{ "cells": [ { "cell_type": "markdown", "id": "8026ec99", "metadata": {}, "source": [ "\n", "*This notebook contains material from [CBE60499](https://ndcbe.github.io/CBE60499);\n", "content is available [on Github](git@github.com:ndcbe/CBE60499.git).*\n" ] }, { "cell_type": "markdown", "id": "62b667e1", "metadata": {}, "source": [ "\n", "< [2.7 Stochastic Programming](https://ndcbe.github.io/CBE60499/02.07-SP.html) | [Contents](toc.html) | [Tag Index](tag_index.html) | [2.9 Supplementary material: data for parmest tutorial](https://ndcbe.github.io/CBE60499/02.09-Parmest-generate-data.html) >

\"Open

\"Download\"" ] }, { "cell_type": "code", "execution_count": null, "id": "0b64e5d5", "metadata": {}, "outputs": [], "source": [ "# IMPORT DATA FILES USED BY THIS NOTEBOOK\n", "import os, requests\n", "\n", "file_links = [(\"data/parmest_20210609_data_exp1.csv\", \"https://ndcbe.github.io/CBE60499/data/parmest_20210609_data_exp1.csv\"),\n", " (\"data/parmest_log_file.csv\", \"https://ndcbe.github.io/CBE60499/data/parmest_log_file.csv\")]\n", "\n", "# This cell has been added by nbpages. Run this cell to download data files required for this notebook.\n", "\n", "for filepath, fileurl in file_links:\n", " stem, filename = os.path.split(filepath)\n", " if stem:\n", " if not os.path.exists(stem):\n", " os.mkdir(stem)\n", " if not os.path.isfile(filepath):\n", " with open(filepath, 'wb') as f:\n", " response = requests.get(fileurl)\n", " f.write(response.content)\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 1, "link": "[2.8 Parameter estimation with `parmest`](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8-Parameter-estimation-with-`parmest`)", "section": "2.8 Parameter estimation with `parmest`" } }, "source": [ "# 2.8 Parameter estimation with `parmest`\n", "\n", "Created by [Kanishka Ghosh](https://github.com/kanishka-ghosh), [Jialu Wang](https://github.com/jialuw96), and [Prof. Alex Dowling](https://github.com/adowling2/) at the University of Notre Dame." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpages": { "level": 1, "link": "[2.8 Parameter estimation with `parmest`](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8-Parameter-estimation-with-`parmest`)", "section": "2.8 Parameter estimation with `parmest`" } }, "outputs": [], "source": [ "# This code cell installs packages on Colab\n", "\n", "import sys\n", "if \"google.colab\" in sys.modules:\n", " !wget \"https://raw.githubusercontent.com/ndcbe/CBE60499/main/notebooks/helper.py\"\n", " import helper\n", " helper.install_idaes()\n", " helper.install_ipopt()\n", " helper.download_data(['parmest_20210609_data_exp{:d}.csv'.format(i) for i in range(1,17)])\n", " helper.download_data(['parmest_log_file.csv'])\n", " !pyomo build-extensions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbpages": { "level": 1, "link": "[2.8 Parameter estimation with `parmest`](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8-Parameter-estimation-with-`parmest`)", "section": "2.8 Parameter estimation with `parmest`" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "# Define the directory to save/read the data files\n", "data_dir = './data/'" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.1 What is parameter estimation?](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.1-What-is-parameter-estimation?)", "section": "2.8.1 What is parameter estimation?" } }, "source": [ "## 2.8.1 What is parameter estimation?\n", "\n", "Given a function $f(x,\\theta)$ where $x$ is the input or array of inputs, $\\theta$ is the vector of unknown model parameters, and $y$ is the array of observed output, parameter estimation is performed to determine the values of $\\theta$ to minimize the error between $f(x,\\theta)$ and $y$. Commonly, parameter estimation is set up as a least squares objective problem:\n", "\n", "$$\n", "\\begin{align}\n", "\\begin{split}\n", " \\min_{\\hat{\\theta}} \\quad & \\sum_{i}^{} (y_i - f(x_i,\\hat{\\theta}))^2\\\\\n", " \\textrm{s.t.} \\quad & \\mathrm{bounds \\ on} \\ \\theta\\\\\n", " & \\mathrm{other \\ physical \\ constraints}\\\\\n", "\\end{split}\n", "\\end{align}\n", "$$\n", "\n", "where $i$ is used to index the datapoints in a dataset and $\\hat{\\theta}$ is the optimal set of parameter values that minimizes the prediction error." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.2 What is `parmest`?](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.2-What-is-`parmest`?)", "section": "2.8.2 What is `parmest`?" } }, "source": [ "## 2.8.2 What is `parmest`?\n", "\n", "`parmest` is a Python package built on the [Pyomo optimization modeling language](http://www.pyomo.org/) to support parameter estimation using experimental data along with confidence regions and subsequent creation of scenarios for [PySP](https://github.com/Pyomo/pysp). `parmest` supports scenario generation for multiple 'experiments' and can be used to characterize estimate uncertainties through, for example, confidence region generations. `parmest` requires the following positional arguments in order solve the optimization problem:\n", "1. Function that accepts an 'experimental' dataset or a list of 'experimental' datasets, each defined as a dictionary, as it's argument and returns the Pyomo model.\n", " \n", " Later in this tutorial, that function is defined above as `create_model()`\n", "\n", "\n", "2. List of datasets where each dataset is a dictionary\n", "\n", " Later in this tutorial, the list of datasets is generated using the function `create_data_dict()` (defined below) and is stored in `data_dict_overall`\n", "\n", "\n", "3. List of parameter names (as they appear in the Pyomo model definition) that are being estimated\n", "\n", " later in this tutorial, the list of parameter names to be estimated is defined below by `theta_names`\n", "\n", "\n", "4. Optional keyword argument to define the verbosity of solver output. Default: False\n", "\n", "More information about the `parmest` package can be found [here](https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html).\n", "\n", "Detailed explanation of the various methods in `parmest` can be found [here](https://www.osti.gov/servlets/purl/1761797).\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.3 Example: Reaction Kinetics](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3-Example:-Reaction-Kinetics)", "section": "2.8.3 Example: Reaction Kinetics" } }, "source": [ "## 2.8.3 Example: Reaction Kinetics" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.3 Example: Reaction Kinetics](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3-Example:-Reaction-Kinetics)", "section": "2.8.3 Example: Reaction Kinetics" } }, "source": [ "Consider two chemical reactions that convert molecule $A$ to desired product $B$ and a less valuable side-product $C$.\n", "\n", "$$A \\overset{k_1}{\\rightarrow} B \\overset{k_2}{\\rightarrow} C$$\n", "\n", "Our ultimate goals is to design a large-scale continous reactor that maximizes the production of $B$. This general sequential reactions problem is widely applicable to CO$_2$ capture and industry more broadly (petrochemicals, pharmasuticals, etc.).\n", "\n", "The rate laws for these two chemical reactions are:\n", "\n", "$$r_A = -k_1 C_A$$\n", "\n", "$$r_B = k_1 C_A - k_2 C_B$$\n", "\n", "$$r_C = k_2 C_B$$\n", "\n", "Here, $C_A$, $C_B$, and $C_C$ are the concentrations of each species. The rate constants $k_1$ and $k_2$ depend on temperature as follows:\n", "\n", "$$k_1 = A_1 \\exp{\\frac{-E_1}{R T}}$$\n", "\n", "$$k_2 = A_2 \\exp{\\frac{-E_2}{R T}}$$\n", "\n", "$A_1, A_2, E_1$, and $E_2$ are fitted model parameters. $R$ is the ideal-gas constant and $T$ is absolute temperature." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.1 Batch Reactor](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.1-Batch-Reactor)", "section": "2.8.3.1 Batch Reactor" } }, "source": [ "### 2.8.3.1 Batch Reactor" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.1 Batch Reactor](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.1-Batch-Reactor)", "section": "2.8.3.1 Batch Reactor" } }, "source": [ "The concentrations in a batch reactor evolve with time per the following differential equations:\n", "\n", "$$ \\frac{d C_A}{dt} = r_A = -k_1 C_A $$\n", "\n", "$$ \\frac{d C_B}{dt} = r_B = k_1 C_A - k_2 C_B $$\n", "\n", "$$ \\frac{d C_C}{dt} = r_C = k_2 C_B $$\n", "\n", "This is a linear system of differential equations. Assuming the feed is only species $A$, i.e., \n", "\n", "$$C_A(t=0) = C_{A0} \\quad C_B(t=0) = 0 \\quad C_C(t=0) = 0$$\n", "\n", "leads to the following analytic solution:\n", "\n", "$$C_A(t) = C_{A,0} \\exp(-k_1 t)$$\n", "\n", "$$C_B(t) = \\frac{k_1}{k_2 - k_1} C_{A,0} \\left[\\exp(-k_1 t) - \\exp(-k_2 t) \\right]$$\n", "\n", "$$C_C(t) = C_{A,0} - \\frac{k_2}{k_2 - k_1} C_{A,0} \\exp(-k_1 t) + \\frac{k_1}{k_2 - k_1} \\exp(-k_2 t) C_{A,0} = C_{A,0} - C_{A}(t) - C_{B}(t)$$" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.1 Batch Reactor](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.1-Batch-Reactor)", "section": "2.8.3.1 Batch Reactor" } }, "source": [ "The following Python code simulates and plots this model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.1 Batch Reactor](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.1-Batch-Reactor)", "section": "2.8.3.1 Batch Reactor" } }, "outputs": [], "source": [ "def kinetics(A, E, T):\n", " ''' Computes kinetics from Arrhenius equation\n", " \n", " Arguments:\n", " A: pre-exponential factor, [1 / hr]\n", " E: activation energy, [kJ / mol]\n", " T: temperature, [K]\n", " \n", " Returns:\n", " k: reaction rate coefficient, [1 / hr]\n", " \n", " '''\n", " R = 8.31446261815324 # J / K / mole\n", " \n", " return A * np.exp(-E*1000/(R*T))\n", "\n", "def concentrations(t,k,CA0):\n", " '''\n", " Returns concentrations at time t\n", " \n", " Arguments:\n", " t: time, [hr]\n", " k: reaction rate coefficient, [1 / hr]\n", " CA0: initial concentration of A, [mol / L]\n", " \n", " Returns:\n", " CA, CB, CC: concentrations of A, B, and C at time t, [mol / L]\n", " '''\n", " CA = CA0 * np.exp(-k[0]*t);\n", " CB = k[0]*CA0/(k[1]-k[0]) * (np.exp(-k[0]*t) - np.exp(-k[1]*t));\n", " CC = CA0 - CA - CB;\n", " \n", " return CA, CB, CC" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.1 Batch Reactor](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.1-Batch-Reactor)", "section": "2.8.3.1 Batch Reactor" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CA0 = 1 # Moles/L\n", "k = [3, 0.7] # 1/hr\n", "\n", "t = np.linspace(0,1,51)\n", "CA, CB, CC = concentrations(t,k,CA0)\n", "plt.plot(t, CA, label=\"$C_{A}$\",linestyle=\"-\",color=\"blue\")\n", "plt.plot(t, CB, label=\"$C_{B}$\",linestyle=\"-.\",color=\"green\")\n", "plt.plot(t, CC, label=\"$C_{C}$\",linestyle=\"--\",color=\"red\")\n", "plt.xlabel(\"Time [hours]\")\n", "plt.ylabel(\"Concentration [mol/L]\")\n", "plt.title(\"Batch Reactor Model\")\n", "plt.legend()\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.2 Experimental Data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.2-Experimental-Data)", "section": "2.8.3.2 Experimental Data" } }, "source": [ "### 2.8.3.2 Experimental Data\n", "\n", "See the notebook *Supplementary material: data for parmest tutorial* for details on how these experimental data were generated (via simulation).\n", "\n", "Experimental data consists of the concentration of species A, B, and C in $\\mathrm{mol/L}$ with respect to time $t$ in $\\mathrm{hours}$ inside the batch reactor. The experimental data is stored in csv files where the first column records the time $t$ in the reactor. Next, the temperature $T$ in $\\mathrm{K}$ at which the reaction was simulated is recorded followed by the initial concentration of species A, $C_{A,0}$, in $\\mathrm{mol/L}$. Finally, the time-varying species concentrations ($C_A$), ($C_B$), and ($C_C$) are recorded in $\\mathrm{mol/L}$. Following is how the pandas dataframe of a single experiment looks like:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.2 Experimental Data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.2-Experimental-Data)", "section": "2.8.3.2 Experimental Data" } }, "outputs": [], "source": [ "# define function to plot\n", "def plot_exp(k, CA0, data, text):\n", " ''' \n", " Plot concentration profiles\n", " Arguments:\n", " k: kinetic parameters\n", " CA0: initial concentration\n", " data: Pandas data frame\n", " text: plot title\n", " \n", " '''\n", " # evaluate models\n", " t = np.linspace(0,1,51)\n", " CA, CB, CC = concentrations(t,k,CA0)\n", " \n", " # plot model-generated and 'experimental' data\n", " # symbols for 'experimental' data\n", " # solid and dashed lines for model-generated data\n", " plt.plot(t, CA,label=\"$C_{A}$\",linestyle=\"-\",color=\"blue\")\n", " plt.plot(data.time, data.CA, marker='o',linestyle=\"\",color=\"blue\",label=str())\n", " plt.plot(t, CB, label=\"$C_{B}$\",linestyle=\"-.\",color=\"green\")\n", " plt.plot(data.time, data.CB, marker='s',linestyle=\"\",color=\"green\",label=str())\n", " plt.plot(t, CC, label=\"$C_{C}$\",linestyle=\"--\",color=\"red\")\n", " plt.plot(data.time, data.CC, marker='^',linestyle=\"\",color=\"red\",label=str())\n", " plt.xlabel(\"Time [hours]\")\n", " plt.ylabel(\"Concentration [mol/L]\")\n", " plt.title(text)\n", " plt.legend()\n", " plt.show()\n", " plt.close()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.3 Pyomo model](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.3-Pyomo-model)", "section": "2.8.3.3 Pyomo model" } }, "source": [ "### 2.8.3.3 Pyomo model\n", "\n", "In the following cell, we define a function to define and return the Pyomo model for the kinetic model to be used for parameter estimation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "nbpages": { "level": 3, "link": "[2.8.3.3 Pyomo model](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.3.3-Pyomo-model)", "section": "2.8.3.3 Pyomo model" } }, "outputs": [], "source": [ "def create_model(data):\n", " '''\n", " function to create Pyomo model\n", " Argument:\n", " data: a single dictionary of data\n", " Return:\n", " m: Pyomo model\n", " '''\n", " # data\n", " exp_data = data['data']\n", " \n", " # This code style matches parmest example found here:\n", " # https://github.com/Pyomo/pyomo/blob/master/pyomo/contrib/parmest/examples/semibatch/semibatch.py\n", " \n", " # unpack 'experimental' data into temporary variables\n", " cameastemp = exp_data['CA']\n", " cbmeastemp = exp_data['CB']\n", " ccmeastemp = exp_data['CC']\n", " tmeastemp = exp_data['time']\n", " \n", " # create dictionaries for 'experimental' data of CA, CB,and CC indexed by timestep\n", " cameas={}\n", " cbmeas={}\n", " ccmeas={}\n", " for i,j in enumerate(tmeastemp):\n", " cameas[float(j)] = cameastemp[i]\n", " cbmeas[float(j)] = cbmeastemp[i]\n", " ccmeas[float(j)] = ccmeastemp[i]\n", " \n", " # define Pyomo model\n", " m = ConcreteModel()\n", " m.T = data['T'] # K\n", " m.CA0 = data['CA0'] # mol/L\n", " \n", " # define 'experimental' data timesteps as Pyomo set\n", " m.t = Set(initialize=tmeastemp.tolist())\n", " \n", " # define 'experimental' data as Pyomo parameters indexed by timestep set and \n", " # initialized by dictionary of experimental data\n", " m.Ca_meas = Param(m.t, initialize=cameas)\n", " m.Cb_meas = Param(m.t, initialize=cbmeas)\n", " m.Cc_meas = Param(m.t, initialize=ccmeas)\n", " \n", " m.R = 8.31446261815324 # J / K / mole\n", " \n", " # Kinetic parameters to be fitted defined as Pyomo variables\n", " # Initialized by 'true' values\n", " m.A1 = Var(initialize=200, bounds=(100,300)) # 1/hr\n", " m.A2 = Var(initialize=400, bounds=(300,500)) # 1/hr\n", " m.E1 = Var(initialize=10, bounds=(1,20)) # kJ/mol\n", " m.E2 = Var(initialize=15, bounds=(1,30)) # kJ/mol\n", " \n", " # Concentration variables indexed by time\n", " m.CA = Var(m.t, initialize = m.CA0) # mol/L\n", " m.CB = Var(m.t, initialize = 0) # mol/L\n", " m.CC = Var(m.t, initialize = 0) # mol/L\n", " \n", " \n", " # kinetic rate constants from Arrhenius equation\n", " m.k1 = Expression(rule = m.A1 * exp(-m.E1*1000/(m.R*m.T))) # 1/hr\n", " m.k2 = Expression(rule = m.A2 * exp(-m.E2*1000/(m.R*m.T))) # 1/hr\n", " \n", " # Constraints to change concentrations based on kinetics\n", " def conc_A(m,i):\n", " if i == 0:\n", " return Constraint.Skip\n", " else:\n", " return m.CA[i] == m.CA0 * exp(-m.k1*i)\n", " m.CA_rate = Constraint(m.t,rule=conc_A)\n", " \n", " def conc_B(m,i):\n", " if i == 0:\n", " return Constraint.Skip\n", " else:\n", " return m.CB[i] == m.k1*m.CA0/(m.k2-m.k1) * (exp(-m.k1*i) - exp(-m.k2*i))\n", " m.CB_rate = Constraint(m.t,rule=conc_B)\n", " \n", " def conc_C(m,i):\n", " if i == 0:\n", " return Constraint.Skip\n", " else:\n", " return m.CC[i] == m.CA0 - m.CA[i] - m.CB[i]\n", " m.CC_rate = Constraint(m.t,rule=conc_C)\n", " \n", " # Initial Conditions\n", " def _initcon(m):\n", " yield m.CA[m.t.first()] == m.CA0\n", " yield m.CB[m.t.first()] == 0.0\n", " yield m.CC[m.t.first()] == 0.0\n", " m.initcon = ConstraintList(rule=_initcon)\n", " \n", " # Objective function\n", " # The objective function for parmest is defined as a 2-stage stochastic optimization objective function\n", " \n", " # First stage cost: independent of scenarios ('experiments')\n", " # expression for minimizing fixed realization \n", " # from model. Eg.: reactor temperature, size, etc.\n", " def ComputeFirstStageCost_rule(m):\n", " # In this case, we do not optimize anything besides the kinetic parameters through \n", " # least square fitting realizations at each timestep defined by m.t.\n", " # Hence, the first stage cost is set to 0 here.\n", " return 0\n", " m.FirstStageCost = Expression(rule=ComputeFirstStageCost_rule)\n", " \n", " # Second stage cost: Realization at each scenario over which the model is defined\n", " def ComputeSecondStageCost_rule(m):\n", " # In this problem, we want to minimize the sum of squared errors between \n", " # 'experimental' data and the model realization of concentrations of \n", " # A, B, and C over each scenario (here, timesteps defined by m.t)\n", " return sum((m.CA[t] - m.Ca_meas[t]) ** 2 + (m.CB[t] - m.Cb_meas[t]) ** 2 \n", " + (m.CC[t] - m.Cc_meas[t]) ** 2 for t in m.t)\n", " m.SecondStageCost = Expression(rule=ComputeSecondStageCost_rule)\n", " \n", " # return the sum of the first-stage and second-stage costs as the objective function\n", " def total_cost_rule(m):\n", " return m.FirstStageCost + m.SecondStageCost\n", "\n", " m.Total_Cost_Objective = Objective(rule=total_cost_rule, sense=minimize)\n", " \n", " return m" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.4 Parameter estimation with a single dataset](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.4-Parameter-estimation-with-a-single-dataset)", "section": "2.8.4 Parameter estimation with a single dataset" } }, "source": [ "## 2.8.4 Parameter estimation with a single dataset" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.4 Parameter estimation with a single dataset](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.4-Parameter-estimation-with-a-single-dataset)", "section": "2.8.4 Parameter estimation with a single dataset" } }, "source": [ "Here, we will estimate parameters $A_1$, $A_2$, $E_1$, and $E_2$ using data generated for a batch 'experiment' at 250 C with an inlet concentration of 0.5 mol/L of A.\n", "\n", "The parameter estimation problem is solved with the least squares optimization scheme\n", "\n", "The parameter estimation problem is solved with the least squares optimization scheme\n", "$$\n", "\\begin{align}\n", "\\begin{split}\n", " \\min_{\\hat{\\theta}} \\quad & \\sum_{i}^{} (y_i - f(x_i,\\hat{\\theta}))^2\\\\\n", " \\textrm{s.t.} \\quad & \\mathrm{bounds \\ on} \\ \\theta\\\\\n", " & \\mathrm{other \\ physical \\ constraints}\\\\\n", "\\end{split}\n", "\\end{align}\n", "$$\n", "where $i$ is used to index the datapoints in a dataset and $\\hat{\\theta}$ is the optimal set of parameter values that minimizes the prediction error.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbpages": { "level": 2, "link": "[2.8.4 Parameter estimation with a single dataset](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.4-Parameter-estimation-with-a-single-dataset)", "section": "2.8.4 Parameter estimation with a single dataset" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 91\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 37\n", "\n", "Total number of variables............................: 31\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 4\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 27\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.6338341e+00 4.02e-01 6.37e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 1.9026209e-01 2.34e-04 4.98e-03 -1.0 4.13e-01 - 9.86e-01 1.00e+00f 1\n", " 2 1.8657853e-01 5.60e-04 2.39e-04 -1.0 1.99e-01 - 1.00e+00 1.00e+00h 1\n", " 3 1.8638673e-01 4.98e-05 1.92e-05 -2.5 7.10e-02 - 1.00e+00 1.00e+00h 1\n", " 4 1.8638598e-01 2.10e-07 1.36e-07 -3.8 4.52e-02 - 1.00e+00 1.00e+00h 1\n", " 5 1.8638599e-01 2.23e-10 3.93e-11 -5.7 9.66e-03 - 1.00e+00 1.00e+00h 1\n", " 6 1.8638599e-01 2.55e-12 2.09e-13 -8.6 1.05e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 6\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 1.8638598612196314e-01 1.8638598612196314e-01\n", "Dual infeasibility......: 2.0857837216213979e-13 2.0857837216213979e-13\n", "Constraint violation....: 2.5491275756905907e-12 2.5491275756905907e-12\n", "Complementarity.........: 2.5255218786493285e-09 2.5255218786493285e-09\n", "Overall NLP error.......: 2.5255218786493285e-09 2.5255218786493285e-09\n", "\n", "\n", "Number of objective function evaluations = 7\n", "Number of objective gradient evaluations = 7\n", "Number of equality constraint evaluations = 7\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 7\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 6\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", "=== Parameter values ===\n", "A1 = 200.209 1/hr\n", "A2 = 400.037 1/hr\n", "E1 = 9.640 kJ/mol\n", "E2 = 14.939 kJ/mol\n" ] } ], "source": [ "# read-in data from csv file\n", "\n", "data = pd.read_csv('./data/parmest_20210609_data_exp1.csv',index_col=0)\n", "# create dictionary of data\n", "# format of dictionary needs to be consistent with input provided to create_model() function\n", "data_dict = {}\n", "data_dict['T'] = data['T'].iloc[0]\n", "data_dict['CA0'] = data['CA0'].iloc[0]\n", "data = data.drop(labels=['T','CA0'],axis=1)\n", "data_dict['data'] = data\n", "\n", "# create pyomo model instance\n", "model = create_model(data_dict)\n", "\n", "# create solver instance for IPOPT\n", "solver = SolverFactory('ipopt')\n", "\n", "# solve model\n", "solver.solve(model,tee=True)\n", "\n", "print(\"=== Parameter values ===\")\n", "print(\"A1 = {:0.3f} 1/hr\".format(value(model.A1)))\n", "print(\"A2 = {:0.3f} 1/hr\".format(value(model.A2)))\n", "print(\"E1 = {:0.3f} kJ/mol\".format(value(model.E1)))\n", "print(\"E2 = {:0.3f} kJ/mol\".format(value(model.E2)))" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.5 Parameter estimation with multiple datasets](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5-Parameter-estimation-with-multiple-datasets)", "section": "2.8.5 Parameter estimation with multiple datasets" } }, "source": [ "## 2.8.5 Parameter estimation with multiple datasets" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.5 Parameter estimation with multiple datasets](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5-Parameter-estimation-with-multiple-datasets)", "section": "2.8.5 Parameter estimation with multiple datasets" } }, "source": [ "Here, we will estimate parameters $A_1$, $A_2$, $E_1$, and $E_2$ using data generated for a batch 'experiment' at 250 C with an inlet concentration of 0.5 mol/L of A.\n", "\n", "The parameter estimation problem is now defined to solve optimization problem where the objective function is the mean of the least square error between observed and calculated data for multiple experiments.\n", "\n", "$$\n", "\\begin{align}\n", "\\begin{split}\n", " \\min_{\\hat{\\theta}} \\quad & \\frac{1}{N}\\sum_{j}^{}\\sum_{i}^{} (y_{j,i} - f(x_{j,i},\\hat{\\theta}))^2\\\\\n", " \\textrm{s.t.} \\quad & \\mathrm{bounds \\ on} \\ \\theta\\\\\n", " & \\mathrm{other \\ physical \\ constraints}\\\\\n", "\\end{split}\n", "\\end{align}\n", "$$\n", "\n", "where $i$ is used to index the datapoints in a dataset, $j$ is an index on the dataset such that $j \\in [1,N]$ and $N$ is the number of experiments conducted." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.1 Generate list of dataset](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.1-Generate-list-of-dataset)", "section": "2.8.5.1 Generate list of dataset" } }, "source": [ "### 2.8.5.1 Generate list of dataset\n", "\n", "In the following cell, we define a function to generate a list of dictionaries containing the 'experimental' data. For this, we read-in the list of file names generated earlier" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.1 Generate list of dataset](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.1-Generate-list-of-dataset)", "section": "2.8.5.1 Generate list of dataset" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'T': 250, 'CA0': 0.5, 'data': time CA CB CC\n", "0 0.000 0.476266 0.000000 0.000000\n", "1 0.125 0.332309 0.133593 0.000000\n", "2 0.250 0.295725 0.362016 0.092655\n", "3 0.375 0.394806 0.131512 0.004404\n", "4 0.500 0.167992 0.271682 0.031808\n", "5 0.625 0.117872 0.313196 0.009108\n", "6 0.750 0.117052 0.582722 0.000000\n", "7 0.875 0.175373 0.224904 0.043844\n", "8 1.000 0.130619 0.360287 0.194820}\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def create_data_dict(files):\n", " '''\n", " Create a list of dictionaries from multiple datasets\n", " Arguments:\n", " files: pandas dataframe of file names\n", " Return:\n", " data_dict_list: list of dictionaries\n", " '''\n", " data_dict_list = []\n", " for index, file in files.iterrows():\n", " # create a dictionary of 'experimental' data for temperature T and initial concentration of A CA0\n", " data_dict = {}\n", " data = pd.read_csv(str(file.values[0]),index_col=0)\n", " data_dict['T'] = data['T'].iloc[0]\n", " data_dict['CA0'] = data['CA0'].iloc[0]\n", " data = data.drop(labels=['T','CA0'],axis=1)\n", " data_dict['data'] = data\n", " # add dictionary to list to be return\n", " data_dict_list.append(data_dict)\n", " return data_dict_list\n", "\n", "# list of temperatures\n", "T_vals = [250,300,350,400] # K\n", "\n", "# list of initial concentrations of A\n", "CA0_vals = [0.5,1.0,1.5,2.0] # mol/L\n", "\n", "# read-in file names from log file created using gen_data()\n", "file_list = pd.read_csv(data_dir + 'parmest_log_file.csv')\n", "\n", "# create a list with all experiments stored separately as dictionaries\n", "data_dict_overall = create_data_dict(file_list)\n", "\n", "# printing a dictionary of data for illustrative purposes\n", "print(data_dict_overall[0])\n", "# Plotting a dataset for illustrative purposes\n", "plt.figure()\n", "plt.plot(data_dict_overall[0]['data']['time'], data_dict_overall[0]['data']['CA'], label=\"$C_{A}$\",marker=\"o\",linestyle='',color=\"blue\")\n", "plt.plot(data_dict_overall[0]['data']['time'], data_dict_overall[0]['data']['CB'], label=\"$C_{B}$\",marker=\"s\",linestyle='',color=\"green\")\n", "plt.plot(data_dict_overall[0]['data']['time'], data_dict_overall[0]['data']['CC'], label=\"$C_{C}$\",marker=\"^\",linestyle='',color=\"red\")\n", "plt.xlabel(\"Time [hours]\")\n", "plt.ylabel(\"Concentration [mol/L]\")\n", "plt.title(\"Batch Reactor Model\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.2 Parameter estimation with parmest](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.2-Parameter-estimation-with-parmest)", "section": "2.8.5.2 Parameter estimation with parmest" } }, "source": [ "### 2.8.5.2 Parameter estimation with parmest\n", "\n", "In the following cell, we perform parameter estimation using parmest to solve the least squares problem defined in the Pyomo model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.2 Parameter estimation with parmest](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.2-Parameter-estimation-with-parmest)", "section": "2.8.5.2 Parameter estimation with parmest" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 1576\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 592\n", "\n", "Total number of variables............................: 496\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 492\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.7039989e+01 2.00e+00 1.40e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 2.2477764e-01 4.80e-06 1.02e-03 -1.0 2.00e+00 - 9.91e-01 1.00e+00f 1\n", " 2 2.2234827e-01 3.02e-04 7.58e-05 -1.7 1.03e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.2228920e-01 7.67e-06 2.49e-06 -3.8 3.78e-01 - 1.00e+00 1.00e+00h 1\n", " 4 2.2211618e-01 1.39e-03 1.28e-05 -5.7 1.20e+01 - 9.13e-01 1.00e+00h 1\n", " 5 2.2210772e-01 3.75e-05 3.59e-07 -5.7 1.88e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.2210762e-01 7.16e-08 5.59e-10 -5.7 1.77e-01 - 1.00e+00 1.00e+00h 1\n", " 7 2.2210762e-01 2.40e-08 2.20e-10 -8.6 4.74e-02 - 1.00e+00 1.00e+00h 1\n", " 8 2.2210762e-01 3.00e-12 2.31e-14 -9.0 1.17e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 8\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.2210762190708977e-01 2.2210762190708977e-01\n", "Dual infeasibility......: 2.3121945602686378e-14 2.3121945602686378e-14\n", "Constraint violation....: 3.0038194154258235e-12 3.0038194154258235e-12\n", "Complementarity.........: 9.0911913344645786e-10 9.0911913344645786e-10\n", "Overall NLP error.......: 9.0911913344645786e-10 9.0911913344645786e-10\n", "\n", "\n", "Number of objective function evaluations = 9\n", "Number of objective gradient evaluations = 9\n", "Number of equality constraint evaluations = 9\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 9\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 8\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", "Total CPU secs in NLP function evaluations = 0.002\n", "\n", "EXIT: Optimal Solution Found.\n", "theta:\n", " {'A1': 185.6087678995809, 'A2': 401.1702352092697, 'E1': 9.866878463449424, 'E2': 14.866030991977437}\n" ] } ], "source": [ "# import parmest\n", "import pyomo.contrib.parmest.parmest as parmest\n", "\n", "# defining the names of the parameters in a list\n", "theta_names = ['A1','A2','E1','E2']\n", "\n", "# create an object using parmest.Estimator() that stores the Pyomo model realizations for the datasets provided.\n", "# This object which will be used to determined the parameter values that best fit all the datasets\n", "pest = parmest.Estimator(create_model,data_dict_overall,theta_names,tee=True)\n", "\n", "# call the method theta_est() for the Estimator() object defined above to solve \n", "# the parameter estimation problem.\n", "# theta_est() returns:\n", " # the overall objective function value\n", " # estimated parameter values (dictionary with keys = parameters names as defined in the Pyomo model)\n", "obj, theta = pest.theta_est()\n", "\n", "print('theta:\\n',theta)" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.3 Plotting fitted model simulation with 'experimental' data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.3-Plotting-fitted-model-simulation-with-'experimental'-data)", "section": "2.8.5.3 Plotting fitted model simulation with 'experimental' data" } }, "source": [ "### 2.8.5.3 Plotting fitted model simulation with 'experimental' data\n", "\n", "Next, we plot the 'experimental' data along with the profiles generated using the fitted kinetic model. The symbols represent the 'experimental' data and the solid and dashed lines are the profiles generated using the fitted model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "nbpages": { "level": 3, "link": "[2.8.5.3 Plotting fitted model simulation with 'experimental' data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.5.3-Plotting-fitted-model-simulation-with-'experimental'-data)", "section": "2.8.5.3 Plotting fitted model simulation with 'experimental' data" }, "scrolled": true }, "outputs": [ { "data": { "image/png": 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\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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\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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\n", 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t1GxiCjUING7s1qJat69hGGHNM884ZQqQkGCt1GxiCjUI5M3rhiRMoRqGEbYktU7j491+fLy1UrOJKdQg0aoV/PWXM5xvGIYRdvi3TpOwVmq2MIUaJJLGUa2VahhGWLJo0enWaRLx8fDTT97IkwMIa4UqIuNFZKeI/JZK/FARWeHbfhORBBEp4YvbKCKrfXFLQys51KnjTBCaQjUMIyxZvtz5nUy+LV/utWQRS1grVGACcG1qkar6oqrWV9X6wKPAd6q61y/J1b74xkGW8yxEXCv1q6/O7lUxDMMwch5hrVBV9Xtgb7oJHT2AqUEUJ9O0bw87d8KyZV5LYhiGYQSbsFaoGUVECuFash/6BSswX0SWiciANPIOEJGlIrJ0165dAZXr2mud95lPPw1osYZhGEYYkiMUKnAd8GOy7t7mqtoQaAfcIyJXppRRVceoamNVbVy6dOmAClWyJFx2GXzySUCLNQzDMMKQnKJQu5Osu1dVt/l+dwKzgKYeyEXHjm6Mf+tWL2o3DCNsCFebuSdOwLFjXkuRI4h4hSoi5wAtgDl+YYVFpGjSf6ANkOJM4WDTsaP7nTvXi9oNwwgbwslm7qFDMHSo8+RRrBhMmeK1RDmCsFaoIjIVWARcKCKxInK7iAwUkYF+yW4A5qvqEb+wssBCEVkJ/ALMVdXPQyf5aWrXhipVbBzVMHI1XtnMVYU//4Tx46FvXxg+3IUXKgQTJrj/gwdD/fqhkSeHk9drAdJCVXtkIM0E3PIa/7ANwMXBkSpziLhW6rhxrlelYEGvJTIMI+SkZDN39Ojg1nnvvTBz5mnlXaoU9Onj/ufJ45R83rBWARFHWLdQcwodOzplumCB15IYhhFygmkzNzERVq6E116DG26ABg1cqxQgXz645hp4+21Ys8at4XvppdN5TZkGHDujIaBFCyhc2HX7tm/vtTSGYYSUtGzmZraVmqQsReCtt+Dxx2Gvb3FDtWpw1VVuklF0NLz8crZFNzKHtVBDQHQ0tG7tFGrS82AYRi4huzZzt22D995z3bXnnw+rVrnwypWhUycXt2mT88Yxbpx74RieYC3UENGxI8yeDatXQ716XktjGEbIyKpt3LVroVs3+M23QKFkSWjZ0rVOAdq1c5sRNphCDRFJXb2ffmoK1TAMP1Sd8vzsM/jiCzfu+dBDrjVavjzceqvr4qpXz5leM8IWU6gh4rzzoHFjp1Afe8xraQzDCAuGDIGPPjrtOLl2bShSxP0vUsQpWCNisM+dENKxI/z8MwTYZLBhGJHApk3w+utw332nw7ZscWtA337bxcfEwN13eyejkS2shRpCOnaEYcNcz86tt3otjWEYQScmxlkh+uQTN4ECoFYtNxO3QAGYMcNb+YyAYi3UENKggev6NatJhpFDOXEC5s2DPXvc/nffwfPPuwlFI0bAH3+4NaEFCngrpxEUrIUaQqKioEMHmD7dzZrPn99riQzDyDYHDzolOmuW+z182C1f6dcPbrkFevSAc8/1WkojBJhCDTEdO8I77zgb2S1bei2NYRhZQtUtX9m1CypWdF/IZco45dm5M7Rq5dIVK+atnEZIMYUaYlq1cr09n35qCtUwIoq9e91i8pkznaKcNg1Kl4Znn4VLL4VLLnE2co1ci42hhpgiReDqq90cBbOaZBgRwCefuLGasmXh9tvdmtF//et0/IMPOjdopkxzPaZQPaBTJ1i//rQBFMMwwohjx1wr9MQJt79kiXtYH3gAli6FDRvgP//xVkYjLDGF6gFdurgJSh984LUkhmEAzlj9l186e7llysBNN7l9gEcfhY0b4YUXoFGj06b/DCMZQR1DFZESGUiWqKr7gylHuFGmjOv2nT7dOZyw59MwPGTTJjcGGhcH55wDN98MPXs6N1FgToyNDBPsSUnbfFtaKiMPUCmlCBEZD3QEdqrqRSnEXwXMAf72BX2kqv/xxV0LvOYr/x1VfS6LxxAUbr4ZBgxwrgzr1/daGsPIRcTFwfvvu/9DhzqbuR06QNu2bhq+eWsxskiwu3zXqmo1Va2a2gbsSSP/BODadOr4QVXr+7YkZZoHGA20A2oDPUSkdgCOJ2DccIObwzB9uteSGEYu4MQJNy7asaNToA89BD/84OKiomDsWOja1ZSpkS2CrVAvzU4aVf0e2JuFepsC61V1g6rGA9OATlkoJ2iUKuWW0EyfbrN9DSPo/Pvfblx0+XLXKl27Fj7+2GupjBxGUBWqqh5PLU5ENqeXJoNcKiIrReQzEanjC6sAbPFLE+sLS0mOASKyVESW7gqx1fpu3ZxP4Ky6SzQMIwUOHYIxY6BJE1i2zIXddZczor15M/zvf1CzprcyGjkSL2f5BmIqzq9AZVW9GBgFzE6j7BTbgao6RlUbq2rj0qVLB0CkjNO5M+TNa92+hpFtVJ0rpzvucOtF77zTKdYDB1x8nTpw7bW2VtQIKl4q1Gx3dKrqQVU97Ps/D8gnIqVwLdLz/ZJWxE2OCitKlnS+hK3b1zCySNKDc+yYm1Q0bRpUruymzrdsaebIjJAS7GUzQ1KLAooEoPxywA5VVRFpivtA2APsB2qISFVgK9Ad6Jnd+oJBt27OhvayZc4BuWEYGSAmBt580z04P/0EhQo5w/RlykC9ek7RTpgATz4J5cp5La2RSwh2C7VoKlsR3JKWNBGRqcAi4EIRiRWR20VkoIgM9CXpCvwmIiuBkUB3dZwCBgFfAGuB6aoaE+BjCwidO0O+fGbkwTDS5eRJ96BceSVcdJHzMlGjBhw54uKbN4dXXoHERLefkOAWehtGiBANYl+jiPQEvlDVtJbGhA2NGzfWpUuXhrzeDh2cZbONG83Ig2GkyvTpbgF3tWowcCD07eumyycRF+fijvvNcyxY0JkKtFZqUBGRZaqa6/vYgt1CrQTMEJEfRGSYiDQTMZWRnG7d3OTDX37xWhLDCCOWL3fjIa+84vY7d4a5c+HPP93SF39lCq41mtQ6TcJaqUYICfaymedUtSXQHlgJ9AN+FZEpInKriJQNZv2RQqdOrtvXZvsauZ7ERJgzx5n9a9jQPRSHDrm4/PmhfXtniCElFi1yfkn9iY93Y6yGEQKC2uWbaqXOalE7oI2qtg25AKngVZcvwHXXOTOEGzem/r4wjBzP7bfD+PFupu7gwa6FWry411IZ6WBdvo5gz/JtmEb0AlUdEcz6I4lu3ZzT8cWLnZ1uw8gVbN8Or78O/fs7Jdq/v1v+0qWLW6RtGBFEsO/YtBSmArZIzMf117serenTTaEauYD16+Gll9zSlvh4N5moXz+45BK3GUYEElSFqqpXB7P8nMQ550C7djBjBowYYd2+Rg4lMRFuvRWmTnUt0NtugwcfdMtfDCPCCclrW0TyichgEZnp2waJSL5Q1B1JdO8OW7fCggVeS2IYAUQVVqxw/6Oi3Jjo0KFuwsDbb5syNXIMoWoHvQk0At7wbY18YYYfnTq5d8348V5LYhgBQBU+/xyuuAIaNDjtBeL11+G55+C887yVzzACTKhG/Zv4DNgn8Y3PupHhR8GC0KMHvPsu7N9vkxuNCEUVPvkEhg+HJUuc/9HRo6FWLa8lM4ygEqoWaoKIXJC0IyLVgIQQ1R1R9OvnDL1Mm+a1JIaRRQ4cgFtugT17nOPu9evh7rvNebeR4wmVQh0KLBCRb0XkO+Ab4N8hqjuiaNQI6ta1bl8jglCF2bOhTx/3v3hx+O47+P13504tf36vJTSMkBAShaqqXwM1gMG+7UJVtak3KSDiTJQuWeLs+xpG2KLqFk83bgw33OAsFcXFubgGDWwdqZHrCNUs3zxAW+AqoBVwTxqu3XI9t9zi3kXvvuu1JIaRCps2ufWi113nBvwnTIA1a6B8ea8lMwzPCNUn5CfAcWA1kJhO2lxP6dLuPTVpkpsMmc8WGBnhwr59cO65zntLgQJujLRPH7tJM8DJkyeJjY3luL83nAgjOjqaihUrks+ud4qESqFWVNV6IaorR9CvH8ya5ZxrdO7stTRGrue33+Dxx+HXX523l+ho+P57r6WKKGJjYylatChVqlQhEp1uqSp79uwhNjaWqlWrei1OWBKqSUmfiUibzGYSkfEislNEUhxNFJFeIrLKt/0kIhf7xW0UkdUiskJEvLF4nw2uvdY1Aqzb1/CUDRugd2+oVw++/db5IU1yqBEX57zCbN/uqYiRwvHjxylZsmREKlMAEaFkyZIR3cIONqFSqD8Ds0TkmIgcFJFDInIwA/kmANemEf830MLX+n0GGJMs/mpVrR+JXhDy5nUW2ubOtfeV4RG//QY1a8LMmc6y0d9/u1ZqwYIu/plnYOFC8zeaCSJVmSYR6fIHm1Ap1BHApUAhVS2mqkVVtVh6mVT1e2BvGvE/qeo+3+7PQMWASBsm9O3r/CNPmuS1JEau4fjx0/5D69SB//zHrSN9/nkoUeJ0urg4132SmOh+7avPMEKmUP8EftPgOl+9HfjMb1+B+SKyTEQGpJZJRAaIyFIRWbpr164gipd5atZ0nmfeffd0L5thBIXERJg82d10bdrA3r1uDdcjj0CFCmenf+YZlwfcV5+1Ug0jZAo1DvhWRB4VkSFJW6AKF5GrcQr1Yb/g5qraEOfI/B4RuTKlvKo6RlUbq2rj0qVLB0qkgNGvH6xd6/ykGkZQ+OEHaNrUrdcqUQLmzDmzNZqcpNZpfLzbj4+3VqphEDqF+jfwNZAfKOq3ZRsRqQe8A3RS1T1J4aq6zfe7E5gFNA1EfaGmWzcoVMgmJxlB4q+/3MSiHTvgvfdg6VJo1SrtPP6t0ySslRpRzJgxg2bNmlGvXj2qV6/O008/7bVIOYKQLJtR1aBcLRGpBHwE9FbVP/zCCwNRqnrI978N8J9gyBBsihWDrl2d+8iXX4bChb2WyIh4Dh+G+fOhSxe44AL46CPXzVuoUMbyL1p0unWaRHz86bFXI6yZOHEio0aNYvbs2VSsWJHDhw/z5pvm/CsQBLWFKiLDspNGRKYCi4ALRSRWRG4XkYEiMtCX5EmgJPBGsuUxZYGFPo82vwBzVfXzbByKp9xxBxw65Ia4DCPLJCbCxInwr3/BTTc5f6TgFjpnVJmCc8OmevaW5J7NCFsOHjzIkCFDmD59OhUrujmcRYoUYejQoR5LljMIdgv1jnSWxwjQHRiWUqSq9kircFW9A7gjhfANwMVn54hMLr8c6teHkSOhf383V8QwMsXSpTBokBuMb9bMtUqrVPFaqlzL/fef9rkeKOrXh1dfTTvNrFmzaNasGdWqVQts5QYQfIU6lvTHSscGWYaIRwTuu88to/nmm/SHuMKVci+VY8eRHWeFly1clu0P2oSWoLF/P1x1FRQp4lqot9wCUaGaPmGEEzExMdSvXz/V+JtvvpmmTZvy73+bM7CsEFSFGqyx09xI9+7w0EPw2muRq1BTUqZphRvZICHBeYK5/nrnTm3WLDeT95xzvJbMIP2WZLAoXLgwx44dSzFuzpw5dOzYka+++irEUuUc7DM1QoiOhjvvdO/Iv/7yWhojrPnlF9et27mz69IAaN3alKlB+/btmTFjBjt2uI/YEydOMHbsWI4fP86MGTPo3bs3Bw4c8FjKyMUUagRx112QJw+8/rrXkhghJaM2c/fvh3vucW7V4uLc1PCWLUMjoxERNGnShGHDhtG2bVvq1q1L/fr12blzJy+++CKHDx9m4MCBxMTEpNqKNdLGFGoEUb68m5w5fryb9WvkEjJiM1cVrr4a3noLBg921kC6d7cZbMZZ9O7dmxUrVrB69WrWrl1L79692bhxI7Nnz+att97illtuYdWqVV6LGZFIcK0B+ioRKQ30B6rgN26rqv2CXnkmaNy4sS5dGt6OaRYvdg2QUaPcpM1IQp5O/eWuT5ltxRSJi4Nq1ZyN3YIFnfeXcuVOx//1F1Sq5PyRzp8PpUpBw4beyWukytq1a6lVq5bXYmSblI5DRJZFohOSQBOqFuoc4BzgK2Cu32ZkkmbN3DZy5NnGasKdsoXLZircIHWbufHx8OyzzoD9yJEurE0bU6aG4SGhcjBeSFUfTj+ZkREGD4ZeveDzz6F9e6+lyTi2NCaTpGYzt0MHePhh516tWzfo2dNbOQ3DAELXQv1URCLo1R/edJFP7uIAACAASURBVO0K5513umESCew+uptv/v6Gfcect709R/ewZOsSfo37ld93/87h+MMeSxiGpGQzNz7eKdT9++Hjj+GDD9zNYBiG54SqhXof8JiIxAMnfWGaEZ+oxtnkzw933w1PPAHr1jmPW16z99heFm5eyA+bfuD3Pb+z7dA2th7ayqh2o+hauyurd6ym1XutWNBnAVdVuYov/vqCXh/1OqOM4tHFqVisotuKVqRK8SrcevGtnH/O+R4dlcekZDM3IcGNk8bEOEPPhmGEDaEyjh8QzzLGaQYMcA2YUaNg9OjQ138k/ggf//4xP2z+ge83fU/MrhgA8ufJT81SNalQtAINyjWgQlHnS7Nx+cZ82+dbLi7nLEK2qNyCT3p8QkJiAofiD7H14FZiD8YSeyiW2IOxrNi+gu2Ht3Pdhddx/jnnMyNmBu8sf4epN06lRMESJCQmkCcqT+gPPJQsX+78kt5/PxQt6s2FNgwjw4SqhYqIXA8k+ST9VlU/DVXdOZEyZdzQ2cSJMHw4nHtu8OtM1ER2H91NmcJlOJFwgp4f9aRI/iI0P785PS7qwZWVr6RJhSZE540+K2/RAkVpUaXFP/sVilWgQrEUHFf7cfDEQQrnc+51Tiae5NCJQxSPLg7AvZ/dyw+bf6BllZa0rNqSFlVa/BOXY/j4Y2fNY/dueOwxtzTGlsEYRtgSEoUqIs8BTYAkfyn3icjlqvpIKOrPqdx/P0yY4Aw9PPFE8OtrPak1iZrIgj4LKFGwBKvvWk3NUjXJGxWc26hYgdNdmj3r9qRn3dOTbxqXb8xf+/5i7K9jGfnLSKIkikbnNeK6f11H55qduajMRUikKp89e5zx5smT4eKLYd48aNDAa6kMw0iHUK1DXQXUV9VE334eYLmq1gt65ZkgEtahJue665wbyo0bXa9gINm4fyPPfv8sr7d/nQJ5CzDtt2kIQrc63cJGWZ04dYJftv7CN39/wxd/fcHPsT+jKNXOrcbdje/m35dFoJHvDRugUSP3xfToo27Q3Ih4bB1qzieUlpL8++PMqGiAeOIJN8z2xhuBK/PYyWMM+3YYtUbXYspvU1i7ey0A3S/qzs0X3Rw2yhSgQN4CXFH5Cp666il+uv0ntv17G2M6jqFmqZocPOE8B55MOMmD8x9k7a61HkubBvv2OYvpqs6Qw8aN8NRTpkwNI4IIlUL9H7BcRCaIyERgGfDfENWdo2naFNq2hREj4MiR7JWlqny45kNqja7F0989TacLO7HunnXUL5e6u6dwo1yRcvRv1J+5Pefy9NXO2dFvO39j9JLR/Ln3TwDiDsWxbvc6L8U8k/nzoW5dGDoUVq92YWbI3ggiM2bMoFmzZtSrV4/q1avz9NPmGCwQhEShqupU4BLgI992qapOSy+fiIwXkZ0i8lsq8SIiI0VkvYisEpGGfnHXisjvvrgcPVb7xBOwaxe8/XbWy1izaw2tJ7Wm64yuFCtQjAV9FjCt67QcsWSlwXkN2PHgDtpVbwfAW0vfotboWjQe05jRv4xm//H93gh25IgzZt+2rVsC8/PPUC+sRkGMHMjEiRN5/vnn+fDDD1m1ahUrVqygUKFCXouVM1DVoG1ATd9vw5S2DOS/0pf2t1Ti2wOfAYJT2It94XmAv4BqQH5gJVA7vfoaNWqkkUrLlqrlyqkePZr5vO8uf1fzP5Nfiz9XXEctHqUnE04GXsAwYtvBbfryTy9r/bfqK8PQ6OHR2vuj3vr9xu81MTExNEIkJqpefrmqiOqQIVm7cEZEsWbNGq9F0AMHDmiJEiX0r7/+ynIZKR0HsFSDqEsiZQv2LN8hwABgRApxCqTpW0pVvxeRKmkk6QS857ugP4tIcRE5D2eEf72qbgAQkWm+tGsyewCRwhNPOGcj48Zlzmj+j5t/pO+cvrSq2oopN06hTOEywRMyTDiv6Hk8cOkDPHDpAyzbtox3fn2HyasnM2nVJC4seSF3NLyD2+rfRqlCpQJf+cmTEBXl/PA98YQbI73qqsDXY4Q9V024Kt00Hf/VkQcve/Cf9LfVv43b6t/G7qO76Tq96xlpv73t23TLmzVrFs2aNaNatWpZEdlIh6B2+arqAN/fdqp6tf+Ga11mlwrAFr/9WF9YauFnISIDRGSpiCzdtWtXAETyhhYt4Ior4Lnn4MSJ9NMnugnXNK/UnOldp/P5LZ/nCmWanEblG/FmxzeJ+3cc468fT8lCJRn65VAenP9g4Cv780+47DJ4/nm336aNKVMjpMTExFC/fspzIho3bsw999xDixYtiImJCbFkOYNQGXb4Cdd1m15YZklpuqmmEX52oOoYYAy4ZTPZlMczRFyDp00btzb1zjtTT7tm1xpunnkzU7pMoW7ZutxU56aQyRmuFM5fmL4N+tK3QV9idsaQP4+bXbs8bjkD5w7k3U7vUrt07awVruq6Du67DwoUCA9bkYbnZKRFmVr6UoVKZTo/QOHChVN0Hr5lyxaaNm3K6NGjefnll4mNjaVOnTqZLj+3E9QWqoiUE5FGQEERaSAiDX3bVUAgRsFjAf9ZMxWBbWmE52iuuca5dvvf/1zPYmoUjy5OwbwFOZGQgaZsLqROmTrUKFkDgH3H93Eq8RTnFXEG6JfHLWfboUzcSnv2wI03Qv/+zpHtqlXQpUswxDaMdGnfvj0zZsxgx44dAJw4cYKxY8eybNky/vjjD/r168c333xD27ZtPZY0Mgn2LN+2wEs4hfYybix1BG5s9bEAlP8xcKtvtu8lwAFVjQOWADVEpKqI5Ae6+9LmaETgySdh0yaYNOns+EVbFpGQmED5ouVZfMdiGpfP9euw06Vl1ZYsG7CMcws6244DPh1AlVer0HtWb36N+zX9Atavhy++gJdegi+/hIoVgyyxYaROkyZNGDZsGG3btqVu3brUr1+fnTt3smzZMkaMGMH48eMpUKAAR7K7Bi+XEipLSTeq6odZyDcVuAooBewAngLyAajqW+IsDLwOXAscBfqq6lJf3vbAq7gZv+NV9dn06otES0nJUYUmTZx3r3XrIK+vU3/2utl0nd6VZ1s+y8OXm2varLJh3wZGLh7JuOXjOBx/mCsrX8n9ze7n+guvP22s/+RJt7a0Qwe3v3u38xBj5GrC2VJS+/btqVy5MlFRUZx77rkMHz481bRmKSl1QqJQAUSkA1AH+Mdyuqr+JySVZ5CcoFABZs+GG26A8eOhb1/4esPXtJ/SngblGvDVrV9RJH8Rr0WMeA4cP8C45eMYuXgkmw5s4oJzL2DIpUPoe85VFLy1Hyxe7Lp369b1WlQjTAhnhZoZTKGmTkgMO4jIW8DNwL24CUM3AZVDUXdupFMnN5b6f/8H367/mU7TOnFhyQuZ12ueKdMAcU70OQy5dAjrB69nxk0z3CSRF+7hZL2LSFgbA9OnmzI1jFxGqEwPXqaqtwL7VPVp4FLOnDRkBBARZ3xnW8Iqrn6nHSf2lOPOQvMpUbCE16LlOPJG5aVr7a4sWtWU6TNhZ+WSnFq2FG66ia83fM36veu9FtEwjBARKoV63Pd7VETKAyeBqiGqO9cxeTK88M566N0GThbm1PiveOjuckyenH5eI2tI3brwyCNU/20bBapfiKpy19y76P9Jf69FMwwjRIRqHeonIlIceBH4FbcmdGyI6s51PPLMTo53uwYkAd5bAPurcBR4/HHo1ctr6XIQ773nLB117+6WxfghInzf93v2HtsLOIP8/T7ux5BLhnBNtWvCymOPYRiBIegtVBGJAr5W1f2+mb6VcTZ+nwx23bmV2FpDofAOmPwZ7D49eWDzZg+FykkcPQq33w59+sD777up1SlQrki5f4xB/Ln3T1ZuX0mb99vQeGxjpsdMJyExIZRSG4YRZIKuUNU5FR/ht39CVQ8Eu97cTMU1L8EHH8G2MyfdVarkkUCRQlycs+G4fXvqadatczO+xo93Tf7Zs92gdTpcWflK/r7vb8ZeN5bD8Ye5eebN1Bxdk7HLxnLilBnYMIycQKjGUOeLyI1i/VxBZdP+TZxKPMWh3nXhlvYwTM7Ydt9WzmsRw5tnnoGFC91vSsTGukW+27fD55/D8OGnF/pmgAJ5C3BHwztYc/caZt40k3MKnMOATwdQbWQ1Rvw0gkMnDgXoQAzD8IJQKdQhwAzghIgcFJFDInIwRHXnCo6fOk7L91pyy0e3cCBhR4ppjsgOEqyXMWXi4uDddyEx0f36t1KTunQrVoT//heWL3fTqLNInqg83Fj7Rpb0X8L8W+ZTs1RNHvzyQQZ9lgk3QYZhhB2hcjBeVFWjVDW/qhbz7RcLRd25hei80Qy/ejj3Nr03zXTvvx8igSKIci+V443ry3P8pJuMfjz+GKOvO49yL5WDLVucX7wkgx/33hsw84EiQusLWvP1rV/z8+0/8+jljwLOecGQL4aw++jugNRjGEZoCJVhh68zEmZkjcPxhwHoUbcHzSs1TzPt44+7OTXGaWT7DvqugGhf6z06AfqugCsW74CGDeHXX9MeVw0AzSo2o2Yp54Xmx80/Mm75uH/i4hPig1q3kfuYMWMGzZo1o169elSvXp2nn37aa5FyBMH2NhMtIiWAUiJyroiU8G1VgPLBrDu3sHbXWiq/WpmPf8+Y7f+tW503GuM0T3wHkmyibv4EmD4TKFMGliyBjh1DJk//Rv3Z8sAWShUqharSYkILbp55Myu2rwiZDEbOZeLEiTz//PN8+OGHrFq1ihUrVlCoUCCcfxnBXod6J3A/Tnku47Sf0oPA6CDXneM5fuo43T/sTpREZdhzTK9ezr91jx5QO4vuPXMal8aebp0mkVdhbzSUWLwYioTeXGOxAm5E5GTiSVpUbsEbS95gesx0rq1+LY9d/hhXVL4i5DIZASYl5/LdusHdd7tupPbtz46/7Ta37d4NXbueGfftt+lWefDgQYYMGcKSJUuo6Bu6KFKkCEOHDs2s9EYKBLWFqqqvqWpV4EFVraaqVX3bxar6ejDrzg288OMLrNqxiomdJ1K+6OkGf9nCZVNMX7ZwWV5+GYoWdQ7IExNDJWl403AgyDDI+8Tp3643QcmH8USZ+pM/T36eu+Y5Nj+wmWdbPsuybcu4csKVXD7+cub+MZdQObcwcgazZs2iWbNmVKtWzWtRciSh9DZzGVAFv1axqr4XksozSCR5m9l8YDM1X6/JdRdexwddP8hU3vHjnV2CMWPOMvCTK5Gnha4xMPwbaHEb7Ch6Ok6fCi+FdfTkUcYvH8+LP73I5gObqVe2Hg83f5hudbqRNypUhs+MrBAO3mYeeugh8ubNy3//+9+z4j744AN+/PFHEhMTKVy4MM8//3yKZZi3mdQJyRMoIpOAC4AVQFLnmgJhpVAjiaFfui6aF1u/mOm8ffs6q3kPPQTXXw9lU27Q5g4SEhj1bSEGfXuUnyqeHpOA1Fv6XlIoXyEGNR3EnY3uZOpvU3lu4XM88MUD3FDzBlOoRroULlyYY8eOnRW+aNEiFi9ezMiRIwGIj7eJcFlCVYO+AWvxtYbDeWvUqJFGAgv+XqAMQ4ctGJblMtauVc2fX7VHjwAKFmns3q3aurUqqA4cqHrihNcSZZqExAT9c8+fqqp6MuGkXjbuMn1/5fseS2WkxJo1a7wWQX/55RetVq2abt++XVVVjx8/rmPGjNEBAwbopk2bMlRGSscBLNUweId7vYXKsMNvQKbN9IjItSLyu4isF5FHUogfKiIrfNtvIpLgm1WMiGwUkdW+uMjox80ApxJPcd/n91H5nMo81PyhLJdTsyY8+ihMneqM/uRKHnkEvvsO3nkH3nzTGbqPMKIkiuolqgOw5+geiuQvQuH8hQHYd2wfcYfivBTPCDOaNGnCsGHDaNu2LXXr1qV+/frs3LmT48ePk9fP6leCWYDJEiEZQxWRBUB94BfgH8Olqnp9GnnyAH8ArYFYYAnQQ1XXpJL+OuABVW3p298INFbVDK+Oj4Qx1DeWvME98+5hxk0z6Fq7a/oZ0uDECbj4YoiPh99+g1wzcz4+3inPfftg/XpnTjAH8uSCJ3n+x+fpc3Efhl42lBola3gtUq4mHMZQUyMmJobhw4dTunRpDh06xCuvvELx4sVTTGtjqKkTqkGXYVnI0xRYr6obAERkGtAJSFGhAj2AqVmSLoJoc0Eb/u+K/+PGWjdmu6wCBeDtt93s/aefdstpcjSJifDUU/D117BgAZx7bo5VpgB9Lu7DriO7eHfFu7zz6zt0qdWFh5s/TJMKOfeYjaxRp04dpk7N8a/PoBMq04PfARuBfL7/S3B+UdOiArDFbz/WF3YWIlIIuBb40L9anFH+ZSIyILVKRGSAiCwVkaW7du1K91i8pnqJ6jzT8pmA+dNs0QL69YMRI2DlyoAUGZ4cPAidOzuD9rlkAe4FJS7gzY5vsun+TTx6+aN8/ffXNH2nKVdPvJp5f84jFL1ThpGbCJXpwf7ATOBtX1AFYHZ62VIIS+0NcB3wo6ru9QtrrqoNgXbAPSJyZUoZVXWMqjZW1calS5dORyTvWL1jNTd8cANbD24NeNkvvgilSkHv3nD8eMCL954//4RLLoF582DUKBg71jXPcwlli5Tl2VbPsvn+zYxoM4L1e9fTYUoH6r5ZlwV/L/BavIBR7qVyyNNy1lbuJfOyZISGUE1KugdojrOQhKr+CZRJJ08scL7ffkVgWyppu5Osu1dVt/l+dwKzcF3IEcu63etYsX0FBfMVDHjZJUo4ByurV7t5OjkKVfelsHMnfPklDBqUIf+lOZGiBYoy5NIhbBi8gfc6v0eURBGdNxqAnUd2cuB4ZLsp3nEkZS9LqYUbRqAJlUI9oar/LGwSkbyk3tpMYglQQ0Sqikh+nNI8y2CtiJwDtADm+IUVFpGiSf+BNriZxhHLTXVu4o9Bf1CiYImglN+unXOk8tprOWTWryqcOuWU53vvOXu8V1/ttVRhQb48+eh9cW9WDlzJpedfCsAT3zxBjVE1OH4qJ3ZRhA+R3s0e6fIHm1Ap1O9E5DGgoIi0xvlG/SStDKp6ChgEfIFbxzpdVWNEZKCIDPRLegMwX1WP+IWVBRaKyErczOK5qhqRakJV+WrDVyRqIvny5AtqXS+8ABdd5EyFRsBwcurEx8PAgc4clCr8619QtarXUoUd/uPwAxsP5Llrnvunxfrkgif5ZesvXomWI4mOjmbPnj0Rq5RUlT179hAdHe21KGFLqJbNRAG341qKglOS72iY3VnhuGzmy7++pM37bZjcZTI96/YMen2rV7uJr61bw8cfR2DvaJLR8O++cwtthw+HqFB9N+YMth7cSu03anPwxEEur3Q5Qy4ZwvUXXk+eqDxei5Ym8nTqN2s4mJA8efIksbGxHI/giQrR0dFUrFiRfPnO/Li3ZTOOUCnUwsBxVU3w7ecBCqhqWHnmDEeF2nJiS37f8zsbBm+gQN7QTKR57TW4/3544w24666QVBkYYmKcLcWtW2HcOOdax8gSh04cYtzycbz686tsOrCJqsWrMrjZYPo16PePJ5xwI9wVak7GFKojVAr1Z+AaVT3s2y+C66a9LOiVZ4JwU6iLYxdzybhLGNFmBEMuHRKyelWd56hvv4VlyyJklcmJE1CjBpw8CbNnQ7NmXkuUIziVeIo56+bwys+v8OOWHymavygJmsDRk2d/C5ctXJbtDwbXEXtalHupXIoTkLyWKzdgCtURKoW6QlXrpxfmNeGmUDtP68wPm39g0/2bKJI/tG7Etm+HevWgfHlYvDiMV5kk3b8i8P33bqz0/PPTzhMhhJuCWLJ1Ca8ufpUpq6ekmsZagrkTU6iOUA0uHRGRhkk7ItIIONvlgfEPMTtjmPP7HO5tem/IlSlAuXLOzdvKlfDggyGvPmOcPOmcMb/yitu/8soco0wh/JaBNKnQhMldJntSt2FEAqFSqPcDM0TkBxH5AfgAN4PXSIXnf3yewvkKc2/Tez2ToWNHeOABeP11t041rNi/3/VLv/WWm4hkhAX7ju1j0/5NXothGJ4QKtODS4CawF3A3UAtVV0WirojkY37NzJl9RQGNBpAyUIlPZXlhRegVSu3CmXxYk9FOc369XDppW4m77vvQgrOkg1veGPJG1QbWY1O0zox/6/5JGqi1yIZRsgIpUfiJkAVX50NRARVNQfjKfDSTy8RJVEhnYiUGnnzwgcfuKU0XbrA0qVw3nkeCnTgAFx2GSQkwFdfuW5eI2zofXFvjp48ythfx/Lx7x9To0QN7m5yN7fVv43i0Sl7LzGMnEKobPlOAl4CLscp1iZArh/ATo2Ly17MI5c/QsViFb0WBYCSJd3E2f374cYb3YRazzjnHGd8ePFiU6YeUbZw2VTDK51TiWdbPcuWB7bw/g3vU6pQKR744gEqvFyB/h/3Z9k265gyci6hmuW7FqgdboYckhNus3zDjRkzoFs3uOMOGDMmhEYfVJ1/uUsvhbZtQ1Sp94TbLN+ssjxuOaOXjGbK6ikcO3WMh5s/zHPXPOe1WEYAsVm+PlQ16BvO1OB5oagrO1ujRo3US/Yf269vL31bj5085qkcafHYY6qg+sYbIarw2DHVHj1cpYMHh6hSIxjsO7ZPRy0epYu2LFJV1XW71umguYN028FtHktmZBdgqYbBO9zrLVSzfEsBa0TkCxH5OGkLUd0Rw/SY6dz56Z2s2ZWaD3Xv+c9/oEMHGDzYLfsMKrt2uRlRU6fCc8/Bq68GuUIjmBSPLs6gpoO4pOIlACzdtpQJKyf8Y9Lwr71/cTj+sJciGka2CFWXb4uUwtU5Gw8bvO7yVVVW7VjFxeUu9kyGjHDggDNEtGMH/PCDM6gfcHbtcj5Mt22DSZOcfV4jx3Ek/giF8xcGoMWEFiyPW06Pi3rQv1F/Gp3X6AwD/kb4Yl2+jlAtm/kOWAcU9W1rw02ZhgMiEvbKFNy8oM8+g4IF3ZDmpswuO4yLgxYtnDmm1ChVCjp3dvYPTZnmWJKUKcB/W/6XLrW6MGnVJJqMbULDMQ0ZtXgUe47u8VBCw8g4oWqhdgNeBL7FeZu5AhiqqjODXnkm8LKFev/n95MvKh8vtnnRk/qzwurVbqJtmTKwcCGULp1+nsmTIXHg3fQ8/DaTiwwkz1ujz7Rh//770LSpc7lm5Er2H9/PlNVTGL98PMvilpE/T346XdiJfg360bpa67D3epMbsRaqj1AM1AIrgTJ++6WBlV4PICffvJqUdOD4AS30bCHt/3F/T+rPDgsXqkZHqzZurHrwYNpp339ftVrBbXqUaFXQIxTUqgXj9P33VTUxUfWJJ1RB9fbbQyK7Ef6siFuh9312n5Z8vqQyDJ24YqKqqiYkJngsmeEPNinJ6baQVAKrk+1HJQ9LJd+1wO/AeuCRFOKvAg4AK3zbkxnNm9LmlUJ9e+nbyjD05y0/e1J/dvnkE9U8eVRbtVI9fjz1dJUrq47mLj1GflXQY+TX17lba1Q6rtqzp7sd+/VTjY8PmexGZHD85HGdETNDDx53X22vLHpFm45t+s++4S2mUN0Wqlm+n/tm+N4mIrcBc4HP0srg85k6GmgH1AZ6iEhKjsR+UNX6vu0/mcwbFoxbPo46pevQtEJTr0XJEh07OkP6X38NvXs7I0YpEb8pjr68SzTxAEQTTz/GM3FzC5gyxZkQfOcdSOa82DAK5C1A19pdKVqgKABlCpehRoka/+y/9vNrzFk3h/iEeC/FNHI5ITE9qKpDRaQLzlKSAGNUdVY62ZoC61V1A4CITAM6ARlZU5KdvCFl9Y7V/LL1F15p+0pEz2i89VY3MffBB6F4cWezPirZ59rzRZ5BDp9p21VIpGrUFpg8Fbp3D6HERiTTs25PetbtCTifra8ufpWN+zdybvS5dKvTjV51e9G8UnOiJFRtBsMI8ixfEakuIs0BVPUjVR2iqg8Ae0TkgnSyVwC2+O3H+sKSc6mIrBSRz0SkTibzIiIDRGSpiCzdtWtXRg4roIxbPo58Ufm4pd4tIa870Pz73/DYYzB2LNx++9kt1Q4lF/3TOk0imnjyVyxjytTIMnmj8vLHoD+Y23Mu7Wq0Y9KqSVw54UqqvVaNx75+jJidMV6LaOQSgv359ipwKIXwo764tEipuZZ8SvKvQGVVvRgYBczORF4XqDpGVRurauPSGZmmGkBOnDrBpFWT6FyzM6UKlQpp3cFi+HAYNgwmTIBbbnEuS5MosXE5k99XBpWaxnEKMKVIfya/r5TYtNwrcY0cQr48+Whfoz2Tu0xmx4M7eP+G96ldujYv/PgCF715EXXfrMuiLYu8FtPI4QS7y7eKqq5KHqiqS0WkSjp5YwF/b9EVgW3Jyjno93+eiLwhIqUykjccmPP7HPYe28sdDe/wWpSAIQJPPeXWqD78sDOkP3UqFCgAqNJr83P02v0YXH45PWf/D7z1TmfkQIrkL0Kver3oVa8XO4/sZHrMdKb9No3zijo3SV/+9SWrdqzinqb3EJ032mNpjZxEsFuoad2tBdPJuwSoISJVRSQ/0B04w1yhiJQT38CjiDTFHc+ejOQNB9759R0qnVOJa6pd47UoAeehh2DkSJg1y7l9O37oJPTv7/qEe/SAL790bmwMI4iUKVyGQU0HsbDfQqoUrwLA5+s/55WfXyF/nvwALPh7AVsObEmjFMPIGMFuoS4Rkf6qOtY/UERuB9L046Sqp0RkEPAFkAcYr6oxIjLQF/8W0BW4S0ROAceA7r4p3CnmDfTBZZfXrn2NLQe35NiJE/feC9HRcOed0Lv9Hj7Y8DlRjz/uDAInn7FkGCFiRNsRPNHiCaIkikRNpNdHvYg7HMclFS/hpto30bV2VyqdU8lrMY0IJKiWkkSkLDALiOe0Am0M5AduUNWw8kHltS3fHElcHO/PL0Offnlo3Xgfkz49N0MWlQwjVPyx5w9mrpnJjDUzWLF9BQBNyjfhhpo30KVWFy4sdaHHEoY/ZinJESrTg1cDSSbUJL6CwQAAGOJJREFUY1T1m6BXmgVCpVATEhMYNG8Qtze8ncblc/A9uGyZW6Tapw8fNX2OXr2gfHmYOxdq1vRaOMM4m/V71zNzzUxmrZvFL1t/AaB26dq83OZl2lbPmi/enOLXNi1MoTpCZRx/gaqO8m1hqUxDyfq965m+Zjp/7/vba1GCx8cfO0O/BQpA79506eLs3B8+7PyEf/211wIaxtlUL1GdRy5/hMV3LGbLA1sY1W4UZQuXpXh0cQAWbl7IoHmD2HUk40vsUlKmaYUbkYsNZHnAhaUuZOuQrXSu2dlrUYLDyJHOU0ydOvDzz+4X5/Jt8WKoUAGuvdYZRTKMcKVisYoMajqIb/p8Q7OKzQBYs2sNH8R88I+XnA/XfMiU1VPYd2yfl6IaYUJIunwjhVB0+SZqIoJEtFWkNNm4EWrVchpz8mQoVOisJAcOwM03wxdfwNChzne4zVEyIoVTiafIG+Xmc7aZ1IYvN3xJHsnDZedfRsd/daRDjQ7ULl37n2dcnk79Wdencsb717p8HfYaCzHz/5pPjVE1WLd7ndeiBJYkCw5VqsBPP8HMmSkqU3D+VD/9FO66C1580S2rOXAgdKIaRnZIUqYAn9/yOYtuX8Sjlz/KofhDPPzVw1z05kVUfa0qg+YNYt6f8zyU1Ag11kL1IxQt1AGfDGDqb1PZNXRXzllUHhvrJh/ddx/07ZvhbKowahQMGQKVK8P06dCoURDlNIwgE3swlnl/zmPun3P5asNXHD15NM30XrZQAzlZylqoDmuhhpCExARmr5tNhxodco4yXbbMOQTfsAHOOy9TWUVg8GD4/nvXwL3sMhg92ilaw4hEKharyIBGA5jTfQ57HtrD/FvmU7JgygZMyhQu4+nYq02WCjwh8TZjOBZuXsiuo7u4sdaNXosSGObMgZ49oVQp18170UXp50mByy6D5cudx5pBg9xs4HfecV3DhhGpROeNpvUFrdn90G4A9hzdw8w1M+lUsxPlipTj7aVvU+rFUjQp34Q2F7ShdbXWXFLxEvLlMfeFkYq1UEPIR2s/okCeArSr0c5rUbLPunVu8POii9zU3Swq0yRKloRPPoEXXnDmChs2dI1fw8gplCxUkjsb30m5IuUAuLzS5Tx+xeOICM/+8CxXTriSEi+UoMOUDry86GVWbF9BoiamU6oRTtgYqh/BHENVVSq9WomG5zVkTvc5Qakj5Eyf7sZOU5l8lFV+/NF5c9uxA554Ah55xHyOGzmbfcf2sWDjAr7a8BXf/P0Nv+/5HXBdyBvv20ieqDzsPbaXc6PPDdgKgUDOPrYxVIe1UEPEkm1LiD0YG9ndvXv2uOUwP/3k9rt1C7gyBWjeHFasgBtvhCefdOtXV64MeDWGETacW/BcutTqwhsd3mDdoHVseWALEztP5J4m95AnKg8A7Sa3o9O0Tv/k2XxgM9YgCi9sDDVEfLT2I/JG5aXjvzp6LUrWWLfOtUa3bIF+/YJeXcmSzu1bt25ueU3jxvB//wePPgr58we9esPwlIrFKnLrxbeeETaw0UCKFigKwNGTR6k+sjolCpbgispXcEUlt9UrW+8fBZweZQuXTXWWr5E1rMvXj2B1+aoq/3r9X1QtXpX5vecHvPyg8+WXcNNNzozg7NnOdmAI2bPHrciZPBnq1XPOyxs0CKkIhhFWHIk/wpTVU/h+8/f8sOkHNh3YBECxAsVofn7z/2/v3oOrqq8Fjn9XAiGAYBSQVwggAkofVIQCCqi9gyi2pQ/v1V6mWkqxFvVaWytW56otg9MHY320XqGI0ikFHRWlPKQWWoECIhUFgQFiMBGIhIcgBEgIWfePdY45eZmTZJ9XzvrM7NmPs/c5v18ee53fb/8ejM4bzRV5VzCsxzDatm5opszm8ypf4wE1QiwD6qo9q8jKzGJ079GBv39MrVtnY/IOGmSthnr3TlhSFi+2qeAOHoSpU+EXv4DzzktYcpxLGkXHilhTuIY1RWtYXbiaHYd2ADD363OZdOkk9h/fz/oP13NNv2s+LeUGyQOq8YAawadvq8PZszac0e23Q4fg/xEb68gRq/qdNcuC6SOPwOTJkBldLZdzaeHQyUNs2LuBoT2G0u2cbsx5ew5T/jqFnXfsZECnAawsWMnWkq2MyB3Bpd0upU2rNs36PA+oxhslxcHMdTPZemBropMRvZISuOEGGwEpM9Oa2SZBMAU4/3x46il4+20bc/+HP4Rhw6xlsHPOdG7Xma8O+OqnXXRuHnwzGyZvoP/5/QFYunspd6+4m5HPjKTjrzoya9OsRCa3xUjqgCoi14rIThHJF5H76nh9oohsCS3rRGRwxGsfiMhWEXlHRBJW7Cw+Xsz9K+9n5Z4Uma9s82ZrAbRsGWxN3i8BgwfbABALF1oV8KhRNsZEQUGiU+Zc8snKzGJ47vBPu9w8Ou5R9v1kHy/910vcNfwuBncb3MA7uGgkbZWviGQCu4CxwF7gLeA7qro94pzLgR2q+rGIXAc8rKrDQ699AAxV1UPRfmasqnyPnj6KIJybneRD/zz/vI3F26mTjYI0ZEiiUxSV0lKbsWbmTKiogB/8AB54AHJzE50y59KDV/maZC6hfhnIV9UCVS0HFgITIk9Q1XWqGh4McwOQlLfQnOyc5A+m8+fbaAqXXQabNqVMMAVo3x6mT4f8fLj1VnjmGbjoIrj7bhscwjnn4iGZA2pP4MOI/b2hY/WZDCyP2FfgbyLybxG5tb6LRORWEdkkIpsOHjzYrATXtPeTvYyaO4qN+zYG+r4x8bWvwUMPwcqV0DU1+6H17GmD6+/aBRMn2kw2F15ofVdLShKdOudcS5fMAbWucbHqrJ8WkauxgDot4vAVqjoEuA64XUTG1HWtqs5W1aGqOrRLly7NTXM1i3Ys4l8f/otz2yRp6XTTJvjGN+DUKejYER5+uEWMmtCnj5VSt2+37P3619bb57bbLNg651wsJHNA3Qv0itjPBfbXPElEvgjMASao6uHwcVXdH1qXAIuwKuS4WrJ7CQM7DWRg54Hx/uiGzZljY/xt3myteVugAQOsJnvHDpvJ5rnn4OKL4Zvf9FbBzrngJXNAfQvoLyJ9RSQLuAlYHHmCiOQBLwPfVdVdEcfbi0iH8DZwDfBe3FIOnK44zerC1YzrNy6eH1tdcTFceSV8FDFZ8KlT1nFzyhS46iqb0qV//4QlMR4GDrR+q4WF1lhp9WprFXz55fCXv0BZWaJT6JxrCZI2oKpqBXAHsALYAbygqttE5DYRuS102oNAJ+CpGt1jugJrReRdYCOwVFVfi2f61324jtMVpxnbb2w8P7a66dNh7Vpbh/3oRzB3ro2OsGyZzWWaJrp2tR9FUZE9Xy0psWetPXvCPfd4dbBzrnmStttMIgTZbebnf/85M9fP5Mi9R2Iy1FeDioutRc7p09C2rUWL3Fxb79xpjZDSXGWltcGaNct6CVVUwNVX22AREyZAdnaiU+hcavBuMyZpS6ip7vWC1xmROyIxwRSsKFYZmpy4rMyqd1XtwaIHUwAyMmDsWHjxRZtE55FHYM8e6z3UrZv1Z/3HP2z0Reeca4gH1Bg4fPIwbxe/zdgLE1TdW1wMzz4L5eW2X1lpDxCLixOTnhQQLqkWFsIFF8AXvmDjXHzlK9ZC+J57rP2WV+g45+rjATUGVu1ZhaKJC6i//CWcOVP9WEYGzJiRmPQkufnzbUCIwkILmCUlNlbwE0/Y0IZDhsDjj9t6wAD42c+slbCXXJ1zkTygxkCPDj24ZfAtDOs5LDEJWLu29t2+vNymYnO1PPAAnDxZ/djJkzY93I032rRxH30ETz8N/fpZcB01Cnr0sEC8bJk9qnbOpTdvlBQhpadvU4Xly2HcOJshZtcuG38vw78zNSQjo+6qXJGqx9CRjh2zH/Urr1gwPX7c2n1deSVcc439Ci65xK53Lh14oyTjd9uAHTl1hPePvB/fDy0pgW9/G66/3jpWgtVNejCNSl5e446fe641XArPdLN8uZVUP/gAfvITm1YuL8+6+y5YAPtrDUfinGuJ/I4bsBe2vcBFT15E/pH8+HzgSy/ZHXzpUvjNb2wOM9coM2ZAu3bVj7VrF90j5zZt4Npr4bHHbESmwkKYPRtGjICXX7ZfR8+eVlnw/e/DvHnWktgrhpxrebzKN0Jjq3y7zezGgdLa05l0bNORo9OOfjr3YMz89Kfw6KM2Q8y8eRZYXZPMn2/PUouKrHQ5Y4YN+tAcZ8/Cu+/CG2/YsmYNHDlir/XoAcOHVy1Dh8I55zQ/H84lglf5Gg+oERobUOUX9QdMfShGP9fTp+1O3b69zbC9caPNU9a6dWw+zwWmshK2bbPgun49vPkmvB96OpCRYd+Hhg2DSy+1ZfBgD7IuNXhANR5QIyR9QH3tNbjzThvGZ+bM4N/fxd2hQ/ad6M03bdm0CQ6HpngQsT6xx49bq+MuXeC+++Cuu6zdmXPJwgOqaZXoBLgo7N4N06bBokXW2GhcAgfcd4Hq3BnGj7cF7Nnqvn02iMS8edaSONwD6uBBq+W/7z74/OetRDtokA3+P2CAdelp2zZxeXEu3XlATXazZsHtt1vrl0cesWakbdokOlUuRkRsyOXcXKuMqGvwiOxsK7n+85/w5z9Xv7ZXLwuu/fvbUM59+1atc3Lilg3n0pIH1GRUVGQP3Pr0gdGj7c46bZoNMOvSRlFR3cdPnLDaf7Dq4N27rdtx5LJgARw9Wv26nBwLrHl5tvTqVX3drRu08jtCWolFY7x05v8+zdC1fVcOlB6g23FY+CLceAMc6GDHm6SgwFrt/vGPNgv2woVWp/e73wWbcJcS8vKsG05dx8M6dLAhEYcMqX3e0aPWRaegwNbhJT8fVq2yYBxJxKa469Gjaune3QJt167Vl1df9RtxqgsPuRkeJayw0PbBf5dN5Y2SIjR5pKSpU6l8+v+YPSyDW9aeoG3rRj7IWrrUOjL+/e9WRJg8Ge6/v/6RBVxaqHnDA+sfO3t2MDe8Y8dslp2iIlvv3197KSmJ7r0yM23mntGj4fzz6146dEjf0aOSsSTYp0/dX9h697ZBShrDGyUZD6gRmhRQI+YdLWudQZuifdFVze7ebddlZlorkwULbL6wSZPsAZpzJP5GXF5ujaEOHLDgeuAA/PjHtauTwYLlZ91OMjJslKmcnKp1eLtjx9pLhw7WbSi8Dm+3b59aVdOx/mLUVI0dcvOzeEA1SR1QReRa4HEgE5ijqr+q8bqEXh8PnAS+p6pvR3NtXZoUUKdORZ95Bikvp6J1Jq2m/BD+8Ifa5x07BqtXW13bqlWwZYsNBHvddVBaai1NvC+ESwGfdSM+eRI+/tgGsIhcDh+2IHzsmK0jl08+qVqivZFnZVlgDS/t2tn66FHr23vqlO1ffjl86UvW+jk725bI7exsa+MXXoe3s7Kq9sPbWVlNC+RBlgSD5CXU4CVtQBWRTGAXMBbYC7wFfEdVt0ecMx64Ewuow4HHVXV4NNfWpdEBNaJ0+qnsbFixwppnXnCB9W3YscP6OVRW2utXXGGjqN9yiz2Qci6FxCpAqFpAjgywpaXWCOvECXvmG94uLbVzS0urlvffh507awflzMzgptrLyLAxVLKyqi+tW9e/rFpV//tNmWJBOnLJzKy9rrld15KRUfd+RkbVErm/ciX89rdQVlaVnqaWnD2gmmSuOPkykK+qBQAishCYAEQGxQnAn9S+FWwQkRwR6Q70ieLa5ps+vfZ/7+nTNu0IwB13wJNPWj+GBx+EMWNg5EgLqs6lqBkz6q7CbO50uyJVJc7u3Rt/fZ8+dZdwc3OtMVZZmf17njpVtS4rqzoeuV1ebtvhdXg5c8aO1VzOnKm9hK/NyrLtmjIzYckSqKiouubsWVsqKuI/3nOPHjYceKKf7aayZA6oPYEPI/b3YqXQhs7pGeW1AIjIrcCtAHmNbQS0fn3d/ym9e8Ozz9qI6GD/OQ891Lj3di5JhW+4ydbIpr5uRkVFFqzDVbzx7o/b1GeoqlXBNTLQhrfDS2Vl/dvh/cjjqtWPh5cxY+zLjGu6ZA6odbUHrPmdrb5zornWDqrOBmaDVfk2JoFs3tyo051rKSZOTHwArSmabkaJ0NQvICJV1cAuNSTz9G17gV4R+7lAzZkl6zsnmmudcy1Ic6bhi7WJE+35cmWlrZPty4gLRjIH1LeA/iLSV0SygJuAxTXOWQzcLGYEcExVi6O81jnXgkycaNWovXtb6a5378R3TXHpJWkrE1S1QkTuAFZgXV/mquo2Ebkt9PrTwDKshW8+1m1m0mddm4BsOOfiKBmrol36SNpuM4nQ5JGSnHMujXm3GZPMVb7OOedcyvCA6pxzzgXAA6pzzjkXAA+ozjnnXAC8UVIEETkI1NE1PCqdgUMBJicVeJ7Tg+e55WtufnurapegEpOqPKAGREQ2pVsrN89zevA8t3zplt9Y8Spf55xzLgAeUJ1zzrkAeEANzuxEJyABPM/pwfPc8qVbfmPCn6E655xzAfASqnPOORcAD6jOOedcADygNpKIXCsiO0UkX0Tuq+N1EZEnQq9vEZEhiUhnkKLI88RQXreIyDoRGZyIdAalofxGnDdMRM6KyA3xTF8sRJNnEblKRN4RkW0i8ka80xi0KP6uzxWRv4rIu6E8T0pEOoMkInNFpERE3qvn9RZ3/4orVfUlygWbCu594EIgC3gXGFTjnPHAckCAEcCbiU53HPJ8OXBeaPu6VM5zNPmNOG8VNoXgDYlOdxx+xznAdiAvtH9BotMdhzzfD/w6tN0FOAJkJTrtzcz3GGAI8F49r7eo+1e8Fy+hNs6XgXxVLVDVcmAhMKHGOROAP6nZAOSISPd4JzRADeZZVdep6seh3Q1AbpzTGKRofscAdwIvASXxTFyMRJPn/wZeVtUiAFVN9XxHk2cFOoiIAOdgAbUivskMlqquxvJRn5Z2/4orD6iN0xP4MGJ/b+hYY89JJY3Nz2TsG26qajC/ItIT+CbwdBzTFUvR/I4HAOeJyD9F5N8icnPcUhcb0eT598AlwH5gK3CXqlbGJ3kJ09LuX3HVKtEJSDFSx7Ga/Y6iOSeVRJ0fEbkaC6ijYpqi2Iomv48B01T1rBVeUl40eW4FXAb8B9AWWC8iG1R1V6wTFyPR5Hkc8A7wFaAf8LqIrFHVT2KduARqafevuPKA2jh7gV4R+7nYt9fGnpNKosqPiHwRmANcp6qH45S2WIgmv0OBhaFg2hkYLyIVqvpKfJIYuGj/rg+pailQKiKrgcFAqgbUaPI8CfiV2sPFfBHZA1wMbIxPEhOipd2/4sqrfBvnLaC/iPQVkSzgJmBxjXMWAzeHWsuNAI6panG8ExqgBvMsInnAy8B3U7jEEtZgflW1r6r2UdU+wIvA1BQOphDd3/WrwGgRaSUi7YDhwI44pzNI0eS5CCuRIyJdgYFAQVxTGX8t7f4VV15CbQRVrRCRO4AVWCvBuaq6TURuC73+NNbqczyQD5zEvuWmrCjz/CDQCXgqVGqr0BSduSLK/LYo0eRZVXeIyGvAFqASmKOqdXa9SAVR/p6nA8+JyFasKnSaqqb0lG4isgC4CugsInuBh4DW0DLvX/HmQw8655xzAfAqX+eccy4AHlCdc865AHhAdc455wLgAdU555wLgAdU55xzLgAeUJ1zzrkAeEB1Lgoi0ik0ddk7IvKRiOwLbZ8Qkadi8HnPiciecL/I0H7cpokTkRtDU3gtiddnOpfqfGAH56IQGk7xSwAi8jBwQlVnxvhjf6aqL8byA0QkU1XP1jyuqs+LyAHgnlh+vnMtiZdQnWuG0KTbS0LbD4vIPBH5m4h8ICLfEpHfiMhWEXlNRFqHzrtMRN4IzdqyohHTY40Rm8C9IFxaDQ0R91sReS/0OTfWTFdo//ci8r3Q9gci8qCIrAX+U0T+R0S2hyaUXhjgj8e5tOIlVOeC1Q+4GhgErAe+rar3isgi4HoRWQo8CUxQ1YOhADgD+H4U790dm8nnYmzM1ReBb2El58HYQP1vhQaub8hpVR0FICL7gb6qWiYiOY3Iq3MuggdU54K1XFXPhMZ/zQReCx3fCvTBBlj/PDYVGKFzoh18/JXQfJzbQ4O1gwXYBaFq2wMi8gYwDGhoirHnI7a3APNF5BUglQf5dy6hPKA6F6wyAFWtFJEzWjVYdiX2/ybANlUd2dT3DpEa65oqqP5IJ7vG66UR29cDY4CvA/8rIp9T1YompM+5tObPUJ2Lr51AFxEZCSAirUXkc814v9XAjSKSKSJdsMC4ESgEBolIGxE5l9A0ZDWJSAbQS1X/AdwL5ADnNCM9zqUtL6E6F0eqWh5qUPREKNC1Ah4DtjXxLRcBI4F3AQXuVdWPAETkBaw6dzewuZ7rM4E/h9IiwO9U9WgT0+JcWvPp25xLQiLyHLAk1t1mGkjDVcA9qvrVRKXBuVTiVb7OJadjwPTwwA7xFmp9/BTwcSI+37lU5CVU55xzLgBeQnXOOecC4AHVOeecC4AHVOeccy4AHlCdc865APw/8lPOgJScXF8AAAAASUVORK5CYII=\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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Parameter values from parameter estimation using parmest\n", "A1 = theta['A1']\n", "E1 = theta['E1']\n", "A2 = theta['A2']\n", "E2 = theta['E2'] \n", "\n", "\n", "A_est1 = [A1, A2]\n", "A_est = np.asarray(A_est1)\n", " \n", "E_est1 = [E1, E2]\n", "E_est = np.asarray(E_est1)\n", "\n", "ctr = 0\n", "for T in T_vals:\n", " for CA0 in CA0_vals:\n", " # generate concentration profiles using estimated parameter values\n", " k = kinetics(A_est, E_est, T)\n", " # plot model-generated and 'experimental' data\n", " # symbols for 'experimental' data\n", " # solid and dashed lines for model-generated data\n", " plot_exp(k, CA0, data_dict_overall[ctr]['data'], 'Model prediction and experimental value at T = {} K and $C_{}$ = {} mol/L'.format(T,'A0',CA0))\n", " ctr+=1" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.6 Using `parmest` with `pyomo.dae`](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6-Using-`parmest`-with-`pyomo.dae`)", "section": "2.8.6 Using `parmest` with `pyomo.dae`" } }, "source": [ "## 2.8.6 Using `parmest` with `pyomo.dae`\n", "\n", "In contrast to the approach above, we will now try to solve the model without the analytic solution for the concentrations using Pyomo.DAE. To recap, the concentrations in a batch reactor evolve with time per the following differential equations:\n", "\n", "$$ \\frac{d C_A}{dt} = r_A = -k_1 C_A $$\n", "\n", "$$ \\frac{d C_B}{dt} = r_B = k_1 C_A - k_2 C_B $$\n", "\n", "$$ \\frac{d C_C}{dt} = r_C = k_2 C_B $$\n", "\n", "This is a linear system of differential equations. Assuming the feed is only species $A$, i.e., \n", "\n", "$$C_A(t=0) = C_{A0} \\quad C_B(t=0) = 0 \\quad C_C(t=0) = 0$$\n", "\n", "In the following cell, we define a function to define and return the Pyomo DAE model (dynamic mode) for the kinetic model to be used for parameter estimation. In this model, the rate equations are presented in terms of linear differential equations." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "nbpages": { "level": 2, "link": "[2.8.6 Using `parmest` with `pyomo.dae`](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6-Using-`parmest`-with-`pyomo.dae`)", "section": "2.8.6 Using `parmest` with `pyomo.dae`" } }, "outputs": [], "source": [ "def create_model_DAE(data):\n", " '''\n", " function to create Pyomo model\n", " Argument:\n", " data: a single dictionary of data\n", " Return:\n", " m: Pyomo model\n", " '''\n", " # data\n", " exp_data = data['data']\n", " \n", " # This code style matches parmest example found here:\n", " # https://github.com/Pyomo/pyomo/blob/master/pyomo/contrib/parmest/examples/semibatch/semibatch.py\n", " \n", " # unpack 'experimental' data into temporary variables\n", " cameastemp = exp_data['CA']\n", " cbmeastemp = exp_data['CB']\n", " ccmeastemp = exp_data['CC']\n", " tmeastemp = exp_data['time']\n", " \n", " # create dictionaries for 'experimental' data of CA, CB,and CC indexed by timestep\n", " cameas={}\n", " cbmeas={}\n", " ccmeas={}\n", " for i,j in enumerate(tmeastemp):\n", " cameas[float(j)] = cameastemp[i]\n", " cbmeas[float(j)] = cbmeastemp[i]\n", " ccmeas[float(j)] = ccmeastemp[i]\n", " \n", " # define Pyomo model\n", " m = ConcreteModel()\n", " m.T = data['T'] # K\n", " m.CA0 = data['CA0'] # mol/L\n", " \n", " # define 'experimental' data timesteps as Pyomo ContinuousSet\n", " m.t = ContinuousSet(bounds = (0.0, tmeastemp.iloc[-1]), initialize=tmeastemp.tolist())\n", " \n", " # define 'experimental' data as Pyomo parameters indexed by timestep set and \n", " # initialized by dictionary of experimental data\n", " m.Ca_meas = Param(m.t, initialize=cameas)\n", " m.Cb_meas = Param(m.t, initialize=cbmeas)\n", " m.Cc_meas = Param(m.t, initialize=ccmeas)\n", " \n", " m.R = 8.31446261815324 # J / K / mole\n", " \n", " # Kinetic parameters to be fitted defined as Pyomo variables\n", " # Initialized by 'true' values\n", " m.A1 = Var(initialize=200, bounds=(100,300)) # 1/hr\n", " m.A2 = Var(initialize=400, bounds=(300,500)) # 1/hr\n", " m.E1 = Var(initialize=10, bounds=(1,20)) # kJ/mol\n", " m.E2 = Var(initialize=15, bounds=(1,30)) # kJ/mol\n", " \n", " # Concentration variables indexed by time\n", " m.CA = Var(m.t, initialize = m.CA0) # mol/L\n", " m.CB = Var(m.t, initialize = 0) # mol/L\n", " m.CC = Var(m.t, initialize = 0) # mol/L\n", " \n", " # Derivatives in the model\n", " #\n", " m.dCA = DerivativeVar(m.CA)\n", " m.dCB = DerivativeVar(m.CB)\n", " m.dCC = DerivativeVar(m.CC)\n", " \n", " \n", " # kinetic rate constants from Arrhenius equation\n", " m.k1 = Expression(rule = m.A1 * exp(-m.E1*1000/(m.R*m.T))) # 1/hr\n", " m.k2 = Expression(rule = m.A2 * exp(-m.E2*1000/(m.R*m.T))) # 1/hr\n", " \n", " # Constraints to change concentrations based on kinetics\n", " def conc_A(m,i):\n", " return m.dCA[i] == - m.k1 * m.CA[i]\n", " m.CA_rate = Constraint(m.t,rule=conc_A)\n", " \n", " def conc_B(m,i):\n", " return m.dCB[i] == m.k1 * m.CA[i] - m.k2 * m.CB[i]\n", " m.CB_rate = Constraint(m.t,rule=conc_B)\n", " \n", " def conc_C(m,i):\n", " return m.dCC[i] == m.k2 * m.CB[i]\n", " m.CC_rate = Constraint(m.t,rule=conc_C)\n", " \n", " # Initial Conditions\n", " def _initcon(m):\n", " yield m.CA[m.t.first()] == m.CA0\n", " yield m.CB[m.t.first()] == 0.0\n", " yield m.CC[m.t.first()] == 0.0\n", " m.initcon = ConstraintList(rule=_initcon)\n", " \n", " # Objective function\n", " # The objective function for parmest is defined as a 2-stage stochastic optimization objective function\n", " \n", " # First stage cost: independent of scenarios ('experiments')\n", " # expression for minimizing fixed realization \n", " # from model. Eg.: reactor temperature, size, etc.\n", " def ComputeFirstStageCost_rule(m):\n", " # In this case, we do not optimize anything besides the kinetic parameters through \n", " # least square fitting realizations at each timestep defined by m.t.\n", " # Hence, the first stage cost is set to 0 here.\n", " return 0\n", " m.FirstStageCost = Expression(rule=ComputeFirstStageCost_rule)\n", " \n", " # Second stage cost: Realization at each scenario over which the model is defined\n", " def ComputeSecondStageCost_rule(m):\n", " # In this problem, we want to minimize the sum of squared errors between \n", " # 'experimental' data and the model realization of concentrations of \n", " # A, B, and C over each scenario (here, timesteps defined by m.t)\n", " return sum((m.CA[t] - m.Ca_meas[t]) ** 2 + (m.CB[t] - m.Cb_meas[t]) ** 2 \n", " + (m.CC[t] - m.Cc_meas[t]) ** 2 for t in m.t)\n", " m.SecondStageCost = Expression(rule=ComputeSecondStageCost_rule)\n", " \n", " # return the sum of the first-stage and second-stage costs as the objective function\n", " def total_cost_rule(m):\n", " return m.FirstStageCost + m.SecondStageCost\n", "\n", " m.Total_Cost_Objective = Objective(rule=total_cost_rule, sense=minimize)\n", " \n", " # Discretize model\n", " disc = TransformationFactory('dae.collocation')\n", " # The DAE model is discretized using a collocation scheme with 20 finite elements \n", " # and 4 collocation points per finite element.\n", " # Increasing the number of finite elements will improve the solution quality but,\n", " # it will also increase the solution time. In our example, oweing to the small problem size,\n", " # increasing the number of finite elements will not impact the solution time drastically.\n", " disc.apply_to(m, nfe=20, ncp=4)\n", " return m" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.6.1 Parameter estimation with parmest](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6.1-Parameter-estimation-with-parmest)", "section": "2.8.6.1 Parameter estimation with parmest" } }, "source": [ "### 2.8.6.1 Parameter estimation with parmest\n", "\n", "In the following cell, we perform parameter estimation using parmest to solve the least squares problem defined in the Pyomo dynamic model." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "nbpages": { "level": 3, "link": "[2.8.6.1 Parameter estimation with parmest](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6.1-Parameter-estimation-with-parmest)", "section": "2.8.6.1 Parameter estimation with parmest" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.7039989e+01 1.98e+01 2.68e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.3426370e-01 2.11e+00 3.16e-01 -1.0 1.74e+01 - 9.58e-01 1.00e+00h 1\n", " 2 2.5138069e-01 2.48e-02 3.29e-02 -1.0 8.43e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.2289282e-01 7.55e-02 4.31e-03 -2.5 1.61e+00 - 9.95e-01 1.00e+00h 1\n", " 4 2.2225974e-01 6.13e-04 3.19e-06 -2.5 1.79e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.2221660e-01 9.42e-04 5.30e-07 -3.8 2.02e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.2211318e-01 1.91e-02 8.81e-06 -5.7 9.02e+00 - 9.36e-01 1.00e+00h 1\n", " 7 2.2210769e-01 7.24e-04 5.48e-07 -5.7 1.66e+00 - 1.00e+00 1.00e+00h 1\n", " 8 2.2210762e-01 2.28e-07 4.65e-10 -5.7 1.24e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.2210762e-01 5.67e-07 2.65e-10 -8.6 4.74e-02 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.2210762e-01 1.87e-11 3.84e-14 -8.6 1.13e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 10\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.2210762193125719e-01 2.2210762193125719e-01\n", "Dual infeasibility......: 3.8434198309153839e-14 3.8434198309153839e-14\n", "Constraint violation....: 1.8748558261449944e-11 1.8748558261449944e-11\n", "Complementarity.........: 2.5059125114858520e-09 2.5059125114858520e-09\n", "Overall NLP error.......: 2.5059125114858520e-09 2.5059125114858520e-09\n", "\n", "\n", "Number of objective function evaluations = 11\n", "Number of objective gradient evaluations = 11\n", "Number of equality constraint evaluations = 11\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 11\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 10\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.086\n", "Total CPU secs in NLP function evaluations = 0.026\n", "\n", "EXIT: Optimal Solution Found.\n", "theta:\n", " {'A1': 185.60880919391812, 'A2': 401.170198669058, 'E1': 9.866878980549787, 'E2': 14.866030768895396}\n" ] } ], "source": [ "# import parmest\n", "import pyomo.contrib.parmest.parmest as parmest\n", "\n", "# defining the names of the parameters in a list\n", "theta_names = ['A1','A2','E1','E2']\n", "\n", "# create an object using parmest.Estimator() that stores the Pyomo model realizations for the datasets provided.\n", "# This object which will be used to determined the parameter values that best fit all the datasets\n", "pest = parmest.Estimator(create_model_DAE,data_dict_overall,theta_names,tee=True)\n", "\n", "# call the method theta_est() for the Estimator() object defined above to solve \n", "# the parameter estimation problem.\n", "# theta_est() returns:\n", " # the overall objective function value\n", " # estimated parameter values (dictionary with keys = parameters names as defined in the Pyomo model)\n", "obj, theta = pest.theta_est()\n", "\n", "print('theta:\\n',theta)" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.6.2 Plotting fitted model simulation with 'experimental' data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6.2-Plotting-fitted-model-simulation-with-'experimental'-data)", "section": "2.8.6.2 Plotting fitted model simulation with 'experimental' data" } }, "source": [ "### 2.8.6.2 Plotting fitted model simulation with 'experimental' data\n", "\n", "Next, we plot the 'experimental' data along with the profiles generated using the fitted kinetic model. The symbols represent the 'experimental' data and the solid and dashed lines are the profiles generated using the fitted model." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "nbpages": { "level": 3, "link": "[2.8.6.2 Plotting fitted model simulation with 'experimental' data](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.6.2-Plotting-fitted-model-simulation-with-'experimental'-data)", "section": "2.8.6.2 Plotting fitted model simulation with 'experimental' data" } }, "outputs": [ { "data": { "image/png": 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\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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\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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gxgHvishmoCcwWkQuC9XJL74YFi1yad0MwzCMwkGBNKiqWldVY1U1FvgAGKKqM0J1/o4d4ehR+OabUJ3RMAzD8JqINKgiMhVYBDQUkXgRGSAig0VksNfaAM47D0qVsnFUwzCMwkQxrwXkBVXtlYuy/YMoJUNKlYI2bWwc1TAMozARkS3USKBjR1i3DuLjvVZiGIZhhAIzqEHCwhAahmEULsygBokzzoDq1a3b1zAMo7BgBjVIiLhu3zlzIDnZazWGYRhGsDGDGkS6d4d//oGFC71WYhiGYQQbM6hBpHNnl3h81iyvlRiGYRjBxgxqEImOhnbtYOZMUPVajWEYRgZs3+7m+f31l9dKIh4zqEGmRw/YsAHWrvVaiWEYRgY8/DB89x2MHOm1kojHDGqQ6d7d/bVuX8MwPEUVNm+GqVPhjjvgrbdc6/Sdd9y+t96yVmo+MYMaZGrVghYtXLevYRhGyPAfZ+rbF2rUgLp1oXdvGD8efv3VtUpTU12ZlBRrpeaTiAw9GGn06AGPPupe/qpX91qNYRgFkv37XZqr775zC8D8+e5vaiq0b+8Cjbdu7SbKJyRAvXpw5Igrc+SIa6U+8oj9UOURa6GGgB493MviJ594rcQwDM8ItPPPvn3H/7/3XqhQwYVoe/JJlzsyLu54K/V//3PLLbdA8+ZQrNiJrdM0rJWaL8yghoAmTaBOHev2NYxCzciRsGBB3g3W3r3urfzuu+Gss6BiRdixw+07+2wYPhxmz4bdu2HZMnj2WRdhJjMWLTreOk3jyBGbOJ8PRG0+xzHi4uJ02bJlQan7jjvgjTdg1y4oUyYopzAMI1zZvt11rx4+DKVLw8aN2XerHjrkWphRUTB9OvTs6VqUJUu6btu2bV2L86STQnIJWSEiy1U1zmsdXmMt1BDRo4d7lixYvmEUQnLi/JOSAkuXwlNPufHOihWdBy647tuHHoJ581wLdP5855gRBsbUOI61UP0IZgv16FH33b/sMjfubxhGIcG/dZpGWiu1UiUXTi0xEWJjXaxSgKZNoUMHuO46170b5lgL1RGRXr4iMgHoBuxU1TMy2H8dMNy3uh+4RVV/CqHEf1G8OHTt6oZAUlKgaFEv1RiGETIycv45cgROPx3OP99NUo+OhiFD4LTTXOu0WjVvtBr5IlK7fN8GOmexfxPQRlWbAiOBN0IhKjt69HBjqIsWea3EMIyQkZHzT0qK67bq1On4tpEj3RxRM6YRS0S2UFX1WxGJzWK/v5vaYqBWsDXlhM6dXUt15kz3YmoYRgEkNRV++AE+/hi++sp5zZYuDaNGwZo1cMklzqHIvBMLHBFpUHPJAODzzHaKyCBgEEBMTExQhZQrdzxY/n//m7VHu2EYEcbatfDcc25cZ+dON65zwQVuaktsLNx6q9cKjSDjWZeviFTKwVIhn+dohzOowzMro6pvqGqcqsZVrVo1P6fLET16wG+/uahfhmFEMLt2wYQJbs4nwMGD8OGHcNFFzjs3IcF548bGeirTCB1etlC3+Zas2mlFgTw1G0WkKTAe6KKqf+eljmDQvTsMHepaqY0aea3GMIxcsXWrmxM6fTp8+63r3r3nHjet5ayzXMu0RAmvVRoe4aVBXauqZ2ZVQER+zEvFIhIDfAT0UdX1eakjWNSu7Z67mTNdYBPDMMKcffvceI0qnHMObNvmPHQffBAuvxzO9P2MiZgxLeR4aVBb57WMiEwF2gJVRCQeeBQoDqCqY4H/AyoDo8UNVCaH0xypyy5zc7Lj4102GsMwwoxNm2DaNHj/fTcG+scfUKSI6+KNjYWGDb1WaIQhYRnYQUS2qGpwPYQyIJiBHfz57Tdo0MD5L9x9d9BPZxhGTvn8c5dtZflyt96yJVx1Fdx2G5Qq5a22MMYCOzjCdR5qgfZ/PfVUlyN16lSvlRhGIWfXLhgzBtb7jQyJuMDymzbBkiUuk4sZUyMHhKtBDb9mc4Dp1cu9BP/2m9dKDKOQceAATJniQpdVr+4iFKWlgurc2cXTvece8841co1nY6giMiyzXUDZUGrxgmuucS++U6fC//2f12oMo5Bw9KiLq7tzJ8TEOMPZq5eLnQs2OdzIF146JUVnse/lkKnwiFq14MILnUF95BF7jg0jKKxdCxMnwrp1MGOGC1U2cqRzKrrgAudoZBgBwkuD+hswO5zmiIaaXr1g8GD46Sdo3txrNYZRQNi7F957z3nk/vCDi1jUpYvLL1q6NAwa5LVCo4Di5etZDDBNRL4TkREi0kqkcLXTevaEYsWOpzw0DCOPpKYeD0D/wQdw882wf79zpf/zTxdXt3RpbzUaBR7Pp82ISDTQAZc95mxgLfAFrvW6I5RaQjVtxp9LLoGff4bNm633yTByzV9/uQTD48fDHXfA7be73KLr1rnoRYXrHd0zbNqMw/OfcFVNVNXpqnqzL3LSE0BVYJLH0kJCr14umtnChdmXNQzDx+zZcOWVLvTYgw86B6MGDdy+6Gg3f9SMqRFivPTyzSoN/XxVfT5kYjykRw/XEzV1qqV0M4ws2b8fyvomADz5pEuFdtddcNNNx42pYXiIl05JWRlMBS4KlRAviY52AfOnTYOXX3ZjqoZh+FB13TejR8OsWbBhg0vAPXmy+1uypNcKDeMYnv18q2o7r84dbvTq5UKGzp0LnTp5rcYwwoADB+B//3OGdNUqKF8eBgxwBhZcF69hhBmej6GKSHERuV1EPvAtt4pIca91hZIuXdzvhYUiNAo9KSnu744dcMstzlNv3DjnqfvSSy6ykWGEKZ4bVGAM0AIY7Vta+LYVGkqWhCuugI8+clPlDKNQoeoScV92mXsQwEUzWrMGVqxwY6Rlynir0TByQDgY1Jaq2k9V5/mWG4CWXosKNb17O2//zz7zWolhhIikJDflpXlzuOgiWLDAhQBM69Zt1Mg8dY2IIhwMaoqInJK2IiL1gBQP9XhCu3bOx8K6fY1Cw8svw403OgM6frybPzZypBlRI2IJB5/Se4H5IrIRFxi/DnCDt5JCT9GicPXV8MYbsGcPVKjgtSLDCDC//+7GQTt0cPPFBgyAs86C9u3NiBoFAs9bqKo6FzgVuN23NFTV+VkdIyITRGSniPySyX4RkVdEZIOIrMpmzmvY0K+f6wWbMsVrJYYRQJYvd2+LDRrA66+7gPUAlSs742rG1CggeG5QRaQo0AloC7QHhmaR2i2Nt3GhCjOjC85InwoMIkKcnFq0gDPPdE6NHkeENIzAcPPNLgTg7NkuX+HmzXD//V6rMoygEA5dvh8Dh4GfgdScHKCq34pIbBZFegCT1AUqXiwiFUTkZFXdnl+xwWbgQJfveNkyFz3NMCKK1FQXiL5jR4iKgrZtncfu4MFublgo2L4drr3WZZwJo2k2R48eJT4+nsOHD3stJc+UKlWKWrVqUbx4oZrZmGPCwaDWUtWmAa6zJrDVbz3ety3sDWrv3i7n8bhxZlCNMMffcFWt6qKTPPGEm+4ybpyb7tKrV+h1jRzpPIZHjoTXXgv9+TMhPj6e6OhoYmNjicTEWqrK33//TXx8PHXr1vVaTljieZcv8LmIXBzgOjP6tmbYiSoig0RkmYgsS0hICLCM3FO+vBtumjrVhS41jLAlzXBddx2cfrp7GxRxTgD9+3ujaft2NxUnNdX9/esvb3RkwOHDh6lcuXJEGlMAEaFy5coR3cIONuFgUBcD00XkkIjsE5FEEdmXzzrjgdp+67WAbRkVVNU3VDVOVeOqVq2az9MGhoEDnTF9912vlRhGJmzbdtxwzZ/vglB/8IELE9i7t3dBqUeOdJrARV0aOdIbHZkQqcY0jUjXH2zCwaA+D7QGolS1nKpGq2q5fNY5C+jr8/Y9B9gbCeOnabRu7V74x43zWolhpOPoUXjzTTjjjOOGq3hxaNPGpVPzMqlvWus0LdH4kSNh10o1CjbhYFB/A37RXGQ6F5GpwCKgoYjEi8gAERksIoN9RT4DNgIbgHHAkECLDiYirpW6ZIl74TcMz0lJgUmT4LTT3Njonj0nGq633/becPm3TtMIw1aqUXAJB6ek7cDXIvI5kJS2UVVfyOwAVc3S08FnnIcGTKEH9OkDw4e7Vuqrr3qtxijUHDzopr6sXevCBHbp4lIjpRlUOG64vHQCWrToRE3g1hcu9EaPUegIhxbqJmAuUAKI9lsKNZUrux60//3PAuYbHqAKP/7o/o+Kgssvhw8/dEEatm8PT8P1449Od/ol7TqMY0ybNo1WrVrRtGlT6tevz2OPPea1pAKB5y1UVbU7mQkDBzpv3w8+cC1WwwgJ333ngi8sWgQ//wyNG8N//nN8vxmoiGbixIm8+uqrzJgxg1q1arF//37GjImI2Ddhj2ctVBEZEYgyBZm2baF+fXNOMkLEqlVwySVw4YUuotHYsS5coFFg2LdvH8OGDeP999+nVq1aAJQtW5Z7773XY2UFAy9bqDdlMz1GgGuBEaGRE36IOP+P+++HdetcNivDCAp798K55zqP3aefhttuc129RlC4805YuTKwdTZv7nIPZMX06dNp1aoV9erVC+zJDcDbMdRxnDhmmn4p6ytTqOnf303pGz/eayVGgWPPHhg92o0zli/vxhY2bnTecGZMCySrV6+mefPmme6/5ppreP7550OoqGDhWQvVxk5zRrVqcOmlMHGiG8YqWdJrRUbEc+QIjBkDjz8Ou3e7lmnz5tA5q3wTRiDJriUZLMqUKcOhTLwcZ86cSbdu3fjqq69CrKrgEA5evkY2DB4Mu3ZZ5CQjn6g6T93TT3d9jmed5RyMsmixGAWLrl27Mm3aNHbs2AFAUlIS48aN4/Dhw0ybNo0+ffqwd+9ej1VGLmZQI4AOHVxgmhdesLRuRj44eNCNjZYuDZ9/DnPmQLNmXqsyQkjLli0ZMWIEnTp1okmTJjRv3pydO3fy7LPPsn//fgYPHszq1aszbcUaWWMGNQIQgWHDnBPm3LleqzEiir/+ggcfdCEDy5SBr7923jCdO1ti70JKnz59WLlyJT///DNr166lT58+bN68mRkzZjB27Fiuv/56VlmItjwhuYj4FxwBIlWBgUAsfmO6qnpjqLXExcXpsmXLQn3aHJGUBHXquF66zz7zWo0RUvKS3zMpCV5+2aVTO3wYvvrKTYcxPGPt2rWcdtppXsvINxldh4gsV9U4jySFDZ4HdgBmAt8BXwEpHmsJW0qWhFtvhUcecekmTz/da0UFh+rPVWfHgR3/2l6tTDX+uicMAqvnNr/nJ5+4MdLff4fu3eH55+HUU4Ov0zAKOeFgUKNUdbjXIiKBwYPhySfdWGokTqMJV8OVkaastoeU9Pk9H3kk61ZqSoorU6IEzJ4NFwc61bBhGJkRDmOon4hIV69FRAJVqkC/fi6+744w+K3PLWFtuMKVnOT3PHjQzan65x8oWhRmznTjpGZMDSOkhINBvQNnVA/7kosHIsF4geXOO93w2OjRXisxgk52+T1VYfp01///8MMwa5bbHhPjWqiGYYQUzw2qL6F4EVUt5fs/EAnGCywNG7phsdGjLQtNgSer/J6bNkG3bnDFFRAd7bx3+/f3QqVhGD48N6gAInKpiDznW7p5rSfcGTbMBXqYPNlrJUZQySq/5333wTffOIejFSugTRtvNBqGcQzPnZJE5GmgJTDFt+kOETlfVe/3UFZY06aNmz7z4osueH6RsHgtOs6RlCP8tf8vtiduZ1viNrbvd3+zYsqqKdQsV5Oa0TWpWa4mUcVDF0u2WplqmTpLeUr6NGnffQc1a0K9ehAf77zTatf2RpthGP/Cc4MKdAWaq2oqgIhMBH4EMjWoItIZeBkoCoxX1afT7S8P/A+IwV3jc6r6VnDkh560QA/XX+8C3lxyibd6Dh49yMSVE1kYv5Dvt3zPpj2b/lWmqBSlbImy7D+y/1/7KpWuxPXTrz9hW8VSFalToQ5NqzWlWbVmNKvWjLgacZQvVT7g+sNiakxW/P23a5FOmOC6dd96C3yptwzDCB/CwaACVAD+8f2f5S+miBQFXgM6AvHAUhGZpapr/IoNBdaoandf4IhfRWSKqh7JoMqI5OqrXVKQF14IvUFVVV7+4WXKlijLTWfdRLEixRg2ZxgVSlXgvNrn0a9ZP13usmMAACAASURBVGpE1zi2nBx9MlWjqlK0SNEM60tKTmLTnk38ue9P/kz889jf33f/zpe/f8mknyYBMO2qafQ8vSfr/17PzHUz6d+8P1XLVA3lpYcWVRfM4fbbXRD7++6D//s/r1UZhpEJ4WBQnwJ+FJH5uByoFwIPZFH+bGCDqm4EEJF3gR6Av0FVIFpEBJcG7h8gOQjaPaN4cfc7O3w4LF8OLVoc3xeM+Z7Jqcn8uP1HWtZsiYgw69dZVCtbjZvOuokSRUuw8faNVC9bHclDOLuSxUrSqEojGlXJOOFrwoEEftrxE82ruyDu3/3xHfd9dR+9mvQC4JP1n7B652ra1W1Hi5NbZGq4I44xY2DoUGjZ0kU6atrUa0VGAWHatGk899xzHDp0iIMHD9KnTx8effRRr2VFPJ4bVFWdKiJf48ZRBRiuqln96tcEtvqtxwOt0pUZBcwCtuFyq16T1qWcHhEZBAwCiImJycsleMbNN8NTT8GIEfDxx8e3B3K+58bdGxm/YjwTf5rIjv07iB8WT/Wy1fm096eULl76WLmTo0/Odd05pWqZqnSo1+HY+oCzBnBpw0uPtU7n/D6HV5e86spGVaVbg250b9Cdjqd0pGyJskHTFRRSU90k45NPdn36IjBokJtfahgBYOLEibz66qvMmDGDWrVqsX//fsaMGeO1rAKBZ7F8RaSRqq4TkbMy2q+qKzI57iqgk6re5FvvA5ytqrf5lekJnAcMA04BvgSaqWqW81vDOZZvZvznP24K4pIlriEDII9l3krUR3N2v/859A+Pf/M4ry19jVRNpUv9Ltx45o10a9CNEkXDb47jjv07mLtpLh+v/5jPf/ucvUl7KVm0JO3qtuPSBpdyxWlXUK2sx05G2bFmjfMy27fPee7aXNICRTjE8t23bx9169Zl6dKl1KtXL091WCzfzPGyhToM1zLMKD28Ahdlclw84O/aWAvXEvXnBuBpdW8LG0RkE9AIWJIvxWHI7be7cdRHHw1M0PyjKUcZu2wsI74ZwZ7Dexh41kAevvBhapULbyeYamWr0btJb3o36c3RlKMs2LKAWb/O4uP1HzPksyF8uPZDvurrEicfOHKAMiXKeKzYj+RkePZZ19VQtqzLPl28uNeqjCDT9u222Zbp1qAb95x7z7Hy/Zv3p3/z/uw6uIue7/c8oezX/b/Otr7p06fTqlWrPBtTI2s8M6iqOsj3bxdVPey/T0RKZXHoUuBUEakL/AlcC/ROV2YL0B74TkSqAQ2BjQERHmZERztflfvvd9MWW7fOe12f/fYZd8+5m3W71tG+bnte7PQiTao1CZzYEFG8aHHa1W1Hu7rteKHTC6xOWM2hoy4Kxs4DO4l9KZax3cbSt1lfj5XioiFdeiksWwY9e7rg9yed5LUqo4CyevVqmmeSUD4uLo5WrVrxyy+/MHr0aBo3bhxidZGP52OowEIgfbdvRtsAUNVkEbkVmI2bNjNBVVeLyGDf/rHASOBtEfmZ4+Oyu4J1AV4zdCg895xrpc6Zk/d6pq2ZRkpqCrOunUW3Bt3y5GAUbogIZ5x0xrH1VE1lSMshxNVwvVPzNs1j6s9TueHMG2hdq3Xor7lKFbe8/z5cdVVoz214Sk5alJmVrxJVJdfHA5QpUybD5OFbt27l7LPP5rXXXuOFF14gPj7eDGoe8HIMtTrOweh/uBZm2i9ZOWCsqmbs8hlEInEMNY3nnoN773Vz/y+YWR3KZuCAtL8a+uyJ/l6L4xdToVQFGlVpxN7DeyldvHRYjpEGi7HLxnLPnHs4cPQADSs3pH/z/vRt1pca0TWCd9Kff3ZdCpMnQ6VKwTuPEVaEwxjq0qVLufbaa1m4cCHVqlUjKSmJSZMmUbVqVUaNGkVMTAw7d+7kk08+ybQOG0PNHC8Naj+gPxAH+FuxROBtVf0o1Joi2aAePOgC6DRu7NJg/vHHv8vUqQObNx9fT0pOov6r9WlarSmf9v40ZFrDjcSkRD5Y8wETVk5gwZYFFJEiXHLqJQyOG0ynUzoFbhpOSop783nkEahY0QWzb5XeQd0oqISDQQWYPHkyzz//PCkpKSQnJ3P99ddz+PBhevbsSbNmzbjyyiuZNGkSZcpk7GdgBjVzPDOoxwSIXKmqH3oqwkckG1Rwvix33QUPPeTCEh48eHxfVBS88QZcdx0cOnqIksVKUkSKsHzbcupVrEfF0hW9Ex5G/Pb3b7y18i0m/DiBHQd2UKd8HR664CEGthiYv4o3bHC59xYudGOlY8a4rl6j0BAuBjUjunbtSp06dShSpAgVK1bkiSeeyLSsGdTM8dygAojIJUBj4Jgzkqo+HmodkW5QDx2CU06BU091Uxcfegi2bHHZvP7zH2dMN+7eyBXvXcFljS5jRNsRXksOW46kHGHmupmMXT6Wi+tdzPDzh5OUnMSi+EW0qdMm92OtPXvC3LnO6ahXLze/1ChUhLNBzQ1mUDPHc6ckERkLRAHtgPFATwrg9JZQULo0PPCAm0rz6KMndu8CrNi+gg6TOqAoZ9c82xONkUKJoiW4qvFVXNX4KtJeOqevm06vD3sxv9982sa2zb6SP/90gRpq14ZRo9z0GIvBaxgFlnDIU3KuqvYFdqvqY0BrTpxnauSCgQNdQpL/+z8XCjaNVTtW0XFyR6JLRrNs4DK6ntrVO5ERRlprtEfDHrxzxTtcWOdCAJ749gnunn03G3dnMCPrgw+gSRO45Ra3Xr26GVPDKOCEg0FNm4N6UERqAEeBuh7qiWhKlXJdvd9/7zLRAKxNWEuHSR0oXaw08/vN55RKp3grMkIpXbw0vZr0ooi4x+bPfX/yypJXqP9KfXq824P5m+aje/fCDTe4KTCnnuoGsw3DKBSEg0H9WEQqAM8CK4DNwFRPFUU4AwZA/fpw992wZsdvtJ/UniJShHn95lGvokVICRRjuo1h8x2befjCh1m0dRFDnr+IP085idRJE0l+6AFYsMAZVcMwCgWeGlQRKQLMVdU9Pk/fOkAjVbUcVfmgRAl4/nlY99cmznv9Io6mHmVu37k0qNzAa2kFjprlavJ4u8fZctcWHrj6VeIrF+eC/krNCm/y2PdPsutggY0nYhhGOjw1qL4MMM/7rSep6l4PJRUYunVTyt/Qh72HDvDBpV/R+CSLehIUNm2CG26gVDL0vfBWWq1L5LGHviSuRhwjvhnBlr1bAEhJTfFYqGEYwSYcunzniMiVUhDi3IURRYoIU6+eCJO/ZNqrzbyWUzB5911o3hymT4fVqwHnwNShXgc+7f0pm+/YzFknuwiaAz8eyLUfXEs4TFMzDCM4hINBHQZMA5JEZJ+IJIpIlmnWjMxJ1VSmrJriUq6dcwq39GjB2LHHfu+NQHDggBuo7tXLhaZaufLEDO8+6lSoc+z/BpUb0KhKI0QEVeWT9Z9wNOVoKFUbhhFkPDeoqhqtqkVUtYSqlvOtl/NaV6Qyc91Mrp9+PZ+sd7E4H3vMZaS5664Tp9EY+eDGG+Gtt5w79bffQmxstofcf/79xwJpLNy6kO5Tu1P/1fq8tPglEpMSg6vXMIyQ4LlBFZG5Odlm5IzLGl3GnOvn0L1Bd8BFtxsxAr78ErKId21khyoc9s3wevxx+OoreOIJKJb72Cita7dm1rWzqFO+DnfNvouYl2J44KsH2J64PcCiDSNjpk2bRqtWrWjatCn169fnscce81pSgcAzgyoipUSkElBFRCqKSCXfEgsEMdVHweSv/X+xbtc6RISOp3Q8ITTekCHQqJGbRnPkiIciI5Xdu+GKK6B/f2dYGzaEiy7Kc3VFpAjdG3bn2xu+ZfGAxXSo14H/LvwvsS/HMmDmANYmrA2cdsNIx8SJE3nmmWf48MMPWbVqFStXriQqKsprWQUCL7PN3AHciTOef3I8fds+YJyqjgq1pkiN5ZuSmkKHyR1Yk7CGTXdsIqr4vx+OL76ALl1cspO77/ZAZKSyZAlccw3Ex8N//wt33hmUOLy///M7Lyx6gbdWvsWh5EMMOHMA4y8dH/DzGN7xrxi4bdv+u9DVV7s34IMHoWsG0cz693fLrl0uPrQ/X3+drYZ9+/ZRt25dli5dSr16eZuTbrF8M8ezFqqqvqyqdYF7VLWeqtb1Lc28MKaRzIivR/D15q/5b4f/ZmhMATp3ds/n44/Dzp0hFhiJqLr0Peef7/5fsMANRAfJGf2USqfw2iWvseWuLYxoM4Lm1ZsDkJyazMx1M23ajREQpk+fTqtWrfJsTI2s8Tw4vqq+KiLnArH46VHVSZkdIyKdgZeBosB4VX06gzJtgZeA4sAuVW0TWOXhwRcbvuCJ757gxuY30q95vyzLvvCCCy97990ut7WRBQkJMHKkewt56y2XvzQEVImqwqNtHz22PuvXWVz5/pV82vtTi79c0MiqRRkVlfX+KlVy1CJNz+rVq2nevHmG+9577z2+//57UlNTKVOmDM8880yu6y/seG5QRWQycAqwEkh7DVcgQ4MqIkWB14COQDywVERmqeoavzIVgNFAZ1XdIiInBfESPGNb4jau/+h6mpzUhFe7vppt+YYN4cEHnedvr14Z9ygVetaudR/USSfBsmXOg9fDKdI9Gvbg414f07l+ZwBeWPQCiUmJDD17KFWiLJ+qkTvKlCnDoUOH/rV90aJF/PDDD7zyyisAHDFnizzhuZcvEAecp6pDVPU233J7FuXPBjao6kZVPQK8C/RIV6Y38JGqbgFQ1QLZyTls9jD2H9nP+1e9n2lXb3oefNBNnbz5Zthns32PowqjR7tADaN8Iw5163qet7RokaJ0a9DtWED+n3b8xIhvRhDzYgxDPx3K7//87qk+I7Lo2rUr06ZNY8eOHQAkJSUxbtw43n77be68885j5UqUKOGVxIgmHAzqL0D1XJSvCWz1W4/3bfOnAVBRRL4WkeUi0jefGsOOuRvn8t7q93jg/AdoVKVRjo8rUQImTIBt22D48CAKjCT274frr4ehQ6FDB/d/mDLxsomsHrKaXmf0YvyP42kwqgFXTbuKH+J/8FqaEQG0bNmSESNG0KlTJ5o0aULz5s3ZuXMnhw8fppjfFLCUFBuzzwueefkeEyAyH2iOSyqelLZdVS/NpPxVQCdVvcm33gc4W1Vv8yszCtfybQ+UBhYBl6jq+gzqGwQMAoiJiWnxxx9/BOjKgseRlCM0HdOU5NRkfhnyC6WKlcp1HXff7cZU58/P2Nmw0LB2rfOWXLcO7r0XFi6E9993+UvDnG2J2xi1ZBRjlo1hz+E9nB9zPve0vofuDbsfa9Ea4UNG3rHhwurVq3niiSeoWrUqiYmJvPjii1SoUCHDsublmzmej6ECI3JZPp4TE5DXArZlUGaXqh4ADojIt0Az4F8GVVXfAN4AN20ml1o8YcX2FWzdt5UPrvogT8YUnL/NzJlw002wapXzgSiU7NoFe/bAnDnw4YcukezIkfDaa14ry5Ya0TV4sv2TPHD+A0z4cQIvLn6Ry967jKUDlxJXo9D/thm5oHHjxkydalkz842qer7g0rZ18P0fBURnUbYYsBGXhLwE8BPQOF2Z04C5vrJRuG7lM7LT0aJFC40Udu7fme865s1TBdV77gmAoEjiyBHVTz89vn7okOq2baqlSrkPpHRp1e3bvdOXR46mHNUvfvvi2PrwL4frU9895aEiw581a9Z4LSEgZHQdwDINA1vi9eJ5v5CIDAQ+AF73baoJzMisvKomA7cCs4G1wPuqulpEBovIYF+ZtcAXwCpcV/J4Vf0leFcROr774ztUlaplqua7rnbtYNAg1/W7dGkAxEUC27e7KEeXXHI8Y0CpUq5Vmprq1lNS3HqEUaxIMTrV7wS4F+WNuzceSx8HsHH3Rq+kGUbhwGuLjpsuUwL40W/bz15oCfcW6vxN85UR6OSfJgeszj17VGvWVD3jDNWkpIBVG54sWKBavbpqVJTqO+8c3+7fOk1bIrSVmp6U1BRVVf1+y/fKCPSSKZfo3I1zNTU11WNlhQ9roRb8xfMWKpCkbvoLACJSDDcP1UjHBTEXMOHSCVzd+OqA1Vm+PIwdC7/84uanFlhGj3beV2XLwuLFbiJuGv6t0zTCoJVa/bnqyGPyr6X6czl3mEpzTmpQuQGPtX2MJX8uof2k9jR/vTlvr3ybpOSkbGowAomzPZFLpOsPNuFgUL8RkQeB0iLSEZcb9WOPNYUdyanJFC1SlBvOvIESRQM7R6xbN5eR7KmnXBKVAkmRIi6Y8dKlLlyUP4sW/TtrwJEjzuPXQ3Yc2JGr7VlRJaoK/9fm/9hy1xbevPRNUjWVG2beQJ2X6vD4N4+TcCAhv3KNbChVqhR///13xBolVeXvv/+mVKm8OUIWBsJh2kwRYABwMS5A/mzcmGfIhYVrcPxNuzfR5u02TLxsIu3qtgvKOQ4cgJYt4Z9/4KefoFq1oJwmtGzeDOvXw8UXH08G63Gghtwgj2WuVR/N3+OhqszdNJcXFr3A5xs+p2TRktzR6g6e6Wjh5oLF0aNHiY+P53BaGsAIpFSpUtSqVYvixYufsN2mzTjCYdpMaWCCqo6DY6EFSwMHPVUVRoz8diQJBxNoULlB0M5Rpgy89x6cfTb07Quff+4adRHL3LkuS0zp0rBhA5Qs6bWisEJE6FCvAx3qdWBtwlpe/uFlKpZ28YpTUlP4YsMXdK7fmaJFinqstOBQvHhx6tat67UMI4iEw0/mXJwBTaM0UFA7HnPNpt2bmPTTJG5ucTM1y6UPCBVYmjRxCVbmzIFnnw3qqYKHqnNbvvhiF5xh/nwzptlwWtXTGNttLPeffz8An/32Gd2mduPT3z71WJlhRBbhYFBLqer+tBXf/4U1zMC/eHrB0xQrUoz7zrsvJOcbNAiuugoeesgNLUYUycnQp48LA3X55e4C6tf3WlXE0eXULky/Zvqx7DbPfv8sQz4dwpqENdkcaRiFm3AwqAdE5Ky0FRFpAfw7HUIhZMveLby18i0GnDmAGtE1QnJOERg3DmJi4NprYffukJw2MBQrBhUqwH/+A9OmQXS014ryRbUyGQ9kZ7Y9UBQrUozLGl1GsSJuRGjXwV1M+HECjUc3pv2k9sxYN4Pk1OSgajCMSCQcnJJa4jLGpIUPPBm4RlWXh1pLuDkl3frZrbyx/A023L6BmPIxIT33kiVw3nnQvbuLyBfWvjzffOMMabNmrss3rMVGJrsO7mL8ivGMXjqarfu2ElM+hlvibmHAmQMCEmTEiGzMKcnheQtVVZcCjYBbgCHAaV4Y03BjW+I2xq8YT//m/UNuTME5Jz31FEyfDq9mn2rVG1RhzBiXIeZ+N/5nxjQ4VImqwv3n38/GOzby0dUfcUrFU3hg7gPUerEW1390PWsT1not0TA8x3OD6qMl0BQ4E+hVENOt5ZZnv3+W5NTkY44iXjBsmGuhDhsGX37pmYyMOXIEBg+GIUOgUyd4912vFRUKihUpxuWnXc68fvNYM2QNN7e4mY/Xf8w/h/4BIOFAAgeOHPBYpWF4Qzh0+U4GTsGFIExLwqeadZLxoBAuXb6qSruJ7YitEMvbl73tqZbERDj3XNi61QUYapTz1KvBY/duuPRSWLAAHnjARTQqatM7vOLAkQNEFY9CRBjy6RA+XPshW+7cQsli5l1dWLAuX0c4zEONA073IpBDuCIizO83n0PJ3vtmRUfDxx+7LuDu3eGHH6BSJY9FlS0L5cq5Vuk113gsxihTosyx//s260uzas2OGdPBnwymda3WXN34akoXL51ZFYZRIAiHLt9fgPDP5hwi9hzew66DuxARooqHx+yh2Fg3lrpli8vFffRoPivcvh3atIG//srdcTNnQkICFC8On3xixjQMOafWOdwcdzPgvsvzN8+n/8z+1HihBnd8fgc/7/jZY4WGETzCwaBWAdaIyGwRmZW2eC3KK579/lnqvVyPvw/+7bWUEzjvPBg/3sVJuPXW45H88sTIka67NqfB51NTYcQIuOwyNyUGzPkoAqhQqgLrhq5jfr/5dDqlE2OXj6Xp2KacM/4cxq8YT2JSotcSDSOghMMYapuMtqvqN6HWEg5jqGsT1vLVxq+4rdVtnurIjAcfdN6/L78Mt+dllHv7dqhXDw4fdmEBN250EY0y48AB6NfPzd3p39+lxrHIRxHJroO7mPzTZMb/OJ41CWsoW6Is1za+lifbP2lTbyIcG0N1eG5QAUSkGs7TF2CJqu70Qkc4GNRwJzUVrrwSZs1yva5duuSygiFD4M03nZduiRJw003w2msZl42PdwO3q1a5WIh33WUt0wKAqrI4fjHjVozjq41fsf629ZQqVoof4n8gtkIs1coWhMwMhQszqA7PDaqIXA08C3yNyzZzAXCvqn4Qai1eGtRUTWXY7GHc0PwGmlVv5omGnLJ/P1x4Ifz6K8yeDeefn8MD/VunaWTVSt21Czp2hCefzIPlNiKB5NRkihUphqrScFRDapWrxbx+8wD3TKTlczXCGzOojnD4tj4EtFTVfqraFzgbeCSrA0Sks4j8KiIbRCTTiZoi0lJEUkSkZ4A1B5w5v8/h5R9eZt2udV5LyZayZV02mlq14JJLYMWKHB6Y00Ten37qWrBVqsDy5WZMCzBp4Q1FhFm9ZvHfjv8FYOeBndR+sTZ3fH4HK7aviNgcokbhIhwMapF0Xbx/k4UuX3q314AuwOm4QBCnZ1LuGVx+1bDn9eWvUzWqKpefdrnXUnJEtWouGXnFii6xy5qcxE3PLpF3Sgrcd5/LeD56tNsW0TnkjNzQqEoj4mq4Rk5iUiLn1j6XscvH0uKNFjQZ04Rnv3+WbYnbsqnFMLwjHLp8n8VFSZrq23QN8LOqZpheRURaAyNUtZNv/QEAVX0qXbk7gaO4sdlPctKF7FWX77bEbcS8GMPdre+OuATPGzbABRe4oc0FC1yPbp5ITITrrnOTXm+5xXk9pUtibIQH1Z+rzo4DO/61vVqZavx1Ty6nQmXD7kO7eW/1e0z6aRKL4hdRRIrQoV4HrmtyHZc3upzokpGdAKGgYF2+Ds9f/1X1XuB1nFFtBryRmTH1URPY6rce79t2DBGpCVwOjM3u/CIySESWiciyhISE3MoPCG+ueJMUTWFgi4GenD8/1K/vwhImJUH79s6PKNds3uzCMX32mXNQGj3ajGkYk5ExzWp7fqhYuiKD4wazcMBC1t+6ngfPf5D1f6+n34x+1HqxFgePHgz4OQ0jr3hmUEWkvoicB6CqH6nqMFW9C/hbRE7J6tAMtqVvZr8EDFfVlAzKnnig6huqGqeqcVWrht51PyU1hXErxtGhXgfqV4rM3J1nnOGck/7+2/kQ7cytj/a+fbB3L3zxhfMCNowMOLXyqYy8aCQbb9/IghsW8FT7p44FP7ny/St5esHTHis0CjtetlBfAjKa2X3Qty8z4oHafuu1OJ76LY044F0R2Qz0BEaLyGV5lxo8vtjwBVv3beXmFjd7LSVfxMU5X6I//nAt1e3bc3BQWgbzpk3ht99c1hjDyAYR4byY8xjS0r18JacmU7JoSYqKi+d8OPkww78cztI/l5ozkxFSvDSosaq6Kv1GVV0GxGZx3FLgVBGpKyIlgGuBEyIrqWpdVY1V1VjgA2CIqs4ImPIA8vry1zmpzElc2vBSr6XkmwsucEOgmza5qTQbN2ZSMCUF7r3XdfNOn+62WbAGI48UK1KMd658h3vPuxeAFdtX8OLiFzl7/NnUf7U+D819iJV/rTTjagQdLw1qqSz2ZRpFW1WTgVtx3rtrgfdVdbWIDBaRwQHWGFS27t3Kp799yo3Nb6RE0RJeywkI7dvD3LkuIcz558Pq1ekKJCbC5ZfDc8/B0KHOo9cwAsi5tc9lxz07ePPSNzml4ik8/f3TnPn6mTQY1YAH5z7Ij9t/NONqBAXPvHxFZCowT1XHpds+ALhYVUMe+TzUXr4JBxJ4dcmr9G/en3oV8+oeG5788oubTpOU5HyNWrXCOR9deqmbY/PKKzZeGqGE0ss3ECQcSGD6uulMWzON+Zvmk6Ip1K9Un2UDl1G+VHmv5RUIzMvX4aVBrQZMB44Ay32b44ASwOWqGvIn00IPBpaNG52T0o4dLlFM+/0z4YYb4P33bbzU8ISEAwnMWDeDFdtXMKbbGADu+PwOqpapysMXPuyxusjFDKrDs3yoqroDOFdE2gFn+DZ/qqrzvNIUShbHL2bH/h10a9CNokUKZnLsevXc3NQBbTbQtWt9pk7twRUbN0KFCl5LMwopVctU/df0tISDCceewVRN5d4593JR3YtoX689pYplNTJlGCfieWCHcCKULdS+0/vy5cYv2XLnFooXLaBzLlNS4IEH0BdfZECjhby9uiVPPgnDh1uMeyM82bh7I83GNmP/kf2UKV6GTvU70aNhDy459RIqR1X2Wl7YYi1UhxlUP0JpUJNTk9nwzwYaVWkUkvOFnHSRjw49/TI33lycd9+F3r1dbtXSmbqeGYZ3JCUnMX/zfGaum8ms9bPYlriNolKU82POp3uD7nRr0I0GlRsg9lZ4DDOoDjOoftgYaoDYvNmlXVu71oUQHDoUcEnJn3oKHnrIzVudMQNq1sy6KsPwklRNZcX2FcxcN5OZv87k550/A7B04FLiasTxz6F/KFuibIHx0s8rZlAdnoceLIx0ndKVccvHZV8wUnn/fReD8PPPjxlTcN28Dz7oDOm6ddCyJSxZ4qFOw8iGIlKEuBpxjLxoJKtuWcUfd/7B691e58zqZwLw6PxHqfNSHVJSXVA2C4VYuDGDGmJW71zN5xs+52jqUa+lBJ4dvqkU994LP//sXHwzoEcPl2CmVCmXV3Xy5BBqNIx8EFM+hkEtBh1zYrr8tMt56IKHjq2fP+F8moxpwn1f3se8TfM4knIkq+qMAoYZ1BAzbc00BOGK067wWkrgSE6Gu+6Cxo1h61bXFK1VK8tDmjRxrdPWraFvX7jxRpe43DAie/MkgwAAF2dJREFUiYvqXsStZ98KgKpyXZPrOKnMSby0+CXaT2pPpWcq0e2dbrzywyus27XOAkoUcGwM1Y9QjKE2Ht2YqlFV+br/10E9T8jYsweuuQbmzIE77nARkIrlfDZWcjI89hj85z9w6qkwdSqcdVYQ9RpGCEhMSmTepnl8ufFLZv8+mw3/bACgdrnaPHHRE/Rt1tdjhYHFxlAd1kINIat3rmZNwhqubny111ICw6+/uhBI8+c7t92XXsqVMQVXfORImDcPDhyAc86BF1+E1NQgaTaMEBBdMpoejXowqusofrvtN36//XfGXjKWljVbUql0JQBW/rWSZmObsWybOUIWFDwL7FAYKXDdvc8844L2zp3rIuPng7Zt4aefYMAAGDbM5Vh9+2046aSAKDUMT6lXsR43x93MzXHHs0olJSdRJaoKJ5c9GYDXl73O5FWTaRvblnax7Whdu/Wx9HRGZGBdvn4Eu8u3QHT3qrpu3ooVXZNy1y6oUyeg1Y8Z44xq+fIwahT07GmBIIyCz5RVUxi1dBRL/1xKiqZQomgJWtVsRdvYtrSNbcs5tc4JWwNrXb4OM6h+BNOgrklYQ+PRjRnVZRRDzx6a/QHhyKFDMHAgrFoFixdDVPAe7l9+gf79YflyF0//tdey9XMyjAJBYlIi32/9nvmb5vP1H1+zbNsyUjWVYkWK0Ta2LXOun4OIcDTlaNhEWTOD6rAu3xAxbbXr7r3y9Cu9lpI3tm51adeWL3eDnqWCG+P0jDOczX7lFXj4YTj9dHj6aRg8GIrYyL8RQeQ2O090yWg61+9M5/qdAdiXtI+FWxfy7R/fcjj58LEITa3fbM1ZJ5/FG93fAGB74naql61uEZw8xAxqiOhyaheiS0ZTvWx1r6XkngUL4MorXQt15kzXZAwBxYq5rt/LLnOGdOhQeOcdGDcOTjstJBIMI99kZEyz2p6eciXLnWBgwU3RubzR5cSUjwFcq7bWi7U4uezJnBdzHufVPo/WtVrTrHqzQh/FKZRYl68fFnowA1Sd6+3u3c6YemTJVF0AiLvucmGCb7sNHnnEEtcY4Y88lnmLUR8NzO9vYlIiE3+ayPdbv2fBlgXE74sHoFSxUsTViOOcmufQunZr2tRpE5Qg/9bl64hIgyoinYGXgaLAeFV9Ot3+64DhvtX9wC2q+lN29QbLoM7eMJvypcpzTq1zAl530Dh82E0SLVvWhREsU8Y5InnMzp0ufOGECVCpEowYATffDMXDYyjJMP5FKAxqerbu3cqi+EUsjl/MovhFrNi+giMpR5hyxRR6N+nNhn828NHaj+jXrB/VylbL9/nMoDoizqCKSFFgPdARiAeWAr1UdY1fmXOBtaq6W0S6ACNUtVV2dQfLoJ71+llEl4zmm/7fBLzuoLBli+vijYmBDz4ISxfbn35y3cHz5kGjRi6eRNeuYSnVKOR4YVDTk5ScxIrtK2hQuQGVoyoz+afJ9J3Rl423b6Ruxbr5rt8MqiMS3TvOBjao6kZVPQK8C/TwL6CqC1V1t291MeCpf+jX/b9mXPcICYb/1VcuVNH69S4mYJhaqGbNnNSZM13a1W7doFMnC7ZvGBlRslhJWtdufay7t0+zPiTcm0BshVhvhRUwItGg1gS2+q3H+7ZlxgDg88x2isggEVkmIssSEhICJPFEypUsR4PKDYJSd8BQdYEaOnWCatVg6VIXxT6MEXH+Ub/84oI0rVjhAjd17Qo//OC1usAwZQrExjrP5thYt25EFtXKZNylmtn2UFElqop5BAcaVY2oBbgKN26att4HeDWTsu2AtUDlnNTdokULDTTXfXidvv/L+wGvN+Ds2KFatarq1VerJiZ6rSZP7Nun+tRTqpUrq4Jqly6qixd7rSrv/O9/qlFR7lrSlqgot90wwglgmYaBffB6icQWajxQ22+9FrAtfSERaQqMB3qo6t8h0nYCaxLWMOXnKTl2j/eEdetcn+lJJ8GyZfDuu84RKUgEs8UVHQ333+/ymz/9tOv+Pecc6NLFhRvWyHIX4KGH4GC69JoHD7rthmGEH5FoUJcCp4pIXREpAVwLzPIvICIxwEdAH1Vd74FGAD5Y84EL5nBaGAZzUIWxY91g5EsvuW0xMUEdM53y/+3de3RV1Z3A8e8vISRgkACCAXnIQ6ngUtRixaUUpOKrrdXRJZU1VurU6sg4ffgYnfpalg7O2CqiDKK1zrQurcupDqICQxlAqow6ykOhKA95SMCiCDExIY/f/PE7d+5NcjE35Nxz70l+n7X2Oo97cs/eJNzf3fvsx1Nw7bWwbZvdfts2Ow67GbO0FG691QLrfffZXBTnnAOnnGLzA9fWhnu/bNm+vW3nnXO5FbuAqqr1wHRgEdac+6yqvici14nIdcFldwJ9gDkislpEcjK4dMH7Czhj4Bn079E/F7c/tMpKuPJKuP56mDjROh9FIOoaV2kp3HKLBaBf/9pWsJk2zb433HOPDcHJZ4MHt+28cy63YhdQAVT1ZVU9XlWHq+qM4NxcVZ0b7P+NqvZS1TFBirw7997qvby1660ms5vkhbVr4bTT4NlnbRHSl1+Gvn0juXWualwlJbaA+Zo11jN47FgbvzpokH2vWLo0P5eLmzGj5XTJ3bvbeedc/ollQI2DJVuWoCjnDT8v11lpqqrK2jyXLrUZEiKcGDfXNS4RmDQJFiywpVx/+ENYuNDOjRgBP/+5zWGRL6ZOhXnzbDEfEdvOm2fnnXP5xwNqlizctJDe3Xrz1QE5HOtcUQFf/zqsW2cLgAOMGwcffGDnI5ZPNa7jj7eJ93ftsvmBhw61qQyHDIGLLrK+WVVV0eerualT7VlwY6NtPZg6l788oGaBqrJ482LOHXYuhQWFucvIvffCq6/C6afDjTcmq19dczNZdj7WuEpK4LvftTXSN2+2SvuaNXauXz+YMgWef95mYnT5z8ftZq78/nLkHmmRyu+P4QIeecIDahas+3gdFZ9X5La5d8sWi1aq1sT7yit5saBoPte4hg2z7yDbt8Py5fC971nL+KWXWnC96iqbmSkfaq6upah6kXcU7V0Fx7XkATULCqSAKSdOYfLwybnJgKo17TY02HFRkXVCchkpKIDx42HOHGsSXrwYLr8cXnzRlpI76iib6vDRR+Gjj3KdW5fg43ZdrsVucvxsiv3ybTU1UFwMu3dbe2pdXfK1bt2s1lruzTmH6+BBa0GfP9+C69atdv7UU226w3PPtYkkctSi3ukVFKSfvEMkP3tx51qYk/b75PjGFxgPWU19DR8d+IjhvYdHe+MVK+Caa+BnP7OJbJtP0NDQYO2ZjzwSbb46kK5drUfwpEk2F8b69cng+otfWC/hI46w/l7f+IalE0/M2/UFOhy5uRzt3rK5UqqPBnZHnyHX6XiTb8iWbl3KiNkjWPbhsmhuWFkJN9xgn+INDVYzff11q06lOngQXnstmjx1AiIwejTcdpv9s37yiXVeuvpq69z0k5/ASSfZs9dLLoFf/tKmQkxtNHDhakwTTL/svHNh8xpqyMaUj2H2BbOjWUx88WL4wQ9gxw740Y+SVaR33sn+vV0TZWX2fPU737Hj7dut5/CKFdZM/MILdr5bN2sWPvNMm2Bi7FgYMCB3+Xad19FHHJ22A1KuV8GJMw+oIRvQYwDTT58ezc1qa21G+D/9yTohucP21FPWeWX7dptoYsaM9vVAHjzYpjmcNs2OKyrs17RypQXYmTOTfcYGDLCRTWPH2iRWJ5/sj7pd9u2+yZvBw+adklK0t1PSrspdLNmyhItHXkzPkp4h5izw6adw113WjnjHHdYDo7ERCnM41rUDSAy3SO0h2r17dsfIVlfD6tW27Owbb9j2gw+Sr/frZ4E1kU46CUaOtD5nLr0wO9m4tvFOScZrqCGav3E+1790PRtu2BBuQK2vh8cesw5Hn30GP/6xnRfxYBqCLxtuka2A2r27NfueeWby3L59FmTXrEmmhx5KPg4vKLCxsqNGwQknJNNxx0GvXtnJp3Mucx5QQ7Ro8yIG9xzMyD4jw3vTpUvtOemWLTBhAsyaZdUVF5p8WSatVy9b/GfixOS5ujp4/32bPXLDButZvGGDzdOR2sGpd28LrCNGJLdDh9psQeXl4U/ZHHYTeRj8maDLNQ+oIalrqOOPW/7IlBOnIO0dJ3HwoPXe7dPHPil794YHHoBvfcvHYGTB4ME2q06687lWVGS9iUePbnq+rs6+Y/35z9ZUvGmTbV991eYmTn2SU1xsnb+PPdbSoEE2aVZiO3Cg9WXLVPMm8sSMRJDboOrPBF2ueUANyaqdq6g8WNm+6Qarq20F7JkzbRjMb38LY8bYQzYPpFkzY0b6Z6j5vExaUZE9Ux2ZpjGkpsaC7YcfNk1bt8Lbb8PevS1/plcv6xzVv3/LVF5uz3T79bPrctFE7lwceEANyaLNiyiUQiYNm9T2H377besB8/TTcOCAPVhLXfTbg2lWJYJAvjVhHq6SEnvOOmpU+tdramzKxB07bL2ExLaiwtKKFbZtPpQZ7JF9ondyc9u2We21d28LvL162X5ZmX0BcK6j816+KdrTy3fsY2MpLixm5fdXZvYDH39sTbqFhXDzzTaD0eWX22xHZ5/tQdTllKp1Kq+osD/V1DRrFnz+edver7TUAmtZGfTsmUxlZXDkkZZ69Gi5LS215ujSUkvFxf5fIx95L18Ty4AqIucDs4BC4HFVndnsdQlevxCoBq5W1bdbe9+2BtTy+8vZU7WH8kp45jm44jLY08M6QbR4nlNTA6tWwbJltpTJypXw0kswebK1wRUV2SeMc3nuUMOMfvUr6ze3b58F4+bb/futk/r+/U3TgQPpa8PpFBZagD3iCLtn8223bpZS9xOppKRpKi5ObhMpcdy1a9Ntly7tC+T52IkrzHx5QDWxa/IVkULgEeBcYCfwpojMV9X1KZddABwXpK8B/xpsQ5XoUXjHcjhru23/cRL03LvH2s369rVxDRs32mDC2lr7X3nKKfDTn1p3TLDlS5yLiWw0kdfWWj+8ykoLsIltVZXVhpun6upkqqqy7SefwBdfJFN1tW3Dmhi/a9dkKipquU2XunSxtSrWrEk2lW/bZksD/uY39vHQpUsyFRYmU2vHqamgoOV+6jaRUo+XLIH77kuu9ZsvncviLHY1VBEZB9ytqucFx7cBqOo/pVzzKLBMVZ8OjjcCE1S14sveu601VLlHKK+ELbOgWz0o0ORL7PTpMHu2/U+6/XZryj3rLGvncs5lnarVfmtrLXCkpi++sPOpqabGtgcPJn8usa2ttd7VdXV2LrFN7KdL9fWwdm36OZwLCuyjoL7eUl2dfVTkemWcIUOsE1tbeA3VxK6GChwD7Eg53knL2me6a44BWgRUEbkWuBZg8GGMk7hjOUjwnaRBYNUxMHcs/O76xckaaGGhfRV0zkVKJNmce+SRucnDocYAq1qtOt35xkYLsg0NTVO6c42Nh95PTYnzifefPDn9cndRj7/uSOIYUNM9yWj+Z5HJNXZSdR4wD6yG2paMlFfCtNVQEjTldFE4dTcsGYYtjumc6/TaOs45MQFatidBy+fx13EVx+XbdgKDUo4HArsO45p2S62dJhSonXfOObDny927Nz2XD+Oc8zVfcRbHgPomcJyIDBWRrsAUYH6za+YDV4k5A9jf2vPTw3H2ri7/XztNKGmA8bviWPF3zmXD1Kk2zHzIEKt9DhmS3YUX4p6vOItdpyQAEbkQeBAbNvOEqs4QkesAVHVuMGzmYeB8bNjMNFVttbdRe1ebcc65zsg7JZlYVqVU9WXg5Wbn5qbsK3BD1PlyzjnXecWxydc555zLOx5QnXPOuRB4QHXOOedC4AHVOeecC0Ese/lmi4j8BUgz1DkjRwFpVprs0LzMnYOXueNrb3mHqGrfsDITVx5QQyIib3W2buNe5s7By9zxdbbyZos3+TrnnHMh8IDqnHPOhcADanjm5ToDOeBl7hy8zB1fZytvVvgzVOeccy4EXkN1zjnnQuAB1TnnnAuBB9Q2EpHzRWSjiGwSkX9I87qIyEPB62tF5NRc5DNMGZR5alDWtSLymoicnIt8hqW18qZcN1ZEGkTksijzlw2ZlFlEJojIahF5T0Riv+pvBn/XPUXkRRFZE5R5Wi7yGSYReUJEPhaRdw/xeof7/IqUqnrKMGHLxW0GhgFdgTXAqGbXXAi8AghwBvA/uc53BGU+E+gV7F8Q5zJnUt6U65Ziqx5dlut8R/A7LgPWA4OD4365zncEZb4duC/Y7wt8CnTNdd7bWe7xwKnAu4d4vUN9fkWdvIbaNqcDm1R1i6oeBJ4BLm52zcXAv6tZBZSJSP+oMxqiVsusqq+p6r7gcBUwMOI8himT3zHA3wH/AXwcZeayJJMyXwn8QVW3A6hq3MudSZkV6BGsr1yKBdT6aLMZLlVdgZXjUDra51ekPKC2zTHAjpTjncG5tl4TJ20tzzXYN9y4arW8InIMcAkwl44hk9/x8UAvEVkmIv8rIldFlrvsyKTMDwMnALuAdcDfq2pjNNnLmY72+RWpWC4wnkOS5lzzcUeZXBMnGZdHRCZiAfWsrOYouzIp74PAraraYJWX2MukzF2A04BJQDfgdRFZparvZztzWZJJmc8DVgPnAMOB/xKRV1X1QLYzl0Md7fMrUh5Q22YnMCjleCD27bWt18RJRuURkZOAx4ELVPWTiPKWDZmU96vAM0EwPQq4UETqVfWFaLIYukz/rveqahVQJSIrgJOBuAbUTMo8DZip9nBxk4hsBb4CvBFNFnOio31+RcqbfNvmTeA4ERkqIl2BKcD8ZtfMB64KesudAexX1YqoMxqiVsssIoOBPwB/HeMaS0Kr5VXVoap6rKoeCzwH/G2Mgylk9nf9n8DZItJFRLoDXwM2RJzPMGVS5u1YjRwRORoYCWyJNJfR62ifX5HyGmobqGq9iEwHFmG9BJ9Q1fdE5Lrg9blYr88LgU1ANfYtN7YyLPOdQB9gTlBrq9eYrlyRYXk7lEzKrKobRGQhsBZoBB5X1bRDL+Igw9/zvcCTIrIOawq9VVVjvaSbiDwNTACOEpGdwF1AEXTMz6+o+dSDzjnnXAi8ydc555wLgQdU55xzLgQeUJ1zzrkQeEB1zjnnQuAB1TnnnAuBB1TnnHMuBB5QncuAiPQJli5bLSK7ReSjYP9zEZmThfs9KSJbE+Mig+PIlokTkSuCJbwWRHVP5+LOJ3ZwLgPBdIpjAETkbuBzVb0/y7e9WVWfy+YNRKRQVRuan1fV34vIHuCmbN7fuY7Ea6jOtUOw6PaCYP9uEfk3EVksIh+KyKUi8s8isk5EFopIUXDdaSKyPFi1ZVEblscaL7aA+5ZEbTWYIu5fROTd4D5XNM9XcPywiFwd7H8oIneKyErgchG5UUTWBwtKPxPiP49znYrXUJ0L13BgIjAKeB34K1W9RUSeBy4SkZeA2cDFqvqXIADOAL6fwXv3x1by+Qo25+pzwKVYzflkbKL+N4OJ61tTo6pnAYjILmCoqtaKSFkbyuqcS+EB1blwvaKqdcH8r4XAwuD8OuBYbIL1E7GlwAiuyXTy8ReC9TjXB5O1gwXYp4Nm2z0ishwYC7S2xNjvU/bXAk+JyAtAnCf5dy6nPKA6F65aAFVtFJE6TU6W3Yj9fxPgPVUdd7jvHZBm2+bqafpIp6TZ61Up+xcB44FvA3eIyGhVrT+M/DnXqfkzVOeitRHoKyLjAESkSERGt+P9VgBXiEihiPTFAuMbwDZglIgUi0hPgmXImhORAmCQqv43cAtQBpS2Iz/OdVpeQ3UuQqp6MOhQ9FAQ6LoADwLvHeZbPg+MA9YACtyiqrsBRORZrDn3A+CdQ/x8IfC7IC8CPKCqnx1mXpzr1Hz5NufykIg8CSzI9rCZVvIwAbhJVb+Zqzw4Fyfe5OtcftoP3JuY2CFqQe/jOcC+XNzfuTjyGqpzzjkXAq+hOueccyHwgOqcc86FwAOqc845FwIPqM4551wI/g/JyQGf+dx+dQAAAABJRU5ErkJggg==\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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Parameter values from parameter estimation using parmest\n", "A1 = theta['A1']\n", "E1 = theta['E1']\n", "A2 = theta['A2']\n", "E2 = theta['E2'] \n", "\n", "\n", "A_est1 = [A1, A2]\n", "A_est = np.asarray(A_est1)\n", " \n", "E_est1 = [E1, E2]\n", "E_est = np.asarray(E_est1)\n", "\n", "ctr = 0\n", "for T in T_vals:\n", " for CA0 in CA0_vals:\n", " # generate concentration profiles using estimated parameter values\n", " k = kinetics(A_est, E_est, T)\n", " # plot model-generated and 'experimental' data\n", " # symbols for 'experimental' data\n", " # solid and dashed lines for model-generated data\n", " plot_exp(k, CA0, data_dict_overall[ctr]['data'], 'Model prediction and experimental value at T = {} K and $C_{}$ = {} mol/L'.format(T,'A0',CA0))\n", " ctr+=1" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.7 Local uncertainty analysis](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7-Local-uncertainty-analysis)", "section": "2.8.7 Local uncertainty analysis" } }, "source": [ "## 2.8.7 Local uncertainty analysis" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.7.1 Covariance matrix](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7.1-Covariance-matrix)", "section": "2.8.7.1 Covariance matrix" } }, "source": [ "### 2.8.7.1 Covariance matrix\n", "The parameter covariance matrix is calculated using the reduced Hessian approach. Using `parmest`, the covariance matrix can be calculated by setting optional argument `calc_cov` to `True`. More information on this approach can be found here: https://doi.org/10.1002/aic.16242" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "nbpages": { "level": 3, "link": "[2.8.7.1 Covariance matrix](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7.1-Covariance-matrix)", "section": "2.8.7.1 Covariance matrix" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: bound_relax_factor=0\n", "honor_original_bounds=no\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.7039989e+01 1.98e+01 2.68e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.3426370e-01 2.11e+00 3.16e-01 -1.0 1.74e+01 - 9.58e-01 1.00e+00h 1\n", " 2 2.5138069e-01 2.48e-02 3.29e-02 -1.0 8.43e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.2289282e-01 7.55e-02 4.31e-03 -2.5 1.61e+00 - 9.95e-01 1.00e+00h 1\n", " 4 2.2225974e-01 6.13e-04 3.19e-06 -2.5 1.79e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.2221660e-01 9.42e-04 5.30e-07 -3.8 2.02e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.2211318e-01 1.91e-02 8.81e-06 -5.7 9.02e+00 - 9.36e-01 1.00e+00h 1\n", " 7 2.2210769e-01 7.24e-04 5.48e-07 -5.7 1.66e+00 - 1.00e+00 1.00e+00h 1\n", " 8 2.2210762e-01 2.28e-07 4.65e-10 -5.7 1.24e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.2210762e-01 5.67e-07 2.65e-10 -8.6 4.74e-02 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.2210762e-01 1.87e-11 3.84e-14 -8.6 1.13e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 10\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.2210762193125719e-01 2.2210762193125719e-01\n", "Dual infeasibility......: 3.8437985232109095e-14 3.8437985232109095e-14\n", "Constraint violation....: 1.8748558261449944e-11 1.8748558261449944e-11\n", "Complementarity.........: 2.5059125114985724e-09 2.5059125114985724e-09\n", "Overall NLP error.......: 2.5059125114985724e-09 2.5059125114985724e-09\n", "\n", "\n", "Number of objective function evaluations = 11\n", "Number of objective gradient evaluations = 11\n", "Number of equality constraint evaluations = 11\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 11\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 10\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.087\n", "Total CPU secs in NLP function evaluations = 0.026\n", "\n", "EXIT: Optimal Solution Found.\n", "theta:\n", " {'A1': 185.60880919391855, 'A2': 401.1701986690201, 'E1': 9.866878980549789, 'E2': 14.866030768895133}\n" ] } ], "source": [ "# defining the names of the parameters in a list\n", "theta_names = ['A1','A2','E1','E2']\n", "\n", "# create an object using parmest.Estimator() that stores the Pyomo model realizations for the datasets provided.\n", "# This object which will be used to determined the parameter values that best fit all the datasets\n", "pest = parmest.Estimator(create_model_DAE,data_dict_overall,theta_names,tee=True)\n", "\n", "# call the method theta_est() for the Estimator() object defined above to solve \n", "# the parameter estimation problem.\n", "# Argument calc_cov=True is used to return the reduced Hessian matrix for parameter sensitivities\n", "obj, theta, cov = pest.theta_est(calc_cov=True)\n", "\n", "print('theta:\\n',theta)" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.7.2 Parameter identifiability](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7.2-Parameter-identifiability)", "section": "2.8.7.2 Parameter identifiability" } }, "source": [ "### 2.8.7.2 Parameter identifiability\n", "\n", "The Fisher information matrix, $FIM$, is calculated as the inverse of the parameter covariance matrix:\n", "$FIM = {(Cov.)}^{-1}$\n", "\n", "Eigen decomposition of $FIM$ following $FIM = v \\lambda {v}^{-1}$ gives eigenvector matrix $v$ and $\\lambda$ is the diagonal matrix of eigenvalues. Column $i$ in $v$ is the eigenvector corresponding to the $i$-th eigenvalue. Further, each element of the eigenvector corresponds to the fitted parameters in order in which they appear in in `theta_names`. The magnitude of each element of an eigenvector denotes the contribution of the parameters on the direciton of the unit vector.\n", "\n", "The eigenvector corresponding to the smallest eigenvalue denotes the direction of least variance in the parameter space. The parameter that corresponds to the major contributor in the eigenvector, then, has the lowest impact on model fit quality. Thus, this parameter is considered sloppy and fixing it's value will not affect overall model behavior." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "nbpages": { "level": 3, "link": "[2.8.7.2 Parameter identifiability](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7.2-Parameter-identifiability)", "section": "2.8.7.2 Parameter identifiability" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "***************************************************************\n", "\n", "Eigen value: 1.559e+01\n", "\n", "=== Eigen vector elements with correspondng parameter names ===\n", "\n", "------------------------------\n", "| Vector element | Parameter |\n", "------------------------------\n", "| 1.254e-02 | A1 |\n", "| -1.789e-03 | A2 |\n", "| 9.985e-01 | E1 |\n", "| -5.373e-02 | E2 |\n", "\n", "\n", "***************************************************************\n", "\n", "Eigen value: 8.808e+00\n", "\n", "=== Eigen vector elements with correspondng parameter names ===\n", "\n", "------------------------------\n", "| Vector element | Parameter |\n", "------------------------------\n", "| 1.519e-03 | A1 |\n", "| 6.805e-03 | A2 |\n", "| 5.373e-02 | E1 |\n", "| 9.985e-01 | E2 |\n", "\n", "\n", "***************************************************************\n", "\n", "Eigen value: 5.745e-05\n", "\n", "=== Eigen vector elements with correspondng parameter names ===\n", "\n", "------------------------------\n", "| Vector element | Parameter |\n", "------------------------------\n", "| -9.794e-01 | A1 |\n", "| 2.017e-01 | A2 |\n", "| 1.263e-02 | E1 |\n", "| -5.644e-04 | E2 |\n", "\n", "\n", "***************************************************************\n", "\n", "Eigen value: 6.451e-06\n", "\n", "=== Eigen vector elements with correspondng parameter names ===\n", "\n", "------------------------------\n", "| Vector element | Parameter |\n", "------------------------------\n", "| -2.017e-01 | A1 |\n", "| -9.794e-01 | A2 |\n", "| 1.150e-03 | E1 |\n", "| 6.919e-03 | E2 |\n", "\n", "\n" ] } ], "source": [ "# Fisher information matrix can be computed using the inverse of the reduced Hessian\n", "fim = np.linalg.inv(cov)\n", "\n", "# Eigen decomposition of the Fisher information matrix\n", "eig_values, eig_vectors = np.linalg.eig(fim)\n", "\n", "for i,eig in enumerate(eig_values):\n", " print('***************************************************************')\n", " print('\\nEigen value: {:0.3e}\\n'.format(eig))\n", " print('=== Eigen vector elements with correspondng parameter names ===\\n')\n", " print('------------------------------')\n", " print('| Vector element | Parameter |')\n", " print('------------------------------')\n", " for j,theta_name in enumerate(theta_names):\n", " if eig_vectors[i,j] < 0.0:\n", " print('| {:0.3e} | {} |'.format(eig_vectors[i,j],theta_name))\n", " else:\n", " print('| {:0.3e} | {} |'.format(eig_vectors[i,j],theta_name))\n", " print('\\n')\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[2.8.7.2 Parameter identifiability](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.7.2-Parameter-identifiability)", "section": "2.8.7.2 Parameter identifiability" } }, "source": [ "Looking at the eigenvalues and corresponding eigenvectors, we can see that parameters A2 and A1 (in order) have the largest contributions to the direction of the eigenvector corresponding to the smallest eigenvalue. Therefore, as discussed above, we can conclude from this analysis that parameter A2 is the least identifiable parameter in this kinetic model, followed by parameter A1." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "source": [ "## 2.8.8 Bootstrap resampling\n", "Bootstrapping is a resampling method by independently sampling with replacement from an existing sample data with same sample size n, and performing inference among these resampled data ([link](https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60)). Bootstrap resampling is often used in parameter estimation problems to determine parameter confidence intervals. More information about bootstrap resampling and confidence interval calculation can be found [here](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/MIT18_05S14_Reading24.pdf).\n", "\n", "`theta_est_bootstrap()` is used to perform resampling with `parmest`. More information can be found [here](https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/api.html#pyomo.contrib.parmest.parmest.Estimator.theta_est_bootstrap).\n", "\n", "`parmest` also provides functions to plot bootstrap parameter estimates along with various confidence intervals [link](https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/api.html#pyomo.contrib.parmest.graphics.pairwise_plot)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.5867818e+01 1.98e+01 2.55e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.1317553e-01 2.14e+00 3.19e-01 -1.0 1.74e+01 - 9.57e-01 1.00e+00h 1\n", " 2 2.6873829e-01 1.33e-02 3.37e-02 -1.0 8.47e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.4267977e-01 7.48e-02 4.37e-03 -2.5 1.60e+00 - 9.95e-01 1.00e+00h 1\n", " 4 2.4236905e-01 7.27e-04 2.31e-06 -2.5 1.82e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.4232473e-01 9.37e-04 4.09e-07 -3.8 2.04e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.4221255e-01 1.64e-02 8.31e-06 -5.7 8.44e+00 - 9.41e-01 1.00e+00h 1\n", " 7 2.4219362e-01 1.56e-03 3.30e-06 -5.7 1.02e+01 - 1.00e+00 1.00e+00h 1\n", " 8 2.4219368e-01 1.52e-05 3.30e-08 -5.7 9.84e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.4219366e-01 3.52e-06 7.46e-09 -8.6 4.73e-01 - 9.97e-01 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.4219366e-01 2.80e-09 5.91e-12 -8.6 1.33e-02 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 10\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.4219366015053306e-01 2.4219366015053306e-01\n", "Dual infeasibility......: 5.9064320719730795e-12 5.9064320719730795e-12\n", "Constraint violation....: 2.8011406527639338e-09 2.8011406527639338e-09\n", "Complementarity.........: 2.5083856818837811e-09 2.5083856818837811e-09\n", "Overall NLP error.......: 2.8011406527639338e-09 2.8011406527639338e-09\n", "\n", "\n", "Number of objective function evaluations = 11\n", "Number of objective gradient evaluations = 11\n", "Number of equality constraint evaluations = 11\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 11\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 10\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.094\n", "Total CPU secs in NLP function evaluations = 0.027\n", "\n", "EXIT: Optimal Solution Found.\n", "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.7498853e+01 1.98e+01 2.54e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.5707852e-01 2.24e+00 3.32e-01 -1.0 1.72e+01 - 9.55e-01 1.00e+00h 1\n", " 2 2.6441294e-01 1.23e-02 3.69e-02 -1.0 8.86e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.2481185e-01 1.22e-01 3.95e-03 -2.5 2.03e+00 - 9.86e-01 1.00e+00h 1\n", " 4 2.2296649e-01 9.62e-04 9.74e-06 -2.5 2.30e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.2285645e-01 2.33e-03 9.46e-07 -3.8 3.14e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.2262528e-01 3.63e-02 1.48e-05 -3.8 1.22e+01 - 1.00e+00 1.00e+00h 1\n", " 7 2.2262252e-01 1.41e-04 1.34e-07 -3.8 8.75e-01 - 1.00e+00 1.00e+00h 1\n", " 8 2.2260420e-01 2.71e-03 1.17e-06 -5.7 3.19e+00 - 9.85e-01 1.00e+00h 1\n", " 9 2.2260208e-01 8.82e-05 1.96e-07 -5.7 2.44e+00 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.2260206e-01 2.22e-08 7.42e-11 -5.7 3.84e-02 - 1.00e+00 1.00e+00h 1\n", " 11 2.2260206e-01 7.04e-07 5.64e-10 -8.6 1.31e-01 - 1.00e+00 1.00e+00h 1\n", " 12 2.2260206e-01 9.65e-11 2.12e-13 -8.6 2.53e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 12\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.2260205708195835e-01 2.2260205708195835e-01\n", "Dual infeasibility......: 2.1180235186969325e-13 2.1180235186969325e-13\n", "Constraint violation....: 9.6474828126247303e-11 9.6474828126247303e-11\n", "Complementarity.........: 2.5059753197903154e-09 2.5059753197903154e-09\n", "Overall NLP error.......: 2.5059753197903154e-09 2.5059753197903154e-09\n", "\n", "\n", "Number of objective function evaluations = 13\n", "Number of objective gradient evaluations = 13\n", "Number of equality constraint evaluations = 13\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 13\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 12\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.115\n", "Total CPU secs in NLP function evaluations = 0.035\n", "\n", "EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.3160828e+01 1.98e+01 2.44e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 3.8848316e-01 2.15e+00 3.43e-01 -1.0 1.73e+01 - 9.57e-01 1.00e+00h 1\n", " 2 2.1823275e-01 2.59e-02 3.26e-02 -1.0 8.32e-01 - 1.00e+00 1.00e+00h 1\n", " 3 1.9588731e-01 9.56e-02 3.26e-03 -2.5 1.79e+00 - 9.89e-01 1.00e+00h 1\n", " 4 1.9426722e-01 5.67e-03 2.98e-05 -2.5 4.99e+00 - 1.00e+00 1.00e+00h 1\n", " 5 1.9381613e-01 9.82e-03 5.75e-06 -3.8 6.41e+00 - 9.93e-01 1.00e+00h 1\n", " 6 1.9250198e-01 2.13e-01 1.33e-04 -3.8 2.84e+01 - 1.00e+00 1.00e+00h 1\n", " 7 1.9247801e-01 1.81e-03 2.17e-06 -3.8 3.35e+00 - 1.00e+00 1.00e+00h 1\n", " 8 1.9248177e-01 1.39e-05 1.14e-08 -3.8 2.16e-01 - 1.00e+00 1.00e+00h 1\n", " 9 1.9226978e-01 1.56e-02 1.12e-05 -5.7 1.38e+01 - 9.08e-01 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 1.9215578e-01 8.01e-03 1.73e-05 -5.7 2.14e+01 - 1.00e+00 1.00e+00h 1\n", " 11 1.9214378e-01 6.14e-04 9.61e-07 -5.7 5.66e+00 - 1.00e+00 1.00e+00h 1\n", " 12 1.9214299e-01 1.37e-05 2.67e-08 -5.7 8.36e-01 - 1.00e+00 1.00e+00h 1\n", " 13 1.9214229e-01 8.20e-05 1.90e-07 -8.6 2.04e+00 - 9.89e-01 1.00e+00h 1\n", " 14 1.9214228e-01 4.19e-07 8.58e-10 -8.6 1.45e-01 - 1.00e+00 1.00e+00h 1\n", " 15 1.9214228e-01 9.01e-12 1.82e-14 -8.6 6.71e-04 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 15\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 1.9214227916791748e-01 1.9214227916791748e-01\n", "Dual infeasibility......: 1.8210932698128006e-14 1.8210932698128006e-14\n", "Constraint violation....: 9.0079055325986701e-12 9.0079055325986701e-12\n", "Complementarity.........: 2.5059130479412850e-09 2.5059130479412850e-09\n", "Overall NLP error.......: 2.5059130479412850e-09 2.5059130479412850e-09\n", "\n", "\n", "Number of objective function evaluations = 16\n", "Number of objective gradient evaluations = 16\n", "Number of equality constraint evaluations = 16\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 16\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 15\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.120\n", "Total CPU secs in NLP function evaluations = 0.039\n", "\n", "EXIT: Optimal Solution Found.\n", "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.6843041e+01 1.48e+01 2.00e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.2887378e-01 1.64e+00 3.10e-01 -1.0 1.30e+01 - 9.57e-01 1.00e+00h 1\n", " 2 2.3856072e-01 1.61e-02 3.12e-02 -1.0 6.34e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.0569218e-01 8.58e-02 1.55e-03 -2.5 1.39e+00 - 9.88e-01 1.00e+00h 1\n", " 4 2.0397947e-01 2.46e-03 1.97e-05 -2.5 4.27e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.0362946e-01 6.28e-03 3.22e-06 -3.8 5.86e+00 - 9.98e-01 1.00e+00h 1\n", " 6 2.0238769e-01 2.52e-01 1.54e-04 -3.8 3.50e+01 - 1.00e+00 1.00e+00h 1\n", " 7 2.0239576e-01 3.87e-03 5.28e-06 -3.8 5.15e+00 - 1.00e+00 1.00e+00h 1\n", " 8 2.0240503e-01 4.29e-05 3.26e-08 -3.8 4.24e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.0225622e-01 2.69e-02 1.77e-05 -5.7 1.01e+01 - 9.43e-01 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.0223592e-01 3.44e-03 1.90e-06 -5.7 4.49e+00 - 1.00e+00 1.00e+00h 1\n", " 11 2.0223489e-01 1.32e-05 1.97e-08 -5.7 8.19e-01 - 1.00e+00 1.00e+00h 1\n", " 12 2.0223483e-01 1.82e-05 1.23e-08 -8.6 3.34e-01 - 9.98e-01 1.00e+00h 1\n", " 13 2.0223482e-01 1.56e-09 4.26e-12 -8.6 1.22e-02 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 13\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.0223482293125566e-01 2.0223482293125566e-01\n", "Dual infeasibility......: 4.2614681659941213e-12 4.2614681659941213e-12\n", "Constraint violation....: 1.5566685718226836e-09 1.5566685718226836e-09\n", "Complementarity.........: 2.5071808827341051e-09 2.5071808827341051e-09\n", "Overall NLP error.......: 2.5071808827341051e-09 2.5071808827341051e-09\n", "\n", "\n", "Number of objective function evaluations = 14\n", "Number of objective gradient evaluations = 14\n", "Number of equality constraint evaluations = 14\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 14\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 13\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.105\n", "Total CPU secs in NLP function evaluations = 0.033\n", "\n", "EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.9317227e+01 1.98e+01 2.60e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 6.1238123e-01 2.24e+00 3.18e-01 -1.0 1.72e+01 - 9.55e-01 1.00e+00h 1\n", " 2 2.7818151e-01 2.30e-02 3.74e-02 -1.0 8.98e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.3911692e-01 9.48e-02 5.53e-03 -2.5 1.79e+00 - 9.91e-01 1.00e+00h 1\n", " 4 2.3810327e-01 5.27e-04 6.57e-06 -2.5 1.65e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.3805062e-01 8.90e-04 4.20e-07 -3.8 1.97e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.3788233e-01 1.11e-02 5.47e-06 -5.7 1.20e+01 - 9.07e-01 1.00e+00h 1\n", " 7 2.3783536e-01 2.86e-03 7.43e-06 -5.7 1.35e+01 - 1.00e+00 1.00e+00h 1\n", " 8 2.3783398e-01 3.89e-05 1.03e-07 -5.7 1.53e+00 - 1.00e+00 1.00e+00h 1\n", " 9 2.3783397e-01 2.27e-08 5.63e-11 -5.7 3.68e-02 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.3783393e-01 3.86e-06 1.01e-08 -8.6 4.82e-01 - 9.98e-01 1.00e+00h 1\n", " 11 2.3783392e-01 1.28e-09 3.27e-12 -8.6 8.73e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 11\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.3783392446349957e-01 2.3783392446349957e-01\n", "Dual infeasibility......: 3.2731232752983274e-12 3.2731232752983274e-12\n", "Constraint violation....: 1.2825678297190279e-09 1.2825678297190279e-09\n", "Complementarity.........: 2.5079696400494811e-09 2.5079696400494811e-09\n", "Overall NLP error.......: 2.5079696400494811e-09 2.5079696400494811e-09\n", "\n", "\n", "Number of objective function evaluations = 12\n", "Number of objective gradient evaluations = 12\n", "Number of equality constraint evaluations = 12\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 12\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 11\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.093\n", "Total CPU secs in NLP function evaluations = 0.029\n", "\n", "EXIT: Optimal Solution Found.\n", "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.2902314e+01 1.98e+01 2.82e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 3.8525024e-01 1.87e+00 3.20e-01 -1.0 1.77e+01 - 9.63e-01 1.00e+00h 1\n", " 2 2.0748122e-01 4.78e-02 2.63e-02 -1.0 7.78e-01 - 1.00e+00 1.00e+00h 1\n", " 3 1.8933165e-01 5.96e-02 1.67e-03 -2.5 1.46e+00 - 1.00e+00 1.00e+00h 1\n", " 4 1.8872332e-01 3.36e-04 6.26e-06 -2.5 1.34e+00 - 1.00e+00 1.00e+00h 1\n", " 5 1.8869519e-01 5.93e-04 2.46e-07 -3.8 1.58e+00 - 1.00e+00 1.00e+00h 1\n", " 6 1.8858365e-01 2.95e-02 1.00e-05 -5.7 1.11e+01 - 9.17e-01 1.00e+00h 1\n", " 7 1.8856721e-01 4.89e-03 1.49e-06 -5.7 5.78e+00 - 1.00e+00 1.00e+00h 1\n", " 8 1.8856667e-01 3.03e-05 3.40e-08 -5.7 1.47e+00 - 1.00e+00 1.00e+00h 1\n", " 9 1.8856667e-01 2.58e-09 5.67e-12 -5.7 1.41e-02 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 1.8856665e-01 4.34e-06 1.81e-09 -8.6 3.65e-01 - 9.98e-01 1.00e+00h 1\n", " 11 1.8856665e-01 2.68e-09 2.73e-12 -8.6 1.39e-02 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 11\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 1.8856664975547494e-01 1.8856664975547494e-01\n", "Dual infeasibility......: 2.7346159208273144e-12 2.7346159208273144e-12\n", "Constraint violation....: 2.6839197531103309e-09 2.6839197531103309e-09\n", "Complementarity.........: 2.5075979185707942e-09 2.5075979185707942e-09\n", "Overall NLP error.......: 2.6839197531103309e-09 2.6839197531103309e-09\n", "\n", "\n", "Number of objective function evaluations = 12\n", "Number of objective gradient evaluations = 12\n", "Number of equality constraint evaluations = 12\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 12\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 11\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.093\n", "Total CPU secs in NLP function evaluations = 0.028\n", "\n", "EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.7354755e+01 1.98e+01 2.98e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 5.2013292e-01 1.86e+00 2.83e-01 -1.0 1.77e+01 - 9.64e-01 1.00e+00h 1\n", " 2 2.7202560e-01 6.97e-02 2.66e-02 -1.0 8.35e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.5654573e-01 2.56e-02 1.19e-03 -2.5 9.75e-01 - 1.00e+00 1.00e+00h 1\n", " 4 2.5651613e-01 9.54e-05 1.45e-07 -3.8 7.33e-01 - 1.00e+00 1.00e+00h 1\n", " 5 2.5650277e-01 2.08e-03 9.86e-07 -5.7 3.12e+00 - 9.83e-01 1.00e+00h 1\n", " 6 2.5650032e-01 3.30e-04 3.07e-07 -5.7 3.02e+00 - 1.00e+00 1.00e+00h 1\n", " 7 2.5650025e-01 1.24e-07 1.53e-10 -5.7 6.35e-02 - 1.00e+00 1.00e+00h 1\n", " 8 2.5650025e-01 1.95e-07 4.47e-10 -8.6 1.13e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.5650025e-01 9.76e-11 2.27e-13 -8.6 2.55e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 9\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.5650025180692537e-01 2.5650025180692537e-01\n", "Dual infeasibility......: 2.2664407575036698e-13 2.2664407575036698e-13\n", "Constraint violation....: 9.7613472860302863e-11 9.7613472860302863e-11\n", "Complementarity.........: 2.5059619932443772e-09 2.5059619932443772e-09\n", "Overall NLP error.......: 2.5059619932443772e-09 2.5059619932443772e-09\n", "\n", "\n", "Number of objective function evaluations = 10\n", "Number of objective gradient evaluations = 10\n", "Number of equality constraint evaluations = 10\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.079\n", "Total CPU secs in NLP function evaluations = 0.023\n", "\n", "EXIT: Optimal Solution Found.\n", "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.5851784e+01 1.98e+01 2.49e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 4.9556238e-01 2.25e+00 3.44e-01 -1.0 1.72e+01 - 9.55e-01 1.00e+00h 1\n", " 2 2.4316470e-01 3.57e-02 3.54e-02 -1.0 8.67e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.1006658e-01 1.17e-01 7.41e-03 -2.5 1.97e+00 - 9.85e-01 1.00e+00h 1\n", " 4 2.0912807e-01 1.42e-03 3.67e-05 -2.5 1.17e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.0908236e-01 7.60e-04 4.78e-07 -3.8 1.80e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.0873382e-01 1.46e-02 1.46e-05 -5.7 2.17e+01 - 8.37e-01 1.00e+00h 1\n", " 7 2.0852924e-01 1.40e-02 2.84e-05 -5.7 2.85e+01 - 1.00e+00 1.00e+00h 1\n", " 8 2.0850600e-01 1.42e-03 2.78e-06 -5.7 8.51e+00 - 1.00e+00 1.00e+00h 1\n", " 9 2.0850448e-01 6.41e-05 1.28e-07 -5.7 1.78e+00 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.0850444e-01 1.31e-07 2.63e-10 -5.7 8.01e-02 - 1.00e+00 1.00e+00h 1\n", " 11 2.0850378e-01 7.84e-05 1.60e-07 -8.6 1.96e+00 - 9.89e-01 1.00e+00h 1\n", " 12 2.0850378e-01 3.53e-07 7.10e-10 -8.6 1.31e-01 - 1.00e+00 1.00e+00h 1\n", " 13 2.0850378e-01 6.04e-12 1.21e-14 -8.6 5.41e-04 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 13\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.0850378243005299e-01 2.0850378243005299e-01\n", "Dual infeasibility......: 1.2149736707217614e-14 1.2149736707217614e-14\n", "Constraint violation....: 6.0387250755411515e-12 6.0387250755411515e-12\n", "Complementarity.........: 2.5059099799274794e-09 2.5059099799274794e-09\n", "Overall NLP error.......: 2.5059099799274794e-09 2.5059099799274794e-09\n", "\n", "\n", "Number of objective function evaluations = 14\n", "Number of objective gradient evaluations = 14\n", "Number of equality constraint evaluations = 14\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 14\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 13\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.105\n", "Total CPU secs in NLP function evaluations = 0.034\n", "\n", "EXIT: Optimal Solution Found.\n", "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 2.3553727e+01 1.98e+01 2.74e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 7.5048522e-01 2.26e+00 2.77e-01 -1.0 1.72e+01 - 9.55e-01 1.00e+00h 1\n", " 2 2.5887790e-01 2.47e-02 3.76e-02 -1.0 9.01e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.0714094e-01 1.07e-01 3.43e-03 -2.5 1.90e+00 - 9.89e-01 1.00e+00h 1\n", " 4 2.0543545e-01 5.18e-04 6.73e-06 -2.5 1.11e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.0539974e-01 3.70e-04 2.10e-07 -3.8 1.30e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.0519893e-01 8.80e-03 3.97e-06 -5.7 1.54e+01 - 8.82e-01 1.00e+00h 1\n", " 7 2.0509916e-01 4.88e-03 5.65e-06 -5.7 1.93e+01 - 1.00e+00 1.00e+00h 1\n", " 8 2.0508880e-01 6.00e-04 8.17e-07 -5.7 7.05e+00 - 1.00e+00 1.00e+00h 1\n", " 9 2.0508828e-01 9.19e-06 1.25e-08 -5.7 8.82e-01 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.0508805e-01 1.96e-05 2.04e-08 -8.6 1.28e+00 - 9.93e-01 1.00e+00h 1\n", " 11 2.0508805e-01 4.71e-08 5.57e-11 -8.6 6.31e-02 - 1.00e+00 1.00e+00h 1\n", " 12 2.0508805e-01 1.46e-13 1.31e-16 -8.6 9.03e-05 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 12\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.0508804642313905e-01 2.0508804642313905e-01\n", "Dual infeasibility......: 1.3064608659185325e-16 1.3064608659185325e-16\n", "Constraint violation....: 9.5923269327613525e-14 1.4566126083082054e-13\n", "Complementarity.........: 2.5059036690133731e-09 2.5059036690133731e-09\n", "Overall NLP error.......: 2.5059036690133731e-09 2.5059036690133731e-09\n", "\n", "\n", "Number of objective function evaluations = 13\n", "Number of objective gradient evaluations = 13\n", "Number of equality constraint evaluations = 13\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 13\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 12\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.100\n", "Total CPU secs in NLP function evaluations = 0.032\n", "\n", "EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "\n", "This version of Ipopt was compiled from source code available at\n", " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", "\n", "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", " for large-scale scientific computation. All technical papers, sales and\n", " publicity material resulting from use of the HSL codes within IPOPT must\n", " contain the following acknowledgement:\n", " HSL, a collection of Fortran codes for large-scale scientific\n", " computation. See http://www.hsl.rl.ac.uk.\n", "******************************************************************************\n", "\n", "This is Ipopt version 3.13.2, running with linear solver ma27.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 42648\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", "Number of nonzeros in Lagrangian Hessian.............: 5680\n", "\n", "Total number of variables............................: 7840\n", " variables with only lower bounds: 0\n", " variables with lower and upper bounds: 64\n", " variables with only upper bounds: 0\n", "Total number of equality constraints.................: 7836\n", "Total number of inequality constraints...............: 0\n", " inequality constraints with only lower bounds: 0\n", " inequality constraints with lower and upper bounds: 0\n", " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 1.1977527e+01 1.48e+01 2.02e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 3.8335150e-01 1.43e+00 3.16e-01 -1.0 1.32e+01 - 9.63e-01 1.00e+00h 1\n", " 2 2.2909936e-01 2.04e-02 2.50e-02 -1.0 5.67e-01 - 1.00e+00 1.00e+00h 1\n", " 3 2.1130723e-01 6.06e-02 2.18e-03 -2.5 1.19e+00 - 9.96e-01 1.00e+00h 1\n", " 4 2.1022439e-01 3.08e-03 1.99e-05 -2.5 4.19e+00 - 1.00e+00 1.00e+00h 1\n", " 5 2.0999394e-01 4.03e-03 2.25e-06 -3.8 4.70e+00 - 1.00e+00 1.00e+00h 1\n", " 6 2.0918544e-01 1.49e-01 8.39e-05 -3.8 2.75e+01 - 1.00e+00 1.00e+00h 1\n", " 7 2.0919272e-01 1.78e-03 3.95e-06 -3.8 3.53e+00 - 1.00e+00 1.00e+00h 1\n", " 8 2.0919573e-01 7.40e-06 5.64e-09 -3.8 1.85e-01 - 1.00e+00 1.00e+00h 1\n", " 9 2.0908787e-01 1.75e-02 1.07e-05 -5.7 8.57e+00 - 9.50e-01 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 2.0905499e-01 2.36e-03 5.62e-06 -5.7 1.51e+01 - 1.00e+00 1.00e+00h 1\n", " 11 2.0905247e-01 1.96e-04 4.92e-07 -5.7 4.55e+00 - 1.00e+00 1.00e+00h 1\n", " 12 2.0905240e-01 6.33e-07 1.64e-09 -5.7 2.61e-01 - 1.00e+00 1.00e+00h 1\n", " 13 2.0905217e-01 3.26e-05 7.85e-08 -8.6 1.85e+00 - 9.87e-01 1.00e+00h 1\n", " 14 2.0905217e-01 2.36e-07 5.69e-10 -8.6 1.59e-01 - 1.00e+00 1.00e+00h 1\n", " 15 2.0905217e-01 2.09e-12 5.22e-15 -8.6 4.75e-04 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 15\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 2.0905216767485774e-01 2.0905216767485774e-01\n", "Dual infeasibility......: 5.2157307674572543e-15 5.2157307674572543e-15\n", "Constraint violation....: 2.0867751970854442e-12 2.0867751970854442e-12\n", "Complementarity.........: 2.5059046785673633e-09 2.5059046785673633e-09\n", "Overall NLP error.......: 2.5059046785673633e-09 2.5059046785673633e-09\n", "\n", "\n", "Number of objective function evaluations = 16\n", "Number of objective gradient evaluations = 16\n", "Number of equality constraint evaluations = 16\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 16\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 15\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", "Total CPU secs in NLP function evaluations = 0.039\n", "\n", "EXIT: Optimal Solution Found.\n", " A1 A2 E1 E2\n", "0 186.769746 382.642388 9.907827 14.726285\n", "1 179.703097 392.070899 9.738017 14.824106\n", "2 156.529846 334.342272 9.464807 14.407121\n", "3 146.617094 406.533938 9.252072 14.878677\n", "4 189.635337 370.602660 9.907778 14.697935\n" ] } ], "source": [ "# create Estimator object\n", "pest = parmest.Estimator(create_model_DAE,data_dict_overall,theta_names,tee=True)\n", "\n", "### Parameter estimation with bootstrap resampling\n", "#\n", "bootstrap_theta = pest.theta_est_bootstrap(10)\n", "print(bootstrap_theta.head())" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "source": [ "Once the parameter estimates are generated through bootstrap resampling, we can visualize the estimates using the `pairwise_plot()` function as follows:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "outputs": [ { "data": { "image/png": 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/zIc//OGfm3/VVVf97P511133lGXWmzYT7gmWJElS5VgES5IkqXIsgiVJkipo5AC0TpCZ7Ny5c1rPcUywJElSxTznOc/he9/7XrPDmFPd3d0ceOCBU25vESxJklQxvb299Pb2NjuMprIIltRS+gcGWb9liKWLeli+uNodtDRbbk+qx7yocUywpJbRPzDI6nUbufCGe1i9biP9A4PNDklqW25Pqse8eJJFsKSWsX7LEDt31Q7U2LlrmPVbhpockdS+3J5Uj3nxJItgSS1j6aIe5nV3ATCvu4uli3qaHJHUvtyeVI958STHBEtqGcsX93LOyiWOVZPmgNuT6jEvnmQRLKmlLF/cW+lOWZpLbk+qx7yocTiEJEmSKsciWJIkSZXT8CI4IhZExH9FxOaIuD0i3lunTUTEORFxR0TcFhEvaXSckiRJ6lzNGBO8G/jDzPxWROwD3BwR/Zk5MKrNycCi4nYM8PfFX0mSJGnWGr4nODPvz8xvFfcfBTYD88c0OwW4MGtuBPaLiKlfDFqSJEmaQFPHBEfEocAS4BtjZs0H7hv1eCtPLZRHlrEqIjZExIahoeqe8FmtyfxUqzNH1erMUZWlaUVwROwNXAK8LzMfGTu7zlOy3nIyc21m9mVmX09PdU/4rNZkfqrVmaNqdeaoytKUIjgiuqkVwP+YmZfWabIVWDDq8cHAtkbEJkmSpM7XjLNDBPA5YHNm/s04zS4H3l6cJeJXgR9m5v0NC1KSJEkdrRlnh3gZ8Dbg2xFxSzHtj4GFAJm5BrgCeDVwB/Aj4B1NiFOSJEkdquFFcGZeR/0xv6PbJPCexkQkSZKkqvGKcZIkSaoci2BJkiRVjkXwFM1fsJCImJPb/AULm706kiRJldaMA+Pa0rat93HaudfPybIuOuPYOVmOJEmSZsY9wZIkSaoci2BJkiRVjkWwJEmSKsciWJIkSZXjgXFSB+gfGGT9liGWLuph+eLeZocjaQbcjpvH976aLIKlNtc/MMjqdRvZuWuYizds5ZyVSxrWifvFIc2Nj1/1XdZc832Gn8iGb8dV18w+dDrsb+eewyGkNrd+yxA7dw0DsHPXMOu3DDXkdUe+OC684R5Wr9tI/8BgQ163lfQPDHLWZZsque6avZH8+fhV32XN1Xcw/EQCjd2O1bw+dDo6tb9tdh9qESy1uaWLepjX3QXAvO4uli7qacjrtsMXR5k69UtJjTE6f9Zc832G88l5XXtEw7ZjNa8PnY5O7G9boQ+1CJba3PLFvZyzcglv/7VDGvozXjt8cZSpE7+U1Dij82f4iaQratO7As48/gX+3N1AzepDp6MT+9tW6EMdEyx1gOWLexvecY98cUxljFonjmVbuqiHizdsZeeu4Y75UlLjjM2fE37xeXx/+6O8cvEBvP9Vhzc7vMppRh86HfX623bvV1uhD7UIljRjU/niaJeDTqZrOv8ESGONzp999urm/OvuYueuYe598C6OWrCf+aSnGN3fdkK/2gp9qMMhJJWqFX7yKsvyxb2cfcoRbfflo9Ywkj+P/nhXx24jKken9KvN7kMtgiWVqhPHsklzyW1E02XOzA2HQ0gqVSv85CW1MrcRTZc5MzcsgiWVrtUPOpGazW1E02XOzJ7DISRJklQ5FsGSJEmqHIdDSGpr7X6uTLU/c1CdqtNz2z3BktpWK1x2U9VmDqpTVSG3m1IER8T5EbE9IjaNM3/fiPjXiLg1Im6PiHc0OkZJra9TzpWp9mUOqlNVIbebtSf4AmDFBPPfAwxk5pHAMuCvI+JpDYhLUhP0Dwxy1mWbpr2nwXNlqpn6Bwa578Ef8bSu2lepOahWNZM+tgr9a1PGBGfmtRFx6ERNgH0iIoC9gQeB3Q0ITVKDzebyn54rU80yOm+f1rUHJxzew5uPOcQcVMuZaR9bhf61VQ+M+xRwObAN2Ac4LTOfqNcwIlYBqwAWLlzYsAClqTA/J1fvJ7fpdLaeK3N2zNGZGZ23Px1+ggXPeYZ5WBJzdHZm08d2ev/aqgfGvQq4BTgIOAr4VEQ8q17DzFybmX2Z2dfT03m76tXezM/JVeEnt1Zmjs6Meds45ujsmKvja9U9we8A/iIzE7gjIu4CfhG4qblhSZprVfjJTZ3HvFW7MFfHN+dFcEQsz8z+WS7mXuAVwPqI6AUOB+6cdXCSWlKn/+SmzmTeql2Yq/WVsSf4c8CEg3YiYh21sz48NyK2Ah8FugEycw3wp8AFEfFtIIAPZOaOEmKVJElSBc2oCI6Iy8ebBew/2fMzc+Uk87cBJ80gNEmSJGlSM90TvBR4K/DYmOkBHD2riCRJkqSSzbQIvhH4UWZeM3ZGRHx3diFJkiRJ5ZpREZyZJ9ebHhEvA749q4gkSZKkks36wLiIOAp4M/Am4C7gktkuU5IkSSrTTA+MeyFwOrASeAC4CIjMPGEOY9MUzF+wkG1b72t2GJIkSW1lpnuCvwOsB16bmXcARMTvz1lUmrJtW+/jtHOvn5NlXXTGsXOyHEmSpFY308sm/y/gf4D/iojPRsQrqJ0ZQpIkSWp5MyqCM/MrmXkatUsZXw38PtAbEX8fEZ7fV5IkSS1tpnuCAcjMxzPzHzPzNcDBwC3AB+ckMkmSJKkksyqCR8vMBzPz3Mw8ca6WKUmSJJVhzopgSZIkqV1YBEuSJKlyLIIlSZJUORbBkiRJqhyLYEmSJFWORbAkSZIqxyJYkiRJlWMRLEmSpMqxCJYkSVLlWARLkiSpciyCJUmSVDkWwZIkSaoci2BJkiRVTlOK4Ig4PyK2R8SmCdosi4hbIuL2iLimkfFJkiSpszVrT/AFwIrxZkbEfsBngNdl5ouA32hQXJIkSaqAphTBmXkt8OAETd4MXJqZ9xbttzckMEmSJFVCq44JfiHw7Ii4OiJujoi3j9cwIlZFxIaI2DA0NNTAEKXJmZ9qdeaoWp05qrK0ahG8J/BS4NeBVwH/JyJeWK9hZq7NzL7M7Ovp6WlkjNKkzE+1OnNUrc4cVVn2bHYA49gK7MjMx4HHI+Ja4Ejge80NS5IkSZ2gVfcEXwYsjYg9I+IZwDHA5ibHJEmSpA7RlD3BEbEOWAY8NyK2Ah8FugEyc01mbo6IK4HbgCeA8zJz3NOpSVXXPzDI+i1DLF3Uw/LFvc0OR2oo819lM8c6U1OK4MxcOYU2Hwc+3oBwpLbWPzDI6nUb2blrmIs3bOWclUvspGfIL7rWM9lnYv6rbObY5Nq172zV4RCSpmj9liF27hoGYOeuYdZv8ejpmRj5orvwhntYvW4j/QODzQ6p8qbymZj/Kps5NrF27jstgqU2t3RRD/O6uwCY193F0kUePT0TftG1nql8Jua/ymaOTayd+85WPTuEpClavriXc1YuacufolrJ0kU9XLxhKzt3DftF1yKm8pmY/yqbOTaxdu47LYKlDrB8ca8d8yz5Rdd6pvqZmP8qmzk2vnbuOy2CJangF13r8TORWl+7bqeOCVY59tiTiJj1bf6Chc1eE0mS1IHcE6xyPLGb0869ftaLueiMY+cgGEmSpJ/nnmBJkiRVjkWwJEmSKsciWJIkSZVjESxJkqTKsQiWJElS5VgES5IkqXI8RZqkttQ/MNiWVyiqKj8vqfVUfbt0T7CkttM/MMjqdRu58IZ7WL1uI/0Dg80OSRPw85Jaj9ulRbCkNrR+yxA7dw0DsHPXMOu3DDU5Ik3Ez0tqPW6XFsGS2tDSRT3M6+4CYF53F0sX9TQ5Ik3Ez0tqPW6XjgmW1IaWL+7lnJVLKj2WrZ34eUmtx+3SIlhSm1q+uLeSnXa78vOSWk/Vt0uHQ0iSJKlyLIIlSZJUORbBkiRJqhyLYEmSJFVOU4rgiDg/IrZHxKZJ2v1KRAxHxKmNik2SJEmdr1l7gi8AVkzUICK6gL8ErmpEQJIkSaqOphTBmXkt8OAkzX4PuATYXn5EkiRJqpKWHBMcEfOBNwBrptB2VURsiIgNQ0PVu+SfWpv5qVZnjqrVmaMqS0sWwcAngA9k5vBkDTNzbWb2ZWZfT0/1Lvmn1mZ+qtWZo2p15qjK0qpXjOsDvhQRAM8FXh0RuzPzX5obliRJkjpBSxbBmXnYyP2IuAD4qgWwJEmS5kpTiuCIWAcsA54bEVuBjwLdAJk56ThgSZIkaTaaUgRn5spptP2tEkORJElSBbXqgXGSJElSaSyCJUmSVDkWwZIkSaoci2BJkiRVjkWwJEmSKsciWJIkSZVjESxJkqTKicxsdgxzJiKGgHsa/LLPBXY0+DWnw/gmtiMzVzTihZqUn5Np9vs/VVWOs+o5OlPtkjMz0Urr1rD8hLbK0Vb6jMrSLus4bo52VBHcDBGxITP7mh3HeIxPE2mX9984NV2d/Fl08rp1iip8Rp2wjg6HkCRJUuV0XBEcEedHxPaI2DSFtn8QEQMRcVtE/GdEHNKIGCVJktRcHVcEAxcAUx2ftBHoy8wXA/8M/NUMXm/tDJ7TSManibTL+2+cmq5O/iw6ed06RRU+o7Zfx44cExwRhwJfzcwjiscvAD4N9AA/An4nM78z5jlLgE9l5ssaG60kSZIabc9mB9Aga4EzM3NLRBwDfAY4cUybdwJfa3hkkiRJariOL4IjYm/gWODiiBiZ/PQxbd4K9AHHNzY6SZIkNUPHF8HUxj0/nJlH1ZsZEa8EPgwcn5k/aWhkkiRJaopOPDDu52TmI8BdEfEbAFFzZHF/CXAu8LrM3N7EMCVJktRAHXdgXESsA5ZRu5LJIPBR4OvA3wMHAt3AlzLz7Ij4D+CXgfuLp9+bma9reNCSJElqqI4rgiVJkqTJdPxwCEmSJGksi2BJkiRVTkcVwStWrEjAm7fp3BrG/PQ2w1vDmKPeZnBrKHPU2wxu4+qoInjHjh3NDkEal/mpVmeOqtWZo5pLHVUES5IkSVNhESxJkqTKsQiWJElS5VgES5IkqXIsgiVJklQ5FsGSJEmqHItgSZIkVY5FsCRJkirHIliSJEmVYxEsSZKkyrEIliRJUuVYBEuSJKlyLIIlSZJUORbBkiRJqhyLYEmSJFVO6UVwRHRFxMaI+Gqdecsi4ocRcUtxO2vUvBUR8d2IuCMiPlh2nJIkSaqOPRvwGu8FNgPPGmf++sx8zegJEdEFfBpYDmwFvhkRl2fmQKmRSpIkqRJK3RMcEQcDvw6cN82nHg3ckZl3ZuZPgS8Bp8x1fJIkSaqmsodDfAL4I+CJCdr8WkTcGhFfi4gXFdPmA/eNarO1mPYUEbEqIjZExIahoaE5CVqaK+anWp05qlZnjqospRXBEfEaYHtm3jxBs28Bh2TmkcAngX8ZeXqdtllvAZm5NjP7MrOvp6dnVjFravoHBjnrsk30Dww2O5SWZ36q1ZmjzWNfOjXmaOdq9jZQ5p7glwGvi4i7qQ1nODEivji6QWY+kpmPFfevALoj4rnU9vwuGNX0YGBbibFqivoHBlm9biMX3nAPq9dttPOWpBmwL1XVtcI2UFoRnJkfysyDM/NQ4HTg65n51tFtIuKAiIji/tFFPA8A3wQWRcRhEfG04vmXlxWrpm79liF27hoGYOeuYdZv8acpSZou+1JVXStsAw0/T3BEnBkRZxYPTwU2RcStwDnA6VmzG/hd4CpqZ5b4cmbe3uhY9VRLF/Uwr7sLgHndXSxd5E9TkjRd9qWqulbYBiKz7lDbttTX15cbNmxodhgdr39gkPVbhli6qIfli3ubHc5s1Rt/XgrzUzNkjnaoDulLG5afYI52mgZtA+PmaCPOE6wOs3xxbzt32JLUEuxLVXXN3ga8bLIkSZIqxyJYkiRJlWMRLEmSpMqxCJYkSVLlWARLkiSpciyCJUmSVDmeIq1kHXIeSElqCvtQqfW163bqnuAStcJ1sSWpXdmHSq2vnbdTi+AStcJ1sSWpXdmHSq2vnbdTi+AStcJ1sSWpXdmHSq2vnbdTxwSXaPniXs5ZuaQtx8lIUrPZh0qtr52309KL4IjoAjYAP8jM14yZ9xbgA8XDx4B3Z+atxby7gUeBYWB3ZvaVHWsZmn1dbElqZ/ahUutr1+20EXuC3wtsBp5VZ95dwPGZ+VBEnAysBY4ZNf+EzNzRgBglSZJUIaWOCY6Ig4FfB86rNz8zr8/Mh4qHNwIHlxmPpMabv07byHsAACAASURBVGAhETHr2/wFC5u9KpKkDlL2nuBPAH8E7DOFtu8EvjbqcQL/HhEJnJuZa+s9KSJWAasAFi70S1KtxfyEbVvv47Rzr5/1ci4649g5iEZjmaNqdeaoylLanuCIeA2wPTNvnkLbE6gVwR8YNfllmfkS4GTgPRFxXL3nZubazOzLzL6envY5IlHVYH6q1ZmjanXmqMpS5nCIlwGvKw5w+xJwYkR8cWyjiHgxteESp2TmAyPTM3Nb8Xc78BXg6BJjlSRJUoWUVgRn5ocy8+DMPBQ4Hfh6Zr51dJuIWAhcCrwtM783avozI2KfkfvAScCmsmKVJElStTT8PMERcSZAZq4BzgL2Bz4TEfDkqdB6ga8U0/YE/ikzr2x0rJIkSepMDSmCM/Nq4Ori/ppR098FvKtO+zuBIxsRmyRJkqrHyyZLkiSpciyCJUmSVDkWwZIkSaoci2BJkiRVjkWwJEmSKqfhp0hT6+kfGGT9liGWLuph+eLeZocjSW3FPlSdqtNz2z3BFdc/MMjqdRu58IZ7WL1uI/0Dg80OSZLahn2oOlUVctsiuOLWbxli565hAHbuGmb9lqEmRyRJ7cM+VJ2qCrltETwF/QODnHXZpo78L2jpoh7mdXcBMK+7i6WLepockaRO1Kn9qH2o2tlE22UVctsxwZMY+Tlg565hLt6wlXNWLumocTHLF/dyzsolHT3mR1JzdXI/ah+qdjXZdlmF3LYInkS9nwM6LRGWL+7tuHWS1Do6vR+1D1U7msp22em57XCISVTh5wBJKpP9qNR63C4bsCc4IrqADcAPMvM1Y+YF8HfAq4EfAb+Vmd8q5q0o5nUB52XmX5Qdaz1V+DlAkspkPyq1HrfLxgyHeC+wGXhWnXknA4uK2zHA3wPHFIXzp4HlwFbgmxFxeWYONCDep+j0nwMkqWz2o1Lrqfp2WepwiIg4GPh14LxxmpwCXJg1NwL7RcSBwNHAHZl5Z2b+FPhS0VaSJEmatbLHBH8C+CPgiXHmzwfuG/V4azFtvOlPERGrImJDRGwYGuq8c9ipvZmfanXmqFqdOaqylFYER8RrgO2ZefNEzepMywmmP3Vi5trM7MvMvp6e6g3qVmszP9XqzFG1OnNUZSlzTPDLgNdFxKuBvYBnRcQXM/Oto9psBRaMenwwsA142jjTVSGdfs1ySSqb/ajqMS9qStsTnJkfysyDM/NQ4HTg62MKYIDLgbdHza8CP8zM+4FvAosi4rCIeFrx/MvLilWtpwrXLJekMtmPqh7z4kkNP09wRJwZEWcWD68A7gTuAD4L/G+AzNwN/C5wFbUzS3w5M29vdKxqnipcs1ySymQ/qnrMiydNWARHxLMi4gV1pr94Oi+SmVePnCM4M9dk5prifmbmezLzBZn5y5m5YdRzrsjMFxbz/mw6r6fJTXS98FbgSbwltTr7UbWL0blqXjxp3DHBEfEmamd32B4R3dQuZPHNYvYFwEvKD09lmOx64a3Ak3hLamX2o2oX9XLVvKiZ6MC4PwZempn3R8TRwBci4o8z81Lqn71BbWIq1wtvBVU/ibek1mU/qnZRL1fPPuUI84KJh0N0FQepkZk3AScAH46I1YxzujK1B38KkaTZsR9VuzBXxzfRnuBHI+IFmfl9gGKP8AnApcCLGhKdSuFPZJI0O/ajahfm6vgmKoLfzZg9xZn5SEScTW1MsNqYP5FJ0uzYj6pdmKv1jVsEZ+atI/cj4ijgzcCbgLuAvy0/NEnTMX/BQrZtvW/yhlNw0MEL+MF9987JsubMHnsSMTeHI3R1P53hXT+Zk2W15HslSZrURGeHeCG1i1SsBB4ALgIiM09oUGySpmHb1vs47dzr52RZF51x7JwsZ049sXtO16+j3ytJ0qQmGg7xHWA98NrMvAMgIn6/IVGp5XiJRUkqj32spsucmb2Jzg7xv4D/Af4rIj4bEa/AU6NVkpdYlKTy2MdqusyZuTFuEZyZX8nM04BfBK4Gfh/ojYi/j4iTGhSfWoCXWJSk8tjHarrMmbkx4WWTATLz8cz8x+KyxwcDtwAfLD0ytQzPMShJ5bGP1XSZM3NjojHBT5GZDwLnFjdVhOcYlKTy2MdqusyZuTGtIng6ImIv4Frg6cXr/HNmfnRMm/cDbxkVyy8BPZn5YETcDTwKDAO7M7OvrFg1Oc8xKEnlsY/VdJkzs1daEQz8BDgxMx+LiG7guoj4WmbeONIgMz8OfBwgIl4L/H6xt3nECZm5o8QYJUmSVEGlFcGZmcBjxcPu4pYTPGUlsK6seCRJkqQRkx4YNxsR0RURtwDbgf7M/MY47Z4BrAAuGTU5gX+PiJsjYtUEr7EqIjZExIahIY+OVGsxP9XqzFG1OnNUZSm1CM7M4cw8itpZJY6OiCPGafpa4L/HDIV4WWa+BDgZeE9EHDfOa6zNzL7M7Ovp8ehItRbzU63OHFWrM0dVllKL4BGZ+TC1cw2vGKfJ6YwZCpGZ24q/24GvAEeXGKKmqX9gkLMu2+QJuiVpDtinajLmyNwrrQiOiJ6I2K+4Pw94JbVLMY9tty9wPHDZqGnPjIh9Ru4DJwGbyopV0+OVaiRp7tinajLmSDnK3BN8ILVLLt8GfJPamOCvRsSZEXHmqHZvAP49Mx8fNa2X2tkkbgVuAv4tM68sMVZNg1eqkaS5Y5+qyZgj5Sjz7BC3AUvqTF8z5vEFwAVjpt0JHFlWbJqdpYt6uHjDVnbuGvZKNZI0S/apmow5Uo4yzxOsDuWVaiRp7tinajLmSDksgjUjXqlGkuaOfaomY47MvYacHUKSJElqJRbBkiRJqhyLYEmSJFWORbAkSZIqxyJYkiRJlWMRLEmSpMqxCJYkSVLlWARLkiSpciyCJUmSVDkWwZIkSaqc0orgiNgrIm6KiFsj4vaI+JM6bZZFxA8j4pbidtaoeSsi4rsRcUdEfLCsOCVJklQ9e5a47J8AJ2bmYxHRDVwXEV/LzBvHtFufma8ZPSEiuoBPA8uBrcA3I+LyzBwoMV5JkiRVRGl7grPmseJhd3HLKT79aOCOzLwzM38KfAk4pYQwJUmSVEGljgmOiK6IuAXYDvRn5jfqNPu1YsjE1yLiRcW0+cB9o9psLabVe41VEbEhIjYMDQ3NafzSbJmfanXmqFqdOaqylFoEZ+ZwZh4FHAwcHRFHjGnyLeCQzDwS+CTwL8X0qLe4cV5jbWb2ZWZfT0/PXIUuzQnzU63OHFWrM0dVloacHSIzHwauBlaMmf7IyJCJzLwC6I6I51Lb87tgVNODgW2NiFWSJEmdr8yzQ/RExH7F/XnAK4HvjGlzQEREcf/oIp4HgG8CiyLisIh4GnA6cHlZsUoaY489iYg5uUmS1IrKPDvEgcDnizM97AF8OTO/GhFnAmTmGuBU4N0RsRvYCZyemQnsjojfBa4CuoDzM/P2EmOVNNoTuznt3OvnZFEXnXHsnCxHkqS5VFoRnJm3AUvqTF8z6v6ngE+N8/wrgCvKik+SJEnV5RXjJEmSVDkWwZIkSaoci2BJkiRVjkWwJEmSKsciWJIkSZVT5inSVOgfGGT9liGWLuph+eLeZoczZzp1vSS1l07pizplPdrRZO+9n01nck9wyfoHBlm9biMX3nAPq9dtpH9gsNkhzYlOXS9J7aVT+qJOWY92NNl772fTuSyCS7Z+yxA7dw0DsHPXMOu3DDU5ornRqeslqb10Sl/UKevRjiZ77/1sOpdFcMmWLuphXncXAPO6u1i6qKfJEc2NTl0vSe2lU/qiTlmPdjTZe+9n07kcE1yy5Yt7OWflko4bS9Sp6yWpvXRKX9Qp69GOJnvv/Ww6l0VwAyxf3NuRG02nrpek9tIpfVGnrEc7muy997PpTA6HkCRJUuWUVgRHxF4RcVNE3BoRt0fEn9Rp85aIuK24XR8RR46ad3dEfDsibomIDWXFKUmSpOopczjET4ATM/OxiOgGrouIr2XmjaPa3AUcn5kPRcTJwFrgmFHzT8jMHSXGKEmSpAoqrQjOzAQeKx52F7cc0+b6UQ9vBA4uKx5JkiRpRKljgiOiKyJuAbYD/Zn5jQmavxP42qjHCfx7RNwcEasmeI1VEbEhIjYMDXnuPrUW81OtzhxVqzNHVZZSi+DMHM7Mo6jt4T06Io6o1y4iTqBWBH9g1OSXZeZLgJOB90TEceO8xtrM7MvMvp4ez92n1mJ+qtWZo2p15qjK0pCzQ2Tmw8DVwIqx8yLixcB5wCmZ+cCo52wr/m4HvgIc3YhYJUmS1PnKPDtET0TsV9yfB7wS+M6YNguBS4G3Zeb3Rk1/ZkTsM3IfOAnYVFaskiRJqpYyzw5xIPD5iOiiVmx/OTO/GhFnAmTmGuAsYH/gMxEBsDsz+4Be4CvFtD2Bf8rMK0uMVZIkSRVS5tkhbgOW1Jm+ZtT9dwHvqtPmTuDIsdMlSZKkueAV4yRJklQ5FsGSJEmqHItgSZIkVY5FsCRJkirHIliSJEmVYxEsSZKkyrEIliRJUuVYBEuSJKlyLIIlSZJUORbBkiRJqpzIzGbHMGciYgi4p8Ev+1xgR4NfczqMb2I7MnNFI16oSfk5mWa//1NV5TirnqMz1S45MxOttG4Ny09oqxxtpc+oLO2yjuPmaEcVwc0QERsys6/ZcYzH+DSRdnn/jVPT1cmfRSevW6eowmfUCevocAhJkiRVjkWwJEmSKsciePbWNjuASRifJtIu779xaro6+bPo5HXrFFX4jNp+HR0TLEmSpMpxT7AkSZIqxyJYkiRJlWMRLEmSpMqxCJYkSVLlWARLkiSpciyCJUmSVDkWwZIkSaqcjiqCV6xYkYA3b9O5NYz56W2Gt4YxR73N4NZQ5qi3GdzG1VFF8I4dO5odgjQu81OtzhxVqzNHNZc6qgiWJEmSpsIiWJIkSZVjESxJkqTKsQiWJElS5VgES5IkqXIsgiVJmgPzFywkIubkNn/BwmavjtTx9mx2AJIkdYJtW+/jtHOvn5NlXXTGsXOyHEnjc0+wJEmSKsciWJIkSZVTWhEcEedHxPaI2DRq2nMioj8ithR/n13neQsi4r8iYnNE3B4R7y0rRkmSJFVTmXuCLwBWjJn2QeA/M3MR8J/F47F2A3+Ymb8E/CrwnohYXGKckiRJqpjSiuDMvBZ4cMzkU4DPF/c/D7y+zvPuz8xvFfcfBTYD88uKU5IkSdXT6DHBvZl5P9SKXeB5EzWOiEOBJcA3JmizKiI2RMSGoaGhOQxVmj3zU63OHFWrM0dVlpY9MC4i9gYuAd6XmY+M1y4z12ZmX2b29fT0NC5AaQrMT7U6c1StzhxVWRpdBA9GxIEAxd/t9RpFRDe1AvgfM/PSBsYnSZKkCmh0EXw58JvF/d8ELhvbICIC+BywOTP/poGxSZIkqSLKPEXaOuAG4PCI2BoR7wT+AlgeEVuA5cVjIuKgiLiieOrLgLcBJ0bELcXt1WXFKUmSpOop7bLJmblynFmvqNN2G/Dq4v51QJQVlyRJktSyB8ZJkiRJZbEIliRJUuWUNhxCmq3+gUHWbxli6aIeli/ubXY4ahA/d0kql/1sjXuC1ZL6BwZZvW4jF95wD6vXbaR/YLDZIakB/NwlqVz2s0+yCFZLWr9liJ27hgHYuWuY9Vu8SlAV+LlLUrnsZ59kEayWtHRRD/O6uwCY193F0kVeJagK/NwlqVz2s09yTLBa0vLFvZyzcoljlirGz12SymU/+ySLYLWs5Yt7K71xVpWfuySVy362xuEQkiRJqhyLYEmSJFWORbAkSZIqxyJYkiRJlWMRLEmSpMoprQiOiPMjYntEbBo17TkR0R8RW4q/zx7nuSsi4rsRcUdEfLCsGCVJklRNZe4JvgBYMWbaB4H/zMxFwH8Wj39ORHQBnwZOBhYDKyNicYlxSpIkqWJKK4Iz81rgwTGTTwE+X9z/PPD6Ok89GrgjM+/MzJ8CXyqeJ0mSJM2JRo8J7s3M+wGKv8+r02Y+cN+ox1uLaXVFxKqI2BARG4aGqnv9a7Um81OtzhxVqzNHVZZWPDAu6kzL8Rpn5trM7MvMvp6e6l7/Wq3J/FSrM0fV6sxRlaXRRfBgRBwIUPzdXqfNVmDBqMcHA9saEJskSZIqotFF8OXAbxb3fxO4rE6bbwKLIuKwiHgacHrxPEmSJGlOlHmKtHXADcDhEbE1It4J/AWwPCK2AMuLx0TEQRFxBUBm7gZ+F7gK2Ax8OTNvLytOSZIkVc+eZS04M1eOM+sVddpuA1496vEVwBUlhSZJkqSKa8UD4yRJkqRSlbYnWJ2vf2CQ9VuGWLqoh+WLe5sdTqX5WUjSzNmHVpNFsKZkbAfx8au+y5prvs/wE8nFG7ZyzsoldhxN0j8wyOp1G9m5a7jhn8VUvzj8gpHUqprZh07H2H7UfnX2LII1qbEdxG+//DDWXH0Hw8XZm3fuGmb9liE3wiZZv2WInbuGgcZ+FlP94miXLxhJ1dSsPnQ66n0Pn3/dXfars+SYYE1qbAfxHwP/87MCGKBrj2DpIk9g3ixLF/Uwr7sLgHndXQ37LOp9ccymnSQ1Q7P60Omo9z1svzp7FsGa1NgO4gXP24eu4rp+XQFnHv8C/wNtouWLezln5RLe/muHNHRvwFS/ONrhC2am+gcGOeuyTfQPDDY7FEkz1Kw+dDrG9qOvXHxAR/Srze5DI3PcKxK3nb6+vtywYUOzw+hII2OP9tmr+2c/wXTtEZx5/At4/6sOb3Z4s1HvMt2l6MT8rPKY4NE/T87r7irzy9McbRMRwWnnXj8ny7rojGNpk+/nhuUnVDtHO21McCv0oY4J1pQsX9zL8sW9nHXZpp/9BDP8RPLoj3c1OTI100hezFW7dtIO4wgldY6x/Wi796ut0Ic6HELT0sk/bUvT4bYgSTPXCn2oe4I1LSNjp9r5JxhpLrgtSNLMtUIfahGsaWv3n2CkueK2IEkz1+w+1OEQkiRJqpymFMER8d6I2BQRt0fE++rM3zci/jUibi3avKMZcUqSJKkzNbwIjogjgN8BjgaOBF4TEYvGNHsPMJCZRwLLgL+OiKc1NFBJkiR1rGbsCf4l4MbM/FFm7gauAd4wpk0C+0REAHsDDwK7GxumJEmSOlUziuBNwHERsX9EPAN4NbBgTJtPUSuWtwHfBt6bmU80NkxJkiR1qoYXwZm5GfhLoB+4EriVp+7lfRVwC3AQcBTwqYh4Vr3lRcSqiNgQERuGhrx2tlqL+alWZ46q1ZmjKktTDozLzM9l5ksy8zhqQx22jGnyDuDSrLkDuAv4xXGWtTYz+zKzr6fHk9WrtZifanXmqFqdOaqyNOvsEM8r/i4E3gisG9PkXuAVRZte4HDgzkbGKEmSpM7VrItlXBIR+wO7gPdk5kMRcSZAZq4B/hS4ICK+DQTwgczc0aRYO17/wKBXvVLbMn8lqRyd3r82pQjOzKV1pq0ZdX8bcFJDg6qYkcTeZ69uzr/uLnbuGubiDVs5Z+WSjkx0tbaZdrT9A4OsXrfR/JWkCcykj61C/+oV4ypoJLEvvOEe1lx9Bzt3DQOwc9cw67d40IEaa3Q+rl63kf6BwSk/d/2WIfNXkiYw0z62Cv2rRXAFjU7s4YSuPQKAed1dLF3kQQdqrNl0tEsX9TCvuwswfyWpnpn2sVXoX5s1JlhNtHRRDxdv2MrOXcPM6+7it19+GI/+eFfHjvlRaxubj9PpaJcv7uWclUs6esyaJM3GTPvYKvSvFsEVVIXEVvuYbT4uX9xrDkvSOGbTx3Z6/2oRXFGdnthqL+ajJJXHPra+GY8Jjoi1cxmIJEmS1CgT7gmOiOeMNwt49dyHI0mSJJVvsuEQQ8A91IreEVk8fl5ZQUmSJEllmqwIvhN4RWbeO3ZGRNxXTkiSJElSuSYbE/wJ4NnjzPurOY5FkiRJaogJ9wRn5qcnmPfJuQ9HkiRJKt+Ee4Ij4o9G3f+NMfP+b1lBSZI6z/wFC4mIWd/mL1jYcjFFxOQvJqmlTDYm+HSeHPbwIeDiUfNWAH9cRlCSpM6zbet9nHbu9bNezkVnHDsH0dTMVUwwt3FJKt9kY4JjnPv1Hk9ZRLw3IjZFxO0R8b5x2iyLiFuKNtfM9LUkSZKksSbbE5zj3K/3eEoi4gjgd4CjgZ8CV0bEv2XmllFt9gM+A6zIzHsjwtOxSZIkac5MVgQfGRGPUNvrO6+4T/F4rxm+5i8BN2bmjwCKvbxv4OfPNvFm4NKRU7Nl5vYZvpYkSZL0FBMOh8jMrsx8Vmbuk5l7FvdHHnfP8DU3AcdFxP4R8QxqV55bMKbNC4FnR8TVEXFzRLx9hq8lSZIkPcVke4LnXGZujoi/BPqBx4Bbgd114nop8ApgHnBDRNyYmd8bu7yIWAWsAli4cO6OGJbmgvmpVmeOqtWZoyrLZAfGlSIzP5eZL8nM44AHgS1jmmwFrszMxzNzB3AtcOQ4y1qbmX2Z2dfT01Nu4NI0mZ9qdeaoWp05qrI0pQgeOdAtIhYCbwTWjWlyGbA0IvYshkwcA2xubJSSJEnqVA0fDlG4JCL2B3YB78nMhyLiTIDMXFMMmbgSuA14AjgvMzc1KVZJkiR1mKYUwZm5tM60NWMefxz4eMOCkiRJUmU0ZTiEJEmS1EwWwZIkSaoci2BJkiRVjkWwJEmSKsciWJIkSZVjESxJkqTKsQiWJElS5TTrYhmV0j8wyPotQyxd1MPyxb3NDkcdyByTpPLYx3Ym9wSXrH9gkNXrNnLhDfewet1G+gcGmx2SOow5JknlsY/tXBbBJVu/ZYidu4YB2LlrmPVbhpockTqNOSZJ5bGP7VwWwSVbuqiHed1dAMzr7mLpop4mR6ROY45JUnnsYzuXY4JLtnxxL+esXOJYIpXGHJOk8tjHdi6L4AZYvrjXjUalMsckqTz2sZ2pKcMhIuK9EbEpIm6PiPdN0O5XImI4Ik5tZHySJEnqbA0vgiPiCOB3gKOBI4HXRMSiOu26gL8ErmpshJIkSep0zdgT/EvAjZn5o8zcDVwDvKFOu98DLgG2NzI4SZIkdb5mFMGbgOMiYv+IeAbwamDB6AYRMZ9aYbxmsoVFxKqI2BARG4aGWu+0Jf0Dg5x12SbPK1hRrZ6f+nlV3F7NUbU6c7T1tWvf2fAiODM3Uxvm0A9cCdwK7B7T7BPABzJzeArLW5uZfZnZ19PTWqct8QTbauX81M+r6vZqjqrVmaOtrZ37zqYcGJeZn8vMl2TmccCDwJYxTfqAL0XE3cCpwGci4vUNDnPWPMG21D7cXiVp+tq572zW2SGeV/xdCLwRWDd6fmYelpmHZuahwD8D/zsz/6Xhgc6SJ9iW2ofbqyRNXzv3nc06T/AlEbE/sAt4T2Y+FBFnAmTmpOOA24Un2Jbah9urJE1fO/edTSmCM3NpnWl1i9/M/K3SAyqRJ9iW2ofbqyRNX7v2nU0ZDiFJkiQ1k5dNliS1lz3+X3v3Hz3XXdd5/PkyTbW1uir9NtAmJbUnpxIrtt2cinRbEI0bo7bi0QM5gqynbKsHTFG7tSyewrLnuP6Cst1dkNLW2hUjAnXhYKlkFWyQgIamoWkiBKU/0pTkWwqCkpUkfe8fM/F8/TLz/ebHzNyZuc/HOXNy597PzPc99/O+d9659zP3nkKSpqMYrgF+xrOXr+Dxxx4dyHtJ08QiWJI0WZ4+zEve/rGBvNW7rn3+QN5n4NrwGaWGORxCkiRJrWMRLEmSpNaxCJYkSVLrWARLkiSpdSyCJUmS1DoWwZIkSWodL5F2DDbv2j+RtwOUppnbpSSdnLbvRz0SvIjNu/azcdN27tr6CBs3bWfzrv1NhyS1ntulJJ0c96MWwYvasmeWg4eOAHDw0BG27JltOCJJbpeSdHLcj1oEL+ryVTOctnQJAKctXcLlq2YajkiS26UknRz3ow2NCU5yHfAfgQDvqKq3zFv+M8Cvdp/+I/ALVbVjtFF2rF29jFs2XNzqMTPSuHG7lKST4360gSI4yYV0CuBLga8B9yb506raM6fZ54AXVNUXk/wIcCvwfaOO9ai1q5e1MjmkceZ2KUknp+370SaGQzwH+HhVfbWqDgN/Cbx4boOq+lhVfbH79OPA8hHHKEmSpCnWRBG8E7giyTOSnA6sB1Ys0P5q4IP9Fia5Jsm2JNtmZ9s3qFvjzfzUuDNHNe7MUQ3LyIvgqtoN/CawGbgX2AEc7tU2yQ/QKYJ/tdfy7vvdWlVrqmrNzEz7BnVrvJmfGnfmqMadOaphaeTqEFV1e1VdUlVXAE8Be+a3SfJc4Dbgqqr6wqhjlCRJ0vRq6uoQZ1XVgSTnAj8JfP+85ecCdwMvr6rPNBGjJEmSpldTt01+b5JnAIeAV3WvAvHzAFX1u8BNwDOAtyYBOFxVaxqKVZIkSVOmkSK4qi7vMe9350y/EnjlSIOSJElSa3jHOEmSJLWORbAkSZJaxyJYkiRJrWMRLEmSpNaxCJYkSVLrWARLkiSpdSyCJUmS1DoWwZIkSWodi2BJkiS1jkWwJEmSWsciWJIkSa1jESxJkqTWSVU1HcPAJJkFHhnxnz0TeHLEf/N4GN/CnqyqdaP4Qw3l52KaXv/Hqs1xtj1HT9Sk5MyJGKfPNrL8hInK0XHqo2GZlM/YN0enqghuQpJtVbWm6Tj6MT4tZFLWv3HqeE1zX0zzZ5sWbeijafiMDoeQJElS61gES5IkqXUsgk/erU0HsAjj00ImZf0bp47XNPfFNH+2adGGPpr4z+iYYEmSJLWOR4IlSZLUOhbBkiRJah2LYEmSJLWORbAkSZJaxyJYkiRJrWMRLEmSpNaxCJYkSVLrWARLkiSpdaaqCF63bl0BPnwcz2NkzE8fJ/gYGXPUxwk8Rsoc9XECj76mqgh+8sknmw5B6sv81LgzRzXuzFEN0lQVwZIkSdKxsAiWJElS61gES5IkqXUsgiVJktQ6FsGSJElqHYtgSZIktY5FsCRJklrHIliSJEmtYxEsSZKk1rEItWsWjAAAGGdJREFUliRJUutYBEuSJKl1LIIlSZLUOhbBkiRJap2hFcFJ7khyIMnOOfPekOTxJA90H+t7vG5Fkg8n2Z3koSTXDStGSZIktdMwjwTfCazrMf/mqrqo+7inx/LDwK9U1XOA5wGvSrJ6iHFKkiSpZYZWBFfVfcBTJ/C6J6rq/u70V4DdwDkDDk+SJEkt1sSY4Fcn+VR3uMS3L9QwyUrgYuATowhMkiRJ7TDqIvhtwPnARcATwJv6NUxyBvBe4DVV9eUF2l2TZFuSbbOzs4OOVzop5qfGnTmqcWeOalhGWgRX1f6qOlJVTwPvAC7t1S7JUjoF8Dur6u5F3vPWqlpTVWtmZmYGH7R0EsxPjTtzVOPOHNWwnDLKP5bkWVX1RPfpi4GdPdoEuB3YXVVvHmV8Ojabd+1ny55ZLl81w9rVy5oOR2qM24JOhvkjNWuYl0jbBGwFLkiyN8nVwG8leTDJp4AfAH6p2/bsJEevFHEZ8HLgRQtdSk3N2LxrPxs3beeurY+wcdN2Nu/a33RIUiPcFnQyzB+peUM7ElxVG3rMvr1P233A+u70R4EMKy6dnC17Zjl46AgABw8dYcueWY9gqJXcFnQyzB+ped4xTsfl8lUznLZ0CQCnLV3C5ascn6V2clvQyTB/pOaNdEywJt/a1cu4ZcPFjmNT67kt6GSYP1LzLIJ13NauXuYOW8JtQSfH/JGa5XAISZIktY5FsCRJklrHIliSJEmtYxEsSZKk1rEIliRJUutYBEuSJKl1LIIlSZLUOhbBkiRJah2LYEmSJLWOd4wbss279ntbTGlCuL2OH/tE0rB4JHiINu/az8ZN27lr6yNs3LSdzbv2Nx2SpD7cXsePfSJpmIZWBCe5I8mBJDvnzHtDkseTPNB9rO/z2nVJPp3ks0luHFaMw7ZlzywHDx0B4OChI2zZM9twRJL6cXsdP/aJpGEa5pHgO4F1PebfXFUXdR/3zF+YZAnwv4AfAVYDG5KsHmKcQ3P5qhlOW7oEgNOWLuHyVTMNRySpH7fX8WOfSBqmoY0Jrqr7kqw8gZdeCny2qv4eIMkfAVcBuwYX3WisXb2MWzZc7Hg2aQK4vY4f+0SCc1acy769jw3kvZYs/UaOHPrngbzX2ctX8Phjjw7kvZrSxA/jXp3kZ4FtwK9U1RfnLT8HmNvbe4HvG1Vwg7Z29TJ33NKEcHsdP/aJ2m7f3sd4yds/NpD3ete1zx/oe026Uf8w7m3A+cBFwBPAm3q0SY951e8Nk1yTZFuSbbOzjhfTeDE/Ne7MUY07c1TDMtIiuKr2V9WRqnoaeAedoQ/z7QVWzHm+HNi3wHveWlVrqmrNzIzjxTRezE+NO3NU484c1bCMtAhO8qw5T18M7OzR7G+AVUnOS3Iq8FLg/aOIT5IkSe0wtDHBSTYBLwTOTLIXeD3wwiQX0Rne8DBwbbft2cBtVbW+qg4neTXwZ8AS4I6qemhYccqL0Wuymb9qmjkoTaZhXh1iQ4/Zt/dpuw9YP+f5PcDXXT5Ng3f0YvQHDx3h3dv2csuGi92Ja2KYv2qaOShNLu8Y13JejF6TzPxV08xBaXJZBLecF6PXJDN/1TRzUJpcTVwnWGPEi9Frkpm/apo5KE0ui2B5MXpNNPNXTTMHpcnkcAhJkiS1jkWwJEmSWsfhEMfAa0BK48ftcrLYX5LGjUeCF3H0GpB3bX2EjZu2s3nX/qZDklrP7XKy2F+SxpFF8CK8BqQ0ftwuJ4v9JWkcWQQvwmtANmfzrv3c9L6dHjVqmWPpd7fLyWJ/Ncf9qNSfY4IX4TUgm+GtSNvpWPvd7XKy2F/NcD8qLcwi+Bh4DcjR63X61D6YfsfT726Xk8X+Gj33o9LCHA6hseTp03ay36XBcXuSFuaR4JYa98sVefq0Xebmo/2uSeF+VJpsQyuCk9wB/BhwoKounLfseuC3gZmqerLHa38JeCVQwIPAz1XV/xtWrG0zKePEPH3aDr3y8Y1XXbj4C6UGuR+VJt8wh0PcCaybPzPJCmAt8GivFyU5B9gIrOkWz0uAlw4vzPbxckUaJ+ajJpF5K02+oRXBVXUf8FSPRTcDN9A5ytvPKcBpSU4BTgf2DT7C9nKcmMaJ+ahJZN5Kk2+kY4KTXAk8XlU7kvRsU1WPJ/kdOkeKDwIfqqoPLfCe1wDXAJx77rmDD3oKOU5sdMzPxZmPzTJHT4x5OzrmqIZlZEVwktOB1wE/vEi7bweuAs4DvgS8O8nLquoPerWvqluBWwHWrFmz0NFlzeE4sdEwP4+N+dgcc/TEmbejYY5qWEZ5ibTz6RS2O5I8DCwH7k/yzHntfgj4XFXNVtUh4G7g+SOMU5IkSVNuwSI4yZIk1yb5r0kum7fs147nD1XVg1V1VlWtrKqVwF7gkqr6/LymjwLPS3J6OmMmfhDYfTx/S5IkSVrIYkeC3w68APgCcEuSN89Z9pMLvTDJJmArcEGSvUmuXqDt2UnuAaiqTwDvAe6nc3m0b6B7GkSSJEkahMXGBF9aVc8FSPI/gbcmuRvYAPT+ZVtXVW1YZPnKOdP7gPVznr8eeP0isUmSJEknZLEjwacenaiqw1V1DfAA8BfAGcMMTJIkSRqWxYrgbUn+1Q0vquqNwO8BK4cVlMbP5l37uel9O9m8a3/ToWgCmT/SwtxGpNFbsAiuqpdV1b095t9WVUuHF5bGydHbg9619RE2btruTlrHxfyRFuY2IjVjsatD3DBn+qfnLfv1YQWl8eLtQXUyzB9pYW4jUjMWGw7x0jnTr523bB1qhV63B/XUneDYTuF6e1lpYZevmuHUJZ2v41OXfIPbSEPOWXEuSQbyOGdFC+5s9w2nTPy6WuzqEOkz3eu5ptT824MCbNy0nYOHjvDubXu5ZcPF3jWphY6ewl0sD7y9rKRJsG/vY7zk7R8byHu969oW3OPr6cMDWV9NrqvFjgRXn+lezzXF1q5exhuvupC1q5d56k7A8Z3CnZs/kv61LXtm+dqRpwH42pGn3adKI7JYEfy9Sb6c5CvAc7vTR59/zwji0xjy9LbAPJAGxW1JasaCwyGqasmoAtHk8PS2wDyQBsVtSWrGYmOCpZ7Wrl7mjlrmgTQgbkvS6C02HEKSJEmaOhbBkiRJah2LYEmSJLWORbAkSZJaZ2hFcJI7khxIsrPHsuuTVJIz+7z225K8J8nfJtmd5PuHFackSZLaZ5hHgu+kx62Vk6wA1gKPLvDa/w7cW1XfBXwvsHsYAUqSJKmdhlYEV9V9wFM9Ft0M3ECfO84l+VbgCuD27vt8raq+NKw4JUmS1D4jHROc5Erg8arasUCz7wRmgd9Lsj3JbUm+eYH3vCbJtiTbZme91aTGi/mpcWeOatyZoxqWkRXBSU4HXgfctEjTU4BLgLdV1cXAPwE39mtcVbdW1ZqqWjMz460mNV7MT407c1TjzhzVsIzySPD5wHnAjiQPA8uB+5M8c167vcDeqvpE9/l76BTFkiRJ0kCM7LbJVfUgcNbR591CeE1VPTmv3eeTPJbkgqr6NPCDwK5RxSlJkqTpN8xLpG0CtgIXJNmb5OoF2p6d5J45s34ReGeSTwEXAb8+rDglSZLUPkM7ElxVGxZZvnLO9D5g/ZznDwBrhhWbJEmS2s07xkmSJKl1LIIlSZLUOhbBkiRJah2LYEmSJLWORbAkSZJaxyJYkiRJrWMRLEmSpNaxCJYkSVLrWARLkiSpdSyCJUmS1DoWwZIkSWodi2BJkiS1jkWwJEmSWsciWJIkSa0ztCI4yR1JDiTZ2WPZ9UkqyZkLvH5Jku1JPjCsGCVJktROwzwSfCewbv7MJCuAtcCji7z+OmD34MMavc279nPT+3ayedf+pkPRlDLH1Gbmv6QTMbQiuKruA57qsehm4Aag+r02yXLgR4HbhhPd6GzetZ+Nm7Zz19ZH2LhpuztpDZw5pjYz/yWdqJGOCU5yJfB4Ve1YpOlb6BTKTx/De16TZFuSbbOzs4MIc6C27Jnl4KEjABw8dIQte8YvRg3PKPLTHNPJGPd96GLM/+k36Tmq8TWyIjjJ6cDrgJsWafdjwIGq+uSxvG9V3VpVa6pqzczMzAAiHazLV81w2tIlAJy2dAmXrxq/GDU8o8hPc0wnY9z3oYsx/6ffpOeoxtcpI/xb5wPnATuSACwH7k9yaVV9fk67y4Ark6wHvgn41iR/UFUvG2GsA7N29TJu2XAxW/bMcvmqGdauXtZ0SJoy5pjazPyXdKJGVgRX1YPAWUefJ3kYWFNVT85r91rgtd02LwSun9QC+Ki1q5e5Y9ZQmWNqM/Nf0okY5iXSNgFbgQuS7E1y9QJtz05yz7BikSRJkuYa2pHgqtqwyPKVc6b3Aet7tPkI8JEBh6YB2bxrv6cgx4R9IU0+t2NptLxjnE6IlyUaH/aFNPncjqXRswjWCfGyROPDvpAmn9uxNHoWwTohXpZofNgX0uRzO5ZGb5SXSNMU8bJE48O+kCaf27E0ehbBOmFelmh82BfS5HM7lkbL4RCSJElqHYtgSZIktY5FsCRJklrHIliSJEmtYxEsSZKk1rEIliRJUutYBEuSJKl1LIIlSZLUOhbBkiRJap2hFcFJ7khyIMnOHsuuT1JJzuyxbEWSDyfZneShJNcNK0ZJkiS10zCPBN8JrJs/M8kKYC3waJ/XHQZ+paqeAzwPeFWS1cMKUpIkSe0ztCK4qu4Dnuqx6GbgBqD6vO6Jqrq/O/0VYDdwzrDilCRJUvuMdExwkiuBx6tqxzG2XwlcDHxigTbXJNmWZNvs7OxA4pQGxfzUuDNHNe7MUQ3LyIrgJKcDrwNuOsb2ZwDvBV5TVV/u166qbq2qNVW1ZmZmZjDBSgNifmrcmaMad+aohmWUR4LPB84DdiR5GFgO3J/kmfMbJllKpwB+Z1XdPcIYJUmS1AKnjOoPVdWDwFlHn3cL4TVV9eTcdkkC3A7srqo3jyo+SZIktccwL5G2CdgKXJBkb5KrF2h7dpJ7uk8vA14OvCjJA93H+mHFKUmSpPYZ2pHgqtqwyPKVc6b3Aeu70x8FMqy4JEmSJO8YJ0mSpNaxCJYkSVLrpKrnPSsmUpJZ4JER/9kzgScXbdUc41vYk1X1dXc2HIaG8nMxTa//Y9XmONueoydqUnLmRIzTZxtZfsJE5eg49dGwTMpn7JujU1UENyHJtqpa03Qc/RifFjIp6984dbymuS+m+bNNizb00TR8RodDSJIkqXUsgiVJktQ6FsEn79amA1iE8Wkhk7L+jVPHa5r7Ypo/27RoQx9N/Gd0TLAkSZJaxyPBkiRJah2LYEmSJLWORXAPSe5IciDJzj7LX5jkH5I80H3cNGfZuiSfTvLZJDc2GON/mhPfziRHknxHd9nDSR7sLts2hNhWJPlwkt1JHkpyXY82SXJLdz19Ksklc5aNZB22RZJvSvLXSXZ0++O/9GjTN6dHHOuSJNuTfKDHsr4504RFYh2L9TntklzX3b89lOQ1PZZPVD/02q8n+Y4km5Ps6f777X1e635zBBb67k1yfZJKcmaf1w71u3dQ+uThG5I8PmdbWt/ntZOVh1XlY94DuAK4BNjZZ/kLgQ/0mL8E+DvgO4FTgR3A6iZinNf2x4G/mPP8YeDMIa6/ZwGXdKe/BfjM/PUArAc+CAR4HvCJUa/Dtjy66/iM7vRS4BPA8+a16ZnTDcT6y8Af9tm+eubMmMY6Futzmh/AhcBO4HTgFOD/AqsmuR967deB3wJu7E7fCPxmj9e532ywj7rzVwB/RudGHj2/X4f93TvMzwi8Abh+kddNXB56JLiHqroPeOoEXnop8Nmq+vuq+hrwR8BVAw2u6zhj3ABsGkYcvVTVE1V1f3f6K8Bu4Jx5za4C7qqOjwPfluRZjHAdtkV3Hf9j9+nS7mPsfhGbZDnwo8BtfZr0y5mRO4ZYNXzPAT5eVV+tqsPAXwIvbjimk9Jnv34V8Pvd6d8HfqLHS91vjsgC3703AzcwhvvW4zUJNdCgWASfuO/vnl7+YJLv7s47B3hsTpu9fH3xN1JJTgfWAe+dM7uADyX5ZJJrhvz3VwIX0zn6OFe/dTV263AadE/dPwAcADZX1fz+gN45PUpvofMl8nSf5eOUG4vFCs2vz2m3E7giyTO6+7n1dI7GzTfp/bCsqp6AzgEG4KwebcZp22idJFcCj1fVjkWajuy7d0he3R2KdkefYTkTl4cWwSfmfuDZVfW9wP8A/k93fnq0bfp/hT8O/FVVzf1f3WVVdQnwI8CrklwxjD+c5Aw6xfdrqurL8xf3eEktMF8noaqOVNVFwHLg0iQXzmvSL6dHIsmPAQeq6pMLNesxb+S5cYyxNro+26CqdgO/CWwG7qVz6vXwvGZt6Yex2DbaqPsfsNcBxzLefCTfvUPyNuB84CLgCeBNPdpMXB5aBJ+Aqvry0dPLVXUPsLQ7EH4v//pIxHJgXwMhzvVS5g2FqKp93X8PAH9C5xTGQCVZSqcAfmdV3d2jSb91NY7rcGpU1ZeAj9A5OzB3fr+cHpXLgCuTPEznFNqLkvzBvDbjkhuLxjoG67MVqur2qrqkqq6gc/p2z7zl09AP+48O++n+e6BHm3HZNtrofOA8YEd3n7AcuD/JM+c3HMV377BU1f7uwZSngXfQO/aJy0OL4BOQ5JlJ0p2+lM56/ALwN8CqJOclOZVOAfr+BuP8N8ALgPfNmffNSb7l6DTww3ROKw7y7wa4HdhdVW/u0+z9wM92f/H/POAfuqf6xmodToMkM0m+rTt9GvBDwN/Oa9Mvp0eiql5bVcuraiWdPv+LqnrZvGb9cmakjiXWptdnWyQ5q/vvucBPMu8//FPSD+8HXtGdfgVz9udzuN9sSFU9WFVnVdXK7j5hL50fhn9+brtRfPcO07zfX7yY3rFPXB6e0nQA4yjJJjq/Kj4zyV7g9XR+TERV/S7wU8AvJDkMHAReWlUFHE7yajq/EF0C3FFVDzUUI3QS9UNV9U9zXroM+JPu98IpwB9W1b0DDu8y4OXAg91xqAD/GTh3Tnz30BnD91ngq8DPdZeNbB22yLOA30+yhE4R8MdV9YEkPw+L5nSj5sXYM2fGxSSszyn03iTPAA4Br6qqL05yP/TZr/8G8MdJrgYeBX662/Zs4LaqWu9+c3R69VFV3d6n7b/0EaP57h2IPnn4wiQX0Rne8DBwbbftROeht02WJElS6zgcQpIkSa1jESxJkqTWsQiWJElS61gES5IkqXUsgjWRunesOZBk0UvMJPnlJLu6d7r58yTPHkWMajdzVJLGm0XwFEry4iSV5LvmzLs3yZeSfKDJ2AboTubd8GEB24E1VfVc4D3Abw0rKC1ufn4muSjJ1iQPdYvAlzQd44DciTk6cZIcSfLAnMeN3fmvTvLZbu5O2g03NEUWyNF3Jvl0kp3d/4QvbTrWcWcRPJ02AB+lc6Hqo36bzrV7p0JV3UfnDlH/Isn53WL/k0m2HC2yqurDVfXVbrOP07mLjZozPz+/CvxsVX03naLxLUdv7jHJzNGJdbCqLprz+I3u/L+ic6OZRxqMTYL+OfpO4LuA7wFOA17ZWIQTwiJ4yiQ5g87NKq5mThFcVX8OfKWpuEbkVuAXq+rfAtcDb+3R5mrggyONSv+iV35W1Weqak93eh+d28LONBbkcJmjE6qqtlfVw03HIfVTVfdUF/DX+J/pRXnHuOnzE8C9VfWZJE8luaSq7m86qGHrFlfPB97dvSMPwDfOa/MyYA2dW0mrGQvmZ/fWtqcCf9dYhENijk6M0+bc6RLgv1XVuxqLRvp6C+ZodxjEy4HrRh7ZhLEInj4bgLd0p/+o+3zqi2A6ZzW+VFUX9VqY5IeA1wEvqKp/HmlkmqtvfnbvTf+/gVdU1dPNhDdU5uhkONivj6QxsViOvhW4r6q2jCqgSWURPEWSPAN4EXBhkqJz7+5KckNN+f2xq+rLST6X5Ker6t3pHGp7blXtSHIx8HZgXVUdaDjU1looP4FvAf4U+LWq+niDYQ6NOSpp2JK8ns5wsmubjmUSOCZ4uvwUcFdVPbuqVlbVCuBzwL9rOK6BS7IJ2ApckGRvkquBnwGuTrIDeAi4qtv8t4Ez6JyGfiDJ+xsJWv3y8wrgT7rL3t1ohANkjkoapSSvBP49sGFKz6YNXKb8AGGrJPkI8BtVde+ceRuB5wAX0vnV6BnAF4Crq+rPmohT7bRAfv4SnR9wPDSn+X+oqgeQRizJEeDBObPuraobu7l6A/BMOj/evKeq/PW9Rm6BHD1M5+olR38Ef3dVvXHkAU4Qi2BJkiS1jsMhJEmS1DoWwZIkSWodi2BJkiS1jkWwJEmSWsciWJIkSa1jESxJkqTWsQiWJElS6/x//lItncR2d1kAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot parameter estimate\n", "pyomo.contrib.parmest.graphics.pairwise_plot(bootstrap_theta, title='Bootstrap theta estimates')\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "source": [ "Confidence regions can be plotted around the bootstrap estimates for various distributions with confidence $\\alpha$." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "nbpages": { "level": 2, "link": "[2.8.8 Bootstrap resampling](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.8-Bootstrap-resampling)", "section": "2.8.8 Bootstrap resampling" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A1': 185.60880919391855, 'A2': 401.1701986690201, 'E1': 9.866878980549789, 'E2': 14.866030768895133}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/kghosh/anaconda3/envs/idaes-pse-dev-pyomo6-1/lib/python3.8/site-packages/pyomo/contrib/parmest/graphics.py:151: UserWarning: No contour levels were found within the data range.\n", " ax.contour(X,Y,Z, levels=[alpha], colors=color)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot bootstrap parameter estimates with confidence intervals\n", "pyomo.contrib.parmest.graphics.pairwise_plot(bootstrap_theta, theta, 0.8, ['MVN', 'KDE', 'Rect'], \n", " title='Bootstrap theta with confidence regions')" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.9 Nonlinear confidence regions](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.9-Nonlinear-confidence-regions)", "section": "2.8.9 Nonlinear confidence regions" } }, "source": [ "## 2.8.9 Nonlinear confidence regions\n", "\n", "The likelihood-ratio test (sometimes called the likelihood-ratio $\\chi^2$ test) is a hypothesis test that helps one choose the “best” model between two models[link](https://www.statisticshowto.com/likelihood-ratio-tests/). Basically, the test compares the fit of two models. The null hypothesis is that the first model is the “best” model; It is rejected when the test statistic is large. In other words, if the null hypothesis is rejected, then the second model is a significant improvement over the first model.\n", "\n", "In the last part of this notebook, we use the bootstrap parameter estimates to determine the goodness of fit using the likelihood-ratio $\\chi^2$ test. More information can be found [here](https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/api.html#pyomo.contrib.parmest.parmest.Estimator.likelihood_ratio_test)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "nbpages": { "level": 2, "link": "[2.8.9 Nonlinear confidence regions](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.9-Nonlinear-confidence-regions)", "section": "2.8.9 Nonlinear confidence regions" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A1 A2 E1 E2 obj\n", "0 186.769746 382.642388 9.907827 14.726285 0.222375\n", "1 179.703097 392.070899 9.738017 14.824106 0.222957\n", "2 156.529846 334.342272 9.464807 14.407121 0.224970\n", "3 146.617094 406.533938 9.252072 14.878677 0.225126\n", "4 189.635337 370.602660 9.907778 14.697935 0.222650\n" ] } ], "source": [ "from itertools import product\n", "### Likelihood ratio test\n", "\n", "# generate arrays of parameter values\n", "A1 = np.arange(180.0, 190.0, 1.0)\n", "A2 = np.arange(395.0, 405.0, 1.0)\n", "E1 = np.arange(5.0, 15.0, 1.0)\n", "E2 = np.arange(10.0, 20.0, 1.0)\n", "\n", "# format parameter values into a pandas dataframe to be provided as input to calculate \n", "# corresponding objective function values\n", "# theta_vals = pd.DataFrame(list(product(A1, A2, E1, E2)), columns=theta_names)\n", "theta_vals = bootstrap_theta\n", "obj_at_theta = pest.objective_at_theta(theta_vals)\n", "print(obj_at_theta.head())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "nbpages": { "level": 2, "link": "[2.8.9 Nonlinear confidence regions](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.9-Nonlinear-confidence-regions)", "section": "2.8.9 Nonlinear confidence regions" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A1 A2 E1 E2 obj 0.8 0.85 0.9 \\\n", "0 186.769746 382.642388 9.907827 14.726285 0.222375 True True True \n", "1 179.703097 392.070899 9.738017 14.824106 0.222957 True True True \n", "2 156.529846 334.342272 9.464807 14.407121 0.224970 True True True \n", "3 146.617094 406.533938 9.252072 14.878677 0.225126 True True True \n", "4 189.635337 370.602660 9.907778 14.697935 0.222650 True True True \n", "\n", " 0.95 \n", "0 True \n", "1 True \n", "2 True \n", "3 True \n", "4 True \n" ] } ], "source": [ "LR = pest.likelihood_ratio_test(obj_at_theta, obj, [0.8, 0.85, 0.9, 0.95])\n", "print(LR.head())" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.8.9 Nonlinear confidence regions](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.9-Nonlinear-confidence-regions)", "section": "2.8.9 Nonlinear confidence regions" } }, "source": [ "The likelihood ratio test results with confidence $\\alpha$ can be visualized as follows:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "nbpages": { "level": 2, "link": "[2.8.9 Nonlinear confidence regions](https://ndcbe.github.io/CBE60499/02.08-Parmest-tutorial.html#2.8.9-Nonlinear-confidence-regions)", "section": "2.8.9 Nonlinear confidence regions" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Objective contour plot for A1 A2 slice failed\n", "Objective contour plot for A1 E1 slice failed\n", "Objective contour plot for A2 E1 slice failed\n", "Objective contour plot for A1 E2 slice failed\n", "Objective contour plot for A2 E2 slice failed\n", "Objective contour plot for E1 E2 slice failed\n", "Objective contour plot for A2 A1 slice failed\n", "Objective contour plot for E1 A1 slice failed\n", "Objective contour plot for E2 A1 slice failed\n", "Objective contour plot for E1 A2 slice failed\n", "Objective contour plot for E2 A2 slice failed\n", "Objective contour plot for E2 E1 slice failed\n" ] }, { "data": { "image/png": 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NN424/datW7n22opnz51wFsGSJEnaxZZt2/jr667n2z/9GX993fX8ctu2Ue9j0aJFfP3rX+fss89m48aNnH766bzpTW9iw4YNAFx88cXMmjWLk046iXXr1rFo0SK6urqYNWsW3d3dnHHGGZxwwgmcfPLJPP3002zZsoVZs2Zx4oknsnDhwnEdn9MhJEmStIufPfwIvVm68nBvJj99+BHe+YbfGdU+FixYwKGHHsoHPvAB3v3ud/Of//mfLF68mGuuuYaTTjqJRx99lOXLl7N69Wo+/elPc/755/PII488P23iqquu4qUvfSlXXHEFS5cu5ZBDDmHWrFlcdNFFZA55VeRhORIsSZKkXbzx4GlMLv/YbHIEbzp4fD967+zsZNKkSUydOpWtW7eyevVqli9fzqxZs/jQhz7E008//aL2vb29fOITn+Ctb30rX/jCF9i4cSMnnHACO3fu5D3vec+w84uH40iwJEmSdvGKPffkb999Gj99+BHedPA0Xr7nnqPeR1tbG729vcCLz96QmRx++OGcfPLJfP7znwdKp23bvHnz8+3vuusutm3bxi233MKXv/xlHn30UXp7e7n44osBOOqoozjrrLPGfHyOBEuSJGkXu++2G/vtsw/vfMPvsN8++7D7bqMfOz3iiCP44Q9/yLnnnrvLuiOPPJL99tvv+Tm+X/3qV9lvv/3YsmULp59+Oh0dHaxdu5Y5c+Zwxx13AHDHHXdw/PHHc+yxx/L2t799XMfnSLAkSZKqYp999uGWW2550bJZs2Yxa9YsAC644AIuuOCCF62/8cYbn79/66237rLPSsvGwpFgSZIkFY5FsCRJkgrHIliSJEmFYxEsSZKkwrEIliRJUuFYBEtqKF2rNnHh9ffRtWpTvUORmp7vJ1XStWoTW3/1G57u2T5ku507d/LMM8+86N/RWrduHTfddBPr1q3jfe9737Dtt27dyrXXXjvo+qeeegqAiy66iK1bt446nv4sgiU1jK5Vm1i4+E6uvv1hFi6+0w9uaRx8P6mSvrx49rleHtnyq0EL4Z07d7J582bmzp3L7rvvzty5c9m8efOoC+G+InikhiuCN2zYwMUXX8yKFSu4+uqrRxXLQBbBkhrGijXd9GwvXSmoZ3svK9Z01zkiqXn5flIl/fNiZyaTJkXFdtu2bWP+/PksX76cHTt2sHz5cubPn8+2bdtG9XiLFi3i61//OmeffTYbN27k9NNP501vehMbNmwA4OKLL2bWrFmcdNJJrFu3jkWLFtHV1cWsWbPo7u7mjDPO4IQTTuDkk0/m6aefJiLITODFV6AbC4tgSQ1j5vQO2tsmA9DeNpmZ0zvqHJHUvHw/qZL+eTEpgrbJlQvJPffcc5eLUtx6663sOcpLJy9YsICzzjqLr3zlK/zyl7/kW9/6Fh//+Me55ppruPfee3n00UdZvnw5X/ziF/n0pz/NggULmD17NsuXL6ejo4OrrrqKm2++mTPOOIOlS5dywAEH8KlPfYqZM2eO65LJ4BXjJDWQ2Z1TuHT+0axY083M6R3M7pxS75CkpuX7SZX05cVePY8z7RUvZffdJldst23bNo4//niWL1/+/LLjjz+ebdu2sffee4/psTs7O5k0aRJTp05l7dq1rF69muXLlz9/9bj999//Re17e3v5xCc+wb333svTTz/Nu9/9bvbdd1+gNCd4vCyCJTWU2Z1T/LCWJojvJ1Uyu3MKq1dv4WXtbYO22XPPPVm8eDHz58/n1ltv5fjjj2fx4sWjHglua2ujt7c0/aL/9IXM5PDDD+fkk0/m85//PADbt29n8+bNz7e/66672LZtG7fccgtf/vKXefTRR0d7qENyOoQkSZJeZNKkSbz61a9m2bJlPPfccyxbtoxXv/rVTJo0utLxiCOO4Ic//CHnnnvuLuuOPPJI9ttvP2bNmsWJJ57IV7/6Vfbbbz+2bNnC6aefTkdHB2vXrmXOnDnccccdE3Voz3MkWJIkSbuYNGnS81MfxjoFYp999uGWW2550bJZs2Y9PwXiggsu4IILLnjR+htvvPH5+wPnJU+kmo8ER8RBEfGDiFgdEfdHxEcrtImIuDQi1kbEPRHxxlrHKUmSpNZVj5HgHcBfZObPImJv4KcR0ZWZq/q1OQWYXr4dC/xT+V9JkiRp3Go+EpyZj2Xmz8r3nwFWA1MHNDsNuDpLfgTsGxH7I0mSpAnR9wO0VpCZ9PT0jGqbus4JjojXAEcDPx6waiqwvt/fG8rLHquwjwXAAoBp06ZVI0xpzMxPNTpzVI3OHK2OV7ziFfz85z+vdxgTqq2tbZfTrA2lbkVwROwFXAP8eWY+PXB1hU2y0n4ycxGwCGDGjBkV20j1Yn6q0ZmjanTmaHVMmTKFKVOKffq8upwiLSLaKBXA38zMSheI3gAc1O/vA4GNtYhNkiRJra8eZ4cI4CvA6sz87CDNlgHvL58l4r8BT2XmLlMhJEmSpLGox3SItwBnAfdGxF3lZZ8EpgFk5mXADcDvAWuBXwF/Woc4JUmS1KJqXgRn5q1UnvPbv00CH65NRJIkSSoaL5ssSZKkwrEIliRJUuFYBI/Q1IOmERETcpt6kOc5lCRJqqe6XiyjmWzcsJ4zL79tQva19IPHTch+JEmSNDaOBEuSJKlwLIIlSZJUOBbBkiRJKhyLYEmSJBWOP4yTWkDXqk2sWNPNzOkdzO6cUu9wJI2B7+P68bkvJotgqcl1rdrEwsV30rO9l2+v3MCl84+uWSfuB4c0MT5z4wNcdvOD9O7Mmr+Pi66efeho2N9OPKdDSE1uxZpuerb3AtCzvZcVa7pr8rh9HxxX3/4wCxffSdeqTTV53EbStWoTF15/XyGPXePXlz+fufEBLlu+lt6dCdT2faz69aGj0ar9bb37UItgqcnNnN5Be9tkANrbJjNzekdNHrcZPjiqqVU/lFQb/fPnspsfpDdfWDd5UtTsfaz69aGj0Yr9bSP0oRbBUpOb3TmFS+cfzfvffHBNv8Zrhg+OamrFDyXVTv/86d2ZTI7S8skB55xwmF9311C9+tDRaMX+thH6UOcESy1gdueUmnfcfR8cI5mj1opz2WZO7+DbKzfQs723ZT6UVDsD8+fE172aBzc/w9s79+MT7zi83uEVTj360NGo1N82e7/aCH2oI8GSxmx25xQuPu2IYQvg8XzltWzZMjo7O9ljjz3o7Oxk2bJl4w17QjTD6JEaT18+n/rGg9n2zws5dvJD/Nnxh/CD/9rMA5ue5cpbf+HUGlXUv79thX61EfpQR4IlVVWlr7xG2tktW7aMefPm0dPTA8Dq1auZN28eS5YsYe7cuVWLeaQaffRIjWVgPq9/aA1P/OO5APT0Hlr6d5TvERVTq/Sr9e5DHQmWVFXjmct23nnnPd9R9+np6eH888+f0BilWhgsn3+09AstN99T1WW/OjEcCZZUVaOZOzzQQw89VHH5gw8+OFHhSTUzWD5v2vAw/zbG94iKyX51YlgES6q6sX7ldeihh7J69epdlh922GETEZZUU0Plc72/FlbzsV8dP6dDSGpYl1xyCe3t7S9a1t7eziWXXFKniKSxM5/VCMzDF1gES2pYc+fOZcmSJXR2drL77rvT2dnJ0qVLOfXUU+sdmjRq5rMagXn4AqdDSGpoc+fOHfIXy81+rkw1v9Hk4HD5LNXCSPOw1ftXR4IlNa1GuOymis0cVKsqQm7XpQiOiCsjYnNE3DfI+n0i4t8i4u6IuD8i/rTWMUpqfI1w2U0VmzmoVlWE3K7XSPBVwJwh1n8YWJWZRwKzgH+IiJfUIC5JddC1ahMXXn/fqEcaxnOuTGm8ulZtYv2WX/GSyaWPUnNQjWosfWwR+te6zAnOzFsi4jVDNQH2jogA9gK2ADtqEJqkGuv7yq1ney/fXrlhVJfPHM+5MqXx6J+3L5k8iRMP7+A9xx5sDqrhjLWPLUL/2qg/jPsCsAzYCOwNnJmZOys1jIgFwAKAadOm1SxAaSTMz+GN5/KfUP/LbjY7c3Rs+uftb3p3ctArXmoeVok5Oj7j6WNbvX9t1B/GvQO4CzgAOAr4QkS8rFLDzFyUmTMyc0ZHR+sN1au5mZ/DK8JXbo3MHB0b87Z2zNHxMVcH16gjwX8KXJKZCayNiF8ArwPuqG9YkiZaEb5yU+sxb9UszNXBTXgRHBGzM7NrnLt5BHgbsCIipgCHA5Uvdi2p6bX6V25qTeatmoW5Wlk1RoK/Agw5aSciFlM668OrImID8CmgDSAzLwP+F3BVRNwLBHBuZj5RhVglSZJUQGMqgiNi2WCrgFcOt31mzh9m/Ubg5DGEJkmSJA1rrCPBM4H3Ac8OWB7AMeOKSJIkSaqysRbBPwJ+lZk3D1wREQ+MLyRJkiSpusZUBGfmKZWWR8RbgHvHFZEkSZJUZeP+YVxEHAW8BzgD+AVwzXj3KUmSJFXTWH8Y91pgHjAfeBJYCkRmnjiBsWkEph40jY0b1tc7DEmSpKYy1pHg/wJWAKdm5lqAiPjYhEWlEdu4YT1nXn7bhOxr6QePm5D9SJIkNbqxXjb5D4HHgR9ExJcj4m2UzgwhSZIkNbwxFcGZeV1mnknpUsbLgY8BUyLinyLC8/tKkiSpoY11JBiAzNyWmd/MzHcBBwJ3AedNSGSSJElSlYyrCO4vM7dk5uWZedJE7VOSJEmqhgkrgiVJkqRmYREsSZKkwrEIliRJUuFYBEuSJKlwLIIlSZJUOBbBkiRJKhyLYEmSJBWORbAkSZIKxyJYkiRJhWMRLEmSpMKxCJYkSVLhWARLkiSpcCyCJUmSVDh1KYIj4sqI2BwR9w3RZlZE3BUR90fEzbWMT5IkSa2tXiPBVwFzBlsZEfsCXwLmZubrgT+qUVySJEkqgLoUwZl5C7BliCbvAa7NzEfK7TfXJDBJkiQVQqPOCX4t8PKIWB4RP42I9w/WMCIWRMTKiFjZ3d1dwxCl4ZmfanTmqBqdOapqadQieDfgTcA7gXcA/zMiXlupYWYuyswZmTmjo6OjljFKwzI/1ejMUTU6c1TVslu9AxjEBuCJzNwGbIuIW4AjgZ/XNyxJkiS1gkYdCb4emBkRu0XES4FjgdV1jkmSJEktoi4jwRGxGJgFvCoiNgCfAtoAMvOyzFwdEd8D7gF2Aldk5qCnU5OKrmvVJlas6Wbm9A5md06pdzhSTZn/qjZzrDXVpQjOzPkjaPMZ4DM1CEdqal2rNrFw8Z30bO/l2ys3cOn8o+2kx8gPusYz3Gti/qvazLHhNWvf2ajTISSN0Io13fRs7wWgZ3svK9b46+mx6Pugu/r2h1m4+E66Vm2qd0iFN5LXxPxXtZljQ2vmvtMiWGpyM6d30N42GYD2tsnMnO6vp8fCD7rGM5LXxPxXtZljQ2vmvrNRzw4haYRmd07h0vlHN+VXUY1k5vQOvr1yAz3be/2gaxAjeU3Mf1WbOTa0Zu47LYKlFjC7c4od8zj5Qdd4RvqamP+qNnNscM3cd1oES1KZH3SNx9dEanzN+j51TrCqY9JuRMS4b1MPmlbvI5EkSS3IkWBVx84dnHn5bePezdIPHjcBwUiSJL2YI8GSJEkqHItgSZIkFY5FsCRJkgrHIliSJEmFYxEsSZKkwrEIliRJUuF4ijRJTalr1aamvEJRUfl6SY2n6O9LR4IlNZ2uVZtYuPhOrr79YRYuvpOuVZvqHZKG4OslNR7flxbBkprQijXd9GzvBaBney8r1nTXOSINxddLajy+Ly2CJTWhmdM7aG+bDEB722RmTu+oc0Qaiq+X1Hh8XzonWFITmt05hUvnH13ouWzNxNdLajy+Ly2CJTWp2Z1TCtlpNytfL6nxFP196XQISZIkFY5FsCRJkgrHIliSJEmFYxEsSZKkwqlLERwRV0bE5oi4b5h2vxsRvRFxeq1ikyRJUuur10jwVcCcoRpExGTg/wA31iIgSZIkFUddiuDMvAXYMkyz/wFcA2yufkSSJEkqkoacExwRU4F3A5eNoO2CiFgZESu7u4t3yT81NvNTjc4cVaMzR1UtDVkEA58Dzs3M3uEaZuaizJyRmTM6Oop3yT81NvNTjc4cVaMzR1UtjXrFuBnAkogAeBXwexGxIzP/tb5hSZIkqRU0ZBGcmYf03Y+IqyzAjcsAACAASURBVIDvWABLkiRpotSlCI6IxcAs4FURsQH4FNAGkJnDzgOWJEmSxqMuRXBmzh9F2z+pYiiSJEkqoEb9YZwkSZJUNRbBkiRJKhyLYEmSJBWORbAkSZIKxyJYkiRJhWMRLEmSpMKxCJYkSVLhRGbWO4YJExHdwMM1fthXAU/U+DFHw/iG9kRmzqnFA9UpP4dT7+d/pIocZ9FzdKyaJWfGopGOrWb5CU2Vo430GlVLsxzjoDnaUkVwPUTEysycUe84BmN8GkqzPP/GqdFq5deilY+tVRThNWqFY3Q6hCRJkgqn5YrgiLgyIjZHxH0jaPvxiFgVEfdExPcj4uBaxChJkqT6arkiGLgKGOn8pDuBGZn5BuBfgL8bw+MtGsM2tWR8GkqzPP/GqdFq5deilY+tVRThNWr6Y2zJOcER8RrgO5l5RPnvw4AvAh3Ar4D/npn/NWCbo4EvZOZbahutJEmSam23egdQI4uAczJzTUQcC3wJOGlAm7OB79Y8MkmSJNVcyxfBEbEXcBzw7YjoW7z7gDbvA2YAJ9Q2OkmSJNVDyxfBlOY9b83MoyqtjIi3AxcAJ2TmczWNTJIkSXXRij+Me5HMfBr4RUT8EUCUHFm+fzRwOTA3MzfXMUxJkiTVUMv9MC4iFgOzKF3JZBPwKeAm4J+A/YE2YElmXhwR/wn8DvBYefNHMnNuzYOWJElSTbVcESxJkiQNp+WnQ0iSJEkDWQRLkiSpcFqqCJ4zZ04C3ryN5lYz5qe3Md5qxhz1NoZbTZmj3sZwG1RLFcFPPPFEvUOQBmV+qtGZo2p05qgmUksVwZIkSdJIWARLkiSpcCyCJUmSVDgWwZIkSSoci2BJkiQVjkWwJEmSCsciWJIkSYVjESxJkqTCsQiWJElS4VgES5IkqXAsgiVJklQ4FsGSJEkqHItgSZIkFY5FsCRJkgrHIliSJEmFU/UiOCImR8SdEfGdCutmRcRTEXFX+XZhv3VzIuKBiFgbEedVO05JkiQVx241eIyPAquBlw2yfkVmvqv/goiYDHwRmA1sAH4SEcsyc1VVI5UkSVIhVHUkOCIOBN4JXDHKTY8B1mbmQ5n5G2AJcNpExydJkqRiqvZ0iM8BfwXsHKLNmyPi7oj4bkS8vrxsKrC+X5sN5WW7iIgFEbEyIlZ2d3dPSNDSRDE/1ejMUTU6c1TVUrUiOCLeBWzOzJ8O0exnwMGZeSTweeBf+zav0DYr7SAzF2XmjMyc0dHRMa6YNTJdqzZx4fX30bVqU71DaXjmpxqdOVo/9qUjY462rnq/B6o5EvwWYG5ErKM0neGkiPhG/waZ+XRmPlu+fwPQFhGvojTye1C/pgcCG6sYq0aoa9UmFi6+k6tvf5iFi++085akMbAvVdE1wnugakVwZp6fmQdm5muAecBNmfm+/m0iYr+IiPL9Y8rxPAn8BJgeEYdExEvK2y+rVqwauRVruunZ3gtAz/ZeVqzxqylJGi37UhVdI7wHan6e4Ig4JyLOKf95OnBfRNwNXArMy5IdwEeAGymdWeJbmXl/rWPVrmZO76C9bTIA7W2TmTndr6YkabTsS1V0jfAeiMyKU22b0owZM3LlypX1DqPlda3axIo13cyc3sHszin1Dme8Ks0/rwrzU2NkjraoFulLa5afYI62mhq9BwbN0VqcJ1gtZnbnlGbusCWpIdiXqujq/R7wssmSJEkqHItgSZIkFY5FsCRJkgrHIliSJEmFYxEsSZKkwrEIliRJUuF4irQqa5HzQEpSXdiHSo2vWd+njgRXUSNcF1uSmpV9qNT4mvl9ahFcRY1wXWxJalb2oVLja+b3qUVwFTXCdbElqVnZh0qNr5nfp84JrqLZnVO4dP7RTTlPRpLqzT5UanzN/D6tehEcEZOBlcCjmfmuAeveC5xb/vNZ4EOZeXd53TrgGaAX2JGZM6odazXU+7rYktTM7EOlxtes79NajAR/FFgNvKzCul8AJ2TmLyPiFGARcGy/9Sdm5hM1iFGSJEkFUtU5wRFxIPBO4IpK6zPztsz8ZfnPHwEHVjMeSbU39aBpRMS4b1MPmlbvQ5EktZBqjwR/DvgrYO8RtD0b+G6/vxP4j4hI4PLMXFRpo4hYACwAmDbND0k1FvMTNm5Yz5mX3zbu/Sz94HETEI0GMkfV6MxRVUvVRoIj4l3A5sz86QjankipCD633+K3ZOYbgVOAD0fEWyttm5mLMnNGZs7o6GieXySqGMxPNTpzVI3OHFW1VHM6xFuAueUfuC0BToqIbwxsFBFvoDRd4rTMfLJveWZuLP+7GbgOOKaKsUqSJKlAqlYEZ+b5mXlgZr4GmAfclJnv698mIqYB1wJnZebP+y3fMyL27rsPnAzcV61YJUmSVCw1P09wRJwDkJmXARcCrwS+FBHwwqnQpgDXlZftBvxzZn6v1rFKkiSpNdWkCM7M5cDy8v3L+i3/APCBCu0fAo6sRWySJEkqHi+bLEmSpMKxCJYkSVLhWARLkiSpcCyCJUmSVDgWwZIkSSqcmp8iTY2na9UmVqzpZub0DmZ3Tql3OJLUVOxD1apaPbcdCS64rlWbWLj4Tq6+/WEWLr6TrlWb6h2SJDUN+1C1qiLktkVwwa1Y003P9l4Aerb3smJNd50jkqTmYR+qVlWE3LYIHoGuVZu48Pr7WvJ/QTOnd9DeNhmA9rbJzJzeUeeIJLWiVu1H7UPVzIZ6XxYht50TPIy+rwN6tvfy7ZUbuHT+0S01L2Z25xQunX90S8/5kVRfrdyP2oeqWQ33vixCblsED6PS1wGtlgizO6e03DFJahyt3o/ah6oZjeR92eq57XSIYRTh64DBLFu2jM7OTvbYYw86OztZtmxZvUOS1ISK3I8OZL+qRrBs2TKu+vgf8Mjfv5tHr/gQO37xk0K+L6teBEfE5Ii4MyK+U2FdRMSlEbE2Iu6JiDf2WzcnIh4orzuv2nEOpu/rgPe/+eCW+gpvOMuWLWPevHmsXr2a5557jtWrVzNv3jw7bEmjVtR+dCD7VTWCvjxc/9Aasnc7O55czxPL/o6etT+ud2g1V4uR4I8CqwdZdwowvXxbAPwTlApn4Ivl9Z3A/IjorH6olc3unMLFpx1RqI77vPPOo6en50XLenp6OP/88+sUkaRmVsR+dCD7VTWCSnn43K+LmYdVLYIj4kDgncAVgzQ5Dbg6S34E7BsR+wPHAGsz86HM/A2wpNxWNfLQQw9VXP7ggw/WOBJJag32q2oE5uELqj0S/Dngr4Cdg6yfCqzv9/eG8rLBlu8iIhZExMqIWNnd3XrnsKuXQw89tOLyww47rMaRNDfzU43OHK0d+9WxMUcnlnn4gqoVwRHxLmBzZv50qGYVluUQy3ddmLkoM2dk5oyOjuJN6q6WSy65hPb29hcta29v55JLLqlTRM3J/FSjM0drx351bMzRiWUevqCaI8FvAeZGxDpK0xlOiohvDGizATio398HAhuHWK4amTt3LkuWLKGzs5Pdd9+dzs5Oli5dyqmnnlqzGFr15PqSiqke/ar9qAaaO3cu5/3dZbzqoENpe0l9Pt8bRdXOE5yZ5wPnA0TELOAvM/N9A5otAz4SEUuAY4GnMvOxiOgGpkfEIcCjwDzgPdWKVZXNnTuXuXPn1uWxW/nk+pKKq5b9qv2oKulatYnFm17Nnu+5lFe1TeZzBc6Lmp8nOCLOiYhzyn/eADwErAW+DPx/AJm5A/gIcCOlM0t8KzPvr3Wsqp8iXLNckqrJflSVmBcvGLIIjoiXRcQuM6Uj4g2jeZDMXJ6Z7yrfvywzLyvfz8z8cGYelpm/k5kr+21zQ2a+trzub0fzeBpeo39F5sn1JTU6+1E1i/65al68YNDpEBFxBqWzO2yOiDbgTzLzJ+XVVwFvHGxbNbZm+IqsCNcsl9S87EfVLCrlqnlRMtSc4E8CbyrP0T0G+HpEfDIzr6Xy2RvUJEZyvfBG0OrXLJfUvOxH1Swq5WrRL1zTZ6jpEJMz8zGAzLwDOBG4ICIWMsjpytQc/CpEksbHflTNwlwd3FAjwc9ExGGZ+SBAeUT4ROBa4PU1iU5V4VdkkjQ+9qNqFubq4IYqgj/EgJHizHw6Ii6mNCdYTcyvyCRpfOxH1SzM1coGLYIz8+6++xFxFKXz9J4B/AL4x+qHJmk0ph40jY0b1g/fcAQOOPAgHl3/yITsa8JM2o2Iifk5wuS23end/tyE7KshnytJ0rCGOjvEayldpGI+8CSwFIjMPLFGsUkahY0b1nPm5bdNyL6WfvC4CdnPhNq5Y0KPr6WfK0nSsIaaDvFfwArg1MxcCxARH6tJVGo4Xas2OZ9IkqrEPlajZc6M31Bnh/hD4HHgBxHx5Yh4G54arZD6zjF49e0Ps3DxnQ17YnhJakb2sRotc2ZiDFoEZ+Z1mXkm8DpgOfAxYEpE/FNEnFyj+NQAvMSiJFWPfaxGy5yZGENeNhkgM7dl5jfLlz0+ELgLOK/qkalheI5BSaoe+1iNljkzMYaaE7yLzNwCXF6+qSA8x6AkVY99rEbLnJkYoyqCRyMi9gBuAXYvP86/ZOanBrT5BPDefrH8NtCRmVsiYh3wDNAL7MjMGdWKVcPzHIOSVD32sRotc2b8qlYEA88BJ2XmsxHRBtwaEd/NzB/1NcjMzwCfAYiIU4GPlUeb+5yYmU9UMUZJkiQVUNWK4MxM4Nnyn23lWw6xyXxgcbXikSRJkvoM+8O48YiIyRFxF7AZ6MrMHw/S7qXAHOCafosT+I+I+GlELBjiMRZExMqIWNnd7a8j1VjMTzU6c1SNzhxVtVS1CM7M3sw8itJZJY6JiCMGaXoq8MMBUyHekplvBE4BPhwRbx3kMRZl5ozMnNHR4a8j1VjMTzU6c1SNzhxVtVS1CO6TmVspnWt4ziBN5jFgKkRmbiz/uxm4DjimiiFqlLpWbeLC6+/zBN2SNAHsUzUcc2TiVa0IjoiOiNi3fL8deDulSzEPbLcPcAJwfb9le0bE3n33gZOB+6oVq0bHK9VI0sSxT9VwzJHqqOZI8P6ULrl8D/ATSnOCvxMR50TEOf3avRv4j8zc1m/ZFEpnk7gbuAP498z8XhVj1Sh4pRpJmjj2qRqOOVId1Tw7xD3A0RWWXzbg76uAqwYsewg4slqxaXxmTu/g2ys30LO91yvVSNI42adqOOZIdVTzPMFqUV6pRpImjn2qhmOOVIdFsMbEK9VI0sSxT9VwzJGJV5OzQ0iSJEmNxCJYkiRJhWMRLEmSpMKxCJYkSVLhWARLkiSpcCyCJUmSVDgWwZIkSSoci2BJkiQVjkWwJEmSCsciWJIkSYVTtSI4IvaIiDsi4u6IuD8i/qZCm1kR8VRE3FW+Xdhv3ZyIeCAi1kbEedWKU5IkScWzWxX3/RxwUmY+GxFtwK0R8d3M/NGAdisy8139F0TEZOCLwGxgA/CTiFiWmauqGK8kSZIKomojwVnybPnPtvItR7j5McDazHwoM38DLAFOq0KYkiRJKqCqzgmOiMkRcRewGejKzB9XaPbm8pSJ70bE68vLpgLr+7XZUF5W6TEWRMTKiFjZ3d09ofFL42V+qtGZo2p05qiqpapFcGb2ZuZRwIHAMRFxxIAmPwMOzswjgc8D/1peHpV2N8hjLMrMGZk5o6OjY6JClyaE+alGZ46q0ZmjqpaanB0iM7cCy4E5A5Y/3TdlIjNvANoi4lWURn4P6tf0QGBjLWKVJElS66vm2SE6ImLf8v124O3Afw1os19ERPn+MeV4ngR+AkyPiEMi4iXAPGBZtWKVNMCk3YiICblJktSIqnl2iP2Br5XP9DAJ+FZmficizgHIzMuA04EPRcQOoAeYl5kJ7IiIjwA3ApOBKzPz/irGKqm/nTs48/LbJmRXSz943ITsR5KkiVS1Ijgz7wGOrrD8sn73vwB8YZDtbwBuqFZ8kiRJKi6vGCdJkqTCsQiWJElS4VgES5IkqXAsgiVJklQ4FsGSJEkqnGqeIk1lXas2sWJNNzOndzC7c0q9w5kwrXpckppLq/RFrXIczWi4597XpjU5ElxlXas2sXDxnVx9+8MsXHwnXas21TukCdGqxyWpubRKX9Qqx9GMhnvufW1al0Vwla1Y003P9l4Aerb3smJNd50jmhitelySmkur9EWtchzNaLjn3temdVkEV9nM6R20t00GoL1tMjOnd9Q5oonRqsclqbm0Sl/UKsfRjIZ77n1tWpdzgqtsducULp1/dMvNJWrV45LUXFqlL2qV42hGwz33vjatyyK4BmZ3TmnJN02rHpek5tIqfVGrHEczGu6597VpTU6HkCRJUuFUrQiOiD0i4o6IuDsi7o+Iv6nQ5r0RcU/5dltEHNlv3bqIuDci7oqIldWKU5IkScVTzekQzwEnZeazEdEG3BoR383MH/Vr8wvghMz8ZUScAiwCju23/sTMfKKKMUqSJKmAqlYEZ2YCz5b/bCvfckCb2/r9+SPgwGrFI0mSJPWp6pzgiJgcEXcBm4GuzPzxEM3PBr7b7+8E/iMifhoRC4Z4jAURsTIiVnZ3e+4+NRbzU43OHFWjM0dVLVUtgjOzNzOPojTCe0xEHFGpXUScSKkIPrff4rdk5huBU4APR8RbB3mMRZk5IzNndHR47j41FvNTjc4cVaMzR1UtNTk7RGZuBZYDcwaui4g3AFcAp2Xmk/222Vj+dzNwHXBMLWKVJElS66vm2SE6ImLf8v124O3Afw1oMw24FjgrM3/eb/meEbF3333gZOC+asUqSZKkYqnm2SH2B74WEZMpFdvfyszvRMQ5AJl5GXAh8ErgSxEBsCMzZwBTgOvKy3YD/jkzv1fFWCVJklQg1Tw7xD3A0RWWX9bv/geAD1Ro8xBw5MDlkiRJ0kTwinGSJEkqHItgSZIkFY5FsCRJkgrHIliSJEmFYxEsSZKkwrEIliRJUuFYBEuSJKlwLIIlSZJUOBbBkiRJKhyLYEmSJBVOZGa9Y5gwEdENPFzjh30V8ESNH3M0jG9oT2TmnFo8UJ3yczj1fv5HqshxFj1Hx6pZcmYsGunYapaf0FQ52kivUbU0yzEOmqMtVQTXQ0SszMwZ9Y5jMManoTTL82+cGq1Wfi1a+dhaRRFeo1Y4RqdDSJIkqXAsgiVJklQ4FsHjt6jeAQzD+DSUZnn+jVOj1cqvRSsfW6sowmvU9MfonGBJkiQVjiPBkiRJKhyLYEmSJBWORbAkSZIKxyJYkiRJhWMRLEmSpMKxCJYkSVLhWARLkiSpcFqqCJ4zZ04C3ryN5lYz5qe3Md5qxhz1NoZbTZmj3sZwG1RLFcFPPPFEvUOQBmV+qtGZo2p05qgmUksVwZIkSdJIWARLkiSpcCyCJUmSVDgWwZIkSSoci2BJkiQVjkWwJEkTYOpB04iICblNPWhavQ9Hanm71TsASZJawcYN6znz8tsmZF9LP3jchOxH0uAcCZYkSVLhWARLkiSpcKpWBEfElRGxOSLu67fsFRHRFRFryv++vMJ2B0XEDyJidUTcHxEfrVaMkiRJKqZqjgRfBcwZsOw84PuZOR34fvnvgXYAf5GZvw38N+DDEdFZxTglSZJUMFUrgjPzFmDLgMWnAV8r3/8a8PsVtnssM39Wvv8MsBqYWq04JUmSVDy1nhM8JTMfg1KxC7x6qMYR8RrgaODHQ7RZEBErI2Jld3f3BIYqjZ/5qUZnjqrRmaOqlob9YVxE7AVcA/x5Zj49WLvMXJSZMzJzRkdHR+0ClEbA/FSjM0fV6MxRVUuti+BNEbE/QPnfzZUaRUQbpQL4m5l5bQ3jkyRJUgHUugheBvxx+f4fA9cPbBARAXwFWJ2Zn61hbJIkSSqIap4ibTFwO3B4RGyIiLOBS4DZEbEGmF3+m4g4ICJuKG/6FuAs4KSIuKt8+71qxSlJkqTiqdplkzNz/iCr3lah7Ubg98r3bwWiWnFJkiRJDfvDOEmSJKlaLIIlSZJUOFWbDiGNV9eqTaxY083M6R3M7pxS73BUI77uklRd9rMljgSrIXWt2sTCxXdy9e0Ps3DxnXSt2lTvkFQDvu6SVF32sy+wCFZDWrGmm57tvQD0bO9lxRqvElQEvu6SVF32sy+wCFZDmjm9g/a2yQC0t01m5nSvElQEvu6SVF32sy9wTrAa0uzOKVw6/2jnLBWMr7skVZf97AssgtWwZndOKfSbs6h83SWpuuxnS5wOIUmSpMKxCJYkSVLhWARLkiSpcCyCJUmSVDgWwZIkSSqcqhXBEXFlRGyOiPv6LXtFRHRFxJryvy8fZNs5EfFARKyNiPOqFaMkSZKKqZojwVcBcwYsOw/4fmZOB75f/vtFImIy8EXgFKATmB8RnVWMU5IkSQVTtSI4M28BtgxYfBrwtfL9rwG/X2HTY4C1mflQZv4GWFLeTpIkSZoQtZ4TPCUzHwMo//vqCm2mAuv7/b2hvKyiiFgQESsjYmV3d3Gvf63GZH6q0ZmjanTmqKqlEX8YFxWW5WCNM3NRZs7IzBkdHcW9/rUak/mpRmeOqtGZo6qWWhfBmyJif4Dyv5srtNkAHNTv7wOBjTWITZIkSQVR6yJ4GfDH5ft/DFxfoc1PgOkRcUhEvASYV95OkiRJmhDVPEXaYuB24PCI2BARZwOXALMjYg0wu/w3EXFARNwAkJk7gI8ANwKrgW9l5v3VilOSJEnFs1u1dpyZ8wdZ9bYKbTcCv9fv7xuAG6oUmiRJkgquEX8YJ0mSJFVV1UaC1fq6Vm1ixZpuZk7vYHbnlHqHU2i+FpI0dvahxWQRrBEZ2EF85sYHuOzmB+ndmXx75QYunX+0HUeddK3axMLFd9Kzvbfmr8VIPzj8gJHUqOrZh47GwH7UfnX8LII1rIEdxJ8dfwiXLV9Lb/nszT3be1mxpts3YZ2sWNNNz/ZeoLavxUg/OJrlA0ZSMdWrDx2NSp/DV976C/vVcXJOsIY1sIP4z1WPP18AA0yeFMyc7gnM62Xm9A7a2yYD0N42uWavRaUPjvG0k6R6qFcfOhqVPoftV8fPIliDWrZsGZ2dnVxyxgweu+JD/GrNj2lvm8xhr96byeXr+k0OOOeEw/wfaB3N7pzCpfOP5v1vPrimowEj/eAY7wdMXx7usccedHZ2smxZ45w2vGvVJi68/j66Vm2qdyiSxqhefehoDOxH3965X0v0q/XuQyNz0CsSN50ZM2bkypUr6x1GS1i2bBnz5s2jp6fn+WW7vWQP3nv+Z/lx7yH0bO9l8qTgnBMO4xPvOLyOkY5bpct0V0Ur5me15wRXysP29naWLFnC3LlzxxX7ePX/erK9bXI1PzzN0SYREZx5+W0Tsq+lHzyOJvl8rll+QrFzdKLmBDdKv9oIfahzglXReeed96I3CMCO3/yaf7/y79nzPZcC0LszeebX2+sRnhrE7M4pI+q0RtpuoEp52NPTw/nnn1/3IrgZ5hFKah0D+9Fm71cboQ91OoQqeuihhyouf2rTow0/d0qtY7A8fPDBB2scya6aYR6hJA3UKP1qI/ShjgSrokMPPZTVq1fvsnz6bx3G5+Yf7WlZVBOD5eFhhx1Wh2herG8eoe8FSc2kUfrVRuhDHQlWRZdccgnt7e0vWtbe3s4ll1zC7M4pXHzaEX7oq+qGysNG4HtBUrNppH613n2oRbAqmjt3LkuWLKGzs5Pdd9+dzs5Oli5dyqmnnlrv0FQg5qEkTSz71RfUZTpERHwU+O+UfrH35cz83ID1+wDfAKZRivHvM/OrNQ+04ObOnVv3Hx9J5qEkTSz71ZKajwRHxBGUCuBjgCOBd0XE9AHNPgysyswjgVnAP0TES2oaqCRJklpWPaZD/Dbwo8z8VWbuAG4G3j2gTQJ7R0QAewFbgB21DVOSJEmtqh5F8H3AWyPilRHxUuD3gIMGtPkCpWJ5I3Av8NHM3FnbMCVJktSqal4EZ+Zq4P8AXcD3gLvZdZT3HcBdwAHAUcAXIuJllfYXEQsiYmVErOzu9trZaizmpxqdOapGZ47+v/buP1qusjz0+PfxECQUWy0cokBiMCsXPaVK6LnUHw1SbXrTVKG4dElu1bYXC/aiwbZchNqF3natFvVWuLRViEAptzRShFauxWhuqxJrsI2ESEiqQeVHCCYH0WJrqkl47h8zscMwcyY5Z/beM7O/n7VmZc/e78w8M/vZ73my5513qyiVzA6Rmddl5qmZeTqNoQ7b25r8GnBbNtwPfAN4YZfnWp2Zk5k5OT7uZPUaLOanBp05qkFnjqoolRTBEXFs898FwOuANW1NHgJe3WwzDzgJ6HyJE0mSJOkQVXXFuFsj4mhgL3BBZn47It4GkJlXA78P3BAR99KYRu1dmflYRbGOvHVbd3nVKw0t81eSijHq/WslRXBmLu2w7uqW5Z3Az5caVM0cSOxnHTGH6z//Dfbs3c8tG3dw1colI5noGmwz7WjXbd3FqjWbzF9JmsZM+tg69K9eMa6GDiT2jRse5OrP3s+evfsB2LN3P+u3+6MDlas1H1et2cS6rbsO+rHrt0+Zv5I0jZn2sXXoXy2Ca6g1sfcnjD0jAJg7Z4yli/3Rgco1m4526eJx5s4ZA8xfSepkpn1sHfrXqsYEq0JLF49zy8Yd7Nm7n7lzxvhvP3Mi3/33vSM75keDrT0fD6WjXTYxj6tWLhnpMWuSNBsz7WPr0L9aBNdQHRJbw2O2+bhsYp45LEldzKaPHfX+1SK4pkY9sTVczEdJKo59bGczHhMcEav7GYgkSZJUlmnPBEfEj3fbBKzofziSJElS8XoNh5gCHqRR9B6QzfvHFhWUJEmSVKReRfDXgVdn5kPtGyLi4WJCkiRJkorVa0zwlcBzumx7f59jkSRJkkox7ZngzPzTabb9cf/DkSRJkoo37ZngiLi4ZfkNbdv+oKigJEmj5/j5C4iIWd+On79g4GKKiN4vJmmg9BoTfA7/MezhUuCWlm3Lgd8pIihJ0ujZueNh3njNF2b9PDef0ZoUvAAAIABJREFU//I+RNPQr5igv3FJKl6vMcHRZbnT/YMWERdGxJaIuC8i3tmlzRkRcU+zzedm+lqSJElSu15ngrPLcqf7ByUiTgZ+HTgN+AGwNiL+NjO3t7R5NvAhYHlmPhQRTscmSZKkvulVBL8kIp6gcdZ3bnOZ5v0jZviaLwLuyszvATTP8p7NU2eb+K/AbQemZsvM3TN8LUmSJOlpph0OkZljmfmjmfmszDysuXzg/pwZvuYW4PSIODoijqRx5bn5bW3+E/CciPhsRHwpIt4yw9eSJEmSnqbXmeC+y8xtEfE+YB3wr8BmYF+HuH4KeDUwF9gQEXdl5lfbny8izgPOA1iwoH+/GJb6wfzUoDNHNejMURWl1w/jCpGZ12XmqZl5OvA4sL2tyQ5gbWb+W2Y+BtwJvKTLc63OzMnMnBwfHy82cOkQmZ8adOaoBp05qqJUUgQf+KFbRCwAXgesaWvycWBpRBzWHDLx08C2cqOUJEnSqCp9OETTrRFxNLAXuCAzvx0RbwPIzKubQybWAl8GngSuzcwtFcUqSZKkEVNJEZyZSzusu7rt/geAD5QWlCRJkmqjkuEQkiRJUpUsgiVJklQ7FsGSJEmqHYtgSZIk1Y5FsCRJkmrHIliSJEm1YxEsSZKk2qnqYhm1sm7rLtZvn2Lp4nGWTcyrOhyNIHNMkopjHzuaPBNcsHVbd7FqzSZu3PAgq9ZsYt3WXVWHpBFjjklScexjR5dFcMHWb59iz979AOzZu5/126cqjkijxhyTpOLYx44ui+CCLV08ztw5YwDMnTPG0sXjFUekUWOOSVJx7GNHl2OCC7ZsYh5XrVziWCIVxhyTpOLYx44ui+ASLJuY50GjQpljklQc+9jRVMlwiIi4MCK2RMR9EfHOadr954jYHxGvLzM+SZIkjbbSi+CIOBn4deA04CXAayJicYd2Y8D7gE+VG6EkSZJGXRVngl8E3JWZ38vMfcDngLM7tHsHcCuwu8zgJEmSNPqqKIK3AKdHxNERcSSwApjf2iAijqdRGF/d68ki4ryI2BgRG6emBm/aknVbd3HZx7c4r2BNDXp+6qnqeLyaoxp05ujgG9a+s/QiODO30RjmsA5YC2wG9rU1uxJ4V2buP4jnW52Zk5k5OT4+WNOWOMG2Bjk/9VR1PV7NUQ06c3SwDXPfWckP4zLzusw8NTNPBx4Htrc1mQQ+GhEPAK8HPhQRv1RymLPmBNvS8PB4laRDN8x9Z1WzQxzb/HcB8DpgTev2zDwxMxdm5kLgY8B/z8y/KT3QWXKCbWl4eLxK0qEb5r6zqnmCb42Io4G9wAWZ+e2IeBtAZvYcBzwsnGBbGh4er5J06Ia576ykCM7MpR3WdSx+M/NXCw+oQE6wLQ0Pj1dJOnTD2ndWMhxCkiRJqpKXTZYkDZdnHEZEVB1Fsfr4Ho87YT6PPPxQX55LGiUWwZKk4fLkPt54zRf68lQ3n//yvjxP39XhPUoVcziEJEmSasciWJIkSbVjESxJkqTasQiWJElS7VgES5IkqXYsgiVJklQ7TpF2ENZt3TWUlwOURpnHpSTNTt37Uc8E97Bu6y5WrdnEjRseZNWaTazbuqvqkKTa87iUpNmxH7UI7mn99in27N0PwJ69+1m/fariiCR5XErS7NiPWgT3tHTxOHPnjAEwd84YSxePVxyRJI9LSZod+9GKxgRHxIXArwMBfCQzr2zb/svAu5p3/xX4jczcXG6UDcsm5nHVyiW1HjMjDRqPS0maHfvRCorgiDiZRgF8GvADYG1E/G1mbm9p9g3glZn57Yj4BWA18NNlx3rAsol5tUwOaZB5XErS7NS9H61iOMSLgLsy83uZuQ/4HHB2a4PM/EJmfrt59y7ghJJjlCRJ0girogjeApweEUdHxJHACmD+NO3PBT7ZbWNEnBcRGyNi49RU/QZ1a7CZnxp05qgGnTmqopReBGfmNuB9wDpgLbAZ2NepbUT8LI0i+F2dtjefb3VmTmbm5Ph4/QZ1a7CZnxp05qgGnTmqolQyO0RmXpeZp2bm6cDjwPb2NhHxYuBa4KzM/FbZMUqSJGl0VTU7xLGZuTsiFgCvA17Wtn0BcBvw5sz8ahUxSpIkaXRVddnkWyPiaGAvcEFzFoi3AWTm1cBlwNHAhyICYF9mTlYUqyRJkkZMJUVwZi7tsO7qluW3Am8tNShJkiTVhleMkyRJUu1YBEuSJKl2LIIlSZJUOxbBkiRJqh2LYEmSJNWORbAkSZJqxyJYkiRJtWMRLEmSpNqxCJYkSVLtWARLkiSpdiyCJUmSVDsWwZIkSaqdyMyqY+ibiJgCHiz5ZY8BHiv5NQ+F8U3vscxcXsYLVZSfvVT9+R+sOsdZ9xydqWHJmZkYpPdWWn7CUOXoIO2jogzLe+yaoyNVBFchIjZm5mTVcXRjfJrOsHz+xqlDNcr7YpTf26iowz4ahffocAhJkiTVjkWwJEmSasciePZWVx1AD8an6QzL52+cOlSjvC9G+b2Nijrso6F/j44JliRJUu14JliSJEm1YxEsSZKk2rEIliRJUu1YBEuSJKl2LIIlSZJUOxbBkiRJqh2LYEmSJNWORbAkSZJqZ6SK4OXLlyfgzduh3Epjfnqb4a005qi3GdxKZY56m8Gtq5Eqgh977LGqQ5C6Mj816MxRDTpzVP00UkWwJEmSdDAsgiVJklQ7FsGSJEmqHYtgSZIk1Y5FsCRJkmrHIliSJEm1YxEsSZKk2rEIliRJUu1YBEuSJKl2LIIlSZJUOxbBkiRJqh2LYEmSJNWORbAkSZJqp7AiOCKuj4jdEbGlZd17I+KRiLineVvR4XHzI+IzEbEtIu6LiAuLilGSJEn1VOSZ4BuA5R3WX5GZpzRvd3TYvg/47cx8EfBS4IKImCgwTkmSJNVMYUVwZt4JPD6Dxz2amXc3l78LbAOO73N4kiRJqrEqxgS/PSK+3Bwu8ZzpGkbEQmAJ8MUyApMkSVI9lF0EfxhYBJwCPAr8UbeGEXEUcCvwzsx8Ypp250XExojYODU11e94pVkxPzXozFENOnNURSm1CM7MXZm5PzOfBD4CnNapXUTMoVEA35SZt/V4ztWZOZmZk+Pj4/0PWpoF81ODzhzVoDNHVZTDynyxiHheZj7avHs2sKVDmwCuA7Zl5gfLjE8HZ93WXazfPsXSxeMsm5hXdThSZTwWNBvmj1StIqdIWwNsAE6KiB0RcS7w/oi4NyK+DPws8JvNtsdFxIGZIl4BvBl41XRTqaka67buYtWaTdy44UFWrdnEuq27qg5JqoTHgmbD/JGqV9iZ4Mxc2WH1dV3a7gRWNJc/D0RRcWl21m+fYs/e/QDs2buf9dunPIOhWvJY0GyYP1L1vGKcDsnSxePMnTMGwNw5Yyxd7Pgs1ZPHgmbD/JGqV+qYYA2/ZRPzuGrlEsexqfY8FjQb5o9UPYtgHbJlE/PssCU8FjQ75o9ULYdDSJIkqXYsgiVJklQ7FsGSJEmqHYtgSZIk1Y5FsCRJkmrHIliSJEm1YxEsSZKk2rEIliRJUu1YBEuSJKl2vGJcwdZt3eVlMaUh4fE6eNwnkorimeACrdu6i1VrNnHjhgdZtWYT67buqjokSV14vA4e94mkIhVWBEfE9RGxOyK2tKx7b0Q8EhH3NG8rujx2eUR8JSLuj4hLioqxaOu3T7Fn734A9uzdz/rtUxVHJKkbj9fB4z6RVKQizwTfACzvsP6KzDylebujfWNEjAF/CvwCMAGsjIiJAuMszNLF48ydMwbA3DljLF08XnFEkrrxeB087hNJRSpsTHBm3hkRC2fw0NOA+zPz6wAR8VHgLGBr/6Irx7KJeVy1conj2aQh4PE6eNwnEhw/fwE7dzzcl+cam/NM9u/9fl+e67gT5vPIww/15bmqUsUP494eEW8BNgK/nZnfbtt+PNC6t3cAP11WcP22bGKeHbc0JDxeB4/7RHW3c8fDvPGaL/TluW4+/+V9fa5hV/YP4z4MLAJOAR4F/qhDm+iwLrs9YUScFxEbI2Lj1JTjxTRYzE8NOnNUg84cVVFKLYIzc1dm7s/MJ4GP0Bj60G4HML/l/gnAzmmec3VmTmbm5Pi448U0WMxPDTpzVIPOHFVRSi2CI+J5LXfPBrZ0aPZPwOKIODEiDgfOAW4vIz5JkiTVQ2FjgiNiDXAGcExE7ADeA5wREafQGN7wAHB+s+1xwLWZuSIz90XE24FPAWPA9Zl5X1FxysnoNdzMX1XNHJSGU5GzQ6zssPq6Lm13Aita7t8BPG36NPXfgcno9+zdzy0bd3DVyiV24hoa5q+qZg5Kw8srxtWck9FrmJm/qpo5KA0vi+CaczJ6DTPzV1UzB6XhVcU8wRogTkavYWb+qmrmoDS8LILlZPQaauavqmYOSsPJ4RCSJEmqHYtgSZIk1Y7DIQ6Cc0BKg8fjcri4vyQNGs8E93BgDsgbNzzIqjWbWLd1V9Uhleb2229nYmKCI444gomJCW6/3Qv3qXyd8rDOx+Uwcn/9B/tVaXBYBPdQ1zkgb7/9ds455xy2bdvG97//fbZt28Y555xjh61SdcvDa2/6q1oel8Oqrv1oO/tVabBYBPdQ1zkgL7nkEvbs2fOUdXv27OHSSy8tLYZ1W3dx2ce31PqsUR217vdueXjXzX9Sy+NyWNW1H21XRb9qPyp155jgHuo6B+TXv/71juu/9rWvlfL6Xoq0ntr3+9e+1jkPd+14kP9bw+NyWNW1H21Xdr9qPypNzyL4INRxDsgXvOAFbNu27WnrFy1aVMrrd/r6tG77oI7a9/uPHXscj+34xtPaLVq0qJbH5TBzf5Xfr9qPStNzOIQ6uvzyy5k7d+5T1s2dO5fLL7+8lNf369N6at/v73jXeyvNQ6mfyu5X7Uel6VkE11SvcWJnnnkmH/3oR5mYmOCZz3wmExMT3Hzzzbz2ta8tJb4DX5++5WXP9yu8GjiQj8BT9vtlb39LpXkoTedQx9uW3a/aj0rTi8ws5okjrgdeA+zOzJPbtl0EfAAYz8zHOjz2N4G3AgncC/xaZv57r9ecnJzMjRs39iP8kdY6TmzunLG6d45R1guZn52Zjz2ZowPIvP2h0vIT6pmjEcEbr/lCX57r5vNf3tfnKqqG7LOuOVrkmeAbgOVPiyRiPrAMeKjTgyLieGAVMNksnseAc4oLs36crkiDxHzUMDJvpeFXWBGcmXcCj3fYdAVwMY2zvN0cBsyNiMOAI4Gd/Y+wvhwnpkFiPmoYmbfS8Ct1doiIOBN4JDM3R3Q+O52Zj0TE/6JxpngP8OnM/PQ0z3kecB7AggUL+h/0CHK6ovKYn72Zj9UyR2fGvC2POaqilFYER8SRwLuBn+/R7jnAWcCJwHeAWyLiTZn5F53aZ+ZqYDU0xgr1NegR5nRF5TA/D475WB1zdObM23KYoypKmbNDLKJR2G6OiAeAE4C7I+K5be1+DvhGZk5l5l7gNuDlJcYpSZKkETdtERwRYxFxfkT8fkS8om3b7x7KC2XmvZl5bGYuzMyFwA7g1Mz8ZlvTh4CXRsSR0Rgz8Wrg6bOLS5IkSTPU60zwNcArgW8BV0XEB1u2vW66B0bEGmADcFJE7IiIc6dpe1xE3AGQmV8EPgbcTWN6tGfQ/BpEkiRJ6odeY4JPy8wXA0TEnwAfiojbgJX0mBswM1f22L6wZXknsKLl/nuA9/SITZIkSZqRXmeCDz+wkJn7MvM84B7g74GjigxMkiRJKkqvInhjRDzlgheZ+XvAnwELiwpKg+dQLw8qtTJ/pOl5jEjlm7YIzsw3ZebaDuuvzcw5xYWlQXLg8qA3bniQVWs22UnrkJg/0vQ8RqRq9Jod4uKW5Te0bfuDooLSYPHyoJoN80eanseIVI1ewyHOaVm+tG3bclQLnS4P6ld3goP7CtfLy0rTW7p4nMPHGn+ODx97hsdIRY6fv4CI6Mvt+Pk1uLLdMw4b+s+q1+wQ0WW5032NqPbLgwKsWrOJPXv3c8vGHVy1colXTaqhA1/h9soDLy8raRjs3PEwb7zmC315rpvPr8E1vp7c15fPq8rPqteZ4Oyy3Om+RtiyiXn83lkns2xinl/dCTi0r3Bb80fSU63fPsUP9j8JwA/2P2mfKpWkVxH8koh4IiK+C7y4uXzg/k+WEJ8GkF9vC8wDqV88lqRqTDscIjPHygpEw8OvtwXmgdQvHktSNXqNCZY6WjYxz45a5oHUJx5LUvl6DYeQJEmSRo5FsCRJkmrHIliSJEm1YxEsSZKk2imsCI6I6yNid0Rs6bDtoojIiDimy2OfHREfi4h/johtEfGyouKUJElS/RR5JvgGOlxaOSLmA8uAh6Z57P8G1mbmC4GXANuKCFCSJEn1VFgRnJl3Ao932HQFcDFdrjgXET8KnA5c13yeH2Tmd4qKU5IkSfVT6pjgiDgTeCQzN0/T7AXAFPBnEbEpIq6NiB+Z5jnPi4iNEbFxaspLTWqwmJ8adOaoBp05qqKUVgRHxJHAu4HLejQ9DDgV+HBmLgH+DbikW+PMXJ2Zk5k5OT7upSY1WMxPDTpzVIPOHFVRyjwTvAg4EdgcEQ8AJwB3R8Rz29rtAHZk5heb9z9GoyiWJEmS+qK0yyZn5r3AsQfuNwvhycx8rK3dNyPi4Yg4KTO/Arwa2FpWnJIkSRp9RU6RtgbYAJwUETsi4txp2h4XEXe0rHoHcFNEfBk4BfiDouKUJElS/RR2JjgzV/bYvrBleSewouX+PcBkUbFJkiSp3rxinCRJkmrHIliSJEm1YxEsSZKk2rEIliRJUu1YBEuSJKl2LIIlSZJUOxbBkiRJqh2LYEmSJNWORbAkSZJqxyJYkiRJtWMRLEmSpNqxCJYkSVLtWARLkiSpdiyCJUmSVDuFFcERcX1E7I6ILR22XRQRGRHHTPP4sYjYFBGfKCpGSZIk1VORZ4JvAJa3r4yI+cAy4KEej78Q2Nb/sMq3busuLvv4FtZt3VV1KBpR5pjqzPyXNBOFFcGZeSfweIdNVwAXA9ntsRFxAvCLwLXFRFeedVt3sWrNJm7c8CCr1myyk1bfmWOqM/Nf0kyVOiY4Is4EHsnMzT2aXkmjUH7yIJ7zvIjYGBEbp6am+hFmX63fPsWevfsB2LN3P+u3D16MKk4Z+WmOaTYGvQ/txfwffcOeoxpcpRXBEXEk8G7gsh7tXgPszswvHczzZubqzJzMzMnx8fE+RNpfSxePM3fOGABz54yxdPHgxajilJGf5phmY9D70F7M/9E37DmqwXVYia+1CDgR2BwRACcAd0fEaZn5zZZ2rwDOjIgVwBHAj0bEX2Tmm0qMtW+WTczjqpVLWL99iqWLx1k2Ma/qkDRizDHVmfkvaaZKK4Iz817g2AP3I+IBYDIzH2trdylwabPNGcBFw1oAH7BsYp4dswpljqnOzH9JM1HkFGlrgA3ASRGxIyLOnabtcRFxR1GxSJIkSa0KOxOcmSt7bF/YsrwTWNGhzWeBz/Y5NPXJuq27/ApyQLgvpOHncSyVyyvGaUaclmhwuC+k4edxLJXPIlgz4rREg8N9IQ0/j2OpfBbBmhGnJRoc7gtp+HkcS+Urc4o0jRCnJRoc7gtp+HkcS+WzCNaMOS3R4HBfSMPP41gql8MhJEmSVDsWwZIkSaodi2BJkiTVjkWwJEmSasciWJIkSbVjESxJkqTasQiWJElS7VgES5IkqXYsgiVJklQ7hRXBEXF9ROyOiC0dtl0UERkRx3TYNj8iPhMR2yLivoi4sKgYJUmSVE9Fngm+AVjevjIi5gPLgIe6PG4f8NuZ+SLgpcAFETFRVJCSJEmqn8KK4My8E3i8w6YrgIuB7PK4RzPz7ubyd4FtwPFFxSlJkqT6KXVMcEScCTySmZsPsv1CYAnwxWnanBcRGyNi49TUVF/ilPrF/NSgM0c16MxRFaW0IjgijgTeDVx2kO2PAm4F3pmZT3Rrl5mrM3MyMyfHx8f7E6zUJ+anBp05qkFnjqooZZ4JXgScCGyOiAeAE4C7I+K57Q0jYg6NAvimzLytxBglSZJUA4eV9UKZeS9w7IH7zUJ4MjMfa20XEQFcB2zLzA+WFZ8kSZLqo8gp0tYAG4CTImJHRJw7TdvjIuKO5t1XAG8GXhUR9zRvK4qKU5IkSfVT2JngzFzZY/vCluWdwIrm8ueBKCouSZIkySvGSZIkqXYsgiVJklQ7kdnxmhVDKSKmgAdLftljgMd6tqqO8U3vscx82pUNi1BRfvZS9ed/sOocZ91zdKaGJWdmYpDeW2n5CUOVo4O0j4oyLO+xa46OVBFchYjYmJmTVcfRjfFpOsPy+RunDtUo74tRfm+jog77aBTeo8MhJEmSVDsWwZIkSaodi+DZW111AD0Yn6YzLJ+/cepQjfK+GOX3NirqsI+G/j06JliSJEm145lgSZIk1Y5FsCRJkmrHIriDiLg+InZHxJYu28+IiH+JiHuat8tati2PiK9ExP0RcUmFMf6Plvi2RMT+iPjx5rYHIuLe5raNBcQ2PyI+ExHbIuK+iLiwQ5uIiKuan9OXI+LUlm2lfIZ1ERFHRMQ/RsTm5v74nx3adM3pkmMdi4hNEfGJDtu65kwVesQ6EJ/nqIuIC5v9230R8c4O24dqP3Tq1yPixyNiXURsb/77nC6Ptd8swXR/eyPioojIiDimy2ML/dvbL13y8L0R8UjLsbSiy2OHKw8z01vbDTgdOBXY0mX7GcAnOqwfA74GvAA4HNgMTFQRY1vb1wJ/33L/AeCYAj+/5wGnNpefBXy1/XMAVgCfBAJ4KfDFsj/Dutyan/FRzeU5wBeBl7a16ZjTFcT6W8Bfdjm+OubMgMY6EJ/nKN+Ak4EtwJHAYcD/AxYP837o1K8D7wcuaS5fAryvw+PsNyvcR83184FP0biQR8e/r0X/7S3yPQLvBS7q8bihy0PPBHeQmXcCj8/goacB92fm1zPzB8BHgbP6GlzTIca4ElhTRBydZOajmXl3c/m7wDbg+LZmZwE3ZsNdwLMj4nmU+BnWRfMz/tfm3TnN28D9IjYiTgB+Ebi2S5NuOVO6g4hVxXsRcFdmfi8z9wGfA86uOKZZ6dKvnwX8eXP5z4Ff6vBQ+82STPO39wrgYgawbz1Uw1AD9YtF8My9rPn18icj4iea644HHm5ps4OnF3+liogjgeXArS2rE/h0RHwpIs4r+PUXAktonH1s1e2zGrjPcBQ0v7q/B9gNrMvM9v0BnXO6TFfS+CPyZJftg5QbvWKF6j/PUbcFOD0ijm72cytonI1rN+z7YV5mPgqNEwzAsR3aDNKxUTsRcSbwSGZu7tG0tL+9BXl7cyja9V2G5QxdHloEz8zdwPMz8yXAHwN/01wfHdpW/b/C1wL/kJmt/6t7RWaeCvwCcEFEnF7EC0fEUTSK73dm5hPtmzs8JKdZr1nIzP2ZeQpwAnBaRJzc1qRbTpciIl4D7M7ML03XrMO60nPjIGOt9POsg8zcBrwPWAespfHV6762ZnXZDwNxbNRR8z9g7wYOZrx5KX97C/JhYBFwCvAo8Ecd2gxdHloEz0BmPnHg6+XMvAOY0xwIv4Onnok4AdhZQYitzqFtKERm7mz+uxv4axpfYfRVRMyhUQDflJm3dWjS7bMaxM9wZGTmd4DP0vh2oHV9t5wuyyuAMyPiARpfob0qIv6irc2g5EbPWAfg86yFzLwuM0/NzNNpfH27vW37KOyHXQeG/TT/3d2hzaAcG3W0CDgR2NzsE04A7o6I57Y3LONvb1Eyc1fzZMqTwEfoHPvQ5aFF8AxExHMjIprLp9H4HL8F/BOwOCJOjIjDaRSgt1cY548BrwQ+3rLuRyLiWQeWgZ+n8bViP183gOuAbZn5wS7Nbgfe0vzF/0uBf2l+1TdQn+EoiIjxiHh2c3ku8HPAP7e16ZbTpcjMSzPzhMxcSGOf/31mvqmtWbecKdXBxFr151kXEXFs898FwOto+w//iOyH24FfaS7/Ci39eQv7zYpk5r2ZeWxmLmz2CTto/DD8m63tyvjbW6S231+cTefYhy4PD6s6gEEUEWto/Kr4mIjYAbyHxo+JyMyrgdcDvxER+4A9wDmZmcC+iHg7jV+IjgHXZ+Z9FcUIjUT9dGb+W8tD5wF/3fy7cBjwl5m5ts/hvQJ4M3BvcxwqwO8AC1riu4PGGL77ge8Bv9bcVtpnWCPPA/48IsZoFAF/lZmfiIi3Qc+crlRbjB1zZlAMw+c5gm6NiKOBvcAFmfntYd4PXfr1y4G/iohzgYeANzTbHgdcm5kr7DfL02kfZeZ1Xdr+cB9Rzt/evuiSh2dExCk0hjc8AJzfbDvUeehlkyVJklQ7DoeQJElS7VgES5IkqXYsgiVJklQ7FsGSJEmqHYtgDaXmFWt2R0TPKWYi4rciYmvzSjd/FxHPLyNG1Zs5KkmDzSJ4BEXE2RGREfHClnVrI+I7EfGJKmProxtou+DDNDYBk5n5YuBjwPuLCkq9tednRJwSERsi4r5mEfjGqmPskxswR4dOROyPiHtabpc01789Iu5v5u6wXXBDI2SaHL0pIr4SEVua/wmfU3Wsg84ieDStBD5PY6LqAz5AY+7ekZCZd9K4QtQPRcSiZrH/pYhYf6DIyszPZOb3ms3uonEVG1WnPT+/B7wlM3+CRtF45YGLewwzc3Ro7cnMU1pulzfX/wONC808WGFsEnTP0ZuAFwI/CcwF3lpZhEPCInjERMRRNC5WcS4tRXBm/h3w3ariKslq4B2Z+VPARcCHOrQ5F/hkqVHphzrlZ2Z+NTO3N5d30rgs7HhlQRbLHB1SmbkpMx+oOg6pm8y8I5uAf8T/TPfkFeNGzy8BazPzqxHxeEScmpl3Vx1U0ZrF1cuBW5pX5AF4ZlubNwGTNC4lrWpMm5/NS9seDnytsggLYo4OjbktV7oE+MPMvLmyaKSnmzZHm8Mg3gxcWHpkQ8YiePSsBK5sLn+0eX/ki2CWgYBhAAABlElEQVQa32p8JzNP6bQxIn4OeDfwysz8fqmRqVXX/Gxem/7/AL+SmU9WE16hzNHhsKfbPpIGRK8c/RBwZ2auLyugYWURPEIi4mjgVcDJEZE0rt2dEXFxjvj1sTPziYj4RkS8ITNvicapthdn5uaIWAJcAyzPzN0Vh1pb0+Un8Czgb4Hfzcy7KgyzMOaopKJFxHtoDCc7v+pYhoFjgkfL64EbM/P5mbkwM+cD3wB+puK4+i4i1gAbgJMiYkdEnAv8MnBuRGwG7gPOajb/AHAUja+h74mI2ysJWt3y83Tgr5vbbqk0wj4yRyWVKSLeCvwXYOWIfpvWdzHiJwhrJSI+C1yemWtb1q0CXgScTONXo0cB3wLOzcxPVRGn6mma/PxNGj/guK+l+a9m5j1IJYuI/cC9LavWZuYlzVy9GHgujR9v3pGZ/vpepZsmR/fRmL3kwI/gb8vM3ys9wCFiESxJkqTacTiEJEmSasciWJIkSbVjESxJkqTasQiWJElS7VgES5IkqXYsgiVJklQ7FsGSJEmqnf8PSisLe/RorVUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyomo.contrib.parmest.graphics.pairwise_plot(LR, theta, 0.8, \n", " title='LR results within 80% confidence region')" ] }, { "cell_type": "markdown", "id": "2e7f3dae", "metadata": {}, "source": [ "\n", "< [2.7 Stochastic Programming](https://ndcbe.github.io/CBE60499/02.07-SP.html) | [Contents](toc.html) | [Tag Index](tag_index.html) | [2.9 Supplementary material: data for parmest tutorial](https://ndcbe.github.io/CBE60499/02.09-Parmest-generate-data.html) >

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\"Download\"" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }