diff --git a/DirectionalCoupler.ipynb b/DirectionalCoupler.ipynb
new file mode 100644
index 00000000..fcc6640f
--- /dev/null
+++ b/DirectionalCoupler.ipynb
@@ -0,0 +1,1364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "# Directional Coupler\n",
+    "\n",
+    "Directional couplers are fundamental components in photonic integrated circuits, enabling efficient splitting and combining of optical signals. By placing two parallel waveguides in close proximity, these passive devices exploit evanescent field coupling to control how light is distributed between channels. It is also possible to control the output by adjusting the relative phase between the inputs, enabling flexible power splitting ratios and switching behavior. Their design and compatibility with silicon-on-insulator technology have made them a popular choice for applications ranging from wavelength-division multiplexing to optical switching and signal processing.\n",
+    "\n",
+    "An important consideration is how fabrication variations might affect device performance. Hence, one common approach is to sweep over different parameters and see how the quantities of interest  behave.\n",
+    "\n",
+    "In this notebook, we will demonstrate how to set up a simple directional coupler simulation, illustrating the common steps of the workflow:\n",
+    "\n",
+    "[1](#1). Importing the geometry using [gdstk](https://heitzmann.github.io/gdstk/). For the process of this geometry, please follow the steps presented in [this](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSCreation) example.\n",
+    "\n",
+    "[2](#2). Placing the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) and [ModeMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) to excite the first TE mode and  analyze the transmittance and losses on both arms\n",
+    "\n",
+    "[3](#3). Carry out the post-process analysis to find the 50/50 splitting frequency and efficiency.\n",
+    "\n",
+    "[4](#4). Create a parametric sweep over different values of the sidewall angle and its effect on the splitting frequency and efficiency, and create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object to run in parallel.\n",
+    "\n",
+    "[5](#5). Use the `Simulation.updated_copy` method to easily create a simulation with two sources at the input ports with a $\\pi$/2 phase difference.\n",
+    "\n",
+    "<img src=\"img/splitter.png\" alt=\"Schematic of the directional coupler\" width=\"400\"/>\n",
+    "\n",
+    "For instructions on how to calculate the full S-matrix, please refer to our [SMatrix](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.Port.html) tutorial. Additionally, if you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we import the relevant structures. We will use gdstk for importing the geometry. To install it, uncomment the `!pip install gdstk` line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install gdstk\n",
+    "#!pip install gdstk\n",
+    "\n",
+    "# Standard python imports\n",
+    "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Tidy3d import\n",
+    "import tidy3d as td\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Importing the Geometry <a name=\"1\"></a>\n",
+    "\n",
+    "We will use gdstk to import the geometry into a [Polyslab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html) geometry, and extrude it along the z-direction (`axis=2`) with a given sidewall angle. For detailed instructions, please refer to [this](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSImport) tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "gds_path = \"misc/coupler.gds\"\n",
+    "\n",
+    "# Load a GDSII library from the file previously created\n",
+    "lib_loaded = gdstk.read_gds(gds_path)\n",
+    "all_cells = {c.name: c for c in lib_loaded.cells}\n",
+    "\n",
+    "# Define waveguide height\n",
+    "wg_height = 0.22\n",
+    "dilation = 0.02\n",
+    "\n",
+    "# Define reference plane and sidewall angle\n",
+    "reference_plane = \"bottom\"\n",
+    "sidewall_angle = np.pi / 6\n",
+    "\n",
+    "substrate_geo = td.Geometry.from_gds(\n",
+    "    all_cells[\"COUPLER\"],\n",
+    "    gds_layer=0,\n",
+    "    gds_dtype=0,\n",
+    "    axis=2,\n",
+    "    slab_bounds=(-4, 0),\n",
+    "    reference_plane=reference_plane,\n",
+    ")\n",
+    "\n",
+    "arms_geo = td.Geometry.from_gds(\n",
+    "    all_cells[\"COUPLER\"],\n",
+    "    gds_layer=1,\n",
+    "    gds_dtype=0,\n",
+    "    axis=2,\n",
+    "    slab_bounds=(0, wg_height),\n",
+    "    sidewall_angle=sidewall_angle,\n",
+    "    dilation=dilation,\n",
+    "    reference_plane=reference_plane,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up Structures\n",
+    "\n",
+    "Next, we assign material properties to the imported geometries and create the [`td.Structures`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/structures.html) with each of those material properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Permittivity of waveguide and substrate\n",
+    "wg_n = 3.48\n",
+    "sub_n = 1.45\n",
+    "medium_wg = td.Medium(permittivity=wg_n**2)\n",
+    "medium_sub = td.Medium(permittivity=sub_n**2)\n",
+    "\n",
+    "# Substrate\n",
+    "substrate = td.Structure(geometry=substrate_geo, medium=medium_sub)\n",
+    "\n",
+    "# Waveguides\n",
+    "arms = td.Structure(geometry=arms_geo, medium=medium_wg)\n",
+    "\n",
+    "structures = [substrate, arms]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up Simulation <a name=\"2\"></a>\n",
+    "\n",
+    "Now we set up the simulation. First, we define the wavelength of interest and set the runtime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wavelength range\n",
+    "wl1 = 1.4\n",
+    "wl2 = 1.7\n",
+    "\n",
+    "# Transforming in frequency\n",
+    "f1 = td.C_0 / wl2\n",
+    "f2 = td.C_0 / wl1\n",
+    "\n",
+    "fwidth = (f2 - f1) / 2\n",
+    "freq0 = f1 + fwidth\n",
+    "wl0 = td.C_0 / freq0\n",
+    "\n",
+    "# Frequencies to analyze\n",
+    "num_freqs = 21\n",
+    "freqs = [freq0] if num_freqs == 1 else np.linspace(freq0 - fwidth, freq0 + fwidth, num_freqs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we add the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html), which excites the first order TE mode (`mode_index = 0`). To analyze the transmittance, we will place [ModeMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) at the end of both output arms. These monitors will carry out mode decomposition and analyze the fraction of power transmitted in each mode. Since we are only interested in the first order TE mode, the number of modes analyzed can be just 1.\n",
+    "\n",
+    "We will also add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to visualize the field profile in the xy-plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Source\n",
+    "source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
+    "source = td.ModeSource(\n",
+    "    center=(-8, -2, 0), size=(0, 2, 1), source_time=source_time, mode_index=0, direction=\"+\"\n",
+    ")\n",
+    "\n",
+    "# Field monitor\n",
+    "field_mon = td.FieldMonitor(\n",
+    "    name=\"field_mon\",\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    center=(0, 0, wg_height / 2),\n",
+    "    freqs=freqs,\n",
+    ")\n",
+    "\n",
+    "# Mode monitor\n",
+    "mode_spec = td.ModeSpec(num_modes=1)\n",
+    "mode_mon_up = td.ModeMonitor(\n",
+    "    center=(8, 2, 0), size=(0, 2, 1), freqs=freqs, name=\"mode_mon_up\", mode_spec=mode_spec\n",
+    ")\n",
+    "\n",
+    "mode_mon_down = td.ModeMonitor(\n",
+    "    center=(8, -2, 0), size=mode_mon_up.size, freqs=freqs, name=\"mode_mon_down\", mode_spec=mode_spec\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we define the grid with our [AutoGrid](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AutoGrid.html) object, which  automatically creates a non-uniform mesh, and place everything together in the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# GridSpec\n",
+    "min_steps_per_wvl = 15\n",
+    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl)\n",
+    "\n",
+    "# Simulation size\n",
+    "sim_size = (17, 7, wg_height + 2)\n",
+    "sim_center = list(arms_geo.bounding_box.center)\n",
+    "sim_center[2] = 0\n",
+    "\n",
+    "### Initialize and visualize simulation ###\n",
+    "sim = td.Simulation(\n",
+    "    size=sim_size,\n",
+    "    center=sim_center,\n",
+    "    grid_spec=grid_spec,\n",
+    "    structures=structures,\n",
+    "    run_time=5e-12,\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
+    "    sources=[source],\n",
+    "    monitors=[field_mon, mode_mon_up, mode_mon_down],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot Simulation Geometry\n",
+    "\n",
+    "It is a good practice to check the simulation and see if everything is correctly set. That can be easily achieved with the `Simulation.plot` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 5))\n",
+    "\n",
+    "sim.plot(z=wg_height / 2, lw=1, ax=ax1)\n",
+    "sim.plot(x=0.1, lw=1, ax=ax2)\n",
+    "\n",
+    "ax2.set_xlim([-3, 3])\n",
+    "_ = ax2.set_ylim([-1, 1])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also see the 3D model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"H4sIAIy/fmgC/+1bX4/cthG/cxLACFAgfWv7ZOxrTYF/RbJPcZOL7SJ2jFweCriGqtvV3QnRrrZa7fnuAr+336AfoR+lX6lP6Qz1b7WSLjwDAeJ2leDWGg6Hw5nfDDnk7j+effnVrz793adH+Dx8ePTx0WdHu8+P9UOf9N+b9r/Wn8f159/rz389aOjHru03Nf3Xtfx9vu++PTlB7h/3nmYc+nH1+fDo8Pw/Ps9OnrzCzz/X7w2e/v2gz/en029eRqffffv85dMeLj9/z3GPjz6pZBw37w97cj8CTfDfvz0+dsj+rG2r+D6BT3x/8KAiP6w1/6gRtBdvR0f//EX74fTlN18et1Y4Onr00QGbh+fwfKjPfwxkoIfdGv70i2++xs9Htr+uIx8+P8zisiw2sz88+uHd40ez8madwL9np+lym8Vlmq9mQJ0nqzIpgP6aBvTxo+bPG2japLfY4TXTSHR/eMA5Ni2TRbpdouT+GKt4iV1W2yyDt/Mi+ds2Wc1voiJeXew0xFmWv40u4nQFtPM42yTYN19l6SqJi2izTuYd8zJf1PruNVylt3uUyyQu90jNrF9UGgNlnRTLtCzTq7S8gRaG85rN89ViO2+JYAGczqYsgLgtEpzg6725XiT5MimLm6EVmjFf5dnNaRaf4ajxdYrt3Nl1kbwFE0RglCypRgNykZwnBVgridZZvHICzvKyzJ3SGxATneXb1cKpQuSOnxZp7c5G0lVSlOm8Uvo1YTxQjx+F4DkF3K9HX0n7TnqEN0OnnsXz7y8K1CTas+SQoYXJnjNOG7vO/rewxANGuRqHE0zp3gB6WnM8BXOuZ12XdBSQ94ed4kJZo7WCv9wac08U1smiSgkeKOQugcgOZ0BgAYxeEVggtOpRQHaPYAPWJ5iAUikkldTKUNi+QB2EnNPQWkup5oJWPSWTijGtKDU2tLLm5DJk0krLFA9BpOOknAtBqbLS2FpkGBgQEGqQZ0NjuEDGUIRcMSVNqLkJDa85FQ2V1SAyNCqkDDmVlGBwpoRQXIWM1ZxMakY1qCOUpsypKbnWDMRyLalgInScKtCgcqioVDCnkLrRuVFMw3wsZZzxmk8qLbnRzFgTCjc0Yxa0ViCV2cYFCswJbldaGZgLt+hMC1Oh0lBOjVEiFI5RBloLZsCwTLBQhcioIT+AYRUVwoLfZM0oKW81VEYhp5K805AJU3NSeOlmzZBTgAm7WVNbTVuAH01nSscJY+9YEqxfc3JuOvcogaxM2c494D3tWHkAM25cDvB3c6JG77icCVmzStbhiFeTolLu4MgY1vD2ASmNY67MyOqkzW3/ccrTuxrv7Dkx5gSvx1x4xelrIH+re3vSAx2s4vPEmy+CfUPCM8Q8I9Y7BXhnFe9E5Zv6vJPpXSl5P3/vJ/jBErC/SOyvIk2HjlBJbN/rEdt3HUAggAXB+OBKcEuvTRltYdrUWrR91QZTlWBAaS0DN9Q2Y1obBcgxEpzDBDICuHQI9pHUoUVVXgAxRnAQh4iV2jEyQI4BuwmuGa/dKjQDJINAwaQAmyMj4CCUMEQYwihC6gpQVhtjOegSSgCqReMgVI3QUgOeQ2MrPkAJFRKyAUwHkOCMKDgwcvCMASFhWDFyHipQxDCYSGgdUJQC6HMJYW0A0JVECYgCaEouGXhcU4OMAkwDecBqC7MLua4YIQAN4xJwZoQUTkWmQBEwjg1DGnJWxySnotVRWJe2YEo7OlJepQ2j+M6kpQtfsP3OrMOaU1rRGRJEuUAXdMeQpk5FAIzONTrUyBlCa+sbSBRVItRKNN4W1rrBleY73q5Tq9AdfCAaXB7EqOzwQ8Mqm/dBWOVWxXcyffVCd18I23ubFkL8dSG+0yP+FiP+biD+viXeeCHeECTeqCbegUK8Y4/4RjPxTxDEN+cQ7yxGvBMjuSu9kv1cTPaTNemn8zfDkulDKHHIcD5kMGcyMMsOZcSKvdaBN8ikOwYuJhM+HoENGcfNGBbJBBhHAE7GET4SNGQyagaRSCZCcSS8yUR8j+QMMpk0BomITGSisfRGJvLbWNYkk2lzmIzJRDYeyfBkPMWPLRtkYt0YW39If60ivXWM9Ja4nbc75NxDIe9Z3sN09/DIPRztDx9/TPoD3T96/EPSO8zvkTu8E5J/kvPPnHdm4EHCHuT0Yd4frA0NYVD7kP3qiOyXTx1hpNbqGkcqNjJeso3UgGS0CBypKsl4WTlSqJKpSnVQ+5Lx4nekmCZj1fSwNicTxfl+rU/Gi/3h4QEZPz0YOY4gU+cRgwMOMnH+NTw0IROnJiMHMWTiJGbkdIdMHO+MHBmRqfOvsaMo0px/NXn/jvOvyVZyd2cyNfAUt8+U+NRB4ZSlyD0cQPzdSnzAwqbOX8nkAawnqIl3nBDfyCO+oUz8swPxTznEP48R79xI/PMtuTNvk0GaJ4OVgAxXC9JfUd68O9zd3e8eGDcX6N7Ryzt3H36zbO7rXmNVCP87cr4t5qMXcn0rtBrki+TU9enfvyMqqt3Q4AaeVnfvFI8HdsaMytQNsDdsvFxnabldJO3t9voy3nQXze3tYrzdbNJ49WoLhp/VfqLYCRCJ2zgNMQP7vgBL6/O36aK8xEbY6MF+lMNWDRJMYCB2Zvn5+SYpoVHVN9nL/CqJFvNoni/X+QqmCG2Am8RZZbuMcCTUl7mqukjmdVk9+/2s8nTSuG/fpNAZm5vOZVxcJGW0Ss7PO0sj03qZNX5Cg52nGZg5WufZDvrwkCAqL5Mybm1T0daXaUtZg3bpplYv3pY5aniWQIQUMUBpsxM8SKxPI3pDFHnZnBs0IC8LiDRnBhSLICjizN3w4lVvlK4WyXW0KZP1bp9dAKF13jW2cuyoscOpO72Ii5sJE14PSevMzcMLvq9efD2rbZzFN4ljZ4iQdVxAh7Ki7InapBfLOMqLhYO6aCnLtDtMqSnxtYOt6g/5CoVvcODv4/W6L6qiVKIqvNcUJ0pUfs0gBtperKXsKlBTXK/6xn4G7QfTjJumUeGPNdwcHG8O4DqA6+cC1+0BXAdw/Vzg2ifurLCrtMyLu75x9VWaZIsXFd/0lypd8dHu6WbPV+fpCreYQNz5d731q/0EGxcQDVse92XNFMVexRms7PG8+m7mY5y3EzzPs3wel0m91ar3c5vqG5yhkAaqLdyyGQ6lI/TRUPdo2OlZR6wu4g1UXniGxt3OL0AIMMMpFFTSQgUDT+C+w2AEVG3QU7tdYID3VlAfWij+QopbRqhBcAijoRJjVjoS1Ed4E2Ycl+QKNREB1sjMMqBQS40bN5D4JQQLFRv8B0SkBo5tsC2F0pIJDhVeyFCVgOIIUIBx2KUK15EHFqRxSi1UvgIK+WrX6mjcQKVmKOoWQjmMXyygMoTeUP65mQUhkkKYWYiHz8gXuAs1PKu0UKXxzp54+AkFtxaysydUsRxrZKY7e4IKDA9FKa3tib6L1/kivW02iftxBpvcsotZ2GV2L82ufC+en3TyWhw7JDk4zE6uEU0nDnont/j3maM8c5Rnt7M7rt5w9zkKddMWKT9VvTTYdntXgHZUfbnxgO4Dut8b3Yea0b9m3EDsJlGVD6Ld8hs1eN/IJ+8X+ov87WFpOwT/Ifh/GcH/xklMFxPWdE3X0z9YeALTfQo87hQvBoXmcVYdQ0p3Wry5dBlj6ucOYKBk8aLiwoZF1tvLw7/dFDd4eh29vULXMDXStEmXUZ2CWP1THqf5zQer+e2HqPnb+CrJktXFbhjnV0kBykK49n5b5RaNFVR46eoCgg5Wg5bsCtKoSKBCSpaAewfNtrWbR7poMQ5jNAHIAxMod9yf5WVUaRNtYRFDCbO/bKk4m7vrgGy7XCeLCBTGQTqltmfr9DrJhhZcQAhB8BTp/M5fgMUFGGXxBFSKL2ByVb6CDJJN9zot47SYx5uGfTQU2yFOvvgiX53nxbLKB3gngF4Bi7lbjPrragL7ZPlmcxP91Ojb4hyW4OdojnhV3VJMCKX9fpWlukzT/tAwanjKdHEjHFLX+QayMGi98xO8bRG7mwIaWOsugnBOYL3ufBulXgLeXfJmCcFLtlmxXTU3ISohjL+rfwf5X1xmb4swQQAA\" ></div>\n",
+       "    <script>\n",
+       "        \n",
+       "        /**\n",
+       "        * Simulation Viewer Injector\n",
+       "        *\n",
+       "        * Monitors the document for elements being added in the form:\n",
+       "        *\n",
+       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
+       "        *\n",
+       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
+       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
+       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
+       "        *\n",
+       "        */\n",
+       "        (function() {\n",
+       "            const TARGET_CLASS = \"simulation-viewer\";\n",
+       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
+       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
+       "\n",
+       "            class SimulationViewerInjector {\n",
+       "                constructor() {\n",
+       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
+       "                        this.injectViewer(node);\n",
+       "                    }\n",
+       "\n",
+       "                    // Monitor for newly added nodes to the DOM\n",
+       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
+       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
+       "                }\n",
+       "\n",
+       "                onMutations(mutations) {\n",
+       "                    for (var mutation of mutations) {\n",
+       "                        if (mutation.type === 'childList') {\n",
+       "                            /**\n",
+       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
+       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
+       "                            *\n",
+       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
+       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
+       "                            */\n",
+       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
+       "                            for (var node of viewers) {\n",
+       "                                this.injectViewer(node);\n",
+       "                            }\n",
+       "                        }\n",
+       "                    }\n",
+       "                }\n",
+       "\n",
+       "                injectViewer(node) {\n",
+       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
+       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
+       "                        // Mark node as injected, to prevent re-runs\n",
+       "                        node.classList.add(ACTIVE_CLASS);\n",
+       "\n",
+       "                        var uuid;\n",
+       "                        if (window.crypto && window.crypto.randomUUID) {\n",
+       "                            uuid = window.crypto.randomUUID();\n",
+       "                        } else {\n",
+       "                            uuid = \"\" + Math.random();\n",
+       "                        }\n",
+       "\n",
+       "                        var frame = document.createElement(\"iframe\");\n",
+       "                        frame.width = node.dataset.width || 800;\n",
+       "                        frame.height = node.dataset.height || 800;\n",
+       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
+       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
+       "\n",
+       "                        var postMessageToViewer;\n",
+       "                        postMessageToViewer = event => {\n",
+       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
+       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
+       "\n",
+       "                                // Run once only\n",
+       "                                window.removeEventListener('message', postMessageToViewer);\n",
+       "                            }\n",
+       "                        };\n",
+       "                        window.addEventListener(\n",
+       "                            'message',\n",
+       "                            postMessageToViewer,\n",
+       "                            false\n",
+       "                        );\n",
+       "\n",
+       "                        node.appendChild(frame);\n",
+       "                    }\n",
+       "                }\n",
+       "            }\n",
+       "\n",
+       "            if (!window.simulationViewerInjector) {\n",
+       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
+       "            }\n",
+       "        })();\n",
+       "    \n",
+       "    </script>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.plot_3d()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before running the simulation, we can check the **maximum** possible Flexcredit cost. If the field decays before the runtime, the simulation will auto shut-off and we will only be billed for the Flexcredits of the effective runtime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:30:37 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'directional coupler'</span> with task_id                    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:30:37 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'directional coupler'\u001b[0m with task_id                    \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82e</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d-4f4b-b43e-aea51a36a600'</span></a>.                                         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=225058;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=926143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=225058;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=419642;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=225058;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32m-ed3dd467-82e\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=225058;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed3dd467-82ed-4f4b-b43e-aea51a36a600\u001b\\\u001b[32md-4f4b-b43e-aea51a36a600'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=960835;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a29728ebcaad48ad97d22c8520859a25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:30:40 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:30:40 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.241\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:30:41 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:30:41 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.241\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Estimating the cost\n",
+    "task_id = web.upload(sim, \"directional coupler\")\n",
+    "cost = web.estimate_cost(task_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can run the simulation and analyze the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'directional coupler'</span> with task_id                    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'directional coupler'\u001b[0m with task_id                    \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4073-9c67-697bf9b13bb2'</span></a>.                                         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=294210;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=939328;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=294210;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=28575;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=294210;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32m-4e016f8d-226\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=294210;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e016f8d-226b-4073-9c67-697bf9b13bb2\u001b\\\u001b[32mb-4073-9c67-697bf9b13bb2'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=2405;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1854c095f8cf4d3f878555d87ef70952",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:30:44 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:30:44 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.241\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:30:45 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:30:45 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6678672dce6a48269e7e50a27062cad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:31:02 -03 </span>loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:31:02 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim_data = web.run(sim, \"directional coupler\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Postprocessing <a name=\"3\"></a>\n",
+    "\n",
+    "Now we would like to analyze the output of each arm as a function of wavelength, and find the frequency where the fields are equally distributed, and the losses.\n",
+    "\n",
+    "The monitor decomposes the fields into mode components, and since the source is [normalized](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/How-are-results-normalized.html), the transmittance of each component is simply $\\text{|amplitude|}^2$.\n",
+    "\n",
+    "For finding the point where the amplitudes of each arm are the same, we will just look at the signal change of the difference between both signals, which is the crossing point. For better resolution, we will interpolate the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Crossing wavelength :1.53\n",
+      "Total transmittance: 0.9912\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "\n",
+    "def postprocess(sim_data, plot=True):\n",
+    "    transmission_up = sim_data[\"mode_mon_up\"].amps.sel(mode_index=0, direction=\"+\").abs ** 2\n",
+    "    transmission_down = sim_data[\"mode_mon_down\"].amps.sel(mode_index=0, direction=\"+\").abs ** 2\n",
+    "    freqs = transmission_up.f.values\n",
+    "\n",
+    "    diff = (transmission_up - transmission_down).values\n",
+    "\n",
+    "    # Interpolate difference to find zero crossing (sign change)\n",
+    "    sign_change = np.where(np.diff(np.sign(diff)))[0]\n",
+    "    if len(sign_change) == 0:\n",
+    "        crossing_freq = freqs[np.argmin(abs(diff))]\n",
+    "        print(\"No sign change found, showing closest point only.\")\n",
+    "    else:\n",
+    "        i = sign_change[0]\n",
+    "        f_interp = interp1d(diff[i : i + 2], freqs[i : i + 2])\n",
+    "        crossing_freq = f_interp(0)\n",
+    "    amp_crossing = np.interp(crossing_freq, freqs, transmission_up)\n",
+    "    if plot:\n",
+    "        fig, ax = plt.subplots()\n",
+    "\n",
+    "        wavelengths = td.C_0 / freqs\n",
+    "        ax.plot(wavelengths, 10 * np.log10(transmission_up), label=\"port 3\")\n",
+    "        ax.plot(wavelengths, 10 * np.log10(transmission_down), label=\"port 4\")\n",
+    "\n",
+    "        ax.plot([td.C_0 / crossing_freq], [10 * np.log10(amp_crossing)], \"*\", color=\"black\")\n",
+    "\n",
+    "        ax.set_ylabel(\"Transmittance\")\n",
+    "        ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "        ax.hlines(-3.01, 1, 1.6, colors=\"gray\", linestyles=\"dashed\")\n",
+    "        ax.set_xlim(1.4, 1.6)\n",
+    "        ax.set_ylim(-15, 0)\n",
+    "        ax.legend()\n",
+    "\n",
+    "        plt.show()\n",
+    "    return float(crossing_freq), 2 * amp_crossing\n",
+    "\n",
+    "\n",
+    "cross_freq, total_transmittance = postprocess(sim_data)\n",
+    "\n",
+    "print(f\"Crossing wavelength :{td.C_0 / cross_freq:.2f}\")\n",
+    "print(f\"Total transmittance: {total_transmittance:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can easily plot the E field with the `SimulationData.plot_field` method. As we can see, the input field is equally split over the two output arms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAHWCAYAAADHMqXsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAADfwklEQVR4nOy9eZwkdX3///x86uh7jp2dnb1hgZUbRUDkUIyiaFAhEg8CEZWfGkQUSWJEEQQPRL8qXkA0BohKQPJVjMaABE8QCeDxFREE5Vz23jn7rOPz+6OO7p7pOXdnd5Z9P3k03V31qapPVfdUv/Z9KmOMQRAEQRAEQVhw6F09AUEQBEEQBKEzItQEQRAEQRAWKCLUBEEQBEEQFigi1ARBEARBEBYoItQEQRAEQRAWKCLUBEEQBEEQFigi1ARBEARBEBYoItQEQRAEQRAWKCLUBEEQBEHY5dTrdW6//XaCINjVU1lQiFATBEEQBGGX88UvvY9XvOIVXH/9h3b1VBYUSlpICYIgCIKwKxkcHGTffffida97Mbfeeg9//OMT5PP5XT2tBYFY1ARBEARB2KV89GN/x5FH7s/V11zAsmV9fPZz79nVU1owiEVNEARBEIRdxmOPPcZBBx3AXb/4Ms997n785Ce/4a9O/RCPPvoYS5Ys2dXT2+WIUBMEQRAEYZfxN39zIo5j86/XfiBddsprP8jeey/lS1/69i6c2cJAhJogCIIgCLuE++67jxNOeBEP/uHfWLmyP13+4IOP84Kj3slvf/s7nvOc5+zCGe56RKgJgiAIgrDTMcbwFy99Pi984UF8/ONvn7D+nL/7DFu2DPPtb/9sF8xu4SBCTRAEQRCEnc73v/993vrWM3n4j9+gu7s4Yf2GDds4YP8zufXW2znuuON2wQwXBiLUBEEQBEHYqfi+z2GH7cc7/+61vPvdr5t03GWXXc8Pf3gvv7jrdyilduIMFw5SnkMQBEEQhJ3K1772ATzP5x3veM2U4y644A08/th6vv3tPTepQCxqgiAIgiDsNMbGxli7di++8IX38LrTTph2/Fe/8j0+85mb+P3v/4zrujthhgsLsagJgiAIgrDT+PT/OY+91yzjr1734hmNf+vb/hLHsbn6mn+Y55ktTMSiJgiCIAjCTmHDhg3st98a/vvWT3PssYfMeLvvf/9uzn7bJ/nTn56ku7t7Hme48BChJgiCIAjCTuEd73gNW7YMc/N/XDar7YwxnHjiBRx99EFc8clvztPsFiYi1ARBEARBmHf+8Ic/8PznP49f/+ZrrF27ctbb33ffw/zFS97Lww8/wqpVq+ZhhgsTEWqCIAiCIMw7r3nNcey11wCf/8LcG66fecZHcV2H66+/dQfObGEjQk0QBEEQhHnlpz/9Ka95zV/yx0e+SX9/z5z38/jjGzjk4LP45S//l+c+97k7boILGBFqgiAIgiDMG2EYcvTRB3HKqcdz4YVnbvf+/vEfr+Z3/+/P3H77vTtgdgsfKc8hCIIgCMK8cdNNN7F+/Vbe+96/3iH7++AHz+T++x/mtttu2yH7W+iIRU0QBEEQhHmhXq9zwAF78+EPn8VZb3nlDtvv5z73Lb7+b7fxq1/9Ecuydth+FyJiURMEQRAEYV74wQ9+gNaaM//25Tt0v+9616msX7+NO++8c8bbrFu3jjPPPJO+vj5yuRyHHnoo9913X7reGMPFF1/MsmXLyOVynHjiiTzyyCM7dN5zQYSaIAiCIAjzQqPRYMlA7w63emUyLn19XdTr9RmNHxwc5LjjjsNxHP77v/+bBx98kM985jP09vamYz71qU/xhS98gWuuuYZ77rmHQqHASSedRK1W26Fzny32Lj26IAiCIAjPboyBMNylU7jiiitYtWoV1157bbpszZo16WtjDFdeeSUXXXQRp5xyCgD/9m//xsDAALfccgtvetObdvqcE8SiJgiCIAjCbkmj0WBkZKTt0cnK9p//+Z8ceeSRvP71r2fJkiUcfvjhfPWrX03XP/bYY2zYsIETTzwxXdbd3c3RRx/N3XffvVPOZTJEqAmCIAiCML+E4Y5/GMNNN91Ed3d32+Pyyy+fcPg///nPXH311axdu5bbbruNc845h/e85z1cf/31QNSDFGBgYKBtu4GBgXTdrkJcn4IgCIIgzB/z6Pp84xvfyJe//OW2ZZlMZsK4MAw58sgj+cQnPgHA4YcfzgMPPMA111zDWWedNS9z21GIRU0QBEEQhN0S13Xp6upqe3QSasuWLeOggw5qW3bggQfy5JNPArB06VIANm7c2DZm48aN6bpdhQg1QRAEQRDmF2N2/GMWHHfccTz88MNty/74xz+y1157AVFiwdKlS7njjjvS9SMjI9xzzz0cc8wx23/+24G4PgVBEARBmF/mw/U5C632vve9j2OPPZZPfOITvOENb+B///d/+cpXvsJXvvIVAJRSnH/++XzsYx9j7dq1rFmzhg9/+MMsX76cU089dcfPfRaIUBMEQRAE4VnNUUcdxXe+8x0uvPBCLrvsMtasWcOVV17JGWeckY55//vfT7lc5h3veAdDQ0Mcf/zx3HrrrWSz2V04cxFqgiAIgiDMJ/OWTDA79+erX/1qXv3qV0+6XinFZZddxmWXXba9E9uhSIyaIAiCIAjCAkUsaoIgCIIgzC+7uDPB7owINUEQBEEQ5o/5cn3OzvO52yKuT0EQBEEQhAWKWNQEQRAEQZhfFkAywe6KCDVBEARBEOYNhUEZiVGbK+L6FARBEARBWKCIRU0QBEEQhPnDIFmf24FY1ARBEARBEBYoYlETBEEQBGF+Cech8H+Wjdl3V0SoCYIgCIIwf8xbC6k9A3F9CoIgCIIgLFDEoiYIgiAIwvwiFrU5I0JNEARBEIR5xIDUUZsz4voUBEEQBEFYoIhFTRAEQRCE+WO+6qjtIVmfYlETBEEQBEFYoIhFTRAEQRCE+WU+6qjtIYhQEwRBEARh/pA6atuFuD4FQRAEQRAWKGJREwRBEARhfpFkgjkjQk0QBEEQhPnDGJS4PueMuD4FQRAEQRAWKGJREwRBEARhftlD3JTzgVjUBEEQBEEQFihiURMEQRAEYX6RGLU5I0JNEARBEIT5Y77qqO0h7lRxfQqCIAiCICxQxKImCIIgCML8Ii2k5owINUEQBEEQ5g9pIbVdiOtTEARBEARhgSIWNUEQBEEQ5hdJJpgzYlETBEEQBEFYoIhFTRAEQRCE+WUPsX7NByLUBEEQBEGYPySZYLsQ16cgCIIgCMICRSxqgiAIgiDML/NRR20P8aaKUBMEQRAEYf4wiOtzOxDXpyAIgiAIwgJFLGqCIAiCIMwj85VMsGf4PsWiJgiCIAiCsEARi5ogCIIgCPOHQZIJtgMRaoIgCIIgzC9Gkgnmirg+BUEQBEEQFihiURMEQRAEYR4x8+P63EMQoSYIgiAIwvwxb3XU9gzxJ65PQRAEQRCEBYpY1ARBEARBmF8k63POiEVNEARBEARhgSIWNUEQBEEQ5g8zX50J9gxEqAmCIAiCML9I1uecEdenIAiCIAjCAkUsaoIgCIIgzC/z0plgz7DSiVATBEEQBGH+MPNU8HbP0Gni+hQEQRAEQVioiEVNEARBEIT5RZIJ5oxY1ARBEARBeNbzkY98BKVU2+OAAw5I19dqNc4991z6+vooFoucdtppbNy4cRfOOEKEmiAIgiAI80fS63NHP+bAwQcfzPr169PHnXfema573/vex/e+9z1uvvlmfvrTn/LMM8/wute9bgddhLkjrk9BEARBEOaR+UommP0+bdtm6dKlE5YPDw/zta99jRtuuIGXvvSlAFx77bUceOCB/PKXv+SFL3zhdk93rohFTRAEQRCEPYJHHnmE5cuXs88++3DGGWfw5JNPAnD//ffjeR4nnnhiOvaAAw5g9erV3H333btquoBY1ARBEARBmE8M85ZM0Gg0GBkZaVuWyWTIZDITxh599NFcd9117L///qxfv55LL72UF73oRTzwwANs2LAB13Xp6elp22ZgYIANGzbMy9xnigg1QRAEQRDml3nq9XnTTTfxmte8pm3ZJZdcwkc+8pEJY1/1qlelrw877DCOPvpo9tprL771rW+Ry+XmZX47AhFqgiAIgiDslrzxjW/ky1/+ctuyTta0TvT09PCc5zyHRx99lJe//OU0Gg2GhobarGobN27sGNO2M5EYNUEQBEEQ5hETBf7v8Ae4rktXV1fbY6ZCbWxsjD/96U8sW7aMI444AsdxuOOOO9L1Dz/8ME8++STHHHPMfF2YGSEWNUEQBEEQnvX8wz/8A695zWvYa6+9eOaZZ7jkkkuwLIvTTz+d7u5uzj77bC644AIWLVpEV1cX5513Hsccc8wuzfgEsagJwh7PT37yE5RS/OQnP9nVUxF2ME899RTZbJa77rprpx/b8zxWrVrFVVddtdOPLSwwkmSCHf2YJU8//TSnn346+++/P294wxvo6+vjl7/8Jf39/QB87nOf49WvfjWnnXYaL37xi1m6dCnf/va3d/DFmD0i1ARhD+Gqq67iuuuu29XTmBM33HADV1555a6eBgBhGPKpT32KNWvWkM1mOeyww/j3f//3GW27fv16PvCBD/AXf/EXlEqlKQXyD3/4Q84++2wOOeQQLMti7733nvVcL7vsMo4++miOO+64WW+7vTiOwwUXXMDHP/5xarXaTj++sMBYAELtxhtv5JlnnqFer/P0009z4403su+++6brs9ksX/7yl9m2bRvlcplvf/vbuzw+DUSoCcIew2RC7cUvfjHVapUXv/jFO39SM2QhCbUPfehD/NM//RMvf/nL+eIXv8jq1av5m7/5G2688cZpt3344Ye54oorWLduHYceeuiUY2+44QZuuOEGuru7Wb58+aznuXnzZq6//nr+7u/+btbb7ije+ta3smXLFm644YZdNgdB2N0RoSbsUZTL5V09hQWH1ppsNovWcjuYjnXr1vGZz3yGc889l6985Su8/e1v53vf+x4vetGL+Md//EeCIJhy+yOOOIKtW7fyxz/+kQsuuGDKsZ/4xCcYGRnhrrvu4rnPfe6s5/qNb3wD27YnlC7YmfT09PCKV7xit7XkCjuIeWshtWc0epc7s7Dbsm7dOs4++2yWL19OJpNhzZo1nHPOOTQaDQCuu+46lFL89Kc/5V3vehdLlixh5cqV6fZXXXUVBx98MJlMhuXLl3PuuecyNDTUdoxHHnmE0047jaVLl5LNZlm5ciVvetObGB4eTsfcfvvtHH/88fT09FAsFtl///354Ac/OO38Z7JdvV7nkksuYb/99iOTybBq1Sre//73U6/XJ+zvG9/4Bi94wQvI5/P09vby4he/mB/+8IcA7L333vz+97/npz/9adqM+CUveQkweYzazTffzBFHHEEul2Px4sWceeaZrFu3rm3MW97yForFIuvWrePUU0+lWCzS39/PP/zDP0wrWgC++93vcvLJJ6ef4b777stHP/rRtm1f8pKX8F//9V888cQT6dyncgO+5S1vmdB4OXl0qq00G7773e/ieR7vete70mVKKc455xyefvrpaSuYl0olFi1aNKNjLV++HMdx5jzXW265haOPPppisdi2fO+99+Ytb3nLhPEveclL0u8ENL8X3/rWt7j00ktZsWIFpVKJv/7rv2Z4eJh6vc7555/PkiVLKBaLvPWtb+34vXz5y1/OnXfeybZt2+Z8LsLuzjy4PUOzp+g0yfoUdk+eeeYZXvCCFzA0NMQ73vEODjjgANatW8d//Md/UKlUcF03Hfuud72L/v5+Lr744tSi9pGPfIRLL72UE088kXPOOYeHH36Yq6++mnvvvZe77roLx3FoNBqcdNJJ1Ot1zjvvPJYuXcq6dev4/ve/z9DQEN3d3fz+97/n1a9+NYcddhiXXXYZmUyGRx99dNrg7ZlsF4Yhr33ta7nzzjt5xzvewYEHHsjvfvc7Pve5z/HHP/6RW265JR176aWX8pGPfIRjjz2Wyy67DNd1ueeee/jRj37EK17xCq688krOO+88isUiH/rQh4Co4vZkXHfddbz1rW/lqKOO4vLLL2fjxo18/vOf56677uLXv/51W52hIAg46aSTOProo/k//+f/8D//8z985jOfYd999+Wcc86Z8jpcd911FItFLrjgAorFIj/60Y+4+OKLGRkZ4dOf/jQQuRqHh4d5+umn+dznPgcwQXy08s53vrOtDQzArbfeyje/+U2WLFmSLtuyZcuUc0solUppuv+vf/1rCoUCBx54YNuYF7zgBen6448/fkb7nU88z+Pee++d9vrPhMsvv5xcLscHPvABHn30Ub74xS/iOA5aawYHB/nIRz7CL3/5S6677jrWrFnDxRdf3Lb9EUccgTGGX/ziF7z61a/e7vkIwh6HEYTdkDe/+c1Ga23uvffeCevCMDTGGHPttdcawBx//PHG9/10/aZNm4zruuYVr3iFCYIgXf6lL33JAOZf//VfjTHG/PrXvzaAufnmmyedx+c+9zkDmM2bN89q/jPZ7utf/7rRWpuf//znbcuvueYaA5i77rrLGGPMI488YrTW5q/+6q/azseY5rUwxpiDDz7YnHDCCROO8+Mf/9gA5sc//rExxphGo2GWLFliDjnkEFOtVtNx3//+9w1gLr744nTZWWedZQBz2WWXte3z8MMPN0ccccTUF8EYU6lUJix75zvfafL5vKnVaumyk08+2ey1117T7q8TjzzyiOnu7jYvf/nL274HRP8en/Zx7bXXts1jn332mXCMcrlsAPOBD3xgxvO6+eab2677VMz2/B999FEDmC9+8YsT1u21117mrLPOmrD8hBNOaPt+JN+LQw45xDQajXT56aefbpRS5lWvelXb9sccc0zHOT7zzDMGMFdcccWM5y88e7jxxhvNMfsuNeG15+3wxwHLes1tt922q09x3hHXp7DbEYYht9xyC695zWs48sgjJ6xXSrW9f/vb345lWen7//mf/6HRaHD++ee3xWW9/e1vp6uri//6r/8CoLu7G4DbbruNSqXScS6JZem73/0u4SxapMxku5tvvpkDDzyQAw44gC1btqSPl770pQD8+Mc/BiIXVxiGXHzxxRPizMZfi5lw3333sWnTJt71rneRzWbT5SeffDIHHHBAen1aGR+w/qIXvYg///nP0x6rtW3L6OgoW7Zs4UUvehGVSoWHHnpo1nMfT7lc5q/+6q/o7e3l3//939u+B7fffvuMHieddFK6TbVa7VhMM7lO1Wp1u+e8I9i6dSsAvb29272vN7/5zW0u2KOPPhpjDG9729vaxh199NE89dRT+L7ftjyZw0wtmIIgtCOuT2G3Y/PmzYyMjHDIIYfMaPyaNWva3j/xxBMA7L///m3LXddln332SdevWbOGCy64gM9+9rN885vf5EUvehGvfe1rOfPMM1MR98Y3vpF/+Zd/4f/7//4/PvCBD/Cyl72M173udfz1X//1lMH5M9nukUce4Q9/+ENa42c8mzZtAuBPf/oTWmsOOuigGV2P6Zjs+gAccMAB3HnnnW3LstnshDn29vYyODg47bF+//vfc9FFF/GjH/1oQmPl1jjAufL2t7+dP/3pT/ziF7+gr6+vbd149+hMyOVyHeOwkvITC61foDHbH8SzevXqtvfJd3/VqlUTlodhyPDwcNu1TuYwl380CM8i5qkp+56ACDXhWc/2/Hh+5jOf4S1veQvf/e53+eEPf8h73vMeLr/8cn75y1+ycuVKcrkcP/vZz/jxj3/Mf/3Xf3Hrrbdy00038dKXvpQf/vCHbRac8XOabrswDDn00EP57Gc/23Ef438odxWTneN0DA0NccIJJ9DV1cVll13GvvvuSzab5Ve/+hX/9E//NCsLZSc+//nP8+///u984xvf4HnPe96E9Rs2bJjRfrq7u9Pv0LJly/jxj3+MMaZNeKxfvx5gTmU05oNEKM1ELCeMP6eEyT7fyZaPF4fJHBYvXjzjuQjPPsx8CLU9RPuJ61PY7ejv76erq4sHHnhgTtvvtddeQFTTqpVGo8Fjjz2Wrk849NBDueiii/jZz37Gz3/+c9atW8c111yTrtda87KXvYzPfvazPPjgg3z84x/nRz/6UeqanIzpttt3333Ztm0bL3vZyzjxxBMnPBKL17777ksYhjz44INTHm+mFo3Jrk+ybPz1mSs/+clP2Lp1K9dddx3vfe97efWrX82JJ57Y0V03W2vMz3/+c/7hH/6B888/nzPOOKPjmGXLls3ocdNNN6XbPO95z6NSqfCHP/yhbV/33HNPun4hsHr1anK5HI899ljH9aOjoxOWbdy4cV7mksxhfAKGIAgzQ4SasNuhtebUU0/le9/7Hvfdd9+E9dO5e0488URc1+ULX/hC29ivfe1rDA8Pc/LJJwMwMjIyId7m0EMPRWudur86lRxIfqw7ucgSZrLdG97wBtatW8dXv/rVCWOr1WqawXrqqaeiteayyy6bYIVqPb9CoTCh/EgnjjzySJYsWcI111zTdg7//d//zR/+8If0+mwviUWmdY6NRqNjy6FCoTBjV+j69et5wxvewPHHH59mjnZiLjFqp5xyCo7jtM3RGMM111zDihUrOPbYY9vm8dBDD+F53ozmvSNxHIcjjzyy498HwN13393WLeD3v/89jzzyyA5xlY7n/vvvRym1yxtbC7uQeWnInuT7PPsR16ewW/KJT3yCH/7wh5xwwglp6Yr169dz8803c+edd7aVjxhPf38/F154IZdeeimvfOUree1rX8vDDz/MVVddxVFHHcWZZ54JwI9+9CPe/e538/rXv57nPOc5+L7P17/+dSzL4rTTTgOiFj0/+9nPOPnkk9lrr73YtGkTV111FStXrpyyTMNMtvvbv/1bvvWtb/F3f/d3/PjHP+a4444jCAIeeughvvWtb3Hbbbdx5JFHst9++/GhD32Ij370o7zoRS/ida97HZlMhnvvvZfly5dz+eWXA1GZhKuvvpqPfexj7LfffixZsiRNTGjFcRyuuOIK3vrWt3LCCSdw+umnp+U59t57b973vvfN9WNr49hjj6W3t5ezzjqL97znPSil+PrXv95RLBxxxBHcdNNNXHDBBRx11FEUi8VJC7m+5z3vYfPmzbz//e+f0C3gsMMO47DDDgPmFqO2cuVKzj//fD796U/jeR5HHXUUt9xyCz//+c/55je/2eYOvPDCC7n++ut57LHH2uq+fexjHwMicQTw9a9/PY37u+iii9Jx/+///T/+8z//E4BHH32U4eHhdNvnPve50xayPeWUU/jQhz7EyMgIXV1dbeuGhoZ46UtfyhlnnMHIyAhf/OIXKZVKPPDAA/zzP/8z73znO2d9bSbj9ttv57jjjpsQIyjsYUiM2tzZJbmmgrADeOKJJ8yb3/xm09/fbzKZjNlnn33Mueeea+r1ujGmWZ6jUwkPY6JyHAcccIBxHMcMDAyYc845xwwODqbr//znP5u3ve1tZt999zXZbNYsWrTI/MVf/IX5n//5n3TMHXfcYU455RSzfPly47quWb58uTn99NPNH//4xynnPtPtGo2GueKKK8zBBx9sMpmM6e3tNUcccYS59NJLzfDwcNvYf/3XfzWHH354Ou6EE04wt99+e7p+w4YN5uSTTzalUskAaSmG8eU5Em666aZ0f4sWLTJnnHGGefrpp9vGnHXWWaZQKEw4v0suucTM5PZy1113mRe+8IUml8uZ5cuXm/e///3mtttumzCfsbEx8zd/8zemp6fHAFOWqjjhhBMmLbVxySWXTDun6QiCwHziE58we+21l3Fd1xx88MHmG9/4xoRxSemSxx57rG35ZHMbf72S72+nR6fyGuPZuHGjsW3bfP3rX29bvtdee5kzzjjD/N3f/Z0plUpm0aJF5qKLLjL/+Z//aUqlknnFK15hjGl+L8aXp5ns7yr5zFtLzgwNDRnXdc2//Mu/TDtf4dnJjTfeaI7ZZ8AE15yzwx8HLO3ZI8pzKGPmwdYtCIIg7HLOPvts/vjHP/Lzn/88Xbb33nvzkpe8ZKe0dbryyiv51Kc+xZ/+9KcFlxEr7BxuuukmPn/he7nzH/9qh+/74Etv5PP/dhOveMUrdvi+FxISoyYIgvAs5ZJLLkm7bexsPM/js5/9LBdddJGINEHYDiRGTRAE4VnK6tWr25IGdiaO4/Dkk0/ukmMLCxCJUZszItQEQRAEQZg/DCLUtgMRaoIgCHsQjz/++K6egiAIs0CEmiAIgiAI84bBSGeC7UCEmiAIgiAI88u8uD73DKUmQm2GhGHIM888Q6lUkubCgiAIwm6LMYbR0VGWL1+O1lL8YaEjQm2GPPPMMwumCbYgCIIgbC9PPfUUK1eunP8DSTLBdiFCbYaUSqX4lQbEoiYIgiDsrhggbPldExYyItRmSNPdqRChJgiCIOzu7NQwHrGozRkRaoIgCIIgzB/GRI8dvt8dv8uFyIKOIvzkJz+JUorzzz9/ynE333wzBxxwANlslkMPPZQf/OAHbeuNMVx88cUsW7aMXC7HiSeeyCOPPDKPMxcEQRAEQdh+FqxQu/fee/nnf/5nDjvssCnH/eIXv+D000/n7LPP5te//jWnnnoqp556Kg888EA65lOf+hRf+MIXuOaaa7jnnnsoFAqcdNJJu6y1iiAIgiDsSZhwxz/2FBakUBsbG+OMM87gq1/9Kr29vVOO/fznP88rX/lK/vEf/5EDDzyQj370ozz/+c/nS1/6EhBZ06688kouuugiTjnlFA477DD+7d/+jWeeeYZbbrllJ5yNIAiCIOzhhGbHP/YQFqRQO/fcczn55JM58cQTpx179913Txh30kkncffddwPw2GOPsWHDhrYx3d3dHH300emYTtTrdUZGRtoegiAIgiAIO5MFl0xw44038qtf/Yp77713RuM3bNjAwMBA27KBgQE2bNiQrk+WTTamE5dffjmXXnrpbKYuCIIgCMJ45quO2h5iVFtQFrWnnnqK9773vXzzm98km83u0rlceOGFDA8Pp4+nnnpql85HEARBEIQ9jwVlUbv//vvZtGkTz3/+89NlQRDws5/9jC996UvU63Usy2rbZunSpWzcuLFt2caNG1m6dGm6Plm2bNmytjHPe97zJp1LJpMhk8ls7ykJgiAIwh7PfAT/7yEGtYVlUXvZy17G7373O37zm9+kjyOPPJIzzjiD3/zmNxNEGsAxxxzDHXfc0bbs9ttv55hjjgFgzZo1LF26tG3MyMgI99xzTzpGEARBEIR5InF9SjLBnFhQFrVSqcQhhxzStqxQKNDX15cuf/Ob38yKFSu4/PLLAXjve9/LCSecwGc+8xlOPvlkbrzxRu677z6+8pWvAKR12D72sY+xdu1a1qxZw4c//GGWL1/OqaeeulPPTxAEQRAEYTYsKKE2E5588km0bhoCjz32WG644QYuuugiPvjBD7J27VpuueWWNsH3/ve/n3K5zDve8Q6GhoY4/vjjufXWW3d5HJwgCIIg7BHMR92zPcSotuCF2k9+8pMp3wO8/vWv5/Wvf/2k+1BKcdlll3HZZZft4NkJgiAIgjAlBsy8uCr3DKW2oGLUBEEQBEEQhCYL3qImCIIgCMJuzh7U8mlHIxY1QRAEQRCEBYpY1ARBEARBmF/2jHCyeUGEmiAIgiAI84cx85NMsIeIP3F9CoIgCIIgLFDEoiYIgiAIwvwiyQRzRoSaIAiCIAjzhjHz0+tzT0Fcn4IgCIIgCAsUsagJgiAIgjC/SAupOSMWNUEQBEEQhAWKWNQEQRAEQZg/JEZtuxChJgiCIAjC/CJCbc6I61MQBEEQBGGBIhY1QRAEQRDmFbOHBP7PByLUBEEQBKEDCtVxuZlBuuFk2851f7s18xSjtqeIP3F9CoIgCIIgLFDEoiYIgiAIMTOxhM3GWrajjjme3c4KJ8kEc0aEmiAIgiAI84a0kNo+RKgJgiAIz3p2tBVsV9PpfHY7K5swI0SoCYIgCIIwr8xL4P8eoktFqAmCIAjPOp5tFrSZ0HrOYl179iBZn4IgCMJujxr33849uNq1jymux8JAQTgPj+3gk5/8JEopzj///HRZrVbj3HPPpa+vj2KxyGmnncbGjRu389y3HxFqgiAIgiDMH3EywY5+zJV7772Xf/7nf+awww5rW/6+972P733ve9x888389Kc/5ZlnnuF1r3vddp789iNCTRAEQdgtmXcL2qwtWnrXPKaY28Kxqi0MxsbGOOOMM/jqV79Kb29vunx4eJivfe1rfPazn+WlL30pRxxxBNdeey2/+MUv+OUvf7kLZyxCTRAEQRCEecYYtcMfoGg0GoyMjLQ96vX6pPM499xzOfnkkznxxBPblt9///14nte2/IADDmD16tXcfffd83VZZoQINUEQBGG3Yd6saNthKVNY8SP+T1k755H+Z3V8pNa2XYxhnlyfBm666Sa6u7vbHpdffnnHedx444386le/6rh+w4YNuK5LT09P2/KBgQE2bNiw4y/KLJCsT0EQBEEQdkve+MY38uUvf7ltWSaTmTDuqaee4r3vfS+333472Wx2Z01vhyBCTRAEQVjwzNqCNmdLUtPR1PGYqpMjSseHbF1nTXsk1XFfk2PGR9BPmF4wboMQjI/Z1f2b5qspO+C6Ll1dXdOOvf/++9m0aRPPf/7z02VBEPCzn/2ML33pS9x22200Gg2GhobarGobN25k6dKlO37ys0CEmiAIgrAgmZE4m5Ug6yyMJhwnFVBTC7BkuZowTk8Y07Z71SoGZx+BNBPhZUyIMSFBWEMF/h5fV+1lL3sZv/vd79qWvfWtb+WAAw7gn/7pn1i1ahWO43DHHXdw2mmnAfDwww/z5JNPcswxx+yKKaeIUBMEQRAEYV6Jgv93HaVSiUMOOaRtWaFQoK+vL11+9tlnc8EFF7Bo0SK6uro477zzOOaYY3jhC1+4K6acIkJNEARBWDBMa0Wb0oI2Q4sZjHNhjreIWel7hZ5gKVNKt1nTkvfR6+a2uoNFLhpnbbdVLWEy65oxIaHxqHnD1MKxeerhNEMMmO0sULsz+NznPofWmtNOO416vc5JJ53EVVddtaunJUJNEARBEIQ9j5/85Cdt77PZLF/+8pcnJCfsakSo7WJmEoOxp8cWCIKwZzDl/bCjJa3dEjV5rFlz7Ph4s8QS1lzXtJZpZUeWMWXH6y2seFmrZUwrB030bCkHFb2LS2SApZx4fhrbJOutdJxuOQ9t5mZdC1W7ZS0kpKbKDFvrqDc27vKEAmnKPndEqO0A5rvy81z3LwJPEITdhc7uyanFWds2Uwbyt7sym+t0KsTaxVkkyLS2UVhYOtMmxCxsNNFrjYVrctjYaKNxw2isg03sDMVRGoVCq/i1AkdH8kwpsOLz1Kp5Tjo+NT3F7T+c4hYfGMOoF/Ck6mWz/n+7NKHAsOtj1HZnRKhtJwu5PcdkcxMBJwjCQmFmAm06cdYc0yrOxseRJcIsEWyRENNoHVvEtB1Zx2ILmaPzWNjYKouFg2VsMuSiZ5PBRmNhkVU2ttLYliKjNbZWOFphKbC0wtWR4LIUuPHyjGVQgK3A0s1KG3Zca1dj4ufZkdjN/FCzrWFhDy3mtypDSGWWexIWCiLUtoOFLNKmonXeItoEQdhV7BiR1irQOicBqNR1qVvcmU7qxrR0ZoI40zhkyGPhkA3zOMbBxiKrXGwUWcvG0QpbK/J2LM4U5GxwtCJrGSwFjgZXg6Oi91krwFaQtUIsZbCUwVEGrUws5gwKg62jZwCtZn+frgcWG2oZyr6NNZrFD9SuSygwardIJlioiFCbL+ajbcc8/JGJaBMEYcEwE5E2aY0za5xAG5+NGYk0rVsEmspgKaejOHNwyYY5XGwKKoNjaRylydkaRysKtopEmAUFOxJjGQ1FO8TRhrwVktEGR4fkrABHhzg6JGsHWDqkK1/DtkOs+KGUQduglIlOUZv0csyyLi4Afk2zcX2JIa8fa6MbX69dXPhWmBMLSqhdfvnlfPvb3+ahhx4il8tx7LHHcsUVV7D//vtPud3NN9/Mhz/8YR5//HHWrl3LFVdcwV/+5V+m640xXHLJJXz1q19laGiI4447jquvvpq1a9fOeo777LMvlhX/qy29ccxdlBljCMMwfYx/nzx838fzvPQxyc7mPA8Q0SYIws5jYuD/+N6a48bN2ILWFGit1rNImNmp9cxSNo6KLWYU28RZBhdHWRQtB9fSFGyNa0UuzJKjcLWh5EBWG1xt6HUDsjokawX0ZBo4OiTvNshlPTJZHzcfoB2DchTKtjDKwmCDbaNdhbIVyop9oyruz2lUiyVKtV+CmVCBnJOnN7OI56j9KNeyGBN1LgiCgD//+Y+z2Nn2syurg+zuLCih9tOf/pRzzz2Xo446Ct/3+eAHP8grXvEKHnzwQQqFQsdtfvGLX3D66adz+eWX8+pXv5obbriBU089lV/96ldpEbtPfepTfOELX+D6669nzZo1fPjDH+akk07iwQcfnHXPr1Kp1CLU2GUNbyPBFom3RqORPtfrdarVKmE4yb+cZvjXMv4mKsJNEIQdRbsLc6JA6yTOOsWeRdmY7aLM0pm0hlkkyuzIYhYnAWRUCcdkcHAphkUyOJQsF9fSuFrR5WqyFmQtRa9ryFuGxZmAnBVQtAP6sjUytk9//yh2LsTOgnLjqdoWgeegHAedt0BlQFlgWRgdzdUE0dg0SUCBavklHv+T0vo2uX3P5GdHK7ALGbIFl2KxgHZKbUJtZzMfyQR7yq/SghJqt956a9v76667jiVLlnD//ffz4he/uOM2n//853nlK1/JP/7jPwLw0Y9+lNtvv50vfelLXHPNNRhjuPLKK7nooos45ZRTAPi3f/s3BgYGuOWWW3jTm940vyc1TziOg+M4QK7j+kajQbVao1qtUqlUqFar1Ov19r/wWfwTJ7lximATBGF7mJ1Im2g9a83QVEmpDDSWdlHKwrGyKKw2gZZRRRyTwSZDISyQMS6ucui2XbKWTsVZzm6Ks7wVMpBtULB9luSrdJeqZAse2b4QlELlXVTWxeCA5WJ0pNZUQFTg1Wm3gCWGssRYqJSKVFiS+tm8BFNcu5lj6RDX9XG03LN3dxaUUBvP8PAwAIsWLZp0zN13380FF1zQtuykk07illtuAeCxxx5jw4YNnHjiien67u5ujj76aO6+++7tE2rpTWbuVaVnzuxiC1zXxXVdurubzWqNMal4GxsbY3R0lEajMXHjKQScWNoEQZgLk7s6pxJozgTrWTNT005dmFrb2DqPpWyyqhtbZXBMhrwpkQkzdKksrrbIWha9GYt8LMqWZA0FK2RZtk7J8Sg5Hsv6Rsh3e1g5g5UDZSv8ugt2FtxuTM5FOTYmEVk6MprpRHhZKl2enqZuF6eqVZzB9CaymYSXjfsZMk6Am4tcslENtV0Xn2aAcD6SCfaQn58FK9TCMOT888/nuOOOm9Cfq5UNGzYwMDDQtmxgYIANGzak65Nlk43pRL1ejyxQMSMjI7M+hx3LdGJw+j9CpRT5fI58PkdfXyR+Pc9jdDQSbalwm4XVTSxtgiDMmk4ibYKbsz05QGu3TaAppXF0Dq1sLOXg6mJkMaOXTJglYyKBlrVsul2brK3IW7AkCwXbULJDVubrdDkNVi0eJt/VwC7E4iyXISSLsTKYbC4SYcns4rh8pVtqZ8TiLBVliRgbf76t9TY6FUhrLYw2fv1s7QFKoVS4q6Jz2jESo7Y9LFihdu655/LAAw9w55137pLjX3755Vx66aW75NhzY/xf8cz+9eQ4DosW9bJoUS8wiXADEWyCIMyZzu5OPXEdGpVU8U87AiTiLHrYOpOKNUfnsFWWqLpZFxlydIfd5JSdCrS8rVicVRRsQ8E2rMx5dDs+XW6DvZYOkin5WN0ZdKkLozIYN4cZJ5J0psVV2WIxS0/F0k0rWSdlNE6cqXFjTHJ/naq67RwRgbT7syCF2rvf/W6+//3v87Of/YyVK1dOOXbp0qVs3LixbdnGjRtZunRpuj5ZtmzZsrYxz3ve8ybd74UXXtjmUh0ZGWHVqlXtg4yJ/yh3tkl5Jv+0msmYifMeL9yq1SqDg0MMDQ1Rq9WaAyf565fMUUEQEqaKR2ta0drdnK0JApZ205IaifUsa3Vjk8FWGYqmh4zJkDVZeq0sOVuzJGdRsBUl27AqH1CyA/YujVHK1untr5DtD0GDUVlC1Y0q5aNgfwXKAW23xI4lljIrzsrsJMQ07QJMKwhNU3y1Wsni1+m9MbkFj7+fjntvwsnXTYaphfgNjRe2CuOdn0SQzmdeOhMsBHPh/LOghJoxhvPOO4/vfOc7/OQnP2HNmjXTbnPMMcdwxx13cP7556fLbr/9do455hgA1qxZw9KlS7njjjtSYTYyMsI999zDOeecM+l+M5kMmUxmu85n/phMGM7WNt46vvM+c7kcuVyO5cuXUavVGBoaZnBwkGq1Gg2YQTybCDZBEJroFgHXdHPqtIdms7+mbeWiGDTl4FoFbJUlr3pxTY5MmKXHlMhqm7xtsThrU3RgRR667YAe12ef0hhd+RpL9i6j80A2RxjmIVtAOXGHTRVbzJLWAfGvYpsb09ZtgqwTqZUsEWmhiW6rZqLfz4wXaC1iLh3aekueTNBNQVg3+J6Fb1TaNkvYPVlQQu3cc8/lhhtu4Lvf/S6lUimNIevu7iaXi7Ib3/zmN7NixQouv/xyAN773vdywgkn8JnPfIaTTz6ZG2+8kfvuu4+vfOUrQPTHc/755/Oxj32MtWvXpuU5li9fzqmnnrpLznP+mGW06YzXRfvNZrMsXZpl6dIBGo1Gamkrl8vNoR1uJGJlE4Q9i8lcnc2EgUSUOW0xaDquc5bUO3OtAo7KY5OhRB85k2ORKZG3bAqOZiCnKTnQ5cCafINFmQb7DWyluKSB063wRsDYOVRvPyabA0tjOcTWMcBRKAuUoye3lgHKat4fU0uZb9pEWFoRKQijRT4QmDg+y2AC2m7RJqQZuxW2HEtPXi/NzMJ5E9SgXrepL4iOAEp6fW4HC0qoXX311QC85CUvaVt+7bXX8pa3vAWAJ598Eq2b3+Jjjz2WG264gYsuuogPfvCDrF27lltuuaUtAeH9738/5XKZd7zjHQwNDXH88cdz6623zrqGGsDDDz+c/stpewveKqVQSqG1Tp/HP5RSWJaF4zi4rovj2DiOMyHGYWa0/pXP5l9YEy1vrusyMLCEgYEl1Ot1Nm/ewtatW5v1eaZxjYpgE4RnH1NldrZmdabJAjQtaJGbM7KcJbFoOdVLhjwuOfrCReS1w5KsS9FRlBzFqnxIjxOwyPVYu2iIvqVlMsvABA4+JfSyYhQ/5kZuTaVU1EwziTGz49gyW3e2liWuysRCBrH4MpjAxNYzIlEWDcT4kSXNBJGwSsSVUtF6ggBCH+0GEAYQGsJGGIu+MBZvYVMEJo9wdkLN1MBbV2DomZCHHn2Ecu0pjPGb57MTMUAoddTmzIISajP58vzkJz+ZsOz1r389r3/96yfdRinFZZddxmWXXbY90wOgVquSVoneAZ0JUlrPvdP+xlU6tG0bx7Zx4jIcjuPEbsrsDF222yvamttnMhlWrlzBihXLGRwcYvPmzZGVbRq3qIg1QXj2MF35jfbOAlYce+ambk5H59DaTl2cDrnUipY3OfrdHCVHszyv6XENPU7Ac0oVFuWqLOqq0HuAB7kCfr2IKmVR8SG1A8qNrWX2OJemlTy33wPbXJcAQSScTAjGD6PlXmwtC0wqyggNBB7Kb2BCD4IA5fmYRoDxfVCG0ANCCO3mJQpja5sJwQQKE0IYPyeWqKRX5lQ/k60/HV7DYtuQzdBYQLkyRrVeTYXaniNxnh0sKKG227Kj/3Uy1f7idb7n4XteM1asBaVUGlvW+rCdyT7uTv9Mm068TcwyVUqliQjVapXNm7ewbdu2ZpeEceclLlFB2P2Z1orW1hjdioWZi9Y2jlVI3Zw5qwdH5emif4I463IUexVhsRtwUPcQfaUKi1ZWcBdrgpomtEqExRJYGrvULJ2h3Ci4X9m6vTRGK1q1CTPjx9as0GA8wBjCetOipXRkLcP3CeuNSJQ1Gpiqh2l4BA2NX9cMDuapehlqfp6Kb+MbTT3Q1MIowL8WaEIiA11gVKoLA6MwJvas0jTkzfZnxjMwWIdHRxp4/lg86V2EYX6asu8hPxsi1LYDg5l4k1oAGGOoVCpUKpW25bZtUygUKHV1USoV07i/zszW4tZuacvlcqxevYqVK1ewbdsgmzZtirJGxSUqCM8KOt77Ooq0pPemk7o6LZ0ItSyWyuDoPDndS9YU6Q17KWqXgmOzIm/R7UKPCweWqizJVXnOoVuxezVhkMOjC704h1ax5cxqujbT2DOtwVZpuEibGzOxgoVhZBELwkicBQYTGsI6kRgwhrBaR3k1MDWUX8cbityeQV1THnEZqxQoew5DdZdaYDHsWTRCTT1UVAOFZyLDXC1U+CH4xhB7TwlacgqS10Eq0Exb4uhMCQyMeQEbzBBhaknbdUiZkLkjQm0XsD1iZHuEoe/7DA8Ppx0fLNumWCxSKpWmEW6JaJu9YNNas3hxH4sX9zE8PML69esjATmFYBOxJggLl0nvQVOItMTVmdRES7I5XV3E0XkccnSHiymYHEucHF2u1WZF6880OHjpFhbtXUP3FvHoAttFu4kgS0RZ9D6taxbHnqWuzbA9G9Mk8WZerJACCGrx8kYdU61hajV0UEdbkdvSKysaVYt1m7qp+jYV32Zbw6EWaGqhYtTXNAIY9RVeaPBDqAUQxMKsHicbeMakIizENBNA49dhy30wnIPKCTBUTJ1BaxPGeLPeXlg4iFDbTna2qJjN8aYTdYHvMzw0xPDQENAUbt3d3fT0dGPb478e412kM80ijbbr7u6iu7uLkZFR1q9f38wWncQlKoJNEBYG04sz6CTQtMqkJTdsK4tWDrZ2ydqRm7ObpRRMgaLJsyybo8fV7FuCPjdgINvg0OWb6Vldx+6zaIzmoGsFJutgO7F7M9MaZ6YmujfjBAHjBbH1LIwC9+OMzLAex5cZMDUP1aigTBXG6oQNw9gWl6GRPOvG+hj1bUY9i80Ni1oAgw2oB9AIoeyF+KHBC0NqoUdgQqrGJyDAJ8BTHoaQUIV4NAgJ0vcAYUt9M0MICsJWV+Uc/n1uCPGoUPG2Yoy3S++n85VMsKcgQu1ZTOsf5kwscUGLxe3JJ6FYLNLb20tPT3fcAH48M7W0tVvZurpKdHWVGB0dY/369YyNjU1Z1kMEmyDsOqZ2ccLENlA6LV6r4j6cSbKApTNRPJrqJUuRRWEvJZ2l5NisKGj6MrB/qcbSXJUlPWP0PdcnNEUapge92I6SA3IqLUSr3di1OVmNs9a4s9itabwwdWmGtQZmtIwOKmjjYUIY3eAwONTFcC3DM9VcKs7KPlR8xXDDUAsMY16AFxo8E1IJvViUhdRVnUD51FW1Kc5MDUOIiUcBBMZLY6xMq1CbTWrnFISEBGGdhl9mV/b5FLYfEWp7CJOJnQk34RbBNDY6ytjoKE89pSgUCqloc1133F5mGs/WbmUrlYqUSmsplyusX7++2U9VLGyCsCCYqrNA2/pYlHVKFrC1GycL9JJRRbKmyJJwCSXLZXWXS29G0Z8JObx3jOWlUVY8v0wwAoFVJHAXoxwbx1XgqmZJjcSK5owrqxEajBemMWemFjYtZ3EyQDjWQAdlqJWpbwoZGc7xp61LGPIchj2LDTVN2VdUfcNQw1ALQka8Op4JqRufMVXGUw0aVPFUnZAAT1cxJiAkxDd1TBgQGj8VYGHoE8b3SWNCTGwtM/MsoMLQJwgnjw3emcxHHbVdf1Y7BxFqeziJ+JnS4mYM5XKZcrnM008/TT6fZ9GiRfT1LcKyrHGDZ2Nli8YWCnn2229fyuUKTz311KQxbBK/Jgg7h8kzOWF8TbS2FlAtrZ8s7eJYWWydx1U5ivSRD0sUTYEBN0eXq9mrCAMZn2W5Bofut4HsCqiPFbD6e7EzFlZOgQVYCuW0uDituFVTi0gzQQi+wXhBFGPmQ1iJBFtY9qE2ig7HYCygNqIZHszzyLYehj2bp6oOox6UfdhWN9T8kFoQMhp4beIsUD4VhgnxaJhInAXGJzReJMAIUlGWCDKTvo6FWotgm5fPLs6yTY4ZhI15Oc6sMGqeXJ97hjtVhJoAzECwtdRxSzJK161bx6JFi+jv7yefH5+IMBPB1u4SLRTyHHDA/mzduo1169bhexMDYMW6Jgg7mU5N1Ftqok2WLGBrl4zVRUYVyagSi4IlFFWWku2wvGDFbs46K/MVli8eIbPCJcgswi5lIhdnBsi2xKDZujmNRKC1FqRtRALNVEKMHwX+h8MVgsFRnGwV40N9WPPo4/1sq2cY8hyerNiM+YpNVUPFN1SDkBGvQd0E1PEY06MEyqemxvBNjQAfL6wQGI/A1JtiyPgTRJlps6Al98P5EWmqpZVBcqtuzmEXluUQdggi1IQ2OgmgNvHWYukyxrB161a2bt1KLpejv7+fRYt62zpHzMwt2j6+r28Rvb09rF+/gY0bN0447vg5iWgThO1nRm7OcZmclnbT97aVxdKZKFkgdnMuMssohgUKKsPqUpZeV9GfNRzeU2F5cYw1R4/ijyqCTC9qcRFLg8rpZhP0JGtzfLPzOPYs9IM09iyshIQ+BGMhuj6MaoxR3ajYsrXIH4eWsKVhM+Jp1lWh7EHZDxmsN6gan2HGqKsaHnWqaoRAefimjhdUMHGsVyTGwtR6FsbvI9E1ncVsx8egdaJdsCX1K/1dfo+MKpzsGdav+UCEmjAtU9aLMwaUolqt8uSTT/L000/T19dHf//iDi26WhraTYPWmhUrlrN4cR9PPfX0pPFrIC5RQdgeZu7mtOPVUbJA1ETdjjsMRB0FLJ3B0TkKuo+86WIxvZRsh56Mxd4FWJwJWJFrcOjqjRRX+zSqXej+HmxHo2wV9d5sjUGzdNpFIP3b95stnEwtjGPSIBiuEgyNYqsK/piiPOTyu2f62VJ3eLxiM+LBqAebqz61IGwTaGU1lFrM6uEYofEIjYcfNtrEWbtAi9sx0Woxm0yQjRNnMxFrkzX8nIIJItCEC+beKOkMc0eEmjAjxv+xT2ZlC4OAzZs3s3nzZrq6uli2bBmFQr5ly+ksbO3u0Ewmw3777cvIyChPPfUU9Xp9wjHHz2eh3JgEYVcx63qLndybMEGcjS9YG/XldHB0jrzuI0uRnCkwEC6i23HYu2TTnzUszfgctWQrS1eP4C5R1LblCEsDOF1OKtC0G3cPiOPPUisaRFazxLUZizMTgD+qUI0yujFE9SnD+s1d/H5wgG0NiyFP8XQZRr2QLfUqVeNTUzVG1BANVcWnRt2MEYQ+fliJAu+NTxDWU7dhGCa9MceLsqDdndlBeE1+H5qhZNlhCQAikXZ3RKgJc2LKmLbYyjYyMsLIyAilUolly5ZRLBbGDZzKwtZMNoCopMdBBx3Ipk2bWb9+/aRtqZI5iVgT9hS2qzvKDF2cSSZn0pcziUFzrCKuysU10frJhwWKKsvyfIbejGJtKWB5tsGKYpnVh48SNBw800tm33xUqHa8mxOatc9MXIQ2Ltcf1gPw4gzOhsHbXMFRQwRlj5HNLr9+aimb6w5/KtuMNAwjnmFT1aMSegyqYeq6Rl1VqIaDBMbDD+v4YRVjQvywnlrKkufEndkqzqJ5TRRone832yuQdpDAWgAZnzBPrs+FcWrzjgg1YbuY1C3aknwwOjrK6OgoxWKRZcuWUSoVWwZOJ9aSMVEP04GBJfT1LWLdumfYunVru0ukBUk6EJ7NzFmcqU7bTUwSUKklzUmTBBIrmlZO6uYs6D5clSdvSiw2iyjZDl2OzaqCYiBrOKR7lFWLh+le1cBXPahFXViOimqhOQqdseJm6ZEFzaTl+Zt9lEwQ9V4y5RATGBrrIwuaY/kMPZnhmW39bKpm+f1IlsGGYl05ZMwLqAQ+WxihrmuMshWPKn5YoxGUU1emFwu11ngzY/ypxRl0EGjzaCXr+JkJexIi1ITtZsrCuq112cbGeOSRRygUCixbtoyurlK8ZnbuUNu22Wuv1fT39/PUU09N2uFg/HxEtAm7I7MSZVP+qDf/ttoTBzpZz+zUxRmV24hizxydw1YZCnoxWVNgiVlMUTkUXYu9ihaLM4b+TMARi7exbMUwdsEQWiXU0gHsggVuVGZDOVYkzizdnHMQ3we8sCnOqnEsWmAww2Mof4jyYzbPbOtlQyXHg6MZttZhqAHrKw3KgcdWPUiNcmQ9M4P4QR0vKKeZmUHYmGA1a/bCnNylOfH+0XLfmoMAm0tty92V+epMsPtfmZkhQk3YoczEJVoul3n00UcpFAqsWrWqQ2mPyWh3h+bzOfbf/zkMDg7x9NNP4yXlPKSPqLAnMak4a/9Hz0RxFo1RqRWtPQYtyuSMenJaOkNWd+GoPK7KsyhYQoEsS7NZulxNj6tYW/JZlm2wrFBh78OHCP0MprsPO+dGoW7ZlkSBlibpyd+rMSaqg9YIMAHgGfyKIdhWwQ62YuoBw+tdfrNuCRtrLpsbmj+NwkgjYMwL2GSGqeoqI2zGMxV8U6cejBAYHz+otsSdNVIxNp1Am9al2eFes733mNl2lJnN/nYZRknW53YgQk2YF2biEi2Xyzz00EP09fWxYsXyuLfodPXX2sUaQG9vD93dXWzcuIkNGzZEt6UpxFoyP2F+GP+5P5uv9WQ/pDvinKf8kZ6hOGvbT1sWYWeBprWNoinQos4CGWyVpUgfWQpkgxxLrS6KjsXqokWva+jP+Dy3d5jl/SOUVoUEbj9hroidM1jZqKtAa7unVKR1qoNWM4SewVQamK1bcaw6WPDIg308U87z+5EsW+tRK6f1lQajQYOKqrJVb8QzFSrhUFROI6ynrs0gbEQuzXExZ9O7NFvWjWfcPWY+vufTlkuaw/bC7ocINWHemKlLdOvWrQwODrJ8+XL6+xfHN/Gp3KHtrlCIynksW7aU/v7FbNy4ic2bNzcTDsYdr9N85IbWZEf8K34+95cwl89svuYy0+NMN+fp+2omzMRaFo1r1tay0tdJ3Fni5tTaTmugaeWk1rMifWRMlkKYo8/OU3IsuhzNmrhx+nN7t7Ksb5TiQANlWwSZHtSSIo6rI6tZ1ooK1Y7rxWmCMG2Snro2q3Em51CI9gax3TIbHs6zbmQR66sZfj/isq1ueGrMYzRoUKbGFms9nq7QMFVq3iB+2MAPqs3EgLARC7PI2p64O6M3k7k1x5e4mP57tjPvH7vjvSpyfe7qWey+iFATdgrTuUTDMOTpp59m8+bNrFq1qiV+DSZPOJgo2GzbZsWK5QwMLGHTps1s2rQpEmyTJB0kzKdlZCGzs4TLfLA7zn374s1m5sqMNm0dGwk0lYo2nVrOEoEWWc+K0bPKU6CXLAV6wx7y2qHg2AzkLHpcxSLXcECpxtJ8hbUHbUXnbUJ3MapUwM3E/ThzcR9O1+pcrDaIaqGFjRB8wDcEZUOwdQw3M0hYD9n6cIbfbe5jfc1hc13x5FjISCNgUzDGmB6lpsqUw61xp4Co+XhofPygRnvsWdBRoE1rNZthbNiz/R6xoxDX59wRoSbsVCYVbPFNsV6r8eijj9Ld3c3KlSvIZDLxgKlcohM7Idi2zfLly9oEWxCMa6Uygxvxrk5G2B3FyIJmt8mgmyrwf9z6liSAaHxTkAFtokwrB6U0js6hlY0VW89slaVgesiZHNkwy2I7R8HRLM9bdDuGXtewf6nMkkKFgf5R8ksDgrqD6utHdRWwLdBFDZaO4tCSZuktcWhJDBphGAm0pNSGB/5wlUxhmLBS4957lvFMNcvGmsVDI4rhesiI5/GM2UZFjTKqN+OFFbywSt0fITR+9EiTBLymMNsOV2bHISLKhF2ACDVhlzBdt4Ph4WGGh4cZGBhg2bKlLW2pputu0G5lsyyLZcuWpoJt48aNTcE2Lph5OnZWMsJuLc52GyHUidlXgp+e2dTCmpk4a3VlJu9bxVlUnLa5LMrgtHCsLCruyZnRRWwVxZ7lTBeZMEsvXeS1TcG2WJqz6HIVe+cD+jIeizN1DtxnM9n+EFwHz+/DWpLHyoBy444CrjWxWXpsQTPGtGVzUjOEvsEf8tH1bWTcGpv+12XDUD+/Hy6wua7YVof1FY/RwGPMVBm2tlA3Y1SDQYKwHrk5w1pclDZpPh6kxWnbq/KLxWxXE87Dfc3szvfKWSBCTdhlzCRDdOPGjWzdupUVK1bQ17coXjmTVlTt67WGpUsHWLKkn8HBITZv3kylUolWTuMWbWU6sbY9sW+7VKDt1gJrOnasAJvqc5r4ec/82HMRZ9Hr6DaeWM5axZkVx58l3QNslcXCJkc3Ljmc0KXHlMhqm76MQ9HRlBxYnTcscn0O6B5hoHeMwqI6zkCe0O2CTBbHjb4yqqCbmZxOMp9xbk6I6qH5IcYz4BmCSkg4OISjRkDByCMWD25czPqay2NlxWDdMNII2epXGFNlKno0dXM2gjJBWCcM/TSDM7GkJVa0jgJtB5bQEISdiQg1YZcznTvU9zyeeOKJNH4takk1k2bvrSRuIE1f3yL6+hZRqVTZsmUL27Zta088mDDBiYkIM83Gmi7LdIeJsx0qtObDstRklwnSOfROnO21mPzMpv++Tm0pi7Ybby2LRJnTJsi0crBVBks5WNhkVAnHZCiaEpnQxVUOvbZLztbkbM2SrKLkGPbK+yxy6yzOVtlnzTacHkNQsyBfwlq8BJ2xmv043aY4Gx+DZoIQ/BATgvHjpAEvzuT0wN82RrZrBK/a4Le/WcIz1Rwbag5/HFEMNQzrqlXGTI2KLjOon6FhqnhBhXowGlnSghomdne2ujmNSUIbJu9aMh0izOaPZ0E5uF3GrIXaY489xs9//nOeeOIJKpUK/f39HH744RxzzDEdmnALwsyZzh1aqVR4+OGH6e3tZeXKFTiOM8cjRSU+8vkcq1evYuXKFWzduo3NmzdTq9UmDp+Bi3Q68TFe3M29F+P2sH0CbMeJyvkVgk1mdhw15/lY494HHUd1nkv7tq3xZc337TFmrSJNawcdL7N0BoXG0fkJ4szCphiUcLDptjJkLYucpenLKgp2JNBWZD0WuT779gyzeMkYme4Qsjl8p4Q9kEc5dG73pFsK1rbGoBkT9eI0QN1gfEPoQTDUQNe2kil4bPu1zcZtfTw4XGJjXTPYgPWVgGHPYyvDVPQoNcaohSN4YTUWaNU0Fi3J5Gx1c0aEO6VshjA75qvg7Z7y0c5YqH3zm9/k85//PPfddx8DAwMsX76cXC7Htm3b+NOf/kQ2m+WMM87gn/7pn9hrr73mc87Cs5hp3aHA4LZtDA0NsXRpFHuWhq/NSog0Y9m01vT3L6a/fzHlcoUtW7YwNDTSknwQ/wi0CLa5iJa51cWabN7bebx00Ez3tyNFz3iBs31Md0w1rQVrKnacqBx/vE7irGktizsEEAuxeLmtMyisyGKGEyUDmCIWNvmwQMa4OMqipB0yliZjaXoyipwVtXQq2iFdTsDehTLd2Tp9i8oUlvooG0LfRnWVUMUSyrGxbYXKxtmbOiq3Ec2z5XsVGqDFihY3Szf1kLAOxoewFqJrW2G0zMYnimwY7eG3QyU21xWPjRpGGj6jgccmtZWKHmU03IQf10FrTRYIglqLQOtgRWsRaCLOhGcTMxJqhx9+OK7r8pa3vIX/+3//L6tWrWpbX6/Xufvuu7nxxhs58sgjueqqq3j9618/LxMW9gymtK4BJgxZv349mzdvZmBggP7+xbFgm+0Pa3sB3UIhT6GwN6tXG0ZHRxkaGmZoaAjfbzQ3mSymrZPYms7eP8OWP5NuPp0Ym1SMTCNutkNwzVU4bd9cEmYuyibb73Tzm7sFbqI4A9A6sgzrODMTiLIz0VixG1MpCws7jjFzcE0O17hYxiZvcpE4sx0yuinOshbkLOjPGAp2wOp8nS63QW+uypLVY1g5wHUIVTeqWMAqulFpDa0gE1nO0lZPLd9T0/qdDk1aDy3J5CSAsA54PqoyjMMYftnw0IP9rK/m2Fhz+PNYVLB2Q7VO2dQpqyrDbMYzVRrhWJos0Gz35E9tRWtBRNrCZH6SCfYMZiTUPvnJT3LSSSdNuj6TyfCSl7yEl7zkJXz84x/n8ccf31HzE/ZgprSuARiD73msW7eODRs2sGTJEpYs6ceyLGZvXWu/2Sul6Orqoquri9WrVzE2NsbgYGTJS1tVzYROom4GBUzbhk9piZudEOssMia3cM1GzHQeuz3WrFmMm+F8Jli1xtcmU7O/Fp3Q08wnspBFx0oC/zVRvFnbMw4Z8pH1DJtcmMMyNjlcHKVxlEXBsSaIs76MoWhFAm1lvkrR9Vi9ahCnaFAFF2P1EmbzKNfBShIDMhpsIoFma7Bi12aSwdmaIBASfa9Nsxcn9TgOre4TbB3CZoygClufzrJ5uMgDQ11saWgGG4pnygGjvs82RinrMjU1RjUcxA/rUdP00IusaGEDCMfFo9GSMDCbrFphVxF/VYQ5MiOhNpVIG09fXx99fX1znpAgjGcmgi3wfdavX8/GjRtZsmQJAwNLYsHWiemK507MKi0WixSLeVatWkm5XGFocJDR0VEq1eo0+wXU+B+TScowQAfhNVNBZHVcP1GItL4fX4ur87ip9jHZuPGCp3XcdCJmquVqBla8qeahO5xz6z7Hz23CeTD1eXSaZ7KNVlb62jaRxUwTW8pMFIeWMRlsNC4ONgpLafKWha0VrtYUHEVGQ5ercLUhb0GvG1C0fZZma5Rcj1K2zuIVkcVMxcIrVFlUoQeVK4C20Q5pYoB24zizpLQGRAKtlTDqwZkKMy8Sa8YLMR6Ru7PhYYXDWLrKtkcdNg/38bvBbrY2LLY1ooK1Y55POfDYzHBUE01txjc1vKCaZnNGyQJhmjDQVrC2U0anKADhWc6csz43bdrUrPrewmGHHbbdkxKETkznDk06HGzYsIFNmzbR39/PwMCSuIdoK1OV95hoXRu/LnKP5qPjGcPYWJnR0VFGR8tpyY/25IHxYmPydj9t4yaIl+nFWCch1klYddpmMmtTq1jpJLjGb69mNM/JxdHE7SYXWxPml4giJm7fKpjax7bMxXSerx63j6nm3rpcmzj4Hw0GLBPtx8YisqlZOMrCQmErjWtpHK3IWBpbg6sVBSd6drWhx4WsNvS6PjkroGgHLM5VKeXq9C0rR+LMUZDJYVQWclnIuWilooQApcAiqn1mdSirkdDS7ydtlu7HddBCoBHVQQsbYOoNGB3C7a7jbQ3Y8mSW363vZ3Pd4U9lm6FGVGpjUy1yc1ZVnWEd1USrBcMEJiq10Wz91OzJOVuRJm7PhYqan2SCPYRZC7X777+fs846iz/84Q9prIJSCmMMSqmJ1d8FYQcybeZk/J0Mw5CNGzeyadMm+vr66O9fTC6Xaxk4nVibjBYhpxRaKbq6SnHLK00YhoyNjTE2Gom3arXaHtMDTN3uB6YTZFOJsc7Zg52WW23LO1qaWtxznedhpduOF2l6/HE6WKLGC6h0fcdt4v2a9rHjxdZ4odYqmNL1Jo4Hi78/yozfRrU9t8+jfV10ju3fQ92yhSL6jmhUbLSKXltaYcXvM1b02taR69LRkfsyo8G1oMsOyVmGnBWwyPXI2z5LusoUCnWcbEimD1Qxg9G9YGUh46J0/NehiQRaUlZDkXYPSGPPxrs2E1pcnCYwmHrU+smEUZkNM1YhHBzFdqroksXG+xy2jPSwoZLn4bEsg6kVLWDM99nCCFVdSd2cDVPFD6v4YYPQeHHj9MTNOQORJgh7CLMWam9729t4znOew9e+9jUGBgYm3KQEYWcx/l/PbcItLphrjGHLli1s2bKFfD5Pf38/vb09caeDqdpSTUUnq1v8I681XV1ddHd1p2vq9TrVWp1qtUq12qBWq1Ov1yGuEg+JEJpedLUXOm0XUVbLvlRLf8dondO2rlVIqVS2NMVOIpYs4kD31DLU4r6Lbx9tViParUaRKFItAioRLapF9LR/ds0xzffp6/hF1J2oZXs1flwiklrfN7drbhM/p8di3D6a+2pdlrxOtlPxdzF5b7Xs29HJexO/NrjaYCtwdEjBCnB0iKMNPW6dvOvRVaxhOwFONsTORt817SqM5WBsF51xUW4RMi5oC+zoSenIhakyCpSK/k2QpEXr5sTTshoQC7Ewbe8ExJazSJARxLFnPhgvgPIotlsmGPHY8HCJLZUBNtcy/Gksw5AX1UJbXwko+z5bzAhVVaWmy5TDLfhhHd8049D8sN3NGb1OYkBn5+oUa9rCZk/pIjAfzFqo/fnPf+b//t//y3777Tcf8xGEOTPBNRqLtYRKpcITTzzB0089xaLYyjb32n+dXaSdrHyZTIZMJkNPTzdR0VJFGBpqNY96zaPR8PC8AM8L8P0A3wsJQ9OxVEMi1qJjje/v2B6QDqSvVSywdCrMrAniK/3P6NTKZMfizTJ2U0DFe2wVXFYqZCJrUSKadCyW2sWRSsVRIqaagmeimIJI6BCvS/RGEkbVKqZawqywlGkTTslzIrbslnHJcey2eZh039FcDDo9lhknAk3ba1tF421lcHWIpQyODsnaAZYKyTk+lg6x7ZBsxkNbJtq/q9Guxi5Y6JyLsm2Ua2O0C5aD0qDCKPYMomftxBN04hpnSqUWs06oFoFGGIu0pDhtENc/80kFGiGYag0qI1hUUbZi8AGbrcM9PDTYzbaGzZCnebIMY15I2Q/Z2KhQUzUG9RYapoIXVqiFI4TGi1o/BdXIeha3f2p2FoDW5ukztaKJSFvYRHXU5me/ewKzFmove9nL+O1vfytCTVhwTNWKKn0NBEHA5k2b2LxpE8VSif7+fnp6uifJ+JuuGXzzB6Q99mySEh7JltqmUHAoFBKrl4pFWCSEjCESbT4EfkjgBShlYUIVZVCFCmNUJLJCC2MUylhR1JOyW8SY3SLEov87xo3EkolC2QGsOGYqEiQqfe0oC1pcdpC8jp4TwZW8jjxryXpa1rcKKNVmrWoVPomAal0HTYvUeCGm4kttxSLKUk3BZLXs22oRTokIS4ScoinAtApjMWmwtImPE8bi0WDpMP6swdJhJK5ioad09FqrMLJsqeYFUPFryzYobWFlImuXdmxCbLBstKPQduQZ17aJYsk0UeC/FV+UWJSlgiz1sSbHopmpmXwHx2VrGkwzYzMRaD5pDTRCCP2oFprtjEF9jHCwwVMPdbOl0sXWeoY/l12GPVhfhbIXUvY9tvk1KqpKXdUY0ZvxqVPzmzFoXlglDKMyG62tn9pcnDCrFlAi0IQ9gVkLtX/5l3/hrLPO4oEHHuCQQw6ZUB3+ta997Q6bnCDsEKYQTGOjo4yNjWFZDt3d3fT29tDV1RVbHVr/FT+T/qLjmEEP0UmLsWqNm7HIZuzYsqbSGltK2ajIftQWz2UpNxZYduzSjIWcSaxgkXvVMU5qF9MmcXu2uiJ1ai2z42crFruJ1SoSSLE1DIXWpA7OVEjppnRts4ipVoEWr6cp6tpdju2iTUeTQMfCTqfrTXqcpuXLpFa55vGTZbFgIxFXzf0k+1LxumSSShlQJrWeGR0JNKOSz9hglCFQhsizGFvJdFRBSmnQOl5mRYJQWeDYYSr0lB2fn0MkupSKlGvsGVe2bndnttJ68WGCxSx1a7bEnRFGgsx48fIwhHIZbSrQqFLfBts2FBgql3hkpBRbzxTrKlDxDZtrHpXQp2YaDOkhGqpKw1SomWH8sI4XlAmMn8agtVvQJsahxVcxPgERac8mJJlg7sxaqN19993cdddd/Pd///eEdZJMIOwKdkRroyDw2LZtG9u2bUNrTXd3id7eXrq6SnE8G3QWa02r2rRZqSmT1+qKknIm3zJxPo4v/9CUW9YkIs1uGaNaxFmyVeJCjUpCqPjMLKVjAdWUlMlrnVjRFFi0W9QUTXEEsRdunBBrFWeJZSzZbrw4S61WtIuzdF/xeouWcbG1LLG4RceMhVLLMS0VCykMWpvmeWqDxqT7iCxqpmVs9LknoizaJraypUINlA7T7bUdL7dMKtRUbEVDg7ZbLgLEFrLkZJquzTa3ZhL0H7+OLGaqrRhtpIlit2YIBHFhWj8uu1GuYCplLKcOChpbDJVBl0c3LGJrPcOgZ/FUxWLEgzEPNlV9KoHPoCnTUHXqukaZQbywQoBHPRiZ1IqWFK6Nvu/txWsn1EYTgfasQWLU5s6shdp5553HmWeeyYc//GEGBgbmY06CMGNm1iYpHjPDekthGDI4OMzg4DBKGbq7u+np6aG7u4tmabbxgi0Sce1iTTdNRuNEXmSxa9+HMSHjM0ATa1qnOmNNqaVjK5sVy7CmSLOwAY2NhTKJKIvWxc7RVKhFbtdEdMXuz1iM2YlQU003ZyLOIHEzJnFpE61mrSIrWd4aL9ZJ1Fmp1YsWN+XkFrNWF+dkosxWYWopi+ZhUgtdKtRSq1xiUUuWNYXYBDGWjIvdpVFgv4liyuJnYmEGifiKr0tiMVMqKjibfn0m+W4bE8WQJa9jF2b6HmJLWvy18+OszVgDmRCM56PqFTQVzFgNfxSeeryH4Vo3Qw2XJysZRn3FxhqUPSj7IVvrdWqhT5UGw3oQT9epmEF8UyMIffywQhj6BMYnCOvNvpwTxJlY0ARhNsxaqG3dupX3ve99ItKEXc707ZNU5/ezKJBpjGJoaIShoRG00uQLeUqlEsVigWKxSHvW89QlPybPkFbx9OIxLbFq7aM6iTXdUcRNGGNaLGimaUlL/1ORjS61qKXuvpYzUs1EANViQUuC/9tEGu0izUrPsoOIa12eHicRW7SJLSu1rDVFWmpdS5/jbbVp2beJ49LCeP8zs5hpRSq+JnNjqtb18YlqK3aTJuIsSbJUTetZB63ezvjoax0Vlo2I16UxaLRY1YjKaPiRWDMhKEJMtRYlBdRrKNXANKBeUTz1ZC9DtQxPlHOM+BZjsUCr+LCtFlALQiqhzzBj1HWdOlUqRF0E6uFY3IvTwwuqseWsmcEZxhazKJuztXH67ESasHuThEgKc2PWQu11r3sdP/7xj9l3333nYz6CMCO2q8H5DErKdNq/QVEu1ymX6yi1DaVsCoUsxWKeUqlAPp+NYpZarGoTxZmVWtOaGZ3JWDsVaCoWYLq1BIdKrF92/DqypKU2sTgVcKLLM7aiodpEWtMN2hRpieBKhFjqZmy5dImEbBVprcafVGy1WNESwdYal6Za9jFBpKmJGiaxXrV+jMn27Z9d0xWZjEsyODuRuiVT4dgSt6aa+0osdZ108fi5mRCUjooeq8SSFRtYU0uXBgLAiueqDcqPI+DGi7gQmuIsLkKbxN4HLc8qHhaGKL8GYQ0zUiUY8xhcn2OonKPs9bKukmPUsxgLFJtqmrJv2Foz1IOQWhAyHNSp4zGmR2lQxdN1auEwgfGiEhthtWk5S12bUfxZ04I2sdRGNP1JMjml7IYgdGTWQu05z3kOF154IXfeeSeHHnrohGSC97znPTtscoIwa2YgwjrTjNGauE/dPkZpwMIYQ7lcp1L22LRxGKUU+UKWUqlILueQzUZlOWY0bfQEkdYUe83CGCpOBZwQl5aMjcVYa10zNaFI7FyvUfxb2mHz1uTaRIMkY0Miy1iiLRI3Z/pznbxQoONtjIEwEWtGEZIcOI4hU1GNPFS0vSIar4AAFe0nKaFhoobQWhlCo9CYaN8GQqVQxmCMwsTiKMoBUVFgvTJE1lCTnpsKI8EWxUar9ESNAZXEGGqVXpPQkFrXWpWvCeNjteiV1DrXIYzRGDA+JEmbad1lbTCeR1j2IKxjWzXC0QZ+Hfya5pn13ZQbDs9Uc5R9izFfs7muKPuKamAYrAfUAsOQ16BufOo0GNOjUdYmY/jUCUKPRjhGaLyoi0Di2kxrn4UtjdPHJQnAuExOmKkFTUTaswU1TzFq87HPhcecsj6LxSI//elP+elPf9q2Tim1Q4Tal7/8ZT796U+zYcMGnvvc5/LFL36RF7zgBZOOv/nmm/nwhz/M448/ztq1a7niiiv4y7/8y3S9MYZLLrmEr371qwwNDXHcccdx9dVXs3bt2u2eq7CAmFKkTTBPTFjeXl5DTxjTdDG2F5NNitUqpalXFY1aFaXqGDMCKiSbdclmHTJZm2w2Qz6fx4pqwdKpJ+TE5t26bT7NuLSJAqy1HVI085ndyJIIu+QapAHpijS8OxI2kVAK45GhibaLTYmEiUUHUpGU7CPeMUY1f5tbY9OS2HebWHEZk0wBTNKSSzX/H4usRGK3ymkTHzsSS8kWClQYlzIx+IlJT+lYJEYTSCx/oFFJN2kVJ1G0uDkNkRtUWy2u4qR8R9g8rrai+Y13iZJcnpaPMb1Ok31sxodGAzwP/Aam0UDbHqYBpgqhp6jVLNZvXkTZcxjzHDbWXMZ8zdaGouIr6qFhsG6o+Yn1rEHDeIypMg3dwKdOlWEC/Cg5wEQN0r0wrn3W0kWgtd1TItBgYhxaOyLS9kTmpY7aAvqK/OxnP5vTdnvvvTerV6+ecsyshdpjjz02p8nMlJtuuokLLriAa665hqOPPporr7ySk046iYcffpglS5ZMGP+LX/yC008/ncsvv5xXv/rV3HDDDZx66qn86le/4pBDDgHgU5/6FF/4whe4/vrrWbNmDR/+8Ic56aSTePDBB7ej4KmwK5jUGjRBpE0V/KMn7mfStk5N16NqEUcqcTMqO11m6QxJYVpLZ9DKxlJOZNGqWphajhoWvsrgOBaZrIu2DbZroW1DaAfYrkZbIdoCk2aThpFhJzazGMIW4aTRkf+MSDoEcQJBhE8QBcSbWMgp3eJBi+Yd7SuxzoWxhSpyg4amJfaM2MAUW4jCxE0aW6nasj5bYlLa3JoKVEBb3FqaDBBv7yf7aflM7XhgM8FAxe7MaEySRGApFbkuaRbSTZIMornoNIYtcXc267i1JBAk807qpJFkdTaTDVrdpEnCQauQa3WZkpxrSwJC9LULsR2TCjNLe3ERsyD+XH1MPcB4HspvEPqGoKoY3ppjtJqh5ucZrGeo+BZbGja1QFEJFEONSJDVfBjxIkFW9n3qJsAzAWOqjKcaeNSpqTF8VcczFYIwKaVRj8pqhF5bvFkiwMaLszbrGXRwc4IINOHZzFlnnTXrbZRSnH/++dMauJSZ0Ihwbqxfv56vf/3rvP/979+u/Rx99NEcddRRfOlLXwKiDLxVq1Zx3nnn8YEPfGDC+De+8Y2Uy2W+//3vp8te+MIX8rznPY9rrrkGYwzLly/n7//+7/mHf/gHAIaHhxkYGOC6667jTW9604zmNTIyQnd3N+1h0cLOZK4ibep4tskF2mRWsyh2rFWoRWNt7ZLEitk6g8LCVpnY/uXgqCyWscmZAhY2GeOSV5nYGxZlWIbG4CU/aA4oh+ifU5aJyoToKPZJx0VUjTaxpUdhlImTApJ+mwpM00Wq46V2ek5JjFrsjYvfp4VsScpvNO1+ChWVo4D0OSl4m9CazZlYp6zYTdgat9YqiKyW8Wmh3GReNAXV+A4FaYZnax21eHyzIG6z0G1zv0lnAYOtk4SCJDauPV7NSpIGkmzPeJ+R+IzX60TgtQq6AK1CLCsKSouOGVmZlCLKxAxDnJyPwoewpbSRBtOA0IP6qEWjZlOuuAxVs9QCmy11l1qgqQWaIU9TC2DYUzRCQyOIxFkjMNTDkHLgUzc+FVXFUw18/EicmToBXiTQTJS1aUyYxp61irOpBRq0ujijqzqJQINpzSAi1HYGBggYHh6mq6trXo900003cen/9/d87uA37/B9v/23X+FfvnsDr3jFK2Y0/uqrr+bqq6/m8ccfB+Dggw/m4osv5lWvehUAtVqNv//7v+fGG2+kXq9z0kkncdVVV+3y5Mk59frsxBNPPMH//u//bpdQazQa3H///Vx44YXpMq01J554InfffXfHbe6++24uuOCCtmUnnXQSt9xyCxBZADds2MCJJ56Yru/u7uboo4/m7rvvnrFQE3YtM4+rmsSVCXSMAh8Xd5a8blrPmuJMtQk1B6U0lrLT17bKkLRsclQeSzk4JhM7JB0yYRbHOOTI4CgLR2uy2kobdDs6EkoZC1ytcDRkLJX2h0yD5k1kdIletp+jGvcjl0RWtbc4ajn7cZcoyaQc39KpNZlA0xRFrfvULZajViEFra2ZWts6tQo103L8ZgZnst+mEEu2MRNe6xahpVr23ewe0L6NanmdnFsiuJrCrGklg3ZrWFK0VlthXIaj5To6SRHbeL+xOtUuzYsZ65ewAYEHYV0xui2D51vUGzZ+qKn5NqMNh0aoqQUWw55FLVQMeZp6AI1QMeYZ6iGMeQFeaGgEhnLg4ZkQD5+yquLpBjVVJsAjMF5a76xZSiNq7WRMENllU9dmu0ADRKQJc2IhfLIrV67kk5/8JGvXrsUYw/XXX88pp5zCr3/9aw4++GDe97738V//9V/cfPPNdHd38+53v5vXve513HXXXbt03rMWaoODg23vgyDgz3/+M3/4wx+46qqrtmsyW7ZsIQiCCep1YGCAhx56qOM2GzZs6Dh+w4YN6fpk2WRjOlGvJ42zI0ZGRmZ+IsIOY7ZWtNnEmSXvm1azZlPzxKWplY3WTurOTERaYilLBJmFQ9YUsI2Ng0M2zGKjyWobR2kcrcjZ0XPeboqxnBU16nY0uNrgKMhaIY4yONpQspOG3SGuFaauueT0HR1ixb0kXdtPxYYJowHNsloqFSStJNXCTUvV8NaMyWSbTlXFO+1vtkwWVqh0+xxUh2OpFoHV6nJM6HROyfVpJWnLNX5OreNar2d6TQMdXWsz8R8AYdkmDBVBoPADq2370CiCUGOMIjCKqmfjhZp6YFENLOphZCnzQoVnoqB/zygaAYz6kdWs7EEjNHhhSNUP8UJDJfTxTICHT1XV8HSDILaetYozY0J8E4kzY8JUoAVhoyVbc3KB1nwNnbM5aa5ru9DTf18ix/VC+EkXno285jWvaXv/8Y9/nKuvvppf/vKXrFy5kq997WvccMMNvPSlLwXg2muv5cADD+SXv/wlL3zhC6fd/7e+9S1OPfVUXNcF4Omnn2b58uVp0fRKpcKXvvSlWRu0Zi3UvvOd73Rc/vGPf5xbbrmFd77znbPd5YLk8ssv59JLL93V09ij2TEibWYCbbzVLBFl492ZFg5aObgqn4ozJ3SxscibXGQpU5qCY+MoRc5WuLFVrGgrMpahZEPWipp1l+yAjI4aduesSJTlbR/XDnCtgHyugeMGWHaI7YYoO67VFZ+alSGqbG8rVE5PMBpGv7fjfvgSk1ZLRXszk0jfqftiT/gnc8d9TrKPjjHns6CTsXS8wSexZCVjJ8S7TzGHqL/q1GNajxt6ijCIHn4jjAVeJNxMItRChR9ogrA5ed/oyP2toszUkMjSGoaRq9lSUfFhS0Vxd1GcoIqshShCpQmNhUZjG5tQhTQziS1UVFQtrqsX9XFVSmNMEP2NJMkfk34gFolYmzVROu3cthV2awzz00Jqe75NQRBw8803Uy6XOeaYY7j//vvxPK/N+3bAAQewevVq7r777hkJtdNPP53169en8fQHHXQQv/nNb9hnn30AGB0d5cILL5x/oTbVBD/2sY9t1z4WL16MZVls3LixbfnGjRtZunRpx22WLl065fjkeePGjSxbtqxtzPOe97xJ53LhhRe2uVRHRkZYtWrVrM5HmDsT3ZaTx6GlY5NKokyMNQPSuDLQaG2noiyJM7O1m7oxHZ3DwsFVxdSFmTMFHBwyoUteubjaouTYuFYkxnpcyFqKrDb0uCZ+9shbAVkroC9XI+d4dPdWcfIBThHsAQeVs1EFF9WdA9eG5cugq4QpFDClErguOA4d+zsmhGH00LptnAo7/OC2Lktet/6Ajt+m0/hJXqvkBz5Zl4i1jsdsGRuOO37LeDV+juG4Y7QeZ/z6Cdt0OE5oWuZiOs89GROaictb9xkE0fuG31w23r8cb2P8ELwAMzYaxatVAmrrQ/yqplGz8bzIBTpWd6kFNjXfYsRzqIWawUbkAm2EMNywaYSGMc+JXJ+hYdTL0ghDPBNSCXvwlEdZlQmUR6B86mqUAD+ytKkKofbTBAJjAkLVIYEgTW6J8n4j9yepL121iLvIKjbOxwvNv+MpBFvr375Y14TpaDQaEzxemczkpZF+97vfccwxx1Cr1SgWi3znO99JBZXruvT09LSNn8771sr4kP8dlAKw44Tab3/7Ww4//PDt2ofruhxxxBHccccdnHrqqUCUTHDHHXfw7ne/u+M2xxxzDHfccQfnn39+uuz222/nmGOOAWDNmjUsXbqUO+64IxVmIyMj3HPPPZxzzjmTzmWqD1rYyUxnQWuxnk0l0FLLWYtQ09pJ48wcnUMrJxJm5HBUlrwp4YQujnHoUlkcZeFami7XSsVZzoKsBX1uSNEOyVsh/dkaOdtncVeZTNYnUwjIrlDoko1e1QelHCxZhFmxHJPLRY/YXJ4KriTsvt6IUiHdpGah6vAcgu8Rd0dvwWopWBY2lzF+Ge1iZbLxxD/IJq6O1rH6wnghFbb/MCvaxQ0hqBCVLjMtv/FhXG8jTE8zNYMpWvab+nib55IIK2j6LMeLu7DFl2nC5rjWUuqt+4wzPAmT69VMIIhmEc/LbVme7jOEIBaHXoBp+BCAKubB91FWg3yJSNiFUW00U6vjD4/ijymqIw6jYxmqnsNgLUPFt6mFmm0Nm0YYZXrWQk0tMBQamkZoqPmGUd/CC0MyoUsDH095OCqDp+oEyqOuHHxTw1MaPc4dqoxO3Z9JQVuFJsRP68SlH4Ei/Q61uzAnEWziCt2j2E6j+aTcdNNNE1yal1xyCR/5yEc6jt9///35zW9+w/DwMP/xH//BWWedNaHU2EJj1kJtfOA+RNap7373u5x88slt6z/72c/OekIXXHABZ511FkceeSQveMELuPLKKymXy7z1rW8F4M1vfjMrVqzg8ssvB+C9730vJ5xwAp/5zGc4+eSTufHGG7nvvvv4yle+ApCmv37sYx9j7dq1aXmO5cuXp2JQWFhMnTgwnUiLMx5b4s1US7xZItTslnizRKC5KoetsjgmEwk041A0eVxlkbUtuhybrK3IWtDrQt5uirOCFbA0X6XoNijk6vQsrWF3g7N3EdVdglIOc8hzoFQk7OmNfvwDIJMlyS9s5jkCXuxe8n3wNMqEmFojPlXVPO9Wa02oJvcFhIlPa/ztssUNio5FmGlfl+4jfjYqus6drHEtn1Fzv1a7MDNh276VUWASK824/Smrxe8Yr1O6XYRFK5qTNLE1JxVzIUm1/ki4jhOEoWk/Bqa5LSb6SsWbEgTEtU5AmehfzOnpxuIsJBV6JgjBj5uim6jnZnotNVFvTwtwgFy0T2UCTL0RZYeWQuxcHavuk8WjNywT1kMq6zSVMZdGw2ZbJUfFjzJBq4FFJVBsa2iqQSTahhoujcAw0rBjK1vAWJjFUx51VcdSDh41LO2kCQZKVdoEm4kFWhLDpolEaWjGGcnShJewg8hq+YxhVmIturoi2HZbTHvM6I7kjW98I1/+8pfblk1lZHFdl/322w+AI444gnvvvZfPf/7zvPGNb6TRaDA0NNRmVZvKo7ezmLVQ+/Wvf91x+VFHHcWmTZvYtGkTEAmkufDGN76RzZs3c/HFF7Nhwwae97znceutt6bJAE8++WQamAdw7LHHcsMNN3DRRRfxwQ9+kLVr13LLLbekNdQA3v/+91Mul3nHO97B0NAQxx9/PLfeeqvUUFuAtMeZJa+nc3NGlqZmTbOJrk2tndS1qZVDRhexsLFVlpzpImOyFMMCGWWTURbdGZu8rViU0eRtQ8GGpRmfkhNQsn1Wdo1SLNTp3quB3e+gF+dRz90Hs3QJpliCQgFsO/rpDhVoGxpB9OxHZ4IGgjhdcHwPpuQ77jiQ23kZUzv0OK2WqFTUjT9g1HOgzd04ToS1utQIWoLKgmCi8GyxgqkgaAqwVstZOjZxXQbN9Z3EZ2JdM5HoalrgTNMKmCwLWvdJtD4IkiCdOG7PNOMDjcF4QfN1IyA0dvQ9MZGYN7ki5OJTCwLwGuSLDQrGw9TrrHQqmIbBeAbjQ1CFzU8VGau7lD2HZypZqoHF5rpNJYCKrxhs5KgHkcVtyPOom4BRytRVHU81qOhhQjzqZiyqpzautlqSeKBa2kaFaaf4IHWTqgnJBuOsa219t6b+9olgEzrhuu52lRgJw5B6vc4RRxyB4zjccccdnHbaaQA8/PDDPPnkk6mHbibcdtttcSmvpkfwgQceAGBoaGhOc9xhddSe7UgdtZ1DU4i1FZForutgQWsmATSTAiztpgHUiUBzdC4toZFT3bgmh2tcSqZERtn0OC5ZK8rO7MsoCrZhaTak2wnodnxWFscoFWoUuhsUDrAicXb4fphlSzHd3ZDLARY0wviHVoG2IqsYNOPHrA79gfZ0WkVdMC6GLR3THiuWumDHGwnHCzk/nLgvYhFlYiGVxJZ1EgF+0BRxrfF0qau4dX7x+hZxFom/WJgl8w9MU7iF4UTRFrZY4bwWUecbjNf07AJo24DnYepRCylNHRoNwoYhqMKmx4uUGy7ry3lGfTtuIWVR9qEawNaaoRoLtlroxz0+x/BoUCESbL6p0zDVtlIe7R0KJraQAtoL4e6A8h0TtxfmhmFn1lG75Oy/54qD3rLD9/3u313D12ZRR+3CCy/kVa96FatXr2Z0dJQbbriBK664gttuu42Xv/zlnHPOOfzgBz/guuuuo6uri/POOw+ICuvPBD1VHHGMUoogmF1Czg6LUROE7aWzy1NPaUVrFp+NrGiJVc3SmTT2zNIZbJUho4rYKoNjMnSFPWSMS1a59LiRQOvLaPK2omgbluV8up2AvQplevI1iqUa3QcZrP4canEP5rgjMN3dkbUDK3p4iZWA6D6YiDIRZtOTWhSt9rTM1pgyHWclxstNEHVkmGh90y0iSkW1Tya4SsEoE7voSOPCOlrd7PgD9eObq6bpXoWmxU0TWUuTEiEmcZsmvkHifSii4DRQloJApRmyyopTPYMkng2U1RRteCYqguzHbtcQTKAwlgs5F1Qh6kdKiApqOKbGisV1wtExBp4ZZXg4R7nhsK5cYNS3GPU1BVtT9hW5mkvVd6gFGbKBS8N4ONrFo4Gn62gzjK/q+KaOUhXC0I5PM0o6CMPouIYQQxTb1imGbWL82uzcoRK3tnsyLy2kZjl+06ZNvPnNb2b9+vV0d3dz2GGHpSIN4HOf+xxaa0477bS2grczJeyUvLUDmJFQe+UrX8lHPvKRadNTR0dHueqqqygWi5x77rk7ZILCs5+p3J1NK1pToLWKs9R6FmdqRmLNwdVRtqZNhgK9OLh0h93klE1W2/TlItdmwVasyId02yF7FcbozdbpKVTpP6CKvTSLfuFzMGv2wvT0RK5ML4zqZlkuaCcSZ4kgc0SQ7TASkWbGLYOJCQG0Jjgky6fYd4tFTLVmfE4Y18E1Cp2FRGLZa01GSLCs9vdhLOASQafDlr+AWHgGpnn8IC5rHCbiLF5vTDT1ICr/QWK8CgzG1xidJwzymAKofEipp0axVgNvlAOsrZgG+GVY93gPQ/UMT5bzjHiaMd9mQ82m4sO2WolKEFALAgZNmYaqU9c1ynowatoeDMfN2T28oJomHTQ7GvgY1dLJIJ6kiq/ZXN2h4gYV5sLXvva1Kddns1m+/OUvT4h529XMSKi9/vWv57TTTqO7u5vXvOY1HHnkkSxfvpxsNsvg4CAPPvggd955Jz/4wQ84+eST+fSnPz3f8xaeJUyVONAq0hI3Z5oQ0JIgkFjPbKvp2syqLjLkscnQE/aQwWGRkyFnRxaEgRx0O9DlBOxTqLIoW2f1qkFyywzW0iz6+CMI996LsLcXsCN3ZtWAnYkLWtlxSX0RZ7MmdV9OIpASq1liwTJTCLNkVZC0amiNi2vGqzVF2bj1rXFprVmn0O7yHJ8J2jovv2V90EFgts691TXaeszxJTyis2w3OFktfy1BiEli5IKoyXxbokKgUEEc4xYYTBAJN5XLR9P0fHS9gttVYe/uIfxR6PlzJNiG6i5FO2rknrU0ZV9T9W2cuqYS5qiFDZTWNFQVLAiMF1vZNGHoE6ik20G7la2ZeKAxxgel0/jDSct5CM8S1IQuKs821qxZM6fY/Jn0+pyRUDv77LM588wzufnmm7npppv4yle+wvDwMBD5Ww866CBOOukk7r33Xg488MBZT1TY8+hsRYPU1dnWvslpJgbEsWe2lYtcm9rG1ZFLM6ujuLOcKdAddpHXDnnLYnHBpujAijz0OAG9ToP9e4fo7alQXOKRfdEA7LeG8HmnprMIq35kNQsy0VxcBe7OujrbyWQ+hjbBM0lw//ixneqPdQjgbwv47xQ31rauJWC/kzUrOVYsZlKBNVnigGlxS4bjBFCyPjmXtjg3msvHj20VXUHLvtrE5bi4s8TSBS3FflvEIjStbp0Yb0nqVKg4aVaqNSoVqS2iLdlVy7GMH83NBC3HDxxM0EXodWEyATpbYVWxzN7WEEorGlsNlUGXRzcsYlvDZahh80TFZcx3GfXybK4WqQQBW8Ox1Mo2prfgm8g12gjHCEMfL6ymrlGVxLM1U2int7BNY12TmmvCQuG6666b03Z77733tGNmHKOWyWQ488wzOfPMM4GosXm1WqWvrw/HcabZWhCaTG5Fa4lHa00WoN2CppWNY2XTVk5Z3YWtsnSHi8maHDlc+twsBVtTdBQr8tDtGPYr1ujL1FjcVWbg8Dr26i5Y3U/4kuOjoP9KHXQmEmh2ZuEE/qcZh/5EK9T4Yq0QZ+JNtb+W1x2FUifhMtGq1fa6Zf2ETMhWWuc6vhjthP23WLuSBIPxY1uzLZPEgU7HTjM3EwsZLYH+bQNbrHRBm/hqnkLr/ExT/CSuydZzaBVmiauyQ7+qjm1ox41BKbCjTgSt3SXSY2rV/i96i0jAJeevm3MyBpSvsFxDWLcwdgmKpSjGplrB7i/TVapyaM8Gtm0oMFTOkrVKDHkWQ57G0TZjnoVVK7Vb2XSVhqmglMZXUQu+wPiouM1WGPoowknrsKlUrMGE+DVhtyX5c3s2c8IJJ8zbvuecTNDd3Z2moArCTJnKkhYta+0sYKVuziRRILGk2TqPrTK4KkeeXnJhgV7TTcGKrGjL8hZdrqLLMexXaNCXqfOcFVsoLvNx9s6hX34M4Zo1kMlDxUPV6lFZDatDuYydhedFz4kgCwNw7KhvkAKCeouwMNEcbTsuwRa/DkOM3xLkPhnpfjqcZxL83mZNG7eMuPRCuk1ToJhx48Zv17EjQQexpsIwLotnwAqZYN1rLaxrxaLLbhFfbaIpjIP9g5bl4zKvxrtLE6E2Tngm8WJtrsxUJMcWtTSWDEw9ygBN6+MmndqtyGocNlTUzsmyWoqS0SytZ8XLdJxsYKnOXSo0GE2bkIsC7+PrqWKhmiQsWCa9LNgQ+gbjaSgWMX6R0A5x+0ZZtqzMksEyuT/6bKtm2VzL4mqXoYbC1i5lz6bsO2hfUVV56qoACnwrEmra1NPEg6QWGyEd6rCFqTu0o3VsmkQDSTJY2DzbXZ/ziWR9CjuNyZIGWrM6o8boSSxaJNBsnU3roGWsLhyVp0gfOVMgG+ZYbBUoOBYrChZdDvS6hgNLFfpzNfp7xug/FvQ+iwlfdXrUjglFOFIFVQDPgnwOk5/nkw+CptVH69jCQTOjMAygUUHVK9BoNIXiljLK86DhQbkSiblyNWpR1PChXI9cW0GLNSmt17UddLKajc+ahLYfztSo1unY4UzHJWNaLFvpwVqsWWHrfsa9Hm/ka1kfBd2rtvWthTjTZWFzeRi0rB9XtLO1obsxirCl+XryFbftEG2FaMtgOZFg0o5Jc2SsDKnxyIRRUorO2VFyiraiWnq4mDjT2Zio+gtE22lHpYJOWWC0Qtm6ReC1WOCIXreehXKji6a8sOkeDQwm1IT1bozTjbJrrCqMsLcaAQsGH3LYNpLnocFutjVshjyHJ8vdjHmGMS9gk9dLTdUYtLfQMBU8U6EWjqSlPfwgcokGYQOIXKPNj9pvcYW2CtJQxJqwRyJCTdgpTBRpnbM6E5GmtRs/2zhWLi2xkVO9ZMjTHS6iQJaC5bA059DlKtYUDIszAf2ZBgev3ERppYe7XxH+6i8IlyyJjlHxwVhQ6N05lrOGB4TgN6IfTr8elVHwGlCNOg2oahVGR1HbhmC0jHl8E1Q9TCMgGGxg6oawBn4NjK8IPEUYaMJQEfi6rdE3gEmeJ3rYZsyEKhUdqoq3ipbW9VNVIG/TXh3GddpPaCaOD1FtcwyN6rgs3RcqNny1L2vd/2TLQ6OmaPig2s7LmKadMf1GK4NWJjZ8mijUDIOlDLY2ZC0fW4doBcVMnUzGJ5uvYzkGyw1JWtRqW2EsB2O7kHHAcVGuS+BZUXcDbVAWKK0wGRNZ35SKnqEZ30Z7QXLj6MjiBpEF00TxbCpUWDoS/UZnIZvF9wIYG6X34DG6x4ZwHwzYUsmzuZZBqwxDDc2Ip6CcY8x3CENDVeVo6OhfQr6po2OXaBh6bZ0OIq9w2LyaE1yhNM9Dyn/uVsyX63NP+RqIUBN2OFO2gIpF2mQJA4lAs60ctnaxdIa81Yer8mRNkcVhP0Xtsqzg0uVGvTb3L/ksydQ5dO+NFPcKcZ7TQ/imN4HrRh6kwVHwnCj2LL+DhVkQ0AxSj61lfg0KOcBHjW2G4RHU8Aj85lHC0Trek1X8UaiP2owMZ6n5NnXPZrjh0gg1Y34PjVDjhYpaqPBCRWDAM9FzXF6rWaUhvt5+iwpqCfuZFS2hT21Mdz+c6U14/I/uVNu1ruo0LvVgTjKmTbRNsWzS48eDpzu3ZPVk47Rqfg461Uwq9Wo6iSdUgasVtoasNtg6Wpe3QrLakLVCFrkeWStgSaFCsTCE4wZke0N00QUnR6izqIwbibN4v8qJRB62ilpWKcC1UuGmLB118HKt1HXblowQf+lMw2AFNqbQQ+j1YFSFgeeMstLdiC5oDv+Ny+bhIhurOR4azbKt4fB0OcOYFzDqe2yml5quUFcVynprlHTgjxCYuCF82MAYnzDuLxolG3SyrrUkGewpv9TPAp7tMWrziQg1YYcyqUgb3w5qnEhL6qJZsVBzrCy2zuOqHAV6yZoCxbDIYjtHl2uxsqDoyxiWZAIOWzRIf/8ovSd1w/6rCY84PBJMtQACG1NcHLuPdiChiUpC+D4YP7KUFbPgVSBsoB98GJ5aT/jgeoLBBv6QYWhdlnrdZkt5CRXfphpYDDZsGqGmHirKgcILoRoo/BB8Y2gE4MeCLCqdZZKi9gAExowPoSJskTh6FlJtvNVtut/AcBL5NtPfzpkKpbZtxlv6WuYwwQrYYX6drtGk84sHz9SVNtn5tDrvkr8PrVT0zxUFVvza0gpHKSwNjlZY8bqcrXE0uNqmy3HIWobecp6sFZK3AvrX1yhlGvT3D2PnBiPLWy6DsXLofBaddwk8UFZkccNWmCBAOZG1LakRHE9s3MRVFNtmKSBILW1ahxidh0yeoNHAHxli4Mg6izZvof+JLNDP5roD2Aw1FPmGBTUYC7NUVR6jQxqmgrECdFzaI7rmmiiEzY8/q07WNUkyEPYsZi3UzjrrLM4++2xe/OIXz8d8hN2YmYi08VmdiUhTqj1ZIIlFy6gi3eEiiiZPyXJZlrfpzSjWFj2W5RoMFMqsOXIY54Bewr85JQ6ydqEeRsE/rr3j3JtJhqUyEDYiy1nOhXoDAg/94J/giWcwG4aoPzhCfVCzeUORsXqJsu+wpe5S8TVDnkUtVDQCGPUVjdDghbGuDA21ICSIBZkXRsVMA2Pw4wCqIBYOIc2Mv5D25/HMRrDpuPLVjC7JLOOBzCx+YDvtu/Oy5j6Nmnz/rduGM5zHTOcbthy3dd+6TaaBjrMfkyxIbTSWsdDoSKxhYaOxlI7ynZUiqy0crbC1ouBoHKUoOjZZy5DRDr1uhrwVsmy0RMH26M7WWbpkBDtXw5TB2IrQzqALOVQu6kGrHBMJOluhMlFAnbIjF2hiZUtxFCYIUY6Oq2pEyk7ZBmUZjOtickvwGh7W4hEG+itw32Y2DxcJ6WJrw2JbXWPIkPNsykGGwPjUyBBqD984KKKYNRVb09Km7wAEJA3fJ4g1cYPuNsxPMsGekaAwa6E2PDzMiSeeyF577cVb3/pWzjrrLFasWDEfcxN2EyYItMn6dAKkZTYyaUanbWWjGDTtkrV6cVWObpZSDEsUVZYVuSy9GUVfBg7trrMyX+GA52/BPbAbdfBehEcfFVViqgWROLNsyOyAP+DQRBazRh0CD7rzkUCrVtD3/gqe3Ez9d4PUNmvKIy5PD3Yz0nAY9RexsbaEcqDYVlfUA0M9hDEvxAsNZd8jMAbPhNRMA5+QQPl4eIQqxKNBGNeY8pUX/2AFqQgxNLMVWwXCZKJCjRMLU9EqLGYqZqY69vZgzMz64U02z9br1L7f2c210/4nXKcJbte4BpyauryLTv7BEqW3YsUJAxY2Ki5NYxkH23ewcMjUs1jY5EwGBxtHaXKWTUZrutwsOTtH1oLFT/eTtwwlO2Rlvk7JabBmxTbcwiBWl0to5fGzBZTjoKP8Gqxc7H+1QbtxX9q4HEjkHlWxa1Sj3NjamHRICAzUXEK/D7/eTc/aIRazhedUt7DtqRybRor8drCLzXWHwYbDk2PLGPF8NtBNTVeoWWOM6U34pk5djRIaLyqeG9ai2msqJAzrcVZolHhgRKwJewizFmq33HILmzdv5utf/zrXX389l1xyCSeeeCJnn302p5xyitRU29MZ7+JkfIcBZ0LJDdcqYOkMjs6R171kTZG+cBEly6XLsVlZ0PRnQpZmfZ47sIW+1RUyrzmA8KjnY3J5GKlBtgRufsdZzzwPGlVQPuQdCBX6kQfh0ScwT29j7L5RKoMuT25ewojnMuJZbKg5lANFxYetdagHhpFGQD0M8UNDJfTw8KmrBh4evvLxVJ1A+YQE+KaOISDAT3/oA+OlwiIRDK0CZjYCaTaCbabMh0Br2/8MRdVkomyu+5vJNom7rtP65LMab1GbbD/JvlRSkiZtmWZhqUi0WXHdQEs5uCaHhYONTdbP4WIz7GXIaIuspRlqWGQtRc6y2dywKNlZttSydLsNerN1lq8axsoNoTIOvsqjSwXAja1kUbilskKUsTBWS9bouGcVl/hQVkhIiPYA2wFrMaHXg84Ms6S3TO/WKt7vNRuqWTbWHLzQIt/QBJVuRsIMjnYJtEfdjBFaHmFo41NPW1Jh/Li/bxAJtLRn6ESxJpmfC49O9ZuFmTOnGLX+/n4uuOACLrjgAn71q19x7bXX8rd/+7cUi0XOPPNM3vWud7F27dodPVdhATJtyQ0gSVtLCtYm/Tm1jto/OVYRV+VwVJ7ucDFFU2DAzdHl6tjN6bM8V2dlaYzlx3voQ/cmfPFx4FuobVVMd8+OyeAMDYQeBDVQARQc1LZR1JOb4eHH8f73GarrFaODGR7buoyhhsszNYcxX1FuEWe1VKAFlE2dBj6BCqjqCgEeDVWNBFgszkITRgLNRAItyYQzBKkIGC/WWplOfCQiYCbM1aI207nMBzO1vMHchWVyXlNdy7mee1OkNcVZ63IraZuGhaUzaHTaKk2jcaw8jskwZgo4oUMmcBn1s7hak9Ga4YZFztZsa2Qp2Rm6nQJbq1l6snUW95QpDgyjvGGCrTaqWEAXSyjPRrsKYwKUrVvqt8UCLflbtxRGGwjjTgkqKvGhjSHExuT7aFR7sUrbOOTAjSx5ssgzoyU8U2BLXeOFGbINm9HAxdc+ts5gCPBUFaWimmsK3YxdS+LWFC31VybGrHUSa5OFZoio2znMx51hT/nktiuZYP369dx+++3cfvvtWJbFX/7lX/K73/2Ogw46iE996lO8733v21HzFBYQk7s69QQXZ7Q6aQFlY2kXS7tk7C4snSGji3SzlLwpUgzzrMjm6XE1+3cblmV9lueqPPe5G8kc1oU6bB/Co44grIdQ1ZDJYhbltu9kggDKZVSjglncBfUyasMG+MEv8Z6usOHBAoNjObbVMzxW3ocRXzPqwaYaVH3DUMOnEYbUTcCYqeIpD081qKgRAuXRYIyQMK4fVY/b6URWspAwLVEA0Q99s8UO6bLW585MtW5mIm0+BMj8s5sIyWnm2WrpnPg5tFjbYktb8x88kfXNUjZaOWnPW0s5uBSxjIPjZyh63TjGoVvlyFoWectiUbZEyemiYC9m1aMBPa7Pvt0jLOkbI9s7DNkcodOFvSiH0iHaAZXTzQQEO/qHkbLiv3mLSMT5Ouo76ihUzkDdYOU1obcY7C6WFraysrCR/R60Wb+1i/u2dbO57rC17vDEaIbRwGOD6qJml6mbUcp6K4Gp44cNPL9MaJruUFQQJfIkGaFJGECLZW0miIATFjqzFmqe5/Gf//mfXHvttfzwhz/ksMMO4/zzz+dv/uZv6OrqAuA73/kOb3vb20SoPUuYaSZnpybq0Y+Km/64OHYhTRZwVY6MKtEbLKKksnS7DisLFn0Zw8FdVVaWRhlYMUr2NfsSHn0EptSN2lrGlHogt50u9iAAr44qD2KKWUw+i77rF/DIOrw/D7P+t3kGxxbz6GiRIc9i1Nesr0LZg4pvGGx41MKAEVNpijM9QmA8Qjzq4VhadsAQRPE2scUsKe6ZiLKJYmyiONuRLsZOLtC5hPjMt9tzLrRes1bRs/MF2mysfAnWBBOBUhpMq7UtumVr3az0H2gbhcbWmfjvzMHX9Ui8KYeGruKYDHXTTcbPkPEdKkGGrKXJ25pRz6bkWIx4i+gbKbF4fY399t6KW9pIMGJBoYS1qIQ2UXFdYylUlkiw0bSuKfX/t/fvYXJVdaL//15r7123vqY7SXdCCAERIYDcwcjMyECeiQ5HRDjK8YeIjgcvh6gYH0Qfb+c44+CoIygT8Hi+CjMOCoNHBcWB0XDzwuUQjMIA4RYSSNKddNK3uu3r+v2xq6qr+pa+VbpCf1556knXrl1Ve1VX7f7UZ631WQpjEye8jCrVBokgjJeqUnYC4y/D3Z+l89QhWnf3kd9is6uQotWx8UKbtKfxix1kVYq8ShJaAX6UR1Eg0n5lsoEhwFAq41GZZFCeUV4bsM2UdKHOrcnqK4rJTTtQW7ZsGVEU8Z73vIfHHnuMk08+ecw+f/mXf0l7e/scHJ6op6l+46y9U+1EgZHgrHS9NAatOjirromWthbhkKbDLKM5aqJVJzmiNUFnSrE4GXF6xyArFg2y/MI0nLqa6ITjiXJ5CCxwLUznkpk3OAzjyQGhCymNGuhBbX+F4OdPktuh2LpjCX1uK/vcTrbnNdkA9hQiCkFEMQwYjAoUlIuvPLJqgEC5uGSJjE8Y+YQmzpiFJiCKfCITEJmgKhCLA7TqQK10YDWHOTaomIsgY+7Hp01susc722ObYDJBI/yNnUmAWBNgln+qXloNwmike1SF8RcjrzKTemRNXKUsbJ1AKwdHpyvr46ZNG46fJOmlaMm1kFY2TyeSNCdStDgpVva10ZGIOLZtmK62YVo7B7GXpAmTLahUGp2I4uK65SxbeSWEUpYNK55nXFksPgKVCjGhwWpuIRhuwqQHOO3sXk4zhqGXHf7fy8vYVXR4dqiZfreJIa+DnUEbeZ0jbw2RtfbgRwXcYJgwcuPsWmlJKmMiMKXi0qYUYFWKwY3+nUz9jVE+R0rANjsGKagyG9MO1K677jre9a53kUqlJtynvb2dbdu2zerARP1MO0Abb01Oxs+glUttlMegaeWQsJpwdBpbJWmmk4xpYQlttCYStCc0RzTDslTAYekix7+hl/TxaaJ3vQ3SaXAN2G3QlJxdo10XwkI8MQCN/vm9RC/sxX3Z5YWnO9lTSPPccIoBXzHkQ0/ekA9C+gOPovFwlUdWD+IrN56ZFmUJjFv6g+FXgrPyH41ygGaqA7VK9iwOzEZn0MaY0yxQ1WNNY8zaAc3JMb52T+Ez+gNfNeZu5LNafg/pmuugMaVMnFIaZeLPolERkQpK47v8+H/LRysHX9kE2q1MTHBVkaRJ4bqtZHyHJtvGDS1aHZtC2MriXIYl+1xW+3tILcmjPAc/WoTVnsEizpgZx6CTcfBYvd5opVu0XEjXimdwWk0anVyEv78FXdxP2+sLHJ/fR8dgM27UQouj2VdU+LkWhsMkSZUiVD6WdjBWRKhsgqi8wkGAIRgp5THZElQw/YKBSMAm5te0A7XLLrusHschDoIpB2jjrjc0qswGVE0ScCrjZ0avLKCVQ9pqx1EZUjTTES2lWaVYVlVy4/jWIiuacyzvGqTp/BWYNadirCSq38U0t866WK3a8yomqSGVRD/3EmzZyvC9e9m/p4nebDvPDjaz39fsyEHWN+T8iD7fpWg8hvQQvnLxVAHXZAmiIqEJCKI8URTE487GBGcREI3q2gzHD8zGCXTq/cdAzVEAKH+0pmsGY+pGZRsrv7tKYf6I8YI2rUYmP0TGrhmYr7VDaOKuUa0cQu1jqySe8kibDGkviR9laLItvEizx03Q7jh4L1os3Z2na/EwTd17CHc60NmObm1CeQajiDNplopXPogPoHxo8coIgUaVJylYCnuRjfGX4g0VWX5WPx3beyk+bbOrkKTVsfAih5RnkfJtfDzyOkVEhK/yaG1jgtrJBuXXq7LAO1EpYCvXXhvn9zCNwK36/Cfv/+mpyxJSc/+QDUlWJphn9fymdsDAbJKAbMxj1HRvVmfP4uAs/gMQl9qoDs4SKkNHuJRm0jRrhxWtDouSitWtPsvTRVa0DnPE+Qb1xlVEZ5xP5IeoooFEKyYxi7b39WFSGjI2atd21JYX6Ls3R09fKz35NM8MH02/pxjyDLvzIfnQoy/MUVQFXFUkp/rjrs0o7tqMjI8fFiuzMuPlbmozZiMLS48TlFWyajULI828gbOwUE5uDWXG/bBV3eKqas3RmscL4/FUhsrnNDR+6S4jY0XDsFi6Hn9WyxMTCno/lrIZtpqwdRJbp+gxi0h5TbxSXERGOTTbNs8MZmhLNNGxazGrtxXpzuR5/eq9mEI/YaIN7Tajk1G8UHwmjAvklicdKDUyhq1UyBdjUE6cQLTSaYJsEtWWZc2f7yIqGPa9nOaxXV3sLtr0uWleHFrOkB/Qy2JyVo6iypK19hBELkFUwAtzpdpr8ULv5S9P8aSDqoDWTBZsRbXnRQnaRAOQQK1BjA6qZvPBP/Bam+MZ1aVZuTKyYHr1Sb+cPVNKY+sUWjuVSQK2StKkF5MyTWTCJrqtVpodi9aE5sgW6EoGnNzZT/fyIZqOteCS84k6OqFowG7CdM6imzOKUP098QzO4WH0H5/Gu20zgy8n+MMr3fS6Dntdi+1ZGPYjsn5IX1CgqIoM6v245AlwKUZD8UzNsFCZBBBG3kjpjHL2rGoywIwCtIM5kGq6K7NPx1y0YzbH1xAD0uqs3EalqA3y9cj7y4Slz2+cURrJuMXZtspEkijepkr7RNomIiKIClg6Gdc0U2lCHZAyGYb9FIFpYsCz6Pc0kUnR6zqY/1QsXzxEc9c+ouEBglQ7ztJmTMFg/Ghk0kF1LTaIs2sRqIQVr41mRdhodKoFv78JS/Wz9MQsb3T30znczK5CEj9y6Hc1JtdGNkqTLS9FpfL4pYkUQeShQl3JbpcnH0QEVIYcMBK0qTGfz1FZN1lTdM7IKzhzEqg1qOl+WzvwzMyyCTJmUBOUxXeNuxx1dR20quyZo9NYOhmPQVMZbJK0sYRUlKabVpoTpeCsGZYkQ5alCpxyRA+tR4U4/783Ea1eHbdvIAeuDYnkzGqh5fIot4jpaAbtoXa/gv+NRxh8KcHufS1s2f869nqaF4dhyAvJ+h57o2HyOodHgazeR2CKeEGuMt4sCAs13ZkQjZoEEFLb7cTUsmYTnPCr7zOjSR5T0eh/bBr9+ObAbDMvivGq8Ic1n/P4Vl0Z66aIKtm2+K4ao/zS3lbVhITyqiE2RT2A1g5Zay+2SuLoDH1RJwkvTabYxK5c/OXrueEOFvUuYskLIad0DNK9eAjbDOAnOzBOM1Y6wEopSCh0amT1+XjCgQKn9G6PDCYRokKDToZEbidRvoVlx+1jhR4C4LgnFrMrn+GpoST73ASDXhM7821kI4+8KtDv7ME3efLRQGXsqB8WSiU9vPE/xyr+HMevXvWXK6v2dzXe0mRm7Gf2YGbWpnKeaJRMnwEimfU5YxKoHQImmyY+7of1AF2aY4OzkdvLWTOg0rVZ3VUSTxKIs2eO1VzKnnWSNBmSJsViFpGxbJY3ObQnFIsShhNaiyxvytO9eIjOt7bAya+Pg7RIofrzmM6u6b8oZWEIQQHTaqO2bUX9v6fwn9rLc493siuXoaeY4NlhzYAHu3LeyEnd6sM1wwRmJHvmh8XK0jU1kwHGnQRQmzGD2Qdok22brboFf1XqlgWuk3r9ERuvLXP1XOXHGfMc1e+vURm38rgtVZXtjbeVC8WWs22lDJSK3/O6FNCEpQk0Roc4KkPRykEIzVEaSDGQ0Ax4Npo29hTSLNuf43Wn7iUqDhHRCSRQQXkNUYVCY7SJJxxUj2FzdLzSgTFoy4CTxNjLCPbnsfx9HHN8H0t3pohYSm/Roc+N1/Ud9m2yfhIdKQqqgNIWvsoTlBZ7j6IAv5w5NAFEuvQalF+H8hev8utHzetVO74NJsu21aOkx2w+G1Ji5LVBArX5Nl5QNc4f9ZlNBJhOcAbV3ZrlZZ5GT/V3rBRWabZYWi8ioTK0RZ1kTJq0sulKpWhxFEe2wJJEQFfK4+SVvbSu8kkct6g0m7MZhl1oacd0Nk2tXePxiqjhPZjODlTfXqLbHyL7bMTunjb+sL+Nva7FPlexIxuQ9UN6zAAFnaeosuSifZVZm15YyqRVxp1NPUAbd4xLtWkEaPU02fNN9w9BPY59qo95KBQnPRjHMmHABvF7ruY8UA7GqgKK8nt5ou7RCCLiLyuhttGRh7EiPF3AV3mwoGhawO0g6ztk/XjM6n7fYk/Rwd4SsWzFEHZxF4HdjN3VAXa5zloYj1fTBlXOoJfWE0UZcEqzQ60I24CVzmD8NNFAls7mAU4xe+gZaGF3IY1SSfo9iwHPQuXbyIUZtNa4qoCnC+RLNeVUqCszs0NGDWGoCdjAlMb3jQ7YRl73cbpHx8muTeX3N565/tLSKMHa/B/BoUsCtXkUfyBHl0qIDhy8TTVjVlOGYWzGrDooK49X0VXBma2TaG1j6wyWsrFVioxaRMKkSZoUnaaNjLLpanZodaDVgWNaApYkPU5c2UvLkSHOsYuILn4XpNNEw3lQGSANLTNcUcDzwStCcwISAWr3bry//yn7tqV4dOeK0sBjzYtDIUO+x3DosVf3xQOPzR6C0CU0bqmbM86cRaUAbexkgANlzWBMYAaTduE1wglztEY8pokcSsd6MEy4TNLo9+CorrvR3aMQxoPsS92jUel8EBmv0i0ahAW0tikoh4I1gKPTDJTW5k25TfQUO2mxHFoTFi/llrJk9xJO7xxkxeJBWq1XCLLt0NSKshVWkw+OQieiePF3zciwB1sBFiphoZIGoige72a1YMJmWoMc7cV+Vlt7Of3VJLv2lycIJRnwkuzKZ8j6IfkwZJ/JUtQFhq39BKZYKa1TrnnohwXK64mOZNHt0msU1XSPxq8vVE9KqLz+43WN1rzgU1vO6oBmMo6zAYYU1Gutz/lv2cEhgdo8GzsWbZwUOxyU4CxegzNeS7AcoNkqSVLFXZyOSdIWdpAmSUY7LE4maHEUK5thkROxOBlwQud+OpfkaHtrJ5x8DNGRR4IXofYMY9o6IDnLemjFIWh2UD2voB7dQvDH3Tz/p05eyTbxzJDDfhf2uxE9bpGsKZDXOQbpwY/yFIOBymoBQVisGbMSn6DL43ammTUrO8AJUYIMUW8H7Bodk2krizNu1eU/4rv4lW7ReK3NAK3KhZvjz4uv8riqGY0mH2TIhykcnSQbKFJWC/1ukhWDWY48c4Bof5YotQhlZ1BePKxfWSYu6VEVsFXWEnUUxiiU1igToHxDYlkzUdBM0JejbdUALV17WbE3gfPqUva6CRxtMeTbZH0Lp9BKPspgGRtXFfF0IV65wcRj2LRy4klDyqss7VbOrCugvALCyGtV20U60j06Xm3CA5y/xztf1HPCjzhkSaA238qBlYnGmfk5eWHSqY41Kwdn8c8jQVl5UoAu1UGzVLxIerl6eTlAazbtJKMkKZNikZWiybZochTLM4r2BLyhuUhXusCStizdbw7Rr+smesc60AkoBJBwME2J2S2a7hVQA72YpYshl8P86NfknnXpfaWVJ/vb2F20eCVnGPAihnyPPrWfvB7GNcMUwv44i1aavl/JolW6P6oCtDkOzsZ/LCHqa+pj2cpqA45y1+hIkKLjoMrExXRhJFALdJJAuaChoJopmBbsfAc53yahbYZ8zX7PIfmHgCVHZEk09VF8MYWzbBFaO3GXqKNQdjyGDRhbNFcZNDbGjtAqQvkRenkT/mAzOp2ltXmAU+0eevtaSOg29ns2/Z4ioR2GfRvH1RSjgAIegzqJr118U6SohkoBW6Eykag8EzbOspeXqSrXRRw1pk3F18sFdmt/BxOdvycovDupWRSpVlFDZNVeu2Wt608CtfmkFEoliWdtjWyuTbNPdNfqD641snDzqGxZ7Ziz2oxZeayZrZLYKonGIUMbSZPCwaHNNJNSFp0phyZb0eTAYWlDRyKiI+Fx3JL9dCzN0/yXHfDG1xMdthyamoiKIehmQMNs1kwPQ/A9SAGJEAo5wi/exeDzdmW5mb2u5oUhw4DnszsYIquHKeosw2EPgXEJwlJ9JRMQhsVRwVl1McyySU4n0zzZSYAm5tuks4nHfT/HM0ir67WVZ40aFVA+r5S7RIOwGI9h1TaencUqzRAdVkvJ+C3s71tMi+PQnrDYkV/Gkl0hh6U9Tjt6F8nWXfhDLej2dpStsdKlxdwTGqXBaB3XYtMqLqSrFcrRmEQEocGEBp2KMH4TRE2kUwUOXzLMUdbL+FnI9yf4z11L2Ocl2JZLMuSnGPYNfcXWeFk4EzBg8njKJesM4psiAS6+yVcKWQdhgYiosupIdRfpmKLWqvT6VV676nNJ9ezwOciaTXV1ERONygiKQ5EEavMq7nas/uZlTIRS1qT3qg7KRraNLNZc3j5S1NLCKp1MLZWsXC+X1EjRjEMSxyRoNk2klU1K2yxK2jTZiq40tDjQ7oS8rrnA4nSBjkU5Ov/MQR21kui8v4BECvIBmCQ02cx6/cYwgOH90J6J66E9/DjRk6/y8v9r5dXhZp4ZTtLnQr9r6Cm45IzLgO6jSBY3yuJFWYLII4zcUldGUBqDVhpvYoKqP2Izz5ZNRII00WgmnYBQs2NtvbaaLNvoLlGjCUvBihfksHX8OdNofFXEIUHRy1AMEyR0giE/zrCltnWxvG+YFW/KEfblCJ12dHfLyEAmq3TRgFGV7BpaxYGOVihlIEVcNNcHqy2Nbk4T5iKs1BBtbcOcmtnNvr1NNFmL2O/ZDPiaJtsm6xsKoU2qaFOMAhJhEl/5uKpIQQ8RqCIhAb5KEhqfULtxkKYjwsgduxKJKk88sjGV88lId2n5fFheMaH29gOZTTYt7jJuhLNRXZJ6jdCwg0ACtXmklIVtZWq2maoPb/Xi3GMzZrVdmlo7lUyZVg4Kja2TKCws5eCQxlIOKdNMwiRwcGiOMiSURYvjkLIUKUuxOKVpsg0ttuGIjE97wuN1S/ppWVwkdZjGPucYzNEnY5Yvx7guZItABqIkZGb5TdH3oVCElIYE6D2vwn9spe+n/ezc08ar+W6eHEyyz4XtwwHDoU/WFOmzenDNMLlwH6FxCSIPvzRZwJhypfIQY4KqDNrcdwdIcCYOBVOuy1j5fFRl2YyheuJBEPrxqgeRRaQ9/NIMcd8uYOsEnpXF0RlSppmBoW6aVJI2x2FnoZnOZDMn7i9yWHOO7sWDOIksUaID4yRRGnQyihd9txRGqbhbVKnSZANQTinjBhhj0Jnyou8K47VjwnYSmTxL24ZYkdmBCcAfULy0rYN+N0W/Z/NKIcGw77CnmKYQGAqBYcD3cU2IZ3yyKoenPYoqS2j8SsatPNa1Mq6tVBR7JGNfWwy7enJS9XndTDPTpaYZtMVZQErB2vydn+K8rIy/mykJ1OaVxrGbaj+4VQsyVytn2ZTS6KqMGYAqLedU7sZUysKilDEzDhYOKZPBNhbNJoOjNAkdL7qcshSLkoomGzI2LE8FtDoBHQmPo5b209zpkjmzDXX0KswRK4iOOALQ4BnQrZhFHbMbewZxgBZFUMzHqTtt0P/+a8Ind5F7NuTJV5axq5Ck17XYkYNBN2RvkCOnCuT1MLmoj8C4+GGOIHKrClyWS2yM7uasDdJmW2xWAjTxWjMm+1Yp+TFS6qM2wxaP51ImXnRdhfHYrqIaIlAuofbZr5O4pomil8bOZRj2NQmdYsC32VdIcWrLLlKH9VLcm8Za3AHGwlIGY5lKcVxUXIcNXRrHZo2sQWwSxPXaABxD3FObwWrLEOYDVGGYRFeWYzL78LOKof0pOvoWMeA7vOI4DAeKXKBpdnXcNRomGfaTeFFIVjXh4+Mpj6LOxucblSckDtSC0ixSQxhn3krdpfFrORK8VV+vvNYzXHtXTaH705gAoyIMxRk9h2gMEqjNE4VCKU3CiuuI1X7LCkftOzIGTZdXC0DHiyqXujZtkig0STJYOFjYpKM0jnGwsMgoh6S2aHasSvasM6VIW4auZESLE9LmBBzROkRrS5GmJR6Z09vh6KOITjkJk8wANhQM2A4kZrdQekUYQuCi8v2Y5gwUcuhHN5O/ezt9rzSxc6CFp4fS7PM0+4qG3rzPcOixX/dTVFlcE3d1BlEBPypUjSEZ6eocE6RVXueJi80eKGCT4EwsODXBWjxYvrYe28gM0TDSpWEcmkj7hMZHWxpPt1AwTTiuRSFwSGqbId9mwLdIPdfF8t5huk8tEOx7lUA3w9I2lGOjnNKMeF36fFrxElQoU/miWKnDBijbYGyD1oBvQNuQWYQJF2ESHolFORYvzdO6ZzfD/Uk6+hYx6DsM+Ta7iha5wCIfGPpdTTE0DPkOXhTiR1ElaCuoHL5yK5m2yPjxMlzaxZiQSAeY0lCLeBmrcsBWvZTVzAO10cYL3KKoNEN3/O//B1U9uj4XyllYArWDaPQff0s3sdQ6Bpg8Ba7QWMRBmW3icWc2Fo5x0CgsY5FSNrbSpG2LhFYkLUXGVqQsSGhFe8KQsQxLkgEtdkiL43P4oiGaWlxajwO9JINavgiz5lxMaxvYNlH/MFhJcJpA2WBZc/eO8XzwC9DkgHZRL7yAemYHe/89T+/+Fp7YFy/7tM+FHdm4Jlo28ujTeylaWXJRH35YGClYGwWEUbGqy8GvGvBbPktNvbtTAjEhxsk218wajWomHWDKAVscpGllE5kg7g7VdmUdUUenyekB0lEr+wcW02IlaHVsXs23sbivldV7C3RnCixdNExnx25CN01ot6NcO+4STRuMpVAW8SoDo8t62Co+WgdUIi7eq4IIwqi0AHySKEhggkUkWj0WZfOcEfWh8CGC3C6Lwf40g4UUu/IZhgOLXjdBIYB8qOh3myiGhqwf4UYRgTHkI5+ACJ8AV7mEOsAlrtEWKD/OuBHGmbbSbNmQUsatKlCLxvk7UN3LcqDxy7qqazQiHk9XDAbwgwGo1ImcHzKdYeYkUDsIJsrO2FaKJdFSIB6yO5ou3S8OzDRKKZLKioMzpUhYGkuBpeKgLKHjmZkpS5HQhnYnDs5SVsSSpEeL49PdlqWpxSXdEZJc3Yzq7sacfgKmtQ2TTEEiDV4IOaBpMVg6DtDmShhC6EECSGjwCujb78bfOsDQNosnd3bTW0zwQtZi0IvLbexxi+Qoktc5svThRQXccIgoCghKkwVqZ2OVxqNB1cwrOU0IMRsGU3suq1kBIRrJsJVmP0blUQYq/vLkU1odwPgoLAJVxNY2bthEMUphqRTZQGGpFH2uQ1chxfHspe2ILE5nEa8vhW5tI8KJM2xaxVm7qjpsBlNTPDdeU5TSalkaFcUrHuhQQWiI7ARWKoGJ2olcH7w8TUcVyORydPtZVuy1GRjIsHO4mVxgl4I2m0KgGPAtiqEVny4DGz8yBJEhH2YITUTBBISEBIT4yidUcZeor+IZpZGKiAgrX9KjUZm2kdd9guEwjD0vV49hM0T4Ko8hJD/9X7doIBKozdJslvtoTi7jzI62mkeoHu6lUGgVn/eSlsFRkLLA1vHPGSsiaRlsFbIoEZCyAhZnCqSSPumMT/OKAKvNQrclUcevgO4lRIcfDrYNjoMZzmLsJDip+JupsgGnFETNuFljFQrgB2BFcQYtLKL/5Rf4zw0wvM3iyR1d9Bbb2etabMsqhnzDrnyBnHHJqQID1h48k8c3edxwiCDy4mnzJiCKars5K4N2a8pulAvYSpZMiNkYsxJC9dVyhq0quxYRggmITLzmplIaX9kEtleabFAgodM4KsNgsYtMIU1vPk2zY9GayPDM8EoWbwtZkvRYvayP9pU7Ue1NBKoNdAKdNHGWLaHAjqBUi6088cA48Rdc5ZSCmkhDwqAiAxHopoh4EngEQRJMgqjQBq0GVXRpai3S5BZZEe0GZTABBHnwsja797aS8x2KgcU+L0Ex1BRCzVBg44UwHKhS8AbFEPzIEBrwQkNoDH4UL14VrwRRfbYyRMZUtkzlK+ZE5Xazpkiv8woD6gUw4bz1FBjqszLBQiGB2jxq1ks4rjX+GJY/aEqNFOuwlIlLCKk4K+YoQ8YOSFgRSR3SnPRwnJBkIqCpw8VpBmdFEtWcQC3tgDeswrQ0x8s3NTVDMgW5IpgEFIBUZ9xt4MzReLNqYTjyf+hCawJQ6N/9Hp7fyf5fZdmzr43eXBP/OZSm31P0u7AzH5ILAvYzTE7n8FSBYjSIZ+JuzvLC6SPrclYHaSNZtAOW3hBCzNqYLBtQnV2Ls0PlCQhxfccICMJCqfr/EIF2SSiXQeXgqia0p8iHDrkgrtHRn7Dp8ywMS1g6UODoE/bhdBaJ3ASh24JuyRAWTRy0BfGkAiJTKpZL3E1aU8pIYSpr0mtUVBrzlijVZrMMhCY+b/qp0inEYPIueEUSrUWSoUdq0X5MAJGvyA4kyeaT5D2HAS9JMdT0ezbFSONHilyo8COFH4EXafwIAmMIo1L53NL/cdAWz2KNzMgYrAMFOePN5woiyPoOftjNNuXIhIJDmARqszDb9do6zDJO7RiIs2bKoJXBUmbkZx2hrfj/ZCrAdkISrRFWGnRaYS1KoFI2qikDr389LGrDLI6Dr8hKQlNL/GmNFGCDb0GilCpz5rhLs1pk4kK1XhZa05CwoX8f+g9P4t37PMOv2Dz1She9xQR9XimL5sVjPvZ6BfKqQL/uwzN5AuNSjIbiemiVKfFxFs1U/hCMrMU3cZFJ+TonRD1UgrVRi8HHM0ODqvVDg7i8UFXJirh4rk+kfZRl4ZIHDbkwTVOYwpgUQ55mIKEwJkVvMUH4J8Xi1hyLDiuQWOwS7deEqgXaWsDSWICxKHXBRiij4tNC9XqiZVqVviWXukojEy8IX4qatB9HTsaPoDmFilLxjNMIrNYAXA/j+yxa6tFWzIPrExQ1oa/o358h7yUoBhb5wMaLNG6kKYQWfqQohIrQxGVPAlMO0uJgzqAqwRtMLRs1ummhgX7Xxhtqj8cLTu/XOufkDDxzEqjV0wGWCDmtqYsTbz4yvqJ1XAjRtksnD41xEqWBsho8L94vnY6vRwZQGNvBaDu+Hqk4MFOl+9jOwfkNR6WujiiKp9BrH8Ih9NZn4cWd5B7Yy0Bvmr6hJp4ePIo+z2J7Fob9ODjr9QoUcCnqAoN6Lz4FisFgqcyGW5nNGdcEql5AfaKuTjhQkCaTBYSYOzXBGlBepNyUFn5XlCf5xHUgTRQRoUtrh8aTDcLIRSuHojWErZMkVIZ9QQcpL01TLsWuXIomR7N1uIP2xCLat0cc3ZynM12kozVPx3EDkMrgD7egmlIoZVD5uCCucnTcLaoorXJAfMUqF88tFdO1SrcTZ7VUstSecrrLGIwfr9SA70DkYCKDCUFHYCKDHfoQ+jSFHsZ3wQ8wXghBAMbEMzBLgWNlBcG4xGMcrPk6Pp2GChOp0tOWltaKav+mKD2qK7qqL9pzbXoGWnhwTzubiq0E4dC8TSioV9fnQjmLS6A2TalUurJgcLwG3UzHqGmO60pgDi8FauXsXBRVgjBse+S2tBo5WaDjE2EQxMWEyt2XmoP7Gy3XP/NcsELIJACD2rULfv4bvJez5Hdqtu7oos9Nss91eDlvMeQZdudDckFIPvLp1wMUyeGSp2D6CaK4Jlp54PHobs6RCQMjQdoIyaIJMR9qukGnUHct/jmIT2tRPNlA6ziQqF4/1NVpiqSJvHYygUNkEvS7imbHwouaaC+kaB9u5phCP51dOVKH5TEDNiEtqJZmCDQ6jNC2ijNtVlSaYKBQpvSl2K7ElhWVLlKozbppjTIGY8V3UKVArXx+NkECogTGNEEwcnpScfSHFYYQBignRJkQE0YYrxQEhnGPSiVqK18A4493PhsnQ1iSKkZ4BLTnLZoyLRiVrgwPMcZQLGan/ssV86phAjXf9/n85z/PL3/5S1566SXa2tpYu3YtX/3qV1m+fPmk9924cSNf//rX6enp4aSTTuKGG27gzDPPrNxeLBb51Kc+xW233Ybruqxbt44bb7yRrq6uaR/nG97wBqxSl6GC6QVqo7oGDn9dJzWLYZa7DKOoNMbLK5XEsMGxa0eMWlZ9xpYdSGTi5Z0w4ObiEhuLWgENw0Poex8kenk/ex/V9A120FdI89xwmgFfMeQrducjckHcxVnJorG3UvE7XpfTrxSuLU8YiAtqjh+kjTsebZIgTbJpQtTHZMFavK0coPmUx6tpwBhNiFfKlMcLvofKBcBTeYokQUPeJIkKLWQsmyZHExhNs23T5tgUo046hlp4/dB+WrpcnLZ+/J39GDuN6sgQpjMoS6MTxMttWoBjQJvSYvNq/O5RGMm4QTypwJjKF3aMQZWDqYj458hgfCqDzkxp8oIJVdwD4thxDEb8XbsygVMT/2GpnlSmKy/bxCmkcf4MqSI4OkN7soM32EeRd1OVUh9hGPLHP26e4MHqwEx6ShYH0DCBWj6f54knnuALX/gCJ510Ev39/XziE5/gggsu4PHHH5/wfrfffjsbNmzgO9/5DmeddRbXX38969atY+vWrSxdGpe++OQnP8ndd9/NHXfcQVtbG+vXr+eiiy7id7/73ewOuhJ0TVQhevRXtNoxYTuL43wbSiZnd0z1UO7a9L142qmOAye1dy/quRcJf/8i7qshw3uTbO3pZL+3nJdyCYZ8RdY39BRCCkFEPgroZxhXFRnW+whwCYwbl9owPqEJKoOM42WfoppuTmBM2Y3RRWxLO43bDAnQhKi/McEaVNVcq+4KjeutGeUDFsYEpTFrQWWpPD8qxOsUK4eC1Y+tUvSrRSSiNMliip2FFlLaJqUtnk0naLITHNbfTOsLEYsSIUc1Z2nPFOk6qg+dAZVOE4QZyDSh7FIXqAbtRKVu0NJpWpdWQSh1kRqrtMYocdCmVLxSgjFx78dIL0vV61A9yKz8OlSviBJVXR/VL2hGf/cc/TgHohTKjkgOBSR1Y5z35nuM3KGsYQK1trY2fvWrX9Vs+6d/+ifOPPNMduzYwcqVK8e93ze/+U2uuOIKPvCBDwDwne98h7vvvpvvf//7fOYzn2FwcJDvfe97/PCHP+Tcc88F4Oabb+a4447jkUce4U1velMdWzX5Eh89uQYoFz2RMIynIqkonrWZsuMuA6+A6utDPfsC4eMv4+/02fl8K/sKafrdBC/mkqXMGQz7EYUwYp9XpICHq1yGdT+eyVM0g5XJAfEYNL9q6aeoavmn8bs5xw26JIsmREOYuObaeNm1+KbIlOqARUBp3eIgLBApe6RLVLlggcswBZ3CNx5JkyIdJPHzaTKWhRdaNDmaZluTDVppyzVxRD5FW7rIosV50l0FVLCfSCUJVRO6OUPoW6V6bECiVBNJm9ouUlUq+TE68NFqJMNWlY1T6JF9y1GKrpp9Wh2cjQrEVNXPlZdwmpGONgbbCXEaJFATM9cwgdp4BgcHUUrR3t4+7u2e57F582Y++9nPVrZprVm7di0PP/wwAJs3b8b3fdauXVvZ59hjj2XlypU8/PDDEwZqruvium7l+tDQ0By0qHam6AO5u4HT5+RxZywy8Vg3RSmtHsUnKFtBmIdsFv3YE7B7gHDXMANPKQYH0wwUUmzLHsagb7G7qBn2IRdAXzGkGIbsD/O4yhuTPfOCLJHx8cNivMxKVXA2fvdm6ZjGBGiSRROikU0WrJWLEJWXoIqvx4P0jSrNBg09tLKpBG2qEC/4HubQOs6y5XQfFg62TrLHtJEI0rQOtZJSCdLKZmvCJmM7LE510GQbmnbAirRPmxPQnnBZtayfZMs+7EVJaE5idIrIS1XWEC3TleCNkcyboqqwbmnR+lIwN+53dD2SkYPS45RZVLJzE5nJaGg7EWIrU1UmZX5IHbXZadhArVgscs011/Ce97yH1tbWcffp6+sjDMMxY826urp49tlnAejp6SGRSIwJ9rq6uujp6Znw+a+99lr+1//6X7NrxDiqT16TLRtVN5UxZpRmj4agwtI3xwgwUCig9vSjHv4Dpi9L/o9ZcvsSDA03sW2wlUHfrgRocXBmyAcRxTBiIHAp4DKsB3FVqbxGKXsWGZ8gLFQyaeVuzepSG8ZUL6syUnJjwhmd8c7jNlWCNCHm14EyayOzRaOqSCQsjaWyKiU9oihe7L28yoGKNFo7RMaP1zxWDqEOsEkS6gCHBEmTpOA2kfZs8oFNylKkbcWw79BsOzTbSfa7SVocn5WdgzS1ZXGah7CaQKWSRCoVL6GXTBNWlSDTyTh7NlICBIxtRkbClBeKrw7WVLwSAkqNrJ4AtePhRkcy4xVHm4EZz3cTDWPeArVbb72VD3/4w5Xr//7v/86f//mfA/HEgne/+90YY7jpppvm5fg++9nPsmHDhsr1oaEhDj/88Dl57HIAcXjqIGTTyoGZoTzSFbwiUJql6ZQ+xa6LGhxEPfoHzN5hor48uWdDcoMJduzrYtBLMBTYvFqwyQVUArRiYBjwfYpRgIvPkB7Co0CBQYIozqL5UaEyBi2M3FHLPVVn0ThAFq20vUy6OoVoeFMJ1qCUXVO6cg5Q5RmiKj4vaCAsfaFTpdIecS02D0vZGBNiKYdQ+9gkcXQK17iVgC2pLFLaIh9YpC1Fk6MZCtI0WSn2FlO07AloTXgc1jlIps3DzrjxuLa8IvQSGCcFiST4CbDt0li2OEBTgYqHsGni4SI6niQwMoy5enZAefYok0dRs/keH8/QwBjVMIP463EYDdK0upu3QO2CCy7grLPOqlw/7LDDgJEgbfv27dx3330TZtMAFi9ejGVZ9Pb21mzv7e2lu7sbgO7ubjzPY2BgoCarVr3PeJLJJMkZD+yf2ifs7JYVM3z8krBqjFsQQBBCqnzM0cg4C13OTkXQ34/KDqP29GH++BJmf5781oDCkEMul2DXcAvDfivZYBG9rk0uUPS5kA/ADQ37XZ9iGOKZkCGVw1ceeTWMr4sEuHhRltD4BFGcOYsnBriVk2pt9mwqXZyl7WUHOOtIgCZE4xk3WIOamhjVNdcq10vdoUppwsqXufjPllKaMPJQSqPQaJ2L10XWA5Wu0SGdxsLBURlslcSJkjTn23CMQxKHNitJytK0JhySVoKMnWFRTzsZy9BkG7qSPk12QHdTnraWAqmmYVKd8cQDlUlAIoHBASeB0Yl4gkRQCjztUVm10s+qFNAppUayiAcK2iYzwVBo40Z4BYdipEsrM2iYYN3Qg6EuXZ8L5HQ/b4FaS0sLLS0tNdvKQdrzzz/P/fffT2dn56SPkUgkOO2009i0aRMXXnghAFEUsWnTJtavXw/AaaedhuM4bNq0iYsvvhiArVu3smPHDtasWTNHrZnZV59F462nWZ5hCVXVEEvXoyh+Y5aKNqLKKXQTj3ewDFhu7fF4XhyYbX0B+gYwO/YT9ruE/SGDryTI55PsGl5M1o8XHN7r2uRCRTGEfW68Rt2gG+JGEcUwZNgUcZWHrz2yaoDQ+HFwRmnWZpSvLJYeF6idKHtWnsVZ28UZt2Z649AqNy+UT60Qh6Dxl5uaZL+q7tDqykblP/jlVQ5UaepmOdNmiNDGQSmv0jUaqLiYrqUcfO1iG4cEaQphE8kwQS5IktCahKUYSFikLEXKUux1kzTZCXqKSZoGW8lYIUt2FknaAUuWDONkXKwkqESpznjCJvTitZRVxoqnkGoblIUpRW5RSClQqzSkNPt05PxVVfnjgCaK78IC+H68CoI4tDXMGDXf9/mv//W/8sQTT/CLX/yCMAwrY8g6OjpIlJY+Ou+883jnO99ZCcQ2bNjA5Zdfzumnn86ZZ57J9ddfTy6Xq8wCbWtr44Mf/CAbNmygo6OD1tZWPvaxj7FmzZoZzfgcHh6uqqM2m4K3UMy5ELhgl8t2VI1dwEDkx5myct0yS8fLMXku5FzU8DAql4OBIXh1D7g+Ue8wJucTDobkd2vcgkOx6LAvlyYf2Oz3lpAPNMVIs8/TFEPoLwVkXmQY8uKgzI8icsbFIyCvc/i4+NrFNdlSN6aPHxZKNc/KqwX4VSsIxCU8ZpU9q7wU0sUpxGtBpZtzdPkOGJtdg8pyU9UTEMrniHKmLR4sBpGJM0cqigO38gLwSmm0slHKQilNXqdL2xwSKh1n2lQGJ0piRQ5NXhOOcXCwabESOErT7FgkLYuEVjQ7TSQtaH11KQltSGnDokRISkekrJD2pEfCCmlKeCSTRZKpgERzgLbKi8fHAVyo7LhEiK1QidKMUa1Q2orHskXEJUGMwhg1asxb9WvKuJmlKA9BVuPmPLLZHLnicE0dtYPJAGamSy6KxgnUdu7cyV133QXAySefXHPb/fffzznnnAPAiy++SF9fX+W2Sy65hL179/LFL36Rnp4eTj75ZO65556aCQbXXXcdWmsuvvjimoK3M/HSSy9SqVBdfuMdMFgbPze9Jb8fCvshCFDlKv++V1oOKoq3eX68AsDe/nhB9VwxXo6k4BP2u5hCRFQ0uP2awNVks0mKboqCb7PfTVIMLfKhZsC3cKO46GwxVKWgLA7Ohv0IL4xwo7ASnPnKp6jzhPgUiTNmUeTjmTg4qw7QyhMDJgvQ4EDZM5hOBk0CNCFey2rHr0FV0DamBzXOqsUFZOOsmkLXziBVI7XZymuMWsom0nHGzVYFLOVgYePpFixj45AkH6VxjEMuTOIoC1srMlb8f5OjSGqFrRUtjiah4zKTLXYKRxkydkRCR6WfAxwdkbAiElaIpSNaM0Vs28eyI+xEhCpl2ZRdqjhXXQS3/BqM86dkTNmO0vWgqMnvbmH/bs1zL7xAvvjqSB3KeTh/yqzPmWuYQG3VqlWl6cmTe/nll8dsW79+fSXDNp5UKsXGjRvZuHHjbA5xYqNWHBirqnZQlVdzHupf78YEEVEhgCBeSmSkirWJ14ALIMhD6Gq8gk0QaPzAIu+2EIQWXqjJ+g5epBkObNxQUYw0Q77GjcCLYNiP/8/5Bj8yeFFEIYiDs7zx8UvBWV7HQVmgfHyTJyTAj/KExscQxXWNSgFY7czN0jT7mgANamqggWTRhBDjd4NWxq2Vt9eeN2u6RMtqEnOjAjalwQQjmbdRgVqoNJYJ0GhCnaxk2QLlopSFhU2xFLTlTZxlswKbdJDAUpqMZ+OUArWMrbG1wlHQ5GgspUhbBluBrSGpDY4GWxmS2mCVAjlLGWxlcLRBYeLlRlVU+T+u9DG181006vUsBhY9xSTPD6tKAXFxaGqYQO2QV5PCHy9oq/6QxCefHjPAK/eAiSy8IIExCj/SGKPiyZomPulERlEMLUKjKYYaP9L4RpEPNKFR+AbyoSY08WxMP4ozZjkffANBZMj5EaEx5MMQ30SEJiJHkVCFFHQ+rnNWFZyFkU9g3Er2LIoCIqqyZ1MO0KrafqAyG6Nfx/FeZgnShHhNmNqYtUql2Mp9oLrrtDbLVhuwhRjKw1TGZtlqgrbSqghx0OZWukrLY9uKKoulnMrYNtvYJMIkdmjhYJPybGwVB2spKw7UkpaKhw4rRaLqZ0fHP6esuDPX0pXSbJXATKm4lOVUOwtrzqqlK75R7HMVr2R9wrA4tQFv9WJkUfbZkEBtFiY80UwpaNP0qJe4d/tZGMCP4m9EYbmQtRl5E0Ymvj004EWK0BhCA24YbwtLwVgQgRvGAZkfGYpRQIAhNBEFXEIV4qoioQqIVIinCvFYMxN3cZooqgRnxkSVWZvl65MHZ9Xdm+U2MusuzurXWgjx2jK1zBqM/qI7+nxQPZatusRHdaAX38OKZ5WaOHiLSpk2re3SzEjQuoBWTmmMW7ZUp83GKm2zVar0fxJLOWgsEiaNjY0ONYkgiY1FnJPTKKVw0GgVh4u2Vtgq/l+jUKUADirD1OI2ceBSaqODn+qrfhQx7IfsCPcRGRdx6JJArd7GC0JKNYQGvB3c13MGxph4WBpmZOm3qo+cMRCUslEB8YLAEQafgNLIMEIVEhHhKy++jo+v45mXofEJcDEmJCzVMyuvCmAIK2PNKtuqArDqbssDdmvCBIEZSHAmhBjPmCxZ5YaJvvCOzcSb6qElpQHz8eONTDyIB3gFGFO1jJOKV0SPSttGT0YYKf1hl8a4WZXMW7zNqnSZ6tJ9bJ2K74OOb0ejVel/LKzIRmOhwtLjoNFGVwJFAH2A5QfjV2G812FkW6ACiirHoNmJibx5PZ8aFk72qx4kUJulmsGuU01Ul05ARa+fLcnniAgrH7DIjD8bx5RmRIX4cQFISoFVpeZQWMp4RaWU/6hMGOWM2Ejl//ECsvK28j6UnrX2+jTHm41q96QvjXychViQJgzYYJIvvGXjBS3VdCWAg3BkdZhSADfy8LoUvAGlIAxAhdXbR4K4+HhrgzqlSt2tpYCu/HP5f4VV9XN5e3nmwORB2rjB2Th/M8rn8DByKfqDmHmsn1Ymkwlm7sBhuxBCCCGEmBeSUZtDk2WDxvuWGEY59njPxPctZafK35hGf6uq/iY1stzSyLek0Vmw0Zmx6mxYZZ+a+0yQNYMx879nO95sNMmiCSHKJs2s1ew4xfOGGukCHXmOiR4njGuXlW+qyqLFD1V9Xh4vM1a7/9j7UJOJm2ifiRgzfo/FeOtGl8/7YTTPEwnKxyPn+RmTQE0IIYQQdWOQWZ+zIYHaQTLeWDYTeeS9fRN+S5rY+N+eap9vbAauep232v1rx5yNPt7Jnrf0YJMd7Ph3WTAfMSHETMxo/O+4D3Sgc03pvFga8zay98i4tsr6o9UPVcqCxdsmy45ZjGeqWbSJHPjvRmmylwnkfHuIk0BtHox8aEKCcHhuHnPSD+3E3Zi1xzPO/uM/2YGPR04MQog5MqYcRz2WIxpzXgvHCd7KqicnxPvWTFCAqmUEAsYz9jQ6UeA2hS/yE3aJmqnd/yCQvwgzJ5MJhBBCCPGadu2113LGGWfQ0tLC0qVLufDCC9m6dWvNPsVikSuvvJLOzk6am5u5+OKL6e3tnacjHiGB2nwyJq5vMweXylpT415KJTtMiBnzLyT+xlV9iY9twstkTSr9E0KIehl7HpvbfyNPNNF5cOx50xCWLqV/5XPulC/+BJcp3JfxL/FahPN/Pi6PUZvry3Q8+OCDXHnllTzyyCP86le/wvd9/uqv/opcLlfZ55Of/CQ///nPueOOO3jwwQfZtWsXF1100dy+GDMgXZ9CCCGEqKu6xIvTeMx77rmn5vott9zC0qVL2bx5M3/xF3/B4OAg3/ve9/jhD3/IueeeC8DNN9/McccdxyOPPMKb3vSmuTzyaZFAbR7F39zmqhDheMnRaYxNmMWnSDJoQojXkilNZJjonKnmYUzYJOfv1/r52fM8hoaGarYlk0mSyeSk9xscHASgo6MDgM2bN+P7PmvXrq3sc+yxx7Jy5UoefvjheQ3UpOtTCCGEEHU1eoDNXFwMcPvtt9PW1lZzufbaayc/lijiqquu4uyzz+aEE04AoKenh0QiQXt7e82+XV1d9PT0zP4FmAXJqM23ucoHT+Vb3Bznnl/r39SEEGK6hcwbYUwYNNb52cxgTNlUXXLJJWzcuLFm24GyaVdeeSVPPfUUv/3tb+tzUHNMArUGMe2TwZgHmPtPQSN90IUQotGMd46sS+mQGRzHQpFIJGhtbZ3y/uvXr+cXv/gFDz30ECtWrKhs7+7uxvM8BgYGarJqvb29dHd3z+UhT5t0fQohhBCiriYrJDDTy/Se37B+/Xp++tOfct9993HkkUfW3H7aaafhOA6bNm2qbNu6dSs7duxgzZo1c/ESzJhk1ObZVL4JTfXb0my+yS3kb2RCCDFX5FzamK688kp++MMfcuedd9LS0lIZd9bW1kY6naatrY0PfvCDbNiwgY6ODlpbW/nYxz7GmjVr5nUiAUigJoQQQog6m+/1EW666SYAzjnnnJrtN998M+9///sBuO6669Bac/HFF+O6LuvWrePGG288yEc6lgRqryHyTU4IIUSjMdRnjsV0HtJM4QBSqRQbN24cMzlhvskYNSGEEEKIBiUZNSGEEELU1Xx3fR7KJFATQgghRB2ZKXU9ivFJ16cQQgghRIOSjJoQQggh6qaeKxMsBBKoCSGEEKKu6hGnLZTYT7o+hRBCCCEalGTUhBBCCFFXden6XCApNcmoCSGEEEI0KMmoCSGEEKJuDDKZYDYkUBNCCCFEXckShzMnXZ9CCCGEEA1KMmpCCCGEqKt6dH0ulBydBGpCCCGEqBtj4ouYGen6FEIIIYRoUJJRE0IIIURdRQumo3LuSUZNCCGEEKJBSUZNCCGEEHUlY9RmrmEzah/5yEdQSnH99dcfcN+NGzeyatUqUqkUZ511Fo899ljN7cVikSuvvJLOzk6am5u5+OKL6e3trdORCyGEEKLMAFEdLgsl9mvIQO2nP/0pjzzyCMuXLz/gvrfffjsbNmzgS1/6Ek888QQnnXQS69atY8+ePZV9PvnJT/Lzn/+cO+64gwcffJBdu3Zx0UUX1bMJQgghhBCz1nCB2s6dO/nYxz7GrbfeiuM4B9z/m9/8JldccQUf+MAHWL16Nd/5znfIZDJ8//vfB2BwcJDvfe97fPOb3+Tcc8/ltNNO4+abb+b3v/89jzzySL2bI4QQQix4xpg5vyyUnFpDBWpRFHHZZZdx9dVXc/zxxx9wf8/z2Lx5M2vXrq1s01qzdu1aHn74YQA2b96M7/s1+xx77LGsXLmyso8QQggh6qO81udcXxaKhppM8A//8A/Yts3HP/7xKe3f19dHGIZ0dXXVbO/q6uLZZ58FoKenh0QiQXt7+5h9enp6Jnxs13VxXbdyfWhoaIqtEEIIIYSYG/OWUbv11ltpbm6uXB588EG+9a1vccstt6CUmq/Dqrj22mtpa2urXA4//PD5PiQhhBDikBRh5vyyUMxboHbBBRewZcuWyuX3v/89e/bsYeXKldi2jW3bbN++nU996lOsWrVq3MdYvHgxlmWNmcHZ29tLd3c3AN3d3Xiex8DAwIT7jOezn/0sg4ODlcsrr7wyq/YKIYQQQkzXvHV9trS00NLSUrn+oQ99iLe//e01+6xbt47LLruMD3zgA+M+RiKR4LTTTmPTpk1ceOGFQDzObdOmTaxfvx6A0047Dcdx2LRpExdffDEAW7duZceOHaxZs2bC40smkySTydk0UQghhBDUp47aQqnN1jBj1Do7O+ns7KzZ5jgO3d3dvOENb6hsO++883jnO99ZCcQ2bNjA5Zdfzumnn86ZZ57J9ddfTy6XqwR3bW1tfPCDH2TDhg10dHTQ2trKxz72MdasWcOb3vSmg9dAIYQQYgEyC6yrcq41TKA2VS+++CJ9fX2V65dccgl79+7li1/8Ij09PZx88sncc889NRMMrrvuOrTWXHzxxbiuy7p167jxxhvn4/CFEEIIIaasoQO1l19+eUrb1q9fX8mwjSeVSrFx40Y2btw4h0cnhBBCiAMyC6ebsh4aOlATQgghxKFPuj5nTgK1KTKVrwPyZhNCCHEoi/+OGUlzHRIkUJui4eHh0k/RvB6HEEIIMReGh4dpa2ur+/PEKxPMfVC4UMJMCdSmaPny5bzyyiu0tLTMWUHeoaEhDj/8cF555RVaW1vn5DEPNmlD43gttEPa0BheC22A10Y76tEGYwzDw8MsX758Th5P1JcEalOktWbFihV1eezW1tZD9iRSJm1oHK+FdkgbGsNroQ3w2mjHXLfhYGTSqpkFk/+aexKoCSGEEKKuZNDQzM3bElJCCCGEEGJyklGbR8lkki996UuH9FJV0obG8Vpoh7ShMbwW2gCvjXa8FtpQv5UJFkZ3qjIyP1cIIYQQdXD77bfz4Us3cEbze+f8sX8//P/x03//EX/1V38154/dSKTrUwghhBCiQUnXpxBCCCHqxiArE8yGZNSEEEIIIRqUZNSEEEIIUVeSUZs5yajNsWw2y/r161mxYgXpdJrVq1fzne9854D3u+OOOzj22GNJpVKceOKJ/PKXv6y53RjDF7/4RZYtW0Y6nWbt2rU8//zz9WoGzzzzDBdccAFtbW00NTVxxhlnsGPHjkOqDWUf+chHUEpx/fXXH3DfjRs3smrVKlKpFGeddRaPPfZYze3FYpErr7ySzs5Ompubufjii+nt7Z3zY/Z9n2uuuYYTTzyRpqYmli9fzvve9z527dp1yLRhKscyWqO9h6699lrOOOMMWlpaWLp0KRdeeCFbt2494P0arR3VvvrVr6KU4qqrrpp0v0Zrw86dO3nve99LZ2cn6XSaE088kccff3zS+zzwwAOceuqpJJNJjj76aG655ZYx+0z3PTobYRjyhS98gSOPPJJ0Os3rXvc6/vZv//aAa242Wjumrzzvc24vC4YRc+qKK64wr3vd68z9999vtm3bZv73//7fxrIsc+edd054n9/97nfGsizzta99zTz99NPm85//vHEcxzz55JOVfb761a+atrY287Of/cz88Y9/NBdccIE58sgjTaFQmPM2vPDCC6ajo8NcffXV5oknnjAvvPCCufPOO01vb+8h04ayn/zkJ+akk04yy5cvN9ddd92k+952220mkUiY73//++Y///M/zRVXXGHa29tr2v2Rj3zEHH744WbTpk3m8ccfN29605vMm9/85jk/7oGBAbN27Vpz++23m2effdY8/PDD5swzzzSnnXbaIdOGqRxLtUZ8D61bt87cfPPN5qmnnjJbtmwxf/3Xf21WrlxpstnshPdpxHaUPfbYY2bVqlXmjW98o/nEJz5xyLRh//795ogjjjDvf//7zaOPPmpeeuklc++995oXXnhhwvu89NJLJpPJmA0bNpinn37a3HDDDcayLHPPPfdU9pnue3S2vvKVr5jOzk7zi1/8wmzbts3ccccdprm52XzrW986pNoxHbfddptptZaZt7RtmPNLRneYe++9d76bWHcSqM2x448/3nz5y1+u2Xbqqaeaz33ucxPe593vfrc5//zza7adddZZ5sMf/rAxxpgoikx3d7f5+te/Xrl9YGDAJJNJ86Mf/WgOjz52ySWXmPe+973Tuk+jtcEYY1599VVz2GGHmaeeesocccQRBwzUzjzzTHPllVdWrodhaJYvX26uvfbayvE6jmPuuOOOyj7PPPOMAczDDz9clzZUe+yxxwxgtm/fPuE+jdSGAx3LaI34Hhptz549BjAPPvjghPs0ajuGh4fN61//evOrX/3KvOUtb5k0UGu0NlxzzTXmz/7sz6Z1n09/+tPm+OOPr9l2ySWXmHXr1lWuT/c9Olvnn3+++Zu/+ZuabRdddJG59NJLJ7xPI7ZjOm677TbTYi0zf972yTm/pBdIoCZdn3PszW9+M3fddRc7d+7EGMP999/Pc889N2mdl4cffpi1a9fWbFu3bh0PP/wwANu2baOnp6dmn7a2Ns4666zKPnMliiLuvvtujjnmGNatW8fSpUs566yz+NnPfjbp/RqpDeV2XHbZZVx99dUcf/zxB9zf8zw2b95cc3xaa9auXVs5vs2bN+P7fs0+xx57LCtXrqxLG0YbHBxEKUV7e/u4tzdSG6ZyLKM12ntoPIODgwB0dHRMuE+jtuPKK6/k/PPPH3Ns42m0Ntx1112cfvrpvOtd72Lp0qWccsop/J//839m1YaZvEdn681vfjObNm3iueeeA+CPf/wjv/3tb3nb29424X0asR0zMfcdn4aFUvBWArU5dsMNN7B69WpWrFhBIpHgrW99Kxs3buQv/uIvJrxPT08PXV1dNdu6urro6emp3F7eNtE+c2XPnj1ks1m++tWv8ta3vpX/+I//4J3vfCcXXXQRDz744CHRBoB/+Id/wLZtPv7xj09p/76+PsIwPGAbEonEmECpXm2oViwWueaaa3jPe94z4cLMjdSGqRzLaI32HhotiiKuuuoqzj77bE444YQJ92vEdtx222088cQTXHvttVPav9Ha8NJLL3HTTTfx+te/nnvvvZePfvSjfPzjH+ef//mfJ7zPRG0YGhqiUCjM6D06W5/5zGf4b//tv3HsscfiOA6nnHIKV111FZdeeukh1Q5xcEmgNgu33norzc3NlctvfvMbbrjhBh555BHuuusuNm/ezD/+4z9y5ZVX8utf/3q+D3dco9tQHij9jne8g09+8pOcfPLJfOYzn+G//Jf/MqVJEfNhdBsefPBBvvWtb3HLLbeglJrvw5uS8d5LZb7v8+53vxtjDDfddNM8HuXCduWVV/LUU09x2223zfehTMsrr7zCJz7xCW699VZSqdR8H86MRFHEqaeeyt///d9zyimn8KEPfYgrrriiYc9JE/m3f/s3br31Vn74wx/yxBNP8M///M984xvfmDTgfC2IpxLM/b+FQspzzMIFF1zAWWedVbl+2GGHcd555/HTn/6U888/H4A3vvGNbNmyhW984xsTdjl0d3ePmXXX29tLd3d35fbytmXLltXsc/LJJ89pG5YsWYJt26xevbpmv+OOO47f/va3Ez5OI7XhjjvuYM+ePaxcubKyLQxDPvWpT3H99dfz8ssvj3mMxYsXY1nWAdvgeR4DAwM1GanqfeaqDYcddhgwEqRt376d++67b8JsWiO0YbrHMtp8vocOZP369fziF7/goYceYsWKFZPu22jt2Lx5M3v27OHUU0+tbAvDkIceeoh/+qd/wnVdLMtq6DYsW7Zs3HPS//2//3fC+0zUhtbWVtLpNJZlTfs9OltXX311JasGcOKJJ7J9+3auvfZaLr/88kOmHeLgkozaLLS0tHD00UdXLr7v4/s+Wte+rJZlEUUTR/9r1qxh06ZNNdt+9atfsWbNGgCOPPJIuru7a/YZGhri0UcfrewzV21oa2vjjDPOGFOC4LnnnuOII444JNrwoQ99iD/96U9s2bKlclm+fDlXX301995777iPkUgkOO2002qOL4oiNm3aVDm+0047DcdxavbZunUrO3bsmPM2pNPpSpD2/PPP8+tf/5rOzs5JH2O+2zDdYxltPt9DEzHGsH79en76059y3333ceSRRx7wPo3WjvPOO48nn3yy5vNw+umnc+mll7Jly5YxQVojtuHss8+e83PSTN6js5XP5+f878N8tGMmjIrm/jLfjTpY5nkyw2vOW97yFnP88ceb+++/37z00kvm5ptvNqlUytx4442VfS677DLzmc98pnL9d7/7nbFt23zjG98wzzzzjPnSl7407lT49vZ2c+edd5o//elP5h3veEfdpsL/5Cc/MY7jmO9+97vm+eefr0wH/81vfnPItGG08WZ9nnvuueaGG26oXL/ttttMMpk0t9xyi3n66afNhz70IdPe3m56enoq+3zkIx8xK1euNPfdd595/PHHzZo1a8yaNWvm/Hg9zzMXXHCBWbFihdmyZYvZvXt35eK67iHRhgMdy6HwHvroRz9q2trazAMPPFDzO8jn85V9DoV2jDZ61mejt+Gxxx4ztm2br3zlK+b55583t956q8lkMuZf//VfK/t85jOfMZdddlnlermsxdVXX22eeeYZs3HjxnHLWhzo8zKXLr/8cnPYYYdVynP85Cc/MYsXLzaf/vSnD6l2TMdtt91mmq1uc2b7lXN+SelFC2LWpwRqc2z37t3m/e9/v1m+fLlJpVLmDW94g/nHf/xHE0VRZZ+3vOUt5vLLL6+537/927+ZY445xiQSCXP88cebu+++u+b2KIrMF77wBdPV1WWSyaQ577zzzNatW+vWju9973vm6KOPNqlUypx00knmZz/7Wc3th0Ibqo0XqB1xxBHmS1/6Us22G264waxcudIkEglz5plnmkceeaTm9kKhYP7H//gfZtGiRSaTyZh3vvOdZvfu3XN+vNu2bStPaRpzuf/++w+JNhzoWA6F99BEv4Obb775kGrHaKMDtUOhDT//+c/NCSecYJLJpDn22GPNd7/73ZrbL7/8cvOWt7ylZtv9999vTj75ZJNIJMxRRx1V83srO9DnZS4NDQ2ZT3ziE2blypUmlUqZo446ynzuc5+r+fJ1KLRjOiRQmz1lzAFKIgshhBBCzMDtt9/OBy/9BMe1XDTnj/3U0I+4899vn7T81WuBTCYQQgghRF0tqCWf5phMJhBCCCGEaFCSURNCCCFEHRkiNfcZtYUybksyakIIIYR4zXvooYd4+9vfzvLly1FKjVka0RjDF7/4RZYtW0Y6nWbt2rU8//zz83OwVSRQE0IIIUTdGKjTygTTy6nlcjlOOukkNm7cOO7tX/va1/j2t7/Nd77zHR599FGamppYt24dxWJx9i/CLEjXpxBCCCHqyDTEkk9ve9vbeNvb3jbubcYYrr/+ej7/+c/zjne8A4B/+Zd/oauri5/97GeV1STmg2TUhBBCCLGgbdu2jZ6enpqlHtva2jjrrLN4+OGH5/HIJFATQhxk3/ve9w5K3aN77rmHk08+edLleYQQ9RdXio7m/ALgeR5DQ0M1F9d1p32MPT09AHR1ddVs7+rqqtw2XyRQE0IcNMVikS984Qt86UtfqvtzvfWtb8VxHG699da6P5cQYjKGiHDOLwbD7bffTltbW83l2muvne8GzykJ1IQQB82Pf/xjWltbOfvssw/K873//e/n29/+9kF5LiHEwXfJJZcwODhYc/nsZz877cfp7u4GoLe3t2Z7b29v5bb5IoGaEGLa9u7dS3d3N3//939f2fb73/+eRCLBpk2bJrzfbbfdxtvf/vaabeeccw5XXXVVzbYLL7yQ97///ZXrq1at4u/+7u943/veR3NzM0cccQR33XUXe/fu5R3veAfNzc288Y1v5PHHH695nLe//e08/vjjvPjiizNvrBBi1urV9ZlIJGhtba25JJPJaR/fkUceSXd3d835a2hoiEcffZQ1a9bM2eswExKoCSGmbcmSJXz/+9/nf/7P/8njjz/O8PAwl112GevXr+e8886b8H6//e1vOf3002f0nNdddx1nn302f/jDHzj//PO57LLLeN/73sd73/tennjiCV73utfxvve9j+rli1euXElXVxe/+c1vZvScQojXjmw2y5YtW9iyZQsQTyDYsmULO3bsQCnFVVddxd/93d9x11138eSTT/K+972P5cuXc+GFF87rcUt5DiHEjPz1X/81V1xxBZdeeimnn346TU1Nk44NGRgYYHBwkOXLl8/4+T784Q8D8MUvfpGbbrqJM844g3e9610AXHPNNaxZs2ZMV8Xy5cvZvn37jJ5TCDF7RtVnZYLpevzxx/nLv/zLyvUNGzYAcPnll3PLLbfw6U9/mlwux4c+9CEGBgb4sz/7M+655x5SqdR8HTIggZoQYha+8Y1vcMIJJ3DHHXewefPmSbscCoUCwIxPem984xsrP5dnZp144oljtu3Zs6cmUEun0+Tz+Rk9pxBibkSEc/6YZpoFb88555yajPtoSim+/OUv8+Uvf3m2hzanpOtTCDFjL774Irt27SKKIl5++eVJ9+3s7EQpRX9//wEfNwzHntQdx6n8rJSacNvochz79+9nyZIlB3xOIYRoRBKoCSFmxPM83vve93LJJZfwt3/7t/z3//7f2bNnz4T7JxIJVq9ezdNPPz3mttEzrV566aU5OcZisciLL77IKaecMiePJ4SYCVO3yQQLgQRqQogZ+dznPsfg4CDf/va3ueaaazjmmGP4m7/5m0nvs27dOn7729+O2X7nnXfyk5/8hBdffJGvfOUrPP3002zfvp2dO3fO6hgfeeQRksnkvM/aEmIhMwYiE875ZbprfR6qJFATQkzbAw88wPXXX88PfvADWltb0Vrzgx/8gN/85jfcdNNNE97vgx/8IL/85S8ZHBys2X7++efzta99jdWrV/PQQw9x44038thjj/GDH/xgVsf5ox/9iEsvvZRMJjOrxxFCiPkikwmEENN2zjnn4Pt+zbZVq1aNCcBGW716Neeffz433nhjTVHKww47jDvuuKNm349+9KOVn8cb/zZ6UPCqVatqtvX19fHjH/94TG01IcTBZhZUV+Vck4yaEOKg+vrXv05zc3Pdn+fll1/mxhtv5Mgjj6z7cwkhRL1IRk0IcVCtWrWKj33sY3V/ntNPP33GxXWFEHPJYOpQnmOhjFGTQE0IMa8eeOCB+T4EIUQdGSCqQ9fnwgjTpOtTCCGEEKJhSUZNCCGEEHUlkwlmTgI1IYQQQtSRwRgZozZT0vUphBBCCNGgJKMmhBBCiLqJq6hJ1+dMSUZNCCGEEKJBSUZNCCGEEHVVjzpqC2OEmgRqQgghhKgrgzH16PpcGKGadH0KIYQQQjQoyagJIYQQom7qtTLBQiGBmhBCCCHqSOqozYZ0fQohhBBCNCjJqAkhhBCirmQJqZmTjJoQQgghRIOSjJoQQggh6sfUqzzHwiCBmhBCCCHqqj4Fb2UygRBCCCGEmEeSURNCCCFE3Zh6rUywMBJqEqgJIYQQor5kjNrMSdenEEIIIUSDkoyaEEIIIerI1GkJqYXR9ykZNSGEEEKIBiUZNSGEEELUjUHGqM2GBGpCCCGEqKP6LMq+MDo+petTCCGEEKJhSUZNCCGEEHVVn0XZF0ZOTQI1IYQQQtSPrPU5K9L1KYQQQgjRoCSjJoQQQoi6kozazElGTQghhBCiQUlGTQghhBB1YzAymWAWJFATQgghRF3VpetzYcRp0vUphBBCCNGoJKMmhBBCiDqqT3mOBZJQk0BNCCGEEPUmY9RmSro+hRBCCCEalGTUhBBCCFE/sjLBrEhGTQghhBCiQUlGTQghhBB1Y6jXouwLgwRqQgghhKir+nR9ymQCIYQQQggxjySjJoQQQog6MkA43wdxyJJATQghhBB1JLM+Z0O6PoUQQgghGpRk1IQQQghRZzKZYKYkoyaEEEII0aAkoyaEEEKI+pIxajMmgZoQQggh6shgFkg3ZT1I16cQQgghRIOSjJoQQggh6kwmE8yUBGpCCCGEqC+zMIKqepCuTyGEEEKIBiUZNSGEEELUkUwmmA3JqAkhhBCiLhKJROmnuQ7U4sdLJpNz/LiNRxkjHcdCCCGEmHuu65JKpYjzQnOZG4qAiCAIsCxrDh+38UhGTQghhBB1kUwm+eEPf0gcWM1VXsgAEffcc89rPkgDyagJIYQQoo6iKCoFVHOVVQsBMAtktQPJqAkhhBCibrTWPPDAA8xNVs0Ahi1b/jDr4zpUSEZNCCGEEHWnVDk3NJvuyoWVTQPJqAkhhBDiIHj66f+knBGbmfi+O3Zsn7uDOgRIRk0IIYQQB8XMs2rxBAJYWNk0kIyaEEIIIQ6S3bt3MbOsWnyfgYH+uT+oBieBmhBCCCEOiu7ubuLQI2TqwVqcTfv2t79NW1tb3Y6tUUnXpxBCCCEOmmw2S0tLC1Mv1xEXt3Vdt2qlg4VDMmpCCCGEOGiam5v57ne/y9TKdcTZtB//+McLMkgDyagJIYQQ4iALggDHcThwVi3OpkVRhFLq4Bxcg5GMmhBCCCEOKtu2+fnPf87kWbU4m/a73/1uwQZpIBk1IYQQQswDYwxal8t0jFeuY+EVtx2PZNSEEEIIcdAppXj88f/H+OU64m3PPbf14B9Yg5GMmhBCCCHmzfhFcCWbViYZNSGEEELMm23bXqI2qxaPW9uzp3f+DqqBSEZNCCGEEPNqJKtWLoarMSacxyNqHJJRE0IIIcS82r9/H9XreeZyw/N6PI1EAjUhhBBCzKtFixYRhySGm2++mUwmM9+H1DDs+T4AIYQQQohiMc9DDz3EueeeO9+H0lBkjJoQQgghRIOSrk8hhBBCiAYlgZoQQgghRIOSQE0IIYQQokFJoCaEEEII0aAkUBNCCCGEaFASqAkhhBBCNCgJ1IQQQgghGpQEakIIIYQQDer/D8OPlFbFF2JdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot field\n",
+    "sim_data.plot_field(\"field_mon\", \"E\", \"abs\", f=cross_freq)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parametric Sweep <a name=\"4\"></a>\n",
+    "\n",
+    "Now, we will investigate how the sidewall angle changes the component performance. To do so, we will create a dictionary where each value is a simulation object with a different coupler sidewall."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sims = {}\n",
+    "sidewall_angles = [0, 5, 10, 15, 20, 25, 30]\n",
+    "for s_angle in sidewall_angles:\n",
+    "    arms_geo_sweep = td.Geometry.from_gds(\n",
+    "        all_cells[\"COUPLER\"],\n",
+    "        gds_layer=1,\n",
+    "        gds_dtype=0,\n",
+    "        axis=2,\n",
+    "        slab_bounds=(0, wg_height),\n",
+    "        sidewall_angle=np.deg2rad(s_angle),\n",
+    "        dilation=dilation,\n",
+    "        reference_plane=reference_plane,\n",
+    "    )\n",
+    "\n",
+    "    arms_sweep = td.Structure(geometry=arms_geo_sweep, medium=medium_wg)\n",
+    "    sims[s_angle] = sim.updated_copy(structures=[substrate, arms_sweep])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object to run all simulations in parallel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4ab786219a044f5b49b7162f7c51b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:31:08 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span> tasks.                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:31:08 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m7\u001b[0m tasks.                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:31:17 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.686</span> for the whole batch.                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:31:17 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.686\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fc47d249914444284fe468315012d52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:31:25 -03 </span>Batch complete.                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:31:25 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b1607ee134c45e397dacab7ad68bfa2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch = web.Batch(simulations=sims, folder_name=\"directional_coupler\")\n",
+    "batch_data = batch.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can easily track the crossing frequency and transmittance using our defined post-processing function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqs = []\n",
+    "transmittance = []\n",
+    "for data in batch_data.values():\n",
+    "    f, t = postprocess(data, plot=False)\n",
+    "    freqs.append(f)\n",
+    "    transmittance.append(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Use scatter for color mapping\n",
+    "sc = ax.scatter(sidewall_angles, td.C_0 / np.array(freqs), c=transmittance, cmap=\"viridis\")\n",
+    "plt.colorbar(sc, ax=ax, label=\"Transmittance\")\n",
+    "\n",
+    "ax.set_xlabel(\"Sidewall Angle\")\n",
+    "ax.set_ylabel(\"Wavelength (µm)\")\n",
+    "ax.set_title(\"Wavelength vs Sidewall Angle (color=Transmittance)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Simulation With Two Ports <a name=\"5\"></a>\n",
+    "\n",
+    "We can use the `updated_copy` method to easily create another simulation with a second source with a $\\pi$/2 phase difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:33:21 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sources at 1 and 2'</span> with task_id                     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:33:21 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sources at 1 and 2'\u001b[0m with task_id                     \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-4a82-822f-e3df54c08e9e'</span></a>.                                         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=382792;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=453561;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=382792;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=89355;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=382792;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32m-3f89ee6b-940\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=382792;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3f89ee6b-940f-4a82-822f-e3df54c08e9e\u001b\\\u001b[32mf-4a82-822f-e3df54c08e9e'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=582459;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "982e7a7048f84d93a64088836a8e4b99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:33:25 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.245</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:33:25 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.245\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:33:26 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:33:26 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3f0e542658f4586b71a5e23afc0c6f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:34:00 -03 </span>loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:34:00 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "source_time2 = source_time.updated_copy(phase=np.pi / 2)\n",
+    "source2 = source.updated_copy(\n",
+    "    center=(source.center[0], -source.center[1], source.center[2]), source_time=source_time2\n",
+    ")\n",
+    "\n",
+    "sim2 = sim.updated_copy(sources=[source, source2])\n",
+    "\n",
+    "sim_data2 = web.run(sim2, \"sources at 1 and 2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, in this configuration, all power is directed to the upper output at the crossing frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot field\n",
+    "sim_data2.plot_field(\"field_mon\", \"E\", \"abs\", f=cross_freq)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "This notebook demonstrates how to import geometries from a gds file to Tidy3D for FDTD simulations.",
+  "feature_imag": "",
+  "feature_image": "N/A",
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "gds, import, 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": "3.11.7"
+  },
+  "title": "GDS File Import in Tidy3D | Flexcompute"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/case_studies/pic.rst b/docs/case_studies/pic.rst
index 237bcd9d..fc2a987a 100644
--- a/docs/case_studies/pic.rst
+++ b/docs/case_studies/pic.rst
@@ -45,3 +45,4 @@ Passive photonic integrated circuit (PIC) components form the backbone of many o
     ../../AnisotropicMetamaterialBroadbandPBS
     ../../SWGWaveguideCrossing
     ../../SiWaveguideTPA    
+    ../../DirectionalCoupler
diff --git a/docs/features/parameter_sweep.rst b/docs/features/parameter_sweep.rst
index c897dc3a..30830eb0 100644
--- a/docs/features/parameter_sweep.rst
+++ b/docs/features/parameter_sweep.rst
@@ -8,4 +8,4 @@ Tidy3D excels at exploring a high-dimensional design space because it is able to
     :maxdepth: 1
 
     ../../ParameterScan
-    ../../Design   
\ No newline at end of file
+    ../../Design
diff --git a/misc/import_file_mapping.json b/misc/import_file_mapping.json
index bd8abfd5..35621808 100644
--- a/misc/import_file_mapping.json
+++ b/misc/import_file_mapping.json
@@ -10,6 +10,8 @@
     "Autograd6GratingCoupler.ipynb": ["grating_coupler_history_autograd.pkl"],
     "CMOSRGBSensor": ["red_eps.csv", "green_eps.csv", "blue_eps.csv"],
     "DifferentialStripline.ipynb": ["stripline_fem_mode.csv", "stripline_fit_mode.txt", "stripline_fem_sparam_long.csv", "stripline_fit_sparams_long.txt"],
+    "GroundedCPWViaFence.ipynb": ["gcpw_sain_experimental.csv", "gcpw_sain_simulated.csv"],
+    "EdgeFeedPatchAntennaBenchmark.ipynb": ["edge_feed_patch_fem.csv", "edge_feed_patch_fem_gain.csv"],
     "Fitting.ipynb": ["nk_data.csv"],
     "FocusedApodGC.ipynb": ["Focusing_GC.gds"],
     "GDSImport.ipynb": ["coupler.gds"],
@@ -28,4 +30,5 @@
     "UnstructuredData.ipynb": ["unstructured_grid.vtu"],
     "MMIMeepBenchmark.ipynb": ["MeepMMI.txt"],
     "Autograd13Metasurface.ipynb": ["logo.png"],
+    "DirectionalCoupler.ipynb": [ "misc/coupler.gds"]
 }