Fitting.ipynb

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    "# Fitting dispersive material models\n",
    "\n",
    "Here we show how to fit optical measurement data and use the results to create dispersion material models for `Tidy3D`.\n",
    "\n",
    "`Tidy3D`'s dispersion fitting tool peforms an optimization to find a medium defined as a dispersive [PoleResidue](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PoleResidue.html) model that minimizes the RMS error between the model results and the data. This can then be directly used as a material in simulations.\n",
    "\n",
    "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. For 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.\n",
    "\n",
    "We recommend using the [FastDispersionFitter](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.FastDispersionFitter.html), with advanced options configurable using the [AdvancedFastFitterParam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.AdvancedFastFitterParam.html)."
   ]
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   "source": [
    "# first import packages\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "import tidy3d as td"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "The fitting tool accepts three ways of loading data:\n",
    "\n",
    "1. Numpy arrays directly by specifying `wvl_um`, `n_data`, and optionally `k_data`;\n",
    "\n",
    "2. Data file with the `from_file` utility function.\n",
    "\n",
    "    Our data file has columns for wavelength (um), real part of refractive index (n), and imaginary part of refractive index (k).  k data is optional. \n",
    "    \n",
    "    Note: `from_file` uses [np.loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html) under the hood, so additional keyword arguments for parsing the file follow the same format as [np.loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html).\n",
    "    \n",
    "3. URL linked to a csv/txt file that contains wavelength (micron), n, and optionally k data with the `from_url` utility function. URL can come from [refractiveindex.info](https://refractiveindex.info).\n",
    "\n",
    "Below the 2nd way is taken as an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam\n",
    "\n",
    "fname = \"misc/nk_data.csv\"\n",
    "\n",
    "# note that additional keyword arguments to load_nk_file get passed to np.loadtxt\n",
    "fitter = FastDispersionFitter.from_file(fname, skiprows=1, delimiter=\",\")\n",
    "\n",
    "# lets plot the data\n",
    "fitter.plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the data\n",
    "\n",
    "The fitting tool fit a dispersion model to the data by minimizing the root mean squared (RMS) error between the model n,k prediciton and the data at the given wavelengths.\n",
    "\n",
    "There are various fitting parameters that can be set, but the most important is the number of \"poles\" in the model.\n",
    "\n",
    "For each pole, there are 4 degrees of freedom in the model.  Adding more poles can produce a closer fit, but each additional pole added will make the fit harder to obtain and will slow down the FDTD.  Therefore, it is best to try the fit with few numbers of poles and increase until the results look good.\n",
    "\n",
    "Here, we will first try fitting the data with 1 pole and specify the RMS value that we are happy with (`tolerance_rms`).\n",
    "\n",
    "We specify weights for the real and imaginary part of the permittivity using `AdvancedFastFitterParam`, although the default weights which are calculated based on the typical values would also work fine here. Also note that, by default, `eps_inf` is optimized, although alternatively a fixed value for `eps_inf` can be specified as an argument to `fitter.fit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "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\">Best weighted RMS error so far: 0.0295 <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Best weighted RMS error so far: 0.0295 \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
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    },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
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       "\n"
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       "<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\">10:48:22 EDT </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Unable to fit with weighted RMS error under               </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'tolerance_rms'</span><span style=\"color: #800000; text-decoration-color: #800000\"> of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.02</span><span style=\"color: #800000; text-decoration-color: #800000\">                                            </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:48:22 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Unable to fit with weighted RMS error under               \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'tolerance_rms'\u001b[0m\u001b[31m of \u001b[0m\u001b[1;36m0.02\u001b[0m\u001b[31m                                            \u001b[0m\n"
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    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "advanced_param = AdvancedFastFitterParam(weights=(1,1))\n",
    "medium, rms_error = fitter.fit(max_num_poles=1, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
    "fitter.plot(medium)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RMS error was above our tolerance and there is room for improvement at short wavelengths, so we might want to try more fits.\n",
    "\n",
    "Let's now try a fit with up to 3 poles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "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\">Best weighted RMS error: 0.0114 <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Best weighted RMS error: 0.0114 \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "medium, rms_error = fitter.fit(max_num_poles=3, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
    "fitter.plot(medium)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This fit looks great and should be sufficient for our simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, if the simulation is narrowband, you might want to truncate your data to not include wavelengths far outside your measurement wavelength to simplify the dispersive model. This is through modifying the attribute `wvl_range` where you can set the lower wavelength bound `wvl_range[0]` and the higher wavelength bound `wvl_range[1]`. This operation is non-destructive, so you can always unset them by setting the value to `None`. \n",
    "\n",
    "E.g. if we are only interested in the wavelength 3-20 um, we can still use the single-pole model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "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\">Best weighted RMS error: 0.000114 <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Best weighted RMS error: 0.000114 \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\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\"></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": [
    "fitter = fitter.copy(update={\"wvl_range\": (3, 20)})\n",
    "medium, rms_error = fitter.fit(max_num_poles=1, tolerance_rms=2e-2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hYCciIiLiIRTsRERERDyEgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdQsBMRERHxEAp2IiIiIh5CwU5ERETEQyjYiYiIiHgIBTsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIeQsFORERExEMo2ImIiIh4CAU7EREREQ+hYCciIiLiIRTsRERERDyEgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdQsBMRERHxEG4NdgkJCXTv3p2goCAaN27MbbfdRlJSUpn7LFy4EMMwnB7+/v7V1GMRERGRmsutwW7dunWMHTuWzZs3s3LlSgoKChg8eDBZWVll7hccHMzx48cdjyNHjlRTj0VERERqLm93fviXX37p9H7hwoU0btyYbdu2ccMNN5S6n2EYREREVHX3RERERGqVGnWOXVpaGgChoaFl1svMzKR58+ZER0dz6623snfv3lLr5uXlkZ6e7vQQERER8UQ1JthZrVYmTJhAnz596NChQ6n12rZty/z581m+fDnvvPMOVquV3r1788svv7isn5CQQEhIiOMRHR1dVV9BRERExK0M0zRNd3cC4MEHH+SLL77gu+++o2nTpuXer6CggHbt2jF8+HCmTZtWYnteXh55eXmO9+np6URHR5OWlkZwcHCl9F1ERESkqqSnpxMSElKu7OLWc+zsxo0bx2effca3335boVAH4OPjQ1xcHAcPHnS53c/PDz8/v8ropoiIiEiN5tapWNM0GTduHEuXLmX16tXExMRUuI2ioiJ2795NZGRkFfRQREREpPZw64jd2LFjWbx4McuXLycoKIjU1FQAQkJCCAgIAGDkyJE0adKEhIQEAJ577jl69epFq1atOHfuHNOnT+fIkSPcf//9bvseIiIiIjWBW4PdnDlzAOjXr59T+YIFCxg1ahQAKSkpWCwXBhbPnj3LmDFjSE1NpUGDBnTt2pWNGzfSvn376uq2iIiISI1UYxZPVJeKnIAoIiIi4m4VyS415nInIiIiInJlFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIeQsFORERExEMo2ImIiIh4CAU7EREREQ+hYCciIiLiIRTsRERERDyEgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdQsBMRERHxEAp2IiIiIh5CwU5ERETEQyjYiYiIiHgIBTsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIeQsFORERExEN4u7sDIiIicvmKioooKChwdzfkCvj4+ODl5VUpbSnYiYiI1EKmaZKamsq5c+fc3RWpBPXr1yciIgLDMK6oHQU7ERGRWsge6ho3bky9evWuOBCIe5imSXZ2NidPngQgMjLyitpTsBMREallioqKHKGuYcOG7u6OXKGAgAAATp48SePGja9oWlaLJ0RERGoZ+zl19erVc3NPpLLY/yyv9HxJBTsREZFaStOvnqOy/iwV7EREREQ8hIKdiIiI1Gj5+fm0atWKjRs3llrn8OHDGIZBYmJilfdn7ty5DBs2rMo/53Io2ImIiEiNNnfuXGJiYujdu3eltjtq1Chuu+22Cu933333sX37dtavX1+p/akMCnYiIiJ1zCsr9zNr1QGX22atOsArK/dXc49KZ5oms2fPZvTo0e7uioOvry933303s2bNcndXSlCwExERqWO8LAYvuwh3s1Yd4OWV+/GyVM2ijH79+jF+/HgeffRRQkNDiYiI4Jlnnilzn23btnHo0CFuvvlmp/Lvv/+euLg4/P396datGzt27HDaXlRUxOjRo4mJiSEgIIC2bdsyc+ZMx/ZnnnmGt99+m+XLl2MYBoZhsHbtWgAee+wx2rRpQ7169WjRogVPPfVUidWqw4YN45NPPiEnJ+fyD0gV0HXsRERE6pjxA1oD8PL5kbnxA1o7Qt2kQW0c26vC22+/zaRJk9iyZQubNm1i1KhR9OnTh0GDBrmsv379etq0aUNQUJCjLDMzk1tuuYVBgwbxzjvvkJyczEMPPeS0n9VqpWnTpnzwwQc0bNiQjRs38sADDxAZGckf//hHHnnkEX788UfS09NZsGABAKGhoQAEBQWxcOFCoqKi2L17N2PGjCEoKIhHH33U0X63bt0oLCxky5Yt9OvXr5KP0uVz64hdQkIC3bt3JygoiMaNG3PbbbeRlJR0yf0++OADrrnmGvz9/enYsSOff/55NfRWRETEc4wf0JpJg9rw8sr9tHnii2oJdQCxsbFMnTqV1q1bM3LkSLp168aqVatKrX/kyBGioqKcyhYvXozVauWtt97i2muv5ZZbbmHy5MlOdXx8fHj22Wfp1q0bMTExjBgxgnvvvZf3338fgMDAQAICAvDz8yMiIoKIiAh8fX0BePLJJ+nduzdXX301w4YN45FHHnHsZ1evXj1CQkI4cuRIZRyWSuPWYLdu3TrGjh3L5s2bWblyJQUFBQwePJisrKxS99m4cSPDhw9n9OjR7Nixg9tuu43bbruNPXv2VGPPRUREar/xA1rj62Uhv8iKr5elykMd2IJdcZGRkY7babmSk5ODv7+/U9mPP/5IbGysU3l8fHyJfV9//XW6du1Ko0aNCAwMZN68eaSkpFyyj++99x59+vQhIiKCwMBAnnzySZf7BQQEkJ2dfcn2qpNbg92XX37JqFGjuPbaa+nUqRMLFy4kJSWFbdu2lbrPzJkzuemmm5g8eTLt2rVj2rRpdOnShdmzZ1djz0VERGq/WasOOEJdfpG11AUVlcnHx8fpvWEYWK3WUuuHhYVx9uzZCn/OkiVLeOSRRxg9ejRff/01iYmJ3HvvveTn55e536ZNmxgxYgS/+93v+Oyzz9ixYwdPPPGEy/3OnDlDo0aNKty3qlSjFk+kpaUBF+a4Xdm0aRMDBw50KhsyZAibNm2q0r6JiIh4kuLn1O1/fqhjWrY6wl1FxMXF8dNPP2GapqOsXbt27Nq1i9zcXEfZ5s2bnfbbsGEDvXv35m9/+xtxcXG0atWKQ4cOOdXx9fWlqKjIqWzjxo00b96cJ554gm7dutG6dWuX062HDh0iNzeXuLi4yvialabGBDur1cqECRPo06cPHTp0KLVeamoq4eHhTmXh4eGkpqa6rJ+Xl0d6errTQ0REpC5ztVCi+Dl3NSnc9e/fn8zMTPbu3esou/vuuzEMgzFjxrBv3z4+//xzXnrpJaf9WrduzdatW/nqq6/Yv38/Tz31FD/88INTnauvvppdu3aRlJTEqVOnKCgooHXr1qSkpLBkyRIOHTrErFmzWLp0aYl+rV+/nhYtWtCyZcuq+eKXqcYEu7Fjx7Jnzx6WLFlSqe0mJCQQEhLieERHR1dq+yIiIrVNkdV0uVDCHu6KrGYpe1a/hg0bcvvtt7No0SJHWWBgIJ9++im7d+8mLi6OJ554ghdffNFpv//v//v/uOOOO7jrrrvo2bMnp0+f5m9/+5tTnTFjxtC2bVu6detGo0aN2LBhA7///e+ZOHEi48aNo3PnzmzcuJGnnnqqRL/effddxowZUzVf+goYZvGxTTcZN24cy5cv59tvvyUmJqbMus2aNWPSpElMmDDBUTZ16lSWLVvGzp07S9TPy8sjLy/P8T49PZ3o6GjS0tIIDg6utO8gIiJSXXJzc0lOTiYmJqbEwgJPtGvXLgYNGsShQ4cIDAx0d3fYu3cvN954I/v37yckJKRS2izrzzQ9PZ2QkJByZRe3jtiZpsm4ceNYunQpq1evvmSoA9uql4uXRa9cudLlahgAPz8/goODnR4iIiJSe8TGxvLiiy+SnJzs7q4AcPz4cf773/9WWqirTG69QPHYsWNZvHgxy5cvJygoyHGeXEhICAEBAQCMHDmSJk2akJCQAMBDDz1E3759mTFjBjfffDNLlixh69atzJs3z23fQ0RERKrWqFGj3N0Fh4sXcdYkbh2xmzNnDmlpafTr14/IyEjH47333nPUSUlJ4fjx4473vXv3ZvHixcybN49OnTrx4YcfsmzZsjIXXIiIiIjUBW4dsSvP6X32+7YVd+edd3LnnXdWQY9EREREaq8asypWRERERK6Mgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIhItejXr5/TnaPK66mnnuKBBx6okrYr6tSpUzRu3Jhffvmlyj/rcijYiYiISI2VmprKzJkzeeKJJyq13bVr12IYBufOnavQfmFhYYwcOZKpU6dWan8qi4KdiIhIXZaRCmsSbM810Jtvvknv3r1p3ry5u7vicO+997Jo0SLOnDnj7q6UoGAnIiJSl2WkwroX3BLsVqxYQUhICIsWLSq1zpIlSxg2bJhTWVZWFiNHjiQwMJDIyEhmzJhRYr///e9/dOvWjaCgICIiIrj77rs5efIkAIcPH6Z///4ANGjQAMMwHLcs+/LLL7nuuuuoX78+DRs25JZbbuHQoUNObV977bVERUWxdOnSK/n6VULBTkRERKrd4sWLGT58OIsWLWLEiBEu65w5c4Z9+/bRrVs3p/LJkyezbt06li9fztdff83atWvZvn27U52CggKmTZvGzp07WbZsGYcPH3aEt+joaD766CMAkpKSOH78ODNnzgRsoXHSpEls3bqVVatWYbFYuP3227FarU7t9+jRg/Xr11fGoahUFb6lWHp6OsHBwS63HTx4kFatWl1xp0RERKQKZaReGKE7vtP5GSAowvaoIq+//jpPPPEEn376KX379i21XkpKCqZpEhUV5SjLzMzkrbfe4p133mHAgAEAvP322zRt2tRp3/vuu8/xukWLFsyaNYvu3buTmZlJYGAgoaGhADRu3Jj69es76v7hD39wamf+/Pk0atSIffv2Od2XPioqih07dlT8y1exCge7m2++mW+++QY/Pz+n8qSkJAYMGFBjV4mIiIjIeVsX2KZfi/t0/IXXfR+H/lOq5KM//PBDTp48yYYNG+jevXuZdXNycgDw9/d3lB06dIj8/Hx69uzpKAsNDaVt27ZO+27bto1nnnmGnTt3cvbsWceIW0pKCu3bty/1Mw8cOMDTTz/Nli1bOHXqlNN+xYNdQEAA2dnZ5fzW1afCU7GBgYHcfvvtFBYWOsp+/PFH+vXrVyLlioiISA3U7V54YJ3tMWyWrWzYrAtl3e6tso+Oi4ujUaNGzJ8/H9M0y6wbFhYGwNmzZyv0GVlZWQwZMoTg4GAWLVrEDz/84DgfLj8/v8x9hw0bxpkzZ3jjjTfYsmULW7ZscbnfmTNnaNSoUYX6VR0qHOw+/vhj0tLSGDFiBKZpsmfPHvr168fw4cMd89MiIiJSgwVFQFRn2yOyk60sstOFsiqchm3ZsiVr1qxh+fLl/P3vf79k3eDgYPbt2+dU5uPj4whcYAt++/fvd7z/6aefOH36NC+88ALXX38911xzjWPhhJ2vry8ARUVFjrLTp0+TlJTEk08+yYABA2jXrl2poXLPnj3ExcWV/4tXkwoHu4CAAFasWEFSUhJ//OMfGTBgACNHjuTll1+uiv6JiIiIh2nTpg1r1qzho48+KvOiwhaLhYEDB/Ldd985ygIDAxk9ejSTJ09m9erV7Nmzh1GjRmGxXIg0zZo1w9fXl9dee42ff/6ZTz75hGnTpjm13bx5cwzD4LPPPuO3334jMzOTBg0a0LBhQ+bNm8fBgwdZvXo1kyZNKtGv7Oxstm3bxuDBg6/8YFSycgW79PR0p4fFYuG9995jy5Yt/OEPf+Cpp55ybBMREZFaJCjCdk5dFY7SudK2bVtWr17Nu+++y8MPP1xqvfvvv58lS5Y4rUqdPn06119/PcOGDWPgwIFcd911dO3a1bG9UaNGLFy4kA8++ID27dvzwgsv8NJLLzm126RJE5599lkef/xxwsPDGTduHBaLhSVLlrBt2zY6dOjAxIkTmT59eok+LV++nGbNmnH99ddXwpGoXIZ5qQlubInZMIwS5fZdDcPANE0Mw3Aa0qyJ0tPTCQkJIS0trdTVvSIiIjVZbm4uycnJxMTEOC0s8ESmadKzZ08mTpzI8OHD3d0dAHr16sX48eO5++67K63Nsv5MK5JdyrUqds2aNZffUxEREZHLZBgG8+bNY/fu3e7uCmC7V+wdd9xRY0Lmxco1YudJNGInIiK1XV0asasrKmvETneeEBEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPUanB7sYbb2TatGk18qa4IiIiIp6uUoNds2bNWLVqFddcc01lNisiIiIi5VCpwW7hwoWsXbuWPXv2VGazIiIiUofl5+fTqlUrNm7cWO2fffjwYQzDIDExsdz79OvXz+keuL169eKjjz6q/M65UCXn2OnCvyIiIlJZ5s6dS0xMDL1793Z3Vy7Lk08+yeOPP+50v9uqUq5bil1s1apVrFq1ipMnT5bo5Pz58yulYyIiIiKmaTJ79myee+45d3flsg0dOpT777+fL774gptvvrlKP6vCI3bPPvssgwcPZtWqVZw6dYqzZ886PURERERc6devH+PHj+fRRx8lNDSUiIgInnnmmTL32bZtG4cOHXIKRPbp0ffff5/rr7+egIAAunfvzv79+/nhhx/o1q0bgYGBDB06lN9++82xn9Vq5bnnnqNp06b4+fnRuXNnvvzyS6fP+/7774mLi8Pf359u3bqxY8eOEn3as2cPQ4cOJTAwkPDwcP7yl79w6tSpUr+Dl5cXv/vd71iyZEk5j9Tlq/CI3dy5c1m4cCF/+ctfqqI/IiIiUkGmaZJTmOOWzw7wDsAwjHLXf/vtt5k0aRJbtmxh06ZNjBo1ij59+jBo0CCX9devX0+bNm0ICgoqsW3q1Km8+uqrNGvWjPvuu4+7776boKAgZs6cSb169fjjH//I008/zZw5cwCYOXMmM2bM4D//+Q9xcXHMnz+f3//+9+zdu5fWrVuTmZnJLbfcwqBBg3jnnXdITk7moYcecvrMc+fOceONN3L//ffzyiuvkJOTw2OPPcYf//hHVq9eXer37tGjBy+88EK5j9PlqnCwy8/Pr7Vz3CIiIp4opzCHnot7uuWzt9y9hXo+9cpdPzY2lqlTpwLQunVrZs+ezapVq0oNdkeOHCEqKsrltkceeYQhQ4YA8NBDDzF8+HBWrVpFnz59ABg9ejQLFy501H/ppZd47LHH+NOf/gTAiy++yJo1a3j11Vd5/fXXWbx4MVarlbfeegt/f3+uvfZafvnlFx588EFHG7NnzyYuLo5//etfjrL58+cTHR3N/v37adOmjcu+RkVFcfToUaxWKxZL1V1GuMIt33///SxevLgq+iIiIiIeLjY21ul9ZGQkJ0+eLLV+Tk4O/v7+l2wrPDwcgI4dOzqV2dtOT0/n2LFjjtBn16dPH3788UcAfvzxR2JjY50+Lz4+3qn+zp07WbNmDYGBgY6H/TJvhw4dKvV7BAQEYLVaycvLK7VOZajwiF1ubi7z5s3jm2++ITY2Fh8fH6ftL7/8cqV1TkRERC4twDuALXdvcdtnV8TFucEwjDJXi4aFhbF79+5LtmWfDr64rLJXomZmZjJs2DBefPHFEtsiIyNL3e/MmTNcddVVBARU7HhVVIWD3a5du+jcuTNAievVVWSOXURERCqHYRgVmg6tTeLi4pgzZw6maV5RzggODiYqKooNGzbQt29fR/mGDRvo0aMHAO3ateN///sfubm5jlG7zZs3O7XTpUsXPvroI66++mq8vcsfo/bs2UNcXNxl97+8Khzs1qxZUxX9EBERESmhf//+ZGZmsnfvXjp06HBFbU2ePJmpU6fSsmVLOnfuzIIFC0hMTGTRokUA3H333TzxxBOMGTOGKVOmcPjwYV566SWnNsaOHcsbb7zB8OHDHat7Dx48yJIlS3jzzTfx8vJy+dnr169n8ODBV9T/8qi6s/dERERErlDDhg25/fbbHeHrSowfP55Jkybx8MMP07FjR7788ks++eQTWrduDUBgYCCffvopu3fvJi4ujieeeKLElKt91K+oqIjBgwfTsWNHJkyYQP369UtdFPHrr7+yceNG7r333iv+DpdimKZpVvmn1CDp6emEhISQlpamO2SIiEitlJubS3JyMjExMaUuLPAku3btYtCgQRw6dIjAwEB3d6fCHnvsMc6ePcu8efNKrVPWn2lFsotG7ERERKRGi42N5cUXXyQ5OdndXbksjRs3Ztq0adXyWZd1SzERERGR6jRq1Ch3d+GyPfzww9X2WRqxExEREfEQCnYiIiIiHkLBTkRERMRDuDXYffvttwwbNoyoqCgMw2DZsmVl1l+7di2GYZR4pKamVk+HRURERGowtwa7rKwsOnXqxOuvv16h/ZKSkjh+/Ljj0bhx4yrqoYiIiEjt4dZVsUOHDmXo0KEV3q9x48bUr1+/8jskIiIiUovVynPsOnfuTGRkJIMGDWLDhg1l1s3LyyM9Pd3pISIiIuKJalWwi4yMZO7cuXz00Ud89NFHREdH069fP7Zv317qPgkJCYSEhDge0dHR1dhjERERsevXrx8TJkyo8H5PPfUUDzzwQOV3qBwq2ueFCxc6zSrOnTuXYcOGVX7HSlGrLlDctm1b2rZt63jfu3dvDh06xCuvvML//vc/l/tMmTKFSZMmOd6np6cr3ImIiNQSqampzJw5k927d7u7K5flvvvuY9q0aaxfv57rr7++yj+vVo3YudKjRw8OHjxY6nY/Pz+Cg4OdHiIiIlI7vPnmm/Tu3ZvmzZu7uyuXxdfXl7vvvptZs2ZVy+fV+mCXmJhIZGSku7shIiIiFbRixQpCQkJYtGhRqXWWLFlSYiqzX79+/P3vf2fChAk0aNCA8PBw3njjDbKysrj33nsJCgqiVatWfPHFF077rVu3jh49euDn50dkZCSPP/44hYWFju1ZWVmMHDmSwMBAIiMjmTFjRon+5OXl8cgjj9CkSROuuuoqevbsydq1a8v8nsOGDeOTTz4hJyenHEflyrg12GVmZpKYmEhiYiIAycnJJCYmkpKSAtimUUeOHOmo/+qrr7J8+XIOHjzInj17mDBhAqtXr2bs2LHu6L6IiEiNYJom1uxstzxM07ysPi9evJjhw4ezaNEiRowY4bLOmTNn2LdvH926dSux7e233yYsLIzvv/+ev//97zz44IPceeed9O7dm+3btzN48GD+8pe/kJ2dDcCvv/7K7373O7p3787OnTuZM2cOb731Fv/85z8dbU6ePJl169axfPlyvv76a9auXVviPP5x48axadMmlixZwq5du7jzzju56aabOHDgQKnftVu3bhQWFrJly5bLOVQV4tZz7LZu3Ur//v0d7+3nwt1zzz0sXLiQ48ePO0IeQH5+Pg8//DC//vor9erVIzY2lm+++capDRERkbrGzMkhqUtXt3x22+3bMOrVq9A+r7/+Ok888QSffvopffv2LbVeSkoKpmkSFRVVYlunTp148sknAdtA0AsvvEBYWBhjxowB4Omnn2bOnDns2rWLXr168X//939ER0cze/ZsDMPgmmuu4dixYzz22GM8/fTTZGdn89Zbb/HOO+8wYMAAwBYemzZt6tSfBQsWkJKS4ujTI488wpdffsmCBQv417/+5fJ71KtXj5CQEI4cOVKh43Q53Brs+vXrV2bSX7hwodP7Rx99lEcffbSKeyUiIiJV5cMPP+TkyZNs2LCB7t27l1nXPnXp7+9fYltsbKzjtZeXFw0bNqRjx46OsvDwcABOnjwJwI8//kh8fDyGYTjq9OnTh8zMTH755RfOnj1Lfn4+PXv2dGwPDQ11WrS5e/duioqKaNOmjVNf8vLyaNiwYZnfJSAgwDF6WJVq1apYERERKckICKDt9m1u++yKiIuLY/v27cyfP59u3bo5Ba2LhYWFAXD27FkaNWrktM3Hx8e5H4bhVGZv12q1Vqh/ZcnMzMTLy4tt27bh5eXltC0wMLDMfc+cOVPiO1QFBTsREZFazjCMCk+HukvLli2ZMWMG/fr1w8vLi9mzZ5dZNzg4mH379pUYJauodu3a8dFHH2GapiP0bdiwgaCgIJo2bUpoaCg+Pj5s2bKFZs2aAbZAuX//fsd0cVxcHEVFRZw8ebJCly45dOgQubm5xMXFXdF3KI9avypWREREapc2bdqwZs0aPvroozIv/muxWBg4cCDffffdFX/m3/72N44ePcrf//53fvrpJ5YvX87UqVOZNGkSFouFwMBARo8ezeTJk1m9ejV79uxh1KhRWCwXolKbNm0YMWIEI0eO5OOPPyY5OZnvv/+ehIQEVqxYUepnr1+/nhYtWtCyZcsr/h6XomAnIiIi1a5t27asXr2ad999l4cffrjUevfffz9Lliy54inVJk2a8Pnnn/P999/TqVMn/vrXvzJ69GjHAgyA6dOnc/311zNs2DAGDhzIddddR9euzotSFixYwMiRI3n44Ydp27Ytt912Gz/88INjlM+Vd99917Goo6oZ5uWuU66l0tPTCQkJIS0tTRcrFhGRWik3N5fk5GRiYmJcLizwJKZp0rNnTyZOnMjw4cPd3Z0K27t3LzfeeCP79+8nJCSk1Hpl/ZlWJLtoxE5ERERqLMMwmDdvntOFhGuT48eP89///rfMUFeZtHhCREREarTOnTvTuXNnd3fjsgwcOLBaP08jdiIiIiIeQsFORERExEMo2ImIiIh4CAU7ERGRWqoy76og7lVZf5ZaPCEiIlLL+Pr6YrFYOHbsGI0aNcLX17fMW3NJzWWaJvn5+fz2229YLBZ8fX2vqD0FOxERkVrGYrEQExPD8ePHOXbsmLu7I5WgXr16NGvWzOlOF5dDwU5ERKQW8vX1pVmzZhQWFlJUVOTu7sgV8PLywtvbu1JGXRXsREREainDMPDx8cHHx8fdXZEaQosnRERERDyEgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdQsBMRERHxEAp2IiIiIh5CwU5ERETEQyjYiYiIiHgIBTsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIeQsFORERExEMo2ImIiIh4CAU7EREREQ+hYCciIiLiIRTsRERERDyEgp2IiIiIh1CwExEREfEQbg123377LcOGDSMqKgrDMFi2bNkl91m7di1dunTBz8+PVq1asXDhwirvp4iIiEht4NZgl5WVRadOnXj99dfLVT85OZmbb76Z/v37k5iYyIQJE7j//vv56quvqrinIiIiIjWftzs/fOjQoQwdOrTc9efOnUtMTAwzZswAoF27dnz33Xe88sorDBkypKq6KSIiIlIr1Kpz7DZt2sTAgQOdyoYMGcKmTZvc1CMRERGRmsOtI3YVlZqaSnh4uFNZeHg46enp5OTkEBAQUGKfvLw88vLyHO/T09OrvJ8iIiIi7lCrRuwuR0JCAiEhIY5HdHS0u7skIiIiUiVqVbCLiIjgxIkTTmUnTpwgODjY5WgdwJQpU0hLS3M8jh49Wh1dFREREal2tWoqNj4+ns8//9ypbOXKlcTHx5e6j5+fH35+flXdNRERERG3c+uIXWZmJomJiSQmJgK2y5kkJiaSkpIC2EbbRo4c6aj/17/+lZ9//plHH32Un376if/7v//j/fffZ+LEie7ovoiIiEiN4tZgt3XrVuLi4oiLiwNg0qRJxMXF8fTTTwNw/PhxR8gDiImJYcWKFaxcuZJOnToxY8YM3nzzTV3qRERERAQwTNM03d2J6pSenk5ISAhpaWkEBwe7uzsiIiIiZapIdqlViydEREREpHQKdiIiIiIeQsFORERExEMo2ImIiIh4CAU7EREREQ+hYCciIiLiIRTsRERERDyEgp2IiIiIh1CwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdQsBMRERHxEAp2IiIiIh5CwU5ERETEQyjYiYiIiHgIBTsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULCrShmpsCbB9iwiIiJSxRTsqlJGKqx7QcFOREREqoWCXVXJSIWtC2yvs09r5E5ERESqnLe7O+BxMlJtj1P7YftCW9mRTbB+OjRsCTE3QFCEW7soIiIinkkjdpVt6wKY1xc+HnOhbP102/PHYy6M4omIiIhUMgW7ypSRCtE94I43oOeDF8pbD7E993zQtv1YoqZlRUREpNIZpmma7u5EdUpPTyckJIS0tDSCg4Mrt/E1CbbFEuXR93HoP6VyP19EREQ8TkWyi86xq0zd7oW2Q22vk7+FlU/ZXseNhB3/hRseg8wT0P73EH6t+/opIiIiHklTsZUpKAKiOtseMTfYyrqMgmtvs70Oa2VbUFGvoRZQiIiISKXTiF1V63avu3sgIiIidYSCXVUJirCdR2dYIOs328hdxgnbtuM7netp9E5EREQqgRZPVLVLLajQIgoREREpgxZP1CTFF1Qc3wmfjoc2N0GPB3SunYiIiFQqBbuq5mqqdf+X0G+KbZGFiIiISCXRqlgRERERD6ERu+pgv39s9mloMxT2f6EFFCIiIlLpFOyqw9YFJRdQfDr+wmstoBAREZFKoGBXHVwtoBg0DU4f0l0oREREpNIo2FUHV1OtQRG2W451u1fTsCIiIlIpasTiiddff52rr74af39/evbsyffff19q3YULF2IYhtPD39+/GnsrIiIiUjO5fcTuvffeY9KkScydO5eePXvy6quvMmTIEJKSkmjcuLHLfYKDg0lKSnK8Nwyjurp7ZewLKHQXChEREakCbg92L7/8MmPGjOHee233VJ07dy4rVqxg/vz5PP744y73MQyDiIhaGIC0iEJERESqkFunYvPz89m2bRsDBw50lFksFgYOHMimTZtK3S8zM5PmzZsTHR3Nrbfeyt69e0utm5eXR3p6utPDbbrdCw+ssz2GzbKVXT/Z9nzHG7btIiIiIpfJrcHu1KlTFBUVER4e7lQeHh5Oamqqy33atm3L/PnzWb58Oe+88w5Wq5XevXvzyy+/uKyfkJBASEiI4xEdHV3p36PcgiJsd5uI6gyRnWxljdrYnsPaaBpWRERErkiNWDxREfHx8YwcOZLOnTvTt29fPv74Yxo1asR//vMfl/WnTJlCWlqa43H06NFq7rELGalwar/ttf35+E44lmh7ZLgOtSIiIiJlces5dmFhYXh5eXHixAmn8hMnTpT7HDofHx/i4uI4ePCgy+1+fn74+fldcV8rVfFz7b6dbnvWuXYiIiJyhdw6Yufr60vXrl1ZtWqVo8xqtbJq1Sri4+PL1UZRURG7d+8mMjKyqrpZ+XSunYiIiFQBt6+KnTRpEvfccw/dunWjR48evPrqq2RlZTlWyY4cOZImTZqQkJAAwHPPPUevXr1o1aoV586dY/r06Rw5coT777/fnV+jYlxd1sR+rt3hDRBzQ/X3SURERGo9twe7u+66i99++42nn36a1NRUOnfuzJdffulYUJGSkoLFcmFg8ezZs4wZM4bU1FQaNGhA165d2bhxI+3bt3fXV7h8rs61274Qru5zYTGFFlSIiIhIORmmaZru7kR1Sk9PJyQkhLS0NIKDg93bmTUJJa9rV5zOtRMREanzKpJd3D5iV6d1uxeie9juRvHbflh/fiFFzwch7Rdo2Mo2qqdROxERESkHBTt3CopwfTeKLXNszz99qlE7ERERKbdadx07j2NfIXvHGxfK7Ctkh06H/Exd105ERETKRcHO3ex3o2h0DVwzzFZmnN+WeRI2zYbkbxXuRERE5JIU7GqKn1bYpl7hwkWL7efcfTwGNrzmnn6JiIhIraFgV1N0uxf+/DF0/CP0/JutLPZPF7Y3aK7bjYmIiEiZFOxqiqAIaDUAQlvAlv+zle1acmH7F5NhXl+N3ImIiEipFOxqmm732hZNAMSNvFBuf92guUbtRERExCUFu5omKALOHbG93vHfC+X2119Mhg/vU7gTERGREhTsaqLef7ddAsU+cgcXzreLGwlHNmilrIiIiJSgYFcT2S+BYh+5gwvn29lH7rRSVkRERC6iYFeT9f77hQsX28+xK75SNqABfPIQHN9V/X0TERGRGke3FKvJgiIg5gZo3ufCSF3xlbJrpl14/fuZ1ds3ERERqXE0YlfTBUXA/5tf9krZkGj46gmdcyciIlLHacSuNrjUSln7yF395rZ6vf9u20dERETqFAW72qL336HjnfDtSxduPXaxLybbnvMyof8UhTsREZE6RlOxtYV9pWzfR+GaYbayYgsqrEVwJqc/ZhGwfSEkr4c1CZqeFRERqUMM0zRNd3eiOqWnpxMSEkJaWhrBwcHu7s7lOZZou71YMb/tDuLU3iD86hcQ2eMcAV16weH1cMebcPqg7Y4WGsETERGpdSqSXTRiVxsFRUCvcU4XMPYLKcDLr4i8cz4cXhnGyeWJWIuA7W/Duhc0eiciIlIH6By72igoAm563hbUzh6BU0kEs5J6jfM5sT2E9JQATv8YRMYvATTu/D2BUWBsXwhXX6fROxEREQ+mYFeb2QPewVVwcCXe1/Shif96gprlkLo1hPwMb35Z35B6jfMIj0vDf/vbtunZhq1s07kGWkErIiLiQRTsPEH4tdD3cVtgO7ye4Ka5XBWex+l9gZxJCiT7pB/JXzUiaN8ewq71vhDwAFreCFsXaBRPRETEA+gcO08QFGG7vEnM9dBlFABera+jcacMWvzuJMHNcgCDjKMBJH/ZmKP/20POaR/bvqcP2c7BO7HXdh7e8V06H09ERKSW0oidJ7EHvKAIx+idb4feNAlcT8P2GZzaG0TGUX8yfw0g89cA/EPzCT33BsFBYPy8DjbOhIYtbUEvIxW6j4afVmg0T0REpJZQsPM09nCXkeo0Petfv5Cmfc6Sl+bN6R8DSU8JIPeML8e+zOSEXzghOxcQEuON35nDGGC7Ft7VfWwhL7oHbHhN5+SJiIjUcJqK9VQupmfp/RB+IYVE3daMVr8/QaOO6XgHFFGU58WZ/YEkf9WY5MfmcfrHqyjItkDqHtt+pw/B5tmwabambEVERGowjdh5uuLTs9E9bNOtcX/BO3UyYddm0rBdJpmpfqQl1yPzV3/y0nw4uTOEkztD8N+wiKAmgQT5LsDXBMPgwjl5mrIVERGpcRTs6oIS07MtHZuMAU8StOafBHVpS1HKbtJTAkg7HEDOKT9yT/uSe9qX33adwzeoMYFRuVyV+xb1vA0s9tG8i6dsty6Aa26Gne9p6lZERKSaKdjVJcUDXq9xtuDVpIttW9xf8EqdTIORY2iwcSYF2RYyj/mT8as/2Sf8yM/w5kxSIGeS0jEsEQSse4d64YFcFZGH/9Gdtjn94qN5m2fb2rVfTkVhT0REpMop2NVF9gsbQ8lRvIgOAPjUszpCXlFoB7ISD5J53J+sE74UZnuT/Zsf2b/5cWoPGKs/wr9BGAH75xDg70/Az/vwsX9W9unyhb3YuzSdKyIicoUU7Oq6i6dpG11jW2yxfaEj5Hn1vIfgM5MJbpaLGd6RgoP7yDrhR1aqH9m/+VKU50XOaV9yTucCobDxPbwDwglomI//qbfwy/PD/8fv8bafp+cq7EV2Kn06V6FPRESkXBTsxMYe8ODCYotG15Q8J6/LSHxPTMZ3iG00zzShINOLnFO+ZJ/2JeeUL3lp3hTmeJHxSwAZv6QADWH9Crx8I/CrX4B/ylz8CgPw3bgW3zwDbz/zQj8uFfrsl12xhz1N8YqIiDgo2ElJxUNeZKzzOXkXTdkaBvj+fgq+a/5JyLCbYc+HWANjyDn0K7lnfMg950PeOR/y0r0pyreQfdKP7JPHgQbw/RogEi+/Iny/S8DXNwS/s/PxzfbD98dd+BSBxatYv7JPlwx7pU3xXhz6igdBjf6JiIiHUrCTS3N1Tl7xKdsGzW3b2gyBPR9iuf5vXJU5mavC86HD/7OFvaAY8o78Qt45b3LP+ZCf5kNehheF2d62qdxfcsnhKvj5CLYRvo+ASLwDrPh8l4Cvd318Ut/AJzsAn6uK8D2agrfVltkcio/2lTbq5+oyLaUFQE0Fi4hILaNgJxVT1pRtvYa28oD6F+rbw951fyMgYzIBoQXQwTayx9V9sR78lvwML/LSvclP97G9zvAmP90bs8hCYY4Xhb/kkkM9OPwL0MDW7pq3wIjEe8WzePuF4R1QhE/yHLyzA/H2+gyfVF+8f/kN7wIDLx+TEopfpqW0AFiR8/8uVaZpYhERqQYKdnL5XE3Z2kfz7FO3ZYQ9uvwZy+F1+DcoxP+Ox2HNPx0jfGbzGyhK+o6CLC8KsrzIz/R2vC7Isr02rQaFWVYKs3xt7f6aCgTD7h+AMFg7G4jE8Lbi/cVzeHuH4bXt33gXheDlb8W7YClex/zx3r4b7zRvvPyseFmtzqOAxZV1/t+lyso7TVzeMo0gioiICwp2UnkuDnpQdtgr7qLpXKPrX/A+8i3e/lYChv3NdseM86EPwIzsQlFyIgU0pvD0WQqzvSjI8bKN8GVbHK+tBRbMQgsFaUUU4Auns4CrbJ+1LxEIhU0fAo1tZcufxcs3HK+vnsXLEmYLe3tewpIfjNfJN/E6Vw+vr1bjddwPL18rXqmn8cozsJQVCKH808TlLSvPYpLyhEONNIqIeBQFO6la5Q17F0/nFnd+oYZjpA8w4h/E+/gYvIc+DF9Mtm2PGwk7/gtX94XD6yCqK9Yj2ynMtVBoNKLw3DmKci0U5looyrNQmOtle59noSjXQlG+F5hQlOdFUZ4VOD8SeCwDCIT9R4H6sH0dcL6fK2cBkbD0WSw+EVh8rHitfA4LYVg2P4+lqAFePlYsR/+DJS0Qr/ylWH4JwLJlH16pvlh8TCzHTuGVY8GSl49hvyRMeVxqMUl5wmFFRhovNxxWRsDUaKWISLko2En1cxX27K8rGvqKT/HGXG8Ldl3+bAt2vf6K5dgYfH2K8LUHwN4P2Ub/wCkAcmwbphWKgq+l8JckrIGtKEo9TFG+QVG+LQgW5Rd/GFjPl1kLLQBYCyxYCywUZhcBvnA6HwiwfdbPx4Bg2JOIbUXwe0CYbdvK14AIWP48EInF28T45BkslsZYvnkOixmGxduK8cO/sBTWx5I0A0t2MJbfFmKcCsTibcXy9QYsKf5YdhzActIXw9vE8utvWLIsGOlZWAoMDC+z7FFFVy41/VxZo48VrX/x4hd3BMyqaFeBVUSukIKd1CyXCn0Xh71LTfHa2QOgffQPnAIgH4/BsID34Al4fzwG7ngYPh5jq9f/Safz/xyBECC8I+ax3RSFXIv1+E8UFViwBrak6ORhrJb6WDMzsBYYtvIC46LXForOP1sLDTANwLC9LjQpwhsyz4dEgBN5QD04kg4EQlIyEGzbtu1rbBeHfgdHYPx6NhABn/4biLSVfTgViyUCY/kzWIzGGF8/h2FthMXLxPhuGhZrKMa2f2Hk17eVHZmL5WwQxrn3MI4H2sq+2YolOQBj816MX/0wdh7EOOmLxWLCz8ewpHljpJ7GyPKyhcmsHIxCMC41XV0RFy9+cUfArIp2S5terynBtba3oVMLpA5QsJPao/hlV+BC8HM1xVvaqF/xy7SU10Xn/zkCIUCf8Rgfj8F78ITzQbDoQii8xDQxx7bZtoV3xEzdjRnaAeuxfVgLDawhbbCePIg1sAXmqSO2ssDmWE8dxVpoYBYaWIvOB8QiWxh0lBcaWAttYdEsMjCtxeKUFaxWy4XgmF0EjhvAFQL+wPkACfDzcSAIftqHI0Ru/xRoAFvex3Zpmv/hCJPf/AdoDF/MAsJtZcteAKLgw2fBiMSwmBhLn8EwwjFWPIuFxhgrp2FYwzAsYKyfhlEUirHlnxiFDWz1d7+AkReCcfAVjMxgDMO0hc1fA8G6EmP/VRhfbMY4WA/DAOPbnXDEH2PzPlv4NMDYdQhO+mIkpWCc9rG1e/QkpHthnDyLkW3BSMvEyDds0+EFhbap8fL/Uq5cadPrNSW41vY2KnsRU00LrmrDfW3UoBF3BTvxHK5G+y4e9YOSl2kpLQCWZyq4LJeYJnaMCJ4Ph0b/h7DYy26bZNt+xyMX6tkDo306udhiEkdQDO8IJ3bbys6/Nht1wDy+F2sRmGGxmMf2YA1pg/nbQaz122KePIC1yMAMbIZ55ihW7xDM7AzMIltoLP5se41zmdXAtFLK64tikWlvxwS8INeK7f+GCnGMTDoCZgGOqWxysC16OQcE2op+PB82d38HhEDiF8D5Y/7Dx9gWxryH43zIb/8LhMHqt4BGtrKvXgfC4fNXgQj4ZDqO0c2PpgFR8P5UjPOBlKXPYBCO8dmzGGZjDAtwftTTWP0cFIbZAuN3/4SChhib/olREAo//AsjvwGGYULi8xh59YkMm3khNO54x/a8/6sLxyrnHFLJKnsRU00LrmrDfW2sewHaDlWws3v99deZPn06qampdOrUiddee40ePXqUWv+DDz7gqaee4vDhw7Ru3ZoXX3yR3/3ud9XYY6nVyhsAK3L+36XKKhoKy+JiMYkjKPYZ7xQY+XgMxvUPYXw8Bos3MGicbbsjOD5ULDhOPj/S+NyFkUZX089RXeDY9gsh8qLRR07svhAqTaBRB6zH92I2bI954kdbsAu7FjN1H2ZIa8xThzAbtME8ecAWBoPOB8zAppjnfrWVeQdh5mTa9jVt4RF7iDz/3h4quei9aRpgf28Wq3fRe9Nqnw6/iGn/DNsUOXhBnj2QAthHPYtNm1MA+J1/to+C2kNqPlCPyJN7wHK+KPn8sbX/eQJsOH8u6IZZtufNc4ttm+X8XFrZ5jlqw74fXAjOqXsQ8VSGaZourt5afd577z1GjhzJ3Llz6dmzJ6+++ioffPABSUlJNG7cuET9jRs3csMNN5CQkMAtt9zC4sWLefHFF9m+fTsdOnRw8QnO0tPTCQkJIS0tjeDg4Kr4SiIlZaRW/nXsonvAO3fY2r/jjfNB7Y1iQa0cZRWt7642hk4vufil1WA4+PWFYFlGwCy17KKQakZ2gV+328Je2Pkg2rAdnPjJFv5C22L+th+zQWv47aCtXoMWcPpnzJAYzDOHbUGyfgvM08kQFI157ihm8NWYZ4/YQudVkZCeSmjfqzFOKmC41eX8ZsrxO6qM36LaqAVt2LfF9LX94+yGydAs3vYP+aCISh29q0h2sZS5tRq8/PLLjBkzhnvvvZf27dszd+5c6tWrx/z5813WnzlzJjfddBOTJ0+mXbt2TJs2jS5dujB79uxq7rlIBdhHCSNjLzzf9DwMef7yyvpPgfBrbSOC8eNKjhKWp6w89StzpPFKuFr8Enun7bnXX52fwTZaWfy5tLJeDzqVGfEPYljA4m3ideMEvP2s+AyeYLuNXVARfsMm4l+/kIDbJxEQVkC9xvlcddfDXBWRT+CIRwhqkkdw01yCR04kpHkOIfdPpn6LHBo8MInQ1tmEtsmi4d8fo2G7TIzrHrrQj97nX3f4fxfKouNtz+Edbc9RXS9ss5fZn0sri+qiNgLL+MvV/hc0XPiL3P5ckbJj253LLqddtVH72rBvs4+4fzvd9o/teX1t/5B3E7cGu/z8fLZt28bAgQMdZRaLhYEDB7Jp0yaX+2zatMmpPsCQIUNKrS/iseyLSa4kHF6qfvi1Vx4OKyNgXrz4xdMUn163636f7dkeRCshuNbJNq5/+EJZ3Ejb89V9bc+XEzBrWnBVG+5rw74t5vzv6YbJ8OeP4YF1toUUbuLWYHfq1CmKiooIDw93Kg8PDyc1NdXlPqmpqRWqn5eXR3p6utNDRMqp+Ejj5YbDygiYF9e/OBS6I2BWRbs1ZYTUk1y8iAlsi5jg8gJmTQuuasN9bdi3xZ3/PV1zC7QaAFGd3bqIokYsnqhKCQkJPPvss+7uhohUlrIWv5RnEcylyiqjjcttt7RrNV7uoh21UXWLmERqKLcunsjPz6devXp8+OGH3HbbbY7ye+65h3PnzrF8+fIS+zRr1oxJkyYxYcIER9nUqVNZtmwZO3fuLFE/Ly+PvLw8x/v09HSio6O1eEJEpK6p7EVMtfWaa2qj1l3HriKLJ9y+KrZnz5706NGD1157DQCr1UqzZs0YN24cjz/+eIn6d911F9nZ2Xz66aeOst69exMbG8vcuXMv+XlaFSsiIiK1SUWyi9unYidNmsQ999xDt27d6NGjB6+++ipZWVnce6/txMORI0fSpEkTEhISAHjooYfo27cvM2bM4Oabb2bJkiVs3bqVefPmufNriIiIiLid24PdXXfdxW+//cbTTz9NamoqnTt35ssvv3QskEhJScFiubDGo3fv3ixevJgnn3ySf/zjH7Ru3Zply5aV6xp2IiIiIp7M7VOx1U1TsSIiIlKb1KoLFIuIiIhI5VCwExEREfEQCnYiIiIiHkLBTkRERMRDKNiJiIiIeAgFOxEREREPoWAnIiIi4iEU7EREREQ8hIKdiIiIiIdw+y3Fqpv9Rhvp6elu7omIiIjIpdkzS3luFlbngl1GRgYA0dHRbu6JiIiISPllZGQQEhJSZp06d69Yq9XKsWPHCAoKwjCMS9ZPT08nOjqao0eP6t6y5+mYuKbjUpKOiWs6Lq7puJSkY+JaXTsupmmSkZFBVFQUFkvZZ9HVuRE7i8VC06ZNK7xfcHBwnfjxVISOiWs6LiXpmLim4+KajktJOiau1aXjcqmROjstnhARERHxEAp2IiIiIh5Cwe4S/Pz8mDp1Kn5+fu7uSo2hY+KajktJOiau6bi4puNSko6JazoupatziydEREREPJVG7EREREQ8hIKdiIiIiIdQsBMRERHxEAp2ZXj99de5+uqr8ff3p2fPnnz//ffu7pJbPfPMMxiG4fS45ppr3N2tavftt98ybNgwoqKiMAyDZcuWOW03TZOnn36ayMhIAgICGDhwIAcOHHBPZ6vJpY7JqFGjSvx2brrpJvd0tpokJCTQvXt3goKCaNy4MbfddhtJSUlOdXJzcxk7diwNGzYkMDCQP/zhD5w4ccJNPa4e5Tku/fr1K/F7+etf/+qmHlePOXPmEBsb67guW3x8PF988YVje138rVzqmNTF30l5KNiV4r333mPSpElMnTqV7du306lTJ4YMGcLJkyfd3TW3uvbaazl+/Ljj8d1337m7S9UuKyuLTp068frrr7vc/u9//5tZs2Yxd+5ctmzZwlVXXcWQIUPIzc2t5p5Wn0sdE4CbbrrJ6bfz7rvvVmMPq9+6desYO3YsmzdvZuXKlRQUFDB48GCysrIcdSZOnMinn37KBx98wLp16zh27Bh33HGHG3td9cpzXADGjBnj9Hv597//7aYeV4+mTZvywgsvsG3bNrZu3cqNN97Irbfeyt69e4G6+Vu51DGBuvc7KRdTXOrRo4c5duxYx/uioiIzKirKTEhIcGOv3Gvq1Klmp06d3N2NGgUwly5d6nhvtVrNiIgIc/r06Y6yc+fOmX5+fua7777rhh5Wv4uPiWma5j333GPeeuutbulPTXHy5EkTMNetW2eapu134ePjY37wwQeOOj/++KMJmJs2bXJXN6vdxcfFNE2zb9++5kMPPeS+TtUQDRo0MN988039VoqxHxPT1O+kNBqxcyE/P59t27YxcOBAR5nFYmHgwIFs2rTJjT1zvwMHDhAVFUWLFi0YMWIEKSkp7u5SjZKcnExqaqrTbyckJISePXvW+d/O2rVrady4MW3btuXBBx/k9OnT7u5StUpLSwMgNDQUgG3btlFQUOD0W7nmmmto1qxZnfqtXHxc7BYtWkRYWBgdOnRgypQpZGdnu6N7blFUVMSSJUvIysoiPj5evxVKHhO7uvw7KU2du1dseZw6dYqioiLCw8OdysPDw/npp5/c1Cv369mzJwsXLqRt27YcP36cZ599luuvv549e/YQFBTk7u7VCKmpqQAufzv2bXXRTTfdxB133EFMTAyHDh3iH//4B0OHDmXTpk14eXm5u3tVzmq1MmHCBPr06UOHDh0A22/F19eX+vXrO9WtS78VV8cF4O6776Z58+ZERUWxa9cuHnvsMZKSkvj444/d2Nuqt3v3buLj48nNzSUwMJClS5fSvn17EhMT6+xvpbRjAnX3d3IpCnZSbkOHDnW8jo2NpWfPnjRv3pz333+f0aNHu7FnUtP96U9/crzu2LEjsbGxtGzZkrVr1zJgwAA39qx6jB07lj179tTJc1LLUtpxeeCBBxyvO3bsSGRkJAMGDODQoUO0bNmyurtZbdq2bUtiYiJpaWl8+OGH3HPPPaxbt87d3XKr0o5J+/bt6+zv5FI0FetCWFgYXl5eJVYcnThxgoiICDf1quapX78+bdq04eDBg+7uSo1h/33ot1O2Fi1aEBYWVid+O+PGjeOzzz5jzZo1NG3a1FEeERFBfn4+586dc6pfV34rpR0XV3r27Ang8b8XX19fWrVqRdeuXUlISKBTp07MnDmzTv9WSjsmrtSV38mlKNi54OvrS9euXVm1apWjzGq1smrVKqe5/bouMzOTQ4cOERkZ6e6u1BgxMTFEREQ4/XbS09PZsmWLfjvF/PLLL5w+fdqjfzumaTJu3DiWLl3K6tWriYmJcdretWtXfHx8nH4rSUlJpKSkePRv5VLHxZXExEQAj/69uGK1WsnLy6uzvxVX7MfElbr6OynB3as3aqolS5aYfn5+5sKFC819+/aZDzzwgFm/fn0zNTXV3V1zm4cffthcu3atmZycbG7YsMEcOHCgGRYWZp48edLdXatWGRkZ5o4dO8wdO3aYgPnyyy+bO3bsMI8cOWKapmm+8MILZv369c3ly5ebu3btMm+99VYzJibGzMnJcXPPq05ZxyQjI8N85JFHzE2bNpnJycnmN998Y3bp0sVs3bq1mZub6+6uV5kHH3zQDAkJMdeuXWseP37c8cjOznbU+etf/2o2a9bMXL16tbl161YzPj7ejI+Pd2Ovq96ljsvBgwfN5557zty6dauZnJxsLl++3GzRooV5ww03uLnnVevxxx83161bZyYnJ5u7du0yH3/8cdMwDPPrr782TbNu/lbKOiZ19XdSHgp2ZXjttdfMZs2amb6+vmaPHj3MzZs3u7tLbnXXXXeZkZGRpq+vr9mkSRPzrrvuMg8ePOjublW7NWvWmECJxz333GOapu2SJ0899ZQZHh5u+vn5mQMGDDCTkpLc2+kqVtYxyc7ONgcPHmw2atTI9PHxMZs3b26OGTPG4/+R5Op4AOaCBQscdXJycsy//e1vZoMGDcx69eqZt99+u3n8+HH3dboaXOq4pKSkmDfccIMZGhpq+vn5ma1atTInT55spqWlubfjVey+++4zmzdvbvr6+pqNGjUyBwwY4Ah1plk3fytlHZO6+jspD8M0TbP6xgdFREREpKroHDsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIeQsFORERExEMo2ImIiIh4CAU7EREREQ+hYCciIiLiIRTsRKTO6tevHxMmTHB3Nxwutz+nT5+mcePGHD58uNL7dLE//elPzJgxo8o/R0Quj4KdiFSpuXPnEhQURGFhoaMsMzMTHx8f+vXr51R37dq1GIbBoUOHqrmX1auyA+Xzzz/PrbfeytVXX11pbZbmySef5PnnnyctLa3KP0tEKk7BTkSqVP/+/cnMzGTr1q2OsvXr1xMREcGWLVvIzc11lK9Zs4ZmzZrRsmVLd3S1VsrOzuatt95i9OjR1fJ5HTp0oGXLlrzzzjvV8nkiUjEKdiJSpdq2bUtkZCRr1651lK1du5Zbb72VmJgYNm/e7FTev39/AL788kuuu+466tevT8OGDbnlllucRvLmzZtHVFQUVqvV6fNuvfVW7rvvPgCsVisJCQnExMQQEBBAp06d+PDDD0vta3nq9+vXj/Hjx/Poo48SGhpKREQEzzzzjFOdjIwMRowYwVVXXUVkZCSvvPKKY5Ru1KhRrFu3jpkzZ2IYBoZhOE2hWq3WMtu+2Oeff46fnx+9evVyKv/uu+/w8fFxCs6HDx/GMAyOHDnieP3RRx9xww03EBAQQPfu3UlJSWH9+vX06tWLevXqMWDAAM6dO+fU9rBhw1iyZEmZ/RIR91CwE5Eq179/f9asWeN4v2bNGvr160ffvn0d5Tk5OWzZssUR7LKyspg0aRJbt25l1apVWCwWbr/9dkeQu/POOzl9+rRTu2fOnOHLL79kxIgRACQkJPDf//6XuXPnsnfvXiZOnMif//xn1q1b57Kf5a3/9ttvc9VVV7Flyxb+/e9/89xzz7Fy5UrH9kmTJrFhwwY++eQTVq5cyfr169m+fTsAM2fOJD4+njFjxnD8+HGOHz9OdHR0udu+2Pr16+natWuJ8sTERNq1a4e/v7+jbMeOHTRo0IDmzZuzc+dOAObMmcO//vUvNm7cyIkTJ/jzn//MCy+8wOzZs1mzZg07d+5kwYIFTm336NGD77//nry8vFL7JSLu4e3uDoiI5+vfvz8TJkygsLCQnJwcduzYQd++fSkoKGDu3LkAbNq0iby8PEew+8Mf/uDUxvz582nUqBH79u2jQ4cONGjQgKFDh7J48WIGDBgAwIcffkhYWBj9+/cnLy+Pf/3rX3zzzTfEx8cD0KJFC7777jv+85//0LdvX6f2K1I/NjaWqVOnAtC6dWtmz57NqlWrGDRoEBkZGbz99ttO/VqwYAFRUVEAhISE4OvrS7169YiIiChxrMpq25UjR4442i5u586dxMXFOZUlJibSqVMnx+vQ0FDee+89GjZsCEDfvn357rvv2Lt3L/Xq1QOge/fupKamOrUTFRVFfn4+qampNG/e3GW/RMQ9NGInIlWuX79+ZGVl8cMPP7B+/XratGlDo0aN6Nu3r+M8u7Vr19KiRQuaNWsGwIEDBxg+fDgtWrQgODjYsTAgJSXF0e6IESP46KOPHCNHixYt4k9/+hMWi4WDBw+SnZ3NoEGDCAwMdDz++9//ulycUZH6sbGxTu8jIyM5efIkAD///DMFBQX06NHDsT0kJIS2bduW61iV1bYrOTk5TqNydomJiXTu3NmpbMeOHY6ynTt3cvvttztCHdiO7V133eUIdfaymJgYp3YCAgIA2/l9IlKzaMRORKpcq1ataNq0KWvWrOHs2bOO0a+oqCiio6PZuHEja9as4cYbb3TsM2zYMJo3b84bb7zhOJeuQ4cO5OfnO9UxTZMVK1bQvXt31q9fzyuvvALYVt4CrFixgiZNmjj1x8/Pr0QfK1Lfx8fH6b1hGCXO9btcFW07LCyMs2fPOpUVFRWxZ8+eEiN227dvd4yEJiYmMmXKFKftO3fuZOLEiY73ubm5JCUlOUb57M6cOQNAo0aNyvmtRKS6KNiJSLXo378/a9eu5ezZs0yePNlRfsMNN/DFF1/w/fff8+CDDwK267IlJSXxxhtvcP311wO2xQAX8/f354477mDRokUcPHiQtm3b0qVLFwDat2+Pn58fKSkpJaZdXalo/dK0aNECHx8ffvjhB8foY1paGvv37+eGG24AwNfXl6Kiosv+jOLi4uJKrFBNSkoiNzfXaYp206ZN/Prrr3Tu3Jn09HQOHz7sFPySk5NJS0tzKtu9ezemadKxY0en9vfs2UPTpk0JCwurlO8gIpVHwU5EqkX//v0ZO3YsBQUFTsGpb9++jBs3jvz8fMf5dQ0aNKBhw4bMmzePyMhIUlJSePzxx122O2LECG655Rb27t3Ln//8Z0d5UFAQjzzyCBMnTsRqtXLdddeRlpbGhg0bCA4O5p577nFqp6L1SxMUFMQ999zD5MmTCQ0NpXHjxkydOhWLxYJhGABcffXVbNmyhcOHDxMYGEhoaCgWy+WdGTNkyBCmTJnC2bNnadCgAWAbjQN47bXXGD9+PAcPHmT8+PEA5Ofns3PnTry8vOjQoYOjHfs5d8XPmUtMTKRly5YEBgY6feb69esZPHjwZfVXRKqWzrETkWrRv39/cnJyaNWqFeHh4Y7yvn37kpGR4bgsCoDFYmHJkiVs27aNDh06MHHiRKZPn+6y3RtvvJHQ0FCSkpK4++67nbZNmzaNp556ioSEBNq1a8dNN93EihUrSpwzdrn1S/Pyyy8THx/PLbfcwsCBA+nTp4/TCtVHHnkELy8v2rdvT6NGjZzOG6yojh070qVLF95//31HWWJiIkOGDOHnn3+mY8eOPPHEEzz77LMEBwcza9Ysdu7cSdu2bZ3OzXO12GLnzp0lpmFzc3NZtmwZY8aMuew+i0jVMUzTNN3dCRERT5aVlUWTJk2YMWNGlVxIeMWKFUyePJk9e/ZgsVgYMmQI3bt355///Gelf9acOXNYunQpX3/9daW3LSJXTlOxIiKVbMeOHfz000/06NGDtLQ0nnvuOcB28eSqcPPNN3PgwAF+/fVXoqOj2blzp+MizZXNx8eH1157rUraFpErpxE7EZFKtmPHDu6//36SkpLw9fWla9euvPzyyyUWIVSF1NRUIiMj2bt3L+3bt6/yzxORmkXBTkRERMRDaPGEiIiIiIdQsBMRERHxEAp2IiIiIh5CwU5ERETEQyjYiYiIiHgIBTsRERERD6FgJyIiIuIhFOxEREREPISCnYiIiIiHULATERER8RAKdiIiIiIe4v8HoUIttqDk85EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitter.plot(medium)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Fit Results\n",
    "\n",
    "With the fit performed, we want to use the `Medium` in our simulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1: direct export as Medium\n",
    "\n",
    "The fit returns a medium, which can be used directly in simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "b = td.Structure(geometry=td.Box(size=(1, 1, 1)), medium=medium)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2: print medium definition string\n",
    "\n",
    "In many cases, one may want to perform the fit once and then hardcode the result in their tidy3d script.\n",
    "\n",
    "For a quick and easy way to do this, just `print()` the medium and the output can be copied and pasted into your main svript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "td.PoleResidue(\n",
      "\teps_inf=3.3946193815575816, \n",
      "\tpoles=(((-1667817350156664.2-206849778478069.44j), (1.004708275108366e+16-2.3076845274369316e+16j)),), \n",
      "\tfrequency_range=None)\n"
     ]
    }
   ],
   "source": [
    "print(medium)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# medium = td.PoleResidue(\n",
    "# \teps_inf=3.394619381557077, \n",
    "# \tpoles=(((-1667817350156741.8-206849778477574.28j), (1.004708275108508e+16-2.307684527443524e+16j)),), \n",
    "# \tfrequency_range=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 3: save and load file containing poles\n",
    "\n",
    "Finally, one can save export the `Medium` directly as .json file. Here is an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save poles to pole_data.txt\n",
    "fname = \"misc/my_medium.json\"\n",
    "medium.to_file(fname)\n",
    "\n",
    "# load the file in your script\n",
    "medium = td.PoleResidue.from_file(fname)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Parameters and Tips for FastDispersionFitter\n",
    "\n",
    "There are a number of advanced parameters for the `FastDispersionFitter` which can be useful if the default settings do not work to find a fit. The advanced parameters are configured by passing in an `AdvancedFastFitterParam` object. When trying to improve a fit, the first thing to try is changing the arguments `max_num_poles` and `eps_inf` to the `fit` function. Importantly, although `eps_inf = None` performs optimization for the value of `eps_inf` as well, this optimization is nontrivial and so it can sometimes be better to provide a fixed value of `eps_inf`. Besides these arguments, the following advanced parameters may be helpful:\n",
    "\n",
    "- `loss_bounds = (lower, upper)` are bounds on `Im[eps]` over all frequencies (not just the fitting range). The default `(0, np.inf)` corresponds to a passive medium requirement, which leads to stable simulations. The upper bound can be used to ensure a good fit for a lossless medium. Setting a small negative lower bound can sometimes be informative; if a lower bound of 0 results in a poor fit but a small negative lower bound results in a good fit, then the poor fit was due to difficulty satisfying the passivity requirement. A negative lower bound, and thus a violation of the passivity requirement, may result in unstable simulations. However, a small violation of the passivity requirement may on occasion be acceptable, if there is loss elsewhere in the simulation to offset the resulting gain.\n",
    "\n",
    "- `weights = (real, imag)` are weights for `Re[eps]` and `Im[eps]` used in the fitting objective function (and when reporting rms error). These can be used if it is necessary to more accurately fit either the real or imaginary part.\n",
    "\n",
    "- `relaxed`, `smooth`, and `logspacing` can usually be left at their default values of `None`, which makes the fitter try both `True` and `False` values and take the better fit. Sometimes, however, it may be clear that specific values are better for a certain medium. `relaxed` controls whether the relaxed version of the fitting algorithm is used. For example, `relaxed = True` could prevent overfitting and also allow for pole relocation further outside the fitting window. The `smooth` and `logspacing` parameters control the initial pole placement. `smooth = True` may be appropriate for fitting metals. `logspacing = True` may be better when the poles are spread over a large frequency range. Again, the default option is usually fine here unless a particular type of model is required, since the default option tries both `True` and `False`.\n",
    "\n",
    "- `num_iters` controls the tradeoff between speed and quality of fit, although a higher `num_iters` doesn't necessarily result in a better fit. Usually, after a certain number of iterations, the algorithm has converged to a certain fit, and increasing `num_iters` further won't significantly help. However, it may be good to try increasing it to `100` or so to make sure that the algorithm has converged.\n",
    "\n",
    "Information on additional advanced parameters may be found in the documentation [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.dispersion.AdvancedFastFitterParam.html).\n",
    "\n",
    "Here are a couple more general tips on dispersion fitting:\n",
    "\n",
    "- If you are unable to find a good fit to your data, it might be worth considering whether you care about certain features in the data.  For example as shown above, if the simulation is narrowband, you might want to truncate your data to not include wavelengths far outside your measurement wavelength to simplify the dispersive model.\n",
    "\n",
    "- It is common to find divergence in FDTD simulations due to dispersive materials. The `FastDispersionFitter` by default should enforce a passivity requirement and produce stable material fits. If there is still a divergence, besides trying \"absorber\" PML types and reducing runtime, a good solution can be to try other fits; for example, using a smaller number of poles, or setting `loss_bounds = (min_loss, np.inf)` for some small positive number `min_loss` (the latter can only work if the data satisfies this minimum loss constraint)."
   ]
  }
 ],
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  "description": "This notebook demonstrates how to fit dispersive material models in Tidy3D for FDTD simulations.",
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