{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# CS207 Final Project Presentation - Group 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Motivation\n",
    "\n",
    "Many of the chemical processes important to industry and to our everyday lives, such as combustion, are complex, involving multiple reactions and species. Chemical kinetics allows us to analyze how reaction rates and species concentrations evolve over time and vary with initial conditions. This analysis often requires numerical methods, motivating the development of a Python 3 library that can:\n",
    "* Compute reaction rates for reaction systems of arbitrary size\n",
    "* Solve coupled reaction rate ODEs to determine how reaction rates and species concentrations evolve with time\n",
    "* Visualize the computed results to facilitate understanding of production yields and how quickly the system evolves\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Library installation\n",
    "\n",
    "It's super easy to install our library. Just one-line pip command:\n",
    "```bash\n",
    "pip install pychemkin\n",
    "\n",
    "```\n",
    "\n",
    "If you want to run tests, do the following:\n",
    "```bash\n",
    "\n",
    "git clone https://github.com/cs207group4/cs207-FinalProject.git\n",
    "\n",
    "cd cs207-FinalProject\n",
    "\n",
    "python setup.py test\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic Functionality\n",
    "\n",
    "The core function of the library is to calculate reaction rates for an arbitrarily-sized system of elementary reactions. Both reversible and irreversible reactions are allowed. Thermodynamic data from all species come from NASA and are stored in an sqlite database. The following example uses an xml file containing irreversible reactions, reversible reactions and species from the extended NASA database. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading xml input file\n",
      "chemical equations:\n",
      "[\n",
      "C + H [=] CH\n",
      "CH + CH3 + CH3 [=] C3H7\n",
      "OH + OH =] HO2 + H\n",
      "CH3 =] H + H + CH\n",
      "O2 + CH =] HO2 + C\n",
      "]\n",
      "species: ['H', 'OH', 'O2', 'HO2', 'C', 'CH', 'CH3', 'C3H7']\n",
      "reversible: [ True  True False False False]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pychemkin import chemkin, ChemSolver, ChemViz, simpleIO\n",
    "rxnsystem = chemkin('demo_xml/demo.xml')\n",
    "\n",
    "print(rxnsystem.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress rates\n",
      "[  1.81881759e+12   1.52699256e+14   1.02542530e+13   1.55398795e+13\n",
      "   6.91351212e+13]\n",
      "Reaction rates\n",
      "[  3.95151944e+13  -2.05085060e+13  -6.91351212e+13   7.93893742e+13\n",
      "   6.73163036e+13  -2.04475681e+14  -3.20938392e+14   1.52699256e+14]\n"
     ]
    }
   ],
   "source": [
    "T = 1500 #temperature in K\n",
    "x = np.ones(8)\n",
    "print(\"Progress rates\")\n",
    "print(rxnsystem.progress_rate(x,T))\n",
    "print(\"Reaction rates\")\n",
    "print(rxnsystem.reaction_rate(x,T))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Chemkin Solver\n",
    "\n",
    "Our reaction rate function returns the right-hand side of a system of coupled ODEs, \n",
    "\n",
    "$$\\frac{d\\textbf{x}}{dt} = \\text{reaction_rate}(\\textbf{x}, t), $$\n",
    "where $\\textbf{x}$ is a vector of species concentrations and $t$ is the time.\n",
    "\n",
    "A natural question is to ask - how do the reaction rates and concentrations vary with time? If one is interested in improving product yields, minimizing the formation of byproducts, or investigating the efficiency of particular combinations of species, it is essential to understand the time evolution of the reaction system. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Chemkin Solver, cont.\n",
    "\n",
    "In order to solve these ODEs, we created a ChemSolver class that wraps around the SciPy library's scipy.integrate.ode function. The user can choose among several algorithms offered by SciPy, including ```vode```, ```lsoda```, ```dop853```, and ```dopri5```. By default, we use the ```lsoda``` algorithm, which switches automatically between methods optimized for stiff and non-stiff problems. Reaction rate systems are often [stiff](https://blogs.mathworks.com/cleve/2014/06/09/ordinary-differential-equations-stiffness/), but in intervals when the system is not stiff, using non-stiff methods may be more efficient.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading xml input file\n"
     ]
    }
   ],
   "source": [
    "# Solve an ODE\n",
    "x_init = np.ones(8)\n",
    "T = 1000\n",
    "\n",
    "# integration end time\n",
    "t_max = 5.e-13\n",
    "\n",
    "# step size\n",
    "dt = 1.e-16\n",
    "\n",
    "cs = ChemSolver(chemkin('demo_xml/demo.xml')).solve(x_init, T, t_max, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.00000000e+00,   1.00000000e-16,   2.00000000e-16, ...,\n",
       "          4.99700000e-13,   4.99800000e-13,   4.99900000e-13]),\n",
       " array([[  1.00000000e+00,   1.00325822e+00,   1.00647891e+00, ...,\n",
       "           8.26670028e-01,   8.26579041e-01,   8.26488078e-01],\n",
       "        [  1.00000000e+00,   9.99031963e-01,   9.98065799e-01, ...,\n",
       "           1.71175230e-01,   1.71146842e-01,   1.71118465e-01],\n",
       "        [  1.00000000e+00,   9.93225497e-01,   9.86564355e-01, ...,\n",
       "          -5.39377464e-14,  -5.49187615e-14,  -5.59179299e-14],\n",
       "        ..., \n",
       "        [  1.00000000e+00,   9.89862168e-01,   9.79988834e-01, ...,\n",
       "           1.51668627e+00,   1.51679196e+00,   1.51689762e+00],\n",
       "        [  1.00000000e+00,   9.88358351e-01,   9.77066515e-01, ...,\n",
       "           1.12134402e-04,   1.11965734e-04,   1.11797319e-04],\n",
       "        [  1.00000000e+00,   1.00507344e+00,   1.00998060e+00, ...,\n",
       "           1.29581710e+00,   1.29581710e+00,   1.29581710e+00]]),\n",
       " array([[  3.27729812e+13,   3.23924469e+13,   3.20226980e+13, ...,\n",
       "          -9.10004133e+11,  -9.09753230e+11,  -9.09502423e+11],\n",
       "        [ -9.68974751e+12,  -9.67099653e+12,  -9.65229992e+12, ...,\n",
       "          -2.83918897e+11,  -2.83824736e+11,  -2.83730622e+11],\n",
       "        [ -6.83222464e+13,  -6.71714499e+13,  -6.60554544e+13, ...,\n",
       "           5.58921338e+00,   5.69126607e+00,   5.79521417e+00],\n",
       "        ..., \n",
       "        [ -1.02735386e+14,  -1.00033398e+14,  -9.74466766e+13, ...,\n",
       "           1.05702058e+12,   1.05671499e+12,   1.05640954e+12],\n",
       "        [ -1.18218456e+14,  -1.14633125e+14,  -1.11224027e+14, ...,\n",
       "          -1.68796287e+09,  -1.68542107e+09,  -1.68288311e+09],\n",
       "        [  5.15914857e+13,   4.98863389e+13,   4.82666794e+13, ...,\n",
       "           9.83901302e+05,   9.81011983e+05,   9.78131127e+05]]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The results are stored as a tuple (time, concentration, reaction_rate)\n",
    "cs.get_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>H-Concentration</th>\n",
       "      <th>OH-Concentration</th>\n",
       "      <th>O2-Concentration</th>\n",
       "      <th>HO2-Concentration</th>\n",
       "      <th>C-Concentration</th>\n",
       "      <th>CH-Concentration</th>\n",
       "      <th>CH3-Concentration</th>\n",
       "      <th>C3H7-Concentration</th>\n",
       "      <th>H-Reaction_rate</th>\n",
       "      <th>OH-Reaction_rate</th>\n",
       "      <th>O2-Reaction_rate</th>\n",
       "      <th>HO2-Reaction_rate</th>\n",
       "      <th>C-Reaction_rate</th>\n",
       "      <th>CH-Reaction_rate</th>\n",
       "      <th>CH3-Reaction_rate</th>\n",
       "      <th>C3H7-Reaction_rate</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.277298e+13</td>\n",
       "      <td>-9.689748e+12</td>\n",
       "      <td>-6.832225e+13</td>\n",
       "      <td>7.316712e+13</td>\n",
       "      <td>6.617938e+13</td>\n",
       "      <td>-1.027354e+14</td>\n",
       "      <td>-1.182185e+14</td>\n",
       "      <td>5.159149e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000e-16</td>\n",
       "      <td>1.003258</td>\n",
       "      <td>0.999032</td>\n",
       "      <td>0.993225</td>\n",
       "      <td>1.007259</td>\n",
       "      <td>1.006559</td>\n",
       "      <td>0.989862</td>\n",
       "      <td>0.988358</td>\n",
       "      <td>1.005073</td>\n",
       "      <td>3.239245e+13</td>\n",
       "      <td>-9.670997e+12</td>\n",
       "      <td>-6.717145e+13</td>\n",
       "      <td>7.200695e+13</td>\n",
       "      <td>6.500751e+13</td>\n",
       "      <td>-1.000334e+14</td>\n",
       "      <td>-1.146331e+14</td>\n",
       "      <td>4.988634e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.000000e-16</td>\n",
       "      <td>1.006479</td>\n",
       "      <td>0.998066</td>\n",
       "      <td>0.986564</td>\n",
       "      <td>1.014403</td>\n",
       "      <td>1.013003</td>\n",
       "      <td>0.979989</td>\n",
       "      <td>0.977067</td>\n",
       "      <td>1.009981</td>\n",
       "      <td>3.202270e+13</td>\n",
       "      <td>-9.652300e+12</td>\n",
       "      <td>-6.605545e+13</td>\n",
       "      <td>7.088160e+13</td>\n",
       "      <td>6.387067e+13</td>\n",
       "      <td>-9.744668e+13</td>\n",
       "      <td>-1.112240e+14</td>\n",
       "      <td>4.826668e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000000e-16</td>\n",
       "      <td>1.009663</td>\n",
       "      <td>0.997102</td>\n",
       "      <td>0.980013</td>\n",
       "      <td>1.021436</td>\n",
       "      <td>1.019334</td>\n",
       "      <td>0.970370</td>\n",
       "      <td>0.966109</td>\n",
       "      <td>1.014729</td>\n",
       "      <td>3.166325e+13</td>\n",
       "      <td>-9.633657e+12</td>\n",
       "      <td>-6.497276e+13</td>\n",
       "      <td>6.978959e+13</td>\n",
       "      <td>6.276736e+13</td>\n",
       "      <td>-9.496838e+13</td>\n",
       "      <td>-1.079798e+14</td>\n",
       "      <td>4.672692e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.000000e-16</td>\n",
       "      <td>1.012812</td>\n",
       "      <td>0.996139</td>\n",
       "      <td>0.973569</td>\n",
       "      <td>1.028361</td>\n",
       "      <td>1.025557</td>\n",
       "      <td>0.960993</td>\n",
       "      <td>0.955467</td>\n",
       "      <td>1.019328</td>\n",
       "      <td>3.131356e+13</td>\n",
       "      <td>-9.615069e+12</td>\n",
       "      <td>-6.392179e+13</td>\n",
       "      <td>6.872932e+13</td>\n",
       "      <td>6.169600e+13</td>\n",
       "      <td>-9.259162e+13</td>\n",
       "      <td>-1.048890e+14</td>\n",
       "      <td>4.526153e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              t  H-Concentration  OH-Concentration  O2-Concentration  \\\n",
       "0  0.000000e+00         1.000000          1.000000          1.000000   \n",
       "1  1.000000e-16         1.003258          0.999032          0.993225   \n",
       "2  2.000000e-16         1.006479          0.998066          0.986564   \n",
       "3  3.000000e-16         1.009663          0.997102          0.980013   \n",
       "4  4.000000e-16         1.012812          0.996139          0.973569   \n",
       "\n",
       "   HO2-Concentration  C-Concentration  CH-Concentration  CH3-Concentration  \\\n",
       "0           1.000000         1.000000          1.000000           1.000000   \n",
       "1           1.007259         1.006559          0.989862           0.988358   \n",
       "2           1.014403         1.013003          0.979989           0.977067   \n",
       "3           1.021436         1.019334          0.970370           0.966109   \n",
       "4           1.028361         1.025557          0.960993           0.955467   \n",
       "\n",
       "   C3H7-Concentration  H-Reaction_rate  OH-Reaction_rate  O2-Reaction_rate  \\\n",
       "0            1.000000     3.277298e+13     -9.689748e+12     -6.832225e+13   \n",
       "1            1.005073     3.239245e+13     -9.670997e+12     -6.717145e+13   \n",
       "2            1.009981     3.202270e+13     -9.652300e+12     -6.605545e+13   \n",
       "3            1.014729     3.166325e+13     -9.633657e+12     -6.497276e+13   \n",
       "4            1.019328     3.131356e+13     -9.615069e+12     -6.392179e+13   \n",
       "\n",
       "   HO2-Reaction_rate  C-Reaction_rate  CH-Reaction_rate  CH3-Reaction_rate  \\\n",
       "0       7.316712e+13     6.617938e+13     -1.027354e+14      -1.182185e+14   \n",
       "1       7.200695e+13     6.500751e+13     -1.000334e+14      -1.146331e+14   \n",
       "2       7.088160e+13     6.387067e+13     -9.744668e+13      -1.112240e+14   \n",
       "3       6.978959e+13     6.276736e+13     -9.496838e+13      -1.079798e+14   \n",
       "4       6.872932e+13     6.169600e+13     -9.259162e+13      -1.048890e+14   \n",
       "\n",
       "   C3H7-Reaction_rate       T  \n",
       "0        5.159149e+13  1000.0  \n",
       "1        4.988634e+13  1000.0  \n",
       "2        4.826668e+13  1000.0  \n",
       "3        4.672692e+13  1000.0  \n",
       "4        4.526153e+13  1000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result as a dataframe\n",
    "df = cs.to_df()\n",
    "#first few timesteps\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We might want to know whether our system has reached equilibrium \n",
    "# is there additional information to be learned by running the simulation for more timesteps?\n",
    "cs.is_equilibrium()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# save solved result as a csv file or an HDF5 file for the user to analyze later as desired\n",
    "cs.save_results('result.csv')\n",
    "cs.save_results('result.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['H', 'OH', 'O2', 'HO2', 'C', 'CH', 'CH3', 'C3H7']\n"
     ]
    }
   ],
   "source": [
    "print(cs.chem.species)\n",
    "# Explore different combinations of temperature values and concentration values at once\n",
    "Temp_vals = [500,1000,1500]\n",
    "concentrations = np.array([[1, 1, 1, 1, 1, 1, 1, 1],\n",
    "                           [0, 0, 0, 1, 0, 1, 0, 0]])\n",
    "gridresults = cs.grid_solve(concentrations,\n",
    "               Temp_vals, \n",
    "               t_max, dt).get_grid_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[500, 1000, 1500], array([[1, 1, 1, 1, 1, 1, 1, 1],\n",
      "       [0, 0, 0, 1, 0, 1, 0, 0]])]\n",
      "(array([  0.00000000e+00,   1.00000000e-16,   2.00000000e-16, ...,\n",
      "         4.99700000e-13,   4.99800000e-13,   4.99900000e-13]), array([[  1.00000000e+00,   1.00254762e+00,   1.00508467e+00, ...,\n",
      "          1.02175902e+00,   1.02169095e+00,   1.02162290e+00],\n",
      "       [  1.00000000e+00,   9.99774842e-01,   9.99549785e-01, ...,\n",
      "          4.70506371e-01,   4.70456520e-01,   4.70406680e-01],\n",
      "       [  1.00000000e+00,   9.93446120e-01,   9.86972129e-01, ...,\n",
      "         -4.98436221e-14,  -4.80602127e-14,  -4.63225756e-14],\n",
      "       ..., \n",
      "       [  1.00000000e+00,   9.94290680e-01,   9.88668968e-01, ...,\n",
      "          2.24712410e+00,   2.24721926e+00,   2.24731438e+00],\n",
      "       [  1.00000000e+00,   9.97040402e-01,   9.94103303e-01, ...,\n",
      "          5.28660499e-04,   5.27940031e-04,   5.27220546e-04],\n",
      "       [  1.00000000e+00,   1.00079988e+00,   1.00159053e+00, ...,\n",
      "          1.14203967e+00,   1.14203967e+00,   1.14203967e+00]]), array([[  2.55292444e+13,   2.54230981e+13,   2.53177394e+13, ...,\n",
      "         -6.80850109e+11,  -6.80586223e+11,  -6.80322452e+11],\n",
      "       [ -2.25209072e+12,  -2.25107668e+12,  -2.25006333e+12, ...,\n",
      "         -4.98559387e+11,  -4.98453747e+11,  -4.98348141e+11],\n",
      "       [ -6.59405191e+13,  -6.51343447e+13,  -6.43440145e+13, ...,\n",
      "          7.38565495e+00,   7.12169709e+00,   6.86449970e+00],\n",
      "       ..., \n",
      "       [ -5.75337547e+13,  -5.66496432e+13,  -5.57829787e+13, ...,\n",
      "          9.51723314e+11,   9.51377187e+11,   9.51031231e+11],\n",
      "       [ -2.97090223e+13,  -2.94822913e+13,  -2.92593360e+13, ...,\n",
      "         -7.20962678e+09,  -7.19978805e+09,  -7.18996278e+09],\n",
      "       [  8.04530048e+12,   7.95208752e+12,   7.86060920e+12, ...,\n",
      "          5.05269460e+06,   5.03914556e+06,   5.02563279e+06]]))\n"
     ]
    }
   ],
   "source": [
    "#first part of tuple stores the parameters of the grid\n",
    "print(gridresults[0])\n",
    "#second part of tuple is a dictionary where the key is the temperature \n",
    "#and the value is a dictionary holding a list of ODE solutions corresponding to each set of concentrations\n",
    "print(gridresults[1][500][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "#save grid results to output file\n",
    "cs.save_grid_results('demogridexample', filetype = 'csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading xml input file\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>H-Concentration</th>\n",
       "      <th>OH-Concentration</th>\n",
       "      <th>O2-Concentration</th>\n",
       "      <th>HO2-Concentration</th>\n",
       "      <th>C-Concentration</th>\n",
       "      <th>CH-Concentration</th>\n",
       "      <th>CH3-Concentration</th>\n",
       "      <th>C3H7-Concentration</th>\n",
       "      <th>H-Reaction_rate</th>\n",
       "      <th>OH-Reaction_rate</th>\n",
       "      <th>O2-Reaction_rate</th>\n",
       "      <th>HO2-Reaction_rate</th>\n",
       "      <th>C-Reaction_rate</th>\n",
       "      <th>CH-Reaction_rate</th>\n",
       "      <th>CH3-Reaction_rate</th>\n",
       "      <th>C3H7-Reaction_rate</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.277298e+13</td>\n",
       "      <td>-9.689748e+12</td>\n",
       "      <td>-6.832225e+13</td>\n",
       "      <td>7.316712e+13</td>\n",
       "      <td>6.617938e+13</td>\n",
       "      <td>-1.027354e+14</td>\n",
       "      <td>-1.182185e+14</td>\n",
       "      <td>5.159149e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000e-16</td>\n",
       "      <td>1.003258</td>\n",
       "      <td>0.999032</td>\n",
       "      <td>0.993225</td>\n",
       "      <td>1.007259</td>\n",
       "      <td>1.006559</td>\n",
       "      <td>0.989862</td>\n",
       "      <td>0.988358</td>\n",
       "      <td>1.005073</td>\n",
       "      <td>3.239245e+13</td>\n",
       "      <td>-9.670997e+12</td>\n",
       "      <td>-6.717145e+13</td>\n",
       "      <td>7.200695e+13</td>\n",
       "      <td>6.500751e+13</td>\n",
       "      <td>-1.000334e+14</td>\n",
       "      <td>-1.146331e+14</td>\n",
       "      <td>4.988634e+13</td>\n",
       "      <td>1000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.000000e-16</td>\n",
       "      <td>1.006479</td>\n",
       "      <td>0.998066</td>\n",
       "      <td>0.986564</td>\n",
       "      <td>1.014403</td>\n",
       "      <td>1.013003</td>\n",
       "      <td>0.979989</td>\n",
       "      <td>0.977067</td>\n",
       "      <td>1.009981</td>\n",
       "      <td>3.202270e+13</td>\n",
       "      <td>-9.652300e+12</td>\n",
       "      <td>-6.605545e+13</td>\n",
       "      <td>7.088160e+13</td>\n",
       "      <td>6.387067e+13</td>\n",
       "      <td>-9.744668e+13</td>\n",
       "      <td>-1.112240e+14</td>\n",
       "      <td>4.826668e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000000e-16</td>\n",
       "      <td>1.009663</td>\n",
       "      <td>0.997102</td>\n",
       "      <td>0.980013</td>\n",
       "      <td>1.021436</td>\n",
       "      <td>1.019334</td>\n",
       "      <td>0.970370</td>\n",
       "      <td>0.966109</td>\n",
       "      <td>1.014729</td>\n",
       "      <td>3.166325e+13</td>\n",
       "      <td>-9.633657e+12</td>\n",
       "      <td>-6.497276e+13</td>\n",
       "      <td>6.978959e+13</td>\n",
       "      <td>6.276736e+13</td>\n",
       "      <td>-9.496838e+13</td>\n",
       "      <td>-1.079798e+14</td>\n",
       "      <td>4.672692e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.000000e-16</td>\n",
       "      <td>1.012812</td>\n",
       "      <td>0.996139</td>\n",
       "      <td>0.973569</td>\n",
       "      <td>1.028361</td>\n",
       "      <td>1.025557</td>\n",
       "      <td>0.960993</td>\n",
       "      <td>0.955467</td>\n",
       "      <td>1.019328</td>\n",
       "      <td>3.131356e+13</td>\n",
       "      <td>-9.615069e+12</td>\n",
       "      <td>-6.392179e+13</td>\n",
       "      <td>6.872932e+13</td>\n",
       "      <td>6.169600e+13</td>\n",
       "      <td>-9.259162e+13</td>\n",
       "      <td>-1.048890e+14</td>\n",
       "      <td>4.526153e+13</td>\n",
       "      <td>1000.0</td>\n",
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      ],
      "text/plain": [
       "              t  H-Concentration  OH-Concentration  O2-Concentration  \\\n",
       "0  0.000000e+00         1.000000          1.000000          1.000000   \n",
       "1  1.000000e-16         1.003258          0.999032          0.993225   \n",
       "2  2.000000e-16         1.006479          0.998066          0.986564   \n",
       "3  3.000000e-16         1.009663          0.997102          0.980013   \n",
       "4  4.000000e-16         1.012812          0.996139          0.973569   \n",
       "\n",
       "   HO2-Concentration  C-Concentration  CH-Concentration  CH3-Concentration  \\\n",
       "0           1.000000         1.000000          1.000000           1.000000   \n",
       "1           1.007259         1.006559          0.989862           0.988358   \n",
       "2           1.014403         1.013003          0.979989           0.977067   \n",
       "3           1.021436         1.019334          0.970370           0.966109   \n",
       "4           1.028361         1.025557          0.960993           0.955467   \n",
       "\n",
       "   C3H7-Concentration  H-Reaction_rate  OH-Reaction_rate  O2-Reaction_rate  \\\n",
       "0            1.000000     3.277298e+13     -9.689748e+12     -6.832225e+13   \n",
       "1            1.005073     3.239245e+13     -9.670997e+12     -6.717145e+13   \n",
       "2            1.009981     3.202270e+13     -9.652300e+12     -6.605545e+13   \n",
       "3            1.014729     3.166325e+13     -9.633657e+12     -6.497276e+13   \n",
       "4            1.019328     3.131356e+13     -9.615069e+12     -6.392179e+13   \n",
       "\n",
       "   HO2-Reaction_rate  C-Reaction_rate  CH-Reaction_rate  CH3-Reaction_rate  \\\n",
       "0       7.316712e+13     6.617938e+13     -1.027354e+14      -1.182185e+14   \n",
       "1       7.200695e+13     6.500751e+13     -1.000334e+14      -1.146331e+14   \n",
       "2       7.088160e+13     6.387067e+13     -9.744668e+13      -1.112240e+14   \n",
       "3       6.978959e+13     6.276736e+13     -9.496838e+13      -1.079798e+14   \n",
       "4       6.872932e+13     6.169600e+13     -9.259162e+13      -1.048890e+14   \n",
       "\n",
       "   C3H7-Reaction_rate       T  \n",
       "0        5.159149e+13  1000.0  \n",
       "1        4.988634e+13  1000.0  \n",
       "2        4.826668e+13  1000.0  \n",
       "3        4.672692e+13  1000.0  \n",
       "4        4.526153e+13  1000.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load from file -- csv or hdf5. Useful when solving ODE takes long time\n",
    "#this only works for single simulation mode\n",
    "\n",
    "# load from file -- csv or hdf5. Useful when solving ODE takes long time\n",
    "cs = ChemSolver(chemkin('demo_xml/demo.xml')).load_results('result.csv')\n",
    "cs.to_df().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Chemkin Solver Visualization\n",
    "\n",
    "We implemented a `ChemViz` class which helps to visualize calculations stored in a ChemSolver object. Reaction rates and concentrations of species can be plotted as a function of time, allowing one to quickly assess:\n",
    "* whether solutions look reasonable (e.g., by examining the smoothness of the curve)\n",
    "* visually check whether the system is at equilibrium or close to equilibrium\n",
    "* identify which species rapidly form or get destroyed versus which species remain relatively steady in concentration\n",
    "\n",
    "Whereas the time series plots focus on the individual species, the network diagrams provide complementary information about how the species interact with one another. \n",
    "\n",
    "We show some examples in the next few slides. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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Y3OqISqlaRAtwGT7hE4oo4h7usTqKslj+qRMc2zCbuHWzOHkw2l50O1xKt4lT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f82ab940>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "cv = ChemViz(cs)\n",
    "#species and time range can be chosen arbitrarily\n",
    "cv.plot_time_series(\"reactionrate\",tmin=1.e-13, species = ['H', 'OH', 'O2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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phJboPDzOJr+6JSAAIiPtCdH93P3NHdPcYjKpJKcoDajkqigOkNXVUFQEhYVn180lytJm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\njjvuKBk9enTcAw880Gqn3KNHj7p6e3t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4UuV4IKWkYd96tm57lm3bn6OjYw8mYSY7+xxm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f4083518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.plot_time_series(\"concentration\",tmax=3.8e-13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gf7OBm06bRP/SIleSN0Bxfh7nHT6Sv761nkUbtthu/hjbDXrkcMzfdmd7T9T4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\nof8PH6ksYUnWSToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f10f6438>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#networks can be plotted for arbitrary time points\n",
    "#species are connected to one another if they react with one another (including backwards reactions)\n",
    "#the lines are weighted logarithmically by the reaction rate coefficients\n",
    "#the bubbles are linearly scaled by the concentrations\n",
    "#red bubbles are decreasing in concentration and blue bubbles are increasing in concentration\n",
    "cv.plot_network([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2KDAUqHJ4Xjds3XMn98xVHPhFugFnFAwY4O11xhl48/Pjfk1fcTGDr74aT35+\nPvDj8P13R0Qoxfa8y/V5lHyawDPbSbhYvOWQkkJOHnQoHo+H2bNnU1ZWdtAxS5cuRURYv379V9/b\nsmUL5eXl9O3bl9zcXPr06cPFF1/M5s2bo16vOC+Ha090PdfmkP4bMEwitqprQWCJ++GoGPwACB02\nZ44tW5hAh553HkAucPAfcNv+BPzNGF53NSgVE03gme0YNxv7/thhjresWrduHePGjWPFihXcf//9\nrFmzhoceeoiPP/6Y8ePH75foW9OjKJ9j+7q6dXkBEJcZcokgQiEwGeeT13oCrxtDtftRqRhcgEhh\n//LyiL3v9rzxdfJGGsBXUsKgq64CO39ippOARbgG+5ryMyfnqfjRBJ6hwvtgd3KzzelD+1HgcMnY\nnDlz8Hg8LFy4kJNPPpn+/fszefJkFi5ciMfjYc6cOVHPz/F4mDigd0fCbk0698DH0LyLmBO5wCLX\no1GOhYeuhxUNGYJ4Wx/F7ugb32iKhw/Hk5sLDiZ0inAlcBkw3RhcreimYqcJPHN1xvn2klGN6l3q\n6PiqqioWLFjAnDlzKDxgkk5hYSGXXXYZzz33HDt3Rt5vIT/Hx0kDXa9D4/rM/EQIT16bgfN7312A\ndcBG14NSsTgcaOw8diwm1Ppt5I6+8Y0mWFdHyVFHAfQJ73YWkQiFItwBXAJ83Rii3/dSCaUJPHPl\n46zAR1Q9ivLJP6BC2oIFCyguLt7vY9KkSV89vnr1aowxjBgxotU2R44ciTGG1atXs2nTJk466SRG\njhzJqFGjeOKJJ746bvQhrm9hnut2gwkyGFtVb29bBx6gM3bymtZNTg0jgVDnMWPwFR+8PYEbb3yj\nEa+XTkcfDXZC5/CIxwkTsVsQ9wQmGKNvAFON7kaWuQrAvRfsTnm5NAVD5Ld4xkyaNInKysr9jlux\nYgVnn3224/Z9Ph+33347o0ePZtu2bYwdO5YZM2ZQVFQUj6Vk6ZrApwANDs/JxS4l/Mj9cFSMigBP\nbrduiOfgPpSTN77Q/Ea6pVCEnj2A5OTgKykB+/qw3zuE8CjPidjh8knAj4zh7+3+yVRCaQLPXK5O\nbW1tomxhYSFDhuy/R8quXc17JQwZMgQRYeXKla0m9ZUrVyIiDBkyhNLSUg45xA6V9+7dm+7du1NV\nVUVRURHi/izdxE77dYEIXbD3LJ1WvuoFPGuM48Sv4kcAoZXkHYuY3kg3X9sjQk/svJBxwLnYvHAX\n8ENjiPtevyp2OoSeuepxMVHVNAbwOnzB6datG9OnT2f+/PnU1tbu91htbS133nknp512GqWl+99b\nX7ZsGcFgkH79+gFQ1+R0xVRRxgOyAAAgAElEQVSbXJ0bkCDHYX+fTm6LCPZv/M24RKRiVQeEmna1\nvjFYyze+rWn5xhea30i3/Ojbt2/Ei5tAgGB1NXUUl/yEd54GPgWuxs4N+TEw0hj+pMk79WkCz1x1\nuPj7/c/eWmLZenbevHkEAgGmTp3Kyy+/zKZNm1i0aBHTpk3DGMO8efP2O76qqooLL7xwvx7Fx5/H\ndq8virTqjYrgw1Zec7rrWHdguTGOthpV8bcKYM+HH9r9uw8Q6xvf9jKBAHs//ph8qkN9+PR4oNQY\nTjaGa43hZZ0rkT40gWeualws4gKw8gvniXTw4MEsXbqUI444glmzZjFo0CBmzpzJiBEjWLJkCQMH\nDvzq2IaGBs466yx++tOfMmHCBAAaA0FeXb/VtZ8hrLbtQ1LKCOxENKdlcQuBF9wPR3XQCiB/97Jl\nEd8UO33j64S3sJA9H3yAwPZXzKxVmrDTl94Dz1xV2GF01yZsvbR2K0f37kauz8t9993X6jHjxo07\n6EWpX79+3HPPPVHbNsYwe/ZspkyZwqxZs776fn0gyOKNTmuWtGm52w3G2SngeO1tIbAL+MT9cFRH\nlBnT6BdZX71y5VCPr/WX4H1vfG+66SZmzZrFF198QY8ePZgxYwaPP/541CHyttRt3EjQ9uyXxtyI\nSgkSy7CoSg9SUfk6cIJb7Q3p1omPLv+242Iu7fHGG28wadIkRo0a9dX3HnzwQXoNHMIhv3mIkHvP\n0wbgZ2Zu+R/cajCeROgN/Bq7htvJf0J/4FFjdMOJVOQX+Q1wxag//zmv74UX4smJ7+ZA+wRqavj0\n5z9n3e23VwNzyox5ICEXVnGhPfDM9ipwPOB404LWrNmxhw/+s4Pj+/dyo7n9nHDCCQctfaltbOLm\nRe+7mbzBjkosc7PBOJuIrWHu5D/BGz7+3bhEpNwwH7hi3R//SJ/zz4cEJXDxeNjcPHr2fwm5qIob\nvQee2ZaAu7Wvr1+4lJpGp1U8YxM0hnuWuD4CXAi873aj8SBCPjCV2Oqev62ziFNXmTEbgdf3fvyx\n2bVkCaGm+P9NBWpr2fzAAzTt2tUI3FNmjNO95FWK0QSe2ZYCeW42+NLaLfzzkw3xWNq1n+qGJi59\n+g121Lo+YXy7mVvudB/tZBmNrajndNlbLvCy++Eol1UA9e/PmkWoIf4LIwJ79rDSbmTSBKTFLSQV\nnSbwzLaVOKx5vvSfb1DTGCAUis/8ifpAkLc3fc7DH66JR/NpMXEnXBHrNOxENCc6YYu9fOZ6UMpV\nZca8C9xdv2lT7cqrriJQHb+N4gI1Nbw/cybBmpoa4CdlxmhN8wygCTyDmbnlBngLF0uqAuyub2Ta\nvc9SE4deeEMgyMZde/n2owtdbxu7fOz5eDQcB4dhJ6I5HQbvitY9Tyf/DezYeM89Zssjj8Qlie+b\nuLbjlVeasLePoi8JUWlDE3jm+yMu3wcH+OA/Ozj5r3521zcSCLqzZ0pdU4DPqvYw8e5/srs+LsXS\nPMAj8Wg4Dk7C+ehJTvictLjHryB8H/pUjNm7/JJLcDuJB2pq+PQXv2Dd7bcHsXMpvl2mS48yhibw\nzLeQOCRwgCWbtzPmzif5cNsOqhs6NgmnprGJxz5ay7F3PcWXtXGZWxMEnjBzy50OSSecCCXABJxP\nXusFvGyM44IvKonKjFkJTMaYPcsvucTsG07vyMS2YH09jVVVvHfeeaz7wx+agM+BCWXGaFW+DKIJ\nPMOZueUh4PfEqfrYZ1V7GX/XP7h+4RJqGpuobnDWadxT38jWPTWc+dAL/NffX6U6fjPcG0ifiTvH\nYpd4Bh2cI9jlY6/HJSIVV2XGvAccjzGbN1ZW1i4aOZKdb75JoKbGUSIP1tcTrKvj86ef5pXBg/ni\n2WdrgI+BseGZ7yqDaCGXLCAVlaXYiU358bxOYY6PmUcP4bpJR9O3czH1TQGKcnPI8Ta/T6xrCtAY\nDFKQ4+OtDZ9z6+sf8vzqzW6v9W7NCjO3/Kh4X6SjRPACt2I3LXFSfa0bsMUYfhOXwFRC+EXysYV7\nfgjkF48cKYOuuII+3/0uJhCwhfHtVqAAmFCIQHU14vFggkE23H03G+68k7qNG4PY2yk/B+4oM8bJ\nm0GVJjSBZwmpqHwUOAeXirq0pUdRPmP79GBcnx707VREvs9LTWMTn365m2Vbt/PBf3ZQ0xjfpWgt\n7AUuM3PLH0rUBWMlwkjgGmCDw1MHAHcYwwfuR6USzS9yLPAr7N7cIl5vXvGIEXQeO5ZORx9NTpcu\nmFCIph072P3+++xeupSatWvBmFrsyOrTwPVlxqxO5s+h4ksTeJaQispx2MpshcmOJQn2AL3M3PKU\nL1whwo+B4Ti7/12A/b3+xBgSU2VHJYRfpB9wGfBNYCC2kiDYJG2wIzUe7ATGVcBDwF/KjNmR+GhV\nomkCzyJSUfk4cAZxHkpPMTXY3nfK13wWoQd2+DyWuudPGsOzcQlMpQS/SC5wBDAM+4YthJ3b8jHw\nqQ6TZx+thZ5dLsGW5syWBN4EvAM8mOxA2mkCNnE7Sd77Jhgsdj8clUrKjNm3RFCXCSpAZ6FnFTO3\nfCdwIem3H3asGoBZ4YI2KU2EXOy2oU6X+fQAlhpDlftRKaVSmSbwLGPmlj8L+Gm+l5apaoA5Zm75\n1mQH0k5HYYdFnRbFzgdecj8cpVSq0wSenS4hs3vhaTV03qLuudNNVkqA7YDONFYqC2kCz0LhofQL\nyNwkXkuaDJ2H9QUGAzsdnleKrXvuTi1bpVRa0QSepczc8ueAq8i8JF4NTEmjoXOArwNOF8X7wucs\ncT8cpVQ60ASexczc8v/FFovIlCReC8wwc8vfS3Yg7SVCETaBO5281gt4zRhH1dqUUhlEE3iWM3PL\nfw3cRvon8VrgLDO3PN1qgY+luTfthA9bmEcplaU0gSvM3PIbgJ9BWu5iZbClUqeZueUvJjsYJ0Tw\nADPA8RKwrsBnwCbXg1JKpQ1N4AoAM7f8Duzs9Dqc7YKVTPXAl8CJZm75W8kOJgZDsEPhex2e1wl4\nzhhHBV+UUhlGE7j6Srjc6NHAcpzthJUMtcD/AUPM3PIPkx1MjE7G+brvPOzP/pH74Sil0onWQlcH\nkYpKL3AFcDOQS4J2MGuneuxM8++aueUvJDuYWInQFbtP+2ZwtAysH/CMMTwVl8CUUmlDE7iKSCoq\nh2J7uUOBoiSHA7bn+TfgcjO33GnRk5Qiwgzg29iNS9p9GnbjkmuNYXtcAlNKpQ1N4CqqcG98DlCB\n3bayJMEhBIBGYC1wVbpNVGuNCD5s77seZxMHewCrjOGOuASmlEormsBVu4QT+anAtcCx2PkTuXG8\nZE34Gk8AfzRzyz+I47USSoSjsbcoNjg8dQBwmzGsdD8qpVS60QSuHJOKyoHAj4CLscu48nEnmddi\n7wfvwq5Nv9/MLd/tQrspRYRrsfeydzg4rQj7huZaY9JmlYBSKo40gauYSUVlPnYP67HYamJjgW7Y\nRFxA9KReix0eL8DeB34beANbGvS9NKpj7ogIhwJzsT+zk59xAPCQMbrzmFLK0gSuXCUVlV2AMdhk\n3gcoxvYe9y1/qgl/rAKWAsvN3HKnS6nSlgjnYvf93uzgNC9wCPATYxzvWKaUylCawJVKEBEKgD9i\nh86bHJx6KLDYGO6NS2BKqbSkhVyUSpzR2JEIJ8kbIAdY5Ho0Sqm0pglcqQQQQbB1z3c5PLUztub5\nerdjUkqlN03gSiXGQKAv4HRWfRfgX1r3XCl1IE3gSiXGZJwPnedia6VnzBp4pZR7NIErFWcidAKO\nBz53eGpP4CVjqHc/KqVUutMErlT8HYtdCuakAIuEz3k9LhEppdKeJnCl4kgEL3bymtPNR7oBK41h\nm/tRKaUygSZwpeLrcKAUW8TGiSLgeffDUUplCk3gSsXXNGzlOScKgD2gm5YopSLTBK5UnIjQEzga\n+NLhqT2ABcYQcD8qpVSm0ASuVPxMxO6u5mQNtyd8/NtxiUgplTE0gSsVByLkYYfPv3B4ak9giTHs\ndD8qpVQm0QSuVHwchb2X7XSntTzQLUOVUm3TBK6Uy1rUPXdaNrUE2AasdT0opVTG0QSulPv6YWuf\nO924pBRb9zzkfkhKqUyjCVwp932d2LYMbQKWuR+OUioTaQJXykUiFAOTiG3y2iJjHBd8UUplKU3g\nSrlrHLaGudM13D7gNffDUUplKk3gSrlEBA928lqVw1NLgTXAFteDUkplLE3gSrlnKLaKWrXD80qA\n54xxVPBFKZXlNIEr5Z6p4Hjv7nxsrfQV7oejlMpkmsCVcoEIpcBYnG8b2gN43hga3Y9KKZXJNIEr\n5Y4J2BrmTtZwe8Ifi+MSkVIqo2kCV6qDRMgBpuO8990deM8Yx7uVKaWUJnClXHAkUIzz+98FwEL3\nw1FKZQNN4Ep13KnAXofnFAM7gE/dD0cplQ00gSvVASL0AYaB4+0/te65UqpDNIEr1TEnYquuOVnD\n7cNOdlsSl4iUUllBE7hSMRKhEJhMbHXP3zDG8bC7Ukp9RRO4UrEbA+QS285ji1yPRimVVTSBKxUD\nEQQ4Def3vrsAG4ENrgellMoqmsCVis1g4FBgj8PzOmMnr2ndc6VUh2gCVyo2U8Bx+dNc7FrxD9wP\nRymVbTSBK+WQCF2A43A+ea0XsNAYGtyPSimVbTSBK+XccYAAQQfnCPbv7Y24RKSUyjqawJVyQAQf\ntvKa0/rl3YAVxvC5+1EppbKRJnClnBmBnUle6/C8IuAF98NRSmUrTeBKOXMKUOPwnEJgF/CJ++Eo\npbKVJnCl2kmEXtidx3Y4PLU7sMAYAu5HpZTKVprAlWq/E7A1zJ2s4faGj38nLhEppbKWJnCl2kGE\nPGAqOJ6E1hN4xxh2uR+VUiqbaQJXqn1GA/nEVrzlZffDUUplO03gSrUhXPd8BrDb4amdgK3AWteD\nUkplPU3gSrVtANAfHA+Dd0Xrniul4kQTuFJtO4nYtgxtBN53PRqllAJ8yQ5AqVQmQgkwEdjm8NR9\ndc+dFnxRKmP4RXpgCx8VYFdj1AE7yoxxug2vaoUmcKWiG49dCua07rkXeD0uESmVgvwihcBk4Fjg\n68DR2MTdSPPSSw+Q6xfZgx2dWgQsAV4tM8bpBNGsJ8bo7bl04BcpBo4BhmIre+Vgt6bcAywHVpYZ\no4VCXCSCB7gV++LjpPpaN2CLMfwmLoEplUL8IsOA/wfMxr7RLaT9ncMmbK/cAHcB88uM2RSHMDOS\nJvAU5RfJAc4CZmJ7gT2x9bd92N6dB/vHsi9p5wNrgNeAv5YZ826iY840IowArgU2ODx1AHCHMbrv\nt8pcfpGJwG3YJZY+bKeiIxqwifx14OoyYz7qYHsZTxN4ivGL9AUuAeZgE3WJwyaC2J75ZuC3wONl\nxuh92BiIcDl28xIn+37nYzcu+Ykxjie+KZXywkPlv8P2uAvicAmDfQ27DfhVmTH6dxSBJvAU4RfJ\nB24BLg1/K9+FZquxCf3iMmOecKG9rCFCd+zw+WZs+dT26g88aQzPxiUwpZLIL3IC8BhQSnySd0s1\n2L+/c7U33jpdRpYC/CLHAv8GfohN3G4kb4BioDNwr1/kmfCMUNU+x2N7Ak6S976/p8Xuh6NUcvlF\nLsduiduH+CdvsCNZw4C3/SLnJuB6aUcTeBL5RcQv8ivsTMwB2Mkf8VCE3QZzjV9kapyukTFEyAWm\nA9sdntoDWGYMVe5HpVTy+EV+AfyGxCTuliR8zXv9Ij9I8LVTng6hJ4lfxAP8GTgXm2DbJadrV3J7\n9sSTk0Oovp66TZsINTQ4uXQdMKvMmCedRZw9RBiDnVW73uGpA4DfGMOnrgelVJL4Ra4FfkksHQwR\nCvr1w1tcDMYQ2LOH+i1bYg2lDns78OFYG8g0msCTwC8iwF+Bc2gjeXuLiugzcyaHfOtbdDrmGHI6\ndybY0ADGIB4Pnvx86jZtYtfixWx+4AG2v/gitP07rQPOLzPmaXd+oswRrntegZ3176QnXYwdbv+p\nMY6G3ZVKWX6R87CvVe1O3kXDh9P/4ovpPmUKxSNGYIJBTMAulpGcHDCGvR9/zJcvvMDGP/+Zug2O\nFnnUATPKjFnk5KRMpQk8CcLD5lcQJXnn9e7NsBtvpM8FF0AwiK+k7cnoTXv3EqyuZu2tt7J+3ryv\n/mgiqAWmlhmj92tbEKEvcDOxLR273xgWuR6UUkngFzkE+JR2roTpccopDLv5ZjodeSTi8+HJzY16\nfLChAUIhdr79Np9WVLBzcbtfir4AhpQZs7e9J2QqTeAJ5hc5DniFKPeS+lxwAUfNn48nL6/NP4LW\nBKqrqd+6lWXnnMPej6JO3twKDNVlZs1EmImtJuVknM+HLZ16pTFUxyUwpRIoPEr4AraiWtT13Tld\nunDU3XfT8/TT8RW1+27gfoK1tWy67z5WXn01obq6tg6vAx4tM+aimC6WQXQSWwKFl4o9ToRZ5p68\nPMb9858cdddd+EpKYkreAL7iYoqGDOGExYsZcNll0Q7til3PqQARCrEblzhZ9w02eb+myVtlkJnY\nlRhRk3enY45h8urV9DrjjJiTN4C3sJC+s2cz+dNPKRw0qK3DC4Dv+EWmxHzBDKEJPLFuwc5UlgMf\n8OTl8bUXX6T7lCn4ios7fCHxePAWFjLi1lsZfO21kQ4rAL7nFzmxwxfMDGOxL1ix7Dz2qvvhKJV4\nfpECYD5tzM/pMn48E159ldzu3fHmd3zlq6+wkLxDD+WEd9+laOjQtg4vBO4PTwbOWln9wydSuMLa\npUSYDDLmiSfoNHZsh97FtsZXVMTQX/yCPrNmRTqkELjb1YumoXDd8xk4m7gGdhTjM0DrN6tMcR5t\n5IbCQYM47sUX2zU3xwmP14uvSxcmvPEGud27t3V4Z+BkVwNIM5rAE+eSSA/0+e536T55Mr7C+CwD\n9xUVcdT8+eT36xfpkAF+kXFxuXj6GAz0BpxOjOkM/MsYdDKJyhTXYVdVtE6EMY8/jtflzsY+Hq8X\nX+fOjPrLX9o6tBi4Ji5BpAlN4AkQ3phkDq3c+87r3dve83Zh2DwaT14eYx6OuHwyD/hJXANIfSdj\nN1NwIg87m3+5++EolXh+kfHYcsARDbz8cooPPxyPL367UXvz8uh+8skc8q1vRTtMgBP9IhF7JplO\nE3hinIXdmOQgw268EU9eXtwD8OTk0Gn0aLpPm9baw17gbL9IadwDSUEidMXu+OZ08lpP4EVjHCd+\npVLVZdg3pq3yFBQwfO7cuHc4wI4cHjFvHshBU4ZaEiBrZ6PH7y2Uamkmrayl9BYV0eeCC2Kebe6U\nt6iIwVdfzZcvvnjQYw3kex/iV3//hjiuPpYJDgUOw/amnSgCikQ40/WIlEqCB+nxrS5sb7WzAdDn\n/PPbUyjKNb7CQnpMn872BQsiHZIHTAVuSFhQKUQTeGKMb+2bfWbOhGDQUUOff/45t9xyC36/n82b\nN9O9e3dGjRrF5ZdfzowZM6KeKx4PpSeeSH6fPgeVM8yhwXc0L+U+xVXrHQWU/jzA4djed9TKNwco\nBLZhN6FRKu0VU+XrxI6oE3EGX3ttuyeuzZ49my+//BK/37/f95cuXcr48eNZt24dhx12WNQ2fJ06\nMfjaa6MlcIBRfhEpy8KiJprA48wvUoIdaj3IId/6lqNZnOvXr2fixImUlJTw61//mqOPPppQKMRL\nL73EJZdcwsaNG9tswwQCdJsyhS0PPrjf9z0YGcDyAe0OJnP0wL6Lr3d4Xg6w1v1wlEqOsTzXK0BO\nUy4NrQ6h55SWUthGwo2H0gkTwOOBUMQKxT6gL1m4EkQTePyNxg7Ndj7wgU7HHOOoocvCRVmWLl1K\ncYt7UCNGjOCCCy5oVxveoiK6Hn/8QQkcoCvbegohTHZNjRgMNDo8Jweb8L90PxylkmMEbx7qIRjx\nj7/zmDEE6+oSMmenpVBjI8WHH071ypWRDmnE1nDIugSeVa/USTKUVt4o5XTtSk7ng3J6RFVVVSxY\nsIA5c+bsl7z36dKlS7vaEY+H0hNOaP0xjBzK6nhtaZqKirE98DZrNx6gAFgFunRMZY6ebOjiIxCx\n8lrnMWPwxmmpazTGGDqPHRvtkDzamDmfqbQHHn+FtDIDPbdnT4INDe1+N7tmzRqMMYwYMaLDAUUq\nkBDCEypkd9TSiRmmPzjeOWzflNitLseiVFLlURd1Nm1Bv36OJ9wuWLDgoA5HKPJQeKu8+fnk9Wz1\nLuQ+PhK/T3lK0AQefzm0MtLhCW+r115uzs+QiOs3xeRSny2jMj5gIM5nnhdjh+p06ZjKKD4ao+YD\niWG1zKRJk6isrNzveytWrODss89ufyMej92GNMoRtFGzPVNpAo+/euCgqeah+nrE0/5cOXToUESE\nTz75xNmTvxWmsfVbvoKROkqczMROZ72xz39nywDsi8V616NRKskayY+6B0CwpsZxm4WFhQwZMmS/\n7+3atctRGyYYJFQfdY5pEOeTUDNCtvS2kmkPrSxPqtu0CY+DDQBKS0uZPn068+bNo7r64E2vnPxR\n1Hz2Wavf9xD07qa70wld6WoIzv/o87C/z53uh6NUctVRXB9tnK9m1SoCMSTxjgrV1VEb4TUrLIDz\nkbSMoAk8/lotsxlqaKBuk7NJk3feeSfGGMaNG8cTTzzBp59+yr///W/uuusuRo0a1a42QoEAVa+9\n1upjjeQ37KBfNgwNd8GuCnD6s+YDq90PR6nk28TI7U3kRnwDv3vZMkwg8QN0nrw8di9bFu2QRiDi\nFPVMpgk8/lYSYf/vXYsXO2po0KBBvPfee0ybNo3rrruOUaNGMWXKFP75z38edJ8pkmBNDbvefbfV\nx7bT7z+OAkpfh+F88poXO1S3zfVolEoBy5n8n2hFS/d89BHegsTPFQs1Nh5UeOoAhcD7CQonpUgW\nFq9JOL/ISuCg6eM9TjmFMX/7Gzkub8kXTaCmhhd69CBUt//KqSCe0Ouct+j3PPJ6woJJjjxgOnbX\nMSdP/hLstqEfxyMopZLNQ0D+Tn6Fl2DEUqrHPvccPU45xdH8nY4INTWx6d57Wf7DH0Y77D9lxhya\nkIBSjE5iS4zXgGEcsJxs+4svEqyuTlgCDzU2svm++w5K3gABcoPvMz2A7Z1mskOwydjpK1ARttd+\nmNsBKZUKQvj4kr57erGha6Rj1t52G6UTJ7q+D3gkpqmJdbff3tZhUcfXM5n2wBPAL3Is8DI2Cexn\n4BVXMPzmmxOyu0+wtpbXRo+mZnWrt3F3Aj3LjMnYWegieIFbsYnYyWycbsAmY7g1LoEplSL8Ijdg\n9wOPOMN2yrp1CSmpGgoG2b1kCW8ef3y0w6qBK8qMaXPz8Eyk98ATYwnQ6k2c9fPmUb91K8ZhcQOn\nAjU1rL/zzkjJux74n0xO3mGHA6U4S95g134/7344SqWcSpqLFbXqg1mzCNbGf9K3aWjgg9mz2zpM\ngEfjHkyK0gSeAOFdcn6Lfbe4HxMIsOycc9pa59ghJhSicft2Pv35zyMdIsDdcQsgdUzD+XKTAuzS\nMb33rTJemTFbgZeIMj+k6o032HT//QTimMQD1dWsuukmaj79NNphTcD9ZcZk5RIy0ASeSI8RYebz\n3o8+YuU118RtjWWwupolZWWEWi/g0gQsCP/hZiwRegBH43wDkh7A88Y42mpUqXR2G22MUq286ipq\nVq0iGIeOR6C2lp2LF7P2ttvaPBT4k+sBpBFN4AkSfpf4AyL8YWyYP5/VN93kahI3oRCBPXt4e9o0\n9n4csQPZAFzq2kVT10TsGygnkz72/X04W++nVHp7FVsuOOLfSqiujrcnT6Zm9WqCrUyKjVWgtpbd\nS5aw5BvfiLZ9KNjkvbjMmKhd9EynCTyByox5AlhEhO0r1956K8svvZRAdTWhpqhVDdsUqKmhbuNG\n3pwwIeK6b+ybicvKjMno9d8i5GKHz79weGpPYIkxWnlNZY/wLb/v0EalwqZdu3jz+OPZvnAhgVaq\nQzoVqKlh62OP8fa0aYQa2qyx1ADM7vBF05wm8MT7L6L8YWx58EEWjRzJrnfeIbB3r+PJbaHGRoK1\ntWyYP59Fw4dH63k3AW8DDzm6QHoahS324LTyWh72fqBSWaXMmI9ox1B6sKaGpWecwYezZ9O0a1dM\nI4iB6moavviCJWecwUcXXYRpu/NSg515nnX7fx9Il5ElgV9kKvA0NqlE1H3aNAZffTWlJ56ICQTw\nFhW1WkAhFAgQrKlBfD4233cf6+64I9Js8332VRQbU2aM015pWhFBgOuxS8Gc9KRLsG9yfmaM46pt\nSqU9v0gOsAIYShsz0wF8nTvTb/ZsBl1zDTklJUhuLt4I+z0E6+owwSAN27ax9re/Zcsjj7R3ZnsA\neAs4qUyTlybwZPGLfAt4kHbsY5vfpw/dpkyh64QJlE6cSG737ojPh2lspOazz6h67TV2vfsuXy5c\n2J4/giBQBYwvM2ZDx3+S1CZCf+AmnO8gNgC41xhaLxyvVBbwi4wA3sG+oW23rhMm0PVrX6P061+n\n06hReAsLwRgC1dXsfv99ql59lZ1vvcXu995z0mwI2AEcU2ZM1Nqq2UITeBL5Rc4EHqGNnriLmrD3\ngSdmQ/IGEGEWMIkI6/Aj8AG9gCuMcbxmXKmM4hc5Dnsr6aBCVAlkgN3AcWXGrEpiHClF74EnUZkx\nTwNTga2Ae1M5W1eDLek6JouSdxE2eX/u8NRewKuavJWCMmPeAU7B+f4Bbglib3+doMl7f5rAk6zM\nmMXYe0z3E589bZuwBWQuBaZl+j3vA4zH9qadruHOwS6lUUoBZca8BZwIbKeN2ekuqwU2AuPKjNFi\nSgfQBJ4CyoypLTPmUuBU4BPskzbYwWbrsbOu/wUMKzPmwWya9CGCB5iBvWfmRFdgDbDZ9aCUSmNl\nxnwIDMGWLo139TODHZW8ExhRZsy6OF8vLek98BTkFxkHXAWche1BF9G+N1tN2Cd9EFuh6O5MX+Md\niQjDgZ8CTm8XDADmGQlbUrMAACAASURBVJO9Oxwp1Ra/yMnYRF6E+3N4arA9/XPKjFnqctsZRRN4\nCvOLlGLvkR+HvZd7BDaRB7DvUD3Y4d7d2A3tXwXeBV7Jgo1JohLhMuAonN3/zgM6AVca03qxHaWU\n5RcpBr4PXIMduSqiHcvNIghhOx9bgN8Aj5QZ47RuQ9bRBJ5G/CICdMcuPcvBDpNXlxmzO6mBpRgR\nSoHfYYfBnazh7gf80xiejktgSmWg8OvS17GJfBr2dclD27PW95VvywGeAn5fZsySeMWZiTSBq4wj\nwunAt7CTX9p9GjaBX2uM4w1PlFKAXyQXO1I4FjgBO3rYCTu6ZbDzcnYCb2L3GFgG/DvbRwxjpQlc\nZRQRcoA/YO+jOZkt2xP4tzHZvbuRUip96Cx0lWmOAIpxvtSlAFjofjhKKRUfmsBVpjmV5ntr7VWE\nXW6W1VsTKqXSiyZwlTFEOBQYjq317kR34DljOrz2XimlEkYTuMokJ2LXwDuZ2OENnxNx03SllEpF\nmsBVRhChAJhCbHXP3zSGve5HpZRS8aMJXGWKY4BcbDU6J3KARa5Ho5RScaYJXKU9EQRb93ynw1O7\nYNeKr3c7JqWUijdN4CoTDAL6AHscntcZ+JcxSdkiUSmlOkQTuMoEU8Bx7fJc7FrxD90PRyml4k8T\nuEprInQGvgY43ee8F7DQmITubayUUq7RBK7S3XHYOuZO1nAL9rn/ZlwiUkqpBNAErtKWCF7gNHC8\n+Ug3YIUxbHM/KqWUSgxN4CqdjcDOJK91eF4R8IL74SilVOJoAlfp7BScJ+9CYDfwifvhKKVU4mgC\nV2lJhF7AUTgfPt9X91z3H1ZKpTVN4CpdTQRCOK97DvCO++EopVRiaQJXaUeEPGAqzuue9wTeNoZd\n7kellFKJpQlcpaOjgQJiK97ysvvhKKVU4mkCV2klXPf8NOxENCc6Af8B1roelFJKJYEmcJVu+gOH\ngeNh8K5o3XOlVAbRBK7SzUnEtmVoE/Ce69EopVSSaAJXaUOEYuAEnNc97wm8YozjNeNKKZWyNIGr\ndDIeuxTM6RpuH/Ca++EopVTyaAJXaUEEDzADqHJ4aimwCtjqelBKKZVEmsBVuhiGraJW7fC8TsAC\nnbymlMo0msBVupgK1Dk8Jx/YC6xwPxyllEouTeAq5YnQDRiD87rnPYHnjXE8a10ppVKeJnCVDo7H\n1jwPOThn33N7sfvhKKVU8mkCVylNhBxgOrDd4ak9gPeMYYf7USmlVPJpAlep7kigGKh3eF4+sND9\ncJRSKjVoAlcpq0Xd870OTy3G3i9f5XpQSimVIjSBq1R2KDAU52u/u2Hrnju5Z66UUmlFE7hKZZOI\nrepaEFjifjhKKZU6NIGrlCRCITCZ2Oqev2aM44IvSimVVjSBq1Q1luZdxJzIBV51PxyllEotmsBV\nymkxec3pve8uwDpgo+tBKaVUitEErlLREOAQnM8+74ydvKZ1z/9/e/ceHlV173/8vSY3SIjcQS5y\nB0UuEQEtRdQiiCieHrVqtbRq1Wj1KC2tcuzUlupJvVRrPQelHavt0R5R8VcFW8FrLXjnIlArICBX\n0QjEAElIMplZvz/WAAIhmT2ZS2byeT1Pn3MIs/detJn92Wvttb5LRDKeAlyao/FAjcdj8nC10lfF\nvzkiIs2PAlyaFWNoB5xCbJPXXrXWc/CLiKQlBbg0N18DDN7qnhvc7/KbCWmRiEgzpACXZsMYsoFz\n8F73vBPwT2s999pFRNKWAlyakxNxE9G87vudD7wc/+aIiDRfCnBpTiYBlR6PyQfKgdXxb46ISPOl\nAJdmwRiOxfXAvW7/2Qm3dCwU/1aJiDRfCnBpLk7D1TD3soY7K/L59xPSIhGRZkwBLilnDK2ACcS2\ndOxda9kd/1aJiDRvCnBpDk7CFWKp9XhcHvB6/JsjItL8KcAlpSJ1z8/FTUTz4hhgG/BJ3BslIpIG\nFOCSan2A48DzMHgHVPdcRFowBbik2pl43zI0B1cr/YO4t0ZEJE0owCVljKEQGAuUejy0K/C6tZ4L\nvoiIZAwFuKTSKbilYF7WcJvIMYsT0iIRkTShAJeUMIYs3OS1nR4P7QCssZbt8W+ViEj6UIBLqhyP\nC2OvpVPbAC/FvzkiIuklO9UNkMxk/IE8XGnUDkBr3MSz6sh/PsV3zdmEfV7fYbcG9gL/imdbRUTS\nkbFWq3Ck6Yw/MAQ3IW0sMAa3PKya+vb1tuQQ9uVR2WoHO9tvZnuXbazut4kdHSsauUwv4P9Zy9/i\n23qR5sn4AwboAYwERgOjgEKgFa6M8D5cDYV3gaXAMltS7PW1lKQpBbjEzPgDrYCLgRlA38iP8z2d\nxGIJ+YIY66Os7SaWDX2b5YM3urlqh/ABPYGfWEtZE5su0mxFRq8uAq4DRuBGr2qBAtwEzvoEgSoO\njlK9BzwEvGRLirXRT4ZSgItnxh84DvgRcE3kR4VxObEFQr5a6rKqWd3/bf4xejlVrfevET8WWGUt\nD8flWiLNjPEHegM3AtdHftTU71UFLtQfAP6gnnnmUYBL1Iw/4ANuAO7BzZ/ITdjFQr4gIV8tr455\nlhWDNwG9gbutZW3CrimSAsYf6A4EgLNwI03x/l7tww1pPQn8yJYU74nz+SVFFOASFeMP9AWeAobg\nhvKSI+QLsqXbGuaNf4Wq1rdYW887dZE0FHm/PRV4GLcxT06CL1mN65VfbkuKX0nwtSQJFODSoMhN\n5jrgftxN5mjv4BIn5Ath2U12+Ju2pPjNpF9fJM4ive4ngFNJ5gOxUwU8C9yk3nh6U4DLUUXC+7fA\n1ST/JlOfKmCqLSl+LtUNEYmV8QfGAAs5uLwyFapxs9fH2ZLi9SlqgzSRAlzqFXnf/UfcbNjmEN77\n7QOusyXFT6S6ISJeGX9gIvA8XldrJEYYtwvgGbak+J+pbox4pwCXI0R63rOAK2he4b3fPtx7vOdT\n3RCRaBl/YDzwAs0jvPezwB5gjC0pXp3qxog3KqUq9bmN5hve4IYe/8/4A6eluiEi0TD+wCnAfJpX\neIObnX4MsNj4A31S3BbxSD1wOYTxB04C3saFZHP3OTDAlhR7racukjTGHzgGWA90TnVbGhDClSg+\nWYVf0odqocsBxh/IBebiyjTGrHe7NkwY0IOxvY5lTK8udMpvTZbPEAyF2VJewaJNn/Hu1i94ad1W\n9tQEGz/h0bXFFakobspJRBLsv3Gb8MTEZwxn9u3GmF5dOaNvN4Z0aU+rnGywloraOlZ8tpN/bPyM\nt7aU8t7WL2K9TBbQH/gxcG+sJ5HkUg9cDjD+QAnwQ2Ic5jtn0HHcOq6Irx3XhVDY0iav/gm2oXCY\nyto6srN8PPPPDdz/5io+LP0y1mZXAefZkuI3Yj2BSKIYf2ACMI8YvlMd8/MoHj2YH44dRqusLFpl\nZ5GbXf8qzupgHcFwmJ1VNdy7aAVPrFhHZW1dLE3eB4ywJcUqmJQGFOACNG3ovFe7Nsy5ZDzDju1A\nYZ63IlLBUIjakOXxDz7mxwveYV8wptG7UtxQemOboYgkTVOGzqeeNJCH/20sWT4f+TneBkoraoLs\nq6vj8qdf59UNn3q9dBj4EA2lpwVNYpP97iWGofMrRgziX9MuZnTPzp7DGyAnK4uC3GyuGDGIddO/\nzcndO3k+B2548qpYDhRJoNvwWM+8batcXvn+ucz+5mkU5uV6Dm+ANnk5dC5ozfNTz+aPF51BTpan\n27wPN5T+Hc8XlqRTD1ww/kAv4GNcpbWo3TquiJ+PP5mC3PjVoqioCXLu4wtYvOlzr4duBXrbkmL9\nQkvKReaT7MDN8I5Kp/xWvHXdN+nVrg2tjjJU7lVVbZAln+5g0p8WUFPnqUP9kS0pHhKXRkjCqAcu\n4DYo8XbAqSfGPbzB9R5e/N7kWHri7YEz49oYkdhdSD174h5Nm9wcFhefT+84hjdAfm4Oo3t05oWp\nk/CZqJsD0Mf4AyPj1hBJCAV4CxfpKfwAD73vYV078OtzTo17eO/XJi+H+d+dROscTzeyAuCWhDRI\nxLsZeBg+n3X+1+ndrpC8OIb3fvm5OYzp3ZUfnzbcy2F5uC2DpRlTgMsFeOgpZPsMcy+bENdeQn3a\nt87j/sljvBxigG8Yf6BbgpokEhXjDwwDBkX7+Qn9e/Ctof1oHcP77mi1yc1h5viRDOrUNtpDsoCL\njD/QPmGNkiZTgMsUPPQUfnDqEHq0LcDnS+yvTn5ONt8bMYihXT3dP4LAGQlqkki0ziPKGhs+Y/jf\nb52ZsNGsr8rL9vHYhZ6+HrXANxLUHIkDBbh46ubOOL2INkm42QDkZhmmj/U07NcG+FqCmiMSrTOA\nqJZknDOoZ9K+T1k+HyO6d/LSC28DnJLAJkkTKcBbMOMP5AG9ov38+H7dOeYoxVkASktLmTZtGv37\n9ycvL48ePXowefJkXnzxRQD69OnDfffdd8Rx9913H3369Dni5zlZWVw6vD+FDVzzMAYYF+2HRRIk\n6slft447iX27v2zwe9OQK6+8kilTphzx86VLl2KMYdOmTYf8PNtnuHnM0Gib5wNOj/bDknwK8JZt\nGK7yUlSmnjSQgpz6w3TTpk2cfPLJvPTSS9x1112sWrWKV199lfPOO4/rr78+5gYGQ2HOGXicl0MG\nR7ZCFUk64w90xJX4bVRBbjbdwlUJ+d4cTW5WFpcN7+/lkOGR3QmlGVIt9JZtJB5+B8b27orPV/93\n+YYb3Eq0pUuX0qbNwbLPgwcPZurUqTE3sE1uNqce14W5H34S7SFhXCGKdTFfVCR2J+MeihsdQh/R\nrRP/ceONQPy/Nw3Jz82mS0FrvqiM6tndhxul25yQxkiTqKfSsvUkyhrNOVk++rSvf65bWVkZCxcu\n5MYbbzzkJrRfu3btYm5gls/H6X2O9XJIHdAj5guKNE0vonwoHnJMLq++/HJCvjcNqQ6GGNkj6joL\ntXh4zSbJpR54yxb17PPuhfnUhsLkZh25fGz9+vVYaxk8eHCj5/H7/cycOfOQnwWDQbp1O/rqr17t\nPFWjhCbupibSBK1wS7Aa1a6qvNHvTXl5ORMmTKCuro66ujqmTZvGtddee8hnFi5ceMQDQDgcPuTP\na9eu5dJLL3V/Zy0frVkD518Nx58U7b9JmiEFeMsW9Q5JedlZHK3srpdyvNOnT+fqq68+5GePPvoo\nc+bMOeoxud5qOYNuOJI6eUQ5slnfw/DhCgsLWbRoEfn5+VRWVjJ06FAuvPBCOnbseOAzp59+OoFA\n4JDjPvzwQy644IIDfz7++ONZsWIFALvKd9Ozd29CfRt/4I7Q96mZUoC3ZJbaaEu4BENhjlbvZeDA\ngRhjWL169SE3jfp07NiRAQMGHPGzhtQd1puIQpM2GRdpgiBuHkajOvTo2ej3Jisri/x895xdU1OD\ntfaIB+b8/PwjvlPl5eVHve4L8+fTc/hI1udGUXwxmNWavQXJWecmnukdeAtlDN1YccL50X5+R2U1\nedn1/7p06NCBSZMmMWvWLCoqjtzRs6GbSTR2VlV7PSTqmfUicVZNlAG+ixzOPvvsRr835eXlFBUV\n0bNnT2655RY6dYppx74Dnp07ly6jT4v+gIWn/c4Y7TPQHCnAWyBj6Assxtg1WGqjOaaiNsiOyqMH\n6UMPPYS1llGjRjF37lzWrl3LmjVrmD17NsOHeyrGcoS3N5dG/2GLDyhr0gVFYrcDN5GyUcs+3cHd\nDzzQ6PemXbt2rFy5ko0bN/Lkk09SWurh+3CYPXv28N577/JZlyiXkuWEauhU/lPgGWP495gvLAmh\nAG9hjKE78BrwICetvRsTfW916bYdR/27fv36sXz5ciZOnMiMGTMYPnw448ePZ/78+Ue8n/OioqaW\nt7d4CXBTwINT2xkT/ft9kTj6AIhqyHnZpzs5YeDAqL83Xbt2paioiMWLF8fcuHnz5nHWhAls3Bv1\nqFYeZ7/9J2Ay8HtjmBzzxSXutB94C2IM2cBbwHxrKTH+QFtcjyGqG853TxrIQ/82lsK8qKpExsW+\nYB0n/PYZtpQfOcRYr8pW5Tz4vedx7yJfBxZby6cJbKLIAZGiJ3txu+M1av30S+nf8eh1X0pLS8nP\nz6ewsJDdu3czduxY5syZw7Bhw2Jq35QpUxh53oXcsS2qQQKAf9mS4qEAxjAGeAH4mrWsj6kBElfq\ngbcsPwH2AL8CsCXFu4Fd0R7siqkktyjTO1tKow9vgLK2m3FFJ3YAZwH/ZQy3GcMIY6J7UBGJlS0p\ntsCqaD9/z+KVVNQc/S3W5s2bGTduHEVFRYwbN46bbrop5vDevXs37y9ZwhvG0/ryN/f/P9byDlAC\nPGaMsqM50P8ILYQxnAj8GLjGWr467LIs2nNU14V4bNkaaupCcW9fffbW1HLv4pXRHxDyBfm885bI\nn4LAp8AWoBtwM/AbY5hiDA1PexdpmkVEOZHtyZXrMeboD8WnnHIKK1asYOXKlaxatYrrrrsu5ka1\nbduWpWs3sGjrzmgPqQDeOexn/43LjZtibojEjQK85bgLuNPaI0oivgRURXuSO/++nH11UQ+/xSwY\nCrHq8zJeWrfN24Gf9Nx62E8sblLbZqASt//5r43hP4zhBPUkJAFew/2uNaqyto7bX1lCRU3iVz5W\n1ga5fp6n9+dZuIeRA6wlBHwf+LkxHFk+TpJKN68WwBh6A6cBj9bz10/i4fdgV1UNV/9lERW1ib3h\n1ITCXPb0a94O2pf3JRt6HX2mnVvisxXYBgwBZgB3G8M3dDOSOIo6wAF++86HrN1ZTl3Ic72DqO0L\n1jF/9WYWfHz4822DltuS4o2H/9BaPsYF+3fi1T6JjQK8ZSgGnrD2yJuKLSneBcwDoh4X/8u/NvLX\nNZupTFCIV9YGuXH+m2zdHfU9EOp8taw6/q0oPx0GSnG9ch/wXeABY/ieMfQ2Jskv+iWj2JLiMHA/\nUY5sWQuXPPUqlcHEjGzVhcKU7avhB/PfbPzDB+0F7mng7x8GbtB3JbUU4C3Dd4FHGvj7+3G90+hP\nOPfvvLX587iHeGVtkDteX87jH8Swmdg7RR/FcMkK3HvyUtwoxUzc8OBoY4iiVJVIvf6Ih/vrJ2V7\nmfjHv7G3Jkg4HL+VQcFQiLJ9NYz9/Tx2V0dV8mG/GqChDclfw+2l0LQiD9IkCvAMZwzH4pa0HDXc\nbEnxEtywctTqwpbzHl/IC2s2x2U4PRwOUxWs45aF73mbuAYQNiE29VhGTV5TujB1wHZcr7w9cAOu\nV36hMXRpwnmlBYplZGvJth2c/sh8yvbVsC8OvfHK2iBbdlcy6uG/sNnLSg73MP+gLSk+atutJYyb\noT66ic2UJlCAZ76RwPLDZp7X56d4eG8HLsQve/p1rnj2Dcqra6iOcXZ6ZW2Qj3ft4dTZzzH7vRg6\n0daEWTTq3ZguXr9yXJDvBs4D7jGGHxnDUGOi22lKBPgZricbtRWf7WLAb57i+Y82xTy6FQ6Hqaqt\n4+H3PuLE3z7j7VWUUwXMiuJzy3D3F0kRFXLJcMbwcyDfWv6z0c/6A38FJgKeK7V0zM/jZ2eezNWj\nTgBso8VewmFLZTDI7upa7lm0kt+9/xF1sQwd1vmCrDz+FV4at8T7wVEzQCfc7m1fAguA961ldwKv\nKRnA+AM/Au4kysIuX3VW/x7ccdZIRnTvRJYx5GY3/Oy4L1iHAV7d8Ck/f20pH2yPusTDV1UB37Il\nxQsa+6AxnAbcby2nxnIhaToFeIYzhv8B1lvLg41+1h/oAqwDjon1eq2ys7h4aD8uHNKXUT060aVN\na2rrwoSx+IwhN8vHxrK9vL2llCdWrOPvn2yP9VIQJsyeNp8x+9uPYn3J+kXOx4V5GHgXV+1tYxQj\nHNICGX/ABywBiohyn/DDDeh4DN8feTxn9e/BkC7t8RlzYIe+nCwf1XUhVn5WxkvrtvLH5Wv5fG/M\ne/lUA8/ZkuLLo/mwMfQHXrWWvrFeUJpGAZ7hjCGAG0L/XVSf9wcuAR4jhh5Dfdrk5tCpoBU5Ph81\noRDb91TG1tOuT8jU8qcLnqe0UyWu8loydyHLArrgRiu24Sb8rLBWO6HJoYw/MABYCfGpz39sYWva\n5OYQtpa9NQ1vMuRRGdDflhRHtX2gMfQAllhL93g1QLzRO/DMV0uUtc4BbEnxM7heZVyCqKI2yKYv\n97Ju1262lFfEL7yhiiw7jdJO1wFPA3lAb1zvOBlLW0LAZ7h35QW4pXoPGMMlxtAtCdeXNGFLitcD\n0/BQMKkhn+/dx/pde/ikbG88w3sfcHG04R2RC9HtZiiJoQDPfF8CnT0e823crHVPE3CSqBIIAL+3\nlnJreQlX5/03wEagF3AcMbzLj9EeXJDvAs4GfmUMtxpDUWQDGWnhbEnxH4D/Ik4hHmf7gO/ZkuLX\nPR7XGXd/kRTREHqGM4aLgKusZYqn49xOZe8A/aBZrYeuBJ4FropsHHGEyNK5scAEoBVuNrmXnkVT\nGaAD0AZXEGMh8I61utm1dMYfuBOYTpyG0+NgH3CdLSl+wuuBxnA9cIq1fD/+zZJoKMAzXKSM6jux\nvKeKhPhrwIlA63i3LQZVuPfzNx8tvL/KGFrhJg+di+uVB3EFW5KzG4vTmoMjIEtw/32uj6yjlRbI\n+APTcTPTUxniFhfel9uS4nmxnMAYHsHN+3gori2TqCnAM1yk1OEXwGhr2eT5eH8gH3gKGE+cJrbF\nwOJmyP4XcFc04f1Vkf8OegPfwPXMs4CdeFz33kQ+3KS3PNxDxIvAsvrK20rmM/7AZODPuBBvleTL\nV+HuCRfbkuKlsZwg8p36J253w3jWYBAPFOAtgDHMAnZZyy9iPoc/cDHwB9zNJlnvlsGF7Ge4m82K\npp7MGApx1aPOBTrieiE7IKnLwApxQ+x1wD8i/9mmpWgti/EH2uFqin+T5PTG9z8IzwJutyXFMc9x\nMYZTcA/2AyM7lEkKKMBbAGMYArwC9LE29lmjkXXijwFnkvje+P6bzf3AnbakOK6zXSMV1QbhCtec\nFLneFyR34l420BW3SmA9rkDMP5vyv5GkH+MPnAs8jnswLkzQZfbiJlnG3Ov+KmP4E/Ava/l1U88l\nsVOAtxDG8AbwB2v5c5PP5Q9cCJTgZnq3IsYCFUdRgwvTRcCMePS6G2MMnXBD6xNxE8/24NbEJlN7\nXAGdKuBl4C1r2ZnkNkiKGH8gD/gW8J8cnDja1O9VLa7g0BLgXmBBQ/XNoxWZJLoa1/vW72gKKcBb\niEjZw2eAYdYSU43FI87pD4zGzaj9d9yNoinDgHsj55gNPGxLij1tXBwPxpALDAMmA/1xQ9ylkf+b\nLHm4d+U+YAVu5GSthilbDuMPjAB+BFyM+92zuAfLxuobhHG762XjJmo+CsyyJcUb4tY29+77L8Aa\na7ktXueV2CjAWxBj+C3Q2Vq+E9fz+gMdgMuBSbjNDToBVVhaUf+WnFW4G1Nr3Prpt4G/Ac/He6g8\nFpGbVE/gDOB03BB3Ge4hI2nNwM1ebx259ou4+uvJbIOkkPEH9r/mGQl8HbfdbT/cUHs2Fgj7DL5w\nBYbVuFGr93CbjGz0OtkzqjYZLsNt0nKytc22TkSLoQBvQYwhH1fS8Q5r8bzuM+rruOVnI1hx/G2E\nzQmMWDMvEuSVuB7CNtxN5sOmTKRJBmMowN1Az8W9r67BvStP5jKwAtxDUQh4C3gD2KRJby2X8Qdc\nb/xXxT8GLgAmJLqMrzEMwP3+TbGWRG4eJFFSgLcwkQltrwHF1jI/gde5GbgZOM1aPk/UdZLFGHzA\nAOAsDu6BnIr66/snvW3lYP31uNXTlPQS+b18Ajd/4luJ6hUbQy/caolfWcsjibiGeKcAb4GMYTTw\nAnBLvHvikeFnP3AVcFYsa8+bO2NoD4zBvTI4BjeqUEZyl6K1BdrhRgReBd70+qD08Z13ds7t2HF6\nXteuZ+T36zc079hjC3y5ucaGQjb45ZfByg0bNtZs3/5W9WefBQbdfvv7ifhHSNMZQw4wh4MhvifO\n5z8Rt0LiN9HsaijJowBvoYxhKPAc8D5wczwmthlDH9zEmTbABdbShL1Cm79InfMhuCAfjBvi/oLk\nbvCQi5v0lg38C3gJ+Mjao0+8W3/PPecUDh16f8czzzyRUMj68vONL/vIku02HCZUWYnJzmbPqlVl\n5e+/f19w1667B82cqZtGMxP5XXwA+DfgWmt5OQ7nzAJ+CNwGTLeWx5t6TokvBXgLFnknXgJcCtwK\nPBPLGuTIe+JrcJNb7gPubyhAMlFkB7JxuIp1ebja67uT2QRcYZoC3DK4BcC71h6sAf/xHXe0PWbY\nsNc6T5o00mRn48uNvh6PDYcJVVVRuW7d3i8WLJg08Kc/fSfe/wBpOmOYiCu49ApursuWGM5hgFNw\nmwPVAd+3lrjNZJf4UYDL/iVmdwInAI8AjzU29B35kg8BrgWmAm8Ct1nLR4ltbfNmDK1xhWHOxc1k\nT2X9dYsbYXn943vvH9bz25c8ndOhQ3Z2Qew1eMLBIOHaWrbPmfNIr2uuKY5TeyWOjOEY4Oe411hv\n4qq9vW4twSiOuxi4AVeX4Ne4Hf9Ut7+ZUoDLAZF3XT/AbSdah5sp/gFuslYtrmhLN9ys7JNx734f\nBwKxPOlnssgDTl9c/fUxpLD++rRJbw379dzhX88uKMD44rODcF1VFaXPP7+gx+WXnxuXE0rcRUbG\nLgOuw21I9BHuO70Ot5QzC1f5bSjuO30cboLrw8DLCu7mTwEuR4iETy/cl/ok3NN4Lm7C1A7cTWCZ\ntZSmrJFpJNKzOQXXK2+Pu3nuJAmT3m486+3BDzw37JKcwvhX6KyrrOSzZ5995rgrrrg07ieXuIqE\n+Um473Rf3ChNGPdAuT/YG5w7Ic2PAlwkSSKTgk7AlWwtwt1AE1Z//di25XnrV1XMyO/Z3cSr5324\nUFUVG2fNumTArbfOTcgFROSoFOAiKWAMXThYfz0fN+Hty3heY9kjC4uHXzauW1PeeTfGhsNUbdoU\n/PSJJ9oO+sUvr9YkYwAACgFJREFUkrkmXqTFS8xjuYg0yFq+sJbncDWvZ+F2iuoD9MAtCWuSq05b\nMqDo8tMTGt4Axucjr2vXnPx+/eYk9EIicgQFuEgKWUuNtSzFrQL4Oa6edVegN249fUymX1d+tqln\nbXciZBcU0Pnss8//+Je/jOeudCLSCAW4SDNgLdZatkQq4/0I+F/cO/LeuECP+rvat9OO/BMu+Hrn\nr67zLi0tZdq0afTv35+8vDx69OjB5MmTefHFFwG48sormTJlyhHnWrp0KcYYNm3a1OA1s/LzfTkd\nOtwabRtFpOmS84guIlGzlgrgDWNYBAwEJuBmD4Ob9NZg7fPbLloyxoZOO/DnTZs2MXbsWAoLC7nr\nrrsoKioiHA7z2muvcf3117NlS9NXAGYVFNDmxBOLgbuafDIRiYoCXKSZiqzDXQusNYYOuPXk5+B6\n5Eetv15URO+s/INbs99www2A6023aXNwVH7w4MFMnTo1Lm01Ph8FAwb0jMvJRCQqGkIXSQPWUmYt\nfwOmAw8Cn+LW6vfE7U52QJ+R3bvur21eVlbGwoULufHGGw8J7/3atWsXtza26t49++M77mgbtxOK\nSIPUAxdJI5FymCuAFcbQAziNg/XXy4A9hT2PPRDo69evx1rL4MGDGz33woULjwj5cPjQYlzl5eVM\nmDCBuro66urqmDZtGtdee637bHU1Jjt7NG53NBFJMAW4SJqylk+Bp41hPq607blAb19ujjn4mejr\nPJx++ukEAoFDfvbhhx9ywQUXHPhzYWEhixYtIj8/n8rKSoYOHcqFF15Ix44dsdbiy8mJf8k3EamX\nAlwkzVnLPuAtY3gb6GdDoStwu5MxcOBAjDGsXr36kCCuT35+PgMGDDjkZ+Xl5Yf8OSsri/zI+/Wa\nmhpsZPo8gDEGGw6rmItIkugduEiGiGTphrryLw9sCduhQwcmTZrErFmzqKioOOKYwwM6GuXl5RQV\nFdGzZ09uueUWOnXqBIAvN5dwdfW6JvwTRMQDBbhIhqncsGHjV//80EMPYa1l1KhRzJ07l7Vr17Jm\nzRpmz57N8OHDPZ+/Xbt2rFy5ko0bN/Lkk09SWur2tAnu2WMH/eIX2jdaJEkU4CIZpmb79rfq9u49\n8Od+/fqxfPlyJk6cyIwZMxg+fDjjx49n/vz5R7zz9qJr164UFRWxePFiAKo2bChrcuNFJGrazEQk\nw6wrKTm13/Tp72a1bh33c5eWlpKfn09hYSG7d+9m7NixzJkzh8F9+/LZs8/++bgrr/xu3C8qIvXS\nJDaRDDPQ73/vy/ffL2s3alSHeG8junnzZoqLiw9MXrvpppsYNmwYdZWV7Nu69SdxvZiINEg9cJEM\ntPF//ue246666lfZ9RRvibdwbS07X311TZdzz218sbmIxI3egYtkoOCuXXdXrlu3NxwMJvxa4Zoa\ndi9fflnCLyQih1CAi2SgQTNn2i8WLJgUrq1t/MNNUFdRwadPPTV74M9+tiKhFxKRIyjARTLUIP9P\nQw/etKS6rqoqIecPVVXxyRsfhvpf/30tHRNJAQW4SAYyhsHA/J/88cyLS+fNWxiKc4iHqqqoWLt2\nz4//M29YKJz1H8ZwTVwvICKNUoCLZBhjyAXmALdby197XHbZ5O1z5z4TqqrCHrY5SSzqKispX7as\ndPszzxz3wocjVgOTgLsjDw0ikiSahS6SYYxhJjAKON/ag/uFb7jvvku6XXTRn3O7dMnJLijwfN5w\nbS3hmhq2P/307Opt224cNHPmgXMbww+AK4Cx1hJq+r9CRBqjABfJIMYwDHgNGBHZrewQH916a/4x\nw4Y92XnixPOzCgp8WQUFNLZWvK6iAuPzseuNN9bsXr78svomrBmDD7eN6N+s5f44/XNEpAEKcJEM\nYgx/Aj6ylnsb+tzHv/xlVk779jPaDBlybcGAAT1bde+eHa6uxlqLMQZfbi7BPXts1YYNZRVr1izY\nt3XrTwbdfntpI9ceggvx3taS2OnvIqIAF8kUxtARWA8MtJadXo79+I472prs7NG+nJxCGw7vC1dX\nr4tlYxJj+Dsw21qe8XqsiHijABfJEMbwE2CYtVyRwjZcDNxoLWemqg0iLYVmoYtkjim42eepNA8Y\nbQzHpLgdIhlPAS6SASKTyEYAS1LZjsi771WRtohIAinARTLDAOBLa9mV6oYAy4CRqW6ESKZTgItk\nhiG4nm9zsBIYmupGiGQ6BbhIZmgD7E51IyL2AN4rxYiIJwpwkcyQBTS9Tmp8hIHsVDdCJNMpwEUy\nQzXQKtWNiGiFa4+IJJACXCQzbAH6p7oREf2BzaluhEimU4CLZIYVwImRnchSbSRuJrqIJJACXCQD\nWEsV8AnNY/a3AlwkCRTgIpnjLdze3CkT2Q0tjIbQRRJOAS6SOR4BrjOGrBS24QfAI1/dh1xEEkMB\nLpIhrGUp8AUwORXXj9Q//zbuQUJEEkwBLpJZ/hvwp6gX/kPgFWvZnoJri7Q42k5UJINENjV5HXjB\nWu5P4nWHA68BJ1vL1mRdV6QlU4CLZBhj6Ae8D4y1lrVJuF4O8B7wkLU8mujriYijIXSRDGMtnwB+\n4Dlj6JTIaxmDAX4HfAo8lshricihFOAiGchafg88D7xsDJ0TcY3IcP0s4ETgMs08F0kuBbhI5vID\nLwKLjeGkeJ7YGDoATwHDgHOspSKe5xeRxinARTKUtVhr+RnwK1xPfGY8Sq0aw/nAP4HPceHdXLYx\nFWlRNIlNpAUwhh5AAOgDPADMsZZKD8f7gPHAzbgh86ut5R8JaKqIREkBLtJCRCacTQJuAMYC/we8\ngatbvuXwd9jG0B44GTgVuAK3RejDwBOR2usikkIKcJEWyBh6A9/FhfMoIBvYBtQAOUAHoBPwAS7g\nnwXe1kQ1keZDAS4iGEN3oCvQCggCu4FPrCWU0oaJyFEpwEVERNKQZqGLiIikIQW4iIhIGlKAi4iI\npCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuIiKQhBbiI\niEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4iIhIGlKA\ni4iIpCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuIiKQh\nBbiIiEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4iIhI\nGlKAi4iIpCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuI\niKQhBbiIiEgaUoCLiIikIQW4iIhIGlKAi4iIpCEFuIiISBpSgIuIiKQhBbiIiEgaUoCLiIikIQW4\niIhIGlKAi4iIpKH/D9xuorJqAG3HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f124eba8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.plot_network([3.8e-13])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading xml input file\n"
     ]
    }
   ],
   "source": [
    "cs = ChemSolver(chemkin('demo_xml/demo.xml')).load_results('result.csv')\n",
    "cv = ChemViz(cs)\n",
    "cv.html_report('report.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "After running the above code, an HTML report will be generated as <a href=\"report.html\" target=\"_blank\">report.html</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['H', 'OH', 'O2', 'HO2', 'C', 'CH', 'CH3', 'C3H7']\n"
     ]
    },
    {
     "data": {
      "image/png": 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AYp4dR+QdQ/HpIMNkCWEv9hyuptZG3kqptsBWjLlHy5VSDwIxWutse8XUnJSX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xdVIeyUNIbloCgnn9NfreKP9xaSn3aZ4FGRNHnrXqrU8zc6mhDCzugiE8mrD3Lm69UkLdsHGvwH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\nx8UJDRUU8509A/k/Bu4IHlcjdMyO+c6eLl95Tnww9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f1301ba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can also plot time series of the simulation grid\n",
    "print(cs.chem.species)\n",
    "Temp_vals = [1000,1500]\n",
    "concentrations = np.array([[1,1,1,1,1,1,1,1],\n",
    "                           [1,1,1,0,0,0,0,0]])\n",
    "t_max = 2.e-14\n",
    "dt = 2.5e-17\n",
    "gridresults = cs.grid_solve(concentrations,\n",
    "               Temp_vals, \n",
    "               t_max, dt).get_grid_results()\n",
    "cv.plot_gridtime_series(\"concentration\", tmax = 1e-14, species = ['H', 'OH', 'O2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## simpleIO\n",
    "\n",
    "We implemented a `simpleIO` module, which allows users to save and load pychemkin objects. We provide 2 methods in `simpleIO`:\n",
    "* `to_pickle(obj)`: method to save a python object to python-specific pickle format\n",
    "* `read_pickle()`: method to load a python object from a pickle file.\n",
    "\n",
    "`simpleIO` allows users to load the saved pychemkin objects (such as ChemSolver or ChemViz) without explicitly preloading dependent objects (such as chemkin), and restart calculations or visualizations from any previous step they want since all the dependent objects have been implicitly loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading xml input file\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>H-Concentration</th>\n",
       "      <th>OH-Concentration</th>\n",
       "      <th>O2-Concentration</th>\n",
       "      <th>HO2-Concentration</th>\n",
       "      <th>C-Concentration</th>\n",
       "      <th>CH-Concentration</th>\n",
       "      <th>CH3-Concentration</th>\n",
       "      <th>C3H7-Concentration</th>\n",
       "      <th>H-Reaction_rate</th>\n",
       "      <th>OH-Reaction_rate</th>\n",
       "      <th>O2-Reaction_rate</th>\n",
       "      <th>HO2-Reaction_rate</th>\n",
       "      <th>C-Reaction_rate</th>\n",
       "      <th>CH-Reaction_rate</th>\n",
       "      <th>CH3-Reaction_rate</th>\n",
       "      <th>C3H7-Reaction_rate</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.277298e+13</td>\n",
       "      <td>-9.689748e+12</td>\n",
       "      <td>-6.832225e+13</td>\n",
       "      <td>7.316712e+13</td>\n",
       "      <td>6.617938e+13</td>\n",
       "      <td>-1.027354e+14</td>\n",
       "      <td>-1.182185e+14</td>\n",
       "      <td>5.159149e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.500000e-17</td>\n",
       "      <td>1.000818</td>\n",
       "      <td>0.999758</td>\n",
       "      <td>0.998296</td>\n",
       "      <td>1.001826</td>\n",
       "      <td>1.001651</td>\n",
       "      <td>0.997440</td>\n",
       "      <td>0.997056</td>\n",
       "      <td>1.001284</td>\n",
       "      <td>3.267681e+13</td>\n",
       "      <td>-9.685055e+12</td>\n",
       "      <td>-6.803120e+13</td>\n",
       "      <td>7.287372e+13</td>\n",
       "      <td>6.588304e+13</td>\n",
       "      <td>-1.020487e+14</td>\n",
       "      <td>-1.173049e+14</td>\n",
       "      <td>5.115686e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.000000e-17</td>\n",
       "      <td>1.001634</td>\n",
       "      <td>0.999516</td>\n",
       "      <td>0.996598</td>\n",
       "      <td>1.003644</td>\n",
       "      <td>1.003294</td>\n",
       "      <td>0.994897</td>\n",
       "      <td>0.994134</td>\n",
       "      <td>1.002558</td>\n",
       "      <td>3.258133e+13</td>\n",
       "      <td>-9.680365e+12</td>\n",
       "      <td>-6.774240e+13</td>\n",
       "      <td>7.258258e+13</td>\n",
       "      <td>6.558896e+13</td>\n",
       "      <td>-1.013695e+14</td>\n",
       "      <td>-1.164030e+14</td>\n",
       "      <td>5.072785e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.500000e-17</td>\n",
       "      <td>1.002447</td>\n",
       "      <td>0.999274</td>\n",
       "      <td>0.994908</td>\n",
       "      <td>1.005455</td>\n",
       "      <td>1.004930</td>\n",
       "      <td>0.992371</td>\n",
       "      <td>0.991235</td>\n",
       "      <td>1.003821</td>\n",
       "      <td>3.248655e+13</td>\n",
       "      <td>-9.675679e+12</td>\n",
       "      <td>-6.745583e+13</td>\n",
       "      <td>7.229367e+13</td>\n",
       "      <td>6.529713e+13</td>\n",
       "      <td>-1.006978e+14</td>\n",
       "      <td>-1.155125e+14</td>\n",
       "      <td>5.030438e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000e-16</td>\n",
       "      <td>1.003258</td>\n",
       "      <td>0.999032</td>\n",
       "      <td>0.993225</td>\n",
       "      <td>1.007259</td>\n",
       "      <td>1.006559</td>\n",
       "      <td>0.989862</td>\n",
       "      <td>0.988358</td>\n",
       "      <td>1.005073</td>\n",
       "      <td>3.239245e+13</td>\n",
       "      <td>-9.670997e+12</td>\n",
       "      <td>-6.717145e+13</td>\n",
       "      <td>7.200695e+13</td>\n",
       "      <td>6.500751e+13</td>\n",
       "      <td>-1.000334e+14</td>\n",
       "      <td>-1.146331e+14</td>\n",
       "      <td>4.988634e+13</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              t  H-Concentration  OH-Concentration  O2-Concentration  \\\n",
       "0  0.000000e+00         1.000000          1.000000          1.000000   \n",
       "1  2.500000e-17         1.000818          0.999758          0.998296   \n",
       "2  5.000000e-17         1.001634          0.999516          0.996598   \n",
       "3  7.500000e-17         1.002447          0.999274          0.994908   \n",
       "4  1.000000e-16         1.003258          0.999032          0.993225   \n",
       "\n",
       "   HO2-Concentration  C-Concentration  CH-Concentration  CH3-Concentration  \\\n",
       "0           1.000000         1.000000          1.000000           1.000000   \n",
       "1           1.001826         1.001651          0.997440           0.997056   \n",
       "2           1.003644         1.003294          0.994897           0.994134   \n",
       "3           1.005455         1.004930          0.992371           0.991235   \n",
       "4           1.007259         1.006559          0.989862           0.988358   \n",
       "\n",
       "   C3H7-Concentration  H-Reaction_rate  OH-Reaction_rate  O2-Reaction_rate  \\\n",
       "0            1.000000     3.277298e+13     -9.689748e+12     -6.832225e+13   \n",
       "1            1.001284     3.267681e+13     -9.685055e+12     -6.803120e+13   \n",
       "2            1.002558     3.258133e+13     -9.680365e+12     -6.774240e+13   \n",
       "3            1.003821     3.248655e+13     -9.675679e+12     -6.745583e+13   \n",
       "4            1.005073     3.239245e+13     -9.670997e+12     -6.717145e+13   \n",
       "\n",
       "   HO2-Reaction_rate  C-Reaction_rate  CH-Reaction_rate  CH3-Reaction_rate  \\\n",
       "0       7.316712e+13     6.617938e+13     -1.027354e+14      -1.182185e+14   \n",
       "1       7.287372e+13     6.588304e+13     -1.020487e+14      -1.173049e+14   \n",
       "2       7.258258e+13     6.558896e+13     -1.013695e+14      -1.164030e+14   \n",
       "3       7.229367e+13     6.529713e+13     -1.006978e+14      -1.155125e+14   \n",
       "4       7.200695e+13     6.500751e+13     -1.000334e+14      -1.146331e+14   \n",
       "\n",
       "   C3H7-Reaction_rate       T  \n",
       "0        5.159149e+13  1000.0  \n",
       "1        5.115686e+13  1000.0  \n",
       "2        5.072785e+13  1000.0  \n",
       "3        5.030438e+13  1000.0  \n",
       "4        4.988634e+13  1000.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the ChemSolver object\n",
    "cs = ChemSolver(chemkin('demo_xml/demo.xml')).solve(x_init, T, t_max, dt)\n",
    "simpleIO('cs.pkl').to_pickle(cs)\n",
    "cs2 = simpleIO('cs.pkl').read_pickle()\n",
    "cs2.to_df().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AYp4dR+QdQ/HpIMNkCWEv9hyuptZG3kqptsBWjLlHy5VSDwIxWutse8XUnJSX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xdVIeyUNIbloCgnn9NfreKP9xaSn3aZ4FGRNHnrXqrU8zc6mhDCzugiE8mrD3Lm69UkLdsHGvwH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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f3e80f60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the ChemViz object\n",
    "simpleIO('cv.pkl').to_pickle(cv)\n",
    "cv2 = simpleIO('cv.pkl').read_pickle()\n",
    "cv2.plot_gridtime_series(\"concentration\", tmax = 1e-14, species = ['H', 'OH', 'O2'])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
