diff --git a/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml b/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml
index 1f28523d81..170ac4917d 100644
--- a/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml
+++ b/Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml
@@ -1,21 +1,21 @@
-OverwriteResults: 0 # Overwrite existing results in output folder or create a new one; 0 = create new folder; 1 = overwrite existing results
-PrintModel: 0 # Write the model formulation as an output; 0 = active; 1 = not active
-NetworkExpansion: 0 # Transmission network expansionl; 0 = not active; 1 = active systemwide
-Trans_Loss_Segments: 1 # Number of segments used in piecewise linear approximation of transmission losses; 1 = linear, >2 = piecewise quadratic
-Reserves: 0 # Regulation (primary) and operating (secondary) reserves; 0 = not active, 1 = active systemwide
-EnergyShareRequirement: 1 # Minimum qualifying renewables penetration; 0 = not active; 1 = active systemwide
-CapacityReserveMargin: 1 # Number of capacity reserve margin constraints; 0 = not active; 1 = active systemwide
-CO2Cap: 2 # CO2 emissions cap; 0 = not active (no CO2 emission limit); 1 = mass-based emission limit constraint; 2 = load + rate-based emission limit constraint; 3 = generation + rate-based emission limit constraint
-StorageLosses: 1 # Energy Share Requirement and CO2 constraints account for energy lost; 0 = not active (DO NOT account for energy lost); 1 = active systemwide (DO account for energy lost)
-MinCapReq: 1 # Activate minimum technology carveout constraints; 0 = not active; 1 = active
-MaxCapReq: 1 # Activate maximum technology carveout constraints; 0 = not active; 1 = active
-Solver: HiGHS # Available solvers: Gurobi, CPLEX, Clps
-ParameterScale: 1 # Turn on parameter scaling wherein load, capacity and power variables are defined in GW rather than MW. 0 = not active; 1 = active systemwide
-WriteShadowPrices: 1 # Write shadow prices of LP or relaxed MILP; 0 = not active; 1 = active
-UCommit: 2 # Unit committment of thermal power plants; 0 = not active; 1 = active using integer clestering; 2 = active using linearized clustering
-TimeDomainReductionFolder: "TDR_Results" # Directory name where results from time domain reduction will be saved. If results already exist here, these will be used without running time domain reduction script again.
-TimeDomainReduction: 1 # Time domain reduce (i.e. cluster) inputs based on Load_data.csv, Generators_variability.csv, and Fuels_data.csv; 0 = not active (use input data as provided); 0 = active (cluster input data, or use data that has already been clustered)
-ModelingToGenerateAlternatives: 0 # Modeling to generate alternatives; 0 = not active; 1 = active. Note: produces a single solution as output
-ModelingtoGenerateAlternativeSlack: 0.1 # Slack value as a fraction of least-cost objective in budget constraint used for evaluating alternative model solutions; positive float value
-ModelingToGenerateAlternativeIterations: 3 # Number of MGA iterations with maximization and minimization objective
-MethodofMorris: 0 #Flag for turning on the Method of Morris analysis
+NetworkExpansion: 0
+TimeDomainReductionFolder: "TDR_Results"
+ModelingToGenerateAlternativeIterations: 3
+ParameterScale: 1
+EnergyShareRequirement: 1
+PrintModel: 0
+TimeDomainReduction: 0
+Trans_Loss_Segments: 1
+CapacityReserveMargin: 1
+ModelingtoGenerateAlternativeSlack: 0.1
+Solver: "HiGHS"
+MethodofMorris: 0
+Reserves: 0
+StorageLosses: 1
+OverwriteResults: 0
+UCommit: 2
+ModelingToGenerateAlternatives: 0
+MaxCapReq: 1
+MinCapReq: 1
+CO2Cap: 2
+WriteShadowPrices: 1
diff --git a/Project.toml b/Project.toml
index 51cd3acab9..f42c8f9bcd 100644
--- a/Project.toml
+++ b/Project.toml
@@ -13,14 +13,25 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
+GMT = "5752ebe1-31b9-557e-87aa-f909b540aa54"
+GeoMakie = "db073c08-6b98-4ee5-b6a4-5efafb3259c6"
HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+Plotly = "58dd65bb-95f3-509e-9936-c39a10fdeae7"
+PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
+ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+TickTock = "9ff05d80-102d-5586-aa04-3a8bd1a90d20"
+VegaDatasets = "0ae4a718-28b7-58ec-9efb-cded64d6d5b4"
YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
[compat]
diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb
new file mode 100644
index 0000000000..58137acefd
--- /dev/null
+++ b/Tutorials/.ipynb_checkpoints/Tutorial_0_Introduction-checkpoint.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f8b3c984",
+ "metadata": {},
+ "source": [
+ "# Tutorial 0: Introduction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5fe615ef",
+ "metadata": {},
+ "source": [
+ "Welcome to the GenX tutorials! In the following tutorials, we outline some important features of GenX and how to run them."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "283cfb5a",
+ "metadata": {},
+ "source": [
+ "Running these notebooks follows similar steps to those in the GenX documentation here. In this tutorial, we go through setting up GenX on Jupyter and navigating to the `Tutorials` file from there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4984f841",
+ "metadata": {},
+ "source": [
+ "First, as in the documentation, open a terminal window on your computer and navigate to GenX. Then, navigate to Julia and open a project using the command `julia --project=.`.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "906b7b33",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14bd5e78",
+ "metadata": {},
+ "source": [
+ "If you don't already have the package IJulia, add it here using `import(Pkg); Pkg.add(\"IJulia\")`:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50fe200d",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e1b2998",
+ "metadata": {},
+ "source": [
+ "After IJulia is added, open a Jupyter notebook with `using IJulia; notebook()`:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af32af4d",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "762ecdbb",
+ "metadata": {},
+ "source": [
+ "This will open a notebook in your default browser. From there, navigate through the GenX directory to the `Tutorials` folder and you're good to go!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97b2b84b",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c6ada492",
+ "metadata": {},
+ "source": [
+ "**For a more detailed explanation of IJulia, Jupyter, and downloading packages, see here.**"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.9.2",
+ "language": "julia",
+ "name": "julia-1.9"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb
new file mode 100644
index 0000000000..c687f7ae89
--- /dev/null
+++ b/Tutorials/.ipynb_checkpoints/Tutorial_1_Configuring_Settings-checkpoint.ipynb
@@ -0,0 +1,373 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "78a5ccd4-f602-4381-aadc-d468cff03ba3",
+ "metadata": {},
+ "source": [
+ "# Tutorial 1: Configuring Settings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c78bfdbd",
+ "metadata": {},
+ "source": [
+ "GenX is easy to customize to fit a variety of problems. In this tutorial, we show which settings are available to change, what their defaults are, and how to change them in your code."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6814a53",
+ "metadata": {},
+ "source": [
+ "## What settings are there?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69655945",
+ "metadata": {},
+ "source": [
+ "There are 21 settings available to edit in GenX, found in the file `genx_settings.yml`. These settings are described here in the documentation. The file is located in the `Settings` folder in the working directory. To change the location of the file, edit the `settings_path` variable in `Run.jl` within your directory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e11cc81",
+ "metadata": {},
+ "source": [
+ "Most settings are set as either 0 or 1, which correspond to whether or not to include a specifc feature. For example, to use `TimeDomainReduction`, you would set its parameter to 0 within `genx_settings.yml`. If you would like to run GenX without it, you would set its parameter to 1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c88b3b9",
+ "metadata": {},
+ "source": [
+ "Other settings, such as `CO2Cap`, have more options corresponding to integers, while some settings such as `ModelingtoGenerateAlternativeSlack` take a numerical input directly (in this case, the slack value). Two settings, `Solver` and `TimeDomainReductionFolder` take in text as input. To learn more about different solvers, read here. For `TimeDomainReductionFolder`, specify the name of the directory you wish to see the results in. For a more comprehensive description of the input options, see the documentation linked above."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bbd18f76",
+ "metadata": {},
+ "source": [
+ "Below is the settings file for `SmallNewEngland/OneZone`:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57ee59c3",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c37c756e",
+ "metadata": {},
+ "source": [
+ "All `genx_settings.yml` files in `Example_Systems` specify most parameters. When configuring your own settings, however, it is not necessary to input all parameters as defaults are specified for each one in `configure_settings.jl`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16ec73eb",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "527e600d",
+ "metadata": {},
+ "source": [
+ "To open `genx_settings.yml` in Jupyter, use the function `YAML.load(open(...))` and navigate to file in the desired directory:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "751b0e46",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dict{Any, Any} with 21 entries:\n",
+ " \"NetworkExpansion\" => 0\n",
+ " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n",
+ " \"ModelingToGenerateAlternativeIterations\" => 3\n",
+ " \"ParameterScale\" => 1\n",
+ " \"EnergyShareRequirement\" => 1\n",
+ " \"PrintModel\" => 0\n",
+ " \"TimeDomainReduction\" => 1\n",
+ " \"Trans_Loss_Segments\" => 1\n",
+ " \"CapacityReserveMargin\" => 1\n",
+ " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n",
+ " \"Solver\" => \"HiGHS\"\n",
+ " \"MethodofMorris\" => 0\n",
+ " \"Reserves\" => 0\n",
+ " \"StorageLosses\" => 1\n",
+ " \"OverwriteResults\" => 0\n",
+ " \"UCommit\" => 2\n",
+ " \"ModelingToGenerateAlternatives\" => 0\n",
+ " \"MaxCapReq\" => 1\n",
+ " \"MinCapReq\" => 1\n",
+ " \"CO2Cap\" => 2\n",
+ " \"WriteShadowPrices\" => 1"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "using YAML\n",
+ "genx_settings_SNE = YAML.load(open(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dcdd15fd",
+ "metadata": {},
+ "source": [
+ "Since all settings have defaults, you only need to specify the settings you would like to change. In fact, you can leave your settings file completely blank and it will still run! Let's try editing `genx_settings` in `SmallNewEngland/OneZone` to contain no parameters:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1ea1703",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_params = Dict() # Empty dictionary\n",
+ "YAML.write_file(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\", new_params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fbc6ce5d",
+ "metadata": {},
+ "source": [
+ "The empty file will look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e24980d7",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67a336a9",
+ "metadata": {},
+ "source": [
+ "Now, run GenX and output the file `capacity.csv` from the `Results` folder:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "236c0180",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Configuring Settings\n",
+ "Time Series Data Already Clustered.\n",
+ "Configuring Solver\n",
+ "Loading Inputs\n",
+ "Reading Input CSV Files\n",
+ "Network.csv Successfully Read!\n",
+ "Load_data.csv Successfully Read!\n",
+ "Fuels_data.csv Successfully Read!\n",
+ "Generators_data.csv Successfully Read!\n",
+ "Generators_variability.csv Successfully Read!\n",
+ "Validating time basis\n",
+ "Capacity_reserve_margin.csv Successfully Read!\n",
+ "Minimum_capacity_requirement.csv Successfully Read!\n",
+ "Maximum_capacity_requirement.csv Successfully Read!\n",
+ "Energy_share_requirement.csv Successfully Read!\n",
+ "CO2_cap.csv Successfully Read!\n",
+ "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n",
+ "Generating the Optimization Model\n",
+ "Discharge Module\n",
+ "Non-served Energy Module\n",
+ "Investment Discharge Module\n",
+ "Unit Commitment Module\n",
+ "Emissions Module (for CO2 Policy modularization\n",
+ "Dispatchable Resources Module\n",
+ "Storage Resources Module\n",
+ "Storage Investment Module\n",
+ "Storage Core Resources Module\n",
+ "Storage Resources with Symmetric Charge/Discharge Capacity Module\n",
+ "Thermal (Unit Commitment) Resources Module\n",
+ "C02 Policies Module\n",
+ "Energy Share Requirement Policies Module\n",
+ "Capacity Reserve Margin Policies Module\n",
+ "Minimum Capacity Requirement Module\n",
+ "Maximum Capacity Requirement Module\n",
+ "Time elapsed for model building is\n",
+ "4.274147083\n",
+ "Solving Model\n",
+ "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n",
+ "Presolving model\n",
+ "36012 rows, 17529 cols, 136567 nonzeros\n",
+ "34549 rows, 16070 cols, 136337 nonzeros\n",
+ "Presolve : Reductions: rows 34549(-6123); columns 16070(-2422); elements 136337(-29881)\n",
+ "Solving the presolved LP\n",
+ "Using EKK dual simplex solver - serial\n",
+ " Iteration Objective Infeasibilities num(sum)\n",
+ " 0 -1.9058295039e-01 Ph1: 12(61.9533); Du: 1(0.190583) 0s\n",
+ " 16420 9.7160052378e+03 Pr: 0(0); Du: 0(3.13359e-06) 4s\n",
+ "Using EKK primal simplex solver\n",
+ " Iteration Objective Infeasibilities num(sum)\n",
+ " 16420 9.7160052378e+03 Pr: 0(0); Du: 1(0.000200549) 4s\n",
+ " 16421 9.7160052378e+03 Pr: 0(0); Du: 0(6.09353e-15) 4s\n",
+ "Solving the original LP from the solution after postsolve\n",
+ "Model status : Optimal\n",
+ "Simplex iterations: 16421\n",
+ "Objective value : 9.7160052378e+03\n",
+ "HiGHS run time : 4.25\n",
+ "LP solved for primal\n",
+ "Writing Output\n",
+ "Time elapsed for writing costs is\n",
+ "0.659734\n",
+ "Time elapsed for writing storage is\n",
+ "0.078838791\n",
+ "Time elapsed for writing nse is\n",
+ "0.317711209\n",
+ "Time elapsed for writing power balance is\n",
+ "0.178140584\n",
+ "Time elapsed for writing emissions is\n",
+ "0.30562725\n",
+ "Time elapsed for writing reliability is\n",
+ "0.141396375\n",
+ "Time elapsed for writing storage duals is\n",
+ "0.326884042\n",
+ "Time elapsed for writing commitment is\n",
+ "0.028738458\n",
+ "Time elapsed for writing startup is\n",
+ "0.028648041\n",
+ "Time elapsed for writing shutdown is\n",
+ "0.024390583\n",
+ "Time elapsed for writing time weights is\n",
+ "0.048884292\n",
+ "Time elapsed for writing reserve margin is\n",
+ "0.124729875\n",
+ "Time elapsed for writing capacity value is\n",
+ "1.293372\n",
+ "Time elapsed for writing net revenue is\n",
+ "0.369404208\n",
+ "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_2\n",
+ "Time elapsed for writing is\n",
+ "7.758552166\n"
+ ]
+ }
+ ],
+ "source": [
+ "include(\"../Example_Systems/SmallNewEngland/OneZone/Run.jl\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "b8ea6030",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
1 | natural_gas_combined_cycle | 1 | 0.0 | 0.0 | 15113.5 | 15113.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
2 | solar_pv | 1 | 0.0 | 0.0 | 31519.9 | 31519.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
3 | onshore_wind | 1 | 0.0 | 0.0 | 26083.7 | 26083.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
4 | battery | 1 | 0.0 | 0.0 | 8609.66 | 8609.66 | 0.0 | 0.0 | 34158.1 | 34158.1 | 0.0 | 0.0 | 0.0 | 0.0 |
5 | Total | n/a | 0.0 | 0.0 | 81326.8 | 81326.8 | 0.0 | 0.0 | 34158.1 | 34158.1 | 0.0 | 0.0 | 0.0 | 0.0 |
"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|cccccccc}\n",
+ "\t& Resource & Zone & StartCap & RetCap & NewCap & EndCap & StartEnergyCap & \\\\\n",
+ "\t\\hline\n",
+ "\t& String31 & String3 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n",
+ "\t\\hline\n",
+ "\t1 & natural\\_gas\\_combined\\_cycle & 1 & 0.0 & 0.0 & 15113.5 & 15113.5 & 0.0 & $\\dots$ \\\\\n",
+ "\t2 & solar\\_pv & 1 & 0.0 & 0.0 & 31519.9 & 31519.9 & 0.0 & $\\dots$ \\\\\n",
+ "\t3 & onshore\\_wind & 1 & 0.0 & 0.0 & 26083.7 & 26083.7 & 0.0 & $\\dots$ \\\\\n",
+ "\t4 & battery & 1 & 0.0 & 0.0 & 8609.66 & 8609.66 & 0.0 & $\\dots$ \\\\\n",
+ "\t5 & Total & n/a & 0.0 & 0.0 & 81326.8 & 81326.8 & 0.0 & $\\dots$ \\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/plain": [
+ "\u001b[1m5×14 DataFrame\u001b[0m\n",
+ "\u001b[1m Row \u001b[0m│\u001b[1m Resource \u001b[0m\u001b[1m Zone \u001b[0m\u001b[1m StartCap \u001b[0m\u001b[1m RetCap \u001b[0m\u001b[1m NewCap \u001b[0m\u001b[1m EndCa\u001b[0m ⋯\n",
+ " │\u001b[90m String31 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float\u001b[0m ⋯\n",
+ "─────┼──────────────────────────────────────────────────────────────────────────\n",
+ " 1 │ natural_gas_combined_cycle 1 0.0 0.0 15113.5 15113 ⋯\n",
+ " 2 │ solar_pv 1 0.0 0.0 31519.9 31519\n",
+ " 3 │ onshore_wind 1 0.0 0.0 26083.7 26083\n",
+ " 4 │ battery 1 0.0 0.0 8609.66 8609\n",
+ " 5 │ Total n/a 0.0 0.0 81326.8 81326 ⋯\n",
+ "\u001b[36m 9 columns omitted\u001b[0m"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "using CSV\n",
+ "using DataFrames\n",
+ "results = CSV.read(open(\"../Example_Systems/SmallNewEngland/OneZone/Results_2/capacity.csv\"),DataFrame)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75d31b5a",
+ "metadata": {},
+ "source": [
+ "As you can see, this runs without a problem! To try with your own parameters, edit the `new_params` dictionary with whatever parameters you'd like to try and run the cells again."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4f9679f",
+ "metadata": {},
+ "source": [
+ "Finally, let's rewite `genx_settings.yml` to put the original settings in the example back:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41b5d0c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "YAML.write_file(\"../Example_Systems/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.9.2",
+ "language": "julia",
+ "name": "julia-1.9"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb
new file mode 100644
index 0000000000..d814592144
--- /dev/null
+++ b/Tutorials/.ipynb_checkpoints/Tutorial_2_Network_Visualization-checkpoint.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "3ce667ff",
+ "metadata": {},
+ "source": [
+ "# Tutorial 2: Network Visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "71b0f263",
+ "metadata": {},
+ "source": [
+ "To run GenX, there are five mandatory input files: `Fuels_data.csv`, `Network.csv`, `Load_data.csv`, `Generators_variability.csv`, and `Generators_data.csv`. Detailed descriptions of these files can be found here in the documentation under \"Inputs\". This tutorial helps visualize the file `Network.csv` using the example system `SmallNewEngland/ThreeZones`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "fa98763b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using Plots\n",
+ "using PlotlyJS\n",
+ "using CSV\n",
+ "using DataFrames"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5f37531",
+ "metadata": {},
+ "source": [
+ "The input file `Network.csv` contains the nodes of your network, how they connect to each other, and some important features about them. Below is the network file for `SmallNewEngland/ThreeZones`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 241,
+ "id": "bcaa924c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "1 | MA | z1 | 1 | 1 | -1 | 0 | 2950 | MA_to_CT | 123.0584 | 0.012305837 | 2950 | 12060 | 0.95 | 0 | 0 |
2 | CT | z2 | 2 | 1 | 0 | -1 | 2000 | MA_to_ME | 196.5385 | 0.019653847 | 2000 | 19261 | 0.95 | 0 | 0 |
3 | ME | z3 | | | | | | | | | | | | | missing |
"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|cccccccc}\n",
+ "\t& Column1 & Network\\_zones & Network\\_Lines & z1 & z2 & z3 & Line\\_Max\\_Flow\\_MW & \\\\\n",
+ "\t\\hline\n",
+ "\t& String3 & String3 & String3 & String3 & String3 & String3 & String7 & \\\\\n",
+ "\t\\hline\n",
+ "\t1 & MA & z1 & 1 & 1 & -1 & 0 & 2950 & $\\dots$ \\\\\n",
+ "\t2 & CT & z2 & 2 & 1 & 0 & -1 & 2000 & $\\dots$ \\\\\n",
+ "\t3 & ME & z3 & & & & & & $\\dots$ \\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/plain": [
+ "\u001b[1m3×15 DataFrame\u001b[0m\n",
+ "\u001b[1m Row \u001b[0m│\u001b[1m Column1 \u001b[0m\u001b[1m Network_zones \u001b[0m\u001b[1m Network_Lines \u001b[0m\u001b[1m z1 \u001b[0m\u001b[1m z2 \u001b[0m\u001b[1m z3 \u001b[0m\u001b[1m Line_\u001b[0m ⋯\n",
+ " │\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m Strin\u001b[0m ⋯\n",
+ "─────┼──────────────────────────────────────────────────────────────────────────\n",
+ " 1 │ MA z1 1 1 -1 0 2950 ⋯\n",
+ " 2 │ CT z2 2 1 0 -1 2000\n",
+ " 3 │ ME z3\n",
+ "\u001b[36m 9 columns omitted\u001b[0m"
+ ]
+ },
+ "execution_count": 241,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "network = CSV.read(\"Example_Systems_Tutorials/SmallNewEngland/ThreeZones/Network.csv\",DataFrame,missingstring=\"NA\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b9aafe7b",
+ "metadata": {},
+ "source": [
+ "Here, MA, CT, and ME are the abbreviations for states Massachusetts, Connecticut, and Maine. However, since the US region of New England contains other states as well, ME in this case is also used to refer to those states."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca18429c",
+ "metadata": {},
+ "source": [
+ "Columns `z1, z2, z3` specify an adjacency matrix forming the network of the three regions. In this case, there are only two network lines, specified in the `Network_Lines` columns. The `z1` column indicates that the first node, MA, is the source of both lines as both rows have value 1. Columns `z2` and `z3` both have values of -1, which means both nodes CT and ME recieve energy from node MA:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a52eb00",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d89f8c96",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "@webio": {
+ "lastCommId": "22b36260144444988f1a70d85ef0b17a",
+ "lastKernelId": "0d15a65c-9b8a-4242-bdb1-4d39948d79a9"
+ },
+ "kernelspec": {
+ "display_name": "Julia 1.9.2",
+ "language": "julia",
+ "name": "julia-1.9"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb b/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb
new file mode 100644
index 0000000000..cf59fac6f4
--- /dev/null
+++ b/Tutorials/.ipynb_checkpoints/Tutorial_3_K-means_Time_Domain_Reduction-checkpoint.ipynb
@@ -0,0 +1,158764 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7054e9e0",
+ "metadata": {},
+ "source": [
+ "# Tutorial 3: K-Means and Time Domain Reduction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "944dff9e",
+ "metadata": {},
+ "source": [
+ "A good tool to reduce computation time of GenX is to use Time Domain Reduction (TDR). Time Domain Reduction is a method that selects a smaller set of time steps from the data in a way that reduces computation time while still capturing the main information of the model. In this tutorial, we go over how TDR works in GenX and how it uses K-means clustering to choose the optimal time steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cb4e61f",
+ "metadata": {},
+ "source": [
+ "### Time Domain Reduction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4456f70f",
+ "metadata": {},
+ "source": [
+ "To see how Time Domain Reduction works, let's look at the `Load_data` in `Example_Systems_Tutorials/SmallNewEngland/OneZone/`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 301,
+ "id": "5f8730d4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n",
+ "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
+ "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TickTock ─ v1.3.0\n",
+ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Project.toml`\n",
+ " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n",
+ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Desktop/GenX/Manifest.toml`\n",
+ " \u001b[90m[9ff05d80] \u001b[39m\u001b[92m+ TickTock v1.3.0\u001b[39m\n",
+ "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
+ "\u001b[32m ✓ \u001b[39mTickTock\n",
+ "\u001b[33m ✓ \u001b[39mGenX\n",
+ " 2 dependencies successfully precompiled in 7 seconds. 128 already precompiled.\n",
+ " \u001b[33m1\u001b[39m dependency precompiled but a different version is currently loaded. Restart julia to access the new version\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First, load all packages needed\n",
+ "using DataFrames\n",
+ "using CSV\n",
+ "using VegaLite\n",
+ "using YAML\n",
+ "using TickTock\n",
+ "using PlotlyJS\n",
+ "using Plots\n",
+ "using Clustering\n",
+ "using ScikitLearn\n",
+ "@sk_import datasets: (make_blobs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 259,
+ "id": "328f601e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "8760×9 DataFrame
8735 rows omitted
1 | 50000 | 1 | 1 | 1 | 1 | 8760 | 8760 | 1 | 11162 |
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8760 | | | | | | | | 8760 | 12287 |
"
+ ],
+ "text/latex": [
+ "\\begin{tabular}{r|ccccc}\n",
+ "\t& Voll & Demand\\_Segment & Cost\\_of\\_Demand\\_Curtailment\\_per\\_MW & Max\\_Demand\\_Curtailment & \\\\\n",
+ "\t\\hline\n",
+ "\t& String7 & String3 & String3 & String3 & \\\\\n",
+ "\t\\hline\n",
+ "\t1 & 50000 & 1 & 1 & 1 & $\\dots$ \\\\\n",
+ "\t2 & & & & & $\\dots$ \\\\\n",
+ "\t3 & & & & & $\\dots$ \\\\\n",
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+ "\t27 & & & & & $\\dots$ \\\\\n",
+ "\t28 & & & & & $\\dots$ \\\\\n",
+ "\t29 & & & & & $\\dots$ \\\\\n",
+ "\t30 & & & & & $\\dots$ \\\\\n",
+ "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n",
+ "\\end{tabular}\n"
+ ],
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+ "\u001b[1m8760×9 DataFrame\u001b[0m\n",
+ "\u001b[1m Row \u001b[0m│\u001b[1m Voll \u001b[0m\u001b[1m Demand_Segment \u001b[0m\u001b[1m Cost_of_Demand_Curtailment_per_MW \u001b[0m\u001b[1m Max_Demand\u001b[0m ⋯\n",
+ " │\u001b[90m String7 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String3 \u001b[0m ⋯\n",
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+ " 1 │ 50000 1 1 1 ⋯\n",
+ " 2 │\n",
+ " 3 │\n",
+ " 4 │\n",
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+ " 7 │\n",
+ " 8 │\n",
+ " 9 │ ⋯\n",
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+ " 11 │\n",
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+ " 8751 │\n",
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+ " 8760 │ ⋯\n",
+ "\u001b[36m 6 columns and 8739 rows omitted\u001b[0m"
+ ]
+ },
+ "execution_count": 259,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loads = CSV.read(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Load_data.csv\",DataFrame,missingstring=\"NA\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62d2def4",
+ "metadata": {},
+ "source": [
+ "The most columns to note in this file are `Rep_Periods`, `TimeSteps_Per_Rep_Period`, `Time_Index`, and `Load_MW_z1`. This file shows the number of time steps used in GenX before applying TDR, i.e. every hour in a year, totaling 8,760 hours. This means that there is only one representative period, as seen in `Rep_Periods`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2fc22b2f",
+ "metadata": {},
+ "source": [
+ "Below shows the load per timestep in megawatts:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 260,
+ "id": "6b684248",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.vega.v5+json": {
+ "$schema": "https://vega.github.io/schema/vega/v5.json",
+ "axes": [
+ {
+ "aria": false,
+ "domain": false,
+ "grid": true,
+ "gridScale": "y",
+ "labels": false,
+ "maxExtent": 0,
+ "minExtent": 0,
+ "orient": "bottom",
+ "scale": "x",
+ "tickCount": {
+ "signal": "ceil(width/40)"
+ },
+ "ticks": false,
+ "zindex": 0
+ },
+ {
+ "aria": false,
+ "domain": false,
+ "grid": true,
+ "gridScale": "x",
+ "labels": false,
+ "maxExtent": 0,
+ "minExtent": 0,
+ "orient": "left",
+ "scale": "y",
+ "tickCount": {
+ "signal": "ceil(height/40)"
+ },
+ "ticks": false,
+ "zindex": 0
+ },
+ {
+ "grid": false,
+ "labelFlush": true,
+ "labelOverlap": true,
+ "orient": "bottom",
+ "scale": "x",
+ "tickCount": {
+ "signal": "ceil(width/40)"
+ },
+ "title": "Time_Index",
+ "zindex": 0
+ },
+ {
+ "grid": false,
+ "labelOverlap": true,
+ "orient": "left",
+ "scale": "y",
+ "tickCount": {
+ "signal": "ceil(height/40)"
+ },
+ "title": "Load_MW_z1",
+ "zindex": 0
+ }
+ ],
+ "background": "white",
+ "data": [
+ {
+ "name": "source_0",
+ "values": [
+ {
+ "Cost_of_Demand_Curtailment_per_MW": "1",
+ "Demand_Segment": "1",
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+ "Max_Demand_Curtailment": "1",
+ "Rep_Periods": "1",
+ "Sub_Weights": "8760",
+ "Time_Index": 1,
+ "Timesteps_per_Rep_Period": "8760",
+ "Voll": "50000"
+ },
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+ "Sub_Weights": "",
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+ "Timesteps_per_Rep_Period": "",
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+ },
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//jAajdi0aRNuueUWAMATTzyBv/71r0hISPDZp7i4mAt/A8CECRNQWlqKoqIiXHXVVQCAhIQEREdHo7i4GMnJyYiLixO0JQiCILzo6vThNoEgiD5KyFby2bVrF1544QU8/vjjWLBgATZu3Ijx48fjpptuwltvveXTnmEYwWuP0HS5XD7r54rbisnJyUFubq5g2/Dhw3HbbbcFcCQEQRC9k8NnGxBtHxluMwiC+IGTnp7usy0kAnP16tU4evQosrOzkZqaCgA4d+4cDhw4gC+//BKVlZU4evQoNmzYgMzMTADA2LFjUV5ezvVRWVmJyZMno7W1FTqdDgBgsVgAAJdffjnq6urAsiyioqJQVVWFjIwMbt/MzEyuX75NUiegJ9HpdGG3IVLsIBsiyw6yIbLsCIYNDieD1mONAfcTCechUuwgGyLLDrIhsuzwaDQxQReYZrMZ//rXv7B27VocPnwYADBu3Di8+uqrXJvf/OY3ePDBB5GZmYk9e/YgJiYG06ZNw9ChQ/HOO+9g4sSJyM7Oxosvvoj09HT89re/xaRJk7Bt2zbMmTMHsbGxmDFjBlatWoW7774b77//Pj7//PNgHwpBEESvpbS2DTaHM9xmEATRRwl6DqbJZMIjjzyCM2fOID8/H/n5+T4zvGfOnMmVLnK5XDh58iQAYNu2bThz5gw++OADbN68GUOHDsWUKVOwYsUKrFmzBnFxcXj55ZcBAG+//TYMBgPWrVuHf//735gwYUKwD4UgCKLX0dBmRGmtHifO1yH4UzgJgiDUEXQP5vDhw/HKK68otpkzZw73N8MwmDlzJgBg9OjRWL9+vWR7/j4AkJSUhDVr1gTBYoIgiB8OG3afxtGiWkT3i8L1Eyj/kiCI8BCyST5queeee8JtAkEQxA8Gh9M9CdLFsAB5MAmCCBO0VCRBEIQK7A4XGtqMaDNYwm2KIg6Xt8qGi2LkBEGECRKYBEEQKjhd2oglr+7Ga58eDbcpivDLuLEMCUyCIMIDCUyCIAgVWO3uGdl8D2Ek4uKJyhAs1EYQBKEKEpgEQRAqsDlcAACXn4Uewg3fPoYEJkEQYYIEJkEQhAqcXZ5LlyuyRVuk20cQRN+ABCZBEIQKnC63BzPSw878EDl5MAmCCBckMAmCIFTgEW6RPm+GLypJXxIEES5IYBIEQajAKzAjW7Ux5MEkCCICIIFJEAShAldXDmaHyYbth4phtNjDbJE0Qg8mCUyCIMIDCUyCIAgVeLRah9GKjdkFaGo3h9cgGYQezNCO5Yr0fAGCIMIGCUyCIAgViMPNTmdklisS2BliD+apiw3kJSUIQhISmARBECpgRN66SM1vZHtwkk99qxHnyptDOwhBEL0SEpgEQRAqEAtKseCMFPh2hdpCu9OFw2eqQjwKQRC9ERKYBEEQKhALzEhd0UdgZYhdmA6nCzlnq7ki9ARBEB5IYBIEQaigV4bIQzyWw8nAaLEjv6Q+xCMRBNHbIIFJEAShAvFkFjZSQ+Q9N8cHDqd7daPzVa2hHYggiF5H/3AbQBAEEel8dvg8iipbBNsiTV46XQysdicYQeg+tFbau2bSR2q6AEEQ4YMEJkEQhB/2HC1Fk94k2BZpIfJDpyuxdvtxwbZQm+js8mBSPUyCIMRQiJwgCMIPkjPGI0xTmW1On22hNtEjsqkWJkEQYkhgEgRB+EFKYEaapPLkQ/IJtfDznBaaRU4QhBgSmARBEH6QyjGMNK9dOOpyesaM1JqgBEGEDxKYBEEQfpDKt4wwfRkWG7kQeWiHIQiiF0ICkyAIwg+SIfIIU5hS5rAhln6e8xJp54IgiPATMoFptVpRVlYGu90u2F5XV4fGxkbZ/fR6PWprawXbGIZBVVUVjEajYLvBYEBlZWXwjCYIgpBAapZ0pEkqSZEXYiNZbpJPaMchCKL3EZIyRe+99x5ee+01XH311Th79iyysrIwevRo3HHHHUhMTERraysmTZqE9evXC/Z79913sW7dOqSmpiI5ORlbtmyB1WrF9OnTMWTIEOh0Orz++uu48847kZWVheXLlyMjIwMmkwl79+5FbGxsKA6HIIg+jnT4ObJUVTjKJnEezB4fmSCISCckAvOll15Cfn4+RowYgbVr1+LNN9/EzTffjHHjxuGjjz6Cy+XC5ZdfjvLycowbNw4AYLPZsHLlShQWFiIpKQl33XUXDh06hPPnz+OGG27AG2+8gfPnz+OBBx7AnXfeieXLlyMrKwvjx4/H0qVLsWXLFjz88MOhOByCIPo4UmIywvSlpD2hFp2e3iNNbBMEEX5CEiL/z3/+gxEjRgAAmpubkZiYiBtuuAHPP/88AMBsNsNutyMuLo7bp6CgAFdccQWSkpIAALfeeiuOHz+OnJwc3HHHHQCAiRMnor6+HtXV1XA4HBg/frygLUEQRCiQLlMUWaIqLB5MCpETBCFDSDyYs2bNQnt7O55//nnodDp8+umnGDJkCADgu+++w9NPP42XXnoJqamp3D4dHR1ISEjgXsfHx6O5uRkdHR1ITEwUbK+trfXZxs/PzMnJQW5uro9dOp0uqMcZCJFgAxAZdpANXiLBDrLBi9gOKfHWUN8IXZyjx2zwh16v99nmcDi6dU797WsyuVc3MhgMIf3sIuG6IBu8RIIdZIOXSLFDTEgE5vfff4/f/va3+P3vf48333wTUVFRYFkWy5YtQ2lpKbZu3YqMjAzBPikpKYLJPW1tbUhNTcWwYcO47SzLwmQyYfz48ZJtPWRmZiIzM1PQ/+rVq5Genh6Kw1WNTqcLuw2RYgfZEFl2kA3ydrAswLInfNqNSB2B9PSxPWKDGoZcMAKoF2yLju4f8DlVY0NsbC2AdsQPHqzY9o9v70fcwAH404M3I3agtp+dSLguyIbIsoNsiCw75ARu0EPkDMPg0Ucfxa5du7BgwQJERUUBAHbs2AG9Xo+srCyBuKypqUFdXR2uvvpqtLa2oqqqCna7Hbt378a0adNw++23Izs7GwCwf/9+TJo0CSkpKUhLS0NeXh5YlsWOHTu4MDpBEEQwkcsvjLSwsGSeaA+NqZSD6XQxKK5sQf6FelhsofP4EgQRWQTdg9nQ0ICBAwfivvvu47bdc889iIuLQ2FhISZNmsRt//TTT5GVlYXOzk688MILWLduHaZOnYqYmBgsWrQIEydOREZGBvbs2YMJEyYgKSkJmzZtAgC8+eabeOihh9CvXz/MmDED06dPD/ahEARBhCW3MRAkF9MJeZki4f9S8Es8OWhJSYLoMwRdYI4aNQp5eXmS7/3+97/32XbLLbegrq4OADB79mzMnj1b8P6AAQPw6aef+uw3ZcoUnD9/PggWEwRByCMnniJt5rS0BzPUa5F7yhQpezC59rSkJEH0GUKSg6mFkSNHYvLkyeE2gyAIQhLZEHkP2+EPSQemHyOzj5XhTFkjZkxOx/UTRmof0yMwGbeQ7B/tm3XFF5UkMAmi7xD2pSLT0tLCbQJBEIQsvSVEHohH9XxVC3LOVqO2xRDQmHy9qKtrl2nDSv5NEMQPm7ALTIIgiEim94TIfbf5E3TOLoXoDDQ3kjfJp7iy2a9hJDAJou9AApMgCEIBOVEUaVopkLXIXV3C0qkxdG2yOrDszX24WNsGwH2OiipbJNvyu6YQOUH0HcKeg0kQBBHJ9JYcTMn10v1Y6dnHpdGDabY6UFrrLezOsCwuVLZJtuWfv0jz+hIEETrIg0kQBKGArCbqBWLJn8NQTZkhKcQh9dpmAzpMVhkb+CFybeMQBNF7IQ8mQRCEAr3Zg+lPOXr20ZobKRaYda2GrhWPgK61NSRNIA8mQfQdyINJEAShQDAcmPWtxqDYooj2FExO8GnNjXQxQoHpORdSk4WEIXJNwxAE0YshgUkQBKGA/FKR6tSS3mBF9vGyYJqE2mYDlr25Dy99cJjbJpmD6cdGz9taPZguGUEqFp7ivkNd+J0giMiBQuQEQRAKyJYpUrn/Z4fPw+F0Bc0eAOg021Baq0fKkDivPdoj5N4QuUYPplx7h5PBoBh5G8iDSRB9B/JgEgRBKNAdD6beYEX2sdKg5x56PIhC4RqIBzOwHEw5gek/RE4KkyD6CiQwCYIgFJCVRCq00uGCKtgcrqDXf/QIOb6gk9JucqFsD563NXswZYSiVIicPJgE0TchgUkQBKFAd7xuNrsTQPDL83BrgPuZoe3PM8ky3pV4tCAnXKU8mJSDSRB9E8rBJAiCUKA7OZgMG5iAU+JoUS2OF9cK+pezx59n0hsi12aDnHCVGo9mkRNE34QEJkEQhAJyXjc1opEJ0EOoxDd55TjWJTD9iTd/HsxABbCccPW3WiXlYBJE34FC5ARBEEp0QxMxAXoIlXC4vBN7hAJS+yQf6X78I9evv1JJJC8Jou9AApMgCEIBOfGlRry5QuDBdLqkvZZSIta9uo782IF6MOWai3MzWzstuFjjXbOcYuQE0XegEDlBEIQC3crBZNSXAWrSm3C4oAojkuIxJkG+nYs3kUaQgynrVQSioyTfCngtcqXSTU4Xg/7Rbt/Fju9K8EVOicAWgiD6BuTBJAiCCAA1okxLncnKxg5szC7AF7kliu2cDL80kf9+ldoEXAdTIUR++EwV91pcYJ5yMAmi70ACkyAIQgF58aVhko9v9R4fHE53I5dLuV+XqPalPy+kkngM3IMpvb2grAkl1a3ca3HZoi3fFuH7whpVY1i7SjwRBNE7IYFJEAShhFyIXIUo84SE1dR/9Igxf14+3xncrPIYCt0FnoMp3T7ryEXszy/Hkld341x5s4+thRXN0NW1qxrj5MUGTTYRBBFZkMAkCIJQoDvFwTnvoYouPKvg+Gsq9khyQ8h4SZU9mB5xqg259k3tJtgcLjS0GdFpskkWZFe7LvvRwlqNVhEEEUmQwCQIglCgO5N8tOQ4ltbqBfvIIRZtLPe/+tqUPu8FKQeTj4thJNvxt5msDjS0GWEw2wVtnC4G+RfqKWeTIHoxJDAJgiAUkBU5Wgqtqxgnv6ReVbe+HkxlL6kaD6bW2d1qi8xLrU3OH2tX7gUseXU3th4oErSxO13oNNmEJY4IguhVkMAkCIJQIPApPv7Fn4eGNiNqWwwAVKy+I/Zg+hlCcRa5xF9qUDNpiWFZyXZ8e2wOd7jcIZoM5Ojann+hXpNdBEFEDiERmIWFhZgzZw4mT56Mp556ChaLBQCwceNG3HzzzZg1axYuXrzos19RURHuvvtuZGZm4tNPP+W2v/7665gyZQruv/9+1Ne7bzi1tbWYO3cubrrpJrzxxhuhOAyCIAj5EHkQJ/mcvKB+QouPwPRM8pGtTSnfF5eDqdWDqXIGvZRNfPttDvdMcacoL9PeNaOeBCZB9F5CIjAffvhh/OY3v8H333+Pzs5OvPHGGzhz5gzWrFmDrKwsPPnkk/j1r38t2IdlWfzqV7/CH/7wB+zcuROrVq1CWVkZvvrqK+zcuRNff/01fvGLX+Cxxx4DACxevBizZ8/GN998g23btuHQoUOhOBSCIPo48h5AVT7Mrj6UW/GFlD8Pps/bGkPqgDvH8XRpI8w2p6ox/dogM66/HExPaSZxK3uXB7OmqVOTXQRBRA4hEZjXXnstZs6cif79++Pmm29GRUUFvvzyS/z6179GcnIy7rjjDrS0tKCpqYnb58KFC4iJicFtt92GlJQU3HvvvdizZw927tyJJUuWYPDgwZg/fz4OHz6Mzs5OnDlzBg899BC3PSsrKxSHQhBEH0c+9Ox/X86D6adxQZn3XqhVMLKi/8VIjW2yOrBiw0E06U3Kg6m0QQoXw0qeI/6+cqWZ7F0eTa3CV8wnX5/DPc9vxSffnOtWPwRBaCckAnPDhg0AgIMHD+Jf//oXlixZgpqaGowZM4ZrM3LkSDQ2NnKvpd5vamoSbI+OjsaQIUNQXFyMESNGICoqSrIvgiCIoCEXetawr1Jbp4sRFBX378Hs/iQf8eQb7R5MFe1Z6X5ZXojcxS2lKWzjEZ6+NT+1YbY5wDAsLDYq2k4QPf8IWf4AACAASURBVE1I1iLv7OzEE088Ab1ej6+++gpjx45FXFwc2tu9BXbNZjMGDx7MvY6Li0NHR4fP+1L7jRgxQrKth5ycHOTm5vrYpdPpgnaMgRIJNgCRYQfZ4CUS7CAbvPDtqGk1S7Zpa2vza2+nwQjAfY+Sa+vJN/TgcDh8bOAjzlcsKLqIoYNjYDJLeyMrK6vQHjdAsE1vFJYFMhlNkuPJ2dDU1Cq5nU9zSwtMJl+bOjoNXL+dBvfEJkNnp2Cs6ib3efMI4UCvi5Y29yx0vb6929dWJFybkWADEBl2kA1eIsUOMSERmPPnz8ecOXOwcOFCbts111yDEydOAABMJhP0ej3Gjh3LvT9x4kTodDrY7XbExMTg9OnTWLx4MRwOBwoKCnDvvfeitrYWqampGDt2LOx2O/R6PYYOHYrTp0/jJz/5CddXZmYmMjMzBTatXr0a6enpoThc1eh0urDbECl2kA2RZQfZIG8HM1APoNCn3dCkoX7tHZzXBKAFgwbFyrZ114DM515HR7tvy3Lto/qdFbz+65YCTJ+cjtjYOAC+q+SkpaUhZUicYFtDmxHAGe51XFy8z3hKn0e5PgqA8o/asGHDENtkA9Ah2J6QMJjrN/ZYA4BWxA9OEIxli24BUMw5jwO9LuJPtQJoxuCEhG5dW5FwbUaCDZFiB9kQWXbICdygC8ympiYcPHgQsbGx2L17NwDg//7v/zB//nysWbMGy5cvR35+Pp5++mlER0fj5ZdfRlxcHJ566ince++9uO+++zBu3Dg0NDRg6tSpuPLKK/Hzn/8cZrMZBw4cwHPPPYfo6Gg888wzmDVrFm655RZ89dVXWLFiRbAPhSAIQn52top9GeXotbuNKFztr18pe5RC1pITbXxKHWkttO6/DSs7ycfXDt+wv6dt90Lknv0D7aegrBETL03plg0E0VcJusBMSEjAwYMHBdtSUlKQkJCAo0eP4ttvv8W8efM4j+Ps2bORk5MDAFi3bh0OHToEs9mMNWvWIDo6GqNGjcKRI0fw3Xff4de//jWuuuoqAMDvf/97ZGZmorGxEStWrBCEyAmCIEKNGlHGlQFSUGQ+yykGMCtcriSQ2wb/fWiXXyqOXaYV3065QvTeNdK1l1AS9MP4P/9KnCtvxlcndLhvcmrgRhBEHyXoAjM2NlYQruaTmJiIe++9V7CtoaEBc+fOBQBERUXhtttu89lv+PDhuO+++3y233jjjd03mCAIQoHuCBw1ddbFAlNroXXPPnJ7OUVFzKX6CJUHU0rY8cfizo9ofKFXN/APwOvBDGx/p4vB4TNVSB0chYyM8IdDCaI3EZIcTC3cfvvt4TaBIAhClkDW+BbvqzWELUVdiwFf55VzJXyEfUBWhzmcEgJTJiStFnXeW7lwvq8dLAtY7U60G62w2l1oaDNJtteKV0gH1onn3LUabIEbQRB9lLALTIIgiIhGRpuoWc0GMh46Pi6Rh1GubV2rEdsPFUu+xzCsrD2qPJhal4pUeej+Cq3zczCPFtXitS1HJfsJFG//ge0vXsKSIAj10FrkBEEQCshqE1WF1v1PMlGbDyklFD1crGmTrRkpuVyjOO0zJB5M6ULrUh5MhmW51XuCiXcpzMAUpqckVHfrcRJEX4Q8mARBEArIiRNVoW3OgynfRJyDKTeeUyLU7aGhzYh2o1XaBEmR170cTDWwrLRnVGqSD1h5Ad2dmeTdC5B3P4eTIPoy5MEkCIIIADWagxM4Sh5MH4Ep3c7hUvbw8VcDEvanHKYGusLZDAujxQ6zzaE4jlyfEq38zmBneR5Mn9n0fOMChJ/jGdD+pCwJImBIYBIEQSggK05UzqSW6sPmcHHiRW2IXLzij1qk+vMRTixQVqfHvFU78Ox/vw2oT58xWP81OPkCUFwPVMtYcnQ3RN7dOpoE0ZchgUkQBKGA7CxylbUgAaFAefLfX2HOC9tRUu1eblFtySDxZCC1+JvJ7bHPE6J2qvDaqa4B6seDyajxYHYDNWWilHDJPAQQBOEfysEkCIJQQE5bqIwSd7X1NvaUvjFZ3aFo35JB6meDq0FtoXWPmFIjZNUcu8Fsh01i4g5fUHv6KShrxPmqFs1j2Z0uDIiORu65arAsi8xrxiIqijdWkDyY3XKjEkQfhQQmQRBEiPDWwfRu8whFc5fAVKt9XDIhZH9Ied98JvkwLNe/GiGrxuTPD5/3a49HbNocLkkx6h5LfrQt3xZh64Ei7vWPL78E8YMGePftZg4my3hsDmx/gujLkMAkCIJQoDuzyD1N9EYrth8sxrAhcdwkmk6zDSarQ2IVG7kQeWAqR83a5QzLcuNaHS5YbNIThngdBGQLIJ2DGehQ4olNFpsD8YMGoLbFgJX/O4w2g8XdR4AuSK99pDAJQiuUg0kQBKGArLTQMMmnrdOCjV8V4F9bj6LT5F4V5r9fnsSiV3b5iCy5XMRA8wAlJ/mIczAZluu/w2jFklez0GaUXr2muqkTzR2WgGwRj61lPXcpxKsaedIPzFYHGtqM3tqagc4ipzJFBBEw5MEkCIJQQkbgqBJHft53uhjfCTdyAjNAlaPVgwkAHSYbOs3SXsx/bv4eFQ3tAdkCCGeLqzukKNl3xPmiHkHos757N89dKOqEEsQPHfJgEgRBKCAnLbQUWpfDyTASHszgFhz3t5oO0LXUpGib1JrnAOCQ2a4WwXGoOCa58kWAvJAUn0NXt8+dvMglCEIa8mASBEEoIKdN1DjF/OX+uYWdrzeRZcGtO555TRriB8UEXipHhQfTxfra4ZCpuxnobHa+OSwL5J6rhtlfrieUz7PcKkhij2WgHkzPOZc7FwRByEMCkyAIQonuhMhV6BqplXycDION2QUAgI3ZBZh18+UYFBPtvzOViM1iGdbHy+cIwdKNgPt4zTYHVm86oqq90nkWeyplQ+QBezC7BGY3RTVB9EVIYBIEQQSAGq+YGlkj1U9pvUHwen9+uaC+oxakbPApUySxzcmbtZ5XUg+r3YlJV1zSnQnkALqKumsIsysNJ+epVDsz3x+e3ajQOkFohwQmQRCEAt3JwWRVCBupfjbuLxO8VrM+uKwNEib4jCma5AMIvYDrd+ShpcOM/yyb2e31uRlG26o9SufZd0Ui6X34NhdWNENvsOKqy4ZjaMIgxbHVfH4EQUhDk3wIgiAUkF/JJ0geTIl+LPbuTaTxi1hfwvc4+SLQM7HH7nR125vHsiycGorGKw0nuwqSz7F4x9v8TSFWbzqCs7omVbYCgXtACaIvQwKTIAhCAbmJOqpEh6pZ0qEVL1L2Swkz3wkz3r89k1z49TIDhWG1FY1XmmDjE+qX8WDyj80zSUnNbHiqg0kQgUMCkyAIIgDUaA5VbUItXlSUKappNkBvEBZP55cH8ogyRmK2uWZzWG0i1aEgRn0i/RJLcwJCD6bnWJxqcmg9DlHKwSQIzVAOJkEQhBLdCZGraHPqYoNWi7qN2KvpdDH4Jr9c1MY7q8gzi5plu+9x7TDZkH2szH9Djx0K59DXEyu9DyPhwZTLr3w36xSOnKvBwjuv5XkwSWAShFbIg0kQBKGAnLRQVWddRZvs4+rFViCU1elR1yKclc5KRJ2rmzqFbXir2PD/7q7YajdasTOnpFt9ePANkUsLQv5Lf6KxvtWIlg4zzLx14klfEoR2+rTANFrsMFrs4TaDIIgIRs6Dpi5EHn5l8tG+s/gi94Jgm6oZ8BI1JT1F4HsSpfEkJsO7/xe34/3tkill5MGTm8k/VhKYBKGdPhsiZ1lg3qodAICdf78f0f1oKTCCiBRcDIvy+nb06xeF9EuSwm2OJEqhW5Zl8V1BNTpNkfEAK159R43A5Dx9PIHJsj0fLlYS6b71PGXEI++153jkIv2WrtWF+BOaKEROENoJqcDcsmUL7rvvPgwYMAAAUFxcjKysLCQnJ+PBBx9EbGysoL3NZsPmzZvR3t6OuXPnYvTo0dx+e/bswdixYzFnzhxERUWBYRhs374dVVVVuOeeezBhwgTVdn2w9wwOnq7kXjucLkTH9FmtTRARh9Fsw7I392FQTH9se+mX4TZHEiXNYbQ48Oqn3/ecMX4IpPC4JweTL66CMclHM4plikRNPR5HhSpI3uOR7thidwtMp4sRpAYQBKGNkITI6+rqsGHDBjzyyCNwONwFgisqKvD//t//w+jRo1FUVIR58+b57Ldo0SLk5eVh0KBBmDp1Kjo7O1FWVobZs2cjOTkZn332GV588UUAwF/+8hd8/vnnSElJwV133YXKykqf/uQwWuxo7fTOmOzu2rqAuxCy0WKnemkEEQTsTvWlZEJNIHUwGQ11HnsCXyGmQmAyviFyNgwhcqUljFTnYPL+9ufBtHZ5MDtMNgqRE0Q3CInb7vTp07h48SKio71r5+bn5yMzMxMPPvggfvGLX+DKK68U7NPc3Izc3FyUl5cjKioKZ86cwa5du1BcXIzHHnsMjzzyCObMmYOJEyfihRdewP/+9z+UlZUhLi4ODQ0N2LRpE5599llV9onvFcEQhc+9cwBldXq89tgduDwtudv9EURfxiMsXYxb0AS6TGIwkAvRKt01tKxU0xP4eDBVmCcZIte4Ck8wkBLDNocL+07oBI4CwPuZyNXHBLzHJSeyLXa3U8S94hAj6JcgCPWExIN511134ZVXXkFiYiK3bcaMGSgqKsKCBQtwxx13+IjB4uJiXHnllYjq+iWZMGECSktLUVRUhKuuugoAkJCQgOjoaBQXFyM5ORlxcXGCtoESjPCHpwdXhHkuCKI3wo8qRGp4UsmuyBeYauyL8tmXYSPj82jtMOOdXSd9Z8fLFkZnfdrIHYe1K0TeZrCgqmtmPWXoE4R2eizx8LvvvoPT6cQjjzyCkpISrF+/HkuWLEFMTAwA35CSR2i6XC7ubw/+wk85OTnIzc312a7T6QAAnZ3Cm5KuogJD4mK0HZAIu80GAKiuqcVAV6dsO48N4SYS7CAbvESCHZFkQ22rmdtWptP1+CQ8/rlobGiXbGMwGGXPWUunLSR2BUqnyNbmpma/+7AsC51OB6PFuw56XX19SOxTtsP32qxuMUu2rautQ0KUCU1NrYLtdrud68PhcAvI1tY2n35dDMutHHSsqIbz3rKIrO9HuIkEO8gGL5Fih5geE5h79uzBo48+iqlTp2Lq1KnYsGEDKisruck5Y8eORXm5t9BvZWUlJk+ejNbWVu7kWSzucMjll1+Ouro6sCyLqKgoVFVVISMjg9s3MzMTmZmZgvFXr16N9PR0AEDCqRYA3hvs2LGXYliicMKRVmJiLgIwY0TqSKSnp0q20el0nA3hJBLsIBsiy45Is8ExoBVAIQDgssvGYUD/nquoJj4XTdZaABd92sXHx8ues5jmTgAFIbJQO2JbS5pZABWK+zAsi/T0dOgNVgCnAQAjRqQCuKC4X7BhwQpsbzdaseuk9LkdMXIk0tMvQbk+CoD3R3fAgBiuj+jocwCApKShPp+fyeoAkAcAsDn4XnRE1Pejr9tBNkSWHXICt8cE5s0334x33nkHU6ZMQXV1NUwmEy699FLs2bMHMTExmDZtGoYOHYp33nkHEydORHZ2Nl588UWkp6fjt7/9LSZNmoRt27Zhzpw5iI2NxYwZM7Bq1SrcfffdeP/99/H555+rtsW3gkXwQuSRED4iiN4Of/3pcH+nZOtgKpgVaSFyqbXH/SG1Kk44yvWIh1y77TjyL0h7Ur1rkYu2S7RxSkTCXLITPiPr8ySI3kBI3QKPP/44V6Jo3rx5WLx4MV5++WVkZWVh165diImJgcvlwsmTJwEA27Ztw5kzZ/DBBx9g8+bNGDp0KKZMmYIVK1ZgzZo1iIuLw8svvwwAePvtt2EwGLBu3Tr8+9//1lSmSEwwJvl4b2x0IyKI7sLPweyN3yl5oRImAplF3vW/oExRBAhng0U+/YALaSsIas/xuCTWOJd7MOiFlyBBhJ2QejCfe+45wev58+dj/vz5gm0Mw2DmzJkAgNGjR2P9+vU+/cyZMwdz5swRbEtKSsKaNWsCsstnFnlQ7h6eG1sQuiKIPo64uHc4kRteqQB4pHsw1ZgnVfInEgSm3SEv3rkZ4gr7e45LyoMp91tgsYe/XBZB9DbCvlTkPffcg2uuuaZnB1UoYdHdLsMdziOIHwLi4t5hRWb4C9VteOuLfMn3IkGI8QkkLcib9uPdFpYQuei1Ut1izoOpcP497zidEgJTZr9OiyPiHhoIItIJu8AMB+LbRHdEod5gRUlVKxxdN71w/xYSxA8B/g99uMWanKeypcOMo0W1ku+FXRSL8F1S0T8eBx///G85UBREq1Qisl2pFJxHfPre4/l/u184JISq3OfGMCzaRDU3CYJQpm8KTIUEcK0cP1+H5W99w9Vj49/I//j2fvxubTbsDgqvEIQW+N+jSI4KyK00FGnerkA8mB4hxxddVY0dQbVLDWJLpXInPdi67rVya5QDXsHslPjslB5mWjqkSyMRRDCRepD564ffYcE/vkBZnT4MFgVOnxSYYr46Xob8ksDqu4lDMQzL4ovcC1j25j4UV7agsrGDu+kRBKEOVwSFyJWGlxMkkbdUpPZZ5C6ZCTM9jXh4peuBW3lHoQ2XziT5nvx+ze0kMInQc1bX5LOtpcMCvcHa65xVfVRgCm8iO74rwbenKoLQk/vm1dBqRGmt90mDVvchCG1EUohcKcQhp0fCbrMf1GhG74zsEBvjD54BDW1GxRxMz2flc/ol8kiljkvpWJvJg0n0AFICUymtI5LpkwJT6iYilfAdCAzL+twAI/3HhiAijXDXXuSjNFtczjZX2FWZEN9Z5Oon+YQ73M8f/Vx5MzpMVhVt5XNOvcfue1xKn3UrCUwixBgtdpRLrBzGlUHUKDBPnK/Dig0HsTOnJBjmaabHCq1HElK3kEB/xHxCKqz0ur/VTZ2objFjdJoLAwdEBzQWQfQVwj1zWS1yIdWIe6hU8OjJ7uJnze5wcOhMlaKXkZXxTkrl9Gr1YDb5CZGbrA7EDRyAKFq4nNDIW1/ko67FgBsmjJR0dnlLhmnrt7XDgtOljUju5kqFgdInPZhSBOtHjJV4BnYxLF7bchRrdhSiQmZdY4IgvPC9ZkqTOnoCZUEjvT3SBGYgHkzPIYRb4POHr1Y5ySiQHEyni5FdIQgAWvwIzHajFY16oyr7CG1szC7AXz/8DtVNnWEZf//JCmw/VIw2g/ZKAizL4vCZKsU2JVWtOF3aiO/OVktGDDxbtH4XPe2LK1uwfkcemtpNmvbvLn1SYErdfAK9h/quGOHbhmG8olMxf4ggCACiEHmEiTU+vSVEHogXkvOahPv8d3kEWRboMMmv4uNu0+WdVGjDfWaic2K2OrBh92nZ/WpbDILcejEOpwu6enIghIKTFxtwvLgOrWEqFfVl7gVszC5AY5t2gfbRvrM4fr5OsY3nStTV6aUFZjejCfWtRmQfL4O+Uz69JBT0SYEphdRNdPuhYnzyzTlNN1iWZX1EptPFcLNKI24JOYKIQPjCrTdOkgu7KBMhfrBV+ztlMNtDYE1gmG0Ovw/oXHalwoO/nAj15x2y2p2obzXIvm9zuKDrZWVkeg3hTtfwTLKRKUsmx+nSRmw/VOz/HsZ7mJNqy+VgBujB9NDT+dR9UmBKehklbkgbswvw6f5CmG0O+b5Er9uNVlhE7R97fS8qGtyhHYZh8dG+s1jy6m4cPF0ZkP0E8UOH/0PiDPckE4WbulJh7khCXCpNzQ81wwLfFVSFPUTuucm2G/17X5TyKz3bZRyYqj4zpVPhcDLQ1ZEHMxQwMp9ZT4+v9Xvd0GYEywKMnzQfwSQ0RQ+mpuF9nV09/LDeJwWmFEoXjuc9s82B06WNKKlu5d4Tf4Dv7T6NnLPV8n2x7htlQ5sRnX7CPQBw6mIDlr25Dx/sPeO3LUH8UOB/HcMt1pRGlxNqkTQxBvD1XKixrlFvQUl1a9jLFHmGV3O/lPP0eD8P/nbt3h0lsW0nD2YICfeEM/e4/8suwL+2HtO6m2zKTElVq0/Uk38dHimsweNrs9God4fmtd4LfSYc9/C9tI/OIpdPopXa4vmQapsNWLHhIC4bmYQ3npohu6e/sT2fuZqaVu1GG0pr9RieFK9pHILozfAXMAi3WPM3yYdl4TNzOOxePzEBTPKpaTVj3Ggm7OffgyoPps8f3pe1LQbsO6HzbhO1UZOKoXQuHE4XWjst6DBaMWTwIL99EepRKo7fk+Pr6vSw2p3q9+v6X07YVTV1dE28kc457zDaBKtnMSyLA6cqkVdSh9t/PA4/vnykqvG516Lrt9NkQ1O7GYnxMRgRAo3RNz2YEp+1Us6O+Nrg5wEF5rLuehpT8TQhtVxbb6LdaMXp0kYuRYAg1CB1vde1GDTd3HsOX1vD7XUVo1R4XAm70xV+D2aXAR1G/x5MLldP4q2a5k58fvg891p8jZlt/q8tpY/V3pWfRxN9go+3OH54Lkb+taJloq6/iXIsC2zeXyismsHw9YXvk9K58iYcPlOFNz4/gWVv7lO810iVTORzpLAGy97chy3fFqk6Hq30SYEpXf9MPoQkzn/ozqQDvjtcjWj0XHiR9oOllnO6ZqzYcBCb958LtylEL0JYtxB4L+sUHl+bjU6zCpERfGuU35V4O9IeCJXW5lbC4XRFzLEYrf4nHOWcrcY3+eWSP8w2u/IEDYeKZfiUBI69q35hb1svujcRCZeiZ6Juq8GGhjZ1ZankNAMLoLqpEzXNnby2PG+mhED0bGrpMKO0Vg+HQn6nUp6x0WKH2eroaheaE0shcs828f2It6FRb0K7yYbS2jYAbrfyxuwC3HTVaM0ue4b1jq5qNY0IWQ84UBg/T3AEIYX4avnqhA5OFxOWmpj+vnoMy6IfokTbQmhQAPiGytTt53BGTohczWd/vqoVQwYPwoTRyT7viVOSApkAoSgwuwRqlcpanYR6vBOzevZadDEsOoxWOHjFz1s7LZj17JauVwX44u/3o18/6er6SgXSN2YXcJpCqsqBeDsAvPrp95I2yiE+X7u/L4Wurh2/+r8rMW/VDm57qO5XfVJgSqEk9v749n7Ba5PVge2Hit3V8TXHyMEruRAc++T4IvcCHE4XZt9yOWL6e1cPyittxbajDWjUm8CyLP62eCriBw3Q3L8avF+wyPiRInoH/Buji2Fgczi5v3vclkD2iTCF6ZsCpM4+d4g8MnJg1c6AtTlcPk4EFqxPiRlxGzUl5NSEyM3WSEzj6N2oqW8aCr7O02H9jjzFNlIPmB489kp9/7YfKpbtj9/OH4zC90K8d/6FepwqbfBZOpI8mMEkSOfS6WI0LwvG92Cq+VC93k5t4wDAx/vOwmp3YsbkDIHArG21IOesd8WKUBZ/Jw8mEQj8y8Vic/LSUyLvOuoNIXK+PefKm1WX03E6mbB7Yz13TLU1hJvbTbhYI8z+stqdOKtrEncswCGxRJ+PLX4m+QBeoUkED89Zb+u09GyxdRXXvoth0V9m9Wc5z6u/iYPev1Wk0Sm0kdqfYVgYLXa/7YJB38zBlNgmVQfTH4EUTffMOhWPcepiA7KPl6FDNFOSe3IL4C7vEY6+TziivI4Q/oKEu34Z0UuRSaoPi8D0c/FKp9xE1gXPN+fbk+WKSyLyYfg3rDCj9rOvbTbgeLFw5RSD2e6zXF/O2Wr86b/e6JSaqh5KgsNzH43Eh6Dejuf79NYX+Vj1weGeG1dFG+WoinQET+kBlP+emktJ8eup8lKkEHkQkV4q0jek4g9+wq1anC4G5fV6bv8j52rwbtYpdJjceR6XDBuM6yRKXPDtq2sxoKXDjLQRQzA0Qb4chncGuthu6XahwGO31FPWufJmOF0Mrh43HP2j++SzDiED/xrlh0bDsRKWv6+4tAczJKYEjKBwvYY8VpZVOx0odER1hR9D8SBs4c0cN1nlF9TwoGRBuGc6/5BhBfeDnju/qjyIMt+nfSd0yD1b09WP+jEdThfmrdqBhXdeCzUKUXEWucoxKUQeRKROZSDn131D0bbjtoPFXMkelmVhstrR0mHm3pe7WPlPNV/kXsCeo6VYMONazL3tR5Lt+Y4H+aLD0u8HEyUP7N8+/A4mqwMf/+VeDIkfGDIbiN4H/xr95OtzvO3hsEYZNQ+s4YZvjpYHShej/SE62HjThIJvCD8s3tzuf51puc91+8FinLzQoNiGCAyWZWHgVY+ItKVjxZ/31gNF+L6wRrBufU1zJ1ZvOoIFM67FJcMGK17LLOue4e1QmZ7CT7mLEuXsqb0WQ6UB+qbbSOJcSpW18EdBWRPsDm0Xe0WDN/eJZSVW2FCRjO/x4ihfPN73/PUZqhC5yergPARKtkbaDYMIP/wbHn9Wbjh+vLUO2WG0RpzI4Nuj5fvujtJExrGEIvRs55UmEi+nqYXPvzuP4soWABQiDzaVjR0C73I4KkkoIf5+NOpNAnEJuFM0cs5We8usqcrtVFfBwfN9rmk2oEPFaldSsCH6Ce5TAlNX3455q3ZI5h/VtRixYsNB1LYYAKjzS57VNcnOBFODO79JYptcW+7vrv9V3sh8PJiiGW9FFS0hKYT+1L+/wrtZp7rG9IWbuBFhNwwiApC5JMIzeUZ5TIvNidJaPaqb3LXszpY3R1yInI8WgSlxi+pxvMWqg/8raONNyPFXJ5Nvi08/vH0jRZD/UCiqaBG87sl7QCCTbJR28Xz3VNfA1pCD6WIYfF9Yo9qWnqBPCUyXi4HRYpecNe1ZZ9xods+uUnuT6M7KIlI3b/HNn+W1FW+Vmz3mFr68FSv8eElf33YsJIXQ/c2G82R3fZB9Bju+K/F5n+i7yN2Aw3HD9DdmaW0blr25D//4OBeA2+OiZt3snoR/CEqzTsUwTPg9mFyIPASq3ckTmHaH/3u53IOyw+USvCaCh8cz7KEnK5KoGckn+KkYrdP43VPTjsv9BXLPVgttUfl4GKpM65AKHPVAuQAAIABJREFUzMbGRp+TXV1djerqapk9AL1ej9raWsE2hmFQVVUFo1FYNd9gMKCysjJ4BqOnPSQsnC7hU7PcbPaqxg6s2HAQZXV6b26lzMV64nw9PvyqwKcP72vf/RiGhdXuRM7ZauSVSM8w7TDZsH5HHj7ad1bpoCSPReq0erYdPlOFc+VNvg0IQkSklf8BvKFVQ1fpD4vVgYs1beE0yRfeadMUIo+AHEwPodAVwiX6/A8gdS4cLuFympF4jfZmxB5MtQ882cfLsOvIxW5dv2r2PVZUKwiJK+3Cle1TGfpWc6xWB4OXPjiMVRu/w1ldkzBMrvbYQ3TJhkxg6vV6/PSnP4XF4q1Z9bvf/Q4LFy7EokWLsHTpUp993n33Xdx6661YsGAB5s6dC4ZhYDab8bOf/QxLly7F5MmTkZ2dDQDIysrC5MmT8dhjj+G2224TjNMdejJ/hmGBs7pm0Tbx+O7XJqvbw9phtMkmvRvMdmzMLvARa5WNHYIlraSuWavdiZLqVqzedARvfZEvaa/JYkf28TIcPKVO1Avyvvx8UUJZi5Pofch7MMOQg+nnfY9gM1nsYFn3mtaeFToiBb6HQss5dNftDbNg6ho+FJ89IxCYau5BvjaI713h9vj+kGjpMKNJNPlKzellWWD9jjy8s+ukT13S/ScrsHrTEW5Slp+e/LZ464t87Dl6EQ1tRvzj41ycUujXc72pOQa1Dyqvf1GEvJJ6tHSY4WJYQZg8zPoyNALzvffew4033ijwRJ48eRJFRUXYv38/9u3bB5fLBYfDm7hrs9mwcuVK5OTkYP/+/TCZTDh06BA2btyIG264Abt378aOHTvw5z//GQCwfPlyZGVlYffu3fjRj36ELVu2+NghRs3n5bnJ9MQ9wu5w4tRF4cXoL9mWX5dOPDPbaLFj+6FiH+/J3z/Kwf/2nFHs93RpI/7y3kH3GBIiu67FgIquyRb+LnxPbqrZxkvMZuTTEwDKwyREyFwOwfjxruvKs1ZtisqHI6eLgclqh8XuUFXyJlxo8bAxKvPAQolH4IZCuPFFZaAeTPG9S6qb81WtyDlbLagYQvjnfFWrzzZ1KR7eNnbR5K3iimbknK0WTLiV7UXlJccwLAxmO74vrEGbQd7ZxVVVUePBZAOLHuSe4wnMMD/shERgLl68GBcuXEBqaiq37dtvv0VmZiaef/55LFu2DMuXL8eAAd7lCQsKCnDFFVcgKSkJAHDrrbfi+PHjyMnJwR133AEAmDhxIurr61FdXQ2Hw4Hx48cL2vpDVW1LzxNGD9xVz5Q1+eRw+ixxJhHe9nowhe8phb74X0p/RyZ1Uf5nZz5e7sox83fR5pXUY2N2gaDG3IXqNsxbtQPneKtpCNdcJYFJeJGf7Nb9vs+UBTcd43RpI/e31e4UXPeRgiCEq+Ekulg2YkK+oQgu8Y9NzUOuVAvx5COpe9n2Q8VYvekIzlf6CiZCmtWbjuAziUm0qlbAU0hZ8LxSEzVTe8mpjXzy8yX9tmUCix7U8x6gVXswQ/Qd77E6mE1NTdi1axfeeusttLS0YPr06SgsLER8fDwAoKOjAwkJCVz7+Ph4NDc3o6OjA4mJiYLttbW1Ptv4+Zk5OTnIzc31saG2ptZnm5i6+noMiTbD2o2SFWqR8qR8lH0auadLcX/mZQCAllbhDam+vgEGg/tYs49dxOHT5fjDfVchNiYaje1WcXccRqMJOp1OlV0Op9OnrdnsffJ2Ol2KfbW3yz8Z1tbXIzHa3Rf/i2+2WFTbF0zCMaYUkWBHJNnQptdLvl9XV4fEfv7rFcphsDhQcKEOVwxXXuOVfy5amlsUWgIHTlVwf5fqKqDv0OYh7QlsdgeO5Bdi5NBYmEzqvWgOhwMNjY3+G4YQlnV/HgZD8M8rywJlZWWIioryyfGXorWlBTqd8Gezwyxcds9ms/l8lzq77tkNjY3Q6QL3bkfCdxQIvR0s615pSQpX17wFORsOnm3A2Urvb1BFRQUSYr3OrM5O93V0oqgK7e163H7tSJ/6kQDQarChqrbZZ7sUHZ0G1KjQF/X1DdD1t8Bs868v2vR6xASwAInN7uDOjb5N+j4qxmhSrw+00GMCMykpCffffz9uu+02AMBbb72FCxcu4IYbbgAApKSkCELqbW1tSE1NxbBhw7jtLMvCZDJh/Pjxkm09ZGZmIjMzUzD+6tWrMWr0aADKZYVSU0ciPf2SrhDXyW4ccWDU6y0YOHAg0tPTAQBn650AvEucDR8xAoOb7QBaYLG7YLG7kJY2FonxAxHd2AFAegLOoEGxXJ/s4QpFG6L6RXNtPQwcVAHA0PV+P5/3+SQUdwKQzkPxnF+dTset0AEAAwcOUuwzFOh0uh4fM1LtiDQbkspMAOp82qSOdF8/gXLkXA1i4+IVj1V8Lgrq1AuC1JGjwEbJT2IMFwaLAx8frsJ/ls1EzMAKeL7L/umH1BGpAEpDZptfWCA9PR1xxxsBBN8DeNm4dET3i0JsXB0A5R/k5GHDfK4dd9jbm4IU3X+AT5uYmCoAHRg+fDjS0y8NyM5I+I72lB1uj9oJmXfdvxtyNuw53YrSeu/1nZY2FsmJsdzrhFMtAJpxvqYD52s68Ou7bkTsQKEUYlngqef8p915iIuLx+jRowEUKbZLTU1FevqorioTyvqif0wcGOXnYEn6RUf7vY+KifdzT/SHnDjtsTJFt9xyC06cOAGGYWAymVBTU4Nx48ahpqYGdXV1uPrqq9Ha2oqqqirY7Xbs3r0b06ZNw+23385N7Nm/fz8mTZqElJQUpKWlIS8vDyzLYseOHVwYvbtEQqhWkGwuLooukZfBzUxTGSL35zj3V2/OX8hM6RzybRRMPAh3ohcRUchdQt39ehZVNAtWb1EDv9C7P5wupluly0JJu9Ed4dCysIHTxUTMdzNUt2ZWQ9hSqo34fmiy+D6QeMKxkXEmIx+lz8Lf5yR+W64yiwfpULm2T4pVORmOC5Gr6DP7eBmyj5VpsgNwLzXZ0GaE2eYIewWIkHowr732WvTr59awP//5z3HgwAFMmjQJ/fv3x9///nckJSXhgw8+QGdnJ1544QWsW7cOU6dORUxMDBYtWoSJEyciIyMDe/bswYQJE5CUlIRNmzYBAN5880089NBD6NevH2bMmIHp06f7tUdtYq3atqHCJRBhQlhGvnamamHn7wvq5yYqXNeYAcOwiBkQLfm+b9/SdoT7i0D0HAazHQlxMYpt5K6h7n4vCytbMCIpDiwL8KNiVrsTg2Kkb4flGhYhYBg2YisieCY7aMmpdLiYkK3yoZquzylU92QtpWOkpIH4wV7qAcPJTR6lG1138Sfk5Bww3v2V35fqwx9Si6bI2ZZ9rAyHzgS3vCIfg9mOJa/uxuP3TlK9T6geIkMqMPfu3St4vXLlSqxcuVKw7ZZbbkFdnduFO3v2bMyePVvw/oABA/Dpp5/69D1lyhScP3/eZ3t3YRgW+07oZPM/egKXwg/U27tOwia6gXlvkPJ9armxSa+tzBuPN9DB05VYt/04brk6DX+ef7NfO9SsVET8sCmsaMZNV45WbCN3NXTnOrHanSiv02Po4EFoajdhWGIs+kf3g9PFQFfXjisvS/Edj2E1eTAZlo3YigiOrvuKFvtYlg37Uq6ejzxkt4iuftVcW5IP36IbnlQ/nnt6JK/wFEkonSa5j6mp3YRtB4pRVCkq/Sc+6eKooMSHovVjUqkvwbAsLta24Vy5utzO7sBC/e9+qL5bYV/JZ+TIkbjnnnt6ZCy1IZAL1a0+5YN6EiXPXofR6vOErMbrKvAcahhfan8+Tqfvk7miJ1WmHT3Z9x34lQTkEJfg4rZ34zI5WlQLF8PC4XTBZHWgqmtpx5rmThgtdsl96loNPmVOlHAx4RdkcngKN2ut9euMEFUUag+mqtnJEtvE51OqH64N3edUofa3jE+H0Ybs42WobTaI2ov2F+33xucnsOvIBdXjS9HaacaZUv+T4dgASw8FAr/iTLjosUk+cqSlpYXbBAFMBHwowifgwJ6qFdv42UEyZCDzvlR4SckTwA+3CftUNIn4AVFe347WTgscThdiBkQjOSHWp00oPJhNevfs806zHeX17WAYBumXJKGsrh0DeSkeYlu1wLCRGyIHAKdLuwCOlOMJmQOT82CqaSsRIveT4wd4BSbd57qP1lvAo2t2445J6Xjyl5Ml9z9xvg6J8QO7ZVNprV6wmo8c7t+/nrkIIiEqGHYPZk+iSjqykfOQuembc8g7L71sIx81de0EnkM/baVD5NK5k1Lb5LxP7rFl+omUk06EHIeLwcsf52LJq7vx949yJNvIeRCMZmlPo6pxu7ztujo91m47xv0glNfpZb2UFRryLwEgp6CKWzoyEnExjOYfHqczvMejpTh1IHAeTFUK03eT2IMpGSKnHExNKE7ykfkFU9pH+FAln8Kgpq/uwKInPZgqr2mE7roMuwezJ1FzDt03h/DeBDw3qJ05JaqKNquZBakpB1PSJum+pDyY6vNnlEUr8cPE4WJQ2rXalM3uK16OnKuRzVHqzlKuDpFQ8iznWFanx5jhiVK7oK5VW+3FvQHM+uxJXAyrqdA6ADS3h3f1GY+1obpHqJkkKbZFan+ujZSXk1vAg1CDkjNI7mNS2kcwcVaimVNcLD9En5TaXM3gjMXCorKiRahs6lMeTDX0ZI6Ekg2A+mR8lgW2HijCtoPyNbjOV7Vi1rNbcPpiAwDl4lqSN1oZASnlXVA6f4xMO7rx9h34HjGHRPj1eHGtz3KnHtQ+KEmFdcVjVdR3wOliUF7fDpuMl84agavydAeH06VZpEdCqA0I3Y++Z61qdZN85MUj91qiDVemiGLk3UbqM3hty1Gs3Sa/mh/fQ6nmISFUP0juMoM9cw2wcC/bHE76mAdT3RNquG8Bnmtdba4Uw7I4cq4GZXX+c0DcfSsfob8QOMOwyD5ehm9PVnDeDYZlYbU7kVdSj0a9/Eorct5Wl8u9VvmgmP7oH8DqBUTvgS/0pEq6SIlOD2rFzoXqNuzL02HpPT/h8ivFHky704XjxXUwWR0wWuwwWR2IHzRA0KZW47rlkY7d6dLswdTaPtiEeha5lklcYooqWvDPzUcE28R22h0u2UlkwaK104Jhib65zL0WRSeF77bqpk7UNHfK7+PnGvY3EShYsD2YgseyrPp0nRDZRL/kIiIhR4bzYGrIn1BrtVShdrnxBdt4fztdDNbvyENxZUvXKhbuL73eYMXqTUdQUCY/m87btXCMysYOzFu1I+xPXOHCvXJU38DJK3QuLrklfl+M2q+n0+XC/vxyvPJJLm+b78778twrUHy6vxAPv/yl4D27w6X4sNQbcTgZzYLREGJx5J/Q5mA6uRJC2vu32p1o7bT4bOd3ZbI6uFQnLSPkldQj52y1qsL9p4NY9aSioQPfFVRBp3GCWzDxIwd9t2iYuKrGCx2qa43twRQ8lmUVSx4K2obIpj4lMNWcQoZF2BMCtYbptdhcWNGMNqPNb385Z6uFT4R++teaTCzXXaTMWO1pso5cDLcJPQb/M5Z6iBLnQ/FRHyJ3t/M8AAHSK1Txy5FZ7U6Bp6mmxRB2712wcboYzbPI5dIVeppQfRIez7bmahwA9AZfcelu527YpDfhS14JHC3CZf2OPKzedAR6g1WxHcOwyPr+ItZuPx6Uz+rAqQr8c/P3yDvvf4nBUKFcpghY9WkBDp32Fiv3d1b9nXY1lQCCwevbjuHAqdAVWefDslqcVKGxoU8JTLV3kHD/pGj9UdNy09p+sBi6BqPf/lZvOoLvC2sV2wn2UWkHd2hR0nmgP7QfdLUcLa5FRUP4PAY9CV9gev4+X9WKktpOWO1OxdxjrTmY/BCR1M1WfL0ZeLPUtRRY7y04Xdpnkde3Kt8vQo/7XhEqr5KLYXG0qFbwMCKH+NehRiaFwtOqtsWA7QeLA7LLK3yVj7uszl0iZ39+Of65+Xv86b/7AxrPg2e4SH7YbzXY0Mn7rmoRkNIpYCKBGXYV0H0YNvw1eftWDqaKNkwP5kjIwarIk+Tz5/9+C6sj+JMRhOuGK6M+TO9eKemroyWS73ZnlnBvpt1gxb4TOjw668fhNiUgWFb2mcEH/lrgnhvgv7YeRX2rEZdnXKr4w6b28vB4Qe1+BKYY/g25ukk+p6u3cupCg+a12MMtNDyfWijvy/x8ci0YTNLRILdgiZLwjKk/CLXrl5/mFfhuaDN2O99Ta4pWKFBzmvyVHuIjrHIiFWLXPn6kw7LqHTahOty+5cFUQU/mSMjaoLFWltnmCInnz99THx9WZf0FhnU/1V+ok/7x7oseTJYF2o1WHDhVGfYf80Cpaurwe414xJ6dN9nGnfTO/n/2zjw+ivr+/6+ZvbKbzX3fJyHhxhDOgFwihyAooFS0nvWo/fawaL1oPVptta209vdta9WvX/16VNraiop4goAHIjdBjpCEJCQkIfe52Z3fH5uZnZmd4zOb3ewmmefjoWRnPvOZz87OfOb9eZ+cD6rTyQj2i2ls7ZL0eRPD+nHyNZgk9xbfT3MkajBfev8wkU9fKBLIwAtxAJjSIPh+uV0qWQYGEzxCWmHooKiCzGA1vezRpP57wUIt9RAfhmFQfr4F1z7yT+w+7F0G2jkSJEoRDMOQWysC9P1HlYBJZCEnbBdIgn1+Fi0VhTq6+1BW1ajaJ+NSFp6HOiVKV68Dp2uagxot3ONwon8gin7PkXPDUsiuaWxHQ6tyQMzxigYwjLdGzOF0caZpp8ulGPn4nz0niUyOrJDK13IQuXAMtG9q6yaqzKETeDq6HehzOAOW4odhGK5sqGpbAHuPegQUuQWhJ/Ldd98+VoBSOqbP4cSJSuG8O+g5lBVsAc3abn9BYg/TYmFzMW6BWS6YUnxvhUKw72DRpMHUfTD9AEmaohB4uYdCuUpA9ACrDKiirhVbtsrnIeP64f1fisr6VpRVqguqfAYzCZ48dxE/fnYHnv3nPp/7GCz88X91ohaNbcFNbO0LLe09qFSpenO8shF1F739+Tq6+rgJvd/pQq+KuweJBo7VYPK1HCRaCrZ9RV0LkU+eTuD595fnsP/k+YDNie3dfWhoIcsWwDAM9hyt5j7L+bixM7i3sEf+LUgSwJdVNXpp/NVu8zc+Po4Nj/4Lr398TPq8POH4SPkFgV/yUEEWLsFI/i3ZVjVNke8LgVBFS7BwoL6u7oMp0SbY95b7xgj2KLSVlyRF7ab/+yfH8e4Xp3H1pYWYmJOIsZlxqn1W1LVgTHqsz+MBENTyfvyo6QOn6rBsRj4So8ODNh6t9PT149CZeoxJi0VJYapsu5PVF3G8wnvx8OqHR7m/nU4XHA7lBQOJGwFXYcpF/hJyt2HHEfznT8dDWWVTwN765bXNmrquvtCGcxfakJEYKe+nyAlpos0azuOpkibfpr3TW/iTu88bWrrw4z99gI6uXjhdjEKVOI/mtNfRj08OVGDl7AJFH+uH/vYpDp2px4ScBGQlR+OOVYH3JdeiqVV774zEp92lwUQeKHljVGkwSVdFwRbuQkC2BBA4f0i1Xju6+/DS9sM4pJBPE3D749Vd7MAJAtO87FgGBhNMszRfCdLe1Yd6CS1fKNPrcOLzY9WouqCswayqb5X8Td/fV8797WQYOJzKwj6JgMlqlrT4aQGel5ZSqiSdoaejuzdgQgBJgQoWBu7778syd4YNOQGT3SrO6fr6x8fwwz+8r34ennmzq8chq7WXEiDk7vN+pwutHT2qpndOgznw34595dh9pEqy7YmqJvz61b2oGPBXPnq2AV8eJ88+IgeZO4sGbTDDqJSfDJGXrh9hQC6E67XIhwjSdBWBxOly4Y7fvhvUMQDaTBCkuNxRHURt1aIYf/aXj1Hf3IkJOQlYObvAp/Gwk07VhVbc+pttuHHZZDCMO9K9dFImLi/J9alfLYgFpuGW3NvR7wTDuN0k5Oju7Sd6rjq7HaouD0QaTJ55kY1wJ3khsfe5vHZHJxj0OwO38C+v1ZAejHHPS1+V1aK7t182EIwda12zcLHY3tWnqh3v63cK7r/7/vIRJucl4eaFmYJ2//WH9yVTSPW73IUwrppXiJQ4u9eY5D6zsO4CbOBmZX0r/vL2AZiMBjz/zkHYLRR+/1/uebGxpQu7jwiDZhz9TtRd7ECEzeJVGYsUMmUQeXu12F2+IHZuQEM93NGiLAuUfD2qBEwSz8YDfqyI4CsMExpCRiCUeoTB5gDUS2WyDwVJMMb2L8/gTG0zlpTkCs3pA304+l2ob+5EZ48DbZ29OHi6HtnJ0YQjHRzi6xwKv70W2Ojw6oY2OF0MDLS3LY0kyhxwVz1REwTVFh4NLV1o4aWPcTEMDJR3yhgp2DZSFYZ0gofT5QrYS7C+mdxi4GIY9DtdOFHVhFPVF1UXO3USAqDaffif3Sfx0vuHBdukBIX2rj5JzSZbytcWZsJNyybz+hC2kxsF63N5vqkDtgEBsbWjB3/bdgD1zZ2IsZt5fXj30trZi9ueegfXL5mI9QvGyZxl8KilHhK3VWrBvzb/3v2twKoyXHGqBNTy0Sv5+IORpwUPKEINpt96Je5LTZBgJ5ievn7VtgdO12H7V2dQK4oWFx/l5AWZDFUeOPEL5yJBGp5gc7GtG1297ojME1VNANxC+ue8AAiWPocT/9lz0mu7FCSpNdQWHpv++0NBpLmnepQGH8wQCPbT8dDS0Ruwl6CWuY3LTTkgaKr12dHtHbWspayh4jbN9bWFG/adqMWvX92Llg5hpSDWH33fiVq8/pEnEIhd+JIGfw4m+JLsWeWrMNXbKlcH4gurI4NT58irOukaTD8wUm6coYJhGOw+cg6/fnWvH/sESH8JLT42vY5+2Czy5hj2AepXSUex81DloGoT+4L4ewYz4IhPV69D9prWN3dix75yrJxTgP/jBelIJXlu6+rFrkPSPlxinC4CAVPFxMgKviwkwRLitsGugKEjpKO7DxaTIdjDwJHyC0TtOF9eCSFU7vbu7HHgR3/cgc4esqhttawI/LmtpaPHS9itbWxHbWM7brh8EqI9lnTVnKCks+JgcvqSnEODfInm9h5F31ChuX1kSApHzpLdq4DugzloWvqtOFbREOxhDCvcVY38e+PVNLYTJcoGtK30+xxOSWHo23NN+PO/93O+SuLkweJT8KOch0rIEAuYjhBJcFxW0YjisSmS+zp7HPhw/1nUNLbjAs+kLyUcavFnJPEbUhJAGcY7OpYk3YugLa1rMEOOEHnpa631LRUs5mIY1F3sQHiYGRE2j7nZ6XRJpvFij/HapupC5Dlmy9av8PW354n6VlvgugiFsUEt0DW4s6iNA3AviP+x64T86cDvi2B8wwAtCppAfedRYyKv6I7xuSbsaGX34Sq8tP2wekMNvPvFaeIoQ/7z8frHx/Dw85/i6FnPIoE/qchNil097kTqbIJdr9JtSpGFMg+ov5MPi8/iCoCAqfXFCLgXA83tPZL7ugauJ0nOUi0CpothCEx/8vulBEO2uRaz23BMds8nOdau3mgY0e9SNkmHGly5RYkx9ztduO2pd7B1ZxlxGi2pXarPCe9vpTlLfF41DSZfsFX2a/T9GSLTYPpPKBQIzYPralii+2AOktF40wyW1s7eoAacnDzXhK07y9DncOLs+RYcPF2Pi23d2PppGVbe/4ZA+JEKymhs7fL2ZxTfCAo3htSui+3dOKrB9ECCWOgNhGn+i+M1mn/L9u4+VNZJR9h2ywTBSL1UtGiC3T6Yym2UtItSL12PiZxcKzLczWQFGbEhYVL2F1X1rT4tkoIFe/soCXYuF4NzDZ5oZaX7Xks6Is9+Mr/Ce/70IR5/eTf3uU9Fg0kavc3/Pj19/ejo7vNaJHxxvAYbHv0Xfv/ml7LnIOl/0OUxXYRfaoQSqPX0qBEwFXLE6oQoZZWNeGn7YXT3Orhn/qnXP/eKsASAbZ+f8tr2s798jGdE1YU0yJde80yfw4nv/uo/2PzCTr/OQeK+pAIDBkt3bz92aIyMbO/qQ6VMGha5OsUMvF+qWszN7shH/2owPSZy9fOzbYa7BtNooFGYGR/sYYx6lHK6XmjpFKTuUrzvpTSYqq4kvMMV2nb1OtDNK6GoZiIXdqXsrsLy+P/uxoZH/+Xlw+rod6Kju8+rhCNJbXh/BuYwMn+PGgIkVI8aAXNU3jQjBIfTpSp01DR41xKXOoQ0F9zAXsEn4cTrvztK/KJobO3y+/Pe0+f2mSQR9lwuBk1t3Wjv6pUVMOVrMDM4Xin0ddaiwSQR7JRerHK+an//5LhkzkC58w9VgFegoADkpsYEexijnn6FgLRzF9rQyVtMKt1zzR09+PDQeYEVQstCjNSvWS06HiD3V5QKnBE/32ybmoZ2bN1Zxvlz1xFYW7REkRN0JvXnqCFQ6+lRE+Sj6zCHL04Xw/n8ycGfnD/6pgLb9p7ExXb1YCItGkzh+QCDn24paV9PBv66Z09VX8QHX58FAHxz8rxiOUcAOHi6Hm1dvahuaIdR5kvK1mBmgP3f1mFyXhK3zd/O5nL9VTe0cSmT+DicLuw8VOWVjkX6/CNDwARFyf52OoHHI1DJC2u1je0CzZ3SLXehuRNvf9WJCQVZSIoJV23PHwPZeN3/kmWw8NxXSuf4/Fg1zl1oxQMbS7nn6e+fHoeBpjBlTLLg+OqGNry0/TByU2Jw9nwLdnytbm0RypeDNJEDaOvsxd5j1V6p7EYFw1GDef3116OnRzipMwyD9evXY+9e79Q3x48fx4oVK1BaWorXX3+d2/773/8es2bNwvr163H+vDsSrqamBuvWrcPMmTPxxz/+MZBfQyfIuFyMIHG2FHzNXHNbN07XNMukCBE9SIorcOFOvvbg1t9sw8s7jiiOiRQpecmfzzv/OpBE8B8pv4APvj6L8tpmVNW3SftVymhmGACHTtcJhECtJd3U23j+3vppGf7wj33L/XdoAAAgAElEQVSobmjDR/srsEXkEgG4U5SQmNz45x/u8iUAUErFo3UCCnv7KFkMnC4Gnby0XkRZDnjPndqzwt+tbk537ycJYBQmOJensbULB0/Xo4/37B2vaEQ1T4ATXx4Xw2DfiVp8VVarbRyDfF4Zxj2//+lfX+Pbc96L1JHOsPLB3L59O5YtW4ZXXnnFawX3u9/9Dp988gm6u4UvOoZhcM0112DTpk1466238Oijj+LMmTN4//338dZbb+GDDz7AFVdcgbvuugsAcOutt2LVqlX48MMP8eabb2Lnzp0qoxoBb4xRisvFoFVFwORPzoppbMR9a2j7fx94BMrG1i5VjdgTr+zB5hd2yqYeUYJ/bke/C20D39/pYvDjZ3fgx8/uIBbc+AImSVJ6h9OFwwM1w3v6+iWDg2SvG+NeDPDrO2vRBmop5wi4tdUffF2OcxfaUN0gXd7NxTDEkf9aAoJCGV20DDJsFLnK/czXYBK5r2gwe2uJstakuReYvtWbu0S5bYULf8arLemTd6KqCS9tP+yXxeBwD+obPMNIgzlhwgQ8/vjjSEhIEGz/5ptvsH//fixbtszrmJMnT8JsNmP+/PmIj4/H6tWr8e677+Ktt97CbbfdBrvdjuuuuw67du1CW1sbDh06hI0bN3Lbt23bpjimUX//DGP6XS60dykLmKQCgXgiURJoxF2KzUdqpzxd24wDp+rQ3tWHZ978Evf/9WPJtD5SY+/p6+fGeqKqEe0Dmo6evn6crmnG6Zpm2UhuMfyAnOffOYjVD/4dB0/Xy9bbFS8KpYKO5K6bi3E76POd+TUJmBpSCQGeYICnXv8cX8ikv3K5GGI/0D6HiztmOENRAK1rMIMGe/eo3Ud7jp7jFqpa7zm15lq601JgQPiMkgnF/EP4AqaUBpN0uiivbcbWnWX4n+2HiJPTy8FgdMsIw0qDmZ6ejuLiYphMnsTXnZ2d2LRpE/74xz9Kmm6qq6uRnp7OfU5OTsaFCxcE2w0GA6KiolBWVobExESun+TkZNTX1ysPSp9shy2tnb2qk6/AJKuklRRbyDUE+YjNrF+W1eDO370nGWDEP/yTAxWorG/F0bMNuPfPH2Hl/W+opvro7O5D3cVO9DmcOHSmnksd0s8bQ3ltMzq6++ByMXh77ylcaJF2jJdK3P7w85/i5R1HJI8Ra1IkTeQy123PkXPo6nEI8pX620TOHx/7t3KeP/KqImy6qxHxstGnvKCjdj+fb+rg8sRqEe4YhkTrRq7tZB8prRYEkufEXUdexmVGcsGv7eH7564TmnLtSsEwTMByQQ4HglLJ58iRI+jrk14ZGI1GTJ48mfhEmzdvRmFhIT7++GNUVFRg165dGDduHFJS3JVCbDYbWls9EatdXV2w2+2w2WxoaWkRbE9MTJRsy7J7927s2bNHcH6GUQ5s0Aldjn57VrVNT08vysvdjuHNF5tl2zU2NXHtAKBOYWHS1t4haNvRKXTraOvsRVtnL37yp/eRmxyB25aMEezvc7g1f2/v9U6hVF5+RtFH7szZStS3dKO1sw9fn7mI1AgGTPdFtHZ5tIkPPPcJAOCna8Zj37HzeGXHIdy5bCwyE8IFfdWel85l+fmxavT39WDj/JyBMQ1cvxZh5Hh1dQ0MfcI+LspcY9Z/6fCZOhwtOwWbxYDzdeT5C+X65dPX5+DG6uhXf7FU19Sgh1DbW3/hAgoSkgRzznCkvb0DBpey1l8ncFRUVCLSZkJfn3rKscrKKnS3huFAufpzUl9fj/JyJ1o61TV2bW3t3HPS06N8L/R096C8vBwX29XvGQYU1++FBvXqeJWVVYLzNzVd5I6vvyC06Jyvq0Nb29AH2TgcDtTUqPt9jlT4c6o/URQwV61ahYqKCsl9iYmJ6lpDHqWlpTh58iTKy8vR1dWF2tpadHV5coAVFhaivLwcfX19MJvNOHjwIG699VY4HA4cPnwYq1evRk1NDZKSkpCZmYm+vj40NzcjJiYGBw8eRHFxseBcpaWlgvNf89ArxGPVCS1e21Wh2sZsNiM3NxcAEFXeBUDaXBoXG8u1Ky8vR9l5eT/K/aebkJEcj1tWTAEAmMynAHR5tevqdaLLQXH9AsCxigb0OOQ1Ejk5uaBpt4B5uGK/1/74xGRUtpzH6QvtqGroREJiMnJzEwfSeBwUtE1NTYX1ZCu6ei+ih7IJxtHY2oWje+QnTtpoQW5uLsrLy5GZlQ2jgYZtfwMAz4vjd/8+jt/csQhFWZ68ipGnOgDI99vrcCEyNhGZSVGo7TABOCPblk9kZBSAOsU2RqOR+44UpR5olZiYhH6nfJk4PrFxcQPjiFQdRygTGRmB2CgblH4jncCRmZWJ2AgrDIajqm3T0tORkRiJj49LpwTjk5CQiNzcbKKctvaICO45MVuUnz+zxT0PWJs6AChXb2MYhuv3ZAMDoEKxfXp6BsyWau5zZFQ0d/zpJgDwKBASExNxrsUFQL1CmD8xGk0Dyq7jQ3reUMFoMgreG1qRE04VBcy3334bl112GaZPn46NGzcK9lksFk0DWLNmDff3yZMnce211yIvLw9PPPEEbDYbfvjDH2L16tW46qqrkJOTg7q6OixYsADjxo3DpZdeiq6uLnzyySd44IEHYDAY8JOf/AQrV67EnDlz8P777+Phhx9WPP+IMHnpyELqFfS3dw7CZDRg+cx8AO5qQUr0Otyar9c/OqaYR1FsYiiraFTUmqmNt9fhRGV9K/r6nXC5GC4SU64UIlfzWLS7qa1b1jcRALp7PRqWdz4/BUe/C3uOVnu1EwdZyZXRFH8HQJsP5qcHK1Xb8PsjMSu2dfURP//s9R3mLpgAdAt5UBm4f4hiZgYaiZONK7Vl5yWVxlJ/yrZlGAYNMm42Ul1TFHl1LP7z5FQoNckEyRlyNJvHgcBdckUBc8KECVi/fj0sFgvWrVunufP33nsPYWFhXts3b96M2NhYAG4t6e7d7jJVW7Zswc6dO9HV1YWnn34aBoMBqamp2Lt3Lz777DNcf/31GD9+PADgnnvuQWlpKerr6/Hwww8LTOSS6LPtiIYv4Kmt7vmpetSii9lEyWfPtygLjKIHVMrvUdieAXtTSvm/9Dr6UVnXCqvF/YiyPphSAtVDz3/CfQ9xT2o+Vf28/hpau/Hv3d9KthOX4iR5sbACphb/HpJclfxSdiQ+Y1295JWRWAFzuEeVUtz/dIIBF+RDKIABZH7CbG99ROmEeMcR+GBebO/Bg3/7VLVfz0gosudEJDQK6q+Lnl8XExxRz+3TGoQThwhB8cEEgPvuu08xWawSkyZNktyelZXF/V1XV8cJrxRFYf78+V7tExIScNVVV3ltnzFjBvlgRvHNMxpgn49+p8u7/rgIvtZObVJnBTr1F4VyQJBXa/7kLzGEPocTtY3tSI2P4D67x+M9Dr6Du3iiUMvpxs9n2aegFdmxrxyXTvE8tyQaPlbL4u+UP01t3WAYBhRFkQUZEAb4ACMn0TpFUaB0CTPoaMnrSiRgstHeRPe0lmAcRrUGuXAcbg3m3mPy1hEWsQZTIPh69UseRe5P9CCfwPSrGkWempqKhx56CL/85S+5m3vHjh1YsmSJXwawaNEiTpsZUPQo8hEN+3yQ5Dvs4zRr6pN6bWM7+p0uTXV/AfV+BZOZxK3Z63DC6WK4qhKsxkJLXW8A2KeSsJg/TiWtSIfIfEeiOezjTOSqTTXDaohJXuAk2h4OzrTp3W9yrB1pCRHkfQURitKnvGDCMAxu/c02Ln+tWtsvjtfIpg3j42LcVX1Iqs3w5zf1nJmq3YnGweBv2w7gTI16YBLjpR6Uj253zxXBEfSG+ZpyUARKg0mUpuitt97CQw89hBtuuAF9fX24ePEiDh06FJAB6ej4AvuAkFRsIZ10AXcy34q6Vs1l2dQEzGrey0Sq716RxpL9XmqCnXgcPSq+WnyTu5IGQ1sNdzec2T4Ak9fZWneUN4mg+80p8mAdRvQvnwVTs7C4OIe4r2BCUZReySeIdPf2EwssH+0/i1d2HEFlvXqQD8Mw2HmoCh99U6Hatqq+FS+8ewh9DidBzkxG83P64f6zRH6jLgbyGkzROf+xswwHT5MHD/sLsrRPI5egaTBZVq5ciVdffRVXXHEF2tuHX63O0XzzjAbYn5dEw3dmQDj56DCZ4MEwjKqmrK2rD7sOVXGf1XwwN/35I0H/YsQ+j6RC8dnzLWho8US69/QpC9z8cSqZ3Rpbu/D6x8e4z2T5KgMnYB4fSFhPMg62KhEJ7FilgpgoikJB+hBYW/yA20SuEyz+s/ckHE4yk/Pbe08RCZcAAIYssA0Ays+34O29J9HZ4yArYEA2AndbDY3dz5S01lLczbkLbWhuV/fD9jf9TteolhEC5R6g6oPJcuutt+LGG2/Exo0bsXfvXoSHh6sfFELoq/mRDfuAkEy+bOnGvSfUc7gBZKvb1o4evPPFKcybnAlAXbPm6HfyIjG997+0XZgqhNQk/P6+cnxxvAYvP7gaFAXV/I9tnb3chK4knLd39eHTA5W4duF4onEA7gCpju4+nKwmz4NJSlllI9bMHUvm4+aDjV7uiPz02AHfz9B+GdEU9CCfILL9S7K0XFpxMYzm+9nhdKrer9UN7bjnTx8Q98mA3FfyhfcOCRa9Ow9V4XhFAzp7HEQa0KGgsbULf/7PN8EeRtAIugYTAK666ip88sknw064BEa3f8VogB/ko4ZLY6QwQzip9/Q5uXQ+6qZsKGo4+kSmfgfPb1QN9xjcDcWaUDHtXX1obHVP/mrCuSCYiOB6OF0unG/qCMjL9nhlIxjG/yUdt391BqfPt0teZwqAzWJC+jDww6QoSi8VOULRGoB2oblLNTsDwzCahL1Dp+sFGSiUOHiqTtB3a0cPTtc043xTB5GP6lBB4gM7UglaFDkAVFRUcELljBkz8PXXX+Ps2bM4c+YM+vv7MXbs2IAMTkeHFE90pRbTLVnfJ6ubvIJcpOhzOLHvRC0WF+cQavhcMBsNRMaJ3n5tKX9cDGCgvOunS0HqXsDXhpLIdS6XW4MZCFo7elTr0/tCQ0sXyuvawTBm750D8lpBRlzIv4zcPpjBHoWOv2EYRnOg3/mmDrR3+fc5fPSlz/zan05wCZT+jUiDGR0dLagrnpGRgXnz5qGqqgpnzgTGFOBvdAXmyIYVktR8HwFePV/Cvv/69gGcJjDztnf1cknNSQTB3j5yobG/nzxqmt+nWBMq2ZZzL1Dum68dJhmz06VNK6IVku/mCw6nS/HeGA5+mDSluwWNRBho19orpR/T0QGCHEU+EtBd3kc2rInnkf/ZRdA2MFGDrZ29OHiqjksxpAabJ5JkGJ4gH7KxsL5aJC8jtk+1fLes8H6sogFlleql3Kob2ohcFnxFS94+LfQ7IXmh2TmkICMuIOf1J3oU+ciE8SHa+8X39IwvOsqEhA/mcGY0J1EdDbAre9afULV9gBL69jqcOHi6jqjv/+w5yY1Fjap6d6qkLlKNIEOeM5NzLyBIgeR0MThX34Yagjx8jn5XQAXMQAQPAcC5hg7F2SIu0hqQ8/oDigJMRhomIw1aly9HHO98cRq7j5zTdAyJm4zO6CaoPpg6OiGPxueDQeAeqi+P1xD1zaYQIhlFR48D7V29eOx/yXyfXBpqn3k0mGR+o6RmehfDcKb9QPDbN74ISL/tPdI5DFmFYCgrBi0mI9585GoA7vQ3OiOLmobhlyJQJ/QJqg/mSECPIh/ZuNNmkP/IJLktfeWrE+cJ7zdNyeQ0aQMZLRpMDSmeTtdc1BR9T+ITG2o4Xcr3UiibnvlDCzUN5g+uKsGMcWnBHoaOjo6IoPhgqgXwZGZmIi8vz68DChSh/FLQGTxNbd14+IWdxO3dz1Ng7onWjh6yxMZsE5KIbIYsBZOnPUNsTuc0mATjqKpvIy79yGgUikMFp0vZoSaUZxJ+aqKhmvPMRgOi7WHc503XzpJsR1Ghfe10dEYrQ+6DyTAM8vPzkZeXh9tvvx1vvvkm2tqEqTny8vKGTYqiUE+MrDM4XC4GLRoqQDBgiCti+DQeIgGTIW4LaE9P8suXdxO148ZBFBBEril2uoangOka1hrMoRcw89Nj8PRdi7nP2clRku303Jw6OqHJkGswKYrCgw8+iIyMDLzyyitYv349EhMTsWTJEvz2t7/F8ePHAzKggKFPbCOeNi15EcldFH2CSFgbaEeUSkirBlNFSBL2zWDbvmpUN6jndnQx5OFyWsccKrR2ObD3aLXXdlZgC+WpxGw0cH8P1ThpioLJSHPnNNDSrxWK+5+Ojk4oEah3oWKQz+OPPw4AcDgcOHDgALZv344XX3wRH3zwAX7zm9+gvn7oi9L7jK7AHPFo8lEEuY+iL5AIdwzD4HRNM/79pbcwI6aprRv/98FRTWMgNmUDaO/uJ66CRCq4fna4Sr1RCOI27UulKRr4N0QlzEunZGHFzHzus2GInDApioI9zIzrFk+A08XIC7YUpaeL09EJQYJSi/zzzz/Hvn378PXXX2Pfvn04efIkaJrGpEmTMH/+/IAMSEdnKGA0aOJ8gdQHk9RM39Hdhz1HydOTaDFlu5uRaztHu7tJiMqXSI4NR1FWPPd5qMzRFACzyYBrF7nr1J9v6pBsR1MANWrCSnV0hg9DrsFkGAazZ8+G0WjEwoULcfvtt6OkpARTp06FzWYLzGgCCQVdi6nD8coHR/1ex5pPQ4t6Pk5XAINgGGioWcwwxNpOV4BdC0KaAXktVP0IxdrBodK00sSaUt0HU0cnFAlKHszLLrsMBw4cwI4dO3Dw4EGUlJSgpKQE06ZNQ0lJCRITEwMyqEAwal+KOpL8e/e3Ae2ftFZ1wMz0WlI2gXyCOVHVOCz9Kv1JqJrIxdZncsHPd+IirUiOtRO1panQFc51dEYzQ67BpCgKO3bsAABUVVXhm2++weeff47nn38ev/jFL5CYmDi8fDApStdg6oQUbDnHQHChpQsOwiTnWlI2SQW/jBa4IJ8gj0MO8bhIhLm0hAicb+rw+T588PpSjBHVZpc7rV6+UkcnNAmKD2ZdXR327t2L3bt3Y+/evfjmm2/gcDgQFxeHVatWBWRAASPAKswx6bE4FaDSdTojlADUQ2f5yZ8+0DAM3a9SC6EqJInHZTCoj/OHV0/HL1/Zg9YO8hRfasgJthQVesnfdXR0guSDmZKSAgCgaRpTp07FT37yEyxevBiXXnopTCZTYEYUKAKowUyMDseKmfl4ZutXgTmBzoiEYTT4SQaQLVu/goEK/jhCHU8UeVCHIYt4WFPHJOOvP12BHz+7A50KSfcH83UkhW05DSZ0DaaOTigSFB/MjRs3YtmyZViyZAni4+OVmoY0nT0OBNJtzBZmGhJ/J52RRXt3H2obg19buFlDgvrRDCsbhawfoWhYNosJNotJVagbzNeRli9lNJh06Gp/dXRGM4EKBVD0wWTN4J988onXfovFMmzM5PXNnXAFsOw6rWcQ1vGBw2fqcfjMMPJj1gEQukKSrGCnMFxqkLkppY6UO5/ZaNBN5Do6IckQazAZhsH69etlD0xMTBw2AmZvX39A+3eXQAvoKXR0dIKOp5IPRYVeZgql4BrZY7j/+XpO74PF22IjrFg5Zwwyk6Lw9bfnfT+Zjo6O30lPiMQ918wMSN+KJnIAsNlsKCkpwezZszFr1iykpqa6DzSqHoo33ngDV111FeevefLkSbz33nswGAzYsGED4uLiBO17e3vx2muvoaWlBevWrUNaWhoAoKysDO+++y4yMzOxdu1aUBQFl8uFrVu3oqqqCldeeSXGjBkjOw6HTCm++CgbGlvV8xWqQdG6b5GOzkiH/4jTFAVnqEmYMijOTINMHUTighltt2DtpUUAht694DuLJ+Bfn51Ad29glQw6OsOVMLMB+WkxAelbsRb56dOn8ec//xlFRUXYtm0bVq9ejauvvhq/+93v8NVX8gEttbW1eP7553HTTTfB4XA7l1dVVWHJkiWIiIhAZ2cn5s2bx+1jufnmm/H1118jLCwMCxYsQFtbG86cOYNVq1YhNjYW//jHP/Dzn/8cAPDQQw/hn//8J+Lj47F8+XJUVlbKjkcu12Beqn8uqkEXLnV0RhWh6HMtt8hV1mAO7nsQBfnw2rDlK01GGhaTAYFmYm6ioD67jo6OiADKL4pqyLy8POTl5eH6668HAHz55Zf47W9/izfeeAMffvghbrvtNsnjDh48iFOnTsFg8DzY27dvxzXXXIObb74ZAPD666/j9OnTKCpyr2wbGhqwZ88enD17FhRF4dChQ3j77bdRVlaGu+66CzfddBPWrl2LwsJCbN68GS+++CLOnDkDm82Guro6vPrqq7j//vslxyOV481ooJGVHIUvy2oILpMyNE2FbGSpjo6Of+A/4iEb6COB2lDF+8PDTIpR54JjJbYpXRuadus0FhfnIDs5Gv/97/1E5/EVtzvD8PmtdHSGmkCulRUjX/bs2YOnnnoKV155JRITEzFnzhx8++23uO2227BlyxbZ45YvX44nn3wSkZGR3Lbvfe97+PWvfw0A+Oyzz9DW1obs7Gxuf1lZGcaNG8dNBmPGjMHp06dx/PhxjB/vrnEbEREBg8GAsrIyxMbGciUr2bZySKWCSU+I8NvKlh6ko7yOjs7wwp8aTANNITbSyn02GsgDEi8pSEa0PQyAvClcUYNJeWsx/+d+ct96SR9Mhc8mIz3wr2FIhHR3aqSAn0ZHZ9gSyAWYYpBPaWkpbDYbpk2bhltuuQXTp09HREQEAMBsNms+WXt7Ox566CEcPnwYO3bsgNXqmVRdLmEeIfZLO51Orwsgbitm9+7d2LNnD/f5osMGQFjWMs5uRHOzfxKj9/b24EKDHg2sozOSabp4EeXl5e4PfvS/jLCacP2l2djydhkAIDk6DNVNZL7hl0+Kx1vd3Wjp6EFzS7NnfDyc/fL+hzU1Neh3evZTFFBRUUE89urqc3B0WAXbOnqE5+vr6+XGFWvpx+zCBCSFM2hsaiQ+j6+cP18L1ygvbaqjo0RvT4/kvOEPVCN1urq6sGvXLuzatUuwXWupyNbWVixcuBA/+MEP8Mwzz3gJjZmZmTh79iz3ubKyEiUlJWhqauK+fHd3NwCgoKAAtbW1YBgGFEWhqqoKeXl53LGlpaUoLS3lPu89Vo3jr3gETgBIiItGfHQ4gMGbyMNtNiQnJwM4w22blJeEMLMBX5XVDrp/HR2d4BMfF4fc3FwAQJjlCLr7pIMHtWIyGZGWlgrALWBOzE9BddMZ5YMGyMrKRMSxiwBaER8Xy41P2P8xANIm7/T0dJiMlQB6AbgX9rm5OQDITNeZmZlIi48QbGvr7AVwgPscFhbGjSs3F1hxqXv7h/vPAqggOo+vpKamwmAM7Dl0dIYzVqtVct7QgpyAqpgH84MP5MvNadVgvvjii8jJyUF4eDi2bt0KAFiyZAn27NkDs9mMxYsXIyYmBn/9619RWFiI7du34+c//zlyc3Nxxx13YNq0aXjzzTexdu1aWK1WXH755Xj00UexYsUKvPDCC/jnP/8pe24pH0wKZKXUSDAaaS9zT3JsOGIiwnQBU0dnBLJqTgH2Hq32S3lYd5ozz/xRkBGH974kEzBpioJxwK9RzuSsZIqmKKG5n1Jp73W8ZJ+U6LP0sQaRm8G8yZk439Th15K74muro6MjJJCPh6IGc/HixYoHf/HFF+jp6cH8+fMl93//+9/nUhRlZWWhrq4O+/d7VsZz586F0+nEN998g8WLF+PNN9/Er371K3zxxRd47bXXEBMTg1mzZuHhhx/G008/jaKiIjzyyCMAgD//+c94/PHHsWXLFvzhD39QTFMkVQaJoijO4XywmI0Grx+JgvdEazEZ0O90yUa16+johC78x3ntpUVo6ej1j4AJ4VxRkB5LfCxNUzAM+GzKzmdKidZF56YobSnXpPNgqrcBAINovDOK0nCk/IJfBUwdHR1lArkAU09mqUB3dzdntpbigQce4P5es2YN1qxZ49XG5XJh2bJlAIC0tDT86U9/8mqzdu1arF27VrAtOjoaTz/9NNE45dyljH5y1DdJaDAB7x/u2rnZ+KaiHUfKL/jlvDo6OsHDX9OyOAtFekKkfOMBMpOiUJQZhwibGcYBS4xYI8j1r6jBFJ6borRFlUoFO4kFSrnzp8ZHYOn0PHx+vAatHT2y/Q0GtwDt1y51dEYUQQnyGSquvPLKgJ9DToNp0BCtqYRbgyk2C3mbZijoBSV1dIYvZKZfrdAUxQlWNE1xAqdSHNHkvCR8b+VUAJ6oczlBTrlUpPA4d9Q1+ReTEmpJNSL5aTHIXzMNlfWtaO3ocZv7/TQnc+hpinR0FAnk0xG4At0hhJRFmoLb1+m7l09CeJhpUP2bTd4mcsB7Yjca6ZBZTuenx6Igg9wUp6Mz2vF+dP3zLNO0ZzHK/qvWM1+uu31VMV7bvAaLinNk+5dDzl+SVCgzSaR6Ex+qJnBy+ylvs/lg8UcCOaOBRoR1cO8IHZ1QJZALsFEhYEprMIHclGisnV+E8DDtKZdYImxmmAwSJnKeZiDCZsaU/CRE2UwhU7M8JzkaE3IS1Rvq6OhIEhANJitoqkwU/JeCxWSA3WrmckxK9S/bj2g/26+cuZ3lsmm5eG3zGkTaLKrnU7tO7LksJgNuXj4Zbz5ytfIBGvAl0fqffrRU8LkgIxbXzM3225h0dEKJQFYlC7qJfCiQEjD5OoLBvCiyk6NhMkmYyOGJUi/IiMMvbpyH8vJy0HRoOLDTVGCde3V0Rhrip8Vfzw/fnUYoaMrbyLW8FJQTrQtN4uyfNE0BClmYWKGW5HxqAt69G2ahu68fsRHufJpSWtHBoPVnihpIXM8dr5fR0BnB+N0thd+30k6l5JsGgwHTpk1TTXoeCkiayHkzxmB8MXNSomE2Grx+JH6UupSGINhQNBWS9ZR1dIYN/tJg0sIvfycAACAASURBVBSnxeNM5IRaP6L+1dIU8YN8IByH0nGk+9TmvCh7GKJ4n/05LfmSpkirBlZHZzijZS7RiqKAOWbMGFkBUmui9eCinBZoMBc4JyUaLR09kn0YeY77LKGiNaT1/HA6OpqQslL4woxxafjyuKfAg9EgDPIB5OcJo4HGDZdPRFFWAvH5lKY3cVAP+6f6nCi/Xzx2rdMrG9nOMMD9G+fgqdf2ot/pW2o3X0zk0pHxPp1eRyekWT4zH7PGpQWsf0UB84knngDDMPj0009x8OBB3HHHHQgLC8Nf//pX2dyXoYhckA/LYBzLs5Ki0N3b79UHP0rdQIWegGmg9Rq9OjqDwVdrxKZrZ2Ht5q3cZ5PBAJqmEB9lw0/WzxjoW/pYA01hzdxCTedTDvJxWzPEbdXMZopCq2jwvpi8aYqCk2GQnRQ10J+PAia0C4di4TpUrE46Ov5m/pQsFGXFB6x/RQHz3nvvhcvlwlNPPYVnnnkGGzduBAAUFxfjmmuuwXPPPee3ZOWBhFGxkcs5x5Ngs5iQkRgJs0k4ibJR6msvLcLYzDip0wYVfmCBjo6OdnxdLIoFGLvVjKjwMDz7o6VcRgu3gOftBOmLsKPkAkRRlGABzAqW31k8AZ/uP42y6la5AxX6dAuqLheDoqx4jNGQOJ4bM03D6XK6tZmajxYOZvAmct3aozMyCfTiSVWycjqd6OzsxCeffIK2tjZ0dnbinXfeQVtbG3p7ewM6OH/hkooi5/29YdF4LJ2e59WGBIoCxqTHIi0+Ar+/ewnyUmO47bkp0fju0kmYyVNBh4pQx0+NEkyKsuID6mSso+MvvCZjHx8fsfWgMCsOFpNBkC6NfTZnjEuT3K4Fo4ISgBUGubYDz+LymfnIT42QO0zV7M0K0T9eNwMbFo3XMNqB/geONxjU83JG2My4pCBZdpxSh+ekRKOQt/CXOjcLxf1PR2dkEWgRQPXNbjKZcPfdd+OFF15AVFQU7HY7nnnmGdxxxx2wWq2BHZ2feOWDI17b+Be2pDAVk/OTfOqbpimEh5lgMtLIT4uB1cIqhaV/OX/nefOVUPHB/OHa6QiXiUbV0QklBhtFvmpOAR67Zb5ba8ibBybmeqcLYwW0q+cVIjbSM8/68sgWZMQhOzlKch8/wOjeDbPwwn0rPfsUz6U8EPb7+bp4nDMhHaUTMwYi1ZXN4+kJkfjeFZfI7JUWUMdmxGFSnvecL048D7hdCII/U+ro+J9AywBEaYqeeuopLFmyBDt37kRvby/mzp2LVatWBXRggUfsiO7bhTaL/IvYyUyuu1DR1hmNtDvxe5ChIPRR1dEZLpDeteFhJqTE2TE+JwFTBhaybBogq8WI/DRvEzKb4kycRkjsikPCzcsn47PDMfjNa5977TPSnhy+WtILqWkw50xIR6/DiXAfE5T/aN0MzzhUrrRYCyveJ/k9JARJ7myi7UZdwNQZoQTaRE4kYLa0tKCxsRGZmZkAgLq6OmzevBmPPvpoQAfnL6QmEu9UGtr6zE6OQmFmPGyiKkCcgClzXKgImGajAaYQGYsuX+oMC7zmDPUbNz0hEiWFqbh5+WTBdlZrOC4rQTJim110uS0N7m3LZ+ajpDDVh4HLj9VAU1hVWoCpBcnIT4sR7FNadKulduMLiINF7TJToBAZbkHpxAycqr6I+uZO3j6ZqHDuf97noij3dXEO+O4bDLQgEEpHZ6QQaJc9IgHz2muvxfvvvy/YlpycPGwETKnJdbDmrin5ybhlxRT5fmX6G0xAkT9JiA4PCcGO0oONdIYJcmUVlfjRuukYm+Ht63f7qmJ8W9WIxJhwyeNS42wIt4XBajFy5y1Ij8W0sSnaBy4x1thIK6YXpsJiNmJGURpmFHmnKlESoENloQy4v1t4mAn3fWc2fv3qXqGAKQpi4m+XVDwM/Gs0uIOMFkzNxriseFCO9kANX0cnaAQyByZAIGD29vbigw8+wIsvvogtW7bg3nvvxalTp1BXVxfQgfkTaQ3m4FJRyLVXW+kaDTQSom1oaOnSdD5/EmE1oXRiBowGCkfKL2DHPvmE+kNBKPiC6uioIb5LB2NeWnRJNlLj7LKLqxsW5CI3N1dwnsEsxMRG3vSESHx/zTTFY6ROZ6Ap5KREIyHa5vNYtKJ2nYV5PL0XAdJ5LaXTtLHHl07MQK/DiTuvLIbVYsSOPYd8GLmOjjbCw0yYkJOIL8tq1Bv7gUC/e1WXoWxFmrS0NJSWluLUqVO47bbb8Nxzz6Gzs1Pt8NBA4hp6RQpqvM5y7dlu5bozGWisnD1G28n8zNzxiTAZaVAUhbvXTENUuHc9YX8jK5BTg6ukpKMzVPiSaF3Je89uNUv6X4ph56rBCLS+BMBLnS8qPAy/v3sJFhfn+DwW7SgH+fBH6f09KUktDQVl16kfrZuB+74zmxe0qaPjLpG6ak6B5mNImZCTiB+una51WETcu2EW7r9ujiBjTqCth6pvdrPZjEWLFuHqq6/GggUL8Ktf/QrTpk2DxWKByeSbA/dQIzWReFWbULjQRgONMLNwopFrrRbkkxxnx5wJGfKD9RNTxiQL0iPxmVPoqQJCUZTfa/9KIfbvYuFHserohDJewsvAfRthM3v5WModwyc9IZLo3ucqgg1KwNR+rPiQibmJWFSc7fMYfEWLBtM7STqZBYvbLjOz61YWHQCwhZlw3WUTNB2TlxZDrMCSe0/6g/AwM2ZPSMf310zjXFwC/e4lUh29+uqreOyxx7B69Wo89NBDKC4uxmuvvQazefimlxH/4EoTyOwJ6Xjw+lLh8TI/jKcf6f0LpmZr9l/yxd8pJzkak/K8058AkAhMIutz/YJxyPchaTIAhTx1VEj5c+noyCEWStgpwGQwICVOPmekfH9k7diUP4MykfsQ1GgUnW9xcQ5uuHySz2PwFTUBk424l2pLURR+uHY6blo2WbRdZs6XOZUuX+oAA+8rXoqxxOhwVSEtwmZBKuH8kJcW43etIpuPOz3BM4bCzDikxkfAZgmskpDozR4bG4s5c+bgySefBE3T2Lx5M1auXKl+YIggqcFUKQc2bWwK/t+PlyEtPgIG2lsIkhNIPSk/pMdiMRk0axOyU6IV96cnROKxW+bjsVvmoyAjljs/+2KaUZQmSvshNDmRjic7OQrRPpjTjQYakyRy/QHu66WbyHWGA+J5n3tuqMAKIHarGXarWZBwXSte8xXBgH0JagoEaqflp4qTmssSY8KREmf36lNKSSBrmVIZg87ogKaF76tnfrAECdHSgXosBori3stq5KXF+KxwyUuN8QoavGnZZNx9VQnWXlok2PfE9xbiL/csR2SA3eOIvsmvf/1rTJ8+HQ8++CAefPBBTJ8+Hb/5zW8COjB/IjUxqpnIrQMlICfkJLiFINF+uVUL750ji9YFSow9DEkS0absjRhtt2BKfhKm5CchKjxs4BwUF7GelRyFSBtf20z2XcSIc/JJYbOYsPm7cwXbJuclySZTpygKRpqSjabV0QkdxBpMitsqZ1r1R565h24oxWub12DqGGkrgC+QPPKhUpNbybr0+7uX4J5rZsq2Nci5F8hFkcv6iusiZrChaSrgGjfVMQzIAhTlVhZZLUZVjaPRSGNMunTVKD6xEVbERlh9NlsvKcnFZFHxgEsKkn0q1eovVAVMl8uFxx57DDfeeCOqqqpQVVWFG264Ab/85S/BSJRgDEWkbgDxKkHKdwcAJuQmgpbQYMpV5OEc8hVuEq2TFU1RKJBIdZKZGMnt57d1n8MzxtKJGYLt4tOTrphIqv+EWYzITfX4kWz+7lzcvHyy7DlomkJaQiTWLxhHNAYdnWAhp8GUi0iWOiZYeFWnIdDJmY1kVpuAo3Da8DCTwD9efL1ZbZOUe4OW30aXL4PPpmtn+VxxbzDER7kzJkzMTeQKHRhoGvkpETAaaNXnYmxGnKSCiA9FubWX7r6F/SXFhOPK0rGq46R4SiWWYPsOq0oWjY2N6OzsxO23346MjAxkZGTgzjvvRFtbGy5evDgUYxw0UgKd2GQiFhjZYybkJMBA04iJCBNEg8mtMtgfVOmH1ZoiiaYpyVVIwUAtXb7Knu2KFYrT4iOQkxIteBmKIV0xiWsoi7l20Xg8fediQZv0hEhkJkXJC5gUhZuWTYbVrEdr6nhz2bTcQZmG/YmcD6b77xDXfImHQTCssWmReG7TCq+5cqhRtAaJLUuiecbICZjevUqnL5I+jx6IGDyiwi2YNT4d6QkRQRGYclKiUZARi42XTcQvb10AwH1fjctwl19V02BOyU9SXaDkpMRwAiYliktIjAnH3InqgcEUBVwxawweuelSbluwc0yrCpgJCQmIjY3Fo48+it27d2P37t145JFHkJiYiLg4dbVvKCD142YnC/0axRMI+zE+yobUeDvio2y4a/U0pMZH4O0nrsHa+UWS5zIQRGeJf/RxWfGIjZCv605TFDKTIr36mDPeXa93Cm9Vx/bNBs/MGbgx2VNKPaCk5edoWjkpenJMOBKibYLExmz71Dg7XnpglVf9X6OBHpI0STrDkzVzxyLKHhbsYQCQCpThWwWUXWaCjbcGUx2TkUZyrJ3zcQy2NgRwX8/kWDt3veU0NhNyEvDYLfO5xYmUP6l0AY7Q/h1HI+OyE/DAxjnITo4WBHQNFTRNYen0fBhoCjER7rnIQFMoYgVMhZsjNsKKjMQo1WdnRlGqIIL83g2zuHRIRgNNJChSFIWMxEiBK02wn1lVtRFFUdiyZQtuv/12vPvuuwAAm82Gl19+OeCD8xfii2y1GJEcK1yVizVs/MmHFYoWXpKNju4+xXNxPj8KN4RY+AyzGHH1pYUAgH0nanHwdD23b/aEdJQUpnjVPE+Lj8CUMcmYIvLL4kecGg00SlkBc+D7Sd1wGYlROHlOXRtN08omciktKb/OcWyElcsrlxFvw8Jp+Qgzh87LSyc0CZV7Qyx8eHww5TX7oaLBHExhCY/bj1+HRH5+3lhNRgOe27QC/95zEo5+J+xhQt9udm6NjbQKFt7irys3l8m7OoTG7zga4V96f2mSYyOtAANcbO9WbUtTFOZNzkRjq6c4SmZSFOIi3IoRpXf95PxExQUoy/SiNETbPYqWWePTcUlBClbOHgOzyYD+fhcKM+Nw/mInWjt6kJkUhTHpsfj4m7NgPRX5bnBGA41+pyvoGVqI7JIbN27EwoULsWvXLgDAvHnzkJrqW03cYCCeHLKTo70mkjCzEXarGT19/eh3ugQ3RC4vilstyaqsU7mgjfeqe+n0PDR39KD8fItg34ZFE5CdHIWyykbB9hyZyHJ2Rc9qPVlBWknwzU2JxkcArrtsAg6cqsPxikavNoA7SbxSxDctcQ7x+djrkhZnw9pLeVpgff7WkYCmQqeUqLc5Vl2DGSqCyWAuITt3GGX8zgMN/xKyf14pMw/TA2MUL8jFv4OJwHdO6XidoSErKQqZSVHcZ7nYB60sn+HWSL70/mHVtjRFwWIyIC3ek+ZnepFH/jEqaFUn5ycP9CHff3yUDXmp3rkyLSaDQBH21J2L8eT/7cWeo+cwa3waNl42ETOKUvH065+jr98lmJ/mTc5EZ49DNrh2qCD+tVJTU3Httdfi2muvxUsvvYSCAvVs9vX19V6BQNXV1WhpaZE5AmhubkZNjbBMksvlQlVVFTo6OgTb29vbUVlZqToO8eQvJZylxNnx2uY1KB6o9cu/IbSs9gvS43DplCzOn0IKL3M8TcFsMiApxjunFvtZ/GAlyqRGYFcsYWahllYpfRIblDM5LwlJMd7+Vjkp0Xj6rsXIT4/1yo0n+B4DnfNXTV6mORlTvT6B60hChU6gjNg8x9fOywnBoXJbDyblEJeUOUjaEErDXMy+7MVuP3x3huRYO6LtYZLmVtmFQqjchKOM+74zG9ct9iQ2J3XnWnRJNtbJuLEBbLohst90XHa817YZRZ4iJiaD/JjyBt6tSkG/STHhxM8jLVJgzRqfjhi7WbANAH68bgYeur406P7rPs0YPT09aG1tVWzT3NyM6dOno7vbrYJ2Op1YuXIlbrzxRpSWluKVV17xOua5557D3LlzccMNN2DdunVwuVzo6urCvHnzcOedd6KkpATbt28HAGzbtg0lJSW46667MH/+fO48kl9S9OPlJMvnleS/NHxh6Yw8/PSamSguSJE/h8iXke+z6JVvk9UeiF9uMjcsW5VHnDuTM51LfK/cVLdGV66qjs1iwtiMONgsJsWqP2zffGHYS+tDSzvdy32f8DDTkJgZQ8WUqSOEQugsPsQaPJp3L4d6kI9a3l8lkmLtSI61By1FjLDWuHLblDg78tNikBovTGzNfn2LyYjnNq3A8pn53O8naCcXvKmn6g0JCjO9hT0pYiKtnObzytKxWDA1S7DfaKAkf38xafERWD4z32t7RqInJkLsB8yHxKKpbbHnbSX0KKFCY67hE5DH5m9/+xtmzJgh0ERu27YNZrMZH374IT799FM8+OCDcDqd3P7e3l784he/wO7du/HRRx+hs7MTO3fuxEsvvYSpU6finXfewb/+9S/87Gc/AwD89Kc/xbZt2/DOO++gqKgIb7zxhvyXFF14pcTl/Nx2geQHV5VgwdRsAMLKOoWZcZzfJOC5aZJi7fjjD5di1vh09zhlbiajgXanPEgValCNAw+BlE+GzWJCcqwdBprm2hUXpGDGQKlJ/o27Zu5Y/PzGeZLnZleE/HPIaWTFD7fcQ/aXe5YPSYR5ZmKkeiOdIUdJOzhUxEVa8d2lkwQvFUD48pDNixvw0ZHhS5APy0+vmYnnNq2Q1OQMBfyhqy02LpuWi9/fvcTLhC4lTEhZY+R+R5NeDCIoiH/uSwqSiRZH7DMZG2nFrSum4IpZ7vvBbDRg7aVFGJeVoGiNY/nu0kmqfowkpZb9NYfREooiksw1wSIgT82tt96KkydPIinJ42T92WefYcmSJQCA+Ph4xMTEoKqqitt/+PBhjB07FtHRbuFv7ty5+Oqrr7B7925cdtllAIDCwkKcP38e586dg8PhQH5+vqCtHGLzchbPp0MMeyME2hy0uDgHkwdKOS68JJvbvmBqNu77zmzupmZ9icLDTMhO9qT7McjcTCYjjZS4CC/VeHiYW4hMS5AuWZWb4i5RtaZ0LJ6+azF+uHY6prHuArxrkRofIZu4lR0rRQE3XD4JRVnxXtfe81IWHit+OOZNzsTv714Cu80yJGbGoiC9PHWUYR3Wg0lMpNWrEgbA98GUD34LGQ3mIIJ8gg0t0GD6Nm4pFyGpOV7Ox89mMeK+78yW9X3XCQziwLqocAsKCBKHsxV32EURa1q3W8347tJJGJsZRyQYjpXIPy3GZKBlTfdyLmHCNuT3tJFT0PA0mKxiR0GTGixkVUMMwyiqkBMTpUv/ydHW1oaICI9wY7fbBT6Vra2tgv3h4eFoaGhAa2srIiMjBdtramq8tvH72r17N/bs2cN9rm1PBuBJdVJbXSUrtHR1dQIAOtrbUV5eruk7ksDv82JTE9LjbAhHB8rLhf6lNyzIhYth0HShFi2NnsH2dLsj2VpamiXH19HWiuQok+S++6/2+KSI90eHuXC+tgYpMVaYADQ3tKKvsxVxERbYjE5B+86efsGxE7OicdWsTEQaurh2xZkWFGfm4HxNlaBtZ6f7+hpoStDnhXqhywXl7AXd24zKiuYhSegfb3UF/Bw62qmprka/QzlzQ6Bx9PVKPk8XG92+5P39Dpw/Xyt5bE3NOXS3+paGy5/zz/mGTsHnrs5Oov4DMQdqhT9VM4zLpzHVXehkO+COb24SxgIYaAoWo/R3NtAUUsMdCDP4PhdtnJ+DL75txOnz7T73Mdo4V30Ove3CNGW5iWH49pzycW0tLWg09CHJ7n7POPpduGv5WNgsRu73zYpy4ambivHE1qO42N4rOD4/JQJLpqZ6vX/FlJeXo6enCwWpETha6R1bUn2uGn3tYTh/oUPiaDfd3d3E9/SkDCuq66PR0ux5/4/LiEas3QJHZzPKy3tVehhaFG2P69atk90XFSWvBZQiLi5OYDJvamoSaDjj4+MF+y9evIikpCTBcQzDoLOzE/n5+ZJtWUpLS1FaWsp9fuC5T3Ck/AL3OS8vV/577W8A0ISY6Cjk5sq384Xy8nJBn+e7TLg6Lh65uTlebeVOHX2gEUAj4uPiJMeXUNmD1GRacezicQBAicOKxJhwgRkwNxdYteASr+M7exwADnCfF5YUYNpk7+8gReyRZgANoGlKMIZWZx2Ak9zn6CjP9TcYDhH17St2qxnFE8bglU/PBvQ8OtrJycnCT1PTUFbZiC1b5a0UgSTMEib5PLW56gGcQpjFjMzMDADHvdrk5eRwufO0IPWMDgYmrBn88YXbw1X79/cYfIWijnF/W8wmn8bkMDUBOA6a9syN6RlOlEwqwCP/8xmqG9pw5+ppuLxEum/2WoSHVwNQjj+QIzExCdaqTgC6gElKZkaGlz/tZeZovLe/RuYIN3FxsUhLi8G48DDkDgTcjpWJSzabvgUgFMxSE2OwbO4UxXOw90TcgUYUZsbjaOU+rzYZmRlIi4+Ay9IMoEyyH5vVSnxP5+YC80omCFIQLQWC/pzKCciyAiZFUfj73//utwEsWrQITz75JDZt2oRjx47BarUiMTER1dXVoGkaEyZMQFNTE6qqqpCcnIx33nkHL7/8MiIjI7F9+3Zcd911+OijjzBt2jTEx8cjIyMDX3/9NYqLi/Gvf/0Ld999t+y5+Skr1NTRJHks/UV8pA05qdpMLuxNlS7jM2gyGhQj2OXITY1Bd6+DqC3fT8looAURdWrkJEdjSn4SEiKFL12v6833MQmwh31aQgTio6xYOj0P35ysw4WWTvWDdIYEI+2uRqWWfzaQyE0Z/JRgYh+9MemxmJyXJPCvDibehSSGk4nc87evqZI4PzVeZ+aBNDBm3u+oxmBcCygqdHxyhwtS1ztTwcWNhaYoRFgtRC4N0hWdyH+pCTmJiLBJpwNie1EKBPLFByzYbkOkBHSUkyZN4oSDRYsWobCwEPn5+diwYQP+8pe/AAC2bt2Kv/3tbzAajdiyZQsWLFiAyZMnY/369SgsLMSGDRvgcDgwZswY3H///XjmmWcAAM8++yw2btyI8ePHIy8vj/PvlOJn181GUbg7ebma3Mj65QzFDzg2M84rX5saRoMBNE1hYq60i4LZSHsF+JAQExHmTj5LAN9PaWKu/MMlxdIZeXjslvmYOVbo8+hVqpP3d6BlfZqiEG0Pw/fXTENWsjbNvE5gmFGUhlcfXoMI20Ay4yAKRHIvGzY4Lj7aBrvVjNKJGVz6sAk5Cfju0kmC8rLBRK4U7nCAHeqauWPx5O0LferDpBDkyPqYk0ThKt2HYWYjzEaDIEiTDwWETt6qYQzJ7xQTaUVuajRRW6k2Wt45M8alcc8TG2TLwm6PsLnnB7auOZ+RfEcENDz3vffe4/6mKArPPvusV5s5c+agttbtv7Rq1SqsWrVKsN9kMuH111/3Om7WrFk4ceIE0TjCzEbuR1SbWIsy49HR1Yd8AkfiYGA0UBiTFiub3yojKcrn3FdhhNHa/JRJsyek+3Qu7z7lo8qH8mWoz/+hgS3MJFi4+DvoLiYiDEYDjYaWLtW2cvdETko0ntu0gvt833dm45k3v8RH33SGnAAnfr5CMaWJHKxwHGGzICHa+wVNAhvQISVgSgVOyKH0s/7gqhIUZsXB6WSw+4i3k6A7GIxwwDqykCRbL0iPJVYSSS8ayH+o8DAT14ctzISocAvOXWgTtImNsOK+78zGb9/4Ap8erMTi4hxkJUfh+XcOBj1LRiAJfP4XFZKTk1FSUhLw87ATvpomZOEl2YKo7lDDaKAxOT9Jdj9JhN1goSgKGy+biIaWTswaR24eV8L7heedhiFQSKVBSUtwR+KTlNDU8T/i28HfaWIWF+cgPsqG//73foKxkN9/4kTIoUKU3YI1cwtxqroJR882DKuXGnspBzPi1IFCGlKCv1GLBlOl0ERidDjqLsoHdITawiPUkTZfu+cDh1M6MNNmMSFdJluKFIPVYPLb0xSFcVnxnIDpVclu4HOY2cgJysNpsaeVoBvyMzKkzQn+xx39N9yfb6OBFtTYFUOqhRws1ywch5uWT0GUXXsAgxTxUTasmlOAlDh3JSEtue8GCyUhzM4sSuNyjuoMPeIXcbQ9DGsvLRKUaxsMBgNNLGRpqtstUc0qFLBZTLh5+WTMlMhtG+r4wy+eoijYrWZJ645xQLspZb4UozgXqaSkUUrIryON3OW6Y1kBLpsmHVianx6r6ZmVso5oXQiwbhY0TWFcdoKnb9k80J7cuf4qfxmKjNxvJoLURB7qhFvNxNUMAo0/y1DFRIThtiumcg7cAh9MhRdLVlIUcfkwOQTC7MC5KJqSffCH08t5uCL+zSNs7vx1SjlstWCgKaI8eIA2zZnZ5F7gKTr1B5G8tFgsnZ6HKQM1kocDrAAQKH9Wk4GGzWKSze/LR1GDyc4dcgImqGGv4Bhq5ATy/JQIpCVIB7pqteJJ3Vdafyd+0QV+QQLx+PlaSwNPKB2phOYsGBDcGszhvoKcnJcUsi8vf8AmkOdP0kraoIduKPWbVgvgJdrnrTDjo2yYM8GjadeTLQce2co4g3x+jQYa+WkxSIiyITclGmsvLUK0ihZeyzmvmJWPx26Zj/lTstQbB4EJOQn4/pppmDc5M9hDIcY88PxbA1Sq0migMSE3gUjrrKRtMsqUweWghr+CY6hRc0mQYkyGNgHzie8txK9vXyTYpvV3MvIq7CTF2Lnt4nmMfXcbDDTGZcXjziuLJUtRjhSC7oM5VHg0mEEdxqDJHuFRzrSEOUxJoFYq00eKlDBroGmu39Q4Oy6fnos9R92O++OzE3C6pnlQ59Rxs3J2AfadqPXyW7OYpKemwa72I8Mt+P3dnowTOSnROHi6Di0diJvv6AAAIABJREFUPbLHaDlnanyEV94+ncFxzdws/Nf62aoLAV8xGmhMzE1Qbwjle4GtqCIn+NC0fMWnYDIhJwFXzB6DJ/9vb7CH4oXS9ZKb932JQ7CYhVpMrb8Sq40MsxhBUW6LC8N4m9+TYu3IT4tBapwdGYmRXuVnRxqjTsAcyerokYCU2UBJs0BrMHXKwb8j5EwY/MlsfE4C/r3HkxQ+OzkKlfWtGIKCQyOGBVPdwXQTchJwobkT9c0dguuXHBsuedxgn1+pPIpq90+o+VOONiKs7nRQgcJooIhdBqTqV6fE2bGkJBcZAyZbOe2XgaZC8v1joN0uAqGIVgHTajES+dKKyUiIhMlIw9HvDhzS+juxY2EVQK8+vEay3ZVzCnDlHJmM7yOQUTNzUpT77aW/LEIbdgLn11pX0mAaaBpTxySjKGsQfqm8c+WlRmNGURoyk6Jgt5pht5oRZQ/jhBCKojA+W6jtSI61Y1wWmQbEaKAxOU8+SGu0kBYfgSn5STAaaBgMFNLihSv5ApkawIPRAK2cPQabb5zrtX1RcQ4WFUsHDAC6z+1IJzEmHOky/nxiwsxGL9/zhOhwQa16udvFQNOaLWhXzyuE3UqWZzglzu5T0CVFUyFrulfWGEvkNPXxe5hNBhSke+YczSbygbHk+pCDeiQzaqQtasAHcyRHbI0EpDSYpoHk8pLtaQobFo3HTcsm+3xOfs8rZo3BQzeUYua4NJROzMBrm9fgp9fM9FRQSohAZLgFU/KTuIAkmqYwf2oWpuQnqa6eYyLCsGnDLJ/HqgZF+Tf4yt8YaArJsXZEhXvqcxsNNMZmeiZ3k5FGdrK0n+tghL34KJtkkNDlJblYXSqtVVg6PQ9zZBJn64wMigtSiAW/W6+Yiuc2XSHYJj7UnxrM5bPyiQtZ3HbFVNy4dBJKJ2ZoEmRDUbMaHubWWitlRZEqUjKY78EvXqJ1nmHPm6v75wsYNdKWhXZi6fQ8XD49+LV1deSZOS4N1y4cL9DyGY20rIbBH5WXSCYlNlKdrZL02C3zsWbuWKTE2TElPxmlEzPwvZWXCCIIJc9FBdYPy2Yx4c/3LA9Y/4Nh3uRM3L6qGM9tWoGlM/K47SYDjbE8jWVOSrSs1nowLxBFbYjMwvOqeYUhG7Cj4x/GymjL5TAYhPcRJbqvxALm2Mw4vPizlZiYm6j52TfQ5Om0aJrC4uIc3Ped2Zrchow8f/NQYUlJLp7btEJxXs9JicaVcwpgtXiE0MEokPh+uFp/J06DqQuYAkaND6aJcuL7a6YFexg6KpQUpqKkMFWwzWSgMSY9FlX1rV7t2Ykx0AKmiTOBeCaQ+VOysJhnWrVbzV4Ttd1qRk9fP8LMRkTbw+BiGFntwvoF43CiqgmHz9T78C3cBCuQIMxshN1qRkd3H3r6+iXbzByXhrmTvKOXDQOR3VtuK8GxOidqGttlz6P1d/7rT1fgma1f4nhFo6LZS67fUHvx6vgf7f52wntF7Jcp7s9iNHCWDa0CkDuIUfmYxJhwLC7O4XxAAfd81edwEp3DoJCSLViQmKhzUqJx6xVT8WVZLbp73UGCg1mAFmbGc36YWk3kBppCXKTVb3mhRwqjRsDUGb4YjTTGpMXgo/1nuW0RNjPau/q4CYUkF2ZxQQqqG9pQ39wp2E4ikLEagZwUj4+NZF1jUV+XFCTDaKDx43Uz0N3bj9/9/QvZyTw7OYqodCGfaxeOR0NrFxpbunDoTP1ALVzp73PLiik4ee4iPjtcpekcaswan44HNs4BAK4UmhSUTGymxWREVnIUqqtauMWEHKSameuXTMT6BeMAeHJTKv3OcmbIUDMd6gQf7+TZKmU4BVkqtAsuaoucjIRIbFg0XrDNZDSApvvhcilHHj5+63xkJESiqa1bcn9UuAVFWfG4fHoeHvmfXZrGPhi0LCT5GuXBLAhZP8xjFdorXRloWve/lCC0li06OhJYzSavh3dSrtuEzk7uafER+Mdja3FJgXw06Mo5YySFFyITuZHMBEKLXjYzitI4wcZqMeK/rp4un+MR5L5Q+emxWF06FmvnF+FHa6fju0snARhIryTzEkuLj8AtK6Zgy39d7jfB6Z5rZuI7iz0vN5JE1GJyUqI4fyqTkVZMM7Jm7lj86rYFXtvZa2q3uhOy810s2ETKSl85PMws+VILNc2OTvDhP7+J0eFIE5UlFC9kpLJUqDEpLwnfWzkVVotJMphFcD6JGzvabsHS6XkSrd0UZMRi3fwiFGXFIzbS6nWO2Egr8tNiMK0wFeFhJk4DGx9lwz2rx8n2G2Y2YnXpWCRGhxP7jrKMy45H0kCglJbSsPy2g31eswfmd625po0GWs+PLIGuwdQJeXJToxFtD0NyrB2tnT3o7u1HdnIUjlU0CCZ7s9EgqeHKSYnG8pn5yE6OlpyMDQQaTOOAmSuSF5wi2U4k3E3ITcDJak898wibGf0yNXRBka/AZ41L4zR0gFvTkJ8Wg9hIq2QqHsA9+cZFWhEXacXNyybjyNkGfHm8RvE8Lz94JX7+4i6U10rn/ZyUm4jYSCvvHNrz1vGF/shwi2IdYXb8BpqCc0A7U1KYins3zMKR8guwW81eGQXYQAGx8M+HotzlKC0mg8BEr5vIdcRQFMWZUp/5wRIvQUqu/jTgtlJMyU/CoTP1imnNxmXHY+Vsd+BZydgU2K1mHDxVJ9lW6h69fVUxvj3X5LU9PsqGF3+20mu7WKBbPiMf1ywch30navH5sWqkJ0Tgfx+4EmaTAWfPVsiOO9JmwS0rpuCWFVPw9t5T+Ovb38h/SRHr54/D/pN1eHvvSRg1CHjswpDyQ07kaWNTYDUbMUljpg+apjj/fB0PuoCpE/KMzYhDSpwdz21agf/31td478szMBpoTMpL9GorJSymxNq51byUmTTKriw0Au4VLckKlZ8gPMLmTnM0dYxwsmLTlYhfMAaaIjINzZucibw04WSWGBPOJRB38sxiRVnxKMiIw9yJGchI8vhoXVk6Fpb/3969h0VV7X0A/w4Dwx3kDiIoBIKIF/CKYoGXc7wkoeKlvKaoaZlHOx2NvOZb6ouGnY72dFIzTyctj4nXNEpRwTQxgTelVBABj6YIiIYCwnr/oBkYhqtuZgbn+3menif2WjP81jBr+9trr72WiXG9CaatlRmcbM1hYiyHmaL+29K1T+iNrVlaF0/n6ie7/T0dmzT/yVhuhIrKCtX/mymMNebuKgX7usLBxlxt/mxdlAn69fx76O7riql/7trkJWLIsHzy5vMwNZHD0kzz+1Hf/tNA1SoVI0J88f5XZ/Df/Hv4NfcO7G3M4eXWBud+vaGqV/Mi8aXBgci9VYy5cd9o/K6efm7o7KW5RFqglxMu17iwBapuAbexrnuOYO0pRsqYA72cce7STRjLjWCnem3dmfGskcFq5+SmJnvmpsZwd7SGlYUCAe0dsf/UpWaNYFYtlO8MudwId+42b4pRbT393NDTz63Zr+MIZt2YYJLeq9lx+wS445szmZDXs55k7SRmyeRQtK+x+1FdJ736EpOaTOTyJl2hKq/exy7fjZnPB0FhLFfdzleSyYD+gR7IvVWMazUeXJIbGamS0vsPyur9HfNG92pw+Q75H+vaCSHw1/F9VevzadRr4CQ+oq+Pal7X2tmD8GvuHfx103eqcmVSV/s9HG0t4GpvpbEzj/I1dan5N2vqbTVjuRFKyyswpKcXendyb7DuwOAOTXrPF0L9VAm3nZUZfNw5IkF1c6gxal+bTFbd/4C6b9suHNcHx1Ov4dcv76CDqy0i+nXEuV9vQCaTob2LDRxt1d/fxkKBXr4OuHb7IW4VVc8hnxPZA85t6unftc51C8f1UdvytiZLMxO8NDgQMhnw74SfVdNszE2N4e+p/pR97XOsnbUZgju6obuPi9rONI3d2lfq3MEJy6c9C6BqMKBqhLjpT8ErTIwx8/kgnEzPwY/36p5L2tIUxkYtuhlAa8UEk/RezcQk2NcNni62MJbL0M1HM8FUntT6dHJHT383BHd0U5tPo/GEp4m8SQufy2RV85aaKqRzO4QHdQBQ93yeRS/1w38SM/DZkfQascswaUgXTBrSBTGfHIPcSIb5Ub2xctsJZN9UT0QbM35gAB49qmzwln7DC9g3/CBD705t8dbE/hqvGxcegHHhAYiI+VI1QutiZ4m1rwxSW/vySdnbmOP3h+UY3tdXskQw0MsJ5/+4DcmHe+hJmMiNUPaoaoS9vpG857q3R0hgO1RWCvySU3U728rcBB/OH6pR19bKDJPCvLHvp3y1hx0bOhc01odrsjJXqC4oi38vU5tOEuSrPq+99l0gH3d7/CWqt8Z7KmqdX4b09EbKr/9F4T31bVlrxmVjaQrvBpYpq0tEP190cLXFb4X2SKkxCqxN+rpQva4xwaRWRSYDIkM74lGFUE0Ir0m5ZEhHD/s6J7nXPsl293Vt0hPoQPPWy5sdEdxoHWUSY2Eqx/C+HeFW4wq45oMsta/mm/Ik6sTBgY3W8fd0wKujeuLLoxeR/8etJXNTY7zQ308j6Tat9Rk19uS93MhINddUYSJvcMTncfh7OiL3VrHkiaAyIefcS3oSxjUSTKMG+qvy4Tbl962hecKAZr9rqB/Wfq+mfqdnjOiOqzeKVD/XvjCs/T71xaBcp/JY6jUU/16KTu2rntDWTDDVX1+1w1fTRzD7BFTdwfBtZ89+q2f4iCS1Os91b69xC0lJOdpZX+JR+8q4T6fGb48rNfaAT01N2U1HeTJsY2mKqUO7oq1j3Q+31NyxQnn7Wwquf8xNrfkPiLW5KSYOCVTbWQeoGoV8e3Iouv6x20VjiV3NUeeWuLpXLoos9ZqfYd3b46+jOmP8wPqflCVqjINtzQffGv9nVtlfGkuQal9cNlRfs27T/rmXG8kavCtQ+1fWF0MH16p1KpUDAXVtlfmXsX1UK2AodfNxafZT3ABgb21e56AD6Q4TTGp1FMbyeidivxDqh17+bes9mdaeB1jfntfaoBxhaOyie+rQrpj5fBCApq8D2RzKk3kv/7aIHOBXZx2FiRx9A9zh6lA1ytpYYrds6gCMDevUpLqPI9CrKtGVesTC1tIUHo4W9c5rI2oK5VxHFzvLJn1HleerxuoqLzb9PB0wdWhXmJnWfxNSea54pq0dPnj9zwis42GgxyGTqS+nZmne8MW08vwiN5JprIUb0N5R48K6cwcnWJg+3na3/u0b3kmNtIu3yKlVqm9UzMPZBsumDkBpPbtY1E7QmvO0otSaugtRp/aOcHOwwpZDqU+0Y1F9lJ9JWPf2eLab5k47NTU2QqzUxdsZJQ/Lm1T3cTi1sYCznSXnSpJe6t/FAzuPXkCQrysqKutZlqwGWytT9O7UFu713MVQUvbVLl7OiHquU4N1lVN/3BysJN/C0ERuhNI/VnEY0rPh7ZdV0wDkmiOYdZ3GFSbyRrfcrU/tB5JIt5hg0lOp9pxBJWO5EUyMjWAkk6G0vALWFtI9eNJcclWy1njdNlZmeLarJ9KeYBvJ+pgYG8HWQoF2znXv916TKsFswqhkNx8XbFowrMbyJtLq4uWkk20xiRrTwdUW7ZxsEOTrqnpwrCEudpZYOmVAo/XsrM0QFdYJI/v5NlrXwcYcr4/phYHBXk2KuTlMjOUoLa+An6eDxrqztc0aGYzV/06uc5ex+i4QG1oloyFci1K/MMEkg2IiN0J3H1dk/rcQ5oqKZu82ISXlU5ZNWegdqNoWMuNavuRxjAr1g5m436RRjqaOYAJV/0h4NCFpfVyB3s6c1E96K7SLBwK9nHAx+7Zk7xnRv2OT6/p5ODTrwcTmMDE2gsJEjsjQuqfU1OThbIP3Xx2C+w/KNDaLrW9TiMfVEnd46PHxr0EGxdjYCP0D20FhLEdbe2mfbG4u5aLsNpZNS3LdnawxqIf0oxHdfV01lhSpj1Obqi3j9GGOYqCXE2+Rk97q38UDFmYmT+W8QBO5HOPCAtCvc7sm1TdTGMPR1kJjBLOpa2VS68S/LhkUM4Uxendyh8JEDncHC53GoryN364ZcTRlxKAlebpULVrfvY41SLXN1d4KtpYtc/ud6El1cLWt2nHMW3PHsdbOxd4SUWGdmn2Bp8wvfdvZY8aI7o/9MA+1DrxFTgYluKMrrC0UUBgboa29bhNM5SR8D6emjwaaN/DUqDa0d7aBpZkJfNo1fdH5lvQ4y5kQaVNzljdrLYb39Xms6SnKEcwOrm10frFMLU9rZ+eff/4ZI0aMQJ8+ffDuu+9qlFdWViImJga9e/dGdHQ0iouLAQAXL17EiBEjEBoaip07d6rqx8XFISQkBOPGjcONG7pZvZ9aH+WtXf0YwTSGTNa8EUxds7UyQ/8uHpz7SGTAQpp4a7w25VmDz+YZBq0lmC+//DKWL1+OxMREJCQk4Ntvv1Ur37x5M7Kzs5GYmAgvLy8sW7YMQgiMHz8eb775JuLj4/HOO+8gMzMTR44cQXx8PBISEvD8889j7ty52moGPSUszRRwstXt7VVThRyu9lYwV0i/tmVLGtmv6Q8aENHT53EvMGV/vI75pWHQWoJZWloKc3NzmJiYQKFQwNhY/VZffHw85syZAwsLC8ycORMHDhzApUuXoFAoEBYWBkdHR0RGRuLQoUOIj4/HzJkzYWVlhYkTJ+LEiROobMJaY0RKPu52jS5w3tIUxnL4uOvHrebm6OBqq+sQiKgVUp5zuXe3YdBagrlw4UKEhISgY8eOuHPnDvr166dWnpeXB3f3qj1FnZyckJ+fj7y8PLRrVz0U7+rqilu3bqkdl8vlsLW1RWFhobaaQk+Bllq+ozlMFfIGt2QjInqaKBNLrv5gGLTyxMDdu3fx9ttv45dffoG7uztee+01bNiwAYsXL1bVsbCwwN27dwFUj3bWPAYAJSUlsLKygoWFBYqKijSOKyUlJSE5OVkjjqysrJZoXrPoQwyAfsShyxhMKysAyHUaw4OyClgYPQSgMPi/hz7FAOhHHIyhmj7EwRiqPW4cZaWlAIDi4uInbos+fBb6EAOgP3HUprUE09LSUjXq2KtXL6Snp6vV6dKlC9LT0xEUFITU1FT06NED/v7+yMrKQllZGRQKBVJTUxEdHY3y8nKkp6cjMjIS169fh4uLC0xNq5/UCw0NRWhoqNr7r127Ft7eDW9p1dKysrJ0HoO+xMEYgEcVlfD0fITbN/MM/rPQlxj0JQ7GoF9xMAZp4jA3vwrgPmxtbZ+oLfrwWehDDPoSR30JrlYSTA8PDwQEBOCll15Cp06d8OWXX2LHjh24efMmhg0bhvPnz2PevHkYM2YMLl++jL1792LTpk2ws7NDZGQkRo8eDS8vL9y8eRPh4eEICAjAc889h5KSEhw7dgwxMTHaaAaRpIzlRrC2UEC6fT6IiPSXcmtX3iE3DFpJMGUyGfbs2YOkpCQUFBRg1qxZcHFxQVlZGQYOHAgA6Nq1K44ePYozZ85gxowZ8PKq2rHkgw8+wPHjx1FSUoJ169ZBLpejbdu2OHXqFE6ePInJkyejc+fO2mgGERERPSblHEw+5GMYtLZqs0wmw4ABA9SOlZaWYsiQIaqfPTw84OHhofG6sLAwjfdzcnLC6NGjWyRWIiIikhYTTMOi020wrK2tMXToUF2GQERERFrAp8cNC/dZIyIiohanXKDdiCOYBoEJJhEREbU4I9Utch0HQlrBBJOIiIhanLG8KuVgfmkYmGASERFRi1OOYMrlTD0MAf/KRERE1OKMjatSDmMmmAaBf2UiIiJqcSZ/JJZyPk1uEJhgEhERUYtzc7ACwBFMQ8G/MhEREbW4YX18YKYwZoJpIPhXJiIiohZnbaFAWFB7JpgGgn9lIiIi0orIUD+YGDP1MARa24uciIiIDJu7ozXMFEw9DAEvI4iIiEhrHGzMdR0CaQETTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpKUVnec37dvH9LT0xESEoJBgwZplJ89exaJiYno0qULhg4dCgAoLS3Fjh07UFRUhLFjx8Ld3R0AkJGRgUOHDsHT0xNRUVGQyWTabAoRERER1UNrI5gbN27Eli1b0K1bN8TExCAhIUGt/NSpU5g+fTpcXFywfv16bNmyBQAwffp0pKSkwMzMDOHh4SguLkZmZiYiIiJgb2+P3bt3Y/ny5dpqBhERERE1QmsjmBs3bkRycjLs7OwQEBCA0tJStfKPPvoIK1euxOjRo9G7d29MmTIFERERSE5OxtWrVyGTyZCWlob9+/cjIyMDc+fOxcsvv4yoqCj4+/vjnXfe0VZTiIiIiKgBWhnBLCoqQklJCV577TX06NEDCxYsgJubm1qdixcvonPnzgAAX19fZGZmIiMjAwEBAarb376+vrhy5YpaXWtra8jlchQXF2ujKURERETUCK2MYJaUlCA3NxdvvfUWAgMDsXTpUqxbtw7vvvuuqk5lZaXG62ofUyaaFRUVDc65TEpKQnJyssbxrKysx22CZPQhBkA/4mAM1fQhDsZQTR/iYAzV9CEOxlBNH+JgDNX0JY7atJJgOjs7w87ODoGBgQCA3r17Iz4+Xq2Op6cnrl69Cj8/P+Tm5sLb21t1TOnatWvo1asX7ty5o/pAHzx4AACwsbFR1QsNDUVoaKja+69duxbe3t4t0r6mysrK0nkM+hIHY9CvOBiDfsXBGPQrDsagX3EwBv2Ko74EVyu3yI2NjTF06FCsW7cOv/zyCzZt2oQXXngBd+/exYoVKwAAL730EmJjY5GWlobly5dj0qRJ8Pb2hp2dHf75z3/ixIkTOHz4MIYPH44XX3wRmzZtwrlz57By5UpERUVpoxlERERE1ARae4r8k08+wcOHD7FmzRpER0cjIiICAJCSkgIAGD9+PMaPH4/Vq1cjODgYr7/+OgBg165dSEtLw7Zt27Bjxw7Y2dkhJCREdZvdwsICq1ev1lYziIiIiKgRWnuK3NzcHEuWLFE7ZmRkhCFDhqh+njVrFmbNmqVWx93dHRs3btR4v6ioKI5cEhEREekhne7kY21tjfnz5+syBCIiIiKSGLeKJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIklpPcEsLy/HlStX6ix79OgRsrOz8fDhQ7XjhYWFuH79utqxyspK5OTk4P79+y0WKxERERE1n9YTzKVLl2LhwoUax3/77Tf07NkT8+bNQ3BwMH766ScAwCeffIIBAwZgypQpGDt2LCorK1FSUoJnn30Wc+bMQa9evXD48GFtN4OIiIiI6qHVBPP777/HDz/8UGdZXFwcJk2ahP379yMuLg4rV65EaWkpVqxYgaSkJHz//ff4/fffcfz4cXz22WcICgrCwYMHsWfPHixevFibzSAiIiKiBmgtwczPz8fq1avx3nvv1VmelJSEIUOGAACeffZZ/Pjjj0hPT4efnx/atGkDABgwYAB+/PFHtbr+/v64ceMGSktLtdMQIiIiImqQsTZ+iRACc+bMQWxsLIQQdda5e/cubGxsAABmZmZ48OAB7t69C2tra1UdS0tL3L59W62u8vj9+/dhamoKoCpZTU5O1vgdWVlZUjbrsehDDIB+xMEYqulDHIyhmj7EwRiq6UMcjKGaPsTBGKrpSxy1aSXBPHPmDE6dOoXVq1ejsLAQFy9exP/8z/9gyZIlqjoODg64fv06vLy8UFRUBEdHRzg6Oqo93FNQUAAXFxdVXaAqef39999Vo5wAEBoaitDQULUY1q5dC29v7xZuacOysrJ0HoO+xMEY9CsOxqBfcTAG/YqDMehXHIxBv+KoL8HVyi3y7t274+TJk1izZg0WLFgAf39/zJgxA2VlZUhNTQUADBo0SPWwztdff40//elPCAwMxJ07d5CTk4OysjIcPHgQgwcPVqv7/fffo2fPnpDL5dpoChERERE1QisjmGZmZqoMu6KiAl27doWbmxtu3bqFF198ERkZGZg/fz7GjBkDf39/uLq64quvvoKxsTE++OADhIeHQ6FQYPr06fD398czzzyDQ4cOwdfXF23atMEXX3yhjWYQERERURNoJcGsydfXF3FxcQAAOzs7jB8/HgBgY2ODhIQEjfoRERGIiIhQO2ZiYoKdO3e2fLBERERE1Gw63cmnoqICr776qi5DICIiIiKJaX0EsyYzMzOYmZnpMgQiIiIikhj3IiciIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJMcEkIiIiIkkxwSQiIiIiSTHBJCIiIiJJaS3BvHDhAqKiotCrVy/Mnz8fDx48UCuvrKxETEwMevfujejoaBQXFwMALl68iBEjRiA0NBQ7d+5U1Y+Li0NISAjGjRuHGzduaKsZRERERNQIrSWY06ZNw4wZM/DDDz+guLgYH374oVr55s2bkZ2djcTERHh5eWHZsmUQQmD8+PF48803ER8fj3feeQeZmZk4cuQI4uPjkZCQgOeffx5z587VVjOIiIiIqBFaSzC7du2KYcOGwdjYGP369UN2drZaeXyKnTzIAAAUQ0lEQVR8PObMmQMLCwvMnDkTBw4cwKVLl6BQKBAWFgZHR0dERkbi0KFDiI+Px8yZM2FlZYWJEyfixIkTqKys1FZTiIiIiKgBWkswt2zZAgBITEzE+++/j5kzZ6qV5+Xlwd3dHQDg5OSE/Px85OXloV27dqo6rq6uuHXrltpxuVwOW1tbFBYWaqklRERERNQQY239ouLiYsybNw+FhYU4cuQIPD091cotLCxw9+5dAEBpaSnMzc3VjgFASUkJrKysYGFhgaKiIo3jSklJSUhOTtaIISsrS+pmNZs+xADoRxyMoZo+xMEYqulDHIyhmj7EwRiq6UMcjKGavsRRm9YSzIkTJyIqKgpTp06ts7xLly5IT09HUFAQUlNT0aNHD/j7+yMrKwtlZWVQKBRITU1FdHQ0ysvLkZ6ejsjISFy/fh0uLi4wNTVVvVdoaChCQ0PV3n/t2rXw9vZu0TY2JisrS+cx6EscjEG/4mAM+hUHY9CvOBiDfsXBGPQrjvoSXK0kmLdu3UJiYiLMzc1x8OBBAMDAgQMRGRmJYcOG4fz585g3bx7GjBmDy5cvY+/evdi0aRPs7OwQGRmJ0aNHw8vLCzdv3kR4eDgCAgLw3HPPoaSkBMeOHUNMTIw2mkFERERETaCVBNPa2hqJiYlqxxwdHWFvb4+BAwcCqHoI6OjRozhz5gxmzJgBLy8vAMAHH3yA48ePo6SkBOvWrYNcLkfbtm1x6tQpnDx5EpMnT0bnzp210QwiIiIiagKtJJjm5ubo0aOHxvF79+5hyJAhqp89PDzg4eGhVkcmkyEsLEzjtU5OThg9erTksRIRERHRk9HpTj7W1tYYOnSoLkMgIiIiIolxq0giIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSFBNMIiIiIpIUE0wiIiIikhQTTCIiIiKSlLGuA3hcFRUV2LVrF65fv47IyEg888wzug6JiIiIiNCKRzAXLVqE/fv3w97eHkOHDsX169d1HRIRERERoZWOYJaVleHzzz9HdnY2zMzMkJubi507d+KNN97QdWhEREREBq9VjmBeu3YNbm5uMDMzAwD4+vriypUrOo6KiIiIiIBWOoJZWVnZYHlSUhKSk5PVjpmYmGDt2rUtGRYRERGRQXFycoK3t7dmgWiFiouLhaOjo6isrBRCCLFmzRoRGxvb4GvWrFmjjdD0PgYh9CMOxlBNH+JgDNX0IQ7GUE0f4mAM1fQhDsZQTR/iqC+GVnmL3NraGgMGDMDq1avx448/Yvv27RgzZoyuwyIiIiIitNI5mACwefNm5Ofn4x//+Ac++ugjeHl56TokIiIiIkIrnYMJAPb29nj//fd1HQYRERER1SJfsWLFCl0HoS2enp66DkEvYgD0Iw7GUE0f4mAM1fQhDsZQTR/iYAzV9CEOxlBNH+KoKwaZEELoIBYiIiIiekq12jmYRERERKSfDOoWeUVFBb766iscOXIELi4usLe313VIknn48CHi4+MREBCgUaZs9+HDh+Hs7Kxqd05ODrZu3YpLly6hc+fOkMvlAIADBw4gPj4elpaWcHV11Wo7nlRycjJ27tyJzMxMBAQEqNqkdPv2bWzduhXp6eno1KkTTExMAACJiYn48ssvIZPJVEP99+7dw7Zt23D69Gn4+PjA3Nxc6+15XMXFxdi5cyeOHTsGa2trODs7a9Q5e/YsvvjiC9y7dw8+Pj4AgNLSUnz++edITEyEh4cHbGxsAAAZGRnYvn07rl+/joCAAMhkMq22RwoHDx7E/fv34ebmpnbcUPrHgwcP8Omnn+LcuXM4d+4cHj58qHFby1D6BwDs27cPu3btQnl5eZ1r+BlC/0hLS8O+fftU34lz587BwsICTk5OqjqG0j+Aqu/5jh07cOfOHfj5+Wn8HVtj/ygtLcW//vUvHDt2DG5ubmjTpo1GnZb6rhvUCObTun/5uXPn8MYbb2Dbtm11lr/11lvYt28fHBwcMGzYMOTl5aGgoAADBw6Eubk5zpw5g9mzZwMANm3ahA8//BAuLi6YNGkSzp07p8WWPJn9+/fj9ddfh6enJ06cOIEZM2aolZeWliI8PBzl5eXIzs5WLW21Z88eLF68GG3btsWCBQvwzTffAAAiIiKQl5enel1FRYXW2/S4Ro0ahStXrsDe3h4REREaO12dOnUK06dPh4uLC9avX48tW7YAAKZPn46UlBSYmZkhPDwcxcXFyMzMREREBOzt7bF7924sX75cF016IpcuXcKLL76In3/+WaPMUPrH5cuX8a9//QuFhYUoLCzEgwcP1MoNqX9s3LgRW7ZsQbdu3RATE4OEhAS1ckPpH0VFRcjKylL998477+DmzZtqdQylf+zZswfLly+Hr68vNm/ejHXr1qmVt9b+ERUVhcuXL8POzg7Dhw/X+Pu26Hddq6tx6lBpaalwcXERDx48EEIIsXLlSrFu3TodRyWNNWvWiEmTJokRI0ZolJWVlam1e9WqVeJ///d/xUcffSQWLlwohBCisrJSuLu7i3v37gl/f39x7do1IYQQ27dvF3PnztVeQ57QlClTxN69e4UQQty/f1/Y2dmple/du1dMmDBB9XOXLl3E1atXRXh4uDh9+rQQQojvvvtOREZGirS0NNG3b19V3ZEjR4pjx461fCMkUFJSIp5//nnVz9OnTxd79uxRqzNp0iSxe/duIYQQGRkZolevXuLWrVuiffv2qg0MXnnlFfH555+Lt99+W7z//vtCiKpNDtq2baullkijtLRU/PnPfxbTpk0Tn332mVqZIfWP3bt3i7i4uHrLDaV/CCFEp06dREFBgRBCiCtXrogLFy6olRtS/1A6cOCA+Otf/6p2zJD6x4IFC8TWrVuFEEIcOnRIjB07Vq28NfaP27dvi06dOql+Xrp0qfj73/+uVqclv+sGM4L5NO9fvmjRIrzyyit1luXk5MDFxUWj3RcvXkTnzp0BQDWsn5WVhRs3bqiG+Dt27NiqPqPPPvsMERERAIBPPvkE/fv3Vyu/cOECAgMDVT/X9Vko21xf3dbA3Nwc+/fvx4ULF/CXv/wF//d//4fBgwer1anZZl9fX2RmZiIjI0Ptlkddn4+1tTXkcjmKi4u126gn8Pbbb2POnDkat8YBw+ofV65cwZYtW9CzZ08EBwfj7NmzauWG0j+KiopQUlKC1157DT169MCCBQs0vhuG1D+Aqik1S5Ys0RiRMqT+MWvWLMTGxmLWrFmYP38+3njjDbXy1tg/TE1N8eDBA9Xo6c2bN3H16lW1Oi35XTeYBLOx/cufVvW1u6KiQmPuxNPwGWVlZWHkyJFIT0/Hv//9b7Wy2u1Ttr+udtdXtzVxcHDAgAEDIITAoUOH1Mqa0+a6viutRUJCAu7du4cXXnihznJD6h8DBgzAjh07kJKSgg0bNmDmzJlq5YbSP0pKSpCbm4u33noL586dQ7du3TRuhxpK/1DatGkTXn75ZVhZWakdN6T+sWPHDgQFBWHy5MkYMWIENm7cqFbeGvuHtbU1Zs2ahZCQEIwYMQIpKSkac0Fb8rtuMAlm27ZtkZeXB/HHqkw5OTl45plndBxVy3Nzc6uz3e3bt0dWVhYAQAiBmzdvwsfHBxYWFrhz545a3dYiNTUVo0aNwt/+9jds3bpVNSlZqWabgapR7WeeeQaenp6qq7qan0/NKz1l3dbgypUrWLVqFVxdXTFmzBjMmzcPx48fV6tTs825ubnw9vZWOwZUtdnHx0ftc1PO26v92eqr8+fPIyUlBT179sS2bduwfPly7NmzR1VuSP3DyMhINYG/d+/e+O2339TKDaV/ODs7w87OTjXC1Lt3b415aYbSP4CqpOHjjz/GpEmTNMoMqX/s378fS5YswYABA7BmzRp8++23auWtsX9UVlYiIiIC3333Hb744gsMGjQIXbt2VavTot91Ke7ztxajRo0S7777rjhz5owICAgQWVlZug5JMklJSWpzME+ePKmaVzFmzBixatUq8eOPP4qAgACRmZkpMjMzhbe3t/jhhx/E+vXrVfNN3njjDTF37lxx/vx50bdvX5GUlKST9jyOqKgoERMTI7766ivx1VdfqeYdbty4UVy6dEkUFBQIDw8PcfToUbF9+3YRGhoqhBBi/fr1Yty4cSI1NVUMHz5c7Nq1Szx69Ej4+vqK3bt3i4MHD4rOnTuLsrIyXTavyYqLi0WHDh3E4cOHRVpamggLCxO7d+8WRUVFYvny5UIIIXbu3CkGDhwoUlNTxZQpU8SGDRuEEEKEhISIjz/+WBw/flz4+fmJgoICcerUKdG1a1eRkpIiFi1aJBYsWKDD1j2+pUuXquZgGmL/mD17tli8eLG4dOmSePPNN1Xz4wytfwghxMSJE0VsbKzIyMgQQ4cOFXv37jXY/nH27FkxaNAgtWOG2D9effVV8dprr4lLly6JuLg4ERUVJYRo3f2jsrJS+Pj4iNOnT4uzZ8+Krl27inv37mntu25QCeadO3fEggULxOTJk8Xx48d1HY6ksrOzxaeffqr6+eTJk2Lx4sVCCCEKCgrEwoULxeTJk0ViYqKqTkJCgnjppZfE3/72N3H37l0hhBAPHjwQy5YtEy+++KL4+uuvtdqGJxUXFycWLVqk+m/ZsmVCCCEWLVqkOtGdOXNGTJkyRbz++uvi5s2bQgghHj16JGJjY8WECRPUPsNffvlFREdHi1mzZonLly9rvT1PIi0tTcyePVtMnTpV7Nq1SwghRFFRkdpFyMcffyzGjx8vNmzYoJrMnZeXJ+bOnStefvll8dNPP6nq7tq1S0yYMEGsXLlSPHz4ULuNkciRI0fEuXPnhBCG2T9+//138d5774nJkyeLDRs2qP7BM8T+UVJSIlatWiWmTp0q/vOf/wghDLd/nD59Wuzfv1/tmCH2j5KSErF27VoxYcIEsWzZMlFUVCSEaP39Iz09XcyYMUNER0eLjIwMIYT2vuvcyecplZycjLKyMoSHh+s6FJ1bv3495syZAwsLC12HolP37t3D1q1bMX/+fF2HonPsH9XYP6qwf1Rj/6j2NPYPbX3XmWASERERkaQM5iEfIiIiItIOJphEREREJCkmmEREREQkKSaYRERERCQpY10HQESkr65evYqEhIQ6y8LCwrBnzx6MGjUKHTt2bJHff+fOHezevRt9+/bVWCC5IceOHUNubi6mTJnSInERETWGCSYRUT1++uknzJ49u86yPXv2YO3atQgMDGyxBDM3NxezZ89GbGxssxLMbdu24ZtvvmGCSUQ6wwSTiKgeY8aMUW2TZ2tri5CQEBw+fBgAkJ+fj48//hjdu3dHfn4+jh07hh49euDbb7+FQqHA5MmTceLECVy8eBGjR4+Gu7s7AODy5cs4cOAAjI2NMXbsWLi6ujYplpKSEhw8eBBBQUFISkpCcXExRo8ejXbt2gGo2iL0m2++gZ+fn8Zrjx8/jtOnT8PV1RUTJkyAQqHAvn37IJPJEBERgZycHJw5cwbdunVrsWSZiAwLE0wioseQkZGBcePG4euvv4azszPGjRuHDh06oLS0FDdu3MDmzZuRk5OD27dvY926dbh8+TJSUlIwePBgeHh44O7du1i1ahXOnz+vSj4bkp+fj3HjxsHDwwOWlpa4evUqYmNjkZmZicuXLyMkJASVlZWQyWQwMzODTCYDAMTGxmLRokUIDg5GRkYGtm7disTERKSmpmLFihXYt28f/vGPf+D8+fP4+eefW/pjIyIDwYd8iIgksmDBAuTm5qJt27bIz8/HtWvXsHTpUuTk5CA7OxvvvfcenJyccOHCBaSlpaGwsBCffvpps37Hn/70J2RkZODtt99GXl4erl69ig8++ADl5eXIyMjAf//7X9jY2AAAKioqsGLFCkyePBkpKSnYunUrTpw4gdOnTyMmJgbdunXDtGnT8O233+Kjjz6Cs7NzS3wsRGSAmGASEUmkXbt2kMvlcHFxQYcOHSCXy+Hg4AAAKC8vR2ZmJnJycmBiYgJXV1c8evQIv/zyS7N+R3BwMADAzc0NAFBWVoYrV66gY8eO8PDwgLW1Nfr37w8AuHHjBkpKSrB9+3bIZDJMmDABAPDrr7/CxMQEy5cvR0FBAbp3744xY8ZI9TEQETHBJCLSBplMBhcXF/Tt2xdCCDx69AgpKSlYs2ZNs97HyEjztO3g4IC8vDw8fPgQAJCVlQUAcHJyglwux5IlSyCEQGFhIVJSUjBq1ChUVFRg7dq1sLCwQGpqKvbu3fvkjSQi+gMTTCIiLZDJZJg4cSJOnz6NkSNHYvjw4ejTpw8yMjKe+L2jo6NRWFiI/v37Y/DgwUhLSwMAmJqaIioqCuvXr8e0adPQr18/DB8+HOXl5YiNjcWZM2ewc+dOdOnSBXPmzEFBQcETx0JEBADyFStWrNB1EERE+u7hw4fo378/goKCAABCCJiZmWHgwIFwdHSEqakpwsPD4ezsjNLSUgQFBSE4OBgVFRVwdHTEoEGD8Oyzz6Jv3764du0aHBwcEBcXh7CwsHp/Z2VlJUxMTBAWFgZPT08AQHh4ONq1a4eKigrY29tj0KBB6N69O3r06IGCggKEhoYiOjoaHTp0wHPPPYcXXngBTk5OyM7ORs+ePbF161Y4ODjg6NGjGDlyJKZNm4YePXpAoVDA0tISXl5e2vg4iegpJxPKNTiIiEjrKisrERMTU2dZTEyM6oEdIqLWhAkmEREREUmKczCJiIiISFJMMImIiIhIUkwwiYiIiEhSTDCJiIiISFL/D0km3KXqxDmYAAAAAElFTkSuQmCC",
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ "@vlplot(\n",
+ " title=\"MW Load per hour, No TDR\",\n",
+ " width=600,\n",
+ " height=400,\n",
+ " linewidth=0.01,\n",
+ " mark=\"line\",\n",
+ " encoding={\n",
+ " x={\n",
+ " field=\"Time_Index\"\n",
+ " },\n",
+ " y={\n",
+ " field=\"Load_MW_z1\"\n",
+ " }\n",
+ " },\n",
+ " data={\n",
+ " values=...\n",
+ " }\n",
+ ")"
+ ]
+ },
+ "execution_count": 260,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loads |>\n",
+ "@vlplot(:line, \n",
+ " x=:Time_Index, y=:Load_MW_z1, title=\"MW Load per hour, No TDR\",\n",
+ " width=600,height=400,linewidth=.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86afb434",
+ "metadata": {},
+ "source": [
+ "As in Tutorial 1, we can open the `genx_settings.yml` file for `SmallNewEngland/OneZone` to see how `TimeDomainReduction` is set. If it's set to 1, this means TDR is being used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 303,
+ "id": "ebb8fde0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dict{Any, Any} with 21 entries:\n",
+ " \"NetworkExpansion\" => 0\n",
+ " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n",
+ " \"ModelingToGenerateAlternativeIterations\" => 3\n",
+ " \"ParameterScale\" => 1\n",
+ " \"EnergyShareRequirement\" => 1\n",
+ " \"PrintModel\" => 0\n",
+ " \"TimeDomainReduction\" => 1\n",
+ " \"Trans_Loss_Segments\" => 1\n",
+ " \"CapacityReserveMargin\" => 1\n",
+ " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n",
+ " \"Solver\" => \"HiGHS\"\n",
+ " \"MethodofMorris\" => 0\n",
+ " \"Reserves\" => 0\n",
+ " \"StorageLosses\" => 1\n",
+ " \"OverwriteResults\" => 0\n",
+ " \"UCommit\" => 2\n",
+ " \"ModelingToGenerateAlternatives\" => 0\n",
+ " \"MaxCapReq\" => 1\n",
+ " \"MinCapReq\" => 1\n",
+ " \"CO2Cap\" => 2\n",
+ " \"WriteShadowPrices\" => 1"
+ ]
+ },
+ "execution_count": 303,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "genx_settings_SNE = YAML.load(open(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ad130ab",
+ "metadata": {},
+ "source": [
+ "To visualize how TDR decreases computation time, let's start by running `SmallNewEngland/OneZone` without TDR. We'll use the package `TickTock` to track the runtime."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2c0a168",
+ "metadata": {},
+ "source": [
+ "To run GenX without TDR, we start by editing the settings to set `TimeDomainReduction` to 0:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 304,
+ "id": "d6893321",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dict{Any, Any} with 21 entries:\n",
+ " \"NetworkExpansion\" => 0\n",
+ " \"TimeDomainReductionFolder\" => \"TDR_Results\"\n",
+ " \"ModelingToGenerateAlternativeIterations\" => 3\n",
+ " \"ParameterScale\" => 1\n",
+ " \"EnergyShareRequirement\" => 1\n",
+ " \"PrintModel\" => 0\n",
+ " \"TimeDomainReduction\" => 0\n",
+ " \"Trans_Loss_Segments\" => 1\n",
+ " \"CapacityReserveMargin\" => 1\n",
+ " \"ModelingtoGenerateAlternativeSlack\" => 0.1\n",
+ " \"Solver\" => \"HiGHS\"\n",
+ " \"MethodofMorris\" => 0\n",
+ " \"Reserves\" => 0\n",
+ " \"StorageLosses\" => 1\n",
+ " \"OverwriteResults\" => 0\n",
+ " \"UCommit\" => 2\n",
+ " \"ModelingToGenerateAlternatives\" => 0\n",
+ " \"MaxCapReq\" => 1\n",
+ " \"MinCapReq\" => 1\n",
+ " \"CO2Cap\" => 2\n",
+ " \"WriteShadowPrices\" => 1"
+ ]
+ },
+ "execution_count": 304,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "genx_settings_SNE[\"TimeDomainReduction\"] = 0\n",
+ "genx_settings_SNE ## Output settings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8706d925",
+ "metadata": {},
+ "source": [
+ "Then we write the edited settings to the file path:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 305,
+ "id": "7768ac6a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "YAML.write_file(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Settings/genx_settings.yml\", genx_settings_SNE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1865ad33",
+ "metadata": {},
+ "source": [
+ "And run it using `include`. (Note: this process will take a few minutes):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 306,
+ "id": "137e9c95",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Configuring Settings\n",
+ "Configuring Solver\n",
+ "Loading Inputs\n",
+ "Reading Input CSV Files\n",
+ "Network.csv Successfully Read!\n",
+ "Load_data.csv Successfully Read!\n",
+ "Fuels_data.csv Successfully Read!\n",
+ "Generators_data.csv Successfully Read!\n",
+ "Generators_variability.csv Successfully Read!\n",
+ "Validating time basis\n",
+ "Capacity_reserve_margin.csv Successfully Read!\n",
+ "Minimum_capacity_requirement.csv Successfully Read!\n",
+ "Maximum_capacity_requirement.csv Successfully Read!\n",
+ "Energy_share_requirement.csv Successfully Read!\n",
+ "CO2_cap.csv Successfully Read!\n",
+ "CSV Files Successfully Read In From /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone\n",
+ "Generating the Optimization Model\n",
+ "Discharge Module\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m started timer at: 2024-01-14T18:50:47.180\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Non-served Energy Module\n",
+ "Investment Discharge Module\n",
+ "Unit Commitment Module\n",
+ "Emissions Module (for CO2 Policy modularization\n",
+ "Dispatchable Resources Module\n",
+ "Storage Resources Module\n",
+ "Storage Investment Module\n",
+ "Storage Core Resources Module\n",
+ "Storage Resources with Symmetric Charge/Discharge Capacity Module\n",
+ "Thermal (Unit Commitment) Resources Module\n",
+ "C02 Policies Module\n",
+ "Energy Share Requirement Policies Module\n",
+ "Capacity Reserve Margin Policies Module\n",
+ "Minimum Capacity Requirement Module\n",
+ "Maximum Capacity Requirement Module\n",
+ "Time elapsed for model building is\n",
+ "0.555159583\n",
+ "Solving Model\n",
+ "Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms\n",
+ "Presolving model\n",
+ "170620 rows, 83017 cols, 646999 nonzeros\n",
+ "161830 rows, 74231 cols, 653419 nonzeros\n",
+ "Presolve : Reductions: rows 161830(-30906); columns 74231(-13381); elements 653419(-134175)\n",
+ "Solving the presolved LP\n",
+ "Using EKK dual simplex solver - serial\n",
+ " Iteration Objective Infeasibilities num(sum)\n",
+ " 0 -4.2035774355e+00 Ph1: 204(998.471); Du: 25(4.20358) 0s\n",
+ " 12911 3.5764275943e+03 Pr: 11955(9388.12); Du: 0(0.00728457) 5s\n",
+ " 27245 4.4018819620e+03 Pr: 15248(18133.3); Du: 0(0.0164783) 10s\n",
+ " 33480 4.6172015768e+03 Pr: 19961(178631); Du: 0(0.00123592) 16s\n",
+ " 41671 6.5841177886e+03 Pr: 19497(1.94677e+06); Du: 0(0.00303983) 21s\n",
+ " 45369 7.2446943919e+03 Pr: 47955(198575); Du: 0(0.00363254) 26s\n",
+ " 48032 7.4871889951e+03 Pr: 41159(54384.3); Du: 0(0.00323346) 31s\n",
+ " 51113 7.7263507185e+03 Pr: 31599(1.02636e+07); Du: 0(0.00359186) 37s\n",
+ " 54158 7.9912685513e+03 Pr: 16427(56826.4); Du: 0(0.00373523) 43s\n",
+ " 57341 8.2279131748e+03 Pr: 25016(70269.9); Du: 0(0.00401519) 49s\n",
+ " 60295 8.4411702548e+03 Pr: 22461(18551); Du: 0(0.00387185) 54s\n",
+ " 63129 8.6669193880e+03 Pr: 46660(9.78873e+06); Du: 0(0.0038402) 60s\n",
+ " 65751 8.8788576826e+03 Pr: 27101(119570); Du: 0(0.00396439) 65s\n",
+ " 68330 9.0772379783e+03 Pr: 15287(17459.6); Du: 0(0.0041059) 71s\n",
+ " 70852 9.2285381286e+03 Pr: 19466(50196.1); Du: 0(0.00439293) 76s\n",
+ " 73853 9.3631903691e+03 Pr: 38866(92264.6); Du: 0(0.00418896) 82s\n",
+ " 76708 9.5539040461e+03 Pr: 16172(14127.5); Du: 0(0.0045444) 87s\n",
+ " 79303 9.7553740900e+03 Pr: 26742(56495.4); Du: 0(0.0042862) 93s\n",
+ " 81856 9.9044159014e+03 Pr: 10467(3884.72); Du: 0(0.00444058) 98s\n",
+ " 84716 1.0129741603e+04 Pr: 62822(3.23315e+06); Du: 0(0.00483053) 104s\n",
+ " 87705 1.0220537743e+04 Pr: 31318(150590); Du: 0(0.00477644) 110s\n",
+ " 90348 1.0279428078e+04 Pr: 10918(5037.23); Du: 0(0.00476901) 115s\n",
+ " 93645 1.0357677034e+04 Pr: 12137(29845.6); Du: 0(0.00488115) 121s\n",
+ " 96623 1.0433817391e+04 Pr: 7282(4202.53); Du: 0(0.00443161) 126s\n",
+ " 99565 1.0485702387e+04 Pr: 25971(1.39873e+07); Du: 0(0.00416339) 132s\n",
+ " 102156 1.0527662238e+04 Pr: 21719(228836); Du: 0(0.0040159) 137s\n",
+ " 105159 1.0568969436e+04 Pr: 28146(190800); Du: 0(0.00408142) 143s\n",
+ " 107981 1.0591229947e+04 Pr: 8709(6382.63); Du: 0(0.00420473) 149s\n",
+ " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n",
+ " 108857 1.0592942277e+04 Pr: 0(0); Du: 0(6.25e-06) 151s\n",
+ "Using EKK primal simplex solver\n",
+ " Iteration Objective Infeasibilities num(sum)\n",
+ " 108857 1.0592942277e+04 Pr: 0(0); Du: 1(0.0001) 151s\n",
+ " 108858 1.0592942277e+04 Pr: 0(0); Du: 0(2.99577e-15) 151s\n",
+ "Solving the original LP from the solution after postsolve\n",
+ "Model status : Optimal\n",
+ "Simplex iterations: 108858\n",
+ "Objective value : 1.0592942277e+04\n",
+ "HiGHS run time : 151.71\n",
+ "LP solved for primal\n",
+ "Writing Output\n",
+ "Time elapsed for writing costs is\n",
+ "0.09726075\n",
+ "Time elapsed for writing storage is\n",
+ "0.021158625\n",
+ "Time elapsed for writing nse is\n",
+ "0.057492916\n",
+ "Time elapsed for writing power balance is\n",
+ "0.054992458\n",
+ "Time elapsed for writing emissions is\n",
+ "0.10993575\n",
+ "Time elapsed for writing reliability is\n",
+ "0.014614333\n",
+ "Time elapsed for writing storage duals is\n",
+ "0.02906075\n",
+ "Time elapsed for writing commitment is\n",
+ "0.02163175\n",
+ "Time elapsed for writing startup is\n",
+ "0.066653583\n",
+ "Time elapsed for writing shutdown is\n",
+ "0.065220833\n",
+ "Time elapsed for writing time weights is\n",
+ "0.002030875\n",
+ "Time elapsed for writing reserve margin is\n",
+ "0.008165709\n",
+ "Time elapsed for writing capacity value is\n",
+ "0.025880458\n",
+ "Time elapsed for writing net revenue is\n",
+ "0.030976584\n",
+ "Wrote outputs to /Users/mayamutic/Desktop/GenX/Example_Systems/SmallNewEngland/OneZone/Results_6\n",
+ "Time elapsed for writing is\n",
+ "1.055933958\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m 153.662797875s: 2 minutes, 33 seconds, 662 milliseconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "tick()\n",
+ "include(\"Example_Systems_Tutorials/SmallNewEngland/OneZone/Run.jl\")\n",
+ "tock()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd41ae1e",
+ "metadata": {},
+ "source": [
+ "This took a little while to run, and would take even longer for larger systems. Let's see how we can get the run time down using Time Domain Reduction. The next sections go over how K-means clustering is used to perform TDR, and how to interpret the resulting files in GenX."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da668a37",
+ "metadata": {},
+ "source": [
+ "### K-means clustering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "379831dc",
+ "metadata": {},
+ "source": [
+ "Let's go over how TDR works. To perform TDR, GenX uses K-means clustering. K-means is an optimization method that clusters data into several groups based on their proximity to \"centers\" determined by the algorithm. Clustering data is "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1505608",
+ "metadata": {},
+ "source": [
+ "To be more specific, k-means finds a set number of groups such that the variance between the distance of each point in the group to the mean of the group is minimized. \n",
+ "\n",
+ "$$\n",
+ "\\begin{align*}\n",
+ "\\mathop{\\arg \\min}\\limits_{\\mathbf{S}} & \\sum_{i = 1}^k \\sum_{x \\in S_i} ||x - \\mu_i||^2 \\\\\n",
+ "\\end{align*}\n",
+ "$$\n",
+ "\n",
+ "Where $\\mathbf{S} = \\{S_1, ... , S_k\\}$ are the clusters, and $\\mu_i$ is the mean of each cluster i.e. the mean of the distances between each point in the cluster. By taking the argmin over $\\mathbf{S}$, the points $x$ are clustered into groups where their distance to the center is the smallest."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8f6a69ea",
+ "metadata": {},
+ "source": [
+ "GenX uses the package `Clustering.jl`, with documentation here. As an example, using the package `ScikitLearn.jl`, let's generate data that can cluster easily."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 457,
+ "id": "69582aa0",
+ "metadata": {},
+ "outputs": [
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+ "data": {
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+ "X, y = make_blobs(n_samples=100,centers=5,random_state=8);\n",
+ "b = DataFrame(X,:auto)"
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+ "cell_type": "markdown",
+ "id": "666045cf",
+ "metadata": {},
+ "source": [
+ "Note that clustering works for data without obvious groupings, but using blobs as an example makes k-means easier to visualize."
+ ]
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",
+ "image/svg+xml": [
+ ""
+ ],
+ "text/html": [
+ " \n",
+ " \n"
+ ]
+ },
+ "execution_count": 458,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plotlyjs()\n",
+ "Plots.scatter(b[!,\"x1\"],b[!,\"x2\"],legend=false,title=\"Before K-means Clustering\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f82515f7",
+ "metadata": {},
+ "source": [
+ "Now we use the function `kmeans`, which is also used in `src/time_domain_reduction` in GenX."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 460,
+ "id": "ad777278",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "KmeansResult{Matrix{Float64}, Float64, Int64}([-0.5983234957845184 7.533257370256171 … 7.527880189472178 -5.971220015686362; -1.2278405241483499 9.450101620955774 … 0.6882845988483258 -9.984381244149827], [1, 5, 1, 1, 4, 2, 1, 1, 1, 4 … 1, 1, 4, 2, 1, 5, 1, 4, 4, 1], [1.3255250419151228, 4.997847745986178, 8.091409099122394, 0.03714790494307074, 3.2626391548434412, 1.9889458740610166, 5.599991619086529, 5.546511424821204, 5.6808996899043525, 0.1286842679680973 … 2.501523413471922, 1.5778169684187557, 3.852127483939782, 0.5750996178271066, 10.710858491505114, 1.8568275420579425, 4.101412120977988, 1.5756217190165671, 0.005038328827794203, 5.487009130161922], [40, 20, 6, 20, 14], [40, 20, 6, 20, 14], 284.066131030526, 4, true)"
+ ]
+ },
+ "execution_count": 460,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "R = kmeans(transpose(Matrix(b)),5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "993370fe",
+ "metadata": {},
+ "source": [
+ "`kmeans` returns three outputs: assignments, centers, and counts. Assignments shows to which cluster each points belongs, centers shows where the center coordinates of each cluster are, and counts shows how many points belong to each cluster."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 470,
+ "id": "c8f956cc",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1, 5, 1, 1, 4, 2, 1, 1, 1, 4, 2, 2, 4, 1, 4, 1, 3, 4, 4, 1, 2, 2, 1, 5, 4, 2, 2, 1, 2, 4, 3, 1, 5, 5, 1, 1, 4, 1, 1, 2, 5, 1, 5, 3, 5, 1, 2, 1, 5, 2, 5, 4, 4, 1, 2, 2, 1, 1, 5, 3, 2, 1, 1, 5, 4, 1, 2, 4, 1, 5, 2, 2, 4, 1, 4, 3, 2, 4, 1, 1, 1, 5, 1, 2, 1, 1, 1, 1, 4, 3, 1, 1, 4, 2, 1, 5, 1, 4, 4, 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "println(R.assignments)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 463,
+ "id": "c7b9ab46",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2×5 Matrix{Float64}:\n",
+ " -0.598323 7.53326 -3.98791 7.52788 -5.97122\n",
+ " -1.22784 9.4501 -8.69267 0.688285 -9.98438"
+ ]
+ },
+ "execution_count": 463,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "R.centers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 471,
+ "id": "15240b4a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5-element Vector{Int64}:\n",
+ " 40\n",
+ " 20\n",
+ " 6\n",
+ " 20\n",
+ " 14"
+ ]
+ },
+ "execution_count": 471,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "R.counts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 453,
+ "id": "822979fe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "data": [
+ {
+ "colorbar": {
+ "len": 0.9039260717410325,
+ "title": "",
+ "x": 0.9934383202099737,
+ "y": 0.48958333333333337
+ },
+ "legendgroup": "y1",
+ "marker": {
+ "color": "rgba(255, 242, 76, 1.000)",
+ "line": {
+ "color": "rgba(0, 0, 0, 1.000)",
+ "width": 1
+ },
+ "size": 8,
+ "symbol": "circle"
+ },
+ "mode": "markers",
+ "name": "y1",
+ "showlegend": true,
+ "type": "scatter",
+ "x": [
+ -5.37432195177643
+ ],
+ "xaxis": "x",
+ "y": [
+ -4.839069518042199
+ ],
+ "yaxis": "y",
+ "zmax": 5,
+ "zmin": 1
+ },
+ {
+ "colorbar": {
+ "len": 0.9039260717410325,
+ "title": "",
+ "x": 0.9934383202099737,
+ "y": 0.48958333333333337
+ },
+ "legendgroup": "y1",
+ "marker": {
+ "color": "rgba(76, 204, 76, 1.000)",
+ "line": {
+ "color": "rgba(0, 0, 0, 1.000)",
+ "width": 1
+ },
+ "size": 8,
+ "symbol": "circle"
+ },
+ "mode": "markers",
+ "name": "y1",
+ "showlegend": false,
+ "type": "scatter",
+ "x": [
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",
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