examples/notebooks/external_optics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solcore import si\n",
    "from solcore import material\n",
    "from solcore.solar_cell import SolarCell, Layer, Junction\n",
    "from solcore.solar_cell_solver import solar_cell_solver, default_options\n",
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 300\n",
    "wavelengths_optics = np.linspace(300, 1200, 800)*1e-9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some materials and layer widths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Si = material(\"Si\")\n",
    "SiO2 = material(\"SiO2\")()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_material = Si(T=T, Nd=si(1e21, \"cm-3\"), hole_diffusion_length=si(\"50um\"),\n",
    "                electron_mobility=50e-4, relative_permittivity = 11.68)\n",
    "p_material = Si(T=T, Na=si(1e16, \"cm-3\"), electron_diffusion_length=si(\"150um\"),\n",
    "                hole_mobility=400e-4, relative_permittivity = 11.68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARC_width = si(\"100nm\")\n",
    "n_material_width = si(\"500nm\")\n",
    "p_material_width = si(\"50um\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are first going to get some optics data which we can then use to illustrate how to provide\n",
    "the relevant quantities to the external optics solver. Obviously, this is a bit circular, since we\n",
    "are first going to use Solcore to calculate reflection and absorption and then feed that information\n",
    "back into solar_cell_solver in a different format, but this way we can show how the data should be\n",
    "provided when using external optics and that everything is working consistently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_cell = SolarCell(\n",
    "    [\n",
    "        Layer(width=ARC_width, material=SiO2),\n",
    "        Junction([Layer(width=n_material_width, material=n_material, role='emitter'),\n",
    "                  Layer(width=p_material_width, material=p_material, role='base'),\n",
    "\t\t ], sn=1, sp=1, kind='DA'),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_width = ARC_width + n_material_width + p_material_width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "options: we are going to use TMM to calculate the optics of a silicon cell with a 100 nm SiO2 anti-reflection coating<br>\n",
    "This will not be very realistic<br>\n",
    "compared to a standard Si cell, which will usually have some textured surface (usually pyramids) and not just a planar<br>\n",
    "surface with an anti-reflection coating, but it gives some physically reasonable data to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = default_options\n",
    "options.optics_method = \"TMM\"\n",
    "options.wavelength = wavelengths_optics\n",
    "# options.position = np.linspace(0, total_width, 100000)\n",
    "options.light_iv = True\n",
    "V = np.linspace(0, 1.2, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving optics of the solar cell...\n",
      "Treating layer(s) 2 incoherently\n",
      "Calculating RAT...\n",
      "Calculating absorption profile...\n",
      "Solving IV of the junctions...\n",
      "Solving IV of the tunnel junctions...\n",
      "Solving IV of the total solar cell...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phoebe/Documents/solcore5/solcore/absorption_calculator/tmm_core_vec.py:1075: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  A_local[:, layer == l] = A_layer\n"
     ]
    }
   ],
   "source": [
    "solar_cell_solver(solar_cell, 'iv', options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to get the relevant data to see how we might construct\n",
    "appropriate functions to provide external optics data to Solcore. If you are\n",
    "using experimental data, this could be loaded from a file, here we will take it\n",
    "from the TMM calculations done above. We get the fraction of light reflected,\n",
    "and the fraction of light absorbed in the Si layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "reflected = solar_cell.reflected\n",
    "absorbed_in_Si = solar_cell[1].layer_absorption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to provide two attributes when defining the cell to give all the required\n",
    "information for external optics: external_reflected and external_absorbed. external_reflected<\n",
    "is a list of the fraction of incident light reflected at each wavelength that is specified in the user options.\n",
    "external_absorbed is a function which takes as input an array of positions (depths)\n",
    "in the cell in m and returns the differential absorption at each wavelength and depth; this is\n",
    "a 2D numpy array where the first dimension is over the wavelengths and the second dimension\n",
    "is over the positions in the cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could take the diff_absorption method from solar_cell, which was constructed during the TMM\n",
    "calculation done above, but in order to show how the absorption profile can be calculated from\n",
    "total absorption in a layer (assuming Beer-Lambert law absorption, i.e. no interference in the layer)\n",
    "we will define a function which calculates the differential absorption from just the total absorption\n",
    "and the absorption coefficient of the junction material."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate how you can use data from e.g. an experiment to perform calculations at different wavelength,\n",
    "we're going to take the data we calculated above but use slightly different wavelength points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolate reflection and total absorption in Si to the new wavelengths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "interp_ref = interp1d(options.wavelength, reflected)\n",
    "interp_totalA = interp1d(options.wavelength, solar_cell[1].layer_absorption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelengths_external = np.linspace(301, 1199, 800)*1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = n_material.alpha(wavelengths_external)\n",
    "A_layer = interp_totalA(wavelengths_external)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make function which returns the absorption profile. The functional form can be found by differentiating the<br>\n",
    "Beer-Lambert law, and making sure it is normalized correctly to give the expected total absorption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "junction_width = n_material_width + p_material_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_absorb_fn(alpha, A_layer, junction_width):\n",
    "    norm = A_layer * alpha / (1 - np.exp(-alpha * junction_width))\n",
    "    def profile(z):\n",
    "        xy = norm[None, :] * np.exp(-alpha * z[:, None])\n",
    "        return xy.T\n",
    "    return profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_absorb_fn = make_absorb_fn(alpha, A_layer, junction_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define a solar cell for the external optics calculation. It's the same as solar_cell,\n",
    "but without the ARC layer; this does not do anything in the electrical calculation, and we must\n",
    "omit it so that the diff_absorb_fn defined above works correctly. This function should describe the\n",
    "differential absorption profile in the WHOLE cell, including any surface layers,\n",
    "but to avoid complexity in make_absorb_fn it just calculates\n",
    "Beer-Lambert absorption in the Si and ignores the ARC. So for the absorption profile to match the actual\n",
    "cell, we need the front surface to be the Si emitter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_cell_external = SolarCell(\n",
    "    [\n",
    "        Junction([Layer(width=n_material_width, material=n_material, role='emitter'),\n",
    "                  Layer(width=p_material_width, material=p_material, role='base'),\n",
    "\t\t ], sn=1, sp=1, kind='DA'),\n",
    "    ], external_reflected=interp_ref(wavelengths_external), external_absorbed=diff_absorb_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "options.optics_method = \"external\"\n",
    "options.wavelength = wavelengths_external"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving optics of the solar cell...\n",
      "0\n",
      "Solving IV of the junctions...\n",
      "0\n",
      "0\n",
      "0\n",
      "Solving IV of the tunnel junctions...\n",
      "Solving IV of the total solar cell...\n"
     ]
    }
   ],
   "source": [
    "solar_cell_solver(solar_cell_external, 'iv', options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the total absorption and reflection are the same. The total reflection will be the same<br>\n",
    "as it is directly supplied by the user, but the total reflection is calculated by integrating the<br>\n",
    "differential absorption, so this is a good check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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s2PeZ1mkUM6tShSDhUjJFRXpNM1jH76RTvp4O6pJRpZxr1/Y/vJZZSKfYvVpHUcysyhSCX7bupUGDBrw98xvtQuRn0vb0Tr6s2wcra3vtcijKXRBWtoz0H4Xr6S2QfFrrOIoZVZlC0LN9MDbO7syc8aFmewXnjiwmzVCgBqVXKo6QMfzl4MSnW1TPuJVZlSkENtZWPPXcZLKSYpk2+1tNMnwUvYgR9eoh64Vqsn5FuWdONTleL5CFObGcvxKldRrFTMxaCIQQ4UKIk0KI00KIV2/RZqgQIkoIcVwIYdYhkj6eMh676vX4YvqH6PVlu1dwNeU0u2QWPV181Z3ESoUyuM3L6IBVuz/QOopiJmb7RhJCWABzgN6AHzBCCOH3jzY+wGtABymlP/CCufKAca/gyUkvk3nhDLN+KNtOtX4/MJMiIegb+GSZrldR/q2aDR+gC3b8lBJBQWGu1nEUMzDnT9PWwGkpZayUsgBYBjz4jzZPAXOklFcBpJSXzZgHgI9feYag575iS7obsgy72v01aQeNDTp8G/cus3UqSmkZ2mQYqTr4Y+8nWkdRzMCchaAucL7E8wTTayX5Ar5CiJ1CiD1CiHAz5gHAzsaaVx/tw9GEdP6IvGDu1QFwJekwhymgj3sQQogyWaeilKZ2oc/RuQBsYjZrHUUxA8tysH4foCtQD9gmhGgupUwr2UgIMQ4YB1C/fv1/vdKHgusx+bU3eWjR85w+doCa7tX+9TJvxyNuN78mJOEQ9qJZ16OFwsJCEhISyMvL0zqKYmYTQ+dDbhrRkUfBwuq2bW1tbalXrx5WVrdvp5QP5iwEiYBnief1TK+VlADslVIWAmeFEKcwFob9JRtJKecB8wBCQkL+9fEcKwsdz43sx+SxS2gbNoiYPX9gaWnxbxd7a2e24OniDbWDzLcOjSQkJODk5ISXl5fa26ns9IXoLx2nwL4adtVu/YNMSklKSgoJCQl4e3uXYUDlfpnz0NB+wEcI4S2EsAaGA+v+0eYnjHsDCCGqYzxUFGvGTMVeHjOU4RNfJ+7gVsIee9Zs60nOSOSlzKOcbFA5LxnNy8vD3d1dFYGqwMKKJBtb4osykQbDLZsJIXB3d1d7iRWI2QqBlLIImAhsBKKBFVLK40KId4UQA0zNNgIpQogo4E9gspQyxVyZ/mnJ59No0eMhtiz7P1743xyzrGNH5GJ+t7fB4Fk5CwGgikAV4mzjgh7Iyrly23bqM1GxmPWCdinlb1JKXyllIynl+6bXpkop15keSynlS1JKPyllcynlMnPm+SedTsfOdUuo4RPEsv0J/Has9Pte333+L9z0epr6qQFozMXCwoKgoCACAgLo378/aWlpt21/5coV2rRpQ8uWLdm+fTteXl4kJyff83q3bt3Krl277nm++13f3YqIiOC3334zy7Id7WtgAaTlXwWga9euHDhwwCzrUspOlb+zycHOlhOHdtGxV38m/nCImWu2l9qypZTsyU2krYUzwsax1JarXM/Ozo6IiAgiIyNxc3Njzpzb791t3ryZ5s2bc/jwYTp16nTf673fQmBu91MIioqK7qqdTmeBi86aTKlHX1RwP/GUcqjKFwIAV0c7Fj/ZhiYWl3h+yAP0Gz0Jw22Ogd6tmITdpApJW4/gUkip3I127dqRmGi8JuHMmTOEh4fTqlUrOnXqxIkTJ4iIiGDKlCmsXbuWoKAgcnOvv0Hq+++/p3Xr1gQFBfH0008X34G+YcMGgoODadGiBd27dycuLo65c+fy2WefERQUxPbt27ly5QqDBw8mNDSU0NBQdu7cCUBKSgq9evXC39+fsWPH3tf9K9OnTyc0NJTAwEDefvttANasWUP37t2RUpKUlISvry/nzp1j6tSpLF++nKCgIJYvX052djZjxoyhdevWtGzZkrVr1wKwcOFCBgwYwAMPPED37t1ZuHAhDz30EOHh4fj4+DBlypTi9T/zzDOEhITg7+/PrBlfI7nz4SGl4tD68tFyw87aglVvPELI9rX8+t1smsWc5o/Vi/GsWf2+l5ke9xc+BQW0a1Y1DgtN+/k4URcySnWZfnWcebu//1211ev1bN68mSefNN69PW7cOObOnYuPjw979+5lwoQJbNmyhXfffZcDBw4we/bs6+aPjo5m+fLl7Ny5EysrKyZMmMCSJUvo3bs3Tz31FNu2bcPb25vU1FTc3NwYP348jo6OvPzyywCMHDmSF198kY4dO3Lu3DnCwsKIjo5m2rRpdOzYkalTp/Lrr7+yYMGCe9oGmzZtIiYmhn379iGlZMCAAWzbto1Bgwbx448/MmfOHDZs2MC0adOoX7/+De/v9ddf54EHHuCbb74hLS2N1q1b06NHDwAOHTrE0aNHcXNzY+HChURERHD48GFsbGxo0qQJkyZNwtPTk/fffx83Nzf0ej3du3dnUM92uDQPuKf3oZRfqhCU4GBnQ+SWNQwc9zK/fPsFjZv488nsuUx8dNB9LS/0Ugyr0yV4dSnlpEpJubm5BAUFkZiYSLNmzejZsydZWVns2rWLhx/+u6fX/Pzbj7S1efNmDh48SGhoaPFya9SowZ49e+jcuXPxpZBubm43nf+PP/4gKurvjtkyMjLIyspi27ZtrF69GoC+ffvi6up6T+9v06ZNbNq0iZYtWwKQlZVFTEwMnTt3ZtasWQQEBNC2bVtGjBhxy/nXrVvHjBkzAOOVXufOnQOgZ8+e172f7t274+LiAoCfnx/x8fF4enqyYsUK5s2bR1FREUlJSZw5e5HQJl5QhnfnK+ajCsE/WFjo+HnBpyx6sA/jn3yCqQvWkerShNHtG1Df3aG4XUz8BbbtPcjowX2wsLjxHgR9UQGGM39i5RsGVeQKirv95V7arp0jyMnJISwsjDlz5jB69GiqVatGRETEXS9HSsmoUaP44IPrO1f7+eef72p+g8HAnj17sLW1vZf4gPEwz7RpxvGB58+fT0hIyHW5XnvtNZ5++ukb5ktISECn03Hp0iUMBgO6m3RoKKXkxx9/pEmTJte9vnfvXhwcHK57zcbGpvixhYUFRUVFnD17lhkzZrB//35cXV0ZPXo0udKCBEtLCvVqGMvKQJ0juIXHB/Qg/tRxxj37PN/sPEvLES/j0bgFvR55Fg/fYHy96jHh+Zd4f83Nr5g4dGIlHWvYc6RWk5tOV0qfvb09M2fO5JNPPsHe3h5vb29WrlwJGL8Mjxw5ctv5u3fvzqpVq7h82djlVWpqKvHx8bRt25Zt27Zx9uzZ4tcBnJycyMzMLJ6/V69ezJo1q/j5tSLUuXNnfvjB2LHu+vXruXr16g3rHjRoEBEREURERFxXBADCwsL45ptvyMrKAiAxMZHLly9TVFTEmDFjWLp0Kc2aNePTTz+9aa6wsDBmzZpVfG7i8OHDt90O/5SRkYGDgwMuLi5cunSJ9evXIyysKdRZUCT1Zdpnl2IeqhDchoerMx8OCWL7lG70adWInKx0fv/hS/Kz0ukxcjx9nv0v3xxIZsuJSzfMu+f0r+QLQcNm93dYSbk/LVu2JDAwkKVLl7JkyRIWLFhAixYt8Pf3Lz5Jeit+fn6899579OrVi8DAQHr27ElSUhIeHh7MmzePhx56iBYtWjBs2DAA+vfvz5o1a4pPFs+cOZMDBw4QGBiIn58fc+fOBeDtt99m27Zt+Pv7s3r16nvuJqVXr16MHDmSdu3a0bx5c4YMGUJmZib/+9//6NSpEx07duTTTz9l/vz5REdH061bN6KioopPFr/11lsUFhYSGBiIv78/b7311j2tv0WLFrRs2ZKmTZsycuRIOnToAICLlQMGIL8g+56Wp5Q/oqJV85CQEKnVdcsGg4GEy6nUr2U8gZxXqKf/jI0cWDqD2f97k0f6PVDc9pGFrRBSz/dPRGiStaxER0fTrFkzrWMoGigqzONU+hncLGyp5drohunqs1G+CCEOSilDbjZN7RHcA51OV1wEAGytLHgn3IvchGgee6gPH3+9FICMrEtEkk9b58ZaRVUUs7O0ssURHen6PKT895dbK9pRheBf6tDSjwN7d2Nl78xHH7wPwP6oZRiEoG2DHhqnUxTzcrNxwUWvx6AOD1VoqhCUggAfLzr1GUxqXDSJl6/SMDWB8WkZtFDdSiiVnKNDDWrpDVjkpWsdRfkXVCEoJQPCe4A0sHDNerwTInjWwRcr+5tfb64olYbOEmnjTFZeGnpD2Y4DrpQeVQhKyWMDe1F35PvkuLixKyWSPK/2WkdSlDKRY+NAvIVQXU5UYKoQlBJXZye6dHuApMu/8XSt6pxwb6B1JEUpE/b21bGSkrT8NK2jKPdJFYJS1Ngmi+iELdgZDPg3G6J1nCrlp59+QgjBiRMnil/bunUr/fr1K/V1jR49mlWrVmk2/52kpaXx5Zdfmm35Jb3zzjt88ulnuFhYkyX1FOlVj6QVkSoEpaiWRSbJ1fKpnaHDytZZ6zhVytKlS+nYsSNLly4163rutrtmLd1PIZBS/qsed11s3QFIz75838tQtKMKQSkK7+yDbV1bsmLNOP6xcoOsrCx27NjBggULWLbs+rGNMjIy6Nu3L02aNGH8+PEYDAb0ej2jR48mICCA5s2b89lnnwHGLiHatm1LYGAggwYNKu4KomvXrrzwwguEhITwxRdfAMYO5kJCQvD19eWXX34BjL2fTp48ubi76P/7v/8DjF+yEydOpEmTJvTo0aO4C4t7sWnTJtq1a0dwcDAPP/wwWVlZxMfH4+PjQ3JyMgaDgU6dOrFp0yZeffVVzpw5Q1BQEJMnTwZu3o11XFwcTZo04fHHHycgIIDt27fTrFkznnrqKfz9/enVq1dxN91ff/01oaGhtGjRgsGDB5OTk3NdPls7N2ykJKswE6XiUZ3OlaKT54xfCCf2VtFL6da/ChePle4yazWH3h/etsnatWsJDw/H19cXd3d3Dh48SKtWrQDYt28fUVFRNGjQgPDwcFavXo23tzeJiYlERkYCFI9o9vjjjzNr1iy6dOnC1KlTmTZtGp9//jkABQUFxSNxjR49mri4OPbt28eZM2fo1q0bp0+fZtGiRbi4uLB//37y8/Pp0KEDvXr14vDhw5w8eZKoqCguXbqEn58fY8aMuetNkJyczHvvvccff/yBg4MDH330EZ9++ilTp07llVde4ZlnnqF169b4+fnRq1cvfH19iYyMLO7r6FbdWNevX5+YmBi+++472rZtS1xcHDExMSxdupSvv/6aoUOH8uOPP/Loo4/y0EMP8dRTTwHw5ptvsmDBAiZNmvR3SCFoYOWMZU4qGIpAp75aKhK1R1CKeqan0WVdAhcOnCPm/EWt41QZS5cuZfjw4QAMHz78usNDrVu3pmHDhlhYWDBixAh27NhBw4YNiY2NZdKkSWzYsAFnZ2fS09NJS0ujSxdjl+GjRo1i27Ztxcu51r/QNUOHDkWn0+Hj40PDhg05ceIEmzZtYtGiRQQFBdGmTRtSUlKIiYlh27ZtjBgxAgsLC+rUqcMDDzzAvdizZw9RUVF06NCBoKAgvvvuO+Lj4wEYO3YsGRkZzJ07t7ib6X8q2Y11cHAwJ06cICYmBoAGDRrQtm3b4rbe3t4EBQUB0KpVK+Li4gCIjIykU6dONG/enCVLlnD8+PEb1mNl745AQm4V/SFUgamyXYos4nbQp4EPX1pE8tPW/Ux+rL/WkcrWHX65m0Nqaipbtmzh2LFjCCHQ6/UIIZg+fTpw4yDqQghcXV05cuQIGzduZO7cuaxYsaL48NCt/LO75pstV0rJrFmzCAsLu27a3Qwbef78efr3N35exo8fz/jx44unSSnp2bPnTc9/5OTkkJCQABgPkTk5Od3Q5lbdWMfFxd2xG+prh4ZGjx7NTz/9RIsWLVi4cCFbt2698U1Y2ZNsaU1B7mXqOLjf8T0r5cc97xEIIRoJId4SQtz4k6AKS06N4f2iBBr36oHflFWkOnhpHalKWLVqFY899hjx8fHExcVx/vx5vL292b7dOPb0vn37OHv2LAaDgeXLl9OxY8fiY+qDBw/mvffe49ChQ7i4uODq6lo83+LFi4v3Dm5m5cqVGAwGzpw5Q2xsLE2aNCEsLIyvvvqKwsJCAE6dOkV2djadO3dm+fLl6PV6kpKS+PPPP29YnqenZ3E31CWLAEDbtm3ZuXMnp0+fBiA7O5tTp04B8Morr/DII4/w7rvvFh+6uVk31DfrxvpeZGZmUrt2bQoLC1myZMnNGwlBkZUNaRjQF6lxCiqSu9ojEELUAYYBI4HmwAfAcDPmqnD2H1/OMmcnBtYLpE0jD/bEpmgdqUpYunQpr7zyynWvDR48mKVLlzJs2DBCQ0OZOHEip0+fplu3bgwaNIhjx47xxBNPFF8lc20gmu+++47x48eTk5NDw4YN+fbbb2+53vr169O6deviwzK2traMHTuWuLg4goODkVLi4eHBTz/9xKBBg9iyZQt+fn7Ur1+fdu3a3dN79PDwYOHChYwYMaJ4lLX33nuPpKQk9u/fz86dO7GwsODHH3/k22+/5YknnqBDhw4EBATQu3dvpk+fTnR0dPF6HR0d+f777286oNKt/Pe//6VNmzZ4eHjQpk2b6wpNSc62bqRkJ6mbyyqY23ZDLYQYB4wA6gIrTH9rpZTeZRPvRlp2Q30705aFsSE3ge2P7OflWSv48uN3idi7HT+vulpHMyvV1bBSkpSSUylR2KMj6wrqs1GO/JtuqGeb2oyUUr4ppTwKVKwBDMrAtr1f8FNeIqFWblha29OqcW0KL5/l25W/ah1NUcqUEAInnTVZ6JGq76EK406FoDawFPhECHFSCPFfwMr8sSqOEwfn81z01/hIS17vYRymcGh4N3TWdvz++x8ap1OUsuds64qzwYChMFfrKMpdum0hkFKmSCnnSim7AN2BNOCSECJaCPG/sghYnsSf3cK6La9zPnGv8YXUWJpumsaHRU58O2Q9tWoFAWBjY42nXwgnD+1S47kqVY6jnTt1DTosivK0jqLcpdueLBZC1JFSXgCQUiYAn2DcO/ClqpwsLsiGw9/zeeR8FliaPtjnf6axQcfEjFy6Cx3hg5eBU+3rZuvcrTuLP9vOXweP0zUkQIPgiqIRIZB21SgsSqIw9ypWdq5aJ1Lu4E6HhuYLIfYIIT4UQnQVQlgCSClPSSnfLYN8moo8vpzUue1h/RQ66C14qXpblrV+h8nV21JNWPKeszVXH5wJrjf2NPrk8Aexb9qJnaduHNheUSq7bEsbrljo2HdontZRlLtw2z0CKWUfIYQt0BUYBMwQQpwDNgAbpJTnzB+x7BUW5vD1L2OYlx7JQzaCqaN+IdS7E6Gm6f7NBvP4HZbRpXUQbca+y6k8e3PHVZRyx97ODQFsPLueDh1euWN7RVt3vKFMSpknpdwgpXzedOnRfzAWkNlCiH1mT1jGrl45wZglHfkq4zh9rGvywogN4N3pvpbV2ceDHYejyMkvLOWUSkkWFhYEBQUREBBA//79i/sOupUrV67Qpk0bWrZsyfbt2/Hy8iI5Ofme17t161Z27dp1z/Pdy/qOHTtGUFAQQUFBuLm5FXcB0aNHD+Li4hBC8Oabbxa3T05OxsrKiokTJwLGbqKFEMU3owF8/vnnCCEw52XYOp0FtsKCzYVXKMzLMNt6lNJxz3cWSynPAnMx3lPQsdQTaSjh/C4e+3kI0RTwsddg/jdyM87O/+I+gLN7iJ39BEs3bC+9kMoN7OzsiIiIIDIyEjc3N+bMmXPb9ps3b6Z58+YcPnyYTp3ur8jD/ReCe9G8efPiO44HDBjA9OnTiYiI4I8/jFekeXt78+uvf1+mvHLlSvz9/W9YRsleWW/WxhzsLO3I0OnYc1gdHirvblsIhBDOQojXhBCzhRC9hNEk4AzwsJSy8oxCcfEYVitGYW8w8HXrqfTu8s6/XuRjA3sBsHLtnfuaUUpHu3btSExMBODMmTOEh4fTqlUrOnXqxIkTJ4iIiGDKlCmsXbuWoKCg4r50rvn+++9p3bo1QUFBPP300+j1xmvhN2zYQHBwMC1atKB79+7ExcUxd+5cPvvsM4KCgti+fTtXrlxh8ODBhIaGEhoays6dOwFISUmhV69e+Pv7M3bs2FK9ksze3p5mzZoV/7pfvnw5Q4cOva7NwIEDWbt2bfE2cXFxoXr16qWW4VZsrB1xMkh+j1Wf//LuTl1MLAauAruBscDrgAAGSSkjzBut7JyJ+Q2vH8dT09qR5QPWIGo0KZXlNmrgiUudhhzY+VepLK8i6Nq16w2vDR06lAkTJpCTk0OfPn1umD569GhGjx5NcnIyQ4ZcP7LbTTs3uwW9Xs/mzZt58sknARg3bhxz587Fx8eHvXv3MmHCBLZs2cK7777LgQMHmD179nXzR0dHs3z5cnbu3ImVlRUTJkxgyZIl9O7dm6eeeopt27bh7e1Namoqbm5ujB8/HkdHR15++WUARo4cyYsvvkjHjh05d+4cYWFhREdHM23aNDp27MjUqVP59ddfWbBgwV2/p7sxfPhwli1bRs2aNYt7OL1w4ULxdGdnZzw9PYmMjGTt2rUMGzbstt1nlBYhdHzlHEzjU1ugMA+sbM2+TuX+3KkQNJRSNgcQQswHkoD6UspKc4HwhQsHeGLHFIa6ODNxxG+IavVLdfnB7Tqzde0PxF9Oo0GNaqW6bMUoNzeXoKAgEhMTadasGT179iQrK4tdu3bx8MMPF7e71k/PrWzevJmDBw8SGhpavNwaNWqwZ88eOnfujLe3sWcVNze3m87/xx9/EBUVVfw8IyODrKwstm3bxurVqwHo27cvrq6lezlleHg4b731FjVr1ryhu+xrrhWLjRs3snnz5jIpBAAtAh+HY2vhzBZoeuOPAKV8uFMhKD7LKaXUCyESKlMRyMm6zKSNT1IE9A37Akq5CAAMG9iPP39cyLxlP/P+c4+V+vLLm9v9gre3t7/t9OrVq9/THsA1184R5OTkEBYWxpw5cxg9ejTVqlUrHpzlbkgpGTVqVHEndNf8/PPPdzW/wWBgz5492Nre+y/fNWvWMG3aNADmz59PSMhNu4S5KWtra1q1asUnn3xCVFQU69atu6FNv379mDx5MiEhITg7l+Ewqg27sMLNg5TDs3hGFYJy604ni1sIITJMf5lA4LXHQog7XgoghAg3dU1xWgjx6m3aDRZCSCHE3X/6/yWDvojX1gzitNAzPWA83g27m2U9jw/ug/eQV7hsV/pFRrmevb09M2fO5JNPPsHe3h5vb29WrlwJGL/kjxw5ctv5u3fvzqpVq4q7aE5NTSU+Pp62bduybds2zp49W/w63Njdc69evZg1a1bx82tFqHPnzvzwww8ArF+/vngIzJIGDRpUfFL4XorANf/5z3/46KOPbrm3Ym9vz0cffcQbb7xxz8v+VyysOF7dm+/yzpOfr64eKq/u1MWEhZTS2fTnJKW0LPH4tj8rhBAWwBygN+AHjBBC+N2knRPwPLD3/t/GvZv/y2i2GDKYXLMTHUInmm09dnZ2DHx4OAeS8tEbVHcT5tayZUsCAwNZunQpS5YsYcGCBbRo0QJ/f//iE6a34ufnx3vvvUevXr0IDAykZ8+eJCUl4eHhwbx583jooYdo0aJF8eGX/v37s2bNmuKTxTNnzuTAgQMEBgbi5+fH3LlzAXj77bfZtm0b/v7+rF69mvr1S/9Hgb+/P6NGjbptm+HDhxMcHFzq676TMJ8HydYJdqiby8ovKaVZ/oB2wMYSz18DXrtJu8+BvsBWIOROy23VqpX81zIvy23TPeXn3/eQBr3+3y/vDpbuOCFdH3hKfv/Ln2ZfV1mLiorSOoJSTl37bBTmZ8uO3/jJyd931ThR1QYckLf4XjXnmMV1gfMlnieYXismhAgGPKWUt+2vWQgxTghxQAhx4MqVUhjwYtdMOmVn8nzYlwid+Ydt7tKkBmnbFvH57LlmX5eilDeW1vb0sKnD1oIr5Oap8YzLI80GrxdC6IBPMd6pfFtSynlSyhApZYiHh8e/Wm/61VjmnPyBdP8HobrPv1rW3apd3ZVGoV05vG09uXlqCD+l6unjM5Cg/DyuxqzXOopyE+YsBImAZ4nn9UyvXeMEBABbhRBxQFtgnblPGC/aMoW5zvZcDn7EnKu5wWOPjESfk8EXi34s0/UqSnkQ2vIp5l0toM6ZbVpHUW7CnIVgP+AjhPAWQlhj7La6+Lo2KWW6lLK6lNJLSukF7AEGSCnN1gFK+tWzLMmIpqeFKz4Ne5prNTf14hMPY2HnxHeLvi/T9SpKuWBpDU37kHzqV/JU30PljtkKgZSyCJgIbASigRVSyuNCiHeFEAPMtd7bWfznK2TrdIxvV8aX0AFO9nYEdu5NwuVUcguKynz9iqK1Ew1C6V7Dia2H1bmy8sas5wiklL9JKX2llI2klO+bXpsqpbzhjhcpZVez7g2kxbMkPYqeumr4Ngoz12pua+asWbg/9BZbTpTCCW9FqWB8AkbgZpBsjFXnCcobzU4Wl7Wc3FRCLKvxdLvXNMvQrpEHNZxsWLH7lGYZKquffvoJIQQnTpwofm3r1q3069ev1Nc1evRoVq1aVWbzt2nThqCgIOrXr4+Hh0dxt9RxcXF4eXnd0IPqtS65wbgNhBDMnz+/eHpERARCCGbMmHHf7+F+WFjb08u2DtsLr5Cde+NNdYp2qkwhqF27JbMe20GTxtrd5m6hE9S5uIvFE8OIPZekWY7KaOnSpXTs2JGlS5eadT1FRWV/WG/v3r1ERETw7rvvMmzYsOI7kL28vADIzMzk/HnjldrR0dE3zB8QEMCKFSuKny9dupQWLVqUSfZ/CvMZRL4QbD38f5qsX7m5KlMIyotH+ndDFuXz/pdl0+lXVZCVlcWOHTtYsGDBdf3ug7Hjt759+9KkSRPGjx+PwWBAr9czevRoAgICaN68OZ999hlg/KXctm1bAgMDGTRoUHFXEF27duWFF14gJCSEL774AjB2MBcSEoKvry+//PILYOz9dPLkyYSGhhIYGMj//Z/xy05KycSJE2nSpAk9evQo7sKitAwdOpTly5cDxi/5ESNGXDe9QYMG5OXlcenSJaSUbNiwgd69e5dqhrsVFPQENfQGNpzdoMn6lZu7U6dzSikb3KMDDjUbsHrp93z9v1fRlcENbWXpiQ1P3PBamFcYw5sOJ7colwl/TLhh+oONH2Rg44FczbvKS1tfum7at+F3Lphr164lPDwcX19f3N3dOXjwIK1atQJg3759REVF0aBBA8LDw1m9ejXe3t4kJiYSGRkJUDyi2eOPP86sWbPo0qULU6dOZdq0aXz++ecAFBQUFPf5P3r0aOLi4ti3bx9nzpyhW7dunD59mkWLFuHi4sL+/fvJz8+nQ4cO9OrVi8OHD3Py5EmioqK4dOkSfn5+jBkz5q636Z0MHjyYJ554gpdffpmff/6ZJUuWsHjx4uvaDBkyhJUrV9KyZUuCg4OxsbEptfXfC52VHR86NsczbjcUFRivJlI0V7m+hSoAnU7HQ4+OIe1cNN/99LvWcSqFpUuXMnz4cMDYn07Jw0OtW7emYcOGWFhYMGLECHbs2EHDhg2JjY1l0qRJbNiwAWdnZ9LT00lLS6NLly4AjBo1im3b/r7m/Z/dOw8dOhSdToePjw8NGzbkxIkTbNq0iUWLFhEUFESbNm1ISUkhJiaGbdu2MWLEiOKxAh544IFSff/u7u64urqybNkymjVrhr39jeNkDx06lJUrV950j6GshbYYTa2cNIj9U9Mcyt/UHoEGpr/2HEtmf8wHMz7jiYe0uYLJXG73C97O0u62011tXe9qD6Ck1NRUtmzZwrFjxxBCoNfrEUIwffp0AIQQ17UXQuDq6sqRI0fYuHEjc+fOZcWKFcWHh27FwcHhhuX887mUklmzZhEWdv3/099+u/MIXefPn6d///4AjB8/nvHjx99xnpKGDRvGs88+y8KFC286vVatWlhZWfH777/zxRdfmH2Izdtq9ABbqnlw8uBnPONbuT7/FZXaI9BATfdqPPraJ+S3eoSk9Nw7z6Dc0qpVq3jssceIj48nLi6O8+fP4+3tzfbtxnGi9+3bx9mzZzEYDCxfvpyOHTuSnJyMwWBg8ODBvPfeexw6dAgXFxdcXV2L51u8eHHx3sHNrFy5EoPBwJkzZ4iNjaVJkyaEhYXx1VdfUVhoHMbj1KlTZGdn07lzZ5YvX45erycpKYk//7zxl7Cnp2fxSeB7LQJg7MZ6ypQpNxShkt59910++ugjLCws7nn5pcrSmsO1mzIvP4H0jMQ7t1fMThUCjbw36TEsHN1YvDte6ygV2tKlSxk0aNB1rw0ePLj48FBoaCgTJ06kWbNmeHt7M2jQIBITE+natStBQUE8+uijxQPRfPfdd0yePJnAwEAiIiKYOnXqLddbv359WrduTe/evZk7dy62traMHTsWPz8/goODCQgI4Omnn6aoqIhBgwbh4+ODn58fjz/+OO3atSv17eDk5MQrr7yCtfWtj7m3b9+egQMHlvq670d4wGMUCcEfB2ZqHUUBhCzFgbTLQkhIiLx20q6iG/j2Qv5c/n/E7tmIe7UyHDWqFEVHR9OsWTOtYyjl0O0+G9JgYMDCFtSwsGPBqH1lnKxqEkIclFLetC83tUegobBm1ck4uYdXPpyjdRRFKVNCp6OPqz/7ZQ6XLh/XOk6VpwqBhp4e2gfner4sWzgPg8GgdRxFKVN9Wo6nUWEhlyNX3LmxYlaqEGhIp9Px+FMTyL4Ux3tfql5JlaqlgVdX1hhq0Txmq9ZRqjxVCDQ2/ZVnsHWvwycfvV9h9woq2nkmxfzu+jMROIzci0fIuHDIvIGU21KFQGO2NtY8M/ktLJp0YVNkxet/yNbWlpSUFFUMlGJSSlJSUrC1tb1j2+wmvelevy6L93xUBsmUW1E3lJUDH708jgPiL2b/FUtY8zo33KxUntWrV4+EhARKZSxppdKwtbWlXr16d2zn4OaNn86B39KimGAwlMkY4sqNVCEoB6wsdDzTyYtJ//2c6fYXmfLkw1pHumtWVlZ4e3trHUOpwPrW68rUhPVERq+iuf9QreNUSar8lhMDg+uSvW8V77/zRoU9V6Ao96N76HNYScn64+qCCa2oQlBO2NlYM+bZ/5CREMN/v1x85xkUpZJwdq5HRwsXNmbFoi8q0DpOlaQKQTky4/WJ2FWvx8fvvU1evvoHoVQdzzR9hFkXL6KL36l1lCpJFYJyxM7GmslvvUvOpXjGv/Wx1nEUpcw0C3oCP2wQkT9qHaVKUoWgnHl74ii8O/Rnx0ULMvIKtY6jKGXDyo6TPl15P3EThQXZWqepclQhKGd0Oh1rly2mqE4gX/55Rus4ilJmLni2YpmDDXsOqfGMy5oqBOVQ83ou9GtWjenvT2Pn4Sit4yhKmegQ/DROBsmGM+u0jlLlqEJQTo1pXZOr+9Yw6pnntY6iKGXC2tqBHnZ12VxwhbycFK3jVCmqEJRTwc0aEzZiHGf2/sEn367UOo6ilIlw3yFk63TsOPCl1lGqFFUIyrGlsz/AzsOTN19+npS0TK3jKIrZtQ4chW+RJPPcDq2jVCmqEJRj1ZwcmPHFbPJSkxg87iWt4yiK2VlaWrOq7gAGxR6EnFSt41QZqhCUcxNGDKDr4y9xxiWY6KQMreMoitmJwIcxGApJO6YGrCkrqhBUAGu++oAa9Rvz6upjFOlVP0RKJVc7iFGeDXjzxDdaJ6kyVCGoAKrZW/NKT2/+mP0aI557U+s4imJeQhDk2oSdMof0lFNap6kSVCGoIIa0boiHvY4f/28G6/7co3UcRTGr3oFPUiQEv++fpXWUKkEVggpCp9OxYdViLGwdePTRx0jPytE6kqKYTbNGYXgZdPycpDqhKwuqEFQgTb09eWf6TDIvnKb/E89pHUdRzEYIwcDqwRzSFRKvLiU1O1UIKpg3nnmMoJ6D2fnbKjYdjtU6jqKYzYNtJjPnUjJ1T/6udZRyoUhvMNvY4GYdqlIIEQ58AVgA86WUH/5j+kvAWKAIuAKMkVLGmzNTZbBx2XwGfb6ZN3+LJahxHWo43XmQcEWpaKrX8KOzZzeI+AEeeBMsrLSOVOoysnM4e+EK0taF5Mx81v/yEzExMVy+dInU5CukpyZj5VqHGn0mcTWngMh3wnCwKf2vbbMVAiGEBTAH6AkkAPuFEOuklCV7UTsMhEgpc4QQzwAfA8PMlamyqOFWjfnP9GTgnO30ffZd/pr3Nva2NlrHUpRSlx00nAWXd9B2/xxat31B6zh3xWAwcP5iCtGx8cTEJXAxJQ2vlp25kpXPr4u+5MyR3WRdTSEvIxl9bhZW7vWpM9bYpcbFJbPITziOztoeGydX7F3cqFnNid4BtajuaIN59gfMu0fQGjgtpYwFEEIsAx4EiguBlPLPEu33AI+aMU+l0qy2M494ZvH2+x/QIz2BXWsXaR1JUUqddeNe/LjHmbMnl5WLQpCclkFE9Gmiz8QTExtP/PkELl28RIuhz3M5M5+9P3zGxb0/I0sMuSms7an/4gqsLXVkJSRRmJ+Ph6c31T3aUqNmLRo29ObBwSFUd7JBN/Y3PGu44ersWKbvy5yFoC5wvsTzBKDNbdo/Caw3Y55KZ+rTw/jtl1/YvW4xUz5qx8evPKN1JEUpVVZWtvRz8eOHjChSLx7BrVYLs6xHSsnVnEKOnjzL7v0HOBOXwLmE81xMTCTlykV8R7xJSoEl8Rvnk7Fn1XXzWtg64tDxUWq7V6NlqxAyazpTq3ZtPOvWwcuzLr7enrQNDsTZ1hIhet8hSTWzvL87Mes5grslhHgUCAG63GL6OGAcQP369cswWfm3ecV8GjQ/yow3X8TPtxGjB/XSOpKilKqBoc+zaMt4ftkznccHfn9fy8jMK+RCWh4HIqPZtGEDcXHnuHTxAqmXL5KZepmaQ96CavXIPPQLqb/PNc0lsHJyxd61BrXtJJ3861JQ+xFy+3aisVd9/Hy8aNGkIdWrOZdY0+1+65ZfwlxnoYUQ7YB3pJRhpuevAUgpP/hHux7ALKCLlPLynZYbEhIiDxw4YIbEFdeJs+dpGdoWfWEBR4+foGk9d60jKUqpGrkolNzCHFaPOoywtL5umsFgIC2ngAvp+RyNiWPd6lXEx8dzMfE8qZcSyU65iHu/l7Fr2IqcmL1cWf1fhIUlNi4eOLrVwK1GbfqOfo6AZk2xzLuKPuMKAb4N8W9cv1KdexNCHJRShtx0mhkLgSVwCugOJAL7gZFSyuMl2rQEVgHhUsqYu1muKgQ3t3XfUZ6a+zt1mgXz4zPtqe5YeT7AStVmMBj4fvNUjp76gR4ez/P1n0mci4/n4oXzpF26QE7qRap1exKnoHAKLp0haeHzCCtb7N1qUa1mHWrWrkevIY/QLjQEN2sDLpZFNPP2xNLSQuu3VqY0KQSmFfcBPsd4+eg3Usr3hRDvAgeklOuEEH8AzYEk0yznpJQDbrdMVQhu7dC5q4z8eg8uV46y7v2nqeleTetIinJX8gqLSEzLI/ZiOt998zWnz5zhwrk4Ui6eJzflIi6tHySqxz72ZdVi4Kd70Nk44OBeG9eadahV15NuvQfQpVMnajpa4aQroJFnLXQ6dZtUSZoVAnNQheD2vtuwl9F9O1C9UXMid/+pioFSbmTmFRKfksPyH1cTeTyas7GxXEyII+1iAlYNWuAe/hxSSs5/OgSEwMGjLtVre1K3vhftOnfjmTon0J/6P/IHriYg4KanE5XbuF0hKBcni5XSMyq8DVEfzObjV58loF03ju3+k1qqGChlJK9Qzx+7D7Fz32Eio09wJuYUF8+fRTp64NJ3MgCJ896k6OoFLB1ccKpRDy//YILadebhh1vQwN0ex2fO4Fu/9g2/6C8lHaZnzjqeiZmrCkEpU4WgEvpoyngAPn71WZq37cqxPVtVMVBKTVGRnkPRZ9hx8AiHj0Zx8uRJUjOyqNn3BS6k55L0w+vknzsKgLWLB66169OoqS+Phjelgbs9ugd/o3mjetStcW8XNdSs3ZL2OkdWXj3G2MJcrKzszPH2qiRVCCqpj6aMRwj46JVn6fOfz9g463U8nNQJZOXuJV5O4c+9ERw4cpzoUzE06jWa2ORs9ix8n/Qjm4rb6aztcKnbiD4NqjHUwxN98w/xdHega+sWeLi63GTJte8700ifh3n21EI275lBeKe37ns5yvXUOYJK7ptftjN9bzbujtZ8/WhLmtV11TqSUs6cTbjIlj2HsazRiPj0Itav/oFDa+ZRkH7l70ZCR7u3VtLU2xND4jF0mRdp1dyfzqGB+DduUGYnZvVFBfRd1IpaOmsWjj5YJuusLNQ5gipsTL9OtGqRxogPfqBFwEg+nzOPCSNve2GWUkldzsjlzJUctu4+wJql33Eu9hSpCWcpzDIOEl/rsU+o1sAPZ9tqNApsjW/TZrQI8KN9q0A6tPTH0f5a54ahmr0HC0trhtdqx5eXdnIldjMeDbtrlqUyUXsEVcS2/ccI79uX3ORE+j3xPGv+b3qVu466qsjIzmX9X/v4a+9BIo4c4eypaJLPxeDaawL2vu3Jiz/KldXv4VLHi7pePjRp1oxWLZozIKwbzerXQqcTWr+F28rOTEI/uzXOvuEw+Gut41QY6vJRBYALV1Lp+uBIYnZvpLZ/W7b8vJKm3vW0jqXcp6IiPXuOnmTzrv3sOxiBqNGInBoBxJw6RcK8pwEQljY41/GmfqOm9Bv2GOHdOtHIw57aLnYV+zr7Da8h982jcNIhrF0baJ2mQlCFQClmMBgY98bHfDN9KrW7juTbLz6kl38trWMpdxB/4QoRsRdIt3DheMJV5r86irSE0xgKcovbeHYbQfjol/Ct4UDq8R107xBCp1bNsbaqfEeAC5NPMerHfrT2aMELD63UOk6FoM4RKMV0Oh3zP3iVAb26MutAJuMWH6StSybvjuiEr1ddreNVeYV6A7FXslm4dAV79u7jdPRxLsefoiD9CnaNQqkx5G1c7KxwcKtBo6YBBAYG0qlNMGEdQ6lVvcSFAOHNtHsTZcCqui917WuwLD2aJzITcXFSn91/Q+0RVGEFRQa+3HKKKSN6IPOzGPP8a8x65z+V8hdkeWMwGIg8Hc+GbfvYe/Aw0ccjuZqdh2P4SxTqJReXTCH/wkkcazagbsMmNPMPoEvH9gwbEEYtZ1uEKN/H8cvCyejVDNn3NhPdQ3i637daxyn31KEh5bZ+2bqXUU8+RWrsMRzrNOLd9z/kxdFDtI5VaSSnZbBxxwF2HTiKR3BPTlzM4Pe575B6+O9r8a2cq1OrkT/j3/sSv9ouuIlsgn3rl7hSR7mZZ79rzVFDDhuHb8feTl0afTuqECh3ZDAYeP3Tr/n8f++Qf/UiPad8xbRxD9O2oZv69XmXCgqLOH81l5jLWfz0829sWr2Ei3Ex5CYngmmQwcYvLsW/kSdWFyKwyblC+9Bgwju1pqGnOk9zPyIivuWxI5/yeq1ujAibqXWcck0VAuWupWdlM/Gdz4lyDiElpwjXhJ30C23MG+MfxdbG+s4LqAKklJxOuMxPm7ay79ARoo5Hkhh7ioykOGo9Oh3rmg3JjtxM9t6V1PL2xbeZP6HBQXRvF0LHYH912W5pkpLNCzrRKSsd60mHwUId1rwVVQiUe5ZXqGflgfNMGBpOVsJJrJzc6dz3Yd6ZPImOwX5axysz8UlX+H3nAXbuO0RkZCTOzbuRbO/Fxai9XF4xFQArRzfcPRvh7duM4aPG0jmkOb41HbG3Vl9KZeLEb7BsBHLAHESwGvb8VlQhUO5bfkEh7321mIXfLCDh2B6QBgIGTmDSiy/Ry68WXtUdtI74rxkMBo7FxLFt/1GydQ7kO9Xh6Ikz/PL+2Ou6WdDZ2NNy2Ev0fHAoDZxAfzmWXh1D8WlQR8P0CgYD277pxBzS+Hb4Fuwda2qdqFxShUApFQeOx/DOjC9Jdvbhol0D8pNOkblxJgGtO9K9axceHRRGM29PrWPeVFGRnujYc8RfyUDvUJ2zlzP5v//+h6RzsWRePIehIAcAp1b9qdt7Al5uNsSt/oSmfn6EtgykR4fWhPg3rtg3YVViR44v59ED7zHW0ZfnB/+odZxySRUCpdSdT81h7orfWDDzIy7HHEMW5QNg5+HJ4Ndm0TbIH3eyqOdsRbB/Y7OO/WowGEi8kkr0mXgup+XgVKchSel5LJr5AfGnoki7lEDu1UvIogLsfdvjMeh1AFKWvIyTszP1vBvTpIkvrZoH0LVdMIE+XuW+mwXlRm/80J31BZdY+8BcPOt31DpOuaMKgWJWWTl5rNzwF+s2bubQ/r3UGTSFpGy4unUhGXtXgdBh7eyOU/XaVHOvweCXp+PmZMvFqP1kXE7AxtoaGxtrbG1scHB0IKRjdwr0Bg7v2UHiubPk5xeQnZNDWloaBp0VLfqPIT23kK0L3icpah/5GanFhci6lg+1R30GQMqqt7EoyMK9Vj3qeNanobc3IcEt6d2jC3Wr2WNnrU7aViZXLkfR79ehtLZwYtbju7WOU+6oO4sVs3K0t+WJh8J44qGw4tcy8wpZv92VLdtaExsXT+L5cyQnJXIpIY7NJ6+QnltI0tpvyT7+53XL0tk64fn8UgCurPmCnFO7/p4odNh6eFIUOAhnO0vsnKpRv2kQHjVqUqt2LerVrUOTxg3p0bUztVxscfqwb5m8f6V88Kjhx9MebfgsZR9HD84jsNU4rSNVGGqPQNGElJIrVzNIvJRMbn4BuXl55OUXkldQSDP/5lhbCtJTkkEWYW9nSzUnR2q6uahj9MptFeRnseebTnQqBDFhD1iqwZiuUXsESrkjhKCGmws13G42gpVJDaeyC6RUCtY2jnTuOR2+H0zalv9Srdd7WkeqENTPK0VRKpfGPdjl35deiWuIiFyqdZoKQRUCRVEqncDwGbhJwRv7PiAnN1XrOOWeKgSKolQ6jk51+G/Qc5zXGfj051Faxyn3VCFQFKVSCg0ex2N2XizPjWPH/tlaxynXVCFQFKXSem7AInz1Ok7u+QJSY7WOU26pQqAoSqVlY+fGkv7LeTKnEJY9AvlZWkcql1QhUBSlUrP1aApDvuVgRiwfr+yPNBi0jlTuqEKgKErl16gb+/37sFifzItLOpGZnqB1onJFFQJFUaqEp/su4OXqbdmqT2fYj705cWKN1pHKDVUIFEWpEoROx6i+X/NNq9fIF5JHd7/Fhc1TIUfdZ6AKgaIoVUpw80dY8eBaHrbyoPb2L+BTPzasGs6ZM79rHU0zqtM5RVGqrkvHKdzzJT1StpJqoaMtdjzYIJwHQiZi71hD63SlSo1HoCiKchtXU8+wasc0ViYfIslCYGeQvGnbkAEtngTvLmBXTeuI/5oqBIqiKHfBoC/icOQSfoteyuDEGPwyk9ljZ8/MmrVp6eBJ0+oBNPPsjFeDLlha2Wod955oVgiEEOHAF4AFMF9K+eE/ptsAi4BWQAowTEoZd7tlqkKgKEqZ0BdCwn52RS7hq8u7iRIFFAjjEKY2BslPha7Uq9WSYw5OJFhZUdvNl9oeAVR3b4aFpZXG4W+kyXgEQggLYA7QE0gA9gsh1kkpo0o0exK4KqVsLIQYDnwEDDNXJkVRlLtmYQUN2tO+QXvaA4WFuZw9t42T57ZxMvk4tXOKIHodP9vBUhcnOGuczVJKahsEPwtPLFw8+ctaECf0ONtUw9nOHWd7D6o51sbHIwCsHSm0sMLSygGh4aBL5hyYpjVwWkoZCyCEWAY8CJQsBA8C75gerwJmCyGErGjHqxRFqfSsrOzwbRSGb6Mw+pd4/fnMJB6+dISklJMkpZ0hKesCWXlXscg3QPxOVtvkscXB7rpl1SgqYvP5CwBMqunBTns7bAwSG8BWgo9BMDfbAoSOKfZ6onWSznodkx/dapbzFeYsBHWB8yWeJwBtbtVGSlkkhEgH3IHkko2EEOOAcQD169c3V15FUZR75uBUGx+n2vg0Dr/p9M/1erJzLpGRecH4l3OZorwMaFkdCrLpfzWS5nmp5OvzyNMXkGcowE3qwNUTpIH6+QkYZB61dQ6gM89XdoUYqlJKOQ+YB8ZzBBrHURRFuWvCwgJHpzo4OtWhzk2m973D/BPNEeofzHlQKhHwLPG8num1m7YRQlgCLhhPGiuKoihlxJyFYD/gI4TwFkJYA8OBdf9osw64NnzQEGCLOj+gKIpStsx2aMh0zH8isBHj5aPfSCmPCyHeBQ5IKdcBC4DFQojTQCrGYqEoiqKUIbOeI5BS/gb89o/XppZ4nAc8bM4MiqIoyu2pTucURVGqOFUIFEVRqjhVCBRFUao4VQgURVGquArX+6gQ4goQr3WOf6k6/7h7uopT2+NvaltcT22P6/2b7dFASulxswkVrhBUBkKIA7fqBbAqUtvjb2pbXE9tj+uZa3uoQ0OKoihVnCoEiqIoVZwqBNqYp3WAckZtj7+pbXE9tT2uZ5btoc4RKIqiVHFqj0BRFKWKU4VAURSlilOFwEyEEBZCiMNCiF9Mz72FEHuFEKeFEMtNXXMjhLAxPT9tmu6laXAzEEJUE0KsEkKcEEJECyHaCSHchBC/CyFiTP91NbUVQoiZpu1xVAgRrHX+0iaEeFEIcVwIESmEWCqEsK1Knw8hxDdCiMtCiMgSr93z50EIMcrUPkYIMepm6yrvbrEtppv+rRwVQqwRQlQrMe0107Y4KYQIK/F6uOm100KIV+81hyoE5vM8EF3i+UfAZ1LKxsBV4EnT608CV02vf2ZqV9l8AWyQUjYFWmDcLq8Cm6WUPsBm03OA3oCP6W8c8FXZxzUfIURd4DkgREoZgLGL9uFUrc/HQuCf4zre0+dBCOEGvI1x+NvWwNvXikcFs5Abt8XvQICUMhA4BbwGIITww/hZ8TfN86XpB6cFMAfjtvIDRpja3jVVCMxACFEP4wh0803PBfAAsMrU5DtgoOnxg6bnmKZ3N7WvFIQQLkBnjGNPIKUskFKmcf37/uf2WCSN9gDVhBC1yzS0+VkCdqZR+eyBJKrQ50NKuQ3j+CMl3evnIQz4XUqZKqW8ivHL8+aDBpdjN9sWUspNUsoi09M9GEd3BOO2WCalzJdSngVOYyyCrYHTUspYKWUBsMzU9q6pQmAenwNTAIPpuTuQVuJ/bgJQ1/S4LnAejIP5AOmm9pWFN3AF+NZ0qGy+EMIBqCmlTDK1uQjUND0u3h4mJbdVhSelTARmAOcwFoB04CBV9/Nxzb1+Hir156SEMcB602OzbQtVCEqZEKIfcFlKeVDrLOWEJRAMfCWlbAlk8/duPwCm4UmrxHXMpsMXD2IskHUAByrgL1lzqkqfh9sRQrwBFAFLzL0uVQhKXwdggBAiDuMu2gMYj5FXMx0KAOOuXqLpcSLgCWCa7gKklGVgM0sAEqSUe03PV2EsDJeuHfIx/feyaXrx9jApua0qgx7AWSnlFSllIbAa42emqn4+rrnXz0Ol/pwIIUYD/YBHSozjbrZtoQpBKZNSvialrCel9MJ4YmeLlPIR4E9giKnZKGCt6fE603NM07eU+B9f4UkpLwLnhRBNTC91B6K4/n3/c3s8brpapC2QXuKQQWVwDmgrhLA3Heu/tj2q5OejhHv9PGwEegkhXE17Wb1Mr1V4QohwjIeWB0gpc0pMWgcMN11J5o3xBPo+YD/gY7ryzBrj9866e1qplFL9mekP6Ar8Ynrc0PQ/7TSwErAxvW5ren7aNL2h1rnNsB2CgAPAUeAnwBXjce7NQAzwB+BmaiswXgFxBjiG8eoazd9DKW+PacAJIBJYDNhUpc8HsBTj+ZFCjHuMT97P5wHj8fPTpr8ntH5fpbgtTmM85h9h+ptbov0bpm1xEuhd4vU+GK8wOgO8ca85VBcTiqIoVZw6NKQoilLFqUKgKIpSxalCoCiKUsWpQqAoilLFqUKgKIpSxalCoJRrQojPhBAvlHi+UQgxv8TzT4QQL5Xi+hYKIYbcueU9L/f1Eo+9SvY2eYf5XhBCPF5KGWYIIR4ojWUplYsqBEp5txNoDyCE0AHVMfa+eE17YJcGue7V63ducj3TncRjgB9KKcMs/tG9h6KAKgRK+bcLaGd67I/xJqxM0x2lNkAz4JAQYqoQYr8w9vE/z3QnalMhxL5rCzL9Ej9metxKCPGXEOKgaS/jhh5Ob9VGCLFVCPGREGKfEOKUEKKT6XV7IcQKIUSUqR/5vUKIECHEhxh7G40QQlzrN8ZCCPG1MI5LsEkIYXeT9/4AcEiaOqO7zXpHCyF+EsZ+/OOEEBOFEC+ZOvnbI4xdNiOljAfchRC1/uX/E6WSUYVAKdeklBeAIiFEfYy//ncDezEWhxDgmDR2vTtbShkqjX382wH9pJQnAGvT7fgAw4DlQggrjL+Oh0gpWwHfAO+XXO9dtLGUUrYGXsDYLz7ABIxjB/gBbwGtTO/hVSBXShkkjd2NgLF7gDlSSn8gDRh8k7ffAWPPpCXdbL0AAcBDQKgpZ440dvK3Gyh5aOmQabmKUszyzk0URXO7MBaB9sCnGLvYbY+xS+adpjbdhBBTMPbv7wYcB34GVmAsAB+a/jsMaILxi/N3U9f+Fhhv8y/pTm1Wm/57EPAyPe6IsYNBpJSRQoijt3lPZ6WUETdZRkm1uX5wo1utF+BPKWUmxr2ldIzvHYzdMgSWaHcZY6+nilJMFQKlIrh2nqA5xkND54H/ABkYxzmwBb7E2A/NeSHEOxj76AFYDqwUQqzG2MNxjBCiOXBcStmOWxN3aJNv+q+e+/t3lF/isR7jXsw/5fL3+7jTeksuz1DiueEf7WxNy1WUYurQkFIR7MLYJW+qlFIvpUwFqmE8PLSLv78sk4UQjvzdiydSyjMYvzTfwlgUwNhhl4cQoh0YDwMJIUqegL7bNv+0Exhqau+HsXBdU2g63HQvooHG9zjPnfhiLKaKUkwVAqUiOIbxaqE9/3gtXUqZLI1DX36N8QtuI8ZueUtaDjyK8TARpnMKQ4CPhBBHMPbw2L7kDHfT5ia+xFg8ooD3MB6eSjdNmwccLXGy+G6sxzjMZ6kwFaLGGHuCVZRiqvdRRSklwjiIuJWUMk8I0Qhjd8pNTEXlfpe5BpgipYwphXyDgGAp5Vv/dllK5aLOEShK6bEH/jT98hbAhH9TBExexXjS+F8XAoz/3j8pheUolYzaI1AURani1DkCRVGUKk4VAkVRlCpOFQJFUZQqThUCRVGUKk4VAkVRlCru/wHQT8UqftLa1QAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(wavelengths_external*1e9, solar_cell_external.reflected, label='Reflected - external')\n",
    "plt.plot(wavelengths_external*1e9, solar_cell_external.absorbed, label='Absorbed - external')\n",
    "plt.plot(wavelengths_optics*1e9, reflected, 'k--', label='Reflected - TMM')\n",
    "plt.plot(wavelengths_optics*1e9, absorbed_in_Si, '--', label='Absorbed - TMM')\n",
    "plt.legend()\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.ylabel('R/A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the light-IV curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Voltage (V)')"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.plot(V, -solar_cell[1].iv(V), 'b', label='TMM calculation')\n",
    "plt.plot(V, -solar_cell_external[0].iv(V), 'k--', label='External optics')\n",
    "plt.legend()\n",
    "plt.ylim(-20, 350)\n",
    "plt.xlim(0, 1)\n",
    "plt.ylabel('Current (A/m$^2$)')\n",
    "plt.xlabel('Voltage (V)') #The expected values of Isc and Voc are 372 A/m^2 and 0.63 V respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, compare the absorption profiles (diff_absorption). These will not be exactly identical,\n",
    "because the profile for the TMM cell also contains the absorption profile in the ARC (= 0 everywhere),\n",
    "so is shifted over by 100 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_plot = np.linspace(0, 200, 100)*1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "absorption_profile_TMM = solar_cell[0].diff_absorption(position_plot)\n",
    "absorption_profile_constructed = solar_cell_external[0].diff_absorption(position_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Differential absorption (m$^{-1}$) in front surface of cell')"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(121)\n",
    "plt.imshow(absorption_profile_TMM, aspect='auto', extent=[0, 400, 1200, 300])\n",
    "plt.colorbar()\n",
    "plt.xlabel('Depth (nm)')\n",
    "plt.ylabel('Wavelength (nm)')\n",
    "plt.title(r'Differential absorption (m$^{-1}$) in front surface of cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Wavelength (nm)')"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(122)\n",
    "plt.imshow(absorption_profile_constructed, aspect='auto', extent=[0, 400, 1200, 300])\n",
    "plt.xlabel('Depth (nm)')\n",
    "plt.ylabel('Wavelength (nm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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