noneq_TI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da3b10b2",
   "metadata": {},
   "source": [
    "# Nonequilibrium free-energy calculation of solids using LAMMPS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56cb454",
   "metadata": {},
   "source": [
    "This notebook demonstrates the application of nonequilibrium techniques for the calculation of free energies of solids using molecular dynamics (MD) simulations. The methods implemented here are based on **Frenkel-Ladd**, and **Reversible-scaling** thermodynamic paths. \n",
    "\n",
    "Calculations presented here are based on https://github.com/jan-janssen/pyiron-free-energy where classes for Frenkel-Ladd and Reversible Scaling are developed and tested within the pyiron package.\n",
    "\n",
    "The main work intriduced here is based on reproducing the results of https://DOI.org/10.1016/j.commatsci.2015.10.050."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7fe93b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron import Project\n",
    "import numpy as np\n",
    "import scipy as sci\n",
    "import pandas\n",
    "from jinja2 import Template\n",
    "import matplotlib.pyplot as plt \n",
    "import scipy.constants as sc\n",
    "from scipy.integrate import cumtrapz\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d487ff0d",
   "metadata": {},
   "source": [
    "## Sumary for the theoretical background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10b4b47",
   "metadata": {},
   "source": [
    "Jarzynski’s equality then connects this distribution to the free-energy difference $\\Delta F$ between the equilibrium states defined by the parameters at the two ends of\n",
    "the path through:\n",
    "\n",
    "$$\\exp(-\\beta \\Delta F) = \\overline{\\exp(-\\beta W_{irr})}$$\n",
    "\n",
    "Another way to avoid the statistical uncertainities is done as,\n",
    "\n",
    "$$\\Delta F = W_{irr} = \\overline{W_{irr}} - \\overline{E_{diss}}$$\n",
    "\n",
    "Then, for a linear-response nonequilibrium process connecting states 1 and 2, we have $E^{diss}_{1\\rightarrow2} = E^{diss}_{2\\rightarrow1}$, then we can write,\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "\\Delta F &\\equiv F_{2} - F_{1} \\\\\n",
    "&\\equiv \\frac{1}{2} (W^{rev}_{1\\rightarrow 2} - W^{rev}_{2\\rightarrow 1}) \\\\\n",
    "&= \\frac{1}{2} (\\overline{W^{irr}_{1 \\rightarrow 2}} - \\overline{W^{irr}_{2 \\rightarrow 1}})\n",
    "\\end{aligned}\n",
    "\\end{equation}\n",
    "\n",
    "The standard way of thermodynamic integration to calculate the free energy is done as,\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "\\Delta F &= F(\\lambda_{f}) - F(\\lambda_{i}) = \\int_{\\lambda_{i}}^{\\lambda_{f}} d\\lambda \\langle \\frac{\\partial H}{\\partial \\lambda}\\rangle_{\\lambda}\\\\\n",
    "&\\equiv W^{rev}_{i \\rightarrow f}\n",
    "\\end{aligned}\n",
    "\\end{equation}\n",
    "\n",
    "When we deal with nonequilibrium process, we introduce the switching time. So, the integeral is estimated in terms of the irriversible work where $\\lambda = \\lambda (t)$ is explicitly time-dependent and varies from $\\lambda_{i}$ to $\\lambda_{f}$ in a switching time $t_{s}$. Then, the integeral is written as,\n",
    "\n",
    "$$W^{irr}_{i \\rightarrow f} = \\int^{t_{s}} dt \\frac{d\\lambda}{dt} (\\frac{\\partial H}{\\partial \\lambda})_{\\Gamma(t)}$$\n",
    "\n",
    "$\\Gamma(t)$ represents the phase space trajectory of the system along the process. The integeral can be performed numerically in the following form,\n",
    "\n",
    "$$\\boxed{W^{irr}_{i \\rightarrow f} = \\sum_{k=0}^{N-1} \\Delta \\lambda(k)(\\frac{\\partial H}{\\partial \\lambda})_{\\Gamma(k\\Delta t)}}$$ \n",
    "\n",
    "The application of the mentioned theoretical background is applied in two specific thermodynamic paths, named **Frenkel-Ladd**, and **Reversible scaling** paths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a3d7a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(\"reproduce_results\")\n",
    "pr.remove_jobs_silently(recursive=True)\n",
    "pr.removedirs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "464c5bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# This script runs a Frenkel-Ladd thermodynamic integration for the calculation of the free energy of bcc iron. See job.sh for the variables that need to be passed in the command line.\n",
      "\n",
      "\n",
      "#--------------------------- Simulation variables -----------------------------#\n",
      "  # Initalizes the random number generator.\n",
      "  variable         rnd equal round(random(0,999,{{ RANDOM }}))\n",
      "\n",
      "  # Simulation control parameters.\n",
      "  variable         t_eq equal {{  t_eq  }}          # Equilibration timesteps.\n",
      "  variable         t_switch equal {{  t_switch  }}  # Switching timesteps.\n",
      "  variable         t_step equal {{  t_step  }}      # Timestep.\n",
      "  variable         T equal {{  temp  }}             # Simulation temprerature.\n",
      "#------------------------------------------------------------------------------#\n",
      "\n",
      "\n",
      "#---------------------------- Atomic setup ------------------------------------#\n",
      "  units            metal\n",
      "\n",
      "  # Create atoms.\n",
      "  read_data        structure.inp\n",
      "\n",
      "  # Define interatomic potential.\n",
      "  include          potential.inp\n",
      "  neigh_modify     delay 0\n",
      "  timestep         {{t_step}}\n",
      "#------------------------------------------------------------------------------#\n",
      "\n",
      "\n",
      "#----------------------------- Run simulation ---------------------------------#\n",
      "  # Integrator, Frenkel-Ladd modifier, and thermostat.\n",
      "  fix              f1 all nve \n",
      "  fix              f2 all ti/spring {{k}} {{t_switch}} {{t_eq}} function 2\n",
      "  fix              f3 all langevin {{temp}} {{temp}} 0.1 ${rnd} zero yes\n",
      "\n",
      "  # Compute temperature using COM coordinates.\n",
      "  compute          c1 all temp/com\n",
      "  fix_modify       f3 temp c1\n",
      "\n",
      "  # Redefine simulation time to capture first and last steps on output files.\n",
      "  # Notice that this does not affect the switching time.\n",
      "  variable         t_eq equal ${t_eq}-1\n",
      "  variable         t_switch equal ${t_switch}+1\n",
      "\n",
      "  # Setup thermo output.\n",
      "  thermo_style     custom step pe\n",
      "  thermo           0\n",
      "  variable         dE equal pe-f_f2\n",
      "  variable         lambda equal f_f2[1]\n",
      "\n",
      "  # Initial temperature to accelerate equilibration.\n",
      "  variable         rnd equal round(random(1,666,0)) # Generates new rnd #.\n",
      "  variable         T0 equal 2*{{ temp }}\n",
      "  velocity         all create ${T0} ${rnd} dist gaussian\n",
      "\n",
      "  # Forward integration.\n",
      "  run              ${t_eq}\n",
      "  fix              f4 all print 1 \"${dE} ${lambda}\" title \"# dE [eV] lambda\" &\n",
      "                   screen no file forward.dat\n",
      "  run              ${t_switch}\n",
      "  unfix            f4\n",
      "\n",
      "  # Backward integration. \n",
      "  run              ${t_eq}\n",
      "  fix              f4 all print 1 \"${dE} ${lambda}\" title \"# dE [eV] lambda\" &\n",
      "                   screen no file backward.dat\n",
      "  run              ${t_switch}\n",
      "#------------------------------------------------------------------------------#\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fl_input = open('fl_input','r')\n",
    "fl_input = fl_input.read()\n",
    "print(fl_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60dc7a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# This script runs a Reversible Scaling thermodynamic integration for the calculation of the temperature dependence of the free energy of bcc iron. See job.sh for the variables that need to be passed in the command line.\n",
      "\n",
      "\n",
      "#--------------------------- Simulation variables -----------------------------#\n",
      "  # Initalizes the random number generator.\n",
      "  variable         rnd equal round(random(0,999,{{ RANDOM }}))\n",
      "\n",
      "  # Simulation control parameters.\n",
      "  variable         t_eq equal {{ t_eq }}            # Equilibration timesteps.\n",
      "  variable         t_switch equal {{ t_switch }}    # Switching timesteps.\n",
      "  variable         t_step equal {{ t_step }}        # Timestep.\n",
      "  variable         T equal {{temp}}                 # Simulation temperature.\n",
      "#------------------------------------------------------------------------------#\n",
      "\n",
      "\n",
      "#---------------------------- Atomic setup ------------------------------------#\n",
      "  units            metal\n",
      "\n",
      "  # Create atoms.\n",
      "  read_data        structure.inp\n",
      "\n",
      "  # Define Interatomic potential.\n",
      "  include          potential.inp\n",
      "  neigh_modify     delay 0\n",
      "  timestep         {{  t_step  }}\n",
      "#------------------------------------------------------------------------------#\n",
      "\n",
      "\n",
      "#----------------------------- Run simulation ---------------------------------#\n",
      "  # Setup thermo output.\n",
      "  thermo_style     custom step pe\n",
      "  thermo           0\n",
      "\n",
      "  # Integrator for barostat and thermostat.\n",
      "  fix              f1 all nph iso 0 0 1\n",
      "  fix              f2 all langevin {{ temp }} {{ temp }} 0.1 ${rnd} zero yes\n",
      "\n",
      "  # Compute temperature using center-of-mass coordinates.\n",
      "  compute          c1 all temp/com\n",
      "  fix_modify       f1 temp c1\n",
      "  fix_modify       f2 temp c1\n",
      "\n",
      "  # Initial temperature to accelerate equilibration.\n",
      "  variable         rnd equal round(random(0,666,0)) # Generates new rnd #.\n",
      "  variable         T0 equal 2*{{ temp }}\n",
      "  velocity         all create ${T0} ${rnd} dist gaussian\n",
      "\n",
      "  # Forward integration.\n",
      "  run              {{ t_eq }}\n",
      "  print            \"$(pe/atoms) 1\" file forward.dat\n",
      "  variable         lambda equal 1/(1+(elapsed/${t_switch})*({{ T_max }}/$T-1))\n",
      "  fix              f3 all adapt 1 pair {{ pair_style }} scale * * v_lambda\n",
      "  fix              f4 all print 1 \"$(pe/atoms) ${lambda}\" screen no &\n",
      "                   append forward.dat title \"# pe lambda\"\n",
      "  run              {{ t_switch }}\n",
      "  unfix            f3\n",
      "  unfix            f4\n",
      "\n",
      "  # Backward integration. \n",
      "  run              {{ t_eq }}\n",
      "  print            \"$(pe/atoms) $(v_T/{{ T_max }})\" file backward.dat\n",
      "  variable         lambda equal 1/(1+(1-(elapsed/${t_switch}))*({{ T_max }}/$T-1))\n",
      "  fix              f3 all adapt 1 pair {{ pair_style }} scale * * v_lambda\n",
      "  fix              f4 all print 1 \"$(pe/atoms) ${lambda}\" screen no &\n",
      "                   append backward.dat title \"# pe lambda\"\n",
      "  run              {{ t_switch }}\n",
      "#------------------------------------------------------------------------------#\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rs_input = open('rs_input','r')\n",
    "rs_input = rs_input.read()\n",
    "print(rs_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a45c09",
   "metadata": {},
   "source": [
    "## Frenkel Ladd Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c0db57",
   "metadata": {},
   "source": [
    "The parametrical form of the Hamiltonian is written in the form,\n",
    "\n",
    "$$ H(\\lambda) = \\lambda H_{f} + (1-\\lambda)H_{i}$$\n",
    "\n",
    "The corresponding forward irreversible work estimator is dettermined as,\n",
    "\n",
    "$$ W^{irr}_{i \\rightarrow f} = \\int_{0}^{t_{s}} dt \\frac{d\\lambda}{dt}\\big[H_{f}(\\Gamma(t)) - H_{i}(\\Gamma(t))\\big] $$\n",
    "\n",
    "The initial Hamiltonian is chosen to be that of the system of interest for which we wish to compute free energy, it takes the form as,\n",
    "\n",
    "$$ H_{i} \\equiv H_{0} = \\sum_{i=1}^{N} \\frac{\\textbf{P}^{2}}{2m} + U(\\textbf{r})$$\n",
    "\n",
    "The other Hamiltonian is assumed to be that of a system with non-interacting particles, known as Einstein crystal, written as,\n",
    "\n",
    "$$ H_{f} \\equiv H_{E} = \\sum_{i=1}^{N} \\bigg[\\frac{\\textbf{P}_{i}^{2}}{2m} + \\frac{1}{2}m\\omega^{2}(\\textbf{r}_{i} - \\textbf{r}_{i}^{0})^{2}\\bigg] $$\n",
    "\n",
    "where $\\omega$ is the oscilator frequency, and $r_{i}^{0}$ is the equilibrium position of particle $i$ in system $H_{0}$, the Helmholtz free energy is written as,\n",
    "\n",
    "$$F_{E}(N,V,T) = 3Nk_{B}T ln\\bigg(\\frac{\\hbar \\omega}{k_{B}T}\\bigg)$$\n",
    "\n",
    "Then, the free energy of interets can be estimated in terms of the irreversible work of the two states (i.e. $W^{irr}_{i \\rightarrow f}$, and $W^{irr}_{f \\rightarrow i}$.) as,\n",
    "\n",
    "$$ F_{0}(N,V,T) = F_{E}(N,V,T) + \\frac{1}{2}\\big(\\overline{W^{irr}_{i \\rightarrow f}} - W^{irr}_{i \\rightarrow f}\\big) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d26aed",
   "metadata": {},
   "source": [
    "### Class for the implementation of Frenkel-Ladd path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32824f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FrenkelLadd:\n",
    "    def __init__(self, pr=None, potential=None, element=None, phase=None, struct_size=None, alat=None, temperature_lst=None,\n",
    "                 latts_lst=None, k_lst=None, fl_input=None, params=None, restart=False):\n",
    "        self.pr = pr\n",
    "        self.job_fl = None\n",
    "        self.restart = restart\n",
    "        self.potential = potential\n",
    "        self.element = element\n",
    "        self.phase = phase\n",
    "        self.struct_size = struct_size\n",
    "        self.alat = alat \n",
    "        self.structure = None\n",
    "        self.temperature_lst = temperature_lst\n",
    "        self.latts_lst = latts_lst\n",
    "        self.k_lst = k_lst\n",
    "        self.fl_input = fl_input\n",
    "        self.T = np.array(temperature_lst)\n",
    "        self.k = np.array(k_lst)\n",
    "        self.m = None\n",
    "        self.natoms = None\n",
    "        self.I_forw = list()\n",
    "        self.I_back = list()\n",
    "        self.T_fl = None\n",
    "        self.F_fl = None\n",
    "        self.t_eq = params[0]\n",
    "        self.t_switch = params[1]\n",
    "        self.t_step = params[2]\n",
    "\n",
    "    def parameterize(self):\n",
    "        kB = sc.value('Boltzmann constant in eV/K')\n",
    "        self.latts_lst = np.zeros(len(self.temperature_lst))\n",
    "        self.k_lst = np.zeros(len(self.temperature_lst))\n",
    "        for i, temp in enumerate(self.temperature_lst):\n",
    "            job = self.pr.create_job('Lammps',f'fl_parameterize_relax_{self.phase}_T_{int(temp)}', True)\n",
    "            job.structure = self.pr.create_ase_bulk(self.element, self.phase, a=self.alat, cubic=True).repeat(self.struct_size)\n",
    "            job.potential = self.potential\n",
    "            job.calc_md(pressure=0, temperature=temp, langevin=True)\n",
    "            job.run()\n",
    "            self.latts_lst[i] = job.get_structure().cell[0,0]/self.struct_size\n",
    "            self.k_lst[i] = 3*kB*temp/np.average(sci.linalg.norm(job.output.total_displacements[-1,:,:], axis=-1)**2)\n",
    "        \n",
    "    def run_jobs(self):\n",
    "        self.a = np.array(self.latts_lst)\n",
    "        self.k = np.array(self.k_lst)\n",
    "        for temp, alat, k in zip(self.temperature_lst, self.latts_lst, self.k_lst):\n",
    "            job_fl = self.pr.create_job(pr.job_type.Lammps, f\"lmp_frenkel_ladd_{self.phase}_T_{int(temp)}\" , self.restart)\n",
    "            job_fl.structure = self.pr.create_ase_bulk(self.element, self.phase, a=alat, cubic=True).repeat(self.struct_size)\n",
    "            self.structure = job_fl.structure\n",
    "            job_fl.potential = self.potential\n",
    "            tm_frenkel_ladd = Template(self.fl_input)\n",
    "            job_fl.input.control.load_string(\n",
    "                tm_frenkel_ladd.render(\n",
    "                    RANDOM=np.random.randint(999),\n",
    "                    t_eq=self.t_eq,             # Equilibration timesteps.\n",
    "                    t_switch=self.t_switch,     # Switching timesteps\n",
    "                    t_step=self.t_step,         # Timestep\n",
    "                    temp=temp,                  # Simulation temperature\n",
    "                    k=k                         # Spring constant\n",
    "                )\n",
    "            )\n",
    "            job_fl.run()\n",
    "            job_fl.decompress()\n",
    "            \n",
    "            dE, lamb = np.loadtxt(os.path.join(job_fl.working_directory, \"forward.dat\"), unpack=True)\n",
    "            self.I_forw.append(np.trapz(dE,lamb))\n",
    "            dE, lamb = np.loadtxt(os.path.join(job_fl.working_directory, \"backward.dat\"), unpack=True)\n",
    "            self.I_back.append(np.trapz(dE,lamb))\n",
    "            \n",
    "    def compute_free_energy(self):\n",
    "        kB = sc.value('Boltzmann constant in eV/K')\n",
    "        eV = sc.value('electron volt')\n",
    "        hbar = sc.value('Planck constant over 2 pi in eV s')\n",
    "        mu = sc.value('atomic mass constant')\n",
    "        \n",
    "        W = np.zeros(len(self.T)) # Reversible work for each temperature.\n",
    "        for i in range(len(self.T)):\n",
    "            W[i] = (self.I_forw[i]-self.I_back[i]) / 2\n",
    "        \n",
    "        # Define harmonic reference system free energy [Eq.(15) in the paper].\n",
    "        species = self.structure.species[0]\n",
    "        self.m = species.AtomicMass\n",
    "        self.natoms = len(self.structure)\n",
    "        omega = np.sqrt(self.k*eV/(self.m*mu)) * 1.0e+10 # [1/s].\n",
    "        F_harm = 3*self.natoms*kB*self.T * np.log(hbar*omega/(kB*self.T)) # [eV].\n",
    "        \n",
    "        # Fixed center of mass correction [Eq.(24) in the paper].\n",
    "        V = (self.a**3/2) * self.natoms # Total volume.\n",
    "        F_CM = (kB*self.T)*np.log((self.natoms/V) * (2*np.pi*kB*self.T /\n",
    "                                                     (self.natoms*self.m*omega**2))**(3/2)) # [eV].\n",
    "        \n",
    "        # Compute absolute free energy per atom [Eq.(16) in the paper] and save data.\n",
    "        self.F_fl = (F_harm + W + F_CM) / self.natoms # [eV/atom].\n",
    "        self.T_fl = self.T.copy()\n",
    "\n",
    "    def plot_results(self):\n",
    "        c = ['#E41A1C','#377EB8','#4DAF4A','#984EA3','#FF7F00','#FFFF33','#A65628','#F781BF','#999999']\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_axes([0.15,0.15,0.80,0.80])\n",
    "\n",
    "        # Plot.\n",
    "        ax.plot(self.T_fl[1:], self.F_fl[1:], 'go', label='Frenkel-Ladd')\n",
    "        ax.plot(self.T_fl[0], self.F_fl[0],'ro', label='$\\mathrm{G(T_{0})}$')\n",
    "\n",
    "        # Add details and save figure.\n",
    "        ax.set_xlabel(r'Temperature [K]')\n",
    "        ax.set_ylabel(r'Free energy [eV/atom]')\n",
    "        ax.legend(loc=1, frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408474fe",
   "metadata": {},
   "source": [
    "## Reversible Scaling Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99d0ff5",
   "metadata": {},
   "source": [
    "The **Reversible Scaling** path is a specific form of the system Hamiltonian $H(\\lambda)$ where each value of $\\lambda$ corresponds to a specific temperature, thus one can calculate the free energy $F_{0}(N,V,T)$ from a single constant temperature simulation. \n",
    "\n",
    "By considering an initial Hamiltonian for the system of interets as,\n",
    "\n",
    "$$ H_{i} \\equiv H_{0} = \\sum_{i=1}^{N} \\frac{\\textbf{P}_{i}^{2}}{2m} + U(\\textbf{r}) $$\n",
    "\n",
    "The free energy in this case is given as,\n",
    "\n",
    "$$ F_{0}(T_{0}) = -k_{B}TlnQ(T_{0}) + 3Nk_{B}T_{0}ln\\Lambda(T{0}) $$\n",
    "\n",
    "where $\\Lambda(T_{0}) = (h^{2}/2\\pi mk_{B}T_{0})^{1/2}$ is the thermal de Broglie wavelength, and $Q(T_{0}) = \\int d^{3N}\\textbf(r)exp[-U(\\text{r})/k_{B}T_{0}]$ is the configurational part of the partition function. \n",
    "\n",
    "The paramtrical form of the Hamiltonian is then described by a scaling factor $\\lambda$ in the potential energy as,\n",
    "\n",
    "$$ H(\\lambda) = \\sum_{i=1}^{N} \\frac{\\textbf{P}_{i}^{2}}{2m} + \\lambda U(\\textbf{r}) $$\n",
    "\n",
    "The configurational part of the partition function of the system described by $H(\\lambda)$ is,\n",
    "\n",
    "$$ \\int d^{3N}\\textbf{r} exp[-\\lambda U(\\textbf{r})/k_{B}T_{0}] \\equiv Q(T_{0}/\\lambda) $$\n",
    "\n",
    "The free energy of these two systems are related, and one can write the free energy as,\n",
    "\n",
    "$$ F_{0}(T) = \\frac{1}{\\lambda}F_{T_{0};\\lambda} + \\frac{3}{2}Nk_{B}T_{0}\\frac{ln\\lambda}{\\lambda} $$\n",
    "\n",
    "where $T \\equiv \\frac{T_{0}}{\\lambda}$, and $F(T_{0};\\lambda)$ is the free energy of the system $H(\\lambda)$ for a specific value of the parameter $\\lambda$. The irreversible work done can be calculated by applying the nonequilibrium technique as,\n",
    "\n",
    "$$W^{irr}_{1\\rightarrow \\lambda_{f}} = \\int^{t_{s}}dt \\frac{d\\lambda}{dt}U(\\Gamma(t))$$\n",
    "\n",
    "The temperature dependence of the free energy described by the system $H_{0}$ is written as,\n",
    "\n",
    "$$ F_{0}(T) = \\frac{F_{0}(T_{0})}{\\lambda} + \\frac{3}{2}Nk_{B}T_{0}\\frac{ln\\lambda}{\\lambda} + \\frac{1}{2\\lambda} \\big[W^{irr}_{1\\rightarrow \\lambda} - W^{irr}_{\\lambda \\rightarrow 1}\\big]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a6e61e2",
   "metadata": {},
   "source": [
    "### Class for the implementation of Reversible Scaling path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ea1b83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReversibleScaling:\n",
    "    def __init__(self, pr= None, potential=None, element=None, phase=None, struct_size=None, params=None, rs_input=None, fl_obj=None, restart=False):\n",
    "        self.pr = pr\n",
    "        self.job_rs = None\n",
    "        self.restart = restart\n",
    "        self.potential = potential\n",
    "        self.element = element\n",
    "        self.phase = phase\n",
    "        self.struct_size = struct_size\n",
    "        self.structure = None\n",
    "        self.fl_obj = fl_obj\n",
    "        self.alat = fl_obj.latts_lst[0]\n",
    "        self.rs_input = rs_input\n",
    "        self.T_rs = None\n",
    "        self.F_rs = None\n",
    "        self.T = fl_obj.T\n",
    "        self.t_eq = params[0]\n",
    "        self.t_switch = params[1]\n",
    "        self.t_step = params[2]\n",
    "        self.temp = params[3]\n",
    "        self.T_max = params[4]\n",
    "        \n",
    "    def run_job(self):\n",
    "        self.job_rs = self.pr.create_job(self.pr.job_type.Lammps, f\"lmp_rs_{self.phase}_{int(self.temp)}\", self.restart)\n",
    "        self.structure = self.pr.create_ase_bulk(self.element,self.phase,a=self.alat,cubic=True).repeat(self.struct_size)\n",
    "        self.job_rs.structure = self.structure\n",
    "        self.job_rs.potential = self.potential\n",
    "        tm_reversible_scaling = Template(self.rs_input)\n",
    "        pair_style = self.potential['Config'].values[0][0].split()[1]\n",
    "        self.job_rs.input.control.load_string(\n",
    "            tm_reversible_scaling.render(\n",
    "                RANDOM=np.random.randint(999),\n",
    "                pair_style=pair_style,\n",
    "                t_eq=self.t_eq,           # Equilibration time: check e.g. double\n",
    "                t_switch=self.t_switch,   # Switching time: check e.g. double\n",
    "                t_step=self.t_step,       # Time step: check e.g. reduce 1/2\n",
    "                temp=self.temp,           # Simulation temperature: check e.g. reduce 1/2\n",
    "                T_max=self.T_max          # Maximum temperature:  \n",
    "                \n",
    "            )\n",
    "        )\n",
    "        self.job_rs.run()\n",
    "        self.job_rs.decompress()\n",
    "            \n",
    "    def compute_free_energy(self):\n",
    "        T0 = self.temp # Reference temperature [K]\n",
    "        kB = sc.value('Boltzmann constant in eV/K')\n",
    "        F0 = self.fl_obj.F_fl[self.T==T0]\n",
    "        \n",
    "        U_f, lamb_f = np.loadtxt(os.path.join(self.job_rs.working_directory, \"forward.dat\"), unpack=True)\n",
    "        U_b, lamb_b = np.loadtxt(os.path.join(self.job_rs.working_directory, \"backward.dat\"), unpack=True)\n",
    "        # Fix adapt also scales the potential energy besides the forces, so we unscale.\n",
    "        U_f /= lamb_f\n",
    "        U_b /= lamb_b\n",
    "\n",
    "        # Compute work done using cummulative integrals [Eq.(21) in the paper].\n",
    "        I_f = cumtrapz(U_f,lamb_f,initial=0)\n",
    "        I_b = cumtrapz(U_b[::-1],lamb_b[::-1],initial=0)\n",
    "        W = (I_f+I_b) / (2*lamb_f)\n",
    "\n",
    "        # Compute free energy [Eq.(22) in the paper] and save results.\n",
    "        self.T_rs = T0 / lamb_f\n",
    "        self.F_rs = F0/lamb_f  + 1.5*kB*self.T_rs*np.log(lamb_f) + W\n",
    "        \n",
    "    def plot_results(self):\n",
    "        c = ['#E41A1C','#377EB8','#4DAF4A','#984EA3','#FF7F00','#FFFF33','#A65628','#F781BF','#999999']\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_axes([0.15,0.15,0.80,0.80])\n",
    "\n",
    "        # Plot.\n",
    "        ax.plot(self.T_rs, self.F_rs, c=c[0], lw=1)\n",
    "\n",
    "        # Add details and save figure.\n",
    "        ax.set_xlabel(r'Temperature [K]')\n",
    "        ax.set_ylabel(r'Free energy [eV/atom]')  \n",
    "        \n",
    "    def plot_all(self):\n",
    "        c = ['#E41A1C','#377EB8','#4DAF4A','#984EA3','#FF7F00','#FFFF33','#A65628','#F781BF','#999999']\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_axes([0.15,0.15,0.80,0.80])\n",
    "\n",
    "        # Plot.\n",
    "        ax.plot(self.T_rs, self.F_rs, '-', c='k', lw=1, label='Reversible Scaling')\n",
    "        ax.plot(self.fl_obj.T_fl[1:], self.fl_obj.F_fl[1:], 'go', label='Frenkel-Ladd')\n",
    "        ax.plot(self.fl_obj.T_fl[0], self.fl_obj.F_fl[0],'ro', label='$\\mathrm{G(T_{0})}$')\n",
    "\n",
    "        # Add details and save figure.\n",
    "        ax.set_xlabel(r'Temperature [K]')\n",
    "        ax.set_ylabel(r'G(p=0,T) [eV/atom]')\n",
    "        ax.legend(loc=1, frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2653e0",
   "metadata": {},
   "source": [
    "## Defining the potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f005e4c",
   "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>Name</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Model</th>\n",
       "      <th>Species</th>\n",
       "      <th>Config</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fe_Potential</td>\n",
       "      <td>[/home/sam/programs/ibrsam_git/FreeEnergyNoneqTI/Fe.eam]</td>\n",
       "      <td>Custom</td>\n",
       "      <td>[Fe]</td>\n",
       "      <td>[pair_style eam\\n, pair_coeff * * Fe.eam\\n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name                                                  Filename  \\\n",
       "0  Fe_Potential  [/home/sam/programs/ibrsam_git/FreeEnergyNoneqTI/Fe.eam]   \n",
       "\n",
       "    Model Species                                       Config  \n",
       "0  Custom    [Fe]  [pair_style eam\\n, pair_coeff * * Fe.eam\\n]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_potential = pandas.DataFrame({\n",
    "  'Name': ['Fe_Potential'],\n",
    "  'Filename': [[os.path.abspath(\"Fe.eam\")]],\n",
    "  'Model': ['Custom'],\n",
    "  'Species': [['Fe']],\n",
    "  'Config': [['pair_style eam\\n', \n",
    "              'pair_coeff * * Fe.eam\\n']]\n",
    "})\n",
    "fe_potential"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a744b85",
   "metadata": {},
   "source": [
    "## Calulations for Fe$_{BCC}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d66fb7a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_100 was saved and received the ID: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_400 was saved and received the ID: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_700 was saved and received the ID: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_1000 was saved and received the ID: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_1300 was saved and received the ID: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_bcc_T_1600 was saved and received the ID: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lmp_frenkel_ladd_bcc_T_100 was saved and received the ID: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/base.py:1014: UserWarning: LAMMPS warning: No dump.out output file found.\n",
      "  warnings.warn(\"LAMMPS warning: No dump.out output file found.\")\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/base.py:591: UserWarning: LAMMPS warning: log.lammps does not contain the required pressure values.\n",
      "  warnings.warn(\"LAMMPS warning: log.lammps does not contain the required pressure values.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lmp_frenkel_ladd_bcc_T_400 was saved and received the ID: 8\n",
      "The job lmp_frenkel_ladd_bcc_T_700 was saved and received the ID: 9\n",
      "The job lmp_frenkel_ladd_bcc_T_1000 was saved and received the ID: 10\n",
      "The job lmp_frenkel_ladd_bcc_T_1300 was saved and received the ID: 11\n",
      "The job lmp_frenkel_ladd_bcc_T_1600 was saved and received the ID: 12\n",
      "The job lmp_rs_bcc_100 was saved and received the ID: 13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature_lst = np.linspace(100, 1600, 6)\n",
    "fl_params = [1000, 5000, 0.002]\n",
    "\n",
    "fl_bcc = FrenkelLadd(pr=pr, potential=fe_potential, element='Fe', phase='bcc', struct_size=15, alat=2.88, temperature_lst=temperature_lst,\n",
    "                     fl_input=fl_input, params=fl_params, restart=True)\n",
    "fl_bcc.parameterize()\n",
    "fl_bcc.run_jobs()\n",
    "fl_bcc.compute_free_energy()\n",
    "# fl_bcc.plot_results()\n",
    "\n",
    "rs_params = [1000, 10000, 0.002, 100, 1600]\n",
    "rs_bcc = ReversibleScaling(pr=pr, potential=fe_potential, params=rs_params, element='Fe', phase='bcc', struct_size=15, rs_input=rs_input,\n",
    "                           fl_obj=fl_bcc, restart=True)\n",
    "rs_bcc.run_job()\n",
    "rs_bcc.compute_free_energy()\n",
    "rs_bcc.plot_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4c88ce",
   "metadata": {},
   "source": [
    "## Calculations for Fe$_{FCC}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a2f32ea",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_100 was saved and received the ID: 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_400 was saved and received the ID: 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_700 was saved and received the ID: 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_1000 was saved and received the ID: 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_1300 was saved and received the ID: 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fl_parameterize_relax_fcc_T_1600 was saved and received the ID: 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity indices. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity velocities. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/units.py:202: UserWarning: Warning: Couldn't determine the LAMMPS to pyiron unit conversion type of quantity steps. Returning un-normalized quantity\n",
      "  \"{}. Returning un-normalized quantity\".format(label))\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/atomistics/job/atomistic.py:866: UserWarning: You are computing displacements in a simulation with periodic boundary conditions \n",
      "and a varying cell shape.\n",
      "  warnings.warn(\"You are computing displacements in a simulation with periodic boundary conditions \\n\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lmp_frenkel_ladd_fcc_T_100 was saved and received the ID: 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/base.py:1014: UserWarning: LAMMPS warning: No dump.out output file found.\n",
      "  warnings.warn(\"LAMMPS warning: No dump.out output file found.\")\n",
      "/home/sam/anaconda3/envs/pyiron/lib/python3.7/site-packages/pyiron_atomistics/lammps/base.py:591: UserWarning: LAMMPS warning: log.lammps does not contain the required pressure values.\n",
      "  warnings.warn(\"LAMMPS warning: log.lammps does not contain the required pressure values.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lmp_frenkel_ladd_fcc_T_400 was saved and received the ID: 21\n",
      "The job lmp_frenkel_ladd_fcc_T_700 was saved and received the ID: 22\n",
      "The job lmp_frenkel_ladd_fcc_T_1000 was saved and received the ID: 23\n",
      "The job lmp_frenkel_ladd_fcc_T_1300 was saved and received the ID: 24\n",
      "The job lmp_frenkel_ladd_fcc_T_1600 was saved and received the ID: 25\n",
      "The job lmp_rs_fcc_100 was saved and received the ID: 26\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fl_params = [1000, 5000, 0.002]\n",
    "fl_fcc = FrenkelLadd(pr=pr, potential=fe_potential, element='Fe', phase='fcc', struct_size=15, alat=3.55, temperature_lst=temperature_lst,\n",
    "                     fl_input=fl_input, params=fl_params, restart=True)\n",
    "fl_fcc.parameterize()\n",
    "fl_fcc.run_jobs()\n",
    "fl_fcc.compute_free_energy()\n",
    "# fl.plot_results()\n",
    "\n",
    "rs_params = [1000, 10000, 0.002, 100, 1600]\n",
    "rs_fcc = ReversibleScaling(pr=pr, potential=fe_potential, params=rs_params, element='Fe', phase='fcc', struct_size=15, rs_input=rs_input,\n",
    "                           fl_obj=fl_fcc, restart=True)\n",
    "rs_fcc.run_job()\n",
    "rs_fcc.compute_free_energy()\n",
    "rs_fcc.plot_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98fb216",
   "metadata": {},
   "source": [
    "## Fe Polymorphism from 100 K to 1600 K Including BCC and FCC Phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f2efd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f170f679f90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NG1iyZAnLly9n/fr1pKam5lo+NDSUDh06OElVkyZNMo2hanJekxwXERYRKYp8nmAZYy4EHgIGAbktd55ojJkL/Nta+5uvYxIRERHvSkpKYs2aNcyfP5/PPvuMv//+O8/yzZo1o2vXrnTp0oV27doRGqpufiJSMvg0wTLG3An8B1dildd0PuWB4cBgY8z91tr/+jIuERERKbwTJ06wdOlSPv30UxYtWsSxY8dyLVu1alU6d+5M165d6dy5M+ecc44fIxUR8R+fJVjGmNuB1zkzyaoFNqVvh9OLVQVa4Oo6aHCN9XnDGJNsrZ3uq9ikQCYBr+RwPgKI9mskIiIScEeOHGH+/Pl8+umnfPHFF5w+fTrHckFBQVx99dV069aNrl270rJlS4KCgvwcrYiI//kkwTLG1AJezjgEPgIettbuyqV8feA54Mb08q8YYxZZaw/4Ij7xnLU2EUjMel7ri4iIlB4nTpzgs88+4/3332f58uWkpKTkWC40NJQuXbrQr18/evXqRdWqVf0cqYhI4PmqBWsMEIar1erf1tpxeRW21u4GbjLG7MI1qUKF9Dom+Cg+ERGfi4yMxFob6DBEzkpiYiKLFy/m/fffZ+HChSQmZvusDYCoqCh69epFv3796NKlC2FhYX6OVESkaDG++ONvjFkPtAZ2AE2stWke3hcE/IprpsEN1torvB6ceIUxJhLXLJAAxMbGEhkZGbB4RESk8FJSUli1ahVz5sxh/vz5HD9+PMdyNWvWpH///vTr14/27dsTHBzs50hFRHwnLi6OqKgo91NR1to4T+/3VQtWfVytVws9Ta4ArLVpxpiFwD+BBj6KTURERNJZa1m/fj1z5szhgw8+4ODBgzmWq1y5Mv3792fw4MG0b9+eMmVyWP1XRER8lmBVSv96OM9SOcu4p6KXYhEREZEsfvvtN2bPns2cOXPYtSvHIdKEhYXRt29fBg8eTOfOnQkJCfFzlCIixY+vEqyjQA2g7lncm3HPUe+FExjGmHOAS9O3y9K/1sy4bq0t8EwRxphuwDDgCqAWcBLYDXwKTLPWHip04CIiUiL99ddfzJ07l9mzZ7Np06YcywQHB9OjRw8GDx5M7969qVChgp+jFBEp3nyVYP2CK5Hoa4x5wFp70pObjDHhQF9c3Qt/8VFsfmGMmYRrgWVv1RcGzAAGZrkUimu6+9bAfcaYW621S731XBE5e1n7cGusogTCkSNH+Pjjj5kzZw5ffvlljhOvGGPo0KEDQ4YMYcCAAVnHHoiISAH4KsFaCFwLVAPeNcYMstbmPKdrOmNMGVwJRHVcCdbnPorNX3Jakv4oULmgFaV/b+YCvdJPpQALgC1AJK6ktD6u7/d8Y0wna+03BQ9ZRERKgsTERBYsWMCsWbNYunRprtOqt2zZkiFDhnDTTTdx7rnn+jlKEZGSyVcJ1lTgX6S3YgGbjDHjcU16keRe0BgTDPQExgPNcSVXB9LrKM5igRXAjxmbtXa3MeZspm0cxZnk6ijQzVr7Q8ZFY8yDwDRgOK7EbpYxpknW77WIiJRcGZNVvPvuu8ydO5e4uLgcyzVo0IAhQ4YwZMgQmjRp4t8gRURKAZ8kWNbaU8aYocASIAS4CNdiw8nGmN+AI7gSqSrAhellwLXIcCIwxFqb4IvY/MVa+6Q36jHGhABPuJ26yz25Sn9WijFmNK4xXs1wzcA4EnjLGzGIiEjRtW/fPmbNmsW7777L9u3bcyxTs2ZNBg0axODBg7n88su1WLyIiA/5qgULa+2a9AkZ3gPOSz8dgisBcOf+v/xe4BZr7Ze+iqsY6oRrMguAP4APcipkrU02xvwHeDf91M0owRIRKZFOnTrFJ598wrvvvsvKlStzHFcVFhZG//79ueWWW7jmmms0rbqIiJ/4LMECsNauNcZcBIzA9Ya/JZB1NcJkYCPwP2CmtTbelzEVQ73d9ufbvFeG/hRIA4KANsaY6tbanBc0ERGRYufHH3/kzTff5KOPPuLEiRM5lrnmmmsYNmwY/fv3Jzw83M8RioiITxMsgPSE6XXgdWNMOaAerokZwDVOaY+19rSv4yjGWrrt5zlxhbX2uDFmG65WQgNcgmscmIiIFFMpKSnMnz+fyZMn8/XXX+dYpmHDhgwbNoxbbrmFevXq+TdAERHJxOcJlrv0RCrnDuKSG/cRyDs9KL+TM90wm6AES0SkWDp69CjTpk3jjTfeYO/evdmuV6pUiRtvvJHhw4dz1VVXaVyViEgR4ZMEyxiTMSnDcmvtdwW8tzXQHcBaO9HbsRUnxphQIMLt1H4PbvvLbb9mrqWyPyenaeXzEpF/ERERKahffvmFV199lffee4+EhOzzPbVp04a77rqLfv36aRFgEZEiyFctWBNwzRJ4EihQggVc7nZ/qU6wgKyd5z0Zn3bKbb+ih88Zh2uafBERCYC0tDSWLFnC5MmTWbEie8eDsmXLcuONNzJ27Fguv/zyAEQoIiKe8msXQSmw8lmOPVnXyn08W9b7RUSkCDlx4gQzZ87ktddeY8eOHdmuV61alTFjxnDHHXdwzjnnBCBCEREpqKKYYAWlf00LaBRFQ9a+ISG41gnLS7k87hcRPypXrhxjx47NdCwCsGvXLl5//XWmT5/O8ePHs11v3rw5Y8eOZciQIYSGFrQHt4iIBFJRTLBqpH89GdAoioas34Mw8k+w3Dvk5zyHb3aTgFc8LJshAogu4D0ipUr58uV55ZVXAh2GFBHWWtauXcsrr7zCggULsq1dZYzh+uuvZ+zYsXTo0EGTVoiIFFNFKsEyxlQHBuEaf/VHgMMJOGttojHmOFAp/dQ5wJF8bqvtth/j6XPIP3HLRH/4RUQ8k5SUxAcffMDLL7/Mpk2bsl2vVKkSI0eO5O6776ZBgwYBiFBERLyp0AmWMWYYMCyXy3cYY3p5UE0ZoDLQ2C2mLwobWwnxC3Bl+n5D4Kd8yjfMcq+IiATA0aNHmTJlCq+99hp///13tuvnn38+9957L8OGDaNiRU/nJBIRkaLOGy1Y9YCOuFqd3BmgQfrmqYxmkYMUvMtaSbWZMwnWVcCnuRU0xlQCLk4/tMAWXwYmIiLZ/f3337zwwgtMmTIlx2nWO3XqxD//+U+6d+9OUFBQDjWIiEhx5s0ugjn1GStoP7K/gcXAU9baA4UPqUT4HBiTvt/PGPOQzdpx/4w+uFoDAb6z1h70eXQikqvTp0/z1ltvOcd33HGHJroowQ4cOMALL7zAW2+9RWJi5l7XISEhDB06lPvuu4/mzZsHKEIREfEHk/t7dQ8rMCYCiHQ/BezC1YLyLPC2B9UkA8ettSV+YgtjjPMNt9bmm4AaY0KAPZxZNHiQtfbDHMoFAxuAjL/cd1tr3yh8xLnGFQnEZhzHxsYSGRnpq8eJFEtxcXFERUU5x/o9KZn279/Pf/7zH/773/9ma7GqWrUqd955J3fccQc1a3q09ruIiARY1r/fQJS1Ns7T+wvdgmWtPQYccz+XPgGCAWKttXsK+4zSzFqbZIx5CshIlt40xuyy1m7IKGOMKQO8xZnkKhqY5tdARURKmT179vDCCy8wffp0Tp8+nelajRo1eOihh/jHP/5BhQoVcqlBRERKIl/NIlg//etRH9Vf5BljquFKevIqMy/rOWvtgByKTgF6Ad2BKsC3xpjPgK24Zhjsy5nJLU4Dt1prPVmUWERECuiPP/7gueee49133yUlJSXTterVq/PQQw8xZswYJVYiIqWUTxIstVoBrjWr+udTJr/rAFhrU40xNwLvAjfg+nfrn8P9h4Hh1tp1BYxVRETy8dtvv/HMM88wZ84c0tLSMl2rVasWDz74IKNGjSIsLCxAEYqISFFQpNbBktylj0/rb4zpgWta/CtwjcuKB3bjml1wqia2EBHxrp9++omnn36ajz76KNviwHXq1GHcuHGMGDGC0NDQAEUoIiJFid8SrPRJGOrjmhDDo79C1tovfRmTL1lroyn4LIqe1LsY10yLIiLiQxs3buSpp57i008/zXatYcOGPPLII9x8882EhIT4PzgRESmyfJpgGddsF8OA24HLgOAC3G5RC5uIiPjZd999x1NPPcXixdk/y2rSpAmPPvoogwYNomxZ/YkSEZHsfPbXwRhTBfgMaJNxylfPEhERKax169bx1FNPsWLFimzXmjdvzmOPPUb//v21OLCIiOTJlx+/fQxclb6/H9eCuQ2Azrhap54CIoCLgXZAOSANmJ5eXkRExOd++uknHnroIZYsWZLt2qWXXsrjjz9O7969lViJiIhHfJJgGWP6Au1xJVJLgQHW2gRjzP/hSrCw1k5wK18NeAFXd8I+QG/3dZ5ERKR0izkZw9sb32btnrWcSDpBxZCKdKzXkZEtR1IjvMZZ1blv3z6eeOIJZs6cmW3yiiuvvJLHH3+c7t27Z6ztKCIi4hFftWANSv96CrjFWpuQV2Fr7SFghDEmDhgLfGSMaVmQFZPFN4wxoeQ8KUmEv2MRkdInITmBsUvHMnPzTJLTkjNdW7FrBRPWTGBEixFM7j6Z0LKezeJ37NgxXnjhBV5++WUSEjL/ebr66quZMGECnTp1UmIlIiJnxVcJ1hW4Wq+WWGsLstjwQ8BAoA6uiTH+44PYpGDGAeMDHYRIcVSpUiV2796d6Vg8l5CcQPfZ3Vm7Z22uZZLTkpm6cSrbj2xnydAllA8un2vZpKQkpkyZwsSJEzl8+HCmaxdccAHPP/88ffr0UWIlIiKF4qsO5dXSv/6S5Xxqxk56y0gm1tokXGO1DK4FdUVEiq2goCDq1avnbBrDUzBjl47NM7lyt3bPWu5bel+O16y1fPTRR1x00UXce++9mZKrGjVq8N///peff/6Z66+/XsmViIgUmq/+2mcsCnIqy/mTbvtVc7l3b/rX+l6NSEREio0DJw8wc/PMAt0zY/MMYk7GZDq3bt062rRpw4033sgff/zhnA8LC2PChAns3LmTf/zjH5pyXUREvMZXCVZGt8CKWc67/+U7P5d7q6d/jfJqRHK2JuH6t8i61QtgTCJSwk3fOD3bmKv8JKclM33TdAB+/fVXrr/+etq3b8/69eudMmXKlGHMmDHs3LmT8ePHEx4e7tW4RUREfPWR3XZciVLDLOe3uu13Bla7X0xfmPia9MOCjN0SH7HWJgKJWc+rG42I+JKnXQOzWvb7Mvb8bw9vv/02aWlpma717duX5557jgsvvNAbIYqIiOTIVy1Y3+IaR3W5+0lr7R7g5/RrdxljWmS570mgGa4JMr71UWwiIn5x/PjxTGOwjh8/HuiQio0TSSfO6r6vvv+KqVOnZkqurrzyStatW8f8+fOVXImIiM/5qgVrKa4ZAesbY5paa392u/YS8A4QDqw3xnyJq7WqBdDIrdzrPopNRMQv0tLS2LNnT6Zj8UzFkKw9zD2TlnDme9yoUSOee+45+vfvr1Z3ERHxG1+1YH2JqzvgfuBG9wvW2pnA+7hasYKBa4EBuJKrjL+Az1lrM3UfFBGR0qND3Q5nd2M0REVF8eqrr7Jt2zYGDBig5EpERPzKJwmWdWlhrT3PWvtEDkVuBu4EtuFKqjL++v0IDLLWPuaLuEREpHgY2WokwUHBBbspFW5tdis7duzgnnvuISQkJP97REREvCwgi7KkJ2D/tdY2A8KAc4Fwa21ra+1HgYhJRESKjprhNRneYrhnha3ryw0NbuDdN96lSpUqPotLREQkP4VOsIwxw4wxua1plS9rbYK1dr+1NqGwsYiISMkxudtkz7oKGleXwtk3z/Z9UCIiIvnwRgvWDOBvY8zXxpiHjTFNvVCniIiUcuWDyzOt4zQaHWsEqTmXCQ4KZnSr0Sy9eSmhZUP9G6CIiEgOvDWLYBmgDXAl8LQx5k9gIfA5sNpaW7DVIkVEpFRLSUnh9ddf5/HHH+fkyZOuzuStgHoQUS2CJo2a0Ltpb0a2HEmN8BoBjlZEROQMbyRYVwK9gN7AJenn6uKaxOJOIN4YswJXsrXIWnvIC88UEZESatOmTYwcOZJNmzadORkPzeKa8e+h/6Zr166BC05ERCQfhe4iaK393lr7hLW2JVAHV1K1BDiNa3bAcKAvMB1XV8JvjTGPGGOaFfbZIiJSciQmJvLoo4/SunXrTMlV5cqVmTp1Kps2bVJyJSIiRZ6x1vqmYmPKA51xtWz1BGq6Xc546F4ydyVM8kkw4nXGmEggNuM4NjaWyMjIgMUjUhTFxcURFRXlHOv3JHfffvstt912G7/99lum88OHD+ff//43Vaue9VxKIiIiBZL17zcQZa2N8/R+b43ByiZ9VsAF6RvGmMtwJVu9gRbpxeoAd6Rvp7J0JTzoq9hERPwhODiY66+/PtOxZJaQkMATTzzBiy++iPsHfvXr12fatGl06tQpgNGJiIgUnM9asPJ8qDG1OTNu61ogY+on6/Z1A65ka6G1dovfg5Q8qQVLRApr/fr1DB8+PFOrlTGGe++9l2eeeYawsLAARiciIqVVYVuwApJgZQrA1ZXwOs50Jazldtm9K+H/WWs/9nN4pZ4xJpQzCbC7CCA640AJloh4KjExkQkTJvDvf/+btLQ053zjxo2ZMWMGV111VQCjExGR0q6wCZY31sEqlPSFhj+31o621tYGWgMTgY24JskwwHnAxQEMszQbh6ulKusWHcCYRKSY+uGHH7j00kt5/vnnneTKGMP999/P5s2blVyJiEix57MxWGfLWvsj8CMwwRhzDq6uhL2AUwENTEREztrp06eZOHEizz//PKmpZ1YNbtSoETNmzKBt27YBjE5ERMR7Cp1gGWPKWGtT8y9ZcNba/cDU9E1EpFhJTk5m0aJFznHPnj1L5UQXmzZtYtiwYfz000+Zzo8dO5Znn32WChUqBCgyERER7yv0GCxjzAFgFvCutfZnr0QlRYbGYImcvdI+TXtycjLPPvssTz/9NCkpKc75Bg0a8M4779ChQ4cARiciIpKzojBNe3XgfuB+Y8xG4B3g/YIEIUWXtTYRSMx63hgTgGhEpLjYtm0bt956Kxs3bsx0/u6772bSpEmaIVBEREosb01ykTEZRSvgdeBvY8xcY0w3o3fiIiKlhrWWyZMn06pVq0zJVd26dVm5ciWvvfaakisRESnRvJFgdQZm45qEIiPRKgcMBBYBe40xzxpjLvDCs0REpIiKiYmhR48e3HfffSQlJTnnR40axdatW7n22msDGJ2IiIh/FDrBstautNbeAtQERgFfpV/KSLZqAQ8BvxhjvjbGjDTGVCzsc0VEpOhYvHgxzZs3Z+nSpc65mjVrsnjxYqZOnUqlSpUCGJ2IiIj/eG0dLGvtSWvtdGtte6AR8DSwhzOJlgGuxDUj4N/GmPeMMfo4U0SkGEtMTOS+++6jZ8+eHDx40Dnfu3dvtm7dSvfu3QMYnYiIiP/5ZKFha+0ua+0T1tr6wLW4ZhmM50yiVQEYCqwwxkQbYyYYY+r7IhYREfGNX3/9lSuvvJLJkyc750JDQ3njjTf47LPPqFatWgCjExERCQyfJFjurLVrrLXDcHUhvA1YC1jOJFvnAY8DO40xa4wxtxpjtCiKiEgRZa1l2rRpXHrppWzZssU536xZMzZs2MCdd96pmUZFRKTU8nmClcFaG2+tnWmtvQZoCDwJ7CJzF8J2wAzggDFmur9iExERz8TGxjJw4EBGjx5NQkKCc/7uu+/m+++/5+KLLw5gdCIiIoHntwTLnbU22lr7pLW2EXANMBM4wZlEKxwYHojYREQkZ+vWreOSSy7h448/ds5VqVKFBQsW8NprrxEamtOa5CIiIqVLQBIsd9batcBdwIPASVzdB0VEpIhISUlh/PjxdOzYkb179zrnO3XqxNatW+ndu3cAoxMRESlaygby4caYdsAwXGtmhQcyFhERbwsPD2f16tWZjoubPXv2MHToUL7++mvnXNmyZXn66af517/+RVBQwD+nExERKVL8nmAZY+riSqpuBTJmDnQfDR0PfIxrLJaISLFVtmxZOnbsGOgwztqHH37I6NGjOXbsmHOuYcOGzJkzh8svvzyAkYmIiBRdfkmwjDFhuFqphuGayCIjoXJPrL7CNRbrQ2vtSX/EJSIi2cXHxzN27FimT88819Att9zCG2+8QcWKWiteREQkNz5NsIwx1+CarOIGXGtfQeakai+uNbJmWmt3+jIWERHJ35YtWxg0aBDbt293zlWsWJG33nqLoUOHBjAyERGR4sHrCZYxpiFnugCel3HarUgi8CmuLoBfWGs1qUURZowJBXKaGizC37GIiO9Ya3nnnXe4++67SUxMdM5ffvnlzJkzh4YNGwYwOhERkeLDKwmWMaYiMAhXYnWV+yW3/e9xJVVzrbXHkOJiHDA+0EGIFEcnT56kV69ezvHChQuL5EQX8fHx3Hnnnbz33nvOOWMM48aN48knnyQ4ODiA0YmIiBQvhU6wjDGzgb6caeVwT6oO4OoCOMNa+1thnyUiUpykpKSwdu3aTMdFza+//sqAAQP45ZdfnHNVq1Zl9uzZdOnSJYCRiYiIFE/eaMEajGvtqozEKgn4HNeEFUustWleeIaIiHjZRx99xIgRI4iPj3fOtW3blrlz51K7du0ARiYiIlJ8eWsBEwNsAsYC51hrB1prFym5KhEmAVE5bPUCGJOIFEJKSgr/+te/uPHGGzMlVw8++CCrVq1SciUiIlII3mjBmoyrC+BWL9QlRYy1NhHXxCSZGGNyKC0iRd2hQ4cYNGhQpgWQIyIimDVrFr179w5gZCIiIiVDoRMsa+0/vRGIiIj41qZNm7j++uvZu3evc65Zs2Z88sknNGrUKICRiYiIlBz+XGi4A3ApUA0IAz6w1i73x/NFREq7pUuXMnDgQE6ePLOO++DBg5k2bRphYWEBjExERKRk8fVCw8G4pvi+C6iU5fI2YHmW8j8C5wM/WWuv9mVsIiKlxfTp0/nHP/5BamoqAGXKlOE///kPY8eOVXdfERERL/PWJBfZGGMigW+Ah3EtSmvcttxMBcKBK40xzXwVm4hIaWCt5YknnuD22293kqvw8HAWLlzIfffdp+RKRETEB3yWYAEf4eoSaIBo4BGgZz73fAAkp+/3yqugiIjkLikpieHDh/PUU08552rWrMmXX35Jt27dAhiZiIhIyeaTLoLGmN5AJ1zrYy0FBlhrE9Kv5XqftTbOGPMD0AZQF0ERkbNw7NgxBgwYwBdffOGcu+iii1i8eDF169YNYGQiIiIln6/GYA1N/xoHDMlIrjy0GbgKaOLlmERE/KpMmTJccsklmY59bd++ffTo0YOffvrJOdehQwfmz59PVFSUz58vIiJS2vkqwboSV+vVZ9baYwW892D612reDUlExL8qVqzI5s2b/fa8rVu30qNHD/766y/n3ODBg5kxYwblypXzWxwiIiKlma/GYFVP/7rjLO5NSv+qdwMiIh5asWIFbdu2zZRcjRs3jv/9739KrkRERPzIVwlWRpJ0Ni1kNdK/xnopFhGREm3mzJn06NGDEydOABAUFMRbb73Fc889R1CQL+cyEhERkax89Zf3QPrXC87i3rbpX/d4KRYRkRLJWsvEiRMZMWIEKSkpAFSoUIHPPvuMMWPGBDg6ERGR0slXY7DWAY2B7saY8p5OcmGMuQrX1O4WWOuj2ERE/CI1NTXTZBPNmjXz2kQXycnJjBkzhnfeecc5V716dRYtWsRll13mlWeIiIhIwfkqwZoLjAQigX8Dd+d3gzHmHOA9t1OzfBKZiIifnDhxgpYtWzrHsbGxREZGFrre48ePM3DgQJYvX+6cu+CCC1iyZAn169cvdP0iIiJy9nzSRdBauxJYiWuR4TuMMbOMMXVyKmuMCTPG/AP4EWiAq/XqQ2vtTzmVFxEpzfbv30/79u0zJVdt27bl66+/VnIlIiJSBPhy9PMQYDeuJGsIsMsY85vb9duMMRuAI8CbnJl5cDsw2odxSQEYY0KNMZFZNyAi0LGJlDbbtm3jyiuvZMuWLc65G2+8kRUrVlClSpUARiYiIiIZfJZgWWsP4VoweBWuJCsIOB9XCxW4FhJuCYSkXzfAaqC9tfaEr+KSAhuHa0bHrFt0AGMSKXW+/vpr2rZty969e51zDzzwAO+//z6hoaEBjExERETc+XT+XmttjLX2OqAXsBA4wZlkKmNLBL4A+llrO1lrD/syJhGR4mbBggVcd911xMXFAWCM4dVXX+Xf//63pmEXEREpYnw1yUUm1trFwGJjjAHqAVXSn30E2GOtTcrjdhGRUmv69OmMHj2atLQ0AEJCQpgzZw79+/cPcGQiIiKSE78kWBmstRbXuKzd/nyuFMok4JUczkegboIiPmOt5dlnn+Wxxx5zzlWqVIkFCxbQoUOHAEYmIiIiefFrgiXFj7U2EVc3zkxcjZEi4gupqancd999vP766865mjVrsnTpUi655JIARiYiIiL5UYIlIlKEJCcnc+uttzJ37lzn3Pnnn8+yZcs0DbuIiEgxoARLRKSISExMZNCgQSxYsMA517p1axYtWkS1atUCGJmIiIh4yucJVvoCw32AS3CtdRWGZ7MXWmttJ1/GJiJSVJw6dYq+ffuyYsUK51znzp355JNPCA8PD2BkIiIiUhA+S7CMMVHA68AgXNOxF+h2zqyXJSJSLFWoUIEZM2ZkOs7J8ePH6dWrF+vWrXPOXX/99XzwwQeUK1fO53GKiIiI9xjXxH5ertSYCsB64CIKnlxlsNbaMt6LquQwxlwF3Aa0B2oDScCfuNYam2atjfZDDJG4FhwGIDY2lsjISF8/VqTEOXr0KN26deOHH35wzt1000289957BAcHBzAyERGR0ikuLo6oqCj3U1HW2jhP7/dVC9Y44GJcrVCngXeAJbimZ49HrVNnxRhTBteU6XeROXGtAEQCzYF7jTF3W2vf9XuAIlIgBw8epHPnzmzdutU5d9tttzF16lTKlNHnSyIiIsWRrxKsAelfTwMdrLU/5FVYPPY6MCZ93wLLcbUUhgLdgWZAODDDGBNvrZ0XkChFJF9//fUXnTp1Yvv27c65u+++m8mTJxMU5MkwVRERESmKfPVXvB6uBGCukivvMMZ040xydRroba3tZq0db619yFrbHHgqozjwtjGmSiBiFZG87d69m3bt2mVKrh566CFeffVVJVciIiLFnK9asI4D1YDffVR/afSM2/4Ea+2irAWstU8YYy4HugIRwIPAQ36KT0SyOHXqFHfeeadz/Oabb7J3716uu+469u3b55x/6qmnePTRR7WAt4iISAngq0kuvgSuBl6y1v7L6w8oZYwxFwK/ph+eAM6x1p7MpWx7YG364V/AedYH/8ia5EIkf1kHyX711VfccMMNHDx40Dn34osvcv/99wciPBEREclBYSe58FVflPdwdVPr4aP6S5s+bvvLckuuAKy1XwKH0w9rA5f6MjAR8Vzv3r2d5MoYw3//+18lVyIiIiWMrxKsWcA24EJjzCM+ekZp0tJt/xsPyn+by70iEkCxsa5G36CgIN59913+8Y9/BDgiERER8TafJFjW2tNAN1zd2p4yxsw0xjT2xbNKiSZu+zs9KO9epkmupUTE78qWLcsHH3zALbfcEuhQRERExAd8NckF1tq/jDGtgZnArcAtxpj9wF4g0bMqbCdfxVfM1HTb3+9B+b9yuTdHxphQXFO9F0SE+8GxY8cKeLtIybdoUea5aEJCQnjvvfe47rrriIuLC0xQIiIikqfCvq/1WYJljKkIvAncgGvKdgOck77leztajNhduNt+vAflT7ntV/Sg/DhgfIEiyqJevXqFuV2kVEhKSuKmm24KdBgiIiLiQz5JsIwxFYDVnBn/Y7J8lYIp77af5EH507ncKyIiIiIiPuSrFqy7gVa4WqFOA+8AS4BoXC0wap0qmAQgLH0/xIPy5bLcKyIiIiIifuCrBGtw+tfTQDtr7Y8+ek5pcZIzCVZYXgXTVXDbP+FB+UnAKwWMKQJXwgxAdHQ0ERERuZcWKQUWLVrE8OHDSUlJASA8PJyTJ8+sqqDfExERkaLv2LFjhRr+4qsEqyGuVqoPlFx5RQxQI33/HCC/72ntLPfmyVqbiGcTjziMydzbMyIiQgsNS6n2ySefZEquKlWqxLx58+jSpYtTRr8nIiIiJZ+vEqxEXC0t231Uf2nzC9A8fb+hB+Xdy/zi/XBExN1HH33E4MGDSU1NBSAyMpLly5fTuHHjTC1WWT+YEBERkZLHVwsN70j/WtlH9Zc2m932r/KgfJtc7hURL3v//fczJVdRUVF88cUXtG7dmoiICOLi4pxN3QNFRERKPl8lWHNxzRjY00f1lzafu+13NcbkOg7LGNMWqJZ+uB/Y4MvAREqz//3vf9x8881OclWlShVWrVrFpZdeGuDIREREJFB8lWBNBTYBFxpjHvHRM0oNa+0vuL6fAJWAu/Io/rDb/hxrrWZsFPGBd999l1tvvZW0tDQAqlatyqpVq2jRokVgAxMREZGA8kmCZa09DfQG1gNPGWNmGmMa++JZpchjbvtPGmO6ZS1gjJkA9Eg/PA4874e4REqdd955hxEjRpDx+UX16tVZvXo1zZs3z+dOERERKel8tdDwqvTdVFxdBW8BbjHG7Af24tmMddZa28kX8RVH1trFxpipwGggFFhsjFmGK4kNBboBl2QUB0Zbaw8HJFiREmz69OncfvvtznHNmjVZtWoVTZo0yVbWWsuxY8ec44iICE10ISIiUsL5ahbBjpxZTDjjq8E1xfg5Htxv0GLEObkTV9J6B67vUbf0zV08cI+19gM/xyZS4mVNrmrVqsXq1au54IILcix/7NgxoqKinOPY2FhN0y4iIlLC+SrBAlcC4Mk58ZC1NhW40xgzGxgJtMeVsCYBfwKLgKnW2t2Bi1KkZMqaXJ1zzjmsXr2axo3V+1lERETO8FWCVd9H9Qpgrf0a+DrQcYiUFkquRERExFM+SbCstXt8Ua+IiL/l1i1QyZWIiIjkxFfTtIuIFHuzZ89m1KhRznGtWrVYs2aNkisRERHJlRIsEZEcLFq0iGHDhjlTsSu5EhEREU8owRIRyWLdunUMGDCA1NRU4MwiwkquREREJD+FGoNljKmTvnvUWnvSC/Hk9pxwoDKAtfZPXz1HsjPGhOJaZyurCH/HIuIPmzZtolevXiQmupbrq1ixIsuWLePCCy8McGQiIiJSHBS2BSsa2I1r8Vtf+kf6c3b5+DmS3TggNoctOoAxifjE77//TteuXTl+/DgA5cqV4/PPP6dVq1YBjkxERESKC1+ug+VtWkNLRHxm3759dO7cmUOHDgFQpkwZPvroIzp06BDgyERERKQ40RgsESn1Dh8+TJcuXfjzzzM9kGfMmEHv3r0DGJWIiIgUR95qwbrMGHOrl+rKsX4f1i15mwS8ksP5CNRNUEqAEydO0L17d3799Vfn3OTJk7nlllsKXXdoaCjjx4/PdCwiIiIlm8mYgvisbjYmDTj7Cgr4OMBaa8v46XmSB2NMJK6xWADExsYSGRkZsHhEzkZiYiI9e/Zk1apVzrnx48czYcKEwAUlIiIiARUXF0dUVJT7qShrbZyn93ujBUtjo0Sk2ElJSWHw4MGZkqt77rknU4uTiIiISEEVNsEa4ZUoRET8yFrL6NGj+fTTT51zN998M6+88grG6DMjEREROXuFSrCste96KxAREX+ZMGECM2bMcI579erFO++8Q1CQ5v0RERGRwilO07SLiBTaO++8w8SJE53jdu3a8eGHHxIcHOz1ZyUmJjJp0iTneNy4cZroQkREpIQr1CQXUnppkgspjpYtW0bPnj1JTU0FoEmTJnz99ddZB7J6TdZBsvo9ERERKfoKO8mF+sOISKmwefNmBgwY4CRXNWvWZMmSJT5LrkRERKR0UoIlIiXe3r176dmzJydPngQgLCyMhQsXUrdu3QBHJiIiIiWNEiwRKdGOHTtGjx492L9/PwBBQUF8+OGHXHrppQGOTEREREoiJVgiUmIlJSXRv39/fv75Z+fcW2+9RY8ePQIYlYiIiJRkSrBEpESy1jJq1ChWrlzpnHv44YcZPXp0AKMSERGRkk4JloiUSBMmTOC9995zjocMGcLTTz8dwIhERESkNFCCJSIlTta1rjp06KCFhEVERMQv9G5DREqUZcuWZeoG2KRJE+bPn0+5cuUCGJWIiIiUFmUDHYAUbcaYUCA0h0sR/o5FJD9a60pEREQCTQmW5GccMD7QQYgAxJyM4e2Nb7N2z1pOJJ2gYkhFOtbryMiWI0mKTdJaVyIiIhJwSrBEpMhLSE5g7NKxzNw8k+S05EzXVuxawYQ1EwjfGU7swVig6Kx1FRERQWxsbKZjERERKdkCPgbLGHONMeYTY0z7PMq0z6+MiJRMCckJdJ/dnWkbp2VLrjIkpyUT2yAWbgbKFp21rowxREZGOpsxJtAhiYiIiI8Za21gAzBmDnA9cI619lguZSKB/cA8a+2tfgyv1MtnDFZ0xkFsbCyRkZF+ikpKk9Gfj2baxmn5F7SAgRapLdg0cZPP4xIREZGSKS4uLuv47ShrbZyn9xeFLoKXA5tzS64ArLVxxphNwJX+C0sArLWJQGLW8/okXvzhwMkDzNw807PC6T+S24K3EXMyhhrhNXwWl4iIiEhuAt5FEDgH2ONBuT3pZUWklJi+cXqu3QJzk5yWzPRN030UkYiIiEjeikKClQRU8qBcJSDNx7GISBGyds/as7pvTfQa7wZylo4dO5ZpDNaxY7k21IuIiEgJURS6CP4GtDXGVLTWnsipgDGmItAW+N2vkYlIQJ1IyvG/BJ/d523W2kxJVaDHvIqIiIjvFYUWrPm4WqemG2OCs15MP/c2UBH42M+xiUgAVQyp6Nf7RERERAqrKLRgvQ6MBPoDrYwxs4HtuOYEuwAYCjQAdgKvBSpIEfG/DnU7sGLXigLf17FeR+8HIyIiIuKBgLdgWWvjga7AT7gSqceAWcD/gMeBhsAWoKu19mSg4hQR/xvZaiTBQdkatvMUHBTMyJYjfRSRiIiISN4CnmABWGt3A61wtWJNA5alb1OBG4BLrbXRAQtQRAKiZnhNhrcY7jrwcPjSiBYjNEW7iIiIBExR6CIIgLU2Ddd4rPmBjkVEio67GtzFO5+9Q+p5qfmW7VC3A5O7T/ZDVCIiIiI5KxItWCIiOYmNjWVgv4GkvpsKG4BccqzgoGBGtxrN0puXElo21K8xioiIiLgLeAuWMeYa4B7gFWvtl7mUaQ/cl1cZESlZUlJSGDRoEDt27HCdWAjTb57OgXMOsCZ6DSeSTlAxpCId63VkZMuR6hYoIiIiRULAEyxgFK5JLkbkUWYr0A04CSjBEikF/u///o8VK87MIDhx4kRuG3QbAI+0eyRQYYmIiIjkqSh0Ebwc2GytPZZbAWttHLAJuNJfQYlI4EybNo1XX33VOb7xxht57LHHAhiRiIiIiGeKQgvWOcD3HpTbA1zi41hEJMC+++477rrrLue4VatWzJgxA2NMAKM6OyEhIQwbNizTsYiIiJRsRSHBSgIqeVCuEpDm41gkC2NMKJDTrAER/o5FSr5Dhw4xcOBAkpOTAahZsyafffYZFSpUCHBkZ6dChQrMnDkz0GGIiIiIHxWFLoK/AW2NMRVzK5B+rS3wu9+ikgzjgNgctugAxiQlUGpqKkOGDGHfvn0AlC1blnnz5nHuuecGODIRERERzxWFBGs+rtap6caY4KwX08+9DVQEPvZzbCLiJxMmTOCLL75wjv/9739z9dVXBzAiERERkYIrCl0EXwdGAv2BVsaY2cB2wAIXAEOBBsBO4LVABSkivrNo0SKefvpp53jgwIGMHTs2gBGJiIiInB1jrQ10DBhj6uNqyWqOK7HKdBnYDNxgrY32b2SSzxis6IyD2NhYIiMj/RSVlCS7d++mVatWxMXFAXDBBRfwww8/ULFirr2Gi42kpCTmzJnjHA8ZMkQTXYiIiBRxcXFxREVFuZ+KSp/V3CN+T7CMMXWAk9bao1nOBwHX41oTq0766T3AMuAzWxQyQXEYYyJxjcUClGDJ2UlMTOTqq69m48aNAISFhfH9999z0UUXBTgy78j6H7R+T0RERIq+wiZYgegiuBuYiatbIMaYd4CvrLXv4GrFmh+AmEQkAO69914nuQLX+lclJbkSERGR0ikQk1yY9C3DcFwzBIpIKTJjxgymTZvmHN99990MHjw4gBGJiIiIFF4gEqyTQM0APFdEiojNmzdz5513OsdXXHEFL774YgAjEhEREfGOQHQR/BnoZIyZAOxKP9fIGHOrJzdba9/zVWAi4ntxcXH079+fxMREAKpWrcpHH32kyR9ERESkRAhEgvUC8CHwuNu5q9M3TyjBEimmrLUMGzaMXbtcn60YY3j//fc577zzAhyZiIiIiHf4PcGy1n5qjLkc6ItrtsDhwB/A1/6ORUT8a/LkySxYsMA5fuqpp7juuusCGJGIiIiIdwVkoWFr7WZca1thjBmOaxbB2wIRi4j4x8aNG3nwwQed4x49evDwww8HMCIRERER7wtIgpXFk8CmQAchIr5z4sQJbrrpJpKTkwGoVasWM2fOJCgoEPPsiIiIiPhOwBMsa+2TgY5BRHzrrrvuYseOHYBr3NXs2bOpVq1agKMSERER8T59fCwiPjVr1ixmzZrlHD/66KNcc801AYxIRERExHcC3oIlIiXX77//zh133OEcX3311YwfPz6AEflXxYoV2bRpU6ZjERERKdm8lmAZY8oDBsBaeyqH612BQR5UtdFa+7q34hKRwDh9+jQ33XQT8fHxAERGRjJ79mzKli09n+uUKVOGFi1aBDoMERER8SOvvNMxxlwC/IgrwfoaaJ9Dsaa4pmS3+VR3szFmtbV2mzdiE5HAGDduXKbWm+nTp1O3bt0ARiQiIiLie976KHkCrvFcicBID8qbPK6VwTWz4IDChyWFZYwJBUJzuBTh71ik+Fi4cCGvvPKKc3zHHXdwww03BC4gERERET8pdIJljKkG9MLVMjXHWrvDg9tG5HL+elwLEPc2xlS11h4ubHxSaOOA0jNoRgrtr7/+Yvjw4c5xs2bNePHFFwMXkIiIiIgfeaMFqx+uVicLTPbkBmvtuzmdN8aswZVklQVuAKZ6IT4R8ZPU1FRuvvlmjhw5AkD58uWZO3cu5cuXD3BkgXHixAnatWvnHK9bt04TXYiIiJRw3kiwrkr/+pe19qfCVGSt3WOM2Qi0AtqgBEukWHnuuedYs2aNczx58mQuuuiiwAUUYKmpqWzZsiXTsYiIiJRs3lgHqwWu1qt1XqgLYBOuMVotvVSfFM4kICqHrV4AY5Ii6Ntvv2XChAnO8cCBA7n99tsDF5CIiIhIAHijBatW+te/vFCXez01vFSfFIK1NhHX5CWZGJPXPCVS2hw7dowhQ4Y4LTR169Zl6tSp+jkRERGRUscbCVbGbHJH8in3BXCPB/UlpH+NPNuARMR/rLWMGTOG6OhowLX205w5c4iMjAxoXCIiIiKB4I0EKwkIBsLzKmSt3QJsyatMuowR4MmFjEtE/OC9995j7ty5zvH48eO56qqr8rhDREREpOTyxhisjJar6l6oy72e/FrERCTAduzYwV133eUct2/fnkceeSSAEYmIiIgEljcSrH24JqVo44W6SK/H4r0xXSLiA0lJSQwePJj4+HgAoqKi+N///keZMmUCHJmIiIhI4HgjwcqYPfAiY0yDwlRkjGkIXJylXhEpgh5//HF+/PFH5/jtt9/mvPPOC2BEIiIiIoHnjQRrefpXAzxRyLoeT68HYEUh6wooY0xVY0xXY8wjxpiPjTF7jDHWbat3lvVeZYx52xjzuzEm3hgTa4zZYox55mzrFCmoL774ghdeeME5Hj16NDfccEMAIxIREREpGgqdYFlr1+CavMIAtxhjRpxNPcaY4cCtuLoH/mytXVXY2ALFGDMGOAQsBZ4BbgDqFLLOMsaY14CvgJHA+UAFXLMtNgceAX4yxgwrzHNE8nPo0CFuueUW57hJkya8/PLLAYxIRAIhOjoaY4zXtoyZSIurjh07Fuj1Lly40KN6rbV8+OGH9OnTh7p16xIaGkrNmjVp164dkydP5uTJkwWKMzk5menTp9O5c2dq165NuXLlqF27Np07d2b69OkkJ3tvjrEJEyY4r3f48OEexTZo0CDnntDQUD799FOvxSPiL96YRRBcLU8L0venGWMaAxOstafzu9EYUw4YD/zL7XRhW8ICLTSHcyfSzwefZZ2vA2PS9y2ulsP16XV2B5rhmslxhjEm3lo77yyfI5Iray0jRozgwIEDAISEhPD+++9ToUKFAEdWNJUtW5YOHTpkOhYR8VRMTAw33XQTa9asyXY+JiaGr776ismTJ/Phhx9y2WWX5Vvfjh07GDhwIFu2ZJ7Uef/+/ezfv58vvviC119/nXnz5tGwYUNvvpR8JSYmMnDgQCfxrFChAvPnz6dLly5+jUPEG7zy195au9AY8yLwf+mnHgRGGWNmAWtwtXAdAU7iSgIqAy2ADsAt6ccGV+LwurX2M2/EFUAngbXAj27b78BuoG5BKzPGdONMcnUa6G+tXeRW5CFjzETOdLF82xiz2lqrmRjFq9544w0WLTrzo/fCCy9wySWXBDCioi08PDzbGyORkqJy5cr8+9//zrPMv/515rPTRx55hKioqDzrKynye60AF110UZ7XT506Ra9evdiwYQMA5cuXp3///jRu3JiYmBjmzZtHTEwMu3fvplu3bnz77becf/75udYXExNDly5dnJbCiIgIBg4cSN26ddmzZw8fffQRx44dY/PmzXTp0oXvvvuOatWqFeyFn6X4+Hj69OnDqlWuzkuVKlVi0aJFtG3b1i/PF/E6a61XNlzdDf8LpAGpbl/z29LctulAkLdiKmobEI0ribRAvQLc96PbfePyKLfUrdzzPn4tkW7PsrGxsVZKtm3bttly5co5/+Y9evSwaWlpgQ5LRIow978Tu3fvDnQ4PtWhQwevvtaHHnrIqa9+/fp2x44dma6fPHnSdu3a1SnTtm3bPOsbNGiQU7ZVq1Y2JiYm0/WYmBjbqlUrp8zNN99c6Ncwfvx4p75hw4blWCYuLs62adPGKVelShW7YcOGQj9bpDBiY2Mz/f8FRNoCvE/2xiQXAFhr06y1Y4ARwIH008aDDSAGGGWtHWmtTfNWTCWBMeZCoFX64QlcXQVz86zb/lBjjMm1pEgBJCcnc8stt3D6tKvXb40aNZgxYwb6ERMR8b6DBw/y6quvOsezZ8+mUaNGmcqEhYUxd+5catasCcBXX33FkiVLcqxv69atfPjhh4CrJWzevHlUr555+dLq1avz0UcfUb58eeeZ27Zt89prysnhw4e55ppr+PbbbwGoVasWa9eu5dJLL/Xpc0V8zWsJVgZr7btAfVxd2hYBx3IpehxYDNyBqzVnurdjKSH6uO0vs9bmOprVWvslcDj9sDag/6HEK5566ik2btzoHE+fPj3bH2cREfGOjz/+mISEBACuvfZa2rTJeanRyMhI7rjjDuf4f//7X47lZs+endH7hMGDB1O/fv0cyzVo0ICbbroJcPVwmjNnzlm/hvzs37+f9u3bs2nTJgDq1q3LunXruPjii/O5U6To83qCBWCtTbLWTrXW9sY1vqo2rpnu2gKXAOcCla21vay1U6wHk2GUYi3d9r/xoPy3udwrclbWr1/Ps8+eaRwdPXo0PXv2DGBExUdKSgpr1qxxtpSUlECHJCLFwOeff+7s57cEhvv1hQsXkpqa6rX6PvvMN0Pio6OjadeuHb/++isAjRs3Zt26dX6fWEPEV3w+pZV1fWTyd/omBdfEbX+nB+XdyzTJtZQbY0woOc98mJeIApaXYujUqVPccsstzh/sBg0a8OKLLwY4quLj5MmTXHPNNc5xbGwskZGRgQtIRPzizjvv5JdffuHAgQOEhIRQs2ZN2rRpw4ABA+jVq1e+3aszWnUArrrqqjzLXnzxxURERHDs2DGOHz/Orl27Mk12kZCQwPbt2z2uz/36r7/+SmJiIqGhBX2LkLvt27dz3XXXsW/fPgCaN2/OihUr1CtCShSftGCJV9V029/vQfm/crk3L+OA2AJu0R7WLcXYgw8+yI4dOwAwxvDee+8RHh4e4KhERKBevXpeW4OrY8eOXo1tyZIl7Nmzh9OnT3PixAl27NjBe++9R58+fbjiiivYvXt3rvfGxcU5S2EA+bbqGGNo0KCBc5zRKpRh+/btpKW5hrdXrlw539kN3cukpaXx+++/51m+ILZu3Ur79u2d5OqKK65gzZo1Sq6kxNGiLEWf+7vZeA/Kn3Lbr+jlWKQUWb58OW+88YZz/OCDD3L11VcHMCKRgklMTCQxMdHj8hEREdlaFo4dO+aMXclPSEhItjXhkpKSOHXqVC53ZFexYkXKlCmT6dyJEydy7PaVk7Jly+pDkAC7+OKLueKKK6hTpw7BwcH89ddfrFq1it9++w2AH374gcsvv5z169dnSowyuCdX4eHhVKpUKd9n1q5d22n1cr8/6/E555zj0WuoXbs2sbGxzv3Nmzf36L68/Pzzz3Ts2NGpt2PHjnz++ef6eZUSSQlW0VfebT/Jg/Lu49nK51pKJA/Hjx9n5MiRznHz5s158sknAxiRSMFNmjSpQD+3OXXhrFu3LseO5TZXU2bDhg1j5syZmc7NmTOHESNGeBzDpk2baNGiRaZz7dq1y7YwbG46dOhQatZee/TRRz3+t8nPeeedV+g6br31VqZOnUrjxo1zvD5v3jxGjRpFXFwchw8fpn///vz4448EBWXuTHTy5Jm5rMLCwjx6tntif+LECZ/Wd7Z+/PFHZ79169YsXrzYmbFQpKQpdQmWMeZT4HovVbfHWlvPS3XlJgHI+B8xxIPy5bLc64lJwCsFiAlcY7CiC3iPFBPjxo1zunAEBwcza9YsypUrl89dIiL+M2rUqECHkMltt92W5/UBAwZQq1YtOnbsSEpKCps3b+bjjz9m4MCBmcplzB4IrlZRT7j//+x+vy/q84bNmzezYsUK+vTpk39hkWJIY7CKPvdp2T356Mm9f4pHHztZaxOttXEF2ch9+n0p5tatW8dbb73lHD/22GNe6R4iIlLaXX311QwdOtQ5/vjjj7OVcW/VSUrypOMKzhqFWe/3RX1na8CAAbRr1w5wra04cOBAFi1a5JW6RYqaUteCBSzlzELIhXXES/XkJQaokb5/DvBjHmXBNSW++70iHktMTOT22293jps2bcq4ceMCGJHI2Rs3bhz33Xefx+UjIrJPjrpnz54CjcHKasiQIfTt29fjGCpWzD50dt26dQUagyVF2w033MC7774LuJbByMp9TFJ8vCdDr8k0zi/rz5C36ztbYWFhLF68mG7duvH111+TlJRE//79+fTTT+nWrZtXniFSVJS6/4mttf8NdAwF9AuuNcQAPFkgwr3ML94PR0qyp556ypkxyhjD22+/7XGXEpGiJjQ0tNDTS+eUdBVESEhIoX+HvPUGt6SZNm2aV8dgDRo0yCt15cd9VsCDBw9mu16jRg1n/+TJk5w4cSLfn4G//jozgbD7/VmP9+/3ZDLivOsrjPDwcJYsWUKXLl347rvvOH36NP369eOzzz6jS5cuXnuOSKCVugSrGNoM3JS+fxX5j5VyX+59s/fDkZJq8+bNvPDCC87x2LFjueKKKwIYkYhI7p555hn27Nnjlbo6dOjgtwQrvzWwoqKiqFmzpjP73x9//JFt4hN31lp27drlHF900UWZrl944YUEBQWRlpbG0aNHiY2NzXOq9tjYWGemv6CgIC644IL8XlKBVKxYkWXLltG5c2e+//57EhMT6du3L59//jmdOnXy6rNEAkVjsIq+z932uxpjch2HZYxpC1RLP9wPbPBlYFJypKSkMHLkSFJSUgDX+jJPP/10gKMSESl53JOh3NZ/ck+ovvnmmzzr++WXX5yWvIoVK2ab+r18+fKZZjb89ttv86zP/XkXXnihVxcZzlCpUiWWL1/OZZddBrgm0ujduzerV6/2+rNEAsHvCZYxZlf69pS/n10cWWt/ATKWdK8E3JVH8Yfd9udYTwcOSKn38ssvs3HjRud46tSpHk/nKyISCNHR0VhrvbL5c2r7Tz75xNm//PLLcyzTu3fvHMvnZP78+c5+r169sq2jVpj6rr/eW5MuZxcREcHy5ctp1aoVcCbJ+vLLL332TBF/CUQLVj2gLlAlAM8urh5z23/SGJNtNKgxZgLQI/3wOPC8H+KSEmDnzp088cQTzvHw4cPp3LlzACMqOcLCwpg/f76zKWkVKd2+++47Zs2a5Rz369cvx3L9+/d3Wo5WrVqV42QY4FoI+80333SO3WcodDd06FCna+L7779PdHR0juWio6N5//33AVdXxiFDhuT9ggopKiqKFStWOC128fHx9OzZk6+//tqnzxXxNXUR9BFjzLysG2e67wG8mUOZajnVZa1dDExNPwwFFhtjlhhjJhhjJhljNgPjM4oDo621h3300qQEsdYyevRoEhMTAddg5hdffDHAUZUcwcHB9O3b19mCg4MDHZKI+MCYMWN46aWXOHIk98mF58+fT48ePZyu2M2aNePGG2/MsWyNGjW45557ANf/00OGDOGPP/7IVObUqVMMGTKEv//+G4CrrrqKnj175ljfJZdcwoABA5z7Bg4cyKFDhzKVOXToEAMHDnRmEBw8eDBNmzbN76UXWuXKlfniiy+c5UBOnjxJ9+7d8+3KKFKUaZIL3+mfz/XuOZx7ADiUw3mAO4FU4A7AAN3SN3fxwD3W2g8KEKeUYtOnT8/U5/21116jcuXKAYxIRKT4OXDgAFOmTOGhhx7i8ssvp0WLFlSvXp2QkBD279/PqlWr+OWXMxP7VqlShU8++YSgoNw/537iiSdYuXIlGzduZNeuXTRr1owBAwbQuHFjDh48yEcffeRMhBEVFcX06dPzjHHy5MmsX7+eP//8kw0bNtC4cWMGDhxInTp1+PPPP/noo4+Ii4sDoH79+rz00kuF/8Z4qEqVKqxcuZJrrrmGn3/+mRMnTtCtWzeWL1+uyZakWFKCVUxYa1OBO40xs4GRQHtc62IlAX8Ci4Cp1trdgYtSipP9+/fzwAMPOMfXX3+98wmniIgUXEpKCt98802eE1O0bduWWbNmUa9evTzrCg8PZ9GiRdx0002sXbuWhISETN0LM9SrV48PPviACy+8MM/6atWqxfLlyxk4cCA//fQTcXFxTJs2LVu5Sy65hI8++sir07N7omrVqk6S9csvv3D8+HG6du3KF1984UyGIVJcKMHyEWtt3vOwnn29XwPqnCyFYq3lrrvucmaeioiI4M0338x3+mAREcnujTfe4KabbuKbb75h48aNxMTEcOjQIeLj44mIiOC8886jTZs2DBo0iA4dOnhcb82aNVm9ejUffPABs2fPZvPmzRw8eJCIiAgaN25M//79uf322z1eK+2CCy5gw4YNvPvuu3zwwQf88ssvHDlyhCpVqnDRRRcxaNAghg0bFrD1D6tXr86qVavo2LEjv/32G8eOHaNLly588cUXzmQYIsWB8fdEc8aYNFzjhKZYa+/068PFa4wxkUBsxnFsbCyRkZEBi0cKZt68eQwcONA5njp1KqNGjQpgRCVTfHx8pkHns2fP1kQXIiIiRVxcXFzW9eKirLVxnt6vFiyRUubo0aPcfffdznHHjh25/fbbAxhRyZWcnMxnn32W6VhERERKNs0iKFLKPPDAA8TExAAQGhrKtGnT1DVQRERExEvUgiV5MsaE4poaPqsIf8cihbdixQpmzJjhHE+cOJFGjRoFMCIRERGRkkUtWJKfcbjGWmXdogMYk5yF+Ph4Ro8e7Rxfeuml/POf/wxgRCIiIiIljxIskVLi8ccfJzo6GoAyZcrw9ttvU7asGrFFREREvEkJlkgp8P333zN58mTn+MEHH6RFixaBC0hERESkhFKCJfmZBETlsNULYExSAMnJydx+++2kpaUB0LhxY5544okARyUiIiJSMql/kOTJWpsIJGY9r1nnio+XX36Zn376yTl+++23CQ3Nad4SERERESkstWCJlGDR0dFMmDDBOR41ahTt2rULXEAiIiIiJZwSLJESylrL3XffTUJCAgDVqlVj0qRJAY5KREREpGRTF0GREmr+/PksWrTIOX7ppZeoXLlyACMqfYKCgqhbt26mYxERESnZlGCJlEAnTpzg3nvvdY6vvfZahg4dGsCISqdKlSo5U+OLiIhI6RCIBGsBYIHNAXi2SKnw+OOP89dffwEQEhLCm2++qYlJRERERPzA7wmWtbavv58pUpps3LiR1157zTl++OGHueCCCwIYkYiIiEjpoQEBIiVIamoq//jHP5w1r84//3zGjRsX4KhERERESg+NwRIpQd566y02bNjgHL/55pta8yqA0tLS+PPPP53jOnXqaKILERGREk4JlkgJ8ffff/Poo486x0OHDuW6664LYERy/Phx6tev7xzHxsYSGRkZuIBERETE5/RRqkgJ8cADD3D8+HEAIiMjefHFFwMckYiIiEjpowRLpARYvXo1c+bMcY6fe+45atSoEcCIREREREonJVgixVxycjJ33XWXc3zZZZcxatSoAEYkIiIiUnopwRIp5iZPnsyvv/4KgDGGN998kzJlygQ4KhEREZHSSQmWSDG2b98+JkyY4ByPGjWK1q1bBy4gEREpstasWYMxBmNMpr8d7jp27OiUiY6OLvQz3esT/6lXrx7GGOrVq5fjdU9+FuTsaRZByZMxJhTIaZ7vCH/HItk98MADxMfHA1ClShWeffbZAEckIqVRvXr12LNnT47XQkJCiIiIoEGDBlx55ZXccsstXHrppX6OUCRnu3btYubMmXz55Zf89ttvxMXFYa2lYsWKnHfeeVx44YW0bt2aTp06cckllwQ6XCkmlGBJfsYB4wMdhGS3cuVKPvjgA+d40qRJVKlSJYARiYhkl5SUxKFDhzh06BDr169n8uTJ3HrrrUyZMkXr9EnApKSkMG7cOF555RVSU1OzXT9y5AhHjhxh8+bNzJ07F4C7776b1157zd+hSjEU8ATLGHMNcA/wirX2y1zKtAfuy6uMSGmSlJSUaWKLK664gttuuy2AEYmIuIwZM4aGDRs6x0lJSezdu5elS5c6Xc7ee+894uPjmTdvXoCilNJu6NChfPjhh87xOeecw3XXXUf9+vUJDQ0lNjaWX3/9lfXr13Pw4EEAEhISAhWuFDMBT7CAUUBXYEQeZbYC3YCTgBIsKfVeeeUVtm/fDpyZ2CIoSEMqRSTwBg0aRMeOHbOdT01N5ZFHHuGFF14A4OOPP2blypV06tTJzxGWXh07dsRaG+gwAu7DDz90kqsyZcrwn//8h3vuuSfHCaLS0tJYv349M2fOpHz58v4O1Wf0s+BbRSHBuhzYbK09llsBa22cMWYTcKX/wpJ0k4BXcjgfAUT7NRIBICYmhqeffto5HjNmDK1atQpgRCIi+StTpgzPP/88a9as4fvvvwdcb3SVYIm/vf32287+Aw88wH333Zdr2aCgINq0aUObNm2UkIjHisJH3ucAOY+MzWxPelnxI2ttorU2LusG5JoQi2898cQTnDhxAoCoqKhMyZYULeXLl+fll192tpL06afI2erTp4+zv3nz5nzL79u3j/Hjx9OmTRtq1KhBSEgI1atXp127djz//PMcP348x/tWrlzpzJLWq1cvj2JLSkqicuXKGGMICwvj5MmTuZb9/vvvueeee2jatClRUVGUK1eO2rVr06dPH2bNmpXjuB53Oc3Wt2bNGm6++WYaNmxIhQoVMMawZs2aTPcdOnSIZ599lvbt21OtWjVCQkKIjIykcePGtGvXjoceeojVq1eTnJyc7ZlnO3PcypUrGTBgAHXr1iU0NJRatWpx/fXXs2jRIo/r8IS1lk8++YShQ4fSsGFDwsPDCQsLo1GjRgwfPpy1a9d65TkbN2509gcNGuTxfZ7MhJiSksL//vc/Bg0aRIMGDahYsaLzs9GlSxcmTZrEn3/+meO9aWlprFmzhnHjxtGhQwdq1qxJSEgI4eHh1K9fn5tuuon58+d7JdE72xkl169fzy233OJ0paxatSpdu3bl448/9vjZf//9Nw8++CAXXXQRYWFhVK5cmUsvvZRJkyYRFxcHwPDhw706m6XfWWsDugFxwEIPyi0Ejgc6Xm3Ov0ckYDO22NhYK763efNmGxQU5HzfJ0+eHOiQRERs3bp1nf+XVq9enWfZqVOnOmUbN26cZ9lnn33WhoaGWve/N1m3qlWr2pUrV2a7NzU11Z5zzjkWsGXLlrWHDh3K93XMnz/fqXfw4ME5ljl58qQdPHhwnjEBtkWLFnbPnj25PqtDhw5O2V27dtm77747x3rcv5/Lli2zkZGR+T4bsCtWrMj2zNWrVzvXx48fn29cu3fvtv/85z/zfM4tt9xiU1JSPHqdedmxY4dt1apVvq9ryJAhNiEhIc+68hMcHOzUt3HjxkLV5e67776zjRo1yvc1BAUF5Xj/JZdc4tG/bfv27fP9ec74naxbt26O18/mZ+GZZ57J9B4k63b77bfbtLS0PONavHhxnj/DDRo0sD/99JMdNmxYpmf7W2xsbNbYIm0B3icXhS6CvwFtjTEVrbUncipgjKkItAV+92tkIkWItZZ//vOfpKWlAXDBBRdwxx13BDgqEZGCiYmJcfarV6+ea7k777yTt956C3C1HHTo0IHLL7+cyMhIDh8+zPLly/n55585fPgw3bp1Y/Xq1Vx99dXO/UFBQQwePJgXX3yRlJQUPvzwQ+688848Y5s9e7azP3To0GzX4+Pj6dixIxs2bABcrdTdu3enadOmlCtXjj179rBgwQIOHDjA5s2badeuHRs3bsx3htdnn32Wt99+m3LlytGjRw+aN29OWloaGzZsoGxZ11u1P//8kxtuuMFZmqNevXp06dKF8847jzJlynD06FG2bdvGV1995fRyKKzJkyfzyiuvEBwcTK9evWjRogWnT59mzZo1fPPNNwDMmjWLkJCQTN3uCmrbtm107NiRw4cPA1CzZk26detGgwYNnOsLFiwgISGBOXPmcOzYMT7//POzXlurRo0a7Nu3D4Bly5bRsmXLs449w/Lly7n++utJTEwEICwsjC5dunDRRRdRoUIFDh48yIYNG/j222+dv+NZZbz++vXrc/nll9OwYUMqVqzIqVOn+OWXX1iyZAmnTp3iyy+/pE+fPqxbty7HcWO+MGXKFCZNmkRoaCg9e/akWbNmWGv58ssvWb16NeDqennllVcycuTIHOv47rvvuOGGG5zvUbVq1ejXrx9169bl0KFDLFy4kJ07d3L99dcX/ynxC5KN+WIDHgLSgA+B4ByuBwMfAKnAw4GOV5vz7xKJWrD86tNPP830acqiRYsCHZKIiLW2YC1YrVu3dsqOHDkyxzLvvfeeU+aiiy6yW7duzbHcm2++6XyiXrduXXv69OlM1zdu3OjUc/XVV+cZ17Fjx5zWsqpVq9qkpKRsZW677Tanvr59++bYipCQkGCHDx+eb0uYe+sA6a15O3bsyDW+Rx991Cl7xx135NpqlJSUZOfNm2d//fXXbNcK2mphjLG1atWyW7ZsyVZuzpw5tmzZsk7ZJUuW5FtfThISEuxFF13klHnyySdz/N7v3bvXXnrppU65KVOm5FifJ0aMGOHUU65cOTt9+vQcn+mpffv22cqVKzt1Xn/99fbAgQM5lj1w4IB97LHHcrz26KOP5vi9znDo0CHbrVs35znTp0/Ptay3W7AA27x58xxbk958881MLVA5SU5OzvTv3LNnT3vs2LFsZf7v//7P+dnLKFscW7CKwhv1MFwtU6nATuBJYAgwGJgA7Ei/th0ID3S82px/NyVYfnT69OlM3Q66du2abzO8iIi/eJJgpaam2gcffDDTm5b169dnK5eUlGRr165tAVu5cmX7999/5/ls9zpnzpyZ7XqTJk2cN2x5vVGbMWOGU8+dd96Z7fq2bducN33t2rXLs1tcamqqbdOmjdMd7I8//shWxv3Na7ly5ez27dvzfJ09e/Z0yh85ciTPsrk5mwTr66+/zrW+Z555xinbtm3bfOvLyRtvvOFcf/jhh/OM/8CBA7ZSpUrOG/mz/Tu4fft2GxYWlulnMSoqyg4YMMC+8MIL9osvvijQ+5q77rrLqadTp042NTX1rOLyRHx8vNP1tU2bNrmW83aCFRERYfft25fr89q2beuUzekDkXnz5jnX69WrZ0+dOpVrXe4/68U1wQp4F0FrbbwxpiswH2gOPJaliAE2AzdYa3MfbSpSgr3++uvs3LkTcM3E9eKLL5511wjxn4SEBB5++GHn+LnnntNEF35greXYMc3DAxAREeH3/ys++OADpwsdQHJyMvv27WPJkiXs3r3bOT9mzBguv/zybPcvWLCAv/76C4B77rmHmjVr5vm8++67z5n6/fPPP2fYsGGZrg8dOpTHHnsMay3vv/9+pt9Jd3PmzMl0T1ZvvfVWxgeMTJw4Mc+uWUFBQdxzzz1Od7DFixdz991351p+0KBBNG7cOPcXCZm6lZ06dYrKlSvnWd4bunbtylVXXZXr9fvvv58XXniBY8eO8dVXXxEdHU29evUK9IyMbqBhYWE8+uijeZatUaMGN910E1OnTmXXrl1s27aNpk2bFuh5AI0bN2bBggUMGjTI6ZYXGxvLvHnznLXZjDE0bdqUvn37MnLkSOrWrZtjXUlJScycOdM5fv311326bEqFChXo3bs3U6ZMYcOGDZw+fZpy5cr57HkZRo0aRe3atXO93q9fP7766ivANXlNs2bNMl1///33nf1//etfef4tnDBhgtcnUPG3gCdYANba3caYVsD1uNbEqpN+aQ+wDPjMZvyvJlLKHDp0iIkTJzrHY8aM4eKLLw5gROKp06dPM3nyZOd4woQJSrD84NixY0RFRQU6jCIhNjaWyMhIvz7zv//9b57Xo6KiuP/++3N9M50xngOgS5cu+T6vVq1aVKtWjUOHDvHjjz9muz5kyBAee8z12e3s2bNzTLBiYmJYtWoV4Br/klNSkRFXaGgo7dq1yzcu9zEkOcXlrnv37vnW17x5c5YsWQK43uzOnDmTGjVq5HtfYfTt2zfP66GhoXTv3p25c+cCrjE2BUmwDh06xM8//wzAVVddRVhYWL73ZP2+nk2CBXDttdeyY8cOXn31Vd577z3++OOPTNettfz000/89NNPPP/88zzwwAM5Jtbff/+9My6udevWXHjhhWcVT1bR0dFs3bqVmJgYTp48mWlWyl27dgGuDy92797ttWfmJb/fxfPPP9/Zz1iY2d13333n7LvPJJqTyy67jFq1avH3338XMMqio0gkWADW2jRcrVjzAx2LSFEyfvx459P4iIiIAk2tKyJS1IwePZpx48bl2rK2detWZ9990gpPHDp0KNu5jITpm2++Ydu2bWzdupXmzZtnKjN37lznDeyQIUOy1ZGamsovv/wCQGJiojPxRGHicnfBBRfkW8eYMWN48803OXHiBEuXLuW8887jmmuu4dprr6VNmzZcccUVXm/JyPp9ykmzZs2cBOv33ws2F9lPP/3k7K9YsaLAra35fV/zExkZyRNPPMETTzzBjh07+Oabb/jxxx/58ccf2bBhA0lJSYCrlerZZ58lOjo600QoAL/99puz37p160LFY63l3Xff5cUXX3QSz/xkTGvua+edd16e18PDw539jIQzQ2JiotMqHRERwbnnnpvv85o2bVqsE6yisA6WiOTi559/ZsqUKc7x+PHjqVq1agAjEhHJ2+rVq51xCKmpqezbt4958+Y5M7U9//zzDBw4MNeZ1I4cOXLWz876xi6De5e/rG+Qs57LqXvg0aNHKUxHmtziyhAREZFvHfXq1WPJkiVOV7Xk5GSWL1/urJkUGRlJ7969+fzzz886zqzym/0wa5nY2NgC1V+Yf2vI//taEOeffz7Dhg3j1Vdf5euvv+bIkSO88847mRKLOXPmMH9+5nYA99dQrVq1s35+WloaN998MyNGjPA4uQKcGfl8LTQ0NM/r7slx1t9t9yTQ066tnvzsFWUBb8EyxlwD3AO8Yq39Mpcy7YH78iojUtJYa7n//vud/6jOP/987rrrrgBHJVL0RUREFPiNXknlyRt3XwoKCqJ27dr079+fnj170rlzZ7766is+/fRTnn32WafrnruUlBRnf8KECR51G8vPjTfeyH333UdycjLvv/8+kyZNct4Q7ty5kx9++AGAVq1a0aRJkzxjqlGjBg888ECBnp/fp/+ejtm5+uqr+f333/nkk0/47LPP+PLLL9m/fz/geqO9cOFCFi5cSNeuXZk3b16mVgVfKUzi6f59veqqq+jXr1+B7s9rfFhhhYeHM2LECHr16sVll13mLA48derUXOMszHjHqVOnOuMAw8PDufPOO+nSpQvnn38+VatWJTQ01Pk5GT9+fKahA0Xd2fyMFPeRQQFPsIBRuMZdjcijzFagG3ASUIIlpcLixYtZsWKFc/ziiy8SEhISwIhEigdjjN/HHUn+QkNDmTVrFhdffDGnTp1i4sSJDB48mIYNG2Yq5/7Jdb9+/TzqppafqlWr0rVrVxYuXMjevXtZt24d7du3B/JvvcoaU1JSUoETLG8KCQnhpptu4qabbgLgjz/+YNWqVcybN48VK1ZgrWXZsmXcfffdmSZfOBuetDAdPXrU2S/o2Ef37+vZJK7+UK1aNf7v//6PsWPHAmSawAUyv4acxh556vXXXwegbNmyrFmzhksvvTTXssVtEh/3nwv3n5e8eFquqCoKXQQvBzZba3P9abHWxgGbgCv9FZRIICUnJ3P//fc7x9dddx29evUKYEQiIoVXr1497rnnHsD1/1xOE124D9jPWMzWG9yTJ/cZAzNmNwsKCnKSlqxCQkKoX78+4OoG5z7uJtAaNmzIqFGjWLZsGfPmzXNaUWbPnl3oBYfdx8Plxn0clftEB55w/7f+9ttvi2yrhXucWZMb9xbPjJbQgjp16hTbtm0DoH379nkmV4BTtrgIDQ11ZiA8duyYs8hzXorba8yqKCRY5+CaLTA/e9LLipR4b775pjNYOCgoiJdeeknTsotIifDAAw9QoUIFAD788MNs4006derk7M+YMcNrz+3Tp4/TZe6jjz4iOTmZDRs2sH37dgCuueYazjkn97cZvorLm2644QYnGUhJSXFmmztbn332WZ7XT58+zdKlS53jNm3aFKj+OnXq0KhRIwAOHDiQqa6ixL1lKuvYoNatWzvdWDds2HBWybd7l+b8xigdPHiQdevWFfgZgXbllWfaSBYsWJBn2Q0bNjhdX4uropBgJQGVPChXCch5RKxICXLkyJFMMwWOHj0623oSIiLFVdWqVRk9ejTgGmeRdSzJDTfc4Kx99f333zN16lSP686rBaRChQrO2JmjR4+yZMkSj7oHZrjzzjud/ddee40tW7Z4JS5fKuyyEEuXLs00vXZWr7zyijOBwdVXX13gNbCATGOL77//fo9b3QrzPV25cmWB7n/nnXec/SuuuCLTtZCQEEaMGOHEdM899+Q6gUtuoqKinA9RN2/enGdsDz30EKdPny5Q/UXB4MGDnf3//Oc/JCQk5Fq2JMyWXBQSrN+AtsaYirkVSL/WFijY/J9SaMaYUGNMZNYNCOzI6RLs6aefdv5gVapUqVgNZBUR8cQDDzzgjCmdN29epu5A5cuXZ9KkSc7xXXfdxQsvvOBMmZ1Vamoqq1atol+/ftlmeMvKPYmaNWsWH3zwAeDqwnTDDTfkeW/Lli0ZPnw44FpEvHPnznnO2BcfH8+cOXO49NJLCz1bHri6ir/++uvOwrg5eeutt/j1118BqFmzZrbxbWdjwIABOc5q98EHH2SapOSRRx45q/r/8Y9/ON3sfvvtNzp27Jhn97C///6b//znP3Tr1u2sngeun4NLLrmEt956K89/m9jYWG699dZMa7Pdfvvt2cqNGzfOaXn64osv6N+/f67jsQ4cOMDjjz+e6VyFChVo1aoV4Jp05fHHH8+WpJ0+fZp//vOfzJw5s1j2aOnTp4/Turp7925uvPFGjh8/nqlMamoq//rXv1i0aFGxfI3uisIkF/OB54Dpxpih1tpk94vGmGDgbaAi8HEA4ivtxgHjAx1EabFr1y7eeOMN5/jRRx8t1LSvIiJFUe3atRk2bBjTpk1zWrEykh2AYcOGsXXrVl566SVSUlJ46KGHeOmll+jSpQsNGjSgXLlyzlio7777zlkPKb9WqOuuu44aNWoQExPDxx9/7LQU9OrVy6MZF99880127NjB119/zaFDh5w3jddeey3nnHMOaWlpHD58mK1bt7J+/fo8P6UvqJ07d3LPPfdw33330bp1a1q0aEGtWrUICgri77//ZsWKFezYscMpn9OiuAU1duxYXnnlFS699FJ69epFixYtOH36NGvXruWrr75yyg0fPpwePXqc1TPKly/Pp59+SocOHThw4AAbN26kWbNmXH311Vx55ZVUqVKFhIQE/v77bzZu3MimTZtIS0vj4osvLtRr++mnn7jzzju59957adasGZdddhk1a9YkLCyMuLg4fv75Z1auXJnp33D48OE5joeuXbs2s2fPpm/fvpw+fZpPP/2UFStW0LVrVy6++GLKly9PTEwMGzZs4NtvvyUtLY2nnnoqUx0PPvgggwYNAuCZZ55hwYIFXHPNNVSvXp29e/fy+eefs3//fqpXr06fPn14++23C/X6/S04OJjp06dz7bXXcvr0aRYuXMj5559Pv379qFOnDocPH2bhwoXs2LGDBg0acMkllzgfmBTLZCtjrYpAbUAYrpapVGAn8CQwBBgMTAB2pF/bDoQHOt7StqX/G9j8ttjYWCuFN2jQIOd7WqdOHZuQkBDokKQQYmNj9XsipULdunWdn/PVq1d7dM/OnTttmTJlLGCDgoLstm3bspV56623bERERL5/gwBbuXJl+9VXX+X73HvvvTfbvfPnz/f4tSYmJto777zTiT2/rVGjRvb48ePZ6unQoYNTZvfu3fk+t1GjRh49r1y5cvY///lPjnWsXr3aKTd+/Pgcy2SNK6fvl/s2ZMgQm5ycnGvc7vXlZd++ffbaa6/16DUC9sYbb8z3e5abQYMG2ZCQEI+fFRoaasePH29TU1PzrPfLL7/M9LuQ21a2bNkc73/44YfzvO+8886z69evt+PHj8/39y0jjrp16+Z4/Wx+FvLiSX3WWrtw4cI8f6fr1atnt27dam+++WbnXExMTJ7P9oWsf7+BSFuA988Bb8Gy1sYbY7riaslqDmRdFMMAm4EbrLUn/RyeiN98//33mT7BfeaZZ/Jd2E+KtsjIyIwPKkQki4YNGzJo0CDmzJlDWloaEydOZO7cuZnKjBkzhsGDBzNjxgyWL1/Ozz//zOHDh0lNTSUiIoJGjRrRqlUrunTpQteuXSlXrly+zx06dCivvvqqcxwVFVWg1pdy5crxxhtvcP/99zNz5kxWr17Nzp07OXr0KGXLlqVKlSpccMEFXHnllfTo0cNrazVt2bKFFStWsGbNGjZs2MAff/zBkSNHsNYSERHBBRdcwLXXXsttt912VmOhcjN58mR69OjBlClT2LBhAzExMURFRdG6dWv+8Y9/eG2G29q1a7Ny5UrWrVvH3Llz+eqrr9i3bx/Hjh2jfPny1KpVi4suuoj27dvTu3fvAs9Y6G7u3LkcP36cVatWsW7dOjZv3syOHTs4cuQIp0+fJjw8nGrVqnHxxRdz7bXXcuONNzrjAvPSrl07fv/9d2bNmsVnn33Gpk2bOHz4MNZaqlWrRtOmTbnuuusYMmRIjvc/++yzdOrUiddff51vv/2Wo0ePEhUVRf369enXrx+jRo2icuXKLF68+Kxfe6D17NmTX375hZdffpmFCxfy559/EhISQr169RgwYAB33HEHlStXztR9sFIlT6ZqKFpMUfnjb4wJAq7HtSZWnfTTe4BlwGe2qARayhhjQoGc3uVHANEZB7GxsVp3phCstVxzzTWsXbsWgBYtWvDjjz96vPikiIiISEnRuHFjduzYQfXq1YmJifH78+Pi4rKu6xZlXctGeSTgLVgZrLVpuFqx5gc6FjnDWpsIJGY9Xyz7wxZhixYtcpIrgH//+99KrkRERKTU2blzJzt37gRwJv8obvQOTiTAUlJSePDBB53jrl27ct111wUwIhEREZHAGD9+vNO9/vrrrw9wNGenyCRYxpggY0wfY8yLxpgPjDFz0/f7pHcfFCmRZsyY4Uyra4zhhRdeCHBEIiIiIt6VmprKqFGj+OOPP3K8npCQwP3338+cOXMA16LL7utnFSdFYgyWMaY1MBvIWLDBvf+ZxTW74FBr7QZ/xyY5S18LKzbjWGOwzk58fDyNGjXiwIEDgGsK2BkzZgQ4KvGWrH249XsiIiKlVUpKCsHBwQC0bt2aK664glq1apGcnMwff/zB0qVLnSUXwDUZScbU9f5W7MdgGWMuBFYC4cB+4CPOTJ5QDxgAnA+sNMZcbq3dHoAwRXzipZdecpKr0NBQLSosIiIiJd4PP/zADz/8kOO1sLAw3njjjYAlV94Q8AQLmIgruZoMPGStzbRUuzHmQeB54L70ssX3uy3iJiYmJlN3wPvuu4/zzjsvgBGJiIiI+EbZsmX58ccfWbp0KStXrmTv3r0cOnSI+Ph4oqKiOP/88+ncuTNjxoyhRo0agQ63UALeRdAYcxg4Yq29II8yBvgNqGKtreq34CRX6iJYeHfffTdvvPEGAFWqVOGPP/4gIiIiwFGJN6mLoIiISPFT2C6CRWHyiPJAnmOr0tfA2pBeVqTY++OPP5gyZYpz/Nhjjym5EhERESkBikKCtR3If3lsqAH87uNYRPzi8ccfJyUlBYC6detyxx13BDgiEREREfGGopBg/Rdob4y5MrcC6dc6AFNyKyNSXGzatIn333/fOX7qqacoV65cACMSEREREW8J+CQX1tqp6TMJLjfGvAHM4cwsgnWBIcBdwKvW2v8GJkoR7xk3bpyz36xZM4YMGRLAaERERETEm/yeYBljUvO4/GD6lpP7jDFjrbUBTwpFztaqVatYvny5c/zcc89RpkyZAEYkIiIiIt4UiGTF5F/EJ/eKBJS1NlPrVbt27ejRo0cAIxIRERERb/N7gmWtLQrjvkT87uOPP860qN7zzz+PawUCERERESkp1N1O8mSMCQVCc7ikOcULIDk5mUceecQ57tu3L23atAlgROIP5cqVY+zYsZmORUREpGQL+ELDUrQZYyYA4/MrpwVU8zZlyhTGjBkDQFBQED///DNNmjQJcFQiIiIiklVJWGhYpEQ7deoUTz75pHM8fPhwJVciIiIiJZQSLBEfmzx5Mn///TcAoaGhTJgwIbABiYiIiIjPeG0MljGmPOmz/FlrT+VwvSswyIOqNlprX/dWXFJok4BXcjgfwZn1yiQXR44cYdKkSc7xPffcw3nnnRfAiERERETEl7ySYBljLgF+xJVgfQ20z6FYU2A4kN+gr5uNMauttdu8EZsUjrU2EUjMel6z33nmhRde4Pjx4wBERkZmmqZdSr7Tp0/z1ltvOcd33HGHJroQEREp4bzVgjUBV3fDRGCkB+XzendeBngSGFD4sEQCJyYmhtdfP9MY++CDD1K5cuUARiT+lpCQwD//+U/nePjw4UqwRERESrhCJ1jGmGpAL1wtU3OstTs8uG1ELuevB/oCvY0xVa21hwsbn0igTJo0iVOnXL1lq1Wrxr333hvgiERERETE17wxyUU/XK1OAJM9ucFa+25OG/BPXIlaWeAGL8QmEhD79+/P1DVs3LhxhIWFBTAiERHxNWMMxhjq1asX6FAAmDBhghPTmjVrAh1OwHXs2NH5fnhDvXr1itS/txQd3kiwrkr/+pe19qfCVGSt3QNsTD/UKqxSbD377LOcPn0agFq1anHHHXcEOCIREd/JeKNZkG3z5s2BDlsKyD1hGz58eKDDESmyvJFgtcDV6rTOC3UBbMI1Rqull+rzO2NMkDGmnTHmSWPMMmPMn8aYBGPMKWPMXmPMQmPMncaYimdR91XGmLeNMb8bY+KNMbHGmC3GmGeMMfV88HKkgP7880+mTZvmHD/yyCOUL18+gBGJiIiIiL94Y5KLWulf//JCXe711PBSfX5ljOkDTAFq5lLk3PStJzDBGDPaWvupB/WWwTVd+l1kniSkAhAJNAfuNcbcnd7dUgLkmWeeISkpCYBzzz2X22+/PcARiYj4z5gxY2jYsGG+5WrXru2HaERE/M8bCVZE+tcj+ZT7ArjHg/oS0r9Gnm1AAXYRmZOr34CvcCWOqUAToA8QBlQDPjbGDLHWfpBPva8DY9L3LbAcWA+EAt2BZkA4MMMYE2+tneedlyMFsWvXLt555x3n+LHHHiM0NDSAEYmI+NegQYPo2LFjoMMQEQkYbyRYSUAwrjf3ubLWbgG2eFBfRre55ELGFUjHgP8C71hrf8960RhTA/gQ13phQcAUY8wqa+2hnCozxnTjTHJ1GuhvrV3kVuQhY8xE4HFcrVtvp68lll/SK1721FNPkZKSArjGJIwYkduEmSIiIiJSEnljDFbGm/jqXqjLvZ7imhwsAupba8fllFwBWGtjgN7A/vRTEcDgPOp8xm1/QpbkKqPOJ4BlbvU9WNDApXB+//133nvvPef4iSeeICQkJIARiYi/xZyM4Zkvn6HLrC60md6GLrO68Oy6Z4k5GRPo0IqkmTNnOpMmTJgwAYC//vqL8ePH07JlS6pUqZLnhArff/8999xzD02bNiUqKopy5cpRu3Zt+vTpw6xZs0hNTc3z+e6zykVHRwOwfv16brnlFurXr09oaChVq1ala9eufPzxx1573fPnzyc0NBRjDOXKlWPevJw7nfz66688+OCDtGrViqpVq1KuXDlq1apF586defPNN53JlIqb5ORkFi9ezNixY7nqqquoVq0aISEhVKpUicaNGzN8+HBWrVpVoDp3797N2LFjOf/88ylfvjzVqlWjTZs2vPbaayQkJORfQRZbtmxh5MiR1KtXj9DQUGrVqsW1117LzJkz8/25EsFaW6gN1+QWacDWwtaVXt9WXF3pvvJGfUV5Aybh6u5ngfdzKXOhW5njQHge9bV3K7sPMD6MPdLtWTY2NtaWdkOHDnW+H40aNbLJycmBDkkCLDY21ur3pHQ4lXTKjlowygZPDLZMINsWPDHYjl4w2iYkJwQ6VJ+oW7eu83O+evVqj++bMWOGc9/48ePt0qVLbeXKlTP93gB22LBhme47efKkHTx4cLZyWbcWLVrYPXv25Pr8Dh06OGV3795tn3nmGRsUFJRrfbfffrtNS0vLtb6McnXr1s21zDvvvGPLlCljARsWFmaXLVuWrUxycrIdO3asUy63rV69enbLli25Pmv8+PFn9e/iSX1Z/008lZKSYqtUqZLvvx1g+/fvb+Pj4/Ot8/3337cVKlTI8+fgzz//zPTvnZcXX3zRli1bNtf6OnXqZGNjY52f+7z+vaV4yvr3G4i0BXif7I0uguuAq4GLjDENrLW7zrYiY0xD4GK3eku63W77lXMp08dtf5m19mRulVlrvzTGHAaqArWBS4ENhY5S8vXLL78wZ84c53j8+PGULeuNXy8RKeoSkhPoPrs7a/eszbVMcloyUzdOZfuR7SwZuoTywZpZNKvff/+dF198kZMnT9KyZUs6depEZGQk0dHRREREOOXi4+Pp2LEjGza4/ryVL1+e7t2707RpU8qVK8eePXtYsGABBw4cYPPmzbRr146NGzdSpUqVPJ8/ZcoUJk2aRGhoKD179qRZs2ZYa/nyyy9ZvXo1AG+//TZXXnklI0eOPKvX+OKLL/Kvf/0Lay1RUVEsWrSINm0yr0qTlpZGv379WLhwIQBly5alc+fOtGzZkvDwcPbv38+iRYvYvXs30dHRtG/fng0bNtCoUaOzisnfrLUcOeLqpNSkSRMuu+wy6tatS1hYGCdOnGDz5s2sWLGC5ORkPv74Y8qUKcMHH+Q+TH3p0qXcfPPNTqvSueeeS9++falZsyb79u1j/vz5bN68mYEDB3r0d3natGn83//9n3PcuHFjevXqReXKlfnjjz+YP38+K1euPOufASklCpKN5bQBHXG1YKUCMwtZ10y3uq4tbGxFfQMmciYznpNLmffdyvzTgzoXuJUf5cPYI92eU+o/mR84cKDzvbjwwgttSkpKoEOSIiA1NdXu3r3b2VJTUwMdkvjAqAWjcmy1ym0bvWB0oEP2Om+0YAG2TJky9p133snznttuu80p37dvX3vo0KFsZRISEuzw4cOdcoMHD86xLvcWDcA2b97c7t69O1u5N9980ynToEGDXGPLKJNTi8YjjzziXK9Vq5bdunVrjnVMnDjRKde2bVsbHR2drUxKSop99NFHnXJt2rTJsa6i2oJ1//332507d+ZaJjo62l522WXOs1auXJljuePHj9vatWs75W6++WZ76tSpTGVOnDhh+/XrZwFrjMmzBevPP/+04eHhTpkHH3ww29/zmJgYe/XVV2eqTy1YJU9hW7C89WZ7k1tiNOIs6xjuVscWb8RV1DfgZ7d/uDtzKbPZrUxvD+p8ya38Sz6MXQlWui1btmT6JZw7d26gQxIRP/n7xN+5dgvMbQueGGwPnDgQ6NC9yj3B8mTLeLOfNcF68skn83zOtm3bnDe17dq1y/PDrNTUVNumTRsL2KCgIPvHH39kK+OeYEVERNh9+/blWl/btm2dsrklRzklWKmpqXbMmDGZErScYrHW2sOHDztd3c4//3x78uTJXOOx1tobb7zRqXfNmjXZrhfFBMtT+/fvt+XLl88zQX711VedeFq1apXrz8Pp06dtkyZNMv2s5eT+++93rvfp0yfX2I4cOWKrVq2aZ0ItxVthEyxvTHIBrtnrMkwzxjxnjCnnyY3GmHLGmGeBaW6nn/BSXEWWMWYYZ7pDHsc1q2BO3Kd8359LGXfu65HlthZX1lhCjTGRBdk4Mz1/qTd+/Hhnv1mzZgwcODCA0YiIP03fOJ3ktIJNepuclsz0TdN9FFHxVaFCBe6///48y7z11lsZH/IxceJEypQpk2vZoKAg7rnHtTpMWloaixcvzrPuUaNG5bk2V79+/Zz9zZs351lXhuTkZIYMGcJ///tfAJo2bcpXX31FgwYNciw/c+ZMTp06BbgWqQ8LC8uz/vvuu8/Z//zzzz2KqbioVasW7du3B+Crr77Ksczs2bOd/SeeeCLXn4eQkBAef/zxHK/lVt/EiRNzLVe5cuV8f1aldPPKIBFr7UJjzItARqfVB4FRxphZwBpc07MfAU7ims69MtAC6ADckn5scGWIr1trP/NGXEWVMaYBrpamDE9baw/nUtx9+vt4D6o/5bZfMddSmY0DxudbSrL58ccf+fTTT53jJ598kqAgb31uISJFXV7jrvKyJnoNj7R7xMvRFA2eLDSc0/V27doRHp7nii/OWKjQ0FDatWuXbyyXXHKJs//jjz/mWbZLly55Xj///POd/YMHD+b77FOnTnHDDTewbJlrgt8rr7ySxYsXExUVles9Ga/Pk3igYK+vqPr999/56aefOHz4MPHx8aSlpTnXDh92vTXau3cviYmJmdaVPH36NBs3bgSgXLlydO/ePc/n9OnTh6CgoEz1u9u1axcxMa7ZPuvVq5fpe5uTfv368cgjJfN3WArPm6PwH8L1hn40rkSpMnBv+pYX47Y/E/inF2MqcowxlYDPODOpxUrgxTxucR8JneTBI9znbNUoah9zb71q2bIlffv2DVwwIuJ3J5JO+PW+4uBsFxq+4IIL8ryemprKL7/8AkBiYmKBJxI6dCjHpSYd5513Xp7X3ZO/+Pi8P+9MSEigc+fOfPPNNwB07tyZ+fPn59sitXXrVmc/r9a0nOT3+rLatm0bS5YsyfV6REQEo0aNKlCdnkpJSeG1117jtddeY/fu3R7dExcXR82aZzrmREdHk5zsaj2+4IIL8l0WJSwsjAYNGrBz584cr+/YscPZb968eb7xNG7cmHLlyhXbqfLFt7yWYFlr04AxxphvgWeBWmROnvISAzxmrfV5nwljzKfA9V6qbo+1tl4Bnh2KK7lqmn7qd2Bw+vcuNwlAxv/Iniyq5N41s+ALP4jHvvvuOxYtOrMk2cSJEzHG0x95KQ2OHz+e6Q/11q1bqVSpUgAjEm+rGOJpRwHv3FeSuc8UmJOjR4863QPPRn5JkXvrSE7c/3/PrRUkw8GDB51WrrCwMP73v//lm1wBzux6ZyO/15fVDz/8wL/+9a9cr9etW9cnCVZCQgI9e/bM1FrnicTExEzHsbGxzn5+M0RmqFq1aq4JVkHrCwoKIioqigMHDnj0bCldvD6PtLX2XWPM+7gmregNtCXn8TrHga+Az3HNPliiPwIwxoQAn+CadRFcU7R3stbm95HTSc4kWPn/7wwV3PY9/Yh0EvCKh2UzRADRBbynRHniiTNDBa+44gp69uwZwGikKEpLS2PPnj2ZjqVk6VC3Ayt2rSjwfR3rdfR+MMVcft2rU1JSnP0aNWrwwAMPFKj+/FqovKlGjRrUr1+f7777jvj4ePr06cPy5cvz/YAl4zWGhITwzDPPFOiZ+SWoRcWTTz7pJFfVq1fnrrvu4tprr6VBgwZERUU5CzADDBs2jPfee89rzy5Mgu6P+qTk8MlCPdbaJGAqMNW4fktqAlWASrje9B8BDuTTcuMrSwFvfdzg0UdNxphgXJNYZHQQ3otrGvp9HtweA9RI3z8HyK+TtXufghhP4rPWJgKJ+RZ0U9pbatatW8eKFWfeVKn1SqR0GtlqJE+ufbJAE10EBwUzsqXW0Cko91aFpKSkAidY/hQaGsqyZcvo3Lkz33//PevXr6dbt24sW7aMihVzb72sUqUKf//9N0lJSYwePdqnLd7Dhw9n+PDhPqs/JykpKUyZMgWAyMhIfvjhB+rUqZNr+WPHjuV6zX0sm6ctf3mVK2h9aWlpxMXFefRcKX18vhKqdaX3f6dvAWet/a8/n2eMKYtrLauMbon7cSVX0R5W8QuQ0cco71HD2cv84uEzpIDcW6+uvvpqOnfuHMBoRCRQaobXZHiL4UzbOC3/wulGtBhBjfAa+ReUTEJCQqhfvz67d+8mNjaW3377jQsvvDDQYeWqUqVKLF++nOuuu44NGzbw7bff0qNHD5YsWZLrZB4XXnghf//terv0zTff0K1bN3+G7HO///67k5TccMMNeSZX4Bonlpt69eoRHBxMcnIy27dvJykpKc9xWPHx8ezatSvX6+6TmLiPhcvN77//rvFXkitNd+ZDxpgywGygf/qpA7iSq5w7AOdss9v+VR6Ud18SfnNuheTsrVq1ijVr1jjHTz31lFqvREqxyd0m06FuB4/KdqjbgcndJ/s4opKrU6dOzv6MGTMCGIlnIiIiWL58OS1btgRc04337NnTmYo9q+L2+grKfZxT5cqV8ygJW7ZsyXW8FLhmDmzVqhXgmlFw6dKleda3YMGCPLtpN2jQgBo1XB98REdH55tkzZ8/P8/rUropwfIRY0wQ/H97dx4mRXX2ffx7sw37qoAoCERNIlHAuIBLBBQeFwgSCBo0AiKgxjcao4jEPNEYRSPRuKABCUgwGg0I4oIKIqgkKIJAwIcEUAiguDHDvg7n/aO6e2qGXmequ6d7fp/rqouq6lOn76lD9czddeoc/gIMDO36Eu+Zq3+nWJV/Yov/MbOYz2GZ2bnA0aHNz4APU3wvScA5V2ouje7du9O9e/csRiQi2VanZh3mXDmHEaeNoGa1mlHL1KxWkxGnjeD1q16ndo34gylIbDfccENk/bHHHmPFihVJH5ut52WaNGnCvHnz6NSpEwDvvPMOvXv3Zu/eI8ehuuaaayKDbUyfPj0yxHsycuF5IH9SFR5iPZrDhw8nNc/UoEGDIuv33HMPxcXFUcsdPHgwqWfa/PXdddddMcsVFhbyxz/+MWF9UnUpwUqDUHI1GQhfqV8DFzrnUu6yFzrmo9BmQ+BncYrf4Vt/1uXCp22OefPNNyPD7kL8iQhFpOqoU7MOE/pMYNMvNnFvj3vp2b4nXY7rQs/2Pbm3x71s+sUmJvSZoOSqgjp37hx5big8FHq8CXZ3797Ns88+y/e///0KjdBXUU2bNmXevHmRUUXffvtt+vTpc8TIeMcccwyjR48GvCSjf//+TJkyJeadlwMHDjB79mx69OgRN2GpLL797W9z9NHe98Dz589n4sSJR5TZvn07gwYNYv78+Ql7hwwZMoRWrVoB8OGHHzJs2LAjzunu3bsZNGgQq1evTljfTTfdFBntcebMmYwZM+aIpO2rr76ib9++fPnll+q9IjGl/RmsqiY0qMcEYHBo1za85OpfFaj2TiA8HvjdZrbSOVfqXriZ3QVcEtrcATxQgfeTKMreverVqxfnnntuFiMSkcqmRf0WjDlvTN5OIlwZPPHEE6xdu5ZFixbx1Vdf8cMf/pDvfOc79OjRg1atWnH48GG+/vprVq5cyfvvvx/1TlE2NGvWjLfeeovu3buzatUq3nrrLfr27cvs2bMpKCiZYeXXv/41q1atYvr06ezevZtrrrmGu+++m549e9KmTRuqVavGtm3bWL16NYsXL44MBJHp71QXLFjAgAEDkip75plnMmrUKKpVq8att97K7bffDsDIkSOZMmUK55xzDk2aNGHdunXMnj2bbdu28Z3vfIcOHTowY8aMmPU2bNiQiRMn0rdvX4qLi5k6dSrz58/nsssuo2XLlmzZsoUXX3yRrVu3cuaZZ1KjRo1SX5KWdfzxxzNu3Diuv/56AMaOHcuLL75I7969adq0KevXr+fFF1+kqKiIfv36sWzZslIjxYqEKcEK3hjgWt/2HKCnmSUaBWG7cy7qU9LOudfMbCLeJM61gdfM7A3g/dD2RUB4ynEHjHDOfV2Bn0GieO2111iyZElkW3evREQyr06dOrz11lvccsstTJgwgeLiYtasWcOaNWtiHnPCCSeUSmKy5aijjookWR9//DFvvvkm/fr1Y9asWZEBGqpVq8bzzz/PPffcw/3338++ffvYuHEjkyZNillvq1atSo2ClwkbN25MOrnwD7F/66238q9//YtnnnkG8OaUXLx4canyHTp0YObMmUl167v00kuZOnUqw4cPZ+/evWzatInHHnusVJlTTz2Vv//971x99dUJ67vuuuvYuXMnY8aM4dChQ/z73//m3/8u/XRH9+7dmTx5cqTbp0hZSrCCd2KZ7StDSyIbgXjDUN0AFAPX403gfFFo8dsN/D/n3PPJhSrJcs5x3333RbYvvfRSzjrrrCxGJCJSdRUUFDB+/HhuueUWnn76ad5++23WrVvHtm3bqFGjBs2aNePb3/42Xbp04ZJLLuHss5MZIyozmjdvzvz58+nWrRtr1qxhzpw59O/fnxkzZpRKsn7zm98wcuRIJk+ezFtvvcWaNWsi3RybNm3KiSeeyBlnnMFFF11E9+7dqV69ejZ/rKRVq1aNadOm0bdvXyZNmsSHH37Ijh07OOqoozjxxBMZOHAg11xzDXXq1Em6ziuvvJKuXbvy8MMPM2fOHLZs2UK9evU44YQTuPzyyxk5ciR169ZNXFHIbbfdRs+ePXnkkUeYP38+W7dupXHjxnz3u9/lqquuYujQoTlzviU7TI/pBMvMnqake2AqNjrn2iZR/znAMOAHePNiHQD+i9eFcKJz7tNyvHfKzKwxEBkOqLCwkMaNG2firbPinXfe4fzzS0YJ++c//0mXLl2yGJHkgqKiolLfKuf7dSIiIpIPyv7+Bpo454qSPV53sALmnBsCDElj/YuARemqX6IbO3ZsZL1bt25KriQpNWvWpG/fvqW2RUREJL/pDpaUS1W6g/XRRx9F5toAeOONN+jVq1cWIxIRERGRdKnoHay0D9NuZp+ElmFl9l8b2r8+3TGIVMT9998fWT/ttNPo2TPReCUiIiIiUlVlootgW7yR7RqV2d/I95pIpbR27VqmT58e2b7jjjs074WIiIiIxKSJhkXi+P3vfx+Z4PGkk06iX79+WY5IRERERCozDXIhcZlZbby5tsoqe0cy72zZsoWpU6dGtm+//XYNyyopOXjwIK+++mpk+9JLL9VAFyIiInlOCZYkMhr4TbaDyIaHHnqIgwcPAnDcccdx1VVXZTkiyTW7d+8uddcznweDEREREY+6CIpE8c033zBhwoTI9i9/+cvIBJAiIiIiIrEowRKJ4vHHH2f37t0ANGvWjOHDh2c5IhERERHJBUqwJJH7gSZRlrZZjCmtdu3axaOPPhrZ/vnPf069evWyGJGIiIiI5Ao9gyVxOef2AfvK7s/noconTZrEtm3bAKhfvz433nhjliMSERERkVyhO1giPgcOHOAPf/hDZHvkyJE0bdo0ixGJiIiISC5RgiXi88wzz7B582YAatWqxS233JLliEREREQklyjBEgkpLi7mgQceiGwPHjyYVq1aZTEiEREREck1SrBEQmbOnMl//vMfAKpVq8aoUaOyHJGIiIiI5BolWCKAc46xY8dGtn/84x9zwgknZDEiEREREclFSrBEgLlz57Js2bLI9ujRo7MYjYiIiIjkKg3TLgKl7l5dfPHFdOrUKXvBSN6oX78+b7/9dqltERERyW9KsKTKW7x4MQsWLIhs33HHHdkLRvJKjRo16NatW7bDEBERkQxSF0Gp8vx3r8455xzOO++8LEYjIiIiIrlMCZZUaatWrWL27NmRbd29EhEREZGKyEQXwXcAB2wqs38TsDAD7y8Sk3/eq1NOOYVLLrkki9GIiIiISK5Le4LlnOsWY/8LwAvpfn+RWD799FOee+65yPbo0aMxsyxGJPlm165d9O7dO7L9yiuvaKALERGRPKcuglJljRs3juLiYgDat2/PwIEDsxyR5JtDhw6xcOHCyHLo0KFshyQiMaxdu5aCggLMjOeffz7b4cQ1d+5czIzq1auzdOnSbIcjImUowZK4zKy2mTUuuwCNsh1bRXzxxRdMnjw5sn3bbbdRo4YG1RQRCdLbb7+NmUWWoUOHBlLnHXfcwbnnnkvbtm1p0KABBQUFNG/enI4dOzJ48GAmTJjAl19+mVK9N998MwcOHKBz586RL9w2bNhQKv6KLhs2bKjwzw/Qs2dPevToweHDh/n5z38eSJ0iEhwlWJLIaKAwyrIhizFV2COPPMK+ffsAaNmyJUOGDMluQCIieejpp58utf33v/+dXbt2lauuV155hU6dOtGjRw/uv/9+Fi1axMaNG9m1axcHDhzgq6++YuXKlfzlL3/huuuuo1WrVlx22WUsX748Yd0LFy7ktddeA2DMmDE50V38V7/6FQD/+Mc/eOmll7IcjYj4mXMu2zFIJWZmdwG/SVSusLCQxo0bpz2eIOzevZvWrVtTWFgIeANdjBo1KstRST4qKiqiSZMmke1cuk5EKmrnzp0cc8wx7N69m9q1a0e+1Jo8eXJKd7KKi4sZPXo048aNi+wzM04++WS6du1K8+bNqV+/Pt988w1btmzh3XffZcuWLaXK7ty5k3r16sV8j27durFw4ULat2/P2rVrqVbN+/55x44dTJw4MW58t912W2R9zJgxpa75skaMGEHDhg0T/szJOv3001m6dCmnnnoqK1asCKxekaqu7O9voIlzrijpCpxzWrTEXIC78EaBjLsUFha6XDF+/PhI3PXr18+p2CW3FBYW5ux1IlJRkyZNivzff/DBB13t2rUd4M4777yU6hk5cmSp6+iqq65ya9eujXvM8uXL3dChQ11BQYED3M6dO2OWXbJkSaTusWPHphSbc65UbJ9++mnKx1fEhAkTIu/95ptvZvS9RfJZ2d/fQGOXwt/P6iIoidwPNImytM1iTOV2+PBhHn744cj2sGHDdEdBRCQNwt0Da9WqxbBhw7j00ksBePfdd1m/fn1SdUydOpUJEyYA3p2op556imnTpnHCCSfEPa5jx45MnjyZlStXcs4558Qt+9hjj0Xqv/rqq5OKq7L4yU9+Qu3atYGSn0NEsk8JlsTlnNvnnCsquwDbsx1bebzyyiusW7cOgGrVqunhYBEJ1hdfwL33Qq9e0LWr9+9993n7q5B169bx3nvvAXDxxRfTpEkTrrzyysjrZZ/Nimb//v2lJn+/9dZbufbaa1OK46STTuKdd96J2T1wz549vPjiiwB07dqVVq1apVR/tjVo0IALL7wQgDlz5vDVV19lOSIRASVYUsU89NBDkfV+/frRvn37LEYjInlj714YMQJat4Y774S5c2HxYu/fX/3K2z9yJISeQ8p3U6ZMiayHE6tLLrkk0mNg6tSpHD58OG4dU6dO5fPPPwegSZMm3H333eWKpVq1ajEHrZgzZ05k0I1evXqVq/5su+iiiwBvWohwsigi2aUES6qMpUuXsnDhwsj2LbfcksVoRCRv7N0LF18MTz0FBw9GL3PwIEycCBdd5JXPY4cPH2batGkANGzYkD59+gBQUFDAgAEDANi0aRPz58+PW8/s2bMj6z/96U+pU6dO4LG+/vrrkfUzzzwz8PozwR/3nDlzshiJiIQpwZIqw3/3qkuXLpx99tlZjEZE8sZNN4Hvy5u4Fi6Em29OazjZNm/ePDZt2gR4PQXCzwgBDBo0KLLuv8tV1uHDhyNdDMEb5S8d3nnnncj6GWeckZb3SLeOHTtSUFAAeM+3iUj2BTazqpnVAQzAObcnyuv/A1yeRFXLnHOPBxWXCHjflr7wwguRbd29kkyoXr06HTt2LLUteWbrVkjieaJSpkyB3/4WWrRIS0jZFq17YNj555/Pcccdx+bNm5k5cybbt2+nUaMj563funUr27eXPOrrv46CsnPnTtauXQvA8ccfz1FHHRX4e2RCrVq1OPXUU1myZAnbtm3j008/pV27dtkOS6RKCyTBMrOOwFK8BGsR8IMoxb4HDMEb6jCeq8zsbefc6iBiEwF4/PHHOXToEOD9Iu3Xr1+WI5KqoEGDBklNcio57M9/jt0tMJaDB73jxoxJT0xZVFRUxKxZswBvEvcePXqUer1atWpcccUVjBs3jr179/L8888zYsSII+r55ptvSm03a9Ys7vsuX76cefPmxXz9e9/7XuRZpbD169eHpyPhmGOOiVt/ZeePf+3atUqwRLIsqDtYd+F1N9wHDEuifLwp0qsDdwMDKh6WCOzatSsyzC/ATTfdRI0agd28FZGqLNmugWUtWJCXCdbf/va3yITCV1xxRdS7tldeeWVk0uApU6ZETbB27NhRart+/fpx3/e9994rNeFvWYMHDz4iwfJPRhxvcuBc0LRp08j65s2bsxiJiEAAz2CZ2dFAb7w7U88659YmcdjQGMssvOSrj5nl5r16qXSmTJkS6WrSoEEDhg1L5jsAEZEk7NyZ2eMquXjdA8M6derEySefDMDixYtZs2bNEWUaNGhQajs80l+Q/EmcP0HJtGnTpnH22WfTsGFDGjZsyNlnn80zzzyTUh3+BHFnnv7fEsklQQxy0Q/vrhPAI8kc4JybGm0BfoGXqNUAfhRAbFLFFRcX88c//jGyPXz4cBo2bJi9gEQkv5RJBNJ+XCX2f//3f3zwwQcAnHjiiZx++ukxyyYa7KJsl8Bt27bFfe8bb7wR51ypJd4gGkCk2zgQGSQi066//nquvvpqli9fTrdu3ejWrRvLly/npz/9KTfeeGPS9fhHWDyYapdVEQlcEAlWeCi2Lc65f1WkIufcRmBZaLNrhaISwRvm95NPPgE0sbBkXnFxMcuXL48sxcXF2Q5Jgnb++eU7Lk2j4mVTMnevwvwJ1rRp0464Nlq2bFnqy7B0PMtYt27dyHphYWHg9Scyffp0/vSnP3HssceyevVqZs+ezezZs1m9ejWtWrVi/PjxkefZEvEnoLEmVRaRzAkiweqEd9cpqLFBP8LrJtg5oPqkCvvDH/4QWR8wYADHH398FqORqmbnzp107tw5sqjrTh4aNgxq1kztmJo1vePySHFxcWTuKyidQEXTrl27yFQZn3/+OW+88Uap16tXr865554b2V5Y3mfd4jj66KMj64nukKXD2LFjAXjggQdKDUrRrl07HnjgAQDuu+++pOryx+//uUQkO4JIsMJD12yJWyp54Xryc/xayZj333+fRYsWRbZ/+ctfZjEaEclLLVvCkCGpHTN0aN4N0T5nzhy2bt0KeBPfnnjiiQmP8d/litadLzxBMXh3ufYGPEGz/wu3TCdYmzZtYtmyZRQUFNC/f/8jXu/fvz+1atViyZIlpQbjiMUfv75IFMm+IBKs8AQW38QtBfOA/wck6qMV/gRtXIGYRHj44Ycj6+ecc06p2e5FRALzyCPJdxU8/3yvfJ552jcXWKK7V2EDBw6MjOg6e/bsI5KcIUOG0CKUiG7bto277747mGBD2rRpE5mDa+3ataWeyUq3jz76CIAOHTqUmog5rE6dOnTo0KFU2Xg+/vhjwLvzFz5ORLIniATrQOjfuGOoOudWOOfGO+fGJ6gv/OSvntKUctu4cSPTp0+PbGtiYRFJmzp1YM4cGDEidnfBmjW9119/HaL8QZ3LvvnmG15++WXA+wP/8ssvT+q4o446il69egFw4MABnn322VKv165dm9/97neR7QcffDDhwBWpMLPIQBz79u1j5cqVgdWdyMaNGwEvyYuldevWAGzYsCFuXZ999hmfffYZ4M335X+2TESyI4gEK3znqnkAdfnrSXRHTDLAzGqbWeOyCyV3Liulxx57LPLQdLt27ejbt2+WIxKRvFanDkyYAJs2wb33Qs+e0KWL9++993r7J0zIu+QK4K9//SsHDnjftV5wwQW0bNky6WMTdRO89tprGTp0KACHDx/mmmuuYfDgwaxbty5uvRs2bOCll15K+P4XXnhhZH3JkiXJhl1h4ecx4w1IEZ77K9Gzm+GRGwF69uwZQHQiUlFBzLa6GTie4Eb964o3aEZQz3RJxYwGfpPtIFKxY8cOnnrqqcj2zTffHHWySxGRwLVo4U0gnIeTCMfi7x74xRdfcNVVVyV97J49eyLry5Yt41//+hennHJKqTITJ06kXr16PP744wD85S9/Ydq0aXTo0IGuXbvSvHlzGjRowN69e9myZQsfffQRy5YtwzkHeHfVOneOPm5Wnz59uOOOOwBYtGgRI0eOTDr2igjHZmYVrsv/rLH/uTURyZ4gEqx3gXOAk82svXPuk/JWZGbfAsKdh4MalVCqmMmTJ0cmkGzUqFHk208REQnWihUrSj0jtGLFClasWFHu+qZMmcJDDz1Ual+NGjV47LHH6NGjB//7v//LqlWrcM6xatUqVq1aFbOuGjVq0Lt3b377298ekbSFdejQgY4dO7JixQpeffVVDh06FHkuLJ3CEynHm0B59+7dpcrGEr5T16ZNG84777yAIhSRigiii+CboX8N+N8K1vXrUD0AcytYl1RBhw4dKjWx8IgRIxL+chIRkfIJ8pkogGeeeSbmRLn9+vVj5cqVzJ07l1GjRtG1a1fatGlDvXr1qFWrFs2bN+eUU07h6quv5sknn2TLli3MnDkzZnIVNnz4cMAbSGPu3Mz86dG2bVvAG00wlvBr4bLRLF26lLVr1wJed8og7oiJSMVZ+DZ1hSox+wjoiNe171rnXMqfuGY2BJgcqmOVc65jhQOTCjOz2kC0hwYaARvCG4WFhTRu3DhDUcU2Y8YMBgwYAHjfXn7yySeRB4VFMq2oqIgmTZpEtivLdSIiJXbv3s1xxx1HUVERP/rRj5gxY0ba33PTpk20adOGgoICioqKjhhJcN++fTRu3Jj9+/ezadMmjjvuuKj13HDDDTz55JMUFBTw3//+l+bNg3ocXqRqK/v7G2jinCtK9vgg7mCBd+cp7CkzG2tmBckcaGYFZnYf8JRvd0XvhElAnHP7nHNFZRdge7Zji+bRRx+NrA8YMEDJlYiIxFWvXj1uvvlmwBsuPjzCXzq1bt2aTp06sX//fmbOnHnE6y+++CL79+/n9NNPj5lcFRYWRiZ3HjFihJIrkUokkATLOfcK8Ae87n0GjAK2mNnDZtbXzNqaWQPzNDCz40P7H8IbJON2oDre3avHnXOJh/4RKWP58uW88847ke2bbropi9GIiEiu+MUvfkGzZs04dOjQEc+Apcvo0aMBGDVqVKmh2Dds2MDtt99eqkw048ePZ9euXdStWzcyUIeIVA6BdBEEMLNqwBPACLxEyUL/JjzUtz4FGO6cOxxIUJI2oaHaC8PblaHr07Bhw5g8eTIAZ5xxBu+//776o0tWlZ3bZ9CgQdSqVSuLEYlILE888QQ/+9nPqF27NmvXro155yhIw4cPZ9KkSdStWzcyZPy8efPYs2cP1113HU8++WTU44qKimjfvj2FhYXcc8893HnnnWmPVaQqqWgXwcASrEiFZoOB+4BjUjjsC+BO59yfAw1G0qayJVhff/01rVu3Zt++fQBMmzYtpaGCRUSkaisuLub3v/89+/fv54ILLsjYiHxTp07lySefZPXq1YA3WfD111/P1VdfHfOYpUuX8vLLL1OtWjVGjRp1xDNcIlIxlS7BAjCzWsAQoA9wLtEnpd0BvAe8DDztnNsfeCCSNpUtwbr//vsjXSRatGjBxo0bKShI6jFAEREREZGIiiZYaZnswTl3AJgITDSvj1ZLoBnQENgJfANsVVdACcKhQ4cYP358ZPu6665TciUiIiIiWZH22fScd4vs89CSNDOr7pwrTk9Ukk9mzZrF5s2bAW9o9pEjR2Y5IhERERGpqtI/XXmKzKwDMBS4ktSe45Kq4osvYNIkWLgQdu7k+DVruAP4M3DhwIEcc4z+20jlsGfPHm644YbI9hNPPEHdunWzGJGIiIikW1qewUo5CO95nkF4idVp4f3OuerZikniy8ozWHv3wk03wdNPw8GDR7x8ACi87DJaPPcc6IFfqQQ00bCIiEjuqZTPYCUj9GzW/+AlVT8EwmMXh8fVzn7mJ5XH3r1w8cXeXasYagEtZs2Ciy6COXOgTp2MhSciIiIiAgFNNJwKMzvJzMYCm4BXgQFAASWTFIsc6aab4iZXpSxcCDffnNZwRERERESiyUiCZWYNzOxaM1sE/B8wCu/5qnBSdQh4De+5q5mZiElyyNatXrfAVEyZ4j2rJSIiIiKSQWlNsMysh5lNwxtBcALQhZKkyoB/AD8DjnHO9XbOPQfsSWdMkoP+/Oeoz1zFdfCgd5yIiIiISAYF/gyWmbXDm2R4MNA6vNtX5GPgWeBZ59yGoN9f8lCyXQPLWrAAxowJNBQRERERkXgCSbDMrC7wY7wBK87zvxT6dzPwN+CvzrkVQbynVCE7d2b2OBERERGRcqpwgmVmf8ZLruqFd4X+LQKm492tWugqw3jwkjIzqw1EG/O8UcaCaNAgs8eJiIiIiJRTEM9gDcVLrgzYD8wAfgS0dM6NcM4tUHKV00bjzXdVdtmQsQjOP798x3XrFmgYIiIiIiKJBDnIxV7gTuBa59ws59yBAOuWqmzYMKhZM7Vjatb0jhMRERERyaCgEizD60b2ILDVzGaYWX8zq5XgOJHEWraEIUNSO2boUGjRIi3hiCTLzGjUqFFk8eZXFxERkXxmFe29Z2bd8LoJ/oiS57DCle7Am9fqr8D8ZLoKhoZ1vxJwzrnqFQpOKizBM1gbwhuFhYU0btw4fYHs3QsXX5zciILnnw+vvw61o4UtIiIiIhJbUVERTZo08e9q4pwrSvb4Ct/BCj1jNRhv4uDhwHuUzHPVCG+49jeBzWY2zsxOq+h7SuY45/Y554rKLsD2jAZSpw7MmQMjRsTuLlizpve6kisRERERyZIK38GKWqnZt4BrgKsomQvL/0ZrgWeA55xz68scqztYOcDMGuMNdgFk4A6W3xdfeJMIL1jgDcXeoIE3oMWwYeoWKCIiIiIVUtE7WGlJsCKVew8cXIjXhfAySrqa+d/0A7yh3J93zn2pBCs3ZDXBEhERERFJk0qdYJV6I7NGwE+AIcCZvpfCARQDbwEtgE4owarUlGCJJOacY/v2kt60GuhCRESk8sv6M1jJcs5td879yTnXBTgZGAdspeR5rRpAL6BjpmISEUmn7du306RJk8jiT7ZEREQkP2UswfJzzq1xzo0CjgP64E1OfICSZEtERERERCTnZCXBCnPOHXbOveqc+zHQCrgZ+CibMYmIiIiIiJRXVhMsP+fcNufco8657wOdgUezHZOIiIiIiEgqamQ7gGiccyuAX2Q7DhERERERkVRUmjtYIiIiIiIiuU4JloiIiIiISECUYImIiIiIiARECZaIiIiIiEhAKuUgF5J7NIGqyJHKXhe6TkRERCq/iv6+NudcQKFIVWJmxwMbsh2HiIiIiEiatXXObUy2sLoIioiIiIiIBEQJloiIiIiISEDURVDKxcwMaOPbFa+zaiNKdydsm6B8MqpynemqV3VWzTrTVa/qrJp1pqte1ak6g6ozXfWqzvyrs5Fv/b8uhaRJg1xIuYT+kyXVF9XLxUrZ7pwrqsj7V+U601Wv6qyadaarXtVZNetMV72qU3UGVWe66lWdeVlnud9PXQRFREREREQCogRLREREREQkIEqwREREREREAqIES0REREREJCAa5EIyYR9wd5lt1Vn55MrPnyt1pkO64syVc1qV2ylX6kxnvUHLlXOaK3WmQy79H82VOtMhV372jJ1PDdMukoPMrDFQ6NvVJIiRlSQ4aqPcoHbKDWqnyk9tlBvUTpmhLoIiIiIiIiIBUYIlIiIiIiISECVYIiIiIiIiAVGCJSIiIiIiEhAlWCIiIiIiIgHRMO0iuSlXhm6tytRGuUHtlBvUTpWf2ig3qJ0yQMO0i4iIiIiIBERdBEVERERERAKiBEtERERERCQgSrBEREREREQCogRLREREREQkIEqwREREREREAqIES0REREREJCBKsEQCZma1zKyXmT1gZvPN7HMz229mu8zsUzObYWY/NbOCJOpaYGYuhaV3kjGamQ00s9lmttHM9pnZVjN718xuMrP6FT8TlZuZPZ3iub0xyXovMrPnzOwTM9trZl+Z2QdmNsbMjk4xxirdTmY2JMU28i/dotSn6ylFZtbKzPqY2V1m9kro8yxyjspZZ6W9RsysppkNM7O5ZrbFvM/uLaHtYWZWM7WfNjOCaiczq2dmfc3sETN7z8y+NLMDZrbDzP5jZs+a2WVmVj3J+jakeM19L8l6q3o75cRnWa62UyCcc1q0aAloAUYC2wCXxPIJcG6C+hYkWVd46Z1EjC2At5OI7fRsn880t9XTKZ7bGxPUVw94IUEdXwIXJRlflW8nYEiKbeRfTohSn66n1M7//YnOUYr1VeprBDgRWJ6gvo+Ab2W7bdLRTniTz+5K8tr4COiQRJ0bUrzmvqd2SqquBSme14x/luVqOwW11EBEgtQZaBJaPwysBBYDnwHVgY7ApUBNoB3wppld5Jx7J4m67wMKE5T5ON6LZlYXeAU4PbRrLzAD+A/eh+uA0L/tgNfNrKtzbm0SseW6PwHrE5R5L9YLoW9z/waEvyU8BMwGVgCNgcvwzunRwEwzu8A594849amdPEuA25Is2wf4QWh9kXNuXYLyup4Sqx1l3zagaaoVVfZrxMxaAG8CbUO7tgN/BzYCxwM/BhoBnfA+t7s4575K9udPs6Da6Sy8JBi89vkQ7xr8IvQeZwIX4vV+6gQsNLNznXNrkqw/mWv583gvqp2iqnSfZTneTsHIdoanRUs+LXh/qG8FfgUcF6PMCcAqSr7BWQ8UxCi7wFeubQDx+b9B+4Qy3/Lj/XJ93Vfm3Wyf0zS21dO+n7NbBeu6zlfXN8AZZV6vAUwp0+a11E6Btuca3/kYFqOMrqfUfr7f4P2RNBbvD6x2of3l+ca9Ul8jeMlfuOxSoHmZ15uH9ofLTMt2+wTdTqHztR74OdAsRpnTgc2+uhclqHNDqv9X1E5J1VWpP8tyuZ0Ca+9sB6BFSz4twBlAnSTKtQF2+z5cfhijXGAfoqEPtD2++rrGKNcY71vEcLmLs31e09RWT/t+xm4VqKcW3h3KcF1XxChXE++OZrjc9WqnwNrybN952A00iFFO11Mw5zvVP9wr9TUCnIrX48CF6m0Xo1x73/seJokucjnWTmcDNZIod4bvfDng1DhlN6QSQ4L3VTuVHFNpP8vytZ1SXTTIhUiAnHNLnHN7kyj3X+Bl366u6Ysqoj9QJ7Q+3zn3z2iFnHNFwJO+XVelOa5cdwFwTGh9PfB8tELOuYPAON+uWOdV7ZS6ob716c65nRl4T7VT8ir7NXIlYKH155xzn8ao7xO8b+YJlR8Uo76c5Jz7h3PuUBLllgAf+HZl4vcXqJ3SRddTGijBEske/4dORftgJ6OPb/3FBGX9r/dOdsSoKsp/Xme60FdzMczC+6YOoKuZNU9Qn9opATOrAwz07ZqSobdWOyWvsl8j5a2vb4Ky+SzTv79A7ZQuup7SQAmWSPYc61v/JonyT4SGvN3nGzJ3amjIV0t8OJ196zEfHg9ZjfdQKkBDvFv5+ezXofO5x7zh9D8xs+fN7EozSzQYUNLn1Tm3A+/cgveNXceK1EfVa6do+uP97OD90bcwyeN0PWVOpb1GQgn6t1Ooz//6d80s2sAFVUGqv78ws5lmtjk0VHehma02swlmdn4Sx6qdYqs0n2VqpxJKsESywLw5sC717Yo5Qp3PxXij7xQADfCGQL0abySu982sXZz3awy09O2KO2Je6BvmT3y7vptEfLmsB975rIP3MG87vLsizwAfm1nnOMf6z02ikevKlil1XtVO5eLvHvh0grsjfrqeMqcyXyPfpuRvoW3OucIE9W2jZMS2asBJ8crnIzM7htLdApP5/QXeSJHH4j2T1xg4GRgBLDCzOWZ2VJxj1U6xVabPMrVTiIZpF8mO0ZR0q9gAzEtQfjXwPvBf4CDeL6kewHdCr58BfGBmZ4X6NZfl/wDdFfqWOJEtlHyz1TJewRz3Cd63aOuB/XgP/J4HfD/0+onAIjPr7px7339g6Nu2Rr5dnyXxflt862XPq9opBWZ2PNA9tOmAqUkequspQ3LgGvFvJxNbuL7wdBwt8QbmqEruo+Tvx3845+IOAY53bS7DG/I9PNjJ8UDP0L8AFwH/DF1z26LUoXaKrrJ9lqmdQpRgiWSYmZ0F3OHbdbtz7kCM4n8BRjjn/hOjrgHAU3jfBh4FzDCz7zvnDpcp6p99fXeSoe7xrTdI8phc8hrwqHNuWbQXzawb3h2sY/HubL1oZic55/znr+ys9smc23jnVe2UmiGUPEw93zm3MUF5XU+ZV9mvEbVlCsysH951B1AM/DLBIeOAWc65zVHqqo53B+thvLsvJ+A9QxntWRy1U2mV9bNM7RSiLoIiGWRmxwIz8X6ZAExxzr0Qq7xzbnKsD9DQ69PxJu4Mj/zUCe+ZlLLq+NZjJXNl7Y9xfF5wzr0QK7kKvb4A75vA8Dd6rYAbyhQre16SObfxzqvaKUmhZwuu9u1KOLiFrqesqOzXiNoySWZ2Kt4f9mG/dc4tjneMc+7xaMlV6LVi59yTwGDf7h+a2RlRiqudfCrxZ5naKUQJlkiGmFkz4A1Khiv+B3BjRet1zi0C/urbFe1D1D90fK0kqy7wrSccej4fhX6BPezbVfbclj0vyZzbeOdV7ZS88yl5wHoHiUerSoqup8BV9mtEbZkEM2sPzKHkDsUM4HdB1O2ce57Sg9PomgtAlj7L1E4hSrBEMsDMGuElVx1Cu5YClzjn9sQ+KiX+Py7PivL6Lt96vSTrrOtbz8S8QpWV/9yeXmZY2l1lyiZzbuOdV7VT8vyDW/wtmfnnUqDrKTiV/RpRWyZgZq2Bt/Du4oPXvXpQlO5mFaFrLj0yfV7VTiFKsETSzMwaAK9TMmjCCqCXc2577KNS5h/5J9q8MV/41uuHYkrEPwzvFzFL5T//ua2Ob84X59w+SroQQskfIPHEO69qpySYWX1Kfxsb9NxXup4CkgPXiH87mdgS1ZdXzKwVMB9oG9o1F+gf57nh8krlmlM7JS/Tn2VqpxAlWCJpFPpDcA7QJbRrNdAzxihJFRF3aOrQUKlbfbu+Fa986PkW//wWiUaJymeJhv32n5u45zVKmVLnVe2UtIGUfDu6JtFzIOWg6ylYlfkaWUPJxMZNzawJcYReD5c5DPw7XvlcZmYt8ZKrE0K7FgB9Q0lz0BJ9zqqdyifTn2VqpxAlWCJpYmZ1gVeBc0K7/g1c4Jz7Kg1v5//A+zJGmeW+9bMT1HcyJUMr76T0vBdVjf/cFgNlk+PlvvW459XMGlLSTdTh3c0sK+n6qLrt5O8eGPTdK9D1FLTlvvVKdY2Eupb6Bwvwz+8Ujf/91qQp2cg6M2uOl1yFJ419D+gdcFdcv7jXnNqp3DL6WaZ2KqEESyQNQrOZvwL8ILRrLdDDOZeu298/8q1/EKPMyzHKR9PPt/6Kc664XFHlB/+5WhrlXPjPa7/QN3yx/BCvmyHAYudctF94aqc4zOwE4NzQZjEwLQ1vo+spWJX9GilvfS8lKJuTQhP+vkXJJLL/xHtmONlht8sjnddcXrZTkrLxWaZ2AnDOadGiJcAFqA28ifftqwPWAcem8f264E0wGH6/K2KUa4E3Qo/DuxV/VoxyjSiZDNIBl2b7nGaxLb+FN8t8+FyMjlKmFvC5r8zAGHXVxPs2PlzuZ2qncrXJvb6f+ZU01K/rKfE5Cv8sLsnylfoaATqG6nF4c/e0jVGubej18Pt+L9ttEWQ7hY5pindHI3zs+0DDNMfZ3x8r0EXtFEi9Wfksy9d2Svn8ZzsALVryaQn9IfGq7wPoE6B1Oev6E3AL0CxOmX54XdbC77cSqBan/O99ZdcD3yrzet0y8S/K9jlNUzvdHVqOi1PmB8B/fediK1A/RtkbfOW+Bk4v83p1YJKvzKdALbVTyu1WrUyb9E/hWF1PwbVDef5wr9TXCPCCr+wS4Ogyrx8d2h8u89dst0PQ7YT3B/SHvuM+BBpX4P1nAMOABjFerwYMp+SPe0eCL03UTrnxWZaP7ZTqYqEfVEQCYGZPAdf6dj0ObEzi0E3OmwvEX9csvBntD+Hd2l+O14f6AN7oPD3w+kOHfYP3zd+6OPHVx5tv5LTQrr3AdLw+082BHwMtQ68VAmc759YkEX9OMbM/AjfhfbAvCy2fAfvwzsO5gH+yy314XTz/GaO+6njdIi4O7TqE191hJdAQuIySh4f34w108m6c+NROUZhZL7zpDsD7/97KJTmama6n1JnZ0cCTUV7yj+A4o+yLzrkBUeqq1NeImR0DLAbahHYVAX/HS+jbhOprHHrtU6CrS1+X75QE1U5mNhe4MLRZDDyAd+4SWeWcez1KXMvx7mbsxTu3K/Guq8N457QXJaMTgveHfRfn3Nex3kjtlBufZbncToHJdoanRUs+LXijLLlyLAui1DUrhePfJcZt+Cj1tkwizk+BM7N9PtPYTn9M4dyuAjomUWd9vF+O8er6iiS7iKmdop6T53w/+yMpHqvrKfXz3bY8n2dx6qvU1wjegA4rE9S3HDgx222TjnYCNpSnHuDpGHEtT6GOWcBRaqek2mlWCsdn7bMsV9spqEV3sEQCZGYLgPPLcehC51y3MnUdC5yHN8rOaXj9pI/GG556O7AJ7+Hj551zC1OM04DLgSuBTnjfUG3H+7ZqBjDJOZc3E/6VFXqIO3xuT8f71u8ovG/Sd+I9L7IYmAm86lL4oDSzS4DBeJM6tsTrY/4p3i/FiS76Q/ux6qrS7eRnZo3x2qV2aFdn59zyFI7X9ZQiM2uL9383Jc65eINYVOprxMxqhWK7HO8uQDO8OwAfA88DU13wc0BVSFDtZGYbgOPLEcJU59yQKHF9C++a64p3J6s53udsbbw7Gp8Ai4BnnHMfpfKGVbydcuazLBfbKShKsERERERERAKiYdpFREREREQCogRLREREREQkIEqwREREREREAqIES0REREREJCBKsERERERERAKiBEtERERERCQgSrBEREREREQCogRLREREREQkIEqwREREREREAqIES0REREREJCBKsERERERERAKiBEtERERERCQgSrBEREREREQCogRLREREREQkIEqwREREKhkzu8vMXLQlgzEMiRWDmbXNVBwiIrlGCZaIiEgUZtY2ToJRnqVtmuPd4HuvbkmU/66ZbfEds9LMWqQzRhGRqqBGtgMQERGRuP4ErA+yQjPrDLwBHB3a9QFwkXOu0FdsCXCbb/t6oH2QcYiI5CMlWCIiItFto3SCEc2DvvX7gMJYBUP1lcfzzrkF5Tz2CGbWFXgNaBzatRDo45zb6S/nnFsNrPYd1xslWCIiCSnBEhERicI5twMYF6+MmfkTrKeccxvSGlQFmVkP4CWgfmjXHKC/c25v9qISEckvegZLRESkCjCzS4FXKUmupgN9lVyJiARLCZaIiEieM7OBwEygdmjXVOAK59zB7EUlIpKflGCJiIjkMTMbCjwL1AztGg8Mdc4VZy8qEZH8pQRLREQkT5nZjcCfgeqhXfc75250zmVsPi0RkapGCZaIiEgeMrPRwGOAhXbd6Zy7I4shiYhUCRpFUEREJP+MAXqG1h1ws3Pu0SzGIyJSZegOloiISP7p6Vu/TcmViEjmKMESERHJb0PM7KhsByEiUlUowRIREck/dwK7QuvfA94ys2ZZjEdEpMpQgiUiIpJ/FgGXALtD26cC88ysafZCEhGpGpRgiYiI5CHn3LvApcCe0K5OwFwza5ytmEREqgIlWCIiInnKObcQ6APsDe06DXjTzBplLyoRkfymBEtERCSPOefmAz8E9oV2nQG8YWYNsxeViEj+UoIlIiKS55xz84C+lCRZZwGvm1mD7EUlIpKflGCJiIhUAc65N4EfAftDu7oCr5lZ/exFJSKSf5RgiYiIVBHOuTlAf+BAaNe5wKtmVjd7UYmI5BclWCIiIlWIc+5V4MfAwdCuHwCvmFmd7EUlIpI/lGCJiIhUMc652cBASpKs7sDLZlY7e1GJiOQHJVgiIiJVkHNuFvAT4FBo1wXAS0qyREQqpka2AxAREclVzjnLdgxhzrm25ThmBlAz+GhERKou3cESEREREREJiO5giYiIVG6Xm9np4Q3n3LhMvKmZdQAu9u1qnYn3FRHJdUqwREREKrfrymxnJMECzgAezNB7iYjkDXURFBERERERCYg557Idg4iIiIiISF7QHSwREREREZGAKMESEREREREJiBIsERERERGRgCjBEhERERERCYgSLBERERERkYAowRIREREREQmIEiwREREREZGAKMESEREREREJiBIsERERERGRgCjBEhERERERCYgSLBERERERkYAowRIREREREQmIEiwREREREZGAKMESEREREREJiBIsERERERGRgCjBEhERERERCcj/B3TORew5bwCXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_tr = rs_bcc.T_rs[np.argsort(rs_bcc.F_rs[rs_bcc.F_rs - rs_fcc.F_rs <= 0])][0]\n",
    "plt.axvline(T_tr, color='k', linestyle='--', label=f'T = {int(T_tr)} K')\n",
    "plt.plot(rs_bcc.T_rs, (rs_bcc.F_rs - rs_fcc.F_rs)*1000,'k', label='Reversible Scaling')\n",
    "plt.plot(fl_bcc.T_fl[1:], (fl_bcc.F_fl[1:] - fl_fcc.F_fl[1:])*1000, 'go', label='Frenkel-Ladd')\n",
    "plt.plot(fl_bcc.T_fl[0], (fl_bcc.F_fl[0] - fl_fcc.F_fl[0])*1000, 'ro', label='$\\mathrm{\\Delta G(T_{0})}$')\n",
    "plt.axhline(0,color='k', linestyle='-')\n",
    "plt.xlabel('T [K]')\n",
    "plt.ylabel('$\\mathrm{\\Delta G = G_{bcc} - G_{fcc}}$ [meV/atom]')\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f2660e",
   "metadata": {},
   "source": [
    "## pyiron job-table \n",
    "\n",
    "It is useful to look at the project job-table, as it has a lot of useful informations about the submitted jobs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fcbb1440",
   "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>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_100</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:14:34.784234</td>\n",
       "      <td>2021-06-30 06:14:46.353651</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_400</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_400</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:14:49.005037</td>\n",
       "      <td>2021-06-30 06:15:01.390647</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_700</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_700</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:15:04.114830</td>\n",
       "      <td>2021-06-30 06:15:16.516955</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_1000</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_1000</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:15:19.129336</td>\n",
       "      <td>2021-06-30 06:15:31.302686</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_1300</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_1300</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:15:33.819625</td>\n",
       "      <td>2021-06-30 06:15:45.340034</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>fl_parameterize_relax_bcc_T_1600</td>\n",
       "      <td>/fl_parameterize_relax_bcc_T_1600</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:15:47.857764</td>\n",
       "      <td>2021-06-30 06:15:59.319379</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_100</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:16:01.928499</td>\n",
       "      <td>2021-06-30 06:18:04.443586</td>\n",
       "      <td>122.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_400</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_400</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:18:05.615711</td>\n",
       "      <td>2021-06-30 06:20:16.545648</td>\n",
       "      <td>130.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_700</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_700</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:20:17.715152</td>\n",
       "      <td>2021-06-30 06:22:32.213172</td>\n",
       "      <td>134.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_1000</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_1000</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:22:33.401971</td>\n",
       "      <td>2021-06-30 06:24:43.640765</td>\n",
       "      <td>130.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_1300</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_1300</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:24:45.010246</td>\n",
       "      <td>2021-06-30 06:26:58.733559</td>\n",
       "      <td>133.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_frenkel_ladd_bcc_T_1600</td>\n",
       "      <td>/lmp_frenkel_ladd_bcc_T_1600</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:26:59.915241</td>\n",
       "      <td>2021-06-30 06:29:12.596718</td>\n",
       "      <td>132.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe6750</td>\n",
       "      <td>lmp_rs_bcc_100</td>\n",
       "      <td>/lmp_rs_bcc_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:29:13.871985</td>\n",
       "      <td>2021-06-30 06:33:33.065907</td>\n",
       "      <td>259.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_100</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:33:35.467338</td>\n",
       "      <td>2021-06-30 06:33:57.158523</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_400</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_400</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:34:01.612796</td>\n",
       "      <td>2021-06-30 06:34:25.748750</td>\n",
       "      <td>24.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_700</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_700</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:34:30.213931</td>\n",
       "      <td>2021-06-30 06:34:54.849564</td>\n",
       "      <td>24.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_1000</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_1000</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:34:59.296570</td>\n",
       "      <td>2021-06-30 06:35:24.100550</td>\n",
       "      <td>24.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_1300</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_1300</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:35:28.981987</td>\n",
       "      <td>2021-06-30 06:35:56.362304</td>\n",
       "      <td>27.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>fl_parameterize_relax_fcc_T_1600</td>\n",
       "      <td>/fl_parameterize_relax_fcc_T_1600</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:36:01.005472</td>\n",
       "      <td>2021-06-30 06:36:27.005017</td>\n",
       "      <td>25.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_100</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:36:31.443327</td>\n",
       "      <td>2021-06-30 06:40:17.794528</td>\n",
       "      <td>226.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_400</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_400</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:40:19.404031</td>\n",
       "      <td>2021-06-30 06:44:31.341318</td>\n",
       "      <td>251.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_700</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_700</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:44:32.892902</td>\n",
       "      <td>2021-06-30 06:48:54.748298</td>\n",
       "      <td>261.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_1000</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_1000</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:48:56.313786</td>\n",
       "      <td>2021-06-30 06:53:21.900080</td>\n",
       "      <td>265.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_1300</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_1300</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:53:23.516221</td>\n",
       "      <td>2021-06-30 06:57:52.651830</td>\n",
       "      <td>269.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_frenkel_ladd_fcc_T_1600</td>\n",
       "      <td>/lmp_frenkel_ladd_fcc_T_1600</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 06:57:54.199637</td>\n",
       "      <td>2021-06-30 07:02:30.070167</td>\n",
       "      <td>275.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>finished</td>\n",
       "      <td>Fe13500</td>\n",
       "      <td>lmp_rs_fcc_100</td>\n",
       "      <td>/lmp_rs_fcc_100</td>\n",
       "      <td>/home/sam/</td>\n",
       "      <td>programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/</td>\n",
       "      <td>2021-06-30 07:02:31.625871</td>\n",
       "      <td>2021-06-30 07:10:34.080323</td>\n",
       "      <td>482.0</td>\n",
       "      <td>pyiron@Lenovo#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    status chemicalformula                               job  \\\n",
       "0    1  finished          Fe6750   fl_parameterize_relax_bcc_T_100   \n",
       "1    2  finished          Fe6750   fl_parameterize_relax_bcc_T_400   \n",
       "2    3  finished          Fe6750   fl_parameterize_relax_bcc_T_700   \n",
       "3    4  finished          Fe6750  fl_parameterize_relax_bcc_T_1000   \n",
       "4    5  finished          Fe6750  fl_parameterize_relax_bcc_T_1300   \n",
       "5    6  finished          Fe6750  fl_parameterize_relax_bcc_T_1600   \n",
       "6    7  finished          Fe6750        lmp_frenkel_ladd_bcc_T_100   \n",
       "7    8  finished          Fe6750        lmp_frenkel_ladd_bcc_T_400   \n",
       "8    9  finished          Fe6750        lmp_frenkel_ladd_bcc_T_700   \n",
       "9   10  finished          Fe6750       lmp_frenkel_ladd_bcc_T_1000   \n",
       "10  11  finished          Fe6750       lmp_frenkel_ladd_bcc_T_1300   \n",
       "11  12  finished          Fe6750       lmp_frenkel_ladd_bcc_T_1600   \n",
       "12  13  finished          Fe6750                    lmp_rs_bcc_100   \n",
       "13  14  finished         Fe13500   fl_parameterize_relax_fcc_T_100   \n",
       "14  15  finished         Fe13500   fl_parameterize_relax_fcc_T_400   \n",
       "15  16  finished         Fe13500   fl_parameterize_relax_fcc_T_700   \n",
       "16  17  finished         Fe13500  fl_parameterize_relax_fcc_T_1000   \n",
       "17  18  finished         Fe13500  fl_parameterize_relax_fcc_T_1300   \n",
       "18  19  finished         Fe13500  fl_parameterize_relax_fcc_T_1600   \n",
       "19  20  finished         Fe13500        lmp_frenkel_ladd_fcc_T_100   \n",
       "20  21  finished         Fe13500        lmp_frenkel_ladd_fcc_T_400   \n",
       "21  22  finished         Fe13500        lmp_frenkel_ladd_fcc_T_700   \n",
       "22  23  finished         Fe13500       lmp_frenkel_ladd_fcc_T_1000   \n",
       "23  24  finished         Fe13500       lmp_frenkel_ladd_fcc_T_1300   \n",
       "24  25  finished         Fe13500       lmp_frenkel_ladd_fcc_T_1600   \n",
       "25  26  finished         Fe13500                    lmp_rs_fcc_100   \n",
       "\n",
       "                               subjob projectpath  \\\n",
       "0    /fl_parameterize_relax_bcc_T_100  /home/sam/   \n",
       "1    /fl_parameterize_relax_bcc_T_400  /home/sam/   \n",
       "2    /fl_parameterize_relax_bcc_T_700  /home/sam/   \n",
       "3   /fl_parameterize_relax_bcc_T_1000  /home/sam/   \n",
       "4   /fl_parameterize_relax_bcc_T_1300  /home/sam/   \n",
       "5   /fl_parameterize_relax_bcc_T_1600  /home/sam/   \n",
       "6         /lmp_frenkel_ladd_bcc_T_100  /home/sam/   \n",
       "7         /lmp_frenkel_ladd_bcc_T_400  /home/sam/   \n",
       "8         /lmp_frenkel_ladd_bcc_T_700  /home/sam/   \n",
       "9        /lmp_frenkel_ladd_bcc_T_1000  /home/sam/   \n",
       "10       /lmp_frenkel_ladd_bcc_T_1300  /home/sam/   \n",
       "11       /lmp_frenkel_ladd_bcc_T_1600  /home/sam/   \n",
       "12                    /lmp_rs_bcc_100  /home/sam/   \n",
       "13   /fl_parameterize_relax_fcc_T_100  /home/sam/   \n",
       "14   /fl_parameterize_relax_fcc_T_400  /home/sam/   \n",
       "15   /fl_parameterize_relax_fcc_T_700  /home/sam/   \n",
       "16  /fl_parameterize_relax_fcc_T_1000  /home/sam/   \n",
       "17  /fl_parameterize_relax_fcc_T_1300  /home/sam/   \n",
       "18  /fl_parameterize_relax_fcc_T_1600  /home/sam/   \n",
       "19        /lmp_frenkel_ladd_fcc_T_100  /home/sam/   \n",
       "20        /lmp_frenkel_ladd_fcc_T_400  /home/sam/   \n",
       "21        /lmp_frenkel_ladd_fcc_T_700  /home/sam/   \n",
       "22       /lmp_frenkel_ladd_fcc_T_1000  /home/sam/   \n",
       "23       /lmp_frenkel_ladd_fcc_T_1300  /home/sam/   \n",
       "24       /lmp_frenkel_ladd_fcc_T_1600  /home/sam/   \n",
       "25                    /lmp_rs_fcc_100  /home/sam/   \n",
       "\n",
       "                                                     project  \\\n",
       "0   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "1   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "2   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "3   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "4   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "5   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "6   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "7   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "8   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "9   programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "10  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "11  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "12  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "13  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "14  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "15  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "16  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "17  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "18  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "19  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "20  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "21  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "22  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "23  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "24  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "25  programs/ibrsam_git/FreeEnergyNoneqTI/reproduce_results/   \n",
       "\n",
       "                    timestart                   timestop  totalcputime  \\\n",
       "0  2021-06-30 06:14:34.784234 2021-06-30 06:14:46.353651          11.0   \n",
       "1  2021-06-30 06:14:49.005037 2021-06-30 06:15:01.390647          12.0   \n",
       "2  2021-06-30 06:15:04.114830 2021-06-30 06:15:16.516955          12.0   \n",
       "3  2021-06-30 06:15:19.129336 2021-06-30 06:15:31.302686          12.0   \n",
       "4  2021-06-30 06:15:33.819625 2021-06-30 06:15:45.340034          11.0   \n",
       "5  2021-06-30 06:15:47.857764 2021-06-30 06:15:59.319379          11.0   \n",
       "6  2021-06-30 06:16:01.928499 2021-06-30 06:18:04.443586         122.0   \n",
       "7  2021-06-30 06:18:05.615711 2021-06-30 06:20:16.545648         130.0   \n",
       "8  2021-06-30 06:20:17.715152 2021-06-30 06:22:32.213172         134.0   \n",
       "9  2021-06-30 06:22:33.401971 2021-06-30 06:24:43.640765         130.0   \n",
       "10 2021-06-30 06:24:45.010246 2021-06-30 06:26:58.733559         133.0   \n",
       "11 2021-06-30 06:26:59.915241 2021-06-30 06:29:12.596718         132.0   \n",
       "12 2021-06-30 06:29:13.871985 2021-06-30 06:33:33.065907         259.0   \n",
       "13 2021-06-30 06:33:35.467338 2021-06-30 06:33:57.158523          21.0   \n",
       "14 2021-06-30 06:34:01.612796 2021-06-30 06:34:25.748750          24.0   \n",
       "15 2021-06-30 06:34:30.213931 2021-06-30 06:34:54.849564          24.0   \n",
       "16 2021-06-30 06:34:59.296570 2021-06-30 06:35:24.100550          24.0   \n",
       "17 2021-06-30 06:35:28.981987 2021-06-30 06:35:56.362304          27.0   \n",
       "18 2021-06-30 06:36:01.005472 2021-06-30 06:36:27.005017          25.0   \n",
       "19 2021-06-30 06:36:31.443327 2021-06-30 06:40:17.794528         226.0   \n",
       "20 2021-06-30 06:40:19.404031 2021-06-30 06:44:31.341318         251.0   \n",
       "21 2021-06-30 06:44:32.892902 2021-06-30 06:48:54.748298         261.0   \n",
       "22 2021-06-30 06:48:56.313786 2021-06-30 06:53:21.900080         265.0   \n",
       "23 2021-06-30 06:53:23.516221 2021-06-30 06:57:52.651830         269.0   \n",
       "24 2021-06-30 06:57:54.199637 2021-06-30 07:02:30.070167         275.0   \n",
       "25 2021-06-30 07:02:31.625871 2021-06-30 07:10:34.080323         482.0   \n",
       "\n",
       "           computer hamilton hamversion parentid masterid  \n",
       "0   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "1   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "2   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "3   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "4   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "5   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "6   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "7   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "8   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "9   pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "10  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "11  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "12  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "13  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "14  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "15  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "16  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "17  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "18  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "19  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "20  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "21  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "22  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "23  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "24  pyiron@Lenovo#1   Lammps        0.1     None     None  \n",
       "25  pyiron@Lenovo#1   Lammps        0.1     None     None  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  }
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