{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surface tension using PC-SAFT Helmholtz energy functionals\n",
    "\n",
    "## Goal of this notebook\n",
    "\n",
    "- Learn how to compute the surface tension for a planar interface using the PC-SAFT functionals.\n",
    "- Learn about the `SurfaceTensionDiagram` that allows convenient calculation of multiple surface tensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feos.si import *\n",
    "from feos.pcsaft import *\n",
    "from feos.dft import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_palette(\"Dark2\")\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "colors = sns.color_palette(\"Dark2\", 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Water parameters for PC-SAFT \n",
    "\n",
    "In this example we will calculate surface tensions for water using the 2B association scheme. The parameters that we use, [were adjusted to vapor pressures, liquid densities and surface tensions](https://pubs.acs.org/doi/10.1021/acs.jced.0c00684). Parameters are available [here](https://github.com/feos-org/feos/tree/main/parameters/pcsaft)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equation of state object.\n",
    "parameters = PcSaftParameters.from_json(\n",
    "    ['water_2B'], \n",
    "    '../parameters/pcsaft/rehner2020.json'\n",
    ")\n",
    "pcsaft = HelmholtzEnergyFunctional.pcsaft(parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first compute the critical point. We will make use of the critical temperature later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|temperature|density|\n",
       "|-|-|\n",
       "|677.34347 K|18.70466 kmol/m³|"
      ],
      "text/plain": [
       "T = 677.34347 K, ρ = 18.70466 kmol/m³"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp = State.critical_point(pcsaft)\n",
    "cp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the model overestimates the critical temperature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surface tension for single VLE\n",
    "\n",
    "To compute the surface tension, three steps are needed.\n",
    "\n",
    "1. We need to compute the vapor liquid equilibrium (VLE) either at given temperature or pressure.\n",
    "2. Then, we need to initialize a density profile. We will use a hyperbolic tangent with the VLE bulk densities as limits.\n",
    "3. We solve the DFT equations to yield the equilibrium density profile and calculate the surface tension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the VLE, we use the `PhaseEquilibrium.pure` method. Here for $T = 300$ Kelvin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "||temperature|density|\n",
       "|-|-|-|\n",
       "|phase 1|300.00000 K|1.51670  mol/m³|\n",
       "|phase 2|300.00000 K|55.38975 kmol/m³|\n"
      ],
      "text/plain": [
       "phase 0: T = 300.00000 K, ρ = 1.51670  mol/m³\n",
       "phase 1: T = 300.00000 K, ρ = 55.38975 kmol/m³"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vle = PhaseEquilibrium.pure(pcsaft, 300*KELVIN)\n",
    "vle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we initialize the density profile. For the surface tension, a 1D DFT calculation in Cartesian coordinates is conducted. Thus, the density profile will be an 1D array (we have a single substance). \n",
    "\n",
    "To solve the DFT equations, the density has to be discretized which can be controlled by `n_grid`, the number of grid points. The surface tension is not very sensitive w.r.t the number of grid points but you should make sure to pick a large enough value. When in doubt, run multiple calculations varying the number.\n",
    "\n",
    "We also have to provide a width of the calculation domain, `l_grid`. The domain should be large enough that the bulk densities can be observed in the limits. You can check the resulting density profile to make sure that's the case.\n",
    "\n",
    "The critical temperature is used to come up with a good initial estimate for the density profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 89 µs, sys: 137 µs, total: 226 µs\n",
      "Wall time: 227 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "interface = PlanarInterface.from_tanh(\n",
    "    vle=vle, \n",
    "    n_grid=512, \n",
    "    l_grid=100*ANGSTROM, \n",
    "    critical_temperature=cp.temperature\n",
    ")\n",
    "initial_density = interface.density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above method does not yet run a calculation. If we try to extract the surface tension, it will return `None`. Let's store the initial density profile for a later comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interface.surface_tension == None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the equilibrium density profile, we have to call the `solve()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 31.2 ms, sys: 1.3 ms, total: 32.5 ms\n",
      "Wall time: 32.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "surface_tension = interface.solve().surface_tension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`solve()` calculates the equilibrium density profile and returns the `PlanarInterface` object so that we can readily extract the `surface_tension`.\n",
    "\n",
    "The `PlanarInterface.density` contains the equilibrated density profile. Let's compare it to our initial density and zoom into the interesting region between 40 and 60 Angstrom. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uFzHVrl2bFStWsGrVKipXroy7u3umEPrwww8zdOhQANufUjj0P5bcMsMwwDCDyYzh4GTvcgCwWixYUxKuBs84LEmxWBKiSIuLxJIQlf4TH01a/LXvo0iLj8ISF05a7GXSYq9kHnVOTcItNYnsrh+9GPzP0zJamMz86OZHrJsvka7eXHHx4pKjOycd3Dhsdoay1TKNVu4LP8/PZw5m+zkMDJqVr8p3fZ62TTseFUaq1cIdnmUUOkVKkFdeeYU33niD559/nqSkJPr378+7776bpV/NmjX58MMPmTFjBs8++yxly5Zl4MCBlCtXjtdee+2m1+vq6sq8efN4++23mTp1KoZh0L59e6ZNm8YDDzyQqW/79u1ZtGgRs2bN4s033yQ2NhZfX1/q1KlD//79b3rdjRs3Zv78+UyZMoWIiAi8vb1p3Lgxb7zxRpZzPK/17LPPcuHCBf79738THx9Py5Yt+frrr23tzZo1w9/fn3LlytGoUaObrkvyz7Bar/mfU2jevDkAu3btsnMlYm9WSxqWuAhSY8JIi7lMWkxYeuCMvpz+Z0wYqZEXSY04R2rEWaxJcTde6FUmd18cy9fEqVwNzrr58bfZlZMuvhxydONsShIX4qJJu3qKQbuKNVnc6wnbvGM2f8uSY7sxGQZVPfwI8q1APb+K1PerRD2/ilTx8NVhdhEp1fbv38+AAQN499138xV4Jf803CGSA8NkxuxZFrPnjU9Gt1qtWBKiSY04mx40w8+SGnHe9j4l/Cypl09iSUg/j9QSF0HSiV0kndiFN9Dx6g+AQ5mqOAbUJ6VCIOFl7oBK1bGmpWJcHZ08E5t+dqnFauVkzBVOxlxhzekDtlrur9mUGR2H2N7HpSTh7uhcINtERKQoO3v2LOfPn2fKlCkEBARkuVG63H4KliIFwDAMzG7emN28cQ6on20fq9WKJS6clEvHSb50jJSwE6SEHiH5YgjJ5w9iiY8EIPXKaVKvnAZWkXEjlKOOLjhVbohL9eZ8XqMFsYEDOOHizZHoyxyMuMiB8AscirhIUloqtbzLZ1pvrx+CSbWkcWe5qrTxr0G7SjWp5llGo5oiUuJ8/PHH/PDDD9SsWZNp06bZbm8khUeHwq+jQ+FiD1arlbSYMJIvHCL53AGSzu4j6cweks/swZIYk+08JlcvXKq3wKV2W9yCOuJQvQWnkhLwdHKx3SopIjGOhgvfyjJvgLsP7SvVpF3FWnQJCMTXxT1LHxERkZulYHkdBUspSqxWK6mXT5J0+m8ST/1J4vGdJJ74HUtcRNbOZkdcqrfArU5HXOt0wjWwAxYHJ/ZcOcfuS6f5/dIpfrtwjIik+EyzfdPjcToFBNrWp5FMERHJLwXL6yhYSlFntVpJuXQsPWQe3U5CyBaSzu7JdAU7pN+z07VOJ9wb9MS9YU8cKwZhxcrB8Iv8ev4oWy4c5WDERbYNfBmnq+dv/nr+CP/Z8SM9q9ajX40mBPne7I2uRESkNFOwvI6CpRRHabHhJBzZQsLhX4k/vJmkk39kCZoOZe7Ao8k9eDS7D9egjhhmByxWS6ZHXb667Xu+OvTPvesal63MwJp3cl+NxjpcLiIiN6RgeR0FSykJ0mKvEL9/HXF71xC3dw1pURcztZvc/Wwh061BD0xO6U8N2nv5HCtO7uHHE3s5HfvPQz8dTWa6Va7DI3Va0zGgdqF+FhERKT4ULK+jYCkljdVqJfnsXuL+/omYP74j6cTvmdoNFw88m/XHs/WDuNXrZhvJ3Bl6kv87upuVJ/cSm5IEwBP12/NGy3vs8TFERKQYULC8joKllHQpV84Q++cPxP7xHQmHN4ElzdZm9qqAZ6vBeLZ+EJcaLTEMg4TUZFafOsC3R3fxXtsBVPX0AyAhNYWP9/zCQ4EtqeThY6dPIyIiRYmC5XUULKU0SYu5TMzvS4jZvoiEkF8ztTlVqod3pxF4tX0425vEf3N4J+N/W4bZMDG4djPGNO5GgAKmiEipZrpxFxEpqcyeZfHp+hRV/r2R6lOPU3bgZJwqNwAg+fwBwha+xLExlTk/8wHi9q3FarH8M69hUN7VkzSrhYUhv9Nh6ftM3P4Dl+Kzv++miBScZcuWERQUxNmzZ23TunbtyoQJE27Y55lnnrmldQcHBxMUFHRLyygMZ8+eJSgoiGXLltm1juz+HhYuXGj3um4XBUsRAcCx7B343TOeapP+pup/fse769OYXL0gLYXYnf/Huam9ODk+iIjV00mLi+SBwBZsGzSet1v3o4KrJ8mWNL44+Bvtlk5h8q5VWe6XKSIFp3PnzixevJjy5cvfUp/8GDRoEIsXLy7QZZZk2f09LFq0iOXLl9uxqttHj3QUkSxcqt2JS7U7KTdkCrG7lhC16XMSQraQEnacsEVjubxsIl7thuHT/VmG123D4NrN+Orgdj7Zu5GIpHhm7t1EGRd3nmzQ8YbrEpGb5+fnh5+f3y33uRnJyck4OTnh7++Pv79/gS23pCvov4eiTiOWIpIjk7MbXu0eocq/N3HH5H14d30aw9kda3I8Ub98yqlXG3J2Sg8sBzbwZIMO/DbwZcY2vYs7y1Xlsbpt7V2+yG137Ngxnn/+eVq1akWDBg249957WbFiRZZ+f/75Jw888AANGzakffv2TJkyhcWLF2c5RBoUFERwcHCW+fNymPt6ufVZuXIld999Nw0bNqRXr1788MMP2c67detWxo4dS8uWLenVqxeQ9VB4boecr/88GfMeOnSIp59+mqZNm9K6dWs++OADLBYL+/bt4+GHH6ZJkyb06NGD7777LsfPd63Q0FCef/55mjZtSrNmzXj++ecJCwvLtu+ff/7JyJEjad68OY0aNWLw4MFs2bIlU5+MOo8dO8aYMWO48847adu2La+88goxMZlP91m1ahUDBw7kzjvvpGnTpvTo0YPJkydn2ZYZfw9du3bl0KFD7Ny5k6CgIIKCghg2bBi7d+8mKCiIlStXZql53rx51KlTh9OnT+dpe9iTRixFJE+cK9WlwiMfU3bg20T/OpfI9Z+QcukY8QfWE39gPU5VGuF391hGtxrC6MZdMz0a8t0/VpOUlspLTe/Cw9HZjp9CihJragqpEefsXQYOvgEYDo43Pd+hQ4d48MEHqVGjBq+//jo+Pj6sXr2asWPHkpiYyKBBgwAICQnh0UcfpWrVqrz33ns4OzvzzTff8NNPPxX0R8mTffv2cfjwYZ577jm8vb1ZvHgx48aNw8HBgd69e2fq+8orr9CjRw+mT59OYmJigdUwZswYBgwYwLBhw9iwYQOzZ88mOTmZjRs3MmLECJ566inmz5/PhAkTCAoKom7dujkuKyEhgeHDh3PlyhXGjh1L1apV2bhxIy+++GKWvlu2bOGpp56iZcuWTJ48GWdnZ7799ltGjRrF7Nmzad++fab+zz77LL1792bw4MEcPnyYadOmAfDOO+8A8Mcff/DCCy8wdOhQxowZg9ls5uzZs+zduzfHej/++GNeeOEF3NzceOONNwDw8PCgVq1aNGjQgAULFtCnT59M8yxcuJD27dtTtWrVvG1gO1KwFJGbYnbzxrfnaHzueo64PauIWDOdhIO/kHxmDxdnP8Llpa/j2+tFvDs+jsnZjSORl5i1dzOpVgs/nNjDtPYDbc8ml9LLmprCyX/XJ+XSMXuXgmP5mlSbvP+mw+WUKVPw9fXl66+/xs3NDYD27dsTERHBhx9+yP3334/JZGLmzJmYTCa++uor2yHRzp07ZwlxheXSpUusXLmSmjVrAtCpUyfuvfdeZsyYkaWmjh078tprrxV4DQ899BDDhg0DoE2bNvzyyy/MnTuXb775xnZ3lgYNGtC2bVt+/PHHXIPl8uXLOXHiBLNnz6ZTp04AdOjQgYSEBJYuXZqp71tvvUW9evWYM2cOJpPJ9hnvv/9+pk+fniVYDhkyhEcffRSAtm3bcvr0aZYuXcrkyZMxDIO//voLT09PJk6cmGm+jF8qslOvXj1cXFzw8PCgSZMmmdqGDRvG+PHjCQkJITAwfT+5bds2jh8/zssvv5zjMouSInkofMeOHbbh4et/jh3LvBPaunUrgwcPplGjRrRp04aJEycSHR1tp8pFSg/DZMKjSR+qjF9H1Ynb8Wh+PxgGqVdOEfbNaE6Mq0n4qmlUcXJh7J134WJ2JDQ+mod+/oL/7vyRpLRUe38EkXxLSkpix44d9OjRAycnJ1JTU20/nTp14vLlyxw/fhyAnTt30rZt20zn2ZnN5iyjUoWlTp06tlAJYDKZuPvuuzl58mSWw8d33XXXbamhc+fOtteGYVCjRg3c3d1toRLAx8cHPz8/zp8/n+uydu7ciZeXly1UZujbt2+m96dOneLkyZPcc889WCwW299XWloaHTp0YP/+/cTFxWWap2vXrpneBwUFkZSUxOXLlwFo3Lgx0dHRjB49mvXr1xMeHs6t6N27N2XKlGHBggW2aQsWLCAgICDL5yuqivSI5dixY2nRokWmaZUrV7a93rFjB6NGjaJbt26MGTOGS5cuMXXqVEJCQliwYIHttxERub1carSg0rPfknzxCBGrpxG95SvSoi9xefE4zKve56HeL3NPn1GM3raCP8JO89n+LWy9cIyPOz1AoE8Fe5cvdmA4OFJt8v5ieyg8MjKS1NRUvvzyS7788sts+0RERNj6li2b9V6w5cqVu/liC0B2tZQpUwZIr/Xaum5Xjd7e3pneOzo64uPjk6Wfk5MTSUlJuS4rr9s3Iwy+/fbbvP3229kuKyoqCnd3d9v762tycnICsNXUvHlzPvnkE+bNm8fo0aNJTU2lQYMGPPfcc/kKgk5OTgwePJh58+YxduxY4uLi2LBhA2PGjCk2maZIB8vq1atnGSa+1vvvv0/t2rX58MMPbRu8XLlyPP7446xevdpuhxlESisn/9pUePR/lOn3OuE/TSHql9mkRV8ibNFYzD+9z+e9x/J1gw5M37+FA+EXuPuHYKa1H0S/Go3tXbrYgeHgiGO5avYuI1+8vLwwmUwMGDCABx54INs+1atXB9LDSUaouVZ2F5c4OTmRnJycZXpGSC0I2dVy5coVIGuQuvZc6Zw4O6efN3193QVZc258fHzYv39/lunXb19fX18AnnnmmSwjkRmyC6g30r17d7p3705ycjK7d+/mk08+4ZlnnmHFihXUqFHjppf34IMP8tlnn/H9999z+fJlzGYzAwcOvOnl2EuRDpa5CQ0NZe/evUyYMCFTim/Xrh0VKlRgzZo1CpYiduLgW4nyD32Ib+9xRKycQtTGz0iLDiV80Tj6lalKl+6jeSYmgQsJsdT0vvkduYi9ubq60qJFCw4ePEjdunVxcMj5v9NWrVqxceNGwsPDbYfD09LSsr36NyAggMOHD2eatm3bNuLjC+6+sIcPH+bYsWO2w+EWi4VVq1ZRrVq1fI1Qli1bFmdn5yx1r1+/vkDqvZFWrVqxatUqNm3alGmU8Por3atXr06VKlU4fPgwo0ePLvA6nJycaN26NSaTiWHDhnHs2LEcg6WTk1OOF0NVqFCBHj16sGDBAqKioujdu7ctFBcHRTpYTpw4keeffx5XV1eaN2/Oc889R4MG6U8FCQkJAaB27dpZ5gsMDOTIkSOFWquIZOXoG0D5h2fg2+dlwn98l6iNn5F65TRui19ifpVGnO/6HA3KBNi7TJF8+fe//227CGXIkCFUqlSJ6Ohojh8/zt69e2232Xn66afZsGEDw4cP5+mnn8bFxYX58+dne4i3b9++fPTRR8yYMYOWLVty9OhR5s+fj6enZ4HVXa5cOZ588knbVeGLFi3i6NGjTJ8+PV/LMwyDe++9l6VLl1K1alXq1KnDnj17+PHHHwus5tzcd999zJ07l3HjxjF69GjuuOMONm7cyLZt27LU+Z///IennnqKJ598kn79+lGuXDkiIiI4fPgwYWFhvPnmmze17hkzZhAaGkqbNm2oUKECkZGRfP7553h5eeV6xLV27dqsWLGCVatWUblyZdzd3TOF0IcffpihQ4cC2P4sLopksPT09GT48OG0bNkSHx8fjh07xuzZs3nwwQeZP38+jRs3JjIyEsh6nkbGtAMHDmS77GtPDM5OTExMgf4DFpH0gFlhWDC+PcdwZclrxOz8ltQzeyj/1ROc/eNbyg2ZgrViXf61aSHPNOxEs/J32LtkkRuqU6cOy5Yt45NPPmHq1KlERkbi7e1NjRo1uPvuu239AgMD+fLLL3nvvfcYP3483t7e9O3bl169evH6669nWubIkSOJi4tj+fLlfPHFFzRq1IgZM2bc8mMYr9WgQQP69OnDxx9/zJkzZwgICGDKlCm3dJTvlVdewTAM5syZQ3x8PK1atWLWrFk5HnIuSK6ursybN4+3336bqVOnYhgG7du3Z9q0aVlOU2jfvj2LFi1i1qxZvPnmm8TGxuLr60udOnXo37//Ta+7cePGzJ8/nylTphAREYG3tzeNGzfmjTfeyHX099lnn+XChQv8+9//Jj4+npYtW/L111/b2ps1a4a/vz/lypWjUaNGN12XPRlWq9Vq7yLyIiwsjHvuuYe6desyd+5cVqxYwdixY1m6dKltFDPDSy+9xPbt29m6dWuW5eQ1WO7atatA6xeRfyQc38nlxRNIOLwpfYLJzIF6PRnvXZNUZw/eb3c/A2o2tW+RIrfZsmXLeOWVV1i/fn2mC1NF9u/fz4ABA3j33XfzFXjtqUiOWGanXLlytG/fng0bNgD/nGCcMXJ5raioqGxHMoEbBsYbBU8RuXWuNVpSecJ64v7+ibBF40i5eJh6+37iGycPZlbvwOhNKYREhvLynT0wGcXjSkgRkVt19uxZzp8/z5QpUwgICLDbLaluRbHaY1ssFtvrjHMrszuXMiQkJNtzL0Wk6DAMA48mfag26S/KDn4Pw9kdr+RYJhxexYy/FrB662LG/7actGv+3YuIlGQff/wxjz76KElJSUybNs12e6PipNgdCq9Xr57tnmH3338/VquVJUuW2K4M37ZtG48++ijTpk3LV9LPGLHUoXCRwpUScY7Li8YRs2MxAGkYLK7SkqguT/N+56E4mMx2rlBERG6kSAbLl156iSpVqlC/fn28vLw4fvw4n332GVeuXGH+/Pk0bNgQSA+RI0aMoEePHgwZMoTQ0FCmTp1KpUqVWLhwIWbzzf9HpGApYl/xB3/h0tfPk3w+/QK8k25lONRrPM/3fcnOlYmIyI0UyWA5e/ZsVq5cyblz50hISMDHx4eWLVvy9NNP256dmWHz5s0EBwdz6NAh3N3d6d69O+PGjcvxHMsbUbAUsT9rajJXfpjE5RXvYLJawDDhe/dLlLnvP5icXOxdnoiI5KBIBkt7UrAUKToST+7m3GePkXZuHwCO/kFUHPUVLjVa3GBOERGxh2J18Y6IlC4u1e6kxn9/x6/fRDA7kHLxMCcmtSds9TT0O7GISNGjYCkiRZrh4ETZ/m9Q4dUtnPcoj9mSSsSicZz/aABpcYXzLGIREckbBUsRKRa8a7TgwKOfs7pCfQDi/vyBUxObkXBsh50rExGRDAqWIlJsPN3sbi71f4v3gu4m0eRA6pVTnJnckYjV03VoXESkCFCwFJFiwzAM/tvyXhxbP8gzdw7jpFsZSEslbNFYQr94Amtqsr1LFBEp1RQsRaRYMZtMzOg4hEo1W/LMnQ+zrnw9AKJ//ZKzH/TWeZciInakYCkixY6z2YE53R6hRrk7mFynNz/W6QlAwsFfOP1WO5IvHbNzhSIipZOCpYgUS15OLnx912MMDWrFiBeW4v/k1xgOTqRcPMyZt9qScGSrvUsUESl1dIP06+gG6SLFV0LIFs59NABL7BUMB2f8n/wazxb327ssEZFSQyOWIlJiXPSvwx9DP8HRPxBrahIX/vcgMTsW27ssEZFSQ8FSREqEg+EXuWfFx7x0+A+ODJ+D8x1NwZLGhVkPE71tgb3LExEpFRQsRaREqOVTjjq+FQF4/o+fSX5yAc7Vm4PVwsXZw4ne+rWdKxQRKfkULEWkRHA0mZnVZSgV3LyITUniie0r8Bn9PS41WqWHyzmPEfXrXHuXKSJSoilYikiJUc7Vk9ldHsbRZOZoVBj//vsXAsatxqVWW7BaCf1iJFGb5ti7TBGREkvBUkRKlGblq/LfVvcC8P3xv/nh/HEqv/QTroHt08Pl3KeI2bXMzlWKiJRMCpYiUuIMC2pF9yp1AHh1+3dcSEsj4MWVuNRqA1YrFz8dRsKR3+xcpYhIyaNgKSIljmEYTG03kLIuHtxXowm+zm6YXDwIGP0djhVqY01J5NyM+0i+GGLvUkVEShTdIP06ukG6SMkRkRiHr4t7pmnJl45x5q12pMWE4ViuBlVe34qDV3k7VSgiUrJoxFJESqzrQ2WaxYJT+ZpUGvM9hpMrKWHHOf9hPyxJcXaqUESkZFGwFJESz2q18sWBrfRdOZPE1BRca7ai4lPfgGEi8fhOLsx6CKslzd5liogUewqWIlLiHYm6xH93ruTvy2d594/VAHjc2Y/yD80AIO7PFVxe8qo9SxQRKREULEWkxAv0qcALTboBMOfAVnaFngLAp/sz+PZ8AYCIn94n9s8f7FajiEhJoGApIqXCs40607BMAADjf1tGytVD32UHv4trYAcALn72GClhJ+xWo4hIcadgKSKlgoPJzHtt+2MyDA5HhvLpvs0AGGYHKj69ALNXeSzxkZz/ZAiWlCQ7VysiUjwpWIpIqdGobGUer9sWgOl/redk9BUAHHwr2S7mSTr5B2ELX7RnmSIixZaCpYiUKuPu7EEld2+S0lL597bvyLiVr1u9rpTp/x8AojbMInrbAjtWKSJSPClYikip4u7ozKTW/bjDswxPN+yIYRi2Nr97XsGtYU8AQuc+RdL5g/YqU0SkWNKTd66jJ++IlA7Jaak4mR2yTE+LucypN5qRGn4Wp8oNqPrGTkyOznaoUESk+NGIpYiUSteGymt/vzZ7lqXiM4vBZCb57D7Cv3/LHuWJiBRLCpYiUqodjwrjgTVz2H7xuG2aa63W+PUZD0D4T1NIPKEjGCIieaFgKSKl2rity9h64Rhv7FhBmsVim+7X9zWcAuqDJY2Lcx7XLYhERPJAwVJESrXXWvQGYH/4Bf7v6B+26SZHZ/xHfpF+SPzcfsJ/mGSvEkVEig0FSxEp1ZqWq8KAmk0BeG/3GmKvGZl0qd4cv94vAxC+8j0dEhcRuQEFSxEp9SY064WL2ZGwhFg+2bMxU5tfv9f/OST++QgdEhcRyYWCpYiUepXcvXmqYUcAZu//lbOxEba2TIfEz+7TIXERkVwoWIqIAM806EQFNy+S0lJ5Z9fqTG3ph8THAVcPiZ/cbY8SRUSKPAVLERHAzdGJV5r1xGQYeDm5ZLpCHMCv30ScKtUDSxqX5j+Pni0hIpKVnrxzHT15R6T0slgtHI+6TC2f8tm2xx/cyNn3ugHg/8RcvNoNK8zyRESKvGIxYhkcHExQUBD9+vXL0rZ161YGDx5Mo0aNaNOmDRMnTiQ6OtoOVYpIcWcyTDmGSgC3up3xaDkIgLBvJ5CWoH2NiMi1inywPHLkCJ999hlly5bN0rZjxw5GjRqFv78/s2bNYvz48WzYsIFRo0Zhue4wlojIzUi1pLH02J8kpqZkml5uyPsYTm6kRV0k/HtdyCMicq0iHSwtFguvvvoqgwYNokaNGlna33//fWrXrs2HH35I27Ztue+++3jvvff4888/Wb16dTZLFBG5sRRLGr1+CGb05sXMP7wjU5tjmSr43fMKABFrZ5B8/pA9ShQRKZKKdLCcO3cuFy9e5IUXXsjSFhoayt69e+nXrx8m0z8fo127dlSoUIE1a9YUZqkiUoI4msy0qlANgOA9vxB33b0rfXu9iGO5GpCWyqUFY3Qhj4jIVQ72LiAnZ86c4aOPPmLq1Kl4eHhkaQ8JCQGgdu3aWdoCAwM5cuRItsvNuDgnJzExMXh6euajYhEpSZ5r3JVFR3ZxJTGOLw78xnONu9jaTE4ulBv6Aedn9Cd+31ri/vwBjzuzngMuIlLaFMkRS6vVymuvvUb79u3p3r17tn0iIyMB8Pb2ztLm7e1taxcRyQ9/Ny8erdsWgFn7NhGVlJCp3b3Jvbg17AlA2MKXsCQnZFmGiEhpUyRHLL/99lv27dvHTz/9dMO+hmHc1PQb3UboRiOaIlJ6/KthJ745vIOo5EQ+3beZl5v1tLUZhkH5odM5+VpjUsJOELHqA8r0e82O1YqI2F+RG7EMDw/n/fff58knn8TV1ZXo6Giio6NJTU3FYrEQHR1NUlISPj4+ANmOTEZFRWU7kikicjP8XNx5on57AOYc2MrlhNhM7U4Vg/DtMRqA8J+mkBoVWug1iogUJUUuWIaGhhITE8MHH3xAixYtbD+7d+8mJCSEFi1aEBwcbDu3MrtzKUNCQrI991JE5GaNqt8BH2c34lOT+fzA1iztfvf+G5O7H9akOMJ/fMcOFYqIFB1F7lB41apVmTdvXpbpkydPJj4+nkmTJlGpUiX8/f1p0KABK1asYPjw4bYrw7dt20ZoaCg9evQo7NJFpATydHJh/J09iE9N5pE6bbK0m9288btnApcXv0zUL5/i2/MFHMveYYdKRUTsr9g80nHYsGFER0fz/fff26Zt27aNESNG0KNHD4YMGUJoaChTp06lUqVKLFy4ELPZfNPr0SMdReRmWZITODk+iNSIc3i1H47/yC/sXZKIiF0UuUPhN6NNmzbMmjWLc+fOMWrUKN599106d+7MZ599lq9QKSKSHyYnV/yuXrgTvfVrks4dsHNFIiL2UWxGLAuLRixFJCdhCTF8tn8LyWmp/KfVvZnarKkpnHy1ASmhR/Fo1p9Kzy2xU5UiIvaT64hlamoq//vf/3jllVfYv38/oaGhPPbYY3Tt2pU33niDpKSk3GYXESlRVp/az8y9m5h7cBvnYiMztRkOjpQd8CYAsX8sJ/H473aoUETEvnINlu+//z7btm3j/PnzjBw5kgULFjB48GBefPFFdu3aRXBwcGHVKSJid4NqNaOCmxepVgsz927K0u7RYhDOVZsAcHnJq4VcnYiI/eUaLFevXs2MGTOYPn06ERER9OvXj7vvvpt77rmHadOm6XncIlKquDg48lSDDgAsOvI7ofHRmdoNk4myAycBEH9gPfEH1hd6jSIi9pRrsIyNjcXX1xc/Pz/c3NyoUaOGrS0oKIgrV67c9gJFRIqShwJb4efsTlJaKrP3/Zql3a1hL1wD08Pn5SWvodPYRaQ0yTVYenp6kpiYCMBTTz2VqS0mJgZHR8fbV5mISBHk5ujEqAbpT+P5+vAOwhPjMrUbhkHZQW8DkHh8J3F/rSj0GkVE7CXXYNmlSxfOnTsHwKhRozK1rV27lvr169++ykREiqjhddrg7eSS49N4XGu3w61h+nPFw1e8o1FLESk1cg2Wb7zxBjVr1sy2rVevXnzyySe3pSgRkaLM08mFx+q1A+CvsDPZBscy9/4bSB+11LmWIlJa3NQN0i0WC7//nn4LDTc3N1xdXW9LUSIiRd2Ium1Z2HME83s8jmEYWdpdA9vjGtQJSB+1FBEpDW4qWCYmJvLII4/crlpERIoNXxd3OlSqnW2ozOB37ysAJBzaSMKRrIfMRURKmpt+pKPOFRIRySoyKT7LNLf63XGu3gKAKysmF3ZJIiKF7qaDZW6/nYuIlDbHoy4zfO1cun33IUlpqZnaDMOgzNVRy/g9q0k8udseJYqIFJqbDpYiIvIPZ7MDG8+FEBofzffH/8rS7t7kXpwqNwAg/EedaykiJZuCpYjILQjw8KFv9UYAfLrv1yynCxkmE373TAAgdtcyks4dKPQaRUQKi4KliMgteqpBRwAOR4ay4ezhLO2eLQfjWKEWAOE/vluotYmIFCZdvCMicovql6lEh0rpwXHWvs1Z2g2TGb8+4wGI2bGI5EvHC7U+EZHCclPB0sHBgXvvvfd21SIiUmxljFpuu3icvy+fzdLu1fZhHPyqgCWNiJ+mFHZ5IiKF4qaCpZOTE++///7tqkVEpNjqWKk2dX39Afg0u1FLByd87x4LQPSWeaRGhxVqfSIihUHnWIqIFADDMHiyQUccTWbcHJywWC1Z+nh3fAyTuy/W1CSifpllhypFRG4vBUsRkQLSr0Zjfhv4MlPbD8RkZN29mpzd8e48CoDI9TOxJCcWdokiIreVQ35nXLFiBd988w2nTp0iMjIyS7thGBw4oNtqiEjp4WgyU9HdO9c+Pt3/RcTqD0iLvkTMtm/w7jSikKoTEbn98hUsZ86cSXBwMGXKlKFp06Z4e+e+IxURKW1SLWmcjL5CLZ/ymaY7+gbg2XIIMdu+IWLNh3h1fFxPNBOREiNfwXLBggW0bNmSOXPm4OjoWNA1iYgUa7+HnmT05m9JSEtm28DxuDhk3k/69nqBmG3fkHz+APH7fsa9YU87VSoiUrDydY5lXFwcd999t0KliEg2Knv4ciE+irCEWJZn85hHlzua4lq3CwARa6YXcnUiIrdPvoJl3bp1uXDhQkHXIiJSIlR09+beq495/PzA1mwfLOHbcwwA8fvWknRmb2GWJyJy2+QrWI4ZM4ZFixaxf//+gq5HRKREGFmvHQCHIi7y24VjWdrdG/XG0T8IgIifPyzM0kREbhvDms9nNK5bt44xY8bQpEkTAgICMJkyZ1TDMJg8eXKBFFmYmjdvDsCuXbvsXImIFHcDfprFztCT3FWlLl92H56lPfKXT7n01TMYDk5Un3oCBx9/O1QpIlJw8hUs//77b0aOHElMTEzOCzYMDh48eEvF2YOCpYgUlJUn9/LkL99gYLBpwEvU8C6bqd2SFM/xl6phib2CX9/XKDvgv3aqVESkYOTrUPjbb7+Ng4MDM2fOZOfOnRw6dCjLT3EMlSIiBaln1XpU9vDBipUvD/6Wpd3k7IZPl6cAiNzwPyxJ8YVdoohIgcpXsDx8+DCPP/44Xbt2xcvLq6BrEhEpERxMZh6r25ZK7t7U8CqbbR+fbumHwi2xV4jZsaiQKxQRKVj5CpZlypTRrYZERPJgeJ02/DbwZR6r1zbbdgcffzxaDAIgct0n2V5BLiJSXOQrWA4YMIAffviB1NTUgq5HRKREcXFwxMFkzrWPT7dnAEg6/ReJR7cVRlkiIrdFvp6806xZMzZu3MjgwYMZOnQolStXxmzOuuNs0aLFLRcoIlJSXE6IJTwpjkCfCpmmu9RshXO1ZiSd/IPI9Z/gWjv70U0RkaIuX1eF16lTJ/NCrnvOrdVq1VXhIiLX+PLAb0za9RNNylZmae+nsrRH/TqX0M9HgNmRGh+c1K2HRKRYyteI5TvvvFPQdYiIlGi1fcqTlJbKjtCT7Ll8lkZlK2dq92w1hLDFL2OJvULUpjmU6feanSoVEcm/fN8gvaTSiKWI3A5Wq5Xu333I4chQ7q/ZlBkdh2TpE/btK0T8NAWzTyVqTD2O4aCLJEWkeMnXxTsiInJzDMNgRP30xzz+cGIPYQlZHzDh0+VJMEykRZ4ndvd3hVyhiMitU7AUESkk/Ws0wcfZjRRLGt8c3pml3bFcNdyb9AEgcv3Mwi5PROSWFclguXv3bkaMGEGHDh1o2LAhrVu35pFHHmHTpk1Z+m7dupXBgwfTqFEj2rRpw8SJE4mOjrZD1SIiuXN1cGJoYPrdMr4+tJ3ktKy3bPPp9i8AEg5vJunMnkKtT0TkVhXJYBkdHU316tWZMGECc+bM4a233sLJyYlRo0axcuVKW78dO3YwatQo/P39mTVrFuPHj2fDhg2MGjUKi8Vix08gIpK9R+q0xmQYhCbEsOrU/iztbvW64egfBGjUUkSKn2Jz8U5qairdunXjjjvuYN68eQAMHDiQ1NRUli1bhsmUnpG3bt3K448/zvTp0+ndu/dNr0cX74jI7TZqw3ziUpIY3aQbLStUy9IesfZjwr4ZjeHkRo3pZzC7+xR6jSIi+VEkRyyz4+DggKenp+1RkqGhoezdu5d+/frZQiVAu3btqFChAmvWrLFXqSIiufqk84N803NEtqESwKv9IxguHliT44neMrdQaxMRuRVFOlhaLBZSU1MJDQ3lo48+4uTJkwwfPhyAkJAQAGrXrp1lvsDAQI4cOVKotYqI5JXjDR7xaHb1wqvtwwBE/jJbzw8XkWIjTzdI/+677/K18Pvuuy9f82UYM2aMbeTRw8ODDz/8kI4dOwIQGRkJgLe3d5b5vL29OXDgQLbLzDjUnZOYmBg8PT1voWoRkbzbHXaaap5l8HNxzzTdp/MoojbMIuXiYRIOb8atTic7VSgiknd5CpYTJkzAMIyb+q3ZMIxbDpbjxo1j5MiRXL58mR9//JExY8bw7rvvcs8992RaT07rFxEpyh7++Qs2ngthXNO7GN2kW6Y256qNcanRisTjO4jaOFvBUkSKhTwFy4yLZQpblSpVqFKlCgBdu3blqaee4s0336R37974+PgA/4xcXisqKirbkUy48UU5NxrRFBEpKHV9K7LxXAjzDu/gmUadsxwi9+7yBInHdxC7axlpMZcxe5a1U6UiInmTp2DZsmXL211HnjRs2JBffvmF8PBw27mVR44coX379pn6hYSE0LRpU3uUKCKSZ8PrtubT/ZsJjY9m1cl99K3ROFO7Z8shhC14CUtCFFFb5uF394t2qlREJG+K9MU717JarezcuRMvLy98fHzw9/enQYMGrFixItM9K7dt20ZoaCg9evSwY7UiIjdW2cOXHlXqAfDFwd+ytJuc3WwX8URt+kwX8YhIkZenEcvsxMfHM2fOHNauXcvZs2cBqFy5Mj169GDEiBG4ubnlu6iXXnqJgIAA6tevj6+vL2FhYSxfvpzt27fz+uuv4+CQXvbYsWMZMWIEL774IkOGDCE0NJSpU6fSuHFjevXqle/1i4gUlsfqtWX16f3sunSKPZfP0qhs5Uzt3p2fIHL9J6RcDCHh0Ebc6naxU6UiIjeWrxukR0ZG8tBDD3Hs2DF8fX2pXr06ACdPniQ8PJyaNWvyzTff2M6DvFnz589nxYoVnDx50naVdoMGDXjooYfo2rVrpr6bN28mODiYQ4cO4e7uTvfu3Rk3blyO51jeiG6QLiKFyWq10v27DzkcGcqgWncyvcPgLH1OT2pP4tFteLYaQsWnF9ihShGRvMlXsHzzzTdZuHAhr732Gg888ABmc/oJ52lpaSxevJhJkyYxdOhQXnvttQIv+HZTsBSRwjb/8A4m/LYcJ5OZnYNfoayrR6b2qC1fETrncTA7UmP6GRy8ytmpUhGR3OXrHMsNGzYwaNAgHnroIVuoBDCbzQwdOpT777+fdevWFViRIiIl2YAaTanq4cfwum2ybfdsMQiTqzekpRC95atCrk5EJO/yFSwvX75M3bp1c2yvV68ely9fzndRIiKliZujE7/eP5Y3Wt6TZbQSrl7E024YcPUinmsuWBQRKUryFSzLli3LwYMHc2w/ePAgZcvqfmsiInllNuW+O/bu/AQAKaFHSTi0sRAqEhG5efkKll26dGHJkiUsWrQo061+LBYLixcvZunSpVkushERkRtLSE1hzan9WaY7V26AS622AERunF3YZYmI5Em+Lt6JiIjggQce4PTp0/j5+dmuCj9x4gTh4eFUrVqVRYsW4evrW+AF3266eEdE7OVMTDh3r/iYyKR4Vt37HA3LBmRqj946j4ufPXb1Ip7TOHiVt1OlIiLZy9eIpa+vL0uXLmXUqFH4+Piwd+9e9u7di6+vL6NGjWLp0qXFMlSKiNhTZQ9fKrh6AvBlNjdM92gxCJObjy7iEZEiK18jliWZRixFxJ4ybj3kbHZg5+AJlHHJfDHPpW/GELk2GMfyNan27iGMG5ybKSJSmLRHEhEpQgbUaIq3kwtJaaksOPx7lnbvzqMASLl0jPiDGwq7PBGRXOU7WK5YsYIHHniANm3aULdu3Sw/9erVK8g6RURKBTdHJx6o3QKAeYe2k2JJy9TuHFAP18D2AERt/KzQ6xMRyU2+nhU+c+ZMgoODKVOmDE2bNs334xNFRCSr4XXbMHv/Fi7ER7Hm1H7uqd4oU7t3p5EkhGwhdvd3pEaF4uBdwU6Viohklq9guWDBAlq2bMmcOXNwdHQs6JpEREq1qp5+3FWlDj+fOcgXB3/LEiw9WgzEtOAFLHERRP86F797xtupUhGRzPJ1KDwuLo67775boVJE5DZ5vF476vr6M6hWM66/xtLk5IpXu0cAiNo0R0/iEZEiI1/Bsm7duly4cKGgaxERkavaVazJz/1G82BgCwzDyNJuexJP2HHiD6wv7PJERLKVr2A5ZswYFi1axP79WZ8OISIit84wjGwDZQbnSnVxDewAQJSexCMiRUS+zrFs2bIlb7/9NkOGDKFJkyYEBARguu5eaoZhMHny5AIpUkSkNDsZfYWzsRG0r1Qr03Tvzk+QEPIrsX/+QGrkRRx8/O1UoYhIunzdIP3vv/9m5MiRxMTE5Lxgw+DgwYO3VJw96AbpIlKULAz5nZe3LsPfzYttg17GwWS2tVmSEzn+QhUsceGUHfg2fvdMsGOlIiL5HLF8++23cXBwYObMmTRv3hwvL6+CrktERIC2FWsAcCE+itWnD3BPtYa2NpOTC17tHyFyzYdEbZqDb++X9SQeEbGrfO2BDh8+zOOPP07Xrl0VKkVEbqM7PMtwV5U6AHx5IOvzw306ZVzEc4L4/WsLtTYRkevlK1iWKVNGtxoSESkkj9VrC8CO0BMcCD+fqc2pUh1cgzoBehKPiNhfvoLlgAED+OGHH0hNTS3oekRE5DrtK9aitnd5AL7IZtTSu/NIAGL/WkFqpG4FJyL2k69g2axZM0wmE4MHD2bJkiVs376d33//PcuPiIjcOsMwbKOWy4//RXhiXKZ2j2YDMHmUgbRUon6da4cKRUTS5euq8Dp16mReyHX3WrNarboqXESkAMWlJNHi23eITk5kQrNePNuoc6b2sIVjiVgzHYey1ag+5Ygu4hERu8jXVeHvvPNOQdchIiK5cHd05uGgVpyLi6TDdfezhPR7WkasmU7q5ZPE71+Le8OedqhSREq7fI1YlmQasRSRoirjaFBOzrzTlYTDm/Bo1p9Kzy0pxMpERNLdtmMlsbGxt2vRIiKlUm6hEsC7S/qth9KfxKOLeESk8OUrWE6aNCnX9tjYWEaOHJmvgkREJHcWq4V1Zw5yLCos03TbRTyWNF3EIyJ2ka9gOX/+fObMmZNtW0JCAk888USxvHBHRKQ4eGzdPB5d9xX/27sp03STozPe7YcDELVpDlaLxR7liUgplq9gOXr0aD744AN++OGHTNMTExMZNWoU+/bt46OPPiqQAkVEJLOulYOA9FsPRVx36yHvTulHizIu4hERKUz5CpZPP/00Q4YM4dVXX+W339Jv1puUlMRTTz3Fn3/+yYwZM+jUqVOBFioiIukG1roTT0dnktJSWRCS+Z7BThWDcK3TGYCojbPtUJ2IlGb5vnjnjTfeoGPHjjz33HPs3r2bZ555hl27dvHBBx/QtWvXgqxRRESu4e7ozAOBLQCYd2g7qZa0TO22J/H8qSfxiEjhynewNAyDadOmERgYyMMPP8yOHTuYMmUKPXvq3mkiIrfb8DptMDA4FxfJz6czn9Oe6SKezV/aqUIRKY3ydIP03B7P+PjjjzNhwgT69OlDuXLlMvVt0aLFrVcoIiJZVPMqQ7cqQaw7c4gvD/5G72oNbG0ZF/FErJ5G1KY5+N0zQU/iEZFCkacbpNepUyfX+6dlLCKjjx7pKCJy+206F8JDP38BwM/9RlPPr6KtLfliCCcn1AUg4MWVuDfqZZcaRaR0ydOIpR7hKCJS9HSsVJsOlWrR1r8mFd28MrU5+QfiWqczCYc2ErXpMwVLESkUeqTjdTRiKSIlRfT2RVyc9RCYzNT44CQOvpXsXZKIlHA66UZEpITyaNYfs2dZPYlHRAqNgqWISAkQkRjHrL2bSU5LtU0zOTrj1e7aJ/Gk5TS7iEiBKJLBctu2bUyYMIGePXvSuHFjOnbsyLPPPsvhw4ez9N26dSuDBw+mUaNGtGnThokTJxIdHW2HqkVE7CM2JYl2S99n0q6f+P7435naMu5pmXrlFPH79CQeEbm9imSwXLhwIefPn+fRRx/ls88+Y8KECZw/f56BAwfy119/2frt2LGDUaNG4e/vz6xZsxg/fjwbNmxg1KhRWPSMXBEpJTwcnely9TGPs/Zt5tpT5538A3Gt2wWASD2JR0RusyJ58c6VK1coU6ZMpmnR0dF069aN1q1bExwcDMDAgQNJTU1l2bJlmK7eo23r1q08/vjjTJ8+nd69e9/0unXxjogUR/uunKPXD+n7xnl3PWZ7njhAzI7FXPjfUF3EIyK3XZ5HLDt16sRbb73Ftm3bSEu7vefpXB8qAby8vLjjjju4ePEiAKGhoezdu5d+/frZQiVAu3btqFChAmvWrLmtNYqIFCUNygTQvmItAGbt3ZSpzf3O+665iEdP4hGR2yfPwbJr166sW7eOxx57jDZt2jBu3DjWrl1LQkLC7azPJjw8nCNHjlC7dm0AQkJCAGzvrxUYGMiRI0cKpS4RkaLiqYYdAfjt4nH2XD5rm25ydMar/aMARG36XBfxiMhtk6cbpAO88cYbvPHGG+zZs4e1a9eybt06VqxYgbOzM23btuWuu+6iS5cu+Pr6FniRVquV119/HYvFwogRIwCIjIwEwNvbO0t/b29vDhw4kO2yMg515yQmJgZPT89bK1hExA46VapNHV9/DkVc5NN9v/JJ5wdtbd6dRhKxaurVi3h+xr3R3XasVERKqpu+eKdRo0a89NJLrFq1ipUrV/LMM89w+fJlXn31Vdq3b8+wYcOYN28e586dK7Aip0yZwrp16/jvf/9LzZo1M7Xl9KjJ3B5BKSJSEhmGwVMNOgDw48m9nI2NsLU5+dfGtW5XACI3fmaX+kSk5MvziGV2atasSc2aNXnyyScJDQ21jWROmTKFd955hzp16vDCCy/QsWPHfK9j+vTpfPHFF7z66qsMGDDANt3Hxwf4Z+TyWlFRUdmOZMKNL8q50YimiEhR1rd6Y9aeOcTAmk2p5J55P+jTeSQJBzcQ99ePpEac10U8IlLgCux2QxUqVODhhx9m7ty5/Pbbb7zzzjsEBATc0rmOM2bMYNasWYwbN45HHnkkU1vGuZXZLT8kJCTbcy9FREo6J7MDn3Z5iLuq1sNkZN7Fpz+Jp1z6RTybP7dThSJSkt2W+1h6eXlx33338fHHH9vOibxZH3/8MTNnzmT06NGMHDkyS7u/vz8NGjRgxYoVme5ZuW3bNkJDQ+nRo0e+6xcRKYkMBye8OjwGQOQvs7Gmpti5IhEpaYrkDdK/+OILgoOD6dKlC23btuWvv/6y/Vx7Uc7YsWM5dOgQL774Itu2beO7775j3LhxNG7cmF69etnxE4iI2N/fl88ybutSEq4JkD5dngTDRFrkeWJ3f2e/4kSkRCqSN0gfNmwYO3fuzLYtICCADRs22N5v3ryZ4OBgDh06hLu7O927d2fcuHE5nmN5I7pBuoiUBFcSY2mx+B2SLWlMat2PR+u2sbWdm3EfcX+uwDWoE1Ve2ZDLUkREbk6RDJb2pGApIiXF+K3L+CZkJ5Xcvdly/ziczOnXa8bt+5lzU9NvN3THW3/hXKWhPcsUkRKkSB4KFxGRW/dMo06YDRPn46JYeuxP23S3et1x9A8EIHLD/+xVnoiUQAqWIiIl1B2eZehfowkAH+/ZSOrVJ+4YJhM+XZ8GIPq3+aTFR9mrRBEpYfIdLL/55hsGDBhA27Ztufvuu5k4cSLbt28vyNpEROQWPduoMwYGp2KusOLEXtt0r3aPYDi5YU2KI3rrPDtWKCIlSb6C5ccff8xbb71FSEgI5cuXx2KxsGzZMh577DFGjBjB5cuXC7pOERHJh1o+5elTrQEAwXs2YLGm357N7O6DV9uHAYhcPxPrNbdtExHJr3wFy8WLF1OjRg1++eUXvvvuO9asWcPWrVt59dVX2bNnD0OGDCE8PLygaxURkXx4vnEXAI5GhbHn8j+P2/Xp9gwAKRdDiD+w3i61iUjJkq9geeXKFQYPHky5cuVs07y9vXn44YdZvHgxERERBAcHF1iRIiKSf/X8KvFWq75sHjCWJuWq2KY7V2mIa1D6I3cj18+0V3kiUoLkK1h6eXmRlJSUbVuNGjUYMmQIa9euvaXCRESk4DxWry3VvMpkme7TLf0inri/fiTl8qnCLktESph8BcvOnTvz/fff5xguq1SpQmxs7C0VJiIit4fFaiHjFsYed/bH7FMRrBYif/nUzpWJSHGXr2D58ssvEx0dzRNPPMHZs2eztG/evJk6derccnEiIlJwrFYrS47+QZfl0/ntwjEADAdHfDqPAiBq0xwsyQn2LFFEijmH/Mzk5+fHF198wTPPPEPPnj1p0qQJDRo0wM3NjW3btnH+/HlmztT5OiIiRYlhGCwM+Z1jUWG8/+da2lasiWEYeHceRfiP72CJvUL0b1/bgqaIyM26pUc6JicnM2/ePNasWcO+fftsh1bKli1L3bp1CQoKIjAwkKCgIGrUqIGjo2OBFX676JGOIlKS/XbhGINXfwbAV90fpVuV9KNLF+c8TvSWr3CqVJc7Ju3BMOn5GSJy8wrsWeHR0dEcOHCAgwcPsn//fg4ePMjJkydJS0vDMAwcHBzYu3fvjRdkZwqWIlLSPbhmDr+eP0p9v4qs6vscJsNE0um/OTXxTgACXvwR90Z327lKESmO8nUoPDteXl60bt2a1q1b26YlJiZy6NAh9u/fz6FDhwpqVSIicgtevrMnv54/yv7wC/x0ch/3VG+Ec9XGuNXrRvyB9USsma5gKSL5UmDBMjsuLi40adKEJk2a3M7ViIjITWhargo9q9ZjzekDTP1zLXff0QCzyYRPzzHEH1hP/P71JJ3Zi3OVhvYuVUSKGZ1EIyJSCo1t2gMDg6NRYSw79icA7g174egfBEDEzx/asToRKa4ULEVESqG6fv70rdGIMi7uGIYBgGEy4dtzNAAx2xaQGhVqzxJFpBhSsBQRKaX+0/Iefhv4MgNr3Wmb5tV2GCZ3P6ypyURu+J8dqxOR4kjBUkSklCrn6om7o3OmaSZnN3y6PgVA1Ib/6YbpInJTFCxFRITwxDjb03h8uj0DZkfSYi4Ts+0bO1cmIsWJgqWISCm34exh2vzfe4z65Rsik+Jx8KmIV+sHAIhYM4MCut2xiJQCCpYiIqVcozIBGIZBZFI8H/61HgCfHmMASD5/gPi9q+1YnYgUJwqWIiKlXFlXD55r1AWAuQe3cTwqDJc7muBWrxsA4T++a8/yRKQYUbAUERFG1GtHVQ8/Uq0W3vr9JwD87pkAQELIFuIPb7ZneSJSTChYiogILg6O/LtF+mMc1545yJbzR3Gt2wWXmumP6Q1f8Y49yxORYkLBUkREAOhzRwNaVagGwH92/ojFasXv3lcAiN/3M4kndtmxOhEpDhQsRUQEAMMwmNjyHgAORVxk47kQ3Bv3walKI0DnWorIjSlYioiITeOylXmxSXfm93icblXqYBgGZa6eaxn7x3KSzh2wc4UiUpQpWIqISCYvNu1O54BA23uPFgNxrFAb0KiliOROwVJERHJktVqJS0vFr8/LAMTsWETypeN2rkpEiioFSxERydaJ6Ms8uOZznvzlGzzbPISDXxWwpBHx0/v2Lk1EiigFSxERydbhiFC2XDjKpnMhrDhzCN+7xwIQvWUuKRHn7FydiBRFCpYiIpKtXnfUp2fVegC8sWMFtH4Qs1d5rKnJRKyeZufqRKQoUrAUEZEcvdWqL+4OTlxOjOWdPZvw7fkCAFEbZmnUUkSyULAUEZEcVfLw4eVmPQFYELKTow37pI9apiQS/v0kO1cnIkWNgqWIiOTq0TptaFy2MgAT/liDd5/xAET9+gXJoUftWZqIFDEKliIikiuzycR7bftjNkyERF5iSfn6OJS5A9JSubL8P/YuT0SKEAVLERG5oQZlAniifnuG12nN0PodKHPfRCD9vpZJZ/bYuToRKSqKZLC8ePEikyZN4sEHH6Rp06YEBQWxY8eObPtu3bqVwYMH06hRI9q0acPEiROJjo4u5IpFREq+V5vfzdtt7sPLyQWvdsNwqlQXrFYuL3nN3qWJSBFRJIPlqVOnWLlyJW5ubrRu3TrHfjt27GDUqFH4+/sza9Ysxo8fz4YNGxg1ahQWi6UQKxYRKfkMw/jntcmMe7/XAYj7eyUJR7baqywRKUIc7F1Adlq0aMG2bdsAWLduHRs2bMi23/vvv0/t2rX58MMPMZnSM3K5cuV4/PHHWb16Nb179y60mkVESgur1crXh3fw3pEDLKvSCOPMHi4veY3KEzZkCp8iUvoUyRHLjJCYm9DQUPbu3Uu/fv0y9W/Xrh0VKlRgzZo1t7NEEZFSKzEthc/2byEqJYmPKqcfVUo4vJn4fT/buTIRsbciGSzzIiQkBIDatWtnaQsMDOTIkSOFXZKISKng6uDERx2HYDZMfO/oRWhAQwAuL3kNq05DEinViuSh8LyIjIwEwNvbO0ubt7c3Bw4cyHa+5s2b57rcmJgYPD09b7k+EZGSrGm5Koxp0pUP/lzHm+Ub8cm5vSSd2k3M9gV4tX3Y3uWJiJ0U2xHLDDmdz6PzfEREbq/nGnXhznJVOehVie3+9QEI+3YCloQYO1cmIvZSbEcsfXx8gH9GLq8VFRWV7UgmwK5du3Jd7o1GNEVEJJ2DycxHHYfQ4/sZfHhHe74OOwKRF7jywyTKDXnP3uWJiB0U2xHLjHMrszuXMiQkJNtzL0VEpGBV8yrDf1vdyyUXL+ZXaQlAxM8zSL5w2M6ViYg9FNtg6e/vT4MGDVixYkWme1Zu27aN0NBQevToYcfqRERKjwdqN+ehwJbc/egnOJarAWkpXPpmNFar1d6liUghK7KHwlevXg3A3r17Afj999+JiIjA1dWVTp06ATB27FhGjBjBiy++yJAhQwgNDWXq1Kk0btyYXr162a12EZHSxDAM3ms3AIDYoR9wfkZ/4vetJW7393g0u8++xYlIoTKsRfRXyqCgoGynBwQEZLph+ubNmwkODubQoUO4u7vTvXt3xo0bl+M5ljeScY7ljc7FFBGRrKxWK2c/6EPCvjU4lL2DapP3Y3JytXdZIlJIiuyI5eHDeTs/p2PHjnTs2PE2VyMiInlhsVqZUq09T+1fC5dPEbFqKmWuPvpRREq+YnuOpYiIFD1mk4k7arZgSeX0oz9hK94h5fIpO1clIoVFwVJERArUi026cbrNI1x28sCUmsTRL0bpQh6RUkLBUkRECpTJMPFBt+F826AvAOYD6zi5fpadqxKRwqBgKSIiBc7H2Y2nH36f38rXASBq0Vgu6d6WIiWegqWIiNwWQX7+1Bv1FVGOrrilJrLro0E6JC5SwilYiojIbdOyVnMSB0wCoNaF/URv/tzOFYnI7aRgKSIit1WHu8fg1moIAGELx+oqcZESTMFSRERuu4rDgjF7+2NJjOHcnMeYf3CbDouLlEAKliIictuZPcpQ4dH/AZB8aBPblr7B27tWKVyKlDAKliIiUig8mvbFo+3DAIw6vpnVO5bx2vYfsFgtdq5MRAqKgqWIiBSaCg/NwMGvCq6WFP67/zuW7NvIy1uXkWZRuBQpCRQsRUSk0Jjdfaj07P+BgxNVEiIYf+gnFof8zuhfvyXVkmbv8kTkFilYiohIoXKp0YIKw4IBaH/lKA+e3sF3x//i6Y0LSE5LtXN1InIrFCxFRKTQeXcaiVfHEQCMOLmF5uEnOBsbSZrOtxQp1hQsRUTELso//BHO1VtgYOXtIz/zWZPOuDo42bssEbkFCpYiImIXJicXKj37f5g9y+KYGE3a549hSU4AYN+Vc2w4q2eLixQ3CpYiImI3jmWqUPHphWCYSDr1JxdnDyc0NpLH1s3j0XVzmbN/i+51KVKMKFiKiIhdudXrSrkhUwCI3bWU+AVjKO/qgcVq5T87f2Ts1iXEpyTbuUoRyQsFSxERsTvfXi/g23scACm/fc2c2BP0rdYQgMVH/uDuFcHsu3LOniWKSB4oWIqISJFQdtA7eHd+AoCYn2fwVsxJXm1+N44mM8eiwrj3x5nM2rtZT+oRKcIULEVEpEgwDIPyj3yCZ8vBAFxZNpEHQ/fzfZ+nqeFVlhRLGpN2/cSqU/vtXKmI5MTB3gWIiIhkMExm/Ed9RVpiNPF7VnNp/nNUc/Nidd/n+c/OH7kYH0XvOxrYu0wRyYFh1eV2mTRv3hyAXbt22bkSEZHSy5IUz7kPepMQ8isYBuUf+gif7s+QnJaKk/mfMZGP/t5Az6r1CfKtYMdqRSSDDoWLiEiRY3J2o9KY73Gp2RqsVi7Nf46wbyfgaPzz39amcyFM2f0zPb+fweRdq3TluEgRoGApIiJFktnNm8ovr8W9aV8AIn56n4uzh2FJSQLAyexANc8ypFotzNy7ic7LP2DFiT26uEfEjnQo/Do6FC4iUrRYLWlcmj+aqA3/A8C1TmcqPbcUs7sPiakpzNy7iU/2biQpLRWABn6VGHdnD7pWDsIwDDtWLlL6KFheR8FSRKTosVqtRPz0Ppf/7xUAnALqU2n0cpzK1wTgRPRl3tq5kp/PHLTN83qL3jzZoKNd6hUprXQoXEREijzDMPDr8zL+o+aB2ZHkc/s5NfFOorfOw2q1Ut2rLF90H84P9zxDh0q1cDE70r9GU9v8KZY0PRpSpBBoxPI6GrEUESna4g9t4sKsh0iLvACAZ8vBlB/+P8zuPrY+Z2LCqeLpZ3v/6b7NfHvkD0bUb0f/Gk1xdXAs7LJFSgUFy+soWIqIFH1pMZe5+MUTxP35AwAOZariP2oebkEdsva1WGi3dApnYyMB8HV2Y1hQKx6u05pK7t6FWbZIiadgeR0FSxGR4sFqtRK16TPCFryINTkBDBO+d79EmXtfxeTqmanvvivn+PzAVr47/jcpljQADAzaV6rJwFrN6H1HfVwdnOzxMURKFAXL6yhYiogUL8nnD3Hh04dJOvUnAGavCpS9/028OjyGYTJn6nspPoZ5h7cz/9AOLifG2qZ/fddjdKkcVKh1i5RECpbXUbAUESl+rKnJhP/4LuE/vY81OR4ApyqNKP/gVNzqdcvSP8WSxsazh/n26B/su3KeLfePw2xKv5711/NH+O74X/SsWp8OlWrrfEyRm6BgeR0FSxGR4isl4hxXlrxK9NavbdPcG/fB795XcK3VJtt5rn9M5NgtS1h0JP3/AFcHRzpVCqRz5UA6VKrFHZ5lbu8HECnmFCyvo2ApIlL8JZ7YRdjCl0gI2WKb5lKjFT49nsez+f0YuYxCfntkF0uO7mZH6EnSrnuKTxUPXx6r25ZRDbJeJCQiCpZZKFiKiJQMVquV2F3LCP/xXZJO7bZNd/ANwKfbv/Dq+BgOXuVznD8iMY71Zw+x9swhtl44RmRS+iH2cU3vYnSTfw6vv7nzRwI8fLmzXFXq+1XMNPopUtooWF5HwVJEpGSxWq0kHtlKxJoPid39PWSMQhomXOt0xrPlQDya9c81ZFqsFvZfucCWC0fpWrkOQb4VAIhKSqD+gv/a+jmbHQjyqUBdv4rU86tIfb+KNC5bWVecS6lR7INlXFwc06dPZ/Xq1URHR1OrVi3+9a9/0a1b1pO180LBUkSk5EoJO0Hkuk+I2vwFloSofxoyQmaz/rjW64JTxTp5es74+dhI3t61ij/CTtnuk3m973o/TfMKdwDpV6WvPLmXmt7lqOldDn83L9tFQyIlQbEPlo899hgHDhxg7NixVK5cmeXLl7NixQpmzZpFp06dbnp5CpYiIiWfJTmR+P0/E7NzCXF//oAlMSZTu9mrPK5BHXGr0xnXOh1xqlgX4wYBMDQ+mr/CznAg4gIHwtN/zsRGcOCh/+Dh6AzA2tMHeGz9PNs8jiYzAe4+VPX0o7KHL1U9fXm6QSdb2EyzWDAZRp5CrkhRUKyD5aZNmxg1ahQff/wxd911F5B+yGPo0KFERkayatWqm16mgqWISOlybciM3/czaTFhWfoYTm44V26Ic5WGOFVphHOVhjhXqofJo0yuoS8uJQn3q6ESYOXJvbyzazWnY8OxZPPfr6uDIyEPv2lb5ucHtjJ51yoquHpRwc2T8m5elHf1TH/t6kkldx/aV6plm99qtSqEil0V6zOM165di6enZ6bD3oZh0L9/f15//XWOHj1KrVq1clmCiIiUdiYnFzya9sWjaV+sVivJ5w+ScHgTCYc2E39oE2nRoViT40k8voPE4zsyzWu4eOBYtjqO5aql/1m2Gg4+/pi9KmD2roCLtz9WBydb2OtTrSF9qjUkKS2VMzHhnI6NyPRnmtWSKRheio8hKS2V07HhnI4Nz1J7kE8F1vd/wfb+jR0rWHRkFz7Orvg4u+Ht5IqPsyveTq54OrkQ6FOBBwNb2Pr/FXaGqOQEPBydcXNwxt3RCXdHJ5zNjjibHXAwTAqqclOKdbA8cuQItWrVwnTd4YmgoPSnJ4SEhChYiohInhmGgXNAPZwD6uHT9WmsVispl46RdPpvks7uJenMHpLP7CUl7DgA1sRYks/uJfns3pyX6eCEyc0Xk6snJhdPTC5emFw8cHf1pL6LJw1dPDG5el1t8yD6t28wHJwwHJx4IDWJ9tWDCE9J4kpKCldSkwlLTuRSchIXkxKoZk0lJeIcBgYYJpKjLuKQEElMQhRRhgEYWDCwGmDBoGNAEA/UaAJGev/gvzew5szBHGu/v2ZTZnQcYnv/1C/fcCjiIs5mB5zMDjibHdJDqMmMs9mR7lXrcn/Nprb+n+zZSFRyAg6GCbPJdPVPMw6GCQeTiRYVqtG4bGVb/x9P7sVisdj6OpjMmE0mHA0TJpOJml7lKO+W/rhOq9XKX5fPYjIMTBgYBumnDWCyva7k7mM7DSHNYuFsXAQmDEyGCePq33f6POl/eju74nj1aU1pFgvxqcm2dsMwbPMAGKSfypDx3mq12m5Pld4zfTPbvge2aSU7qBfrQ+E9e/akWrVqfPrpp5mmnzx5kp49e/LGG28wdOjQTG0Zh7pzEhMTg6enpw6Fi4hIjtISokkJPUpK2AlSLp+8+ucJUi+fJjXqIpa4rKOLRZUFsGJgMQysGFgNg4xg4GCYcDT/81jMpLRU0nJJDY4mc6b+CakpWK1WrDlkKSezI07XPHYzNiXJtu7sVuPq4Iij6Z8xsejkxEzt16/HzcEpPSgaRvqz5a/rf/16PJ1ccDCunt+KleikrP2v5e3sgulq/1SLhZhsln8tH2dXW7BMsaQRm5yUa39fFzfb6+S0VOJSk3Psa2Dg4+xqe5+YlkJCakqmPi0+i71+tgJXrEcsIffkX9J/KxAREfswu3phrnYnLtXuzLbdmppCWkwYqdGhpEWFkhYfgSUxBktCTPqfttfRWBJjsCbG/vM+JRHSUrCmJl/9SYLbOAaUHousmDPWcd2qrGn/vL7hTZMsKVhT/3nrcqP+aSlcewt6txw7ZvRPztTf40b9U5My9fe6Yf9EW38D8L5h/wRbf1Me+ltTE2yb15yH/pbYBNtrh7z0T4m3vXYiD39ft0GxDpY+Pj5ERkZmmR4VlX4LCW/vrH8FNxqJvNGIpoiIyI0YDo44+FbCwbdSgSzPakmzBc1/QmcSVqsFLJb0e3Narenvr/5YM6ZjBYsla9vV6Vivacvol2MhOQXcXIJvAc2TfrGTFdPVQ8pW0kfxrFixWtPvNUrG66vLdjE74nj1dDmL1UpEUjxYrViwYgWslvRlZvQv4+KOk8kBsJKclsaFuMj0fulLttWVUV4VT1/bDfETUlM4Fxt5dcnY/rxWDa9yOFytJyY5kXNxUbluoyBff9vh9IjEeC7E/3OLLNvI7tV5zYZBXb+Ktvaw+FguXtc/MNu1FKxiHSxr1arFzz//jMViyXSeZUhICACBgYWxCUVERG4vw2TGcHIFJ9cbd5Yc3XAE8jo382R4TyDnW+xn3/9mfu3wBKreZP8aN9G/oBTru7LeddddREdHs2HDhkzTv/vuO6pXr64Ld0REREQKUbEesezUqROtWrXi1VdfJTIyksqVK/Pdd9/xxx9/MHPmTHuXJyIiIlKqFOurwgFiY2OZNm0aa9asyfRIx+7du+drebpBuoiIiEj+FPtgWdAULEVERETyp1ifYykiIiIiRYeCpYiIiIgUCAVLERERESkQCpYiIiIiUiAULEVERESkQChYioiIiEiBULAUERERkQKhYCkiIiIiBULBUkREREQKhIKliIiIiBQIBUsRERERKRAKliIiIiJSIBQsRURERKRAKFiKiIiISIFQsBQRERGRAqFgKSIiIiIFQsFSRERERAqEgqWIiIiIFAgHexdQ1Hh6etq7BBEREZFiybBarVZ7FyEiIiIixZ8OhYuIiIhIgVCwFBEREZECoWApIiIiIgVCwVJERERECoSCpYiIiIgUCAVLERERESkQCpYiIiIiUiB0g/R86tKlCzExMfYuQ0RERCRPPD09+eWXX27rOhQs8+nChQtYrVY9qec6GWFb2+Uf2ibZ03bJnrZL9rRdstI2yZ62S/ZiYmKIjY297etRsMwnDw8PAHbt2mXnSoqW5s2bA9ou19I2yZ62S/a0XbKn7ZKVtkn2tF2yl7FdbjedYykiIiIiBULBUkREREQKhIKliIiIiBQIBUsRERERKRAKliIiIiJSIBQsRURERKRAKFiKiIiISIEwrFar1d5FiIiIiEjxpxFLERERESkQCpYiIiIiUiAULEVERESkQOhZ4VcFBwfz8ccfU6dOHb7//vtMbVu3bmXGjBkcOnQId3d37rrrLsaOHYuXl1eelj1v3jy++eYbzp07h7+/P0OGDGHEiBGYTEU/12e3XWJjY5k3bx6//fYbx48fJyEhgSpVqjBgwACGDh2Kk5PTDZcbFBSU7fT//Oc/PPjggwX6GW6HnL4vw4YNY+fOnVn69+7dm+nTp+dp2cX1+5LdNjl79izdunXLcZ4hQ4bw5ptv5rrc4vZd2bFjB4888ki2bT/99BM1a9a0vS8t+5a8bJPSuF/J63eltO1X8rJdSuO+JcOOHTv49NNP2bNnDykpKQQEBDB8+HCGDBli62PPfYuCJXDkyBE+++wzypYtm6Vtx44djBo1im7dujFmzBguXbrE1KlTCQkJYcGCBTfcyDNnziQ4OJinnnqK1q1b8+eff/Lhhx8SFRXF2LFjb9dHKhA5bZfz588zb948+vXrx2OPPYabmxvbt29n6tSp7Ny5k5kzZ+Zp+b1792b48OGZplWpUqXA6r9dcvu+AFSrVo333nsv0zRfX988Lbu4fl9y2ibly5dn8eLFWfovX76cRYsW0b179zwtvzh+V8aOHUuLFi0yTatcubLtdWnct+S2TUrzfuVG3xUonfuV3LZLad23LF++nFdffZVBgwbx6KOP4ujoyPHjx0lJSbH1sfu+xVrKpaWlWQcNGmR98803rQ8//LC1b9++mdrvv/9+a79+/axpaWm2aVu2bLEGBgZaV65cmeuyw8PDrQ0bNrS+9dZbmaZPmzbNWq9ePeuFCxcK7oMUsNy2S1xcnDUuLi7LPMHBwdbAwEDroUOHbrj8wMBA66RJkwq05sJwo+9LdtPyqrh+X260TbJz7733Wjt27Jjp31VOitt3Zfv27dbAwEDr2rVrc+1XmvYtedkmpXG/ktfvSmnbr+R1u2SnJO9bzp8/b23UqJF19uzZufaz976l6I2BF7K5c+dy8eJFXnjhhSxtoaGh7N27l379+mVK+O3ataNChQqsWbMm12X/+uuvJCUl0b9//0zT+/fvT2pqKuvXry+YD3Eb5LZd3NzccHNzyzK9YcOGAFy8ePG212cvuW2XW1Vcvy83u0327t3L4cOH6d+/f5E8DFcYSvO+JSeleb9yO5XE70pOSvq+ZcmSJUD6qRE5KQr7lpK35W/CmTNn+Oijj5g4cSIeHh5Z2kNCQgCoXbt2lrbAwECOHDmS6/KPHDmCYRhZ5q9WrRouLi43nN9ebrRdcrJ9+3YMw6BWrVp56v/999/TqFEjGjZsyKBBg/jpp5/yW3KhyOt2OXHiBC1atKBevXr06NGDmTNnZjpMkZPi+H3Jz3dl6dKlGIbB/fffn+f1FLfvCsDEiROpV68ezZo148knn2Tfvn22ttK6b8ltm+SkpO9XIG/bpTTtVzLc7PelpO9bfv/9d2rWrMnPP/9Mz549qVu3Lh07dmTq1KkkJycDRWPfUmrPsbRarbz22mu0b98+x3MxIiMjAfD29s7S5u3tzYEDB3JdR2RkJK6urtmedO7l5WVbflGSl+2SnT179vD111/Tr18/AgICbtj/3nvvpVOnTlSsWJFLly6xcOFCXnjhBcLCwrKc71IU5HW7NGvWjN69e1OjRg3i4+NZt24dH330Efv37+eTTz7JdR3F7fuSn+9KUlISK1eupGXLlnk+j6m4fVc8PT0ZPnw4LVu2xMfHh2PHjjF79mwefPBB5s+fT+PGjUvdviUv2yQ7JX2/ktftUpr2K5C/70tp2LdcunSJS5cuMWnSJEaPHk2tWrXYvn07s2fP5sKFC3zwwQdFYt9SaoPlt99+y759+/L024lhGDc1Pa9udf7b4Wa2S4ZTp07x9NNPU6NGDV5//fU8zTN16tRM73v16sWwYcP48MMPGTJkCC4uLjdV9+2W1+0yZsyYTO+7dOlC2bJlmTVrFrt27aJ58+b5rqGofV/y8135+eefiY6OvqkRheL2XalXrx716tWzvW/evDldu3blnnvuYfr06cydO9fWVlr2LTezTTKUhv1KXrdLadqvQP6+L6Vh32K1WomLi2PatGn06dMHgFatWpGYmMgXX3zB888/b+trz31LqTwUHh4ezvvvv8+TTz6Jq6sr0dHRREdHk5qaisViITo6mqSkJHx8fACyTehRUVHZ/kZwLR8fHxISEmxD1NeKjo6+4fyFLa/b5VpnzpzhkUcewcvLiy+//PKmDp1fy2Qy0bdvX+Lj421D+UVFfrbLte677z4A/vrrr1zXU5y+L/ndJkuXLsXT05OePXvme91F+buSk3LlytG+fXv+/vtvgFK3b8nO9dvkWqVhv5KT3LbLtUrifiU3N9oupWHfkrHfaN++fabpHTt2BGD//v1FYt9SKoNlaGgoMTExfPDBB7Ro0cL2s3v3bkJCQmjRogXBwcG2cwyyO6cgJCQk23MYrlWrVi2sVmuW+U+dOkViYuIN5y9sed0uGTJ2/s7OzsydO5cyZcrc0votFgtAkTvp+ma3y/Xy+rmK0/clP9vk3LlzbN++nT59+tzySEBR/a7kJqNmoNTtW3Jy7TbJUFr2K7nJbrvk1Kck7VduJKftUlr2LYGBgbm2m0ymIrFvKZWHwqtWrcq8efOyTJ88eTLx8fFMmjSJSpUq4e/vT4MGDVixYgXDhw+3fdG2bdtGaGgoPXr0yHU9HTt2xMnJie+//5769evbpi9fvhwHBwe6du1asB/sFuV1u0D6P+SMbfLVV19RoUKFW1q3xWJhxYoVuLu7F7kd3c1sl+xk3Cw8p/PIMhSn70t+tsmyZcuwWq03dagqO0X5u5KTsLAwfvvtN5o0aQJQ6vYt2bl+m0Dp2q/kJLvtkp2SuF/JTW7bpbTsW+666y6+/fZbNm3aRN++fW3TN23ahGEYNGzYsEjsW0plsHR3d6dVq1ZZpmfckf7atrFjxzJixAhefPFFhgwZQmhoKFOnTqVx48b06tXL1i/jSQHPPvsszz33HJB+89onn3ySmTNn4unpSatWrfjrr7+YM2cOjzzyCBUrVrzNn/Tm5HW7XLlyheHDh3PlyhUmT55MaGgooaGhtv5Vq1bFz88PyH67fP7555w4cYLWrVtTrlw5Ll++zMKFC/njjz+YOHEizs7Ot/uj3pS8bpddu3Yxe/ZsevToQUBAAPHx8axfv55ly5bRq1cvmjVrZpu3uH9fbubfEKSfG7R8+XICAwNp1KhRtsssCd8VgJdeeokqVapQv359vLy8OH78OJ999hmJiYm8+OKLtn6lad+Sl21S2vYrkLftUpr2Kxny+m8ISte+pWPHjnTs2JE333yTiIgIateuzfbt25k3bx4PPPCA7QI3e+9bSmWwvBlt2rRh1qxZBAcHM2rUKNzd3enevTvjxo3DbDbfcP5//etfeHh4sGDBAj799FPKly/Pc889xxNPPFEI1d8eR48e5cyZMwBZ/pEDvPPOOwwYMCDH+atXr8769etZt24dMTExuLq6Ur9+ff73v/8Vm9+es1OuXDkAPvroIyIiIjCZTFSvXp0JEybket+xa5XE7wuk3zLm3LlzvPLKKzc1X3H8rgQFBbFy5Urmz59PQkICPj4+tGzZkqeffjrToazStG/JyzYpjfuVvGyX0rhfyeu/IShd+xaAGTNmEBwczJw5c4iIiKBixYqMGTOGkSNH2vrYe99iWK1Wa74/oYiIiIjIVUXv7FQRERERKZYULEVERESkQChYioiIiEiBULAUERERkQKhYCkiIiIiBULBUkREREQKhIKliIiIiBQIBUsRkSLkv//9Lx06dCCvtxiOioqiYcOGBAUF2R7zJyJiLwqWIiJFhNVqZf369XTr1g3DMPI0z4oVK0hJSaFy5cosWbLkNlcoIpI7BUsRkSJi7969hIaG0r179zzPs2TJElq1asXw4cP5/fffOX369G2sUEQkdwqWIiIFICUlhQYNGhAUFJTtz7PPPnvDZaxduxZPT09atWqVp3Xu37+fgwcP0r9/f+69914cHBxYunTprX4UEZF8c7B3ASIiJUFKSgpvv/12lulfffUV+/fvp0uXLjdcxtq1a+nUqROOjo55WueSJUtwc3OjR48euLm50blzZ7777jtGjx6NyaRxAxEpfAqWIiIFwM3NjX79+mWaNmXKFPbv38/48eO5//77c53/2LFjnDhxgjFjxuRpfUlJSaxcuZKePXvi5uYGwH333cfatWv59ddf6dSpU74+h4jIrdCvtCIiBcxqtfLWW2/xxRdfMHHiRB5//PEbzrNu3TqcnJzo0KFDntbx888/ExUVxX333Web1rlzZ8qUKaPD4SJiNxqxFBEpQBaLhYkTJ7J06VImTZrEwIED8zTf2rVradeuHe7u7nnqv2TJEvz8/PD39+fUqVO26W3btmX16tWEh4fj5+eXr88gIpJfCpYiIgUkLS2NCRMm8NNPP/H+++9zzz335Gm+ixcvsm/fPt5666089T9z5gw7duzAarXSs2fPbPv88MMPPProo3ktXUSkQChYiogUgJSUFF566SU2bNjA9OnT6dGjR57nXbduHYZh0K1btzz1X7ZsGVarlUmTJuHp6Zml/cMPP2Tp0qUKliJS6BQsRURuUXJyMs8//zy//fYbH3/8MZ07d76p+deuXUuzZs3ydOjaYrGwfPlyAgMDGTRoULZ9jh49SnBwMHv27KFRo0Y3VYuIyK3QxTsiIrfo5Zdf5pdffqFPnz5ERUXx/fffZ/qJjY3Ncd6oqCh27dqV55uib9myhQsXLuR4CBywjZbqSTwiUtg0YikicgusViubN28G0g9RL1u2LFO7yWRi9+7dOc7/yy+/kJqamufD4Blh8a677sqxT2BgINWqVeOnn37i3//+Ny4uLnlatojIrTKsVqvV3kWIiJRW//rXvzh79izff/+9vUsREbllGrEUEbGjJk2aMHjwYHuXISJSIDRiKSIiIiIFQhfviIiIiEiBULAUERERkQKhYCkiIiIiBULBUkREREQKhIKliIiIiBQIBUsRERERKRAKliIiIiJSIBQsRURERKRA/D8Qh9VyLPoTkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(interface.z / ANGSTROM, (initial_density / (KILO * MOL / METER**3))[0], linestyle=\"dashed\", label=\"initial density\")\n",
    "plt.plot(interface.z / ANGSTROM, (interface.density / (KILO * MOL / METER**3))[0], label=\"equilibrium density\")\n",
    "\n",
    "plt.xlim(40, 60)\n",
    "plt.ylim(-5, 60)\n",
    "plt.ylabel(r\"$\\rho$ / kmol m$^{-3}$\")\n",
    "plt.xlabel(r\"$z$ / A\")\n",
    "sns.despine(offset=10)\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison to NIST data using `SurfaceTensionDiagram`\n",
    "\n",
    "We can use the above steps to calculate multiple VLE's and then - for each VLE - calculate the surface tension. We provide a utility object, the `SurfaceTensionDiagram`, that you can use to do exactly this task. Let's load some experimental (correlation) data obtained from the NIST Webbook and see how the water model compares to that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (K)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Density (l, mol/l)</th>\n",
       "      <th>Volume (l, l/mol)</th>\n",
       "      <th>Internal Energy (l, kJ/mol)</th>\n",
       "      <th>Enthalpy (l, kJ/mol)</th>\n",
       "      <th>Entropy (l, J/mol*K)</th>\n",
       "      <th>Cv (l, J/mol*K)</th>\n",
       "      <th>Cp (l, J/mol*K)</th>\n",
       "      <th>Sound Spd. (l, m/s)</th>\n",
       "      <th>...</th>\n",
       "      <th>Volume (v, l/mol)</th>\n",
       "      <th>Internal Energy (v, kJ/mol)</th>\n",
       "      <th>Enthalpy (v, kJ/mol)</th>\n",
       "      <th>Entropy (v, J/mol*K)</th>\n",
       "      <th>Cv (v, J/mol*K)</th>\n",
       "      <th>Cp (v, J/mol*K)</th>\n",
       "      <th>Sound Spd. (v, m/s)</th>\n",
       "      <th>Joule-Thomson (v, K/MPa)</th>\n",
       "      <th>Viscosity (v, uPa*s)</th>\n",
       "      <th>Therm. Cond. (v, W/m*K)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>275.0</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>55.502</td>\n",
       "      <td>0.018017</td>\n",
       "      <td>0.13978</td>\n",
       "      <td>0.13979</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>75.903</td>\n",
       "      <td>75.915</td>\n",
       "      <td>1411.4</td>\n",
       "      <td>...</td>\n",
       "      <td>3271.50</td>\n",
       "      <td>42.830</td>\n",
       "      <td>45.115</td>\n",
       "      <td>164.06</td>\n",
       "      <td>25.580</td>\n",
       "      <td>33.980</td>\n",
       "      <td>410.33</td>\n",
       "      <td>558.89</td>\n",
       "      <td>8.9986</td>\n",
       "      <td>0.016879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285.0</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>55.479</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.89678</td>\n",
       "      <td>0.89681</td>\n",
       "      <td>3.2139</td>\n",
       "      <td>75.397</td>\n",
       "      <td>75.533</td>\n",
       "      <td>1454.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1704.30</td>\n",
       "      <td>43.078</td>\n",
       "      <td>45.445</td>\n",
       "      <td>159.52</td>\n",
       "      <td>25.736</td>\n",
       "      <td>34.170</td>\n",
       "      <td>417.48</td>\n",
       "      <td>408.81</td>\n",
       "      <td>9.2941</td>\n",
       "      <td>0.017535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>295.0</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>55.384</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>1.65110</td>\n",
       "      <td>1.65120</td>\n",
       "      <td>5.8154</td>\n",
       "      <td>74.765</td>\n",
       "      <td>75.361</td>\n",
       "      <td>1487.7</td>\n",
       "      <td>...</td>\n",
       "      <td>934.36</td>\n",
       "      <td>43.324</td>\n",
       "      <td>45.773</td>\n",
       "      <td>155.38</td>\n",
       "      <td>25.898</td>\n",
       "      <td>34.374</td>\n",
       "      <td>424.46</td>\n",
       "      <td>304.01</td>\n",
       "      <td>9.6018</td>\n",
       "      <td>0.018214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305.0</td>\n",
       "      <td>0.004719</td>\n",
       "      <td>55.233</td>\n",
       "      <td>0.018105</td>\n",
       "      <td>2.40440</td>\n",
       "      <td>2.40440</td>\n",
       "      <td>8.3264</td>\n",
       "      <td>74.038</td>\n",
       "      <td>75.300</td>\n",
       "      <td>1513.1</td>\n",
       "      <td>...</td>\n",
       "      <td>536.20</td>\n",
       "      <td>43.568</td>\n",
       "      <td>46.099</td>\n",
       "      <td>151.59</td>\n",
       "      <td>26.069</td>\n",
       "      <td>34.596</td>\n",
       "      <td>431.28</td>\n",
       "      <td>231.32</td>\n",
       "      <td>9.9196</td>\n",
       "      <td>0.018918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315.0</td>\n",
       "      <td>0.008145</td>\n",
       "      <td>55.034</td>\n",
       "      <td>0.018171</td>\n",
       "      <td>3.15730</td>\n",
       "      <td>3.15750</td>\n",
       "      <td>10.7560</td>\n",
       "      <td>73.235</td>\n",
       "      <td>75.301</td>\n",
       "      <td>1531.7</td>\n",
       "      <td>...</td>\n",
       "      <td>320.58</td>\n",
       "      <td>43.811</td>\n",
       "      <td>46.422</td>\n",
       "      <td>148.10</td>\n",
       "      <td>26.252</td>\n",
       "      <td>34.843</td>\n",
       "      <td>437.93</td>\n",
       "      <td>180.66</td>\n",
       "      <td>10.2460</td>\n",
       "      <td>0.019646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature (K)  Pressure (MPa)  Density (l, mol/l)  Volume (l, l/mol)  \\\n",
       "0            275.0        0.000698              55.502           0.018017   \n",
       "1            285.0        0.001389              55.479           0.018025   \n",
       "2            295.0        0.002621              55.384           0.018056   \n",
       "3            305.0        0.004719              55.233           0.018105   \n",
       "4            315.0        0.008145              55.034           0.018171   \n",
       "\n",
       "   Internal Energy (l, kJ/mol)  Enthalpy (l, kJ/mol)  Entropy (l, J/mol*K)  \\\n",
       "0                      0.13978               0.13979                0.5100   \n",
       "1                      0.89678               0.89681                3.2139   \n",
       "2                      1.65110               1.65120                5.8154   \n",
       "3                      2.40440               2.40440                8.3264   \n",
       "4                      3.15730               3.15750               10.7560   \n",
       "\n",
       "   Cv (l, J/mol*K)  Cp (l, J/mol*K)  Sound Spd. (l, m/s)  ...  \\\n",
       "0           75.903           75.915               1411.4  ...   \n",
       "1           75.397           75.533               1454.3  ...   \n",
       "2           74.765           75.361               1487.7  ...   \n",
       "3           74.038           75.300               1513.1  ...   \n",
       "4           73.235           75.301               1531.7  ...   \n",
       "\n",
       "   Volume (v, l/mol)  Internal Energy (v, kJ/mol)  Enthalpy (v, kJ/mol)  \\\n",
       "0            3271.50                       42.830                45.115   \n",
       "1            1704.30                       43.078                45.445   \n",
       "2             934.36                       43.324                45.773   \n",
       "3             536.20                       43.568                46.099   \n",
       "4             320.58                       43.811                46.422   \n",
       "\n",
       "   Entropy (v, J/mol*K)  Cv (v, J/mol*K)  Cp (v, J/mol*K)  \\\n",
       "0                164.06           25.580           33.980   \n",
       "1                159.52           25.736           34.170   \n",
       "2                155.38           25.898           34.374   \n",
       "3                151.59           26.069           34.596   \n",
       "4                148.10           26.252           34.843   \n",
       "\n",
       "   Sound Spd. (v, m/s)  Joule-Thomson (v, K/MPa)  Viscosity (v, uPa*s)  \\\n",
       "0               410.33                    558.89                8.9986   \n",
       "1               417.48                    408.81                9.2941   \n",
       "2               424.46                    304.01                9.6018   \n",
       "3               431.28                    231.32                9.9196   \n",
       "4               437.93                    180.66               10.2460   \n",
       "\n",
       "   Therm. Cond. (v, W/m*K)  \n",
       "0                 0.016879  \n",
       "1                 0.017535  \n",
       "2                 0.018214  \n",
       "3                 0.018918  \n",
       "4                 0.019646  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "literature = pd.read_csv(\"data/water_vle_nist.csv\", delimiter=\"\\t\")\n",
    "literature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `SurfaceTensionDiagram`, we need to provide the VLE's. We compute those using the `PhaseDiagram` object (here for 50 temperatures between 275 Kelvin and the critical temperature) from which we get a list of `PhaseEquilibrium`s via the `states` filed. The `SurfaceTensionDiagram` is nice, because we can reuse equilibrium density profiles from prior iterations as input for the next iteration. It's therefore typically faster and more stable than an \"naive\" implementation by hand.\n",
    "\n",
    "The `SurfaceTensionDiagram` takes the same arguments `n_grind`, `l_grid` and `critical_temperature` as discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.24 s, sys: 3.05 ms, total: 1.24 s\n",
      "Wall time: 1.24 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "vles = PhaseDiagram.pure(\n",
    "    pcsaft, \n",
    "    275*KELVIN, \n",
    "    50,\n",
    "    critical_temperature=cp.temperature\n",
    ")\n",
    "sfts = SurfaceTensionDiagram(\n",
    "    vles.states, \n",
    "    n_grid=512, \n",
    "    l_grid=100*ANGSTROM, \n",
    "    critical_temperature=cp.temperature\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now can extract all surface tensions via `surface_tension` as well as the liquid and vapor states via the `liquid` and `vapor` getters, respectively. Let's store the results in a pandas `DataFrame` to make plotting easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dft_data = pd.DataFrame(\n",
    "    np.array([\n",
    "        sfts.liquid.temperature / KELVIN, \n",
    "        sfts.surface_tension / NEWTON * METER\n",
    "    ]).T, \n",
    "    columns=[\"Temperature (K)\", \"Surf. Tension (l, N/m)\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Surface tension of water\")\n",
    "sns.lineplot(data=literature, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"NIST\")\n",
    "sns.lineplot(data=dft_data, x=\"Temperature (K)\", y=\"Surf. Tension (l, N/m)\", label=\"PC-SAFT (2B)\")\n",
    "sns.scatterplot(x=[cp.temperature / KELVIN], y=[0.0], clip_on=False, color=colors[1], label=\"PC-SAFT (2B), critical point\")\n",
    "plt.ylabel(r\"$\\gamma$ / Nm$^{-1}$\")\n",
    "plt.xlabel(r\"$T$ / K\")\n",
    "\n",
    "plt.xlim(250, 700)\n",
    "plt.ylim(0.0, 0.08)\n",
    "sns.despine(offset=10)\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concluding remkars\n",
    "\n",
    "Hopefully you found this example helpful. If you have comments, critique or feedback, please let us know and consider [opening an issue on github](https://github.com/feos-org/feos/issues)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}