, 0, 1, 1, ],\n", + " ],\n", + " [\n", + " [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, ],\n", + " [0, 1, 3, 1, 1, 1, 1, 1, 1, 1, ],\n", + " ],\n", + "]) * 4 + 1 # refractive index\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_2d_m, \n", + " thickness=thickness, type_complex=type_complex)\n", + "mee.pol = 0.5\n", + "\n", + "result = mee.conv_solve()\n", + "\n", + "res = result.res\n", + "res_te_inc = result.res_te_inc\n", + "res_tm_inc = result.res_tm_inc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RCWA result" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time: 0.04631304740905762\n" + "R_s, R_p, T_s, T_p, de_ri, de_ri_s, de_ri_p, de_ti, de_ti_s, de_ti_p\n" ] } ], "source": [ - "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_z=100, res_y=1, res_x=100)\n", - "print(f'time: ', time.time() - t0)" + "attrs = vars(res)\n", + "print(', '.join(\"%s\" % item for item in attrs))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### ZX direction (Side View)" + "Attributes of subresult instance.\n", + "\n", + "\n", + "R_s: reflectivity coefficient (Rayleigh coefficient) from TE component
\n", + "R_p: reflectivity coefficient (Rayleigh coefficient) from TM component
\n", + "T_s: transmittivity coefficient (Rayleigh coefficient) from TE component
\n", + "T_p: transmittivity coefficient (Rayleigh coefficient) from TM component
\n", + "de_ri: diffraction efficiency of reflection in total
\n", + "de_ri_s: diffraction efficiency of reflection from TE component
\n", + "de_ri_p: diffraction efficiency of reflection from TM component
\n", + "de_ti: diffraction efficiency of transmission in total
\n", + "de_ti_s: diffraction efficiency of transmission from TE component
\n", + "de_ti_p: diffraction efficiency of transmission from TM component
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diffraction Efficiency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " Incidence Backward Diffraction \n", + " (Reflected)\n", + " || \n", + " || -1th 0th +1th\n", + " || order order order\n", + " || \\ | /\n", + " || ... \\ | / ...\n", + " || \\ | / \n", + " || \\ | / n_top:refractive index of superstrate\n", + " ____________________________________\n", + " | Layer 1 |\n", + " |____________________________________|\n", + " . z-axis \n", + " . |\n", + " . |\n", + " ____________________________________ |_____ x-axis \n", + " | Layer N |\n", + " |____________________________________|\n", + " n_bot:refractive index of substrate\n", + " / | \\ \n", + " / | \\\n", + " ... / | \\ ...\n", + " / | \\\n", + " -2nd -1th 0th +1th +2th\n", + " order order order order order\n", + " \n", + " Forward Diffraction \n", + " (Transmitted) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 Field construction result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1D TE" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "pol = 0 # 0: TE, 1: TM\n", + "\n", + "n_top = 1 # n_superstrate\n", + "n_bot = 1 # n_substrate\n", + "\n", + "theta = 20 * np.pi / 180\n", + "phi = 50 * np.pi / 180\n", + "\n", + "wavelength = 900\n", + "\n", + "thickness = [500]\n", + "period = [1000]\n", + "\n", + "fto = [30]\n", + "\n", + "type_complex = np.complex128\n", + "\n", + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, fto=fto, \n", + " wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness,\n", + " type_complex=type_complex)\n", + "\n", + "result, field_cell = mee.conv_solve_field(res_z=100, res_y=1, res_x=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ZX direction (Side View)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -480,17 +680,54 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### YZ Direction (Top View)" + "### 1D TM" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "pol = 1 # 0: TE, 1: TM\n", + "\n", + "n_top = 1 # n_superstrate\n", + "n_bot = 1 # n_substrate\n", + "\n", + "theta = 20 * np.pi / 180\n", + "phi = 50 * np.pi / 180\n", + "\n", + "wavelength = 900\n", + "\n", + "thickness = [500]\n", + "period = [1000]\n", + "\n", + "fto = [30]\n", + "\n", + "type_complex = np.complex128\n", + "\n", + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, fto=fto, \n", + " wavelength=wavelength, period=period, ucell=ucell_1d_s, thickness=thickness,\n", + " type_complex=type_complex)\n", + "\n", + "result, field_cell = mee.conv_solve_field(res_z=100, res_y=1, res_x=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ZX direction (Side View)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -507,7 +744,7 @@ " title = ['1D Hy', '1D Ex', '1D Ez', ]\n", "\n", "for ix in range(len(title)):\n", - " val = abs(field_cell[0, :, :, ix]) ** 2\n", + " val = abs(field_cell[:, 0, :, ix]) ** 2\n", " im = axes[ix].imshow(val, cmap='jet', aspect='auto')\n", " # plt.clim(0, 2) # identical to caxis([-4,4]) in MATLAB\n", " fig.colorbar(im, ax=axes[ix], shrink=1)\n", @@ -520,17 +757,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1.3 Example: multilayer 2D" + "### 2D" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "grating_type = 2\n", - "fto = [10, 9]\n", + "fto = [4, 2]\n", "thickness = [100, 200, 400, 245]\n", "period = [1000, 2000]\n", "\n", @@ -551,98 +787,29 @@ " [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, ],\n", " [0, 1, 3, 1, 1, 1, 1, 1, 1, 1, ],\n", " ],\n", - "]) * 4 + 1 # refractive index\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_2d_m, thickness=thickness, type_complex=type_complex)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time: 10.542928695678711\n" - ] - } - ], - "source": [ - "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", - "print(f'time: ', time.time() - t0)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Diffraction Efficiency of Reflection:\n", - " [[0.028 0.056 0. ]\n", - " [0.183 0.036 0. ]\n", - " [0. 0. 0. ]]\n", - "Diffraction Efficiency of Transmission:\n", - " [[0.063 0.004 0. ]\n", - " [0.025 0.052 0. ]\n", - " [0. 0. 0. ]]\n" - ] - } - ], - "source": [ - "center = de_ri.shape[0] // 2\n", + "]) * 4 + 1 # refractive index\n", "\n", - "print('Diffraction Efficiency of Reflection:\\n', np.round(de_ri[center-1:center+2, center-1:center+2], 3))\n", - "print('Diffraction Efficiency of Transmission:\\n', np.round(de_ti[center-1:center+2, center-1:center+2], 3))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time: 3.1625919342041016\n" - ] - } - ], - "source": [ - "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_z=40, res_y=40, res_x=40)\n", - "print(f'time: ', time.time() - t0)" + "mee = meent.call_mee(backend=0, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_2d_m, \n", + " thickness=thickness, type_complex=type_complex)\n", + "result, field_cell = mee.conv_solve_field(res_z=100, res_y=100, res_x=100)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### ZX direction (Side View)" + "ZX direction (Side View)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -694,12 +861,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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p+88XZdtxY7NKlE0swQhRnmyapCqbHRXlUu2ekPojv48AM9axvh5pcUDB0XSkLWgIECkd8TItHfths79I2YSLvNtt4ooAY2DLWGHAlKc8LTZZjutIrWAXPfw+F/FLPwlpkzT0a0pNS3eEEOJbNB2hhhBCbPH7XIQPO4R4mPbQBykHLiHEFk1HqCGEEFv8PhfxSz8JaZOcXNteEvoi1oSQtoqmI9QQQogtfp+L+OhhR3oLan7cNjESsh0tz4OyJnqpjL+ROVQA0x87kuvvS594mdMGMG9Fzb9bHn9fxUbzI5do68bnB9kXoPvO5rqLz402LGqSRIzOWq1PMtZJu/42t7w2pOW9pcVVRZ6GXh37RWCizz/gznAtr2OquUmSiNkpTTNtTsi4lveVfecrdTbxJ+FkM9DaDTWHSyj7t/GJ12J/bLRFap0WV6XFLEq91cao7JN23izGdr7MuwWYx5av2Gj+/RJ5Luv3scJiexNNR6ghtjQUrVD//5LQ4zNbN36JKxIaIkMsAQx4xZ1Q8YubfmoaLX9JVBhJGBvARleDbWND5HL++UVHfPSwQ0jbw+9BgYSQ6NOWFygghDQdv89F/NJPQtokDf2aEm5ueEJI20PTEWoIIcQWv89FPPyw8zVqvQRPIpdR1dwY5Cs/zWXAxh0pX6mTl1l7xpUucakW+9dc3bR+27hfSDeK75sms0X5FqWZIuXcPibq1qQqG74sytpxaMNFuP8tNN3YjNfJ2rLaVtdIQ7r2aK6N8t6yWTJWXiNtvfLGiY8DOgaU3jgI16OljXEG3PeFHEd/MTcpki6i2rLONu5I2rLS8p7QxoPNfSvvP83dQVt6OjVIGTA1Kl+xkW68msuajRup4ieCse5ivOJq20PZ7JC0076G5bnVzr90o9NcfW1c/TRXY9knzdVY3jd76/0dnnuUpiPUEFtOoHF3opZ20Wrr2OhjuNdkp7v4mLns/Bepgy3akXMozY0tUstR2yyrLW1Cf0Tx+1zEww87hJAO7YAOisB08EsmL0JI1NF0hBpCCLHF73MRPuwQ4mE6xgEdY8z6qhqEm1+QENLG0HSEGkIIscXvcxE+7BDiZeIAKALjm0xehJDoo+kINYQQYovP5yIeftiRfrLSB3Kcso200eIq9opyfgP7ltjE40gfeW3/8pRr8SG2SzZLtDgmwQ5Rzldstil1BWJ5aHVDiXZ7acemxUQIpNu8Fv5ySJ5Lza9e65M8b1ocgUSL9QoWRxFGKF97+Fpgoo/MDiDvPy3WRF6nVIv9aOPYJq5Li/2R96h238h7SxtX2nYyRlCLAZHnRGomYB5buEtYf6HUiXNSPsQ0OaTUGedNi1kS2/UYYZpscBfnj/wPw+Qr9DHq/t+rd7or7lZ2v0/GQ8mUBoB5beufxzB9RjQdoYYQT2Ebw2IzbQ1HQ7W6xaZJluynHNNA9Jceb4Y4Mp/PRTz8sEMIQSyAdkq9Xxa3J4REH01HqCGEEFt8Phfhww4hXiYevhYYQogH0HSEGkIIscXncxE+7BDiZdpBFxhCCLGFOkIIaQo+1xAPP+z0hNtXWfpoW+ReOKrFsEifdc33+yylTsSVJCkmRbJCW1td5JRRff0nmlVdRJ9Slc0kBUrdFllxSDHaqdR9JMo2/vg28QgAMMZdvEoxuU6Uuyg2S4a6y2u1/W9X6uQ9YRPXo103eU5kLFIYfrxxDXQnxPCf7OxsvPzyy9i9ezc6duyICy+8EI888ggGDRpUZ1NeXo677roLq1atQkVFBTIyMvDUU08hOdkiFsyz9ALQuV5ZXgPN31qixXBJjdDakWMdMONftLgem/tE+o1rN4mWw0vbn8QmHuhsUVZiX9RYvHxR1s6R7KPWZy3WRZ43LR5L9EnL1yXiGpeM/IlhUrDiDHO7G+WSREuUxuW5lTl9APO7rf74K1PsLdB0xC/ZAD1HsBwmfsU2Zqa5jtcmXwxgd+PKm13TR21/YjxOutWw+Okbj7vKf8UEw2ZX4DxR8xtlX9pxyDmE9l0QpfstQnORaKGFGxFCvEJcI58Q2LRpEzIzM7Ft2zasX78eVVVVuPzyy1FWdmryNGfOHLz22mtYvXo1Nm3ahK+++gpTp06N2KEQQqJEBDSEENKGidBcJFp4+M0OIQTtEZFRum7dOld5+fLl6NWrF3Jzc3HxxRejuLgYTz/9NFauXIkJE2p/qVq2bBmGDBmCbdu24YILLmh6Jwgh0SFCOkIIaaP4XEP4ZocQLxML/ZeU2KY1W1xcDADo1q3WHS83NxdVVVWYOPGUC+XgwYPRv39/bN26tWk7I4REF01HmqghhJA2RDPNRVoKDz+ndYTbf1H4hCthLUYchxGfAuCo9P/Ot+tO/Gh3eZTF/ndNNm3yzxUVWsyMEiOQIspanpl9MgfDy4qRjCPS/D81X3vpW27jj2/rWyqcPosUExl/lKrYGP73Wn4QLY5H+gVrsQ7Sl1ZzVJX+tRb5g4IRD91t+btAwZISd6xIXFwc4uIaf69cU1OD2bNnY9y4cRg+fDgAoKCgALGxsUhKSnLZJicno6BAC/7yC13hHpg2sWZSFpUYPvmi65ByrQ/1UtqW96TWH5t7zSZfl7ad1D8t1sjGd1+OI5sxA5gDV7Ox6eMGpc4m94+4JuWKT/wtbpuCWzTNeNNi/wMUGyXW1ECek/rlMH+f1HTEx8HGLUt7uE+eHHutJWbHlpaMWWrpcyt0fLBpMVaN+3Wzq8f57oqjmhZp8TjNdW/J/YexhFqQuYjX4ZsdQrxMu0Y+APr164fExMS6T3Z2dtAmMzMzsWvXLqxatarZuk0I8RCNaAghhAQlyFzE63j4zQ4hpMHXxN/9THHw4EEkJJz6NSrYW52srCysXbsWmzdvRt++p35lTklJQWVlJYqKilxvdwoLC5GSIl8rEkJ8haYj/KmTEGJLkLmI1/FJNwlpowRZASUhIcH1aehhx3EcZGVl4ZVXXsHbb7+NtLQ01/9Hjx6NDh06YOPGjXV1e/bswYEDB5Cent4MB0YIaTF8vIoSIcQDcDW2KBGv1Mk8O1p6jKMip4vqD77PrCoXfvtJio/+jaKs5YLZldx4uYHdy9wPKNfiUWQuHI1Z7uIo5ThSlc1kqM/uTxSjTy32r/mg5oiicmw5slNajMImUdau7TVmVXtxbbUXGTJG6uh+xcjm/IdIQyug1ITWTGZmJlauXIlXX30VXbt2rYvDSUxMRMeOHZGYmIibb74Zc+fORbdu3ZCQkIBZs2YhPT3d5yuxHQbQqZH/a3FV0pc61zTZJmPv8pV25P0ImLEtMl8NYOb+0mJG5P5sfbvlzaTFzMhz0lWxkfFA7yo2Wp3Uu1TFRhvbEi33Tji5v7R8PfJcRjLXhU2eJ9l293p/y1w+lmg6EqKGtF06wj3hsMlDFc794cU8N34l3GMRubkWmnORmTnPuyvylWaKXhIVNvncgMjlWAu2TcCuO3I3EZiLRAu+2SHEy0RoBZTFixejuLgY48ePR+/eves+zz9/SrgXLFiAK664AtOmTcPFF1+MlJQUvPyytsgFIcRXNMNqbA8//DACgQBmz55dV1deXo7MzEx0794dXbp0wbRp01BYqC1eQQjxFRGai2zevBlTpkxBnz59EAgEsGbNmkbtc3JyEAgEjE+oCyf5980OIW2BeOiviUP8YcZx5Ep9yq7i47Fo0SIsWrQotMYJId5G05Ewftw9yd/+9jf84Q9/wIgR7lVS58yZg9dffx2rV69GYmIisrKyMHXqVLz7rvaWjxDiGyI0FykrK8PIkSPx4x//OKSk5Xv27HHFJ/fqpa122jB82CHEyzS02olPVkAhhHgATUfC1JDS0lLccMMN+OMf/4iHHnqorp6JiQlpxURoLjJ58mRMnqykZQlCr169jNQYoeDdh53EG4BAPd9xmZ4gX9lG5tUp0hqWvotaYI8WeyJ8N3eNNk1kXI2R9wVGeAp2KTbqOu428SDS116JB2ovbK5SmlHWlsdCWaHFEcj92fqpSl97LY5C1mn7l7EFN5gmtyttTxLlfKXpx2SFFjMgfqVIEjetUwIU36Fs1whx0OPTiCUlcMeXyBgZGR8DmONfy+nygShr+VM0bbHZf3elTiLHjOwPoN+jUvK1Psq8QjLHlsZOpU7rU74oa771Iqea2kftvMmcZXJfgBlrlGqapAht/6HSzFGlbpXUuxWK0ReirB2//I6qXw4zZieCOpKZmYkf/OAHmDhxouthJ1hiYv8+7FShZX5dak0xM37FYgznpYoKTZ+0+Uk4aHFc4Uzb5b0Vxr0W5bnIqFGjUFFRgeHDh2P+/PkYN25cSNuHHLMTzN/OcRzcd9996N27Nzp27IiJEydi714tmJ4QEhSfr4CiQQ0hpIVpRENKSkpcn4qKigabWbVqFT744AM1n1dLJyamjhDSggSZi4SiI6HQu3dvLFmyBC+99BJeeukl9OvXD+PHj8cHH2g/pjVMyA87J/3tGvLr/81vfoMnnngCS5Yswfbt29G5c2dkZGSgvFwuZ0UICUr7Rj4+hRpCSAvTiIbYJiY+ePAg7rzzTqxYsQLx8dF/3UwdIaQFCTIXCSfBuQ2DBg3CT37yE4wePRoXXnghnnnmGVx44YVYsGBByN0Picb87RzHwcKFC/GLX/wCV155JQDg2WefRXJyMtasWYPrrrvOfkdz4H5lliP+v05bMlQGQaYqNtJFQluu9EulTrg/7FPc2NaKsuqiliPK+YpNqlmVdKu7LJe51nhOqSsSlfO1162XKHXStSZVsbFxrVFuuS7iYF40TXpmHHCVv14hryOAG8eKCiUof5RZ1eEi96vrqi2Kq9sh6SOZb9oMF/eEvEblAOabmzXKyRVQJD5Z7lGjxTQEAPA9uNwbBwtXS80DIWeou1wqlxEFTLctTUc0Vytpp7l6irGmeci1F/d/vraEteZ+J9e111zd5K/fmh5KNwhtxS2bZVQ1G+mOZos8FumyBpj6oyxzfZ4o3624fLSvNusKhG7k2LhIay670h24/jLnYbrHaDrynYbYJibOzc3FkSNHcO65p/pTXV2NzZs348knn8Sbb77ZoomJW05HTkC/lxpDuh9F20UtUstjh4umj/KcRrI/NudfOycipUC8cn8Zz8qahstxrdlo2iuxOSc2NvJYQ72fEXQuEmqC86YwZswYbNki52SNE9Glp/fv34+CggKXz25iYiLGjh2LrVu3RnJXhLQN4hv5tEKoIYQ0A41oiG1i4ssuuww7d+5EXl5e3ee8887DDTfcUPe3VxITU0cIiTBB5iK2OhIJ8vLy0Lt375C2iagzzEm/3ORk9y9TjfnsVlRUuHz7Skpsg9oJaQO0sdXYwtEQgDpCSKNEYDW2rl27Yvjw4a66zp07o3v37nX1XklMzLkIIREmQnOR0tJS7Nt3ysNg//79yMvLQ7du3dC/f3/MmzcPX375JZ599lkAwMKFC5GWloZhw4ahvLwcS5cuxdtvv4233norpP1G3fM/OzsbDzzwQLS7QYg3aWgFFB+7sTUH1BFCGkHTkWbQkAULFiAmJgbTpk1DRUUFMjIy8NRTT0V+R80ANYSQRojQXGTHjh249NJL68pz584FAMyYMQPLly/H4cOHceDAqbCFyspK3HXXXfjyyy/RqVMnjBgxAhs2bHC1YUNEH3ZO+uUWFha6XjEVFhZi1KhR6jbz5s2rO1ig9teUfv36Ac/D/cS463mxpfKry3AR17LW9GXs1tftf/7PSWlmOxs0f0bhczpcMZF12tnNGy8qNP90xf+9SJS1GMuJopyk2DwkA0nkeQWAI0qdPCeav6n0R9f8+BU/9pnu4n9nmGu9DsUnrvKEH75t2BTPFH7hJ5REdk9eZFRVrRP+tWs+MWzM+IdbTBN5aDJmSnHzD0pDK6+F05YPCEdDgEZ0BL3g8p+WY3S80pi8jgunKUYydkLGwgB6rIv0Xdf8xsVSz4e02J9UUbb9FVrGyGjbaeNfIrVVOw65FDygx6gE279crlnbv9YHTYAt4gpl7GWO8h3RXjneIrkstBJXaIi0htTW+jFcWuyDBZqOREBDcnJyXGWvJCaO6FzEl9iMBUlzxvCEESNijaY9NuNEOyfu8XnF8dWGxaV4x1W+62rlYX6NTTxOuEQp/itCc5Hx48c3muR8+fLlrvI999yDe+65J7SdKEQ0ZictLQ0pKSkun92SkhJs3769QZ/duLg4w9ePEPIdrXA1tsYIR0MA6gghjdKGNATgXISQiOPzuUjI3Qzmbzd79mw89NBDOOuss5CWloZf/vKX6NOnD6666qpI9puQtkFDK6A05w9lzQw1hJAWRtMRH2sIQB0hpEXx+Vwk5IedYP5299xzD8rKynDbbbehqKgIF110EdatW+eJdfkJ8R0NrbzmYzc2agghLYymIz7WEIA6QkiL4vO5SMBpzHkuCpSUlCAxMRHARgCd6/3nI7fhiyI+B4DTNeAqv5Rhtj/tX93ls19/37DZFSjSeuYuDlf8+JNEWV0GPEeUtbgWzY9e+v/LXAwAMEaUNb9VGVsg42wa2k7mo9DyY8iEIAMUG2V/P3Sfy/dXmzlDuuOoq3zmZ1+Z7YxyX3+Ublf2r/m7ymugxCx0+am7rOQCMvIq3S33XwbgMhQXFwd1kTg5DorfBBI6K/8vAxIzYNVWW+SUjvwO7vt5vNsw6Sxz4y6irKZ9kYNb5t0B9Lg2m1gXG59sec2VfDEq8l63yYVjg41mAGa/tWOVfdLa1vRPop1/efxa3hoZayTzdzVQJ+UvVdmsVJTzZJwPALwsysfF33daj/vGdIQa0jinNOTXcM/y5P2o3cPy525tnEVKCzTCidnRfqKPdn4gDe08ScK5RgDa/9xV/GuVmU/xoj+74/wCHylT6Id/bbF/jXDyM4VzPsoB/LxNzUV84m1HSBuloRVQfPLqmBDiATQdoYYQQmzx+VyEDzuEeJmGVkDx4g9uhBBvoukINYQQYovP5yJ82CHEyzS02glHLiHEFk1HqCGEEFt8PhfxcDd7wO07Pc7137Om/d3c5N/dRc2LfppImVCo+n4XKXXC/3u3YmLwhlInYo8wxTRJGRq8S+V7lbZlPI4WbCDfOY4zTYabuWiMtDI9lKZTg3QHMHLqAABedPutL1Vy2Pw7VrrKN5651LB5TsZxTdJ87R9X6nq5izI+B8CAY+4L/keYMWO3ZvzRVf7ibtGuGh8QhIZWQKkMvam2SQe4fZrFmCx6zdykSFZoMin9pG1jX2xibWSd5get5bCRaPlyZDygTb9tYgm0drT9yzgam/w8qYqNlsNG9kHTSNlvzQdDjlOZYwsAlBxeh2zyIwkbNT5TfifVj9cM02dE0xFqiCVylifjHzR9sMmzYhNXo9XJe0D7aT1SP7dHMo6oufZvE7OijRvl3J5wt/17/Mwwuahkptsm25yvzHpRzE/2/dGw0e8ROfY1XQ0nh08Efunw+VzEww87hJAGV0DxyatjQogH0HSEGkIIscXncxE+7BDiZXz+6pgQ4gHoxkYIaQo+n4v4pJuEtE1OxAInlFfHJypavi+EEH+i6Qg1hBBii9/nIh5+2EmF21fdnddi7/fMuJJef/3CVb5wpelXHXj3elGh7fu/lbpr3EXTTdPMs7BhsmmTJ+q02BftVWGBrNB8OQeKsrJ/2bZ2ByghO/hhuas48vQ8w0Tmwnk75QdKQ1qMlDtu4v89fadhse/mM13lTtrxG+dSy2GhnTfRJ3kdARRVJLnKn8SZcVUViBU1IkBM9c9vnIq4ACrizJu0Is4B4KkUWR7larh1ROZ50vJMyVgT7Z6RcR3au3yb3DNazI5NPJCN37o2uOX40/oojy3cXDzh+jfI/eUrNlr8mzxerd+ponyjaSK1XTFBnlI3W1a8pBjJ+83MKWbkgnKdxxIAdyjbNI6mI9QQW46j8fOk3WeRyl8VKcLNs6MRTi6Y5sRm/7Z9dOfQeWHFDMPi+QkzXeWs3U8bNrNmipidX2hxlzYapl23cM63vB/LVavG8PtcJCbaHSCENEx1+/YNfkJh8+bNmDJlCvr06YNAIIA1a9a4/j9z5kwEAgHXZ9KkSRE8EkJItIiEhhBC2i6RmotEC3/0kpA2SlW7WFS2M39NqWrnIJRfeMrKyjBy5Ej8+Mc/xtSpU1WbSZMmYdmyZXXluDht6RVCiN/QdCRUDSGEtF0iNReJFnzYIcTDVCAWscoL2ArUACizbmfy5MmYPFlxa6xHXFwcUlJSQu0iIcTjaDoSqoYQQtoukZqLRAsPP+x8CKBLvbLwb9xSaGzxdb/+rvKrSf0NG+ySFTLvDQDMMase6uQqnvvzLYaJjCPZMun7ZjtLRDlH2f0uzf9R5gPRYg1EXpn4s0yTJFEu+MS0WbJJqXPHA/0dyrHJkCEjzgiQPrG1iF8FlHiot+df4a7QUggZJ3O7ZqQgcyjtNyyKU9zxN7MH/sFsxri3ckW51LI/p6hGO1QrAlP9XbBZSYk7DiguLi7sNzI5OTno1asXTjvtNEyYMAEPPfQQunfvHlZb3iEHQKdG/q/5Uss6U2v0HDISmxw6WhyXbFuTaTm2tZwuWnycRDs2maFM05pwf8mT22ntyNgBzbf9S6VOxhLIPFcAcK67+JBpcfjnSa7ykkCxYTNRafl9x52f666znzKNdsl8Gy8oLclcPPWvoxaHGBxNR6r1gFVicALue9ImXi9SRKrtMPMzqUT7V3ybmKFw43hEbq41Zq6+0Tf81VX+YIUS5PwL+d2vJR3U9h96XK8d8vqHfj8Em4t4HcbsEOJhKhHb4AcA+vXrh8TExLpPdnZ2WPuZNGkSnn32WWzcuBGPPPIINm3ahMmTJ6O6ujqSh0MIiQKNaQghhAQj2FzE63j4zQ4hpBJxqFB+k6hEDQDg4MGDSEg49SYi3Lc61113Xd3fZ599NkaMGIEzzzwTOTk5uOyyy8JqkxDiDTQdOakhhBASjGBzEa/DNzuEeJhqxHz3+lh+aoduQkKC6xOpRQXOOOMM9OjRA/v2aa/fCSF+QtcRfv0TQuwINhfxOh5+s/Mu3ElhpB+z4u8o3RC1WOskUd41wrTpYlbJ/DSfFJt5VsqPnuauWK60s1RWmLE/uj+lXCddsxH+puUyiAZAkUgik2Qeh2EDwPTbzzFN9kn/fy0eQMvrIfzotWt7SAbpaDETXYO0C+i+s9L/X8ZHASgS53uH0kzQ3CdhrG2PWHRAO6W+ed3LDh06hG+++Qa9e/du1v00P4PgHtAiPssqZsQmz44mpdp28l6z8dEeoNRJPdTQ2pbxSFrMkowrkuMKMI8tXD9+2/xA4aBppDgnRpwd8Aj+w1VekPOfhs22S0YadXf9ScTo7PpG2b/UJG3REKmR9e/Z0OP+AF1HmltD2g7aPRzOeIh2LEy09x8ukTr/gKFrynThg1wRo/Oi1k6+KNtqWjj5kFrmukVrLhIpPPywQwipQhwqFYGpClFgSktLXW9p9u/fj7y8PHTr1g3dunXDAw88gGnTpiElJQWfffYZ7rnnHgwcOBAZGRlNPgZCSHTRdCRUDSGEtF0iNReJFnzYIcTDnHxVbNaHxo4dO3DppZfWlefOnQsAmDFjBhYvXoyPPvoIf/rTn1BUVIQ+ffrg8ssvx69+9Svm2iGkFaDpiD+mKIQQLxCpuUi08PDDTjzcr/OlS5RcHhVAgXC/Gq80e56yG8kGxf3qbrcbVfndmqtXvijvVmzk0qHjTZMkZbnfIlnxktK2PCdvmCbl40RZcWMbqLi2xIslGDWPLOlhUaC50eQodWKJ3IuU19Lj3Us/Y2KaaSPdDx9TdrVKc22T501b11q6H2nLCsvzJl2mQqcCHdBeGaYVIS4dOX78eDiOtqR5LW+++WbIffMHp8HthiXvSe1aSxc1zR1NorlSaNfIZtla6aKmuZrJ+1FbZl1zGZVLqaYqNnLJZm3/4d7bNktPB3MHBey+ujT9EUvfrzKPY+GqH7vL+KHSTo5SN1+UzWVrAaG/6rltDO1cBEfTkVA1pO1yAv518TpJS/ffZnloG6Lc7zzFFfVuMT9TpWi8KGt6qdVJzdbGu43rczCdDX3sR2ouEi08/LBDCKl9dWwO0yrlFxZCCNHQdIQaQgixxe9zET7sEOJhGn513PBbGkIIqY/uxkYNIYTY4fe5CB92CPEwFYhFO+VVtrbePSGEaGg6Qg0hhNji97mIhx92ZsDtz/yu+L8sA8BH7uKqG0yTQyLWRl2eOtmsKxLLOmvxMDL2RFtWNEuULzJNjNAfAFgibZQ+Gn78mi/lhiBlAPsmmnVdRGyPtjx3D1mh+KMXKNtJ5DkCkHLt565yZbUZOP/P/D7uivMCZkNHlXgo6V+rHZtcaloL9TCQfrIlAO6y2bCOSsSivSIwlVCOjSg8DXdgnpQ8m2VLNRvpW60tz6wh/bQ1v205jrVl1mUftRiyIUqd1A1t/zLWUPMRTw3SLqDHDEm0tmXMpIxzAuyWDNe2k3XaUvSbRPlsxeYWs2qwGJOa/O6Tx/ucYvSFUneS0JevB3QdoYbY0h5uDfBa/E54cVwmkTyuSLVlc2yR7LfUcUXDcmR8pDYXldtpOqfFgkrRCHeKHuy8hb6sgN/nIh5+2CGEVKMdTqivjv2RtZgQEn00HaGGEEJs8ftcxB/vnwhpo1QiFpWIUz6x0e4aIcQn6DpCDSGE2BGpucjmzZsxZcoU9OnTB4FAAGvWrAm6TU5ODs4991zExcVh4MCBWL58ecj958MOIR6mVmD0DyGE2EANIYQ0hUjNRcrKyjBy5EgsWrTIyn7//v34wQ9+gEsvvRR5eXmYPXs2brnllpDTZXjXje3VDkDn+v6B493/XyLKAPCiDKRQ4mq2yPw4SlyNFsfTXgTXnFCCbVJFebbSzihRXqPYPKnUncgVFZo/uuyA5o8vfUc1304ZMwCgVMTflCq5iIx4HC2OQNkO7msy4FotP5HY1b1nmJXrRFlzb09S6saLspZWQyJjeADT3Xa4OLdlHYArLdquR8Ovjv2x3GP0+RcAneqV5ZjQ/KalLGrxKDIeZoRpkqRsViQrPlKM5BjRYl9SRVnJ+6XFnpXKlXNyFCORi0aN/Zkq9qXoiMy7BUCPY5PI49fiahSNMq6ljS+/dm5FLpwkResvUDaTerNL25+8t5T7xriW9XN9yDxtduhubNQQO4Ll2Qk3D5T8wrCNPZH7C2dfoewvmtj00eb82+aCSXUX13QyLM660p2cce9Zc81m9j0uKmxytQF2ecgiQei5cSI1F5k8eTImT1bm3Q2wZMkSpKWl4be//S0AYMiQIdiyZQsWLFiAjIwM63a8+7BDCEEl4hCj/HLil6BAQkj00XSEGkIIsSVac5GtW7di4kT3olkZGRmYPXt2SO2E5MaWnZ2N888/H127dkWvXr1w1VVXYc+ePS6b8vJyZGZmonv37ujSpQumTZuGwkLt13xCSDAq0AEViFU+kVqBp+WhjhDSsug6Qg0hhNgRbC5SUlLi+lRUVERkvwUFBUhOdntXJCcno6SkBMeP274xC/FhZ9OmTcjMzMS2bduwfv16VFVV4fLLL0dZWVmdzZw5c/Daa69h9erV2LRpE7766itMnTq1kVYJIQ1RjfYNfvwKdYSQloUaQg0hpCkEm4v069cPiYmJdZ/s7Owo99hNSGq3bp07KGL58uXo1asXcnNzcfHFF6O4uBhPP/00Vq5ciQkTJgAAli1bhiFDhmDbtm244ALN0bkBEuHyOZ8weq3r33GXVRqbvPEnIWQzNb/AFaKs+IwXnKtsd7rWSzf5wv97vvJ6r0hWbAneLgDTt3u0YiP98V9WbGTMwiWKjfaL305R/lSxkecyVbFRcu+I2IIvvjjTtCkQfcpTmjZ85GWcE6D6qu4Q1ztPOX7ptq/FQ8jRlBTk/xZUIVZ9dVzlk6zFGi2qIzgB9zWXsW5azIi8/qmKjRiPSm4onKfUyfCTDUrMhrTR7rXBopyk2Gi5oLZITdJ+GZNxLJr2iXNUqsWSSM0AzCRiNvknNP9ybTDJ62bzq5+mdWJ/RcpYW6e5bsj4q3yL/dnkZzrRwN/2aDpCDbElHu5cXePF/8co28jv2RzFJnhsavjxQPa/eEcPeWwDFZvxSp3UmvcVG/kGzyanjcnwK/9m1O38uft6/2nvNYbNzMDzoubXQfdVi9QDrd/hXFup6aG7ngWbixw8eBAJCafmd3FxZi7EcEhJSTHeyBYWFiIhIQEdO9rkcqulST/tFBcXAwC6dasNRM/NzUVVVZXLv27w4MHo378/tm7dqgpMRUWF63VXSYn25UdI26T2FbEpMBU+WdveBuoIIc2LpiPUEDfUEEIaJthcJCEhwfWwEynS09PxP//zP6669evXIz09PaR2wl56uqamBrNnz8a4ceMwfPhwALW+dbGxsUhKSnLZJicno6DAWKoLQK3vbf1XX/369Qu3S4S0OlqjG1t9qCOEND/UkFqoIYSER6TmIqWlpcjLy0NeXh6A2qWl8/LycODAAQDAvHnzMH369Dr722+/HZ9//jnuuece7N69G0899RReeOEFzJkzJ6T9hv2wk5mZiV27dmHVqlXhNgGg9sCKi4vrPgcPHmxSe4S0JqoaWNe+qpXkyKCOENL8aDpCDXFDDSGkYSI1F9mxYwfOOeccnHPOOQCAuXPn4pxzzsF9990HADh8+HDdgw8ApKWl4fXXX8f69esxcuRI/Pa3v8XSpUtDWnYaCNONLSsrC2vXrsXmzZvRt++pnAApKSmorKxEUVGR6xeVwsJCpKRoyWtq/fpU375suFw6377lCte/h1wpc0FASTVh48+nrc6ixaPItrQcMsJ3tEjLoXEkSLsAcLZSl6/USeTllPsCgF7u4kQl9kdzZ14nYgt2yKABAHhXlLUcEgqln7jLPxxq2sj4B829V7JByT1ixGwBRr83KDFbG8a7y0rqDXMbUQ5jyfwKxMKBOT4qW4ELSovoCMrh9k+WY0Qbf2GsUqWlgrGxy1NsyoVP+lEz14OR06WH0k6+1gEx1lT9k77sWr4u2Y6W90sLGpLn/yzFRub1UfIcad9cJ/aLik2KkczPo7kq5Yiy1DUg/PwXNvFQ0qa+j76WPCw4mo5QQ9w0rCEd4YrZiR/r/vdjyia7u7vLT2rfhVIMtHtKi+mSY0azkXEd+YqNlquqJZH9nmiajOpu1skhsFvTnnxRto1zcc/hdk3+qWHx8hvuWPBU7dxeIcprte8Z7XrbuIGFE4vY9JidSM1Fxo8fD8dpOF5w+fLl6jYffvhhSPuRhPRmx3EcZGVl4ZVXXsHbb7+NtLQ01/9Hjx6NDh06YOPGjXV1e/bswYEDB0L2ryOE1CbsaujjV6gjhLQs1BBqCCFNwe9zkZDe7GRmZmLlypV49dVX0bVr1zrf18TERHTs2BGJiYm4+eabMXfuXHTr1g0JCQmYNWsW0tPTQ1z9hBAC4LtXxNoKKNUt35kIQR0hpGXRdIQaQgixxe9zkZAedhYvXgyg9pVSfZYtW4aZM2cCABYsWICYmBhMmzYNFRUVyMjIwFNPPRWRzhLS1qhALGqUV8d+ERgN6gghLYumI9QQQogtfp+LhPSw05if3Uni4+OxaNEiLFq0KOxOAQC+gbt3z7n//Wm5ElexTVZoeR6kP7qtz76I/5ikmKwTvryqP7z0L9X2rwUAyLXkNT9d2Zbmjz7DVeqy5mvDIrvzPKNu+6/cx/bcL281m35IxhZox6bFQ4n4qx3Kdrvdbgpq7pGjskI7R1o8lPTJ1/oototXfIll3ES+KIeRIqMa7RCjvCb2y6tjjRbVEfQH0Lleea/4v81F0cbxG+7iugGKjeZ/LdvKV2ykT7YSe1YggtYKtH1pbW8XZS0eR8YjauNY9lHzP1dijYwxOc40uUj4k2vxeflKXY7QCFVHZT+12Mvx7mJfi7gBADgq45jWKUbyvGnXLVWU61+jMoSDpiPUEFv+F6g/ySsXY3+tks8vSVZo48NGe7S5iIhtuVexkZKRpeXBWiDK4cahhYvUFUVn87RcgTL+KZKrCoo507rHDYtpATn50xLVynht2+sv52za9Zd1NtdN6nyFatUYfp+LtI61JwlppdT+mqK9Og4vuSAhpO2h6Qg1hBBii9/nImEvPU0IaX5qGljXvibE3yk2b96MKVOmoE+fPggEAlizZo3r/47j4L777kPv3r3RsWNHTJw4EXv3yrcghBA/oulIqBpCCGm7RGouEi34sEOIh6lEB3Vt+8oQl0cuKyvDyJEjG3Tp+M1vfoMnnngCS5Yswfbt29G5c2dkZGSgvDy8pW4JId5B15EwllgnhLRJIjUXiRbefSSbC7fLd6nFNoaLthazIv0btTXLFR9IuTS/lmYmT5SlX73aJ+0VoOaDeYkoKzFLhs+r4o8/3u1HP7Tz3wyTKfiLUZd15GlXeeevzNiXvz8pVrkp0vJsaPEP8pwo6/+XivirUu26ye2mmCY9vm/WHZXXIF9p2+JVrVxbX8YVfQvg34M3U58KxKFaCQo8EeKr48mTJ2PyZMW3HLVvdRYuXIhf/OIXuPLKKwEAzz77LJKTk7FmzRpcd911oXXaU+yAK0dGWDkKtG3k2NLivDRs/KttvjykdGvb5Ct1WoxOsP1r28hxrJ0j7R61yH22ReTZ2aK1o51vqTda/g153pQcPgNFjE6W0kyBUvewzA+m5IIzvqS042gsF1C4eXZMHQlVQ9oup8GtIeKardOmUamirMUP26CMqyT3+PyvbDOT/E+wxFXuvk9pZ6Eciy0dsyPvP01DxptVKSLPUIHWbxmHa3uviznbjdMMizP++2NX+fM5SkzlwhxRYZvnR2qtFtMn7zft+KWNjJUMXUciNReJFnyzQ4iHqWlgXfua74ICS0pKXJ+KitADD/fv34+CggJMnHgq8DUxMRFjx47F1q1bI3YshJDooOlIjU8Ciwkh0SfYXMTr8GGHEA+jvzau/QBAv379kJiYWPfJzs4OeR8nc1QkJ7t/5U5OTq77HyHEvzSmIbZkZ2fj/PPPR9euXdGrVy9cddVV2LNnj8umvLwcmZmZ6N69O7p06YJp06ahsFB7m08I8RPB5iJex7NubO3TSxCo9+YtLt79i3Xphp7mRsbSwzbLStu4dQAoEK5Wa5XXi8b+ldebxutsbf+a+4VcIlVzf5CvSk83TYrcxfdfku5xwO+n/cyoe6zsl67yv+J/DJu/DxZubNtsX91KO7nMNmC6us1QbLTtBE8qdfOFe9fulxSj9e7iBrnMOIC+7tfrHR5z99k5VhLyC98KxKKd8uq4+rtX1wcPHkRCwql7MS7OtG3bnIDbhUFqgub+ZSOLNi4YkXIL0dq2WcI+XGRbQxQbeY60ZZ4Vd1RD77QxK+sieW7lsSk6uk/cE7O149dcpKXbmqbt8n7TFKGx76TwXEY0HakO8Rxu2rQJmZmZOP/883HixAn853/+Jy6//HJ88skn6Ny5dnn3OXPm4PXXX8fq1auRmJiIrKwsTJ06Fe++K92K/MS/wL18vXR91I5N3sOapticf+XeF7fAdDxrmHT7gXBTukdp2nBj0+7p5kTuT3H1mzTTrHtM2igafigycSTxT5pj8UeB4a7yfLniPYDAFWJp9LW/VloP8/obGmajCVLDQl/CPthcxOt49mGHEHJyDfuG17ZPSEhwPeyEQ0pKbUBaYWEhevfuXVdfWFiIUaNGNaltQkj00XQk1PwY69a58wYtX74cvXr1Qm5uLi6++GIUFxfj6aefxsqVKzFhwgQAtUk+hwwZgm3btuGCCy7QmiWE+IBgcxGvQzc2QjxMJWJRoXwi+eo4LS0NKSkp2LhxY11dSUkJtm/fjvT09IjthxASHTQdaaqGFBcXAwC6dav1OsjNzUVVVZUr9m/w4MHo378/Y/8I8TktMRdpTvhmhxAPU4lYxCivjmtQGVI7paWl2LfvlKvR/v37kZeXh27duqF///6YPXs2HnroIZx11llIS0vDL3/5S/Tp0wdXXXVVUw+BEBJlNB05qSElJW53ori4uKDusDU1NZg9ezbGjRuH4cNr3XoKCgoQGxuLpKQkly1j/wjxP5Gai0QLzz7snPhTAhB/yj2nSr4B36VsZBWzI5fgs1lWFgCedxf3jVNs5NKj2umV+7P1Rz9isZ30U5VxPgB2i/Iq0+TNaRlG3X+kPeIqq0/zxvLgmg+w5ksr3bC0YxNLQg5WHGV3S99VZf/akuHjZTtarNV2UV5hmiy/xVWsGizaCWPV2Gq0g6O8Jg51BZQdO3bg0ksvrSvPnTsXADBjxgwsX74c99xzD8rKynDbbbehqKgIF110EdatW4f4+PiGmvQJZ8C9hr1NHJnUDZt4GNtxLLVFi8+TbWk28t6Wugao499mmXdjWWnNRratHb9NnaaRUiO0Y9P81GXb2vmXNtqxybo3FBubeFANed5svqPqH+u3Fvsw0XTkpIb069fPVX///fdj/vz5jbaXmZmJXbt2YcuWLWH1x1+cBncegRHi/9p8Id/CxjamVVDqTvb8YzxjmPzpdRHTqoWMeA7lfEwyq64Z9idX+YWBSvzuITn2beNK3POs8oWmhv480V3esl9pJlOU14Ybs6URTkypPI7Q38ZEai4SLTz7sEMIASorYxGoNIXJUeoaY/z48XAcp8H/BwIBPPjgg3jwwQdD7iMhxNtoOnJSQ0Jd5CQrKwtr167F5s2b0bfvqR90UlJSUFlZiaKiItfbncLCwrq4QEKIP4nUXCRa8GGHEA9TWR6LQKw5+XDKQ8+nQwhpm2g6clJDbBc5cRwHs2bNwiuvvIKcnBykpbnfro8ePRodOnTAxo0bMW1abTLGPXv24MCBA4z9I8Tn+H0uwocdQjxM9Yl2CJwwXxM7Sh0hhGhoOhKqhmRmZmLlypV49dVX0bVr17o4nMTERHTs2BGJiYm4+eabMXfuXHTr1g0JCQmYNWsW0tPTuRIbIT7H73MR7z7sbIXbBVq+BU9Sthkuyvu0/Aj5oqz5fmu+lNK3WvklTIZ6HOpk2oR9ymWfbPzDFT9hGTdixNkAnxTK2CNgZfK/u8qv4GpzQyOOSsu9oSHPiXaOuruLRVo7Mh5B5r0AMHG0WXf0I1GRo7Rts5a9yBOwRdwQYbjoVlXEAeWKW0kF8+nY0QvuHBkyHkW7KHJsa7Ev8h7VfhnXtpPxIPLe02xsYl+02I9UpU7m3tLydclxK+PVAFN/tDEbXk4Y85rY5v+QfbLRSJtBaZvDSF4T7fqL2EMjZwtgnsv6cQSh58cAGtCREDVk8eLFAGpdYuuzbNkyzJw5EwCwYMECxMTEYNq0aaioqEBGRgaeeuqpsPrsHU6DK47qbvHvvuPNTZaLcp6Wu03JK2OFO4bsrd5mXrzey4vcFUpsbuSmf9o4sxlXcrufGhZv3fk9o+77/+WOE3vhdiVmJ8dm7Fv0e6Fp8VqROy/f9/BX0+hsWRFmfJZV3KFmEyx+O/Q8RH6fi3j3YYcQApTHAh0Un9hyf/jJEkI8gKYjIWpIYzF/J4mPj8eiRYuwaNGikNomhHgcn89F+LBDiJepDgAnAno9IYTYoOkINYQQYovP5yJ82CHEy5RDH6VhLGNNCGmjaDpCDSGE2OLzuYh3H3a+B6Beio8zZnzs+ney4XsP7JzpdpQsndjTbHfbWaJC8wdPNavai+1uVDa7QpRzFJsn5f5tfTml37jm6y19N7UcEqJT68YYFjVZnY26/7vkUfeecrQYhb2irO1fwyb3xjfuYoF23WSMkrL/o1rCAenPeo1iI/2rtWMTSXzGi3+XA3hT2awxygF1GXufCEz0OYLG8+zYSKDmE33IYrtUpU76SmvjSPZR86+WbWv76q7UyWPR4lFkn7R7XcsbItH6LfdnE+ujnaNeSp2MkfxGsbHJcyTPUST97W3ijxrLFxZenh1VR6ghlnRD/Xsw5dHPXf+9BUuNLR4a+F/uiiu03G02MTsW+bMKlDxQt7jjSrTYXPtYtGCEmy8m+Ljqg6/Myk2ifFmYu1cRY6/IdN38Nf7TVe6h6cwuTXsihU0spLwm8rsg9Jgdv89FvPuwQwgBqr/7aPWEEGKDpiPUEEKILT6fi/BhhxAv4/NfUwghHoBvdgghTcHncxE+7BDiZSqgC4w/8ngRQryApiPUEEKILT6fi3j2YWfkrK1ol3AqduQvuNL1/9OPmD7jf+rljrWYeePzZsPbZJ4DzR9dxtUAmOkuxj9m7n9i4kZXee3wfzPbeVL64Gq+kzIXhman2cg+mXFNZlyNEnvw4lijqipfnBPVB1jmDLH1dZfnxNy/eatqbdvk2dBuebFO/y1KfqRV7mzhKM01bVLcNufe5c4HUF1Shr//Qtl9Y5yA7qIbbgqTNsfncAX/GX7qWi4Um/g4afOuYqP55MvtNL95WZeq2MhYOy2nlxbfIfPqaLmwpI5oY80m75c21mzyE8k67RxpsQyyn1p8jM3ACTeWQbatxQOFk1ul/rkN82dUTUeoIZYkoP49OQLvuP6rxmwYw0q7F+WYscmXApjfj8p8pYcoq9c6UjE74SKP9xPDYhLWGXX/+aaIu51smMAuNk4730JX7jZXGss94s7983IvswNv4fsW+w83P1E4aDobIj6fi3j2YYcQgtr5m7ayY7gx04SQtoemI9QQQogtPp+L8GGHEC9TCf3VcWVLd4QQ4ls0HaGGEEJs8flcJCbaHSCENMKJRj6EEGIDNYQQ0hQiOBdZtGgRUlNTER8fj7Fjx+L9999v0Hb58uUIBAKuT3x8fIP2DeHZNzuZeAod6/k0nj5b+JH3M7dJvkvEqCRpLdv4ySqkuIt9Eg8bJp2kj/wurSHps629A7TJIaPZyLa0tmWdli8jx6zaYeOjL+OftNtL61Oqu9hDidmRMUKq67qMR9D2r/muvuYurrpW2b+Mf1COY5S7OAcLxRZVuE3Ze6OUQ3917JMVUFoHqWZVvLj/y4co2yn5L7BblLXxJ+/RcP0EtHhEGUygxfVJtPgY+Q2n9VGLWZF2mo2Njthgo3/hYvO9YZNnyMZvPwJPJZqOUEPsSITr3BWJicWv8XNzm4dkhRYbZ3MPad/zom6wYpJqsXvP+R8dMWoO/XS8UXfHqOXuinX7lbbCPTZ3fGb8L0x9/iTJXZ76gKLzqSKGMj/M7qhIPbSI9UoS/3YAFIe42wjNRZ5//nnMnTsXS5YswdixY7Fw4UJkZGRgz5496NVLy58GJCQkYM+ePXXlQEDrSOOE9GZn8eLFGDFiBBISEpCQkID09HS88capC11eXo7MzEx0794dXbp0wbRp01BYaPNlSghRqUCtmMiPT1ZA0aCOENLCaDpCDSGE2BKhucjjjz+OW2+9FTfddBOGDh2KJUuWoFOnTnjmmWca3CYQCCAlJaXuk5ysLRrUOCE97PTt2xcPP/wwcnNzsWPHDkyYMAFXXnklPv74YwDAnDlz8Nprr2H16tXYtGkTvvrqK0ydOjXkThFCvqOqkY9PoY4Q0sJQQ6ghhDSFIHORkpIS16eiwnwKqqysRG5uLiZOnFhXFxMTg4kTJ2Lr1q0N7rq0tBQDBgxAv379XOM8FELyD5gyZYqr/Otf/xqLFy/Gtm3b0LdvXzz99NNYuXIlJkyYAABYtmwZhgwZgm3btuGCCy4IqWPd8A061+/eOPf/t/zbucY2/xePuivWaC3LpZfzFRtlKccTbteSaiVSax/OdFds0/YvX4vaXgIbtxGbpSRtXGS0dqS7ieZ+Y+NGp7ltaHUCq9MklwAdZ5oMv8is2yWWvCzdYtoYx6L8SnjC3XYPHHWVy8JxR6mA/urYx7/KtqSOAKfBfT/b3Ejy2irLSpfL1+02Llva/rUZp3Rdke6ZgLlkvDaGbJZf1dw65XLcNm1rLnPasck6G43Q3Oi0Ptnojw02LruaRko7m3tN04TGXN3CPCZNR6ghdnSH62fhT8qGuv5dentPc5uj0rXJZixqaNdbjLVDo00TufS0mioi2m5scrwMME2SlM3yZIXqoxcm7rFfvsFMTTB0iqioVpoRYQ+6G5vNrw2aztjM4YT2yPuhBqG7sQWZi/Tr544tuf/++zF//nxX3dGjR1FdXW28mUlOTsbu3dLFu5ZBgwbhmWeewYgRI1BcXIzHHnsMF154IT7++GP07dvXuvthx+xUV1dj9erVKCsrQ3p6OnJzc1FVVeV6Yhs8eDD69++PrVu3hjFJIYQ0OCHx8USlPtQRQloATS+oIYQQW4LMRQ4ePIiEhFMPYnFxcRHZbXp6OtLT0+vKF154IYYMGYI//OEP+NWvfmXdTsgPOzt37kR6ejrKy8vRpUsXvPLKKxg6dCjy8vIQGxuLpKQkl31ycjIKCgoabK+iosL1uqukJNxfQAhphVRBdzb1sQsKQB0hpEXRdIQa4oIaQkgjBJmLnIyfa4wePXqgXbt2RvxcYWEhUlLk6zCdDh064JxzzsG+faG90Qt56elBgwYhLy8P27dvx09/+lPMmDEDn3xiZr61JTs7G4mJiXUf+SqMkDZNJWp/OZGfENe2nz9/vrF84+DB2jI+LQN1hJAWRNMRn+THaAhqCCEtSATmIrGxsRg9ejQ2btxYV1dTU4ONGze63t40RnV1NXbu3InevXvb7xhhvNmJjY3FwIEDAQCjR4/G3/72N/zud7/Dtddei8rKShQVFbl+UQn2xDZv3jzMnTu3rlxSUoJ+/frhJUxFbD1/xXf+7VLXdr95936zsfmivEHGcADm0s/arzfSHx4A3H66qYoT5kDhO/rBYCU+xPA/1/zBlaWXjZ/htNUoZBxNvmIjfVC7KzbaeZO+kdrKNnJ/tjEqIo7qqHZbyn4rcVVG3IRy/rOUze52X1uUPqcYyWUxFT9Z8UPDKriXsK7EcajxH41xHLpPcBgTlWHDhmHDhg115fbto7fyfEvpSO164J3rWcqxbS53ao4jm3gU7V6P1E/nmkbZxJVo11f2U4u9k/uzWR45Mku91jJQlE9XbLT95YvyF4qNza/10k9e+7VS86WXdTZL35sxAaZN/WtUpthboOmIzx92WkxDvoErVqF0jYjR0b4ujDi7cMeHRYxrqTJf2WITy2ARKxs2Nik+xHg5L82wuDH7j0bdm8hwlb9eoi37H+L3bB3i+2C+abF3p/vcnnVQOf8vh7l7K6SuWNwjR8W/nTB2G6G5yNy5czFjxgycd955GDNmDBYuXIiysjLcdNNNAIDp06fj9NNPR3Z2NgDgwQcfxAUXXICBAweiqKgIjz76KL744gvccsstIe23ybOdmpoaVFRUYPTo0ejQoQM2btyIadOmAQD27NmDAwcONPrEFhcXFzHfPkJaHdXQBUarC0L79u2tXxW3NNQRQpoRTUfC0BAvQw0hpBmJ0Fzk2muvxddff4377rsPBQUFGDVqFNatW1e3aMGBAwcQE3PK6ex///d/ceutt6KgoACnnXYaRo8ejffeew9Dhw5taBcqIT3szJs3D5MnT0b//v1x7NgxrFy5Ejk5OXjzzTeRmJiIm2++GXPnzkW3bt2QkJCAWbNmIT09nQGBhIRLBWpXTpGE8dJg79696NOnD+Lj45Geno7s7Gz079+/qT0MGeoIIS2MpiM+jtmhhhDSwkRwLpKVlYWsLM3NBsjJyXGVFyxYgAULFoS+E0FIDztHjhzB9OnTcfjwYSQmJmLEiBF488038f3vf7+uUzExMZg2bRoqKiqQkZGBp556qsmdJKTNUg79l5N6a9vXp6FfJ8eOHYvly5dj0KBBOHz4MB544AF873vfw65du9C1q7a0b/NBHSGkhdF0xMcPO9QQQlqYIHMRrxNwHCcc771mo6SkBImJicD8YiC+nu+yXERloba1DE7U8lNoftwSJT/LfHcczfz7/8Mw+XesdJWnKo6buwLyl3QlPmag8npOurFrIQIbZEWuYpTqLsYrMTvaI/B5oqyFNe2TuQU+UozCjW2Qvu03KDZy/8qxXTHZrFsr/fiXKm1Ln2vNZ1/4kD4sfO/LS4D5iSguLg66akndOJhUDHRQbKtKgHWJRrW2tr1GUVERBgwYgMcffxw333xzUHu/UXf+UAR3DMQ6YSnzbgF2/vXST1q7H7T7WrZtE9emxXXIOBbNxiauRYtZkv22yd9lO65lW1o8jozH0/avxQzKOu3YbK6tfPjXzq0W6yiPJdU06SLG8iilGXkqt9X/ii4BkGSlIUAQHflOQ2zbamuc0pAPAHQ59Y/B4v7crX3Pyi/jcGN2tHtfzk+0mEJhk6LE8BQ8Lyq0+ZINNvGC2vGLcTXzZ4bF1mWjjLrN+J6r/B+n/d5suugRi/1r/RZj/yKzT86zItmMDAMHEMgW0+pt85V92WCT40uLQ5TbXSPKpQDOjehcxOs6Er0IZUJIcMqhzyO/qwt3bfukpCT8y7/8S8jLNxJCfIimI2HkOCaEtFGCzEW8TshLTxNCWhBtqceTH5xa2/7kx/Zhp7S0FJ999lnIyzcSQnxIIxpCCCFBCTIX8Tp82CHEy5xo5BMCd999NzZt2oT8/Hy89957uPrqq9GuXTtcf/31ke4xIcRrREBDCCFtmAjNRaKFd93Y5u9A6PkxbLDxJVVyT5S6i0koMkzO2u/u46i0PMNmV/z57opyxd9S8ywS+zdimAAA60VZy6HRt9EiAGC8UvdDUTbigwA8Jn3WNR9g7XynivIYxUbm0NF8Q2XODmX/a5WYHcPnVQZIaTayP4ARM3Rvqvh/GDkyKqCLSYjLPR46dAjXX389vvnmG/Ts2RMXXXQRtm3bhp49ewbf2M8kBoBAPR/rIpv4E+nvrMmkTcyOVie308aDjHWxybOjjXWtbZs8M3IcaeNBHocWxKf1SW6n+aTL2ButHS1OIRxsrq12HrXjledWabu9iMfU8vrGi/LuevevEwCKlW2CoelIK1t6uvn4K1xjebf8gtbi/uTJtslVpaFtJ+9HZcIQ787xpqWcw4vNGV9hMwMWx7HFtPgL/o9R11V+92pjaJvUXtsZuZjDPGdeo+dT3eVrtZR/wyx3Z2CTn8gGuV2OKIcRQxahuUi08O7DDiGk1k+2nVIfosCsWrUqEr0hhPgRTUd8MkkhhHiACM1FogUfdgjxMiegr22v1RFCiIamI9QQQogtPp+L8GGHEC9TAT2yzicCQwjxAJqOUEMIIbb4fC7i4Yed9QDqryxl40dv44cocybY+IzDcHl8HtcaJn3SvnKVN+Aysx0jRkcLfpH5YgAU2PiR2xy/8ElNlf75AJTEtiNHbnOV/16uZKJ+LFVU2N5e57qLVyh5hqRb8m4tz4Y8t5pPsrad9O8dq9hIH31lcX0jjkDGDIWxbMlx+Fpgok53uM9fkcyhot0j0t9ZGSOGjmjOzEoOLeMetYnZ0bDxQdeOzSZmScbeKXnH0EmU9ys2u5U6efw2uYi0c2RzHDZoOXSGWNhoOcTk8SqxRkUiP0+Bcm/JEKke9f6uQXgxO5qOUEMsOQz3XETqvG2OrXDQ7n35ZagkhR4vyupX8bfhdEghQse6b7tRlT3vQaOuw91CQ8q1xrR5nQ1uzZww4E3DwjiVZjpF4Jwwd29FOOdb3rNtby7i4YcdQghqAGhpfz2VCpgQ4mk0HaGGEEJs8flchA87hHiZcgABpd4nAkMI8QCajlBDCCG2+HwuwocdQryMzwWGEOIB+LBDCGkKPp+LePhhpyfcvrDSb1yLvZAxElouFJs8D4qf7A53cev2CYbJ0LGfuMoFe85Q2s4VZe04NF97eSyKny4GiPIXwdtR1t+/ZOQ6oy5V+OT/PUWJ2TH6rZ1brU44qc9UTPJF+W6bmC0ZewCo/vepok+pyma7hG/9Uc2XepMoS3/8MNa2B3wjJp7kG+gCXYdNfhzNRsbMaPlrtFxgchyHm2tDot1bWlyPjeTLMaK1LWN2NGzicTSktmkxUzZta/l5gu0LMAVA2792vWUshWYj6vKVtuUlOlrv76ZoAXUkTHrDpQFJt7r/LXPQAcBS+b2+XDEK8/vAuPdnGRZdXvzaVS5druRTWxXtbJByvqDkK3r4XaOq6uG5oka7seU5so1zcZ+Tv35zsWGxyuniKgdylXnmiect9hVu7iUbZNu9RLntzUW0cCNCCCGEEEII8T0efrNDCKn9pUf7tSdSvwARQlo/mo5QQwghtvh7LsKHHUI8zQno7kjRdkEghPgHTUeoIYQQW/w9F/Hww04fuP3Cpd+8jT+2jV+39GUE1JgZcaZiUssMk3/HSle5YlCcYfMchL8v3lf2r/mR28TsiBwOasyA8MfXQm8U/vTlTe6K+ZqVjEfSfNa1W05c2y6KieznefJYAeyQcV3aPaL4yQ4W5b7KZvmifFSLB5P3m4wZCievwXFYx5URk+KtADrXq8gXBjaxLjJHgbadpjXhxgPJMaLleZE3qbZ/rd9yTNhsZ6NRWuyhpj82OYzksWnnSNMW7Xgl8rpp4zhflG1/uZQJcix88ncpmrBL9rF+LKr5vWOHpiPUEDtGov6X0sj/deecyzuSbmwRuF0EN5w3QmlX5pXR7hft3hfzk+FmUOLvOt/pKi/InGPY7MqSY0/GPNsSbuyJmHsNn2aa7NK2WyzK2lwo3HvbnRut6l5zLnj6w0Jn7tXakRpum/dH2kXqrYnIZYjSMNrw91zEww87hJBaIQk3gS4hhAC6jlBDCCG2+HsuwocdQjyNv18dE0K8AN3YCCFNwd9zEQ8/7ByD+yRuEP/XniZt3Aiku4XmIqK4VgjPivOSdxgmE97a6ip3vdx0kXjuPOHGtkP2uYH9q24iEnm82uVtH9Rk08eTzEpZdShHaVu+BtcGgfZaXri7rFWO/4ogZQDYkSYqPlCMFLeVdUNEhebauFOU8xUbeS9JV5twfgEph34dy8Noqy2SCyC+XtnG/Uyi6Yi8R1IVG20JeXlP2IxrbczI4/hGsdHcODX3r2Bta+3IPtl+lchzoumvbNvWZU32U9vOZslwiyXs4xVf1/LRokK69QKmRkpd0fZf/7jCHfeajlBD7DiM+i71n3wz3v3vCnOLAaN3u8pfqN8pEltXWHGfHzItRotcGdfCXAp5F/6vqDGXedbR+hQOQgt+YVrEjDfdNmtSrhE1v1fatnUbk4hzu9Q8uVVL5di3cU+PZBC/jaubrJN6GY5Lvb/nIh5+2CGE1E582jVQTwghNmg6Qg0hhNji77kIH3YI8TQnoP9y449Xx4QQL6DpCDWEEGKLv+cifNghxNMch5771x+/phBCvICmI9QQQogt/p6LePhh5yu4fe2ln+hEZZuzRVlbSnGTKGsXSnl6FW6J7392sWHy5OU3u8pLcYvZjhHqo/msa3E8sp+Ko67Vsqpif0mKieaCWSArtBgBed4slpkGYMS/PKfE7MjlqFU30Y9EeZ9io11vLbZHEs5QkX6y4fi2lkMXGH/4yfoT6RM9QLEZK8panJ1NHI12P8pxazuObGzkMq3ar3LhxJrZ7Aswj0WLB8q3sNH6ZOOnLzVK00ipB0pcVbmm2xKbuKJIxT8EQ9MRaogdH6P+XKSqhzvFQWC2WGYaAHJkhRkzY4fF/KTInK/MxeOu8k5oS1/bxO/ZEG48itj/c6ZFTUFnsxLVFm2H2yd5vt9QbOQY1rS/ORNthtO2jA1se3MRDz/sEEL8vgIKIcQLcDU2QkhT8PdchA87hHiahlZNCWc1FUJI20TTC2oIIcQWf89F+LBDiKcpB2BmyPbLq2NCiBfQdIQaQgixxd9zEQ8/7JQDqO8LK+NYFB/UHqJ89CylXRnHka/YKD7aBcL//oruhsms1KXuCpkaCIDpzCtzvAD11/Q/hbxUmj+49D9XYg3Oc+eiuWT0OsPkbCP2BXhy1T3uihunmW2XbxEVWsyUhvBB1d6KyvGkheNgryhr/vDnKnWyn7sVG4lN7hPpW6skZAiKv18d+xM51rRYEHmt8xUbmzotZkRikwvM5n7U6mxiFrV7TdpofuQ2sT7advJ8W+ZCM9rSYn1s+i3rbGMbwom/0b6Cw80R0hh0YwsfuQrVEve/F9rEb4UbH2MTn2HGlbwd+D8W7SwPY1+RRObXk/MHAGu1MaXlpooUcuxpc0g5P9TmOS0Vi2dLJFZi9PdcxMMPO4SQhieM/lgBhRDiBWwefAkhpCH8PRfhww4hnqahV8T+eHVMCPECml5QQwghtvh7LuK5hx3HOem6Jl1+ZBCU8lq4RlZor2VlO9qF0gKuhLtJteJqIN/mKStSAmWNtwtAfwVaKso2X17KE3e1+7ydKJH9ASq1tr8V59vq2Gyf+MV2jnJt5e2gvnG3ubbyPNpuJ7HxXZU3RO1BnLrHbSiFfrDhuMS1HU6dY3lNgl0jwLy2mh5o95FE2y5YfzS0cSTbNsSvgbZlWzY6otnYuC7YLBGrtSO3s9UROU60fjfnuNE0IZxt5DkpN/4OTUMAXUeoIY3R8FwknPuzOe9FTWfkd6iNm6ltf2zGtQ1y/+ZcRN9XuP22QV4nrU/yfGv7l+Paa2Ot7c1FAk7oqtmsHDp0CP369Yt2NwhpNg4ePIi+ffs2alNeXo60tDQUFBgJjupISUnB/v37ER8f36BNW4U6QlozNhoCBNcRakjDUENIa6ctzUU897BTU1ODr776Cl27dsWxY8fQr18/HDx4EAkJCdHumhUlJSW+6zPgz377rc+O4+DYsWPo06cPYmK05FxuysvLUVlZ2eD/Y2NjPS0u0eSkjjiOg/79+/vmHjmJ3+5twJ99BvzV71A1BGhcR6ghDcO5SHTwY7/91ue2OBfxnBtbTExM3ZNmIFD7KjAhIcEXN1B9/NhnwJ/99lOfExMTrW3j4+M9LyBe5aSOlJTUunP46R6pjx/77cc+A/7pdygaAlBHwoVzkejix377qc9tbS5i99MQIYQQQgghhPgMPuwQQgghhBBCWiWeftiJi4vD/fffj7i4uGh3xRo/9hnwZ7/92GfSsvj1HvFjv/3YZ8C//SYthx/vET/2GfBnv/3Y57aG5xYoIIQQQgghhJBI4Ok3O4QQQgghhBASLnzYIYQQQgghhLRK+LBDCCGEEEIIaZXwYYcQQgghhBDSKvHsw86iRYuQmpqK+Ph4jB07Fu+//360u+Ri8+bNmDJlCvr06YNAIIA1a9a4/u84Du677z707t0bHTt2xMSJE7F3797odPY7srOzcf7556Nr167o1asXrrrqKuzZs8dlU15ejszMTHTv3h1dunTBtGnTUFhYGKUe17J48WKMGDGiLmFXeno63njjjbr/e7HPxBt4WUf8qCGAP3WEGkLCxcsaAvhTR/yoIQB1xM948mHn+eefx9y5c3H//ffjgw8+wMiRI5GRkYEjR45Eu2t1lJWVYeTIkVi0aJH6/9/85jd44oknsGTJEmzfvh2dO3dGRkYGysvLW7inp9i0aRMyMzOxbds2rF+/HlVVVbj88stRVlZWZzNnzhy89tprWL16NTZt2oSvvvoKU6dOjVqfAaBv3754+OGHkZubix07dmDChAm48sor8fHHH3u2zyT6eF1H/KghgD91hBpCwsHrGgL4U0f8qCEAdcTXOB5kzJgxTmZmZl25urra6dOnj5OdnR3FXjUMAOeVV16pK9fU1DgpKSnOo48+WldXVFTkxMXFOX/+85+j0EOdI0eOOACcTZs2OY5T28cOHTo4q1evrrP59NNPHQDO1q1bo9VNldNOO81ZunSpr/pMWhY/6YhfNcRx/Ksj1BASDD9piOP4V0f8qiGOQx3xC557s1NZWYnc3FxMnDixri4mJgYTJ07E1q1bo9gze/bv34+CggLXMSQmJmLs2LGeOobi4mIAQLdu3QAAubm5qKqqcvV78ODB6N+/v2f6XV1djVWrVqGsrAzp6em+6DNpefyuI37REMB/OkINITb4XUMA/+iI3zQEoI74jfbR7oDk6NGjqK6uRnJysqs+OTkZu3fvjlKvQqOgoAAA1GM4+b9oU1NTg9mzZ2PcuHEYPnw4gNp+x8bGIikpyWXrhX7v3LkT6enpKC8vR5cuXfDKK69g6NChyMvL82yfSfTwu474QUMAf+kINYSEgt81BPCHjvhJQwDqiF/x3MMOaRkyMzOxa9cubNmyJdpdsWLQoEHIy8tDcXExXnzxRcyYMQObNm2KdrcIadP4SUeoIYR4Dz9pCEAd8Suec2Pr0aMH2rVrZ6xgUVhYiJSUlCj1KjRO9tOrx5CVlYW1a9finXfeQd++fevqU1JSUFlZiaKiIpe9F/odGxuLgQMHYvTo0cjOzsbIkSPxu9/9ztN9JtHD7zridQ0B/Kcj1BASCn7XEMD7OuI3DQGoI37Fcw87sbGxGD16NDZu3FhXV1NTg40bNyI9PT2KPbMnLS0NKSkprmMoKSnB9u3bo3oMjuMgKysLr7zyCt5++22kpaW5/j969Gh06NDB1e89e/bgwIEDnjv3NTU1qKio8FWfScvhdx3xqoYArUdHqCGkMfyuIYB3daS1aAhAHfENUV4gQWXVqlVOXFycs3z5cueTTz5xbrvtNicpKckpKCiIdtfqOHbsmPPhhx86H374oQPAefzxx50PP/zQ+eKLLxzHcZyHH37YSUpKcl599VXno48+cq688konLS3NOX78eNT6/NOf/tRJTEx0cnJynMOHD9d9vv322zqb22+/3enfv7/z9ttvOzt27HDS09Od9PT0qPXZcRzn3nvvdTZt2uTs37/f+eijj5x7773XCQQCzltvveXZPpPo43Ud8aOGOI4/dYQaQsLB6xriOP7UET9qiONQR/yMJx92HMdxfv/73zv9+/d3YmNjnTFjxjjbtm2LdpdcvPPOOw4A4zNjxgzHcWqXfPzlL3/pJCcnO3Fxcc5ll13m7NmzJ6p91voLwFm2bFmdzfHjx5077rjDOe2005xOnTo5V199tXP48OHoddpxnB//+MfOgAEDnNjYWKdnz57OZZddVicujuPNPhNv4GUd8aOGOI4/dYQaQsLFyxriOP7UET9qiONQR/xMwHEcp3nfHRFCCCGEEEJIy+O5mB1CCCGEEEIIiQR82CGEEEIIIYS0SviwQwghhBBCCGmV8GGHEEIIIYQQ0irhww4hhBBCCCGkVcKHHUIIIYQQQkirhA87hBBCCCGEkFYJH3YIIYQQQgghrRI+7BBCCCGEEEJaJXzYIYQQQgghhLRK+LBDCCGEEEIIaZXwYYcQQgghhBDSKvn/U34WfiPSL3IAAAAASUVORK5CYII=", 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", 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", 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WKasNNGBUzuSPgbmkt3iJNiX2+QpDzjGmxwFlznFAhRElpjQpMbVC4ElcHFrEGcpXfeCeMSriI4ZSsoEqDKJM1jkOMR9TGIP0zzpME9g7iUuKHteh8KD3kIGCkERyl/oIsKkDDXPJAqaLVshAjmwdsuGTkPV73WmkasIrhS2dw2c1MqQOMGNlgUyQ90i/S6S9tL6louqtgYtmHxXbBDEAI59RwEwo6wyDfT/YD9Vv4XtIk8tzavbdz5GBmxDkCOmyz+EDx6Etp4wCN6ndV8n4nDTQhDjPM/uTuknF+Mich/hUzEOER4D5oGP7f9vwhgFZ/wBfQVqxlwPZN69jOkMP843L+POtkAyKcA9xYDOJHAu/KePzHv3Mknp20RyveSOR4/BaefasMqUeR5UnJG0Xk01S/B4eyj9638DXXlhl1nTRyAznMIrzkb20a36mBYfDRUYtGNXUI7qY8f0MBsT2MNjBffebGBlikYzpCC+vmjrtN0w/2oV0b5Hte8fs8HW0WeAGVylT58JL+jT2DljYM7cNm1Bp1qhRoUlCnQp1qrBRgdWaw2zstTHala6tj/TD57csctvrtbq6SqlUYmtryzu/tbUVXbugWq1SrVZz5wEqjGmwT4cuA+qMqdBd6rAFDLeXTUv3Ma2ekrW+KN7DySsENEe9fXh/eFyomCirCyZkAzJ68QkpBlSSYH/S8mJMVZcvjFD2+rewLpp5CsDTQE/AV11dlwT37xNn9mXyjS7Pu2mPBUzuw3AdhhaMiOVGP0obgdx7S73C59u6DcuGR2wDm6bMZ/70BfBCuMwlUko0GDC1UlmbHhXG9v+Tf3sRa7+W6KQ8BGbxEWEEkFcKJJh+k2AmmjpmZgr7aShYFE3eKZnWXYB6CGjCY02xCVnzDqn3QnZ5TW0dMnAjewE3WqGjcZcI4yKAJ2ovx+ErJ0TGT8EkKWMuUce6zkmwCen6afYWkmYHR4GafvB7rPmlTi38bqOPW8G18rsuO8FvHzmW+SkN22uiNs3/Qj4U09odTXM+ktHJZRH5wPsYIVHmtgawDNtlf5zEUGSC/eYynvU8I6Tn15hQHiotNcncp/mLpvB8bH49Snl5nPnrNDKIfn74XrpNTiquhopdKTtU1t7MLum3ob+QKUO7dr9p96tkiiOpkvDQbQyP2bTHbqjq/qIVuOBklW7D3HcF+AJs1u7jiy/6M4ypsMoON+hAFdrVHp3lLlPrEzKgwZgKABVGtOnR6vTob9RMHTaA7QUjC7Fl3/kmJ2/LO4+H3HZwU6lUePWrX81HP/pR3vSmNwEmMO+jH/0ojz766InKKpFSZUSDAQ32qTOgTY/RUoXN1WXTR1btxatk46pPfnLRdBxQU3QuJs97pCetmKYl9vCJOj+LMZy04uXgXJEZOyz/KCZ33DpojZH+PSHPxAnu0w0uDC4J7g2vTzHCKWRt34Z+w7fchEKed32ooQpvIBMGuxgG0i3TbXXornSoM6BHmwojKowpKT+laaGmvphEX/+1RLeTh5i+UVLHoZSsLYgiXJbVdftkXyDsF/oZ0pH2C/YhyIF8P5aydR0Lxrz+uRZsLbVpC44uSgMBmYTFuBrKJy3y7KlI0RPWLwQwIajRxyHN4rW6HiGwGAZbPziv79H1lffWSpBQGazrHeMnGvjp+7vkwVD05UL+eVSfOR7N+cibgFvlIxrk3MQJielK1rdkDEG+f6VyIF9D5jb9DH2Dlh1CsHPcOod0HJBTVJbmi6hjDUpOCphiFPLoEJTIOf1NZsk2Rb/pD7WvzrcNIOjbf0UhIWAn5Kky1gXQdMmURY7kYvnuul5pxqe27ba5wNaL1qkw5ioXmFLivFXyT5U5e0CD1P6fMKXCiHpzn74otzq2rsM2xnJ0k5P1oYzuNB5y28ENwDve8Q7e+ta38g3f8A38D//D/8BP//RPs7e3x/d///cfu4wpJfuxxjQsqOlQZ4UdppS4trrHAU3TERIyQJNG/i+aeDXpfp6QZ1BH7XMUSgPHAS8a5MyicLCGgyVkAjFAEROsJsE9mmEVWW/C54XnhHFobYm26MQExqPc4HS9tFAZXp9iNOnrkNaha4GnFlhyDCgUMHU72PL7ZOCmBmzCJFlke2WVCmN22KHElKoCN1VGpwI3s8Tp5zPdDh5iaEjmKgo+aNF9Wv4XsCOuCzGhItZf0+A4FEyP44Kqfxctn35G8NyYsC2TmmgZEzILjmYtMnRkEtaTtEzquvyhuk/2USWBuleXUWTBSSKbpll8VwOaENTod+tHro1RQmax0SBE/x+CSflfygyfLwCoFpQD5AXCsK/o/qMfcHKaxWWfz3R7+EhC5sojwCbBWHEAFo1FX/qZ3CJ73UdS7T6ekvGbcM6WmzWwiXk7gN+hRYY4jhXmVnpACHa07KCvOWk5Rb9L2UfJSOEHoOB/XT+RHbTltA3DOjy1YL5dl8w6Ho5/eaR8/y4K3MicL3OLyCTyXTH/y33bGOtNCy7ffwnW4SnuZUDdysBdpiSUSEmYsk+dsfWHLDGlwT4NBob3dzD7VWC7Yd6JRVuPk/ORO00WeVbAzXd/93fzzDPP8E/+yT9hc3OTP//n/zwf+chHcoF9s2hKQtUi0TY9RlSZUqJPmxIpvfU23VaHfvcui0zJ0HSXTADV1ptZuCKdcV2o6TtS0ZFEjjVi1ygsRFOaijS7RVSkqQnrE14XCvExRnMc7U7IZPU5zUhCpl1k1dLlaVc/KU+3m5Qrvw1wJl83iSybcoZF7ajdkMLvo96rb13TNu2pp8xllzcuMV0xbmlgLI8lpkwp0abHOB+JfSQVaUtue/72M0a3g4f4FGorQ+tMWR2H50KXkBjNAjKhIFGEBPQzUc8NBV18IVtr6TqYyayFcUmoAZ0h55IplVrW/6ZpwjQtcdBvwHAh8xvfJhPKNYgX/joLLMRe6yiQA3GQo8sLn6GfHbPWSP10PYFMeNFtqdwUUyWgStvq4R9axTpBfVF1kOsFOHZVuV43CvtVTLFzHN47m2J85PnOQ+B28ZE6Zj7R7lE9MnemRWDZzAuQ9euw77ewCrYGLnbOCdPSJ3VfkPEvAKjIehPOUUWDUitdboVCQCN0XFClr4e8h8cssHOUDHSca2N1lLl+n+zbJjjgutnGeeWE4CbkVw7Y6LLrwf9h/SbQLRuZ4ilzycFGkz/tv4TOi7p06VBlTIcuPdrUGVBlbP1DMldKMQSwiuH/F8msQdsXbSWvF7TBbLrTZJFnBdwAPProo6cw/WY05RwlpiTWu7DKiDoDWvRIKdGhS9os0V+9y9zQsTdq7YmMjyHx8R8ex7BGGmwnpuO4pYUU04IcRZrhhIwC8gLdLNJMZ9a58Pmx38PJWjdwWNej9IshJ4m5a2httyatMVu05cTiBJSw43UE9f5pOROiRPirwaRrAHev2qZLhzY92vQokVJhxPRrgKHcTrpVHjKbjvoW2jdaJr2jdFcxTXvR/+H9RdZYrYlVPSFmRdBApwV0DqE2otXpUamNqZQycDOeVpmmJQbJ1IAcmn5ciH4G5ONxQnATylKhPuW4W0hFfFhbR+R/ESr05towFCI1j1Vtn5bj/F7XMWa5Ibg2BDghgMs1UkghX7w1gfRrFdzA7eAjNfKKC1GclXHJSMQ9TX82DYz12HFfQ+ar2PcN3aTDfXid7rRHAY2QL0k5x52jZilAZw3kojKK5JZZdFz5qGicxdzzRLGl5YkyBuzYUTSsG4WQFB1TcLjXTfG/Y2w2tzypX87kiU271RbYetEaAGtcY0TFKvwrNNh3sTdTSpSUp9O5zh4Hnaav9NpexFhvTudgdqfJIs8auLlVmtoMJSWm1FWA9j4NEqZ0OU+JKf2LbfrJqgkCi5n+NUMJQY8+lsmoaB458dwSzu4Qd405ik4iEBcBnOPUUSh0TwvrEXN9C6+ZJciFQCbcz2KQ+vmhukSepyefMDsJaq81YgJyEnVOrom8gwhVXXupWHCuLLDLOlsv2qHCmDr71O03brBPegpwUyROn9mBe+ZIWrCo7UMNuYzLWe5oRZP6UcAmfJ5QkdJBrL2R8RQK2gJq1Nba2KZSG9MpdW38YuYmOS2VmJZK9KptUkp0ax3GwyoHtWYmjAt4b5H5khdZboqUxUlkHwoERcBGSD8rBDUxQONAjU1n6JKNaHAjJN930Z4PBFT9HtLerWBf09+l7NehhW8JylluQhIXSf2/tlKfbuTH+MichxyX2mRzCmovlhuJ8wxib4TCsToEUkk5r9N+y8XhHKnd0opcucHnczIgNZAPqQhQnZRC2eAkwlIIxkL5IiZH3Q6SZ+n20fWWbxy6CIqInwANSOtmiypLtdjfUGXp7xQoUbfJeYRsbtxHf6NNozlghR0AOnQ5Tzfj51Y+NkaAfTqrXa5vNI31Riw4V7CJBb5M1u+OT3eaLHJW68U+DTrsOT/Cqa1qx37QHm2qjOg125QuTtntb/ggRSicjKF4Eg7HZKHFRq/rEDahzg2tNSr6+pTM73Iff+LSDxPUfxI6SkMiFApUGiyEoEbfe1zGFWMY+v+YEFknC+AOtdlFmu1Ym+nnixYmljJb7tdr72hNmQhEkTqIsAcK3AAscHX1BVSXRi4ZBhhws8ABJ6UibcnJS/papTpZuk842sIYngs1IUUUcyM6jWuRBuIy4UfYdAhsBNysAhsTap0eK80dGgzo0LXOCyNvMjQW8AYjKrSXeuwvNeh12gz6DSatxbxbmuxDcDPLghPWedZeN4HsYxYbXYcuBaBGYiNStdcuaVpoUesHybpooRJct7cDkxNIptRaZoxP04RJMoVhNdPshpY1DexSbTWKTcUxnn2yFNBCMT4y5yHHpTWyzqXnFQE1AnIGGIGXuPUGMnbi3NMWmW2l0/ORzJU6HlADi3DO0wrDoxQsMYr1yXCu1deESoNZdFJANEsW0O8ett8sBWtRnWLXaAWXzqQpihHZx8BnuEyHVsLq73holPTb9vKnMN2us0C/exdfevW99CQsgzb7NJx7miQVqLNvYnNKXQYbdYYXlw2P7GIAzlMNSNdReaqPTXeaLHJmwY02tUnWNDCpdWUvsQzTaondzorxjeyQDyyNTaIx641cE45fr58fqhMxn9OQFvAHBsFx0SfQQs1RAz+sR2i1meWmFpI0QFldM0vbPItmWXBmPV+3SRFTmyWsyrlY2kW9FwalAV0IcsIyFWlBq4thSi0YdtvcWOo4/9gGA5uu8eRasSKl9pkduGeORCo9zoQm/4d9+yiXzqLJNPbMmAAQjtUYoJFrAneIEOC0oNbp0V7q06anwM3It9xYzlq1acoTq0QqVadUqmN6Scqw34BuLbPiiNZZW0tCcDNLgST1jh2HNEvRFAIrB2xk0dQemWJCx92FmevAV6ho3hfUU2+2zc/VxpSSKdWaCArjLDKjVvMtNke9L5D1sVl84vSWm/DOOQ85LrXwU7LreVH6lepHuTTfZCxI94UU/MQCMSuyBjUJvvCsv6ooSUNQU+QdMmvuntUzivhSeH6Wa33MnTzkhbfDLe2osXRakCNtLSBTK6Z1JjwNBXQKcA1+pC+psS+8VkDOprnl2tY6rBsFP0DVApSpanuRldv0aC+1Ga4u+4kFusD2MlnWmOPTnSaLnNV62ZR3Q5d1CnDrhZSYsoJx/REk293osIsFOLGJdpZGMaSYFQfwEbdQiIys5s8jWahLFy5WG229iTGiWYJOEYUCW0woiyG7kwjf2lXiOBS7TjMFyLuEccRxqO0K43v0NeEzpP560tAk31AHfgYkgl7AhFgtc622TmN9365zM6JDl/IptCU14tqSaeTcnGKkW1ALEdJfwr4D/oSr+09CtB+43zUdRzs66xphWtrqaPtoCGo6qAlswurSDm16rLFFmx6rlldmiS6m3joJIyouaUubHgPq9JbaxpKzai05/brxCS+KbYlZcE7Kd+WaECDp5gifKxYcJhgN+r7a603z7VCxIry3nP9Ns3bPcnNIpTaiWhtTr/ouHqMkZdhS4CYhb7UpZJ1HwZDTuA3F+cichxyX1jCrM4Lv2ijz9TIGUMv6ag0fxGgXNTnn+rCeX2Z4CjgBWIRnATfaS0T6uOZbUm5M1IspWsL5N6ZgPcoaUmTNmUUxnqvL06AtphjW9QwVsiexWoXK0nBuENKugsI7ZH6xC357IFTLgLMU5GS44wrWcmPOHbSabPbvI3nR1MrHCSts06bnZGTxcurQZUyFnYsrHHSb2fo7KbC9cox2yNOdJoucWXCzT52Um3ahagE4GbgZUKfE1KHYneoq09WSyZ7WJ9NkygQjsTfgy9RHGUfc+UN8oUjfoIWno5pUd3KC62PqytAHVd+jtRwxwV7+nzUhloN9eBz7P8ZAYqRncH1cBL608Kg1HyEzE6YRmqI1QJwEv+u9uBPo9L9BtaVI90kibSjCVt9ev41LDX3QarLTWWG1uk2dAQMalE/h51pkCj6rDOXskUw0kI1bra3XrpHhJHhS14mTTxh5KtI2Kj4RujopgNNa7dLBbOtco8GANa65hCySoEXc0vbtOgldOoyp0mDAiApdzpuVxaoNs63U2R81GA0rDLttSJNssbsYwDlOPE74/zA4DkFACG4csDkkW3dELDca1ISLYkphCc6VKOTroawWWm6SlGptTKU6doIFwLRqstCRHEJtoVjdeSTYC2+UPnFyHgJxPjLnIcelC2BjHbI+pvuRVlLuQ9rwp73wU0qfFiWB+zrarYygAPABTqhA1UK/XptL100o7Hx6fo0d62tCJVAMpRdZc45D2kquXepigL8cOa/romWE4yphw7Ji30T+l3aVOUWDMMhA6Cxld+AOCxkf7Nq9gJJVIF3gausCrGMt8fJePcvbU2utv8GUEmvrW2z27zO8WsDSU9jU0CejO00WObPgZkzF5UoDY7UBvBiGKYnVOBrN46DZoN+yk0pMYyYUWhI5zm9akxp29JjmX/0UTtAQXD8Dxbvfi0DOLFQmL30c97lZ50PmEoIWPaBjPsCzgJBmZjEri35m2MYatMQ+mmZsYXuEK8hb5qI1bRpDhrFculjtmtbC+bj2um16621atBlQp8LJGUpRzzqzA/fMkWbJsf4RcwcBf7zNUhDcDkBTVO6MrxxaEmpAa0i9ue+y9MnW4QZVa7kpKXADMGCfMRUSpoyoUmLKmApVaxWv23UTBjQYVPcZVysMWvuMhxUG/QYHw4qxlocgR4BHkSUH/OEuSiit5yiSWzR4AvKuZyJ8DtS5UCGlCz2mAOSBnCmlRCBiNr2XSCklU0iKQOosKgLRWrg9XW6iWdxzTkfRAsY6EwIF+U46TjTSh2L9Wff1VITdcB4sKmTBP62r4sV3aB5ykr5YBBw0HUfBGcoNug6z7tNuf0Wzn3bPi5G8f6gA1XU7Ls26T9dVK7kj5YssSnBZCjkAJDwUMs+QbXP/wUaTbqtDu9lzVpsG+yRWYoYRDfYZ0+M8XbqrNxhuLJvEAn2MImxCZow8Jt1psshZrRf7NOwkO1JxN2ObVnfMgAYl0sxyw6rROHY6TDqLfpaf2EQZyueh7J4jbTbWTCy8MdCwhmVrgdhjIAISYoNHc7CQQczSnOjYktgA1YxPum5R+doqogVAPbjBn6BjQyHGFEPAFgIl3QZST23VCYUTEXDk3lD7clP9n+A9XwuLusoxbbT8Jkyoa4vrAC1jvdleX6XOwCbAiCGk2VSkLTmNHuprk84DTXuswaxMflooTtQ1cv1xJ+TbQXosxZiQrUuBS9ryxg6rbLPCDitsc4Gr1j3tmvPDFnADJu5G1kno0WZMhR5tBjTs3kCbHm1GVNinwYgqvVKLaTOh1zT3jKiyv1dnNKwyGVZMQH1/wR8bsyw54fjSLEBb3MPrUzAARiw1N8nWqJiQxd7o7w5xnq2Fk8jPmv0kh2bdoNIo0J5i1HHJNP+YWP093hQ+FHx+LHWr5i8/BsX4yJyHHJMuAFfXMS22hd+XtMJzH2dZ010tTCahQwA72DWQ2sT7QzmyqXK08jTFjDvPEqmtQsFc55GeV4vAg7y3PFC7Y8nvut7he8T+n6X01HULQVKC754npMe6HkeD4JqjaJbVKuYlUyfjO3X1uz1uEc+WGLN4SxVFQbRpz7eyfZ+7uHr/1LnE1hlYq80+FQC2KTFlRIV0qcRT9ydMNhdN2U/Zan32GM0QvOGdJIucYXBTY0SFutUwJnZBTznWiQVGVGgwoME+1dqISchMYqSZjx4Hs4whHiMTCoV6VU4RA5Lyh2AQe5H7VQgEwkEcCkJFgKHIPUsLb8I4w4k/BCJ6scyQuWmKNbwGSGEdhULJB/KZzUK3PnkPDeb0/6FmXmt4JfhPFae/m9CQzL1x6N/imFKfbDXjbWAVerstukvn6dNmfKzU3z7VqlCPxKdODuEUa4J+DdJ5Mv9UyCa+0LUEii2AGsxrerbBjiY7NrVFWgBOC+hMaJd6ziXtPJl72irbLouOrGwNkFKyOdQM/0wpUee8B3IG1F1GNQOADBCaUnKAaECDUbPCfrPBYNrIrDppCfq1/OQd44OxZpcxF8MkqT7QwmXMFU3zgSJ+hbrmaHIAJiDJWuTXkTiw8wQxfbHmyaEQVzlW/UKK8ZE5DzkmXQCurpAtL6AthbqPhd+RYjlAhFiZM/raelMEQhTY0Eo4LVtImch6bjJHH4d3SQVDkKN/14rOIn4p5Z/EVnjUuAt5sHbRC8vVyioollNC2W2WBlz+j8kU+pnynEBRnZDxai2XSl8IN12stuBYt3dasNvp0L37PFXGrFBlzJi2vbhh6zm2Sqn9lQZX7l00ZW9gDMAnBDd3mixyhsFNgzFV55pWUia3KQlVRkwpUWdAw6XEG1GpjWeDmuNQ9N6iiSj8TZURY2qQ9y33mApkA0gL7kIxoDPLnS32f4zZaU1NVIpQ/8t2EotNUb1mgbBQg62Zv9ZoynXhhwuBnzxXW2wm6rdy8XdLyMdtaS2LMKEaXgrdYbfNYKnLgAYLp9C6lktQjjCU8lldOevMUY1sDRPIhBDwP6b8PwuAhmNhlvbxOG4gpwRHkZibcmvfQhGzQnULA3Tadi/nJX4xc0truKxpI+WeJgkIRsqqI/fJ+fD3AfvUSwP2mw0qtbEBOcnUBzkCVvT4CceY/F4LrgGf/bg21GNYW+NS9VvY5rc29U3T0tEXhfXN1T1Gut9oATMmbB6fYnxkzkOOSSvYPrqM4SU3yVss9HxiKQk23fdrap/oG45BWrkRghs3trSlR1swivibvj5MWqCvCV294Wg+FtahiIqUnSGFDRvzONH3x3hBUZ1jCmZmnAuteBEXWC1PtNT/unp9MgWpfoTwyQS3WLhL7dytMbi77ni4XutGqEPXKqkaXFm9BBs1A25OntvojpNFziy46dNiQMOlfAaUv/iUlrLcmEBYs2BipTQyi6olqrMfNbmExgrZe60zIT+R6guDptQMKOzMMmlLRx6K9SbUTsTczrRfp35YqFFIiQ9gDQZkwkzU/2Gw4iFZ4LXQRJUj/8fUq+H/Wsuj3yumBZG9vEdovbEmVsdwhalcxwSRX7fXisuKLjvU9Krf5Vu1yL6fBqXagtMnz4T6uJTQtIDNMjutVborHSanSShQhfq5/PnJAaeNLf4aozWyvqKVEHqBR52tUPcnuV4rGcKJE07niayVAAXWX0IBpR5f26YDnZUuK+ywyg7rbLFi9x26vICrNNins9snmcKCAumT6nXGtXMMqg1nsdFWGnFNMyCmxZTEc2GTa/ftBOp+K7UZNSv0mm2mJOzv1UnTklkoNC0Z17V0IRs3eoLX7FTGW5TNSurn2FYgaBxrylPBvbE5w6b6Neq2qUvFOrUquHEsDimqmdWWppCENwtPljU0TgeIY3xkzkOOSS8E7gWebGBib25i5hch6SDS7w5dH8nNJ/ryhKzPd6HYwhJ4dggP6OCDo1T9tgmkYrm5mS/Do5gMsEjeKqKtE+H5WYhdK031vSl+rJLwWS1XCIgM3cTCeobgRsbWTXVe1hbUk7aAUs1nJdOZJqlXuHZWqGSWeSSI5dRuxMK7NY/r4ylF3bH8Di7mhitZeTsbq1RXTHxkRYVtiKIry5CZ0r27w+XkEgdPNk8l+d9pssiZBTc9Wtafu+KhUsBZcSqMSdyvU8/lwqMicBOTw7XsHSUtcEOhq0NoAdAxHKHG37mnaSYzy/++TFzQijEYfU7uCT0nNbOYldlDv/M+PkOJNViMQegG1lldYpN2+O7CHBvkNUua8WqGJuUIQNNqLq3tVWZ0DUxbqhgtmOhihsHvAZOadNv0VtoceoUdk6pAhKGc2ZWzzhw1yLKlQfatNbguB3vdp0LlwnHpKA1l7Pn63nBs2XNRt7RDCy0GKolA5pZ2ftql3p9Qvm4fOcqKLVehnBzQaPYZVaHSHDOmQoN9BtRpU2Vg4x8lk1qVEam1mu/ToMrYWG2s9dzEQ05dooKUEtXmyKy0UxszTUuk6SAPdCDPJ7WGOrTieBTTzN5mt0E1fxwMK0ynJUalqpegYTyqMBpWi0GNY9NaSRYqouQF68FWJp9X+JgU4yOn4CFf/vKX+ZEf+RF+4zd+g8FgwP33389jjz3GN3zDN5yuXncCrZHFtnWXMXE3Mp+E/CRiudF9GHzLjZ7y06L5UhcYlKeFZM2mWljAJP0mBCRCMYvNIkZBKOeEQguo1jYkwe8azISWx5jCM7QoFY1deaaWA0K5Rb+TnuNjrmnhRxBgE5MvpIwBmeK0aH5QfUH3A6WQcueFN3TJMqRp4CPFiWzRzbZJt81gpeHkZMACnBEVRiR22ZQpJdbZYrReYXPj62Y7KBTRHSaLnFlws29x55gqaQBswGRPE/cJk3QgADYxQKO3EMTo/6NaQik0tNxAzpIRMjUNcvQzpS564nakNRah/2oRkEiID+CYu5fW5MgWLBCo60iZTAMuGqoQhIV1C4VGPQnIddo39iiTMfiTfYyhSRtI5eUZ+jmo32e4E4bgRn8n0bhpTCkMKAQ43QUr8MXC8Y6ghDuKoZx9Cic+DaBFWAn7D5wc5BSx1lALGXtOWF/ZJ3mFSQ3OtXxgowFOmx6LOxPYw2SzneLzmhpQgoUlqCVQXTIgp9407r4mHqfheK1xS9t3bmoD9qkwtsCmTcKUOvsO3EhQqyijxtUKadVaOCzQGfSt6xo1f0y1yMZTIU8OFRRhex71zQpAaNG84epnLDSV2phRycTBTEkYDSvGZS0GajzLjbY2hwIY+IKhFuIkjf0JKcZHTshDbty4wWtf+1r+0l/6S/zGb/wGd911F3/0R3/E+fPnT1enO4VWMGl4t4HuIpkVrcjNS43xcD6Rn7VbmpYNomUFFM5PWikr5MoUQf0oUU/6WwiotWJIq+d1vw2BTViubBpo6XI0/4U4j4yVq+qXk1lEFgjH1lEKDyl3UR1LoVKGJA+QOmtX51BGtPdocNPC9Cc5l5K5tEsfEdADPv/w5Aqgu0Bvt8V4qWoXCjckMTdVxpjYygor7DCgYcDNaSwtd5gscmbBzR4tN00bt4i+i72R7DQ6DacPbBJ/MpJOIZ1EKInsheF4GhVdM7EQpMFNgcYm1K7ELDdSnBOaY8BEzieRYykg9PsMXdP0b/q+gOkk+BoFuUwG37CMMcuH2hZdbiCMFdZXBzrqSSJ0ddPoQWuBFgrMuw3Mwmhte524D4grgTxf6h+0kbx7yIS0hqVGZjLuB1WUtupjJsNtU0532uHgFKsC24We8nRWk8ufNWriKx/dRBH2Pdknkb3uI3pgHDUBzyKtuCiicMwv+C4pduuotW06dF22tBV26Oz24SoZuBmRF4JKmOEhIKcGteYQqkMmVdhvlRmV8m5pXTou5qZn+XXL8usqxlJTZ2Dd2xrKEp+Y42qVabVEq9ljPK0y6NeNNSdp+mN6yOzMl7k4AM07YhRqsmPuQKqIEKj0ARYY9BukaYlpreRicIb9hokvEh6ghRE5Bvy1eIL5I3RF1EJcldMF78b4yAl5yE/+5E9y6dIlHnvsMXfuvvvuO0Vl7jD6OuAi5hs+JVaNNn7szQkVZXKZljWcsk6XB5kyciHqkppTnIo8kWLXM4lZb7RXiBboFzFz/DK+ElFcQHv4yh8tD2kAoPtunSxwSUCDbgidaU7Ae0yBrOd/mePx5QBphz42Y+OyKkOyJ4ZlCklbLQLrqu6a58t6Wj2MPLFD3PW9QCbsYGSKi6rewmO6ZC7t2/Y+kSM0uJFrNs3xsHOe3lLbJdlKmHpp/yXOsksHgCfvfxHD04j+d5gscmbBzYC6mwxNws0KKSVK0daF1EKdlBKeL3eoOdOURI71PgpuhIriRALrhwY5uizp/5q5pXIgDDOmxZXfQ9IxKeHL6TprLqiZBb4mSGuZdLu5+KAwDqaonmEgrDCtcnBO3j1kPqG1w5alGbwGHy2sRqOBYaiQMbWYe53WsKhHaJCjv6VuC5msQpeTUBDqw6BfJ1k4hUuJCJ8hnVGGcuaoiZ9gSnf/IYGGD/LjWtwQwgKOowUsotAtrZDJkHU+23dDa3DrkErJLNBZV9abBvu09/qUb2LWM9gjcxPXwvHIljXF9LMpRoC2IL7chPJowqQ6obSUMiWhRMrYJscYKM1uohRNU0pubbIxY2fJqVB1U64AnQF1pqV9SktT9pMp/WHFvK8GNSEfndm2x6UiVx3yfDoc14lxTRumJabWnWgqiRNCYCOKNdkzwfQpHXOj6x1a1e3/8s1PA25ifMTykJs3fTfearVKtZpPfvKf/tN/4o1vfCPf+Z3fycc//nHuvvtu/t7f+3v8nb/zd05RoTuIlsiABJCP8RCK9L1QDoidLwTsERkgVL7JBj6e954X9nPNw+R36WdtMpCj31FSTOv63cSfLAnuiVmCxN1NrhfXdOmDoXJWlxWWuxCXA7TednuBLGucdk2P8XmCetpnOGxTxsgU8v3DeoZl2w+i66OVplJvyHidttz01T4mXzj+smDXhSy5+L+SzS4sSWCmlFxygc5Sl+2VSuGMU0h3mCxyZsFN307THaXxG1NFYmyEdCyOicRJ4to25w6AD8TDsSkCbHgeKBSGQyE8Bmw0A5IBGD7f623hABSG0FC/y3ntXhNaQsK6hl3alqsZZWhCl/aQ/11Oft1AIfPUjE3+l5cM75X66YBugt+kXNvOmqlp4DhU/2+LG911W45ohWLZlIJ20RoyYUra8ifCMfhWHPCtN31c1rRSpc2JqUScoczpeFTDn5PDcdfHKEO8ybwc3AB5QELk/6NIu2BIvy4H56XcUFhIsp22BtfMgm0N9mnT90BObRcDbATc7GL6rJ6MSmR9uUYGbkb4IKcK56dD0hKUljJwU7HAxaxDZqTuqgU1ZlFQc22Fcc5yU7eTsVh5AKpNU0afDnTK2cQegroEXJwOkLdKx0CO5kO6vWNuguViYCP8sF+GpMxEMqelSRzYaICTgg9qtMY/1gdUXWucdpmbmXzk0qVL3v/vete7+LEf+7HcdX/yJ3/C+973Pt7xjnfwoz/6o3z605/m7//9v0+lUuGtb33rKSt2B9D6EFYXs2DwflEQu9rrTxdTcOr/j5QxVN8Oy+sE5Qq19Hnd70MKAYiKtwnrPMR6RaTq+hQ/uYJUUis4pUwBTXVVqHhxhG0QcyOXslWjiQwQtoM+7kodrpNXlmreq+trJ41V9ThnYWngKyBi6aEnalPFa+tNC2obpu2macKk24aOUo537X2y76s6BLxlZK3j2aL3Bti0lRvrCjuMqHCeLpPlKjuckO4wWeTMgpvd0ZLnQd6m5wJVweTvHlstoPmMxrqzv1fPBV15kwtknUyOQ3Aje2++CRnZjMlUMyANGvQAiVlucsH8+tkCbIosIXJdSj7YX/+uSTQUCxmD2CCf2UVMplLXTb1is5QTalm0BkQzA6mzZDKROu3gB2nKw3XbWuCWkNX3Ilk767p2yABOetHeq/1kdT3kmSpLkmZEunzpE9Kf5BjiiQW69t7tMnvVJU5MVeKj9MRql69RatrNA+f4QuoQI6jmwI0GHjKu4OQLws2yJsy6P3DvEJAt/bEDrU4+zkbWuHFeE9cpdksT3jPCTFwtTJ9r2v0erv0WRlAuwfJoyKQ6pLQ0dUkEqozoWaCjY24EuMg6OsKz9bFMysbNrUKpOaVSG3OdNeM+mJp3zVly+tJGJ3EP1AogzUcDCq2v8jwROmSs14CaAkJdMteRbmRzGu+e3cSCEyOlodbzybUTvK5QjI/YfnD58mUWFxezSyNWG4CDgwO+4Ru+gX/xL/4FAF//9V/P5z73Of63/+1/e16Dm/Pr17mxsWYE0g4K3GipF3JjOQQiWsEZBTeH+LFYEd6gZYpVMguAVEPK65ItZN7VMoPu73KxBjXLQCMrV9dZ5rO+9JUeGdDRwlJZlSkubutZ2eDzY7DXbhG3iGiSd2hkbdDByC0a0Mi7y3O68n56LZpw3JVxLocy90u5Ig9JG2wvQPcimRdLgu8Oq461QrRjtnMX92h3elyoXnUybW+lTW/a5nrrbpMRTfid8B0tX7h6mDaQRZfFw0liHCX9f4Uxq2wzpcQa14BTgJs7TBY5s+BmsFdncJfJAaRd0yRoKlWuDXJ+hM1UI4Knnpy05UaAiyYZ51rzn9OqxCjyZWMam1Zw+ZHPKLCwOGYkQEhrQEKLSEhaS6EmdyUseakKpRqh5aZPsOiYBloxX/GY76r229XZgGJMR1Pia687ZIworKtoOraXbd1E6xROSIoZpeW81q2lytcARwBP2J+0AK2BdXqKGI0ihnIHaVC+qqSFCMi+Dep/UFr1WPY+uUgmsgY+4D4KvMQUDUWkgZUeqwu+5cJuldrYZUqThYzrDGiMBuadRnYLj4Wk74oFR/e1lMzKU7XX1EwZZTDPqOLAivBkwCYTSEmte1qJ1Fpsxu76KfvOfU34uiSRoQSDTp1hfzkDFy1OwJdDmgFkci9t+ZhWROm5RJQZWpMqt/YLNjf/hK5oOn4zrEtwOuH0lpsYH7E8ZHFx0QM3RfSCF7yAl73sZd65l770pfxf/9f/dcpK3RnUKvW5oZWUUaAQoXAe0VgoarURCpV6EZdpreSIKU7l94Qj6qrlCmuxkPlOly3zqntGzCtDW5m1DCAgp5FfYkHGVCprkQn412ND80G1D+UWDUK6qr4yBlOtHA4bXs5ZYKYBpFZu6nYdAsMVjPZIgybwZbLgES1otAZ0qiY+0sTEjOjRplvqML5Yoc9dBuCk6tl6DtNybR/29+qMm1XLV82FwmUr1qLeoucMBf3TZAG4w2SRMwtu0n6dHi32qdukAl1G1mcbcOsqjK3ecGCdMyb9ej6jhJ5cZvVrYUJDtc+1UNhpy8GxKkczn9B0HGpuvOfE3NG0X6y28Cyo86Zl4q4XoflVlakZhFhuNMMU4UJev2v3w3ZQbqrK1QyuTXFmM6mbuI0JQCoSGBd8praqjnX9NODYLmOYq9RXAystZCihRgMbDW6EamTmYs18tLCshZsueYv4cehUgtycHFXJCxZaw4k6P8QmGggVAHKRTjCgP7YG90Wk1XenjAsJgA01aJQGzhVNA5zG3oGREXYx/e8mxgojIEe/t35FqapsU3UO9X8TGolZ4GBazSZUE5MjMTfGjTil5NJHy6LMGcBJnFVH1mcA4zM+WqqwKQH6HfKaTK99Zg0S/Zu+vkjoS/12EP4Xghot9Mn1XfwEArKXe7yA5DChSRufyUh98QXk09Bt4COvfe1reeKJJ7xzX/ziF3nhC194awWfcWrR8+dxJO5U+lCBglP2IcAJ+Y8jDWoK0lkJsAmVkTX8catBRKHQoz1CrMtYQl4OgEwmEj7ZLXI7B99zQyw3jbwlRAvpm1igkJJZhEQRAH6DKiu2tIHUV8rVdRf5b7tt27VBXIEq/GAhbxnrqHboqudcAYaiPBX3d52QJuD19t07zS4rbLPOluN7JgyjC03Yun/M9c27zT1PqXsFvOq268Kg32DQbDhruGkpA2yE6xpQ07NeUEcp2SJ0h8kiZ7eqO2W6nOcGXfq0uUGHuu2Q4QJykqmnR9sIstvk3QKkU2jtJxQznj6BUKSbKlT/BhQCm1D4hmKgFS1XM4uFfH1TrC+suF4VBTzq8qxvqWZmG5jFyqS+Gtxsq/qKpnJTLCF6IURtuRFT8IKfqEDKdHWeqHK0D24s8JrMUiP17pD5xoqA0VG3bEtdJdPbFr5vs1r0Lw1SSzogJb7UU0hqfk76DlnfGuIDaRFwtvFjP45LNrY6R7GUjHPKkx4rYVcKwY0bTwu4TDyOUvy+qguTCUxLuUV0HGCjFRp2H4Jtu7nkAfRp0TPxNqMeCwJq9shAzZ6tmoAbiauZ2sfIPqSSemVJPAAsVKHBAdNkwLRkTko8jSk+WxNHUpPqNcsE3AyoW5Bj1tYRTeaUEuONKtfTNdgsZ+BGC27DWIVjADIUviD+skpZI8KD9B9RYmjhLOTDWpmhtz4YwUqATZhQAPL9KXilhNNbbmJ85IQ85O1vfzuvec1r+Bf/4l/wXd/1Xfy3//bf+Lf/9t/yb//tvz1lpe4MatHPezQcBaZDUKOVmbG5PwXfWiEgR5GWKzr4yj0NbsCABQ+MFdTTKR+XgcVMDriIL7dIv5a6DoHhIplrufAr8K01K2bT5WpLiAYLm0B3nQzYyV4zbWVZkfe/SCa3aEAjMou0y7YkGdKeIuFYs8BJgM1F9RypyrZ6TgJ8QUDZl/HllzQ7llew7y4LLl/iMnX2LeCo06dNhRHtUo//94GOyRz5lL1XntnFd03rwkG3SW+97dRbsnyKXgOywb6TlPdOkwXgDpNFTlStd7/73Tz44IO0223W1tZ405velNPkDIdDHnnkEVZWVmi1Wjz88MNsbW2dvGZ7BsRoC43+f6SO92U91mljtktAio98hUKAE9uORUm+rEDTOrvsw4JyI0JOuLnrilI/RupKkrdOyKDeUNuq2joETF4/K7QyWS1IQk4oc2bkaJ2PIP3eUp6ud+y4A/5qxvo5CuiE/cI+51xtTLm1T601gNZhPAFD2NRuEiBjRiel2Lf2vvmdR88pHwnHWi1yHI5LINPOSn+RH0PgEfZ3woJugVS5sbrXDt06X1UdeTg8yFzQBMzoY83/0oL/NYXWG3V+IYVSOnWuZ1XlKOxc5OzkHVtgNIsRuuGdc1nfSgPKrX1/nOl2yPEeTbHvEAKc8Fjo0G8bGb/h1i04H849QD6RgD4OhdmgsW+1O90GHvLggw/yoQ99iP/wH/4Dr3jFK/in//Sf8tM//dO85S1vucXKnZyeSx5SYRzhESd0SztKpkjlj1htwmUWInO/nkv1nJQbI2F9y8E5FdPXIZsvZS9bOOfN5H1Sro1fickVF8nmaFFSJhCfp4PypS1C2SUmt8hvNcjHLOtvGCk7Vq4u39VZsquFXiEpnlxny5Z1yTp0WbUWnHWu8QKuss411rjGyvoObBzmEyZI8UN/G4+yEI0pCeH6kFlwx8hlsjwR3WGyyIlY5sc//nEeeeQRHnzwQdI05Ud/9Ed5wxvewOc//3mazSZgtDsf/vCH+eAHP8jS0hKPPvoob37zm/nEJz5xsprtQnevQ6fZNb6IdJzLQoUxPVqMqTqrTZfzdLc7mdVGb1qoPArYtMhn5/FaKZIFQ5NmQCEz0GV1g3JdncJMKUKBn2kLXyshWkM3eHWMS1iOMJ6GP3gv2m0VWB1yLrGL63VrCsyQuaVtYgOxdZyCFv6U+VgzXci0V9sEZl2dDGFGG4dg7KKpbymZMunYrCMiVFyxz+tKEgSJnZBnKEEj1j9q0O70KCVTSqWpyWyS1I2VMMXvM/LMUCDqcjqta1GGkjO6cNZx6DnlIyGwSfEnCPl2ejwlqGB1yLsyym9yQ0wDGMuMdpJKawtoPcpTZPHOugURFUY0GFAOY2yKgA34fSsl66NT/LbS18hvqbm/lB5QqqY27Whiby9Z17SROxeSWHBMIGzVLgrasNWaOnfj0UqVZzqLWZCw5iX9kEGXyWddjAlI8n9YN/nW9rv2yXiWzCNddU4en6rfRButN89So0FObH2MEEUGzzoNxfjIKXjIt33bt/Ft3/Ztp6zE7aPnkodUBdzoObzgEznylBD4c18IcNwn15abiIwRCt0dYPWQc60BldrIrBHVb/rxJ4X9JeAvLPsARCwWMu/LOJA5rYXKGqZfSIEaVoCGL1vcj68c7ZLVV9rhiszT4mGhx6tyG+vge5x0Dqmt3gAw6021auZWAQCrwBXrseK5GGuq+54h0g73qkukvn17+1NYzxBpSyE1xrVHSG3igM0aWxbg7DiF/ZSECiOu8gKm95a4fvFuP5mKVFu+RddsvW6bwboYAyqO7+oU/QJwyqdhJHeYLHKiN/zIRz7i/f8Lv/ALrK2t8ZnPfIZv/MZvZHd3l/e///08/vjjfPM3fzMAjz32GC996Uv51Kc+xV/8i3/x+A/bhX63TbfZccCm7oEbkx1ih1WzXN1ohYPNZgZoNslc0xyjsINDzx+aAenWiAKbWRRoWGMaFiERhHNlaw2eSBaKaWig0CEPbgD6okGYVXFhavim3YvA/RNaq11WmjvOd7630eZ6ZwWSWgZIUjLQkOp4H80wyz4zlmNpA6miYzhizhamJlSQiU7qvnHIXXdfo2r9S3udNt1Wh4Nh01z/lH3ekw31HC1UaLc0/IBry4wq1bErf9opMUhShp3lfByA5pchA9J94LhUxV+nReiMmoKPQ88pH4G8xjQlm0y1IgO1d/1AJmv5qGGQbwzYEFyjKS04lrKSyL4e5SeNVhZrU1WWEg/QDAselQTH4SaxSpJIQJ8vBdcCydQA/4rzeZPHZrOhTuMvvAVw2dLaNuBVrh1TcRmAnll9gYmH6pAJV06ZE7rFxEgLSGEDCGnFkuVl6YIPajRb1seQKZgE3PTV//TI3Hf14p16IprgK4pOCopnUIyPzHnIsegcU0gOIVlQ33vW/BqsdReCGX2rN3+recgp4chu0uO/A6xCudMzirfSlHFtzG6/AcMFX6HqKOKCRR0DQhZ8OUCE+o69tEsm0Hcp8NyQ+qrFQKXM++3+AVVuSuayrfnxpiy+uagaJ2hErdy8COV7b9JZMVYQgMFSg53OCv3krmwcbkj99QKsQnIciendgNq910kSw7v6nQ7Uyhm4kXL7ApqkrRPctxw21GtMXbr+VXZYYYcLXHXLnUiM4mUuMS0lXN+425TfYabyNHNNE3t53absHzneq/cnpjtMFrmlau3u7gKwvGxWgf3MZz7DZDLh9a9/vbvmgQce4J577uGTn/zkyQrvA/0ag6kXJuucG4y+0vzfp82g38gnEZBJyU0qM3yZRWiOMaQE/IFQpGFLijU2hZYgXVRRCkRltZGBp0GTpxmSxbZCc7F+YXs+HMSrsLSxw0pzx5pJ7VbaYnljxzfF6jpEBYwkX+eirQaZWVfVL0Zh+3YMg+9wI3N5KXXprHZ9s3RHqhQuIqaF1kmhEKgzj1RKI6q18exvK990qLY+J6fqjO0E9L73vY9XvvKVLjPSQw89xG/8xm+432+bG9gp6FnlIzE6SrDPjVGtnQyBR6zQIooB9iIBNnhOhJdUamPlZjBywaM519siLXMocIlmLlH/y1gLAU3kdbPJM/Vc08QtraXc0rR72grbOVc17ZpWx7qmxcYbOmujbruiFz6KtGXeCirhGO4Hx7FN3+PK1cqrELwEfMirT3DJaeg28JCzTM8JDzlO99HXJpFjTbnvqfuFHsBqvg62RmtAo2QtuNUB1EYzx6hfQQVKtDWog+/WLeda+HO/k4v0gzTQKefLuwhcPKR88SbnLu5lrmnyrFWUTBF7iUAWsuV3Voxr64oFC+tssdLc4dzGXl5uyS1NEZQtPM+1ySGdpS6dptlaq13jLqbr68lBQkpOVJ/zXDJ1uX+1q64BOtsO8AgfzLe5Kj7gQ/tWLh5bG42fvOUW05rdJlnkuaKTDFePDg4O+KEf+iFe+9rX8opXvAKAzc1NKpUKnU7Hu3Z9fZ3Nzc1oOaPRiNEo0/a51ZJ3gW3odjrsrK/QsG4XJkh1jDhkSHeYXFk0loRNtQ3BLO4gS3MvYyw3qhOHwrLwk5g1B5gtKSz4zCcU4qWDx0BOKn8G5AUeBRQ6BMI6vjCzDfnUy0Iy8KyFpIPHdM7dv8e91adYYZsLfAUwriM92rRLPZ68H65zt2nb1N6bYvPop2QMo4HHhHQbdFSdpXpd7No5kode3MYiQoq0mytvaDU2O3bNDdM32qUe43ttSsWLZGbpzYZ9js5Go7SpOaEEcMzILJg4JYEq7LYOobUQ7ysa3Ajovp3Z0k5oCr548SLvec97ePGLX8zh4SH//t//e/7aX/tr/P7v/z4vf/nLb58b2AnpWecjQklkn+Bbb1B7/f1c/I0WNkI6yg1NC6+hFUhUelK2CB4qiUiOnwydUCMAQmJunNvYlPyCnfLucizpPWXftG3QxLfe6HPN7J5JFabJOdJSNnGaRTzH9pFmeq1ORyY2Z3hg3OYUGz2swagK7WZmuSmRekqtlZVtNvt13MKeTrEC8YxNs6a2ou8H2Yffz65Ny5llVveToTon7yPApkvGl71sjPr7C9ApqlMaPTwVxfjIGXUnOSk9ZzzkyG+glHuzFCbCd6RMN9doi14kmY4ncMO51T061a5bqLFChW5rwFDcsbxvHrM4i/vYog8+7gXuh3OdPRqtAWlaYrh9HpKFzI2sg3ILDQCNJChokbmMPWDKXP7zX6ZdMsL8lBLd9Q5bq+tMWMzmyg2gKwsr60RFgVJWwMXFIZd4mlV2eAFXARhTpUWPyvqIP7r/ldBfgC9g6n9lmSzxkm4TS4HSt7WxzTqZZ0i72WP7Rfs8073H1PmirfemLBQqrrHCR25Cuq74AS5Dmih31q8bcJ6WYLxUpcSUS1xmTJXaxesMu8umPl38eUuATRfYxlsbsmXXhpxSom4zUqa3AnBukyzyXNGpwc0jjzzC5z73OX73d3/3lirw7ne/mx//8R/P/7CHZ2rr0nH+25ITfEDDuaTl3NG2wXRgieOATHAOGFAINqKgBvLatTAwj7h1RQMnLdSnwZZzVVDla4FeBp4GNzJwWkC/KD2j7K3Q1EExtUMurF/lXr7kmISkce3SMYvxlapM7y2x++SGGVCrBMJfQdCfFsw6qs7SBh2UWbeHN0l4DaWKt+XKIoYr7FBlRIsefdqmjzTbjDaqTDYWM6bcBYZFSQWUpjbFY0bmsdmKHlXGkKTGPB2bwEJw0+d0TKDCbdGMfPu3f7v3/z//5/+c973vfXzqU5/i4sWLt9cN7AT0rPMRLUToMZ2oc0WbjNkUTB/XHzdGRZaZUKgNBVxdKfD94RfzY78FNeuSVgksNxVGGbDRxepNFCyyaGeJDLi08BfxlP+rwbUJDJsG2IyqFWez0W5nYNbCKaUH1Pbw19uR8T+FhSbUElhf26WxNGBcMr4P+zTo0mGFHZPGdLVLv3VXYPGVF5zlmnbUNDcLmNpyxT1N+oySOT1FiFzjWWr195/laqb5XLBPybHBE9Ft4iNnkZ5tHnJAyXx/IaeIPIJCYVD6TQhsHAAWYVjzBymknptH252eS/8OZsxVa2OTQDDKqsKsjoq/CFCwAKd18RlazZ5V5pXYSab007tmWG4077LlBoCpdv917i19ySURSSmxwyr1lQF/dP9LTGzvJhl46uqELkq20O2wAcsbOzYIf4tLXCax8XrSLt0XnTdAZAMrF4obvNS7OLapvHqTTrPrUjZXGVFnQIkpz2ysmTqv2vo67xPd3jbWLpAlxLrdtr5HC9ey1ltb2mJKiVV26NKhs9Rls7Pst7uWUQTcdOEGHVbcwvdmn609li12fyqQc4fxkFO5pT366KP8+q//Ov/lv/wXLl686M5vbGwwHo/pdrve9VtbW2xsbETLeuc738nu7q7bLl++bH5QQmFvqnPttDx02qNNr9vOp35OIXNH025paV6eKAI53v6Y/s+6nKJ9SK4+MXcF5ZaiwUIInDzrQTjJ6xdJst+VhqK1se3SE65xjQtcVdtXTPYOtlmp7uRd0xyT00xoId+2MauWfo9j+c6rV6n5ixhqtxfn+rLS9c3qNcgH/gVuAKEgkfqMwMUOJNOIhkyRLie0Bh2XwnbT2ylpOp3yS7/0S+zt7fHQQw89+25gBfSc8BHwx3tMJokNj5AHAP5Efow+mhNmtdBSREqYkedFxk9VuaRVGZNYeJFMp8XvqMdiaJXR1hnZWvHjSdMAm3GtzKhacWsraLeHEinV6YjK0AKbXbtdxxjTrwPX7LHdL1yDxa2JP37pusxC9WaQNc19p9O4CMYo/Fai8NgHDuPuaf3gnL4mxdyXs9Qdtx7BXBCC1pPQbeYhZ4WeCx4yonICcJl4u6jSS/Zemdq6Fz7EBtEH836lOrZrXO07wbuU2PigHIVlCg+r5zOOrR7SafpZDFvNHuc6e/kESd5Y00qZchC3MmF96RoX+AoX+AovsHLFBZsd7K67r+UzsubcvGydpT1bQOuQdqnnZBcBOetsscY11rlm4nA2hlnZTgbQ7vvBY2xbN1qDXKbH85Y/La12fdnCW/9I2tzOAYonHKSSptkAnMZokPGSXWjvDpVbbt+k0W9NjueWNtKhGw2n+tLHKSUmp0Epz4Is8mzSiWaBw8ND3va2t/GhD32Ij33sY9x3333e769+9aspl8t89KMf5eGHHwbgiSee4Omnn+ahhx6KllmtVqlWIw29izO1dbc7VNZHdk3rhsuW1qPNta11Dq40jUvaFTKXNHYwgenXydRtsoijevsQ2NTI56GPaj80Kc2KFtz1JlqbUBCWDurKFusNZAJ4JMhtFd8aJFsLa9IV5hX6oovWAk+zcql5mXv5En+GJ1jnGvfylFtVfIcVpwUBuHz/JSbDxSyQroVN3BD4XSkNiOfTq9siJbOqbFsTuWOQEQrAUruUWW6E+Qyoe1qLZy7eY56xap/VFfOxVqWpDEb9cpBCPPEWxhIv1nPJlIOYQKwnwb7anyY+uChDiT0Xuk4Ujifgs5/9LA899BDD4ZBWq8WHPvQhXvayl/EHf/AHJ3bhuBV6TvmICJmyyScPKSaM1MJ7QoYgYysGYFJ1POucLlfGp/iELwILfhrWjtna1Z5b40aSrVQYU0qnWRWl70gSAE2hq9mS2tfsowX0NDPXsXGtbCy4ypdbk3NDG5mU1GVZPHQHY7GR/Z56fXGJ2wVW4O7kOqW1KQPqbqLuYLJmPtO5J5+WtqvdYnRb6g9dBEbDTiH8N0w6kppGGZLPljYMLtNa1dy31hROv/p/7SZ3aILEpT+ehmJ85IyuLH4cei55yJB6BLQWKSItFSm8IA9shuADG+0ZogL11fgvr960caZ9Ggzc/FQpjSBJs7p4ilN5oJQbuKTda7ble69yicu06VFh7Mqeric802n6CgYv5k3GjbUGWdmC++GFL/xjXsSTvIQnbDzJDSdbtOkxpkL3gQ6TzcUMhFwJU/Hbhq1l7WBcxozF5gJXeQlPuGQl4s3Ro83g7gZ/eu8DRgm+ClyRxEIic6lnaPmiKlnNrjk+K7x2p7rC4N6G8QzZsPcMF1VZSkHi9R9/kU234LLtW+UlWEmus9bcMpYbbtDSVmthVwJsEpysvLu5ws4Le2yzQp0B26zQoEGbHjfo0HfAJ5YZ4Ag6QhY5a3QicPPII4/w+OOP82u/9mu0220n+CwtLVGv11laWuIHfuAHeMc73sHy8jKLi4u87W1v46GHHjq5a4t1SxPXtH6rTbU5ZkyFCmO6dBjQMBnSNvFTP6fgrwI9IbeCYiiUzmJGgM90NJNQICJmoXCgaQJpAklg3vaYpfa3FVIudMJUtPYE8ilS3X2xF7L19bQqhzYI75rTplziaaqMSW0WoxJTJ2isrGyzubHoay2GkFtQLwSNUneNKbSQ4jQfDTL3tBlUg4o1E4t219Q1JWFqgU4DOhNYLQcaIWGaWqsqjKjsM6PhAuKvmjrx7Yi6aQ2dMKHTCCYihBbQpUuXvP/f9a538WM/9mPRa1/ykpfwB3/wB+zu7vLLv/zLvPWtb+XjH//4KSp1a/Sc8hEtQGgAE1NkauWDvtbRQngCv/8UfeCQdxRpZwXUq61AWSITbMPunctkepC3TAt40O8jcTMa1FQxiZPU/4c1GDTPeRaaEZkbmpBAHXFNK6U2tmYPp5FkD6NT6NtjiQ3S9RuZuq1Ud9lZkuQCN9zY9iywniZTK3NkXBch2ZCKwKoAHO1uWzdAQ+NSPT610OqRrp8cHwV4FBNJy8e0HBTQEXzkTqPnkocMqGfKLvcNij5ExAogl4eARm+e8iMsoJ6b/xutbIW/BgO30pVHrq5iPZzky6XtKUzLGzdZL225gPYSqV1yw6gLn5E6ROUl6dcqQcEGcPHQAZB7MTG94pYmStNtVnh65VImV7h5Wj8oH/DfaRoeIcmPLnDVucRWMPLiVS5wjTX+9OIDRgHewcYNy/hWz9B8soYDM0aRZPaSaWyFHbZXVk2aeqnzMLTciNIU9b3LuXVonLtuCvShtguN5r5TYNWb+/ms91q26MpWprfRpl9tu9QswqsF2OzTYHgacHOH8ZATgZv3ve99ALzuda/zzj/22GN83/d9HwA/9VM/xblz53j44YcZjUa88Y1v5Od+7udOXjPpCPbD9Vtt6s19N3n2ptYdrUvBmgKSelNS/g1woz2c6LVAk3LCVlEF6EHvAZwJJFMDboRymhvtcxthnIFJ2u3T4HepQ6qZrJ6cLfNRlqDa6g1W2GbNMocLXOXS7qapcgmqS4ZJXGONHVZYZYed1VUmnUVf0BhGGHusPaSdo6bN0IdX2iZerniRaoCjwU2PNrVOz6RtdiAq1Abptt8HFv0JKEWcfvJM6SjyvvEpqCgbifU8uHz5MouLi9nlBVYbgEqlwv333w8YzeanP/1pfuZnfobv/u7vdi4c2nozy4XjVug55SMiQEN0WM2kkC8cizSQ0Zrdo0CQjE15oAI3IcCpYVM/D9zimWY/9YsLgVqNLOtZU+1Dy00VDpeMpWbQrFmHF1mDQQB+Nj5lvJnp0iYSkH4vQEbAzY7d72HGxJRs0pR2XoZyEzpLXTe5O/eM2gRa5QK+EaOTMPNQqST/iwVHeEQjG9M1iuMovbKkLhrgaP4TcY/RdSoETcekGB8pWjP6DqDnkoeMqMzIfiekvt8sZWkIctw3DRWnUpCar9W8L0sTSIZEUb1Fn+P6rTynnpUrluEOsCpZx0yQuwjyI6rZOoM5pUJISVZfW25t9YZzEbvAVc9yY8qvsMoO5+myKXVxys5wTCi3NAs+sqyLxqW+OrVJIUrQo8WadVFj9RBWF5TFVys5g1ew5UuKfb3oJsCIqv3/RmZNrtn65cayuLUuuD6kFaQL+ltJbOIIx98rFrp67S6fWnhPHycr97pteuttWhbMgHnegIbajuNWHdARsshZoxOJ8YeHR79FrVbjve99L+9973tPXSnAZUujZvdJjWdYo1wbU0pSk72jv2AXUCJzS+uDcUfbIrPeSCcucEk7DiPKMR4pRAXox7SstUPO1cyEfxCaiz0zdxgbFDxGl221LA7c9MmC8oX5uCwm+sWkru2snA1YX7rGfTzFfTzFS3iCC9evs/BZYGru2LiwS+PFn2dUMtqhJ3gJvZU2f6oD9YdY95DDfPtqQaQVVGkYtFc/Fuyv21+1SYJbuFD7xDYYMGCfG3QYUGd1aYcrq+cNY+uAsQyFKaFlcrkJrBnXPs0wdluUllKnyZpSMr6zWpuXE0Cs0CIg/TT+8kV9056T1M6noYODA0aj0alcOG6FnlM+Im0fKh6KqIgnRAGOBi4a1Igwoa2xoT+9CLTgu5/IWkzWvaFDNt5Fu7p602kSJZ9YRdYy0HWs2sc1VTWlHTSoaeIsNpNl2G+VGZRkedCGWtc6FluTLQwHWK9uMqvNTQwvvxbsd8k0lTVbl749b+u4tnydrSWT1tX4/neMe0b3rrwFZ6gtXvLxivxAY0kDigCn/l7gBTjL2A+FPa8oDVZTcvOQp00Tnq3rqdxmRLt7GorxkRMr8c4OPZc8ZJdOpjgdgh9HFVIyW5YILTZ9KVO5RQOeXEHDd+vuHLroY1HsTSk5iwJp4pfvra2ky17JMppdxCQV4qp18fqKUxTKIupdOpzr7HFQaxYAHOFjCxnfugiXlozL+/3WLW2Na3R2+0wT6DRvUGLKDqtc5hJfungvw9XlLPGQxyNt+yrFrKRNNlahL3HXH/UdX1m88KeU1kzZXTrc9aLLPLN5j3nXbeyi3hOMxkV53yiZReQK7fYuae6v8gJ6tPmjDTJXOmcRAj9mr2cSJlmZYuxKqXKY2JWRpI9Y3imW+YaNqYqOYeFB27ZNNuGg1WRrfZ0SU1bZYZ8GdQZ0Oc8+dbp02D3N4D9CFjlrdEarRZZRRwZoH+jWmNSqTJLUCJ9dskWg5NhZbTSzKPoqBaejwqoIL7EC7MAILREJkKQmyA84SKbmuqggrF3StOCT5MGB/l/K0MJbAnlwoOuqtBcdlNZji7Xd6yw8DVwm0woMYbE6Yf2+La6x5kzWf9oh01g4gVEtYBY+Otxi7RW13CgK8KXEwEje+AYDp8GS9TXa9Ch3ekxa1tJkf80/Qwmkw4YXqDceVtmvNahWx84th2F1hiYuUu/TCCZFGUpOmHntne98J9/6rd/KPffcQ6/X4/HHH+djH/sYv/mbv3l73cDOGokArb/LkZp2RdIVZ2rMZ4GcIr4RPkAEYJWeVbSfnsLk0C3eWbegxgEbpiRTW5Reo0aDanEBk5ia5Ww/qUJ3qcWAOn3ajKg6gGPc0aqFrmjyf8umpSW1zx2SWWokeWUIbrTlBoyssQjlm9Be6lm3EBNbUClaXyp0ET5W2wtpfitgI3Th0dcpATRUtOdIxyRoJZto0DW40e8Q1o9bswDH+MgZTeF61qi/2/RjML0xDv4YVtYbuVQDmxDkeMpT8G9WSj6nLIVzrQFVO+bFJdU8yloD0gX/eR5oSlXZfjKB2uoNZwURV1AhiTcpSZypLsqjJKtvB1iF83YNlzWuscY17rrWh2tQrsL6PdfoVdtZAP1Sn2FtWVlyI+NByQ51ZVFZne7AVTK+AlxYusp6dcslFnhm9Z4MJGrwKPXWeoZEK28yJSoYS8h5W2c6h9BRC6f2Q6Wy7SsK1IpaakyFURVqEYVa5gSf+nVzbX8ILGQK7m62iaXNuKUZ3q2TcfVPI/rfJlnkuaKzC248UIOK3bCTxbb9TQObPvjuaAJwZrhLQbwVcsJqGOwHHgARjY0MPLs/Zy1NU3FJS4PynSYoXAdhRuIDDXJksouai/U/Zf+clLE6dGboVXYoXwaeBv6ETCiyfvBr922xwgVW7aJ73gJWRdrwsJ1rkf9Ve2V1lS0M1vZJImCyVLhjZDFBo93uGJe11oDdlnKjG4owqYUa0bDvQ1+BmyEc9BsMkimV6pjptMQ0LWW+94XgRoC1NUefhgl4wFHRCU3B165d43u/93v5yle+wtLSEq985Sv5zd/8Tb7lW74FuI1uYGeNtEDo9TH1+y3RRO31FoKcyLh2AyGIsxGrjQY1HbOda5nF+sRVIkvuGbil6eK1/CyuBZI4wFpubq6YRAFda+0U32xxR0sp2dWuyeJqmBp3CXCBx+a31Ac2QzJAs4sBLxrcJPhJD67beu1A+1LPAZs6ZsHC660h1Go+wOnrsRzyvVkAJwJYcgBHM+0BWbKTsv+zfqwnACbqHgE3ukx9UwhsUv/wtP01xkfOqDvJWaPRXiOwshS5lgbARlMM5DiwJBeE/dTyg2Der9RGTu9fCuoxnZbyFiLtku8qaPua4i2dpSw72nm6tOg51ylZQ66UpExy76fBul/Xc509pzxdZ4u7rvYNALlmrmkmB6zet+MW8s3F9aTheFCv0EJ5bNxgcWdiyhXXrhSaSwes3XfNLOrJDrWN68ZFvQOGz97M2kIDHLuJVbrOvgNSYHhgK6Y4raG8ZuS7WrlCKUuz7GVVxrUytdpkpvIz/M4ZaLVykpaVu9AddWhUDbhJHbjJsg0POAXdJlnkuaKzC2728NGoaE+lcQXUbKqtD1meUbHe6E5hO3DRJJTga1T0sSeoyI0JBvU38KwhSuAvJSb3/GgIEwniCk3Trq4CyHTFyhlz04KODKQaGfjT1pvop1UWpg6wanLES7rne3kK/gizfU696o7ZLa8PufeBL/ECrrLFuk2vWMvWj9FgK9BCHN9yE9ZdmITSXgYkYEbiEET40q5qnWqX3dUNFfi3iG+9ESBiwXF3JbMKtoDtBSbpIl0wwAay/pnTwkHmuiAuJeXTgZvblKHk/e9//8zfb5sb2FkjLUAEWjmPcsB0FmmrTLiFSooiYKNjLzSoWcZbAE+2VbNfWTcpT7NUoT0LckaUSI3/tryjAIYE35LTxIGavTWTLGCHVbso8ipjKtygw9habsS7XwSdLMbG/JJalxhvAh6SZUW7brctjGCzC5PrcLMP6RTqVajX7Egf2SZomntWdnfpLGUCV5serU6PfqsWZG3SlhFt/U0pjsfRICbgMx4lwW9HWIN0P+uDsd5IFiXhM1JHjTyFQrdc6Tvl07ulPc+ypT2ntFXLlKl9yLuO6+8VfMsQxCgBNx/DIyRA2PKDDl7ynkZr3415MPOfKB8G/Xo2L3n1vYkP4C1o6uDc0nRCoTWu0abHgDol0kyZIuvoyLvlXPTKSnFq+NUa17jEZS5x2cgWCtwAXFi6zvryNReL46eaDtsGJTsc0qZvFsEcXTNK2T8isxT3zbX33/MkO6UVLnGZq0sX+JOLyzajbtm2seIbgYySLd5trMeSXAmMRWqFHTorXZ5ZVQmWtvVinuKadtM0UxeTAdjByA43Sh0WF5/J3s+6DOukLS7G0ZunlMtz11r6N03b7D61AffiFnmtM3AKq53RCrtXT7EKzPM5W9pzSsIUZIBKZxdm0CUbwLJnghFOQyASIf3moTVFa1dSgh81aeGEPMCoHVJKIohbP8NpgvbJP8MWVAQGBEzoZ878ovYCpVlplDKzbmdv1zAd2bRQaMOYVl+8w/mSGTC11oBhp5Z/76MExBjQydU9nNzVPvIpSk5vbBiPBFuKdrsiAXmeMCRChtayiFsaOW0ICUxqdVxiCD1BRfuLYsrD8uk0HEUZSs6oKfjMkbi3Qta/iqyMQscGOEWbBjVFwEYqJEL5Is4dLVSUdMgEGxUSmi3iaa2V02lWrPAL/S42rTMt85hJE3rVNgPrhz2wi2YOaNCjZcFNwy36FoKbutX/VRgzsiuCO5JEDtL+e9l2uAvXdzNxqz6CxgjWm7bekj56F8p70Fjat+PZeqrHXNNcu+oxLZmQjvNBNcAJ/9f8p6z21i1EU8h/ncJHypL4OClTp5uWZybBOeWqPOR0FOMjcx5yPBJBuY/9bEX9KQJsYvN9TnGqJwbNG+KxvJXSyJvrpk69V2I8rEaAUwjGbNkefxmGqwe6TGZjqi5xgfde7p9A1lLKWC8edtTNFB3XMX1yDRZ2oL3cc3N1PJ5HSK2fVxN3sX2auwdZubv23W32x8WtCZ0LmbudpxweCrixfCNQyErLVqzyyCwkX8osybKIqlJo55MUiHx3aOLE+zjX356FTCw9kzWn5dPZKmbWHTiUU/WHkHiebrb1um2665lb2r6tca/bhr1TBADfYbLI2QU3Ily2yHJ5o/ZdfIAzBN8VTXpCOKgDLV4a2efAh7iNFZWXxIFHkpIkUxdzQ1rKlz0EPz5on8L1YnIgR014oZXEeykhFTRnmY8269auYbQqVzFakBEZKl8y5zWjaC/1GbaWTwZuovVU9yYUmKIjwCZqxUmR9Xl0HI5ZCIsj0mWrAEANbFpk1sNEuaKEGjg3WUlZqoIpp9NwFGUoOe1ifl9rpEGnjDvIg2lPIzbjXOEFepIPQU6MNLDR69os+2navc24kLZcMoF9m1HHZk1KVaco4WvaErwFOvdWzjGoGjDTo80OKwxouH2PtnNJ01YbU3TqEgiAUSTk0qMLsOmTZUuzyQWu7xpjsKihJCFrexcaJTL3NQuGstQGRpColEYF4EZbRORYLCXHodDdWF4kFFT0Xj1aH4f/96XcxciFwjTlphhfsv3qtOM+xkfmPOR4tEsAGELFqe5v5ThfkTl6GJTlCakanAu4WfTnrZqxJmhwY6w2hgsc9JULnVP4hopTy2+U4qTV6XnWYB/cVEjU87z3yslFvvJUL6rd3DnIFKdb9p2si6pW1jj5JEe+q365lmVJdaBmi4x3gJFbrsHahS3n+uZ4awujdPQSkeCN3yw3pHiG7Ls2b9PPXNU6QxBrsvt20hGksWxSgS50Oe8sNz3app5gxuSSUTxJIheXxCU3H6kQCZFHujjr0EGtyfb6qsvsZtzR6hxsN2HHXx/vWHSHySJnF9yIxq9rN/D5/yZZzM02GACiwU3oOqA0IkXMBwrMxtrVRJclQkkj6pJm4m2sdkW0/UMiwrDOZiKTsfTgsj+JOyY34VxtzIFeO0ePpSgCsExTaWwyf9hrGaj5E9j6kvHULQMXR5jBdxm4CmsXrllGcYNnVl8AnXI+NbWmEDgWgRo3z4eTvtAkJ09m9ppwMcHUBQKK+0z4fUyGpXLwHJvZhEOTtGKbzNwbyqpd8gDbgWERchWdxqUkFJT0+TkdTSJgy3eTSTPWrjnNWPjbIXkAI8LDAF9BEVOGQAZoEoylpo7xw2oD6+Z36ybi74csb+y42Dgd9Ntg3wg8ssaNvFtTvWvN/H/YhN5Sme3SCvs02GKdAXWusZ4DN2O7srUeXyXljmaapUSDfQuA7OJ002k2FsRiYwHLzg5sTY18I5Yb4aL1PbiYQFnH5+xCe9qjXeq5NSdyigpnxdZuaRogxEDLLNJxOCGwmTHwtIJHSOOWIVaYWrb17JElwAktfGFdTwLSIhTr73MecjzaQnmHQNwlTQMTihUqUfkidEkTHrGMW2RTgEjr0FkRIHNdkjHr5qxtW2fPRX+iyq57KaBXmoa36NiXOgNSW7azEoUKWsfvFOALlKcdzFITDtRcJUsJv2aq2HJ8bBQBNpoZHxp5J4FqzVht6gxMeQKcBOhMMTzwabhw3zOsL5l01FzEZNldle8gLqP1ICbYWNSE54kSSdpdW6VanR79Tk3F8sh3lDnCxoFvL8I27GytcG19nS3WaNNjc+0pOs1dajUYLkO3ueSAT482g6kfA2xIzzMD6DacWxpXzNlnOi+g12nRXmqzv1dn0LfX7IRtfAy6w2SRM1otssEj+z5+bbV2wgGQiEDpTXIFk1uoWQk3TzMrpAXw4N+iTpAmEauQ1D2m6Q3qm6h9EoHLOWAR8w1Xlptalm6wPep5AsUO2bBZ3IVFYRg3UZqdfQOwknL8vfXrhArvW+15tuxUCV4hwIEs24mXStExMK1dEdLWGz9jWk7RGk5S7n218HuLVJShpMggMKc8hUIGxDWDRYaW6LgK1Wg5tVrkRm1V0Jl6xB2t7PvXr2b7VqdHp9T1MgBKitCqrHURKpP1u1YzYNMrGd9rN3HaBAIiIMl+n7qX/lkEHIlrE03r2MYSOgCUTo1gMQq2Idyc+ilfNGfdB/aHmDTSotwaQWU4odoUB41xJgCFFmMvDXQYtxJrmNtEmu+FfDDWJVJZrFj3nQTfjU4JytGCTkgxPjLnIcejPSLB/0IzJnw9zyfB/zl2ofusjsHDV2omxiUU8OY9iY3z5qQ+GPAczkUJ0PDc3TJL8L4d1yYbW6LU8lNKpAJuPMVv0DfVPFuxY7bBvr++lVhWbFu4RYhzZoDZllLJlOq5v/YxskoTx2gk82KbXgYUPaVIYHVzYl1Wn8S6/k5LWQKjasxVNipXpGiPkINuk95620YSGlUVTeiwS7e55DkIjqgwHlaCPhOCyn3zTftkCtcW0C0zpM00TZgMKzBUfeSkdIfJImcX3EhnLfoQXXzB07N8zBIq1IQRMh+CMt1zYz6rihEl5DWJCZk7GqLxWIgAJ63pjcUKJT7/VJoFk5YxxblKyTu5zq/3ZIU4K9ChG0LOZ/UaHF6DL5OBm/oIXraDQTw7Am5MBqNGa0C/1YxrLcM6HSULFlLQLmqCmJI4NiOuMaLVculxxaTuWb6wWU0aZH70WgvfM7917bXbqq7yjl3irmnRPnjKlCKOYQZ0Rk3BZ5KGBeeLwHisv3oXhDE2g+D/IkUL5IWXRcwiM8sZoNlQe7utNHecBlQ0q85Ny2oVkymmX2hXtBJQ84FNlw7brNKj5Sw1W6yzT51tm1CgS8dzi4Asrk2ATYMGVcb48ThTY0ESAU6SCvThcM8HNjo6cmL/vzmCRRlTFuRUR1BpZprTCmMf3LjxvEBmB0rtd4lRQfB37vcQHJWDY2UxVwqjaL/SvLsPDCXJgLgga2tR1GzILSlLYnxkzkOOR54SNYyPSfGtN+T5R2i58eYKuQGyTHrSL9pxbxAv1kbiKRr0pu28N4G3LIZUwvIfKbcz8VzRBOToOFZ3pNfQSSHv1YIaC4dZjOBo4Ftx9+x1exgFhkpp7cgNg0l4wslX4plBn2xNrZtwcxcWq2RLHu5A55KBEeXVm2YB8g42GZLy4CiQYaTNS+mUUil1ihb3fqUB10W2CN3ctGW/i5ElNmHn4grXmsZyc5ULDGgwalZcLI623PS77Yi1T881drH6rnVj3CbzOBmWmXTK2f3bGFnvpHSHySJnF9xA9iFDcJOSFyyjwEZAiBZiVRkxq5AgXwduRHjRcTwioFiNqxaYPXBjrp+mJQ6GwSrHrt5hnFA4sakAuihwSAoEsiL0kLm5nWsNstztympzfdfwA2e5AV58Hco2YE8YYZ0B9eY+fT2pe3UjH0A5JHNfi2qwZpHyL7X3i8ZqrPQoJatlTpVQ5q3wm9OwyKa1IDvm9+1F89Omqm8M3PTV74XmqlMAnDssQ8nZo8PMyhZSbEzNBDhhLM2si4vc0QTUKLcT1oyrhYCZi8C9dn8RWhefcalMZdE68WN3AsneMFvpGrLsaAlMFs3CnDdKHfq0uUGHHVbo0XZuafL/DituTQTJkpY1l3HPEBrRd/E43vo3KZnlRsbFHvT2srxN2lFGyIlgU3X/EBaGWUrWqOXGCRWoNk4xPD8GCPRHL3JVCwGQBjxBx9HARe81ad5RQwWoi5uawDsRWMB3i5P3OKWSZJ4t7fQkAepD8JUWScGGL1vIfAf5+cKR8AfI3BYXvGQi1DKFqVHqZfPeDTp0tzuZS5pTxol8keK7vLWzeJvVrktrLDEseg0rsamMqDKRbGweuAmUOQ7sZ8qIyvDAWWJdshE1PWZ2G2UZ8sATZLzXjIlSotLRizJ8Fwa7sDOCurXYiNJWFENedrMWGD4R+aTgZAjN3zLrzdgpluo6qQBimRXXU3AApGu/zRXor97F5ZdfosTU8XexmPdoc5ULbLFGl/PQrQX9RoPW1P5f9rOmJWQLrPfJ2rVLFupxErrDZJFT5IN7DkkDkBAUeNoD8IGNtqxA4QQWald0+a4TxTRp2gpU9wVmp7kLJiEBIbreOWYZTsSJ/7hAEHPWoKh8pbUp2o87cyHT+fK1Wffm1I8CEncRqbv4/FYlz36Et/vvTvxbzhQkZ5BifiJYieVGGGTMRY3kMKhnoHFzbSUAJ0gs0I9sw2BzZYTf8pRaV8lQEm4x8/CcIpT6h8ftg9G+KCdDy024RRQKrtOFVhuVHW2VnDvaudU9Os1M+BAwI/Enbm2nWH0ToArj2jlGpaoLye97Lmgma4/WEurF3vadHrfhFvXMsqf5SQTESprIGjdDMpAyhUmaX6o4CgnDbzDNrLAukFpbS/TeU2iFx5pBheO+TJ4fJAX7oI2T4DhUosSAWEtd58VhFXgZODqWFihPMT4y5yHHI5kbXdPr+JrIHCLXxRR7Id9xFOEP0kdiClNltRlTYX/UMMkEuijFrI4D1IDMAifbDys1Xz2oXcNknLuMXcOymusO8flenNziwlJspAsLL0mjgfPFgoGXwc0+I02VzNLHfT+xtOTibzXF5JcZJG5xifAkN6ZFmS4FWqWFlie2YZsVa0lfsWorE1Vp+HHL7HdbQeIsKU/kFPnO+74cK8/pkpdhThP/e5tkkXe/+908+OCDtNtt1tbWeNOb3sQTTzzhXTMcDnnkkUdYWVmh1Wrx8MMPs7W1daLnnPBTPseUExrxNSDeb7GZHfzMI/Y4BmikiLAT5NbLkYlIfOXrUZc0lNXGlB24pLmOKgts6Wccg9KSycBXxDxj5mJhoHYQVmtGC1pllJmMR1lIvegPb2LdRew1jb0hjea+Y4bee7v6zaqbpZnMPvfCOOuHALo+LsTYBD+bnEqyuJljyO71U6ipBA0587E8R7SnN6G/bjQtHfWzaGU1EBbNiKMYwz+FYFKUoeQ2hPN8bZBNwSkLqUJcVpXjmQAnJpLHLDmaNP9R1l6WMQkEFvNuaBexlptDLqxfZQ2zwvaKnQLFamPWfenTGA1MnIpoROVdqjCpwqDacIBF1lfILDWr9GizjUkwIKmgBzQYjyqOf5WSKfWqSdU6ouoW7RTSwMNZkATkpHA4hP1pvgUlTF4cchwFzajLN25pEzOWQxDRr6vSJA9baMEJQUPRNBhaa0KhFh/QhJsuukbm/pyQzTmiSU3FTa1I0RVxzTkJxfjInIccj4S3e00fguYA4Oh5PiH79lop5sqTESBkLbodcpabJJlasFFxKdv3abC73YHNMJmAOIFql7Qy0Mi0+i2zHISkYRY7jZC4fcs8mw8FiKwnaJvkXCKqx6DP6mazwnGmZq0GcoK23KTeTqjE1LMG7VsZxsksfbxYYZPdbJIlQmqpMkM5hgxIppSYJprnpU6dWmHsy4GOD0nbi5r4ELYXTMB/B65ygSpj1tiiR5tVdlyi/69wgWusM9xc9herd9l7dTiDPU7VdTXzjJxyuU8W83QSuk2yyMc//nEeeeQRHnzwQdI05Ud/9Ed5wxvewOc//3mazSYAb3/72/nwhz/MBz/4QZaWlnj00Ud585vfzCc+8YljP+cMg5tDXzjWpM+n9lpHMoD1LK8Xa0whLfuMJ81+8oBNCnmtR4KfurURATaqqqn1U41p/FPIkHc4qRVo7lxHTcxWCB6K3Nwyy00pyUyrThhJfc1q2AKkxu1E538vtNzEQGQ/cj7UoHt1ngTHqdEe2TbcHzUYVI0Zt87AZXcya29UvCxOjoSpetrSMj4j6uGEpG7ZN+NqAUUDVdcXYwKItOoJKWxTfX5OxyBRHNhvLEIGwT4NbslhFRHJY1aaUHupLaUiuCyqvV2oU7SzFzGT0L0YcHOv2ZbvNcDmAl9xbgsrbHNerSDeGA1o7B346/nYd5pYq42Afue/bUFOV4GcLh32aZiYm1GFQb9h3GnTEud0OvtqNtkLZZO8DQhOyepjLTfp1Ac1miKQIdq/9XNIpr6iwoEbcQkRcCMfNCywyB0trEAIbIT3Z3w0CmzCuUCUIsLvNA9xvFCeEdZX96tTOrjH+MichxyPRhRYWcSdScsX5AVJ3fZ6/nfj1QIO1yeD5CKB8lBcxAA3ptmuHeGSJs/Jx/JIYpAsviZRAErzjpZvBfCATcxibWhKQlqCcolMQG7abRFYgizRu3J7c0J8KA+U848Kpnj3s3JvFWt3mx7l1j6TVjkDjvLNgnGSeYYY4JWWsije7NHTzIPFU7KEDGAfuA7bK8ZtrAObf3oJXohTWnXt4skjKtYtbd1cK+Cmr9td5EYp+6Y511exwt2gUaxS+FQJBW6TLPKRj3zE+/8XfuEXWFtb4zOf+Qzf+I3fyO7uLu9///t5/PHH+eZv/mYAHnvsMV760pfyqU99ir/4F//isat7dkkziQR/ZvRmSd3TYy4JR5St/9fMxxtcMYHFTp5FExomBbSXPjEH2GLTfRL/V7/7cCHTCEXBTcxq42sbS6V83vrDdIYIHsyr+cwmiqSuIvTPstykal9IaoJPy+6e0bDCfrXhmI8OgDaWm8SJREDwfUSc0v1FdMlKG0I5m6T0Nw7Bmffit4mKMpSMI+fmFCE9vhJgIc5HYqAcCj6nXDDLHUNr/aWPiRtaG1jONGsdcmvalFdv0il1nZVGr0HRcr7x+zT2DliQPijD0b7LNIFpUkLss5m2t62Wn2uo1M9V9vfqjIbWt96mrz9IUg6SKdPaiGm1eMpwWlrtfhJpP12C5tBuaMYUJeoZYLTCB+G17nrRmMo30FnIwhoUkXZNCwFOUOGjNrk2BqAh44lDyPz15aLb5N4a4yNzHnI88vqxhuFh3wiu1/OD9IOol0LYWSKgXYBNaoRrmef2abA/akQSCYRxwvo5da/szOUzdXOnxNoJ6BjbpAW+fKQTqcye96YJlLXwD9mCwk0cH9qnoeSEmJo1KFcrWmwbJSX8ZLKp/CwK2THV2oiJbt9CcCMu7wL4quq5iXPLdW5pHhMLFShWpkjJQOhmmZ3WKtdW1tw3FQfBbVboTjvZdxWwnIvRDu3h5L2EtAxZwJePpCNkkZs3/bVzqtUq1erRPmu7u7sALC8vA/CZz3yGyWTC61//enfNAw88wD333MMnP/nJ5wO4SX2hVzRcQjGLjutQMjlI56rjdTT90UWjJh+8q5+lTaJaWBEBJVjfRg9cMmBzMKzE4zScAF3EHMquKXIMU76c1Ff2OcZW4JaW+GZVoYUkY90y1l3rSZCyV+I03os8IEY2wKRtNZCUNnFNEOp4A0lUWYGG/Qa9JZNSscqYLh3nMiORCQJyvGaoqfbwXALkOcJAbppruiroUCYbDcycJqRI4D16AohS0Ke84uZ0DNLfQ3r0gj+GCI5DxUehpSYNNiEt8Igrms2IxhrGVFM2u1WMpUb2G1C7/zrrS9e4xGUucZl1rrHGFqt2Pao2PVanO9T7ExZ27aO1D3UVKME0MbE2Mg50Bp685eY8+3t1+tsdnF+9UK0MSUKaDphOS0xLOnuaSU0rLi3V0djcK25y0p6qmTWHBt+u1ZD623egZt9FB/RiLEkHoQAoPHgoJeoA5KNAKME1MXCjUsbKT1rZEdYlBGhaeJJN+KGn5IlZlW5xwMf4yJyHHI88tr1A1oNDhaf62FrGQP2klWGuXHFJtBdppUcHL2HGNE1cGHuJKdu7Kwy7baPdFw3/NvguaRNVZxXjZzdx4waccC2gQXjFDivcoBMAqEgygVzTmfLGtTK1pYl5vPAHYYdrsGNjT8xaPVK+TrZUdiVKu01Tw4tSSlnsR9WAqPI0kFmm/no19eY+fS27yTdRQJJ0wbnLiVue9gTJxfbKvTX5plruFFnvuvkGm9Zq/yRMWOTJzv10Sl0vS+Xl3UvGJe0KvvVGx9h4bmll22Z16Jf9xAEa3GhF2EnoCFnk0qVL3ul3vetd/NiP/djMIg8ODvihH/ohXvva1/KKV7wCgM3NTSqVCp1Ox7t2fX2dzc3NY1f3DIMb22LyMQSAyLmo9iOiRckFh06yyTucWATceMBDD9y62tsc9CGwER6ngY1+nqf5iAnCkQBF/c6eII0PbGSfMxXrz5y5U3ipF6Xe1Uwck5ZsYNMqNnEBytoPNVdPfazfWyZz+Z5DfLDnWbOKZt6JD/K6NQYbDXqlFg0G9Gg7cCOB0DIZuKQOjjQDCq18Ku6GOgwbvhuj7D3LzSkzGc2iUEDS5+d0DApBpbgLLGTfL2zLHG8pAjX6dyGlfSUhW6jTWmtYMb+tksXaXFT7ixPWl65xgausW0CzwjbrXPMtOLsTY7HZU9UJ3mNUzfzYtduHXkPBbbstxsOqmRT1WJQyawuuXB3/Ius8yHpSpfQg838n2yelTLwKI910aoW2uKvUcPzmsAY63TSYwOqJ1E8DixbGqu14tLiaijA6yx0tVHLIA4Q/KGFFA5uQ9+tNCwMhuJE2kraWa/vgW5GFZlkKj6AYH5nzkOPRlKD9dFyXkKgDleZcz9MhmM3puVS/7JCBmgAkT9OSWdDRHg+3z5uFOzWw6UIe2Kh6B31WLwg6stZd838Jz3a82wnco7RwnaeDVKzGFXqlNotL1w37Aw/c3Fwv26WJV+judZRQ7vInWkq9w2laIq1aa4pyd2s3oT2CugTAJzi3NVHGlEjz8XHhXJDirN77lnfuq7bRINCRN+Y16JWPbhPhC7i5Yn6+vnqBXqdNb6VtFEhpieFTyy5ttGv3Ifi5JgvcpXXihyLrzUnpCFnk8uXLLC4uutPHsdo88sgjfO5zn+N3f/d3T1Gh2XRnsLeYgrTw45SDH0OhVTEfEUp133MTewhsQg2eb9qNuaQdpCWicTFOKxxFacXk1c+SgAMNEjyNim4HW/+gnlNKDthQ8xzuKGPFM/We0yQY0LOqr4GM1FMzk7BdCgGCAmvBtxr06+wvZe41dbu+hZi6JZOaCdq15PX8mPui7mj7fn3l/iLFvVfurVFagTTCI9LTZDv5mqUi8IEPDGYy/6NATUiKT3ji+2LUDc0kEziktdp1KUslf8555ZLWpkdjOmBhj6w/ajCR+PvU+otLUPCYqgM52i1t2G/gFngrAn2WSp5qw+yrdtE/L2tb0K5abROCG2e9EYFECSZpKfN9j7qYhpt7WghWYuDlOBRRgMSeGQM2s/pVrWDvhOJy5KbTUYyPzHnIaUnGtp5j9WCxSW9kDMX6gL5NHycUZNQz1x2kJcbDShbL213wg81FkVg4/+flFr0oqAAccX3TCpChrLXi+mcxsDHvmGVaG9CA5nVYUu+/YrYbpY5LeOKt51IYpWdOpRY8jal4yo2FPWhct4oSsQCrzyMOZTlwI6Tm9iwrXSV7D0ujIFW+uzfR/4SWYasw7ZIBlxpwZYHJcJEdG+fIsOqD1S4R75CQ2ar4m2HDlxdjFpcT0lGyyOLiogdujqJHH32UX//1X+d3fud3uHjxoju/sbHBeDym2+161putrS02NjaOXf7ZBzdakI2dd6T1gpCzVgCZJuzQaPe6FDAdibUZBPfLJGcDglcp1LAY5lY1Aa7CEPSWQpw5SCGBD29oARFLlu78XblBB5tp7V+CWzdH0ZRSFuS3BOtLcHE381RfB8pr5jeWoFdqO9AwpjpbyNf1RrWTBjxdFEOLZXkTmuC+SR/HIIad82wvrVBiSocbNGiwb604WpCL80kJBpX+EwqvAhQHkDaybxcVWGIPiADrE9CousCouhA5f8izYil63tGUrD8p1wbAjQc5Ff08oQW3KHkA+CBZRPYVjHrgbnPcwa1f4xII3G/2Gy/6EqvscInLXOArdm8sOBe4SoseK7u7lPcwyzDpoP0IyaowOp2zFlZc+ufdlllHQVuzNTBRE2OplEEMSR/bYN8lJimPyLdlCRasNrW9l7W8kKzqsbYMC+K9t4LjN/utsrO+TkPmpbXQIhC2bLukEvy/z/EpNn61SzI54TBqxQnPS9G6eOGFw8i+r553KxYbSzE+MuchxyQRjuVbpg1MnxJZQ/MW4RcNPwurjKVY95L+IWCmg++OpvtPv0Y/tSmZU4zmv4vvlsYhLrjcPUTc6VUWNlW2gJouHZdpdECDq1wwge3Tddgsm2d0wXeNCrVDloZlp3Ds0mG4doXaJZzTCytw88Vl63x7D1e5AFdq6j16ZHKMlD1x42g8rDJespYmydGybH5bH+H4B0tAEwfawMYLh+BGz+v2/4HimeL2LvdLqnyJkfG+aQ3rHittL2NY3Mbst3uKDHx04GC1mfGJp2xbX1Hf2VslTMuPOrFA6gMb2SKf6CR0u2SRw8ND3va2t/GhD32Ij33sY9x3333e769+9aspl8t89KMf5eGHHwbgiSee4Omnn+ahhx469nPOMLhJ84eFsqHuQKJViZEwIcuYhvkPlfevl/K1JnYZz29VT2ZSzxScb78W8D3LSwzYhG50qjxtXhRLR5cA2et4G6l/4K6nOrkwNZ29pLwIa7uZF+fdTbwETwIYZM3wuGBoO7skPhBGL/UPB5+nDZoFAuxvXrsu0KNNg30XGC3B0xKkOJBAxVzR0i6y1xdogTbFgWLIu0nmysv9cCqaJgnTJN9Pp4msMTCno0lmrdAtScZ0jA9Ato6D1orFOnvoBqt5hbijLZvTq/gpn1fNvrxxk1V2vLTPq2xnC8/RpT3tmYV0R7i07Z4lUc2vh0mmdZSgWBm3+9a9YkCd/b26tdrg86aIQrpaGztQ4y+bO3JbrnkUQGo0YWUvbyddBhZLCtgsYQQgy5MGJd8Cm4ufiwEcsDxlAW+RviMpNkkHFl9tkTnuJveG7AV8y02q9kO5SfOl02VLi/GROQ85Ael+1gdfkapJ8Yu07Mf1xqQt+fYJeYtNaFWQ+UvKFcWg1vD3IZ8hTfqQTUOv5ZUEJC5GeEKJ8+xbhaCsveIWCO0SZEoLybeI61i/neYyd1+4bsZ1AqzA1dIFm/J4jR1WCtzeIjw3NVaskbMKYfjGCtnYWSLjJ00yK4+morEqAMpavAXc9GgDxr1N2kiWoohT6L0hAOSmWXBz056+QqD4JgOS0iYOtM5w19OniqarU9LtkkUeeeQRHn/8cX7t136Ndrvt4miWlpao1+ssLS3xAz/wA7zjHe9geXmZxcVF3va2t/HQQw8dO5kAnGlwA259iiJwExUqQ3AQziYD9ZsWbGRSCy0HunOqgLyY9i5U0sfQsxMgtOAkFGgIdVkhEJCJsk/EHB2zCMVcrzJTtPNxtwxi5XImCjaEQdhNfE9HVBlPY5YbnQ5Z+X4KyAnBjTBqd18oUOqGsNcNF7J7uzhw06VDg32bUCCz2oypFIAb8AFObEjoupR9hjGTcdz68JqUKoxLeYYyKc0Fk5ORBjix82EbS1+UDxxqz2PjC3wrYLBQ5yp5l7QNA2xWVradC9oKO6yq9WwE2LR3J9l6VBJrMyJLgxpoHjOXtAyCiA+5ZEAaDavGwixjUb+yFsqTQ0pJFmOjS5UsRImsN6Gztun0r1XrKrLnj4z1pnVHW4psTZwiRfu4T9MjAM5Rk3s4NN01RUAXXwAqAjjhdTGWIvdL3WrBee+ehUgBJ6cYH5nzkGNS2Lf6kMkaolCVOQK8dWXEPU3KKdqHzwj7gcyX8qiu/V8Dm65UuIf/XYNot0AZKy6fYqUwJRirhHCgg+2meY6TM3QaYv0cRUOsBSgDOGtr1ynXgCrcXCmzxTpbrJvMYHud4BkFik4395ayeFrhF5KwYA+PhxhwU8lc1GPtH8pvQ7xMcQOrPJUYJfEMGdlFjfO8swjYSCxv26x5k2CAjMhBGtyIfNc1T8wscqECXtMkz/f03FAk5hxBt0sWed/73gfA6173Ou/8Y489xvd93/cB8FM/9VOcO3eOhx9+mNFoxBvf+EZ+7ud+7kT1PePgxlJMkPS+qWQwScmn0dTcYV/9r83KOptOKFRr06J1oGjhryQumpYA9TsmpDto4cBNgmM1MJQQ76w28hwx4Tp0fx1/4S7t4d7Iik8y4WdAneEy1NaBC6bcdcnCVLPn7rPbPXCNdcf0Bv16RDDSbWgZfDf715vcNThzZmg9UQjpiWM/y+W+DbRgZ2cVVlBwZt/FGHTpGMYZakYSjBbMMf7QhzrkdhbuFWpDRHi+fcNqRIUK5yLnDzjdSlxfixSa7gW2628dTkT6WumTsX4Z3idZj6yZk7uBhr84p2z3Axcn3LvyJc7T5V7ELe1p5562xhar0x0Wr05gF+OOpsGNVH+KJySnpUxxIcBGLJhmUq7Tm7aZdNuZ66y23OQErxGV0khF6ey7cSbrZLj1suS+EkawmGKEjMRwoMaSWdRzQZ6xbOt+CaN5vYDLonR9rUaX81766jHZ4qI5wbBFJgRqACEUstnYnBIb3zFgExNEZ21awNDPkDZvkSmA5HwKvmIqSOByTIrxkTkPOSbVMEoE/b2HMm+IGxpkH1SvW1fPLC3SZyDfn7TFpkNephAZQB7RJXNt2ka5cg3I+JV9vls4eAXnlqbKltiaLh0vQ9o+Db7EvcZd7EmyrF1dMHO1HuxaTsKBA1ko+CoXaDCgtDSlvjRwwOmL/Bn+mBfxx9xP/8m7jCvWphSt5ZiAhuYbSF2vr9VYvjCEe1TbNTF85B7gAtygwz51P1amSAlheWFv2qZb6rBtMyFUGbnsstus0qVDX9YayilPpT1ELoAM2GyZf69czPhVS76hLUeA3hVp8x38tYVmalazckIS3nxCul2yyOHh0S5stVqN9773vbz3ve89SRU9ytf0BPSe97yHhYUFfuiHfsidGw6HPPLII6ysrNBqtXj44YfZ2to65RNS/3Dm94zNJqHPso6hkE6m9zpWRZfZwC3YyUI+m0loPtZWCWFK/eBcVFCCvLAULGaqgVK4RRe9kvewzCensUmM0NOsZfxPBIxwW4fJsmFYN+gYUNRX7l5ucIeWl0O/HfTmtYl8nxkaCbkmaI9Jt01/L0txq5lOb9pmoF1vvH4kHE27E8WE3eOQgOxbTyQgpF2Lwu35QM8+DwlJ982QJ4QuqbofHmW10SBZr2fTMGNuFd8lbQPYOGRpY8e6o12z+YJ2XDIBZ7XZtcBGQM0o2Ar4YrYAXdVlLRqTZVAbDyuZZjnGXxUrPRdYbQTgVFXp3krkWnDTVhkrXzkXtDV7Tvb63BIOiGlg4/zbC+rqpecXRVS4reJb0eR8eH3I42NbkYBE8H8IGEPhNgae3A23xlOezzwEnmU+IjE3+lsB2XfRcoYoUUO+QnyMhUBZg5oQCMuc2cV3V+oSUZpK3XSi9Xb2TCUHiCU3S/u8yg6rbLFujndWAwWqvKv2bgkSblh5QJe7bcv8io3jucoFnuZStljlFf2MQfAM3d5++c7ipFJLO5llDeeaJvKAi93T2ExTqsrvG94jCx97sYo2CcKAhvFgKfQMCdtMyZ9ifdtUe62wdu1xSH7R1BNYXWPi8QnpTpNFTvGKhj796U/z8z//87zyla/0zr/97W/nwx/+MB/84AdZWlri0Ucf5c1vfjOf+MQnTvkk65oG2Zid2YEkpiaMn0Cd31fHRROHZlyKMYQTZ8iM1MBwDGkYbB4A0M8Kjy1pQV4/Q6P7IfgpAsOy7XsEHVu0uj3aLC8NDTNIyTTDTUxGAcsodpaWHHAwi3rVZlhu9rPnDu076XfQbQQclTM/ozQPlrYX6Ccdes2uJ8xNSRj06xwUghvwgY1yKciliC4QMLw+mQR7KAZrR5MRJ/M6iLFZwvCOpueOhxzV9lo4CQUVHWsT+44xcKzWtkmIuqKxAUsXt1ipilf7tnVH2/Zc0tq7Q7OWTQhuhkEVdB8s2QU8rWVWZ/vR/4+G1Tiw0RaGBKhNqNTEwS1LIKDhUokpyXTq1cEBG4nnkzmwSWYVLpG5joggYrfhshFI+hbgiNZ1TMVkFArfX4S2lPgEHhuaQvrdwy28/7iWmhDMhBQDObHz6cKMQo5HMT7yfOAh8BzwkRB0us8Qc/UuEjoTnIt9EbjVoEMrS0Wzr2ULmfe7RMBN6FIvAKeRV8amflyJWz+HFfq02dpdY3JlMRO0+/KO2qKiX8b3ONnfq9NrttlhxVhuSF3sSpeOAzrPfHktWMsljOmJ9P0Up/jo0mF1ZYfm2kF2qcQKrxsLsHYjG48qroxYuSIryDp64q5XZ4AsetrlvPFeoZH3YHHtL22irXupfb8yMIBhw7z3EGy+AnNJV9r7EOORoxXzs+a0fGy1o1k88Ai602SRU3HLfr/PW97yFv7dv/t3/LN/9s/c+d3dXd7//vfz+OOP883f/M2A8aN76Utfyqc+9akTBQP5Qq5C64UkYCYGbPYjxyGw0ZoO1PGiORYhpYMRTkTDF2EWHqDpBptjQqEQr4Ukbcq0af2EkQ3ts2QQbUp5A4zZUue3D9+j7ltupiUGpYYzHa/es0PzvgO3vgRTMqHjpXDzpWWe4l4uW23L9m4QAOgBN6nDTbtfxstQFwIcBqruRYKkfN+BAjW2rE0gLXO1doFGa0C9uc94ZNxXhpvLBfXU7RNmVdLufPqYYgaRgr/CuJw8vdbViJB3DkM5Lj03PCSkmKuj9DUZd6n6bRI5Foq5tUmykTrRhTovYlzR7ofyvTe5VL3MinU/W2GbS1ymww0ucNUAnt1dytcw85qAm13MuBSBRx5f8v+fJudU+tK6gyJuxfFplcmwEpmQyQld5dY+jdY+bXouFYFOJl21kT2l1AbbCLAZYSwwwnOGGCCDfYeqPb+M4TP32N+/Dg4vwNXmhvPJ32GVG5iUsSY5SDUP8ETzrQVB+U2/Vzh+9bVDtY8BnLB9tHA6S5iI/V+bsRfw576N9LfT8ZIYH7nTeQg8R3ykhlmFXb6JbEP5mGGsDeQ7jyg+FrJ/taUmpiyVYobBsYCbLpnL0jb4cbzCz9T6WiLDyDNsmYNpg27pvK1W5nLVo83wC3YRySfJLCtO0NbvpWQMNbf3u212mitc5hKpdXmTTGM7rPB5XsYT05fA52rmGU/J++6QH3x5V/2+LecrXKBaHfPAn/lTwyOX7Xsuw9595/iKtQ7JosUD7XGiHyHylchb2zV2khWurl+gY81W4pbmFkIerRgZQ2SSGM9wJH3FLg7Ol00lt1eCuCl9fM1eLyEHRRYtxYCS4GfNr8AHz8ekO00WOZVb2iOPPMJf/at/lde//vXe+c985jNMJhPv/AMPPMA999zDJz/5yVurqVBacOwoFsQl+/2CTQs54A9YW56AmQ4+sJHJNHxUaK3RgAfwO2fErKvrr8uQQaf3fcibLHVnVwsXqkl4avPQuxSx1bZv1r1k99ZndatkA/+sWdblvc8JSNpyo82wEfc0Dwyl+N8jaAe3L2iTLhxsN+lvd+judOh122bFdd1OUcuNfAO9nkUdH9So7xJq3mJCzS1oWTVNOVdgCr4lj9KvOn1VeUjUGiOxNdo1DeKdRStENLDRk3w5859fJbDeTOismKlx1WZDMzrAG3To0rJr2ZRvEndHK+zDOOuIWYtKdIzZXlamMS5pKgg2lMPUVrXxNlXGDtTUGXgLeMqE7+7TFptFfLc0cUMTPrOGsQ6vZduN5ZprlZ6z3DQcSGO44L+/BhlaWOyEbR9ssd86kS0UPjWwCcFSkfIjBFmRti4GS1Emc2yK85E7m4fAc8RHjuT14POUULbQMTCRMkOrTfiMgrnOn/9Rz9fyi/Cjdn7JCiunDPrZwr43rPvYtb21zJqyqZ7n5mshzfuCig+Bfs2VK65uV7ngFKRXucD1K2sRlzTtJhwR3u2rilvaDQs0Ji8g506/VV1z8TE3bCzRRCwtIf8LZbc+HPQz1zrTPisO2PRo09Pr/3i8WcuT+htF3NMkCYD+tkgZN/FBTZHGRcmQRf1V/j+FJ9mdJoucmFv+0i/9Er/3e7/Hpz/96dxvm5ubVCoVb+EdgPX1dZfuLaTRaMRolK0mdvPmTfWrnm0X8qdzFL5OqFHZD34TbVg4G6kBm1Dstx1OdFI3GSAhAJHO79UtrLsGOErIEgamtaCpnJcBIDFDQtpVZtG/HwNuRtWKM+vusMLGhV3fEmUtN89canGVC54GhG7Zt4YMwdceCaPX+zZeBrxcesPjuHCpNunaum7bn1pAv8ykpQL/t8l/A+8RWkgV0kCnYfcLx5jkwO8Msf3xaUSFcoQTjU6ZEvYs0O3mITCLj0jbndQ1UK4tmlDKaq+FCbuujQY2q3ixNksbO9YescUK5ljc0M7T5fy0y+KOirPpk4EbqYbOSJZixqmlwwTSkr+C9shBERMlk8oCwyFAEHIgYUKlNlYJBAYqFfTYWW0S3R9FYEsxgEYsNLrekKWEFcvNBdN8z6y1shS0qmXErWR/pNasEvatBcVwMg+Fxpr6LdCZ5OIHj2PBCcsM2zIEM2nkWt1mYblDyBYgPp3lJsZHbpWHvOc97+Gd73wnP/iDP8hP//RP31JZp6HnTBYRS2RoLfVIK0o0ybxif0sDl6FaZAtlCTmWuatLZH07ibfVz9VLVzQKLUPDbptpmjDtlIzCMy3Rv3KXecYVgkQCWhGpeV8DF5OsgBN96I467FRXmZI49y6RN/70y/fCk+XsOdtgLBQ6YYFux3zMjcQJNdjn6tJdtJd6nG8OGTTP0au2uapSTXfp0Bu1oV/Ou6mHAEfki2SBnY0VRk3jyiYLFwtPOthu5uWgmVONyENljOwj76zCH4BMYV20to1QyBPKxQpY+e6nGPp3mixyInBz+fJlfvAHf5Df+q3folY7hV0rQu9+97v58R//8cgv2pIiHH9Gmk53nVAY3CfHmvTAFKr751bxta8djJCiTcpaEyIDo0s8+H+o6xbOcuGx0i6n5cyly3vVm2rrqXuEGQhza+eYW5pmVhvxie1c6NJe77G4Ztp+b+kc21VjVv4if4YnuZ/LXOJpLvlMryt1Cq1i0jA38QehnNfALAzslrbScRDqmr5qk00y4CjvGYIbqWcO4GhrjTRwYGqXOsS0qvJcmQBdP03IM6KT0YQq4whDmZxRhnIUPRs8BGbxEcgPmiL3tPB7Fc1QWsKRCV4SCCybcxu4NWzYwGVIq917nUtVszjnC7jq1rY5T5d1rtGhy+LWxAyJHTKAIxabTPbyq5NgQEQNRlXjQpBaUCORMSOCVMppJJZRC1wt65JW0hnSjHuauKZVGPvApmrLWSQPajS70zE5K0AT9i5kwsgOK067u2UFkx1W6E47xqXEUxSpeocATZRT+vfwOlRZfTJBMtTinhTgFFEoIId1E5Aj/KRm6+Pxp5NRjI/cCg8pinN5rug5lUWKBERHMo9pWSPBXyDaXpOU/f6iLY3hvBJaEvoodykyt+wUsvTPev5qZPsWmQyjFaR9YLvMpF/m+vZi9qwr9jlfwMytMtfnXNJ0uvt2dhqc+9zu5gqXL07plbI0yjvTVbN2zh/UjDvaFzAuaQ7ciKVC3iWYn+0Y7U477JRWTEY3oEOXBgPayz2XaOApm/HtMpf4ChfY3VzxFZ56jEvdu3a/aZ7Vb91Fv9Nmf7VBKTHjptdtGwuQtI0ATs2XHEnczUQdl8lktkSdB1/WkRTQodu+buzAgvYsgJs7TRY5kT3pM5/5DNeuXeNVr3oVSZKQJAkf//jH+df/+l+TJAnr6+uMx2O63a5339bWFhsbG9Ey3/nOd7K7u+u2y5cvR646iYAYCsbgg5xZFgIt5FKcdUeDhCKrTdGWq6M8F3LaCc98OfGfwSF+nIpsUg7kTNMyySvmbPLbZ2bpbVbYLq3wzFqLzbUlrlaNtUZbbHZYobvbiVhtpILatUcz/qM2fc8sEsBH3FS/TV6zpRlZTkgJXfj0JueYzTBCgeWUQkhId1qGkqPo2eAhMIuPhFqtMI5GKRBy6jvw+2P4TbXmUtKtluNuUXbrLHVp0zPJAujbveTd6dEYDTJrzTEyorm+pzI6TRPjPjC2azrIPnNPM9rZQkHdCV0TqrWRylU2IEwioF3SpknJv79J5oome3E9kyQl1h1tuAbb1RV0eoWY1WY8rBiBIvxMASiLtv8q0cQO7jj8vROUE/L90CIEJxv2MZAzi7fcAt1OHqLjXM6fP3/rlTsFffVkkZA075i1D0iPtaL+EwM1ssk5JwtoPqWBTdsHT6G8oi0UMm9uks/a1bXXetlkQ3fcej4RwhDolulum9iUnekq10brXH/qAgdXmnlgEMo7uQZTdU+zbGbCK66xxhZrzlpzTSlGbli3spzHSRrsi9p8u8budoeu3Sbbi7BdjssYrpKzSLtCaytNLItvkTtapG10QoHYJafkKXeaLHKiV/zLf/kv89nPftY79/3f//088MAD/MiP/AiXLl2iXC7z0Y9+lIcffhiAJ554gqeffpqHHnooWma1WqVarRY8MQ2OE4623ujrU3z/zdByI9fpGBWbvrWDb7HZwJ8ohWEkQVF6cHTJd/yo+Vj7lYZ1E4Q/CX6XdxJNimhutLZDp6VdzFlupmni0hxus+JSvHZt0PCUkvMzvcwlt21N1xleWY6sjKxN1nrT3FS/dwz4pJFrdXvIO/bM+3WtGVw0WDJhtNQtXXxrWtRsXI/8LynAy3lNm/7uWsPqytZuJEcxuWIaUSaJDNPRLZT51aRng4fALD5SxkQDF9FxFSehMiLBt9rYrUbGNzbUdhFaF59xlhpxR9MLdrZHPZq7B/E4m/BzC5gpBcdVGFUrOWuNLF6X2s0jPdmpPn6uNqbe3HcWmyzWZuSATimo2KRqudSSrXOTTEuoU+qWYNKEce0c3arJOLTFmsnQZJUoWTKBDOj0u9alJBzHUq5YPBL8dUNyAl6w1oLE8Ei54V6OhccTebY+PmpmlbrGhNwQZDph6fQLesb4iPAQ3xX8qDnZj3PRQfzPJT2nskhhk+u5Ss99WqseKOvCbxv79nK59G8taPcJwEa4/INYi+z8lZCXW+R9QguDftZT9hlPEQEeuuNCZrVZyIOnrrn/gCa73Wb23Cv2tyftM57CWoa28C0UGkBZ13Ape2hc6rpLBtSYtzfW5AYDlwX2Mpe4ZlNQXxutZ94mfXxAosd13x6LXCH8Y1jLwuflXg3OHLiJKWrLwXmtkNa/oX7Xni2T4Bq5L9ySYn6k+9cJ6U6TRU7EKdvtNq94xSu8c81mk5WVFXf+B37gB3jHO97B8vIyi4uLvO1tb+Ohhx46RZaj8CPqrxNbTVx/+NAKUARupEydUcwyBA1qVvE1eR0ypgSzQY1sDtxo4V+oHOx1G8Tqq99JQI0OWixwlcmBG1nEs8EOq4h7SYN9SkwduOlacPMl7uUp7uP6k3cbZiRaF3k/F/MT+obKXptl5Xz4fWL3hW0iz+lB18YStfDdw7QGqUvGaHPaFWlX3fYhQyWusRUSYJMG5wuzbx2fjCk4P0wnZ1RbchQ9tzwEzAc5JK8oiVGoQNAkk4/uK1qBsAwsxNezuQi1i9dZaxq94jrXFLDZdvaJ5s5BBmr2KPbfFpBQC7YWUEUF3lfdPksLbda28ppH8zLp560J7U7mfiZuaW363qKdwjNSSoxKVWjBuDallB5QsvWeJrI/x6iaRers27SsXc6zT91maGq5RYINuFlzgci93RZs1+JjWE/gIahZNftznT0qtRHV2phKdUyJ1LWFZFYc9BscDCtGu6uFnz7ZGBfBJxQOY4JESFq4CK+rqX0IfBzIOR24ifER4SGXLl3yzr/rXe/ix37sx6LlzIpzeS7pOeUjIetw07fMu1oxF7rAL6qblZKsCNTq8qWPb6t9lyCwP7RyqHlLK1o65BMgafAkMkyXPLjpgptvPWFceKFdsDhU+Io8tE02foSnXbHnv0AWb+MWtwwX75S4V6WAlHp3y+ysrrDVXHfrX4l1WRYndW6ue5dMLJG2Ssn7al6bqDZvkY33lrSFvUb4ggA1UfamkAe6IclcIn1F3Pa1vLAf2Ydyo3QcLbMs5PsVnJZ1qBrfWbLILb5unn7qp36Kc+fO8fDDDzMajXjjG9/Iz/3cz92GkvWMoNa+cYKLx3WIu0eFs4oci6ZDuaN11NYKjkPhWT9SA51wA/IxJUIxoSqUamQwhO5eWkMgHb5B5irT8N01VIefkrjErl06VBizb+ONUkp0Oe+0qNdYZ2cUpH/2tB56QIdaC7FAhcAu4noXZQoTzPfRjb0Pw0U/Y4wW9oSkjrIPq+DaTe9Vf9AaNs00hG77KMqoyOw75ehVfu9UenZ4yHE/0lEAR8oKXTIWZrqjtZfMEnLnrRta3Y06G6g/HWRWGolPibkya2AjVpuq3UpwWLNAw1psdFIBwO9L4aSnBK5zFgCIDUhbahJrA9LkEheUqpRKKbLOpgMPVEiti5wsxCkpqmWtCEnTalzSzvsZiXZbWXZGEcbCcSz1D8FNZ0K5tU+706NSGlnlTYqOFRpVq4yrFSq1MeNhhX7Shlotb00JrTZyfCsChLD18DvIdx7qck+XUCDGR4SHXL58mcXFTAgvsto8W/FyzxbdVj4SihapPoi5YWtFpKLYNw77TEyOEKWp9H1PGagLt8BGxoHewnlxGBxrIKKB1BAMsNHJitSztPtbaBnS826ijgVcyDYE32KjB4RekkG1kS27323TbXaAzHJTImVM1cUTb7FuMqfq9wrlF5ENamTyRNeea6nfhTT41EoQT9kekh6/uq8UKXK1vDeLIoznNssld5oscsuv/7GPfcz7v1ar8d73vpf3vve9t1hyUecIAUrIXGKxHPqaUEOvrTaL8QX3OmRr22hwEGIqGcjCiPQ2hMyErIPGiiarEMHr8yG40WWFwGbZN00Lk0vgIDXCzz51djDZTEZUneAigse2zSN/ee8S/afuynLea+1HCpkWSxpEM8OGqjNkmnDwAdos64b+xpIn/hD6C4pBkjF2IWFcXXxm6ygUWqUNyQd8huAmJpTk6qzrfjIaUaEU6SOxfPN3Kj17PAQyZUhM4Ih9j3CiCZNgyLnA7VMsBJpvyH5jyKqJZmPFuqCtWk/wDjc4P+3S3p34FpvQFS3sYwJomv42aJ7DOJI17IJ1GZiQ2BuPtPDsBKLMatO2sUB165qWLeCZxdxIPM+AehZ/E8T4DGi4/dhai+VYgxtxT+vRdhac7l4nW0eiSx7gaMVDaLFpDbnr7mtUGblg43oE3Ej9eqU2o2aFbvM8+3t1+q02dGvZs6WtRHOMqkf4nfReU3guBDP6fYbB7+nppuwYHxEesri46IGbItJxLkLT6ZTf+Z3f4Wd/9mcZjUaUSl89Le6zxkfCOd4JuUWyBuSFUfsBw74aAhz9DOnnGnAAvrVISGf3JO9W38FPWiD9V8stXfz1c/rAUM+3+v10sh1rte7gg5sUJRtY6uJbhp6U99qym05YAHkX4KCdusB2jasbF+iVskVIS0xd+vit6TrXN1dMVrZN/LTTUh9pc63AkP9bZKBH5D9U+20GbecSAITyjDS8plS9r75ey3lapoopxSMJkWL8JwfOT0Z3mixym7Hd7SSdXickXe3QfzFEuyHy1a4lCX6mj3Ic1HTsOc0cIJvcIM8k9ObQvI6LmdW7JpFjXf9ZQKCMB2xYzrvVqXcwGlQTkGfWvKnbp5lMal2V977/5F2ZO5oGN0PwGW4sYYMAHRmIuv6hG+EsEKBBXc9ubRN7E9O8yC2a0brJSTQOuj8FDEKDmnBC0gLNTGYh73PyrCJjKiQRhjI+duzZ1zqJK4OWUGIfTJQDGq1q0prDYP0IrTwIAc7GhOWNHS+2RjYHHPoTFkLtoTxSU5U8uFnCBe4Pl2BQbXjAQYBNCGpKyZSD5BCSwIWhNaHW6VGvDmxWtGzRzqrLvTbyQIyJ4anYzZA8U4MZqUuWVDqzGI+pOnCTWXBW6e50TODuJn52qFCLKkJjC8fnahvXabT2WWXbJXGoW/c6nb5aW5QkcUGVMYNmg0ptTK9msyIlZR/QJAV1iX27onP6Nw1wYqAtxShyTkExPnJSHnJUnMtXE9g8qySZCnNeGKFLmp67Qm2EjRfR3zlUhoUAqqv2ngwhmcQ0r1ILaLbI5JUOxWvyhVahbTIBfRv7jC0ycKMZk+J/LOQtRKh20mNE5IUr+jkDfGCjFdEJmUta3a+/8IMaXL+yxqBTZ7RUceN6MG2YjGabi+a6p8gnSpB3D79Bl4wvCoiRthXS76SVH4Uy0CyKKdzCfhUq3WIdSY3xWN+SY0+5ezy602SRMwxuhI7qHPrDTyL/hwKzCrrysmE1Mq1f0abNunpyC5lSaI51HT50jxPSVgxNYd1nmSmlg2tT8SIe49EWCPvlp9MSg1KDis0xO7ICivjD77BKd9TJfFU1AxTNkhfUqNsdit+rTP69Qs1FEelrrdZjuJgXOJLg8iHx5gf8ySgyCYVatmONHP3up1CVoIXH8PzJVgV+97vfza/8yq/whS98gXq9zmte8xp+8id/kpe85CXumuFwyD/4B/+AX/qlX/LcONbX109V97NBIlgcx2pWBHBCNZgeY4E7mhYoOlDr9GiXem7xS7GGZGBhTFm7o8Xwr66KAJwquVibca3sAI04lAnIiLoUJCkuNS1A7ZBya5+qXddGEoxkpflxNkI6jkevqzPFrEgue22xGVCnT5uRtQ6PqbDNqoq9aWfApkvchSRsHw0EWhPaS32T3t6CGtm36TnLk7SJWG5KTKnYBBQlpsbtr2PVUkM7sWteoi0rx9HFxX4Lu9qs7ZQU4yMn5SHHiXN5XlI4vzvFWEzWiJGyOhwFbEI5Qvf73FonKf7imUk+Bk9vup+GIEo2mdtdjI3O2CXvoi0F9WK3Ny1E62cJKHHPkdTPofVLeLcGiEF7OetNmWF6nh1l2Zz06yb5iMgthfIL+HGZCS7BiMz9ECT3CJ7vvtEE34NFKMGPs9GU4s87GoUcx+NDy7Sqn+niwzqfInvz7ZJFnis6w+DmqI8amu+KTMSxGUdrHpaBddNxN/DXp+jYfQtYPbTCwBTSEgzLec2EDJYuBVabcOVdTaEApjt2GtlLG2igJgzHvpO2RMnWwQM342GFQdNot3WszT4NBtOGWT24W/YtNlfwVy52zEln9SgCYVqADL9h7PoiH1Vhtjs4Bp/WYXvBZ0j6Vk9bEQaZSztaLYSeILTFRlvuhGYKHjHAd3wyZvZK5PzJtCUf//jHeeSRR3jwwQdJ05Qf/dEf5Q1veAOf//znaTabALz97W/nwx/+MB/84AdZWlri0Ucf5c1vfjOf+MQnTlzvs0OSYSpg/m5CmfVNwjgsPdZU0K4eXxt4CQVWl3acO5q4oknq5xY92tNePCOaVLOk/hdQk2CsNcpys7dyjl6p7WJUBtbmkoGcigduSknKtDY201KaQG3EuWRKozWgXZXE8H1nXRILTtVabcR1Vaw2Munti3sXbVKbkER830dU6Nu6mTUoWl7MjXNP21rhoN8wY7mL79orWtSY4CHjdXVIq9NzFpsLXKVFj1Xb9nW7EGmFkQNlEv/TxqyP0WDgViWvlkY0VgZsJynDfsM8aKieq+sU0znpffiblmdEeNIKlaF6t9PpR4A4HzmrGtczR3vkM2vlBP9QKBfSblWczB2tS5CCeYfMY6FA0dLBX9NmlejCnc7y0cUX/ofY54jFRrxN5F0CN34oXh5DKwHkPa6QBeEzAb6MkR80wNHPC5Zl0OUKSJIxtL3ApLWYvaO8n4CZp/Bd7jbtYzxLGDger+WJPnm5QtqxK7cKIBQL1IC4TKOBWkzRK4Xre4vK0O56kRCHEDRj94P8pUfR7ZJFnis6w+CmUMWO3yG01j8GCGKmPO1WsmL2oXCit9Yh5U4PMELBsG+tPJTjTElvUWBT1GH1u+v3KAIMulNrwKbihzrks6UoDUualhhPq1AyE/yUhOm0RHe7Y7IGXSnnVyoWZtiVqoRgMgRjsffTVGS+LUqyEAM5shhWG9IF3y0tR1rrphmEHaTCxMKJKAZswtdxx/oZsQuPR8UM5WT0kY98xPv/F37hF1hbW+Mzn/kM3/iN38ju7i7vf//7efzxx/nmb/5mAB577DFe+tKX8qlPfeqUmcrOColrWhisGgKcMPZKjuV/ESTUejYd4uupdIDO0AMJ2hmrwb6xHqTTPKvTwKaqzgm4qeGBm2ETetW2AzWyjVymND9DWomUam1sFvKsjYExldqIJJnSrvr1rLPvuaKVlMpPrDRjdTywFl9tlRnZuBpZMDhmuRnQMJaaYQU2a75wJ8daWIJ4VrEWtDo9Ws2ec/8TQLnGlvse1cC1TtzmKozYp0GJ1K3lIzRaqpIkU/rDqnEP0/XJKU+OoBDYaBCktxDwVIkv5HoExcHNrVMY5/K8pJwXxiF5S4OeT8CfV+xxEajRzwnBTR/1LG250TzKurwJuO+oTeb8ENjo52yj3Mu3MOBGBPTQYiOyk12wUz9DP0eeFbPY9MEHAqFiVJ4nDaWStui2kjaC/PysnyuyyhX1v5PNxOUulKtE7lo0yp+ukg30M9Dl6DaLKdeP60EQyodFcpSem+RYpcoOgY3Uu3/MagR0u2SR54rOMLg5jqY75oYWCtea9EBRi1zBDHe0CedqYxqtDOqOh1UO9CJ4ehBrBjWEeGc/ri9mDOTIewtpBhcwHmF0eh+YjqdpwnhYMQvwAaNhxbzfdjPTbnTJm3S78graalbkClhU59AF7STCv9wrbaJXNNYDXpOUH+tXAfMKBYxwUgqrO7Pqs4D6bCo2Bd+af/vu7i4Ay8vLgAkWnkwmvP71r3fXPPDAA9xzzz188pOfvMPBjTb5xz5iLGmAHIeAR6UHD90/OqjxdkirI1aPDNDUA8BQGR74XUOvBaOF3tAdzYKbYRN6zZZyemt4rmnjwGJj3mRKKZlSrY1IU/Nb1WVHG7t66kU7qzYDUQhuhOQqeb6AmAzcmHTPYhOS1cPHVOjudUwK5u2mn32oS15jHsof+lPZ71Fv7juLU7ZgqrHcaLe0CmP3DnUGjF0q2bEChVnGuQENk7ShNeCAhnFdkS6khQcoHu5FM274zWdtpwA3cbe052mMzO0miblxCsvQQ2RWPITwDOLsR0gLofpZTnkoAEAr9nSh5F3SZC9jIxR4c4rYmxhgI6BDyykRPhjyvxBgFCl+h+CDmqIsaeVgi7SXVmKGFqMQ3GjZxVnf9DfcV88JNQ+LZp8uRBSYN9U+7BchFc01mmEUyU2xMmadI//Nwbz7KcDNsyWLPFt0hsFNLKGAdDTt1iR76VAxf0fwzZuyVPY60DCuZxdxa1JkbmmHLG3sUKmalRnkw45qFYZpydQjHLhdlD/pIT6a1wCliEKwpu+NWW0EsInFxr7TKv5aGx0yE7UwPuxaN8Mq07RkAFvXulxskpmPt/ETCIgGJMeciuoae0dNRw3m8NpEPUcsNqjnJ7hsZ9EuHjIClT9fCUk5q02MQubhXi0G+E6i2jU0psq5Gabgky7AB3BwcMAP/dAP8drXvtb5ym9ublKpVOh0Ot616+vrbG5uRkq5Q6gKjERj1lA/zBqHYYYeUYa08dLFr5JPImDHXW31Bp1mljzAbDece1eFMcl0SqJ9n8Ou2rTnSmR9sWn+Hy6ZGJteKQMK4pImlhCxomiAI9aXSmkENZMCoFSaOhAjdRTXOW3FqSpLhsmsiAMAsm6NBjUDm4XRJA0w4OaGWHBGbXrdtrEOb5czUKP3XXyNuWYbLbWX8dkya9l0uMF5uqxxjQ43uMBV2vS4xGXqDDhP16WLlXeRzEqZ5WZKg4G7psSUEVUqjBh3KgySKZPhYh5knUaPEQM1wn9Ct7Qqxk3qhBTjI2fVneTM0Q6ROT2c90KkXcYLhIfibJsh2OiilIey9kuRi5gFGlrBInxJg46EDAhoq4coLBlgJvZrZABHniXzo/BCm0SgQ3xR8wRf0dtV21A8WbbUXt5NkxQUrDcnbSbl6vfRY7FP/tmiNElFrtqy7yjvGvuGEtMk82woO0hZGuSEclCR1SVGMbe0IoBTAPzkdmn/8FFdTplQYLYsctboDIObWZabEOWGHSok3VElw8cisBbPcrQKrJqsQe1qzwaZjvKpVIu0La7jaM2A7uyaQiuGfie9hR1dazc002kXJ0YImR0mHfRBWoJh1WgluvjaU7HcbKvNaUuKNFjhO4ZuP7HfTiIRaAlCt0eo1tVtpPcaJAdUpC0Ff7SEQkwO3OhvdhqJx9CIMgsRhjKymd5OsgCf0COPPMLnPvc5fvd3f/dUdbqjyGm662SauaO+RwzYyHGQGUgEimCTdW0EHIj1Q9yhKoyMS5p+ZJW4K5oCNZMmjGvnbFa0ugcozD604FStvUXF21hIQinLGCZxKFl2tH33v7hnxVzSJHnAPnVGNrbGi59RGdAGNOhNDaiZ9Ou4hTK7xMGNBjZ6Mg6VDQoMNFqm7i1ltQkz1HXommxpU5strVSiQpuGBbw9Z7kpOYvUlBJtK4BVqmOmaYlJza63JnWJ6eJmUQiMwm8u7zVUx6ecsWN8ZHRG16c4c+RZN2ROF6VakTeFFszrs63/MRmiDwZwaBAVC5RQLm+hxSbw0nAUPssBNokjknec4K8tpwCbtthoZaCm2HvRIwNP2mKj20+ntQ5jJSPlpv7Phe59Xf2u0rYalGi3QlGgyjlt1dGk5TIBSVoWOsm8fxpZSL94JOGCtJUuss+prL9HySJnjc4wuIGs187S5ofuXjEBOxZrYzOJdYhqYGudHo2Wcc9I1MQurguIW5oeaHpQeVaXWZaXopiM8F1iwCjUKjfwggplH2NCyWH2HmBcLETTEdN46P9TyJhDLN5G3qUI5ByHQrAS0iTYhwGdmuNpICQMMyXHrGYBmxiF+NO9vh7sRX3yeJRbUd6dN/3xuAvwCT366KP8+q//Or/zO7/DxYsX3fmNjQ3G4zHdbtez3mxtbbGxsXHiep8Zck23QDZRa7eOGMWSdCREk03EgE7rUGVH2/cAjrYY5OoYAmlxUauaxTlHVRg0a9b9q06WpNkE52dxLFkKaJNIIHtXSQZQAUo2O1iJ1IKZkat3lilt5MWnlEgDd7SqlwVNAFaYJOAGHfZHDXa3O9Cv+e4hIaiR8wJuNFsRIV/O6faqTajUxi4jnQYzJvZmmwb7dPZ2qY4w6bcTOEwmJEtTRiXzLoCXwrrBgBEVGgwY2z1N6NdG5uMkKrvi6XQY8fu9d7O/FVmQj6AYH5kyLbh6Th5JQgEgcxHTCr3wownvUDxEg9OQ9YgMoYGNc5sKvSK0u7UqP+RH2koUPisEB+45oTVKbtAAw/LBEEyF7mj6vTzLa/g+MXc0oYJgef0O4Fsn5DnShl117Lx6dsi8PjQY0WBVFFyhIjREpqh7B0EZRcwgfM+YXDALGMW0rnKsFrjXbaS/TZ9TBcocJYucNTrD4GYfOEcmkGqgowOzZHBIxwq1sjoWZQUDAtbNJm5oF4F7yf7vDFldyvyzhSR4FoA0ybujyeYG8Q55ZhGS/gQauYdbCNa0m10b52rXwXdJE8DWQaWztmWlJfMewxkWmyv4MTd9ebeQEYZMogi0xbhtERV1TxFOtUlY94sgQ42niZdrtYuSMqvOAjXSBYfqf824nXa5iHmfXPKZUImagicWQB13Ab7Dw0Pe9ra38aEPfYiPfexj3Hfffd7vr371qymXy3z0ox/l4YcfBuCJJ57g6aef5qGHHjpxvc8MNcl85rX7IZAHwHoiTfDj8tSEvkreLc2Nt0NaG9vBWjZ9B0NkOU23xkoJyhJPEwAbATTT5By9apZSWfbGUtJyICcDFuetYF53VgdxqS2RUsUkETD/m18lyF6nS/YX7cwAjpSl14aRTVzjtlinR4trrJusi5srxuW1ix9crMGM/C/CSOiOpoX8lvqMVsgqt/ZplzIws2qz1K2zRYcu66NrNHcPjOJ2aPtFCRYSWFyaQHMCa8aKJe8oMTcpJfr0SNU3qLUGplvValn3CbtTOORD/UtIoWJF3leOZ+suCinGRyZnVON65mgHSEXjL+uxhHMf+EH3DbJFLst+xjLdR0TY1HJEH7LsaOGzwOdTyk023IRfhVp8/ayhvKBkLNMLUIIP0qxiOCHvDRKCmxBA9bHtdNM+RzK/hd42YSKBwO1Kz7N6zEkR+pmOdwioua72UheRB2IyFhQDG026DIhb82YpasOyNBV5+oTHAUkbSTtIG6X8/9t791hJsvo+/NNT1c/bfW/PvZe5M7M7yy5v8wi2DF4BDsHKymhlWxAsx1aIBCEJMV5sYyI/iAyLN7ZXEMVBthAWVsRixSSxFQOJpWDZOIBIMGRJcH5o7YXFC7vL7Mxw72zPdN/u6u7q278/zvlWfc+3vqf6MZeZ7tn6SHWrb3V11anH+Z7v5/s65rkvkXMzSxdZNawwuQH8nptYfOYvJ9+XXkxOAuzEltRBufK/C6BtyoiS1bVqlQBurZzEIZLEMumxcTqx9CpJEGnj4NeQl0xGnZ4EqBIH6xN2FOgfB+519Ni6A4/Q5dfms15x+EZ3H3wVRWRH9mkP8jvtFad3qp7dPAtyoJAEB0DWLb2852aIMqC6gherLX/PPffgYx/7GD75yU+i1WoleTRbW1uo1+vY2trCP/2n/xTvfOc7sb29jc3NTfzsz/4sXvGKV6x3MQGuKMYlOApBQpLJOkk/kJWBmMdG9qk2nL52otlHfYPKJo+cpPwkHMxiEgaYhEcoU/usp2Yamq5J4WdZUmPyW4aWWKQeHENHyONA+TA838YgjUmgPJoq0nlsKBytmnht0kICfD4bd96ahuOpoZwbKhaA/ZprAKJFeoslueFGXa7IcF3BLiYkLZ1LSBYV2Dg4Sm1OQ3ZcEu0x0NroYbIRoIEBBszblpK8UTIXThhOcCKc4EgSknlsGLNsOnkkZwlocmRRGfK0xRDQvSharo3UOWYUE+BjCL33SfUtHtZO51FefHo/nMgMZN9HOV4l59LC5/k1cXlYVqJAkO2bcpwElHPwjsy96ZzQcHKhHVOct8e/55OeSo8bz83W9Beue85jnJW5Mto+82KWnsDvD9eLqK1l12NDC/dqLUFujksXuV5YYXJDAzHvzNpLI0O3OHzhaGUlxwZAOy0jShV16uhjgtCWwbMDPIWkSVKTCAwpAMmrRKDbzmM8+TXwDuJLRuPCcxPJJKRS6WpDCKOxGZApHM1nZSFCQ0uPzk3XpJE3aQFZtFMT+D3JsU7Mdfy8XuwhOFqvkHxaIzjJMWNkBWeMZWbO8ruCF+u6H/rQhwAAr3nNa5ztH/nIR/DmN78ZAPDv/t2/w4kTJ/DjP/7jziSeaw0a+AH7DmvVaqQ1jCsorDKaZh0V/a3VdifsTEPR0rlhCHEQIAgnGFft4FAFJqHx1AyrVH2snqlCxj03adBbI7MvzXHDkYaXmTV5ZciYwz1MnJBp+Tac1HQFnSCS09tvI5lIrwd3Ar0OUo9NDJfc8L5Fj0pTEJnyX6kaed208/LwvJvWsGsMtlfsQt48frwYqG0BrbCLRrWPPurJFfZRZzMGDTFEBUFoqs4dAbNH0jz9aBZ8174A9LC0FR7+VwoUISAJjrT0S1LD8m00rw2tpWHRCROThIMrtdbw4iM2HHKc9+oqNI77iqogG46Wd06HRGnngriPIVsLYkPgngjt+gCk5IXnEGmRJj59JfRs599rhmneIO03eZAXtAzss5P3g5ObyPPTGTguXeR6YTVbBcA8pD78ii7vGNwCAKSXRS7VbaSVxE654Wh8OR1hZ+MgCSWhEA4TvUDJsxUzwZxU/mnBFGnCnLQQ8LZp1ytJAr8munbuJqZJSLfTcBkZjmZJm1HCIpywAzKAtJw1JzbaNXWoKVqoHQkJwC8oloUUbHn3joPawfNr+O9juNaPHEiDGZCN600sbnQv6JnTc+eDxmIYo6Im8Y0XtJZMp7Ndx7VaDR/84AfxwQ9+cKFjrzQ2kMYXJ4KdBmwtSZRbKu2/NJDvsnUT7oSduwB2I7Sqaa5Hk5WBrmLokIQYgcnfqwKTME1sJ0MKeWeo4hn31NCkmNyTI4kQT4aPLUXhxKZi20PkpmmNOWkRhEFCcvh8MEA6HxYvHkDLPnbQwUkcHO6g12mZebJ6SCfOk+SG1jFbE7nhfU6G2MjtzbEzp1AbHZyEmeNm5/AyapdhCkFxckPG2cAuE/PdBo7QOmUm80xLYrdsQQgTUljFKJGjXrHE25onDn3fad6bawhLk3JkURny9MUlZOdlkeHQIdJqirxoUSmb3C89DdyYGAP+cDQgNbiEABqpfKJFIxsyuqQDpfKbjMDgBh66nm09GkSSNkmiHMKmhbFLCBkswRV2p+9wL5TmrZGRJlr4GJRt2jixDEjg+L6T59VC0qRlh7Zzi+sYiMuiuBU7RRIauBiOSxe5XlhxcsNDlGS4kuYGlBYAGS+6DSd0ixT/NpJ5KbQkWiCt8T1BCMSlrAUkYwkZw1Vwee4Qb+MiCi9JEem12ZxdQKA2dYiNuXWh36KTua4pXNIlR+0l/Jzq9V0LpHWLsGDb8pQS6a1xLCGaBYy/m8uEpVUwVTSa0YoKlJUD99w4ry4vMCB/ICZz5X0px3tTa/Zt1bFhogjnVRmbWIIzCSbJ/zGChMgQUSHvDJ8cU3ppiADFbF6WEXtvuNcoQJwQG1PueGKzZ9I5bUj+haLtZs0JWN36qfgcNnVDbCjHRhIZ/llarXk4mnwsmg3LPqMTtVESRkfEjEou1A6RJoXT5wiGzFC+06FdW9JDTyNkTyu9f+xzGLu9OlYWCd+2PGMvJzhLQJMjhQyZFzQJt6+6F1fGRS6sJKhcIedjb6KIcsOY9Npwb4bVbbTjE7TzZHQVblSVxlTSNahKWln31tBC46A8H/i5fGHsZbFoYMny/PoApNExeaRmHo+NhkX1NI5lSNE87ckz9DKPV4b80T26ikWxbrrICpMbn7Tn3wP+ZDBJamw4WhtqSFrZhqOR1bIiyklM7FA/Glayk1/RAsBf/pkr3r7r0sLSCFyocWtKK70uIjh8aQJoToHaEJWaibWfxKH12ih5Q1qoXaxdl1TigfmFhbyuebGMoOCxvOSxWQD0OLhyxUlNIrzJ7c4LWywqRLPgORNye4E5QHPEAOntT54b5eAo4IM4EZi2srbLifYhWls9S0UGScWxCstZyXhtkCrKCdGx4V7cc0PhaNxzw0nNyJlq05hm6JjurUiVdem5oRA6HpJGBIe3kcgTD2LroYUOTtpwtJPoXmmmOTb7SD02mucmguu5ccCUGWmBFB6NSs20mTwtPEgu8dYQsblijzFhx9pASnqGSAo/8JBCSXIAm39JbeKQxhFJynzwERxNeV0AmhwpZMi8oMFQG9eBbOiW4rXRiEcMd5xNPA+08DFWy0OpZ8kGoJ9HGg8yhthY/JjrT6RnwFN5VZwvQ6KALLHJy9MFXLnMcyPlBfLoCPKujeF6cLihGWw9j2E2T1/TsEwHvVYDMelgdI/I0yQfDE8peGrhs6ybLrLi5GZWvgTtx8EFDSXb7wDYdibZc5cpdnb2cRKdpGoQn+DNTFhnrKX9XsMfupUw4rzY3HmuWSM2ZEGha9pGUv65iWwYWhtMEA1RrpkEWEAMyD5Sw62pALJk5lqT5rRXbx7ikfe7WcfUpL8AV4B9AxK/VzGgh6OR0CWyM0uY6xijAi2Jb7yi5RdXDtzSLQ2gPk+A9Ni0kfYxWrfhzInV3k3LDrdszgcVJAkz5CBLPmg7D0GjOWx84WhEhCgEjRcQ0GaSJjJDpZ8DTNBCFwFi1JGWgqYCAzqxqTp5NmZqUhOSdoAddA7aGO9vptUWLyCtvijJTcYwRJZFHoZjZR+fGVw+pxrQaA6csLRkmTBycxUpyeHkpgrjsaF1DDMHjjJmO5632CxqojaXFcBi5EbzWtFyDWFpUo4UMmRePAXdqMfHHR4mvoOkqhiXI/y5a+OtWpJZ029y8nm4HY8TqA5b0+fcim9cz9hGomdwcqONj2rUxxh+rxchj8HTvR6wffmYysddIjcyX0mGz9NxZ0HbZ1Ye8LxqNX+2Up/NC9fLO15f7EMRT9yrdRmKJWkm1k0XWWFyI5GnTPOOSWtOcqx3Q/NstE2FI7LSkQ00xITZ6mwIyaSKcVSZUUhAS6yX7eQSiFsk8joSXQ+3qGymMbc1ZW0XE44mhCSfp0fzSPCBOQlJkwr6cgq7/xoJ876Ws5LD+X7ymB4hwWWTvDSpxCTfax4tn/t9MQxRwZGi0ayqQFk5cCXYZ03nz7yGrFIiPTdiKTcHqATDJLcmrTKWDWkyBGTiJGfyyTDTCmR8Hpu6JTzZvBry1HBSIxM83fCq9DNvJ6/oxvNsePvIs0R11VwPUx39SQPjTitbiITWvgVAarWWfYZCB0V/FeSmEgyTcOJ6UshhiEo0TosHUJ8dslPQXELmQlWQ116S0QlNgCzlKJehmudGM0Dz73wi4xpGa02OFDJkXvRhHqhmqOTjMV/KWeIBuO8DH0sA6GMHrfn4T/pNSecE2rsoibezI78WAr8WVkjAR6TkeZ1zDdgXvpC0WeC6E783MgVAhp7Jc84yms/CrLbO6qRa556H2EjCIsP36B2Jxf7cwMrvEXeFz4d100VWmNzkSXmCj93y0C1rdahBr5DGrK4UmsHj052Sp726O1dDByIRkNyi3GLArZCzrkMbAemauHWIFRJoI5s/5FhYxqjUhonXBqBqb6EriHpinVHepbDl934RBT6PZISe7fMeT7rtJanxJeRZcPlHnhvfYBQDaSiaL2mRD4bzvM9ZTBDgxBq5glcOJIvp9odIny1tJ5CyzMkNJzW0djw3EVrt1FNQt1QkDetKTxCDe1UqSOdSCa0Xpp54Z0jmkOeGcm1Sj009yX2JhfLNSz/LXBEeikaV3CqWEIQsXE3LN6RgtZ4tJNBF06bt2/T9/TawX3In/uXrHluTnHFIDZcz3MsqLJaC2KA5TjxlbtW3QZpvcwhDaojY0GOpQu2acZDeS74MEzIZYhhVjZFIs1ZzcrMoWdH2J1lUU76bA5ocKWTIvOghG9oEZEPFW8iEcPkIjoyWSHJ68iYIlUZbcXw6Nsk4PrZnDAryesrsx9xzY6Ne2sifIFQjUSpp8xEcfn0EHmrFFX/+eznu+or5UCMXITaLhsHPaUB1sHzIevY49JLJ+0v3gHu1uvIAM7FuusgKkxueKzEPZJ4NEYDNHGID1NpdtAKX2KRhJKFTZjXiVkkatDtgwkLGycrwLV75jUMydmnak6WsyVVcyoaitcGE0BQnam7uUJxYG0u69ykjnChUJE8ozQsfEcnbpkETGtJzQ+8DDT5g/+cIHS0cDXDJDYBsZRZuGdESJ5cvKHCkuoKvVRg+TRDCpG1wkkpeHM1+4CjNcEnNabHejbC120E7MCq+rDJGeXt8jhhK9g8wSQYFXjxAem6yuTZ1K5dScmOOHdjLCxL5ReeQ3hlqm5xUlDzWsjoahaPJkLSurQn3FNroHLRxtL9h5CItHWRJTQcsnJMnAcuwEeqr4kEpz+hEbcTmt+mlpbiH/dRrI703gCtmQxgvTtV8JvqX+oMq9vmQd61uvPi9sj+cd15y4/PgaPsvOWJrcqSQIfOii3QcJMj8V6ZvUK4NEQFJAiTpSM7ByxbzkDQunJhHRXpQ6Pg0htE5OmIBkPVCyeNT3tA2nKiXWdeUCbXr28UXzk7wjck0rpLSzrdrHhtuKAFmk4dlQ+F9WIQQafdinugdCU70+DZpkObFFhYnN+umi6wwuZkH3MIHuO466qitnApHU1RroyQMo8omt4uFtW4wbJiBTJKBRDhpLmX50KVrdZ5kNa0Ki6e+PV9CADYUjbw2cRyYfJuoqlsX5f8x3WNfeN0iyCMxPlKTJyh8+0rvjaZBeLbT6yQfSUZJ4TX7pcuX3y9JWhfHEfTa8kfr3nWvF4jcANnZuuV+XHHmykkbWdnRttXRqul8NqYQyZB5P7ihxBCa1FuThpCR92WAehJ6loakGcIzsGFoaUW0NETMHM99H0JLbOS8NuS94dXQUkmXTtZJ4CFZPByN2jRAA4NhA+NePT/0jMvLGPCHkkiMoU6EyGSdKSZAYXVxWlohOkqrommgW0aloOnY1bTAA91nfr/Jc+PI0Wvx3GgiihOwY9AdNDlSyJB5ESMNzQZcwxkfmxtISIcM3+KeFTnmYozU4yCnjuBGXqHf+Axw9Fm+j9QPnbAtPjbx66CCRWVzHk5qNEIlzxfzL/P0Ip/CLosc8BwnGZbGP8sQNL4GFtMpfFimsJGEFvlyLeFyUnnhx+ReLB6utxjWTRdZzVYBWPwBcMsDWR2sFaUNtUpaud1Fq9p1quKYM6eJszQDeFfGknPrpFMmkoSTlqPCrQ/SEgFlX1mFRcxr01YWEkI1AKL08yQObQJsSbeySOGUCELO/ucB72Q+EhLieEiOL++GH78O937mQAtHc0BuXWkJIeGhDRzeg83ECGXVWhJfkyB8GkE6ADSrPa1pEKc1Lbsw/Snx3IxRbg6wu3XgJq/bQgIkT3gZZQpFCxAkdRhl6WfKqzEyp+6QG/LgEMnheTYcMgyNh6Zx5Z9yayrMRyHLPlP7KFyOh6PxYgJX9tvAftn12uwjWyUtITZ8gj3NKMAfnvhXMeQ0mkSz+vb+2+uZwA1DmyiH5seqAtgApjVkJiilUteUX9S90gR6JX9OkSNDkRXt86zl7wC1yME80ORIIUPmxQDujSehwnUNCkdrZEO4NM+KYxglSzp5baSXg5Mo0gdYvg0dl79vQPoukp5CC64iS6IouoEXErA5vfJ6ZBgcf9cdj5QsJKDlLGmIkSbCA65HV+oj3Lio7QNkLZaaDnAcpCcP/PzLEJvQ81nLReLf8XvE9Rb+osyHddNFTiz6g29/+9v4x//4H2NnZwf1eh0veclL8OCDDybfT6dTvOc978GZM2dQr9dx11134etf//qxNjqLsljIVewvIoAm0GimiaeyOhqfX6I7bOGoo4RcJJ2YV0eTphlAt+TL/TTFlwtRkeDHr0sKHiFQ4zhAHAcYRxUcUUEEjdDI/50250Hr/NK8wz1qofJZ+7+BpL6+14MVss9l8TmES2z4dp5wJ6A+ErKucULjq6XPBey1mV3T9PTsss64bnKELPJaqBlfuOHjtFjT59MATk/R3O2gvdNRiE3qxZFekAlCxInlP82nodpeFIJGoV5p6FczmSyTSBAVHqDPsT1bLMiOS3R4Zk7MQtCy7ycvcDCwoVhS2e+hZcLRrrRNHiKXi3zhCn9CbLQQHBmKI5QR6bFJyOcYjYDuOwWQGZ9LSY7f/F2wHppk2bDH2wC6W2XrPatDZvF00ULvsIWIV80kguP7X96LDrKkaFnvz5woZMi16CLS8q8Rm00kk2pqURR0GFoSLwon+pxw0DnluEhjGNzhlZObvPcPgD93iBtQrXG4jdm5Nvy8fMnkwtDOvlxpDs07I8dbXolU5rgCWcMn6QOaPhGKRRSHUHXMvO/n8e5cY6fOkCR+v2QOkjQkLX7uddNFFiI3Tz31FF71qlehXC7jv//3/46HHnoI//bf/lucPHky2ef9738/fvu3fxu/+7u/iy9+8YvY2NjAa1/7WkTRokyRv7TaIiE7P0u405T/2hRB6IaOAGkBAapClJR/lh2XD0oZbw1vo4/Vyn3muS4ibeJ6pOAJAYTpjPTGYxPCJL+Ws5YW6a1RFft5OoOvQ2seGvmZkxpJdnyCiVuz+PfyszyfDGFL71UWFI6gkRgpRKTFiKB58ebDkUhopuVoRZP45sH1lSNQQ5lmTsrZ1pYxTjT7aG7wAgI0p03q/a0qXhCzDm0YmktOiOyQQYXn3/DUfzmXTRpUpocLEPhknHJiTtoGwCFHvPgBzwUasJC5wbCRKvk++egQGz4XlOw/XBZKZRKuGKil6xM1ft/5vDT2GqXBNkSaW1MTa+u1GQZVJywwJTh186x6DaBX84fd9eAai3o53/kMSz5is6QRWZMjhQxZFHxMYmFofFzWSI0kA45Xj7wO/GFruoA2lonjamM6f69iQI8s0BT2Vno9mn5B59SuKbm9Upme18IvZQG/RzIqZpayPkun8BEXuZ8kPXnH5LoGIa/j5t0XX2i9/D03oJMhlj9r+W7d/LrIQqLyfe97H86dO4ePfOQjybY77rgj+TydTvGBD3wAv/qrv4rXve51AIDf//3fx97eHj7xiU/gp37qp46p2UAag8k7vZjgsg19csumKf9cCdLKQFSileybyTwOV9rpvA37SNdkpUyS2DTrI39pZY5NqOxD/0svBL8uxRvFlTJhWUnKlQJu8msHulXREYTUnrzOFyKNC47hJzgayQjhEg0pFOTrKY8dKt9px+DvifZbIEtwuPeFkxtJdLjA5b8Bri2G1mCECgLFFTxZUVfwPLiuckSON6GyPYQejkbLLoCmKR5Qr/axiwM00EfbFhFoo8Ns+/2sgg0kif8AkjlpuAFFVknjeTcjx1tTzRyP59YA7kwErk+HZxJmPUt8zUs9d9HEAA100EYfdexjB120bDhaLRuOJj04PSANUZEeG95vpNGBHlTdS0Qbzb5z39Py1nE2DI13eVs4AFswXpsd8/mp7Zqt/pYuTznrk6kXn65vH7onJlFgkRX7vD01ttZ0pFm6zRzQ5EghQxYFKbc8DG0T5uVpZZPua+ynknz06As5r42meMooBJaDRsem94zW9B52IMLned/jxydCw4oitOEJdxfXpJF3JyRNznEjiRUH6RKyIABBG2M14pSnO2h6g4Svs2n7S/aqQRq7F9URNNlIxyEdTBIc6flajtQQ1k0XWchz81//63/Fy172MvzET/wETp06he/7vu/D7/3e7yXfP/roo7hw4QLuuuuuZNvW1hbuvPNOfOELX1CPORwOcfXqVWfxI+/BSKtKKevZYB00CFO7pwzDGNj46i5apkIaCQc5aAPIukrzXiLNjaiZ5/g1SZKDfI8NBxGbqGosjZrrWBNMieVFVoiRREd2OE0gcAuJJBry+jR3ry8EzUcAZWxyw3Nu50axZZaHRs4eLT02Psvbckit+3LJCpl1wXWXI/QKat6aNvTQtF0kYWjl3aumKlo1LXzcTCRELyE2daZgE4EA3HLC3Pvikpg61fhy5rXhxIaoCX2mxQfppZEeG4Izl5etBJZ6LRpOyByF0fUOWymx6XgWkikAUjmphaJpXm7eX5XiKVb2VWrcpzVUiWXy/HkIGi1bbNmECDRUpgW90tSvM+9/fi98v/XJZenFWRK6HClkCEe+LiLHKp5030JSIU16bqTe6zxLaTTTvJZcp2GRKfK4nGR0kH3XYsD0PVkqWY6pShEmzRslz829RACy/XvWmBgrn7nXQTOG5CnrmidGXqf00PhCzXz7y9/xc81roM3DvPtKPTKP2Cyvl6ybLrIQufnbv/1bfOhDH8Jzn/tc/Omf/ine9ra34ed+7ufw0Y9+FABw4cIFAMDe3p7zu729veQ7ifvvvx9bW1vJcu7cObGHVP59JIBeKHq54KkgBiCcIgjjhNgAaTjakCsdh3WgU9YHICccTXbi44AkBbYDaaE10uJHoFA0mt2bExnNdc2FrzkA5u8UvDPzRRIKbbsWSsb/12JjpRCSv2mI7Zqw0cIduTCN4X/Gmstdvpt5xHU+aG5gLZF8nXBd5Yh8HWcRHGcZo9zumvyaahdtpPNhUTZMWii+b2trucSGEIMm6ayCh6ON2JpID/kf+ASdvEJaHqEhAiNJTVotLW0XhbQRaUrzedJQuDShPjX6DIYN9DqtrMKuKeUAdCUuL9RYygp45V4lSO9UhtTQIagaGv2OQtE23CXaAiOaDbHUFwvDy1ukkSkvPG3W0DcnChmC5P/FdRE5non8V41859nPMv2CjzeaATHHeEjvBL0r2vuVECnuudE8HXRNcxA1fk0yFC65Ljmm8rFSgzQSSgWd7pP8XrtfGimUOoVPt5DERQtdk7nAUtfgOge/x8cNnw4j79G1ERtg/XSRhe720dERXvayl+E3f/M3AQDf933fh69+9av43d/9XbzpTW9aqgHvete78M53vjP5/+rVq1ao5CmFMdLqHtoLVXeVmIzgSS2qpHQQyGraudJGtH/ShKHJZd8uTiEBLbZRg7a9LD7LjkeV38p+i4oUqERoUE6FToet5dJj3/eonT7CRu2VzyfPZat5awD3OWqhafwY2jm0/WbFupaR7+7mQkKGohHhoe/A1tLlLI+/uHYyQgUnFMuIVrVkXXB95QiyxIbkQghDYmhds+vmGLV211ThCijszKwrGKGNDqoYooVuMl9MOgmmO1cWn6iTPC5EHqhAAJ/DhoencY+NJDYThBkClQ0/izPDEIHCz4LEY5PKQ2oLeWw6aKOLFvaxa8LRntgDOiVXFu4j/b9j144BiHtuuGyha+CEhuSDLSjCyWgbaZhMe2rLb48YwWHhdryAgLnoFOTFseFoh6dOoFttYR87OMAuDrAjll0ThneBeasuIFuNiiuXjiLLQJdaY+uQrendjOG2P8QSJYAMNDlSyBAXfhlCSi4vHkBzzW1mjSWcDEhS4xAAOaeNNJBxBZsr02xMkwZJ7kXpgPXBy3aRIWmAq2Ow/tZGKhfpnfRdE3/vveGn8yrX/B5oY2qeDqXpG5pesIgHxad/1JElVnnXuAy54CRJC0mjfegznSPPY1OGvz6+H+umiyxEbs6cOYMXvvCFzrbv+Z7vwX/5L/8FAHD69GkAwMWLF3HmzJlkn4sXL+J7v/d71WNWq1VUq8tWWwjFZxoUS+5gwRcGPs8E/d9FC6NJ1U7YWZoz1IJPUrUMeLw5XQvvmDkWTJ9FJWKf+UCbZ0nMWFu5sPUJF95uKZj595zMSMEjLR3yt7PiXBeNp+XgQoETG5lPI4kNf955pObaYARKtn8cYaTsvR647nJEIzfS+NE2n0+0D9GwRQMaGDiV0IjctNBFFUM00QUPHCNCIcO/0hplAbinpm/LOrsem0qGnmjEJr20lLhwrw2fw0aCW9uGrI0AIOfaIXLTRwOdSduWxC/pYbpSptgjZmPu83IL6IExA4j3ufHACJM/6dz7EGnxAHORKTbsdgpHqyohaHYh7w06NV1+atfegwDl9JXSy6Z9iMSArTnx4XaRJfUITY4UMsSFX4Zwyz8vImDHMm5o9OgaWc8NH1d8IVbSIyC8Afx4dD5pyExIFDfC0nileTmQ1S/kdXGVgJOrpM/z8VFe2zyhaWXlXsyjW+XpFFo4+jyYZSjlN8SHZfRC2V55bq4zSh0GyppDe0FnY910kYWu8FWvehUefvhhZ9vXvvY1PPOZzwRgEvpOnz6NT3/604kAuXr1Kr74xS/ibW972/G0OAPpti2nHzViY9eTOHSIDQ32g2HDVMPplPO9G5nY1Vkem2VAAscKV66gea7LeZc164pv4WEQAHRr0qxOrBELeiZ5IWiaIJLH49u1c87rAtauQcao0hpwFTHuscmL9z0+TBBgqrh9V7VCyTy4IXKEExxuYW3TeooTzT7aux1UgiFO2mIBDQwynhqzHrI5VVJvjesdCZzPRDeIzPCJMbUwNSo8ALgeIMANL6P/pYdGJzYhaIJP3kb6JRURSOe0YYp+p4WxnO+LFilPAGQ9nppMkYYdqXDBG5JWrlFdy1Hm3gPANARK9MzBThkiJTebwHhD5to0MwTHzGuDLKHRrj1R8qg6nDw5XWcIoOR6dzQ5TtuX0c0sNDlSyJB5wck2Jd5bLweg57/Sz2KxJGPsPMRG/s/eGYIcmrmhElNkpyzgOgofb+2i9bVZZI3rGADcss+cmfvGS05qAL8O5VPUNT2CjKk+w2neMeXxCYt4e/ixpQFYI28c857HF33i+56OvZznZt10kYXE5S/8wi/gla98JX7zN38T//Af/kN86Utfwoc//GF8+MMfBgCUSiW84x3vwK//+q/juc99Lu644w68+93vxtmzZ/H617/+GpsqO6SEcNn6yE0MICpjUhthNKxgEgYYBlVMJgFGUQW9/bapKkYhFlpIGsYwLt68yj/LQAvdYt4oLkCl4JGyJEQqSDv28z7SMApad+AqKMmArCXvSdCJfaQjj9TUPfvQb4H8Ti5f3ZK6l4spazPgKlqSuGieGxnP+t3z2BBGowpKo6y5dqpsWxdcdznCjQJEbNpQK6ERqSEbfuqxGaKFXkJyAkzQQF8N+QKosswkyZmRxQH4BJ1EKAbMk0PERp+oM7aXZb4lrwWVoaZtsipa6q2pitybNGSuhxZoIlFS8g8mu4bYPLFp5MQTSMOyaE2yZB9WySGF6jJMP6EJ5Ljnkz8grjxSmAybELEtluY0qZSmFRKYIMCwCtQop4bybMwNTOa0Odwx4WgHtgIchaXtYyetmDa0Icr7yJ/Pp2MvDVO44XcQ1ytlnpWFPSvDYuiEjNq+BDQ5UsiQeVGDG45G4WnIVhLj5EZT/h2Ppi/pno+D5C0S45302tD/PTDydBnAAVLPDfVBDhqT7bgsjT/ymrTzZyI++DUtEpI2b0iXNJxKvQLQjaca+P3wRaTMIjj8gYfQ+/yY7RvD1Z185/WBfs//p3P4PtOxuZVvMaybLrIQuXn5y1+Oj3/843jXu96F++67D3fccQc+8IEP4I1vfGOyzy/90i/h8PAQb33rW9HpdPCDP/iD+NSnPoVabfGbmSLvpecvb+hu5hCu1KOogmFUQRiawXAYVTGJA0NsaKDii2OVlC7eawlJ812T4tGQRE0Dv04tHjbPcwNAJzax+CzbGov/Ae8ALqu+qJ6bZYjLMtDIirxe6VqX6+8uRlEFpUrWFTyNhsre64HrLkfodeJhFjSANyPUmn20ql1LaszcNW08ZSuh0TazDjFJyI3MrwFSD0sgSEkaYpYWCaACJtyrQ9/Rb2QBAUlsAocCxdCIlmwbHdttlyFdXUtuKM+mixY6+223/DEZRzrwhKPx+Wy4oWCWhZqEm1IZMrMMUakqldHAvGThCUzDI5SqcK3pIYBNM6cNhaORlyqZw4cXE+g1gKiUTZyWi0NspHLHMYAr88bsmpknh9oaIQ1PWxKaHClkyLygnBdRaUsL3dKQcWJQ//B5bQDX8Me30e9LOrlJDsUnBc3z2sD9XzMKz9Cl3PPyUO1ldCJO7mbpfbSeNzJEu4hZkIbWeVRmzROlXZMkONqz9pEd2X5JpDix0cJ9Zd7VbByXLvK5z30O/+bf/Bt8+ctfxpNPPomPf/zjjsFhOp3i3nvvxe/93u+h0+ngVa96VVI8ZBEsRG4A4Ed/9Efxoz/6o97vS6US7rvvPtx3332LHvqYUHbfew7H2lFG1GvghCU3R1EFiMp+r0YHLNxCC7UAlh99tLAMxcWaJ3T4tRHIiqORmw5chcQRulolsDxolg2fpyaP3Hy3SIyEHFA0UjPLY6NZfBZ1W8/GJA5QirPm2qmybZ1w3eUIN1gl5GaKZruL+sYgITBUEY0CkyjvhkhOgIlTPAAwhENL0Ce4k3gGDomZIFQro9G++qXwIgGxSnJCtqY2BIghy0dTPg95kbiiTzk2R/sbbo6NJDcd9n+i3Muw3TwjUFksQnlsQhBSE5JGuTb8XvB7PqxWEMQRakQMqMBA1YSiDZrlTG4NeazSyTvrGPfq/upmGcs1ERs+27yUF2TdrSM7dpSB2BrYuJzPKJGLQZMjhQyZF5xwk1exlPVs+IhAZmyWRkPAef7OebmhUzm2PEcyhlNUiZZvo5GmMHtKfj38HZTndM59DS9pgnlJkQxhJQKqkRu6CGo8EQq6sDGyD4/OwX+bdywo39FaIzzaefIgCRA/jvTSaJD3YzEcly5yeHiIl770pXjLW96CN7zhDZnvafLdj370o4nH9bWvfS0eeuihhQwTC5Ob1YD2os3R+WnAIKU/ruGI9qVB64L9zEMuKOyiBxhBQSFp2sRby3pwpEThA32oCxt+nZoVhwbdDrLhaFw5iajdJBQ5afOFYnGUxTqP1Gyy/wWZWcQociyI4cYH8wFAq4i26HOWD2lxjIdVM0+RxHDZIhxPQ0hS0wbQNhXRdjbMhJyncAkN9LGDA7SQLftMVdHc4gEpcUhDvky+DJ8/K7a5NURqeJ4NZY24Nc1SYiPLPktiQ7QordSWLSQwsZRrxJJBZRU247GpoIOT6KNuqkV2WsB+OZWFTyArGztISU8MpBWZZCUozVIoDTg8n6HkCUdDMnmnrJLGr5fC+7ABBHEEa8NCHACTEOhuNM11UrEE5qniy2DYSL352uJ4bUiGUsld6bXi1w2kHhySkeTBsZUxyZhG3ptrGK1VOVLIkDnBq6TZZyPzbGRuihyDEwIsDYga4efjvyQidPCywiOmSPsfrbmewhVsbpCzC8nJPF2DX5vXczNPDtE8kAYBOo6UGSGS54IW3D6l3b9FdDTtvBxc54nhvgD8fHK7JFe+c0pobec6ie++0wOle7a4YeO4dJG7774bd999t/rdcU6+u6bkBpi76VzAyM98H+7l6CDr3egBrjtZupWPIyzNY02h6m95xIYWTm64x0YKWb4NQH7ir891Lj1O1DhtvpoG+8y8NHlClF8j2D6zeELu99w7I13omgcHyA5AxxmCOANRBSgrMa3Rasa5riToHWOKSLk5MKWebRnnNL/mqUyVNJfcZEPRACReF5Osryf0ax6cvOLN7iWkx3S9M/xo/tA0WUBgqHhsqCx190rT5Jh0SnpenvZ/DGTDYKRi71PiuAHElrwVz0sulWo6pxCvkkZ3gPuzRrUTmMTGjDUJT2BYrbCJUisYoJ6QTHM/Uk/aMKpkFTg5djiKHW3MC+flShDYZ/q9vR9Uzv84jOGaHClkyJwI4CrTyI+iIKgEYJahcFlI0pQXgZHXaPj1DL6Wn50NPAyM+vgiL/AsZZ/rFtxjU1bWnJDIMTwUn/POK4lSnuFSKiz8+jWPzzxKjQ/zGFslWc559nm4DrrIrMl3nybkJgeaUCGFHkitE3zQIGWfezc6cOcucFy8/OC+wXseyA4oLTYsFpav5WCreW04udFIW4faQEoJrX05JnnXALjWEp9FRcT28lhlzUKkrX2yQJMfueAKiHye9J2sdrSox4ZvW6K7TUpGydG2F5gf/H2jZPQgnYyTz2XTQhcn0bGTdA6S6mjksakmBZQBCiEzxQMqSfiXCVXjOS5UKS0t9UxeHjoGrfmxOaTHxi0gkK3YxhV+3tYJOzfNrdNBG/1Jw+TX9BrABVbumWQh92qTjCSjUOKtkCEwsoAAKfZa+CpTHps5Sw1OlboskQuTogwAEFTjpBw0FU+gnBqqCscLPFAu1AgVTOLQNYhxWaTKF6lASflJoSW0nQsrEmwkcxqpLKddloUmRwoZMifEhNGceGti3ad/xEA21Fl7kaTyDrjvCSnEJaQlxjmZuSrWtMhGivPlGVD5mDtXP6DjDrD02KeCG0zLUHOhkm20vyQkA/aZ90d+kT5i4/Oq8O3cSAFkjam0vyRBeZCygiDD0vj2PDK4RL7dDF3k6tWrzuZlpnlZZvJdH1aY3PCmzRuPSOtyKlA4qUkGYqQCijonEQE+cHfAJuwcw52cSs5ts8zIo3UQ6kiKdUUTolF2t0yVtA48nihAV0q0ym9aWAV95m0WcwBgk31GdqI6n/yWa984IOVD7mOQxIX/L5UxKZDG4ji8wUBWgB4TfOEokbKtQBacQFvl+IQtINBO62FhF/tooos9XMqEpdXRRxUj1NG3h0wn6CTvQN8OZEQeQkySvBs+z42bISN9L9nqaHkeGzqaRmwovwaiDZLUkMfm4GAX46hiJqnsQCc1TyBLbgCkcjGveiQNzlxWcG8umySRhw82lc/NKCmWLUMEuefLhNy5nYeun66bExtTta5qvVqWbObFk3O5lcgdUujkZyArS7gCopGbuilkwMepZaHJkUKGzIka0pBJuKRGpgBohMaJFpHhzRK8j/hA7whXpEknkeM474v89zN0Kvm+aYQtcwm8b/OwejmgE2YZCuX4yj28RGJaSGUHrWkfwA19lxZtvuYEh59TEhteYppQh0uSuNzj3/FzS8/RPPDdP43sEDjp4rrZErMBz9BFkkmzLe699168973vXfw8x4QVJjeLQBEUXMDQQyFyQ/KDh6d1oHs5EkIkwyy0TuJDniCRAoG2id9KS4o8NR+opFDtsc/8fydG3JdAr3U86qya9ZV7nDjJgTvTcQ3u4CCvT14nxPehWC8MLhjkzZRxw1LKS/ieLx98lkQEPTy2UEwWA3vfKrWhncMmrYSmzGySITdEIgicMPCcm7x5ZszvspXUtOIBPPSNe2MkyXG3u4n1sp0UcjWwinx30sIoqpj5a3qlrOea1rSoxhEth4AvPpDM44OuSNQmQsNDCmtpSJpvklLyjFUxSjw4AJyJUinfKJvzpDyPcMYCwDVKkTIjQ2KkNUYSHIhtys+WgSZHChkyJ5Tcl3kM7SoRkAruPNC8NhDbpCdIFvLQDHIevWmR/9W2yn4Qiu/pQPwdl14NDukxkWRDLiH04kQl1q55dTauk8mwNDoHTTHBPK7oI61KRrJA5t7Mev6KHHC+o+PIbbzd1FaI/4+wMGboIo8//jg2NzeTzYt6bYDlJt/14SYhNwQmOLhXg4R4D65A5+94B25YWgdi7hc5GdYsCwxhFrGhtbQSCGHKL5Ffn2TTktRoS3IPtOuha+qLE/L2ymuQlhpuSbHX0IZLbprsp3QYjbBxkirvQx7BUR+L5rHx5dtwEjvPc6aLALKeLTr3EphAn29r8Tm4np7IeG4itLZ6aGCQhKNRGFoLXexgHy30bCloE5bWmPRRicYIYmQS00e1MhCwSYCZeixB3pr0/yzJ0cC9MdzvUxGhadmKYeZcVDwgzTFppCWfeRgaeWU6cD03fO312PAiAtxIArh9h3tteHw8y7Vp5iy1Mao1d16bLJlLPWMy+GLo+HzSCnU+UhOEExxJIqPlAUUAYpKBDaTV0LR74LPAyn2tAnitxAbQ5UghQ+YEU5q5PJHDoW/ccsiNJP0+hZT/LwkRH1Nou+apkfk2PPeDQ4xNKilTvick96KELOmg4w/EAbjhT/7PwfUi7rkh/cKjb1C76PAJSsjXyTik18OjkyUkpyGugW6Uz3tDyDOCyufN4SM2vP2SCFo5u4znZoYusrm56ZCbZXCck++uOLnRtFZfk6kDDwC0jDufrIsdmIFxH+58AdxLSUo/JzcJseGhFr5CAosqr5oiTNul1s8ukQvNkK010tNB1huVkASyuMoY+TzSRkxCs2ZwgUbVZZgVts3WcoCQQoh71Oh6SIGA2BdiX/ldgin7LL02PsLD950F3/ME276EllJ4bq4NAZzKRjU78SORmnZCakyVtF1eLW3SRb03RvkQRoDTPQ+BcgiUq0AQj4FmP3lGNIGm+aw/79hRy7Nz2bjN18PRUuXeV0DALTudThxqEum7wxb6vYYp80ykhpMbWnegTNBJcuIA2cpoWo6NvWm54Wgs16YNL7kpNweo1Eb2HgyT+8Kvm+7ICBVU4EaXc2+Ndu/pXlKOVbU2xJh7kWK7BtKcxYj9H1NYjOYF5zIiT6mle8wUm4yXaEEcg+fm/vvvxx//8R/jb/7mb1Cv1/HKV74S73vf+/D85z9/yUatC6iGeEkdlgFkow0isQDIelF8Y4svt0Mbk3hYtdYHfZEXdbEtNuRc6hg09vLr1FSykP/TsJ/5Oeid5x6leTwoXG5IDw1VsKPPrEiR1uaFhl+6IBmNwr6WRuUYcHUjGX4aI5uDNE+jtGc46x2SkHraEgrEMekivV4PjzzySPL/o48+iq985SvY3t7GbbfddmyT764wudFM+j5IsjEA0Mh6bjix4Yfm5EZ6OdTKP/P2FJ+iKwWX9Noov5fKPCc4sq9Iwcpjfh3lRHoyuFICZDuODLXg1yMFTym1wtbEeh7rFyc687wKcxEbTekCsl6buQ5q4Xles9z/82IIXaAskQ/4tISwuFdro2RWe5rJRIaiNdFFa9JF68oYpUMAh3BfDfbelgFMwjGGG67HxletbBHwY8hwNL5dgjw2svraiHkt+r2GCUXrIFtkRFsSjy95sYnUcMVKyhDtvfd5qOvZcDT+v73nQRijEgzBK6TJe0XXztcccfKdK3wkkaxgiCCcALUpULOlqSNkSQ79X7P3KWO1nmfc0IZiZfxYdmoaTY4sKEM++9nP4p577sHLX/5yxHGMf/Wv/hV++Id/GA899BA2NjaWbNi6ICckjWx+kuBklGpfqDMddN5xQo7R0jCn7UNr6QVga0luNFIw8/UtIfVU8IR67brp3DzfRSpotJYenLr4bImNT6/Ia7vTFg5OCBQjgxziE6Ms3WPusZE/0krjz4tF9Al5XhlutwCOSRd58MEH8UM/9EPJ/+985zsBAG9605vwwAMPHNvkuytMbjhmabU8ppEG27qJIQfM4MxzO3pwyQAnBGSdJK+PN9ke0AnBrOsgcIbPOy995wlJ4+3lh6S1zK3hHpvkmnzlIrXrIcktIS0BJGjEPABttvZ5bgg8fHAegTqv4M18yZ+fIuQzhI+2+8iL7zPftiTB8elD18CXnlZwcjcitKrdxDPTRgcn7Zo8Nzs4QGvYxcalI0NqejDCmwvwKtJHumEOP6oNMQkCVDEEVUyT880A85McTpL0iTrT7RroW148oI8GupMWup0WxvubrleGPDdPwPXg0D4xkHp7+RwavuIBHFI5KcNNBLZW1zZcT438vwk0mgOW/6TP6WO2TqB5w/hdTFuX3tMqhhihgjoGGKKKVrWLfruBMVrpeELjB1m1SUTS0isB8Q5SmUEhvlyB4wMQvy+KxV4Q9KWgyZEFZcinPvUp5/8HHngAp06dwpe//GW8+tWvXrJh64AcRVAbg1SD4hTuWMKPza0mEnJc4ifi2zXj6ywjrHJs6W2SegadWhp66N2MgKxXiHYkXY10NNo2y4OlVUZr2f+tvkHn53oFN5AuBC2vB6mxlusvBE5uOnSMTfYl3WMZwXENusFMvVMaXrnOuQSOSRd5zWteg+l06v3+uCbfXWFyw9iy49LnIOsAt7xTAlcXwGY6EHFywz/TA+uxdQ9I68bzhFktHG2R6yHIsC7+2SNIOalxBAn0TkZkRnWNa3P1SMuPFKSSRdFnaUERcfNtu95F1iIrHykPR+Mucb5duy+yud6dJFmRA8CsZyoJjrSCKIqJgyW62wB6buS1GH6eTqgieRebbVM8oG7LO/NqaZRj0z68gtoVmIgr8tpEcGONq3CVzBCoRFnvDSGPhGjg+/pC23zg9GeU0ICU2PR79dRj00F2Yl++pn0SYqORGmkUySM2smwrhZSUBAmF7vGtTREE1qOSk9vE74W5h/rzoNC2CYIklLCOPmIEaKGbeLuGO1V0AIx3N1NS04Gf3NTs/YvLALaRykhe4YrfL81CbRd53GXJjSZHrAxZtoTrlStXAADb29tLNmpdwEkn3GFDemzk98nYxY2vEqHYLseJvBDpWPl+Ed2EG/JEu0P2WXvvJOGmdUT3i3tm6P2nH9Jay8XhJ2CeXUduUKU0u79GOqQdIQOp81Bb6bxKkZO2OA/XIbme0oPt/3SMAVtrHrRlkJeroxnKuSF9CUK1ZrrIEllFqwJNIZUuWssOpSeDezTktsRr4KsAdK3Q2LPs0NKlwSA7krQUSULjkJspuw5p3clzmWvXIIkZC8WQSkqeNZb+lyEofCCXt0JyktzmkoVAeqMk+MHks5bPXfPUaMTG94wXwAip54Avo7wfZfG5z30OP/ZjP4azZ8+iVCrhE5/4hPP9dDrFe97zHpw5cwb1eh133XUXvv71ry/X5lUCe68qtZEpEGAroPG1mc+mZ4jNVRhScwUpweHLEJnZ6YPYJTGa12YWskp4tkKaDJ2S0HJ5aM6WUVRB1GsYz8I8cjCRhb7JOWV8v4/YAK7SXmf/193+7ltCADVTqFkrrL0oeDFuqiFnPDfmHTFhi+n70Wp3gfbYyCtadj2faWnSdfPZ7cljxZcy3HtCCgmbvJnuwbJhaZocsTLk3Llz2NraSpb7779/5uGOjo7wjne8A6961avw4he/eMlG3QSQw6i2qD+SkHkd2njB+5kct/MGw7xjcn1gmvU4cT3Dd00q+abwNF7BjNYyJFUh9ZltZXEMoW/w80vyodllZ+o49EOrzUuDi0+v4UYaQFz/PFiCdGSwpK4xC8eki1wvLGFKvl4Q1hIAfnJBAy39jqMORA23slhN/BRQvBtUN54P5j6BMgvyOrgizDvvDPc392RwiwHfhwsloYDpFVWWJW1c2JBFtpRaN2jZReq5ISHAnwG/DmovtxzVoIcR5g4eBL/rU4c8GLeKAVmvDRe6QDaBUh5rCaHju8YFZeDh4SFe+tKX4i1veQve8IY3ZL5///vfj9/+7d/GRz/60SSJ77WvfS0eeuihhWNdVwobsAPQGK2gm/HY7OLAhqXt4+TlCLgEQ2ouw/XcEOh9pdC00OxTrpp8nqAaJ9XLgKznxaeIm0AqMxmoCWubrbDHbD9e6YsXEBihgi5aGAwb6HVaQKeWLRJA655YJ8RGemx4rg3gJza0lpZXsr7akJJZho8mgOYU5RpNsWnISJVN4kn32XffXG9Y+rmOPgJMbPEB8uKMkjWRoEbQB24F+u06ImyncqwNI6M6SIvW9JDKrQ4MmYyI2PB7x41xXJ6wAgvSe5UoTQtCkyP2/2VKuN5zzz346le/is9//vNLNmjNwcdj+p9vd8ZdYHGdQZ4M0NU1bozzsY5YbONtqtvvra4TN1LdIYaep6ydghObJuy1k17QRTY8k36cp3vQeErH4XPZ2FBW3jekbifhPAta+wwyVk6FcMPqd+GSKK6/cN0s0b04yYvZ8TONygHfn0cxkU4xj/52DKr+Meki1wsrTG7oJfAplwTe8cn1GcKtL24HjsiycB7OlXlgZIkkYsMJzbV6b2RMNSc1muWfgRMXaju1Xyr+0mMTy4PkXcOsN1UjZEIQaAoKCYU2su5j3sZQXKPWrLmIzfWAtIjNimldortF0F3BkbItB3fffTfuvvtu9bvpdIoPfOAD+NVf/VW87nWvAwD8/u//Pvb29vCJT3wCP/VTP7XYyVYJltzU2t1kXht1uRKhdAWG2NBC5IbeNW4FBFLLVQxgAgTxEYKqO+kmgMTDooWYkVeG54BwYqR5ZwBYIhQ7v4utKk7ztlCp48GwgX6vAfRqeuEAzYMTA6kSruXmyRAYKYykfJPkhjwVyBYSaIptNRivTW3o9dhoBQZkhTm6pxOEqGKU7B/a+1+xlEkjmBWMEAcBGlsNXLw1xLhn29+Bew2k2ACuwtWBGX/iTaQVOGWIs8gx0DxYG1gOmhyxMmTREq5vf/vb8Sd/8if43Oc+h1tvvXXJBq0brB4hxx45LgHuPrH8UiLM+V6O08sOeD5SRAsPmWKGYEAvWiQPS0o9vf/UzB5gXrqWcg30Y1k+WZ6AZAc1QBQs0iI++Gm8t0w7p5RVpfzwevnoOnB1mab9HJMBmHRSXxyXbKymC8pUjVhs+y55bYBj00WuF1ac3ADpw/JRRiIwNEgQsaHf0jYKDyh5XnxK+NO8NMcRkjbPS6cRnNj9yAmMFDqS3DjClYekAf4ExUXaxwWOJ26eCwb6LC0skmSStUgKVN+gklzfvJhlMZrFnLQwQv6/71kv8Q4NoQsUm+C+bLw8x6OPPooLFy7grrvuSrZtbW3hzjvvxBe+8IX1Jjc1AM0pqrURC0HrO+FpLXRR5qFoV5GSGyIvgMm7iZFWho1gPDh2nyBOyQrgkhPNo5AlNXGmgpfvt6Y5Yeb/dFLR0FRFQx3DqIJxVHErQnLvbg/udz0gzTlcJNdQIzaylConN5gzHA04EU4QhGkoGZEV/pnuofudS3ImCBDaYgNUNjqw/5MHRz4XegYjmhR0p4pBbYhe9Aw334AreySHyaILpLk6cQnG+iwt7pwMivvASd8y0OTIglWOptMpfvZnfxYf//jH8ZnPfAZ33HHHko25SSCHCI3cOJB9R+ah+MYcTn5j9pm+035HYxw3EGvqHtdvYgBT836SfkGkRXISfpoQ7jvP+0JCcHj1QFqTIZp7I7S8Vk5qWP8IPQvBO4TnMR8REip1GFpzTxV/1pwYNu3nHk0e6gsPlDKAY14VnZOcRX87J2boIquGFSc3FczOlfB1XHrjuDuQOpMMeaNBO0bquZHWymVdBdot1uJMpQeHYwqACRxpSeZykQ+qiQV2yn6UJwxjuAfj38nYeVJUGul3bWRD0k7DdHJat5G1sHCBwC3HvkfqWAp4FRreXomy2M93r2cRkJCt5XOToYX8vo2xVHDqGHqcvW3muXPnnM333nsv3vve9y50igsXLgAA9vb2nO17e3vJd2uLLaC2+xTa1Y4TjraDfezgALs4wM7hZeAiDKm5jDQsLYIhOECqpPJXhhEbDIHykJcT1j04WhU1+mwU7DjxymjQqq3RNj5ZZ4wAXbQwGlYQdVpAr+xOzrkP13ND60RmaBUVNYKjDai8T5BCwqujzQhH44q8XVdqQ1SqaVhaUqqZlVCg7XyCU5qvhs8/RPfMeHCGiGGq3A1RRQVDjFBFA/3Er9dAH300km0N9NHdaGH/+QN0rzQRNbfN/WvbpWPb3UMankbWbdreg028VgwhJB/p3vBlK7v7XNDkyIK2lnvuuQcf+9jH8MlPfhKtViuRDVtbW6jXtQpZNwtI+ReeG43c8PE3Y1icBT72SoUf0Mcr39jn01e0ECfSd/owHkVWGZCrBXLok//z/Xj0RQ8wyfXcqEhrny4CpPNhUT9hE/8S8ZCGEGlnJDin4TqQ5jVtpf2P9JZbYfrfabieG7rWGGweMKT9nfQwx/ukNop9R8+oLrZzzDI0aWHxhCUN9TN0kVXDCpMbn/KpSRRNIc9TeBvIQlonZcnnRaFZ730kwZdvo7geuSWFCxNpNcqEoy3ifeIH5PdNU9wVC6xUUtoQpaCnQGiPH4dAWHKFhJQB/Loygwbv5PIekpkhFGtOdJbpApoCB7aWlidqn/bezcAMa8ky8fJPK2yZ8sEUklZPksTttkkXNQo/68ENR4vgem4A87g32PbY/Zym8bshZRpZ8SXDy30pRGqCEKHwKvD5XIZ2gkrKuRkNKzYcrex6Z/KWuWSGzzPJ5RhP/OWGEE84mhZewhSWMMwWEXDLYrtkkiqhcRJUtcYF7pkZ2hC+EBObfxNjBNOHKjawL7DfmauitbG4V7eGRqeptYCw7BpuSE7T/z275kqRc8/FtUuidy1hacfgufnQhz4EwJRy5fjIRz6CN7/5zUs2bJ1gjYx55IZ/jrUvNHClX45HMseCf56FWWObNHiSESNEknvD9Qmf15BOw/US7u1JFHzuwSGjse96NAMwG2v5ppr4P/fW8AJDUrax3GFpWJBGWkluyPDMDUTcwOEzZKjg+oqmB8dsuyQ586YcLF6EpfDcHBv4y8Af2CJERnspfIo77+Sc6MxiyMtg0dtOAq7kei64tQTIsSpp3hr+P1fAtTZKQcMJmd2PD8jS6kifawCaEcq1EYIwxiQ2v03i18nakUdsEq8NDyPk0ARIye7PiSW5v/PqGEoLtSQ1XHmT94cfI7TnWaLUkU9w2O2LxstrOH36NADg4sWLOHPmTLL94sWL+N7v/d5rOvYNRxNoBCmhaaHn5NrUe+OUzFBoGhEdyqnhmMC8gwFcgmO7kyz7LBXvUCjjHBQupYEIjvY9z7WJWa5Nv9cwfYuHovXEwhXsmZZm+V5LEm+uwo2Tp/7By0BjNrHhikttjEptlOTJuFXjXDKjeXC45yacTBDEE0xCcx8rwTDJwRmighATDG3ejbmLhiD1rWGigQYmCNFAHxMEhhBtTdCpjXAlbANhzSUznNzQWiM3fEjyGYmauDZyM8+2HOTNTXFzY8b4r5GajILt0yO0sZf/xqet5xkYgCxp8B2HDLk0dvXtWuTecOLCD8/Har4P39cxtpJhkTqBJkM4cvQNbgTVDKKEDNHUnoVoD/W3XbjVEGUECpC9Tx2k5Ib6csSvg0OSWq6jSL2CX4NMwaDtswjUPKTYgxm6yKphhclNDW6lal6ZB+KzDDniYRSkwIZif/kycTepJDnyvISZZgKxr1z7wtM4uKRgBKeHbIeOxRqA692QsZ3ynuS1XYajsUUOwG1khcLuGOXmAK12F0EwScJvJpMA/TBGFNqEZ24RkbcgEZA8H4BA7wBdhzQxlNg10D3gQj1POsp7QUobWaOp3n6eICoDOJrj+AJj6AXbj5Fv33HHHTh9+jQ+/elPJ2Tm6tWr+OIXv4i3ve1tx3eiG4Bw56pTFa2Np5zwtPJlmDltKByNPDdXkbWqA6mnJmSf6f2cAOFkAgSAL/fG/J9uD2woGgAnHyQ9XZD8ltR48uLwbTRh5xAVDIYNk2fTseElHegFBDSSk1wkgctWPvGeDEPi8pSTGllEoJSVF1KZzxCeiZUZk0xIWtUSECI+9D956KoYoTXpIognaBweoZQoOrYvhqYjjavAJAT6GzWMUEUXLfRRRxcttNBNwtL6aKCCEQaoo44Bumga0lztonVLF51221Slq9VSGU3WW77W7juJeLruNlw5ugtgB8tBkyMrGk6ymqAXR4SmAbpx0YFPd5DGM20/GtPinH0keJ8lI4NslAx744WY6HcNP0H3baPTJLIErhcj5so7kOpp1EZJduT4XHL/9Q3dkmQleoPUg+g8ZHxBGk6/C+B2u36BXd8aodbso7XVS+R6f9JAv1dPw1PJQNtBWnkSlHeTBy47eY4R83YDcPVTeid4eX4Jur9jpGkaS3T+66CLHCdWmNzUkd5JbiXU7qTcRvvz3iZzd0K4QoD/lhOc44Ds0LOsFbwtgNt72f6qIOXQ8lF8beP3mP9GviJSooi5GKT3xi61dhfV2giNoJ9YWycIMAkCTGoBJnGIca3mFkrg15ixvgyQvbZ53L4kZCRhmwfSqsLDbJhVOkNuaNDw+fVz4EvTWTB9p9fr4ZFHHkn+f/TRR/GVr3wF29vbuO222/COd7wDv/7rv47nPve5SSnos2fP4vWvf/3ibV4hNDbceW1MOFq6TZ3DZoiU2JBViizt0jrJEdOu2Xwb+b8vTE3LqZHeGq1CGhUSGE2sxyaq2BLE0AsJcI9BrhzRLA0+CyvghmjyftFId5MeGhGCJv8/4Q1Jcz04lI9TSYiO/T8aozoESofsWumx2FtZrgHlAAjiCJMwQrCR5uuQB4iXie7bvKY0R8oecAMIwgmuRHtAWNLL93PxqYWmSa9NKVB11QAATCNJREFUW/y/DDR5saLzU6weZoTwaH1nLtVhnsiBsrKmE2hjM/2OD6A+cOLEQ9PYtqicvrfcxipPKbfV2L7y/U/GXM04LY2v9D2TOfJcPi02I9/oAzdk885YzxpoiejcCmA3wulbztsIgG4iw7tBC62tKs7HIca1FrBfSvPrSBdKroPgI7xcZm6y/zWDqVaMQipOfBudd0k2cky6yPXCCpObGox1jQbLPvwKOMH3wkjQS+3r+DLhTNuPmDAXKIuAW/hnKdiSkVtSkQufx0a2IWaffXG/ZbHQNk++TSakYmzyHoK+FQopuRmigmG1ikkcYOwTVDGYhZO8NtL6wH9YcjfTV4k1SdacpzUX7BLSMs0tLOS54SFqBCqzCSwV5zrw/GxBgfLggw/ih37oh5L/3/nOdwIA3vSmN+GBBx7AL/3SL+Hw8BBvfetb0el08IM/+IP41Kc+td5z3ACoVftoopsMSFQtrY4+GoeRm18TwSU5RG4C+5nWXDmW/1v4JuTkMPkzaQljjdgQtHA0HuQ2RBWjYcV4bHp1o5RwUsOJDf+sEhxp+CElyCczZd/gfYENzCE8ZZ7hJznhNFMlTcu5MZ6cOPHmJJ6b4cjkVA2RlvWOkO1Tdt6i8qEhOtVhD/2NPvrVQZJzE2CCge3LdRuWlubxpPclqE4w3K0gqjWAuJYqdzIsLY/ckOemDSfEt7R1uPDsXQB0ObKiSslqgqzei+K4QvmkfpGnL/DvtAFVRr/Qy8gLLrGKs/SOcpLDIQkOJzMayUkMjKRP+A6Yc12azUWSsIxck3nV/LhlOLk2RGxuBXAaqN1+Ge2tDs7ifBLaDJh+30ULAzQw2QnQqQ3Ru/CMlNxQX1ahtYEbg6QHhy6QroHrYlxW+zyAs/TnGTgmXeR6QXMyeTGZTPDud78bd9xxB+r1Op797GfjX//rf+3E4h7fTOdNpJM28Y4gId9icnNq1X5oErq+/XzVsx8nNhoW9cDI34bifw3cyiAT/nh51qmyzPI+cTIlFXzfIiwcJAxIaElLIy27EZq7HbSDNBSI5zyQFb1SGwG1adZrwy3NALt+PsGqQnJIkZJLDUhr73PPi+/9grhX3KrSsssOgG0Ae3Ytl027TwsLY5KzLIDXvOY1mE6nmeWBBx4AAJRKJdx33324cOECoijCn//5n+N5z3ve4u2dA9dTjrRxJZm4k0/g2UIXNQpB4+Fo/H3jBIfuOScy8hnEQBDrJAZwK6n55q/hSFX6kE1baRbuj+oOW+heaaLbaZnKaJ1yNhSNFs17kzbUghJ/ZYWzTWTf7T2Yd5vefeoL1D+snJBhZxrJkU7hEEAY2/ltsh6big3Eo7C0hnNXBmgcRti4cpQ+3wOkIYgHMBXyaDnvLqXzwMb5IzzjUg9nh+dxFk9iDxdxCpdwFuexZ9fpZ/r+IvZwEXtbl7B9+gA4PTaWX23ZXXxp71yZ+d6oOAYZskq4vrrIDMNlnvdgKXCNfCz+9xld5Tgtx26pd/BjaGMq6UZTdxzOEAZ4+i2yfdqLvC+54cQDfmtoieC2G4CrL3CjbhlJhTTuqbkdwAuAEy84xPO3vobn42t4If4aL8RDyfJ8fA3PwTfwbHwDt+ObOLfxOHDrOO3fbTCCk0faSLfg+sQtMHL1VvuZr/cAnLJrktNcV+Y3RxKeJYgNcGy6yPXCQl3yfe97Hz70oQ/hox/9KF70ohfhwQcfxD/5J/8EW1tb+Lmf+zkAxznTeQOGEnIrIo8dlNBctst4U/jx5HcxWy8K2Tk1b43839d+6px5j493YF97pbtS/s/bpJEduEJMKitNmFnFa5QNMLQlWk17SFWpYIhKUAHCGAhF2B2tk0sgISwtEdQYVsKSC1d+zMR6VEeaQDkL/Lqp/LXMJ9AEcAOp9W4Jy98QeqrOisa5zoPrKUfqNgyNrPkV+x5Wh6M0/IwENIWkEYGh9+4aFJd08kh9nht/yefQrgNnDQBJKNokwCQOMIwqGEVVHPUaQJQTiiaVFIhrdJoiOw3gf3+l5wZw+qH0ymS8M54FAEKdGOpFBdJQsgqGrseGe274cyfQ3EW8vLddNnCEYKOH0YaZA2eECgBTaQ0A+mggnTTVbOuiBQRAv11HhBbQtLJB3nu6Vs1zw4lgE0BzinowwFOep5ALTY4UMmQ5aO/pdwVS+GjhabOgjelQtmkGYhpnyzqp8Z1ONlt6dpaCaDtvD+8/GsFJfu8z+NpcQN7XrDHhxOlDnNozBosdHFjPTTfx3ABgZeObmCBArd1F1N7OGm5UcP2KdAsiOWTcbyj7y1yqgVhr8Hl15sSa6SILvW7/63/9L7zuda/Dj/zIjwAAbr/9dvzH//gf8aUvfQnAcc903kTW1eYLHcpL+qftZfaZk595tBfeY/n+3DXIP3PMchHLfbT25n2nmV61e8P30e4TCU26xrx2My+HVF74wNwEGs0+WoHp+hWMHKFAM6lXMcIAE6PIELnhAiqxvnArlvTYiOZyyzAXrvTzHpAKjq5yjbNiYrnwsdXKuKJGjyACzKR9LX9786CF0Piatya4nnKkaUMImomX0C6HR26uDVf6RZGAuTGHNOW5I+k2vQoaqewAku+J1IyiCuLY5qpFFSCqpjk2HbhrSXR8iorz7lIBDpIDWnhNTmisZsX1haPlLTA5LHk5N9KDU8HIkFdOavhzPoRLXnl7q3ah/TbMvrUhsIPLqGykuTe8uhqBntMAHQSYoL9lFJMo2nblAr0r84Sl2aXc7qJmS1EvDE2OFDJkeeSRG42w5h4o70FwD8M88O2n6RRcD5IMhCvLoRnDuNyQhh+uenA16ZqJzYybKOWY5rkBkA3PpzBD1sg20opopwHcDpzdM97Z2/FN7OAAd+BRNNHFSXSSvn4Je0ksSogJHt86hyd2G0C7luohIZD1qBDIa0MLj/aAW2Qp0V/K6feJl42OxfU4ulaJm18XWeiVe+UrX4kPf/jD+NrXvobnPe95+Ku/+it8/vOfx2/91m8BWG6m8+FwiOEwrSWXzrjO2Spn3D5iI1+WWXdcdvZZXhR5bCnBNGnmk3wacZhHuebCSMZOznPN81h9pAeHfqdoHtL6KogNamPUq5TjMEjyHQhkte6jjgpGOBFOjGFAE1AxkIYSXmXXzYmYXTfhhsBwRYILwx7PCdDc9gS6UC6AKESn4SojUgiR8tIhZXFBxNB1yLkGztXE9ZQj6XuXvn8N9E1yOQ8942FnElKJCdiavrPbqMTwtYBnk0gvzSQOEMeB8dTEgSE10lvTQZbc9KDn2sjrq4nvEigvoewuXJTx49F6EWJjj3MinKg5N5UkQG9ovcKjdA6jYd+EoxGh4eGH5LWh525uuOu5CWFIzQZS5SgCagEQxD2MtowHp406qEx0mvtUwQQhBnb86qMBbME8L2wsTm52zfpE+xCtdhetw172OcwDTY4UMsSBXxcBkvEh732ld8WRF/KmyzFaPgTtoczjLuGfpXGSxnxOlKTeNGD/82R1S26ihjl0hFRGyCbmaZO5lzDvwex18LE1ZGvAHXN7dGiZpiBzXeppOD0Rm+cA5edcxbPxDZzFeTwfD+MULuE5eCQxlpHhqY0OOmjbSopDnMJF9E83cLl9S0pMMtdB4AbTTaShZptuvh3pV2SY7cGGGe/Y39LzonfWV114aZa5drrIQlf6K7/yK7h69Spe8IIXIAgCTCYT/MZv/Abe+MY3AlhupvP7778fv/Zrv+Y5Yx3pA8lTPiWkwq+FknEyIM0OGmi7zzWseUi0z4S8HA+JRd6ea3h5nWuUkHkpwnOjLCdq6RwTVaRx82S1prK23vyDjGuZk1xqI4XK2PdDC5Hj7eLCuUcXMM9zoGvnBMcS8DZSZQRwFcQe+9+XkJcHqfgQVlSgzIPrKUcaGCSWfaqkFWDiemVUZd4iEJ85mZHKfEiHSz0uk2vojxqxGUam+MZRVAHi0CU2RF44iZFkRi4ETT+S8JEZbZu8N3N6aXKPbaFVoCM5EmKCSnSUkhjywPAqeOS5icSB6bplWw/t94dWAmz1EVuP8ygJRhs6hIvetSqGGKGCIJzgqDYGauVUHvDIKD5kKWFpldoQlWCYTB66MDQ5UsgQB/m6CJAJtVTI+PFCM0RK4qKBd2A+vnE9RwMfV2O44y37WSx24d4aebhY+Z1zLh51Q9vK7Ic8ogQwOUA5E6lmDKJANk+ZGkxjughJawPYnaK908Eu9rGHiziD89jFAc5QWNqVCJMQmIQnMKmaebKexD76qKOFHhpBH5el/uFFiJTc2KiQEMaw0bRrOgbJDTpuBCCmUDa6X0vKiFlYM11koe74h3/4h/iDP/gDfOxjH8OLXvQifOUrX8E73vEOnD17Fm9605uWasC73vWupHITYKwl586dA7YAXOHKpJyThCOvs3ISMy/48aUrV7oWeS7OcSGv3Zr3Zhlov9WuEcjeb8WKpZCKSs0M+g30UcEo47mhKk+Z3AOpgPWobbwABG8zNUIIKVo4seEdtAMg9iXi8Wvl7x2FpNkwkzZSIdSGK4So7XTOBoBLyinyQMnsEisqUObB9ZQjNZZknlr5h34lnyCVfWnZD5X/AyAOUjbE82TyyA7Pq4mlus7yapIQtDgwXkf+fmnhZz6yoxGbWQPwLFuNb/ssQjPjuIHIuSHvDScUNNkmeW7KPPyMliFSzw3l3kjvFWC8NUPPd4fmc3U4wqQ6SCbybGCQFHmQpIY8So1m39T77JXTiGsum4BUtEsPeHOM1lYvCalcCpocKWSIA68uwg2rPu8jKZ0R3Hc7BvJDxXzgSr/P6yKPrXUmrif45kDhEB6bRO/aTEkDN95x4hIq28G+T7ZNxUYZXq+F3pN10I7H0mvDz+2M89JrI5mWvU80fp8GcCuwfft5nMNjpkgAHsdz8A2cwkU88/HvJMVJyjUAwRGe86wnsL/dwUWcQowAu9jHAXbwRBuenBvFc5ToFXtArZQWNWjD6BeczPRgdBd6DvsAIgpRI4cAv/Ha5yWwZrrIQuTmF3/xF/Erv/IriUv3JS95Cb71rW/h/vvvx5ve9KalZjqvVquoVqvZL7ZgB6EGjFZIL2ceM9U6PfU6+l4KCv67vNuhCSjNZCPbwPeZRUa0cDOw/7XryFPKNfiOP+s7RRvRLFdsCcM0wIasmbwMtJpQLQVUYl2VFe3ohNRu23ZpgSHrJ5BV8Jpg4WKcxGgoI0322zGfd6HPWkxCh1vUY5j3eVFyk8fb1xTXU45UmUWd3r9wMnFD0Bg5Sd6VGKnXhkgMDVRVGCW4yn5nj0G2e05SYkZezDp0CAxtc+esqTq5NaOoarw10RykpqNszwz67NpIMYPYnreWn33vo/yd9IoAGbEiEQSupyaEG6ZG89BUKddG5tlwsiPD0uQ7INtI+1ftvkOgEh1hWJVPmH92/w9hQuuCMMa4Zi3PNXFu/lmQm3LTFMRooI/ysnVX18jiOg+uqy4CwIlU4M+Hv7c0VvkMJgm0pHYZPiS/m9fIqnVQqe/I80gFmAiHqMrqy7vRfi7HcGc/KgrEPSla3jTXd6iTWB2Qjs93gzzXFMYgSlXg6PekM9k1jd27SKql7QUXcRZP4nZ8E7fjUTx/8jA2z4+B/w+G3FxGMiaUJsAzzvZw9tyTGKGaVOPMhMVnro8aT14bq1fciiTnJ6nexglcB4bQkJEEAJ7YRJpHlDelhe9dmANrpossVAq63+/jxAn3J0EQ4OjIlFDgM50TaKbzV7ziFYu1bANswOXlegH9wWgCgSCtAvzzrCdTFovcLtuT18a87fOAS45Fr2MRSAE5y5MDRVmZ2kRgNxTNVQbcmduPYqtNSgEJwC3xLZMDWZsyVk9lyVhT5jFb00JWFrgkqo1UOPL/6TPtuyh4eA1fhnk/Wm1cTzkS4Mgh0E7VstDZ0X3MXMGtsnWgfGcJz7gKh7TI9HcfeDGBpHdMRH5NEoYGd+nl/E+fY7iDvhQV8rp5H8rrS3JfuU0SmVkEaUG4c93Yux0f6crXUHymPsRzrfIUtyXEqxM+F0wQhhMgtAfS3jPt/auNEYQxMwwtKec1OVLIkAXAxhf53HzPcOF3O+/Zat4NvuaQuso8hlU6Fl+4rhGnTczrI7OWZP49/kMZmsY/cwLGiBagj4uJQZTmw5MhaRwsAoXkVdssbXSwg33sYB+7ODDE5jyAx+3yGNLS8bacfBtPoY1O4kFOprbIBQ93b7lE6zRbbkVKemjh+gWArJ7MoV3/glgzXWSh7vdjP/Zj+I3f+A3cdttteNGLXoT/+3//L37rt34Lb3nLWwCYuTKObabzHSAprNVrwTBw6qTEvhd5WNKqIf/XoLl8yS28SHLWHOQgwTLXJI+v5QRJaG1YZOAs6YdIFJg4Q27IqgnoM7QnTZCWH/Tt0kUqrOTJtbhZuJ4bbsmG/a4DmNA0n9eG7i+f76OcCp9dGKHTRNbCEvFzAKxQ3PyIAFUvXtHa8vPgesoROdkj+8KAe2QmSAkMt6bX7DYyuGywpYmkwtaodsIJllJUb499P3TWo0nVLfEcVdwwNLLe0f+a54bvyz03Uq/QPDN5C9+XIAmAjxD4PDU+0iWgl34eMc/cEFXKraGFh6VpnhsCyYgQM9sxsW2Xxam5J46+V6HdT/6ZEcYTtRGqNV4NbkkFRZMjhQyZEwEyc7ppnhtChDnJjWak8xllQ7GNXtR58m9kZAqteQSMxlCofUQOyu64TN7HCO61+ghHDLhEg47NCZTmwZJljgdIyjdH8IDrCjIsjZOAulv++TTQvPU7OIvzOIfHcQe+iWfjG8BfwxAa7rmhMSAAcAjsveQSutVW6rmpDYFajd0bThiRnj+pkNZIPTa3A3gBUv2iBiCcmhzLfQAX7DbSZZ4A0OETf/IXcB5ddw6smS6y0BX/zu/8Dt797nfjZ37mZ3Dp0iWcPXsW/+Jf/Au85z3vSfY5tpnOt2AU1BimxKn60I4LmpCQSXjys+93y1ak0F7AecgGCS+59oWqLTI4Hu99zpu4cIIQiIOsOzsC/BYY8Yw0r00bruJCyl/E9u/lhaMRGMEhC88uZpMbOkeEBf2kFjH02vLatjXB9ZQjnEQnxCIIgHCcEpshkFTzpUGYvw7kudlCGpJGi/1/vAEMq9mUcsryScPNKiL9PVXVR6gYwiOJTVRO28WJChEaSW4kmZGkRtOZuBUaWMwa7bPcakRhHhE3J9FJP8dJuGGJrpfIjSz5DOQPxtKDx++L9dCNamWl+HSFfa46z5k8cXEcGO8bvxfyXXPOa73fLO9oaWhypJAhc4KFPEuvJh2Kv2Mk75PnGoqdpBKvQXYArhDz/3kHXtTI6dMHuPGWpwLUjXLNSQ2dmjfX600BdLJBYWOALpzi9PxJm6j9mv5FpIkma+fnotAtAjOKts3S3uhgBwdJAYFnnO8Bj8KQm6/DGEkOYMaDDRhDfAhsXDpC+1wnmZy8XBthHGrvmgy9s5VX23A9Nc8BsAts3X4BlaqZH3AwbOBKu52Spo69rF26zy24z0x7/iFyy/j7sGa6yELaa6vVwgc+8AF84AMf8O5DM53fd99919ayLZgXjpjpXDPJz4t5k/E1gSGPQ/sBKdnxJfzNCynYfIKQj45yvXpww2/CZFti4YzDrOcmBtwBgT7z8D8RD91ENnyGg94rUuBy7xeLzUUIx9LTFssuawO3VpEQWkYIDKGTohUVKPPgusoRC9feHzrhZMkCpAnlvCuFbB/pubHenFHthK7UyjwaQWiynpzQVkYLbanncjZunRMYSWz4vhqp0UiDT5nXPvPfaJ4a37nmFUuifZM4aypMQ1wVT7CPFOVdPwddZ8AWXjSiCsRBwOqhVRKSQ+R1mBCcaurBs8/UaaOP+In7FASTayM2gC5HChmyGCTR9ZGbpeyvsrP4oOXczAtfHgY/Jg8Di8X3MRCXs31cem68coBC0gbsC+mp0jxXvE2kuHNIEjRGNs9Gy9MWRtEagOYULXTRRgdtdLA7PDB5shdhQtAuAbgCTK/ATCewZb/bMNtb57qJFzkIY4x50zLXx/WKRrYc9ekpmqf3cap6Mak626/WUbllhO/gFBDVjM7RQaqTRD5D7SzBNwfWTBdZTS0YMKW+aTqTfQA9WdWKOuq81WP4pc4K15KFAPhvpHDhbmVZqYJjHmHkIzXzvJiLCLt5PTh5GolNMNR+EgOI7YAepAoc4M68Ttbr2Fqtk7K2fOkBWQsMhaWxtnHrC6134XpuEk8N0pCxJkRRAQ5OoCgsrewkHiZu5DZw4tZDBOEE1doQw6hqKlvVarYefc6tzMMAayVQVg0ThI7iSbMujTd6KA9hBicgtfST0s7jiKXnZhvmvdkyy3gT6FbNJG4DNNBFy5nZaYQK+qgLZTirGI8mVYyiCvq9Ruqx4SQmhhtyJr010nNDa02x1zw2UnGTxEZT2jRFhlt1NcxDilh7J5MgEw7BPXLkEQ7iiZ5Dw3mBtDLT9dAzJsJLIYdkZLMRqdEW0EE7ec7dZNaLFjpoo4sWemjZmZXq6KNh3rtew062WsoST34vJLEJYxwLNDlSyJA5UXYNZ9yA5iivFrz/aN5LANmcFiA7JmtjdCi+o4NrkSZ8O+Am0/vaFIrP3Atgx0gam6Wxg5ojvco9wBCbLrLjOM+l5dfDD0rfXUVq5Kb7J6+XjkdhaVqFVdIl7X2gZ7kL1Hafwg4OsIeL2MNFbDx2lHpsHgf6XwcuH5qotO3LZmmcgnneB8DJyxFa28ZzE4barLlSEFu9IoTrsXkB8MxnP4wd7OMcHk/JDeo4wC4at/TxeHgOR50Nc7hH7H3e30Tq5crTjZcgx2umi6wuuaFBhRYnNG0eQqPly/jg+54rvFwbIIEkBQ2HZ7RSIT1JGqnRLDZaG+ZNHMyDRrKkaYZ9VJeSITdVHnZTRdVW+5GW9BhKSFpyGu5S9iQGciWtiXzPjRx8APifE9fq6mbFyRORnHaEnb2DZD6f4UYFo0kVl+NTQK1sCLo2yfssHHl+t8yxnoYYocxChgypGKCBQbOMcDJG6YrdkXIxAFc5549/E+a9oXCELWC6BXS3ajaFNFVmXWLTENNNupZ9CkejymhOKNqsRXpspJeHX4vUBeRnn8dG9hWuyJA4lIRGU/qkSJS/V0jPkeK54cgLdXW8MFr7+VxFG/YzhRoSodmw6y1DbLobTYfE8oVPFdtFM30HhhVDbHo1/zPy3LNJbMuBBzRJ6JJRC5ocKWTInAizXk0+hgDp+y+NAQDcECA5fsmxTHorOHxkR45d80amaNA6rBh3NUMGJ3FqOJqWA8NzjrTrlh2DkyzNWM1zeMgyzs/F97XHFc+00TS9lrw3uIx0uQh8+9Ac+TK1/hC44yqSiYJLUVo0ybmMmD7IqB5LbtpwvDblW6/iLM7jFC5myE0VI6M37QV44vRzjcGLfo8WTMwc3TdfesISnuA100VWm9y0kYYPAdDJxnGBe4Ro4YLDJyzkCzvrHD6QQJol1KRbcxESlQefhZBrIGJ7XE53UZY45nXRAgTMgyMTrE3ODbJCMzmBFIL8ukM9XIBb2ng7tcEpFySEymrRghO7ZvbwXezbJOehUV6DCianA1wJ28BuDUtVcY2gh8euqEBZNfC8iJR0DNANWgg2OtjYOjLvAM+7mcAotQRSkLeQkhsbntbfOGFnHyHF1nhshiIPQyM1PBxtNKkmldGS8MxZpMa3D98OZPUVjdTwzz5iI40EmhckT4zIz5LYaN0bNjQtcOcD4mGtDnj5bt6uCdvGP9P+3GPD86rIyLZhiA09X0lsyHtDCyc2/V5DJzaSdMp7RAaiOMAwqKKOvlOwYCFocqSQIfNDEhveN4B0XInh9iMvuN6wiA6Rdyx6ifKMtfOeJ0YaHcMXG7HBPTd03fSzjIFyijRUzFdMQOo4MkQO7H9ZUCgW31FbJYEK9evn5CYgM5Xxu9KcNjgAcNVEoHWR0gcAuMMSGypYooaQxrz9CsHhBtPTwN7ORZzCRezhEs7iycRo2kcDISZJFMITp28H9stpxApKMFNWaO8AFzD5RiMVa6aLrC65uQXmDeohZaWdFhILupMLAfjzXDSGL7dD7EOSiZMc+Vs+IlFn7MPFoqFfvs6seCqS/a4l/4g6/Vj8z8/Jjx/DEaRxOatMMeUr6jUw2GqgihEGaAAAArueILAhQmYZHNb1MraJEOQWGKlBlbOEps2WGvW+Uup5abL9k+vULB0s14aXarQWFtwKnN07jzY6OIvzifpKFvywOkH9lj4udJ61VA7fugmUVUOEOrpoIkCMOvqJpT9AjGG1gr2zl9A4PEIphLnXWzCvlxaWZvNrsGkKCHS2jLLLw5E6OIkB6ujgJEaoJBb8AX/XrYeni5YhXMMGhlEFUadliE2v5J+Is2PbJ7fLz1J5lpDiL1SWPM+NYutI9tG+09rBt3sXOUdQdlBO5hEKAyA8Ms8rQjo3DT0/btwgkPeGSM0OW2/DhB2eMt65SziFPhq4iD100cJFnEIXLVyy/x9gJ3kX9rGL0bCCKxd2jBeuA/0Zccj7HAFHUcUUl6haj6OTDL0ACnJzDVCMZ3L8oP5G7xfvR85zlgxeY/2LhKPTPnyG+jzQeDZma+2YY2U/GnvLLrmRl8ANLxjD+DquwtACnwcn71rpRAO2nzTsxmwfn85Qhyv4snpDHX0rya3n5gCJ5+bgYkpuLvPmXUE6QfDQjC2JR9kx2vB7SO1umcWSGtwKbD/n28nEoWdxHrfjm2igjwATdNFCG08lBOriM0/hQu9Z5rcdex1RC+nckIRrIc8Wa6aLrC65Ic9NG8z6TqFpPBciD7OIDVdoNVcnJznyVvHj8YmTFgGRIk5wZCeXbloukK6F2OQJkzxPERMkmqfFITp8TviqPcLE/jRIQnJGMOE4asiGY+mRZAxw7oFUyGowxKZmNdVaTR98VIuzRnKUctPtKEk+3MEBaMLSOgYYWCsvAFzaPcRRhOWwosJjHWBscEOEdmCgdP66NURUqyMMq32cRIRShDSaQXokmAJ8uHUCw2pKXLrJcNhKiIvJsak6pCa1BzbSxPNJFf1ew3gnKBSNK8CzPDNan8nxgjhiRno3Zn3m//u8NBnvg9IeuW8uuTG5e3E1EJ4bOZ1niDgIMA3HhqiSJyaGTbSFeX4xjOdGTtDatGsKOdwGsGPCDjtbTXTsLBY9S2L6aKCDk+iimRCaLlp4ivJxrjSNF65T1kmpdj+oPYzcICoj6jXQbw4QBJPlyQ1QyJHjgvTi0HOUnhtA9B/fuKoZFfMgw8T573wkR9NfSGeRBk4Z+s73sV4T8t7IQ3M9IDPXjMyxyRvTNdLHr3MeciPJhLy3Ybqyiwn/ihP/epKLGQGDSbbO2wDANIap0siaGYOqI4L1ddnpSe8sOZ6bdtDBKVy0yyWcxfnEMNdFCwASeXMSHaNbtDdSPTmSJI7jGknOGsmQ1Sc3Xgu71ll9D85HbDRwMqN5bug8XMBILcJnXYD4XnpFCNJjIzt6qOzL/1/msS760jOCwwdrR+EqYTSsYFStJoNyheXcUMjQEFUbjoOskpbRdjQvU+i3ONeGKNfMOce1KhCV3H0ScjPP+4AMuWm208oqbXSSrArKtSBS12p3cbU7XSfZcFOghya6VhXu4GRCbioYYoIQoXX1Y3sf4WSCVm2cGagQAtMQGNoywN3AeFw6OIk+6gmx0XIviNSk4WrVJBeHPDYm0byan0/DraE+csMXQBdHGrTtkuBoIpegkSiNuMyzj0KE4tgNZQVkzl6QVCUbVoEakRoaM4ZwozD4/ajBncNo237eA8abhtgcYMcpGDBr3TtsGS9cr5xfzU57LpLc9ACEttDERiMxlhS4zvB5NblxTO4zcxjOy7/xGR/pRL5OnZdv4/PU+MBfUk5CLHkiOSRVm6Tps4iNj+Hza9D0mT5cAsTvRx8p7cgzhmaJDUJY4+QomTQ3aeIw5yom7h1Piibx8OKYroUWMlKwCcHbZtm1E4fu4RL2cBFncD4Jbm4yctPBSezgAO3dDi63N1J9ucOdAD4r1M2PlSU31bOXMbywaSxeuzAJ2U0AvU0Y82oDrqIvrRWSxGheHK64hvaYLL/CqaoRKr+j8xKWsapplhK+lqSHOjWRLN/1L3J+rmXkucSpp/fT83LlSy4doNtpIdidoBH0E+UkwMSGpdWNtfuwbqp+9MSxAOjz2yhtkwOPXcq1EarWczOu2eplcmBKoBWPYDlYIRxic2L3EDsbprJKGpZmLD6U3wEAVQyxX91B9ZSpJLkY5ADItxeYhcvYRhW1pERzSjoraKGHAeqoYIQuWqgGQ9S3+8kAR6DcmJGtakbhZFQVKyU19SQs7Sm0MUIVHbQzCeYUhplUResJj40kNXIdwyU7ed4bIKsPQfzv6+rSdqPtI5dIrPl2rS3UX73HKmESh/beDxBbo0jslF2uWNJYR7ARo7YVmeNSHhV93hDtD5GW96ZKeDvAdAN4artmzRUnE3JDYWhP4iz6aOA8zqKPehKW1hm20e20jCzbt+3ft9fS8dwXageQyiUiZsl+JfTCNoZRFeXytudhzIImRwoZsjC0cYaeo/TcqNqV9CYA+eOuFpIuf8u3kQ7iIzllZI+hgesT1F4ai60+FJd0fpF4bKg6GoWkSaLDr08KJ0n0YqReKToZz72RJEwLd/MYwB1yk+4fIE4rLypItEcKba0iyQ0cwVZL5cYmJ6yOjuDm25RPX8UpXMIZO4noOTyOZ176TpLPs70doXpqlMi7sziPTtDG5dO3mIk927Cyh3TX48R66SIrS26aW4cYtpH13vTsC5G8kXkddV5iIwsV8KUs1vI4nIlzsjErjpT20Tqc7PC+F8onvK4VvnZLMjVFMkuwVLSsMnbUa6BfG6K/ZRR9HpY2oGpSvYbHqkmJiJKAKQiVdThFEMbJJHgnwgmONMtayH/oO7iIz60BjWYabER2+wqGaGCQVDXpooUJArTQxQSlJciNz7r19LTGLArjVTH1K3llrQATjKxXrYohJghQwSiJba6ypBvK6Rigkby3Q1RE6eeGJeuGxPTQwhBVdNEyXhqN2PQaxpPos+7L/2X/mBWOBrjdJu8V932vESP+nSQ1GkGR7dDa5ruG2BQUcEPRgoTs8AINA5j8vnE1QjlGSliApACEo4yFSGcYt0UiDk+dQL/acLwx9PkAu+ijkYSeGeJad4nN/kZi2FFJaR650Qgl/d8pYxyH6G02lQcxDzQ5UsiQ+TDjPqljj9jmPa7WIQjzjvvcE8BfnFlenFkkh0eo8DXz3qhNJ/Ii55qh/6V+o72X/Mb59CQgS4JkZAf/Xir72QQSXrBjYthO8jyJLtDVJEFlFAJrCS8VkAGF2ieXp11vOSXKTRPhQboERYPgEgy5scfaqV7B7pbx7rRhJg1N84vtEjGjbIb4jbH8jOLro4usLLnZQgcHbSglfcm7wkPGuDTJG8V9xEYjOtxjUxe/pRdEeodoLfNvroXZasTnOAgM4Nc0+Hk19y9dvxVwMbJeFxrgeyVEYQvd5gCTwE0G7qOB/oTm9EBWgVOFVQ60wQVIZ/gOJzgKp0BYWiB0gB2cW+yaQH1jYBMQTa2kNjoJuWmgnyizANBGB6WlBIovl2uecugFrhxuobxZT4gMWf4nCGw2TsWS0Uby7KgyDSVtUigUTc7YRyPx4PDqWeTFGVnik/l/0kC/V0fUa5gwtF5Jz8dYJCzNRyyAxcecHIKRu69sxyzSJQmTj9zY5YhVXaQcPf6ZvHF19NFHHZWtIRphZIYLWUyAwMlNFYi2TMjhfrCDgSUwPZtDc4DdpGAAkRz6v48GDg530Ou0gP1aOqfVPrLPVj4r3hZ+X+Q9T+5XCVfHW1gOmhwpZMjc0N4dbfyYi9ho4WjzEJs8LKtjcB2Gj7WcLGnLgP2eSAKfpNNXNICHo8l8GI20cSOy7zv+PT8urbnBOWfAt03h4a5J7l4VqAdA3UaqtdiShLRaWULTDiBioXtOSBrB6pdESJqmWlsLXZxEBzvYx+7lnpk89NC2bwiUQ2Bn6wA7SBengFITQIc7AKQnbFmsly6ysuRmB5fxzdOHxsW/i3RCxicAM/EAeUpI+dYIhyQfofKZ1vQy1MU2Ijb8ofJj8/PK0Zo+a1YH6nTaSycJxvVCnqWIX9OArUOjpIUwAzmtiYxeABCX0am10a/VMdyqJq7fwbCBbqdlkm47cCciTASCtL7kIM8IlodQ+0chvIzYoAZbD7+XCKM2OkmGxQhVtNA1k5MC2MMlnLCfF4Mse8m3F5iFw8d2MaoYi1g/MOFhLfTQRRMNDNBGG1UMLTHNem4ohBJAQoqI3PDKZ/S5az02HbQTUjNEFd0rTUNqqCQwvevSMKCRnBjZcDSN7CxDbDjR4CSAjsm/k7/TyI3WboCFZsAVlb6QNIckBclkrENUUcEIfTSS0qgmh2pkn5UpLjDY6KNV6yKIJ6hERwgnJul3aq9jWAUmofHSpM+pYr0zdVYs4CQjNRSetmfC0YZ7ZnLOC5upMYdIDa35s+T3ij8jurcUlkb3vcmWyK5btbynmQNNjhQyZD7EfqLOoRGbmRrWMqGCfB95Aq7kz6vU8g6pnYN7cOR7xD/TWK1N1CnT8BcZ233CjG/Xol2kNUXCRp6wZ0ohruQ/Ob15xXh1d4CdQ+DWS+bqWjDzbZ4CTJ6eLUBydaeceHoTnSaCuH5BcEinaMLx2OwOD1C6hJTcREgmnT69fQUHp8xEo+dxBrXTlxHtbrOS0Fx3lVXTYixXunW9dJGVJTd1DNBo9tFrbrhhaTWweEK62Xkm+DySwz012ray+D0HzWovjyvZMgkOjfH6Op22H+G78chmCVjuFg7FegBEIqysJ5YacNTZQNQMnFl7h1HVhOZoylwEZK07vvaJXcTukzhIvDf5yLu3YebxVjBEgDiJ/KdiAg0MkFblMiFrhggtM9GNbyRdhL09jXEFGO9vohMHwC4wCdLcmQH6STjaENWkyp303BCIqHJyQ54bKhKQ8dgMW2mZ56jsEngfudFIggxH0xStZV8J+XvtlePkh+8zi9zkKYPyON4lxGhYSSYElh6bCoY2DJQIaWzypIIA1WCESnXonJJC2ygvjkIMR6iySmjZsLR97GJgS3/3YQwzR71GqsTQWj7jjFxT7ockmJzD8Hu+jAhxbrTcVmA2xv53U8PCxCYW/x8XuH6x7LPmOgwfj6mt3GAsCwXQ/3J+m2tpD7VJQt5D/v+M8Dv2PPm8ZH3Kh9lCMpHv9qX0ircB7FCFRVtlsR+kc5w5stBL5sJ0FSLJIKxghEp0ZOTHIVuApPR081QawtZoDhCRnuyQG2otz/cpwtJuKNq4guZGF732LtAuuS63iHtVZKwp79Aa+aD/uXdGEhz5YvhAv4nZ/z7pN4/1gSNPCMxwr87EPBYT6T6VQo6EFwA0HCKTeG7I+gj7OaqhFwcAEY2oaip7dOBaORLrpoynldvEd5nBxyQjT2IbXhQHQOyzWEjP3IB9FvfaenBCm3hOYWhNdNGwZGaECioYoY2nMEGANjpJaNRiIDOutr3ATHwbQGDI9eVeA912F41mH91qCw300UULFYzQQjchN6YU6MiZaZoUYoCTm3oSpkaKdi6p4cRfGgNiZMmNJAySSCxCcPLsP/x3/HXTLNG0bx658bVTtkUjStoSmcksh1WjcHCPjfk/nVeCvGsmxLCRPEuCVhxigiBTAIKTnDTnJg1D6/caOLqwkYagdZAlOIuSG1oSI579fRPp+7GBJaHJkUKGzIexv8/NIjiLnOOaQA3RCgpcq/JJ+gJ1Ws0TALiVyqS3hi/atfLx13fz8q5jSaLIZViEJIeOKmDiFMxcN2cBTIDbLwFXe8DVCXDrFlDehtnHLlxeOHJAzTUqp5dr+z1lEDbQR/kQxk1E8+hQaFoTwGVgd3iAdtWeMejgcvsW5rmh6Capv9IFLzvp3vroIitLbrbQwUl00Nl9yrjbdsEm8wQQb8K8LFQak7w4chSVnhvNW8PJDK+UFrqHorWjSJTY73jMKvfeLIrjsG7kHZdDnmNWaBqQXhsJOTtrcQ/u4Aykg3QI29HLQGg7HA3eHXjC0rTRg4iWEGLSamyPNa4ZYR+Escnt8SlczvFpHbrbhYKYRubGSYWtKoZooJ9YX1poYYIQbXQwzEzxPg8G0GcTXk1X8MrhMQBHsKS7hHFzE1fam7jSbqPW7KO11UvC0gJM7LMbJd4bIJ1xmp42KdC8Shf57XqHLQyjKsadlukTHbhKLn+/faRG89LQ+OFTsDRlWfvfR3IkOaLzeeVeTju1dnObE9jv88LS2LGHURWV2giDwMzQ3U9Kyw+TJOAhqhjYvBuyv5IHFQDrqWHy7AaoO4UfiNzwuWto6UzaJmfqwnZKaviaZJnvec9Sijm54XK0htRwtHQBJE2OFDJkPkTpuyzHKN6f8shO5uW/lrHdF00CuB4VLRR+XsjOznNwtXPGbC1DsGyERybnhKARm1l5xb5r8+lbXO9jv6GJyCMAHaB32EJno42L2MMODjA+B0MyngVTffUQ2IlMiBp2YDjEs8zynVNNXMSe1VzbIiyNrl0+g5LzDvHSKTS/juq5OQA2Do7QPktnewrl3asYtzctueE6LN1THlq4bP7v+ugiK0tukunupLuN1h3pXaE6Fhya50YjNqHyXQiH3fosmM7x+f98gixnZ/gFxfXOsbkWcGvOAEDDVdpoMCbC00FKePhgoFmyk0HCJwwJTKgKC0yqRIQYRxUzeznVnZ9JbBbvFlwomfh/IwQa6GOEig1gWmaWG5+l67iJ702KA6QeRLJ6RQCiGqJaDaOoikptiOGWCUvro44weYZxRjkGUu8AD0akOWuSYgGdkq7kync9z9uhKcScLOQqUwzzeGJ4n4TyHT+X1iYfydHEnzz+rO9iYBxVMIoqiDcCS0qMUauBhn1GceJdMyGH6TMk8O95cQgiNTxXKpmM04am9ScNdPbbaRiaVYZmEldNrmn3RSrJobJ/Daktb2FocqSQIfPBKm/ae67dQu9tlUr89Rzvx2K9KGK4yf1ckMhkfr5oN4n/f63E5hrfYaE39HsN9DfSft/ZauIZp3rGMxMDOANDOg5h4tK2YLw6jtemaQoJ8b6fJ+jY5gknD7HYh0rbR0gmF61giDr6xtvT7ONKbTONlkED2ZxxekYaSZmF9dJFVpbcNHHoutu456YHoEOem01krfnAbG8NX/PkKyI7pfQwfOFIrJn04nCXMOUEZXZm8AmaVXxZNIHCrTQAenbkJasjfSaFjHtxaDspCbRwRcALWSUlVr02psBBCWjWTJW0Xim/alHSYFrz92RsrDwzlMrUkxMnngAzp0oXPcyT9yPhs7KsprVk5fAYzK2qIXXZN9PPR/sbiGobiNrbQG2McnOAIIxRrY0QhBMEwSSrIE8CEyYVVTCx5NkpFBAjrZLVQdZLMw+pAdx+4FOK5Xvo89jQZ/k/QQ6kvu95G3zKniRh/Bh5pEojTMm9qqLfm6C7kU5iFyOwc0pVE69NF60k9y0QN4c8NhSWRjlSMQJLaqpJBTwq892fmNyaca8O7Fsr7z5SuSW9NZzoxMpaIzgccrwhL07TrpcOS9PkSCFD5kMEoA9ELPyaFiAd63yk9digGVF9oM6VR2bmIViSFJGOJOepoXOSl4Z0A0l08tpCx5fb8qwfsp3zwraVPDe2Hx/tb+BgbwcXcQotdPFN3A7c8U0843t6hswAhlj0kObaPBfAOeA8zuISTuEAuzg43FEKCsi8I8ApaiAhL58IDvPktGwQXRsdtKpdXGmfTp0AEa/4yzHLaOzDeukiK0xu0oAA7EbAbi2tmkZCBdswdSvIe6Ala+WRGk5uGkhHlLp7iDxyA9iXj45HFg422RWAbHGBayE2eUJt0XwcKeDy2iaPP4Ar6Opp5TS61DxyQ8pLB9lE3JjOxdspCSKNJn2XKLWReoqSdpRcBSRXyZAVRpRTxlQauJIoTRNhDTHFBka2jtYAzaXITQRdoETKtgIZfBtmQKghzQXjSxvmHWkDqJUxbpYxrgERvTvhFAjFCxKHSCawkx4YSWKksjvLYwO2nofMSGjkZd61ZmDVupuPiGh9SrM3yXP6zuHc1xKOQuMhQxWoYpT0twYGNucp63UzhzT7jRKPW5o0bAoKVNFFMyE3Tnhhr26qOXJPjVxrHrlY+azdK+2+awSnh8zQtBg0OVLIkPlgJ6SMG64M6Ynd8rw5GSPrIsjzbvjCxEgX4R1tUc+d9pLSMWjc5+cfsH25N2cRUqPpLrNITp6+wu+7hD0eIzfYB/av7ODJrbNoYIBv4nZMEOAZL/4r470JkZKLbRhjw3OBy+dqeBzncB5ncYAd9PbbqV7jbeM4bYaVDRTm3Ec9nWC4Zs8nDUMRknzfhp2SwikJfaEFY/y/yn5M5HOZKJL10kVWltzwRO1as4+oWcsqJr0SzMMDMjkyCeYhNjJMLcdrw/uZo2uXxE6c6FA7ZrHleYhNHq610MA8x8/LvWHhaTQYU+fuwPXm0L0jRZArB85tmHVPrEWIKxPSgkrn05QNB1wIKhYPobxNEForcEpsaB1ObL5GENuk5iGqSyXx+QaHa31Xnia4DHOrasjKD1IaiXTTPtxSHpaQ5Ijx/i4VVvosyUweuZHK7jKk5lrIjCYqJNGQa0k+gKzC7msrFxv0v0ZyvGSnjGFUQRBO0A+Mlzgt/2xCzdJCEKkhgc9bwct5UxEIWmdIjVbdLu9ZLrP47hO/V/RuhjA520tBO2EhQ+aD9dxgbN4J/ty5fjBLj88gx4iWfM/hIwJS6yWDJR+rCXk5KfMYNXlODz8fJzSah0Kei3d+DTI64zggLdQiX9cSnGj/JDpbbRxgBxexhwpGuHBqCzvVKyhHSMmNrZB2+VwNT1pSs2/LxaPDisgAyB/H04iQkQ2XHaGKaQ0o2Tl2UEM6oSj9bEIGVDKzjtz0jUQnFXlGKtGdB+uli6wsualZJtpGB+2tDi7sbgOnkXpudu2OvW2knZhbEni1kLqy5lYUTnI8xMZndXQIDpGjsfghd+lej5eDH89nJeJt8bVr1jnkPe+aVdRIw3KANN+BWyBr7BBcacgIBAkuSOmztURQ5bUmTOgIfU3KgWZljflxAfcZiuvlSl3PVFbpJbPTN5KytDSzcTiZJIUG6uijhqO8G+qBT5tZWst5euHbMASHC/0a0tmcm+z/UOyTeG+Q1R9oLUmKJDXa4iM1nNzIcwFZGcR1BN5GaUTIIzMcksTwz7PIzDLiw7edn4/3WQBRp4VRVAV2gX5QTwhNF02EDo2ZOAYH12OTrgeHdcRxYKraxSGSiVUXWWYRF3lNzrXTxIeyiIx9WHHZtCtaxjDCocmLQobMhwGMEGkA+3uufOCPS/Zr8O+kYVUaCeV4k+et4f/ndWquA3Gy4QsRK4t9+P80znJwIkX/y1ybPIJD59T+l+3i55wXOYbK5Fg2XL0DozM8AaBZwqPPvgMxApzCRXTsXGg7Wwc497LH0Zj0Ue+NMWiW0Q8aeATPxiXs4Zu4HY/jHC4e7Jn5/TqwciuP6I1TI0oHTgGTp7Zr2N6JDImiMUWQ6aqlQmmUE1LPTQ1AtAnz7vIIGy3KaR6sly6ysuSmgYjND9LFhXYEtGspuWnDPnDKsOzDHfG5/z6P1HByIwoIaHdHKhQquPDihQW4Qn69wAUcQWsDZ2xcIGrHA1zBx2crtt/R7LxkeQTcsDQ+MEjLaAyYQV8T+HIbKzkZNbLkhi6JDz5EojJKWVn5zO6F+H3vsIX+Bk3baYLPqhhihIopKBCkE0FS2ejFEUEv27iaruCVw3AK0DQnXCnpwCU1bbieG05uOFkAsoq/z4OjrSWp0YiED5rMCcV3vgXKbwl0XikWtHZq/6ugsIcllXKN5ABArYyjOEQ3nKBaq2CyEaqluwlOns2kmuRKRb0GEAdAr5wlo/JZ5l2/hFA+8sEVQb7m4MrfPN5/HzQ5UsiQ+RDBjDFXAeylIUxN+zXvY953owxX76CFnqlmlJ1FAOYhOcddREJenEag8kgNx6zr4ueE+G5WmL92LOkNsiFaNNfevl0uAN/51hngmcA3cAldtBJDexctNII+6lv9JKT1m7gdB9jB4ziHJ3HWTOq7D1EpTWubyBfuAd1hC91qOr/W9vaFNNeHQtOq6WWlRYyGqGCYjU6IWnAn86TrXqbvr5cusrLkhqpO0SSIzXYXvXbNjZWPYRhy3EBaWADQFXcZega4IWlKZbS8zyqkJkHnOy5cj8fFR+W8wVTmw8j1prE6duAqjDH7DLiKRCIM6At+fM2SxS1FNiSOBp42UksH/4kkUZmBSNMGWWwss9gOoyr6GxQ8aQhOHYPEmjxhz8u4jZetlqYNSNeTIK8znkKiVFBICRHumrKmd5W/sz5yoCm8nMTMqyzP+yhjZImWJnKkxynPECPPLYlWHqnJtFt7v23CrMSsNmnnBuz9LWEc1jGOKohjMzHwIJwkBSAAYDKxXhtZ+CGqGi/ILI+aJh+0ds0io3zt7E/Gmxiu0uEjOHSQZfL2Midn2wrMxhgmKmETQD9Vhml8keOZHLocosuNqzQYgu2QR1zyvDnyszRqLlNhjIei8fPN0gmkh0jL6Z2XwMlQe824Kv/XwvY0PYyRC3qe+7D5KmV8JzyF87ecxQSBLbfcSSZ9pkJBI1TxOM6hgzb2sYt97LrEJgLyBcU4HTd6plpbt9q0ZzuJqzsH2NwZm26/gfR9YiFq6TQULCytCftONpAlv4sMOhzrpYusLLmpJ2FpT6GNDnY2DtA73QZuLaekJgSbI2UHxntDLzl/0X3haYLU0CYurGhbHhLhVYJLorjwkhYH+uwcYMZJgPxEQijHkUJAIyXa9lnxt9Jzo2kAdeMWpU5OSiNXGOknXMBgCj0Wmd83stjYZE9cNl/t76S7MqHhDD4dtt0xOtBza0BVBDgJ2wfGFzbRabexH+ygghE6aCPAxPE4UmiMIevLkBtfTPZqVihZPXwLqVywVRHjMtBpIen7ktRwDw+XBT6DouaN6Yn/VYvuVBwoz/pYSs9F7zYXLby9kpj5iIRGXPgxfcp8xlBHoVUaZgjPWW2L2H4x+2zny4pqNXsM1rdidq9o7SMz/Dnx38wSx/xeAe54wdvPvdUZRZeIDZ/8cJble1lyo8mLQobMhx6M16YO4CIQ3w7sl7JhafR/BPf9CekPjS+h+D/ZSaDs2SfPK+Eb0wH3vZrXo0Mvurwg2r5MqL3UXfJC02QUCTdgy3C1WeSP/ufCbADEm0YnuIDUG9cEsF/D/xe+BOf3zqKPBlro4nGcS+I0yCP8JM6iixYexvNw6eKeCW17AiIszXftA6eYwfjCJp7cOYs9XMLjOIdqMMSLbvtb463h48cWgKoJuaXCRS10cWL3EEftDUPQdgE8sQ3j+tmEW8VuGUKyXrrIypKbAEfJbK0UT9jc7aC3+ww356Zt1xFgPDjc/UaQ5EYhBnQn8khN7PmcAbWBPvOOJy0M8xCceR6T7Px8+zzeI83qI0mMdlwfWeJtqpvwDyCVifw+U6clRUMd5DVByhMXbUWQeCdbKS0W/xOxyVhf5cBD29i1cBLWATr7bXT2TqKBQUJu2ugAgBP3vzx8Lt/VdAWvHnowZi4efkr9MwSwCUShseaHSImwj9xIQ6um+GvrBJy48/db9v0QbsVFFjariQlulJEkR1WskX3/OVnL+93cyDEeyW7G76tGpBLZwNrjHIOdRx6DE5g8Tw39VmsvxDoU+2i6n3YfnUZybw2F15IXWmsMkMZYLgpNXhQyZD7QM7mKxJAW2XEGcI118t1LUEI2LD4WP6RzyVBEPhb5lPY8HUEaT3wEZ9bv58W8+8tOBOgkjetP8rN2LAInh5oVhfqfyLsJYchJBBy1N/CdzgaC58dooWvC0mycBhUyuYhT6KKFC98+C1yomWN0wGQWF1whVGMzIzj72MUBdnDJlqN+3pm/RTmACUuLYewbtphBnBhPzdQTldoQUXMjnfaAG/WS+5B5OefEeukiK0duplNjgRtejTBBDyfwFE7gCqr4DqpooIcqUIF5VhWYBziBkTk9+zmxiPo6L2OagVhPYe7KCZiZzUO7ljiy+9KSgARhZNc9u+3QrifIJt1xaKOr7AwhO+kR3E48YZ/5hUkiQu2gQVb7H5gtCLln7BDmwRwinU6bRviW2Y90TdIt+SUnl34VKYug+8ZnPY7YdXbtvaAXoW5yb67CNdKwOFVESCuejOzPk9CDkV249++E/e6qMbI+BTNL8EXgaGOKXv0IVzDFd1ABEKCJMmIEGLPuFWGI4VXzLOgdnw/0/kgsq+Q8PZDe44swD5wTnBrScDUqJc8SyntIQwDovdHGRiCrzAzZOm0N0n7Ftei8vkX9ik4s5t+awnT9E3ZTie0a2OWEXah7luDqBSfYEsCVYxO7HLFFS4Pzem3oZjHSIdt1ZM9B987qGRjZ7w/Zd8QJR3Cfh2aAks+CxEWPbY/Y91wMT5CKTHkeCgWZsO8n7Hcldl0B27/Mrjm5xyRvBmzNmRaXP1wJNmPXYjIE0OVIIUPykN7jQ5jIgCmMQLDols0zrbubnXeO3gUAZvCgl28M8zJzSzp/CfngyPOHtVAx2GP5IC0xXPfg//N9CUOkHYDaRe8jLyakkTR+TtkWfhxJ8qTOk3fMCfzyU7aXjkFKXSf9Lm6YbkiTPj8G89gbAC4CF8ZbeGrrBKKtMcpooGHl+AQncIAWomEDeGQEHIyMB+iy/f0AMAcmPYZeDPLYdgBcTV+x88CVq8B3UMGTaKCCTXyztInt2lWUyQtcQiIeulePcIgJYnusyugAEUJz+YkBuWSvl84f2/bc3LpIabq4lPyu4oknnsC5c+dudDMKFPiu4fHHH8ett96au08URbjjjjtw4cIF7z6nT5/Go48+ilpNxsQUKORIgZsZ88gQYLYcKWSIH4UMKXCz42bWRVaO3BwdHeHhhx/GC1/4Qjz++OPY3Nyc/aMVwdWrV3Hu3Lm1avc6thlYz3ZPp1N0u12cPXsWJ05ok2G5iKIIo5HfIlepVFZKmKwS1lWOrON7DRTtvl5YVIYA+XKkkCF+rKsMAdbvvQbWs83Aerb76aCLrFxY2okTJ3DLLbcAADY3N9fmZeFYx3avY5uB9Wv31tbW3PvWarWVExjrgnWXI+vYZqBo9/XAIjIEKOTIslh3GQKsZ7vXsc3A+rX7ZtdF5jP9FChQoECBAgUKFChQoMCKoyA3BQoUKFCgQIECBQoUuCmwkuSmWq3i3nvvRbVanb3zCmEd272ObQbWt90Frh/W8R1ZxzYDRbsL3JxY1/djHdu9jm0G1rfdNztWrqBAgQIFChQoUKBAgQIFCiyDlfTcFChQoECBAgUKFChQoMCiKMhNgQIFChQoUKBAgQIFbgoU5KZAgQIFChQoUKBAgQI3BQpyU6BAgQIFChQoUKBAgZsCK0luPvjBD+L2229HrVbDnXfeiS996Us3ukkJ7r//frz85S9Hq9XCqVOn8PrXvx4PP/yws89rXvMalEolZ/npn/7pG9Rig/e+972ZNr3gBS9Ivo+iCPfccw92dnbQbDbx4z/+47h48eINbDFw++23Z9pcKpVwzz33AFjN+1xgNbDKMgRYTzmyjjIEKORIgeWxynJkHWUIsJ5ypJAh64eVIzf/+T//Z7zzne/Evffei//zf/4PXvrSl+K1r30tLl26dKObBgD47Gc/i3vuuQd/+Zd/iT/7sz/DeDzGD//wD+Pw8NDZ75//83+OJ598Mlne//7336AWp3jRi17ktOnzn/988t0v/MIv4L/9t/+GP/qjP8JnP/tZnD9/Hm94wxtuYGuB//2//7fT3j/7sz8DAPzET/xEss8q3ucCNxarLkOA9ZUj6yZDgEKOFFgOqy5H1lWGAOsnRwoZsoaYrhh+4Ad+YHrPPfck/08mk+nZs2en999//w1slR+XLl2aAph+9rOfTbb9vb/396Y///M/f+MapeDee++dvvSlL1W/63Q603K5PP2jP/qjZNtf//VfTwFMv/CFL1ynFs7Gz//8z0+f/exnT4+OjqbT6Wre5wI3HusmQ6bT9ZAjN4MMmU4LOVJgPqybHFkHGTKd3hxypJAhq4+V8tyMRiN8+ctfxl133ZVsO3HiBO666y584QtfuIEt8+PKlSsAgO3tbWf7H/zBH2B3dxcvfvGL8a53vQv9fv9GNM/B17/+dZw9exbPetaz8MY3vhGPPfYYAODLX/4yxuOxc99f8IIX4LbbbluZ+z4ajfAf/sN/wFve8haUSqVk+yre5wI3DusoQ4D1kSPrLEOAQo4UmA/rKEfWRYYA6y1HChmyHghvdAM49vf3MZlMsLe352zf29vD3/zN39ygVvlxdHSEd7zjHXjVq16FF7/4xcn2f/SP/hGe+cxn4uzZs/h//+//4Zd/+Zfx8MMP44//+I9vWFvvvPNOPPDAA3j+85+PJ598Er/2a7+Gv/t3/y6++tWv4sKFC6hUKmi3285v9vb2cOHChRvTYIFPfOIT6HQ6ePOb35xsW8X7XODGYt1kCLA+cmTdZQhQyJEC82Hd5Mi6yBBg/eVIIUPWAytFbtYN99xzD7761a868aIA8Na3vjX5/JKXvARnzpzB3//7fx/f+MY38OxnP/t6NxMAcPfddyef/87f+Tu488478cxnPhN/+Id/iHq9fkPatAj+/b//97j77rtx9uzZZNsq3ucCBRbFusiRdZchQCFHCtycWBcZAqy/HClkyHpgpcLSdnd3EQRBpjLGxYsXcfr06RvUKh1vf/vb8Sd/8if4H//jf+DWW2/N3ffOO+8EADzyyCPXo2lzod1u43nPex4eeeQRnD59GqPRCJ1Ox9lnVe77t771Lfz5n/85/tk/+2e5+63ifS5wfbFOMgRYbzmyTjIEKORIgfmxTnJknWUIsF5ypJAh64OVIjeVSgXf//3fj09/+tPJtqOjI3z605/GK17xihvYshTT6RRvf/vb8fGPfxx/8Rd/gTvuuGPmb77yla8AAM6cOfNdbt386PV6+MY3voEzZ87g+7//+1Eul537/vDDD+Oxxx5bifv+kY98BKdOncKP/MiP5O63ive5wPXFOsgQ4OaQI+skQ4BCjhSYH+sgR24GGQKslxwpZMga4QYXNMjgP/2n/zStVqvTBx54YPrQQw9N3/rWt07b7fb0woULN7pp0+l0On3b29423dramn7mM5+ZPvnkk8nS7/en0+l0+sgjj0zvu+++6YMPPjh99NFHp5/85Cenz3rWs6avfvWrb2i7/+W//JfTz3zmM9NHH310+j//5/+c3nXXXdPd3d3ppUuXptPpdPrTP/3T09tuu236F3/xF9MHH3xw+opXvGL6ile84oa2eTo1FWpuu+226S//8i8721f1Phe48Vh1GTKdrqccWVcZMp0WcqTA4lh1ObKOMmQ6XV85UsiQ9cLKkZvpdDr9nd/5neltt902rVQq0x/4gR+Y/uVf/uWNblICAOrykY98ZDqdTqePPfbY9NWvfvV0e3t7Wq1Wp895znOmv/iLvzi9cuXKDW33T/7kT07PnDkzrVQq01tuuWX6kz/5k9NHHnkk+X4wGEx/5md+Znry5Mlpo9GY/oN/8A+mTz755A1sscGf/umfTgFMH374YWf7qt7nAquBVZYh0+l6ypF1lSHTaSFHCiyHVZYj6yhDptP1lSOFDFkvlKbT6fS6uooKFChQoECBAgUKFChQ4LuAlcq5KVCgQIECBQoUKFCgQIFlUZCbAgUKFChQoECBAgUK3BQoyE2BAgUKFChQoECBAgVuChTkpkCBAgUKFChQoECBAjcFCnJToECBAgUKFChQoECBmwIFuSlQoECBAgUKFChQoMBNgYLcFChQoECBAgUKFChQ4KZAQW4KFChQoECBAgUKFChwU6AgNwUKFChQoECBAgUKFLgpUJCbAgUKFChQoECBAgUK3BQoyE2BAgUKFChQoECBAgVuChTkpkCBAgUKFChQoECBAjcF/n+GhNSLO2YDAgAAAABJRU5ErkJggg==", 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" ] diff --git a/tutorials/02-autograd-and-optimization-jax.ipynb b/tutorials/02-autograd-and-optimization-jax.ipynb index 9d92bc1..5f42bc2 100644 --- a/tutorials/02-autograd-and-optimization-jax.ipynb +++ b/tutorials/02-autograd-and-optimization-jax.ipynb @@ -12,24 +12,14 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yongha/miniconda3/envs/meent/lib/python3.10/site-packages/jax/_src/api_util.py:190: SyntaxWarning: Jitted function has static_argnums=(1, 2, 3, 4), but only accepts 4 positional arguments. This warning will be replaced by an error after 2022-08-20 at the earliest.\n", - " warnings.warn(f\"Jitted function has {argnums_name}={argnums}, \"\n" - ] - } - ], + "outputs": [], "source": [ "import jax\n", "import optax\n", "\n", "import jax.numpy as jnp\n", "\n", - "import meent\n", - "from meent.on_jax.optimizer.loss import LossDeflector" + "import meent" ] }, { @@ -97,6 +87,25 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [], + "source": [ + "class Loss:\n", + " def __call__(self, meent_result, *args, **kwargs):\n", + " res_psi, res_te, res_ti = meent_result.res, meent_result.res_te_inc, meent_result.res_tm_inc\n", + " de_ti = res_psi.de_ti\n", + " center = [a // 2 for a in de_ti.shape]\n", + " res = de_ti[center[0], center[1]+1]\n", + "\n", + " return res\n", + "\n", + "\n", + "loss_fn = Loss()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -118,7 +127,6 @@ "\n", "pois = ['ucell', 'thickness']\n", "forward = mee.conv_solve\n", - "loss_fn = LossDeflector(x_order=1, y_order=0)\n", "\n", "# case 1: Gradient\n", "grad = mee.grad(pois, forward, loss_fn)\n", @@ -152,14 +160,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 3/3 [00:05<00:00, 1.90s/it]" + "100%|██████████| 3/3 [00:06<00:00, 2.12s/it]" ] }, { @@ -189,7 +197,6 @@ "\n", "pois = ['ucell', 'thickness']\n", "forward = mee.conv_solve\n", - "loss_fn = LossDeflector(x_order=1, y_order=0)\n", "\n", "# case 2: SGD\n", "optimizer = optax.sgd(learning_rate=1e-2, momentum=0.9)\n", diff --git a/tutorials/02-autograd-and-optimization-pytorch.ipynb b/tutorials/02-autograd-and-optimization-pytorch.ipynb index fdf789c..00588fc 100644 --- a/tutorials/02-autograd-and-optimization-pytorch.ipynb +++ b/tutorials/02-autograd-and-optimization-pytorch.ipynb @@ -21,7 +21,7 @@ "import torch\n", "\n", "import meent\n", - "from meent.on_torch.optimizer.loss import LossDeflector\n", + "# from meent.on_torch.optimizer.loss import LossDeflector\n", "from meent.on_torch.optimizer.optimizer import OptimizerTorch" ] }, @@ -41,7 +41,7 @@ "n_bot = 1 # n_transmission\n", "\n", "theta = 0 * torch.pi / 180 # angle of incidence\n", - "phi = 0 * torch.pi / 180 # angle of rotation\n", + "# phi = 0 * torch.pi / 180 # angle of rotation\n", "\n", "wavelength = 900\n", "\n", @@ -122,13 +122,17 @@ } ], "source": [ - "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, thickness=thickness, type_complex=type_complex, device=device)\n", + "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, \n", + " thickness=thickness, type_complex=type_complex, device=device)\n", "\n", "mee.ucell.requires_grad = True\n", "mee.thickness.requires_grad = True\n", "\n", - "de_ri, de_ti = mee.conv_solve()\n", - "loss = de_ti[de_ti.shape[0] // 2 + 1]\n", + "result = mee.conv_solve()\n", + "res = result.res\n", + "de_ri, de_ti = res.de_ri, res.de_ti\n", + "loss = de_ti[de_ti.shape[0] // 2, de_ti.shape[1] // 2 + 1]\n", "\n", "loss.backward()\n", "print('ucell gradient:')\n", @@ -149,6 +153,25 @@ { "cell_type": "code", "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class Loss:\n", + " def __call__(self, meent_result, *args, **kwargs):\n", + " res_psi, res_te, res_ti = meent_result.res, meent_result.res_te_inc, meent_result.res_tm_inc\n", + " de_ti = res_psi.de_ti\n", + " center = [a // 2 for a in de_ti.shape]\n", + " res = de_ti[center[0], center[1]+1]\n", + "\n", + " return res\n", + "\n", + "\n", + "loss_fn = Loss()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "collapsed": false }, @@ -169,16 +192,14 @@ } ], "source": [ - "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, thickness=thickness, type_complex=type_complex, device=device)\n", + "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, \n", + " thickness=thickness, type_complex=type_complex, device=device)\n", "\n", "pois = ['ucell', 'thickness'] # Parameter Of Interests\n", "\n", "forward = mee.conv_solve\n", "\n", - "# can use custom loss function or predefined loss function in meent.\n", - "loss_fn = LossDeflector(x_order=1) # predefined in meent\n", - "# loss_fn = lambda x: x[1][x[1].shape[0] // 2 + 1] # custom\n", - "\n", "grad = mee.grad(pois, forward, loss_fn)\n", "print('ucell gradient:')\n", "print(grad['ucell'])\n", @@ -215,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -236,18 +257,21 @@ } ], "source": [ - "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, thickness=thickness, type_complex=type_complex, device=device)\n", + "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, \n", + " thickness=thickness, type_complex=type_complex, device=device)\n", "\n", "mee.ucell.requires_grad = True\n", "mee.thickness.requires_grad = True\n", "opt = torch.optim.SGD([mee.ucell, mee.thickness], lr=1E-2, momentum=0.9)\n", "\n", "for _ in range(3):\n", + " result = mee.conv_solve()\n", + " res = result.res\n", + " de_ri, de_ti = res.de_ri, res.de_ti\n", "\n", - " de_ri, de_ti = mee.conv_solve()\n", - "\n", - " center = de_ti.shape[0] // 2\n", - " loss = de_ti[center + 1]\n", + " center = de_ti.shape[1] // 2\n", + " loss = de_ti[0, center + 1]\n", "\n", " loss.backward()\n", " opt.step()\n", @@ -270,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -279,7 +303,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 3/3 [00:00<00:00, 145.39it/s]" + "100%|██████████| 3/3 [00:00<00:00, 169.86it/s]" ] }, { @@ -305,15 +329,19 @@ } ], "source": [ - "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, thickness=thickness, type_complex=type_complex, device=device)\n", + "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta,\n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, \n", + " thickness=thickness, type_complex=type_complex, device=device)\n", "\n", "\n", "def forward_fn():\n", "\n", - " de_ri, de_ti = mee.conv_solve()\n", + " result = mee.conv_solve()\n", + " res = result.res\n", + " de_ri, de_ti = res.de_ri, res.de_ti\n", "\n", - " center = de_ti.shape[0] // 2\n", - " loss = de_ti[center + 1]\n", + " center = de_ti.shape[1] // 2\n", + " loss = de_ti[0, center + 1]\n", " return loss\n", "\n", "pois = ['ucell', 'thickness']\n", @@ -343,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -352,7 +380,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 3/3 [00:00<00:00, 196.12it/s]" + "100%|██████████| 3/3 [00:00<00:00, 163.67it/s]" ] }, { @@ -378,13 +406,14 @@ } ], "source": [ - "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, phi=phi, fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, thickness=thickness, type_complex=type_complex, device=device)\n", + "mee = meent.call_mee(backend=backend, pol=pol, n_top=n_top, n_bot=n_bot, theta=theta, \n", + " fto=fto, wavelength=wavelength, period=period, ucell=ucell_1d_m, \n", + " thickness=thickness, type_complex=type_complex, device=device)\n", "\n", "pois = ['ucell', 'thickness']\n", "\n", "forward = mee.conv_solve\n", - "loss_fn = LossDeflector(1, 0)\n", - "\n", + "loss_fn = Loss()\n", "opt_torch = torch.optim.SGD\n", "opt_options = {'lr': 1E-2,\n", " 'momentum': 0.9,\n", diff --git a/tutorials/03-device-and-datatype-jax.ipynb b/tutorials/03-device-and-datatype-jax.ipynb index cfb89c1..e1e114d 100644 --- a/tutorials/03-device-and-datatype-jax.ipynb +++ b/tutorials/03-device-and-datatype-jax.ipynb @@ -183,27 +183,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -241,27 +241,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -313,27 +313,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -375,27 +375,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] } diff --git a/tutorials/03-device-and-datatype-torch.ipynb b/tutorials/03-device-and-datatype-torch.ipynb index ef2b106..9f07209 100644 --- a/tutorials/03-device-and-datatype-torch.ipynb +++ b/tutorials/03-device-and-datatype-torch.ipynb @@ -188,27 +188,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -250,27 +250,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -312,27 +312,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] }, @@ -374,27 +374,27 @@ " thickness=thickness, device=device, type_complex=dtype)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti = mee.conv_solve()\n", + "mee.conv_solve()\n", "print(f'time for efficiency, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "field_cell = mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.calculate_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for field, 2nd: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 1st: ', time.time() - t0)\n", "\n", "t0 = time.time()\n", - "de_ri, de_ti, field_cell = mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", + "mee.conv_solve_field(res_x=res_x, res_y=res_y, res_z=res_z)\n", "print(f'time for efficiency and field in one step, 2nd: ', time.time() - t0)\n" ] },