{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy volume curve\n",
    "## Theory \n",
    "By fitting the energy-volume curve, we can determine several equilibrium properties, including the equilibrium energy $E_0$, equilibrium volume $V_0$, equilibrium bulk modulus $B_0$, and its derivative $B_0'$. These properties are then utilized in the Einstein model to provide an initial estimation for the thermodynamic properties, namely the heat capacity $C_v$ and the free energy $F$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing matplotlib, numpy and the pyiron project class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:08.037567Z",
     "start_time": "2019-09-04T12:58:06.149845Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "model_id": "dd260ca2d4ea4a9296c26b7e12a1ce1a",
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       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pyiron import Project\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we create a project, by specifying its relative path/name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:09.049344Z",
     "start_time": "2019-09-04T12:58:08.040033Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pr = Project('thermo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Atomistic structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse the energy volume dependence a single super cell is sufficient, so we create an iron super cell as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:09.222395Z",
     "start_time": "2019-09-04T12:58:09.051675Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "122085497932408fbab5cda44864e959",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis = pr.create.structure.bulk(name='Fe', cubic=True, a=2.75)\n",
    "basis.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure energies by looping over different volumes\n",
    "\n",
    "Let's first see how we can get the energy as a function of the volume mainly based on the knowledge you acquired in the [first tutorial notebook](https://pyiron.readthedocs.io/en/latest/source/notebooks/first_steps.html). For this, we are going to vary the volume of our initial structure by up to 5 %.\n",
    "\n",
    "Energy volume curves are commonly calculated with ab-initio simulation codes. In this example, we use [GPAW](https://wiki.fysik.dtu.dk/gpaw/), but the functionality below would work just as well with any DFT code, just by changing the job name prefix `Gpaw` to the DFT code of your choice (e.g. `Vasp`, `Sphinx`...). In this tutorial, we select an energy cutoff of 320 eV, but the simulation would run without changing any parameter. For the rest of the DFT parameters, you can look up functions starting with `job.set_*`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Launch calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Exponent of 1/3 to translate volume strain of 5 % to line strain\n",
    "line_strain_list = np.linspace(0.95, 1.05, 7)**(1 / 3) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sample_m0d01695243 was saved and received the ID: 19694133\n",
      "The job sample_m0d01123691 was saved and received the ID: 19694134\n",
      "The job sample_m0d00558671 was saved and received the ID: 19694135\n",
      "The job sample_0d0 was saved and received the ID: 19694136\n",
      "The job sample_0d00552497 was saved and received the ID: 19694137\n",
      "The job sample_0d01098989 was saved and received the ID: 19694138\n",
      "The job sample_0d01639636 was saved and received the ID: 19694139\n"
     ]
    }
   ],
   "source": [
    "for strain in line_strain_list:\n",
    "    dft = pr.create.job.Gpaw(job_name=('sample', strain))\n",
    "    dft.set_encut(320.0) # Optional, among other plane wave dft parameters\n",
    "    dft.structure = basis.apply_strain(strain, return_box=True)\n",
    "    dft.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your environment allows you to launch calculations to a queueing system, it might be useful to set\n",
    "\n",
    "```python\n",
    "dft.server.queue = \"name_of_the_queue\"\n",
    "dft.server.cores = number_of_cores\n",
    "dft.server.run_time = run_time_in_seconds\n",
    "```\n",
    "\n",
    "Queueing system setup can be found in the pyiron docs, the homepage for which is [here](https://pyiron.readthedocs.io/en/latest/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = {'energy': [], 'volume': []}\n",
    "for strain in line_strain_list:\n",
    "    dft = pr.load(('sample', strain))\n",
    "    results['volume'].append(dft.structure.get_volume())\n",
    "    results['energy'].append(dft.output.energy_pot[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('Potential energy eV')\n",
    "plt.xlabel(r'Volume $\\mathrm{\\AA}^3$')\n",
    "plt.grid()\n",
    "plt.scatter(results['volume'], results['energy'], label='DFT results');\n",
    "coeff = np.polyfit(results['volume'], results['energy'], 3)\n",
    "volume_mesh = np.linspace(np.min(results['volume']), np.max(results['volume']), 1000)\n",
    "energy_mesh = np.polyval(coeff, volume_mesh)\n",
    "plt.plot(volume_mesh, energy_mesh, 'r--', label='3rd order polynomial fit');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the results look good enough for us to start extract physical parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equilibrium volume: 21.19 A^3\n",
      "Equilibrium bulk modulus: 1.753 eV/A^3\n"
     ]
    }
   ],
   "source": [
    "equi_volume = np.roots(np.polyder(coeff)).min()\n",
    "print('Equilibrium volume:', np.round(equi_volume, decimals=3), 'A^3')\n",
    "equi_bulk_mod = np.polyval(np.polyder(coeff, 2), equi_volume) * equi_volume\n",
    "print('Equilibrium bulk modulus:', np.round(equi_bulk_mod, decimals=3), 'eV/A^3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translation of eV/A^3 to GPa takes a factor of around 160, meaning the bulk modulus measured here is around 280 GPa, which is comparable to experimental results.\n",
    "\n",
    "So far, we examined the procedure for conducting an energy-volume curve analysis through the manual creation of jobs. It is great that it requires only around 10 to 20 lines of code, but since it's such a common routine, we also offer the master class `Murnaghan` for this purpose, which offers an array of enhanced functionalities designed to streamline the process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Murnaghan Job\n",
    "\n",
    "In order to launch a set of DFT calculations with varying volumes and do the analysis afterwards, the Murnaghan job can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.644025Z",
     "start_time": "2019-09-04T12:58:37.532255Z"
    },
    "tags": []
   },
   "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>Parameter</th>\n",
       "      <th>Value</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num_points</td>\n",
       "      <td>11</td>\n",
       "      <td>number of sample points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fit_type</td>\n",
       "      <td>polynomial</td>\n",
       "      <td>['polynomial', 'birch', 'birchmurnaghan', 'mur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fit_order</td>\n",
       "      <td>3</td>\n",
       "      <td>order of the fit polynom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vol_range</td>\n",
       "      <td>0.1</td>\n",
       "      <td>relative volume variation around volume define...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>axes</td>\n",
       "      <td>[x, y, z]</td>\n",
       "      <td>Axes along which the strain will be applied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>strains</td>\n",
       "      <td>None</td>\n",
       "      <td>List of strains that should be calculated.  If...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parameter       Value                                            Comment\n",
       "0  num_points          11                            number of sample points\n",
       "1    fit_type  polynomial  ['polynomial', 'birch', 'birchmurnaghan', 'mur...\n",
       "2   fit_order           3                           order of the fit polynom\n",
       "3   vol_range         0.1  relative volume variation around volume define...\n",
       "4        axes   [x, y, z]        Axes along which the strain will be applied\n",
       "5     strains        None  List of strains that should be calculated.  If..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft = pr.create.job.Gpaw(job_name='gpaw')\n",
    "dft.set_encut(320) # Again, optional\n",
    "dft.structure = basis.copy()\n",
    "murn = dft.create_job(job_type=pr.job_type.Murnaghan, job_name='murn')\n",
    "murn.input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In principle you can run the simulation without touching the input, but since we chose 7 points and volume strain of 5%, let's take these parameters here as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.933789Z",
     "start_time": "2019-09-04T12:58:37.931262Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "murn.input['num_points'] = 7\n",
    "murn.input['vol_range'] = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.529725Z",
     "start_time": "2019-09-04T12:58:38.466857Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn was saved and received the ID: 19694140\n",
      "The job murn_0_95 was saved and received the ID: 19694141\n",
      "The job murn_0_9666667 was saved and received the ID: 19694142\n",
      "The job murn_0_9833333 was saved and received the ID: 19694143\n",
      "The job murn_1_0 was saved and received the ID: 19694144\n",
      "The job murn_1_0166667 was saved and received the ID: 19694145\n",
      "The job murn_1_0333333 was saved and received the ID: 19694146\n",
      "The job murn_1_05 was saved and received the ID: 19694147\n"
     ]
    }
   ],
   "source": [
    "murn.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.850020Z",
     "start_time": "2019-09-04T12:59:39.628014Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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3VrVlZVxPYmKi8Pb2FoULF5aNgdu/f7+wt7cXAwYMEPv37xeHDh0SI0eOFBYWFrKwnPqFXLlyZZGcnKxqf/DggbCzsxO9evVSe85///1XXLhwQezdu1cMGzZMmJmZyWo9deqUKmzdvXtX1a5UKkWtWrWEp6enrP62bduK4sWLi5UrV4rff/9drF27VpQvX17UqlVLvHz5UuPrTkpKEu7u7hrHNiYkJIhy5cqJokWLil9//VW8ePFC7N+/X7i5uQlzc3O131lt31NNzp07J8zMzMSoUaMy3Oa7774TAMTWrVtl7Vn5nU394yx1Sf1DIfXze/Tokdrztm7dWlSsWDHDuurUqSMKFy4s4uPjM32N+RlP+ZHeKRQKDBgwAD/++COWLVuGChUqqE7p5JSdnR0cHR01ritcuLBam7W1texS6C+++AJBQUHo1KkTdu/ejXPnzuHChQuoUaOGbLvnz5/Dzc1N7Xia2rR97h07dqBbt24oVqwYfvzxR5w5cwYXLlzAwIEDER8fn61jauv58+dITk7GkiVLYGlpKVtSx5I8e/ZMto+Hh4fGY2n6DJ4/fw53d3coFApZu6urKywsLPD8+XOtjm0oAwcOxOzZs7F+/Xp4enqiRIkSuH79uup0V7FixVTbbt26FY0aNUK3bt1QsGBBNG/eHB9//DFq1qwp265w4cKIj4/Hmzdv1J4vOjoahQoVUj0eMWIE3NzcsHPnTrRv3x7NmzdHcHAwJk2ahKlTp6pO6y5cuBC3b9/GlClT8PLlS7x8+VJ1JVt8fDxevnyZ6TQLlpaW6N69O54/f46IiAgA0jQQAwcORNOmTbFmzRq0adMGLVu2xOLFi/HJJ59g5MiRqivtUn8GW7ZsCXNzc9VxPTw8UKNGDVy+fFntOUuUKIE6deqgXbt2+OGHH/Dpp58iICAAT58+lR2zUqVKKFmypGo/hUIBPz8/3L9/H0+ePAEgnQrbv38/duzYgcGDB6NJkybo27cvDhw4gMuXL2PhwoUaX/e+ffvw6NEjDB48WG2dlZUV9u/fjxIlSqB169YoWLAgunTpgi+//BIFCxaUfabavqcZqVevHipUqJDplAOrV69GkSJF0LFjR1l7Vn5ny5YtK1s/ffp0AO/e6/S/f4D6z2Raf/75Jy5evIjevXtrPDVOEk6bQAbRv39/TJ48GcuWLcOMGTMy3C51UGn6gcvpv9xTpf/Czqoff/wRffv2xcyZM9WeL3WgKyD9Q/T48WO1/dPOZZOd5y5dujQ2b94sex3pX7s+FCxYEObm5ujTpw9GjBihcZv04yQyeq81tRcuXBjnzp2DEEK2/smTJ0hOToaLi4tWxzakiRMnYsyYMYiIiICDgwNKliyJoUOHokCBAqhdu7ZqO1dXV+zbtw9PnjzBo0ePULJkSdja2mLp0qXo0qWLarvUsVN//fUX6tevr2p/9OgRnj17JpurLCwsDD179pSFFACoW7culEol/v77b5QpUwZXr15FTEyMxkv0g4KCEBQUhCtXrqBmzZoZvk4hBIB3g/gfP36Mhw8fYujQoWrb1q1bF+vWrcPdu3dRpUoVtbE36Y+rzYUB9erVw7Jly3D79m0UKVIEZcuWlV0MkFmtYWFhMDc3R61atWTblSlTBoULF85wDNDq1athZWWFPn36aFxfrlw5nDlzBlFRUYiOjkbZsmURExOD0aNHo2nTpu99TenrfN+2GW135coVXLlyBWPHjpVNvwFk7Xd29+7dsn9HihYtCkD+M1m5cmXV+uTkZPzzzz+qi4jSW716NQBoDKT0DgMVGUSxYsUwfvx4/PPPP+jXr1+G26UOQP7zzz/h5+enas9s0GdOKBQKtb+49u7di6ioKJQrV07V1qxZM3z77be4fv267B8iTRMkZuW5raysZGHi0aNHGq/y0zU7Ozs0b94cV65cQfXq1WFlZaXT47do0QJbtmzBrl27ZPPopA62T70SMLextrZWBZ3IyEhs3rwZQ4YMUbv6DpCCVerA/MWLF+P169eyixTatGkDGxsbhIaGygJV6qS3nTp1UrUVLVoUFy9eREpKiixUpQ709vT0BABMmjRJbcLSR48eoWfPnhg2bBi6d+8u+7lNLykpCZs3b4aLi4tqu4IFC8LGxkZjr8mZM2dgZmam6kGsX78+PD098euvv8pqffDgAf744w+1uZk0OXbsGMzMzFCmTBkAgIWFBTp27Iht27bh7t27qn8DhBA4cOAAypYtqwrgRYsWRUpKCi5cuCB7T2/cuIHnz5+r3qf078++ffvw8ccfa+zlTatYsWKqHqnAwEAUKFAAgwYNynQfTe9pRs6ePYuIiAiMGjVK4/rU4KLpObPyO5v+QohU9evXh4eHB0JDQ2UXBW3btg2vXr3SeMFIQkICfvzxR9SrV48TFr8HAxUZzOzZs9+7jbu7O1q2bIlZs2ahYMGCKFmyJI4cOYIdO3bopab27dsjNDQUlSpVQvXq1XHp0iXMnTtX7R/mMWPGYM2aNWjbti2mT58ONzc3bNy4Ef/88w+A7F2y3759e+zYsQOfffYZunTpgnv37iE4OBgeHh7vPXWQGQsLCzRr1gxHjhzJdLtFixahcePGaNKkCYYPH45SpUohLi4ON2/exO7du3H06NFs19C3b198//336Nevn+oKrpMnT2LmzJlo165dpldjpZX6BXXz5s33bvvbb7+pTiOlpKTg33//xbZt2wBIgbhIkSIApCvkpk+fjiNHjqBZs2YApKuftm/fjjp16sDa2hp//PEHZs+ejfLly6tdSbdy5UoA0mmVly9fYv/+/Vi9ejVmzpwp6zkpVKgQAgMDERQUhEKFCqkm9pw6dSoGDx4sC+aff/45Ro0ahQ4dOmDo0KGws7PDkSNHMG/ePLRs2RI1atQAIJ0Wq1Spkqyeu3fvqupJe3XnF198gaSkJDRq1Aju7u64d+8elixZgrCwMISEhKjCkLW1NT777DPMnz8fffv2Rffu3WFubo5du3Zh48aNGDRokOpUkJmZGRYsWIBu3bqhY8eOGD58uOpqQysrKwQEBKie/9NPP4WjoyPq1asHNzc3PHv2DFu3bsXmzZsxfvx41ecBAMHBwdi/fz/atGmDqVOnwtHREatWrcIff/yBLVu2qLYbMGAAFixYoJqAtGLFirh9+zZmzpyJAgUKaJyIcu3atUhOTs60d2XOnDlwd3dHiRIl8PjxY9UfA+vXr5ed8tP2PQWAGjVqoHfv3vDy8oKNjQ3Onz+PuXPnwt3dXeOVm/Hx8di4cSN8fHzg5eWlsc6c/s6am5tjzpw56NOnD4YOHYqePXsiIiICEyZMQKtWrdCmTRu1fXbt2oXo6Gj2TmnDeMO3KC9LOyg9M5om9nz48KHo0qWLKFSokHBychK9e/cWFy9e1DgovUCBAhqPC0CMGDFCrb1kyZKywcovXrwQgwYNEq6ursLOzk40btxYnDhxQjRr1kytrqtXr4qWLVsKGxsbUahQITFo0CCxdu1aAUB2dVNGE3v269dPlCxZUtY2e/ZsUapUKWFtbS28vLzEypUrVRMCZuf1pG6r7WSpd+7cEQMHDhTFihUTlpaWokiRIsLHx0d8/fXXqm1SB6WnHySb+poy+gyeP38uhg0bJjw8PISFhYUoWbKkCAgIUBvUmtFrS3196d+zjKReXalpSXvRQOr7m7YtPDxcNG3aVBQqVEhYWVmJcuXKicDAQLWJNoUQYvny5cLLy0vY2dkJe3t70aRJE7Fr164M61q0aJGoUKGCsLKyEiVKlBBTpkwRiYmJattt375dNG7cWLi4uIgCBQqIKlWqiODgYI01pJXRoPTVq1eLevXqiUKFCgkLCwtRsGBB4efnJw4ePKh2jJSUFLFy5UpRp04d4ezsLBwdHYW3t7f47rvvNNa6a9cuUbduXWFjYyOcnJzERx99pHYF7Jo1a0STJk2Ei4uLsLCwEM7OzqJZs2ZqV9Ol+uuvv8SHH34oHBwchI2NjWjQoIHYvXu32nYRERGiT58+qt+bEiVKiO7du6s9f6oKFSqIUqVKqV0gkda0adNE2bJlhbW1tXB2dhZt2rRRXTiTVlbe0x49eohy5cqJAgUKCEtLS1GyZEkxbNgw8eDBA401pE6uqemq5bS0+Z19n40bN4rq1asLKysr4e7uLkaNGiXi4uI0btuqVStRoEAB2RXGpJlCiP9O/hJRln366afYtGkTnj9/rvPTZkREZDp4yo9IS9OnT0fRokVRpkwZvHr1Cnv27MGqVasQGBjIMEVElM8xUBFpydLSEnPnzsX9+/eRnJyM8uXLY/78+Rg9erSxSyMiIiPjKT8iIiKiHOLEnkREREQ5xEBFRERElEMMVEREREQ5xEHpBqJUKvHgwQM4ODjkittsEBER0fsJIRAXF4eiRYtmOokzA5WBPHjwAMWLFzd2GURERJQN9+7d03h7o1QMVAbi4OAAQPpAHB0djVwNERERaSM2NhbFixdXfY9nhIHKQFJP8zk6OjJQERERmZj3DdfhoPRcZOpUIN19WPUmOFh6PiIiIso5BqpcxNwcmDxZ/6EqOFh6njQ3RiciIqIc4Cm/XCQoSPrv5Mnyx7qUGqamT9fP8YmIiPIjBqpcRp+himGKiEg7KSkpSEpKMnYZZACWlpYw18EpGwaqXEgfoYphiojo/YQQePToEV6+fGnsUsiAnJ2d4e7unqN5IhmocildhiqGKSIi7aSGKVdXV9jZ2XEi5jxOCIE3b97gyZMnAAAPD49sH4uBKhfTRahimCIi0k5KSooqTBUuXNjY5ZCB2NraAgCePHkCV1fXbJ/+Y6DK5XISqhimiIi0lzpmys7OzsiVkKGlfuZJSUkMVHlZdkIVwxQRUfbwNF/+o4vPnIHKRGQlVDFMERERGRYn9jQhQUFSSNI0+acQ0n8ZpoiIKKtCQ0Ph7Oxs7DK0MnXqVNSsWTNL+ygUCuzatUsv9aRiD5WJSd9T9eGHwPz5gL09UKwYwxQREeVt48aNw8iRI41dhhoGKhMUFAQ8fSqFp9RgZW4OpKQwTBERUd5mb28Pe3t7Y5ehhqf8TNRXX8nvxZeSAvj6MkwREemKUin98WrMRanUrlZfX1/4+/vD398fzs7OKFy4MAIDAyH+Gw/y4sUL9O3bFwULFoSdnR3atm2LiIgIjce6e/cuzMzMcPHiRVn7kiVLULJkSQghcPz4cSgUChw5cgR16tSBnZ0dfHx8EB4eLtvnhx9+QNmyZWFlZYWKFSti/fr1svUKhQLLly9H+/btYWdnBy8vL5w5cwY3b96Er68vChQogIYNG+LWrVuqfdKf8rtw4QJatWoFFxcXODk5oVmzZrh8+bJ2b5wOMVCZKDc3oF8/edu1a8Dbt8aph4gor3n+HHB1Ne7y/Ln29a5duxYWFhY4d+4cFi9ejAULFmDVqlUAgP79++PixYv45ZdfcObMGQgh0K5dO4231ylVqhRatmyJkJAQWXtISAj69+8vuyLuq6++wrx583Dx4kVYWFhg4MCBqnU7d+7E6NGjMXbsWFy9ehVDhw7FgAEDcOzYMdlxg4OD0bdvX4SFhaFSpUr45JNPMHToUAQEBKhCnb+/f4avOy4uDv369cOJEydw9uxZlC9fHu3atUNcXJz2b54uCDKImJgYAUDExMTo7JgjRgghDUd/t6xcqbPDExHlK2/fvhXXr18Xb9++FUII8eSJ+r+xhl6ePNGu9mbNmgkvLy+hVCpVbRMnThReXl7ixo0bAoA4deqUat2zZ8+Era2t2LJlixBCiJCQEOHk5KRav3nzZlGwYEERHx8vhBAiLCxMKBQKcefOHSGEEMeOHRMAxOHDh1X77N27VwBQvX8+Pj5iyJAhsjq7du0q2rVrp3oMQAQGBqoenzlzRgAQq1evVrVt2rRJ2NjYqB5PmTJF1KhRI8P3Ijk5WTg4OIjdu3fLnmfnzp0Z7pP+s09L2+9v9lCZqOBg4PvvgbJl5e1ffvnuij8iIso/GjRoIOs9atiwISIiInD9+nVYWFigfv36qnWFCxdGxYoV8ffff2s8VqdOnWBhYYGdO3cCANasWYPmzZujVKlSsu2qV6+u+v/U27ak3sbl77//RqNGjWTbN2rUSO050x7Dzc0NAFCtWjVZW3x8PGJjYzXW+uTJEwwbNgwVKlSAk5MTnJyc8OrVK0RGRmrcXl8YqExQ2qkRvvtOvu7pU/VTgUREROkJITKc0NLKygp9+vRBSEgIEhMTsXHjRtnpvFSWlpaq/089ljLNwK/0x9f0nJqO8b7jptW/f39cunQJCxcuxOnTpxEWFobChQsjMTFR4/b6wqv8TEz6eaaEACpXBq5ff7fN+vVA+fIcoE5ElBOFCwP/dbYYtQZtnT17Vu1x+fLlUblyZSQnJ+PcuXPw8fEBADx//hw3btyAl5dXhscbPHgwqlatiqVLlyIpKQkff/xxlmr38vLCyZMn0bdvX1Xb6dOnM33O7Dhx4gSWLl2Kdu3aAQDu3buHZ8+e6fQ5tMFAZUI0TdqpUABjxgCffirfNic3VCYiIsDMDChSxNhVaO/evXv44osvMHToUFy+fBlLlizBvHnzUL58eXTs2BFDhgzB8uXL4eDggEmTJqFYsWLo2LFjhsfz8vJCgwYNMHHiRAwcOFB1E2FtjR8/Ht26dUOtWrXQokUL7N69Gzt27MDhw4dz+lJlypUrh/Xr16NOnTqIjY3F+PHjs1yrLvCUn4nIbAb03r0BFxd5W61ammdUJyKivKlv3754+/Yt6tWrhxEjRmDkyJH49L+/tkNCQlC7dm20b98eDRs2hBAC+/btk51a02TQoEFITEzUeLrvfTp16oRFixZh7ty5qFKlCpYvX46QkBD4+vpm5+VlaM2aNXjx4gW8vb3Rp08fjBo1Cq6urjp9Dm0o/hv9TnoWGxsLJycnxMTEwNHRMUv7anM7mSlTpPWprK2lnqtvvuFkn0RE2oiPj8edO3dQunRp2NjYGLucLPH19UXNmjWxcOFCnR53xowZ+Omnn/DXX3/p9Li5TWafvbbf3+yhyuW0vTffZ58BVlbvHickALa2Gd/7j4iIKCOvXr3ChQsXsGTJEowaNcrY5ZgEjqHKxbJyo2M3N6BXLyDtPGzffw/8+6/0/xxTRURE2vL398emTZvQqVOnbJ3uy48YqHKprISpVJ9/Lg9UT58CP/6ofkNlhioiorzl+PHjOj1eaGgoQkNDdXrMvI6n/HKh7IQpAKhWDWjdWt42b550L6igIJ7+IyIi0hcGqlwmu2Eq1dix8sfh4cDevdL/M1QREb0fr9XKf3TxmTNQ5SI5DVMA0KqV1FOV1rx57/6foYqISLPUKQTevHlj5ErI0FI/8/dNI5EZjqHKRVJScj7FgUIh9VL17/+u7bffgAsXgLp1pcepx09Jyf7zEBHlNebm5nB2dlbdi87Ozi7DW7NQ3iCEwJs3b/DkyRM4OzvD3Nw828fiPFQGkpN5qLIqMREoXRp48OBdW/fuwE8/6fVpiYhMnhACjx49wsuXL41dChmQs7Mz3N3dNQZobb+/2UOVB1lZAaNGAZMmvWvbtg24exdId6NwIiJKQ6FQwMPDA66urkhKSjJ2OWQAlpaWOeqZSsUeKgMxZA8VALx4ARQvDrx+/a5tzBhgwQK9PzUREVGewZnS87mCBYHBg+Vtq1YB7MUmIiLSPQaqPGz0aOlu6alevQKWLTNePURERHkVA1UeVro00KWLvG3RIuk+f0RERKQ7DFR53Lhx8sePHkm3oyEiIiLdYaDK4+rWBZo3l7d9+610OxoiIiLSDQaqfGDCBPnjf/4B9uwxTi1ERER5EQNVPuDnp347mjlzjFMLERFRXsRAlQ8oFMD48fK2U6eA06eNUw8REVFew0CVT/ToIU30mdbcucaphYiIKK9hoMonLC2Bzz+Xt/38szSeioiIiHKGgSofGTwYcHJ691gIYN4849VDRESUVzBQ5SMODsBnn8nb1q0DHj40Tj1ERER5BQNVPjNqFGBl9e5xYiKwcKHRyiEiIsoTGKjyGXd3oH9/edsPP/CmyURERDnBQJUPjR8vv2lyXJwUqoiIiCh7GKjyoXLlgP/9T962aBHw9q1x6iEiIjJ1DFT51MSJ8sePHwNr1xqnFiIiIlNnMoFqxowZ8PHxgZ2dHZydnTPcLjQ0FNWrV4eNjQ3c3d3h7++f6XF9fX2hUChkS48ePTRum5CQgJo1a0KhUCAsLCwHr8b4atcGWrWSt82dCyQnG6ceIiIiU2YygSoxMRFdu3bF8OHDM9xm/vz5+OqrrzBp0iRcu3YNR44cgZ+f33uPPWTIEDx8+FC1LF++XON2EyZMQNGiRbP9GnKbSZPkj2/fBrZtM04tREREpszC2AVoa9q0aQCkHihNXrx4gcDAQOzevRstWrRQtVepUuW9x7azs4O7u3um2+zfvx+//vortm/fjv3792tfeC7WvDlQpw5w8eK7tm++Abp3l+7/R0RERNoxmR6q9zl06BCUSiWioqLg5eUFT09PdOvWDffu3Xvvvhs2bICLiwuqVKmCcePGIS4uTrb+8ePHGDJkCNavXw87Ozut6klISEBsbKxsyW0UCvVeqrAw4OBBo5RDRERksvJMoLp9+zaUSiVmzpyJhQsXYtu2bYiOjkarVq2QmJiY4X69evXCpk2bcPz4cQQFBWH79u34+OOPVeuFEOjfvz+GDRuGOnXqaF3PrFmz4OTkpFqKp78zcS7RqRNQoYK8bfZso5RCRERksowaqKZOnao2IDz9cjHt+ahMKJVKJCUlYfHixfDz80ODBg2wadMmRERE4NixYxnuN2TIELRs2RJVq1ZFjx49sG3bNhw+fBiXL18GACxZsgSxsbEICAjI0msLCAhATEyMatGmp8wYzM2BCRPkbb/9Bpw6ZZx6iIiITJFRx1D5+/tneEVdqlKlSml1LA8PDwBA5cqVVW1FihSBi4sLIiMjta6pVq1asLS0REREBGrVqoWjR4/i7NmzsLa2lm1Xp04d9OrVC2szmGvA2tpabZ/cqk8fYOpU4P79d22zZgF79hitJCIiIpNi1EDl4uICFxcXnRyrUaNGAIDw8HB4enoCAKKjo/Hs2TOULFlS6+Ncu3YNSUlJqoC2ePFifP3116r1Dx48gJ+fHzZv3oz69evrpHZjs7ICxo0Dxox517Z3rzSeqmZNIxVFRERkQkxmDFVkZCTCwsIQGRmJlJQUhIWFISwsDK9evQIAVKhQAR07dsTo0aNx+vRpXL16Ff369UOlSpXQvHlzAEBUVBQqVaqE8+fPAwBu3bqF6dOn4+LFi7h79y727duHrl27wtvbWxXQSpQogapVq6qWCv8NOCpbtqwquOUFgwcD6bMtx1IRERFpx2QC1eTJk+Ht7Y0pU6bg1atX8Pb2hre3t2yM1bp161C/fn18+OGHaNasGSwtLXHgwAFYWloCAJKSkhAeHo43b94AAKysrFRzVVWsWBGjRo1C69atcfjwYZibmxvldRpLgQLyHioA2LIFuHHDKOUQERGZFIUQQhi7iPwgNjYWTk5OiImJgaOjo7HL0ejlS6BECelmyakGDQJWrTJaSUREREal7fe3yfRQkf45OwMjRsjb1q0DcukFikRERLkGAxXJjBkD2Ni8e5yUBMybZ7RyiIiITAIDFcm4uUmn+dJasQJ48sQ49RAREZkCBipSM348YJFmQo23b4GFC41WDhERUa7HQEVqSpYEeveWt333HRAdbZx6iIiIcjsGKtIoIAAwS/PTERcHLFlivHqIiIhyMwYq0qhCBaB7d3nbokVAbKxx6iEiIsrNGKgoQ19+KX/84gXwww/GqYWIiCg3Y6CiDFWtCnTuLG+bNw94/do49RAREeVWDFSUqa++kj9++hRYudI4tRAREeVWDFSUqdq1gbZt5W1z5wLx8caph4iIKDdioKL3CgqSP37wAAgJMU4tREREuREDFb1Xw4bABx/I22bPBhITjVMPERFRbsNARVoJDJQ/jowE1q41Ti1ERES5DQMVacXXF2jcWN42c6Z082QiIqL8joGKtKJQAFOmyNvu3gXWrzdKOURERLkKAxVprUULaTxVWjNmsJeKiIiIgYq0pqmX6vZtYONG49RDRESUWzBQUZa0bg3Uqydv+/prIDnZOPUQERHlBgxUlCWaeqlu3gR++sk49RAREeUGDFSUZW3bAnXqyNuCg4GUFOPUQ0REZGwMVJRlCgUwebK87cYN9lIREVH+xUBF2dK+PeDtLW+bPp1jqYiIKH9ioKJs0TSW6sYNYNMm49RDRERkTAxUlG0ffQTUqiVvYy8VERHlRwxUlG0KBTB1qrzt5k3OS0VERPkPAxXlSPv2mq/4Yy8VERHlJwxUlCMZ9VL9+KNRyiEiIjIKBirKsXbt1GdPDw7mPf6IiCj/YKCiHNPUS3X7NrB+vVHKISIiMjgGKtKJNm2A+vXlbcHBQGKiceohIiIyJAYq0gmFApg2Td529y6wZo1RyiEiIjIoBirSmdatAR8fedvXXwPx8caph4iIyFAYqEhnFAopQKUVFQWsWGGceoiIiAyFgYp0qnlzaUlr5kzgzRvj1ENERGQIDFSkc8HB8sePHwPff2+cWoiIiAyBgYp0rlEj6aq/tL75BoiLM049RERE+sZARXoxfbr88fPnwKJFxqmFiIhI3xioSC/q1gU6dpS3ffst8OKFceohIiLSJwYq0pv0vVQxMcC8ecaphYiISJ8YqEhvqlcHunWTty1cKA1SJyIiyksYqEivpk0DzNL8lL1+DcyaZbx6iIiI9IGBivSqUiWgXz952w8/AJGRxqmHiIhIHxioSO+mTAEsLd89TkxUH19FRERkyhioSO9KlgSGDZO3hYYC4eFGKYeIiEjnGKjIIL78ErCze/c4JUXquSIiIsoLGKjIINzdgdGj5W2bNwNhYUYph4iISKcYqMhgxo8HnJzkbV99ZZxaiIiIdImBigymYEFgwgR52759wIkTxqmHiIhIVxioyKBGjQJcXeVtkyYBQhinHiIiIl1goCKDsrcHgoLkbadPA7t3G6ceIiIiXWCgIoP79FOgdGl525dfSlf+ERERmSIGKjI4KysgOFjedu0a8OOPxqmHiIgopxioyCh69gRq1JC3TZ4MxMcbpx4iIqKcYKAiozAzU79JcmSkdJ8/IiIiU8NARUbTpg3QtKm8bcYMIDbWOPUQERFlFwMVGY1CAcyeLW97/hyYM8c49RAREWUXAxUZVcOGQMeO8rb584EHD4xTDxERUXYwUJHRzZoljalK9fYtMHWq0cohIiLKMpMJVDNmzICPjw/s7Ozg7Oyc4XahoaGoXr06bGxs4O7uDn9//0yP6+vrC4VCIVt69Oihtt3evXtRv3592NrawsXFBR9//HFOXxL9x8sLGDRI3rZ6NfD338aph4iIKKssjF2AthITE9G1a1c0bNgQq1ev1rjN/PnzMW/ePMydOxf169dHfHw8bt++/d5jDxkyBNOnT1c9trW1la3fvn07hgwZgpkzZ+KDDz6AEAJ//fVXzl4QyUydKs1D9fat9FiplG5J8/PPRi2LiIhIKwohTOsuaqGhoRgzZgxevnwpa3/x4gWKFSuG3bt3o0WLFlofz9fXFzVr1sTChQs1rk9OTkapUqUwbdo0DErfjZIFsbGxcHJyQkxMDBwdHbN9nLwsMFC6yi+tEyeAxo2NUw8REZG2398mc8rvfQ4dOgSlUomoqCh4eXnB09MT3bp1w717996774YNG+Di4oIqVapg3LhxiIuLU627fPkyoqKiYGZmBm9vb3h4eKBt27a4du1apsdMSEhAbGysbKHMTZgAuLjI28aP542TiYgo98szger27dtQKpWYOXMmFi5ciG3btiE6OhqtWrVCYmJihvv16tULmzZtwvHjxxEUFITt27fLxkelnjKcOnUqAgMDsWfPHhQsWBDNmjVDdHR0hsedNWsWnJycVEvx4sV192LzKEdHabb0tM6eBXbsME49RERE2jJqoJo6daragPD0y8WLF7U6llKpRFJSEhYvXgw/Pz80aNAAmzZtQkREBI4dO5bhfkOGDEHLli1RtWpV9OjRA9u2bcPhw4dx+fJl1XEB4KuvvsL//vc/1K5dGyEhIVAoFNi6dWuGxw0ICEBMTIxq0aanjIChQ4GyZeVtAQFAUpJx6iEiItKGUQel+/v7a7yiLq1SpUppdSwPDw8AQOXKlVVtRYoUgYuLCyIjI7WuqVatWrC0tERERARq1aql8bjW1tYoU6ZMpse1traGtbW11s9LEisrYOZMoHv3d20REcCyZcDIkcari4iIKDNGDVQuLi5wST9oJpsaNWoEAAgPD4enpycAIDo6Gs+ePUPJkiW1Ps61a9eQlJSkClK1a9eGtbU1wsPD0fi/0dFJSUm4e/dulo5L2uvaFZg3Dzh//l3btGlAnz5AJjNmEBERGY3JjKGKjIxEWFgYIiMjkZKSgrCwMISFheHVq1cAgAoVKqBjx44YPXo0Tp8+jatXr6Jfv36oVKkSmjdvDgCIiopCpUqVcP6/b+pbt25h+vTpuHjxIu7evYt9+/aha9eu8Pb2VgU0R0dHDBs2DFOmTMGvv/6K8PBwDB8+HADQtWtXI7wTeZ9CAXz7rbzt+XP1mykTERHlGsJE9OvXTwBQW44dO6baJiYmRgwcOFA4OzuLQoUKic6dO4vIyEjV+jt37sj2iYyMFE2bNhWFChUSVlZWomzZsmLUqFHi+fPnsudOTEwUY8eOFa6ursLBwUG0bNlSXL16NUv1x8TECAAiJiYm2+9BftO5sxDSNX7SYm0txJ07xq6KiIjyE22/v01uHipTxXmosi4iAqhcGUhOftf2ySfAhg3Gq4mIiPKXfDcPFeU95csDn30mb9u4UT62ioiIKDdgoKJcLSgIcHKSt40bx8k+iYgod2GgolzNxQX46it524kTwK5dRimHiIhIIwYqyvVGjgTSz1AxYQKQyQT4REREBsVARbmejY36lAk3bwLffWeceoiIiNJjoCKT0KMH0KCBvG36dODZM+PUQ0RElBYDFZkEhQJYsEDeFhMDTJ1qlHKIiIhkGKjIZDRoAPTsKW9btgy4ft049RAREaVioCKTMnu2NKYqVUqKNI0CERGRMTFQkUkpUUI9QO3fDxw4YJx6iIiIAAYqMkETJwIeHvK2sWPlt6ghIiIyJAYqMjn29sCMGfK269el8VRERETGwEBFJqlfP6BWLXnb5MnA8+fGqYeIiPI3i6zukJCQgPPnz+Pu3bt48+YNihQpAm9vb5QuXVof9RFpZGYGLFwING36ru3FCylUff+90coiIqJ8SiGEdreZPX36NJYsWYJdu3YhMTERzs7OsLW1RXR0NBISElCmTBl8+umnGDZsGBwcHPRdt8mJjY2Fk5MTYmJi4OjoaOxy8oyePYGffnr32MwMCAsDqlUzWklERJSHaPv9rdUpv44dO6JLly4oVqwYDh48iLi4ODx//hz379/HmzdvEBERgcDAQBw5cgQVKlTAoUOHdPZCiDLzzTeAre27x0olMHo0oN2fCURERLqh1Sm/1q1bY+vWrbCystK4vkyZMihTpgz69euHa9eu4cGDBzotkii9qVMBc3MgKEi66i/tjOnHjgE7dwIff6yb5woOlua74qzsRESUEa16qEaMGAEzM+3Gr1epUgWtWrXKUVFE72NuLo2XCg4Gxo8HiheXrx87FoiPz/nzBAdLz2NunvNjERFR3qX1VX4eHh4YN24crvM+H5QLBAVJN0eePBmYNw+YO1e+/u5dqT0nUsPU9OnS8xEREWVE60D1xRdfYPfu3ahWrRoaNmyI1atX49WrV/qsjShTaUNVeDjQpIl8/YwZQGRk9o7NMEVERFmhdaAKCAhAeHg4jh8/jkqVKmHMmDHw8PDAgAEDcOrUKX3WSJSh1FA1ZQpQtSqgULxb9/Zt9u7zxzBFRERZleWJPZs0aYKQkBA8evQICxcuxM2bN9GkSRNUrFgRc+bM0UeNRJlKDVU//ADUqSNft3UrcPSo9sdimCIiouzQeh6qzOzduxd9+/bFy5cvkZKSoou68hzOQ6V/qWHI1lbqnUpVpQpw5Qpgaand/gxTRESUSqfzUGny5s0bhISEoGnTpvjoo49QuHBhzEh/gzUiA0rtqUobpgDg2rX3z57OMEVERDmR5UB14sQJDBw4EO7u7vD390fp0qVx7Ngx3LhxA5MmTdJHjURaCwrSPF/UlCnA48ea92GYIiKinNI6UM2cORMVKlSAr68vrl27hrlz5+Lhw4dYu3Ytmqa9oRqRkU2ZAgweLG+LjQUCAtS3ZZgiIiJd0HoMVZEiRdC7d28MGjQIVatW1XddeQ7HUBlezZrAH3/I206fBho2lP6fYYqIiN5H2+9vrW49AwAPHjyAZbpRvfHx8bCxscl+lUR6tH8/UKoUkJj4ru2zz4ALF4BZsximiIhId7Q+5ZcappRKJYKDg1GsWDHY29vj9u3bAICgoCCsXr1aP1USZYOHhxSc0goLAzp2ZJgiIiLdyvKg9K+//hqhoaGYM2eO7GbJ1apVw6pVq3RaHFFOjRwpTZuQ1r590v3/GKaIiEhXshyo1q1bhxUrVqBXr14wT3PH2OrVq+Off/7RaXFEOWVpCSxdqt7+6JHhayEiorwry4EqKioK5cqVU2tXKpVISkrSSVFEutS0KVCjhrxt/Xrg99+NUw8REeU9WQ5UVapUwYkTJ9Tat27dCm9vb50URaRLwcHS1X7W1vL2Ll0A/g1ARES6oPVVfqmmTJmCPn36ICoqCkqlEjt27EB4eDjWrVuHPXv26KNGomxLOzWCszMwatS7dU+fAh06AAcOGK08IiLKI7LcQ9WhQwds3rwZ+/btg0KhwOTJk/H3339j9+7daNWqlT5qJMqW9PNMDR8uzU2V1sGDwLhxRimPiIjyEJ3cHJnejxN7GlZGk3aeOQP4+KhvzykUiIhIE73fHDkzzGhkTJnNgN6wITBkiPo+kydL+xEREWWHVoHKy8sLGzduRGLaKac1iIiIwPDhw/HNN9/opDiirNLmdjKzZwMuLvI2JyeGKiIiyj6tBqV///33mDhxIkaMGIHWrVujTp06KFq0KGxsbPDixQtcv34dJ0+exPXr1+Hv74/PPvtM33UTqdH23nyFCgHz5gH9+r1ri4kBGjeW9gd4+o+IiLImS2OoTp8+jc2bN+P333/H3bt38fbtW7i4uMDb2xt+fn7o3bs3nJ2d9Viu6eIYKv3K6o2OhQCaNwd+++1dm4UFMHQo8P33HFNFREQSnd8cGQB8fHzgo2lEL5ERZTVMAYBCASxbBlSv/m4uquRk6V5/06axp4qIiLJGL4PSiQwlO2EqVaVKwMSJ8rZTp6SbKk+fzjFVRESkvSxP7EmUW+QkTKX68ktg40bg9u13bRMmAH//Lf0/e6qIiEgbDFRkknQRpgDA1hb44QfAz+9d28uXwJgxwE8/SY8ZqoiI6H0YqMgkpaTobuB469ZAr17Ahg3v2jZvBvr0eXf8lJScPw8REeVdnCndQHiVX+725Ang5QVER79rK1ECuHYNsLc3Xl1ERGRcepsp3dfXF+vWrcPbt29zVCBRbuLqCnz7rbwtMhKYMsU49RARkWnJcqCqXbs2JkyYAHd3dwwZMgRnz57VR11EBte/P+DrK29buBC4dMkIxRARkUnJcqCaN28eoqKisG7dOjx9+hRNmzZF5cqV8e233+Lx48f6qJHIIBQKYPlywNr6XZtSCQwe/G6uKiIiIk2yNQ+Vubk5OnbsiF27diEqKgqffPIJgoKCULx4cXTq1AlHjx7VdZ1EBlGhgvpA97Aw6VY1REREGcnRxJ7nz5/H5MmT8e2338LV1RUBAQFwdXVFhw4dMG7cOF3VSGRQ48cDVarI26ZOBW7cMEo5RERkArJ8ld+TJ0+wfv16hISEICIiAh06dMDgwYPh5+cHhUIBADh8+DA6deqEV69e6aVoU8Sr/EzLuXNAw4bSPf9SNWkCHD8OmPH+AkRE+YbervLz9PTEqlWr0K9fP9y/fx/btm1DmzZtVGEKAOrVq4e6detmr3KiXKB+fWD0aHnbiRPAihXGqYeIiHK3LPdQnThxAk2aNNFXPXkWe6hMz+vXQNWqwN2779ocHIDr1wFPT6OVRUREBqS3HiqGKcovChQAVq6Ut8XFAcOGyU8FEhERZfnWM97e3rLTe6kUCgVsbGxQrlw59O/fH82bN9dJgUTG1LIlMGAAEBLyrm3vXmDTJuCTT4xXFxER5S5Z7qFq06YNbt++jQIFCqB58+bw9fWFvb09bt26hbp16+Lhw4do2bIlfv75Z33US2Rw8+YBbm7ytpEjAU67RkREqbLcQ/Xs2TOMHTsWQekm6/n666/x77//4tdff8WUKVMQHByMjh076qxQImMpWBD4/nugS5d3bdHRwGefAdu2SROCEhFR/pblHqotW7agZ8+eau09evTAli1bAAA9e/ZEeHh4zqtLY8aMGfDx8YGdnR2cnZ0z3C40NBTVq1eHjY0N3N3d4e/vn+lxfX19oVAoZEuPHj1k29y4cQMdO3aEi4sLHB0d0ahRIxw7dkwXL4tMxP/+B3TtKm/bsQPYutU49RARUe6S5UBlY2OD06dPq7WfPn0aNjY2AAClUgnrtPfv0IHExER07doVw4cPz3Cb+fPn46uvvsKkSZNw7do1HDlyBH5+fu899pAhQ/Dw4UPVsnz5ctn6Dz/8EMnJyTh69CguXbqEmjVron379nj06FGOXxeZju++A1xc5G0jRgBPnxqnHiIiyj2yfMpv5MiRGDZsGC5duoS6detCoVDg/PnzWLVqFb788ksAwMGDB+Ht7a3TQqdNmwZA6oHS5MWLFwgMDMTu3bvRokULVXuV9FNea2BnZwd3d3eN6549e4abN29izZo1qF69OgBg9uzZWLp0Ka5du5bhfpT3uLpKoSptB+azZ4C/P7B5s/HqIiIi48tyD1VgYCBWrlyJ8+fPY9SoURg5ciTOnz+PlStX4quvvgIADBs2DLt379Z5sZk5dOgQlEoloqKi4OXlBU9PT3Tr1g337t17774bNmyAi4sLqlSpgnHjxiEuLk61rnDhwvDy8sK6devw+vVrJCcnY/ny5XBzc0Pt2rUzPGZCQgJiY2NlC5m+bt2Azp3lbVu2ANu3G6ceIiLKHbLUQ5WcnIwZM2Zg4MCB6NWrV4bb2dra5riwrLp9+zaUSiVmzpyJRYsWwcnJCYGBgWjVqhX+/PNPWFlZadyvV69eKF26NNzd3XH16lUEBATgjz/+wKFDhwBI00EcOnQIHTt2hIODA8zMzODm5oYDBw5kOpZr1qxZql41yjsUCmDpUuC336SB6amGDweaNgWKFDFebUREZDxZ6qGysLDA3LlzkZKSopMnnzp1qtqA8PTLxYsXtTqWUqlEUlISFi9eDD8/PzRo0ACbNm1CREREpgPIhwwZgpYtW6Jq1aro0aMHtm3bhsOHD+Py5csAACEEPvvsM7i6uuLEiRM4f/48OnbsiPbt2+Phw4cZHjcgIAAxMTGqRZueMjIN7u7A4sXytqdPpav+OOEnEVH+lOUxVC1btsTx48fRv3//HD+5v7+/2hV16ZUqVUqrY3l4eAAAKleurGorUqQIXFxcEBkZqXVNtWrVgqWlJSIiIlCrVi0cPXoUe/bswYsXL1RTzi9duhSHDh3C2rVrMWnSJI3Hsba21vnAfMo9PvlEOtX3yy/v2rZtk8ZSvedHmoiI8qAsB6q2bdsiICAAV69eRe3atVGgQAHZ+o8++kjrY7m4uMAl/WVT2dSoUSMAQHh4ODz/u9FadHQ0nj17hpIlS2p9nGvXriEpKUkV0N68eQMAMDOTd+aZmZlBqVTqonQyQQoFsHw5cPKk/NTfiBFAs2bAfz8+RESUT2T55sjpg4XsYAqFzk4HphcZGYno6Gj88ssvmDt3Lk6cOAEAKFeuHOzt7QEAnTp1ws2bN7FixQo4OjoiICAAt2/fRlhYGCwtLREVFYUWLVpg3bp1qFevHm7duoUNGzagXbt2cHFxwfXr1zF27FjY2triwoULMDc3x7Nnz1CpUiU0a9YMkydPhq2tLVauXIlFixbhwoULqFGjhlb18+bIedOWLUD37vK29u2lnitO+ElEZPr0dnNkpVKZ4aKvMAUAkydPhre3N6ZMmYJXr17B29sb3t7esjFW69atQ/369fHhhx+iWbNmsLS0xIEDB2BpaQkASEpKQnh4uKrXycrKSjVXVcWKFTFq1Ci0bt0ahw8fhrm5OQCpF+3AgQN49eoVPvjgA9SpUwcnT57Ezz//rHWYoryrWzdpSWvPHmDtWuPUQ0RExpHlHqq04uPjVZN5UubYQ5V3PXsGVK0qv7efoyNw9SpQvLjx6iIiopzTWw9VSkoKgoODUaxYMdjb2+P27dsAgKCgIKxevTr7FROZKBcXYMUKeVtsLDBgAMBhdkRE+UOWA9WMGTMQGhqKOXPmyOZ2qlatGlatWqXT4ohMxUcfAf36yduOHAGWLDFOPUREZFhZDlTr1q3DihUr0KtXL9U4IwCoXr06/vnnH50WR2RKFi5UP8U3cSJw/bpRyiEiIgPKcqCKiopCuXLl1NpTJ9Ykyq+cnYH0t5pMSAB69wYSE41RERERGUqWA1WVKlVUUxaktXXrVp3fEJnI1HzwAfD55/K2K1eA6dONUw8RERlGlif2nDJlCvr06YOoqCgolUrs2LED4eHhWLduHfbs2aOPGolMysyZwMGD8lN9s2YB7doBPj7Gq4uIiPQnyz1UHTp0wObNm7Fv3z4oFApMnjwZf//9N3bv3o1WrVrpo0Yik2JjA/z4I/Df9GcApKv9+vQB4uKMVxcREelPjuahIu1xHqr8Z9Ys4Msv5W39+wMhIUYph4iIskHb7+9sB6rExEQ8efJE7X52JUqUyM7h8jwGqvwnJUW6r9+pU/L2zZvVZ1cnIqLcSdvv7yyPoYqIiMDAgQNx+vRpWbsQQq/38iMyNebmwPr1QI0a8lN9Q4cCDRtyFnUiorwky4Gqf//+sLCwwJ49e+Dh4QEF7wBLlKHSpYGlS6XxU6levpQeHzkihS4iIjJ9WT7lV6BAAVy6dAmVKlXSV015Ek/55V9CAL16AZs2ydtnz5Ym/iQiotxLb/fyq1y5Mp49e5aj4ojyE4VC6qVKP7wwMBC4eNE4NRERkW5lOVB98803mDBhAo4fP47nz58jNjZWthCROmdnaSoFszS/ccnJQM+enEqBiCgvyPIpP7P/vhHSj53ioPTM8ZQfAVKv1IwZ8ra+fYG1a41TDxERZU5vV/kdO3YsR4UR5WdTpgCHDwPnzr1rW7cOaNVKuucfERGZJk7saSDsoaJUd+4ANWsCac+Q29sDly8D5csbrSwiItJAb4PSAeDEiRPo3bs3fHx8EBUVBQBYv349Tp48mb1qifKR0qWBFSvkba9eSeOpEhONUxMREeVMlgPV9u3b4efnB1tbW1y+fBkJCQkAgLi4OMycOVPnBRLlRd27A4MGydsuXVK/VQ0REZmGLAeqr7/+GsuWLcPKlSthmeburz4+Prh8+bJOiyPKyxYtAtJP5zZvHrB3r3HqISKi7MtyoAoPD0fTpk3V2h0dHfHy5Utd1ESULxQoAPz0E2BtLW/v2xe4d884NRERUfZkOVB5eHjg5s2bau0nT55EmTJldFIUUX5Ro4bUK5VWdDTQoweQlGScmoiIKOuyHKiGDh2K0aNH49y5c1AoFHjw4AE2bNiAcePG4bPPPtNHjUR52mefAf/7n7zt9GkgKMg49RARUdZla9qEr776CgsWLEB8fDwAwNraGuPGjUNwcLDOC8wrOG0CZSYmBvD2lqZUSGvvXqBdO+PURERE2n9/Z3seqjdv3uD69etQKpWoXLky7O3ts11sfsBARe9z8SLg4yM/1Ve4MHDlClC8uPHqIiLKz/Q6DxUA2NnZoU6dOqhXrx7DFJEO1KkDfPutvO35c46nIiIyBdkOVESkeyNHAp07y9tOnwYmTjROPUREpB0GKqJcRKEA1qyRZlNPa8ECYNs249RERETvx0BFlMs4O0vhKf38VAMHAjduGKUkIiJ6DwYqolyoVi1gyRJ5W1ycNL3CmzfGqYmIiDLGQEWUSw0eDPTrJ2+7ehUYPhzI3rW5RESkLwxURLmUQgEsXQpUqyZvX7cOWLbMODUREZFmDFREuZidnTSeysFB3j56tHT1HxER5Q4MVES5XIUKQGiovC0pCejSBXj0yCglERFROgxURCbg44+BSZPkbQ8fAl27ctJPIqLcgIGKyER8/TXQsqW87eRJYNw449RDRETvMFARmQhzc2DTJqBECXn74sXAjz8apyYiIpIwUBGZEBcXYMcO9Uk/hwwBLl0yTk1ERMRARWRyatdWnzYhPh7o1Al4/NgoJRER5XsMVEQmqH9/YMQIedv9+9JM6omJRimJiChfY6AiMlELFgDNmsnbTp0CRo40Tj1ERPkZAxWRibK0BLZuVR+kvmIFZ1InIjI0BioiE1akCPDzz4Ctrbx95Ejg+HGjlERElC8xUBGZuJo1gZAQeVtysjSe6tYto5RERJTvMFAR5QHdu6vPpB4dDXToAMTEGKcmIqL8hIGKKI/4+mvgo4/kbX//LYWt5GTj1ERElF8wUBHlEebm0ozp1arJ2w8e5O1piIj0jYGKKA9xcAB27wZcXeXtixYBy5cbpyYiovyAgYoojylZEti5E7CykrePGAEcOmScmoiI8joGKqI8yMcHWLlS3paSAnTpAly9apyaiIjyMgYqojyqb1/1K/9iY4H27YFHj4xTExFRXsVARZSHzZgBdO0qb/v3X+lqwDdvjFMTEVFexEBFlIeZmQFr1wLFisnbL1wAevcGlErdP2dwMDB1qu6PS0SUmzFQEeVxtrZSeEpv507dT6cQHAxMnixN4UBElJ8wUBHlA7NnS/f3S2/BAmnRhdQwNX06EBSkm2MSEZkKBiqifGLxYqB/f/X2sWOBrVtzdmyGKSLK7xioiPKRkBDg44/lbUIAffoAJ05k75gMU0REDFRE+c727UDLlvK2hASgY0fp3n9ZwTBFRCRhoCLKh379FahbV9724gXQpg1w/752x2CYIiJ6x2QC1YwZM+Dj4wM7Ozs4OztnuF1oaCiqV68OGxsbuLu7w9/f/73HPnPmDD744AMUKFAAzs7O8PX1xdu3b1XrX7x4gT59+sDJyQlOTk7o06cPXr58qYNXRWQcCgVw5gxQsaK8PTJSClXR0ZnvzzBFRCRnMoEqMTERXbt2xfDhwzPcZv78+fjqq68wadIkXLt2DUeOHIGfn1+mxz1z5gzatGmD1q1b4/z587hw4QL8/f1hZvburfnkk08QFhaGAwcO4MCBAwgLC0OfPn109tqIjMHcHLh8GfD0lLdfuwZ06JDxxJ8MU0REGggTExISIpycnNTao6Ojha2trTh8+HCWjle/fn0RGBiY4frr168LAOLs2bOqtjNnzggA4p9//tH6eWJiYgQAERMTk6X6iPTt6VMhChcWQhqe/m758EMhEhPl206fLq2bPt04tRIRGZq2398m00P1PocOHYJSqURUVBS8vLzg6emJbt264d69exnu8+TJE5w7dw6urq7w8fGBm5sbmjVrhpMnT6q2OXPmDJycnFC/fn1VW4MGDeDk5ITTp09neOyEhATExsbKFqLcyMUFuHQJcHCQt+/dCwwe/G42dfZMERFlLM8Eqtu3b0OpVGLmzJlYuHAhtm3bhujoaLRq1QqJiYkZ7gMAU6dOxZAhQ3DgwAHUqlULLVq0QEREBADg0aNHcHV1VdvX1dUVjzK5w+ysWbNUY66cnJxQvHhxHbxKIv0oWVIaU2VjI29ft06ap2r6dIYpIqLMGDVQTZ06FQqFItPl4sWLWh1LqVQiKSkJixcvhp+fHxo0aIBNmzYhIiICx44dy3AfABg6dCgGDBgAb29vLFiwABUrVsSaNWtU2ykUCrV9hRAa21MFBAQgJiZGtWTWU0aUG1SpAhw5AlhYyNsXLgSmTGGYIiLKjMX7N9Eff39/9OjRI9NtSpUqpdWxPDw8AACVK1dWtRUpUgQuLi6IjIzUeh8A8PLyUu3j7u6Ox48fq+379OlTuLm5ZViPtbU1rK2ttaqdKLfw8QF27ZIGpQshX5f+lCAREb1j1EDl4uICFxcXnRyrUaNGAIDw8HB4/nfZUnR0NJ49e4aSJUtq3KdUqVIoWrQowsPDZe03btxA27ZtAQANGzZETEwMzp8/j3r16gEAzp07h5iYGPj4+OikdqLc5MMPgfXr1W+o/PnngJMTMGCAceoiIsrNTGYMVWRkJMLCwhAZGYmUlBSEhYUhLCwMr169AgBUqFABHTt2xOjRo3H69GlcvXoV/fr1Q6VKldC8eXMAQFRUFCpVqoTz588DkE7ljR8/HosXL8a2bdtw8+ZNBAUF4Z9//sGgQYMASL1Vbdq0wZAhQ3D27FmcPXsWQ4YMQfv27VEx/SQ+RHnEf8ML1QweDGzbZthaiIhMgmEuOsy5fv36CQBqy7Fjx1TbxMTEiIEDBwpnZ2dRqFAh0blzZxEZGalaf+fOHbV9hBBi1qxZwtPTU9jZ2YmGDRuKEydOyNY/f/5c9OrVSzg4OAgHBwfRq1cv8eLFiyzVz2kTyFSknRph1iz16RQsLYXYvdvYVRIRGYa2398KIdKPlCB9iI2NhZOTE2JiYuDo6Gjscog00jQ1QkAAMHu2fDsrK2D3bqB1a8PXSERkSNp+f5vMKT8i0q+M5pmaORNIf4OCxETpZsrHjxu0RCKiXIuBiogynbRToQC++w7o31/eHh8PtG8PnDplsDKJiHItBiqifE6bGdDNzIBVq4BPPpG3v34NtG0LnDun/zqJiHIzBiqifCwrt5MxNwfWrgX+9z95e1ycNJbqv4tniYjyJQYqonwqO/fms7AANm6UJv5MKzYWaNWKPVVElH8xUBHlQzm50bGVFbB1K9Cmjbw9NlbqqTp7Vnd1EhGZCgYqonwmJ2EqlbU1sHOn5lDl58dQRUT5DwMVUT6iizCVysZGClX/3aVJJbWn6uTJnB2fiMiUMFAR5RO6DFOpbGyAHTuAdu3k7XFxUk/V0aO6eR4iyj+mTpX+vTKE4GDp+XSBgYooH9BHmEplYwNs364eqt68kW60vH+/bp+PiPI2c3Pp3yt9h6rUfxfNzXVzPAYqonwgJUU/YSpVak/VRx/J2+PjpRnVd+3Sz/MSUd4TFCT9e6XPUKWPPzItdHMYIsrNdNWlnRlra2DbNqBXL+kqwFRJSUCXLsD69UDPnvqvg4hMX2rImTxZ/lgX9NVjz0BFRDpjaSnNU2VjIwWoVCkpUtCKiQGGDTNefURkOvQRqvQ5/IGBioh0ysICCA2VQtXKle/ahZBusvziBTBpknSPQCKizOgyVOkzTAEMVESkB2ZmwPLlgJ0dsGiRfN2XX0qh6ptvGKqI6P10Ear0HaYABioi0hOFAliwAChYUH0M19y5Uqj64QepR4uIKDM5CVWGCFMAAxUR6ZFCAUyZAjg7A2PGyNetWgU8fQps2gTY2hqjOiIyJdkJVYYKUwADFREZwOjRUk/VwIHSAPVUP/8MtGwJ7N4NFCpkvPqIyDRkJVQZMkwBDFREZCB9+wKOjkCPHkBCwrv206eBJk2AAweA4sWNVx8RmYaMQlVCgjR9C2D4MAUwUBGRAXXqBPz6qzQBaEzMu/br14GGDaVQVbWq0cojIhORPlRVrw6MGAEcOiTNh2foMAUACiGEMNzT5V+xsbFwcnJCTEwMHB0djV0OkVH99Zd0U+WoKHm7o6N0G5uWLY1TFxGZlmnT5Be9uLgAz57pNkxp+/3NW88QkcFVqyad6vPykrfHxkpBKyTEOHURkel4/Bj47Td527NnUi93YKDh62GgIiKjKFECOHkS8PGRtycnS4PXg4KkyUCJiNI7dQrw9gaOHVNfV60akJho+JoYqIjIaAoVAg4flu71l97XXwN9+sgHsBNR/iYEsHAh4OsLPHyoeZtKld4NTjckBioiMipbW2DzZmD8ePV1GzYAH3wAPHli+LqIKHd58QL4+GPg88+lnuy0HBykYQTTp0tz3wUHG74+XuVHREZnZgbMmQOUKSNdqaNUvlt3+jRQt640V1X16sarkYiM5/x5oHt34O5d9XVlygBnzwJFikhXCwO6vaGytthDRUS5xrBhwJ49gL29vD0yUhpr9csvxqmLiIxDCOl+oI0baw5TTZsCN25IYSpVUJDUUzV5smF7qhioiChXadtW6pUqWVLe/vq1NI/V7NkcrE6UHzx/Lv3OjxkDJCWpr+/dW7rKz9xcfZ0xQhUDFRHlOtWqSV386a8AFAIICJBmW3/92ji1EZH+HT8O1KiRca/02LHA+vWZH8PQoYqBiohyJVdX4OhR6ZY16W3ZIo2VuH3b8HURkf4kJUlzSH3wgfrEv6mmTgW+/Va74xkyVHFQOhHlWtbWQGgoUKUKMGmS/FTfX38BdeoAP/0EtG5ttBKJSEdu3ZKmSjlzRn2dnR3w5k32ZkDPyg2Vc4I9VESUqykUwIQJwN69gLOzfN2LF9KYqxkz5FcGEpHpEEK6O0LNmprDVNmy2Q9TqQzRU8VARUQmoW1b4MIFqbcqLaVSOkXw0UdSwCIi0/H8uTSx78CBwKtX8nUWFlLv861burk3n75DFQMVEZmMcuWkv2A//lh93d69QK1awKVLhq+LiLLuwAHpApQdO9TXlSkjhaxff9XtjY71GaoYqIjIpDg4ANu2ATNnShOCpnX3LtCoEbBiBadWIMqt4uKAoUOlXmdNt48ZNAjo2VP6PdZlmEqlr1DFQEVEJkehkKZPOHRIPqEfIN37b+hQ6R/kmBjj1EdEmh0/Lt3xYMUK9XWFCwM7d0pz0M2YoZ8wlUofoYqBiohM1gcfAFeuSL1S6W3eLJ0CvHDB8HURkdybN9IEnc2ba57xvE0b6crdTp2AlBT9hqlUqaEqJUU3x1MIwY5xQ4iNjYWTkxNiYmLg6Oho7HKI8pSkJGDiRGDBAvV1lpbS7OpjxqifIiQi/Tt2DBg8WPO8cQUKAPPmAZ9+KvU850bafn/znxciMnmWlsD8+cCuXUDBgvJ1SUnSrMrt2mker0FE+hEbCwwfLvUkawpTTZsCf/4pnaLPrWEqKxioiCjP6NgRCAvTfArw4EHpiqJduwxdFVH+s38/ULUqsGyZ+jpra+kPoGPHpKv58goGKiLKU0qUkAa+fvml+l+9z58DnTsDQ4aoz3mTaupUw91MNThYej6ivOLxY+mCkHbtgHv31Nc3agT88Qfw+ed57xR8Hns5RETShIAzZkhXARYtqr5+1SppVuaTJ9XXmZsb5r5fwcHS85ib6/d5iAxBqZR+rypVkm4HlZ6dHbB4MfD770DFioavzxAYqIgoz2rRQhqj8b//qa+7dUsawzF2LPD27bt2Q9yiIjVMGeJKJiJ9u35dunpvyBDg5Uv19R98IF3BN3Jk3uuVSisPvzQiImlum61bpXuF2dvL1wkhjeXw9gbOnXvXrs9QxTBFecWrV9LVtTVqSD1P6RUsCKxeDRw+nLfGSmWEgYqI8jyFAujfXxqw7uOjvj48XGofN06aLwfQT6himKK8QAhg+3bAywuYMwdITlbf5pNPgH/+kW4fkxeu4NMGAxUR5Rtly0p/SX/7rXSlUVpKpTQfTrVqwJEjUpsuQxXDFOUF//wj3TKmSxfg/n319aVKSVf4bdgAuLoavDyjYqAionzF3FwaN3XlClC3rvr627eBli2lv6yjo3UTqhimyNS9fAl88YX0B8fBg+rrLS2Br74Crl2TZj3PjxioiChf8vICTp+Wrga0slJfHxIibbN+PRAYmP1QxTBFpiwlRbp6r0IF6U4Emk7vtWwpDTr/+mvpar78ioGKiPItCwtpvqo//gAaN1Zf/+QJ0LevdJVSly5ZD1UMU2TKDh+W7oc5ZAjw9Kn6+qJFpSkSfv01706FkBUMVESU71WqBPz2G7B0KeDgoL7++HHpSqa3b6XeKm1CFcMUmarr14EPPwRatZKmHUnPykr6QyQ8HOjePf8MOn8fBioiIkjz4wwfLo0B+egj9fVJScCsWUBoKNC1a+ahimGK0jOFGfgfPJDuq1e9OrBvn+ZtOnaUAteMGerTkOR3DFRERGkULw78/LO0lCihvv7+fWleqxIlNIcqhinSJDfPwP/yJRAQAJQrB6xYIY2bSq9aNenU3q5d0tWypIEgg4iJiREARExMjLFLISItvXolxKRJQlhYCCHNvqN5GT9e2n76dOnx9OnGrZtyJ33/fGT1+K9fCzFnjhAFC2b8s+3uLsTKlUIkJ+unZlOg7fe3QgghjB3q8oPY2Fg4OTkhJiYGjo6Oxi6HiLLg+nVg1Kh381NpYm4u/WXPninKjL56MLNy3Ph4YPly6RT248eat7G1lSa6nTCBp/a0/f62MGBNREQmqXJl6UbLP/8szcVz5476NqmnSYoUkS4tt+C/rqRBatiZPFn+OCe0DVMJCdKtYGbMkMZLaWJuDgwaBEyZovnG4pQxjqEiItKCQgF06iT1Vs2cCRQooHm74cOl+as2bpRmXydKz9Az8L99C3z3nTRGasSIjMNU167SRRnLlzNMZQcDFRFRFtjYSAN4IyKA2rU1b3PzJtCrlzTVwq5d0mgUorQMMQP/q1fSbZZKlwZGjtR8qxgA8PMDzp8HtmzhfFI5wU5pIqJsWLUKuHQJ8PeXblej6TLzq1eBzp2BOnWkL7wOHThnD72Tk9N/mYWpp0+lHqnvvpNun5SRFi2AadOARo2yVjdpxkBFRJRFmr7Mjh4FJk4ELl5U3/7iRWn+npo1pYlBO3eW5r0iyk6oyihM3bwJzJ8v3TYpPj7j/Zs2lfZt1iz7dZMGBrnmUAe+/vpr0bBhQ2FrayucnJwy3C4kJERUq1ZNWFtbCzc3NzFixIj3Hvv06dOiefPmws7OTjg5OYlmzZqJN2/eCCGEuHPnjhg4cKAoVaqUsLGxEWXKlBGTJ08WCQkJWaqf0yYQ5Q2ZXZquVAqxc6cQVatmPs1ClSpCrFsnRGKiwcunXErbKQ/Sb6dUCvH770L8739CmJll/nPn5ydtS1mj7fe3yQSqyZMni/nz54svvvgiw0A1b948UbRoUbFhwwZx8+ZNcfXqVfHLL79ketzTp08LR0dHMWvWLHH16lVx48YNsXXrVhEfHy+EEGL//v2if//+4uDBg+LWrVvi559/Fq6urmLs2LFZqp+Bisj0afull5IixMaNQhQqlPkXXPHiQsybJ0RsrGHqp9ztfT9fadfHxwsRGiqEt3fmP2OAEB07CnH+vEFfSp6S5wJVqpCQEI2BKjo6Wtja2orDhw9n6Xj169cXgYGBWdpnzpw5onTp0lnah4GKyLRlZ1LGpCQhPv74/V94Tk5CTJwoRGSk3sonE5HRz1lq+xdfCPHVV0K4umb+M2VpKUT//kJcvWqc15GXaPv9nWfO4h86dAhKpRJRUVHw8vKCp6cnunXrhnv37mW4z5MnT3Du3Dm4urrCx8cHbm5uaNasGU6ePJnpc8XExKBQoUKZbpOQkIDY2FjZQkSmKbuTMVpYANu3v7uvmqur5u1iYoBvvpGuxurSBfj9d14ZmF9puvpv2jTpcYUKwIIF0jxST55o3t/BARg/XporLSQEqFLFcLXnewYKeDqTUQ/VrFmzhKWlpahYsaI4cOCAOHPmjGjRooWoWLFihuOdzpw5IwCIQoUKiTVr1ojLly+LMWPGCCsrK3Hjxg2N+9y8eVM4OjqKlStXZlrnlClTBAC1hT1URKZFV7cLST1Oz55C+Pi8v9eqRg0hli/n6cD8KvXnxdz8/T8rgBClSwsxf74QL18au/K8xyRO+WUUOtIuFy5ckO2TUaCaMWOGACAOHjyoanvy5IkwMzMTBw4c0Pj8p06dEgBEQECArL1atWpi0qRJattHRUWJcuXKiUGDBr33tcXHx4uYmBjVcu/ePQYqIhOj63uvpT3eqVNCdOokhEKR+RdlgQJCDB4sjYFRKnVTB+Ver18L8eOPQnzwgXZBqnlzIXbtyt/32tM3bQOVUadN8Pf3R48ePTLdplSpUlody8PDAwBQuXJlVVuRIkXg4uKCyMhIrfcBAC8vL7V9Hjx4gObNm6Nhw4ZYsWLFe+uxtraGtbW1VrUTUe6jj3uupb9EfudOIDwcWLIECA0FXr9W3+f1a2nOq1WrpIlC+/cHevYE3Nx0UxMZX3KyNO3Gjz9KPxOvXmW+vYMD0LcvMHQoUK2aYWqk9zNqoHJxcYGLi4tOjtXov5nJwsPD4enpCQCIjo7Gs2fPULJkSY37lCpVCkWLFkV4eLis/caNG2jbtq3qcVRUFJo3b47atWsjJCQEZpxAhihP09cNbAHN8w599500LiY0VApXt25p3vePP4DPP5duWtumjfSl2qGDdCNbMi1KJXDmDLB1K/DTTxnfpDgtDw9pPFXPnrxhca5koB6zHPv333/FlStXxLRp04S9vb24cuWKuHLlioiLi1Nt07FjR1GlShVx6tQp8ddff4n27duLypUri8T/Jnu5f/++qFixojh37pxqnwULFghHR0exdetWERERIQIDA4WNjY24efOmEOLdab4PPvhA3L9/Xzx8+FC1ZAWv8iMyDbo+zZfV50lJEWLfPul0oDbjZ+zthfjkE+m0z3+zvRjMlCn6f59STZ8uPZ8pS04W4sQJIUaNEqJYMe1O6Tk7CzFihBDDhhnm55LUmcQYqqzo16+fxjFWx44dU20TExMjBg4cKJydnUWhQoVE586dRWSa65Dv3Lmjto8Q0oB2T09PYWdnJxo2bChOnDihWhcSEpLh+K6sYKAiMg25KSRERQnx9ddClCyp3Zevo6MQffoIsX27EGn+1tRr/cYMn6bg9Wsp7A4c+P6pDlIXhUKIVq2E2LRJiLdv3x3LlN8HU6bt97dCCF6cawixsbFwcnJCTEwMHB0djV0OEZmQlBRpjM369dI0DG/evH8fa2ugZUvpljcffggULaqf2vR5etQQx9eHmzeBAweAgweBw4czvw1Mem3aAGvWSKf3NDHF98PUafv9zXv5ERHlcubmQKtW0rJ0KbBjB7BuHXDsmDQWR5OEBGDvXmkBgKpVAT8/6Qu7cWPAxkY3teXkBr/vYyrh4dkzae6ww4elEHX7tvb7Vq4szU92/Lh2r1Of7zflDAMVEZEJsbeXBqP37Qs8eiT1WG3ZApw4kflkoFevSsu8eVKY8vEBfH2lpV49qUcru/TxJZ+bw9Tjx8Dp08Bvv0mh9s8/s7Z/1apA167SJK7bt2f9dTJU5U4MVEREJsrdHRgxQlqioqSeq59/lr7ok5Mz3i8+XjqFePSo9NjGRgpVDRsCDRpI/83qtAy6/JLPTWEqMVEKoufPSyHq9OmMr8LMiLk50KSJdPq1QwegbFmpPSevk6Eq92GgIiLKA4oVA0aOlJaXL4H9+6VwdfCg9Dgz8fHSKavff3/XVrIkUKsW4O0tLTVrSs+hUGR8HF18yRszTMXEANeuAX/9BVy+DFy6JP1/YmLWj+XmJp1iTT3Nmv5uZbp4nQxVuQsDFRFRHuPsLM1V1LOn1FN1/rwUrA4elP5fm0uR/v1XWnbufNdWsKA05sfL691/y5aVwpeVlbRNTr7kDRGmlErg/n0gIkJabtwA/vlH6oXK5Nav72VnBzRqBLRoIQWo6tUzDp+6fJ0MVbkHr/IzEF7lR0S5QXS0NN7q+HFp+eOPnN+I2cwM8PQEypSRwlWxYtK4oj17pNm8J04EXFyk8V/6DBlv30o3DX70SBrn9PixdCo0MlK+JCRk/7WmKlBAOj3q6ws0bw7UrfsuVGZGX6ExN50mzWu0/f5moDIQBioiyo2io6VxQWfPSjN3nz///lufZJelJVC4sNTTZW8v3ULF3h64c0c6tVa7NlC/vrSdmZnUm5S6pKRIgSnt8uqVdDrzxQvpv7oIShkpV06qzcdHWqpWBSyyeI6HU0yYJgaqXIaBiohMQUqKNI7o8mXgyhVpCQsD4uKMXZlhWFgAFStK98irXVtavL2l06g5Yaiww1Cle5yHioiIsszcXBr/U726dCNmQOohunsXuH4d+Ptvabl+XRqDFB1tzGqzz94eKF/+3VKlitTrVLGidqfusiolxTAhJ/X4KSn6fR5Sxx4qA2EPFRHlRS9fShNZpi737wMPHkhjl6KipPFMSUmGrcnKSrrKzt1dWkqUeLeULAmUKiW1Z3bFIlEq9lAREZHeOTtL0yvUqqV5vRDSWKdnz4BvvgGWL5dOqyUnA23bSlfGvXolha60S0qK1FtmZvZusbGRrqaztZWWAgWk8VjOzu+WIkUAJyeGJTI89lAZCHuoiIikGdkTE6VeJH0OIifSFW2/v80MWBMREeVjwcHvwlRiovSYKK9goCIiIr1Le/VZQoL038mTGaoo7+AYKiIi0itNl/Jzhm/KaxioiIhIbzKbF4mhivISBioiItILbSaZZKiivIKBioiIdC4rM3YzVFFewEBFREQ6lZ3bnzBUkaljoCIiIp3Jyb3kGKrIlDFQERGRTujixrwMVWSqGKiIiCjHdBGmUjFUkSlioCIiohzRZZhKxVBFpoaBioiIsk0fYSoVQxWZEgYqIiLKFn2GqVQMVWQqGKiIiChbUlL0G6ZSpR4/JUW/z0OUEwohhDB2EflBbGwsnJycEBMTA0dHR2OXQ0RERFrQ9vubPVQGkppbY2NjjVwJERERaSv1e/t9/U8MVAYSFxcHAChevLiRKyEiIqKsiouLg5OTU4brecrPQJRKJR48eAAHBwcoFApjl5OrxcbGonjx4rh37x5Pj5oAfl6mh5+ZaeHnZVxCCMTFxaFo0aIwMzPLcDv2UBmImZkZPD09jV2GSXF0dOQ/HiaEn5fp4WdmWvh5GU9mPVOpMo5aRERERKQVBioiIiKiHGKgolzH2toaU6ZMgbW1tbFLIS3w8zI9/MxMCz8v08BB6UREREQ5xB4qIiIiohxioCIiIiLKIQYqIiIiohxioCIiIiLKIQYq0qtZs2ahbt26cHBwgKurKzp16oTw8HDZNkIITJ06FUWLFoWtrS18fX1x7dq19x57+/btqFy5MqytrVG5cmXs3LlTXy8j39DX5xUaGgqFQqG2xMfH6/Pl5HnafF47duyAn58fXFxcoFAoEBYWptWx+fulH/r6zPg7ZnwMVKRXv/32G0aMGIGzZ8/i0KFDSE5ORuvWrfH69WvVNnPmzMH8+fPx3Xff4cKFC3B3d0erVq1U9z/U5MyZM+jevTv69OmDP/74A3369EG3bt1w7tw5Q7ysPEtfnxcgzfL88OFD2WJjY6Pvl5SnafN5vX79Go0aNcLs2bO1Pi5/v/RHX58ZwN8xoxNEBvTkyRMBQPz2229CCCGUSqVwd3cXs2fPVm0THx8vnJycxLJlyzI8Trdu3USbNm1kbX5+fqJHjx76KTyf0tXnFRISIpycnPRdbr6X/vNK686dOwKAuHLlynuPw98vw9HVZ8bfMeNjDxUZVExMDACgUKFCAIA7d+7g0aNHaN26tWoba2trNGvWDKdPn87wOGfOnJHtAwB+fn6Z7kNZp6vPCwBevXqFkiVLwtPTE+3bt8eVK1f0V3g+lf7zyi7+fhmOrj4zgL9jxsZARQYjhMAXX3yBxo0bo2rVqgCAR48eAQDc3Nxk27q5uanWafLo0aMs70NZo8vPq1KlSggNDcUvv/yCTZs2wcbGBo0aNUJERIT+XkA+o+nzyi7+fhmGLj8z/o4Zn4WxC6D8w9/fH3/++SdOnjyptk6hUMgeCyHU2nSxD2lPl59XgwYN0KBBA9XjRo0aoVatWliyZAkWL16su6Lzscw+r+zg75f+6fIz4++Y8bGHigxi5MiR+OWXX3Ds2DF4enqq2t3d3QFA7S/fJ0+eqP2FnJa7u3uW9yHt6frzSs/MzAx169blX886ktHnlV38/dI/XX9m6fF3zPAYqEivhBDw9/fHjh07cPToUZQuXVq2vnTp0nB3d8ehQ4dUbYmJifjtt9/g4+OT4XEbNmwo2wcAfv3110z3offT1+el6XnCwsLg4eGhs9rzo/d9XtnF3y/90ddnpul5+DtmYMYYCU/5x/Dhw4WTk5M4fvy4ePjwoWp58+aNapvZs2cLJycnsWPHDvHXX3+Jnj17Cg8PDxEbG6vapk+fPmLSpEmqx6dOnRLm5uZi9uzZ4u+//xazZ88WFhYW4uzZswZ9fXmNvj6vqVOnigMHDohbt26JK1euiAEDBggLCwtx7tw5g76+vEabz+v58+fiypUrYu/evQKA+Omnn8SVK1fEw4cPVdvw98tw9PWZ8XfM+BioSK8AaFxCQkJU2yiVSjFlyhTh7u4urK2tRdOmTcVff/0lO06zZs1Ev379ZG1bt24VFStWFJaWlqJSpUpi+/btBnhFeZu+Pq8xY8aIEiVKCCsrK1GkSBHRunVrcfr0aQO9qrxLm88rJCRE4zZTpkxRbcPfL8PR12fG3zHjUwghhH77wIiIiIjyNo6hIiIiIsohBioiIiKiHGKgIiIiIsohBioiIiKiHGKgIiIiIsohBioiIiKiHGKgIiIiIsohBioiIiKiHGKgIiIyAXv27EGZMmVQt25d3LhxQ7YuLi4OdevWRc2aNVGtWjWsXLnSSFUS5V+cKZ2IyARUqFABS5cuxbVr13DmzBn89NNPqnUpKSlISEiAnZ0d3rx5g6pVq+LChQsoXLiwESsmyl/YQ0VE+YKvry/GjBlj7DIy9fz5c7i6uuLu3btq61xcXFCuXDmUKVMGTk5OsnXm5uaws7MDAMTHxyMlJQVp/1bu0qUL5s+fr9faifI7BioiytU6dOiAli1balx35swZKBQKXL582cBV6cesWbPQoUMHlCpVSm3dgAEDULZsWQwZMgQzZ85UW//y5UvUqFEDnp6emDBhAlxcXFTrJk+ejBkzZiA2Nlaf5RPlawxURJSrDRo0CEePHsW///6rtm7NmjWoWbMmatWqZYTKdOvt27dYvXo1Bg8erLYuOTkZixYtwoQJExAXF4eCBQuqbePs7Iw//vgDd+7cwcaNG/H48WPVuurVq6NUqVLYsGGDXl8DUX7GQEVEuVr79u3h6uqK0NBQWfubN2+wefNmDBo0CACQkJCAUaNGwdXVFTY2NmjcuDEuXLiQ4XFLlSqFhQsXytpq1qyJqVOnqh77+vpi5MiRGDNmDAoWLAg3NzesWLECr1+/xoABA+Dg4ICyZcti//79suMIITBnzhyUKVMGtra2qFGjBrZt25bp69y/fz8sLCzQsGFDtXXLli1DmTJlMGLECLx58wYREREZHsfNzQ3Vq1fH77//Lmv/6KOPsGnTpkxrIKLsY6AiolzNwsICffv2RWhoqGxc0NatW5GYmIhevXoBACZMmIDt27dj7dq1uHz5MsqVKwc/Pz9ER0fn6PnXrl0LFxcXnD9/HiNHjsTw4cPRtWtX+Pj44PLly/Dz80OfPn3w5s0b1T6BgYEICQnBDz/8gGvXruHzzz9H79698dtvv2X4PL///jvq1Kmj1v7ixQsEBwfjm2++gaenJ5ycnBAWFibb5vHjx6rTebGxsfj9999RsWJF2Tb16tXD+fPnkZCQkIN3g4gywkBFRLnewIEDcffuXRw/flzVtmbNGnz88ccoWLAgXr9+jR9++AFz585F27ZtUblyZaxcuRK2trZYvXp1jp67Ro0aCAwMRPny5REQEABbW1u4uLhgyJAhKF++PCZPnoznz5/jzz//BAC8fv0a8+fPx5o1a+Dn54cyZcqgf//+6N27N5YvX57h89y9exdFixZVa588eTI6d+4MLy8vAEDlypXxxx9/yLa5f/8+mjZtiho1aqBx48bw9/dH9erVZdsUK1YMCQkJePToUY7eDyLSzMLYBRARvU+lSpXg4+ODNWvWoHnz5rh16xZOnDiBX3/9FQBw69YtJCUloVGjRqp9LC0tUa9ePfz99985eu60wcTc3ByFCxdGtWrVVG1ubm4AgCdPngAArl+/jvj4eLRq1Up2nMTERHh7e2f4PG/fvoWNjY2s7fr16/jxxx9lr6Fq1apqPVS1a9dWa0vP1tYWAGQ9aUSkOwxURGQSBg0aBH9/f3z//fcICQlByZIl0aJFCwBQnQpUKBSyfYQQam2pzMzMkH4avqSkJLXtLC0tZY8VCoWsLfX4SqVS9t+9e/eiWLFisn2tra0zfH0uLi548eKFrO3zzz/Hy5cv4enpqWpTKpXw8PDI8DgZST31WaRIkSzvS0Tvx1N+RGQSunXrBnNzc2zcuBFr167FgAEDVGGmXLlysLKywsmTJ1XbJyUl4eLFi6pTZekVKVIEDx8+VD2OjY3FnTt3clxn5cqVYW1tjcjISJQrV062FC9ePMP9vL29cf36ddXjPXv24NKlS7hy5QrCwsJUy+rVq/HgwQM8ffo0S3VdvXoVnp6esukUiEh32ENFRCbB3t4e3bt3x5dffomYmBj0799fta5AgQIYPnw4xo8fj0KFCqFEiRKYM2cO3rx5o7oKML0PPvgAoaGh6NChAwoWLIigoCCYm5vnuE4HBweMGzcOn3/+OZRKJRo3bozY2FicPn0a9vb26Nevn8b9/Pz8EBAQgBcvXsDe3h5jx47F+PHjUbNmTdl2jo6OAIA//vgjw/m5NDlx4gRat26d7ddFRJljoCIikzFo0CCsXr0arVu3RokSJWTrZs+eDaVSiT59+iAuLg516tTBwYMHNc7ZBAABAQG4ffs22rdvDycnJwQHB+ukhwoAgoOD4erqilmzZuH27dtwdnZGrVq18OWXX2a4T7Vq1VCnTh1s2bIFr1+/xsuXL+Hv76+2XfHixWFnZ4ewsDCtA1V8fDx27tyJgwcPZvs1EVHmeC8/IqJcYt++fRg3bhyuXr0KMzPdjcj4/vvv8fPPP6sG8ROR7rGHiogol2jXrh0iIiIQFRWV6XirrLK0tMSSJUt0djwiUsceKiIiIqIc4lV+RERERDnEQEVERESUQwxURERERDnEQEVERESUQwxURERERDnEQEVERESUQwxURERERDnEQEVERESUQwxURERERDnEQEVERESUQwxURERERDnEQEVERESUQ/8HZ4N8aTvRNjEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "murn.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "In the first section of this notebook, we took a look at a polynomial fit (because it's simple!). In pyiron, we offer multiple ways to fit the points: Vinet, Murnaghan, Birch-Murnaghan and polynomial. We show the comparison below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fit_dict = {\n",
    "    \"Vinet\": murn.fit_vinet(),\n",
    "    \"Murnaghan\": murn.fit_murnaghan(),\n",
    "    \"Birch-Murnaghan\": murn.fit_birch_murnaghan(),\n",
    "    \"Polynomial\": murn.fit_polynomial(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = list(fit_dict.keys())\n",
    "volumes = [d[\"volume_eq\"] for d in fit_dict.values()]\n",
    "plt.ylim([min(volumes) - np.std(volumes), max(volumes) + np.std(volumes)])\n",
    "plt.ylabel(r\"Equilibrium volume $\\mathrm{\\AA}^3$\")\n",
    "plt.bar(names, volumes);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the fitting method gives a slight difference in the results. It is important to keep in mind that in DFT calculations the equilibrium volume can easily vary by more than one percent, depending on the type of pseudo-potential used. In this regard, we can consider all of these results to be acceptably correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = list(fit_dict.keys())\n",
    "volumes = [d[\"bulkmodul_eq\"] for d in fit_dict.values()]\n",
    "plt.ylim([min(volumes) - np.std(volumes), max(volumes) + np.std(volumes)])\n",
    "plt.ylabel(\"Equilibrium bulk modulus GPa\")\n",
    "plt.bar(names, volumes);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T19:12:50.900916Z",
     "start_time": "2018-10-16T19:12:50.896333Z"
    }
   },
   "source": [
    "## Energy cut-off dependence\n",
    "\n",
    "We launched the DFT calculations above using the energy cutoff of 320 eV. However, in order to obtain a physically meaningful result, the energy cutoff (among other plane-wave DFT parameters) should be varied to see the convergence of the output values we are interested in. As the energy cutoff refers to the maximum kinetic energy that is allowed for the plane wave basis functions used to describe the electronic wavefunctions, choosing a higher energy cutoff means including more plane waves in the basis set, which can improve the accuracy of the calculations but also increases the computational cost. Conversely, choosing a lower energy cutoff reduces the computational cost but can lead to less accurate results. Therefore, the energy cutoff is an important parameter in plane wave DFT calculations, and it must be carefully chosen to balance accuracy and computational efficiency for a particular system or calculation. Here, we take a look at the partial convergence of the equilibrium volume and bulk modulus. For the sake of quick DFT convergence the energy cutoff is chosen between 270 eV and 320 eV (and not higher), but in the scientific context you should see how far you have to go in order to see a true convergence, which also applies for other plane-wave DFT parameters (e.g. k-point mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "encut_lst = np.linspace(270, 320, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_270d0 was saved and received the ID: 19694148\n",
      "The job murn_270d0_0_95 was saved and received the ID: 19694149\n",
      "The job murn_270d0_0_9666667 was saved and received the ID: 19694150\n",
      "The job murn_270d0_0_9833333 was saved and received the ID: 19694151\n",
      "The job murn_270d0_1_0 was saved and received the ID: 19694152\n",
      "The job murn_270d0_1_0166667 was saved and received the ID: 19694153\n",
      "The job murn_270d0_1_0333333 was saved and received the ID: 19694154\n",
      "The job murn_270d0_1_05 was saved and received the ID: 19694155\n",
      "The job murn_280d0 was saved and received the ID: 19694156\n",
      "The job murn_280d0_0_95 was saved and received the ID: 19694157\n",
      "The job murn_280d0_0_9666667 was saved and received the ID: 19694158\n",
      "The job murn_280d0_0_9833333 was saved and received the ID: 19694159\n",
      "The job murn_280d0_1_0 was saved and received the ID: 19694160\n",
      "The job murn_280d0_1_0166667 was saved and received the ID: 19694161\n",
      "The job murn_280d0_1_0333333 was saved and received the ID: 19694162\n",
      "The job murn_280d0_1_05 was saved and received the ID: 19694163\n",
      "The job murn_290d0 was saved and received the ID: 19694164\n",
      "The job murn_290d0_0_95 was saved and received the ID: 19694165\n",
      "The job murn_290d0_0_9666667 was saved and received the ID: 19694166\n",
      "The job murn_290d0_0_9833333 was saved and received the ID: 19694167\n",
      "The job murn_290d0_1_0 was saved and received the ID: 19694168\n",
      "The job murn_290d0_1_0166667 was saved and received the ID: 19694169\n",
      "The job murn_290d0_1_0333333 was saved and received the ID: 19694170\n",
      "The job murn_290d0_1_05 was saved and received the ID: 19694171\n",
      "The job murn_300d0 was saved and received the ID: 19694172\n",
      "The job murn_300d0_0_95 was saved and received the ID: 19694173\n",
      "The job murn_300d0_0_9666667 was saved and received the ID: 19694174\n",
      "The job murn_300d0_0_9833333 was saved and received the ID: 19694175\n",
      "The job murn_300d0_1_0 was saved and received the ID: 19694176\n",
      "The job murn_300d0_1_0166667 was saved and received the ID: 19694177\n",
      "The job murn_300d0_1_0333333 was saved and received the ID: 19694178\n",
      "The job murn_300d0_1_05 was saved and received the ID: 19694179\n",
      "The job murn_310d0 was saved and received the ID: 19694180\n",
      "The job murn_310d0_0_95 was saved and received the ID: 19694181\n",
      "The job murn_310d0_0_9666667 was saved and received the ID: 19694182\n",
      "The job murn_310d0_0_9833333 was saved and received the ID: 19694183\n",
      "The job murn_310d0_1_0 was saved and received the ID: 19694184\n",
      "The job murn_310d0_1_0166667 was saved and received the ID: 19694185\n",
      "The job murn_310d0_1_0333333 was saved and received the ID: 19694186\n",
      "The job murn_310d0_1_05 was saved and received the ID: 19694187\n",
      "The job murn_320d0 was saved and received the ID: 19694188\n",
      "The job murn_320d0_0_95 was saved and received the ID: 19694189\n",
      "The job murn_320d0_0_9666667 was saved and received the ID: 19694190\n",
      "The job murn_320d0_0_9833333 was saved and received the ID: 19694191\n",
      "The job murn_320d0_1_0 was saved and received the ID: 19694192\n",
      "The job murn_320d0_1_0166667 was saved and received the ID: 19694193\n",
      "The job murn_320d0_1_0333333 was saved and received the ID: 19694194\n",
      "The job murn_320d0_1_05 was saved and received the ID: 19694195\n"
     ]
    }
   ],
   "source": [
    "volume_lst = []\n",
    "bulk_modulus_lst = []\n",
    "for encut in encut_lst:\n",
    "    dft = pr.create.job.Gpaw(job_name=('dft', encut))\n",
    "    dft.set_encut(encut)\n",
    "    dft.structure = basis.copy()\n",
    "    murn = dft.create_job(job_type=pr.job_type.Murnaghan, job_name=dft.job_name.replace(\"dft\", \"murn\"))\n",
    "    murn.input['num_points'] = 7\n",
    "    murn.input['vol_range'] = 0.05\n",
    "    murn.run()\n",
    "    volume_lst.append(murn.fit_vinet()[\"volume_eq\"])\n",
    "    bulk_modulus_lst.append(murn.fit_vinet()[\"bulkmodul_eq\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.grid()\n",
    "plt.ylabel(r\"Equilibrium volume $\\mathrm{\\AA}^3$\")\n",
    "plt.plot(encut_lst, volume_lst, \"-o\");\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.grid()\n",
    "plt.ylabel(\"Equilibrium bulk modulus GPa\")\n",
    "plt.xlabel(\"Energy cut-off eV\")\n",
    "plt.plot(encut_lst, bulk_modulus_lst, \"-o\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}