{ "cells": [ { "cell_type": "markdown", "id": "804aac2b", "metadata": {}, "source": [ "# Atomically thin waveguides based on MoS2 monolayers" ] }, { "cell_type": "markdown", "id": "83e3ff78", "metadata": {}, "source": [ "Common waveguides in integrated photonics have a thickness in the order of hundreds of nanometers. However, a novel research `Myungjae Lee et al., Wafer-scale δ waveguides for integrated two-dimensional photonics. Science 381, 648-653(2023)` [DOI:10.1126/science.adi2322](https://www.science.org/doi/10.1126/science.adi2322) demonstrates ultrathin, wafer-scale monolayer molybdenum disulfide (MoS$_2$) waveguides that can efficiently guide visible and near-infrared light over millimeter distances with a low loss. The extreme thickness on the order of a single atom enables light confinement analogous to a quantum mechanical $\\delta$-potential well. This allows single-mode, broadband operation unconstrained by conventional waveguide design requirements. Furthermore, microfabricated dielectric and metallic components integrated on the waveguides enable manipulation of the guided waves. Overall, this work establishes a flexible integrated photonics platform using atomically thin two-dimensional materials.\n", "\n", "In this notebook, we replicate the key findings from the above publication. We have used the [FastDispersionFitter](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.FastDispersionFitter.html) and [Medium2D](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium2D.html) features to model the $MoS_{2}$. Our simulations confirmed that the real part of the effective index is approximately equal to the refractive index of the surrounding environment, while the imaginary part follows that of the $MoS_{2}$. Ultra-low loss waveguide mode can be achieved at energies below the bandgap of $MoS_{2}$ when absorption is low. The profile of the waveguide mode is analogous to that of the solution to the $\\delta$-potential well in quantum mechanism. We also simulate the propagation of the waveguide mode and visualize its out-coupling into free space, reproducing Fig. 1E in the [publication](https://www.science.org/doi/10.1126/science.adi2322).\n", "\n", "\"Schematic\n", "\n", "For more simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). 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. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it." ] }, { "cell_type": "code", "execution_count": 1, "id": "54dd42ae", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.web as web\n", "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter\n", "from tidy3d.plugins.mode import ModeSolver" ] }, { "cell_type": "markdown", "id": "ca9dc32f", "metadata": {}, "source": [ "## Simulation Setup" ] }, { "cell_type": "markdown", "id": "2958f7e2", "metadata": {}, "source": [ "In our [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html), we provide a built-in option of [MoS2](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#molybdenum-disulfide-mos2), which can be used by `MoS2_medium = td.material_library['MoS2']['Li2014']`. However, for maximum consistency, in this notebook we will use the experimentally measured refractive index provided in the [reference](https://www.science.org/doi/10.1126/science.adi2322). The refractive index is stored in a .csv file and we will use the [FastDispersionFitter](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.FastDispersionFitter.html) to fit it with the pole residue model. Using 4 pole residue pairs, we are able to fit it very nicely. The fit can be further improved by using even more pole residue pairs, but it is unnecessary in this case. In addition, we can also manipulate the relative weights of the real and imaginar parts of the refractive index. Since in this case, both $n$ and $k$ have the same order of magnitude, we will use the default value `AdvancedFastFitterParam(weights=(1,1))`. " ] }, { "cell_type": "code", "execution_count": 2, "id": "7083ed4a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab5540097460463eb66cc76be73db876", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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c36fkw1mIRx/MaT1QFJV1KbPJMGcA8toSoqGSe+zqiYnhoZzO3cmfyQdRrTr277+u1rs9LzXBJYxg3YkWRDEbjSEZ1xYf0cr6H2auvriPdM+Khqb8HJEF1gK+OfwNAM00NzF7zfFLtqZX9tjE8FDM1snMP/UE2YYLvLntTYLM4+S1JUQDJcGuHsl23QBAQWo/9Grl3Z416Z+6c/fEGHhsyEwOWyPYlbiLk5oPaRt6DzNXw5y1xzFbrPLGIxqU4tsmwBbIVp5cSWJOIm4ab5ZvCWLK0CtrXXt6WBdSlz/D0qT/8ceJP8g958WUYf+S15YQDZB0xdYTCdkJbIrdDICa0eeq5qy7WqVb4p694RrmDZvHrSG3oihW4nQLcW28DLOlkEv1EMm9QaI+Kr182Ow1USw4tACAtITeTBna8aqC2Bs334IlZQgAroG/8WB//5ooWYgaNWjQICZNmlTt41544QUeeeSRWjl3dV24cIGAgADOnTtX69e6EhLs6onn//wSUGli6EjUyw/YdSHn8l20Bq2B1/u/zpPXPAmA3mcT7sFfY1XyKq1R7g0S9VlxuIvY8gdRqVGoVgPju9131a1rEZHR5J4fjDW/MYoui7HLptdQxULYV0JCArNnz2batGk1et7169ejKAppaWnVOs7Pz48HH3yQl156qUbrqSkS7OqB2WuOsePCKgCe6HUfUHFh8bpU2U3fiqKQkzSQ3HP3ocWA1niU4I5foOiTy9Qo992JhmBieCiuvhsBsKT34plh3a/qfCWvm6EdWDjqfUDheM4Gnvn9hxqoVtR7mQmw7i3bfx3Q559/Tr9+/WjRooW9SykxduxYvvvuO1JSUuxdSgUS7OqB2LyDaAwpGPVGhrYYWrK9Nuasu1LFbzwT+9zJdyO+JsAtgLTCs/iGfYzWPYaZq6MImybLkYmG4Y1Vm9B4RKOqCnnJ/a7qw1f5D0Nd/bvyQIf7AVie8CHvrd5XU2WL+iozATbMsEuw++OPP/Dy8uK7776rcp9FixYxcuTIMtuys7N58MEHMRqNNGnShPfff7/Ccd988w09evTAZDIRGBjI6NGjSUpKAuDUqVMMHjwYgEaNGqEoCmPGjAFg5cqVXHfddXh7e+Pr68stt9xCTExMmXN37NiRoKAgfv3116t5+rVCgl09oPHaAcDwVsNx07mVeawm56y7GqW7Zzv6deT7W76nk28n8q1ZeLT4EoP39pJlkyTUifosIjKar/bb3gz6NOnF5EF9rqplvbLR6RO6TcCkbYxGn862tG8rrUHuYRX2tnDhQu69916+++477rvvvkr3SUlJ4fDhw/To0aPM9meeeYYNGzawdOlS/vzzT9avX8/u3bvL7FNQUMBrr73Gvn37WLJkCadOnSoJb8HBwSxevBiAY8eOER8fz+zZswFbaJwyZQo7d+4kMjISjUbDqFGjsFqtZc7fq1cvNm7cWBM/iholo2KdXIY5gzWnbZObjmozys7VVK18uAxwD2D+TfN5cfOLrDi5Apcmv6BzTSAnYQT3zNtCvxC/KiczlrVmhbOyta4dI7jzQdIKYUTrEYwKvfKl/qDy0enuencGNHqM5Rde4mjOSnYm3E2PwB6lapCl/Rq8zISLLXTx+8r+F8AUaPuqJXPnzmXatGksW7aMgQMHVrnfmTNnUFWVoKCgkm1ZWVl88cUXfPvtt4SHhwPw1Vdf0axZszLHPvTQxTWUW7duTUREBD179iQrKwuj0YiPjw8AAQEBeHt7l+x75513ljnPl19+ib+/P4cPH6ZTp04l24OCgtizZ0/1n3wtk2Dn5FacWEG+JZ823m3o5Nfpnw9wIK46V5qaHyY/yYpLwCq0jTYTFpjBtgO3s/WE7b6F0m9y8oYknJ3FqvLAQB1LkmJx0bowrMUw4OLfeU3eNvH2iDuI+Xkjx7LX8GTk/4i8+zfmbTgrtzsIm53zbd2vpS2bePH/Bz4Pg6fWyqV//vlnkpKS2LRpEz179rzkvrm5uQC4urqWbIuJicFsNtO7d++SbT4+PrRt27bMsbt27eLll19m3759pKamlrS4nTlzhg4dOlR5zejoaF588UW2bdvGhQsXyhxXOti5ubmRk5Nzmc+67khXrJP75bht+bA7Qu9AUZxrFGlEZDSz1kQz4ZpHmT14Nu46d+LzD9K4/SdoXM/JoApR70weFobR19YqMjh4MEaDseSx2rht4stbX8Fd40NGYTzdP3xKXkPioh5j4ZENtq+REbZtIyMubusx9tLHX4Xu3bvj7+/Pl19+iape+sOMn58fAKmpqdW6RnZ2NjfeeCOenp5899137Nixo+R+OLPZfMljR44cSUpKCp999hnbtm1j27ZtlR6XkpKCv7/jTSskwc6JzCp3H86xlGMcTj6MTqMj/lwHp7tnpvS9QUOaD+H7Ed/T0rMl2ZYLeLb6lLZtjsqgClGvFFoLWXFyBQC3tL6l1q/nafDk7UGvAKBttBEX4wl5DQkbUyAEdbN9Nelq29ak68VttdgNGxISwrp161i6dClPPPHEP+7r6enJ4cOHy2zT6/UlgQtswS8q6uJ74NGjR0lOTmbGjBkMGDCAdu3alQycKGYwGACwWCwl25KTkzl27BjTp08nPDyc9u3bVxkqDx48SPfuVzeivTZIsHMixbPWF4e7X4/bPn0Eu/Tko7UJTjfvW/lpUVp7t2bhiIUMajYICwXE6RfgHvgbZkuBDKoQ9cLW+K0k5yXTyKUR/Zr2q5Nr7o9qijm1J4qiogv8gXdXO949QaLhCQsLY926dSxevPiSkwprNBqGDh3K33//XbLNaDQybtw4nnnmGdauXcvBgwcZM2YMGs3FSNO8eXMMBgMffvghJ06c4LfffuO1114rc+4WLVqgKAq///4758+fJysri0aNGuHr68u8efM4fvw4a9euZcqUKRXqysnJYdeuXdxwww1X/8OoYRLsnEjpuelmrTnM7yd+B+DQsXb1pjXLZDAxe8hsHuv6GADaRpvxaDmPAiXVbitpCFFTil+zN7W6Cb1GX+vXK76F4ZGOk2huao5Gn84XR95n9hrnat0XtcwUaLunrhZb6SrTtm1b1q5dy/fff89TTz1V5X4PP/wwixYtKjMq9d1332XAgAGMHDmSoUOHct1113HttdeWPO7v78+CBQv46aef6NChAzNmzOC9994rc96mTZvyyiuv8Pzzz9O4cWMmTJiARqNh0aJF7Nq1i06dOjF58mTefffdCjUtXbqU5s2bM2DAgBr4SdQsRf2nDm4HlpGRgZeXF+np6Xh6etq7nDoTERlNxNafcWu2EGuBF+Nbfs6koe3sXVaNioiMZvaWJXg3/xmzmoNWNZJ59m6e7DeiQoCVkbLCGeQU5DDox0HkFuby3c3f0cW/S61er/x9qfvP7+f+5Q+gYiU39m4m9r67wuAkeR05j7y8PE6ePEmrVq3KDCyoj1RVpXfv3kyePJl7773X3uUA0KdPHyZOnMjo0aNr7JyX+p1WJ+9Ii50TmhgeiqHRLgCsGT3qZaibuTqKJ/vezpLbf6adTzssShZuwV8yZ89cPlhztMK+ztYNLRqeyDOR5Bbm0sKzBZ39Otf69crPb9fFvwvXeN4FgEfQb6SbE0v2ldeRcGSKojBv3jwKCwvtXQpgWyv2jjvucJiQWZ4EOyf03uq9aNyPA5CX1q3edVGWfkMK9gzmm+HfcGfonSiKiot/JIvOvkhKXgr3zNtS5aAKmYBV2Fv5wU7LYpYBtrnrPlx7vNb/Pitb2u/z254jwNAWq5LLX2kfYrFaZMS5cArdunXjgQcesHcZgG2k7rPPPuuwM1FIsHMyEZHRfLLjDxTFQmuv1kwe2N8u68HWpvJvSK46V17u9zJvXPcGWsVAtuYI1383kh0JOyo9XlofhCMoPdgpKSeJbQm2EXzJCZ3s9vep0+hYMGIWesWVBPNhOs18XkKdEPWMTFDsRIoDS5drznAyF8KbhzPxmqubtd6Z3BpyK+182jHq5/+gMZzHrfnnXOv5b2autg1VnxgeKq0PwmEU//3NXB3F/syjWFUrjQ1t+Xxdhl3/PoM9g3mh7/94cfOLaP3+xCWvBRPDR9ilFiFEzZNg50QsVpWJQ1vyQ+JewBbsoHZmrXdUK3crZJ+YgHuTZWi9drI780dCux5k1rpbmbP2OGaLVUKdcBjFf4efHJ+N1hVOn27vEH+fZ053pCC9G3qvveiafMsbq7oz7cb+dq1JCFEzpCvWiUweFsa1bS+QU5hDY/fGdPC9uCRKbcxa72hKWuOGdubwxPkM9pmEanEhwXwYj9YRWN0OyHx3wuHc2N2K1jUeVdWiZHe1+99n8Yovj3R4jvY+7dHosvnu1KvMXH3QrnUJIWqGBDsnE3kmEoAhzYc47I2btaGyLtaIkeO4u+lMLLlNUbQ5uAV/g+K/mPdXH7BztUJc9HzkhwBYszpiLnCz6/2wpV9HU4Z2Yvbg2bhqPNG6xjHvyIwK89vJICQhnI8EOydSaC1k/dn1wMVu2Iai/NQNxbx0Tcg59V98C22LqRsabePLkxN5YcVye5QpRBlvrtpMdPZGAH66538lE4zbK9yVfx01MTZhqO8zqKoGvdc+9mUuLdlXBiEJ4ZzkHjsnsjdpL6n5qXgaPLm28bX/fEA9Ulk388XWhw5MDL+NrfFbeXLNc+S4XODXxKmc/XUvn9/2LDqNrmR/mYBV1JWIyGi+OvQ1Bl8rvQN709GvIx2LPo/Za7BTZX/7b918OylLz7A57TN2ZnzL5tj+7DzqL4OQhHBS0mLnRIq7YQcFDyoJKw1Z+daHPk36cGfgLAoyuqAoVnZlfM/YlWM5m3FWWh9EncspzMDdzzaR+NhOY0u2Fy8N6EiDnT659QnC3MNRsfLIqkl8sOFvCXXCoZjNZtq0acPmzZvr/NqnTp1CURT27t172ccMGjSozBq4ffr0YfHixTVfXCUk2DkJVVVZe2YtYLu/TlQ+Aeuzw67hv+1fIjf2bvSKO3vP7+XWJXfw4Y6vmTw0VN6oRJ1pFLiTQjWPto3a0i+oX5nHHG2wk6IofH/HO1hzm6Noc3Fv/gX/6u1h77KEKPHJJ5/QqlUr+vXr9887O6Dp06fz/PPPl1nvtrZIsHMSR1OOEpcdh6vWtcKbhCjryaFhTOx9N6nRE7DmtKZQzcO1yRJWXHiVhOyECvvLDeKipuUV5rHw6ELA1lrnDAOdPll/mpyz/4c1PwBFn86/l47hQu4Fe5clBKqqMmfOHMaNG2fvUq7Y8OHDyczMZMWKFbV+LQl2TmLtWVtrXf+m/XHTudm5Gsc3MTwUvdWP7NMPU5h0K1rFQGz+Pm5efBtLji9BVW3dYNJFW3+UX8Kr9LbKwnttBvqlx5eSkpdCkEcQN7a8sVauUZOKXweTh3Rn7X0LMWoDyCiMZ/iiB0jPT690f/kwJK7EoEGDmDhxIs8++yw+Pj4EBgby8ssvX/KYXbt2ERMTw4gRFyfSLu4e/fHHHxkwYABubm707NmTqKgoduzYQY8ePTAajQwfPpzz58+XHGe1Wnn11Vdp1qwZLi4udOvWjZUrV5a53vbt2+nevTuurq706NGDPXv2VKjp4MGDDB8+HKPRSOPGjXnggQe4cKHqD0JarZabb76ZRYsWXeZP6spJsHNQ5d+kiu+vC28eLv+oXoaIyGjMFisGrY7c5H7cETCTAEMYBWoOL2x6gSfWPsGd85bLWrP1SPESXlOXL+GmxTdx17K72JHxFbO3/MbMyP1lwnttBnqL1cKCQwsAeLDjgw5/P2z5qYQaezTmh1vno1O9yNOc44aF/0dOQU6F/eXDkGNRVZWcghy7fBV/UL5cX331FR4eHmzbto133nmHV199ldWrV1e5/8aNGwkLC8NkMlV47KWXXmL69Ons3r0bnU7H6NGjefbZZ5k9ezYbN27k+PHjvPjiiyX7z549m/fff5/33nuP/fv3c+ONN3LrrbcSHW17v83KyuKWW26hQ4cO7Nq1i5dffpmnn366zDXT0tIYMmQI3bt3Z+fOnaxcuZLExETuuuuuSz7vXr16sXHjxur8qK6IY/+L04AVv0kBjOrpSnRqNFpFy5ETTfko0vaPsKhc+Teq4u8nDX2D/ZlL2Z72PRvObUDVbUXvdQuqWjHUFR8vnMfE8FBO5W5jWeL7KJpCYokFjuAeDKqqYUliCB4HRvDnljC2nah8Wa8rHTk9qyjoTAwPZc2ZNZzLOoe3izej2oxy+NHYlU0l1NyzOT/c9iV3LX2AHE0Mdywez2//ns8n60/LaFkHlVuYS++Fve1y7W2jt+Gud7/s/bt06cJLL70EQGhoKHPmzCEyMpJhw4ZVuv/p06cJCgqq9LGnn36aG2+0tYo/+eST3HvvvURGRtK/v20llXHjxrFgwYKS/d977z2ee+457rnnHgDefvtt1q1bxwcffMDcuXNZuHAhVquVL774AldXVzp27Mi5c+f473//W3KOOXPm0L17d958882SbV9++SXBwcFERUURFlb5az0oKIizZ89itVrRaGqvXU2CnYMqu87kYQAaGzryUWS8/KN6CZVNZFz6Zzll2B1s2t8YbeMf0bqdxTVoMZ8c20+W5Smm3dhf1pp1AqVDVGk/R/3MmuR3UTRWCjI7QFZXVNfj+PmdJcOSwPmCaD7Y/QEWS3MU/T0Vzns1gb74g5iqqmzO+xKAe9vdy+d/xTr8h4SqAmdYozC+HvEZ/7diHLH5++n+6b1kn72ffiFNKn1tOHqAFY6jS5cuZb5v0qQJSUlJVe6fm5uLq6vrP56rcePGAHTu3LnMtuJzZ2RkEBcXVxL6ivXv3599+/YBcOTIEbp06VLmen379i2z/759+1i3bh1Go7FCPTExMVUGOzc3N6xWK/n5+bi51d4tVRLsHFjJOpNRH6N1hxOnW0ng+AdVTWRc/P3mmAvk5/pjOPs4BV5/4dF4DRij+T52It+8czN5Kb2YMqyd/IwdWOnW7Inhoaiqyqf7P2Xu3rkAtPUYyv6ocMwWBYO2O5smDSchO4FBcz9C57cCrfsZfELnELE1FrizTKtuvxDfKwotxcfM3vwH7s0P46p1JTOpFx+vde4PCV38u/DJsDmMW/lfNB5RuLf4jM2nxhARWfbnJK3c9uemc2Pb6G12u3Z16PX6Mt8rinLJ0aJ+fn4cOFD5ikKlz1U8SKn8tpoeiZqVlcXIkSN5++23KzzWpEmTKo9LSUnBw8OjVkMdSLBzePf182He2TMAKDkdnfYNoq78U2vB1hMppbpow5i1vgNtOiwnwXwYfeMlaEx7SC2YAkiLhKMq3QJrVS1kGxez6JjthuTupn9xree97LREY9BqMFusJfeq5qX0xpDdFl3gQszuZ3Br9h1zDxxnzrpbMRdq6Rfiy+aYZCIio68otNzRy41v4xaTa4XsC9fw8cFEpw51xbYd9iHn9Hjcmy9A63YWv9DPmbVuDECZUFwfnqszUxSlWt2hzqR79+58/PHHqKp6VSPMPT09CQoKYtOmTQwcOLBk+6ZNm+jVqxcA7du355tvviEvL6+k1W7r1q1lznPNNdewePFiWrZsiU53+THq4MGDdO/e/Yrrv1wyeMLBvbT6ZxRFxZrXFHO+p13XmXRmVXXRTh7Uj+h995OXcBuq1YDW/TQ/JUzh/359HbPFXOF4uWHcMRRP8vvR3k9YdGwRqqrQz/theniNZtaaaKYMCyPqjeElS3gV/+6jXrmPR0LfI//CIMC2BJ0uOAIXt0QWju9TYcmvyw0tqXmp/HfNf8m1pmHNa0JO0g0YtBqnDzol96cOGMrSOxbiofUhX4mnUZtPmbVhE2HTVkioE7Vu8ODBZGVlcejQoas+1zPPPMPbb7/NDz/8wLFjx3j++efZu3cvTz75JACjR49GURTGjx/P4cOHWb58Oe+9916Zczz++OOkpKRw7733smPHDmJiYli1ahVjx47FYrFUee2NGzdyww03XPVz+CcS7BxYRGQ0a05vAOCx3iPtvs6kM6uqi9ZGw7WNRrDmrj9o7toDRbGwO+MHwhfdxt6kvdIi4aAevr4pBp9NABQm3klH482X9XuaNLQ9E7pNJOfMQ1gLjWhdE9E3j2Dcknd5fHDrktfZ5YaW3MJcJqydwKmMUxi1fuScHYtBcS/TWuiMyv/dh3iH8Ovti/DSNaVAScGjxScU6k/XiwArHJuvry+jRo3iu+++u+pzTZw4kSlTpvDUU0/RuXNnVq5cyW+//UZoqO1v2Gg0smzZMg4cOED37t2ZNm1ahS7X4lY/i8XCDTfcQOfOnZk0aRLe3t5VDoqIjY1l8+bNjB07ttLHa5KiVnecsgPJyMjAy8uL9PR0PD097V1OjbL9o3oYnw5vUKDm8v2I7+nk10lCRg2q7GepqiqTfv+KNUmfotFloaoKBWm9eLjjBJ4Z1s2+BYsyHln6AVvSvsBq9iM7Zgp9WvvRL8SvzOtiVqlW1tLd6MW/+x6tdQSGLGPDOdsHqMaG9sy/5X3CZxwsmi5HQ9Qbw6usodBayOT1k1l/dj0uipGU448waeB19aKLsqpBKil5Kdz0/YPkak6jWnXkxd/JxN53O+VzdGZ5eXmcPHmSVq1aVTmwoD7Zv38/w4YNIyYmptJBC47uueeeIzU1lXnz5lW5z6V+p9XJO3KPnYOyWFXuHlDI8gu5+Lr60sG3A3Dx/iJHWmfSWVXWiqcoCrNHjuGd1V2Yf/hDdN47MTTaxh8px+l08jluanmTU6wiUN99sOYom5J+RWOAFwf8h8TW7YoGP/iV2a+y+yHLBy5VvYFfj//K61veItF8hFsWj0I1jcCQ2buk1a34b6R02FFVlTe3vcn6s+vRoif15P30atauTFc/UGaghzOp6l7SbzclkxT9EB26LuVs3m7cmv7A3P3nsKpPMmlo+zquUjQUXbp04e233+bkyZNlRr06i4CAAKZMmVIn15Jg56AmDwvjnR1L4AIMaDYAjXKxedfZ3iAc1aUGQbhqTOTG/wuXzGvRBvxCMud59q9nWXJ8CdN7TyfYM7hkXxlUUbciIqOZs20pbs1S8HLxYmTISNzb2W4av5wQVT7QK4rCHaF30CuwF3f+PJEcTTSuTX4hOGwH2alhzN50HIs6jMlDO9hG5K45xNm83RzP2cjxnA0oKAQVjONwbssKwbK+fRC7GIo78/jg2/ho30fM2z8Pg88mPo2KI8/6As/f0LPM/vLaEDVlzJgx9i7hij311FN1di0Jdg7sr3N/AXB9s+vtXEnDUrZFZwSz1gzh472fY/Bdx+a4zYz6bRSPdHmEjIT+7D6dyeaY5AojJuUNrfZYrCotW+8i0Qx3hd1VMhLwckNUVb+TX7bnknhsLEP7HGNf1g+czTwLurO4t4AvznzDuh+voYWfC43ab2HlhfyS4/p6j2PVlpZVdrnWpw9i5UPxE92f4GRsI/5M+gCdx0l+TXyaEefn0Nm/s0yBIoSdyD12DupU+ilGLhmJTqNj490bMRqc754CZ1TVfVERkdHMWr8Z1yZL0HkcB8BFDST1zC30btKbheP7/OM5RM04cP4Ao5ePRqfRserOVQS4B9TIeUt3s2YXZLM1bivrz61n1Yl15FrLrpdqLfCkMKsdZHclPzOkwf+uX125jkVnXkPrch6dRoev+VaOR/eodE5I+dBTMxraPXYNQU3dYyejYh1UcWvdtY2vlVBXhy41wfHkQf3oonuG3Ni7UQuN5CsJuLf4nN25c5nxp21i0HvmbZH1Z2tQ+TWTAb45/A0ArVyv47tNaTV2rcmlfmceeg/CW4TzWv/X2Hr/X9wW8Db554dSeOEmsk9M5JGW81HP/4v8zBAZFQq8eNNgHmo1i4KMjhRaC0nU/YJbi3lkFCaW2U+mDRKi9kmwc1B/xdqC3cBmA/9hT1GTJl+i5WVieCg/PNIPTfa1ZMU8hSWtHwoKeq+9fHvucdq9O52tJ85Xeqy8oV2Z4lUmisNdfFY8f57+E4C9B7vUyc9To2h4ffjNkHoDuecHoStshqIoJaNmnX1ak5ry9LCuWBMeJDfuTlSrAZ37KRbFPsnjv32CqqrSki1EHZF77BxQljmLXQm7ALm/ztFEREYXvaF7kBN/Kw+EjSLasoBDyYfQByxF67mD24MnlLmJX97Qrlz5kaUFXr9hUS0UZocw6fpBdfbzvPh7twW50r/P4t9v6XobItvPSMWQ1ZvsEyG06fA7CebD/JU6l44frSc3/jb6tG55yeOli1aIqyfBzgFtid9CoVpIS8+WtPBsYe9yRJHyAa34+74hE8lLXoFLwCo0rnH8dv5/XHvtYGatzWLO2uOYLVb6tPa55HnlDa1qJeEu8gDGNotQtDCied3Nm1Z+EMDMct3pzj6tSU2o/LXhTXifI2xLW4jWeAiP1jF4+t7HzNUhABXuYZWBFkLUDOmKdUAbztomS5XWOsdR1ZJk/UJ82RKTSk/fEfw1eiVt3cMBiMpZh0fI+6iemzBooV+IX6WrhkgX7eWZGB6KW6NdKNo8rGZ/3hlxd51ct/zvvfgezPKrwBQvcVZfpjWpjqpeG1OGtSNya0dyTk7AmheMos1jS9oXtOzyGR9sjKz2sm1CiMsjwc7BWFUrG2M3AhLsHEllgyoiIqPZHJNMvxBferb0wcfVh5///QG3+r+FJS8IRZuLa+BSdM1nk5B/5IrXIRUwe80xNN625cPMyf2ZszamTq5b/vdefA9mZUFuYnhog2x1vfRyfdCraUf2j19Gf+9HUS2uJBecxKPVR8w98DZhLyyW10ADM2jQICZNmlTt41544QUeeeSRmi/oMlS35gULFuDt7V3y/SeffMLIkSNrvrAqSFesAyg9zcKhC4dIyUvBqDdyTcA10k3nICr7+Vf1htbYpS05JycQ2uYA2R6/k0k8y85Po437QP4z5AFmro4q6aKVN7R/FhEZTcTmP3FvkYyH3oMx195VZ92el3rdye/N5nJW9wD45LYJvP1nb+YfmYPeezeGRttQPfejTRnCo4OG1nXZwokkJCQwe/ZsDhw4YO9SrshDDz3Ea6+9xsaNGxkwYECtX09a7BxA6ZF/xWtW9g3qy8frT0k3nQOrbATtxTe0dqx++H/8Pup37gy9E1A4nrOBHxOewM33b8yWAi71a5WpUWyKf57d2tvmDryx5Y08NaxzhdZP4Viq+tDz3A09eazTdFqan8aSF4iizUXn/wcDv7+JpceXYrFa7FSxcGSff/45/fr1o0UL57zn3GAwMHr0aCIiIurkehLsHEBxt87M1VEsPrIaAHNGO+micELl39B8XH14ud/LLLz5O/z1bShQc9EF/I5H69kobtFy390/sFhVnghvzjnzVgBuC7kNaNj3tDmDS00bBHAgxo9HQj7ktf6voVMbkW25wPRN0/n37//mr3N/UTxvvnzAuXyqqmLNybHL19Wsc/DHH3/g5eXFd999V+U+ixYtqtCVOWjQIJ544gkmTZpEo0aNaNy4MZ999hnZ2dmMHTsWk8lEmzZtWLFiRZnjNmzYQK9evXBxcaFJkyY8//zzFBYWljyenZ3Ngw8+iNFopEmTJrz//vsV6snPz+fpp5+madOmeHh40Lt3b9avX3/J5zly5Eh+++03cnNzL+OncnWkK9ZBTAwPJduSwsL4E6iqwh/bvCTUOaGquu46+3fmtoAZzNnxPd7NVpPnkoR7iy8oyOjIrPUjAFuI2XEqpWSJsvL38zXELvnJw8JYFrOMnLgcgk3BdA/oXvKYvDacT8Uu2nacONWGT3Z/jcFvHdGp0Twe+Th++hA8cm7i4PFmTBnWtuT4Wauj2HEqhZ4tfSq8Fhrqa6SYmpvLsWuutcu12+7eheLuXu3jFi5cyH/+8x8WLlzILbfcUuk+KSkpHD58mB49elR47KuvvuLZZ59l+/bt/PDDD/z3v//l119/ZdSoUfzvf/9j1qxZPPDAA5w5cwZ3d3diY2O5+eabGTNmDF9//TVHjx5l/PjxuLq68vLLLwPwzDPPsGHDBpYuXUpAQAD/+9//2L17N926dSu57oQJEzh8+DCLFi0iKCiIX3/9lZtuuokDBw4QGlr5v0s9evSgsLCQbdu2MWjQoGr/rKrDYVrsZsyYgaIoV3RTZX0R2vIsANa8ZujxlDeueiQiMppZa47zRK/7WHPXcu5rfx9aRYve8xAerWcyZ89c5qw7XDIYo7KpIBpqC97SmKUA3BpyK4rSMH8G9UVlXbRThnZiwrXjyYp5Bt/CYbhqXblQEMNp/Vz8286lbcgJrKoVoOSDz45TKWXO29BfI85o7ty5PPbYYyxbtqzKUAdw5swZVFUlKCiowmNdu3Zl+vTphIaGMnXqVFxdXfHz82P8+PGEhoby4osvkpyczP79+wH46KOPCA4OZs6cObRr147bb7+dV155hffffx+r1UpWVhZffPEF7733HuHh4XTu3JmvvvqqTIvemTNnmD9/Pj/99BMDBgwgJCSEp59+muuuu4758+dX+Tzc3d3x8vLi9OnTV/FTuzwO0WK3Y8cOPv30U7p06WLvUuxq/p4/AFCzO5TMZi/hrn4o/4b2fK/nuTP0TmZsn8H2hO24+Edi9d5FQdIINsd0Kvnd3zNvC1tPpFS5RFl9b6GIz4pne/x2AEaG1N2oMlE7qvpbvTgXoAcGQ1/w+gs33y3kac7x9Ianae3VmqaaG9l8Ioh+IYFsjkkueY3I6HIbxc2Ntrt32e3a1fHzzz+TlJTEpk2b6Nmz5yX3Le66rGw93NKZQavV4uvrS+fOnUu2NW7cGICkpCQAjhw5Qt++fct8QOzfvz9ZWVmcO3eO1NRUzGYzvXv3Lnncx8eHtm0vthofOHAAi8VCWFjZv+X8/Hx8fX0v+Vzc3NzIycm55D41we7BLisri/vuu4/PPvuM119/3d7l2M3MNQc5nbMPRQNLxo5n1R5Ng57wtL6p7A0ttFEon9/wOZN//5rViZ+h0afh0vQ7fJq05YO/hpeMnK1MQ5nQddmJZaio9ArsRVNjU3uXI2rRxPBQ29+82R1Dys38NeF1Jv4Rwa603ziRfoITfIx/B0/6dbqXLi16lRldXr6Vu1hD+PBTTFGUK+oOtYfu3buze/duvvzyS3r06HHJlng/Pz8AUlNT8ff3L/OYXq8v872iKGW2FZ/Xaq3839ErkZWVhVarZdeuXWi12jKPGY2XXtc9JSWlwnOoDXbvin388ccZMWIEQ4f+83D3/Px8MjIyynzVBxGR0czduhJFU0CgRyBhjcLKDKiQkX/114drj7Nkkx/jWnzCf7r+By16sjXHcG8VgeK3BIM+r8HMfzer3N+6qqr8FvMbAG75veUm+nqu/LJtX/2dxFd3vID55FTyEm9GLfAmz5rBp/s/5cfE/+Ae9BOF+jNoFaWkBa/8+aR71jGFhISwbt06li5dyhNPPPGP+3p6enL48OGrvm779u3ZsmVLmcEemzZtwmQy0axZM0JCQtDr9Wzbtq3k8dTUVKKiLv7b0717dywWC0lJSbRp06bMV2BgYJXXjomJIS8vj+7du1e5T02xa4vdokWL2L17Nzt27Lis/d966y1eeeWVWq6q7lmsKte0i+NoNgxqNqjkU0bxm7aM/KufKga0TtwWcht3/zyNDM1uDD6bsXru5UjWA0waOrTez39XPO0P2P72953fx+mM0+gUV37f4seUofIGXV9VtVzf1hPJmAtcMKQPIiulP7f3T+WCdjX7zu9D67ULD69dWHKb0lw/hJmR+YCsz+wswsLCWLduHYMGDUKn0/HBBx9Uup9Go2Ho0KH8/fff3H777Vd1zccee4wPPviAJ554ggkTJnDs2DFeeuklpkyZgkajwWg0Mm7cOJ555hl8fX0JCAhg2rRpaDQX28DCwsK47777ePDBB3n//ffp3r0758+fJzIyki5dujBixIhKr71x40Zat25NSEjIVT2Hy2G3YHf27FmefPJJVq9eXWnfeWWmTp3KlClTSr7PyMggODi4tkqsM08ObcOyn/YCMCh4UJnH5B+l+quyG8l/2Z5L7LG76NJmIKluP5PGWf5O+5Qj2X/iYryJ/KwWGLQaLFa1ynswnbX7qfyaqyluSwDITe3AlKGd5bVQT1W1JNnWE8klg4kWju9Tsl+/kAlkx+6hS4cDnMnfCm6xxPINXmEuzD3QjYiNPSnMa8qUYW0b5H2pzqRt27asXbuWQYMGodVqK51aBODhhx9m/PjxvPPOO2VCVnU1bdqU5cuX88wzz9C1a1d8fHwYN24c06dPL9nn3XffJSsri5EjR2IymXjqqadIT08vc5758+fz+uuv89RTTxEbG4ufnx99+vS55CCQ77//nvHjx19x7dWhqFczAc1VWLJkCaNGjSrTR22xWFAUBY1GQ35+foX+6/IyMjLw8vIiPT0dT0/P2i651hy6cIh7/rgHd507G+/ZiEFrsHdJwg7Kv8EVWgv54dgPzNzxIWY1GwBLRldyE4fTt0VIldOiOHtLRURkNDPXHMIY+gaKNo8Rfq8wY8Qd9i5L1JLSK+8UuxjifMtMbTL6s61lwl5qXiq/xfzGZ3sXkl4YV3K8Ja8xfRvfyIwbxuDv7l/mnM782igtLy+PkydP0qpVq8tuHHFWqqrSu3dvJk+ezL333mvvcqrt0KFDDBkyhKioKLy8vKrc71K/0+rkHbu12IWHh1dYHmTs2LG0a9eO55577h9DXX2y/tx6APoF9ZNQ14CVb8HTaXTc1/4+zp1ty5eHP8HgvR2t5z68vY6yM/F6+oTcXqbrsr68cU0MD+WjHT+haPNQCxrx5s2327skUYuqs1xfz5Y+Zf7byLUR/9fx/3iww4Pc9sVXROWsQWc6hNY1ke3pXxP+07f0a9oXfc61LNvqQ7+QpvWqlbuhUBSFefPmOe2SYvHx8Xz99deXDHU1yW7BzmQy0alTpzLbPDw88PX1rbC9vttw1raM2MDggXauRNhTVWtufrw2kSnDnmNYt0JmbJ/B7qTduASs5oB5Jx3b3MPM1Wq9Gh0YERmNYrJN22BO686ctTFOHVRF9VX1d1rV9g/XHmf/cX+mDHuJMQMa89yKr9kQtwKt+xk2xW4CNuEZpmd3ZlsmLTvGjOF346pzLZnwuLj1uzRnes00BN26dSszSbAzuZzBoTXJ7qNiG7qE7ASOpBxBQeH6ZtfbuxzhYEq3XLT3bc+CmxbwzvXvEOAegMaQyhn9x3i0+IJCXVy9GB0YERnNrHXb0Rtta8M+2PlfMjJcXFL5lmpPgycf3zaB/4R9QFbMU+SfH4rV7I+qFKD3PEhkynv0WziAZzY8w+ozq9h8Mq7MB6JZq6MY/dnWSl8zssSZcAZ2n8eutH9aa60+Km6t6+rfFR9XHztXIxxN+dYCRVEY3mo4A5sN5IuDX/DF/vngfhz3VhEUpPahh/Eup+2eLa61T8/9HMqy0jOwJ9NvHICnLlrmdBRVqqrbFkA1+2NJHob5Qjj/N9CAd8AhfjyyjCzLeVaeWgk68AzTsSerDY//Fs5rN9xV0oJXPuxJy55wFg4V7Bqi4vvrpBtWVIe73h015SbSj3vTqfNfnMrdisFnMwcL99I2dBQzV1udrnvWYlV5dIgfPyeuAeCRLo8AMu2PuLSqbmGobPqUKcNuYfN9k2j3+jxU9wPoPQ+iMSSjMx3lr9SjXL/oI6yFwbQJvYatZ1oye40PTw4NqzTslb+Ovdhp/KOoBTX1u5SuWDvKKcgpWS5pULNB9i1GOJXiN5TJg/qw7K7P+OyGz2jj3QaNLoc43Xd4tPoQi8txp+qenTwsDI33esxWM90DutM78OKyPhPDQx0mgArHVtX0KcUTfd/3+Tbys5tBygiyY57mzsazeLzb41jzglAUFa37GRJ1S/Bo/QHzTo2jw+xH2J74F71DPMq8lkqP3K3qg1NtdtsWr7BQF0tUibpR/Lssv6JGdUmLnR1tid+C2WqmqbEpId61P2mhqD/Kdz/1adKHn0b+xI/HfuSdbbPBNR73FvMoyOhMd9f7Ku2edbSWvPM55/kp6icA/tPlP5dcZkiIqlTVNVt6brzyLXn9QrqRfTIYg0sGqtsRwlqdJbHgAPmkgfcW3Ly3EKV8T2jXMObsDmbuprbk5wTSL8S/zLq1xeqiJU+r1eLt7V2yDqq7u7u8ZpyUqqrk5OSQlJSEt7f3Vc8KIsGuDpWfr6n4/rpBwYP4cO1xh+oWE46tsr8TnUbHhbiepEc/Rc/u2zmWsxq95wGOWqcTEnYTM9eYy3TP2usNqSoLDi0g35JPF/8u9A3qW+fXF/VDVf+GRkRGV5j7seJEyCMu3usZMoYdiTtwMUWjuB+j0JBMgvkwLgGHgVXoLe4Etu7PTQEtmbXhPKqq8uTQMO6Zt4WtJ1IqDZc1/aGpeAmr4nAnnJu3t/cllyW7XBLs6lDpJZMmDAlhwzlbsEs9H8onG+v/gu6idl0MZd2ZGH4Xx1KO8c6Od9iesJ0kfscj5C/yk27GkN29zEz+UPcDLcp/yLmQe4Efj/0IQJB6Kx+siZYPOaJGVdaSVxz2iidChvJh7zoWjn/a1q26fgthLWM5nbsHnUcMijaH1adXA2AMgXmnPuPTD0MoyG6FYmiFqlYMdTX9oUlRFJo0aUJAQAAFBQU1dl5R9/R6fY3N3yvBrg6VXjIpMf8YKXkp6BV3ftioc4pRi8KxlX/jauvTls9v+JzIM5E8u+51CvTJuDVdhCVnM9OWW3njZtvyN/ZYg7b8urBfHfqKPEse/vpQftrowZRh0qUkatblToR8ybAX7Uu/kMF89X/X8vLKVfx0aB0tg+NILojCrM8A/R60nnsAmHd6Hmt/7Mw9XQZx9KQvX23IZ8qwdrXy+tJqtQ1qUn9xaRLs6ljxi3rO3g9x8YOc9DZMGdZBQp24apW9cSmKwuHjLUg5NonBvY5wKOdXct3P8Nv5qRz6cTkf3/wCc9ZqMFusXGocRU13IZX+kJNrSWPx+R8AOB3Tv9I1PoWoDVca9vQaPW/cfAuNXdoWddua2BG/B4PxJLiewMXjHOgyOZG7mTe3bQbAq50rBy3d+Xhvd7oFdKOzX2c+2xDHjlMpZZZNm1VqUFP515yjjWQXjkmCnR1MDA/l05jDtm+yJdSJ2nOx+6cjE8Nv53zOw0TsiWDJ8aXE5G7kxp9HgM8ADKmDMRfqK50vrrbuu7v4IWc2Ln65WHKbMbHvSHk9CLuqzhJnZQdk3HzxloY1h7h3ACw5shFcT6J1O4NVm8eW+C1sid8CgIKCwRpEZnYQKWfaMzJ1FK28WpVpzZ5SKuzJPHrickmws4NXV65D45KIqmrJy2hb4QZ2IWpK+Tckf3d/Xuv/Gve2u5eHfnuBbE0ULn5r8W22j3au/2LlluZ1et/dA/39mHfG9kZnTRnKk0PlzUk4nuoOyACKXkdDMGg15FoK6d4mj4PJ++jYKoVcTQxx2XHka2IxNIollh2M+u1r3HXueGla4RLgiyW3GRmFXqhqG4eeR084Hgl2dSwiMppvD/yOSwAMaNaX9sFdZVZ9UWuqekNas1dPwrGxjOqfwlHz95zLOsemvE9p1jmIhFNDyqw/W1uhrtBayIO/PYuiMWPNayofcoTTudSqFwB9Wvuw6JG+paZVuYXNu5MxaDUUkM6ovhaaNznP8uhtxOVGkVOYQw6HMPjajv8hYSGL5rtjMTeldVgI2+N9eH1VAdNuuI4P1x6vdNqiyrpypXu3YZFgV4eKX9ytuhznQgEMbT6UO8NKf7qTcCfqhu0NyXYvW4HlXn6M+pFP931Kan4cbs2+xZLTnPyk4WjyWlV5jqt5Q7CqVv69eBKncreiQcf8215n8yEveR0Ip3I5q15A2VY8raJgtlgxaL2YectwACZdC2HT/qBQl4DBPZb7B2o4cOEAhy4cQ9HmoDNGc55o3JoVhz1XLHlNaBnWmu2JjXhpRQbTbxiMXquvtCu3sm3laxX1hwS7OmSxqjw82IsfEk6gUTQMbj4YkCWTRN0r/Yak1+q5r/193BZyG/MPzefLA1+B+xncW35KYXZrZm8agqoOL9NNejVvCKqqcs/iZzmeswEFDbMGv0+PwB70KJq+ScKdcGaXcz+eQWsbsFTcQh0RGY3ZAgaCyEsNxDM7jP5ud7Pt2GFc3BKxGs7RrU0WWtc4Dp4/iqLNQ+dxkmRO4hYEvyT9zJJvdbRp1BprfhP0Pu5Y85uQY/GrcpmqupxvT9QtCXZ1aPKwML46tAUSoEfjHvi4+pQ8Jm9iwt6MBiNqyk2kRfnRs9suonMjweMEOo8TfBr9J+fyHuSdEXeVdAFdThdt+fnqVFXl/Z3vcyR7FagK1zeayJDmQ0r2lw85wtld7v14xR+OqloNAyiaMeG2MqvFZMUk4uJ2AYshlp5hubgZE9mXeBizmk1UahQQhWtj2zW/i/+Sb+e7Y8lrTO8e7Wikb87sv08xZ/1+zAWuldZZOvBV9hwk8Dk+CXZ1rHhCy6Ethtq5EiHKutgK14OJ4feSkJ3A/IPz+eHoz+B+hpXJr7N83nzMqT3p2XrgJc9T/I9/+fnqPt73MV8d/gqAvPg7CAu+vsLx8iFH1DdVdc+WXvXiUn/3ZfdtzMLxt5U55xcPtKHtywux6OMwuCUyogdEpUZxIu2UrSvX4ySHs08C4N7Sdk5DoYmmHi2Zu9/E4azOjOvdm7X7YeuJZKDi3EfSbes8JNjVoaScJPad3wfAkOAh/7C3EHWrfBdSoEcgU3tPZXyX8Ty5/AP2ZSxH4xqLa5NYovmDA4fbcTp3JDNu/hd6jW3R6vL/+E8MD8WqFjJ703L+SjnJsZxIAPISRvJEr/skxIkGoaru2eIJkIv/W3rf4v+HqidNBkpa/cz53hgKfcjN6kDzwjCam2DmtkO21j1dPL3bmkktPMPpzBNo9Gkoukzi8g9g8IFNaZvZtOpTAPw6GvHWNWXuQRP7Mztw/7U9+euQwpfr05kyrL28Zp2AolbVAe8EMjIy8PLyIj09HU9PT3uX84++P/o9b257k67+Xfn25m/tXY4Qly0iMppZa/fg2mg3imkXWteEksdcNZ70aNKFw2f0xF1w59YOnXmkXw9OZpxk3Zl1bIzdSKY5s2T//KQbmXDNo/IGIcRlKn9LQ2mjP9t6ia7cyrc9NKAJMyL/YtHeHfQOK2RPwlEUQyKKPg1FqSISqFpaegXT0rMlLTxb0MKrBS1MLViz34Kr4sOUG9rW2vMX1cs70mJXh9acXgPAsBbD7FyJEJev/Bq0s9dEMXvjBnp3OUlMzl/kWtP5O/Zv0IJrY/gz+Q/+XFb2HNZCD6xZ7TFndEGbVzvLKglRX1VnHr3LYTQYeX34zQQYQosCXx/bgA5rHv93vQd921k5kX6CORs3gyEJjeECiqaAUxmnOJVxqsL5dIoLm35rQXNTc4I9gzl+zhWjtjGTB/WjsUdjNIrmCp+5uBIS7OpISl4KOxN3AhDePNzO1QhxeSq7N+jJoWEoisLM1UFMGvoAH2+NxKo7j84llbCm+USnnMbdPZ0gT39Mlq5sPRhIr6DubIlPrTAaUAhx5Srr4q2sK7eybaWVn2/P1xAGhJAT26LoNVtIjxAte+KjGNpVQ+smuSw5tJeMwng0+lQKyScqNapo8MZFKxaDXqOnqbEpwaZggk3BNDM1I9gUTFNjU5oam+Kud6+ln07DJcGujqw/ux6raqW9T3uamZrZuxwhLsulpm4A2BxzgfysVhi0IeSmWQnvEka4r+2+n4yiENcvxLfKriIJd0Jcucpa8i532z/NtwcVu3L7hfRh1Zbi6Vo62V7bR5MYN8iLgZ0Uvt25mw0nj+DjnYGvdyaxmbEUWKtu6QNw03hh1AbQo2kbYi+4kpxmpFNAS54Y2JsmHk2Yu/aUTK5cTRLsrlRmAuycDz3Gginw0tszE1i9+xNARsMK5/JP/2CWnger9BtFccucVlH+YcklCXdC2MPlrJpx6QmWNSwc36fkdf/NRg1mSyj9Qvqw+Vgy9w4L47HbWpGQncCHf23llwP7UfQptAvOJ7MwifN5cSjaPHKt6eRa01lxKtp2YR3EpsCqX23fumsakZllwlrQiB7NQvjxWEe2Rlk4lWhg9wmFKeGdgcpX3CjW0AKgBLsrlZkAG2ZA2+EVg92GGXyX1pFkT9sIooyUGLbmxoGiMLTF0Ab3Rybqn8v5tG8o1WJXVYufzFcnhH1U9f5zJRMsFy8/WD7sFR/X1LUrBWluAAztZjv36M+2svn4Wbq3tvLYMB9+2LOPjSej8PXOIq0gEYNrGhbVTI41Fa17KlrOsC9zH/u2/mIrSA+mtrAoycSG35qQl+vJ8XgdaoEXN7Vrx86EDAI9AvlpWwaz15xsUNO0SLC7HJW0wllycslLNFCwfB3m7A0UxMZSEBtHwdnTaHIDCNv6MXvMnqz6oRnGgGS6W1VSuwbx+06LzAUknF5118is7J46aakTwvFcyQTLxaGu/P2zM1dHlVlzuvy2fiHBbD6ezOMnCzFb2jNl2G2lzn0MgyGXQk0Kt17rSpblPBtPRqM1pIEuFYNLOhYlh0xzZsmoe0MjW63rUlaxbtXF2hu182RTbjNi1gbS2KMx0bFajDpfPLS+uGl8eG5Ybz5ed6bSFr9Zq6PYcSqFni19ymxz5NZBCXaXIzMBdd0McjN9yT50huxdB8k9Eg0WP1g3r5IDdBgzTzEAIG4/AM8A2X8msDH4dd6980b+3SEX1r1VsStXCCdwpffsSJgTwvlczgTLlbXUlW7JKz6ufOte2LQVFfYpOdbsjkFr5IORtjV1w6atIKdo38NvDOe91fv5aONOW8jTptInTEu25QJHzp9Bq08HXTqKppBCJYPDyYc5nHy40uf383fgohjJyfVALfSkQ0Awuj1tCHALIPJ0KodjFQpowRPW1ug0Oodfe1eC3T+w5uURO+1dcnYEYv1xVpnH9B6FGDwL0XtYMHhY0HsUone3YLUqWPI0FOZpyc3XsExjousJ8M4q4KYj21He3Ebs8iB8fPfjVr4rVwgn9U8DLaTbVQjnVNlr+7ImTS7XkgeU2Tb6s62VtvbZ1s699LERkdE8PawL89bHkpfZGINWwzd3XAyAuRYrBq3Cthf6Mmvddr7btZ9hnV3oEAzrY6I5cv4sGl06BtdMCtV88tUstK5ZQCJROdFE7V9re6J68GgFR4FrvtHg6+aDWmjCLdiAtdDEzvRW/HC0LefOtufDyLPVnnqmNsgExVXJTLB9Acfv/S8FiWloja64dw7Bo3NrPFp7Ydj7DoyMAK9mkJMM7r6Qfg6WTYRhr4EpkGUp+/nf6aWE5BQwYkt3hmZmkXfkeMll/B68E7+pr6EoFZdwEUIIIRzR1U6aXLxP+da+S42irzhK17fMPX+lu3tLbyt9TMV9j2HQmylU0vhXbxPZlmRWH4tCZ8jEqs0gyMeMos/gfM4FVKxV/jwyj73ElPCutRbqZILimrBzvm1wBBDYzgVtVyuujQpQlBOQAWjG2PZr0hWCul08Lm6v7b+troegbny06HcAbszPZkPTzhj7dOZ2k5mUbxeSsfkwF75ejCUzi8aPP4jiFSStd0IIIRze1UyaXLq1b3NMcklL3ZWtnVuxG7iqqZWq7BoucMGgbcI7Iyp2+a6dYNtmsVpo//LPFGrSMRiyeONfLTife56I9buwarIwKO52b6krJsGuKj3G2ka8Asb4fbZWuJERtiAHtha63QsueYq3/9zB2bx9KAqMyMrhuhZRdN7zNgBNm4NbnjuJu71J/XUV1j2/0uTpR1B6j6t8GhUhhBDCwV3upMmlw1fxtqtZO7cy1e0armrbxPBQ5q47gdlsxKD1JC/PSuxZW0058cEON/G6BLuqmAIrBqvSrXOZCTDw+Yr7mAJh4PN8vi+Xz/f/jmuglc6N2tG8+WhoN4Lvt9/Jt1tPM6VTLuG8jrbPA8R98gfpp9yx/HiCpm3OoKlsGhUhhBDCwV3uBMnFSgehyz32csNj8fmLW/eqs55u+ZG/1Tne3uFOgt2VMgXC4KlVbs9cHUXz5kdJMsPNbW6DDvcDcO9tXThvjCYh4wgAXv8ajabTcGInTybrr82czUijWYiCti6fixBCCOEkqhMer3Q9XajY5ftP3cPgGDMASLC7HEWtcNVpQftXHze+/CUKjaLhplY3lXlsYngoxGXDvqLT92xP8IxnOTftPXL2HiYuqRHNRu2lZDhFZa2HQgghhLikq11P93K7h8FxZgCQUbG15NN9nzJn7xz6NunLvBsqmeuu9KTHRQM1cpP1nIr0A6tC0/4peAbn2fYd+HzlrYNCCCGEqPdkVKydqarKHyf/AODm1jdXvlPprtyigRpugK/HhyQv2Uzi4RZ4TJ2N1t1NWuuEEEIIcVk09i6gPjqacpST6ScxaAwMbT70nw8wBdoGZQR1w+/RR9EbCylMzeTCT+tt2yXYCSGEEOIySLCrBctPLgdgYPBAjAZjtY7VuBgIvDYdgJRvviXvcOVLoAghhBBClCfBroZZVWtJsBvRekT1T2AKxHjPZDyHDQarlfiXXkZNi7WtK1u0EoYQQgghRGUk2NWAWaujSiY23JW4i6ScJEwGEwOaDiAiMppZRcOfL0vRvXcB019GYzSSd+AAad8vtK2CIcFOCCGEEJcgwa4GaDUKM4vC3R8nbIMmbmhxA5+sP83MovX0qkvfOAD/SZMASPp8EYW58qsSQgghxKXJqNgaUDIx4ZrD+LZfCUBeahdmboy6okkRAchMoNHA9qT/1Jq8YydI3ONJ0/h9Fx+Xue2EEEIIUY40A9WQieGhjOqfgVnNRi30ZNFG/ZWHOoCd81G+GExgi22ASsYZd/K/mwLzBtq+ds6v0fqFEEII4fykxa4Gmd222v6b1h2DVnd1S4qUmtvOeOEFsnYfJ9V6M4GPPGV7XFrrhBBCCFGOtNjVkMTsRDae+xsAJbMXZou1ZEDFFSk1t12ju+4EIH3jYazeYTK3nRBCCCEqJcGuhjz/55eoWAk0dODYyw8yZVhYyYCKq+XRowt6YyHW7BzS//ijBqoVQgghRH0kwa4GzF5zjO0XVgEwoedowHbPXU2FO8UriEY39gYgdeH3OPHyvkIIIYSoRRLsakBs3iE0hmQ89B4MazGsZHtxuLNYrzKImQLxevpDFBcX8o8cIXfv3qs7nxBCCCHqJQl2NUDrtQOAm1rehLvevcxjE8NDmTws7KqvoWvUCM+bbwYg9fvvr/p8QgghhKh/JNhdpUxzJqtPrwbgjtA7avVajUbfa7vmipUUpqTU6rWEEEII4Xwk2F2lFSdXkG/Jp413Gzr7da7Va7l17oxr586oBQWkLV5cq9cSQgghhPORYHeVfon+BYBRbUahKNVfOqy6Gt1ra7VL+34hauQbsn6sEEIIIUpIsLsKx1KOcSj5EDqNjltCbqmTa3rePByNlxcFcQlkLZotwU4IIYQQJSTYXYVfj/8KwODgwfi4+tTJNTWurnjfYbuXL/W4R51cUwghhBDOQZYUq4ZZq6PQahQmhoditpj5/cTvgK0bNiIyGotVrZERsFXKTIDMBBoN6ULKfMiOd8G8bwOG4sdNgbIihRBCCNGASbCrBq1GYebqKADCWseQnp9OY/fG7DrqzwdrophSm6EOYOd82DADA+DR2IfsRFcyvnwHv44v2h4f+DwMnlq7NQghhBDCYUmwq4aJ4aEAzFwdRVi3bwEI0g7ggzUxTBkWVvJ4rekxFtoOB8Bk/Jrsz1eQmRWG3yOzbI9La50QQgjRoEmwq6aJ4aGkFpzl58SDqKrCxt1t6ibUQZmuVtPwdBK+WE7eyQQKVH/0TZvW/vWFEEII4dBk8MQV0HpvBqAwqz161aduQl05ukZeuPuZAchcs6bOry+EEEIIxyPBrpqyzFn8ErUUADWtP2aLlYjI6LovxBSIadB1AGSulmAnhBBCCAl21fbUii8pUPPw1jXl6LQnmDIsjJmro+o+3JkCMT32NgA5u3ZReOFC3V5fCCGEEA5Hgl01zF4TxcZEW2vdf655AEWxTX1ir3Cnb9IE106dQFXJXLu2Tq8thBBCCMcjwa4azuUdQOuShLvOndtCbivZXhzuLFa1zmsyDRsGSHesEEIIISTYVUuhx98AjAwZidFgLPPYxPDQ2p2cuArFwS5761YsGRl1fn0hhBBCOA4JdpcpITuBtWdt3Z33tL3HztVc5NK6FYY2IVBQQNaGDfYuRwghhBB2JMHuMv147EesqpWegT1p06iNvcspo6Q79s/Vdq5ECCGEEPYkwa4Ks0oNhjBbzCyOXgzYWusiIqOZVbS0mCPwLAp2WRs3Ys3NtXM1QgghhLAXCXZVKF4XNiIymj9P/0lKXgoB7gEcPh7MzNVRaDWKvUss4dK+PfqmTVHz8sj6+297lyOEEEIIO7FrsPv444/p0qULnp6eeHp60rdvX1asWGHPkkqUnsZk1rYFAATrhvDBmhN1t4TYZVIU5WJ37PJlsO4tyEywc1VCCCGEqGt2DXbNmjVjxowZ7Nq1i507dzJkyBBuu+02Dh06ZM+ySkwMD+Wx8CYkZJ1HVbVs2BnicKGumOmGou7Yvzahrp0hwU4IIYRogBRVVet+8rVL8PHx4d1332XcuHH/uG9GRgZeXl6kp6fj6elZazWFTfuDQl0susJgot4YXmvXuRqq1Ur0wIFYzl8geGAyxpf+hKBu9i5LCCGEEFepOnnHYe6xs1gsLFq0iOzsbPr27WvvckpEREZjtoCuMNh+68L+k8wElIT9mPp0s317zhXi90HcXtuXtN4JIYQQDYLO3gUcOHCAvn37kpeXh9Fo5Ndff6VDhw6V7pufn09+fn7J9xm1PCFvRGQ0M1dHlXS/Fn8POFZ37M75sGEGpnQX0vAlK94F9beJKMXjOwY+D4On2rVEIYQQQtQ+uwe7tm3bsnfvXtLT0/n555/5v//7PzZs2FBpuHvrrbd45ZVX6qSu8qEOLoY5hwt3PcZC2+G45+WjbBlHYQ6Yr30Rlx7htsdNgfatTwghhBB1wuHusRs6dCghISF8+umnFR6rrMUuODi4Vu6xm1U0pUll4S0iMhqLVbXLEmL/5Mz9d5O9cz8B/70f3yen2bscIYQQQlyl6txjZ/cWu/KsVmuZ8Faai4sLLi4udVLHpUKbw7TUVcLYqyvZO/eTvX0fvvYuRgghhBB1yq7BburUqQwfPpzmzZuTmZnJwoULWb9+PatWrbJnWU7NY/BQ+OgbcvYfxZqTg8bd3d4lCSGEEKKO2DXYJSUl8eCDDxIfH4+XlxddunRh1apVDCuabFdUn6FTT/RBQRTExZGzYwfGgQPtXZIQQggh6ohdg90XX3xhz8vXS4qi4DFgAGk//EDWXxsl2AkhhBANiMPMYydqjvH6AQBk/b3RzpUIIYQQoi5JsKuH3Hv3AZ2OgtNnMJ8+be9yhBBCCFFHJNjVQ1qjB+7XXANA1sa/7VyNEEIIIeqKBLt6qrg7NnujdMcKIYQQDYUEu3rKY0BRsNu+HWsV8wIKIYQQon6RYFdPuYSFoQsIQM3NJXfXLnuXI4QQQog6IMGunrJNe3IdAFlrVsK6tyAzwc5VCSGEEKI2SbCrx4xF3bFZmzbDhhkS7IQQQoh6ToJdPebRty9oNJhPx1KQrbV3OUIIIYSoZXZdeULUoswEtNkJuLVvQ+6hKLISXGgUv+/i46ZA25cQQggh6g0JdvXVzvmwYQZGjZFcPMmOd6HRsokXHx/4PAyear/6hBBCCFHjJNjVVz3GQtvheByL4fx/p5Od4II6fBZKsG3iYmmtE0IIIeofCXb1VVFXq2tgF7Reb2NJzyQ31R333t3sXZkQQgghaokMnqjnFI0Gj2s7A5C1Y98/7C2EEEIIZybBrgHwuP56ALJ3H7ZzJUIIIYSoTRLsGgBj+M0A5B2NpjAlxc7VCCGEEKK2SLBrAHT+/ri0aweqSvamzfYuRwghhBC1RIJdA2G8rj8A2X//bedKhBBCCFFbJNg1EB7XFa0bu2kTqtVq52qEEEIIURsk2DUQbtdcg+LujuXCBfKPHbN3OUIIIYSoBRLsGgiNwYBHr14AZEl3rBBCCFEvSbBrQIq7Y7P/3mTnSoQQQghRG6od7DIyMqp87Pjx41dVjKhdxQMocnbvxpqdbedqhBBCCFHTqh3sRowYQX5+foXtx44dY9CgQTVRk6gl+hYt0DdrBgUFZG/bbu9yhBBCCFHDqh3sjEYjo0aNorCwsGTbkSNHGDRoEHfeeWeNFidqlqIoeBRPe/LDbMhMsHNFQgghhKhJ1Q52v/zyC+np6dx3332oqsrBgwcZNGgQ9957L7Nnz66NGkUNMg4YAEDWrkMS7IQQQoh6ptrBzs3NjT/++INjx45x1113ER4ezoMPPsjMmTNroz5Rw9x79watloIsHebYRHuXI4QQQogapLucncoPmNBoNPzwww8MGzaMO++8kxdeeKFkH09Pz5qvUly9zATITEALuIc1JefIGbL+/B2fpo1tj5sCbV9CCCGEcFqKqqrqP+2k0WhQFKXC9uJDFUVBVVUURcFisdR8lVXIyMjAy8uL9PR0CZT/ZN1bsGEGAMlHPUja64VH4zyaD06xPT7weRg81Y4FCiGEEKIy1ck7l9Vit27duhopTNhRj7HQdjgAxl1rSdr7GdkX3LHcvxCtu5u01gkhhBD1wGUFu4EDB9Z2HaK2lepqNagqeuPHFGTpyD6ZheewvnYuTgghhBA1QVaeaIAURcEYlAdA1vr19i1GCCGEEDVGgl1DZArEdNPtAGRt+AvVarVvPUIIIYSoERLsGiJTIO4Pv4/G3R3LhQvkHTpk74qEEEIIUQMk2DVQisGAx3XXAZC1br19ixFCCCFEjZBg14AZi9b2zdqwwb6FCCGEEKJG1GiwGzJkCK+99ho5OTk1eVpRS4zX25YXyzt0iILEJDtXI4QQQoirVaPBrnnz5kRGRtKuXbuaPK2oJTo/P1y7dAEg6y9ptRNCCCGcXY0GuwULFrB+/XoOHjxYk6cVtcg4yDZHYdZ6CXZCCCGEs6uVe+xkeS/nYSq6zy5782as+fn2LUYIIYQQV+WyVp4oLzIyksjISJKSkrCWmwPtyy+/rJHCRN1wad8eXUAAhUlJ5GzfgXHAdfYuSQghhBBXqNotdq+88go33HADkZGRXLhwgdTU1DJfwrkoinJxdKysQiGEEEI4tWq32H3yyScsWLCABx54oDbqEXZgHDSQtB9/JGvlUtQnx6F4NrF3SUIIIYS4AtVusTObzfTr1682ahF24tGnD4peT0FyFuYDO+1djhBCCCGuULWD3cMPP8zChQtroxZhJxp3d9y7dwQgc8suO1cjhBBCiCtV7a7YvLw85s2bx5o1a+jSpQt6vb7M4zNnzqyx4kQty0ywfQGmzoFkb4eMNevxG73X9rgp0PYlhBBCCKdQ7WC3f/9+unXrBlBhvjpFUWqkKFFHds6HDTMAMOVrSFAak38qEfP7QzCYLDDweRg81c5FCiGEEOJyVTvYrVu3rjbqEPbQYyy0HQ6ALn4fHlteJjvBlYzAifjdd7u01gkhhBBOplYmKBZOwhQIQd1sX0264hmcB0DGpn22bRLshBBCCKciwU6UMDbLBa2W/KNHyT9x0t7lCCGEEKKaJNgJG1Mguhuew6PXtQBkrlpp54KEEEIIUV0S7ISNKRAGT8Vz5O0AZKyQYCeEEEI4Gwl2ogxT+BDQ68mPiiI/Jsbe5QghhBCiGiTYiTK0Xl4Yi1YWkVY7IYQQwrlIsBMVmIbfBEDGyhV2rkQIIYQQ1SHBTlRgGjIERa/HfDyG/Ohoe5cjhBBCiMskwU5UoPX0xOO66wDpjhVCCCGciQQ7USnP4u7Y5b+jrn2zZE1ZIYQQQjguCXaiUsYhQ1AMBsynzpD/2/sS7IQQQggnIMFOVEprNOIxYAAAGWfc7FyNEEIIIS6HBDtRUWYCxO3Fs097ADJOu6HG7oW4oi9pvRNCCCEcks7eBQgHtHM+bJiBqVBBo29MQbaO7C+exRiYb3t84PMweKp9axRCCCFEBRLsREU9xkLb4WgAr/R3SV29m7Tc/hgfecn2uCnQruUJIYQQonIS7ERFpsCS8OZ9112krt5N5q5oCl2C0fn62rk4IYQQQlRF7rETl+Qa0gJXHzMUWkhfssTe5QghhBDiEuwa7N566y169uyJyWQiICCA22+/nWPHjtmzJFGeKRDv4QMBSPvxJ1RVtXNBQgghhKiKXYPdhg0bePzxx9m6dSurV6+moKCAG264gezsbHuWJUozBeI15UM07u6YT58mZ/sOe1ckhBBCiCrY9R67lSvLLle1YMECAgIC2LVrF9dff72dqhLlaTw88LzlFtJ+/JG0n37Co3cve5ckhBBCiEo41D126enpAPj4+FT6eH5+PhkZGWW+RN3w/ve/Acj8808KU1PtXI0QQgghKuMwwc5qtTJp0iT69+9Pp06dKt3nrbfewsvLq+QrODi4jqtsuFw7dcSlfXtUs5mMH7+DdW/JRMVCCCGEg3GYYPf4449z8OBBFi1aVOU+U6dOJT09veTr7NmzdVhhw6YoCo3usrXapf6yBHX9DAl2QgghhINxiGA3YcIEfv/9d9atW0ezZs2q3M/FxQVPT88yX6LueN5yC4qbG+bTseQm6+1djhBCCCHKsWuwU1WVCRMm8Ouvv7J27VpatWplz3LEpWQmoM2MwfN628CJtOMeEL9P1o8VQgghHIhdR8U+/vjjLFy4kKVLl2IymUhIsIUDLy8v3Nzc7FmaKK9o/Vhvi550/Mk460rAz0+icyma107WjxVCCCHsTlHtOOOsoiiVbp8/fz5jxoz5x+MzMjLw8vIiPT1dumVrW2YCZCagqionH5pE/ukk/O64Dv8Jj9seL7UMmRBCCCFqTnXyjl1b7GQVAydSFNwUwO/Be4h9LYLUNXvxnRaKxsPD3tUJIYQQAgcZPCGci+n63uiNhVgyskj7+Wd7lyOEEEKIIhLsRLUp3kH43nYdAMnzF6CazXauSAghhBAgwU5cCVMgXs99is7fn8KEBNKXLbN3RUIIIYRAgp24QhqDAZ+iAS7Jn32OarHYtyAhhBBCSLATV8777rvReHlhPnWKzNVr7F2OEEII0eBJsBNXTGv0wOe+0QAkf/oR6to3ZaJiIYQQwo4k2Imr0uiBB1Dc3Mg7EkX2jx9IsBNCCCHsSIKduCq6Ro3w/ve/AEg+bLRzNUIIIUTDJsFOXLnMBIjbi+/wXqDVkJPkQs5fy2X9WCGEEMJO7LryhHByRevH6gGv5l6kn/Qgac7ntDjyNoqCrB8rhBBC1DEJduLK9RgLbYcD4N9vIxmT55B7wYXM9u/geX1vWTtWCCGEqGPSFSuunCkQgrpBUDf0nQbg2y4bgKQvFmP16yDBTgghhKhjEuxEjfFtl4XO15uCs2dJ/fY7e5cjhBBCNDgS7ETNMAWiGfoc/o89AsCFjz+mMDXVzkUJIYQQDYsEO1EzTIEweCpe9zyIS/v2WDMzuTD3I3tXJYQQQjQoEuxEjVK0Who/9ywAqd9/T/6B7bDuLZn6RAghhKgDEuxEjfPo0wfj4MFgsZA0czZsmCHBTgghhKgDEuxErQh45hnQ6cjaspvsRIO9yxFCCCEaBAl2ouZlJuDimk6jkeEAJOzywnp6l6xIIYQQQtQymaBY1LyiFSn8FYUM1wDMGXouvPMSAV0zbY/LihRCCCFErZBgJ2pe0YoUWqBJy585N3MxyUc9MU34ALf2bWTiYiGEEKKWSFesqHmlVqQw3fIvPFvkgKoSN2sBVt/2EuyEEEKIWiLBTtS6xteko23khfl4jMxtJ4QQQtQiCXaidpkC0d3wHE3+9zQAyZ9/Tu6BA3YuSgghhKifJNiJ2lW0IoVp5L/wHDECrFbin3sW65+vy+hYIYQQooZJsBN1pvH0aWh9fck/cYoLn3wiwU4IIYSoYRLsRJ3RNWpE4EsvApB8xEj27oN2rkgIIYSoXyTYibqRmQBxe/HsFIDXwM6gKsS+OouCfWtl0mIhhBCihsg8dqJuFE1aDBDoD3ne/uSn5RD72HhaDLmAMkQmLRZCCCGulgQ7UTeKJi0G0MTvo1nuZE6ua05uMiRaHiKwx1g7FygcSmaC7cNAj7Ey76EQQlSDdMWKulFq0mKadMVgshD0vycASP1lJel/7bJrecLBZCbYWnhLd9FnJsC6t6TbXgghLkGCnbAbU79r8X30UQDip08nf+Fz8qYtqlZZ2BNCCFGGdMWKumcKhIHPgykQ/4lPkLt/HzlbtnLug8W07HkbWul6a1iKu13bjQDVimqxkv7Td5j3m1A++giNTzCKiwEFMy7nDbjbu14hhHBgiqqqqr2LuFIZGRl4eXmRnp6Op6envcsRV6gwJYWTt42k8HwK7td0ovmC71AMBnuXJepK3F6YNxCuGQO7F5C4x5OUY8Yqd28+9R48ht1m+8YUKPfgCSHqverkHemKFfZTNAWKLu8MzZ68FY3OSs7ug8RN+i/qud3S5dbQdLiVFP//lYQ6r1bZeF/jg1fLHEzBubg2MgMQH/Et1rmDbGFw53w7FiyEEI5HumKF/ZSaAsUNaHqdC2c3+JCxdjO62D9pPOkxmQKlvspMuBjc4/fZNq1bR+LcXwDw/79b8cv/BO6fD+6+AFhObOfkxHcpyNaRZPk/Ah+XEbNCCFGetNgJ++kxFh7ZYPsaGYExMJ+gR0cCkHLMSHKUUUZC1lc759ta3OYNhGUTyU3RE/vBT2BV8W6djW+HXNt+7r4lo6m1rXsR2CsdgNQlf5ITVyjBTgghypEWO2E/ldwf5XXXfRR4hHH+/ZkkzfoInYcWr+MzbHPgyZt4/VFqXsOCA39x9rk5qBYNHj26EPjmMyh6A3g2qfA7Nwbm433zYNKWryNu2jRaf/spmoPfy3x3QghRRIKdcDi+Dz9MYWISqd9+S9yMj9Be50LVt9ILp1QU6i2ZmZz94FkseVpcWgfT9JMvUIxFv+0mXSoeM/B5Atr9m6xdhyk4fYbzEXNorP1Mgr8QQhSRrljhGEpNgaJkJdL4/4Zjur43FFo4t9GHrFW/2kZPyrqy9UrCq6+Rf/IsOlcLwW8+h9Z4iQhvCoTBU9E2aUPgKy8DkLJ4ObkX9HVTrBBCOAEJdsIxFL1pYwqEnfNRvhhMUONfMQbloVoUzr37A1kv3yAjIeuRrA0byFi2DDQamj1xC/qQjpd3YGYCpjBvvIYNAKtK3HZvrKd3SfAXQghkHjvhiEqNmFTP7iZ22qtknnMDnZZmL03CdF0vOPqH3FflbEqt/2pRTJwYOZLC+Hh8xoyh8fPPXf551r0FG2ZgyVeIWRGAJU9LQNcMfNtn2R4f+LyMphZC1CvVyTtyj51wPKUGVShA036pxCYOInPDNs69Mpum0yfgeVQGVDid4iXB2g7n/OefURgfj75ZM/wnPlG98xQNvNACAQHfEv/pHySfbkKjtz9CYzDI34QQokGTrljh8BQNNJ3+BJ633AKFhcS+GkH6aVd7lyWuUM6Bo6QuXAhAk9deReNezUXCTIElU6B43XkPOvdCLGnZpO84bdsuwU4I0YBJsBOOrWhQhaLTE/TkvXjdMACsVuK2NCL583mosXvkvipHVrS6CHF7IX4fVgvEz/gQAK/hg/Do1OqqTq/odPi0zQYg5YsvUa3WqyxYCCGcmwQ74diKB1Uc/QPli8E08f6BRqHZgELSwnUkPHob6icyoMJhlZuI+MIhE+b4FLSuFhq7fX/1vzdTIN4P/geNyYj51Cmy1q6tmbqFEMJJyeAJ4RxKD6iI20vK+/8jaa8XqODRuxtNX5yE9sxaGVDhaEr93vK2/snJaV+CqtD05cl4Xt+r0kmqr0TSrA9I/vRT3Lp2pcWi71EU5arPKYQQjqI6eUda7IRzKHVflRLUDd+22TR9eQqKqyvZ2/Zy+smXKFjxjnTJOpqi35vq14G4L9eBqmAa0AvPex6p0fvhfB64H8VgIHffPnJ37ZKl6IQQDZYEO+G0PAf0pMXXX6H19SX/+GlOrfYn93C0vcsSlTg/Zy75x0+hNVhpPHFMjZ9f5+eH16hRACR//sXFEbgS7IQQDYwEO+F8ilepUDS4+VlpGfEihiBfCnO1nHryZVLmzLANqojfL602DiBn1y6SP/8cgMD/G4y+ZftauY7v2DGgKGStX0/eybO1cg0hhHB0EuyE8yk1oIJ5AzEs+zct+x7EFJwLFiuJc74idswoLBs/lVYbO7NkZRH37HNgteJ1++14PvVJrd0DafB1xTSgJwApC762bYzf5xgrUkjXsBCijsgExcJ5FU1UC6CN30dT3URSDSNJXLiOzLNu5M87RNNOOmTGOzsoWmUi8Y8kCmJj0TdtSuPp02r3mjvn4+vyB5n4k/73AfxHatAvm3jx8bpakaLUChslIbbU5MwyuEcIUZsk2AnnVW5EpaKAz92349prALGvzsZ8LpFT8X40bvoV3qNV20jJ4v3Lv/GKmpWZQMa3H5C+yQcUhaC3Z6A1Gmv3mj3G4tZ2OO5Jr5Kz7wgpx4w0fvF1aNLV9nhd/a4lxAkh7Ei6YkX9cvg33DeNp9V1h/EIzEO1aEj4YiVn77+Dgg8G2wKd3Fhf6woupJCwwxsA3/Hjce/Ro/YvWjQC1/fxSQCkRntgpknJaOo6D1k5yWUmZwYcp2tYCFFvSYudqB+KB1S0GwE9xqIDgu/YS8rM/3H+oA/Z8a6cWBtCYM9gPMNUZJazWlA0Z51qtRL/+ntYzBpcWzbG/84BtiBTQ3PW/ROPAQNw796RnD2HSPp0Ic3m3VDr1yw9X19JiNs+D8vBVWScdSPznCvu/kZ81YmUTLFXV13DQogGRSYoFvVX3F6YN5D8GxcS985n5B05BoCpZ1sCm25A9+8PLnbTKRrbYAzpnr1y696CDTNI3ONJyjEjilal1Q3ncfEqtD1eh0Emb89mTo5+GFSV5l9/hUevXpXf+1ZTip47gNUCWfGuZJxyIyvOFdV68WOE78g++E+aePG2APlbE0JcBpmgWIhSXFo0peXjPfHrlAGKSuaOY5xYHkDqB8+jflq03NWOL6R79mr1GEuK31RSjtnupWvSMw2X+2fCIxtsXz3G1lkprt374X33XQAkvjUD1WKp3S74HmPhkQ2kh7zJ8RWtif3bh8xzbqhWBZdWwXjd0BeA5GVbOb9oHWrxBwoZKSuEqGES7ET9Vdw9awpE6T0O/4g/aPXJW7g098di1pCww5tT+68nd9AC6HCrvat1epnbDpI41zbNiP/Dd+PVMtfWImqne9z8J05EYzKRf+QIab/8UvMXKD2FiSmQ5JV7iHtjDpasPHRuFnzuvoVWS5fQesWfBD0/kcbXpAGQ/NlnnJ/1AWpGvHyYEELUOLnHTtRfxfPdlfreNagbrVo3J/XZkZw/2pi8I8c59dg0GoV3x9+koC2+Pwqke/ZyZSaQ++M7xEZEgqrifffd+N57G3w2y65l6Xx88Bt3P0kffMz5me/hGTgOLVy8Bw6urju0qAVQDb2RpDkLSPnqKwB87hxOgPYLlEfvg6C2JdfxGT8RYrxIfO9DkufNg+wk/FXkfk8hRI2SYCcaHEWrxadtNqabe5H0zUoyzriTuno3GS4B+J2aSqOQbBQtcM0Y2L1Apq34B+aj+zgb8TtqvhaPgdcT+MJ0lNwLJa2l9uQTkkGaqRBzagbJ779KQDegBue2s1og/s25ZKzdDEDAM8/g8+/hKLv8yz73og8ZPj0SIC+DxDlfkfzdEpSOJvxrKmgKIQQyeEI0RMU30bcbAaqV7N0HSZj5Cea4ZAD0TQLwf+guPHuHoSz8l+3+sKBuFY+XljwKk5M5ffe/MJ9LwDW0FS0W/YTGw8PeZV2UmUDmn8s5N+1dFJ2W1jfGYRg9q+zcdtX5HZYa/WqJ2c656TPISXQBrZagZx/F645/X/p8RYMsUo55kLjHC4CWN5zHzafA9riMlBVCVKI6eUeCnRCAemYnaf8byfmY5lhSMwBwaRFAQMsjeIx/B6U42JkCbW/s8wZWDHwNSWYC+Qd2cnbauxTEJ6F3L6Tlu5PQte9ve9yBWp5UVeXsw+PJ3rQJY9Ncmn39K0rT7ld2sqJgVpCt5exGH/LT9Cg6K836p2Jskv/PwaxUMIz936tkbD1Co2HXEDj1GdvjDvRzE0I4jurkHemKFQJQdDoatcnB6/YepPywhOSjRvJPJ3H2tC+uB1/Dr30WxqZ5KIOeL1nGrIwG1oqX/c0bnPv4T6wFGvTGQpoPTEa38X+wsWgHB2p5UhSFxlOf58Stt5EV60bC+58R+E4Eik5X/d9bj7HkWkM5O/09LGnpaF0sBE9/CLf+N9ke/6dzlApuXqNuJ2PrEdK3HiPArwMag+Eqn6kQQkiwE8KmaAStpt0I/PqNxzs9g+R5n5O6ajt5yQbO/e2DS3AAvs3A02OP7Yb30vdG5STX/2WkikJQ6rkAEuauA4sGt05taTbhRnTrn4eREXW/fNdlcmnThibTniL+9fdIW76OQvMkmr7/PppqLv+V/tcu4qe+hmo249I6mOCOO9H3v+mKWm49rumEzs1CYWY2WWvX4nnTTVfwzIQQoiy7Tnfy119/MXLkSIKCglAUhSVLltizHNGQFY+gbdIFgrqha389jZ+ZQptbE/G991Y0rnryzyYR9/7XxPz3TVKOeWBZ/KStS3beQDj8m72fQa1T0+NImj2XhDfeA4sFz5Ejab7wR3Rhtjna7Dm1yeXwvu8hmkbMRjEYyFoTydmHx2PJyrmsYy1Z2SS99RpxTz2NajZjHDyYFhGvoPewXHE9incQXgO7AdTOdCxCiAbJrsEuOzubrl27MnfuXHuWIUSVdK5WAsbfS5vlv+A/7m603p4UZOtI3ONF9O/BxCfdQm63V8C3je2A0muBxu+vNxPQ5p88yZln3yL5qAkAvwkTCHrnbafrPvQcNozgiLfReLiRs2MHpydMpSBXU+UaroWpqZyP+JDjQ4aQ/NVCAHzuvoVmcz5E27jV1Y38NQXiPekdALL/3kRBYuJVPjshhHCgwROKovDrr79y++23X/YxMnhC1KpK7r+y5uaSvmAOqQs+JT9dX7Krq6+ZRiHZmILz0OqLXlLF06U446jaohqtrYaR/N2vJC/6DbWgEEWj0uQ/t+L1r9G2/Yrrd/TnU9q6t8hb8h5nNvhiydOicy/EGJSPwaMQvdGC/voH0Pa4h9SP3iJ18wnU3DwADMFB+AcfxPP1VTU6aObUffeTu2sX/lOm4PfI+Bo7rxCi/nDKUbES7ITTiNuL+ulAcq95l9RV28j4axsU2rrkFL0OY79r8Qrvj0fnYDQ/3F0x2BWtYVtmu6OFvbi9ZL1yAwkHm1GQnAuAR2AegdemYzCV6n50oEESl61oZKo5NpEzT71MQVLaJXd3adMSv/tvxxRiQPljUsV7Ca/y95W2eDHx06ZjaNmS1iuW29aRFUKIUurtqNj8/Hzy8/NLvs/IyLBjNaLBMgWiDHoe9x634n7bIzS+cIG0BXNI//FbzBmQuWEbmRu2oXF3wRTohanpz3iE56Nxdak6BJS/ib86Qa8GQ6GqquRs20bKpx+StcUXyEXn50PjCQ9iauOC8vuTDj1I4rIUhTFDELT63Ejm63diDnuIgrQCCs6exRwbi+XCBdz88/G7tS8eWb+jHN0MR4uOr8EJjgFMN95EwutvYD51itw9e3G/5gqnYhFCCJws2L311lu88sor9i5DNHTllirT+fnhN/p2fDNnkR94B+mr1pBx2o3CnHzST3iQ/v5ilA9+xj0gH2PvbpgGDUYPFUfVllbZaM2qAlx19q1MZgKWjZ+SfsqN1KV/Yj4da9uuqPgM743fY/9B6+52scbiQRL1gNbogXfrXHjorouBGlDP7UFZPgn69QbTbbadMxNh9fQaD7ZaoweeN95I+pIlpP/4He7pKx2n5VYI4XScKthNnTqVKVOmlHyfkZFBcHCwHSsSokhRK55ruxG43vYkARYrOet+J/Onz8hKa0rB+XSy413JXnKUxCVH0RsDcN/6Im7+Ztz9zRiuHVp2CpULURWvUZ2pOS4j7FmyssnZsZ2s5YvJWLEaa6FtLJVGZ8WzZS4+Ydm4eC6Bb5fYjr9mzFX/mBxO0TQ3mAJtP5sNM4BS67eufuHivsXPvxaCrdeoUaQvWULGn2tprDuBpj5PmyOEqFVOFexcXFxwcXGxdxlCVFSuFU8BPIZq8DjxPur43zCfzyMzcjVZW/eQe/AYBVk60rN0pJ9yB0AbuQdXHx9c903DxasQF+8CXDxBqapVr9QKBiVh8FItgIAl8ST5P88ke0s22QdPkrt/PxQWFj2qwRAcSKPbb8LrhgFoM6JtXY6lW6cUTf1bGaH0763H2IuTT8fvq/j8c5Jtg2FqgXvPHuibNaPg3Dkyz7niVStXEUI0BHYNdllZWRw/frzk+5MnT7J37158fHxo3ry5HSsTouYoioJLt764dOuLH2CJ2kzu+7eTEzia3KNnyD1yHEs+ZMdryY53LXWgim7ZG+g9LOjdC9E3a4Euzx3Nj9+iyT6H5sxaNFoVRaeCqsf6+dNYCxWshQqqXxcKTxkxv/wm5hQz5tgELKnpgB+wuOQS+iYBeLRrgieRuI977uLSaUoz23/Lt0416VK7Pyx7qiy0ln7+mQlXN71JVTITUDIT8BrahwsLfibthDtepUN6fQvTQohaZddgt3PnTgYPHlzyfXE36//93/+xYMECO1UlRA0p3c1XitbojrFJPsZHxkBQN1Szmby/lpI3fyJ5PkPJj88k//hJrHlmCnO1FOZqycUAZ84D3rD7j6Iz+V7i4ucAT+BA2Wu7WnDv2hGPZho88tdhMMYBe20P/v7kxR3rY7fr1SrXKltjirqAvbO1XCCAnCQXzN9PxmAsGn3sjCOPhRB2Y9dgN2jQIBxkthUhal5VQaBc4FMMBtw6hOIWmgOPPGULe7F7KIwYQuH171GQXkBB0gUKTh6l8NDfWH06YFUNWHPzUc0WrDnZKLlJaHyDUdzc0bi5oCEfbdoBDC1boc8/gsFkwWAsRGtQgaKJcPXYAlzTaxpGt2t1VBHKa0VRF7AecD/xPDmHTpPhfhd+j/znYi1CCHGZnOoeOyHqhcoCX/mwpyjo3azou1+LW3FXYNxemLcMHnmzinnxfrq4vXjb/S+Ae1HLXmX3jZUaCdqgul3/SW21zlV1raLfu+ewcHIOfUnWoXj86snIYyFE3ZJgJ4QjKB8kqtNidKl93X0rjuAsH+DqwZJn9YWxzzXAl+QejqYwNRVdo0b2LkkI4WTsulasEKIKxUGvdFirKsBVZ9+qrlVX3Y7ikvQhHXFp1ghUleyNG+1djhDCCUmwE8JZVBbgqrNvdYKhsA9TIMYRdwGQtX69fWsRQjglCXZCNBQS4JyCcdBAALL+3oRaUGDnaoQQzkaCnRBCOBC3Ll3QNmqENSODnD177F2OEMLJSLATQggHomi1GK8fAEDWhg12rkYI4Wwk2AkhhIMxDhoEQNZ6CXZCiOqRYCeEEA7Go39/0Goxx8RgPnvW3uUIIZyIBDshhHAwWk9P3K+9FpBWOyFE9UiwE0IIB3SxO3a9XesQQjgXCXZCCOGAiqc9ydm+HWt2tp2rEUI4Cwl2QgjhgAytWqFv3hy1oIDsLVvsXY4QwklIsBNCCAekKArGgbZWu8zVK2DdW7KurxDiH0mwE0IIB1XcHZv992bU9TMk2Akh/pEEOyGEcFDuPXuicXenMDmNvFS9vcsRQjgBnb0LEEIIUYnMBDSZCXhc05HMv3eQFeeCW/y+i4+bAmXdXyFEBRLshBDCEe2cDxtmYDS7kUkjMmNd8V828eLjA5+HwVPtV58QwiFJsBNCCEfUYyy0HY4xPQPu/A/5qQbMPV/B0H2Q7XFprRNCVEKCnRBCOKKirlZdEHhc05nsnfvJOJyG34hu9q5MCOHAZPCEEEI4OM9BfQDIWL/VzpUIIRydBDshhHBwpptGgEYhP+Y0+SdO2rscIYQDk2AnhBAOTtssDI/rrgMgY+UKO1cjhHBkEuyEEMIJeA6/GYDMFRLshBBVk2AnhBBOwBQ+BEWvJz/6OPnR0fYuRwjhoCTYCSGEE9B6el7sjl2x0s7VCCEclQQ7IYRwEp7DbwIgY+VKVFW1czVCCEckwU4IIZyEccgQFIMB84kT5EdF2bscIYQDkmAnhBBOQms04nH9AAAylssgCiFERRLshBDCiXgOHw5AxvLfUde+CZkJdq5ICOFIJNgJIYQTMQ0ahOLqSsHZWPKWzJRgJ4QoQ4KdEEI4EY2HB8aBAwHIPOtq52qEEI5Ggp0QQjiLzASI24tn73YAZJxxQ43bC8Vf0nonRIOns3cBQgghLtPO+bBhBsZCBY2uMQXZOrI+fRZT03zb4wOfh8FT7VujEMKuJNgJIYSz6DEW2g5HAzTK+4Dk37eRHN8Z48vvoSgKmALtXaEQws6kK1YIIZyFKRCCukFQN3zGjEHRqOQejyPnrNm2XYKdEA2eBDshhHBCOh9vvENyAEie96mdqxFCOAoJdkII4YxMgfj+3wOg1ZK9eQu5+/fbuyIhhAOQYCeEEM7IFIj+X2/gdeutAFz4dJ6dCxJCOAIJdkII4cR8x48HRSErMpK8Y7J+rBANnQQ7IYRwYi6tW2G68UYAkj+KgHVvyXx2QjRgEuyEEMLJ+T0yHoCM1Wsx//6uBDshGjAJdkII4eRcO3TA4/oBYFVJPmK0dzlCCDuSYCeEEM6saJkxv3+FA5B2yp2CgxtlmTEhGigJdkII4cx2zod5A3Hf/Aju/vlgVYh7fRbqJwNh3kDb40KIBkOWFBNCCGdWtMwYQOC1azk59VNykly44PEk/g/cIatRCNHASIudEEI4s1LLjLlcO4Qm16YDcOGrxWSfM0uwE6KBkWAnhBD1iFerXLxuHAhWK3FPPU1hcrLtPjuZBkWIBkGCnRBC1BemQBj4PIHTnsPQJoTC8+eJe+551PQ42DBDgp0QDYAEOyGEqC9MgTB4KpqAVjSbNQvF1ZXsv/8medEye1cmhKgjEuyEEKIecgk0ETjhQQDOf/kD2UkGiN8n06AIUc/JqFghhKiPds7H6+wMslt4k3HanbMbfAnKexbP5nm2x/tMABejbVStDLAQot6QFjshhKiPeoxFeXQDTT76AWO3EFSLQuxmH84bnkAdvx7aDJH77oSohyTYCSFEfVQ0DYompA/N3n0Vn7ZZAFz4ejGx732NVWuyc4FCiNogXbFCCFHPKVoNjbtn4HLjo8R/tIjMFSs5fWwfzTpr0MfvK7WjBo7+Id2zQjgxCXZCCFHfFU2D4m3OQn99IrF/NyLvRDwnzgXge+p/+IRmo9GpcM0Y2L3AtpKFBDshnJJ0xQohRH1XNA0K/Z7AY/pqWn75IS7NA7CaNZzf58nxyHak+E3FGjrc3pUKIa6StNgJIURDYQoEUyCGIGj1hT8Z02/m/OlQChIukDjnK5K//xW/Vu54xmxDW/oYgJ3zpYtWCCcgLXZCCNEAKVoNXq1yCXmyG4E90tC5WShMziBhpzdRj87izH2jSHv+JgrXf2wbOVt+BG19W6assudztc+xNs4pxD+QYCeEEA1R0X13Sp/xNHpnBSG/fEvj+8MxeBaAVSE73pX47Y2Ifv5nTj/9BucPGsnasQ9LWprt+MsNe1UFmcvd92qOr+6xlT2fyw20V3POS51XiGqSYCeEEA1R8X13TbrYpkVp1Qufhx4m5ObztJ7/Hv7/eQiXkBZgsZKz+yAXDnpy9rkZRPXpy/Ehg4h9+T0uHDKSvnYzuQcOYElLQ82Ir16QudwgdaXHV+fYy1Ub56zq+KsNuqJBknvshBBClOHSoikuefvxS9uCuZ2WrDhXcpP15KYYKMjSURCXSEFcIuAJBz4EPgRA4+GGXu+P7vhb6Jq2ROvrh06Xh/akG9pNO9G2LEBjMqE1mdBkZ6OxgmLPJ5qTbFteDWzLrQGc/AsuRNn+PzOx7GPFx9T0Oau6b7E47JUepVzZtkvtW/7eyMvdVt1964K9n489n3s1SLATQghhU9Q9iynQ9ubVdjgGwCd+HyybCGE3YTm4itwUA3nJesyZOsxZWgqydBTmabFm55KPnvyd+2Hn/lInbgTb3q/kgkEov9yH4uKCxkWPRq9ByfdH2f4kGhc3FIMOBQtKSiOUs6+juBlRKERx9UCx5KKcNMH776K4mUCjQSnMQYnxgLnvopgaQ+4FlBh3WPg1isfvUJiD4uoJ+akoZ1zhq3dR4vfA/7d391FRl/n/x5+fGblVQJAbByQVKtOD90nhHWC27pqu2Z5d3dRl29Jz9ujPDb/p6qaZ5M2e9XiXlqSp1WZLrbrVbzFv4oBBmpoKR+gn3qBSKmSKgAgMzFy/P0ZGRsAvw93E8H6cw5G55vpcn/eHq7GX1+cGTYEGmuYGHyxHs74GNFfY8T+W7wF6DUcr6gRHD6J5Z0NlMXT2Rbv9I9zWw4FNaBfTLIlVA03TwX+XoqFqtWnwn7n39jEkDnoMsYRce0Lk/6axwdDesNiYANlQ+/1t9m7r6ONpbF8HkmAnhBDCoub0bO3XtUXOQh+ziC5Al1phj7P7MFdrVJXpqSrTU12pw1Shp7pCR3WFDlOFDlOVDnOVDlOVhtmoQ5ktSUlVm1HV5ZjLyu/uxAVu/XhfYR7wfU59BcOZk/e1+UBW7bau8O0X9WzrB1y++6c9zgGBsH97Pe8FAWcAO/8H/8lB4CBggI9X3Q2UCnSd0OgOn8eh6fSg0wFmtKogODALrZMLoEBvCcCUBaBl/B9w9bAEXWWGIn/IWoLm7gV6HVrVHbjWDe3cSvDwsbQZb0O+L9qVdeDpi6bXWR5WXVmMdr4r3EqELv6WGipuov0/H9j0PprXF6DTo5X/BFkfwZASNJ9gy771OigrhONb0Z40gW8PNJ0OSq/B4TfRzrqAX0+0kh/gqw1ol7tAt96W7XQ6uJWP9uV6uBGIFvAw6PRQlIdW6AqZ36Fdrbbs++Z5uOECZy+iFbtZ2m78ACWd0H64BtV+lja9Dq7fRCvXQVExeBRZ6im7g1alQaURzWi07LvkGlp9wbA+DYVIB9KUUsrRRTRVSUkJPj4+FBcX4+3t7ehyhBDCOV3NhC3RMOsQBA+ybZu+Bzy7Wdpqwt7Tb9Ra0SiEg4th4pvg08OyCuXZDfP1S5g//x9U1HzMLl0xV1ahigoxf7MNNWAaSu+FqjZhLrmJyv4M1XcyysULdacEdG6osluos1+iekSB3h1lNqPKy+DKSZR/P5RLZ6goQf14FgIjUHp3qKoETY8ylsP1cyi/cNB7oMxmqDaiblwAv0dA54pSZjCWQ1E+qkt3UBrKrKC6Eu7cQrl5AxqYTCgFmExQXYHSuVr6KjOYFZjNbT5dooXoLCFT02mWP1FgqkBz8wS9i6VNU1BeRPiuD9CFP9lqpdiTd34WK3ZvvfUWq1evpqCggIEDB7Jx40YiIyMdXZYQQgiwPUV7P89u98Jejd6jbQMggGGgTT+dZyY6NwWPP2Pb9/LbMDnuvgD5Ebw403Y/VzNhyx6Y9be6YXPWKkub9fXyeraNhll76tl2dz1t/66n7ZMGxqwbftXMNOg+AMxm1JVT8O4vUH/8AoIiLG1mM1zJQv1zMkx+F9y7WtoLv4Mvl6GGvwwe3UApVOl1OLIJNWoBeAbAnSJLyCwpRH29AUIiUd8fBwUoLKFTaSio1abVeu/u+3eXeGr622x7/1g1y0E1Y3Xtjbp5EXpFg0tnlPGOJRxX3Ib8b6D7IEuwNptRVeVwLRvV7RHo1BlVUQo38sD/MUsoNhlR6MFYATcvotx8oLzYtmbqr6v2+zX96zuORl/ZaTZbar53uIAOqiqAilod9VBwGjzcLS/vPi/SURwe7D7++GPmzZtHYmIiTzzxBOvXr2fcuHHk5uYSGBjo6PKEEELcf4q2pq2hsNce1Xc8zT3GmkfKeBtArwe9Hq3bQ/DUXyGoN3h53etb3hU8zPBQeK1gGABHTRD1a9uweGY9DB9XN1ieWw1T5jVuBbURq6oU/9Dw9ndPwVtkQS+A/1v3Z2AASLNtCwc4dl/Hww38EH+69+2jv4KzXzT7eNTYBOgSZAltJYWW8Bw+Fs59aRt84V7w7fcbCOyHSnkDeo5CXcyw7Otuf23/K/eeMxK9sO7npQ05PNitXbuWmTNn8sILLwCQmJhIcnIy27dvZ+HChQ6uTgghRL0aG/YaCkeN7duc7e3dtr7jaWygbc6YDxrXXo1dQW3EquoD+0bOgpi7x1ETIO0JhqPmQ8CjlruFv1rd8LYT37TUBZb3zn7R7OPRwqKt7drVTEh7HUb9GcYtrns8NfuuudHj+OvwzFzwXPbgvg7k0GBnNBo5ceIEixbd+49cp9MxduxYjhw54sDKhBBC2M3eINPYINXU7e3ZtrFaY8yGtm9u0G1N9QVIe4Jh3wn3Tpd/tbrhbWu317S1hsYcT80zAhvT14EcGux++uknTCYTQUFBNu1BQUGcOXOmTv/KykoqKyutr0tKSlq9RiGEEMIhWiLotvaqaFty9PG0k8sPHHpX7NWrVwkJCeHw4cNERUVZ2xcsWMChQ4c4evSoTf/XX3+dZcuW1RlH7ooVQgghHKStnmPn6ONpbt9msOeuWIcGO6PRiKenJ7t27eLZZ5+1tsfFxXHr1i0+++wzm/71rdiFhoZKsBNCCCGE07In2Dn0d8W6uroydOhQUlJSrG1ms5mUlBSbFbwabm5ueHt723wJIYQQQggLh98VO2/ePOLi4nj88ceJjIxk/fr1lJWVWe+SFUIIIYQQjePwYDdlyhSuX7/Oa6+9RkFBAYMGDWLfvn11bqgQQgghhBAPJr9STAghhBDiZ6zdXGMnhBBCCCFajgQ7IYQQQggnIcFOCCGEEMJJSLATQgghhHASEuyEEEIIIZyEBDshhBBCCCchwU4IIYQQwklIsBNCCCGEcBIO/80TzVHzbOWSkhIHVyKEEEII0Tpqck5jfqdEuw52paWlAISGhjq4EiGEEEKI1lVaWoqPj88D+7TrXylmNpu5evUqXl5eaJrWavspKSkhNDSU77//Xn512c+YzFP7IPPUPsg8tR8yV+1Dc+ZJKUVpaSnBwcHodA++iq5dr9jpdDp69OjRZvvz9vaWD007IPPUPsg8tQ8yT+2HzFX70NR5+t9W6mrIzRNCCCGEEE5Cgp0QQgghhJOQYNcIbm5uLF26FDc3N0eXIh5A5ql9kHlqH2Se2g+Zq/ahreapXd88IYQQQggh7pEVOyGEEEIIJyHBTgghhBDCSUiwE0IIIYRwEhLs7nrrrbfo1asX7u7uPPHEExw7dqxR2yUlJaFpGs8++2zrFigA++bpvffeQ9M0my93d/c2rLbjsvfzdOvWLWbPno3BYMDNzY1HH32UvXv3tlG1HZc98xQTE1Pn86RpGs8880wbVtwx2ft5Wr9+PX369MHDw4PQ0FDi4+OpqKhoo2o7LnvmqaqqioSEBMLDw3F3d2fgwIHs27evZQpRQiUlJSlXV1e1fft2lZOTo2bOnKm6du2qCgsLH7jdxYsXVUhIiBo1apSaNGlS2xTbgdk7Tzt27FDe3t7q2rVr1q+CgoI2rrrjsXeeKisr1eOPP67Gjx+vMjIy1MWLF1VaWprKzMxs48o7Fnvn6caNGzafpezsbKXX69WOHTvatvAOxt552rlzp3Jzc1M7d+5UFy9eVPv371cGg0HFx8e3ceUdi73ztGDBAhUcHKySk5PVhQsX1Ntvv63c3d3VyZMnm12LBDulVGRkpJo9e7b1tclkUsHBwWrVqlUNblNdXa2GDx+u3n33XRUXFyfBrg3YO087duxQPj4+bVSdqGHvPG3evFmFhYUpo9HYViUK1bS/92pbt26d8vLyUrdv326tEoWyf55mz56txowZY9M2b948NWLEiFats6Ozd54MBoPatGmTTdtzzz2npk2b1uxaOvypWKPRyIkTJxg7dqy1TafTMXbsWI4cOdLgdgkJCQQGBvLiiy+2RZkdXlPn6fbt2/Ts2ZPQ0FAmTZpETk5OW5TbYTVlnj7//HOioqKYPXs2QUFBREREsHLlSkwmU1uV3eE09fNU27Zt25g6dSqdO3durTI7vKbM0/Dhwzlx4oT1NGBeXh579+5l/PjxbVJzR9SUeaqsrKxzaZCHhwcZGRnNrqfDB7uffvoJk8lEUFCQTXtQUBAFBQX1bpORkcG2bdvYunVrW5QoaNo89enTh+3bt/PZZ5/x4YcfYjabGT58OD/88ENblNwhNWWe8vLy2LVrFyaTib1797JkyRLWrFnD8uXL26LkDqkp81TbsWPHyM7O5qWXXmqtEgVNm6fnn3+ehIQERo4ciYuLC+Hh4cTExPC3v/2tLUrukJoyT+PGjWPt2rWcO3cOs9nMwYMH2bNnD9euXWt2PR0+2NmrtLSUGTNmsHXrVvz9/R1djniAqKgo/vCHPzBo0CCio6PZs2cPAQEBvPPOO44uTdRiNpsJDAxky5YtDB06lClTpvDqq6+SmJjo6NJEA7Zt20b//v2JjIx0dCniPmlpaaxcuZK3336bkydPsmfPHpKTk3njjTccXZqoZcOGDTzyyCM89thjuLq6MmfOHF544QV0uubHsk4tUF+75u/vj16vp7Cw0Ka9sLCQ7t271+l/4cIFLl26xMSJE61tZrMZgE6dOpGbm0t4eHjrFt0B2TtP9XFxcWHw4MGcP3++NUoUNG2eDAYDLi4u6PV6a1vfvn0pKCjAaDTi6uraqjV3RM35PJWVlZGUlERCQkJrliho2jwtWbKEGTNmWFdT+/fvT1lZGbNmzeLVV19tkeAgbDVlngICAvj000+pqKjgxo0bBAcHs3DhQsLCwppdT4efYVdXV4YOHUpKSoq1zWw2k5KSQlRUVJ3+jz32GKdPnyYzM9P69etf/5rY2FgyMzMJDQ1ty/I7DHvnqT4mk4nTp09jMBhaq8wOrynzNGLECM6fP2/9BxLA2bNnMRgMEupaSXM+T//+97+prKxk+vTprV1mh9eUebpz506d8FbzjyYlv0G0VTTn8+Tu7k5ISAjV1dXs3r2bSZMmNb+gZt9+4QSSkpKUm5ubeu+999R3332nZs2apbp27Wp9NMaMGTPUwoULG9xe7optG/bO07Jly9T+/fvVhQsX1IkTJ9TUqVOVu7u7ysnJcdQhdAj2zlN+fr7y8vJSc+bMUbm5ueq///2vCgwMVMuXL3fUIXQITf17b+TIkWrKlCltXW6HZe88LV26VHl5eal//etfKi8vTx04cECFh4er3/3ud446hA7B3nn65ptv1O7du9WFCxfUV199pcaMGaN69+6tioqKml1Lhz8VCzBlyhSuX7/Oa6+9RkFBAYMGDWLfvn3WCyHz8/Nl+fpnwN55KioqYubMmRQUFODr68vQoUM5fPgw/fr1c9QhdAj2zlNoaCj79+8nPj6eAQMGEBISwl/+8hf++te/OuoQOoSm/L2Xm5tLRkYGBw4ccETJHZK987R48WI0TWPx4sVcuXKFgIAAJk6cyIoVKxx1CB2CvfNUUVHB4sWLycvLo0uXLowfP55//vOfdO3atdm1aErJ2qwQQgghhDOQZSghhBBCCCchwU4IIYQQwklIsBNCCCGEcBIS7IQQQgghnIQEOyGEEEIIJyHBTgghhBDCSUiwE0IIIYRwEhLshBBCCCGchAQ7IYQQQggnIcFOCCGEEMJJSLATQji1mJgYXn75ZUeXYdXUem7cuEFgYCCXLl1q8ZruN3XqVNasWdPq+xFCtDwJdkKIZktMTMTLy4vq6mpr2+3bt3FxcSEmJsamb1paGpqmceHChTausm21dKBcsWIFkyZNolevXi02ZkMWL17MihUrKC4ubvV9CSFalgQ7IUSzxcbGcvv2bb799ltrW3p6Ot27d+fo0aNUVFRY21NTU3nooYcIDw93RKnt0p07d9i2bRsvvvhim+wvIiKC8PBwPvzwwzbZnxCi5UiwE0I0W58+fTAYDKSlpVnb0tLSmDRpEr179+abb76xaY+NjQVg3759jBw5kq5du9KtWzcmTJhgs5K3ZcsWgoODMZvNNvubNGkSf/rTnwAwm82sWrWK3r174+HhwcCBA9m1a1eDtTamf0xMDHPnzmXBggX4+fnRvXt3Xn/9dZs+paWlTJs2jc6dO2MwGFi3bp11le6Pf/wjhw4dYsOGDWiahqZpNqdQzWbzA8e+3969e3Fzc+PJJ5+0ac/IyMDFxcUmOF+6dAlN07h8+bL1+927dzN69Gg8PDwYNmwY+fn5pKen8+STT+Lp6clTTz3FrVu3bMaeOHEiSUlJD6xLCPHzI8FOCNEiYmNjSU1Ntb5OTU0lJiaG6Ohoa3t5eTlHjx61BruysjLmzZvHt99+S0pKCjqdjsmTJ1uD3G9/+1tu3LhhM+7NmzfZt28f06ZNA2DVqlV88MEHJCYmkpOTQ3x8PNOnT+fQoUP11tnY/u+//z6dO3fm6NGj/OMf/yAhIYGDBw9a3583bx5ff/01n3/+OQcPHiQ9PZ2TJ08CsGHDBqKiopg5cybXrl3j2rVrhIaGNnrs+6WnpzN06NA67ZmZmfTt2xd3d3dr26lTp/D19aVnz55kZWUBsHnzZlauXMnhw4cpLCxk+vTp/P3vf2fTpk2kpqaSlZXFjh07bMaOjIzk2LFjVFZWNliXEOLnp5OjCxBCOIfY2FhefvllqqurKS8v59SpU0RHR1NVVUViYiIAR44cobKy0hrsfvOb39iMsX37dgICAvjuu++IiIjA19eXX/3qV3z00Uc89dRTAOzatQt/f39iY2OprKxk5cqVfPnll0RFRQEQFhZGRkYG77zzDtHR0Tbj29N/wIABLF26FIBHHnmETZs2kZKSwtNPP01paSnvv/++TV07duwgODgYAB8fH1xdXfH09KR79+51flYPGrs+ly9fto5dW1ZWFoMHD7Zpy8zMZODAgdbv/fz8+Pjjj+nWrRsA0dHRZGRkkJOTg6enJwDDhg2joKDAZpzg4GCMRiMFBQX07Nmz3rqEED8/smInhGgRMTExlJWVcfz4cdLT03n00UcJCAggOjraep1dWloaYWFhPPTQQwCcO3eO3//+94SFheHt7W29MSA/P9867rRp09i9e7d15Wjnzp1MnToVnU7H+fPnuXPnDk8//TRdunSxfn3wwQf13pxhT/8BAwbYvDYYDPz4448A5OXlUVVVRWRkpPV9Hx8f+vTp06if1YPGrk95ebnNqlyNzMxMBg0aZNN26tQpa1tWVhaTJ0+2hjqw/GynTJliDXU1bb1797YZx8PDA7Bc3yeEaD9kxU4I0SIefvhhevToQWpqKkVFRdbVr+DgYEJDQzl8+DCpqamMGTPGus3EiRPp2bMnW7dutV5LFxERgdFotOmjlCI5OZlhw4aRnp7OunXrAMudtwDJycmEhITY1OPm5lanRnv6u7i42LzWNK3OtX5NZe/Y/v7+FBUV2bSZTCays7PrrNidPHnSuhKamZnJokWLbN7PysoiPj7e+rqiooLc3FzrKl+NmzdvAhAQENDIoxJC/BxIsBNCtJjY2FjS0tIoKipi/vz51vbRo0fzxRdfcOzYMf785z8Dluey5ebmsnXrVkaNGgVYbga4n7u7O8899xw7d+7k/Pnz9OnThyFDhgDQr18/3NzcyM/Pr3PatT729m9IWFgYLi4uHD9+3Lr6WFxczNmzZxk9ejQArq6umEymJu+jtsGDB9e5QzU3N5eKigqbU7RHjhzhypUrDBo0iJKSEi5dumQT/C5evEhxcbFN2+nTp1FK0b9/f5vxs7Oz6dGjB/7+/i1yDEKItiHBTgjRYmJjY5k9ezZVVVU2wSk6Opo5c+ZgNBqt19f5+vrSrVs3tmzZgsFgID8/n4ULF9Y77rRp05gwYQI5OTlMnz7d2u7l5cUrr7xCfHw8ZrOZkSNHUlxczNdff423tzdxcXE249jbvyFeXl7ExcUxf/58/Pz8CAwMZOnSpeh0OjRNA6BXr14cPXqUS5cu0aVLF/z8/NDpmnb1y7hx41i0aBFFRUX4+voCltU4gI0bNzJ37lzOnz/P3LlzATAajWRlZaHX64mIiLCOU3PNXe1r5jIzMwkPD6dLly42+0xPT+cXv/hFk+oVQjiOXGMnhGgxsbGxlJeX8/DDDxMUFGRtj46OprS01PpYFACdTkdSUhInTpwgIiKC+Ph4Vq9eXe+4Y8aMwc/Pj9zcXJ5//nmb99544w2WLFnCqlWr6Nu3L7/85S9JTk6uc81YU/s3ZO3atURFRTFhwgTGjh3LiBEjbO5QfeWVV9Dr9fTr14+AgACb6wbt1b9/f4YMGcInn3xibcvMzGTcuHHk5eXRv39/Xn31VZYtW4a3tzdvvvkmWVlZ9OnTx+bavPputsjKyqpzGraiooJPP/2UmTNnNrlmIYRjaEop5egihBCivSsrKyMkJIQ1a9a0yoOEk5OTmT9/PtnZ2eh0OsaNG8ewYcNYvnx5i+9r8+bN/Oc//+HAgQMtPrYQonXJqVghhGiCU6dOcebMGSIjIykuLiYhIQGwPDy5NTzzzDOcO3eOK1euEBoaSlZWlvUhzS3NxcWFjRs3tsrYQojWJSt2QgjRBKdOneKll14iNzcXV1dXhg4dytq1a+vchNAaCgoKMBgM5OTk0K9fv1bfnxCi/ZBgJ4QQQgjhJOTmCSGEEEIIJyHBTgghhBDCSUiwE0IIIYRwEhLshBBCCCGchAQ7IYQQQggnIcFOCCGEEMJJSLATQgghhHASEuyEEEIIIZyEBDshhBBCCCchwU4IIYQQwklIsBNCCCGEcBL/H26P7RjpnN8YAAAAAElFTkSuQmCC",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitter = FastDispersionFitter.from_file(\"misc/MoS2_nk.csv\", skiprows=0, delimiter=\",\")\n",
    "advanced_param = AdvancedFastFitterParam(weights=(1, 1))\n",
    "MoS2, rms_error = fitter.fit(max_num_poles=4, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
    "fitter.plot(MoS2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0f5f4b",
   "metadata": {},
   "source": [
    "The MoS$_2$ medium defined from the fitter is a conventional [Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html) meant to be used with 3D geometries. Due to the atomically thin nature of MoS$_2$ monolayer, we will be modeling the waveguide as a 2D layer to enjoy the computational cost saving. To do so, we first need to define a [Medium2D](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium2D.html) for MoS$_2$, which can be done simply by using the `from_medium` method and providing the thickness of the layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21653c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 0.00065  # thickness of the MoS2 monolayer\n",
    "\n",
    "# define MoS2 as Medium2D\n",
    "MoS2_2D = td.Medium2D.from_medium(medium=MoS2, thickness=t)\n",
    "\n",
    "n_env = 1.46  # refractive index of the environment\n",
    "env_medium = td.Medium(permittivity=n_env**2)  # define environment medium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18d38fc",
   "metadata": {},
   "source": [
    "Define the simulation wavelength range. Here, we are interested in simulating the waveguide response between 500 nm to 900 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d32011b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 0.7  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(0.5, 0.9, 50)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.4 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f92cf0",
   "metadata": {},
   "source": [
    "Define the waveguide structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcf42ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = 150  # length of the waveguide\n",
    "\n",
    "# define the waveguide structure\n",
    "MoS2_monolayer = td.Structure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(l, 0, td.inf)), medium=MoS2_2D\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b5cb58",
   "metadata": {},
   "source": [
    "As discussed in the [paper](https://www.science.org/doi/10.1126/science.adi2322), the waveguide mode can be excited by a [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) or a [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html). However, for simulations with a waveguide, the more natural source is a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html). We will define a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) at the beginning of the waveguide here and later use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to investigate the waveguide mode profile and effective index.\n",
    "\n",
    "In addition, we also defined a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to help visualize the waveguide mode propagation and out-couple into free space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bea141c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
           WARNING: Default value for the field monitor           monitor.py:261\n",
       "           'colocate' setting has changed to 'True' in Tidy3D                   \n",
       "           2.4.0. All field components will be colocated to the                 \n",
       "           grid boundaries. Set to 'False' to get the raw fields                \n",
       "           on the Yee grid instead.                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor          \u001b[0m \u001b]8;id=860062;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\components\\monitor.py\u001b\\\u001b[2mmonitor.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=187255;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\components\\monitor.py#261\u001b\\\u001b[2m261\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has changed to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D    \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be colocated to the  \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mgrid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw fields \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mon the Yee grid instead.                              \u001b[0m \u001b[2m              \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a mode source\n",
    "mode_spec = td.ModeSpec(num_modes=1)\n",
    "mode_source = td.ModeSource(\n",
    "    size=(0, td.inf, td.inf),\n",
    "    center=(-l / 2 + lda0, 0, 0),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    "    direction=\"+\",\n",
    "    num_freqs=7,  # using 7 (Chebyshev) points to approximate frequency dependence\n",
    ")\n",
    "\n",
    "# define a field monitor\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(td.inf, 30, 0),\n",
    "    freqs=[freq0],\n",
    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "    interval_space=(1, 2, 1),\n",
    "    name=\"field\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b920b27f",
   "metadata": {},
   "source": [
    "Now we are ready to define a Tidy3D [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html). This is a 2D simulation so the domain size in $z$ is set to 0 and periodic boundary condition is applied in the $z$ boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e03b7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = 200  # simulation domain size in the x direction\n",
    "Ly = 80  # simulation domain size in the y direction\n",
    "run_time = 1.5e-12  # simulation run time\n",
    "\n",
    "# construct simulation\n",
    "sim = td.Simulation(\n",
    "    center=((Lx - l) / 2 - lda0, 0, 0),\n",
    "    size=(Lx, Ly, 0),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
    "    structures=[MoS2_monolayer],\n",
    "    sources=[mode_source],\n",
    "    monitors=[field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
    "    ),\n",
    "    medium=env_medium,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30edac77",
   "metadata": {},
   "source": [
    "Visualize the simulation to see if it is set up correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c8e575a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim.plot(z=0)\n",
    "ax.set_aspect(\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab700aee",
   "metadata": {},
   "source": [
    "## Perform Mode Solving "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc47caf",
   "metadata": {},
   "source": [
    "Before running the simulation, we use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to inspect the waveguide mode profile. The mode solving is performed on the same plane as the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) defined earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7548982d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[09:55:49] WARNING: Use the remote mode solver with subpixel  mode_solver.py:141\n",
       "           averaging for better accuracy through                                \n",
       "           'tidy3d.plugins.mode.web.run(...)'.                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09:55:49]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel \u001b[0m \u001b]8;id=723226;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\plugins\\mode\\mode_solver.py\u001b\\\u001b[2mmode_solver.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=761706;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\plugins\\mode\\mode_solver.py#141\u001b\\\u001b[2m141\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31maveraging for better accuracy through             \u001b[0m \u001b[2m                  \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.               \u001b[0m \u001b[2m                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define mode solving plane\n",
    "mode_plane = td.Box(\n",
    "    center=mode_source.center,\n",
    "    size=mode_source.size,\n",
    ")\n",
    "\n",
    "# define mode solver\n",
    "mode_solver = ModeSolver(simulation=sim, plane=mode_plane, mode_spec=mode_spec, freqs=freqs)\n",
    "\n",
    "# solving for the mode\n",
    "mode_data = mode_solver.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8584b16",
   "metadata": {},
   "source": [
    "After running the mode solving, let's plot the real part of the effective index, $n_{eff}$, as a function of wavelength. It shows that $n_{eff}$ is constant and equal to the refractive index of the environment medium. This is not surprising since the MoS$_2$ waveguide is atomically thin so most of the mode field is in the environment medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a6659de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_eff = np.real(mode_data.n_complex.values)\n",
    "plt.plot(ldas * 1e3, n_eff)\n",
    "plt.xlabel(\"wavelength (nm)\")\n",
    "plt.ylabel(\"n_eff\")\n",
    "plt.ylim(1, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3bee33",
   "metadata": {},
   "source": [
    "Similarly, we can plot the imaginary part of the effective index, $k_{eff}$. The shape of $k_{eff}$ largely follows the imaginary part of the MoS$_2$ refractive index since MoS$_2$ is the only source of loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d9b2318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_eff = np.imag(mode_data.n_complex.values)\n",
    "plt.plot(ldas * 1e3, k_eff)\n",
    "plt.xlabel(\"wavelength (nm)\")\n",
    "plt.ylabel(\"k_eff\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db128446",
   "metadata": {},
   "source": [
    "Lastly, we can visualize the mode profiles by plotting the `Ez` component of the mode at different wavelengths. We see that the mode profile resembles that of the solution to the [δ-potential](https://en.wikipedia.org/wiki/Delta_potential) problem in quantum mechanics. We can also observe that the width of the mode increases with wavelength from a few µm at 500 nm to over 10 µm at 900 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7de9dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(8, 3))\n",
    "mode_data.Ez.sel(mode_index=0, f=freqs[0]).abs.plot(ax=ax1)\n",
    "ax1.set_title(rf\"$\\lambda$={ldas[0]} $\\mu m$\")\n",
    "mode_data.Ez.sel(mode_index=0, f=freq0, method=\"nearest\").abs.plot(ax=ax2)\n",
    "ax2.set_title(rf\"$\\lambda$={lda0} $\\mu m$\")\n",
    "mode_data.Ez.sel(mode_index=0, f=freqs[-1]).abs.plot(ax=ax3)\n",
    "ax3.set_title(rf\"$\\lambda$={ldas[-1]} $\\mu m$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a95ab0",
   "metadata": {},
   "source": [
    "## Submit the Simulation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a5f4cf",
   "metadata": {},
   "source": [
    "Now we are ready to submit the simulation to the server. Although this simulation is 2D, the large simulation domain size and the fine grid still results in a good amount of computation. We can expect this simulation to finish in just a minute or two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92a6a8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[09:56:04] Created task 'mos2_wg' with task_id                     webapi.py:188\n",
       "           'fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1'.                       \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[09:56:04]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mos2_wg'\u001b[0m with task_id                     \u001b]8;id=284593;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=133023;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
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       "
           View task using web UI at                               webapi.py:190\n",
       "           'https://tidy3d.simulation.cloud/workbench?taskId=fdve-              \n",
       "           e05b98f7-0517-4f67-806a-50cc8ad299b7v1'.                             \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=943506;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=418340;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=875514;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=269545;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=875514;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=910795;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=875514;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=875514;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e05b98f7-0517-4f67-806a-50cc8ad299b7v1\u001b\\\u001b[32me05b98f7-0517-4f67-806a-50cc8ad299b7v1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
      ]
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       "Output()"
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      "text/html": [
       "
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      "text/html": [
       "
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      ],
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "
[09:56:06] status = queued                                         webapi.py:361\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[09:56:06]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=443272;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=265742;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "model_id": "",
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       "version_minor": 0
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
[09:56:10] status = preprocess                                     webapi.py:355\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[09:56:10]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=392319;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=99695;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#355\u001b\\\u001b[2m355\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "
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      ],
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     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/html": [
       "
[09:56:15] Maximum FlexCredit cost: 0.890. Use                     webapi.py:341\n",
       "           'web.real_cost(task_id)' to get the billed FlexCredit                \n",
       "           cost after a simulation run.                                         \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[09:56:15]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.890\u001b[0m. Use                     \u001b]8;id=337726;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=789677;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\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 FlexCredit   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \u001b[0m\n"
      ]
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "
           starting up solver                                      webapi.py:377\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=402560;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=277843;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#377\u001b\\\u001b[2m377\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    {
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           running solver                                          webapi.py:386\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=387250;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=451993;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#386\u001b\\\u001b[2m386\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "
           To cancel the simulation, use 'web.abort(task_id)' or   webapi.py:387\n",
       "           'web.delete(task_id)' or abort/delete the task in the                \n",
       "           web UI. Terminating the Python script will not stop the              \n",
       "           job running on the cloud.                                            \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or   \u001b]8;id=66882;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=577034;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "version_minor": 0
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
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[10:00:35] early shutoff detected, exiting.                        webapi.py:404\n",
       "
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      ],
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       "\u001b[2;36m[10:00:35]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=700433;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=844775;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#404\u001b\\\u001b[2m404\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
       "
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      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
       "
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       "
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      ],
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       "\n"
      ]
     },
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    },
    {
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[10:00:36] status = postprocess                                    webapi.py:420\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[10:00:36]\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=507698;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=427428;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#420\u001b\\\u001b[2m420\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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[10:01:03] status = success                                        webapi.py:427\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[10:01:03]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                        \u001b]8;id=79537;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=330362;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#427\u001b\\\u001b[2m427\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    },
    {
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      ],
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     },
     "metadata": {},
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       "Output()"
      ]
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    },
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       "
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      ],
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    {
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[10:01:11] loading SimulationData from data/simulation.hdf5        webapi.py:591\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[10:01:11]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/simulation.hdf5        \u001b]8;id=28627;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=70796;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#591\u001b\\\u001b[2m591\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"mos2_wg\", path=\"data/simulation.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd1c0ae",
   "metadata": {},
   "source": [
    "## Visualize the Result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea3f955",
   "metadata": {},
   "source": [
    "After the simulation is complete, we can plot the field distribution to visualize the waveguide mode propagation in the MoS$_2$ and out couples to free space in the end. The field stays guided when propagating on MoS$_2$ and starts to diverge after entering the free space. The result is consistent with Fig. 1E in the [publication](https://www.science.org/doi/10.1126/science.adi2322)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f2c7038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[10:01:13] WARNING: Evaluating permittivity of a 'Medium2D' is    medium.py:3582\n",
       "           unphysical.                                                          \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[10:01:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Evaluating permittivity of a \u001b[0m\u001b[32m'Medium2D'\u001b[0m\u001b[31m is   \u001b[0m \u001b]8;id=318566;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\components\\medium.py\u001b\\\u001b[2mmedium.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=276334;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\components\\medium.py#3582\u001b\\\u001b[2m3582\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31munphysical.                                           \u001b[0m \u001b[2m              \u001b[0m\n"
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax)\n",
    "ax.set_aspect(\"auto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7843ed5c",
   "metadata": {},
   "source": [
    "In conclusion, the discussed method for creating 2D material waveguides can be applied to various atomically thin materials, enabling ultra-slim photonic circuits. These waveguides can guide a broad spectrum of light, making them versatile for multi-octave photonic systems. They also offer high-power, dispersion-free operation, ideal for 2D nonlinear photonics and pulse manipulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "affeb8e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Nanophotonics"
  ],
  "description": "This notebook demonstrates how to model a monolayer MoS2 waveguide in Tidy3D FDTD.",
  "feature_image": "./img/MoS2_waveguide.png",
  "features": [
   "Material fitting",
   "2D material"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "TMD, waveguide, MoS2, 2D material, 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": "3.11.0"
  },
  "title": "Monolayer MoS2 waveguide | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}