diff --git a/Manifest.toml b/Manifest.toml index ea5ffe8..6a43e79 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -543,12 +543,6 @@ git-tree-sha1 = "81690084b6198a2e1da36fcfda16eeca9f9f24e4" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" version = "0.21.1" -[[JSON2]] -deps = ["Dates", "Parsers", "Test"] -git-tree-sha1 = "66397cc6c08922f98a28ab05a8d3002f9853b129" -uuid = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3" -version = "0.3.2" - [[JSON3]] deps = ["Dates", "Mmap", "Parsers", "StructTypes", "UUIDs"] git-tree-sha1 = "f17f647d78ade849298039b75bbd48c05da77900" @@ -843,9 +837,9 @@ version = "1.3.4+2" [[OpenBLAS32_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "a459bb2c511b679d726cfd6c4c370149c6837fe3" +git-tree-sha1 = "ba4a8f683303c9082e84afba96f25af3c7fb2436" uuid = "656ef2d0-ae68-5445-9ca0-591084a874a2" -version = "0.3.12+0" +version = "0.3.12+1" [[OpenBLAS_jll]] deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"] diff --git a/Project.toml b/Project.toml index 71cca75..d8ead83 100644 --- a/Project.toml +++ b/Project.toml @@ -12,7 +12,7 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6" InfrastructureSystems = "2cd47ed4-ca9b-11e9-27f2-ab636a7671f1" Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9" -JSON2 = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3" +JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306" Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" diff --git a/notebook/2_PowerSystems_examples/add_forecasts.ipynb b/notebook/2_PowerSystems_examples/add_forecasts.ipynb index aea4352..d704bd3 100644 --- a/notebook/2_PowerSystems_examples/add_forecasts.ipynb +++ b/notebook/2_PowerSystems_examples/add_forecasts.ipynb @@ -2,27 +2,28 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Add time series to `System`" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**Originally Contributed by**: Clayton Barrows" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "An example of how to parse add time series data to a `System` using [PowerSystems.jl](github.com/NREL-SIIP/PowerSystems.jl)\n", "\n", @@ -30,93 +31,56 @@ "doesn't contain any time series data. So a user may want to add time series to the `System`\n", "### Dependencies\n", "Let's use the 5-bus dataset we parsed in the MATPOWER example" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Info: Precompiling SIIPExamples [2c79006f-6450-48c4-b124-fbadab4f299d]\n", + "└ @ Base loading.jl:1278\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Project SIIPExamples v0.0.2\n", - "Status `~/Documents/repos/SIIPExamples.jl/Project.toml`\n", - " [336ed68f] CSV v0.8.0\n", - " [9961bab8] Cbc v0.7.1\n", - " [41994980] D3TypeTrees v0.1.1\n", - " [a93c6f00] DataFrames v0.22.0\n", - " [2cd47ed4] InfrastructureSystems v1.0.4\n", - " [b6b21f68] Ipopt v0.6.3\n", - " [2535ab7d] JSON2 v0.3.2\n", - " [98b081ad] Literate v2.7.0\n", - " [f0f68f2c] PlotlyJS v0.14.0\n", - " [91a5bcdd] Plots v1.7.3\n", - " [5f7eddb3] PowerGraphics v0.6.1\n", - " [e690365d] PowerSimulations v0.8.0\n", - " [398b2ede] PowerSimulationsDynamics v0.3.0 `https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl.git#jd/perturbation_refactor`\n", - " [bcd98974] PowerSystems v1.0.2\n", - " [c3572dad] Sundials v4.3.0\n", - " [9e3dc215] TimeSeries v0.19.1\n", - " [f269a46b] TimeZones v1.5.0\n", - " [0f1e0344] WebIO v0.8.15\n", - " [ade2ca70] Dates\n", - " [56ddb016] Logging\n", - " [44cfe95a] Pkg\n", - " [9a3f8284] Random\n", - " [10745b16] Statistics\n", - " [8dfed614] Test\n", - "┌ Info: extending matpower format with data: areas 1x3\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/matpower.jl:332\n", - "┌ Info: extending matpower format with data: gen_name 7x4\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/matpower.jl:332\n", - "┌ Info: extending matpower format by appending matrix \"gen_name\" in to \"gen\"\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/matpower.jl:665\n", - "┌ Info: reversing the orientation of branch 6 (4, 3) to be consistent with other parallel branches\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1216\n", - "┌ Info: the voltage setpoint on generator 4 does not match the value at bus 4\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1679\n", - "┌ Info: the voltage setpoint on generator 1 does not match the value at bus 1\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1679\n", - "┌ Info: the voltage setpoint on generator 5 does not match the value at bus 10\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1679\n", - "┌ Info: the voltage setpoint on generator 2 does not match the value at bus 1\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1679\n", - "┌ Info: the voltage setpoint on generator 3 does not match the value at bus 3\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1679\n", - "┌ Info: removing 1 cost terms from generator 4: [4000.0, 0.0]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 1 cost terms from generator 1: [1400.0, 0.0]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 1 cost terms from generator 5: [1000.0, 0.0]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 1 cost terms from generator 2: [1500.0, 0.0]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 3 cost terms from generator 6: Float64[]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 3 cost terms from generator 7: Float64[]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: removing 1 cost terms from generator 3: [3000.0, 0.0]\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1860\n", - "┌ Info: Constructing System from Power Models\n", - "│ data[\"name\"] = nesta_case5_pjm\n", - "│ data[\"source_type\"] = matpower\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:39\n", - "┌ Info: Reading bus data\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:149\n", - "┌ Info: Reading generator data\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:431\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:588\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:650\n", - "┌ Info: Reading DC Line data\n", - "└ @ PowerSystems /Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:622\n" + "\u001b[36m\u001b[1mProject \u001b[22m\u001b[39mSIIPExamples v0.0.2\n", + "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/sandboxes/SIIPExamples.jl/Project.toml`\n", + " \u001b[90m [336ed68f] \u001b[39m\u001b[37mCSV v0.8.0\u001b[39m\n", + " \u001b[90m [9961bab8] \u001b[39m\u001b[37mCbc v0.7.1\u001b[39m\n", + " \u001b[90m [41994980] \u001b[39m\u001b[37mD3TypeTrees v0.1.1\u001b[39m\n", + " \u001b[90m [a93c6f00] \u001b[39m\u001b[37mDataFrames v0.22.1\u001b[39m\n", + " \u001b[90m [0b91fe84] \u001b[39m\u001b[37mDisplayAs v0.1.2\u001b[39m\n", + " \u001b[90m [2cd47ed4] \u001b[39m\u001b[37mInfrastructureSystems v1.0.4\u001b[39m\n", + " \u001b[90m [b6b21f68] \u001b[39m\u001b[37mIpopt v0.6.3\u001b[39m\n", + " \u001b[90m [0f8b85d8] \u001b[39m\u001b[37mJSON3 v1.4.0\u001b[39m\n", + " \u001b[90m [98b081ad] \u001b[39m\u001b[37mLiterate v2.7.0\u001b[39m\n", + " \u001b[90m [f0f68f2c] \u001b[39m\u001b[37mPlotlyJS v0.14.0\u001b[39m\n", + " \u001b[90m [91a5bcdd] \u001b[39m\u001b[37mPlots v1.9.1\u001b[39m\n", + " \u001b[90m [5f7eddb3] \u001b[39m\u001b[37mPowerGraphics v0.6.1\u001b[39m\n", + " \u001b[90m [e690365d] \u001b[39m\u001b[37mPowerSimulations v0.8.0 `https://github.com/NREL-SIIP/PowerSimulations.jl.git#master`\u001b[39m\n", + " \u001b[90m [398b2ede] \u001b[39m\u001b[37mPowerSimulationsDynamics v0.3.0 `https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl.git#master`\u001b[39m\n", + " \u001b[90m [bcd98974] \u001b[39m\u001b[37mPowerSystems v1.0.2\u001b[39m\n", + " \u001b[90m [c3572dad] \u001b[39m\u001b[37mSundials v4.3.0\u001b[39m\n", + " \u001b[90m [9e3dc215] \u001b[39m\u001b[37mTimeSeries v0.19.1\u001b[39m\n", + " \u001b[90m [f269a46b] \u001b[39m\u001b[37mTimeZones v1.5.1\u001b[39m\n", + " \u001b[90m [0f1e0344] \u001b[39m\u001b[37mWebIO v0.8.15\u001b[39m\n", + " \u001b[90m [ade2ca70] \u001b[39m\u001b[37mDates\u001b[39m\n", + " \u001b[90m [56ddb016] \u001b[39m\u001b[37mLogging\u001b[39m\n", + " \u001b[90m [44cfe95a] \u001b[39m\u001b[37mPkg\u001b[39m\n", + " \u001b[90m [9a3f8284] \u001b[39m\u001b[37mRandom\u001b[39m\n", + " \u001b[90m [10745b16] \u001b[39m\u001b[37mStatistics\u001b[39m\n", + " \u001b[90m [8dfed614] \u001b[39m\u001b[37mTest\u001b[39m\n" ] }, { - 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Resolution: 0 seconds

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"execution_count": 1 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "using SIIPExamples\n", + "using PowerSystems\n", + "import JSON3\n", + "using Logging\n", + "logger = configure_logging(console_level = Logging.Error, file_level = Logging.Info, filename = \"ex.log\")\n", "pkgpath = dirname(dirname(pathof(SIIPExamples)))\n", "include(joinpath(pkgpath, \"test\", \"2_PowerSystems_examples\", \"parse_matpower.jl\"))" - ], - "metadata": {}, - "execution_count": 1 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Define pointers to time series files\n", "For example, if we want to add a bunch of time series files, say one for each load and\n", "one for each renewable generator, we need to define pointers to each .csv file containing\n", "the time series in the following format (PowerSystems.jl also supports a CSV format for this file)" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\n", - " {\n", - " \"simulation\": \"DAY_AHEAD\",\n", - " \"resolution\": 3600,\n", - " \"category\": \"Generator\",\n", - " \"component_name\": \"SolarBusC\",\n", - " \"module\": \"InfrastructureSystems\",\n", - " \"type\": \"SingleTimeSeries\",\n", - " \"name\": \"max_active_power\",\n", - " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", - " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", - " \"normalization_factor\": 1.0,\n", - " \"data_file\": \"./gen/Renewable/PV/da_solar5.csv\"\n", - " },\n", - " {\n", - " \"simulation\": \"DAY_AHEAD\",\n", - " \"resolution\": 3600,\n", - " \"category\": \"Generator\",\n", - " \"component_name\": \"WindBusA\",\n", - " \"module\": \"InfrastructureSystems\",\n", - " \"type\": \"SingleTimeSeries\",\n", - " \"name\": \"max_active_power\",\n", - " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", - " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", - " \"normalization_factor\": 1.0,\n", - " \"data_file\": \"./gen/Renewable/WIND/da_wind5.csv\"\n", - " },\n", - " {\n", - " \"simulation\": \"DAY_AHEAD\",\n", - " \"resolution\": 3600,\n", - " \"category\": \"ElectricLoad\",\n", - " \"component_name\": \"bus2\",\n", - " \"module\": \"InfrastructureSystems\",\n", - " \"type\": \"SingleTimeSeries\",\n", - " \"name\": \"max_active_power\",\n", - " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", - " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", - " \"normalization_factor\": 1.0,\n", - " \"data_file\": \"./load/da_load5.csv\"\n", - " },\n", - " {\n", - " \"simulation\": \"DAY_AHEAD\",\n", - " \"resolution\": 3600,\n", - " \"category\": \"ElectricLoad\",\n", - " \"component_name\": \"bus3\",\n", - " \"module\": \"InfrastructureSystems\",\n", - " \"type\": \"SingleTimeSeries\",\n", - " \"name\": \"max_active_power\",\n", - " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", - " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", - " \"normalization_factor\": 1.0,\n", - " \"data_file\": \"./load/da_load5.csv\"\n", - " },\n", - " {\n", - " \"simulation\": \"DAY_AHEAD\",\n", - " \"resolution\": 3600,\n", - " \"category\": \"ElectricLoad\",\n", - " \"component_name\": \"bus4\",\n", - " \"module\": \"InfrastructureSystems\",\n", - " \"type\": \"SingleTimeSeries\",\n", - " \"name\": \"max_active_power\",\n", - " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", - " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", - " \"normalization_factor\": 1.0,\n", - " \"data_file\": \"./load/da_load5.csv\"\n", - " }\n", - "]\n" + " {\n", + " \"component_name\": \"SolarBusC\",\n", + " \"normalization_factor\": 1,\n", + " \"name\": \"max_active_power\",\n", + " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", + " \"data_file\": \"./gen/Renewable/PV/da_solar5.csv\",\n", + " \"resolution\": 3600,\n", + " \"module\": \"InfrastructureSystems\",\n", + " \"category\": \"Generator\",\n", + " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", + " \"simulation\": \"DAY_AHEAD\",\n", + " \"type\": \"SingleTimeSeries\"\n", + " },\n", + " {\n", + " \"component_name\": \"WindBusA\",\n", + " \"normalization_factor\": 1,\n", + " \"name\": \"max_active_power\",\n", + " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", + " \"data_file\": \"./gen/Renewable/WIND/da_wind5.csv\",\n", + " \"resolution\": 3600,\n", + " \"module\": \"InfrastructureSystems\",\n", + " \"category\": \"Generator\",\n", + " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", + " \"simulation\": \"DAY_AHEAD\",\n", + " \"type\": \"SingleTimeSeries\"\n", + " },\n", + " {\n", + " \"component_name\": \"bus2\",\n", + " \"normalization_factor\": 1,\n", + " \"name\": \"max_active_power\",\n", + " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", + " \"data_file\": \"./load/da_load5.csv\",\n", + " \"resolution\": 3600,\n", + " \"module\": \"InfrastructureSystems\",\n", + " \"category\": \"ElectricLoad\",\n", + " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", + " \"simulation\": \"DAY_AHEAD\",\n", + " \"type\": \"SingleTimeSeries\"\n", + " },\n", + " {\n", + " \"component_name\": \"bus3\",\n", + " \"normalization_factor\": 1,\n", + " \"name\": \"max_active_power\",\n", + " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", + " \"data_file\": \"./load/da_load5.csv\",\n", + " \"resolution\": 3600,\n", + " \"module\": \"InfrastructureSystems\",\n", + " \"category\": \"ElectricLoad\",\n", + " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", + " \"simulation\": \"DAY_AHEAD\",\n", + " \"type\": \"SingleTimeSeries\"\n", + " },\n", + " {\n", + " \"component_name\": \"bus4\",\n", + " \"normalization_factor\": 1,\n", + " \"name\": \"max_active_power\",\n", + " \"scaling_factor_multiplier_module\": \"PowerSystems\",\n", + " \"data_file\": \"./load/da_load5.csv\",\n", + " \"resolution\": 3600,\n", + " \"module\": \"InfrastructureSystems\",\n", + " \"category\": \"ElectricLoad\",\n", + " \"scaling_factor_multiplier\": \"get_max_active_power\",\n", + " \"simulation\": \"DAY_AHEAD\",\n", + " \"type\": \"SingleTimeSeries\"\n", + " }\n", + "]" ] } ], - "cell_type": "code", "source": [ "FORECASTS_DIR = joinpath(base_dir, \"forecasts\", \"5bus_ts\")\n", "fname = joinpath(FORECASTS_DIR, \"timeseries_pointers_da.json\")\n", + "\n", "open(fname, \"r\") do f\n", - " for line in eachline(f)\n", - " println(line)\n", - " end\n", + " JSON3.@pretty JSON3.read(f)\n", "end" - ], - "metadata": {}, - "execution_count": 2 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Read the pointers" - ], - "metadata": {} + ] }, { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "5-element Array{InfrastructureSystems.TimeSeriesFileMetadata,1}:\n InfrastructureSystems.TimeSeriesFileMetadata(\"DAY_AHEAD\", \"Generator\", \"SolarBusC\", \"max_active_power\", 1.0, \"/Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/data/forecasts/5bus_ts/gen/Renewable/PV/da_solar5.csv\", Dates.Millisecond(3600000), Float64[], SingleTimeSeries, nothing, \"get_max_active_power\", \"PowerSystems\")\n InfrastructureSystems.TimeSeriesFileMetadata(\"DAY_AHEAD\", \"Generator\", \"WindBusA\", \"max_active_power\", 1.0, \"/Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/data/forecasts/5bus_ts/gen/Renewable/WIND/da_wind5.csv\", Dates.Millisecond(3600000), Float64[], SingleTimeSeries, nothing, \"get_max_active_power\", \"PowerSystems\")\n InfrastructureSystems.TimeSeriesFileMetadata(\"DAY_AHEAD\", \"ElectricLoad\", \"bus2\", \"max_active_power\", 1.0, \"/Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/data/forecasts/5bus_ts/load/da_load5.csv\", Dates.Millisecond(3600000), Float64[], SingleTimeSeries, nothing, \"get_max_active_power\", \"PowerSystems\")\n InfrastructureSystems.TimeSeriesFileMetadata(\"DAY_AHEAD\", \"ElectricLoad\", \"bus3\", \"max_active_power\", 1.0, \"/Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/data/forecasts/5bus_ts/load/da_load5.csv\", Dates.Millisecond(3600000), Float64[], SingleTimeSeries, nothing, \"get_max_active_power\", \"PowerSystems\")\n InfrastructureSystems.TimeSeriesFileMetadata(\"DAY_AHEAD\", \"ElectricLoad\", \"bus4\", \"max_active_power\", 1.0, \"/Users/cbarrows/.julia/packages/PowerSystems/eF3Pv/data/forecasts/5bus_ts/load/da_load5.csv\", Dates.Millisecond(3600000), Float64[], SingleTimeSeries, nothing, \"get_max_active_power\", \"PowerSystems\")" - }, - "metadata": {}, - "execution_count": 3 - } - ], "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ts_pointers = PowerSystems.IS.read_time_series_file_metadata(fname)" - ], - "metadata": {}, - "execution_count": 3 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Read and assign time series to `System` using the `ts_pointers` struct" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "System\n======\nSystem Units Base: SYSTEM_BASE\nBase Power: 100.0\nBase Frequency: 60.0\n\nComponents\n==========\nNum components: 30\n\n\u001b[1m9×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m Count \u001b[0m\n\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\n─────┼────────────────────────────────────────────────────────────────────\n 1 │ Arc Topology <: Component <: Infrast… 6\n 2 │ Area AggregationTopology <: Topology … 1\n 3 │ Bus Topology <: Component <: Infrast… 5\n 4 │ Line ACBranch <: Branch <: Device <: … 5\n 5 │ LoadZone AggregationTopology <: Topology … 1\n 6 │ PhaseShiftingTransformer ACBranch <: Branch <: Device <: … 2\n 7 │ PowerLoad StaticLoad <: ElectricLoad <: St… 3\n 8 │ RenewableDispatch RenewableGen <: Generator <: Sta… 2\n 9 │ ThermalStandard ThermalGen <: Generator <: Stati… 5\n\nTimeSeriesContainer\n===================\nComponents with time series data: 5\nTotal StaticTimeSeries: 5\nTotal Forecasts: 0\nResolution: 60 minutes\n", "text/html": [ "

System

\n", "

Base Power: 100.0

\n", @@ -293,43 +281,73 @@ "

Total StaticTimeSeries: 5

\n", "

Total Forecasts: 0

\n", "

Resolution: 60 minutes

\n" + ], + "text/plain": [ + "System\n", + "======\n", + "System Units Base: SYSTEM_BASE\n", + "Base Power: 100.0\n", + "Base Frequency: 60.0\n", + "\n", + "Components\n", + "==========\n", + "Num components: 30\n", + "\n", + "\u001b[1m9×3 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m Count \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\n", + "─────┼────────────────────────────────────────────────────────────────────\n", + " 1 │ Arc Topology <: Component <: Infrast… 6\n", + " 2 │ Area AggregationTopology <: Topology … 1\n", + " 3 │ Bus Topology <: Component <: Infrast… 5\n", + " 4 │ Line ACBranch <: Branch <: Device <: … 5\n", + " 5 │ LoadZone AggregationTopology <: Topology … 1\n", + " 6 │ PhaseShiftingTransformer ACBranch <: Branch <: Device <: … 2\n", + " 7 │ PowerLoad StaticLoad <: ElectricLoad <: St… 3\n", + " 8 │ RenewableDispatch RenewableGen <: Generator <: Sta… 2\n", + " 9 │ ThermalStandard ThermalGen <: Generator <: Stati… 5\n", + "\n", + "TimeSeriesContainer\n", + "===================\n", + "Components with time series data: 5\n", + "Total StaticTimeSeries: 5\n", + "Total Forecasts: 0\n", + "Resolution: 60 minutes\n" ] }, + "execution_count": 6, "metadata": {}, - "execution_count": 4 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ - "add_time_series!(sys, ts_pointers)\n", + "add_time_series!(sys, fname)\n", "sys" - ], - "metadata": {}, - "execution_count": 4 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "---\n", "\n", "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" - ], - "metadata": {} + ] } ], - "nbformat_minor": 3, "metadata": { + "kernelspec": { + "display_name": "Julia 1.5.3", + "language": "julia", + "name": "julia-1.5" + }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.5.2" - }, - "kernelspec": { - "name": "julia-1.5", - "display_name": "Julia 1.5.2", - "language": "julia" + "version": "1.5.3" } }, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/notebook/4_PowerSimulationsDynamics_examples/03_inverter_model.ipynb b/notebook/4_PowerSimulationsDynamics_examples/03_inverter_model.ipynb index 14ac15c..77d10e8 100644 --- a/notebook/4_PowerSimulationsDynamics_examples/03_inverter_model.ipynb +++ b/notebook/4_PowerSimulationsDynamics_examples/03_inverter_model.ipynb @@ -2,69 +2,96 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "Line Modeling simulation with [PowerSimulationsDynamics.jl](https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl)" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**Originally Contributed by**: José Daniel Lara" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "This tutorial will introduce the modeling of an inverter with Virtual Innertia in a multi-machine\n", "model of the system. We will load the data directly from PSS/e dynamic files" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The tutorial uses a modified 14-bus system on which all the synchronous machines have been\n", "substitued by generators with ESAC1A AVR's and no Turbine Governors." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "In the first portion of the tutorial we will simulate the system with the original data and\n", "cause a line trip between Buses 2 and 4. In the second part of the simulation, we will switch\n", "generator 6 with a battery using an inverter and perform the same fault." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Load the packages" - ], - "metadata": {} + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "using Revise" + ] }, { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Info: Precompiling SIIPExamples [2c79006f-6450-48c4-b124-fbadab4f299d]\n", + "└ @ Base loading.jl:1278\n", + "┌ Info: Precompiling DisplayAs [0b91fe84-8a4c-11e9-3e1d-67c38462b6d6]\n", + "└ @ Base loading.jl:1278\n", + "┌ Info: Precompiling PowerSimulationsDynamics [398b2ede-47ed-4edc-b52e-69e4a48b4336]\n", + "└ @ Base loading.jl:1278\n", + "┌ Info: Precompiling Sundials [c3572dad-4567-51f8-b174-8c6c989267f4]\n", + "└ @ Base loading.jl:1278\n" + ] + }, + { "data": { - "text/plain": "Plots.GRBackend()" + "text/plain": [ + "Plots.GRBackend()" + ] }, + "execution_count": 1, "metadata": {}, - "execution_count": 1 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "using SIIPExamples # Only needed for the tutorial, comment if you want to run\n", "import DisplayAs # Only needed for the tutorial\n", @@ -74,214 +101,97 @@ "using Sundials\n", "using Plots\n", "gr()" - ], - "metadata": {}, - "execution_count": 1 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Create the system" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Info: The PSS(R)E parser currently supports buses, loads, shunts, generators, branches, transformers, and dc lines\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/common.jl:25\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 4 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 1 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 12 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 20 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 2 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 6 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 11 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 13 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 5 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 15 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 16 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 14 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 7 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 8 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 17 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 10 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 19 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 9 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 18 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 3 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: Constructing System from Power Models\n", - "│ data[\"name\"] = 14bus\n", - "│ data[\"source_type\"] = pti\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:39\n", - "┌ Info: Reading bus data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:149\n", - "┌ Info: Reading generator data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:431\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:588\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 04-i_4 is larger than the SIL 44.88379907931658 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 01-BUS 05-i_2 is larger than the SIL 46.30200500787965 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 03-BUS 04-i_6 is larger than the SIL 43.40068938825592 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 09-BUS 14-i_13 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 05-i_5 is larger than the SIL 43.107319591166274 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 12-BUS 13-i_15 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 13-BUS 14-i_16 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 10-BUS 11-i_14 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 11-i_8 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 13-i_10 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 12-i_9 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 03-i_3 is larger than the SIL 46.39663623797776 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:650\n", - "┌ Info: Reading DC Line data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:622\n", - "┌ Info: Generators provided in .dyr, without a generator in .raw file will be skipped.\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/psse_dynamic_data.jl:192\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n" - ] - 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5DynamicGenerator{PowerSystems.RoundRotorQuadratic,PowerSystems.SingleMass,PowerSystems.ESAC1A,PowerSystems.TGFixed,PowerSystems.PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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7LoadZoneAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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9TapTransformerACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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8PowerLoadStaticLoad <: ElectricLoad <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
9TapTransformerACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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"execution_count": 2 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "file_dir = joinpath(\n", " dirname(dirname(pathof(SIIPExamples))),\n", @@ -291,94 +201,46 @@ ")\n", "\n", "sys = System(joinpath(file_dir, \"14bus.raw\"), joinpath(file_dir, \"dyn_data.dyr\"))" - ], - "metadata": {}, - "execution_count": 2 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Define Simulation Problem with a 20 second simulation period and the branch trip at t = 1.0" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Serialized time series data to /var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_JrRFju/sys_time_series_storage.h5.\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/time_series_storage.jl:55\n", - "┌ Info: Serialized System to /var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_JrRFju/sys.json\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/base.jl:233\n", - "┌ Info: Loaded time series from storage file existing=sys_time_series_storage.h5 new=/var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_w0Icec\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/hdf5_time_series_storage.jl:82\n", - "┌ Warning: Rate provided for BUS 12-BUS 13-i_15 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 05-i_5 is larger than the SIL 43.107319591166274 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 01-BUS 05-i_2 is larger than the SIL 46.30200500787965 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 10-BUS 11-i_14 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 13-i_10 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 09-BUS 14-i_13 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 03-i_3 is larger than the SIL 46.39663623797776 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 11-i_8 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 04-i_4 is larger than the SIL 44.88379907931658 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 03-BUS 04-i_6 is larger than the SIL 43.40068938825592 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 13-BUS 14-i_16 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 12-i_9 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: Initializing Simulation States\n", - "[ Info: Unit System changed to SYSTEM_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: PowerFlow solve converged, the results have been stored in the system\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "[ Info: Attaching Perturbations\n", - "[ Info: Completed Build Successfully. Simulations status = BUILT\n" + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInitializing Simulation States\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to SYSTEM_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPowerFlow solve converged, the results have been stored in the system\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mAttaching Perturbations\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCompleted Build Successfully. Simulations status = BUILT\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": "Simulation()\n" + "text/plain": [ + "Simulation()\n" + ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 3 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "sim = Simulation(\n", " file_dir, #path for the simulation output\n", @@ -387,18 +249,19 @@ " BranchTrip(1.0, \"BUS 02-BUS 04-i_4\");\n", " console_level = Logging.Info,\n", ")" - ], - "metadata": {}, - "execution_count": 3 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Now that the system is initialized, we can verify the system states for potential issues." - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -478,6 +341,24 @@ "====================\n", "====================\n", "Differential States\n", + "generator-1-1\n", + "====================\n", + "eq_p 1.0604\n", + "ed_p -0.0111\n", + "ψ_kd 1.0563\n", + "ψ_kq 0.1134\n", + "δ 0.1683\n", + "ω 1.0\n", + "Vm 1.06\n", + "Vr1 0.0049\n", + "Vr2 1.951\n", + "Ve 1.4049\n", + "Vr3 -0.0585\n", + "x_g1 0.3144\n", + "x_g2 0.3144\n", + "x_g3 0.3144\n", + "====================\n", + "Differential States\n", "generator-3-1\n", "====================\n", "eq_p 1.0649\n", @@ -536,72 +417,55 @@ "Vr2 3.2875\n", "Ve 2.4472\n", "Vr3 -0.0986\n", - "====================\n", - "Differential States\n", - "generator-1-1\n", - "====================\n", - "eq_p 1.0604\n", - "ed_p -0.0111\n", - "ψ_kd 1.0563\n", - "ψ_kq 0.1134\n", - "δ 0.1683\n", - "ω 1.0\n", - "Vm 1.06\n", - "Vr1 0.0049\n", - "Vr2 1.951\n", - "Ve 1.4049\n", - "Vr3 -0.0585\n", - "x_g1 0.3144\n", - "x_g2 0.3144\n", - "x_g3 0.3144\n", "====================\n" ] } ], - "cell_type": "code", "source": [ "print_device_states(sim)" - ], - "metadata": {}, - "execution_count": 4 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We execute the simulation with an additional tolerance for the solver set at 1e-8." - ], - "metadata": {} + ] }, { - "outputs": [], "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], "source": [ "execute!(sim, IDA(); abstol = 1e-8)" - ], - "metadata": {}, - "execution_count": 5 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Using `PowerSimulationsDynamics` tools for exploring the results, we can plot all the voltage\n", "results for the buses" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=14})", - "image/png": 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" + "image/png": 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RDrKzd4iJoTQBH5kXQ39/v6qghvRx6O9u4+p9PGqI/iEYzjboKF9sz0XNyqmM7iGotZM6fBX/v2l0Qx4ht+Eh//cSo0tJibuA1X8Hv0EQBEE6sEY4mDplVFEtOcoXAwDQMDB1BO30bbyf/VUacLeYjBrSZ8vh1JG0LiU4nfZnJjIl9cNp9eQwmqPVI/zfaQcQwigIQRDUGwyEgymzlAxwQXUTowR7Yp0yqryxz6mA7hQRnnYo17jPJkcMBf+aTE8vIX69ohFJqKJa8tMjan9nbPRQ2OsGQRA0OGAgHEz3K4hA1z9PKYqAsYG0S1l9Lq94I4+I8h8gpJlykPfmMEgSfPKb+vA1zcRQ2owIWLODIOgfRyKRPNL+KpVKoxl4IMVTAAPhoNHgoKyB9HH8S2AL98Xyq0lxl54BL9UtpFRO+TkO/F/AYSILx9K/WsrcuIAZ5gXrghAEAQBAdHQ0n8/n8/n29vZTpkwpKioarJxv3rypzdnc3NzPz2/Tpk0URbW0tPD5/La2Nt1uS5Ys+eijj5RK5Wuvvcblcr28vExNTefNm9cjt9OnT48bN04oFM6fP1+75eLFiwEBAVwul8vlxsbG1tXVDVbJHw8MhIOmvJG0Ne855yGbAcK80Ks5eiqFV3OIKH8MPk4JQdDjkUgkn376aXl5eXp6ulAoXLhw4WDlrNFoMAwrLy8vKyvbv3//jz/+eOTIEZIkOzo6us/BIpPJlErll19+WVhYWFVV1dDQ0NTU1DsQstnshQsXzpgxQyaTabegKPrDDz90dXWJRCIej/fGG28MVskfDwyEg6a4jtQ71/OYQNr1PEL910EzXQoqs5SM7HuYDARB0IA4HA6Px7OxsZk+fXp1dTUAYNOmTRcuXNB+mpaW9uabb8rl8sWLFzs7Ozs5OU2YMMHA2cQQBOHxeDweb9iwYYGBgVVVVX3tWVBQEBERweVyteUZP358jx3i4uLmzJkjFAp1W2JjYyMjIzEMY7FYiYmJz3wxdtjbNGjKGsixgXrOpw0PcROiqQ+IMd1GuFzKJoZ7oCb9zqAGQdA/X6McUI8w38bj4zMRVq8753v37rFYLJFItHv37tWrVwMA8vLydCGntbU1MzNzx44dHR0dBQUFKIrevXvXwFm9VCrVsWPHNBpNYWFhcXHxDz/80NeecXFx//73v0mSjI6OjoyMfNSp/k6ePBkdHf1ISQYdDISDQzuzpatQ/y9sUhj23SlNuA+mXTBBIqeu5hAfzDVg1hkIgv7Zws7gGvJpBMI9kbR4u55XmPz8fLVarR110tf0mwiCNDc35+bmhoSEdF8OQUtbQewdHdVqdUpKCgCgoaFBIBD0U49ctGiRlZXV/v37ly5dKhKJ1q9f/8EHHxj4pX766adbt25lZmYauP8TAgPh4Ghsp0w5iN5pPwEAjlaonyN65Ibm5bF0CoADl/EIP8xioPUEIAj656ue/SyvoosWLVqwYAEAoLm52c7OTruwaw/Lly9va2ubP3++RCJZsmTJ5s2bdWEvKSkpMTERADBkyJBbt251T2ViYvLTTz9pX69fv37VqlX79u0DAKjVat0+KpWKTqcDACZMmDBhwgQAwLlz56ZOnTpu3Ljg4OABC//rr79u3rz56tWrFhYWj/XtBw3sIxwclc2kc79zWM+JoZc3kDsvaLaeVkvk1ICzo0EQBBnOwsKCTqfX1dVpF2fQbmxoaAAAsNnsTz75pLS0NDk5ec+ePVeuXNGlSkhIkEgkEomkRxTswdbWtra21tzc3NjYuLy8XLe9rKzMycmp+54TJkwQCATa3sr+nThx4p133rl48aK7u/ujfNEnAl6ODTPQrNuVTaSTdX81PDYDvDubeSMPx1A0so851SAIgh5Jfn5+SkqKRqM5duwYj8cLDg4uKirav3//1KlTW1patm3bZm5ufvbsWRsbGz8/PyaTCQAwMzMzJGdd02hzc/OWLVu09c5XX3117dq127dvt7S0PHToUENDw+TJkz/++GNbW9uRI0dyOJzDhw+LxeKwsLDuWTU0NBQUFJSVlbW0tKSkpNja2lZVVc2bN+/TTz9tbGxsbGzEMCwmJuYJnB5DwUA4OKqbqZA+lonQYTNAfBA84RAEDY7Q0NDMzMzMzEwOh+Pt7X379m0zM7PFixcXFhbGxcV5enq+//77d+/eVSqVa9eura2ttbS0/Pjjj4cPHz5gznw+Pzg4+PPPPwcACASCDRs2aB8B/OKLL7Zs2bJixYrOzk5/f/8bN25oh5Xu3bv3q6++IknSy8srOTnZzs6ue255eXlbtmwBAHA4nM8//3zChAnGxsYRERHnz58/f/48AIDNZj/bQPjMFuZ9PM9qYd5vflePHUob0mu5di2SAm9sVX79OsuQRZf+58EVYp8JeNqfArgw77P15BbmhX2Eg6CxneIZIzAKQhAEPY9gIBwENS2kw6OsBQFBEAT9c8DL9yCoaaEcrOCzEBAEQc8lGAgNgqEA73MxJVDdQj7S6oAQBEHQPwe8fBuEa4R0yvSPKqIAqG2FNUIIgqDnFQyEBuGZIO1S/YFQ1Ekx6ADOGgpB0FM2b9684cOHDx8+fNSoUStXrmxsbBysnDMyMob/1/jx4/fv3w8AEIlEw4cP7+jo0O323nvvbd++nSCIDRs2eHp68vl8X1/ft99+u0duBEG89dZbAoHA0dHx+++/7/5RcXFxcHDwt99+O1glfzzwsTaDmJsg+dX620arW0hHSxgFIQh62goLC+fPnz916tSOjo5PPvlk/vz5ly9fHpScpVJpZWVleno6juOFhYWvvPKKubl5UFBQZmYmjv+5kk5lZSWCIFu3bj1x4sSpU6fc3d0rKyuvXbvWI7cdO3akpKRkZWW1t7ePHj16yJAh2qcGSZJcsmSJRqOpr68flGI/NlgjNIiFGdLaqb9GWN1COfY7uRoEQdATYmlp6eLiEhQU9MYbb+Tm5gIAvv76a90kahkZGR9++CGO45s2bYqOjo6Kilq+fLmBOWMY5uLi4uHhMWXKlFGjRmkz1+vOnTuTJk3y9vam0Wju7u5LlizpscPPP//89ttvCwQCX1/f11577eeff9Zu//7774cPHx4UFPTIX3uwwRqhQay5SIu4j0DYTMYMhacRgqBnQDvFmkql+vbbb2fPng0AuH37tqmp6ejRowEA9fX1165ds7GxuX79+o4dO+h0elpaGkVRhkwzoptirbKyMiMj46OPPuprz9DQ0M8++0wgEMTExPj6+vbOvKSkJCAgQPs6ICAgOTkZAFBVVbVr1667d++uWrXqcb/9oIFXcIOYGSE4QXUpqR7rS1AAVDZRr9nAplEIekHVvz2ZIvCB9/vbzF/9gO0X1mPjH3/8kZmZqdFoamtrly1bpjdhR0cHm802MzMTCoVubm4GHk6hUGinWGtubg4KChIIBCSpv3to5cqVDAbjwIEDb731loWFxUcfffTqq6/qPpXJZAqFQjftkampaWtrK0VRS5cu/eyzz4yNjQ0szxMFA6GhbC3QulbKy/4vMa+pneKwgCkHBkIIekHZfn5qgCn5Bwuipwtm7dq12umwS0tL/fz8SkpKeu+zfPny/Px8b29vDw+PpUuXdm+6vHz58qJFiwAAPj4+SUlJ3VOZmZldunRJ+/qNN95YuXLl1q1bAQDd+wg1Gg2GYRiGrVixYsWKFXK5fN++fUuXLg0NDfX19dXuY2RkxGazJRKJ9q1EIrGwsDhw4IBCoRAKhZmZmW1tbWq1uqioyMvL62+cnb8FBkJDOVoh1S2kl/1ffovljaSLAHYQQtALDP1HXAHc3NzodHpxcTGLxVIqldqNIpEIAMDj8Q4ePKhWqy9evLho0SI/P78RI0Zod4iMjMzJyQEA0Gj9xQJ/f/+tW7daWFiw2eyamhqBQKDdXlNTk5CQoNuNw+EsX778008/zc/P1wVCAICrq2t+fr6/vz8AID8/393dXVtNfP311wEA1dXVGIbRaLQ9e/YM4gl5JDAQGsrZBr1f3rNloLqZdIRPEEIQ9Iy0trZWVFSoVKrDhw/T6fTAwMDs7OwzZ84sXbq0vb19x44dHA4nLS3Nzc3NysoqMjLS1NRUo9HoktPpdB6PpzdngiAqKioAAPX19du3b4+OjkYQZPr06Zs3b96/f7+pqemJEycKCwsTEhJ++uknd3f3ESNGMBiMEydOtLS0BAYGds9q0aJFX3/99bhx48Ri8S+//LJ79+74+HjdsJ3XXnuNz+d/+eWXT+wkDQwGQkO526JHb+AUAN3jXmUzNXyg1ZcgCIKeBFtb223btm3bts3U1NTb2/vy5cuWlpbLly+/efOmlZWVi4vLwoUL7969m5ubO3/+fKVSyWazFy1aFBkZOWDObDaby+XGxsaiKCoUCmfMmLFmzRoAwPfff7927Vp/f3+ZTObt7X327FlbW1sOh7N+/fqioiIAgLu7+5EjR3qstbty5crS0lIXFxc6nb569er4+Pjun1paWhq4ROKTA5dhegTrflG9MYlh/9+nBhUq8NYu5XfLWHR4O9ENXA/omYCn/SmAyzA9W3AZpn8Ef2c0r5LQvS2sJVwFKIyCEARBzzUYCB9BoCuWUfpnN2FOBennBE8gBEHQ8w1exx+Bpz3aKafq2ygAgEINssrIUE/YQQhBEPR8g4HwEaAIiB6CJWfhAIBb+YSfE8o1hkNGIQiCnm8wED6aMYFYXhV5NYe4mIlH+sHqIARB0HMPBsJHw2EiKyfTs8qI8cE0bwd49iAIembWrl2bmJiYmJi4YMGCL7/8UiqVDlbO+fn5if+1fPly7SzeYrE4MTFRN0cMAOC777777bffAAB79uyJjY0dNmxYQkKC3jWVvvvuu5CQkKioqJMnT2q3VFVVLVy4cPjw4TNnziwvLx+skj8eOOTxkTnboG9NZzzrUkAQ9KK7dOlSXFzc2LFjJRLJN998c/v2bV2Y+ZtaWlqSk5OPHz9OkmRhYeHkyZPPnz/v4eFx7Nixbdu26Xa7e/eui4vL3r17N2zY8NNPP7m5uVVWVqalpfXI7ddff/3222+PHDkiEonmzp3r6Ojo4+MzduzYuXPnbtiw4fjx4/Hx8UVFRf3PbvNEwUAIQRD0vPL19R07diwAgMvlzpo1CwBw8OBBDw+P4OBgAEBBQUFqaurSpUv3799/5coViqJ8fHzWrVtnSM4MBkObc1xcXEpKyq1btzw8PPTumZycPG/ePO1ca25ubrGxsT122L59+7vvvhsSEgIAeOWVV3788cd58+ZJpdINGzagKPrOO+/8/PPP586dmzJlyuOfiL8HNu5BEAQ9r7RTrOXn5//888/apZdOnTqlnT4UAFBaWnro0KGDBw/+8MMPc+fOffnllzUajYGTqGinWKuoqLh48WJ6enpERERfe3p5eR09evTMmTOdnZ16d8jLy9MGZgBAcHBwbm6uQqFgsVjof6dp5XA4ujI/E7BGCEEQ9PgWnF2Ok8TA+/1tb4WsGC4Y2mPjDz/8cOjQIbFYLJfLDx8+rDdhWVmZp6dnVFQUi8UaM2aMgYeTSCSJiYkEQdTU1MTExAwbNqx772B377zzjkwmW7ZsWXNzc3Bw8MaNG7tPoiaXy7u6urhcrvYtj8fT7iYWi0+dOjV16tQrV67k5eWFh4cbWLAnAQZCCIKgx/d5zIdP50B8lp7ZsTdt2qRdhiktLS02NlbvqJPFixfPmTNHIBCMHj16yZIl3deLSEtLe+eddwAAbm5ue/fu7Z6Kx+NlZGQAAHAcX7BgwapVqz7++GMAQPdVCQmCQBCExWJ9/vnnn3/+eVlZ2e7duydPnlxYWOji4qLdh81mMxgMuVyufasNihYWFkeOHFm9evWyZcv8/f1jY2OtrKz+1tn5e2AghCAIenxCY5tnXQQAABg5ciSKojk5OXQ6Xbe+hLYOZ2dnl5qa2tDQcPLkycTExNTUVN168X5+ftrBL2w2u6+caTRaTEzM999/b25uzmAwGhsbra2ttR81NDTExMTo9nRzc/v007RBz0QAACAASURBVE8PHjyYkZGhC4QIgjg6OpaWlmoXZiotLXVycgIAJCQkaOfppijK29t78eLFg3xGHgXsI4QgCHq+KRSKXbt24Tju5+fn7u5+9epViqIUCoV2hb+ioiK1Wi0UCpctW2Zra9vS0qJLaGpqGhAQEBAQ0NdAGABAdXX1wYMHg4KCMAyLi4v75ptvtGvz3rp1KzMzc8yYMSdPntTVRG/fvt3c3Ozt7d09h3nz5m3bto0gCIlEsnfv3nnz5gEAamtrAQAkSX722Wc4jk+cOPEJnBhDwUAIQRD0XDI1NV21ahWfz3d0dNy7d+/JkydtbW1XrFhRVFTk6uoaGBgYHBxsYmJy8uRJBweHwMBANze38PBw7Zia/tHpdBzH+Xw+n8+Pjo52c3P75ptvAAA7duxoamqysbFxcnKaNWvWL7/84u7uXl1dPWrUKGtra2dn56lTp37zzTdDhgzpnttbb71Fp9OFQqGTk9OYMWOmT58OAFi+fLmVlRWXyz19+vT58+dZLNYTOkuGgMswQYMMrgf0TMDT/hQ8R8swtbS08Hg8Op2ufavRaNra2vh8PpPJ/PuZq1Sqrq4uc3Pz7hvFYjGO4xYWFn2lEolEDAaj+69UJBIBAHrk048ntwwT7COEIAj6X9Nj7AmdThcIBIOVOZPJ7B1QdeNC+9I74BkeAp802DQKQRAEvdBgIIQgCIJeaDAQQhAEQS80GAghCIKgFxocLANBEPRc+v777xsaGgAAdDrdz89v+vTpg7WAQ3l5+a5du7SvTU1Np02b5uXlJZVKP/744/fff183YvPw4cN8Pj8+Pv7KlSuHDh1qa2uzsrKKjY2dOXNmjwxPnTp18OBBFou1YsWKESNGqFSqY8eOXblypbOzMyAgYOXKlTyennlznhpYI4QgCHou7d27t6WlxcXFhcfjbdy4cdmyZYOVc01Nzfbt211cXFxcXFpbW4cNG5adnS2TyT7//HPdZGkAgLNnz6ampp4+fToxMTEkJORf//pXSEhISkpKj9zOnTu3ZMmSWbNmhYeHJyQkFBcXl5WV7d27Nyws7OWXX75z587YsWMJ4mnM19oXWCOEIAh6XsXExGjnGh0yZIh2xpZLly7Z29t7eXkBAKqrq3NzcydNmnT16tVr166RJOnj4zNnzhxDcmaxWEuXLtW+Li4uvnjx4iuvvKJ3z+PHjy9evFi3c+/J0r755pv33ntP+/x3dnb29u3bv/vuO128HD16tJmZWXl5eT+z2zxpsEYIQRD0fOvq6jp37px2BtGdO3fevHlTu/3+/ftbtmw5e/bs66+/bmdn5+rqql2V0PCcKYoqLCzMy8vz9/fvax+BQJCcnNzPOkrp6emjRo3Svh41alR6enr3T2tqalAUtbS0NLxUgw7WCCEIgh7fgx+rKPJpzM/lOM7a1JnTY+Py5ctXrVolk8m4XO758+f1JkxPTx8zZsySJUsAAH3V6nrTzkRDUZRYLJ41a1Z8fHxra6vePd99992amprQ0FAjI6OxY8euX79eN6k3AEChUEgkEj6fr31rbm7e2Nio+1StVi9dunTNmjXPto8QBkIIgqDHZz/W8unMU8m20jM72o4dOxYsWEBR1NGjRxMSEkpKSnrvM2fOnPHjx3t4eEycOPGVV17pXrfLzs7esmULAMDR0fGTTz7pnsrCwkI7Pbc2EK5bt27NmjUAgO4VSu2STDwe77fffuvq6rp3797u3bsjIiKKioqEQqF2HyaTSaPRVCqV9q1CoTA2Nta+xnF8zpw5lpaWmzdvfvzzMhhgIIQgCHp8Zm6DOenl40EQZNasWYsXL05PT8cwTDfwRDuwxdvbu7y8PD09/cSJE+Hh4ZmZmbreOKFQOGvWLACAmZlZX5lzudwZM2Z89913n3zyCY1Ga21t1c3f1traqmvzNDY2Hj16dExMjK2t7a1bt3QDR1EUFQqFVVVV2iUpqqqq7O3tAQAEQSxcuFCpVJ48eXKwBrs+NthHCEEQ9NxLTk5WKBSurq6Ojo5ZWVkAAJIkjx8/DgBobW1FUTQ0NPSLL75wcXHpvnivtbX15MmTJ0+eHBUV1VfOSqXy/PnzHh4edDp9xIgRuvV7S0tLb9++HRERcePGDd3i9ZWVlR0dHQ4ODt1zmDlzpnZBKLVaffDgwZkzZ5IkuWjRotbW1hMnTgzKPOB/E6wRQhAEPa/WrFmzYcOGrq4uOp2+c+dOV1fX119/PSIiIioqSiKR+Pn5AQA+++yzc+fOeXt7NzQ0WFlZRUdHG5Jze3u7q6srRVGtra1hYWG7d+8GAPz4448zZ868cOECn8/Pz8/fvHlzQEDA+vXrp0yZ4u7uzmazc3JyVq9eHRoa2j2rd955Z8yYMUFBQVKp1M3Nbe7cuXfv3j1w4ICJiYmuBfX06dMRERGDfHYMBpdhggYZXA/omYCn/Sn4py3DJJFItK2gDAaj+7JEMpmsqKjI3t6ex+MplUoTE5OGhoba2loLCwtXV1dDcsZxXCqVal+bmJh0b7okCKKqqkoikbi5uel+clKptLKyEsdxFxcXvctQEASRk5PDZrO1DaTd89d7FL3gMkwQBEHQX5iamurdbmRkFBQUpH2tXZJQKBTq6l6GoNFofQ3jxDCsdzQ1MTHp5/kKbaphw4YZkv8zAfsIIQiCoBcaDIQQBEHQCw0GQgiCIOiFBvsIB1lZ5cXsvF9QBAsautTJPvpZFweCIAgaAAyEg+n+g71Zubujwz8kSTzl+rsjQ9b4eEw3PHl23i93s7Zq1F1e7lOjwz+k05/9g7oQBP1j7d27t7m5GQBgZmY2dOjQsLCwwcq5pqbm8OHDAAAMwwQCQUJCgrm5uUwm27p168qVKzmchzO9nTlzhsvlRkZGFhcXHz16tLW11draOioqSveUvVZXV9f9+/fv378vFAqnTZum27hz586qqqrw8PDExMRnO7z/iTSNKpXKtLS0bdu27dixo/eneXl5b7755qpVq+7evavb2NTUtHnz5tdff/3jjz/ua0a7f7iGpsx7WdtmTj7i4jjWzTlh+sSDqbc/bm0rMDD5rbtfFBSfmDXl+NKXMylAnfhjHo4rDD96RXXKweMTduzxP352TnNLn7Pf9ut5epAGgqDvv//+5s2bHR0dhYWF06ZNe/fddwcr5/Ly8k2bNnV0dLS1tZ08edLNza20tFQqla5bt04mk+l2++2335KTk2/cuBESEqJWq4cOHapSqT777LMeuX300UfLly//6aefuj95kpCQcO3aNT8/v40bN/5vTrF26tSpTZs2WVpaVlVVLV++vPtHJSUlo0aNWrt2LYfDiYuLu3TpUkhISHl5eXh4+KRJk0JCQiorK2tra5/tTOSPgSA1l66/MzriIxPjh2OUeVyXyBH/Sb62Zu70swiC9Z+8rPJiSfkfc6afYTG5AIC46C+Srrx5OfU/8TFfGXL0zNxd9/P2jon82MYyoLLm6qkLr8aM2uThOsHAwje1ZN+6t6W+4S6KMVydYkeFrtV9C0N0yRqLSs+IJVUmRgJPt8kYam54Wi2JtI4kNSbGthjGeNS0EPQiS0xM1C7DFBsbu2TJkk8//TQ3N9fc3NzW1hYA0NraWl1dPXz48JKSkhs3blAU5ePjEx4ebkjORkZGupA2ZsyY33//feHChXr33LNnz+LFi/sJZp/9l67yc/369bKystraWjqdHhQUFBsb+/bbb7PZ7Ef67oPoiQTC2bNnz549++LFi7oVqnS2bt06e/bs9evXAwA6Ojq+/vrr33777a233lqwYMGXX375JArzdOQ82Gdm6uDmHN99o7fHtPziY7kFhwJ8F/STVqkSX0n9z6T4ndooCAAAABkb+cmvx8eXVSa5OSf0f+ji8rM5eftmTT1hbGSjPailhc/JP+azWFwH24F/8TkP9t/N+mFU2LopCbtxQnn/wb6DJyYmxHzt5BA9YFoAqKzc3feytrm7jLO2GNIuLjtyapq7y9To8PUoOvBPi6LIB4W/pWdvJ0g1DWMp1RJfzxmhQauYDIMeDFeru7If7CkuPS3urGKz+Q52EcP8F1uaexuSFgCg0chKys/VNqSpVFKumaObc7ytIAQAQ9tnNLi8oTFdIq2n0VjWlv58npuBCXVHb+8oU2tkxkY2PK6z4ceFIL3y8vK0E5tt3rw5Pj5euyhgWlra119/vXnz5jlz5rz66qsYhh0/fjwpKemR2iHFYnFtbW2PWdO6MzY2zsrKEolE5uaG3gSnpqZGRkZqn3EMCgpCECQvLy8kJMTwUg2up91HePPmzbVr12pfjx49evfu3RRFXbp0adWqVVu3bpXL5VOmTPH09HzKpfqb1Oqu9OwdMyb/1vujqJEf/H5ugbf7SwyGcV/J0+5tcXcZL7AO7L6RRmPHRW85d2mFvW14P4GhQ1xx9eaHMyYe1EZBLQu+14TYbX8kr5gz7bSpiV0/Jc95sD8rb/fsl06ZmtgCAGg0VljQKke7iLMXl0aHb+y/TkmS+KXra9s7yuZMP2NmYq/dGBy44nzK/508N39S3E4mU//TvloKheh8ykqCUI8b+4P2u0u7Gu5mfn/gaNz4sT8IbYb3kxYAUFV77dK1dQ524XExX1qY+8jlbaUV50/+Md/Lfeqo0LUD1SypvMLfbqd/JbAe5uI4lsXiijpKL994j8EwiQnfYG0V0G9aIFeI7mVtLSw5aWHuxTNz1uDytPQtLCY3OHCFh+uEAUNafVN6Vs6umvpbXFMnJtO0U1JDECpvj2mBQxYZGwn6TwsAVVt/p6T8j4bmzK6uBhqNzee5OdlHeblPBWDgu+kOcUVx2ZnahtvizkqSxI2NBEKbIHeX8XbC0AGLTRDqqtprNXW32tqLNBoZhjHNee72tiOdHGIMuXGhKLKlLb+p5X6npEat6WIxzcxMHQVWQy3MvQy5CSBJXNRe0i4uVatlAAAWi8vnufO5rghiUOcOTqjaO8qkXQ0EocYwhrGRDZ/nSqf1XNKoHzJ5i1ze1imtpSiyx0dZl9aQFG54Vo/NbehirpVfj43aKdY6OzsxDEtKStKbMCUlZc6cOY/a/KidYo0giKampqlTp86aNUvbH9nbunXrZs6caW1t7efnFxsbu3r1am19tB9NTU3dm/0sLS27r8309D3tQNjY2GhhYaF9bWlp2dzc3NjYKJfLV69enZiYKJfLw8LCzp07N3LkSL3J5XL5119/ffTo0e4bN2/erJ3OvIe20vv4yWODVXLWlMk8b/190Rk52+2FEWymrXau9+6M2I62gvDbGVuDh/5bb1pRR3FJ+fnZUy/0Tss19ba3jbyR9ll4yHt605Ikfj5lddCQFRy2Y4/kPLMhAb6Lz1xcNiX+176iQlVNyt2sbVMSfqVhvO7JzUy8xo/ZdS5lMY5TTvaj+zr05dS3cEI1YewvNIzdLTknIuTLvKJtR07NGD/2Zw7bQm9ycWfFhSvLXJ3GBw9diSCYNjmGckcGf2AnvHEmaenwoSt9PGbpTUtRZEbO1pLyU6NHfS6wDgYAqFUEDeN5u89zcZx44/aHB09Mio381szUUW9yuaLt6q11arV0/JhdfN7DWy6B1Shfj5dLK8+euvCau8uk4QEraTSWvkMTBcVHMnK3uTtPmDHptBHHSlekuoZb97J/SM/+KTzkPSsL/VNstItL7mRskUirA3xfiwz7WDcYqlNSXVDy2/6jCe7OEwOHLOWw9fQLEKSmrOLs/fzdKEr3cJkcGbbZ1MROo5G3d5RU1qbczRwrsB4xfOgyc57+m8j6xtv3839p7yhyd54U4LOYx3VFUbq0q66xOeNy6gckofb1muvp+pLe8VmdkqqCkiMlFWf4XDdHu+ihflEMugmOy0UdJQXFv6fcWO9gG+XjkSiwHq4vpFGNLVllFX9U1iSzWHwbq2GmxvZGbFulWlxbfy89+0cclzvaxTg7xgmsgzGU3iOxTN5UU3ejuu5aQ/M9YyMBn+vGYJgCAJTKjnZxqVLZLrAJsROMtLUJ45o590irweXNrffrG283NKW3i4tNTRxNjIQ0GhPHVV3yRomkxsTYTmgTIrQJsbYM7P1DlStam1vvN7VktrTlidqLaDQ2h22RX9iME+499jS3DekdHZ8EBlvPVCybNm1KTExUKBSHDh2aPn16Xl5e730mTJgwbty4nJyciRMnJiYmCgR/3m8VFRXt378fACAQCFauXNk9FY/Hy8jIAADU1tYuWbLkiy++ePnll3vkrJ2h09bWNi0traysLDU1dd++fUFBQQUFBboFCPWi0+kKxZ9jIDQaDYMxcLcIRZGV1bd5PBsM5RleqWWxWCg6wA1Tf4Hw999/f+89/Zfg3i5cuODoqP/S0x2TydRoNNrXarWaTqdrpx5funTpG2+8AQAgCOKrr77qKxDS6fSQkJAeNWgrKysWS89ly9hCKB3uPyhjkbCULKqzTe9RlCpxQfHhWS+d0vspAGBU6NuHTk4c5v+K7rrZ3Z3Mz8OGrzY11d8nGjni3V+Pxft5J1pbDun96b3srSyWadDQRXrvqUOGLW1py76XvSVm1Kbenza35qXe3TBl3F5LCz1zDwoFAZMTfjmTtIjDNnGw6zkTLkFqzqesAoCanLCr98VLo9GMjth4L2vr2Yvzp4zbyzVz6rFDXcPtC5f/HRG23sv9pd6H9nCNs7LwOJu8RNxZGjXygx5RXKFsv3jtTYJQzZ12ls3u2Q7DYrEmxf+YW3DgzMV5kSM+8HSb3GOHiupLV1L/4+c1O2TYyt6Nt/4+s9ydx167teHkuelR4R842v1lPv76xnupdz6m0zkzJx3p3RDq7hrr7jq2sOTkpeurhDYhwYErLPh/xiRRe0lmzk/VdTdChq0c4j23x6FZLE9rqw/Dgv6Vcf/HY2enuDuP8/aYYWMVoK3utHeUlVace1B0mM/zHBPxkZ1wRPe0FubOHm7xKrU08/7epCvLuGbOvp4zHewitFf2TmltVc2VB0VHAEUN81/s4Ta5+/8Xj2vrYBcaGvRGQ1PG/Qd7M3O2OjmMdrKPNue502hsmby5uTW3vCq5U1Lj4zlzzks9WxecHSOGD31NqRIXlZ5Ky/hUrZa4OMbaWAUacawAoLpkTY3NWZU1VxgMEy+3KSFBp3TNBt2JO6vKq5KzcreJOkqsLfzN+R5sljlOKDsltW2ifLmy3dEu0sfzpYTRX7FYPcOAXCGqa0irrkvNyf+ZIFTmPE8OxxLDGEqluFNaI5HUWln62QlHRI5Yb2M1tMcPiSTx1rb8+qZ7JRW/37jzIYbSuaaO2kMolR0dnRUURdlYDRXaDHd3SbC08H1Y61X9eunSpR7FcPRJ7P29nhoOh8Pj8Xg83po1azZu3JiWloaiqHaZQACAdhXA0NDQioqK5OTkEydObNy4MScnR1dzYLFY2rjYe1gGgiDaWdB4PN5rr722devW1atXoyja3t6u21kkEunm13Zzc3Nzc5s/f76tre3Vq1enT+9vwLxQKLx+/br2tUajaW5uHrASCQAAgMrK/YEEkpjwLSzWgM0nDw0YBUH/gVAsFotEori4uAFzOXjwoG7dxf7Z2dnV1dVpX9fW1trZ2fH5fA6H4+7+8D7Lw8Pj2rVrfSWn0+mjRo0ycNJtFs+KP2nRoIzKrbx1n4Egek9oVu5Od5fxPLM+bwLMTO38vGbdzvgqLrpnJ2hZZZJS2RHgO6+vFh4Omx8Rtv5y6ro50870CDktbQ9yHuydN+McivY5Eidh9NeHT07OL/ptiM/c7tvFkuo/kpeOjfrcxqrP6QEF1gGT4nedvbi0x7gbparzj+TXWUze+LE/aC/olEpN1jcBnEAFloiJMYqiKIqGDf+3sZHVsTMzR0d85O4yTpuWJPGM+z/ef7BvQuw2O2EYAIBsEWkycsmGZoTJwFwcaEFDEBaTz3OZM+3MpWtvH/59UtSI9x3tIwBACFJTXHbm1t3PvT2mjQxeg6I0skWkuXEXL60Eag3CM6P5edLDAhEOe6jfy7aCkAuXVxWWHA8OXGErCKEA1diUmZHzk1hcNTHuR6FNECXtUqfewu8XkI0tgIZhDkJ6kD8tLNDIyHJC3LbK6is3bn9Ep3/j6hRrbGQjk7dUVKXIFW0jg9d4uU8GFMCLyvGMXKK2gZIrUVNjzNOFHjIUFVj5es30cB1//8G+0xdeZjF5Vha+FKBa2vJVKom/z7zREZsZDGOqS45n38fLqilxJ6DRMIEV5uVG83Y1MrKMCn8/eNiKvMJDV26+J+6sMuJYKlViJsPExTH2pQn7LfheAACypY0oqiCbWymVGjExQu0ENC9XtpFZgO+rI0JWlFUmlZSdvZ62kSBxQFFMpomDXUTUyPcdbEcCgFCSLryokKxroqRdgEZDrS0wd2fMQWgnDLEThsgVotKK8+VVSen3txO4ksOxtLLwDQ1a6WA7CkVpgCSJylqipoFqFwOKQsxMUTsbzNWRw+YP8391mP+rovaSyporFdXJMnkbgiBGHGsbK//AIQce3jQQBFHbSLa0UTIFgqKImQlqa41a8Pk8Fz5vWXDgMqWqs6klu72jTKkU02lsF8eY0GErzPmeuj8NqqOTbBdTKjVixEG5poiZibGRpZf7FC/3KQCALlmTqKNUoRDhhIrN5JqY2FnwPf+84aAosqOT6pIDkkSMOSjXDKUxBDaBApvA4UNfBwB0yRo7JbUKZTsAgM3im5k6dO9rAABQShUlVwDDrnLPRG5urkwm084pWlxcrN2YkpICAFCr1VwuNzExMTExMSgoqHsgdHJy6lER7I2iqDt37giFQiaTGRAQcObMmbfffhsA0NraeufOnQ0bNhQUFHh6emIYBgDo7OyUy+W6Zr++TJ48edOmTS0tLVZWVufPn9c2qw74HREEmz7pkHbSbUPCm+H6W31iz549e/bsuXHjxoC5YBhWWFioW+xRSztYprq6Wvs2KSkpKCho3759SUlJly5dQhBk3rx5tra2X3zxxYIFC5ycnLRN2K+88gqDwdi5c6feAz2r1Seq3l2FjglxGDuvx3ZpV8Ovx8cvmJnU48+mB7W6a9+RMRNitwttgnQbVWrpgaNx48Z8aysI7SctAOBM0hKumVPkiD9r5yqV5NDJyeEhazxcJ/afViypPnY6cWTIGl/Ph+tktrUXnTq/KCxolZ/3bO0WsqlVczuLkkgRUxN6sD9q9+etVquo8OzFpXaC0AC/hUyGaW3D7TsZ33q6TY4IexdBUEqpUp1O1qSmo1bmgE4j65swDxciIdLE42Ets7E5O+X6OhqN7WQfqVJLK6oumfM9tGNrqS6Z8sgfeG4hPTgAc7KjlCq8oJQoq2YmRDHiIwGGAQBKK87fyfxeoRAZGws6O6ssLXzCQ9YKrAOpLrnqxHlN5gN6RDDNzxNhs8hWEZ6Zh+eXMhKimPFRgIaRJP6g6MiDwsNtoiIEQfk8Nz+v2X4+czAcqC5cVafcpA3zo4cMxeyFFEEQFTWaWxlEZS1zwmhGzAiAYRRF1tTdrK5LVSjbOWxzO+EIJ/tIBKCarAeq3y8iKEIfEYS5OQIWixJ34gVlmjtZmL2QOXEM5uEMAKAosrk1t72jDABgzvewsvBFEIyorlcnXcPzimi+npiXK2rOBRqCqGvEHxSTrSJGZCgjZgTCe7g+qgaXy+VtDIYxm8UHAFByheZWpubmPVLSRfNxR4XWCItJSbqI6nqipAJzd8aDfE1HDgf/nb9fre5CUPRhN5hao8nM1dzMIKrqME8XzNEOMTMBGpxsbMaLyoFKTQsdSh8RhNnru8UmSbyoXHMnG8/OR/lmmLM9Ys4DKEqJJUR1PVnXiHk404MDaIG+CEdPJyXV0am5X4Bn5xOllYgFH7W2QE2MKJKkOiREbQMAgObtRvP1oPl6INxePco4TlTW4gVlREkFUVkLmAzUnIcwGZRMTnZ0AoLEnO0xN0fM3RlztkfYf22PoSiyVUSUVxNl1URFDVHfhBgbocZGAEWoLhkp6UItzTEnO8zVEXN1xGxtAO0vd5OUWkPWNRKVtURlLVnXSDa1AQAQDuvwg+zrXPqvR4/08xf3NA0bNkytVgsEArFYXFJS8tZbb33wwQe5ubnR0dHjx48XiURsNlskEvn6+paXl/v4+NTW1hYWFqalpfWzDK/W1atXExISIiMjCYKorKxksVhHjx4dMmRIWlrajBkzhg4damFhcfny5ZkzZ3777bdLly69ePFiSEgIm81OSUmJjo7+9ddfuweqs2fPbty4sampSSaTubq6Tps2bf369f/617+SkpJGjBiRlJS0Y8cOQ67qRhxOY0a2kZmZ0ohtxB3gKzyS/gJhRkZGRkbGsmXLBsxl3bp1a9as0d0FlJSUzJ07VyKRVFdXDxkyxNfXd9++fWZmZkePHg0LC4uKijI2NuZwOBUVFTdv3rSxsSkrK4uJiQkKCpLJZLW1tSkpKXZ2+od4PKtA2LThw2r3rtB5PR9muHD532amjiOD3xowh7LKpNQ7n82b/odu1MyFy/9mMkxHR3w0YFqlsuPQycnDh77u7zMfAKBWd526sMjGKiByxH8e7qHWqC6l4jkFCIbRQgIYUWGg26+wXVx++sJrFnxPJ/uodnFZYempmPAPPd2mAAAASSqPndekZdJHDUctzSlRh/pmOs3PkzV3iu7KotbIMu7/WFaZpNHIrC39h/kv1o5kISpqFDt+pfm6M6fEPbx8qzXq63eVZy8xR49kThqrDWYURdTU3axvSmfQTRzsRllZ+AIA8AfFit1H6GGBzMmx3S9hZFOr8vAZqkPMejURc3p40yqR1ssVLaYm9trmPk1GrvLgKXpwAPOl+B6XP7K5TXnkLNnYwpr3Es3v4W0ZRREURWkrB/j9AuWh05iTHXPWJNS852IxZF2j8ug5slXEnJZAH+4P/vrLwR8Uq36/CAiCOW0czd+r538STmjSMlXnLiM8LjMughbgrf36AABApLkYuwAAIABJREFUEHhukfryLbKhmREXSY8K7XnVBoBsalVfvqW5nYV5udJDh9K8XBETYwAAJZMTxRWajFw8p5Dm70WPCqN5uvQoGKXW4Jl5yut3QH0zbZgfzc8DsxcixkZUl4yoqcfzivHsfMzVkR4RTA/wAfSejUBkQ7PmdpbmdiZgsWhDPDEne8TECJAk2dZBlFXhuUWoBY8eFkgLDkB5PS89lEKJ5xRqMnKI/FLU0Zbm5oRamQMWi+qUks2tREkl2SGm+XvTAn1pvh56vnVbO1FQij8owQtLERYLtbVBuSYAQSiFimxuIxubUVsbzNOF5uWKuTohRn8JtJSki6isIUqr8JJKsqYeMTFGzLmIsRHQ4FSnhGwRIRwW5uKIuTlhLg6ogxBhdGtQIQiioYWsrMXLq4iKWrKlDeWaIabGCINOqdVUh4SUdmFCa8zJHnO2Qx2EqLWltvD/tGWYCgsLtf3rLBbLyclJtzJRXV1denq6q6urg4NDc3Ozs7NzVlaWdhmmUaNGacdq9k8qlZaUlGhfW1lZ2dra6gKbVCrNyMiQSCS+vr5ubg/7CEpLS4uKikiS9PT09PLq+dchEomqqqp0by0tLbVjUNPT06urq4ODgw3pVgMAGLHZlW9u4lAUsmiGkbuLIUkM9ETWI5TL5YWFhbq3RkZGXl5e2vWxjIyM1Gr1zZs3cRyPiIjQPTgik8lSU1ONjIxCQkL6WbD4WQVC6ZfbL9NOxCze272fo6rm2pXU/yyYlWzg8LOrNz9oay+eGLeDyTBNvfNpfeO9xCnH/jIoQ63BSypQPhcVWvdIK5ZU/37uZQu+hwXfq6DkhKtTbHT4h9quQapTKv9qJ2pjRR89Emg06os3KIWSs/KV7rfYOK7MLz7W0vbAxEjg5z3r4ehEtUa+bR8ACHvJHMT44VeglCrV8fN4XhH7Xy9j9n0+Tai5k608fJr9ykxaoG/Pc1XXgB27QHXJ2K/PRa16tZAQhOpkkuZ2FmvpXJqX/qXRNHezlYfP0EMCmJPjdAUDAJAtIuWRs2RTK3tRIubW518OnlOoPHwateQzYiNoPu6ARgNqDZ5forp4g5J2seZNpfn0HO/wl+QFpaqTSZS0ix4cgNoLAYKQDU2a9FyAIMzJY+nBAaCfXxRJatJz1FfSyLomzMkOMeJQMjlRWYvaCRhRofTQwB41jx4opQq/l6PJzCPKqoD2D5OiMFdH2jA/euhQxKi/n5lUKjVSE3hmHl5URjY0U11yxIiD2lrTfD3oQUP01Ld6HpsiKmvx/BKytpGSyQGKIjwzzMWe5ueJWvQ36uEhtQYvqSQqa8i2dqBQIWYmqKU55uqAOdsDQ5qwKIpsbiMbW8hOKQAAYTFRK3PU1gZhGvZcKUmSbR1Uh5iUyhA6HTExQq0t+j9df4ETZFs7JZVRGg1CpyNcE9Scp7fY/7RA+KJ5cusRGhoIQ0ND29ra9H5UXl4+iAXq37MKhPKvdlW7yXLIa9MnHtSOrOsQVxw7kzghdtuADZs6FEXevPt5XsFBBMGENsPjR3/V7cFBQNY3yb/ZjVjwqBYRNsSLvXDan1UKAAAAGlxeVHpa2tXgZB+ta2KlVCr5x9toQUOYU2J1h1H9cVlzM53zzvLelZ4/4bj8218QMxP2a7N6/81r7uUoD/7OmvcSPaTXswQUpTqVrLmdyVn1Kmqrp0FYKpWaGBurL99Snb7EiI9kxEboLmd4YZnq8BnEnMt+NVFb4+nzXHXJVSeTNPfu0/y9MEdbQJB4SSVRXsVIiGbGR4KBFvAEBKG5naW+fpesqQd0GtDgqLP9w1BkWNcCUVWLZxeQDc0AQVAbS9oQT8y957jE/sov7SKq6ymFEmGzMSdbxPiR/2gphRIA0LsW1Re4MO9TAAPhs/XsF+aNjIyUSCS6t3K5PC0trbW1ddYs/WPc/9cgiLtLfKNYdOjk5KCApRqNPD17+6iwdw2PggAABEEjwt4NGfYvglD1GK5Ndcnl3/zMnDmBHhpIqdSKHQcUvxxlL5nTfR86jTPEe85fswTKvcdRJ7s/oyAAAEGYk8YiLKb8ix+N3l2hvypAkvLtBxAjjt4oCACghwSgAkvF1v1EYRlz5nhd9w8p6lDuOUbhhNH7/+4vkiEIY+woWoC36kRS1/9twtydERaDqG0EADCnxusJrr0zMOawFk5jTonFcwqI+maAIvSwQPby+YZWETCMPiqYPioY4Dil0iBM+sCxs0cGTva6ttnHgJgY0/z+1uOwhodACIL+JkOvDr2nfdFoNAsWLDDk4Y//Fcj/s3fe8VEU/R+f3b3eS3KX3kMKSQgBQgsSSgJIF6WjgMCDdHhQwEeFHzZQRMCCIPgoHQJSRR6kE2qAEEoS0nu7JNf77s7vj8UjXNqhQHge9v3KH3ezM7Nze5f97Mx8S99eH+cX/ZFbcBxDmcMHbmnV87pJ2CwhAM5P7pZ9xxhx0cyuHQEACJvFm/Wm8bNvbacvsfq1FBrGfjWdKK0UfDS/8SFWUi9gx41f/MB/b6azFpKkedNOQELujHEtTI8wXy/+igXWlOOGJZ8z4qJQmYQsq8Kz81gDE9mDEl2ZV6Hucu7MCdBgJHKLoNXGerUv5ufV0rpiIxCxkPlK19Y3NFqAwUCeUAJpaGheNv76PYLJZC5fvjw6Onr58uUKRRMecv+TBAckBQcktVKJJIncQtTbw8UFMaK0Er+TJVi19FERi8mdNcn4ybeMiJDG+4UUUKu37D7CWzQNsJpWCtarfQAAxs++5c6d7Njtg0aTedMugKG8OW86Lb02BuFyOG++xhraD0+/D7V6RmwkZ8obTRoHttSJgN94H5GGhobmxeFvPSwLBAKCIMrLy18eIWwdnDCu2QTMFlKr5y2ajvm1Hr3aduwUe1AiwnnMRAhVuLFHDjBv3cv/15wmp1/mX/azErth/i15obJe7YPIpaYvNzM7RWNhQWSVynb+KrNbHOeNV13cKgMAoFIxq2/T8Q1oaGjakD/++EOj0QAA+Hx+TExMc8b2f4GamhrK4R1BEE9Pzy5durBYLIvFcvTo0WHDhjnsGa9du8bn86Oiompra48fP15dXe3h4ZGQkBAY6LyhXlxcfOTIETab/frrr1NBZ27evHnr1i2dThcSEjJ48GBGm67c/HWfRL1ev3z5cgzDHBa0NAAA28kLCIfDX7GQM36E+fttwN5KEEJSVYdn5TMTmwjexkrshvA41mOnGx+yp6bBWjV7aP9Wx8PsGiv4ZDHiJsVvZ0KbjbdoOmfMENdVkIaG5oVlyZIla9asSUlJ2bx5c3R09IYNG55Wz/fv3580aVJKSsq+ffsWLlwYERFRUVGh0WhGjx7d0Fhk/fr1e/bsuXPnTlhY2KlTp0wm0+nTp0ePdg61c/fu3djY2Nzc3PPnz3fs2JGyu1y0aNGtW7eqq6tXrlzZq1cvm832tAb/F3BVhJ2sRq1Wa3V1NUEQH330EW2r9gg7bv3PBf7SdwCCMOM74GkZ1pMX2IObDtdJYTuVynolHmnSYwRBuG+PNfzfOiw0kBHx6GmDLK+ypPzGf29my7b4j7oRCVoeAw0NzX8pc+bModIwpaSkLFy4cN68edXV1Twej7otm0wmjUbj5eWlVqtv3LhBkmT79u1dnDiKRCJHVOcePXrs2bNn/PjxTdb89ttvx4wZ8/3331NvCYJwqrB69WoqWikAYMiQIT/++OOyZcscIdYsFotSqbxx40ZzkTWfA3/RapTFYvn5+SUlJcXGxj6bgf1XYk+/h/l5op4PF4rZowYZP/+O1a+n07KnA2i12S/f5K9Y1FyHiETEnTHe/MNO3oKpWKAvAICsUpm+3sIZP6JJ1wUaGpqXE61WS4UGnTNnjiMN0x9//LF27dp169YNHjy4f//+KIpevXo1KyvriZzKcBw3mUwSSbO+WCRJVlRU2O12ylUfa2R8QIU5pV4PGzZs165dDXMIV1VV4Tju5fUEOVCfOn/dapSmMfbrt5nd4hxvUQ93RkSo/fxV1oDeTde/mo6FBbXk7QcAIyKEO+UN09dbmZ2iAJNhv3qbM3owsyv9/EFD80KwMjObeAZhSRozzs8nvNHy2+rVq7dt21ZfX19eXn7kyJEmGx48eHDixInUhMx1tFptUlKS3W7Pzc1NSEiYNGlSXV1dkzUXL148aNAgT0/Pnj17JiUlvfXWWw2XCW02W21trSPlhaenZ0VFBfV64cKFZ8+eLS4u/umnnwICAp5oeE+XJ9grSk1NHTp0qK+vL4ZhXl5eycnJv//++7Mb2X8fdpzIzGPEPmYhyRqUaPvjImi0VvCwxelLrL6t585lxEYKVi5CvZSIRMz/cC4zocvTGTANDc3fBkWQ5/OHNJVkZsyYMatWrfrmm2/GjBkze/ZsR26fhiQkJPz444/Tpk07ePCgU762kpKSTZs2bdq0af/+/U6tqAz1X3311Q8//HDnzp2dO3c2dwXCw8NzcnL27t0bFxf37bffdunSxWg0Oo4iCIIgjyK3QAgd89HZs2dv2rRp8eLFCxYsKC0tde16PxNcnRHu3bt3woQJbm5ugwcPViqVdXV1J0+efPXVV9evXz9vXtOZ9l428JwC1MfTKSIi5u+NKtzsaXeY3To61SceFECCaLj51wKIRMRKck6HREND0+Z8ENGWicQDAgI6deoEAOjevbtAIEhNTQV/pgkEAOA4DgBITk5OS0s7ePDg6tWrFy5cmJaW5sijZDAYcnNzAQANpYuCwWBQPXfq1KmsrGz9+vWjR49GEESr1TqaazQaKn43k8ns169fv379Fi9e7Ovre+rUqeHDh1N1mEymTCarqqqiUgxVVVU5ZodU5qauXbueOXNm//79CxcufHYXqmVcEkKCIObPnz9w4MD9+/c7su7hOD5z5sylS5dOnjxZJGotkuFLAJ6Zy2jfRBBL1sDe1l9PMLvGOvmSW09eYPVPeCIHcxoaGpomKS8vt1gsIpFIqVSWlJRQhVevXqVehISEvPvuu++++27Hjh2vXr06dOhQqjwyMnLNmjWtdp6fny8Siah8eefPn6c8BUwmU1pa2sKFC1UqlUMaqcmfI4g0RXJy8rFjx3r16gUAOHbsWHJyMkmSjijeNputrKysbX3wXBLCmpqa6urqFStWNMw9y2AwPv74461bt+bk5HTu3PmZjfDFAAGgtV0AIjufM2Zo43JGdLh1/+/4nWxGhwhHIVleReQX8/7hnNeJhoaGxnW+/fbbo0eP2u32S5cuTZ48OS4uzmw2Dxs2jCRJlUqVkZHB4XDee++9+vr66Ojo4uLihql0W0an01GOEFVVVVlZWZS1y1dfffXWW29Rc8ojR4688sorSUlJ48aNKyoq6tmzJ4/HO3z4cHR0dGJiYsOulixZ0rt3b4vFUldXl5GR8dNPP2VkZMyYMaNHjx4Yhp0+fVoul7ecyPdZ45IQSqVSNpvtyHrsgCrx8KDNFwG0WsnyaiyoqeiUCMIenmz99XdGdJjDgc9y8AR7YGJzQWFoaGhoWmX16tWUQz2Px/vqq6+CgoIAAAkJCadOnTp37lyPHj1WrFiRk5MTERFx9uzZ0tLSDh06fPjhh5Rxacu0b9/eEVvc09MzNjZWIBAAAIYMGZKenn769GmdTrdu3bo+ffoAAH7++efU1NTMzEySJL/88kvKPLVhbzExMbdv3z5y5Ej79u03bNggk8kkEslXX32VkZFBkuQXX3yRlJT0dBPtPimuZp+YN29ednb2wYMHHTG/bTbb22+/bTQaf/3112c5wsdos+wTX29h9UtoIgvdn+CZudZDJ/nvz262hy9+YMREsAb2BgDg6fcte44KPlncODnc/wB0GoQ2gb7szwE6+0Tb0vbZJ+Ry+c2bN/38/AYMGODh4VFbW3v69GmNRjN9+vSlSx8GyRw1alSXLi+pQSORX4yFBrRQgTN1tPHTbwEDQ4R8y67DvHlT/idVkIaGhua/Dlfvxbt27aLmjidOnHAUstnsbdu2Od62b9/+ZRZCVq+WVt5RNxl/yTuWfceA3c6bMxkLdikjMw0NDQ3Ns8ZVIXzw4MEzHceLDoKCRlukDSEKSrHJb7TcB+rhzps35akOi4aGhobm79LS/uS1a9fWr1/vSi/z589XqVRPaUgvIohYSGr1zR0lVXUIk9F0ClwaGhoamheblmaEmZmZBw4cmD+/ibyvTnz77bezZ892uJL874HKxLBe09xRoqAUC/J7nuOhoaGhycrKoiLFcDicoKAgJ++9v4Ner8/JyaFeKxQKX19fAIDdbr9z506HDh0cKZMKCwvZbLaXl5fNZrt8+bJKpfL09IyLi+PxeE4d6nS6c+fOsdnsPn36UOncMzMzzWYzdVQgEISFtWVcglaWRrOyst54o5UVP/CnH8X/MKhcimfnN3eUKCylImLT0NDQPDcmTJhgs9k8PT3r6+sLCwt/+OGHximQ/ho3btwYOHDgK6+8Yrfb8/Ly/P39f/vtN4vF0rlz55qaGsec51//+ldQUND06dMTExN9fX1DQkIKCwtramru37/fsDfKy7BTp04ajWbZsmUXLlwQCATjx483m82UqXNMTMxPP/30VEb+12hJCN3c3Pz9/QsLC1vtpVOnTg197f/3QN1l5IXrzR0lCkoYIwc8z/HQ0NDQAACWLFlCpWHasmXLkiVLRo8ebTQaGQwGlTvXbrdbLBahUIjjeH5+PoQwMDCQ3WTSt0aIxeI//vgDAIDjeOfOnbdv397cpOjLL79MSEhweJWo1WqnCqtXrx48ePDmzZtJkkxMTPz3v/89d+5cAMAPP/xAeSK2OS0J4dChQx2ReF5yUIUbqWo68jogSbK0Agt4armhaWhoaJ4UsVhM5T+aPHmyIw3T8ePH165du3Xr1oEDB3p5eTGZzAcPHpSWlj6RdzWDweDxeC04vOv1+oaplxo77B8+fJiSSRRFR48efejQIUoI8/LymExmZGQklbO+DaFd2VwCkYiAxQrNFoTrPPElyqsQmaRxOQ0NzcuA7WKWi2FJ/ibM9r6o3DlmAhVizWq13rt3b/PmzU023Llz57Bhw9auXQsAsFgsLqqgI8RaYWGhj4/PW2+9ZTAYmqz5zjvvDBo0KCYmJjExMSkpadCgQY5NRAAAjuPV1dXULiMAwMfHp7y8HADAZrP//e9/AwDu3LmzcuXKRYuaTcv6HKCF0DUQBPVSkuXVWIiz/x9tKUND8zKD51VB4nkYSTD83UEjIYyPj6d28hgMxtdff93kSmNkZOS8efMkEsngwYPj4uIaHlKpVDdu3AAAiESinj0fSwnHZrOptdDq6uq1a9eeO3euuZjS3bp1y8vLO3z4cGpq6tSpUyMiIk6fPk0l6QUA4DhOkqRjyshkMm02GwDgwoUL1CLt9evXe/fuPXDgwMjIyCe/Kk8HWghdBfXxIMoqmhLC4qZDjNLQ0LwE8Ka05S5XfHw8JVdvvPGGSCS6cuVKw6OUGeMbb7yhVCpTUlKGDx/u5eV1+vRpRzS+kpISaloWEBDQnBBS/XzyySfUlqHBYHAYy+j1eirUmVwunzp16tSpU6urq0NCQk6dOjVo0CCqDofDkUqlKpUqODgYAFBTU0Mlo3dsVcbHx0dERNy6dasNhbAt45z+d4H5eZPF5Y3LibxiLIgOE0NDQ9OW6PV6HMcRBJHL5VVVVVThnTt3qBevvPLKN998U1JSYjQaz58/72jVqVOnffv27du3r+X89fX19QiCCIVCHx+ftLQ0qtBut6enp0dGRlIzPAq5XM7lconHU5H36tXr9OnT1OvTp09T+Zgc6HS64uJiSh3bCnpG6CpYgI/9orPhKDSYoEaH+Xq2yZBoaGhecvbt23f//n2r1XrixIk+ffrEx8fX19dPmzZNqVRWV1enpKTI5fLVq1fb7fbIyMiysrK6urqYmBhXejYajVQc6fLy8sOHD//8888AgI8//nju3LkFBQVyufzAgQO+vr5Dhgx56623cBzv0aMHj8fbv3+/XC53SsO0ePHiIUOGcLncurq633//PT09PTMz88MPP0xISIAQbt++PSYmpnfv3k//6rjMEwihyWQ6dOjQ/fv3cRxfvXo1ACAtLU0oFIaHN5uT4X8JzN+brKqFFivCeWR8TOQWYiH+oE0TiNDQ0LyczJs3r7q6GgAgEomGDx/eu3dvBEGGDBny448/njp1Kjw8PCUlJT09PTY29siRI2fOnFEoFBcvXvTza92mITg4+KOPPgIAoCgaHR392WefUdYukydPjoyMPHHiRFZW1pgxYyZOnIhh2JdffnnixInMzEwcx0eNGjVu3DgqZ5ODXr16/fHHH3v37uVyudevX/fz8zMYDAMHDszKykJRdPHixWPGjGlod/r8cTUNU2FhYXJycl5enlwu53A4ZWVlAIBly5adPn36+vVmHeyeOm2Vhulhh6u+Zw/px4h6FAHBsvcYwuOwh/Z/Wqf4H4DOB9Qm0Jf9OUCnYWpbnl0aJlenMtOnT+dwODk5Ofv373cUvv766zdu3GjsPvm/CiM8BM/Ka1hC5BQw2gW21XhoaGhoaP4+LgmhXq8/e/bsV199FRoa2nCCFRwcDCGkZocvA4yodsTdR1k4oMFEVtZgwQFtNyIaGhoamr+Lq0JIkqSPj3PwFIvFAgDAcfzpj+uFBAvyI7U6su7hDBjPyMQiQwGjLZe2aWhoaGj+Ji4JoUKhEIlEqampAICGM8Ljx48zmczQ0NBnNboXDRRlxkXbr92m3tmvpjO7uGR/RUNDQ0PzwuKS1SiDwZgyZcqyZcukUqlEIgEAmM3mX3/9dfHixRMmTHAyEPrfhvlKvHnjdvaAV4iKGqKskhcX3dYjoqGheUnR6XSUxx6bzW6c+ejvgOO4Xv8wA6tIJKJMOkmS1Gq1EonEMR0yGo0oilLpnwoLC2tra5VKZXNWqVSEtoZ6YbfbDQaDWCxuIZDp88HV069atapnz56jR48eOHBgVVWVSCSaOHFi+/bt161b90zH96KBBfqiXkrLvt+se46wB/QGTNoRk4aGpm1ITEwMDw/v3Lmzv79/aGjouXPnnlbPFy9edHd379y5c8eOHcVi8fjx4y0WS01NjUwmq62tdVSbPn36p59+WldX16NHj759+y5dujQxMbFbt25OvW3ZsiUgIEAoFFKJMgAAarW6a9euIpFIJpO5kuDoWePqfZzD4Rw5cuTcuXMnT55UqVRCoTAxMXHIkCFtruTPH+7U0eZfDqCeClb/nq3XpqGhoXlmrFmzhlKXzz777J133snKymqupsVigRC6nrxXJpPl5+cDADQaTXx8/LZt24YNG9ZkzVWrVsnl8tTUVEoOHjx44FShc+fOhw4dOnbs2M2bN6kSNpu9cuXK6Ohob29vF8fzTGlJCO/evXv8+HGnQrFYLBaLAQBZWVnURV+yZMmzG98LCCIS8uZObutR0NDQ0DyiQ4cOGzduBABMnjy5X79+lDr+/vvv33333datW1977bXy8nIGg4EgSE5OzhN5V0skEsoFvrkKlZWVAQEBjklR41zzsbGxAIATJ044Sng83oABAxrGZmtbWhLCGzduUCF2WuZlE0IaGhoaB9qiq+C5pGHiK8MZPOdUf1SINZPJdP78eSpeqNFotFqt1FGbzabX63/88ceoqKhLly4BACoqKlxUQUeItdzcXJIk33zzzeYcBCZMmDBy5MjCwsI+ffr079+/Q4cOf/kzthUtCeGUKVOmTJlCvb58+fLYsWMXLFjw+uuvK5XK2tra48ePf/rppxs2bHgu46ShoaF5Eam4tBmSz8OFzDdxgbCRELq7uwcFBWm1Wsqwf9y4cY0benp67tix45dffhk0aJBTbGuDwVBSUgIA4HA4QUFBDQ9hGBYUFEQQBEmShw4dys3NDQxsOnjIoEGDbt++vXfv3uPHj7///vuvvfbarl27nmJIr+eAS3uEEMIpU6asWLFi6tSpVIm3t/f06dN9fX3Hjx9fUVHB4dBpaWloaF5GIib81IZn79OnD7UKOmvWLJlM5rhFU1ARNKdMmcJisVJSUmbNmjVw4MDdu3ezWCyqwu3bt//5z38CAEJDQ3fs2NGwLYfDmTFjBvVaLpcvXbr08OHDAACz2eyoYzKZqJt/eHj48uXLAQDZ2dkdO3Y8e/Zs3759n9mHfvq4ZOqiUqlycnKcslUBABISEtRqdXZ29jMYGA0NDQ2Nq1AzMMobwRH2Mi8vDwCAouikSZOOHDlSWVl5584dKq0gRUJCwrVr165du+akgk6gKGo0GiUSiZub2927d6lCkiTv3bvn5EceFhYmlUp1Ot3T/XTPGpdmhBwOB0XR1NRUp11QysX+6QY/paGhoaFxkbNnz5rNZsqxOzw8vGvXrmVlZcuXL+/QoUN1dfXWrVsVCsWPP/7I4XDCwsIqKyu1Wm1ISIgrPVssls2bNwMASkpKvvvuu1WrVgEAFi9ePH/+fJ1OJ5PJ9u3bh6Lo8OHDZ82aJRKJunXrxufz9+zZgyCIU8bBBw8enD9//vr160VFRZs3b46IiOjVq9cvv/xiNBoBAHv27HF3d58yZYojqf3zxyUhFIlEI0eOnDdvnlqtHjVqFLVH+Pvvv3/wwQc9evR4iSLL0NDQ0LwwTJ48uaKioqCggIp5MmbMGA6HM27cOLVavXHjxnbt2v3888+3b98ODw/fvXt3SkqKu7v7r7/+2tiqszF+fn6zZs0qKCgAAIhEohMnTnTt2hUAsGTJkpCQkOPHj2s0mujo6GvXrnE4nJkzZx44cGD37t04joeHh1+9elUulzfsTa/XFxQUtGvXrl27dgUFBVSC+6KiIrPZvGTJEr1er9frXcyD9IxwNQ2TVqudNGnS0aNHGxb26tVr7969np7PLy1t26ZhonEFOh9Qm0Bf9ucAnYapbXl2aZhcdagXi8VHjhy5f/9+enp6eXm5UqmMiYmJi4t7ikOhoaGhoaF5/rg072hIAAAgAElEQVQqhO+++27D/c+CgoIrV65Qrzdt2vT0x0VDQ0NDQ/NccFUIL1y40DDEnE6nq62t5fF4Hh4ez2ZgNDQ0NDQ0zwNXhfDatWtOJZmZmePHj3/vvfee9pBoaGhoaGieH389ZHZkZOSGDRtmzpxpMpme4oBoaGhoaP7rsNvt1dXVdrvd9SZqtdrh8ti2/K3cEWFhYXq9vnGscRoaGhqaZ01iYqJMJpPJZEqlsl+/fg5X979Pamqq7E8CAwOXLVtGkmSTaZg++eQTk8k0duxYd3f3nj17yuXyUaNGOfW2f//+3r17KxSKiRMnUiUZGRlKpTIiIiI0NLRdu3aXL19+WiP/a/wtIaS8KZ6n+wQNDQ0NDYVOp1u/fn19fX1BQUF0dPTkyZOfVs92u53BYNTX19fX1x8/fnzHjh27du0iSVKtVjf0uDMajRaLZfXq1ZWVleXl5Xl5ebW1tXPmzHHqTS6XL1q0aNKkSZQHPQDA39//9u3bVVVVKpVq0qRJTpHhnj9/0WoUx/G8vLyLFy8mJSXR9jI0NDQ0bQifzx88ePCePXsAAEuWLOnevfuIESMAAOfPn9+xY8fatWtnzJhx+fJlkiRDQkLOnDnzRN7VERER0dHR5eXlzVXIzc2Nj4+nHPtYLFafPn2cKlAlWVlZlIc+AEAikUgkEgAAFYbmm2++ebIP/LT5i1ajDAbDx8dn1apVjcWfhoaG5uUhz6CB4HlERfHiCPgM5yBkVIg1g8Gwc+dOKiNeQUGBI9qXRqPJycnZuHEjhDAvL4/BYKSlpbmoglSINQhhdnZ2cXHx2LFjm6s5ZMiQadOmmUymxMTEPn36uLm5udI/hPDHH39UqVQHDx5cu3atK02eHX/dapSGhoaGZtilQ3aSfA4n2hjXv7/Sz6lQpVIVFBRQS46ONIROsNnsgoKCS5cuJSQkxMfHNzxEEITFYgEAYBjmlESIIIiCggIIYUVFhUAgMJlMbDa7yf7Hjx/v4eGxffv2ZcuWFRUVLV68mApM2ioFBQX19fV6vZ4aQxviqhCOHj16xYoVkZGRDQszMzPnz5/fMJY5DQ0NzUtF5oDJbXj20aNHU2mYamtrPT09hw0b1rjOzJkz9Xr9/Pnzi4uLp06dumbNGkc2+TNnzlDN27dvf/r06Yat+Hy+Q88++uijRYsW7dy5Ezwut1arlYqU3bdvXyrv0rlz55KTk0eMGNGtW7eWR44gCNX/3bt3O3XqNHLkSKcIpc8TV41lLly4oNFonAo1Go3TtaOhoaGhef5IJBImk1lZWSkQCAwGA1VYVlYGAGCz2R988EFGRkZaWtr+/fsb3rSTkpKqqqqqqqpavpO7u7tXVlbKZDKRSJSbm+sof/DggVM638TERKVSSZ3XRfz9/XEcd4y5TXB1RtgkxcXFLi4HtwlWElYZrU8r6LYnh8nF/paRLQ0NDc3T5f79+6dOndLr9Xv37lUoFPHx8bm5uZs2bRowYIBKpfrhhx9kMtmBAwfc3NwiIiIMBoPdbqeSP7SKzWY7deoUhLCgoGD16tUzZ84EAMyYMePdd99dv369TCbbs2dPbW3t8OHDP/roI09Pz27dunE4nH379un1+h49ejTsqqioKC0t7e7du+Xl5SkpKUFBQeXl5WazOSoqqr6+/quvvoqPj/f3938mF8g1WhHCAwcO/PDDDwAAtVq9YMECsVjsOGQymW7dujVkyJBnO8C/wfV6+7ibRU+lKwNh3xQXNDVY3HpVGhoamudCUlJSQUHB5s2bWSxWZGTkunXrhELh1KlTCwoKJk2aFBoaunbt2tTUVAzDvvzyy/Lycrlcvn79+tjY2FZ7VigUycnJVD5CuVz+9ddfU96Bn3/++YYNG1asWKHVaqOioi5fviwSiXr37r19+/Zt27YRBBEeHn7u3DkvL6+GvRUVFaWkpAAAAgICUlJS+vfvHx4evn379lWrVgmFwp49e27duvXZXCFXaSUNk0MIL1y40KFDh4ZCyOfzo6OjFyxY8DwXdp8oDVNhnWlvKfupzAg/fpD3fqTy/ajWhdBQZi48XGU34l695B7dZX//1P910PmA2gT6sj8H6DRMbUubpWEaNWoU9SCQnJy8Zs2amJiYp3juZ42CA5d0QJ+KEK7Jd6masdJyf3Nx0HAPjhs7d28ZYSO9ez/B0rGhzKzO1HPkLLdYMYLRaRRpaGhongctCeGJEydWrVo1Y8aM8ePHCwSCefPmNVnt3Llzz2Ro/21AEubsLAsc5uHeSQIAaD8j4PbX+eIQgcCb02pbAED5udryc7WKLtLq65qK1Pr20/0ZPOwZD/npQEBQoIM2EgQKEd4TbjrXW8GRYjJXB7kY0lWB9PV6ggeAeivYW0BeroYaG/QTIAO8kUG+KNO1bVwLAQ4Vkb+VwgdayEBArBwZGYD280JQ186ep4OHimFGHdTYoJKLJCiR4f6otGnbcmdwElyogldrYLkJslEQJkH6eCLtxK5+bIMdXFPBPB002IGCC6KkSAeZq8PGSXCrDt5XwxozYGMgRIR0UyBuLv08AQCg2gwy6mGpAUIAPLhIjAz4CZ7gcU1jA/k6qLYCDAUeXBAiQlz8siggACozMOJQykYkrCdo6EBnBwQJJGxAP2PSONHSfQvDMC6Xy2AwAAAcDofL5T6vUf1XUnVFzRQwFJ0l1Fu2hBkwWJl/oKLD3KBW//NU6drKS/Wxi0JYIgaAoPBoVebW4uhZga7Lgr7IVJ+lRxmoWwcRV+HaLflPivRwTwEs1MMAITIq4AluymYcfHmX3JhJ8BgIBwPlJjjCH303DGnvwhKdwQ4+Tid+fEAmeaPRUsRMwA9vktMvghVx6FvtWrmxm3CwOoP4NpMc6IMmeSNSNlKoB2vukvOvkv8Xh04Kbak5CcHWB+TKdDJSAkYHoXMiUTsJ0mrh0jTCSoD/i0NfC2yp+dUauDKdSK+FrwWiyT6ImImUm8BvpXDhVfvrgeh7MWho81ev3Ai/zSR/yiEDBEiiJxIpQawEuFYDP0kn3TngHxHoxBCU38x/pJ0Ex0rIn3PhuQoyRo5ESBAhE6TXgdUZZLUZvhGIjvJG+zVz2SEAFyrhtlzycDHpxUfi5IgHD5hxcKKMnHgOtpci44LRMUGoezOK+EAL9+TDg0VkiRHGyRE/AYIioNJE3q4DLAwM9kVGBaKveDT9U7UQ4EwFPFJMnq2ElSYYLELcOAAnQYUJlBphRznSxxNJ8ka7KxFWU6JYbIDnK+GFKpimgg+0UMwCfAZSb4U2EkRIkM5uSHcF0kPZ9C/WhIObtfBKDbyugnfrYaEecjCAIUBvB74CJEICOrshXRVoV/emHwUqTeBGLXmzFt5Tg1wtrDBBrQ1g14hBeNNXieY5AAGotQAJ8+8ZeTZFK3uELxpPtEdoNBp5PN5TWRp1/zVnYbuW9ghJHN74NCdymv9j8z8Ibn+d79vfXR4jaqFzq8Z+e21+1MwAvhfH0TBzazHfi+P/qrLVsUESZu+vLL9nSJWL2JDsXqML7Ofm38+lJVmcBB/dJLY8IMcFo5FSJFsDd+eTrwWgq+IxkXMIC2cy6uHo00SsHFnZCQ0TIwCAWgvYmEWuv4cvjWUsikJb0JPL1XDSOeIVT+Szzpgn71H5lRq46CrBQMGWXlhYM4pypgLOSCW6uCNfxKO+/MfqXK6Gi68REIBvemCd3ZpofqUGzr1M8BlgbTesU6MKJ8rg+2kEGwOfd8ESPZ2P3q6Dy2+RGXXwg47opBCU/fh0vc4Kvr1PfpdJ9PVC50eh3RWPNb9WAzdmkUdLyEmh6JxINET02FESgjMV8Pss8kIlOS4YHR2EdlM8nC3hJLhZC/cXkjvzyVAR8nYYOiIAdfpqig1wVz78dzaOouiYICTZB42SImIW0NlBRh08UUbuyYc8BpjSDh0dhPg8fsVsJDhdDnflk8dKyK4KZKAP2lGO+PCBlQT5Oni1Bv5WAlUWMDoIeT0Q7aZwVrssDTxUDA8UkiUGSOlZoBBho0Bnhw+04FIVebEKxrkhQ/zQAT5IpOSxmasJB9dU8EwFebIMZmlgrByJkiLefISBgFoLzNeDGypoI2GiJ/qKB9JNgURKEc6f19yIg/tqeF0FL1fDy9VQZ4ftpUigAOEyAAlBjQXkaWGhAcbIkG4KpKs70kGOBAsR6ivDSVBsgPfU8GYtvFoD01RQwESCRUDBQUQsoLWBKjN8oIEAgM7uSJwc6SBHQkWINx8Rs8Av27afP31yB71H2EaweXzRN+U2Bv9YX3svX17rDVzGVSGEEB47duzgwYMVFRUEQTQ89Dwd6ttKCH33Zr3T3qsFIay8XK/O0ke+7WwBrM7SFx2r7rg4pIVJYfYvpTwPtt8ARY0ZnCgjffhIHy8E1+O31uS1nxHQysoqBOm/lF0rxa93954axTATYOtNy8gbZRFxopgRrYioCQevn8YxglyDaQ3pGpvOzpGxxF1kq1HR2Sqwvx8WI2t20PsLyVmXiA3dsbHBzk/y96sMc25yAAA/98b8Gy2dERB8fpv8LpPY3Asb6tfELICEYGMW+X+3iJkR6HsxmKDBTb/CBP91gzxbAb/vib3q2/TYIADbcsn308heHsi89mh3JYIAQEJwqRquu0emqeDqeHRscLMaTUKwO59cmU6KmGBMMBotRVAEZKrhwWIyTwfejUZnRjhLYEP0drDlAbk5m7QQoIcCkbBBvRVcrYFsFEwNQ6eHtbJ8WmqEv+TAg8VktgZ68RAGCkoNMFiEDPNHJoWgLc/U9Xp9pllwsJg8WwEzNdBgB3wGiJIhfT2R1wPRuKYeCxpiwsHvpeTZSni7DlaaAAcD/kLQ2Q0Z4IN2V7S+9FpqhKfK4XUVLDFAGwmETCRYCLopkD5eqMyF5QmdHdxQwUwNrDRBAgI5GwkQgo5yxOmJoTlUFnBfDUsM0EIAAICCCwKFSISk6VlmY4r0sNAAai1QYwUSNlBykXZixKOp9S/aWKZteXbGMq4K4cKFC9etW+fr6xsSEoJhj90JXgYhDNtx781Y3381I4SQhDdX5bYb7yMKaOIhJWN9vneiu1uHpieF2nxj7u7yuCUhF2uR0Wfwvl7ofTX044O9/RjGdE3FpbrY+cEtiGjO8Zor1/TqkQELYh99KevT7J4HCyP6y6P7NmvQa8bBkJN4BLBPuVfGV3J8+rlx5CxTlbXkRA1pJ7P7+MzJQJrUORKClenEzznwcDLWQYYYyy1lZ2u1eQZIQL4X1zNBxgpA+ALhV3fJL+8Q73XAZkeg3D8XMi5Vw4VXCTEL/PwK5v3n1ASS0K7HURbK4D76COVGuCyNPFFGjghA4+SIlQBXauAf5eQ/wtFlsZiwtdmqCQebssmtD8gyI5SzkVoLDBAi08LQaWGPBtMCJAR/lMOjJWS2BiIICBYiA32RwS5vQAIAMjXwZi3U2YCUDeLkSLjkyX6ERhxUGCEBga8AaW6x1AnaavQ58KIJ4dChQzMzMwEAYrE4NjZ2xYoVfn7OMdj+GlevXp0wYQIAAMMwT0/PsWPHvvPOOyqVqlu3bmlpaTLZQ3v4BQsW+Pr6zp8///3339+9e7darZbJZMOGDfv2228b9ma32+fNm7dnzx4Wi7Vw4cKlS5cCADZt2nTixInMzMwlS5a4mH2izaxGKQiC2LRp05w5c9atW+ekgi8JLJxErERzR+vv6VkCRpMqCADwTVIUH692ixE1oWcQFB6pChiiLLUiY87ge/oy+ngiOAlmpBKvn8KPJUuqr6srL9d79mzaDUOdY8hPVWclB6yKfexLmd+F+TP0KzlQxHRnh0cLGje0EuC1U3h7wjbxVqlXf3dH/6JAXtTMgNLTqqDDRf8Z6zfmFnKqAn4Rjzke6vN18B+phJ0E14YzFGxYfLy6+rrau49bwGAlykS0ecaSEzWYEImYxHs3Bhvuj/zrBvlpuj3BAxGzkPRaaCXB8jh0YsjDCYZNh5ecrKlN16JMhLCSHBnL6xW5Ml4KEODNR7YlYsUG9FARTK+DLBQk+yA/JDCdrCQMZWZtvtFuIDhylixSyBI9/D3zGGBhFLowCq23Ao0NSlmIi5YsFCgCBvggA3z++k89UoJEPqH4NYTPAC3sNdLQUJSXl8+ePXvEiBFms/nTTz+dNGnS+fPnn0rPZrNZo9GkpaUBAHJzcydMmEC5zBcUFDRcEaypqREIBOvWrfvjjz8uX77s6+tbVlbW2Hzyu+++S0tLKygo0Gg0CQkJcXFxycnJZrN52LBhFRUVWq32qYz57+CSENbW1prN5ilTprycKggAQADAqpsNC1t+rtYrsdk9OVmksORkTe0dXeNJYc0tDYICtw7iySfxBVFYH08EAMBAwY+9sEEn8P9LJ5a+7nX3u0J5tMhxi3dg1+O3t5XtCPP6+ZUm7vGT4zk7dN55O8qQ2YFhfo9VsBDgjdO4r802Mb00cIiSsnFt+FF9+7uzRIzincWp0wI+K2WE7LMP9EF9+SBLAy5Xk0s6YIuiUWgl7v9YCgDouDiEKXg4NrdYsTxalHu4LOPr/Ii3/dp5clL6YTVm7HINqbOB+e3Rzu6PpufV19VFx6qVXaVx74WwxEwAgTbfWHyipuqqOmyiD0fOAgD4C5D5UU3rgb7YVHikyqbDpRFCloihyzcWHauStxf5D1GyhI+ulYwNZOwmesBNRNWV+toMnbHCAhAg8OG4xYo9uskwtquTPmOFRV9ixk04S8gUBfI4bk9syEjYSAQBTzDNpKFphLu7OxXkbNq0aWPGjAEArF27NjY2lor8eePGjaNHj37wwQcffvjh2bNnIYSRkZE///yzKz1jGEb1HBQU1L1796ysrObCh968eXPQoEG+vr4AAB8fH0f2XQc//fTTsmXLpFKpVCqdOnXqTz/9lJycvGDBAgAA5Wjf5rgkhG5ubh4eHqWlpXFxcS72CyHMz8+32+0RERFOhywWy8WLFxkMRs+ePVmsx24fGRkZUqn0ac3uny6Iqmkh1BWabAZcHvVoVapID/+dQyZ6oX3+NLjwH6gsOFQpjxI2NDYgrGTxb9Xhb/qeqYQ5OnAw6dENEUPAjkRG7EH7AB+Wb4Isd295+2n+DSeUkITp/y79VSxZPkLY3I10Yn/hAa3brU3FldODEgMeftFlRjjmDNEB2KfcLvV7VeGsgn+ijJeiDKTwx8JPpgcs68D5rZSsMoEJIWBHH6aICSz1tswtJZJQfuBwD6ftIwRDPJMk0gDRvY1F7cb7SMMFCi4Y4f/YEEkbmbe/wlhuiZ4VyPP4U6QRIA7hx8wOrLxUl7G+IHiUV3OLySQOi49Xq25pAoZ4KDpJHJeFsJClp2rSv8wLGuHpHtd86AMIKlLrSv9QySKFQSM9hX5cSEJ9sbnqqvrmmRy/gQqPbrKWrHwhqLmpKTtTS9pJcQifyWcYKwxFv1Vx5Cyfvu6yVu1lIVBn66vTNNo8I2EjAQQMLippJ1B0kUpCXVrqsdTbNDlGU5WFtEOMhfK9OJJwQUPtb/nsxiqLodRsN+AIinBkLFEQz/Ec4wpWjd1Sa4MkZAoYXAUbZdDTVkCS+PNJw4ShDNDop0mFWNPpdF9//fWUKVMAAFeuXBGJRJQQlpeXnzt3zs3N7fbt2/v378cw7MqVKxBCVzaMqBBrBEFkZ2ffunWrhYQSPXv2XLFihUQiSUxM7NSpk9N8CUKYm5sbFRVFvY2Kijp+/PiTfvZnjUv/AxiGrVu37qOPPoqJiQkMDGy1/p49e2bOnGmxWKKiom7cuNHwUHV1dUJCgp+fn9VqNRgM58+fd0SrOXjw4Ouvvz5jxoyNGzf+hU/yrMEqTCQOG//nl55S+SS6OfSgygwSjhGvByJvniO+iEfHBaMAAGm4gCNjll+o8+nzaOJYfLxa0k4gDOCt/A1f3tF5YqDggh96YpPPE7eGuas3F5aeUvkm/RkhEIK8A5V39Ui74YqWDShGjXI7Z8NLtxSN7+Yb7cssMcCUAnKlp7VzapnfIKWyi7SFtu5xEpSJ3t9UFDTCc2oDXam7q8vbX+GX5O6Z0OwGpHucmC1jZv9S6tld6tPfvaFYGsrMOTvLhH68DguCmpgMIcAzQS4M4GX/UqrNNQQO80AfN3gwlJpz95RzFay4d0MZ/Mf+3zAOGjDEwy1WnLOzrD5TFzTSi8l3XsAwVljyUspRBho9O5CnfKjBCIaIQ/jiEL6xwpK/v6ImTRP8utcjC94GqLMNRUerGDwsaKSHJPTRmjMkYd09ffHv1WWnVQHDPJpcJIckVKVry06pUBbq0V0WNMKTmuVb6mzqLH3hoUqAAt9+7m4dxE3KMCRgbYa28rLaXGOVhgv43hyMhRIWsj5LX3C4UujPk3TkCjsJm5Nwq9pedbW+5oYGwRBRAI8lYkKC1OYZc/eW8704ynipW6yo2bkpBNoCoypdW39PBwDgurMRDLHrcXOdTejHlUeL3DuKW1ZTQ7lFnaXXFRhNVVabHgcQMAUYT8kW+vMk7QSiIF5L1jgQmKosukKTocJiVdtJO4lgCEvE4HlwRP5cgR+vZTGGJDRVWQ3lZnONDTcRAACUgbAlTJ4nR+jLZTT6hTR56UzVVpvWbjfi9Zl6SDhr3nc/RZGkvdV+/j5Dkn8IDkhyKjx79mxBQYHZbFapVM0FPDEajSRJ2u12X19fKkCKK1D5CAEApaWlUVFRUmmzt4vZs2cLBILt27evXLmSzWavXLly9uzZjqMmk8lisTg2sEUiUcPUti8Irj4MHjhwoLq6OiwsLCwszCklfWNjmZ49e2ZkZFy8eHHdunVOh77++uuOHTvu27cPQpicnLxp06b33nsPAKDRaJYvX/7GG2/81Q/yPKi5rvbo8dh2na7QZK62KqY8msK+n0ZMCkE+74K93Q72O4739kS8eAgAIHiUV8b6AkkIX+DLBQDU3dXV3dN1XBxyrQaWGMCYoCbuQcP80cPFcGEa+f1kvzvfFJAE9O3vTlrJgsOVeUW2X2N8jrZvfVUtcZxH/kmV34XCvFqpl5Q9EzNaL+pDxnjLIls3rJBHizgy1oOdZZWX6+XRQgBB3V0dbiIipvg1tyHqQBTAi10YnLunLH1NnlcvOd+ba9fjtbe1mhxD4PAWZ2wACHy4sf8MLvi18uaqXJ++brL2IoyDmiotVVfVmgetNBf4cGMXBRf/XnPri1yfPm6KLlJKDo2VlsqLdXX39QGvKqltyMbwvTgxc4Oqrqnv/VAkjRB49ZILvLkAASQO1Vn6igt1Nj0eMFgpj3aeqiIo4hYjcosW1dzU5Owo47izvBLkknZ8SlqsGntthrYytZ4tYQaN9JS0e2zXliNneSbIPXvK67P0ZadURcerPbrJ5NEirjsLAAAJaCiz1N7Rqm5oeJ4c70S5LELo5MRA4rA2Q1t2rqbyPxplV6lbBzHVFgBAWEn1A0NNmlpXZFJ0kkS+7e8k8JCA9Zn6qqvqgiOV7h0lbjEiYcBDaYEENJRb6u/rVLe0KBNVdJbEzA2ilqwfntdOanKNtenakhM1okCeWwexpB2fJWY6juqLzfVZ+ro7OoAAeXuhZ08534vDEjEAAux63Fhp1RUaC49WWepsknYCSTuB0JfLljMZHAy3EJZam6HMoiswah4YUBYqDuYLfLiySCHGQkkc2rR2Y6Wl8IjWVG0V+vFEwTyhL5fjxmYKMACAXY9b6myGMouuyKQvNLHEDIEvl+vO5siYAADSDq1qmzrbYCg1s8QMUSBP4MvleXDYEibGQSEB7QbCXGM1Vlr0JWZ9iQnFEJ4Hhy1lMvkYaSMbGxfOnZbd3K/xOTBnzhwqj1JxcXFoaKhTtGuKWbNmFRQUxMfHKxSKf/zjH/Pnz3cc+s9//jN+/HgAQFRUlNP+okgk2rdvH/V64cKF8+bNo8Jt2u2PVN9ut2MYhiDI5MmTJ0+ebLfbd+/ePXXq1F69ejlUmc/nc7lcnU5HvdVqtS5G/W4MhKD6qloos7N9MPA0bWWexDExOjraxZrUYvHFixcbHzp48OAXX3wBAEAQZNy4cVu2bKGEcP78+f/85z9v3bpls9lcH9LzBG8nKjlZ49ZB/OgpEoKio1V+AxWOZ9JKEzhcTOaPYQIAomXI9HD0wxvk1lcwAABHzgod431/S7FfkgI34RWX6ttP92dwsXVXifntUUYzira+O9blML69Ep00Nyj/QMXVD7IQBFgiJHP8fFL7MV2MJxKc7O4RLfS9prZp9QJvjsdw57lUC/C9OR0XB9fd0+vyjQAB3oluskjnG3FzsESM9jMC1NkG1U1N1TU1g4NJIwRBozwZnNbPzuBg7cb76ItMFan1padrCQvBdWe7xYqDR3m1uo2HMtHAYR7KLpLSM7WlJ3MwLkrYSIyFKuOlnZaGNrRNbQIEeHSTusWKKlPrH2wvsxtwBg+z6XGhL9eju9QtVtzS3AUBis4S945iVbq2/Fxt9vZSJh8j7RAAIIsQhk30Efo3//SAAFmkUBYp1Bebqq9r7m0sxM0ExsFwM8F1Y8kihdFzghzy5vx5GYiik4TbDkPUjOo09b2NhaSdZEmYpI206XBhAM89Thw+yRdtypkAwRB5tEgeLbKq7TU3NEXHqoyVVpaQASG0GwmuG0saLgyf7NekDw/KRKkxEzay/p6+7q628GgVJCCDh5F2EjeTfG+ONEwQMcWv8fSaJWayxExpuMB/kNKmw9VZem2+seJCnVVtI6wkxkE5MhbfiyMO5vsNVHBkze6/EhZSW2DUFZrKz9dZ6h7O+Rh8jCNjCXw4Ht2k7cb7NF4YoIAkNFVadYVGfZG5+rrapsUJKwlQwBIwOG4svhfHs4csdKx3w2VnN7UYrXlBV4P9/PxYLFZ+fj6Hw3HkuVWpVAAAkUi0efPmjRs3nj17dvz48XFxcb169aIqJCcnV1ZWAgAcGQqbJCws7NSpU3K5nMfjFRcXO2JqFxYWDho0yFGNyWS++eabH3zwQc96jx4AACAASURBVHZ2dsPpabt27e7cuUOV3L17Nyws7C9/TGOlxVYGFW5P2UDaVSF0PBr8TcrLyymZBAD4+flRaat+//33srKyN99889atWy03t9vtFy5ccEpnPGjQIImkib0um9FueKB+Ku4TGAkJEcs9jpm9oyR8ii+lBOVnagEK5LFChxnVthz4WgAixB4W/LM9CDtA5mpgkBABAIjDeeFTfKsvq1E2EjXbnyNnlerxk2Xw++6ok2umAy4Kfu0L+p0gQBwy+U1v0k6erESmXoYH+qJKNtlMoybgKJj+QxWOt82drjmk7fnS9g8fwEhAghZbEwTRsH9RKFcUynWq4OJ5eb7skHGej5dBF5uzFcyQsZ6Q8LDpcJSJONbuXGmOMIFXH5lXHxluJHALwRIzqWcdErby2SnkHYXyjkIShzadHWWgDkMnV07N82EH+igDgZKwkLiFYPIxx4ply80JghB68wK8lQEjlHY9btMTKANw5Czqtwpbu24MEerVV+bVVwYJaNXYERRhClw9NcCArINA1kEAALAbcMJKogyEKWQ4Hhpabo7xEbfOIrfOzQadaKk5E4jDeOKwltYnWmjO8WByPCSK7i20fqw5+Vwy0T8RVIZ6jUaza9cuDocTFxeXkZGxf//+yZMn19XVfffddyKR6OzZswEBAYGBgbGxsTwer6HmIQjiZKjhgMpQTxBEfn7+hg0bBg0ahCDI2LFjP/roo59++snNzW337t15eXmDBw/esGFDUFBQjx49uFzunj17VCpV586dG3b19ttvr1mzJikpqb6+fuvWrbt27QIAFBUV1dXVabXasrKymzdvBgYGOrwymgNBgP8wBeU+4fptBEVbjzjtqhAmJyfX19c3LpdKpQEBAaNGjRo4cGCrnUAIbTYbldEYAMBkMi0Wi06nW7hw4W+//eaKYtnt9hs3blRXVzcsjI+PbzL8m1lnqU83PxUhtGMIwYSK7qLCPZZ7m4qUCRJDoVmTaWo3zdNqe5SvOaWQ8UlH0mp9+N/CAWBqMPplBrKuy8PvjKlAfEZQXza0Wq0b76Oj/RE2aW+Q89kZfw74rS8y8SK2/h7gMUCVBexOwOPEsIUmbYvNZrO+UIPjARIAa/PeLy3BAIgA2AmbK/rXGIQPICD+4qkRALjATpLAtWv52GVnAYYcAABsuA08eUgwRAAAgK6f+jGYAGECCKDN/oIu7fwdcPzFCrAWERGxa9cuSgLDwsLOnz8vl8tnzpyZlpYWHBwcHBz8zjvvpKWlFRUVLViwoK6ujs/nz507t2fPnq32LBQKAwMDR48eDQBQKBRTp06dO3cuAGDdunUffvhh37599Xp9VFTUiRMnlEqlh4fH2rVrZ8yYQZJkWFjYoUOHnLL1zpo1q6ioqGPHjiwWa9myZZQhz88//3zs2DEAwPnz58+fP//JJ5+4IiJWqxXDMKvVSsX+dAUOh9O6CkDXmDZtmlQqZbPZCQkJI0eO7Nq1K4ZhPj4+Q4YMoRIqfvjhh05Ntm/f3qlTJ6dCX1/fM2fOUK9TUlI6dOjw9ddfR0dHL1myZMmSJZ06dYqNjV27dm1zwxg1alRKSoqLYzYYDCRJuli5ZdwOPPjkrgZCSBJk+YXae5sKc1PKrTp7wzoVRijfZrMTjzWsNkHZNluNuYk+LTj02GHLUrs0QpyEl6rI0+WklWi9ctui0+naeggvI/Rlfw5s27Zt4sSJbT2Klxcej0fd1Q0Gw9Pt2VVRDQgICA0NPXTokKfnw6Wq3NzcQYMGjR079uDBg++9997nn38+c+ZMp3yMToqLIEiPHj3OnDnTp08fAMDZs2d79uyZmJjI4z1c2RCLxTiOO9ZOXyioPXIERbx6yb16NWEw+Z8ysp+3826fggteD0S/yyRWxDlvVOzOJ2NdjjmCIaCH8gXdnLCRMN9gBQAEC9gsF/ctG1BishUZbQIGGinicLAnc6ojIMzWWSwk9OOx3NlPHInXiJN5BquQifrzWNjTWDmgoaH5b8Sle4fNZlu9evXx48cdKggACA0NXb58+SeffDJhwoTPP/9848aNV65coWxz8/LytmzZcv/+/fLy8qVLl4aFhU2YMIHNZt+4cWPRokVJSUnu7u5ms3nHjh3Xrl0LDw93ZEy+f/++zWZzMYLa84TaZmmZUxUwybuJm+niaLTnUXxx9GNhM0kI1twlv+72mDqqbcTVOkO4iBPIf7L0ERVm+6dZlWdr9O5sxiR/+dRAN9f1qMRk+ziz8mCZps6G+/NYkwLk74YpRUyXrGnqbPjH9yt3FNe7sRkAAJUVH+MrXeDnaqivA2Xqz7Kqys32MCFbaydKTLaxvrL3Iz18uK07p5eYbF9kV+0qqVewmXwGWmi0BvLZ7wS7vxUgb9WIiIBwb6l6U77qRr0pWMDW40S9jRjuJZkb6t5F1rotWq0V/6Wo7miF9q7WrMcJTw6zp5tgrJ90sKfYFTW9qzUfKtfcVJvKzTY+hoUJ2X0UwsFeYiGj9WsOAbilNl2qNeQbrBaClLEYMRJuX4VQyWkt6BwAAIAqi/28ypCpNVdZ7DwGGirg9HDjd5DwXPmxEBDe0ZgztOZqix0AIGcxosXcOCnPRZMtEoICozXfYNXZCRQBHhxmpIgrZT1BgA4DTpabbWaClDAZHhzGkz4zGXFSbccJCNxYDH5zxmk0LyuuRpbR6/WNDVIkEklhYSEAgM1m+/r6Go3Gh50yGFKpNCEhISEhAQAgEAgYDMbnn3/u4+OjVCpPnjy5c+dODMNSU1PDw8Mbdjh06NAnteN4cThfCf8vrol/sFAxkuSNbrhPvh/76OiBIpLHAA2F82SVbuK1whgJ967GPC3I7ZPopkS1Ka7WGUdeyp8SKN/bPajcbP88q3JXSf3+HkEyVutf7qFyzYwbxbNC3O8MiPTiMu9rzWtzaiJP3P8lPrCfshU1+61SOy2t+A1facaASG8uEwBQabGvz6npcan402h8RrB7C+MvMdmm3yhWWfFPo7wGeIipe2mlxb4hp6bjyaz3wj0WtlMwmhEVKwk/z6r8Lk/1jyD3ewPae3GZAAASgnMq/ersqi+yq9bG+g7xata/4mSVbtHtMikL+2eY8lVPMTWFVVnxHcV1oy4XxEl5n0d7R4iaDnRebbGvzq7+pahumJd4aYRHFxlPyMBKTLazNfrV2dUL0sveDVdOCZA3eY/GITxQplmXU11htr/uI53kL/PjsYw4eU9n3llSP+tWySgf6axg947Spu0+8gzWnwvrthfXcTG0r0IYJuJwMVRlte8rVc++VRIj5o324E0M4TX5BKOxE3tL6ncU12fqLK+4C2IlvDgpz4CTtzWm9bk1ZoIc7Sud4Cdr8tR2Ep6u0aeUqo9UaDw4zI4SHnXBc/TW7/NVhQZrP6XoNR/JEE9xk6euMNuPVWqPV2rP1+hlLEawgC1hYQSEFWZ7ps7iwWH2UQiTlMJEhVDe6OdqJ+EttelireGiynBTbVTbCB8ei4shGhtRbcW9uczOUl43uaC7nB8r5bEb6XGF2X693nitzpimNt7VmPU4KWNhGIKorDgXQyNFnE5SXrycHy/jBwucgw/hED7QWW6qTbfUpvs6S77BWmmxWwgSXCsagf+33qD+B4AAFBqtXgw29rSTJrkUdNtms7m5ub311lvffPONo5AkyREjRuTl5WVmZkIIJRLJnj17GprSPgvaKui24teceaHKD6Kbvb0W6OErx4iycU1rT54O9jiC336NQfkUWggQfQDf2BPr/6fY3dOa+57LOdQzuIeboNaKD0nN66sQfhbt3erA7mvN/c7n/ruL/yDPh2MjIVh6p+z3Kt3pxHaKFlcLN+WrPsmsOpQQ3OnxO+DZGv2ka4WzQxRLIzyavHYkBCvuV/xSVLerW2BPN+dYpjer6mbdU7mzGVu6+Hs0NVPZWVy/6HbpwjDl4jBlY7UrMFrfuVmisuIb4/y6yp3nZyerdHPTS6PF3PUdfSn1deI/VboF6aWBAvbn0d4dJI+ZUN3RmP91r/yBzrIm1meYVxNmxlYSfp9XsyqraqCn+J/tlDENmucbrN/nqX4pqpvoL1sS4eHZ1Oe6Wmf8LKsyrd40LchtjK+0vZiLAAAByNSa95dpthbWBgvYC9spmpw41ljxrQW1m/JV7hzGGz7SV9yFQQI2AKDIaE2tNfxapskzWCf6yyYHyKPEznZhVhKeqNRuyau+WG/uqxD2V4qixVw+AzXiZIbWdLJKd1FlSPYQvRkgT1aKGk/gMnWW3SX1u0vqUQCSPUQdpTwfLstCkHkG67V64+lqfYSI84avdKS3xI/nPFOvs+FHK7S/lqnP1Rg6Srnd5AI/HouJIiacfKC3XKo1VJjtAzxEQ73EfZUip18jBOCe1ny6Wn+qWnex1uDBYbYXcdzZDAaKaO1Ert6aqTMHC9i93YU93QRd5Xz/BmcnIMwzWNPqTVfqDFfqjNk6SwCf5cdjiZiYjYQqK56rtwAAusj4XeX8eBk/Wsxt+GtRWfF7WvNNtelanfGG2qiy4kF8tpLDRBGAk7DSYi8y2nx5rE5SXpyUFyPmhgjZnhwmF0O3b9/+n5N0GqY2g83jeW0/r8WYx+J9e3g1G9DjL+Bq9onVq1cvXbo0ISFh6NChcrm8srIyJSXl7t27u3fvHjNmzKlTpwYMGFBVVfWXPSVd5ImEsLbwZu7/s3fegVEUbx+f6z2X63fpvZIQEggECDX0FkMVBKUpKqh0sANiBRSQDooiERCQLr0mpkESEtJ7u8v13su+f6zvGZMjOTQQfrqfvzazO7Nzm7t5dmae5/v89GK33HeFz9pJgZHvDnMRrArzY5XjUhN0bAQGAHBdrPmhXv6CNyPN58/RdkO+I0PsuDQGi0OD1zLsGiv4efgfb9AOCAy4Uf5aMHth4B+6M3KLrd+18m1xPqneriXQYDRWe99rZR9Ge73k397teEOJ6FSz8tbwsI4v2jDfVku2VUhuDAt1uQwrNFrTfq/xIeG/6+ff7k1fZrbNy63X2+y/DAx2aWi1Wi2JSt1UItpbI/0o2mtRENu5cVimMa0pam7QW35IDHjc1AfmaINiXVFLXyb5lQBWHwYZAiBLpttfK2sxWrfF+UwQdBaPb3VAe2ukn5W1htIIY/l0FgErN9sut6qrtOa1kfzXgzmdb2Sqrfbd1dI9NVIKBg3bwnKtqdVonRfAWh7G83JlfdtSpjEdqJWdE6paTVYeAScxW/lE3AQBfX4gu51h7ogdgm5JtGdaVNlyfZPBAgDwJuMHMCkTveijXNmwtmi1WiuBdFGkviPRlWtNBruDjEFHeRCHc2njBXS6G2vdhSrDLYm2SGUUmqxENDqQgk9gkkfxPFy+zbTDaHdkynR5CkOz0WJzQGQsOphKSGRSEhhkd5aL4b3eCq1ZZrFZHRAdhwmmEnrRie4sFwMALA6oSmtqNFh0NgcOjWLhsbDpcqcuAEBjtdfpLVKz1QEBLBrFI2CDqASSq2n985Z94r9Gz6dhAgAcPHjwq6++qqysBACg0ejY2NgNGzZMnjwZAGAymUwmk8tgvu6lp2aEvY+cnunL78QQLsmwRzFQb0Wjs+X61Myaj6IFW8rFG3r9aaLsEJh1016jgTzxwGAHV8dhnelVjzTI99bIMkaEt+0ovOBZMDqyk2FoTnYdHYfZneBamvXd4pbLrZprQ0M72sKvK8XfVklvDg/z7/CO78TsgFYVNp8Tqj6P9Z7mw8ChUSa746cGxYePhHMDWJtjvB63dOnMB1SkMq4rbrmv0I/kedCw6BKNqVprWhXBfzuU645PjdHu+LFefrpFVao2AQDiGKQX/ZgzfBmPu287LA7ocqs6Q6pTW+10HGYwhzqWT3fflwcC4KHKUKoxYVCoIAq+D4Ps5n2daKx2mcXGIWDdHM3/IUgapmfA82YIX3/99erqagAAhUKJj49ftmxZJ0JoT8TDhw9XrVoFAEChUAKBYMaMGRMmTFAoFDNnzjx58qRTF/PTTz8VCASvvPLK7t27f/75Z4lEwufzx40bt379+ratQRD0+eefp6enEwiE5cuXz5kzRywWt9PmXrp06ZQpUzrvVQ+nYYJZtGjRokWLTCZTa2url5dX2zBMIpFIJHaaP/bfTrYEWhiOBgC8XdD0TZzPLD9mMps64nZlCo8GWzIMCpwYibncBBls0CT/P1U+7BC0sUR0sJ9/u1F2AIuyMJC95EHjmUHBLu+Y3qgoVBnuj2qvae7k0xhvFADJNyvODAoOo/3x37E6oHVFLZdE6tvDw3wfbwUBAAQ0ame873RfxkclwtcfNAqIuCajZSiHdmZwcKIbHiUAgFhP0qXkkAaDJUOq09nsM32ZyRyq+6aIhEG/Fsx5LfhvrjHg0ajJXp4ul0DdAQVAnCc5zvPvZ8H2wGHc9DlCQPh75OTkjB49OiUlxWKxfP3113l5eefPn++WlhUKRV5e3smTJwEAdXV1s2bNOnv2bFRU1PXr19uKfz169MhgMBw8eHDbtm0//PBDUFBQfX19RkZGu9YOHz783Xff/frrr3K5/IUXXggODo6Li1u7di18ViKRvPTSS2333Z497hpCCIL279//888/19bW6nS6tqdcBtr/y0ChQCdR0TorqNFCvVmou/CI78cEAPSik14OYG0sETlnbCgAxvmi2unHn2hSepFwQzku3uU/jBb0u172Q7385YD2q+F1evPygqYrQ0NdLuA42RzjHUAhDLpZMT+ANZxLazRYvq2WBlLwmSPD3XGlAQAM4VBvDQtTWGytJpsfGU99cnc7fzLev8PKLQICQrcQHR2dkpICACASiXAapqNHj4aFhfXr1w8AUFpaeu/evVdffXXv3r03b950OBzR0dEbN250p2U8Hg+3DAA4d+5cTk5OVFSUyytv3bo1a9Ys2DXSy8uro97pnj171q9fDyegWLBgwb59+77//ntn41u3bh04cGA7x8lnjLuGcNOmTR9//PHkyZM9PT19fHwiIyNv3Lghl8sXL178VPv3T5CYjccbS7plaVSKwTeaH6uU8UAGxTJReDQ4XC9bHPSnt+TaCH7EbyXvRQlcunXAbK0Qb+jlOvgSj0b91D9w5O3KASxKOO3PCbfR7piRVftulMCd+criIPZovse+GumOKgmXiPsy1ntcpxtsLmHisW4aTgQEhGcJLLGmUCh27doF+yqeOXNmzJgxsCGsqqqCFyR//PHHr776Co1G5+TkQO6lYYIl1mw2W2lpaV5e3nvvvfe4K3v16rV///6YmJjhw4fzeLyOF5SUlDhT+CUkJGzbtq3t2e+++w5ehu1B3B3ddu3atX79+s2bN8+fP9/b2/uTTz4xm82zZ8+WSCRPtX//BDvkUNm6R+tLjsE3mx5rCHOkUH8OyuqAzraoP+n1p6snm4CdF8DaXin+srePy4p3pTq9zTH+8ZYphk76ItZn4r3qO8PDYTcNiwN6MbsugkZ8O5T7uFrt8Cfj3XFARUBA+Bv4XSi2Op5FPsLvEwPG8tsrsu7cuTM9Pd1sNiuVysdl3G1sbPT19e3duzeNRnOZnsIlGo0GllgTiUQDBw6MiooyGAwur1y1apXVan3//fdrampiYmI2bdrUdrfPYDAYDAbntqKnpycsBQ6TlZXV2NjY47HjbhlCpVIpkUjgh4JGo41GIwCAQCB8+eWXYWFh27Zt43LdHZSfJQIi5bNeg7tlRri37LLO9th2cqXQtADUPZkulEZo51X4dhi377WyD6IFLj0mvq4Uvx3G7bx/8wNZCout//Xy96L4njjM1kpxIIVwqF/A3/sgCAgI3UtuSsSzMIMAuFyV2bhxI5yGKT8/f9CgQTU1NR2vefXVV19++WUvL6/BgwcvXrw4LS3NeerevXtwVqawsLBjx461rcVgMOBssg6HY/78+W+99RacOKhtqLfdbodluz/66KOPPvpIJBIdOHBg+vTpJSUloaGh8DUkEolAIDijzLVabVuPnu+++27mzJk97uflliGE1crhvMM8Hg8OogcA8Pl8h8PR1NT0fBrCbgQFgObxqTdzJNCXiei9tepx/PZzO38yPoXncahW/k5Y+0dUrTP/LtMdHdB1ouOV4bz+LMq+GqnO5lgexnvRj4mogSEgPCe4E17yDIiPj8discXFxXg83unPolarAQB8Pv/KlStKpfLMmTMLFizw9/dPSEhw1jp+/DgAgEB4rJoVGo0eNGjQjh072Gw2gUAQCoXOlLTNzc0jR450XikQCD788MODBw8WFBQ4DSEKhQoICKioqID3CCsqKpyS3Hq9/sSJE7/99lv3Poq/gVu+D3Q6ncvlwoETcXFxV65cqa2tBQDs3bsXhUI9n9Kg3QsKAL0VWF3lYBEaILMdCqKhrku0o1ypsawM431dKe64eLKlQrwkmEN2TylqMJt6pH/gr4OCZ/8braDF8ffVOpqN2ocqqdr6d9bAm43aW5Kme7IW1ZNXd0CQ0Kgr0yjkFlPXV7vCaLfZoOcurQ/C/xYGgwFesduxY4fD4YiNjY2IiLh27RqsTH3gwAEAQFFRkcFgYDAYs2fP5vF4KpXKWZ1CoYSGhoaGhvr5tY/CgiBIqVQqlcqysrIffvihf//+aDR6/PjxX331FWxob9y4UVhYOGrUqJ9//rm4uBiudfPmTbFYDNs8J3Pnzt25c6fFYpHL5YcPH4ansACAEydOCASCpKRO82A9E9zdI0xNTb1w4UJaWlpaWtqGDRtCQ0PpdLpSqVywYMG/fjoIQ8OBKg0U1UEjO0cC9eeilBZ7rc7sUqmyL5Mc4UH8oV6+KIjtLGw0WE42KcvHRT/dTruNyW5rUrf60vlEzJM5xaispgMFlzMljVbIEePJmR87ygvdRdi4E4lZ/1nuudOSFqEDhQEgFodeGJqwIHIwrtMcoTBKi+mrgiuHm6utNqunwyzEkiPxuDcikuaF9cOiuqhudTi+q8zeUZ4rspiDzCobGlWLZ/YmkVfEDkv1i+ryPeOhSvrNw+vnpSKUw0aBrAo0gY/DzPCNfDM6WUDsIrBEZ7Om1z08VvXgvkFvgSAHQHlh0cOY/JfCk0bw/NBdLeNXaJUnagvviKpqjQaTA/LEYqJpnmP8Y1/wjehSnlViNvzaWHal8VGJVi2x2QhoVAiBNJgfMDkgNonl1fmNrQ7HbWnT5cZHBbKWRrMRAIiHI8YyeCP8okbzAui4LqRxVVbzPWlLjri2QtUqNxmwGKw3idaL7TuA45fI5Hf571ZYTA+U4lKVVKRTmGwWEhbnQ+dGerASGLwubw0AqNdrHmlk1VqF3Ki1QRCHSPWjekZ5sMJpzC6D/R0Q1GDQVOtUQqPe7LBlS5qeq3cXDw+P9evXr1+/nkwmR0ZGXrhwQSAQvP766xMnThQIBEwmc/r06fn5+deuXRs1ahSbzVapVFOnToVzHnQODoez2+3BwcEAAIFAMHLkyE2bNgEAdu/e/cYbb3C5XCKR6OHhkZ6eHhgYeOnSpbS0NLFYTCaTiUTi3r172/mXrlixoqCggMfjQRC0YMEC5w7i6dOnlyxZ0i27V/+QJwiod6JUKo8fP15bWxsfHz9jxozOUxt3Lz0VUM88cTkahXozftSs4PYfdm2unYZDxfI0u6ull4eEuqyeLdfPyKotGxvtVPudm1MXSCFsfIy/6N+gurn4UuXvJCxhSuworucTuMZUSetXZhy/bkUxbSYFljgMB74aNC2aG9R1TQB23r/wcc2jJJtiNIOLR6OzldLzaNoYGn3XyPkMQmcerRAAOwqufFxVON4iWRgYFe8TYzDpLtfk7ZcrJGT2nn4Txvi5dtSG6+4ruft+aXayQfhWQHhi2BAMkaZXNJ4rvbVLpVdTODsTJ4z1eawr9oXG0mV5l3n61mUM6vjoFCovHLJb5S1Fxx/d2GshkKisrYmTRghcf/xHauna7DP3VbKZpqY5Ab0i/PpgCFSTWvR7Xd5RccsVauALPJ/348cFU10ELzYZtFuLbv7QVBVvaJlCQI3wiRAwfGxW86PWyivi+vM4jonEeCM0YUFYIgvfPiRXa7P8XFe8vzyryagfpq0bQqVEe/LJeIJMry5QiW/bMDkUv0E0+msxQ8cLgvDov+xGG+22C8Lqg6UZ2RrVAH3jUKwjgcH39uDozIYyVWumWnGb4mfBU2f7R84OSYil/yVq0+yw35E2H63IviBt8TarBhmF8TR6AJmGwRJbdMqHGkU2zrOIyB/gQZ8ekjDeK9Sb9KfeHgRAmUZ+qbniTF3xQ6M2xiTrZZKGEEksPNFqMwutlnIrVEL1bsLSkmj00X5Rg7gB0XQWDYsHAFgdjnqD+oFSfKe57LakqdlqiTSrAoxiDgoioFEmBxBDqHoyrwLn6YPDDWR7JXmFxtC5/hQPIhoDAJCYjTU61QNZU1ZrTZ5GhXZYQ8wKL6OUARxYCFIAtJjIqCWy5WhCHJmSxPWL5wYGUz25BDIdh9fYLEKjvlKryBfX3Zc3PzQY6A6rj1XDNqtxdnNTdnWgnnPyWPdkKX+q6HQ6CoXSdvRTq9U0Gq1bhmuHw2E2m9tlgTWbzQ6Hw2VqWBij0YjBYB6XB9gdiGTSa1fS6R6ec70iQjmCriu4TReGcM+ePRqNpvMmnHGRz4AeNISDiehI/sgvEtv7vAy5YPuwD+aKrMUTj3kv8rH/m5dz66lY9K54PwDABaF6WUHjozHR7VTw7WatpC7bg+FL4T1BSI0Dsq+/sGWfwTEEjzY6HPdt4F0WY3XKa+0CFl3yy8PflpQVLqJiVw96kc3wUSibvs48tktve0fg+8GQlzqpr7eaX7y8u0ajONCr/8DeE53louaiVXd/uoFlHUgYOSm4r8u6EpPuxWuHpBrJvrDeSX2ngTYTOJtJ8+PN3R+obUNZ/B1D57AJ7X9UlRrZ/NvpGo3ka1/vkYMXoTB/2Z6x6qSHb+zeZED18uRtGzQ1woPd9uwjlWTF76cre+IHrgAAIABJREFU1eIPyY6XRi7FU9uH6htkNftv7t9mpwZ5sNbHj0nhBzlnDBnSpi0FVzNU0oXmluX9p/LDhrWr67Bbqgp/3Vp85yTZP5HOmBs+cBDHj47Dq6zmLGlzemV2pko+UVf3VkjvuL4zcOT2gZX61rIreccPyRQZVL9EmsdIn4hgTz4Oha5Utd5prrinUcUbWl4kY6bGjmOFJLf71DajqqHk4k8lGecxjCoCayCdHsPwYpBoapO+QN6cpVVHmiSpQD87aoh35Bgs6a/b2JBD21yYWXTx59amW7RAE4bYm0LzpXiY7LZanfqR0RhiVaSYxTP8o6OjxlD47dUbzGphU8X1s1V5VyyoB1RfIgbrhycSMGitzV5jMdFs5n66xjFU4riQRG7gQCLT/693tutFjxpqfr/WXJpptJRSvOswFDQKhUUBgwPwIEuIUdLHrk1m8ZMC+tC9exOZAaj/t/GQ3WqQ1aiFRfcbi39XSB5BmHoyX4whWVFoAICnwyqwqEOMkngyaSAvMNQnlsyLIHh6oVB/VLdoWg3Sqlbho0xRdb5WVYmliQhMFYagBRgKsHPsJm+jLBQy9qHRE3nBAn44kRmAp3ExBNqRIz9evXr1yJGfAEJPQCIR52+dacVh3hqyKCbcXfdXd+jCEAYEBDQ0NHTexN+YU/5tetAQTvFEN9pG3Bj/l5VDsx1wfrIKZ+NG3y3/NMZ7GPexvk9qq33gjfKhHFoYjfhZmejs4JABf1WUbm7IfeXO8XskL0+7eR3R8s6k99qNd48BWnRqU7YNc3HMAn9PAQCgSFg2/e6p/njU96lrMejO1jn355x8r67yeEziiOiUtuVljQUzM3/1ItJ+mbCUhnfxflenlky4eijCpvtx4ttUWvuFca1GfbXw+FKhZAyTu334PDruL/ObE5W5ywpvpdrlX49/h0x3PSGWND9cdfuHi3j+m/7hr8aO8CHRAAClGtn2/MsnJC1LHLL3Ry+jMF0LywEA5C0PP7v1/fc4fhyVNs4vyp/hXadovtRY9sigXWhtXTtsPtO7dyePRdH0YG9m+lE7WYinhxMJWBS63GTytOqm2aRL+072iRzVyRsG5LA3l/72U9H1K1ZUBYlnQGGpDluYSZyCdcyOHhoQM6nz/6ndrK1/dOF8dV6eTt+KIdlRKC+rLp5MnhgUF9xrIq6D5W6LVqvFWeSVpZdvCysq9Hqdw0FGoaMo1BE+kcHRY4mMLjbyIYdN05BbVpNdIGsWWUxENNqPTEnkh/uEDCZzwzqvCwCwGVXq+uzypkcNOrnFAVExmDAG3883zsOvLxrX9VK5w2Y2SCqMslqFRmoHDjqRSmH4knmRHV9WXGK36I2yWoum1WpQonEEHJlBZPgRGX6gq0XyPzpvUBqVDTa93GGzoLEEHJVDZPpjie0DFcDzJ7H2X6PHtEYDAgIkEklqaurLL7/s9AJqh9MF6BnQU4aQfeLCS1zi4cahspdwbWdxd1uhNbn2OxMxrDOF4sm9O89zprDYPitrVVjsb4Vy2+kva5VNgy/u7e8btWPwzGKlaOrN9AUY/cdTN3Q5q/vq6rf7lOrcyUuZbd70VQZ1yvkdITjUzy+8i3rMWPB97um1teW/9RuZENy/41mjUTX/3LZcQPwlOS3B6y/T07MVmYsK7y4hOjZOWotyZWhh0ctWYcnbtw9fx3IWCnwmhw9mECl5wvJ95TkSg3qrl1fq0MWdD1IQZM/KOba1Mu8WgQ+hsRAKkGzmiTbp6rixYb3Gdv5MYMTV9449vPy7ViWzAzYGDPbwnBk3nhs0yJ26AACTor687GaZvNEBUBEMfkTokI7zoU6wmTQGcblVL8dRWGRehMtRtXPsZi0AAENw160c0Rp9BiCGsGfpMUMoFAp/+eWXQ4cOFRcXJyQkzJ07d+7cuUxmjylm9ZQh9D5xagyH+UCZfDAZ069Nor1PChxqC5QWYlqW39iJ7GeXLD2+vpLMvzLpbbjpOo2s/+WDu/js6UMWdVIru/zO+Ie/3xs2I5rXXo9UY9IOP/tNBB79U+r6jrbwx/tnVlSXXuo7IjFkwOObh3Ze2/2xXDOZTlvSZ1wgQ1Akqv7m4fVCg25XQNCUpJceV+3PERly/J7/6/6KnDw72oAmBNh1aUzOwuS5ZLrbW5iQQysqaRFXoVEoH+9eZI7rVzEEgBjCZwJiCHuWns8+8eDBgx9//PGnn34yGAyTJk2aO3fu+PHj4cjCZ0lPGcKgEz/18vQLpA0SkFHrev9pV8Zetr0eia4xS+t0lp3xf6w+NWZ9d+XhxXiviD7jP0Rju/Zqe1R+M7kor3jCaz6UP50sLtc+eDnn8v0hU3y9e7msZTSq4858/VZQzJv9p7q8QG1Ujzy33RuDOjZ5JQn/p+vK9ntHPmlpOpswbGBo14vsTaLyz7NOXDHaZBiij03/Ao2yYshcRqfrbB1HZMhhd9hMGHx3fnER2oEYwmcAYgh7lp7PPpGQkJCQkPDZZ5+dOnXq8OHDqampU6dOPXHif8B7qlsgAKPEhnnVG7212O40hEYbyBJDx0egjz7QT/n/LAeisqtT6uoc/uOEOuX3FzdPnNK1vu3G/KsLvCLbWkEAwNighGl1hQvvpF+ZucHlxtLKy9/6E0iPs4IAADqJfid15Yzz38Sd3rIxNHZ4aFKdrOmTgquPLPbrAyf19o9z54P7CiJ2pX0IALCbdRhC+xy8boJCYxAriIDQ7XzyySdNTU0AABKJFBcXN3v27H/ik9mWiooKpyIoj8dLS0uLi4vTaDSrV6/esmWL85Xr4MGDHA5nypQpFy5cgNMwwbEWL7/8ctvW6urqrl+/XlRUFBUV9frrr7c9dfPmzePHj69atepxW2/PhidzpUWhUBgMBovFgmfrI9PjEIG+1YoZ6YXKl0GK/4+9vi50JLBRdDzIkumT2BQAAIAcm3LP+nCCCsct+rbfuDeMOHFdVuctV1RlXMUy3k2a3vHUtuGv1OM8tl/Z3vHUxQe//mJB/zC6s4VTAACFQLk47b01AZFfVD8M+e37mbm/hZHIRalvu2kF2/K3rSACAsJT4vTp03COiMjIyG+++WbRoi4GBPcRCoXHjh1LSUkZOnQoBEEDBw7My8szGAz79+83mf5UkLh58yacrWnhwoXjxo3btGnTuHHj8vPzO/bz6tWrpaWlV69ebVuuVqvfeeedn376SSQSdVfP/x7uzgjhpdGjR4/q9fpJkyadOXMGVjr/j0AChjIrloABY3zQp+ociyPQAIDjtVBaILrZaLE4HEEUAgCgpfrezyS//IFpKABmBvQ6VpG1OfP4jsDOdBO2FVye7unLchV1R0Bjjg+ZPvL2iQFFlwbEjneWNwjLFlWV7I0ZJKC2T8/kkoUDpi/sZCsQAQHhf5bExMTp06cDAIKDg2fPng0AOHPmTFBQUGxsLACgqqoqLy9v9uzZly5dun79OgRB0dHRbtpLAoEAtwwAyM/Pv379+vz5811eCSu3wYl2BwwY8OKLL7a7YOXKlQCAzz//PCcnp1358uXL16xZ80Qf+WnQxYxQKBRu3749Nja2b9++mZmZH3zwQXNz84kTJyZNmgTPC/8jYIDNA+2oN5gXhKP3lDkgAJr10OUmx+xgdJZMP4D1x2zpSPGNAVRqIOUPB84tA6cewQsaK28/rlmlvO6kg7K6f+rjLujDC/4iPH5qyYPisptwiVBUPv72z3N53lOju5aHQEBA+HcDS6y1traeOnUKTr109OjR3Nxc+Gxpaem+fftOnz69atWqhISEAQMGFBcXu7mY55RYy8/PLyws7NvXdVgwAMDPz+/ixYtZWVk2m839nt+8ebO2tvaVV15xv8rTowtjNnDgQLFYPGXKlC+++CI8PBwAoFarYSFXJ88yfKIH8SNYi1TGKT6E9++DPaWObAm0OALNJIAsuX4gmwIAgOzWMzrjitg/J1/BNOYYT8a2Bxe/6RB/DXMg60QfIiGM7iKDl5PFcWN0VvPwhzlzH92kYHHfmdHTeQFfDnf9aoaAgPCMufCd0vFMZNcSR1H5/u3dBVauXLl+/Xqr1Uomkx+Xnr6wsHDgwIEvvvgiGo2Gk/e6g1wuhyXWdDpdWlrasGHD5HK5yyvXrVsnFotHjRoFQdCIESM++OCDxMTEzhvX6/VvvfXWmTNnngd9NeDO0qjJZDp+/DisUO6S/8hmYSDelq80pHp7HhmGmXrd7ksBOwZiAQCZMt2W3j4AgOb6nBISd2LgX4K1309MHX754ActRSzv2HYN2s26gxr99qQXurz18n6Th/n33l98Q2m3HIkfNNI/pvs+FgICwj+i/xjqsxkC6SwXw/WePXtgDeuLFy+OGTOmurq64zXz5s2bNGlSQEDAuHHjFi5c2NZK3b9/f/PmzQCAwMDAdvly2Ww2nG7WYDDMmjVr1apV7777LgDA0cbsw8ceHh6HDh3avXt3fn7+oUOHhg8fXl5e3nkyhnXr1s2bNy8kJORJHsBTpAtDuHbt2i4l1v4jhBCteQo9ACDSE1U67Y/nprHayzSmRBYFAHCpMqcfHkf+q2h1L09uHJW2N+uX96a1N4S/Zv0M4aljA/u4c/c+XP89Ixd0w8dAQEDoVjjez0UapgkTJthstvv372OxWOcSJZwFMCQkpKysrKio6PTp0yNHjszNzY2M/CPoOSAg4M033wQAdBJ7QyaTJ02atH379q1bt+JwOKlU6kxDLxaLk5OT4WMCgZCUlDRgwIDffvstKyurc0N4586d4uJipzzn0KFDP/300/Xr1/+jR/AP6MIQtnN1/c+CAiCUaN7brLdDUFvF+nsyXSKLTECjAAA3FOIxYS6ES95PGD/31tF35HUU1p+pBx12y9dNNctjRjwX6wIICAj/45w5c8ZkMoWHhwcGBubk5CxZssRms8G5dltaWry8vGJjY2NiYk6dOtXQ0OA0hGw2OyUlpdOGgU6nO3PmTFRUFBaLTU5O3r9///bt21Eo1KNHj3Jycnbu3Hn9+vW4uDg2mw0AqKiokMvlXe6XFRUVOY85HM6pU6eGDBnyjz7/P+PZJY74X8cD4/Ah4wtVxraF96S6ZDYNAGA363JQ5LEhLlbGhwqCvSgeu+8calt4NetILZG5MHZkx+sREBAQ3OT1119nMpk0Gm3NmjU//PBDQEDAkiVLbt261a9fvz59+gQEBAAAdu3aFRgYOGbMmNjY2KCgIHfSMAEAZDIZk8lkMpleXl44HO6bb74BAOzduzczMzMkJCQxMXHYsGFbt27t1atXZmZmaGhoTExM//79k5KS3nvvvXaeNenp6Uwmc+PGjRcvXmQymStWrHgKT+If8R/y/PznjOTSrok1CYw/Qx3uSLWbY7wBANV1ORosOZbhOvvEF/0nz76dPq8uixeYBACw6KTv11W/HzuSgH7W0jwICAj/Gm7fvm232wEAFArFGUrv5+dXWVlZVVXl6+tLIpFMJhONRlu5cmVLSwuXy3Uml++c5ORk2DUGjUbT6X/qGIeGhj548KC1tVWlUgUFBcE3/eijj9avX9/Q0GC32/39/TtmYpo2bVrbcDsC4S96W1VVVVRqD4cpI4bwCRgroG8uFa2L+OObJDHbyjWmwWwqAOBmw8N+RNzj1jmH8oMGcXyX3Tn6o4cAR2VtOrvJQgtcgkwHERAQ/gEeHq7F3PF4fHT0H0m/cTgcAIDFYrFYboUdw2CxWAaD8bizfD6/nUHF4/GdSMPg8fhOJG88PV3k73zGIIbwCRjOpc3OrhWZrAIiDgBwXqgaw/fAo1EAgCyFZJB/fCd19w+ZlfSbdua5LQyb8TIz+l7Ky11mx0ZAQEBAeAYge4RugUJh7HYzAY1K9fY82qCAC7+rk8/yYwIAIIc934YeGtCZbpknjpA9bnGffjM5fV/MHbc4lNpjGTwQEBAQENryBDPChoaGAwcOlJSUmM3mS5cuAQDS09MZDMZ/QWuNisWrdAoAwOvBnOm/1y4N4WTIdHKzbZIXHQCgaC2rJTISeV04StFxhI97JT+L7iIgICAguI27hjAnJ2f06NF4PN7Pz08sFsOFzc3NGzdu/C8YQh8SpUmnAgD0Y1KS2NR5ufWlatMHUQJ4eTO7Lj8YDbWLIERAQEBA+J/A3bF7yZIliYmJp0+ffvDgASyuCgCYMGHC2rVrxWKxM77y34o/jVEubIWPD/T131wqGsXzmOP/x/JmlqSuL9UtXywEBASE7uLw4cPwtASPx/fu3XvEiBHd1XJjY+PPP/8MHzOZzIkTJwoEAr1e/+233y5btoxM/sNz/ty5c56enkOGDCksLDx27JhcLufxeMOGDesYm3j37t2ff/6ZSCQuXrw4KioKAKDT6Q4ePFhWVsZkMl955RVYwrOncGuPUKFQFBYWbtiwgUajtZWG8/f3BwAIhcKn1bvnhmBPr0arFT6mYtGfxXovDmI7z97X6wYJnhetIAQEhP8IO3bsKCgoAADIZLL58+d3Y3xeTU3Npk2bAAAOh+PevXvh4eFlZWVarXbdunWwWg3MsWPHrl69euPGjaFDh3p4eKSkpFCp1H379rVr7fbt25MnT46NjWWxWIMGDaqvrwcATJ48+cqVK6NHjyYQCP369aurq+uuzv8N3JoRWiwWAEDHUA840OS/kIYiUhDS8BAHQXYUqn3kn82kLsbQvgno7bIiAgICwtNjwoQJsNbo4MGD58+fv23btqysLD6fHxgYCAAQCoWVlZXDhg0rKiq6ffs2BEFRUVGjRo1yp2UymeyUQBs9evT58+fnzZvn8soff/zx1VdfhZVIXbJly5Z169bBOmVVVVV79uz58MMPb926VVdXFxAQMHXq1MuXL9+6dQvuc4/g1oyQy+VyuVxY2rztjDA9PZ1KpfbslPbZEEbnibA0nbyh46m6hgc6LCncg93xFAICAsIzwOFwZGdnw8kitm3bduPGDbg8Ly/vo48+un79+sSJE3U6ncVi2bVr15OmSZBIJDU1NZ2opjEYjKysrJaWlsddkJmZ6Vy2HT58eGZmJplMjoiIuHv3LgCgpaWlvr4+Lu6Js4V3I25N5tBo9MqVKz/44AObzebj4+NwOIqKio4dO7Zly5bVq1d3Ein5r4GIwfJQjpLmogHs9t+GjIZHffAYJCQQAeG/yb5L1meThmlsX0wgv/3U5fXXX3/77bdNJpOHh8dvv/3msuLdu3enTp0Kz9hWr17t5u1giTUAgFqtnj59+tSpU51uku1Yv379Sy+95OvrGxoampKSsmrVqrZzO5PJpFKpYCVSAACHwxEKhSgU6uTJk6NHj163bp1Sqfzqq6/i4zuLw37auLuquXr1aoVCsXnzZqvVCgDo3bs3CoVauHDhxx9//BR79zwRRSDki2oHdHhryVaIBnRIsYSAgPAfISEE/WwS0TGoLt634TRMKpUqPT198uTJZWVlHa9JTU0dPXp0bm7uhAkT5syZA/t2wJSUlBw4cAAA4O3t3c5GwmmY7HZ7XV3d/PnzN23a9Oqrr4K/5t2Dj3k83rVr14RC4b179w4fPtyvX7+ysjIOhwNfg8PhMBgMvL8GALBYLCQSyWg0pqWlLVu27JVXXikvL589e3ZsbGwP6m67awhRKNTnn3/+1ltv3bp1SygUMpnMIUOGdKKp8++jD5Nf2FTYrhBy2O9boE3+iCFEQPiP0jes5xWDPT0933jjjbVr12ZlZaHRaGfKQJPJBACIj49vbGy8efPmqVOnYmNjHz58CItxAwA8PDxiYmIAAI8TYMNgMCEhIXPnzt25c+e6deswGIxCoeByufBZmUw2YMAfqci9vLxmzpw5bdo0b2/vO3fuTJs2zdkCj8drbm6OiIgAADQ1NXl7e+fl5Uml0nXr1gEA+Hx+amrqsWPH/gcM4erVq9slJszNzQUAUKnUwMDACRMm9OA+57NhoF+vjbX5kMOOaqOUrRA+rCCyEJdRBASEniUvL0+v1/v6+vr4+JSUlMCFsPKJwWAgk8kTJ06cOHHiw4cPS0pKnIbQ19d34cKFnbfscDju3r3r5+eHx+Pj4+NPnTr13nvvAQCEQmFWVtbmzZsLCwujoqLgPTKpVKrVatsF1L3wwgvp6ekpKSl2u/348eNz5szh8XharbapqcnX1xeCoJKSkpEje1J72V1DePfu3ZKSEr1eT6PROByOSCQyGo10Op1GowmFwpUrVx46dMgZX/ivZCA/pIzIVotKPNsshN6ryg3Coug4QicVERAQEJ4SH3744fbt241GY0tLyxdffBEREbFw4cIhQ4a0tLRIpVJYz3r16tVFRUXh4eGtra0oFMrNiZdSqYSzKTU1NXl5ecFhhbt3705LS7t27RqLxcrMzHz77bcTExOXLVt24sSJhIQEMpl8586d2bNnDx48uG1Ta9euHTJkyJgxY5RKJQaDefnllykUyhtvvJGYmJiSklJWVmY2m+H8wD0Fyk0PomPHjr3//vuHDh0aOnQoAACCoHPnzr355pvp6emRkZGLFi26efNmU1PT09YRnzZt2qxZs5yT7s7R6/VkMhnVfdrWkae2bmWSxg9/w1my4sR6Mydi1/CXu+sW/wK0Wm0n2a4RnhLIY38GHDly5OrVq0eOHOnpjvxBS0uL2WwGAGCxWIFAACeaAABIJJL8/Pzg4GBvb2+VSsXj8YqLi5ubmzkcTr9+/dDoroMFjEajSCSCj+l0etuFU4PBUFxcrFaro6KifHx8nD0pLy+32WwRERFt9yDbNnjv3j0SiZSUlOSMuKutrS0vL2ez2QkJCRhM1yvMFApFIpGQyWSDwUChULq83n3cmhHa7fZ33nnnu+++g60gAACFQk2ZMqWpqWnVqlW5ubmHDx/mcrkZGRkTJ07sxs49bwxj8a8J88f//58OuyXHaF0e0Kcn+4SAgPBfxdvb22U5l8sdO3YsfAyrwMTFxT1RfAKJRHpcvASZTO7fv3/HnjyuM84GR48e3a4wKCioy1z2zwa34gglEolYLIaDVNoSEhJSXFwMAGAwGP7+/nB8/b+YiSH97tlxVsMf2SfENZlFZF6Kz78/jBIBAQHhX4xbhpBOp+Px+HPnzrUrP3v2rNN9SK1Wd5LI8d/BSEFQJZFT/egS/Of5RzdjiSRPZIMQAQEB4X8Zt5ZGyWTyggUL3n333fr6+okTJ8IRkcePH09PT//8888BAAUFBTKZrHfvf7nMGBGDHc3kHC2590m/2WaN6FeNZmb/8V1XQ0BAQEB4jnHXa3T79u14PH7fvn27d++GS2g02ieffLJmzRoAAJPJvHnzpss90n8Zr/Ua+qas6bWMfTWS6hyqf3owskGIgICA8L+Nu4YQj8dv375906ZNFRUVra2tPj4+4eHhzmQc/v7+/wUrCABI4fkzGD7bGwrKCawlYfEMPLGne4SAgPAf5dq1ayqVCgCAw+Gio6O7UeFEIpHcuXMHPqbT6cnJySQSyWQynT9/fvLkyQTCH/tBOTk5FAqlV69eQqHw7NmzMpmMy+UOGTIkMjKyXYOVlZWnT58mEokvvviiM8pQLpf/8ssvEokkNDQ0LS3N2eyz5wkSR2RmZh4/fry2thZ22HVy7dq17u5V9yA1yk8+OtxdrU2LmBxA90UBcHTglFnZWB8SdV3UwO5qHAEBAeFJWbt2raenZ2hoqE6nW7Bgwbp16+Alun9OSUnJvHnz4HQTZWVltbW1v//+Ox6PnzFjhkQiccqnbd++PSgo6IUXXhg5cuTMmTODg4MLCwuPHDmSkZHRtrX8/Pzhw4e/8cYbNTU1ffr0KSgo4PF4paWlI0aMGDZsWERExOnTpwcNGuTn59ctnf8buGsIjxw58vLLL4eEhBiNRjKZTKfTi4uLiUSimxk9egQCBh/GDO6WOMKT5eeG+CUF0H0BACFUz/spc/55mwgICAj/kPnz58NpmE6fPr1s2bI1a9bU1tZ6eHjAItcqlUosFsOh9Lm5uQ6HIyoqKiwszJ2WaTSaM7NgcnLyL7/8MmeO63EP1jvduXMn/GfH2PQvv/xy6dKlmzdvBgCkpaXt27fvww8/fO2115YuXfr+++//rc/dzbhrCD/66KPZs2f/+OOPCxcu9Pb2/uSTT5qamqZOndpxCvz84IGnTQoZ0y2G8G5j1j9vBAEBAeEp0draCk/U1q5dO2bMmEWLFgEA7ty5s23bti1btkyZMmXy5Mk4HG7jxo0PHjx4olHRbDYrlUpn+oiOYDCY2tpak8lEJBLBX1P1wVy/fn3ZsmXw8bhx43766ac333wzIyPj8OHDv/zyCwqFGj16tIeHx5N+5G7ELUOo1+vr6+uPHTsGSxLAOuK+vr779u1LSkpasWIFnU5/ut1EQEBAeC4x/3IRcjyL/BP4wX3R3vx2hbDEmk6nU6lUFy5ccFnx/PnzL7300pdffvlEt3NKrNXW1o4aNWrOnDkymczllatXr4ajCZKSklJSUhYvXtw2lM5isSgUCue+IJ/PFwqFdXV1RCJx1qxZgwcPrq+vX7VqVU5OTjuF0meJW4bQaDRCEARbbDabLZVK4fKIiAiz2VxTU9OzqaQQEBAQegqUBxX1TAwhwLkYrpctW5aamqrT6Y4ePbpgwYK8vLyO1wwfPjwtLa25uXnChAmTJk1qO/eqra09c+YMAIDD4cBLrE48PDxOnDgBQVB9ff3bb7996NChKVOmuOxXSEhIaWlpTk5ORkbG0aNH9+3bV1hY6BT8Q6FQKBTKmRDD4XBgMBiHw2EymdasWTN9+nQAwMSJE3fs2AGvnfYIbhlCFotFo9Hq6+sjIiLCw8OPHTum0Wg8PDyuXLkCAOhkyoyAgIDw7wY/ZmgP3p3D4cAqZTExMbt27crIyECh/lSQhtPHDh8+vKio6Pz584cOHVq9ejXsqwJfYLPZ1Go1AIBEIrVrGYPBwC0HBwcvXbp0z5498+bNQ6PRKpXK6SyjUCgSEhIAAGg0OikpKSkpaenSpT4+PjfNS3UvAAAgAElEQVRu3EhNTYWvweFwbDa7tbUV3psUiUQCgcDLywsA0KfPH+Fn8fHxlZWVT+8pdYlbyjIoFColJeXUqVMAgFmzZpnN5oiIiKFDh86cOXPEiBE96OqDgICAgAAAqK+vNxgMTCaTx+PV1dXBhbD3JgRBvr6+b7zxxs2bN/l8PpxBDyYsLGzDhg0bNmx4/fXXO2m8tLSUyWSSSKTw8PDr16/DhRqNJi8vr0+fPs3Nzc4rbTab3W6nUqltq48dO/bs2bPw8dmzZ8eNG+fj49O7d+/Cwj8yvBYUFPRsdlt3nWUOHjxoMBgAAFQq9c6dOzt37mxoaFi5ciWcWfH5RK+uyzm7sluaitEKzdxQIEBWgBEQEJ4jvv322/Pnz+t0uuzs7KVLl/bp0+ell14aO3asyWSSSCR1dXVYLHbFihUSiSQyMlIoFCqVyoED3Yr70mg0M2bMgCCorq5OKBSePn0aAPDNN9/Mnj07OzubyWReunRp/PjxI0aMmDNnTklJSVJSEolEunTpUmJiojM9Awychkmr1cpksurq6qNHjwIAPv300/nz52dnZ9fX15eVlX3//fdP4/m4ibtpmO7evdu7d+92TjFqtTovLy8lJeXp9M0FT5SGSaOW2Ywt3eI1eu7MvMDEpUP6LPrnTf3rQfIB9QjIY38GPG9pmHJycrRaLQAAj8eHhITA640AgNLS0oyMjJCQkJiYmIaGhqCgoMzMzKamJjabPX78+HbTNZcolcoHDx7Ax0wmMyoqCvYIBQCIRKJ79+5pNJpevXrB6entdntOTk5FRQWchmnw4MEdR12RSHThwgUSiTR58mTnJmV1dfWNGzfYbPa4ceOc8iyd0MNpmAAAM2bMOH36dLtXidLS0lGjRrlpSp89GCyJxovtFkNow+D+eSMICAgI3UjHdEgwUVFRUVFR8DG8nzdp0qQnapnBYDxuhiMQCGbMmNG2BIPBDBw4sPOJpkAgWLx4cbvCkJCQkJCQJ+rYU8KtPcLH4QwcQUBAQEBA+B+lixlhY2Mj7MxjsVju378PbxPCWK3WAwcOBAYGPt0OIiAgICAgPE26MISnTp1asWIFfPz222+3O0uhUA4dOvRU+oWAgICAgPBM6MIQzp49e8iQIQCA0aNHb9myJTY21nmKQCAEBAS4s++KgICAgIDw3NKZIRSJRGVlZfDxihUrGAwGHHrpRCKRAABGjBjx9PqHgICAgIDwVOnMEF6+fHnBggVdNvHceo0iICAgICB0SWeGcNKkSW01CBAQEBAQEP59dGYI2Wx2Ox1RCIKam5ubm5sFAoGvry8Gg3nK3UNAQEBAQHi6PEGG+h07dnzyySfO1BN0On358uXvv//+f8UcIgvAj0dmkEMAYpGYaNQTR6YqjMoWnYiEJfp5+OAx+Ceqa4fs9eomvUXPo3B5FM6T3lpr0Ql1rVg01o/mjXtyzQQIQGabmYhFQmn/Q1RUVHz77bc93Yv/KDab7Sm17K4h/Oabb5YvX56cnPzhhx8KBAKZTHbu3LkNGzYYDIYvvvjiKXUOwU3skP1q7a18cREFRxkZMCSG8wTZko0206mKC3caM8V6qa+HV0rA0IkhY3Bot74YJpv5RPmZs1WX7Q47Bo2xO2yjAoZN9h/rptZXtvD+kUcnGjUt/h4+eqtBapCP8E9+qdd0LrnrfCZiveRoyanr9Xc4ZBYNT23RtVJxlCmh4yaFjiF0ZU3tkP1q3e1zVZcb1E1eVL4dsgt14gR+7NTwSQn83l3eWmKQXay+ltmcU69uRKPQGDSmFztyhP/g4f7JRCyh87oQgIokpRlN2aXyCoVRhcfgvGmCPrzYoX4D3fnUVoetQFxULCkT6cRWh5WMIwV6+ifwewd7BnRZFwBQq2rIby2qUzfoLHosGiug8qLZEX14MV12GwBgsBofSkpqVPVSgwyCICbJM8gzIIYTxSC6lYvUYDVWKWuFWpHaosWhsUwiw4/uE0D3xaDceo22OexCnUhhVNohBwaN4ZBYAirP/bcuq93aqpcYbEYAABFD4FI4pCd5fYEApDFr9VZD8vihpeWl5eXlf7ZsteJw/2bZKQhAKNAN4lzdwuLFi5/S03ZLa9ThcAgEgilTpuzfv79t+YYNGz777DOpVPrMRA6fSGtUr9eTyeRukVg7dLBfaPxrQ+Kfotao0qT6MvvbbOF9b5pgWcKi/l4JblZUGJXv3tlMxpFGBQ7TmLW/Vl6M58Uu77fEnSlOhaL6o3tf9GJHTgkb50PzqlXVnyw/16wVvjdwRQSrCzH4UlnFpsytkeywub1mBNL9AACtesnJ8nNXa2+/2mfehJBRnfx+5Ebl1txdLVrRot4vDfRJhEdDhVF5quL8+eqrMyJTZ0W+gEW7HiItdstPJSfPVF5KDRv3QtgEBtETAAABqERacazs10pF9ZI+r4zwT37crX9vyduT/x2bzHoxMq2vIA4eTI02062GjGNlv9IJHkv6vBzNjnBZV6yX/PjoxN2mrJSAoSP9h4SzQnBorMaiLWgtvlx7s0xe8ULYhBfCJ3jgXfwcTDbzlbqbJ8vPYdDYkf7JMdwoLplttlsa1E15ooJ7TdlR7LDUsPGJggS0q29sqazyt9rrtxsy/ek+8fxYXw9vHBqns+irlLW5wnwsGpMsSJoSOZZH4XasK9KJr9bdulp32w7Z+wn6hDKCPAg0i93aohUVSUsq5NX9vRJSAoYmesV3fAHSWLSZTTm3GjMfScsiWKHhzBAuhY1GoeVGRZWirlha6k0TDPFNSvYd4Ofh064uBKBKRU1WS152y4N6dWMwI8CX5u1BoNkcdrlRUadulBnk0ZyIfvy4PvzYYM+AdoZNaVI9kpYVSh4VS8vqVY1cCodFZGDQGJvDJjHIlCZVkGdAFDssmh0RyQrjU7ltv29mu6VGWV8uryqTV5TLq1r1UjaJScNT4f+1WC/xJNLDmMERrLBIVmgII5BO+Et6dJlRUausr1TUVClra1UNIl0rCUei4iiwRbQ6bN40gb+HT5CnPwfHCuEGccgsDwINg8JY7BaNRSc1yITa1iZtS5NGKNSJJHqZ0qS2Q3YAAAaF8SR6cMhsAZXnTeULqHwehcMkMUhYIhlHMliNFrtVa9FKDHKpQSbWS6UGmcqk1ln0ZrsFAIBCoRhEOpPI4FE4PjQvb5qAT+HyKVznj91oM0kN8gZ1U6OmuVHT3KBulhpkarPW6rCiUSgqnsomMQVUnjfNy5fmxadyOWQ2AYMn40hmm1luVMmN8hZda4tW1KwVinRipUllsVsdkAMAwCDSWSSmF5XvQ/PypvEFVL4HgUbBkQEAdoddZdbIDPJWvaRFKxLpxRK9TGlS6a16BwSRcSQyjiyg8LxofF+alw/Ni0Nm8ykcApYAADDbzFqLTmKQteolIp24VS9RGJVai85kMwMAyDgSneDBIjE4ZDabxGKRGHQCjYglcrBMFp3V8av+t3HLELa2tgoEggcPHrRLwCsWi/l8fsdyAMCGDRvu3r1bX1+/e/fuMWPGtD2Vk5OzYMGC2tra0NDQw4cPx8fHnzx58rPPPistLSUSiWlpaTt27HicoOq/1RAarMY3rqwe7Dvg5V4zi6Vlm3/f9mbCwk5Gcycai3bZ1XUj/JPnxcyEBwKTzfRZ1na1WfPZsA86f+3NFeZ/mvX1isQ3hvgmtS2/3Zj5Td7eV2JeTA0b/7i6v1Ze/KH4+JoBSwd6J7Y79aildEfRQRaJsWbAMthKteNuU9bXeXsnhYyZ22tGx5G3VS/5Onev1CBbnrgkhhPV7myeqOCbvH0hjMBlfRezScyOjRdJSnbcP0DCkV6Nm9duZlwurzrw8IjUIF+asDDRVSIRB+S4Unfr+6L0YM/AmZFTYrm9nDapSln7a8XFe83ZqaHjp0dOdmnqmrXC9JJT95qyh/oNHOGfHMUOJ2IJZrulTF55p/H3G/V3e3Ojp0dMjuVGd6xrsVtuNNw7W/mbzKgY6pfUm9tLQOVhUJhWvfiRtPxeUxYEwLigkWMCh7PJLn7/ZfLKCxVXM4Q5AiqvryAukO7nSaRrzNoaVX2eqECslwz3Tx4TONzly43GrL3VmHGz/l6Nqj6WGxXKCGaTmRa7VayXlMoqalUN/QR9hvkNGuDdt+PXyQ7ZC8WP7jVl/96SCwAqmh3uReOTsSSlSdWiFZXIKjyJ9CSvvknefaM5kR3/11qLrkBc/KD1YYG4WKyX+nl4M4kMAhavNKmFulazzRzNjujNi47hREUwQ9q92JlspkpFTYmsokRWXiGv1lh0PAqHiCE4IIfSrFabNIGefhGs0ChWWDgr1J/u03bqCQFIpBNXyKvL5JUV8upaVYMdstMJHjQ8VWvRKUwqEpYQ5BkQxgwOZQYHefr7UAVt726ymZu1wgZNc62qvk7RIDbK5Ea52qxxQBAOjfMgUNlkFmww/Dy8vWkCHpnjSfSEX+ysDpvarJHoZSJda4tOJNKJxXqpwqQy28wGq5GMI+EwOA88jU1mwtaCS2Z7EulUHAU2GxAEKU1qhUkp0ombtcIWrahVL5HopQAAIpZotlvQKDSLxPD38PWn+/h5+ATQfblktgeBhsfgHRCktWilBjlct1krhA2txW41WI14DI5JYrBIDG+qwJsm8KF5Cag8FonhXPlXmlQyo6JFK2rStIh0YpFerDHrDFYDAACDxngSPFgkJo/C8aYJBFQej8xhkhgUHBmNQuutBr3VINKJhVpRk1bYohVJDDKJQWa2mQEABAyehqdyyGw+lcuncPlUHovEoOGp8JfNYDUoTWqlSSUxyOVGudyoVJs0RptpeZ8lkfywjl/mv41bhlClUjEYjGvXrrWTYS0rK4uKiiopKXEKvDr57LPPQkND16xZs2XLlrS0NGe53W4PCQlZv3794sWLv/322127dpWVlaWnp3t7eyclJSkUiilTpgwdOvSrr75y2ZMeM4SHEkPjFg9JaC8a6xKNWZsvLorhRLFIDDfb/yrnWwDA6v5L4T/rVA3Lb7z/5fCPw5jBndRyQNCaWx8Hefq/Eb+gXfnW3F3NGuEXwz963KrX7y15X2bv/HToe1Hs8I5nhbrW9+5sDmeGvNNvSbsWTDbTtry91YraT4a+60Xld6yr1WpJFPLh4p8vVF+dH/vihODRzrmdUNe6r+CHGlX9u0nvOO9rt5mN2hYsnkKk8JyN3GrI2FPwfZCn/7iglBBGIAqFKpGVX6i+JjcqliYsHODV13mlQdtiMakoHr64/3+pd0COK7U3f3x0gkag9hckcClsqUGWJyqQG5VzoqdNDBntHBNtVoNeVYfG4Kmegaj/H6atduul2utnK39TmFTBjAAIgpq1QjQKPT44JTVsfDsTCDlsqL+O7wqj8rfaG5nNuZWKagwaa3fYQ5lBA7wSxgSN4LuarrWjXt2U0ZxTIi2XGGR2yM4hsyJZYYO8E8NZXWgTw4+9SFJSKHnUpGlRmzU0PNWf7hPHjenNi3ZnBVJlVheKH9Wq6hVGFQ6D5ZI54cyQXpwINzduGzUtlYpqkU5ssBoZJE8BhRvJCnNptl1isBqbNC0Kk8psNzOInnwKx+Xs9nEYbSaxXmq2m1EA5Umks59wu1pvNajNGq1FR8VRGERPMq59ltrH8Zwk/bA6bCabCY/Bd7kv8C8AgqBuzz7hbhqmvn37Wq3WCxcu+Pr6wiVyuXzGjBmVlZUNDQ1otOvvXHR09KZNm9oawuvXr8+bN6+5uRmNRtvtdj6ff+bMmUGDBjkv2Lp165UrV65eveqyweffEMqNytevrA6g+1YqaraM+DiEEdRllQp59bt3Nx+ZuLvtz+9WQ8ahoqMHx33diS/Gj4+O57cWbRu5CY1Cm/QShegBBkdieydhsAQHBH2Zs1Osl3w29P2OLdxryt6au/vzYR/AUwRpU0Zz5TmDVkSh+/tGpLIEfcH/G7wSafmbCQsHePVFo1AOCMpsztmd/11vXq93+r5GxBIAgJoqztY+/EEpLoQgB50d6R81nR2QSvdkAQCqlXX7Cn6oUtb05fehEai1qoZaVf30iMkzI1+Af65KcVFF7nZx410CmW2z6DBYon/UzND4V2GTZrVbr9XfudP4e4OmCYKgEEbgiIAhw/wGwmO62SCryt/fUHocoNAEEkuvbvBghQfFzvOLSIMtkwOCCsXFDyUlcqOCSWLEcCIT+L3hwRGCHC1VF2uLflCI8qmeAQ67xahr5QelhMQtZHn1cz4lsV7aoG5CoVB8CtfXw9tZbtKL60uOiWquqmUlDrsVR6CxvPr7hE32Dp2I+etLg9Fm6jiLkgvzWqovKVsLjToRBkuiMYLYPkneoRNJrt4q2uGwW6XNmbKWHIO60eGwYnFUD3YE13cQnRPtzoislpZIm37XyCusFg0KjaN4+DIFCVy/wRhs1+O+1aKVNWeppWVGvQhADgKZ48EKZ3sPILpnrqxmjUpSpFc3mo0KNBpLpPJojBAPdgQa7daWj8Nu0SlrTQap3WrA4qlECo/i6e9mXQCA3WbUqxstJhUE2fFEBpnmjfvrWmjnQA6b2SCz280AACyOgicxUf9vZd03hFaLFoVCY3HdOXy7j82q/3u3tlq0kMOBwRIwf8sprOOb4j+nJw1hdnZ2SkqK1WpNSkqCnWWysrIsFsvp06cnTpz4uFodDeGePXtOnTrlzHE8ePDgRYsWvfLKK/CfEASlpKQkJyd//PHHLhucOnVqampqampq20IKheLSEveIIdyY8ZU3zWth7zk3Gu59X5T+/fgdXe7Vrbr50TC/gRNDxshaskW11yl0f/+oGRgs4dOsb0hY4vJ+S1zWKpVVvnd384FxX7NJjLKcb6rzD7C8E21mjVZVFzd8s3fIeAcEfZWzs17dtHnIu8w2c9NfKy8eeXTi82EfhjGDzUbFg6vv6FT1QTFzKZ7+GnlFXfFPntyYPiM+I5A5AICslvvfF6VLjXI+hSvStQqovPmxs+F1RYOmOe/yMofdHJ74FtcvGYXCKkT3qwsOKiWP+o7awvX/IzOnSCcukpRoLXpvmiCBHwtPL2wWXfG9jaLa6xGJb/tGvACPShpZeVXBAVHttV6D1gX0ehE8ZosRguzVBYcqcrf7RqQFx82negbBhZKGu5UP9hi1wtihG/iBIx/3tKVNGQ/vfIzFksL6vsEPTEFjcAAAi0ndVPFrVf4+Ct0/ZvB7ntwYl3WNutby3O3NFWd8wib5hKcy+X0wWJLZIJU0ZTaW/qKSPgqOWxDce77LQdZu+z/27jPAiup8GPiZ3m4ve7c3ll06S5EuICgqihpF7Ens+Rs1iYlRY0s30RhLjIktdsSKiooCKlXaAgvbWLb33dvL9HbeD8uLm2V3uSiW6Pw+wcw8M+eevXeeOWVm5Pa6Nxr3PQUAyC0915czg7HnmLqSiBwMtm/pblzrzZ5ePPnHgYKFyFBNmWjv3raaVZ2H1tg9ozPy5tk8JShKaGoyHqzua/0UQbFA8bLS8stYR97RsUKirb3ujfa6NyE0A4ULXf4JBOU0TU2It4S7dsZ6KwOFC/PGnJ9ZuAg9qj2hyrHuxrWdh9ZEe/Z4sqa6ApMZWxaCoLIQTIRqwl07OGd+dsnS7FFnOryDu6ogNGN9+3ua1/W1fpKKNbv8E2yuIpLxQKhLfG8qckhIdniypgXyT/bnzXX5Jww6Y0p8b6R7V7hrZ7hrJx9rZh25NBfACVZXeYnvlfhuh7fMkzXNkzXNk1nOOQsH1puuCYlQbay3Mtq7N9q7TxZ6WUc+xbgRBFOkqJjsxEmbOzDJHSj3ZE5x+sbS/3UVAsVkVyJcGw9WxYPVichBKdVF0p7+TKCrvKYmWUeew1vm8JaSbL43cwxjyyZpJ4YzmprSlZSY6uLjralYIx9rFhJtEt+riGGc5ACEuiZQjJexZbKOPM5VyDkLOEcuxfpI2n2k5KoUlfheMdkl8V39saqS0FW+/3qFpF00l8HYs+2uUTZ3EevI45z5Ry5lVDku872pWGMq2piMHErFGiW+R5WiGE7rmojhFOvIYx15Nmeh3VPCOvIYezZB2gAApqn3H5ePNwvxVj7eIiTaFCmCYhSKYoauoBjJ2DI5Z4HNXcw5CzhHHsX6cNKOorgqJ3SNl1I9YrKDT7SKiXYx1SULIUOXEARDEJSgXZwjl3MWcK5Cu3sU68ijWT9OcihG62pSkWIy3yMk24Vkh5joUMSwKsdMUwcAYDhF0m6K9TP2LNaeS7E+mvGiOENwRU7Xcc8SH8ExEuHmzZvnzJmD4zgAoLGx8cEHH9yyZUs4HHa73bNmzfrFL34x8OmjRzs6Ed533307dux45513+v+7dOnSU0899chzvf/0pz+tXLly9+7dw72kccmSJVu2bBl0w8ann346duwQ8yRFUWQY5oQkwlWrTikc/+NZE3808mZdfM+vt/7+mVMf7u9O/OOuv4/1lF5QMuyFAgCgPtb0l92PPHXqQx3Vz3bUvpRbdlE8WKmIvdPO+I9OsD/99PYbJ181LWPwVEZRl27e+Jurx186O2t6zZa7+Fhj+eJHKS4AAEiE9u//+Oc5ZctHTfkpBHBV/er3WzdcUHL2BO+YuJJY07wuriR+M+PnmWyGlOqo+ODKjMIlo0/6xZGLa0NXmiv/2XXorcmLHnJnHm4eBcVQRI77GY+POdzTFe7cUrXx10WTrimYeOWgE3dn4/rm3X/05y8qnXnbkFeR8b69Bzbe6s2eVTbrDpwY/KzaVKSuZus9GEaOnftbm3vwmFYitL92670E5Ro373eso+DonYc6NtVv/xPrLBh90i/tnv+a88LHGhp2/z0VrS+bdXugcMnRsdDUO+tfb973T2dGeeGkq10ZUwbENrbXvtTb9F5O2YqiydeQ9BDDk0K8qWX/k8G2jzNHnZ016iyHbxKGU6apJUNVfS0fdje+68qYUjDxx56sId4hZ+hyb/P7HbUrVSkcKDrdnXkSY88FCCrz3bG+vcHWdRCauaUXZI3+wcAO5AHVcqCt5rVIx3rGnuvNnWtzjyYpt6YkUtH6SNc2ie/KGnV2Vsm5Tv8QCV5TEn0tH/Y0rklGat2Z0x3ecRSXaRqKzHfF+yr5WIMvb35m8Zm+vAVHNxwhNGK9FX0t64JtHwNgujLKaVs2gmCmoYiJ1niwkmID/vyF/vxTXBnlR7cMdDUV69kV6d4e6doupTpYRwHJeBEU01VeTLYBCJ2BKZ7MGe6saXbvuEHtP0NXUpGaeF9lPLgvGa5WpTDFBgjKbhqaKsd0NWlzlzr9k5wZk53+Sex/p0kAgJTqSkXr4n2VyXBVKlpvaCLJeAFAADBVKUxQLpunzO4d5/RNsHvLGFvuwMKbhiom24V4Ix9riIfrNbFHFvt0JWnoEkZwBOWguSzGnsu5ijlHIePIZ2xZ/3/nAACoSlFZ6JNSHVKqXUx2SHyPJodV+fDTK3GCJWg3zQUoLovhsmhbJsl4cdKBE5xpKAAAVY6rUkhKdQqJVinZLqY6Zb7L0GWM4ExDwXCG5gKcs5hzjeJcozhXMWPLImg3gmD9XzMp1SmlOsVEq5BolvgeRezTVR4AgKAYSXso1s86ClhnAesoYO35BO050smhqylZ6BWTbWKiVUx1yHy3KkU0NQVNg6SdGM7StiyKy+Ic+Yw9j7Flk6y//wtjGpqmxCS+S0p2CIkWMdku892KGDZ00TRknLQTlJNiA4w9j7HlMI5civERlKv/CtXQZVWOqVJY5ntlsUeTooocNVShZOa93swhxtqHxLLscH2WRxwjERYWFiqKsmLFiquvvnrknDekoxPhU089tXLlyk8//bT/v3PmzPm///u/K664AgDwj3/84+GHH968eXNOTs7Qu/uGu0avmT/tupE3+/e+51AEva78h/3/bU923rz+jlfOfWqESSt3b75vauak2SSzb8OvT7nk/f7TXO1n9/e2frJgxTtVkUN/2Pbgk2c+NHC4EQL42y33OynHLTP+r37Xo91NH568/A2c+PzSQRFDW968OKv4tPFzbwcANMaa3zi4piHW4qBsC/Pn9g/a8bHmzW9eOGbGz4snXXF0qYJtm3Z/dHPZ9BtLpl5zVMsMHtz1aNP+52YufcJ31EwZAEAqlaJJWPnJHdHefZNP+WNm4eePolXlRN2OBzsPvTt18V+zRp1+dOzhA0Cz+cDzdTv+nlm4qGD8RS7/BABAtGdPc9WLsd59E06+O3/M+cPFAgBMQ2s+8Hz97sfs7lGBwoUk7VbleF/bxmTkUNn0n44qv/Lods9Ahi61VL3UfOAFXRWc/vEIiqUihwxdLhh/UUn51dSxbnKQ+N62mlXdTR8lQjUYwRia5PSPzSw6tWDcCs45ROYeJBmp7276MNqzR0x2QWgwtixP1tSs4tPcgfKRA1OpFMfRka6doc4dqVijKsUIyuHwlvpz5/hyZqbTPaVI0XDXjkSoRhaCGE4z9mx3YLI3a/rI1XWEEG+N9lVKfA80DQynOEe+O3NKmh2n4P+P1ypSxDR1grRzjjw6jb7iIwxdlvhuTeFRDCdpD2MLDNejMEy4JAtBAACCoBTrT78b8FsyRggA0FUexan0u4v/d30VXaMAjuiVV1455ZRT+nPJ7Nmzn3zyyUQiMXLIQOPGjXvzzTcHLtm8ebPP59N1HUKoqqrT6dy1axeE8JlnnikoKGhubh55hxdccMHrr7+e5tF5njdNM/3SjuDpp0/aVPHEyNuY0Fz+1pUt8fae5g073r++tXoVhPB3W+5/tXb1cCGdye5zXr+cl2IfPDUt2L514Kqd7//f3g2/hhC+WPXadWtv4VXhyKp/7X32ho9uVXU12LHtvSfLpVTP0XuWxcj6FxbVfHb/kMdNRhvff3JKS/UrI3wcIdH+8ctnbF19OR9v/Tww0rDp9eWfrlomDnXQw9skk/3/6GnesO75BetfXFy15U91Ox/etfan7z5etnfDrbIYGeG4R6hyolWy44EAACAASURBVH73Y5+sXPrOY6Pffqxk46vnNFY+q2tSOrEQQsNQuxs/PLDpd3vW/+rApt91NXxg6Eqasf1Ssebuxg+7Gz9MhA+apnFcsf0UKQ7hifkGHtORard8naxq//qZpsnz/Ind5zESYb+Ojo6//OUvo0aNAgDQNH3hhReuX79+5BxTX19fUVFRVFT0wAMPVFRUpFKpl19++Z577jFNc+zYsX/84x/j8fg999xTXl4OIXzhhRc4jnvttdcqKioqKipqamqG2+23OREejDRcseaGUOfO956Y1FK9ct3z8xv3PXMo2rT8rSs1Qx8y5KFd/36q8sWDOx/Z8f71g1ZpSmrtMzO7mz4yofnI7id/9N6NO7oqDoYbfrvl/ms/+EVCTipS/IOnpvW2bhyuPLIQWv/Cov2bfjvoJB7t3ffek+WtNa8d81Mbunpw16PvPj528xsXVqy7ZeOr563594RDe54wzaE/Tr+BpwbTNIIdn9XteOjA5j80H3hR4vuOeVDLF2Odkb8RVrV//b6xRHhERUXFdddd1/8Owry8vNtuu224Ntz1118/bYDKysqnn376lltugRDW1dUtWrQoEAicdtppDQ0NEMLbb7994MYrVqwYrgDf5kT43IFXHt/zzIaXTu08tAZCyMfb1vxrHB9v/cWGuz5s+vjo7RNy8uzXLg3GO9b8e0Iy2nj0BqHOne89Wd7fftrQsumnH9125Xs3/Wf/SllXIIQ73ru2cuPdIxdJkeKbXr9g8xsXxoPVEEJVTtTtfHjNvyd0N61L73NDCKGuST3NG5oPvNTbulHX5GNub50avhFWtX8jrGr/+n0ViTDdWaMDpVKp119//bnnntu6dSuCIIZhnMi+2hF9m8cIb1x32yWBcdKB50+94uP+8YmDux5JhOuwKT95bO8zz5716KAnrTx74JWwFDkPJ2J9lTOW/mvIfVZv/XMyUj/n3OcGDXi0VL3UtP+5RZd8cMzxGwiNpn3/adj7hKYkITSzipdMmHfHkHMLT5Rvz6jJ94pV7d8Iq9q/fvArGCM87kck95fDNM3+xtYJSTPfAZIuN8Vbse6dRRMvP5K0SqZcG+7cXkqyNEZtbt8+cHteFd4+9MGlY89t3PdU6fSfDrfbcXNuVaVo3c6HBy4Md+2s+ez+WWc9mc4sBgTBSqZee+Y1FWdeU7HshroZSx//SrOgxWKx/M85jkRomubWrVuvv/76nJyca6+9tr29/bbbbmtsbPzqCvctApGRG85Vwdox7uJQ6ye5pcuOLMQJdlT5VYf2PH7V5Euf3v+iZn7+6PQXql+dlzfT7KngXIWujAnD7RZFiVnLnmmrfa1ux98hNAAAPc3rd7x3zYwzH7Olcav+QATl+D7MKLNYLJbjldY9/x0dHStXrnzyySebm5tpml62bNl11123ePFiqzl4xP5g9VSCcnjL+u9DP6J40o8/enb2qXNuy3PkPF+16prJlwMAasOH1rVsfPasf+x766Kxs24Zec80l7Fwxdu7P7xp7dMv4qQNmsbsZf8Z+AAUi8VisXwZx0iEK1eufOKJJ7Zs2QIhnDt37m9+85sVK1Z8P/vE4YgvJDwQqjtHi2cWDn6gCUk7C8Zd2Lj3qVtn3nz9h7/CECzXkfWvvc/+etaNeqha14TMolOH3OFANBc4+YLX+HiLoYkO3xgkvTfXWCwWiyUdx+ga/c1vftPQ0HDTTTft379/69atV1999fczCxIoJmrScGs1Q2uINpmhmiPPFRto9LSftNas4gD855K/9AnBT1q33jnnljk5Mw7u/kfp9BuGfJ7WkGyuIqd/vJUFLRaL5cQ6RovwxRdfnD17dv8j1r7PSJyMKfHh1jbEmks4rxqtGnK0j7Fl5Zaefaji8Qnz7rx99s/6F4a7dgjxlvyxaU1/tVgsFstX5xjNkZNPPtnKggAACqNi0rCJsDpUNwkjvDkzh2uujZ15S2v1Sj7e3P9faOr7N949fu4d1uwVi8Vi+cZ9p5OcomktHSdkRk9GLOsQnhxubXX44Elq0p87a7gNaFvm2Fm/3PXBDfMvfBMnuKotf6C5QF7ZuV++YBaLxWL5kr7TiTApqx/sOyF7Km2YXDGhcri1NaGDc/hW74ybRtjDqPKrUtHGDS8uormAaWjzzn/luB4KbLFYLJavyHc6Efrt3K3nnJAWYfL2+xFD5lXBRg5+nEGvEMRNTUl2ujKO8XaO8kV/jvVVqnLcnzfX6hS1WCyWb4nvdCI8cVCc8mNkc7x1Usbgl2BVh+qmMS5XxgT0WC/gBQAc82U6FovFYvmafZFHrH0PYTjtAqAh1nz0qqpQXRHUPFnTv/5SWSwWi+XLsxJhWjCcYQ2lPtp09KqqYK1TDHqzrURosVgs/5OsRJgWnGARNVkdqhu0PCYngmJIidR7s6Z9IwWzWCwWy5dkJcK0oBiJQFOT42ExMnB5ZV/VTEc2QbsGPWLUYrFYLP8rrESYLs6RN8Pm39tXNXDh/mDNGAyzmoMWi8Xyv8tKhOniXIWlGLGre+/AhXt6K31y1Js945sqlcVisVi+JCsRpsvmKnariZ09e3TT6F/SnuyUdUUN1fqsRGixWCz/s6xEmC67ZzTft7/Akbur53Cj8NO2rQszxmlqyuEr+2bLZrFYLJYvzEqE6WJsmRCap2dOWNPwEQBANdQ1jetmUJw/b671sDSLxWL532U9WeY4ZOTNdQHz+Vjz/mBNbbh+rHc0Gmv05sz+pstlsVgsli/uO50ITdMU+RPyrFFoGNA0AgULu5s+/PXMG+/a9GcSIx469Q81r/2gdPoNX37/FovFYvmmfJcTodrd2f7CUydkV0SsxN/uzjxtceWnd551xmMvnfMvEiOVaAOKU3b3qBNyCIvFYrF8I77LiVBh8w4V3HVCdhXHpbl8VwntdgfKe5rX55YuAwA01r+TU3LWCdm/xWKxWL4p3+VEaPcg51zrPiFdo+/ddfhlhIXjL2o+8Hxu6TJNTbXVvnrKRWu+/M4tFovF8g2yZo0en5zSZVKqp7vpw/pd/8gqOpVzFX7TJbJYLBbLl/JdbhF+FVCUOOmMRz9754ck4z35/FXfdHEsFovF8mVZifC4ebKmnXXdfoAgCIJ902WxWCwWy5dlJcIvAkGterNYLJbvCGuM0GKxWCzfa1YitFgsFsv3mtXF9y2ysr3j1Y6uYo67fUxpgKaOK7ZbkndGowGanuVxoyfijhGLxWL5nrASYdpguhuqlS0trb2+eeO9Pkf6u7+3sjrjo6rHQ2ofS1xd0vzI+aeOsnHpBJoQ3ltT90ZNw5USvk1R78ziHlkwc5LTmeZxeV1/vKnlna7uqKpNcDquLS5cEshIv9hBRXmxrb0iGgcATHO7rijIZ9MPBqBbktf09LQKooMgTvZ55/q8x5XD65KpndGYZBgFLLPA7+fw45i+BAGoTSYPpXgGw8pdzkyaPp4jAwBASteTmuajKAq1elYslv9h3+VEWJ1Knbnh0xOyq0eQwlZVKEljy9S7u9s27N/vpad8uL/+F2fOKStIZ/8fdHbPe2nn9IIsx89n+tvCT6/c/Ft13YM/PueYZ3YIwLV79k3Y0bqtNkJNLAAmxq9tfehQOPrDJQszfMc8bkUsdtm2nb/q1V5rTtIxQbBRqwLNz83If2zWdA9JjhxrQvj3Q42P1hy8N4HdE5IxzTjIYiu8VQsmldw9aQJxrNwQVtQ7q2vWtHf9QqMX8noSgf/G628KcH+ZNP70zMAxS76+L3h3dW1ElC81KZ9ivo0bVyLqJUX5d40d46OOUXLRMB5vbH68qRlFkDkUq6j6BkWY4HTcUlpyTnbWMTNxA8//q6nl7a7uoKI4cCKpa1Ndrkvy864oyLPhx/hBmRB+2Nv3Zlf39ki0V5ZpFCu12xZn+C/Ozx1tsx3zU4uG8VFv39ZwpIHnJcP0kmS5y7kkkDHV7TpmrAnhjmhsQ1+wNpmKqCqFoqV221yvd0lmhv1YxQYAhBRlQ19ofyLRIUoQwCyanuR0LszwFbBpXfmEFbUiFjuU4pO6TiCIj6LGOuxTXE4GS+vaJa5pB5OpXlnRoEmiaB7DjHHY2fRiAQBBRWkRxJSmAQA4HC9g2WzmOK57RMPoluSEpgEAvCSZSVN02ofux+u6ZkIAAI4i6dT2iZXSdVE3SBS1Ezh+PN1FsmGEFBUAYMNxJ4EfV1cTBCCu9lc4Rn7rrxQRCNNu6XwLLF++/OKLL16+fHk6Gyd5XifIE/JkmbW/P+gtSZ7+47kjb2a0h3v+8tbvlo9/ctHc2jc/C2+tKfvz5ZkMM3KUZBj/ue+VpbS96JfnAAQBABhtoa773nrhvPF3nTFv5Nj7Dh7yvrt3RQo4fn4W6rUDAMxwqveR995H1ck3L5vh9YwQ+1Fv3x0bd7y9P+XxO+mzpmG5XiOYENdVRqvabz3J/9sfnDLeMWyLNqqql+zYPaUl9us9QWp0FjF9FMKSRktQ3FizzU0+elLmEwtmFXPDtmjXdPfeULHvgShyekUXnudFCgOIrht1nbys/rbMLk3Of6R80nCZOKgoN+3b39ITfjqE5u9th5lew2knIlEzIWwc6781E721fPy1RYXD/Whf7ei89UD1DwDzy07FXt0pQQrDEEoXu8dk3hPAu3zM3ydPnD1MvTULwr01dev7gnfRnvN6JGdnDPIy5KiuLMd/fPgLQLqppPjm0aOcBHF0rKAbz7a2/f1QQyZF/gyxz4zIzqRimLDHQa53Yg9qyfFOx82jRy3NDAxZ8h2R6FMtrW91dk/3uM+jbJNFg9FhFIGfkfBlIY4g4NKszGtKS3KG+rI18cKzrW0vtLU7CeIcr2+egvpFTUVADY28Y0hbo9ElgYwrCvJPz8w4+oQVVtQ3Orte7eisjCdOyfDPsdlLZYiZZgcKt5rKhlAoQNHLc7MvyM0Z57APijUh3BOLv9vds6ant1UQZ3jcZXabkyB0CIOyUp1M1iVT09yuJYGMxYGMqS7noIunDlHaEg5vDIU3h8LdkjzWYc9maAJBFdNsF8VDKb7Yxs32eub5vDM9nhIbhw2ot5iq7YvHd0Si2yPR3bGYZsJijnORBAAgpWktgqia5jS3a5bXM8PjnuR0FnDskWAdwlZBrIzH98TiFbF4TSIZ17RshnYRBAQgoqi9shyg6TF222SXs4gkJvp8uQxz5I8eUdVOSWrk+foUfzCZauD5DlFCEYRAEQCAZkIdwjyGKbZxJTZuFMcVcVwGTbkIgsZQA8KYqoUVpUuS20SxXZTaRDGkKDFVU0wDAIAAJIOiMmmqkGNL7fbRNq6I4/JYxkOSOIIophlWlE5Jrk0m65Kp2mSqLpnqlCQSRVkM06CZ1PQATRVzXKndVma3F3FsDsNkUBSKAEE3+mS5S5IbeL6B5xtSQrMgSIaRQVEIAnhdF3Qjg6JKbFyp3VZisxVznJ8inQRhQAgB6JPlblluEcSGFN8iiP3FdpNE/zcfRZBiji3iuDK7rdRuy2fZfJbpvwZSDDOqqh2S1MwLLYLYIgi9shJR1f7Py2CYlyQzaTqPZfJZNoumMyiKw9AJDJ2VdqdXOr7LiVAQBJZlT0giXHlXVTqJMPT3d/8MhDt+en4GRQEIDtz10mclnp9cuXTkqOc/3nXyW5VFD/wIYT8fF4zvqG99cRN/xznzcjOHC9wajqx+af2dvYb77uUI93ksVLT2v7y1FlEW33L+cO2MD3v7/rh+++rdEcfZ05H5k9rrFTFl2lxo3mgKHOqI/vujP5fal150ypCNs+pEcvnW7Y82SjO6Utry0xpCXLBTgybwBPDicYRj1w5+d9N1UzyXnzrjkvzcQbGSYdx6oHp/Y8er1QLG2JtLZrR3IJJgAgC8WXixX8ravrGeQ68fa797ZvmFuTkDY00In29r/82BmodSxOId3R1jpreagVQSEhSiKTAzCykUWnwNVQ9N9m0odDw4eeJcn3dgeEUsduv+apiSn+3U1Qa9LX9Sj8DQLGYYEBowxyYUNe3ls5Gr88msXO9tZaWzBqTD6kTy4YbGd7p7HiDcZ+4OdijeHndBQqVUHWEp6CXF7L4GH5N4frznfkK+oiD/0vzcqW4XhiAGhHtj8Vc7ul5oaz/d6bo7CIldoW4mO24LyAiJIqYdUbzxriypq7U84x4PPAT1FXk5izL8/R3jrYK4JRx5s7NLh/AWh/8HrUJifzyMuES720AxwtQciVAGElcnuJ/KwJ6Sk5NdziWBjMkup4ckg7JSGY+v7e1r4oWrsrKvCxt4ZaQvhKYcfo2iUdNkpaSHD/pKqO0lrgcJqZrnF/h909wuH0Xp0GwTxG2RaHUieVZm4FrITG5I9tXzcYmQbU6IorQs2qRYIAcLl7pWZuAvxSIAgNlez2i7jcOwkKI28Pz2SNRPkedlZq6QsIKWRLhZ5OOmogIEQoYwnE7EXsg05NhWO5C18VhDih9tt/kpyoZjIUU9lOJRBMz1ek9zuhYnzMzuVKJDkJKmJhskCVg7as+iewLcNhexTpd2RWM9styfq2TDDCqKYhqTnM4ZHvdijJkSU7muFN8ny5IJIULhhs1NGFncQQ/9KQu2p5LViWSvLAdoikBQ2TQiiprDMBOdjmku11yIj01qzogohmVD1IAJcY6knETSTTc4iF0U2JdMtKtqhyildK3/q+IhyWyaHm23ldntk0xstKAHRB1LKmpKBQDgFAI5IsxRbSxWQyGHNLlFEEOKEtc0xTBRBLgIMoOmsmiqgGVHkVSxDP2qYVcMQtQAhABDYjQeZPAGAlQbSgMvtApipyRFVdWAkERRH0Vm08w4h32cwz4ZIcYmdb+kgbisCRphIxAcidupThavZZBqTWkVxG5JCiqKCQGLY36KymWYEhs3hqDGSkY2r9t4RY5rwDAQDCVcZIwj2li8igH1itIsCBFFTWh6fzvRT1E5DF3IcWUEWSqDbEFzSJqRUg2IEATUWbKXwZsZrAo360WhQ5TaRVE2TAAAiaJukshjmSKOK2aZUgPNlgynYpA6BAAoCIyTWB+BNNNIm6Z2y3JQVgTD+NuY0skZ/uFOjF+AlQjTsvKuKk9J8owRE6EZSvbc/coj1866f9rk/iWppp7uB942/njRON+wLTNBM/be+p/cc2cWnTIJAKApECcQBAUAgLpH1+xIJC+985Ihh6CSmn7Fqvef2hny3bsibrK71/PdLSqGI0Xj6emLOBrXW3/32psu7JKbzzu6lbC2t+++9dvf2hm2X7ag3sg5sE3MKiQcXiweMvo6tElz2HElWuRvb690Y+IPpt82tuxIdwoE4JmW1vv3Vn1QI/o4e03xnPYWfdxJTHYxiWFIX4dWt1vCSXPOOAm8su7pYtv2k/J+N2F8ucsJAFBN862u7juram+KmVfs6GmZMr8+7CybRpdMZlw+zNBhT6tWu0uMB42pjk7nvorfl3t3F7quKy46yeM2IPwsEnmiqXW0ZDxalYgYgWqmLKuEHncS488hEBRoCmyvV6q2i0DVJoX26UTyutGMkuE4Kyszg6LCirK2t6+LF56W2MLtkarMaYbNNnEuVziWIigEACAkjKZqpWaH6MWEspYdHePstwTQIIFMdjkJFKlKJEXd+C3hWron3CBmtjN5+eOZ4vG0PwcnKFRMGT2tWmOlHOuVS8TmAqRnXbnvQVprkEQ7gSc0vcTG/ZhxXdzCB/crTZ4xgKGLyrlAPsE5UNMAsZDe1aR2HJT9WKqgu9o2hllbaH+DMZtFAQFIDkOfwtovjOjsnlgz7+q05Tk9WHYZ6/JhGI4okhnp1bvqJURVc2MtBc5U5wT3Gj9ZYShRVfVT1GSWO5eH+dXJ5hbQ4SgEFJk7hvZmkxSDGgZMRo3eZjncpWeakbxkq3ciV5lv2+rA+xATR9AinJzPG0XNqc5quYvKiuIufybmL2Y4B4YgQOTNaJfa26ayUMqKt+e6ZTDOUZnBVbNIAkMyEGycCsqjqlyb7OxEgmwWj7FeD2IPkIyLgCYQo1q0T+WT0AN4f7wzw6W5SmyhABO2UyI0PQiWw+tYO9/XqvQJdJQLCAhj4yBnR3EaVSVT5E1eQByo5BZCXj3uy8a5Qi7honiGJCBwqQYZVCIdYqQPRjBXjPaogLCxJsUgKAJkGQgiQCB0m0l3KuhhFW8uxWSxKQepoyiOIHZBF3qkaLcajSEJyp0gnTIkadJAUQQgwDCAqqEMotqNpIOP2AjN6UZZL0U7CITEgaZLvCHGtUQcpgSUJ+wC5ZAQUjNxAjcBALqBYKjJApXVBU6I21CFsyM0i1J2HEOgCRFVB7IAJcEQRFQCpEzbZZzSEEKDOEAAjhgENBhdZGTeZggOB7B5cFuApjkMYUlTNeSEKka0eFCLp1CesKdopwgoHaIEBjUDQQBgEZU1RZuYsAHRbgeMA2dYBOCYYQA5pQspmBKQFGAEximijGISBG4AgEATgRDQqMJpok2K2RDF5gAMhxEsDgECIZB4QxIgLyI8pCXGIWCMDAkEAAyBmolSqMEAmVN5mxy3UzrnxGxODONIAIChQ0UyxbjB81BQMImySwSjoiQAKIKYCIQkUGldoaUkA1SGhjSLYBTmWzbKOTorzbN3OqxEmJZ0EqH41s6naw4t+fl5Y+yf9xHt/dNrewPMNdcsGy7q7Xe2FWw5NOWBK8WUuXF1MtSlESQy4zRbyWQaSmrTrc9tWTr+yqVDHPenW3f+8tWqvKsW93DZW99JTVvElUymNQVWbxcb9suLLnRmuPW2e1c9n8defu3ZA+fdvNze8cymitd2RdlLF2xr9auKueA8h811eMwjGTW2f5DSFLhgKaX8Z221JNw9PXDh+JIyu61FEP/T2jYhKN6/K6hPmbA1WRTIJ2edbuvPJf2gCSq3xWp3GBOnYPm7NgYF4ZYye42P9pBkiyCchdC/r08RcbQiazbrJucuc3COwTm+u0X97P2Ui9EnNm0VaP2VMs9qB6IjYKlJ/qhTYmti+wvmmXZu7tl2b9ZRYy0QNNfIu9cLHpwf07hDLmQ3FtpbHaTPQBZGVf++cJ1tbITxTT/NUTKJBkd9Lwwd1u2WDmwVMpD4qPb9dDHdme3QCSxH0LC6ZBOS1cbkj57KTprHsfYhLk2iffqBrWJnvZRn9OZEmj2FpOlicdWMdqidmqeDy/fnkRPm27OLyKMPrauwuVqu2ylIMS1b7vYmexweHOAon4IRnet25CM0WTrDVjKFPbrGAAChLq12V7LzoM4aoj/Z7QQCxWCKgSQkss+RLRPsqAlMyXTOlz3E6JQiwdY6uWmvEOnRPTDuTIZpoEATCBgbswVSKJtbgBWf5MgtIXFicLmhCfo6tLZaua1a0BXTo8UZIY4auo6TPO2MES47B3PHsgUTuf7rlUE0Bfa2ad1NSvchMZEAHKrQhgwg1BBcQBgMQzICaNY4LlBIezLxQVeDpgEjvXqwUw82ieEuVRBRCtVQaCIAKoCAAHW7oC+Pyihh/TmEw40NqnMxZYZ7tHCHFmwW4mEoqSiFaBhiGgBVTZwhDZcX9RUy/gLanYHbXAO7XYFpAD5hxIJ6vE8PtaWkOBRFoOr98+kQmjQZGti9mDOHdQVIhwfjnBjFfB6vylBIGqmYkYoZyV45FdZkwdRUoBsARQBJQJoBjB3n/KTdT7EOjOFQkkZIGgUA6KqpyFBMmnzCSPQoiV6FT5qihCg6CgBAEEjhJkNBlxdz5dLubNrpxTgnSjHokUrj42YqbiSjRrxb5kOqxJuSDAAAOAYpCnB2zJFJObNpuwdzuDGKRY9Uu65BIWGmYkYirCf7lFRIVSRTUyCCAASBNIMwdszuJx3ZjN2F2Zwoa8eO/MUlweyPTYbVRLfMR3VRBIYOAQAoCkgCsHbE4SNsAcbuxRkbSjEoigFoAtMAsmjKosknTCmiCHFNThm6Zk47nckc7T76y/yFWYkwLS/fVeUvTiy5aqQRu95fPf+rGRkvrThz4MJkY2/n31Zzf728wDl4BAUAoBtm1c+eJi8/eXT5mHefjo6eTE+ay8VD+obXEuNOYsbPYnv3NUWfWG/cff7EnP+aybmqvZP994aF40dJ82d9vCpx2qVOf87n41Kdjerm1ckF5zuyPFrXH1572UfQ589cmpMV07RHG5roqo6/VkbYKxZ/Wuuyu7F5yxzooIF/CKo+E6t3iAvOtbv37+M/PrB1jH+nh85FkNO7pYzuROjUxTtrmZlLbCWTh5hxkEqlEIPdtDoJTTAtM8Ru3KkhQPbZWF6BMalt8ry6mHvaKdyYaczR+aCfocMDW8XaXVKhV8zrOWhvbTYhjPvz2rPG9iiO8oW2sdOZo8+qn9eqBmt2iNXbJQ8lBcQeKpmQSTbIZkZN+/i53IRZLE6O9JXQFHiwQjq4WzRk3YWJKDSTBqOh+Ojp3PiZLGM7xrC/kDAOVcrttVI0aOAY1E3E7Qb5420l5bTDc+wZFrGg3l6vBNtUPqqaBrA5UH8xk19GD5nDBkqlUhxnD7ZrPa1qvFtWBINiUGc2lT2KDuQNkYSOpkhmX7sW7dXlpI6gwO4jvVm4P4cY/PUYBp8wwt26kDAMHRAUYndjvmycZtOdJWHoMBUzRN4E8HB4+rEAANMAEm8YBjBNQLPIccUCAEwDygI0DIjhCM2iaX5kAEAqlbLbh/hpW746EEJRFLnhpyB8AVYiTMtz91blB6KLblgw3AZGR6T1/tWrb1zwq7LRg1bt+e3KmlHeH15x+tFRm979DN9WP/evV255J4nhyJyzDv+ihKS55unovHMcuSXk3qc+6mvqnvXby9z04ckj++LxD/79/tUK7vz5BW8/Ez/5HEduyeB5JX3t2sevJhac78jOMPv+8X4wmny5wIaQ+IVhbVRUpq5d8vFnlDsDn3uWfbhs1NOqbnwrOWoCPXmCaX5Wa7SFEIowxxQc4LO72vTFK5xDNMgAAP//1AAhqN8j7dskeAJ4tlcjVDmuQpqdkwAAIABJREFUkC2dmD+bmHm6ze4+9plGTJm1u8S2OiUeNjAccfqw4gn02OkMSaf1BzU02HpQ6WvXZNFkODQjjygYQx3dphlBPKQnIgaEwOHB3H48nVwyiCJBikaGq+ETyzojfyOsav/6fRWJ8Lt8+8QJpKKAFLWRNqhoei+T+kFONoBgx0d83W4pt4RceL6DoJCc5XPxf66NLpc9/z1jG+qG76Oq+OXzQp1aV5N6wY2fz+zgHOjCC5yfvpE473rP1KtO2/P7VR//5bWTfnFugdO+oTe49YUN14W0wL3nr3uXL5vCHJ0FAQCBfGLxRc6PX03MXWYvuOMC776We/e1QE3H52QbU0rXvy74c/DZZw6bBQEAWYXkedd7dq3j33hZLRw73jYTS0b0tj1q8QT0Bz/xDOwOHRKCgDHTmZLJdHu90teOqSblCGBnLKTcGel+5Vg7On2xbfpiGwDANCCKHV8+wQhk1ER61MTjvjvwCJcfd/m/1A9kYIeYxWL51rISYVpUDCFEfYQNEnsad5c5f2bjDlZIwQ7t0l96d28QNr2dPPUiZ+aEgnYf98nqrcsvPXVgyN63Pws7qSWzx3/4QnzqQo747/66zAKibCqz5Z3kkktdU+9cgT74duTOle9ksVPDyjU2LvPuC2troarA8gXDXhYF8okll7nWr4pHevTJJxeyU4sAAN3N6pbnUqPL6akLj309xXDogh84UjGj7aAi8qYvm5h2io0daoxqODiBFE+giyd88WzU73izoMVisaTPSoRpUTBAJYdtEZoJEYSSucsnGhrct0k4/XIXxaJzzrK9/USs7aBSMIbKvHS+8+EPwktn+VyHb2bQ4oJ7fXXsJ4tCHVoqZpSUD3H7V/l87r3/xGp3S+NmMFPuWC4e7Mo61OUtCJCT8iN9xv4t8WXXuEe+UdWXjZ97rWfH2tSqv0e8WbiYMg0Dzj3Lnjv6GLecD2R3YxNmH9fjYiwWi+V/iZUI06KhgBQ0YJgAGyLz6NXtFQFuSXZmS63izcQ9ARwAgGLIjCW2nR/x+aVU/tiCdWWBzsffX/ybiwAAAMJDj723rcx33ZSyDa8mJs5hh8xnKAYWXuB475mYPxv35xLsmBx2TA4AQBbNT15LzF5qT2ekjbWji1Y4haQZC+o0i3qzjuvJEhaLxfLd921/8s23BA1MkcaMvsSQa4XK1ne8+Dyft2G/XDrl87ZdbglJMUhLrQwAmHX9UiaYqnzsPb25r/3hNW0J/pSrTk9GjGC7Nrp82J5Dhwc7+VzHhlcT0d7DHbOyaK57OVE8gS4afxxP5eYcaG4J6cu2sqDFYrEMZiXCtLCIGWQJoz00xDoTqjXtypgcKINwt5b3372Ok0/mDmwVAQQOjvbc8YPd0di+f7z3qpgkbj5ztMtRs1Msm8aMPJUxr5ScfaZ97Qvx7WtTFR8Lq/8VzS0hp51yImdMWSwWy/eZ1TWaFhs06lnslNYQmFU6aJXe0hdh8Bmjcjsb1axCEvvvrJZXQlZsAJ1Nam4JOSbgLbjz0t3R2DVOp5skFNFsqlIu+OlIjwPtVziO8ucSjQdkQ4NLLnN5M62/msVisZww1ik1LTZormMxtaHn6Dkt6oH2tV7q3MxA5zo17+hJKAiYNI/dv0Xov8mBwbD5/sMvhajZJRWOo455g3Y/zoFOnmfNWLFYLJYTz+oaTQsCoOqh9Y4wVAffRJHa27QrhyvhuO4WNbt4iNmYxeNpkTd7WtWBC1UZ1u2WJs21cpvFYrF8w6xEmK5Sluz12YzG3oELzShvhpNZE4riEQPDkSGncSIomLqQ271eGPgMn8otQsEYKp1nblksFovlK2UlwnSVM+RGP6lVtQ9cqO1t3ppjX5qb1duqZhYM8Ra6fqMm0BgOaneK/f8Nd+uNlbI14cVisVi+DaxEmK5xFP66m5D2NQ9cyO9qfMNLLPT7etu1zPxhEyFAwPzzHAe2iTU7pbaDyvpV8bnL7GmODlosFovlK2Wdi9OCAohCpHBsvigqRmekf6EZTmkdIfuUQhJF+9q1QP5Ij2uxu7GlP3L3tKg1O6STlzkKxhzHXYAWi8Vi+epYiTAtNCpIMnZpYd7b+TZlc23/QuWTqrWFjgsK88WkqWvQ6T3GgJ/Th516sXPpj13H9YQzi8VisXylrESYFhZLCgJ+aoZ/ZZFd3HbQjAlmKClsqvl3se20QEZfp5aRS3w9b9uxWCwWy4ll3UeYFgZLhQUcRZAflo99vVu67MF3AQRvTPBfNHksjiDBDi2QN/wAocVisVi+xaxEmBYOj7fwOADg6qKCWWNbHbkoieH/JOWKkmIAQLBDO+k02zddRovFYrF8EVYiTIsNS6QSOICAQNF35s66fk+lZpqrp86iUNTQYTSo+7JPUE2aBkCtmwstFovl62MlwrQQqELgJp8wbC4sl2Henzf7yKpwt+724wMfnG2KKSMaJHKKwfG868FIRGKrHpbr92JOr+uca5gp89OPharCb35brt2NkBRTfjI3YwkY+UWFg8Nl+eAePdSF+3PoMdMQ8vjeo2tKvNbRADUVz8zHvVnHFQsAgLpmxIIISWMOz3HV2IBdwC8YaLFYLFYiTJ/LpUX7dJtrcHOtr0PLGDBAqLYfijx1L0KzmN3tu/4PCDXEG3ePZgrJ0D9+xc5c4r3mXq2zMfrCXwwhaZt3djqxerQv/MTdZE6R48zLoSqnNq4Wdq73XnUXZnenEy5sX5t4/zkyr5TIzBcaq2KvPuI443LbvGXppBZTSCY+eF7at4nILkYoRus4hPtyiMUXgfEnpVXyvo7kulfkmh2ozQlVBaAoN+M028ILUDaNfmYIpZqdwva1SsN+qMqYw0OVTbXNPZssHJPOoaGqiJWb5artWm8bQBAiq4iZNJuZNA8h0prQa4q8XLdb62gwFQllOLJoHD1mepqxAABTSKptB414GGAY7gmQBWOO7+LDNPR42BSSmN2NOb3HexFgSrwpphAEQ50eBDvuMwDUNWDoaX6xLZb/CVYiTJfHrYa79fyywff/9bVrJZMOn8Wgpkafv8+14iZmwuzYqw/HXnvUc8Vt6ew8tuohZtJcx2mXAADIgjG+n/419MgtREYuVVo+cqCRjIX/ebttwXm2+ef2L6HHzUx+9HLo0V/5b7wfc3pHCjaN2KuPqJ1N/hvvJzIL+pdpve3x1x6R63Z7Lr9t5ISkHKqMvvwAU35y5m+eQW1OAAAwTXHfxvhrj4DJc53Lrh4hMUBDT334Mr99rX3Rctf5P0E5BwBA62vnN67uu+9a5znXsCctHuHQWmdj7PXHgGnaTjnfc/mtKGMzokGpalvk+T+TuSXOH1yPewIjfGr+s7WpdSvJ/FJm6kJnbgk0Da2jUaz4JPHuM/bTLuFmnzFCetAjvakNr0r7t1CjJpFF43CaNYSEsPW92CsPcTOX2BaeP1KdQyhVbRc+e19tPUgWjsE8AWDowo51ek8LVTqFm7OULps6QlYzJV6q3CLt36q21iGMDbM5jUQEqgpVOpkpnw8LJ45QY8A05UP7pMot8qG9UORRzgkN3RQSRM4oevwMdsoC3Jc9QrQRC0oHPpMP7lE7GqDEA5wApkFkFpCjJjLjTqJKJo3cn6/1tSsN+9W2g3qwy+QTAAGo3Y37c8j8UmrURCKrcKSSQ6j1tKptB7XediMRhrKI0Czm8OAZuWRuCZEzauTrD6jKameT1tNihHtMRQIAICiGuXx4IJ/MK8Fc/pEODYApi3pvux7qMpJRU0wBAFCGQx0e3JNJZBX0f29HOrquGZFePRY0U3GoKQAAgGIo58BcXtyTecxwAIApC2YyZoo8VCUAAAAIanehNmc6l7nQ0PVgpxEPmXwSagpAUZThMIcX82ZijmO89MaIh/Vor5mKm0ISAAAQBLW5MLsb92Ud/rGPEJuMGrGgkYhCVYaagjI2hKQxTwD3BBDyGPdPQ001U3FDiENJAAAgNIuyDszhPt5uquOFwIFPwPzWW758+cUXX7x8+fJ0NhYEgWVZ5ER0moWfuDteclFTT+7pl7sGLocQvHx/+PwbPKwdBQDwm95Wmg54r7oHAAA1te+vP3GvuIkqnTLyzqWqz5LvP59x6z8Hnn+VhsroSw8Efv2vEX4t0NBD/7iVHjvdcfplg1alPn5d2LU+4+a/DRcODT363J+hoXt/fOfgb6dpJN59RqrZ6bvu97g/Z8jw1Cdv8JtWe664jSqZNGhVMtSrf/CsFuzw/vAOPJB3dKze1xF58a+42+9acfPRv2etszH6ykOYy+++8EbM5RtcbFVJfviiWPGx8+yr2JNOHZQ2oK7xn76Z2viW/ZQLbAvPR/DBU3mVxgPxt/6F2pyuc68jcoqPOnRT4r3/6NE+59lXMhPnDNq5EQ2mPn5NrNxsm7fMdvI5g04HRjzEb1wt7FrPTJlvX3j+oHqDiiTs/pjftBrlHLb55zIT5ww8fZuyIO3bzG99D+qqbe7ZTPl8zDGgWkxDaTwg7P5Yrt5BlU1hpyygyqag9OHn85l8XK6rEPduUlrr2Cnz2akLqeIJAzvGta5mcd8mcfcGzOljpi5gxs88UjaoqWprnVT1mVS5GfNkslMX0mOnf15yCLVgh1xXIe3fqoe6mAmz6fEzyPyy/kwPVVnrblEa9ktV2/VIDzNxNj1uBlk07sgf1BRTamudXL9XrtkJTYMunUoWjSUC+ajNBQAwUzGtr11tPag07oeqQpWWU0Xjiewi1OlFac7k43q0T+tqUltqleZq1O6mCsbgWYW4y48wHJQEIxXT+tq19kNaXweRU0wWjCGyi3BvZv/p0hSSRqRX7WpS2w/pwU4is4DIHYV7s1CG6/+SGPGQ1tumdTYCgJAFZUR2MZ6Rg9ndCGODimSm4nqkR+tuVjubjESECOThGbmYw9P/UzIlwUhGjXC31tsOUAzNyKUy8zGnD7M5+78wppgyEhE93KOHOo1EBHP5cW8manMePpUbhikk+jcAAOC+LMwTwJxelGYRigEQmhJv8kkjFTOifXosiCAIanejrP3/N8GhmYobqThURNyfg/tzcG8m5s5AWTuC4dA0TD5hJKN6sEPrbTdiQcyTiXsyUM6BkDQwDVPkjUREj/RCTTkc6/KhNieCYlBTTT5hJCN6qFuP9KA0i/tyULvr8AkEQpOPG4moHu4CAODebMyXidlcKMMBBIG6bkq8kYgY0T493I1QLO4JYE4vQtIISZkSDxVJj/YZ0T6Uc+L+HNztxzwBhCARDIe6drjGokEj2msKSdTuRjk7yjoAAFAWTCFppGIIRmAuH+b0onYXQlD4nHPsuYXgxLESYVrCT9xNzDzv3Q/zL/+1Hxkw+hbu1jetTh5+pyCEvX+6yvPD28n8sv610oFtyXWvBH75jxEu86Gh9/3leveFNx6dLxPvPq1H+7w/vnO42PjqJ4xor/eqe4bcf/L95+WDFb4b7kOZwQ07qKmRZ/+IEKTnh7cP1/oRdn6UfO8598U/p8fPHLjclIXYqoeNSJ/36nuOTlQAgFQqZbfbhc8+SHzwvH3RhbYF5x05BNRUfvPbqU/fdC79ETdn6XCfCxp6av0qfsu7tvnn2uYt6/8pQlUW925MfrSSGjXBdd71I1yW6tG+xNtPaJ1NtgXnMZPmYu4MUxaUhgPC1jV6uMd5ztXM5HnDxQIA5IN7ku8/b6oSO20RWVCGoJge7ZVrdipN1dzsM+2LlqOsfbhYk0/wW94Rtq/F3AGqZBLmcJuyqLY3qE1VVNlU+8LzyKLxIxxaaa4Wtq+Va3Zi7gARyAM4YSYiasch3JfNTlvETl80wlVRoqsNPbhTqtyih7rwrCLM7jIlQe9tQyiamTyPPenUI43+ocptyPV7pcotcv1eqMqYOwBMw4iFUM5BlU1hJs2lS8tHaPMZsaBUtV0+WKG2HoSGjtlc/c0IIn80PbqcHj+TyC4a4VPrkR6l4YDaWqv1thnJKJRFlHNibj+RVUQVjSNHTRih9QNVWW0/pLbVa72tRqQXaioAAGFsuCdAZBeR+WVE7qijr4cGllxtr9e6mvVIr5GKQUlAKBrlXLgvk8gsJPJKiIy8EYbbjUQk2VJPCjEjETb4RP9ClLVjdjfuz+7PNCPUmykk9UhPf+PJlASoygBBUIbrb/Bh7gzMnYHSQ7+gBiqSHurWw916pMeIhUwxBU0DQVGUc6ION5GRhwfycX/2cL9uUxb0YJcR7TMSYZNPQtNACBJl7ZjTg/uycV/2CF3fppDUw916pNfkE2Z/uw3HUZrDXF7M5R8pFkI9FtRDXUY8ZMRCUFOhrh0+rsPT32QcrqvfFHkjETYSETMVN1UZjJ5iyxipD+N4WYkwLeEn7rbNP+eDT0fNP88xcIJo1WciHzdmL7UDAJTGqvjqfwVuffzzMAj7HrzJccblzIRZwxZy+1px32b/DfcdvQpqavDBG+1LLmWnLjx6rVS9I/HWvzJ+9c8ROjDjq59Qm2t81/9hYNowRT7y7B8wu9tz+a0j92ipLbXRlx4gi8baT7mAyC42FVHauyn50cvM5HnOc64Z7vzSnwgBAHq4O776Sa3jEDNpDuYOGNE+qeozsnCM85xrcd+x59To4e7U+lelA9swpwegmBHtI0dNcpx2EVk49pixAAC17SC/dY1Sv9dIxhCKIfNLuZNOZaadkuaomNpSI+3fpnY3Awgxl48aXc5MmjvcWWkw01CaqtS2eiMVQymGyC6mSstHSJ+DQEPXOhv1cA80dMzuJvNGH7MzCgyodlPktd42k4+jNItn5B6z929w2YWkEQ8BFMNcvqMvoY4dLoumkEA5x5E263fbkWq3fG0ghKIoctyJ/IJZiTAt/Ymwsm0sw6GTT/78bLj+lUTJZLpoHAUAiL/+GOYJ2BdfODBQ2r819ekbGT9/eMjdQkPv+9PVnh/ePtzJXetsDD9xt/8XDw8a9NIjPaGHb/Fecy9ZcIy5IckPXxJ2rXOdfwMzfiYAQK7dFX/r38ykOc5zrklnkgVUpNSmt8Wd64x4CGAYPWaaffGKkQ866NSg93VIdbvNZBRzeOgB/XJpgrqmh7qAoWO+7HTz0PeSdUb+RljV/vX7KhKhNVkmLQhJQVXOLSErNwtHEqGhw942df55dgD650F85r/5wUGBzKS5iQ+eVxoPHD2WBgAQd2/AM3JHaOIQuSX20y6OPPP7jJv/dqTDwRSSkafutZ9x2TGzIADAccblZNG45HvPxV56AACI+3Ncy39Kj52e3ucGCMU4llziWHIJ1NT0Z0UOhAfy7EONFKZbAJw4xmQKi8Vi+XKsRJgWzOUz4uHsCcTGNw0xZfZPjelsVL1ZBMWgAAC14xDK2obo8UMQ++IVqfWrjk6E/SNhx5xWapt/rh7sDD1+h+dHd+CegNbbFn3+PnriHNvctG6uAADQZVPpsqmmmAIASevOhKF8sSxosVgs335WIkwL5s7Qo0EUQwrGkE1V8sQ5LACg8YBcPP7wfEu5djc9bsaQsey0U1Ifvay21JJF4wYuF7avxTPy0hnxcl1wQ+rTN4N/+ylCs1BVnWf/mJt1xvF+hPTHqCwWi+V75StJhIlE4pNPPtmzZ48kSQ8+OLi3cNu2bY8//rgsy5dddtn555/fv/CFF1546623XC7Xz372sylTjnG/wdcP9wTU/9fevQc1ce59AH/CBgIJSUiIQeAFolhgBLQ6iBYRLHOKSociKpQWa6fHzrRMp68dx2JLpReVOl46DLal7eDxZRjhDGq1VlFfDwjO9LUKqICIXApNDSAXESEhF7ns+8d2ODlaBSeQJdnv56/dJ5vNl2Unv9nsPs/TepMQEhTmcvHY4Pxwl8H7o913hqPX/vkUn7HxmvTlN//yvTyKL16VOnD6H7PePzB+W25Mr9NeKFK8kzWpj+fxxDEbXFe8MjrYx5cpMQYbAMAUmpZpmK5evXrw4MHGxsaCgoJHXmpsbFyzZk1UVFRqauq777575swZQkh+fv6OHTs2b968YMGCmJiYzs7O6UhlCb7Ca6S3kxAyy9tR4cX/vzPayyXaBcuFfCceYcYYu6t+ypPxoiV/o0eGh67+73jLg59+cFm44vGubE/Bc3Tiu3uiCgIATK1pKYSxsbHl5eUffPDB4y/l5uampKS8884769at27FjR3Z2NiEkOzt7z5498fHxW7dujYmJOXTo0HSksgRf4TVyv4uMjRFCVrwioSie5xyn+eF/PjVj+q1OMGf+U7orER5P9vq2gTP5xqbrhKYH//XP4TvN0vi/Wyc8AAA8hbUn5q2uro6M/LM7c2RkZHV1tclkunnzpnljVVWVlVNNiOckoFzdRu53EUKcnHnL48WLV4rGr81MzTUTDh/jONvX/e87+v+Z3flJkrGhWpH25XQPGgQAAJNh7Ydluru75fI/h7lzd3cfHBxUq9U0TZs3dnV1PentOp0uMzMzJyfHvPG7775TqVSPb6zX68fGxqakHyEhhMz6L+3vjY7OfzG0h77xuijpv3U63QR7UKrEW7+hDTqeSGoghEy4vW0aGhqasmMOk4bDzgocduujadpgMEy+B7xQKHSYaDYeaxdCoVBoNBqZZYPBQFEUUwJNJhPTL9VgMLi6PvERf2dn55SUlOjoaPPGOXPm/GXnSh6PN1Ud6gkhY74BpK/z8WyjA32D+kHpc6GTnQRAMvEoITaNpumn/AdhmuCwswKH3fpomnZwcLDtDvU+Pj5qtZpZVqvV3t7eCoVCJBL9/vvvCoWCafTxeWL/az6fHxwcvHLlSquE/Q+OPvOGrv7r8XZTc41g3gLMhwcAYKOsd4/wyJEjarU6OTm5sLDQZDIRQg4fPpycnMzj8ZKSkg4fPkwI0Wq1x44dS05OtlqqyXPyDXz4RyN57Hrc2HzDeaIbhAAAMGNNSyFsa2uTy+VxcXF9fX1yuTwiIoIQ8uGHH16/fv3111/38vIKDg5etGhRc3Nzeno6IeTzzz8vLS0NDw8PCQlZtmxZXNwT5yVgEeWm4Dk6jdz7z64dNG1quiYIWsxSKAAAsNS0/DSqUqlaW1vHVymKIoTcvn1bJBI5OjqWlJQ0NTWZTKaQkBDmHqafn9/t27dv3rwpkUj8/f2nI9KUEMwNMbXeNB82+qGmheci5rtPPJcCAADMTNNSCB0cHGSyR6cQc3P795S2gYGBj+bg82fggDKPEAQsNDVdNx/ezNhQ6RL81yOrAQCATbB2P0Kb5hwUZmy6wXSrZxhvVT5piFEAALAJKITPgJK6891nm1rrmNWR3o7RgXuCuSHspgIAAEugED4bl8UrhypLmeWhX88Jw2LIRF01AQBgJsOX+LMRLfmbsaFy9H7P6IN7Q1cvuK54he1EAABgEcxH+GwcRBJxzIa+/9lNKMo1ai0lU7KdCAAALIJC+MzEMUkOQjGhadGyVWxnAQAAS6EQPjseT/TCGrZDAADA1MA9QgAA4DQUQgAA4DQUQgAA4DR7LoRXrlzp7OyceDuYUmVlZVqtlu0UnPPTTz+xHYFzdDpdaWkp2yk4p6ur69dff53afdpzIczOzq6qqmI7Beekp6d3dHSwnYJz3nzzzTGzwf/ACu7evbtt2za2U3DOtWvXDhw4MLX7tOdCCAAAMCEUQgAA4DQe/diU6zNZWlpaTU2Nu7v7ZDa+deuWp6enXC6f7lRgrrq6Ojg42MXFhe0g3PLLL79ERkaynYJbDAbDrVu3wsLC2A7CLf39/R0dHSEhk53t4Ouvv54zZ87Tt7GxQqjVai9dusR2CgAAsA1RUVESieTp29hYIQQAAJhauEcIAACchkIIAACchkIIAACchkIIAACcZp/TMI2OjmZlZZWUlEil0oyMjJUrV7KdiBMyMzObmpqYZT8/v/3797Obx461tbWdPHmytrbW29t7z549TKNGo9m+fXtzc3NoaOjevXuVSswaPcVaWlpOnTpVV1fn5+e3a9cuQojJZHrjjTfGN4iPjzdfhSlRXFx8/PhxtVrt6en5/vvvv/TSS4SQ+vr6Tz75pKOjY8WKFbt37xaJRJZ8hH1eER44cODkyZPff//9pk2bEhIS2tvb2U7ECRcvXvT3909KSkpKSoqNjWU7jj2rrq5uaGgYGRkpKysbb1y7dq1SqSwoKKAo6rXXXmMxnr2qrKxsbGw0mUzl5eVMy8jIyLFjx9auXcuc9gsXLmQ3oV06f/58QkLCDz/8EB8fn5CQUFVVZTQaY2Njly9fnp+f39zcvGXLFks/g7ZHPj4+JSUlzPL69eu/+OILdvNwRERExOnTp9lOwSEFBQVLlixhli9fviyTyR4+fEjTtE6nc3FxaWhoYDWd3crLy1u+fDmzrNPpCCFGo5HdSNwRFxf35ZdfFhYWhoaGMi0tLS3Ozs79/f2W7NYOrwgfPHig0WjCw8OZ1fDw8NraWnYjccfevXtffvnl7du39/T0sJ2FW+rq6hYtWuTo6EgIEYlE8+fPr6urYzsUV7z66qvr16/PyckZHh5mO4s9e/jwYUNDQ0BAQF1d3fg3/Lx584RCYXNzsyV7tsN7hMxXsJubG7Mqk8nwpWwdGzdu9PLy4vP5+fn5y5Ytq62tFYvFbIfiip6envFznhAil8u7u7tZzMMRfD4/Kytr8eLFAwMDWVlZlZWVhYWFbIeyW9u2bfP19U1MTCwpKTEfO1Mmk1l4ttthIZRKpYQQvV7PDKuj0+nMvyBg+qSlpTELq1evDggIOH/+fFJSEruRuEMqler1+vFVrVaL094KBAJBRkYGs/z8888HBQV9++23OPLTYdeuXeXl5RUVFQ4ODlN+ttvhT6MKhUIkErW0tDCrLS0tKpWK1UScQ1GUh4fHwMAA20E4RKVSjZ/zY2NjbW1tOO2tzMvLixCC03467Nu3r6ioqLS0lJlxwfxs7+/vv3//voVnux0WQoqiUlJSDh48SAjp7Oz88ccfU1NT2Q5l/wYHBxsaGpjln3/++caV7eXtAAAECElEQVSNG5gMwZpWrVo1MDBw9uxZQkhxcbFQKMTxt4LW1tbe3l5CyMjIyM6dO/39/X19fdkOZW9ycnLy8vLKyso8PDyYluTk5MuXLzMPf+Tm5kZERPj4+Fj0GVPxIM+Mc/fu3aVLl3p7e7u5uWVmZrIdhxM0Go2Hh4e7u7tSqfT29i4qKmI7kT27ePGizExKSgpN02fOnFEqlXPnzvXy8qqoqGA7ox06d+6c+WHftGnT0aNHxWKxt7e3WCwODw+vqalhO6Mdeu6558wPe3p6Ok3TeXl5MplMpVIFBATU19db+BH2PPtEd3e3WCwWCoVsB+GQ3t5eHo+nUCjYDsJRw8PDvb29Hh4eFEWxnYUrhoeH7927J5FILOzTDc/KZDL19fV5enryeDwLd2XPhRAAAGBCdniPEAAAYPJQCAEAgNNQCAEAgNNQCAFsTE1NTVFREdspAOwHHpYBmFk+/fTTI0eOPOnVjz76qL29/auvvhoaGrJmKgA7ZodDrAHYtBdeeMHJyYlZvnHjxokTJ95++20/Pz+mJTQ0NCgoyNXVlb2AAPYGV4QAM1d+fv5bb7116dKlqKiop2/Z09MjlUoFAgGzOjo62t/f/3iHzuHh4b6+PolEgv61AONwjxDAxuzfv9/f358QkpGRERgYyMyH7OHhIZVKd+7cSQj55ptvlErlrFmzZs+ezQy6RggxGAxbtmxRKBSenp4SiWTNmjUajYbNPwNgxsBPowA2RqvVdnV1EUIMBkN7e/t77723e/fuefPmHTp06LPPPtNoNPX19UVFRa6urh9//HFqaqparZZIJBs2bLhy5cq+ffsiIyPv3LmzdevW2NjYmpqa8YtIAM5CIQSwYXq9Pjc398UXXySELFiwoLi4+OjRo21tbcwg/dnZ2WFhYRUVFUKh8OzZsydOnEhMTCSEBAcH+/n5BQcHnz59esOGDSz/DQBsQyEEsGHOzs7R0dHMskAgUKlUcrmcqYKEkMDAQEKIRqP5448/KIoSCASlpaXj75VIJPX19SiEACiEADZMKpU6OPz7Tr+Tk5NMJjNfJYSYTKbu7m6apjdu3Gj+XoqiMHkeAEEhBOACqVTq6OjY1dU13jEDAMbhqVEA+xcdHW0ymU6dOsV2EICZCFeEAPYvMTFx6dKlaWlper0+Li5OIBD89ttvx48fX7duXVhYGNvpAFiGK0IA+0dR1Llz51avXr1582alUimVSsPCwi5cuIC5ZAEIRpYBmMlomh4bG3tkunmapmmaNn9GZvIGBwebmpqcnJx8fHzkcvkUxQSwbSiEAADAafhpFAAAOA2FEAAAOA2FEAAAOA2FEAAAOA2FEAAAOA2FEAAAOA2FEAAAOA2FEAAAOA2FEAAAOO3/Acd4rOqhVMHLAAAAAElFTkSuQmCC", + "text/plain": [ + "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=14})" + ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 6 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "p = plot()\n", "for b in get_components(Bus, sys)\n", @@ -615,30 +479,32 @@ " )\n", "end\n", "img = DisplayAs.PNG(p) # This line is only needed because of literate use display(p) when running locally" - ], - "metadata": {}, - "execution_count": 6 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We can also explore the frequency of the different generators" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=5})", - "image/png": 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" + "image/png": 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", + "text/plain": [ + "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=5})" + ] }, + "execution_count": 14, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "p2 = plot()\n", "for g in get_components(ThermalStandard, sys)\n", @@ -652,689 +518,333 @@ " )\n", "end\n", "img = DisplayAs.PNG(p2) # This line is only needed because of literate use display(p2) when running locally" - ], - "metadata": {}, - "execution_count": 7 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "It is also possible to explore the small signal stability of this system we created. However,\n", "Since a simulation has already taken place, we need to reset the model." - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Rebuilding the simulation after reset\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/simulation.jl:127\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: Initializing Simulation States\n", - "[ Info: Unit System changed to SYSTEM_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: PowerFlow solve converged, the results have been stored in the system\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "[ Info: Attaching Perturbations\n", - "[ Info: Completed Build Successfully. Simulations status = BUILT\n", - "┌ Info: Simulation reset to status BUILT\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/simulation.jl:133\n", - "┌ Warning: No Infinite Bus found. Confirm stability directly checking eigenvalues.\n", - "│ If all eigenvalues are on the left-half plane and only one eigenvalue is zero, the system is small signal stable.\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/small_signal.jl:89\n", - "┌ Info: Eigenvalues are:\n", - "│ -1000.0000000000003 + 0.0im\n", - "│ -1000.0000000000001 + 0.0im\n", - "│ -999.9999999999997 + 0.0im\n", - "│ -999.9999999999992 + 0.0im\n", - "│ -999.9999999999984 + 0.0im\n", - "│ -51.80750209272119 + 0.0im\n", - "│ -51.66123302456198 + 0.0im\n", - "│ -51.49203886277387 + 0.0im\n", - "│ -51.481903988513096 + 0.0im\n", - "│ -51.38460545442782 + 0.0im\n", - "│ -43.60716425261336 + 0.0im\n", - "│ -36.89387419725479 + 0.0im\n", - "│ -33.31102183032896 + 0.0im\n", - "│ -30.408684873845043 + 0.0im\n", - "│ -28.016564054568867 + 0.0im\n", - "│ -23.88120337382463 + 0.0im\n", - "│ -20.770730197277874 + 0.0im\n", - "│ -18.29943504995121 + 0.0im\n", - "│ -15.81821914155917 + 0.0im\n", - "│ -12.464313199228197 + 0.0im\n", - "│ -6.62036491475575 + 0.0im\n", - "│ -5.287336108463023 + 0.0im\n", - "│ -5.0 + 0.0im\n", - "│ -5.0 + 0.0im\n", - "│ -4.48510748483644 + 0.0im\n", - "│ -4.473100690495934 - 10.72463570060822im\n", - "│ -4.473100690495934 + 10.72463570060822im\n", - "│ -3.7756911474823878 - 10.22427100013456im\n", - "│ -3.7756911474823878 + 10.22427100013456im\n", - "│ -3.746054742811385 - 9.923043365706413im\n", - "│ -3.746054742811385 + 9.923043365706413im\n", - "│ -2.6700477300949417 - 8.718506099264511im\n", - "│ -2.6700477300949417 + 8.718506099264511im\n", - "│ -2.467976122455335 - 7.970276155972672im\n", - "│ -2.467976122455335 + 7.970276155972672im\n", - "│ -2.3481895181659898 - 8.539428521852784im\n", - "│ -2.3481895181659898 + 8.539428521852784im\n", - "│ -2.2759313438720015 - 8.931079340123352im\n", - "│ -2.2759313438720015 + 8.931079340123352im\n", - "│ -2.10698375724919 + 0.0im\n", - "│ -1.6801206992544258 + 0.0im\n", - "│ -1.3274692800441712 - 8.790583490268638im\n", - "│ -1.3274692800441712 + 8.790583490268638im\n", - "│ -1.2310370753428266 + 0.0im\n", - "│ -1.1607992556394304 - 0.12285093395719408im\n", - "│ -1.1607992556394304 + 0.12285093395719408im\n", - "│ -0.9765594618186264 - 0.098920784571056im\n", - "│ -0.9765594618186264 + 0.098920784571056im\n", - "│ -0.9310246491365664 - 0.4760254454829673im\n", - "│ -0.9310246491365664 + 0.4760254454829673im\n", - "│ -0.6462941404591245 - 0.18899029828771308im\n", - "│ -0.6462941404591245 + 0.18899029828771308im\n", - "│ -0.5 + 0.0im\n", - "│ -0.4168402299583835 + 0.0im\n", - "│ -0.3365197752596957 - 7.603682407384889im\n", - "│ -0.3365197752596957 + 7.603682407384889im\n", - "│ -0.18628447010834612 + 0.0im\n", - "│ 0.0 + 0.0im\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/small_signal.jl:94\n" + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInitializing Simulation States\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to SYSTEM_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPowerFlow solve converged, the results have been stored in the system\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mAttaching Perturbations\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCompleted Build Successfully. Simulations status = BUILT\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": "The system is small signal stable\n" + "text/plain": [ + "The system is small signal stable\n" + ] }, + "execution_count": 15, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "res = small_signal_analysis(sim; reset_simulation = true)" - ], - "metadata": {}, - "execution_count": 8 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The eigenvalues can be explored visually" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in GENERATOR are missing: O2, F2, O3, F3, O4, F4, WMOD, WPF\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: The following fields in BRANCH are missing: O2, F2, O3, F3, O4, F4\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/pti.jl:1220\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 4 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 1 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 12 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 20 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 2 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 6 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 11 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 13 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 5 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 15 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 16 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 14 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 7 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 8 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 17 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 10 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 19 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 9 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 18 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: this code only supports positive rate_a values, changing the value on branch 3 to 0.0\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/pm_io/data.jl:1118\n", - "┌ Info: Constructing System from Power Models\n", - "│ data[\"name\"] = 14bus\n", - "│ data[\"source_type\"] = pti\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:39\n", - "┌ Info: Reading bus data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:149\n", - "┌ Info: Reading generator data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:431\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:588\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 04-i_4 is larger than the SIL 44.88379907931658 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 01-BUS 05-i_2 is larger than the SIL 46.30200500787965 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 03-BUS 04-i_6 is larger than the SIL 43.40068938825592 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 09-BUS 14-i_13 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 05-i_5 is larger than the SIL 43.107319591166274 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 12-BUS 13-i_15 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 13-BUS 14-i_16 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 10-BUS 11-i_14 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 11-i_8 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 13-i_10 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 06-BUS 12-i_9 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Data for line rating is not provided, PowerSystems will infer a rate from line parameters\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:88\n", - "┌ Warning: Rate provided for BUS 02-BUS 03-i_3 is larger than the SIL 46.39663623797776 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Info: Reading branch data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:650\n", - "┌ Info: Reading DC Line data\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/power_models_data.jl:622\n", - "┌ Info: Generators provided in .dyr, without a generator in .raw file will be skipped.\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/parsers/psse_dynamic_data.jl:192\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n" - ] - }, - { - "output_type": "execute_result", "data": { - "text/plain": "System\n======\nSystem Units Base: SYSTEM_BASE\nBase Power: 100.0\nBase Frequency: 60.0\n\nComponents\n==========\nNum components: 77\n\n\u001b[1m11×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m C\u001b[0m ⋯\n\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m I\u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ Arc Topology <: Component <: Infrast… ⋯\n 2 │ Area AggregationTopology <: Topology …\n 3 │ Bus Topology <: Component <: Infrast…\n 4 │ DynamicGenerator{PowerSystems.Ro… DynamicInjection <: Device <: Co…\n 5 │ DynamicGenerator{PowerSystems.Ro… DynamicInjection <: Device <: Co… ⋯\n 6 │ Line ACBranch <: Branch <: Device <: …\n 7 │ LoadZone AggregationTopology <: Topology …\n 8 │ PowerLoad StaticLoad <: ElectricLoad <: St…\n 9 │ TapTransformer ACBranch <: Branch <: Device <: … ⋯\n 10 │ ThermalStandard ThermalGen <: Generator <: Stati…\n 11 │ Transformer2W ACBranch <: Branch <: Device <: …\n\u001b[31m 1 column omitted\u001b[0m\n\nTimeSeriesContainer\n===================\nComponents with time series data: 0\nTotal StaticTimeSeries: 0\nTotal Forecasts: 0\n", "text/html": [ "

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1ArcTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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3BusTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
4DynamicGenerator{PowerSystems.RoundRotorQuadratic,PowerSystems.SingleMass,PowerSystems.ESAC1A,PowerSystems.GasTG,PowerSystems.PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
5DynamicGenerator{PowerSystems.RoundRotorQuadratic,PowerSystems.SingleMass,PowerSystems.ESAC1A,PowerSystems.TGFixed,PowerSystems.PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
6LineACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
7LoadZoneAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
8PowerLoadStaticLoad <: ElectricLoad <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
9TapTransformerACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
10ThermalStandardThermalGen <: Generator <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
11Transformer2WACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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ConcreteTypeSuperTypes
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1ArcTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
2AreaAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
3BusTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
4DynamicGenerator{RoundRotorQuadratic,SingleMass,ESAC1A,GasTG,PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
5DynamicGenerator{RoundRotorQuadratic,SingleMass,ESAC1A,TGFixed,PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
6LineACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
7LoadZoneAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
8PowerLoadStaticLoad <: ElectricLoad <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
9TapTransformerACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
10ThermalStandardThermalGen <: Generator <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
11Transformer2WACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
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\n" + ], + "text/plain": [ + "System\n", + "======\n", + "System Units Base: SYSTEM_BASE\n", + "Base Power: 100.0\n", + "Base Frequency: 60.0\n", + "\n", + "Components\n", + "==========\n", + "Num components: 77\n", + "\n", + "\u001b[1m11×3 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m C\u001b[0m ⋯\n", + "\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m I\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ Arc Topology <: Component <: Infrast… ⋯\n", + " 2 │ Area AggregationTopology <: Topology …\n", + " 3 │ Bus Topology <: Component <: Infrast…\n", + " 4 │ DynamicGenerator{RoundRotorQuadr… DynamicInjection <: Device <: Co…\n", + " 5 │ DynamicGenerator{RoundRotorQuadr… DynamicInjection <: Device <: Co… ⋯\n", + " 6 │ Line ACBranch <: Branch <: Device <: …\n", + " 7 │ LoadZone AggregationTopology <: Topology …\n", + " 8 │ PowerLoad StaticLoad <: ElectricLoad <: St…\n", + " 9 │ TapTransformer ACBranch <: Branch <: Device <: … ⋯\n", + " 10 │ ThermalStandard ThermalGen <: Generator <: Stati…\n", + " 11 │ Transformer2W ACBranch <: Branch <: Device <: …\n", + "\u001b[31m 1 column omitted\u001b[0m\n", + "\n", + "TimeSeriesContainer\n", + "===================\n", + "Components with time series data: 0\n", + "Total StaticTimeSeries: 0\n", + "Total Forecasts: 0\n" ] }, + "execution_count": 17, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "sys = System(joinpath(file_dir, \"14bus.raw\"), joinpath(file_dir, \"dyn_data.dyr\"))" - ], - "metadata": {}, - "execution_count": 10 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We want to remove the generator 6 and the dynamic component attached to it." - ], - "metadata": {} + ] }, { - "outputs": [], "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], "source": [ "thermal_gen = get_component(ThermalStandard, sys, \"generator-6-1\")\n", "remove_component!(sys, get_dynamic_injector(thermal_gen))\n", "remove_component!(sys, thermal_gen)" - ], - "metadata": {}, - "execution_count": 11 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We can now define our storage device and add it to the system" - ], - "metadata": {} + ] }, { - "outputs": [], "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], "source": [ "storage = GenericBattery(\n", " name = \"Battery\",\n", @@ -1354,77 +864,94 @@ ")\n", "\n", "add_component!(sys, storage)" - ], - "metadata": {}, - "execution_count": 12 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "A good sanity check it running a power flow on the system to make sure all the components\n", "are properly scaled and that the system is properly balanced. We can use `PowerSystems` to\n", "perform this check. We can get the results back and perform a sanity check" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Info: Unit System changed to SYSTEM_BASE\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/base.jl:282\n", - "┌ Info: The System has no islands\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/network_calculations/ybus_calculations.jl:137\n", - "┌ Info: PowerFlow solve converged, the results are exported in DataFrames\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/power_flow.jl:339\n", - "┌ Info: Voltages are exported in pu. Powers are exported in MW/MVAr.\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/power_flow.jl:175\n", - "┌ Info: Unit System changed to SYSTEM_BASE\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/base.jl:288\n" - ] - }, - { - "output_type": "execute_result", "data": { - "text/plain": "\u001b[1m14×9 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m bus_number \u001b[0m\u001b[1m Vm \u001b[0m\u001b[1m θ \u001b[0m\u001b[1m P_gen \u001b[0m\u001b[1m P_load \u001b[0m\u001b[1m P_net \u001b[0m\u001b[1m Q_gen \u001b[0m\u001b[1m Q\u001b[0m ⋯\n\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m F\u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 1 1.06 0.0 193.33 0.0 193.33 1.12086 ⋯\n 2 │ 2 1.04 -0.0711029 30.0 21.7 8.3 27.0157\n 3 │ 3 1.01 -0.178704 20.0 94.2 -74.2 21.719\n 4 │ 4 1.01285 -0.145825 0.0 47.8 -47.8 0.0\n 5 │ 5 1.01648 -0.123536 0.0 7.6 -7.6 0.0 ⋯\n 6 │ 6 1.06 -0.194906 15.0 11.2 3.8 14.8004\n 7 │ 7 1.04377 -0.181188 0.0 0.0 0.0 0.0\n 8 │ 8 1.08 -0.165561 10.0 0.0 10.0 22.2916\n 9 │ 9 1.02628 -0.210231 0.0 29.5 -29.5 0.0 ⋯\n 10 │ 10 1.02453 -0.212546 0.0 9.0 -9.0 0.0\n 11 │ 11 1.03837 -0.205887 0.0 3.5 -3.5 0.0\n 12 │ 12 1.04362 -0.210542 0.0 6.1 -6.1 0.0\n 13 │ 13 1.03723 -0.211859 0.0 13.5 -13.5 0.0 ⋯\n 14 │ 14 1.01263 -0.229125 0.0 14.9 -14.9 0.0\n\u001b[31m 2 columns omitted\u001b[0m", "text/html": [ "

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bus_numberVmθP_genP_loadP_netQ_genQ_loadQ_net
Int64Float64Float64Float64Float64Float64Float64Float64Float64
111.060.0193.330.0193.331.120860.01.12086
221.04-0.071102930.021.78.327.015712.714.3157
331.01-0.17870420.094.2-74.221.71919.02.71896
441.01285-0.1458250.047.8-47.80.00.00.0
551.01648-0.1235360.07.6-7.60.01.6-1.6
661.06-0.19490615.011.23.814.80047.57.3004
771.04377-0.1811880.00.00.00.00.00.0
881.08-0.16556110.00.010.022.29160.022.2916
991.02628-0.2102310.029.5-29.50.016.6-16.6
10101.02453-0.2125460.09.0-9.00.05.8-5.8
11111.03837-0.2058870.03.5-3.50.01.8-1.8
12121.04362-0.2105420.06.1-6.10.01.6-1.6
13131.03723-0.2118590.013.5-13.50.05.8-5.8
14141.01263-0.2291250.014.9-14.90.05.0-5.0
" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccccccc}\n", + "\t& bus\\_number & Vm & θ & P\\_gen & P\\_load & P\\_net & Q\\_gen & Q\\_load & Q\\_net\\\\\n", + "\t\\hline\n", + "\t& Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 1 & 1.06 & 0.0 & 193.33 & 0.0 & 193.33 & 1.12086 & 0.0 & 1.12086 \\\\\n", + "\t2 & 2 & 1.04 & -0.0711029 & 30.0 & 21.7 & 8.3 & 27.0157 & 12.7 & 14.3157 \\\\\n", + "\t3 & 3 & 1.01 & -0.178704 & 20.0 & 94.2 & -74.2 & 21.719 & 19.0 & 2.71896 \\\\\n", + "\t4 & 4 & 1.01285 & -0.145825 & 0.0 & 47.8 & -47.8 & 0.0 & 0.0 & 0.0 \\\\\n", + "\t5 & 5 & 1.01648 & -0.123536 & 0.0 & 7.6 & -7.6 & 0.0 & 1.6 & -1.6 \\\\\n", + "\t6 & 6 & 1.06 & -0.194906 & 15.0 & 11.2 & 3.8 & 14.8004 & 7.5 & 7.3004 \\\\\n", + "\t7 & 7 & 1.04377 & -0.181188 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\n", + "\t8 & 8 & 1.08 & -0.165561 & 10.0 & 0.0 & 10.0 & 22.2916 & 0.0 & 22.2916 \\\\\n", + "\t9 & 9 & 1.02628 & -0.210231 & 0.0 & 29.5 & -29.5 & 0.0 & 16.6 & -16.6 \\\\\n", + "\t10 & 10 & 1.02453 & -0.212546 & 0.0 & 9.0 & -9.0 & 0.0 & 5.8 & -5.8 \\\\\n", + "\t11 & 11 & 1.03837 & -0.205887 & 0.0 & 3.5 & -3.5 & 0.0 & 1.8 & -1.8 \\\\\n", + "\t12 & 12 & 1.04362 & -0.210542 & 0.0 & 6.1 & -6.1 & 0.0 & 1.6 & -1.6 \\\\\n", + "\t13 & 13 & 1.03723 & -0.211859 & 0.0 & 13.5 & -13.5 & 0.0 & 5.8 & -5.8 \\\\\n", + "\t14 & 14 & 1.01263 & -0.229125 & 0.0 & 14.9 & -14.9 & 0.0 & 5.0 & -5.0 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m14×9 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m bus_number \u001b[0m\u001b[1m Vm \u001b[0m\u001b[1m θ \u001b[0m\u001b[1m P_gen \u001b[0m\u001b[1m P_load \u001b[0m\u001b[1m P_net \u001b[0m\u001b[1m Q_gen \u001b[0m\u001b[1m Q\u001b[0m ⋯\n", + "\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m F\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ 1 1.06 0.0 193.33 0.0 193.33 1.12086 ⋯\n", + " 2 │ 2 1.04 -0.0711029 30.0 21.7 8.3 27.0157\n", + " 3 │ 3 1.01 -0.178704 20.0 94.2 -74.2 21.719\n", + " 4 │ 4 1.01285 -0.145825 0.0 47.8 -47.8 0.0\n", + " 5 │ 5 1.01648 -0.123536 0.0 7.6 -7.6 0.0 ⋯\n", + " 6 │ 6 1.06 -0.194906 15.0 11.2 3.8 14.8004\n", + " 7 │ 7 1.04377 -0.181188 0.0 0.0 0.0 0.0\n", + " 8 │ 8 1.08 -0.165561 10.0 0.0 10.0 22.2916\n", + " 9 │ 9 1.02628 -0.210231 0.0 29.5 -29.5 0.0 ⋯\n", + " 10 │ 10 1.02453 -0.212546 0.0 9.0 -9.0 0.0\n", + " 11 │ 11 1.03837 -0.205887 0.0 3.5 -3.5 0.0\n", + " 12 │ 12 1.04362 -0.210542 0.0 6.1 -6.1 0.0\n", + " 13 │ 13 1.03723 -0.211859 0.0 13.5 -13.5 0.0 ⋯\n", + " 14 │ 14 1.01263 -0.229125 0.0 14.9 -14.9 0.0\n", + "\u001b[31m 2 columns omitted\u001b[0m" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "res = solve_powerflow(sys)\n", "res[\"bus_results\"]" - ], - "metadata": {}, - "execution_count": 13 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "After verifying that the system works, we can define our inverter dynamics and add it to the\n", "battery that has already been stored in the system." - ], - "metadata": {} + ] }, { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Warning: struct DynamicInverter does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n" - ] - } - ], "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], "source": [ "inverter = DynamicInverter(\n", " name = get_name(storage),\n", @@ -1455,136 +982,123 @@ " filter = LCLFilter(lf = 0.08, rf = 0.003, cf = 0.074, lg = 0.2, rg = 0.01),\n", ")\n", "add_component!(sys, inverter, storage)" - ], - "metadata": {}, - "execution_count": 14 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "These are the current system components:" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "System\n======\nSystem Units Base: SYSTEM_BASE\nBase Power: 100.0\nBase Frequency: 60.0\n\nComponents\n==========\nNum components: 77\n\n\u001b[1m13×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m C\u001b[0m ⋯\n\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m I\u001b[0m ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ Arc Topology <: Component <: Infrast… ⋯\n 2 │ Area AggregationTopology <: Topology …\n 3 │ Bus Topology <: Component <: Infrast…\n 4 │ DynamicGenerator{PowerSystems.Ro… DynamicInjection <: Device <: Co…\n 5 │ DynamicGenerator{PowerSystems.Ro… DynamicInjection <: Device <: Co… ⋯\n 6 │ DynamicInverter{PowerSystems.Ave… DynamicInjection <: Device <: Co…\n 7 │ GenericBattery Storage <: StaticInjection <: De…\n 8 │ Line ACBranch <: Branch <: Device <: …\n 9 │ LoadZone AggregationTopology <: Topology … ⋯\n 10 │ PowerLoad StaticLoad <: ElectricLoad <: St…\n 11 │ TapTransformer ACBranch <: Branch <: Device <: …\n 12 │ ThermalStandard ThermalGen <: Generator <: Stati…\n 13 │ Transformer2W ACBranch <: Branch <: Device <: … ⋯\n\u001b[31m 1 column omitted\u001b[0m\n\nTimeSeriesContainer\n===================\nComponents with time series data: 0\nTotal StaticTimeSeries: 0\nTotal Forecasts: 0\n", "text/html": [ "

System

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Base Power: 100.0

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Components

\n", "

Num components: 77

\n", - "

13 rows × 3 columns (omitted printing of 2 columns)

ConcreteType
String
1Arc
2Area
3Bus
4DynamicGenerator{PowerSystems.RoundRotorQuadratic,PowerSystems.SingleMass,PowerSystems.ESAC1A,PowerSystems.GasTG,PowerSystems.PSSFixed}
5DynamicGenerator{PowerSystems.RoundRotorQuadratic,PowerSystems.SingleMass,PowerSystems.ESAC1A,PowerSystems.TGFixed,PowerSystems.PSSFixed}
6DynamicInverter{PowerSystems.AverageConverter,PowerSystems.OuterControl{PowerSystems.VirtualInertia,PowerSystems.ReactivePowerDroop},PowerSystems.CurrentControl,PowerSystems.FixedDCSource,PowerSystems.KauraPLL,PowerSystems.LCLFilter}
7GenericBattery
8Line
9LoadZone
10PowerLoad
11TapTransformer
12ThermalStandard
13Transformer2W
\n", + "

13 rows × 3 columns (omitted printing of 1 columns)

ConcreteTypeSuperTypes
StringString
1ArcTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
2AreaAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
3BusTopology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
4DynamicGenerator{RoundRotorQuadratic,SingleMass,ESAC1A,GasTG,PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
5DynamicGenerator{RoundRotorQuadratic,SingleMass,ESAC1A,TGFixed,PSSFixed}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
6DynamicInverter{AverageConverter,OuterControl{VirtualInertia,ReactivePowerDroop},CurrentControl,FixedDCSource,KauraPLL,LCLFilter}DynamicInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
7GenericBatteryStorage <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
8LineACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
9LoadZoneAggregationTopology <: Topology <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
10PowerLoadStaticLoad <: ElectricLoad <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
11TapTransformerACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
12ThermalStandardThermalGen <: Generator <: StaticInjection <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
13Transformer2WACBranch <: Branch <: Device <: Component <: InfrastructureSystemsComponent <: InfrastructureSystemsType <: Any
\n", "\n", "

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\n" + ], + "text/plain": [ + "System\n", + "======\n", + "System Units Base: SYSTEM_BASE\n", + "Base Power: 100.0\n", + "Base Frequency: 60.0\n", + "\n", + "Components\n", + "==========\n", + "Num components: 77\n", + "\n", + "\u001b[1m13×3 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m ConcreteType \u001b[0m\u001b[1m SuperTypes \u001b[0m\u001b[1m C\u001b[0m ⋯\n", + "\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m I\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ Arc Topology <: Component <: Infrast… ⋯\n", + " 2 │ Area AggregationTopology <: Topology …\n", + " 3 │ Bus Topology <: Component <: Infrast…\n", + " 4 │ DynamicGenerator{RoundRotorQuadr… DynamicInjection <: Device <: Co…\n", + " 5 │ DynamicGenerator{RoundRotorQuadr… DynamicInjection <: Device <: Co… ⋯\n", + " 6 │ DynamicInverter{AverageConverter… DynamicInjection <: Device <: Co…\n", + " 7 │ GenericBattery Storage <: StaticInjection <: De…\n", + " 8 │ Line ACBranch <: Branch <: Device <: …\n", + " 9 │ LoadZone AggregationTopology <: Topology … ⋯\n", + " 10 │ PowerLoad StaticLoad <: ElectricLoad <: St…\n", + " 11 │ TapTransformer ACBranch <: Branch <: Device <: …\n", + " 12 │ ThermalStandard ThermalGen <: Generator <: Stati…\n", + " 13 │ Transformer2W ACBranch <: Branch <: Device <: … ⋯\n", + "\u001b[31m 1 column omitted\u001b[0m\n", + "\n", + "TimeSeriesContainer\n", + "===================\n", + "Components with time series data: 0\n", + "Total StaticTimeSeries: 0\n", + "Total Forecasts: 0\n" ] }, + "execution_count": 22, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "sys" - ], - "metadata": {}, - "execution_count": 15 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Define Simulation problem using the same parameters:" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Serialized time series data to /var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_pQ1j5S/sys_time_series_storage.h5.\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/time_series_storage.jl:55\n", - "┌ Info: Serialized System to /var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_pQ1j5S/sys.json\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/base.jl:233\n", - "┌ Info: Loaded time series from storage file existing=sys_time_series_storage.h5 new=/var/folders/bb/jpwk1tws6bj6j5nj0gwmzndh0000gn/T/jl_JKUV7o\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/hdf5_time_series_storage.jl:82\n", - "┌ Warning: Rate provided for BUS 12-BUS 13-i_15 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 05-i_5 is larger than the SIL 43.107319591166274 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 01-BUS 05-i_2 is larger than the SIL 46.30200500787965 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 10-BUS 11-i_14 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 13-i_10 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 09-BUS 14-i_13 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 03-i_3 is larger than the SIL 46.39663623797776 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 11-i_8 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 02-BUS 04-i_4 is larger than the SIL 44.88379907931658 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 03-BUS 04-i_6 is larger than the SIL 43.40068938825592 in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 13-BUS 14-i_16 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: Rate provided for BUS 06-BUS 12-i_9 is larger than the SIL Inf in the range of (min = 12.0, max = 13.0).\n", - "└ @ PowerSystems /Users/jdlara/.julia/packages/PowerSystems/eF3Pv/src/utils/IO/branchdata_checks.jl:147\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicInverter does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicInverter does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "┌ Warning: struct DynamicGenerator does not exist in validation configuration file, validation skipped\n", - "└ @ InfrastructureSystems /Users/jdlara/.julia/packages/InfrastructureSystems/6GZHV/src/validation.jl:51\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: Initializing Simulation States\n", - "[ Info: Unit System changed to SYSTEM_BASE\n", - "[ Info: The System has no islands\n", - "[ Info: PowerFlow solve converged, the results have been stored in the system\n", - "[ Info: Unit System changed to DEVICE_BASE\n", - "┌ Warning: Initialization failed, initial conditions do not meet conditions for an stable equilibrium.\n", - "│ Trying to solve again reducing numeric tolerance from 1.0e-9:\n", - "└ @ PowerSimulationsDynamics ~/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/simulation_initialization.jl:117\n", - "[ Info: Initialization succeeded with a relaxed tolerance of 1.0e-6. Saving solution\n", - "[ Info: Attaching Perturbations\n", - "[ Info: Completed Build Successfully. Simulations status = BUILT\n" + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInitializing Simulation States\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to SYSTEM_BASE\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mThe System has no islands\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPowerFlow solve converged, the results have been stored in the system\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUnit System changed to DEVICE_BASE\n", + "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mInitialization failed, initial conditions do not meet conditions for an stable equilibrium.\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mTrying to solve again reducing numeric tolerance from 1.0e-9:\n", + "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ PowerSimulationsDynamics ~/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/simulation_initialization.jl:117\u001b[39m\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInitialization succeeded with a relaxed tolerance of 1.0e-6. Saving solution\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mAttaching Perturbations\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCompleted Build Successfully. Simulations status = BUILT\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": "Simulation()\n" + "text/plain": [ + "Simulation()\n" + ] }, + "execution_count": 23, "metadata": {}, - "execution_count": 16 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "sim = Simulation(\n", " file_dir, #path for the simulation output\n", @@ -1593,119 +1107,208 @@ " BranchTrip(1.0, \"BUS 02-BUS 04-i_4\");\n", " console_level = Logging.Info,\n", ")" - ], - "metadata": {}, - "execution_count": 16 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We can verify the small signal stability of the system before running the simulation:" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Warning: No Infinite Bus found. Confirm stability directly checking eigenvalues.\n", - "│ If all eigenvalues are on the left-half plane and only one eigenvalue is zero, the system is small signal stable.\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/small_signal.jl:89\n", - "┌ Info: Eigenvalues are:\n", - "│ -2274.463684330781 - 6834.713359114738im\n", - "│ -2274.463684330781 + 6834.713359114738im\n", - "│ -2102.3188794698017 - 6526.791717154522im\n", - "│ -2102.3188794698017 + 6526.791717154522im\n", - "│ -1604.8831830348008 - 293.477470183822im\n", - "│ -1604.8831830348008 + 293.477470183822im\n", - "│ -1000.0000000000009 + 0.0im\n", - "│ -1000.0000000000006 + 0.0im\n", - "│ -999.9999999999995 + 0.0im\n", - "│ -999.9999999999993 + 0.0im\n", - "│ -984.5412410510447 + 0.0im\n", - "│ -500.0000000000003 + 0.0im\n", - "│ -471.01006997968807 + 0.0im\n", - "│ -223.6029815536152 + 0.0im\n", - "│ -57.046208215175604 - 289.2276816950161im\n", - "│ -57.046208215175604 + 289.2276816950161im\n", - "│ -51.8509747265928 + 0.0im\n", - "│ -51.53766580827428 + 0.0im\n", - "│ -51.4680552047486 + 0.0im\n", - "│ -51.39701520571893 + 0.0im\n", - "│ -50.352236463882974 + 0.0im\n", - "│ -50.28045625471906 + 0.0im\n", - "│ -43.11101673357476 + 0.0im\n", - "│ -36.865974852579676 + 0.0im\n", - "│ -33.28333962683253 + 0.0im\n", - "│ -29.727404795934763 + 0.0im\n", - "│ -24.863868907400594 + 0.0im\n", - "│ -20.757178390721794 + 0.0im\n", - "│ -17.549656744652037 + 0.0im\n", - "│ -13.172417107322506 + 0.0im\n", - "│ -11.31001248578639 + 0.0im\n", - "│ -11.17698953744912 + 0.0im\n", - "│ -6.633090449167979 - 26.402404835684763im\n", - "│ -6.633090449167979 + 26.402404835684763im\n", - "│ -6.483441550635695 + 0.0im\n", - "│ -5.364519981636358 + 0.0im\n", - "│ -5.0 + 0.0im\n", - "│ -5.0 + 0.0im\n", - "│ -4.91577463892175 + 0.0im\n", - "│ -4.586965784044188 + 0.0im\n", - "│ -3.8541595668179127 - 9.99896553053981im\n", - "│ -3.8541595668179127 + 9.99896553053981im\n", - "│ -3.6151478161496318 - 10.479301983227577im\n", - "│ -3.6151478161496318 + 10.479301983227577im\n", - "│ -2.5403701746129967 - 8.668414072341996im\n", - "│ -2.5403701746129967 + 8.668414072341996im\n", - "│ -2.4842482267067947 - 8.097776757645004im\n", - "│ -2.4842482267067947 + 8.097776757645004im\n", - "│ -2.17673979948575 - 8.96192264465768im\n", - "│ -2.17673979948575 + 8.96192264465768im\n", - "│ -2.051920203783915 + 0.0im\n", - "│ -1.5998576699575109 - 8.816079134039873im\n", - "│ -1.5998576699575109 + 8.816079134039873im\n", - "│ -1.5940844354185164 + 0.0im\n", - "│ -1.2700316575381947 + 0.0im\n", - "│ -1.1024588340149295 - 0.4095510543015172im\n", - "│ -1.1024588340149295 + 0.4095510543015172im\n", - "│ -1.0747488616728131 - 0.05369218880364683im\n", - "│ -1.0747488616728131 + 0.05369218880364683im\n", - "│ -0.7359328902986594 + 0.0im\n", - "│ -0.5 + 0.0im\n", - "│ -0.38007176920659924 - 7.46134780193091im\n", - "│ -0.38007176920659924 + 7.46134780193091im\n", - "│ -0.31174250195237185 + 0.0im\n", - "│ -0.2528008128430094 + 0.0im\n", - "│ 0.0 + 0.0im\n", - "└ @ PowerSimulationsDynamics /Users/jdlara/.julia/packages/PowerSimulationsDynamics/4A5EO/src/base/small_signal.jl:94\n" - ] - }, - { - "output_type": "execute_result", "data": { - "text/plain": "The system is small signal stable\n" + "text/plain": [ + "The system is small signal stable\n" + ] }, + "execution_count": 24, "metadata": {}, - "execution_count": 17 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "res = small_signal_analysis(sim)" - ], - "metadata": {}, - "execution_count": 17 + ] }, { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "Plot{Plots.GRBackend() n=1}", "image/png": 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"image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "text/plain": [ + "Plot{Plots.GRBackend() n=1}" ] }, + "execution_count": 18, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "scatter(res.eigenvalues)" - ], - "metadata": {}, - "execution_count": 18 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We execute the simulation" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2081,34 +1523,35 @@ ] } ], - "cell_type": "code", "source": [ "execute!(sim, IDA(); abstol = 1e-8)" - ], - "metadata": {}, - "execution_count": 19 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Using `PowerSimulationsDynamics` tools for exploring the results, we can plot all the voltage\n", "results for the buses" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=14})", - "image/png": 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" + "image/png": 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", + "text/plain": [ + "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=14})" + ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "p = plot()\n", "for b in get_components(Bus, sys)\n", @@ -2122,30 +1565,32 @@ " )\n", "end\n", "img = DisplayAs.PNG(p) # This line is only needed because of literate use display(p) when running locally" - ], - "metadata": {}, - "execution_count": 20 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We can also explore the frequency of the different static generators and storage" - ], - "metadata": {} + ] }, { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=5})", - "image/png": 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DDz9s3749MjIyLi5Oo9EEBgY2b9782LFjABAbGzt48GC+/piYmMzMzD179iiVyoCAgLNnz9rZ2QmFQicnJwDYtm1bQEDA2LFjAeCDDz5YvXr1oUOHAKBDhw5Dhw7la+BTVPGZnKqrq69du1ZQUGDJPGy99Okeu+bx48enT58OAHq9Xq1We3p6tmnTRqfT3bhxo3379i91ol8U+gtGGroKTdWFnCv5OVeg7I6dWSlnqwlMyWIUZSfRO2A6EtNjBCuQScXuPo4x0f7d/HwiSWdPeI6rAPKK6+WHlQ4TPHs9W6uoqJBIJA4O96fp8fLy0mg0RUVF6enpqampfGFISIjJZAIAd3d3vkQqler1+sLCwtTU1Pfee89SG0mSAODn52cpWbp06ebNm/v37+/o6MiyrEqlsrP7exK7srIyf39/y9fAwMCSkhLrGtq2bcvH4J07dyYkJERGRv70008AMH/+/OnTp7dt23b58uUAMGHChG+//fYphzlx4sTff/8dAObOnTt79mxLeXFxMT+sJjk5+aeffjpy5AiO497e3oWFhSgQIkjt4lhOU2jQFhp0ZUaj0mxW03qt3szeMUnOMpLzfkBhVFgN8ZamUefods2b+Dga6fuZSGUCGY7CHmI7Pj4+FEUVFRXxsefu3bs4jvv7+/v5+fEx5in8/f3Dw8P/+usv68IlS5ZY32lcsWLFkSNHmjVrBgDnzp0DAIIgLINZfHx88vLyLCvn5OQMHTq0sLDQUsOlS5ceu+uwsLC9e/d++eWXX3755fMc5po1a9asWfNoubOzs0qlAoCSkpLS0lIA4DhOpVK5uro+T7W1AQVC5A3HsVx1hrbiek1VuoZ0FIC3VO1A32V262Gvl6QQZwQeWqdy4h2q7bhuLXway/8OeAI0EzRSO0Qi0fjx48eOHTtv3ry0tLTDhw/36tVr4sSJHTp06NSpU7du3fh7p+PGjXt02yFDhixcuHDZsmUjR440m82nTp166623HlrHxcXl2LFj/v7+e/fuPXbsWKtWrRo1apSamnrhwgW5XN6rV69p06b9+OOP77333u+//15RUfH2228/NmIBwOrVqyMjIz09PTMzM7/88svExMSHVjhx4gQ/WObs2bNubm5du3Z96BHjo7p3775x48bOnTtv27ZNIBCcPn26oKCA47i2bds+7xm0NRQIkTcTq1NXXb+bd1VnKJOaBUU6UXq1LNOIF5EVCrtytQRkvkZXV/kQeVTv0Jg2XUVoJCdSp3744Yfvvvtu+fLlERERY8eONZvN4eHhR48eXbRo0dy5c+VyeadOnTAMa9asmeU2pouLS0xMjKOj4/nz5//973+vXbuWJMmYmJhu3bo1atRIIvl7/rz169fPmjVr1apV3bt3X7x4sZOTU2xs7IQJE77++mu5XP7LL7+cOHHi008/XblyZXh4+IkTJ2QyWUBAAP887yFarfajjz5SKBTe3t6TJk2aMmXKQyt8//33BoMhPj5+/fr1ANClS5dnBsJFixZNnTo1Pj5++PDh3bt3nzx5skAg+OOPP6zv39Yx7LEH/7qbM2eOs7PznDlznmdljUZj/YooUqtq72wbaOMNRUpG2W3j7Wuyiio5bcSICrNAgYHeSIq1uLCaEAoknn4uEbEh/d19WuB2b3giBTTF2kNmzpzp6+s7c+bM+m4IAIBWq+Wv+3q9/q233vrss88sQ12QJ/n888+lUunnn39u88sI6hEiryWW5gzlJpOKorR0ieJeRkm6SX3OHrvjQGY40f5qLKTCL8a/mY+PkwcudhLgpKvUxVPm/k+TviJILdm/f/+8efMCAwPT0tJ69uz57rvv1neLGjQUCJHXibbYUHlLXZ2p1d8ziV0EjJzONh2j2CMy7LY951lFR+c3/b5v+7BIFzS2BXmlJSYm9ujRo7y83NvbG92RqncoECKvAZbmKq5Vl55RMiZW1sKB6+qZx+VnpC3wqboiBy5A7VJp/z7bc+zgFj7iN3ZaYORN4+TkxL/Yh9Q7FAiRV5q5vDL7cKEyDdcLDcWyHBWRRuZkyPIz7NiaEM4pyNjCscm7QV16iRzRBQVBkBeEAiHyyqk0VOVXZCsunKKzy4UMoxNmmT2KSKxajrFmQlotcvJwjo0PTXRp1PKNH/CCIEgdQIEQeVWojNX70o5kpV71r8p3ZvJwXK2VBKicnUkXX0bS3M4x0NM5uL1ruJ/965rqBUHqS0pKSk5OzoABA/7phnq9fubMmQcOHGAYJi4ubvny5Q+99v7VV19duXKlqKhozZo10dHRtmtynUKBEKknHOgVJk2R3qAw6SqNZaXllCHdSbarteAuaWx90+3fcX16DfBG/30iiA1cvnw5OTn5BQLhV199lZqaeuPGDZFINGLEiI8//njDhg3WKygUin79+n300UcajcZ27a1r6EKD1DVdqfHeRZUypQYX4PJAaY2d6rhpvZ/TZYm0yrXa86Dbqt69u83zRMM+kTeZXq+fP3/+xYsXW7Zs2bFjx6qqqsmTJ9+9e3fp0qW5ubmRkZHz5893cXFZsWJFQEDAmTNnLly4EB0dvXjxYplMplAoFi1axKdh+ve//x0UFLRnz5579+4plcqDBw8uWbJEIpGsXLkyJyfH3d198uTJYWFhP/30U1lZ2Xvvvefu7r5y5cri4uKFCxfevXs3PDx8/vz5Pj4++/fvz8/P1+l0+/bt++KLL7p168a3Mzs7OyEhge8F9u3bd+PGjQ8dCD8n3GefffaPjn3s2LE//vijs7MzX3Lo0KErV67Mmzfv5U/si0GvVSF1R1toyP1FcWdtgdCOiPhXY6/p/n84/nihZJi/Mdn/noCFieKxe5eOi++EoiDyppswYUJeXt7atWu7du06ffr0K1euFBQUxMbG9ujRY+3atc7Ozn379uU47vLly9OmTWvTps2aNWtu3rz5zTff6PX6Dh06NGrUKCkpqUuXLnFxcVqtNj09ffbs2SKRaPXq1aGhoWq1evjw4Zs2bRo3btyIESOqqqp69uwZGho6Z86ciRMnGo3GTp06eXt7//TTTx4eHp06dTIajRkZGZ999hnHcatWrWratKmlnePGjdu6deuBAweOHj36448/Tpo06eWPffv27VlZWZYoCABNmjRZvHhxRUXFy1f+YlCPEKkLZjWds/deZaYuLVR+IVBaqqxstP3rVsx+f4ZtVONl7xznP/79Tn6B9d1MpGExpJ5XblhUBzvy+uI3Qv73wGaDwbBly5aSkhIPD4+wsLBDhw5RFLVq1aoRI0bwiZAWLFiwadOmu3fvAsCgQYP4SWemTJmSlJS0ffv2kJAQfn6c0NDQHTt2HD58GACioqI++ugjvn5PT8+amprMzEyRSNS6deuzZ88GBATcuXMnKioKAHbv3i2TyRYsWAAACxcu3LFjR3JyMgC0aNHik08+eajlrVq1Cg4O5qOsVCrt2LHjy5+N5ORkS4+TFxAQ4Ofnd+zYsUfnMq0bKBAitYuuqszadkuZI8eJv+RkctNcrWu5nsEN9wSOwaLotiEDZC07E87u9d1MpCGSNG/v+599dbEn/H/eby0rKxOLxR4eHvzXxo0bZ2RkZGdnnzlzZs+ePfe3wPHKykoAsMw1am9vr1ars7OzL168GBQUZKlNrVaDVSp5ANi4ceOCBQs6duwok8mKi4vLy8utkzQVFBSEhYXxnzEMCw8PLygosK4hPj4+MzMTADZt2rRixYqQkJD9+/cDwH/+85/BgwfPmDGDj5dxcXGbNm16ykEvX778v//9LwAMGjTou+++s5Tn5eV17twZAFJSUgYOHJidnQ0AAQEB1jkx6hgKhIjtccBlq/LulKRqT12wr9IaRamcbx4lkNSI7As5scyzTecWIwYHdEK5jZB6hmGA1cMUDJ6engaDoaKiws3NDQDy8/P5wvHjxy9ZssR6zaSkpEe37dy58969e60LlyxZQhB/H8iCBQt27NjBj+HkMxfiOG6ZVtrd3b2srMyycmlpqYeHR2lpqaWGXbt2MQwDAHK5/IMPPhg9ejRfHhcXN3fu3HfffbdXr14AIBA8I5XjhAkTRo0aBQAikci63MHBgU9YoVAoKioqWJbFcVyj0Tg6Oj69wtqDnhEitkSzzO7MA+N2/OvILwuZ5IUS/ZFcV4O5XV9Jj+U+fdd3eee3jwYfmNc3KTawM4qCSIMllUoHDRo0Y8aMwsLCgwcP7tq1CwBGjx69du3ay5cvAwBN0wcPHqRp+tFtBw4ceP78+Z07dwIAx3EXLlxQKBQPrSMQCPje1dmzZ/nOnLe3d2ZmZllZWU1NTffu3dPT0/nyP//8MzMzs3v37tab29vb87PekCQZHR29ceNGvV5PUdTPP/8cExMjEon4pfyk4cXFxbm5uQzDlJWV5ebmms1mSz1isZhfUyqVWtffsWPH3bt3UxS1e/duV1fXY8eOZWdn37x50yb3XV8M6hEiNkAbGEO5OTe/8PyNK16Gon6Cs0La7YJwWot3Rk5v/D8/tl7rMdYIYitr16799NNPBw0a1KJFi9GjR1MU1bZt2w0bNkyePLmgoEAkErVr1y4hIcHNzc2SyF4qlfr4+Hh7ex86dOijjz6aMmUKhmEtWrTYsGGDk5MT38firVixYsqUKdOnT4+JiZkyZYqTk1N8fHyXLl369OljZ2d36tSpP//8c+bMmWPHjuVzFrq4uDg6OvLd04csW7Zs6tSpjRo1Ylk2Jibm0VGjEydOTE9Pl8vl/JjPw4cPW9+kfawZM2bcuHHDy8urbdu227dvT0xM1Gq1CxYsaNGixcuc0peB0jChNEwviKU5Zaq6Kk2tztPTBsZkb8qFC672ewlTuVDdZHOr776J9/CSPrwVOtt1BqVhesgrlYaJvx/If+jSpcvYsWPHjBlT34161aE0TMgrhKW50tPKklOVdj5i15YOfj3c1uVvqr67o1lNkbQmsJAZJE2c+UsYuv4iyBNt3rx58+bNjRo1unbtmpub27Bhw+q7RQ0aCoTIP6MtMmT8Vix1F7WY3MjgKEpXGXacntXs3klPxjOwJLyk1aghgwbK0H9WCPJUo0aNioqKUigUs2bNatKkCYYemdcrdMVC/oGyc1UFh8uLOnj+SsgvHmX9TMffM34cTdUE6pt5E418pk5r7du4vtuIIK8BHMdbtGhRj0/FEGsoECLPhWO4rN9z7mWos4SXfK5nfyzMuUdm6DE9QXl3dxouT+gtbhID6FctgiCvIRQIkWczlpTeXn3XwNwsdTnvISvW0+U5ItdSeZNRHT4L849C8Q9BkNcaCoTIExWqiy+UXC27eTMou0rvfFQrZEtlbjmkg6dPz7dDekzybYfeBUSQ10JeXl5ZWVmHDh3+6Yb5+fk7d+68deuWu7v7f/7zn4eWVlZWbtu27fr160aj8bfffrNRY+sBCoTIY5Rq7/1wNamgMuedEsqfvlpg1wbafJXQpLmDyN5N6kLUx2QcCIK8sGPHjiUnJ79AILx169adO3cwDDty5MijS/Pz8y9cuODq6vr777/bopn1BgVC5GEnC8+tOb95ONeqfeluoSlgjcu2Gf0i47xQ5w9BbIZhmB9//PHSpUstW7aMjo6urKwcPHhweXn5qlWr8vLyIiIipk6dKhKJtm3b5uXldePGjcuXL0dHR0+dOpUkSYPBsGrVqpSUlMDAwOnTp7u4uJw8ebKqqkqv1ycnJ8+YMcPBwWHz5s15eXlubm7jx493c3PbtWtXTk7OJ5984uzsPHv2bI1G88MPP2RkZISFhU2bNk0ul585c0ahUFAUdeDAgSlTprRv355vZ//+/fv37//HH3/cuHHj0aOIjo7+9ddfb968+dNPPz3/sd+5cyc5OXnWrFmWknXr1jVp0uQF4rStoCnWEOBYTp2rKzlVmbOr7PgPV7Q/0WOzHZl7Kzx0rT7x+fmLQSgKIoiNzZo1a8eOHaNGjSJJcvjw4QcPHqysrIyOjpZIJKNGjUpPTx80aBAA7N+/f9SoURRFDR06dOPGjcuWLaNpumHKZOkAACAASURBVFOnTkVFRaNGjcJxvGPHjmaz+cKFC5MmTbp06VJiYqKLiwufqnD8+PGhoaFxcXFVVVUBAQGOjo5RUVHNmzdnGCY2NjY3N3f48OHZ2dldunRhGOby5ctTpkw5ffr0kCFDHjvFjA199913RUVF1iUqlWrhwoW1utOnQz3CBo2ludJTlaWnlUJHgX2gNB/PPyE9HNs0BfRVfgUt/93yq4Nvu/rIUBRE3kxXym58d3l1HexoTc//OIjsLV9NJtPq1aszMjICAwO7d+9+7do1AFixYkWvXr0siR38/f1zcnIAoHfv3nx+JbVavXnzZj8/P7FYzKfDjY+PP336NJ+GKTAwcMWKFXz9jRs3BgCFQhEQEHD8+PHTp09HRUWVl5fz6ZwOHjyoVquTkpIIgoiPjw8ODuZr8PHxWb26Ls7Gn3/++euvv1qXvP3223PmzNFqtfz8pXUPBcKGy6g0p28oFLsIW0xpJHEXXSq99vvpb/uyBUpti8Z50hO9vt7TyVOAbhkgb64Wbk3+260uOiJy4f9c30tKSkiSDAwM5L82a9YsMzMzLS3t5MmTR48e5QuVSmVubi4AhIaG8iUuLi4qlSotLS0lJcWShqmiooJfrXnz5pb6Dx06NGXKFB8fH2dn5/T09ObNm/v6+lqWZmdnR0RE8LkmCIJo2bJlVlYW3wx+hZEjR/Jzdv/nP/9p167dQ8fy2WefnT59GgBmzJjBd1ufZNKkSampqQBgnfJepVJVVlbyoTopKamsrGz+/PlBQUEsy+bk5LRs2fI5TqftoUDYQOnLjLd/yvdLcPd8y/l6Jbf3cnbV7Y9jNaUFdJ94lVI+5duP/Lzqu40IUrvEpNjbzrPu9+vq6mowGCwTZvLpI5ycnKZOnTp//nzrNTdv3vzQpDNOTk49e/bcvn27deGSJUuskyJNmzZt7dq1cXFxADBy5EiO4zDs72mlHR0dVSqVZeWqqionJ6eKigpLDXPnzjUajfCgZ/mQDz/8cMiQIQDg4+Pz9MOcNWsWPxV4QECApVAikWAYxiepyMnJKS4uBgD+60NJKuoS+sHfEBmV5ttJBYH9vY56ODb5g55w9K7d9cHtzdq2WNdhIqzJJ8v8URREkFpjb2+fkJAwf/58iqLS0tK2bt0KAIMHD167di2fIxcA0tLSHpuGqW/fvkePHuWzNQFAUVGRdVTjGQwG/kNWVtaff/4JAG5ubsXFxXyWwbi4uKtXr/L3Yy9fvnzjxo2uXbtabx4WFtayZcuWLVs+dmLrwMBAfqmrq+vTDzM4OJhf0zrRoFgsbt68+ZEjRziOO336dFpamtFoPHz4sLu7u3W8rGOoR9jg0EbmztoC5y5uoxQyTYH5O/kaVeYyQhDQqdTDvlu8vPM7gKOfRwhSuzZs2DBx4sTAwMBmzZrxj+66d+/+2WefdejQwcXFRa1Wu7m5nTt3TiaTWfKHCAQCuVweHBz8yy+/DBs2TCgUUhQlEAj2798vkUisu1OLFi0aOHBgYGCgSCQaOHCgWCxOSEj4+eefg4KCAgICTp06tW7dun79+jk6OlZXV69fv97X11csFj+2Q3b+/Pk+ffrwn52dnWNjY3fv3m1ZqlAomjRpAgASicTZ2dnPz+/WrVvPPPZly5YlJiauWLGiT58+Hh4eYWFhWq32xx9/FAqFL3FGXwpKw9TAEgNxcGddAQ3aM8oTramTVaKbpSIMl8ckNh0jjeyEiSS1vf+GdbbrFUrD9JBXKg2Ttb59+/bq1Wvy5Mn81/LycrlcLpE8449RqVSKRKInjS4xmUwqlcrT82k3fisqKmp7gOiTMAxTXV3t4uICACqVSi6Xk+Sze2UoDRNiG3m7cwx5BXp8pbdHlYJUnbZr3D56yuDw/vXdLgRpWLZv356cnNyoUaPLly/n5+ePHj3assjd3f15auCjyJOIRKKnR0EAqK8oCAAEQVja7+TkVF/NsECBsAEpO5RadPlujtdqIaEocA9Pl4TNajs10r35s7dEEMSmevXqJZPJSkpK/vWvf8XFxYlEovpuUYOGAuGb73LZ9SO5J2vSKzvdq9Z6HkyThzmHjXnL7605fm3RZGkIUi/kcnnv3r3ruxXIfSgQ1gqWpcx6pbg+RmZbUxpUi89/pzJWD9SHMKrDOuewzT4X/+zjKULhD0EQ5AEUCGuFsuQyx9L1GwhLtfdmHJ3b169HSGpuWeVqcB7/pcP0sz1JFAURBEGsoUBYK8ry/pI7hViXmHJvC9x9cTvHJ21iK5SWrsnWVZXWHE87NxEfZ0xfpRRkS2UTJtv962Qvwhk9iUCQhqekpESpVEZERPzTDS9durRz586srCx3d/dx48a1adPGeml1dfWJEydu3bolkUiec5T+qwm9MVYrqhWpBk3J399ZVrluYfWepFrdKW1ksreXXFuSVX6j+lTeeXdfknFeKHNRSQ0d5nlOOtOX8EOzhiJIg5ScnPzFF1+8wIbffvutTCYbM2ZMYGBgly5dLl26ZL30xIkTK1asuH79+po1a2zU0vqBeoS1gjKrzaaav78qCoFjTVnPftX0hZnV9O3VeQ5Bsuh5ob9kbs8uvuRUfCkPbxuVU3Gwz2enO4jE6I4ogrxK9u7dy6dhCg8Pr6qq6tKli8Fg2LJlS35+fosWLQYNGoRh2IkTJ9zc3LKzs/k0TAMHDgQAlmV37tzJp5gYMWKESCS6efOmRqPhOO7w4cPDhg1zdXXdt29fXl6eq6vr0KFDhULh6dOn8/LykpKS5HL50KFDaZreunVrZmZmaGhoYmIiSZIpKSkqlYokyeTk5MGDB1vm/NyxYwf/oX///rdu3dq9e3fbtm0thzBgwIABAwYcOnRo0qRJz3/gGRkZd+/e7d//77e2tm/f3qZNG8vkq3UP9QhrBWXW0maN5SutKBIFt+QoE6tT18buWDOb9nO+W5Rj0CDv3/JzDt7cHpZ1/I5oSLfse37j583r5IqiIIK8Ur766qtPP/00MDDw/Pnz77zzzqZNm7RabZs2ba5evRoYGLh+/foPPvgAANavXz948ODjx497eXnNmjVr7dq1HMf169dvy5YtgYGB586d69atG8uyycnJY8aM+f777728vADgwIED+fn5ISEhCoWidevWD83BxnFcnz59fvvtNz8/v19++aVv374A8Ndff40dO/brr7/29PR8aHZTi4KCAm9v75c/9gULFly9etW65MSJE99+++3L1/zCUI+wVjC0kaYMlq+08h7p6sVUV9IVJUKZ/VM2fDE5u8tk3mLPrm4fnjHW5M4dpElv89a/xfsPOg6ZLAkJt/nuEOTNoM7TFyQr6mBHTd73JyV//xqlaXrJkiUXL17kEz7w84uuWrUqIiLixx9/BIBhw4b5+/vzSfvatm37ww8/AIBMJtu1a5eHh0dpaenVq1dxHB87dmy7du34hBVSqXTXrl18DLPkkQCA4uLikydPdu7cWafTffjhhwBw4sSJ1NTUvLw8oVA4atSowMDAEydOAIBAINizZw/+hBkWV69erVAoxo0b95KngqKoPXv28PkrLPr37z9y5MhVq1a9ZOUvDAXCWsHSRobSW74yNZWkqzfp6kkr7wkDm9h2X6p0TU22ruWs4BEnGft7CwdornWK+QLbu9++z/uSVp1tuy8EeZNI3IR+Cc81jctLIoT/E134+a8t4SomJiYrK+v69es3btxISEjgC41GI58dyXKX0sfHp6Ki4vr16wqFokePHnxhYWFhZmYmAERHR1t6clevXp06dapSqZTJZGVlZSEhIdaZItLS0mJiYviJPUUiUUxMTFpaGgBERUXxUXDOnDl8UojZs2fze9+5c+fChQuPHTsmk8kWLFjAN2zq1KmPJmmy9n//93/l5eUAMHfuXH5KUgDIy8szGo0hISEAsHz5cnt7+/fffz80NLSysrIep3xDgbBWMLSJYUx/f1VXiRo1I5w8GFW5bXfEmtmcnWXBQ7yXpINUsSG24rcWLu9iB5Odhs0Sh0fZdl8I8oYR2JGOIfVwDbS3tzeZTEajkZ8Jtrq6GgAkEsmQIUOsp0K1s7PbsGEDnzjQQiKRdOjQISkpybrk+++/t56bZty4cXPnzuXn8h47dizLstY1SKVSvf7vn+l6vV4qlZpMJksNvXr1UqvVAMDfaN23b9+UKVMOHTrUtGlTAIiPj2/dujUA+Pv7P/0w+/Tpw6dhsp40znpO0dTUVOuJ4qwzSdUx9IywVrCMiaGNf3/VVONyJ8LBhVFX2XZHhX9VyBtJsyQAJ+fHl38pMzfzc2rpMecnFAUR5JXl7OwcExPDJ5RXKBTbtm0DgF69eu3YsYMgCCcnJycnJ5ZlHwqBvO7du584caK6uppfTSQS8cmVrJWXlzdq1AgAKioqDhw4AABOTk6VlZX80o4dO164cIFPvZuTk3Px4sWOHTtab96lS5d+/fr169fP3d39yJEj48eP37dvX2RkpGVzfukznxd269aNX9M62vn6+jo5OZ0/f55l2ZSUlAsXLgDA+fPn/f39rbM11THUI7Q9lqU4jmWtA6GuBrezJxycTTmpNtyRocKkuFgV0rW45qeloa45JYF9ZvZZjhHo3xRBXnUbNmxITExctWqVk5NT165dxWLxkCFDbt68GR4eHhMTU1VVVVRUlJGR8eiGkZGRS5Ys6dChQ2RkJEVRmZmZR44ceWidGTNm9O3bt23btnl5eXzvLT4+/osvvggLCwsLC9u7d++iRYvat28fFRV17dq1xYsXh4aGPqmdM2bMqK6uttyJfe+996zflEhJSenSpQtFUXq93tnZOSoq6q+//nr6gQuFwkWLFo0ePTo4OLhNmzYKhaJz585paWn8z4L6gtIw2T4xEE3p/1wZJHcO7j76DF9SOjfRY/ZqRllavXed+/TvbLWj22vyJWxaVfW6DFnqdb/2X/X7WUy+6i/MozRMdQalYXrIK5iGib8tOXTo0JiYGL5hNTU1WVlZcrk8JCQEx3GdTkeSJH/TkqIoo9HI//no9fqMjAyhUBgSEiIUCo1GI8uy1gkFCwoKFApFREQEf4XnkzrpdDqaph0cHACguro6JycnKCiI74cZjUaGYWQy2UMtVKvV1j1OoVBovQ7DMPxNVB5Jks/5111aWlpRUREREcH3C318fJ4n5wZKw/Q64VgaAFjG/OA7xxm0uEzOmZ1Z9cO5pF9Y+dVqc3kVId6ULrt92j3iq57fvfpREEEQ3sGDBy9fvsynYTpz5szKlSv5cgcHh+joaMtq1lFHIBBYnqJJpdJWrVpZFj36cycgIODRhO/WtTk6OkZF/f0A5Uk/mOztnzbKnb+R+5QVnsTb25u/s0oQhPWB1Bf0jND2+BDIPAiEnNkIpAAjSELuyGhsEwiNSnPenlKCWXZDfO2YU4uFfVe5S11tUjOCIHWgdevWbm5umZmZLVq0uHXr1tOTCyK1DfUIbY9lKYIUsfT9UaOsQYeLpQCAiSTAsZzZiAlf6m4VbWDS1uWXOuzUik4fsOuxqP9/vZ+QpRpBkFeTp6fnlClT6rsVyH0oENoey1CkQGbpEbJGHS65f0cClzsymmrS5cWzUuTmFmb/UnZHdtpDsD7V/V9Nw2YGO6BuPYIgyItDgdD2WJYiBDLKrOW/cgYdJrnfYyNkjqxODf88EJprqIo71Tcu3RGVSnRR+UGlP4m9hu7kZtxugaIggiDIS0GB0PY4liYFEn7IDACwRj1/axQAcDt7Vlfz5E0fgzYwObvKqtLVOQ7Zem9dn8Etbu1b6s4GD2QXrutESNA/IIK8biorK9etWwcAOI57enrGx8fzr64/qrS01MHBgR/kotFo9Hq9h4dHnba1YUD9CdvjWBrDBRhGsCwFAJzJgIkk/CJcZs9q/8G827SRSV2Vh4m5jW3XGxKqB8c3vXlwpKfaY6PrgmFBWLwPSquEIK8fhULxySefqFQqpVKZnJwcGhr60CTUFsOGDbO8Jrht27bJkyfXYTMbENShsD2WpXGcxAkBy5hxXMCa9PjfgdCB+Sc9wpw/SnFfyVzBehxvLKpxPXFhhMBlTGVhIdcxZnE0yiiBIK+xpUuX8h8SExO3bt0aHR1dVlZ2+vTpmpqa5s2bd+jQITs7W6VSpaSkyOVyDw+P9PT08vLyo0ePSiSSt956CwBu3rx57do1T0/Pnj17EgTBZ991cHA4dOhQdHS0RqPp0qULvwuj0Xju3LmuXbs+KbOEhdlsPn36dGxsrOVVDf69/rCwsFo6D68C1CO0PY6lsfuBkAYAzvi/PcLnzsRUnamtyDeMoK9ozerOZiN+e94ujw0+qXfF3Ueu7EDgqDeIIK8/nU6Xm5vLv/P3wQcfXLp0qaysbOLEiZ999llWVpZKpbp169bRo0fT09MtgfDcuXMA8Pnnn48ePbqoqCgpKSkhIYFhmOTk5MTExMTERH7DESNGXLt2jd/Lli1bFi1a9MwoCAB//vnn1KlTraf9vHbtGj9t6RsM9Qhtj2UZHCcwXMixZgBgzQZc9OAZoUxOlVY+swZDynnt6T35ysErXZ2Cya1TcQw3Z7Qbc/Cdm1cMXi5unaKfWQOCIM9UXZ6afXNdHeyoZZcvBcKHZ0IJCgriOK6srKxTp058Ytv9+/fzi6ZNm+br67tgwYLGjRuPHDlywIABAKBWq5OTk/l+ZGpq6saNGzMyMuzs7AAgLi5uz549AFBZWXn58mW+cOzYsT///DP/1vzPP/88bdq052nqli1bBg0aZF3Sv3//0aNHp6WlWWd3esOgQGh7HEdjGInjJMvSAMCZjJbBMoTU3vSsHiGtKFJtX050nmk6avLwmtFZlevWLLFZx085jboq+Ve3KUtr/QAQpGEQip1cfZ6WSMhWcPwxeRX454L37t2bNm3anDlz/vvf//7xxx8rVqyorKyUSqVms7mkpORJFV68eJEgiFmzZvFflUrl7du3fXx8oqKi7B68VTxx4sTmzZt/8803BQUFWVlZfDS12Lt378WLFwEgNjbWMpUoANy+ffvdd98FgNTU1J07dy5YsEAmk/n7+6ekpKBAiPwDVrdGzQDAmY2YvTO/CJfJWZ3mqVuD5vgf8i4Dc3M9//Q+2NaQ2kzQJTR6JhgMynUL5bHvCLwCa7v9CNJASO19A5sl1tfe+cnJnJycJk+ePGnSpAkTJvzrX/86fvx4s2bNOI6TyWQ0TT9lcz8/Pz7RLgB8+OGHnp6eycnJ1tONent7x8bGbt269fbt22PGjLHO0wQADg4O/FDVhyZRY1mWT1WYnZ29b9++BQsWAIBQKHwol9MbBgVC2+NYBsMJHBc86BEaMNH9qWRwqT2rf1og5CizIeW8w+TVypN3I9y/bhL/rWeG6t7C0QAg69RXHj+kDtqPIEhdunjxore3d15eno+PD9/rOnLkiMFgAAC5XK7V3n8jWS6XazT3rx4dO3acM2eOh4eHr68vX2I2mx+teeLEibNnzy4qKuI7f9ZiY2NjY2Mf3SQ0NPTWrVuDBw++evVqXl4eP093Xl5eeHi4jQ73VVSLgZCfmNzBwYHPemytrKysvLy8SZMm/E8PXlZWFsuyD41N0mg0JEnyU6fTNG357wAAZDKZ9eavDn7UKIYLOIYCANZsxIUPBstI5U8PhKacVIFXQGUqW+T8jcqtfaum70FTcHh7FMdxlvurCIK8ARISEjiOKygooGn6t99+a968uUqlGjhwoIuLS05ODp8j4t133/3000937tw5bNiw2NjYWbNmJSQkhIWFrVy5csGCBe3atevVqxcAXLx4cfny5Y/uonv37tOmTWvVqtVTEi095KOPPnr33XdLS0vPnj07derU3r17GwyGuLg46xm63zy1EgjLy8sHDhx48+ZNnU5XXFzs4+NjvfTTTz9du3ZtYGBgeXn5gQMHmjdvbjAY+vXrl5ubS5Kku7v7wYMH5XL5l19+uXbt2sLCwo8++ujbb78FgFOnTvXo0cPSkd+4cWO/fv1qo/0vw1BhLjp6D/MgcELw4D1C4989QtmzAmHWLWFIq9tXjoPD3ZH9LvCFmEiChogiyBujcePGlhcHXVxcfH19+bztKSkpJ0+eFAqFcXFx6enp/v7+ISEhffr0KS4udnd39/T0zMnJycnJ4e9STps2bdCgQTdu3GBZdt68ef7+/hEREXFxcdY7wjDM3t5+woQJz9+2rl27Xr9+/datW1999ZWHh8eJEycAoFOnTjY7+FdSrQRCiUTyySefhIaGPvrqSUpKypo1a/jnul988cXHH3+cnJy8du1anU539+5dgiB69OixcuXKTz/9NDY2tm/fvsuWLbPePCIi4vr167XRZltRptRoCrRCB8DxB4HQbMQE9+/OYyIJ0BTH0E9Kn2vKTWObjFJKpt32GjZahhJKIMgbSCKRPLaDJZfL+/bty39u2bIl/8HV1dXV9f6lQCQSNW3a1LK+JZnRo2sCQG5u7pYtW/he5j9qXqNGjfgE9wDQtWvXf7Tta6pW3iOUy+V9+vR57KRBW7duffvtt/k+4gcffHDkyBGVSrVt27YxY8YIBAIcx8eOHbtt2zYA6Ny5c2RkpPXrLDyFQmE0Gh+t+RWhV5hwITAGwAny/nuEZhMm/PsxNS598ngZlqFKc1PTLhoEqk5t5tZNgxEEeSPt3bs3Nzd3z549fHcTeYq6PkGFhYVBQUH8Z29vb5FIVFRUVFBQ0LhxY74wKCiosLDwSZunpaXFxMRUVFTEx8evX7/ezc3tsatxHJeXl3fmzBnrwujoaP5ZY60yVVMST1Jvxkg7Acf3CCkTJhQrCk56BMQCYJhUzuo1hP1jsllSiiKtfbCO2rTFaep2n8eMt0YQBHlOM2bMqO8mvDbqOhDqdDrrUbxisVir1ep0Okt+ZL7ksdtGRUVVVFTY29vX1NQMGTLk//7v/3799dfHrsmybHJyckpKinXhunXrLCOsrD1pdy/GWG0mg4BWsDgLOp1GrNEwRoPObLq4/8Omby3wDOrLiaTaSgUpczLcM9fc0Ts0lUo87w/5oXLv3CaAEuEGz5GcUaN5dfu9L862Zxt5CpqmaZqmKKq+G/KqQKfiDWAymTQazT+6jEilUoJ4xoSUdR0IPTw8qqqq+M80TdfU1Hh6enp6eloKq6qqPD0fn6XI0dGR/+Dg4DBr1qzRo0c/aS8EQUyePHnOnDnP2Sq5/OFJH14Yo2ft3YRVJbhAKBGLSLlcrmXMOGmizRp9zV25fJhJ7igGhgTJnV9L3Fs75v1SETzY26WFPQCcy83HyUPpwSt6+Arl8pdK3vsqs+HZRp6CD4SWn5jIo89ZkNeOSCTiLyC2vYzU9VyjUVFR58+f5z9fvHjR1dXVz8+vdevWlsJz5849zzjd0tJSS1x8dXAsxxhZoQPOmvC/Z5YxmwwmJYbhuuoCeDBwtOS00i3SoVF/z+YTAnN2lpadVapLdcq8QkLmu8vYsZs3GiWKIAhSR2qrR7h+/Xq+9/rbb785OjqOHz9+zJgxkZGRH3744bx58xYuXPjWW2/Nnj17ypQpAoFg6tSpPXr0aNmypVAo/O6773bs2AEAV65cuXHjxt27dyUSSVJSUtu2bc+ePSsSiQICArKyshYsWDBv3rxaavwLY4wsIcJxEoDFMFzAMvwzQrPZXC118DdoS+HBYJmK69XNxgcAgMxH3GJK45ydpRmH84zOu6LeWl1yi2vtigIhgiBIHamtQHjjxg2z2fzhhx/m5OQAwPjx49u1axcQEGBnZ3fq1Kmvv/764sWLI0aM4OeBbdu27bZt237++WeGYTZu3Mi/ClNaWnrt2jV+rPC1a9cCAwMbN268bdu27du3e3l5rV279hV8iZA2MKSE4IDGBQKOZjmWApbhOI4yqR1cwqvuXQcAXCrXl9MYgUm97t+zkrgJpYnYzv2fv13KXZF0ivXECRQHEQRB6kptBcIVK1Y8VGJJKRkWFrZ+/fqHlvbo0cN64lcA6N+/f//+/R9ajZ9G4ZVFG1lCgnMsQwhJYBmWZTjKjAmEZmO13Dn4Xv6xSwr6cK6sl5Z2CrGzbMVy3LeXViQA7sU12q0g4tB9UQRBkDqE3i+xJcbIkGKC41hcQHA0cCzFUSZMIKLMapHERSC0/7/TFVPc5RX5Jvv2f7/I8WfWQTFGEqpMD8d3j5Vw05uhJJEIYnsYhh0+fLim5h9kxkZeKWfOnHmov2QrKBDaEmNiCRHOcQwpFHA0x7I0ZzZjAiFt1sgcAliRC0mp3o1wuHrB+IdezL8wrzSoNqZsXdTsnfL8M5RDYz3NNXVCPUIEsb0lS5YsWbKklio3m80CgeB5Mt8iL6xbt26dO3eujZpRILQlxsTiwvu3Rlma4ViKo82YQEiZtAKhXQ3nHO9WhQk9cBZfdY+YzYIQhzU3NvQJToCKNGfSL53wivdAmecRpFYIhcL58+fXUuU6nU4ikTyaYAB5LaB/NltizSwhwoFjcRHJURjLMvytUZrSkkK5gnNuKasy6qUCXNnEGd9fyKZWpN8qTxvZ/L3ywtP2RvlZs2cPXxQHEQRB6hTqEdoSY2YJIc5wLCEgTBTBsRQ/WIY260ihrMjsHC9UGdUCAVs2KgTfnMW60z9PiByNmbU6dZFE5XeA9Jjmi36aIAiC1Cl02bUl1sziQpzjGEIk4GicZej7gZDSV9ESPeEsopWGKk4AFQN96ZR7xzFM0DWwU3nhaTef9oxKae/h4YamAUEQBKlbKBDaEmNiCSHOcSwhJFgzxj7oETK0Ps8gkctcTAalocIkFGlZdf5Q4xd+st4YYGW5f7l7tNMI7N8JEj17HwiCIIhNoUBoSyzF4UKM7xGyJoLjaI6mMFJAU/pcncTJ3sVkUBorzUKZef+J2eGMxiFloUFfdS//uJ20WTbhMaQx+udAEASpa+jKa0sMxeICnGNZUkxyFMYyND9YhqH02XqJu4OrSa80VprVTmam4lbnvuv0Ap8DmxN8Q/udu7rGvQAAIABJREFUydSzzl5e0vo+AARBkIYHBUJbYimWEOAcx+IEAUCyFPWgR2jI1ol9nF1NukpciP8uzZPiZIBPTOe+a9YL/33d64vU3LKWQR713XwEQZCGCAVCW2LNHC7AOI7BMIIUCWiTmaPMGClgGVOWVhzo4m7UlpvtjByn8pAFA0B7L8mH3foeKBW951ju4e1V381HEARpiFAgtCWWZnEBzgdCQiRkTGZgKJbAcUJQoMMaubpQlCaTudsNk9sL3PlN3gnAdycQbgYF4Yx6hAiCIPUABUJbYikOF2DAcYBhhEjAUDRHmVkCwwixXAASEmNA5uyOi1iNjLW33pCpuke6oB4hgiBIPUCB0JbY+4NlGAwnSJGQNps5muJwjiMk/jJs8+2tHOfcxNdRbSyVmv9+YZCjzKxeQzi41GPLEQRBGiwUCG2JpTicvP+MUCAWMhTFUWYOBwYXuxGnk3OOORAhJjaN4yiBjrFsRVfdIxzdAE3XiyAIUh9QILQlluYDIYthOCEWsZSZoymWAAPDmfSbvombT5r8y0p3Orq2YDUqy1aM8h7piu6LIgiC1A8UCG3pwWAZFsPw+z1C2pzPqDS0KibgswB7P0LdRKfO8QxKYGqUlq3oilLS1bsem40gCNKQoUBoSxzNYQ9ujZJSEcvQetr4S9UFTODT1CWE0tESrFXTDrMbR47BBEJWr+G3opVlqEeIIAhSX1AgtCX+1ihwLGC4UCZmafNvTE6Y2M2EuXlJMVMVJXIUNGn7fwQpJhxcLZ1CuryYdPOp35YjCII0WCgQ2hJLsxiJ8aNGBTIhQ1NnoDzBIVzLir2lYKymRM4Cfk3C0ZVRVfCf6YpSFAgRBEHqCwqEtsTRHE7iHMdhGCaQiViW6ka7iAmBlhV6SjGTyixyfBAIndxplQIAOMrMqJWki2e9NhxBEKThQoHQdjhgGQ4n7j8j5EiMBbq7wc3MMUZO5CwCk4oSOd0PhKSzB1OlAAC6vIh09QacqNemIwiCNFwoENoMHwUBA37UaGplFhCUg0GipVmcEGEAJhUldhLyK5NuPnRFCQBQpfkCr4B6bTiCIEiDhgKhzXA0hxEYAPA9wiuKWxjOUiaBjqaFAjEAmKrMlmeEpIcfpSgGAHNJjsAnqB6bjSAI0sChQGgzLMPhJAYAwHE0x9xWZuEkx9AiPcMKBUIAMKookVWPkFEpOMpsLrgr9A+rx2YjCII0cCgQ2gxHsxiJAwDHsXk1RT72vhjOUJRIS9FigZgxshzDCWT3nwViBCnwDDRl3qDL8oUB4fXacARBkAYNBUKbuf8SIQDHMZmqvJYeLQFjaEZqYBixUGisMoudhdbri5vGqHauEoW0xISiemoygiAIggKh7XDM/WeEwHGZqtxIzwgOaJqW6mlKJpIYlWaxy/8EQrtO/USBTe17v18/zUUQBEEAAICs7wa8OSw9QjNjVJv1jZ1DcoGiGXsjTbuIRIbKhwMhLrN3HvVJPTUWQRAEuQ/1CG2Gn2gUANQmbWOnQJIQcMBQnIOZoeRikbHSLHYTPrMSBEEQpI6hQGgz998jBNCa1EGOjXFCwLJmlhPTtFkuEhkqTBI39CwQQRDklfO0W6NFRUU3b958zoq6du0qk8ls0aTXleUZod6sC3cKxHEBy9IkqEiz0V4s1t8zST1QIEQQBHnlPC0QHj16dOzYsc9ZUUZGRmhoqC2a9LrinxHqKL2ZMfvZ+2I4CRwrwCoEtNEBBEqOE9qjJ7IIgiCvnGdcmqOiojZt2vTMWiIiImzUntcY3yO8q8ySkiICJwEAwwkBVi5ljLias/OW1HcDEQRBkMd4RiCUSqXNmjWrm6a87vhnhOmVmVJSAhgGADguFJDFcs5oVIC8kbS+G4ggCII8xtMGywwfPvzAgQPPU0tlZWVwcLCNmvS6ut8jrMoSEwIMIwAAxwUCQYmENVZnUs7hdvXdQARBEOQxntYjFAqFQuH9Ef/79u0zGo2PXW3w4MFOTk62b9rrhg+EmVU5nUghhv1/e/ceF1W1/g/8efaG4SZyUeQiCiKKecsUL+UBX3YxMzvqsY6mHU3T0vKS9jI7HOurpywvp9K0i6VmlikapdnP7GRqhqaloujxkmbeU4TAAYbL7L3X74+NI2nCGHuY2czn/RcsNsPjwGt9XGuvtbZERJLko8r2EotSLyQ4OB4jQgAAT+Ts8o3Ro0dfvHjxD78khDCuHhMTqlBIKSov9mHJEYSKJJf7qol/i3d3dQAA8MecDcJdu3apqur41Gazbd26dfbs2fPnz3dNYeajqeKyUpgY0Ywu5hJLRMTso0iSD5XJMjZOAAB4KGeDMC7u2ofHtm3bNjw8/Nlnn+3fv78kYWM+CUVYFWvzsGbiwi7HiLCcJV+tRPLxd3d1AADwx2oUYHfeeefPP/986NAho6oxNaGJAvvl5qFx+hPqqSIImbVy2QcjQgAAD1WjIDx8+DAR+ftjuENEJFSRX365WaUgZPYtY5ZEmYSpUQAAT+Xs1Og1q0bLy8t/+umnd955JyEhISEhwTW1mYyqagX2gviQpueEpt8jlFguI43YR89FAADwQH9+1aivr+/dd9/92muv4QahrrjUJstSkG/g76dGVZYxYgYA8Fx/ctWoxWKJjo6WZdk1VZnSZVthcEA9IhIkmJiIJPIpI9WClTIAAB7s5laN/u9//9u3b9+5c+ciIyPbtWvXsWNHV9ZmMkWlRfUDgomIrk6NSnZSAnxxyigAgOdyNgitVuvw4cPXrl1bubFHjx7p6emRkZEuKKz2rFs2I7BsU81fhwVrfncS/VVoKksyEUnkY+dyX4wIAQA8mLNB+Nhjj3311VevvPLKwIEDIyMj8/LyNmzY8MILLwwcODAzM9OlJbpap3sfy8u5u+av8+uPrwYGlhGR4x4hkS9xkS9GhAAAHsypILRarZ9++umiRYtGjRqlt9SvX/+pp55q1qzZ/ffff+zYsRYtWriySNeKjY69peUtNX+dtVnvCGKqdI9QFZIslfn4htf8xQEAwEWcWvBZWlqqaVpKSso17ampqURUVFRkfF0mJLOkahrR1XuEmiZLUpnsi+O2AQA8l1NBGBERkZiYuGPHjmvat2/fHhoa2qpVKxcUZj4ssaZpVGlqVNV8ZLLJPpgaBQDwXE5NjTLzsmXLhgwZUlBQMHDgwKioqNzc3A0bNsycOfODDz4ICEBHT0TELGnidyNCVZMltvla8CRCAADP5exe+IEDB54+fXry5MlxcXF+fn6NGzcePXr0yZMn+/Xrx1csXrzYpbV6OIlZE4IqjwiFj0Q2TI0CAHgyZ1eNTps2rbi4uOprunTpUuN6TExiVrXfBaGiykSaryXY3aUBAMANVRWEiqIoiqKfqT1u3LgqrrTZbAEBAcxscHWmIkksKhbLCGImIk31JSIfTI0CAHiwqqZGP/zww169ejnzKsHBwceOHTOoJLOSmK/kYMWI0K76EJHFP8y9hQEAQBWqmRotLS09ceJE7ZRidpJ03T1CxSIRWfxD3V0aAADcUDVB+OOPPzZv3rx2SjE7mVnoq0ZJ04faiuJv8aGA4Bj3FgYAAFWoKgh79uy5evVqJ18oJsbbu3uJWVw7IvQLDG0TGtHW3aUBAMANVRWE8fHx8fHxtVWJ6cmydGVDvdDXDamKkpQ0SZJ93V0aAADcEJ6paxgfiVVBROR4+gQp9pD6WCkDAODREISGkVgioRIJIiLico1koQRanN2pCQAAboEgNAzLLJOw2SvmRfNKKYBUlhGEAAAeDUFoIPYhYS1XmWUiyisTAWxn3CAEAPBsVY1XysvLqz1WjYjCwnAbjIiIiX1YFJVrdHVEqLAPRoQAAB6tqm56xYoVI0eOrPYl9D0DQMwyi0J7xd6J3FIRQwphahQAwLNV1U3fcccdixYt0j+2Wq0zZ85s2rTpgAEDoqOjL1269OWXX2ZlZaWlpdVKnSbALPmwZrULIiaivDKKJwX3CAEAPFxV3XRSUlJSUpL+8aBBg/r16/f+++87TtaeNm3av/71rw0bNkybNs3lZZoCk8xUWFYxIswrIwsWywAAeDynFssUFRVlZGRMnjz5mudLTJo06fvvvz9+/LhrajMbZh8SVrvqmBq1aJgaBQDwdM4GoaqqVqv1mna95fp2L8Xsw8JaVvEMpksl5CPs7INVowAAHs2pIIyMjExMTHz66afPnTvnaMzLy3vyySfDw8Nbt27tsvLMhFmSSbtcXrGPMKdUyJpKsuzuugAAoCpOTdwx89KlS/v06ZOQkNClSxd9scwPP/ygKMqqVav0J/fClRGhou8jvFRKsrDjHiEAgIdzdkN9SkrKwYMHJ0yY4O/vf+DAASJ67LHH9u3bN2DAAFeWZy4ssygor1g1mlNCrGKxDACAp7uJbjouLm727NmnTp0qLCxs376962oyK2aZ6HKZRiwJokulglU74R4hAIBnc3ZEqGna9OnTQ0NDExIS+vTpozdOmDBhzJgxLqvNZJgkibX8MpVZ+q2MgnwqPYYCAAA8lbNBOH369FmzZo0ZM+bll192NN5zzz0rVqwoLy93TW1mw+xD4rdSwcwXbCI2QGVJpt9vOAEAAE/jVBAqijJ//vyXX355zpw5d9xxh6O9Q4cORUVFZ86ccVl5ZsLMEov8Uo1YOm+jpv7YRAgAYAJOBWFOTo7Vau3du/c17aGhoUT022+/GV+XGTHLVHHW6LliEeun4sRtAADP51QQBgcHS5L066+/XtN+8OBBIoqKijK+LjOSJBKigZ9QBZ8upiaBCkkIQgAAT+dsEHbv3v3FF18sKipynLJ2+fLl55577tZbb23SpIkrKzQPZhIiOlCoQjpZKOL8VcZuegAAj+fskOWNN97o0aPHLbfc0qZNG6vVOnLkyI0bN/72229ff/31jb7FZrPZ7faQkJBr2oUQBw8e1DStXbt2klSRxGVlZfv372/YsGFCQoLjSv1ct3r16vn6VmxC0DQtOztbluW2bduyx61DYRJaTICmCOm4VTwWobCPxd0lAQBANZxdNdqhQ4fdu3enpqbu2bOnsLBwzZo1nTp12rFjR0pKyvUXb9q0KSkpqV69esnJydd8qbi4OCUlZfDgwcOGDevWrdvly5eJ6PDhw4mJiZMnT05NTX3iiSf0K/v06RMaGhoeHr5x40a9paCgoGvXrsOHDx88eHCPHj2ceWhwrWKJSMTXE+WCj14WzYJwvhoAgAk4G4RE1KJFixUrVuTk5OTn5xcWFq5fv75jx45/eGVCQsLy5cvT09Ov/9K7774rSVJ2dnZWVlZ4ePjChQuJKC0t7R//+EdmZmZ2dvb69eszMzOJaOLEiUeOHElMTHR874IFCxo0aJCVlZWdnU1Eixcvvql/qssxkxAt6osihYkowhfHygAAmMBNBGF6enrnzp2DgoLatm2rt7zwwgszZ868/sqEhISuXbv+4Rmka9asGT58uCzLkiQ9+uija9assdlsX3zxxciRI4koPDy8X79+q1evJqJ77723cePG13zviBEjJEmSZXnYsGH6ZZ6DmYUQyQ3JpvCdMZJQ7Ng+AQDg+ZwNwvfee2/w4MExMTHDhw93NCYlJc2bN09VVed/3unTp5s1a6Z/3KxZs9OnT58/f15V1fj4eEfjjTYmXv+9N/opQohjx459/Xs2m835Ov8MZhJaVICIDpJf6iSRiu0TAAAm4FRPrWna888//+yzz86ePfvbb79dv3693n777bfn5uaePXs2Li7OyZ9XUlJisVQsIfH39y8uLrbZbLIsy1dup+mNTn5vFQVv3br1xIkTlRvfeuut2NjY6y8uLi42ZN2N3a4ITSsuLgr1pUip2FZcqAkuKiqq+SvXJUa921At5Qp3F+IVbDabqqqO1X/gUjfVjQQGBlb7e3EqCHNyci5evDh06NBr2iMjI/WvOh+EUVFRjg34eXl50dHRUVFRiqJYrVZ9fWleXt6NNiZe/703+imyLI8ePXrq1KnOlCSEqFevnpP1V8Hi52cnCgzwl2SfevXqlfn62v38DHnlusSodxuqpacgnpJWO5g5ICAAQVg7DO9GnPq1+fn5EdH1IzB9yBUWFub8z0tOTtbXwhBRZmZm586dIyIi4uPjKzd26dLFme+90WVuw0xCCFHxYF6h2lnGoycAADydUyPCsLCwdu3azZ8/v2vXro4BqaZpM2fOjIuLa968+TXX//rrr8uXLz98+HB+fv7s2bNjY2OHDh3apEmTlStXjh8//q677mrdurXFYnn99dfXr1/PzJMmTZo8ebIQIisr68CBAxkZGUS0YsWKs2fP5ufnZ2RkHDp0aNiwYRMnTuzXr19iYmJ5efmiRYu2bNli7HtRY0wkhNCYJSISKjbUAwCYgLOrOebOnXv//fefOXOmbdu2JSUlc+bMWbNmze7du9PT06+fq1UUJT8/PyoqatSoUfn5+fqc59ChQ6OiohITE9euXbt48WJN09LT0//yl78Q0fjx4wMDA5cuXRoREfHdd9/pR5gWFhbm5+ePGjWKiPLz8xVFSU1NXbVq1bJlyyRJWrdu3Y02b7gNSyQEkVYxzlbthBEhAIDHYyGEk5du2rRp6tSpe/fu1T9NTEx85ZVXHnzwQZfV9udNnTo1PDzcyXuEhYWFwcHBNf+h/9v6UvH3/y9xxFv7t/yr58MbbHu2lB76IfwfTtXgPYx6t6FauEdYm4qLi3GPsNYY3o3cxPr+u+++e8+ePZcuXbpw4UJ4ePg1m/xAP3SbhEbEREQqHsMEAGACN9FTHz9+fP78+VlZWefOnYuKimrbtu24ceNuvfVW1xVnLsySEEIITX8qvVAVnCwDAOD5nB3Ib968+dZbb128eHFAQED37t1DQ0PT09OTk5M97XgXd7p21aiCxTIAAJ7PqSGLEGLUqFHt2rVbt26dvneQiAoKCh555JEnnnjigQceCAgIcGWR5sAsCdIqLZZRMTUKAOD5nBoRXrx48Zdffnn11VcdKUhEoaGhCxcuLCgoOHz4sMvKM5VrRoSKHVOjAACez9kH88qyHBQUdE273qLvdgAirlgso+8j1BRsqAcA8HxOBWFQUNCgQYP+85//aJpWuX3OnDmpqamVH6Xr1ViqvKGeVDyPEADABJydu0tJSXn++edbt249YMCAqKio3NzcL7/88ujRo2lpae+++65+TWpqaqtWrVxWqqfTH8N0dUSoKpIFW7gAADyds0E4ffr03Nzc3NzcWbNmVW5PS0tzfPzee+95cxCSxPoZaxUH7WAfIQCAGTjbUx89evSaedHrBQYG1rgek2NJaErFiFCxsw/uEQIAeDpng1A/L1S3a9euvXv3BgcH9+nTJzw83DWFmRQLVb1y6LaCe4QAAJ6vmiBcsGDB1q1b16xZ4zhDb/z48QsXLtQ/btCgwTfffIPDZXRMLCQmoTqOWMOqUQAAz1fNqtFly5aFhYU5UnDjxo0LFy5MSUnZsGHDkiVLiOipp55yeY3mwcxCteOINQAAE6mqpxZCHDhwYPLkyY6WVatWWSyWNWvW6Dvrmfmxxx7D8wQqMBOx0FS+eug2pkYBADxdVSPCgoICu93epEkTR8uWLVtuv/12x/kyKSkpQogzZ864tkYTYRaaShUnyyhYLAMA4PmqCsLg4GBfX99Lly7pn54+ffr06dPdunVzXKCfJVZeXu7SEs2EWWhXF8tgahQAwPNVFYQ+Pj4tW7Z89913FUUhouXLlxPRfffd57jgp59+IqLY2FgXF2kWTL9fLIN9hAAAnq+annrKlCmPPvpoy5Yto6Kidu7c2blz55SUFMdXP//884SEhIYNG7q4SNNgljRFqVgsg32EAABmUM2q0eHDhy9dujQsLCwnJ+fhhx/+7LPPHCtIbTbb9u3bH374YdcXaQ7MLIiEUJgwNQoAYBrV99QjRowYMWLE9e2BgYHZ2dkuKMnMWBKapt86xdQoAIApOPuEenACExNpGl19Qj2CEADA0yEIDSVJQlUqVo0qCvkgCAEAPB2C0DjMTCyurhq144g1AADPhyA0DBOLyvsIFYUxIgQA8HgIQiMxs9DszDIRFssAAJgDgtBQEmOxDACAuSAIDcTErGkqV5w1akcQAgB4PgShoZhJU/Un1JOqEE6WAQDweAhC4+iPYdJXjQohNFU/aw0AADwZgtBIzEyayixX3CDUj5gBAAAPhiA0DBML6cr2CSwZBQAwCQShcZiIJH2xDB49AQBgFghCQ7HEmkosYe8EAIBZIAgNxCyREBrhYYQAAOaBIDQMMwtioSnMMu4RAgCYBYLQQExM+vMIMSIEADALBKGhWNJHhEKxY0QIAGAKCELjMDOz0AQxkYpHTwAAmAOC0EAsmISoGBFiahQAwBQQhIZiSWgaEQtVwVN5AQBMAUFoGCYmiUmozBIpdpy4DQBgCghC47DEJIRQGRvqAQDMA0FoKJZInxrFPUIAAJNAEBqGmQWzJjRmxtQoAIBZIAiNw0zEFWeNYkQIAGASCEIjccWIEEEIAGAaWNBhGGZJ6FvpsVgGAMA8MCI0kL59QiNsnwAAMA8EoWGYJSISQj90uxxTowAApoAgNBATs9DUK/cILe6uBwAAqocgNAxLsiBBAvsIAQDMBAs6DKM/mJewfQIAwFQwIjQQs0T6EWtkLydfTI0CAJgAgtAwzLIgITR9HyEWywAAmAOC0DBX7xEyY7EMAIBZIAgNwywJoooRod3OvhgRAgCYAILQMExMTIJUJn1qFCNCAAATQBAahyVi0k+WEfZyxmIZAAAzQBAahiVZCFGxod6OESEAgDkgCA3DxIJJCMHMQrET7hECAJgBgtA4LDERaRoxk70MI0IAAFNAEBqGWRIkBGkVi2V8/dxdEQAAVA9BaBhmFkQVZ42Wl7EFQQgAYAIIQgMxkRASk6Zh1SgAgFkgCI3Dkr53glVFCA1PqAcAMAUEoWGYWQhBkqSVlUoWf3eXAwAATkEQGoiJBElM5aW4QQgAYBYIQgPpQShppaXsixEhAIA5IAgNUzE1yixKSzAiBAAwCwShcZhJCCExldrYL8Dd1QAAgFMQhIZhYkGCmdVSG2OxDACASSAIjVMxIpSp2CphRAgAYBIIQgMxkSAmraSI/QPdXQwAADgFQWiYq/sIi6xSQJC7ywEAAKcgCA3ERIIlSSssQBACAJgFgtBggplUTQoMdnchAADgFJyHaSAmEiRLRCQFhbi7GAAAcApGhIapuEco+xIJOSTc3eUAAIBTMCI0EBMJ8vGRZOEb3czdxQAAgFMwIjQOMwlBktRg9AypHqZGAQDMwYUjwpycHLvd3rhx42vaFUXZvXu3LMudOnWSpIoktlqtWVlZ0dHRLVu2dFx5/vz5I0eOtGnTJjIykohKSkp+/fVXx1cjIyODgjxocSYTCSImIQfWd3ctAADgLJeMCDMyMmJjY6Ojo/v163fNl/Ly8m677bYJEyaMHj26e/fuRUVFRLRr164WLVq89NJLd9111/jx4/Ur33nnnfbt28+dO7dNmzYrVqwgoh07drRu3fqeK7Zt2+aK4v88/WQZoTFjnA0AYBou6bJvu+22jRs3Ll269PovzZs3LyEhYdeuXXv27LFYLO+99x4RTZky5dlnn/3666/37Nnz8ccfZ2VlWa3WKVOmbNy48csvv1y9evWkSZPKysqIqHXr1j9fcd9997mi+JoSGiEIAQDMwyVddkJCQtu2bWVZvv5Ln3zyyfDhw5lZluVHHnkkIyPj4sWLmZmZw4cPJ6JGjRr16dMnIyPjq6++iouLS05OJqI777zT399fH/+pqnrkyJELFy64ouwaYyIhhGBmd1cCAADOqu1Vo2fPno2Li9M/jo+PP3PmzNmzZ4OCgho2bKg3xsXFnTlzJiwsLD4+3vFdcXFxp0+fjo+PP3ny5KBBg86cOdO6dev09PTrb0DqhBCHDh1at25d5caePXvWq1fv+os1TdM0reb/NKEJIQSREBoZ8oJ1klHvNlRLu8LdhXgFvNW16abebcdKlCrUahAKIUpLSy0Wi/6pn59fSUlJ5Ra90WazXd9YUlLSrVu3S5cuWSyW0tLSIUOGTJw48ZNPPvnDH6Rp2q5duy5dulS58ZZbbomJibn+4pKSkj8cvN6s0rIyTVOJRGlZmY/NVvMXrJOMerehWoqiKIqC3rl2lJSUCCGc6XOh5m6qGwkMDKz291KrQcjMkZGReXl5+qe5ublRUVGRkZGXL19WFMXHx0dvjI6OjoqKclzmaHSsEfX393/qqaeGDh16ox8ky/KIESOmTp3qTFVCiD8cKd4srSxIklgIERQUbMgL1klGvdtQLT0I/f3xaMzawMwBAQEIwtpheDdS27+2rl27OlZ7btu2rVu3bnFxcREREdu3b6/c2LVr1927dxcXFxNRXl7ekSNHOnfuXPl1Tpw4ERERUcvFOwOrRgEAzMUlI8KTJ0+mp6dnZWVduHBh9uzZCQkJ/fv3DwsLy8zMnDRpUt++fRs3blxaWvr+++9nZmb6+vpOnDhx7NixL7744rZt2woKCgYOHOjn55eamjpkyJARI0a8/fbbAwYMaNq06Zw5c5g5Li7u+PHjc+bMmTdvniuK/9MqjlgTgrBYBgDAPFwShIqi5Ofnx8fHx8fH5+fnFxUVSZL09NNPN2rUqEOHDp999tmHH34oy/J///vfdu3aEdHUqVMbNWr06aefRkdHf/fdd35+fkS0evXqefPmrVq16s4775wwYQIRde3ade3atT/++GNMTMz69etTUlJcUXwN6KtGMSIEADATFkK4uwbjTZ06NTw83Ml7hIWFhcHBBjw1qTD/5+/XDdc0e8rANUEhTWv+gnWSUe82VAv3CGtTcXEx7hHWGsO7EfzaDCY0lSWsigQAMA0EoWGYWGBqFADAbNBlG4eJBAkSTFgsAwBgGghCA+lJiLNGAQDMBF22wYSmYmoUAMBE0GUb5uo9QiyWAQDwFTSyAAAK7ElEQVQwDwShcfQn1JOGdxUAwETQZRuIiYTQNMZeIgAA80CXbRj9iDUhVGZMjQIAmAaC0GDYRwgAYC7osg2Es0YBAMwHXbZxmEkgCAEATAZdtmEqtk/grFEAAFNBEBqnYvsEEY5YAwAwDwShgVgIHCsDAGAy6LUNwxXPYMJbCgBgJui1jSSEgk2EAADmgiA0DrOmYTc9AIDJIAgNxHj0BACA6aDXNgwzC6HgHiEAgLmg1zaSwNQoAIDZIAgNxEJohCAEADAVBKFxmIlIwrEyAACmgiA0EBMRpkYBAMwFQWgYZiYiHDQKAGAuCEKDYUQIAGAuCEID6VOjeEsBAMwEvbaBMDUKAGA+CELD6PcI8ZYCAJgLem2DYUQIAGAuCELjYB8hAIAJIQgNw9hHCABgQghC4+j3CLFqFADAVNBrGwjbJwAAzAe9tsEwNQoAYC4IQsPgiDUAADNCEBoIU6MAAOaDXttACEIAAPNBr22YiqlR3CMEADAVBKGBsH0CAMB80GsbBotlAADMCEFoHMY9QgAA80GvbRg9AnGPEADAXBCEBsLUKACA+SAIjcUYEQIAmAuC0EjMEu4RAgCYC3ptIzFLmBoFADAXBKGhmCXJx91FAADATUAQGolZxj1CAABzQRAaiSVZki3urgIAAG4CgtBIkuQj+/i7uwoAALgJCEIjteme5msJdncVAABwExCERgqNaO3rH+ruKgAA4CYgCI0UHN7C16++u6sAAICbgCA0kq9f/eDwFu6uAgAAbgKC0GAhDZLcXQIAANwEBCFt2LChvLzcqFdjbKi/MavVunnzZndX4S1+/vnn7Oxsd1fhLXbt2nXu3Dl3V+EtjO20CUFIRGPHji0sLHR3FV7h1KlTaWlp7q7CW2zcuHHZsmXursJbvPHGGzt37nR3Fd7iySeftFqtBr4gghAAALwaghAAALwaCyHcXYPx5s2bt2zZstjYWGcu3rlzZ3Jyso8P7u25nM1mO3LkSMeOHd1diFc4f/58SUlJ8+bN3V2IVzh8+HBERETDhg3dXYhXuKlOe8GCBc2aNav6mroZhJqmbdiwwd1VAACAm6WmptavX8327roZhAAAAE7CPUIAAPBqCEIAAPBqCEIAAPBqCEIAAPBqXr1nIC8v77nnnsvKymrevPmsWbOqXWILf9pvv/02ZswYx6eDBw/+29/+5sZ66qT169dnZmb+8ssv48aNS01N1RtXrlz59ttvq6o6YsSIUaNGubfCuuSjjz7as2fPuXPnZsyYccsttxDR0qVLN27c6Lhg5cqVsiy7r8C649ChQ2+//XZWVpYsy/fee+8zzzzj5+dXWlr6wgsvbNmyJTIy8t///ncNN2V5dRA++uij9evX/+CDDz788MO+ffsePHiQmd1dVN1UUlKybt26jz76SP9U7zjAWBkZGdHR0T/88MOpU6f0lu+++27cuHErV64MDAz8+9//3qhRo7/+9a/uLbLOSE9P79Sp05IlS8aPH6+37Nu3j5kffPBB/VNJwnybMbZv3x4ZGTlnzpyysrKJEydevHhx/vz5//znP/fv37906dJvv/22V69eJ06cqHaPRFWEtzpx4oTFYsnLyxNCqKoaHR29adMmdxdVZ509ezYgIMDdVXiF5OTk5cuX6x8PHjw4LS1N//i1117r1auX++qqmyIiIrZt26Z/PH78+OnTp7u3njpv1apVSUlJNputfv36e/fu1Ru7d+++aNGimrys9/6f5eDBgwkJCeHh4UQkSVJycjKO6ncpu93ev3//hx56aNGiRaqqurscr5Cdnd2lSxf94y5duuAv3NU+++yz3r17jxkz5vDhw+6upW7av39/y5YtT548WVJS0qFDB72xS5cu+/fvr8nLeu/UaE5OTmhoqOPTsLCwnJwcN9ZTtwUEBLz88svt27fPzc2dMWNGdnb2m2++6e6i6r5Lly45/sj1v3AhBOb/XaRHjx7du3cPCQnZvHlzcnLynj17WrVq5e6i6pTvv//+zTff3LFjR05OTkhIiOMvOSwsrIb/yfPeIAwJCbHZbI5Pi4qKQkJC3FhP3RYeHj5lyhT948TExNTU1Ndff91isbi3qjqvfv36jj9y/S8cKeg6AwcO1D/o3bv38ePHly1bNmvWLPeWVJfs27dvwIABK1asaNOmzb59+67pvSuPav4E750ajYuLO3nypOPpjsePH4+Pj3drRd4iJiamvLy88t8xuEh8fPyxY8f0j48dO4a/8FoTExNz+fJld1dRdxw8eLBPnz5vvvlm3759iahJkyalpaWOJyHXvPf23iBMTk6OiYn54IMPiGjr1q2nTp164IEH3F1UnXX06NH8/HwiKi8vf+mllzp27FjD/8GBM4YOHbp48WKbzWa32995552hQ4e6u6K6bNeuXUIIIjp48GB6enrPnj3dXVEd8dNPP/Xu3Xvu3LmOMXeDBg3uu+++BQsWENHRo0e//vrrhx9+uEY/o+bLeMxrx44dTZo0SUhIaNiwYUZGhrvLqcuWLFkSFBQUGxsbFBSUkpJy5MgRd1dUBw0aNCisks2bN9vt9kceeaRBgwYRERH9+/e32WzurrHuuOOOOyq/28ePH2/fvn1wcHBsbGxISMiMGTPcXWDd8X//93+V3+qmTZsKIY4dO9amTZu4uLjQ0NA33nijhj/C258+oarqxYsXIyIifH193V1LHVdeXp6bmxsWFhYQEODuWrxLQUGBpmn6AmlwKavVWlRUFBMT4+5CvMX58+fDw8P9/f1r+DreHoQAAODlvPceIQAAACEIAQDAyyEIAQDAqyEIAeqClStX7t27191VAJgSFssAeDq73Z6UlFTFBUeOHGnUqNHYsWNfeeWVWqsKoM7w3iPWAMxCluWRI0c6Pn3rrbeI6Mknn6x8wbRp0zp16uSG4gDMDyNCAJPp2LGjECIrK6vqy8rKyqxWa0REhKPFZrOpqhocHHzNlUVFRUVFRREREXiQLHgn3CMEqAtatWqlz4s2bNhw7ty5EydODAkJadSoUVJS0v79+3Nycvr27VuvXr3Q0NA+ffrox90R0Q8//NC9e/f69etHR0c3atRo7ty5+J8xeCEEIUBdcOHCBavVSkT5+fmvvvpqTk7Opk2bNmzYUF5ePmTIkIceeqhTp047d+5csmTJ1q1bZ8yYQUT79+/v2bNnUFDQN998c+DAgWeeeSYtLW3+/Pnu/qcA1DbcIwSoa6Kjo1esWCFJEhGlpaU9/vjjY8eO1cOvS5cuW7ZsWbt27bx586ZNmxYTE/P555/rJ1S1bdv29OnTc+fOffrpp938DwCoXQhCgLrmnnvu0VOQiFq0aEFEvXr1cny1ZcuWH3/8sd1u/+abb+6+++7MzEzHl8LDw8+fP5+Xl9egQYNarhnAjRCEAHVN5Udc6U8/DgsLq9yiKEpBQUFJScmmTZsqByFdeZA9ghC8CoIQwBsFBQXJsvz444/PmzfP3bUAuBkWywB4I4vFcvvtt3/xxRclJSXurgXAzRCEAF7qpZdeOnXq1IABA/bs2VNSUnL27Nm1a9c+88wz7q4LoLYhCAG8VI8ePdavX//LL78kJycHBgY2adJk2LBhdrvd3XUB1DacLANgMpqmEZFjXaijkZmZ+U+84M8//5yXlxcWFhYXF6cvrgHwKghCAADwapgaBQAAr4YgBAAAr4YgBAAAr4YgBAAAr4YgBAAAr4YgBAAAr4YgBAAAr4YgBAAAr4YgBAAAr/b/AW7BEi3kjZ4uAAAAAElFTkSuQmCC" 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", + "text/plain": [ + "DisplayAs.Showable{MIME{Symbol(\"image/png\")}}(Plot{Plots.GRBackend() n=5})" + ] }, + "execution_count": 21, "metadata": {}, - "execution_count": 21 + "output_type": "execute_result" } ], - "cell_type": "code", "source": [ "p2 = plot()\n", "for g in get_components(ThermalStandard, sys)\n", @@ -2161,33 +1606,31 @@ "state_series = get_state_series(sim, (\"Battery\", :ω_oc))\n", "plot!(p2, state_series; xlabel = \"Time\", ylabel = \"Speed [pu]\", label = \"Battery - ω\")\n", "img = DisplayAs.PNG(p2) # This line is only needed because of literate use display(p2) when running locally" - ], - "metadata": {}, - "execution_count": 21 + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "---\n", "\n", "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" - ], - "metadata": {} + ] } ], - "nbformat_minor": 3, "metadata": { + "kernelspec": { + "display_name": "Julia 1.5.3", + "language": "julia", + "name": "julia-1.5" + }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.5.3" - }, - "kernelspec": { - "name": "julia-1.5", - "display_name": "Julia 1.5.3", - "language": "julia" } }, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/script/2_PowerSystems_examples/PowerSystems_intro.jl b/script/2_PowerSystems_examples/PowerSystems_intro.jl index 127cad9..cf4de22 100644 --- a/script/2_PowerSystems_examples/PowerSystems_intro.jl +++ b/script/2_PowerSystems_examples/PowerSystems_intro.jl @@ -42,7 +42,9 @@ Pkg.status() using SIIPExamples; using PowerSystems; using D3TypeTrees; -IS = PowerSystems.IS +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") # ## Types in PowerSystems # PowerSystems.jl provides a type hierarchy for specifying power system data. Data that diff --git a/script/2_PowerSystems_examples/US_system.jl b/script/2_PowerSystems_examples/US_system.jl index a1bd035..ab9af12 100644 --- a/script/2_PowerSystems_examples/US_system.jl +++ b/script/2_PowerSystems_examples/US_system.jl @@ -23,11 +23,14 @@ using Dates using TimeZones using DataFrames using CSV +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") # ### Fetch Data # PowerSystems.jl links to some test data that is suitable for this example. # Let's download the test data -@info "downloading data..." +println("downloading data...") datadir = joinpath(dirname(dirname(pathof(SIIPExamples))), "US-System") siip_data = joinpath(datadir, "SIIP") if !isdir(datadir) @@ -57,8 +60,8 @@ initial_time = ZonedDateTime(DateTime("2016-01-01T00:00:00"), timezone) # # First, PowerSystems.jl only supports parsing piecewise linear generator costs from tabular # data. So, we can sample the quadratic polynomial cost curves and provide PWL points. -@info "formatting data ..." -!isnothing(interconnect) && @info "filtering data to include $interconnect ..." +println("formatting data ...") +!isnothing(interconnect) && println("filtering data to include $interconnect ...") gen = DataFrame(CSV.File(joinpath(datadir, "plant.csv"))) filter!(row -> row[:interconnect] == interconnect, gen) gencost = DataFrame(CSV.File(joinpath(datadir, "gencost.csv"))) @@ -171,7 +174,7 @@ timeseries = [] ts_csv = ["wind", "solar", "hydro", "demand"] plant_ids = Symbol.(string.(gen.plant_id)) for f in ts_csv - @info "formatting $f.csv ..." + println("formatting $f.csv ...") csvpath = joinpath(siip_data, f * ".csv") csv = DataFrame(CSV.File(joinpath(datadir, f * ".csv"))) (category, name_prefix, label) = @@ -239,7 +242,7 @@ end # describing the column names of each file in PowerSystems terms, and the PowerSystems # data type that should be created for each generator type. The respective "us_decriptors.yaml" # and "US_generator_mapping.yaml" files have already been tailored to this dataset. -@info "parsing csv files..." +println("parsing csv files...") rawsys = PowerSystems.PowerSystemTableData( siip_data, 100.0, @@ -253,7 +256,7 @@ rawsys = PowerSystems.PowerSystemTableData( # time series, we also need to specify which time series we want to include in the `System`. # The `time_series_resolution` kwarg filters to only include time series with a matching resolution. -@info "creating System" +println("creating System") sys = System(rawsys; config_path = joinpath(config_dir, "us_system_validation.json")); sys diff --git a/script/2_PowerSystems_examples/add_forecasts.jl b/script/2_PowerSystems_examples/add_forecasts.jl index 27efad3..579c8f2 100644 --- a/script/2_PowerSystems_examples/add_forecasts.jl +++ b/script/2_PowerSystems_examples/add_forecasts.jl @@ -11,6 +11,10 @@ # ### Dependencies # Let's use the 5-bus dataset we parsed in the MATPOWER example using SIIPExamples +using PowerSystems +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") pkgpath = dirname(dirname(pathof(SIIPExamples))) include(joinpath(pkgpath, "test", "2_PowerSystems_examples", "parse_matpower.jl")) @@ -22,14 +26,9 @@ include(joinpath(pkgpath, "test", "2_PowerSystems_examples", "parse_matpower.jl" FORECASTS_DIR = joinpath(base_dir, "forecasts", "5bus_ts") fname = joinpath(FORECASTS_DIR, "timeseries_pointers_da.json") open(fname, "r") do f - for line in eachline(f) - println(line) - end + @JSON3.@pretty JSON3.read(f) end -# ### Read the pointers -ts_pointers = PowerSystems.IS.read_time_series_file_metadata(fname) - -# ### Read and assign time series to `System` using the `ts_pointers` struct -add_time_series!(sys, ts_pointers) +# ### Read and assign time series to `System` using these parameters. +add_time_series!(sys, fname) sys diff --git a/script/2_PowerSystems_examples/loading_dynamic_systems_data.jl b/script/2_PowerSystems_examples/loading_dynamic_systems_data.jl index 46612da..fe35591 100644 --- a/script/2_PowerSystems_examples/loading_dynamic_systems_data.jl +++ b/script/2_PowerSystems_examples/loading_dynamic_systems_data.jl @@ -10,6 +10,10 @@ using SIIPExamples using PowerSystems const PSY = PowerSystems +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") + # # Step 1: System description diff --git a/script/2_PowerSystems_examples/network_matrices.jl b/script/2_PowerSystems_examples/network_matrices.jl index 35cb6df..ce4b471 100644 --- a/script/2_PowerSystems_examples/network_matrices.jl +++ b/script/2_PowerSystems_examples/network_matrices.jl @@ -13,6 +13,8 @@ # ### Dependencies # Let's use a dataset from the [tabular data parsing example](../../notebook/2_PowerSystems_examples/parse_matpower.ipynb) using SIIPExamples +using Logging +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") pkgpath = dirname(dirname(pathof(SIIPExamples))) include(joinpath(pkgpath, "test", "2_PowerSystems_examples", "parse_matpower.jl")) diff --git a/script/2_PowerSystems_examples/parse_matpower.jl b/script/2_PowerSystems_examples/parse_matpower.jl index 9060dae..5ddadde 100644 --- a/script/2_PowerSystems_examples/parse_matpower.jl +++ b/script/2_PowerSystems_examples/parse_matpower.jl @@ -16,6 +16,9 @@ Pkg.status() using SIIPExamples using PowerSystems using TimeSeries +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") # ### Fetch Data # PowerSystems.jl links to some test data that is suitable for this example. diff --git a/script/2_PowerSystems_examples/parse_psse.jl b/script/2_PowerSystems_examples/parse_psse.jl index cd15459..c2f8ae1 100644 --- a/script/2_PowerSystems_examples/parse_psse.jl +++ b/script/2_PowerSystems_examples/parse_psse.jl @@ -16,6 +16,9 @@ Pkg.status() using SIIPExamples using PowerSystems using TimeSeries +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") # ### Fetch Data # PowerSystems.jl links to some test data that is suitable for this example. diff --git a/script/2_PowerSystems_examples/parse_tabulardata.jl b/script/2_PowerSystems_examples/parse_tabulardata.jl index 5b86daa..aa4a485 100644 --- a/script/2_PowerSystems_examples/parse_tabulardata.jl +++ b/script/2_PowerSystems_examples/parse_tabulardata.jl @@ -19,6 +19,9 @@ using SIIPExamples using PowerSystems using TimeSeries using Dates +using Logging + +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") # ### Fetch Data # PowerSystems.jl links to some test data that is suitable for this example. diff --git a/script/2_PowerSystems_examples/serialize_data.jl b/script/2_PowerSystems_examples/serialize_data.jl index c47cc25..31dc4fe 100644 --- a/script/2_PowerSystems_examples/serialize_data.jl +++ b/script/2_PowerSystems_examples/serialize_data.jl @@ -10,6 +10,8 @@ # ### Dependencies # Let's use a dataset from the [tabular data parsing example](../../notebook/2_PowerSystems_examples/parse_matpower.ipynb) using SIIPExamples +using Logging +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") pkgpath = dirname(dirname(pathof(SIIPExamples))) include(joinpath(pkgpath, "test", "2_PowerSystems_examples", "parse_matpower.jl")) @@ -17,10 +19,10 @@ include(joinpath(pkgpath, "test", "2_PowerSystems_examples", "parse_matpower.jl" folder = mktempdir() path = joinpath(folder, "system.json") -@info "Serializing to $path" +println("Serializing to $path") to_json(sys, path) -filesize(path) / 1000000 #MB +filesize(path) / (1024 * 1024) #MiB # ### Read the JSON file and create a new `System` sys2 = System(path) diff --git a/script/3_PowerSimulations_examples/01_operations_problems.jl b/script/3_PowerSimulations_examples/01_operations_problems.jl index 7b581d2..22a4d6c 100644 --- a/script/3_PowerSimulations_examples/01_operations_problems.jl +++ b/script/3_PowerSimulations_examples/01_operations_problems.jl @@ -22,6 +22,7 @@ using D3TypeTrees # ### Data management packages using Dates using DataFrames +using Logging # ### Optimization packages using Cbc #solver @@ -30,6 +31,7 @@ using Cbc #solver # This data depends upon the [RTS-GMLC](https://github.com/gridmod/rts-gmlc) dataset. Let's # download and extract the data. +logger = configure_logging(console_level = Error, file_level = Info, filename = "ex.log") rts_dir = SIIPExamples.download("https://github.com/GridMod/RTS-GMLC") rts_src_dir = joinpath(rts_dir, "RTS_Data", "SourceData") rts_siip_dir = joinpath(rts_dir, "RTS_Data", "FormattedData", "SIIP"); diff --git a/src/SIIPExamples.jl b/src/SIIPExamples.jl index 4ecb672..da6fc52 100644 --- a/src/SIIPExamples.jl +++ b/src/SIIPExamples.jl @@ -4,7 +4,7 @@ export print_struct # using Weave using Literate -using JSON2 +import JSON3 repo_directory = dirname(joinpath(@__DIR__)) @@ -24,7 +24,7 @@ end function read_json(filename) return open(filename) do io - JSON2.read(io, Dict) + JSON3.read(io, Dict) end end