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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4314b34d",
+ "metadata": {},
+ "source": [
+ "# Joint optimization of grating coupler geometry and Gaussian beam parameters\n",
+ "\n",
+ "Grating couplers are commonly used to couple light between an optical fiber or free space beam and an on chip waveguide. For background on grating coupler modeling in Tidy3D, see the [uniform grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingCoupler/) example and the [focused apodized grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/FocusedApodGC/) example. For a closely related optimization workflow, the [inverse design of a grating coupler with the adjoint method](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin6GratingCoupler/) shows how gradients can be used to improve grating coupler performance with a freeform design region.\n",
+ "\n",
+ "When modeling coupling from a fiber or free space source, we often represent the incoming light with a Gaussian beam profile. This beam has its own center position, angular tilt, and waist radius. The coupling performance therefore depends not only on the grating geometry, but also on how the Gaussian beam is placed above the device. A source position, tilt angle, or waist that works well for one grating can be suboptimal after the grating geometry changes during optimization, so it may be less effective to optimize the grating first and tune the source afterward, or to fix the source first and optimize only the grating.\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "\n",
+ "This notebook demonstrates a joint approach: instead of prescribing the beam settings ahead of time, we include them directly in the design variables for a simple 1D grating coupler. Tidy3D Autograd computes gradients with respect to both the grating dimensions and the Gaussian beam parameters, allowing the optimizer to reshape the grating and reposition the beam in a single optimization loop.\n",
+ "\n",
+ "We show two related inverse design problems. First, we optimize a partially etched grating coupler together with the Gaussian beam center position, incident angle, and waist. Second, we add a lower silicon reflector grating separated from the top grating by a silicon dioxide gap. The reflector is parameterized by a single period and duty cycle, while the top grating and Gaussian beam parameters are again optimized. Both objectives maximize coupling into the backward propagating waveguide mode."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "eeed0530",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import autograd.numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import tidy3d as td\n",
+ "from tidy3d.components.autograd.utils import get_static\n",
+ "from tidy3d.plugins.autograd import adam, optimize\n",
+ "from tidy3d.web import run\n",
+ "\n",
+ "np.set_printoptions(precision=4, suppress=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b31c078",
+ "metadata": {},
+ "source": [
+ "## Problem Setup\n",
+ "\n",
+ "We work with a compact 2D grating coupler model. The top grating uses individual tooth and trench widths as design variables. The Gaussian beam source is also part of the optimization through three parameters:\n",
+ "\n",
+ "- lateral source position `x_center`,\n",
+ "- source tilt angle `tilt`,\n",
+ "- beam waist `waist`.\n",
+ "\n",
+ "The second design adds a lower silicon reflector grating. To keep this reflector easy to interpret, it uses a global period and duty cycle rather than independent widths for every reflector tooth."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "829598de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Center wavelength and frequency for the single frequency coupling objective.\n",
+ "wavelength = 1.55\n",
+ "freq = td.C_0 / wavelength\n",
+ "\n",
+ "# Top silicon grating parameters for the compact 2D model.\n",
+ "min_feature_size = 0.100\n",
+ "n_periods = 20\n",
+ "h_si = 0.220\n",
+ "n_si = 3.48\n",
+ "\n",
+ "# Gaussian beam source initialization and bounds.\n",
+ "source_z = 2.0\n",
+ "initial_tilt = np.radians(8.0)\n",
+ "initial_waist = 2.25\n",
+ "waist_bounds = (2.0, 2.5)\n",
+ "\n",
+ "# Lower reflector geometry. The top of the reflector is this far below the\n",
+ "# bottom of the top grating layer, so the gap represents silicon dioxide.\n",
+ "reflector_gap = 0.400\n",
+ "reflector_thickness = h_si\n",
+ "n_reflector_periods = 40\n",
+ "\n",
+ "# Bounds for the normalized optimizer variables.\n",
+ "single_layer_bounds = {\n",
+ " \"teeth\": (min_feature_size, 1.0),\n",
+ " \"trenches\": (min_feature_size, 1.0),\n",
+ " \"x_center\": (0.0, 10.0),\n",
+ " \"tilt\": (np.radians(0.0), np.radians(20.0)),\n",
+ " \"waist\": waist_bounds,\n",
+ "}\n",
+ "\n",
+ "reflector_bounds = {\n",
+ " **single_layer_bounds,\n",
+ " \"reflector_period\": (0.20, 0.45),\n",
+ " \"reflector_duty_cycle\": (0.20, 0.80),\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca42f658",
+ "metadata": {},
+ "source": [
+ "## Parameter And Design Helpers\n",
+ "\n",
+ "These helper functions convert between normalized optimizer variables and physical simulation parameters. The normalized representation lets the optimizer work in a unit box, while the physical representation is used to construct Tidy3D geometry and sources."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "23fb7bd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def to_norm(value_phys, min_val, max_val):\n",
+ " \"\"\"Map a physical scalar or array into the optimizer unit interval.\"\"\"\n",
+ " return (value_phys - min_val) / (max_val - min_val)\n",
+ "\n",
+ "\n",
+ "def to_phys(params_norm, bounds):\n",
+ " \"\"\"Map normalized optimizer parameters back to physical units.\"\"\"\n",
+ " return {\n",
+ " key: bounds[key][0] + value * (bounds[key][1] - bounds[key][0])\n",
+ " for key, value in params_norm.items()\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def physical_to_normalized(params_phys, bounds):\n",
+ " \"\"\"Normalize a dictionary of physical parameters using matching bounds.\"\"\"\n",
+ " return {\n",
+ " key: to_norm(value, bounds[key][0], bounds[key][1]) for key, value in params_phys.items()\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def calculate_initial_widths(wavelength, tilt_angle_rad, n_clad=1.0):\n",
+ " \"\"\"Return a simple Bragg style starting tooth and trench width.\"\"\"\n",
+ " n_slab = 2.85\n",
+ " n_etch = 2.25\n",
+ " n_eff_grating = 0.5 * n_slab + 0.5 * n_etch\n",
+ " period = wavelength / (n_eff_grating - n_clad * np.sin(tilt_angle_rad))\n",
+ " return period / 2.0, period / 2.0\n",
+ "\n",
+ "\n",
+ "def make_initial_design_phys(include_reflector=False):\n",
+ " \"\"\"Construct the physical starting point for either optimization branch.\"\"\"\n",
+ " init_tooth, init_trench = calculate_initial_widths(wavelength, initial_tilt, n_clad=1.0)\n",
+ " params = {\n",
+ " \"teeth\": np.ones(n_periods) * init_tooth,\n",
+ " \"trenches\": np.ones(n_periods) * init_trench,\n",
+ " \"x_center\": 6.0,\n",
+ " \"tilt\": initial_tilt,\n",
+ " \"waist\": initial_waist,\n",
+ " }\n",
+ " if include_reflector:\n",
+ " params[\"reflector_period\"] = 0.32\n",
+ " params[\"reflector_duty_cycle\"] = 0.50\n",
+ " return params\n",
+ "\n",
+ "\n",
+ "def compute_grating_edges(params_phys):\n",
+ " \"\"\"Compute top grating tooth start and end positions.\"\"\"\n",
+ " teeth = np.asarray(params_phys[\"teeth\"], dtype=float)\n",
+ " trenches = np.asarray(params_phys[\"trenches\"], dtype=float)\n",
+ " periods = teeth + trenches\n",
+ " starts = np.cumsum(np.concatenate([[0.0], periods[:-1]]))\n",
+ " ends = starts + teeth\n",
+ " return starts, ends"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8f3bb77a",
+ "metadata": {},
+ "source": [
+ "## Simulation And Figure Of Merit\n",
+ "\n",
+ "The simulation builder creates the top grating coupler, the Gaussian beam source, and optionally the lower silicon reflector grating. The reflector sits below the top grating across the grating region and is intended to scatter downward radiation back toward the waveguide, improving useful unidirectional coupling."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ebdab4a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_top_grating_structures(params_phys):\n",
+ " teeth = np.array(params_phys[\"teeth\"])\n",
+ " trenches = np.array(params_phys[\"trenches\"])\n",
+ " periods = teeth + trenches\n",
+ " starts = np.cumsum(np.concatenate([[0.0], periods[:-1]]))\n",
+ "\n",
+ " structures = [\n",
+ " td.Structure(\n",
+ " geometry=td.Box.from_bounds(\n",
+ " rmin=(-10000, -10000, -0.5 * h_si),\n",
+ " rmax=(0, 10000, 0.5 * h_si),\n",
+ " ),\n",
+ " medium=td.Medium(permittivity=n_si**2),\n",
+ " )\n",
+ " ]\n",
+ "\n",
+ " grating_geometry = 0\n",
+ " for start, tooth_width in zip(starts, teeth):\n",
+ " center_x = start + tooth_width / 2.0\n",
+ " grating_geometry += td.Box(\n",
+ " center=(center_x, 0, 0.25 * h_si),\n",
+ " size=(tooth_width, td.inf, 0.5 * h_si),\n",
+ " )\n",
+ "\n",
+ " structures.append(\n",
+ " td.Structure(geometry=grating_geometry, medium=td.Medium(permittivity=n_si**2))\n",
+ " )\n",
+ "\n",
+ " structure_start_x = starts[0]\n",
+ " structure_end_x = starts[-1] + teeth[-1]\n",
+ " structures.append(\n",
+ " td.Structure(\n",
+ " geometry=td.Box.from_bounds(\n",
+ " rmin=(structure_start_x, -10000, -0.5 * h_si),\n",
+ " rmax=(structure_end_x, 10000, 0.0),\n",
+ " ),\n",
+ " medium=td.Medium(permittivity=n_si**2),\n",
+ " )\n",
+ " )\n",
+ " return structures, structure_end_x\n",
+ "\n",
+ "\n",
+ "def make_reflector_structure(params_phys):\n",
+ " \"\"\"Build the lower silicon reflector from a global period and duty cycle.\n",
+ "\n",
+ " The reflector is a repeated silicon grating located below the top device\n",
+ " layer. It is deliberately lower dimensional than the top grating: the\n",
+ " optimizer controls only one period and one duty cycle, which are repeated\n",
+ " across enough periods to cover the top grating region.\n",
+ " \"\"\"\n",
+ " period = params_phys[\"reflector_period\"]\n",
+ " duty_cycle = params_phys[\"reflector_duty_cycle\"]\n",
+ " tooth_width = period * duty_cycle\n",
+ " starts = np.arange(n_reflector_periods) * period\n",
+ "\n",
+ " reflector_top_z = -0.5 * h_si - reflector_gap\n",
+ " reflector_center_z = reflector_top_z - 0.5 * reflector_thickness\n",
+ " reflector_geometry = 0\n",
+ " for start in starts:\n",
+ " reflector_geometry += td.Box(\n",
+ " center=(start + tooth_width / 2.0, 0, reflector_center_z),\n",
+ " size=(tooth_width, td.inf, reflector_thickness),\n",
+ " )\n",
+ " reflector_end_x = n_reflector_periods * period\n",
+ " return td.Structure(\n",
+ " geometry=reflector_geometry, medium=td.Medium(permittivity=n_si**2)\n",
+ " ), reflector_end_x\n",
+ "\n",
+ "\n",
+ "def build_2d_sim(params_phys, include_reflector=False, include_field_monitor=False):\n",
+ " \"\"\"Construct the Tidy3D simulation for one set of physical parameters.\n",
+ "\n",
+ " Parameters in ``params_phys`` define both the geometry and the Gaussian\n",
+ " beam source. Setting ``include_reflector=True`` appends the lower reflector\n",
+ " grating to the single-layer grating coupler. The field monitor is omitted\n",
+ " during optimization to keep the simulations lighter and added only for the\n",
+ " final visualization runs.\n",
+ " \"\"\"\n",
+ " structures, structure_end_x = make_top_grating_structures(params_phys)\n",
+ "\n",
+ " if include_reflector:\n",
+ " reflector_structure, reflector_end_x = make_reflector_structure(params_phys)\n",
+ " structures.append(reflector_structure)\n",
+ " sim_end_x = get_static(np.maximum(structure_end_x, reflector_end_x) + 1.5)\n",
+ " else:\n",
+ " sim_end_x = get_static(structure_end_x + 1.5)\n",
+ "\n",
+ " sim_start_x = -10.0\n",
+ " sim_center = (0.5 * (sim_start_x + sim_end_x), 0, 0)\n",
+ " sim_size = (sim_end_x - sim_start_x, 0, 7.0)\n",
+ "\n",
+ " source = td.GaussianBeam(\n",
+ " center=(params_phys[\"x_center\"], 0, source_z),\n",
+ " size=(td.inf, td.inf, 0),\n",
+ " source_time=td.GaussianPulse(freq0=freq, fwidth=freq / 10),\n",
+ " direction=\"-\",\n",
+ " angle_theta=params_phys[\"tilt\"],\n",
+ " waist_radius=params_phys[\"waist\"],\n",
+ " pol_angle=np.pi / 2,\n",
+ " )\n",
+ "\n",
+ " monitor = td.ModeMonitor(\n",
+ " center=(-2.0, 0, 0),\n",
+ " size=(0, td.inf, h_si * 5),\n",
+ " freqs=[freq],\n",
+ " mode_spec=td.ModeSpec(num_modes=1),\n",
+ " name=\"mode_mnt\",\n",
+ " )\n",
+ " monitors = [monitor]\n",
+ "\n",
+ " if include_field_monitor:\n",
+ " monitors.append(\n",
+ " td.FieldMonitor(\n",
+ " center=sim_center,\n",
+ " size=sim_size,\n",
+ " freqs=[freq],\n",
+ " name=\"field_mnt\",\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " return td.Simulation(\n",
+ " center=sim_center,\n",
+ " size=sim_size,\n",
+ " grid_spec=td.GridSpec.auto(min_steps_per_wvl=15),\n",
+ " structures=structures,\n",
+ " sources=[source],\n",
+ " monitors=monitors,\n",
+ " run_time=2e-11,\n",
+ " boundary_spec=td.BoundarySpec(\n",
+ " x=td.Boundary.pml(),\n",
+ " y=td.Boundary.periodic(),\n",
+ " z=td.Boundary.pml(),\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def coupling_efficiency_from_sim(params_phys, task_name, include_reflector=False, as_float=False):\n",
+ " \"\"\"Evaluate the differentiable coupling efficiency objective.\n",
+ "\n",
+ " The figure of merit is the power coupled into the backward-propagating\n",
+ " waveguide mode measured by ``mode_mnt``. During optimization we return the\n",
+ " value that Autograd can differentiate; for reporting cells, ``as_float=True``\n",
+ " converts it to a plain Python number.\n",
+ " \"\"\"\n",
+ " sim = build_2d_sim(params_phys, include_reflector=include_reflector)\n",
+ " sim_data = run(sim, task_name=task_name, verbose=False)\n",
+ " amp = sim_data[\"mode_mnt\"].amps.sel(direction=\"-\").data\n",
+ " ce = np.sum(np.abs(amp) ** 2)\n",
+ " return float(np.asarray(ce)) if as_float else ce\n",
+ "\n",
+ "\n",
+ "def field_data_from_sim(params_phys, task_name, include_reflector=False):\n",
+ " \"\"\"Run one simulation with an x-z field monitor for visualization.\"\"\"\n",
+ " sim = build_2d_sim(\n",
+ " params_phys,\n",
+ " include_reflector=include_reflector,\n",
+ " include_field_monitor=True,\n",
+ " )\n",
+ " return run(sim, task_name=task_name, verbose=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc06c046",
+ "metadata": {},
+ "source": [
+ "## Optimization Driver\n",
+ "\n",
+ "Both branches use the same optimization helper. The objective is differentiated with Tidy3D Autograd, and Tidy3D's Adam optimizer updates the normalized design parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "945eebb0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def format_progress_line(step, epochs, value, grad_norm):\n",
+ " return f\"step {step:02d}/{epochs:02d} | objective = {value:.6f} | grad norm = {grad_norm:.3e}\"\n",
+ "\n",
+ "\n",
+ "def gradient_l2_norm(gradient):\n",
+ " \"\"\"Compute the L2 norm of the flat gradient dictionary used here.\"\"\"\n",
+ " squared_norm = sum(np.sum(np.abs(np.asarray(value)) ** 2) for value in gradient.values())\n",
+ " return float(np.sqrt(squared_norm))\n",
+ "\n",
+ "\n",
+ "def optimize_params(initial_params_norm, objective_fn, epochs, learning_rate, label, print_every=5):\n",
+ " \"\"\"Maximize an objective over normalized parameters with Tidy3D Adam.\n",
+ "\n",
+ " The optimizer works in normalized coordinates bounded between 0 and 1.\n",
+ " Each objective maps those normalized values back to physical dimensions\n",
+ " before building the simulation. The returned history stores both the\n",
+ " objective value and the gradient norm for plotting.\n",
+ " \"\"\"\n",
+ " optimizer = adam(learning_rate=learning_rate)\n",
+ " params0 = {key: np.array(value, copy=True) for key, value in initial_params_norm.items()}\n",
+ " grad_norm_history = []\n",
+ "\n",
+ " print(f\"\\n{label}\")\n",
+ " print(f\"{'=' * len(label)}\")\n",
+ " print(f\"epochs = {epochs}, learning_rate = {learning_rate:.4f}\")\n",
+ "\n",
+ " def print_step(_params, gradient, _state, step_index, objective_value):\n",
+ " step = step_index + 1\n",
+ " grad_norm = gradient_l2_norm(gradient)\n",
+ " grad_norm_history.append(grad_norm)\n",
+ " should_print = step == 1 or step == epochs or step % print_every == 0\n",
+ " if should_print:\n",
+ " value_float = float(np.asarray(objective_value))\n",
+ " print(format_progress_line(step, epochs, value_float, grad_norm))\n",
+ "\n",
+ " params_norm, _, opt_history = optimize(\n",
+ " objective_fn,\n",
+ " params0=params0,\n",
+ " optimizer=optimizer,\n",
+ " num_steps=epochs,\n",
+ " bounds=(0.0, 1.0),\n",
+ " callback=print_step,\n",
+ " direction=\"max\",\n",
+ " )\n",
+ " history = {\n",
+ " \"objective\": [float(np.asarray(value)) for value in opt_history[\"objective_fn_val\"]],\n",
+ " \"grad_norm\": grad_norm_history,\n",
+ " }\n",
+ " return params_norm, history"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c4c9cae",
+ "metadata": {},
+ "source": [
+ "## Plotting Helpers\n",
+ "\n",
+ "These helpers only format cross sections and summary plots. They can be skimmed or skipped if you want to focus on the simulation setup and optimization code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "402d5c86",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.rc(\"figure\", facecolor=\"white\")\n",
+ "plt.rcParams[\"axes.grid\"] = True\n",
+ "plt.rcParams[\"grid.alpha\"] = 0.25\n",
+ "\n",
+ "design_labels = {\n",
+ " \"single\": \"Single layer\",\n",
+ " \"reflector\": \"With reflector\",\n",
+ "}\n",
+ "design_colors = {\n",
+ " \"single\": \"black\",\n",
+ " \"reflector\": \"tab:red\",\n",
+ "}\n",
+ "design_markers = {\n",
+ " \"single\": \"o\",\n",
+ " \"reflector\": \"s\",\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def plot_design_cross_section(params_phys, ax, color, title, include_reflector=False):\n",
+ " starts, ends = compute_grating_edges(params_phys)\n",
+ " structure_start_x = float(starts[0])\n",
+ " structure_end_x = float(ends[-1])\n",
+ " left_limit = min(-5.0, structure_start_x - 0.5)\n",
+ " right_limit = structure_end_x + 0.5\n",
+ "\n",
+ " ax.add_patch(\n",
+ " plt.Rectangle(\n",
+ " (left_limit, -0.5 * h_si),\n",
+ " -left_limit,\n",
+ " h_si,\n",
+ " facecolor=(0.83, 0.83, 0.83),\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=1.0,\n",
+ " alpha=0.8,\n",
+ " )\n",
+ " )\n",
+ " ax.add_patch(\n",
+ " plt.Rectangle(\n",
+ " (structure_start_x, -0.5 * h_si),\n",
+ " structure_end_x - structure_start_x,\n",
+ " 0.5 * h_si,\n",
+ " facecolor=color,\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=1.0,\n",
+ " alpha=0.25,\n",
+ " )\n",
+ " )\n",
+ " for x_start, x_end in zip(starts, ends):\n",
+ " ax.add_patch(\n",
+ " plt.Rectangle(\n",
+ " (float(x_start), 0.0),\n",
+ " float(x_end - x_start),\n",
+ " 0.5 * h_si,\n",
+ " facecolor=color,\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=1.0,\n",
+ " alpha=0.85,\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " if include_reflector:\n",
+ " period = float(params_phys[\"reflector_period\"])\n",
+ " duty_cycle = float(params_phys[\"reflector_duty_cycle\"])\n",
+ " tooth_width = period * duty_cycle\n",
+ " reflector_top_z = -0.5 * h_si - reflector_gap\n",
+ " reflector_bottom_z = reflector_top_z - reflector_thickness\n",
+ " for start in np.arange(n_reflector_periods) * period:\n",
+ " if start > right_limit:\n",
+ " break\n",
+ " ax.add_patch(\n",
+ " plt.Rectangle(\n",
+ " (float(start), reflector_bottom_z),\n",
+ " tooth_width,\n",
+ " reflector_thickness,\n",
+ " facecolor=\"tab:orange\",\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=0.8,\n",
+ " alpha=0.8,\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " ax.axvline(float(params_phys[\"x_center\"]), color=\"black\", linestyle=\"--\", linewidth=1.0)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(\"x [um]\")\n",
+ " ax.set_ylabel(\"z [um]\")\n",
+ " ax.set_xlim(left_limit, right_limit)\n",
+ " ax.set_ylim(-1.05, 0.75 * h_si)\n",
+ " ax.set_aspect(\"equal\")\n",
+ "\n",
+ "\n",
+ "def plot_history(ax, history, title):\n",
+ " objective_history = history[\"objective\"]\n",
+ " grad_norm_history = history[\"grad_norm\"]\n",
+ "\n",
+ " steps = np.arange(1, len(objective_history) + 1)\n",
+ " objective_line = ax.plot(\n",
+ " steps, objective_history, color=\"black\", linewidth=2, label=\"Objective\"\n",
+ " )\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(\"Optimization step\")\n",
+ " ax.set_ylabel(\"Objective\")\n",
+ " ax.tick_params(axis=\"y\", labelcolor=\"black\")\n",
+ "\n",
+ " grad_steps = np.arange(1, len(grad_norm_history) + 1)\n",
+ " ax_grad = ax.twinx()\n",
+ " grad_line = ax_grad.plot(\n",
+ " grad_steps,\n",
+ " grad_norm_history,\n",
+ " color=\"tab:orange\",\n",
+ " linestyle=\"--\",\n",
+ " linewidth=2,\n",
+ " label=\"Gradient norm\",\n",
+ " )\n",
+ " ax_grad.set_ylabel(\"Gradient norm\", color=\"tab:orange\")\n",
+ " ax_grad.tick_params(axis=\"y\", labelcolor=\"tab:orange\")\n",
+ " lines = objective_line + grad_line\n",
+ " ax.legend(lines, [line.get_label() for line in lines], loc=\"best\")\n",
+ "\n",
+ "\n",
+ "def plot_width_comparison(design_params, title=\"Optimized top grating widths\"):\n",
+ " fig, ax = plt.subplots(figsize=(12, 3.8), constrained_layout=True)\n",
+ " period_indices = np.arange(1, n_periods + 1)\n",
+ " for design_key, params_phys in design_params.items():\n",
+ " color = design_colors[design_key]\n",
+ " ax.plot(\n",
+ " period_indices,\n",
+ " params_phys[\"teeth\"],\n",
+ " color=color,\n",
+ " linewidth=2,\n",
+ " marker=design_markers[design_key],\n",
+ " label=f\"{design_labels[design_key]} teeth\",\n",
+ " )\n",
+ " ax.plot(\n",
+ " period_indices,\n",
+ " params_phys[\"trenches\"],\n",
+ " color=color,\n",
+ " linewidth=2,\n",
+ " linestyle=\"--\",\n",
+ " marker=design_markers[design_key],\n",
+ " markerfacecolor=\"white\",\n",
+ " label=f\"{design_labels[design_key]} gaps\",\n",
+ " )\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(\"Grating period index\")\n",
+ " ax.set_ylabel(\"Width [um]\")\n",
+ " ax.set_xticks(period_indices)\n",
+ " ax.legend(ncol=2, fontsize=\"small\")\n",
+ " return fig\n",
+ "\n",
+ "\n",
+ "def plot_field_cross_sections(field_data_by_design):\n",
+ " fig, axes = plt.subplots(\n",
+ " len(field_data_by_design),\n",
+ " 1,\n",
+ " figsize=(12, 4.0 * len(field_data_by_design)),\n",
+ " constrained_layout=True,\n",
+ " )\n",
+ " axes = np.atleast_1d(axes)\n",
+ " for ax, (design_key, sim_data) in zip(axes, field_data_by_design.items()):\n",
+ " sim_data.plot_field(\n",
+ " field_monitor_name=\"field_mnt\",\n",
+ " field_name=\"E\",\n",
+ " val=\"abs\",\n",
+ " ax=ax,\n",
+ " )\n",
+ " ax.set_title(f\"{design_labels[design_key]} field magnitude\")\n",
+ " ax.set_aspect(\"auto\")\n",
+ " return fig"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1878352b",
+ "metadata": {},
+ "source": [
+ "## Start From A Uniform Seed Design\n",
+ "\n",
+ "We begin from a uniform top grating generated from a simple Bragg style estimate. This seed also sets the initial Gaussian beam position, tilt, and waist."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b3ac98ed",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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epvX79u0zp06dinQ4iGCzFc2brGmW6G4R31QWjh07Zv7888/LKgvasasZ1OW0GtIB4nJcbgxuiiPYGHSAPn/+vMmbN6/3AO2WdRGKOKIhhoyKw19ZyOgY0iKtceh8x3Ov2p1IxRFINMQQKI5LlYWMiiMY0RBDtMQRqhiKFSsWVDlw6zaJhhgiHYfzf/mDzhkLFChw2XGk1aXOT+Mqmfd46KGHSOLimHbGpUuXNjt37rQHa8SvCxcumBw5cpg9e/Zc1vvoqunXX3+drh27DipNmjSx/aIiFYPb4oiGGNwSRzTEEC1xREMMwcZx9uxZe9+7d2+TLVu2iMURSDTE4JY4oiGGaIkjlDEsWLDAZM+ePWbXRajiiIYYIh1HpkyZTIUKFczWrVtN7ty5LyuOUIq5ZP7VV181o0ePNnv37jU1a9Y0EyZMMNdff31Q79G9e3dz1VVXhS1GRDddWVMSrx8lNfPxbfny5WbOnDmmdu3a9ipreqhmf8WKFfYqb3p26nqdDirXXXedSUxMjEgMboojGmJwUxzREEO0xBENMQQbx6FDh8y8efPseVLBggUjFoc/0RCDm+KIhhiiJY5Qx6Da+VhdF6GIIxpiiIY4EhIS7H5UXbV/+umny4ojbpP5GTNmmMcee8xMmjTJ1KtXz4wfP940a9bMbN682RQpUiTN71O8eHF7ZQXxm8yriYyuqpHMx7ft27fbezWfK1SoUERj0UEl0jEQB+uCcuGu34hq46+55hp73qP9XKTiCLdoiCFa4oiGGKIljmiIgTjctS4SExPTfTEiXGJqRI+xY8eabt26mQceeMBcffXVNqnPlSuXmTx5cqRDAwAAiCpK4G+99dawJfIAgMiKmWRe/b5WrlxpmjZt6n1OzaT1eNmyZRGNDQAAINqcO3fOHDx40N4DANwnZprZ62CkwaqKFi3q87web9q0ye9rzpw5Y29Jh/j3NLPWDfHJs/0pAxB1tdC+IUuWLOnuw6X3OHnypP1/sPQ6vT49rw1VDG6KIz0xaAwNdb3JnDmzvUjslnURijiiIYaMjCN5WYhEDJcSTByePvPNmzdP0Wc+0tskGmJILY7UykJGxpFW0RBDtMQR6hjSWg7CHQe/1ciui4SEBO8sSCoLnjG4wiWt7x0zyXx6jBgxwgwdOjRgn2nEL6alg2jcBM1ssH///suaLkUDamrsjvSMiq8LCXq9LljqFokY3BZHNMTgljiiIYZoiSMaYgg2Dp24yu+//26OHj0asTgCiYYY3BJHNMQQLXGEMoZffvnFDrodq+siVHFEQwyRjiMhIcFWEut1eg8l24cPHzbhktYR9xOcGKmeVDN79Y//8MMPTatWrbzPd+7c2R6gPv744zTVzJcqVcq+h0bCR3xiADx4rF+/3kycONF219G+4XKmSilcuHC6X3/gwIGQTNdyOTG4KY5gY9A+QccYDRbmGRTTLesiFHFEQwwZFYe/spDRMaRFWuPYtm2befLJJ83w4cNNuXLlIhZHINEQQ6A4LlUWMiqOYERDDNESR6hi0CBpwZQDt26TaIghGuLIlCmTnWtercVbt25t4wkX5a358+e3LQFSG/ckZmrm9SOqU6eOndPPk8zrioge9+nTx+9rNC+kv7kh9WNkFPP45ikDlAN45g2tUqVKxFZGnjx5omJDxGscOmnXwTlnzpzefUK8rotojSGj4vBXFjI6hrRIaxye7kOqRdLAwZGKI5zCFcOlykJGxRGMaIghWuIIVQzBloNwxXG5oiGOaIjhcuLwlAXln5qvXuePwXa9CEZa3ztNyfy1114b1IersM+dO9dceeWVJpQ0LZ1q4jU3oOZM1dR0ai6v0e0BAADgez6WNWtWLlwDgEulKZlfs2aN6d+/f5quZOiqxciRI32at4dKu3btbNOIQYMG2f4rtWrVMl988UWKQfEAAADiXdWqVe05HADAndLczP7xxx83RYoUSdOyL774ogkXNakP1Kw+rTRoQrQ09UDG84w+qeYrNLOPb7t377b3uvjIoJjxy9M3VvsF9gnxjbIAygLYJyC144P6y8dcMq8BVIIZbODnn382JUqUMNHqjTfe4IQtjimJ1wjmO3fuDOuUEogNKgtK5j2JPQC4xa5du2yXxH79+l3WIJ8AgP9Pg7KrC1PMJPNlypQJ6k2j/YDx1ltvhXX0QUS3yx3MBO4qC7rCqn1WOAcxQXTTRT2NFpuYmEg5iHNuKwtqYv/bb7/Zbom6IX7LAtKHcgB/ZUEDrOfIkcNEgyzpnaN77dq1dm7m5DWbLVq0MNFOo7rmy5cv0mEgQjzzQhYoUIADdJzzlAVd3ONkLb7Lwblz5ygHcF1ZyJ07t/eeSoz4LgtIH8oBor0sBJ3Ma8C5Tp06mYMHD6b4m2o5o60fAQAAAAAAbhP0ZYWHH37YtGnTxg4ipysUSW8k8gAAAAAARGEyv2/fPjvfO9PBAQAARK9y5cqZmTNn2nsAgPsEnczfc889ZvHixeGJBgAAACGRP39+25pS9wAA9wm6z/wrr7xiDwzffvutqV69eoph+R955JFQxgcAAIB0UGvK6dOnm/bt29OiEgBcKOhk/v333zfz58+3w/Grhj7p1F76P8k8AABA5O3evdv079/fNGrUiGQeAFwo6GT+qaeeMkOHDjUDBw6MqmH5AQAAAACIF0Fn42fPnjXt2rUjkQcAAAAAIFaS+c6dO5sZM2aEJxoAAAAAABD6ZvaaS37UqFHmyy+/NDVq1EgxAN7YsWODfUsAAACEWGJiornzzjvtPQDAfYJO5tetW2dq165t/79+/XqfvyUdDA8AAACRU6FCBTN37lw2AQC4VNDJ/KJFi8ITCQAAAELm3Llz5ujRoyZfvnwpWlICAGIfw9EDAAC4kFpTFilSxN4DANwn6Jr5W265JdXm9AsXLrzcmAAAAAAAQCiT+Vq1aqVowrVmzRrbf14j3QMAAAAAgChL5seNG+f3+SFDhpiTJ0+GIiYAAAAAAJARfeY7dOhgJk+eHKq3AwAAAAAAoaqZD2TZsmUmR44coXo7AAAAXIaaNWuaY8eOmdy5c7MeAcCFgk7m7777bp/HjuOYPXv2mBUrVphnnnkmlLEBAAAgnTJnzmzy5s3L+gMAlwq6mX1iYqLPrUCBAqZRo0bms88+M4MHDw5PlAAAAAjKli1bTLNmzew9AMB9gq6ZnzJlSngiAQAAQMicOHHCzJ8/394DANwnZAPgAQAAAACAKErm1ZT+4MGDaX7T0qVLmx07dlxOXAAAAAAA4HKa2R89etR8/vnnto98Whw6dMhcuHAhTcsCAAAAAIAw9Znv3LlzkG8NAACASClVqpR55ZVX7D0AIE6T+YsXL4Y/EgAAAIRM4cKFTe/evVmjAOBSDIAHAADgQocPHzbTpk2z9wAA9yGZBwAAcKHt27ebjh072nsAgPuQzAMAAAAAEGNI5gEAAAAAiDEk8wAAAAAAuD2Zb9y4sRk6dGiK548cOWL/BgAAgMjLnTu3ueGGG+w9ACCO55n3WLx4sVm3bp1ZvXq1mT59uvcAcfbsWbNkyZJwxAgAAIAgVa5c2Sxbtoz1BgAula5m9gsWLDB79+61V3sZIRUAAAAAgBhI5osXL25r4atXr27q1q1ra+sBAAAQPVatWmUSEhLsPQDAfYJO5nVQkOzZs5v33nvP9O3b19x2223mtddeC0d8AAAAAADgcvvMO47j8/jpp582VatWNZ07dw72rQAAAAAAQEYk89u2bTOFCxf2ea5169amSpUqZsWKFemJAQAAAAAAhDOZL1OmjN/nq1WrZm8AAAAAACAKB8CLhOeff940aNDA5MqVy+TLly/S4QAAAES1q6++2mzZssXeAwDcJ2aSec1j36ZNG9OrV69IhwIAABD1cuTIYSpWrGjvAQDuEzPJ/NChQ82jjz5qp8MDAADApcc56tChg70HALhP0H3mY8mZM2fszeP48eP2/uLFi/aG+KRtr1kZKAOgLIB9Atx8fDh06JCZPn266devX8AxjxAfZQHpQzlApMpCWj/H1cn8iBEjbI1+ckeOHGHnHMf04zhx4oT9QWbKFDONUxAGlAVQDuDmfcKxY8e894cPH450ODHFbWUB6UM5QKTKgj4r6pP5gQMHmhdeeCHVZTZu3GinvUuPJ554wjz22GM+NfOlSpUy+fPnZxC9OP8xJiQk2HLAATq+URZAOYCb9wmJiYne+wIFCkQ6nJjitrKA9KEcIFJlIUuWLNGfzPfv39906dIl1WXKly+f7vfPnj27vXnoSoqcPHkyzSsI7vwxqgxkzZqVA3ScoyyAcgA37xNOnTrlvdf3QvyWBaQP5QCRKguefbYnfw0kohlt4cKF7S0j+44J/cYAAEC8aNiwYaRDAACks7m9p5WVPzFTPb1z507b30v3Fy5cMGvWrLHPa8qVPHnypOk9PE3M9B6prRS4m6e7xa5du0zevHkjHQ4iiLIAygHYJ4DjAzhPQLSdM6pGXol8iRIlUl0uZpL5QYMGmXfeecf7uHbt2vZ+0aJFplGjRml6D0+TCCXyJHFQGaAcQCgLoBwgKfYJoCyAfQIifXxIS+VzzHQCmjp1qr1CkfyW1kQeAAAAAAC3iJlkHgAAAAAAxGEyr5HtBw8e7DPCPeIP5QCUBbBPAMcHcK4AzhkR6/lDgnOp8e4BAAAAAEBUiauaeQAAAAAA3IBkHgAAAACAGEMyDwAAAABAjCGZBwAAAAAgxrg6mS9btqxJSEjwuY0cOTLV15w+fdr07t3bFCxY0OTJk8e0bt3a7Nu3L8NiRuht377dPPjgg6ZcuXImZ86cpkKFCnY0yrNnz6b6ukaNGqUoPz179mQTxZhXX33V7gty5Mhh6tWrZ3788cdUl581a5apUqWKXb569erms88+y7BYEXojRowwdevWNVdccYUpUqSIadWqldm8eXOqr5k6dWqK377KA2LbkCFDUmxX/dZTw/4gfs4PddP5nz/sE9zhm2++MXfeeacpUaKE3d5z5szx+bvGBB80aJApXry4PV9s2rSp2bJlS8jPMxDdZeHcuXNmwIAB9hwwd+7cdplOnTqZP/74I+THmFBwdTIvw4YNM3v27PHeHn744VSXf/TRR80nn3xiD+BLliyxG+7uu+/OsHgReps2bTIXL140r7/+utmwYYMZN26cmTRpknnyyScv+dpu3br5lJ9Ro0axiWLIjBkzzGOPPWYv3qxatcrUrFnTNGvWzOzfv9/v8kuXLjX33XefvfizevVqm/jptn79+gyPHaGh/bhO0H/44Qfz1Vdf2YP0rbfeav78889UX5c3b16f3/6OHTvYJC5QrVo1n+363XffBVyW/YF7/fTTTz7lQPsGadOmTcDXsE+Ifdrv6zxAybc/Osd7+eWX7Tni8uXLbSKncwZV9IXqPAPRXxZOnTplt+Uzzzxj7z/66CNbCdCiRYuQHmNCxnGxMmXKOOPGjUvz8kePHnWyZs3qzJo1y/vcxo0bNXWfs2zZsjBFiUgYNWqUU65cuVSXadiwodO3b98Miwmhd/311zu9e/f2Pr5w4YJTokQJZ8SIEX6Xb9u2rdO8eXOf5+rVq+f06NGDzeMS+/fvt/v0JUuWBFxmypQpTmJiYobGhfAbPHiwU7NmzTQvz/4gfuhYX6FCBefixYt+/84+wX10HJg9e7b3sbZ9sWLFnNGjR/vkBdmzZ3fef//9kJ1nIPrLgj8//vijXW7Hjh1OqI4xoeL6mnk1q1eT+dq1a5vRo0eb8+fPB1x25cqVttZGzWo81DyidOnSZtmyZRkUMTLCsWPHTIECBS653PTp002hQoXMNddcY5544gl7tQ6xQd0o9JtO+nvOlCmTfRzo96znky4vusLO799dv3251O//5MmTpkyZMqZUqVKmZcuWtlUPYp+azKrJZPny5U379u3Nzp07Ay7L/iB+jhXTpk0zXbt2tc1iA2Gf4G7btm0ze/fu9TkHSExMtM3mA50DpOc8A7F77pCQkGDy5csXsmNMqGQxLvbII4+Ya6+91p60qbmckjE1eRg7dqzf5fUjzpYtW4oNVbRoUfs3uMOvv/5qJkyYYMaMGZPqcvfff789mdePcu3atbb/jJrZqLkNot/BgwfNhQsX7O83KT1W1wt/9Dv3tzy/f3dQd5t+/fqZG2+80V6gC6Ry5cpm8uTJpkaNGvYArn1FgwYNbEJfsmTJDI0ZoaOTcvV91vbVucDQoUPNTTfdZLvRaEyF5NgfxAf1lT169Kjp0qVLwGXYJ7if5zgfzDlAes4zEHtOnz5tcwB1w1R3m1AdY+I2mR84cKB54YUXUl1m48aNtkZdfVg8dFKmRL1Hjx52QKTs2bNnQLSIlrLgsXv3bnPbbbfZfnHqD5+a7t27e/+vQTA0IEqTJk3M1q1b7SB6AGKL+s7roHqpPmz169e3Nw8l8lWrVrXjbjz77LMZECnC4fbbb/c5J9CJly7Yzpw5046Tgfj09ttv27KhC/eBsE8A4tO5c+dM27Zt7eCIEydOjMpjTMwl8/3790/16qmoaYM/WqlqZq/RzXXVJLlixYrZJjO6Qpu0dl6j2etviO2yoMEMb7nlFnti/sYbbwT9eSo/npp9kvnop+4RmTNnTjEbRWq/Zz0fzPKIHX369DGffvqpHcE22Nr1rFmz2q5a+u3DPXScr1SpUsDtyv7A/TSw5YIFC4Juccc+wX08x3kd81V546HHtWrVCtl5BmIvkd+xY4dZuHBhqrXy6TnGhErM9ZkvXLiwrWlN7aYaeH/WrFlj+7JoeiJ/6tSpY3fQX3/9tfc5NatWf4ektTSIvbKgGnlNNadtPGXKFFsOgqXyI0l38ohe2vba3kl/z2pmrceBfs96PunyolGO+f3HLl1NVyI/e/ZsezDWFJXBUjPKdevW8dt3GfWBVkurQPt09gfup/MBnRM2b948qNexT3AfHRuUgCc9Bzh+/Lgd1T7QOUB6zjMQW4n8li1b7AU/jb8W6mNMyDgutXTpUjuS/Zo1a5ytW7c606ZNcwoXLux06tTJu8zvv//uVK5c2Vm+fLn3uZ49ezqlS5d2Fi5c6KxYscKpX7++vSF2aTtXrFjRadKkif3/nj17vLdAZeHXX391hg0bZsvAtm3bnI8//tgpX768c/PNN0fwmyBYH3zwgR2JdurUqc7PP//sdO/e3cmXL5+zd+9e+/eOHTs6AwcO9C7//fffO1myZHHGjBljZ7LQyKSa4WLdunWs/BjVq1cvOzL94sWLfX77p06d8i6TvBwMHTrU+fLLL+2xY+XKlc69997r5MiRw9mwYUOEvgVCoX///rYcaJ+u33rTpk2dQoUK2RkOhP1BfNGo4zrfGzBgQIq/sU9wpxMnTjirV6+2N6VAY8eOtf/3jFA+cuRIe46gc761a9c6LVu2tDMf/fXXX973aNy4sTNhwoQ0n2cg9srC2bNnnRYtWjglS5a0eWTSc4czZ84ELAuXOsaEi2uTeZ2AaUopncTpJKxq1arO8OHDndOnT3uX0crWBly0aJH3Of1g//GPfzj58+d3cuXK5dx1110+SR9ij6aU0Xb2dwtUFnbu3GkT9wIFCtidtC4GPP74486xY8ci+E2QHtrR6oQtW7ZsdgqZH374wWf6wc6dO/ssP3PmTKdSpUp2+WrVqjnz5s1jxcewQL997RcClYN+/fp5y0zRokWdO+64w1m1alWEvgFCpV27dk7x4sXtdr3yyivtY1249WB/EF90wU77gs2bN6f4G/sEd9I5nr/jgWf/r+npnnnmGbvf17mfKoGSlw9Ne60L/Wk9z0DslYVt/5cT+LslzRmTl4VLHWPCJUH/hLfuHwAAAAAAxHWfeQAAAAAA4h3JPAAAAAAAMYZkHgAAAACAGEMyDwAAAABAjCGZBwAAAAAgxpDMAwAAAAAQY0jmAQAAAACIMSTzAADAr+3bt5uEhAR7q1WrVtjX0uLFi72f16pVK7YKAACpIJkHAACpWrBggfn666/DvpYaNGhg9uzZY9q2bcsWAQDgEkjmAQBAqgoWLGhv4ZYtWzZTrFgxkzNnTrYIAACXQDIPAEAcOHDggE2Uhw8f7n1u6dKlNoEOtta9UaNGpl+/fj7PqVl8ly5dvI/Lli1rnnvuOdOpUyeTJ08eU6ZMGTN37lwbR8uWLe1zNWrUMCtWrAjBtwMAIP6QzAMAEAcKFy5sJk+ebIYMGWIT6BMnTpiOHTuaPn36mCZNmoTlM8eNG2duvPFGs3r1atO8eXP7eUruO3ToYFatWmUqVKhgHzuOE5bPBwDAzUjmAQCIE3fccYfp1q2bad++venZs6fJnTu3GTFiRFg/r0ePHuaqq64ygwYNMsePHzd169Y1bdq0MZUqVTIDBgwwGzduNPv27QtbDAAAuBXJPAAAcWTMmDHm/PnzZtasWWb69Okme/bsYfssNaP3KFq0qL2vXr16iuf2798fthgAAHArknkAAOLI1q1bzR9//GEuXrxop55Lj0yZMqVoGn/u3LkUy2XNmtX7f003F+g5xQIAAIJDMg8AQJw4e/as7a/erl078+yzz5qHHnooXbXi6n+vKeQ8Lly4YNavXx/iaAEAQGpI5gEAiBNPPfWUOXbsmHn55Zdtf3X1W+/atWvQ79O4cWMzb948e9u0aZPp1auXOXr0aFhiBgAA/pHMAwAQBxYvXmzGjx9v3n33XZM3b17bVF7///bbb83EiRODei9dAOjcubMdib5hw4amfPny5pZbbglb7AAAIKUEh/lgAACAH+pTX65cOTu1XK1atTJsHWm+etX0z5kzh+0CAEAA1MwDAIBUNWjQwN7CTa0E8uTJY0fZBwAAqaNmHgAA+KUp7Dwj3msKu1KlSoV1Tf31119m9+7d9v9K6osVK8aWAQAgAJJ5AAAAAABiDM3sAQAAAACIMSTzAAAAAADEGJJ5AAAAAABiDMk8AAAAAAAxhmQeAAAAAIAYQzIPAAAAAECMIZkHAAAAACDGkMwDAAAAABBjSOYBAAAAADCx5X8Bl9VZdG2qJOQAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initial x_center = 6.000 um\n",
+ "Initial tilt = 8.000 deg\n",
+ "Initial waist = 2.250 um\n"
+ ]
+ }
+ ],
+ "source": [
+ "initial_single_phys = make_initial_design_phys(include_reflector=False)\n",
+ "initial_reflector_phys = make_initial_design_phys(include_reflector=True)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(10, 2.8), constrained_layout=True)\n",
+ "plot_design_cross_section(\n",
+ " initial_single_phys, ax=ax, color=\"0.35\", title=\"Uniform single-layer seed\"\n",
+ ")\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"Initial x_center = {initial_single_phys['x_center']:.3f} um\")\n",
+ "print(f\"Initial tilt = {np.degrees(initial_single_phys['tilt']):.3f} deg\")\n",
+ "print(f\"Initial waist = {initial_single_phys['waist']:.3f} um\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bac49900",
+ "metadata": {},
+ "source": [
+ "## Stage 1: Single-Layer Grating And Beam Optimization\n",
+ "\n",
+ "The first optimization updates the top grating geometry together with the Gaussian beam source parameters. This demonstrates how to include source position, source tilt, and source waist directly in a Tidy3D Autograd optimization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "9be9a5e9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Single-layer grating and beam optimization\n",
+ "==========================================\n",
+ "epochs = 90, learning_rate = 0.0030\n",
+ "step 01/90 | objective = 0.084270 | grad norm = 1.229e+00\n",
+ "step 05/90 | objective = 0.140750 | grad norm = 6.235e-01\n",
+ "step 10/90 | objective = 0.145501 | grad norm = 1.005e+00\n",
+ "step 15/90 | objective = 0.165367 | grad norm = 8.510e-01\n",
+ "step 20/90 | objective = 0.176042 | grad norm = 1.053e+00\n",
+ "step 25/90 | objective = 0.199274 | grad norm = 6.669e-01\n",
+ "step 30/90 | objective = 0.214997 | grad norm = 7.155e-01\n",
+ "step 35/90 | objective = 0.234634 | grad norm = 8.797e-01\n",
+ "step 40/90 | objective = 0.253888 | grad norm = 7.545e-01\n",
+ "step 45/90 | objective = 0.274034 | grad norm = 9.434e-01\n",
+ "step 50/90 | objective = 0.288923 | grad norm = 1.119e+00\n",
+ "step 55/90 | objective = 0.307696 | grad norm = 8.266e-01\n",
+ "step 60/90 | objective = 0.317485 | grad norm = 1.209e+00\n",
+ "step 65/90 | objective = 0.329971 | grad norm = 4.592e-01\n",
+ "step 70/90 | objective = 0.334242 | grad norm = 9.036e-01\n",
+ "step 75/90 | objective = 0.342667 | grad norm = 7.190e-01\n",
+ "step 80/90 | objective = 0.344083 | grad norm = 9.081e-01\n",
+ "step 85/90 | objective = 0.352045 | grad norm = 4.737e-01\n",
+ "step 90/90 | objective = 0.354362 | grad norm = 4.982e-01\n"
+ ]
+ }
+ ],
+ "source": [
+ "def objective_single(params_norm):\n",
+ " params_phys = to_phys(params_norm, single_layer_bounds)\n",
+ " return coupling_efficiency_from_sim(\n",
+ " params_phys,\n",
+ " task_name=\"grating_beam_single\",\n",
+ " include_reflector=False,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "optimization_epochs = 90\n",
+ "learning_rate = 0.003\n",
+ "print_every = 5\n",
+ "\n",
+ "single_initial_norm = physical_to_normalized(initial_single_phys, single_layer_bounds)\n",
+ "single_norm, single_history = optimize_params(\n",
+ " single_initial_norm,\n",
+ " objective_fn=objective_single,\n",
+ " epochs=optimization_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " label=\"Single-layer grating and beam optimization\",\n",
+ " print_every=print_every,\n",
+ ")\n",
+ "single_design_phys = to_phys(single_norm, single_layer_bounds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24ce0f5c",
+ "metadata": {},
+ "source": [
+ "### Single-Layer Result\n",
+ "\n",
+ "After the first optimization, we evaluate the optimized design at the nominal source condition and plot both the final cross section and the optimization history. The printed source parameters show how the Gaussian beam moved during the optimization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "3aa8819a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Single-layer nominal CE = 0.352976\n",
+ "Single-layer x_center = 3.569 um\n",
+ "Single-layer tilt = 9.690 deg\n",
+ "Single-layer waist = 2.379 um\n"
+ ]
+ }
+ ],
+ "source": [
+ "single_nominal_ce = coupling_efficiency_from_sim(\n",
+ " single_design_phys,\n",
+ " task_name=\"grating_beam_single_nominal_final\",\n",
+ " include_reflector=False,\n",
+ " as_float=True,\n",
+ ")\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(14, 3.5), constrained_layout=True)\n",
+ "plot_design_cross_section(\n",
+ " single_design_phys,\n",
+ " ax=axes[0],\n",
+ " color=design_colors[\"single\"],\n",
+ " title=\"Single-layer optimized design\",\n",
+ ")\n",
+ "plot_history(axes[1], single_history, \"Single-layer optimization history\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"Single-layer nominal CE = {single_nominal_ce:.6f}\")\n",
+ "print(f\"Single-layer x_center = {single_design_phys['x_center']:.3f} um\")\n",
+ "print(f\"Single-layer tilt = {np.degrees(single_design_phys['tilt']):.3f} deg\")\n",
+ "print(f\"Single-layer waist = {single_design_phys['waist']:.3f} um\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d34dee2",
+ "metadata": {},
+ "source": [
+ "## Stage 2: Add A Lower Reflector Grating\n",
+ "\n",
+ "The second optimization adds a silicon reflector grating below the top grating, separated by a 400 nm silicon dioxide gap. The reflector is intentionally simple: it uses one global period and one global duty cycle, repeated enough times to cover the top grating region. This lower grating is optimized together with the top grating geometry and Gaussian beam parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "3b1209e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Reflector grating and beam optimization\n",
+ "=======================================\n",
+ "epochs = 90, learning_rate = 0.0030\n",
+ "step 01/90 | objective = 0.198748 | grad norm = 2.288e+00\n",
+ "step 05/90 | objective = 0.280728 | grad norm = 1.845e+00\n",
+ "step 10/90 | objective = 0.308899 | grad norm = 1.517e+00\n",
+ "step 15/90 | objective = 0.345474 | grad norm = 2.142e+00\n",
+ "step 20/90 | objective = 0.377360 | grad norm = 2.077e+00\n",
+ "step 25/90 | objective = 0.418240 | grad norm = 1.794e+00\n",
+ "step 30/90 | objective = 0.451548 | grad norm = 1.873e+00\n",
+ "step 35/90 | objective = 0.475962 | grad norm = 4.308e+00\n",
+ "step 40/90 | objective = 0.517014 | grad norm = 2.262e+00\n",
+ "step 45/90 | objective = 0.549722 | grad norm = 2.061e+00\n",
+ "step 50/90 | objective = 0.582705 | grad norm = 1.334e+00\n",
+ "step 55/90 | objective = 0.609034 | grad norm = 1.604e+00\n",
+ "step 60/90 | objective = 0.624934 | grad norm = 1.807e+00\n",
+ "step 65/90 | objective = 0.630303 | grad norm = 3.563e+00\n",
+ "step 70/90 | objective = 0.660135 | grad norm = 1.067e+00\n",
+ "step 75/90 | objective = 0.668581 | grad norm = 1.005e+00\n",
+ "step 80/90 | objective = 0.671168 | grad norm = 2.370e+00\n",
+ "step 85/90 | objective = 0.680527 | grad norm = 1.543e+00\n",
+ "step 90/90 | objective = 0.685291 | grad norm = 1.049e+00\n"
+ ]
+ }
+ ],
+ "source": [
+ "def objective_reflector(params_norm):\n",
+ " params_phys = to_phys(params_norm, reflector_bounds)\n",
+ " return coupling_efficiency_from_sim(\n",
+ " params_phys,\n",
+ " task_name=\"grating_beam_reflector\",\n",
+ " include_reflector=True,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "optimization_epochs = 90\n",
+ "learning_rate = 0.003\n",
+ "print_every = 5\n",
+ "\n",
+ "reflector_initial_norm = physical_to_normalized(initial_reflector_phys, reflector_bounds)\n",
+ "reflector_norm, reflector_history = optimize_params(\n",
+ " reflector_initial_norm,\n",
+ " objective_fn=objective_reflector,\n",
+ " epochs=optimization_epochs,\n",
+ " learning_rate=learning_rate,\n",
+ " label=\"Reflector grating and beam optimization\",\n",
+ " print_every=print_every,\n",
+ ")\n",
+ "reflector_design_phys = to_phys(reflector_norm, reflector_bounds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88ca0d45",
+ "metadata": {},
+ "source": [
+ "### Reflector Result\n",
+ "\n",
+ "We now evaluate the two-layer design at the nominal source condition. The summary includes the optimized Gaussian beam parameters as well as the reflector period and duty cycle, which are the two additional design variables introduced in this stage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "52875fe8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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BBx/EI488YiIsmTzrkEMOcQuThOLgnDlzzHB8CsiMVubQe6dXKkU1ethSfKToymH8HIJPEfLFF180YiXrzeRb9CimOGcnv4uUc845x0Tzsv433XST2deJJ55oxEAKdU4oErJel1xyiRHaKZBy/75tx7peccUVRkymjQDXKS8xmNHYu3fvNoK7L/THZRtSvOY6tCigaE2BnqI27UR8xXIK7mxndiYwKR3L8CcGM8r2008/NW3BMrkeBXVeE9dddx3iAQq27FCggMtOBV5DI0eOxJ133mnqT7iM1x9tVs477zwT7TxjxgzzPeF6FHLZwcFzS6Gf7czIeLZVONAq4vPPPzfXvzO5nxCiYpDgCtXhXAghhBBCCCGEEBUOisb02t21a1esqyIqAYyYZlT7999/bzpOhBAVC3kGCyGEEEIIIYQQQoiQeOGFF0yUNq1ThBAVD9lECCGEEEIIIYQQQoigvPPOO/jrr7+MLQe9qZlITghR8ZAYLIQQQgghhBBCCCGCwiSFNWrUMH7El156qVpLiAqKPIOFEEIIIYQQQgghhBCiCiDPYCGEEEIIIYQQQgghhKgCSAwWQgghhBBCCCGEEEKIKoA8g4UQQgghhIgT8vPz8ccff6Bx48ZITFTchhBCCCFEvFFYWIiNGzeiT58+SE6ueNJqxauxEEIIIYQQlRQKwf369Yt1NYQQQgghRAn88ssv2H///VHRkBgshBBCCCFEECZMmIBHHnkEGzZsQK9evfD0008HFGwHDRqE7777rtjyYcOG4YsvviixnRkRbD9cNG3aNKoRLDt27ECdOnUUcVzF0Lmvmui8V1107qsmOu/ly/r1681/Qft/W0VDYrAQQgghhBABePfdd3Httddi4sSJ6N+/P8aPH4+hQ4di0aJFaNSoUbH1P/roI+Tm5rrfb9261QjIp5xySkhtbFtDUAhu0aJFVB8S09PTUa9ePYnBVQyd+6qJznvVRee+aqLzHhsSK6ilV8WstRBCCCGEEOXA448/jgsuuABjxoxBt27djChMUXXy5Ml+16fY2qRJE/f09ddfm/VDFYOFEEIIIYQoSxQZLIQQQgghhB8Y4fvbb7/hlltu8YoAGTJkCGbPnh1Sm7300ks47bTTkJGR4ffznJwcM9lkZWW5I3w4RQuW5XK5olqmqBjo3FdNdN6rLjr3VROd9/Jv74qMxGAhhBBCCCH8sGXLFhQUFBTzg+P7hQsXlthm9P39+++/jSAciAceeAB33XVXseX092VEcTQfWig0UxCuqEMaRWTo3FdNdN6rLjr3VROd9/Jlx44dqMhIDBZCCCGEEKIMoAjcs2fPgMnmCKOO6Ulss3btWmNHwURvtJzwBwXd/Px8I1SH85DIbSgwSwyuWoRy7lNSUpCUlFTudRNle94TEhJQt25dfeerGDr3VROd9/IlOzsbFRmJwUIIIYQQQvihQYMGRiDbuHGj13K+px9wMHbv3o133nkHd999d9D10tLSzGSTmZlpXina+RPuaF3BDNbhPoTYFhGMZKFAJKoOoZx7LmfCwho1apR7/UTZwfMa6F4iKjc691UTnffyI7GC31clBgshhBBCCOGH1NRU9O3bF99++y1OOOEEs4yiGt9ffvnlQdvs/fffN17Ao0aNilrbct/Lly83AnWzZs1M/UIVdu1o4uTkZInBVYySzj0/37x5M9asWYOOHTsqQlgIIYSo5EgMFkIIIYQQIgC0cBg9ejT2228/Y/cwfvx4E/U7ZswY8/nZZ5+N5s2bG+9fX4sICsj169ePWtsyKpiCcMuWLcP2E5YYXHUJ5dw3bNgQK1asQF5ensRgIYQQopIjMVgIIYQQQogAjBw50kRN3nHHHdiwYQN69+6NadOmuZPKrVq1qthQwUWLFmHWrFn46quvyqRdK/rQRBF/yDpECCFERYUWWwsWLEC1atXM/zRRMhKDhRBCCCGECAItIQLZQsycObPYss6dO5toTCGEEEIIER2ysrLw999/G+H3n3/+cb/S5sjuwGe+BlEyEoOFEEIIIYQQQgghhBBxA0VejrT68ccfzfTnn38au6xAUBwWoSExWAghhBBCCBEXtGnTBldffbWZSrNOaXnllVdM+Tt27CizfQgRMkunAynpQPV6QMNOajghRFxAP/p58+aZUVJz5swxy+rVq2emunXruuebNGmCrl27mvlA0LP+r7/+wuzZs81EEZhWXCVRp04ddO/eHd26dZNFRBhIDBZCCCGEEEKUOatXr8a4ceOM5/KWLVvQtGlTk2SPfszhJNr79ddfkZGREbV6+ROXOdR02LBhUduHEBFTkAe8fqI13+pA4NxpakwhRKlhFO2nn35qBF0mEfWd+DtLgdZ32rRpE77//nsjAP/www/GuiFUmG+Boq09cT+//fabEX/5275nz56g3vY9e/ZE//790aNHD7M9RWAKzfK9Dx+JwUIIIYQQQogyZdmyZTjwwAPRqVMnvP3222jbtq3x+bvhhhvw5ZdfmoiiYBFDTvjwWNZUr17dTELEnLxsz/yq2ZY4nJQSyxoJISoo7Ijlb/Brr72GuXPnlvv+N27caKYZM2aUuG56eroRfg866CAcfPDBOOCAA1C7du1yqWdVQKmIhRBCCCGEEGXKZZddhtTUVHz11VcYOHAgWrVqhaOPPhrffPMN1q5di1tvvdW9LqOMTj/9dBOV1Lx5c0yYMKFYJO/48ePd72nlcP755xuRuFatWhg8eLDxFXTy2WefYf/99zeZxhs0aIATT7QiLQcNGoSVK1fimmuuMZFFdnQRbSI49JT8999/ZvnChQu9ynziiSfQvn1793smteEx1ahRw0Q/nXXWWebBW4hSkecTKZcTehSeEELwN/Xjjz82I3E4IufKK6+MmhDcqFEjnHrqqXj22WfNbyB/T2kbMX36dHzwwQd44YUX8NBDD+HSSy81v7dcPxD8bedv/1NPPWWihPnbznLuueceDB06VEJwlFFksBBCCCGEEBWY/fbbDxs2bCj3/XJoZigPlNu2bcP//vc/3HfffcWibVnGmWeeiXfffdc8TJJHHnkEY8eOxV133WW2u+qqq0xE8RFHHOG3/FNOOcWUywhjRg09//zzOPzww42Iy2jjL774woi/FJwZDZWbm4upU6eabT/66CP06tULF154IS644AK/5XPfbOM333zTPJTa8P0ZZ5xh5vnQShGaojRFYg51vemmm8xDMh9mhYiaGJy7C0gPLYpeCBFbXC6XETZpj0RRlp2iKSkp5tWe2PHYu3dv46nLz0pDTk4OfvnlF2O9wP1y+vfff009fOnbt6/ptORIHVo/bN682Wvi75hdV77aEyN22blKcbdz587FLBrY2RsMdpKyTrSpYJQwrR84coj/B0T5ITFYCCGEEEKICgyFYEbXxiuLFy82D6J80PUHl2/fvt08fBIOCb355pvdQiwziFNg9ScGM8EMH3z5IJuWlmaWPfroo5gyZYqJSqLISxH6tNNOM+KyDQVgQrE4KSkJNWvWDPogSsH6mWeecYvBFJr5sP3GG2+Y9/ysT58+uP/++93bTJ48GS1btjTr8jiEiAhFBgtRoaCvLj11GY3L36JQf5/5G0ZhdN999zVTly5dzG/bihUrsHz5cvPKidG33EdycnKxiUIrPwtEs2bNMGrUKJx99tnGbzcWcHTOIYccYiYROyQGCyGEEEIIUYGJVTRNuPv1F5nkD0YI+b532kI4oR3Erl27iiWgY0TT0qVLzTyHrAaK+g0VisnXX3+98TambyGjgu2Hdbse9ECkRYQvrIfEYBE9MXiXGlOIGEI7BHYG7t271z0xIpevTJTK0Sjs4AwXlsHRNqFaOAQTfW0oEO+zzz7o16+fGSHDUTPsABXlzIwHgO8e9F5WvyNwRfn7NttIDBZCCCGEEKICE8qDI4VYZgzng2F5Z93u0KGD2SeHhdpevU64vG7duhElhqMQTA9EZjX3xfb8jUYiOArftIF46623jBjM10suucSrHscee6zxRvSF9RMiKgnkSK48g4WIBb///rsZYfLpp5+GvA0tFoYMGWJ++2ipQAGXVkX2KwVgRg6z7D/++MOMJAnWcUrf+9atWxtPff6m21NBQYF55ecUfjnRyoGjYLhMxAENuwJnf+J5nxhbOVZisBBCCCGEEKLMYNQuLR7oCcxEbU5xlhYXjLLlkFVbpGb0rRO+D2QxwehclkGRm8ln/MGoqG+//RZjxowJ+LDOB+mSoFXEjTfeaBLcLFu2zEQLO+vx4YcfmjqwLkJEjfy93u8VGSxEXIvAtB0aNmyYEYCZVJSJTUOFvsIcacJ9clQJOyLp6cvfFr4yAVugDt3CwkLj0U/7o8TExJD3KcoJir81GyNe0D8VIYQQQggh4gw+1HHyXcaIIX+flYRzu/KODCZPP/208Qc86qijcPfdd5uH2n/++cd4A9NX99577zV14wPs7Nmz8fDDD+P444/HN998Y0RWPoTbx2w/5PI9o3UHDBiAESNG4IEHHjB2DOvWrTMJ4pg5nQly7rjjDhx55JFo3749Ro4caaKnmGzuhhtuMOWwLj/88IP5jJ6NtuUE9+NsZ5Z32WWXmYn75UO6/Tkzpb/00ksmoRztJPgwvmTJErz33nsmoV0sh+WGcu5Lc22JMiZ3N5yyTmFOFk9YiZs5z6moWujclx5+d2gFQR/4zz77zOu3p3nz5mZkCH8DGHXLib8dfGXELjsgbQ97+3yECrfnbxqnQPUKFDms816+FBadVwr4mZmZ7uU8987z72bbUuDRzkByGtCyH3D4OKBOS8QKicFCCCGEEELEGXywYISPEw4ppRfujh07TDRrOPDhkdGvFCVjIQZTHGV07iuvvILbb7/dHB+XUfAdPXq0qR+Pt127diaSislyGInLB2MKyfvtt5+7PbgOH7rt90zi9uKLLxoxmG3Dcjk0lttyHSbkeffdd/H6668bUdl+WLe3v/XWW/HYY4/huOOOM0N36f3L6F4Ky77ngNHFtKRgJLPzMz74MVs8hd+rrrrKnCsKBRyqyzrFos3DOfelubZE2ZK2YwtqOt5nb9+IvT7XZSChgiIFz7+iBKsWVfncM4Ea78eMzg2XrVu3mohcisCcWBbp1q2bO/EZf5cY9RvsPrl7924zlTdV+bzHgswiAdi+PmzGjRuHO++803vlFvsBJzxr+QTv2gDMfAh4+Wjg0tlAWvjXajRIcIWayUEIIYQQQghRpqxZs8ZEyjJbeIsWLbw+Y3IaZhLncNFwPQBj6Rkcbdg+HLJ77rnnxroqFYJQzn1pri1RDiz8HInvnWVmCw+7FTjk+pCEISaxoh+3hKGqRVU89xTm2LE3ceJEc68bOnQoRo0ahWOOOSagb/zOnTvx/fffmw4+dlZytIo/GAnMUSzsDPQb8RknVMXzHuv/a61bt8aCBQvMNVJiZLCTPTuA8T2BofcB+56NWKDIYCGEEEIIIeIMPsj5PszxPcU8f5+VhB0pZJdREcnOzsaPP/6I9evXo3v37nrYjeK5L821JcqB5vsCw8ebCLLEJj15wkLaTOe06lKVzj1HfNCqh4nY7JEOtHbgRL/ek08+2QjDffr0Mb8hHP3BiQnbAsVGUkA+9NBDje/vOeecE9cicFU977EmsaiNGYUeji+0oXodoH57YNsyxAqJwUIIIYQQQoi4Z9KkSbjnnntw9dVX48ADD4x1dYQoP2q3APbznwBRiKoKk4deeeWVeP/9993LaANUp04dtzDMiOHJkyebqSRhb//998eQIUPMxN+YiiIAiwpIzi5g23JgH08i2vJGYrAQQgghhBAi7qEIzEkIIUTVhdG8L7/8Mq677jrjc25DawjaRNBK6LvvvjN+8h988IHx0fUHveMPO+wwDBo0yEwUkYUoE/53K9D5aKB2SyCLnsH3A4lJQM+TESskBgshhBBCCCGEEPHIv58Ba34FkqsDfUcDtZrFukZCRJWNGzcae4NGjRqVGAnMRKCM8l24cKF7ef369fHkk0/ijDPOcFvhDB482EwTJkwwdhFvvfWWsRhiUk8KwAMHDjTbCVEuZK4DPjgP2LMNSG8AtDoAOP8bIKMBYoXEYCGEEEIIIYQQIh5ZOh2YWzTEvVZToFkfoGmvWNdKiKhAcZfJQJnksmPHjjj44IPdE98XFBRg6tSpRgD+/PPPzXsn9AJ+/PHH0bBhw4Dev6eeeqqZhIgZp7wcd40vMVgIIYQQQgghhIhH8vZ45j+7yhKDL5wZyxoJEbXEb2PGjHELvIsXLzYTLSAII4UZ6cvIYV+Y3O2WW27BUUcdpbMhRARIDBZCCCGEEEIIIeKRvOziiYeEqODQ05fRurYQ3KlTJ6xYsQK5ubnudTZt2uS1TbNmzXDOOeeYiVHDQojIkRgshBBCCCGEEELEI3l7vd/nSgwWFZvff/8dxx57LHJycsx7ev3SLoJC8Ny5c/HDDz9g1qxZ+PHHH5GdnW3WPe+883DkkUciOVkSlhDRQN8kIYQQQgghhBAiHlFksKhE/Pfff8baISsry7wfNmwYXnnlFSQmJqJatWpuv2BSWFhoXvmZECK66FslhBBCCCGEqJRwOPEJJ5zgfj9o0CBcffXVMa2TEBF7BpPcLKpkakRR4Vi9ejWOOOIIbN682byn6Pv+++8jJSXF7/oUgSUEC1E2SAwWQgghhBBClDkbNmzAVVddhQ4dOpgIsMaNG+Oggw7Cc889Z4YClwcfffQR7rnnnjIVnIUoUzHYLNutRhYVii1btmDo0KFYtWqVed+rVy989tlnSE9Pj3XVhKiSyCZCCCGEEEIIUaYsW7bMCL916tTB/fffj549eyItLQ3z58/HpEmT0Lx5cxx33HF+t83LywsYORYu9erVQ2WCHpupqamxroYoS/L9iMFMIpdWU+0uYsK6deuMtUONGjVw9tlnm/t6IPLz8/HSSy/hjjvucCeEa9++PaZNmxZ0OyFE2aLIYCGEEEIIIUSZcumll5rEP0wOxAzyXbt2Rbt27XD88cfjiy++MAmCbBISEky0MMXhjIwM3HfffSbjPBMItW3bFtWrV0fnzp3x5JNPeu2D61x77bVGYKhfvz5uvPFGuFwur3V8bSKYwOj66683YjT31b9/f8ycOdP9OQUPlve///3P1JniB/0u169fbz6/88478eqrr+KTTz4x9ebk3N5331deeaWpF0XpJk2amO2dMGqObcL91KpVy7TVxo0b3Z9z/d69e+PFF180bcEIa7vNnn/+eQwfPtxE2rGus2fPxpIlS3DYYYeZY6AYv3Tp0gjPoIiryGAlkRMxYOHChTj//PPNvefWW281Iz1atGiByy+/3HgBO+G998svvzQRwBdffLFbCG7atCm+/vprc/8TQsQORQYLIYQQQghR0fnpGWD2hKCrJMMFNO0FnPGu9wdvnQas/7PkfRx4GTDg8rCrtnXrVnz11VcmIpiCqz8oZjqh6Pnggw9i/PjxRkRmIiGKDvSXpND7008/4cILLzTCAgVT8thjjxnxdvLkyUYM5fuPP/4YgwcPDlg3ihgLFizAO++8g2bNmpn1KfYyYrljx45mHVpYPProoybbPf0rR40aZQTkN99807z++++/yMzMxMsvv1xi9DGFYwrWP//8sxFraTFBkZY+mjxGWwj+7rvvTETdZZddhpEjR3oJzBR4P/zwQ2N5kZSU5F5O+4vHH3/cTDfddBPOOOMMI7jffPPN5tguuugic7wUaEQFomV/YPcWYOUsz7IcK/mWEOGwa9cuY9fAkRYcUcBXe573Wd/7sA3vtw8//LDp9PJl9+7dmDBhgpmOOeYY09nWqFEjc2+k6OvklFNOMffSVq1a6cQJEWMkBgshhBBCCFHRoTiUtS7gx/Yjvqt2i+IfZm8Juq3XPiKA4iWjxBjN66RBgwbYu3evmafo+dBDD7k/o5A5ZswYr/Xvuusu9zwj0yimvvfee24xmMLxLbfcghEjRpj3EydONBG9gWAULgVcvlIsJRQwOHyZyyle2zYVLItDmwkF1bvvvtvMU7hlpDIjjEOJdNtnn30wbtw4M0+x+ZlnnsG3335rxGC+UoRevnw5WrZsadZ57bXX0L17d/z666/Yf//93dYQXN6wYUOvstledltQDD7wwANx++23G59OCsuMSj733HNLrKOIM0591Xr95k5g1hPWvCKDRRisXLnSdK6xo4z3D3+wo8sekcCpZs2a5nX79u1mRIeT2rVr45JLLkFWVpa5V9qe7xzlwckXjrhgJ9WAAQN03oSIEyQGCyGEEEIIUdGhf2hNS9D0h2WW4ALS6xf/ML1B0G299hFFfvnlFxMNe+aZZxox1cl+++1XbH1GnlHMoHi7Z88eI2rQMoHs3LnTWDdQdLBhpBvL8bWKsKHwSmuJTp06eS1nXRh9bEPbBVsIJoxGtoc8hwvFYCfOshhhTBHYFoJJt27djMUDP7PF4NatWxcTgn3LZnI+Qm9m5zKK74xipsgjKhgHXQUccCmQWgNIqR7r2ogKADvi2MnGziN2CAWD92LeGzgFgnY611xzjRmVQbHYHpFA25qnn34aq1ev9lqf9yqK0BzdECjqWAgRGyQGCyGEEEIIUdGhfUMwCweXy4gBFEiLccY7ZVq1Dh06GCFg0aJFXstpYUAYWeuLr50EbRwYtUvrB0a8Uoh45JFHjN1CaYZM02bht99+87JbIIyQs/FNXsdjCSQwl4S/sijChEMgqw1n2bbw4m9ZuPsTcUL1urGugahA3r602qGdjPP7zvvakUceae5fHPHAiZ1qfGVHESN9bUGY9g/OTil6nZ9++unFElbWrVsXN9xwgxGJabPz1FNPYcWKFbjiiivMaATb11wIEV9IDBZCCCGEEEKUGYyypQ0CLREoEAQSM4Px448/miHGTERn40yGxmHLjLKlOHzooYeaZRS/KfTuu+++fsvs06ePiQxmZO4hhxyCSKE4wnJKC32OGVnHyY4Opp/xjh07jBgjhBCB4OgI+ojTy3z69OleHVa8PzLZG6dgnuZOeE9jhxlHSnAkQkmRvexopCcwJyFE/CMxWAghhBBCCFGmPPvssyZRGm0bGLFGSwN6VNILl1Fsffv2Dbo9/XU51JkewPQLZjI3bst5GwodHJLMdbt06WI8KimkBoL2ELSoOPvss03EMcXhzZs3G+9e1o/JkEKhTZs2pl6MfKbwTeHFNwI4FIYMGWJsHVgn+h9TzKb4PXDgQL+2GaIKsHMN8NoJli1E56OBw8bGukYijmA079SpU/HWW2/h888/L2a3Q+GXCSvpc877UjhwtES42wghKg4Sg4UQQgghhBBlCj13//jjD5OUjUne1qxZg7S0NBPxSvsHZ8SvPy666CKzve09yeHK3ObLL790r3PdddcZ3+DRo0cboZnJ0k488UQTMRcIJj+69957zbZr1641Se0OOOAADB8+PORju+CCCzBz5kwj2DKSbsaMGRg0aBDChcf1ySefmOhpRjfzGI466ijjxSmqKDm7gK2Lrfn0elYCOS5r2R/odGSsayfKCUb58t7233//mU4n+3XWrFl+72+8344aNcoIwfIHF0L4I8EVqeGVEEIIIYQQIqpQJKVFAK0CWrRoUSwKbPny5SYaNlwfRpfDM1iJfKoWoZz70lxbogxZ+zvwwmHWfNNewPo/rfn+lwBHPxh0U3rFbtu2zUSHsmNBVCx4/j799FM8+eSTmDt3ruloCkajRo1MZxlHFrBjavv27Tr3VQx95+Pn/1pFQJHBQgghhBBCCCFEvJG/1zOf0dAzn5sVk+qIsofJ3N5++2089NBDxjM8GIz6Pf74440AfPjhh7sThCpJpBCiJCQGCyGEEEIIIYQQ8UZetme+RmPPPK0iRKUiOzsbL730Eh599FGsWrXK67NWrVqhR48exuecU+fOnc1r8+bNNdJDCBEREoOFEEIIIYQQQsSeHauA5T8AO1YCHYcCLYInFqz05O0JEBksMbiyUFBQgOeff94k1mQCSycHH3yw8Vg/+uijJfoKIaKKxGAhhBBCCCGEELFn9S/AJ0XJBFMzJAYHEoNzZBNRGfj1119xySWX4LfffvNafswxx+Dmm282YrAQQpQFcpIXQgghhBCiAqH8z6LSXlN1Wnvmt6+MZU3iTwxOqwkkFyX3k01E3MJEjMcddxyGDBmChx9+GH///Xex7xeTu1166aXo37+/lxDMBHB//vknPv/8cwnBQogyRZHBQgghhBBCVABSUlLc3pLVq1ePdXVEJSI3N9e8JiUlxbYidVt7W0ZUdZxicEo6kFrDSiqnBHJxyVdffYXTTz8d27ZtM++//fZb3HTTTWjZsiWGDRtm7B4oBN94441elhD0A37uueckAAshyg2JwUIIIYQQQgRgwoQJeOSRR7Bhwwb06tULTz/9NPr16xewvXbs2IFbb70VH330kREEWrdujfHjxxshoLRQqKtTpw42bdpk3qenp4fsI8nItPz8fJNtPtRtROWgpHNfWFhohCleT1wnpkwa5Jmnb3BVx5lALqU6kFYDyN6iyOA4g9+hBx98ELfddpvfKPvVq1cbX2BOTmrUqIG77roLV1xxhbuzTwghygOJwUIIIYQQQvjh3XffxbXXXouJEyea4bwUdYcOHYpFixahUaNGfqMrjzjiCPPZBx98YDK9r1y50gi40aJJkybm1RaEQ4UCBQWLxMREicFVjFDOPT9r1apVbK+Ngjwgc513ZDCFtarcedFuIJB8vyUKN+oKpNa0liuBXNyQmZmJ0aNHY8qUKe5lxx57LO6++258//33mDp1KmbOnImcnByv7U455RQ8/vjjaNGiRQxqLYSo6kgMFkIIIYQQwg98UL/gggswZswY856i8BdffIHJkyeb5D6+cDmjgX/66Sd3lFebNm2i2rYU65o2bWoE57y8vJC3oxi4c+dO1K5d2wh/ouoQyrlPTU2N/XWxewula8972iHs2gTUbIwqS/O+1mTTpAeQnGZFCBcWAIkxtvWo4ixYsAAnnngi/vvvP/f9mSLw2LFjzfepd+/euPLKK7F7927MmDHDCMNr1qzBZZddZjoWhRAiVkgMFkIIIYQQwk+ULxP73HLLLe5lfLhnUqDZs2f7ba9PP/0UBx54oHnQ/+STT9CwYUOcccYZxjMykBcro8WcEWNZWVluAY9TICg6UMALFZZFC4C4EP1EuRLquQ92vZULWRuKZTcv3LYcyGgYowrFIcc/6/0+yDnj+bSjwkV04X2alg8Ufin0krp16+L11183vsB2+9vQ451WQU67oLI8Lzr3VROd9/Jv74qMxGAhhBBCCCF82LJlCwoKCtC4sXdUIt8vXLjQb3stW7YM06dPx5lnnmkiwJYsWWIyxjOCd9y4cX63eeCBB4xnpD/vYXq4RvOhhQIGxSGJwVWLinLuUzYsRW2fZbvXLEBORocY1ahiU1HOe0WCIz9efPFFvPDCC+YebdO9e3e88sorZiSInTwulujcV0103suXHY57QEVEYrAQQgghhBBRehCjfcOkSZNMJHDfvn2xdu1ak4AukBjMyGP6Ettw/W7duhmf4Xr16kW1bowmZvSahKGqRYU596v2FFuUkb8VGVH8HlQ4sjYArgIguTpQvQ6QkFj5znsFYP369XjiiSeMVZAdCUzYvueccw6eeuqpqHbelRad+6qJznv5kp3tSPBZAZEYLIQQQgghhA8NGjQwgu7GjRu9lvO9ncTNF3r50ivYaQnRtWtXbNiwwdhO+LN1SEtLM5MzGRGheBNtAYfCRVmUK+KfCnHud/skRWw3CIm1W/DLgCrLF9cC/31pzd+wFMhoUPnOexySn5+PefPm4YcffjATR3o47Xx4jx81apTxju/SpQviEZ37qonOe/mRWMHvqxKDhRBCCCGE8IHCLSN7v/32W5xwwgnuqBu+v/zyy/2210EHHYS33nrLrGc/JDCxEEXicPx9haiSMFmczblfAa36o0KzcCrw80RgwBVAxyMiKyPPEXmWXA34813g1xeB3F3AsEeANgdHrbpVEd6raQm0bt06MyqDPvGzZs0yvvC7du0qtj477s477zzccMMNUU8OKoQQ5YnEYCGEEEIIIfxA+4bRo0djv/32Q79+/TB+/HgzRHjMmDHm87PPPhvNmzc3vr/kkksuwTPPPIOrrroKV1xxBRYvXoz777/fZJMXQoQhBteoBEnj3jndel3+HXDnzsjKyHNYZ6RUt6Kn1/xSvL1ESFDopdXD0qVLjQBM+wd6upcEbXsuuOAC85sQaGSIEEJUJCQGCyGEEEII4YeRI0di8+bNuOOOO4zVQ+/evTFt2jR3UrlVq1Z5DRNs2bIl/ve//+Gaa67BPvvsY4RiCsM33XST2leIknCKmxmNKnZ7uVze73N3A6kZ4ZeTXyQGJ6UBiUlAag1HmcUjV4V/FixYYCwdPvvss5CaqFmzZjjkkEPMdPDBB6NHjx5e9j9CCFHRkRgshBBCCCFEAGgJEcgWYubMmcWWHXjggZgzZ47aU4hwGTIO2L4C2L0FSHOInhWRHMv7283WpUDTfSKPDGZUMEmr6diHxOCSWLNmjUne+corrxhLCCcNGzY0oi877fjKqUOHDkb8pQUEvVeFEKKyIjFYCCGEEEIIIURsadnPmsi8t4BfXgB2rARGvgG0HlCxzk72Vu/3GRHaXgQTgxUZHJAdO3bgwQcfxJNPPom9e/e6l1P4vfvuu3HmmWd6Je4UQoiqhsRgIYQQQgghhBDxw96dwLrfrfntKyugGLzNM9/vQqBW09IlkLPFYKdNRE5WaWpYaZk7dy5GjBiB1atXu5fVrl0bt9xyi/FyT09Pj2n9hBAiHpAYLIQQQgghhBAifqjTyjO/YxUqHM7I4PT6kZfjjgwuEjCd9hkSg4tBO4iLL74YOTk55n1qaqoRgCkE169fivMghBCVDInBQgghhBBCCCFiB32C1/8J1GgM1GkJ1Gnt+YxWEVVRDKbHbf7ewJHBsonwNEVurknc+eyzz7qXDRgwAG+++abx/xVCCOGNJ/2xEEIIIYQQQghR3qz+BXhjBDDxIODn570jg2kTUdGgt2/z/YC6ba2o3jVzAZcrvDJsIZgkV/OUa6MEcoYNGzZg8ODBXkLwJZdcghkzZkgIFkKIACgyWAghhBBCCCFE7Ni9yTNfoxFQrRZQvS6wZ3vFtInoeqw1fXAu8Mml1rJr/wVqNQu9DArAV/xuWUUkpVrLFBnsxZw5c3DSSSdh3bp1VvOkpuK5557DueeeG7VTKYQQlRGJwUIIIYQQQgghYscupxjc2HpldDDF4Mw1QEEekJSCCkeNJp75LYvDE4MTE4H67b2X0S7ikOssUbheW1RV/vjjDzz00EN4//33UUg7DQAtWrTAhx9+iH79+sW6ekIIEfdIDBZCCCGEEEIIETt2bfTMZzSyXukbTB9hVyGwc03FFD8bdPDMb10MtBtYuvISEoDD70BVxOVy4fvvv8eDDz6IadOmeX02cOBAvPfee2jUqOjaEUIIERR5BgshhBBCCCGEiA8xmDYRpK4ziVwFtIog9Tt65rcsiWVNKiyZmZmYMmUKDjroIAwaNMhLCKb4ywjhr7/+WkKwEEKEgSKDhRBCCCGEEELEjl2bi4vBjAy22VHBksi9NRLYuxNITPaODA6HzHXAwi+sBHRNegJN90Flh96/tICYN2+eeeW0bNmyYuu1adMGN954I8455xxUr149JnUVQoiKjMRgIYQQQgghhBCxjwyuVgdITrPm2xwMHHmf5R3cYr+KdXbWzAWytwC1WwKpNYHcLGDLf+GVsXkhMPV6a/7QGzxicH4OkJNlTXXbWNYRFZy//voLV111FWbOnBl0vR49euDmm2/GyJEjkZwsKUMIISJFd1AhhBBCCCGEELFPIGdHBZNGXa2posGEZnu2WfPp9YGMhsC634Edq4G8PVYSuFDgujbJ1Tzzb50KLCsSTW9eDVSrhYrKtm3bcMcdd+C5555zJ4JzwqjfXr16oXfv3hg+fDiOPvpoJDKxnhBCVGR+eBz49i6g/yXA0Q/GpAoSg4UQQgghhBBCxIacXUDebmu+RuOKfxZydlpJ79xicANLDIYL2LYMaNw9fDGYVhE2qTU887m7KqQYXFBQgBdffBG33nortm7d6l7evn17jBgxAn369DFTx44dkZSUFNO6CiFEVFn7G/Dby0DjHoglEoOFEEIIIYSIMxgl5y9SrjTluVyuqJYpKgZxf+53b0FCSjoS8rLhymgIV7zWM1R2bXFnaXel14OrXgf3+8LN/wENQ4x2zs32bMfI4KJ2SUitAdsYonBvJlCjSYU677NnzzaWEPQDJoz0zcjIwNixY83ytLQim5Ai4q3+FYF4PfeibNF5L18Ki75fWVlZJtGlDe9hvvcxr87PDy8Ajn0K+P5RxBKJwUIIIYQQQsQZfLDgEOpoPrTwgYUCgYZZVy3i/9zXAC78CwmMDi7Ig8tx3Sfs3YGknauQmLUWuW0HA0kBHrDjiOQNy1GnaH5vYgbyqjWBHbubvXEJ9jYJ7XtdbecWtoxhd24hcoraJcOVAttoInPzOuQnNqgQ53337t2YNGkSPv30U/O+W7du5vXwww/HRRddhIYNG5p1OInSEW/nXpQPOu/lS2aRAGzfy2zGjRuHO++80/9G9IHvNBRof5jEYCGEEEIIIYQ3tWrVQr169aL6kJiQkIC6detKHKhiVJxzX7/YkoQPb0DCPx+Z+cJLfwHqNUXcsznPPZtWtznSeg5HYateQP32SE9Jh8PwITipnnOVUachMoruBwm1PO1Uq1oiEOA+EU/n/fPPP8fll1+OtWvXupfRC3j8+PE4+OCDY1q3ykg8nXtRfui8ly/Z2dnmdcGCBWjevLl7ecCo4PkfAOv/BC6YgXhAkcFCCCGEEELEGXyAj/ZDPMWBsihXxD8V9tzXbeOeTcxcDTTqjLhn73b3bGIGPYPrWVO45O/1lJOawZuC9cbhEZzIaOog5zTW533Tpk248sor8e6777qX0RLi/vvvx2WXXSY/4DIk1udexAad9/Ijsei7VbNmTdOBH5Sda4BpNwNnTQFSHAlBY4jEYCGEEEIIIYQQ8UedVp757StRIcj2JEQzCeQiJc+KOjM4xYPUmt7+k3EI7QneeOMNXH311V52N0OHDsXzzz+P1q1bx7R+QghRrqybB+zeDDx/qGeZqwBY+SPwyyTg9s1AYvkmy5QYLIQQQgghhBAiNsydDGz8B8hoBPS7AEh3RNHWdYiGO6qaGLzHM59iuwRzDHINhxjsSVoUL2zYsAEXXHCBsYawoeUNLSFGjRplIheFEKJK0W4gcMls72WfXAo06AQcdHW5C8FEYrAQQgghhBBCiNiw+Gtg0VRrft+zvT+r4xSDV6FC0HEoUK2OJQrXa2st2zAf+O9/wNYlQP+LgWa9Sy6nWm2gdisgfw+Q6hCAnfO58RUZ/OGHH5pkcFu3egTxkSNH4qmnnkKjRo1iWjchhIgZaTWBxt6J5pCSAVSvV3x5OSExWAghhBBCCCFEbNi1qWgmAcho4P1Z7RbWcrgqjk1Em4OsycmqOcD0e6z5lv1DE4MH32ZNvnhFBseHGLxjxw5cccUVxhrChuLvpEmTcPzxx8e0bqIULP7Gum7ZSbP/eWpKISoREoOFEEIIIYQQQsRWDKalQlKK92fJaUCtZkDm2ooTGeyPBh0984wOLg3N+wLnT7dE4Rqxj7b9+uuvMWbMGKxdu9a9bMSIEZg4cSIaNmwY07qJUvLmSdbrF/OA/c5ldjI1qRDRYswXiCUSg4UQQgghhBBClD8uF7BrozVfo7H/dWgVQTE4e4sVCeuMjK0o1HeIwVv+K11ZtI9o0RexZNWqVZg6darxBf7iC4+gUbt2bTzzzDM488wz5Q1c0XFGnTftJSFYiEqGxGAhhBBCCCGEEOXP3p1AQY41XyNAFGmdVsCqn6z5nauBRl0R12xdClSva/kGJyZayxjdTH/IvN3AlsWoaOTl5WH27NlG+KUI/PfffxdbZ8iQIZg8eTJatmwZkzqKKLPhL898sz5qXiEqGRKDhRBCCCGEEEKUP7s3e+YDRQbXLUoil9EI2LMDcU1hAfA0o3ZdQPP9gAu+tZZzeH2DDsD6P4EdK4H8HMsCIxhfXA9krQfSagEnPodYUFhYiNdffx1jx47FunXr/K7TpEkT3HbbbbjkkkuQaIvfouKz7g/PvMRgISodEoOFEEIIIYQQQpQ/tkUECeR/e+DlwEFXA6npZV+fhVOB2ROAAy4Bug4Pf3sjVrus+fR6xa0iKAa7CoFty4FGXYKXtWwmsHWxJQbDRwz++yMgJwtIzQB6noyy4Mcff8TVV1+NuXPnei1PSEhA//79MWzYMBxzzDHo3bu3RODKiMRgISo1EoOFEEIIIYQQQsRYDA4QGVyNYmg58c7p1uvqn4E7toS/ffZWzzwT4gVMIre4ZDE4f6/1mlK9+GefXAbkZQONukVdDF65ciVuvvlmvPPOO17LKf6efvrpGDp0qBLDVTUx+OfngX4XAs16x7JGQgjbz5udiqX8nZQYLIQQQgghhBCi/Nm1yTNPG4hY06CTleCtMC+y7Z1icHXfyOAOnvlQfIMp9gYSg1NrWJ8zOjhMnn76aUyaNAnVqlVD3bp1zVSnTh3zmp2djRdeeAF79xYJ0QB69uyJJ554AocffnjY+xIV2Mt76xLP+3lvAu0GSQwWIlZsXwFMvQFYMcvTUWgnYaUN0bjtYRcpMVgIIYQQQgghRPlTrx3Q42Rg9yagXtvYn4Fqtb39fxOTShEZXC9wZHBIYvAe6zXZjxicVsNqszDFYPr/XnnllSGt26BBA9x7770477zzkJws2aBKQTsTfwKxECI2fHShJfwe/4zVcUoBuJTori6EEEIIIYQQovzpNNSaSuKXF4B186yEc2e+V3b1SavpmafQWr1O9GwiGBncop8lCrcdGLwcPvSXFBlMcnd5IsNKYNasWTj//PO9vH9d3NaHlJQUIxgzKRwjhkUVt4iw2RvnyRuFqMxs+Bu46DvvTsVSIjFYCCGEEEIIIUT8suATYMUPniRt4Yq0oWKStZWRGMxkb+d/HVo5+Tme+ZT0wKJ1Yb61bkq1oMUtW7YMJ554InJzc837iy++GBMmTEBWVhZ27NiB7du3m1e+79OnD1q0aBFaPUXlJHM9uws8yRCJIoOFiB3N9wV2rpEYLIQQQgghhBCiilC3tUcM3rEy+mLw2t+Ar8d59kFyMsMvZ8+2wGJwONhRwYEig50RzIwODiIGU+QdPnw4tmyxEuIdccQReOqpp5CYmIjatWubqXXr1pHXVVQ+jn4QOGws8OfbwJc3WsskBgsRO457Cvj8GiBrPdCoK5CY4v15kx5hF6nIYCGEEEIIIYQQ5U+IFgeo4xArd6wCmvaKbj2WTvcWgkkEydmQHS0xuMgvmPgTem2bCLueGQ38FpOfn4+zzjoL//77r3nfpUsXvPfee8YKQoigVKsFdB4mMViIeGD3VmDbcmDKpZ5l/O0sRQK5xOjWUAghogN9zO688041Zwm0adMG55xzToVve+6P+42UQYMGmaksWLFihanbK6+8UiblCyGEEFWSwkLgvqbAY12BDz1etiWKwdtXRr8uy74rviwiMTiITYST3N3ewrEvzmzxfm0ifMRgP9APeOzYsfjmm2/M+/r16+Pzzz+XD7AIHWcEviKDhYgdn1wGNN0HOP8b4Ko/gav/8n6NAEUGC1GFCVV8mzFjRpkJbaJkfvrpJ3z11Ve4+uqr9QdeCCGEEPHFliWWH26Nxtb7xMTQLRXy9wBZe4A9JUQ11WnlmadNRDRhFO7qX4ovj0T8OuE5K8kdReH0ev4Tc71zJpC5FjjwcmDoff7LYXv2v9iyi2ixv5/Pa3jbRPjh6aefxssvv2ytnpqKKVOmoH379uEfk6i68DpLSARchZZXtxAiNuxcDZz+NlA/evdwicFCVGFef/11r/evvfYavv7662LLu3btWs41E75i8F133WUigH2zOi9atMh4vkWTPXv2IDlZPw829NFjm2hIpRBCCOGHZ/p65gffDhx6fWjNtGuTZ94WkgNR25HQLHNddE/D6p+BAkfCttJEBlMA9icCuz9vYAnBZOuSwOvVbAIc/VDgz2kLwTYzorB3cMd///2HG2+8EZ988ol72QsvvICDDz44jAMRVZZpY63OmWZ9gL6jgWq1rfeKDBYidrQ9FNj4t8RgIUR0GDVqlNf7OXPmGDHYd7nwZu/evSbCItoibCSkpaVFvcxq1YJnpK6KEfRqEyGEECIEwhFQd230zNdoFHxdiqNG9HRFXwx2WkQMvR9o0c9K0FarGaJOreZAcnUrInrL4sjLOfgaa3KwefNm3H333Zg4caLxCrahVcTZZ59dmlqLqsSCT4DMNcC/nwL7nw+0HWhFzzs7ZIKRn2NFx4e6vhCiZDodZXXUbFwANO5WPIFcl2EIl9grGUKIuGXEiBHYd999vZYde+yxRhz79NNP3ct+/vlns+zLL790L1u2bBlOOeUU1KtXD+np6TjggAPwxRdfRFyXlStX4tJLL0Xnzp1RvXp143vG8unn6twn6/HEE0/4ja7lZ2+//bZ72dq1a3HuueeicePGRlTt3r07Jk+e7LXdzJkzzXbvvPMObrvtNjRv3twcT2Zm4AzTu3fvxnXXXYeWLVuaclnnRx991Hi3OWG5l19+Od58802zDgXHvn374vvvv/fy0r3hhhvMfNu2bc02nOzj9vUMpq8tP581axauvPJKNGzY0EQTX3TRRcjNzTUZpflAULduXTMxcsRfvWzPYNsvN9DkhNfBUUcdZbJSs40GDhyIH3/8sVj7sG7777+/OV4OV3z++ecRDpMmTTLb8Tro168ffvjBJ+FLETk5ORg3bhw6dOhgzgPPB4+Xy52wA4TRMmynGjVqmHPBB6eSPIPff/99dOvWzRxHjx498PHHH5tzwXPiuy3Pv11v1oXH/+uvv4Z13EIIIUTcQe9bJ+FED4YTGZyU4hGMmU09miz3/O9Cj5OBlvsDjbpYCbSiDQMJ6new5revAPJzS10kRy89+OCD5v/OM8884xaCmzZtiieffNIIxEKE/J2kEGwuoN7W9Xrqq8CZ7wHDHy95+4I84LkBwBPdgb/eU6MLES0+v8YaVfLdQ8B7o4F3zvBM754ZUZEaByyECMghhxxihphR+KxVq5YRDSnuMSKWAtxxxx1n1uM8lx100EHm/caNGzFgwABkZ2cbQZLC7auvvmrW/+CDD3DiiSeG3eoUzijonnbaaWjRooUR2Z577jnjZbxgwQIjPrZr187UgeLqNdd4R0twWc2aNXH88ce760iB2hZkKZpSzD7vvPPM8dKf18k999xjooGvv/56IyZy3h9sIx4nfZZZVu/evfG///3PCLoUn32F6u+++w7vvvuuaSeKhM8++6wRVH/55RcjMFKQ53A/itjctkEDK1s06xuMK664Ak2aNDH2Eoz4phBJsZNt2KpVK9x///2YOnUqHnnkEbOfQBEj3I+vbUheXp5pX2cbTJ8+HUcffbQRsynA8nqgT93gwYPN9UHRlsyfPx9HHnmkKZeCMx9YuD4F+VB46aWXjLDN64vniB0AbG92OlDstSksLDTLKTxfeOGFxuqE+2Ybsj3pm0f++ecfDB8+HPvss495WOI5WLJkiV8R2wk7NkaOHImePXvigQcewPbt2835ZmeBP9566y1kZWWZuvOae/jhh825Zf1lPyGEEKLCsrPI8iASMXi3QwzOCP6/xlCzqRVNzKkgH0iKwqMs67vud2u+YVegZuNSlJUJzJ1sJY5r1BVosZ//9Rp0BDbOB1wFliDcsFPxdeyO+hLye/A/MP9/rVq1yr0sIyMDN910k/mfxP+spUnQK6oY6+Z55pv1Dn/7tb957E8+ugDY59To1U2IqsydZeDZ7RJCiCIuu+wy/vN0t8evv/5q3k+dOtW8/+uvv8z7U045xdW/f3/3escdd5yrT58+7vdXX321We+HH35wL8vKynK1bdvW1aZNG1dBQUGJbc7tx40b536fnZ1dbJ3Zs2eb9V577TX3sueff94s+/fff93LcnNzXQ0aNHCNHj3avey8885zNW3a1LVlyxavMk877TRX7dq13fubMWOGKa9du3Z+6+DLlClTzPr33nuv1/KTTz7ZlZCQ4FqyZInXMXKaO3eue9nKlStd1apVc5144onuZY888ohZb/ny5cX217p1a6/jevnll826Q4cOdRUWFrqXH3jggWb/F198sXtZfn6+q0WLFq6BAwcGbXtfLr30UldSUpJr+vTp5j3307Fjx2L7ZHvxnB9xxBHuZSeccII5Ph6nzYIFC0x5Jf0k8Tw2atTI1bt3b1dOTo57+aRJk8y2zuN4/fXXXYmJiV7XIJk4caJZ98cffzTvn3jiCfN+8+bNAffLduc6bFubnj17mrbjdW0zc+ZMsx7Pie+29evXd23bts29/JNPPjHLP/vss6DHLISoeqxevdrcH/gaTfjby3tdKL/BonJRpud+8Tcu17hanun1EaFv+79bPdst+77k9Wc84HJNudTl+vZelyu35P9kIbFwqqcOX9xQurI2/usp6+NLAq/31e2O4/7O/zq/v+Fy3VnX5bqvmTXvB/7n6tevn/v/JP/3XHjhha7169ebz/Wdr7pEfO5nPOi5Nv96P/wdL53hfT8Q5Yq+85Xj/1ox8nOt34MN/7iiiWwihBAB6dOnjxk2b9sWMMKTUbmMIv39999N5C+1Q0ZfMorYhhGnjAR1JqpgOYzQZEQvoxjChZYAzsjUrVu3muFwjHZlXWxOPfVUM2yfkcA2jMzdsmWL2wuZdf7www+N5QXn+Zk9DR06FDt37vQqk4wePdqrDoHgsSclJZlIXye0jeC+nFYa5MADDzTRtDaM2mX0MutcUFCASGGUqjMSpH///mb/XG7Deu63334mOjVUmGSQ0cuMbD3ssMPMsnnz5mHx4sU444wzzHmx25J2GYcffri5fhipy+PhcZ1wwgnmOG0Ytct2L4m5c+di06ZNuPjii72ikmnNQGsKXwsHltulSxev88tIZcLIbWIn5GMEPOsYCuvWrTNRxvwe8Lq2oS0GI4X9wShi2nLY2N+XcNpeCCGEiDt2Fg0pj8gmYnPoNhFk0M3A8ROAwbcCKSX/JwvbL7jdQMvv9J+Pgd9fAxZ5/2crkeytnvmSksjZ7N7ifx16CjNyOHcXkODnkX3rUqwefyRubz8fF++Xgk6dOuGvv/4y1lscGSZERKz7wzPPBHLhwkRzvtHyQojSQZskenDzNyGKSAwWQgS+7yQlGbHS9mTlK0UsirwU9mg/QGF327ZtXmIw/X3pu+oLxTn7c8LtNmzY4J4owgbzQ7vjjjvcPry0S6DVAD1wndtR3KPIy2H5NhSGOXzfFgKZYIPb0TqBZTinMWPGmHUoOjqhX28o8NiaNWtmLCmCHbtNx44di5XBP/QU2lnPSHGKrcQWS51WCvZyWhyEAkVfCrGnn346rr32WvdyCsG2YO7bni+++KIZoshzxOPhefR3zP6uF1/stvPdnjYLtAhxwjrRAsK3Pmxb5/mlSEtrkfPPP99YVdCG5L333gsqDNv1YGeEL/6W+TsftjAcatsLIWLLhAkTjB84OxvZuUYrn0DY3u3OSUkoRaWFHoYRi8FhJJArKw66CjjxeaDXGUDrg4CCXOD9c4BPrwB+nlgKMbh+4PUyQhCDmbDLxo/wnZe9E612/oLhnVLQu0mSscFi7gshoiIGp9UG6hX9t54zEXh6P+CRjsCqn4NvX9eTN8OweZFOiBDR4NDrgW/vBrK3IVrIM1gIERQKv/fddx/27t1rxOBbb73VCK70meV72+vVKQaHCj1T6ZlrQzHRN0mX0wOXHrT0P6NATRGTD9gU73yFO0ZsMjKU/riM1GSyOyafo48tsddnpDD36Q96yDoJJSo43oT8UJf7JpDzB0XLk046yYipFHid2O1J/2F6JPuDEbS+idvKEtaJ5/7xx/0nu7BFcZ5XRi4zUpg+wNOmTTMezuw4+OqrrwK2Y7gEKieUthdCxBbeE9gBNnHiRCMEjx8/3oxmWLRoERo18i9g0Wefn9vIs1NUWkoVGVzU8Z6UClTzHuFTbtRqCvQ6zZqI+U/DkVUuICcrcjG4epDIYKc/cra3GMz/xRyBNLLJbk/UVkp6sSLe/+RLnFE036FFIww++ujw6iqEL5nrgV0bPH7B9ghDfg+2LvYf+esLo4mPegiYdpP1ftMCKyGjEKJ0/DIJ2LYceKwLUKdl8d+Fi/0nVA+GxGAhRFAo8ubm5poEZkyAZou+hx56qFsMpkDoTADWunVrr4dgm4ULF7o/J4899phXZCQjagPBxHMUbrmNDQVqRvj6wgRsjAJlRDAf3Blle9ZZZ7k/52eM3GV085AhQ6J6BfDYvvnmG5MszBkd7HvsvlG1TpjgjAnx7CRxsRYRKKyeeeaZpq15bKybk/bt27vFj2DtyeOh+OrvmP1dL77Ybcft7Shv2zZk+fLl6NWrl1ed/vzzT2NTUVL7sZOA63GieMzkeuz0oEDs73jsejDRnC/+lgkhKja8L1xwwQXukSMUhdl5NHnyZNx8881+t+F9R0O1RZVg5+rIxeBjx1vb59AKIYz/Onl7gfy9QHXL6imqMHAgrSaQkxn+EPdQI4Odnzkig/lfm5ZbpNY1++OYWkUfpFTz2nzXrl24/9GncMY51vt9e3SO+X9FUYktIpwdNaF8v5k80WbTv9GqnRBVmy7Do16kxGAhRFAopnIY/kMPPYR69eq5h6BRFGakLqOEKb46GTZsmImcmj17toniJfSPpS0Dh9l269bNLHN65YYSWekbRfn000/79dVNTk42Vga0ivj3339NhKgz0pdlMcqVn//9998mytkJ7QxsITZceOw8zmeeeQa33HKLezmH7/GPOjM+O2Eb0Z943333Ne9Xr15t/GvZpnY0KbNCE3/Cd3lw1113Ga9f+h37s8vgeaT4+uijj5qHGKePrrM9eTyMppsyZYrJem1bJ/AcsfySoL8xy6EQQ1HG9g1mNLlv29A7mv7NL7zwgvGqdkKrCgrcbFdalfC6dmJHNweKZGanBa8Z+ifzHNvHy2geegn7Cv5CiNjBDpqlS5eaDkx2RvF3JBzRhJ2hv/32m9f9nB1I7Cji/TsQFGt4L+C9hvd3djIFGsLNe43zfsPORMJtQ/UyDwWWxeOPZpmiYlCW5z5h51oTR+smfy8Kc7OBZG8B0y/N97Mmq5Ilr799JRJeHIyEPdvg6nkqXLR3KAMS0mogIScTrpwsuMJos4TdW91tUVi9buBjqtceOO0dyy6CPpCFhSb/BnMg2Cxe8BdwQJpVVlI1r7LYQbVkDaOqLbW4TvUkv+dW3/mqSyTnPmHt757rt2lvzzWXVtMdpV7IyOCSymzYxazvqtPKRC+G8x0SpUPf+fKlsDyvbXrmRxmJwUKIoDAKlGIf/YHpxWs/RPPBmgIvJ1+LCEZKMbqBwicTqVFse/XVV030JhO32XYN4TB8+HC8/vrrxh6CYjIfwhmlWr++/8gLWkU89dRTJrqTQrYvDz74oPmMYjcjvlgmhUEKsyyX85HANmJiNUaWMlkeo1VpN0CBlxYXdhStDUVFCqRsJ3ohMzmbLcDa2KI5y6QtBsV57scWicsSipv33HOPOd/02X3jjTe8PqfVBs8nrSN4vil2UKilRzMjydnGjBj+7LPP3MdFKwZeM7TuyM/PN6I+t2Pik2DwuO+9915cdNFFJjKYfr+8ptgp4esZzEhwev/S45h1oC8wOw4Yoc3lFJ8pLt99993GJuKYY44xwg2PkeeAiRKdCRB9obDDRH8sl8fLCHd2APB8UgQSQsQWJrPkPWL69Onmd4sjCnifYBJNenY7R5kEg4knee9wjn4hfG+P+PDngc6oYXZC0i+dHWUDBgwwPua8t/jywAMPeN3zbdjJ5TsSo7QPLRSaKRBE8jssKi5leu5Hfo7E3ZuQ9t8nKKjTFq7UWsjbvhNIyo7ufihW5Sag/h7r/1netlXIjPC/mk36z+OR37A78pr3hyvNDsMF6iRnWA/JOZlh/R+ssWM9bAl8Z14yCoJt26Bo6HwusGzuXJx44omm88ldtxSPxL4zO89dFjvYmcQ3pwDIK3AhJSkBBXt2Yoeffek7X3WJ5NwnNTsUqQMSkbxpPnZntEdh0TWVkpcEOzZ4z/YN2BPsuqbXdXI1JFz4J1ypRcEhpfyeitDRd7582RGLQC1G8G/+z5pv1AVo6hkZGy4Sg4UQJULhjmKwUxzj8Fcmy2LUla8YzIdk+vXedNNNRuijnQMfiikIUnSLhCeffNJEltL6geVRhKNoSyHVHxRQKTAy6pQWB76wjkwARDHwo48+MgIghWVu4088DhX+4aJHMZPd0WeSQiWjoemne9111xVbf+DAgSZ6mkIAo2UpSjPS1RnJvP/++xtBlhGxFFL5Q08RtDzEYAoq/CPJqFenv7NTDCaDBg0yAj3rSVGUgiivEYrtFG9teFwUYum/yTaiMMJjX79+fYliMGGUL4UZtucNN9zg9oS+/fbbi50HRiAzIpsRvB9//LERVSgGXXXVVe5Ecscdd5wR7SncUPRhYkKeE9bJTrrnD4rx7PC48847TecHk9rxvLHTg4KPECK2XHPNNWaUCO+rdgJPQoGY959QxeBI4D3dHhVDKASzDs8//7y5R/rCqGNnUk52pPG3gCNvfEculAb+dlAYpxguMbhqUebnvmEToI13roWyoR5cqTWRkJuFlD2bS/f92L4Sib8+bWZdbQ6B6+xP3R8lpNcBtgEJedmoV6c2kBha/oCEAk9ncO2m7YD0kutHsZkd2LboTMsq/qdK//0+9zqLlq9Cv07WPYX/TxiIQXIT0pCCXCQV7PHbFvrOV10iOvf1DgY6W896XgYsu5q7Z9MT8lA9yPcu4cUhwPp5Jurddc0CIEEdj+WJvvPlS3Z29Ds9A7JrM/DBGGDFLI91C21b2h4CnPyyd2LSEElwKXuNEKKS0qdPH/Pn+Ntvv0U8wj9pl112mRFPReWAFhO0svj6669jXRUhqjTsjGLHE0dn0L+dHuLsDFq2bJnplAo1gp+ReuxIom/9CSec4F5OD3tGhHDURyiccsopRpxmJ1JJrFmzxiS5pG2Qv0ji0jwk2tY4EoOrFnF57neuBTb+A9RoCNRtG7r/7zP7A1v+s5LnjF0Xntewk99eBT670po/7DZg4A2ez14fASwt+u9408rQ6/bCYGDtb1YCuju2ligi8/5y5JFHujvb2QnEYAqOqJp3Sw/0qWYl5+v2UiI+/maOuYewY4m5EnhfyryrFZJ2rQMyGgE3LK4Y512UC1E99xsXAM8VdXD2OQs4Pshzy5O9ge3LAdqk3LSidPsVYaPvfPmypoz+r/nl/XOA7SsA2iM17Gwt27QQmHIxUK8dcPLksIvUr4IQolIyd+5czJs3z9hFCBFt+CBGiwsnM2fONIITI3qEELGFkXP+LBb4cExLnlChNzlHmjg7FfmwxffO6N9gcDQDLXeaNm0a8n6FqPQsmwm8dQowaRAw//3Qt6tZ9D3Kyw4vWZ0vyx2jndoN9P6MCeRsmEguVGq3BOp3AOq1LVEIdq37A5OuGoZuu39CYgLQqFEjk5iSo5IYLNCre2fPqODNO0xOCo5s4v8PwtFmSYxgJrmypxJliLMzpKTvXHaRJUT16I1qEUIAWPItcMxjHiHYtokY9hiw+JuImkg2EUKISgUTwjHZD4cA88GbQ4KFiDYcxs0EUrTJYEI5eofSxoPRiPQpFkLEFtoX0SLGtmWguEIRl16b9HUPB1o4MBKYPuP9+vUzCVIpNtMvnLDTkT7p9P4ltB864IADjJUSo4dpa7Ny5Uqcf/75ZXCkQsSQJd8AK2dbSdBa9rP8QvfuABp0Buq0DL7tro2e+RqNQt9nrWae+az1oUftOmFC4uXfW/OpNYFmVhJfN9U8/sHIsRI6hsSpr4a86pJJY3B54+XAMdXx2dIkfPDpp8ZWzCbx8NuR3eNMjLv1JuzKzcTOZcvMyAZCS6vrr78eeG+2RxgvyAeS9GgvImT1r5alQ+PuQIpP8kd7SHpJYjCvwZyizwvygCmXAZsWAK0HAEM9tidCiAhwFQKJKcWX877PzyJAvxhCiEoFh/LyQZwJfDgct1q1ELJZCxEm9GBjtCAT5zGZC/2b6YfNxISBkhoKIcoPir703uQoEQ7FvvHGG42fNyODf/zxx7DKYqciv+f0Od+wYYOxg6F/u51Ujr7EzmG4TCjJxKRc175XcOg3h4ALUalYMh2YM8Ga73UG8Odb1vzw8cB+VmdJQHZv9szX8E7QGLIYnLkOaOTxBA+ZrUs8+29zUHERNb2BZb3ACOHCAkSbSZMmIeWPRejYJ9W8f+nph0yOBS+a9UF6sz64alJ/vNW/P9atW+f+iPciWkmgyzBLvGM9XaynHu1FhMy4D1g2A0hMBq5daNm32NCShcsL863OnkDs2e6Zr90cmFeUdDo59NE4QogAtD0UmHYzcNJLQK2mnt/AaWOLj24JEf1iCCEqFUzoxakiIMv2iguHcTJBoBAiPunRowf+++8/48lOz2B6BI8YMcL4tEdi13D55ZebyR+0iHHCxJWchKj07FztmacoaROKfUOkkcG2TYT9IBwJOy0vXkMjP500Q8ZZUxl1VDHB8kNDPALZkQP6BFyfPpS0j+BoB97H6H3uTsx78DVlUkdRxWCk/Lo/rPn0+sUTUdGXe+gDlqjr7IzxJXurZ54+4DtWAZlrrehg7iNSf28hBDDsEeDt04DxPa3OFtt7nx2iIyZF1EISg4UQQgghRKXstLn11ltjXQ0hKi9uUZWmt13C89ndtckzzyjcSG0iIsEpWkWQgT3SAICxY8eaEURkc7bLUZ8tQbflaIRZs2aZEW/nnXee8TIXImpQsLUjfpv29i/a9r+w5HL2FPkFk/R6lkhlyt5pfVeDCclCiODQjumiH6wI/i1FCUMbdALah2d9VmXFYHrFcYgNI0ToHSeEEEIIIeIHCiZZWVnGi7s0GdDp10tP7zPPPBMdO3aMah2FEEVQ6LGjdWmtEE5ksG3TwCHoaTXKNzLYTnJlR0JGgzW/Af+7xSpvn1OB7id6JZHkyALmFrA59KgTgYIvrTe7/YjBS6dbQ/Or1QGa7oNevXqZSYio47RsoeAUKdl+xGD6ihNGB0sMFqJ0UMNsP9iaokCVEoMpBLdsWUIyAyGEEEIIEVNWr15thkdHCu0g3nrrLeMhT89eCsP0/mWSRyFEFMjP8Vg9cMiqM+laKGKwHZ0bbmRug47AyDctz8Q6noRrYeGMxHWK2KW1zFj9szXf6gD3YnqWMwHlO++8Y94zIIn2NccOaQ+8FUQMfvdsIDfLSsZ3+S/B983EXUQJ5MqXv94Dls6wvHKPebR0Qmos8RVxIy7HEXFfvZ53x82mhUCHIZGXLYQAls0Eln1ndeDQesXJCUX+/dEWg/fd1yfDagnwR+7TTz81mZXjCUYEk+nTp7vnRemid/bs2YPq1asr0jpK7ZmZmWkSUZU2cr1OnToReSL6sn79epMJvaz2U1L5ke6D5TKBT0nXZ7hlh1LfSMr+5ptvcNJJJ+GVV15Bly5dolZuOPUtqWyOrGCbMhnSxo0bo1ZupPUtizJDLTca9XS2J6MfY33ssSwznHJDvUbDadPyrmc8lRms3PJoT3/wd5Ad96X9n3bNNdeYib7Bb775JiZMmIDrr78ehx12mBGGzz777FKVL0SVx44KJhTBGMEaqhhcWOgRoCgahQOTpXUdXrrmr9kEaHmAJQr78yve/B/w/SNAThbQ5Rhg37Owe/duPProo9i6dav5r+47dcueA9v5d8GKjdhS8D1SUlJwzz334MsvLdE3KSkJr732Gs444wxg7W+BbSL4kJ+Xbc2nVA98HHMmAt/cCeTvAUa+AXQ9tnTtIsJjzVxP0sRDb6i4YrAz8Vug7yPX2bXZ+m7THzw13c86PqJynVae95v+jWaNhah6zHwQ+O4hk1wUNZpExYM7JDF43rx5uO6661CjRo2QBC16IeXk5KAs4J/5Rx55xGRo5lCZp59+Gv369QtpW1sQuvrqqyVeRgE+HLZq1cpk0eZDoygdHEJWIzUVmx3ZgiMlvXZtTJs+3QyzLU0k/SnHH4/snTvLZD+hlB/JPuxy92ZloW2HDli+ZEnA6zOcskOtb7hls9ybrr3WzN923XVIS0mJWrnh1LekstmG+fn55mEomuVGWt+yKDOUcqNVT7s9mQ2cv2exPvZYlRluuaFco+G2aXnXM57KDFZuWbdnSUTLzqtTp0646667zDRnzhxccsklGDNmjMRgIUqLMwkbRbC0MCKDc3YCroLo2jSEw37nWlMg6Hk8/z1rvm5r83zL6N4PP/ww4Ca3HpKKPoOrmfmb734En/33gNfn1apVw/vvv4/hw4cXj0j2jQwuyPO0D200ApGYZAnBps67Aq8nog8F+1+e9y+oVjScEb2BIoO/vgP4/TVr/uIfgSY9iq/T63Sgxf5WRw9fq9W2/MThsmwihBCRM3cycMJzQK/TEC1Ctom44YYb0KhRaOb+jz32GMoCZm6/9tprjd9S//79MX78eAwdOhSLFi0KuW7kwgsvlH9cFOAfIz4sUhSWB3Pp+fnnnzH1o49wbeMmaFsjI+JyVmfvwcPr15mIrtI8jHN7Pvjf2LQZWqZXj/p+Sio/0n24y23WHM2bNUNCYSESfIdRRFB2KPWNpGyus3eX9Qf+xhYt0CFIp1u45YZa33DKjodyY1nXWNezspUZTrkqM/rf/Wif+2j9/kSbX375xVhG8H8kI49POeWUWFdJiIoPs5jb1G5pWRSk1gByd5UsBvNze91YiMGhRB/b7M3ECy+8EFQIJg3SPR1YW5zJ4YpGp3722WcYOHCgZ6HTHsMpxhE7KpikVAutnmxLUX74XuMVWgzeVnJksBF2iwj0/WbEPScn9doC25YBmxdaIwJKkQtAiCpNQS7QMrQg2KiKwcuXL0fDhg1DLnTBggVl8hDw+OOP44ILLjARHYSi8BdffIHJkyfj5ptvDrkcDl9s37591OtXFcVgRglGw9ZAACtWrDDN0LxaGjrViB8bEz74l2V9yqr8FunV0bx6OjJq1PQrBsdTfRMTEtC/bl10r1UbrdODRIDEUftWpHJVZvy3Z1mVqzLjvz3LCtse4u233zb/YwcPHoyHHnoII0aMCGmkmxAijMjgWs09glEoYnDdNsDYtUDeXusBN1yYSX3jP0DWeqDnqUBGlAVlh8iauWUtrrrqFfd7Pn9yxAGfgTjt2rXLvB6+8x2g4B+zzslnX4iD82qY0RXp6elmJAK38SI1w4qmpg2EU2gj+Xs988EigymoO6OZRfnhtEQge0O3JIs7fO0dIhWD/dGomyUGs4Njx0pLHBZChM++ZwPzPwAG3ohyFYNbt24dVqFlkaSN5vu//fYbbrnlFvcyRqQOGTIEs2fP9rsNrSqcdhWMBrFFTE6idNjtqLaMHgmJiViTk4PkrKyIy1izd48R5/nndGcYw+N94fYsZ9WePSh0RX8/JZUf6T7sclfv2YOC7GwkZGX5FYPDLTuU+kZSNtdJTUnBmFatsKegAAuDnPtwyw21vqGUzVEA9kNPNMuNpL5lUWao5UarnnZ70j8w1sceyzLDKTfUazScNo1FPeOlzJLKjbQ92blljxyKhGjZTtF/ff/99zeJ5E477TQ0btw4KuUKIYqo0xJofziMd3Dd1h7BiO9DFXgZ9Ros8jUQv74E/PycNd+0N5BxYHRPi8PyYsEfP2PvXkucvfTSS3HRRRf53+aN74Allhh87a33hpaI6+ZV/n0fvSKDg4zwSHOKwYoMjlk0bUWPDN7r6EioXtf/OuF4gjvpeQrQYj9LFM4IPbhQCOEnaetvr1hJ5OjbnehjLXnU/Sgzmwgn/EH866+/sGnTpmJ/2o877jiUBVu2bDGeqr5/5vl+4cKFfrd54IEHjEdcoIhWUXp4LSgqODowwrp5q1b4cMMGoLDIJywSUlPRrlMnrFywANtXr464mMysLFPOu+xQ8VefUu6nxPIj3Idd7vt5eWjqKsR6V6F/cSHMskOqbwRls9w2HTrg9exsJOfnmU6uaJUbcn1DKJv6j4uiUWZmVMuNqL5lUWaI5UarnnZ7sqMiK9bHHsMywyo3xGs0rDaNQT3jpswSyo24Pbt0Mffcbdt8HpRDhPuKBrQQ69ixY1TKEkL4gb6Fvt6FY760IlmTU8u2yWo5klRmhZlrgwECE/pZ4hZFqqO8vX3dUbtFXqeJedYzY48ePUwCuYDYVg8Jid7CWTACjazMK/IBLkkMTpVNRMzwtfaoyGLwiROB4Y9bAnfNppFHBv/7mSVQ1WgINO9rLet+QhlUWIgqyMZ/gCb7RDUhY9hi8LRp08xQF4qzvlAUpGAbLzCKmB7DvhmqWU8Kb6J02FHBHP4kQbj08JrMzczCOUcdhValjK6vXaMmmtQv5ZC5uvXQ8dLLsHNXVtnsJ4TyI9pHUbk7dmUhPzkZyfn55u98qcsOsb5hl123HvKHHYMzb7kZz99xBzqVMBIjnHLDqW9JZfO7nlVQgJq160S13EjrWxZlhlRulOrpbs+kJCTEwbHHrMwwyw3pGg2zTcu7nvFUZrByI2nPPTm52JmYYEaU0SMzEpKTI4pTKIaEYCFiQPUQRdDSYttSkMz14W1LIWvLfw7R1w8JCchLqo6UgmzUTrOSv73zzjuoXr16yeIgPVdL64vqFIOTFRlcMSKDK7BNBDsl+F0I9H0IVQz+7GogewtQuxVwzfzo11OIqsw5n0e9yLD/cV9xxRUm8cYdd9xRrkPuGjRoYIYqbty40Ws53zdp4mNUXkRaWpqZfKFwKfEyOthtqfaMDkmJiejath26de6MeKBWGfsqllX5LLfQ5cKO3FzUSU01w5ajVW5Z0Lqp1QvfvV179O7SJWrlRrO+bM+C3FzUTE1F7QhFnli0b7yW6WxPXp/xWs/yKDNa5apNY9+eWbt3Y/7WLWaEQ9BRDkGIdDtSr1494xXM/4x169YN+t8k0shlIUQU+Os9YNVsK3lc3zFAbYe4GwrO6MXMdZFHdAZIXrdmzRpgRzZa1ARqpSXgiSeeQPfu3Usot+ieEo2EeCFHBjvuy7nRGVUhqmBkcCiUJAZzFKbdBqFYpAghYk7YYjDFV0bblrf3WmpqKvr27Ytvv/0WJ5xgDTfgMES+v/zyy8Mqa/369UoeEgVsT0A+uEkMLj1r11pZmffk5JgHalH665MevMl5eXF/fe7es8f9Gq/nviK1Z0VA7ak2rYzXaHaRr2asoGBjRyRzXvcqIeKU5d8Bf7xhzXcfEb4YXBqbCGdEZ0aDYh9zlOuoUaPwdNcCtKiZhLoZyYF9gp3WE4feYAmE4URHL/oS+PsjK5ry8DuAZn083pBFNhVBE8g5Et3JMzjGCeSquhicsxNwFfgXg9m5sXmRNbS958lAko/XqRCiYojBJ598MmbOnIn27dujvKEIPXr0aOy3337o168fxo8fb7x/x4wZE1Y5kyZN0gNCFKAI3KpVK6xatSpqCV+qOu2aNwdj33O2FrdhEeFBv8vChAQkulwBbSLihSU7d7hfE+L03Fek9qwIqD3VppX1Gk2pWRMpKbF50ON/RJtzzjknJnUQokqwYT7w6rFA7RZA71HAARdby1f8CCz+CsjJBPY7D2jSo2RBNpJIWq/I4DBtIii8Btj39u3bceONN+K7775DZjtLhK2WWGh5pScFeWxmh9nBVyNsKJDNf8+a33e0RwzuOAQYt71IFEZokcE5igyOWWQwkxh2HR7adru3ABv/BtocAiQmIebwGvvfWOu7wKRU3Y4PIYHcjuDfaVqlOJlyKfDPR9Z8832BhvExAlaIqk7YYvAzzzxjbCJ++OEH9OzZs9gf/iuvvBJlxciRI7F582ZjUbFhwwb07t3beBiHG6X84osvRuxlJ3yihvbsMf5Zir6JTnsyGoG+1qUZJiss2EGxc+dO1K5dO+7bs9q8eea154AB5r4Wj1Sk9qwIqD3VppX1GuX/Qvprxhpai3EkWKNGjbyWb9261SyLpxwXQlQ4dq6xIiE5OYWhtb8BP4635tseGkQMdlo1RDCknNYJ1eta+w87Mrj4vvk8w2fc+++/Hzt2WMfz+eICtDvgGDRt0wkozA8uBkdKRkNHvbYUF5hTSriX8t586mtASoaVtEuUH87r6LQ3rY6RkijIB14cAmxfDgy6BRh0M2IORdxfX7TmOx8TRAwuITLYGRnt28HTqJtHDN60QGKwqJr8+iLw62RgxyrrfaMuwMCbgI5HxKxKYf+qvf322/jqq6/MH31GCDtFQM6XpRhMaAkRri2Ev6QideqUU4KDSoydLZwefRKHotee7KhQe0anPfPy8ipEew4YMMCILkwiSAEjHqlI7VkRUHuqTeOdin6NsoPVHzk5OcZ6TAhRSjHYXzK3UJJMOYW0tNqRDxnnfo0YvMHyKw31PuUQ8Qqq1cOrkydj3Lhxlk9wEbxHtDrjCTS98BKUKU6bCkaMRkIg8U6ULa0OBBKSrOsp1Oj2rUssIZjMfCA+xGCn3UV63eAdMFf8bkUIV6sVXgcPRS8bWkV0P7FUVRaiQlKrOTDkTqB+e8ta6M+3gLdPBy7+AWjUteTtOfKmZf/iHZPsZFr9M9DmoLIXg2+99VbcdddduPnmmyvkw4EQQsQbFIBr1fLzx0oIIURYPPXUU+4ABY4Eq+FIfMdo4O+//x5dopioU4gqyc7VnnlnRKSXGJwZeHtbOCpNoilaRXC4fUGuVV4IkbH5+fnYtW4p7JCg8664Aa9+t8z9Oe8btJvhsy6t8EKGFg0cbk+hLJwI4vQoiMEiNhx4mTWFQ14c5gVxiri+9g5OGABIEStgOUFsIhgZbMPIYCGqIp2P9n5Pn/hfXwLW/BqaGPzqcOC6/4r/1tGWiZ/RWqisxeDc3Fxj1yAhWAghosPixYvNiAcOUeTIBSGEEJHBxHF2ZPDEiRO9Rlsw2q9NmzZmeUWJzo5mTgaWZSf/FVWLaJ/7hB2r3V7ihYx2sstNrQk7VMi1Zwdc/vZXmI+EPTus9Gjp9f2vE0odajUDajQ2orCLYqyf6EzaCz755JOYN28eli1bhuXLl2Pi0UkY09uKRv594Sr3M+0xxxyDe++9Fz16WNYWYbXVvLeR+OUN1nYnPg/0PDW07arX87TX7i2etlj8NRKWzTDRmK5eZwQX4YKg73w5Ql9pCqYJQYLldm/1nO/eoyK+9qP6nXfUqZDWK5HWKTtIObVbISG5GhLy98K16d8yPe6qjr7z5d/eJCsrC5mZng7QtLQ0MwW9X/zzMZCXDbToh5BgNLG/hM7siKFVUASELQazt/Tdd9/F2LFjI9qhEEIIb/gDQvsdvgohhIgcij3ksMMOw0cffYS6dYMMe41z+GBB+6hoPrTwd4YCgYI6qhbRPve1t62Ebe6wrSAdKLpOk3MT3FG3e3duwm4/12/Cnq2ob1JUAnnJNZEZ6TV+4O3AgDuseRbnKIfH+8UXX5ik4bt27XL7mXfq1AltGm3m2HizrF6Ljjh5YE9ceOGF6NWrl3U8vvXhQzt3kBj4sbn61rWwH8Wz8pKQF+ox5SbCjg3O27nO3Rbpi2cife5zZj6zfm/kJQS+jyVtW4zEXRuRkLsLuW0P97Ld0He+7En/6WFU+/stJOZmYftpU1HQIHBytLRNK2FnLdpdsx32RvH+7kuo577altWwx8/sLkxDToR1St+6BlbKRSArP6XYd6BO3fZI3vwPsG0Ztm1aDyQHEcpExOg7X75kFgnA3bo5ot8BYz105513Ft9g4z/Ai0cA+XutBKAj3/S2UfHHO2darxSCp1wCJDmszlyF1giZliEKyqUVgznE7uGHH8b//vc/7LPPPsUSyD3++OMRVUQIIYQQQohoMGPGjArfkLQPYl6GaD4kchg8BXKJwVWLaJ/7hN0bzKsrvQHqNWrm+cDV0j1bDXuR5u/63Uwx1iKlTpOoXuNk/vz5uPTSSzFnzhyv5enp6Wjfvj1+yu2M3blpaF63Gu57/FoccNAh/hNh//YKEr66DQl5u1E44kWgx0kB95mAbPd8zUatgVCPyVUXrqKIyZTcne62oBWtu7x6jYOWl/DVeCT8N83MF167CKjhWVff+bInIS0VCblWMEft1MLg5z4xxz2b3rAV0qN87TsJ+dwneOqU0bAVMoLVidfZpn+RsHcnXIfeAKR6ohETqqfDlVYLCTmZqNm4+HcgoWlPYPM/SHAVol7hFqBez1IeofCHvvPlS3a2de9fsGABmjf3+OcHjAqu39HyCKa1w4JPgCkXA+dMDS4I2/ZLjAymgEz/bht2/rU4B9j3nPIRg/kD26dPHzP/999/e33m94dUCCGEEEKIcuSkk05Cv379cNNNN3ktZ0DDr7/+ivfffz/uzwcf4KMt2vK/elmUK+KfqJ17JqvJWm+VWbsFEpzlORJQJezN9P7MZq/H1zAhvb7/dSJg9+7dJhKLVjEMXrI566yzcPfdd6N169bhPavyIbvI45VRn0ET1Dn8UhMz6oeezI5kNDQezAnZWz1tkb/HU15qevDy0uxYUyCR9fVZV9/5MmD7CuDZAy1v3ExP4sHEnB0lXCcef95EJg8s4/twSOd+747Qr92/3gUWTLHK7nc+UM1z7WHIHdZUkIdEWmX4ltPYEzmZuHkh0MyKxBfRR9/58iOx6DpnsuWQ8v8kp3psf5r1Adb+Dvz8HHDsk4G3OeFZ67VOK2DAFV6dMKUluSpGWgghhBBCiMoLE8X5G6J39NFH47HHHotJnYSoFFAI5tBU3+RxJM3xMLx3p//tuQ49dSmMORNLlYJp06bhoosuwqpVq9zLaAnx3HPPYfDgwZEVWs1xLPQkDjUJlx/v4qBwfSbkYxn0n6S4kOcRg5GSXvL2NpnrIvYXFmHAc0WvT060DynMt5bv2VHydjZ/fwi02B9I8yQ5jQnBEr/54pUgMsD322FTUqZJ5DLXA/PfB7oeC9RrW/ryhIgF/C3Nzw1t3UE3R333YYvBQgghokvLli1N8ji+CiGEKD30CWXCOF9ob+ZM8iGECJPMtZ752i2LRz1RvKRIFkgsatIDOOmF0jd73l4UfHIFlvzxA1bPX4lVq/a6h+cytw1HBQRN4FMSjojbkMVg+js4BbNQ6DIcaL4vkN4AKMwDEtOs9rNxDgn2R0OHRy0jLtseEt7+RekE1HrtgS2LrPk920MXg/94HTj4mtiLwXscx5IeBTE4EI26Wq81m3l7nkZCbjbwyjDjP4z57wEXzypdeUKUB9/cCXQ4wupEzd1ldWasmAWc9VFo2+/aBHx1G7DsO2A37ZYs730340q4/0RDDGZCjmBDbKZPnx52JYQQoirTsGFDXHbZZbGuhhBCVBp69uxpEh7fcUdRgqki3nnnnWKJPoQQYdCgE3Da28DONUDj7sU/bzfIipSs26ZMm/XvhYvR8c/30DkFyG9hmewOGTIEzz77LDp27Oh/o/wcYNtyK5q2el0gKcijsDPKuSQx2BYBWW64tokDbyi+zCsyuAQxONoRlyI8Mbh+GGLwKa8CH57ntlpwWjTEjKa9rO8FhWp+J8pKDK7VHLhpRcn7CIXp91pCMNkwv/TlCVEeUMD9+GJg1wbr94W/nxSC24c4eoXJ4/i7y9+MGk3C/62Jhhjcu3dvr/d5eXmYN2+e8Q8ePXp0qSskhBBVDWaunjp1KoYNGxb1RCpCCFEVuf322zFixAgsXbrUPUz822+/xdtvv10h/IKFiFsYPdhlWODPT3+7THfvcrkwYcIEXH/99fjnwhS0r5eI5rUS8dRTT+Hyyy8P7gu8dQnw3ABrvs8o4PgJoUUG780MLeIzXIuIQDDTfMiRwY7EQ5sWRmf/IvQIX6ctR0niLjsf6rVzrB8Ho1QOGxv6uoHEYCa2en+0R+A64JLi2/J7GQ0heNXPwJwiD1WbvL1ASrXSly1EWRLs9yYUVs0BxnwJNN0nWjUKXwymKb8/6MvGIXlCiMhh5woTYDiN/jmk1d9Q19zcXLO+k0Drhrt+acuOVj1KUzazqe7Zs8csq1atWly33+LFi02Ck1mzZpkOt3g5j5X9GinrYyypbF6fzu97ZTzG8jyPSUmO9OtRLrsqtF+wejiTMZX1MXJZNDj22GMxZcoU3H///fjggw9QvXp17LPPPvjmm28wcODAqOxDCFG+bNq0CWPGjDEd6GRNZpIRg+tUS8AVF51bcqSUl7dvgzBsIjKDD1m3bR1KGmYfKk6biOQSRK7qdayoS9p3MDKYwpySupeftQJtItzLt4fpRR0HYnA4VKvjXwzm9brgE2u+zSH+xeBosfz74sPjKcKnNCm7fQpv6E2+8ieg01Dv+6QoW3if973248UzeNSoUSZr86OPPhqtIoWoUvABeOwNV+Pveb/CxQQSRdRv1BSfffWd14M01z32yIHYusnK5hxs3XDXj0bZ0ahHactmRuZWrdthd3Y2Pv3fzLhuv6svvcDMXzDqZKRXT4ub8+hcPzk5udJdI+XZfr5ln3D0YGSkp2PVymXu73tlO8byPo8NGjfDy2996LVM7Ve680ihltfqlo3ryu0aqV2vIaLFMcccYyYhRBzxzpnAml+tZFXn/S9kj90vvvgC5557rhGEbWo0ox3Eck9CqQYdgheye4tnvqQo3lBtIsLxXA0GrSGYTIiZ4m2bCPovhyLs0o+VYjBFsawNQK2mkddDRB4ZHIoY7JVksaKJwQEig706Wcp4hCOHyLfoC7x+onddakoMLhf4zPLaCZY1Ss9TgJNeLJ/9CuCoByzf4eHjgbqt40sMnj17tt/oOyFEaPChO3P7Vnw+uglqpFmRgrtzCjDs5fXmM98HdD5ATx3TFBlpSUHXDXf90pYdrXqUtuxCJGBDYn2cMXFZ3Lffzm00gQdeOLkxOjaqHhfn0Xd9pxhcWa6R8my/YmVv3oCnL94fTQpzkAhX5TzGcj6Px7y6Afn5RRm91X5ROY9sT16r5XmNDH3RkZyqlOzYscNEBS9btswMKacNz++//47GjRujeXNGWAghwmbxN1Z0I5PHRSI6UqzctdFKhpOSUeLqHEFz3XXX4fnnn3cva9SoEV555RX0TfoR+OlpayHF0JLEYC/RqgQxmKIsKMS6govBNRoDl/9micIlWTr4Y+kM4N1RVkKhQWOBQTcBLftb5SalhFYGrSKWfGPNMzpYYnD5eQbXaWUlDnQVAHuC2ERQsPz+UWD5dxU4MjiQGOxoD3byBGLe28CSry0R/JhHI/cVp8dq/0uAn58rXhdRtqya7fHIZgI0icHlxwdjrI7Cp3pbHYWJPlLuzSvLXgym/5qvb9P69esxd+5c488mhCgd6WlJyCgSg0uCD9D2Q3S016/oZVMMrobECnOMpHpqYkjbxFtbV+ayy7Ie1VISkYEkIwZX1mOsCmXHSz3Ksux4qUc4/PXXXyaZVO3atbFixQqcf/75Rgz+6KOPsGrVKrz22mtR36cQVYIPz7XElzqtgav/Kv75j08Cf7xhrXPm+1aCKn+CLO0NgiVwA/DLL7+Y0ae007JhtP9LL71kOnUwZ4Vn5SzvUQZ+cYpWGSXYRDAil1GcOTuDi8EUbEsSoYORWsMSgk39iiKXjx0fXhlMIpeYAjTsbCXvE2WLr93IiEnWeazZOPA2jFz/6SnvZbGODN66FHhluBXN22MEcMh1kYnBXtHxQTpZ1v8J/F00goudQaVJMlmaZHYicuzzJ8qfox6MepFhi8H8U+2EXoedO3fG3XffjSOPPDKadRNCiCoB76P92tRERmr0BREhhKiKXHvttTjnnHPw8MMPo2ZNj6cdE3WeccYZMa2bEBUWiqK28MLIYH/w8y3/BR42bwuyQUQjjkq47777cM8997g9y9PT0/H444/jwgsv9CSJq9nU28eyJGyxtYT9uznxOUtkzYhSYjh/OEVpp41FOPQ4Cdjn1NAjiUXpsK/rpDQrgrznyeEJyPESGczrLWudNbU9tOT1KRo37GoJsXXb+u9kCWYT4fRLDkcI/+5hoEYjYN/RHtuUfc8COh9t1cV5HxBlR0E+sGBKcc/01HS1ennQ+4zYi8Evv/xy1CshhBBVmWppKZh+8T7u4fFCCCFKx6+//uo1rNyG9hAbNmxQ8woRCTsdNi61m4cfsVeQZ0Xa+oixHGm6evVqY+Py22+/4fPPP8e8efPcnzMvzeuvv45OnTp5l1erWZiRwWF6m3YpB89xLzHYsg0LmxRZNZYrRz9kRfoycVqoyfqcHRHxEhns/D4Es3ewoSB72Rw/5YRoE+F1bwhiqeFkzVxg5gOWn/aiL4HT37HavHYLaxLlBy1OnNcMRXieR4nB5ce2ZcAfbwLblwNHPQTUaAgs/tr6LtA7PlaewUIIIYQQQsQDaWlpyMws/qD933//oWHD6CWpE6JKsXONZz6QEOOVIGtnQNEoqzAVD912m7EapAC8ZUtxsSwpKQm33XYbbr31VqSk+Il6dYrBIUUGh+EZHCoLp1pCNOvSdmD4wgjtBRhhWpDjP3pUxB9tDg5/G99zS69hWqXEEi97h7pRKqd+iEkZQxDCCwuATy6zhGBCL+1QxXcRff7+yDN/yqtA9xPUyuXJilnAGycDrfoDK38CBtOityGwYT7w+2vAyNfLRgymxxr/PDdoUIK3UhGtWrXCDz/8gNato5PlToiqRHZOgdtDtKQoUefnoUSUhrN+pGVHux6Rlk3P4L2JhWVSj1DWD2fd7D05qHHVj5h1fS+TQC5W9ahq10is229vXiF2F1rf98p6jPFyHsuy7KrQfvF0jKFy3HHHGQuz9957z7znsHJ6Bd9000046aSTorYfIaoUO1eXLAYHiwx2CGLvf/4t7vv0i4C76tatGyZPnoz+/fsHrg+TrO17NlCzGdB0n5Lrv7to/7R+cApTpeH3V4H/plnz1y0KXwymuJXREMhcYw3bz9kFTDwISK5uiY5MtCXimx2rgR0rLfsI2i04vwO+1x457W2gyzDEnFAjeqMVce9lExGCz+/mhdZE6D0+4MrI6yhKT80mQEYjK4lZp6Fq0fLmmzuBwbcBAy4H7neMzGEn5C8vRFRkcqjZmL/88stifsGB2Lp1q9vfSQgRGox4qFW3Poa/ugyuQo+IWb9R02LREHzP5czQ7sTfuuGuH42yo1GP0padkJiIVq3TUL9hk7hvv9r1GgIrNuCCDzYivXpa3JzHyn6NlPUxBi27YRPc/OVWrFq51v19r3THWM7nsUHjZkhOLv63Ru0X+Xlke/JaHfbyunK7Ruo1bAz85xCcIuSxxx7DySefjEaNGmHPnj0YOHCgsYc48MADjRepECICMp02ES3DEoMZATxl/HW4tyjX2uZsT/JUBhz17dvXTPvuu695ZVCR2xs4EPTIPe7p8EUrRi+GEmHIIbk7VlnD+dsN8hazfO0pEpIsUTcS6ElMMZj1YzK57UWJ8eo5fFlLYtlM4NeXgE3/AkPvk1hTnjAx3C+TrPnzpwMt+pZPVHppCTWiNyxROUiEsde9IYTIYGebtTvMO+Ekt//vf5ZNARPRdTwi7GqLMDn8duCwscDWJUBKycFLIspsXACc9KJ/q6EIR5UkuGjSFEJyo3BZsmQJ2rVrh3iCwwUpaG/fvh116sR4WEYloLCwENu2bTOR45FcI6J4e27cuBE1atTwak8+MKemphZrrtzcXOTl5XktC7RuuOuXtuxo1aM0ZbM92ZHF4cDVqlWL6/b7+eefccABB2DWrFno3bt33JxH5/q+3/fKcI2UtH5ZXiN79+7F5s2bzW+R/X2vbMdY3ueRw4l37drl9zdJ7Rf+ubG/8/xN8u3gL8trhMIt79s7d+5ErVqlj9zjffWvv/4y1wZFpiFDhiDeWbNmDVq2bGk8VFu0iJ4nov63VV2idu4/vhj4821r/tI5/j0K1/wGvDjYmu93EX5vcpqxeZg2bRpO6pqMD061Imfvmp2M9CE3Y+TIkeZ6L1H4jQaM3KRwlbs7tEjiT6+0In/JxbOAJj2Lr/NIR2D3Jis6+bp/I6vX6yOApd9a8xfMAF44zJMY7uTJoZUx/wPgw/Os+cPvAA65Tt/5soDXz/p5lnjKDhFGws64H/juIevzUR8CHfz8znx4PjD/fWv+it+B+u0R8+/8p1dYw8uDXd++TLsFWP2LZfPAe0BiEvDbK8Cqny1xecQk/5HRZO3vnmt7//OBYx4Lvq8FnwLvnWXND7kTOPgaz2fbVwJPFn2Hu58InPJKyXWvAuh3vnL8X/PLY12t65w2EYwM5neWHYb/fgZ8dRtw1Z8ok8hgXlRCiLKHD8wZGRkh/VHng3Wgh/HSrl8ZyuZ9KycnJ+C28XSMdsRc9erVzfmPVT1Udvm2n32+o/19r6rnMdh/FbVf6dojHPGotG0d7ZFlBx98sJmEEFH2DK5VcgK5LWuXYsCIAeb/GGmQ7hF8b777EaT1P7d8TwujFoNFLvrijATOySr+ORPi2UnfOIQ6GknknG0cTvSdU5hndLAoG9b9DrxRZDV06A3WsO1qjiCzPTuik7wwXm0iNi8C1s615ikI8/vU9xxrKolgFjL+cK7jbGNT3zrhlSWiR9YG4MenrE6wFvsD/S9S65YHPUYA34yz/JqRYHlpr5pjCcG9To+oSCWQE0IIIYQQFZ6nnnoKF154oRkNwvlgMOK5e/fuwf1IhRD+bSLot+vPMsFH8Plj9ky3ENymTRscMuZC5B3cESm5O5FGb9VokZtt2TXQpiFQvSIhrQQxeNdGoCjPh1cyu3Bx2ks4fZnpGxwq9TsCiclAYT6wqchnVUSf7O3FrRWcHQyMPvcH/aBtO5E5E4GNfwN52cBZH8fuLDnrGqpA7SvohtW5EqZNBC0g/G1LUmtaghi/f4EEeBEdti4F0moCNRpZ7/NzgDkTrPmCXInB5cXh44Cp1wFPdLOSK07oD7gKgJ6nWB1TESAxWAghYgyTpCxevLjsh5cIIUQl5oknnsCZZ55pxGDOB4MC1aZNm3DNNdfgkUceKbc6ClGhYSQSBZhgwqdDjE1z7TWv9OqeMWMG0tLSol8nRqh9zazqAEa+AXQ9NnplUwAJJgYzQs6mZtPI99P7TMsTld7BtmgYbmRwcipQv4OVcGvLIqAgnwk0Iq+TKDnC146m9RKDAwiTTICWTNs6F7BiFrBylrU8by+QUtzOrlwjg9npEOq1Fm50r2/nSo+TrTKa9Ch5fWdbOiOBCUcs8V7DOigyuGz5+g5g0VSgzSHAiBc8ojDZVTQyQpQ9vMfTI//QG63RH/SX532lFJYzEoOFECLGULjo0KEoo4oQQoiIWL58ud/5QHz99dc444wzJAYLESr0JKQ1QjDxJTkNX+QPwJQvv8XKHYVo3LgxPvjgg7IRgn0tFjK9k10WG95Ob0VGc7YeADTsHJ4Y7O+YnfsrjU1E427WRBZ/41meYvkrh0zDLpYYzGg9Jr+jOCzKPumaU6gMFBl8/DOe+bcdQ7pptRArMZje0oz25/USKr5iMNNPcQrFTopi1skvhb4vr8jgOv7rIjG4bGH7Lv7K6gikAMn7LX2iGZmdm2VZRYjypU5La4oCFUYMZubnL774AvPmzTP+ckwMJYQQlQGKFrfffjvuuecetG0bRuZoIYQQEUMv4dtuu00tKEQ4JKV4C7A+fPzxxxhx3zRr1aQkTJ/+Hpo1K4WFQkk4I3KDicFMXjX9Hmv+6EfCF4NLigwujU2EE1oHRBIZTBp1AxZMseY3LZAYXBZ4ef/WDd0mIpD9CO0SnJGW5UmXYeFv4ysG0yrl8a6WWMtEbsMfj179Wh1oCdXcj7/OFrsutihdHkkoqxoLv/B0FvD8UggmNRoC27IUGVzWTBsLDL4VSM2w5oNx1P1hF19hxo4w4/Qpp5yCSy65JNZVEUKIqLJ9+3a8+eab5lUIIUR0+PbbbzF8+HC0b9/eTJz/5htP1B2TOF511VVqbiFCID8/H9OnT8eDDz6ITz75xO0F7GTRokUYPXq0+/2jjz6KQw91eAOv/AlYMxfYVnLkfsg4E9nRNzgQkSTwKskzOCtKkcFO8i1rjYgig5VErnyTrvnzDHZGswbC6Wtd0SwOfMVgtgejRhkxXVD8nlAqep5sDYs/9TX/nS12tHBhHpC3J7r7FhZ/f+hpiR5FiRNJRlEHRs5Oy+pElA0b/rJG49jzwabyiAwePHgwBg4ciHHjxnktp4hx0kknmT8JZcFdd91lXl955ZWQt+GfFOcflczMTHfG8WBZx0VosA1dLpfaMkqoPatue9p1jOd7U0Vqz4qA2lNtGu/E6hqN1v6effZZI/SefPLJbsF3zpw5GDZsmPETvuyyy6KyHyEqM3l5ecbrlzYPjPjdssXjZ1u7dm2MGDECp512mnk+3Lt3r3mflWWJpiNHjize2fLRRcDOVUB6A+DGpdGpZK0QI4OzHV68QSKbwxKDKUY17GqJwjVLERmcnwus+snyC/7309JFBtswMliUj2ew08Ig3MhgimkVWgz20x6hXvMcZVCaaF7fuqSG2XkigrN7K7B0hjVfuxXQsp/nM0YGu9fbHDXbAuHDOZ/7n48SYYvBM2fOxPz58/HHH3+YSLaMjAx35O53332HeOKBBx5wi8i+wrUEjdLDNuQfPj4sJobiEyTUnuVIRbo+d+7c6X7dts0RcRBHVKT2rAioPdWm8U6srlFbSCot999/vxF9L7/8cveyK6+8EgcddJD5LFwxeMKECcZbeMOGDejVqxeefvpp9OvneDAKwDvvvIPTTz8dxx9/PKZMKRq+LUScM3fuXNOhwmvWHrV0dq8UDD6oGtZkufD83Fys3rkTL7/8spkaNmxokuAuWGAJkAf27oqXHr4FCbRmaNLT8goltnBkR1RGA1o52P6VQcVgZ2RwiPv3somwgoq8OPhqa4pGNPBrx1vzNRpbNha0i2i+b3jl1GsLJKVZEZr0DhZl5xmcmOK5Pnh9p2RY59EkWfRh2XfAF9da112/C30ig/1cV+UBxdOtS60oeUZ5hiqkOoVvluHloRyCGPzBuZb1ANvqphXeUdXh4lsXZ8eQKD3/fgK4Cqz5Hid6C/d2ZDChb7DE4ApJRJ7BHGJ30UUX4YADDsBnn32GNm3aIB655ZZbcO2117rfU2hp1aqV8a9KTq4wdslx/aCYkJBg2lLikNoz3qhI1yfvSfZrvN6bKlJ7VgTUnmrTeCdW16h9P6QIXRqYW+Koo44qtvzII4/ETTfdFFZZ7777rvk/OXHiRPTv3x/jx4/H0KFDzZD4Ro0Cez2uWLEC119/PQ455JCIjkGIWMDrfdSoUcYWwsngdmkYvY91L8huOxRPvTsdu3btMu83b95sJlKrVi3878ruyJh8qCfpXN021jDuvN3RF4MJhQhGwu5cbQ2pZcRh0OH9oUYGF4l9icn+Rb5owf0kpVrenKxb/wsjK4d+ngNvtBKaNeoe7VoK53VE4dMpjl2/CEit4T/SlZ0UW5dYU4+TfSKDYyQGr/kVeKNo2P+hN1q+pJHaRNiE8r3mb7tthcLtg4nBhQUej1p/1GwM1Gph1ckWLUX0+Psj/xYRxOlzvcu694sy4J0zQ1/3tDfDLj4i1aFp06YmCnjMmDHYf//98f7776Nr165hl3PzzTfjoYceCrrOv//+iy5dukRSTZO11pm51h7apARNQoh45LDDDot1FYQQIi5ghDCHoUfKcccdZ4a133DDDV7L6XVK7+BwePzxx3HBBReY/72EojCTGk+ePNn8l/VHQUEBzjzzTDNC7Ycffgia+NjX1syOjo62dZDsaaouoZ57RvleeOGF7vVq1Khhvi+0Ajxh12vACmsU6D3jX8LYJ6tj6tSpRjzm94E2EexAoqVfjeSZwMqifWfvAGoXGgsEu1vJlV4Prihe2wkNOyOBYnBBLgq3LAYaFn92TNi9GbZMV8iIwlD2n9EQuGUdkFzNEvnK0DYnIaMBEjLXwZW9pXRtc7AnEErf+eiTkLvbXEfFrmFGBlPo9NeR6bj2Cyl+JiR43u/ZWSbXVYnn3rdOodahdgskHHwtXBRgm+0LrP7ZU061kstJSKvp+R7y2Hlv8IfLhYT7m1iR7i37wXXmB8XXOew2a7KRlV30vvNZ65GwYpZ1rdfvAFejHt7tm97Ac96ZRLCKtn1hWR+3s/OF95aFn1udSc16W8vW/2l1qnQ9NqLiwxaD+SNPKLK+9dZbuPfee03kRbhRFuS6667DOeecE3Sddu3aIVrUq2cNXVi1alWpHjCEx4O5ZcuWWL16tYkCEKVD7Rld1J5qz3hG16faNN6J1TXKhxiKoc2ahe+/+dRTT7nnu3Xrhvvuu8/Ymx144IFuz+Aff/zR/P8MFdqg/fbbb2a0mQ0jpYcMGYLZs2cH3O7uu+82UcPnnXeeEYMjsTWjgJyeHj0PRNnTVF1COfcvvPACxo71ZCtndDCvzWrVqlkL3nrQvLiS0rBtjwtI2GM6sTkxQvj77783AUN9+vTBnp9mw75yszavRl5aCyRtXg47BjAnMQO7omiLVb1GG1jGhcCuZb8iN6l4xH6drM3mwbcwpQa2ZTJCuShKOSTKPjlVnbS6SMY6Y2exbesWIKH0IzL0nS8DzpsL5O9BQl42XCFew+lb13i+DwWpQGE+bCVi744NyC4Di7iSzn21LWtQo2h+d2EqckKuQwrQ22OzlLF9Cmxn68z8ZOSXUE66K9XdFpmb1yA/1ZEA0kleNhowUr4gF7l7dyEzTm304o1ofedTl85ELVgdG3vaHIFsnyTnydWao3q7oShMb4CclEYlnvfKyo4gHf1R4YRnPfNf3wF0PwEYPt4TMc/oeVrQOC2NylIM9h22d9ttt5moYGfm2FChvxSn8sL+QlAIlngZPdiWak+1Z7yi61PtGc/o+lSbxjuxuEYj7bCnR7CTunXrGg9T28eU1KlTx0T08v9rKHBUGaN8Gzdu7LWc7xcu9O/JOWvWLLz00kuYN29eRLZma9euNWI262oHMkTT+oPtIrufqkVJ5/7BBx/Erbd6hokz8dtjjz3mDgIiCbs3WjO1mqFefe/h4LxOKR67qeP5vtRMdXEFYIfHdiKtbjOkRvHaRpv9gJ+t2Rr5W639+ZCQY4kZCTUaROd7tX0FEt47C6jZFK4uw4F9zy5VcQkc8r75HyQU5qNezmrTzmbYPS0qyuk7n/DWKZa3cq0WcJ36WsT7FT7t6sp2z9ds3MZEmrt6nWG8g6u1OxTVovldCPHcJyR4RqNkNGiJjAjr4Dy2Wjy2kspx3Btq2fcGf2Tu9cjPNRtG9bewMhO13/nNteFqxOSYG1GtRc/i12i9oUCPoWbWMw6/6pGd7bn+y5w/3gDO/Z+3dQrnD7wceOkI4Mh7wy4y7F+X5cuXFxNwOXSIVg5MNlBWMJqXiZX4yj/l9h/sDh06mCFMQgghhBCi6sL/qL7YFmENGoToEVpKGJFz1llnmSjLUPfpa2vGqGzCB7loi7Z8SCyLckX84+/cM8iHIjAjgG1uv/12E6nuFIKRs8vtbZpQqzkSSrp+6FlbRCK34/qORFPGEiGa12Cbg4Ax04BGXZHo2LebgnxgjxXBlZBePzr7zlwLbPzbTAmNuljHWBpqeJ6vE18cbM1c9ivQsFP4Ze3eanko52QhoUG/0L/zG+YDHPK97g8kvH4CcMoroSUFE8CiacDy74A924FBtwB1W/tNXphIr1UmOjvxOfPej8Nw+dzvWU+7ThkNIr9+HZ7BIZXjdW/ICry+w0s5oVqd6N4vKjlR+Z3vOtyayvgaregklud1WZgPbPkPaNDRezmXRehpH7YY3Lq148bmoHv37mYqK+644w68+uqr7vccgkRmzJiBQYMGldl+hRBCCCFExRq2R4GLXqbbi4Y2MkrmtNNOM/ZmjLgNFQq6TGq3cWNRVGQRfN+kSZNi6y9dutQkjjv22GOLecoxGR+TzrVv374URydEdOB1efXVV+Ppp592L2MulxtvvLH4ylnrPfOMWA03yRRxJpqiaBRN6Hna2rKD8Uv+HqD1AOOTinphfv9+ngRsXWwlwDv+Gf9tUrMpSo2/pHYp9gD8MOAo3id7AblZSKjTGhg1PbTtONx4tyMRFIXNxV8BvU4Lvt2qn4F/Pgb6XwTUa4sqy8ofgTlFQ7r7nBVQDI4bcd3RORN2nfL2Wt/r3F3e5QRLBhfs3uCPvY7h9/46eMjWpcC3d1vldDoKOODikKovRIWk9yjgk8uBbcuB5n2tZWvnArOesD6LgPhMW+8HJiPgVBoYdTFu3Div6Auh9owXdH2qPeMZXZ9qz3hH16jak3AUGT2CabXABG52gmNaRfB/5LfffouffvrJiMOhkJqair59+5rtTjjhBLeIxveXX355sfU5Um7+/Pley2hJwYjhJ5980vgwCxEPMPmhUwieMGECLr300sBRsDaMaiwJJmgrJgY7BTFvm4kyh36KY6ZGtu3fHwKr51jzw58AklLKRgzOqB8dMZgR3Q07G5EgYcdKIJfeyCGIfRTKfaPLFk0NLgbvzQTePNmK4lzzK3DBt6jUUPie/551/XY5Bmjay79g6Yi6NWRbI1RM4qfkONEhSiNQP3+IFY3I4zn7EyBznXUNJIUgLXEbP9G/xSiK5C92P3HCDpoFU6z5OvptjRnsHEgp8pYXZQdtIDiyYPYzQNYGa1nNJsCAK4EBV1RuMThaD4p33nlnrKtRaVB7qj3jGV2fas94Rten2jTeqajXKBO3UcBlhK6vzy8/O/LII82rr79wMOjny9wY++23H/r164fx48dj9+7dGDNmjPn87LPPRvPmzd3Jtnr06OG1vR2J7LtciFjBZIuPPPKIe5grfbSD5n+h2GNTK0DCp5Ki/xhFGG/RkaHgTMzDYe123TPjNDKY0OuTEWN82N++BGgSglC2q0hccLLkWyA/J7CAOf99j6DH/TFirTJHB6//E/j1RWu+blsfMbhuEDF4q//rnlHc+XsjP8+lwY7Upye1U6ANBfv7zXPPNmi+bxjb1goxMnhnyZHBzuXByqpK8HrKo49tOdxjXxkOrP3Nuj/ctKLs91fVSUwEDr7amtgR5/t9iqTI6NRMCCGEEEKI2DJlyhQ8+uijxYRgQluHhx9+GB9//HFYZY4cOdKUScuy3r17m7wV06ZNc++D+SzWr3cIQ0LEMR988IGxh7B59tlnS04E7iUGR2gTMfQ+4LbNwHX/AY17IursXAPMmQh8egWw6MsyEoMzA1hnRCMyuCGQlOq9LDlSMbibezZp63+hbZPlbYXjFvBX/OB/fQqZv73svezfz1CpCRbd7hSDnRYHxq96e3HB/8newN31gGeD2JuUJXadWG+nP3gohGr1UJptnW3o3CZa9aiM0CP8mf1Q/4W+wJpS5vJ6/UTgjZOB/3kSixaDHUUUnnktFeSVbn8iPCgCl1IIrnKRwUIIIYQQovJCUTZYDgtG527Y4CcCrgRoCeHPFoLMnDkz6LaltTkTIlp8//33GDVqlEkcZ1uYXHTRRSVv2GQfYN/Rligciueul0jjEFCTU4GaxTtqogL9Q6fdZM2npAOdj45Ouc4HbkYG+xODaxT3Dw8bep7etgmYeLCVmC65WuRJvZjQrojkbYtD24aJ42zaHQYsm2HNU1jvMKT4+mt/txLOOVnwCXDQlai0BPPZdVoZOCODab1x3DOWkOwUkLmcU6xETDsyOBLLFl8RNpxI/zqtgJNftr5XddqUziYitQaQkGi1o3P9qsqiaUiwLX1ePx64NcJOauY5WPYd4Crwvi/468Cyod94KB2FonT8M8XyaGfnZ0Gu92cXB+i4C4LEYCGEEEIIUSlgwjcmcGvRooXfz5cvX4569SrQEHUhosTChQtx4oknIicnx7w/55xzjGVKSHQ60ppChQniLv/NEo2iEL0UEo0dnUAb//H+jEP7571lCV+H3Qo06x25TYSvGFy9XnT8Mm3h1wzx5lN6KcqMJDLYaRPR63QrIRrFBorBwx4tHj3qGxW837lAt+NRqQnmsxvIJoIdIPueVbws+3vBaHN2zoQbnVtablxmidv03S2NGPzn20D7wQCTFYYSIc/vU48RJa8XSgI5thnrwvZWZLBlEWE3De8jjNa1Pc7Dvc4pBNuetIGo4RCDd22SGFzWcOTL9HuA3mdYfu69zwS2LwfW/gH0Oz+iImUTIYQQQgghKgVDhw7Frbfeitxcn4gJPnPn5OD222/HUUcdFZO6CRErmFCRdic7dlgCC78DkyZNQkJZCVCJSUCDDpZYUF4JsyhAZzSy5jctsAQ2Z9QwvS0Xf2UNbQ4Hp5+qHeXMst0JfKJgEeGbjMmObo6UGo3d4mTStlDF4E2e+frtgbYDrXlGGtIr1wmFNybWs9tn7DoruV67QajU2NG0didAQDE4hCjVtCJBtTA/MkG2tLADg5GcPNelEYO/ewiYPBT46amoVg/7XwCc8R5w4iSgfoeS6yIx2NuXnayYFVnbO6OBmbAsEPb91o4MFmULOzWPfRIY9ohlKXTQVVYCx/4XeY/ACQNFBgshhBBCiEoBIx2Z6K1jx4647LLL0KVLFzMk/t9//zXeqBSEX3/99VhXU4iygRGxf74DHHG3O9Jw27ZtOOaYY7BuneX727dvX7z//vtISYkgYixSKJ5+ca0l3DToDPQ+vWz2w8RpyzdZkW0UJ2whY/cWzzrhDot3isF2ZDBFQXuIbjT8gp3YkcGlSSrGc8/o4JU/Imn3RhRSnMwoYUSELW4TthttNpZ8bb1ndLAzmprD80951YoOrt0SSM1AlcCODE5IKu5j64xe9U0g5w/n9owOTi2F+F/e+PPw9RXHSws7kziVWJc6HjE4FhHW8YRvJ4TzvhexGBzE1scpFDs7k0TZQGuIlv08I0ds8b/XacCLhwPHPBp2kZU6MrhNmzamx9s5Pfjgg0G32bt3r3l4qF+/PmrUqIGTTjoJGzcG8UqpQnDY5XnnnYe2bduievXqaN++PcaNG+c3+sbJoEGDip2Hiy++GFWRCRMmmOuS2cb79++PX375Jej6/LPOB1mu37NnT0ydOrXc6hrPMGP7/vvvj5o1a6JRo0Y44YQTsGjRohI9G32vQ7arAO68885ibcPrLhi6NsP//eHE3xddn6H5Wh577LFo1qyZaTcmBXNCcY/JvJo2bWp+j4YMGYLFixdH/R5cFdozLy8PN910k/mNycjIMOucffbZbuEomveN8oD2ELNnz0a3bt1wyy23mN8HDo1ntDCX/fjjj2jZsmWsqxlf5GYDC6cC0++NdU1Eafj2HuC5AVaE3n/T8Ndff+GSSy5B69atMX++5e3arl07fPHFF+YZJyz/SF4jpRU4504GZj0BzHsT5W4VEWx4fyQJ5Dj0+uhHgIOvja41wowHPMPjSyMGk4aO+/Hmf0tef//zgKEPWNFmFICcnssckuwb+U3bkNPfBo5+CFUGdyK4esVFR3YaUCR2rmcn5tu8CNi9FSgsGnpPnPYpEUb1xZUYnO6IjC6J9X8BS74FFn4RvbrQ1iB3N6o0DmuNwjHTgH1OiYIY3CREz2CJwWUOxXf73lK7BbDmV2t++wrvkTBhkFwVIkQuuOAC93uKR8G45pprzJ8kCh21a9c2yUJGjBhhHh6qOvQaKywsxPPPP48OHTrg77//Nm27e/duk2U7GFzP6UuWnl6Bej+jxLvvvotrr70WEydONCLE+PHjzXBWipgUNH356aefcPrppxvhc/jw4XjrrbfMQ+3vv/9uEuBUZb777jsjqlEQzs/Px9ixY3HkkUdiwYIFRswIRK1atbxE4zIbHlkBYcKlb775xv0+OTnwz4OuzZL59ddfUVDg+dPP++URRxyBU04J/MdM16cH/q706tUL5557rvkN9uXhhx/GU089hVdffdV0UHLoP++nvAcE6uQJ9x5cVdozOzvb/K6wDbnO9u3bcdVVV+G4447D3Llzo3bfKE94TXz55ZfmWOxOAv5vkVdwAN482fIHJX1GAXWDJPUR8Uvzvu7ZRc+fg15PeD+cM9CFQQWNG4eZwG3nauDJfawIPHoUHnV/aNv9+zmwdbEVscfkczaRJKyKwCvXWEW0P8yaz97iiOgM4D8ajhhMIa//hYg6ixziGJNslYZGXeFKSEJBnbZIDMWGgBYPTpsHWggccCnQsLOV3C4Qzv/SjERc+LklIHWuhHY8dqeCv2uY7dBpqCWU1+/oWf77q8CM+6z509/xiOxeEeflLAavmwf8+5klatPvlxH1pRaDw/heT7kU2DgfSEoDbi+liOibzC4tjI6uyhwZXJpI7VBtIrwig2UTUea0PdQapdG0l/VfbdpYK2nnuj+ArsdGVGR8/GsvQyj+NmkSWobXnTt34qWXXjKi2+DBg82yl19+GV27dsWcOXNwwAEHoCpDfzGnzx6jC/gQ/dxzz5UoBlP8DfU8VFYef/xxI4qPGTPGvKcgwY6HyZMn4+abby62/pNPPmna+4YbbjDv77nnHnz99dd45plnzLZVmWnTphWL+qWY89tvv+HQQw8NuB3F36p+HQaCIk6obaNrs2QaNnT0lgNmVApHUwwcWOTB5wddnx6OPvpoM/mDUcEUcm+77TYcf7wVkfXaa68ZgYMRr6eddlpU7sFVpT3Z8c3fFif8nenXrx9WrVqFVq1aReW+EQvq1q1rjkOUQIfDPWIwI4QPvFRNVsFgJP+E12dh5I4E7NPQhc619mJ4p2R8/l++6SQfNWoULrzwQmOfEjaZ6zxRZ67C0Leb+xKwdLo17xQZy1IMbuwQgzcuKO71SvHLTtQWKozAomBGUbheBP6q4ZDeIDQRJhR6nwlX71HYkbk78o6wox4Ifd1ty4Gn97WukTaHVD4xmIK6beERSGhjpLQvXlHp9QNEBu9EuUL/7B+Knt2PeyY6YnA44qN97AU5lke2vwSM/31l+Y0z+tT5vfalwxAryRnrVNpo+opOpyPhSq+PnJ0bkeqM2g0Xp+VDsARyXp7Bigwuc459yvMb3O8Cy6d89S9A52FAX+vZJlwqtU2E/QDOnvA+ffrgkUceMVGEgaCQxOGSHG5qwyGPfBDikEPhX0AP5Q/Gm2++aTJ8M6KVwzYZiVSVoJUGry/ntZWYmGjeB7q2uNy5PmEUm65F/9chKela3LVrlxkuySHCFJH++ccn23QVhtFzHB7OTp4zzzzTiECB0LUZ/vf/jTfeMFGZwaLRdX2GxvLly7Fhwwav+yMFTUb7Bro/RnIPrur3VF6rderUidp9Q8QxnY8JPBxcxDV//PGHsXUZd1x7bJ72KL5Z4skm/+BRtTHhmWeMUEy/7GAdO0HJWucdLRqJYLRtWfmIwQ0pahX9zm7yYxPhFFtDpfm+wFkfA6e+BnQ7DmWeBM+fiBgJ9KCNdvI+Rh6+eSrwzxSgIM/7M44oqNvWmmfnUqR+pfEKPaK7j7A6NnhNhEogv+pYRgbv2Ra5bQppsT9wwXRL9I+kHF+/ZH+8Pxp47TjggxJErr6jrYRag2+L7FgqEz1OguvIe7Hr8Ic8HtaRWJD4+ocHgglCbeQZXLYU5APfP+Idtd3zZGDYw1YCueTUiIqt1JHBV155Jfbdd18jEHFYM0XI9evXm+ggf/DhMjU1tdjDD6ON+JnwZsmSJXj66adLjAo+44wzjADHB0b6l9GbkBHFH330UZVp0i1btpgh475D8/ie9hv+4DXnb31di97QuuTqq6/GQQcdFNQ+o3PnziYCcJ999jFCB6/bAQMGGEGYHpNVGYpojK5mG/Eeedddd+GQQw4x1gb+rHV0bYYHo1WZwf2cc84JuI6uz9Cx74Hh3B8juQdXVZg7gb/TtCmidUm07hsijuEw8HrtLMFu5U9WFGVVf6iO8/89HNXwxBNPYMaMGWbZwssy0LlBKrLzXFixtxbaVMtE97p56H54W3oQmW0ixo4MrghiMAXQem2t/W1aaHm05jP6MDu6+96+0rIDoLcu/YOjhZcH52agfhlHItvk7AK2LbWOh3Xgsfnjr/eAxf+zpgFXAkfe4/mMnd0Uy+kLzeg12kX0Dfy/p8LB6/mUCKw7QooMLmcxOHt76ewE2Ba0pXF+x8P5bqX5REX7Co75uZ7vbLi2LsLin4+tBI+rZgNXzgPqhJErwSnsBksgx/N44vNWJ1btCDsbRWgkJQM/Pmkli4siFU4M5lDOhx4KblTPjNGM6KU3oA0FIAq9F110kfFgTUuLck9pBSacNrVZu3atsTCg/6XTk9kfHJZmwwQ1TPhz+OGHY+nSpWbYtBClgd7BFB9mzZoVdL0DDzzQTDYUgmkBQw9sWnBUZZzDx3mvpMjDDpz33nvPJI0UpYP2Q2xjdogFQteniAc4OurUU081Vhy0gAqG7huVCIo4HGY4+xkrCc/ir6L+wCFKD7+Xn3/+OW688UavTqykBKBdXUu8S2nSFW2OGAe8c7r14cwHvBOBxUwMXl4+YjDpdDSQtd7yD2Y0Z2mSxwXiyxtNkj4ThXz9f6W3dLBJSPSO0m9VThaF6+cBrxSNEDjwcmBokcetLcwx0pf1+WWSZ3mvomvMCZPpUQwm9LKsTGJwpNh+1YnJ3t+JdocBp71lCWrskKtIkcG+9iuR2kQEEsIdidDcEa4iPLYuAVb8YM3TZzYcj3NGW7fqb53f1Izg/x30X6H8aDfQuhfXbV11xeDrrrsuaHQV4XBFf1DgoE3EihUrTCSLL/S941BSRnA5o4M3btwY15545d2mHG522GGHGTFt0iTHn4IQ4XmwI4urihhMi4ykpCRzLTkJdm1xeTjrV0WY4JEPRt9//33Y0b0pKSnGPobXofCG979OnToFbBtdm6GzcuVKk2Ar3JEQuj4DY98DeT9k56IN3/fu3Ttq9+CqKgTzmp0+fXrQqOBI7hsizuky3BKDCSP69IAXVzA5JpNcf/XVV17L+Z274/JRSNlqjdJLadzZEn+b7ANs+MuaKAQESwAWjhhc03PPjTwyuIyjzn0T3NlinK8NQ2mw24TibTTFbTsaksyeABzhSb4dEb++iJr/TUfCno3AOV9YkdMlDQv39QhdOQt4/UTvZS37+/dxbdobqNMK2LEKWP591RtlQCF87mTLTuPsKVb0rNuvur53sj0KOlEUdSIWcUtz/dqickq6f9/fkJK+OYRffx7KoUQGu1yWpzMJdI1XBXjdpRYl0OM9f2bRvZAdOeGIwfucWjb1E6WD/tjf3Als/Ado1sf63jnpMqzyewYzKQ8jVINNjAD2x7x584xHYKCs4X379jUP4N9++617Ge0M6IHnjCisbITTpowIHjRokGkrJtdje4YLzwNxPsRXdth+bDPntcUhe3wf6Nricuf6hEl+KvO1GE50DIXgjz/+2IgWzBwfLhwyPn/+/Cp1HYYKvWsZuR+obXRthg7vk/zNOeYYhydnCOj6DAy/7xRwnffHzMxM/PzzzwHvj5Hcg6uiEEwPYHZeMNdCtO8bIs5p2c/jp7pkupXUR8Sc7du3GyssjtpxCsG0xvrss8/MyL0zh1pBFob6HSzBadAtnmWMDqZYEjdicBlHBvtSowlw5H3AwddaD9OR8NzBwOPdgVeGe4unFE4DWSpEgvPhvjBwnptQSVg9B2lLv0TCut+BzLWBV3T6UPoOC299MJDqY/0TKFkRr72ux3nqz46IqkRutiWE0wd3z3bre2d7BkfiV11WOKPlI7VhWPApsHlhZN/pkvySKWoGS1bnZMk3wD0Ngfubejo0qyIUwx9qjYR7GqDWZ+cDjXt4rBtWzCr/JIUi+nxxnWXhwY7CD88H3jnDM717ZtWIDA4VJoThgyEjWOldx/fsUWc2XWaXtoVN2hUwCzmzTTMBDYdE016CPsOMirniiivMg+IBB5TTMJ04xhaCOXycfqubN292f2ZHVvm2KR8M33rrLQwbNsw8XNIzmOfh0EMPNX9sqxK8rkaPHo399tvPtM348eOxe/dud2Z7JgBp3ry5sTEhV111FQYOHIjHHnvMCEnvvPMO5s6dG1E0dmW0huB19cknn5jvt+0Tyu9w9erV/bbn3Xffbb7HHTp0MNH/TCjJCLjzzz8fVZ3rr78exx57rPluM/J/3LhxJoqSnqFE12ZkUGykGMzvfXKy98+trs+ShUVnhCmTxrEjkb/NTIJEceTee+9Fx44djTh8++23GxuOE044wb0Nf4tOPPFE03EUyj24qrYnxduTTz4Zv//+uxlpwY4I+57Kz+3OYN/2LOm+ISoYFLQYSTTvDSBvN7D8O6DT0FjXqvJAUYgiESNJQ/BuzMrKMolHeW/butUj3Nj/wU866SRPQlIOB3aKwcQZHUz/VvrPRvrYR8sFW8wKK/rPITLl742dGFyrKTDAum9FzM7VVgQjE7IxcZppTz9RtKWFGeJtQYtenKWltuNa4/XXoGP4YjATE3HIOEU3m+6e39pidDvBcwy0iugTmUgRd8waD/z8vBXpPPwJqwPNl+qWxuAWNHN3AwU51vt4ipC2I3optNKLNBKm3uDpbLk4uFVf8MjgnaWziUjJAAqLkhlWZcGzSEBPgAsunlP+PjBS9OeJVvss/tpKOBZtdq4Ftvxn3RNpfeJMKieiy51+ouhLSaUVg+kJTPHszjvvRE5OjnlYpAjp9BFmJAwjf7OzPUNymIyB0a78k8Xthg4dajLwCisqlQ+TnHyH5DNS01+b8iGSUUb2Q3fLli1N2952221VrklHjhxpBPQ77rjDPGhzOPO0adPcCY0Yge6MtKYNBwVPttXYsWON6MFEVMGSpFUVbC9Ldk44ofBmW574tieja+hvzbZnhxCjBJlYsls3P8Pcqhhr1qwxAg4fODlS4OCDD8acOXPMPNG1GRm897Htzj333GKf6foMDju+2JlrY/92U8xl0jJ6ZvI3hZ707NzhNcv7abVqHqGCnZFMHBfqPbiqtif/J3366afmva/NBpNT2fdZ3/Ys6b4hKiB8cKQYTBZ+ITE4WqybB7wxworG638JcPSDxVahZQ1zH/zwww9mYmeNM+kbO7qZCJudMHantxsm/rKpV2S/RiFg6P1WdCItQMx6jqHhocIEbLYYTFE1UsEnuRrQdqDVBuUlBjNx3PYV0fFjZSQjBSpGMRrh1BV+pHQo1G0DnPe1FcXb5dhSF+eq3ZKuxh5BOxBZDjHYn8C9z2keMXj/84EUn2vQCa0RajW3jmHpdEugKym6syLAaPCsddYUCKdwye9eMIuSgnxg9c/WNZVWE2hzMMrdJiKS5HHOY921AcjdFb6vb0mewV6RwXVKZzlRVeD1VoQrrahNOheJwbZVRChiMJNJ8jvLRJLsCCqJuS8BPzxmzY/6COhweGT1FzGh0orB++67r3koCUabNm3cIqYNHyQnTJhgJuENRbaSvIV925Ti73fffaemLIIRVXZUlS8zZ84stowJ+jgJb3y/t6G0Jzt6OInisOMsnLbUtRkaRx55ZMBrVddncChABvueMyKO0f6cAsH8AOHcg6tye4ZyT/Vtz5LuG6ICwqiepr2A9oOt6D4RHeihag/L5nB9x/eOwRLs4KZFSyDOOOMMPPjgg+Y/tV/8RQaTtod45h3Cclgw2su2K6DAFw5OkWa/c4GjrJFa5cJ7ZwP/fm6JtmPXBRcvQ4FiHcnJAjKLxPGyEIOJv4jTqEQGBxGDKerZ+EuGx0jgZTOt62HQ2OD7ZCBG12M9EYmLpgG9RqLC47RWCCSi+kYGeyUv9OkEYXLDV4o8PtscApzD67UcoAhtR9CWpmPG/n5TDGaZ4UQY2zYR7CSyo3ojjQwuKcq4quBos0JbDG49wGoftgsjg5kMsiSBlx04751lzdNep6RRFRmO+4U9YkJE3wJk2XdA5yLvf/oG81w677mH3RbeyJ3KLgYLIYQQQgghQoSJdy76Xs0VbTg8nBGfjFJd/5cRTgqQYCyvnn/+eb+dXRwFxmh7WgqVaFW3tSgymA/90R6KTtGL1wR9g21BNFQoNDXsYgk/4QrJ0fDedRVY83yIbtbbOpZQIt2CRTLS7mLHSs/ycKOlyxunJUmwyGD6UJKkNP+RmEkpwAlhBEqxM2ndH0C34707JSoyXsJuCGIwxbmmfYAbl1vb+nZI8H1istXZUp4iJq9hdvjRKqJR18jLcYqwjG4O597DUQK3bbJsV/zhFINLiip3isVVWQx2RFO77Dbj97bjkcD8961ztPJHoL1nlFiJljHO6zkQTlsI+z4iosu8t4DFX3nE4F9esH5b7XsKbTrYMXngZWEXLTFYCCGEEEIIIcoCk0SraLB+/h7krf8bo657AO+9955b/KXge8ghh5iJyeHs/CYhRQztXOOdPC4ACRR/clKA6mEM2ad4ymhxTuFSvz1w2c+ICY0cFmBvO6JSr/kHqO1tdRcSTiF8iyOKu2YzxDWhRga7E+I1DnoNhUzrA4HzPAkPKwW2zy59vwNZFziXc9g+I/YokvoTStnO7ChhueUpYqbVAM76qPTlsHPLeY8LxxvaRBEHkaEY9ZiQZHXolGQTwY4fW1R32ktUNRwCutsmwraKoBhsW0WEIwb7+oeXGBksMbhM4Pk76CrvZSe9CNRra83/+S7w6wsSg4UQQgghhBAiLqBY+/ZpXoueuuVcvPfeH2aeyUWZdDnixIu0LWDiP1pFNAwQ5bf4GyR8fTvqb1qAwuOfrTwJvYLROEA+iEiHxXuJwf955qOdQC7apGagsFpdJO7dHjgymMKbLXSGIv5UVezIYEZLOnKSBLaJ8Hi4Bo04N500fnxz4x27A4Gs+CG695XBtwKHjbUS8AWKHnaK6oyE5fmp0pHBnuut0LbhIB2GAIkplh2H7f8e6nll51BJOG1ldskmIij0Vv73M6tDkRYpLfsDR9wVOLGnzbZl3h2c/E6wU8rp0z71ekSCIoOFEEIIIYQQjoeP5VYUEaOK7OgTET521K6Datv+tV6rVcOHH36IYcOKfEMjgQ/iZxT5dwfy/k6phoRNC8xsQrRFm1CvpRcPB9IbAD1GAINuLvt9NupefFlKRuTewYHE4FpxHhlMYahmM0sMptWHP29Xp8+nxODAZG8vuUPBK4FcCFGq9nB+JlHj9zcaUdnlBZNL2jCKN9qYyOkaoa0rMdjbJsIZGcwOh9PetEZ3hNJ55bR6CCky2GETocjg4Kz4Edj/AqD5vlYk+7d3A6+faI2gSc0IvB07OegxbnPjMu/PXYVWwtQIkBgshBBCCCFEnFFYWGimaJbHpGUllvnHG0j87AprG0YN+g5PFKGzbQV8Ywj3a5ZkbCA++eQTYwkRzXPsVxBu1hcJydWQQK/Q5T+gsKAgdNGJSYeYIIp+hIw+ogdluOzajERG7WVvhWv3FriiebyByGiEhGp1kOAcOp1eL+J9J6TWsI0+UHjEPZYFBiPoareKPDlfOcBrq6BGMyRv/scMuS/MXOttHUF4bm9eY4lAvC6ifTwb5iNhzrNw0bu0+4mokOTnIDE3y8y6qtcNfB0lpiAhJR0Jedlw7dkG1/wPLTsFCshMwueM2OR1lVbLuq54bnJ2BReEyup+HynDH0fixxdZ++p3YdjXTcLMB4DsLaZNXIePK1VVEqrVNu3oysmEix0ezqjJKkLCnu2ee1RqLe/z3uGIog9KPkcJuzZYbZmQBFe1uiVvk1YbCbzuC/Pg2rWpfO7vcURh0fFmZWUhM9MT4Z+WlmYmL3ztWU54DnikPbBuHtDmoMA7YacjO3QDRRBv/DvijkmJwUIIIYQQQsQZfLDYtq1o+HaUHlr4wEKBIDHQMGfqGXW6w3a4LPjnE+zs+v/27gO8ifr/A/g73XTRBQXK3qAsWbIUBEWW4kQBQUDFPRBR9C84+IkDEVGGoihDBbeIAgICCoJsAZkCZRYodAAtdND8n89drrm0TZukSZvxfj1Pnl7W5XKX0Xzuc++vaWRxGxgyzyLw9HZk1+hS8uG9Xk7W88V//kDBvuqWVQKwZPFPqNewiVO3b3Ei41sh6MR6GM4fR2riP8iTIqYt9/tjMoJObFCmzz30D4xBNnbqmYT/PhYhu9VsZJFpCMWlMnrOFWMaIvDkxvzzuUEVke7gY1fIC4RWpruQehY5Uc2B0HrA+Qxph4NbFyqqdEB4eFXkRVZH1sVsGK9YWwcVAdmX4MTt45+8G9EL+ynTV5J2Iq3KdZ7V/Wrid/F0/mdidkAELhSzjoI7/x/gH4S8sMoI2ToXwYdXKJenxLVFXniuxW0j/EKgfUqmnTqKPCd2Zlv7vA/Z9SVCdsxTCn0ZHZ5FbtXWjj1A1a4I7vGOEkOSE5Rg9+smZtOn8Lt0DlciEpDaSt356KhI/1DI0JAGYx5STh+DMcjOwS69QHj6GYSYpi/kBiAnJaXY73lros+fgvR551WIRWqabbEb0RVi4Z9xCsYLp8vsO81dnDcVgJs2tYwmGj9+PF555ZXi76zFmpQ0UJ/sSFv1BtCgp3KkT6EoqjVvAQ17OrD0LAYTEREREbmdyMhIxMTYMUK7DcUBGaxMulKL/ZEYEwNjpcYwJO9FwKntiAnKtcwFtMaYB8PX/ZRIAmO1VjDe95Pl4fXOsm4KENsAqNYSiEyAO5F1vGnTJvzwww/48ccfMaLWMTzfScoUwOmLeYgP90OwvxFta0Uo67nUbDy03Fi/K3BivTIdlbYTqNPSptkbLqkRAlJcia5iWwHZ4v5pusHW5DdvbAIqOPE1XexjJzQHdMXggMh4x99PzfohLzZBeT1HyOBokWXzHJzxeky9ZgiCTO/50LJegOhOMFZtCUPSdqU7OebC3uI74NxVjjlrNSiqavGvoy6P5E8atnyYPx2VUE8pEusZIuLM11cwOOczoYTPe0P2WRhS1KiTyNDg0j1m3AMO39UgkRqXzsEv52Kh9WlY9pLSLW2Uz/eONhSKbxyPvOxMJaYjunKCOqCcr7n5NeR1eAjGzBRUiKmP6JgY+4vB8h0u3dqyA6RiCa9zHUNkPJBxShmkNCaqIuDngtgQN5WZman83b17NxISzP+PFOoKLkh21C0dC9S41nrGvabLs8C/PwAftgHaPagOFiske3jjLDVyQm7jAB98pxARub/ExETUqaP2E7Vo0QLbt2936eOtXr0a3bqpI8zeeuutyo9YIiIqP/JDzpHOnuJIccCm+TbuAyTvhQFGGA4sA1oPLXnmKYnqoYzyOCe3wfD1YGDQt87tEM44C6x8VZ2u2REYvgTuYsGCBRg9ejROnDiRf1mt5uaM2g3n43FreLKSXet34QSQYFtBtljvt1RzNau3Uw7btiavThdgzURl2i9xrW3bUwrNkjOr/NivBoMjr0UtE9XELyzO+uBbzhZvmRtsCItz7DmI6q3Vkwey+T3vKlLM+26EMum3YRpQtws8TsUE4I5PgcwUGOIa2P460gbmC46EX8GOvgLvDz+JYymLz3vJj9YeMyy27N6PVp67IeuCGm+gX47tXwBZ6TBI0auzDTFFNdu7bjk9RUxt5aTsBDB1BVts972/qOMAJO8H7pwNRBWIixEXzylFeGEIj7f9dR5WGfAPhiG8Mgw5F0vudPUifqZ1FBERoezAt9mvzwJn9gDDl5Z8W9kZP+I3YPEoYMUr5jgo2RFctxvQ513bdtgXgcVgIiI3tmLFCrRs6YQfjCXo2LEjkpKS8NRTTyEry7EQeiIi8hKN+qgjX2s/Im0pHsbUBUb+AXx0nXr+8B9qEeiuOc7rFJJsPX2x79BqtbOmqEJLGcnNzcWYMWPw3nvvFfqR2LxmVH6MwK1vrwKyM9TcP2esDxl0KvWwOl0gi7SQhNYwBlSAIfcSIIPI2dJRLHm7cvvSDJRWoBhc7OBbrh5ETgawK63zScCmWeYMZRkIyNPt/BY4959aTLj6jsLbrLSa3gosHw+cPw7sX6J2s1nLvnRXoTFAszsd23lV3OteWdcyUFqkwwNA2S1Tdxh/hXLscJeBzbTBr6QQrp2XgemyTIfPh+gG5KPSObZRGQ9AkbzXSjH4tHnanuLigPnqTl8PjIApF7+MBvYvA4b9qu5oskV0bTVzWN6/MiirkMF95bOpFHwvXZuIyIPExsYqJ1cLCgpClSpVUKGCgyNtExGR96jWCgg3jTwuBdcsdfCkEsmI5SNWAIGmA9L3/Awsfqbogc0ccXKbeVqKcnNvVYub5eTMmTO48cYbLQrBcn727Nk4ffo0mlY1rQcpHsqP78qNnVcYTzloWYgvjn8QcrRs0AtJwDndfa0xdQV7bDG4SjOgvfmQ/dL+aFacO6DuJPl1NPBvgcGA3Jm8/6QwWdR23/U9IIN5yftUdlY4mww6eO3D5vPrp8GrSbHm1E7g4Cp1h0pxr/vrngPGpQBjjwLSvV8WLqU69z3hKP1nQ9b5wjmqQqIkyDkqNTZPSzHY2m2e3gU8sBLo+KTt85adsSwE2/Y5LIXgvYuBoT+rBV57yXtWO1LFCe9fFoOJiFwsOTlZKbS+8cYb+Zf99ddfSgF25cqVds2ra9euePrppy0u69+/P+6///7887Vr18aECRMwZMgQhIeHo1atWli0aJGyHBIBIZc1b94cmzdvdsKzIyIiryOHPkpUhLiSBez+yfb71mgLDJgH+AWq57fOAVa+5vxisGa/DYdZuoBkA7dp00aJWRKBgYGYMWMGli1bhmHDhiEuMhTIUDN3EVXL+QugL+xpGYLFyEm41nwm8Q/vLwYHhQKVmzjnsa/kqOt7n+61FuHgOilrRiMM7zZUR63/6p5iugENQFgl1yzDNUMAbVCvf74yd8x6o7WTgZmdgXn9zZdJPIq1QnlZxzRknlP/yg67wHJsANEfzaAvAGsFdGFrl/qF08CB5WqXu8Qg+GKRcdsX6lE8Sf8UfZtKjUouBvsHqDstq7exvD05xy/PAju+Bu74BJDBWOV1KycZBK6csBhMRORilSpVUrqEZFRRKcDK6L733XcfHn/8cXTv3t0ljyldSp06dcK2bdvQp08f5fGkODx48GBs3boV9erVU87LKMNERESFtLjXPL39y5IHQ9Gr3wO4baZaYNIKJH+ZB1MqdTHY4GceJEgOtyzj77LPPvsMXbp0wbFjx5TzssNXisIPP/ywktOpkEKwZCmKKPsHX3N6Mbi6qRhs8Lcs9JZlMbisD0tvfjfw5Ha1061Rb8fnk5oIfHANIJm3mghT57y7k9ej1kGWdqzwe0UrBkuxXIqTriCvAy1qJvcysOlTeBSJtjixFUg9AlzJLf62ReWlluVOEFtjIsozIqLgZ4NE3hRVGLY1JkIiib64U40lOmhfk41XyMkEfnoUWDAQhuUvF32buIbmaV8smLuDzZ+qESif9wFkB512kqMzygkzg4mIykDv3r3x4IMPYtCgQUonUVhYGCZOnOjSxxs5cqQyPW7cOKVbqW3btrjrrruUy55//nl06NBBOYxVfsQSERFZkO4g+QF5dj9wZB2Qcsh6HMHsm9RM1ib9gJYD1QKUZGzKIclySL347SUgth7QqJdjK/rCKeCCqUBZq5NauDr4O5B+TB24rsCAYa4gg/OMGjUK77//vkXm/rfffouqVata3ji6FvDcAUBGudc6fyTrTzojpagt+awtiujUtJXkvGpkvZYgt3Iz5N37NfxqdwSCTV2athaDHe2CLVjMKetsZ+l8lFxFOZVGUevL0QJ5eahYQ30fSwa0dIZqnapSGNaKwa4ubrd/GNgwQx2gSiJeOj1VrlnfdvljErBjgTr92Cagkq6wVlBRBUx3KQbL9tYGtQst50G+rMVEXEqzPybCorCsKyb7Cn30h7UCugw0Kp8D8n2ZvM+23HhbyU4SiX+Rz5K6XYE2w5wzX2/zivu9NtkZTERURiZNmqQMNPPNN9/giy++QHCwE0dYL0BiIDTx8fHK32bNmhW6TPIOiYiICpEfiq3uU3/c3f6JmntrrWvy+CZ1cKhNn1j+wGz3IND1RXW6wU1AneudM3icZBo3vLlMoyLkSJonnnjCohD86KOPYtWqVYULwQXjCsJMxSA5PH7NW8CB34DDpcw6zi8GG4BoG4qd0knd4EbbCsHi/Anndgbb0L3stooaoM9TOoNFxerm6bSjlkWkK9nqtIOj0dtMDj+/qr+5e1aKUp5Ci1awpbBrT2ewDEi49EXgp8eArXPhcpL9npfrhp3BpYyJ8PlisI0FdK07WLpT9YPFaXZ9B2ycpcZC2TOgoWSNb/wI2P2jOlCd3XnmuvcXlSkWg4mIysjBgwdx8uRJpbMoMTHRoXnI6OQFox1ycnIK3U6yCzXaIatFXSbLQkREVKSOTwBDfgKa32U9X3LPYvO0dAYXdP0Y4LaPgXu+VAujzsgLlmKwFJc1+ixXZ5FR7SUr1lQIfuaZZzB9+vT87+JZs2Zh2rRpSv6/zapcrcY0WMs/tpX8H6ANICdFNld0WAaFqTsAJJIj0sYRz4srojYfAI8lr31tu2ms7RxxQ0bpCNToi7D6gpA2YKQryYBpA78GHv0biGsAj6F108qOl5K6VQsWg6UApy/G62VfVKNHts0HEtfCaaS4um0+/JP/tfI83KBbOaaeGickR0jodzbpC5u2xkT4ejHY1gJ6SYPISXyLHMnz9RDzTgNb6HckZdjZZLRgIPBOXbX7nsocYyKIiMpAdna2ktc7YMAANGrUCA888AB27tyJypUr250/nJSUlH/+ypUr2LVrF7p16+aCpSYiIp9my2Gke34uvhgs82jhhEJgwWKwHPovP27lR610JkvXrbWBmuwtAm+eDax6Q+mmNT6wHGPemJbfESw7U+fMmaN8pztUVKzcFDi9E0jeo0ZIOFIgl05FreghRRVX6PWWepKMVL8ChVBbScGv8yi1QFGrIzyWvIalo1orukhXZYDrju5yOtlhoJHcYH30isbVncFCBvTTD+onnHm4uqs7g6UQXNJ7QV8sbjMC6DvZxkHUdFEJpc0E/rwP/M7shpSljXI0Ruengbrd1Pdhn8lqUVifIVseGvRQTwXpi7m2xkTob+eLxWBdAd1YVGd6kYPI7VOP+tHTdg7JYI+yM9BW8nkoO8skAuaincXgmteqmdy/v67ufPakz1UvwGIwEVEZeOmll5Ceno6pU6ciPDwcv/76K4YPH47Fi3UdVTa44YYblLzCX375RRkEbvLkyUhL0+0RJiIiKisyEvaxv9VpKcy6sttPCsBSjJNiVnRt9TKJilA6nIzqaPItdYPeOUIiL74ZBpzcmn/R6snDMWnSqvxCsAwIa1MhePEo4EqWuqxdRpsLXtVaqsVgYx5waidQs30p84LtjF/Y8Q1wYBlwZg8w8k9pcy7+9jLCvKMky7jHeHgFfTHYk/KCRWR1K53BZ8o/9kK6EGUHSacn7StAlaXMVNu7afXFOH2Wa1FCIovOzS1NDIQMpCYZ6iaGw2sAOVVpruY0XzO0dO9pV5Nc+qa3qgVO/eu2OOwM1q2LKNuLwQVpnwcRapSgzeQ7RHbESjFZBk61h3wHnjugTv+3EmhcioE+yW6MiSAicjEZYXzKlCmYN28eIiMjlcNLZfrPP/9UBnazhxSQhw4diiFDhuD6669H3bp12RVMRESuJd2y/60AvnvA8jDefb+ohVjRuG/J85FOWImVsDdXUHQbC4z4DXjmX3Nh1dm5wVLs0XdLSj3zrHlZP/74Y9x///22zUvyE+Xw782fWXY+SlFb42hUhBTeB30L3Pwm0PQW++4ruZA7vwFO71K7k8k2Wraup+UFF9cZfFHfGWxnAcgZDqwA9iwC1rwJfNAG2P6V5JfBrUhBVzJWbc3Z1ReD9YfvFyUgBPAPck5ncM5l9ZD7E1uUs8awyrhSsZb5+lM7gO9GANvnw63J59ndc4Ghi4DqrW27j3496r+ffIWtmcFSDG52N3DDy+oArwW/m7UdEo58FoSZjiyQYnCBOMNiNVMHNlfs+tb+x6VScePdQkRE3qFr166Fcn1r166tdArbS3J/JbNQyy0sSlF5xAVzhuXxC15GRERUpN8nAGtNhzvX7AC0HWFbXrCedKJ+3A3IvQQ07Q/UaOfYytZ3slZvqxZfpGtTulBLS+Zz80TkrXoDKclnEGdIRfN4f9SqaMDzE6cpEU82ybpoPrQ8qqbldc4oBssPfhkMTk72qtNFHexPyCB28Vc5tgy+xj/YsnvRkyjZz6bDuPWdwXK5xAhIR5+1XFtXkq5+GdhQ8kkvnAR+fFgdiOrW6UB8U9veZ/LZJB34smPEFYeYL3nBPF0w4qIocoi95GxL539JncFK/EgkkHnWXHB2lEQ/aIX+kCgYB3+PVP/KiDmzDn7r3geStqvFbH3xzVvIepTuYClE+mRMRKptncHyXXnHrKKvs8gPdyAyJrwSILOQ97IsT6iNAxRKfIksl9xn3xJ1MDp3PULAC7EzmIjIjXXs2FE5uZp0KUt8xRdffOHyxyIiIg9zVX/z9PYvzN1IcvixqFgTqNqi+HnENjDn40qkQ86l0i+XHO786AbgqR1A93H23//cwfwuJsngX7VqFR6cshjx4/fjvVXmfP45L96DRx55xPb56gtuUbruPCHFV7/A0g8i56jaXczTiX8WfZvjm4HP+wLfP6RuKwIq6wZful5XIPQEUnDVBgHUvzab3612YD72t+M7Z0qj4+Pq+7dhL/Nl8p74vA9wpogBrvRys4CFg4G/Z6gZ33JyNslD37FAnQ6uCFz/vG07q7SCnDyXBYNsi4oobWewRJcMXwpUbwcM/s70OeOv7nh7aDUwZBHQ6233KbRJpMWH7YB3G5e8juyJinCkGCzfAalH7OtodSf6DvTiMoOLo4+McWQwSa0zWNgSFbF+OrB2ilqEllgQkZOpFoSpzLAzmIjIDVWvXh0HDqgZSsHBrg/Tb9OmDbZv365MS1GYiIgonxR645upWbdyGLIUapL+MY843qRvyYNASeG2US81OiEnAzi4yvZ8QOkasvYj19FD9tOPAzM64nxUU3xypCbe/XIFTp48mX/1on1++N8N6vT1VTLtm3faUfN0wc5g6V6UQo106p3drxZFpCO5rMRfbe7ESlyrHpZfMDdYMom1QnG1axzrQPY2+sG+5HDqMBvyY93JXZ+rr7Py6AAujuSMD1ygfh4sHatGl0iX67z+wLAl6kCRBcmghhJ5cGhV0TEeziDvi9//Zz4vgylWNBXUS/LIOmBae/V1IjtWbHldyedAaQfTk89CidKReejjNuR83evhVgJDgbP7CnellqYYHBSuvsaL+kwrzi/PAps/BVrcC9w2Ex5HIjLkM10K4cV1BhfHIjLGkc7gypaFZX0+cVGxU9KtLo8pA7UOmAds+Vy9bue3hSMsyGXYGUxE5IYCAgJQv3595VSjhi7rzUUqVKiQ/3hVqnhYFh4REbleq0GW3cFSzNTYkhcsmujybffaOICqFEimtgImNQJ+fMzWpS1hlkYkffkEkHsZkWe34so/Cy0KwbJTtFXPgbgcYvqBK0VTezr3pMvMWjHYIirCCCTtsG/hpdAh2apH/wYyU2A3KZLU6mTuKJMCf0HnT5inI6va/xjeqOcbwKi9wIsnzQMYehLJX63U0Nyd727qdQNGLDMfYXAhCZh7C5Cuey1qnweLn1K7djXDl6mDozmTvE+G/qzG3zTqA7S4x/b7SvxG7mV1WgbWsqUzWCI85BB5e/z7g5oVrFeaYnJZkq5lidQo2M370XXAe1cDn/Wxb34jVgAvngBG/WtfIVheT1IIFv98VXK0hzu6eSLwfCLw8jnbPpvkqBwZuC3lkJXO4PjSFYMzdPMqypF15uJz/R5A/RuBCNOgnDI2gSPfa+QQFoOJiIiIiKh4MvCMFm+wYyFw4+vAk9uAnhOBmtfatvYkn1S6t8S+X9UOv5KkHVF/oMuPx5J+ZMrgb8X8kJQi8MqVKzGsdzvEn1qpXJZyyYg3/sxSMvlvueUWLFiwAKdPn8bcufMQ0ry/+TD70//a9hy1ZS6uGNywJ9D2QTUbVToj7aFlq86+CfjRjugKvTrXmaclN7ig8+aIjPx4AV8XEa8WxuUwe08puHka6e4c/IM6QKLWYb9srGXh7rf/U48uEPJ5JJEItn7+OJKDevc84M7Z9m3z7IvmTuWSslMLdpzbKuOsOqDnl3epXcWeqKiIDDliQ6JMzh+3b172FID1zpt3AioO/g6PJeugpNfp0Q3A/6oCMzsDG3X5wfrubPmss5cc7XLVbUC7kSUXpKX7V3P17epyy1+Rl6MOKkllgsVgIiIiIiIqnhwW3+hm8w9H6eCRgbQ6PKp2edkiMARocJM6LQVe6RAqiT5XVz/4mt6xjcDHXYF3G5kzjQsUgZcvX44uXbqgR48eGBCzG36mH82z9kbizSkzcerUKfz0008YMGAAQkNN3ZOthwEDvwaePwzU6gCHYiKiC2QGC4nL6DNJ7ba295BcyTnWxNaHS3KD9QUS6XIk7yMdpdJtP7ML8NvLcKvPmft+BKLrAAmtgb5TzNf9OQlY/6E6LYO0yWBY0lnoSvI5IZ9b9pBCrSa0hM5gGQRT6Ty+Vx3kz1ZyZIbE9Bz+Qz3U3hNphXCtM1iK/ZJFr88ALotBDPUOrIBXk/eVHJFS8LnL4IISBSXZv45kBte7QY2i6f22+r61Jjcb2P2TOSpEvguFPhpCXywml2JmMBERERERlazlYPPh2dvnm4vD9pDDrv/9Xp2WeZWUZWlLMVjyErXb7V8GdHwi/6r09HQMHDgQv/76q3K+ex1/9Gqg/gS6GBCD0V/vgn+wlUGV4puqJ3tpxWApWDm7s1byfDWx9RybR+UmapEq86zaGSwd2pLpXDAmQpbfkUOGyf1Ix7wMziRdl7L9q7ZUu+3lJDt13Il0YEtEgxQEte5R6WL8fYL5NlIklk5EPemQlSMPHO3clmKkZJm2GlxyvENx9J2NJXXtdn7ascc4tcs8XaU5PJJW8M29BFzJUaM1JC5Duc7B7Ft7Fcwrlp2c9mYOexLZ+SjrViKCkk2ZzUJ26srJ1STnWxvwrlFv84CG8nkkOzelKC0DypU2P5ts4qWvciIiIiIicirpwtNGDd+3FMg4Z/88ZDAyf9PAqHt/sRzoqKRisPxgLIr8iNQKWkf+yu8uO3r0KDp37pxfCJafllP7mYsM4f3etF4ILg0tJkIKwf6maA1n0XcGxzhYDJYf2bU7q9PdX7aM35CijFYMlg4xfZGYPFfmOeCnR4HVE4HdiwocFu6GY0VE1dDl6RqBQ6vN10lETeuhltm5kjH7Vm3LApe95KiCFeOB6dcC+39zfD67vnMs+sEep/XF4KvhkbTtq0VFaF3BooKdxWDZqfXzU8DXQ9UoBFu1HKhm7WpHWchn4Sk7c9zLk7w3FgwCfnwUWD/Nts9+bXA3+Zy3JwvfGfRdv/puYFmuh9YADywH2j3IQnAZYTGYiIiIiKgY06ZNQ+3atRESEoL27dtj48aNVm/7/fffo02bNoiKikJYWBhatmyJefPmecf6lcJgw5vMWZj6goStZLR3GSxKy789udX6baVQfPIfc1yBtcHM5IdkQ1OXsnSWLXoC2zb8oWyrXbvUZYyJicG6mU+jaXSOersqzYBmd9n/w7skssztH1G7qKULujgyaI90MusH8ilJihNiIoSyvgzq4emRpsF7tIJYRrI6zcHjvEfF6uZp6Q6WfG2NvVElZU3e33fNUV+rnUcBnZ60vF4GmTuyVo1N+G+5Y48hWbVLXlCn5fWvdag6Qg5/12gZ6c6mdQZLbnKcqbjnafRRENItqh9Izt7O4LP7gS2fA7t/tDx6wtbvtfYPW3avegrpPJfBWOVzW75LbKEVg8XZA85dHvmOtDYQYnamugNY2/YSLaEX7KL3ClnFXb1ERERERFYsXLgQo0aNwsyZM5Xi4pQpU9CzZ0/s27cPlSsXLqJI0fGll15C48aNERQUhMWLF2PYsGHKbeV+Hu+650ydhWcc73pt3FctgEqxtLhCVOphICu9+IgITfMBwN8zAWOecph2xF8/obIxA1Lyql+/Ppb8/APqL7nXsrvQ1kOB/1mo/uCWTqoHSxhgSObZ9XnbOqS+G2HuuJbB9UJj1ZMcoi5/o2oVXkat0CEFp9Lk+Ta/GwisULgDTz+Akr5ITJ5NtnVYJbXQmXbMsjPYkYzQsiYFOxlwsahDx+Vog99eUqcPLLeIibHZspeAbFOkQ4uB5ixTR9w8Uc0wFze9DpfkPUvxUyvsBQTBIxUcPE9fRLQ3M9iisKwrKtuzc0y+0yTTPuEaeAwtcsGebmr9zoOz+4DqxWT82kNe86d3q9/pzxSxo3j/UiDHtI2b3AIEmI4QonLDYjARERERkRWTJ0/Ggw8+qBR0hRSFf/nlF8yePRsvvGDqJNPp2tVUBDB56qmnMGfOHKxdu9Y7isEyUvhT/wA5l4CKDubhthwEXHNfybezJS84//qWwID5yFo4HMHGy6gfbcDfD4Th/f3VMWLaGsQl/wWkm7J863U3dyfbYvNs4NgGc0yDo1m9BUdf1+dUyqkgiZmQQeyuf049L9m+qYnqtMRilCbXUgb9u6p/4culYChdeTmZ6nYi71GxhloMvpCkdsJqPCUX2trrPa4hEFVTzeqWmJisi/Z1GcogbNJRKmQnzM2lHJBNPqseXqsOCFdS5rjEXyx6Ui2GdhkNdHy85PnLwF9a53K8h0ZEFIqJSFe3m6MxEfpOYkeKwRJLcoNph4In0Udr2NpNXamx5WtJBjycc4taxJUdk7a8Bq0NDnclSy2qF5X5K3EuRUVEFCT3Pb5JfU+4eoBIH8diMBERERFREbKzs7FlyxaMHTs2/zI/Pz/06NED69evL3GdGY1G/P7770oX8VtvvVXkbbKyspST5sIFtTstLy9POTmLzEuWxynzlB+dcirNvGy4r+HEViXnV7l5lRbF3kee16jpy7B4XjK+visUbar5IyTAgOebnoBx21Tk9XgVGPIzDCtfhbH7ePuWvWFP+JmKwXkyCNe1ThhoJ7YBDDU7wHC0mNfR+RMwphyCUVvW1ET4yaHw8tqKqWe+3Jnbvtc7wM1vqR3WfgGl28ZUrgpud0PFGjAosSxGGPXvLdkB4OHb2VC/Bwyy0yYvB3lSYJXBqWyRlwvDkjHmdXHDOCC4YunXR2XTzp6S5pOXBz9Txrgx85xt7+lTO/OzPvNkp1IR93Hq572rNOyt7qCQrl7pVv1vhfl52bsNgiPy72u8lGbbepT3wF/vKzsTjI36qDsUPU1mqvl5h1S0bbvHNTDf58xeGM+fhN+Zf4Ez/8IYmWDz90pBhrBK6vvoShbydiwErrpD3emo6TcVqH8jDAdXwlizU9HbNzsDho86w5CaCGNcIxgf6ebW+cF57vz+sgGLwURERERERTh79iyuXLmC+HjLzjk5v3fvXqvrLD09HQkJCUqR19/fH9OnT8eNN95Y5G0nTpyIV199tdDlaWlpCA3VZU864UeLFJrlh6IUtD1B5PFt0A6ATq1QC8aUFKu3fe211/DBBx8o051mZ2DFc+3RJXgPjH6BSE/ohly5b2RToP8C9cdlMfMqyD++A6JN07n//ozzDe+xelvDpRQYJcIhIKTkGfebC/9zB+B3+RwMl1LhdznV9DcF/mmJCDy2Dufr9EKOaVkDj2xHeEQ1+F84iUuh1ZBp43PwxG1PpVdwu4cFx6GCdqV03pmk5gYX+97yBEHx1yISs5XprF2LkVHpWpvuF/LPHISf2aNM51RuhvRave36bCitgCxA6+e8nJ6MDBseO+zI5vzteCG0Vv7ng8e950PrAnVMA39mAyHnTkLr5754JQDZ9nxGZyH/Mzor/Qwu2nDfkP/WInzPIvXx/KOQFVITnibo7HFo/dWZxmDl9VPidjeGIjYwFIacTOSd2YOLSQehhWxcCoi0+XuloPAK8dC+9fx+GInc1e8gs+1jyK7fx1wUlveXnNKsd29XDIlDIBJhOLsPaQf+wpW4JnBXaWm6zmwPxGIwEREREZETRUREYPv27bh48SJWrlypZA7XrVu3UISEkK5juV5z4sQJNG3aVBmATvKHnUWKAwaDAdHR0e5VHJDs4CPrgFZFxEYM/RF5chhr8h5EV29odRYff/xxfiFYntuHM2ai04gRyJPDUi+nI7JJ4fVul+h2MEbXgSH1MAKTNiMm1N9qpqXh66dg2LsYxvB4GGV09JIOwY+Ns3qVMSMZEXLYusG0vQI7AMYxwOKnEVK9OUJsfH247bYnlyq03eMb5F9nyL2k/DUa/BFdrZ5lB58nCu8N49IgGK5kI+T4nwiOji65ozDjLAyb3s8/6993MmKKeT+6hNE8sF8IshBsw3vakGYeIC2ifgcgLMY73vMt+iOvUk3lMzu8fhcJ4Lf9vkHqERNCYoKCbFmPmcfyp8NqX4MwuY9kMR/4DYbjm2C88zPzZ6+7SswxJ2vEVFNePzZtd8maPrkNfnk5iDCez784JK6Wzd8rhXQfC+P5wzAcVwfYDUj9D5G/PQPj1hkwdnm2cKewNS0HAEmblcmoo8thbNgJ7iozMxOejMVgIiIiIqIixMXFKZ29p0/rBlsClPNVqlgfdEl+hMmgZaJly5bYs2eP0gFcVDE4ODhYOWnOnz+fPw9n/4iXH4mumK/DvnsQ2Pm1Ol21ReHDdP2CgWpyeQurs1i6dCkef9yccShFYcl4VjS7w3nLKgNKbZgOgxxWfnCl9cxDyS2VdS2FJjn8vjTrOqJAIVkymo9uAKpdA78mfeyat9tteyoTFts9umbRh3YHODgQpDsJiQBqdQIOrYIh/TgM5/YDlUvoKFw1wZwv23IQ/Gq2Q5mroPWzAoas8zDY8v7sPw1I2gHIUQURlb3nPV+5kXpyhC5j2HA53bb1mLwvf9JPXitynxWvAPuXqPM5vbPkrPrypg2wKs8hNFp5DjZt9zs+VV57htAYGNa+Z55HRBXHv7Ni6wAjflNzsNe8BZgikAxn98Pww0gg9zLQ+v6S53P17cDSF5RcbMO/P8Bw46tuGxXh5ynvLSs8e+mJiIiIiFwkKCgIrVu3Vrp79R1Xcr5Dhw42z0fuo88FJhN9sWZWN+D7h4Dk/Tavnh07duDuu+9WojyEdFg/+qgT8nytjTavkdxga0z5n0rh1t8FfTe3fwQ8tMpqZzKRVZLPqpHM715vA9Kx5y0a6KJ4Diwv+fZS6JNibFAEIDni5T6ImrlDs8RBPJve4vnbTgYcO/sfcGILcPZA6eYVGGKO5rF1ADmtGCw77UJN3bANdAOWHShiUE9vGEBOyCCo2nOWAd80UgwuDSnayuCsw5YAQxYBNTuql4dVBprdbds8wuKAOtep0zLoqzZoKjkdO4OJiIiIiKyQAuPQoUPRpk0btGvXDlOmTEFGRgaGDRumXD9kyBAlH1g6f4X8ldvWq1dPKQD/+uuvmDdvHmbMmMF1XNA1Q4Atn6sFVBmwTAad2fG12hl03XPFdvadPHkSffr0yR9w77bbbsM777zjunVcq6M6sJR0Yv23HLiSA/gHFv5hrhUiomq5blmIHBFVQy0kSlG4wU1A+5HetR7r3wgse1GdTj1c8u3bDAOa3gqc2lG4C7+sBAQD/sHKoFvIsrEY7C0kImh6e3W6xUDgtlJ+R8oOsouXbSsGZ6YAGaYiaKXGlq8hjXzOX/8cyk3qEWD/MqBJPyCyatG3uZxWZHe0XS6cMk+HW+80t7soXPd6taib+Ke6voPsGANBjhQ6tMr8Oomp45zlIgssBhMRERERWTFgwAAkJydj3LhxOHXqlBL7INEE2qByR48etThUUArF0p16/PhxVKhQAY0bN8b8+fOV+VARHUAPrwU2zQL++hC4JAPXGIFd36knKZLc8yVQv7vFYaKSxdy3b19lHQsp0ss6dukhm1L4la4xWS4pNkhcQ50ulrdJN2dQshhMbke6YJ/6B14rroF6+HvtLrYXd6U7sm4pM8Wd0R2ckWx7Z7C30B/dIIXwk9vVjF65PNqBnWmNegM5mSXntAvJBtbn52rkceMaAWf3qYMsShFT66AtawsGARJVse9XYMiPRd+mxrVqh7UUhcMd7OrVdwbbsu7sId/bWpevPWJMAwtqxWB0d+pikYrFYCIiIiKiYkgmrT6XVm/16tUW5ydMmKCcyI5CiBzu3G4ksPlTYN1UIPOsep10y/36rEUBKz09HYMHD8a2bduU87Vr18aiRYsQGmpH15GjGvZSi8HiyF+Fi8GmvGBFlOeNTE/k0aTwZC3LW2M0ul/+aLCpGKzLf7Vq57fq3yrNgNgGpcskL28WERnpwPcPqkXaoHDgxRP2z6/fFNtvKwOTavSdwVrciBSD5WiVg7+X/JpyBXmdat8n0iFr7XXbYoB60uTl2f4Ymz4FTu0EjqxVz0tcSlAY3ILEWFgUg8kVPPjTg4iIiIiIvEJwONDpKeDpHbjS43VcCa2kXPwPmuD5559H7969UbNmTURFRWHx4sXKdRUrVsQvv/yS36XtctIZ3ONV4LGNwPVjij6sV8NiMLmr9ONqTmv6CTXuxFcc2wR81AX48THLbkh36ZDNuqAW/Yqz+k3guxHAzC5qsdKTBYYCfgHmYrCWf2tP9q2jdIPHIa6h5XX1dbnB/5VTbrAUfmuaIjTEhSTnP4YUg7d85vyICGco1BlMrsDOYCIiIiIicpm0tDQlN1lyfiVH+fLly8pJm5bc33PnziElJUX5K+eD/IFqEQYkpi0s+kdMQAC+//57NG3atGwPs+/8tPXr2RlMnkCyuVe+pk7fPU8djMwbaYVViW9Z8Sqwy9RVK92QkgcrRxwEVkC56/yMWgiWTlkp8Br8i75ddiaQclCdrtzYNQNUlnXBU7qiJR5IYiK0rF9Hs2/tUVxnsOTDB4YBORlqMVi6bcujAztG1x177iAQWc2586/UEDjzrzp91W3qgIruQiIvpFNZoqScHV1B+Tz8E4SIiIiIiNzVmjVrcN999+HYMV2erg2yrwCJaZZdctIJfPXVVyunESNGoG3btnAr+mKwI5mXRK629xdzIVhEOJgz6s6Obwb+WQAc+E0dxEoGpcy9bL5eMmElUsAdCsHC1mL8mT3mbuD4ZvAKIaZisHRqSyxQwSzh0uwEKC4OpPk9arFVdhQU7IiVQf3kdSNZvRLfkbQdSLgG5RuVcLBwLJEoTaFaXwS/6nb32ikkz+n5RM/f4eHmuHaJiIiIiMipsrOzMX78eLz11lswlnTos4zP5u+PmJgY5RQbG6v8rVSpEpo0aYJmzZopBeCEhAQY3CnvUzr1JOe43UNqAUErBsuhzxFWRn8nKk85lyzPu9Oh4c5ybKM6KKXYOtd8eYUYoNuLQOthnllkksHENFWuhlfQCr8y8Fv+ZQ52Bv/1AbDufbXDeNC3akHXmoJZuwVJVIQUg4V0B5dlMTjjnPr6zTUVx7XO4KIKwRMqA0GhQK3OwL1f2vc4+ngMyUh2N574HvUwXMNEREREROQ0+/btw8CBA7F169b8y7p27YoXX3wR4eHhCA4ORkhISP4pLCwMkZGR7lXoLUniWuCHR4D0o2ruapdR6ojvaUeAi8mAn5VDvYnKU8Esa288BLvBTcCysebzfoHAtQ8DXUaXTQSBq5zaZZ6O95JisMREFOToNsq7onbyCi1ywlEyiFyTW9S/8noqS0fWAasnWl5WVDE4+wKQl6M+V30x3ZHOYH2GMvkMFoOJiIiIiKjUpAP4o48+wqhRo3DpktqBGBgYiAkTJuDZZ59Vun+9hhQxzh9Xp/94B2g+AKiYoGYcErmrijUsz7tLVIKzD69v3BfYuxho0g+48TXLAancjQycdv4EcPk8EFsfCFcHzyzk9C7v7Qy2uCyq9PMqbTFYdpoMmIdyceSvwpdpWdF62oB7jhbQ9TEUSTvsvz95vHJIwiYiIiIiIm8rBN9///145JFH8gvBjRo1woYNGzBmzBjvKgSLqs2BNiPUaenK+u3/ynuJiErmjZ3ABckRBgPmAy+eVP+6cyFYbP8SmNER+Oxm4NDqom8jUTunTYN9RVZXB7P02mJwRdcWg1MTgYyzcFtHtWKwQR1ITaQcVmMh9C6llq6ALtFG+pgIicNwJ7Kdvh0OfNwVWPVGeS+NV2IxmIiIiIiISuWDDz7A3LnmfM6HH35YiYm45ppyGHinrNzwEhAaq07/+z1w+I/yXiKikgdmuuFlIKwy0H+GdxeEg8LgMYOoabKsFDEljzzrvHd1BQvp2h59AOg3tfQxERbFYF3XbEG/jgHeqQe8Xdf9isJSxD610xwFktBanZbB9bQjUfJvq+8MdsLOgUA3e78Y/IFd3wEnt5nXCTkVYyKIiIiIiMhhGzduxOjRo/PPf/nll7j33nu9f43KD/AerwCLnlDPz+kH3PkZULcrEBpT3ktHVLTrRgNdnlULplT+LIqYpoJvcRER3pIXLLRYndzLToiJiLKtMzh5r+kxs8w786x1Y184ZRpczVD8gHQlyc4A/pwMVG4CNLuz+MEPjaYO4FodgMpN1diQmHpAYKhzYyLEvQuBhYOBqi2AGu3gViITAP9gtRCecqi8l8YrsRhMREREREQOSU1NxYABA5CTk6Ocl6KwTxSCNS0HA5s/A06aBsv7dhgwbKn6Q57IXbEQ7J6DqGndvwUFhAC1u6hFYW/qDNa0ewhoOUgt4uo7pZ0dE5GdqXZZi0qNin8fZF8EJpsGWavRvnTF4CVjgG3z1eno2kD1NiXnBdfqCFx1m/V5ljYmQjS6GXj+sNoVLEcNuBNZnpg6avFei8lwt2X0cFybRERERETkUE7wsGHDkJiYqJzv0KED3njDx7L95Mdpn0mWl0XXKq+lISJPoy9+WusMrt8duH8xMOYw0OQWeB0pygaHq4NwBkc4Ng99d6y1YvC5A/LNpU5XMhV6rZHlkHxmkbxP7RR2lD63euMs67fTF4Nrdix+nhYxEQ4Wg7Xn6a5FVm29KTEZJ8p7abyOm251IiIiIiJyZ++//z5++uknZTomJgYLFy5EYGAgfI7kOrZ/xHwItzboDxGRMzqD9UVTPy8ajDP1CLDufWDla8DB3523Hq0Vg6Woq4lrWPI8KzU0F14vnnF82a591Ny9++8PQGZK4dvkXAJObFGnJRYiooTBHvUxEY52Brs7fRGdURFOx2IwERERERHZnRM8ZsyY/PMyeFyNGjV8dy32fAN44Hdg+DL37bIiIs/MDPZWqYnA8nHAn+8Ch/8s3bwCgsy5uvpCaVF5wbZ0Bhe8jf6+9gqsoMZgaF2uWmSEnhSC83LMERF68nxO6XKjndkZ7M5YDHYpZgYTEREREZHNUlJScPfdd+fnBEtRuE+fPr69BqUAXN008jsRkbM6g6/kqt3A3pjzrC+Er50MhFUC2j/s+A61flPVonBY5ZI7gyUzuCT67uGz+0uXG9xmOLBhmjq9eTbQ4XHL5ykDkrYepkZF1O5svvzzvkDin4BfAPDSacA/wNxt3KCnWhSWHGJvxGKwS3G3NRERERER2ZUTfOTIEeV8p06dMGHCBK49IiJHSPFSBoizFm+w5TPg7TpqUfDIeu9axwUHi1vxSumOrGh+F9D0VusDeGrF4IAKQFRN13YGXzgFfNYb2LdUzRuOqw/U7apel3oYOLTK8vbxVwH9pgCPbwSaDzBfHhqj/s3LBdKP6patEdC4N9ByoGVR3ZuwGOxSLAYTEREREZFNvv76ayxatEiZjo2NxYIFC3wzJ5iIyNndwVkXCl93ehdwKVXtDvU2wQWKmK6MO8jNMufOxjWwLXtZ3z2s7yq2xV8fAEfWAV8NUKdFmxHm6zd9av2++i5wyQ/WnDsIn1KxOuAfpE4zM9jpGBNBREREREQlys3Nxbhx4/LPz549G9Wrm0ZbJyIix4xco+bdBkcUvk6fFSvdo97cGezKgdDSjtkXEaF15Up0RUayfcXgjLNqFISQrm+t07dRbyCiKnAhCdi/BEg/rhY8ixNboBjc4Eb4DCnYdx6lvi9s3WZkMxaDiYiIiIioRPPmzcP+/fuV6euuuw79+vXjWiMiKq3IapbnD65SYwwkC/bMbvWyqFqFi6eezj9Q7fy8ku2czuDzJ9UCq8Rt1Ghvub4kpuGlU0DKQcBgxwHyEhUhxeCMM0Bmijm2oTjrPwRyMtXp1vcDEfHqtOT9XjMUWPMmYMwD9v4CtB+pFnkl6iEsrvC89J3BsuwaiZ8IDgfC49VOZ2/VbWx5L4HXYjGYiIiIiIiKlZ2djVdffTX/vOQEG7xxQCMiovKUlwcsegJIP6YWIrWiYpVm3rldJE9XU9rs2z8nA5tmqdPtHwF6vmGZQSz5zJWb2DdPGUTu6AYgtj6Qea7kYrAUjDealkEK3Z2esry+9VAg7SjQ9gEg4Rr1smUvqZ3CcY2A+xcD4ZWtdwZrr5Gv7pGVB1S7BnioQP4wkQ1YDCYiIiIiomJ98skn+YPG9ezZE126dOEaIyJytuOb1EJwwUHL4q/20nWtLwZHOa/D+u8ZwLG/gZvfBGq2d3yePV4Ber2tdvXa4u+ZQPZFdbrV4MJd33L+thnm81LYPWoaGFA6kCWWQk/OB0UA2ReAc/+pl2Wlm9dbhWgHnxiVucR1wF9TgZPbgYungAFfAE36orxwADkiIiIiIrIqMzNT6QTW6KeJiMiJYuoAN00Aqra0vLxWB+9czRKXoCkqM9kebUcAjXXFtZNbgdk3Ad89oMZHOEKiJmwtBEs8xYaZ6rRfAND5mZLvk7wHuJymTtfqaDl4nJDzWnew7CTIzVYHFCyLQffchTzfE1uApB3waDmZ6k6dPpPgDtgZTEREREREVk2fPh1JSUnK9G233YY2bdpwbRERuYJEBHR8Qj1JLMC+X9XuzzrXe+f6jqwOpB9VpwMrlG5eEjNxzxdq5vLSsWqhVez8Rj1VrAHcMhWodwNcYuPHpq5dAC3uUXOfbekW1UgxuChSDE7arhbOUxPNnceuHnTPHcjzfb+FOt2kHzBgPjxWgxvdagBAFoOJiIiIiNxMXl6ecnLm/IxGo93zvHDhAt5++234+fkpGcGSG+zM5SLXc3Tbk2fjdvcC0XWAax8zZ+vq83W9Zdt3Hwe/7x9QJvPCq6ixCaUlhfORfwBbPodh9UQYLqWol6cfg3HLHBjrdIXTGY0wbPoU0tdrNPjB2OmZ4p9LziVgx0L4LXku/6K8Gh2KvI8huq4yX+U2Uhw1+Ocf4m8MqQij6T4etd1tFVENBr8AGPJyYUw5lP9c9QzLxsIoAyw27AVE1yqzRcszLYv8n3T+/Pn8y4ODg5WTu2MxmIiIiIjIzcgPi5QU0w9YJ/1okR8s8kNRCru2mjt3LuLj45VT9+7dUbVqVacuF7meo9uePBu3u+/ypG0fmBuACtU7wi8rHZn+0ch25vdLvdthSOiO0E1TEbJzvlJQvFi1I7LsfIzg3V8j6Mga+Kf8h/S7voMxKLzQbQyZZ1ExJAYBF5KQU/M6nEcUUMzjGC6nIWbpC/nnjQZ/pARVK/I+fnVvgSGhK65UrAkEhiLowC+INF2XaQzGJdN9PGm72yMqsgYC0g4D5w4h5dw5iygNw6VziPn7I/jBiNyNnyJt0LIyW67zpgJw06ZNLS4fP348XnnlFbg7FoOJiIiIiNxMZGQkYmJKGLXcDvIjUTp7o6Ojbf6RmJqaitdff135wePv74/vv//eqctEZcORbU+ej9vdd3nUto+5FWh5qzIZ7poHAG59D8Zuo2HMOIuwqi0QZuccDKm7YTi4VJmOvnIWiCki/kG+Gx9Zi7yLpxFw+bwN35UxQO3OwMGV6tnQWMTEVbZy0wLzCsjNn6wQUw0VTNd71Ha3gyGuPpB2GIbcS4gJygEiqpiv3L4UBtNgev5N+5bp/yiZmZnK3927dyMhISH/ck/oChYsBhMRERERuRn5IefsH3PyI9Ge+U6aNAlpaerANsOHD0eDBg2cujxUduzd9uQduN19F7d9AVE11JMjKjXOn/Q7tx+oUUxufmRV9WSL7i8Dh1YpWcCG656DwdbPZxmoTlue0Gj5h8G7t7tkJv+3XJn0S0sEKlYzX7d/Sf6koVFv29ehE2jrOCIiQtmB72lYDCYiIiIiIgunT5/G1KlTlemgoCC8/PLLXENEROR7dMVgJO913nyrtQIGfwekHQNaDrT9fpdSfWcAORFT1zydcsg80F7OZeDg7+p0aBxQnYPb2oPFYCIiIiIisjB58uT8QyBHjhyJmjVtGBWdiIjI21RqaJ5O3l/4+twsIMDBaIB6N9h2u8N/AMc3AecOAv6BgMFP6ShGhWj4XDFYk/gnkKP+n4KGNwN+/nBrWRctlz/tCJC0Q92GjnatlwKLwURERERElC83Nxdz5sxRpgMDA/Hiiy9y7RARkW+KrA4EhgE5GUV3BstAcNKhWr8HcP0LQHgl5y/DtvnAjoXq9CPrgT7vAdkX1OXypWKwFMM1+341TzfqBbd3chswp6/5/DLT/1YtBgK3zSjzxWExmIiIiIiI8i1fvlyJiRD9+vVDlSq6wVqIiIh8iWTDxjUAkrar3Zw5l4DACup1RiPw3wog7SiwdS5w42uuWYbY+ubpc/8B8U2BkIrwCVE1AYM/YLxi7qyV9b7PlBfsHwzU6wa3V6cL8Io577m8eVGqNBERERERldbcuXPzp4cMGcIVSkREvk3LDZZoBinGamRaCsFCsmyDwsogKkHXHesLJBZDCsIi97L6VwrzF5LU6brXu269ezF2BhMRERERkSI9PR0//vijMh0bG4tevTzg0EsiIiJXqtTIPJ28D6jSTJ2WrmCNxES4Smy9oqMSfMWQn4CwSkBQqHpe6wr2lIgIN8RiMBERERERKb799ltcvqx23gwcOBBBQUFcM0RE5NsKFoM1ZVUMjtEVg7fNA4Ij1AJx2wfgE6JrWZ5vMxyIqKoWhWXwOLIbi8FERERERKTQBo4TjIggIiICUKU50PZBtSgscRBCsoMT16rTkQnmKAlXCIlUO2MzktXzG6YD1dv6TjG4oIgqQJth6okcwmIwERERERHh0KFD+PPPP5U10aRJE7Ru3ZprhYiIKKoG0GeS5Xo4ss6cYVu/O2AwuHY9SXewVgwWIVHcLuQwFoOJiIiIiAjz58+36Ao2uPqHLRERkaf6b2XZRERoYusDxzaYz1fwoWJwdobaDZ1yWB1MrusL5b1EHo/FYCIiIiIiH2c0GjF37lxlWorAgwcPLu9FIiIicl9aXrDBH6hzvesfL7au5Xlf6gz2CwRWvQEY89TzNa8FanUC/APLe8k8ll95LwAREREREZWv9evX4+BBdYTy7t27o3r16twkREREehln1Zzg41uA9BPqZTXalU2Xbnwzy/O+1BkcEARUrGE+P/dWYPm48lwij8fOYCIiIiIiH6d1BQsOHEdERFTAnp+BhaajZrr9H/B8ohrbYDSWzapqeBNwx6fAdyPU8xWifWsTxdQF0o6UbTSHF2MxmIiIiIjIh12+fBkLFy5UpsPCwnDbbbeV9yIRERG5F8ns1STvVbtV61xXtstwOc03YyJEdC3L87U7l9eSeAXGRBARERER+bCff/4ZaWnqD8w77rgD4eHh5b1IRERE7iWmnpoPLM7uK59luJTmmzERwj/IcjoguDyXxuOxGExEREREVIxp06ahdu3aCAkJQfv27bFx40art501axa6dOmC6Oho5dSjR49ib+8OGBFBRERUAukElqgCcfYAkHel7FeZvjM4yMd23La41zx915zyXBKvwGIwEREREZEVEp8watQojB8/Hlu3bkWLFi3Qs2dPnDlzpsjbr169Gvfeey9WrVqlDMpWo0YN3HTTTThxwjTQjJuR57FkyRJlWpa1W7du5b1IRERE7qlSI/Vv7mV1ALPL6WX7+DmXzdMRVeFTEq4BhvwEDPwGaNy7vJfG47EYTERERERkxeTJk/Hggw9i2LBhaNq0KWbOnInQ0FDMnj27yNt/8cUXePTRR9GyZUs0btwYn3zyCfLy8rBy5Uq3XMdfffUVrlxRu5sGDx4MPz/+PCAiIiq2GCzWfwjkZpftiuoyCrj6TuCm/wGVGsLn1O2qDqRHpcYB5IiIiIiIipCdnY0tW7Zg7Nix+ZdJsVSiH6Tr1xaZmZnIyclBTExMkddnZWUpJ82FCxeUv1JAlpOzyLyMRmOheeojIgYNGuTUxyT3YG3bk3fjdvdd3PYuFFHVoqMyLzRWVjjKTHgV4PZZpge3fFxu97KV5+HfqSwGExEREREV4ezZs0rXbHx8vMXlcn7v3r02rbPnn38e1apVUwrIRZk4cSJeffXVQpfLgG7SgezMHy1SaJaioNb9u2fPHiX6QrRq1Up5XikpKU57THIPRW178n7c7r6L2951/GKvQbTBDwZjHjKuHY1LbvSdye1ettJMA+96KhaDiYiIiIhc4M0338SCBQuUHGEZfK4o0nUsmcQayRaWOIqoqCir3cSO/kg0GAzKoHZaQbBt27b47rvvMG/ePCXX2JmPR+6jqG1P3o/b3Xdx27tQTAyMA76EMeUgKrQZgQoBwXAX3O5lKzMzE56MxWAiIiIioiLExcXB398fp0+ftrhczlepUqXYdTZp0iSlGLxixQo0b97c6u2Cg4OVk+b8+fPKXynaObtwJwVB/XylQH377bcrJ/JuBbc9+QZud9/Fbe9CjXvBXXG7lx0/D/8+9eylJyIiIiJykaCgILRu3dpi8DdtMLgOHTpYvd/bb7+N119/HUuXLkWbNm24fYiIiIjIbbAzmIiIiIjIColwGDp0qFLUbdeuHaZMmYKMjAwMGzZMuX7IkCFISEhQsn/FW2+9hXHjxuHLL79E7dq1cerUKeXy8PBw5UREREREVJ5YDCYiIiIismLAgAFITk5WCrxS2G3ZsqXS8asNKnf06FGLQwVnzJiB7Oxs3HnnnRbzGT9+PF555RWuZyIiIiIqVywGExEREREV4/HHH1dORZHB4fQSExO5LomIiIjIbTEzmIiIiIiIiIiIiMgHsBhMRERERERERERE5ANYDCYiIiIiIiIiIiLyASwGExEREREREREREfkAFoOJiIiIiIiIiIiIfEBAeS8AERERERGp8vLylL9JSUlOn29aWhoyMzPh58d+EF/Cbe+buN19F7e9b+J2L1tJpv/TtP/bPA2LwUREREREbuL06dPK33bt2pX3ohARERFRCf+31axZE57GYDQajeW9EEREREREBOTm5mLbtm2Ij493agfvhQsX0LRpU+zevRsRERFc1T6E2943cbv7Lm5738TtXrby8vKUQnCrVq0QEOB5fbYsBhMRERERebnz58+jYsWKSE9PR2RkZHkvDpUhbnvfxO3uu7jtfRO3O9mDgWFEREREREREREREPoDFYCIiIiIiIiIiIiIfwGIwEREREZGXCw4Oxvjx45W/5Fu47X0Tt7vv4rb3TdzuZA9mBhMRERERERERERH5AHYGExEREREREREREfkAFoOJiIiIiIiIiIiIfACLwUREREREREREREQ+gMVgIiIiIiIiIiIiIh/AYjARERERkZebNm0aateujZCQELRv3x4bN24s70UiJ5o4cSLatm2LiIgIVK5cGf3798e+ffssbnP58mU89thjiI2NRXh4OO644w6cPn2a28GLvPnmmzAYDHj66afzL+N2914nTpzA4MGDlfd0hQoV0KxZM2zevDn/eqPRiHHjxqFq1arK9T169MCBAwfKdZmpdK5cuYKXX34ZderUUbZpvXr18PrrryvbWsPtTrZgMZiIiIiIyIstXLgQo0aNwvjx47F161a0aNECPXv2xJkzZ8p70chJ1qxZoxR6N2zYgOXLlyMnJwc33XQTMjIy8m/zzDPP4Oeff8Y333yj3P7kyZO4/fbbuQ28xKZNm/DRRx+hefPmFpdzu3un1NRUdOrUCYGBgViyZAl2796Nd999F9HR0fm3efvttzF16lTMnDkTf//9N8LCwpTPftlBQJ7prbfewowZM/Dhhx9iz549ynnZzh988EH+bbjdyRYGo34XAhEREREReRXpBJauUfnxKPLy8lCjRg088cQTeOGFF8p78cgFkpOTlQ5hKfped911SE9PR6VKlfDll1/izjvvVG6zd+9eNGnSBOvXr8e1117L7eDBLl68iGuuuQbTp0/HhAkT0LJlS0yZMoXb3YvJZ/e6devw559/Fnm9lHmqVauGZ599FqNHj1Yuk8+B+Ph4fP7557jnnnvKeInJGfr27atsw08//TT/MjnKQ7qE58+fz+1ONmNnMBERERGRl8rOzsaWLVuUw4M1fn5+ynkpApJ3kqKPiImJUf7Ka0C6hfWvg8aNG6NmzZp8HXgB6Qrv06ePxfYV3O7ea9GiRWjTpg3uuusuZcdPq1atMGvWrPzrDx8+jFOnTlm8JipWrKjsHORnv+fq2LEjVq5cif379yvn//nnH6xduxa9evVSznO7k60CbL4lERERERF5lLNnzyoZg9JJpCfnpTOUvI90fktmrBxCfvXVVyuXSVEoKCgIUVFRhV4Hch15rgULFijxLxITURC3u/c6dOiQEhcgEUAvvviisv2ffPJJ5X0+dOjQ/Pd1UZ/9fM97dkf4+fPnlZ15/v7+yvf7//73PwwaNEi5ntudbMViMBERERERkRd1ie7atUvpFiPvduzYMTz11FNKTrQMDkm+tdNHOoPfeOMN5Sa24rAAAAtoSURBVLx0Bsv7XvKBpRhM3unrr7/GF198oUT+XHXVVdi+fbuy808iQbjdyR6MiSAiIiIi8lJxcXFK99Dp06ctLpfzVapUKbflItd4/PHHsXjxYqxatQrVq1fPv1y2tUSGpKWlWdyerwPPJjEQMhCk5AUHBAQoJ8mJlkHDZFq6QLndvVPVqlXRtGlTi8skA/zo0aPKtPb5zs9+7/Lcc88p3cGS+dysWTPcd999yiCREydOVK7ndidbsRhMREREROSl5JDh1q1bKxmD+o4yOd+hQ4dyXTZyHhksSgrBP/zwA37//XfUqVPH4np5DQQGBlq8Dvbt26cUjvg68Fzdu3fHzp07le5A7STdonLIuDbN7e6dJAZG3sN6kiNbq1YtZVo+A6QwqH/PS7zA33//zfe8B8vMzFRy//Vkh698rwtud7IVYyKIiIiIiLyYZErK4aNSGGrXrh2mTJmCjIwMDBs2rLwXjZwYDSGHDf/000+IiIjIz42UAaNklHn5O2LECOW1IIPKRUZG4oknnlCKQtdeey23g4eSba3lQmvCwsIQGxubfzm3u3eSblAZTExiIu6++25s3LgRH3/8sXISBoNBiQ+YMGECGjRooBQJX375ZSVOoH///uW9+OSgfv36KRnBMvinxERs27YNkydPxvDhw5Xrud3JViwGExERERF5sQEDBiA5ORnjxo1TioQtW7bE0qVLCw0sRJ5LBpISXbt2tbj8s88+w/33369Mv/fee0pH2R133IGsrCz07NkT06dPL5flpbLD7e6d2rZtqxwJMHbsWLz22mtKsVd29GkDiYkxY8YoO/4eeughJSKmc+fOymc/86U91wcffKAU9R999FElIkaK+yNHjlS+3zXc7mQLg1GOKSIiIiIiIiIiIiIir8bMYCIiIiIiIiIiIiIfwGIwERERERERERERkQ9gMZiIiIiIiIiIiIjIB7AYTEREREREREREROQDWAwmIiIiIiIiIiIi8gEsBhMRERERERERERH5ABaDiYiIiIiIiIiIiHwAi8FEREREREREREREPoDFYCIiIiIiIiIv8sorr6Bly5almkdiYiIMBgO2b98OV+ratSuefvpplz4GERGZsRhMREREREREVIaOHTuG4cOHo1q1aggKCkKtWrXw1FNP4dy5c3bPSwq2P/74o8Vlo0ePxsqVK0u1jDVq1EBSUhKuvvpqOMPq1auVZU1LS7O4/Pvvv8frr7+O8lJWRW8iInfBYjARERERERFRGTl06BDatGmDAwcO4KuvvsJ///2HmTNnKsXbDh06ICUlpdSPER4ejtjY2FLNw9/fH1WqVEFAQABcKSYmBhERES59DCIiMmMxmIiIiIiIiKiMPPbYY0o38G+//Ybrr78eNWvWRK9evbBixQqcOHECL730Uv5ta9eurXTN3nvvvQgLC0NCQgKmTZtmcb247bbblO5W7XzBmIj7778f/fv3xxtvvIH4+HhERUXhtddeQ25uLp577jmlIFu9enV89tlnVjtmZR5yvuBJOn7FvHnzlCK3FHaliDxw4ECcOXMmf17dunVTpqOjo5X7yfyKiolITU3FkCFDlNuFhoYq60YK55rPP/9cWf5ly5ahSZMmSuH75ptvVrqYrZF5Dho0CJUqVUKFChXQoEGD/Odap04d5W+rVq2U5ZLl0XzyySfKY4SEhKBx48aYPn16ofWzYMECdOzYUbmNdFGvWbPGjlcDEVHZYzGYiIiIiIiIqAxI168UMR999FGlKKknBVQpWC5cuBBGozH/8nfeeQctWrTAtm3b8MILLyhxEsuXL1eu27Rpk/JXCptSDNXOF+X333/HyZMn8ccff2Dy5MkYP348+vbtqxRd//77bzz88MMYOXIkjh8/XuT933//feUxtJMsR+XKlZUiqcjJyVEK1//8848SWyHFUq3gK5ET3333nTK9b98+5f4yv6LIfTZv3oxFixZh/fr1yrro3bu3Mn9NZmYmJk2apBSg5fkcPXpUicaw5uWXX8bu3buxZMkS7NmzBzNmzEBcXJxy3caNG5W/UoyX5ZLYCvHFF19g3Lhx+N///qfcRwrpMp85c+ZYzFuK6c8++6yyfaSzu1+/fg7FfRARlRXXHu9BRERERERERArpcJXipnSbFkUuly7W5ORkpdAqOnXqpBSBRcOGDbFu3Tq89957uPHGG5VOVyGdslJMLo50/06dOhV+fn5o1KgR3n77baWo+uKLLyrXjx07Fm+++SbWrl2Le+65p9D9K1asqJyEFEw/+ugjpYCqPa5kIGvq1q2rPFbbtm1x8eJFpXtXHl/I85LltbZ+pAgsz1G6bbWirBSTpcB81113KZdJYViiNerVq6ecf/zxx5VOZ2ukWCydv9K5LLQOaqGtQ4nV0K9DKZa/++67uP322/M7iKWgLM976NCh+beTx77jjjuUaSkyL126FJ9++inGjBlT7PYgIiov7AwmIiIiIiIiKkP6zt+SSLdpwfPSqWqvq666SikEayQuolmzZhYZwVIQ1aIdrJEO2Pvuuw8ffvihUqjWbNmyRemKldgLiYqQCAytEGsreV6SUdy+ffv8y2SZpHitf84SH6EVgkXVqlWLXe5HHnlEiXOQ6Awp0v7111/FLkdGRgYOHjyIESNGKIVs7TRhwgTlcmvbR5ZdCs6ObB8iorLCYjARERERERFRGahfv76SM2utWCiXS2yD1q3qTIGBgRbnZTmKuiwvL8/qPE6dOoVbbrkFDzzwgFIo1RdPe/bsicjISKWTV+IqfvjhB+W67OzsMnkuxRXYJXf4yJEjeOaZZ5SojO7duxcbKyHdzGLWrFlKZrJ22rVrFzZs2ODEZ0JEVPZYDCYiIiIiIiIqA9LlKvEOMhDZpUuXChVapZA6YMAApbipKVh8lPP6mAkpjF65csXly3758mXceuutSkawZA7r7d27V8nJlZiJLl26KLcp2Kkrg+aJ4pZVnpcMaicZxhqZr+QMN23atFTLLwV2iXeYP38+pkyZgo8//tjqcknXdLVq1XDo0CGlgK8/aQPOFbV9ZNmlQ9paDAgRkTtgZjARERERERFRGZF4BcnDlU5aiR2Q4uK///6rDESWkJCgDFimJ/m5ku/bv39/ZeC4b775Br/88kv+9ZJ/u3LlSiWyITg4WOksdgUZXO7YsWPKY0mmsUaygCUaQoqqH3zwgTIQnXTQymByerVq1VKK3IsXL1YGhJMB9CR6Qa9BgwZKwfnBBx9UsnklbkLykmW9yOWOkoHgWrdurURlZGVlKcugFWwlw1iWRbJ+q1evjpCQECUb+dVXX8WTTz6pTN98883K/WRgO8l0HjVqVP68p02bpiy3zE+ynOV6fX4yEZG7YWcwERERERERURmRwqEUFWWQtbvvvlvJvn3ooYfQrVs3rF+/Pn+gNc2zzz6r3F4GQJPisXTlSiFZI4OcSZFYBlmT27jKmjVrkJSUpHToSkavdpL8Xem6/fzzz5VCtVwvHcKTJk2yuL8UdKXAKsVd6byVgdeK8tlnnymF2759+yp5vBL/8OuvvxaKhrCHFKplgLzmzZvjuuuuU/KRJUNYy/mVwe6k+CzdwFrRWaIwPvnkE2V5JFtZMpDlORbsDJbnKqcWLVoog+/JAHhxcXEOLysRkasZjPYk1xMRERERERFRmZCu36efflo5kXtJTExUCsMyoJ4MTEdE5CnYGUxERERERERERETkA1gMJiIiIiIiIiIiIvIBjIkgIiIiIiIiIiIi8gHsDCYiIiIiIiIiIiLyASwGExEREREREREREfkAFoOJiIiIiIiIiIiIfACLwUREREREREREREQ+gMVgIiIiIiIiIiIiIh/AYjARERERERERERGRD2AxmIiIiIiIiIiIiMgHsBhMREREREREREREBO/3/5e9Il5+SvTTAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reflector nominal CE = 0.685303\n",
+ "Reflector x_center = 3.762 um\n",
+ "Reflector tilt = 9.858 deg\n",
+ "Reflector waist = 2.373 um\n",
+ "Reflector period = 0.339 um\n",
+ "Reflector duty cycle = 0.417\n"
+ ]
+ }
+ ],
+ "source": [
+ "reflector_nominal_ce = coupling_efficiency_from_sim(\n",
+ " reflector_design_phys,\n",
+ " task_name=\"grating_beam_reflector_nominal_final\",\n",
+ " include_reflector=True,\n",
+ " as_float=True,\n",
+ ")\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(14, 3.5), constrained_layout=True)\n",
+ "plot_design_cross_section(\n",
+ " reflector_design_phys,\n",
+ " ax=axes[0],\n",
+ " color=design_colors[\"reflector\"],\n",
+ " title=\"Two-layer optimized design\",\n",
+ " include_reflector=True,\n",
+ ")\n",
+ "plot_history(axes[1], reflector_history, \"Reflector optimization history\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"Reflector nominal CE = {reflector_nominal_ce:.6f}\")\n",
+ "print(f\"Reflector x_center = {reflector_design_phys['x_center']:.3f} um\")\n",
+ "print(f\"Reflector tilt = {np.degrees(reflector_design_phys['tilt']):.3f} deg\")\n",
+ "print(f\"Reflector waist = {reflector_design_phys['waist']:.3f} um\")\n",
+ "print(f\"Reflector period = {reflector_design_phys['reflector_period']:.3f} um\")\n",
+ "print(f\"Reflector duty cycle = {reflector_design_phys['reflector_duty_cycle']:.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e4784ea",
+ "metadata": {},
+ "source": [
+ "## Final Comparison\n",
+ "\n",
+ "We finish by comparing the two optimized designs from several viewpoints: the numerical coupling efficiency, the optimized beam parameters, the grating geometry, and the final field profiles. This makes it easier to separate the effect of source co-optimization from the added degrees of freedom provided by the lower reflector."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "9516eab3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final design summary\n",
+ "====================\n",
+ " Single layer | nominal CE = 0.352976 | x_center = 3.569 um | tilt = 9.690 deg | waist = 2.379 um\n",
+ " With reflector | nominal CE = 0.685303 | x_center = 3.762 um | tilt = 9.858 deg | waist = 2.373 um\n"
+ ]
+ }
+ ],
+ "source": [
+ "design_params = {\n",
+ " \"single\": single_design_phys,\n",
+ " \"reflector\": reflector_design_phys,\n",
+ "}\n",
+ "nominal_by_design = {\n",
+ " \"single\": single_nominal_ce,\n",
+ " \"reflector\": reflector_nominal_ce,\n",
+ "}\n",
+ "\n",
+ "print(\"Final design summary\")\n",
+ "print(\"====================\")\n",
+ "for design_key, params_phys in design_params.items():\n",
+ " print(\n",
+ " f\"{design_labels[design_key]:>16} | \"\n",
+ " f\"nominal CE = {nominal_by_design[design_key]:.6f} | \"\n",
+ " f\"x_center = {params_phys['x_center']:.3f} um | \"\n",
+ " f\"tilt = {np.degrees(params_phys['tilt']):.3f} deg | \"\n",
+ " f\"waist = {params_phys['waist']:.3f} um\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfe5ef01",
+ "metadata": {},
+ "source": [
+ "### Coupling Efficiency\n",
+ "\n",
+ "The bar plot below compares the final mode coupling efficiency for the single-layer grating and the two-layer reflector design. This is the primary figure of merit optimized in both stages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "4740b182",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(6, 3.5), constrained_layout=True)\n",
+ "ordered_keys = [\"single\", \"reflector\"]\n",
+ "x_positions = np.arange(len(ordered_keys))\n",
+ "x_values = [nominal_by_design[key] for key in ordered_keys]\n",
+ "bars = ax.bar(\n",
+ " x_positions,\n",
+ " x_values,\n",
+ " color=[design_colors[key] for key in ordered_keys],\n",
+ " alpha=0.85,\n",
+ ")\n",
+ "ax.set_xticks(x_positions, [design_labels[key] for key in ordered_keys])\n",
+ "ax.set_ylabel(\"Nominal coupling efficiency\")\n",
+ "ax.set_title(\"Optimized coupling efficiency\")\n",
+ "for bar, value in zip(bars, x_values):\n",
+ " ax.text(\n",
+ " bar.get_x() + bar.get_width() / 2,\n",
+ " value,\n",
+ " f\"{value:.4f}\",\n",
+ " ha=\"center\",\n",
+ " va=\"bottom\",\n",
+ " )\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ea0e56de",
+ "metadata": {},
+ "source": [
+ "### Geometry Comparison\n",
+ "\n",
+ "The next two plots compare how the optimized devices differ geometrically. The cross sections show the additional lower reflector layer, while the width plot shows how the top grating tooth and gap widths changed in each optimization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e5845830",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ ""
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+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(2, 1, figsize=(12, 5.5), constrained_layout=True)\n",
+ "plot_design_cross_section(\n",
+ " single_design_phys, axes[0], design_colors[\"single\"], \"Single-layer optimized design\"\n",
+ ")\n",
+ "plot_design_cross_section(\n",
+ " reflector_design_phys,\n",
+ " axes[1],\n",
+ " design_colors[\"reflector\"],\n",
+ " \"Two-layer optimized design\",\n",
+ " include_reflector=True,\n",
+ ")\n",
+ "plt.show()\n",
+ "\n",
+ "width_fig = plot_width_comparison(design_params)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "059181ac",
+ "metadata": {},
+ "source": [
+ "### Field Profiles\n",
+ "\n",
+ "Finally, we rerun each optimized design with an x-z field monitor. These field plots show how the incident Gaussian beam scatters from the grating region and how the lower reflector changes the field distribution in the two-layer design."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "8eb462ed",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "field_data_by_design = {\n",
+ " \"single\": field_data_from_sim(\n",
+ " single_design_phys,\n",
+ " task_name=\"grating_beam_single_fields\",\n",
+ " include_reflector=False,\n",
+ " ),\n",
+ " \"reflector\": field_data_from_sim(\n",
+ " reflector_design_phys,\n",
+ " task_name=\"grating_beam_reflector_fields\",\n",
+ " include_reflector=True,\n",
+ " ),\n",
+ "}\n",
+ "field_fig = plot_field_cross_sections(field_data_by_design)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3c32ec5",
+ "metadata": {},
+ "source": [
+ "## Takeaways\n",
+ "\n",
+ "This example shows that Gaussian beam source parameters can be treated as first class design variables in a grating coupler optimization, but which parameters are practical to tune depends on the packaging setup. In some systems, the source position or tilt angle may be adjustable during alignment, while the Gaussian waist may be largely fixed by the fiber mode profile or by the packaging optics. In the single-layer case shown here, the optimizer adjusts the grating geometry together with the source position, tilt, and waist to demonstrate the full source parameter optimization workflow.\n",
+ "\n",
+ "Adding a lower silicon reflector grating introduces another physically meaningful design handle. The reflector period and duty cycle are optimized jointly with the top grating and source parameters, allowing the lower layer to work with the top grating to redirect more of the incident Gaussian beam into the desired waveguide mode. As the geometry becomes more complex, it becomes even less intuitive where the source should be placed or how it should be tilted, which makes joint optimization of the grating and source parameters an even more powerful technique."
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "This notebook demonstrates how to use Tidy3D FDTD and automatic differentiation to co-optimize a grating coupler geometry and Gaussian beam source parameters. A single-layer grating coupler is compared with a two-layer design that includes a silicon reflector grating below the top grating.",
+ "feature_image": "./img/grating_coupler_beam_optimization_thumbnail.png",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "keywords": "grating coupler, Gaussian beam, source optimization, inverse design, reflector grating, Tidy3D, FDTD",
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ },
+ "title": "How to optimize grating coupler beam parameters in Tidy3D FDTD",
+ "applications": [
+ "Passive photonic integrated circuit components"
+ ],
+ "features": [
+ "Adjoint inverse design",
+ "2D simulation"
+ ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/features/autograd.rst b/docs/features/autograd.rst
index 2ff02ce3..89004f1a 100644
--- a/docs/features/autograd.rst
+++ b/docs/features/autograd.rst
@@ -39,4 +39,5 @@ The adjoint method is an extremely powerful tool for photonics optimization, all
../../Autograd28FiberLens
../../Autograd29SourceGradients
../../Autograd30ParallelAdjoint
+ ../../Autograd31GratingCouplerWithBeamOptimization
../../RFAutograd1RectangularPatchAntenna
diff --git a/img/grating_coupler_beam_optimization_thumbnail.png b/img/grating_coupler_beam_optimization_thumbnail.png
new file mode 100644
index 00000000..4624d41a
Binary files /dev/null and b/img/grating_coupler_beam_optimization_thumbnail.png differ