{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pure substance phase diagrams\n",
    "\n",
    "## Goal of this notebook\n",
    "\n",
    "- Learn how to generate and work with phase diagrams.\n",
    "- Learn what `PhaseEquilibrium` objects are and how to use them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feos.pcsaft import *\n",
    "from feos.eos import *\n",
    "\n",
    "import si_units as si\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context('talk')\n",
    "sns.set_palette('Dark2')\n",
    "sns.set_style('ticks')\n",
    "colors = sns.palettes.color_palette('Dark2', 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read parameters from json for a single substance and generate `PcSaft` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|component|molarweight|$m$|$\\sigma$|$\\varepsilon$|$\\mu$|$Q$|$\\kappa_{AB}$|$\\varepsilon_{AB}$|$N_A$|$N_B$|$N_C$|\n",
       "|-|-|-|-|-|-|-|-|-|-|-|-|\n",
       "|hexane|86.177|3.0576|3.7983|236.77|-|-|-|-|0|0|0|"
      ],
      "text/plain": [
       "<PcSaftParameters at 0x7f4f29144650>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = PcSaftParameters.from_json(\n",
    "    ['hexane'], \n",
    "    '../parameters/pcsaft/gross2001.json'\n",
    ")\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcsaft = EquationOfState.pcsaft(parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## The `PhaseDiagram` object\n",
    "\n",
    "A `PhaseDiagram` object contains multiple thermodyanmic states at phase equilibrium.\n",
    "**For a single substance** it can be constructed via the `PhaseDiagram.pure` method by providing \n",
    "\n",
    "- an equation of state,\n",
    "- the minimum temperature, and\n",
    "- the number of points to compute.\n",
    "\n",
    "The points are evenly distributed between the defined minimum temperature and the critical temperature which is either computed (default) or can be provided via the `critical_temperature` argument.\n",
    "\n",
    "Furthermore, we can define options for the numerics:\n",
    "- `max_iter` to define the maximum number of iterations for the phase equilibrium calculations, and\n",
    "- `tol` to set the tolerance used for deterimation of phase equilibria.\n",
    "\n",
    "A `Verbosity` object can be provided via the `verbosity` argument to print intermediate calculations to the screen.\n",
    "\n",
    "Starting from the minimum temperature, phase equilibria are computed in sequence using prior results (i.e. at prior temperature) as input for the next iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_diagram = PhaseDiagram.pure(pcsaft, min_temperature=200*si.KELVIN, npoints=501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|temperature|density|\n",
       "|-|-|\n",
       "|200.00000 K|12.21572 mmol/m³|"
      ],
      "text/plain": [
       "T = 200.00000 K, ρ = 12.21572 mmol/m³"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase_diagram.vapor[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stored information\n",
    "\n",
    "A `PhaseDiagram` object contains the following *fields*:\n",
    "\n",
    "- `states`: a list (with length of `npoints`) of `PhaseEquilibrium` objects at the different temperatures,\n",
    "- `vapor` and `liquid`: so-called `StateVec` objects that can be used to compute properties for the vapor and liquid phase, respectively.\n",
    "\n",
    "The `to_dict` *method* can be used to conveniently generate a `pandas.DataFrame` object (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a `pandas.DataFrame`: the `to_dict` method\n",
    "\n",
    "\n",
    "The `PhaseDiagramPure` object contains some physical properties (such as densities, temperatures and pressures) as well as the `PhaseEquilibrium` objects at each temperature.\n",
    "\n",
    "Before we take a look at these objects, a useful tool when working with phase diagrams is the `to_dict` method. It generates a Python dictionary of some properties (with hard-coded units, see the docstring of `to_dict`). This dictionary can readily be used to generate a `pandas.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>specific entropy liquid</th>\n",
       "      <th>specific entropy vapor</th>\n",
       "      <th>mass density liquid</th>\n",
       "      <th>molar enthalpy liquid</th>\n",
       "      <th>density liquid</th>\n",
       "      <th>density vapor</th>\n",
       "      <th>temperature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>molar enthalpy vapor</th>\n",
       "      <th>molar entropy vapor</th>\n",
       "      <th>mass density vapor</th>\n",
       "      <th>specific enthalpy liquid</th>\n",
       "      <th>specific enthalpy vapor</th>\n",
       "      <th>molar entropy liquid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.867028</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>735.916212</td>\n",
       "      <td>-37.322054</td>\n",
       "      <td>8539.589591</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>20.312763</td>\n",
       "      <td>-0.000147</td>\n",
       "      <td>-1.879119e-07</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>-433.086018</td>\n",
       "      <td>-0.001703</td>\n",
       "      <td>-0.074718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.864466</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>735.319382</td>\n",
       "      <td>-37.281784</td>\n",
       "      <td>8532.663964</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>200.638669</td>\n",
       "      <td>21.816282</td>\n",
       "      <td>-0.000157</td>\n",
       "      <td>-2.001300e-07</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>-432.618726</td>\n",
       "      <td>-0.001819</td>\n",
       "      <td>-0.074497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.861915</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>734.723484</td>\n",
       "      <td>-37.241570</td>\n",
       "      <td>8525.749142</td>\n",
       "      <td>0.013994</td>\n",
       "      <td>201.277337</td>\n",
       "      <td>23.418684</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>-2.130365e-07</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>-432.152081</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.074277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.859376</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>734.128506</td>\n",
       "      <td>-37.201412</td>\n",
       "      <td>8518.845002</td>\n",
       "      <td>0.014967</td>\n",
       "      <td>201.916006</td>\n",
       "      <td>25.125608</td>\n",
       "      <td>-0.000179</td>\n",
       "      <td>-2.266637e-07</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>-431.686083</td>\n",
       "      <td>-0.002073</td>\n",
       "      <td>-0.074058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.856848</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>733.534438</td>\n",
       "      <td>-37.161309</td>\n",
       "      <td>8511.951422</td>\n",
       "      <td>0.015999</td>\n",
       "      <td>202.554674</td>\n",
       "      <td>26.942961</td>\n",
       "      <td>-0.000191</td>\n",
       "      <td>-2.410451e-07</td>\n",
       "      <td>0.001379</td>\n",
       "      <td>-431.220733</td>\n",
       "      <td>-0.002212</td>\n",
       "      <td>-0.073841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   specific entropy liquid  specific entropy vapor  mass density liquid  \\\n",
       "0                -0.867028               -0.000002           735.916212   \n",
       "1                -0.864466               -0.000002           735.319382   \n",
       "2                -0.861915               -0.000002           734.723484   \n",
       "3                -0.859376               -0.000003           734.128506   \n",
       "4                -0.856848               -0.000003           733.534438   \n",
       "\n",
       "   molar enthalpy liquid  density liquid  density vapor  temperature  \\\n",
       "0             -37.322054     8539.589591       0.012216   200.000000   \n",
       "1             -37.281784     8532.663964       0.013078   200.638669   \n",
       "2             -37.241570     8525.749142       0.013994   201.277337   \n",
       "3             -37.201412     8518.845002       0.014967   201.916006   \n",
       "4             -37.161309     8511.951422       0.015999   202.554674   \n",
       "\n",
       "    pressure  molar enthalpy vapor  molar entropy vapor  mass density vapor  \\\n",
       "0  20.312763             -0.000147        -1.879119e-07            0.001053   \n",
       "1  21.816282             -0.000157        -2.001300e-07            0.001127   \n",
       "2  23.418684             -0.000167        -2.130365e-07            0.001206   \n",
       "3  25.125608             -0.000179        -2.266637e-07            0.001290   \n",
       "4  26.942961             -0.000191        -2.410451e-07            0.001379   \n",
       "\n",
       "   specific enthalpy liquid  specific enthalpy vapor  molar entropy liquid  \n",
       "0               -433.086018                -0.001703             -0.074718  \n",
       "1               -432.618726                -0.001819             -0.074497  \n",
       "2               -432.152081                -0.001943             -0.074277  \n",
       "3               -431.686083                -0.002073             -0.074058  \n",
       "4               -431.220733                -0.002212             -0.073841  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(phase_diagram.to_dict(Contributions.Residual))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "ax[0].set_title(f\"saturation pressure of {parameters.pure_records[0].identifier.name}\")\n",
    "sns.lineplot(y=df.pressure, x=1.0/df.temperature, ax=ax[0])\n",
    "\n",
    "# axis and styling \n",
    "ax[0].set_yscale('log')\n",
    "ax[0].set_xlabel(r'$\\frac{1}{T}$ / K$^{-1}$');\n",
    "ax[0].set_ylabel(r'$p$ / Pa');\n",
    "ax[0].set_xlim(0.002, 0.005)\n",
    "ax[0].set_ylim(1e1, 1e7)\n",
    "\n",
    "ax[1].set_title(r\"$T$-$\\rho$-diagram of {}\".format(parameters.pure_records[0].identifier.name))\n",
    "sns.lineplot(y=df.temperature, x=df['density vapor'], ax=ax[1], color=colors[0])\n",
    "sns.lineplot(y=df.temperature, x=df['density liquid'], ax=ax[1], color=colors[0])\n",
    "\n",
    "# axis and styling \n",
    "ax[1].set_ylabel(r'$T$ / K');\n",
    "ax[1].set_xlabel(r'$\\rho$ / mol/m³');\n",
    "ax[1].set_ylim(200, 600)\n",
    "ax[1].set_xlim(0, 9000)\n",
    "\n",
    "sns.despine(offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `PhaseEquilibrium` object\n",
    "\n",
    "A `PhaseEquilibrium` object contains two thermodynamic states (`State` objects) that are in thermal, mechanical and chemical equilibrium.\n",
    "The `PhaseEquilibrium.pure` constructor can be used to compute a phase equilibrium for a single substance. We have to provide the equation of state and either temperature or pressure. Optionally, we can also provide `PhaseEquilibrium` object which can be used as starting point for the calculation which possibly speeds up the computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "||temperature|density|\n",
       "|-|-|-|\n",
       "|phase 1|300.00000 K|8.86860  mol/m³|\n",
       "|phase 2|300.00000 K|7.51850 kmol/m³|\n"
      ],
      "text/plain": [
       "phase 0: T = 300.00000 K, ρ = 8.86860  mol/m³\n",
       "phase 1: T = 300.00000 K, ρ = 7.51850 kmol/m³"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vle = PhaseEquilibrium.pure(\n",
    "    pcsaft, \n",
    "    temperature_or_pressure=300.0*si.KELVIN\n",
    ")\n",
    "vle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two equilibrium states can be extracted via the `liquid` and `vapor` getters, respectively. Returned are `State` objects, for which we can now compute any property that is available for the `State` object and the given equation of state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saturation pressure      p_sat(T = 300 K) =   0.22 bar\n",
      "enthalpy of vaporization h_lv (T = 300 K) = 365.83 kJ/kg\n"
     ]
    }
   ],
   "source": [
    "liquid = vle.liquid\n",
    "vapor = vle.vapor\n",
    "\n",
    "assert(abs((liquid.pressure() - vapor.pressure()) / si.BAR) < 1e-10)\n",
    "print(f'saturation pressure      p_sat(T = {liquid.temperature}) = {liquid.pressure() / si.BAR:6.2f} bar')\n",
    "print(f'enthalpy of vaporization h_lv (T = {liquid.temperature}) = {(vapor.specific_enthalpy(Contributions.Residual) - liquid.specific_enthalpy(Contributions.Residual)) / (si.KILO*si.JOULE/si.KILOGRAM):6.2f} kJ/kg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to compute a boiling temperature or saturation pressure without needing the `PhaseEquilibrium` object you can use the\n",
    "\n",
    "- `PhaseEquilibrium.boiling_temperature` and\n",
    "- `PhaseEquilibrium.vapor_pressure`\n",
    "\n",
    "methods. Note that these methods return lists (even for pure substance systems) where each entry contains the pure substance property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$300\\,\\mathrm{K}$"
      ],
      "text/plain": [
       "300.0000000000137 K"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhaseEquilibrium.boiling_temperature(pcsaft, liquid.pressure())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `states` method returns all `PhaseEquilibrium` objects from `PhaseDiagram`\n",
    "\n",
    "Once a `PhaseDiagram` object is created, we can access all underlying `PhaseEquilibrium` objects via the `states` field.\n",
    "In the following cell, we compute the enthalpy of vaporization by iterating through all states and calling the `specific_enthalpy` method on the vapor and liquid states of the `PhaseEquilibrium` object, respectively.\n",
    "\n",
    "Note that this is merely an example to show how to compute any property of the states. The total value of enthalpy of vaporization may not be correct, depending on the ideal gas model used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add enthalpy of vaporization to dataframe\n",
    "df['hlv'] = [\n",
    "    (vle.vapor.specific_enthalpy(Contributions.Residual) - vle.liquid.specific_enthalpy(Contributions.Residual)) \n",
    "    / (si.KILO * si.JOULE / si.KILOGRAM) \n",
    "    for vle in phase_diagram.states\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dd5KTNAudiARKgR4GCvS6YW9BHq+smMO/lsxgzZ7tpe1dG7fg6l7DGX34AA5pkB5ghSJSjynQw0CBXrcUlxQz9Wtj3JLpfLphZWl747QMLu42mCt7DqVjo2YBVigi9ZACPQwU6HXXsm838a8lM3j9qy/IKyoEIDkpiZM79WJsrxEc1bqLZqETkdqgQA8DBXrdtyN3Hy8un83TS2eyef/u0vY+zdoxtvdwzjz0SNJTUgOsUEQSnAI9DBToiaOguIh31yziqSXT+HLr+tL2lplZjHFHM6bHEFpmNgqwQhFJUAr0MFCgJ6a5W9bxryXTeWfNQgpLigFIS07h7K5Hck2v4fRp3j7gCkUkgSjQw0CBntg27ttVOgvdt3n7S9uHtD6Ua3oN5+ROvUhJ1mVvInJQFOhhoECvH3IKC3hj1ZeMWzwd27m5tL1jVlOu7DmUi7oNJjs9M8AKRaQOU6CHgQK9fikpKWHaxpWMWzKdKeuXlbY3TE1j9OEDuabXMLpmtwywQhGpgxToYaBAr79W7drG00tn8MqKOewrzC9tP76D4/rexzCs7WG67E1EKkOBHgYKdNmdn8vLKz7n6SUzWbd3R2n7Ec3bc0OfkZzWpY/u9iYi5VGgh4ECXSKKiot5f/1Snlz8KbM2rylt75TVjGt7j+DH3QbRsEFacAWKSFgp0MNAgS5lmbtlLf9Y+AmT1y0pvdtbk/SGXNlzKFf1HErzjKyAKxSREFGgh4ECXcqzatdWHl/0Ka9FTS+bnpLKj7sN4treIzi0cYuAKxSREFCgh4ECXSpja84enl4yg2eWfcau/BzAmzf+1M59uKHPSPq37BhwhSISIAV6GCjQpSr2FeTx7+Wf8+TiaXyzb2dp+9FtDuXGPqM4voPTmfEi9Y8CPQwU6FIdBcVFvL16If9Y9DFLdmwsbe/VrC0/O+I4TuvcRzPQidQfCvQwUKDLwSgpKeHTDSt5bOHHTNv43f3ZuzZuwU+OOJbzDutPA13yJpLoFOhhoECXmjJv63oeXvAhk9ctKW1rf0gTbugzkou6DyYztUGA1YlIHCnQw0CBLjXNvt3MIws+5M3V8yn2/x5bZGRxbe8RXN7jaBqlZQRcoYjUMAV6GCjQJV7W7N7OPxZ+zKsr55JfXARAdloGV/UazjU9h9E045CAKxSRGhKOQHfOTa2gSwmQA6wD3gPeNLOECTYFusTbxn27eGLxpzxvs8gpLAC8m8GMcUO4oe9IWmY2CrhCETlIoQn0NUAmELnF1E7/sYn/uBVIBprjBdp04FQz23dwpYaDAl1qy47cfYxbMp2nl85gd34uABkpDbiix9EKdpG6LS6BXp3rZI4F9gP3Aa3NrJmZNQNaA/cD+4BBQAvgb8AI4I4aqVakHmmWcQi/GvAjZo3+Lb8deDJN0xuSW1TA44s/Zeir9/K/s99ha86eoMsUkZCozgj9DWCfmV12gOUvAJlmdp7/9USgp5kdfrDFhoFG6BKUvQV5jF86k8cXfcK3efsBjdhF6qjQjNCPBz4tZ/mnfp+IKUCHaryOiETJapDOT484lpmjf8NvB56iEbuIfE91p6bqUcGy6HcfxXgnyYlIDahMsG/L2Rt0mSJSy6oT6FOAG51zF8UucM5dDNwAvB/VPABYU63qROSAygv2Ya/dy31fvMeuPL2XFqkvqnMMvTMwDWgHbAQi81ceDrT124ab2VrnXAbwLjDRzB6osaoDpGPoElaRY+z/XPQJO/1j7NlpmdzUdxRX9xpGZmpawBWKiC8cl60BOOeaAb8FzgAO9ZvXABOBe8xse00VGDYKdAm73fm5PLH4U55c9Cn7CvMBaJXZiJuPPJ6Luw8mLSU14ApF6r3wBHp9pkCXumJ77l4eWfARzy77jLyiQgA6ZTXj1v4ncm7Xfrq7m0hw6k6gO+camVlCnm6rQJe6ZsPenTww/wNeWTGXopJiALo3acWvBvyIUzr11v3YRWpfOC5bc849VMHyRsDkalckIjWqXVYT7ht+Ph+eewtnHXoEAMt3buHaqc9z9jv/4PPNa4ItUERqRHX2uf3MOfershY45xoCk4B+B1OUiNS8rtkteezYS5h81s85oYN35ekXW9dx7rv/5Nqpz7Fq17aAKxSRg1GdQP9/wN3OuUujG51zmXhntA8Czq+B2kQkDno3b8czJ13Jq6dex5EtvDmfJq1dzPFv/I3ff/Ym23N1DbtIXVTds9z/AVwFnGFmU/zL094GjgEuMLOJNVtmeOgYuiSS4pJi3lq9gHvmTmb93m8BaNQgnZ8ccRzX9BpOZmqDgCsUSUjhOSnOOZcMvI43xeupeDdfOR642Mxer9EKQ0aBLokor6iQ8Utn8Pf5U9nl39mt3SHZ/HrAjzjvsP4kJ+mMeJEaFJ5AB/BH5VOAo/Gmd73MzF6pbiHOuUHA7Xgzy7UCdgHzgDvNbEZM32HAvX7f3cDLwG1mtj+mXzpwJzAGaArMB243sw8Oos6doECXxPRt3n7+Pn8q45fOpKC4CIA+zdpx59FncVTrLsEWJ5I4ggl059zIchY3B8YDLwEvRC8ws0+qUohz7sfAZcAsvNnmmgCXAkfg3U/9fb9fP2AmsBh4Cu/GL78E3jezM2PW+W+84/kP4s1odyXeMf5RZjazKvVFrXMnKNAlsa3ds5175k7mrdULStvOPvRIbh90Ku2ymgRXmEhiCCzQiyk/mCKFlUR9XWJmKQdbnH/W/Cpgjpmd4be9ixfyPcxsr982FngSOMHMpvptR+G9ObjFzB702zKARcAGMyvvjUp5Ne0EBbrUD3O3rOOOWW8xf9vXAGSmNuAnfY/l+j4jdXxdpPriEuiVmQPyqni8cGWY2X7n3Fa80TrOucbAScB9kTD3PQs8AFwITPXbLgAK8EbxkfXlOufGAXc559qa2cb4fxciddfAVp2YeMZNvLbyC/46979szdnL/V++z0srPuf3g0/n9M59NDGNSEhUGOhm9kxtFBLhT0yTjrc7/wqgD95xcIC+eDXPiakx3zk3D+gf1dwfWBYT/ACz8d4d9cPbtS8i5UhOSubCboM4tXMf/j7/Q55aMo2v9+7khg9fYFibrvxxyJn0atY26DJF6r0w3qXhab67jj0f+CfwF//ryH+NsoJ4IzA06uu2wDcH6Afe3eJ+ILJLvRzZeCfsidQrjdIyuH3wqVzcfTB3fv42U9YvY8amVZzy1t+5vMfR/HrAyTROywi6TJF6K4zXovwJ+BFwNTAdb7QeOViX6T/mlfG83Kjlkb4H6kdMXxGppK7ZLRh/4pU8d9JVHJbdkuKSEsYvncmo/9zPG1/NQzd8EglG6EboZrYQWAjgnHseb/f6eLxj4jl+t/QynpoRtRz/8wP1I6Zv9Os3Ka++SozgReqF4zo4RrQ7nHGLp/O3eVPYmrOXn33yEi+vmMNdQ8/msOyWQZcoUq+EcYReyswKgDeB8/ypZSO7y8s6YNcW2BD19cZy+hHTV0SqoUFyCjf0HclH597KKZ16AzBt40pOmvAg933xHjmFBQFXKFJ/hDrQfZl4J7E1wrvkrBDvWvJSzrk0vJPc5kU1zwN6OOeyYtY3xH+cX/OlitRP7bKa8NQJY3jmxCvpmNWU/OIiHpo/lRPeeIAP1i8LujyReiE0ge6c+8H+Of8ytdHAejPbYma78GanGxMT1GOALODVqLbX8I69j41aXzreZXjTzUwjdJEadkLHHkw99xZ+fsRxNEhOYd3eHVwxZTzXf/gCW/bvCbo8kYRWqalfnXPrgQn+x0dmVlTThTjnpuKdsDYD2AR0xAvfDsBFkWllnXMD/D6L+G6muF8AH5rZaTHrfAU4B+8a9a/wLoMbDBxnZtOrWedO0MQyIhVZuXMLv5s5gRmbVgGQnZbJHUedzoWHD9S161LfBTeXu3PuEeBsoD3wLd5tUv8DTI6dP726nHNXA5cDvfDmXd8JfAbcb2Yfx/QdAdzDD+dy3xfTLwP4X7wpZZsCC4DfmdmUg6hzJyjQRSqjpKSE17/6gj/Ofoeded6/imPaHc7dw86lc6PmAVcnEpjgb87iT6d6Lt6o1+GdKT4FeAOYaGbb41BjqCjQRapuW85e7pj1Vunc8JmpDfj1gB9xdc/hpCSH5sifSG0JPtCjOed68F24D8K749o0vHCfYGbraqjGUFGgi1Tfe+uW8LuZE9i0fzcA/Vp05P4R59OjaZuAKxOpVeEK9GjOufZ8F+4jgRS8s8h/Z2b/PegXCBEFusjB2Z2fy1/mTOJ5mwV4l779z5HH85MjjiU1+aDv6SRSF4Q30KM555oCZ+KF+wwzu79GXyBgCnSRmjFz0yp+Pf0/rN69DYAjW3TgoWMu5PAmrQKuTCTu6kagJzoFukjNySnM5565k3lqiXfRSXpKKrcNPIWrew0jOUnH1iVhKdDDQIEuUvNmbPyKW6e9ytd7dwJwdJtD+duI0XRq1CzYwkTiIy6BrrfAIhK4YW0PY8o5t3BJ96MA+GzTak6a8CAv2Gzd7EWkkjRCryKN0EXi64P1y/j19NfZnOPNLHd8B8ffRoymRWbsLM4idZZG6CKS+E7o2IMp597C2V2PBGDq18ZJbz7IR98sD7gykXBToItI6DRNb8ijoy7m0VEX06hBOltz9nLZe//iztlvk1dUGHR5IqGkQBeR0Dq765G8d/bNDGrVGYAnFk/jrLcfZeXOLQFXJhI+lZ3L/dYqrrcIby72hWb2RTXqCi0dQxepfYX+7Vgfmj+V4pISMlMb8KchZ3Jxt8G60YvURYHenKW4musvAb4AzjKzjdVcR6go0EWCM3vzGn728Ut8s28nAGd06ct9w8+nUVpGsIWJVE2ggT6qqusFGgND8W5t+pqZXVL18sJHgS4SrF15Ofx2xhtMXOPd6OXQxi14/LhL6dWsbcCViVRa3ZlYxjnXyMz2+J//H3CFmbWo8RcKgAJdJHglJSW8YLO5Y9Zb5BcXkZ6Syl1Hn81F3QcHXZpIZYTjsjXn3EMVLG8ETI5qmlOd1xEROZCkpCQu6zGEN0+/iU5ZzcgrKuSX01/n1k9fJacwP+jyRAJRnaD9mXPuV2UtcM4dAkwC+kXazOzfZqb5G0WkxvVt0Z5JZ/2Mkzv1AuCVlXM5Y+KjfLVra8CVidS+6gT6/wPuds5dGt3onMsE3sG7N/r5NVCbiEiFstMzeer4Mdwx+HRSk5KxnZs57a2HeXvNwqBLE6lVVQ50M7sLeAIY55w7EcA5lwFMxDsJbrSZTarRKkVEypGUlMR1fY7h1VOvo03DxuwrzOeGD1/gnrmTKSqu7kU6InVLdY9t/wRv1/rrzrlhwARgJHCJmU2sodpERKpkcOsuTD775wxt0xWAhxd8yFUfPMOuvJyAKxOJv2oFupkVAxcDC4FPgOOBy8zs9RqsTUSkyppnZPHiyddwdc9hgDcX/BlvP8oKzS4nCa7Cy9accyPLWdwcGA+8BLwQvcDMPjnY4sJIl62J1B2vrJjDbTMnkFdUSFaDdB465kJO7tw76LJEgrkO3Z8lrrxOkcJKor4uMbOUgy8vfBToInXLl1vXc+3U59i0fzcAv+x/EjcfebymjJUgxeWXL7USfa6KxwuLiNSG/i078u6ZP+O6D59nzpa13P/l+6zavY37hp9Pekpl/gWK1A1xmSkukWmELlI35RcVctvMN3h5xVwAjmrdhaeOH0OzjEMCrkzqoXDMFCciUhelpaRy//AL+O3AUwDvRi9nvv2YJqGRhKFAF5F6IykpiZ8ecSz/PO5S0lNSWbtnO2e9/SjTN34VdGkiB02BLiL1zhld+vLaqdfTMjOLXfm5XDp5HK+smBN0WSIHRYEuIvVS/5YdmXjGT3BNWlNYUsyt017joXkfoPOKpK5SoItIvdUhqykTTr+RUe26AXDfl+/z+8/e0nSxUicp0EWkXmuUlsHTJ17BuV37AfDMspnc9PG/yS0sCLYwkSqq1EWYzrn1ePO1TwA+MrOiONYkIlKr0lJSeWjkhbTMzOKJxdN4Z81CduTuY9wJl9M4LSPo8kQqpbIj9DeBc4D3gS3Oueecc+c65xrGrTIRkVqUnJTMHUedwe8HnQbAzE2rOP/df7LZn2FOJOyqNLGMc+4o4Fy8cHdADjAFeAOYaGbb41BjqGhiGZHE99rKL/jltNcoLCmmc6PmvHzKWDpkNQ26LEkcwczlfiDOuR58F+6DgGJgGl64TzCzdTVUY6go0EXqhw+/Nq6d+jy5RQW0OySbl06+lq7ZLYIuSxJDuAI9mnOuPd+F+0ggBZgP/M7M/nvQLxAiCnSR+uOzTau44v3x7CvMp1VmI/598lhc09ZBlyV1X3infjWzb8zsETM7EWiNd0OXNUCfmli/iEgQjm7TlZdOuZbstAy25OzhgkmPs3DbN0GXJVIm3ZylijRCF6l/luzYwMWTx7E9dx+N0zJ47qSrGNiqc9BlSd0V3hG6iEgi69WsHa+dej2tGzZmd34uF08ex6xNq4MuS+R7FOgiIpXQrUkrXj/1ejpkNWF/YT5j3n+azzevCboskVIKdBGRSurSuDmvnnId7Q/xQv2y9/7F3C1rgy5LBFCgi4hUScdGzXjl1Gtpd0g2+/xQ/3Lr+qDLElGgi4hUVedGzXnllOto07AxewryuPS9cczf9nXQZUk9V61Ad86lOOcOc861q+mCRETqgi6NvVCPnCh3yeSnWLJjQ9BlST1W5cvWnHPDgdeAVn7THuBL4Iuoj2VmlpCXbOmyNRGJ9tWurYye9ARbcvbQMjOL10+9QTPKSUVCc9naQ0AW8BfgD8B/gQ7ALcBzwCJgV00VKCISZodlt+TFk68hOy2TrTl7ueS9p9i4T/8CpfZV6vapMXoC95jZndGNzrlsYAAw0H8UEakXejRtw7MnXcXFk5/i6707uWTyOF4/7XqaZRwSdGlSj1RnhL4J2BrbaGa7zOxDM7vfzC45+NJEROqOga06Me6EMaQlp7Bi1xbGvP80ewvygi5L6pHqBPrLwLE1XIeISJ13TLtuPDLqYpKTkpi/7WuunvIMuYUFQZcl9USFJ8U55xYA84AF/sdq4FXgXjN7Md4Fho1OihORiry0/HN+Of11AM4+9EgeHvVjkpN0lbCUistJcZU5hp4PjAYu47uAygOec86djzdin2Vmmi5JRAS4qPtgtufu469z/8ubq+fTPqsJvxt0atBlSYKr1GVrzrkUoBfQD+jvP/YDmvhdSoBv+e7ytblm9kpNFxsGGqGLSGWUlJRw+2dv8uyyzwD469BzGNPj6ICrkpCIywj9oG6f6pzrjBfwA4Aj/c87ACVmllLFdQ0GrgSOAzoD24EZwO/NbGVM32HAvf7r7sbbS3Cbme2P6ZcO3AmMAZoC84HbzeyDKn2j31/nTlCgi0jFCouLGDv1OaasX0ZyUhL/OuFyTuzYM+iyJHihuQ69lL+bfY6Z3WFmZ5tZJ6AF8KNqrO43wHnAFOBm4Am8k+++dM6V/gU45/oBHwAZwK3AU8D1eKEeazze9fHP++ssBiY554ZWoz4RkSpJTU7hsVGXcETz9hSXlHDjRy+yQFPESpwc1AgdwDm3F7gLuN/Mqn06pz/qnmNm+VFt3YCFwEtmdqXf9i5wBNDDzPb6bWOBJ4ETzGyq33YUMAu4xcwe9Nsy8Ca+2WBmI6tZ507QCF1EKm/L/j2c9c6jfL13Jy0zs3j7jJ/SPqtJ0GVJcMI3QvedB1wKLHbOnVbdlZjZjOgw99tWAIvxJrPBOdcYOAl4NhLmvmeBvcCFUW0XAAV4I/jI+nKBccAI51zb6tYqIlIVrRo24rmTriY7LYOtOXu5+oNn2F+QX/ETRargoAPdzN7DO37+CN6Z7xOdc10PujLAOZcEtAa2+U198c7MnxNTQz7epXX9o5r7480pHx38ALPx3h31q4kaRUQqo1uTVjx+3GWkJCWzeMdGbp32Kge7h1QkWnWmfv0BMysC/u6cex74MzDfOfcg8BczyzmIVV8KtAdu97+OjKo3ltF3IxB9bLwt8M0B+gGUeae4yC71cmSjuepFpBpGtDucPxx1OnfMmsjbaxbSc/5Ubu53QtBlSYKo0ZkOzGyHmd0EDMML12XOuQuqsy7nXA/gUWAa3k1fADL9x7LmU8yNWh7pe6B+xPQVEakVV/UcxsXdBwNw35fvM2ntooArkkRRIyP0aP5u8jzgH8Bv8c4+r+olbG2Ad/CubR9tZsX+oshoP72Mp2VELY/0PVA/YvqWquhkt0qM4EVEDigpKYm7jj6br3ZtZfbmNdz8ySt0Pr05vZrptB45OAcd6M65U4A+/kdfoAfQAFiJd4b6G1VcXzYwCW/X9nAz2xS1OLK7vKzf/LbAhpi+B+pHTF8RkVqTlpLKE8ddxmkTH2bDvl1cO/V53j3zp2Sna8ehVF9N7HJ/DjgN2Im3i3wk0NjMepnZj83sL5VdkX9Z2USgO3CGmVlMl0VAITAo5nlpeCe5zYtqngf0cM5lxaxjiP84v7J1iYjUtBaZWYw7/nLSU1JZu2e7TpKTg1YTZ7m3NLPjzex/zOxfZjbXvzysSvzpZV/GO/Y+2sw+K+O1duFNPDMmJqjHAFl4N42JeA1vT8HYqNdIB64CppuZRugiEqi+Ldrz56PPBmDyuiX8c9EnAVckdVmNH0OP5h9P72VmiyvR/f+As/BG6M2cc5dFLdtrZhP8z2/HmxL2I+fcU3hTzf4CmGRmUyJPMLNZzrlXgXv9a86/Aq7Am1b2yoP6xkREasjF3QczZ8saXl4xl7vnTqZfy44MbVMjV/5KPRPv+/ml4d1ytTL6+Y9n4u3Gj/54MNLJzL4ATsQ78e4B4Fq8WeJGl7HOy4GH/Me/443YTzOz6VX7NkRE4ufPR59Dr2ZtKSop5qaPXmTz/t1BlyR10EFP/Voefxd3jpklzI2ANfWriMTDmt3bOW3iw+zOz2VI60N55ZRrSUlOmH+d8n3hnPrVOXeic65pOV0UaiIiFejSuDkPjPB2NM7avJqHF3wYcEVS19TEMfQJQKZzbi3+vdCjPmKnXRURkQM4uXNvru45jH8tncED8z5gRNvDGdS6c9BlSR1RE/tzGuPd/eyPwNd4l7C9DmzBuxZdREQq6XeDTqVn0zYUlRTzs09eYnd+lS8aknoqLsfQnXMNgU5414sPMLNba/xFAqJj6CISb8t3bubUtx4mr6iQs7seySMjLyIpKS6HXSUYcdmYNRrozrmBwDXAj82seY2tOEQU6CJSG55b9hm3zZwAwIPHXMgFhw8ItiCpSXEJ9JqY+rUZcBlwNd7Ur0koyEREDsplbggffbOcyeuWcPvMCQxq1ZkujRNynCQ1pNrH0J1zJzvnXsa7RemDwKHAeODFGqlMRKQeS0pK4r7h59O6YWP2Febzi2mvUlxSXPETpd6qUqA757o45+70z2h/FzgP+AjvvuWtzewavBuyiIjIQWqWcQj3Dz8fgFmb1/CvJTMCrkjCrFK73J1zl+LtUh+F9yZgEd7May/E3A1NRERq0HEdHBd3H8y/l3/O3XMnc3yHHnTNbhF0WRJClT2G/hyQizeN6nNmNi9uFYmIyPfcMfh0Pv5mORv27eIX017ltVOv1yxy8gOV/Y3IAzLwbp5ypnNOMx2IiNSSRmkZ/N+ICwD4fMtaxi3R7Sjkhyob6G2BnwN7gD8Bq5xzHznnrnbONYpbdSIiAsAx7boxxg0B4J4vJrN697aAK5KwqVSgm9lOM3vEzAbgTRbzT7zZ4Z4CNjnnXnTOneKc0z4gEZE4uX3wabQ/pAl5RYX8buYE4nlzLal7qhzAZvaFmf0Eb9R+GfAZ8GPgHbypXy+s0QpFRASArAbp3DX0bAA+3bCS/6yaF2xBEirVHlGbWZ6ZvWhmJwCHAXcBBUD/mipORES+78SOPTmtcx8A/jTrbb7N3RdwRRIWNbKL3MzWmNkdQBe+uzmLiIjEwZ1Hn0VWg3R25O3jz3MmBV2OhESNHvM2sxIz+6+Zabe7iEictGnYmN8OPAWAl1fMYeamVQFXJGGgk9hEROqgMW4I/Vt2BOB3MyZQUFwUcEUSNAW6iEgdlJKczN1DzyU5KYkVu7bw7LLPgi5JAqZAFxGpo3o3b8cl3Y8C4G9fvs8OnSBXrynQRUTqsF8NOInstAx25edy3xfvBV2OBEiBLiJShzXPyOKWficC8MLy2SzZsSHgiiQoCnQRkTruip5D6ZbdiuKSEv4w623NIFdPKdBFROq4Bskp/GHIGQDM3LSKd9cuCrgiCYICXUQkARzbvjsndewJwF/n/FeXsdVDCnQRkQTxu0GnkpyUxJo923lp+edBlyO1TIEuIpIgujVpxejDBwLwwLwP2F+QH3BFUpsU6CIiCeQX/U4kPSWVLTl7GLdketDlSC1SoIuIJJB2WU24sucwAB5b+JHuxlaPKNBFRBLMT484lsZpGewpyOORhR8HXY7UEgW6iEiCaZrekJv6jgLgmaUz2ZazN+CKpDYo0EVEEtCVPYfRJL0huUUFPL7o06DLkVqgQBcRSUBZDdK5rvcIAJ5ZNlM3bqkHFOgiIgnqyp7DyE7LYH9hPk8s1ig90SnQRUQSVOO0DMb6o/Snl8zQGe8JToEuIpLAru45nEYN0tlXmM+Ti6cFXY7EkQJdRCSBZadncnWv4QCMXzaTvQV5AVck8aJAFxFJcNf0Gk5GSgN25+dqjvcEpkAXEUlwzTIO4cfdvDnen1w8jULdiS0hKdBFROqBsb1HkEQS3+zbyTtrdL/0RKRAFxGpBw5t3IJTO/cG4J+LPqGkpCTgiqSmKdBFROqJ6/uMBGDh9m+YuWlVwNVITVOgi4jUEwNbdWJgy04AjF86M+BqpKYp0EVE6pEreg4FYPK6JWzYtyvgaqQmKdBFROqR07v0pXnGIRSVFPOCzQq6HKlBCnQRkXokPSWVS7ofBcCLy2eTX1QYcEVSUxToIiL1zGVuCMlJSWzN2cuktYuDLkdqiAJdRKSeaZ/VhJM69gTg2WWfBVyN1BQFuohIPXR5j6MBmLV5Nat3bwu4GqkJCnQRkXpoRNvDadswG4BXV8wNuBqpCQp0EZF6KCU5mdGHDwDg1ZVfUFRcHHBFcrBSgy4gmnOuLXAzMAQYBGQBx5nZR2X0PQv4I9AL2AKMA+4ys8KYfk2Ae4FzgYbALOBWM5sXp29DRKROGN1tIH9f8CEb9+9i2saVjGrfPeiS5CCEbYTugN8AHYAFB+zk3KnABGAH8DP/8zuAB2L6JQPvABcBDwO/BloDHznnDqvx6kVE6pBDG7dgSOsuALyi3e51XtgCfS7Qwsy6AfeV0+9+4EvgZDN70sx+DvwVuMk51y2q3wXAMOByM7vTzB4FjgVKgD/E4xsQEalLLuw2CID/rlvMzrz9AVcjByNUgW5me8xse3l9nHO98HazP25m0Tf1fQzv+zk/qu0CYAPwZtRrbAVeAc5xzjWoqdpFROqiM7r0pWFqGnlFhby9ZmHQ5chBCFWgV1J//3FOdKOZbQC+jloe6TvXzGLvEzgbaAQcHq8iRUTqgkMapHNyp14ATFx9wCOdUgeE6qS4SmrrP24sY9lGoF1M36kH6Iffd2n0AufczgpePxvQHQ1EJGGc3fVI3lg1jxkbV7F5/25aN2wcdElSDXVxhJ7pP+aVsSw3anmk74H6EdNXRKReGtmuG9lpGZRQwjva7V5n1cUReo7/mF7Gsoyo5ZG+B+pHTF8AzKxJeS9eiRG8iEidkpaSyqmd+/DSijm8tXoBV/caHnRJUg11cYQe2V3etoxlbfFOgovue6B+xPQVEam3zu56JABztqzlm707gy1GqqUuBvo8/3FQdKNzrh3e9evzYvoOdM4lxaxjCLAXWBmXCkVE6pihbbrSNL0hAJPX6Q5sdVGdC3QzWwwsA65zzqVELboRKAZej2p7De/Et7MjDc65FsBo4E0zK4h/xSIi4ZeanFJ6B7bJ65YEXI1UR+iOoTvnfu9/2tN/HOOcGwHsNLNH/LZfAW8Bk51zLwN9gJ/iXZu+PGp1rwGfAc865+4HtgE34b2R+WNcvxERkTrmR5168crKuXy2aTU78/bTxB+xS90QxhH6//ofl/hfX+1//ctIBzN7GzgPaI43pet5wJ+Bn0evyJ945jS8iWR+jjf73Fa8+eG1u11EJMqo9t3ISGlAUUkxH3xtQZcjVZRUUhI754qUJ3KWe0Vnw/v0wxWROuXqKc/w3vqlnN6lL48fd2nQ5SSq2PO6akQYR+giIhKQkzv3BuDDr428osIKekuYKNBFRKTUCR16ALC/MJ85W9YGXI1UhQJdRERKtcjMok8zbwbtT75ZEXA1UhUKdBER+Z6R7b27UH/8zfIKekqYKNBFROR7RrXzAn3Rjg1sy9kbcDVSWQp0ERH5nkGtu5CZ2gCATzfoCt+6QoEuIiLfk56SytA2XQH4ZIN2u9cVCnQREfmBkf5u9082rETzldQNCnQREfmB4W0PB2Dz/t2s3bMj4GqkMhToIiLyA65pK7LTMgGYtXl1wNVIZSjQRUTkB5KTkjmqdRcAZm1SoNcFCnQRESnTkNaHAhqh1xUKdBERKdOQNl6gr92zg437dgVcjVREgS4iImXq07wd6SmpAHy5dX3A1UhFFOgiIlKmBskp9G3eHoB52xToYadAFxGRA+rXogMA8zRCDz0FuoiIHFC/lh0BWLD9G4qKiwOuRsqjQBcRkQPq7wf63oI8vtq9NeBqpDwKdBEROaBOWc1omt4Q0G73sFOgi4jIASUlJZWeGLdkx8aAq5HyKNBFRKRcPZq2BmDZt5sDrkTKo0AXEZFy9WzaFoCl32qEHmYKdBERKVePpm0A2J67j605ewKuRg5EgS4iIuXq1qQVyUlJACzdsSngauRAFOgiIlKujNQGdG3cAoBl3yrQw0qBLiIiFdJx9PBToIuISIUOb9ISgFW7tgVciRyIAl1ERCrUtbEf6LsV6GGlQBcRkQp1zfaOoX+bt59vc/cFXI2URYEuIiIVOtQ/KQ40Sg8rBbqIiFSocVoGzTMOAWDtnh0BVyNlUaCLiEildMhqCsDXe78NuBIpiwJdREQqpaMf6OsV6KGkQBcRkUrRCD3cFOgiIlIpkRH6uj0K9DBSoIuISKW0OyQbgM37d1NSUhJwNRJLgS4iIpXSMrMRALlFBewpyAu4GomlQBcRkUpp1bBx6ee6jWr4KNBFRKRSWvjXoQNsUaCHjgJdREQqJS0llWbpXqhv2a9ADxsFuoiIVFqrht5xdO1yDx8FuoiIVFrLzCwAtubsDbgSiaVAFxGRSjskNR2AnML8gCuRWAp0ERGptMzUBgDsV6CHjgJdREQqLTM1DYCcooKAK5FYCnQREam0yAg9t1CBHjYKdBERqbRIoOco0ENHgS4iIpWWmRIJdB1DDxsFuoiIVFrpMXSN0ENHgS4iIpVWustdJ8WFjgJdREQqrfSytQLtcg+b1KALiDfnXDpwJzAGaArMB243sw8CLUxEpA7K0GVroVUfRujjgVuA54GbgWJgknNuaJBFiYjURTopLrwSeoTunDsKuAi4xcwe9NueBRYB9wAjg6tORKTuiexyzysqpLikmOSk+jAurBsSfUtcABQAT0UazCwXGAeMcM61DaowEZG6KHKWO0BuYWGAlUisRA/0/sAyM4u9LdBsIAnoV+sViYjUYZEROkBOkXa7h0lC73IH2gLflNG+0X9sF7vAObezgnVmA7sOriwRkbopOtD3F+TTPCPAYuR7Ej3QM4G8Mtpzo5ZXVQmVD/SkaqxfRCS0OjdqztdX3R10GVKGRA/0HCC9jPaMqOXfY2ZN4lmQiIhIPCT6MfSNeLvdY0XaNtRiLSIiInGT6IE+D+jhnMuKaR/iP86v3XJERETiI9ED/TWgATA20uDPHHcVMN3MNEIXEZGEkFRSUhJ0DXHlnHsFOAd4APgKuAIYDBxnZtMDLE1ERKTGJPpJcQCXA//rPzYFFgCnKcxFRCSRJPwIXUREpD5I9GPoIiIi9UJ92OUeCOfcWrxZ5URERKLtMrPONb1SBXr8dMSbKU7TxAYr8qZK2yFY2g7B0zYIh2ygcTxWrECPn92gmeeCFpmbX9shWNoOwdM2CIdK3C+k2nQMXUREJAEo0EVERBKAAl1ERCQBKNBFREQSgAJdREQkASjQRUREEoACXUREJAFoLncREZEEoBG6iIhIAlCgi4iIJAAFuoiISAJQoIuIiCQA3ZylEpxzg4ErgeOAzsB2YAbwezNbGdN3GHAvMADvBi0vA7eZ2f6YfunAncAYoCkwH7jdzD6I6zdTRznnBgG34/1cW+HdMWoecKeZzYjpq21Qi5xzvwbuAeabWb+YZdoWceCcOxb48ACLe5rZsqi+2gZx5OfDH4FhQAPgK+ABMxsf1ecsv08vYAswDrjLzApj1tUEb1udCzQEZgG3mtm8ytSiEXrl/AY4D5gC3Aw8ARwLfOmc6xnp5JzrB3wAZAC3Ak8B1+P9AcUaD9wCPO+vsxiY5JwbGqfvoa47DO8N6JPAT4H78IL9E+fcSZFO2ga1yznXBvg9sK+MZf3Qtoi3B/ECOPpjQ2ShtkF8OedOBabjBfn/A36BlxMdY/pMAHYAP/M/vwN4IGZdycA7wEXAw8CvgdbAR865wypTjy5bqwT/He4cM8uPausGLAReMrMr/bZ3gSOAHma2128bixdCJ5jZVL/tKLx3XreY2YN+WwawCNhgZiNr6Vur05xzDYFVeNvmDL9N26AWOefGA53wBgdNokfo2hbxEzVCP9fMJpTTT9sgTpxz2cByvAy4uZx+i4Fc4CgzK/Lb/gzchrddVvhtF+K90Srdps65lv5rTDSzyyuqSSP0SjCzGdFh7retABYDPQGcc42Bk4BnI384vmeBvcCFUW0XAAV475Yj68vF2w0zwjnXNh7fR6LxdxluBZqAtkFt80PgMryRX+wybYta4pxr5Jz7weFTbYO4uwTvf88dULodkqI7OOd64e1mfzwS5r7H8PL3/Ki2C/D2rrwZaTCzrcArwDnOuQYVFaRAryZ/w7UGtvlNffF2Cc+J7ue/EZgH9I9q7g8si/kjA5gNJAH9ar7ixOD/0bRwnr8AffB2KYK2Qa3xf/8fBp45wPE9bYva8RzecfEc59x7zrm+Ucu0DeLrRGAZcJpzbj3edtjhnLvbOZfi94n8jGO3wQbga364DeaaWexu89lAI+DwigpSoFffpUB7vHdPAJF3sBvL6LsRaBf1ddty+hHTV77vabxR+TK841X/BP7iL9M2qD2X4408fn+A5doW8ZUPvIZ3rPts4E/AUcA051x3v4+2QXwdjnesfLz/cT7wBt45V//n96nVbaCz3KvBOdcDeBSYhvcOGSDTf8wr4ym5UcsjfQ/Uj5i+8n1/Ah4HOuCdAJSOd0JKHtoGtcI51wi4G7jbzMr6BwTaFnHlX9kRfXXHW865iXgjwT/gDTi0DeIrC+9qgN+a2T1+23+cc1nATf5x8oq2QcOorw96G2iEXkX+Wb3vAN8Co82s2F+U4z+ml/G0jKjlkb4H6kdMX4liZgvN7H0zexo4GRiI9+4YtA1qy+/xRoh/K6ePtkUtM7P5eGdYn+A3aRvEV+Rn8u+Y9hfwBhlHUcvbQIFeBf5ZjZOAbOBkM9sUtTgyUinr5JG2RF1K4vc9UD9i+soBmFkB3gkk5znnMtE2iDv/5Kj/wdtD1do518U51wXvn06a/3VTtC2Csh5o5n+ubRBfkZ/v5pj2yNe1/negQK8k/xKOiUB34Awzs5gui4BCYFDM89LwTiiZF9U8D+jh75qJNsR/nF8jRdcPmXgn7TRC26A2tAbS8CaSWR31MQTvio/VeMcQtS2C0RXvHBPQNoi3uf5j+5j2Dv7jVr77Gcdug3Z+v3lRzfOAgbFnyuNtg73ASiqgQK8E/4zFl4GheLvZP4vtY2a78HZ3jYn5oxiDd6zl1ai21/B2yYyNeo104Cpgun8GpETxr8eMbWsMjAbWm9kWbYNasRpvFqvYj8XAGv/zZ7Ut4usAfw8j8GaznAz6n1QLIj+/ayINfhiPxZto6TMzW4x3Au91UWe+A9yIN3HP61Ftr+Gd+HZ21Ppa4P2Pe9PfI1kuTSxTCc65B/HOJp3Id2e1R+yNmgRgAN6JKovwrufsgHcm9odmdlrMOl8BzsGbLegr4ApgMHCcmU2P07dSZznnpuKdHDID2IR3dulVeD/ji8zsFb+ftkEAnHMf8cOJZbQt4sT/e9iP9/Pdhnf55nV4UyIPNrN1fj9tgzhyzj2D9wZpHPAFcLr/8Wszu8/vcwbwFjAVb2DYB2+2y8fN7KaodaXgnWjdG7gfb7vehPe/bmDsNONl0Qi9cvr5j2findUe/fFgpJOZfYF3bWIe3h/FtXgzMo0uY52XAw/5j3/He3d8mv5wDuh5vDNCfw78A+8XfT7eP5vSN1naBuGhbRFXE4CWeOH8KN4lUy8SFeagbVALrgXuwjtB9yG8S9luiIQ5gJm9jTd1eHO8uRvOA/6M97+MqH5FwGl4g8af401vvRXvf1yFYQ4aoYuIiCQEjdBFREQSgAJdREQkASjQRUREEoACXUREJAEo0EVERBKAAl1ERCQBKNBFREQSgAJdREQkAeh+6CLyA/7c37uo/Jv+5ma2o5z1PYI3z3sHMytzNivn3LHAh8CvzOz+mGWj8KbP3I93p8MFlaxLpN5QoItIWVLx5vKOdiMwDPgl379lZF4FYZ6EN0f4mwcK8/L4c2G/ijeH/4lm9lVV1yFSHyjQReQHzGwn3vz5pZxzt+DdIOchMyuswuoG491ickJV63DOXQI8AxjwI931S+TAdAxdRCrknGuAd5eoBVUMc/B2te/E251elde8Ee9NxRfASIW5SPkU6CJSGb2BNODLajz3XODdytzPOcI5dxvwGN6bgBPK26UvIh7tcheRyujvP35RlSc553oCDvh9FZ52I9AVbxf9RWaWV5XXFKmvNEIXkcoY4D9WdYR+Dt5x9/9W4Tlt/cevFOYiladAF5HK6A8UAgur+LxzgSlmtrcKz7kbmAr8wjn3f1V8PZF6S4EuIuVyziUDRwJLzSy3Cs/rAAyi6me37wfOAD4AbnXOPVDF54vUSwp0EalINyCLKh4/x9vdXoI3IUyVmFkOcCYwBfgf59yDVV2HSH2jQBeRilT3+Pm5wDQz21qdF/VD/SzgfeBm59xD1VmPSH2hQBeRilT5DHfnXDNgJNWYTCZaVKi/B/zcOffwwaxPJJEp0EWkIv3xdp3Pr8JzzsC7LPaNg31x/7j92cBk4KfOuUf96WRFJEpSSUmVp1YWESmXc+4N4FAz6xd0LSL1hSaWEZF4mAk8GXQRIvWJRugiIiIJQMfQRUREEoACXUREJAEo0EVERBKAAl1ERCQBKNBFREQSgAJdREQkASjQRUREEoACXUREJAH8fzPjGhRJbvOgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.lineplot(y=df.hlv, x=df.temperature, ax=ax)\n",
    "\n",
    "# axis and styling \n",
    "ax.set_xlabel(r'$T$ / K');\n",
    "ax.set_ylabel(r'$\\Delta_{LV} h$ / kJ / kg');\n",
    "ax.set_xlim(200, 600)\n",
    "ax.set_ylim(0, 500)\n",
    "\n",
    "sns.despine(offset=10);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "feos",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}