d7dee2e579
Use 2.5.0a2 (git hash 471041a2) to run the examples. Update some formatting in comments and docstrings. Move imports to the top of Notebooks. Set the matplotlib magic before importing matplotlib. Fix deprecation warnings from Pandas about argmax and set_value.
2001 lines
340 KiB
Text
2001 lines
340 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Non-Ideal Shock Tube Example\n",
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"## Ignition delay time computations in a high-pressure reflected shock tube reactor\n",
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" \n",
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"In this example we will illustrate how to setup and use a constant volume, adiabatic reactor to simulate reflected shock tube experiments. This reactor will then be used to compute the ignition delay of a gas at any temperature and pressure. The example very explicitly follows the form set in batch_reactor_ignition_delay_NTC.pynb, which does very similar calculations, but with an IdealGasReactor. All credit is due to the developer of that example. This example generalizes that work to use a Reactor with no pre-assumed EoS. One can also run ideal gas phases through this simulation, simply by specifying a cti file with that thermodynamic EoS.\n",
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"\n",
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"Other than the typical Cantera dependencies, plotting functions require that you have matplotlib installed, and data storing and analysis requires pandas. See https://matplotlib.org/ and http://pandas.pydata.org/index.html, respectively, for additional info.\n",
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" \n",
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"The example here demonstrates the calculations carried out by G. Kogekar, et al., \"Impact of non-ideal behavior on ignition delay and chemical kinetics in high-pressure shock tube reactors,\" Combust. Flame., 2017.\n",
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"\n",
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"The reflected shock tube reactor is modeled as a constant-volume, adiabatic reactor. The heat transfer and the work rates are therefore both zero. With no mass inlets or exits, the 1st law energy balance reduces to:\n",
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"\n",
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"\\begin{equation*}\n",
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"\\frac{dU}{dt} = \\dot{Q} - \\dot{W} = 0.\n",
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"\\end{equation*}\n",
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" \n",
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"Because of the constant-mass and constant-volume assumptions, the density is also therefore constant:\n",
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"\n",
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"\\begin{equation*}\n",
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"\\frac{d\\rho}{dt} = 0.\n",
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"\\end{equation*}\n",
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"\n",
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"Along with the evolving gas composition, then, the thermodynamic state of the gas is defined by the initial total internal energy $U = mu = m\\sum_k\\left(Y_ku_k\\right)$, where $u_k$ and $Y_k$ are the specific internal energy (kJ/kg) and mass fraction of species $k$, respectively. \n",
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"\n",
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"The species mass fractions evolve according to the nety chemical production rates due to homogeneous gas-phase reactions:\n",
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"\n",
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"\\begin{equation*}\n",
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"\\frac{dY_k}{dt} = \\frac{W_k}{\\rho}\\dot{\\omega}_k,\n",
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"\\end{equation*}\n",
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"\n",
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"where $W_k$ is the molecular weight of species $k$ $\\left({\\rm kg}\\,{\\rm kmol}^{-3}\\right)$, $\\rho$ is the (constant) gas-phase density $\\left({\\rm kg}\\,{\\rm m^{-3}}\\right)$, and $\\dot{\\omega}_k$ is the net production rate of species $k$ $\\left({\\rm kmol}\\,{\\rm m^{-3}}\\,{\\rm s^{-1}}\\right)$."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Runnning Cantera version: 2.5.0a2\n"
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]
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}
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],
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"source": [
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"from __future__ import division\n",
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"from __future__ import print_function\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"import time\n",
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"\n",
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"import cantera as ct\n",
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"print('Runnning Cantera version: ' + ct.__version__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib notebook\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams['axes.labelsize'] = 16\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"plt.rcParams['figure.autolayout'] = True"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Define the gas\n",
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"\n",
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"In this example we will choose a stoichiometric mixture of n-dodecane and air as the gas. For a representative kinetic model, we use that developed by Wang, Ra, Jia, and Reitz (https://www.erc.wisc.edu/chem_mech/nC12-PAH_mech.zip) by [H.Wang, Y.Ra, M.Jia, R.Reitz, Development of a reduced n-dodecane-PAH mechanism and its application for n-dodecane soot predictions, $Fuel$ 136 (2014) 25–36].\n",
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"\n",
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"To fun a different model or use a different EoS, simply replace this cti file with a different mechanism file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"gas = ct.Solution('data/WangMechanismRK.cti')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Define reactor conditions : temperature, pressure, fuel, stoichiometry"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define the reactor temperature and pressure:\n",
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"reactorTemperature = 1000 #Kelvin\n",
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"reactorPressure = 40.0*101325.0 #Pascals\n",
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"\n",
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"# Set the state of the gas object:\n",
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"gas.TP = reactorTemperature, reactorPressure\n",
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"\n",
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"# Define the fuel, oxidizer and set the stoichiometry:\n",
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"gas.set_equivalence_ratio(phi=1.0, fuel='c12h26', oxidizer={'o2':1.0, 'n2':3.76})\n",
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"\n",
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"# Create a reactor object and add it to a reactor network\n",
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"# In this example, this will be the only reactor in the network\n",
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"r = ct.Reactor(contents=gas)\n",
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"reactorNetwork = ct.ReactorNet([r])\n",
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"\n",
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"# Now compile a list of all variables for which we will store data\n",
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"stateVariableNames = [r.component_name(item) for item in range(r.n_vars)]\n",
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"\n",
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"# Use the above list to create a DataFrame\n",
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"timeHistory = pd.DataFrame(columns=stateVariableNames)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Define useful functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def ignitionDelay(df, species):\n",
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" \"\"\"\n",
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" This function computes the ignition delay from the occurence of the\n",
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" peak in species' concentration.\n",
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" \"\"\"\n",
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" return df[species].idxmax()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Computed Ignition Delay: 4.093e-04 seconds. Took 3.98s to compute\n"
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]
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}
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],
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"source": [
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"# Tic\n",
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"t0 = time.time()\n",
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"\n",
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"# This is a starting estimate. If you do not get an ignition within this time, increase it\n",
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"estimatedIgnitionDelayTime = 0.005\n",
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"t = 0\n",
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"\n",
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"counter = 1;\n",
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"while(t < estimatedIgnitionDelayTime):\n",
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" t = reactorNetwork.step()\n",
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" if (counter%20 == 0):\n",
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" # We will save only every 20th value. Otherwise, this takes too long\n",
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" # Note that the species concentrations are mass fractions\n",
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" timeHistory.loc[t] = reactorNetwork.get_state()\n",
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" counter+=1\n",
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"\n",
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"# We will use the 'oh' species to compute the ignition delay\n",
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"tau = ignitionDelay(timeHistory, 'oh')\n",
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"\n",
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"# Toc\n",
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"t1 = time.time()\n",
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"\n",
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"print('Computed Ignition Delay: {:.3e} seconds. Took {:3.2f}s to compute'.format(tau, t1-t0))\n",
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"\n",
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"# If you want to save all the data - molefractions, temperature, pressure, etc\n",
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"# uncomment the next line\n",
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"# timeHistory.to_csv(\"time_history.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Plot the result"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import modules and set plotting defaults"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Figure illustrating the definition of ignition delay"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"/* Put everything inside the global mpl namespace */\n",
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"window.mpl = {};\n",
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"\n",
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"\n",
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"mpl.get_websocket_type = function() {\n",
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" if (typeof(WebSocket) !== 'undefined') {\n",
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" return WebSocket;\n",
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" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
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" return MozWebSocket;\n",
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" } else {\n",
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" alert('Your browser does not have WebSocket support.' +\n",
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" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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" 'Firefox 4 and 5 are also supported but you ' +\n",
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" 'have to enable WebSockets in about:config.');\n",
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" };\n",
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"}\n",
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"\n",
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"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" this.id = figure_id;\n",
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"\n",
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" this.ws = websocket;\n",
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"\n",
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" this.supports_binary = (this.ws.binaryType != undefined);\n",
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"\n",
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" if (!this.supports_binary) {\n",
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" var warnings = document.getElementById(\"mpl-warnings\");\n",
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" if (warnings) {\n",
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" warnings.style.display = 'block';\n",
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" warnings.textContent = (\n",
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" \"This browser does not support binary websocket messages. \" +\n",
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" \"Performance may be slow.\");\n",
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" }\n",
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" }\n",
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"\n",
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" this.imageObj = new Image();\n",
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"\n",
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" this.context = undefined;\n",
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" this.message = undefined;\n",
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" this.canvas = undefined;\n",
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" this.rubberband_canvas = undefined;\n",
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" this.rubberband_context = undefined;\n",
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" this.format_dropdown = undefined;\n",
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"\n",
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" this.image_mode = 'full';\n",
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"\n",
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" this.root = $('<div/>');\n",
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" this._root_extra_style(this.root)\n",
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" this.root.attr('style', 'display: inline-block');\n",
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"\n",
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" $(parent_element).append(this.root);\n",
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"\n",
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" this._init_header(this);\n",
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" this._init_canvas(this);\n",
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" this._init_toolbar(this);\n",
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"\n",
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" var fig = this;\n",
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"\n",
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" this.waiting = false;\n",
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"\n",
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" this.ws.onopen = function () {\n",
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" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
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" fig.send_message(\"send_image_mode\", {});\n",
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" if (mpl.ratio != 1) {\n",
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" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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" }\n",
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" fig.send_message(\"refresh\", {});\n",
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" }\n",
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"\n",
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" this.imageObj.onload = function() {\n",
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" if (fig.image_mode == 'full') {\n",
|
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" // Full images could contain transparency (where diff images\n",
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" // almost always do), so we need to clear the canvas so that\n",
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" // there is no ghosting.\n",
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" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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" }\n",
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" fig.context.drawImage(fig.imageObj, 0, 0);\n",
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" };\n",
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"\n",
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" this.imageObj.onunload = function() {\n",
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" fig.ws.close();\n",
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" }\n",
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"\n",
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" this.ws.onmessage = this._make_on_message_function(this);\n",
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"\n",
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" this.ondownload = ondownload;\n",
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"}\n",
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"\n",
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"mpl.figure.prototype._init_header = function() {\n",
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" var titlebar = $(\n",
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" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
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" 'ui-helper-clearfix\"/>');\n",
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" var titletext = $(\n",
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" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
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" 'text-align: center; padding: 3px;\"/>');\n",
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" titlebar.append(titletext)\n",
|
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" this.root.append(titlebar);\n",
|
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" this.header = titletext[0];\n",
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"}\n",
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"\n",
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"\n",
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"\n",
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"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
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"\n",
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"}\n",
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"\n",
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"\n",
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"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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"\n",
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"}\n",
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"\n",
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"mpl.figure.prototype._init_canvas = function() {\n",
|
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" var fig = this;\n",
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"\n",
|
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" var canvas_div = $('<div/>');\n",
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"\n",
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" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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"\n",
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" function canvas_keyboard_event(event) {\n",
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" return fig.key_event(event, event['data']);\n",
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" }\n",
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"\n",
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" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
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" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
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" this.canvas_div = canvas_div\n",
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" this._canvas_extra_style(canvas_div)\n",
|
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" this.root.append(canvas_div);\n",
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"\n",
|
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" var canvas = $('<canvas/>');\n",
|
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" canvas.addClass('mpl-canvas');\n",
|
||
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
|
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"\n",
|
||
" this.canvas = canvas[0];\n",
|
||
" this.context = canvas[0].getContext(\"2d\");\n",
|
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"\n",
|
||
" var backingStore = this.context.backingStorePixelRatio ||\n",
|
||
"\tthis.context.webkitBackingStorePixelRatio ||\n",
|
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"\tthis.context.mozBackingStorePixelRatio ||\n",
|
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"\tthis.context.msBackingStorePixelRatio ||\n",
|
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"\tthis.context.oBackingStorePixelRatio ||\n",
|
||
"\tthis.context.backingStorePixelRatio || 1;\n",
|
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"\n",
|
||
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
|
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"\n",
|
||
" var rubberband = $('<canvas/>');\n",
|
||
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
|
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"\n",
|
||
" var pass_mouse_events = true;\n",
|
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"\n",
|
||
" canvas_div.resizable({\n",
|
||
" start: function(event, ui) {\n",
|
||
" pass_mouse_events = false;\n",
|
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" },\n",
|
||
" resize: function(event, ui) {\n",
|
||
" fig.request_resize(ui.size.width, ui.size.height);\n",
|
||
" },\n",
|
||
" stop: function(event, ui) {\n",
|
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" pass_mouse_events = true;\n",
|
||
" fig.request_resize(ui.size.width, ui.size.height);\n",
|
||
" },\n",
|
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" });\n",
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"\n",
|
||
" function mouse_event_fn(event) {\n",
|
||
" if (pass_mouse_events)\n",
|
||
" return fig.mouse_event(event, event['data']);\n",
|
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" }\n",
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"\n",
|
||
" rubberband.mousedown('button_press', mouse_event_fn);\n",
|
||
" rubberband.mouseup('button_release', mouse_event_fn);\n",
|
||
" // Throttle sequential mouse events to 1 every 20ms.\n",
|
||
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
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"\n",
|
||
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
|
||
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
|
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"\n",
|
||
" canvas_div.on(\"wheel\", function (event) {\n",
|
||
" event = event.originalEvent;\n",
|
||
" event['data'] = 'scroll'\n",
|
||
" if (event.deltaY < 0) {\n",
|
||
" event.step = 1;\n",
|
||
" } else {\n",
|
||
" event.step = -1;\n",
|
||
" }\n",
|
||
" mouse_event_fn(event);\n",
|
||
" });\n",
|
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"\n",
|
||
" canvas_div.append(canvas);\n",
|
||
" canvas_div.append(rubberband);\n",
|
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"\n",
|
||
" this.rubberband = rubberband;\n",
|
||
" this.rubberband_canvas = rubberband[0];\n",
|
||
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
|
||
" this.rubberband_context.strokeStyle = \"#000000\";\n",
|
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"\n",
|
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" this._resize_canvas = function(width, height) {\n",
|
||
" // Keep the size of the canvas, canvas container, and rubber band\n",
|
||
" // canvas in synch.\n",
|
||
" canvas_div.css('width', width)\n",
|
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" canvas_div.css('height', height)\n",
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"\n",
|
||
" canvas.attr('width', width * mpl.ratio);\n",
|
||
" canvas.attr('height', height * mpl.ratio);\n",
|
||
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
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"\n",
|
||
" rubberband.attr('width', width);\n",
|
||
" rubberband.attr('height', height);\n",
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" }\n",
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"\n",
|
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" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
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" // upon first draw.\n",
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" this._resize_canvas(600, 600);\n",
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"\n",
|
||
" // Disable right mouse context menu.\n",
|
||
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
|
||
" return false;\n",
|
||
" });\n",
|
||
"\n",
|
||
" function set_focus () {\n",
|
||
" canvas.focus();\n",
|
||
" canvas_div.focus();\n",
|
||
" }\n",
|
||
"\n",
|
||
" window.setTimeout(set_focus, 100);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_toolbar = function() {\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" var nav_element = $('<div/>')\n",
|
||
" nav_element.attr('style', 'width: 100%');\n",
|
||
" this.root.append(nav_element);\n",
|
||
"\n",
|
||
" // Define a callback function for later on.\n",
|
||
" function toolbar_event(event) {\n",
|
||
" return fig.toolbar_button_onclick(event['data']);\n",
|
||
" }\n",
|
||
" function toolbar_mouse_event(event) {\n",
|
||
" return fig.toolbar_button_onmouseover(event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" for(var toolbar_ind in mpl.toolbar_items) {\n",
|
||
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
|
||
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
|
||
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
|
||
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
|
||
"\n",
|
||
" if (!name) {\n",
|
||
" // put a spacer in here.\n",
|
||
" continue;\n",
|
||
" }\n",
|
||
" var button = $('<button/>');\n",
|
||
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
|
||
" 'ui-button-icon-only');\n",
|
||
" button.attr('role', 'button');\n",
|
||
" button.attr('aria-disabled', 'false');\n",
|
||
" button.click(method_name, toolbar_event);\n",
|
||
" button.mouseover(tooltip, toolbar_mouse_event);\n",
|
||
"\n",
|
||
" var icon_img = $('<span/>');\n",
|
||
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
|
||
" icon_img.addClass(image);\n",
|
||
" icon_img.addClass('ui-corner-all');\n",
|
||
"\n",
|
||
" var tooltip_span = $('<span/>');\n",
|
||
" tooltip_span.addClass('ui-button-text');\n",
|
||
" tooltip_span.html(tooltip);\n",
|
||
"\n",
|
||
" button.append(icon_img);\n",
|
||
" button.append(tooltip_span);\n",
|
||
"\n",
|
||
" nav_element.append(button);\n",
|
||
" }\n",
|
||
"\n",
|
||
" var fmt_picker_span = $('<span/>');\n",
|
||
"\n",
|
||
" var fmt_picker = $('<select/>');\n",
|
||
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
|
||
" fmt_picker_span.append(fmt_picker);\n",
|
||
" nav_element.append(fmt_picker_span);\n",
|
||
" this.format_dropdown = fmt_picker[0];\n",
|
||
"\n",
|
||
" for (var ind in mpl.extensions) {\n",
|
||
" var fmt = mpl.extensions[ind];\n",
|
||
" var option = $(\n",
|
||
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
|
||
" fmt_picker.append(option)\n",
|
||
" }\n",
|
||
"\n",
|
||
" // Add hover states to the ui-buttons\n",
|
||
" $( \".ui-button\" ).hover(\n",
|
||
" function() { $(this).addClass(\"ui-state-hover\");},\n",
|
||
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
|
||
" );\n",
|
||
"\n",
|
||
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
|
||
" nav_element.append(status_bar);\n",
|
||
" this.message = status_bar[0];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
|
||
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
|
||
" // which will in turn request a refresh of the image.\n",
|
||
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.send_message = function(type, properties) {\n",
|
||
" properties['type'] = type;\n",
|
||
" properties['figure_id'] = this.id;\n",
|
||
" this.ws.send(JSON.stringify(properties));\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.send_draw_message = function() {\n",
|
||
" if (!this.waiting) {\n",
|
||
" this.waiting = true;\n",
|
||
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
|
||
" var format_dropdown = fig.format_dropdown;\n",
|
||
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
|
||
" fig.ondownload(fig, format);\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
|
||
" var size = msg['size'];\n",
|
||
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
|
||
" fig._resize_canvas(size[0], size[1]);\n",
|
||
" fig.send_message(\"refresh\", {});\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
|
||
" var x0 = msg['x0'] / mpl.ratio;\n",
|
||
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
|
||
" var x1 = msg['x1'] / mpl.ratio;\n",
|
||
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
|
||
" x0 = Math.floor(x0) + 0.5;\n",
|
||
" y0 = Math.floor(y0) + 0.5;\n",
|
||
" x1 = Math.floor(x1) + 0.5;\n",
|
||
" y1 = Math.floor(y1) + 0.5;\n",
|
||
" var min_x = Math.min(x0, x1);\n",
|
||
" var min_y = Math.min(y0, y1);\n",
|
||
" var width = Math.abs(x1 - x0);\n",
|
||
" var height = Math.abs(y1 - y0);\n",
|
||
"\n",
|
||
" fig.rubberband_context.clearRect(\n",
|
||
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
|
||
"\n",
|
||
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
|
||
" // Updates the figure title.\n",
|
||
" fig.header.textContent = msg['label'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
|
||
" var cursor = msg['cursor'];\n",
|
||
" switch(cursor)\n",
|
||
" {\n",
|
||
" case 0:\n",
|
||
" cursor = 'pointer';\n",
|
||
" break;\n",
|
||
" case 1:\n",
|
||
" cursor = 'default';\n",
|
||
" break;\n",
|
||
" case 2:\n",
|
||
" cursor = 'crosshair';\n",
|
||
" break;\n",
|
||
" case 3:\n",
|
||
" cursor = 'move';\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" fig.rubberband_canvas.style.cursor = cursor;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
|
||
" fig.message.textContent = msg['message'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
|
||
" // Request the server to send over a new figure.\n",
|
||
" fig.send_draw_message();\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
|
||
" fig.image_mode = msg['mode'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.updated_canvas_event = function() {\n",
|
||
" // Called whenever the canvas gets updated.\n",
|
||
" this.send_message(\"ack\", {});\n",
|
||
"}\n",
|
||
"\n",
|
||
"// A function to construct a web socket function for onmessage handling.\n",
|
||
"// Called in the figure constructor.\n",
|
||
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
|
||
" return function socket_on_message(evt) {\n",
|
||
" if (evt.data instanceof Blob) {\n",
|
||
" /* FIXME: We get \"Resource interpreted as Image but\n",
|
||
" * transferred with MIME type text/plain:\" errors on\n",
|
||
" * Chrome. But how to set the MIME type? It doesn't seem\n",
|
||
" * to be part of the websocket stream */\n",
|
||
" evt.data.type = \"image/png\";\n",
|
||
"\n",
|
||
" /* Free the memory for the previous frames */\n",
|
||
" if (fig.imageObj.src) {\n",
|
||
" (window.URL || window.webkitURL).revokeObjectURL(\n",
|
||
" fig.imageObj.src);\n",
|
||
" }\n",
|
||
"\n",
|
||
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
|
||
" evt.data);\n",
|
||
" fig.updated_canvas_event();\n",
|
||
" fig.waiting = false;\n",
|
||
" return;\n",
|
||
" }\n",
|
||
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
|
||
" fig.imageObj.src = evt.data;\n",
|
||
" fig.updated_canvas_event();\n",
|
||
" fig.waiting = false;\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" var msg = JSON.parse(evt.data);\n",
|
||
" var msg_type = msg['type'];\n",
|
||
"\n",
|
||
" // Call the \"handle_{type}\" callback, which takes\n",
|
||
" // the figure and JSON message as its only arguments.\n",
|
||
" try {\n",
|
||
" var callback = fig[\"handle_\" + msg_type];\n",
|
||
" } catch (e) {\n",
|
||
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" if (callback) {\n",
|
||
" try {\n",
|
||
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
|
||
" callback(fig, msg);\n",
|
||
" } catch (e) {\n",
|
||
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
|
||
" }\n",
|
||
" }\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
|
||
"mpl.findpos = function(e) {\n",
|
||
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
|
||
" var targ;\n",
|
||
" if (!e)\n",
|
||
" e = window.event;\n",
|
||
" if (e.target)\n",
|
||
" targ = e.target;\n",
|
||
" else if (e.srcElement)\n",
|
||
" targ = e.srcElement;\n",
|
||
" if (targ.nodeType == 3) // defeat Safari bug\n",
|
||
" targ = targ.parentNode;\n",
|
||
"\n",
|
||
" // jQuery normalizes the pageX and pageY\n",
|
||
" // pageX,Y are the mouse positions relative to the document\n",
|
||
" // offset() returns the position of the element relative to the document\n",
|
||
" var x = e.pageX - $(targ).offset().left;\n",
|
||
" var y = e.pageY - $(targ).offset().top;\n",
|
||
"\n",
|
||
" return {\"x\": x, \"y\": y};\n",
|
||
"};\n",
|
||
"\n",
|
||
"/*\n",
|
||
" * return a copy of an object with only non-object keys\n",
|
||
" * we need this to avoid circular references\n",
|
||
" * http://stackoverflow.com/a/24161582/3208463\n",
|
||
" */\n",
|
||
"function simpleKeys (original) {\n",
|
||
" return Object.keys(original).reduce(function (obj, key) {\n",
|
||
" if (typeof original[key] !== 'object')\n",
|
||
" obj[key] = original[key]\n",
|
||
" return obj;\n",
|
||
" }, {});\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
|
||
" var canvas_pos = mpl.findpos(event)\n",
|
||
"\n",
|
||
" if (name === 'button_press')\n",
|
||
" {\n",
|
||
" this.canvas.focus();\n",
|
||
" this.canvas_div.focus();\n",
|
||
" }\n",
|
||
"\n",
|
||
" var x = canvas_pos.x * mpl.ratio;\n",
|
||
" var y = canvas_pos.y * mpl.ratio;\n",
|
||
"\n",
|
||
" this.send_message(name, {x: x, y: y, button: event.button,\n",
|
||
" step: event.step,\n",
|
||
" guiEvent: simpleKeys(event)});\n",
|
||
"\n",
|
||
" /* This prevents the web browser from automatically changing to\n",
|
||
" * the text insertion cursor when the button is pressed. We want\n",
|
||
" * to control all of the cursor setting manually through the\n",
|
||
" * 'cursor' event from matplotlib */\n",
|
||
" event.preventDefault();\n",
|
||
" return false;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
|
||
" // Handle any extra behaviour associated with a key event\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.key_event = function(event, name) {\n",
|
||
"\n",
|
||
" // Prevent repeat events\n",
|
||
" if (name == 'key_press')\n",
|
||
" {\n",
|
||
" if (event.which === this._key)\n",
|
||
" return;\n",
|
||
" else\n",
|
||
" this._key = event.which;\n",
|
||
" }\n",
|
||
" if (name == 'key_release')\n",
|
||
" this._key = null;\n",
|
||
"\n",
|
||
" var value = '';\n",
|
||
" if (event.ctrlKey && event.which != 17)\n",
|
||
" value += \"ctrl+\";\n",
|
||
" if (event.altKey && event.which != 18)\n",
|
||
" value += \"alt+\";\n",
|
||
" if (event.shiftKey && event.which != 16)\n",
|
||
" value += \"shift+\";\n",
|
||
"\n",
|
||
" value += 'k';\n",
|
||
" value += event.which.toString();\n",
|
||
"\n",
|
||
" this._key_event_extra(event, name);\n",
|
||
"\n",
|
||
" this.send_message(name, {key: value,\n",
|
||
" guiEvent: simpleKeys(event)});\n",
|
||
" return false;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
|
||
" if (name == 'download') {\n",
|
||
" this.handle_save(this, null);\n",
|
||
" } else {\n",
|
||
" this.send_message(\"toolbar_button\", {name: name});\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
|
||
" this.message.textContent = tooltip;\n",
|
||
"};\n",
|
||
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
|
||
"\n",
|
||
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
|
||
"\n",
|
||
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
|
||
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
|
||
" // object with the appropriate methods. Currently this is a non binary\n",
|
||
" // socket, so there is still some room for performance tuning.\n",
|
||
" var ws = {};\n",
|
||
"\n",
|
||
" ws.close = function() {\n",
|
||
" comm.close()\n",
|
||
" };\n",
|
||
" ws.send = function(m) {\n",
|
||
" //console.log('sending', m);\n",
|
||
" comm.send(m);\n",
|
||
" };\n",
|
||
" // Register the callback with on_msg.\n",
|
||
" comm.on_msg(function(msg) {\n",
|
||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||
" ws.onmessage(msg['content']['data'])\n",
|
||
" });\n",
|
||
" return ws;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.mpl_figure_comm = function(comm, msg) {\n",
|
||
" // This is the function which gets called when the mpl process\n",
|
||
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
|
||
"\n",
|
||
" var id = msg.content.data.id;\n",
|
||
" // Get hold of the div created by the display call when the Comm\n",
|
||
" // socket was opened in Python.\n",
|
||
" var element = $(\"#\" + id);\n",
|
||
" var ws_proxy = comm_websocket_adapter(comm)\n",
|
||
"\n",
|
||
" function ondownload(figure, format) {\n",
|
||
" window.open(figure.imageObj.src);\n",
|
||
" }\n",
|
||
"\n",
|
||
" var fig = new mpl.figure(id, ws_proxy,\n",
|
||
" ondownload,\n",
|
||
" element.get(0));\n",
|
||
"\n",
|
||
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
|
||
" // web socket which is closed, not our websocket->open comm proxy.\n",
|
||
" ws_proxy.onopen();\n",
|
||
"\n",
|
||
" fig.parent_element = element.get(0);\n",
|
||
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
|
||
" if (!fig.cell_info) {\n",
|
||
" console.error(\"Failed to find cell for figure\", id, fig);\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" var output_index = fig.cell_info[2]\n",
|
||
" var cell = fig.cell_info[0];\n",
|
||
"\n",
|
||
"};\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
|
||
" var width = fig.canvas.width/mpl.ratio\n",
|
||
" fig.root.unbind('remove')\n",
|
||
"\n",
|
||
" // Update the output cell to use the data from the current canvas.\n",
|
||
" fig.push_to_output();\n",
|
||
" var dataURL = fig.canvas.toDataURL();\n",
|
||
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
|
||
" // the notebook keyboard shortcuts fail.\n",
|
||
" IPython.keyboard_manager.enable()\n",
|
||
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
|
||
" fig.close_ws(fig, msg);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
|
||
" fig.send_message('closing', msg);\n",
|
||
" // fig.ws.close()\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
|
||
" // Turn the data on the canvas into data in the output cell.\n",
|
||
" var width = this.canvas.width/mpl.ratio\n",
|
||
" var dataURL = this.canvas.toDataURL();\n",
|
||
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.updated_canvas_event = function() {\n",
|
||
" // Tell IPython that the notebook contents must change.\n",
|
||
" IPython.notebook.set_dirty(true);\n",
|
||
" this.send_message(\"ack\", {});\n",
|
||
" var fig = this;\n",
|
||
" // Wait a second, then push the new image to the DOM so\n",
|
||
" // that it is saved nicely (might be nice to debounce this).\n",
|
||
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_toolbar = function() {\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" var nav_element = $('<div/>')\n",
|
||
" nav_element.attr('style', 'width: 100%');\n",
|
||
" this.root.append(nav_element);\n",
|
||
"\n",
|
||
" // Define a callback function for later on.\n",
|
||
" function toolbar_event(event) {\n",
|
||
" return fig.toolbar_button_onclick(event['data']);\n",
|
||
" }\n",
|
||
" function toolbar_mouse_event(event) {\n",
|
||
" return fig.toolbar_button_onmouseover(event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" for(var toolbar_ind in mpl.toolbar_items){\n",
|
||
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
|
||
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
|
||
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
|
||
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
|
||
"\n",
|
||
" if (!name) { continue; };\n",
|
||
"\n",
|
||
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
|
||
" button.click(method_name, toolbar_event);\n",
|
||
" button.mouseover(tooltip, toolbar_mouse_event);\n",
|
||
" nav_element.append(button);\n",
|
||
" }\n",
|
||
"\n",
|
||
" // Add the status bar.\n",
|
||
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
|
||
" nav_element.append(status_bar);\n",
|
||
" this.message = status_bar[0];\n",
|
||
"\n",
|
||
" // Add the close button to the window.\n",
|
||
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
|
||
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
|
||
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
|
||
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
|
||
" buttongrp.append(button);\n",
|
||
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
|
||
" titlebar.prepend(buttongrp);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._root_extra_style = function(el){\n",
|
||
" var fig = this\n",
|
||
" el.on(\"remove\", function(){\n",
|
||
"\tfig.close_ws(fig, {});\n",
|
||
" });\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
|
||
" // this is important to make the div 'focusable\n",
|
||
" el.attr('tabindex', 0)\n",
|
||
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
|
||
" // off when our div gets focus\n",
|
||
"\n",
|
||
" // location in version 3\n",
|
||
" if (IPython.notebook.keyboard_manager) {\n",
|
||
" IPython.notebook.keyboard_manager.register_events(el);\n",
|
||
" }\n",
|
||
" else {\n",
|
||
" // location in version 2\n",
|
||
" IPython.keyboard_manager.register_events(el);\n",
|
||
" }\n",
|
||
"\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
|
||
" var manager = IPython.notebook.keyboard_manager;\n",
|
||
" if (!manager)\n",
|
||
" manager = IPython.keyboard_manager;\n",
|
||
"\n",
|
||
" // Check for shift+enter\n",
|
||
" if (event.shiftKey && event.which == 13) {\n",
|
||
" this.canvas_div.blur();\n",
|
||
" event.shiftKey = false;\n",
|
||
" // Send a \"J\" for go to next cell\n",
|
||
" event.which = 74;\n",
|
||
" event.keyCode = 74;\n",
|
||
" manager.command_mode();\n",
|
||
" manager.handle_keydown(event);\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
|
||
" fig.ondownload(fig, null);\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.find_output_cell = function(html_output) {\n",
|
||
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
|
||
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
|
||
" // IPython event is triggered only after the cells have been serialised, which for\n",
|
||
" // our purposes (turning an active figure into a static one), is too late.\n",
|
||
" var cells = IPython.notebook.get_cells();\n",
|
||
" var ncells = cells.length;\n",
|
||
" for (var i=0; i<ncells; i++) {\n",
|
||
" var cell = cells[i];\n",
|
||
" if (cell.cell_type === 'code'){\n",
|
||
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
|
||
" var data = cell.output_area.outputs[j];\n",
|
||
" if (data.data) {\n",
|
||
" // IPython >= 3 moved mimebundle to data attribute of output\n",
|
||
" data = data.data;\n",
|
||
" }\n",
|
||
" if (data['text/html'] == html_output) {\n",
|
||
" return [cell, data, j];\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"// Register the function which deals with the matplotlib target/channel.\n",
|
||
"// The kernel may be null if the page has been refreshed.\n",
|
||
"if (IPython.notebook.kernel != null) {\n",
|
||
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
|
||
"}\n"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
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c2+77bZQXXT1uU2TylXDyYwAAggggAACCCCAAAIIIIAAAp4UsClWQQAwG1+xqlWrypYtW8yW2BMnTqRbNed7rP9KPt3S+8YbbzhuUZMmTdIu2tCAY8WKFTOV/eGHH+Syyy4LGOQ7ffq0lC5dWo4cOWKupd69e3fAZ/uv4NNVgrpa0JemT5+ediHId999J3qzcKB06NAh8ywNSE6ePFlatmzpuJ9OMto0qZy0lzwIIIAAAggggAACCCCAAAIIIOBtAZtiFQQAs/Fd0/PvOnXqZJ7w6aefpq2283+kXpLRu3dvyZ07tzlbT2/mdZoWLVok9evXN9nfeecd6dixY6aikyZNEr2BV5PeStygQYN0efTZvos6tm/fnrZq0T+T9kH7cu6558quXbtEt/H6ktbpuxBEzxnUFYLBkgYADx48mG4bstO+hspn06QK1VY+RwABBBBAAAEEEEAAAQQQQAAB7wvYFKsgAJiN79upU6fM+Xy6TVaDaGPGjMn0tOrVq5vz+9q3by/jx49P9/ny5culVatWJvA2e/bsgMFB3/l7GghcuHBhpvp9F33UrVtXlixZkunzw4cPmy28GpjTQGCvXr3S5dE+6CUleg5gv379pG/fvuk+/+9//2u2LuvW4p49e8rAgQMDiuoKSN2yrNuV9QxA/4tCojEENk2qaPSHOhBAAAEEEEAAAQQQQAABBBBAIL4FbIpVEADM5ndp/fr15ly8PHnyyNatW6VChQppT5w2bZq0aNHCbNFdu3ZtupV1mknL6fl7mnTLrG6dzZj0zEDdaqxnAGY8g2/16tVmRV6xYsVM/YG2CGt9c+fONWcA6go9vSykaNGiaY8ZMmSI9OjRQ2rUqCHLli0z5whmTBrY1Jt+9WxBbYOeeZgx+W5FnjhxYtqKxGjS2zSpotkv6kIAAQQQQAABBBBAAAEEEEAAgfgUsClWQQAwBu/Q/PnzzTl5upJu+PDhJhCnZ+fpirorr7xS5syZI+XLl8/Ukpo1a4oG8TTpSkDdzhsoaXCvUaNGZlXdyJEjpVq1amY1oAbudMWdrh7UlYhZJd1C3KVLF0lKSpLXXntNSpQoIePGjZOhQ4eaW4NnzJiRKUDpX5+u/HvhhRfk/PPPN1ua77nnHnPm4bZt28y5hrpdWW8J1stAsiPZNKmyo3/UiQACCCCAAAIIIIAAAggggAAC8SVgU6yCAGCM3h0NhOkWW92Gqxdi6Lbb5ORk6dy5c9DtsCtXrjQr//QW32BbgH3N1221GoT7+OOPZe/evWalYevWrc2FHbqF2Elat26dDBgwwAQdf/75ZxNI1PbpGYK5cuUKWcWmTZvMTb/aR22DltHLRW6//XZ58skn5eqrrw5ZR7gZbJpU4faBcggggAACCCCAAAIIIIAAAggg4B0Bm2IVBAC9814ldE9smlQJPRB0HgEEEEAAAQQQQAABBBBAAAEEjIBNsQoCgLyUnhCwaVJ5ApROIIAAAggggAACCCCAAAIIIIBARAI2xSoIAEY0lBS2RcCmSWWLCe1AAAEEEEAAAQQQQAABBBBAAIGcE7ApVkEAMOfeA54cRQGbJlUUu0VVCCCAAAIIIIAAAggggAACCCAQpwI2xSoIAMbpS0Sz0wvYNKkYGwQQQAABBBBAAAEEEEAAAQQQQMCmWAUBQN5HTwjYNKk8AUonEEAAAQQQQAABBBBAAAEEEEAgIgGbYhUEACMaSgrbImDTpLLFhHYggAACCCCAAAIIIIAAAggggEDOCdgUqyAAmHPvAU+OooBNkyqK3aIqBBBAAAEEEEAAAQQQQAABBBCIUwGbYhUEAOP0JaLZ6QVsmlSMDQIIIIAAAggggAACCCCAAAIIIGBTrIIAIO+jJwRsmlSeAKUTCCCAAAIIIIAAAggggAACCCAQkYBNsQoCgBENJYVtEbBpUtliQjsQQAABBBBAAAEEEEAAAQQQQCDnBGyKVRAAzLn3gCdHUcCmSRXFblEVAggggAACCCCAAAIIIIAAAgjEqYBNsQoCgHH6EtHs9AI2TSrGBgEEEEAAAQQQQAABBBBAAAEEELApVkEAkPfREwI2TSpPgNIJBBBAAAEEEEAAAQQQQAABBBCISMCmWAUBwIiGksK2CNg0qWwxoR0IIIAAAgggkJgCI0aIvPmmyIkTIuedJ9K1q8jjjyemBb1GAAEEEEAgJwVsilUQAMzJN4FnR03ApkkVtU5REQIIIIAAAggg4EKgY0eRd98VSU3NXChXLpEOHUTGjnVRIVkRQAABBBBAICIBm2IVBAAjGkoK2yJg06SyxYR2IIAAAggggEDiCNx4o8i6daH7m5QksnZt6HzkQAABBBBAAIHIBWyKVRAAjHw8qcECAZsmlQUcNAEBBBBAAAEEEkhAV/6NG+e8ww8/zEpA51rkRAABBBBAIHwBm2IVBADDH0dKWiRg06SyiIWmIIAAAggggEACCOj2Xrcp0DZht3WQHwEEEEAAAQSyFrApVkEAkLfVEwI2TSpPgNIJBBBAAAEEEIgLAb3w44kn3Dd11CiRzp3dl6MEAggggAACCDgXsClWQQDQ+biR02IBmyaVxUw0DQEEEEAAAQQ8JlCmjMj+/e47deGFIvv2uS9HCQQQQAABBBBwLmBTrIIAoPNxI6fFAjZNKouZaBoCCCCAAAIIeEwgTx6Rs2fdd0rLnT7tvhwlEEAAAQQQQMC5gE2xCgKAzseNnBYL2DSpLGaiaQgggAACCCDgMYFwzv/zEXAOoMdeBrqDAAIIIGCdgE2xCgKA1r0eNCgcAZsmVTjtpwwCCCCAAAIIIBCOQO7cIuEE8jRwGM7KwXDaSBkEEEAAAQQSVcCmWAUBwER9Cz3Wb5smlcdo6Q4CCCCAAAIIWCxQsKDIyZPuG1iggMivv7ovRwkEEEAAAQQQcC5gU6yCAKDzcSOnxQI2TSqLmWgaAggggAACCHhMoEIFkZQU952qWFFkxw735SiBAAIIIIAAAs4FbIpVEAB0Pm7ktFjApkllMRNNQwABBBBAAAGPCbRoITJtmvtOabmpU92XowQCCCCAAAIIOBewKVZBAND5uJHTYgGbJpXFTDQNAQQQQAABBDwmMH26SPPm7js1Y4bI/fe7L0cJBBBAAAEEEHAuYFOsggCg83Ejp8UCNk0qi5loGgIIIIAAAgh4TGDCBJHkZPed0nLt2rkvRwkEEEAAAQQQcC5gU6yCAKDzcSOnxQI2TSqLmWgaAggggAACCHhMoG9fkZdfdt8pLdevn/tylEAAAQQQQAAB5wI2xSoIADofN3JaLGDTpLKYiaYhgAACCCCAgMcEOAPQYwNKdxBAAAEEPCVgU6yCAKCnXq3E7YxNkypxR4GeI4AAAggggECsBZo2FZk71/1Ttdzs2e7LUQIBBBBAAAEEnAvYFKsgAOh83MhpsYBNk8piJpqGAAIIIIAAAh4TaNVKZMoU951q3Vrk/ffdl6MEAggggAACCDgXsClWQQDQ+biR02IBmyaVxUw0DQEEEEAAAQQ8JjB0qEj37u47peX++lf35SiBAAIIIIAAAs4FbIpVEAB0Pm7ktFjApkllMRNNQwABBBBAAAGPCaSkiFSo4L5TWq5cOfflKIEAAggggAACzgVsilUQAHQ+buS0WMCmSWUxE01DAAEEEEAAAY8JEAD02IDSHQQQQAABTwnYFKsgAOipVytxO2PTpErcUaDnCCCAAAIIIBBrgQkTRJKT3T9Vy7Vr574cJRBAAAEEEEDAuYBNsQoCgM7HjZwWC9g0qSxmomkIIIAAAggg4DGBYcNEunVz3ykt17Wr+3KUQAABBBBAAAHnAjbFKggAOh83closYNOkspiJpiGAAAIIIICAxwRYAeixAaU7CCCAAAKeErApVkEA0FOvVuJ2xqZJlbijQM8RQAABBBBAINYC06eLNG/u/qkzZojcf7/7cpRAAAEEEEAAAecCNsUqCAA6HzdyWixg06SymImmIYAAAggggIDHBDp2FBk3zn2ntNw777gvRwkEEEAAAQQQcC5gU6yCAKDzcSOnxQI2TSqLmWgaAggggAACCHhMoFkzkVmz3HdKy334oftylEAAAQQQQAAB5wI2xSoIADofN3JaLGDTpLKYiaYhgAACCCCAgMcEWrQQmTbNfae03NSp7stRAgEEEEAAAQScC9gUqyAA6HzcyGmxgE2TymImmoYAAggggAACHhPo1Utk4ED3nXrhBZG//c19OUoggAACCCCAgHMBm2IVBACdjxs5LRawaVJZzETTEEAAAQQQQMBjAsOGiXTr5r5TWq5rV/flKIEAAggggAACzgVsilUQAHQ+buS0WMCmSWUxE01DAAEEEEAAAY8JTJggkpzsvlNarl079+UogQACCCCAAALOBWyKVRAAdD5u5LRYwKZJZTETTUMAAQQQQAABjwmkpIhUrCiSmuq8Y7lyiezcKVKunPMy5EQAAQQQQAAB9wI2xSoIALofP0pYKGDTpLKQhyYhgAACCCCAgIcFSpcWOXjQeQdLlRI5cMB5fnIigAACCCCAQHgCNsUqCACGN4aUskzApkllGQ3NQQABBBBAAAEPC+gKwAoV3HdQy7EC0L0bJRBAAAEEEHAjYFOsggCgm5Ejr7UCNk0qa5FoGAIIIIAAAgh4ToAzAD03pHQIAQQQQMBDAjbFKggAeujFSuSu2DSpEnkc6DsCCCCAAAIIxFaAW4Bj683TEEAAAQQQcCNgU6yCAKCbkSOvtQI2TSprkWgYAggggAACCHhOgBWAnhtSOoQAAggg4CEBm2IVBAA99GIlcldsmlSJPA70HQEEEEAAAQRiK7Bypcgtt7h/pparVct9OUoggAACCCCAgHMBm2IVBACdjxs5LRawaVJZzETTEEAAAQQQQMBjAqwA9NiA0h0EEEAAAU8J2BSrIADoqVcrcTtj06RK3FGg5wgggAACCCAQawHOAIy1OM9DAAEEEEDAuYBNsQoCgM7HjZwWC9g0qSxmomkIIIAAAggg4DEBVgB6bEDpDgIIIICApwRsilUQAPTUq5W4nbFpUiXuKNBzBBBAAAEEEIi1QEqKSIUK7p+q5cqVc1+OEggggAACCCDgXMCmWAUBQOfjRk6LBWyaVBYz0TQEEEAAAQQQ8JgAAUCPDSjdQQABBBDwlIBNsQoCgJ56tRK3MzZNqsQdBXqOAAIIIIAAArEW6NVLZOBA90/Vcv37uy9HCQQQQAABBBBwLmBTrIIAoPNxI6fFAjZNKouZaBoCCCCAAAIIeEygcmWRb75x36lrrhH517/cl6MEAggggAACCDgXsClWQQDQ+biR02IBmyaVxUw0DQEEEEAAAQQ8JlCypMjhw+47VaKEyE8/uS9HCQQQQAABBBBwLmBTrIIAoPNxI6fFAjZNKouZaBoCCCCAAAIIeEzgwgtFDhxw3yktt2+f+3KUQAABBBBAAAHnAjbFKggAOh83closYNOkspiJpiGAAAIIIICAxwSuvlrk22/dd0rLbd3qvhwlEEAAAQQQQMC5gE2xCgKAzseNnBYL2DSpLGaiaQgggAACCCDgMYFatURWrXLfqdq1RVascF+OEggggAACCCDgXMCmWAUBQOfjRk6LBWyaVBYz0TQEEEAAAQQQ8JhAjRoia9a479TNN4cXOHT/JEoggAACCCCQuAI2xSoIACbue+ipnts0qTwFS2cQQAABBBBAwGqB+vVFFi1y30Qtt3Ch+3KUQAABBBBAAAHnAjbFKggAOh83closYNOkspiJpiGAAAIIIICAxwRatBCZNs19p7Tc1Knuy1ECAQQQQAABBJwL2BSrIADofNzIabGATZPKYiaahgACCCCAAAIeE+jTR+SVV9x3qm9fkX793JejBAIIIIAAAgg4F7ApVkEA0Pm4kdNiAZsmlcVMNA0BBBBAAAEEPCYwYYJIcrL7Tmm5du3cl6MEAggggAACCDgXsClWQQDQ+biR02IBmyaVxUw0DQEEEEAAAQQ8JpCSIlKhgvtOably5dyXowQCCCCAAAIIOBewKVZBAND5uFcaOYIAACAASURBVJHTYgGbJpXFTDQNAQQQQAABBDwocPfdIh995Lxjmn/ePOf5yYkAAggggAAC4QnYFKsgABjeGIZVav/+/bJgwQI5cOCAlC9fXho3biznnXdeWHUFKnTs2DH56KOPZPfu3XLRRReZ+kuWLOmq/p07d8rChQtF67rqqqvkrrvukvz587uqIycy2zSpcqL/PBMBBBBAAAEEEldg3TqR2rVFTp0KbZAvn8jKlSJJSaHzkgMBBBBAAAEEIhOwKVZBADCysXRU+uzZs/LSSy/JoEGD5Morr5QbbrhB1q5dK/oivPHGG9KhQwdH9WSVaeTIkdKzZ0+54IILpHbt2rJ161bZsmWL9O3bV3r06CG5c+fO8hknT56Up59+WsaMGSNJSUlSqVIlWbp0qejXx44dK3frr4qDpH/+858mUOg01a9f3wQZo5lsmlTR7Bd1IYAAAggggAACTgRmzhRp2TLrIKAG/6ZMEWnWzEmN5EEAAQQQQACBSAVsilUQAIx0NEOUT01NlZYtW8oHH3wgXbp0keHDh5tgnAYF27ZtK5MnT5aBAwea4F246bnnnpNXX31VmjZtKlOnTpUCBQqYqrTOwYMHS+fOnWXUqFFBq9cgX4MGDWTFihUyYMAAef75501e/XrDhg3N1ydOnCitWrUKWEezZs1k1qxZjps/ZMgQ6d69u+P8TjLaNKmctJc8CCCAAAIIIIBAtAXee0/k6adFjh7NXHOpUiJDhnDxR7TNqQ8BBBBAAIGsBGyKVRAAzOZ3tX///tK7d2+z8u/rr7+Wc845J+2Jus1WtwIfP35cPvnkExOEc5smTZokbdq0kaJFi0pKSooUKVIkrYrTp0+blXw7duyQ0aNHyyOPPBKw+k6dOplVfvXq1ZPFixeny/P999+bOjRouWHDBrnmmmvSfa7bmi+99FLRZxUqVEguvPBCyZs3b8Dn/PDDDyaftrNs2bJuu5plfpsmVVQ7RmUIIIAAAggggIADAV0B+OCDIr//Hjyzfos2dSorAB1wkgUBBBBAAIGoCNgUqyAAGJUhDVzJrl27TODv1KlT8vrrr0u3bt0yZdTVeRqc00CgbtstWLCg4xadOHFCKlasKD/99JM89dRTZjtxxqTbjnVFnwYGv/32WxOg80+rV6+WmjVrmi/pKr577703Ux26ClADlDVq1BDN7590xeCbb75ptg7rNuFgW40PHTokZcqUMXWs1INnopxsmlRR7hrVIYAAAggggAACWQroGYD67dzp06Gh9HfRq1ZxBmBoKXIggAACCCAQuYBNsQoCgJGPZ9AannjiCRkxYoT5/LvvvpMrrrgiU97Zs2fLfffdZ74+fvx4ad++veMWDR06NG0rrZ6pp2frZUwbN26UatWqmS/reYD9+vVLl0WDdnpxiK5M1JWIuoovY9LAop4PqGnJkiVSt25d82fd3nzzzTfLjBkz5JJLLsmy3XpG4eOPPy5vvfWWqEu0k02TKtp9oz4EEEAAAQQQQCArAb0A5PPPnRtp/hUrnOcnJwIIIIAAAgiEJ2BTrIIAYHhjGLKUrvrT1XZHjx6V888/39yqmytXrkzl9u7dKxdffLH5eq1atVytjrv22mvNtmJNhw8fluLFi2eqX88aLFy4sDnPT7fq6qpEXzv0NmJ99pkzZ6RKlSqyefPmgP1atWqVaZum1q1by/vvv2/+rCsQddXiTTfdFNLj1ltvFa1nz549Urp06ZD53WawaVK5bTv5EUAAAQQQQACBcAVSUkQqVHBfWsuVK+e+HCUQQAABBBBAwLmATbEKAoDOx81VzuXLl0udOnVMmapVq8qmTZuCltcAoQbT8uTJIz///HPAVXgZC//4449p5+hpeV29Fyzp8/VGYE3btm0z25I1/eMf/5B27dqZPzdp0kTmzJkTsIojR45IiRIlzGe6jVeDlm6SBv00+HjbbbfJokWL3BR1nNemSeW40WREAAEEEEAAAQQiFBg6VCScu9W03F//GuHDKY4AAggggAACWQrYFKsgAJhNL6veyqu382pq1KiR2WYbLGlAbvv27eZjPR/Pt9ouq6ZNnz5dmjdvbrJUrlw5bSVgoDJ6ucinn35qPtLVe7qKT5NuydWtuZoee+yxtO3KgerInz+/OctQk77AvlWLTvhee+01+etf/2ouGnn44YedFHGdx6ZJ5brxFEAAAQQQQAABBMIUaNVKZMoU94X128E/NnW4L0wJBBBAAAEEEHAkYFOsggCgoyFzn+nRRx81F2No0qCXBr+CpRtvvFHW6enNIvLee+/JQw89FPKBAwcOlF69epl8t99+e5Yr6zRQqAFDTS+99JL06dPH/PnOO+8UPTtQ0yuvvGJuKw6WSpUqJXqRh6Zly5aJbul1mrR/ugJSbwwuVqyY02Lp8umkySrt27dP9DmadHVkqDMJw2oEhRBAAAEEEEAAAcsECABaNiA0BwEEEEAAAT8BAoAJ8Do0bdpU5s6da3oa7IZeH8Mtt9ySdvbfsGHDpGvXriGFtE69fVeTPksvEwmW2rZtm3Zun17moSvyNF133XVp5/4Fu6XYV6du4fUF4XSrsG4ZdpJ++OEHueyyy8wNwfPmzXNSJGCeQOcnBquMAGDYzBREAAEEEEAAgTgT0N8HDxzovtFarn9/9+UogQACCCCAAALOBQgAOreK25x6I6/vvLuePXuKrtgLlvwDgKFW4vnq6NSpU9qqwgcffFCmZLH3wz8A6L8aUW8l/v77702Vb7/9tuiqxWDJPwA4ceJEadOmjaOxGTBggLzwwgsyadIkaaW/og4zEQAME45iCCCAAAIIIOBpgcceExk1yn0XH39cZPhw9+UogQACCCCAAALOBQgAOreK25wNGzaUTz75xLT/xRdflJdffjloX2rUqCFr1qwxnw8ePDjt7MCsOt+5c2cZPXq0yaIBPr3QI1jSAOEHH3xgPu7SpUvauX+VKlUyl4JoGjdunHTo0CFoHXqjsd4arEnr8p0/GGqAdJWhnm948OBBOffcc0NlD/o5W4DDpqMgAggggAACCHhYoEYNkT++jXTVy5tvFlm1ylURMiOAAAIIIICASwECgC7B4jH7Aw88IDNmzDBN18tANLAXLPlvxR01apRocC9U6t69uwzV69tETDDOF+ALVM5/O3KPHj1k0KBBJltSUpJ8+eWX5s96GYgGB4OlIkWKmBuKNS1YsEA0wBkqffPNN+aCErWYNm1aqOwRfW7TpIqoIxRGAAEEEEAAAQRcCNxwg8hXX7ko8EdWLffHt4HuC1MCAQQQQAABBBwJ2BSr4BIQR0PmPtMzzzwjeq6eJr1td3gWeyzKli1rLq7QpLcF663BoZKe/6fnAGpq3LixzJ8/P2gRvbBjxYoV5vMRI0aYG381NWvWTGbNmmX+rLcWP/vsswHrOHPmjOTNm1dSU1PN519//bUJ7IVKffv2NSsfP/zwQ/Os7Ew2Tars7Cd1I4AAAggggAAC/gJ3363fP7o30XIRHM/s/oGUQAABBBBAIAEFbIpVEADMphfw3XffNbf/arr33nvTAm2BHleoUCH59ddfzUcpKSlSrly5kK1avHixuf1X0/XXXy8bNmwIWubqq6+Wb7/91ny+dOlSqVOnjvmz3gasZw5q6tatW1rAMmNFuvVXtwBryp8/vxw/ftz8O1TSLcZ6O6+WL1CgQKjsEX1u06SKqCMURgABBBBAAAEEXAiEewnICy+I/O1vLh5EVgQQQAABBBBwLWBTrIIAoOvhc1Zgx44dcvnll5vMusV348aNAQvu379fypQpYz675JJL0lYChnrKL7/8IiVLlpSTJ0+Kbs89duxYwCK6aq9w4cImwJgvXz45evSoaMBR02effSZ33HGH+XNWNwl/8cUXcrMeFCMitWvXTltNmFUbNSBZvXp1eeihh+S9994L1Z2IP7dpUkXcGSpAAAEEEEAAAQQcCgwbpr/IdZjZL5uW69rVfTlKIIAAAggggIBzAZtiFQQAnY+b65xVq1aVLVu2mO2zJ06cCLhqzn8ln27pfeONNxw/p0mTJjLvj70bGnCsWLFiprI//PCDXHbZZQGDfKdPn5bSpUvLkSNHRG/53b17d8Bn+69m1G3NulowVNKzBocMGSIff/yx3HXXXaGyR/y5TZMq4s5QAQIIIIAAAggg4FBgwgSR5GSHmf2yabl27dyXowQCCCCAAAIIOBewKVZBAND5uLnOOXbsWOnUqZMp9+mnn6attvOvqH///tK7d2/JnTu3OVtPt806TYsWLZL69eub7O+884507NgxU9FJkyZJmzZtzNf1VuIGDRqky6PP1jZo0tt6fasW/TNpH7Qveovvrl27pHjx4iGbWKFCBRP01BWO55xzTsj8kWawaVJF2hfKI4AAAggggAACTgVSUkT0d8B/HNXsqFiuXCI7d4o4OHXGUX1kQgABBBBAAIHAAjbFKggAZuNbeurUKXM+n96Gq0G0MWPGZHqabpPV7bLt27eX8ePHp/t8+fLl0qpVKxN4mz17dsDgoG7h1a28GghcuHBhpvp9F33UrVtXlixZkunzw4cPmws9Dh48aAKBvfQgGb+kfdBLSvQcv379+ole7BEqrV69WmrWrCmPPvqovP3226GyR+VzmyZVVDpEJQgggAACCCCAgEMBtxeBcAGIQ1iyIYAAAgggEKGATbEKAoARDmao4uvXrzfn5+XJk0e2bt0qujLOl6ZNmyYtWrQwW3TXrl2baWWdltPz9zS1bNlSJk+enOlxenuwbjXWMwBXrVqVdlafZtRAnJ7ZV6xYMVN/oC3Cmm/u3LnmDEDdDqyXhRQtWjTtObqNV7fz1qhRQ5YtW2bOEQyVdCuz3lKsAUcNPMYi2TSpYtFfnoEAAggggAACCPgE1q3Tc5pFTp0KbaLfyq1cKZKUFDovORBAAAEEEEAgMgGbYhUEACMbS0el58+fL82bNzcr6YYPH24CcdOnTzcr6q688kqZM2eOlC9fPlNduopOg3iadCWgbucNlDS416hRI3PW4MiRI6VatWpmNaAG7vSCEF09qCsRs0q6hbhLly6SlJQkr732mpQoUULGjRsnQ4cONbcGz5gxw9HW37Nnz5rLTHLlymUuNNGtzbFINk2qWPSXZyCAAAIIIIAAAv4Czz0n8uqroU26dxcZMiR0PnIggAACCCCAQOQCNsUqCABGPp6Oati2bZvZYqur4g4dOmS23SYnJ0vnzp1N4C5QWrlypVn5p7f4BtsC7Cu3Z88eGThwoLl0Y+/evWalYevWrc2FHbqF2Elat26dDBgwwAQdf/75ZxNI1PbpGYIa0HOSli5dKvXq1RO3F5o4qTurPDZNqkj7QnkEEEAAAQQQQMCNACsA3WiRFwEEEEAAgdgJ2BSrIAAYu3HnSdkoYNOkysZuUjUCCCCAAAIIIJBJgDMAeSkQQAABBBCwU8CmWAUBQDvfEVrlUsCmSeWy6WRHAAEEEEAAAQTCFuAW4LDpKIgAAggggEC2C9gUqyAAmO3DzQNiIWDTpIpFf3kGAggggAACCCCgAhMmiCQnu7fQcu3auS9HCQQQQAABBBBwLmBTrIIAoPNxI6fFAjZNKouZaBoCCCCAAAIIeExg2DCRbt3cd0rLde3qvhwlEEAAAQQQQMC5gE2xCgKAzseNnBYL2DSpLGaiaQgggAACCCDgMQFWAHpsQOkOAggggICnBGyKVRAA9NSrlbidsWlSJe4o0HMEEEAAAQQQiLVAOGcAahu1XLlysW4tz0MAAQQQQCCxBGyKVRAATKx3z7O9tWlSeRaZjiGAAAIIIICAlQJubwHOnVvkiy9EkpKs7A6NQgABBBBAwDMCNsUqCAB65rVK7I7YNKkSeyToPQIIIIAAAgjEWmDdOpGbbhJJTXX+ZA0azpvnPD85vSmgK0GXLhU5flykSBGRunVFypf3Zl/plXcFeI+9O7Ze6JlNsQoCgF54o+iD2DSpGA4EEEAAAQQQQCCWAvrDb4UK7p9YubLIRReJFC8uUqmSyHnn/a+OvHkzB4Oy8wfs7KzbvUpilNCg8UsviXz8cfrAca5cIo0aifTtywrRxHgT4ruXvMfxPX6J0nqbYhUEABPlrfN4P22aVB6npnsIIIAAAgggYJlAuBeBhOqGBoNq1vxfrlWroh8o4of3UCOQPZ/PnCnSsqXIqVPB68+XT2TKFJFmzbKnDdSKQKQCvMeRClI+VgI2xSoIAMZq1HlOtgrYNKmytaNUjgACCCCAAAIIZBDo2lXkrbdyhiXcQBE/vOfMeGnQtXbtrIN/vpbp2K5cyUrAnBkpnpqVAO8x70c8CdgUqyAAGE9vDm0NKmDTpGKYEEAAAQQQQACBWArceKOI/kCcU8ltoIgf3nNqpETcXhjDWZE5N1Y8ObgA7/GfNlmd/Rrss2h9XVsRrbq83I89e/4tVapcagbtxx9/lEsuuSTHpjcBwByj58HRFCAAGE1N6kIAAQQQQACBeBKoUkXkX//K2RbrqrKhQ//3w6D+c/Zs8D+/8ILImjXO23vDDSLPP/9nfb5n+H5g9P/vaPzZ90NtNOrK7ra6MThxQuTDD527+3I2bixSqND//sv/eb7PM34t2H8HKp+Ief0DHdgFfq+yei/OnBH59Vf377H+okKPNfBP8RS8ct9jStgj8G8RIQBoz3jQkrgXIAAY90NIBxBAAAEEEEAgTIFq1UQ2bgyzMMUQQAABBBBAIBsFCABmIy5VJ6IAAcBEHHX6jAACCCCAAAIq8Je/iKxfjwUCCCCAAAII2CdAANC+MaFFcS1AADCuh4/GI4AAAggggEAEAqVLixw8GEEFFEUAAQQQQACBbBIgAJhNsFSbqAIEABN15Ok3AggggAACiS2QkiJSoYIdBnrGlu+crdy5/3felv7j/+fffhP5+Wf37b3wQpGiRf+sU2vw1R/Jv6NRTzTq8J1NFklffGWDtUfdx451b//kk3/a++oO9O+sPvPvn38+G8vY2Cb/s+uCWcaTcST92btX5P77g18+EegN1+fNmydSpkzmTzOeC+jLEa2vZ3zf/VsQrWcEqycWz+YZod+pvXv/LX/5C2cAuv+/DyUQCCJAAJBXAwEEEEAAAQQSUWDCBJHk5Jzvuf4AunOnSLlyWbdFA5YVK7r/4d1J3TmvYH8LuD3V/jGihaEFeI9DG5HDHgGbYhXcAmzPe0FLIhCwaVJF0A2KIoAAAggggAACrgSGDRPp1s1VkWzJrD+Q6wobJ4kf3p0oZU+edetE9MbmU6dC16+rOVeuFElKCp2XHAjEUoD3OJbaPCtSAZtiFQQAIx1NylshYNOksgKERiCAAAIIIIBAQgjYsALQbaCIH95z9tWcOVOkZcusg4A6plOmiDRrlrNt5ekIBBPgPebdiBcBm2IVBADj5a2hnVkK2DSpGCoEEEAAAQQQQCBWAuFsqY1m28INFPHDezRHwX1dGoR9+WWRjz5Kvx1bt3I3bizSpw8r/9yrUiLWArzHsRbneeEI2BSrIAAYzghSxjoBmyaVdTg0CAEEEEAAAQQ8LeB2S61TDA0G1ar1v9yffx79QBE/vDsdiezLpwHkZctEjh8XKVJEpG7d0Oc4Zl9rqBmB8AR4j8Nzo1RsBGyKVRAAjM2Y85RsFrBpUmVzV6keAQQQQAABBBBIJ6CBtJtvFjlzxhmMBnpKlRI591yRkiVFihcXqVTpf/+tQb+8eTMHg7LzB+zsrNuZCLkQQAABBBDIHgGbYhUEALNnjKk1xgI2TaoYd53HIYAAAggggECCC7z3nkj79s4R9NzAdu2c5ycnAggggAACCIQnYFOsggBgeGNIKcsEbJpUltHQHAQQQAABBBDwuEDp0iIHDzrvpK7+O3DAeX5yIoAAAggggEB4AjbFKggAhjeGlLJMwKZJZRkNzUEAAQQQQAABDwusXClyyy3uO6jlfOf7uS9NCQQQQAABBBBwImBTrIIAoJMRI4/1AjZNKuuxaCACCCCAAAIIeEagY0eRcePcd0fLvfOO+3KUQAABBBBAAAHnAjbFKggAOh83closYNOkspiJpiGAAAIIIICAxwSaNROZNct9p7Tchx+6L0cJBBBAAAEEEHAuYFOsggCg83Ejp8UCNk0qi5loGgIIIIAAAgh4TIAVgB4bULqDAAIIIOApAZtiFQQAPfVqJW5nbJpUiTsK9BwBBBBAAAEEYi0wfbpI8+bunzpjhsj997svRwkEEEAAAQQQcC5gU6yCAKDzcSOnxQI2TSqLmWgaAggggAACCHhMYMIEkeRk953Scu3auS9HCQQQQAABBBBwLmBTrIIAoPNxI6fFAjZNKouZaBoCCCCAAAIIeExg2DCRbt3cd0rLde3qvhwlEEAAAQQQQMC5gE2xCgKAzseNnBYL2DSpLGaiaQgggAACCCDgMQFWAHpsQOkOAggggICnBGyKVRAA9NSrlbidsWlSJe4o0HMEEEAAAQQQiLVASopIxYoiqanOn5wrl8jOnSLlyjkvQ04EEEAAAQQQcC9gU6yCAKD78aOEhQI2TSoLeWgSAggggAACCHhY4O67RT76yHkHNf+8ec7zkxMBBBBAAAEEwhOwKVZBADC8MaSUZQI2TSrLaGgOAggggAACCHhcYN06kZo1RU6fDt3Rc84RWbVKJCkpdF5yIIAAAggggEBkAjbFKggARjaWlLZEwKZJZQkJzUAAAQQQQACBBBEgAJggA003EUAAAQTiTsCmWAUBwLh7fWhwIAGbJhUjhAACCCCAAAIIxFKALcCx1OZZCCCAAAIIOBewKVZBAND5uJHTYgGbJpXFTDQNAQQQQAABBDwmwCUgHhtQuoMAAggg4CkBm2IVBAA99WolbmdsmlSJOwr0HAEEEEAAAQRiLTBhgkhysvunarl27dyXowQCCCCAAAIIOBewKVZBAND5uJHTYgGbJpXFTDQNAQQQQAABBDwmMGyYSLdu7jul5bp2dV+OEggggAACCCDgXMCmWAUBQOfjRk6LBWyaVBYz0TQEEEAAAQQQ8JgAKwA9NqB0BwEEEEDAUwI2xSoIAHrq1Urcztg0qRJ3FOg5AggggAACCMRagDMAYy3O8xBAAAEEEHAuYFOsggCg83Ejp8UCNk0qi5loGgIIIIAAAgh4UIBbgD04qHQJAQQQQMATAjbFKggAeuKVohM2TSpGAwEEEEAAAQQQiKXAunUitWuLnDoV+qn58omsXCmSlBQ6LzkQQAABBBBAIDIBm2IVBAAjG0tKWyJg06SyhIRmIIAAAggggEACCcycKdKyZdZBQA3+TZki0qxZAsHQVQQQQAABBHJQwKZYBQHAHHwReHT0BGyaVNHrFTUhgAACCCCAAALOBd57T+Spp0SOH89cplQpkSFDRNq1c14fORFAAAEEEEAgMgGbYhUEACMbS0pbImDTpLKEhGYggAACCCCAQAIJsAIwgQabriKAAAIIxI2ATbEKAoBx89rQ0KwEbJpUjBQCCCCAAAIIIBBLAc4AjKU2z0IAAQQQQMC5gE2xCgKAzseNnBYL2DSpLGaiaQgggAACCCDgQQFuAfbgoNIlBBBAAAFPCNgUqyAA6IlXik7YNKkYDQQQQAABBBBAIFYCKSkiFSuKpKY6f2KuXCI7d4qUK+e8DDkRQAABBBBAwL2ATbEKAoDux48SFgrYNKks5KFJCCCAAAIIIOBRgQkTRJKT3XdOy3EhiHs3SiCAAAIIIOBGwKZYBQFANyNHXmsFbJpU1iLRMAQQQAABBBDwnMCwYSLdurnvlpbr2tV9OUoggAACCCCAgHMBm2IVBACdjxs5LRawaVJZzETTEEAAAQQQQMBjAqwA9NiA0h0EEEAAAU8J2BSrIADoqVcrcTtj06RK3FGg5wgggAACCCAQawHOAIy1OM9DAAEEEEDAuYBNsQoCgM7HjZwWC9g0qSxmomkIIIAAAggg4EEBbgH24KDSJQQQQAABTwjYFKsgAOiJV4pO2DSpGA0EEEAAAQQQQCCWAuvWidSuLXLqVOin5ssnsnKlSFJS6LzkQAABBBBAAIHIBGyKVRAAjGwsKW2JgE2TyhISmoEAAggggAACCSQwc6ZIy5ZZBwE1+DdlikizZgkEQ1cRQAABBBDIQQGbYhUEAHPwReDR0ROwaVJFr1fUhAACCCCAAAIIOBfQlYCPPy6i//ZPuXKJNG4s0qcPK/+ca5ITAQQQQACByAVsilUQAIx8PKnBAgGbJpUFHDQBAQQQQAABBBJUYN48kSZN/uz8ueeKLFjwvy3CJAQQQAABBBCIrYBNsQoCgLEde56WTQI2Taps6iLVIoAAAggggAACWQroyr/HHhP58sv02XQFYKNGIn37sgKQVwgBBBBAAIFYCtgUqyAAGMuR51nZJmDTpMq2TlIxAggggAACCCAQRIAzAHk1EEAAAQQQsE/AplgFAUD73g9aFIaATZMqjOZTBAEEEEAAAQQQCFuAW4DDpqMgAggggAAC2SpgU6yCAGC2DjWVx0rApkkVqz7zHAQQQAABBBBAQAXuvlvko4+cW2h+PSuQhAACCCCAAALZK2BTrIIAYPaONbXHSMCmSRWjLvMYBBBAAAEEEEBAUlJEKlYUSU11jqFnAu7cKVKunPMy5EQAAQQQQAAB9wI2xSoIALofP0pYKGDTpLKQhyYhgAACCCCAgEcFJkwQSU523zkt166d+3KUQAABBBBAAAHnAjbFKggAOh83closYNOkspiJpiGAAAIIIICAxwSGDRPp1s19p7Rc167uy1ECAQQQQAABBJwL2BSrIADofNzIabGATZPKYiaahgACCCCAAAIeE2AFoMcGlO4ggAACCHhKwKZYBQFAT71aidsZmyZV4o4CPUcAAQQQQACBWAtwBmCsxXkeAggggAACzgVsilUQAHQ+buS0WMCmSWUxE01DAAEEEEAAAQ8KcAuwBweVLiGAAAIIeELAplgFAUBPvFJ0wqZJxWgggAACCCCAAAKxJLXVyAAAIABJREFUFFi3TqR2bZFTp0I/NV8+kZUrRZKSQuclBwIIIIAAAghEJmBTrIIAYGRj6ar0/v37ZcGCBXLgwAEpX768NG7cWM477zxXdWSV+dixY/LRRx/J7t275aKLLjL1lyxZ0lX9O3fulIULF4rWddVVV8ldd90l+fPnd1VHxsy///67rFmzRrZt2yZqcMMNN0jDhg0jqjNjYZsmVVQ7RmUIIIAAAggggIADgZkzRVq2zDoIqMG/KVNEmjVzUCFZEEAAAQQQQCBiAZtiFQQAIx7O0BWcPXtWXnrpJRk0aJBceeWVJgC2du1a0RfhjTfekA4dOoSuJESOkSNHSs+ePeWCCy6Q2rVry9atW2XLli3St29f6dGjh+TOnTvLGk6ePClPP/20jBkzRpKSkqRSpUqydOlS0a+PHTtW7ta9JS7Thg0b5NVXXzVByWrVqknTpk3luuuuM/+UKFHCZW1ZZ7dpUkW1Y1SGAAIIIIAAAgg4FNCVgI89JvLll+kL5Mol0rixSJ8+rPxzSEk2BBBAAAEEoiJgU6yCAGBUhjR4JampqdKyZUv54IMPpEuXLjJ8+HATjNOgYNu2bWXy5MkycOBAE7wLNz333HMm0KYBtqlTp0qBAgVMVVrn4MGDpXPnzjJq1Kig1WuQr0GDBrJixQoZMGCAPP/88yavfl1X6unXJ06cKK1atXLURC3XvXt30aCkBjv133/5y18clQ03k02TKtw+UA4BBBBAAAEEEIhUYPZskfvu+7OWUqVE1q4VKVcu0popjwACCCCAAAJuBWyKVRAAdDt6LvP3799fevfubVb+ff3113LOOeek1aDbbHUr8PHjx+WTTz4xQTi3adKkSdKmTRspWrSopKSkSJEiRdKqOH36tFnJt2PHDhk9erQ88sgjAavv1KmTWeVXr149Wbx4cbo833//valDg5a6ou+aa67Jsom6xfeee+6RL7/80gQ433nnnYi3EDsxsWlSOWkveRBAAAEEEEAAgewQyBgAvPxyke3bs+NJ1IkAAggggAACoQRsilUQAAw1WhF8vmvXLhP4O3XqlLz++uvSrVu3TLXp6jwNzmkgULftFixY0PETT5w4IRUrVpSffvpJnnrqKbOdOGPSbce6ok8Dg99++61ceOGF6bKsXr1aatasab42a9YsuffeezPVoasANUBZo0YN0fzB0pEjR8z242+++casetTgZC7dcxKDZNOkikF3eQQCCCCAAAIIIBBQgAAgLwYCCCCAAAL2CNgUqyAAmI3vxRNPPCEjRowwT/juu+/kiiuuyPS02bNny31/7NMYP368tG/f3nGLhg4darbaatKLO+rXr5+p7MaNG835e5r0PMB+/fqly6Nn++kZfboyUVciFipUKFMdGljU8wE1LVmyROrWrZspz6+//ip33HGHrFq1Sq6//npz6Uc+PWk6RsmmSRWjLvMYBBBAAAEEEEAgwPeW6bcA67ef330HFAIIIIAAAgjkhIBNsQoCgNn0BuiqP11td/ToUTn//PPNrbqBVsPt3btXLr74YtOKWrVqycqVKx236NprrzXbijUdPnxYihcvnqmsnjVYuHBhc57fpZdeKroq0dcOvY1Yn33mzBmpUqWKbN68OeCzNainbdPUunVref/99zPle/zxx81Zf7pV+KuvvjIXfcQy2TSpYtlvnoUAAggggAACCPgLZFwBSACQ9wMBBBBAAIGcE7ApVkEAMJveg+XLl0udOnVM7VWrVpVNmzYFfZIGCHU7b548eeTnn38OuAovY+Eff/xRypYta76s5XX1XrCkz9cbgTVt27bNbEvW9I9//EPatWtn/tykSROZM2dOwCp0a6/v1t4yZcqIBi39kwYOq1evbgKJzZs3NxeexDrZNKli3XeehwACCCCAAAII+ARmzRJp1uxPDwKAvBsIIIAAAgjknIBNsQoCgNn0HuitvHo7r6ZGjRqZbbbBkgbktv9xOrOuAPSttsuqadOnTzfBNk2VK1dOWwkYqIxeLvLpp5+aj3T1nq7i0+Rbtad/fuyxx9K2KweqI3/+/OYsQ036AvtWLep/33777WmXh+gZgXpWYKyTTZMq1n3neQgggAACCCCAgE+AACDvAgIIIIAAAvYI2BSrIACYTe/Fo48+KmPGjDG1P/zww+aW3WDpxhtvlHXr1pmP33vvPXnooYdCtmrgwIHSq1cvk08DcIsWLQpaRgOFGjDU9NJLL0mfPn3Mn++8805zdqCmV155xdxWHCyVKlVKDh06ZD5etmyZ3HrrrebPugVZtyJrKleunOjKxylTpshnn31mzj3UswH1ghNtg56JqIHEcJJOmqzSvn37RB016erISy65JJzHUAYBBBBAAAEEEIhrAQKAcT18NB4BBBBAwGMCBAA9NqCButO0aVOZO3eu+SjYDb2+crfcckva2X/Dhg2Trl27hhTSOt98802TT5+ll4kES23btk07t08v83jttddMVj2nz3fuX7Bbin116vmBviCcbhXWLcOaXn75ZXO5iCY9g1C3JetqQr0NWM89nDBhQlogVLci//Of/xTdRuw2ublNmACgW13yI4AAAggggIBXBDIGAPXkl23bvNI7+oEAAggggEB8CRAAjK/xCqu1eiOvb1Vez549RVfsBUv+AcBQK/F8dXTq1CltVeGDDz5oVt0FS/4BQP/ViHor8ffff2+Kvf3226KrFoMl/wDgxIkTpU2bNiar3vyrq/006YpEbb9eBOKfBg0aJM8//7z5UlJSknz++eeSN29eV64EAF1xkRkBBBBAAAEEElSAAGCCDjzdRgABBBCwUoAAoJXDEt1GNWzYUD755BNT6YsvvmhWygVLembemjVrzMeDBw9OOzswqxZ17txZRo8ebbJogE8v9AiWNEDou5ijS5cu5rZeTZUqVTKXgmgaN26cdOjQIWgdeqOx3hqsSevynT+oW2337Nljvq598G3D9a8oNTXVXITyr3/9y3z5nXfekY4dO7oCZwuwKy4yI4AAAggggECCCsycKXL//X92nhWACfoi0G0EEEAAASsECABaMQzZ24gHHnhAZsyYYR6il4FoYC9Y8t+KO2rUKNHgXqjUvXt3GTp0qMkW6uZd/+3IPXr0EF2Rp0lX43355ZfmzxoU1OBgsFSkSBFzQ7GmBQsWiAY4NRUoUEB+++038+eMl4P416X166Ujmm677ba0VYOh+un0c5smldM2kw8BBBBAAAEEEIi2AAHAaItSHwIIIIAAAuEL2BSr4BKQ8Mcxy5LPPPOM6Ll6mjTwNXz48KD59dw8PbdOk94WrLcGh0p6/p+eA6ipcePGMn/+/KBF9MKOFStWmM9HjBhhzujT1KxZM5ml+0RERG8tfvbZZwPWcebMGbNlV1fyadKLP/TmYU0FCxaUkydPmj9rgPC8884LWMcPP/wgl112mflMzwo8fPhwqC66+tymSeWq4WRGAAEEEEAAAQSiKEAAMIqYVIUAAggggECEAjbFKuI6AKhBtWrVqpnLLM4999wIhyW6xd99911z+6+me++9Ny3QFugphQoVMrflakpJSTG36YZKixcvNrf/arr++utlw4YNQYtcffXV8u2335rPly5dKnXq1DF/1tuA9cw+Td26dUsLWGasSLf+6hZgTXqL7/Hjx9Nu89W27t6923z2008/SYkSJQK2Q4OHhQsXNv3UYOKpU6dCddHV5zZNKlcNJzMCCCCAAAIIIBBFgYwBQP0WbvVqkfLlo/gQqkIAAQQQQAABRwI2xSriOgCol03o5RD6j64u02Cg/z8XXHCBowHJjkw7duyQyy+/3FStAcqNGzcGfMz+/fvTbsXV8/R8KwFDtemXX36RkiVLmtV3uj1Xb9wNlPwDb/ny5ZOjR4+KBhw16eUdeomHpqxuEv7iiy/k5ptvNvn0dl/fakL9bw1CajBS06ZNm8xZf8HSxRdfLHv37jXBxH379oXqoqvPbZpUrhpOZgQQQAABBBBAIEoC69aJ6Iku69enrzBXLhHdYNK3rx4BE6WHUQ0CCCCAAAIIhBSwKVbhiQCgBrkC3RJbpkyZTEHB8jH89acGw7Zs2WJWvJ04cSJt1Zz/G+K/kk+39L7xxhshXyBfhiZNmsi8efPMf2rAsWLFipnK+m+9zRjkO336tJQuXVqOHDkiesuvbyVfxkr8VzPqtmZdLehLf//739O2Doe6SVgDsrpK8J577pG5c+c67qeTjDZNKiftJQ8CCCCAAAIIIBBNAV3517KlSFabLPLlE5kyRY+BieaTqQsBBBBAAAEEggnYFKuI6wBg69atzdbX7777Ts6ePZvJO1BQUFfL6ZZZ/5WCukVWVxNGO40dO1Y6depkqv3000/TVtv5P6d///7Su3dv83w9W09v5nWaFi1aJPXr1zfZg92sO2nSJGnTpo3Jo7cSN2jQIF31+mxtg6bt27enrVr0z6R90L7oNutdu3aZM/x8SV9mXemoF4HcfffdaQHJjH3Q8wGLFi1qzhGcMmWK6M3E0Uw2Tapo9ou6EEAAAQQQQACBUAK68q927ayDf746NAi4ciUrAUOZ8jkCCCCAAALRELApVhHXAUDfYPz3v/8120+/+uqrtH+2bt0qv//+e8DxyhgY1HPtqlSpki4oeOONN0Y81nrOnQYbv/nmGxMIHDNmTKY6q1evboKY7du3l/Hjx6f7fPny5dKqVSsTeJs9e3bA4KBu4dWtvBoIXLhwYab6fRd91K1bV5YsWZLpc72MQy/0OHjwoAkE9urVK10e7YNeUqLnAPbr10/66t6RDElvOdZLRNRV+3rVVVdlyvP+++9L27ZtzRbh9evXyznnnBOxr38FNk2qqHaMyhBAAAEEEEAAgRACd9+tF8k5Z9L8f2wicV6InAgggAACCCDgWsCmWIUnAoCBRkCDf5s3b5Ynn3xS9Aw7DU7pJRT/+c9/Ag6Yf1BQ/6zbY6ORNNil5+flyZNHNChZoUKFtGqnTZsmLVq0MOcXrl27Nt3KOs2k5bTtmlq2bCmTJ0/O1CQ9M1CDanoG4KpVq9LO6tOMq1evNmf2FStWzNQfaIuw5tPtuLo9WLcD62UhulLPl4YMGSI9evSQGjVqyLJly0TPEcyYNAB72223yZo1a8zz9aIR/3yHDh2Sv/zlL6aNen5gVucEhmtu06QKtw+UQwABBBBAAAEE3AqkpIjoKTCpqc5L6pmAO3eKOLh3znml5EQAAQQQQACBTAI2xSo8GwD0qT/wwAPy4YcfmgDgmTNnzDZXXXHn/48GqPyTL2+03t358+dL8+bNzUo6vblYA3HTp083K+quvPJKmTNnjgQ6m7BmzZomiKdJVwLqdt5ASYN7jRo1MmcNjhw50qxk1NWAGrjTLc+6elBXImaVdAtxly5dJCkpSV577TVzm++4ceNk6NCh5tbgGTNmZApQ+tenZ/vpKkS97KRevXqmb9pPDYB2797dBF61z7Vq1YoWa7p6bJpU2dJBKkUAAQQQQAABBAIITJggkpzsnkbLtWvnvhwlEEAAAQQQQMC5gE2xioQLAAYaJr2ZNmNQUC/PiGbatm2b2WKr23A14KjbbpOTk6Vz584mcBcorVy50qz805WLwbYA+8rt2bNHBg4cKB9//LG5aVdXGuoZiXphh24hdpLWrVsnAwYMMEFHPbNPA4naPj1DMNB5ihnr1BuJ33rrLbNSUfur/dIg4H333WdWYupKxOxKNk2q7Ooj9SKAAAIIIIAAAhkFhg0T8bufzTGQluva1XF2MiKAAAIIIIBAGAI2xSoIAIYxgBSxT8CmSWWfDi1CAAEEEEAAAa8KsALQqyNLvxBAAAEEvCBgU6yCAKAX3ij6IDZNKoYDAQQQQAABBBCIlQBnAMZKmucggAACCCDgXsCmWAUBQPfjRwkLBWyaVBby0CQEEEAAAQQQ8LAAtwB7eHDpGgIIIIBAXAvYFKsgABjXrxKN9wnYNKkYFQQQQAABBBBAIJYC69aJ1K4tcupU6KfmyyeycqVIUlLovORAAAEEEEAAgcgEbIpVEACMbCwpbYmATZPKEhKagQACCCCAAAIJJDBzpkjLllkHATX4N2WKSLNmCQRDVxFAAAEEEMhBAZtiFQQAc/BF4NHRE7BpUkWvV9SEAAIIIIAAAgg4F9CVgJ07i3z1VfoyuXKJNG4s0qcPK/+ca5ITAQQQQACByAVsilXEdQDwiy++kKpVq0qhQoWCjsoDDzwgH374oeTKlUvOnDkT+ehRg5UCNk0qK4FoFAIIIIAAAggkhMD06SLNm//Z1TJlRFavFilXLiG6TycRQAABBBCwSsCmWEVcBwBz584tefLkkSuuuEKqVasm1atXN//Wf4oVK2YGnQCgVe9+tjXGpkmVbZ2kYgQQQAABBBBAIIRAxgBg5coiX38NGwIIIIAAAgjkhIBNsYq4DwDqyr5AqWzZsiYQ+PXXX8v27dtZAZgTb3oMn2nTpIpht3kUAggggAACCCCQTmDaNJEWLf78EgFAXhAEEEAAAQRyTsCmWEVcBwCvuuoq2bFjh5w9ezbTaAYKDOqqQN8KQd+/tY5gQcSce0V4slsBmyaV27aTHwEEEEAAAQQQiJYAAcBoSVIPAggggAACkQvYFKuI6wCgDsUvv/wimzZtkg0bNshXX31l/r1161Y5depUupHSIF9qamqmYF/BggXNOYL+gcEqVapIPr0mjRQ3AjZNqrhBo6EIIIAAAggg4DkBAoCeG1I6hAACCCAQxwI2xSriPgAY6D34/fffzdZf/6Dg5s2b5T//+U/A1ybjCsC8efPKyZMn4/gVS7ym2zSpEk+fHiOAAAIIIICALQIEAG0ZCdqBAAIIIICAiE2xCk8GAAO9ZLr6T88C9K0S1OCg/nP48OFM2bkxOP6mqU2TKv70aDECCCCAAAIIeEUgYwDwmmtE/vUvr/SOfiCAAAIIIBBfAjbFKhImABjsFdHByBgU1K+dOXMmvt6qBG+tTZMqwYeC7iOAAAIIIIBADgp88IHIgw/+2QACgDk4GDwaAQQQQCDhBWyKVSR8ADDQ23jkyBEpXrx4wr+o8QRg06SKJzfaigACCCCAAALeEiAA6K3xpDcIIIAAAvEtYFOswrMBwH379slPP/0kep5f6dKlRW8AJnlXwKZJ5V1leoYAAggggAACtgsQALR9hGgfAggggEAiCdgUq/BUAHD58uXy1ltvyZIlS+To0aPp3qnChQtLvXr15M4775QWLVpIiRIlEumd83xfbZpUnsemgwgggAACCCBgrQABQGuHhoYhgAACCCSggE2xCk8EAH/55Rdp166dzJo1y7xOeuFHoOS77bdgwYLSsWNHee655+Siiy5KwFfQe122aVJ5T5ceIYAAAggggEC8CGQMAF57rciWLfHSetqJAAIIIICAtwRsilXEfQDw5MmTcuutt8r69euDBv6CBQOLFCkiI0aMkJYtW3rrDUvA3tg0qRKQny4jgAACCCCAgCUCU6eK+H9rSwDQkoGhGQgggAACCSlgU6wi7gOAjzzyiIwdO1Z0dZ+u/GvYsKG0bdtWqlevLhdccIGcPn1aDh8+LJs3b5bPP/9cPvzwQ9HzAX1JyyUnJ8uYMWMkd+7cCflCeqHTNk0qL3jSBwQQQAABBBCITwECgPE5brQaAQQQQMCbAjbFKuI6ALh9+3apXLmynD17VgoUKCBTpkyRJk2aZPnWaJBw9uzZ8sorr8jGjRtNXg0C/t///Z98oHsmSHEpYNOkiktAGo0AAggggAACnhAgAOiJYaQTCCCAAAIeEbApVhHXAcDevXvLgAEDTABv/Pjx8tBDD7l6RfTCED0H8LfffjN1dO/eXQYNGuSqDjLbIWDTpLJDhFYggAACCCCAQCIKEABMxFGnzwgggAACtgrYFKuI6wBg7dq1ZdWqVXLFFVfItm3bwhrvL774Qho1aiTHjh0zW4D1BuFbbrklrLoolHMCNk2qnFPgyQgggAACCCCQ6AIZA4BVqohs3pzoKvQfAQQQQACBnBGwKVYR1wFAvcH3wIED0qVLFxk+fHjYo7l8+XK5/fbbzVZiPTtw3bp1YddFwZwRsGlS5YwAT0UAAQQQQAABBESmTBFp1epPCQKAvBUIIIAAAgjknIBNsYq4DgDquX+///67/O1vf5Pnn38+ohF99tln5bXXXjNbgZctWya6upAUPwI2Tar4UaOlCCCAAAIIIOA1AQKAXhtR+oMAAgggEM8CNsUq4joAeO6558qvv/4q/fr1kxdffDGid0JvCtYVhXpr8FNPPWWCgaT4EbBpUsWPGi1FAAEEEEAAAa8JEAD02ojSHwQQQACBeBawKVYR1wHAyy+/XHbu3CnJyckyduzYiN+J+vXry2effSY33XSTrF69OuL6qCB2AjZNqtj1OjGfNG3aNOnWrZsULFhQJkyYkC1ndurlQO+8845ce+215nZw/eUAyTsCGzZsEL0ESm+OHzVqlLRv3947nbOwJ4sXL5aOHTvKyZMnZcSIEXLfffdZ2Mr4a9KmTZvMLy1vuOGG+Gt8DrV4wYIFct1113n+73QCgDn0gvFYBBBAAAEEAgjYFKuI6wDgvffeK3PnzhUNBH733XcRv2z6Q//QoUOlRIkScujQoYjro4LYCdg0qWLX68R70tGjR+WSSy6R//73v6bzF198sfz4449m63600ieffCINGzZMq65Vq1YyadKkaFVPPTkkoIGSmTNnyptvvimff/55Wiv0BnkCgNk3KHq2ru+8Xn3KeeedJ/r39fnnn599D/V4zfr9iR57sn79epk+fbr5HojkTGD+/Pnml8Z67Iv+kydPHmcF4yxXxgBg1aoimzbFWSdoLgIIIIAAAh4RsClWEdcBQF1J8OSTT5of/vX23ltvvTWiV2Tw4MHmm+q8efPKb7/9FlFdFI6tgE2TKhY9v/POO0VXMYUKVOsPN6VLl5Z69erJ+++/H4umZesz9IKeG2+8Md0zDh48KBdccEHUnvvqq6+K/jLAlypXrixff/111OqPh4quv/56+emnn2TPnj2OmnvOOeeInsmq/xQtWtQEZjVQW6VKFUlKSpIaNWpIoUKFHNWVHZkWLVpkgnz79++XM2fOpHtEIgUAr7jiCvn++++zg9jUuXnzZjPm/mnfvn2ZVlt99dVXUq1atWxrh5cr1r8DmzVrJnfddZdZTanfr2jSlawDBgww77gGu7NK+ktOPUJF69K/O3Wl87Fjx8Ka78WKFTPzXf+pWrVq2nzXFdpOU2pqqujfIRoszq6kvzTytUlXTuovkC+88EITQNW/q7yWJk8Wad36z14RAPTaCNMfBBBAAIF4ErApVhHXAUA9t0+/cTt16pTZAqPbdiP5bW6fPn3MhSK6QuH48ePx9E4lfFttmlSxHIyUlBTp1auX2cqYMT3zzDPSt29fT6200R9Sdc7/8ssvprv65927d0d1BeCnn34qDRo0SONs3bq1J4Kn4byXe/fulYcfflj++c9/BixepkwZMwYlS5Y0qzI1+KArMn0rNH2FNNjQtGlTefrpp3Nku+KRI0fM/yc0ONm4cWPRLam+lEgBQA14HDhwwHS9YsWKZvu8BoDy58+fbnz79++f7r+feOIJKVKkSNrX1FLHWi/M0vnnS7qysmbNmunKalBH3xENBGpiBWA4M/F/ZSZPniwdOnQwv/jUX1QESjr39GiEv/71r2bLtX/S75Pee+89ueaaawKW1YC/ro7TvwMDJV3JqYE+ne/6d7C+Szr+ehazf9L5rgE2/X+Qk0Dvf/7zH/Ne+JIGkfUXPRqo9AU49TNdAT5y5Mi0fPrLX/3/n3/StujfWxr0119i+JK2tVSpUmn/re3Wy970l726Lbh69erhD4yFJQkAWjgoNAkBBBBAIGEFbIpVxHUAUN8g/YFy2LBhJgDQqVMnefvtt8N+sfS36rNnzzY/GGXnKomwG0jBoAI2TapYD5OuaLrssstk165daY/WM442btwY66bE5HkffvihdO3a1QQt9AfdSFf+Bmq0/lCpK2o4A1DMyiD9gd9/5Vz37t3N370aAMyY9AfqpUuXyowZM0ywQW9q9yX9e7pt27YyfPjwdD/wx+TF+eMherbjI488kvbIRAoAFi5c2ARn9ZddenGWrroKlDJuqdezdsuXL58pq67c0jn4+OOPmyCQbp/3D577Cuj7oIFkzcMZgOG97XPmzJH777/fBNZ0boVKPXv2FN3V4J+2bNli/k7LKukvVjVQ5r8aT+vSy9E0gBxovusODN989199qO+RrrzVbfcaFAyWfKtEc+fOLWPGjDHvSqCk35fpKlZf0l/4BlvtqH/vDBkyxLzr2pcdO3aY7+3807Zt28yZz/oeazBbVz57JREA9MpI0g8EEEAAAS8I2BSriPsA4M8//2y2HCmqpjZt2phvIDOuaAj14ug2Qv3mUH9AeeCBB2Tq1KmhivC5RQI2TaqcYLnnnntEzzbyJQ1wjB49OieaYu0zdbXTxIkTAwYyrG20JQ276qqr0p2z6jRopmezauAg4wpCrU9X3VSoUCHmPdRzY3U1oi857UvMGxrlB2oQRAMm7dq1M0G7rJLTAKCvjnfffdcEbTQIpEEqUnQFVqxYYQKruoL1m2++Mf8OlfTyogcffDAtmwZ/daWdk6S/UPrhhx/Ssurfm/q9VaikATX95czChQvTZb366qvNfC9XrlzAKvTvCf07QXdgvPDCC0Ef4yYA6KtEf5kzcOBA8wsx/cVYxqRHY+gvJXR14xdffCGXXnppqG7GxecEAONimGgkAggggECCCNgUq4j7AKC+M2vXrpXbbrstbRuK/oZYV5jccccdjl8pvZVQf8OuP/joDzP6QxIpfgRsmlQ5oaY/nPlfVKE/9GTcxpcT7bLlmVu3bjXb3oKtZLKlnba2Q7d1+t+M7iZopoEn3QqoK7X9kwYZVq5cGXBVUXY6aHBCz9D0JTd9yc52ZXfdeqxF8eJYaB3UAAAgAElEQVTFTSBX7bNKbgOAWpee/6bbTvl/Z3RHUre96t9dukpu3LhxZguwk6RBdz0n0JfKli2bbpV4VnXo9ls9H9CXnAYANb/Odw0C6kpP/6Tfl+l899+G6/tcLzO5/fbbzS9ys1opGE4AUFck60rlefPmSa1atQJ22/cLND2rVNsYyVEyTsYmFnkyBgA19unRTQGx4OQZCCCAAAIIRCRgU6zCEwFAHQ09s0a38Or2Jt3OoT/A6A+tjz32mDRp0kT0t9+Bkv7GWs/T+eyzz8zHer6NbhXxP3cmotGmcEwEbJpUMelwhofoNivdbulLevZfv379cqIpVj5TV0Tq1k8CgOENT926dc0WOV8KJ2im24b1lnX/VKdOHXOBUzRvcQ7VQz3/T4MNkfQl1DNs/Fz/jtRflGkAMFQKJwCoN6rqNmE9L5AUPQE9g1TP/tOVaboqL9i27YxP1DPw6tevn/ZlDfo6PdpEz8bzvynbTQDQ90AN+r/++uvpmqXzTr9Xy/h+6RZx3a778ccfZwkXTgBQK9Rt0507d053u7v/g3RrtK4O1O8d9f+b+v/PeE96cb3/ok0CgPE+orQfAQQQQCCeBWyKVXgmAKgvhP4WuWXLluabXP9vMPPly2dWJ+hWYT3LSr+B1sOhNb/epKrf9Ok/+ltfXQXYqFGjeH6/ErLtNk2qnBgAAoDB1fWHOz38Xs+EIgAY3tsZjQCgntWlAb9Vq1ala4SuDNQVQ7FKGmzQW7F9KZxgZqzaGs3n6OUseiyAbrMMlcIJAGrw5sSJE9KiRYtQ1fO5QwE91kFXp2l65ZVXpHfv3g5LirkEIycDgPr3rR67sGbNmnRt1pWB+otZ/6R59GboLl26ZNm/cAOA+ssf3Yasgc1gybcKUL9f1G3WGc8LdAxvSUYCgJYMBM1AAAEEEEBAxOxy8B0zot+T6wV5OZU8FQBURD3DT1ci6Dd8GQ+jDoSsgT9NGhR866235NFHH82pseC5EQjYNKki6EbYRQkABqbT7V8333yzCfRrIgAY3isWjQCgPll/gNezvvwvGNBfyuht1sFWaYfX4uClEjUA6MYxnACgm/rJG1pAvzfRG3Q3bdpkMm/fvl0uv/zy0AX/yJHTAUBthu6wqFy5crr5Xrp0afP3cMGCBR33xZcx3ACgkwf5n5mo51g6uWjFSb05lYcAYE7J81wEEEAAAQQyC9gUq8ixAKAG2vSWTb31LTvS7t275dVXXxU94FnPPsoq6bkvujVNtwyT4lPApkmVE4IEADOr6w/Qegj+tGnT0j4kABje2xmtAKA+/f/+7/9Eb3L2T2+88Ya5LCQWiQBgaGUCgKGNsjvHzJkz0y5UqVSpklmV5ibZEADU9ur2W91Z4Z/0jGa9Odptys4AoF4op2dk6m3n+v7rubHqHq8pYwBQLzj+4/dg8dol2o0AAggggEDcCtgUq8ixAKAG/nR7yvTp0+X888/PtsHUlSZ6eL2eM6WrTPbv32+2++qqk2uvvdacBVW9evVsez4Vx0bApkkVmx6nf0o0AoA6V3SlnP6jW+T1HEw900u3TemqDbfp8OHD5mIS3X6vAST/pM9Yvny5+WFLf8jSSxmcHrx+8uRJE9R7++23zQUS+oNyxqQrgZOTk0VXdfgnpwHAQ4cOydixY825ih07djSrip0m/ftFjxfQLW3qqH+/6dmiegB9oAPwQ9X75ZdfmrOxdJus/+2furpRD/rXwECJEiXMSkf9Oy07UjQDgHqj6a233pqumTfddJO5gdNJUlM9j3DPnj3mzFc11fJ6UYKTFG4A8P/ZOxNwm6r3j7/InCE0UJn6SRqEulIR/ZIypLrJlFmKf5kzJEMpU4aoEJEibkWGTEnJ1IR+phLJ2EOKjIUu8n++K/u0775nn7P3Oefes/Y+3/d5euKetdZe67PWOu75nnfAvm7atEndD5wP/B33AncEe+s0N5uTOca7TawFQHjjQwTCl35//vlnmoIydmvFe3pKSop6b8B7iNm+//57dQYg2sArrm7durYeZdgn5LNDKgC8L8ADFWNGsl/oj+fid4nDhw/LJZdcolKL4O45ff9yurfmQhzIXwd2bkwXARC/eyH3pNlwX1Bsw61lpACIuSBVBN63Ycgbi5B5r9q774o0b/7v7CkAenUnOW8SIAESIAE/ENBJq4irAIgPGcjLgjw3+BBFI4FICeh0qSJdQzT9ohEA8QEZIfOoGgzPWavhg+2DDz4ow4YNcxSCBpHk1VdfVeIfxDpzQZKtW7eqPE8QYcyGfEvvvfeeJCUl2WJAAnyIfqjSDXERhpxy1rEgLOIDsxOPGWt/CAXjxo1T4V+pqanqGU4LqqA9PqSPGjXKliOEtCFDhoRcJ54JwQRzAEej+q5ZvIQw2atXL/n111/T8GrYsKEgp12ePHmiOU7p+sZSAMTaIGRCCDLb/v37VbVOO8O5wppRxdccQmy0h0iEQgLmCr/BxopEAJw6darKwWZXROHSSy+Vdu3aqQIC1gJS6Ne/f3/bdeXKlUseffRRwTOshrXCixWVYM2GPGXwUApXzTfSQxArARB7ivcW/AfBFhauGMWaNWsEHqH4chBnxVyAAncanmMQlcwGIRb8atWqlebnEB179uyZrvAJKuKiPe6/E8M969evn3pPg+hsNXwRgfx8yG0Xi4I2VqELX0a0bdvWyVQDbXQRAPG+iPsO8dQwMAJT3Bs3ltECIP7dMES/fPnyyW+//Sa4n140CoBe3DXOmQRIgARIwK8EdNIq4i4AQnzAL4Fz585V36LTSCASAjpdqkjmH22fSAVAeJFBYMD9gyE0v2vXripJKT5sQ9CAQG9nEAfRt3bt2srDB4KVuVos+hkC2kcffSRNmzZNJ/wYY8MrF549Vi85CEUIG12yZInyujJbMAEQwtmGDRtUM4iB8DgzDBVKCxQoEPh7qVKl1Adr5Nlq0aKF8vCymhMBEB5BDz/8sHrutddeK88995zUrFlTeSXBowTCFMQcGLyfn3322aDFGOBZNnHiRCUkGoKJMR8IgEgYC88UiHx21rx586BiUjRnLJYCIOYBNkbldWNeCxcutC3AhEIhqCoKz6E+ffoI0jbg7ELMe+GFFwJiL4QFCNnga2duBECcN/CGAAODUAchCII19gehjMgdaxg80YLdFxQ5gEAILzSz4VxDiAqXDw3CJ84QrE2bNuqeZWTOxGgFQHDDXcddNOfixfyDCYDwBJ4zZ46qGmstEmMIgDgfKDBiFY4Nnjlz5lReZbfeeqv60cCBA0NWcwU/3E3c11AGsTE5OVmFh0LIhcBs3Gt8KYL3JcPwHoAvMiDQRmM4w+aCH1gXzr4b00UAxJyt7x/4GbhZBdtw68toARD3uWPHjoFp4EwihNmLRgHQi7vGOZMACZAACfiVgE5aRdwEQHO4DD4s4FvWyZMnqyq+NBJwS0CnS+V27rFoH6kACPHPCJNFmCvyZpoNnlb4ADR//vzAjxG6j/A0GMLoWrZsKTt27FBtEA43dOhQ9XfDIKDBOwsf3vHhH/+H8IYKSBAyENJpGPLAwfvHaggPRagwhDEIQIYFEwDNfSFgQiAyzC4EGB5WOEPFihVTAgvESvP8MY6dwcsJghTWgxAyCEwXX3xxuubdu3dX3oGG4e/IPWq23r17q7BShDZi3mbBEx9+8To+lKJSOSrZwrMGAgVEErNB2EABgVhZrAVAiMzWfca5gdBlNYgsWDc8v3BerHlj//jjDyVAm0MKIapBCA1mbgTAkSNHBsK/EWYMccr6fHi0wjPVMDvRAOfj5ptvTuPNZ/ZuC7VX8PjEHcf9QghyrMNNrc+OVgDEeGvXrlUew/hiAOH4hgUTAOF1hbyQENYg9EEQNAyM8CUhvJDxHgARFkI4imLg/QChuIYhtBxfQODu4M5CDIQohxB8w4MYXl2G4bVgKQSM15ctW6bOFt7vMC9ruhLcT7xnmUVgfIliPg+R3EHMG2kEDMMXA/iCxI3pJABCVIO4Zja89+E90I1ltACIPa5Xr15gSvhyyBD/3cxTh7YUAHXYBc6BBEiABEiABP4hoJNWETcBEL90w+PGCAvBL9L40AGxIFS4FA8RCQQjoNOliscORSIAwnMPH4BhCBn95ZdfgubjhKcTBLszZ86otqjqCE89O8MHdtxtw+6//34lBrz00ksqNNds3333nVSoUCHwgR8f9M0f0K3PgPePWVyLlQBofo71Q2AoD0C8b2EO8DLE+xc8CO3y8JnbGs9DSCE8pYIZ8pNCgDAMAgg8DcEXQpDZIJBBKDMsmJgbzbmMtQAYLCw22Jwh6uHZOCMIDbUrGmWtLoyciGAVTIh1KgBC/EZoJ8QXGIRneGZZDV595hx1EAQRRh7MIExCMDcMeR3h3RjOkIcSX5AhlBZ/zmiLhQBoniO854ww5nAhwFZBFZ6TuAd4/8AXFmaDqAdxzkgJgNdwtiAUQ3BCSLzZIMJCGDfa4wsMCIgI97Qa9h37ivecH3/8UZ2FYIYQV+QVxHkzDGc1VDqDUPsHz1bMx3i/BTvz+pzuvU4CIN5D4ZFpfc9COgQ3ltECIMLqzblE4R2KSsZeNKsAiO+DLqQ39OJyOGcSIAESIAES8DQBnbSKuAmA2EEkt69fv34gj5UhAsILEHm+og2j8fQp4eRdEdDpUrmaeIwaRyIAwoPMyJ9neM7YTcdayRF3F95uwQwffuEtZRi81FCswvwzcz942OB1wyA4whPPzhDCCw85WEYIgPCwMhcGCiUAmsXOcAwxXwiEELMMzz58uIeIYQ5LNtaNfGLmxP8Im4U3VbD8fihuZM6fBy9NI+Q4Fkcs1gLg2LFjBeHYZoPQg/Bns+HM4DyFEkqN9hBhINQYBq8yhO9azakAiBxlZtEHXmXBvpyCtxrCSSHcwCDUGiH11mdj37H/Rqg59h/vXaFCgOHhaNwHeJsGEzVjscfmMWItAEJUgbgCCycAvv3222k8BiF2I1zULj+kVVzCFxQIw8aXFsHM6omLHJvw4LUaQs4RkhxK0DX6wFMMv7MYFk0YPt5bzeIhBEujMIWbfdZJAITI3aVLlzTTd8LVut6MFgDxhTTustnz2ij24oa9Dm3hFG76Hk4oAOqwK5wDCZAACZBAohLQSauIqwCIA4Bv5BFyAS8KfOgwRMA77rhDfYiCJweNBMIR0OlShZtrRrzuVgBEXi6E3RuhdvCsQcVNOzOHQqINhBoINsEMuQPxIdww5Eyzen+Y+0FUgdeOYQh9C1WZG0IABC9YRgiA8PhAqKFhdgIg3quwThQ2gcE7zByebMcSFTHNRQwQdh2syrA57xvGQlg1cs/ZGQob4P0UBtEkWD7DSM9erAVAiHNWb1CECcJzyzCE2xp5z3C/EcYZyqpVq5YmDBjeX9Yq0OjvVADEHYEAaHhfYSyrR5kxH/OZhFC7dOlS26kiR5w51QXEEXgC2pnBKpJKsJHud6wFQAhaELZg4QRAs2cy2ocLk0YuSTA3LFx7iMnNmjULtMfzINqaDaIrzhu+aMB+IW1BKEPOSYSwGwbB1prD0+lewNPT7OXp5IuFYGPrJAAGE/yRBsCcEsEJn4wWADEHVHY+evRoYDrI32mkvHAyR13aUADUZSc4DxIgARIgARJgCHC6M4AQG/yCvWjRooAIiEb4sItwPHh20EggFAEKgK0EecIMC1e4wuoxBkFixowZtoitH8pDiV0QqpD7y+lc4OUGbzfDEE5btWpV27mYha6MEADhkWeurmrHEuGpEJ0MQw5Ec/4ouwXAU8hc0RN54YyiJeY+EE4R9miYXf5C43XDWw5/DyeyuH03ibUAOHjwYFUoxWxWDzu8jnaRGvKoIfTcak4FQPRDPjkIdAgFhVBtF4JcokSJQOVnsLJWqTXPAaI7BGajojDy2eHO2Hm8QwyHV2qs8zqG4hprARBf6BnVrMOdTXgDwyvYsHCCHtIRmMPuw7VfvHhxmmIzeN+z5h62tonkDKICOgqTuDXcedx9w+wKy4QbVycBMFhBFqdfmJjXmRkCIIRfeNoahi/GrKHn4djr8DoFQB12gXMgARIgARIggX8I6KRVxN0D0DgUyLeEEBHk7TE+fMDDBuGDM2fOFOTDopGAHQGdLlU8dsmtByDCFRFKinsHQzVGczVL6xqsHyZRwAHJ74MZhCqzp1o4MRIVbVF4wzCIJxBR7KxkyZKyZ88e9XJGCIDI5WUOH7Sbv9XrBx5rTiqZw2OwXLlygeXh/Q5eZvA8MZs1tDGcAAgWqHoMgyBlzkkW7ZmMtQAIj0d4lZoNXkJmIdj8THhWWkWpcGtC0QRr2CH6uBEAQz0DIcLI6YfzC69Xw8KdSbRDYQGzB61duDK8jxCeCg86hEJnlsVaAISg/8UXX6jphxMAEbqOSruGhRP0rKJQuPbW97Jg7VGB18j3CG9B3Ce3hveHSCo1W+8GvhyFF6Jb00kAxD205rq0O/Oh1pkZAmCZMmUC4jzmgnyeCFf2mlEA9NqOcb4kQAIkQAJ+JqCTVqGNAGhsOH7ZgrBgCBMQAbNnz66EQbuQQz8fFq7NGQGdLpWzGce2lVsBEE9HgnNU0oQh1BFFQOwMVXHNYXLw0jF/SDf3cyqgGX2sOb+8IgAizx4+ZBuGgibmBPJ2LPHehmqi8Hw2LJh3l9MKxsYYZsFMdwHwgQceUHnazGYVUM1edbHMwxWtAIh5ouorqtbCew/enPg7BEGYEwEQhSMghOF9CwbBGfkLUZTCbKiei/sBwdDsNRrbd4/0o8VTAHQi0JlnnBEC4GOPPRbwiMZ7H85rZplR8MV4Hs6AOb+g03noJABa87xiDZEUSskMARA5Ojdu3BjA/PLLL0uPHj2cYtemnVUArFRJxFRYWpt5ciIkQAIkQAIkkAgEdNIqtBMAcQAgLiDk4sSJE+o8GHkBkZQb+bJoJGAloNOlisfuRCIA4oMN8swZ9vXXX9sW6kCFWVSahcFTDWKhXXhbogiAyP9n9vxy84EWIdII+zQMecyQG9BsfhUA8X6O3K5GVVisGYUt8HezAIZKqMjFBoPXZKxSQUQqAEKYRhgwQr/hkY4cZhA2EBbsxivV2GN40WIMwxDCb66ejTxkCEcEE4QkRuJNFul7UaILgGbBKrPFV0P0NfYuXHoGuz3WRQDEFx74N8Mo3IT5ougRvJ6zZcvm6ohmhgAIL278W2iYXY5WVxOPQ+OpU0VMBceFAmAcNoGPJAESIAESIIELBHTSKrQUAMEJ3jTIp7V37940xUFQNRhJvINVwuQJS1wCOl2qeOxCJAIgPpAhb5xRxKJOnToq56bV8AEOecgMrwiEbkKMt7NEEQCtVWcRugivQCeGCsrmyp4Ij0SeNLP5VQBEvkNUNjUbPKzgaWU25MQ7c+aM+pFVHHPC2K6NWwHwt99+UwVL5syZI1dffbW88cYbaXLI4TmRCIAnT55UoaWHDh1SU0VeQOSzM/IMGpVTERaN8OjMtEQXAM2FeoJVp87IvUB1bPN+h6oqHWoeugiA1qrGmPPDDz8ss2fPdo0xMwRAI+emMbk333wzTVEW15OOUwcKgHECz8eSAAmQAAmQQBACOmkV2gqA4IYPXhD84FljrhCMD49IuI/KizQSAAGdLlU8diQSARDzhBcaKmga+eKQ+wrFGAwRAuIfwp+Mao34MIoPbnbFEDBmogiAVk8RhIHiw7sTs1asRcVQVA41m18FwCeeeELwodpsqJprruSK1y677DI5ePCgahZpHrRge+FGANyyZYsKdcf7CwQ69L388svTDRuJAIhBrAUfzJWGEU6O50MwRaGYzLREFwCTk5OV4AtD0SEUBnLrrRbpfpnzD2IMfKmALxfcmi4CIPK7Ik+m2cKlebBba2YIgNYcgMhB3aBBA7f4496eAmDct4ATIAESIAESIIEAAZ20Cq0FQBBDsYLmzZvLrFmz0lQIRmgUPnBXrlw53QdnnrXEI6DTpYoH/UgFQMwVuegaNmyoqnDDENqKD50Q//DBc9u2beruwQsKXknIyRnKEkUAtH6wRY42hAs6MbMHIIQ/CIBW86MAiC914PWGCqmG2VXrhei1adMm1QxhsBCrIcY4MYQXwnMOgprVnAqAyDuIOeC9BecfFYWxb8EsUgEQYb7gYYRHotowBD8Uc0FOR3jomsMRnaw9Fm0SXQB86qmnVPEHwz744AN59NFHHaNF9AJyREZSBXjy5MlpPM7uvPNOFXbu1nQQAFFtHncDv8cZhqI2RkVot2vKDAEQ+XCNfJ6YHyqB33XXXW6nGvf2FADjvgWcAAmQAAmQAAkECOikVWgvABrUUAVy6NChaURA40MKqj3CK9D8H77FpSUOAZ0uVTyoRyMAwtMFSe8xBsIS8YEHOf5y5cqlQh4hRCAMDuKEE0sUARBhqWBmGL6UMIo6hOOEog+G1yU84lAR02p+FAAhouDLHMPgVYXzBpHDak8++aRMnDgx8GN4CEKQdlINGF6r8B5EAQ2rORUA4QmLPYDh3xMU6bCzSAVAjGetJj1v3jxJSUlRlV9R/AE54TLbEl0ARGVgcz5GiPRIgYDfNcIZ9q979+5pKsmG62N+HeIvCskYhmJN+BLGrekgACLUd+7cuYGpQ8hftWqVqmwdiWWGAIh8pOYCTchNWrBgwUimG9c+VgEQ312sWxfXKfHhJEACJEACJJCwBHTSKjwjAEKQQFjON998E/gAiGTyhlk/sCBhOgQLsyh44403hvVeSthT6fGF63Sp4oEyUgEQni1NmzZVIsSLL74Yk6nrLAAilA/iWyhzOn8UKYLoZxQrwphOvEVOnTqlil4Ylc7xngZPZqv5TQCERxU8q8yGENjnnnsu6HbAg8oaegchGjn4QoWgI7ywVq1aygMLKSSs5lQAhCiJir8wVAZdv3697bExC4DwFsI5cGrwikR/nAsY/p2C2Jg7d25V/CMe+W4TXQAEd3ibnjt3LrCNEK3gJY2CFnYGT96kpCRp1qyZoMhSJIbCNxCcjGfDixBnw4nwbX5evAXAV199VTp37pwGAb7ENReecssnowVAeOKiQIlh+LcC/2Z40d55R8T0/ZRQAPTiLnLOJEACJEACfiGgk1ahvQCIb92RfwyhXEZCeLuDYBYE0cb6CzO+fUZ4I0RBfKDr1KmTX85Uwq9Dp0sVj82AiAevIcOQRyqcoIf8Ykh4jvAsCA6x8prdtWuXlC5dOjCXAQMGBDypgrGBl5bZyylcfiiETKI4EAyeMhB07AwMULnVMBTeMBegwIdtvC/A29EwN/PHh1nzB32E/iE/aShDlXNUGYWhmiw+qAczcBs4cGCaeUEosjN4ahrCExgZHoaxOI9mMQzjIaeX2fsx3DPgyYeQXLOggi90kF/LTsw7e/asoFrynj170gx/9913y+uvv67ey82G9394ZUJkRH5YFLcxVxU22uJ8mSsu23nZwfNq+/btqhvOB8IC8+fPn26p+HcJvPElFSySsN2OHTuqNZkN+SSR5iKzLTU1NV3oarTvD+bzc80114T0jkM+SAi4hsEbD4KanVlFoXDtrcKYXfvGjRunCyHH3LFP999/f7rpIEwX6UogAuLsmd8D3e6htUjQzz//LFdddZWrYSBW1q1bN9AH7x14b3NiOMPIvex0D6xjjh8/XuVDNb7kwOvw/sXvcW6FTPPY+DcLuTHNhmdEM6Z5LIT5m7+MwZzxJZkXjQKgF3eNcyYBEiABEvArAZ20Cm0FQFQjhfBnfLg3xD3jFz2r2IfDEuyXwGCiIH6GtuYPo349bImyLp0uVTyYo1CBOVG8E/GgZcuWMhVxQiJKMMEYyH+E6qsQZXBH8H+IKPBCyZcvn2qHD6doZ2ebN29OEy6M8P1BgwbZtrcKgLj7qEhsZ5deemmgcio+rMGDzs6MSqrG66NHjw54pcCrBoU8EAJt9gpEJVZ4YRnWs2dPGTZsWNBHIFQMbc1iG6rZoqqtnT3yyCOqkApEJXzgND/L3AdhhEbxFfw8nAhjFgDBCJ5lsbJy5coFqkVjTKcCIPhAyETlaLNB8IUoGEygM7eDqA1xO5hBpMBZhLcUco199tlnAbEQYYcoWBPMcL4g1BqGiqsQJ61WtWpVQXVmw5DjEcVLzP/O4HX0NXIVoq1Z4IK3Iqo7hwufh6ANsdP8JRfGvOmmm2K1hY7HAUtrgS3kbIs0bBMPhuiO3IYwu5yXxgQhoJs9N5FXE+ztDGIbzqdh4cKmzQI8+thVecW4OF9Ii2A17DHuGwrCHDt2THmKGh6izzzzjAwfPtwx72ANrYVAEBaMwkFuzBrGjHuCcFYnZi2EEU5UNcbEFyr9+vUTvM+a7fHHHxeIguHue7i5QWS1ckCov5PQ7HBj4/V3331XibiGTZ8+3fb9x8l48WxDATCe9PlsEiABEiABEkhLQCetQisBEInh4cGBXx6NfEvBhD+E5uDDMT5k4xth/OKNDxf4D+Ea4TwBKQD670rqdKkymy6EbISimhOXQ7xArqVQBmEikmTsEEAgoMATJlhRBuR4MyfMxwcqQ2gMNh9rqBhEOzvvXBRmwIc9445D6MK67TxArF4wCKuE1x76Ie8evMjMnpOYHwRBeKcZBsHOnLfOugZ4KcMTEUIADPODFxO8jK1mfCjHfCFUhPKiA2OIVYZB4DJ7rlnHhjenIUJgfHzYN4ezRXousS6sCR55hmHP4LVmZ9gThOAixNfwjENbCCYjRowI6dFlHdOaCzDcOjp06JCmgIO1Pf596dq1a+DH+LNZaDVesIZg4+fwPkRYMrxmkesNHpfIFYcvqgyvVLSD5ym8tiAQoogHQr7DGURRI2chhGkj/Dhcv1i/bhVIMb6dSOrk2Tg3hQsXDhQ6Qd5HnCmk6Qhm1v2BBy/yMdqZ1aNv8ODBKqWBneHemQXFUF9Q4H0LX5Q4NQiG2PNIin+Yn4H3FPP7B8RyhL+7sS5duqiiTWbDFxXwVg1leLFa+JcAACAASURBVI9FBW7zF6QI38e9sjOIxniPxBc9+LNhEJIh/jdp0sTN1G3bWr/QQcMlS5ak8RiN5kFm4RX/VuBLFCd3N5pnZlRfqwCIbYezfAgn8oyaCsclARIgARIggYQnoJNWoYUAiA+LEBPwgRzVG+28/fAtP7xxULDA7ptkfANtiIGGMAiR0FyFDieQHoD+uoc6XarMJAvvB9yJYMUOIKLhwy1El2Bm5P+L1BMWnjwIE4P4aBj24aGHHpJvv/028DN4ui1evFh5zFgNXjYIhzV70MEbD146CME0G74ggOhkrbQLDzOsE56LVoOXH7xZglXZxfwREmzmg/cieB/i54ah6jFEQXM4nfU5+NAP0dMQu+AtiUIUCCPEc7A+vL9BSMEHS3iG2YU14v0PXlAQmsweYQijhOgEMcVsCIGDqGEVCFBc5JVXXokqhxyEGoiU5kT+xrMLFSqkquRCBDYEQvBDtV7svzn8D95tqCKNOYGNG8M4yCVmDZENNgaEBog2dv8+4AsihF2bzxvmA64Q98x26NAh5YFnFjTMr0PkgViFdUGYRWix2SB+wGPJaSgoCj1AkMZ6cZ/dCE9ueIZqi/sMj0vsodkgCKE6LUJf3Xhx4fwijB1CsNkg6kJ0teY3xPsBnmEO+8b9hKAezBsSoivEevP7DUJl8d4GEdVqENZwrxA2bBi+DMD+B2uPNvhSEh5sZgE8GEPcBXhhg1UsDAIg5guzKxQU7DmYJwrI4P3AXG0bbfElAd6D7Lw5UZUagnawNAbGfYeAiPcgPAd3w7jv5i9e8d6Ne4E5xEJAw9gQ+vC7HwRKs+FZuC9YU7ShwPhSecGCBWp4L4f/rl0rAr34wvEJ4MqSRQTO9QMGiCQlxeKUcgwSIAESIAESIAEnBHTSKuIqACJUEB8C8Msq8g7ZCX8I8+vdu7cSFiIx/KIKEdAsDOIXa+svkpGMzT56ENDpUmUGEYTrfvfdd+oDmFlosT4bH4jwARqiB8KbrAYPJniaQTiPxBo2bKjyOuEDO+5pqLBTeKMhtA8CDbxC4KkT6g7iQybGxtwR7on7i/eJYAYxBusI5qkHcQ7hoPhywTB8WEf+OSPvIc7PrbfeqirH2vGEUASvsG7dugWdAzzuIETOmDEj4O1kbgghFLwwhl0hEnCBx1OwsEOMhZBsCAzwCMKHVXDBObBrD/ESH5DRxo3BgxRnAoKM9csTu3EwN4ib2GeIMHgumEIci0UoKz6Y498BhGhbDQIkvMQgOgTLKQiPcqwJ+2tnECoQOj9kyJBAE/w7AYHJWggA4gxEB5wjmDV0EOvGWQznbWWeC8QXiMU4yyhCAZaZYWCDnHsogBAuRBQiO8L/ccaDVW42zxcVYCHkW0Uoow3OJjjB0xHvQ/C0Nd9R69rxfgDhEF5mYIvQa6f7iTBffFECxnaGc4vQdszbarg/iDqAEGmNMMB+YS44f27F7VD7a64yft1118kPP/wQ8jggxBZFNsDEKChj1wG5LFHMBCkIIIBCPMM+ubnv8OTEexq4oVq8+b7bpTVwe54hqEL8xL8T5mJLwcbBfcF7I0R3t/kSMR7e93HGjDMCMRwFXbxms2eLwOHS5p9KtRx8V4aUwSZHd68tk/MlARIgARIgAU8R0EmriJsAiA8cCGmD2Ql/NWvWVKE8Vs8MT+02J5spBHS6VJmy4Bg9BLntIMTBAwZiIQQ8fABC7jZ8IIR4Dg9B/B9CED6gozAC8u4ZxQIgHATzvovRFGMyDOYILzYIKxCj4HUYqpJsNA8FNwgaEEXxYRzCUtmyZZVgEkuBIJo5erkvxBh8ODfC8yCOwLvUjWeam/XjnEPIwpdICFuFKBDMgwoh97gXKFIALza33kgo+AGvXfxnDd10M1+2zTgCeP9AuDc8iiGAobAGfk8JVhwm2lng3OF9w/BWRQGPUEWAon1eovfHF0X4kgAGL3Bz+gWvsIHnH5YQSvwz1gIRcPVqegJ6ZW85TxIgARIgAW8T0EmriJsAaP7wbS7sgZ/jG3gIfwhXoZGAEwI6XSon89WhjVF8A1528ExzY8hvdtddd6kPp/hQbC0a4GYstiUBEhBVDAbejRA4rZVOyScxCSCUuVGjRmrx+LIGXoy0jCFg5EzE76AQ8uGd6jVDbSNT2tiw00f7MEXrw47BBiRAAiRAAiRAAuEJ6KRVxFUANAt/8CBCTiwk6DfC8sKjZAsS+IeATpfKC3uC4h8Q8FAIBN5qkRg+jKKgA8LNok16H8nz2YcE/ELAqG4KL1H8mUYCBgFESyD0GOHkCNfW3dvaizuHFAoIG0YIPFI8WKuWe2FNu3eLlC6NiBrns0VOwF27RMLUhXE+IFuSAAmQAAmQAAkEJaCTVhFXARB0EFKFHC/Ir4P8RzQSiISATpcqkvlnZh982IG3EULKQlXcDTcneA0iFA6hUzQSIIHICSD/3ezZs1V+TFTNppGAQQAe1uXLl1fpF6Kpxkyi9gRQLAnCHwrNoQAUcht6zd5+W6R1a/ezRj8Xha7dP4A9SIAESIAESIAEtHJWipsAiMTTyHWEpOtIRk0jgWgIUAB0Tm/48OGBUDJU6OzXr5/zzhdaosI2ClBMmzYtEKLmehB2IAESUDnlUCgFBQyQN5LetDwUVgLwAEQVcuQQRfE0flkauzOCat8oYIJctytXrgwU9ondEzJnpDFjRLp0cf8s9OvUyX0/9iABEiABEiABEnBOQCetIm4CILyQ8uTJ45waW5JACAI6XSrdNwqVeOdfSPwDD9zp06erKrlODQUY0B45Or2YKN3pOtmOBGJFAEV02rZtK3PmzFFVuRs0aKDybiLnH74IQ+jhyy+/LD169IjVIzmOzwigKnDr1q1VxeZFixZlWBEjn2ELuRwUoEM1dRSkQ6Gf6tWre3b59AD07NZx4iRAAiRAAglAQCetIm4CYALsM5eYiQR0ulSZuOyIHtW/f3958cUX0/RFLkCEHqLi9jXXXJOuoioqAyMx+owZM+Tdd9+VW265RT799NMMqX4Z0aLYiQQ0JoB789hjj9nO8Prrrxd41TK/m8abqMHUpkyZIu3atZNWrVrJpEmTNJiRt6eA1DMTJkyQ9957T+qhIoaHjTkAPbx5nDoJkAAJkIDvCeikVVAA9P1xS4wF6nSpdCf+119/KQ+kBQsWBJ1q9uzZpUiRIspDF2FRJ06cUPmnDGvSpIlMnjxZcufOrftSOT8S0IKAkWMs2GQKFSqk8mhed911WsyVk9CbwLJly6Rx48aSnJwsr7/+erova/SevR6zg0cuqv4uWbJEeeUiJ64fjFWA/bCLXAMJkAAJkIAfCeikVVAA9OMJS8A16XSpvIAfoU8oONC3b1+VlNSJ3XDDDYKcgfjgSSMBEnBOYOvWrSq3WGpqappO8LadO3eubwQI50TYMhoCv/76q3To0EEOHz4ss2bNUl/Y0JwR2LZtm/KihNft0KFDpWDBgs46eqDV2rUiVauKWN5mgs48Rw4RFBxPSvLAwjhFEiABEiABEvA4AZ20CgqAHj9MnP4/BHS6VF7aE3j4ffLJJ8obcN26daoy8LFjx5RXCQr1XHnllVKtWjW57777VH6krFmzeml5nCsJaENg8eLF0qtXL9m+fbuUKlVKmjVrJp07dxbk4aSRQCQEUBwEntp33nlnJN0Tss/7778vN910kxIA/WizZ4s0aRJaBIT4l5Iiwu/y/HgCuCYSIAESIAEdCeikVVAA1PGEcE6uCeh0qVxPnh1IgARIgARIgARIIAYE4AnYrp3Ixo1pB8uSRaRuXZH+/en5FwPMHIIESIAESIAEHBPQSaugAOh429hQZwI6XSqdOXFuJEACJEACJEAC/ibw1lsibdv+u8ZSpUQ+/1ykRAl/r5urIwESIAESIAEdCeikVVAAzMQTcuDAAUEYGPL3lCxZUurWrSv58uWL2QxQqXXhwoWyd+9eKVasmBrfbW4ghIAiJBRjlS1bVmrXri05c+aM2RwzaiCdLlVGrZHjkgAJkAAJkAAJkEA4AlYBsHJlkW++CdeLr5MACZAACZAACWQEAZ20CgqAGbHDljH//vtveeGFF1TC6WuvvVZuueUWWbNmjcpbN3r0aGnTpk3Usxg3bpz07t1b5W2rWrWqbNmyRTZv3iwDBgxQeafC5W47ffq0dO3aVSZOnChJSUmqIuXy5csFP580aZLUQ3k5B3bo0CEpUaKEnDx5Mmhr5L5CDqxs2bI5GM15k0y9VLt2iXz4ocivv4o8/LDIHXc4nyhbkgAJkAAJkAAJJB6B8+dF5swRWblSpFw5kUaNRDKoCMnkySKPP/4v4ttuE/n668RDzhWTAAmQAAmQgA4EMlWrCLNgCoAZfCJQbbVJkyaCxNOo2vf6668rMQ6iYPPmzWXGjBkyZMgQJd5Faj179pThw4fLgw8+KO+9957kypVLDYUxhw0bJu3bt5fx48fbDg+Rr1atWrJq1SoZPHiwPPvss6otfn7//fern0+bNk2aNm0adop9+vRR67GzMWPGSKdOncKO47ZBplyqnTtFBg8WeecdkbNn/5kikuq8/77Io4+6nTLbkwAJkAAJkAAJJAqBXr1EXn7539UWKCDSpcs//8VYCKQAmCiHiuskARIgARLwAoFM0SocgqAA6BBUpM0GDRokffv2VZ5/33//vaquahjCbBEKjKqrS5YsUSKcW5s+fbqqJlmwYEHZvXu3FMAvlBfs7NmzypNvx44dMmHCBHniiSeCDt+uXTvl5Xf33XfLsmXL0rT56aef1BgQLdevXy833HCD7RSxjuLFi0vhwoUlB8rMWSx37txKTLz44ovdLjNs+wy9VMGEP/OMbrlFZN26sHNkAxIgARIgARIggQQk8OefIpdcInLmTPrFZ4AQSAEwAc8Yl0wCJEACJKAtgQzVKlyumgKgS2Bumu/Zs0cJf6mpqfLKK69IF3zLazF450GcgxCIsF2IZE7txIkTUrp0aUHYbefOnVU4sdUQdgyPPgiDW7dulSuuuCJNk6+++kruuBDCOmfOHHnooYfSjQEvQAiUVapUEbS3sxdffFGmTJkiP/74Yxqh0+l6ommXIZcqnPBnTBg5Ek+fjmb67EsCJEACJEACJOBXAijJW6FC6NXFUAikAOjXg8R1kQAJkAAJeJFAhmgVEYKgABghOCfdnn76aRk7dqxqClGsTJky6brNnTtXHkYeORElnrVq1crJ0KrNiBEjpEePHurPKNxx7733puu7YcMGqVixovo58gE+//zzadogtx8Kh8AzER58efLkSTcGhEXkB4R9/vnnUqNGjXRt/vjjDyViIoTYztPQ8cIiaBjTS+VU+DPmSQEwgh1jFxIgARIgARJIEAJOBEADRQyEQAqACXKuuEwSIAESIAFPEIipVhHliikARgnQrju8/uBtd+TIEcmfP7+qqpsF+eIstn//frnyyivVT++8805ZvXq14xndeOONKqwY9vvvv0uhQoXS9UWuwbx586p8fldffbXAK9GYB6oR49nnzp2Tm266STZt2hT02V9++aWaG+yxxx6Td999N107iJEvvfSS7Ny5M+g8HC8qwoYxuVRuhT9jrhQAI9w1diMBEiABEiCBBCDgRgA0cEQhBE6aJNKu3b9cq1QRCRHAkQAbwCWSAAmQAAmQQPwIxESriNH0KQDGCKR1mJUrV0r16tXVj8uXLy8b8cufjUEgRDgvKuMeP348qBeetevPP/+s8u3B0B/ee3aG56MiMGzbtm0qLBk2depUadmypfpz/fr1Zd68eUGHOHz4sMrrBytatKhAtDQbxEVU9z1w4IDKFYg8gShI0qJFi6BejxmBPKpLFanwZyyEAmBGbCnHJAESIAESIAF/EIhEADRWHoEQSAHQH8eGqyABEiABEvAHgai0ihgjoAAYY6DGcKjKi+q8sDp16qgwWzuDILd9+3b1MjwADW+7UFObOXOmNGzYUDW5/vrrA56AwfqguMjSpUvVS/Degxcf7KmnnpJx48apP//f//1fIFw52Bg5c+ZUuQxhOMCG1yL+jsrGHTt2TNcNYiBCmlEV+LLLLssg0v8MG9Glilb4M1ZEATBD95aDkwAJkAAJkICnCUQjABoLdyEEUgD09Gnh5EmABEiABHxGICKtIoMYUADMILBPPvmkTJw4UY3etm1bVWXXzipXrixr165VL7/zzjvKcy6cQVTr06ePanbPPffIp59+atsFQiEEQ9gLL7wg/fv3V3++7777VO5AGAp4oFqxnUHAO3jwoHp5xYoVctdddwWaYr4//PCD7Nu3T3755Zd0Q1x11VWCAiO33npruGVF/LqrS3XihAjyGP7vfxE/L01HhHYHye8Ym8E5CgmQAAmQAAmQgKcJ/PWXyJ49sVlCtmwiycki778vEiS1DB5CATA2qDkKCZAACZAACcSCgCutIhYPDDEGBcAMAowQ2I8++kiNbleh13h0tWrVArn/xowZI506dQo7K4z56quvqnZ4FoqJ2Fnz5s0DeftQzGPUqFGq6c033xzI+2dXpdgYE/kDcXBhCBVGyHAwg0gIb0cIlCh8YljBggXl66+/lrJly4ZdW7AGxrPtOkN4hJAKQ3g0REdbK1FCZO/eiObBTiRAAiRAAiRAAiQQdwIPPSQyZ07QaVAAjPvucAIkQAIkQAIkECBAATABDgMq8hpeeb1791aCmJ2ZBcBwnnjGGO3atQt4FTZu3FhSUlJsxzcLgGZvRFQl/umnn1S/N954Q+C1aGdmAXDatGnSrFmzkLt45swZee2116RXr15y9uxZ1TYpKUm++eaboMVQwh2JYAVU7PqEFADhoVisWLjH8XUSIAESIAESIAES0JdA7twiJ08Gnd+bb4o88cS/L91+u8iXX+q7FM6MBEiABEiABPxMgAKgn3f3wtruv/9+WbJkifpbv379ZODAgbarrlKlihLGYMOGDQvkDgyFqX379jJhwgTVBAIfCnrYGQTC9xEqIiIdOnQI5P277rrrVFEQ2OTJk6VNmza2Y6CiMaoGwzCWkX8w3FYi9LhJkyaq0jDs448/VqHHbi1mAiCKpRQs6PbxbE8CJEACJEACJEAC+hDIl0/k+PGg86EAqM82cSYkQAIkQAIkQAEwjmfgiy++UN5umzZtUp5p11xzjQqhhYiWI0eOmM3s0UcflVmzZqnxUAwEwp6dmUNxx48fLxD3wlmPHj1kxIgRqhnEOEPgC9bPHI4Mj7yhQ4eqZvDIW7dunfozioFAHLSzAgUKqArFsMWLFwsETqdmLojy9NNPK89AtxbTEGDkIvz2W7dTYHsSIAESIAESIAES0IMAiq9dSAVjnRAFQD22iLMgARIgARIgARCgABincwAh6tlnn5Xz58+r//Lnz6/+f+LECSlVqpTMmDFDbrvttpjMrlu3boK8ejBU20WlXDsrXry4ylsHQ/48VA0OZ8j/hzyAsLp168qCBQtsu6Bgx6pVq9TrY8eOVRV/YcnJyao4BwxsnnnmmaBjwHsve/bsihXs+++/V5WHnRqEVvDFwQ83V6djWtu5ulTwRkQI8+zZIhcqG0f6XNUva1YRF4JoVM9iZxIgARIgARIgAW8RwBeoq1fHZs6oBtytm8iFgm7BBqUAGBvUHIUESIAESIAEYkHAlVYRiweGGCNhioBs3LhRKlWqFBCx8uXLJ4cPH5Zs2bKpCrwvvfSSfPbZZypEtWrVqlFjf+utt1T1X9hDDz0UENqCDZwnTx45deqUemn37t1SAkUqwtiyZctU9V9YhQoVZP369bY9ypUrJ1u3blWvL1++XKpXr67+jGrAyDkI69KlS0CwtA6E0F+EAMNy5swpx44dU/93Y0bRknAFS9yMaW4b0aVCNeCxY0XgSfn775E+GlBETp+OvD97kgAJkAAJkAAJ+JfAxo34ZS269V17LXLKiDRuLHLRRSHHogAYHWr2JgESIAESIIFYEohIq4jlBExjJYwAiHBZhOIilxw82fB/eNuZQ1lHjx6tPOE2b94shQoVigr5jh075D//+Y8aAyG+GzZsCDregQMHpGjRouo1VK41PAHDPfzPP/+UIkWKyOnTpwXhuUePHg3aBWvNmzevEhgR4nzkyBGB4AiD4FmzZk3151DCHKr33o4M0iJKHDW8CcPN0fw6QpvheRhKaHQznrVtVJcqWiGQAmA0W8e+JEACJEACJOBvAtEIgC6EPwPixIki5rpud9wh8sUX/kbM1ZEACZAACZCArgSi0ipivKiEEQDfffddadGiRRoBcOTIkUqQMlvXrl3l77//ljFjxkSNunz58kpMRPgswoyDec2ZPfngJQcR0qnVr19f5s+fr5pDcCxdunS6rjt37lR5DmFWkQ+huZdffrnyhESV37179wZ9tNmbEWHNVmZO5ou8f506dZLPP/9catSo4aSLqzYxuVSRCoEUAF3tFRuTAAmQAAmQQEIRiEQAjED4owCYUKeKiyUBEiABEvAIgZhoFTFaa8IIgPCUu+WWW+SHH35Q6CDGISz4WvyCZTJ41iFkFq9dcsklUWGeNGmStGvXTo2xdOnSgLededBBgwZJ3759JWvWrCq3HirzOrVPP/1U7r33XtX8zTfflMcffzxd1+nTp0sz5LsTUVWJa9WqlaYNno05wLZv3x7wWjQ3whqwlosvvlj27NkTkXdky5YtVcXhr776SomwsbaYXiq3QiAFwFhvJ8cjARIgARIgAf8QcCMARiH8GcDoAeifo8OVkAAJkAAJeJ9ATLWKKHEkjAAITn/88YfAmw1hsI888ojceOONQfGlpKQoIa5ixYpR4U1NTVX5+SA6QkSbiN/ILIa8hMjf16pVK5kyZUqaV1euXClNmzZVwtvcuXODioMI4UUoL4TATz75JN34RqEPeN3B+85qv//+uyro8dtvvykhsE+fPmmaYA0oUoI8gM8//7wMGDDANROIhrfeequaJ7wiM8Iy5FI5FQJz5xY5eTIjlsUxSYAESIAESIAEvE5g82aRcL//xED4owDo9YPC+ZMACZAACfiRQIZoFRGCSigBMEJGUXX79ttvVf48FBvZsmWLqoZr2AcffCCNGjVSIbpr1qxJ51mHfsi/B2vSpImqUmw15AyEqIYcgF9++WUgVx/awdsOOfvgyYjxg4UIo91HH32kwoMRDoxiIQULFgw85uWXX5ZevXpJlSpVZMWKFSqPoNlQNAX5/SpXrqyqCFvDnA8dOqSqDUNYNOdbjApqkM4ZeqnCCYEoGnOhynKs18XxSIAESIAESIAEPE4gNVUEUSXBviyMofBnUKIHoMfPC6dPAiRAAiTgKwIZqlW4JJWwAiBCgnPlyuUSV2TNFyxYIA0bNlSedK+//roS4mbOnKk86hCCPG/ePClZsmS6we+44w4l4sHgCYhw3mAGca9OnToq1+C4ceOU5yK8ASHcoUAIvAfhiRjKEELcoUMHSUpKklGjRknhwoVl8uTJguIpqBo8a9asoKG/CO2dOnWqGhpC5nPPPSfVqlVThVYQogyvP6zTztsyMqLpe2XKpQomBML7b8kSkWrVYrUUjkMCJEACJEACJOA3AuPGiTz11L+rygDhzxh8wgSR9u3/fRRq0i1dKhLkV02/UeZ6SIAESIAESEA7ApmiVThcdcIKgMWKFZP9+/c7xBR9M+S/Q4gtwnAPHjyowm5bt24t7du3V8JdMFu9erXy/EMVX7sQYKPfvn37ZMiQIbJo0SK1LngaPvbYY6pgB0KIndjatWtl8ODBSnQ8fvy4EhIxP+QQtMvbd+zYMVVdGZ6AeG7u3LlVNWOIl40bN5Z77rknQ3L+WdeTqZcKQiDCqQ8cEKldW+Tqq53gZRsSIAESIAESIIFEJrBp0z8RA8j3XL26yEUXxZzG2rUibduKIOrYbEi/XKeOCDK5JCXF/LEckARIgARIgARIwIZApmoVYXYhIQVAiFZFihSRM2fO8JD6hIBOl8onSLkMEiABEiABEiABDxGYPRspY0QQcWxnyOSSkiKSnOyhhXGqJEACJEACJOBhAjppFQklACLs96efflL56BYuXCjnzp3z8DHi1M0EdLpU3BkSIAESIAESIAESyEwC8PxDSuJQ4p8xH4iAq1fTEzAz94fPIgESIAESSFwCOmkVnhQAUbQC+fA2btwoBw4ckJMnT7oS85CfDiGtFAD9cwl1ulT+ocqVkAAJkAAJkAAJeIFAvXoiCxc6nynaz5/vvD1bkgAJkAAJkAAJREZAJ63CUwIgKso++uijsnLlSkUeQl6kRgEwUnJ69tPpUulJiLMiARIgARIgARLwI4Hdu0VKl8bvxc5Xh5yAu3aJlCjhvA9bkgAJkAAJkAAJuCegk1bhKQGwZs2asmzZMkXcKEoRqQhIAdD9wdW5h06XSmdOnBsJkAAJkAAJkIC/CLz9tkjr1u7XhH4tW7rvxx4kQAIkQAIkQALOCeikVXhGAFyzZo1UqVIljfCXL18+qVChghQtWlRy5crlqNosqtsi/x8KgDAE2Pmh1b2lTpdKd1acHwmQAAmQAAmQgH8IjBkj0qWL+/WgX6dO7vuxBwmQAAmQAAmQgHMCOmkVnhEAx48fL0899ZQS+S666CJ59dVXpW3bturPbm3BggXy4IMPUgB0C07j9jpdKo0xcWokQAIkQAIkQAI+I0APQJ9tKJdDAiRAAiTgKwI6aRWeEQCHDx8uvXr1UgLg008/LWPwtWUUVqBAATl27FgUI7CrTgR0ulQ6ceFcSIAESIAESIAE/E2AOQD9vb9cHQmQAAmQgLcJ6KRVeEYAXLp0qdx3331KAJwyZYq0aNEiqlOAYiIzZ86Magx21oeATpdKHyqcCQmQAAmQAAmQQCIQyJFD5MwZ5yvNnl0kNdV5e7YkARIgARIgARKIjIBOWoVnBMC///5bypQpI7t375aXX35ZunfvHhl99vIlAZ0ulS8Bc1EkQAIkQAIkQAJaEli9WqRaNfdTQ787qzu50gAAIABJREFU73Tfjz1IgARIgARIgAScE9BJq/CMAAi8KN7xwAMPyH//+1/59NNPnRMP0nLChAny5JNPRjUGO+tDQKdLpQ8VzoQESIAESIAESMDvBBo1EvngA/erRL/33nPfjz1IgARIgARIgAScE9BJq/CUAAjE/fr1k8GDB8vKlSvlzii+tkTl4F9++cX5rrGl1gR0ulRag+LkSIAESIAESIAEfEXglltE/vc/90tCv3Xr3PdjDxIgARIgARIgAecEdNIqPCcAAnO3bt1k3rx5snbtWilUqJBz8hda7t27V0qVKsUqwK7J6dtBp0ulLyXOjARIgARIgARIwG8EqlQR+eYb96u6/XaRL7903489SIAESIAESIAEnBPQSavwjAAI0Q75/wx75ZVXZM+ePTJq1CjJmjWrI/pnzpyRn3/+WYYNGyY//vgjBUBH1LzRSKdL5Q1inCUJkAAJkAAJkIAfCPTpIzJkiPuVoN+gQe77sQcJkAAJkAAJkIBzAjppFZ4RAIcMGSJ9+/ZNQ/n8+fOqKrBbM/qdO3fObVe215SATpdKU0ScFgmQAAmQAAmQgA8J4PvxUqXcLwz9SpRw3489SIAESIAESIAEnBPQSavwjAAIj71y5coJxDvDIP6Z/+58C0QJhxQA3RDTu61Ol0pvUpwdCZAACZAACZCA3wjcfLPIpk3OV4X2GzY4b8+WJEACJEACJEACkRHQSavwjAAI1LVr15YlS5Yo8S5S4c8sHlIAjOwA69hLp0ulIx/OiQRIgARIgARIwL8E3nlHpFUr5+t7+22Rli2dt2dLEiABEiABEiCByAjopFV4SgBctGiR1KtXT3LlyiVNmzaVsmXLSr58+SRHjhwqD2CocGAIhn/88Yds27ZNZsyYIUePHqUHYGTnV8teOl0qLQFxUiRAAiRAAiRAAr4lUK+eyMKFzpeH9vPnO2/PliRAAiRAAiRAApER0Emr8JQACNwQ/f7v//5POnfuHBl9Efniiy/krrvuogAYMUH9Oup0qfSjwxmRAAmQAAmQAAn4lQBy+ZUuLWLKkhN2qUihvWsXcwCGBcUGJEACJEACJBAlAZ20Cs8JgK+99pry5Hv22Wej2oaCBQsqL0CaPwjodKn8QZSrIAESIAESIAES8AIBhPO2bu1+pgwDds+MPUiABEiABEjALQGdtArPCYAQ/yZOnCjdunVzyz1N+w8++EAaNmwY1RjsrA8BnS6VPlQ4ExIgARIgARIgAb8TGDNGpEsX96tEv06d3PdjDxIgARIgARIgAecEdNIqPCcAOsfMlolEQKdLlUjcuVYSIAESIAESIIH4EqAHYHz58+kkQAIkQAIkEIqATlqFLwTATZs2yYoVK2Tnzp1y5MgRVSSkZMmSkpSUpHL9Zc+enSfS5wR0ulQ+R83lkQAJkAAJkAAJaESAOQA12gxOhQRIgARIgAQsBHTSKjwtAKKa77Bhw+S7776zPWSoEty8eXPp3r27EgVp/iSg06XyJ2GuigRIgARIgARIQFcCrAKs685wXiRAAiRAAolOQCetwpMC4KFDh5So98knn6izdN5U9iwLyppdMOPn+FmOHDmkZ8+eMmDAAMmaNWuin0HfrV+nS+U7uFwQCZAACZAACZCA1gTWrhWpWlUkNTX8NHPkEFm9WiQpKXxbtiABEiABEiABEoiOgE5ahecEwN9++03uuece2bJlixL+DMHPLALabQ/a1qpVS+bOnSs5c+aMbhfZWysCOl0qrcBwMiRAAiRAAiRAAglBYPZskSZNQouAEP9SUkSSkxMCCRdJAiRAAiRAAnEnoJNW4SkBECLf7bffLmvWrFHCnyH65c6dW8qXLy8VK1aUK6+8UgoUKCB58uSRkydPyuHDh2Xr1q0qR+Avv/yi+jVp0kTefffduB8ETiB2BHS6VLFbFUciARIgARIgARIgAecE4AnYpo2INTsOAmTq1hXp35+ef85psiUJkAAJkAAJRE9AJ63CUwLga6+9Jp07dw6Ifyjy8fTTT8sjjzyiBL9wNn/+fNUeGzBv3jyph4QpNF8Q0OlS+QIoF0ECJEACJEACJOBJAq+9JtKp079Tv+46kY8/FilRwpPL4aRJgARIgARIwNMEdNIqPCUA/uc//1GVfvPmzStjxoyRNviK06Xt27dPKleuLFdffbV8/fXXLnuzua4EdLpUujLivEiABEiABEiABPxPwCoA3n23yLJl/l83V0gCJEACJEACOhLQSavwjAC4YcMGqVSpksrdt3z5crntttsi3tu33npL2rVrJzt27GBl4Igp6tVRp0ulFxnOhgRIgARIgARIIJEIUABMpN3mWkmABEiABHQnoJNW4RkBcPr06aryb9++fWXgwIFR7fGvv/4qRYsWlRkzZkjjxo2jGoud9SCg06XSgwhnQQIkQAIkQAIkkIgEKAAm4q5zzSRAAiRAAroS0Emr8IwAOGLECOnVq5eq/lu2bNmo9vbYsWNyySWXyMsvvyzPPPNMVGOxsx4EdLpUehDhLEiABEiABEiABBKRQLAcgIsXi5QsmYg0uGYSIAESIAESiC8BnbQKzwmAJ06ccFTwI9QWf/HFF1KtWjUZPny4dO/ePb6ngU+PCQGdLlVMFsRBSIAESIAESIAESMAlAVQBbtVKZMuWtB1RBbhOHZEBA1gF2CVSNicBEiABEiCBqAjopFV4RgCcOXOmCtf9/vvv5TqUM4vCkpOTVRXgadOmSdOmTaMYiV11IaDTpdKFCedBAiRAAiRAAiSQOARmzxZp0kQkNdV+zTlyiKSkiCQnJw4XrpQESIAESIAE4klAJ63CMwLg/v37VeXenj17ypAhQyLeP/R97rnnJEuWLLJ+/XopX758xGOxoz4EdLpU+lDhTEiABEiABEiABBKBADz/qlYNLf4ZHCACrl5NT8BEOBdcIwmQAAmQQPwJ6KRVeEYAxLbVrFlTvvzyS1m6dKnceeedrnbywIED0qlTJ/nwww/l/PnzUq5cOeVNSPMHAZ0ulT+IchUkQAIkQAIkQAJeIVCvnsjChc5ni/bz5ztvz5YkQAIkQAIkQAKREdBJq/CUALhq1SqpUaOGZM+eXbp27SpPPvmklAyR0fjs2bOyYsUKSUlJEVQRTk1NVeIfvP/eeustadmyZWQ7yF7aEdDpUmkHhxMiARIgARIgARLwLYHdu0VKlxY5f975EpETcNcukRIlnPdhSxIgARIgARIgAfcEdNIqPCUAAnW3bt1k9OjRSsSDlSlTRoXxFi5cWPLnzy8nT56Uo0ePyrZt21TF4FOnTql2EP5g6FenTh2Zz6893Z9cjXvodKk0xsSpkQAJkAAJkAAJ+IzA22+LtG7tflHox+/C3XNjDxIgARIgARJwQ0AnrcJzAiCEvHbt2ikPPkPQCwbfEPzMbfCzWrVqyZw5cyR37txu9oxtNSeg06XSHBWnRwIkQAIkQAIk4CMCY8aIdOnifkHo16mT+37sQQIkQAIkQAIk4JyATlqF5wRAA/PkyZOld+/e8vvvv4cUAvEihL9s2bKpsOFBgwapEGKavwjodKn8RZarIQESIAESIAES0JkAPQB13h3OjQRIgARIINEJ6KRVeFYAxCE6duyYjBs3TnkD7tixI+i5ypUrlzRq1Ei6d+8uN954Y6KfPd+uX6dL5VvIXBgJkAAJkAAJkIB2BJgDULst4YRIgARIgARIIEBAJ63C0wKg+Uz9+OOPsm7dOtm3b5+cO3dOChUqpCr9Vq5cWXLmzMnj53MCOl0qn6Pm8kiABEiABEiABDQjwCrAmm0Ip0MCJEACJEACFwjopFX4RgDk6UpsAjpdqsTeCa6eBEiABEiABEggswmsXStStapIamr4J+fIIbJ6tUhSUvi2bEECJEACJEACJBAdAZ20ioQVANesWaO8A2n+IKDTpfIHUa6CBEiABEiABEjASwRmzxZp0iS0CAjxLyVFJDnZSyvjXEmABEiABEjAuwR00ioSVgC87LLL5LfffvPuKeLM0xDQ6VJxa0iABEiABEiABEggHgTgCdiqlciWLWmfniWLSN26Iv370/MvHvvCZ5IACZAACSQuAZ20ioQUAJEvEPkBkSuQ5g8COl0qfxDlKkiABEiABEiABLxIYPRoka5d/5359deLLFokUqKEF1fDOZMACZAACZCAtwnopFXEVQD8+++/ZcqUKbJhwwapWLGitGrVSrJmzRp0d7/++mtJdZLYJMTZOHXqlKoWPHLkSNm9ezcFQG/fozSz1+lS+Qgrl0ICJEACJEACJOAxAlYBsGZNkaVLPbYITpcESIAESIAEfEJAJ60irgJg69atZerUqYFthQA4efLkoNtcvHhxVeE3Fnb+/HnJkiULBcBYwNRkDJ0ulSZIOA0SIAESIAESIIEEJPDKKyLduv278HvvFfnkkwQEwSWTAAmQAAmQgAYEdNIq4ioAFixYUE6cOKG2BKJc/vz55ejRo0G3qGPHjjJ27NiYbR8FwJih1GIgnS6VFkA4CRIgARIgARIggYQkQAEwIbediyYBEiABEtCUgE5aRVwFwAceeEAWLlwY2KYHH3xQ5syZE3TbfvjhB7nhhhuU554hGEazvxQAo6GnX1+dLpV+dDgjEiABEiABEiCBRCFAATBRdprrJAESIAES8AIBnbSKuAqAx48flyFDhsjmzZulQoUK0qtXL8mXL5/tHv73v/+V5cuXS8mSJaVBgwZSrFgxyZMnj2TPnt02d6AxGDwMT548KSgA8vbbbyvPQxYB8cJ1cTZHnS6VsxmzFQmQAAmQAAmQAAnEngAFwNgz5YgkQAIkQAIkECkBnbSKuAqAbgHOnDlTGjduLDt37pQSUZQy+/bbb6Vy5coUAN1ugMbtdbpUGmPi1EiABEiABEiABHxOYNQoke7d/10kcwD6fMO5PBIgARIgAa0J6KRVeEoAPHv2rBL+vvnmG7nqqqui2uQiRYrIoUOHohqDnfUhoNOl0ocKZ0ICJEACJEACJJBoBKwCYK1aIkuWJBoFrpcESIAESIAE9CCgk1bhKQEQ25eSkiJVqlSRUqVKRbWbH374oTzyyCNRjcHO+hDQ6VLpQ4UzIQESIAESIAESSDQCFAATbce5XhIgARIgAZ0J6KRVeE4A1HljObf4EdDpUsWPAp9MAiRAAiRAAiSQ6AQoACb6CeD6SYAESIAEdCKgk1aRMAIgCoAg91+1atV0OgucS4wI6HSpYrQkDkMCJEACJEACJEACrglQAHSNjB1IgARIgARIIMMI6KRVeEoALF26tHTs2FG6du3qenNOnz4tDz30kKr++9Zbb0nZsmVdj8EO+hLQ6VLpS4kzIwESIAESIAES8DuBkSNFnnnm31UyB6Dfd5zrIwESIAES0JmATlqFpwTArFmzSt++fWXgwIER7e/ff/8td999t2zcuFE+++wzueWWWyIah530I6DTpdKPDmdEAiRAAiRAAiSQKASsAuB994l8/HGirJ7rJAESIAESIAG9COikVSSUAIhjMGnSJHniiScE3oQ//PCDZM+eXa/TwdlERECnSxXRAtiJBEiABEiABEiABGJAgAJgDCByCBIgARIgARKIEQGdtIqEEwB79OghI0eOlCxZssjMmTMlOTk5RtvKYeJJQKdLFU8OfDYJkAAJkAAJkEBiE6AAmNj7z9WTAAmQAAnoRUAnrUI7AXDlypW2u1WjRg1p3ry5tG3b1tWOnj9/Xo4ePSoY+7XXXpOzZ88qAXDIkCHSs2dPV2OxsZ4EdLpUehLirEiABEiABEiABBKBAAXARNhlrpEESIAESMArBHTSKrQTAFu0aCEffvihoGiH1SDkQbiLxjAGDOO8//770qBBg2iGY19NCOh0qTRBwmmQAAmQAAmQAAkkIIERI0R69Ph34cwBmICHgEsmARIgARLQhoBOWoV2AiB26ciRIzJ+/HgVqos/x8oM8RAiYKlSpVQOwBw5csRqeI4TRwI6Xao4YuCjSYAESIAESIAEEpyAVQC8/36RxYsTHAqXTwIkQAIkQAJxIqCTVqGlAGjsy4EDB+Txxx+XRYsWKY89w3sv2n2rVKmSTJ8+XcqWLRvtUOyvCQGdLpUmSDgNEiABEiABEiCBBCRAATABN51LJgESIAES0JaATlqF1gKgsYPPPPOMjBo1SomADzzwgDz88MOuNzdbtmySP39+KVOmjJQrV851f3bQm4BOl0pvUpwdCZAACZAACZCAnwlQAPTz7nJtJEACJEACXiOgk1bhCQEQG9ytWzcZPXq09O3bVwYOHOi1Ped8M5iATpcqg5fK4UmABEiABEiABEjAlgAFQB4OEiABEiABEtCHgE5ahWcEwL///luqVasm99xzDwVAfc6yNjPR6VJpA4UTIQESIAESIAESSDgCw4eL9Oz577KZAzDhjgAXTAIkQAIkoBEBnbQKzwiA2L/vvvtOfv31VyUC0kjATECnS8WdIQESIAESIAESIIF4EbAKgLVriyxaFK/Z8LkkQAIkQAIkkNgEdNIqPCUAJvax4epDEdDpUnGnSIAESIAESIAESCBeBCgAxos8n0sCJEACJEAC6QnopFX4WgA8dOiQXHzxxZIrVy6eQ58T0OlS+Rw1l0cCJEACJEACJKAxAasAeNNNIh99JFKypMaT5tRIgARIgARIwKcEdNIqPCkA7t+/X9544w3ZvXu3NGrUSOrWrRv0qHz00UfSvXt3VfW3U6dOUrNmTZ8eKS5Lp0vF3SABEiABEiABEiCBeBBYu1akRQuRrVvTPj1LFpE6dUQGDBBJSorHzPhMEiABEiABEkhMAjppFZ4TAFesWCHJycly9OhROX/+vOTIkUN27dolRYsWDXqaUlNTZcSIEapwyN133y1vvvmmXHXVVXE5eQcOHJDFixerPIYlS5ZUwmW+fPliNhcwWbhwoezdu1eKFSumxi9SpIir8cHyk08+UXzLli0rtWvXlpw5c7oaIx6NdbpU8Vg/n0kCJEACJEACJJDYBGbPFmnSRCQ11Z5DjhwiKSkiycmJzYqrJwESIAESIIHMIqCTVuEpAXD79u1SqVIl+fPPPwN7lSVLFvn6668lKczXmStXrlRiVuHCheWzzz6TMmXKZNZ+CyoYv/DCCzJ06FC59tpr5ZZbbpE1a9YIDsLo0aOlTZs2Uc9l3Lhx0rt3b7n00kulatWqsmXLFtm8ebMMGDBAevXqJVmzZg35jNOnT0vXrl1l4sSJiuV1110ny5cvF/x80qRJUq9evYjmePDgQSlfvrxA/IS4COEzI0ynS5UR6+OYJEACJEACJEACJGBHAJ5/VauGFv+MvhABV6+mJyBPEwmQAAmQAAlkBgGdtApPCYAQ8JYsWSIQ/WDwALz88stl586dkjt37rB7N378eHnqqaeU+Pftt9+q/IAZbZhjkyZN5P3335cOHTrI66+/rsQ4iILNmzeXGTNmyJAhQ5R4F6n17NlThg8fLg8++KC89957gZyHGHPYsGHSvn17wdrtDCJfrVq1ZNWqVTJ48GB59tlnVVP8/P7771c/nzZtmjRt2tT1FCEcwisRRgHQNT52IAESIAESIAESIIGwBPA97YVft8K2RQO0nz/fUVM2IgESIAESIAESiIIABcAI4O3Zs0dKlSqlxD/8B68yeAN27txZbkJ2YwcG0Q1jYAO6dOkiI0eOdNAruiaDBg2Svn37Ks+/77//Xi666KLAgAizhUfcsWPHlLAJEc6tTZ8+XZo1ayYFCxZUORELFCgQGOLs2bPKk2/Hjh0yYcIEeeKJJ4IO365dO+XlhxDpZcuWpWnz008/qTEgWq5fv15uuOEGx1N89dVX1f4YRgHQMTo2JAESIAESIAESIAFHBHbvFildGl+MO2quGuG79F27REqUcN6HLUmABEiABEiABNwToADonpnylIPQdfPNN8u8efOkePHiEYwiArFr8uTJymMQufgy0gsQoiWEP+QhfOWVV5ToaDV450GcgxCIsF0nnozGGCdOnJDSpUsLqh1DaEM4sdUQdgyPPgiDW7dulSuuuCJNk6+++kruuOMO9bM5c+bIQw89lG4MeAFCoKxSpYqgvRPbtGmT3HbbbWpdeC6MAqATcmxDAiRAAiRAAiRAAs4JvP22SOvWztsbLdGvZUv3/diDBEiABEiABEjAOQEKgM5ZBVoixBUhrZ9//rncddddEYzwT5fnnntOhdzCixCCV/369SMeK1zHp59+WsaOHaua/fjjj0HzDs6dO1cefvhh1WbKlCnSqlWrcMMGXkdxkx49eqi/o3DHvffem67vhg0bpGLFiurnyAf4/PPPp2ljhOjCMxGeiHny5Ek3BoRF5AeEgX+NGjVCzvHUqVNy6623Ks9MeA8i/yGMAqDjrWVDEiABEiABEiABEnBEYMwYkSDfMYfti36dOoVtxgYkQAIkQAIkQAJREKAAGAG8UaNGKbELIlU0Xntt27ZVQhsEQAhohrAVwZRCdoHXH7ztjhw5Ivnz51dVdY3cheaO+/fvlyuvvFL96M4775TVyMrs0G688UYVVgz7/fffpVChQul6Iuw5b968Kp/f1VdfLfBKNOYBD0g8+9y5c0qsg9deMPvyyy/V3GCPPfaYvPvuuyFniFyH8BhEyDA8HykAOtxQNiMBEiABEiABEiABlwToAegSGJuTAAmQAAmQQCYSoAAYAeyUlBQVAvzLL7/IZZddFsEIogpvICQVGwARDF6F3bp1i2iscJ1Qdbh69eqqGfIVbty40bYLBEKE82bLlk2OHz8e1AvP2vnnn38OhEGjP4RRO8PzUREYtm3bNhWWDJs6daq0vBD7AU9IhFYHs8OHD6vqybCiRYsKREs7wxiPPvqoKhyCEGB4HFIADHda+DoJkAAJkAAJkAAJREaAOQAj48ZeJEACJEACJJAZBCgARkAZBS6Q7w6iFYTASAxFKYw8fBAAUZm3QYMGkQwVtg/ERVTnhdWpUydQCTdYRwhy27dvVy/BA9Dwtgv1kJkzZ0rDhg1Vk+uvvz7gCRisD4qLLF26VL0E7z148cFQEXncuHHqz//3f/8XCFcONkbOnDlVLkMYDrDhtWhuu2/fPpWj8ZlnnglUNaYAGPaosAEJkAAJkAAJkAAJREWAVYCjwsfOJEACJEACJJBhBCgARogWeeXgjbZu3bqg4a6hhoXY16JFC0Fl3PPnz0uOHDmUN+Ell1wS4WxCd3vyySdl4sSJqhHCjlFl184qV64sa9euVS+/8847ap7hDHkM+/Tpo5rdc8898umnn9p2gVAIwRAGb7z+/furP993330qdyDsxRdfVNWK7QxelwcPHlQvr1ixIl0eRnhX1qxZU3kxIvwXVYNhFADD7SRfJwESIAESIAESIIHoCODXyKpVRS58VxtysBw58IWzSFJSdM9kbxIgARIgARIggfAEKACGZxS0xccff6y86cqVKyfTpk2TSpUqhR0JsPv166c8ByH8weD9B5ENuQAzyh588EH56KOP1PB2FXqNZ1erVi2Q+2/MmDHSyUFGZowJj0YYnoViInbWvHnzQN4+5DxEPkUYvPWMvH92VYqNMZE/ECxhCPO1Fk+BIIkxMJ650nCsBEDj2XZrhJgLIRWG8Oirrroqo7aW45IACZAACZAACZCAdgRmzxZp3FjkzBn7qWXPLvLeeyLJydpNnxMiARIgARIgAV8SoAAYxbYiZx3EP3ia1a5dWxo1aqSEn2LFiknu3LlVDr29e/cqL0EIVfBGO3PmjBL/IPzh/5dffrnKiVekSJEoZhK6KyryGl55qF4MgczOzAJgOE88Y4x27doFvAobN24syJFoZ2YB0OyNWKZMGfnpp59UtzfeeEPgtWhnZgEQ/M1h2GvWrBGsAVWVIdCaLVYCYLACKnZzpQCYYceaA5MACZAACZAACWhKAAJgo0YiZ8/aT/Cii0Tef58CoKZbyGmRAAmQAAn4kAAFwCg2FWJe3bp1lbjmRBQye/3hzwUKFJDly5cr77eMtPvvv1+JjzB4IA4cOND2cVWqVJFvvvlGvT5s2LBA7sBQ82vfvr1MmDBBNYHABw9HO4NAiBBoGCr0Gnn/rrvuOlUUBDZ58mRp06aN7Rjw6kPVYBjGMvIPonhJxYoVlUeg4VloHoQCYEaeMo5NAiRAAiRAAiRAAiIMAeYpIAESIAESIAE9CVAAjHJfzp07Jz169JDRo0eHHMkQCA0RECHDM2bMCFTBjXIaIbujEu6sWbNUGxQDgbBnZ+ZQ3PHjxwvEvXCG9Y8YMUI1gxhnCHzB+pnDkXv16iVDhw5VzZKSkpSnJAyiIMRBO4NwCu9K2OLFiwUCJwzi45YtW+Srr75SeRWtFisBkCHA4U4EXycBEiABEiABEkhUAiwCkqg7z3WTAAmQAAnoToACYIx2aOPGjTJo0CBVYffUqVNBR4UIiOIhHTt2FHjCXYTYh0ywbt26qZx4MFTbff31122fWrx4cZW3Doa1WMNog3VE/j/kAYTBI3LBggW24991112yatUq9frYsWNVxV9YcnKyCtuFoWoxqvcGMwiu2bNnD+RQ/P7771Xl4enTpyux8n//+58gnDiYxUoADLdlOl2qcHPl6yRAAiRAAiRAAiQQKwK7d4uULi1yIdW1o2GzZBHZtUukRAlHzdmIBEiABEiABEggQgI6aRVZzhvucREuRoduqampygNt586dcujQISVUobovCkEgvDajKv2GWvtbb72lqv/CHnrooYDQFqxPnjx5AgLm7t27pYSD38aWLVumqv/CKlSoIOvXr7edDoqmbN26Vb2O8Ofq1aurP6MaMHIOwrp06RIQLK0DIfTXKOyRM2dOOXbsmOD/5hyCbs/Brl27pGTJkm672bbX6VLFbFEciARIgARIgARIgATCEHj7bZHWrd1jQr+WLd33Yw8SIAESIAESIAHnBHTSKnwhADpF/8ILLyjRCYVEMtp27Ngh//nPf9RjEOK7YcOGoI88cOAu735kAAAgAElEQVSAFC1aVL0GwdLwBAw3vz///FMVMTl9+rTKa3j06NGgXSCG5s2bVwmMCNE9cuSIQHCEffbZZ1KzZk3151CVhL/++mu5/fbbVbuqVasGvAnBcs+ePeGmGvR1CoARYWMnEiABEiABEiABEkhDYMAAkRCppm1pod/zzxMmCZAACZAACZBARhKgAJiRdEOM/dtvvykhC5WDBw8enOGzKF++vKo2jPBZFMuA15zVzJ58COkNl9fQ3B+FN+bPn69+BMGxNOI/LAavyGuuuUb91CrynT17VlVEPnz4sKDKL6onBzOzNyPCmuEtaDwTRVlCGUKfEXYMQ+GWK6+8Uv0ZcwKXWJlOlypWa+I4JEACJEACJEACJBCOwOOPo5hbuFbpX0e/N9903489SIAESIAESIAEnBPQSatIKA9AbBHEqE6dOsnIkSMDQpbzrXPXctKkSdKuXTvVaenSpQFvO/MoyGHYt29fyZo1qyC3HirzOjUIavfee69q/uabb8rj+E3OYsjT16xZM/VTVCWuVatWmhZ4NuYA2759e8Br0dwIa8BaLr74YuXxV6hQIadTFOYAdIyKDUmABEiABEiABEjANYH+/UUuZHRx1ZcegK5wsTEJkAAJkAAJRESAAmBE2NJ2Qq6//fv3C0Jh4ckWKpUhXvvrr79UfsAxY8bI2rVrJXfu3ErwKlasWAxmE3wI5CZEfr4ffvhBCYETJ05M1xCViZG/r1WrVjJlypQ0r69cuVKaNm2qhLe5c+cGFQcRwotQXgiBn3zySbrxjUIfNWrUkM8//zzd67///rsq6AHvSAiBffr0SdMGa0CREuQBhJg3AL8tujAKgC5gsSkJkAAJkAAJkAAJuCTAHIAugbE5CZAACZAACWQiAQqAUcBGsQ9U1UUF4EgNgiCqA48fP16eeOKJSIdx1O/bb79VYcfZsmWTLVu2SKlSpQL9PvjgAxWOjHDYNWvWpPOsQz/k34M1adJEZsyYke6ZyBmIUGPkAPzyyy8DufrQEKyQsw9FUDB+sBBhtPvoo49UeDDCgVEspGDBgoHnvPzyy9KrVy9VTGXFihUqj6AbowDohhbbkgAJkAAJkAAJkIA7AqgCbPr10nFn9HNQd87xeGxIAiRAAiRAAiSQngAFwAhPxbp165SghbxzkRYvhvBn2BtvvBEI0Y1wSo66LViwQBo2bKg86ZATD0LczJkzlUfdtddeK/PmzQtaEfeOO+5QIh4MnoAI5w1mEPfq1KmjcuqNGzdOKlasqLwBIdyhQAi8B+GJGMoQQtyhQwdJSkqSUaNGSeHChWXy5MkyYsQIVTV41qxZrkJ/jWdRAHR0RNiIBEiABEiABEiABCImUK+eyMKFzruj/YU00s47sSUJkAAJkAAJkIBrAhQAXSP7pwOEqFWrVinvPasAGOxn1seY25QrV0551+XLly/C2bjrtm3bNhViizDcgwcPqrDb1q1bS/v27W2LYaxevVp5/qGKr10IsDGLffv2yZAhQ2TRokUqNBqeho899pjKc4gQYieG0GgUR4HoePz4cSUkYn7IIWgWTp2MRQHQDSW2JQESIAESIAESIIHICaxdK1K1qkhqavgxEMyxerVIUlL4tmxBAiRAAiRAAiQQHQEKgBHwQ6hryZIlVc9cuXJJ7dq1lUB16aWXykUXXSRPP/20tGzZUoWqBjNU4e3evbu0adNGedMhvBb9aP4goNOl8gdRroIESIAESIAESMBLBGbPRsqY0CIgxL+UFJHkZC+tjHMlARIgARIgAe8S0Emr8EwV4NmzZ0uDBg1UvjxUvy1hSVoCbzcU+kCoqp2h4i3CWv/3v/9J0aJFvXuCOPN0BHS6VNweEiABEiABEiABEogHAXgCtmghsnVr2qcjA07duiKoGEzPv3jsDJ9JAiRAAiSQqAR00io8IwAit13Hjh1VKOwDDzyQ7uxAFKxbt67s2bNHrrjiiqBn648//lChsQi/RShu1qxZE/UM+m7dOl0q38HlgkiABEiABEiABDxDYPBgkeee+3e6SAM9dy4LfnhmAzlREiABEiABXxHQSavwjACI/Hbw4Dty5Ijkz58/6IEoW7asCu8dMGCA7YHp1KmTjB07VhXg6Nevn68OViIvRqdLlcj7wLWTAAmQAAmQAAnEl4BVAKxfX2TevPjOiU8nARIgARIggUQloJNW4RkBENVzO3fuLL/++qsUKVIk6Nl55ZVXVBGLHTt22IqEU6ZMkbZt26o8giiWUbBgwUQ9h75at06XyldguRgSIAESIAESIAFPEaAA6Knt4mRJgARIgAR8TkAnrcIzAuDixYtViO/06dNVZdxghkIfxYsXl0cffVQmTpwYtE2fPn1k6NChqqotxmrcuLHPj1tiLE+nS5UYxLlKEiABEiABEiABHQlQANRxVzgnEiABEiCBRCWgk1bhGQEQob/I7QePvalTp0rNmjUlW7Zs6c7Qc889pwS+V199VZ566qk0r2/dulVuu+02gVAIARBhxT179kzUc+irdet0qXwFloshARIgARIgARLwFAEKgJ7aLk6WBEiABEjA5wR00io8IwDiTKDSb0pKihLvYPh/uXLlZNq0aVIBGY5F5Pjx46pS8OHDh6V+/frSqFEjyZs3r3zzzTdKFPzzzz/l/Pnzqi9yAbZv397nxy0xlqfTpUoM4lwlCZAACZAACZCAjgQoAOq4K5wTCZAACZBAohLQSavwlAD4yy+/yM033yyHDh0KnB0IeQ0aNJD3338/8DOIhBALDaHQeMEQ/vB/eA/CIxBiIc37BHS6VN6nyRWQAAmQAAmQAAl4lQAFQK/uHOdNAiRAAiTgRwI6aRWeEgBxGNavX688+/bt2xc4GwjjRdiv2bp37y4oCgIR0BD+zEIgXh8+fLgfz1dCrkmnS5WQG8BFkwAJkAAJkAAJaEFg0CCRvn3/nQqrAGuxLZwECZAACZBAghLQSavwnACIM4MwX+QB3LJli1x77bXSoUMHyZkzZ7rjBIHvpZdeUjn/DEM4cO/evQXFQKweggl6Hn2xbJ0ulS+AchEkQAIkQAIkQAKeJGAVAB98UGTuXE8uhZMmARIgARIgAc8T0Emr8KQA6OYEIOffV199JQcPHpTLLrtMqlSponIC0vxFQKdL5S+yXA0JkAAJkAAJkICXCFAA9NJuca4kQAIkQAJ+J6CTVuF7AdDvh4nr+4eATpeKe0ICJEACJEACJEAC8SJAATBe5PlcEiABEiABEkhPQCetwlMC4JgxY6R///5y0UUXycSJE+WRRx7h+SIBCoA8AyRAAiRAAiRAAiRwgQAFQB4FEiABEiABEtCHAAXACPZizZo1KnzXsCuuuEL2798fwUjs4kcCOl0qP/LlmkiABEiABEiABLxBwCoAVqwoMnu2SMmS3pg/Z0kCJEACJEACfiKgk1bhGQ/AkSNHSo8ePQKFOypVqiRr167107ngWqIgoNOlimIZ7EoCJEACJEACJEACERPAr8bNm4ts25Z2iCxZROrUERkwQCQpKeLh2ZEESIAESIAESMAlAZ20Cs8IgDNmzJBmzZop1FmzZpXPPvtMqlev7hI9m/uVgE6Xyq+MuS4SIAESIAESIAF9CcDLr0kTkdRU+znmyCGSkiKSnKzvOjgzEiABEiABEvATAZ20Cs8IgH/99Zdcf/31snv3bmncuLFMnz49qjPx+OOPy6RJk6Iag531IaDTpdKHCmdCAiRAAiRAAiSQCATg+Ve1amjxz+AAEXD1anoCJsK54BpJgARIgATiT0AnrcIzAiC2bfPmzfLf//5XbrjhBlm2bJnyBIzEUlNTpUCBAnLq1KlIurOPhgR0ulQa4uGUSIAESIAESIAEfEygXj2RhQudLxDt58933p4tSYAESIAESIAEIiOgk1bhKQEQuHft2qU8AIsUKSJTp06VwoULu94FeP49+eSTcu7cOdd92UFPAjpdKj0JcVYkQAIkQAIkQAJ+JLB7t0jp0iLnzztfHXIC7tolUqKE8z5sSQIkQAIkQAIk4J6ATlqFZwTAvXv3qvBf2JkzZ+S1116TL774Qvr16ycVKlQIuwtnz56V33//XVasWCFvvvmm4O8UAMNi80wDnS6VZ6BxoiRAAiRAAiRAAp4n8PbbIq1bu18G+rVs6b4fe5AACZAACZAACTgnoJNW4RkBcMiQIdK3b980lM+fPx+oCuwcP74h/acfBUA31PRuq9Ol0psUZ0cCJEACJEACJOAnAmPGiHTp4n5F6Nepk/t+7EECJEACJEACJOCcgE5ahWcEwK1bt6oiIGaDiAcxz40ZfSgAuqGmf1udLpX+tDhDEiABEiABEiABvxCgB6BfdpLrIAESIAES8CMBnbQKLQRAVPj99NNPVT6/KlWq2O45CoAsX75cee+5Ff6sg1IA9NfV0ulS+YssV0MCJEACJEACJKAzAeYA1Hl3ODcSIAESIIFEJ6CTVhF3ARB5/WrVqiU7duxQ5+LFF1+UPn36BD0jH3zwgSoAguq/zZs3l/Lly0vevHkle/bsjioCI+/fzz//LGPHjpXDhw8zBNhHN1GnS+UjrFwKCZAACZAACZCABwiwCrAHNolTJAESIAESSEgCOmkVcRUAUczjtttukw0bNgQOwg033CCbN28OejAg4BUvXlzq1KkjqOQbqX3yySdSu3ZtCoCRAtSwn06XSkM8nBIJkAAJkAAJkICPCaxdK1K1qkhqavhF5sghsnq1SFJS+LZsQQIkQAIkQAIkEB0BnbSKuAqAEyZMkA4dOqQJ6cXP2rVrZ0u4f//+8ueff8rIkSOj2gV4DmIcmj8I6HSp/EGUqyABEiABEiABEvASgdmzRZo0CS0CQvxLSRFJTvbSyjhXEiABEiABEvAuAZ20irgKgNWqVZMvvvhC7eTll18uEP/q168fcmcBb9asWdIlknJnppFfeeUV6dq1q3dPEWeehoBOl4pbQwIkQAIkQAIkQALxIABPwGbNRH78Me3Ts2QRqVtXpH9/ev7FY1/4TBIgARIggcQloJNWEVcBsECBAvLHH39IzZo1Zfr06VKkSJGQp2Lq1Kly8803q/9oJGAmoNOl4s6QAAmQAAmQAAmQQLwIvPjiP0KfYZUqicA7sESJeM2IzyUBEiABEiCBxCWgk1YRVwEwT548ctVVV8l3330nORCTEMZQ/KNv374ycODAcE3Dvl6vXj1ZsGBB2HZs4A0COl0qbxDjLEmABEiABEiABPxIAL8mDxjw78oefvgfAZBGAiRAAiRAAiSQ+QR00iriKgCWLVtWFfRAOK4Ti6UAWLp0adm5c6eTx7KNBwjodKk8gItTJAESIAESIAES8CkBqwCIfH8ffujTxXJZJEACJEACJKA5AZ20irgKgCgAcvz4cRX+68RiJQD+/fffkj9/fhV+TPMHAZ0ulT+IchUkQAIkQAIkQAJeJEAB0Iu7xjmTAAmQAAn4lYBOWkVcBcCNGzdKlSpV5OOPP5bq1auH3e9YCYAzZsyQ5s2by7lz58I+kw28QUCnS+UNYpwlCZAACZAACZCAHwlQAPTjrnJNJEACJEACXiWgk1YRVwEQG9i5c2cZN26c3HfffVKjRg254oorlHderly5JHv27ALRLwtKl4mo1yHctW3b1vXep6amysGDB2Xt2rWq2vDp06cpALqmqG8HnS6VvpQ4MxIgARIgARIgAb8ToADo9x3m+kiABEiABLxEQCetIu4C4NmzZ6V+/frKC9AQ+uw28/z582HbODkIxjj0AHRCyxttdLpU3iDGWZIACZAACZAACfiRAAVAP+4q10QCJEACJOBVAjppFXEXALGJEAF79eolY8aMEeTny0iDyEgBMCMJx2dsnS5VfAjwqSRAAiRAAiRAAiQgQgGQp4AESIAESIAE9CGgk1ahhQBobM22bdvktddek7lz58r+/fszdMcgBNIDMEMR/z979wJtY7X+cfzZiNKFXLrKrSLVUardkXZRJ+UW0nFXbNGhi1REjlySW0lxRJQosd0SNjkuKeyIrdJNKXJpuJRIpdJG/uOZ/7NW+7LWXu+71l5rz3et7xzjjJy953zfOT/zfccwfuY7Z0wvbtNLFdOBczMEEEAAAQQQQCCbwODBIoMG/fUDTgHm8UAAAQQQQKDwBGzKKqwKALNPiSJt3brV7Nvn26+vc+fO5nPhO+64w/XsHT16VA4ePCjr1q2TBQsWmE+JCQBdM1rbwKaXylokOoYAAggggAACcS+QOwC8806RuXPjftgMEAEEEEAAASsFbMoqrA0AA81cQZ0C/Mwzz0jfvn0JAK18PcLrlE0vVXgjoBUCCCCAAAIIIBC5AAFg5IZcAQEEEEAAgYISsCmrSMgAUE8EPvPMM+XXX38tqDnlOoUsYNNLVcgU3B4BBBBAAAEEEliAADCBJ5+hI4AAAghYJ2BTVpGQAaA+EVWqVJHt27db93DQofAEbHqpwhsBrRBAAAEEEEAAgcgFCAAjN+QKCCCAAAIIFJSATVmFpwLAVatWSaVKlaRy5coRz8Xu3bvl/PPPj/g6XMAOAZteKjtE6AUCCCCAAAIIJKIAAWAizjpjRgABBBCwVcCmrMJTAaCtE0q/Cl/Appeq8DXoAQIIIIAAAggkqgABYKLOPONGAAEEELBRwKasggDQxieEPrkWsOmlct15GiCAAAIIIIAAAgUkMGiQiIaAvsIpwAUEy2UQQAABBBAIQ8CmrIIAMIwJpIl9Aja9VPbp0CMEEEAAAQQQSBSB3AHgP/8pMmdOooyecSKAAAIIIGCXgE1ZBQGgXc8GvQlTwKaXKswh0AwBBBBAAAEEEIhYgAAwYkIugAACCCCAQIEJ2JRVEAAW2LRyocIUsOmlKkwH7o0AAggggAACiS1AAJjY88/oEUAAAQTsErApqyAAtOvZoDdhCtj0UoU5BJohgAACCCCAAAIRCxAARkzIBRBAAAEEECgwAZuyCgLAAptWLlSYAja9VIXpwL0RQAABBBBAILEFCAATe/4ZPQIIIICAXQI2ZRUEgHY9G/QmTAGbXqowh0AzBBBAAAEEEEAgYoGBA0WefPKvy3AISMSkXAABBBBAAIGwBWzKKuI6AFyxYoXMmjVLFLxs2bJy8803S/v27aVEiRJhTx4N7RSw6aWyU4heIYAAAggggEAiCOQOAFu2FJk9OxFGzhgRQAABBBCwT8CmrMIzAaCi7dq1K+BsVqxYUSpUqOD/XVZWlqSmpsrMmTPz1L/wwgslPT1dqlevbt+TQY/CFrDppQp7EDREAAEEEEAAAQQiFCAAjBCQ5ggggAACCBSggE1ZhWcCwClTpkiXLl3yTMM111wjQ4YMkVtvvdX/u3bt2gUM/3wVzj//fNm0aZNZFUiJDwGbXqr4EGUUCCCAAAIIIOBFAQJAL84afUYAAQQQiFcBm7IKzwSAuqrv5JNP9j8T+ilvv379pEaNGjmeE131pwFgUlKS+fmJEydEV/3dfvvt8ttvv8mCBQvk+++/l06dOsnkyZPj9RlLuHHZ9FIlHD4DRgABBBBAAAFrBAgArZkKOoIAAggggIDZku6CCy4wEt9++22Or1djzeOZAHDnzp1SpUoVKVKkiLz22msm5Mtdjh49KtWqVTOfCmvwpyGg1tPVg8WKFTPVNfyrV6+ebNu2TXbv3i3lypWLtTn3i4KATS9VFIbHJRFAAAEEEEAAAUcCBICOmKiEAAIIIIBATARsyio8EwAuW7ZMGjRoIF27dpWJEycGnChd0ae/963+q1mzpmRmZvrDP1+jRYsWSdOmTc0BIS11Z2SK5wVseqk8j8kAEEAAAQQQQMCzAgSAnp06Oo4AAgggEIcCNmUVngkAX3zxRbn//vtl/fr1ovv+5S664u+SSy6RrVu3mtV/ulJw3bp1kpycnKfugQMHpHz58jJy5Ejp3bt3HD5iiTckm16qxNNnxAgggAACCCBgi8CAASJDhvzVG/2rsJ4CXLmyLT2kHwgggAACCCSOgE1ZhWcCwOHDh0v//v3l559/llNPPTXP0zJnzhxp3bq1f/Vfq1atJC0tLeBTdejQISlTpozoNfv06ZM4T14cj9SmlyqOmRkaAggggAACCFgskJkp0r69yNdf5+ykbo3dqJGIrg4M8G/jFo+IriGAAAIIIOBtAZuyCs8EgM8995z06tVL9u7dK2eddVaOJ0BX/F1xxRXy+eefm9V/RYsWlc8++0yqV68e8El5++23pX79+jJu3Di57777vP000XsjYNNLxZQggAACCCCAAAKxFpg3T6RtW5GsrOB3Ll5cRP99vEWLWPeO+yGAAAIIIJCYAjZlFZ4JAJcsWSKNGzeWefPmSfPmzXM8OePHj5cHHnjAv/qvQ4cO8uqrrwZ9unTfP73O7Nmz5c4770zMpzDORm3TSxVntAwHAQQQQAABBCwX0JV/KSn5h3++IWgImJHBSkDLp5TuIYAAAgjEiYBNWYVnAsDff/9dKlasaE7tXb16tdnDT8tbb71lDvI4cuSIWf1XsmRJ2bx5s6kbqLzxxhumvh4UsmHDBrn66qvj5LFK7GHY9FIl9kwwegQQQAABBBCItUCTJiKLFzu/q9ZPT3den5oIIIAAAgggEJ6ATVmFZwJApdbTf7t3727CvxtvvFF27dolGzduzDELo0aNkocffjjgzLz++uvmlOA//vjDBIB6GEjp0qXDm0VaWSVg00tlFQydQQABBBBAAIG4FtixQ6RqVZETJ5wPU/cE3L5dpFIl522oiQACCCCAAALuBWzKKjwVACr1wIED5amnnjKr/XKXzp07y8svv5zjx7/++qssXbpUxo4dK2vWrPG30wDw+PHj7mePFlYK2PRSWQlEpxBAAAEEEEAgLgWmThVJTXU/NG3XsaP7drRAAAEEEEAAAecCNmUVngsAlVlX/U2YMEE+/PBDE+JdfPHF0qVLF2nYsGGeWdCVgkePHg04O+vWrXM+a9S0WsCml8pqKDqHAAIIIIAAAnElMGaMSM+e7oek7Xr0cN+OFggggAACCCDgXMCmrMKTAaBzamomioBNL1WimDNOBBBAAAEEECh8AVYAFv4c0AMEEEAAAQSCCdiUVRAA8pzGhYBNL1VcgDIIBBBAAAEEEPCEAHsAemKa6CQCCCCAQIIK2JRVEAAm6EMYb8O26aWKN1vGgwACCCCAAAJ2C3AKsN3zQ+8QQAABBBJXwKasIq4DwBUrVsisWbNEwcuWLSs333yztG/fXkqUKJG4T1+cjtymlypOiRkWAggggAACCFgqkJkpkpIikpUVuoPFi4tkZIgkJ4euSw0EEEAAAQQQiEzApqzCMwGgou3atSugfMWKFaVChQr+32VlZUlqaqrMnDkzT/0LL7xQ0tPTpXr16pHNIq2tErDppbIKhs4ggAACCCCAQEIIzJsn0rZt/iGghn9paSItWiQECYNEAAEEEECg0AVsyio8EwBOmTLFnPSbu1xzzTUyZMgQufXWW/2/ateuXcDwz1fh/PPPl02bNplVgZT4ELDppYoPUUaBAAIIIIAAAl4T0JWA7duLfP11zp4nJYk0biwyYAAr/7w2p/QXAQQQQMDbAjZlFZ4JAHVV38knn+yfef2Ut1+/flKjRo0cT4Ou+tMAMEn/piMiJ06cEF31d/vtt8tvv/0mCxYskO+//146deokkydP9vaTRO/9Aja9VEwLAggggAACCCBQWAL9+4sMHfrX3a+9VmT2bJFKlQqrR9wXAQQQQACBxBWwKavwTAC4c+dOqVKlihQpUkRee+01E/LlLkePHpVq1aqZT4U1+NMQUOvp6sFixYqZ6hr+1atXT7Zt2ya7d++WcuXKxexJ3LdvnyxZskS+++47qVy5sjRu3FhOP/30Arv/oUOHZPHixWb85513nrm+2/Ft375dli1bJnot/Uy6YcOGrvZMXL9+vWzYsEEOHz4s55xzjtl3sVIM/sZp00tVYBPKhRBAAAEEEEAAAZcCuQPANm3+/7NfCgIIIIAAAgjEXsCmrMIzAaCGUg0aNJCuXbvKxIkTA86arujT3/tW/9WsWVMyMzP94Z+v0aJFi6Rp06bmgJCWLVtG/Qn4888/ZfDgwTJixAgTUF599dUmJNMH4fnnn5fOnTtH3Ifx48dL3759pXz58pKSkiKbN2+WTz/9VAYOHCh9+vQxwWl+5ciRI/Lwww/LpEmTJDk5WS655BJ59913RX/+8ssvSxM9Xi6fsnXrVnPAio4re9H7du/e3YzTF8JGPNgAF7DppYrG+LgmAggggAACCCDgRIAA0IkSdRBAAAEEEIiNgE1ZhWcCwBdffFHuv/9+0RVmuu9f7qIr/jS00iBK/6zB07p160yYlbscOHDABGUjR46U3r17R3XWtS9t27Y1YaMGYePGjTN901DwrrvukhkzZsjw4cNNeBdueeyxx+SZZ56RZs2amb0PfZ9K6zV1jN26dZMJEyYEvbyGfLqH4po1a2TYsGHy+OOPm7r6cw1d9efTpk0LuOpS6+kDff3118uPP/5o5ua0004z86SrLX1FQ85ofnJt00sV7jzSDgEEEEAAAQQQiFSAADBSQdojgAACCCBQcAI2ZRWeCQA1JOvfv7/8/PPPcuqpp+aZjTlz5kjr1q39q/9atWolaUG+d9DPW8uUKWOCN10dF80ydOhQ029d+ff555/nWAWn/dBPgX/66SdZunRpjoNMnPZp+vTp0qFDByldurTs2LFDSpUq5W967NgxE4rq5866avLee+8NeFldNamr/G666SZZuXJljjoaqOo1NLT86KOP5LLLLstzDQ3/rrrqKtGxnnHGGeb3f/zxhwkSn3vuOX99bX/llVc6HZqreja9VK46TmUEEEAAAQQQQKAABQgACxCTSyGAAAIIIBChgE1ZhWcCQA2SevXqJXv37pWzzjorxxToKrsrrrjCBGz656JFi8pnn31m9rALVN5++22pX7++WY133333RTidwZvrvoUa/IkixLwAACAASURBVOkBJtr/nj175qmsq/M0nNMgUD/bPeWUUxz355dffpGqVavKDz/8IA899JD5zDZ30c+ONYjTYPDLL780+/JlL7pKsk6dOuZHb775pjRv3jzPNXQVoAaUtWvXNqsqs5c33njDfGat9wlUbrvtNrOnoJZRo0bJo48+6nh8bira9FK56Td1EUAAAQQQQACBghQgACxITa6FAAIIIIBAZAI2ZRWeCQD18Aw91GLevHl5Qird/+6BBx7wr/7TFXGvvvpq0FnSff/0OrNnz5Y777wzstnMp7X26YUXXjA1vvrqK7n44ovz1J4/f77ccccd5ud6WImeTuy0aKDm+4RZQzYNNXOXTZs2Sa1atcyPdT/AQYMG5aiie/vpwSG6P5+uRCxZsmSea2iwqPsDannnnXfMISq+smXLFrnoootM6BqovP766+ZTZy06T/oZdDSKTS9VNMbHNRFAAAEEEEAAAScC//63yLBhf9XkEBAnatRBAAEEEEAgOgI2ZRWeCQB///13qVixojnVdvXq1WYPPy1vvfWWOchD96vT1X8aYOlKOq0bqOiKNa2vB4XogRV6IEc0iq7609V2ui+efharn/v6DifJfr89e/bI+eefb36kn9JmZGQ47s7ll19uVj1q0X0N9bPm3EX3GtRPptXnggsuEF2V6OuHnkas9z5+/Lj87W9/k08++STgvdeuXWv6pkUP+tBQz2nRlYO6glDv+cUXXwRdlen0esHq2fRSRToW2iOAAAIIIIAAAuEK5A4A//53kZkzRSpXDveKtEMAAQQQQACBcAVsyio8EwAqtn4qqyvINPy78cYbZdeuXbJx48Yc86Cr4nyr1XJPkAZXut+d7k+ngZSGZrp3XjSKhpR169Y1l9bTiD/++OOgt9GAUD/n1VV0usdhoFV4uRt/++23/pBT2+vqvWBF768nAmvRFXv6WbKW1157TTp27Gj+rKciL1iwIOAlDh48KGXLljW/O/fcc0VDS6dFTxX+17/+ZVY26grHaBWbXqpojZHrIoAAAggggAAC+QlkZoq0ayeydWvOWklJIo0a6dcgIgHOxwMVAQQQQAABBKIkYFNW4akAUOdDP2N96qmnzGq/3EVPmtXDLLKXX3/91exfN3bsWHOara+dBoC68i1aRU/l1dN5tTRq1Mh8ZhusaCD39ddfm1/rCkDfarv8+qaHnuhBJ1ouvfRS/0rAQG30hN/ly5ebX2kIqqv4tOipyvpZrhbdC9H3uXKga5QoUcLsZahFH2DfqsVQfnqwiK6CfO+99wIe3hKqvdPf2/RSOe0z9RBAAAEEEEAAgYISmDdPpG1bkf/9dS3gZYsXF9Ez8lq0KKi7ch0EEEAAAQQQyE/ApqzCcwGgwuqqvwkTJsiHH35oQjzdW69Lly7SsGHDPO66UvDo0aMB5yP3gRYF+djqqjdd/ablnnvuyRNMZr/Xtddeaw7S0KJ7F959990hu6InGPfr18/U+8c//iErVqwI2kaDQg0MtQwePFgGDBhg/pz9gI4hQ4aY04qDFT14Zf/+/ebXq1atMiswQxUNF1955RVzCEigz5NDtc/+e31p8it6OIw6atHVkRUqVHBzeeoigAACCCCAAAKeFdC/Rqak5B/++QanIaDuOMNKQM9ONx1HAAEEEPCQAAGghyYr3K42a9ZMFi5caJoHO6HXd+0bbrjBv/ffmDFjpEePHiFvq9fUVY1a9F56mEiwoodw+Pbt08+jR48ebarqycm+ff+CnVLsu6buH+gL4fRTYf1kOFjRFX8aNOpYdP9BvWefPn3ktNNOCzmuYBUC7Z8YrC4BYNjMNEQAAQQQQAABDwo0aSKSz8cmeUak9dPTPThQuowAAggggIDHBAgAPTZh4XRXT+T1rcrr27ev6Iq9YCV7ABhqJZ7vGrqXoe9z5zZt2kiafs8RpGQPALOvRtSVk1v/t0nMiy++aPbqC1ayB4DTpk0TPWk5d9GTjnV1oR7MonsaZi/XXXed+RT79NNPD4cz4AEqwS5EABgWMY0QQAABBBBAwIMCO3aIVK0qEmB3nKCj0T0Bt28XqVTJgwOmywgggAACCHhIgACwkCZLV6VVrlzZf/BFNLuhJ99q4KXliSeekCeffDLo7WrXri3r1683vx85cqR/78D8+tetWzdzKIoWDfj0QI9gRQPCWbNmmV/rISq+ff8uueQScyiIlsmTJ4vuoRis6InGemqwFr2Wb//B7PV1f0Xdc3H79u3mQBHdU3Dfvn3+Ki1atBA9hTmcwifA4ajRBgEEEEAAAQTiXWDqVJHUVPej1Hb/OwvOfWNaIIAAAggggIAjAQJAR0wFX+n7778XXYnWunVrGTZsWMHfINsVW7ZsKXPnzjU/0cNANNgLVrJ/iqt7G2q4F6r07t1b9MRjLRrG+QK+QO2yf46sn+KOGDHCVEtOTvafoqyhoIaDwUqpUqXMCcValixZIhpwhioaBuqqwunTp/urfvnll1K9evVQTV3/3qaXynXnaYAAAggggAACCIQpMGaMSM+e7htrOwe7zri/MC0QQAABBBBAwC9gU1bhyUNAInmWdFWa7rH37LPPSs9w/rbk8OaPPPKI6L56WvS03XHjxgVtWbFiRXNwhRY9LVhPDQ5VdP8/3QdQS+PGjWXRokVBm+iBHXoCshYdv574q0VX5L355pvmz3pqca9evQJeQw9aOemkk/wnKH/++efm5GEn5c8//xT9xHnt2rWmuq5avPfee500dVXHppfKVcepjAACCCCAAAIIRCDACsAI8GiKAAIIIIBAlAVsyio8GwD+8MMPsmfPHvPJ6bFjx/zhVKC5009T//jjD9E2ejCFnrh7yimnyNdffy3nnXdeVKZbT7/V/fa0NG/e3B+0BbpZyZIl5ffffze/2rFjh1RysCHLypUrzem/Wq688kr56KOPgo6jRo0aoivvtLz77rtSt25d82fdr0/3HNSiYagvsMx9If30Vz8B1lKiRAn56aefzH+dlvT0dP+hIUOHDvWfXuy0vZN6Nr1UTvpLHQQQQAABBBBAoCAEXnhB5IEH3F9pwgQRBx+duL8wLRBAAAEEEEDAL2BTVuG5AHDdunVmRd3HH38c9iOlgaCeKquf20ZjNZp2bNu2bXLRRReZPuonvps2bQrYX90j79xzzzW/q1Chgn8lYKjBafBZrlw5OXLkiOjnuYcOHQrYRMeqJ/FqwFi8eHHRE3o1cNTy9ttvyy233GL+nN9Jwu+//775dFpLSkqKfzVhqD76fp+VlWXuqSsJs69AdNreST2bXion/aUOAggggAACCCBQEAI33ijyvw89XF1O261a5aoJlRFAAAEEEEDApYBNWYWnAsCNGzeaAOro0aP5rvjLbz40+PMVPflWT9ONVqlZs6Z8+umn5vNZPRU30Kq57Cv59JPe559/3nF3mjZtKrq6TosGjlX1CLhc5ZtvvpELL7zQ/DR3yKcrJ88++2w5ePCg6Cm/u3btCnjv7KsZdZVgOJ9Oa8ipYacednLttdc6HqPTija9VE77TD0EEEAAAQQQQCBSgZo1RT791P1VtF0E/57u/oa0QAABBBBAIAEFbMoqPBUA6qerupedhni6si17CfSz3M9W9jr6WayubDv99NOj9gi+/PLL/oBx+fLl/tV22W+on8T2799fihQpIrq3np7M67SsWLFC6tevb6q/9NJL0qVLlzxN9QCODh06mJ/rqcS33nprjjp6b+2DFv0k2rdqMXslDUl1LKeddprs3LlTypQp47SLpp4GtmeccYb5tHnz5s1mrAVdbHqpCnpsXA8BBBBAAAEEEAgmoP+umpnp3kfbrV/vvh0tEEAAAQQQQMC5gE1ZhWcCQD0ko3Llykb55JNPloYNG0qtWrWkfPnyUqxYMXnggQekY8eOUrt27YAzoSvwHn30UencubO0a9fOfNKq7aJZ9NNX3Z/viy++MEHgpEmT8tzuqquuMvv3derUSaZMmZLj96tXrzZ91eBt/vz5AcNB/YRXP+XVIHDZsmV5ru876KNevXryzjvv5Pn9gQMHzIEeekJyoP35dAx6SInuAzho0CAZOHCgazI9DVlPRV6wYIF/L0DXFwnRwKaXqqDHxvUQQAABBBBAAIFgAnq2m+7n57bcf79IPmfUub0c9RFAAAEEEEAggIBNWYVnAsB58+bJP//5T/M5q658y31QRvv27c1BHxo2BSu62m3y5Mny4Ycf+vfdi/YT+sEHH5iwsWjRomb1W5UqVfy3nD17trRu3dqMacOGDXlW1mk7XaWopW3btjJjxow83dVgVD811j0A9aRd3159WlH3S9RPps8880xz/UCfCGu9hQsXms+D9XNgPSykdOnS/vs8/fTT0qdPHxOsrlq1yuwjmL3o6cN6sIgGjE2aNMnTP/2sWPvUrVs3eeKJJ6LGbdNLFbVBcmEEEEAAAQQQQCCXwJgxepibexZt16OH+3a0QAABBBBAAAHnAjZlFZ4JAMePHy8PPvigWQl3++2359HWULBx48bmE1XfibW5Kx0+fNgEcLriTVfDReNT1ECPgYZkrVq1Mivpxo0bZ4K4OXPmmBV11apVMyvjfKsbs7evU6eOCfG06EpA/Zw3UNFwr1GjRmavQXXSlZG6GlCDOz0gRM10JWJ+RT8h7t69uyQnJ8vo0aOlbNmyJiwdNWqUOTVYg9VAn/7qCkw9XVnLNddcI4888oj5r66u1M+edby9evWSu+++2/kbEkZNm16qMLpPEwQQQAABBBBAICyBqVNFUlPdN9V2HTu6b0cLBBBAAAEEEHAuYFNW4ZkAcPjw4WavPD3FVveTC1SqV69ugrL8PlPt0aOHOYlWw7dorkjL3b8tW7aYT2w1eNy/f78JIVNTU83KOA3uApWMjAyz8k9P8Q32CbCv3e7du0WN3nrrLdmzZ48JOnVVpB7YoZ8QOymZmZkybNgwEzr+/PPPJkjU/ukegtkPT8l+LQ1e1fKzzz4zB53oab8aCurJx7fddptp6/T+TvoYrI5NL1Uk46AtAggggAACCCDgRmDHDhE9By7X9tj5XkLPxNu+XaRSJTd3oi4CCCCAAAIIuBWwKavwTACoK8n0lFzdi65cuXIBzfWEWg2w9ETcYCGh7rN3zz33mH0ENSjL/rmr24mkvj0CNr1U9qjQEwQQQAABBBBIBAHdhWXxYucj1frp6c7rUxMBBBBAAAEEwhOwKavwTAC4ZMkS84mvfgarq+ICFV2Bpp/Z6oETgQ7c0Db9+vWTESNGmBVteq02bdqEN4u0skrAppfKKhg6gwACCCCAAAJxL6CnAKekiGRlhR6qbueckSGSnBy6LjUQQAABBBBAIDIBm7IKzwSA+umv7u2nK/Zee+010dNv9WCN3OXf//63CfjGjh0r9+vxZtmKHnDx97//3XyqqgGgfjL72GOPRTabtLZCwKaXygoQOoEAAggggAACCSUwb54eGpd/CKjhX1qaSIsWCUXDYBFAAAEEECg0AZuyCs8EgDpbuqddWlqafz86DfFq1Kgh06ZN8x9yoXvX6am6Bw8elKZNm5pTdnUPvfXr15tQ8Ndff5UTJ06Ya+hegLrHHcX7Aja9VN7XZAQIIIAAAggg4EUBXQmoIeC2bTl7r3v+NW4sMmAAK/+8OK/0GQEEEEDAuwI2ZRWeCgD37t1rDpfwnTqrj4AGef/85z9l1qxZ/idCQ0INC3MfXOEL/vS/unpQVwRqWEjxvoBNL5X3NRkBAggggAACCHhVoG9fkZEj/+r9ddf9/6o/Dvzw6ozSbwQQQAABLwvYlFV4KgDUSf/oo4/Myj499dZX9DNe/ew3e3n00UdFDwXRENAX/Pl+r/9ff//MM894+Tmi79kEbHqpmBgEEEAAAQQQQKCwBHIHgB06iEybVli94b4IIIAAAggktoBNWYXnAkB9dPQzX90HcPPmzVKtWjXp3r27lChRIs9TpQHfU089Zfb88xX9HLhv377mMJDcKwQT+7H09uhteqm8LUnvEUAAAQQQQMDLAgSAXp49+o4AAgggEG8CNmUVngwA3TwQuuffunXrZP/+/XLWWWdJ7dq1zZ6AlPgSsOmlii9ZRoMAAggggAACXhIgAPTSbNFXBBBAAIF4F7Apq4j7ADDeHybG9/8CNr1UzAkCCCCAAAIIIFBYAgSAhSXPfRFAAAEEEMgrYFNWkbAB4BtvvCF33nknz2ecCNj0UsUJKcNAAAEEEEAAAQ8K9Okj8vTTf3WcPQA9OIl0GQEEEEAgbgRsyioSNgA888wz5ccff4ybhyrRB2LTS5Xoc8H4EUAAAQQQQKDwBHIHgHXqiEyfLlK5cuH1iTsjgAACCCCQqAI2ZRUJGQC+9957cuONN8rx48cT9RmMu3Hb9FLFHS4DQgABBBBAAAFPCGRmirRpI/LNNzm7m5Qk0qiRyMCBIsnJnhgKnUQAAQQQQCAuBGzKKjwbAOpJwJ999pl899138ttvvzkK844cOSLbt2+XyZMny4EDBxy1iYsnLgEGYdNLlQDcDBEBBBBAAAEELBOYN0+kbVuRrKzgHSteXCQtTaRFC8s6T3cQQAABBBCIUwGbsgrPBYBbtmyRPn36yFtvvRV2gHfixAlJSkoKu32cPpeeHpZNL5WnIek8AggggAACCHhOQFf+paTkH/75BqUhYEYGKwE9N8l0GAEEEEDAkwI2ZRWeCgDfeecdadq0qVnxpyGe26KhnxYCQLdy9te36aWyX4seIoAAAggggEA8CTRpIrJ4sfMRaf30dOf1qYkAAggggAAC4QnYlFV4JgA8duyYXHjhhfLtt98adQ3zwgkBfVPGCsDwHl5bW9n0UtlqRL8QQAABBBBAIP4EduwQqVpV/4Hb+dj038S3bxepVMl5G2oigAACCCCAgHsBm7IKzwSAixcvlttvv90f/JUvX15SU1PlqquukjJlyshJJ50UciY0RNyzZ4889dRTsnXrVj4BDinmnQo2vVTeUaOnCCCAAAIIIOB1galTRVJT3Y9C23Xs6L4dLRBAAAEEEEDAuYBNWYVnAsDRo0dLr169jPI555wjGzdulPPOO8+5eraan376qVx55ZUEgGHp2dnIppfKTiF6hQACCCCAAALxKDBmjEjPnu5Hpu169HDfjhYIIIAAAggg4FzApqzCMwHgxIkTpXv37mYF4MCBA2XAgAHOxQPULFu2rDkJmBIfAja9VPEhyigQQAABBBBAwAsCrAD0wizRRwQQQACBRBWwKavwTAC4du1aSUlJMQHg1KlT5a677oro+ZkyZYr5hJgSHwI2vVTxIcooEEAAAQQQQMALAuwB6IVZoo8IIIAAAokqYFNW4ZkAUB+WGjVqyFdffWX28Hv88ccT9flh3AEEbHqpmCAEEEAAAQQQQCCWApwCHEtt7oUAAggggIBzAZuyCk8FgIsWLZKmTZvKNddcIxs2bHAuHqBmnz59ZOTIkRFdg8b2CNj0UtmjQk8QQAABBBBAIBEEMjNFUlJEsrJCj7Z4cZGMDJHk5NB1qYEAAggggAACkQnYlFV4KgBU9qFDh8oTTzwhs2bNkpYtW4Y9E6VKlZKffvop7PY0tEvAppfKLhl6gwACCCCAAAKJIDBvnkjbtvmHgBr+paWJtGiRCCKMEQEEEEAAgcIXsCmr8FwA6AsBx44dK+vXr5fKlSu7ntF169aZ/QSPHz/uui0N7BSw6aWyU4heIYAAAggggEC8C+hKwDZtRL75JudIk5JEGjcW0TP0WPkX708B40MAAQQQsEnApqzCkwGgLwScPn269O3b1/HcHj16VPbt2ycTJkyQvXv3EgA6lrO/ok0vlf1a9BABBBBAAAEE4lWgd2+RUaP+Gt3114tMny5SqVK8jphxIYAAAgggYK+ATVmF5wLAzMxMeeSRR0RPBQ63nDhxwpwmzArAcAXta2fTS2WfDj1CAAEEEEAAgUQRyB0AduwoMnVqooyecSKAAAIIIGCXgE1ZhacCwKVLl0rz5s0lKytLfCGe779uppgA0I2WN+ra9FJ5Q4xeIoAAAggggEA8ChAAxuOsMiYEEEAAAa8K2JRVeCYA/P3336VKlSry/fffm9V7WjTIC7ewAjBcOTvb2fRS2SlErxBAAAEEEEAgEQQIABNhlhkjAggggIBXBGzKKjwTAM6dO1datWrlD//KlSsnDz74oNx4441yzjnnSIkSJULO/7Fjx0TxH3vsMdm4cSOfAIcU804Fm14q76jRUwQQQAABBBCINwECwHibUcaDAAIIIOBlAZuyCs8EgCNGjJB+/fqZedfwT/cCrBTmbsbffvutOT2YPQC9/Brl7LtNL1X8qDISBBBAAAEEEPCaQK9eIs8++1ev2QPQazNIfxFAAAEE4knApqzCMwHgiy++KPfdd59ZAdi/f38ZPHhwRM/E2WefLd99911E16CxPQI2vVT2qNATBBBAAAEEEEg0AQLARJtxxosAAgggYLOATVmFZwLAL774Qi677DITAE6dOlXuuuuuiOZ4wYIF0qxZs4iuQWN7BGx6qexRoScIIIAAAgggkGgCuQPATp1EpkxJNAXGiwACCCCAgB0CNmUVngkAderq168vK1eulCFDhvg/B7ZjSulFYQvY9FIVtgX3RwABBBBAAIHEFSAATNy5Z+QIIIAAAvYJ2JRVeCoA3LJliyQnJ8tFF10kH374YUQz+/jjj8vw4cMjugaN7RGw6aWyR4WeIIAAAggggECiCRAAJtqMM14EEEAAAZsFbMoqPBUA6qT+97//lRYtWsiUKVOkdevWYc9z+fLlZf/+/WG3p6FdAja9VHbJ0BsEEEAAAQQQSCQBAsBEmm3GigACCCBgu4BNWYXnAkCd3BUrVpgDQdasWSN6mIfb8sknn0itWrU4BdgtnMX1bXqpLGaiawgggAACCCAQ5wK5A8Drrxd5/XWRypXjfOAMDwEEEEAAAQsFbMoqPBMAami3adMm/3RmZGTI+++/L730bzkOyx9//CG7du2Sl156yaz+O378uMOWVLNdwKaXynYr+ocAAggggAAC8SmQmSmiH8hs355zfElJIo0aiQwcKJKcHJ9jZ1QIIIAAAgjYKGBTVuGZAHDUqFHSp0+fHPN54sQJcyqw2+JrRwDoVs7e+ja9VPYq0TMEEEAAAQQQiFeBefNE2rYVycoKPsLixUXS0kRatIhXBcaFAAIIIICAXQI2ZRWeCQD37NkjVapUkWPHjokGeFo0/PP92ekU+9rofwkAnarZX8+ml8p+LXqIAAIIIIAAAvEkoCv/UlLyD/9849UQMCODlYDxNP+MBQEEEEDAXgGbsgrPBIA6ne3bt5e0tLSwgr/cjwMBoL0vSDg9s+mlCqf/tEEAAQQQQAABBMIVaNJEZPFi5621fnq68/rURAABBBBAAIHwBGzKKjwVAH7wwQeSnJwsxYoVk27dusnf//53KV26tJx88slSpEgR879gRVcKHjlyRLZt2ybPPvus7Ny5kxWA4T2/Vray6aWyEohOIYAAAggggEBcCuzYIVK1qsj/PpBxNEbdQUf3CaxUyVF1KiGAAAIIIIBAmAI2ZRWeCgDV+8Ybb5Ry5crJPN3oJMzy1VdfSY0aNQgAw/SzsZlNL5WNPvQJAQQQQAABBOJTYOpUkdRU92PTdh07um9HCwQQQAABBBBwLmBTVuG5AFCDv4kTJ8rSpUudiweoWb58eXMSMCU+BGx6qeJDlFEggAACCCCAgBcExowR6dnTfU+1XY8e7tvRAgEEEEAAAQScC9iUVXguAPzzzz9lypQpcs899zgXD1Dzvffek+uvvz6ia9DYHgGbXip7VOgJAggggAACCMS7ACsA432GGR8CCCCAgJcFbMoqPBcAenni6Xv0BGx6qaI3Sq6MAAIIIIAAAgjkFGAPQJ4IBBBAAAEE7BWwKasgALT3OaFnLgRseqlcdJuqCCCAAAIIIIBAxAKcAhwxIRdAAAEEEEAgKgI2ZRUEgFGZYi4aawGbXqpYj537IYAAAggggEBiC2RmitSpI3LsWGiHYsVE1q4VSU4OXZcaCCCAAAIIIBCZgE1ZBQFgZHNJa0sEbHqpLCGhGwgggAACCCCQIAIEgAky0QwTAQQQQMBzAjZlFQSAnnt86HAgAZteKmYIAQQQQAABBBCIpQCfAMdSm3shgAACCCDgXMCmrMKKAPDjjz+WK664wrlgAdQsjHsWQLe5RBABm14qJgkBBBBAAAEEEIiVAIeAxEqa+yCAAAIIIOBewKaswooAsGrVqvLNN9+4l4ygRWHcM4Lu0jSEgE0vFZOFAAIIIIAAAgjESmDqVJHUVPd303YdO7pvRwsEEEAAAQQQcC5gU1ZhRQB42mmnyeHDh50LFkDNwrhnAXSbSwQRsOmlYpIQQAABBBBAAIFYCQwcKPLkk+7vpu0GDXLfjhYIIIAAAggg4FzApqzCigCwSJEismzZMrnlllucK0ZQc/ny5dKgQQM5fvx4BFehqU0CNr1UNrnQFwQQQAABBBCIb4EuXUQmT3Y/Rm330kvu29ECAQQQQAABBJwL2JRVWBMAXnjhhTJx4kS56qqrpFSpUpKUlORc1EHNEydOyKFDh2TTpk3SpUsX2bFjBwGgAzevVLHppfKKGf1EAAEEEEAAAe8LDBggMmSI+3GwAtC9GS0QQAABBBBwK2BTVmFNAFjQgV9+k6JhoN6PFYBuH11769v0UtmrRM8QQAABBBBAIN4E2AMw3maU8SCAAAIIxJOATVmFVQGgBnOxKgSAsZKOzX1seqliM2LuggACCCCAAAIIiOgpwFWquJfQdpUquW9HCwQQQAABBBBwLmBTVmFVAOicMLKarACMzM/G1ja9VDb60CcEEEAAAQQQiF+BJk1EFi92Pj6tn57uvD41EUAAAQQQQCA8AZuyCqsCQFYAhvdA0UrEppeK+UAAAQQQQAABBGIpkJkpUqeOyLFjoe9arJjI2rUiycmh61IDAQQQ4UbBXwAAIABJREFUQAABBCITsCmrsCYALFu2rAwfPlxq1aplDgE56aST8lXWsLBq1ary4IMPyiOPPBJyRrKysuSHH36Q999/X4YMGSI//fQTewCGVPNOBZteKu+o0VMEEEAAAQQQiAcBAsB4mEXGgAACCCAQjwI2ZRXWBIAzZsyQNm3auJrvIkWKSP/+/eXJJ5901S49PV2aN29OAOhKze7KNr1UdkvROwQQQAABBBCINwE+AY63GWU8CCCAAALxImBTVmFFAHjKKafI4cOHpWjRoq7mONwAUFcPli5d2qwCpMSHgE0vVXyIMgoEEEAAAQQQ8IKAHuZRtaqIm7P0kpJEtm/nEBAvzC99RAABBBDwtoBNWYUVAWDlypVlh/7txWUJNwDU21SpUkW26998KHEhYNNLFRegDAIBBBBAAAEEPCEwdapIaqr7rmq7jh3dt6MFAggggAACCDgXsCmrsCIAXLBggTRr1sy54P9qRhIAhntP152kQUwEbHqpYjJgboIAAggggAACCIjImDEiPXu6p9B2PXq4b0cLBBBAAAEEEHAuYFNWYUUA6JwuZ81IAsBw70k7OwVseqnsFKJXCCCAAAIIIBCPAqwAjMdZZUwIIIAAAvEiYFNWQQAYL09Vgo/DppcqwaeC4SOAAAIIIIBADAXYAzCG2NwKAQQQQAABlwI2ZRUEgC4nj+p2Ctj0UtkpRK8QQAABBBBAIF4FOAU4XmeWcSGAAAIIeF3ApqyCANDrTxP9NwI2vVRMCQIIIIAAAgggEEuBzEyRlBSRrKzQdy1eXCQjQyQ5OXRdaiCAAAIIIIBAZAI2ZRVWBIDz5s2TFi1auFaNZA/AcO/pupM0iImATS9VTAbMTRBAAAEEEEAAgWwC8+aJtG2bfwio4V9amkgYf+3GGgEEEEAAAQTCELApq7AiAKxSpYps377dNWUkAWC493TdSRrERMCmlyomA+YmCCCAAAIIIIBALgFdCdiqlYjuC5i9JCWJNG4sMmAAK/94aBBAAAEEEIilgE1ZhRUBYMmSJeXw4cOigZ6bEm4AeOzYMTnjjDPkt99+c3M76losYNNLZTETXUMAAQQQQACBOBd46CGRsWP/GmTduiKvvipSqVKcD5zhIYAAAgggYKGATVmFFQGgBnmzZ8+Wf/7zn66mK9wAMC0tTTp06CDHjx93dT8q2ytg00tlrxI9QwABBBBAAIF4F+jRQ+Q///lrlF27ikyaFO+jZnwIIIAAAgjYKWBTVmFNAFi2bFkZOnSoJCcnS6lSpaRYsWL5zt6JEydEP+N98MEH5dFHHw050xr2HTp0SDZt2iS9e/eWH3/8MeYB4L59+2TJkiXy3XffSeXKlaVx48Zy+umnh+y70wo6vsWLF8uuXbvkvPPOM9cvV66c0+amnn6KvWzZMmNVvXp1adiwoZQoUcLRNY4ePSqrV6+WTz/9VI4cOWL6cPPNN0uFChUctY+kkk0vVSTjoC0CCCCAAAIIIBCJQO4A8N57RSZOjOSKtEUAAQQQQACBcAVsyiqsCQCTdHMSl0VDwEjaxWoF4J9//imDBw+WESNGSLVq1eTqq6+WDRs2mJNrn3/+eencubPLkeetPn78eOnbt6+UL19eUlJSZPPmzSaIGzhwoPTp0yfk59Ua2D388MMyadIkE8Jecskl8u6775og7+WXX5YmTZrk28f09HR54IEHTPiYvegqzfbt28t//vMfE+xGq9j0UkVrjFwXAQQQQAABBBAIJUAAGEqI3yOAAAIIIBA7AZuyCqsCQA30YlU0OIxFAKhjatu2rcyaNUu6d+8u48aNM2GchoJ33XWXzJgxQ4YPH27Cu3DLY489Js8884w0a9ZMZs6cKSeffLK5lF5z5MiR0q1bN5kwYULQy2vId+utt8qaNWtk2LBh8vjjj5u6+vMGDRqYn0+bNk3atWsX8BrTp083YznppJPkb3/7m1nxt3XrVvn888/99S+99FLJyMiQM888M9xh5tvOppcqKgPkoggggAACCCCAgAMBAkAHSFRBAAEEEEAgRgI2ZRVWBYAx8hffysFYBID6WXP//v3Nyj8NxLJ/2qyf2eqnwD/99JMsXbrUhHBui4Zvup9h6dKlZceOHTlW2elhJ7qSb9u2bTJx4kS5V78BCVC6du1qVvnddNNNsnLlyhw1NMjTa2ho+dFHH8lll12W4/e7d+82nwprUDh69GipWLGi//f6KbGGhgcOHDA/u/POO2Xu3Lluh+iovk0vlaMOUwkBBBBAAAEEEIiCAAFgFFC5JAIIIIAAAmEK2JRVWBUAxtsKwJ07d5rgLysrS5577jnp2bNnnkdGV+dpOKdBoH62e8oppzh+rH755RepWrWq/PDDD/LQQw+Zz4lzF/3sWFf06ee3X375pZxzzjk5qqxbt07q1Kljfvbmm29K8+bN81xDwz0NKGvXri1aP3vRa2vAqCsPA53irJ86X3/99aJhpBbtgwaGBV1seqkKemxcDwEEEEAAAQQQcCpAAOhUinoIIIAAAghEX8CmrMKaAFCDLA3CatWqZcKqokWLFuhM6AEVuhJt48aNJojTwy6ivQJQ98R74YUXzDi++uorufjii/OMaf78+XLHHXeYn0+ZMkU6derkeNyjRo0yB5po0dV29evXz9NWDz1RUy26H+CgQYNy1NG9/fTgEF2ZqCsRS5YsmecaGizq/oBa3nnnHalXr56/zrXXXmsONtFDXIIVXaGoKxW16KfIGnoWdLHppSrosXE9BBBAAAEEEEDAqQABoFMp6iGAAAIIIBB9AZuyCmsCQF1hFijAisZ0rF+/3qx6i2YAqKv+dLWdnjZ8xhlnmFN1Ax1YsmfPHjn//PPNMHWlnO6T57Rcfvnl/n32NNwsU6ZMnqa61+Cpp55q9vO74IILRFcl+vqhpxHrvdVB9+775JNPAt567dq1pm9a9ECP119/3fxZV2zqyj/d4zC/ovV1j0Atke53GOw+Nr1UTuePeggggAACCCCAQEELPPigyLhxf121bl2RqVNFKlcu6DtxPQQQQAABBBAIJWBTVmFFAHj66aeLfs4ay6Ir1nx700XjvqtXr5a6+jcuEalZs6Z8/PHHQW+jAaGOX1c9/vzzzwFX4eVu/O233/r329P2unovWNH764nAWrZs2WI+S9by2muvSceOHc2fmzZtKgsWLAh4iYMHD/pX+J177rmioaWboqsTb7vtNtPk1VdflbvvvttNc0d1bXqpHHWYSggggAACCCCAQAELZGaKtGwpsnNnzgsnJYk0aqRfg4gkJxfwTbkcAggggAACCAQVsCmrsCIArFKlivkkN5Yl2vfUU3n1dF4tjRo1Mp/ZBisayH399dfm17oC0LfaLj+POXPmSKtWrUwVPWE3+4m7udvp4SLLly83P9bVeLqKT8v9998v48ePN3++7777/J8rB7pviRIlzF6GWvQB9q1adDJnaWlp/hOE2QPQiRh1EEAAAQQQQAABdwLz5onoRxn/++tawMbFi4ukpYm0aOHu2tRGAAEEEEAAgfAECABzuX322Wein7PGskT7nv/6179k0qRJZkj33HOPOWU3WNF99DL1n2xdrJDTT2n79etn2vzjH/+QFStWBL2+BoUaGGoZPHiwDBgwwPxZV+Xp6jwtQ4YMMacVBytnnXWW7N+/3/x61apVcuONNzqerl69esmzzz5r9iL88MMPHbfLXlFfmvzK3r17RR216OrIChUqhHUfGiGAAAIIIIAAAl4T0L9GpqTkH/75xqQhoO44w0pAr80y/UUAAQQQ8KIAAaAXZ81ln5s1ayYLFy40rYKd0Ou75A033ODf+2/MmDHSQ3dvDlH0mmPHjjW19F56mEiwovvv+fbt08M8Ro8ebapeccUV/n3/gp1S7Lum7h/oC+H0U2H9ZNhJ0X0C9dRfXeGo+wW2bt3aSbM8dQLtnxjsQgSAYRHTCAEEEEAAAQQ8KtCkiUg+H5vkGZXWT0/36GDpNgIIIIAAAh4SIACMYLJ0rzsNk3bv3i2//vqrOb1W9xDUgEo/pS2u/6xpQdEDTXyr8vr27WsOvwhWsgeAoVbi+a7RtWtX/6rCNm3aiH5mG6xkDwCzr0bUU4m3bt1qmr344ouiqxaDlewB4LRp00RP9nVSFi1aJLfffrvUrl1b9DARN0Fe9uu7aUcA6GRmqIMAAggggAAC8SCwY4dI1ap6OJvz0eiegLr7TqVKzttQEwEEEEAAAQTcCxAAujTTAyw0dNJ99PQQC11VFqicdNJJctVVV5kVcRpQudmnzmWXQlZv0KCB6MnGWp544gl58skng7bRcExPJtYycuRI/96B+d2kW7duMnHiRFNFAz490CNY0YBw1qxZ5tfdu3f37/t3ySWXGE8tkydPls6dOwe9hp5orKcGa9Fr+fYfzK+PerpwcnKybN68WTZu3BjRZ958AhzykaMCAggggAACCCSggJ7wm5rqfuDa7n9nwblvTAsEEEAAAQQQcCRAAOiISUT36evdu7d/n7pgwV/2y/lWiunKwLZt28rQoUMLJQhs2bKlzJ0713RNDwPRYC9Yyf4p7oQJE0TDvVBFXUaNGmWqaRjnC/gCtcv+OXKfPn1kxIgRppqGcxrMadHDQDQcDFZKlSplTijWsmTJEtGAM1TRz4ofeeQRc7iIHjISzWLTSxXNcXJtBBBAAAEEEEAgu8CYMSI9e7o30XYOdp1xf2FaIIAAAggggIBfwKaswopTgAM9G0899ZRZNaeryLIHf6E+Bc1d99RTT5Xnn38+39Vt0Xg2NfjSAEyLnrY7bty4oLepWLGiObhCi65y1FODQxXd/0/3AdTSuHFj0U9tgxU9sGPNmjXm19nDuBYtWsibb75pfq6nFuthHYGKzoGurvTZ6onDevJwfkUP+6hTp445cVhXF0a72PRSRXusXB8BBBBAAAEEEPAJsAKQZwEBBBBAAAF7BWzKKqwLAP/44w+zck8PmvAFTrlDv/xWAgaqqz/TVXUawoUKEAvqsXnllVfM6b9amjdv7g/aAl2/ZMmS8vvvv5tf7dixQyo52JBl5cqV5vRfLVdeeaV89NFHQbteo0YN+fLLL83v3333Xalbt675s54GrHsOaunZs6c/sMx9If30Vz8B1lKiRAnRfRj1v8HKgQMHzOrCCy+8UN566y0THka72PRSRXusXB8BBBBAAAEEEPAJsAcgzwICCCCAAAL2CtiUVVgVAB49etTs3/ff//7XH9TlXtGn4ZiGUXrwxxlnnCGnnHKKHD58WH755RcTTOkBIfpfX9HAT6+h/9W98qbqP5PGoGzbtk0uuugicyf9xHfTpk0B77pv3z4599xzze8qVKjgXwkYqot6AEq5cuXkyJEjop/nHjp0KGATHbuugtSAUQ9I+fHHH0UDRy1vv/223HLLLebP+Z0k/P7778t1111n6qWkpPhXEwa6ofZHg0n9r4aNOk+xKDa9VLEYL/dAAAEEEEAAAQR8ApwCzLOAAAIIIICAnQI2ZRVWBYD33nuvOdnWF9rp9NWqVUvatWtnAqiaNWvKaaedFnJWd+7cKR988IHZFy89Pd2EUVr0uv/+97/zPZAj5MVdVND+6gEmugJOA8pAq+ayr+TTT3r1c2WnpWnTpmZ8WjRwrKpHwOUq33zzjVmJpyV3yHfs2DE5++yz5eDBg+YU5V27dgW8dfbVjPpZs64WDFT0U2Hdj/CTTz6R9957T8466yynQ4m4nk0vVcSD4QIIIIAAAggggIALgcxM/Udakays0I2KFxfJyNC9oEPXpQYCCCCAAAIIRCZgU1ZhTQD4xhtviB6c4Qv/dH863QdQT6qNpOjBFfqpq+59pwFVkSJF5J133pEbbrghkss6aqthZteuXU3d5cuX+1fbZW+sh5T079/f9Ev31nMz3hUrVkj9+vXN5V566SXp0qVLnn5Nnz7dnIisRU8lvvXWW3PU0XtrH7To6knfqsXslXQMOhYNXzVcLVOmTJ77/Pnnn9KxY0fRQFPDv8qVKwc10ro63oIsNr1UBTkuroUAAggggAACCDgRmDdPpG3b/ENADf/S0kRatHByReoggAACCCCAQKQCNmUVVgSAuu+frlLbs2eP+az11VdflYYNG0bqnKP9+vXrpUmTJqL70+kBFroyL9r7AWZlZZn9+b744gsTBE6aNCnPmK666iqzf1+nTp1kypQpOX6/evVqs/pRg7f58+cHDAf1E179lFeDwGXLluW5vu+gj3r16pngM3fxeXz//fcmCOzXr1+OKjoGPaRE9wEcNGiQDBw4MM819DNjHZ/2cdWqVXLZZZcFnTu10NOLC/pgEJteqgJ9cLkYAggggAACCCDgUEBXArZsKbJzZ84GSUl6aJzu/8zKP4eUVEMAAQQQQKBABGzKKqwIAMePHy8PPPCAlC9f3oRZl19+eYFA576I7sN30003ia4KTEtLM5+rRrvop8j6+XLRokVl8+bNUqVKFf8tZ8+eLa1btzbh54YNG/KsrNN2uv+eFj0YZcaMGXm6q6cH66fGugfg2rVr/Xv1acV169aZPfvOPPNMc/1AnwhrvYULF5rPg/VzYD0spHTp0v77PP3009KnTx+pXbu2Cfd0H8HsRVfz6cpDXWmoAaYGmrmLrrzUfRm1fyNGjDDjyL0SMdJ5sOmlinQstEcAAQQQQAABBMIVuP9+kfHj/2p9000i+m/MDs6YC/eWtEMAAQQQQACBIAI2ZRVWBIB6YuyHH34oGojdeeedUX1wdOWZrlbTlXOBVsxF4+aLFi0yYaOupNOTiDWImzNnjllRV61aNXPicaBPZuvUqWNCPC26ElBDtkBFw71GjRqZvQY1TNV9E3VsGtzpASG6Mk9XIuZX9BPi7t27m9N7R48eLWXLljWr9HS1np4aPHfu3DwBpe4hqJ8X616LTosabN++nU+AnYJRDwEEEEAAAQQQcCGQOwDs3j1nIOjiUlRFAAEEEEAAgQgFCACzAeqnp3qq7/XXX5/v6bIRmudorgGZ7renh184OVSkIO69ZcsW84mtfoa7f/9+8xlyamqqdOvWzQR3gUpGRoZZ+aen+Ab7BNjXbvfu3TJ8+HB56623zKfUutKwffv25sAOp2PMzMyUYcOGmdBRV0mqk/ZPQ75An0v36NFD/vOf/7ji0f0YBw8e7KqNk8o2vVRO+ksdBBBAAAEEEEAgGgIEgNFQ5ZoIIIAAAgiEJ2BTVlHoKwA1sNK9+TRIul//xhKD8uyzz8pjjz1mPjfWvfEo3hew6aXyviYjQAABBBBAAAGvChAAenXm6DcCCCCAQDwK2JRVFHoAqJ/EPvTQQ/Lxxx9Hbe+/3A+R3ktXt7344oty7733xuMzlnBjsumlSjh8BowAAggggAAC1ggQAFozFXQEAQQQQAABsSmrKPQAUD85feKJJ0Q/BdZ952JRfvjhBznrrLPMJ7O6Tx7F+wI2vVTe12QECCCAAAIIIOBVAQJAr84c/UYAAQQQiEcBm7KKQg8Ax44dKw8//LDZc073uotFOXz4sJxxxhny1FNPSb9+/WJxS+4RZQGbXqooD5XLI4AAAggggAACQQXuu09kwoS/fs0hIDwsCCCAAAIIFJ6ATVlFoQeAM2fONIdV6CEZF110UUxmZevWreb03TFjxsiDDz4Yk3tyk+gK2PRSRXekXB0BBBBAAAEEEAgukDsAvPlmkcmTRSpXRg0BBBBAAAEEYi1gU1ZR6AHgmjVrpG7duqJBYKtWrWIyF9OnT5e7777bnKx7++23x+Se3CS6Aja9VNEdKVdHAAEEEEAAAQQCC2RmirRsKbJzZ87fJyWJNGokMnCgSHIyeggggAACCCAQKwGbsopCDwD/+OMPKVOmjDRq1EjmzJkTkzlo3Lix/Pe//5WdO3dKhQoVYnJPbhJdAZtequiOlKsjgAACCCCAAAJ5BebNE2nbViQrK7hO8eIiaWkiLVogiAACCCCAAAKxELApqyj0AFDBGzZsKMuXL5f33ntP/v73v0d1DpYtWyYNGjQwnwB/+eWXUb0XF4+dgE0vVexGzZ0QQAABBBBAAAERXfmXkpJ/+Odz0hAwI4OVgDw3CCCAAAIIxELApqzCigBwxowZ0qFDB6levbqsWrXKnNAbjfL1119LvXr1ZN++fTJ48GDp379/NG7DNQtBwKaXqhCGzy0RQAABBBBAIIEFmjQRWbzYOYDWT093Xp+aCCCAAAIIIBCegE1ZhRUB4IkTJ+Tyyy83K/Jq1Kghb775plx88cXh6QZptXLlShMyavinpw3v2LFDypYtW6D34GKFJ2DTS1V4CtwZAQQQQAABBBJNYMcOkapVRU6ccD5y3RNw+3aRSpWct6EmAggggAACCLgXsCmrsCIAVEI9kKNFixaSlJQkxYsXl379+kmvXr3klFNOcS+crcW3335rrqWrDDVo1Os/9dRT8vjjj0d0XRrbJWDTS2WXDL1BAAEEEEAAgXgWmDpVJDXV/Qi1XceO7tvRAgEEEEAAAQScC9iUVVgTACpfjx49ZNy4cUZSg7rTTjtNWrZsKW3btjV7A+r/d1J27dplPiV+7bXX5N1335U///zTH/7Vrl1bVq9eLUWLFnVyKep4RMCml8ojZHQTAQQQQAABBOJAYMwYkZ493Q9E2/Xo4b4dLRBAAAEEEEDAuYBNWYVVAeCxY8fkpptuMoeBaADoW7HnCwSrVq0ql112mZx55plSqlQpOf300yUrK0t+/vln87+9e/fKpk2b5NChQ/7Z0Gv4SpUqVcy1zznnHOezRU1PCNj0UnkCjE4igAACCCCAQFwIsAIwLqaRQSCAAAIIxKmATVmFVQGgzvfhw4fNqcDZQ8Dsz4EGg/mV7IGfLzjUn+megkuXLpXKlSvH6WOV2MOy6aVK7Jlg9AgggAACCCAQSwH2AIylNvdCAAEEEEDAnYBNWYV1AaBS6qq+Bx98UF566SWzEjB7yR3w5aYPVL9JkyYydepUKVOmjLuZorZnBGx6qTyDRkcRQAABBBBAIC4EOAU4LqaRQSCAAAIIxKGATVmFlQGgb86XLVsmjzzyiGzevNn8KNTqP187X0ioq/2GDRsmbdq0icPHiCFlF7DppWJmEEAAAQQQQACBWApkZoqkpOg/ooe+a/HiIhkZIsnJoetSAwEEEEAAAQQiE7Apq7A6APQxL168WF599VXzCe8vv/ySr74eFHLbbbdJu3btpFmzZlKkSJHIZovWnhCw6aXyBBidRAABBBBAAIG4Epg3T6Rt2/xDQA3/0tJEWrSIq6EzGAQQQAABBKwVsCmr8EQA6JtJPc33s88+ky1btsiePXvk119/9Z8WfP7550u1atXk0ksvJfSz9tGPXsdseqmiN0qujAACCCCAAAIIBBfQlYDNm4vs2ZOzju6o07ixyIABrPzj+UEAAQQQQCCWAjZlFZ4KAGM5SdzLWwI2vVTekqO3CCCAAAIIIBAPAhr+DR4ssnhx3tHcdJPIyJGEf/Ewz4wBAQQQQMBbAjZlFQSA3np26G0QAZteKiYJAQQQQAABBBCIpYB+/qtbXh89GvyuJ50kMnMmn//Gcl64FwIIIIAAAjZlFQSAPI9xIWDTSxUXoAwCAQQQQAABBDwhoCv/6tQROXYsdHeLFRNZu5aVgKGlqIEAAggggEDBCNiUVRAAFsyccpVCFrDppSpkCm6PAAIIIIAAAgkkoKf/vvee8wFr/TVrnNenJgIIIIAAAgiEL2BTVkEAGP480tIiAZteKotY6AoCCCCAAAIIxLHAjh0iVaq4H6C2q1TJfTtaIIAAAggggIA7AZuyCgJAd3NHbUsFbHqpLCWiWwgggAACCCAQZwKjRon07u1+UNru0Ufdt6MFAggggAACCLgTsCmrIAB0N3fUtlTAppfKUiK6hQACCCCAAAJxJtCsmcjChe4Hpe3mz3ffjhYIIIAAAggg4E7ApqyCANDd3FHbUgGbXipLiegWAggggAACCMSZQO3aIuvXux/Uddf9/2EgFAQQQAABBBCIroBNWQUBYHTnmqvHSMCmlypGQ+Y2CCCAAAIIIJDgAk2biqSnu0dgBaB7M1oggAACCCAQjoBNWQUBYDgzSBvrBGx6qazDoUMIIIAAAgggEJcC7AEYl9PKoBBAAAEE4kjApqyCADCOHqxEHopNL1UizwNjRwABBBBAAIHYCXAKcOysuRMCCCCAAALhCNiUVRAAhjODtLFOwKaXyjocOoQAAggggAACcSlAABiX08qgEEAAAQTiSMCmrIIAMI4erEQeik0vVSLPA2NHAAEEEEAAgdgJTJ0qkprq/n7armNH9+1ogQACCCCAAALuBGzKKggA3c0dtS0VsOmlspSIbiGAAAIIIIBAnAmMGSPSs6f7QWm7Hj3ct6MFAggggAACCLgTsCmrIAB0N3fUtlTAppfKUiK6hQACCCCAAAJxJsAKwDibUIaDAAIIIBB3AjZlFQSAcfd4JeaAbHqpEnMGGDUCCCCAAAIIxFpA9wCsWlXkxAnnd05KEtm+XaRSJedtqIkAAggggAAC4QnYlFUQAIY3h7SyTMCml8oyGrqDAAIIIIAAAnEs0KSJyOLFzgeo9dPTndenJgIIIIAAAgiEL2BTVkEAGP480tIiAZteKotY6AoCCCCAAAIIxLlAZqZISopIVlbogRYvLpKRIZKcHLouNRBAAAEEEEAgcgGbsgoCwMjnkytYIGDTS2UBB11AAAEEEEAAgQQSmDdPpG3b/ENADf/S0kRatEggGIaKAAIIIIBAIQvYlFUQABbyw8DtC0bAppeqYEbEVRBAAAEEEEAAAecCuhLwySdFFi3K20Y/+x0wgJV/zjWpiQACCCCAQMEI2JRVEAAWzJxylUIWsOmlKmQKbo8AAggggAACCSigAeDgwYH3A2zcWGTgQALABHwsGDICCCCAQCEL2JRVEAAW8sPA7QtGwKaXqmBGxFUQQAABBBBAAAFnAnwC7MyJWghsGWsCAAAgAElEQVQggAACCMRawKasggAw1rPP/aIiYNNLFZUBclEEEEAAAQQQQCCAAIeA8FgggAACCCBgr4BNWQUBoL3PCT1zIWDTS+Wi21RFAAEEEEAAAQQiEtD9/RYvdn4JrZ+e7rw+NRFAAAEEEEAgfAGbsgoCwPDnkZYWCdj0UlnEQlcQQAABBBBAII4FduwQqVpV5MQJ54NMShLZvl2kUiXnbaiJAAIIIIAAAuEJ2JRVEACGN4e0skzAppfKMhq6gwACCCCAAAJxKjB1qkhqqvvBabuOHd23owUCCCCAAAIIuBOwKasgAHQ3d9S2VMCml8pSIrqFAAIIIIAAAnEmMGaMSM+e7gel7Xr0cN+OFggggAACCCDgTsCmrIIA0N3cUdtSAZteKkuJ6BYCCCCAAAIIxJkAKwDjbEIZDgIIIIBA3AnYlFUQAMbd45WYA7LppUrMGWDUCCCAAAIIIBBrAfYAjLU490MAAQQQQMCdgE1ZBQGgu7mjtqUCNr1UlhLRLQQQQAABBBCIQwFOAY7DSWVICCCAAAJxI2BTVkEAGDePVWIPxKaXKrFngtEjgAACCCCAQCwFMjNFUlJEsrJC37V4cZGMDJHk5NB1qYEAAggggAACkQvYlFUQAEY+n1zBAgGbXioLOOgCAggggAACCCSQwLx5Im3b5h8CaviXlibSokUCwTBUBBBAAAEEClnApqyCALCQHwZuXzACNr1UBTMiroIAAggggAACCDgX0JWATz4psmhR3jb6mfCAAaz8c65JTQQQQAABBApGwKasggCwYOaUqxSygE0vVSFTcHsEEEAAAQQQSGCBW24RefvtvwA6dBCZNi2BQRg6AggggAAChShgU1ZBAFiIDwK3LjgBm16qghsVV0IAAQQQQAABBJwJ6ArAwYNFFi/OW79xY5GBA1kB6EySWggggAACCBScgE1ZBQFgwc0rVypEAZteqkJk4NYIIIAAAgggkIAC7AGYgJPOkBFAAAEEPCFgU1ZBAOiJR4ZOhhKw6aUK1Vd+jwACCCCAAAIIFJQApwAXlCTXQQABBBBAoOAFbMoqCAALfn65YiEI2PRSFcLwuSUCCCCAAAIIJKiAHvAR6LPfYBxaPz09QbEYNgIIIIAAAjEWsCmrIACM8eRzu+gI2PRSRWeEXBUBBBBAAAEEEMgpsGOHSNWqIidOOJdJShLZvl2kUiXnbaiJAAIIIIAAAuEJ2JRVEACGN4e0skzAppfKMhq6gwACCCCAAAJxKjB1qkhqqvvBabuOHd23owUCCCCAAAIIuBOwKasgAHQ3d9S2VMCml8pSIrqFAAIIIIAAAnEmMGaMSM+e7gel7Xr0cN+OFggggAACCCDgTsCmrIIA0N3cUdtSAZteKkuJ6BYCCCCAAAIIxJkAKwDjbEIZDgIIIIBA3AnYlFUQAMbd45WYA7LppUrMGWDUCCCAAAIIIBBrAd0DsEoV93fVduwB6N6NFggggAACCLgVsCmrIAB0O3vUt1LAppfKSiA6hQACCCCAAAJxJ0AAGHdTyoAQQAABBOJMwKasggAwzh6uRB2OTS9Vos4B40YAAQQQQACB2AqMGiXSu7f7e2q7Rx91344WCCCAAAIIIOBOwKasggDQ3dxR21IBm14qS4noFgIIIIAAAgjEmUC7diJpae4H1b69yOuvu29HCwQQQAABBBBwJ2BTVkEA6G7uqG2pgE0vlaVEdAsBBBBAAAEE4kygWTORhQvdD0rbzZ/vvh0tEEAAAQQQQMCdgE1ZBQGgu7mjtqUCNr1UlhLRLQQQQAABBBCIM4HWrUVmz3Y/KG03c6b7drRAAAEEEEAAAXcCNmUVBIDu5o7algrY9FJZSkS3EEAAAQQQQCDOBAYMEBkyxP2gBg4UGTTIfTtaIIAAAggggIA7AZuyCgJAd3NHbUsFbHqpLCWiWwgggAACCCAQZwJTp4qkproflLbr2NF9O1oggAACCCCAgDsBm7IKAkB3c0dtSwVseqksJaJbCCCAAAIIIBBnAjt2iFSp4n5Q2q5SJfftaIEAAggggAAC7gRsyioIAN3NHbUtFbDppbKUiG4hgAACCCCAQBwKNGkisnix84Fp/fR05/WpiQACCCCAAALhC9iUVRAAhj+Prlvu27dPlixZIt99951UrlxZGjduLKeffrrr6wRrcOjQIVm8eLHs2rVLzjvvPHP9cuXKubr+9u3bZdmyZaLXql69ujRs2FBKlCjh6hqFUdmml6owxs89EUAAAQQQQCAxBV59VaRTJ+dj5/Nf51bURAABBBBAIFIBm7IKAsBIZ9NB+z///FMGDx4sI0aMkGrVqsnVV18tGzZsEH0Qnn/+eencubODq+RfZfz48dK3b18pX768pKSkyObNm+XTTz+VgQMHSp8+faRIkSL5XuDIkSPy8MMPy6RJkyQ5OVkuueQSeffdd0V//vLLL0sT/edih+XYsWMyc+ZMM95evXpJJzd/K3V4j9zVbHqpwhwCzRBAAAEEEEAAAdcCrAB0TUYDBBBAAAEEYiZgU1ZBABjlaT9x4oS0bdtWZs2aJd27d5dx48aZME5DwbvuuktmzJghw4cPN+FduOWxxx6TZ555Rpo1a2aCt5NPPtlcSq85cuRI6datm0yYMCHo5TXku/XWW2XNmjUybNgwefzxx01d/XmDBg3Mz6dNmybt2rULGSK+8sorpi87dHMZEZkyZQoBYLgTSzsEEEAAAQQQQCAfAf3rVtWqIidOOGdKShLZvp09AJ2LURMBBBBAAIHwBQgAw7fzXMuhQ4dK//79zcq/zz//XIoVK+Yfg35mq58C//TTT7J06VITwrkt06dPlw4dOkjp0qVN6FaqVCn/JXQlnq7k27Ztm0ycOFHuvffegJfv2rWrWeV30003ycqVK3PU2bp1q7mGhpYfffSRXHbZZQGvsXHjRnnhhRfkjDPOMKHmDz/8QADodjKpjwACCCCAAAIIuBDgFGAXWFRFAAEEEECgEAQIAAsBvTBuuXPnThP8ZWVlyXPPPSc9e/bM0w1dnafhnAaB+tnuKaec4rirv/zyi1StWtWEbQ899JD5nDh30c9wdUWfBoNffvmlnHPOOTmqrFu3TurUqWN+9uabb0rz5s3zXENXAWpAWbt2bdH6ocp//vMf6dGjBwFgKCh+jwACCCCAAAIIRCAwZoxIgL9ehryitvvfX9VC1qUCAggggAACCIQvQAAYvp2nWj7wwANmVZyWr776Si6++OI8/Z8/f77ccccdYYVlo0aNkt69e5u2enBH/fr181x/06ZNUqtWLfNz3Q9w0KBBOero3n56cIiuTNSViCVLlsxzDQ0WdX9ALe+8847Uq1cv33lYtGiR3H777WGNKdwJtumlCncMtEMAAQQQQAABBNwIsALQjRZ1EUAAAQQQiL2ATVkFewBGaf511Z+utvvxxx/NZ7H6uW+SbrqSq+zZs0fOP/9889Prr79eMjIyHPfo8ssvN58Vazlw4ICUKVMmT1vda/DUU081+/ldcMEFoqsSff3Q04j13sePH5e//e1v8sknnwS899q1a03ftLRv315ef/31fPv49ttvyy233GLqsAeg4+mkIgIIIIAAAggg4EqAPQBdcVEZAQQQQACBmAsQAMacPPY3XL16tdStW9fcuGbNmvLxxx8H7YQGhPo5b9GiReXnn38OuAovd+Nvv/1WKlasaH6s7XX1XrCi99cTgbVs2bLFfJas5bXXXpOOHTuaPzdt2lQWLFgQ8BIHDx6UsmXLmt+de+65oqFlfkVPD9b9BLUQAMb+2eOOCCCAAAIIIJA4ApwCnDhzzUgRQAABBLwnQADovTlz3WM9CVdP59XSqFEj85ltsKKB3Ndff21+rSsAfavt8rvpnDlzpFWrVqbKpZde6l8JGKiNHi6yfPly8ytdvaer+LTcf//9Mn78ePPn++67z/+5cqBrlChRwuxlqEUfYN+qxUB1CQBdPy40QAABBBBAAAEEwhLIzBTR7ZyPHQvdXM+iW7tWJDk5dF1qIIAAAggggEDkAgSAkRtaf4V//etfMmnSJNPPe+65x5yyG6xce+21kql/exORV199Ve6+++6Q4xs+fLj069fP1PvHP/4hK1asCNpGg0INDLUMHjxYBgwYYP582223mb0DtQwZMsScVhysnHXWWbJ//37z61WrVsmNN94YtC4BYMjpowICCCCAAAIIIFAgAgSABcLIRRBAAAEEEIiKAAFgVFjtumizZs1k4cKFplPBTuj19fiGG27w7/03ZswY/wm6+Y1Irzl27FhTRe+lh4kEK3fddZd/3z49zGP06NGm6hVXXOHf9y/YKcW+a+r+gfrgatFPhfWT4WAlGgGg797B7rl3717RIFWLfh5doUIFux4IeoMAAggggAACCERBgE+Ao4DKJRFAAAEEECggAQLAAoK0+TJ6Iq9vVV7fvn1FV+wFK9kDwFAr8XzX6Nq1q39VYZs2bSQtLS3o9bMHgNlXI+qpxFu3bjXtXnzxRdFVi8FK9gBw2rRp0qFDh6B1oxEABjpAJVgHCABtfjPoGwIIIIAAAggUlACHgBSUJNdBAAEEEEAgOgIEgNFxteqqDRo0kKVLl5o+PfHEE/Lkk08G7V/t2rVl/fr15vcjR4707x2Y34C6desmEydONFU04NMDPYIVDQhnzZplft29e3f/vn+XXHKJORREy+TJk6Vz585Br6EnGuupwVr0Wr79BwM1IAC06lGkMwgggAACCCAQpwJTp4qkprofnLb73zlw7hvTAgEEEEAAAQQcCxAAOqbybsWWLVvK3LlzzQD0MBAN9oKV7J/iTpgwQTTcC1V69+4to0aNMtU0jPMFfIHaZf8cuU+fPjJixAhTLTk5WTZu3Gj+rIeBaDgYrJQqVcqcUKxlyZIlogFnsBKNAJBPgEM9EfweAQQQQAABBBJNYMwYkZ493Y9a2/Xo4b4dLRBAAAEEEEDAnQABoDsvT9Z+5JFHRPfV06Kn7Y4bNy7oOCpWrGj2rdOipwXrqcGhiu7/p/sAamncuLEsWrQoaBM9sGPNmjXm9y+88II58VdLixYt5M033zR/1lOLe/XqFfAax48fl5NOOklOnDhhfv/555+bk4eDlWgEgKE8bHqpQvWV3yOAAAIIIIAAAgUhwArAglDkGggggAACCERPwKasIumEL9WJ3ngT8sqvvPKKOf1XS/Pmzf1BWyCMkiVLyu+//25+tWPHDqlUqVJIs5UrV5rTf7VceeWV8tFHHwVtU6NGDfnyyy/N7zWcq1u3rvmzngasew5q6dmzpz+wzH0h/fRXPwHWUqJECfnpp5/Mf4MVAsCQ00cFBBBAAAEEEEAgYgH2AIyYkAsggAACCCAQVQECwKjy2nHxbdu2yUUXXWQ6o5/4btq0KWDH9u3bJ+eee675nZ5c61sJGGoUv/76q5QrV06OHDki+nnuoUOHAjbRfPfUU081AWPx4sXlxx9/FA0ctbz99ttyyy23mD/nd5Lw+++/L9ddd52pl5KS4l9NGKyPBIChZo/fI4AAAggggAACBSPAKcAF48hVEEAAAQQQiIYAAWA0VC28Zs2aNeXTTz81n8/+8ssvAVfNZV/Jp5/0Pv/8845H0rRpU0lPTzf1NXCsWrVqnrbffPONXHjhhQFDvmPHjsnZZ58tBw8eFD3ld9euXQHvnX01o37WrKsF8ysEgI6nkIoIIIAAAggggEBEApmZ+g+0IllZoS9TvLhIRobuAx26LjUQQAABBBBAIHIBAsDIDT1xhZdfflm6du1q+rp8+XL/arvsnR86dKj0799fihQpYvbW05N5nZYVK1ZI/fr1TfWXXnpJunTpkqfp9OnTpUOHDubneirxrbfemqOO3lv7oOXrr7/2r1rMXknHoGM57bTTZOfOnVKmTJl8u0gA6HQGqYcAAggggAACCEQu8Nhjup9z6Ov07i3y9NOh61EDAQQQQAABBApGgACwYBytv0pWVpbZn++LL74wQeCkSZPy9Pmqq64y+/d16tRJpkyZkuP3q1evlnbt2pngbf78+QHDQf2EVz/l1SBw2bJlea7vO+ijXr168s477+T5/YEDB8yBHt9//70JAvv165ejjo5BDynRfQAHDRokAwcODOme/dPiyZMnS+fOnUO2ibSCTS9VpGOhPQIIIIAAAggg4FSAFYBOpaiHAAIIIIBA7AVsyio4BCTK8//BBx+Y/fOKFi0qmzdvlipVqvjvOHv2bGndurX5RHfDhg15VtZpO91/T0vbtm1lxowZeXqrewbqp8a6B+DatWv9e/VpxXXr1pk9+84880xz/UCfCGu9hQsXmj0A9XNgPSykdOnS/vs8/fTT0qdPH6ldu7asWrXK7CMYqsybN0/uvPNOU2306NHy8MMPh2oS8e9teqkiHgwXQAABBBBAAAEEHAqwB6BDKKohgAACCCBQCAI2ZRUEgDF4ABYtWiStWrUyK+nGjRtngrg5c+aYFXXVqlWTBQsWSOXKlfP0pE6dOibE06IrAfVz3kBFw71GjRqZvQbHjx8vtWrVMqsBNbjTA0J09aCuRMyv6CfE3bt3l+TkZBPalS1bVnT13qhRo8ypwXPnzs330189bERXEe7du9fsEahhoZbLLrvMfD6s4ytfvrwJQqNRbHqpojE+rokAAggggAACCOQW4BRgngkEEEAAAQTsFrApqyAAjNGzsmXLFvOJrX6Gu3//fvPZbWpqqnTr1s0Ed4FKRkaGWfmnp/gG+wTY12737t0yfPhweeutt2TPnj1mpWH79u1NGKefEDspmZmZMmzYMBM6/vzzzyZI1P7pHoJJSUn5XuKHH34wAV9+RT+FdrPHoZM+++rY9FK56Td1EUAAAQQQQACBcAWmThVJTXXfWtt17Oi+HS0QQAABBBBAwJ2ATVkFAaC7uaO2pQI2vVSWEtEtBBBAAAEEEIgzAd2a+ckn3Q9K2w0a5L4dLRBAAAEEEEDAnYBNWQUBoLu5o7alAja9VJYS0S0EEEAAAQQQiDOB1q1FZs92PyhtN3Om+3a0QAABBBBAAAF3AjZlFQSA7uaO2pYK2PRSWUpEtxBAAAEEEEAgzgSaNhVJT3c/qGbNRObPd9+OFggggAACCCDgTsCmrIIA0N3cUdtSAZteKkuJ6BYCCCCAAAIIxJmABnkLF7ofFAGgezNaIIAAAgggEI6ATVkFAWA4M/h/7d0J3FXT/sfxn8xDQmYZQuYi5B+SmVTKTBO6ZB4KFXHLlGS6IlOmXMqQSwMiQjJnLjIUJTKUqQgp/V/fxT7Oc54z7H2ec56zzj6f9Xrd1723Z6+113qvtc/wO2sgj3cCPj1U3uFQIQQQQAABBBCIpQBLgGPZrTQKAQQQQCBGAj7FKggAxmhgVXJTfHqoKrkfaDsCCCCAAAII1J5A375ml10W/X4cAhLdjBwIIIAAAgjkI+BTrIIAYD49SB7vBHx6qLzDoUIIIIAAAgggEEuBoUPNunaN3jTlO+646PnIgQACCCCAAALRBHyKVRAAjNZ3XO2pgE8PladEVAsBBBBAAAEEYiYwY4ZZw4bRG6V8G28cPR85EEAAAQQQQCCagE+xCgKA0fqOqz0V8Omh8pSIaiGAAAIIIIBADAXWWcfs22/DN2zttc2++Sb89VyJAAIIIIAAAvkL+BSrIACYfz+S0yMBnx4qj1ioCgIIIIAAAgjEWIAZgDHuXJqGAAIIIBALAZ9iFQQAYzGkaIRPDxW9gQACCCCAAAII1IYAewDWhjL3QAABBBBAIH8Bn2IVBADz70dyeiTg00PlEQtVQQABBBBAAIEYCwwaZNa9e/QGKt9ZZ0XPRw4EEEAAAQQQiCbgU6yCAGC0vuNqTwV8eqg8JaJaCCCAAAIIIBAzAWYAxqxDaQ4CCCCAQOwEfIpVEACM3fCqzAb59FBVZg/QagQQQAABBBCobQHtAbjppmZLloS/81JLmX32GacAhxfjSgQQQAABBPIX8ClWQQAw/34kp0cCPj1UHrFQFQQQQAABBBCIuUCLFmYvvRS+kbp+4sTw13MlAggggAACCOQv4FOsggBg/v1ITo8EfHqoPGKhKggggAACCCAQcwECgDHvYJqHAAIIIFDWAj7FKggAlvVQovKBgE8PFb2CAAIIIIAAAgjUhgBLgGtDmXsggAACCCCQv4BPsQoCgPn3Izk9EvDpofKIhaoggAACCCCAQIwFOAQkxp1L0xBAAAEEYiHgU6yCAGAshhSN8OmhojcQQAABBBBAAIHaEBg0yKx79+h3Ur6zzoqejxwIIIAAAgggEE3Ap1gFAcBofcfVngr49FB5SkS1EEAAAQQQQCBmAswAjFmH0hwEEEAAgdgJ+BSrIAAYu+FVmQ3y6aGqzB6g1QgggAACCCBQ2wIvvmi2xx7R76p8u+8ePR85EEAAAQQQQCCagE+xCgKA0fqOqz0V8Omh8pSIaiGAAAIIIIBAzAT69DEbMCB6oy680Ozyy6PnIwcCCCCAAAIIRBPwKVZBADBa33G1pwI+PVSeElEtBBBAAAEEEIiZQPPmZq+9Fr1RyvfKK9HzkQMBBBBAAAEEogn4FKsgABit77jaUwGfHipPiagWAggggAACCMRMoHFjsylTojdK+d57L3o+ciCAAAIIIIBANAGfYhUEAKP1HVd7KuDTQ+UpEdVCAAEEEEAAgZgJ7LKL2aRJ0RulfPnMHIx+J3IggAACCCBQ2QI+xSoIAFb2WIxN6316qGKDSkMQQAABBBBAwGuBrl3NdBJw1KR8d90VNRfXI4AAAggggEBUAZ9iFQQAo/Ye13sp4NND5SUQlUIAAQQQQACB2AkcfbTZQw9Fb5byPfBA9HzkQAABBBBAAIFoAj7FKggARus7rvZUwKeHylMiqoUAAggggAACMRNo185szJjojWrf3mzkyOj5yIEAAggggAAC0QR8ilUQAIzWd1ztqYBPD5WnRFQLAQQQQAABBGIm0KKF2UsvRW+U8k2cGD0fORBAAAEEEEAgmoBPsQoCgNH6jqs9FfDpofKUiGohgAACCCCAQMwENtzQ7IsvojeqQQOzWbOi5yMHAggggAACCEQT8ClWQQAwWt9xtacCPj1UnhJRLQQQQAABBBCImcB665l9/XX0Rinf7NnR85EDAQQQQAABBKIJ+BSrIAAYre+42lMBnx4qT4moFgIIIIAAAgjETGCjjfKbyad8M2fGDIPmIIAAAggg4KGAT7EKAoAeDhCqFF3Ap4cqeu3JgQACCCCAAAIIRBfYZx+z557LL9/48dHzkQMBBBBAAAEEogn4FKsgABit77jaUwGfHipPiagWAggggAACCMRM4OyzzW64IXqjlO/666PnIwcCCCCAAAIIRBPwKVZBADBa33G1pwI+PVSeElEtBBBAAAEEEIiZQNu2Zo8/Hr1RBx9sNnp09HzkQAABBBBAAIFoAj7FKggARus7rvZUwKeHylMiqoUAAggggAACMRPYZhuzqVOjN2rbbc2mTImejxwIIIAAAgggEE3Ap1gFAcBofcfVngr49FB5SkS1EEAAAQQQQCBmAg0amH35ZfRGbbCB2RdfRM9HDgQQQAABBBCIJuBTrIIAYLS+42pPBXx6qDwloloIIIAAAgggEDOB9dc3++qr6I1SvnwCh9HvRA4EEEAAAQQqW8CnWAUBwMoei7FpvU8PVWxQaQgCCCCAAAIIeC2w+eZm06dHr2KjRmYffxw9HzkQQAABBBBAIJqAT7EKAoDR+o6rPRXw6aHylIhqIYAAAggggEDMBJo2NXvnneiN2nFHszffjJ6PHAgggAACCCAQTcCnWAUBwGh9x9WeCvj0UHlKRLUQQAABBBBAIGYC++9v9swz0RulfOPGRc9HDgQQQAABBBCIJuBTrIIAYLS+42pPBXx6qDwloloIIIAAAgggEDOBtm3NHn88eqOUb8yY6PnIgQACCCCAAALRBHyKVRAAjNZ3XO2pgE8PladEVAsBBBBAAAEEYibQrl1+gbz27c1GjowZBs1BAAEEEEDAQwGfYhUEAD0cIFQpuoBPD1X02pMDAQQQQAABBBCILtCxo9n990fP16mT2X33Rc9HDgQQQAABBBCIJuBTrIIAYLS+42pPBXx6qDwloloIIIAAAgggEDOBa64x69kzeqOU79xzo+cjBwIIIIAAAghEE/ApVkEAMFrfcbWnAj49VJ4SUS0EEEAAAQQQiJnAiy+a7bFH9EYp3+67R89HDgQQQAABBBCIJuBTrIIAYLS+42pPBXx6qDwloloIIIAAAgggEDMBZgDGrENpDgIIIIBA7AR8ilUQAIzd8KrMBvn0UFVmD9BqBBBAAAEEEKhtAfYArG1x7ocAAggggEA0AZ9iFQQAo/UdV3sq4NND5SkR1UIAAQQQQACBmAkQAIxZh9IcBBBAAIHYCfgUqyAAGLvhVZkN8umhqsweoNUIIIAAAgggUNsCffqYDRgQ/a7K179/9HzkQAABBBBAAIFoAj7FKggARus7rvZUwKeHylMiqoUAAggggAACMRNgD8CYdSjNQQABBBCInYBPsQoCgLEbXpXZIJ8eqsrsAVqNAAIIIIAAArUt0K+f2aWXRr+r8l18cfR85EAAAQQQQACBaAI+xSoIAEbrO672VMCnh8pTIqqFAAIIIIAAAjETOPFEszvvjN4o5bv99uj5yIEAAggggAAC0QR8ilUQAIzWd1ztqYBPD5WnRFQLAQQQQAABBGImcPbZZjfcEL1Rynf99dHzkQMBBBBAAAEEogn4FKsgABit77jaUwGfHipPiagWAggggAACCMRMgBmAMetQmoMAAgggEDsBn2IVBABjN7wqs0E+PVSV2QO0GgEEEEAAAQRqW6BvX7PLLot+V/YAjG5GDgQQQAABBPIR8ClWQQAwnx4kj3cCPj1U3uFQIQQQQAABBBCIpQCnAMeyW2kUAggggECMBHyKVRAAjNHAquSm+PRQVXI/0HYEEEAAAQQQqD2B05OstHEAACAASURBVE4zu+WW6Pc74wyzG2+Mno8cCCCAAAIIIBBNwKdYBQHAaH3H1Z4K+PRQeUpEtRBAAAEEEEAgZgJNm5q98070RinfW29Fz0cOBBBAAAEEEIgm4FOsggBgtL7jak8FfHqoPCWiWggggAACCCAQM4FNNjGbOTN6o5Tvs8+i5yMHAggggAACCEQT8ClWQQAwWt9xtacCPj1UnhJRLQQQQAABBBCImUC+MwB33NHszTdjhkFzEEAAAQQQ8FDAp1gFAUAPBwhVii7g00MVvfbkQAABBBBAAAEEogvkuwfg6aebDR4c/X7kQAABBBBAAIFoAj7FKggARus7rvZUwKeHylMiqoUAAggggAACMRPgFOCYdSjNQQABBBCInYBPsQoCgLEbXpXZIJ8eqsrsAVqNAAIIIIAAArUtMGiQWffu0e+qfGedFT0fORBAAAEEEEAgmoBPsQoCgNH6jqs9FfDpofKUiGohgAACCCCAQMwEmAEYsw6lOQgggAACsRPwKVZBADB2w6syG+TTQ1WZPUCrEUAAAQQQQKC2BY4/3uyee6LftU8fs/79o+cjBwIIIIAAAghEE/ApVkEAMFrfcbWnAj49VJ4SUS0EEEAAAQQQiJnAWmuZzZ0bvVGdOpndd1/0fORAAAEEEEAAgWgCPsUqCABG6zuu9lTAp4fKUyKqhQACCCCAAAIxEpgxw6xhw/waRAAwPzdyIYAAAgggEFXAp1gFAcCovcf1Xgr49FB5CUSlEEAAAQQQQCBWAvnu/ycE5T333Fhx0BgEEEAAAQS8FPApVkEA0MshQqWiCvj0UEWtO9cjgAACCCCAAAJRBdq3Nxs9Omquv67X7MGNN84vL7kQQAABBBBAILyAT7EKAoDh+40rPRbw6aHymImqIYAAAggggEBMBJo2NXvnneiNWXlls59/jp6PHAgggAACCCAQXcCnWAUBwOj9l3eOr7/+2saOHWvffPONbbLJJtamTRurW7du3uWlZvzxxx/t8ccft88//9zWX399V/6aa64ZqfzPPvvMxo0bZypryy23tIMOOsiWX375SGVMmjTJXnzxRVtqqaVsxx13tJYtW0bKn8/FPj1U+dSfPAgggAACCCCAQBSBbbYxmzo1So6/rt10U7Pp06PnIwcCCCCAAAIIRBfwKVZBADB6/0XO8eeff9oll1xiV155pW2xxRa200472euvv24aCNdff73961//ilxmaoabb77Zzj//fFtrrbWsRYsW9sEHH9jkyZOtX79+1rt3b6tTp07We/z222/Wo0cPGzJkiDVr1sy22more/75503/fscdd1jbtm1z1lGBx+OOO84F/w488EBbddVVbcyYMS6QOGzYMPffxUo+PVTFaiPlIoAAAggggAACgUC+AcBttzWbMgVHBBBAAAEEEKgNAZ9iFQQAi9zjS5YssQ4dOtiDDz5op556qg0ePNgF4xQU7NKliw0fPtwGDBjggnf5pl69etnVV19t7du3twceeMBWWGEFV5TKHDhwoJ1yyil2yy23ZCxeQb4DDjjAJk6caFdccYVdcMEF7lr9e6tWrdy/33vvvdaxY8eMZUyfPt0FHjVz8IknnrC9997bXTtjxgxr3ry5LVq0yCZMmGDb6lNnEZJPD1URmkeRCCCAAAIIIIBAFQECgAwIBBBAAAEE/BfwKVZBALDI46V///520UUXuZl/77//vi2zzDKJOypYpqXAP/30kz311FMuCBc1aWZd586dbbXVVnPBtnr16iWKUNBNM/kUnLvtttvspJNOSlt8t27d3Cw/Be2effbZKtdMmzbNlaGg5dtvv502gLdw4UK31Fft00zHvn37Vinj7rvvdrMcN9tsMzcrccUVV4zazJzX+/RQ5awsFyCAAAIIIIAAAjUU2GQTs5kzoxfSsKHZp59Gz0cOBBBAAAEEEIgu4FOsggBg9P4LnWPmzJku8KcA2X/+8x/r3r17tbyanafgnAKBWrYbJTg2f/5823TTTW3u3Ll29tlnu+XEqUnLjjWjT4HBDz/80NZdd90ql7zyyiu22267uX979NFH7ZBDDqlWhmYBKkCpmXy6PjVpBmOfPn1s6aWXNu1zmLrv4O+//27rrLOOC3RqObLqVOjk00NV6LZRHgIIIIAAApUioNNpn3/e7KefzPSb5l57mSnQlSul5tt8c7NXXzV74QWzb781W2cdsz32MDviiOrlhb1npuvS3XvatH/aoLqE/f9//PFXS+fNM/voI63GMNOhHYGBlu7OmmU2f37++/jJ9ccfc4nydwQQQAABBBAohIBPsQoCgIXo0QxlnHHGGXbTTTe5v3788cfWqFGjaleOHDnSDj30UPfvmil3/PHHh67RNddcYz179nTX6+CO/fffv1red955x5rqmDgztx/gxRdfXOUa7e2ng0M0M1EBupVWWqlaGQosan9Apeeee8720qfxv5OWCTdo0MC+++47F0h86aWX0tZfgcVRo0bZKqusYl999ZX770Km5IdqjTVmWcuWDWz33c2WXTb9F4iwH/Zz1bFQ5eS6T7q/l/Le2errY72oU7gR5qOTal7b9crnfmHyZLsmTP7UXswnTzGf3eT6KHDxxht/BTGWLDHT70x77hk+mJNcz0K3Mx/HQtShpmXUNH8+7U5+/jRjLDkgteOOVYNpNa3fpElml1xi9sQTf42ZIC21lFnr1voMY9asWfURnClftrGuzwf/+c9fV4S5Z7Z7rL222Zw5Vesc7hW3dFfp/Dk9myQEEEAAAQQQKL4AAcDiG5f8Dpr1p9l2P/zwgzsMQ8t9dSpuapo9e7ZtsMEG7p933313d4BG2LTddtu5ZbdKCsCtscYa1bJqr8GVV17Z7ee34YYbmmYlBvXQacS69+LFi61x48b23nvvpb31yy+/7Oqm1KlTJ7vvvvsS1z388MN25JFHuv9/5pln2g033JC2DO0teOGFF7q/3X777XbiiSeGbWao65IfKrNZZtagSr7gC4SqOmJE9C8YqZXI94tKqMbkuKiU985WNR/rRZ3CjTgfnVTz2q5XPvcLk0dtyRRk+HsCtr38cvigR5h7pguUZBoNNS0vagCmTZvMwZzkOta0XrlGf5jys/VdtqBUIdsRpp7F6O/gvo8/nl2ySRMzBZSijOHUEh95xKxDB7OFCzPfa7nlzO6/3+yww/65Jky+TCXqbDT9Z9Gi7Pc8+2yzQYOy1y3XWPPt76uu+tfsRBICCCCAAAIIFF+AAGDxjUt+hxdeeMH21HQHM2vSpIm9++67GeukAKGW82oJ7bx589LOwkvNPGvWLNtoo43cPyu/Zu9lSrq/9t5T+uijj9yyZKX//ve/7tRepXbt2rkZeunS999/b/Xr13d/Wm+99UxByyBpbz/NXFS67rrrEjMFU8t55JFH7PDDD3f/rENRdPhJIVOuAGDYe6X7glG9Lfl9UQlbh2zXhfmyE6YNhahLchk+1os6hetlH51U89quVz73C5Mn2PY1W5AhW0+lPs9h7hnlNaCm5YXJn659ueoYptxcZdT0tTRM3+WqQ03bUdP8+b5/FTLolctIgcYWLcIF2FSWfidVwDNKvnCvhpVzlWYtfvNN5bSXliKAAAIIIFBKAQKApdSvpXvrVF6dzqvUunVrt8w2U1JA7pNPPnF/1gzAYLZdtqqOGDHCjjrqKHfJNttsk5gJmC6PDhd5+umn3Z80e0+z+JROP/10u/nmm93/Pu200xLLldOVsfzyy7u9DJU0gINZizrVV3sXKj300EOJ2YCpZSTPItx8880T7S1UdxQqAKj6JH/BSK1flC8c2crJp92lvHe2+vpYL+oUboT56KSa13a98rmf6hk2aBGuNzJfFbyWRLlnmNeffNqdPNMsSv50rctUxyjlhmlnTV7Hw/RdsdpRaIco5YVpd5RrsvVT27ZmuWYZJt9L148ZYxY1X5T6xv3a7bYz+/t34bg3lfYhgAACCCBQcgECgCXvguJX4OSTT7YhQ4a4G51wwgnulN1MaZdddrFJ+mRuZvfcc48de+yxOSsYHLyhC/fdd1975plnMuZRoFABQ6XkU3oPPPBAt3eg0mWXXeZOK86U1l57bZujTW7MbMKECdayZUtbsmSJm62o5cVKEydOtBb6RpwmKUioYKGSThTWCcXplkTnbHiGCwoZANQtNtvM7O+tGavcUbMxopyct+mm/yxXSt7TKJ92Pvpo9HunOdMl7a1rUreRI80++yx8i3T6YPv24a/Pp26azKr9qMImba7erl3Yq/+5Lkrd9IU1ap30BbcmKVf9Hnss2gmSG29spuWbhUjZ6qY9wKKcbKl6HXRQ/rXS/T7/PHz+vydfR8oTvvT0V+ZzT+U58MDMd37yyb8OEwibNtywanlPPRUtf7r7qMwDDqj6F70tRa1Xmi1wq90uGHP6PeyLL8K2Otx1DRqY7bdf1Wv1thzlPipj333/KWP8+PzyZ3q2VN6XX4ZrTzGu0m4n++xTdan7zz+b6T0kalJ///27ZtSsXG9mRx9t9sADUCCAAAIIIIBAbQgQAKwN5RLfo3379jZ69GhXi0wn9AZV3GOPPRJ7/w0aNMjOOuusnLVXmcF+e7qXDhPJlLp06ZLYt0+HeWiprtL222+f2Pcv0ynFQZnaP1ADV0lLhbVkWPsbJu87+Pbbb9sOO+yQthrTp083zfwLkpYsa+ly2BTcO9P1OlhEgdS/UvU9AMPeh+sQQAABBBBAAIE4C2gZ9d9bO8e5mbQNAQQQQAABLwQIAHrRDcWthE7kDWblnX/++aYZe5lScgAw10y8oIxu3bolZhUec8wxdr92xs6QkgOAybMRdSrxtGnTXK5bb73VNGsxU0oOAN57773WuXNn+/LLL90JwEH68MMPbcstt0xbRGoAUHsYJufN1RvRZgsSAMzlyd8RQAABBBBAoPIEtLflH39UXrtpMQIIIIAAAqUSIABYKvlavG+rVq3sKa2PMrN///vfdumll2a8e/Pmze21115zfx84cGBi78Bs1T3llFPstttuc5cowKcDPTIlBQgffPBB9+dTTz01se/fVltt5Q4FUbrzzjtNB3pkSjrRWKcGK6ksLSv++uuv3aEgQVKQb1OteU2TFBzceuutE39RWVpWHDYRAAwrxXUIIIAAAggggEB6gT59zPr3RwcBBBBAAAEEakuAAGBtSZfwPkceeaQ9/PDDrgY6DESBvUwpeSnuLbfcYgru5Uo9e/a0a665xl2mYFwQ4EuXL3k5cu/eve3KK690lzVr1szeeOMN9791GIiCg5lSvXr13AnFSmPHjjUFOH/55RdbZZVVElm0z19ykC+5LC0P3nHHHRP/tGDBAltxxRVzNTPx91IsAdbymKRVy6bJki+9FLrKiQuTy1lqqej5lUNnxORzb23J2KhRuHvmUzfVa+LEcOUnX9WyZfh6KV+Uun38sdkLL0Svkw7t/vuA7EiZw9RNcfYJEyIV6y7eay+zDJNqQxeWqX4ffmj2/POhi0lcuPfeZlttFT1fuhzp6qZ6Pfts9PK1d1o+9Zo6Nb/7Ra9haXJor7Sk316SXq/Nsmwdm7GyKm+bbcx09lOh9mDTHoAqU+n99/MrV2X8vc1sVmjV++/f5greIa1a/VOHKVPyu4/K0AENyq89GqOmIH/qs6Xyxo6NWlrhr2/d+q/2BfX76SetQIh+n44dzYYPj56v0nMsu2y405Yr3Yn2I4AAAgggUEgBAoCF1PS0rHPOOce0r56STtsdPHhwxpputNFGpiWxSjotWKcG50ra/0/7ACq1adPGHtNu/hmSDuzQAR1KN910kzvxV+mwww6zR3WyhJnp1OLzzjsvbQmLFy+2ZZdd1h36ofT++++7k4eVtAeg9gJU0kEmO++8c9oynn32WXdYidJaa61l3377ba4mRvp7oQ8B0ZcTHWyhwwWCpAMcNMEx18EKyRVPV06khv19cSnvna2+PtaLOoUbYT46qea1Xa987qd66tmO8loQrleyXxX1ntlef/Jpd3J5+eRP17rUOuZTbpTX2XzKD9N3hW5HPvUsdH+HaXeUazLVL+ppvpwCHEW96rVDh5odd1z++cmJAAIIIIAAAtEFCABGNyu7HHfddZc7/VfpkEMOSQTa0jVEJ+n++uuv7k8zZsywjZOjThlanhxQ08EbmmGXKWlWnpbgKj3//PO2p6Y6mVnfvn3d6b9K3bt3TwQsU8vRcl0tAVZafvnlTQd46L+VkoOLOohEsw3TJc1Q1FJkpb322suee+65gvZpoQOAwReM1Erm+0WlEI0t5b2z1d/HelGncCPORyfVvLbrlc/9FPx7/PFwzoW4SnWMes9Mr2NBffJpt06zzjd/Ood0daxpvXJ5Ry0/V3nBmE22KcQ4jlrPQvd3mHZHuSZT/SZNMtNs9YULc5e23HJmOsCiWTP96Bg+X+6S439Fz55mV10V/3bSQgQQQAABBHwTIADoW48UoT7Jh15oie8777yT9i7J++jpUIxgJmCuKmn57Zprrmm//fabaXnujz/+mDaLZu2tvPLKLsC43HLLudl6CjgqjR8/3vbbbz/3v7OdJPzqq6/arrvu6q5r0aJFYjah/r/2N7z88svd37KdJKxlxxdccIG77qKLLkoEHnO1M+zfCxkATP6CkXr/KF84spUTtl3J15Xy3tnq62O9qFO4Eeajk2pe2/XK535/vR6GC1qE643MVwWvJVHuGeb1J592K/ASpCj507UuUx2jlBumnTV5HQ/Td8VqR6EdopQXpt1RrsnVT488YtahQ/bnSWXovLPDDvvnzmHyZapnnTpm+s+iRdmfPS22GDSodp71KKZhr1Ub77qLmX9hvbgOAQQQQACBQgsQACy0qKflNWnSxCZPnuyWz86fPz8xay65uskz+bSk9/rrrw/dmnbt2tmYv6ccZDqA49NPP7XNNtvMlZka5Fu0aJGts8469v3335tO+f3888/T3jt5NmNqkO+tt96ynXbayeXLdhjJscceazo9WEmzFTVrsZCpUAHAdF8wUusZ5gtHmHLyaX8p752tvj7WizqFG2E+OqnmtV2vfO4XJo9O3FTKFmTI1lOpryVh7hnl9aem5YXJn659ueoYptxcZdT0NStM3+WqQ03bUdP8+b5/FTLolcsoqKMClDovTTNrk5fXa+lwmzZatfDXzL/UlClftv5X8P666/66Isw9s91D55nNmVP7WwKEeYU//nizu+8OcyXXIIAAAggggECxBAgAFkvWs3LvuOMO69atm6vV008/nZhtl1zN/v37uxlxderUcXvr6WTesOmZZ56x/bUju5ndfvvtduKJJ1bLOmzYMOvcubP7d51KfIB2Sk9KurfqoPTJJ5/Y5smnXvx9ndqgtujAj5kzZ7p9/5LTHnvsYS+++KJpBqOCiOlO7G3UqJFNmzbNzSR8+eWXwzYx9HW5AoDBF4gjjjDT2SxRv2CkViTfLyqhG5TlwlLeO1v9fawXdQo34nx0Us1ru1753C9MHrUlU5BBhwQp6ZCfsEGPMPdMFyjJNBpqWl7UAIyWgmYK5iTXsab1yjX6w5Sfre+yBaUK2Y4w9SxGfwf3zbLFsGvm9tub1a0bbQxn6xvtf6hDk3RASL16fx2GFGJnFLd/aHI+/fb56qt/LRnWtsMK1O2xh5k+B6SWF/aema5Ld+/p0/9pg+oS9v9rKbQ+s+jcNR0g9dtvZjpvLaizDsrR77Xz55stWGC2ePFfPzDov3W22oYb6vA5s8MPz/UE8HcEEEAAAQQQqA0BAoC1oezBPRYuXOhmuk2dOtUFAocMGVKtVjoZVzPijj/+eLs75WfaF154wTp27OgCb9pfL11wUEt4tZRXgcBx48ZVKz846CPTvnvfffedO9BDh3IoENinT58qZagNOqRE+wBefPHF1q9fv2r3UD1VvpYbv/TSS7bbbrtVuea9994zLYNW0t5/urbQKfmhWmONWbbXXg1M1dCJd+m+QIT9sJ+rnoUqJ9d90v29lPcuxpe3fAzC5vHRijqF7b2/DgbJJyAQ/g5Vr8znfmHyZLsmTP7U9uSTp5jPbnJ9tO3sm2/+FcT4808zBTq1/WzYYE5yPQvdznwcC1GHmpZR0/z5tFt5gvt++mnVgFTTplWDaYWuX77PL/kQQAABBBBAAAGfBAgA+tQbRa7Lm2++6Wa9Lb300vbBBx9Yw4YNE3d86KGH7Oijj3ZLdF9//fVqM+uUT/vvKXXo0MGGDx9erbbaM1BLjbUHoGbWBXv16cJXXnnF7dm3+uqru/I31RG2adLo0aPd8mAtB9ZhIauttlriqquuusp69+5tzZs3twkTJrh9BNOlc88916677jpr1aqVPfHEE4lZgAoKHnzwwe504x49erhripF8eqiK0T7KRAABBBBAAAEEEEAAAQQQQACB8hLwKVax1BJFaEhFFXjsscfsqKOOcjPpBg8e7AJxI0aMcDPqtthiCxs1apRtsskm1eqgmXQK4ilpJqCW86ZLCu61bt3a7TV48803W9OmTd1sQAXudECIZg/m2nNPS4hPPfVUa9asmQvS1a9f3+6880675ppr3KnBDz/8cLUAZXJdFi9e7JYgDx061Lp27Wq9evUyHVSiNqr9OmX42muvdUudi5F8eqiK0T7KRAABBBBAAAEEEEAAAQQQQACB8hLwKVZBALCWxs5HH33klthqCeycOXPcslsFyk455RQXuEuXtK+eZv7pFN9MS4CDfF9++aUNGDDAzb6bPXu2m2nYqVMnF3jTEuIwadKkSXbFFVe4oOO8efNcIFH10x6C6fb1S1emApuDBg2yKVOmuDxa7nveeefZ7sFGV2Eqksc1Pj1UeVSfLAgggAACCCCAAAIIIIAAAgggEDMBn2IVBABjNrgqtTk+PVSV2ge0GwEEEEAAAQQQQAABBBBAAAEE/hHwKVZBAJCRGQsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yLg00Pliwn1QAABBBBAAAEEEEAAAQQQQACB0gn4FKsgAFi6ccCdCyjg00NVwGZRFAIIIIAAAggggAACCCCAAAIIlKmAT7EKAoBlOoiodlUBnx4q+gYBBBBAAAEEEEAAAQQQQAABBBDwKVZBAJDxGAsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yLg00Pliwn1QAABBBBAAAEEEEAAAQQQQACB0gn4FKsgAFi6ccCdCyjg00NVwGZRFAIIIIAAAggggAACCCCAAAIIlKmAT7EKAoBlOoiodlUBnx4q+gYBBBBAAAEEEEAAAQQQQAABBBDwKVZBAJDxGAsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yLg00Pliwn1QAABBBBAAAEEEEAAAQQQQACB0gn4FKsgAFi6ccCdCyjg00NVwGZRFAIIIIAAAggggAACCCCAAAIIlKmAT7EKAoBlOoiodlUBnx4q+gYBBBBAAAEEEEAAAQQQQAABBBDwKVZBAJDxGAsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yLg00Pliwn1QAABBBBAAAEEEEAAAQQQQACB0gn4FKsgAFi6ccCdCyjg00NVwGZRFAIIIIAAAggggAACCCCAAAIIlKmAT7EKAoBlOoiodlUBnx4q+gYBBBBAAAEEEEAAAQQQQAABBBDwKVZBAJDxGAsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yLg00Pliwn1QAABBBBAAAEEEEAAAQQQQACB0gn4FKsgAFi6ccCdCyjg00NVwGZRFAIIIIAAAggggAACCCCAAAIIlKmAT7EKAoBlOoiodlUBnx4q+gYBBBBAAAEEEEAAAQQQQAABBBDwKVZBAJDxGAsBnx6qWIDSCAQQQAABBBBAAAEEEEAAAQQQqJGAT7EKAoA16koy+yIwY8YMa9iwoavO66+/buutt54vVaMeCCCAAAIIIIAAAggggAACCCBQgQJfffWV7bLLLq7ls2bNsgYNGpRMgQBgyei5cSEFJk2alHioClkuZSGAAAIIIIAAAggggAACCCCAAAI1FSAAWFNB8iNgZo899pgdfPDBWCCAAAIIIIAAAggggAACCCCAAALeCRAA9K5LqFA5CkybNs0aNWrkqv7yyy/bhhtuWI7NoM61LJA8HZul47WMX8a3Y9yUceeVuOqMnRJ3QJnennFTph1X4mozbkrcAWV6e8ZNmXZciavNuMneAYsWLbI5c+bYWmut5Zb/LrPMMiXrMZYAl4yeGxdSwKeNNQvZLsoqrgDjpri+cS2dcRPXni1+uxg7xTeO4x0YN3Hs1eK3iXFTfOM43oFxE8deLX6bGDfFNy7UHQgAFkqSckoqwItOSfnL9uaMm7LtupJWnHFTUv6yvjljp6y7r2SVZ9yUjL6sb8y4KevuK1nlGTcloy/rGzNuyqf7CACWT19R0ywCvOgwPPIRYNzko0Yexg1jIF8Bxk6+cpWdj3FT2f2fb+sZN/nKVXY+xk1l93++rWfc5CtX+/kIANa+OXcsggAvOkVArYAiGTcV0MlFaCLjpgioFVIkY6dCOrrAzWTcFBi0Qopj3FRIRxe4mYybAoNWSHGMm/LpaAKA5dNX1DSLAC86DI98BBg3+aiRh3HDGMhXgLGTr1xl52PcVHb/59t6xk2+cpWdj3FT2f2fb+sZN/nK1X4+AoC1b84diyDAi04RUCugSMZNBXRyEZrIuCkCaoUUydipkI4ucDMZNwUGrZDiGDcV0tEFbibjpsCgFVIc46Z8OpoAYPn0FTXNIsCLDsMjHwHGTT5q5GHcMAbyFWDs5CtX2fkYN5Xd//m2nnGTr1xl52PcVHb/59t6xk2+crWfjwBg7ZtzxyII8KJTBNQKKJJxUwGdXIQmMm6KgFohRTJ2DfvzRAAAIABJREFUKqSjC9xMxk2BQSukOMZNhXR0gZvJuCkwaIUUx7gpn44mAFg+fUVNEUAAAQQQQAABBBBAAAEEEEAAAQQQiCxAADAyGRkQQAABBBBAAAEEEEAAAQQQQAABBBAoHwECgOXTV9QUAQQQQAABBBBAAAEEEEAAAQQQQACByAIEACOTkQEBBBBAAAEEEEAAAQQQQAABBBBAAIHyESAAWD59RU0RQAABBBBAAAEEEEAAAQQQQAABBBCILEAAMDIZGRBAAAEEEEAAAQQQQAABBBBAAAEEECgfAQKA5dNX1BQBBBBAAAEEEEAAAQQQQAABBBBAAIHIAgQAI5ORAQEEEEAAAQQQQAABBBBAAAEEEEAAgfIRIABYPn1FTRFAAAEEEEAAAQQQQAABBBBAAAEEEIgsQAAwMhkZEEAAAQQQQAABBBBAAAEEEEAAAQQQKB8BAoDl01de1PTrr7+2sWPH2jfffGObbLKJtWnTxurWrVuwuv3444/2+OOP2+eff27rr7++K3/NNdeMVP5nn31m48aNM5W15ZZb2kEHHWTLL798pDImTZpkL774oi211FK24447WsuWLSPlX7RokavDe++9Z6uttprtvfferi6Vmhg3ldrzNW83Yye34U8//WTjx4+36dOnu9eszTbbzPbdd19bddVVc2eO6RWMm9wdu3DhQjdu3n//ffvzzz9t0003tf3228+9Z1VqYtxUas/XrN2Mm5r5VWpuxk3+PT9v3jz3PW3GjBk2d+5cO+qoo2yrrbbKv8Ayy8nYKbMO86y6BAA96xBfq6MvB5dccoldeeWVtsUWW9hOO+1kr7/+un3xxRd2/fXX27/+9a8aV/3mm2+2888/39Zaay1r0aKFffDBBzZ58mTr16+f9e7d2+rUqZP1Hr/99pv16NHDhgwZYs2aNXNvBM8//7zp3++44w5r27Ztzjoq8Hjccce5N5UDDzzQfYEeM2aMC94NGzYsVBBvwoQJ1rVrVxeAPOCAA0xvUk899ZQdffTRdsstt1i9evVy1iMuFzBuwo+boM81bm666SYbNGiQe8YUaK/ExNjJPXaWLFliAwcOtP79+9vPP/9cZZjohxm9nuo/uV474zS+GDe5x436+8knn7QTTjjBZs+eXW3cXHfddXbiiSfGaVjkbAvjJty4yQSpzz377LOPbbjhhu4LeaUkxk20cfPEE0+4H/YzpWOPPdbuueee2A8fxk20cZM8IEaOHGk33HCDvfnmm7b//vtb69atbZtttrHtt9/eVlxxRcYO38fd5159Ng6b9Bm6T58+YS+PxXUEAGPRjcVthL5kdujQwR588EE79dRTbfDgwe4Lpd7AunTpYsOHD7cBAwa4By7f1KtXL7v66qutffv29sADD9gKK6zgigoe4lNOOcUFzzIlBfkUbJs4caJdccUVdsEFF7hL9e+tWrVy/37vvfdax44dM5ah2TMKPCoAow8pmrWnpA+zzZs3N83q04fcbbfdNmMZemPSr1Abb7yxm1mx0UYbuWtVngKQO++8s/v3Qs6azNe82PkYN+HHjfpCv+YpmK5xrqCxkmazVmIAkLETbuycdNJJdvvtt7sfFZo0aeJmbulDcXJQR6/d+vFCMwPjnhg34cbNM888476E68c2fWnSTMCXX37ZFixYkBgi//3vf937eyUkxk24cZNpLPzwww/u9Uc/COuzT6UEABk30cfN7rvv7l5rMqV33nnHvSbFOTFuoo8bjQe9vmiCxrPPPuu+y+k7o1aKVVJi7OQeO3/88Yf7IUorFcMmTbbQxKFKSgQAK6m382yrIuMXXXSRm/mnpULLLLNMoiQFyxSg0BI0zXJTEC5q0pfTzp07uy+v+uCYPENOQTfN5FNw7rbbbjN94U2XunXr5mb5KWinN4fkNG3aNFeGgpZvv/122gCevgBpqa/ap5mOffv2rVLG3Xff7WY5ammdZiWm+5Xpk08+sR122MF9iVKgMHXZsN649KVKX8gVNI17YtyYhRk3Ggf33Xefm62qNHToUFu8eLH735UaAGTs5B47o0aNskMPPdS9VvXs2dNWXnllN2Y0djR7VD+qBOPoxhtvtDPOOCPuLzluJiTvVdnfq+bMmWPbbbedXX755e49bemll3bj4ttvv3Wz1IPXIQUHv/zyS1t22WUZN3zGyToGDj/8cHvkkUfcNZUUAOT1Jvf7VPLAee6559yMrc033zzteNLKIn0fiHti3EQbNxoPr7zyipsg8t1337kfys8888y4D5O07WPs5B47Dz/8sB155JHOb/XVV3fbiKVbBaP4gmIL+l6vOEGlJQKAldbjEds7c+ZMF/hTgOw///mPde/evVoJmp2n4JwCgVq2G2UK9vz5892+Q9q/4eyzz3Yv7KlJy441o0+BwQ8//NDWXXfdKpfojWG33XZz//boo4/aIYccUq0MzQJUgFIz+XR9atIMRk3/1ZchzcRK3Xfw999/t3XWWccFOrUcWXVKTVoyrH3/9OulfsVMTa+++qrtuuuu7p+1j6LqFNfEuPmrZ8OMm9QxkPxlqhIDgIydcGNHryWdOnXKGNjT6/U555zjCtOv5Pr1PM6zABk34caN3mf1XqVlU6lJ72/6cq73Y6U33njDbfcR58S4CTduMo0Bbbmiz4UKGGsLlUoJADJuoo8b7Uu7wQYbuB/CKzUxbqKPG70P7bXXXvbLL7+4FTL6zlmJibETbuxoIpJmpd95551uZnqmpB+t9H3rwgsvdD+IVloiAFhpPR6xvZo1ov3IlD7++GNr1KhRtRK07FUzUZQ04+n4448PfZdrrrnGzV5RUvBMvw6mJgXTmjZt6v5Z+wFefPHFVS7R0lodHKKZifoCs9JKK1UrQ4FF7Q+opF8h9WYSJC0TbtCggftlSYHEl156KW39FVjUrJtVVlnFvvrqK/ffQdIbVDB9WIFE/UqTmrRkWoFFvTDtueeeiZkWobHK6ELGzT+dlW3cpOvS8847z6699lr3p0oMADJ2co8dzdZq166d6UeFTEmz//TLpj40Kk2dOjXWG2QzbnKPG12hWe7ZtrHQ3n/64KykH/S23nrrMnrniV5Vxk24cZNOVj/IKkCs9ytt3aKVD5USAGTcRBs3eq/S8l+9/lTSQQ2pzw3jJtq40cwsfS/TzPXTTjst8X00+it9+edg7OQeO/rOpJUNmvCz3HLLZe10bdc1YsQIt6pPqyIqLREArLQej9BezfrTbDsFrHQYhpb7pptBov2m9Kuekt7gdYBG2KSHTh8IlBSAW2ONNaplVeBMy9sUqNO6fn2hDeqhNf66t77sNm7c2J26my5pzxHVTUmzZrTkMkjJ04U1rVyby6ZL2ltQvxQoad+t5E3Sk1+Y//e//9lhhx2Wtgz9MvH000+7v2UKqIa18/U6xk3Vnsk2btL14b///e/Er1GVFgBk7IQbOxoXmqkczCjO9FqQHMzRzGfNgI5jYtyEGzdh+l6z7TXDXfvXfvrpp4klwmHylts1jJv8x41mt+v1RCs49JlHP6pWSgCQcRN93OiH+l9//dV9MU/eRqjcXjNqUl/GTbRxo+912ppJ3+v0fqQfpIKtTmrSD+WYl7ETbuxojGi1YBCTyNTXmk269tprW8OGDW3KlCnlOCRqXGcCgDUmjG8BL7zwgpuppqRptO+++27GxipAqOW8WkKrAwzSzcJLzTxr1qzEIRnKr9l7mZLuryi90kcffeSWJStpKYH21lPSjBjN0EuXvv/+e6tfv77703rrrVdlk3z9WqCZi0o6/TCYKZhaTjBdWP+euo+fPgTrS7nSW2+9lZixmFrGWWedZdqPSynbnoblPKoYN1V7L9u4SdfPmuGqfSiVKi0AyNip2dhJHU+ajaztDZT0w0lwKFE5v76kqzvjpnDjRpur33///W4v0uC9NW7jJWgP4yb/caNlv9pyRSs0tM9SJQUAGTfRxk3yKh5tEaTD8DT7Rp+jg8/lcX2NSW4X4ybauNGBk8FefzfffLM7hLJSE2Mn2tjJNU60D78mA1122WVu3+hKTAQAK7HXQ7ZZJyxpI3kl7RekZbaZkgJyOgRDSTMAg9l22W6lqbf6EKCkI9yDmYDp8iTPnNPsPT24SqeffrrpjUEp1/Tw5Zdf3u1lqKT9sIJfCLQcSr8aKD300EOJzUNT65E8i1D7JAXt1dR0/ZIQJC3P03446VLybDDNztFMwrglxk3VHs00bjL1eyUHABk7NRs7qWPq5JNPNu3RpT0A9YNLuo2Q4/D6w7gpzLjRj3h6X9RhIHF8b0od64yb/MaN9jDWD646MCb4rFdJAUDGTbRxc8QRR7hZoqmpbt26dv7557ttgCrhsCHGTfhxowkhmp2lFWg6IFLbLq2wwgpx+LiSVxsYO+HHThhgvX+NGTPGfY/PdChRmHLK+RoCgOXce0Wue/DlUbc54YQT3Cm7mdIuu+xikyZNcn++55577Nhjj81Zu+DgDV2ozYGfeeaZjHmCtfq6IPmU3uDgDf17rki+gnQK1ikFp/TqSHXNVtTyYqWJEydaixYt0tZDQcJg7yR9kdYJQlqKnHwIiZY2KMiYabN9BSsVtFRKd2JxTrQyuIBxU7WTMo2bTF1ZyQFAxk7Nxk7qmNJMizfffNPNatbs5rgmxk3Nx4222tDsP30p1+z0uAaLk6UYN9HHjbZd0YoMbXui7SqCVEkBQMZN+HGj1TfaEkeBHB0So62EUtMee+zhAoSZfjiPy/sW4yb8uNHe83qNUdJMdM0E1D6j2qM92NdYW0h169bNFGCOe2LshB87ucaCXoN0qKfex4K4Ra48cfw7AcA49mqB2qQj10ePHu1Ky3RCb3ArvYEHe/8NGjTItNQ1V1KZwX57upcOE8mUunTpkti3L/nLrE7cDfb9y3RKcVCm9g/UzD8lLRXWLwD6UJK87+Dbb79tO+ywQ9pq6Ljw5F8K9AuVli5rGUyw55/2Hkj3AScoUJurB3sH6mATLReOW2LcVO3RTOMmU79XcgCQsVOzsZOcW691WvKrmRX6lTOuy3/VZsZNzcaNxoeWV40fP97ts6sf2bp27Rq3t6Zq7WHcRBs3+sFUq0G0l9uzzz5bJUhcSQFAxk20cZN8tQ510EobzWhK/qysw2S0zDHM9kHl+sLEuAk/bvbZZx93YKOSgjXaC1BBMB0go71pNX6Cv2spuSaexHkWKWMn/NjJ9fpw1113uUlNOoT03HPPzXV5bP9OADC2XVvzhulE3mBWnqbpB3tJpSs5OQCYayZekF+/3ASzCo855hi371CYAGDybESdSqwPFEq33nqre4PIlJIDgPfee6917tzZvvzyS3cCcJB0qt2WW26ZtojUQI6W1CnvsGHDXFlKOjRFU9UzpeQAoE7oDOpe897ypwTGTfY3qmDcZOqxSg4AMnZqNnaScwenSWsbh4EDB/rzAlGEmjBu8hs3TzzxhOkHO73PawZgctLsi2C/2iJ0mRdFMm6ijRvNItY2JtrTLflzk0qppAAg4ybauEn3sM+dO9ct/dVeo0HSe5YCO3FNjJtw40aHf+iwDx00pKQtB1q1alUls67RJA69hynlmqRS7mOKsRNu7ITpZ20pps88mpGc+j4WJn9criEAGJeeLEI79IKrE7uUtNTj0ksvzXgXnQb32muvub/ry2awd2C2ap1yyiluqZGSZvjpQI9MSQHCBx980P1ZMxWCff/0a5AOBVFScE0HemRKCs5p+YqSytKyYp2kqUNBgqQgnw70SJcUHNx6660Tf1JZWlasaen6BUpJQUa9qGRKClIGG9mqrGDvwSJ0X8mKZNxUpc80bjJ1UCUHABk7NRs7QW69BmlfVb226fCmOM+qUJsZN/mNGwX9NAtH++/qfUy/jAfbYahEzdAPNmEv2RtKEW/MuAk/brQ6QqeOa/aWvninpkoKADJuwo+bXI9v8kog7dOtz9VaSRPHxLgJN240MUKTO5T02UUntqZLmsCh72vadklbVuj7VKYJHOU+nhg74cZOrn7WNmDaE1vvZZpxXMmJAGAl936Oth955JH28MMPu6tyzSJJXop7yy23mIJ7uZJ+/dMUXCUF44IAX7p8ydOfe/fubVdeeaW7rFmzZvbGG2+4/53rlCh9qNAJxUrBL0p6Y1lllVUSt9QbSHKQL7ku+gCsaehBWrBggelEMx2O0rZtW/fPCggGQcZ07dAy5XPOOcf96f/+7//s1VdfzcVUdn9n3FTtskzjJlPHVnIAkLFTs7ET5NbrpX680QE0ya9ZZfdiErLCjJvCjJsZM2a47Sz0mqWkD8paSp5pT9uQ3ePtZYybcONGn3X0OqJZKJlmhVZSAJBxE27chH3w9fk5OGRQ3zkOP/zwsFnL6jrGTbhxo+9FCtAoJR+4mK6zk/eH1ySV5H1Jy2pw5KgsYyfc2MnV54pPBAeG6r8rOREArOTez9F2BaoUsFLSwRU6kj1T0v5SWtqopDdy7ROTK2l2gX79U2rTpo099thjGbO0bNnSHdChpM1hgwdXX1a0B5+Slg5oCUG6pOni2h9Ce9goacaDZsgoaQ9A7QWopA1BtXF+uqQ9b3RYiZI2K9Zpv0rag1ABUKVsv1bp73379nWHlSjpBV2/psctMW6q9mimcZOp3ys5AMjYqdnYCV4ftXxTM6o1s7oSEuOm5uMmKEE/YGkWhfa4VdIM+y222CKWw4hxE27caN9ifTZ6/fXXTbO00qVKCgAybsKNm7AvGlOnTnUH7OnzebbP8WHL8/U6xk24caPVZFpVpqQfHnSQWaYU7Oemv+v7YLrTpn0dD1HqxdgJN3Zyme65557uIJnZs2e7CTuVnAgAVnLv52h78gvrIYcckgi0pcumwJc2hlbSLIKNN944p2xyYEQHbwSzDtJl1Kw8LaVUev75500PsVJyQK179+6JgGVqGfpSoyXASvoAqy83wQfZ5OCiDiLR7Jl0STMUtRRZSR92gw1otU+FZhHqVGAlLanKtIRBy3+1DFhJgZ5+/frldCq3Cxg3VXss07jJ1K+VHABk7NRs7OiXc702XnDBBe71pVIS46Zm4yZ1nGhj7ODUaH1Y3m233WI5lBg3ucdN6j7JUQaCPgfq82DcEuMm97iJ2ufBKqJch/lFLden6xk34cbNZ599ltiKSSf9Tp48OWM3JgcLNUN53LhxPnV5werC2Ak3drKBK+inbbr222+/xPZmBeugMiyIAGAZdlptVTn50Au9OWvj53QpeR89bagZzATMVU8tv11zzTXdnkPZTs/Vr4LaEFYBxuWWW87N1gv2tNKphXqYlbKdJJw8pbxFixaJ2YTKpynjl19+uSsj24cPLTvWF2uliy66KDGTT/8/+RCUbCcJJ+/joE1IgxmFuazK6e+Mm6q9lW3cpOvXSg4AMnbyHzuy07KZQw89NLG3ajm9btSkroyb/MdNOvfkH+eSZ8vXpI98zMu4yT1uFMBr2LBhXt0X1wAg4yb3uIk6YI4++mi3Iibbj/BRy/TtesZNuHGjPf1WW201951v9dVXt++//z5jV+oE+2CGeq7DJH0bD1Hqw9gJN3aymQZbcCmY2rVr1yj8sbyWAGAsu7VwjWrSpIn79UXLZ+fPn592+Ufyl4WoJzFpM+kxY8a4Cmc6gENHvuvEXKXUIJ9m3emIeL1BZDuAI/nXk9Qg31tvvWU77bSTKz/bYSTHHnus6fRgpdQgn2ZLBMeJZzuMJFgqrcCnTgteZpllCtdZHpXEuPmnM7KNm3RdVskBQHkwdqKPHW1HoB829Gv5iBEjbOmll/bo1aB2qsK4iT5uMvWMlv3qgK1VV13Vvvvuu9i+T/F6U3UEpHuv+uOPP9xns1xJebVMWPtG6odZJX1uDD675cpfbn/n9aZwrzcqSfv+ad9ardbRD/5xTYybcOPm4IMPTmwLpdVfmQ730A9U+tyjdO211yb2WI/j+GHshBs7mfpe++5rIpNeYxRgrvREALDSR0CO9t9xxx3WrVs3d9XTTz+dmG2XnK1///5uRpxOYdKLsb44hE2aBadp20q33367aa+Z1DRs2DDr3Lmz+2d9QNAR3slJ91YdlPRrkDaNTU1qg9qipbozZ850+/4lp2AGn2Yw6gTNdJue61QqnU6lWTbaXD85aVaifu1WkLRTp0523333VatD8lIazSS84oorwjKV3XWMm3+6LNu4SdexlR4AZOxEGzsK0Oy9995Wv359e/LJJzPu0aVSdeqrXqfjmBg30cZNtjGg/Xa1NYbej/W+HOfEuCnMuKmkPQAlxrgpzLgJStHnZ80CvOqqq+L8csO4SerdbJ+NdRiM9klX0mGRwQSL1MGhveMVLNRkCk0W0USQuCZec/J/zQmWlWvS0ahRo+I6RCK1iwBgJK7Ku1hTsbU/nzbpVRBtyJAh1RC0SatmxB1//PF29913V/m7jtnu2LGjC7xpan+64KCW8OoX40z7NwQHfSTvu5d8E30B1oEemgWjQGCfPn2q1EFt0Mw7Rf0z7buneqp8LTdOt+dR8kEf2vtP16YmHe6hPQm1nFl7DQTLlIPrgkNP9EVdMyz033FNjJu/ejbMuEkdA8lL0jPNio3ruFG7GDvhx472G9U2Aprxp9fQunXrZhwa99xzj1tSE+aE9nIcX4yb8OMmV//qEBn98Pbuu++69844J8ZNYcZNpQUAGTeFGTcqRUt/tR+2ZpDqu0KcE+Mm/LjRQSDa40/vQfosnG7FlH6k0qqr4GRXxg7fx9ONgQEDBrjYwP3335/Yyz/OYyVM2wgAhlGq8Gt0ApNmvelL5gcffFBlPxi9cetXOy3z0AlxqTPrlE/77yl16NDBhg8fXk1TewZqarO+zGpmXXD8uy585ZVX3NI27QOh8jfddNO0vTF69Gi3PFjLgTVdPHl6r35R7N27tztVasKECW4fwXQp2Phc+/Q98cQTiVmACgrqFyadbtyjR4/E5uipZWg58u677+7qqReb888/P3GJliirjQoMPvLII6ZDVeKeGDfhxk3qODjrrLPsxhtvdP+s5elNmzaN+1Cp1j7GTu6xox8+NBtaP2zodTj1tVeoWr43Z84c0+ujtkHQa22mA4riMMgYN9nHjd7L9EOUfiw76qijEqfXJ/e9ZpEG+3EdeOCBcRgWOdvAuMn9epMLsdICgPJg3OQeN1qW+eKLL7rXG30HSE0yPPnkk00zvjbZZJNcwywWf2fc5B436uiPP/7YHUClzzo6BVdjKTlpsoZed3RIpP53th9AYzFweM1xk3TCfB9P7W+dY6AVfPrsE+ctBqKMcwKAUbQq+FpNs9YbuH6JGTx4sAvEaa8pzajTBqyaUpvuzVsv3griKWkmoGYVpEsKmrVu3drtGXPzzTe7oIdOc1LgTl9YNXtQMxGzJS1V0im7zZo1c0E6zbDTL0OaPq6TMfUBI92X5KDMxYsXuyVPQ4cOdRuE9urVy3RQidqo9uuUYb0BZVtCpy/bBx10kJs5ocCjNuTX7EkFF7WZtuqoJcKVkhg34caNfhWeO3euGyvaS0mBYiUtgdD403H12jeykhJjJ/PY0cFLmjmtLRfCJm2jEOxhGjZPOV7HuMk8bpL3S9I2F5pdf9JJJ7mZ+dq+Qr+OKwB4yy23uPfRSkqMm3DvVZnGRCUGAGXBuMk+brS0V9vqKGmM9OzZ0xo3buyCOvpMrh+w9FlZP/JXUmLchHu90QzANm3auPGi73d6v9JY0d7xmtGl4J++f6677roVM3wYO+HGTjAgNClI40Q/bD7wwAMVM05yNZQAYC4h/p4Q0LJVLbHVElgFurTsVoEyLSlT4C5d0i9/+tVPEfdMS4CDfNojTzPnNPtOARCdPKdgmQJvYZcFaAmB9tZT0HHevHkukKj66ctvun390tVZgc1BgwbZlClTXB59aDnvvPPc7L4wacGCBS5Iqi/bmrauoGPbtm3djMBK+YUz2Ylxk3vcJO93km6MaWargj6Vlhg76cdOsBl0lPGgw5q0V2AlJMZN5tcczQDUD1HaL+m3335zP7Dp9UUz7/XLumbSx3WfyFxjn3GT+72KAGB1AcZN5nGjHzX1Q74+k2sljII3CgpqdrEmBQQHOOR6NuP4d8ZNuNcbfR8cOHCgC7Z/8cUX7j1r2223dd8P9R20Eg89Y+yEGzt63Qj2VX/00UcrYvVd2NdKAoBhpbgOAQQQQAABBBBAAAEEEEAAAQQQQACBMhQgAFiGnUaVEUAAAQQQQAABBBBAAAEEEEAAAQQQCCtAADCsFNchgAACCCCAAAIIIIAAAggggAACCCBQhgIEAMuw06gyAggggAACCCCAAAIIIIAAAggggAACYQUIAIaV4joEEEAAAQQQQAABBBBAAAEEEEAAAQTKUIAAYBl2GlVGAAEEEEAAAQQQQAABBBBAAAEEEEAgrAABwLBSXIcAAggggAACCCCAAAIIIIAAAggggEAZChAALMNOo8oIIIAAAggggAACCCCAAAIIIIAAAgiEFSAAGFaK6xBAAAEEEEAAAQQQQAABBBBAAAEEEChDAQKAZdhpVBkBBBBAAAEEEEAAAQQQQAABBBBAAIGwAgQAw0pxHQIIIIAAAggggAACCCCAAAIIIIAAAmUoQACwDDuNKiOAAAIIIIAAAggggAACCCCAAAIIIBBWgABgWCmuQwABBBBAAAEEEEAAAQQQQAABBBBAoAwFCACWYadRZQQQQAABBBBAAAEEEEAAAQQQQAABBMIKEAAMK8V1CCCAAAIIIIAAAggggAACCCCAAAIIlKEAAcAy7DSqjAACCCCAAAIIIIAAAggggAACCCCAQFgBAoBhpbgOAQQQQAABBBBAAAEEEEAAAQQQQACBMhQgAFiGnUaVEUAAAQQQQAABBBBAAAEEEEAAAQQQCCtAADCsFNchgAACCCCAAAIIIIAAAggggAACCCBQhgIEAMuw06gyAggggAACCCBQTgILFy60E044wUaPHm1777233XeoybMkAAAYZUlEQVTffbbKKquUUxO8rKtcR4wYYZ06dSpY/YYPH26HHnqorbjiigUrk4IQQAABBBBAoPQCBABL3wfUAAEEEEAAAQQQiLXATTfdZGeccUaijX379rVLLrkk1m0uduMeffRRu+CCC6xLly524YUXFux26puhQ4ea+uzggw8uWLkUhAACCCCAAAKlFSAAWFp/7o4AAggggAACCNSawLBhw6xz585Fu99hhx1m//vf/6qVf/LJJ9uQIUMS/96uXTsbNWpU0eoR54L/+OMPO+ecc9zMv5EjR1rz5s0L3tz777/fzdjs0KGDCwSusMIKBb8HBSKAAAIIIIBA7QoQAKxdb+6GAAIIIIAAAgiUTODWW2+1U0891d1/2WWXtX333dc233xzq1u3rtWpUydRrxdeeMEmTpyY+P9NmjSpNhvs+++/t2nTptlzzz1nixYtctfuv//+Nm7cuGrtu+WWW+y0005L/Hu/fv3s4osvLplDud74xx9/NAVPP/nkE9c/6rtipaeeesrda4cddrCxY8faGmusUaxbUS4CCCCAAAII1IIAAcBaQOYWCCCAAAIIIICADwJXX3219erVywWOtB/f1ltvnbZaCs4lL9E97rjj3LLQdGn27NluVqECgbvuuqu9/PLL1S7TrLVu3brZI4884vYA1ExE9gCMNiJ+/fVXO+CAA+z111+3l156yXbeeedoBeRx9QMPPOBmATZu3NgFHOvVq5dHKWRBAAEEEEAAAR8ECAD60AvUAQEEEEAAAQQQqAUB7e82cOBAmzx5sm2xxRYZ7xglAKhCFJxq2rSpm1WoskmFFVi8eLFpebWCtgMGDLDzzz+/sDfIUtqJJ55od955p5stqpmA6mMSAggggAACCJSfAAHA8uszaowAAggggAACCOQl0KNHD9PS3XvuuSdr/qgBQBWm2X3nnnuuffbZZ3nVjUyZBa688kp34Mdmm21mH3zwgS233HK1xvXzzz9bo0aN7Ouvv7bzzjvPNIuUhAACCCCAAALlJ0AAsPz6jBojgAACCCCAAAJ5CWg21z777GMdO3YseABw3rx5bmnxt99+m1fdyJReYMqUKbbTTjvZwoULTXs46kCV2k6DBw+2M88805ZaaikbP368W8ZNQgABBBBAAIHyEiAAWF79RW0RQAABBBBAAIG8Bfr3729HHnlk1uW/KjyfGYDKd+yxx9p///vfvOtHxqoCS5YssV122cXeeOMNd1CLZuGttNJKtc70+++/2wYbbGDfffedbbXVVm6Z9zLLLFPr9eCGCCCAAAIIIJC/AAHA/O3IiQACCCCAAAIIxFIg3wBgLDFK2Cgtqz788MNdDTp16mT33XdfyWqj06M1A1HpxhtvtDPOOKNkdeHGCCCAAAIIIBBdgABgdDNyIIAAAggggAACsRYgAFj67tXsPx2s8u6777rK6ETeo48+umQVe/zxx61t27bu/g0aNHB7PTILsGTdwY0RQAABBBCILEAAMDIZGRBAAAEEEEAAgXgLFCMAOGPGDLvttttcIOuaa65JzGzLJPnnn3+6U28///xzO+uss6pc9ssvv9hTTz1ln3zyia288spuT7ptt902Y6fMmTPH7V2nstZcc03bc8893YEa+aSPP/7YXn/9dbccV3viaWnsXnvtZeuuu24+xWXM88QTT1ibNm0Sf//qq68i3+PHH3907f70009NJwnXr1/f7dO45ZZbWr169ez222837Qu5yiqr5Kz7Tz/9ZKuvvropMKk0fPhw69ChQ858XIAAAggggAACfggQAPSjH6gFAggggAACCCDgjUChAoAK4j355JN2880329ixY03/X+nee++1zp07p23v/Pnz7e6777ZBgwa5wJUCddOmTXPXKv91111n2stQwa3kpINN7rjjDltxxRUT/6wTj3v27OmWzuoQjSDVqVPHunbt6uoV9kRdLcft27evvf/++9XqrUBgu3bt3Am5OjG3EEk+w4YNc0Vpxt2sWbNCFyunyy67zK666ipbsGBB1nxRAova/++jjz5y5e2///42bty40HXiQgQQQAABBBAorQABwNL6c3cEEEAAAQQQQMA7gUIEABUcOuWUU9xS0dSULgCoGYLaW+7OO+80zTYLUhAA/PXXX92sQQUSMyX9/eGHH3Z/VvDwwAMPTAQP0+Xp1q2bDRkyJKu/DsDQ4SYjRoywE044wbVJM+g0q/B///ufXX755Yn6rrrqqqZA4b777lujPv3tt99s7bXXNgVDlfbbbz97+umnQ5cZBA91gIjqt+OOO9rSSy9t77zzjgusjhw5MlFWlADgEUcc4dqspOW/yqsZlSQEEEAAAQQQ8F+AAKD/fUQNEUAAAQQQQACBWhUoRADwyy+/NAXttET34IMPtjfffDPRhtQA4KJFi2yPPfawjTfe2F0XzPhTBgUANeuuffv29uKLL9pRRx3lTsZVUrAtNTD27LPPumWuLVq0sB9++MHtm9esWTO3dPWxxx5z/0lOCoptv/32aX01k65169b2zDPP2P333+9OUE5NWhK86667mmYbKikIqCXCChLmm1RHmQXp9NNPt8GDB4cqTkustTR3m222sUmTJqU9NfiSSy5xJz0rRQkAajallm8HKdtMzlCV5SIEEEAAAQQQqDUBAoC1Rs2NEEAAAQQQQACB8hAoRAAwuaWadda9e/dQgaPZs2fbhhtumFgurKDgDjvs4AKJ1157bbV98Hr06GHXX399omwF/D744APTclUFzTSTLjn16tXLLdUNkv7/wIED03aMltFq2a8CX1pOmylpL72TTjop8Wft3ZcaaIzS87qn7h2kG264wc4888xQRbRs2dImTpxo2dqlYOhuu+1mr776aqQAoJZMKxgZJLVZ+zqSEEAAAQQQQMB/AQKA/vcRNUQAAQQQQAABBGpVoNABQC2f1cy9IOWaObbJJpvYzJkz3eXaX08BxEwBMB0IoiBfsNedrtcegRdccEFaM+0dqGWrOhRDScuEtU9hatIS34022sjtHfjFF1/Yeuutl7EPtD+frk1O06dPt0033TSvftNpuzp1N0ijRo1yewyGSTrQQybHHHOMm7WYKemAFc2qjDIDUE4HHXRQosgmTZokTikOUzeuQQABBBBAAIHSCRAALJ09d0YAAQQQQAABBLwUKHQAUMEsBbWClCsAqBl/7777rrs8+RCQTFhaPqzlwWGv10EdwTJj3evtt9+uVvSll15q/fr1czMJp06dmrWftE/gCiusUOUa7S2oPQbzSZoBqaBjkLSsWScdh0krrbSSW3q97LLLuuXUjRs3TptNAdB11lnHpkyZEvp0YfWJvIKkeynYSEIAAQQQQAAB/wUIAPrfR9QQAQQQQAABBBCoVYFCBwB1IIhm2gUpVwCwefPm9tprr7nLwwQAtTdfcPhHmOv/7//+z+3Tp7TFFlskTrZNRk6+Jh/8888/3wYMGJBPVhe8076IQdJefjvvvHOosjQrb/Lkye7a+vXruyW6OhwlXRo+fLibWahZg2HSJ5984rySU5QZhGHuwTUIIIAAAgggUBwBAoDFcaVUBBBAAAEEEECgbAUKHQDUIRr7779/wiNXAFD7073yyivu+jABveDU27DX64CQl156KWP5mkFXr149++OPP9zS3i5dukTuSwUQkw/yCFvAzz//bHXr1q1yuWYgaiZimKT9As8+++wql6q92lcwuQ/ClJV6jQ52adCgQZV/1n6LW2+9dT7FkQcBBBBAAAEEalGAAGAtYnMrBBBAAAEEEECgHARKHQDMFaBLNSx0ADB5ptuOO+5Y5QTjYveflv5qCXBy0n6IqXsMZqqHgpatWrUyLRtOTTvttJPbG/HQQw+1OnXqRG6K9k9cffXVq+TTTEqdskxCAAEEEEAAAb8FCAD63T/UDgEEEEAAAQQQqHWBSg8AavmxliEracabDvmoraRgnw5BSU4fffRRtaW32eqjg0v69OnjTkcODjtJvl77Al533XW23377RWpWur0OoyxPjnQzLkYAAQQQQACBggoQACwoJ4UhgAACCCCAAALlL1DpAcAXXnjB9txzz0RHaunr+uuvXysdO3fuXFtrrbWq3EuHlCQfvhG2Ijq0QzP+xo4dmzZLz549beDAge6k5TBp3rx5bml0ctJsyc033zxMdq5BAAEEEEAAgRIKEAAsIT63RgABBBBAAAEEfBSo9ADge++9Z9tvv32ia26//XY78cQTa6WrtIR3+eWXtyVLliTup/0KtS9ivkn7Kf773/+28ePHVyvioosusssuuyxU0en2AJwzZ46tueaaofJzEQIIIIAAAgiUToAAYOnsuTMCCCCAAAIIIOClQKUHABXUWnvttRN9s91225lm04XdN++xxx6z6dOnVzuMI2xnaw9A7QUYpKeffjryct1093r88cfttNNOs88//zzxZ7Xpww8/tEaNGuWsnq5LPvBDpxXrwJSll146Z14uQAABBBBAAIHSChAALK0/d0cAAQQQQAABBLwTqPQAoDpEpw9/+umnib654oor3HLaXOnPP/+0XXbZxbp37246nCSftM8++9hzzz2XyDp8+HDr0KFDqKK0VPidd97JeO1PP/1k7du3twkTJkRu2xtvvFHlwA/dS8uTSQgggAACCCDgvwABQP/7iBoigAACCCCAAAK1KkAA0OzUU0+1W2+9NeG+zDLL2F133WVdunTJ2hf9+vVz++rNnj3b1lhjjbz6rXfv3nbVVVcl8vbv398d6hEmaTnuM888k3XPwO+++84aNmxo8+fPd0VqVuBNN92Us/jRo0e74GGQunbt6kxICCCAAAIIIOC/AAFA//uIGiKAAAIIIIAAArUqoGDTgAEDEvfs2LGjDRs2LO86aAnrAQcckMh/7733Zp0dt/vuu9vLL7/srtdMvGnTpmW9t2baBfULc32LFi1M++plK3/q1Km27bbbVtmLT4dlKFh26aWXVgvu/fzzz26fPZ28e8IJJ9gdd9yRt9eYMWOsXbt2ifxRylMAUHlzBeYOPvhg01JlJdVbbcqVrr76auvVq1fiMgUN5UFCAAEEEEAAAf8FCAD630fUEAEEEEAAAQQQqFWBU045xW677bbEPQ888EB78skn867DqFGj7JBDDknkHzJkiHXr1i1jeTvttJO99dZb7u8NGjSwWbNmZb33McccYw8++KC7ZqONNrKZM2dmvb558+b22muvuWs22GCDKvvtJWdU4C1dIE2HdOy777625ZZbugM7FKAcN26c6ZTcVVdd1e2pt9566+XtpWCiTgL+7bffXBlaEpzuAI90N1AA8IcffnBLfBXozJRU/2effdb9WcuN99prr5z11UEod955p7tOwVDtJaj+ISGAAAIIIICA/wIEAP3vI2qIAAIIIIAAAgjUqkCzZs1M+70Fad1113VBsnwPe0idOaYlrldeeWXaNmkPPQW/vv/+e/d3HTShpaoKtGVKyTP6VlxxRXd9trpuvPHGiYMwtLRXgTvlS00LFiwwBQsnT54c2v/hhx+2ww8/PPT1mS489NBDbeTIke7P66+/vukE3jBJAUAt8dXyYy3Z1WzK1PT+++/bzjvv7AKMLVu2tOeff94F9HKlZGf974kTJ+bKwt8RQAABBBBAwBMBAoCedATVQAABBBBAAAEESi2gmWc67CJ5+W9QJ+19d80111Q5HTdMfT/55BM3gy35VFsFpzSjUIHG5LRw4UK7/PLL7bLLLqvy7zpQQ/vqLbfcclX+fdGiRW5GmmYsJqcePXqY9uKrV69elX/XibUKPKYud1V+7blXt27dak1SMK1t27b26quvZm2uAmhaEqu9AwuRFPxTEDBI2lMwzKzCIACofApuap++448/3nSS8S+//OJm+2kZrwKKWuKs/QIV4M2VFJhVv+kQESWW/+YS4+8IIIAAAgj4JUAA0K/+oDYIIIAAAggggECtC2gfN+07980339gff/yR9f4KMGm56IgRI7Jepz32tOx37ty5Ga9bbbXV3BJbBbq0J532Cvz999/TXq/gn2YG6tRZ/feuu+7qTrsNlsmmZtIMwPr169uUKVPc9Vquq1N9FTRMlxQsU0Ay2Hsw+RqZ3HDDDS54mK49jRs3tkGDBtnee+9dsL5TwG2LLbaw6dOnuzIfeOABO/roo3OWnxwAzHTxSiut5AKVl1xyia288so5y9QFWpKtpdlK8pwxY4apHBICCCCAAAIIlIcAAcDy6CdqiQACCCCAAAIIIFBigcWLF7tlr9rjT/vsKdimoOEOO+xQlJppr8STTz7ZlX3cccfZ0KFDQ91nyZIl9u2337qApwJ1+t+a/acgaqNGjVwAN3V2ZK6CdRLxRRdd5C7TbMmePXvmysLfEUAAAQQQQMAjAQKAHnUGVUEAAQQQQAABBBBAIBBQwLFp06ZuD8LVV1/dtAx4hRVWKAmQgpzvvvuuWy6sQ0/CzhwsSWW5KQIIIIAAAghUEyAAyKBAAAEEEEAAAQQQQMBTAc041Iw9LQm+9dZbEzMCa7O6r7zyiu22227uljpYRMu1SQgggAACCCBQXgIEAMurv6gtAggggAACCCCAQIUJXHDBBW7/wQ033NB0qEq2E5GLQdO+fXsX+NNhInfffXcxbkGZCCCAAAIIIFBkAQKARQameAQQQAABBBBAAAEEaiKgQ0hat27tTuzt3bu3CwbWVho/frztt99+1qRJE7f/4aqrrlpbt+Y+CCCAAAIIIFBAAQKABcSkKAQQQAABBBBAAAEEiiEwb948a9mypdsP8LnnnnP/u9jpu+++c3sQ6oRkneq83nrrFfuWlI8AAggggAACRRIgAFgkWIpFAAEEEEAAAQQQQKCQAt9//721atXKHcIxYcIEa9y4cSGLr1LWggUL3L108MhTTz1lm222WdHuRcEIIIAAAgggUHwBAoDFN+YOCCCAAAIIIIAAAggURECBuZNPPtmefvppe+yxx2znnXcuSLnJhcydO9cOPfRQq1u3rg0bNsydQExCAAEEEEAAgfIWIABY3v1H7RFAAAEEEEAAAQQqUOD++++3Xr162RVXXGFdunQpmMDYsWNduTrwo0ePHlanTp2ClU1BCCCAAAIIIFA6AQKApbPnzggggAACCCCAAAII5C3w448/2tChQ6179+55l5Ga8YYbbrBOnTpZ/fr1C1YmBSGAAAIIIIBA6QUIAJa+D6gBAggggAACCCCAAAIIIIAAAggggAACRRMgAFg0WgpGAAEEEEAAAQQQQAABBBBAAAEEEECg9AIEAEvfB9QAAQQQQAABBBBAAAEEEEAAAQQQQACBogkQACwaLQUjgAACCCCAAAIIIIAAAggggAACCCBQegECgKXvA2qAAAIIIIAAAggggAACCCCAAAIIIIBA0QQIABaNloIRQAABBBBAAAEEEEAAAQQQQAABBBAovQABwNL3ATVAAAEEEEAAAQQQQAABBBBAAAEEEECgaAIEAItGS8EIIIAAAggggAACCCCAAAIIIIAAAgiUXoAAYOn7gBoggAACCCCAAAIIIIAAAggggAACCCBQNAECgEWjpWAEEEAAAQQQQAABBBBAAAEEEEAAAQRKL0AAsPR9QA0QQAABBBBAAAEEEEAAAQQQQAABBBAomgABwKLRUjACCCCAAAIIIIAAAggggAACCCCAAAKlFyAAWPo+oAYIIIAAAggggAACCCCAAAIIIIAAAggUTYAAYNFoKRgBBBBAAAEEEEAAAQQQQAABBBBAAIHSCxAALH0fUAMEEEAAAQQQQAABBBBAAAEEEEAAAQSKJkAAsGi0FIwAAggggAACCCCAAAIIIIAAAggggEDpBQgAlr4PqAECCCCAAAIIIIAAAggggAACCCCAAAJFEyAAWDRaCkYAAQQQQAABBBBAAAEEEEAAAQQQQKD0AgQAS98H1AABBBBAAAEEEEAAAQQQQAABBBBAAIGiCRAALBotBSOAAAIIIIAAAggggAACCCCAAAIIIFB6AQKApe8DaoAAAggggAACCCCAAAIIIIAAAggggEDRBAgAFo2WghFAAAEEEEAAAQQQQAABBBBAAAEEECi9AAHA0vcBNUAAAQQQQAABBBBAAAEEEEAAAQQQQKBoAgQAi0ZLwQgggAACCCCAAAIIIIAAAggggAACCJRegABg6fuAGiCAAAIIIIAAAggggAACCCCAAAIIIFA0AQKARaOlYAQQQAABBBBAAAEEEEAAAQQQQAABBEovQACw9H1ADRBAAAEEEEAAAQQQQAABBBBAAAEEECiaAAHAotFSMAIIIIAAAggggAACCCCAAAIIIIAAAqUXIABY+j6gBggggAACCCCAAAIIIIAAAggggAACCBRN4P8B4Qv/D9Ihr8kAAAAASUVORK5CYII=\" width=\"640\">"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"plt.plot(timeHistory.index, timeHistory['oh'],'-o',color='b',markersize=4)\n",
|
||
"plt.xlabel('Time (s)',fontname='Times New Roman')\n",
|
||
"plt.ylabel('$\\mathdefault{OH\\, mass\\, fraction,}\\, Y_{OH}}$',fontname='Times New Roman')\n",
|
||
"\n",
|
||
"# Figure formatting:\n",
|
||
"plt.xlim([0,0.00075])\n",
|
||
"ax = plt.gca()\n",
|
||
"font = plt.matplotlib.font_manager.FontProperties(family='Times New Roman',size=14)\n",
|
||
"ax.annotate(\"\",xy=(tau,0.005), xytext=(0,0.005),arrowprops=dict(arrowstyle=\"<|-|>\",color='r',linewidth=2.0),fontsize=14,)\n",
|
||
"plt.annotate('Ignition Delay Time (IDT)', xy=(0,0), xytext=(0.00004, 0.00525), family='Times New Roman',fontsize=16);\n",
|
||
"\n",
|
||
"for tick in ax.xaxis.get_major_ticks():\n",
|
||
" tick.label1.set_fontsize(12)\n",
|
||
" tick.label1.set_fontname('Times New Roman')\n",
|
||
"for tick in ax.yaxis.get_major_ticks():\n",
|
||
" tick.label1.set_fontsize(12)\n",
|
||
" tick.label1.set_fontname('Times New Roman')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Illustration : NTC behavior\n",
|
||
"In the paper by Kogekar, et al., the reactor model is used to demonstrate the impacts of non-ideal behavior on IDTs in the **N**egative **T**emperature **C**oefficient region, where observed IDTs, counter to intuition, increase with increasing temperature."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Define the temperatures for which we will run the simulations"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Make a list of all the temperatures we would like to run simulations at\n",
|
||
"T = [1800, 1600, 1400, 1200, 1100, 1075, 1050, 1025, 1000, 975, 950, 925, 900, 850, 825, 800,\n",
|
||
" 750, 700]\n",
|
||
"\n",
|
||
"estimatedIgnitionDelayTimes = np.ones(len(T))\n",
|
||
"\n",
|
||
"# Set the initial guesses to a common value. We could probably speed up simulations \n",
|
||
"# by tuning this guess, but as seen in the figure above, the 'extra' time after igntion \n",
|
||
"# does not add many data points or simulation steps. The time savings would be small.\n",
|
||
"estimatedIgnitionDelayTimes[:] = 0.005\n",
|
||
"\n",
|
||
"# Now create a dataFrame out of these\n",
|
||
"ignitionDelays = pd.DataFrame(data={'T':T})\n",
|
||
"ignitionDelays['ignDelay'] = np.nan"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Run the code above for each temperature, and save the IDT for each."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"scrolled": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Computed Ignition Delay: 6.357e-07 seconds for T=1800K. Took 2.36s to compute\n",
|
||
"Computed Ignition Delay: 1.585e-06 seconds for T=1600K. Took 2.67s to compute\n",
|
||
"Computed Ignition Delay: 5.785e-06 seconds for T=1400K. Took 2.71s to compute\n",
|
||
"Computed Ignition Delay: 3.911e-05 seconds for T=1200K. Took 3.00s to compute\n",
|
||
"Computed Ignition Delay: 1.326e-04 seconds for T=1100K. Took 3.14s to compute\n",
|
||
"Computed Ignition Delay: 1.839e-04 seconds for T=1075K. Took 3.32s to compute\n",
|
||
"Computed Ignition Delay: 2.533e-04 seconds for T=1050K. Took 3.45s to compute\n",
|
||
"Computed Ignition Delay: 3.365e-04 seconds for T=1025K. Took 3.97s to compute\n",
|
||
"Computed Ignition Delay: 4.093e-04 seconds for T=1000K. Took 4.06s to compute\n",
|
||
"Computed Ignition Delay: 4.289e-04 seconds for T=975K. Took 4.01s to compute\n",
|
||
"Computed Ignition Delay: 3.911e-04 seconds for T=950K. Took 3.94s to compute\n",
|
||
"Computed Ignition Delay: 3.407e-04 seconds for T=925K. Took 4.10s to compute\n",
|
||
"Computed Ignition Delay: 3.145e-04 seconds for T=900K. Took 3.74s to compute\n",
|
||
"Computed Ignition Delay: 3.233e-04 seconds for T=850K. Took 4.12s to compute\n",
|
||
"Computed Ignition Delay: 3.439e-04 seconds for T=825K. Took 4.26s to compute\n",
|
||
"Computed Ignition Delay: 3.852e-04 seconds for T=800K. Took 4.51s to compute\n",
|
||
"Computed Ignition Delay: 6.824e-04 seconds for T=750K. Took 4.37s to compute\n",
|
||
"Computed Ignition Delay: 2.056e-03 seconds for T=700K. Took 4.67s to compute\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for i, temperature in enumerate(T):\n",
|
||
" # Set up the gas and reactor\n",
|
||
" reactorTemperature = temperature\n",
|
||
" reactorPressure = 40.0*101325.0\n",
|
||
" gas.TP = reactorTemperature, reactorPressure\n",
|
||
" gas.set_equivalence_ratio(phi=1.0, fuel='c12h26', oxidizer={'o2':1.0, 'n2':3.76})\n",
|
||
" r = ct.Reactor(contents=gas)\n",
|
||
" reactorNetwork = ct.ReactorNet([r])\n",
|
||
"\n",
|
||
" # Create an empty data frame\n",
|
||
" timeHistory = pd.DataFrame(columns=timeHistory.columns)\n",
|
||
"\n",
|
||
" t0 = time.time()\n",
|
||
"\n",
|
||
" t = 0\n",
|
||
" counter = 0\n",
|
||
" while t < estimatedIgnitionDelayTimes[i]:\n",
|
||
" t = reactorNetwork.step()\n",
|
||
" if not counter % 20:\n",
|
||
" timeHistory.loc[t] = r.get_state()\n",
|
||
" counter += 1\n",
|
||
"\n",
|
||
" tau = ignitionDelay(timeHistory, 'oh')\n",
|
||
" t1 = time.time()\n",
|
||
"\n",
|
||
" print('Computed Ignition Delay: {:.3e} seconds for T={}K. Took {:3.2f}s to compute'.format(tau, temperature, t1-t0))\n",
|
||
"\n",
|
||
" ignitionDelays.at[i, 'ignDelay'] = tau"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Figure: ignition delay ($\\tau$) vs. the inverse of temperature ($\\frac{1000}{T}$). "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"scrolled": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"/* Put everything inside the global mpl namespace */\n",
|
||
"window.mpl = {};\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.get_websocket_type = function() {\n",
|
||
" if (typeof(WebSocket) !== 'undefined') {\n",
|
||
" return WebSocket;\n",
|
||
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
|
||
" return MozWebSocket;\n",
|
||
" } else {\n",
|
||
" alert('Your browser does not have WebSocket support.' +\n",
|
||
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
|
||
" 'Firefox 4 and 5 are also supported but you ' +\n",
|
||
" 'have to enable WebSockets in about:config.');\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
|
||
" this.id = figure_id;\n",
|
||
"\n",
|
||
" this.ws = websocket;\n",
|
||
"\n",
|
||
" this.supports_binary = (this.ws.binaryType != undefined);\n",
|
||
"\n",
|
||
" if (!this.supports_binary) {\n",
|
||
" var warnings = document.getElementById(\"mpl-warnings\");\n",
|
||
" if (warnings) {\n",
|
||
" warnings.style.display = 'block';\n",
|
||
" warnings.textContent = (\n",
|
||
" \"This browser does not support binary websocket messages. \" +\n",
|
||
" \"Performance may be slow.\");\n",
|
||
" }\n",
|
||
" }\n",
|
||
"\n",
|
||
" this.imageObj = new Image();\n",
|
||
"\n",
|
||
" this.context = undefined;\n",
|
||
" this.message = undefined;\n",
|
||
" this.canvas = undefined;\n",
|
||
" this.rubberband_canvas = undefined;\n",
|
||
" this.rubberband_context = undefined;\n",
|
||
" this.format_dropdown = undefined;\n",
|
||
"\n",
|
||
" this.image_mode = 'full';\n",
|
||
"\n",
|
||
" this.root = $('<div/>');\n",
|
||
" this._root_extra_style(this.root)\n",
|
||
" this.root.attr('style', 'display: inline-block');\n",
|
||
"\n",
|
||
" $(parent_element).append(this.root);\n",
|
||
"\n",
|
||
" this._init_header(this);\n",
|
||
" this._init_canvas(this);\n",
|
||
" this._init_toolbar(this);\n",
|
||
"\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" this.waiting = false;\n",
|
||
"\n",
|
||
" this.ws.onopen = function () {\n",
|
||
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
|
||
" fig.send_message(\"send_image_mode\", {});\n",
|
||
" if (mpl.ratio != 1) {\n",
|
||
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
|
||
" }\n",
|
||
" fig.send_message(\"refresh\", {});\n",
|
||
" }\n",
|
||
"\n",
|
||
" this.imageObj.onload = function() {\n",
|
||
" if (fig.image_mode == 'full') {\n",
|
||
" // Full images could contain transparency (where diff images\n",
|
||
" // almost always do), so we need to clear the canvas so that\n",
|
||
" // there is no ghosting.\n",
|
||
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
|
||
" }\n",
|
||
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
|
||
" };\n",
|
||
"\n",
|
||
" this.imageObj.onunload = function() {\n",
|
||
" fig.ws.close();\n",
|
||
" }\n",
|
||
"\n",
|
||
" this.ws.onmessage = this._make_on_message_function(this);\n",
|
||
"\n",
|
||
" this.ondownload = ondownload;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_header = function() {\n",
|
||
" var titlebar = $(\n",
|
||
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
|
||
" 'ui-helper-clearfix\"/>');\n",
|
||
" var titletext = $(\n",
|
||
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
|
||
" 'text-align: center; padding: 3px;\"/>');\n",
|
||
" titlebar.append(titletext)\n",
|
||
" this.root.append(titlebar);\n",
|
||
" this.header = titletext[0];\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
|
||
"\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
|
||
"\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_canvas = function() {\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" var canvas_div = $('<div/>');\n",
|
||
"\n",
|
||
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
|
||
"\n",
|
||
" function canvas_keyboard_event(event) {\n",
|
||
" return fig.key_event(event, event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
|
||
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
|
||
" this.canvas_div = canvas_div\n",
|
||
" this._canvas_extra_style(canvas_div)\n",
|
||
" this.root.append(canvas_div);\n",
|
||
"\n",
|
||
" var canvas = $('<canvas/>');\n",
|
||
" canvas.addClass('mpl-canvas');\n",
|
||
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
|
||
"\n",
|
||
" this.canvas = canvas[0];\n",
|
||
" this.context = canvas[0].getContext(\"2d\");\n",
|
||
"\n",
|
||
" var backingStore = this.context.backingStorePixelRatio ||\n",
|
||
"\tthis.context.webkitBackingStorePixelRatio ||\n",
|
||
"\tthis.context.mozBackingStorePixelRatio ||\n",
|
||
"\tthis.context.msBackingStorePixelRatio ||\n",
|
||
"\tthis.context.oBackingStorePixelRatio ||\n",
|
||
"\tthis.context.backingStorePixelRatio || 1;\n",
|
||
"\n",
|
||
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
|
||
"\n",
|
||
" var rubberband = $('<canvas/>');\n",
|
||
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
|
||
"\n",
|
||
" var pass_mouse_events = true;\n",
|
||
"\n",
|
||
" canvas_div.resizable({\n",
|
||
" start: function(event, ui) {\n",
|
||
" pass_mouse_events = false;\n",
|
||
" },\n",
|
||
" resize: function(event, ui) {\n",
|
||
" fig.request_resize(ui.size.width, ui.size.height);\n",
|
||
" },\n",
|
||
" stop: function(event, ui) {\n",
|
||
" pass_mouse_events = true;\n",
|
||
" fig.request_resize(ui.size.width, ui.size.height);\n",
|
||
" },\n",
|
||
" });\n",
|
||
"\n",
|
||
" function mouse_event_fn(event) {\n",
|
||
" if (pass_mouse_events)\n",
|
||
" return fig.mouse_event(event, event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" rubberband.mousedown('button_press', mouse_event_fn);\n",
|
||
" rubberband.mouseup('button_release', mouse_event_fn);\n",
|
||
" // Throttle sequential mouse events to 1 every 20ms.\n",
|
||
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
|
||
"\n",
|
||
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
|
||
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
|
||
"\n",
|
||
" canvas_div.on(\"wheel\", function (event) {\n",
|
||
" event = event.originalEvent;\n",
|
||
" event['data'] = 'scroll'\n",
|
||
" if (event.deltaY < 0) {\n",
|
||
" event.step = 1;\n",
|
||
" } else {\n",
|
||
" event.step = -1;\n",
|
||
" }\n",
|
||
" mouse_event_fn(event);\n",
|
||
" });\n",
|
||
"\n",
|
||
" canvas_div.append(canvas);\n",
|
||
" canvas_div.append(rubberband);\n",
|
||
"\n",
|
||
" this.rubberband = rubberband;\n",
|
||
" this.rubberband_canvas = rubberband[0];\n",
|
||
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
|
||
" this.rubberband_context.strokeStyle = \"#000000\";\n",
|
||
"\n",
|
||
" this._resize_canvas = function(width, height) {\n",
|
||
" // Keep the size of the canvas, canvas container, and rubber band\n",
|
||
" // canvas in synch.\n",
|
||
" canvas_div.css('width', width)\n",
|
||
" canvas_div.css('height', height)\n",
|
||
"\n",
|
||
" canvas.attr('width', width * mpl.ratio);\n",
|
||
" canvas.attr('height', height * mpl.ratio);\n",
|
||
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
|
||
"\n",
|
||
" rubberband.attr('width', width);\n",
|
||
" rubberband.attr('height', height);\n",
|
||
" }\n",
|
||
"\n",
|
||
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
|
||
" // upon first draw.\n",
|
||
" this._resize_canvas(600, 600);\n",
|
||
"\n",
|
||
" // Disable right mouse context menu.\n",
|
||
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
|
||
" return false;\n",
|
||
" });\n",
|
||
"\n",
|
||
" function set_focus () {\n",
|
||
" canvas.focus();\n",
|
||
" canvas_div.focus();\n",
|
||
" }\n",
|
||
"\n",
|
||
" window.setTimeout(set_focus, 100);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_toolbar = function() {\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" var nav_element = $('<div/>')\n",
|
||
" nav_element.attr('style', 'width: 100%');\n",
|
||
" this.root.append(nav_element);\n",
|
||
"\n",
|
||
" // Define a callback function for later on.\n",
|
||
" function toolbar_event(event) {\n",
|
||
" return fig.toolbar_button_onclick(event['data']);\n",
|
||
" }\n",
|
||
" function toolbar_mouse_event(event) {\n",
|
||
" return fig.toolbar_button_onmouseover(event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" for(var toolbar_ind in mpl.toolbar_items) {\n",
|
||
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
|
||
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
|
||
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
|
||
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
|
||
"\n",
|
||
" if (!name) {\n",
|
||
" // put a spacer in here.\n",
|
||
" continue;\n",
|
||
" }\n",
|
||
" var button = $('<button/>');\n",
|
||
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
|
||
" 'ui-button-icon-only');\n",
|
||
" button.attr('role', 'button');\n",
|
||
" button.attr('aria-disabled', 'false');\n",
|
||
" button.click(method_name, toolbar_event);\n",
|
||
" button.mouseover(tooltip, toolbar_mouse_event);\n",
|
||
"\n",
|
||
" var icon_img = $('<span/>');\n",
|
||
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
|
||
" icon_img.addClass(image);\n",
|
||
" icon_img.addClass('ui-corner-all');\n",
|
||
"\n",
|
||
" var tooltip_span = $('<span/>');\n",
|
||
" tooltip_span.addClass('ui-button-text');\n",
|
||
" tooltip_span.html(tooltip);\n",
|
||
"\n",
|
||
" button.append(icon_img);\n",
|
||
" button.append(tooltip_span);\n",
|
||
"\n",
|
||
" nav_element.append(button);\n",
|
||
" }\n",
|
||
"\n",
|
||
" var fmt_picker_span = $('<span/>');\n",
|
||
"\n",
|
||
" var fmt_picker = $('<select/>');\n",
|
||
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
|
||
" fmt_picker_span.append(fmt_picker);\n",
|
||
" nav_element.append(fmt_picker_span);\n",
|
||
" this.format_dropdown = fmt_picker[0];\n",
|
||
"\n",
|
||
" for (var ind in mpl.extensions) {\n",
|
||
" var fmt = mpl.extensions[ind];\n",
|
||
" var option = $(\n",
|
||
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
|
||
" fmt_picker.append(option)\n",
|
||
" }\n",
|
||
"\n",
|
||
" // Add hover states to the ui-buttons\n",
|
||
" $( \".ui-button\" ).hover(\n",
|
||
" function() { $(this).addClass(\"ui-state-hover\");},\n",
|
||
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
|
||
" );\n",
|
||
"\n",
|
||
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
|
||
" nav_element.append(status_bar);\n",
|
||
" this.message = status_bar[0];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
|
||
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
|
||
" // which will in turn request a refresh of the image.\n",
|
||
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.send_message = function(type, properties) {\n",
|
||
" properties['type'] = type;\n",
|
||
" properties['figure_id'] = this.id;\n",
|
||
" this.ws.send(JSON.stringify(properties));\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.send_draw_message = function() {\n",
|
||
" if (!this.waiting) {\n",
|
||
" this.waiting = true;\n",
|
||
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
|
||
" var format_dropdown = fig.format_dropdown;\n",
|
||
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
|
||
" fig.ondownload(fig, format);\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
|
||
" var size = msg['size'];\n",
|
||
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
|
||
" fig._resize_canvas(size[0], size[1]);\n",
|
||
" fig.send_message(\"refresh\", {});\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
|
||
" var x0 = msg['x0'] / mpl.ratio;\n",
|
||
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
|
||
" var x1 = msg['x1'] / mpl.ratio;\n",
|
||
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
|
||
" x0 = Math.floor(x0) + 0.5;\n",
|
||
" y0 = Math.floor(y0) + 0.5;\n",
|
||
" x1 = Math.floor(x1) + 0.5;\n",
|
||
" y1 = Math.floor(y1) + 0.5;\n",
|
||
" var min_x = Math.min(x0, x1);\n",
|
||
" var min_y = Math.min(y0, y1);\n",
|
||
" var width = Math.abs(x1 - x0);\n",
|
||
" var height = Math.abs(y1 - y0);\n",
|
||
"\n",
|
||
" fig.rubberband_context.clearRect(\n",
|
||
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
|
||
"\n",
|
||
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
|
||
" // Updates the figure title.\n",
|
||
" fig.header.textContent = msg['label'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
|
||
" var cursor = msg['cursor'];\n",
|
||
" switch(cursor)\n",
|
||
" {\n",
|
||
" case 0:\n",
|
||
" cursor = 'pointer';\n",
|
||
" break;\n",
|
||
" case 1:\n",
|
||
" cursor = 'default';\n",
|
||
" break;\n",
|
||
" case 2:\n",
|
||
" cursor = 'crosshair';\n",
|
||
" break;\n",
|
||
" case 3:\n",
|
||
" cursor = 'move';\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" fig.rubberband_canvas.style.cursor = cursor;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
|
||
" fig.message.textContent = msg['message'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
|
||
" // Request the server to send over a new figure.\n",
|
||
" fig.send_draw_message();\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
|
||
" fig.image_mode = msg['mode'];\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.updated_canvas_event = function() {\n",
|
||
" // Called whenever the canvas gets updated.\n",
|
||
" this.send_message(\"ack\", {});\n",
|
||
"}\n",
|
||
"\n",
|
||
"// A function to construct a web socket function for onmessage handling.\n",
|
||
"// Called in the figure constructor.\n",
|
||
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
|
||
" return function socket_on_message(evt) {\n",
|
||
" if (evt.data instanceof Blob) {\n",
|
||
" /* FIXME: We get \"Resource interpreted as Image but\n",
|
||
" * transferred with MIME type text/plain:\" errors on\n",
|
||
" * Chrome. But how to set the MIME type? It doesn't seem\n",
|
||
" * to be part of the websocket stream */\n",
|
||
" evt.data.type = \"image/png\";\n",
|
||
"\n",
|
||
" /* Free the memory for the previous frames */\n",
|
||
" if (fig.imageObj.src) {\n",
|
||
" (window.URL || window.webkitURL).revokeObjectURL(\n",
|
||
" fig.imageObj.src);\n",
|
||
" }\n",
|
||
"\n",
|
||
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
|
||
" evt.data);\n",
|
||
" fig.updated_canvas_event();\n",
|
||
" fig.waiting = false;\n",
|
||
" return;\n",
|
||
" }\n",
|
||
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
|
||
" fig.imageObj.src = evt.data;\n",
|
||
" fig.updated_canvas_event();\n",
|
||
" fig.waiting = false;\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" var msg = JSON.parse(evt.data);\n",
|
||
" var msg_type = msg['type'];\n",
|
||
"\n",
|
||
" // Call the \"handle_{type}\" callback, which takes\n",
|
||
" // the figure and JSON message as its only arguments.\n",
|
||
" try {\n",
|
||
" var callback = fig[\"handle_\" + msg_type];\n",
|
||
" } catch (e) {\n",
|
||
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" if (callback) {\n",
|
||
" try {\n",
|
||
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
|
||
" callback(fig, msg);\n",
|
||
" } catch (e) {\n",
|
||
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
|
||
" }\n",
|
||
" }\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
|
||
"mpl.findpos = function(e) {\n",
|
||
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
|
||
" var targ;\n",
|
||
" if (!e)\n",
|
||
" e = window.event;\n",
|
||
" if (e.target)\n",
|
||
" targ = e.target;\n",
|
||
" else if (e.srcElement)\n",
|
||
" targ = e.srcElement;\n",
|
||
" if (targ.nodeType == 3) // defeat Safari bug\n",
|
||
" targ = targ.parentNode;\n",
|
||
"\n",
|
||
" // jQuery normalizes the pageX and pageY\n",
|
||
" // pageX,Y are the mouse positions relative to the document\n",
|
||
" // offset() returns the position of the element relative to the document\n",
|
||
" var x = e.pageX - $(targ).offset().left;\n",
|
||
" var y = e.pageY - $(targ).offset().top;\n",
|
||
"\n",
|
||
" return {\"x\": x, \"y\": y};\n",
|
||
"};\n",
|
||
"\n",
|
||
"/*\n",
|
||
" * return a copy of an object with only non-object keys\n",
|
||
" * we need this to avoid circular references\n",
|
||
" * http://stackoverflow.com/a/24161582/3208463\n",
|
||
" */\n",
|
||
"function simpleKeys (original) {\n",
|
||
" return Object.keys(original).reduce(function (obj, key) {\n",
|
||
" if (typeof original[key] !== 'object')\n",
|
||
" obj[key] = original[key]\n",
|
||
" return obj;\n",
|
||
" }, {});\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
|
||
" var canvas_pos = mpl.findpos(event)\n",
|
||
"\n",
|
||
" if (name === 'button_press')\n",
|
||
" {\n",
|
||
" this.canvas.focus();\n",
|
||
" this.canvas_div.focus();\n",
|
||
" }\n",
|
||
"\n",
|
||
" var x = canvas_pos.x * mpl.ratio;\n",
|
||
" var y = canvas_pos.y * mpl.ratio;\n",
|
||
"\n",
|
||
" this.send_message(name, {x: x, y: y, button: event.button,\n",
|
||
" step: event.step,\n",
|
||
" guiEvent: simpleKeys(event)});\n",
|
||
"\n",
|
||
" /* This prevents the web browser from automatically changing to\n",
|
||
" * the text insertion cursor when the button is pressed. We want\n",
|
||
" * to control all of the cursor setting manually through the\n",
|
||
" * 'cursor' event from matplotlib */\n",
|
||
" event.preventDefault();\n",
|
||
" return false;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
|
||
" // Handle any extra behaviour associated with a key event\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.key_event = function(event, name) {\n",
|
||
"\n",
|
||
" // Prevent repeat events\n",
|
||
" if (name == 'key_press')\n",
|
||
" {\n",
|
||
" if (event.which === this._key)\n",
|
||
" return;\n",
|
||
" else\n",
|
||
" this._key = event.which;\n",
|
||
" }\n",
|
||
" if (name == 'key_release')\n",
|
||
" this._key = null;\n",
|
||
"\n",
|
||
" var value = '';\n",
|
||
" if (event.ctrlKey && event.which != 17)\n",
|
||
" value += \"ctrl+\";\n",
|
||
" if (event.altKey && event.which != 18)\n",
|
||
" value += \"alt+\";\n",
|
||
" if (event.shiftKey && event.which != 16)\n",
|
||
" value += \"shift+\";\n",
|
||
"\n",
|
||
" value += 'k';\n",
|
||
" value += event.which.toString();\n",
|
||
"\n",
|
||
" this._key_event_extra(event, name);\n",
|
||
"\n",
|
||
" this.send_message(name, {key: value,\n",
|
||
" guiEvent: simpleKeys(event)});\n",
|
||
" return false;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
|
||
" if (name == 'download') {\n",
|
||
" this.handle_save(this, null);\n",
|
||
" } else {\n",
|
||
" this.send_message(\"toolbar_button\", {name: name});\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
|
||
" this.message.textContent = tooltip;\n",
|
||
"};\n",
|
||
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
|
||
"\n",
|
||
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
|
||
"\n",
|
||
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
|
||
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
|
||
" // object with the appropriate methods. Currently this is a non binary\n",
|
||
" // socket, so there is still some room for performance tuning.\n",
|
||
" var ws = {};\n",
|
||
"\n",
|
||
" ws.close = function() {\n",
|
||
" comm.close()\n",
|
||
" };\n",
|
||
" ws.send = function(m) {\n",
|
||
" //console.log('sending', m);\n",
|
||
" comm.send(m);\n",
|
||
" };\n",
|
||
" // Register the callback with on_msg.\n",
|
||
" comm.on_msg(function(msg) {\n",
|
||
" //console.log('receiving', msg['content']['data'], msg);\n",
|
||
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
|
||
" ws.onmessage(msg['content']['data'])\n",
|
||
" });\n",
|
||
" return ws;\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.mpl_figure_comm = function(comm, msg) {\n",
|
||
" // This is the function which gets called when the mpl process\n",
|
||
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
|
||
"\n",
|
||
" var id = msg.content.data.id;\n",
|
||
" // Get hold of the div created by the display call when the Comm\n",
|
||
" // socket was opened in Python.\n",
|
||
" var element = $(\"#\" + id);\n",
|
||
" var ws_proxy = comm_websocket_adapter(comm)\n",
|
||
"\n",
|
||
" function ondownload(figure, format) {\n",
|
||
" window.open(figure.imageObj.src);\n",
|
||
" }\n",
|
||
"\n",
|
||
" var fig = new mpl.figure(id, ws_proxy,\n",
|
||
" ondownload,\n",
|
||
" element.get(0));\n",
|
||
"\n",
|
||
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
|
||
" // web socket which is closed, not our websocket->open comm proxy.\n",
|
||
" ws_proxy.onopen();\n",
|
||
"\n",
|
||
" fig.parent_element = element.get(0);\n",
|
||
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
|
||
" if (!fig.cell_info) {\n",
|
||
" console.error(\"Failed to find cell for figure\", id, fig);\n",
|
||
" return;\n",
|
||
" }\n",
|
||
"\n",
|
||
" var output_index = fig.cell_info[2]\n",
|
||
" var cell = fig.cell_info[0];\n",
|
||
"\n",
|
||
"};\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
|
||
" var width = fig.canvas.width/mpl.ratio\n",
|
||
" fig.root.unbind('remove')\n",
|
||
"\n",
|
||
" // Update the output cell to use the data from the current canvas.\n",
|
||
" fig.push_to_output();\n",
|
||
" var dataURL = fig.canvas.toDataURL();\n",
|
||
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
|
||
" // the notebook keyboard shortcuts fail.\n",
|
||
" IPython.keyboard_manager.enable()\n",
|
||
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
|
||
" fig.close_ws(fig, msg);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
|
||
" fig.send_message('closing', msg);\n",
|
||
" // fig.ws.close()\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
|
||
" // Turn the data on the canvas into data in the output cell.\n",
|
||
" var width = this.canvas.width/mpl.ratio\n",
|
||
" var dataURL = this.canvas.toDataURL();\n",
|
||
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.updated_canvas_event = function() {\n",
|
||
" // Tell IPython that the notebook contents must change.\n",
|
||
" IPython.notebook.set_dirty(true);\n",
|
||
" this.send_message(\"ack\", {});\n",
|
||
" var fig = this;\n",
|
||
" // Wait a second, then push the new image to the DOM so\n",
|
||
" // that it is saved nicely (might be nice to debounce this).\n",
|
||
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._init_toolbar = function() {\n",
|
||
" var fig = this;\n",
|
||
"\n",
|
||
" var nav_element = $('<div/>')\n",
|
||
" nav_element.attr('style', 'width: 100%');\n",
|
||
" this.root.append(nav_element);\n",
|
||
"\n",
|
||
" // Define a callback function for later on.\n",
|
||
" function toolbar_event(event) {\n",
|
||
" return fig.toolbar_button_onclick(event['data']);\n",
|
||
" }\n",
|
||
" function toolbar_mouse_event(event) {\n",
|
||
" return fig.toolbar_button_onmouseover(event['data']);\n",
|
||
" }\n",
|
||
"\n",
|
||
" for(var toolbar_ind in mpl.toolbar_items){\n",
|
||
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
|
||
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
|
||
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
|
||
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
|
||
"\n",
|
||
" if (!name) { continue; };\n",
|
||
"\n",
|
||
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
|
||
" button.click(method_name, toolbar_event);\n",
|
||
" button.mouseover(tooltip, toolbar_mouse_event);\n",
|
||
" nav_element.append(button);\n",
|
||
" }\n",
|
||
"\n",
|
||
" // Add the status bar.\n",
|
||
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
|
||
" nav_element.append(status_bar);\n",
|
||
" this.message = status_bar[0];\n",
|
||
"\n",
|
||
" // Add the close button to the window.\n",
|
||
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
|
||
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
|
||
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
|
||
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
|
||
" buttongrp.append(button);\n",
|
||
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
|
||
" titlebar.prepend(buttongrp);\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._root_extra_style = function(el){\n",
|
||
" var fig = this\n",
|
||
" el.on(\"remove\", function(){\n",
|
||
"\tfig.close_ws(fig, {});\n",
|
||
" });\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
|
||
" // this is important to make the div 'focusable\n",
|
||
" el.attr('tabindex', 0)\n",
|
||
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
|
||
" // off when our div gets focus\n",
|
||
"\n",
|
||
" // location in version 3\n",
|
||
" if (IPython.notebook.keyboard_manager) {\n",
|
||
" IPython.notebook.keyboard_manager.register_events(el);\n",
|
||
" }\n",
|
||
" else {\n",
|
||
" // location in version 2\n",
|
||
" IPython.keyboard_manager.register_events(el);\n",
|
||
" }\n",
|
||
"\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
|
||
" var manager = IPython.notebook.keyboard_manager;\n",
|
||
" if (!manager)\n",
|
||
" manager = IPython.keyboard_manager;\n",
|
||
"\n",
|
||
" // Check for shift+enter\n",
|
||
" if (event.shiftKey && event.which == 13) {\n",
|
||
" this.canvas_div.blur();\n",
|
||
" event.shiftKey = false;\n",
|
||
" // Send a \"J\" for go to next cell\n",
|
||
" event.which = 74;\n",
|
||
" event.keyCode = 74;\n",
|
||
" manager.command_mode();\n",
|
||
" manager.handle_keydown(event);\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
|
||
" fig.ondownload(fig, null);\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"mpl.find_output_cell = function(html_output) {\n",
|
||
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
|
||
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
|
||
" // IPython event is triggered only after the cells have been serialised, which for\n",
|
||
" // our purposes (turning an active figure into a static one), is too late.\n",
|
||
" var cells = IPython.notebook.get_cells();\n",
|
||
" var ncells = cells.length;\n",
|
||
" for (var i=0; i<ncells; i++) {\n",
|
||
" var cell = cells[i];\n",
|
||
" if (cell.cell_type === 'code'){\n",
|
||
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
|
||
" var data = cell.output_area.outputs[j];\n",
|
||
" if (data.data) {\n",
|
||
" // IPython >= 3 moved mimebundle to data attribute of output\n",
|
||
" data = data.data;\n",
|
||
" }\n",
|
||
" if (data['text/html'] == html_output) {\n",
|
||
" return [cell, data, j];\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"// Register the function which deals with the matplotlib target/channel.\n",
|
||
"// The kernel may be null if the page has been refreshed.\n",
|
||
"if (IPython.notebook.kernel != null) {\n",
|
||
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
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"}\n"
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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\" width=\"640\">"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_subplot(111)\n",
|
||
"ax.semilogy(1000/ignitionDelays['T'], ignitionDelays['ignDelay'],'o-',color='b')\n",
|
||
"ax.set_ylabel('Ignition Delay (s)',fontname='Times New Roman',fontsize=16)\n",
|
||
"ax.set_xlabel(r'$\\mathdefault{1000/T\\, (K^{-1})}$', fontsize=16,fontname='Times New Roman')\n",
|
||
"\n",
|
||
"# Add a second axis on top to plot the temperature for better readability\n",
|
||
"ax2 = ax.twiny()\n",
|
||
"ticks = ax.get_xticks()\n",
|
||
"ax2.set_xticks(ticks)\n",
|
||
"ax2.set_xticklabels((1000/ticks).round(1))\n",
|
||
"ax2.set_xlim(ax.get_xlim())\n",
|
||
"ax2.set_xlabel('Temperature (K)',fontname='Times New Roman',fontsize=16);\n",
|
||
"\n",
|
||
"for tick in ax.xaxis.get_major_ticks():\n",
|
||
" tick.label1.set_fontsize(12)\n",
|
||
" tick.label1.set_fontname('Times New Roman')\n",
|
||
"for tick in ax.yaxis.get_major_ticks():\n",
|
||
" tick.label1.set_fontsize(12)\n",
|
||
" tick.label1.set_fontname('Times New Roman')\n",
|
||
"for tick in ax2.xaxis.get_major_ticks():\n",
|
||
" tick.label1.set_fontsize(12)\n",
|
||
" tick.label1.set_fontname('Times New Roman')"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.7.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|