lipid_gamd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import simtk\n",
    "from simtk.openmm.app import *\n",
    "from simtk.openmm import *\n",
    "from simtk.unit import *\n",
    "import numpy as np\n",
    "from simtk import unit\n",
    "import copy\n",
    "sys.path.append('../')\n",
    "\n",
    "import itertools\n",
    "import custom_forces\n",
    "import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "topology.Topology.loadBondDefinitions('./popc.xml')\n",
    "topology.Topology.loadBondDefinitions('./chl1.xml')\n",
    "output_directory = './'\n",
    "\n",
    "#Load up restart files:\n",
    "pdb = PDBFile('./step5_assembly.pdb')\n",
    "saved_state = XmlSerializer.deserialize(open('lipid_equilibrated_state.xml').read())\n",
    "saved_system = XmlSerializer.deserialize(open('lipid_equilibrated_system.xml').read())\n",
    "\n",
    "####\n",
    "###Setup\n",
    "####\n",
    "#after equilibration, standardMD and GST-MD will run for this many seconds:\n",
    "number_ns = 200\n",
    "one_ns = int(5e5)\n",
    "number_steps = number_ns*one_ns\n",
    "dcdstride = 50000\n",
    "\n",
    "TEMPERATURE = 323*kelvin\n",
    "FRICTION = 1/picosecond\n",
    "TIMESTEP = 2*femtoseconds\n",
    "\n",
    "SIGMA0 = (MOLAR_GAS_CONSTANT_R * TEMPERATURE ).value_in_unit(kilojoule_per_mole) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanning forces:\n",
      "0 <class 'simtk.openmm.openmm.HarmonicBondForce'>\n",
      "0 <class 'simtk.openmm.openmm.HarmonicAngleForce'>\n",
      "0 <class 'simtk.openmm.openmm.NonbondedForce'>\n",
      "Found the torsions - setting group to 2\n",
      "2 <class 'simtk.openmm.openmm.PeriodicTorsionForce'>\n",
      "0 <class 'simtk.openmm.openmm.CMMotionRemover'>\n"
     ]
    }
   ],
   "source": [
    "forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')\n",
    "pdb.topology.setPeriodicBoxVectors((Vec3(5.75760367, 0.0, 0.0),\n",
    "                                           Vec3(0, 5.75760367, 0.0),\n",
    "                                           Vec3(0.0, 0.0, 6.0)))\n",
    "system = forcefield.createSystem(pdb.topology, nonbondedMethod=CutoffPeriodic,\n",
    "        nonbondedCutoff=1*nanometer, constraints=HBonds)\n",
    "\n",
    "def set_dihedral_force_group(system, g=2):\n",
    "  \"\"\"Sets the dihedral forcegroup to a number other than 0,\n",
    "  which will be used by serial tempering\"\"\"\n",
    "  print('Scanning forces:')\n",
    "  for f in system.getForces():\n",
    "    if isinstance(f, PeriodicTorsionForce):\n",
    "      print('Found the torsions - setting group to 2')\n",
    "      f.setForceGroup(2)\n",
    "    else:\n",
    "      f.setForceGroup(0)\n",
    "    print(f.getForceGroup(), f.__class__)\n",
    "\n",
    "set_dihedral_force_group(system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomGaMDLangevinIntegrator(CustomIntegrator):\n",
    "    def __init__(self, temperature, friction, dt, ktot, Etot, kgrp, Egrp, forceGroup):\n",
    "        self.ktot = ktot \n",
    "        self.Etot = Etot \n",
    "        self.kgrp = kgrp\n",
    "        self.Egrp = Egrp\n",
    "        self.forceGroup = str(forceGroup)\n",
    "        \n",
    "        CustomIntegrator.__init__(self, dt)\n",
    "            #lew added:\n",
    "        self.addGlobalVariable(\"ktot\", self.ktot)\n",
    "        self.addGlobalVariable(\"Etot\", self.Etot)\n",
    "        self.addGlobalVariable(\"kgrp\", self.ktot)\n",
    "        self.addGlobalVariable(\"Egrp\", self.Egrp)\n",
    "        self.addGlobalVariable(\"groupEnergy\", 0)\n",
    "        \n",
    "        self.addGlobalVariable(\"allEnergy\", 0)\n",
    "\n",
    "        \n",
    "            #normal langevin:  \n",
    "        self.addGlobalVariable(\"temperature\", temperature);\n",
    "        self.addGlobalVariable(\"friction\", friction);\n",
    "        self.addGlobalVariable(\"vscale\", 0);\n",
    "        self.addGlobalVariable(\"fscale\", 0);\n",
    "        self.addGlobalVariable(\"noisescale\", 0);\n",
    "        self.addPerDofVariable(\"x0\", 0);\n",
    "        \n",
    "        self.addPerDofVariable(\"fgrp\", 0)\n",
    "        \n",
    "            #normal langevin:                                                                  \n",
    "        self.addUpdateContextState();\n",
    "        \n",
    "        self.addComputeGlobal(\"groupEnergy\", \"energy\"+self.forceGroup)\n",
    "        self.addComputePerDof(\"fgrp\", \"f\"+self.forceGroup)\n",
    "        \n",
    "        self.addComputeGlobal(\"vscale\", \"exp(-dt*friction)\");\n",
    "        self.addComputeGlobal(\"fscale\", \"(1-vscale)/friction\");\n",
    "        #original line:                \n",
    "        self.addComputeGlobal(\"noisescale\", \"sqrt(kT*(1-vscale*vscale)); kT=0.00831451*temperature\");\n",
    "        self.addComputePerDof(\"x0\", \"x\");\n",
    "            #original langevin line:                                                                                      \n",
    "        #self.addComputePerDof(\"v\", \"vscale*v + fscale*f/m + noisescale*gaussian/sqrt(m)\");  \n",
    "            #GaMD:\n",
    "        dof_string = \"vscale*v + fscale*fprime/m + noisescale*gaussian/sqrt(m);\"\n",
    "        dof_string+= \"fprime= fprime1 + fprime2;\"\n",
    "        #fprime2 is the dihedral force modified by the boost. Boot calculated using group only. \n",
    "        dof_string+= \"fprime2 = fgrp*((1-modifyGroup) + modifyGroup* (1 - kgrp*(Egrp - groupEnergy)) ) ;\"\n",
    "        #fprime1 is the other forces modified by the boost, but the boost is calculated using TOTAL energy. \n",
    "        dof_string+= \"fprime1 = ftot*((1-modifyTotal) + modifyTotal* (1 - ktot*(Etot - energy)) );\"\n",
    "        \n",
    "        dof_string+= \"ftot=f-fgrp;\"\n",
    "        dof_string+= \"modifyGroup=step(Egrp-groupEnergy);\"\n",
    "        dof_string+= \"modifyTotal=step(Etot-energy);\"\n",
    "        self.addComputePerDof(\"v\", dof_string); \n",
    "            #normal langevin                                            \n",
    "        self.addComputePerDof(\"x\", \"x+dt*v\");\n",
    "        self.addConstrainPositions();\n",
    "        self.addComputePerDof(\"v\", \"(x-x0)/dt\");\n",
    "        self.addComputePerDof(\"veloc\", \"v\")\n",
    "        \n",
    "        \n",
    "        #after integration, record the values for fast retrieval:\n",
    "        self.addComputeGlobal(\"groupEnergy\", \"energy\"+self.forceGroup)\n",
    "        self.addComputeGlobal(\"allEnergy\", \"energy\")\n",
    "\n",
    "\n",
    "        \n",
    "    def setKtot(self, newK):\n",
    "        if not is_quantity(newK):\n",
    "            newK = newK/kilojoules_per_mole\n",
    "        self.setGlobalVariableByName('ktot', newK)\n",
    "        \n",
    "    def setEtot(self, newE):\n",
    "        if not is_quantity(newE):\n",
    "            newE = newE*kilojoules_per_mole\n",
    "        self.setGlobalVariableByName('Etot', newE)\n",
    "        \n",
    "    def setKgrp(self, newK):\n",
    "        if not is_quantity(newK):\n",
    "            newK = newK/kilojoules_per_mole\n",
    "        self.setGlobalVariableByName('kgrp', newK)\n",
    "        \n",
    "    def setEgrp(self, newE):\n",
    "        #if not is_quantity(newE):\n",
    "        #    newE = newE*kilojoules_per_mole\n",
    "        #    print(newE)\n",
    "        self.setGlobalVariableByName('Egrp', newE)\n",
    "          \n",
    "    def getGrpBoost(self, grpEnergy):\n",
    "        kgrp = self.getGlobalVariableByName('kgrp')/kilojoules_per_mole\n",
    "        Egrp = self.getGlobalVariableByName('Egrp')*kilojoules_per_mole\n",
    "        if not is_quantity(grpEnergy):\n",
    "            grpEnergy = grpEnergy*kilojoules_per_mole # Assume kJ/mole\n",
    "        if (grpEnergy > Egrp):\n",
    "            return 0*kilojoules_per_mole #no boosting\n",
    "        return ( 0.5 * kgrp * (Egrp-grpEnergy)**2 ) # 'k' parameter should instead be per kj/mol\n",
    "    \n",
    "    def getTotBoost(self, totEnergy):\n",
    "        ktot = self.getGlobalVariableByName('ktot')/kilojoules_per_mole\n",
    "        Etot = self.getGlobalVariableByName('Etot')*kilojoules_per_mole\n",
    "        if not is_quantity(totEnergy):\n",
    "            totEnergy = totEnergy*kilojoules_per_mole # Assume kJ/mole\n",
    "        if (totEnergy > Etot):\n",
    "            return 0*kilojoules_per_mole #no boosting\n",
    "        return ( 0.5 * ktot * (Etot-totEnergy)**2 ) # 'k' parameter should instead be per kj/mol\n",
    "        \n",
    "    def getEffectiveEnergy(self, totEnergy, grpEnergy):\n",
    "        if not is_quantity(totEnergy):\n",
    "            totEnergy = totEnergy*kilojoules_per_mole # Assume kJ/mole\n",
    "        if not is_quantity(grpEnergy):\n",
    "            grpEnergy = grpEnergy*kilojoules_per_mole # Assume kJ/mole\n",
    "        \n",
    "        group_boost = self.getGrpBoost(grpEnergy)\n",
    "        total_boost = self.getTotBoost(totEnergy)\n",
    "        \n",
    "        return totEnergy + group_boost + total_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "integrator = CustomGaMDLangevinIntegrator(TEMPERATURE,\n",
    "                                         FRICTION,\n",
    "                                         TIMESTEP,\n",
    "                                         1,\n",
    "                                         -1e6*kilojoules_per_mole,\n",
    "                                         1,\n",
    "                                         -1e6*kilojoules_per_mole,\n",
    "                                         2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instantiating simulation object:\n",
      "loading state:\n",
      "setting positions:\n",
      "setting velocities:\n",
      "setting periodic box:\n"
     ]
    }
   ],
   "source": [
    "print('instantiating simulation object:')\n",
    "sim = Simulation(pdb.topology, system, integrator)\n",
    "print('loading state:')\n",
    "\n",
    "print('setting positions:')\n",
    "sim.context.setPositions(saved_state.getPositions())\n",
    "print('setting velocities:')\n",
    "sim.context.setVelocities(saved_state.getVelocities())\n",
    "print('setting periodic box:')\n",
    "sim.context.setPeriodicBoxVectors(*saved_state.getPeriodicBoxVectors())\n",
    "#simulation.loadState('lipid_equilibrated_state.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_statistics(pe_trace, boost_trace=0):\n",
    "    arr = np.array(pe_trace)\n",
    "    arr = arr + np.array(boost_trace)\n",
    "    Vmax = arr.max()\n",
    "    Vmin = arr.min()\n",
    "    Vavg = arr.mean()\n",
    "    Vstd = np.std(arr)\n",
    "\n",
    "    return [Vmax, Vmin, Vavg, Vstd]\n",
    "\n",
    "def calc_parameters(Vmax, Vmin, Vavg, Vstd, mode='low', sigma_0=SIGMA0, verbose=False):\n",
    "    E = Vmax\n",
    "    k_0 = min(1, sigma_0/Vstd * ((Vmax-Vmin)/(Vmax-Vavg)))\n",
    "    k = k_0 * (1 / (Vmax - Vmin) )\n",
    "\n",
    "    if verbose:\n",
    "        return E, k, k_0\n",
    "    else:\n",
    "        return E, k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "equilibration = dict()\n",
    "equilibration['grp_nrgs'] = list()\n",
    "equilibration['tot_nrgs'] = list()\n",
    "\n",
    "equilibration['grp_boosts'] = list()\n",
    "equilibration['tot_boosts'] = list()\n",
    "\n",
    "def report(sim, recorder, record_boost=False):\n",
    "    #grp_nrg = sim.context.getState(getEnergy=True, groups={2}).getPotentialEnergy()/kilojoules_per_mole\n",
    "    #tot_nrg = sim.context.getState(getEnergy=True,).getPotentialEnergy()/kilojoules_per_mole\n",
    "    grp_nrg = sim.integrator.getGlobalVariableByName(\"groupEnergy\")\n",
    "    tot_nrg = sim.integrator.getGlobalVariableByName(\"allEnergy\")\n",
    "    recorder['grp_nrgs'].append(grp_nrg)\n",
    "    recorder['tot_nrgs'].append(tot_nrg)\n",
    "    \n",
    "    if record_boost:\n",
    "        grp_boost = sim.integrator.getGrpBoost(grp_nrg)/kilojoule_per_mole\n",
    "        tot_boost = sim.integrator.getTotBoost(tot_nrg)/kilojoule_per_mole\n",
    "        recorder['grp_boosts'].append(grp_boost)\n",
    "        recorder['tot_boosts'].append(tot_boost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10788\r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-82173af3464e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msteps_per_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m500000\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps_per_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mequilibration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mnum_steps\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0msteps_per_iter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;34m\"\"\"Advance the simulation by integrating a specified number of time steps.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendStep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrunForClockTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstateFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointInterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36m_simulate\u001b[0;34m(self, endStep, endTime)\u001b[0m\n\u001b[1;32m    200\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mendTime\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mendTime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstepsToGo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstepsToGo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0manyReport\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/openmm.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m  11764\u001b[0m             \u001b[0mthe\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtime\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mto\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11765\u001b[0m         \"\"\"\n\u001b[0;32m> 11766\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCustomIntegrator_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  11767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11768\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgetPerDofVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "num_steps = 0 \n",
    "steps_per_iter = 1\n",
    "while num_steps / 500000 < 0.05:\n",
    "    sim.step(steps_per_iter)\n",
    "    report(sim, equilibration)\n",
    "    num_steps+=steps_per_iter\n",
    "    print(num_steps, end='\\r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f843b56bf50>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(equilibration['grp_nrgs'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# initial estimate of parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Etot: -416469.0376032861\n",
      "ktot: 9.462818474970943e-06\n",
      "Egrp: 31932.34375\n",
      "kgrp: 0.0001576235666710725\n"
     ]
    }
   ],
   "source": [
    "#if doing total boost as well:\n",
    "Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration['tot_nrgs'],0)\n",
    "E,k = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0=SIGMA0*5)\n",
    "print('Etot:',E)\n",
    "print('ktot:',k)\n",
    "sim.integrator.setEtot(E)\n",
    "sim.integrator.setKtot(k)\n",
    "\n",
    "#for dihedral boost:\n",
    "Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration['grp_nrgs'], 0)\n",
    "E,k = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0=SIGMA0*7)\n",
    "print('Egrp:',E)\n",
    "print('kgrp:',k)\n",
    "sim.integrator.setEgrp(E)\n",
    "sim.integrator.setKgrp(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iteratively update parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "equilibration2 = dict()\n",
    "equilibration2['grp_nrgs'] = list()\n",
    "equilibration2['tot_nrgs'] = list()\n",
    "\n",
    "equilibration2['grp_boosts'] = list()\n",
    "equilibration2['tot_boosts'] = list()\n",
    "\n",
    "ks = list()\n",
    "k_0s = list()\n",
    "vmaxs = list()\n",
    "\n",
    "gks = list()\n",
    "gk_0s = list()\n",
    "gvmaxs = list()\n",
    "\n",
    "num_steps = 5\n",
    "steps_per_iter = 5\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-414653.88 1.3424166510362146e-05 0.045727233915511034\n",
      "\t 32228.443359375 0.0003328408554131209 0.3188394380091856\n",
      "-414287.98 1.1208160055071607e-05 0.04227990514173325\n",
      "\t 32298.28515625 0.00029751593497915604 0.3057795573956062\n",
      "-414010.76 9.456828151627994e-06 0.038295065421330335\n",
      "\t 32310.71875 0.00030779964507377455 0.32017595733662146\n",
      "-414010.76 1.0039681896097422e-05 0.04065530946062607\n",
      "\t 32322.42578125 0.000322851385370574 0.33961254413738634\n",
      "-414010.76 1.0659866827580854e-05 0.04316672471991602\n",
      "\t 32334.279296875 0.0003314977829840369 0.3526372424468244\n",
      "-414010.76 1.130833248739579e-05 0.045792661711466835\n",
      "\t 32334.279296875 0.0003537144585747474 0.3762706651084205\n",
      "-413903.63 1.1084264618217366e-05 0.046072709256444716\n",
      "\t 32361.6171875 0.00034426544370458567 0.3756305820000752\n",
      "-413709.6 1.0113686170857777e-05 0.0440007867535873\n",
      "\t 32380.45703125 0.000340324211913147 0.37774192970325104\n",
      "-413709.6 1.0422483391657592e-05 0.04534424555119805\n",
      "\t 32428.4453125 0.0003091316109654924 0.35795448214807035\n",
      "-413633.26 1.0344691022593115e-05 0.04579555316263042\n",
      "\t 32452.95703125 0.0002998345347910639 0.35453852691749277\n",
      "-413596.16 1.0482028010563557e-05 0.04679235650418977\n",
      "\t 32452.95703125 0.00030772812907091574 0.3638722859189807\n",
      "-413596.16 1.0709035220819192e-05 0.04780572932675792\n",
      "\t 32452.95703125 0.0003168324179899379 0.3746376274905599\n",
      "-413596.16 1.1039799470550305e-05 0.04928227934901139\n",
      "\t 32452.95703125 0.0003268291660997952 0.38645825499533953\n",
      "-413023.69 8.418341403972771e-06 0.042399182001216874\n",
      "\t 32452.95703125 0.0003363657575981119 0.3977347715713342\n",
      "-413023.69 8.482180468100168e-06 0.042720709006221394\n",
      "\t 32480.77734375 0.0003186698836702444 0.38567582951478896\n",
      "-413023.69 8.64328000026692e-06 0.04353209073296219\n",
      "\t 32551.63671875 0.000273224633759632 0.3500354435821736\n",
      "-412992.29 8.587139507946095e-06 0.04351902762298101\n",
      "\t 32551.63671875 0.00027542635928606764 0.3528561335056528\n",
      "-412992.29 8.659327074448894e-06 0.04388486920477482\n",
      "\t 32551.63671875 0.0002792205721226468 0.35771700185058236\n",
      "-412992.29 8.775015816930602e-06 0.044471171730204914\n",
      "\t 32551.63671875 0.0002832749544700632 0.36291118036919334\n",
      "-412992.29 8.914693205056302e-06 0.045179047048466994\n",
      "\t 32551.63671875 0.0002875831977656904 0.3684305869918461\n",
      "-412992.29 9.056678767156642e-06 0.04589862003239154\n",
      "\t 32576.423828125 0.0002752780978473287 0.35948954009970824\n",
      "-412992.29 9.157696165099574e-06 0.04641056920096068\n",
      "\t 32576.423828125 0.0002792532514120208 0.3646807490552039\n",
      "-412992.29 9.303422826875037e-06 0.04714910181864352\n",
      "\t 32576.423828125 0.000284033484298949 0.37092332242202647\n",
      "-412992.29 9.444169178092865e-06 0.047862394567738206\n",
      "\t 32576.423828125 0.000286909646308618 0.3746793428471662\n",
      "-412992.29 9.578088527444609e-06 0.048541088544736856\n",
      "\t 32581.8984375 0.00028678494340910187 0.37608652711638657\n",
      "-412992.29 9.701163380792211e-06 0.04916482336790729\n",
      "\t 32654.53125 0.00024658685153434106 0.341281501203962\n",
      "-412992.29 9.844633484889585e-06 0.04989191990773968\n",
      "\t 32654.53125 0.0002474125406729018 0.34242427271421255\n",
      "-412992.29 9.986751622989504e-06 0.050612164777637726\n",
      "\t 32654.53125 0.00024896344671673895 0.3445707600048916\n",
      "-412992.29 1.010656410082564e-05 0.05121936610791816\n",
      "\t 32654.53125 0.000249951981911388 0.3459389139560191\n",
      "-412992.29 1.018335957719713e-05 0.05160856025742957\n",
      "\t 32654.53125 0.00025244395627786504 0.3493878600269925\n",
      "-412992.29 1.0244880142529227e-05 0.05192034221690414\n",
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      "\t 32729.7109375 0.00027421441803343827 0.40013400115809344\n",
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      "-412379.19 9.314631740002295e-06 0.05291665031653126\n",
      "\t 32736.197265625 0.00027345327687560697 0.4007970507664679\n",
      "-412379.19 9.36005887214415e-06 0.053174722963258654\n",
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      "\t 32794.48828125 0.0002436857301351443 0.3713718181956197\n",
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      "\t 32794.48828125 0.00024374307933569574 0.37145921714536145\n"
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      "-411944.6 9.24538882534297e-06 0.05654126782771243\n",
      "\t 32825.3515625 0.000277824713563697 0.43197347788575186\n",
      "-411944.6 9.260143387537655e-06 0.056631501096262866\n",
      "\t 32825.3515625 0.0002785994080105191 0.4331780051943558\n",
      "-411944.6 9.277486766373402e-06 0.05673756657888502\n",
      "\t 32825.3515625 0.00027937838552323323 0.43438919199284326\n",
      "-411944.6 9.289256648921965e-06 0.05680954668637978\n",
      "\t 32825.3515625 0.00028008030342817244 0.4354805632920253\n",
      "-411944.6 9.298061905676377e-06 0.05686339627452022\n",
      "\t 32825.3515625 0.0002807543656496482 0.4365286234101742\n",
      "-411944.6 9.316411215006936e-06 0.056975613643947536\n",
      "\t 32825.3515625 0.00028145071622711547 0.43761133839588523\n",
      "-411908.51 9.171925531518627e-06 0.05642304356081233\n",
      "\t 32825.3515625 0.0002821466679986117 0.43869343330140226\n",
      "-411908.51 9.191074498730141e-06 0.056540842490536196\n",
      "\t 32825.3515625 0.00028286283903966147 0.4398069659723983\n",
      "-411908.51 9.209919770466605e-06 0.056656773173188145\n",
      "\t 32825.3515625 0.00028340345961273556 0.44064754543767287\n",
      "-411908.51 9.223631615364732e-06 0.056741124492802275\n",
      "\t 32825.3515625 0.000283958564425079 0.44151064560361303\n",
      "-411908.51 9.243413310398248e-06 0.05686281578180667\n",
      "\t 32825.3515625 0.00028451522433646043 0.4423761637025415\n",
      "-411908.51 9.264499449390294e-06 0.056992531634251074\n",
      "\t 32825.3515625 0.00028520717614156005 0.44345203929310406\n",
      "-411908.51 9.28472562674901e-06 0.057116957250470675\n",
      "\t 32825.3515625 0.0002858938486631055 0.44451970643291944\n",
      "-411908.51 9.29809603945736e-06 0.057199208177615354\n",
      "\t 32825.3515625 0.00028659791089538346 0.4456144118218818\n",
      "-411908.51 9.31400976471194e-06 0.05729710483085009\n",
      "\t 32825.3515625 0.00028723692260653746 0.4466079739414505\n",
      "-411908.51 9.328465963131992e-06 0.05738603519889281\n",
      "\t 32825.3515625 0.00028790504056382517 0.44764679166916616\n",
      "-411908.51 9.345069576635771e-06 0.05748817584588905\n",
      "\t 32825.3515625 0.00028851591867714985 0.4485966104948289\n",
      "-411908.51 9.36602530060399e-06 0.057617089422677695\n",
      "\t 32825.3515625 0.0002891877362043718 0.4496411804685226\n",
      "-411890.19 9.303034210810605e-06 0.05739994819914486\n",
      "\t 32825.3515625 0.00028975849053810833 0.45052861316448395\n",
      "-411890.19 9.315547401103265e-06 0.057477154888739744\n",
      "\t 32825.3515625 0.0002904333326931768 0.4515778859559846\n",
      "-411890.19 9.329647115705302e-06 0.05756415047205591\n",
      "\t 32825.3515625 0.00029109263634165847 0.4526029988279253\n",
      "-411890.19 9.340258008589044e-06 0.057629619940196275\n",
      "\t 32825.3515625 0.0002916402572203403 0.45345446266097555\n",
      "-411890.19 9.361085489866128e-06 0.05775812600814495\n",
      "\t 32846.0859375 0.00027854027732519815 0.4388614248957859\n",
      "-411890.19 9.370408767777548e-06 0.05781565086047107\n",
      "\t 32846.0859375 0.0002790312237405658 0.43963494837133044\n",
      "-411890.19 9.384319871369969e-06 0.05790148270925327\n",
      "\t 32846.0859375 0.00027949003429279004 0.44035783934652417\n",
      "-411890.19 9.404382355539245e-06 0.058025268726370614\n",
      "\t 32846.0859375 0.0002800581283150942 0.44125291475356165\n",
      "-411890.19 9.420998383680512e-06 0.058127790025655525\n",
      "\t 32846.0859375 0.00028060845441087574 0.4421199954388804\n",
      "-411890.19 9.434950453770922e-06 0.05821387463873044\n",
      "\t 32846.0859375 0.00028103002440370647 0.44278421107592814\n",
      "-411890.19 9.440772141143073e-06 0.05824979459194364\n",
      "\t 32846.0859375 0.00028140261935722944 0.443371262807844\n",
      "-411890.19 9.441874664126263e-06 0.058256597185666226\n",
      "\t 32846.0859375 0.0002818277509003954 0.4440410899387481\n",
      "-411890.19 9.452456787175874e-06 0.05832188914322765\n",
      "\t 32846.0859375 0.0002823542102344701 0.44487056672293307\n",
      "-411890.19 9.470282639097784e-06 0.058431875084775\n",
      "\t 32846.0859375 0.0002828677861973807 0.44567974477445066\n",
      "-411890.19 9.481029327059791e-06 0.058498182411863484\n",
      "\t 32846.0859375 0.0002834821872240204 0.44664778039428216\n",
      "-411890.19 9.493399178322208e-06 0.058574504696142386\n",
      "\t 32846.0859375 0.00028388567656128247 0.44728350858117677\n",
      "-411890.19 9.513046892891576e-06 0.05869573157470238\n",
      "\t 32846.0859375 0.000284408112159952 0.44810664566372055\n",
      "-411890.19 9.529406091521854e-06 0.058796668229634405\n",
      "\t 32846.0859375 0.0002848956603537562 0.4488748149823303\n",
      "-411890.19 9.549004160803903e-06 0.05891758879555962\n",
      "\t 32846.0859375 0.0002852516730146821 0.44943574004909354\n",
      "-411890.19 9.563588030178429e-06 0.05900757162564189\n",
      "\t 32846.0859375 0.00028568423255193823 0.45011727055051853\n",
      "-411890.19 9.581272573035647e-06 0.05911668568681026\n",
      "\t 32846.0859375 0.00028608412888530866 0.45074733768608266\n",
      "-411890.19 9.598245217551324e-06 0.05922140731783149\n",
      "\t 32846.0859375 0.00028650921227686726 0.4514170889504614\n",
      "-411890.19 9.614720680302617e-06 0.059323061325228583\n",
      "\t 32846.0859375 0.0002869666948779492 0.45213788763747303\n",
      "-411890.19 9.628011547701592e-06 0.059405066301555155\n",
      "\t 32846.0859375 0.0002874149459818338 0.45284414139743545\n",
      "-411890.19 9.64674466681828e-06 0.05952065010384375\n",
      "\t 32846.0859375 0.00028781130196787384 0.4534686304460926\n",
      "-411890.19 9.651098114009472e-06 0.059547510979295726\n",
      "\t 32846.0859375 0.0002883145458207729 0.45426152947123233\n",
      "-411890.19 9.661549676868817e-06 0.05961199737729579\n",
      "\t 32846.0859375 0.0002887407185149694 0.4549329970148954\n",
      "-411890.19 9.672325472647853e-06 0.05967848430032596\n",
      "\t 32846.0859375 0.000289091245695743 0.45548527928981897\n",
      "-411890.19 9.69002986867398e-06 0.059787720856031186\n",
      "\t 32846.0859375 0.00028960711571847587 0.45629807180733784\n",
      "-411890.19 9.706879738609153e-06 0.05989168496489337\n",
      "\t 32846.0859375 0.00029016632645696483 0.4571791509240351\n",
      "-411890.19 9.718408688317005e-06 0.05996281886605136\n",
      "\t 32846.0859375 0.00029072204231705403 0.4580547235935891\n",
      "-411890.19 9.737531074421615e-06 0.060080804455158746\n",
      "\t 32846.0859375 0.0002912490316663761 0.4588850354571706\n",
      "-411890.19 9.753159409740948e-06 0.06017723166561941\n",
      "\t 32846.0859375 0.0002918477072558465 0.4598282944528474\n",
      "-411890.19 9.770353019315253e-06 0.06028331665644687\n",
      "\t 32846.0859375 0.00029243646688371537 0.4607559300956668\n",
      "-411890.19 9.786649501299277e-06 0.06038386634814039\n",
      "\t 32846.0859375 0.0002929933198027511 0.4616332942882071\n",
      "-411890.19 9.80308309980113e-06 0.060485262052096395\n",
      "\t 32846.0859375 0.00029346405463300586 0.4623749728717769\n",
      "-411890.19 9.815778794094055e-06 0.060563594795828464\n",
      "\t 32846.0859375 0.00029400652686050317 0.4632296791893408\n",
      "-411890.19 9.8298042717736e-06 0.060650132335520475\n",
      "\t 32846.0859375 0.0002945186857426546 0.4640366245221754\n",
      "-411890.19 9.845964282280235e-06 0.06074983999486677\n",
      "\t 32846.0859375 0.0002951054904969983 0.4649611801128422\n",
      "-411890.19 9.84973917040956e-06 0.060773131146782455\n",
      "\t 32846.0859375 0.0002956502148949286 0.4658194348964838\n",
      "-411890.19 9.855174074277155e-06 0.06080666463632985\n",
      "\t 32846.0859375 0.00029626609455735993 0.4667898002196409\n",
      "-411890.19 9.87344679575169e-06 0.0609194077739265\n",
      "\t 32846.0859375 0.00029685057296135824 0.46771069046812747\n",
      "-411890.19 9.888101325556589e-06 0.061009826580589226\n",
      "\t 32846.0859375 0.0002973652803676142 0.4685216511948241\n",
      "-411890.19 9.902885646657722e-06 0.06110104620271836\n",
      "\t 32846.0859375 0.00029793684007453067 0.46942218705196875\n",
      "-411890.19 9.920589290594391e-06 0.06121027811801327\n",
      "\t 32846.0859375 0.0002983932316605754 0.47014126676168055\n",
      "-411890.19 9.931219661011877e-06 0.06127586776301236\n",
      "\t 32846.0859375 0.00029889520586271915 0.47093216535533894\n",
      "-411890.19 9.937629760754485e-06 0.06131541823491641\n",
      "\t 32846.0859375 0.0002993870153017638 0.47170704859042795\n",
      "-411890.19 9.943903917605796e-06 0.06135412993586232\n",
      "\t 32846.0859375 0.00029994536454251166 0.47258677035180896\n",
      "-411890.19 9.960898665464504e-06 0.06145898794504967\n",
      "\t 32846.0859375 0.00030051216671890047 0.47347981035719966\n",
      "-411890.19 9.97693380351459e-06 0.061557925138290503\n",
      "\t 32846.0859375 0.0003010917990174351 0.47439306519737323\n",
      "-411890.19 9.989582874249445e-06 0.06163597021353104\n",
      "\t 32846.0859375 0.0003015042234200986 0.4750428712604417\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-411890.19 1.0004280207721839e-05 0.06172665311986719\n",
      "\t 32846.0859375 0.0003020124854809375 0.4758436768544558\n",
      "-411890.19 1.001662724486054e-05 0.06180283464044581\n",
      "\t 32846.0859375 0.00030241305389438897 0.47647480290338373\n",
      "-411890.19 1.0027432797851176e-05 0.06186950516619802\n",
      "\t 32846.0859375 0.0003028761868009999 0.4772045040771716\n",
      "-411890.19 1.0042944072967459e-05 0.061965210112348026\n",
      "\t 32846.0859375 0.0003033789336030735 0.47799661996087284\n",
      "-411890.19 1.0056367365539203e-05 0.06204803215522354\n",
      "\t 32846.0859375 0.0003038652942358772 0.47876291818666317\n",
      "-411890.19 1.0069971950931371e-05 0.06213197277922703\n",
      "\t 32846.0859375 0.0003042391492233424 0.47935195519803875\n",
      "-411890.19 1.0079654377831908e-05 0.062191713589584044\n",
      "\t 32846.0859375 0.0003046422686313758 0.4799871005332541\n",
      "-411890.19 1.008268053284031e-05 0.062210385039864535\n",
      "\t 32846.0859375 0.0003050138358336566 0.48057253296479835\n",
      "-411890.19 1.009474983809257e-05 0.06228485294792343\n",
      "\t 32846.0859375 0.0003054984265545725 0.48133604255958534\n",
      "-411890.19 1.0106574489497228e-05 0.06235781133576979\n",
      "\t 32846.0859375 0.00030597216672894023 0.4820824562917389\n",
      "-411890.19 1.01148251918814e-05 0.062408718370907605\n",
      "\t 32846.0859375 0.00030640348580549003 0.4827620323528283\n",
      "-411867.32 1.0012379147229858e-05 0.06200565694734698\n",
      "\t 32846.0859375 0.0003068603922953854 0.48348192333277984\n",
      "-411867.32 1.0025444316445054e-05 0.062086568226115385\n",
      "\t 32846.0859375 0.00030735155586256077 0.48425578894753907\n",
      "-411867.32 1.0035205556768946e-05 0.06214701860559101\n",
      "\t 32846.0859375 0.00030784501631601674 0.4850332734816001\n",
      "-411867.32 1.0035863761590683e-05 0.06215109480184542\n",
      "\t 32846.0859375 0.0003083653758861065 0.48585313962297366\n",
      "-411867.32 1.0038581970472887e-05 0.06216792839603745\n",
      "\t 32846.0859375 0.00030885341668097444 0.48662208507208354\n",
      "-411867.32 1.0052858711782756e-05 0.06225634281892022\n",
      "\t 32846.0859375 0.00030937514658553605 0.48744411027980367\n",
      "-411867.32 1.00659119376922e-05 0.06233718013399743\n",
      "\t 32846.0859375 0.0003099482017319531 0.48834700231579753\n",
      "-411867.32 1.0078929886063282e-05 0.06241779897882803\n",
      "\t 32846.0859375 0.00031050782826809914 0.4892287365533772\n",
      "-411867.32 1.0094670551751263e-05 0.06251527933813322\n",
      "\t 32846.0859375 0.00031106240179738846 0.4901025093937382\n",
      "-411867.32 1.0109559015769324e-05 0.0626074820982167\n",
      "\t 32846.0859375 0.0003115557677852955 0.4908798450901309\n",
      "-411867.32 1.0124898157098457e-05 0.06270247585755553\n",
      "\t 32846.0859375 0.0003120240276172077 0.49161762412527754\n",
      "-411867.32 1.0135491851879409e-05 0.06276808154374994\n",
      "\t 32846.0859375 0.00031249931360371563 0.4923664734022111\n",
      "-411867.32 1.0150852152533991e-05 0.06286320634066293\n",
      "\t 32854.390625 0.00030668082468620944 0.48574588929711576\n",
      "623570\r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-04905341ef9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m500000\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps_per_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mequilibration2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecord_boost\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnum_steps\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0msteps_per_iter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;34m\"\"\"Advance the simulation by integrating a specified number of time steps.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendStep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrunForClockTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstateFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointInterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36m_simulate\u001b[0;34m(self, endStep, endTime)\u001b[0m\n\u001b[1;32m    200\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mendTime\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mendTime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstepsToGo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstepsToGo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0manyReport\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/openmm.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m  11764\u001b[0m             \u001b[0mthe\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtime\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mto\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11765\u001b[0m         \"\"\"\n\u001b[0;32m> 11766\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCustomIntegrator_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  11767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11768\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgetPerDofVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "while num_steps / 500000 < 10:\n",
    "    sim.step(steps_per_iter)\n",
    "    report(sim, equilibration2, record_boost=True)\n",
    "    num_steps+=steps_per_iter\n",
    "    print(num_steps, end='\\r')\n",
    "    \n",
    "    if (num_steps) % (2500)==0: #5 picoseconds:\n",
    "        Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration2['tot_nrgs'], equilibration2['tot_boosts'])\n",
    "        E,k,k_0 = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0 = SIGMA0*5,verbose=True)\n",
    "        sim.integrator.setEtot(E)\n",
    "        sim.integrator.setKtot(k)\n",
    "    \n",
    "        ks.append(k)\n",
    "        k_0s.append(k_0)\n",
    "        vmaxs.append(E)\n",
    "        print(np.around(E,2), k, k_0)\n",
    "\n",
    "        #larger SIGMA0 for dihedrals group, i.e. below calculates up to an StdDev of 6kT :\n",
    "        Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration2['grp_nrgs'], equilibration2['grp_boosts'])\n",
    "        E,k,k_0 = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0 = SIGMA0*7, verbose=True)\n",
    "        sim.integrator.setEgrp(E)\n",
    "        sim.integrator.setKgrp(k)\n",
    "    \n",
    "        gks.append(k)\n",
    "        gk_0s.append(k_0)\n",
    "        gvmaxs.append(E)\n",
    "        print('\\t',E, k, k_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-411434.19 1.0807989553114458e-05 0.0716140026384158\n",
      "\t 33063.44140625 0.0002525978381484522 0.4528906573082083\n",
      "-411434.19 1.0810748209898054e-05 0.07163228156561202\n",
      "\t 33063.44140625 0.0002527050801146446 0.453082934822996\n",
      "-411434.19 1.0814340739303958e-05 0.0716560857531613\n",
      "\t 33063.44140625 0.00025278809862413777 0.45323178133573216\n",
      "-411434.19 1.0815287152283665e-05 0.07166235670866998\n",
      "\t 33063.44140625 0.0002528952266779519 0.4534238546134121\n",
      "-411434.19 1.0814023034151684e-05 0.07165398063106608\n",
      "\t 33063.44140625 0.0002529747550149278 0.45356644348539793\n",
      "-411434.19 1.081807273361281e-05 0.07168081403856233\n",
      "\t 33063.44140625 0.0002530708789006021 0.45373878704180026\n",
      "-411434.19 1.0822113009687683e-05 0.0717075850064712\n",
      "\t 33063.44140625 0.0002531866560843381 0.4539463676182131\n",
      "-411434.19 1.0826933695509241e-05 0.07173952698841404\n",
      "\t 33063.44140625 0.00025328882526612687 0.4541295499773185\n",
      "-411434.19 1.0832077865303429e-05 0.07177361238305688\n",
      "\t 33063.44140625 0.000253335952545701 0.4542140459681786\n",
      "-411434.19 1.0837644891896333e-05 0.07181049963717136\n",
      "\t 33063.44140625 0.00025343056239491863 0.4543836749607071\n",
      "-411434.19 1.0837431853242202e-05 0.07180908803784809\n",
      "\t 33063.44140625 0.0002534849261075032 0.45448114538130374\n",
      "-411434.19 1.0840948212888683e-05 0.07183238752271187\n",
      "\t 33063.44140625 0.0002535764035685692 0.45464515821599283\n",
      "-411434.19 1.0845381025261507e-05 0.0718617594457141\n",
      "\t 33063.44140625 0.0002535876721551161 0.4546653620213999\n",
      "-411434.19 1.0849221797427445e-05 0.07188720849585181\n",
      "\t 33063.44140625 0.00025358428672819745 0.45465929218234774\n",
      "-411434.19 1.0852081768867314e-05 0.07190615873643394\n",
      "\t 33063.44140625 0.0002536228033270713 0.4547283498113011\n",
      "-411434.19 1.0855904442484113e-05 0.0719314878651432\n",
      "\t 33063.44140625 0.0002536411663428743 0.4547612734434197\n",
      "-411434.19 1.0857020115748608e-05 0.0719388803434311\n",
      "\t 33063.44140625 0.0002536482635152657 0.45477399818838565\n",
      "-411434.19 1.0859767053441632e-05 0.07195708161964708\n",
      "\t 33063.44140625 0.00025368582064617007 0.4548413355568547\n",
      "-411434.19 1.0863966360207385e-05 0.0719849063287962\n",
      "\t 33063.44140625 0.0002537666079663032 0.4549861816995828\n",
      "-411434.19 1.0868720178414435e-05 0.07201640524429286\n",
      "\t 33063.44140625 0.00025387022698548456 0.4551719635180286\n",
      "-411434.19 1.086778029983617e-05 0.07201017758588767\n",
      "\t 33063.44140625 0.00025395019726278095 0.4553153447587998\n",
      "-411434.19 1.0868553525775352e-05 0.07201530099983774\n",
      "\t 33063.44140625 0.00025401953252872844 0.45543965815118587\n",
      "-411434.19 1.0869394573178794e-05 0.07202087380046575\n",
      "\t 33063.44140625 0.0002540468806732326 0.4554886915048813\n",
      "-411434.19 1.0869967054995622e-05 0.07202466707896027\n",
      "\t 33063.44140625 0.00025408607947555195 0.45555897227797987\n",
      "-411434.19 1.0872876806027247e-05 0.07204394716033265\n",
      "\t 33063.44140625 0.00025417367285573533 0.45571602122114535\n",
      "-411434.19 1.0877028954090732e-05 0.07207145939476924\n",
      "\t 33063.44140625 0.00025425003643112686 0.45585293589193976\n",
      "-411434.19 1.0880440950137165e-05 0.07209406736387124\n",
      "\t 33063.44140625 0.00025434741419417753 0.4560275275647684\n",
      "-411434.19 1.0883664302718297e-05 0.07211542537676673\n",
      "\t 33063.44140625 0.0002544450447996651 0.4562025725668032\n",
      "-411434.19 1.0885315814212877e-05 0.07212636833224878\n",
      "\t 33063.44140625 0.00025453786454493254 0.45636899202531267\n",
      "-411434.19 1.0887868763629522e-05 0.07214328423740957\n",
      "\t 33063.44140625 0.00025462625464748044 0.4565274694372172\n",
      "-411434.19 1.0891011345632736e-05 0.0721641070624839\n",
      "\t 33063.44140625 0.000254687661023022 0.4566375668710899\n",
      "-411434.19 1.0894354519326135e-05 0.0721862590129945\n",
      "\t 33063.44140625 0.00025476163212353927 0.456770191997974\n",
      "-411434.19 1.0896997424881714e-05 0.072203770969728\n",
      "\t 33063.44140625 0.00025480294205384947 0.45684425787925265\n",
      "-411434.19 1.090155198205579e-05 0.07223394957675554\n",
      "\t 33063.44140625 0.00025487680186647455 0.45697668347455317\n",
      "-411421.1 1.0830703113423322e-05 0.07190631776242834\n",
      "\t 33063.44140625 0.00025494147918414886 0.4570926454840824\n",
      "-411421.1 1.0833202503184293e-05 0.07192291150638776\n",
      "\t 33063.44140625 0.0002550327493017332 0.4572562865660821\n",
      "-411421.1 1.083116366104895e-05 0.07190937539253475\n",
      "\t 33063.44140625 0.00025510854009826997 0.4573921742835425\n",
      "-411421.1 1.0827558127939896e-05 0.07188543783218179\n",
      "\t 33063.44140625 0.0002551807337803641 0.45752161262069047\n",
      "-411421.1 1.0831872628615955e-05 0.0719140823119861\n",
      "\t 33063.44140625 0.00025527372278299495 0.457688335546083\n",
      "-411421.1 1.0833481592432575e-05 0.07192476441288523\n",
      "\t 33063.44140625 0.0002553269869908444 0.457783834629847\n",
      "-411421.1 1.0837010934482665e-05 0.07194819613179473\n",
      "\t 33063.44140625 0.00025540475600902007 0.45792326916348336\n",
      "-411421.1 1.0841983657833423e-05 0.07198121062971417\n",
      "\t 33063.44140625 0.00025552511140436614 0.4581390581603664\n",
      "-411421.1 1.084612387240311e-05 0.07200869800346404\n",
      "\t 33063.44140625 0.00025564894389092725 0.45836108134411907\n",
      "-411421.1 1.0847595843714732e-05 0.072018470594935\n",
      "\t 33063.44140625 0.0002557540164020443 0.45854946917425105\n",
      "-411421.1 1.0852562648369075e-05 0.07205144579792958\n",
      "\t 33063.44140625 0.00025583681262296616 0.4586979171387736\n",
      "-411421.1 1.0854863622411126e-05 0.07206672223648561\n",
      "\t 33063.44140625 0.00025593100940832377 0.4588668055360364\n",
      "-411421.1 1.0859913314133434e-05 0.07210024773651862\n",
      "\t 33063.44140625 0.00025603937171482234 0.45906109174441134\n",
      "-411421.1 1.0862867627219839e-05 0.07211986177019086\n",
      "\t 33063.44140625 0.00025614404099693564 0.45924875661250264\n",
      "-411421.1 1.0866628876821392e-05 0.07214483315073751\n",
      "\t 33063.44140625 0.0002562469178162402 0.4594332077173027\n",
      "-411421.1 1.0867200958225197e-05 0.07214863126677644\n",
      "\t 33063.44140625 0.00025636415591725613 0.45964340761853656\n",
      "-411421.1 1.0871622893493705e-05 0.07217798901753485\n",
      "\t 33063.44140625 0.00025645719992063075 0.45981022915650305\n",
      "-411421.1 1.0875281779988396e-05 0.07220228079731947\n",
      "\t 33063.44140625 0.0002565472004434203 0.4599715939418195\n",
      "-411421.1 1.087806231606079e-05 0.07222074110486161\n",
      "\t 33063.44140625 0.00025661617124178576 0.46009525386874434\n",
      "-411421.1 1.088331616122058e-05 0.07225562200368953\n",
      "\t 33063.44140625 0.0002567108731233384 0.4602650478689586\n",
      "-411421.1 1.0885190033724444e-05 0.07226806286466578\n",
      "\t 33063.44140625 0.0002567947250702875 0.46041538867808324\n",
      "-411421.1 1.0889995006979928e-05 0.07229996364988078\n",
      "\t 33063.44140625 0.00025685621032066233 0.46052562752914034\n",
      "-411421.1 1.0894527205600215e-05 0.07233005345206074\n",
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      "\t 33120.5078125 0.00025072408162520976 0.46383906224258115\n",
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      "\t 33120.5078125 0.0002509502521148334 0.4642574772074858\n",
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      "\t 33120.5078125 0.0002512777940992498 0.46486342924014246\n",
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      "\t 33120.5078125 0.00025168143877453864 0.4656101711025578\n",
      "-411205.13 1.0642117632255618e-05 0.07295264131145063\n",
      "\t 33120.5078125 0.00025176853220740367 0.46577129378357734\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\t 33121.69921875 0.00027370441406196223 0.5066787256020471\n",
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      "-411205.13 1.1405714126704006e-05 0.07818716164764121\n",
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      "\t 33121.69921875 0.0002741367403615304 0.5074790434897223\n",
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      "\t 33121.69921875 0.0002742163237615711 0.507626367440817\n",
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      "\t 33121.69921875 0.0002744364841585262 0.508033926046474\n",
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      "\t 33121.69921875 0.0002744811825567003 0.5081166712499116\n",
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      "\t 33121.69921875 0.00027454752342822474 0.5082394807718351\n",
      "-411205.13 1.1433731635471161e-05 0.07837922410533625\n",
      "\t 33121.69921875 0.0002746039808016266 0.5083439940662375\n",
      "-411205.13 1.1434473442168092e-05 0.07838430925471662\n",
      "\t 33121.69921875 0.0002746676672565068 0.5084618897600554\n",
      "-411205.13 1.1436721318248809e-05 0.07839971864062044\n",
      "\t 33121.69921875 0.00027471533599870453 0.5085501336330326\n",
      "-411205.13 1.1439222801188799e-05 0.07841686652358638\n",
      "\t 33121.69921875 0.00027477305543789393 0.5086569832503147\n",
      "-411205.13 1.1441451700941768e-05 0.07843214582511418\n",
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      "-411205.13 1.1444308575380774e-05 0.07845172994769416\n",
      "\t 33121.69921875 0.00027487917131554677 0.5088534238442289\n",
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      "\t 33121.69921875 0.00027494617120156397 0.5089774533268334\n",
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      "\t 33121.69921875 0.000275086932314289 0.5092380288146431\n",
      "-411205.13 1.1454278961643803e-05 0.07852007781208713\n",
      "\t 33121.69921875 0.000275138002054417 0.5093325685791316\n",
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      "\t 33121.69921875 0.00027520199350580376 0.5094510288792666\n",
      "-411205.13 1.1459379819878203e-05 0.07855504463861673\n",
      "\t 33121.69921875 0.0002752547401961817 0.5095486729963775\n",
      "-411205.13 1.1462154653405777e-05 0.0785740663636184\n",
      "\t 33121.69921875 0.00027530740103850977 0.5096461581924835\n",
      "-411205.13 1.1463968589571735e-05 0.07858650105369135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t 33121.69921875 0.00027537461123006314 0.5097705769904793\n",
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      "\t 33121.69921875 0.00027776679860920094 0.5141989690455246\n",
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      "-411205.13 1.2075098937524794e-05 0.0827758527043094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t 33121.69921875 0.000294906066643658 0.5459270013288646\n",
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      "\t 33121.69921875 0.00029830058366572137 0.552210895451172\n"
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      "\t 33121.69921875 0.00030310518849753975 0.5611051292601162\n",
      "-410902.01 1.0659702564807003e-05 0.07630440989241329\n",
      "\t 33121.69921875 0.0003031269485099856 0.5611454111657367\n",
      "-410902.01 1.0661144409780587e-05 0.07631473092428043\n",
      "\t 33121.69921875 0.00030317742461228876 0.5612388519941427\n",
      "-410902.01 1.0661852895743338e-05 0.07631980241693946\n",
      "\t 33121.69921875 0.0003032189171415092 0.5613156625267731\n",
      "-410902.01 1.0663286134527841e-05 0.0763300618438814\n",
      "\t 33121.69921875 0.00030325777290104793 0.5613875918991688\n",
      "-410902.01 1.0663923456696573e-05 0.07633462393102226\n",
      "\t 33121.69921875 0.00030328596950687174 0.5614397891585885\n",
      "-410902.01 1.0664953547130653e-05 0.07634199753663935\n",
      "\t 33121.69921875 0.0003033303196458901 0.5615218896683486\n",
      "-410902.01 1.0665924241107042e-05 0.07634894596982537\n",
      "\t 33121.69921875 0.0003033788462494202 0.5616117216051798\n",
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      "\t 33121.69921875 0.0003034287124005226 0.5617040332983537\n",
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      "\t 33121.69921875 0.00030347534880000525 0.561790366109381\n",
      "-410902.01 1.0668335626596285e-05 0.07636620717816221\n",
      "\t 33121.69921875 0.0003035272816791482 0.5618865037077138\n",
      "-410902.01 1.0668837132065855e-05 0.0763697970605891\n",
      "\t 33121.69921875 0.0003035700625489491 0.5619656992028437\n",
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      "\t 33121.69921875 0.0003036213834041879 0.5620607038289778\n",
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      "\t 33121.69921875 0.00030364564793601827 0.5621056220744772\n",
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      "\t 33121.69921875 0.00030367832022134075 0.5621661046645972\n",
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      "\t 33121.69921875 0.0003037795568688256 0.5623535128790669\n",
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      "\t 33121.69921875 0.00030382523414799345 0.5624380701766796\n",
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      "\t 33121.69921875 0.0003038754937584266 0.5625311102376183\n",
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      "\t 33121.69921875 0.0003039243808445101 0.5626216094959516\n",
      "-410902.01 1.0676101331710462e-05 0.07642179574102625\n",
      "\t 33121.69921875 0.00030398114329518506 0.5627266875461855\n",
      "-410902.01 1.0677717017435635e-05 0.07643336116183246\n",
      "\t 33121.69921875 0.00030402452305556833 0.5628069917012473\n",
      "-410902.01 1.0679145823449877e-05 0.0764435888580655\n",
      "\t 33121.69921875 0.0003040722694697001 0.5628953793596899\n",
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      "\t 33121.69921875 0.00030412049918364327 0.5629846616976479\n",
      "-410902.01 1.0682109652126567e-05 0.07646480457180818\n",
      "\t 33121.69921875 0.00030414281226642933 0.5630259674412511\n",
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      "\t 33121.69921875 0.00030419916072609 0.563130279115684\n",
      "-410902.01 1.0684691140967523e-05 0.07648328341598375\n",
      "\t 33121.69921875 0.0003042557020020515 0.5632349477296203\n",
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      "\t 33121.69921875 0.00030436688720567485 0.5634407726063244\n",
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      "\t 33121.69921875 0.0003044209477475758 0.5635408489115226\n",
      "-410902.01 1.0690996415416903e-05 0.07652841790666438\n",
      "\t 33121.69921875 0.00030447387250986886 0.5636388226734852\n",
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      "\t 33121.69921875 0.0003045714073376012 0.563819378118258\n",
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      "\t 33121.69921875 0.00030462053946224904 0.5639103309893982\n",
      "-410902.01 1.0697066377880881e-05 0.07657186798428776\n",
      "\t 33121.69921875 0.0003046768715232891 0.5640146123068777\n",
      "-410902.01 1.069890323688281e-05 0.07658501661027856\n",
      "\t 33121.69921875 0.00030472585251871333 0.5641052854091925\n",
      "-410902.01 1.0700044513956048e-05 0.07659318611342067\n",
      "\t 33121.69921875 0.00030477866921088133 0.564203059112879\n",
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      "\t 33121.69921875 0.00030481730806298775 0.5642745869485228\n",
      "-410902.01 1.0701538285427533e-05 0.07660387884616371\n",
      "\t 33121.69921875 0.00030485315382196466 0.5643409442396133\n",
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      "\t 33121.69921875 0.0003048875598495757 0.5644046363151787\n",
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      "\t 33121.69921875 0.00030492619390182717 0.5644761552653815\n",
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    {
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     "text": [
      "\t 33121.69921875 0.0003050947026646389 0.5647880969104837\n",
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      "\t 33138.03515625 0.00029735190205838215 0.5553122281492996\n",
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      "\t 33138.03515625 0.0002973835145608339 0.5553712653004068\n",
      "-410896.44 1.0755318509088553e-05 0.0770486755430847\n",
      "\t 33138.03515625 0.0002974163384845145 0.5554325648114222\n",
      "-410896.44 1.0756431843692345e-05 0.07705665122103286\n",
      "\t 33138.03515625 0.0002974417501193468 0.5554800216847833\n",
      "-410896.44 1.0757381267191433e-05 0.07706345267680309\n",
      "\t 33138.03515625 0.0002974573020430577 0.5555090652972442\n",
      "-410896.44 1.0758852284738118e-05 0.07707399071466565\n",
      "\t 33138.03515625 0.0002974933896122094 0.5555764597490549\n",
      "-410896.44 1.0760158261716917e-05 0.07708334643913083\n",
      "\t 33138.03515625 0.0002975289160875533 0.555642806343931\n",
      "-410896.44 1.076116670447099e-05 0.07709057069552981\n",
      "\t 33138.03515625 0.0002975663427994783 0.5557127016788777\n",
      "-410896.44 1.0762392559896217e-05 0.07709935244725954\n",
      "\t 33138.03515625 0.0002975886388159835 0.5557543400558937\n",
      "-410896.44 1.0763970150509608e-05 0.07711065395053053\n",
      "\t 33138.03515625 0.0002976191511289716 0.555811322575239\n",
      "-410896.44 1.0765045707574091e-05 0.0771183589987092\n",
      "\t 33138.03515625 0.00029765233408609595 0.5558732925903281\n",
      "-410896.44 1.0766282104325331e-05 0.0771272162661205\n",
      "\t 33138.03515625 0.0002976939293629762 0.5559509728261866\n",
      "-410896.44 1.0766930338771614e-05 0.07713186007146065\n",
      "\t 33138.03515625 0.0002977317514866484 0.5560216066026122\n",
      "-410896.44 1.076773107480359e-05 0.07713759636375839\n",
      "\t 33138.03515625 0.0002977771075433327 0.5561063101902572\n",
      "-410896.44 1.0769034864481717e-05 0.07714693641889497\n",
      "\t 33138.03515625 0.00029782606666464343 0.5561977425925897\n",
      "-410896.44 1.077069087952508e-05 0.07715879973709078\n",
      "\t 33138.03515625 0.0002978731488763589 0.5562856698185901\n",
      "-410896.44 1.0770423792100518e-05 0.07715688638303257\n",
      "\t 33138.03515625 0.0002979085421500574 0.5563517676560108\n",
      "-410896.44 1.0768509145042952e-05 0.07714317028343094\n",
      "\t 33138.03515625 0.0002979550317541989 0.5564385881723178\n",
      "-410896.44 1.0769348632801312e-05 0.07714918418435461\n",
      "\t 33138.03515625 0.0002979983514781442 0.5565194888568615\n",
      "-410896.44 1.0770206306650928e-05 0.07715532836633351\n",
      "\t 33138.03515625 0.0002980333498894775 0.5565848492787879\n",
      "-410896.44 1.0770998896817846e-05 0.07716100630349157\n",
      "\t 33138.03515625 0.00029807501774560394 0.5566626650582318\n",
      "-410896.44 1.0772134426711456e-05 0.07716914098348904\n",
      "\t 33138.03515625 0.0002981076170406726 0.5567235450696101\n",
      "-410896.44 1.0771932090708215e-05 0.07716769149400611\n",
      "\t 33138.03515625 0.00029813003938155517 0.5567654193606089\n",
      "-410896.44 1.0772835646103998e-05 0.07717416436103149\n",
      "\t 33162.3046875 0.0002828488127113537 0.535091948596467\n",
      "-410896.44 1.0773621669478664e-05 0.07717979525517123\n",
      "\t 33162.3046875 0.0002828627892755149 0.5351183893896244\n",
      "-410896.44 1.0773909508174249e-05 0.07718185726665384\n",
      "\t 33162.3046875 0.0002828924697767576 0.5351745388112643\n",
      "-410896.44 1.07751339863965e-05 0.07719062915241214\n",
      "\t 33162.3046875 0.0002829295737755315 0.5352447319678638\n",
      "-410896.44 1.0775486779349039e-05 0.07719315648153875\n",
      "\t 33162.3046875 0.0002829652384512889 0.5353122022204846\n",
      "-410896.44 1.0776815832562146e-05 0.07720267751894082\n",
      "\t 33162.3046875 0.00028299017935982815 0.5353593853046986\n",
      "-410896.44 1.0778097784539669e-05 0.07721186113371657\n",
      "\t 33162.3046875 0.00028302802817436546 0.53543098749998\n",
      "-410896.44 1.077912173867479e-05 0.07721919651015022\n",
      "\t 33162.3046875 0.0002830376084878868 0.5354491114884852\n",
      "-410896.44 1.0780314586583998e-05 0.07722774180348126\n",
      "\t 33162.3046875 0.00028305300665259494 0.5354782416583435\n",
      "-410896.44 1.0781007278192067e-05 0.0772327040898995\n",
      "\t 33162.3046875 0.00028306983954758553 0.5355100860436696\n",
      "-410896.44 1.0781124369733329e-05 0.0772335429072868\n",
      "\t 33162.3046875 0.000283076489393631 0.5355226661886744\n",
      "-410896.44 1.078117519930815e-05 0.07723390703889435\n",
      "\t 33162.3046875 0.00028309945512911374 0.5355661126505231\n",
      "-410896.44 1.0782020360020464e-05 0.07723996157958042\n",
      "\t 33162.3046875 0.00028312698583922094 0.5356181951082017\n",
      "-410896.44 1.0782487234336163e-05 0.07724330616185748\n",
      "\t 33162.3046875 0.00028316031738237734 0.5356812515524071\n",
      "-410896.44 1.0783547489397525e-05 0.07725090158993772\n",
      "\t 33162.3046875 0.00028317477598749813 0.5357086042682058\n",
      "-410896.44 1.0783803677551895e-05 0.077252736864083\n",
      "\t 33162.3046875 0.0002831745684482689 0.535708211646545\n",
      "-410896.44 1.0783879748801204e-05 0.0772532818213524\n",
      "\t 33162.3046875 0.0002831925330429311 0.5357421969755672\n",
      "-410896.44 1.0785229506053955e-05 0.07726295117782392\n",
      "\t 33162.3046875 0.00028322639414668374 0.5358062552398214\n",
      "-410896.44 1.078656355799097e-05 0.07727250802495642\n",
      "\t 33162.3046875 0.00028323323437778745 0.5358191955543132\n",
      "-410896.44 1.0788029192037026e-05 0.07728300749663493\n",
      "\t 33162.3046875 0.0002832565614308154 0.5358633255548605\n",
      "-410896.44 1.0787748046315797e-05 0.07728099343211076\n",
      "\t 33162.3046875 0.0002832933933443189 0.5359330039819262\n",
      "-410896.44 1.0788419784157643e-05 0.07728580561047466\n",
      "\t 33162.3046875 0.0002833234169243605 0.535989802438297\n",
      "-410896.44 1.0789740113461922e-05 0.07729526415176188\n",
      "\t 33162.3046875 0.00028335925485901313 0.5360576004612164\n",
      "-410896.44 1.0790347149857793e-05 0.07729961282356261\n",
      "\t 33162.3046875 0.00028339551707394506 0.5361262011354152\n",
      "-410896.44 1.079123754218317e-05 0.077305991393321\n",
      "\t 33162.3046875 0.0002834260933580894 0.5361840451946029\n",
      "-410896.44 1.079220555173144e-05 0.0773129259953548\n",
      "\t 33162.3046875 0.00028344454543913845 0.5362189527478981\n",
      "-410896.44 1.0792762193486119e-05 0.07731691365131439\n",
      "\t 33162.3046875 0.0002834597866975512 0.5362477860832229\n",
      "-410896.44 1.0793397223611315e-05 0.07732146286387699\n",
      "\t 33162.3046875 0.0002834801242774718 0.5362862606137154\n",
      "-410896.44 1.079439277557004e-05 0.07732859477352609\n",
      "\t 33162.3046875 0.0002835113717706167 0.5363453744626844\n",
      "-410896.44 1.079532300169494e-05 0.07733525870363829\n",
      "\t 33162.3046875 0.000283527508253812 0.5363759013797101\n",
      "-410896.44 1.0796290162186595e-05 0.07734218722322204\n",
      "\t 33162.3046875 0.00028355376278593075 0.5364255695703462\n",
      "-410896.44 1.0797591172016393e-05 0.07735150736415229\n",
      "\t 33162.3046875 0.00028358634562162003 0.5364872096135644\n",
      "-410896.44 1.0799024169978962e-05 0.07736177303829096\n",
      "\t 33162.3046875 0.0002836145172400222 0.5365405045383034\n",
      "-410896.44 1.0800301765723661e-05 0.07737092544599747\n",
      "\t 33162.3046875 0.00028365290969703674 0.5366131351936644\n",
      "-410896.44 1.0801316301665448e-05 0.0773781933526187\n",
      "\t 33162.3046875 0.0002836825849424521 0.5366692746723568\n",
      "-410896.44 1.080290112873968e-05 0.07738954669626269\n",
      "\t 33162.3046875 0.00028371785134620155 0.5367359914760138\n",
      "-410896.44 1.0804405673040386e-05 0.07740032491222788\n",
      "\t 33162.3046875 0.00028374243009547 0.5367824894291572\n",
      "-410896.44 1.080570679770281e-05 0.07740964587579316\n",
      "\t 33162.3046875 0.00028377354748421494 0.5368413571472824\n",
      "-410896.44 1.080644139659529e-05 0.0774149083765432\n",
      "\t 33162.3046875 0.0002838103471452929 0.5369109745593732\n",
      "-410896.44 1.0807161244525805e-05 0.07742006520472934\n",
      "\t 33162.3046875 0.00028385128433899766 0.5369884193346918\n",
      "-410896.44 1.0807430778970744e-05 0.07742199608868952\n",
      "\t 33162.3046875 0.0002838717343510859 0.5370271065637885\n",
      "-410896.44 1.0808612793304361e-05 0.07743046377272893\n",
      "\t 33162.3046875 0.00028388727747952994 0.5370565109753067\n",
      "-410896.44 1.0810142522925558e-05 0.07744142241064864\n",
      "\t 33162.3046875 0.00028391438339684077 0.5371077898121288\n",
      "-410896.44 1.0811181205137679e-05 0.07744886329570345\n",
      "\t 33162.3046875 0.00028394520709016096 0.5371661019187399\n",
      "-410896.44 1.0811222084917173e-05 0.07744915614922182\n",
      "\t 33162.3046875 0.00028397439524907386 0.5372213199296586\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\t 33162.3046875 0.000283997970597519 0.5372659196542162\n",
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      "\t 33162.3046875 0.00028416189564727873 0.5375760322315287\n",
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      "\t 33162.3046875 0.0002842020964922513 0.5376520839860522\n",
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      "\t 33162.3046875 0.0002844607867168005 0.5381414728401541\n",
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      "\t 33162.3046875 0.00028450660951953236 0.5382281601858385\n",
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      "\t 33162.3046875 0.00028458409361955004 0.538374744213066\n",
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      "\t 33162.3046875 0.00028462421291642936 0.5384506416953204\n",
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      "\t 33162.3046875 0.0002846627930445622 0.5385236273859507\n",
      "-410896.44 1.0830018403816409e-05 0.07758380873761776\n",
      "\t 33162.3046875 0.0002846971503271491 0.5385886243188057\n",
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      "\t 33162.3046875 0.00028505127737420675 0.5392585600694435\n",
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      "\t 33162.3046875 0.00028508482524953517 0.5393220257697542\n",
      "-410896.44 1.0845111580669909e-05 0.07769193285177738\n",
      "\t 33162.3046875 0.0002851131186981767 0.5393755511723216\n",
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      "-410896.44 1.0845115838597426e-05 0.07769196335460958\n",
      "\t 33162.3046875 0.0002851952947113629 0.5395310113370725\n",
      "-410896.44 1.084654252221184e-05 0.07770218379418249\n",
      "\t 33162.3046875 0.00028523863746396323 0.5396130069365029\n",
      "-410896.44 1.0847747296053246e-05 0.07771081452220147\n",
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      "\t 33162.3046875 0.00028649107011044094 0.5419823526617525\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\t 33226.6640625 0.00028302930102560377 0.5536489843941882\n",
      "-410706.38 1.0886331217243789e-05 0.08005630435657127\n",
      "\t 33226.6640625 0.0002830425563454914 0.5536749138451921\n",
      "-410706.38 1.088644018874107e-05 0.08005710571519047\n",
      "\t 33226.6640625 0.00028306336661163353 0.5537156219368025\n",
      "-410706.38 1.0886797558195535e-05 0.08005973375187643\n",
      "\t 33226.6640625 0.00028308580376626283 0.5537595124733236\n",
      "-410706.38 1.0887049454913356e-05 0.08006158615925829\n",
      "\t 33226.6640625 0.0002830986918740013 0.5537847236007026\n",
      "-410706.38 1.0887659446616734e-05 0.08006607193876346\n",
      "\t 33226.6640625 0.0002831210284239284 0.553828417338903\n",
      "-410706.38 1.0888393666268037e-05 0.08007147127033773\n",
      "\t 33226.6640625 0.0002831365005812278 0.5538586832659436\n",
      "-410706.38 1.0888941092516957e-05 0.08007549695369438\n",
      "\t 33226.6640625 0.0002831489037621589 0.5538829458016629\n",
      "-410706.38 1.0889649577359421e-05 0.08008070703568146\n",
      "\t 33226.6640625 0.00028316626519166356 0.5539169074366528\n",
      "-410706.38 1.0890081937131028e-05 0.08008388653892975\n",
      "\t 33226.6640625 0.00028317973683050324 0.553943260040905\n",
      "-410706.38 1.0889961135309162e-05 0.08008299818203292\n",
      "\t 33226.6640625 0.0002831951035023857 0.5539733196221936\n",
      "-410706.38 1.0890604216565995e-05 0.08008772729671772\n",
      "\t 33226.6640625 0.0002832170658359629 0.5540162813354718\n",
      "-410706.38 1.0891034204808261e-05 0.08009088936012601\n",
      "\t 33226.6640625 0.000283238172820763 0.5540575698545609\n",
      "-410706.38 1.0891769430962591e-05 0.0800962960933594\n",
      "\t 33226.6640625 0.00028326798611069757 0.5541158892498812\n",
      "-410706.38 1.089262471239477e-05 0.08010258569352668\n",
      "\t 33226.6640625 0.0002832950621831686 0.5541688542254884\n",
      "-410706.38 1.089291421545512e-05 0.08010471465181841\n",
      "\t 33226.6640625 0.0002833163049801663 0.5542104084141921\n",
      "-410706.38 1.0893675594383338e-05 0.08011031370828581\n",
      "\t 33226.6640625 0.00028333125377789074 0.5542396505691494\n",
      "-410706.38 1.089441268668088e-05 0.08011573416483204\n",
      "\t 33226.6640625 0.00028334928520581395 0.5542749228244271\n",
      "-410706.38 1.0895105517455083e-05 0.08012082912935409\n",
      "\t 33226.6640625 0.000283364676841995 0.5543050312397357\n",
      "-410706.38 1.0895331681392588e-05 0.0801224923020662\n",
      "\t 33226.6640625 0.00028338120485143847 0.5543373625764888\n",
      "-410706.38 1.0896368246242448e-05 0.0801301150309222\n",
      "\t 33226.6640625 0.0002834075259551395 0.55438885071669\n",
      "-410706.38 1.0897367487145102e-05 0.08013746328555749\n",
      "\t 33226.6640625 0.00028343026842688146 0.554433338500594\n",
      "-410706.38 1.0898129410875006e-05 0.08014306634840762\n",
      "\t 33226.6640625 0.0002834584537932591 0.5544884734262473\n",
      "-410706.38 1.0898935454640968e-05 0.08014899386280755\n",
      "\t 33226.6640625 0.00028348687554546523 0.5545440707590557\n",
      "-410706.38 1.0899620927401676e-05 0.08015403471769836\n",
      "\t 33226.6640625 0.00028351305227712614 0.5545952764852695\n",
      "-410706.38 1.0900407012028397e-05 0.08015981545584337\n",
      "\t 33226.6640625 0.00028353989947575676 0.5546477937483295\n",
      "-410706.38 1.0900934401153908e-05 0.08016369378946227\n",
      "\t 33226.6640625 0.0002835676574761247 0.554702092680125\n",
      "-410706.38 1.0901741948440189e-05 0.08016963236050541\n",
      "\t 33226.6640625 0.00028359361104568254 0.5547528618668313\n",
      "-410706.38 1.0902579835375515e-05 0.08017579404437965\n",
      "\t 33226.6640625 0.0002836187973519302 0.5548021301681136\n",
      "-410706.38 1.0903096628371358e-05 0.08017959445578905\n",
      "\t 33226.6640625 0.0002836476063101586 0.5548584849356476\n",
      "-410706.38 1.0903687589841216e-05 0.08018394029006047\n",
      "\t 33226.6640625 0.0002836718404770704 0.5549058907054734\n",
      "-410706.38 1.0904295560475235e-05 0.08018841120695117\n",
      "\t 33226.6640625 0.00028369332799369574 0.5549479236035291\n",
      "-410706.38 1.09050252926784e-05 0.08019377753856367\n",
      "\t 33226.6640625 0.00028371877241662813 0.5549976968208743\n",
      "-410706.38 1.0905673922613634e-05 0.08019854745732452\n",
      "\t 33226.6640625 0.00028374156131027946 0.555042275413196\n",
      "-410706.38 1.09063984551174e-05 0.08020387555119565\n",
      "\t 33226.6640625 0.0002837658192974657 0.555089727779154\n",
      "-410706.38 1.0907321634825328e-05 0.08021066446420949\n",
      "\t 33226.6640625 0.00028379446391338603 0.5551457610677754\n",
      "-410706.38 1.0908266375455716e-05 0.0802176119327308\n",
      "\t 33226.6640625 0.0002838237506479428 0.5552030504395288\n",
      "-410706.38 1.0908312817566312e-05 0.08021795346043804\n",
      "\t 33226.6640625 0.00028385013705954567 0.5552546663320629\n",
      "-410706.38 1.0908964582511612e-05 0.08022274643355162\n",
      "\t 33226.6640625 0.0002838763148920717 0.5553058742117387\n",
      "-410706.38 1.0909888391197445e-05 0.0802295399719664\n",
      "\t 33226.6640625 0.00028389791470164393 0.5553481267721254\n",
      "-410706.38 1.0910917189069457e-05 0.08023710558373401\n",
      "\t 33226.6640625 0.0002839169835498043 0.5553854283814617\n",
      "-410706.38 1.0911623248009158e-05 0.08024229782602779\n",
      "\t 33226.6640625 0.00028394049570867706 0.5554314217921568\n",
      "-410706.38 1.091232974862908e-05 0.08024749331636591\n",
      "\t 33226.6640625 0.0002839573514860209 0.5554643942934976\n",
      "-410706.38 1.0913211557012186e-05 0.0802539779914041\n",
      "\t 33226.6640625 0.00028397985610403907 0.555508416798817\n",
      "-410706.38 1.0913937274359249e-05 0.0802593147984196\n",
      "\t 33226.6640625 0.00028399849758702544 0.5555448824159303\n",
      "-410706.38 1.0914791384688647e-05 0.08026559578648804\n",
      "\t 33226.6640625 0.0002840116441970522 0.5555705992136721\n",
      "-410706.38 1.0915522059275946e-05 0.08027096904823977\n",
      "\t 33226.6640625 0.0002840327141218933 0.5556118152377633\n",
      "-410706.38 1.091636955912842e-05 0.08027720142393897\n",
      "\t 33226.6640625 0.000284056675251182 0.5556586869038683\n",
      "-410706.38 1.0917290970963827e-05 0.08028397733630661\n",
      "\t 33226.6640625 0.00028407731841059963 0.5556990681089409\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-410706.38 1.0918132766588011e-05 0.08029016776404126\n",
      "\t 33226.6640625 0.0002840959947210383 0.5557356018539246\n",
      "-410706.38 1.0918909722348011e-05 0.08029588136998919\n",
      "\t 33226.6640625 0.00028411739573719504 0.5557774655437167\n",
      "-410706.38 1.091953748629493e-05 0.08030049784367493\n",
      "\t 33226.6640625 0.0002841409845567509 0.5558236089145366\n",
      "-410706.38 1.0919959492819922e-05 0.0803036012108361\n",
      "\t 33226.6640625 0.00028416681629979927 0.5558741397897925\n",
      "-410706.38 1.092081697741688e-05 0.08030990701271787\n",
      "\t 33226.6640625 0.00028419149350872194 0.5559224122181531\n",
      "-410706.38 1.0921646933320303e-05 0.08031601037307692\n",
      "\t 33226.6640625 0.00028420724788859065 0.5559532302160864\n",
      "-410706.38 1.0922422241772772e-05 0.08032171186499394\n",
      "\t 33226.6640625 0.0002842351138781365 0.5560077403913767\n",
      "-410706.38 1.0922627401350894e-05 0.08032322057507267\n",
      "\t 33226.6640625 0.0002842612218404153 0.556058811594068\n",
      "-410706.38 1.0923612819196664e-05 0.08033046717720199\n",
      "\t 33226.6640625 0.00028427813440139177 0.5560918951729561\n",
      "-410706.38 1.0924252633663156e-05 0.08033517226844339\n",
      "\t 33226.6640625 0.000284301937131948 0.5561384569866995\n",
      "-410706.38 1.0924840643062842e-05 0.0803394963936722\n",
      "\t 33226.6640625 0.00028432613421391873 0.5561857902126582\n",
      "-410706.38 1.0925311486785758e-05 0.08034295890162221\n",
      "\t 33226.6640625 0.0002843539883843866 0.5562402772680083\n",
      "-410706.38 1.0925806928637732e-05 0.0803466023002017\n",
      "\t 33226.6640625 0.0002843711815610182 0.556273909774417\n",
      "-410706.38 1.092665553824818e-05 0.08035284283688005\n",
      "\t 33226.6640625 0.0002843899766627043 0.5563106758934103\n",
      "-410706.38 1.092745477492912e-05 0.08035872029308687\n",
      "\t 33226.6640625 0.00028440064824830256 0.5563315511614725\n",
      "-410706.38 1.0927448532163651e-05 0.08035867438480772\n",
      "\t 33226.6640625 0.00028441893887831216 0.5563673304560263\n",
      "-410706.38 1.09280345536002e-05 0.08036298389087984\n",
      "\t 33226.6640625 0.0002844382119821954 0.5564050316210731\n",
      "-410706.38 1.0928855072992344e-05 0.08036901785667393\n",
      "\t 33226.6640625 0.00028445517083951633 0.5564382057627546\n",
      "-410706.38 1.092962897911711e-05 0.08037470903610172\n",
      "\t 33226.6640625 0.0002844612651550121 0.5564501271842208\n",
      "-410706.38 1.0930479716040744e-05 0.08038096521669431\n",
      "\t 33226.6640625 0.00028447207875943536 0.5564712802630183\n",
      "-410706.38 1.0931357158525285e-05 0.08038741778562648\n",
      "\t 33226.6640625 0.00028448773338824124 0.556501903132481\n",
      "-410706.38 1.0931891583954134e-05 0.0803913478630726\n",
      "\t 33226.6640625 0.00028450402941892036 0.5565337806829765\n",
      "-410706.38 1.0932519423431007e-05 0.0803959648921933\n",
      "\t 33226.6640625 0.00028452580349430006 0.5565763741341719\n",
      "-410706.38 1.0933059303922262e-05 0.0803999350852787\n",
      "\t 33226.6640625 0.0002845432806273722 0.5566105621031929\n",
      "-410706.38 1.0933711737736361e-05 0.08040473297714436\n",
      "\t 33226.6640625 0.00028456041430866127 0.5566440782275315\n",
      "-410706.38 1.0934462416708863e-05 0.08041025334788243\n",
      "\t 33226.6640625 0.0002845782560556598 0.5566789794376528\n",
      "-410706.38 1.0935175507792357e-05 0.08041549730341484\n",
      "\t 33226.6640625 0.00028458614222359475 0.5566944059989738\n",
      "-410706.38 1.093582715627349e-05 0.08042028942006993\n",
      "\t 33226.6640625 0.000284609369321906 0.5567398417872257\n",
      "-410706.38 1.0936420141643796e-05 0.08042465013777518\n",
      "\t 33226.6640625 0.0002846312015085299 0.5567825489129812\n",
      "-410706.38 1.0937060313164787e-05 0.08042935785473714\n",
      "\t 33226.6640625 0.0002846584082060687 0.5568357694113766\n",
      "-410706.38 1.0937758705631463e-05 0.0804344937190386\n",
      "\t 33226.6640625 0.00028467780835325 0.5568737190927174\n",
      "-410706.38 1.0938668847639136e-05 0.08044118675493103\n",
      "\t 33226.6640625 0.0002846990230730781 0.556915218358145\n",
      "-410706.38 1.0939261968929098e-05 0.08044554847216727\n",
      "\t 33226.6640625 0.00028472693034468097 0.5569698092875086\n",
      "-410706.38 1.093992793904154e-05 0.08045044590776333\n",
      "\t 33226.6640625 0.00028475146244837183 0.557017797867646\n",
      "-410706.38 1.0940477145192028e-05 0.08045448468022585\n",
      "\t 33226.6640625 0.0002847757809914503 0.557065368690273\n",
      "-410706.38 1.0940831762690665e-05 0.08045709247947745\n",
      "\t 33226.6640625 0.00028480003141206786 0.5571128062548552\n",
      "-410706.38 1.0941638621347333e-05 0.08046302598645073\n",
      "\t 33226.6640625 0.0002848278376522824 0.5571671995512342\n",
      "-410706.38 1.0942497976314902e-05 0.08046934554273294\n",
      "\t 33226.6640625 0.00028485825899697553 0.5572267083954853\n",
      "-410706.38 1.0943429047204015e-05 0.08047619248620626\n",
      "\t 33226.6640625 0.0002848856392483729 0.5572802683920075\n",
      "-410706.38 1.0944391353628287e-05 0.08048326913071105\n",
      "\t 33226.6640625 0.00028491575628985616 0.557339181972226\n",
      "-410706.38 1.0945319289598195e-05 0.08049009302049981\n",
      "\t 33226.6640625 0.00028494219963429856 0.557390909234179\n",
      "-410706.38 1.0945693146688505e-05 0.08049284230457085\n",
      "\t 33226.6640625 0.00028495977671455416 0.5574252927153128\n",
      "-410706.38 1.0945815402394883e-05 0.08049374135309728\n",
      "\t 33226.6640625 0.00028497831373647785 0.5574615539904689\n",
      "-410706.38 1.0946479312800958e-05 0.08049862364194865\n",
      "\t 33226.6640625 0.0002849983011084227 0.5575006523740564\n",
      "-410706.38 1.0947102084456459e-05 0.08050320340313748\n",
      "\t 33226.6640625 0.00028501543174840407 0.5575341625491278\n",
      "9195570\r"
     ]
    }
   ],
   "source": [
    "while num_steps / 500000 < 20:\n",
    "    sim.step(steps_per_iter)\n",
    "    report(sim, equilibration2, record_boost=True)\n",
    "    num_steps+=steps_per_iter\n",
    "    print(num_steps, end='\\r')\n",
    "    \n",
    "    if (num_steps) % (2500)==0: #5 picoseconds:\n",
    "        Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration2['tot_nrgs'], equilibration2['tot_boosts'])\n",
    "        E,k,k_0 = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0 = SIGMA0*5,verbose=True)\n",
    "        sim.integrator.setEtot(E)\n",
    "        sim.integrator.setKtot(k)\n",
    "    \n",
    "        ks.append(k)\n",
    "        k_0s.append(k_0)\n",
    "        vmaxs.append(E)\n",
    "        print(np.around(E,2), k, k_0)\n",
    "\n",
    "        #larger SIGMA0 for dihedrals group, i.e. below calculates up to an StdDev of 6kT :\n",
    "        Vmax, Vmin, Vavg, Vstd = get_statistics(equilibration2['grp_nrgs'], equilibration2['grp_boosts'])\n",
    "        E,k,k_0 = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0 = SIGMA0*7, verbose=True)\n",
    "        sim.integrator.setEgrp(E)\n",
    "        sim.integrator.setKgrp(k)\n",
    "    \n",
    "        gks.append(k)\n",
    "        gk_0s.append(k_0)\n",
    "        gvmaxs.append(E)\n",
    "        print('\\t',E, k, k_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f843c8822d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seaborn import kdeplot\n",
    "s = 10000\n",
    "for i in range(int(len(equilibration2['grp_boosts'])/s)):\n",
    "    \n",
    "    kdeplot(equilibration2['grp_boosts'][i*s:(i+1)*s], label=i, alpha=1)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boost magnitude histogram\\n(should be gaussian)')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(10)\n",
    "ax[0,0].plot(gvmaxs)\n",
    "ax[0,0].set_title('Vmax convergence:')\n",
    "\n",
    "ax[0,1].plot(gks)\n",
    "ax[0,1].set_title('kgrp convergence:')\n",
    "\n",
    "ax[1,0].plot(equilibration2['grp_boosts'])\n",
    "ax[1,0].set_title('Boost magnitudes:')\n",
    "\n",
    "\n",
    "ax[1,1].hist(equilibration2['grp_boosts'][-15000:],bins=55)\n",
    "ax[1,1].set_title('Boost magnitude histogram\\n(should be gaussian)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boost magnitude histogram\\n(should be gaussian)')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3L0FOMbMRgMPmNmR7v5W3C6vAfuF7qkfBR4EDk5wntnAbICZM2f2qlVS6yiKiOQPJYoi0iO3v7yO2c+t6tExzS3Olt31XHjsVMYOK+6jyPLDmUdOynQIkmPcvdLMngHOAt6KK6+Ke/6Imf3OzMa7+46+imXvOorqBSsikvOUKIpIjzyzbDs19c18+JAJPTpu4qjB/O9HDmHQIPVNE+ktMysFmkKSOAQ4HfhZu30mAdvc3c3sRKLhJhV9Glf4qRZFEZHcp0RRRHpk954mDp00kms/fUymQxEZyCYDc8ysgCgBvNfd/25mlwG4+03AJ4D/NLNmYA9wkffxdKR711Hsy4uIiEi/UKIoIj1SVd+k5RtEMszd3wCOTVB+U9zzG4Ab+jOuvesoKlMUEcl5mvVURHpk954mLQgvIglp1lMRkfyhRFFEekSJooh0RZPZiIjkPiWKItJtDc0t1De1MnKwEkURSU5dT0VEcp/GKIoMMK2tTqs7rR596+8efahr9agNwMM2EpTtrG0EYNRQJYoi0pG6noqI5A8liiIDxNbd9Vzz6FL+9sYWWlp793W/1kIUkUT2TWajJkURkVynRFGknT88t5onlm7LdBhpt2FnHZt313POuydz6KQRmEWLY5vBIIs+3g0Kr23v6+j5IAPCz8GFBZx+2MQM342IZKO9y2MoTxQRyXlKFCXrVNU38dunVlJZ19Tv13acB1/fzJTRg5k4cnC/X78v7TduGFf/y7v58KETMh2KiOSpWM9T5YkiIrlPiaJkzPqKOn7yyFKaWlrblG/eXc+yrVVMHJGZRG3a2CHM+dKJTB87NCPXFxHJVaZBiiIieUOJomTMfQs38PjbWzl8ysg25QWD4PvnHM6/vX//DEUmIiK9oa6nIiK5T4midLBxVx2fv/kVahua+/Q6lXuaOH6/Mdx32Xv79DoiItI/9nU9VaYoIpLr0pIomtn/AD8HSt19h5mNA+4HTgBudffLw35DgfuAA4EW4G/uPitsKwFuA44HKoBPu/vasO1i4Hvhcle5+5xQvj9wNzAWeA34vLs3puOeBrKV22tYs6OW0w+bSOmIvp3d8oJjpvbp+UVEpP9oMhsRkfzR60TRzKYDZwDr44rrge8DR4ZHvF+4+9NmVgw8aWZnu/ujwCXALnc/yMwuAn4GfNrMxgJXADOJxscvNLOH3X1X2OdX7n63md0UznFjb+9poGtsjsYMfv30gzly6qgMRyMiIrkiNkZReaKISO4blIZz/Ar4JnH1grvXuvsLRAkjceV17v50eN5I1Ao4LWw+H5gTnt8PnGZRjXMmMM/dd4bkcB5wVth2atiXcOwFabifAa8xTC5TXJiOPw8RERlw1KQoIpLzepUJmNl5wCZ3X5zCsaOBjwFPhqKpwAYAd28GdgPj4suDjaFsHFAZ9o0vT3a9S81sgZktKC8v72m4A0pDU5QolihRFBGRHjJTi6KISD7osuupmT0BTEqw6bvAd4CP9PSiZlYI3AVc7+6rY8UJdvUUyhNy99nAbICZM2eqDuuEWhRFRCRVWiBDRCQ/dJkouvvpicrN7N3A/sDiMCZhGvCamZ3o7lu7OO1sYIW7XxdXthGYDmwMieQoYGcoPyVuv2nAM8AOYLSZFYZWxWnA5q7uR7oWG6NYXKBEUUREek49T0VEcl/KmYC7v+nuE9y9zN3LiBK647pKEs3sKqIk8OvtNj0MXByefwJ4yt0deAz4iJmNMbMxRC2Yj4VtT4d9Ccc+lOr9yD4NzS0AlBQVZDgSERHJNWam5TFERPJAn62jaGZrgZFAsZldQJTgVRF1WV1G1PoIcIO7/xG4GbjdzFYStSReBODuO83sSuDVcOofu/vO8PxbwN0h+Xw9nEN6SS2KIiKSKkMtiiIi+SBtiWJoVUz6Ok7C4QvuXg98Msm2W4BbEpSvBk7sSZzStcbmVsygqEAjTUREpGc0mY2ISH5Qk5F00NDcSnHBoL3rYYmIiHSXYWpRFBHJA0oUpYOG5lbNeCoiIqkxNEZRRCQPKBuQDhpbWikp1EQ2IiLSc+qLIiKSH5QoSgcNTa2UqEVRRERSpQZFEZGcp2xAOohaFPWnISIiPafJbERE8oOyAemgsblFYxRFRCQl0WQ2ShVFRHKdsgHpQJPZiIhIqsy0jqKISD5I2zqKkru27q5nVXnN3tfl1Q0MLdZkNiIi0nOGup6KiOQDJYrCJXNeZcnmqjZlZxw+MUPRiIhILjPTOooiIvlAieIA5+6sKq/h/GOm8NmT9ttbfsjEERmMSkREcpWWxxARyQ9KFAe4HTWN1De1ctyMMZy4/9hMhyMiInnA1flURCTnKVHMQbv3NLFk0+60nGv1jloApo0ZkpbziYjIAKfJbERE8oISxSzW0NxCc0vH2va/71nEk8u2p/VaB5YOT+v5RESk75jZYOA5oISoLr/f3a9ot48BvwY+CtQBX3T31/o8tr6+gIiI9AslillqzY5azrzuORqbWxNu/9zJM/jYUVPScq1RQ4soGz8sLecSEZF+0QCc6u41ZlYEvGBmj7r7y3H7nA0cHB4nATeGn30qmsxGTYoiIrlOiWKW2lK5h8bmVj538gxmjB3aZltRwSA+cfw0RgwuylB0IiKSSR5lYrF1jYrCo312dj5wW9j3ZTMbbWaT3X1LX8ZmpuUxRETygRLFLNXYErUk/sux0zh+vzEZjkZERLKNmRUAC4GDgN+6+/x2u0wFNsS93hjK2iSKZnYpcCnAjBkzeh8XGqMoIpIPBmU6AEksNjaxqECjPUREpCN3b3H3Y4BpwIlmdmS7XRJVIB1SOHef7e4z3X1maWlpr+OKhkaKiEiuU6KYpZpCi2JRgX5FIiKSnLtXAs8AZ7XbtBGYHvd6GrC5X2JS51MRkZynLCRLNbWqRVFERBIzs1IzGx2eDwFOB5a12+1h4AsWORnY3dfjE0FdT0VE8oXGKGappma1KIqISFKTgTlhnOIg4F53/7uZXQbg7jcBjxAtjbGSaHmML/VHYJrMRkQkPyhRzFLNrVGiWKhEUURE2nH3N4BjE5TfFPfcga/0Z1wRU4uiiEgeUBaSpRo1mY2IiOSgaC4bZYoiIrlOiWKWao5NZjNIvyIREckdGqMoIpIflIVkqb2znhbqVyQiIrlDq2OIiOQHZSFZqil0PS0cpBpXRERyi1oURURynxLFLKV1FEVEJBcZpnUURUTygLKQLNXc4gwyKFCLooiI5BAztSiKiOQDJYpZqqmlVa2JIiKScwzNeSoikg+UiWSpphZXoigiIjnHTOsoiojkA2UiWSpqUVS3UxERyT0aoygikvuUKGap5tZWCtWiKCIiOUbLY4iI5AdlIlmqsdkpVqIoIiK5SA2KIiI5T5lIlopaFPW1rIiI5BYz5YkiIvlAiWKW0qynIiKSiwzDNZuNiEjOUyaSpZpanEKtoSgiIjlGLYoiIvlBiWKWampppbhQvx4REcktBloeQ0QkDxRmOgBpa8W2al7fUMmmXXsYMVi/HhERyS1mphZFEZE8oEwky/zv/W+waEMlAB9996TMBiMiItJDGjQhIpIflChmmY279vCxo6fwrbMOYeLIwZkOR0REpMc0mY2ISO5TophFmlpaqaht4IDxw5g2ZmimwxEREek5TWYjIpIX0jJbipn9j5m5mY0Pr8eZ2dNmVmNmN7Tb9x9mttjMlpjZTWZWEMpLzOweM1tpZvPNrCzumIvNbEV4XBxXvn/Yd0U4tjgd95Mp26sbcIdJo9SSKCIiuclAmaKISB7odYuimU0HzgDWxxXXA98HjgyPeJ9y9yozM+B+4JPA3cAlwC53P8jMLgJ+BnzazMYCVwAziaqehWb2sLvvCvv8yt3vNrObwjlu7O099Zfahmb+8tpGVmyrAWBnbSMAk9TlVEREclQ0mY0yRRGRXJeOrqe/Ar4JPBQrcPda4AUzO6j9zu5eFXftYvZ973g+8MPw/H7ghpBMngnMc/edAGY2DzjLzO4GTgX+NRwzJxyfFYniwnU7+fWTK5OO03CHRRsqqWloZkhRAUOKCwCYMXYoh08Z2Z+hioiIpI2WxxARyQ+9ShTN7Dxgk7svjnK6bh/3GHAi8ChRUggwFdgA4O7NZrYbGBdfHmwMZeOASndvblee7JqXApcCzJgxo9uxpmJXbSPXP7mShWt3csikEUn3O6FsDJ8+YTpnHjGJnrx/IiIi2cpMiaKISD7oMlE0syeAROs0fBf4DvCRnl7U3c80s8HAnUStgvNIPKO2p1Ce7JqzgdkAM2fO7LMqrLXV+dDPn6aqvpkvva+MKz52RF9dSkREJOuYFsgQEckLXSaK7n56onIzezewPxBrTZwGvGZmJ7r71m6ct97MHibqcjqPqEVwOrDRzAqBUcDOUH5K3KHTgGeAHcBoMysMrYrTgM1dXbevNba0UlXfzJFTR/L/znhXpsMRERHpdxqjKCKS+1Ke9dTd33T3Ce5e5u5lRAndcZ0liWY23Mwmh+eFwEeBZWHzw0BsRtNPAE95NMDvMeAjZjbGzMYQtWA+FrY9HfYlHLt3nGSmNLdGleN5R09h5OCiDEcjIiLSv9T1VEQkP/TZOopmthYYCRSb2QVECV4F8LCZlQAFwFPATeGQm4HbzWwlUUviRQDuvtPMrgReDfv9ODaxDfAt4G4zuwp4PZwjo1paotqxYFBaVh4RERHJOcoTRURyX9oSxdCqmPR1nBOSHF9PtFRGom23ALckKF9NNClO1mhqbQWgqEBjNEREZOAxM7UoiojkATV7pVlL6HpaqBZFEREZgKKvSZUpiojkOmUzadbUErUoFg5Si6KIiAw8GqMoIpIflCim2d4WRXU9FRGRAUjLAouI5AclimnWtHcyG9WUIiIyMKlBUUQk9ylRTLNYi2JRgd5aERHpG2Y23cyeNrOlZrbEzL6WYJ9TzGy3mS0Kjx/0S2wYrr6nIiI5r8+WxxioYmMU1aIoIiJ9qBn4hru/ZmYjgIVmNs/d32633/Pufm5/BmamFkURkXygZq80a97boqhEUURE+oa7b3H318LzamApMDWzUUUMTWYjIpIPlCimWUtrrEVRb62IiPQ9MysDjgXmJ9j8HjNbbGaPmtkRSY6/1MwWmNmC8vLydASkFkURkTygbCbNYpPZFKnrqYiI9DEzGw78Bfi6u1e12/wasJ+7Hw38Bngw0Tncfba7z3T3maWlpb2PKTpnr88jIiKZpUQxzfYtj6G3VkRE+o6ZFREliXe6+1/bb3f3KnevCc8fAYrMbHzfx9XXVxARkf6gbCbNNJmNiIj0NTMz4GZgqbtfm2SfSWE/zOxEojq/ov+iFBGRXKZZT9OsRZPZiIhI33sf8HngTTNbFMq+A8wAcPebgE8A/2lmzcAe4CLvhz6hmsxGRCQ/KFFMs9gYRbUoiohIX3H3F4hyss72uQG4oX8i2sfMcE1nIyKS89T1NM2aw6ynRRqjKCIiA5BaFEVE8oOymTSLdT1Vi6KIiAxEZkoURUTygRLFNNu3PIbeWhERGXgMdT0VEckHymbSrCV0PS3UZDYiIjIQqUVRRCQvKFFMs1iLYqG6noqIiIiISI5SophmsTGKhZrMRkREBiADdTwVEckDymbSrKkl6nqqyWxERGQgMmWKIiJ5QYlimsVaFIs0RlFERAYgTWYjIpIflCimWbOWxxARkQFMy2OIiOQHJYppFut6quUxRERkIDJTz1MRkXygbCaNXl+/i+ueWAHAILUoiojIAGQYriZFEZGcV5jpAPLJTc+uorhwEKcfNiHToYiIiGSE6XtSEZG8oBbFNGlpdV5cWcGFx07ld589PtPhiIiIZIzaE0VEcp8SxTTZXLmH6oZmjpk+OtOhiIiIZJR6noqI5D4limmyfmcdADPGDc1wJCIiIpljZmpRFBHJA0oU02RdRUgUxypRFBGRgctATYoiInlAiWIaNDa38p0H3gRg8qghGY5GREQkc7Q8hohIflCimAa76hoBOPXQCRRoWQwRERnADDUoiojkAyWKaVBd3wzA+cdMyXAkIiIimWVaH0NEJC8oUUyD2oYoURwxWMtSioiIuDqfiojkPCWKaVATEsVhxUoURURkYFPXUxGR/KBEMQ1iieJwtSiKiMgAZ6ZEUUQkHyhRTIOaMEZxeIkSRRERGei0jqKISD5QopgGtY2h66kSRRERGeCiFkWliiIiuU6JYhpUq0VRREQEiMYoiohI7lOimAa1Dc0UDjJKCvV2iojIwKbVMURE8oMymzSoqm9ixOBCrR0lIiKCJrMREckHShTToLy6gfHDSzIdhoiISMYZpnUURUTyQFoSRTP7HzNzMxsfXo8zs6fNrMbMbkhyzMNm9lbc6xIzu8fMVprZfDMri9t2sZmtCI+L48r3D/uuCMcWp+N+eqq8uoEJI5UoioiIaHkMEZH80OtE0cymA2cA6+OK64HvA/+T5JgLgZp2xZcAu9z9IOBXwM/CvmOBK4CTgBOBK8xsTDjmZ8Cv3P1gYFc4R78rr2mgVC2KIiIiUaKY6SBERKTX0tGi+Cvgm8TVC+5e6+4vECWMbZjZcOC/gavabTofmBOe3w+cZtGgvzOBee6+0913AfOAs8K2U8O+hGMvSMP99Ii7U17dQOkIJYoiIiKGaXkMEZE80KtE0czOAza5++IeHHYl8Eugrl35VGADgLs3A7uBcfHlwcZQNg6oDPvGlyeL9VIzW2BmC8rLy3sQbnJ/eG41+3/7EeqbWpk4cnBazikiItIVM5sehngsNbMlZva1BPuYmV0fhnS8YWbH9U9walEUEckHXS78Z2ZPAJMSbPou8B3gI929mJkdAxzk7v8vfgxibHOCQzyF8oTcfTYwG2DmzJlpqcOWbq1i5OBC/uNDB3LBsUlzVBERkXRrBr7h7q+Z2QhgoZnNc/e34/Y5Gzg4PE4Cbgw/+5Tm/xYRyQ9dJorufnqicjN7N7A/sDgsCzENeM3MTnT3rUlO9x7geDNbG649wcyecfdTiFoEpwMbzawQGAXsDOWnxJ1jGvAMsAMYbWaFoVVxGrC5q/tJp/qmFiaOHMxXPnxQf15WREQGOHffAmwJz6vNbClRr5r4RPF84DaP+oG+bGajzWxyOLaPA+zzK4iISB9Lueupu7/p7hPcvczdy4gSuuM6SRJx9xvdfUrY//3AOyFJBHgYiM1o+gngqVC5PQZ8xMzGhElsPgI8FrY9HfYlHPtQqveTivqmVgYXFfTnJUVERNoIPXSOBea325Rs6Eb749M6NMPMlCeKiOSBPltHMbQaXgt80cw2mtnhXRxyMzDOzFYSTXYzC8DddxKNa3w1PH4cygC+Bfx3OGZcOEe/2dPYwuAiLUUpIiKZESaI+wvwdXevar85wSEdcjh3n+3uM919Zmlpae9jis7Z6/OIiEhmddn1tLtCK2HS1wn2XwscGfe6Hvhkkn1vAW5JUL6aaMmMjKhvbmF4SdreQhERkW4zsyKiJPFOd/9rgl1iQzpi+mWIhpbHEBHJD2oO64WoRVFdT0VEpH+FJaJuBpa6+7VJdnsY+EKY/fRkYHd/jE+MWhT7+ioiItLX1BzWCw3NrQxRoigiIv3vfcDngTfNbFEo+w4wA8DdbwIeAT4KrCRakupL/RFYNEZRmaKISK5TotgLGqMoIiKZ4O4v0MVKFGHSt6/0T0T7aHkMEZH8oCynF+qb1fVURESkPXU9FRHJfUoUe2FPY4u6noqIiMQzJYoiIvlAiWKKWludhuZWSpQoioiI7GXqfCoikheUKKaoobkVQC2KIiIiccy0jqKISD5Qopii+qYWAE1mIyIiEsfQOooiIvlAWU6K6ptjiaJaFEVERGJMYxRFRPKCEsUUNTRFXU9LCvUWioiIxGiMoohIflCWk6LGlihRLFaiKCIi0oar86mISM5TlpOixjCZTXGB3kIREZEYdT0VEckPynJSFJv1VC2KIiIi+5hpMhsRkXygLCdFjUoURUREEjC1KIqI5AFlOSlqCLOeajIbERGRfcxAbYoiIrlPWU6K9o1R1PIYIiIiMYbGKIqI5AMliimKzXpaUqS3UEREJMa0OoaISF5QlpMizXoqIiKSmBoURURyn7KcFGkyGxERkY4GmdHSqlRRRCTXKctJUazrqRJFERGRfQoHDaJViaKISM5TlpMitSiKiIh0VFhgNCtRFBHJecpyUtSgMYoiIiIdFAxS11MRkXygLCdFShRFREQ6KhxkNLe2ZjoMERHpJWU5KWpsbqW4YBCDBmkecBERkZiCQUaro3GKIiI5ToliihqbWzU+UUREpJ3C8AVqiytRFBHJZcp0UtTY0qJEUUREpJ2CQVHdqHGKIiK5TZlOimJdT0VERGSfWIuiZj4VEcltynRS1NTiFBVqfKKIiEi8gljX0xYliiIiuUyJYoqaWlopGqS3T0REJF5hQaxFUTOfiojkMmU6KWpqaaVIXU9FRETa2NuiqK6nIiI5TZlOippbfO+3piIiIhLRGEURkfygRDFFTa2uFkUREZF2NOupiEh+UKaToqbmVorUoigiItKGWhRFRPKDEsUUNbe2UqjJbERERNrYN0ZRk9mIiOQyZTopamxxigr19omIiMRTi6KISH5QppOi5pZWigap66mIiPQ/M7vFzLab2VtJtp9iZrvNbFF4/KC/You1KDZrHUURkZxWmOkAclVziyazERGRjLkVuAG4rZN9nnf3c/snnH1iM4JrMhsRkdymTCdFTS2tWh5DREQywt2fA3ZmOo5EYrOequupiEhuU6KYoqbWVrUoiohINnuPmS02s0fN7IhkO5nZpWa2wMwWlJeX9/qihYPUoigikg+U6aSoqdm1PIaIiGSr14D93P1o4DfAg8l2dPfZ7j7T3WeWlpb2+sJ7xyhq1lMRkZymRDFFza2tFKpFUUREspC7V7l7TXj+CFBkZuP749pqURQRyQ9pyXTM7H/MzGOVkJmNM7OnzazGzG5ot+8zZrY8bia2CaG8xMzuMbOVZjbfzMrijrnYzFaEx8Vx5fuHfVeEY4vTcT/d0dTiFCtRFBGRLGRmk8zMwvMTier7iv64doGWxxARyQu9nvXUzKYDZwDr44rrge8DR4ZHe5919wXtyi4Bdrn7QWZ2EfAz4NNmNha4ApgJOLDQzB52911hn1+5+91mdlM4x429vafuaGpp3futqYiISH8ys7uAU4DxZraRqJ4sAnD3m4BPAP9pZs3AHuAid++XzK0wTGbTouUxRERyWjqWx/gV8E3goViBu9cCL5jZQT04z/nAD8Pz+4EbwrehZwLz3H0ngJnNA84ys7uBU4F/DcfMCcf3S6LY3OLqeioiIhnh7p/pYvsNRMtn9Du1KIqI5IdeZTpmdh6wyd0X9/DQP4Vup9+PdY0BpgIbANy9GdgNjIsvDzaGsnFAZdg3vjxZrGmb1c3daWxppViT2YiIiLShdRRFRPJDly2KZvYEMCnBpu8C3wE+0sNrftbdN5nZCOAvwOeJFgxOlHV5CuUJuftsYDbAzJkze1V7xSo/tSiKiIi0pVlPRUTyQ5eZjruf7u5Htn8Aq4H9gcVmthaYBrxmZomSyvjzbQo/q4E/AyeGTRuB6QBmVgiMIlpMeG95MA3YDOwARod948v7XKw7jdZRFBERaSs2fr9ZYxRFRHJaypmOu7/p7hPcvczdy4gSuuPcfWuyY8ysMG5m1CLgXOCtsPlhIDaj6SeAp8LA+8eAj5jZGDMbQ9SC+VjY9nTYl3Ds3nGSfamxJfqWVOsoioiItFWg5TFERPJCOiazSSi0Mo4Eis3sAqIEbx3wWEgSC4AngD+EQ24GbjezlUQtiRcBuPtOM7sSeDXs9+PYxDbAt4C7zewq4PVwjj4X+5ZUs56KiIi0FZv1VJPZiIjktrQliqFVMenrOMcnOb4e+GSSbbcAtyQoX82+rqv9pinWoliorqciIiLx9rUoaoyiiEguU6aTgr2J4iC9fSIiIvFiwzKaNEZRRCSnKdNJQWzcRYG6noqIiLRRUlgA7BvPLyIiuUmJYgqUKIqIiCRWHIZlNDQpURQRyWVKFFPQ6koURUREEikYZBQVGA3NLZkORUREekGJYgqa1aIoIiKSVElhAQ3NalEUEcllShRTEOt6OsiUKIqIiLRXUjhILYoiIjlOiWIKYjN+q0VRRESko5LCQRqjKCKS45QopqBl7xjFDAciIiKShUqK1PVURCTXKdVJQWwR4QKtoygiItJBSeEg6pvU9VREJJcp00lBbGmoAo1RFBER6SAao6gWRRGRXKZEMQV7J7PRuyciItJBNOupWhRFRHKZUp0U7F1HUS2KIiIiHZQUqUVRRCTXKVFMQaxFsbBAiaKIiEh7mvVURCT3KVFMgdZRFBERSS6a9VRdT0VEcpkSxRTEEkWtoygiItKRJrMREcl9ShRTEFtHUS2KIiIiHUWT2ShRFJHec3d21zWxrao+06EMOIWZDiAXtapFUUREJKlojGIL7k5Ti1Nc2Pvvpd0d0xe0kmXcnZ21jWyurGfz7j1MHT2EI6eOynRYOcHdqdrTzLbqerZXNbCtqn7v8+2xsup6tlU10Bi+ePr7V9+v97cfKVFMQXNsMhsliiIiIh3EZj392T+Wc9Ozq1hx9dkUFaSeLLo7F81+mf3GDeX/PnF0GiOVgc7d2VpVz57GFg4oHZ5wn/qmFtbvrGPNjlrWV9SxfmcdG3ZFPzft2tOm9Xzq6CH8c9ap/RV+VmptdXbWNbZJ+LZX17O9uqHt8+p9CWC8ESWFTBhZwoQRgzl+xhgmjBzMkKICfv3kCt7atFuJYj9SopiC2PIYg5QoioiIdBDrenr7S2sBeH19JSfuPzbl8/35lfXMX7OT+Wt28t1zDud3z6zkPz90IKOHFqcpYsl3jc2trKuoZcX2GtbsqGXtjlpWltewclsN1Q3NAFx0wnQ+cHApmyrrWLOjjnUV0X5bquoJH/0AGDm4kBnjhnLopBGcfthEJo8azJTRQ/jHW1uZ++aWvG39bm5ppaK2cW/rX5TstUsAqxrYUdOwt1El3sjBhUwYOZgJI0qYud8YJo4cTOmIEiaGsokjBzNhZAlDizumJy2tzo3PrmL1jtr+uFUJlCimYO9kNnn4n4CIiEhvlYSuptPHDmXZ1mpeXbuTE8rGcNovn+XyUw/iwuOmdftcG3fV8ZO5SwEoKjC+88CbzH1jC8fNGMOZR0zqk/gld9U3tbC6vJZ3tlWzYns1K7fXsHJ7Desq6tokL6UjSjiwdBj/ctxUpo8Zyq0vruXuVzdw96sbABgztIj9xg3jpAPGUTZuGGXjh7L/+GHsN3YYo4YWJbz26vJaHnh9E3uaWhImO9mmpdWpqGlga1U9W3fXs7O2kT1NLVTUNFJV30RNQzO7ahvZVhW1/lXUNrRJmGPGDitmwogSJowczLsmjoieh9fR8ygBHFxUkHKsBYOM/ccNY/Zzq/nie8uYMnpIL+5cuiv7/4qzkGY9FRERSS6WKFbWNYWfjWyq3MPqHbX84KEl3U4U3Z1Zf3kTgP/44AH8/rnVzH1jC0DCLmsycDS3tLK2opZ3ttWwfGs172yrZvm2atbuqCWWDxYOMvYbN5QDS4dz1pGTOGjCcA4qHcEBpcMYVtL2I/CXP7A/6yrqqNzTxP7jkieDnRkTjtlV15TRRNHdqWloprx6XxK4taqebeHn1qoGtu2up7ymYe9n2niDDEYMLmJ4SSFjhhUxadRgjpo2qm3yF36OH16SljHI3XHG4RNZvq2aJZurBnSi2NLqVO1pYmhJASWFqSff3aFEMQWxrqdKFEVERDoqCS0HW8MshVV7mlmyuQqAgyYkHgeWyG0vreOFlTu48oIjmTJqML9/bjVl44aytqJOieIAUlXfxNLNVSzdUsXbW6pYuiVKCmN/A2ZQNm4Y75o4nHPfPZmDJ47gkEkjKBs3rNtJjJlRNn5Yr+KMdYXeVdvI1DQnMq2tTnVDMzUNzdQ2NFO1p4mK2kY2V+5h0649bKrcw/bqBsrDY09Tx3VMRw4uZOLIwUwaNZiDJ4xn4sgSJo0cvLds3PASBhcOYvTQ4qz8jHvRidO54emV7KxtyHQoveLu1De1Urmnkcq6Jirrmtgde75n3+vde5qorm+muj76vdfEfoau0rdfciIfOLi0T2NVopiCZrUoioiIJFXS7sP57j1NvLGxEoADk0wY0t49r67nyr+/zamHTuBzJ82guqGZi06YzidnTufjN75IY4sSxXxTWdfIqvIatu5uYPnWKt7eUs3SLVVsqtyzd5+xw4o5fPJILn7Pfhw6aSSHTBrBQROG96pbY7rEWhRjLenxquub2FS5J0rsKuvZtCt6vrlyD1t213PRCdM57bCJbNhVx+bKPWzdXc/m3fVs3b2HzZX1bKuqTzjuD2Bw0SCmjB7CpJGDOXbGaCaMKKE0tPZNGjWYSSERzIXusJ0ZOyxKxHfWdnx/M6Gl1amub2J3SO6iJK+Rqvooqauub2JXXSO7apvYWddIZd2+ZLCzL7qKCozRQ4sZNaSIEYMLGTG4kCmjBzO8pHBvS++oIUWUjevdFxvdkdt/MRkSWx5D6yiKiIh01D5RrKpv4vkVO4DuzRj+yJtb+PZf3+TIqaP4xSePxswYObiIaz5+FBU1UWuCWhRzl7uzeXc9b2+uYsnm3eFn24RwkMEBpcM5dsZo/vWkGRw+eSSHTxnJhBElWTtRzJiQyPz+uVXcOX9d1LpXE7Xw1TW2beErKjAmjxrC1NFDGDmkiF/Oe4dfzntn7/biwkFMHjWYyaMGc+L+Y5k8ajBjhxUzcnARQ0sKGDG4iDFDi5gyegjjhhVn7XuSTkOLCxlcNCitLYqxbpy7kzw621bT0JxwzGZM4aAo4Rs7rIjRQ4vZf/wwxgwtZtTQIkYPKWb00CJGDylq+3poEUOKCrLm96lEMQUaoygiIpJc/LiZgkHGi6sq9r5u6qIlsLy6gf++dxEzxg7lli+esLcVISbWlVCJYm5obG5l2dYq3ty0myWbq/aOJ6yuj7rPmcH+44Zx7IzRfO7k/Th00ghKR5RkTSthT0wZPYTSESW8sXE3pSNKKB1ewtHTRlMaJneZOmYIU0ZHyWHp8JK9s+dX1jXy0KLNe/eZOnoIYwdI8tdTY4cWJ21RbGxuZWdtIxW1DdHPmkYqahupqIleR1062yaBsRlvkykuHMSoIUV7HxPDhD2jhhQxMpSNHlK0N8kbNaSIkYOLGD64MKsSvlQpUUxBS/j2QLOeiohIJpjZLcC5wHZ3PzLBdgN+DXwUqAO+6O6v9Vd8g4v2tSgWmNFCVHGOGlLUZZfRm55dRWNzK3/60omMH17SYfveRLGPu55W1zcx+7nVHDZ5JB999+Q+vVa+cHc27trDog2VvL6+kkUbdvHW5qq9Sf3IwYUcOnkkFxwzlXdNGsERU0Zy6KQROd8lMmZ4SSGvfOe0HicHo4cWc/F7y/omqDwzdngxr67dyTfuXUxFbQMVNY3srG3sNOkrGGSMHVYctd4NKWLyqMEcOmnE3kSvzWNo29e59mVFuuXHv8x+1tIa/YdXUKBEUUREMuJW4AbgtiTbzwYODo+TgBvDz34R36IYS+juv+w9fO/BtzptCZz7xhZu+ecaPnX8dPZPMrFIcUGUKDb0UYtiY3Mrf31tI9fOe4ft1Q0cNW3UgE8UG5pbWLGthqVbqjh44giOmT4aiJLpNzbu5vX1u1i0oZJFGyrZUdMIRN2Pj5w6iovfsx/HTB/DUdNGMW3MkJxvYelKvt9fph0zfTT3LtjIS6t2MG54CeOGF3PwhOGMHFLE2GHFjBtezLhhxYwdVrL3+cjBRVr7PEVKFFMQ+xJTLYoiIpIJ7v6cmZV1ssv5wG3u7sDLZjbazCa7+5b+iK8ktCgWFw7iqx8+iF/Oe4djZ4yhqGBQ0q6nVfVNzPrrGxwzfTQ/PO+IpOc2M4oLBqW16+k/V+7gnlc38M62ajbt2kN1QzPvnjqKwyaPZMHanWyvqueB1zdx74INVNY1ce9l7+n2pDy5pq6xmaVbojGDb23azVubqlixvZqm0J1qREkhB00cTmVdE2sraveO0TqgdBgffFcpx04fzTHTx3Do5BEUFfTPsgkycFx1wbu58vwjlZD3EyWKKYgtjzFI//+JiEh2mgpsiHu9MZR1SBTN7FLgUoAZM2ak5eKxyWwmjizhq6cdzOWnHoSZUVRgexOO9m5/aR3V9c38+LwjGVLceXev4sL0JIpvb67i+idX8I8lWykqMI6bMYZjZ4zh1EMncPphE7hj/nqefaecE3/yZJvrrtxekxeJ4u66JpZsjsYOvrV5N29t2s3qHfuSv7HDijliykg+dMgBHDFlJCWFBfz8sWUMKSpg8uTBXHDMVI6dMZqjp41Oad1BkVQoSew/ShRTsHcyG/2hiohIdkpUQSXM0Nx9NjAbYObMmZ3M4dd9sa6nE0cMjoIJ9WVx4aCEYwvrm1r40z/X8MF3lfLuaaO6PH90no7rxHVXU0srNz2zil8/uYIhxQX8f6ccyH+ddnCH8UjvPXAc+40bGi3RcfJ+AJz2y2epT7BGXS6oqGngxVUVvLiqgpdW7WBtRd3ebVNGDebwKaP42NFTOHLKKI6YOpJJIwd3+FB+xuET+ztsEckQJYop0KynIiKS5TYC0+NeTwM299fF97UoDm5TXlQwaO9i0fHuXbCBHTWNfOWUA7t1/t50PX1m+XaueHgJ6yrq+NjRU/jxeUfsXdagvQNLh/Ps/3547+vNYfmG9ksdZKOmllbe2FjJiysrWLyxkjU7allVXgtE3UdPOmAcnz5hBkdOHckRU0Z1mF1WRESJYgpaWp1BpqZvERHJWg8Dl5vZ3UST2Ozur/GJsG+M4oSRbWctLU4wRrG5pZXZz63muBmjOXH/sd06fypdT2sbmvn5Y8u59cW1HDxhOLd8cSanHtqz1rGhoUvsnixMFBubW3lnWzUvrargxVU7eGXNTmobWzCDg0qHc0DpcC48bhrvO2g8R04ZSaHGD4pIF5QopqDFXa2JIiKSMWZ2F3AKMN7MNgJXAEUA7n4T8AjR0hgriZbH+FJ/xjesuJCCQcbU0UPalBcVDKKpuW3v1seWbGPjrj18/9zDu/0FbHHhoG7PelrX2MwNT63kjpfXUVXfzBffW8assw9Nadr72NjJPRnqerqpcg+rttdw/H5jWLa1moXrdrJw3S7e2tR2sfoDS4fxL8dN5b0Hjuc9B4xL2mIqItIZJYopaG11Bqk1UUREMsTdP9PFdge+0k/hdDCspJB7Lj2ZwyaPbFNeVNi2RdHd+cPzq9lv3FBOP6z7rXvJup7uaWyhubWVEYOL2LCzjj88v5rHlmxlW1UDZx85iS9/YH+O3697rZbJrjvIouQz3fY0tvDK2p28uHIHL6+JZlq9/ZIT2VxZz7PvlPPM8u17u47G22/cUI7bbwyfmjmdsvFDOfmAcR26/IqIpEKJYgpaWp1CtSiKiIgkNbOsY0JWVGBtWgIXrIvW3/vReUf0qKdO+0lxnl6+neufXMEbG3dTYMaZR07iibe30eLOMdNH87vPHterBDHGzBhaXMiext7PuNrc0sr8NTtZsHYXr2/YxYurKmhsbqWowDh2+hjKqxs4/drngOh+T9p/LJ85cQYtrU5FbSPHzRjN8fuNpXRESRdXEhFJjRLFFDS3uhbuFBER6aH4MYruzlV/f5vSESV84vhpPTtP6Hra0NzC1XOXcttL6zhg/DC+8J79uO2ldcx7eyunHzaR73z0MKa06/7aW0OKC9jT1PMWRXdnXUUdy7ZW8dSy7cx7exu76poAKBs3lM+fvB8ffFcpJ5SNYWhxIXNeXMuaHbV86F2lnHzAuC6XDBERSTcliilo1RhFERGRHiuKSxRfWlXB4o27+emF72ZYSc8+jlTXR4vCH/6Dx2hpdb78/v3537MOoaSwgEvevz+jhxYzvIfn7K4hRQXdnvW0vqmF51fs4NE3t/DCyh1sr24AYHhJIacdNoGzj5zEBw4uTXj/F7+3LJ1hi4j0mBLFFLS0utZQFBER6aHiwkE0tUST2dzw9EpKR5TwL8dO7fF5po4ezNItVXzmxOmcdthEPnzIhL3bpo0ZmrZ4ExlaXNDprKc1Dc28vKqCR97cwry3t1Hd0MzooUV84OBSTj5gLEdMGcWhk0akNJmOiEh/UqKYglZX11MREZGeiq2j+D/3LebFVRV875zDUkqYfnrhUVzR1ML0sX2bFCYSdT2NEsXq+ibW7Khl/c46lmyu4uXVFbyxcTctrc7IwYWcdeQkzjlqMu87aDxFWo5CRHKMEsUUaDIbERGRnisuiOrO+xduZPzwEv71pBkpnSeTE7gMLizg+RU7OPpHj7N7T9Pe8sJBxtHTR3PZhw7gPQeM58T9x1JcqORQRHKXEsUUNGt5DBERkR5rDN1Oxw8v4aHL38fQ4tz7GHLBsVMoKRrE9DFDmTJ6CAeUDmPG2KGUjRumCWdEJK+k5X9oM/sf4OdAqbvvMLNxwP3ACcCt7n553L7FwA1ECwW3At9197+YWQlwG3A8UAF82t3XhmMuBr4XTnGVu88J5fsDdwNjgdeAz7t7YzruqTNTRg3hkEkj+voyIiIieeW0wyawdkctP7nw3YzN0UXgP33CDD59QmotoSIiuaTXiaKZTQfOANbHFdcD3weODI943wW2u/u7zGwQUZIHcAmwy90PMrOLgJ8BnzazscAVwEzAgYVm9rC77wr7/Mrd7zazm8I5buztPXXlf848pK8vISIikndOKBvLCQnWVxQRkeyTjs7zvwK+SZTEAeDute7+AlHC2N6/AT8N+7W6+45Qfj4wJzy/HzjNzAw4E5jn7jtDcjgPOCtsOzXsSzj2gjTcj4iIiIiIyIDWq0TRzM4DNrn74m7uPzo8vdLMXjOz+8xsYiibCmwAcPdmYDcwLr482BjKxgGVYd/48mTXvtTMFpjZgvLy8m7dn4iIiIiIyEDUZaJoZk+Y2VsJHucTdSP9QQ+uVwhMA/7p7scBLwG/iF0qwf6eQnlC7j7b3We6+8zS0tIehCwiIiIiIjKwdDlG0d1PT1RuZu8G9gcWR71AmQa8ZmYnuvvWJKerAOqAB8Lr+4jGFULUIjgd2GhmhcAoYGcoPyXuHNOAZ4AdwGgzKwytitOAzV3dj4iIiIiIiHQu5a6n7v6mu09w9zJ3LyNK6I7rJEnE3R34G/sSv9OAt8Pzh4GLw/NPAE+F/R8DPmJmY8xsDPAR4LGw7emwL+HYh1K9HxEREREREYn02QJGZrYWGAkUm9kFwEfc/W3gW8DtZnYdUA58KRxycyhfSdSSeBGAu+80syuBV8N+P3b3neH5t4C7zewq4PVwDhEREREREemFtCWKoVUx6eu48nXABxOU1wOfTHLMLcAtCcpXAyf2PFoRERERERFJJh3LY4iIiIiIiEgeUaIoIiIiIiIibVg0J8zAYmblwLpenmY80cyr+UD3kn3y5T5A95KtBsq97OfuWhOpm9JUP0L+/H3ly32A7iVb6V6yT77cB3R9L53WkQMyUUwHM1vg7jMzHUc66F6yT77cB+hespXuRfpSvvxO8uU+QPeSrXQv2Sdf7gN6fy/qeioiIiIiIiJtKFEUERERERGRNpQopm52pgNII91L9smX+wDdS7bSvUhfypffSb7cB+hespXuJfvky31AL+9FYxRFRERERESkDbUoioiIiIiISBtKFEVERERERKQNJYopMLOzzGy5ma00s1mZjqczZnaLmW03s7fiysaa2TwzWxF+jonb9u1wX8vN7MzMRJ2YmU03s6fNbKmZLTGzr4XynLsfMxtsZq+Y2eJwLz8K5Tl3LwBmVmBmr5vZ38PrXL2PtWb2ppktMrMFoSxX72W0md1vZsvCv5n35OK9mNkh4fcRe1SZ2ddz8V4GglyqHyF/6kjVj9l5LzGqI7PyXnK+juyX+tHd9ejBAygAVgEHAMXAYuDwTMfVSbwfBI4D3oor+z9gVng+C/hZeH54uJ8SYP9wnwWZvoe4uCcDx4XnI4B3Qsw5dz+AAcPD8yJgPnByLt5LiO+/gT8Df8/xv7G1wPh2Zbl6L3OAL4fnxcDoXL2XuHsqALYC++X6veTjgxyrH0PMeVFHovoxK+8l7p5UR2bfveRVHUkf1Y9qUey5E4GV7r7a3RuBu4HzMxxTUu7+HLCzXfH5RP9ACD8viCu/290b3H0NsJLofrOCu29x99fC82pgKTCVHLwfj9SEl0Xh4eTgvZjZNOAc4I9xxTl3H53IuXsxs5FEH4BvBnD3RnevJAfvpZ3TgFXuvo7cv5d8lFP1I+RPHan6MTvvBVRHZuO95Gkd2Sf1oxLFnpsKbIh7vTGU5ZKJ7r4FosoFmBDKc+bezKwMOJbom8acvJ/QFWURsB2Y5+65ei/XAd8EWuPKcvE+IPow8riZLTSzS0NZLt7LAUA58KfQ3emPZjaM3LyXeBcBd4XnuX4v+Shf3vuc/ttS/Zhd94LqyJhsupd8rCP7pH5UothzlqAsX9YYyYl7M7PhwF+Ar7t7VWe7JijLmvtx9xZ3PwaYBpxoZkd2sntW3ouZnQtsd/eF3T0kQVnG7yPO+9z9OOBs4Ctm9sFO9s3meykk6k53o7sfC9QSdT9JJpvvBQAzKwbOA+7ratcEZVl1L3ks39/7rL8/1Y/7TtEngfWQ6sgOsuVe8qqO7Mv6UYliz20Epse9ngZszlAsqdpmZpMBws/toTzr783MiogqwTvd/a+hOGfvByB0d3gGOIvcu5f3AeeZ2Vqibmanmtkd5N59AODum8PP7cADRF0ycvFeNgIbw7fwAPcTVYq5eC8xZwOvufu28DqX7yVf5ct7n5N/W6of98qme1EduU823Uu+1ZF9Vj8qUey5V4GDzWz/kMFfBDyc4Zh66mHg4vD8YuChuPKLzKzEzPYHDgZeyUB8CZmZEfUnX+ru18Ztyrn7MbNSMxsdng8BTgeWkWP34u7fdvdp7l5G9G/hKXf/HDl2HwBmNszMRsSeAx8B3iIH78XdtwIbzOyQUHQa8DY5eC9xPsO+bjWQ2/eSr/KhfoQc/NtS/Zid96I6MjvvJQ/ryL6rH5PNcqNHpzMLfZRoRrFVwHczHU8Xsd4FbAGaiL5JuAQYBzwJrAg/x8bt/91wX8uBszMdf7t7eT9RE/kbwKLw+Ggu3g9wFPB6uJe3gB+E8py7l7j4TmHfjG45dx9EYxYWh8eS2L/tXLyXENsxwILwN/YgMCaH72UoUAGMiivLyXvJ90cu1Y8h3ryoI1U/Zue9tLsv1ZFZcB9xseVFHdnX9aOFg0REREREREQAdT0VERERERGRdpQoioiIiIiISBtKFEVERERERKQNJYoiIiIiknfM7CYz+34az3ermV2V4rFuZgcl2fZZM3u8d9GJpJ8SRREREZEcZGZrzWyPmdWY2S4zm2tm07s+slfXfMbMvtyX10gXd7/M3a8EMLNTzGxjpmNKxN3vdPePdLVfbxJVkVQoURQRERHJXR9z9+HAZGAb8JsMxyM5yMwKMx2DZB8liiIiIiI5zt3rgfuBw2NlZjbKzG4zs3IzW2dm3zOzQWHboPB6nZltD/uNCtsGm9kdZlZhZpVm9qqZTTSzq4EPADeEVswb2sdhZmWhm+WXzGxDaOm8zMxOMLM3wvluiNv/QDN7Klxrh5ndaWaj47YfZ2avm1m1md1nZvfEWtVirYRm9o1wD1vM7Etxx95qZleFBeIfBaaEuGvMbEr7Frr2rY5mdqyZvRaufQ8wuN29nmtmi8I9vWhmR3XxazrdzFaE9+S3ZmbhPF80sxfCczOzX4X72R3esyPN7FLgs8A3Q/x/C/sfFlp5K81siZmdFxffODP7m5lVhd/hVbHrhO1uZl8xsxVEa+5hZr8Ov7cqM1toZh+I2/+H4XdwR3hP3jSzd5nZt0O8G8ysy5ZRyR1KFEVERERynJkNBT4NvBxX/BtgFNFi6R8CvgDEEqkvhseHw/bhQCyBuzgcN51o8e7LgD3u/l3geeBydx/u7pd3EtJJwMEhpuuIFvo+HTgC+JSZfSgWOvBTYApwWLjmD8M9FQMPALcCY4G7gH9pd51JIdapwCXAb81sTPwO7l4LnA1sDnEPd/fNncQeu/aDwO3h2vcBH4/bfhxwC/Af4T36PfCwmZV0ctpzgROAo4FPAWcm2OcjwAeBdwGjid6/CnefDdwJ/F+I/2NmVgT8DXgcmAB8FbjTzA4J5/otUBveo4vDo70LiH5XsS8YXiVajH4s8GfgPjOLT5A/Ft6TMcDrwGNE+cRU4MfhfZA8oURRREREJHc9aGaVQBVwBvBzADMrIEoyvu3u1e6+Fvgl8Plw3GeBa919tbvXAN8GLrKoC2ITUfJzkLu3uPtCd6/qYVxXunu9uz9OlKzc5e7b3X0TUbJ5LIC7r3T3ee7e4O7lwLVESS3AyUAhcL27N7n7X4FX2l2nCfhx2P4IUAMcQu+dDBQB14Vz30+URMX8O/B7d58f3qM5QEM4Lplr3L3S3dcDTxMlZO01ASOAQwFz96XuvqWTGIeH8za6+1PA34HPhN//x4Er3L3O3d8G5iQ4x0/dfae77wFw9zvcvcLdm939l0AJbd/P5939MXdvJkqeS8P1m4C7gbL4FmHJbUoURURERHLXBe4+mugD/eXAs2Y2CRgPFAPr4vZdR9TyA1ELXvtthcBEohajx4C7zWyzmf1faL3qiW1xz/ckeD0cwMwmmNndZrbJzKqAO0LssRg3ubvHHbuh3XUqQtISUxc7dy8lunb8+7Uf8I3Q5bMyJOvTw3HJbO0qzpDs3UDUGrjNzGab2chOYtzg7q3tYpxKlMAV0vb9av/edSgL3XiXhm6vlUSttePjdmn/e9zh7i1xr0l0X5KblCiKiIiI5LjQqvVXoAV4P7CDqHVqv7jdZgCbwvPNCbY1A9tCC9qP3P1w4L1EXSa/ELtUmkP/aTjnUe4+EvgcUXdUgC3A1NhYviDVWV0TxV0LDI17PSnueaJrz4h7vgG42t1Hxz2GuvtdKca3L1D36939eKJuuu8C/jfJPWwGplsYdxoX4yagnOj3OS1uW6L3bu85w3jEbxF1ix0TvoDYzb7fhwwwShRFREREclyYBOV8orFjS0Mrz73A1WY2wsz2A/6bqMUOovF+/8/M9jez4cBPgHvcvdnMPmxm7w7dF6uIEs5Yq9E2ojGN6TKCqLtopZlNZV9SBPBSuO7lZlYY7u/EFK+zDRhnYcKeYBHwUTMbG1phv97u2s3Af4VrX9ju2n8ALjOzk8J7P8zMzjGzESnGB4BFk/6cFFpwa4F6kr/388M+3zSzIjM7hWgM4d3h9/9X4IdmNtTMDmVfsp/MiHDP5UChmf0ASNaaKQOAEkURERGR3PU3M6shSuiuBi529yVh21eJEonVwAtEk5PcErbdQtTF9DlgDVFC8tWwbRLRDKpVwFLgWfYlmL8GPmHRzJ3XpyH+HwHHEbVczSVKbgBw90bgQqJJaiqJWhv/TjQWsEfcfRlRcrw6dBWdQnT/i4G1RBPC3JPg2l8EdhGN94yPbQHROMUbwvaVYd/eGkmUhO4i6kZaAfwibLsZODzE/2CI8TyiiXp2AL8DvhDuFaKuyKOIurzeHu6/s/fuMaLZYd8J164ncXdVGSCsbddrEREREZHsZGbzgZvc/U+ZjiXXmNnPgEnunmj2U5EO1KIoIiIiIlnJzD5kZpNC98+LgaOAf2Q6rlxgZoea2VGha+yJRC2zD2Q6LskdhZkOQEREREQkiUOIxloOB1YBn+hkuQhpawRRd9MpwHai5VEeymhEklPU9VRERERERETaUNdTERERERERaUOJooh0i5ndZGbfT+P5bjWzq9J1PhERSQ8z+6mZfb0b+601s9P74PpfNLMXOtn+jJl9Ocm2H5rZHYm25TIzm2FmNWHJkt6e61ozuywdcUl+U6IoEoQKb0/4j3iXmc01s1QX9u3uNZNWdtnG3S9z9ysBzOwUM9uY6ZhERCS9zKyUaL2932c6FtnH3de7+/CwPmJv/Rz4rpkVp+FckseUKIq09TF3Hw5MJlrY9jcZjkdERKQ/fRF4xN33ZDoQ6RthMqBlRGswiiSlRFEkAXevJ1ps+PBYmZmNMrPbzKzczNaZ2ffMbFDYNii8Xmdm28N+o8K2wWZ2h5lVhEVyXzWziWZ2NfAB4IbQinlD+zjMrMzM3My+ZGYbQkvnZWZ2gpm9Ec53Q9z+B5rZU+FaO8zsTjMbHbf9ODN73cyqzew+M7sn1v0z1kpoZt8I97DFzL4Ud+ytZnaVmQ0jWpB3Soi7xsymtO9K2r7V0cyONbPXwrXvAQa3u9dzzWxRuKcXzeyouG3fMrNN4djlZnZaz3+rIiLSDWcDz8ZemNl4M/t7+L95p5k9H6v7gmNCfbQ71CmD4479dzNbGY572KJF7uPrtsK4fTvrTnqGmS0L17gBsC7uYXCIpTrUO0fHnWuKmf0l1OVrzOy/kp3EzMaZ2d/MrCrU3VdZXJdYM/t1qJurzGyhmX0gbltXdWLCes3MTjSzBeGc28zs2kTvWfhcsDQcv9rM/qP9tZLV58EzwDldvI8ywClRFEnAzIYCnwZejiv+DTAKOAD4EFHXnNh/vF8Mjw+H7cOBWAJ3cThuOjAOuAzY4+7fBZ4HLg/dSS7vJKSTgINDTNcB3wVOB44APmVmH4qFDvyUaCrsw8I1fxjuqZho/aRbgbFEU2b/S7vrTAqxTiVab+m3ZjYmfgd3ryX6ILE5xD3c3Td3Envs2g8Ct4dr3wd8PG77ccAtwH+E9+j3wMNmVmJmhwCXAye4+wjgTGBtOO79ZlbZ2bVFRKRH3g0sj3v9DWAjUApMBL4DxE+Z/yngLGB/ojUOvwhgZqcS1UefIuqlsw64u6fBmNl44C/A94DxREtkvK+Lw84nqmfGAn8GHjSzopDg/g1YTFTPnQZ83czOTHKe3wK1RHXjxeER71XgmLjr3BefKHdyT0nrNeDXwK/dfSRwINHSIIlsB84FRhJ9FvlVqEtjuqrPlwJHI9IJJYoibT0YEo8q4AyifvxYNHj808C33b3a3dcSrUf0+XDcZ4Fr3X21u9cA3wYuCt/8NRElPwe5e4u7L3T3qh7GdaW717v740SV1l3uvt3dNxElm8cCuPtKd5/n7g3uXg5cS5TUApxMtHbq9e7e5O5/BV5pd50m4Mdh+yNADdEaVr11MlAEXBfOfT9RBRvz78Dv3X1+eI/mAA3huBagBDjczIrcfa27rwr3+4K7j05DfCIiEhkNVMe9biJK9PYL/38/723XVrve3Te7+06iJOyYUP5Z4BZ3f83dG4jqxfeYWVkP4/ko8La73+/uTURflm7t4piFcftfS9SD5WTgBKDU3X/s7o3uvhr4A3BR+xOEev/jwBXuXufubwNz4vdx9zvcvcLdm939l0R1VXfqzKT1GtH7fZCZjXf3Gnd/OdEJ3H2uu6/yyLPA40S9lIg7T2f1eTXR71okKSWKIm1dEBKPEqJv+541s0lE32IWE30jGrOO6Js6iFrw2m8rJPr29XbgMeBuM9tsZv9nZkU9jGtb3PM9CV4PBzCzCWZ2d+jOUgXcEWKPxbipXQW/od11Kty9Oe51XezcvZTo2vHv137AN0LXpsqQrE8Hprj7SuDrRC2j28P9TUlDTCIi0tEuooXaY34OrAQeD10cZ7XbPz5pi68z2tSL4UvUCvbVm901hbi6KtQj7euu9uL3byVqEZ1CVNdMaVfXfIeorm6vlKgej79Wm+uGrp1LQ5fYSqIWvPF0oYt67RLgXcCy0N313ETnMLOzzezl0K23kiihjr92V/X5CKCyq1hlYFOiKJJAaNX6K9G3fu8HdhB9O7df3G4zgE3h+eYE25qBbeHbvB+5++HAe4m6inwhdqk0h/7TcM6jQreVz7FvLMcWYKqZxY/tSHVW10Rx1wJD415Pinue6Noz4p5vAK5299Fxj6HufheAu//Z3d9P9B478LMU4xYRkc69QZSoABB60XzD3Q8APgb8t3VvnHibetGi8e3jiOrN2lCcrM6It4W4uirUI13VXfH7DwKmhXg2AGva1TUj3P2jCc5RTlSPT0ty3g8A3yLqWjsmfMm8m311bmd1YtJ6zd1XuPtngAmh7P7w3u1lZiVE3XF/AUwM134k7trdcRhRF1yRpJQoiiRgkfOBMcDSMB31vcDVZjbCzPYD/puoxQ6i8X7/z8z2N7PhwE+Ae9y92cw+bGbvDt1YqogSztj01tuIxjSmywii7iWVZjYV+N+4bS+F615uZoXh/k5M8TrbgHEWJuwJFgEfNbOxoRX26+2u3Qz8V7j2he2u/QfgMjM7Kbz3w8zsnPBeH2Jmp4aKsZ6oBTUd04OLiEhHj7BvyEJsorGDQoJWRfT/b3f+D/4z8CUzOyb8//0TYH7oZllOlDB+zswKzOzfiMbjJTIXOMLMLgzDOf6L5EllzPFx+3+daCjDy0TDLaosmkhmSLj2kWZ2QvsThHr/r8APzWyomR3Kvi95Iapvm4kSykIz+wHReMGYRSSpEzur18zsc2ZWGlpCK8Mh7d/vYqKeT+VAs5mdDXyki/ekvQ8RTUwnkpQSRZG2/mZmNUSV4dXAxe6+JGz7KtE3hKuBF4gqwVvCtluIupg+B6wh+o//q2HbJKIZVKuIBo8/y74E89fAJyyazfT6NMT/I+A4om815xJVcgC4eyNwIVG3lkqi1sa/E1WgPeLuy4iS49Wh+84UovtfTDQg/3HgngTX/iJRt6ZPt4ttAdE4xRvC9pVhX4gqw2uIWnW3En3L+h2IvtENvy8REUmP24gSnCHh9cHAE0RfQr4E/M7dn+nqJO7+JPB9opavLUSJYPxYwH8n+jKzgmhitheTnGcH8EmieqAixPPPLi7/EFE9s4toLoELQ++eFqJW0WOI6uodwB+JuowmcnnYtpWojruLfXXmY0SJ1jtEXWzrads1NWmdSCf1GtHEQEtC3fZr4CKPZmKPf0+qiRLme8M9/ivwcBfvyV5mNploVvcHu3uMDEzWdsiQiAwkZjYfuMnd/5TpWEREJDuY2U+A7e5+XaZjySZm9jNgkru3n/00p5jZL4FV7v67TMci2U2JosgAYtEyGsuJvsX8LHATcIBHi++KiIhIELqbFgNvEs2Y+gjwZXd/MJNxifSXwq53EZE8cghRV5XhRGtRfUJJooiISEIjiLqbTiFat/CXRN1aRQYEtSiKiIiIiIhIG5rMRkRERERERNoYkF1Px48f72VlZZkOQ0RE+tjChQt3uHtppuPIFaofRUQGjq7qyAGZKJaVlbFgwYJMhyEiIn3MzNZlOoZcovpRRGTg6KqOVNdTERERERERaUOJooiIiIiIiLShRFFERERERETaUKIoIiIiIiIibShRFBERERERkTaUKIqIiIiIiEgbShRFRERERESkDSWKIiIiWcTMbjGz7Wb2VlzZz81smZm9YWYPmNnouG3fNrOVZrbczM6MKz/ezN4M2643M+vnWxERkRymRFEkiyzeUMmX5yyguaU106GISObcCpzVrmwecKS7HwW8A3wbwMwOBy4CjgjH/M7MCsIxNwKXAgeHR/tzioiIJFWY6QBEZJ+v37OINTtqWb+zjgNKh2c6HBHJAHd/zszK2pU9HvfyZeAT4fn5wN3u3gCsMbOVwIlmthYY6e4vAZjZbcAFwKN9G730h7JZcxOWr73mnH6ORETymRJFERGR3PJvwD3h+VSixDFmYyhrCs/bl3dgZpcStTwyY8aMdMcqXVDSJyLZSl1PRUREcoSZfRdoBu6MFSXYzTsp71joPtvdZ7r7zNLS0vQEKiIiOU8tiiIiIjnAzC4GzgVOc/dY0rcRmB632zRgcyiflqBcRESkW9SiKCIikuXM7CzgW8B57l4Xt+lh4CIzKzGz/YkmrXnF3bcA1WZ2cpjt9AvAQ/0euIiI5Cy1KIqIiGQRM7sLOAUYb2YbgSuIZjktAeaFVS5edvfL3H2Jmd0LvE3UJfUr7t4STvWfRDOoDiGaxEYT2eSQZGMXRUT6ixJFERGRLOLun0lQfHMn+18NXJ2gfAFwZBpDExGRAURdT0VERERERKQNJYoiIiIiIiLShhJFERERERERaUOJokgWSrjYmYiIiIhIP8mZRNHMDjGzRXGPKjP7upmNNbN5ZrYi/ByT6VhFUpVohWwRERERkf6WM4miuy9392Pc/RjgeKAOeACYBTzp7gcDT4bXIiI5q76phT2NLV3vKCIiItJHciZRbOc0YJW7rwPOB+aE8jnABZkKSkQkHWZe9QSH/eAfmQ5DREREBrBcTRQvAu4Kzye6+xaA8HNCogPM7FIzW2BmC8rLy/spTBGRnqtpaM50CCIiIjLA5VyiaGbFwHnAfT05zt1nu/tMd59ZWlraN8GJiIiIiIjkgcJMB5CCs4HX3H1beL3NzCa7+xYzmwxsz2BsIiIiIh2UzZqb6RBERHok51oUgc+wr9spwMPAxeH5xcBD/R6RiIiIiIhIHsmpFkUzGwqcAfxHXPE1wL1mdgmwHvhkJmITERERyaRkrZZrrzmnnyMRkXyQU4miu9cB49qVVRDNgioiIiIiIiJpkItdT0XynnumIxARERGRgUyJokg2sUwHICIiIiKiRFFERERERETaUaIoIiIiIiIibShRFBERERERkTaUKIqIiIiIiEgbShRFRERERESkDSWKInnsf+9bzDE/fjzTYYiIiIhIjlGiKJKV0rOQ4n0LN1JZ15SWc0nm1TU2s76iLtNhiIiIyACgRFEki2gZRenM529+hQ/+/OlMhyEiIiIDQGGmAxCRjjw9DYqSZxau25XpEEQkB5XNmpt029przunHSEQklyhRFMkiZmpTFBHJZZ0lZSIiuURdT0VEpF/trG1kVXlNpsMQERGRTihRFBGRfvWhnz/Nab98NtNhiIiISCeUKIqISL+qrm/OdAgiIiLSBSWKIpKXWlqd219eR1NLa6ZDEREREck5ShRFJC/d8+oGvv/gW/zh+dWZDkVEREQk5yhRlKzi7lz197c10YX0WlV9EwC765oyHIlIz5jZLWa23czeiisba2bzzGxF+Dkmbtu3zWylmS03szPjyo83szfDtutN0yqLiEgPKFGUrLKuoo4/vrCGL89ZkOlQJMtt2FlHZV1jpsMQ6Qu3Ame1K5sFPOnuBwNPhteY2eHARcAR4ZjfmVlBOOZG4FLg4PBof04REZGklChKVmrVivPShQ/839Ocfq1mzpT84+7PATvbFZ8PzAnP5wAXxJXf7e4N7r4GWAmcaGaTgZHu/pK7O3Bb3DEiIiJdUqIokoWUJnfPjhq1KCbz6tqdVNQ0ZDoMSZ+J7r4FIPycEMqnAhvi9tsYyqaG5+3LOzCzS81sgZktKC8vT3vgIiKSm3IqUTSz0WZ2v5ktM7OlZvaezsZtiOQaDSCSdPnkTS/x8RtfzHQY0vcS/bfhnZR3LHSf7e4z3X1maWlpWoMTEZHclVOJIvBr4B/ufihwNLCUJOM2REQGurUVdZkOQdJnW+hOSvi5PZRvBKbH7TcN2BzKpyUoFxER6ZacSRTNbCTwQeBmAHdvdPdKko/bEBERyRcPAxeH5xcDD8WVX2RmJWa2P9GkNa+E7qnVZnZymO30C3HHiIiIdKkw0wH0wAFAOfAnMzsaWAh8jXbjNsxsQqKDzexSotnfmDFjRv9ELJJEXWMz//qH+fz0wndz2OSRmQ5HJGXl1Q0s2bybUw5J+F+vpMDM7gJOAcab2UbgCuAa4F4zuwRYD3wSwN2XmNm9wNtAM/AVd28Jp/pPohlUhwCPhodIG2Wz5iYsX3vNOf0ciYhkm1xKFAuB44Cvuvt8M/s1Pehm6u6zgdkAM2fO1FwhklGvrt3Fog2V/OSRpdx+yUmZDienbauqZ+LIwUm36x973/rMH15m5fYaVv3koxQM0ijbdHD3zyTZdFqS/a8Grk5QvgA4Mo2hSZAsuRIRySc50/WUaLzFRnefH17fT5Q4Jhu3ISIDwHcfeKtD2ad+/xLXPLosA9EMPKvLazIdgoiIiPSBnEkU3X0rsMHMDglFpxF1tUk2bkNyWHl1Az99ZCktrWoPkq50/Bt5ZU37Jehyk7vz2vpd/X7dd7ZVUzZrLsu3Vvf7tUVERCQ75EyiGHwVuNPM3gCOAX5CNG7jDDNbAZwRXksOcnceeH0TAHWNLfz+udW8uGpHhqOSTNhZ28juuqZMh5Fxt7+8jgt/9yJPLdvWr9d95M0tAMwNPzPhK3e+xt8Wa5JOERGRTMmpRNHdF4W1no5y9wvcfZe7V7j7ae5+cPiZH00JA9Ara3by6ydXtClTg+LAdNyV8zj6x4+n5VzuuftH9IOHlgCwYeeeDEfSNXfnp48uZV1FbbePeXl1BWWz5rJ4Q2WHbXPf3MJX73o9jRGKiIhIT+RUoij5raahOdMhZI0czm0GlIXrdrJeaxUCsHpHLb9/djX/ftuCNuVls+byo78tSXjM08ujIeUvrqro8/hERESkZ5QoimQR06SROeXjN77EB3/+dKbDyAqxLzcSjSv+0z/X9m8wIiIi0mtKFEUkp6n1Nf1aQ7JXq1Z+ERGRAUuJoohklfmr1Q2xvf4eZ/nYkmjynJtfWNOv1xUREZHsUZjpAERiEn0WzuWJSCQ1n579co/2119I+tU3t/TJee+cv25vEioiIiLZTYmiiOQ1fdfQP7wbKft3H3irHyIRERGRdFCiKJKFuvOhO9v99NGlnFg2ltMOm5jROLZW1Wf0+gONaUYmkbxQNmtuwvK115zTz5GISKZojKJIFjHy50P2759dzSVzFnS9Yx97Y+PujFy3rrGZx5dsTcu5uvu1wRNvb2N3XRML1+1Ky3VFRERk4FKiKCI5ratxrOt3Zmadw2//9U0uvX0hy7dW98v1yqsb+PJtC/jyba/y8Rtf7JNrvLZ+F6f+8hnNhioiIjIAKFGUnLauopZrH1+es5PezF+9k2vnvZPpMKQPrKuIEtTaxt4nVT/629td7tMQJqBZXV7b6+sla9f+2aPLWF1em7FWWhEREek/ShQla6SS6n3pT69y/VMr2bhrT9rj6Q+NLa1c/+SKDuU5mvdKhizbWsU9r27IdBgpy4cxuSIiIvlGiaJkta4+PjY0t/ZLHJ15Y2Pl3tac3tI8IB1d8dBbzH1jS6bDSJuHF2+mbNZcKusa03bOs657nt88tTKlY92dldvbdo9NZUKaVL7cyKcxuSIiIvlGs56K9MLGXXWcd8M/ueiE6Vzz8aMyHU5emvPSOua8tI5zjsqtmfaS5U23hEXsV++o5bgZxX0ex8OLN3Pe0VOSbv/D86v5ySPLePjy93HUtNHdOmdnLYA9Sf3UkijZLtnMnyIiA4FaFKXflVc3pLU1pS89vWx7pzNI7t7TBMDiNI/ZOvvXz/OmxoF1S3NrdicbmW4z+6+7XmdXbfJ/b6+vrwToVvft7jQ0pvLbUMuiiIhI9lGiOID9462t/Hn++n6/7glXP8GxV87r9+um4ku39t0Mkl3504trMnLdXLOsn2YV7S99MTFTSz8MelW3aRERkfyirqcD2GV3LATgX0+a0e/XTvfnVn1IlWyzeENlwvJ1FdGspMn+Ddy7oHeT0lR00nrYXV0mq3Gb2zfouju/fHw5x+83hv3HD+t1LCIiIpIZShQla6TSkpKry2L0hLvz8Rtf5LIPHchHjpiU6XD6Vaxrb3e9uXE3Y4YV9VE0qWk/Mcyuus7vacHa5F2dM6W5pZWXV+/stIto7J9iXWNLyhPriIiISPZQoih5IZVZGjOpJwluQ3Mrr62v5PK7Xuedq87uw6hS89iSrUwdPYQjp45K+7mP/tHjPdr/Yze8kPYYequv/jJfX7+L6WOHMn54Sa/Ok+hPsf2/pxueXsl1T+xbxiX/v54RERERjVGU7Nb9HnA55aePLMt0CGnzH7cv5NzfdD9Bq2lo5tLbFrC9uh6AzZV7+NvizSlfPxsalf+2eDNH/+hxGvtxuZZ/+d2LnHt9+hLjZAmtu7dJEvtaa6sz58W1/XY9ERERSUyJouSF3GpPhOXb0j8By1E/fIxr572T9vOm218WbuTxt7dxQ+ie+IkbX+Srd73O8VfO44cPL+n3eLburu/1DLM/+tvb7N7TROWenowPTD3DjSWkW6vqe3zs9up6ymbNZd7b27h67tvMW7qtwz7x/57Kaxo6bO/L5PzBRZu4IgN/ByIiItKWEkWRNOiTsZI9PGVVfTPXP9l/LT/psnl3lOxU1DZya0otSb1779//s6cy0mU12Z9Md+7mw794pkfXik/8lmyuAuCOl9fxh+fX0JKm5UX2NLUAqSWR8esp1jQ0pyUeERER6Z2cGqNoZmuBaqAFaHb3mWY2FrgHKAPWAp9y9+ybDUJSUlHbSNmsudz42eM4+92TMx1OB7HJPZZtreb83/6Th77yvr67WBZ0sUxF2ay5PPM/p1AWZsDMtgmIsm0dxu68PZsqu17zsCsbdtb1+hzxvnHvYgB29mDWVa2fKJJ7ymbNTVi+9ppz+jkSEelrudii+GF3P8bdZ4bXs4An3f1g4MnwWoKdtY38/tlVGf9wXtPQTNmsuTz65pak+ySKcEXoovmtv7zBbS+t7XhMOKg/57K55YU1/GdYWiT+usmWQ+itHJunJ6GXVld0KOvpbT2zfHvC8uzIO5MHcf5v/5nwbzfTVu+obfO6zd9ZF78cT3C/a8L5Glv6b5ymiIiI9J1cTBTbOx+YE57PAS7IXCjZ55v3L+anjy7jtfWZbWSNrR13fSfT5teHrmuJVNU384OHOo5bin1gbd8y8dam3X2WuP3472/z6Ftb+fsbqU/Akm2+/+BbfPWu1zMdRqe++KdXE5ZnRZ4YJGshu/uVjmsjpivuy25fmKYzRbHvrmtidXltF/uKiIhIvsu1RNGBx81soZldGsomuvsWgPBzQqIDzexSM1tgZgvKy8v7KdzO7axt7PE6cT1xxrXP8sTSqBWmqSWbPk4n9rW7F3UoSxZ1eXUDZbPmsq2q40QbAOf+5gXO/+0/0xdcAje/sKZPz99eolacdLn95XW9mnm0Kw+8vmnv8764i5Xba/ja3ZlLdLtq1Wx1p7q+b/6t/2PJ1rSe7/K7Xkvr+XoqO1qIRUREJNcSxfe5+3HA2cBXzOyD3T3Q3We7+0x3n1laWtp3EfbAcVfO6/E6cT2xYntNn50705ZvTT5raFUffSDPlFgrVS5/gH5lzc4OZela+9Ld+cZ9i3loUeeJ7vqKOn76yNI+7Yad7JaWba3m3T98nIq4GURbk4yN7IsvBGLv9fbqer5yZ+eJYIexiwnCSddb2JdffoiIiEjv5FSi6O6bw8/twAPAicA2M5sMEH4mHsgkLNm8m4cWbep6xxx3/4KNmQ4hLRrCEgj5MEYxkb+8tpHX09Alurupxn/csZDfP7ead7b1/guUhuYWmuLG4nU3hh01+yZ6aenNtKcp+tW8d6hrTN7Fu7vSHaImtek+M/t/ZrbEzN4ys7vMbLCZjTWzeWa2IvwcE7f/t81spZktN7MzMxm7iIjklpxJFM1smJmNiD0HPgK8BTwMXBx2uxh4KDMRZr9zrn8hYffOVC3dUsVZ1z3XrS51fd0alo/J1NxOJv7JZbG/her6Zv7ldy+m7XxdaWmNErvm1tZeLwlxyPf+wRnXPtuhvEd/hmpMkx4ys6nAfwEz3f1IoAC4iCSTupnZ4WH7EcBZwO/MrCATsYuISO7JmUQRmAi8YGaLgVeAue7+D+Aa4AwzWwGcEV5LP/jl48tZtrWal1d37FaYTE/zuWzP/7pKUOe8uJb3/PTJLs9T09DMFQ+91emEPpIe51z/Aqf84ukO5e/t5PfU2Ny6twWxsi5qFVxb0bvlJbJsVQ7uX7iRhes6tvD25ThqSUkhMMTMCoGhwGaST+p2PnC3uze4+xpgJVFPHBERkS7lzDqK7r4aODpBeQVwWv9HlFsq63r3Ye+qv7/NH19Y0+t1kpIlVn98fnWSA3p1uT7VnZasKx7uOFNrIjc9s4o5L63rZUT9b+vu+kyHkJINOzuuQ7i5k3t51/ceZeLIEuZ/53SeWd5xMqxMLz+TDk8s3cYTS7dRNm5om/KahuYO++bD/eYid99kZr8A1gN7gMfd/XEzazOpm5nFJnWbCrwcd4qNoayNMDncpQAzZszoy1sQEZEckjOJovROQ3PvWqr+2MsZPrtKKK6au7RX589EPlld38RZ1z2flnN1Z9H3bPxoHt/t+Kll2zCMaWOGMKeTdQPTfR9NLa3dytp31vbuy5JkM+zG68kEPckmcsm233Nn8cRaV3t/jX1XURKaXBh7eD6wP1AJ3Gdmn+vskARlHd5gd58NzAaYOXOmfgEiIgIoUZR+8uXbFgApdGNL8pGlw+fxuNf99Slnw66OrVJJ991ZR3lNA8fNGNP1zu2kIwkumzW3163BXfm3W6Pf8X7jhrKul90ye6K7k7PsqOk60UtVKn9zSeey6YNEyYiSuvXtZzTtpUvmLOj2vonWHd1V2zHRVJbSqdOBNe5eDmBmfwXeS5jULbQmxk/qthGYHnf8NKKuqiIiIl3KpTGK0gt9+SV9Tz7YJlsSIKks7nraEx/4v6e5MIWJW1Zu37cMiFpasl+rO3WNHbtqJtLT3+aq8tRna3XgtF8+yz9XVqR8jvbn62lMl/+54zqX9yaYofiuV9anGtZAsB442cyGWtR8fRqwlOSTuj0MXGRmJWa2P3Aw0Rh/ERGRLilRlF4I6/v14IjOxoElks25UWNza9c79dLbW6rTtt5gIq2t3usZQHviR39bwpV/f7vfrtdT33ngTX7x2PKk2xeu63zipm/e/waH/+Cxbl3rv+7qmDh15rRfdpxltbv++PxqKhK03iXS20l6eisdy5fkK3efD9wPvAa8SVSHzybJpG7uvgS4F3gb+AfwFXfXjFkiItItShQHiL7INfpjSYoFaxN/MG9NUwb55/nreXHljrScKyaVcVvdWXi81aMupMu2VgHw1qbdrNhW3cVRHTU2t+6doOSj1z/Pgd95pMfn6Exnv5o//XNtWq7R05bpexds6NZ+f56/nhueXpl0+8dvfClheeyen1rW/WVc+3M20dd6sV7l7Tk4yVI+c/cr3P1Qdz/S3T8fZjStcPfT3P3g8HNn3P5Xu/uB7n6Iuz+aydhFRCS3KFHMM48v2co7CZKHbG6ZS0Vtu5kYU12w+zsPvMm//nF+OkICohanY348j0fTuAZi+ztbtL4SgHN/8wJn/Oo5mlt61rL5hVvmc+QVUavXsq09TzQBWlo96XXTlcR3pruzycZ88/43enyN+asrePD1Td3at7tJX3e+XMm2f6oPJHoPsi1IERERSTslilmgIo2TbFx6+0I+8qvnenTMzb2c0fQ/bl/IR36Vere4dOiP1k2ANzZWdrr9zY27AXh5dXrGgnXH9x96K2F5slaknqx7WVXftHf9wHgn//RJjvrR4wmP2diDSX5S1b6FsC/ylk/Pfpmv37OoD87cub7Is9Pdu7g7LeAiIiKS25QoZoHjr3qi0+0X/u6fnPyTrhdt70xnH+z+8FySNQx7oK/GFSWPOnlm2JtJXypqGpj39raE25Zvrea8G/6Z8rlT0Z17eWxJ4nhTmTynvaN++Dhfu7vjWLry6gbqGlv6LUHvC9c98U6/X7M740H7okX2lTXd/3JAREREBJQo5oTX1leytap3C5vnatfT7sZd19DS67UiAf7t1lf599sWtFkfMKYvl1forT3dXCIiFY+8uTUt56lvSl+MDWmYSOi6J1akIZKe+f6DHVt/axqa+dRNL7FmRy0Ab27a3d9hZQ3NeCoiIpI9tI5iHnB3rntiBZ8+YXrSfXbVpX/ijGxqTPrgz5/m3VNH8bevvr9X54nN+NifM4ECPeo7magVb1fcBDq79zQxakhRSmHc8NQKLj/14JSO7Up/Tt7SXc+9U97jY9ZW1KZ8vQXrOnYHfmrZdl5Zu5NfPr6cG/71uP7/20tB7AucdP0fEBtj/O2/vpmmM4pIfyubNTfptr5ex1dE+oZaFPPA8m3V/PrJFfznna8l3SddSxLUN7VwxUNvUV3flOFuhx0/TGdDS0xsNtE3ehhLstRgZ20j1z3ZseXrLws7rj8X86GfP92ja8f7xeP93x0zk75wS8+XlOvrlshcaP2PxdgXX0CJiIhIdlCLYh6ItUA0xHXtu2h24mn8E+nJxBR3vLyOOS+tY3BxQfcD7MSGnXWdfvBOdzK6raqe0UOLKBw0iLtfTd7NbcHaXSktcL4wtBi9HmYmbW9VeQ33JVhkPJkf/S1xgv+N+xa3eb29el+32Ep9eM8pexrbzuC7qbLvJwPqrSeXbef9B49P2/k0OY6IiEj2UaLYx/Y0tnDclfP41aeP4awjJ/X6fE0trTy7vJzTD5/YYVv8Ugc9mdlyW1X3x97FJtro6Tp2ycz66xv8c2XyGUKT54mpZZAn/eRJzjh8IqcdOoHvPrBvvNiTS7dxyKQRe19/+bYFKZ0//l2pabeEB/Ru0fSY5gTv/eznVvX6vLmsL1vhFq7r24lgvvWX3Otuedcr6/neOYdlOgwRERHpQ+p62sc2Vdaxp6mFnz+2LC3n+82TK/jybQt4Zvm+hb2ztavaz/6xjMUbKnt1DuuD/q3z3t5GVbvJai6Zs4D3/yz1LpuJbOmjlqHfP9t2ltrezPLaXWWz5vJ//+jd33Bf9lRu//vsrfiJkT5+Y/db5weSxV0sFSMiIiK5TYlijomtUVdR09jFnn1je1zrY/tF7nfVto3pxmdWcf5vO19OorPWxOgaScr7KOvo7XlTPfzvizd3e99EM+A+s7znk7JU1iX/G1pVXtNh8pnfPZOo1bL7d/z/7l3U7X17al2YhChdmlv659uXv7+x7/f+5NLtaZm5t7+kcxZbERERyT5KFKVH/vjCmqTbjr1yHs+vKKei3TISv5qX/glSsrUVdWsPuvHG27y7d8uf1KWwPMYxP56XdNtpv3yWC7pI8nuqqy8FBqLL/7xvjco9TS389JH09DzINU3NWfoPWkREZABTopgHepo0vbOtuuudUnTxLa9w/FVPUB43ucqvE8za2VudtYbFu3fBBn7TB9dPZumWqr6/SDd+34uSdPntSXyxdf3aXDpbM/Q8sXFXeltG+9Jjb21L27meWpa+c4mIiEh6aDKbPFC5p2fdUM+5/nlWXP3RXl/3uRUduzvG5lmpqE3P4vSJ1p0D+F6ChcsBtlfXc9XcpXtff/P+NwD46mlt1wbc09izBdubW3q/wHt/qk0wkQ7APa9u6NV5//D8ai794IF7X9/wVP8uWl82ay4Lv3d6v14zFcu29sMXBhl2z4Le/S2JZJPO1gAUERmo1KLYx/qqASZ22qeXb+fzN/dsLbimTsZfNbW08ofnVtPUjcQole6O6ZJo5k9IvpxEe796omfdYXekMCb0s3+c3+NjuqUbwwL7aqzb/Haz6T64qPtjK9NlbZrHI/aFs657PtMhiIiIiPSKEsV+krbZO9udZsHa9E7dP+fFtVz9yFL+9M/kYxG7ozsJcp8kM71MzNM5R0782ob9rWpP4hbF7tAkJdLflmyu4um4mZxFREQk85Qo5pB0LwGQSHV9lGDUNvR9srC6vOMYOOlaS5rWsEzmy3NSW0NSeu+JpQMzWWpudb70p1czHYaIiIjEybkximZWACwANrn7uWY2FrgHKAPWAp9y98QD27LECyt28HyC8X1daWjKnXFy3WlRPPvX6e+et3J7Ta+Ob2xO/h5nyzwufZ0ovrByR9JtTy7bPiDG8nz3gTczHYKIiIhIRuVii+LXgKVxr2cBT7r7wcCT4XVW+9zN8/n9c6uTbq+ub2J7defLJfTV7JPdPeuuur5v3UzF8l7O6FqfJFHMte6YfbXOZDb4+I0v9vk1MjH2UkRERCSb5FSiaGbTgHOAP8YVnw/MCc/nABf0c1h7ba+q55k0jLM57ZfPcuLVT3YoP+HqJ/jra5sAWFleQ9msuTzxdt90Vesq0bh/4cZOt3tvBwtmmVN+8Qyt2dKkmKL/vmcRt764NtNhiIiIiEgOyKlEEbgO+CYQ3+wz0d23AISfExIdaGaXmtkCM1tQXt7zbp/dceGNL/LFduNsUkktujMJyoK1Ue/a3ragdZCmZOjeXi7DkCmd5cdfuKVns8tmm7++vinTIYiIiIhIjsiZRNHMzgW2u/vCVI5399nuPtPdZ5aWlqY5usjGXXuSbkt3T8CFSdYXTBfrZcRLNmfXOnLJFqDPVy+sSD7OUERERESkKzmTKALvA84zs7XA3cCpZnYHsM3MJgOEn1k7beDLqyuyfiKQdHWuzLZOmhf89p/d2i8Xx/Yleq/v66JrsIiIiIhIZ3ImUXT3b7v7NHcvAy4CnnL3zwEPAxeH3S4GHspQiG24O2+3a1V7aFHudP3LxYRpoMrxoZMiIiIikoVyJlHsxDXAGWa2AjgjvM64O+ev56PXP8/zA7QLYF/lmcu3VlOe4kL2j7y5Jc3RZIfde7JzBloRERERyV05t44igLs/AzwTnlcAp2UynkSWbolaE9fu6N6i8ku3VHHY5JEdyl9aVZHWuJJ5etl2PnzohL2tU71N9Bb00RjKM697juElqf3Z/n93vpbmaERERKQryYbdrL3mnH6ORER6Ih9aFHNCV105Y4vPXxw3s2ZNQzOf+cPLfRnWXo+/vRXYt6xFXVMLNQ3N/XLtnurLuJpa1I9TRERERESJYj+qb0q8mHu8Z9/Zt3THlX97uy/DaePl1TvbvL7xmVUcecVj/XZ9ERERERHJHjnZ9TRX9XTB9uqG/ht7tiZJF9nHl2ylpVWtbCIiIiIiA4kSxT5QNmsuhycYb5gL2ueyl96e0rKVIiIiIiKSw5Qo9pG3t7RdGuOdbTW8s62m02O0SLqIiIiIiGQDjVHsYy+s7H7y97mb5/dhJN2jTqYiIiIiIqJEsY8lG/uXafNXJ152Q4u3i4hkLzMbbWb3m9kyM1tqZu8xs7FmNs/MVoSfY+L2/7aZrTSz5WZ2ZiZjFxGR3KJEMYv1ZdL26dkdl91obO56VlYREcmoXwP/cPdDgaOBpcAs4El3Pxh4MrzGzA4HLgKOAM4CfmdmBRmJWkREco4SxTQomzWXr+TBYu51jdm5bqKIiICZjQQ+CNwM4O6N7l4JnA/MCbvNAS4Iz88H7nb3BndfA6wETuzPmEVEJHcpUUyTuW9uyXQIIiKS3w4AyoE/mdnrZvZHMxsGTHT3LQDh54Sw/1RgQ9zxG0NZG2Z2qZktMLMF5eXl7TeLiMgApURR2nBNZyMikq0KgeOAG939WKCW0M00CUtQ1uE/eXef7e4z3X1maWlpeiIVEZGcp0Qxiz361tb+v6jyRBGRbLUR2OjusSmy7ydKHLeZ2WSA8HN73P7T446fBmzup1hFRCTHaR1F2avVobFFE9qIiGQjd99qZhvM7BB3Xw6cBrwdHhcD14SfD4VDHgb+bGbXAlOAg4FX+j/y7FA2a26mQ5B2kv1O1l5zTj9HIiKJKFGUvY67cl6mQxARkc59FbjTzIqB1cCXiHoH3WtmlwDrgU8CuPsSM7uXKJFsBr7i7i2ZCVtERHKNEkUREZEc4e6LgJkJNp2WZP+rgav7MiYREclPGqMoIiIiIiIibShRFBERERERkTaUKIqIiIiIiEgbShRFRERERESkDSWKIiIiIiIi0oYSRREREREREWkjZxJFMxtsZq+Y2WIzW2JmPwrlY81snpmtCD/HZDpWERERERGRXJZL6yg2AKe6e42ZFQEvmNmjwIXAk+5+jZnNAmYB38pkoCIiIiKSmrJZc5NuW3vNOf0YicjAljMtih6pCS+LwsOB84E5oXwOcEH/RyciIiIiIpI/ciZRBDCzAjNbBGwH5rn7fGCiu28BCD8nJDn2UjNbYGYLysvL0xZTY3Nr2s4lIiIiIiKSDXIqUXT3Fnc/BpgGnGhmR/bg2NnuPtPdZ5aWlqYtpj/PX5e2c4mIiIiIiGSDnEoUY9y9EngGOAvYZmaTAcLP7f0ZS2OLWhRFRERERCS/5EyiaGalZjY6PB8CnA4sAx4GLg67XQw8lJEARURERERE8kQuzXo6GZhjZgVECe697v53M3sJuNfMLgHWA5/sz6AM68/LiYiIiIiI9LmcSRTd/Q3g2ATlFcBp/R+RiIiIiIhIfsqZrqfZytSgKCIiIiIieUaJooiIiIiIiLShRFFERERERETayJkxitlmyebdnHP9Cxw1bVSmQxEREREREUkrJYop2LCzjnOufwGANzbuznA0IiIiElM2a26mQxARyQvqepqCbVX1mQ5BRERERESkzyhRFBERERERkTaUKIqIiIiIiEgbShRT4JkOQEREREREpA8pURQREREREZE2lCiKiIiIiIhIG1oeQ0RERERyQrLlT9Zec04/RyKS/9SiKCIiIiIiIm0oURQREREREZE2lCiKiIiIiIhIG0oUU+BaH0NERDLEzArM7HUz+3t4PdbM5pnZivBzTNy+3zazlWa23MzOzFzUIiKSa5QoioiI5JavAUvjXs8CnnT3g4Enw2vM7HDgIuAI4Czgd2ZW0M+xiohIjlKiKCIikiPMbBpwDvDHuOLzgTnh+Rzggrjyu929wd3XACuBE/spVBERyXFKFFNglukIRERkgLoO+CbQGlc20d23AISfE0L5VGBD3H4bQ1kbZnapmS0wswXl5eV9ErSIiOQeJYop0BhFERHpb2Z2LrDd3Rd295AEZR1qMHef7e4z3X1maWlpr2IUEZH8UZjpAERERKRb3gecZ2YfBQYDI83sDmCbmU129y1mNhnYHvbfCEyPO34asLlfIxYRkZyVMy2KZjbdzJ42s6VmtsTMvhbKk872JiIiki/c/dvuPs3dy4gmqXnK3T8HPAxcHHa7GHgoPH8YuMjMSsxsf+Bg4JV+DltERHJUziSKQDPwDXc/DDgZ+EqY0S3hbG99ydX3VEREssc1wBlmtgI4I7zG3ZcA9wJvA/8AvuLuLRmLUkREckrOdD0NA/Rjg/WrzWwp0aD884FTwm5zgGeAb2UgRBERkX7h7s8Q1Xe4ewVwWpL9rgau7rfAREQkb+RSi+JeZlYGHAvMJ/lsb+2P0axuIiIiIiIi3ZBziaKZDQf+Anzd3au6e1w6Z3UzrY8hIiIiIiJ5LKcSRTMrIkoS73T3v4bibWGWN9rN9tZnNEZRRERERETyWc4kihY1490MLHX3a+M2JZvtTURERERERFKQM5PZEK0f9XngTTNbFMq+QzS7271mdgmwHvhkZsKT/7+9u4+PqrrzOP75kWcSSEISIA8EEgjPCoFAeEZFIIAtumoXbSlaEbutXR9wW6hdq6112Qd9de223WWrbdVarVVbW227Fu26dlWgFQREFgRUHiRUVFAEAzn7x9yEmWRmkgnJTO7wfb9e85o7Z+7ce345k3vnN+fcMyIiIiIikhx8kyg6554HIl0cGHa2ty6rSzx3JiIiIiJRDVrxZNjy3asWxLkmIsnDN0NPRUREREREJD6UKIqIiIiIiEgI3ww97U704xgiIiIi3Z+GpIp0nHoUO0DXKIqIiIiISDJToigiIiIiIiIhlCiKiIiIiIhICCWKIiIiIiIiEkKJooiIiIiIiITQrKciIiLiO5FmsxQRkc6hHkUREREREREJoUSxA5x+H0NERERERJKYEkUREREREREJoURRREREREREQihRFBERERERkRCa9VREREREzijRZs3dvWpBHGsi0n2pR1FERERERERCKFEUERERERGREEoUO+CtQ0cTXQUREREREZEuo0SxA7786CuJroKIiIiIiEiXUaIoIiIiIiIiITTrqYiIiIiIJ9KMqJoNVc406lEUERERERGREL5JFM3sXjOrN7PNQWV9zOxpM9vu3ecnso4iIiIiIiLJwDeJIvAjoK5F2QpgjXOuCljjPRYREREREZHT4JtE0Tn3HHCoRfFC4Mfe8o+BC+NZJxERkXgxswFm9qyZbTWzLWZ2nVcecXSNma00sx1mts3M5iau9iIi4je+SRQj6Oec2w/g3feNtKKZLTOz9Wa2/uDBg3GroIiISCc5ASx3zo0AJgFfNLORRBhd4z23CBhFYETO98wsJSE1FxER3/F7othuzrnVzrka51xNUVFRoqsjIiISE+fcfufcn73lI8BWoJTIo2sWAg85544753YBO4CJca20iIj4lt8TxQNmVgzg3dcnuD4iIiJdzswGAdXAS0QeXVMKvBX0sj1eWcttacSNiIi04vffUXwCWAKs8u5/mdjqiIiIdC0zywEeBa53zh02s4irhilzrQqcWw2sBqipqWn1fKJF+k07ERHpWr5JFM3sp8A5QKGZ7QG+TiBB/JmZXQW8CVyauBqKiIh0LTNLI5Ak/sQ595hXfMDMip1z+1uMrtkDDAh6eRmwL361FUkukb602L1qQZxrIhIfvkkUnXOXRXhqVlwrIiIikgAW6Dq8B9jqnLsr6KlIo2ueAB40s7uAEqAKWBu/GouIiJ/5JlEUERE5w00FFgObzGyDV/ZVIoyucc5tMbOfAa8SmDH1i865k3GvtYiI+JISRRERER9wzj1P+OsOIcLoGufct4BvdVmlREQkafl91lMRERERERHpZEoURUREREREJIQSRREREREREQmhRFFERERERERCaDIbEREREZEOivT7iqDfWBR/U4+iiIiIiIiIhFCiKCIiIiIiIiGUKIqIiIiIiEgIJYoiIiIiIiISQomiiIiIiIiIhNCspyIiPvFX1aU89vLeRFdDRETaKdKMqJoNVfxAiaKIiE+k9LBEV0GkS0T7eQEREUkMDT0VEfEJa5EnpqUocRQREZGuoR5FEUlKK+cN5x9+81qiq9GpcjLSQh7PGdmfJzftT1BtWvt0bTk/eenNRFdDRKTb05BU8QP1KIpIUrpm5uBEV6HTpbQ4Yl9eW95p266t6HPa2/j7C0Z2Qk1ERESkO1CiKCLSjd1w/lAAzh1WhAWNPR1XnsfUIYWdtp/Kopzm5d/fODPiemeV5oYt33TrHDLTUjqtPiIiIpJYShRFYnDXp8bEZT+fqimLy34629JpFZ2+zV3/ML/Tt+kHA/pkAXBWWe+wz/fPzYz6+plDi2LaX2FOevPykL45EddbPHlgq7KS3Ex6ZYYOi51eFUhib5ozNKZ6iIiISPegRFE6LDcrjd2rFnDNjEoAJlW2Hrr24NLamLc7pix8j0WwnXe0nTxUl+fFvO+21I3uz7XnDun07QZ79G+m8E+XxCch7SxD+wUSi5nD2pec9DC4/6qJ7VrXWs7gAhTmZLS/cglSkJ3e9kpRPP6FqfzsmslhnxvevxerLj4bgBdWnsd5w/u2Wufa82J7n37pvKqQx2uWt+5VfO2bdWH3tWTKoObldG987OrFNTyzfCZXTaukT3Y6n585mO3fmtdmPVbOG841MwPHlNystDbWFhFJLoNWPBn2JpIIShSlw5qGoC2fM4wfXTmBB66q5ZLxp3rCstJSmNKBoXHXz47eA1Gcm0mPdvxMgHOty0rzspqXZwV94P2r6tKI2/ns5IGsvXkWG78+h57pqdw0d1ib+z4d4wfmd8p2Kouy+Y/F4ztlW215eNlkHvvCFIy22+U/P1vDltvqmF51KqkcWNAzpv3dGaeeXYDzR/Tr0OvWf+3809pvYU4GEyv6MHVIIZ8YU8I3Fo5u/uteWF1Kb68Hrzg3i6unBxKr4CRufHn499H0qsKwkyWkpwZOB1ne8NHBQUNRm3o3M9NS2kzSf/WlaXxtwQiy0lOoLMohKz2FP//9bFbMG05a0EWWj3y+dRL83N+dyzUzB7Ny3gg2fn0O/7vivKj7EhERka6TFImimdWZ2TYz22FmKxJdn0QZXRoYonbZxNAJLr554eiIr7k9ynMQmH2r6fb0DTOayx+8upbvfWYcEPiAec6wvqSm9OBfLj31Ab7pg2Cql9Q19ThmpLZ+210alGCmtpEENvVe3Onta0jfHH54xYTm5wcXZQNw4dgSfnjFhOaEcFJlHx5aNql5vXuumMCjfzOFL9cNC6l3SzfOHkrfXpkx9W48e9M5fHJMSfPjOy46q12vu+2To5qXxw7IAwKJakesuXEmc0f1b26/rnLNzErys9MZV55Pz4y2r1GbOKgPWemB9e6/aiIr5w3ne58eF/U1TdfpNRk7IC+m9ujfO/owzWDBQzA33DKb0rzIr60uz+PhZZPYvWpByHsrtYeF9IR+c+GocC9vl4zUFL5zWTUD+kROpicPLuD+qyayevF4Zgwt4uyyXMzgt9dPb11n730VzosrZ/Hiylmtyn9/40y2fqMu6PEMfn/jTJZ5owmCDevfi6XTW5c3+fWXpvHDKycwYVDoCISzSnMpD/rCIDcrjewMTcwtIiKSKL4/C5tZCvBdYDawB1hnZk84515NbM1au3BsCb/YsK9Dr91821y27H2fv179Ykj5+IH5/OmNdwG4ckoFyx/ZyFfqhnHe8L5cfd96aiv60LdX5B6AS2vK+NovNrerDlX9egGBxHDK4LZ7Ckd7PY7V5Xms2/0u180ayi0XpFFRmM3hYw3M/9f/4Z0PP+bKqYP4St1wLqstJyO1B4c+/Djqdpt6NC4eX8bFQQnmLReMxAyunFpBw8nG5g/rGWk9WPNaPQAD+vRk5x3zOel1N44fmN9mD164XrINt8wmMy2F9JQeVH71KQAeXjaJv179ItnpKVQUZnP3ZdUsnV5BblYaAwuy+erjm0K2cXltOYsnDWREcW+m/eMz7Hn3o5AeoSVTBrLh4fe4cmoFv938NvVHjjNreF/WvFbPdy8fx/b6I3z799sZ3r8Xr719JGTbm2+bG3bIZjhD++Xwfwc+aHO9Xpmp/PzzU5j77eeay+646CwuDbqeclx5PndeOobhxb041nCSi7//Qsg25o7qR27PUwne9KoiplcV8cHxE1H3fd35VZTmZ3HTIxuBQBKx8etzmofj7F61oHl53uj+jCgOva7vgaW1/HLDXr7zzI6w27/1EyN5eP0ebr9wNJWF2VR/82kA8nqms3L+CMYNzOfxl/fyh20HuetTY5gxtIgbHt7A3YuqyfeGmE6qLODpG2bw2Mt7ueDs4pDtL548iHED83ngxTf56do3KeqVwcEjx6PGHM5nJg3kN5vf5sKxrXvAm3po7/vcqSG9w/v35rm/O5cZ//wsAL+7fkbz9Ye7Vy3gR3/cxa2/epWvLRgBtL7u8cm/ncYre94nIzX0C4AhfQPHgoVjS1j93E5mj2x/r+vooMlwHv/CFN48dJTrHtpASjtGCYiInKmiDT/VT2pIV/F9oghMBHY453YCmNlDwEKgWySKy2cPpVdmKicaHUunV3L1jEoW3P08EJiO/qVdhwCYNqSQ53f8pfl1N88fwbee2grAf90wg5yMVGorC9j6jTpG3PJbAP5w0zkMKszm9l+/yoSKPswd1b85cRreP/BBbvbIfpTlnxpuGWzb7XWtPgAGuyZMb0F7DkbfuayaisLs5sezRvRj3e53GdAni7L8QI9BVnoKkyoLeHLTfpbPGUZmWgrjvKFy2w8Ekp5FEwawbEYl63YfYsbQIq776Qbe/6gh4jDRzwVNpBI8xK2f15tUMzDQg9Gjh9EjTPI3ZkAegwp68ssN+xhV0purp1fy+Mt7QxKbJnk9T/U6LZowgGe31TPG66kJHm57dlle8/LgomxeP/ghAHdfVs0nzi5uTubuWTKB+17YHTI09qLqMuafVUxGagrP3HQOxxtOUhA07K/+SD6/3fw23/30OGbd+d+snDeci8eXcazhJDlhemI+N7WCe/+4C4CqvjksHFvC4smDWLfrEEvvW8/y2UO5ekYlqT2MP77+DkvuXcsPPlvD1v2HmTWiHyV5mZxsPDWeN9J7ITh5f3BpLQ2Nju0HjnD7k1tZNDH8zznkZKRyUXUpj3tJVqNzPLXpbSqLTr2PFpxVzE2PbKRf71N/g19dO43MtFNtffG4spBhqc9/5VwaG6G8oCfL5wzDzLh7zXYeXFrL5T94qXm9qUMKuWLqqfdPfs803j3aAASGWy4cW0pBdgZ/2HaQqUMKKczJ4P6rWl9/W9WvF1+pGx5Sv7W7A//jo0py+cbCUby06x1unj+C3Kw09r1/jOLcTPr3zuSBF9/gP57bGfbv02RAn5489+Vzo67TUnlBTy6vLWdceT7DvONCkyumVoTE3dKoklxGlUS+ZnhUSe5pfUCpLs9nTFkem/e+H3Kdo4iIiCSeuXAXcvmImV0C1DnnlnqPFwO1zrlrW6y3DFgGUF5ePv6NN97o8D4PHD5G7R1rmh9vunUOS+5dy/Di3lQWZjN3VH9ONjo+/PgEI/r3bnU93YfHT9DU2fN6/Ye89e5R5o3uj5nxlw+O03CykeLcLHbUf0BpXlbzML0mh481sG7XIWa1ce3Ue0c/JjcrDTNjR/0RKgtzeHHXO0yuLKDhpGu+Jqn+yDEOf9RAfs90CnIyONno2Pb2EUaWhJ9tMVbOOd472tDc89LkWMNJ9rx7tLl3ItjW/Yep6ptDassfjuugnQc/YGBBdrt6LXb95UOKemWETbbas5+SvKywPxNw+FgDew59RHZGCgMLssO8ums1nGxk41vvcayhkbHleSHx7X//I4pzQ79QeOeD4yGJaZMXXn+HkrzMmGJwzrFl3+GQ3qRw6zQ6mttozdYDnDOsb0ibHf34BJmpKWGvUf34RKAXOdr1qycbHTsPftDcO+6cY9/7x0ISdIAjxxo4fqIx7pPmHDnWwBvvHCUnI5VBhfF/jyQjM/uTc64m0fXwi5qaGrd+/fq471eTdYh0PvU0SlvaOkcmQ6J4KTC3RaI40Tn3pUivSdSJUERE4kuJYmy6+vyohFAkfpQoSlvaOkcmw2Q2e4ABQY/LgI5dCCgiIiIiIiJJcY3iOqDKzCqAvcAi4PLEVklEREREJHEi9eCrp1Hay/eJonPuhJldC/wOSAHudc5tSXC1REREREREfMv3iSKAc+4p4KlE10NERERERCQZJEWiKCIiIiIibevIpFIarnpmUqIoIiIinUqzm4okl1j/p5VYJodkmPVUREREIjCzOjPbZmY7zGxFousjIiL+oB5FERGRJGVmKcB3gdkEfk5qnZk94Zx7NbE1E5EzVayzsUbrzVTPZddSoigiIpK8JgI7nHM7AczsIWAhoERRRLpMR4afd+Zr4pF0ngk/P2LOuUTXIe7M7CDwxmluphD4SydUpztQLN1PssQBiqU7SpY4oO1YBjrniuJVme7GzC4B6pxzS73Hi4Fa59y1QessA5Z5D4cB205zt8n0/gqWrHFB8saWrHFB8saWrHFB94wt6jnyjOxR7IwPDWa23jlX0xn1STTF0v0kSxygWLqjZIkDkiuWLmJhykK+IXbOrQZWd9oOk7RNkjUuSN7YkjUuSN7YkjUu8GdsmsxGREQkee0BBgQ9LgP2JaguIiLiI0oURUREktc6oMrMKswsHVgEPJHgOomIiA+ckUNPO0mnDdPpBhRL95MscYBi6Y6SJQ5Irlg6nXPuhJldC/wOSAHudc5t6eLdJmubJGtckLyxJWtckLyxJWtc4MPYzsjJbERERERERCQyDT0VERERERGREEoURUREREREJIQSxQ4wszoz22ZmO8xsRaLr08TMdpvZJjPbYGbrvbI+Zva0mW337vOD1l/pxbDNzOYGlY/3trPDzO42M/PKM8zsYa/8JTMb1Il1v9fM6s1sc1BZXOpuZku8fWw3syVdEMetZrbXa5cNZja/u8fhbW+AmT1rZlvNbIuZXeeV+6pdosThu3Yxs0wzW2tmG71YbvPKfdUmbcTiu3aRAOum58aOsBjPSX4R5XiYDLHFfHz0EzNLMbOXzezX3uNkiSumz65+YWZ5ZvZzM3vN+3+b7Mu4nHO6xXAjMBnA60AlkA5sBEYmul5e3XYDhS3K/glY4S2vAP7RWx7p1T0DqPBiSvGeWwtMJvD7W78B5nnlXwD+3VteBDzciXWfAYwDNsez7kAfYKd3n+8t53dyHLcCN4VZt9vG4W2zGBjnLfcC/s+rs6/aJUocvmsXb7853nIa8BIwyW9t0kYsvmsX3br3ubGD8bT7nOSnW5TjYTLEFtPx0W834EbgQeDX3uNkiWs37fzs6qcb8GNgqbecDuT5MS71KMZuIrDDObfTOfcx8BCwMMF1imYhgTcr3v2FQeUPOeeOO+d2ATuAiWZWDPR2zr3gAu/k+1q8pmlbPwdmNX1zf7qcc88BhxJQ97nA0865Q865d4GngbpOjiOSbhuHF8t+59yfveUjwFagFJ+1S5Q4IumWcXj1d865D7yHad7N4bM2aSOWSLptLAL479wYVYznJN/owHHdNzpwfPQNMysDFgA/CCr2fVxR+Do2M+tN4MumewCccx87597Dh3EpUYxdKfBW0OM9RP/QGU8O+C8z+5OZLfPK+jnn9kPgBAH09cojxVHqLbcsD3mNc+4E8D5Q0AVxNIlH3ePVntea2SvecKamoQa+icMbsldN4Bta37ZLizjAh+3iDT/aANQTSHZ82yYRYgEftoucEX/TSP9nvtTO47qvxHh89JNvA18GGoPKkiEuiO2zq19UAgeBH3rDhX9gZtn4MC4lirEL14MW7VvweJrqnBsHzAO+aGYzoqwbKY5o8XWX2Duz7vGI6fvAYGAssB+48zTqFPc4zCwHeBS43jl3ONqqHahb3OIJE4cv28U5d9I5NxYoI9CjNjrK6n6MxZftIvqb+kkMx3VfifH46AtmdgFQ75z7U6Lr0kVi+ezqF6kEhq5/3zlXDXxIYKip7yhRjN0eYEDQ4zJgX4LqEsI5t8+7rwceJzAU6IA3NAvvvt5bPVIce7zlluUhrzGzVCCX9g+z7Ih41L3L29M5d8A7eTUC/0mgXXwRh5mlEfgw8RPn3GNese/aJVwcfm4Xr/7vAX8gMGTSd20SKRa/t8sZ7Ez4m0b6P/OVGI/rvtTO46NfTAU+aWa7CQzpPs/MHsD/cQExf3b1iz3AnqBRMj8nkDj6Li4lirFbB1SZWYWZpROYIOGJBNcJM8s2s15Ny8AcYDOBui3xVlsC/NJbfgJYZIFZASuAKmCt1xV+xMwmedfyfLbFa5q2dQnwjHdNUFeJR91/B8wxs3xviNscr6zTNB0UPBcRaJduH4e373uArc65u4Ke8lW7RIrDj+1iZkVmluctZwHnA6/hszaJFosf20WAbnpu7GSR/s98owPHdd/owPHRF5xzK51zZc65QQT+r55xzn0Gn8cFHfrs6gvOubeBt8xsmFc0C3gVP8blusGMOn67AfMJzBT2OnBzouvj1amSwCxzG4EtTfUicD3OGmC7d98n6DU3ezFsw5sl0CuvIfCP+jrwb4B55ZnAIwQmkVgLVHZi/X9KYJhZA4FvYq6KV92Bz3nlO4AruyCO+4FNwCsEDhLF3T0Ob3vTCAwdewXY4N3m+61dosThu3YBzgZe9uq8Gbglnv/ncYrFd+2iW/PftNudG08jlpjOSX65RTkeJkNsMR8f/XYDzuHUrKe+j4sOfHb1y43A5RPrvffjLwjMsO27uJpOpiIiIiIiIiKAhp6KiIiIiIhIC0oURUREREREJIQSRREREREREQmhRFFERERERERCKFEUERERERGREEoURUREREREJIQSRREREREREQnx/6OjGbQwMPy5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(10)\n",
    "ax[0,0].plot(vmaxs)\n",
    "ax[0,0].set_title('Vmax convergence:')\n",
    "\n",
    "ax[0,1].plot(ks)\n",
    "ax[0,1].set_title('kgrp convergence:')\n",
    "\n",
    "ax[1,0].plot(equilibration2['tot_boosts'])\n",
    "ax[1,0].set_title('Boost magnitudes:')\n",
    "\n",
    "\n",
    "ax[1,1].hist(equilibration2['tot_boosts'][-20000:],bins=55)\n",
    "ax[1,1].set_title('Boost magnitude histogram\\n(should be gaussian)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a trajectory reporter and do some production run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "sim.reporters.append(DCDReporter('gamd_traj.dcd', dcdstride))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-76547711d2fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mone_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;34m\"\"\"Advance the simulation by integrating a specified number of time steps.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendStep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrunForClockTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstateFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointInterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36m_simulate\u001b[0;34m(self, endStep, endTime)\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0mstepsToGo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnextSteps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstepsToGo\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Only take 10 steps at a time, to give Python more chances to respond to a control-c.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m                 \u001b[0mstepsToGo\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/openmm.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m  11764\u001b[0m             \u001b[0mthe\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtime\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mto\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11765\u001b[0m         \"\"\"\n\u001b[0;32m> 11766\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCustomIntegrator_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  11767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11768\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgetPerDofVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sim.step(int(200*one_ns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_k(nrgs, threshold, kt=1):\n",
    "    arr = np.array(nrgs)\n",
    "    k = 0.2\n",
    "    while np.std(arr - (arr - threshold)*(k)) > kt:\n",
    "        k+=0.001\n",
    "        #print(k,np.std(arr - (arr - threshold)*(k)))\n",
    "    return k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vmax, Vmin, Vavg, Vstd = get_statistics(tot_nrgs, 0)\n",
    "E = Vavg\n",
    "k = calc_k(tot_nrgs, np.mean(tot_nrgs), kt=10*SIGMA0)\n",
    "\n",
    "#E,k = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0=SIGMA0*10)\n",
    "sim.integrator.setEtot(E)\n",
    "sim.integrator.setKtot(k)\n",
    "\n",
    "Vmax, Vmin, Vavg, Vstd = get_statistics(grp_nrgs, 0)\n",
    "E = Vavg\n",
    "k = k = calc_k(grp_nrgs, np.mean(grp_nrgs), kt=10*SIGMA0)\n",
    "#E,k = calc_parameters(Vmax, Vmin, Vavg, Vstd, sigma_0=SIGMA0*3)\n",
    "sim.integrator.setEgrp(E)\n",
    "sim.integrator.setKgrp(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "production = dict()\n",
    "production['grp_nrgs'] = list()\n",
    "production['tot_nrgs'] = list()\n",
    "\n",
    "production['grp_boosts'] = list()\n",
    "production['tot_boosts'] = list()\n",
    "\n",
    "def report(sim, prod):\n",
    "    grp_nrg = sim.context.getState(getEnergy=True, groups={2}).getPotentialEnergy()/kilojoules_per_mole\n",
    "    tot_nrg = sim.context.getState(getEnergy=True, groups={0}).getPotentialEnergy()/kilojoules_per_mole\n",
    "        \n",
    "    grp_boost = sim.integrator.getGrpBoost(grp_nrg)/kilojoule_per_mole\n",
    "    tot_boost = sim.integrator.getTotBoost(tot_nrg)/kilojoule_per_mole\n",
    "        \n",
    "    prod['grp_boosts'].append(grp_boost)\n",
    "    prod['tot_boosts'].append(tot_boost)\n",
    "        \n",
    "    prod['grp_nrgs'].append(grp_nrg)\n",
    "    prod['tot_nrgs'].append(tot_nrg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "etot = list()\n",
    "ktot = list()\n",
    "egrp = list()\n",
    "kgrp = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23f8d82f715f413f93e727ea855e7c63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=200000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-73120bd6ad4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mkgrp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0msim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;34m\"\"\"Advance the simulation by integrating a specified number of time steps.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendStep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrunForClockTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstateFile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpointInterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/app/simulation.py\u001b[0m in \u001b[0;36m_simulate\u001b[0;34m(self, endStep, endTime)\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0mstepsToGo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnextSteps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstepsToGo\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Only take 10 steps at a time, to give Python more chances to respond to a control-c.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m                 \u001b[0mstepsToGo\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentStep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_conda/lib/python3.7/site-packages/simtk/openmm/openmm.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m  11764\u001b[0m             \u001b[0mthe\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtime\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mto\u001b[0m \u001b[0mtake\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11765\u001b[0m         \"\"\"\n\u001b[0;32m> 11766\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_openmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCustomIntegrator_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  11767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11768\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgetPerDofVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for _ in range(20):\n",
    "    sim.step(500)\n",
    "    report(sim, production)\n",
    "    \n",
    "    \n",
    "for _ in tqdm.tqdm_notebook(range(200000)):\n",
    "    report(sim, production)\n",
    "    if _ % 30 == 0:\n",
    "        nrgs = production['tot_nrgs']\n",
    "        E = np.mean(nrgs[-100:])\n",
    "        k = calc_k(nrgs, np.mean(nrgs[-100:]), kt=10*2.6)\n",
    "        sim.integrator.setGlobalVariableByName('Etot', E)\n",
    "        sim.integrator.setGlobalVariableByName('ktot', k)\n",
    "        etot.append(E)\n",
    "        ktot.append(k)\n",
    "        \n",
    "        nrgs = production['grp_nrgs']\n",
    "        E = np.mean(nrgs[-100:])\n",
    "        k = calc_k(nrgs, np.mean(nrgs[-100:]), kt=10*2.6)\n",
    "        sim.integrator.setGlobalVariableByName('Egrp', E)\n",
    "        sim.integrator.setGlobalVariableByName('kgrp', k)\n",
    "        egrp.append(E)\n",
    "        kgrp.append(k)\n",
    "    \n",
    "    sim.step(500)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0c85b9490>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(production['tot_boosts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0c92e0750>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(production['grp_nrgs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0c88b95d0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(production['tot_nrgs'])\n",
    "plt.plot(np.repeat(etot,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43964.12876032861"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = max(production['tot_nrgs'])\n",
    "k = 0.2\n",
    "1 - (E*k)/2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff0c88e4a50>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(production['grp_nrgs'])\n",
    "plt.plot(np.repeat(egrp,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}