{ "cells": [ { "cell_type": "markdown", "id": "759b55ee", "metadata": {}, "source": [ "# 2023-04-11 • Nto1 AdEx sims: conntest method comparison" ] }, { "cell_type": "markdown", "id": "47e11f3e", "metadata": {}, "source": [ "Data generated in `2023-02-24__multisim-winline.jl`" ] }, { "cell_type": "code", "execution_count": 1, "id": "5168f13b", "metadata": {}, "outputs": [], "source": [ "using CSV" ] }, { "cell_type": "code", "execution_count": 2, "id": "e4271f9b", "metadata": {}, "outputs": [], "source": [ "using DataFrames" ] }, { "cell_type": "code", "execution_count": 3, "id": "2372ab94", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
90×13 DataFrame
65 rows omitted
RowNNᵤδ_nSdurationseedmethodTPRₑTPRᵢTPRFPRAUCAUCₑAUCᵢ
Int64Int64Float64Float64Int64String15Float64Float64Float64Float64Float64Float64Float64
151005.0600.01STA_corr_2pass1.01.01.00.080.960.960.96
251005.0600.01STA_height1.01.01.00.040.9850.9850.985
351005.0600.01fit_upstroke1.01.01.00.051.01.01.0
451005.0600.02fit_upstroke1.01.01.00.051.01.01.0
551005.0600.02STA_corr_2pass1.01.01.00.060.9950.9950.995
651005.0600.02STA_height1.01.01.00.050.970.970.97
751005.0600.03fit_upstroke1.01.01.00.051.01.01.0
851005.0600.03STA_corr_2pass1.01.01.00.050.9850.9850.985
951005.0600.03STA_height1.01.01.00.060.990.990.99
1051005.0600.04STA_corr_2pass1.01.01.00.060.990.990.99
1151005.0600.04fit_upstroke1.01.01.00.051.01.01.0
1251005.0600.04STA_height1.01.01.00.060.980.980.98
1351005.0600.05fit_upstroke1.01.01.00.051.01.01.0
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
7965001000.02600.02fit_upstroke0.1005770.2384620.1281540.050.4971830.4619420.638146
8065001000.02600.02STA_height0.01557690.03384620.01923080.050.2603120.2520320.293431
8165001000.02600.02STA_corr_2pass0.01576920.001538460.01292310.050.09094460.1053770.0332154
8265001000.02600.03STA_height0.02250.05538460.02907690.060.2465650.2374640.282965
8365001000.02600.03STA_corr_2pass0.004807690.002307690.004307690.040.08948080.1014110.0417615
8465001000.02600.03fit_upstroke0.05115380.1415380.06923080.050.4595090.4255230.595454
8565001000.02600.04fit_upstroke0.1107690.2246150.1335380.050.4528850.4227060.5736
8665001000.02600.04STA_height0.02788460.060.03430770.050.2453650.2357210.283938
8765001000.02600.04STA_corr_2pass0.003076920.0007692310.002615380.070.08182150.09373170.0341808
8865001000.02600.05STA_height0.020.04384620.02476920.050.2422680.2362670.266273
8965001000.02600.05STA_corr_2pass0.009230770.002307690.007846150.040.1083680.1203880.0602846
9065001000.02600.05fit_upstroke0.1101920.2069230.1295380.050.4783450.4489630.595869
" ], "text/latex": [ "\\begin{tabular}{r|cccccccccc}\n", "\t& N & Nᵤ & δ\\_nS & duration & seed & method & TPRₑ & TPRᵢ & TPR & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Int64 & String15 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 5 & 100 & 5.0 & 600.0 & 1 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t2 & 5 & 100 & 5.0 & 600.0 & 1 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t3 & 5 & 100 & 5.0 & 600.0 & 1 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t4 & 5 & 100 & 5.0 & 600.0 & 2 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t5 & 5 & 100 & 5.0 & 600.0 & 2 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t6 & 5 & 100 & 5.0 & 600.0 & 2 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t7 & 5 & 100 & 5.0 & 600.0 & 3 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t8 & 5 & 100 & 5.0 & 600.0 & 3 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t9 & 5 & 100 & 5.0 & 600.0 & 3 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t10 & 5 & 100 & 5.0 & 600.0 & 4 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t11 & 5 & 100 & 5.0 & 600.0 & 4 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t12 & 5 & 100 & 5.0 & 600.0 & 4 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t13 & 5 & 100 & 5.0 & 600.0 & 5 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t14 & 5 & 100 & 5.0 & 600.0 & 5 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t15 & 5 & 100 & 5.0 & 600.0 & 5 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t16 & 20 & 100 & 2.3 & 600.0 & 1 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t17 & 20 & 100 & 2.3 & 600.0 & 1 & STA\\_height & 0.8125 & 1.0 & 0.85 & $\\dots$ \\\\\n", "\t18 & 20 & 100 & 2.3 & 600.0 & 1 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t19 & 20 & 100 & 2.3 & 600.0 & 2 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t20 & 20 & 100 & 2.3 & 600.0 & 2 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t21 & 20 & 100 & 2.3 & 600.0 & 2 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t22 & 20 & 100 & 2.3 & 600.0 & 3 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t23 & 20 & 100 & 2.3 & 600.0 & 3 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t24 & 20 & 100 & 2.3 & 600.0 & 3 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t25 & 20 & 100 & 2.3 & 600.0 & 4 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t26 & 20 & 100 & 2.3 & 600.0 & 4 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t27 & 20 & 100 & 2.3 & 600.0 & 4 & STA\\_height & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t28 & 20 & 100 & 2.3 & 600.0 & 5 & fit\\_upstroke & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t29 & 20 & 100 & 2.3 & 600.0 & 5 & STA\\_height & 0.6875 & 1.0 & 0.75 & $\\dots$ \\\\\n", "\t30 & 20 & 100 & 2.3 & 600.0 & 5 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m90×13 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m N \u001b[0m\u001b[1m Nᵤ \u001b[0m\u001b[1m δ_nS \u001b[0m\u001b[1m duration \u001b[0m\u001b[1m seed \u001b[0m\u001b[1m method \u001b[0m\u001b[1m TPRₑ \u001b[0m\u001b[1m TPR\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Flo\u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ 5 100 5.0 600.0 1 STA_corr_2pass 1.0 1.0 ⋯\n", " 2 │ 5 100 5.0 600.0 1 STA_height 1.0 1.0\n", " 3 │ 5 100 5.0 600.0 1 fit_upstroke 1.0 1.0\n", " 4 │ 5 100 5.0 600.0 2 fit_upstroke 1.0 1.0\n", " 5 │ 5 100 5.0 600.0 2 STA_corr_2pass 1.0 1.0 ⋯\n", " 6 │ 5 100 5.0 600.0 2 STA_height 1.0 1.0\n", " 7 │ 5 100 5.0 600.0 3 fit_upstroke 1.0 1.0\n", " 8 │ 5 100 5.0 600.0 3 STA_corr_2pass 1.0 1.0\n", " 9 │ 5 100 5.0 600.0 3 STA_height 1.0 1.0 ⋯\n", " 10 │ 5 100 5.0 600.0 4 STA_corr_2pass 1.0 1.0\n", " 11 │ 5 100 5.0 600.0 4 fit_upstroke 1.0 1.0\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 81 │ 6500 100 0.02 600.0 2 STA_corr_2pass 0.0157692 0.0\n", " 82 │ 6500 100 0.02 600.0 3 STA_height 0.0225 0.0 ⋯\n", " 83 │ 6500 100 0.02 600.0 3 STA_corr_2pass 0.00480769 0.0\n", " 84 │ 6500 100 0.02 600.0 3 fit_upstroke 0.0511538 0.1\n", " 85 │ 6500 100 0.02 600.0 4 fit_upstroke 0.110769 0.2\n", " 86 │ 6500 100 0.02 600.0 4 STA_height 0.0278846 0.0 ⋯\n", " 87 │ 6500 100 0.02 600.0 4 STA_corr_2pass 0.00307692 0.0\n", " 88 │ 6500 100 0.02 600.0 5 STA_height 0.02 0.0\n", " 89 │ 6500 100 0.02 600.0 5 STA_corr_2pass 0.00923077 0.0\n", " 90 │ 6500 100 0.02 600.0 5 fit_upstroke 0.110192 0.2 ⋯\n", "\u001b[36m 6 columns and 69 rows omitted\u001b[0m" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = CSV.read(\"../data/Nto1_AdEx.csv\", DataFrame)" ] }, { "cell_type": "code", "execution_count": 4, "id": "f8583338", "metadata": { "scrolled": false, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
15×13 DataFrame
RowNNᵤδ_nSdurationseedmethodTPRₑTPRᵢTPRFPRAUCAUCₑAUCᵢ
Int64Int64Float64Float64Int64String15Float64Float64Float64Float64Float64Float64Float64
165001000.02600.01STA_corr_2pass0.003461540.0007692310.002923080.060.08518310.09708850.0375615
265001000.02600.01fit_upstroke0.1213460.250.1470770.050.4899550.4586170.615308
365001000.02600.01STA_height0.01596150.04307690.02138460.050.2519280.240990.295677
465001000.02600.02fit_upstroke0.1005770.2384620.1281540.050.4971830.4619420.638146
565001000.02600.02STA_height0.01557690.03384620.01923080.050.2603120.2520320.293431
665001000.02600.02STA_corr_2pass0.01576920.001538460.01292310.050.09094460.1053770.0332154
765001000.02600.03STA_height0.02250.05538460.02907690.060.2465650.2374640.282965
865001000.02600.03STA_corr_2pass0.004807690.002307690.004307690.040.08948080.1014110.0417615
965001000.02600.03fit_upstroke0.05115380.1415380.06923080.050.4595090.4255230.595454
1065001000.02600.04fit_upstroke0.1107690.2246150.1335380.050.4528850.4227060.5736
1165001000.02600.04STA_height0.02788460.060.03430770.050.2453650.2357210.283938
1265001000.02600.04STA_corr_2pass0.003076920.0007692310.002615380.070.08182150.09373170.0341808
1365001000.02600.05STA_height0.020.04384620.02476920.050.2422680.2362670.266273
1465001000.02600.05STA_corr_2pass0.009230770.002307690.007846150.040.1083680.1203880.0602846
1565001000.02600.05fit_upstroke0.1101920.2069230.1295380.050.4783450.4489630.595869
" ], "text/latex": [ "\\begin{tabular}{r|ccccccccc}\n", "\t& N & Nᵤ & δ\\_nS & duration & seed & method & TPRₑ & TPRᵢ & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Int64 & String15 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 6500 & 100 & 0.02 & 600.0 & 1 & STA\\_corr\\_2pass & 0.00346154 & 0.000769231 & $\\dots$ \\\\\n", "\t2 & 6500 & 100 & 0.02 & 600.0 & 1 & fit\\_upstroke & 0.121346 & 0.25 & $\\dots$ \\\\\n", "\t3 & 6500 & 100 & 0.02 & 600.0 & 1 & STA\\_height & 0.0159615 & 0.0430769 & $\\dots$ \\\\\n", "\t4 & 6500 & 100 & 0.02 & 600.0 & 2 & fit\\_upstroke & 0.100577 & 0.238462 & $\\dots$ \\\\\n", "\t5 & 6500 & 100 & 0.02 & 600.0 & 2 & STA\\_height & 0.0155769 & 0.0338462 & $\\dots$ \\\\\n", "\t6 & 6500 & 100 & 0.02 & 600.0 & 2 & STA\\_corr\\_2pass & 0.0157692 & 0.00153846 & $\\dots$ \\\\\n", "\t7 & 6500 & 100 & 0.02 & 600.0 & 3 & STA\\_height & 0.0225 & 0.0553846 & $\\dots$ \\\\\n", "\t8 & 6500 & 100 & 0.02 & 600.0 & 3 & STA\\_corr\\_2pass & 0.00480769 & 0.00230769 & $\\dots$ \\\\\n", "\t9 & 6500 & 100 & 0.02 & 600.0 & 3 & fit\\_upstroke & 0.0511538 & 0.141538 & $\\dots$ \\\\\n", "\t10 & 6500 & 100 & 0.02 & 600.0 & 4 & fit\\_upstroke & 0.110769 & 0.224615 & $\\dots$ \\\\\n", "\t11 & 6500 & 100 & 0.02 & 600.0 & 4 & STA\\_height & 0.0278846 & 0.06 & $\\dots$ \\\\\n", "\t12 & 6500 & 100 & 0.02 & 600.0 & 4 & STA\\_corr\\_2pass & 0.00307692 & 0.000769231 & $\\dots$ \\\\\n", "\t13 & 6500 & 100 & 0.02 & 600.0 & 5 & STA\\_height & 0.02 & 0.0438462 & $\\dots$ \\\\\n", "\t14 & 6500 & 100 & 0.02 & 600.0 & 5 & STA\\_corr\\_2pass & 0.00923077 & 0.00230769 & $\\dots$ \\\\\n", "\t15 & 6500 & 100 & 0.02 & 600.0 & 5 & fit\\_upstroke & 0.110192 & 0.206923 & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m15×13 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m N \u001b[0m\u001b[1m Nᵤ \u001b[0m\u001b[1m δ_nS \u001b[0m\u001b[1m duration \u001b[0m\u001b[1m seed \u001b[0m\u001b[1m method \u001b[0m\u001b[1m TPRₑ \u001b[0m\u001b[1m TPR\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Flo\u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ 6500 100 0.02 600.0 1 STA_corr_2pass 0.00346154 0.0 ⋯\n", " 2 │ 6500 100 0.02 600.0 1 fit_upstroke 0.121346 0.2\n", " 3 │ 6500 100 0.02 600.0 1 STA_height 0.0159615 0.0\n", " 4 │ 6500 100 0.02 600.0 2 fit_upstroke 0.100577 0.2\n", " 5 │ 6500 100 0.02 600.0 2 STA_height 0.0155769 0.0 ⋯\n", " 6 │ 6500 100 0.02 600.0 2 STA_corr_2pass 0.0157692 0.0\n", " 7 │ 6500 100 0.02 600.0 3 STA_height 0.0225 0.0\n", " 8 │ 6500 100 0.02 600.0 3 STA_corr_2pass 0.00480769 0.0\n", " 9 │ 6500 100 0.02 600.0 3 fit_upstroke 0.0511538 0.1 ⋯\n", " 10 │ 6500 100 0.02 600.0 4 fit_upstroke 0.110769 0.2\n", " 11 │ 6500 100 0.02 600.0 4 STA_height 0.0278846 0.0\n", " 12 │ 6500 100 0.02 600.0 4 STA_corr_2pass 0.00307692 0.0\n", " 13 │ 6500 100 0.02 600.0 5 STA_height 0.02 0.0 ⋯\n", " 14 │ 6500 100 0.02 600.0 5 STA_corr_2pass 0.00923077 0.0\n", " 15 │ 6500 100 0.02 600.0 5 fit_upstroke 0.110192 0.2\n", "\u001b[36m 6 columns omitted\u001b[0m" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.N .== 6500, :]" ] }, { "cell_type": "code", "execution_count": 5, "id": "63ba6829", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

GroupedDataFrame with 18 groups based on keys: method, N

First Group (5 rows): method = "STA_corr_2pass", N = 5
RowNNᵤδ_nSdurationseedmethodTPRₑTPRᵢTPRFPRAUCAUCₑAUCᵢ
Int64Int64Float64Float64Int64String15Float64Float64Float64Float64Float64Float64Float64
151005.0600.01STA_corr_2pass1.01.01.00.080.960.960.96
251005.0600.02STA_corr_2pass1.01.01.00.060.9950.9950.995
351005.0600.03STA_corr_2pass1.01.01.00.050.9850.9850.985
451005.0600.04STA_corr_2pass1.01.01.00.060.990.990.99
551005.0600.05STA_corr_2pass1.01.01.00.060.970.970.97

⋮

Last Group (5 rows): method = "STA_height", N = 6500
RowNNᵤδ_nSdurationseedmethodTPRₑTPRᵢTPRFPRAUCAUCₑAUCᵢ
Int64Int64Float64Float64Int64String15Float64Float64Float64Float64Float64Float64Float64
165001000.02600.01STA_height0.01596150.04307690.02138460.050.2519280.240990.295677
265001000.02600.02STA_height0.01557690.03384620.01923080.050.2603120.2520320.293431
365001000.02600.03STA_height0.02250.05538460.02907690.060.2465650.2374640.282965
465001000.02600.04STA_height0.02788460.060.03430770.050.2453650.2357210.283938
565001000.02600.05STA_height0.020.04384620.02476920.050.2422680.2362670.266273
" ], "text/latex": [ "GroupedDataFrame with 18 groups based on keys: method, N\n", "\n", "First Group (5 rows): method = \"STA\\_corr\\_2pass\", N = 5\n", "\n", "\\begin{tabular}{r|cccccccccc}\n", "\t& N & Nᵤ & δ\\_nS & duration & seed & method & TPRₑ & TPRᵢ & TPR & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Int64 & String15 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 5 & 100 & 5.0 & 600.0 & 1 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t2 & 5 & 100 & 5.0 & 600.0 & 2 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t3 & 5 & 100 & 5.0 & 600.0 & 3 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t4 & 5 & 100 & 5.0 & 600.0 & 4 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\t5 & 5 & 100 & 5.0 & 600.0 & 5 & STA\\_corr\\_2pass & 1.0 & 1.0 & 1.0 & $\\dots$ \\\\\n", "\\end{tabular}\n", "\n", "$\\dots$\n", "\n", "Last Group (5 rows): method = \"STA\\_height\", N = 6500\n", "\n", "\\begin{tabular}{r|cccccccccc}\n", "\t& N & Nᵤ & δ\\_nS & duration & seed & method & TPRₑ & TPRᵢ & TPR & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Int64 & String15 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 6500 & 100 & 0.02 & 600.0 & 1 & STA\\_height & 0.0159615 & 0.0430769 & 0.0213846 & $\\dots$ \\\\\n", "\t2 & 6500 & 100 & 0.02 & 600.0 & 2 & STA\\_height & 0.0155769 & 0.0338462 & 0.0192308 & $\\dots$ \\\\\n", "\t3 & 6500 & 100 & 0.02 & 600.0 & 3 & STA\\_height & 0.0225 & 0.0553846 & 0.0290769 & $\\dots$ \\\\\n", "\t4 & 6500 & 100 & 0.02 & 600.0 & 4 & STA\\_height & 0.0278846 & 0.06 & 0.0343077 & $\\dots$ \\\\\n", "\t5 & 6500 & 100 & 0.02 & 600.0 & 5 & STA\\_height & 0.02 & 0.0438462 & 0.0247692 & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "GroupedDataFrame with 18 groups based on keys: method, N\n", "First Group (5 rows): method = \"STA_corr_2pass\", N = 5\n", "\u001b[1m Row \u001b[0m│\u001b[1m N \u001b[0m\u001b[1m Nᵤ \u001b[0m\u001b[1m δ_nS \u001b[0m\u001b[1m duration \u001b[0m\u001b[1m seed \u001b[0m\u001b[1m method \u001b[0m\u001b[1m TPRₑ \u001b[0m\u001b[1m TPRᵢ \u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float6\u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ 5 100 5.0 600.0 1 STA_corr_2pass 1.0 1. ⋯\n", " 2 │ 5 100 5.0 600.0 2 STA_corr_2pass 1.0 1.\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 5 │ 5 100 5.0 600.0 5 STA_corr_2pass 1.0 1.\n", "\u001b[36m 6 columns and 2 rows omitted\u001b[0m\n", "⋮\n", "Last Group (5 rows): method = \"STA_height\", N = 6500\n", "\u001b[1m Row \u001b[0m│\u001b[1m N \u001b[0m\u001b[1m Nᵤ \u001b[0m\u001b[1m δ_nS \u001b[0m\u001b[1m duration \u001b[0m\u001b[1m seed \u001b[0m\u001b[1m method \u001b[0m\u001b[1m TPRₑ \u001b[0m\u001b[1m TPRᵢ \u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ 6500 100 0.02 600.0 1 STA_height 0.0159615 0.043076 ⋯\n", " 2 │ 6500 100 0.02 600.0 2 STA_height 0.0155769 0.033846\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 5 │ 6500 100 0.02 600.0 5 STA_height 0.02 0.043846\n", "\u001b[36m 6 columns and 2 rows omitted\u001b[0m" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = groupby(df, [:method, :N])" ] }, { "cell_type": "code", "execution_count": 6, "id": "9fba5dc3", "metadata": {}, "outputs": [], "source": [ "using Statistics" ] }, { "cell_type": "code", "execution_count": 7, "id": "5b956577", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
18×9 DataFrame
RowmethodNTPRₑ_meanTPRᵢ_meanTPR_meanFPR_meanAUC_meanAUCₑ_meanAUCᵢ_mean
String15Int64Float64Float64Float64Float64Float64Float64Float64
1STA_corr_2pass51.01.01.00.0620.980.980.98
2STA_height51.01.01.00.0520.9830.9830.983
3fit_upstroke51.01.01.00.051.01.01.0
4STA_corr_2pass201.01.01.00.060.9850.9850.985
5STA_height200.91.00.920.050.899050.8793120.978
6fit_upstroke201.01.01.00.051.01.01.0
7fit_upstroke1001.01.01.00.050.999960.999951.0
8STA_height1000.740.860.7640.040.752010.728350.84665
9STA_corr_2pass1001.01.01.00.050.9860.9860.986
10fit_upstroke4000.9668751.00.97350.050.9935950.9920440.9998
11STA_height4000.5056250.6250.52950.050.5476120.5303750.616563
12STA_corr_2pass4000.936250.99250.94750.060.966110.9643810.973025
13STA_corr_2pass16000.000156250.00.0001250.0580.007225630.008995310.000146875
14fit_upstroke16000.4610940.8318750.535250.050.8148030.7807550.950994
15STA_height16000.2040620.41250.245750.0540.4059680.3923380.460484
16STA_corr_2pass65000.007269230.001538460.006123080.0520.09115950.1035990.0414008
17fit_upstroke65000.09880770.2123080.1215080.050.4755750.443550.603675
18STA_height65000.02038460.04723080.02575380.0520.2492870.2404950.284457
" ], "text/latex": [ "\\begin{tabular}{r|cccccccc}\n", "\t& method & N & TPRₑ\\_mean & TPRᵢ\\_mean & TPR\\_mean & FPR\\_mean & AUC\\_mean & \\\\\n", "\t\\hline\n", "\t& String15 & Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & STA\\_corr\\_2pass & 5 & 1.0 & 1.0 & 1.0 & 0.062 & 0.98 & $\\dots$ \\\\\n", "\t2 & STA\\_height & 5 & 1.0 & 1.0 & 1.0 & 0.052 & 0.983 & $\\dots$ \\\\\n", "\t3 & fit\\_upstroke & 5 & 1.0 & 1.0 & 1.0 & 0.05 & 1.0 & $\\dots$ \\\\\n", "\t4 & STA\\_corr\\_2pass & 20 & 1.0 & 1.0 & 1.0 & 0.06 & 0.985 & $\\dots$ \\\\\n", "\t5 & STA\\_height & 20 & 0.9 & 1.0 & 0.92 & 0.05 & 0.89905 & $\\dots$ \\\\\n", "\t6 & fit\\_upstroke & 20 & 1.0 & 1.0 & 1.0 & 0.05 & 1.0 & $\\dots$ \\\\\n", "\t7 & fit\\_upstroke & 100 & 1.0 & 1.0 & 1.0 & 0.05 & 0.99996 & $\\dots$ \\\\\n", "\t8 & STA\\_height & 100 & 0.74 & 0.86 & 0.764 & 0.04 & 0.75201 & $\\dots$ \\\\\n", "\t9 & STA\\_corr\\_2pass & 100 & 1.0 & 1.0 & 1.0 & 0.05 & 0.986 & $\\dots$ \\\\\n", "\t10 & fit\\_upstroke & 400 & 0.966875 & 1.0 & 0.9735 & 0.05 & 0.993595 & $\\dots$ \\\\\n", "\t11 & STA\\_height & 400 & 0.505625 & 0.625 & 0.5295 & 0.05 & 0.547612 & $\\dots$ \\\\\n", "\t12 & STA\\_corr\\_2pass & 400 & 0.93625 & 0.9925 & 0.9475 & 0.06 & 0.96611 & $\\dots$ \\\\\n", "\t13 & STA\\_corr\\_2pass & 1600 & 0.00015625 & 0.0 & 0.000125 & 0.058 & 0.00722563 & $\\dots$ \\\\\n", "\t14 & fit\\_upstroke & 1600 & 0.461094 & 0.831875 & 0.53525 & 0.05 & 0.814803 & $\\dots$ \\\\\n", "\t15 & STA\\_height & 1600 & 0.204062 & 0.4125 & 0.24575 & 0.054 & 0.405968 & $\\dots$ \\\\\n", "\t16 & STA\\_corr\\_2pass & 6500 & 0.00726923 & 0.00153846 & 0.00612308 & 0.052 & 0.0911595 & $\\dots$ \\\\\n", "\t17 & fit\\_upstroke & 6500 & 0.0988077 & 0.212308 & 0.121508 & 0.05 & 0.475575 & $\\dots$ \\\\\n", "\t18 & STA\\_height & 6500 & 0.0203846 & 0.0472308 & 0.0257538 & 0.052 & 0.249287 & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m18×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m method \u001b[0m\u001b[1m N \u001b[0m\u001b[1m TPRₑ_mean \u001b[0m\u001b[1m TPRᵢ_mean \u001b[0m\u001b[1m TPR_mean \u001b[0m\u001b[1m FPR_mean \u001b[0m\u001b[1m AU\u001b[0m ⋯\n", " │\u001b[90m String15 \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 Fl\u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ STA_corr_2pass 5 1.0 1.0 1.0 0.062 0. ⋯\n", " 2 │ STA_height 5 1.0 1.0 1.0 0.052 0.\n", " 3 │ fit_upstroke 5 1.0 1.0 1.0 0.05 1.\n", " 4 │ STA_corr_2pass 20 1.0 1.0 1.0 0.06 0.\n", " 5 │ STA_height 20 0.9 1.0 0.92 0.05 0. ⋯\n", " 6 │ fit_upstroke 20 1.0 1.0 1.0 0.05 1.\n", " 7 │ fit_upstroke 100 1.0 1.0 1.0 0.05 0.\n", " 8 │ STA_height 100 0.74 0.86 0.764 0.04 0.\n", " 9 │ STA_corr_2pass 100 1.0 1.0 1.0 0.05 0. ⋯\n", " 10 │ fit_upstroke 400 0.966875 1.0 0.9735 0.05 0.\n", " 11 │ STA_height 400 0.505625 0.625 0.5295 0.05 0.\n", " 12 │ STA_corr_2pass 400 0.93625 0.9925 0.9475 0.06 0.\n", " 13 │ STA_corr_2pass 1600 0.00015625 0.0 0.000125 0.058 0. ⋯\n", " 14 │ fit_upstroke 1600 0.461094 0.831875 0.53525 0.05 0.\n", " 15 │ STA_height 1600 0.204062 0.4125 0.24575 0.054 0.\n", " 16 │ STA_corr_2pass 6500 0.00726923 0.00153846 0.00612308 0.052 0.\n", " 17 │ fit_upstroke 6500 0.0988077 0.212308 0.121508 0.05 0. ⋯\n", " 18 │ STA_height 6500 0.0203846 0.0472308 0.0257538 0.052 0.\n", "\u001b[36m 3 columns omitted\u001b[0m" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfm = sort!(combine(df2, Between(:TPRₑ, :AUCᵢ) .=> mean), :N)" ] }, { "cell_type": "code", "execution_count": 8, "id": "bde9738f", "metadata": {}, "outputs": [], "source": [ "using WithFeedback" ] }, { "cell_type": "code", "execution_count": 9, "id": "2f158452", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import PyPlot … ✔ (4.8 s)\n", "using Sciplotlib … ✔ (4.7 s)\n", "using PhDPlots … ✔\n" ] } ], "source": [ "@withfb import PyPlot # mpl wrapper\n", "@withfb using Sciplotlib # pyplot wrapper\n", "@withfb using PhDPlots # local plotting funcs" ] }, { "cell_type": "markdown", "id": "bc082060", "metadata": {}, "source": [ "## Plots" ] }, { "cell_type": "code", "execution_count": 10, "id": "0278e26a", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "function plotrates(m)\n", "\n", " df3 = df[df.method .== m, :]\n", " df3m = dfm[dfm.method .== m, :]\n", "\n", " # jitter(x) = x .* (0.95 .+ 0.1.*rand(length(x)))\n", " jitter(x) = x\n", "\n", " fig, ax = plt.subplots()\n", " clip_on = false\n", " plt.semilogx(jitter(df3.N), df3.TPR, \".\", ms = 8; clip_on)\n", " plt.semilogx(jitter(df3m.N), df3m.TPR_mean, \"C0-\"; clip_on)\n", " plt.semilogx(jitter(df3.N), df3.FPR, \".\", ms = 3; clip_on)\n", "\n", " Ns = unique(df.N)\n", "\n", " plt.xticks(Ns, Ns)\n", " plt.minorticks_off()\n", "\n", " plt.ylabel(\"Detection rate\")\n", " plt.xlabel(\"Number of inputs (N)\")\n", " plt.title(m)\n", "end\n", "\n", "plotrates(\"STA_corr_2pass\")\n", "plotrates(\"STA_height\")\n", "plotrates(\"fit_upstroke\")\n", ";" ] }, { "cell_type": "markdown", "id": "be5bce52", "metadata": {}, "source": [ "## What's up with that N=100, zero detections point" ] }, { "cell_type": "markdown", "id": "70951f9c", "metadata": {}, "source": [ "> **This has been fixed by now**" ] }, { "cell_type": "markdown", "id": "9e5cad51", "metadata": {}, "source": [ "Which seed is it?" ] }, { "cell_type": "code", "execution_count": 11, "id": "46acced2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
3×13 DataFrame
RowNNᵤδ_nSdurationseedmethodTPRₑTPRᵢTPRFPRAUCAUCₑAUCᵢ
Int64Int64Float64Float64Int64String15Float64Float64Float64Float64Float64Float64Float64
11001000.75600.01fit_upstroke0.01250.20.050.050.50.46050.658
21001000.75600.01STA_height0.00.00.00.00.340.3250.4
31001000.75600.01STA_corr_2pass0.00.00.00.00.50.50.5
" ], "text/latex": [ "\\begin{tabular}{r|cccccccccc}\n", "\t& N & Nᵤ & δ\\_nS & duration & seed & method & TPRₑ & TPRᵢ & TPR & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Int64 & String15 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100 & 100 & 0.75 & 600.0 & 1 & fit\\_upstroke & 0.0125 & 0.2 & 0.05 & $\\dots$ \\\\\n", "\t2 & 100 & 100 & 0.75 & 600.0 & 1 & STA\\_height & 0.0 & 0.0 & 0.0 & $\\dots$ \\\\\n", "\t3 & 100 & 100 & 0.75 & 600.0 & 1 & STA\\_corr\\_2pass & 0.0 & 0.0 & 0.0 & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m3×13 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m N \u001b[0m\u001b[1m Nᵤ \u001b[0m\u001b[1m δ_nS \u001b[0m\u001b[1m duration \u001b[0m\u001b[1m seed \u001b[0m\u001b[1m method \u001b[0m\u001b[1m TPRₑ \u001b[0m\u001b[1m TPRᵢ \u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float6\u001b[0m ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ 100 100 0.75 600.0 1 fit_upstroke 0.0125 0. ⋯\n", " 2 │ 100 100 0.75 600.0 1 STA_height 0.0 0.\n", " 3 │ 100 100 0.75 600.0 1 STA_corr_2pass 0.0 0.\n", "\u001b[36m 6 columns omitted\u001b[0m" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.N .== 100 .&& df.seed .== 1, :]" ] }, { "cell_type": "markdown", "id": "5b8f54ee", "metadata": {}, "source": [ "First let's inspect the simulation" ] }, { "cell_type": "code", "execution_count": 26, "id": "fc66cf24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using Distributed … ✔\n", "using Revise … ✔ (2.0 s)\n", "using SpikeWorks … ✔ (2.4 s)\n", "using SpikeWorks.Units … ✔ (1.0 s)\n", "using ConnectionTests … ✔ (0.2 s)\n", "using DataFrames … ✔\n", "using MemDiskCache … ✔ (2.0 s)\n" ] } ], "source": [ "include(\"2023-03-14__[setup]_Nto1_sim_AdEx.jl\");" ] }, { "cell_type": "code", "execution_count": 13, "id": "ec8c9ce5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/run_sim/_ N=100 δ_nS=0.75 duration=600.0 seed=1 _.jld2] … ✔ (4.7 s)\n" ] } ], "source": [ "sd = sims(; N=100, seed=1, duration=10minutes, δ_nS=0.75);" ] }, { "cell_type": "code", "execution_count": 14, "id": "de132795", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAIcCAYAAAB2CVHHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAB7CAAAewgFu0HU+AACuEElEQVR4nOzdd3gU1d4H8O9syW5CeoEkJJRAQi+hI0pHEPQKKqBgQUHsrwV7b/eqV6+9I4IiKmBB7PTeEjqhhJJCIBBIb5vsZuf9I+6S2ZZNspvdyX4/z+MjmZ2Zc2bOzuxvzpwiiKIogoiIiIiIZEvh6QwQEREREVHTMKgnIiIiIpI5BvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeiIiIiIimWNQT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeiIiIiIimWNQT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERERDLHoL4FGDlyJARBwIsvvujprDTYiy++CEEQMHLkSE9nxS0yMzMhCAIEQUBmZqansyMrGzZsMJ87kpdFixZBEAR06NDB01nxahcvXkRYWBiioqJQVlbm6ew0WFlZGaKiohAWFob8/HxPZ4fI5zGo9xKiKGL58uWYMmUK2rdvD39/fwQGBqJTp064/PLL8cgjj+Dnn39GSUmJp7MqC/v27cOLL76Id99919NZIaI6NmzYgBdffBGLFi3ydFY87qWXXkJRUREef/xxBAYGNkualZWVCAkJgSAIuP/++53ebvPmzeaH7J9++gkAEBgYiHnz5qGoqAgvv/yyu7JMRE5iUO8FioqKMGrUKEybNg0rVqxAdnY2DAYDNBoNsrOzsXXrVrzzzju47rrrzDfTutq1a4cuXbogMjLSA7n3Tvv27cNLL73EoJ7Iy2zYsAEvvfSSzwf16enp+PTTTxEVFYX77ruv2dL19/fHTTfdBAD49ttvUVVV5dR2X375JQAgKioK11xzjXn5/fffj8jISHzyySc4ceKE6zNMRE5jUO8Fbr31VmzcuBFKpRLz5s1Deno6qqqqkJ+fj8rKSuzfvx9vvPEG+vTpY3P7r7/+GkePHm1QrQsREXnO22+/DYPBgNtuuw0BAQHNmvbs2bMBAIWFhVixYkW965eVlWH58uUAgFtuuQVqtdr8WWBgIGbOnAm9Xs9KFCIPY1DvYcePH8evv/4KAHj11Vfx1ltvITExEQpFbdGoVCr07t0bjz/+OPbt24fp06d7MrtERNREpaWlWLJkCQDg5ptvbvb0Bw4ciF69egG4VAPvyLJly1BeXg7g0gNBXaZj+Oabb1BRUeHCnBJRQzCo97B9+/aZ/33ttdfWu76/v7/VMkcdZTt06ABBELBo0SJUVFTgxRdfRLdu3RAQEIDY2FjccsstyMjIMK9/8eJFPPHEE0hKSoK/vz+io6MxZ84cnD9/3mZ+Zs2aBUEQMGvWLLt5bmynuYqKCnz33Xe49dZb0bdvX0RFRUGj0SA2NhaTJ0/Gn3/+aXM7QRBw++23AwCysrLM7UBN/9k6TxcuXMCzzz6L5ORkhISEQKvVIiEhAbNnz0ZaWprDfJ45cwZ33XUX4uPjodFoEBcXh9tvv73Jr6Itz9vmzZtxzTXXoHXr1mjVqhWSk5OxYMECyTa///47xo0bh6ioKAQEBGDgwIFYunRpvWlt3boVN998M9q3bw+tVouQkBAMGjQIb7zxht0OfJZlv2jRIgwdOhQhISEICwvD2LFjsWnTJvP6BoMBH3zwAfr374/g4GCEhIRg4sSJ2LNnj1PnIzU1FTfccANiYmKg1WrRuXNnPPbYYygqKrK5vuX5W79+PSZPnoyYmBgolUrJdzYjIwNvvPEGJkyYgKSkJLRq1QqBgYHo3r07HnroIWRnZ9vNV93rTxRFzJ8/H4MHD0ZwcDCCgoIwdOhQfPPNN/Ue36FDhzB37lwkJiYiICAAgYGB6N27N5555hlcvHjRqXNUV58+fSAIAh555BGH661btw6CIEChUNg8zg0bNmDq1Klo27YtNBoNIiMjMWbMGCxcuBA1NTVO58fUafyll14CAGzcuNHq2qzbJKewsBALFizAtGnT0KtXL4SHh0Or1aJ9+/aYMWMGduzYUW+amzZtwjXXXIPIyEj4+/ujS5cueOaZZ1BWVubUfakp9wVHvv/+e5SVlaF79+5238BaDiKwcuVKjBkzBhEREQgODsZll11mVcu+ePFiDBs2DGFhYQgMDMTw4cOxdu1am/s3Bedr1qzB6dOnHebXFPgPGTIE3bt3t/p8wIABSExMRHFxsVP3GyJyE5E8atmyZSIAEYC4atWqRu1jxIgRIgDxhRdesPqsffv2IgDx3XffFXv16iUCELVarejv729ONyYmRszIyBBPnjwpduzYUQQgBgQEiH5+fuZ1EhMTxeLiYqv933bbbSIA8bbbbrObv4ULF4oAxPbt21t99sILL4gAxBEjRtjdDoAoCIIYEhIiBgQEmJcBEOfNm2e1XZs2bcTg4GARgKhQKMQ2bdpI/nvzzTcl669evVoMDQ0171OtVoutWrUy/+3n5yd+9dVXNo9t9+7dYlhYmHldf39/MTAwUAQgBgcHi0uXLjV/lpGRYfcc1Xfe5s+fLyoUCvN5qHsOnnzySVEURfH55583H7PlOp988onNNGpqasT/+7//k6wbGBgoKpVK899dunQRMzMzrbatW/amf6tUKjEoKMi8rUqlEn/99VdRp9OJV155pfl81j2/AQEBYmpqqtX+169fb15nxYoV5u9jcHCw5LvZvn17m+e27vl79913RUEQRABiSEiIqFarJd9Z0zVkyl9ERISoUCjMy0JCQsTNmzfbPIembZ999lnx2muvNR+36Tto+u/555+3W9ZvvPGGJD3L6y8mJkbcs2eP3e1tefPNN0UAYnR0tGgwGOyuN2vWLBGAOHLkSKvPHn74Yck1GBoaKvlujB49WiwpKbHaztY1n52dLbZp08Zc9mq12ura/P77783rm+4NAESlUimGhYWJGo1Gkp/33nvP7nG9//775jI3laHpnHbr1k1855137N6XRLFp94X6XHfddSIA8a677rK7Tt17o6Nr+9NPPxWNRqPda1CpVIq//fab1f4vXrxoPh+vvPKK3Xykp6eb9/X555/bXW/27NkiAHHatGkNOxlE5DIM6j0sIyPD/MPTq1cv8dixYw3ehzNBfWhoqNihQwdx1apVYk1NjWgwGMRVq1aJUVFR5hvxoEGDxL59+4rbt28XRVEUq6urxaVLl5oD6WeeecZq/+4M6lesWCE++uij4pYtW8Ty8nLz8rNnz4ovvfSSqFarRQDiL7/80qA06zpw4ID5AefOO+8UDx8+bA6AsrKyxHvvvdf8Q5mSkiLZtqSkRGzXrp0IQGzXrp24atUq0Wg0iqIoitu2bRN79OghCQoaG9SbArz/+7//E/Py8kRRFMX8/HzzuVcoFOIbb7whKpVK8dVXXxWLiorM52nChAkiALFVq1bm5XU9++yzIgCxdevW4kcffSTm5+eLolhb9uvXrxeTk5NFAGK/fv3Empoaybam9ENDQ0V/f3/xs88+EysqKkRRFMWjR4+K/fv3FwGIHTp0EO+//34xPDxcXLZsmVhdXS0ajUYxNTVV7NSpkwhAHDZsmFXe6gb1ISEh4siRI8XDhw+LoiiKer1eXLp0qfmBauDAgVaBq+n8abVaUalUirNmzRKzs7NFURRFg8Egnjhxwrzugw8+KH700Udienq6+Tj1er24c+dO8zmMjY01H19dpusvLCxMDAkJERctWmRe7/Tp0+I111xjLqf09HSr7b/44gvzw9S///1vMTc315zH1NRUcfTo0SIAMS4uTiwtLbXa3p6zZ8+aA/A///zT5joVFRXmAPDLL7+UfPbBBx+Yz//cuXPN+SorKxPfeecdUaVSiQDE6dOnW+23sdd8XZ999pn4wgsviKmpqWJVVZUoiqJoNBrFU6dOiQ8++KAoCIKoVCptPuxs3brV/JA0btw4831Vr9eLy5cvF8PDw83fHVt5bMp9wRmm++6CBQvsrmM6TyEhIaJSqRT//e9/m6/hnJwccfz48SIAMSgoSHz++edFf39/8dNPPzXfK9PT08UBAwaY70+W168oiuK0adNEAGKnTp3M9y5LTz75pPkeYusBzuTzzz83P0QSkWcwqPcCd955p6T2KTk5Wbz33nvFBQsWiAcPHrR7szVxJqj39/cXjx8/bvX5ggULzGm3adNGvHjxotU6zz33nPnGb8mdQX19TDWRY8aMaVCadZkCpqeeesruOqaa7GuvvVay/I033jDX2JmCzbpyc3MltfiNDeoBiHPmzLH63GAwmN+sABBfffVVq3WKi4vNtYuLFy+WfJaRkSEqlUrR399f3Ldvn808lJSUiHFxcSIA8eeff5Z8Zip7AOI333xjte2JEyckNYq2arrXrl1r/vz06dOSz+oG9UlJSTYD6tWrV5vXWbZsmeSzuufvuuuus3l8zjAYDGLv3r1tnkNRlNbyr1u3zupznU4nxsbG2iyjkpIS84PfX3/9ZTN9vV5vfkB65513GpR3U+B300032fz822+/Nd8f6gZsFRUVYnh4uMNt33//ffNxW75pcdc1X9d9990nAhBnz55t9dmYMWNEAGL37t1FnU5n9fm6devMebeVx6bcF+pz8uRJu+etrrpvKuq7tp25Bm1df3///bf58/Xr11t9bjAYzN/dWbNmOTyulJQU875OnTrlcF0icg+2qfcCH3/8MZ577jm0atUKoihi7969+PjjjzF79mz06tUL0dHReOSRR+y2a3fG9ddfj86dO1stHz9+vPnfc+fORUREhN11Tp48ae4s5Q0mTZoEANi+fXuD2vaaZGZmYt26dVCpVHj00UftrnfrrbcCqG17Wjed77//HgAwdepUdOvWzWq76Oho3H333Q3Oly1PPvmk1TKlUokxY8YAALRaLR566CGrdYKDgzF06FAAwIEDBySfLVq0CDU1NZgwYYLddr1BQUGYPHkyAODvv/+2uU67du0wY8YMq+WdOnUyf+euuOIKXH755VbrjBgxAhqNxmb+6nrsscds9icZO3YsLrvsMgCXysOWp556yu5n9VEqlZgwYQIAYMuWLXbXGzZsGEaNGmW1XKPRmK8hy2P88ccfUVRUhOTkZMm1WJdKpTIPQWivDOy55ZZbAAArVqxAaWmp1eeLFy8GAEyePBlBQUHm5atXr0ZBQQEA2J3U7t5770VMTAyA2qERm5vp+rcsk4KCAqxbtw5A7ffG9P2qa9SoUbjiiits7rep94X6nD171vzvqKioetd35tp25hq0dX2NHTsW7dq1AwAsXLjQ6vO///7bnN877rjDYT7rDqlc9xiJqPmoPJ0Bqv3RfvnllzFv3jz8+uuv2LhxI1JSUnDkyBFUV1cjLy8P77zzDhYvXozff/8dgwYNanAa9rZp06aN+d8DBw6sd52ioiK0atWqwek31vnz5/Hxxx9j1apVSE9PR3FxsdUPaEVFBQoLCxs8Tv/WrVsBAEaj0WbnLxNTeuXl5cjPz0fr1q1RXV2NgwcPAgBGjx5td9vRo0fjtddea1C+LIWHh6NTp042PzOVTffu3e2Wi2mdwsJCyXLT8a9atQrR0dF20zd1lM3KyrL5+YABA+zO+tqmTRucOHHC7ndLqVQiMjISZ86cscpfXfWd423btiE1NdXm5/7+/ujXr5/d7U02b96MBQsWYMeOHcjJybH5AJuTk2N3+8GDB9v9LDY2FgDMgbKJqQyOHDnisAwqKysB2C8De6ZMmYKgoCCUlpbixx9/lHQOPn/+PFatWgXgUoBqYjqX8fHxSEpKsrlvpVKJ0aNHY8mSJXbPfVOdOnUKH3/8MdavX4+TJ0+itLQURqNRso5lmezduxeiKAKofWi0Z+TIkdi8ebPV8qbcF5xx4cIF87/Dw8PrXd+Za9uZa9DW9aVQKDBr1iy8/PLL+OGHH/Dhhx9KHu5MHWSTkpLsPgTZOpa6x0hEzYdBvRcJCQnBzTffbB4eTKfTYcuWLXj//ffx66+/4uLFi7j++utx/PhxaLXaBu277o26LpVK1aB19Hp9g9Jtiu3bt2PixImS0U0CAwMREBAAQRBQU1NjHhWkvLy8wUG9qTbJaDQ6/RbENFxbQUEBDAYDAKBt27Z214+Li2tQnmyxVy7ApbJxZh3LsjMdf3l5uVNvYOwNVeeu/NXl6BybPsvLy7P5eUREhHmIWHueeOIJ/Pe//zX/rVQqERYWBj8/PwC1Dzb1naemlIFOp4NOp3OYR8B+GdgTEBCA66+/HosWLcLixYslQf13332HmpoaREdHY9y4cZLtTOfS0XkHLn2/7Z37pvj5559x0003SSZHCg4OhlarhSAIqK6uRmFhoVWZ1A0oTQ9Tttg7tqbcF5xRt5xtvUWw5O7r6/bbb8crr7yCiooKfP/997jzzjsB1I6EZhpuub5aekA6Mpsz32Uicj02v/FiWq0WY8eOxcqVK3HbbbcBqK2V+uuvvzycM/czGAy46aabUFRUhL59++KPP/5ASUkJSktLcf78eZw7d04ypJ2pZq4hTDVtbdq0gVjbv6Te/xo6LKc3Mx3/E0884dSxb9iwwbMZbiSlUunw89WrV5sD+nvvvRcHDx5EVVUVCgoKcO7cOZw7dw4PP/wwgMZ9zxwxlcH06dOdKoPMzMwGp2Gqhd+wYYNk6EJT05sZM2bUe46aW35+PmbNmoWqqiqMHj0aGzZsQEVFBYqLi83Xv2kyJEfs1V474u77Qt0mjo7eTjWXDh06mJvx1W2Cs2TJElRXV0OpVFq9ybGl7lsoW804icj9GNTLxNy5c83/PnbsmAdzImWqBXJUM1NcXNzg/W7fvh1ZWVlQKpX47bffcNVVV1nVRp07d67B+63L1Nzh4sWLDe4rEB4ebg6Ezpw5Y3c9R595mun4G9qkwxOcOcfONn+wZGqLP378eHz00Ufo2bOnVZDb1O+aPc1RBiNHjkR8fDyMRqN5wqPDhw+b5wewFbCZzqWj5kZ1P2/subfH9BAfFhaGX3/9FSNGjLDqU2GvTOq2U3fUttved6op9wVn1M2fZXMsTzGNWb99+3YcPXoUwKUAf+LEiea+E47UPRZn+goQkesxqJeJwMBA87+deWXbXMLCwgDA4eQlO3fubPB+TfuLioqy+5p8zZo1drc3NbdwVLM6bNgwALU1c/YmsrLHz88PvXv3BlA7qZE9pg573sh0/GvWrPH61+WOzrHpswEDBjRq36bvWnJyss3PRVF0WzmaymD37t3Izc11SxqCIJib9Jlq503/7927t81O0qZzmZOTg/T0dJv7rampMZ97e30mbHHm2jSVSZcuXRAQEGBzHXvXf3JysrmG3tHbJXufNeW+4IzExERzZcipU6dcvv/GmDJlirlN/Jdffondu3dj//79AGzPIGuLaRJDlUqFxMRE92SUiBxiUO9hGRkZdn806/rqq6/M/3am019zMQUEKSkpNgP7I0eO4KeffmrwfkNCQgDUduaz1a41JycH77//vt3tg4ODAcDubKNA7Y+rabbGZ555pt43Cpa1atOnTwcALF++3Obbk7y8PHz66acO9+lJd9xxB1QqFS5evIgXXnjB4brV1dV2Z5ZtDm+99ZbNB4/169ebOzaayqOhTN81UxBj6dNPP3Vb8DV16lSEhoZCr9fjkUcecRjoGo1Gh99nR0y18YcPH0Zqaqq5xt5es4px48aZm1DYG/3ms88+M9eEm0bncYYz16apTNLT022W+759++yOuBMeHm4eheh///sfqqurrdbZtGmTzU6yQNPvC/UJDAw038N37drVoG3dRaPRYObMmQBqH/jmz58PoLYJkmmUofqYKm/69+/frIMpENElDOo9LC0tDd26dcOkSZPw9ddfS9rM6vV67N27F7fffjvefvttALWj2NgaGtBTrrnmGgQGBkKv12PatGnm4Fav1+OXX37B2LFjG3WDv/zyy81DfE6bNs384FNTU4O///4bI0eOdNhetmfPngCAkpISLFu2zO56H3zwAQIDA5Geno4hQ4bgl19+kQQRZ86cweLFizFmzBg88cQTkm3vuecexMXFoaqqChMmTMDatWvNQdnOnTsxduxYq5E6vEmnTp3w3HPPAQD++9//4tZbb8WhQ4fMnxsMBuzbtw8vv/wyOnfujH379nkop0Bubi4mTZpk/n4ZDAb88MMPuOGGGwDUPuhed911jdq3abjKP//8E6+88oq5yUVRURH+85//4IEHHnBbG+HQ0FC8++67AGqbAU2aNAk7d+40f2+MRiOOHDmC//3vf+jRowd+++23RqXTtWtXc+37Pffcg9OnT0OpVNocBhGo7fRoCua/++473H333eaH64qKCrz//vvmYRanT5+O/v37O50X07WZlpaGbdu22VznyiuvhEKhQEFBAWbOnGluKlNdXY1ly5bhyiuvdNg59KWXXoIgCDh06BD+9a9/4fjx4wBqvzc//fQTrr/+evNbRluacl9whumhoTFvMd3FVCN/7tw5c1B/6623SgZKcMR0LI5GHCIiN3P90PfUEH/99Zdkgh78M5lReHi4ZIpz/DOr55kzZ6z24czkUwsXLrSbBziYfEQUaycpMq1jawKlL774QpLXoKAg8/TjQ4YMET/88MNGTUTzySefSI4/MDBQ1Gq1IgAxMjJSXLlypcN8mSagMeWpffv2Yvv27a0m8NmyZYsYHR1tXlepVIoRERHmGSVN/9maAColJUUya2xAQIAYGBhoTnPp0qVNnnzK0QRazkzk42iCMKPRKD733HOS8vP39xcjIiLMs5Ga/tuyZYvT+zVx9N00sfcdrTv51IoVK8wzCIeEhIgajcb8Wbt27WxOduPsBGTV1dXiFVdcYd6fIAhiWFiYeUbSSZMmmWfetXWenTnG+srpk08+MV8zAESNRiNGRESYj9n0n60JhpxVd7IoAOL48ePr3ebhhx+2Oi+mmWQBiKNGjbI5y6ijc6/X68UuXbqY9xEWFma+NpcvX25e74knnpDkNyQkxHw+OnbsKC5ZssT8mS3vvPOOZPvQ0FDz96Znz57mz7t06WJz+6bcF+qzd+9e87VWXFxsc52mXtsmznw/TUyTnJn+O3LkSL3biGLtRFime7O9ieyIyP1YU+9h48ePx/Hjx/Hee++ZJzHSaDQoKipCQEAAEhMTMW3aNHz//fdISUlxOESbp8yePRu///47Ro8ejeDgYBgMBiQlJeH111/Hxo0bG/0q9u6778bvv/+OkSNHIjAwEAaDAW3btsUDDzyA/fv3o1evXg63/+GHH/Dwww8jKSkJer0eWVlZyMrKsnrtP2zYMKSnp+Ott97C8OHDERoaiqKiIiiVSnTr1g0333wzlixZYq5RrWvAgAE4cOAA5syZg7Zt28JgMCAkJAS33XYb9uzZ06g5BZqTIAh4+eWXceDAAdx7773o1q0blEoliouLERYWhssuuwyPPfYYtm3bZm5r7AnXXnsttm3bhuuvvx5arRaiKKJjx46YN28e9u3bh44dOzZ632q1GqtWrcILL7yApKQkqNVqiKKIQYMG4ZNPPsHKlSvdPjrM3XffjWPHjuHRRx9Fnz59zPeAwMBADBgwAA888ABWr17doGYulm666Sao1Wrz386MaPL2229j3bp1uP7669GmTRuUlZUhKCgIo0aNwpdffonVq1c7rDG3RaVSYe3atZgzZw46duyI8vJy87VZt4nX66+/jq+//hqDBg2Cv78/9Ho9OnfujKeffhp79+6t91740EMPYcOGDZg4cSLCwsKg0+nQoUMHPPvss9ixY4f5rVpoaKjN7ZtyX6hP3759MWjQIFRWVjaqeaK71G0/f9lll6Fr165Obffjjz9Cp9Nh8ODBdieyIyL3E0TRQSNOIiKiFmjmzJn49ttvcccdd2DBggXNnv7XX3+N2267DaNGjfLqDvXOGD16NNavX4+vvvrKqYdFInIPBvVERORT0tPT0adPH+h0OixbtgxTp05t9jzU1NSgd+/eOHz4MHbu3On1b/Xs2blzJ4YMGYIePXpg//79XjfnAZEvYfMbIiJqcZ5//nl8+OGHyM7ONnc8Li8vx9KlSzFq1CjodDp07doVkydP9kj+lEqledIzeyMMyYEp7//9738Z0BN5mHPd2omIiGTkwIED+OWXX/DAAw9ArVYjKCgIRUVF5gC/bdu2WL58uaSfQXObNGkS3nnnHRQXF6OsrEwyH4kclJWVYciQIZgwYQImTpzo6ewQ+Tw2vyEiohZn48aNWLp0KbZt24bc3FwUFBSgVatWSEpKwtVXX43777/fPOESEVFLwKCeiIiIiEjm2KaeiIiIiEjmGNQTEREREcmc7IP6zZs349prr0Xr1q2h1WrRuXNnzJs3DwUFBZ7OGhERERFRs5B1m/pPPvkE999/P4xGI2JjYxEdHY0jR46gsrIScXFx2LJlC9q3b+/pbBIRERERuZVsa+r37t2LBx54AEajER988AFycnKwe/du5OTkYMyYMcjJycH06dM9nU0iIiIiIreTbU39ddddh59//hkzZszAkiVLJJ9dvHgRCQkJKC0txW+//YZJkyZ5KJdERERERO4ny5r6srIy/PnnnwCAe+65x+rzyMhI3HDDDQCApUuXNmveiIiIiIiamyyD+r1790Kn08HPzw+DBw+2uc6IESMAANu3b2/OrBERERERNTuVpzPQGOnp6QCA9u3b253iu1OnTgCAU6dOQa/X1zsVuMFgwLlz55Cbm4uoqCioVI5PTVxcXCNyTkRERETkerIM6k3DVTqa4tv0mdFoRElJCSIiIhzu89y5c4iPj3c6DzLtikBEREREDWA0GqFQeH/jFlkG9TqdDgDg5+dndx2tVmv+d2Vlpdvz1Bjnzp3zdBaIiIiIqB6RkZH1tuLwNO/OnR2mgL26utruOqbAHwD8/f0btP9du3YhJiamcZlroJCQELd9SYxGI6qqqqDRaNz2hMk0nGMwGFBcXMzy9pE0WN6+lQbL27fSYHn7Vhqm8pZDCw1ZBvVhYWEAgPz8fLvrmJroKBQKBAcHN2j/MTExzdZmXqVS1dvev7GMRiMMBgPUarVbLyam4TyWt++kAbC8fSkNgOXtS2kALG9fSkMuZHn0Xbp0AQBkZ2dDr9fbXOfkyZMAgISEBLdddERERERE3kCWQX1ycjI0Gg2qq6uxY8cOm+ts3LgRADB06NDmzBoRERERUbOTZVAfGBiICRMmAAA+/fRTq88vXryIH374AQAwbdq0Zs0bEREREVFzk2VQDwDPP/88FAoFvv32W3z44YfmDgwFBQW48cYbUVpaioEDB2LSpEkezikRERERkXvJNqjv168f3nvvPQiCgAceeABxcXHo378/4uLisHbtWsTGxmLp0qUQBMHTWSUiIiIicivZBvUAcP/992PDhg245pprUFVVhUOHDiEmJgYPPfQQDhw4gI4dO3o6i0REREREbifLIS3rGj58OIYPH+7pbBAREREReYysa+qJiIiIiIhBPRERERGR7DGoJyIiIiKSOQb1REREREQyx6CeiIiIiEjmGNQTEREREckcg3oiIiIiIpljUE9EREREJHMM6omIiIiIZI5BPRERERGRzDGoJyIiIiKSOQb1REREREQyp/J0BrzR6NGjoVarJcvS0tLckpYoijAajW7bN9PwjjREUYQgCLI+BqbRsP2zvH0nDZa3b6XB8vatNEz7lwNBlFNu3SgnJwfx8fEAgMTExGYJ6svKyiAIgsv3S0RERESuUVpaioiICKvY0Nuwpt6GdevWIS4uzu3plJWVITw8HCqVe4pBFEXodDpotVq3PTwwDecYDAYUFhYiLCyM5e0DabC8fSsNlrdvpeHL5X2uWIcP1p+AQhDwf6M7IypI4/I0Gqo5ylsuGNR7mCAIUCjc07XB9BqKaXg+DdOrWjkfA9NwHsvbt9JgeftWGr5c3nd8lYqj50oBAIfOFOOX+y93eRoN1RzlLRfsKEtEREREDmVcLDcH9ACwP6cY50t0HswRWWJQT0REREQOlemsm6Ho9DUeyAnZw6CeiIiIiEjmGNQTEREREckcg3oiIiIiIpljUE9EREREJHMM6omIiIjIIRHWc5UKkM9wj76AQT0RERERNZitQJ88h0E9ERERETnEWnnvx6CeiIiIiEjmGNQTEREREckcg3oiIiIicojt570fg3oiIiIiajC2s/cuDOqJiIiIqMFYe+9dGNQTERERkUOslfd+DOqJiIiIiGSOQT0RERERkcwxqCciIiIih9h+3vsxqCciIiKiBmM7e++i8nQGvNHo0aOhVqsly9LS0tySliiKMBqNbts30/CONERRhCAIsj4GptGw/bO8fScNlrdvpeGr5W00WtfUG0Wjw2298Tgau385YFDvQYGBgdDr9dDr9W5NR6fTuXX/TMM5LG/fSoPl7VtpsLx9Kw1fLO+qKuv1Kit1qKysv7bem46jJWNQb8O6desQFxfn9nTKysoQHh4Olco9xSCKInQ6HbRaLQTBPa/ImIZzDAYDCgsLERYWxvL2gTRY3r6VBsvbt9Lw1fLWaqttLNPA39/fZWk0RnOUt1wwqPcwQRCgULina4PpNRTT8Hwaple1cj4GpuE8lrdvpcHy9q00fLW8FYL1OgqFwuG23ngcDeWuhxF3YEdZIiIiInLI1ug37CjrXRjUExERERHJHIN6IiIiIiKZY1BPRERERCRzDOqJiIiIqME4y6x3YVBPRERERA6xU6z3Y1BPRERERA5x9Bvvx6CeiIiIiEjmGNQTEREREckcg3oiIiIiIpljUE9EREREJHMM6omIiIiIZI5BPRERERGRzDGoJyIiIiKSOQb1REREREQyx6CeiIiIiEjmGNQTEREREckcg3oiIiIickgUrZcJQvPng+xjUE9EREREDWYr0CfPUXk6A95o9OjRUKvVkmVpaWluSUsURRiNRrftm2l4RxqiKEIQBFkfA9No2P5Z3r6TBsvbt9Lw1fI2rV+XUTQ63NYbj6Ox+5cDBvUeFBgYCL1eD71e79Z0dDqdW/fPNJzD8vatNFjevpUGy9u30vDF8q6qsl5Pp9OhsrL+NjjedBwtGYN6G9atW4e4uDi3p1NWVobw8HCoVO4pBlEUodPpoNVqIbip4RvTcI7BYEBhYSHCwsJY3j6QBsvbt9JgeftWGr5a3hpNtdUyrVYLf39/l6XRGM1R3nLBoN7DBEGAQuGerg2m11BMw/NpmF7VyvkYmIbzWN6+lQbL27fS8NXyViisA2aFoHC4rTceR0O562HEHdhRloiIiIgc4ug33o9BPRERERGRzDGoJyIiIqIGk9HAMD6BQT0REREROcSmNt6PQT0RERERkcwxqCciIiKiBmPtvXdhUE9EREREDrH9vPdjUE9EREREJHMM6omIiIiowVh7710Y1BMRERGRQ2w/7/0Y1BMRERERyRyDeiIiIiJqMNbeexcG9URERETkENvPez+PBPX5+fn48ssvMWPGDCQlJcHf3x/+/v5ITEzEXXfdhSNHjtS7j6ysLMydOxft2rWDRqNBbGwsZs6ciUOHDjXDERAREREReQ+PBPWTJ0/G7Nmz8d133+Hs2bNISkpCQkICsrOz8fnnn6Nv3774+uuv7W6fkpKC3r17Y/78+SgpKUGvXr2g1+vx7bffYsCAAfjjjz+a8WiIiIiIiDzLI0G9QqHA9OnTsXbtWhQVFWH//v1IS0vD2bNnMX36dFRXV2P27Nk2a90rKipw3XXXoaSkBDfffDPOnj2L1NRUnD17Fo8//jiqqqpw44034vz58x44MiIiIiKi5ueRoP7HH3/E999/j9GjR0OlUpmXR0REYPHixejevTsMBgMWLFhgte0XX3yBnJwcdOzYEV988QUCAgIAAGq1Gq+//jouu+wylJaW4q233mq24yEiIiJqydgp1vt5JKiPjIy0+5larcbYsWMBAEePHrX6fNmyZQCAO+64AxqNRvKZIAi46667JOsRERERUdOwo6z3U9W/SvOrrKwEAHMtvElNTQ1SUlIAAMOHD7e57YgRIwAA2dnZOHPmDNq2bdvg9HNzc+tdJy4ursH7JSIiIiJyB68L6svLy/HLL78AAK644grJZ5mZmaiurgYAdO7c2eb28fHxUKvV0Ov1OHbsWKOC+kGDBtW7juiiR1aDweCS/dhiNBpRU1MDvV4PhcI9L2WYhnNM5czy9o00WN6+lQbL27fS8NXytnW8eoMeer3eZWk0RnOVtxx4XVD/1FNPIS8vD61bt8bs2bMlnxUUFJj/HR4ebnN7hUKB0NBQXLhwAYWFhW7NqysUFxe7PY2Kigqm4SVpsLx9Kw2Wt2+lwfL2rTR8rbyLi8utlhUVFiHAWOmyNJqiOdLwdl4V1C9evBgffPABAGDBggUICgqSfK7T6cz/9vPzs7sfrVYL4FIznobatWsXYmJiGrVtQ4WEhEg6C7uS0WhEVVUVNBqNW5+QmUb9DAYDiouLWd4+kgbL27fSYHn7Vhq+Wt4hldbHGhoWioiwABtrNy6Nxmiu8pYDrwnqf/31V3PN/FtvvYWrr77aah1TsA4A1dXVkr/rMgX//v7+jcpLTExMs7WZV6lUUKvVbtm30WiEwWCAWq1268XENJzH8vadNACWty+lAbC8fSkNwPfK29YDjFqldngOvPE4WjK3BPVTp0612dn0gw8+QHJystXyv/76C1OnToVer8crr7yCefPm2dxvWFiY+d/5+fk228sbjUYUFRVZrU9ERERE1FK5JahPSUlBVlaW1XJbry/WrFmDKVOmoKqqCk899RSeffZZu/vt2LEj/Pz8UF1djRMnTtgM6k+fPm3utNGlS5cmHAURERERkTy45T1FZmYmRFG0+m/kyJGS9davX49//etf0Ol0eOSRR/Cf//zH4X6VSiUGDhwIANi0aZPNdTZu3AigdhScxox8Q0REREQkNx5rfLR582ZcffXVqKysxH333Yf//e9/Tm13ww03AAAWLlyIqqoqyWeiKOKzzz4DAEybNs21GSYiIiIi8lIeCep37NiBSZMmoaKiAnfeead5xBtnzJ07F7GxscjIyMCcOXPMQxjp9Xo8+eST2LZtGwIDA/Hoo4+6K/tERERERF7FI6Pf3HbbbSgtLYUgCEhLS7OaZMokJiYGy5cvlywLCAjATz/9hHHjxuGbb77Br7/+is6dOyMrKwsXL16En58fvv32W0RHRzfHoRAREREReZxHgnpTsxlRFLFt2za767Vv397m8sGDB+PAgQN49dVX8ffff+PgwYMIDw/HjTfeiKeffhq9evVyS76JiIiIiLyRR4L6zMzMJu+jQ4cO+OKLL5qeGSIiIiIimfPtUfqJiIiIiFoABvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeiIiIiJySPR0BqheDOqJiIiIiGSOQT0REREROSR4OgNULwb1REREREQyx6CeiIiIiEjmGNQTERERkUPsKOv9GNQTEREREcmcytMZ8EajR4+GWq2WLEtLS3NLWqIowmg0um3fTMM70hBFEYIgyPoYmEbD9s/y9p00WN6+lYavlrdoYx1RNDrc1huPo7H7lwMG9R4UGBgIvV4PvV7v1nR0Op1b9880nMPy9q00WN6+lQbL27fS8MXy1lVV2dy2srL+cXG86ThaMgb1Nqxbtw5xcXFuT6esrAzh4eFQqdxTDKIoQqfTQavVQhDcMxgV03COwWBAYWEhwsLCWN4+kAbL27fSYHn7Vhq+Wt5arXVQr9Vq4e/v77I0GqM5ylsuGNR7mCAIUCjc07XB9BqKaXg+DdOrWjkfA9NwHsvbt9JgeftWGr5a3grBeh1BUDjc1huPo6Hc9TDiDuwoS0REREQOyadlue9iUE9EREREJHMM6omIiIiIZI5BPRERERGRzDGoJyIiIiKH5NNd1HcxqCciIiIikjkG9URERETkEEe/8X4M6omIiIiIZI5BPRERERGRzDGoJyIiIiKSOQb1REREREQyx6CeiIiIiBzikJbej0E9ERERETnE0W+8H4N6IiIiIiKZY1BPRERERCRzDOqJiIiIiGSOQT0RERERkcwxqCciIiKiBhPZe9arMKgnIiIiIpI5lacz4I1Gjx4NtVotWZaWluaWtERRhNFodNu+mYZ3pCGKIgRBkPUxMI2G7Z/l7TtpsLx9Kw1fLW/RxjpG0ehwW288jsbuXw4Y1HtQYGAg9Ho99Hq9W9PR6XRu3T/TcA7L27fSYHn7Vhosb99KwxfLW1dVZXPbysr6p6XypuNoyRjU27Bu3TrExcW5PZ2ysjKEh4dDpXJPMYiiCJ1OB61WC0Fwz1xwTMM5BoMBhYWFCAsLY3n7QBosb99Kg+XtW2n4anlrNdZBvVarhb+/v8vSaIzmKG+5YFDvYYIgQKFwT9cG02sopuH5NEyvauV8DEzDeSxv30qD5e1bafhqeQs21lEICofbeuNxNJS7HkbcgR1liYiIiMghObUt91UM6omIiIiowUQw0PcmDOqJiIiIyCE5NUPxVQzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERE5BBHv/F+DOqJiIiIiGSOQT0RERERNRgr770Lg3oiIiIicohDWno/BvVERERERDLHoJ6IiIiISOYY1BMRERGRQxz9xvsxqCciIiIikjkG9URERETUYKy79y4M6omIiIjIIY5+4/0Y1BMRERERyRyDeiIiIiIimWNQT0REREQOcfQb76fydAa80ejRo6FWqyXL0tLS3JKWKIowGo1u2zfT8I40RFGEIAiyPgam0bD9s7x9Jw2Wt2+l4avlbSuoNxqNDrf1xuNo7P7lgEG9BwUGBkKv10Ov17s1HZ1O59b9Mw3nsLx9Kw2Wt2+lwfL2rTR8sbyrqqqsl+l0qKysv9GHNx1HS8ag3oZ169YhLi7O7emUlZUhPDwcKpV7ikEUReh0Omi1Wrf1WmcazjEYDCgsLERYWBjL2wfSYHn7Vhosb99Kw1fLW6OxDur9tFr4+/u7LI3GaI7ylgsG9R4mCAIUCvd0bTC9hmIank/D9KpWzsfANJzH8vatNFjevpWGr5a3rXUU9WzrjcfRUHIaypMdZYmIiIjIITm1LfdVDOqJiIiIiGSOQT0RERERkcwxqCciIiIikjkG9UREREREMsegnoiI7CqsqMZnm05heeppGI3sKEdEl/CO4F04pCUREdlUYxQxfcEenCmqndQl/XwpnpnU3cO5IiIiW1hTT0RENv2dds4c0APA/M0ZHswNERE5wqCeiIhsOnau1NNZICIiJzGoJyIiIiKSOQb1REREREQyx6CeiIhs4sgWRETywaCeiIiIiBpM5JO/V/GaoN5oNGLIkCEQBAGCIGDRokUO1z948CBmzJiB2NhYaDQatGvXDnPnzkV2dnbzZJiIqIXjDzYRkXx4TVD/7rvvYufOnU6tu3LlSgwYMADfffcd9Ho9evXqheLiYsyfPx+9e/fGnj173JxbIiIiIiLv4RVB/cmTJ/Hcc89hwIABiIuLc7hubm4uZsyYgerqajzxxBM4e/YsUlNTkZubi5kzZ6K4uBhTpkyBTqdzuB8iIiIiopbC40G9KIqYM2cOqqurMX/+fCiVSofrv/nmmygvL8ewYcPw+uuvQ61WAwACAgKwYMECdOzYEdnZ2ViwYEFzZJ+IiIiIyOM8HtR//vnn2LBhAx5++GH07du33vWXL18OALj77rutPtNoNJg1axYAYOnSpa7MJhGRz2GTeiIi+fBoUJ+Tk4PHH38cCQkJePHFF+td//Tp08jJyQEADB8+3OY6I0aMAACkpKSgpqbGZXklIiIi8lV8yPd+Kk8mftddd6GkpAQ//PADAgIC6l0/PT0dAODn54f4+Hib63Tq1AkAoNPpkJWVhYSEhAbnKzc3t9516mv7T0RERNSyMdT3Jh4L6r/55hv88ccfuPnmmzFu3DintikoKAAAhIWFQRAEm+uEh4eb/11YWNiovA0aNKjedUQXjfVmMBhcsh9bjEYjampqoNfroVC456UM03COqZxZ3r6RRkspb1tvO/V6vUvTYHk7pyWcp5aShq+Wd42N49XrDQ7vCd54HA3lznJ2NY8E9Xl5eXjooYcQGRmJd955x+ntTCPa+Pn52V1Hq9Wa/11ZWdn4TDaT4uLiJu/jt7SL+GJHLkL9VXhmXHskRknfelRUVDQ5jfowDee4orzr0xLOU0tJQ+7lbWsUsfz8fLekxfJ2Tks4Ty0lDV8r7+LicqtlRcVFyFdWuSyNpmiONLydR4L6+++/H/n5+fjqq68QGRnp9HamgL26utruOnV/hPz9/RuVv127diEmJqZR2zZUSEgIVKrGF0NhRTVeW7sHNUYR50qr8cn28/jq9gEAap9eq6qqoNFo3PqEzDTqZzAYUFxc3OTydqQlnKeWkkZLKW+NxjqAj4iIcGkaLG/ntITz1FLS8NXyDqmwHp0wNCQUERGBLkujMZqrvOXALd/GqVOn2myX/sEHHyAnJwfLly/H2LFjceuttzZov2FhYQBqm9WIomizCY6piU7d9RsqJiam2drMq1Qq87CcjfHj3mzUGC81Bdp2qsC8P6PRCIPBALVa7daLiWk4r6nl7UhLOU8tJQ1A/uWtUFrv19XHw/J2Tks5Ty0lDcD3ytvWA4xa7fgceONxtGRuCepTUlKQlZVltby4uBi7d+8GUFsbHh0dbbXOhQsXAAAPPvggnnzySfTs2RNr1qwBAHTp0gVAbU19dnY22rdvb7X9yZMnAdTW6tv6vKWpNhg9nQUiIiJq4dgl1vu55ZEmMzMToiha/Tdy5EjzOiUlJTh//rzVf0ajUfL5xYsXzdvEx8ejbdu2AIBNmzbZTHvjxo0AgIEDB9Y7kVVLIPIyIyIiIvJ5zf6e4sUXX7QZ8Jv+M9WuL1y4EKIoYt++fZLtb7jhBgDAZ599ZrXvqqoqLFq0CAAwbdo0tx4HEVFL56JBvoioheI9wrvIrvHR448/joCAAGzduhVPPvmkeSiliooKzJkzBxkZGYiPj8fs2bM9nNPmwQuKiNyFbwKJyMT2QOLkTWQX1MfGxmLJkiVQq9V44403EBsbiwEDBiAmJgbffPMNgoOD8dNPPzV65Bu54U8uEREREckuqAeAyZMnIzU1FdOnT4dKpcLBgwcRHByMOXPm4MCBAxgwYICns9h8WFVPREREbsZow/t5bEZZezIzM51ar3fv3vj+++/dmxkiIiKiFmZZ6ml8vS0TcaEavDK5F6KCfaN1Q0vndUE9NQyfnInIbXiDIWpxMi+W4/EfDgAADp0FIoJO4N9Tenk4V+QKsmx+Q5ew9Q0RETW3/LIq/Lr/LI6dK/V0VqiB3lmTLvl7yc7sRu+LIYh3YU09EREROe1CaRUmvr8ZF0qroFYK+OK2gRiRFOXpbJGTCsqrG7UdR7/xfqyplzkOOUdE3qTaYMSJvDKU6PSezgq5yWcbT+JCaRUAQF8j4vEf9ns4R0QEsKaeiIjsaGiVQalOj5lf7MSBnGK0DtLgqzsGoVtMsFvyRp7z894zkr/Pl1R5KCfUGI1ttssqRO/HmnqZY5t6InKXht5flqXm4EBOMQAgr7QKb/19zA25Ik8z8odH1viGv+ViUC9zvDSJyFu8s1raAW/t0TwP5YTcib878ubomayyugZ5pTqIfHCTJTa/kTled0TkLViD6xtYzPJm7zo9mFOM2V+lIK+0CmO7tcGnN/eDSsm6XzlhaRERkUswqPcNrMWVN3vF98ZfR5H3TwfoNUfOY2P6hUbvizyDQb3MsW0cEblLQ+8v/IH3DSxnebNXfFtOXJT8/dH6E+7PDLkUg3q5482ViLwEb0e+geUsc04WoEKQjkzPhznvx6CeiIhcgs0yfAPLWd6cfQNnGdSzeZ33Y0dZG0aPHg21Wi1ZlpaW5pa0RFGE0Whs9Pa2LjLT/kw33qam4QjTcH7/giDI+hiYRsP235LKuy5HaRlt/ObXlzeWt/NpmP7v6TQaU84NTaMpWN6OOVt+giBdbjDU2NzOUf5aSnnLBYN6DwoMDIRer4de3/iZFw0Gg9WyyspKyd86na7R+3cW06ifK8rbGXI/Ty0ljZZQ3rZ+xC3vL3XZ+vFztH5dLG/neMN5ako5O5uGK7C8baupce66NhqNkuUVOutJxqqqdKisrD+UlHt5ywWDehvWrVuHuLg4t6dTVlaG8PBwqFSNLwal0npbf39/ALU3Xp1OB61WC8HiNZqrMA3nGAwGFBYWIiwsrEnl7UhLOE8tJY2WUt5KpdJqmen+YoutGkBH6wMsb2d503myVW9ZXzk3NI2mYHk7plBYt7y2VX4qpVKyXKUus1pHo9E6LPuWUt5ywaDewwRBsHmBNYVpf6bXUO5Iw4RpOMf0qlbOx8A0nNdSyhs2fiAbmlZ967O8neNN58lWawRn8+RNx9FYci9vWw9lttJQKaVpG2F9PxAUjvPXUspbLthRVubk09KLiOTGaKvqnXweh1KWN2cva8tglvcD78egXuZk1H+DiGTGwB9xsoG/OzLnZAFa1k/X8H7g9RjUy1yNm3qTExHxR5xs4dCG8uZs6SksonqWu/djUC9zev7oEpGbGFhpQDbwZ0feDDXOFaDSIqqv4e3A6zGolzkDrzIichNnf/yJSD6qbAxVa4tlm/oa1tR7PQb1MscfXSJyF95fiFqeaicrAzUqaYhYpXfuYYA8h0G9zDl7cRIRNZSezW+IWpwqvfV1bWtCMa1aOk9FpY2gnpX33oVBvcxVG/ijS0TuUVnNmjmilsZWZaDexls5f8ugnvcDr8egXuZsPTkTEblCOX/EiVocWzX1tmIJfz9pUF/B+4HXY1Avc7zIiMhdyqvkMz06ETnHVkdZW7XwWos29axE9H4M6mWuuFLv6SwQUQtVUW0d1Ntqe0tE8mFrSFJb17pGbVlTz4d8b8egXuYKyqs9nQUiaqFKdfwRJ/IFtmIJP6U0RLxQWtVc2aFGYlAvc7YuRNakEZErFFY4/ybQ1iv90AC1K7NDXsDWLMNqpWBjTZITZwL2c8W6ZsgJNQWDehmzF7wzpieipmro/SW/zLqCITJQ48oskRewFfy1DtJ6ICfkSmeKKq2WiZBe7OdKrIN6xhvehUG9jOXxVRgRucn5kobdX3KLrYOCYK3KVdkhL5GVX261LCLQzwM5ocaw1y4+46J1udYN2Et1+gbfE6j5MaiXsfTzpTaX88GZiJrqRF6ZzeX27i/Hztlen1qWlMwCq2VsfCMfZ4usa9tD/dU4buN6r3utH8m1HW+Qd2FQL2N7soo8nQUiaqEOnilu0Pq2gj1qWURRxE97zlgv90BeqHHO2mhmo1QIOJBT5HC7tLMNux+QZ/DdqIzZ+xGtbQvLuhMiarzUBtxfKqtrsCrtnPW67sgYecze00U4VU8zDfJutprJ5dsZRa9uue7K4EO7HLCmXqZO5JVi68mLns4GEbVARqOI3dmFTq+/5sh5m7PPekuwd6G0Cmm5pRwZrIl+3J3j6SxQE9l6KLPH1FG2otqADccuuCtL5EKsqbdh9OjRUKulQ7GlpaW5JS1RFGE0Wk/ZXJ9PNpy0+4NZYzRCIVwavaKxaTiDaTi/f0EQZH0MTKNh+5dzeR/JLUGRneEsTfeXun7ZZ90kw9m8ubss/k47h3u/3QtRBAZ3DMdnN/dDsL9rh9qUe3k7k0aVvga/7j9rdztn8+Tp43DV/uVa3gdznG9GYzTWpr0q7Zzd2WRF0egwfy2lvOWCQb0HBQYGQq/XQ69v2KywZ4oqsWKv7R9RAKisrIShzqQROp37x5ZlGvVrbHk3lNzPU0tJQ87l/UNqtt3PLO8vRZV6u7V4RqMRlZXWr/ttccdxVNcY8czPh8wVIDszCnD3N6mYP7MPFIJrmyjKubydSeOvtDyU2JmMrKYB5ewoDVfj9S1VYxRxqAF9ZfR6PSorK7Fij/03NJU6HSor639Ilnt5ywWDehvWrVuHuLg4t6dTVlaG8PBwqFQNK4bFq0+hxsGDo1brDz+VAqIoQqfTQavVQnDxD5gJ03COwWBAYWEhwsLCGlzezmoJ56mlpCHn8jbUGPHboTy7n/v7+0NdJ6hfcfACDLbmnQcgKBTw9/d3mJ47y2LDoXMosHjjsCOjCD8duIBbhrR3WTpyLm9n0/g1zX7zC4UT5exMGq7C69u2g2eK7T6Y2aJSq1ENFbaest+eXqvROiz7llLecsGg3sMEQYBC4XzXhsLyaiyvp12joKjdp+k1VEPTaAim4RzTq1o5HwPTcJ6cy3tr+kWHs0sKgkKS3o82RkOpq768ubMsvk85bXP5+2tP4Ib+8Wilcc1PoJzL25k0jp8vxebj9vtwiWL95VxfGq7E69u2bScb1tlVALAzoxB6B7WIpnjDnpZS3nLBjrIy882OLOj07mmXRkT0s4OmfZb2ZBdiT3aR+zLTBMfO2Q9E88ursWhbZvNmSKZEUcRLvx5GjZ23MQBHOZKLVYetR6hyRBSBnRn5bsoNuQODehnR1xjx1fYsybLkdqFW68moTwcRATh1oQzHz3t+dJbyKgNWHz7vcJ26U8d/uSXD8boePJwFW045/Hzh1kwYalhBUp91R/Ow5YT04SguzLmmNuQ9Mi6WY28DH8BFADsdNL0h78OgXkY2pV/AxTLpa/G5VyR4KDdE1FTlVQbc+XUqRv9vI8a9swkv/3bYo/lZffi83VEuLJ0v0eGvQ9Kav6Q2gZK/RQ/V4eaV6rBir3SkluGdwyV/XyyrwtFznCWzPu+vOyH5OyZEi/tGdZYs8/TDKNXvu132O7/bU1ypx5FzJW7IDbkLg3oZ+cnitXhyu1B0jw32UG6IqCl0+hrc9uUuSc34wq2ZOObBQPP3g7mSvztGtrJaxxS/fb09U9JB1l+txI0D29lct7m9v/Y4quvUwvspBbxyTVe0C5fWMB8+y4DFkf2ni7D/dJFk2WPjuyDAT+mZDFGjVFQb8H0jgvqUzALJNeynsg4Z+TznXRjUy0SpTo81Fq/Fr0tuC4Ezx1Ijlej02H+6COuOnsfX2zNxJJcBTnN66dfDSM2ynuDJ1syszaHaYMRWi2YWV/eOsbluflkVFm3NlCybnNwWoQGuHf+9MTIuluO7XdIOslOS2yIy0A89YkMkyw+ddX54P1/0lUW/g9gQLf7VJ9ZqPQZ23u3H3TkNGvXG5IDFmPbJ8aFQKxlzeDOOfiMTW45fRJXhUs2TSiHg6t6xKLVxofIGS46cLqjA638exZ+HclG375ufUoFFdwzEZZ0iPZc5H7H5+AW7r8MPNmAcaVdKzSxARZ1ZYQUBGJkUiQ8sml8AwMcbTkpmkBUEYPblHayCAE/ci77eninp1KlRKfB/Y2qbi/SIDcafdZoMNWTMbl9zuqACv1hMNjVzSHuolApZjQbi684WVeLNv49JlgVqVCiraniQPzghAnsaMNM0NT/W1MuE5eQuQztFIKyVH3hvpYbYm12Iaz7cgt8PSgN6oHainpdWHobRwSgX1HQ6fQ2e/8X+DNVpHmoSsiFdeo/p3TYE4a38rNZ77Y8jWGDRQXZK37bo3DrI6n7U3N+kKkON1eg9tw5tj5iQ2mY3PSyaKx7JLXU4qosvW7AlQ3JuaptXxdtc11N9J8gxQ40R9yzZY1VLf8ewDjbXH9Qh3OZykyEdHX9OnsegXgZEUcSGdOlkMCOSouyvzxss2XC2qBJ3LEpBUYX9GRCPnS+V1GSSa1UZajDnq1RkXCy3u86ZokoUlFc3Y65qbT8pHbpuRFKUzRpZyxG41EoBD41NAgCr5oBHckvQ5dk/ce2HW6yaD7rD5vSLVt/vmYMvTTLVPUYa1Ffqa5Bxsczt+ZKbwopqLLUY4//GQfGICNQAgFWjT74d9k6LtmVa9YkYkhCOyzpbv43tHhOMqCCN3X2plQKS24W5OovkYgzqPUSnr0FeabVTtURHz5XifIl01JuRXVrbXZ83WN9zuqACi7Zl4s3VJ/DRhpPYk10oGZFCFEXMW7YfhRYBj603PU/+eAC7bbT1psarMYpYmpKNrs/9ZTU8YEJUK/gppbfi5m4WUlZlQJpF+/KhTjbDuntEJ7SLCABg+/tUZTBif04x5nydis83nWxyXh3ZZvFgMrBDGDrU6ewbFaRBdLBWss6hM+xLYum9NSckoyApFQJmX97R/LdlOR/PK8N3u7L51sOLVFbX4OMN0uutQ0QAPprRDwobF+rliY6v9x6xIfBnB2mvx6DeA07klWLM/zbiXwsO4taFqSjV2a85BWrb09fVNtQfnaJqf6hs/Yiyw2PLk51fgVVp57D2yHnkFlealxtqjHjtjyMY+dYGvPzbESzakYP/rUrHdR9vw/WfbDMHh6sPn8f2U9KA54rESOx7/kq8d2NfyfLSKgNu/mIndmVwfGJXKCivxvTPtuOJHw9aPXC38lPi3el90aOttAa5udvV780ulDTHUisF9I0Prbcb/uWdI62GN3TkP38cxdoj7quxt/yOD0+0fqPZ0+Jcs1291Im8ciyx6O9xde8YxIUFONzuqZ8O4uYvdjaqrTa53vLdp63e+L05tQ8iAjXQ2Ri2dmhChPUrmDr6t7ddS19lcG4IXGoeDOo9YOHWTJwpqg3MdmUW4u3V6Q7X33Rc2tZ1eJ3X4iobUyLPmL8Tu7NaVkAmiqJPjYVcWV2Dd9ek44ZPtqHDk79j+JvrMXfxbsz+KhVDX1uHWxbsxKEzxXjw+334bNMpmzVke7KLMOXjrViyMwvvrjku+axtqD8+mtkPIf5qXN07FgMsbtiV+hrcvnCX1atbapjyKgNmzN9hc5QbrVqBJXcOQe+4UPRqKx2VxbLW3N1SMqX569m2tlZOZWekC41KgYWzBmLx7EHQqi/V3lm+cbDlyZ8OothBE7DGKqqoxlGLMbWHdIqwWs9yBBxP9WHwVh9vsu5o/OiVXSTr2PrdAWofqu5anMoaey9g2XxqRFIUBv7TZr7uNWsyoEMYVAr7Uf3EXtEArMv+252nba1OHsKg3gMsh31bvD0LJ/Ks23VmXizHtM+2W011fkWd12RtgjXoECGtQamuMWLOV6k4XVDhwlx7zvLdORj17nb0fmk1pn26HV9uycDx894xaUyNUcSGY3nYduKiSx86/vv3Uby75rjNYBAANh+/iKs/2GI1rrglfY2IZ34+hMMWb28en9AFwdra76FSIWDBbQMl3ysAKK+uwZ1fp+Jcsa4JR+LbXvrtiM0JjrRqBd6dnoy+8aEAgJ4Wgea+Bs782FQpFm9lTB3mooO16BQpvb90jQ7CgtsGYlTX1lZt7od2ipAMeadUCFY14xdKq/DJRtc3w9lxSjqmtlatQJ+4UKv1LDvLHjpb7FMVBo6cL9FhzVFpJdJdwxMQHy79DvSND7U5ZjkAbD2Rj883OZ7Nl9zrRF6p1cNq3eZTveNCEKy9NPjhxJ7RCNKqMcBOR9mp/ePQv33tZ5bNdFbuP4Ps/JYRa7QEDOo94MaB7SQ3RINRxKu/S2eSvFhWhZk2mkAoBOCyOrVPgiBg4e2DrGr6Civ0GPHWRhyR+YyJGRfL8fTPh3ChrBrl1TXYlVmAl387jHHvbMKQ/6zFiyvTPFabXGMUMffrVMxamIIZX+zEbQtTUO6CV886fY1VLYsz/JQCxnaNrHcK9/hwf1zdWzrWdEiAGl/OGohx3dtIlueVVmHO1ymorOYr1obam1OKn/dJH7piQrT4/Jb+2P3sOEzoGW1e3rddqGS9s8U689s8d6s2GLH3tPTh0VSjJwgCPp/ZG9f2jcWgDuH495Se+PPBK+y2vw0N8MOP91yG6/q1xT0jO+Hvh67Ar/dfjlFdpM1gFm3LQF6Jax8Wd1g0vRnQPtxm4NnT4l5ZqjMgu4VUgDTV8tQc1NR5vvFXKzFnuPWs5dEhWnx9xyD8q08sru8XhxB/aUXVO6vTGeh50E97pCNAtQ7SYFidzrFatRJL7xqKG/q3xe1D4/HK5B4AgBmD2mH6AOkIR5P7xuLVKT3Nfz8xoavkutLXiPhgnfRNMHkOg3oPiA8PwJ1XdJQs23DsAtYfrR3hRhRFPPDtXps/6oM6hiM0QDrMXMfIVlh+91CbI+I89pO8hyhcsiPL7qvccyU6LNqWiWs/2op1R90/soalTzeexNqjl0Yl2pR+ATO/2FlvH4n6bD+VLxkv3CTEX40gje2pJRQCMP/WAXhvak+seXg47rLxQ2wyc3B7KG28ZlUrFfhwRrLkoRGo7Uj42A/7PV6baTSKKNXpoa8zU6i3EkURH2zOkSwL0qjw7Z1DcGWPaLSyKMfOUYFWgVFqZvM0oUs7WwydXnpO67afjQ7W4p1pfbDs7qGYObh9vWOU944LxdvT+uKJCV3/GeZSwPPX9JB853R6I77anunS47AM6ofaaHoD1D5YWQ7VuY/NzAAAf1lMfDY5Odb8Rs/SkIQIvH9TMv43rQ++uG0A6t5SqmuM+O/fR92ZVbLDUGPEj3uk955r+sRa3fO7xQTjv9f3xqNjOyHsn5hCqRDw+vW9sHj2INx5RUe8O70v3p7WFxrVpeY6nVsH4tYh7SX7+mnvGWTl2x/Ri5oPg3oPuXdkZ0S2kt4sX/n9MKoNRvywO8eqwxdQ23ntsfFdrJYDtU/eH85IRmLrQMnyjPxKu004vEXGxXI89dNB3LJgJ/6u86Oi09dg+e4cB1te8r9V6c0adB7JLcE7NvpC7DtdhLsW725S5yFbHQlnX94RW54YhZRnx+L/xiRatX18bHxXc/MZP5UCT03shheu6W61n0CNyqompi6NSomPZ/azatL124FcfGhjEqLmUqLT47aFu9DrxVWY+N5mnLrg3cMQ7sosxOHz0prKxyZ0Qcc6I7HUpVAIVv0aUpopqLdMJ6lNIMJsjE/fFB0jW2Fq/zjJsm93ZrvsDVB+WZVVM6chCbabEgiCgOR/mj2Z7G3m5k7e6HRBBQ7nSs/htX3bOrXtwA7huO2yDpJlvx3Ibfa+IVTbB89ytLxpDu75lgRBwBWJUXhmUndMTm4LhY0KoLtHdoJ/nXb5NUbRo78PdAmDeg9ppVHhnmHSG+apC+W44dNteOyHA9br+ykx/9YB5nZttgRp1Vh4+0Cr5b8dcNzu2pMOnSnGvz7cgu92ZWPz8Yu4a/Fuc7D86/6zKK6U1nq3jwiwOSFO2tkSq9ks3cVoFPH0zwdhsPMGYdvJfNz25S4UVTg/1nhxhR4frT+Bq97bjG92SEeeeHxCFzx3dXcEadXQqpV4ZFwSfrl/GKYkt8X4Hm2w4LYBuGdkJ6t93j6sI167rpdkhKRHr0yqN2ALDfDDF7cNRJBWWpv8v9Xp+NeHW/HHofMwNHNt+au/HTb3LTmeV4a7v9ltcwQHb7HEovNY+4gA3DSoncNtBlpM7JKa2TwP45bDl9prV9tUcyzeThZW6LFi3xk7azeMZTPFAD8lettoT2/Sz+IBirNkAmssKhPCAtRWD5qOPDgmUdJOGwDb1nvAshRpRVjvuBB0iQ5yaRqRgRrcMpS19d6IQb0HXdUtHL0tOpHZCkwXzhqItJcnOByb3iQuLAD/NyZRsuzPQ+eaPQhzxsWyKsxamIJSi9nu3lt7HBuO5eGbndLg9orESGx8bBQ2PDbSZvOS+Zub5wdk7dG8emv2dpwqQN+XV2P8O5uweHsmqg32z39KZgHGvL0Bb/59zOZwpGO7tbFa1iM2BO9M74vPbhmAMTY+N7lpUDv8ct8wzB2egA9nJGPWsI52162rc+tAfHBTMiwraQ6dLcFjPx/B1R9sbbax7I+dK8WyVOkPVfr5Mqt+KN6isLwaq45IJ4u7bWgHqOsZGWZgB2kAdex8aYNGiSmu1KOwEZNWWY7T3s9NE8x0bh1k1USwMX1HbLFsetO/fZjD851s0Yfh8NkSn+83YhnUj+7aBionRjMyCQ3ww51XSO/Lvx3IbTEDNshBWZUBay2aojaklr4h5g5PsKqtf38ta+s9jUG9BykEAc9O6moVONV1RWIkRnWtP5iv67pk6RuA/PJq7DjVtFf5FdUGfLszG4//sB9P/XQAy1JPN6lTqCiKeGFlGi6WVdn8fNbCFKsOsDMH19Z0BmvVeGpiNzx6ZZLk898O5KLDk7/jseX7sWLvGZfW5IqiiL/TzuGeb3bjzq9TJZ91iAjAj/cMRSsbE3McO1+K535Jw3Wf7sDxC9IfN9OwlVM/3Y6LZbaDsaEJEVZNqhqqd1wonp7YzapzbH1GdmmNpyd2s/lZel4Zpn9WOxKRu71pp23uNzuyJc21vMXqw+cl/UC0agWut2h6YkvPtiHQ1OmAJorW467bs2LvGVz22lokv7IaL/962OmmaIXl1VZ9dyw73bvSrRa1e/tOF9kc+auhtlpMOjUkwXZ7epM+caGS+67BKOJATlGT8yFXJTo9dlr8Rozr3rDfHQC4ZWh7BPhJA72FWzObmj1y0tYTF6Gv09NZrRRwTZ+G3fedZau2/ue9OVYzU1PzYlDvYcnxoXhmUne7gX1DJnYx6RDZyuqH+df9ZxuTPQC1P7xj/rcRT/98EMtSc/DdrtN4/IcDGPnWBqxqxNTvoljbfOX3BjQLig7WYLTFCBrTBsTbHBd7+e4cPLR0H6Z8vM0lgX1ldQ3uWrwbdy3ejT8PWQeRD4xORP/24Zh/6wC7w7wdO1+GW5YcwU1f7MJbfx/DTZ/vQLfn/7IaP76u+HB/vD29T70dE91p9uUdcZvFjdvEYBTx8m+H8cZfR93Sn0EURVz74Rassaj1ruuJHw9IJuPyBn8ckn6vRya1tuoEa4tGpTSPOmOyMf2CnbUv2ZNdiHnL96P8n5rmL7dm4I+Dzj3sHLJo86xRKcwT27nDiKQoRAZKp6L/cU/TmuCcL9FZPRhYdva21EqjQtdo6VvSPTJsV++q625T+gVJc0I/ZW276oYKDfDDjQOlzcx+2H3aq5vKtSQbjknvlYM6hjt172msucMTJA9xRhF4/Mf9nJDKg2wPpeHjRo8eDbVaeiGkpaW5JS1RFHH7Ze0xoH0oVuw7i5/3nEHRP+3Ir+4dg4HtQ2E0NrzpzKRe0ZJZKf9KO4eX/tXdbtBpz7FzpZg5f4c5YKjrQmkV7v5mDx4dm4B7RiU5nc/31h7Hd7ukr9391UoIAmyO+gIANw9qC6VCkKQRGeiHp67qgpd+O2JzmyO5Jfhx9+l62zIDl34cRVGUpFFjFHHblzuxy0775pgQLSb1iobRaMSQhHB8fnM/PPrDAbs176lZRUjNKrKbj8s7R+C6fm2hUSkxrHMEgrVqp8+rvWNoquev7oabBsVj7+kiLN6eZdWZ7pMNJ1FtqMHTV3V1yQOI6Th+2JOD/fX0kyiq0OOh7/fhm9mDbI7oU18arj5XxZV6bD0hnVdiQs82TqcxPDESW+psvzE9DzU1NTbPqyiK0NcY8fTPh6xGiPr3H4cxMimy3mndD1rUTneLCYJCgDm/rj5PCgGYkhyL+ZsvveFZsfcM7rsivtFpbLGYnC9Iq0KPmCDJvmwdR3K7UMn8DXuyCmA0Otc8zZIoihAEweXfJ8s0TP+v0hvw5t/p+G5XNtqG+ePjmf2QYKcTtjPWWFTODEmIgL9a0ahjuXVoO3y59VL5lugM+HX/GVzfL87qOJrjXLkjjeYub2fSEEXRPIKeyYikKIfbNvU8hQeocf+oTvjv35cGjThdUIlvtmfh9mEdXJKGM5qjvOWCQb0HBQYGQq/XQ6/Xo3O4Hx4d3QH3XRGPPdnFUCoEDO4QCp2ucWM5j0kMw+t/Xfq7uFKPX/dmY2JP++2vLVUbjHjw+702A/q63lpzCn4qBWYOrL+JwdFzZVa95BUC8PLVSUiIbIU7l+xHgUU74vHdonDzoDib52JachtU6Krx5hrbk9n8uv8MJvdyvsapbhqfbMrEhxsz7a6rUSnw0qQk1OirYOrPOzA+EBseGooSnQGHc0vx5pqTOHbeuc5DA9uH4MNpPS61BRYNqKxseBOnxn5nHIkPViG+RyQmdYvARxsz8dmWLMnnC7ZkIlyrxKyhrmu/+ZONkY/+O6Ub1hy9iFVHLgVyOzMK8P6ao7j7ig4NTsPV52r5rhyr199D2wehstK5twmD20k7tJ0t0iHtdL7d2vPle3KRft66+crZIh0+WncM941wHKTusRj5pkvrVjbz6srzNKl7hCSoP19ahV2ZhRia0LgHwvVHpG8lBrYLgb66CrZ6I9Q9jp7R0hGe9mQXoqKiotEPpnXv5+5UWVmJp385ipUHawPx9PNluGfxbvw4d0CDHmxNaowi1lvU8A7vHOb0d9ZSlL+AYZ3CsPXkpYqQZSnZmNhN+vbEHfcpS+5Mo7nK29lj2JlZiHMWo94Maefcvacp5+mWgTFYlXYO+3IuPSAv3JqBqX1bS4exlXl5ywWDehvWrVuHuLj6A9SmKisrQ3h4OFSqS8Xg7w+M7dm0NtQA0MnfH/3ahUpeKb/29wkM7xqDqCCN/Q3rWL49C+l50oC0d1wIWvmprNr6vv73CXRqE4JR9XTm/d+6g5LJTZQKAR/e1Bfje9ROxLPmkeH4cc8ZVNcY0S8+FB0jWyEqSAOdTgetVmvzB/ee0UnoEReGNUfysO1kPk5dvJTnXZlFKK9RWL3ytySKoiSN9cfybAb0wVoVHh6biKggDbrFBNsdojAgAIgOD8awLtF44seDWLnfflOj/u3DcFlCOO4akYAAv8ZfkpbH4A6iKOL/RnVEQusgPPXzIdStIH5zzUnERQQ2uQ2nKIo4k1+CFIs3GgmRrXD9gPYY17MtDn2wBWeLLt3AP9qYics6t8bgetpS103D1edKFEUs3ysNMMd0jUJUqPMjT/Rsp0V0sEby47wjuxQ921lP9qQ31GDRDvsdTRdsO40ZQzoiNtT2ZGSGGiN2WpzjAR0j4O9/aX13nKfe7f3RMzYYh+rMeLnqyAWM6h7b4DRqjCK2npK+RRvRtY3kGADbxzG4c2sAl/pr5Jfrcb5CREeLGXSdYTAYUFhYiLCwMMn93JVMx7DuRJE5oDc5fqEc604U4V+NuPYOny1BsUXlwZU9Yq3OYUPMHNxBEtTvzi5GpVGJ8FZ+zXafcmcazVnezhzDqQtluO/7Q5JlHSIC0CM+wuG2rjpPT17VDTfO32n+O6dIh+1ZpRjXvU2LKW+5YFDvYYIgQKFwT9eGW4a0lwT1BRV6PPXzISy4bUC9X/yiimp8YFGj3jU6CMvuGgqNSoHPN53Ca39e+kE0isCjyw/gr4eGo02w1uY+D50pxjaLTjT3jeqMq3pd+iEKD9TizuHS4RlNr9McnauRXdtgZNc2qKyuQb9XVqPynzacRhFYdTgPNw+x3S7cXhofrLOu+RcE4NNb+uOyTrZn07TF30+B925MRlLrQHyzIwsRQRrEhPhjd1Yh/FQKPDGhK67r55oHSGfOk6vSmDogHmqVEo8s2y/5/PEfD6JT6yCrWTsbmsamE/mwHDH0jwevgFKpRFgrJd67MRnTP9tuXscoAg8u3Y/f/+8Kpx5a3XGudmcV4LhF2+6bBsY3eP8jklpjaeqlYP2vQ+cwd7j1kKUrD+TgTJH9mqkqgxG/7M+12y/n4Okiq5Gnhie1luTXXd+pSb1jJUH9mqMX8apRhJ/acXMhS3tPF6DQ4s3emG5trPJq6zg6RgYiKkiDC6WXHqB2ZRaiU+uGD/9naorRHNfe19uzbX7+3toTuLp3bINGrAGAPRYDEsSHaREXHtCk4xjdrQ381UrJfXjt0TxMH9iuWe9T7kqjOcvbXhqiKGJj+gX8fiDX5nwud1zeEUql4+vJVedpcEIEerYNloyktXz3GYzvGdNiylsu2FG2BftXnxiMTpLWXK47moe/bHT2rGvHqXxM/mgr8i2Gx3vpXz2gVSshCALuGtEJD42VDp1ZWKHHw0v32Z0B9pON0kA5MlCDe22Mr94U/n5KjOkmfVuwsoGdhA+dKbY5w+Qb1/duUEBvIggC7hreEStm98KKe4bii9sGYu/zV2LHU2NcFtB7wnX94vDkVV0ly6oMRtz9ze5GDa1Y1w6L2tcJPaKhrRPwDewQjofHSkc/yiutcvj9c7c/LTqnxodqMNTOBEiOTOgZLfl7T3aRZFjAyuoaLNmZhcd+OChZr098KKYNkH6fthyXtu+va1O69LMubYLsPpC72sRe0mMsqNDb7bfiyDqLNsRJbQIRF+ZcTbsgCFaj5Hj7yB3VNUbstzNKT8bFcquZRJ1hOR9CcnzTRz/SqpUYniS9Vy7ZmS2rtsne7FyxDjd8uh2zFqbYDOi7xwRjhhN9yVxFEATcOrSDZNnG9LwGzdVCrsGgvgUTBAEvX9MFkYHSyYbuWbIHjy3fj6ve24yxb2/ETZ/vwHUfb8W1H21Fhyd/x42f70BmvnT4xcs6RVg1bXhwTCKu7C5to7/tZD6ueGMdNh+/ILmBHz9fij8OSpug3D6sgyRQcxXLoRt3ZRTgRF6pnbWt/WDjJnn0lQkuH+9XTk//9tw1PAE3D5H+eOQUVuL/vt/b6OBaFEVsz5AGGpcnWj9M3Teqs3kWXZMtJy7io/XNP1ayKIr4+7A0qB/XJbxRZTyscyRCA6Qd9W9ZsBMXSquw9sh5jHxrPZ75+ZDVdv83ujPGdZcGy7uzCu2Ov27ZodfyXLpT+4hW6BErHX3G8v7gjPVHpZ1kGzr871CLe9qOU/leHXiezCuX9Nmw9P7aEw0aeUQURav5JlwR1APAlGTpA+aBHNuVJdQwheXVmPbZdrvzhHSKaoVPb+7f4Dc2TXVVz2jJQBz6GrHeCkRyPQb1LVxYgB+ev7q71fLlu3NwJLcEJ/LKsP1UPvZkF1mNC28SFaTBW1P7WC0XBAGvX9cT0cHS5g5ni3W4ZcEu3LtkD1IzC7AnuxCP/3gAdX8rgzSqepvENNaorlGIsJg19ZMNp6x+rFMzCzDnq1T0fXkVuj73F675eBde/vUwFm3LlKz3wOjObnn4aAkEQcDzV/ewmnly8/GLeG+t/eE6HUk/X4b8cmmTClsBp0Ih4J3pfdHG4vv37pp0bDtpv4baHY7kluJ0gbRD2ohOoY3al59KgWst2kZn5ldg4L/XYPZXqVZTwAPAld3bYEy3NhiSEC7pnFZdY8Qui86wAFCq02OvxfVu68HJnSb2ipH8/Xfa+QY9CJ4v0UlGrwGA0U5M0FfXEIs3KXmlVZI+Od7m8DnHY/qfKarE97ucn9BrT3aR1TwF/eJcE9SP7dYabS36c3xlcW+lhhFFEY8s24dsOxN6DescgV8fuBztIhreL6SpgrRqq+vv90Y8qFPTMKj3AZN6RTe6Fq5NsAYLbhtgt7NdaIAf/julm81RF/48dA43fLod1328zWoG1tuHdXDb+LkalRJTLWrVf9yTg8/+mbJcFEW8v/Y4bvh0O9YcOY+iCj2qa0Scyq/Aou1ZVvu7XsZNZJqDn0qBj2f2s2rL/uG6441qzmBZA9U21B/twm3/SEUGavD+jdKZb40i8OD3+yRtpd3tl/3SsdbbhmqRFNX4joYPjEm0esNmT9tQf7wyuSeA2h/WvvGhks832xjrfvvJfEkA7adUYFDHhjcVagrLoD6/vLpB3xfLpkVBWhX6Wzxc1qdjZCurh0LL2Wm9yZFz0jeOE3tFY5DF3AYvrExz2OyqLssKjNhQLRKiXBMQqpQKq4qb3w/mNut12dKsPZKH9cek13NcmD/uHdkJ79+UjK9uH9SkwRaaynKQhB2n8lHWhEkqqeEY1PsAQRDwyrU9oVU7X9x+SgWeu7o71j86Er3jQh2u279dKF66pjvUSueaGgRpVLjj8saNB+2sm4e0sxqT//U/j+K1P45gyGtr8fbqdDtbSl2RGIkOTRj/2Ve0Dtbik5n9oKoTXRtF4KGle1HQwPb1lmPTD+gQ5rAZy+CECMy7sotk2YXSKgz89xroa9wzLnJdOn0NVuyVBvWTekU3qXlVZKAGr1/X2+E6gzuG47Vru2LVQ1dI2sJf3ln6AL/myHmrt1SWk1r1ax/a7MFAx8hW6B4jbYKzYp/zE1FttXgbMzQhosFNDgRBsNEEp2mzb7vTEYua+h6xIZhnMbM2ANzy5U68vTrdYVOiI7kl+P2AtL/RrUPaQ+HCZoE3DoyXzJKsrxHxfYrzbxLoElEU8ebfxyTLooI0+PGey/D4hK74V5+Gd5J2teFJkZI4QF8jWjXzI/diUO8jOkS2woc39UOw9tIP9+S+sXhmYje0jwiAv1qJ6/vF4a2pffDvKT2x+pHhmH15R6d/6GcMboedT4/Ff6b0kqRhyU+lwLs39kVogHO1kI0VFxaA/15vHRR9tumUzeYL9rj74aMlGdAh3Cq4Pl9ShUeX729QO+UDZ6RBfZ96HioB4J4RnTCyi/V8BM4+vDWWKIqYt2y/1XfqX71j7GzhvLHd2+C5q7tbta8HatvPfztnEP7VO9pqgqmx3aT9XDLzK3Ds/KUa3srqGqu2riMb2GzFVaYkt5X8/dOeHKxKq78driiKVrX6wzo37m2krc6y3tiuvtpgxFGLoL57bDAGJ0RYvYkVReD9tcfx/C9pMNpo0iSKIl5cmSYZYcpfrcT0ga7tNxTWys9qmM2lqac91pldznacKpBcxwDw9MSuzda53RlBWrXVG791R+ufFZtch0Na+pCx3dsg5dmxyLhYjgC1ytzu7s7hCdDXGC9NetRI4a38MGNwO1yRGIl5y/ab2/IqhNpa2zbBGnw0ox8GdGie1/yTk9viQmkV/v2H7RlnTR4b3wX924Xix93Z+HFvrvmH7qZB8RiZ1PCp0n3ZXcMTsO3kRWyu8/p/3dE8fLk1E7OdeEAqrzLguMUPVx8nOu4pFALentYXE9/bjHMll4Z5XLg1A3Mu74iIeuYpaChRFPHz3jN4e3U6cgqlbZKHJISjS3QQ8vOb3oxj9uUdccewDqgxisi4WI7DuSVIiAxEr7gQuzMn9mwbjLah/pK20n8ePIeu0cEoqzJg7tepViNbjW5gB1NX+VffWLz25xHJ0KR3fbMb/72+t1UTurqyCyqQWywdznNYZ+fmKLA0tJN0u4tlVTieV4akNg0f2tKdDueWQGeQlnnffx54357WF7cs2ImjFs1zFu/IQpBWhccnSEepWn8sDzszpG8k7hqRgBB/daMmvHPklqHtJSO0nC3S4cCZElyW1PztvuVKFEW8/NthybKEyFa4tk9bO1t4zuiubbD1xKV734b0CzBe5dpR7sg+BvU+RqNSomt0sNXypgb0dcWHB2DpXUOQcbEcwf5q+KuVOF1YgQ4RrZq9w+mdwxNwvkSHL7Zk2Pz8/lGdcd+ozjAajegV7Y/bLkvAqsPnkRQdhEm9YlrECDXNyRRcX/XeZlwsu1R7/fqfRzCwQ1i9TbkOnSmW1B4qFQJ6xDrXcS+8lR/+e0Nv3PrlLvMynd6IRdsyrd4gNIW+xoh5y/bbHCpVEIBnJ1l3TG8KQRCgUgpIbBOERCcCTUEQML5HNL7ceuk7v3L/WVzTJxY3zd9h1aZ5aEKExwLYNsFaTBsQL2mSIYowd6y/oX8cFDb661jOdxEZqEGnqMZN2tcuPMDqIWjjsQteF9SnWHR4TmwdiLB/BgSICtLgtwcux5dbM/DGX8ckNeEfbziJvNIqPDa+i7lW98stmZJ9tQ31x90j3BN49Y4LRWLrQMkcDvtzSnBZUrSDrcjkRF4Z7vpmDzIsOnDfPKS9zWvD00Z3bY1X6jyAXCitQvr5ciR35ENcc2DzG3ILQRCQEBWIyEANWmlU6Bod7LERZJ6e2M3mLKevXdfLqj1q99hgPHJlF1zdu+GzW1KtqCAN3pkuHS1JXyPi/77bW2+nqYMWTW+6tAlq0PdmeFKUVRvpj9afwKkL9kcNKSyvxlfbMvHAd3sx84sduHvxbry/9jgOWeQFqK0xe2jpPrtzH9w4ML5JE2+5ylUW48BnXCzH2Lc3WgX0fioFnp7YrTmzZuXZSV0xIlFaZqbAfson26xGZwFg1XxoSELjhg8Fau9Vwy3eyFmOf+8NUizGkx9o0cxBpVRg7vBO+PCmZKuBC37YnYPB/1mLDk/+jg5P/o4tFu2cHxyT6Nb784AO0g7M+8+U2FmT6krLLcV1n2yzCuijg7WY5uKmUq7SMbKV1cAGOxsxBwU1DoN6avEUCgFvTe2NB8ckIrldKP5vTCJO/WcibhrUjoG7m1yRGIV7LCYWy8yvQM8X/sazKw7ih905NicmWXX4vOTv3o0YXu/la3tI/jaKwFXvbbYK7EVRxOIdWbj8jXV4YWUaft1/FltP5OOvtHN4e3U6rv5gCyZ/tBXrj+XhdEEF/v37YXR86g/8fsB6mLb4cH/MvrwjXrimh9VnntCvXRg6t3Zccx3eyg/fzhmMXi4awrCxAvxU+Gh6T9wxrIPVZ/tPF2HqJ9twtk5gX1yptxqy9MoeTav1HWXRHyMlswA5hbaHDfQEo1G0miTKctQbk6t6xVhdA46EBajxr77WlR6ulBwvDeoPMKivV35ZFe77/iDKqqznHXj9+l4I1HhvQ4vLLJq07cws8kxGfJD3fiuIXEijUuLhcUl4eJz1SBHkHo+MS8L2k/lWE858syMb3+zIhp9SgRsHxWPu8ATsyS7Cu6vTrcYI79GIWu/ENkG4qmc0/qxTm1tlMOLDdSfw9vS+AGoD+tf+PoElKY5HW9l3ugi3L0yx+7m/Wol3pvfB+B5NG+3G1ZQKAe9O74sbPt0Gnd667X2QVoVldw1B59be0cREEAQ8OaELjueVSfpjALXzXtyxKAW3XdYBGRfLsfrweckETH5KRZP7BAzrHAmtWmE+VwajiE83nsSrk3s1ab+ucuJCGYoqpXM3WNbU1zVzcHtkXCi32+ywrtmXd3T7W9TkdqGSv8+VVOFcsQ6xTs7+64v++3c6LpRJKz5iQ7R4eFySxzq2O2topwhJk7rUrCIYaozwU7Ae2d14honILdRKBd6/MdlujVJ1jRFfb8/C5W+sx/99t9cqoNeoFBjfo43NbevzzKRukuE1AeCPQ7ko/icw+nxzRr0BvTM+vrkfJvT0zr4XPduG4N3pfa2Gso0J0eKLWwd4TUBvolIqMP/WAXj+6u6IC5OO8X/0XCme+ukgPt90yqopwvCkyCbXWrbSqHDjQOnMyMtScpBbbN30xxN2WXRqbRvqbzWxk6VnJnXDY+O7oH1EAFr52Q7ar0iMxJwrElyWT3s6RQUiyKKMbE2KRrWOnSuVdC4GapuYrXt0pMMO5N7CsvN5eXUNDp3l25nm4PGgPjc3F48//jh69OiBoKAgBAUFITExETfffDM2bdpkd7uDBw9ixowZiI2NhUajQbt27TB37lxkZ2c3Y+6JyJF2EQF47bpeaEzM+9ykbmgd1Ljh2uLCArDivmGSZTq9ESv3n8WhM8X43yrroS4n9YrBMxO7YdZlHRAbUn+6T0/silFeXmM2oWcM1jwyAt/MHozNj4/C4ZfHY/tTYzA4oXEjxbibVq3EHZd3xK/3X44OTs6KeevQDi5J++4RneBXZ8CA6hoj3l19HLsyCiTt+muMItLPl+LQmWJUVls3jXCHgzbmbqiPIAi4b1RnbHxsFHY/Nw5PT+yKduEB6B4TjP9M6YV180bgq9sHNUtfJ4VCQH+LPP9xsP6hS33V5/9MlGjSyk+J925Mls3M5q2DtEi0aP5n2bmd3MOjzW9WrlyJm2++GaWlpQgICEBiYiIAICcnB0uWLEF0dDSGDx9uc7upU6eiuroakZGR6NWrF44fP4758+dj2bJlWLduHfr169fch0NENlzTJxZtgrXYcvwCRNTWum44lidpQmHp+uQY3DSoaTVSPduGYEKPaPxVZ9zz73Zm49d9Z2GoMzqIIADv3ZgsGU/72Und8NPeM/jvX8fMo/gIQu3kPFf2iEZim8BGP3A0t7iwAMTJrJlDWCs/fHJzf0z+aCuqDPYnEEtuF9ro2bItRYdoMX1gPBbvuDSr9NLU01iaWtuM4Ib+cYho5Ycf95wxfye0agWmDYjH/41yb2330XPSWs5eDWyWplUrMXd4J8wd7rmhBSf2jMGGOrOhbjiWhxKdHsFa98wsLle7Mgrw4x5pLf3sKxK8ajx6Z1zWKUIy4tH2U/m4f3SiB3PkGzwW1G/ZsgU33HADlEolPvnkE9xxxx3w87s0IdGRI0dQWlpqtV1ubi5mzJiB6upqPPHEE3jllVegVqtRUVGBuXPnYsmSJZgyZQqOHTsGrVZeFwFRSzWoY7hkUpKzRZX4cP0JLE25NBFN1+gg3Dq0A3rGBiEhTO2SJi3TB8VLgvrDudavgO8b2dlqghyVsjZYm9AzGstSTuN0QQWm9ItD3/jQJueJnNMtJhjv35SMR5ftR+k/oya1CdagsFyP6hojukYH4aMZ/Vza9OnukZ3wfUq2zQfOHyyaQwC1b3++3p6FrScu4otp7glYaoyi1aRDtoYl9nbje0bjmRUHzee2ukbEqrTzuKF/nIdzZu1EXhkO55ZAo1KgX7swRAW5dp4Le8qrDPi/7/ZKlvkpBdwypH2zpO9KQztF4qvtlx6Q92YXuWQ+HHLMI0G9wWDA7bffDr1ej++++w7XX3+91TrdutkeZu3NN99EeXk5hg0bhtdff928PCAgAAsWLMC2bduQkZGBBQsW4L777nPbMRBR48WG+uM/U3ph7hUJWHPkPFoHazGhRzT8VAoYjUZUVrqmLfPwxCi0Cw9AdoHtkUzahvrjgTGd7W4frFU3S5tjsm18j2j0ezQMuzIKoFYKGNOtDQorqpFTWImescFQuThAaBvqjxv6x+O7XQ1rxnnyQjm+35uHR69yfXOsrPxyq87O3WK8qz+EM0L81RiR1Bprjlwa4erX/We9KqjPLa7E4z8ckHTWVioEXNM7Bk83oTmgsz7bdEoyeR4A3DuyU7M9VLjSQIvmVhXVNTiSW1LvXCXUNB55ZFq5ciVOnDiBvn372gzoHVm+fDkA4O6777b6TKPRYNasWQCApUuXNjmfROReHSJbYc4VCfhXn1j4qVx/O1IqBMy5wv5MtvOuTIRGJY92qr4qKkiDSb1jcGWPaCgVAiIDNegbH+rygN7kkXFJVuNsO2NtunvG4j6SK62ljwrSuHyG5OZyTZ8Yyd9bTlyUTFLnSuuOXcQNn27HsNfX4aP1JxyuK4oiNhzLw9DX1lmNvlRjFLFi31lMeHcz9mS7b7z1Up0eCzZL29IP7hCK+0bZr3TwZhGBGiREtZIss5xrgVzPI0H9ihUrAADjxo1DRUUFPvjgA0yZMgVjx47FHXfcgZ9//hmiaP368/Tp08jJqX0FaqutPQCMGDECAJCSkoKamubpxERE3mvGoHaYkmw9nfrtQ+NxrY1Jyci3RQVpsOK+Ybh3ZCeM7dYaMRadpoM0Kvx7Sk98OCNZsvzExUqcvCAdmccV0s5KO8l2jZZfLb3J2G5tJKMx1RhFPPPzQdQYRZRVGfDZxpO48fPtePnXw/hkw0mrY3eGKIp47c+jeGDZIezJLsKZokq8+fcxqwnLTPadLsLE97dgloOhawGgoLwaM+bvwM5T7unw+cPuHJTX6XitEICnxydaTSQmJwPbS4dd3Z3FEY/czSPNb1JSai8etVqNfv364dixY5LPFy5ciNGjR+Pnn39GcPCltoPp6bUjVvj5+SE+3nYnuk6dajsC6XQ6ZGVlISGh4a/Oc3OtJ5exFBfnPa8Micg+lVKBd6b3xd0jOuGbHVnIzC/HlORYXJkU5pVDUZLnhbfyw+MTupr/rjGKEEXR3MFaq1aixigiKuiwZJbePw+dQ9fYUJfmZXeWtHazR6z82tObtNKoMK5bG/xaZwK3v9POI/GZP1Cn7zp2nKoN/t74C7iqZzTemd7X6ZFfvt6ehfmbrcfn/9+qYxjXvY0kSN524iJmLUpBtY3O2MFaFQxGERV1Am2d3og5X6fiu9kDEenC/r0XSqvw/trjkmVju7VB59at7GwhD/07hJk7mgO1NfWiKPK+60YeCepNQfObb74JlUqFzz77DNOmTYNKpcLKlStx3333Yd26dbjzzjslzWgKCmov9LAw+z/G4eGXngwLCxv3qmfQoEH1rmPrTUJjGAwGl+zHFqPRiJqaGuj1eijcNOkD03COqZxZ3p5LIyFCi+cndTGnUVVVxfJmGg1iCiv1/7Rxn9C9NRbvvBS0/HHoHO4f5boRZqoNRqvJ2/q2DYJer7e9QRM1R1ncM7wDVh8+D12dQNro4Of0z0PnEBV4GM9N6mp/pX+cKarEa38esfnZ8bwy/LQ7G5P/mT33YlkV7lmy22ZA3z48AMvvGgSjUcRjPx7C5hOXaudLdQbcvWQvFt7YBSEuur6fW3EQhRXSMr1pQKxsrgt7kuOkb5UulFbhVF5Jo5q3OdJcv99y4JGgvqysdpgjvV6P999/H3PnzjV/NmPGDPj5+WHq1KlYtmwZnn/+efToUTvltU5X24Gk7ig5luqOeOOqznbuVFzc8NeLDVVR4f7pzpmGc1jevpUGy7tlpzGsnT8W77z09/G8cqSm56BjhOOJoZy1/2yZ1ZCeHYOB/Hz3jvntzrIIVwGPjorHq6uz6l/5H8t35+D2/uHwd1Bbb6gR8b91WTZnUDZ5Z3U6hsSqoVYq8MKfGSiulAZrof4q9I5phYdGxMNYWduX4bWr2uOZP2qw8WSReb2cIh1eXZ2F169WNrnW+ej5cvyVdl6ybHhCCLqGCaioqJDldWESKIoIC1ChsOLSed6YloOJ3d0zT0ZznCtv55GgXqvVory8HCEhIZg9e7bV59dffz06duyIjIwM/PXXX+ag3hSwV1dXW21jYgr8AcDfv3E31l27diEmJqb+FV0gJCQEKpV7isFUG6nRaNz6pM806mcwGFBcXMzy9pE0WN6+kcaosHC0/isLeXWa4GzPqcKAJNc0z9yVIu202TkqAB1iomR3nizTmDYwAAq1Fl9szcLZYl2921Tqjdh9zoBr+0pHFxJFEb8dPIf5mzNx5Jz1ENgxIRrkFl8qm7Ml1bjig71W6wHAmK5R+PDGPjY7YH84Mwy3LEzFvtOXHtI3nizCn8fLcEsTJz/7dtVpyd9hAWr8d1oywvxVsr0u6hrQPgyrj1yan+BYgR63RLg2qG+u+7kcuOXXZurUqTbbpX/wwQdITk5GeHg4ysvL0blzZ6jV1g3TBEFAt27dkJGRgYyMS23jwsJqh0gqLLTfLsvURKfu+g0VExPTbG3mVSqVzXPgCkajEQaDAWq12q03BabhPJa376QBsLx9IY2JvWKwaFum+e+/Dp/HI+PrbypSH1EU8VdanmTZmC5Rsj1PlmnMujwBdwzvjNTMAvz7jyM4fLYEQztF4OGxSegTH4rbvtyFjemXgsFfDpzDDQMvjdcuiiJeXJkmGQu9Lj+lgJ/uuQz3f7fPql+CpSCNCm/c0Af+WtujCqnVanw8sz8mvb9Z0kzmzdUnMK5nW8Q3sjlJeZUBG9KlD25zh3dCdGgr2V8XJgM7RkiC+j3ZxS6/JzbX/VwO3HL0KSkp2Lp1q9V/piedrl1rb3gajf1huUyf1R3BpkuX2vaw1dXVyM62PY7wyZMnAdTW6rdvL78JG4iISD6u7i19q5t+vgzp561rjRvqQE4xzhRJm5CO7x7V5P16mwEdwvHzvcNw+OUJWHT7IPT5Z4K36/pJR6zaeuIiztWp1V+0LdNuQA8AtwyOQ5tgLR4b36XePMwY0g6R9QwTGhvqj7en9ZUsq9Qb8eVW6065ztp8/IKkTb9KIWDG4HaN3p83GtBeWrl6PK8MheX2W1tQ07glqM/MzIQoilb/jRw5EgAwbNgwAMCpU6fs7sMUnNetMY+Pj0fbtrUX+qZNm2xut3HjRgDAwIEDoVRy/GkiInKffu3C0CZYGhD+fqD+EdTqU3eSJgDoEBGAJJmPhuKI5dCNV3aPRqDmUmMCowj8su8MAOBcsQ6v/3nU7r5Gd43CAyNr56cYkhCBVyf3hJ+deQ20agVuv8z+XBZ1jeraGlMtJstalXa+0QNnrDosLeMhCREI8XfPmz1P6R4TDK3FHCTuHO/f13nkPcX06dMhCALOnTuHP//80+rzlJQUHDhwAAAwduxYyWc33HADAOCzzz6z2q6qqgqLFi0CAEybNs3FuSYiIpJSKARc1aONZNnvB3ObPELaDovx0Md1b+NTQwH6+ykxsVe0ZNny3TkQRRHvrT1u1YH4lck9sfzuoVg6dwjm39If6jpB/M1D2mPDYyMx67IOkm3USgGvXdcL0SHOzxR7x+XSB4AzRZU4nFvi9PYmhhoj1h+VNq8a2831MxJ7mp9KgV5tpaPg1NccihrPI0F9165dccsttwAA7rvvPqSlpZk/y8jIwB133AEAGDNmDAYPHizZ9vHHH0dAQAC2bt2KJ5980jy0V0VFBebMmYOMjAzEx8fb7IBLRETkahN7SoPPE3llSD9f1uj9VVbXYP9pace8IQnhdtZuua7rJ60VP5FXhrVH8vDjnhzJ8psGtcMtQ9pjYIdwDE6IsPnwExvqjxf/1QOZr0/C/uevxHd3DsGqh0dgSnLD+s91jQ5CXKj0IWDl/rMN2gdQG9haDmM5tnsbO2vLW9+4EMnfDOrdx2M9Cj766CMMHjwYGRkZ6NWrF3r16oW+ffsiMTERhw4dQvfu3bF48WKr7WJjY7FkyRKo1Wq88cYbiI2NxYABAxATE4NvvvkGwcHB+Omnnxo98g0REVFD9IkLQZsgabOJFf80FWmMvdmFqK65VBOtEKzbJvuCwR3D0TFS2uRoztepknboaqWAh8YmNmi/IQFqDO0UYbVvZwiCgHEWwffSlNMo0TVs7gDL5lXdYoIRF+ba8du9RXK8NKjfn1MEfY39oUep8TwW1AcGBmLz5s1466230LdvX2RmZiI9PR3du3fHK6+8gp07d9odVnLy5MlITU3F9OnToVKpcPDgQQQHB2POnDk4cOAABgwY0MxHQ0REvkqhEDA6URp0/7A7p9GBy5YT0hFRerUNQZC2ZbW1doYgCLhpkO3Z402u7B6NNsHON59xhWn9pZ14iyr0uPLtTagy1NjZQkoURay2aE9v+aDQkvRpK50FWac34vDZhjdZovp5dOwftVqNefPmYc+ePSgtLUVFRQUOHDiAZ599FoGBgQ637d27N77//nvk5uaiqqoKp0+fxvz58zniDRERNburu0dK/r5QWoW1R/LsrO3YOou21kM7RdpZs+W7vl+c3U6uADBtoOOg3x06tw7EiE6hkmXnSnR47Q/7nXfrOnmhDJn50omSxnVruUF9aIAanaKkb0VS2QTHLXx7QE8iIiIX6BTpj37tQiXLvttle+hlR84WVeKoxURKo7u2vA6UzooI1GC8RZ8Fkx6xwRie6JkHnnuGtYXFgD1YsjMLOYX1z2pqOepNdLAWPS1qs1ua/hbNx/YwqHcLBvVEREQuYNksY9PxC04FeXWtPyatpQ/xV1s9LPiam+2M3f7Q2CSPjQjUIVyLu66QjoSjrxHx6caT9W5r2fRmbPfWLX5kI8vvcGpWQZNHiCJrDOqJiIhcYGLPaARpL42tLorAkp0Nq61fZ9FkZ0RSFFQOmp/4gsEJEXhwjLQzbN/4UI8PAfnIuEQM6igdleibHdkO24sfP1+KvdlFkmXjutt+E9GSWHb0Pl9SZTW5GjWdb98piIiIXMTfT4kpydLa+m93ZqOi2uDU9jp9DbaelHaS9eWmN3U9PC4JS+YMxtW9Y3DToHb4/Nb+XlG7/f6NyVZt/ie+vxmPLNuH0wXWb2kWbcuU/B3Ryg9DEyLcmUWv0DGyFUIDpJ29ObSl6zGoJyIicpFbh3aQ/F1cqcePu3Nsr2xh+6l86PTSoSxHJEW5MnuyNqxzJD6c0Q+vXdcLrYOad8Qbe6JDtJg6wHqs+5/2nMGEdzfhQE6Redn5Ep3VGPs3DoqHn6rlh2KCIKB/O2ltPYN612v53yQiIqJm0rl1oFXt+pdbM2E01t9+eOOxC5K/+7ULQ1grP5fmj1zv4XFJiAy0Lqfy6ho8+P0+HDtXitf+OILB/1kreWhTKQTcMqRDM+bUs/p3YFDvbgzqiYiIXGjO5dIOlBkXy62GqbTFcnz6UWx6IwuRgRosmTMEV3ZvY9UUJ+NiOca/uwmfbTpltd31/eIQHeIdbxyag2VN/ZHcEpRVOdc0jZzDoJ6IiMiFhnaKQNfoIMmyBVsyHG5zvkSHE3llkmXDOvvu+PRy0yU6CJ/fOgDbnhqNcCferoQGqPHEVV2bIWfeo098KFR1xgE1isD+00Wey1ALxKCeiIjIhQRBwJwrEiTLtp/Kdzi85TaLDrJBGhV6xrbssctboshADd6e1sdqDPu61EoB/72+t1PBf0uiVSvRo22IZBmb4LgWg3oiIiIXu6ZPDEL8paN9OGqCs/NUgeTvwQkRPj+UpVyN7NIaC24biDbBGvMyP6UCdw1PwKc398P2p8bgyh4tfxhLWyyb4HBmWddS1b8KERERNYRGpcSYbq3x054z5mXrj+ZZjY5jYjl2+ZCEcJvrkTyM6toaqx4agR/25OB8iQ5T+8chsU1Q/Ru2cAM6hOHLrZeaou3NKoTRKELh6NUGOY1BPRERkRuM7CIN6muHrKyBVq2UrFdWZUB6XqlkWbKPzyLbEoQEqDHbotO0r+tvMQlV6T/f/a7RbGrmCgzqbRg9ejTUaulr07S0NLekJYoijEZj/Ss2ct9MwzvSEEURgiDI+hiYRsP2z/L2nTTslfflncKhEGo7BAKATm/E9pMXrcae35ddCLHOiJcqhYBu0UGSfbWE89RS0uD13fg0ogL90DbUXzKbbGpGAZJaB7osDVcTxfqHo/UWDOo9KDAwEHq9Hnq93q3p6HQ6t+6faTiH5e1babC8fSsNW+WtEYDebYOxL6fEvGxNWi4GxUsDmNQM6fj0XdoEQjRUo9LGaH9yP08tJQ1e341Po29ckCSo33o8D1N6N32SteY4Dm/HoN6GdevWIS7OeoY4VysrK0N4eDhUKvcUgyiK0Ol00Gq1bptOm2k4x2AwoLCwEGFhYSxvH0iD5e1baTgq7zHd2kiC+nXp+Xjp2l6SNsTH8iol2/RtFwZ/f/9mPQam4Txe301LY2inKPx+6FKn8U0nCiCo/KyapTUlDVcyGOQzlj6Deg8TBAEKhXtGODC9hmIank/D9KpWzsfANJzH8vatNByV95U9YvC/1cfNf+cW67D/TIm5bbEoitiTLR0BpE9cqNV+WsJ5ailp8PpuWhrje8bg+ZVp5mZp5dU12HT8Iib0jHFZGq7krgced+B4WURERG6S1CYQnS3aC/924Kz53ycvlOF8SZXk834WnQmJWpKoIA2GJERIlv12IBeiKEJf4562/b6CQT0REZGbCIKASb2kNZB/HMyF8Z9qys3HpZNOxYRo0SmqVbPlj8gTJvWWXhO/HchFx6f+QNKzf+L6T7Zh56l8D+VM3hjUExERudHVFgHM+ZIq7PgnaNliEdRf3jlSVq/7iRpjQo9om7PuimLtLLMzvtiJn/bkNH/GZI5BPRERkRsltglC12jpxENLU09DX2M0B/cmlydGNmfWiDwiIlCDyzrZ/67XGEU8/sMB7M4qsLsOWWNQT0RE5GY39JeOqPbnoXP4ee8ZlFfXSJYP68ygnnzDNX0cd4w1GEU8tHQfdPoah+vRJQzqiYiI3Oy6fnFQKy+1N6g2GPH4Dwck6/SJD0VkoKa5s0bkEVOS49A95tJMspGBfhjbrbVkndMFlfh4w8nmzppscUhLIiIiNwtv5Ycre0Tj9wO5dte5ZUj7ZswRkWf5qRT46d7L8Mu+MyjVGXBt37YIb+WH6Z9tR2rWpWFe5286hduGtkdEAx94TxdUYGP6BdQYRfRvH4aebUNcfQheh0E9ERFRM7hxYLzdoD68lZ9Vh1qilk6rVmL6wHaSZa9M7olJ7282j2Nfqa/Bgi0ZeHxCV6f2KYoi3l6djo/WnzDvAwDGdG2NN6f2QXgrP1dl3+uw+Q0REVEzGNYpEgl2hqucNiC+0TNqErUk3WKCMTm5rWTZV9syUVypd2r7t1YdwwfrpAE9AKw9mocpH29FXqnOVVn1OgzqiYiImoFCIeD2YR2tlrcO0uDOK6yXE/mq+0d1lgx5WV5dgx921z/E5e6sQodt8LPyKzDnq9QW2/mWQT0REVEzmdo/Dl3aXBrespWfEl/OGtjg9sJELVlCVCCu6iltjvbNjizzpG32vPn3UYgWq2hU0lD3QE4x3l6d7pJ8ehsG9URERM1Eq1bi5/suw+vX9cKjVyZh9SMjfKIDH1FD3TpU2nE842I5Np+4aGdtYNvJfOw4JR3X/okJXbHp8VFoFx4gWT5/8ynsrtMZt6VgUE9ERNSMAvxUuHFQO9w/OhGxof6ezg6RVxrUMVzyVgsAFm/PsrluebUBT/10ULKsTbAGd1zeAW2CtZh/6wD4KS+FvKIIvLgyrd6af7lhUE9EREREXkUQBNxiUVu/7uh5nCmqNP9dVmVAeZUBb/x9AqcLKyXr3jeqMzSq2s7nXaKD8MiVSZLPD54pxi/7z9Sbjyp9DfLKqmXxAMAhLYmIiIjI60xObovX/zyKsioDAMAoAh+vP4Fr+sTiP38cwYGcYpvbDeoQjpsHSx8IZl/eEctTT+PkhXLzsjf/OoaresbYHXkqNbMA//fdXpwt1mFghxwsun0QWmm8N3RmTT0REREReZ1AjQqTk2Mly5bszMaNn++wG9CrFAL+c10vKOoOnwNArVTgqau6SZadLdbhq22ZNveTlV+O2xem4Gxx7RCYKZmF2Hz8QiOPpHkwqCciIiIirzTrsg5QWgTojjw9sRs6tw60+dmYbq0xNCFCsuyj9SdQVFEtWSaKIp788SBK/3lDYBKoUTudD09gUE9EREREXqlz6yC8cE33etcL1Cjxxa0DcMfl9ud8EAQBT14lnZm2RGfAQ0v3QV9jNC9bdzQP20/lS9Yb1601hnWWPhB4G+9tGEREREREPu/WoR1w6EwxlqVKJ6C6qmc07hjWAWfyS3F5l2hEBmnr3Vef+FBM6h2D3w/kmpdtOHYBic/8iVFdoqBRKfFX2jnJNtFBfnjz+p4QBOffGHgCg3obRo8eDbVa+oolLS3NLWmJogij0Vj/io3cN9PwjjREUYQgCLI+BqbRsP2zvH0nDZa3b6XB8vZMGs9O6oa92UU4nlcGAGgX7o9/T+6BEH81urfWQqtVO53GvHGJ+PvQORgsRrRZf8x2m/k7h8Z6dQdZE0E0nXEfl5OTg/j4eABAYmJiswT1ZWVlXv/UR0REROQNiiv1WH30Agw1IsZ3j0JYgF+j9/Vd6hm8+ufxeteLDtZg+W3dEd06yio29DYM6v9RN6g/ffo04uLi3J7muXPnEB4eDpXKPU9/oihCp9NBq9W67eGBaTjHYDCgsLAQYWFhLG8fSIPl7VtpsLx9Kw2Wd8tJIyWzAA8t3Y/cf0a4sRSoUWLBLf3QPtCIiIgIrw/qvf9dQgsnCAIUCvf0Vza9hmIank/D9KpWzsfANJzH8vatNFjevpUGy7vlpDE4IRLL7x6Kp38+hJ2n8tE3PhSRgRrsOJWPmFAtXp3cCz2iWyE/P7/+nXkBBvVERERE5JPiwgLw9R2DUGMUzUNnmhqxCIIAvV7vyew1CIN6IiIiIvJpdcfCl2t/R45TT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeiIiIiIimWNQT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeiIiIiIimWNQT0REREQkcypPZ8AbjR49Gmq1WrIsLS3NLWmJogij0ei2fTMN70hDFEUIgiDrY2AaDds/y9t30mB5+1YaLG/fSsO0fzkQRDnl1o1ycnIQHx8PAEhMTGyWoL6srAyCILh8v0RERETkGqWlpYiIiLCKDb0Na+ptWLduHeLi4tyeTllZGcLDw6FSuacYRFGETqeDVqt128MD03COwWBAYWEhwsLCWN4+kAbL27fSYHn7Vhosb99Kw2AwuHyf7sKg3sMEQYBC4Z6uDabXUEzD82mYXtXK+RiYhvNY3r6VBsvbt9JgeftWGnJqUcGOskREREREMsegnoiIiIhI5hjUExERERHJHIN6IiIiIiKZY1BPRERERCRzDOqJiIiIiGSOQT0RERERkcwxqCciIiIikjkG9UREREREMsegnoiIiIhI5hjUExERERHJHIN6IiIiIiKZY1BPRERERCRzHg3qV61ahSlTpqBt27bw8/NDYGAg+vbti2eeeQb5+fkOtz148CBmzJiB2NhYaDQatGvXDnPnzkV2dnYz5Z6IiIiIyDt4LKh/8sknMX78eKxYsQJlZWXo0aMHoqOjcfDgQfznP/9Br169cOzYMZvbrly5EgMGDMB3330HvV6PXr16obi4GPPnz0fv3r2xZ8+eZj4aIiIiIiLP8UhQv2XLFrzxxhsAgGeffRZ5eXnYu3cvTpw4gbS0NHTv3h25ubm46667rLbNzc3FjBkzUF1djSeeeAJnz55FamoqcnNzMXPmTBQXF2PKlCnQ6XTNfVhERERERB7hkaB+5cqVAIC+ffvilVdegUajMX/WtWtXfPjhhwCATZs2obS0VLLtm2++ifLycgwbNgyvv/461Go1ACAgIAALFixAx44dkZ2djQULFjTT0RAREREReZZHgvqKigoAQKdOnWx+3rlzZwCAKIowGAySz5YvXw4AuPvuu62202g0mDVrFgBg6dKlrsouEREREZFXU3ki0eTkZABAamoqqqqqJDX1QG3zHABISkpCWFiYefnp06eRk5MDABg+fLjNfY8YMQIAkJKSgpqaGiiVygbnLzc3t9514uLiGrxfIiIiIiJ38EhQf8stt+C9997DwYMHMWXKFLz88svo3r07SktL8ccff2DevHnQaDR4//33Jdulp6cDAPz8/BAfH29z36baf51Oh6ysLCQkJDQ4f4MGDap3HVEUG7xfWyzfRLiS0WhETU0N9Ho9FAr3vJRhGs4xlTPL2zfSYHn7Vhosb99Kg+XtW2m4s5xdzSNBvZ+fHzZv3oznnnsOixcvxsCBAyWfT5gwAf/+97/Rr18/yfKCggIAQFhYGARBsLnv8PBw878LCwtdnHPXMRqNAIDi4mK3p2Vq7sQ0PJ8Gy9u30mB5+1YaLG/fSoPl7VtpuKoy1508EtQDwIULF3D27FlUVFQgMDAQnTp1QmFhIU6fPo2NGzdi4cKF6NmzJ/z8/MzbmEa0qbvMklarNf+7srLS6fxER0fj9OnTyM3NRVRUFFQq954ahUKByMhIWXxJiIiIiHyZaWAWb+aRoP7YsWO47LLLUFhYiDfffBMPPvigOYhOS0vDLbfcgg8//BBnz57Fjz/+aN7OFLBXV1fb3XfdoSz9/f2dzpNKpUJcXFyztpV394MDEREREfkGt0SVU6dOtdnZ9IMPPkBycjKefvppFBQUYPbs2Zg3b55knR49euCHH35Aly5d8NNPP2Hnzp0YPHgwAJg7zRYWFkIURZtNcExNdOquT0RERETUkrklqE9JSUFWVpbVclP7s82bNwMAxo8fb3P7hIQEJCYm4siRI0hJSTEH9V26dAFQW1OfnZ2N9u3bW2178uRJALW1+rY+JyIiIiJqadzSFTkzMxOiKFr9N3LkSACwmlDKkbrNaeLj49G2bVsAtRNT2bJx40YAwMCBAxs1nCURERERkdx4ZPKppKQkAMDff/9t8/OTJ0+ah6801c6b3HDDDQCAzz77zGq7qqoqLFq0CAAwbdo0V2WXiIiIiMireSSov/XWWwEAX375Jd5++23JGKBpaWmYOnUqampqEBsbi7Fjx0q2ffzxxxEQEICtW7fiySefhF6vB1A7lNGcOXOQkZGB+Ph4zJ49u/kOiIiIiIjIgwTRA2Mq6vV63HDDDVi5ciUAICgoyDykZXZ2NkRRRFBQEH777TebM8euWLEC06ZNg16vR2RkJNq3b4/jx4+jpKQEwcHBWLt2LQYMGNDch0VERERE5BEeCeqB2kH8v/vuOyxevBh79uxBQUEB/Pz80LFjR1x55ZV46KGH0K5dO7vbHzhwAP/5z3+wceNGFBQUoHXr1pgwYQKeffZZdpAlIiIiIp/isaCeiIiIiIhcwyNt6omIiIiIyHUY1BMRERERyRyDeiIiIiIimWNQT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvUekJOTA0EQIAgCcnJyPJ0dcjOWt29hefsWlrdvYXn7FrmVN4N6IiIiIiKZY1BPRERERCRzDOqJiIiIiGSOQX0L1qNHD/To0YNpeEka7tZSzlNLScPdWsp5ailpuFtLOU8tJQ13aynnqaWkIRcM6omIiIiIZI5BPRERERGRzDGoJyIiIiKSOZWnM+CLDAaD+d+5ubluS0ev1wOAW8dWZRr1q1vGLO+WnwbL27fSYHn7Vhosb99Ko24Z143dvJUgiqLo6Uz4mpSUFAwaNMjT2SAiIiIiJ+zatQsDBw70dDYcYvMbD4iJifF0FoiIiIjISXKI3VhT7wEGgwHnzp3zdDaIiIiIyAnR0dFQqby71TqDeiIiIiIimWPzGyIiIiIimWNQT0REREQkcwzqiYiIiIhkjkE9EREREZHMMagnIiIiIpI5BvVERERERDLHoJ6IiIiISOYY1BMRERERyRyDeg/YvHkzrr32WrRu3RparRadO3fGvHnzUFBQ4OmskYVz587hm2++wYMPPohhw4YhICAAgiCgb9++Tm2/aNEiDBs2DCEhIQgMDES/fv3w7rvvoqamxuF2+fn5mDdvHjp37gytVovWrVvj2muvxZYtW1xwVGTPgQMH8PLLL2Ps2LGIjo6GWq1GaGgohg4dirfeegsVFRUOt1+5ciXGjRuHiIgIBAQEoEePHnjppZdQWVnpcLuKigq89NJL6NGjBwICAhAREYFx48bh119/deXhkYU1a9aYr+34+Hj4+/sjICAAXbp0wd13342jR4863J7Xt7zt27cParUagiBAEASH67Ks5efFF180l629/z799FOb2xoMBrz77rvo168fAgMDERISgmHDhuGrr76qN92srCzMnTsX7dq1g0ajQWxsLGbOnIlDhw65+hCtidSsPv74Y1GhUIgAxNjYWLFfv36iv7+/CECMi4sTMzMzPZ1FquOdd94R8f/t3XlUVNcdB/AvMMAIDgwKkaAYZSm0BEUWl+COWeqWpIl1TYix2nTxKEdCNUZJrcc2bWISTTVGTW0brYm2UaO1RC2iuG+45GhkdTRGAwqCCIFhfv3DM68MMwMDsj3y/Zwz58C99/fmXi5v5vd2wOrVt2/feuNMJpNMmTJFaR8WFiaRkZHi5OQkACQhIUG+++47m7F5eXkSEBAgAMTDw0Oio6OV352dneXDDz9sgZFSTk6OxRwHBARIbGysdO/eXSn7wQ9+IFeuXLEZP3/+fKVdr169JCoqSlxdXQWAREZGSnFxsc24W7duSUREhAAQV1dXiYqKkl69einLev3111tw1N9vzz33nAAQjUYjPXr0kNjYWAkODhaNRqPMx8aNG63iuH6rX3V1tfTr189inbeFc61eqampAkAeeughiY+Pt/natm2bVVxlZaUMHz5cmafIyEgJCwtT/gdeeOEFMZlMNt/z+PHj4uXlJQDE29tbYmJixNfXVwCIu7u77Nq1q0XHzKS+FZ0+fVpcXFwEgKxcuVL5p7h165YkJCQIABkwYEAb95JqW79+vYwaNUrmz58vW7dulWXLljmU1L/33nsCQPR6vWRkZCjl58+fl8DAQAEgKSkpVnEmk0liYmIEgIwaNUpu3bqllK9YsUIAiIuLi5w7d65Zx0ki2dnZ4ufnJ2+88Ybk5uZa1GVmZsojjzwiAGTgwIFWsdu3bxcA4ubmJlu3blXKDQaD9OnTRwDIT3/6U5vv++yzzyr/UwaDQSnfsmWLslHw73//u5lGSbVt3rxZdu/eLeXl5Rbl169fVxJ+rVZrMS8iXL87AvNnuXn9s5fUc67Vy5zUJyYmNiouKSlJAEjPnj3l/PnzSnlGRoZ4e3sLAFm1apVVXHl5ufTo0UMAyLRp05TPlaqqKklJSREAotPp5MaNGw80rvowqW9F5g+PKVOmWNUVFhaKTqcTALJz58426B054i9/+UuDSX11dbX4+fkJAJt7YtLS0pRkoaioyKLOnBzqdDqrOhGRyZMnCwB5/vnnH3gsZKmiokLu3r1rtz4zM1P58s/KyrKoM+/xe+2116ziLl68qBydq/0FISKSlZWl7A26ePGiVeyCBQsEgMTGxjZxVNRUlZWVotfrBYCsXr1aKef6rX4XL14Ud3d36d+/v+zbt89uUs+5VremJPXffvutuLm5CQD54osvrOrXrFkjAMTf31+MRqNFnXkDsHfv3lJZWWlRZzKZ5LHHHhMAkpyc3KTxOIJJfSspKysTrVYrAOTgwYM220yfPl05tEPtkyNJ/Z49ewSAeHp6SkVFhc02QUFBAkDWr19vUT516lQBIDNmzLAZl5GRIQCkU6dO9Sag1DLMe2k2b96slGVnZytJQd09umYjRowQALJo0SKL8oULFyqH8G25cuWKsuy6Rw+o5UVHRwsAeeutt5Qyrt/qVlNTI4899phoNBo5e/aspKen203qOdfq1pSkfu3atQJAgoODbdZXVFSIh4eHAJB9+/ZZ1MXHxwsA+d3vfmcz9q9//atyBKCl8ELZVnLmzBlUVlbCzc0NAwYMsNlm2LBhAIAjR460ZteomZnnr3///tBqtTbbDB061KJt3VhzfV0DBw6Em5sbKioqkJWV1Uw9JkcYjUZUV1cDADw8PJRy85z17t0bgYGBNmPtrdsNzXfPnj3Rq1cvm7HUsoqKipQLZePi4pRyrt/q9v777+Pw4cNITk5Gnz596m3Lue4Yzp49i6lTp2LkyJF4+umnsWjRInz55Zc22zY0b1qtFv3797doCwA1NTU4ceJEvbHm7wGDwYCvv/66aYNpAJP6VnL58mUAwCOPPAJXV1ebbYKDgwEAeXl5SvJA6mOe65CQELttzHP91VdfKWVVVVUoKCioN9bNzU1JHGvHUsvbtm0b7t27B41Gg0GDBinlTZ3vB42lllFUVIS0tDQ89dRTuHfvHiZPnmzxJc31W70KCgrw2muvITg4GIsXL26wPee6Y8jKysKmTZuQnp6OHTt2YOnSpYiMjERSUpLV3YuaOucFBQWoqqqqNzYwMFDJ/1pqzpnUtxLz7Sq7dOlit425zmQyobS0tFX6Rc2vMXNdXFyslN25cwcmk6lJsdSySkpKMG/ePADAzJkz4evrq9Q1db4fNJaaz969e5Vb3Pn5+eGpp55CUVERVq9ejY0bN1q05fqtXjNnzkR5eTnWrFmDTp06Ndiec61uAQEBWLJkCY4dO4bCwkJUVlbi3LlzeOWVVyAiePfdd7FgwQKLmKbOee1bktuLdXZ2hl6vt4ptTpoWWSpZqaysBHB/69ye2of3GrqvNbVfjZnr2vNsjmtKLLUco9GISZMmwWAwICgoCH/4wx8s6ps63w8aS81Hr9cjPj4eIoLr16/j2rVrMBgM2LhxI4YMGYKIiAilLddvdVq/fj327t2LxMREJCQkOBTDuVa3WbNmWZVFRkZi9erV6N27N37zm9/gnXfewS9/+UvlNEe1zzn31LcS80SaD8/YUvufwpG9CNQ+NWaua89z7Y26xsZSyzCZTEhMTERaWhr8/Pywa9cueHl5WbRp6nw/aCw1n9jYWGRmZuLQoUPIz8/H1atX8fLLLyMzMxODBg1Cfn6+0pbrt/pcv34d8+bNg5+fH95++22H4zjXHde8efMQEBAAo9GIHTt2KOVqn3Mm9a3Ex8cHwP2ny9ljPnzj7OxslTiQejRmrs1tAcDb2xvOzs5NiqXmJyKYMWMGNm3ahC5dumDPnj0IDw+3atfU+X7QWGo5/v7+WLduHZ544gmUlZVh2bJlSh3Xb/WZPXs27ty5g+XLl6Nr164Ox3GuOy4XFxflpiXZ2dlKeVPnvPbP9mJNJhNKSkqs2jcnJvWtJCwsDMD9q57tXQSbm5sLAAgKCrJ7MS21f+a5zsnJsdvGPNfmtsD9Q3bmQ4D2YquqqnD16lWrWGpeIoJZs2Zhw4YN8PLyQlpaGvr27WuzbVPn+0FjqeWNGzcOAHDq1CmljOu3+pjnLzk5Gf7+/havn/zkJ0o7c9natWsBcK47OvNpMkajUSlr6pz37t1bWZ692KtXryr5X0vNOZP6VtKvXz+4u7ujqqoKR48etdkmIyMDACzurEHqY56/EydOWJxSVduBAwcs2taNNdfXdfToUVRVVUGr1SIqKqqZekx1/epXv8K6devQuXNn7N69G7GxsXbbmuesoKBA+ZKuy9663dB8GwwG5S4aAwcObNQY6MGZv+xr3yGD67d63bx50+pV+4JFc1l5eTkAznVHd+HCBQBAjx49lDLzvB08eNBmTGVlJY4fP27RFri/599861t7c27+HggMDET37t0fsPd2tNgd8MnK008/7dATZT///PM26B05wpGHT1VVVTn0FEJ3d3cpLCy0qPvss88cegrhc88998BjIdtmz54tAMTDw0P279/vUEzfvn0deqJs3UfCnzlzxqEnysbExDRtMPRABg8ebPXwGq7fHUt9D5/iXHdcO3fuVOb9+PHjSvnNmzcdeqJst27dpLq62qLunXfeceiJsvPmzWuZQQmfKNuqTp06pXy5r1y5Ukwmk4iI3Lp1SxISEgSAxMXFKeXU/jiS1IuIvPvuuwJA9Hq9ZGRkKOXnz5+XwMBAu4+KNplM0q9fPwEgo0aNklu3binlK1asEADi4uIiWVlZzTouuu/VV19VHvu+d+9eh+O2bdsmAMTNzU22bt2qlBsMBunTp0+9j4N/5plnlP+p2k+k3bJli7i6unJDv4VcunRJkpOT5fz581Z1BoNBSbpcXFzk9OnTFvVcvzuO+pJ6Ec61Wl24cEFmzZpl9fetqamRTZs2iZeXlwCQsWPHWsXOnTtXefJr7c+HjIwM5ani77//vlVceXm5BAQECACZNm2alJeXi8j9jcOUlBQBIJ07d5ZvvvmmmUf7f0zqW9nKlSvFyclJAEhAQIBER0dLp06dlN/z8vLauotUi8FgkK5duyqvzp07Kx/Gtct//etfW8TV1NTIxIkTlS+LsLAwiYyMVDbqhg8fbrUlb5aTkyMPP/ywsrc4Ojpa+aBwcnKS1atXt8bQv3cOHz6szNdDDz0k8fHxdl91Hwkv8v8NAvOemqioKCUpj4iIkNu3b9t836KiIvnhD38oAMTV1VWioqKkV69eyrIWLFjQ0kP/XjIfJQEgPj4+0q9fPxk4cKAEBQUpn9Genp6yefNmq1iu3x1HQ0k951qdaq/fXbp0kX79+klcXJz4+Pgo5UOGDJHi4mKr2IqKChk6dKhyFDUyMlLCwsKUuClTptjd+Xr06FHlrAtvb2+JiYkRX19fZafPjh07WnTcTOrbQEZGhowbN066du0qbm5uEhQUJHPnzrV5iI7aVn5+vrIi1/eqfXjezGQyyfr162XgwIGi0+nE09NToqKiZPny5VaH7eoqLCyUuXPnSlBQkLi7u4uvr6+MGzdODhw40EIjpdpf7g29UlNTbS7js88+k5EjR4perxetVivh4eGyePFiZY+NPXfv3pXFixdLeHi4aLVa0ev1MnLkSNm+fXsLjJRERMrKymT16tUyceJECQsLE71eLxqNRnx8fGTAgAGycOFCiyMndXH97hgaSupFONdqVFxcLEuXLpUxY8ZIUFCQ6HQ6cXV1lW7dusmPf/xj+fvf/y5Go9FufFVVlbz99tsSFRUlHh4eotPpZNCgQfLRRx81+N75+fkyY8YM6dGjh7i5uYm/v79MmjTJ6vTLluAkIgIiIiIiIlIt3v2GiIiIiEjlmNQTEREREakck3oiIiIiIpVjUk9EREREpHJM6omIiIiIVI5JPRERERGRyjGpJyIiIiJSOSb1REREREQqx6SeiIiIiEjlmNQTEREREakck3oiIiIiIpVjUk9EREREpHJM6omIiIiIVI5JPRERERGRyjGpJyIiIiJSOSb1REREREQqx6SeiIiIqJU4OTnByckJ+/fvb+uutJhLly7B1dUVjz76KEwmU1t3p0EGgwHu7u4ICQlBVVVVW3enyZjUExERETnAnJA35bVhw4a27n6refXVV2E0GpGamgpn5/afavbs2RPTp09Hbm4u/vznP7d1d5rMSUSkrTtBRERE1N75+/vbLL979y7Ky8sBAN26dbPZ5r333sPEiRMRHh4OAPjb3/6G/v37t0xH21B6ejpGjhyJRx99FOfOnYOTk1Nbd8khBoMBISEh0Ol0yM3NhV6vb+suNZqmrTtAREREpAY3btywWf7GG2/gt7/9bb1tzC5dutTs/WpP3nzzTQDAK6+8opqEHri/t3706NHYvn071q1bh+Tk5LbuUqO1/2MiRERERNTu5efn44svvoCrqysmTpzY1t1ptGnTpgEAPvzwQ6jxRBYm9UREREStxN6FsgUFBUpdQUEBrly5gpkzZ6Jnz57QarUIDg7G66+/rpzmAwAXLlzAtGnTEBgYCK1Wi9DQUCxduhTV1dX19qGgoABz585FREQEOnfuDA8PD4SHh2POnDkwGAxNHtu6desgInj88cfh6+trs81LL70EJycnvPTSSwCADRs2YNCgQfD29oaPjw9GjRqFAwcOKO2NRiNWrlyJmJgYeHl5wdvbG6NHj8bp06ft9uPatWtISkpCREQEPD094e7ujoCAAMTExCApKQknTpywGTdu3DjodDpkZ2er80JmISIiIqImS01NFQDiSFplbpeenm5Rnp+fr9T985//FL1eLwDEy8tLXFxclLohQ4ZIVVWV7Ny5Uzw8PASAeHt7i5OTk9Jm4sSJdt//448/Fnd3d6Wtu7u7dOrUSfldp9NJWlpak/4O0dHRAkB+//vf222TmJgoACQxMVH5WaPRiE6nU/qg0Wjk888/l8rKSnniiScEgLi5uYmnp6fSxsPDQ06ePGm1/KysLPHx8VHaubi4iI+Pj8XfJzEx0W7/EhISBICkpKQ06W/QlrinnoiIiKgdmTFjBmJiYvDll1/izp07KCsrw4oVK+Di4oKDBw9iyZIlmDp1KsaNG4eCggKUlJSgtLQUCxcuBAB88skn2Lt3r9Vy9+zZgxdffBE1NTVISUlBfn4+KioqUF5ejkuXLmHChAkoKyvDhAkTGr3HvrS0FGfPngUAhy4A3r59Oz799FOsWbMGpaWlKC0txaVLlxATEwOj0YjZs2cjOTkZJ0+exKeffoq7d++irKwMJ0+eRHBwMO7du4c5c+ZYLXfevHkoLi5GdHQ0jhw5gurqaty+fRuVlZW4fPky3nrrLURERNjt14ABAwAAGRkZjRp/u9DWWxVEREREatbce+ojIiKksrLSKvaFF15Q2jz++ONiMpms2gwZMkQAyIwZMyzKa2pqJDQ0VADImjVr7PZv/PjxAkDmzJnT4Fhq27dvn9K3oqIiu+3Me+cByMcff2xVn5OTo9QDkIMHD9b7XlevXrWoMx91OHz4cKP6b7ZlyxblaMF3333XpGW0Fe6pJyIiImpHkpKS4O7ublX+5JNPKj/Pnz/f5t1lzG3OnTtnUX7gwAFkZ2fD19cXP/vZz+y+94svvggASEtLa1Sfr1+/DgBwcXFBly5dGmzfs2dPTJkyxao8ODgYISEhAIAhQ4Zg8ODBVm2GDRum/H3qjtN8K8pvvvmmUf03M18LYDQaUVhY2KRltBXe0pKIiIioHbF3+krte+DHxcXV26a4uNii/NChQwCAO3fuICAgwO57m5+oeuXKFcc7DCgJsF6vd+hWlrGxsXbbdevWDTk5OXbH6OLiAl9fX3z99ddW4xw7dizWrl2LxMREHDp0COPHj0dcXBw8PDwcGkftDZLCwkJ0797dobj2gEk9ERERUTui0+lslms0Gofb1L0DjnlPenV1NW7evNlgHyoqKhzqq1llZSUA2DzCYIu9/gP/H4MjbeqO849//CNycnKQnp6O5cuXY/ny5XBxcUFUVBTGjBmDWbNm1Zuod+rUSfnZPCa14Ok3RERERB1cTU0NgPsXgoqIQ6/G6Nq1KwDrIwStTa/X47///S8OHjyIlJQUxMfHQ6PR4NSpU1iyZAlCQ0Pxj3/8w2787du3lZ/NY1ILJvVEREREHZy/vz+Axp9W4yg/Pz8A9/fwt4c93IMHD8abb76JzMxMlJSUYPv27YiMjERFRQVefvllu0craif15jGpBZN6IiIiog4uPj4eAHDjxg2cPHmy2Zf/ox/9SPk5Ly+v2Zf/ILRaLcaPH49//etfAO6fVpOZmWmzbX5+PgDg4YcfVi66VQsm9UREREQd3IgRI5S7yiQlJSkXxNpTe4+1I8LCwpSLdI8fP960Tj4go9EIk8lkt772+fLOzrZT4GPHjgEAhg4d2rydawVM6omIiIg6OI1Ggw8++AAajQaZmZkYOnQo9u3bZ3GhaV5eHj744APExcVh1apVjX6PYcOGAfh/Ytzarl27htDQUCxduhRnzpyB0WhU6s6dO4dp06YBADw9PZW+1mXuu7369oxJPREREdH3QEJCArZs2QKdTodjx45h1KhR8PT0hK+vL7RaLYKDg/GLX/wCJ0+edOi2lHVNnjwZALBr165GX2jbXPLy8rBo0SJER0dDq9Wia9eucHd3R9++fbF//364ublhw4YNNu+lf/nyZXz11Vdwc3PD888/3wa9fzC8pSURERHR98QzzzyDnJwcrFq1Crt370Z2djZKSkrg6emJ8PBwxMXFYcyYMRg9enSjlz127FgEBATg6tWryMjIwPDhw5t/APXo3r07duzYgfT0dBw5cgTXrl3Dt99+C41Gg5CQEIwYMQJz5sxBaGiozfiNGzcCAJ599lnVXSQLAE7SVptSRERERNShLFmyBKmpqZg+fTo++uijtu6Ow0QEoaGhyM3NRUZGhirPqWdST0RERETNorS0FCEhIbhz5w5yc3PRo0ePtu6SQz755BNMmjQJTz75JP7zn/+0dXeahOfUExEREVGz8PLyQmpqKqqqqrBs2bK27o5DTCYTlixZAmdnZ/zpT39q6+40Gc+pJyIiIqJm8/Of/xwlJSVwdnaGyWSye/vI9uL69euYMGECevfujcjIyLbuTpPx9BsiIiIiIpVr35tORERERETUICb1REREREQqx6SeiIiIiEjlmNQTEREREakck3oiIiIiIpVjUk9EREREpHJM6omIiIiIVI5JPRERERGRyjGpJyIiIiJSOSb1REREREQqx6SeiIiIiEjlmNQTEREREakck3oiIiIiIpVjUk9EREREpHJM6omIiIiIVI5JPRERERGRyjGpJyIiIiJSOSb1REREREQqx6SeiIiIiEjl/gd3iiubrMb4rgAAAABJRU5ErkJggg==", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "plotsig(voltsig(sd) / mV, [0,500], ms, hylabel = \"Simulated membrane voltage (mV)\");" ] }, { "cell_type": "markdown", "id": "69044ebb", "metadata": {}, "source": [ "We need to fix the spike peaks for aesthetics. But otherwise seems fine." ] }, { "cell_type": "code", "execution_count": 15, "id": "20b1c3d0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.856666667832444" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spikerate(sd) / Hz" ] }, { "cell_type": "markdown", "id": "138778ea", "metadata": {}, "source": [ "Maybe sth went wrong during calc and wrong data was saved.\n", "Otoh, both the STA methods and the upstroke fit (which uses the sim directly) have bad perf.." ] }, { "cell_type": "markdown", "id": "ee32d9ed", "metadata": {}, "source": [ "Ok but first, is this very different from another seed?" ] }, { "cell_type": "code", "execution_count": 16, "id": "232b4a82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/run_sim/_ N=100 δ_nS=0.75 duration=600.0 seed=2 _.jld2] … ✔ (0.5 s)\n" ] } ], "source": [ "sd2 = sims(; N=100, seed=2, duration=10minutes, δ_nS=0.75);" ] }, { "cell_type": "code", "execution_count": 17, "id": "f05dc0f2", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "plotsig(voltsig(sd2) / mV, [0,500], ms, hylabel = \"Simulated membrane voltage (mV)\", ylim=[-80, 0]);" ] }, { "cell_type": "markdown", "id": "7b1a2fd1", "metadata": {}, "source": [ "So no, looks similar." ] }, { "cell_type": "markdown", "id": "275eb851", "metadata": {}, "source": [ "Let's then look at some STAs." ] }, { "cell_type": "code", "execution_count": 27, "id": "e8597405", "metadata": {}, "outputs": [], "source": [ "kw = (; N=100, duration=10minutes, δ_nS=0.75, Nᵤ=100, batch_size, part=1);" ] }, { "cell_type": "code", "execution_count": 19, "id": "154e45c6", "metadata": {}, "outputs": [], "source": [ "# reals, shufs = STA_sets(; kw..., seed=1);" ] }, { "cell_type": "code", "execution_count": 20, "id": "26153cfe", "metadata": {}, "outputs": [], "source": [ "# ylim = [-59, -56]\n", "# plotSTA(reals[1]; ylim);" ] }, { "cell_type": "markdown", "id": "b6489a53", "metadata": {}, "source": [ "Seems proper" ] }, { "cell_type": "code", "execution_count": 21, "id": "c417d0ef", "metadata": {}, "outputs": [], "source": [ "# reals2, shufs2 = STA_sets(; kw..., seed=2);\n", "# plotSTA(reals2[1]; ylim, color=C1);" ] }, { "cell_type": "markdown", "id": "41ae3dc5", "metadata": {}, "source": [ "Not too different. " ] }, { "cell_type": "code", "execution_count": 22, "id": "100309c6", "metadata": {}, "outputs": [], "source": [ "# plotSTA(shufs[1][1]; ylim);\n", "# plotSTA(shufs2[1][1]; ylim);" ] }, { "cell_type": "markdown", "id": "a4e9f85a", "metadata": {}, "source": [ "So only difference is a lower avg Vm." ] }, { "cell_type": "code", "execution_count": 23, "id": "003afc7b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-57.73967079215508" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean(voltsig(sd)) / mV" ] }, { "cell_type": "code", "execution_count": 24, "id": "e6598886", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-58.51984240174548" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean(voltsig(sd2)) / mV" ] }, { "cell_type": "markdown", "id": "da14f51a", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "id": "70639aad", "metadata": {}, "source": [ "Or maybe, let's delete the cached data and recalculate, see if it stays the same." ] }, { "cell_type": "markdown", "id": "e13c3496", "metadata": {}, "source": [ "Nope, it stayed the same." ] }, { "cell_type": "markdown", "id": "358e09f6", "metadata": {}, "source": [ "Huh!" ] }, { "cell_type": "markdown", "id": "6f58a6db", "metadata": {}, "source": [ "## Solution" ] }, { "cell_type": "markdown", "id": "44857f0a", "metadata": {}, "source": [ "(Hit me offline)" ] }, { "cell_type": "markdown", "id": "b50c0db3", "metadata": {}, "source": [ "N = 100, Nᵤ = 100, and the seed used to generate both is the same." ] }, { "cell_type": "markdown", "id": "e4422294", "metadata": {}, "source": [ "So, add Nᵤ to the seed, e.g." ] }, { "cell_type": "markdown", "id": "ae9acd80", "metadata": {}, "source": [ "Next:" ] }, { "cell_type": "markdown", "id": "02efaf83", "metadata": {}, "source": [ "## Why is AUC < 0.5" ] }, { "cell_type": "markdown", "id": "ad2146ca", "metadata": {}, "source": [ "First, let's plot em" ] }, { "cell_type": "code", "execution_count": 24, "id": "b316b93d", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAIfCAYAAADe2AL7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAB7CAAAewgFu0HU+AACk8UlEQVR4nOzdeXxU1f3/8dedJZnse0gggbCjIJvsyK5WRUWtCnWvWlsVrNaltl/rUu2vdanVurZq3Reqte677DsCgqDsBBKW7Oskk9nu74+YlJAEAmQmmeT9fDwgZO65934un5nJJ2fOPccwTdNERERERESCxtLWAYiIiIiIdDYqwkVEREREgkxFuIiIiIhIkKkIFxEREREJMhXhIiIiIiJBpiJcRERERCTIVISLiIiIiASZinARERERkSBTES4iIiIiEmQqwkVEREREgkxFuIiIiIhIkKkIFxEREREJMhXhIiIiIiJBpiJcRERERCTIVISLiIiIiASZinARERERkSBTES4iIiIiEmQqwkVEREREgkxFuIiIiIhIkKkIFxEREQlhCxYswDAMDMNgwYIFbRrLvffeWx9LoLSn6z0eKsJFRESkQ3A6nTz77LOcddZZdOvWDYfDQXh4OCkpKYwcOZKrr76a5557jpycHKBhwXisf6666qpGcZimSa9everbXHfddUH+n5BQoCJcREREQt7y5cs58cQTuf766/n000/Zt28fNTU1uN1uCgsL+eabb3jxxRe57rrrGDlyZEBjWbx4Mbt27ar//u2338blcgX0nHL0gtFrfzi2NjmriIiISCvZunUrP/nJT6ioqADg3HPP5cILL6Rfv36EhYVRWFjI+vXr+fLLL5k/f379fjfccAMXXnhhk8d8//33ueuuuwB44IEHmDFjRpPtEhISGj32yiuvABAdHU1lZSWlpaV88MEHXHzxxcd1naHg3nvv5d57723rMEKCinAREREJaf/3f/9XX4C/+OKLTQ4ROe2007jtttsoKCjg3//+NwCpqamkpqY2ecxvvvmm/t/dunVj0KBBLYrF5XLxzjvvAPDLX/6STz/9lO+//55XXnmlUxTh0nIajtIOlZSUEBERUf8RybZt2w7bfvLkyRiGweTJk4947KO5mWHDhg389re/ZdSoUXTp0oWwsDDi4uI48cQTufLKK3n//ffxeDxHcWUiIiKty+fz8fHHHwMwYsSIJgvwg6WkpHDjjTcGLJ733nuPsrIyAC699FIuu+wyAD7//HPy8/MDdl4JPSrC26HXX3+9wdixf/3rX0E9f0VFBZdeeilDhw7loYceYvXq1RQUFBAVFUVNTQ0//PADr7zyCueddx4nnHACK1euDGp8IiIidQoKCqiurgagT58+bRzN/4ainHjiiQwbNoxLL70UwzDwer288cYbQYvj3//+N9OmTSMlJYWIiAj69+/PHXfcQXFxcYv2f++997jooovo3r07DoeD+Ph4RowYwX333UdJSUmz+7V0nPWSJUv46U9/SlpaGg6Hg169evGrX/2K7du3A0fXwXi01/vSSy9hGAb33Xdf/WNN3XSbnZ3donMfKxXh7dALL7wAwJw5cwB4+eWX8fl8QTl3SUkJY8eOrX+jmDVrFgsXLsTlclFSUoLL5WLv3r08//zzDB48mB07drB8+fKgxCYiInKosLCw+n//8MMPbRgJ5OXl8cUXXwDU94B3796diRMnAv8r0APJ7/dz+eWXM3PmTObNm0dhYSEul4utW7fy8MMPM3r0aA4cONDs/iUlJUybNo3zzz+fd955h5ycHGpqaigrK2PNmjXce++9DBgwgBUrVhxzjA8++CATJ07k3XffJS8vj5qaGnbt2sU//vEPhg8fXv9/GIzrbUsqwtuZtWvX8u233xIfH89DDz1Ez5492b9/P5988klQzn/ppZeyadMmbDYbc+fO5c0332TixIkN3uS6du3KNddcw7fffsvTTz+Nw+EISmwiIiKHSkxMpEePHgCsX7+eBx98EL/f3yaxvP766/h8PgzD4NJLL61/vK4gX7duHZs2bQpoDH/4wx947bXXOO+883j33XdZs2YNn3zyCdOnTwdg+/bt3HLLLU3uW1NTw6mnnsq8efOwWq1cfvnlvPnmm6xYsYLFixfzpz/9iaSkJPLz8znrrLPYvXv3Ucf373//mzvvvBPTNElMTOTBBx9k2bJlLFu2jAcffBCbzcasWbPYv39/wK73vPPO47vvvuP666+vf+y7775r9Kdbt25HfX1HxZR25YYbbjAB85e//KVpmqZ5zz33mIB53nnnNbvPpEmTTMCcNGnSEY8/f/58EzABc/78+Q22ffLJJ/Xb/vjHP7Y4Zp/P1+K2IiIire2RRx6p//kFmFlZWeZNN91kvvXWW+bOnTuP6Zgvvvhi/fFefPHFFu0zZMgQEzAnTpzY4PHS0lIzPDzcBMzbb7/9mOI5nIN/tgPmAw880KiN3+83Tz/9dBMwbTabmZ+f36jN73//exMw4+PjzW+++abJc2VnZ5vp6ekmYF5yySWNttfVLU2VmC6Xy+zSpYsJmMnJyea2bdsatdmyZYuZmJhYf4ymapvWut7DxRoM6glvR1wuV/0wkCuuuKL+q2EYfPTRR+Tl5QX0/E8++SQAcXFx/OY3v2nxfhaLnkYiItJ2brnlFq6++ur677Ozs/n73//OrFmz6NWrF2lpacyaNYsPP/wQ0zQDEsN3333H+vXrgf/1fNeJi4vjnHPOAWp7ywPZU3/yySfz+9//vtHjhmHU/2z3er2NhpJWVlby1FNPAXD//fdz8sknN3n8Hj168Ic//AGonf/c6XS2OLb33nuvvpa59957mxzD369fP+65554WH/NYr7c9UPXUjvznP/+htLSUPn36MG7cOAB69erFKaecgtfrDehYMq/Xy6JFi4DaaZyioqICdi4REZHWZLFYeOGFF/jiiy8444wzsNkazsCcl5fH3LlzOffccxk1ahQ7duxo9RhefvllAMLDw7nooosaba8rzPft28fXX3/d6uevc8kllzR7U+TBhfXOnTsbbFu4cGH9rC7NzZ1ep26Mu8fjYc2aNS2O7auvvgJq83XwcJ1DXXbZZS1eQOdYr7c9UBHejtTdkFnXC16n7vtAzpKye/duKisrARg2bFjAziMiIhIop512Gp9++ilFRUV88skn3HfffZxzzjnExcXVt/nmm2+YMGFCi8cct4TP5+P1118HYPr06cTHxzdqc9ZZZ5GYmAgE9gbNAQMGNLut7vxA/bzqdQ6eFz09Pb3J2ULq/hw8Z/rR3PS4ceNGoLaDsan/o4Pj7NWrV4uOeazX2x6oCG8ndu7cWT+H9+WXX95g28UXX0xERASbN29m2bJlATl/UVFR/b8PftKKiIiEmtjYWM4880zuvvtuPvjgA/Ly8vjXv/5Vv7rl/v3764dUtIYvvviivhg9dChKHbvdzsyZMwH473//W9/x1doiIyOb3Xbw8NFDZ1071jnMq6qqWty2bmrDlJSUI7ZtSRs49uttD7RiZjvx4osvYpomEydOJCsrq8G22NhYzjvvPN58801eeOGF+qEqIiIicmTh4eH8/Oc/p2vXrpxxxhkAvPvuu/zzn/9slfuaDu7ZvuCCC47Y3ul08p///Icrr7zyuM/dWg4uUteuXYvdbm/RfhkZGYEKqcNTEd4O+P1+XnrpJaDxUJQ6V155JW+++Sb//ve/efzxx4mOjm7VGJKSkur/3dKJ/EVERELJT37yEzIzM8nJyaGkpISioqIW97g2p7y8nPfff/+o93vllVfaVRF+cB2QkpISkOK67pOIgoKCI7ZtSZtQpyK8Hfj888/Jzc0F4Nprr+Xaa69ttm1lZSX//ve/G9wFHhERAVC/YtjhHPyxUd1+UHu3c3R0NJWVlaxbt+6or0FERCQUdO3alZycHIAW3/x3OG+//Xb9z98//vGP9O3b97DtP/roI15//XUWLFhATk4OmZmZxx1Dazj4frClS5fWD51pTQMHDmTFihXs3LmTkpKS+qL8UMXFxUG5kbI18n88VIS3A3U3ZB5N+4OL8OTkZAD27t17xH0PbnPwb/82m42JEyfyySef8OWXX+J0OjVDioiIdChVVVV8//33QO1Qz4N7f49V3VCUhIQE7rzzziMO4xgwYED9NIWvvfYav/vd7447htZw6qmnEhkZSVVVFX//+9+5+OKLW71InTZtGi+88AJ+v5833niDG2+8scl2r732WsCmkjzYwYsN1tTUEB4eHvBzHkw3ZraxgoICPvjgAwDeeecdKioqmv2zatUqAJYtW8aWLVvqj1E3Bc/evXvJzs4+7PmWLFkC1N582bNnzwbb6l4MZWVlPProoy2+hrZamUxERKSyspLRo0fz0UcfHfbnkd/vZ86cOfWzZJx77rnHXWRmZ2ezePFiAGbMmNGicdRDhw6ld+/eALz66qvHdf7WFB8fz+zZs4HaOuOWW2457P9nXl4ezz///FGd4/zzzyc1NRWonSe8qakit23bxn333XdUxz1W6enp9f8OxLSVR6IivI29+uqreDye+on8o6Ojm/0zcuTI+ql4Du49v+CCC+rnRP3zn//c7Ll27NjBO++8A9TOAXrom89ZZ53F6aefDtR+pFbX9nD++c9/8txzzx3dRYuIiLSiVatWcc4559C9e3dmz57N66+/zpIlS1i/fj0LFy7kscceY+jQofVT/cbFxXH//fcf93lfffXV+h7bn/70py3er67tDz/8wOrVq487jtbyxz/+kdGjRwPw+OOPM3z4cJ566imWLl3Kt99+y/z583nyySc577zz6N69O88+++xRHd/hcPDYY48BUFhYyOjRo3n44YdZsWIFK1as4KGHHmLMmDH4/f76YT2BHDJy8EQXt9xyC4sWLWLbtm1s376d7du34/V6A3ZuQMvWt7UTTzzRBMwrrriiRe3/8Ic/mIDZpUsX0+Px1D9+66231i+9eu2115pbtmyp3+Z0Os23337bzMzMNAEzMTHR3L17d5PHLywsNE844QQTMA3DMC+55BJz0aJFptvtrm+zb98+86WXXjKHDx9uAubf/va3Y7t4ERGR41RdXW2mpaU1WMb8cH/69u3b7JLsB2vJsvV9+/Y1ATM2NtasqalpccyrVq2qP/bs2bNbvF9zDl7Gff78+YdtW9funnvuaXJ7eXm5ecEFF7To/3LKlCmN9m/JUvAPPPCAaRhGk8eMjIw0P/74Y3PChAkmYJ5xxhkBvd6LL7642evbtWvXYY99vNQT3oZWrFhRPzatqdW1mlLXLi8vj48//rj+8b/85S9cc801ADz//PP079+fqKgokpKSiImJ4aKLLiInJ4e0tDQ++ugjunfv3uTxk5KSWLFiBRdffDEAb7zxBhMnTiQ8PJzExEQiIiLo2rUrV111FWvXruWEE05gwoQJx/x/ICIicjwcDgd79+5l6dKl3HfffZx55pn06tWLqKgorFYrsbGxDBgwgJkzZ/LGG2+wcePGZpdkPxrLly9n27ZtAJx99tmEhYW1eN+RI0fW/xx+66238Hg8xx1Pa4mJieE///kPixcv5tprr6V///7ExMRgs9lITExk5MiR3HjjjfX3kB2L//u//2PhwoWcd955pKamEh4eTo8ePbj66qv55ptvOOussygvLwdosNBSILz22ms89NBDjBo1iri4uFaZsrKljB9/S5A28Itf/ILnn3+euLg48vPzW/wCPvHEE/nhhx8455xz6seT11m6dCnPP/88y5YtY9++fbhcLhISEhg4cCDTp0/nF7/4RYuf0OvXr+e1115jwYIF7N69m5KSEiIiIsjIyGDUqFFcdNFFnHHGGVit1qO+dhEREZGm1A3Tra6u5q677mqVoUPtkYpwEREREWk33n777fpP5D/66COmT5/exhEFhopwEREREQma7du306dPnya3ZWdnM2HCBHJzc+nSpQu5ubn1k090NB3zqkRERESkXRowYABnnXUWZ599NgMHDiQqKor8/Hzmz5/Ps88+S2lpKQCPPPJIhy3AQT3hIiIiIm1q69atuN3uo94vNTW1ft7tUHKkaQctFgsPPPBAu1nIKFBUhIuIiIi0oaysLHbv3n3U+91zzz3ce++9rR9QgH300Ud8+umnLFu2jLy8PIqKiggPD6dbt25MnjyZG2+8kUGDBrV1mAHXcfv4RURERKTdOfvsszn77LPbOow2p55wEREREZEg02I9IiIiIiJBFpJF+IEDB3jttdf49a9/zfjx44mMjMQwDIYOHdrWoYmIiIiIHFFIjgl/6623uOWWW9o6DBERERGRYxKSRXhsbCynnnoqI0aMYMSIEWzdupXf//73bR2WiIiIiEiLdIgbM1966SV+/vOfM2TIEL799tu2Dqfd2FVQybKdRVS6vEQ7bIzrlUTPlOi2DktERESk0wvJnnA5vFW7inhpWTZb8yoxTfCbJhbD4MWl2fTrEs1V47IY1TOprcMUERER6bRUhANer5cDBw6wf/9+UlJSjrhEakZGRpAiO3rvf7uXp+Zvp6rGR2m1B5fHV7/NYbficvu4672N3DilDzOGdmvDSKU5JU43OwsrcXn8OOwWeiVHkxAV1tZhiYiISCtSEU7tbCuZmZktbt9eR/Cs2lXEU/O3U1TpprTKjQnYLBYMA0wTqtxeqt1e4iPDeGr+dtLjHOoRb0dyiquYtzmfLXkVtZ9g+E0sFgPDgP5dYpg6IJXMxMi2DlNERKTN+f1+LJaQnOSvnorwNnLgwIFWP+azX++guNJFidOLYYDVAL/px/Rz0Pe1Pa0Gfv4xbzPdp/du9Tjk6G3Jr+KDjYVU1vgocLoprfbi94PFAvERNopKK9mQnc+5g5Lpn6pCXEREJBjS0tICdmwV4YdYtWoV6enpAT9PcnJyq/ao7yp0sqO4hjKXD5Panm+T2uIbav/tN2sfByhz+dheVEO56aBnclSrxSFHL7ekmi+251PuMdhVXIPNaqFrXCRWfPiwUljlYUdxDT2To/hieyXd05LJSIho67AlQLxeL2VlZcTFxR1xaJy0D8pZaFLeQldHyV3oRh4g6enpQRnz3dpPmtV7ynC6fXh9JiZgMagfhmKaYPC/gtxvgtdn4nT7+GZPOf3S41s1Fjk6i7bvpdLtY1dRFUnR4WQlR4FpUuNyEe5w0C3JILvQya6iKqLCbSzeUcwVY7PaOmwJMJvNht1ub+sw5CgoZ6FJeQtdoZ670B5MI/UKyl1Uu2t7weuYZm3hfXBBXr8NqHb7KKhwBTtUOUiJ082WvAryyl3YrRaykqOw1P229COLYZCVHIXdauFAuYvNByoocbrbKGIRERFpDSrCO4gqjx+f/39Vdl0Z5/ODzwS//8cHDirEfX6Tavf/Zk+R4NtZWDuNZLHTTUp0GBbDoMLlYUdBJdsKq9hRUEmFy4PFMEiJDqPY6cY0YWehs61DFxERkeOg4SgdRLd4B76Durp9hww3rxsnfjCP30+X2PDAByfNcnn8+P0mPj9Ue3ws2V5AWZWnNl9+P4bFzda8SuIi7aTFOvD5a+d9r/HolycREZFQpiK8g7BZLYRZLXh8LS/OfH74y2dbWL6zmMn9U5nUP4Vu8brhL5gcdgsWi4Gzxsv+sioMDNy+2k81aocT+bFaDHyVJkWVNcQ6anvLw+3Wtg5dREREjoOK8A4iKSqMqHArzqMcXlLj9fPF93l88X0eAH1To5nUL4XJ/VMZ2TOBcJuKvUDqlRxNQYWLsmo3bp8f/4/TSdp+nB/cNMHrN6ny+bBYoKzaTUGFi16a0UZERCSkhWQRnpOTw7Bhw+q/r6mpAWDjxo0kJyfXP/6zn/2MJ554IujxtYWUmHDC7VYsP84Ffqy25VeyLb+S55fsIsJuZVzvJCb1T2Fyv1S6J2l+6taWEBXGvtJqwMDnr53LPSLMimEYmH4Tw2IQZtaO3ff5wbAa7C+r1gqaIiIiIS4ki3Cfz0dRUdERH6+oqAhmWG0qMTIMq2FgtRj4fQ1v0DQP+no0qj0+vt6cz9eb84FN9EyOYlK/FCb1T2FsryQcGhJx3HYVVNZOLen3YzHAYjFwe2uHoNQN5Pf9uHKm6Tfx+P1U1vjYVVBJz5Totg5fREREjlFIFuFZWVntdun4tlJc5SYpOpz8ihpMs7aIM/1+fOb/piq0GmBYLPj8JmE2C31+HHqSXVTF4m0FlFZ5DnuOXYVOdhU6eWlZNuE2C6N7JTH5x6K8V3IUxiFT68mRLdtZhM1iwTTBZrEQZrPg9fvx1o8JB6vFwGax4Pb68ZsmNouF5TuLVYSLiIiEsJAswqUxl8dPeqyDbXmVRERYcdZ4MQ2DMKtR3xXu85sYQFyEHY/PpHtiJGN7JXHr6f3x+U3W55aycEsBC7YWsCG3tNFsKger8fpZtLWARVsL4CPITIyoHUveL5WxvZOICtdTqyUqXV78polhQEy4DcMCbi+Y1rrZUSwYQJit9sbbihoPpmnirDn8L0wiIiLSvqlS6iAcdgsxEXbiI214fCbRDgfOGi/Vbh9+TCyGQVSEjahwG26vn6hwgxiHvX6WDavFYHj3BIZ3T+CW0/pR7HSzeFsBC7cUsHBrAUVHWBwmp7ia11bs4bUVewizWhjZM6F26Eq/VPp1iVYveTOiHTYshoGBgc80SYoIx+c3cXt9+Hw+rFYrYTYrVotBUWUNBgaGYRAVHrorhImIiIiK8A6jV3I0hgFd4yMoqHATF2HDbrEQ6zDx+bxYrbXFXmyEjbJqLykxYRgGzc6ykRgVxoyh3ZgxtBt+v8mmfeUs2JLPwq0FrN1TctibP90+P0u3F7F0exH/75PNpMc5fpxxJYVxfZKJdaiArDOuVxIvLs3GYbdQ7fHhN/1YLRYcdit+C1istTdp+k0/1R4fDrsFw4CxvRLbOnQRERE5DirCO4iEqDD6d4nBWeOlsNKN3WphUEYsFdUeXK4aHI5wYiLs7C91YRiQFutgQFpMi2bZsFgMTsqI46SMOOZM60tZlYcl2wvri/L8iprD7r+/zMVbq3N4a3UONovB8B4J9UX5iemxnbqXvGdKNP26RONy+8ircFFY6SY5OgyD//2f+E0/hZVu/KZJrMNO/y7RGg8uIiIS4lSEdyBTB6SyPb+S3slR7Ch0Uu7ykhRlx2oxqPH62XegEo/PT+/kKGIcdqb0Tz2m88RF2pk+OJ3pg9MxTZPNBypYsKWAhVvz+Sa7BO9husm9fpNVu4pZtauYhz/fQkpM+I/DVlKY0DeZ+MjON/XeVeOyuOu9jST4wiipcrOv1EWE3fLjdJNeqj21N2QmRIYRGW7lynFZbR2yiIiIHCfD1DQj5ObmkpmZCdTOQZ6RkdHGER27jXvLmLs6hwqXhwPlLooq3Xg8Xux2G0nRYaTFOohx2Jk5MpNB3eJa/fwVLg/LdhTVFuVb8tlX5mrxvhYDhmbG167e2S+Fk7rFYbF0jl7y97/dy1Pzt1NV46Pc5aldzt6sHcvvsFuIddiJDLdy45Q+zBjara3DlQDyeDwUFRWRlJSE3a6hW6FAOQtNylvo6ii5UxFOwyK8b9++jRK6adOmVj+naZoBm2Yxt6SKeZsL2JJXgd/vrx+OYrFY6N8lhqkDUshICPzCO6Zpsj2/koXbClm4tYDVu4px+1p+zYmRdib0S2FSv2Qm9EkmKTo8gNG2vdXZxby8bDdb8yrwmyZenw+b1YrFMOjXJYYrx/VgZJbGgnd0Xq+XkpISEhISsNn0YWUoUM5Ck/IWuoKZO4vFErBjqwinbYpwp9PZ6sc8VGmVh+ziKlwePw67hazESOIj2+43xiq3j1XZJSzZUcySHcXklLS8l9wABnaN4ZTeiUzok8hJXWNrF7TpgHYXVbFqdymVNV6iw22M6hFPD61WKiIiEnRRUU1PYNEaVITTNsNRAtkTfrD2+pu+aZpkF1WxcGsBC7cWsmJnETVef4v3j4uwM6FPMhP7JTOxbzKpsY4ARht87TVvEnjKfehRzkKT8ha6OkpPuJ51bcQwjKDMCmIYBqZpYhhGQJ9Ix6J3agy9U2O4+pReuDw+Vu4qrp9xZWfB4T8pKKv28NF3+/nou/0AnJgey6T+KUzul8LwHgnYre3rWo9We86bBJZyH3qUs9CkvIWujpI7FeHSLjjs1vpZUgByiqtYsLX25s5lO4qocvsOu//3+8v5fn85zyzYQUy4jfF9kpnUv/Z4XeMjgnEJIiIiIi2mIlzapczESC4f04PLx/SgxutjTXbJj0V57Q2nh1NR4+WzTQf4bNMBAPp1ia6fcWVEVgLhNmswLkFERESkWSrCpd0Lt1kZ1yeZcX2S+f1ZJ7CvtJpFWwtYsKWApdsLqajxHnb/rXmVbM2r5J+LdhIZZmVc7yQm9U9lcr8UMhN1w6OIiIgEn4pwCTld4yOYNao7s0Z1x+Pzs3Z3CQt/LMq/319+2H2r3D6++iGfr37IB6BXclT9sJUxvZJw2NVLLiIiIoGnIlxCmt1qYXSvJEb3SuKOMwaQX+76ccaVAhZvK6Ss2nPY/XcWOtlZ6OTFpdmE2yyM6ZXE5B+L8p7JUUG5eVZEREQ6HxXh0qGkxjq4aEQmF43IxOvzsz63jIU/zriyYW8Zh5sVssbrry/gAbonRjKpXwqT+6cwtncSkWF6uYiIiEjrUFUhHZbNauHkHgmc3COB35zen6LKGhb/uHrnoq0FFDndh91/T3EVr67YzasrdhNmtTCqZ2J9Ud4nNVq95CIiInLMVIRLp5EUHc55w7px3rBu+P0mG/eVsWBLbc/3uj0l+A/TS+72+VmyvZAl2wv50yc/0C0+gok/Tqk4vk8SMY62W4lUREREQo+KcOmULBaDwRnxDM6I56ZpfSmtcrNke2F9UV5QUXPY/feWVvPmqj28uWoPNovByT0S6qdBPCE9Rr3kIiIiclgqwkWA+Mgwzh7clbMHd8U0Tb7fX14/48qa3SX4DtNN7vWbrNxVzMpdxTz42WZSY8J/HLaSyil9komLVC+5iIiINKQiXOQQhmEwsGscA7vGccPkPpS7PCzbXlhflO8vcx12//yKGt5ek8vba3KxGDC8e0J9UT6waywWi3rJRUREOjsV4SJHEOuwc8agdM4YlI5pmmzLr2TBjzOurNpVjMfXfC+534Rvdpfwze4S/vrlVpKjw5jYN4VJ/VOY0DeFxKiwIF6JiIiItBcqwkWOgmEY9OsSQ78uMVw3sTfOGi/LdxSxYGs+C7YUkFtSfdj9CyvdvLtuL++u24thwOCM+PoZV4ZkxGNVL7mIiEinoCJc5DhEhds49cQunHpiF0zTZGehk4VbCliwtYAVO4twe/3N7muasD6nlPU5pfz9623ER9qZ0Ld2xpVxPeOxBPE6REREJLhUhIu0EsMw6J0STe+UaK4+pSfVbh8rdxWxYEvtvOQ7C52H3b+0ysOH6/fx4fp9AAzpGs3PT+nF9CHdsFtVkouIiHQkhmkebg3BziE3N5fMzEwA+vbti93ecDaLTZs2tfo5TdMkGP/1Xq+XkpISEhISsNn0O1db2lNcVb8i5/IdxVR7fC3ar0tsOJeO6s6sUZkkR4cHOEppa3rNhh7lLDQpb6ErmLmzWALXCaYinLYpwp3Ow/eKSsfm9vpZs6eUJTuKWby9mB2FVUfcx241OHNgKpeO7MagrrFBiFJERKRzi4qKCtixVYTTsAjPyckhIyMj4OdUT7gcbG9pNYu2FrBwayHLdhRSWXP4XvJhmfFcMbYHZw5KI8ymoSodiV6zoUc5C03KW+jqKD3heta1EcMwgrKqomEYmKaJYRgBfSLJ8clMjOLSMVFcOiYLt9fPpxv28sKSHWzY1/QnJutySlmXU8r/+3Qzl47uziWju5Ma4why1BIIes2GHuUsNClvoauj5E5FuEg7E2azcNZJaYzuaudAjZ3XVuXywfp9Tc60UlBRw2NfbeOp+duZflI6V47LYlj3hDaIWkRERI5G6P76INIJDOwayyMXDWH5nVO5/Sf9SY9rurfb4zN579t9nP/0MmY8tZT/rsulxtuyGz9FREQk+FSEi4SApOhwbpzSh8V3TOHpS4czqmdis23X55Ryy9z1jP/LPB79cit55a4gRioiIiItoeEoIiHEZrVw1knpnHVSOt/vK+flZdm89+1eapoYqlJY6ebvX2/j6fnbOfOkdK4al8Xw7vFBuRdBREREDk894SIh6sSusTx44WBW/G4avz1jAN3iI5ps5/WbfLh+Hz99ZhnnPrmU/6zRUBUREZG2piJcJMQlRIVx/eTeLLx9Ms9eNpwxvZofqvLd3jJufXs94/48j0c+38KBMg1VERERaQsajiLSQdisFs4YlM4Zg9LZfKB2qMp/1+3F5Wk8VKXI6ebJ+dt5ZuEOzhiUxlXjshjRI0FDVURERIJEPeEiHdCAtFj+fEHtUJXfn9X8UBWf3+TjDfu56NnlnP3EEv79TQ4uj4aqiIiIBJqKcJEOLD4yjOsm9mbRHVP45+UnM75PUrNtN+0r5453NjD2z1/z0Geb2VdaHcRIRUREOhcNRxHpBKwWg9MHpnH6wDS25lXw8rJs3l27l+omer1Lqjw8vWAH/1i0k9NP7MJV47IY1TNRQ1VERERakYpwkU6mX5cY/nT+SdzxkwG8vSaHV5bvZk9xVaN2Pr/JpxsP8OnGAwxIi+GqcVnMGNqNiDBrG0QtIiLSsWg4ikgnFRdp59oJvZh/22Sev2IEE/omN9t284EK7nz3O8b+5Wv+/OkP5JY0LtpFRESk5dQTLtLJWS0Gp57YhVNP7ML2/ApeXrab/6zNpcrdeKhKaZWHfyzcyXOLdnLaiV24clwWY3slaaiKiIjIUVIRLiL1+qTGcP95g7j9jP68800uLy/PZndR415vvwmfb8rj80159O8Sw5XjsjhvWFciw/SWIiIi0hKGaZpmWwfR1nJzc8nMzASgb9++2O32Bts3bdrU6uc0TZNg/Nd7vV5KSkpISEjAZlOBFCraS978fpOF2wp4edluFm0rPGzbWIeNmSMzuWx0dzITI4MUYcfTXnIvLaechSblLXQFM3cWS+BGbqsIp22KcKfT2erHFAmkXYVVvPnNXt5bfwBnE0NV6hjA5H5JXDYqg9FZ8RqqIiIiISsqKipgx1YRTsMiPCcnh4yMjICfUz3hcjjtOW8VLg/vrt3LKyv2sKvw8L9M9k2N5vIx3Tl/WDeiwtvXdbRX7Tn30jTlLDQpb6Gro/SE61nXRgzDCEoPoWEYmKaJYRgBfSJJ62rPeYuLDOfnp/TiynE9WbStgJeXZTN/S0GTbbflV3L3B9/z8BdbuXhEJleM7UGPpMD1KnQE7Tn30jTlLDQpb6Gro+RORbiIHBOLxWBy/1Qm909lV6GTV5Zn8843uVTUeBu1rXB5eWHJLv61dBdT+6dy5bgsJvRN1lAVERHptFSEi7QzJU43Ww+UkV9UTqrTSr+0OBKiwto6rMPqmRzFPecM5NbT+/Pu2lxeXpbNjoLGQ1VME77enM/Xm/PpnRLFleOyuGB4BtEaqiIiIp2MfvKJtBM5xVXM25zPlrwKfD4/1dXVRERUY7UeoH+XGKYOSG33s45Eh9u4YmwWl4/pwZLthby8LJuvN+fT1O0POwqc3P3+Jh7+bAsXjsjgirFZ9EzWUBUREekcVISLtAMb95Yxd3UO5S4PeeUuiirdeDxe7PYakqLDcNZ42Z5fycyRmQzqFtfW4R6RYRhM6JvChL4p7C5y8ury3cz9JocKVxNDVWq8vLg0mxeXZjO5fwpXjctiYt8ULBYNVRERkY5LRbhIG8sprmLu6hzyy13sKHRit1roEhuO1W/FZ7FR5PRQWFlO7+Qo5q7OIS7C3u57xA/WIymKu84+kVtO68d/1+3l5WXZbMuvbLLtgi0FLNhSQM/kKK4Y24MLT84gxmFvsq2IiEgoC91bSkU6iHmb8yl3edhR6CQpKozBGXF0i48gIdJOt/gIBmfEkRQVxo5CJxUuD/O35Ld1yMckKtzGZWN68MUtE3n92tGcdmIXmrsvc1ehk/s+/J4x/+9r7nl/IzsKmi7aO5oSp5u1e0pZvaectXtKKXG62zokEREJEPWEi7ShEqebLXkV5JW7sFstZCVHYTEM/AcNorYYBlnJUZS7vBwod7H5QAUlTne7v1mzOYZhML5PMuP7JJNTXMWrK3bz1qo9lDcxVMXp9vHy8t28vHw3E/ulcNW4Hkzul9rhhqp0hPsBRETk6KgIF2lDOwsrMU0odrpJi3VgaaZr2GIYpESHcaDcRc9k2Fno5OQQLcIPlpkYye/POoGbT+3Le+v28fKybLbkVTTZdtHWAhZtLSArKZLLx2Zx0YgMYjvAUJWOdj+AiIi0jIpwkTbk8vjx+018fnDYrYdtG2634vOD3zSp8TS/bHwoigyzccno7vxsVCYrdhbz0rJdfPl9Hv4mZlXJLqri/o++569fbOGnwzO4clwP+qTGBD/oVtDR7wcQEZHmBWVM+OLFi5kxYwapqak4HA769OnDrbfeSnFx8TEdr7q6mkceeYQxY8YQHx+P3W4nJSWF008/nTfeeCMoy8GLtAaH3YLFYmC1gOsIhXWNx4fVUtsrHn6Egj1UGYbB2N5J/OPyESy6Ywq/mtSb+Mime7ur3D5eXbGbUx9dxGXPr+Sr7/PwNVW1t2Od5X4AERFpLOBF+DPPPMPkyZP54IMPsNvtDBw4kH379vHoo48yZMgQdu/efVTHKywsZNSoUdx+++2sXLmS2NhYhgwZgmEYfPnll1x66aVcfPHF+P3+AF2RSOvplRyNYUBiVBgFle4GY8EP5jdNCirdJEaFYRjQqxPMp52REMmdZw5gxe+m8eBPT+KE9Nhm2y7ZXsi1r3zD5Efm8/zinZRVe4IY6bFp7n6Ag9XdD2C3WhrcDyAiIqEvoEX4unXrmDNnDn6/nyeeeILc3FzWrFlDbm4u06ZNIzc3l5kzZx7VMe+88042btxIYmIiixcvZs+ePXzzzTfk5eXx0ksvYbFYeOedd3jllVcCdFUirSchKoz+XWLoEuvA4/OTXehsVIj7TZPsQicen5+0WAcD0mJC9qbMY+GwW5k5sjuf3HQKc68bw1knpWFt5sbMnOJqHvj4B8b8v6/5/X+/Y2sz48vbg4PvB0iJDjvi/QDFTjemWXs/gIiIhL6AFuH3338/Pp+PSy65hNmzZ2P8+EMmMTGRt956i5iYGFauXMnHH3/c4mN+8MEHAPzhD3/glFNOqX/cMAyuvPJKLrvsMgA++uijVrwSkcCZOiCVWIed3slRFDndbMgtY29pNcVVHvaWVrMht4wip5veyVHEOOxM6Z/a1iG3CcMwGN0riacvPZnFd0zhhsm9SWhmqEq1x8cbK/dw+t8WcclzK/h804F2N1RF9wOIiHRuASvCKysr+fTTTwG4/vrrG21PTk7mwgsvBGDu3LktPm5VVRUAffr0aXJ73eMeT/v/OFoEamcImTkyk9RYB4O6xhIXYSOvvIY9JTXkldcQF2FjUNdYUmMdzByZqRvzgK7xEdxxxgCW/24aD184mIFdmx+qsmxHEb98dQ2THp7PPxbuoLSqfQznaOp+gBqPj8LKGgoq3RRW1tQX3J3hfgARkc4mYLOjrFu3DpfLRVhYGKNHj26yzaRJk3jxxRdZvnx5i487bNgwlixZwpIlSzj77LMbbV+yZAkAY8aMOaa49+/ff8Q2GRkZx3RskeYM6hZHXISd+Vvy2Xyggh6JfqqrXUREOLBaLQxIi2FKf80VfSiH3cpFIzK58OQM1uwu4cVl2Xy2sele79ySav786Wb+9tVWzh/WjSvHZTEgrfniPdAOvh9gb6mLyhov5dVe/KaJz+vFavNhMaqJjbBRVu0lJabz3A8gItIZBKwI37p1KwA9evTAbm/6I+PevXsDsHPnTjweT7PtDvbAAw9w+umn89e//pX4+HguvfRSUlJSyM7O5tFHH+WLL75g0KBB3HTTTccU96hRo47YpjVmX/F6vUGZxcXr9Tb4Ku1XWoydn43oRkmVm+15FRSXlZMYF0ufLjEkRNaOAdcnPM0b0i2Gxy46iQM/6cubq3J565scip2N/79cHj9vrsrhzVU5jMpK4Iox3Zk2IAWbNbgLCEeHGfRJjiS32ElhhYuSKgtWA2q8tcNULBYf4TYLxVVufD4/GfHh9E2JJDrM0POgndH7bGhS3kJXMHPXktr0WAWsCK+bfjAxMbHZNnXb/H4/5eXlJCUlHfG4kyZNYsGCBdx77738/ve/53e/+139NofDwV133cXtt99OVFT77i0qLCwM6vnKysqCej45Pr1ioVdsbS+tv7qCouo2DiiE2IErhiUw86Q4vt5awr+/zWdzflWTbVdll7Aqu4QuMXZ+OjiVcwclEx8RvOUTukb4yS12YrNAebUHw4AIu4Uwm4HPb1JW7cE0IdZhJafYSbojiaKioqDFJ0dH77OhSXkLXcHIXVpaWsCOHbCfNi6XC4CwsOZncXA4HPX/rq5ueZWRnZ3NgQMHME2Trl270qVLF7KzsykpKeH1119n2LBhXHDBBccU96pVq0hPTz+mfY9GcnJy0HrCy8rKiIuLw2bT2kyhQnlrHZd3SeGyU/rybU4Zr6zYw2eb8vA2MVQlr8LD00v38sLK/ZwzOJ3Lx2Ry4mGmRGwt+7Y5yUiMojCnjNgIGxYDarwmbq+JxWIQF2HDb4LHZ5KZGM1+l4VpLeiskODS6zU0KW+hq6PkLmCR1xXYbnfzN0HVFeoAERERLTruI488wu23306vXr1Yvnx5/dhv0zR55ZVXuO6667jwwgv573//y4wZM4467vT09KCM+Q72k8ZmswX0IxUJDOWtdYzqncKo3inklbt4feUe3li5h8LKmkbtarx+3lm7l3fW7mVUViJXjsvi9IFdsAdgqEqJ0832wircPpPUGAdxETbKXN7aGVN8PqxWa20h7qgdE17j87OtoIpKt9mppqgMJXq9hiblLXSFeu4CVgkmJCQAHPaj07ohKxaLhdjYI/c65efnc/fddwPw8ssvN7j5sm6Kwt27d3PPPfdw5513HlMRLiIdV5dYB785rR83TunNJ9/t56Vlu1mfU9pk21XZxazKLiYt1sHlY3swa2QmSdHhrRbLwfOEd4130C0hErfXR1mVm+qaGiLCw4mLDCPMZmVvSRUHyl30TK6dJ/xkFeEiIiEvYHci9e/fH4A9e/Y0exPRjh07AOjVq1eLfpP55ptvqK6uJioqivHjxzfZ5owzzgBg8+bNVFS034U6RKTthNusnD8sg/dvHM97N47n/GHdsFubXiznQLmLhz/fwti/zOO2t9ezcW/rjEFsap7wMJuVpOhwUqLCSIoOJ8xW+7jmCRcR6XgCVoQPGzaM8PBw3G43K1asaLLNwoULARg7dmyLjnm0RXVNTeOPm0VEDjY0M56/zRzK0juncsup/UiJabq32+31886aXM5+Ygk/fWYZH67fh8fnP+bzNjVPeHM0T7iISMcTsCI8Ojq6vlf62WefbbS9sLCQd955B4CLL764Rcfs168fAE6nk6VLlzbZpm6BoOTk5BbNtiIiApAa4+DXp/Zl6W+n8visoQzvHt9s2zW7S5jz5jpOeXAef/96GwUVR/8L/8HzhBdUuvGbJm5vw8V63F4fftOkoNJNYpTmCRcR6UgCOjHu3XffjcVi4Y033uDJJ5+snw2kuLiYWbNmUVFRwciRI5k+fXqD/U455RSysrJ47LHHGjw+dOhQBg8eDMCVV17ZoIfdNE1efvll/t//+38AXH755RhG0x8vi4g0J8xmYcbQbrx7w3g+mD2eC4Z3I6yZGzPzymt49MutjP/LPH4z99tmx5c3JSEqjP5dYugS66Da7WXFziK+zSllV2EVuWU17Cqs4tucUlbsLKLa7SUt1sGAtBjdlCki0kEEtAgfPnw4jz/+OIZhMGfOHDIyMjj55JPJyMjg66+/pmvXrsydO7dRsZybm8vu3bspLS1t8LhhGLz++uukpqayc+dOxo4dW3/MpKQkrrrqKtxuN+PGjeP+++8P5KWJSCcwOCOeRy8eyrLfTeXW0/rRJbaZoSo+P++u28uMp5Zy/tNLef/bvbi9Rx6qMnVAKl6fic9vUlTpZn+Zi/1l1eRXeNhfVs3+MhdFlW58fhOPz2RK/9TWvkQREWkjAV8ibvbs2SxYsIBzzjmHmpoaNm7cSHp6OjfffDMbNmygZ8+eR3W8QYMGsWnTJu666y6GDRtGeXk569evxzAMJk+ezLPPPsvChQvb/WI9IhI6kqPDmTOtL0t+O5UnfjaMET0Smm27bk8pv37rW8Y/OI/HvtpKfoWr2bZNMQwDw0Cf5ImIdHCGGYwVY9q53NxcMjMzAcjJyQnKPOHB4vF4KCoqIikpKaTn0uxslLf2b+PeMl5als0H6/cdttfbbjWYflI6V47LYlj3hsX7y8uyWbunhG9/HMZisxiYJvh8XqxWG4ZB/eJCwzLjGd4jgSvGZgXqkuQY6fUampS30NVRche6ywyJdFAlTjdbD5SRX1ROqtNKv7Q4jQNuhwZ1i+ORi4bwuzMH8NbqHF5bsZv9ZY17vT0+k/e+3cd73+5jSEYcV43P4qyT0qmq8bElr4K8cheRYTYGZ8Th8fopq3bjctXgcIQTFxGG3WZhQ24ZB8pdbD5QQYnTreeDiEgHoCJcpJ3IKa5i3uZ8tuRV4PP5qa6uJiKiGqv1AP27xDB1QCqZiZFtHaYcIik6nBun9OGXE3vxxfd5vLQsm1W7iptsuz63jFvmrudPH//A5P4peHwmxU43abGO+ukHk63h1NhMwh3hWCy1IwZTosO0WI+ISAejIlykHdi4t4y5q3Mod3nIK6+9Gc/j8WK315AUHYazxsv2/EpmjsxkULe4tg5XmmCzWjjrpHTOOimd7/eV8/KybN77di81TQxVKax0886avRgGRIXZiI+wY5pms+PAtViPiEjHoyJcpI3lFFcxd3UO+eUudhQ6sVstdIkNx+q34rPYKHJ6KKwsp3dyFHNX5xAXYVePeDt3YtdYHrxwMHceNFRlb2l1o3amCZU1XlZll7A930nfLtFkJkQ0aqfFekREOp6Az44iIoc3b3M+5S4POwqdJEWFMTgjjm7xESRE2ukWH8HgjDiSosLYUeikwuVh/pb8tg5ZWighKozrJ/dm4e2TefaykxnTK7HZtsVVblbuKubDDfv5Ic9ZvxqnFusREemY1BMu0oZKnO76m/PsVgtZyVFYDAP/QZMWWQyDrOQoyl1e3ZwXomxWC2cMSuOMQWlsPlA7VOW/6/bi8jQeqlLj9bOloJq95W5G90yissaLx+fXYj0iIh2MivBDTJ06tdF0N5s2bWr185imSTBmh6wbZ2qaJn7/kRcPkeDanl+B6TcprnSTFueo/WjKNAETDGq/miYWfrw5r8xFzySTHQUVDI9ofq5qab/6pUbzp/MGcfvp/Xhu8S5eWbEbZ03jcd6VNT6+3pxPfKSdET3iiQ63Mblfsl7H7ZDeZ0OT8ha6gpm7uhvkA0FFeBupqqoK2rmio6PxeDx4PJ6gnVNaptxZjdvjwev3Y7eYeH3e+m12ux2/34+f2jcYm8XE6/fj9ngoq6ymutrRVmFLKwg3YPbE7kzuHc8TC3ex+UAlhc7Gr9HSKg/fZJfwhzNTSHIYVFc3HlsubU/vs6FJeQtdwcpdIBd/VBF+iHnz5gVlsZ7IyMig9IR7vV5KSkpISEjAZlO625vYKBdhdjs2iwWP38Bmrc2R3/RTU1NDeHg4FqP2t3Cv34PNYiHMbicuOoKIiMY38EnoGdkngjk2Oy8t283GvWXkV9Q0mlGltNrL797/gYIqH9ec0hOrRatptid6nw1Nylvo6ii5C93IQ1zt0tSB/0Fa93GNYRgB/UhFjk2f1BgMi0FidBgFlW7S4yOwGAaYBpgABvw4Rryg0k1idBiGxaB3Sozy2UFs3FvGRxsOEGaz0CXWgd80KatyU+luWIi7fSZ/+WwL8zYX8NeLh2iGnHZE77OhSXkLXR0ld6EbuUgHkBAVRv8uMXSJdeDx+ckudDa4KRNqZ8fILnTq5rwO6ODpKTftK8fp9tEzOYqTM2IYmhlHuK3xW/Sq7GLOeGwRb63aE5RP00REJDDUEy7SxqYOSGV7fiW9k6PYUeik3OUlKcqOxe/F7zIpcnrw+Pz0To4ixmFnSv/Utg5ZWsmh01NmJUeBaVLjcpHmcNAnNYal2wvZX+ZqsJ/T7ePOd7/jy+/z+PNPTyI1RvcHiIiEGvWEi7SxzMRIZo7MJDXWwaCuscRF2Mgrr2FPSQ155TXERdgY1DWW1FgHM0dmahhCB9Hc9JQHs1stTOyXQo+kyCZ7xb/enM9P/raIzzbuD1bYIiLSStQTLtIODOoWR1yEnflb8tl8oIIeiX6qq11ERDiwWi0MSIthSv9UFeAdyM7CSkwTip1u0mIdjQrwOhbDoEdiJHaLgQnsKHA22F5S5eFXr63lgmHduOfcgcRF2Js8joiItC8qwkXaiczESK4Ym0WJ0822A2XkF5eQmphA37Q4jQHvgFweP36/ic8PjiMsRR9ut2JiMCIrgfOHdeOlZbsprKxp0ObddXtZsbOIhy8awvg+yYEMXUREWoGGo4i0MwlRYQzrHs+IzFiGdY9XAd5BOewWLBYDqwVcnsaL9RysxuPDaqntFR/bO5nPb57AGQPTGrXbV+bi0udXct+Hm454TBERaVsqwkVE2kCv5GgMAxKjaqenPHRWnDr101NGhWEY0Cs5iqTocJ65bDiPXjyEmPDGH2i+uDSb6X9fzPqc0gBfhYiIHCsV4SIibeB4p6c0DIMLhmfw2S0TGdc7qdHxdxQ4ueCZZTz21VY8Pi3JLSLS3qgIFxFpI1MHpBLrsNM7OYoip5sNuWXsLa2muMrD3tJqNuSWUeR0H3Z6ym7xEbx2zWjuOefERjOo+Pwmj321jQufWcb2/MpgXZaIiLSAinARkTbSWtNTWiwGPx/fk49vOoXBGXGNtq/PLWP63xfz4tJd+P1a4EdEpD3Q7CgiIm2oNaen7JMaw3+uH8dT87fzxLzt+A4quGu8fu778Hu++iGPhy8cQtf4iEBeloiIHIGKcBGRNtaa01ParRZuPrUfU/qncsu/v2XnIfOKL91exE8eW8QfZwzkvKHdMJqZn1xERAJLw1FERNqJ1pyeckhmPB/PmcBV47Iabatwebll7npueH0txU73cUQsIiLHSkW4iEgHFRFm5d5zB/L6taNJj3M02v7pxgOc/rdFzNuc1wbRiYh0birCRUQ6uPF9kvns5omcP6xbo22FlTVc/dI3/O7dDVTWeNsgOhGRzkljwg8xdepU7HZ7g8c2bdrU6ucxTROzmcU5Wvs8hmFgmiZ+v+YKDhXKW+cVqNzHhFv560WDmTYghT+8v4mSKk+D7W+uymHJtkIeuWgwI7MSW+28nYFer6FJeQtdwcydxRK4/mrDDEYl2M7l5uaSmZkJQN++fYNShDudziM3EhEJgILKGu75aAsLtxU32mYAPx+byZzJPQmz6cNSEencoqKiAnZsFeE0LMJzcnLIyMgI+DmD1RPu9XopKSkhISEBm00ffIQK5a3zClbuTdNk7je5PPDxD1S5fY2290+L4dGLBnNCemzAYugo9HoNTcpb6Apm7gLZE65nXRsxDCMoU4PVfVxjGEZAn0jSupS3ziuYub9kdA9O6ZPCrW9/y+rskgbbthyo4Lynl3HLaf345cTeWC2ayrA5er2GJuUtdHWU3IVu5CIicty6J0Xy1nVjufPMAYRZG/5I8PhMHvpsCzP/sZzdRRpCJyLSmlSEi4h0claLwa8m9eb92eMZkBbTaPs3u0s48/HFvLFyT1CG0YmIdAYqwkVE2okSp5u1e0pZvaectXtKKQnyQjonpMfy/uzxXD+5N4eOPqly+/j9f7/j6pdWk1/uCmpcIiIdkcaEi4i0sZziKuZtzmdLXgU+n5/q6moiIqqxWg/Qv0sMUwekkpkYGZRYwm1WfnvGAKYNSOU3/17PnuKqBtvnbyng9McW8afzTmL64PSgxCQi0hGpCBcRaUMb95Yxd3UO5S4PeeUuiirdeDxe7PYakqLDcNZ42Z5fycyRmQzqFhe0uEZkJfLpryfwp09+4I2VexpsK63ycOMba/ny+67cd+4g4iLtzRxFRESao+EoIiJtJKe4irmrc8gvd7FpXzll1V66xIbTIyGcLrHhlFV72bSvnPxyF3NX55BzSK90oEWF2/h/55/Ei1eNJCUmvNH2977dx08eW8SSbYVBjUtEpCNQES4i0kbmbc6n3OVhR6GTpKgwBmfE0S0+goRIO93iIxicEUdSVBg7Cp1UuDzM35LfJnFOGZDK5zdP5KyT0hptO1Du4rIXVnLvB5uobmK+cRERaZqKcBGRNlDidLMlr4K8chd2q4Ws5Cgsh6wdYDEMspKjsFstHCh3sflARdBv1qyTGBXGU5cM5/FZQ4l1NB7J+NKybKb/fTHf5pQGPzgRkRCkIlxEpA3sLKzENKHY6SYlOqxRAV7HYhikRIdR7HRjmrCzsO3m6zYMgxlDu/H5LRM5pU9yo+07C5389JllPPrlVjw+fxtEKCISOlSEi4i0AZfHj99v4vODw249bNtwuxWfH/ymSY2n7Yd8pMdF8MrVo7jv3IE47A1/jPj8Jn//ehsXPL2M7fkVbRShiEj7pyJcRKQNOOwWLBYDqwVcRyisazw+rJbaXvHwIxTswWKxGFw5LouPb5rAkMz4Rtu/21vGWX9fwgtLduH3a4EfEZFDqQgXEWkDvZKjMYzasdYFlW78zaxE6TdNCirdJEaFYRjQKzkqyJEeXu+UaP7zq7H85rR+2A5Z4cft9XP/R99z6fMr2Vta3UYRioi0TyrCRUTaQEJUGP27xNAl1oHH5ye70NmoEPebJtmFTjw+P2mxDgakxZAQFdZGETfPZrVw07S+vHvDOPqkRjfavnxnEWf8bRH/WZOrZe9FRH6kIlxEpI1MHZBKrMNO7+QoipxuNuSWsbe0muIqD3tLq9mQW0aR003v5ChiHHam9E9t65APa3BGPB/NOYWrx/dstK2ixsutb6/nV6+toaiypg2iExFpX7Ri5iGmTp2K3d5w9bdNmza1+nlM0wxKj5BpmhiGgWma+P2arSBUKG+dQ7d4BxeP6Mbc1bk47FbyKlzklbvweH3YbV6SosPpEuMgxmHj4hHd6BbvaPfPhzCrwV3TBzBtQAq3vbOB/WWuBts/35THN9kl/OWCQUw7oUsbRdm69HoNTcpb6Apm7iyWwPVXG6Y+GyQ3N5fMzEwA+vbtG5Qi3Olsu2nGRKR92VtazcJtxWzLr8QE/H4Ti8XAAPqmRjOpbyLd4iPaOsyjVuHy8ufPt/H+hrwmt/90aBp3nN6H6HD1B4lI+xQVFbj7cFSE07AIz8nJISMjI+DnDFZPuNfrpaSkhISEBGw2/aALFcpb51RS5Wb7gXIKS8pIToijT1osCZHtbwz40fp80wH+778bKa7yNNqWkRDBIxcOZlTPxDaIrHXo9RqalLfQFczcBbInXM+6NmIYBkYzi3O09nnqPrYJ5BNJWpfy1jklRTuI7WGlKNpPUlJCo0/lQtWZJ3VlRFYSv3t3A1/9kN9gW25JNT97fiW/mNCL35zW74hzprdHer2GJuUtdHWU3IVu5CIiEjJSYsJ57ooRPPTTwUSFNSy0TRP+uWgnM55cyqZ9ZW0UoYhIcKkIFxGRoDAMg4tHZvLZzRObHH6yJa+C855aylPzt+PVsvci0sGpCBcRkaDKTIzkzV+M4f/OOoEwa8MfQx6fycOfb+Hifywnu1A3sItIx6UiXEREgs5qMfjFxF58OOcUTkyPbbR97Z5Sznx8Ma+t2K0FfkSkQ1IRLiIibaZ/Wgzv3TieG6f05pBV76n2+LjrvY1c9eJq8spdTR9ARCREqQgXEZE2FWazcPtPBvD2r8aRlRTZaPvCrQWc/rdFfLh+XxtEJyISGCrCRUSkXTi5RwKf/HoCl43p3mhbWbWHOW+u46Y311Fa5W6D6EREWpeKcBERaTciw2w8cN5JvPTzkaTGhDfa/sH6ffzksUUs2lrQBtGJiLQeFeEiItLuTO6fyhe3TOTswemNtuWV13DFv1bxh/c2UuX2tkF0IiLHLyhF+OLFi5kxYwapqak4HA769OnDrbfeSnFx8XEd96uvvuLiiy8mIyOD8PBwUlNTGTVqFLfffjslJSWtFL2IiLSF+MgwnrxkOH//2TBiHY0XeH51xW6m/30Ja/fo/V5EQk/Ai/BnnnmGyZMn88EHH2C32xk4cCD79u3j0UcfZciQIezevfuoj+nz+bjmmms47bTTePvttzFNkyFDhhAXF8d3333HI488wt69ewNwNSIiEmznDunKF7dMYkLf5EbbdhU6ufCZZfz1iy24vVrgR0RCR0CL8HXr1jFnzhz8fj9PPPEEubm5rFmzhtzcXKZNm0Zubi4zZ8486uPOnj2bf/3rXwwdOpSVK1eyd+9eVq1axbZt2ygrK+Pzzz8nPb3xR5giIhKa0uIcvHL1KO4/bxAR9obL3vtNeGLedi54Zilb8yraKEIRkaMT0CL8/vvvx+fzcckllzB79mwMo3YS2MTERN566y1iYmJYuXIlH3/8cYuPuXDhQp599lkyMjKYP38+o0aNarA9LCyM008/naSkpFa9FhERaVuGYXD5mB588usJDM2Mb7R9495yzn5iCc8v3onfrwV+RKR9C1gRXllZyaeffgrA9ddf32h7cnIyF154IQBz585t8XH/+te/AnDbbbcRHx9//IGKiEhI6ZkcxTu/Gsttp/fDdsgKP26vnwc+/oGfPbeCnOKqNopQROTIGt/p0krWrVuHy+UiLCyM0aNHN9lm0qRJvPjiiyxfvrxFx3S5XHz22WcAnHbaaWzevJnnnnuOjRs3YrPZGDx4MFdeeSUDBgw45rj3799/xDYZGRnHfHwRETl+NquF2VP7Mrl/KrfM/ZZt+ZUNtq/cVcyZjy/m7nNO5KKTM+o/iRURaS8CVoRv3boVgB49emC325ts07t3bwB27tyJx+Nptl2d9evX4/F4AFi2bBmzZ8+mpqamfvsnn3zCww8/zCOPPMLNN998THEfOrylKaZ5/B9zer3eVjlOS85z8FcJDcpb56XcH53+qZH891ejefSr7by4fDcHv61W1ni5450NfLFxPw/MOJGk6MbzjrcG5Sw0KW+hK5i5O1JtejwCVoTXTT+YmJjYbJu6bX6/n/Ly8iOO4z64l/qGG27gpJNO4sknn2T48OEcOHCABx98kGeeeYZbbrmF/v37c+aZZ7bClQRGYWFhUM9XVlYW1PNJ61DeOi/l/uhcNyqZk9PDuf+LbA5UNFxR86vNBazZvZTfndqDib3jAxaDchaalLfQFYzcpaWlBezYASvCXS4XUHujZHMcDkf9v6urq494zMrK/33cGB4ezmeffUZKSgpQ2+P+9NNPs2fPHj7++GPuvvvuYyrCV61aFZSZVZKTk4PWE15WVkZcXBw2W8DSLa1Meeu8lPtjd3pSEmMHZPCnTzfzn7X7GmwrqfZyx4c7uGBYV+46awAxTcw7fqyUs9CkvIWujpK7gEVeV2C73e5m29QV6gAREREtPibAFVdcUV+AH+z222/n448/5ptvvqGwsJDk5Mbzyh5Oenp6UMZ8B/tJY7PZAvqRigSG8tZ5KffHJtFu568XD+MnA9P53bvfUeRs+DPo3XX7WLmrhEcuGsLY3q07i5ZyFpqUt9AV6rkL2OwoCQkJABQVFTXbpm7IisViITY29ojHPHhoywknnNBkmxNPPLH+39nZ2S0JVUREOpjTB6bx+S0TOf3ELo227S2t5pLnV/DAR9/j8vjaIDoRkQAW4f379wdgz5499TdTHmrHjh0A9OrVq0W/yRw860l4eNM32Bz8uM+nN1cRkc4qOTqcf1x+Mg9fOJjo8IafPpomPL9kF+c8sYSNezUmWESCL2BF+LBhwwgPD8ftdrNixYom2yxcuBCAsWPHtuiYXbt2JSsrC6idUaUpdYU9aCpBEZHOzjAMLhqRyWc3T2BMr8YTBWzLr+S8p5byxNfb8Pq07L2IBE/AivDo6GjOOOMMAJ599tlG2wsLC3nnnXcAuPjii1t83FmzZgHw+uuvN5iesM7zzz8P1Paad+vW7ajjFhGRjicjIZI3rh3DXdNPIMzW8Eef12/y1y+3cuGzy9lZUNnMEZpW4nSzdk8pq/eUs3ZPKSXO5u+DEhE5WECXrb/77ruxWCy88cYbPPnkk/WzgRQXFzNr1iwqKioYOXIk06dPb7DfKaecQlZWFo899lijY952220kJCSQk5PDr371K6qq/rci2htvvMFzzz0HwP/93/8F7sJERCTkWCwG107oxUdzTmFg18b3IX2bU8pZf1/Mq8uzjzh7VU5xFS8vy+bhL7bw7rp9fLmlmHfX7ePhL7bw8rJsrdYpIkcU0CJ8+PDhPP744xiGwZw5c8jIyODkk08mIyODr7/+mq5duzJ37txGK5nl5uaye/duSktLGx0zKSmJd999l6ioKF566SXS0tIYNWoUPXr04NJLL8Xj8XDLLbdw2WWXBfLSREQkRPXrEsN/bxjPTVP7YD1k2XuXx88f3t/EFf9axYEyV5P7b9xbxj8X7WTtnhJ2FlSydk8pG/Y5Wbun9MfvS/jnop0aay4ihxXQIhxg9uzZLFiwgHPOOYeamho2btxIeno6N998Mxs2bKBnz55HfczJkyezYcMGrr32WhISEli/fj2VlZX85Cc/4cMPP+TRRx8NwJWIiEhHEWaz8JvT+/POr8bSMzmq0fbF2wo5/W8Lef/bvQ0ezymuYu7qHPLLXWzaV05ZtZcuseH0SAinS2w4ZdVeNu0rJ7/cxdzVOeoRF5FmGWYwVoxp53Jzc8nMzAQgJyenQ93Q6fF4KCoqIikpKaTn0uxslLfOS7kPviq3l798uplXlu9ucvvZg9N54LxBxEeG8fKybNbuKWHTvnKSosLISo4C06TG5SLc4QDDILvQSZHTzaCusQzvkcAVY7OCe0HSInqtha6OkruA94SLiIi0Z5FhNv44YxCvXD2KtFhHo+0fbdjP6X9bxEfr97Elr4K8chd2q4Ws5Cg8Xj+FlTUUVLoprKzB4/WTlRyF3WrhQLmLzQcqdLOmiDQpdNf6FBERaUUT+6Xw+c0TufuDjbz/bcNl7/Mrapj95jr6pEaDaZIYFcb2/EqKnTXUeP14vT5sNjfhNguJUeHEhFspdrrpmQw7C52cHBXWRlclIu2VinAREZEfxUXaeXzWME47sQt3vbeR0qqGi81tz6/EajEoqfJgmlDl8eLzm5imiWF4sVoMyqq8hNstWA0Dv2lSo1U5RaQJKsJFREQOcfbgrozMSuS3/9nAgi0FDbb5/CZFTjcGYPDjXyZgmPj9JuU+D1a3gd1qocrtI9xuDf4FiEi7pzHhIiIiTegS6+DFq0byp/MHEdFEIW0CdWtsGkbtH79ZW6S7vX68Pj/ZhU56NTH7ioiIinAREZFmGIbBpaN78OmvJzA4I67JNn4TfCb4/LWFuEntH5/fpKxaN2WKSNM0HOUQU6dObTTdzaZNm1r9PKZpHnFFttY6j2EYmKaJ3+8/8g7SLihvnZdy3z51T4zgpim9ueXtDVS4vM228x/0tu71m3j9Jt/mlDCpX0oQopSjodda6Apm7iyWwPVXqwhvI1VVwVvAITo6Go/Hg8fjOXJjaTeUt85LuW+ftueVYz9khc3DMYEaj49t+0sZlRkduMDkmOm1FrqClbuoqMANJ1MRfoh58+YFZbGeyMjIoPSEe71eSkpKSEhIwGZTukOF8tZ5Kfftl8c0qPEdXa9bZY2Xah9EREQEKCo5Vnqtha6OkrvQjTzEGYaBYbS8R+V4zlP3sU0gP1KR1qW8dV7KffvlsFvx+f7XeWKhdgy47zD9KTVek+eXZBPtCONnozKJDNOP3fZCr7XQ1VFyF7qRi4iIBFFCVBgN6u0fZ0SxWeBwo1QqXF7u/+h7TnlwPk/O20ZZtYY+iIiKcBERkRap8fqJDPvfVIWmWXsjpkntVOEW48d5w5tR7HTzyBdbGf+XeTz42WYKK2sCHbKItGMqwkVERFogKSqMGIe9QbFtmuD3/1iMt/A2n8oaL88s2MH4v8zj3g82sbe0OmAxi0j7pSJcRESkBVJiwkmJCcdht2IxwGoxagvyHytyw6jtDbdZDKwGRIZZ6d8lhpN7JDR5vBqvn5eWZTPpofnc8c56dhZUBvFqRKStqQgXERFpgV7J0WTERxDjsOGwW7EaYLEY2CwGdmvtV8uPBbjDbiXGYWNAWgzPXzGCj286hemD02nqfnyv3+Tf3+Qy7dGF3PjGWr7fVx78ixORoNNt2iIiIi2QEBXG8B4JlFV7WJdTgsNuB0xcHj9+08RiGDjsFsDA6/dzQlosJ2clkBAVRkJUGE9dMpydBZU8u3AH767di9ffcPyKacLHG/bz8Yb9TB2Qyo1TenNyj8Q2uVYRCTz1hIuIiLTQ1AGpZCZGMjgjHsuP0xNGh9uIc9iIDrfhM2uHpAzOiCczMZIp/VMb7N8rJZqHLhzCwjumcNW4LMJtTf8Ynrc5n58+s5yZ/1jO4m0FQVlXQkSCS0W4iIhIC2UmRjJzZCZZSVGM75NMn5Ro7DYLJiZ2m4U+KdGM75NMVlIUM0dmkpkY2eRxusVHcO+5A1l651RumNybmPCmP5heuauYy19YxYynlvLZxgP4/SrGRToKDUcRERE5CoO6xREXYWf+lnwiw230TomiutpFRIQDq9XCgLQYpvRPbbYAP1hydDh3nDGAX07qzavLs/nX0myKne5G7TbklvGr19bQNzWaG6b05pzBXbFZ1Y8mEsoMU59xkZubS2ZmJgA5OTlBWbY+WDweD0VFRSQlJWG329s6HGkh5a3zUu5DS4nTzbYDZeQXl5CamEDftDgSosKO+XhVbi9vrsrhuUU7OVDuarZdZmIEv5zYmwtPzsBhtzbbTpqn11ro6ii506/RIiIixyghKoxh3eMZkRnLsO7xx1WAA0SG2bjmlJ4svGMyf7ngJHokNd2bnlNczV3vbWTiQ/N5btFOnDXe4zqviASfinAREZF2JtxmZdao7nz9m0k8Pmso/bvENNkuv6KGP33yA+MfnMfjX22jtKrxUBYRaZ9UhIuIiLRTNquFGUO78emvJ/D8FSMYmhnfZLvSKg9/+2or4/8yjz9/8gP5Fc0PZRGR9kFFuIiISDtnsRicemIX/nvDON64djTj+yQ12c7p9vGPRTs55cH5/OG9jeQUVwU5UhFpKc2OIiIiEiIMw2Bcn2TG9Ulm7Z4Snp6/g69+yGvUzu318+qK3byxag8zhnblhsm96ZPa9JAWEWkbmh2FhrOj9O3bt9Gdtps2bWr1cy5btowVK1YcsV1aWhqzZs1q8Nhbb73FgQMHjrjvmDFjGDFiBCUlJSQkJODz+XjmmWdaFN/MmTNJT0+v/37r1q188sknR9wvLCyMG264ocFjX331FRs3bjzivn379mX69OkNHnv++eeprKw84r7Tpk3jpJNOqv++sLCQ11577Yj7AVxzzTXExPzvh9PatWtZtGjREfdLSkri8ssvb/DYf//7X3bv3n3EfYcNG8akSZMaPPbYY481+N7v92OxNP6w6rzzziMrK6v+++zsbN57770jnhPg5ptvbvD9woULWbdu3RH369GjB+eff36Dx1599VWKioqOuO/EiRMZPnx4/fcVFRW88MILLYr3sssuIzk5uf777777jq+//vqI+0VHR3Pttdc2eOzjjz9m27ZtR9x30KBBnHrqqQ0ee/rpp3G7jzzW9qyzzqJfv3713+/fv5+5c+cecT+A66+/nvDwcLxeLyUlJWzbto1Vq1Ydcb/jfY8YM2ZM/fc1NTV6j2hCS94jmnq9Bvo9ok6hN4w1ziS21sRgYjRzRJPe4ZWMjCwi1V7T7Hk703vEVVddVf/z0Wazhcx7RJ0VK1YEpY5or+8RTb3mAvEe8Zvf/OaI+x4r9YS3kcrKSioqKo7YLjo6murq6mPat7KyEo/HQ3R0NB6Ph5qamhbtB1BVVdXgvFVVVS3aNyws7LjiPXTfioqKFr14Do23urq6xddaXV2Nzfa/l4LT6Qz4tTqdziavtSWONTdAo3O29Fqby82xXOvR5uZYrtU0zVZ/HrbkB+zx5sbv9wO1r/uW7nu87xEH76v3iKa19/eIcGCcpYhB4eF8501jp5mC139oK4MdNTHsqImhm6WMwbb9pFkb/791pveIg38+ejyekHqPqItD7xGHjxeC8x5xrFSEH2LevHlBmSc8Ojq6Qc/K4dpFREQc8752u73+N32LxdKi/QAiIyMbnDcyMrJF+4aFhR1XvIfuGxMTg2E017PTfLwREREtvtaIiIgG+0ZFRbVo35iYmGO+1qioqCav9WDN9YQfa26ARuds6bU2l5uW/NA59Fq9Xu8x56al13q8r5tjvdbjzc3BPeFtca16j2haS94jmnq9Bvo94lAxQDeKGX/qWD7b7eXNVXtweRpV4+z1x7HXHUdXexUjIovpEeak7r+wM71HHPzz0Wazhcx7xMFxdOb3iKZec231HnGsNBwFLdYj7Y/y1nkp96GnveasqLKGl5Zl89KybCpczc8jPrBrLDdO6cNPBqZhtRy5WOko2mve5Mg6Su40O4qIiEgHlBQdzq2n92fpnVO544z+JDWzkNCmfeXc8PpaTv/bQt5Zk4vH17j3XERan4pwERGRDizWYeeGyX1Y8tup3HvOiXSNczTZbkeBk9veXs/khxfwyvJsXB5fkCMV6VxUhIuIiHQCEWFWrhrfkwW3T+GhCwfTKzmqyXZ7S6u5+/1NnPLgfJ5duIMKlyfIkYp0DirCRUREOpEwm4WLR2Ty5W8m8eQlwzghPbbJdoWVNfzl082M/8s8Hv1iCyXOI994KCItpyJcRESkE7JaDM4e3JVPbjqFF68ayck9EppsV+7y8vd52xn/4Dwe+Oh78spdQY5UpGNSES4iItKJGYbBlAGpvPOrsbx13Rgm9E1usl2V28fzS3Yx4cH5/O7d79hTVBXkSEU6Fs0TLiIiIhiGwZheSYzplcSG3FKenr+DzzY1XlXR7fPz5qo9zF29h3OHdOX6yX3onxbY+ZRFOiL1hIuIiEgDgzPiefbyk/nylolcMKxbk/OH+01479t9/OSxRVz3yjeszykNfqAiIUxFuIiIiDSpb5cYHp05lPm3TubS0d0JszZdNnzxfR4znlrK5S+sZPmOIrQOoMiRqQgXERGRw+qeFMmfzj+Jxb+dwi8m9CQyzNpku8XbCvnZcyv46TPL+PqHPBXjIoehIlxERERapEusg/+bfiJLfzuVX0/rS1xE00uGr91TyjUvf8OZjy/mw/X78PlVjIscSkW4iIiIHJWEqDBuOa0fS++cyu/OHEBydHiT7TYfqGDOm+s49dGFzF29B7fXH+RIRdovFeEiIiJyTKLDbfxyUm+W/HYK9583iG7xEU2221Xo5Lf/+Y5JD8/nxaW7qHb7ghypSPujIlxERESOi8Nu5fIxPVhw+2T+etEQeqdENdluf5mL+z78nlMenMdT87dT7vIEOVKR9kPzhB9i6tSp2O0Nx7ht2rSp1c9jmmZQblgxTRPDMDBNE79fHwOGCuWt81LuQ49y9j9WA84f1pUZQ9L54vs8nl6wg437yhu1K3K6efjzLTyzYAdXjO3Bz8f1IKmZIS2BoryFrmDmzmIJXH+1YerWZXJzc8nMzASgb9++QSnCnU5nqx9TRESkPTFNk6U7S3huyW6+2VPWbDuHzcKFw9O5akwm6XGOIEYocnhRUU1/qtMaVITTsAjPyckhIyMj4OcMVk+41+ulpKSEhIQEbDZ98BEqlLfOS7kPPcpZy6zOLubpBTtZuLWg2TZ2q8H5w7rxq0m9yEoKXPEDylsoC2buAtkTrmddGzEMA8NovAJZIM5T97FNIJ9I0rqUt85LuQ89ylnLjO6VzOheyWzcW8bTC7bz6cYDHNoX5fGZ/PubXN5Zk8v0wV25YXJvTkiPDUg8ylvo6ii5C93IRUREJOQM6hbH05eezJe3TOLCkzOwWRp3SPlN+HD9Ps58fDHXvryatXtK2iBSkcBSES4iIiJB1yc1mkcuGsKC2ydzxdgehNmaLkm++iGfC55exs/+uYKl2wu1Cqd0GCrCRUREpM1kJETyxxmDWPLbKfxqUm+iw5seKbt8ZxGXPr+S855exhebDuA/jlU4S5xu1u4pZfWectbuKaXE6T7mY4kcK40JFxERkTaXGuPgzjMHcP2k3ry8PJt/Ld1FaVXjecTX55Ry3atr6N8lhhum9Gb6SenYrC3rU8wprmLe5ny25FXg8/mprq4mIqIaq/UA/bvEMHVAKpmJka19aSJNUhEuIiIi7UZcpJ2bpvXlmlN68uaqPTy3eCd55TWN2m3Jq+DXb33LX7/Yyq8m9eanJ3cj3GZt9rgb95Yxd3UO5S4PeeUuiirdeDxe7PYakqLDcNZ42Z5fycyRmQzqFhfISxQBNBxFRERE2qGocBvXTujFojum8P/OP4nuzfRQ7ymu4vf//Y6JD83n+cU7qXJ7G7XJKa5i7uoc8stdbNpXTlm1ly6x4fRICKdLbDhl1V427Ssnv9zF3NU55BRXBfryRFSEi4iISPsVbrNyyejuzLt1Eo/NHEq/LtFNtssrr+GBj39g/F/m8fevt1F20FCWeZvzKXd52FHoJCkqjJ7JkVS7fRQ6PVS7ffRMjiQpKowdhU4qXB7mb8kP1uVJJ6bhKCIiItLu2awWzhvWjXOHdOWrH/J4av521uc2XoWzpMrDo19u5Z+LdnLZmB5cOLwbW/IqyCt34fb62VtazQ/7yzEB0+/HsLjZll9JbIQdi2FwoNzF5gMVlDjdJESFBf9CpdMISk/44sWLmTFjBqmpqTgcDvr06cOtt95KcXFxqxz/22+/xW63B20BHBEREWkbFovB6QPTeO/G8bx2zWjG9kpqsl1ljZdnF+7grL8vZvWuYnbkV1JYWUOx0021x4ezxovT7cdZ46Xa46PY6aawsobsQiemCTsLnUG+MulsAl6EP/PMM0yePJkPPvgAu93OwIED2bdvH48++ihDhgxh9+7dx3V8r9fL1VdfjdfbeAyYiIiIdEyGYXBK32TevG4M/7l+HNMGpDbZzu0z2ZZfSUGlG5fHR7Xbh89vYhhgMcAwwOc3cXl8eP0mRU4PeeXV1Hh8Qb4i6WwCWoSvW7eOOXPm4Pf7eeKJJ8jNzWXNmjXk5uYybdo0cnNzmTlz5nGd4+GHH2bdunWcf/75rRS1iIiIhJKTeyTwwlUj+eSmCZwzpCtNLMIJ1K7Eaf741e//8Xvzf1/dXj+mWVu0h9ubn2lFpDUEtAi///778fl8XHLJJcyePbt+qEhiYiJvvfUWMTExrFy5ko8//viYjr9582buu+8+Ro0axezZs1szdBEREQkxJ3aN5YmfDePrWyczc0QmdmvzQ1RN/leQ130P4DdNyqo8HGZXkVYRsCK8srKSTz/9FIDrr7++0fbk5GQuvPBCAObOnXvUx/f7/VxzzTX4fD6ee+45LBZN9CIiIiLQMzmKBy8czMLbpzCud9NjxpvjN2uHqHy/vyJA0YnUCljlum7dOlwuF2FhYYwePbrJNpMmTQJg+fLlR338J598kmXLlnHbbbcxePDg44pVREREOp6u8RFM6peCw3Z03dpurx9nTePVOkVaU8CmKNy6dSsAPXr0wG63N9mmd+/eAOzcuROPx9Nsu0NlZ2fz+9//nt69e3P33Xe3TsA/2r9//xHbZGRktOo5RUREJDBcHh8+88jtDlbj9ZNf0XiVTpHWFLAivG76wcTExGbb1G3z+/2Ul5eTlNSyj4x+8Ytf4HQ6+cc//kFERMTxB3uQUaNGHbGNaR7lq7kJXq+3VY7TkvMc/FVCg/LWeSn3oUc5a9/8/oaznBzcJ97cT2G/Cf9amo3f7+e6CT1J1Hzh7UowX3Mt7SA+FgErwl0uFwBhYc0/cR0OR/2/q6urW3TcF154ga+++oorr7ySadOmHV+QbaiwsDCo5ysra7yggbR/ylvnpdyHHuWsffK5a7BZDDwHdYdbDMConRHFpPZro/38Ji8s3c2bq3L42fAuXDK8C1HhmjGlPQnGay4tLS1gxw5YEV5XYLvd7mbb1BXqQIt6tPft28ett95KSkoKf/3rX48/yCasWrWK9PT0gBz7YMnJyUHrCS8rKyMuLg6bTQukhgrlrfNS7kOPcta+pSZWER9RSLWnBoPaottn/nhT3I83YR7up3GVx88LK/fznw2FXDcxi8tGdSciTMV4W+oor7mARZ6QkABAUVFRs23qhqxYLBZiY2OPeMw5c+ZQVlbGq6++2uKhK0crPT09KGO+g/2ksdlsAf1IRQJDeeu8lPvQo5y1TxP6pvLKihwcVR5cXj9Wy//mBgfAPHjRHrAaNDmGvLTaw0Ofb+OlZXuYM60vM0dkEmbTzGxtKdRfcwF79vTv3x+APXv24PE0fYfxjh07AOjVq1eL/hPXrFkDwG233UZaWlqDPxdccEF9u7rHnnvuueO9DBEREQlhPVOi6dclmuTocKwGGBhYLQZ2q4HNAnZr7fcGBlYD0uMimNI/mQuGdcNoYlKV/Ioa/vDeRqY9uoD/rMnF5w/8p9rSMQWsCB82bBjh4eG43W5WrFjRZJuFCxcCMHbs2KM6dl5eXqM/JSUljbY7nc5jvwARERHpEK4al0VkuJWk6PDawto0sRk/FuJG7eBww4Ck6HAiw61cP7kPj84cyuc3T+SMgU2PCc4prubWt9dzxmOL+Gzj/qAMMZWOJWBFeHR0NGeccQYAzz77bKPthYWFvPPOOwBcfPHFLTpmdnY2pmk2+Wf+/Pn17eoeu/nmm4//QkRERCSkjeqZxI1T+hAfaadLjIOIMBs+Ezy+2qEnEWE2usQ4iI+0c+OUPozqWTvktV+XGJ69/GTev3E8E/omN3nsbfmV/Oq1tZz75FIWbi1QMS4tFtDBTHfffTcWi4U33niDJ598sv6JWVxczKxZs6ioqGDkyJFMnz69wX6nnHIKWVlZPPbYY4EMT0RERDqJGUO78cB5gxiSGUdqrIOMhAi6xoaRkRBBaqyDIZlxPHDeIGYM7dZo3yGZ8bx6zWje/MUYTu6R0OTxv9tbxpX/WsXMf65gdXZxoC9HOoCA3h04fPhwHn/8cW666SbmzJnDn//8Z9LS0vjhhx+orq6ma9euzJ07F+OQQVe5ubns3r2b0tLSQIYnIiIincionkmM6pnEroJKlm7LJ6+knC4JsYzvm0rPlOgj7j+2dxLv/GosC7YU8PDnW/h+f3mjNqt2FXPRs8uZ0j+FW0/vz6BucYG4FOkAAj5Fx+zZsxk8eDCPPPIIy5YtY+PGjWRkZHDuuedy1113BWyWExEREZGm9EyJJiM+nKKiIpKSko5qhg3DMJgyIJVJ/VL4ZON+Hv1iKzsLG9+DNn9LAfO3FDD9pHRuOa0ffVKPXORL52KYGrxEbm4umZmZAOTk5HSoZek9Hs8xvclI21LeOi/lPvQoZ6GptfLm9fl5d91eHv9qG3tLm1540GLAT4dn8OtT+5KREHnM55JaHeU1pwkuRURERI6RzWrh4hGZzLttEveecyLJ0Y1XCveb8PaaXKY8soB7P9hEfoWriSNJZ6MiXEREROQ4hdusXDW+J4vumMIdZ/Qn1tF4xK/HZ/LSsmwmPbSABz/bTGlV86uKS8enIlxERESklUSG2bhhch8W/3Yqs6f0IbKJJe6rPT6eWbCDCQ/N58l523DWeNsgUmlrKsJFREREWllchJ3bftKfRXdM4erxPQmzNi65KlxeHvliKxMfms8LS3bh8vjaIFJpKyrCRURERAIkOTqcu885kfm3T2bWyEysFqNRmyKnm/s/+p4pjyzgrVV78Pj8bRCpBJuKcBEREZEA6xYfwV9+Opgvb5nIOUO6Ntlmf5mLO9/9jtMeXcj73+7F7+/0E9h1aCrCRURERIKkV0o0T/xsGJ/cNIFTT0htsk12URW/futbzvr7Yr76Pg/NJt0xBXyxnlAzderURnNObtq0qdXPY5pmUF5UpmliGAamaeL36+OtUKG8dV7KfehRzkJTW+dtQFo0/7z8ZNbuKeGRz7eyYlfjpe43H6jg2le+YVhmPLed3o+xvbXAIQQ3dxZL4PqrtVgPDRfr6du3b1CKcKez8epaIiIi0vmYpsmKXSU8Pn8X3+2raLbdmJ7x/HpKLwZ3iw1idJ1bVFRUwI6tIpy2WTEzWD3hXq+XkpISEhISsNn0wUeoUN46L+U+9Chnoak95s00Tb76IZ+/frmVrXmVzbY77YRUbjmtHwPSYoIYXfsRzNwFsie8fTzrOiHDMDCMxndIB+I8dR/bBPKJJK1Leeu8lPvQo5yFpvaat58MSufUE9P4cP0+/vbVVnYXVTVq8+UP+Xy1OZ9zh3TlllP7kZUcuN7a9qi95u5ohW7kIiIiIh2Q1WJw3rBufPWbSfy/808iLdbRqI1pwvvf7mPaowv53bvfsb+sug0ileOhIlxERESkHbJbLVwyujsLbp/MXdNPICHS3qiNz2/y5qo9THp4Afd/9D1FlTVtEKkcCxXhIiIiIu2Yw27l2gm9WHTHFG45tR8x4Y1HE7u9fl5YsouJD83n0S+2UO7ytEGkcjRUhIuIiIiEgBiHnV+f2pdFd0zhl5N64bA3LuOcbh9/n7edCQ/O55kFO6h2+9ogUmkJFeEiIiIiISQhKozfnXkCC2+fwuVjemC3Np7ooazaw4OfbWbiw/N5ZXk2bq/msG9vVISLiIiIhKAusQ7uP28Q826dzE+HZ2BpYtK1gooa7n5/E1P/uoB31uTi83f6manbDRXhIiIiIiEsMzGSv148hM9vnsiZg9KabJNbUs1tb6/nJ48t4pPv9uNXMd7mVISLiIiIdAB9u8TwzGUn8+HsU5jUL6XJNtvzK7nh9bWc+9QSFmzJD8rCgdI0FeEiIiIiHchJGXG8fPUo5l43hpFZCU222bi3nKteXM3Mf6xg1a7iIEcooCJcREREpEMa3SuJf/9yLC/9fCQDu8Y22WZVdjEX/2M5V/5rFd/llgU5ws5Ny9aLiIiIdFCGYTC5fyoT+6bw2aYD/PWLLewocDZqt3BrAQu3FnDWSWn85rR+9EmNaYNoOxf1hIuIiIh0cBaLwVknpfP5zRN5+MLBdIuPaLLdJ98d4PS/LeLWf68np7gqyFF2LirCRURERDoJm9XCRSMymXfbJP44YyDJ0eGN2vhN+M/aXKb+dQF3v7+R/HJXG0Ta8akIFxEREelkwm1WrhibxaI7JvPbMwYQF2Fv1MbjM3ll+W4mPjyfP3/6AyVOdxtE2nGpCBcRERHppCLDbFw/uTeL7pjCTVP7EBlmbdTG5fHzj4U7mfjQfP7+9TYqa7xtEGnHY5iaIJLc3FwyMzMB6Nu3L3Z7w98GN23a1OrnNE0zKHNzer1eSkpKSEhIwGbTfbihQnnrvJT70KOchSblrWmFlTU8u3Anr63c0+xS94mRdq6f3JvLRncn3N64aA+0YObOYglcf7WKcNqmCHc6G9+ZLCIiItIeHCh38ezi3by7bj++ZirFLjFh/GpiFucPScNu7ZiDK6KiogJ2bBXhNCzCc3JyyMjICPg51RMuh6O8dV7KfehRzkKT8tYy2UVOHvtqGx9u2E9zZUuPxEhuPrUv5wxOx2IxAh5TR+kJ17OujRiGgWEE/olqGAamaWIYRkCfSNK6lLfOS7kPPcpZaFLeWqZXSgx//9lwbphSzl+/2MqX3+c1arO7uIpb/r2eZxfu5NbT+3HaiV0CWuN0lNyFbuQiIiIiEhQD0mJ57ooR/PeGcYzvk9Rkmy15FVz36hrOe3oZS7cXBjnC0KMiXERERERaZFj3BF6/dgyvXzuaoZnxTbZZn1PKpc+v5Gf/XMGa3SXBDTCEqAgXERERkaMyvk8y/71hHM9fMYIBaU0vcb98ZxE/fWYZ1768mh/2lwc5wvZPRbiIiIiIHDXDMDj1xC58ctMEHp81lKykyCbbffVDPmc+vpg5b65jZ0FlkKNsv1SEi4iIiMgxs1gMZgztxpe/mcRfLjiJ9DhHk+0+XL+P0/62iDv/s4F9pdXHdK4Sp5u1e0pZvaectXtKQ3oVT82OIiIiIiLHzW61MGtUd84b1o3XV+7h6fnbKTqkSPb5Td5ancO7a/dy2Zge3DClN8nR4Uc8dk5xFfM257MlrwKfz091dTUREdVYrQfo3yWGqQNSyUxsuie+vVIRLiIiIiKtxmG3cs0pPZk1MpMXl+7iH4t2UuFquNS92+fnX0t38dbqPVw9vie/mNiLuAh7k8fbuLeMuatzKHd5yCt3UVTpxuPxYrfXkBQdhrPGy/b8SmaOzGRQt7hgXGKr0HAUEREREWl1UeE2Zk/ty+I7pnD95N447I3Lziq3jyfnb2fCg/N4esF2qtwNi/Wc4irmrs4hv9zFpn3lFDvdRNgtxDqsRNgtFDvdbNpXTn65i7mrc8gprgrW5R03FeEiIiIiEjDxkWH89owBLLpjCleNy8JubbyQT7nLy0OfbWHiQwt4aekuarw+AOZtzqfc5WFLXgUenx+f36SyxkeZy0tljQ+f38Tj87Mlr4IKl4f5W/KDfXnHTEW4iIiIiARcaoyDe88dyLxbJ3PRyRk0tcJ9YWUN9374PVMfWchLS3fxw4FydhZUUlbtwTRNyqo9HCh3kVdR+/Xgx3cUONl8oCJkbtZUES4iIiIiQZOZGMnDFw3hi1smMf2k9Cbb7C2t5t4Pv+ej9fvZnu/E7zfJL6+hrNqD1+fH7zfx+vyUVXvIL6/BNGFPcRWVLi87C51BvqJjoyJcRERERIKuT2o0T106nI/mnMKU/ilNtqms8VLtqR1+4vb58fpMPD4Tt7/2q89v4vWbVLg8uL1+ckuqqPH4gnwlx0ZFuIiIiIi0mUHd4njx56N4+1djGZWV2Gw7vwkmtX/48avfBK/fxGdCjddHkdON2+cPRtjHTUW4iIiIiLS5kVmJzP3lGF6+ehQnHcNUg34Tyqs9AYgsMDRP+CGmTp2K3d5wnspNmza1+nlM08Q0zSM3bIXzGIaBaZr4/aHxm6Eob52Zch96lLPQpLy1XxP6JHFK77H85bMtPL94F0dTLbk8Pqrd3lbLqcUSuP5qFeFtpKoqePNYRkdH4/F48HhC57dDUd46M+U+9ChnoUl5a9/iwg1sFvAcRT3t85vsyi+nulfrLNoTFRXVKsdpiorwQ8ybN4+MjIyAnycyMjIoPeFer5eSkhISEhKw2ZTuUKG8dV7KfehRzkKT8tb+VXsBw4DD9IUfutUEqjwQERER2OBagZ51bcQwDAyjiQkyA3Ceuo/cAvmRirQu5a3zUu5Dj3IWmpS39q/a48dyUBF+cNVkwv/mGTf/V4hbDaN2vxDIafuPUEREREQ6naSocOwHrehjUNsxbhg0WOjn4OLcZjFIig4LWozHQ0W4iIiIiLQ7J3aNxRFm/V/Bbfyv4K4b0WvU/1VbmDvCrJzYNTa4gR4jFeEiIiIi0u4MzYwnPc5BmNWCwf/mCTcMsFhqv9bNFW4AYVYL6XEOhmTEt2ncLaUiXERERETanYSoMKad0IXE6DCsFgPrjz3epvm/PwBWA6wWg8ToME49oQsJURqOIiIiIiJyzH46PINhmQmkxIRjs1rqx4QfPD7cZrWQEhPOsMwELhge+BnuWotmRxERERGRdikzMZLrJ/fmhSW72FFQyb7SaqrdPvx+E4vFICLMStf4CHqnRHPNKT3JTIxs65BbTEW4iIiIiLRbg7rF8ZvT+jF/Sz7rc0opq3JT5aoh0hFOXGQYQzLjmdI/NaQKcFARLiIiIiLtXGZiJFeMzaJksJttB8rILy4hNTGBvmlxITMG/FABHxO+ePFiZsyYQWpqKg6Hgz59+nDrrbdSXFx81MfasGEDf/zjHzn11FNJS0vDbrcTHx/P2LFjeeSRR4K6FLyIiIiIBFdCVBjDusczIjOWYd3jQ7YAhwAX4c888wyTJ0/mgw8+wG63M3DgQPbt28ejjz7KkCFD2L17d4uPtWPHDoYMGcI999zD119/jdVqZejQoURHR7NixQpuv/12hg0bxp49ewJ4RSIiIiIixy9gRfi6deuYM2cOfr+fJ554gtzcXNasWUNubi7Tpk0jNzeXmTNntvh4pmmSkpLCvffey44dO9i7dy+rV68mNzeXJUuW0KNHD7Zu3XpUxxQRERERaQsBK8Lvv/9+fD4fl1xyCbNnz8Ywaid3TExM5K233iImJoaVK1fy8ccft+h4GRkZ7Nq1i3vuuYdevXo12DZ+/Hhef/11AFasWMH69etb92JERERERFpRQIrwyspKPv30UwCuv/76RtuTk5O58MILAZg7d26LjulwOIiKimp2+/jx44mLiwNg8+bNRxuyiIiIiEjQBGR2lHXr1uFyuQgLC2P06NFNtpk0aRIvvvgiy5cvb5Vzer1ePB4PAJGRxz5Fzf79+4/YJiMjdCaCFxEREZH2JyBF+NatWwHo0aMHdru9yTa9e/cGYOfOnXg8nmbbtdR7771HVVUVNpuNsWPHHvNxRo0adcQ2Zt06qcfB6/W2ynFacp6Dv0poUN46L+U+9ChnoUl5C13BzN3x1qeHE5AivG76wcTExGbb1G3z+/2Ul5eTlJR0zOcrLS3l1ltvBeAXv/gFycnJx3ysYCksLAzq+crKyoJ6PmkdylvnpdyHHuUsNClvoSsYuUtLSwvYsQNShLtcLgDCwpqfu9HhcNT/u7q6+pjP5fV6mTVrFnv27KFXr1785S9/OeZjAaxatYr09PTjOkZLJCcnB60nvKysjLi4OGw2rc0UKpS3zku5Dz3KWWhS3kJXR8ldQCKvK7DdbnezbeoKdYCIiIhjOo/f7+fKK6/k888/JyUlhY8//pjY2NhjOlad9PT0oIz5DvaTxmazBfQjFQkM5a3zUu5Dj3IWmpS30BXquQvI7CgJCQkAFBUVNdumbsiKxWI5psLZNE2uueYa3njjDRITE/nyyy8ZMGDAsQUsIiIiIhJEASnC+/fvD8CePXvqZyw51I4dOwDo1avXUf8WY5om1113HS+99BKxsbF8/vnnDBky5PiCFhEREREJkoAU4cOGDSM8PBy3282KFSuabLNw4UKAY5rJ5MYbb+T5558nOjqaTz/9lBEjRhxXvCIiIiIiwRSQIjw6OpozzjgDgGeffbbR9sLCQt555x0ALr744qM69k033cQzzzxDZGQkH330EePGjTv+gEVEREREgihgy9bffffdWCwW3njjDZ588sn6mUCKi4uZNWsWFRUVjBw5kunTpzfY75RTTiErK4vHHnus0THvuOMOnnjiCRwOBx988AGTJk0KVPgiIiIiIgETsCk6hg8fzuOPP85NN93EnDlz+POf/0xaWho//PAD1dXVdO3alblz52IYRoP9cnNz2b17N6WlpQ0eX758OQ8//DAAsbGx3HPPPdxzzz1Nnvvqq6/m6quvDsh1iYiIiIgcr4DOkzd79mwGDx7MI488wrJly9i4cSMZGRmce+653HXXXUe1QE9NTU39v/Pz88nPz2+27amnnnpccYuIiIiIBFLAJ6ueOHEiEydObHH77OzsJh+fPHlyUBa3EREREREJtICNCRcRERERkaapCBcRERERCTIV4SIiIiIiQaYiXEREREQkyFSEi4iIiIgEmYpwEREREZEgUxEuIiIiIhJkAZ8nPNRMnToVu93e4LFNmza1+nlM0wzKvOemaWIYBqZp4vf7A34+aR3KW+el3Ice5Sw0KW+hK5i5s1gC11+tIryNVFVVBe1c0dHReDwePB5P0M4px09567yU+9CjnIUm5S10BSt3UVFRATu2ivBDzJs3j4yMjICfJzIyMig94V6vl5KSEhISErDZlO5Qobx1Xsp96FHOQpPyFro6Su5CN/IQZxgGhmEE5Tx1H9sE8iMVaV3KW+el3Ice5Sw0KW+hq6PkLnQjFxEREREJUSrCRURERESCTEW4iIiIiEiQqQgXEREREQkyFeEiIiIiIkGmIlxEREREJMhUhIuIiIiIBJmKcBERERGRIFMRLiIiIiISZCrCRURERESCTEW4iIiIiEiQqQgXEREREQkyFeEiIiIiIkGmIlxEREREJMhsbR1AezN16lTsdnuDxzZt2tTq5zFNE9M0W/24TZ3HMAxM08Tv9wf8fNI6lLfOS7kPPcpZaFLeQlcwc2exBK6/WkV4G6mqqgrauaKjo/F4PHg8nqCdU46f8tZ5KfehRzkLTcpb6ApW7qKiogJ2bBXhh5g3bx4ZGRkBP09kZGRQesK9Xi8lJSUkJCRgsyndoUJ567yU+9CjnIUm5S10dZTchW7kIc4wDAzDCMp56j62CeRHKtK6lLfOS7kPPcpZaFLeQldHyV3oRi4iIiIiEqJUhIuIiIiIBJmKcBERERGRIFMRLiIiIiISZCrCRURERESCTEW4iIiIiEiQqQgXEREREQkyFeEiIiIiIkGmIlxEREREJMhUhIuIiIiIBJmKcBERERGRIFMRLiIiIiISZCrCRURERESCTEW4iIiIiEiQqQgXEREREQkyW1sH0N5MnToVu93e4LFNmza1+nlM08Q0zVY/blPnMQwD0zTx+/0BP5+0DuWt81LuQ49yFpqUt9AVzNxZLIHrr1YR3kaqqqqCdq7o6Gg8Hg8ejydo55Tjp7x1Xsp96FHOQpPyFrqClbuoqKiAHVtF+CHmzZtHRkZGwM8TGRkZlJ5wr9dLSUkJCQkJ2GxKd6hQ3jov5T70KGehSXkLXR0ld6EbeYgzDAPDMIJynrqPbQL5kYq0LuWt81LuQ49yFpqUt9DVUXIXupGLiIiIiIQoFeEiIiIiIkGmIlxEREREJMhUhIuIiIiIBJmKcBERERGRIFMRLiIiIiISZCrCRURERESCTEW4iIiIiEiQhXQRvnjxYmbMmEFqaioOh4M+ffpw6623Ulxc3NahiYiIiIg0K2SL8GeeeYbJkyfzwQcfYLfbGThwIPv27ePRRx9lyJAh7N69u61DFBERERFpUkgW4evWrWPOnDn4/X6eeOIJcnNzWbNmDbm5uUybNo3c3FxmzpzZ1mGKiIiIiDQpJIvw+++/H5/PxyWXXMLs2bMxDAOAxMRE3nrrLWJiYli5ciUff/xxG0cqIiIiItJYyBXhlZWVfPrppwBcf/31jbYnJydz4YUXAjB37tygxiYiIiIi0hK2tg7gaK1btw6Xy0VYWBijR49uss2kSZN48cUXWb58+VEff//+/Udsk5GRcdTHFRERERGpE3JF+NatWwHo0aMHdru9yTa9e/cGYOfOnXg8nmbbNWXUqFFHbGOaZouP1xyv19sqx2nJeQ7+KqFBeeu8lPvQo5yFJuUtdAUzd4ZhYLMFplwOuSK8bvrBxMTEZtvUbfP7/ZSXl5OUlBSU2FrK7/dTWFgY1HOWlZUF9XzSOpS3zku5Dz3KWWhS3kJXsHKXmpqKxdL6I7hDrgh3uVwAhIWFNdvG4XDU/7u6uvqIx0xLSyMnJ4f9+/eTkpISsN946lgsFpKTk4PSEy4iIiLS0fj9/oAUxocyDCNg5wm5IryuwHa73c22qSvUASIiIo54TJvNRkZGRlDHege60BcRERGR9ivkZkdJSEgAoKioqNk2dUNWLBYLsbGxQYlLRERERKSlQq4I79+/PwB79uzB4/E02WbHjh0A9OrV66huyhQRERERCYaQK8KHDRtGeHg4brebFStWNNlm4cKFAIwdOzaYoYmIiIiItEjIFeHR0dGcccYZADz77LONthcWFvLOO+8AcPHFFwc1NhERERGRlgi5Ihzg7rvvxmKx8MYbb/Dkk0/WzzJSXFzMrFmzqKioYOTIkUyfPr2NIxURERERacwwQ3SevCeffJKbbroJ0zTp2rUraWlp/PDDD1RXV9O1a1eWLFlCz5492zpMEREREZFGQrYIB1i0aBGPPPIIy5Yto6KigoyMDM4991zuuuuudrdAj4iIiIhInZAuwkVEREREQlFIjgkXEREREQllKsJFRERERIJMRbiIiIiISJCpCBcRERERCTIV4SIiIiIiQaYiXEREREQkyFSEi4iIiIgEmYrwDiw3NxfDMDAMg9zc3LYOR1pIeeu8lPvQo5yFJuUtdHWk3KkIFxEREREJMhXhIiIiIiJBpiJcRERERCTIVISLiIiIiASZinARERERkSBTES4iIiIiEmQqwkVEREREgszW1gF0Nl6vlwMHDgTlXPv372/y39K+KW+dl3IfepSz0KS8ha62yF1aWho2W+uXzIZpmmarH1WalZubS2ZmZluHISIiIiItkJOTQ0ZGRqsfV8NRRERERESCTD3hQRbM4SgiIiIicnw0HEVEREREpIPQcBQRERERkSBTES4iIiIiEmQqwkVEREREgkxFuIiIiIhIkKkIFxEREREJMhXhIiIiIiJBpiJcRERERCTIVISLiIiIiASZinARERERkSBTEd7B3HvvvRiGcdg/zz77bFuH2Slt2LCBP/7xj5x66qmkpaVht9uJj49n7NixPPLII1RVVR12/w8++IDTTjuNpKQkIiMjGThwIPfddx/V1dVBugJpzoEDB3jttdf49a9/zfjx44mMjMQwDIYOHdqi/V966SXGjx9PXFwc0dHRDB8+nMceewyfz3fY/YqKirj11lvp06cPDoeD1NRUZsyYwZIlS1rhqjqnb7/9FrvdXv9+eTjKW2Ac7+vJ5/Pxwv9v787joir3P4B/BmRmGIZlWI1NREgUBVxQyuvGoterLZZhrngzuWjRVdHUci3z5oaklpaZmma5dA13U0LLNHMJRa/kxqJpibuIwzbf3x+8zvnNOAvDMgPi9/16zesF53m+c55znnlmvnPmOeesXImYmBh4enpCJpPBx8cHsbGxWLBggdG44uJizJo1C6GhoVAoFHBzc0NcXBy2bdtW5Tqzs7MxePBgeHt7QyaTwd/fH4mJiSgoKDB3sxudffv2IT4+Hr6+vpDJZPD09ESnTp0wceJE3L59W6dujx49qsxdcnJyjK6rNmPqp59+wgsvvABPT0/I5XIEBQUhJSUFt27dqvU+qBKxRmXGjBkEgDw9PalLly4GH9999119N/OJc+HCBQIgPry9valjx47k4+MjLnv66acpPz/fYPzkyZPFegEBARQREUF2dnYEgNq2bUu3b9+27gYxHYsWLdLpX+ERHh5uMk6j0dDgwYPF+i1btqS2bduSRCIhABQTE0MlJSUGYy9dukTe3t4EgBQKBbVv317838bGhj777DMLbGnjVlZWRu3atdPpQ0O43yyrpuOJiOj69esUGRlJAEgikVBwcDBFRkaSn58f2drakpubm8G4mzdvUmhoKAEgOzs7ioiIoICAAHHdU6dONbrO9PR0kkqlBIDc3d2pQ4cO5OTkRADI2dmZjh8/XtNd8VgqLy+n1157TefzLjIykoKCgkgulxMAys7O1onp3r07AaDg4GCjuYuxz8fajKlPPvmEbGxsxHa2b9+e7O3tCQD5+vpSXl5ene6bR3ES3sgISXhCQkJ9N4VpOX/+PHl4eNDMmTPp4sWLOmUHDx6kZs2aEQCKiorSi01PTycAJJVKafPmzeLygoICCgsLIwAUHx9v8W1gxq1cuZJiY2Np8uTJtHnzZpozZ45ZScNHH31EAMjFxYUOHDggLs/OziY/Pz8CQG+//bZenEajoQ4dOhAAio2NpZs3b4rLFy9eTADI1taWTp06Vafb2dgJ/da/f3+TSTj3m2XVdDyVlJRQeHi4+J5YUFCgU37r1i1KT083GCv0eXh4uE7cpk2bxAMeO3fu1Iu7evUqOTg4EACaNGkSlZaWEhHRgwcPaMiQIQSA/P396eHDh9XcC4+vpKQkAkARERF05MgRnbKSkhLas2cP3bhxQ2e5kISvWrWqWuuqzZg6ceIE2draEgBasmQJaTQaIqr8QhYTE0MAqHPnztVqT3VxEt7IcBLeMD18+JCKioqMlh88eFD80M/KytIpE47MvfPOO3pxZ8+eFb/FP3pkgdWfVatWVZk0lJWVkYeHBwEweKRmz549BIDkcrneB5bwxczR0VGvjIho0KBBBIAGDBhQ6215Upw9e5ZkMhl16tSJMjIyjCbh3G/WZ854IiKaOXMmAaDnnntOTKjMkZWVJR41PXv2rF75lClTCAB17NhRr2zcuHEEgLp06aJXplarqXnz5gSAli5danZ7Hmf79+8XjyJX5xfamibhtRlTwhevwYMH65UVFhaSo6MjAaDt27dXq03VwXPCGbMCuVwOBwcHo+XCvFIAOvPeLly4gN9++w0AkJSUpBcXEhKC7t27AwA2btxYl01mFrZ//34UFhbCwcEBw4YN0yvv1asXAgMDoVarkZ6erlMm9HV8fDzc3Nz0YoXXyo4dO/DgwQMLtL5x0Wg0GDlyJCoqKrBixQrY2Bj/aOR+a5hKSkqwdOlSAMD7779f5Xx+bZs2bQIA9OzZEyEhIXrlQr8cO3YMly5dMhhr6P1ZJpNhxIgRAIANGzaY3Z7H2cKFCwEAEyZMgIuLi8XXV9MxVVRUhF27dgEARo8erRfn7u6OAQMGALBs33ES3kidPHkSQ4YMQXR0NF544QVMmzYNZ86cqe9mMSPKy8tRVlYGAFAoFOLyw4cPAwCaN28OPz8/g7FCEi7UZY8Hob86deoEuVxusE63bt106j4aK5Q/KioqClKpFA8fPkRWVlYdtbjxWrp0KQ4dOoQJEyYgLCzMZF3ut4bp559/xo0bN+Dh4YHw8HBkZGTgtddeQ2xsLF566SUsWLDA6Il2VfWLv78/AgICdOoCwOXLl3HlyhWTscL789GjR6s8Yfdxp1arsXv3bgBAXFwccnJykJKSgt69e6Nv376YMmWKyZMrAWDz5s148cUXER0djfj4eKSmpqKwsNBo/ZqOqd9++w1qtRpSqRSdO3c2GGuNz1ZOwhuprKwsrF+/HpmZmdi6dStmz56Ntm3bYty4cY3+jeBx9N1336G4uBhNmjTBM888Iy4/d+4cACAoKMhobIsWLQAAv//+u2UbyepUTfu2tLQUeXl5JmOlUqn4pY1fF6bl5eXhnXfeQYsWLTB9+vQq63O/NUxHjx4FAAQEBOCtt95CbGwsVq1ahYyMDGzZsgUTJ05EcHAwfvzxR73YmvapEKfdb8bi1Go18vPza7Blj4+TJ0+KB5MOHTqEiIgIpKam4vvvv8fOnTvx4Ycfok2bNkhLSzP6HDt27EB6ejoyMzOxadMmpKSkoHnz5li3bp1e3dqMKaHvmjVrBjs7O4OxQt9dunRJ3K66xkl4I+Pt7Y333nsPR44cQWFhIdRqNU6dOoWkpCQQEdLS0jBlypT6bibTcufOHaSkpAAARo0aBXd3d7FMOHLj6upqNF4oe/SST6xhq2nf3r17FxqNpkaxTN+oUaPw4MEDfPrpp7C3t6+yPvdbw3Tt2jUAlQeglixZgkGDBuH8+fMoKSnBiRMn0K1bN9y6dQsvvvgirl69qhNb0z4V4lQqldHpL9rP2dj7VOgDABgzZgxCQ0Nx6NAhqNVq5OXlYfTo0aioqMC4cePEqSCC9u3bY/ny5Th9+jTu37+P+/fv44cffkB0dDQePHiA4cOHY8eOHToxtRlT1elzjUaDe/fumbMLqo2T8EYmMTER06ZNQ6dOneDu7g6ZTIa2bdti2bJlmDt3LgBg0aJF4rdHVr/Ky8vx6quvoqCgAIGBgfjwww91ytVqNYDKb/PGCD+J8/XCHy817VshriaxTNfKlSuxb98+JCQkICYmxqwY7reGqaioCABQVlaGqKgofPXVVwgKCoJUKkW7du2wc+dOeHl54fbt21i0aJFObG371Jy4R2MbI6EPgMr58Lt378YzzzwDmUyGZs2a4ZNPPkHfvn0BQO9Xp9TUVPzrX/9CaGgolEollEolevbsib1796Jfv34gIowdOxZEJMbUZkw1lL7jJPwJkpKSAm9vb5SXl2Pr1q313ZwnnkajQUJCAvbs2QMPDw/s2LEDTk5OOnWEN4HS0lKjzyO8mZhzFI81HDXtW+0PBn5d1NzVq1eRkpICDw8P8WQyc3C/NUza+3f8+PF6R6YdHBzEE/CEecuPxta0T82JezS2MdLug+HDh8PDw0OvzsSJEwFUnuR648aNKp/TxsYG8+fPB1B5oYLs7GyD63tc+46T8CeIra2teALC+fPn67k1TzYiwsiRI7F+/Xq4urpi7969Bs/KV6lUACrvBmaM9k+i7PFR0751dnYWr97Br4uaS05Oxt27d5GammrwqgrGcL81TNrTClq1amWwTuvWrQEAubm5Ostr2qfC37dv39Y5Qmso7tHYxqg6fQDA7F/kQ0JCxH2nnbvUZkxVp89tbGz0DpDVFU7CnzDCTy/l5eX13JInFxEhMTERq1evhpOTE/bs2YPw8HCDdVu2bAmg8giAMRcvXtSpyx4PNe1bqVQqXqnBWGxpaSkuX76sF8v+3/HjxwFUXkqtadOmOo+XXnpJrCcsW7FiBQDut4ZK+yCGTCYzWEdY/ujFCWrap8LfpaWlRm9PL8TJ5XI0a9bM5DY87qrTB4B+P5hiKHepzZgS/i4oKDB60qXQd4GBgUZP3qwtTsKfMKdPnwYA+Pr61nNLnlxvvPEGPv/8cyiVSuzatQsdO3Y0Wle4UkpeXp74RvKoAwcO6NRljwehv44eParzs6c24UoOj/at8L+hKz0AwC+//ILS0lLI5XJERETUUYsbp7/++kvvoX0Cl7BMuMYw91vD9Oyzz4p/P3otb4GQVD36+VdVvxQUFIhHbaOiosTlfn5+8PHxMRkrvD9HRkbC1ta2qs14rHl7e4tJcVV9AJifh1y/fh3Xr183GFPTMdWuXTvIZDKUlpbil19+MRhrlc9Wi90GiDU427dvF+8C9+uvv9Z3c55IycnJBIAUCgXt37/frBjhNsxV3THzSb/VdUNizh3+SktLzbrzokwmo8LCQp2yLVu2mHWXuJdffrnW2/IkyszMNHrHTO436zP3jplRUVHiLesfVVFRQW3atCEAlJSUpFP222+/mXXHzA4dOuiV/fvf/zbrjplLliypYisbh8mTJxMA8vPzI7VarVc+ZswYAkAhISFmP+eECRMIAKlUKiopKdEpq82YeuGFF8y6Y+a2bdvMbmt1cRLeiJw+fZoSExP1bnteUVFB69evJycnJwJA/fr1q6cWPtkmTpwo3s563759Zsd99913BICkUilt3rxZXF5QUEBhYWF8m+sGyNykIS0tjQCQi4sLHThwQFyenZ1Nfn5+BIAmTJigF6fRaKhdu3YEgGJjY+nmzZvi8sWLFxMAsrW11XsvYOYxlYQTcb9Zm7njKSMjgwCQRCKhjz/+WLx1fUlJCY0dO5YAkL29PZ07d04v9sUXXxTXUVBQIC7ftGkT2dnZGU3G/vjjD1IoFASAJk2aRKWlpURE9ODBAxo6dKiYkBYXF9diDzw+bty4QSqVigDQiBEj6MGDB2LZV199Je7LtWvXisvXrFlDs2fPpitXrug8V1FREU2bNo0kEgkBoAULFuitrzZj6vjx4+JBrCVLloivl5s3b1JMTAwBoMjISHG5JXAS3ogI3+YBkKurK7Vr144iIyPFAQGAunbtSrdv367vpj5xDh06JPaBp6cndenSxehj5cqVevFCAg+AmjdvThEREeKbWWhoKN26dasetooJCgoKyM3NTXwolUrxzV97+ZtvvqkTV1FRQQMHDhT7tmXLltS2bVvxg6FHjx4GjyYREV24cIGeeuop8ZeV9u3bk7e3t5iELFu2zBqb3ihVlYRzv1lWTccTEdHcuXPFpO2pp56iTp06iZ+BUqmUNm7caHCdN27coFatWhEAsrOzo4iICAoICBD7eMqUKUbbu2XLFvH92N3dnTp06CAe9HJycqKjR4/W2b55HGRmZpKDg4N4hDoyMpL8/f3FfTlu3Did+osWLRLLfH19KTIykiIiIkgul4vLk5OTja6vNmNqyZIl4uvF29ub2rdvT/b29uL/ly5dqrP9Yggn4Y3I7du3afbs2dS3b18KDAwkR0dHsrOzIy8vL+rTpw+tXbuWysvL67uZTyTtD/WqHjNmzDD4HFu2bKHo6GhycXEhuVxOISEhNH36dJ0jDax+5ObmmtW3CQkJerEajYZWrlxJUVFR5OjoSA4ODhQREUGpqalUVlZmcr2FhYU0duxYCgwMJJlMRu7u7vTcc8/Rjz/+aKEtfTJUlYQTcb9ZUm3GE1Fl/z3//PPk4eFBdnZ25O3tTUOHDqXs7GyT6y0qKqLp06dTSEgIyeVycnFxoejoaEpPT6+yzSdPnqSBAwdS06ZNSSqVkq+vL73++uuUl5dXk13w2Lt48SK9/vrr5O/vT1KplFxdXal3794Gf004c+YMvf3229StWzfy8/Mje3t7ksvlFBAQQEOGDNH5tcmY2oypAwcO0HPPPUdubm4klUopMDCQxo4da3B6S12TEBm5rg5jjDHGGGPMIvjqKIwxxhhjjFkZJ+GMMcYYY4xZGSfhjDHGGGOMWRkn4YwxxhhjjFkZJ+GMMcYYY4xZGSfhjDHGGGOMWRkn4YwxxhhjjFkZJ+GMMcYYY4xZGSfhjDHGGGOMWRkn4YwxxhhjjFkZJ+GMMcYYY4xZGSfhjDHGGGOMWRkn4YwxxhhjjFkZJ+GMMcYYY4xZGSfhjDHGGGOMWRkn4YwxxtgTpkePHpBIJJg5c2Z9N6VeFRcXY9q0aWjVqhXs7e0hkUggkUiQlZVlVnxAQAAkEglWr15t0XY+qTIyMiCRSNCnTx+LraOoqAgeHh5QqVS4efOmxdZjCCfhjDHGGICZM2eKSZhCocDVq1eN1s3LyxPr7t+/33qNZHVq4MCBmD17NnJyciCRSODl5QUvLy/Y2dnVd9PqVVZWFmbOnIm0tLR6a4NGo0FKSgoAYNasWXrldTVelUolUlJScOfOHbz33nt1ug1V4SScMcYYe8TDhw8NfvCzxiMnJwfbt28HAGzYsAHFxcX4888/8eeffyI0NNSs52jRogVatmwJZ2dnSzbV6rKysjBr1qx6TcLXrFmDkydPom/fvujUqZPJurUdr2+++Sbc3d2xbNkyXLhwocbPU12chDPGGGMGfPHFFzh37lx9N4NZSHZ2NgDAzc0N8fHxNXqOjIwM5OTkoH///nXZNAZg3rx5AIDRo0ebVb8241WpVGLIkCEoKyuz6hcPTsIZY4wxLX5+fggLC0N5eTneeeed+m4Os5Di4mIAlQkYa1j279+PnJwceHh4oHfv3ibr1tV4HTp0KABg3bp14mvD0jgJZ4wxxrTY2NjgP//5DwDg22+/xa+//lqteO35p3l5eUbrGTup79H4/Px8jBo1Cv7+/pDL5WjRogWmTp2KBw8eiDGnT5/G0KFD4efnB7lcjuDgYMyePRtlZWVVtre0tBQffvghwsLC4ODgAJVKhbi4OOzatavK2NOnTyMxMRHBwcFQKBRQKpUICwvDu+++ixs3bhiMEeby9ujRA0DlPu7Vqxc8PT1hY2NT7ZNF1Wo10tLS8Oyzz0KlUkEul6NZs2YYPny4wRMshfWPGDECAJCfny/ub+3l5jB1Yqb2HOT79+9j6tSpCAkJgb29Pdzc3NCvXz8cOXLE4PM++ho4f/48RowYAV9fX8hkMvj7+yMpKcnoPOjVq1dDIpEgICDAaNuNvU4lEgn++c9/AtDfN4ZO5t2zZw9eeukl+Pr6QiqVwsnJCYGBgejVqxcWLFiAW7dumdyHhqxYsQIA8Morr6BJkyYm69Z2vAo6duyI4OBg3L17Fxs2bKjRc1QbMcYYY4xmzJhBAKhZs2ZERNS9e3cCQD179tSrm5ubSwAIAGVmZhoty83NNbq+Zs2aEQBatWqV0fhvv/2WXFxcCAA5OTmRra2tWNa1a1cqLS2l7du3k0KhIADk7OxMEolErDNw4ECD6xa2bcqUKdS1a1cCQE2aNBHXJTxmzJhhtP1z584lGxsbsa5CoSCpVCr+/9RTT9GJEyeM7ufu3bvT+PHjCQBJJBJSqVRka2trcp2PunLlCrVp00Zcp52dHTk7O4v/29jY0OLFi3Vi5s+fT15eXuTk5CTW8fLyEh9vvfWW2es31odEJLZh/fr1FBQURABILpeLfQWApFIp7dmzRy9W+zXwzTffkKOjIwEgpVJJ9vb2YpmrqysdP35cL37VqlU6r2VDjL1OTe0bLy8vmj9/vlh31qxZOq8XhUJBSqVSZ9mj46MqGo2G3NzcCAB9/fXXRuvV1XjVNnLkSAJA8fHx1WpzTXESzhhjjJH+h/rhw4fFD+5du3bp1LVWEu7i4kIxMTF05swZIiIqLi6mxYsXi8n41KlTydnZmQYOHEh5eXlERHT//n169913xefYu3ev3rqFhMXZ2ZlkMhktX76cHj58SEREBQUFNGDAADE+PT1dL/7zzz8Xk8IPPviArl27RkRE5eXldOzYMYqOjiYA5OvrS/fv3ze4n4VkbdKkSXT9+nUiIlKr1eJ2VKW8vJw6d+4sbse6deuopKSEiIguXrxI/fr1ExP8nTt36sWbk6hWxZwkXKVSUevWremHH36giooK0mg09Ouvv1LLli3F9VdUVOjEar8GnJ2dKSwsjI4cOUJElUnqnj17yN/fnwCQv78/3bt3r9rbZup1ak58Xl6e+CVs/Pjx9Mcff4hld+7coZ9++onGjBlDx44dM/ochpw+fVps18WLF43Wq6vxqu2zzz4jANS0adNqtbmmOAlnjDHGSP9DnYiof//+BIAiIiJIo9GIy62VhIeGhpJardaLHTZsmFgnLi5Op20C4Qj3yJEj9cqEJBwArVy5Uq+8oqKCunXrJrZB271798Qj5rt37za4bWVlZdShQwcCQIsWLdIpE/azkLzV1DfffCM+j6GjyWVlZWKS3qZNG71yayXhHh4e9Ndff+mVnzp1Sqxz8OBBnTLt14Cbm5vB+P/973/iLw/z5s2r9rbVNgnfsGEDAaCnn37aaJ2aWLlyJQEgR0dHk/XqarxqO3r0qFjv0qVLtd2UKvGccMYYY8yIOXPmwNbWFllZWfj666+tvv5x48ZBJpPpLdc+WW3y5MmQSCRG65w6dcro8/v5+Ynzf7XZ2Nhg6tSpAIAzZ86IVxIBKufd3rlzB+3atTN60lyTJk0waNAgAJVzhg2xsbHBpEmTjLatKsK83WeeeQa9evUy2IYZM2YAqJy7rr0N1pSYmAhPT0+95W3btkXz5s0BmO6jpKQkg/GtWrXCgAEDAADffPNNHbXWfC4uLgCA+/fv65yfUFvCPHd3d/dqx9Z2vGqv09R1x+sKJ+GMMcaYESEhIWKSOm3aNLNOdKxLxq6P7OXlJf4dGRlpss7t27eNPr9w50xDunbtKp4Ud+zYMXH5zz//DAA4e/YsmjZtavQh3PgkPz/f4PMHBQUZTC7NJbQpNjbWaJ2ePXvC1tZWbxusqXPnzkbLvL29AcDkyYvR0dFVlp06dapeXpvu7u64du0aOnfujKVLlyInJwdEVKvnLSwsBAC4urpWO7a241V7nUI7LImTcMYYY8yEmTNnwt7eHpcuXcLy5cutum5HR0eDy7WvGFFVHVOJiI+Pj9EyuVwONzc3AMD169fF5cIRQrVajb/++svo4969ewBg9HJvtUnAtdtU1TYIRze1t8GajPUPUPs+EsrKy8trdBWS2nBxccHXX38NDw8PnDlzBsnJyWjVqhVUKhWef/55rFu3rkZfDNRqNQAY/AXIHLUZr/b29nrtsCROwhljjDETfHx8kJycDACYPXs2ioqK6rlF9auiogJA5S3fqfLcMpMPY5dpFI5Qs8dXbGwscnNz8eWXXyIhIUG8xN+2bdswbNgwtGvXDn/88Ue1nlP44mfqFxxTajNetb/ICO2wJE7CGWOMsSpMnjwZKpUK169fx8KFC03W1T5Kbepo2t27d+usfTVlKkEqKSnBzZs3AegetW7atCkA49NMrEVo05UrV4zWUavVBrfhcWKqj4SyJk2a6EylEF6D1nj9OTg4YNiwYVi9ejXOnTuHK1euYO7cuZDL5eIR8urw8PAAYHqKTlWqM161aa9TaIclcRLOGGOMVUGlUmHy5MkAgIULF5qcL6pSqcS/L1++bLDOuXPncOfOnTptY00cOHDA6Bzen376CeXl5QAqb2Qi6NKlCwDg+PHjuHbtmuUbaYTQpoyMDKN19u/fL26DsbnzDV1mZmaVZWFhYbCzsxOXC6/B69evo6SkxGCssRsFAZUnzQKo0fxuHx8fvP3220hJSQEA7N27t1rxrVu3BlA5J7umvzpVZ7xqy83NBVD5JSY4OLhG664OTsIZY4wxMyQnJ8PX1xf379/H+++/b7Seg4MDWrRoAaDySiKGfPDBBxZpY3UVFBRgzZo1ess1Gg3mzJkDoDIpatu2rVj2yiuvwMXFBWVlZRg/frzJRE2j0Vjsy8arr74KADh8+DC+//57vfLy8nLx5NA2bdqgTZs2FmmHpS1fvtzg3Ud///13bN68GUDl1CBt4eHhACqT6C1btujFPnz4EIsWLTK6TicnJwAw2XfGknuBML9aSOjN9eyzz8LW1hYajaZWJ9OaO161CV9MOnToAAcHhxqv21ychDPGGGNmsLe3F2/ZvW3bNpN1hcvzffHFF/jkk0/w8OFDAJVHxl9//XVs2LABCoXCou01h7OzM0aPHo0VK1aIUxcuX76MQYMGiUdZZ8+erRPj4uKCtLQ0AJWXxuvbty+OHDkCjUYDoDLxPnv2LBYuXIjQ0FBs377dIm1/+eWXxSuPxMfHY/369eKJgLm5uXj55Zdx+PBhAMC8efMs0gZrKCsrQ1xcHI4ePQqgMrHet28fevfujZKSEvj5+SEpKUknxtfXF3/7298AAOPHj8e+ffvEufzHjx9HbGysyRNVhS8s9+7dw8aNGw3WmTt3Lvr06YO1a9fqTAkqKSnBxo0bMX/+fABA3759q7W9jo6O6NChAwDTR+urUp3xKhDW17179xqvtzo4CWeMMcbMNGLECISEhFRZb9KkSWjdujXKysrwxhtvQKlUQqVSwd/fH19++SVWr15tlTmnVRkzZgw6duyIxMREODk5wdXVFf7+/mLiNXXqVPTv318vLiEhAcuWLYNUKsWuXbsQFRUFhUIBd3d3yOVytG7dGhMmTEBOTo7RSyDWlq2tLb799luEhobi7t27GDJkiLifAwMDsXXrVtjY2OCjjz5Cnz59LNIGa/j0009x8eJFdOrUCY6OjlAqlYiLi0N+fj5cXFzw3//+VzxyrW3JkiVwcnLCtWvXEBcXB6VSCaVSiY4dO+LixYtYu3at0XUGBQUhJiYGQOVRdicnJwQEBCAgIED8AqbRaLB7924MHz4cfn5+UCgUcHNzg729PQYOHIi7d++iVatWSE1NrfY2C19it27dWu1YbeaOV6DyC8eBAwcAAIMHD67Ves3FSThjjDFmJltbW3GahilKpRIHDx7E+PHj0bx5czRp0gR2dnbi0VlhKkV9k0qlyMjIwJw5c9CyZUuUlJTA2dkZMTEx2LFjh8mf8ZOSkvD7779jwoQJCA8Ph0wmw507d8RELzk5GXv37hUTKkvw8fHBsWPHkJqaiqioKNjb26O4uBh+fn4YNmwYjh8/jrfeesti67eGzp0749ixYxg+fDicnZ1RXl4OHx8fjBo1CtnZ2Trz9bVFRETgyJEjePXVV+Hp6QmNRgN3d3e88cYbyMrKEudeG7N582aMGzcOTz/9NMrKypCfn4/8/HxxikpiYiI+++wzDBo0CG3atIFCocC9e/egUqnQtWtXpKWl4cSJE+KJvNWRkJAAuVyOQ4cOifO0a8Lc8QpUTh1Tq9Xo3LmzOJ3H0iRU26uqM8YYY4yxOpOXlyfeTTM3NxcBAQH126B68Nprr2HVqlWYNWsWpk+fbvH1RUdHIzMzE2vWrMHw4cMtvj6Ak3DGGGOMsQaFk/DKfRASEgInJyfk5uZa9ETJI0eOICoqCqGhoTh58qTVrmHP01EYY4wxxliDEhAQgOTkZBQWFuLjjz+26LqEEzjnzZtn1ZtINam6CmOMMcYYY9b17rvvQqlUWvQoeFFREaKiovD3v/8d//jHPyy2HkN4OgpjjDHGWAPC01GeDJyEM8YYY4wxZmU8J5wxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSvjJJwxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSvjJJwxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSvjJJwxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSvjJJwxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSvjJJwxxhhjjDEr4yScMcYYY4wxK+MknDHGGGOMMSv7P6hzHfGQ2iy7AAAAAElFTkSuQmCC", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "plot_dots_and_means(\n", " df, ycol;\n", " xcol = :N,\n", " xlabel = (xcol == :N ? \"Number of inputs (N)\" : string(xcol)),\n", " hylabel = string(ycol),\n", " color = C0,\n", " ax = nothing,\n", " kw...\n", ") = begin\n", " clip_on = false\n", " isnothing(ax) && (fig, ax = plt.subplots())\n", " ax.set_xscale(\"log\")\n", " x = df[:, xcol]\n", " y = df[:, ycol]\n", " plot(x, y, \".\"; clip_on, ms=8, alpha=0.6, color, ax)\n", " xu = unique(x)\n", " ax.set_xticks(xu, xu)\n", " ax.set_xlim(xu[1]/1.4, xu[end]*1.4)\n", " ax.minorticks_off()\n", " set(ax; xlabel, hylabel, kw...)\n", " dfm = combine(groupby(df, xcol), ycol => mean => ycol)\n", " ym = dfm[:, ycol]\n", " plot(xu, ym, \"-\"; clip_on, color, ax)\n", "end\n", "\n", "plot_AUC(df, method) = begin\n", " fig, ax = plt.subplots()\n", " ax.axhline(0.5, c=\"gray\", lw=1, ls=\"--\")\n", " plot_dots_and_means(\n", " df[df.method .== method, :],\n", " :AUC,\n", " title = method,\n", " ylim = [0, 1];\n", " ax\n", " )\n", "end\n", "\n", "methods = unique(df.method)\n", "for m in methods\n", " plot_AUC(df, m)\n", "end" ] }, { "cell_type": "code", "execution_count": 31, "id": "51bcdc1f", "metadata": {}, "outputs": [], "source": [ "kw = (; N=6500, duration=10minutes, δ_nS=0.02, Nᵤ=100, seed=1, method=\"STA_corr_2pass\");" ] }, { "cell_type": "code", "execution_count": 59, "id": "83251d44", "metadata": {}, "outputs": [], "source": [ "conntest = conntest_tables(; kw...);" ] }, { "cell_type": "code", "execution_count": 34, "id": "c049eeea", "metadata": {}, "outputs": [], "source": [ "using ConnTestEval" ] }, { "cell_type": "code", "execution_count": 42, "id": "cf0e4194", "metadata": { "scrolled": true }, "outputs": [], "source": [ "sweep = sweep_threshold(conntest);" ] }, { "cell_type": "code", "execution_count": 58, "id": "3a77d283", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "plot_ROC(sweep) = begin\n", " fig, ax = plt.subplots()\n", " ax.set_aspect(\"equal\")\n", " plot(\n", " sweep.FPR,\n", " sweep.TPR;\n", " ax,\n", " xlabel = \"FPR\",\n", " ylabel = (\"TPR\", :loc=>\"top\"),\n", " xlim = [0, 1],\n", " ylim = [0, 1],\n", " ) \n", "end\n", "ax = plot_ROC(sweep);" ] }, { "cell_type": "markdown", "id": "b5886e42", "metadata": {}, "source": [ "For the last point on the graph:" ] }, { "cell_type": "code", "execution_count": 60, "id": "332be240", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Predicted\n", " exc inh unc\n", " exc 1349 3851 0\n", "Real inh 1139 161 0\n", " unc 58 42 0\n", "\n" ] } ], "source": [ "print_confusion_matrix(sweep[end])" ] }, { "cell_type": "markdown", "id": "2366465c", "metadata": {}, "source": [ "So the problem is that we only count a detection as correct if it's the right type: exc or inh.\n", "Hence why, even with a threshold as low as possible, we do not detect all real connections." ] }, { "cell_type": "markdown", "id": "0f16ba74", "metadata": {}, "source": [ "## Two-pass STA template for high N\n", "\n", "Finally, what's the template look like for the high N 2 pass case.\n", "\n", "I suspect it's zeros: not enough (or no) connections detected in the first pass, with a high threshold." ] }, { "cell_type": "code", "execution_count": 62, "id": "da5ef7bb", "metadata": {}, "outputs": [], "source": [ "kw = (; N=1600, duration=10minutes, δ_nS=0.08, Nᵤ=100, seed=1, method=\"STA_height\");" ] }, { "cell_type": "code", "execution_count": 70, "id": "d0b23287", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.99" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table = conntest_tables(; kw...)\n", "maximum(table.t)" ] }, { "cell_type": "markdown", "id": "8e8c703a", "metadata": {}, "source": [ "So there is indeed not one connection with t-value 1.\n", "So no excitatory STAs for the template.\n", "\n", "We must change the method.\n", "Auto-choose a threshold at which we do have at least some detections." ] }, { "cell_type": "markdown", "id": "9acc9f54", "metadata": {}, "source": [ "We can do the full sweep: and then start from high thresholds, and pick the first where we cross a detrate;\n", "or we have a fixed integer number of exc detections." ] }, { "cell_type": "markdown", "id": "3aa870b1", "metadata": {}, "source": [ "Aha wait no ofc, we don't have the groundtruth.\n", "\n", "So rather we'll, maybe, go in a loop and lower the thr?\n", "You need to know the range then..\n", "that's np, just sorted unique.\n", "ok" ] }, { "cell_type": "code", "execution_count": 77, "id": "7fad963e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(θ, N) = (0.99, 0)\n", "(θ, N) = (0.98, 305)\n" ] } ], "source": [ "ts = sort!(unique(table.t), rev = true)\n", "for θ in ts\n", " predtype = ConnTestEval.predicted_types(table.t, θ)\n", " N = count(predtype .== :exc)\n", " @show (θ, N)\n", " N > 5 && break\n", "end" ] }, { "cell_type": "markdown", "id": "1e60af05", "metadata": {}, "source": [ "Something like that.\n", "\n", "Now to insert it in the code." ] }, { "cell_type": "markdown", "id": "a8ef0e00", "metadata": {}, "source": [ "---\n", "\n", "Wait, it's still so low after that change.\n", "Ok then so how many do we have and what's template like\n", "(now that we should have at least some)." ] }, { "cell_type": "code", "execution_count": 27, "id": "2c977be2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using Distributed … ✔\n", "using Revise … ✔\n", "using SpikeWorks … ✔\n", "using SpikeWorks.Units … ✔\n", "using ConnectionTests … ✔\n", "using DataFrames … ✔\n", "using MemDiskCache … ✔\n" ] } ], "source": [ "include(\"2023-03-14__[setup]_Nto1_sim_AdEx.jl\");" ] }, { "cell_type": "code", "execution_count": 28, "id": "02a631a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_size" ] }, { "cell_type": "code", "execution_count": 29, "id": "f0df62ba", "metadata": {}, "outputs": [], "source": [ "simkw = (; N=1600, duration=10minutes, δ_nS=0.08, Nᵤ=100, seed=1);\n", "m = TwoPassCorrTest();" ] }, { "cell_type": "code", "execution_count": 30, "id": "0f818445", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=1 _.jld2] … ✔ (0.4 s)\n", "First pass … Testing connection 1 / 300 … ✔\n", "Testing connection 2 / 300 … ✔\n", "Testing connection 3 / 300 … ✔\n", "Testing connection 4 / 300 … ✔\n", "Testing connection 5 / 300 … ✔\n", "Testing connection 6 / 300 … ✔\n", "Testing connection 7 / 300 … ✔\n", "Testing connection 8 / 300 … ✔\n", "Testing connection 9 / 300 … ✔\n", "Testing connection 10 / 300 … ✔\n", "Testing connection 11 / 300 … ✔\n", "Testing connection 12 / 300 … ✔\n", "Testing connection 13 / 300 … ✔\n", "Testing connection 14 / 300 … ✔\n", "Testing connection 15 / 300 … ✔\n", "Testing connection 16 / 300 … ✔\n", "Testing connection 17 / 300 … ✔\n", "Testing connection 18 / 300 … ✔\n", "Testing connection 19 / 300 … ✔\n", "Testing connection 20 / 300 … ✔\n", "Testing connection 21 / 300 … ✔\n", "Testing connection 22 / 300 … ✔\n", "Testing connection 23 / 300 … ✔\n", "Testing connection 24 / 300 … ✔\n", "Testing connection 25 / 300 … ✔\n", "Testing connection 26 / 300 … ✔\n", "Testing connection 27 / 300 … ✔\n", "Testing connection 28 / 300 … ✔\n", "Testing connection 29 / 300 … ✔\n", "Testing connection 30 / 300 … ✔\n", "Testing connection 31 / 300 … ✔\n", "Testing connection 32 / 300 … ✔\n", "Testing connection 33 / 300 … ✔\n", "Testing connection 34 / 300 … ✔\n", "Testing connection 35 / 300 … ✔\n", "Testing connection 36 / 300 … ✔\n", "Testing connection 37 / 300 … ✔\n", "Testing connection 38 / 300 … ✔\n", "Testing connection 39 / 300 … ✔\n", "Testing connection 40 / 300 … ✔\n", "Testing connection 41 / 300 … ✔\n", "Testing connection 42 / 300 … ✔\n", "Testing connection 43 / 300 … ✔\n", "Testing connection 44 / 300 … ✔\n", "Testing connection 45 / 300 … ✔\n", "Testing connection 46 / 300 … ✔\n", "Testing connection 47 / 300 … ✔\n", "Testing connection 48 / 300 … ✔\n", "Testing connection 49 / 300 … ✔\n", "Testing connection 50 / 300 … ✔\n", "Testing connection 51 / 300 … ✔\n", "Testing connection 52 / 300 … ✔\n", "Testing connection 53 / 300 … ✔\n", "Testing connection 54 / 300 … ✔\n", "Testing connection 55 / 300 … ✔\n", "Testing connection 56 / 300 … ✔\n", "Testing connection 57 / 300 … ✔\n", "Testing connection 58 / 300 … ✔\n", "Testing connection 59 / 300 … ✔\n", "Testing connection 60 / 300 … ✔\n", "Testing connection 61 / 300 … ✔\n", "Testing connection 62 / 300 … ✔\n", "Testing connection 63 / 300 … ✔\n", "Testing connection 64 / 300 … ✔\n", "Testing connection 65 / 300 … ✔\n", "Testing connection 66 / 300 … ✔\n", "Testing connection 67 / 300 … ✔\n", "Testing connection 68 / 300 … ✔\n", "Testing connection 69 / 300 … ✔\n", "Testing connection 70 / 300 … ✔\n", "Testing connection 71 / 300 … ✔\n", "Testing connection 72 / 300 … ✔\n", "Testing connection 73 / 300 … ✔\n", "Testing connection 74 / 300 … ✔\n", "Testing connection 75 / 300 … ✔\n", "Testing connection 76 / 300 … ✔\n", "Testing connection 77 / 300 … ✔\n", "Testing connection 78 / 300 … ✔\n", "Testing connection 79 / 300 … ✔\n", "Testing connection 80 / 300 … ✔\n", "Testing connection 81 / 300 … ✔\n", "Testing connection 82 / 300 … ✔\n", "Testing connection 83 / 300 … ✔\n", "Testing connection 84 / 300 … ✔\n", "Testing connection 85 / 300 … ✔\n", "Testing connection 86 / 300 … ✔\n", "Testing connection 87 / 300 … ✔\n", "Testing connection 88 / 300 … ✔\n", "Testing connection 89 / 300 … ✔\n", "Testing connection 90 / 300 … ✔\n", "Testing connection 91 / 300 … ✔\n", "Testing connection 92 / 300 … ✔\n", "Testing connection 93 / 300 … ✔\n", "Testing connection 94 / 300 … ✔\n", "Testing connection 95 / 300 … ✔\n", "Testing connection 96 / 300 … ✔\n", "Testing connection 97 / 300 … ✔\n", "Testing connection 98 / 300 … ✔\n", "Testing connection 99 / 300 … ✔\n", "Testing connection 100 / 300 … ✔\n", "Testing connection 101 / 300 … ✔\n", "Testing connection 102 / 300 … ✔\n", "Testing connection 103 / 300 … ✔\n", "Testing connection 104 / 300 … ✔\n", "Testing connection 105 / 300 … ✔\n", "Testing connection 106 / 300 … ✔\n", "Testing connection 107 / 300 … ✔\n", "Testing connection 108 / 300 … ✔\n", "Testing connection 109 / 300 … ✔\n", "Testing connection 110 / 300 … ✔\n", "Testing connection 111 / 300 … ✔\n", "Testing connection 112 / 300 … ✔\n", "Testing connection 113 / 300 … ✔\n", "Testing connection 114 / 300 … ✔\n", "Testing connection 115 / 300 … ✔\n", "Testing connection 116 / 300 … ✔\n", "Testing connection 117 / 300 … ✔\n", "Testing connection 118 / 300 … ✔\n", "Testing connection 119 / 300 … ✔\n", "Testing connection 120 / 300 … ✔\n", "Testing connection 121 / 300 … ✔\n", "Testing connection 122 / 300 … ✔\n", "Testing connection 123 / 300 … ✔\n", "Testing connection 124 / 300 … ✔\n", "Testing connection 125 / 300 … ✔\n", "Testing connection 126 / 300 … ✔\n", "Testing connection 127 / 300 … ✔\n", "Testing connection 128 / 300 … ✔\n", "Testing connection 129 / 300 … ✔\n", "Testing connection 130 / 300 … ✔\n", "Testing connection 131 / 300 … ✔\n", "Testing connection 132 / 300 … ✔\n", "Testing connection 133 / 300 … ✔\n", "Testing connection 134 / 300 … ✔\n", "Testing connection 135 / 300 … ✔\n", "Testing connection 136 / 300 … ✔\n", "Testing connection 137 / 300 … ✔\n", "Testing connection 138 / 300 … ✔\n", "Testing connection 139 / 300 … ✔\n", "Testing connection 140 / 300 … ✔\n", "Testing connection 141 / 300 … ✔\n", "Testing connection 142 / 300 … ✔\n", "Testing connection 143 / 300 … ✔\n", "Testing connection 144 / 300 … ✔\n", "Testing connection 145 / 300 … ✔\n", "Testing connection 146 / 300 … ✔\n", "Testing connection 147 / 300 … ✔\n", "Testing connection 148 / 300 … ✔\n", "Testing connection 149 / 300 … ✔\n", "Testing connection 150 / 300 … ✔\n", "Testing connection 151 / 300 … ✔\n", "Testing connection 152 / 300 … ✔\n", "Testing connection 153 / 300 … ✔\n", "Testing connection 154 / 300 … ✔\n", "Testing connection 155 / 300 … ✔\n", "Testing connection 156 / 300 … ✔\n", "Testing connection 157 / 300 … ✔\n", "Testing connection 158 / 300 … ✔\n", "Testing connection 159 / 300 … ✔\n", "Testing connection 160 / 300 … ✔\n", "Testing connection 161 / 300 … ✔\n", "Testing connection 162 / 300 … ✔\n", "Testing connection 163 / 300 … ✔\n", "Testing connection 164 / 300 … ✔\n", "Testing connection 165 / 300 … ✔\n", "Testing connection 166 / 300 … ✔\n", "Testing connection 167 / 300 … ✔\n", "Testing connection 168 / 300 … ✔\n", "Testing connection 169 / 300 … ✔\n", "Testing connection 170 / 300 … ✔\n", "Testing connection 171 / 300 … ✔\n", "Testing connection 172 / 300 … ✔\n", "Testing connection 173 / 300 … ✔\n", "Testing connection 174 / 300 … ✔\n", "Testing connection 175 / 300 … ✔\n", "Testing connection 176 / 300 … ✔\n", "Testing connection 177 / 300 … ✔\n", "Testing connection 178 / 300 … ✔\n", "Testing connection 179 / 300 … ✔\n", "Testing connection 180 / 300 … ✔\n", "Testing connection 181 / 300 … ✔\n", "Testing connection 182 / 300 … ✔\n", "Testing connection 183 / 300 … ✔\n", "Testing connection 184 / 300 … ✔\n", "Testing connection 185 / 300 … ✔\n", "Testing connection 186 / 300 … ✔\n", "Testing connection 187 / 300 … ✔\n", "Testing connection 188 / 300 … ✔\n", "Testing connection 189 / 300 … ✔\n", "Testing connection 190 / 300 … ✔\n", "Testing connection 191 / 300 … ✔\n", "Testing connection 192 / 300 … ✔\n", "Testing connection 193 / 300 … ✔\n", "Testing connection 194 / 300 … ✔\n", "Testing connection 195 / 300 … ✔\n", "Testing connection 196 / 300 … ✔\n", "Testing connection 197 / 300 … ✔\n", "Testing connection 198 / 300 … ✔\n", "Testing connection 199 / 300 … ✔\n", "Testing connection 200 / 300 … ✔\n", "Testing connection 201 / 300 … ✔\n", "Testing connection 202 / 300 … ✔\n", "Testing connection 203 / 300 … ✔\n", "Testing connection 204 / 300 … ✔\n", "Testing connection 205 / 300 … ✔\n", "Testing connection 206 / 300 … ✔\n", "Testing connection 207 / 300 … ✔\n", "Testing connection 208 / 300 … ✔\n", "Testing connection 209 / 300 … ✔\n", "Testing connection 210 / 300 … ✔\n", "Testing connection 211 / 300 … ✔\n", "Testing connection 212 / 300 … ✔\n", "Testing connection 213 / 300 … ✔\n", "Testing connection 214 / 300 … ✔\n", "Testing connection 215 / 300 … ✔\n", "Testing connection 216 / 300 … ✔\n", "Testing connection 217 / 300 … ✔\n", "Testing connection 218 / 300 … ✔\n", "Testing connection 219 / 300 … ✔\n", "Testing connection 220 / 300 … ✔\n", "Testing connection 221 / 300 … ✔\n", "Testing connection 222 / 300 … ✔\n", "Testing connection 223 / 300 … ✔\n", "Testing connection 224 / 300 … ✔\n", "Testing connection 225 / 300 … ✔\n", "Testing connection 226 / 300 … ✔\n", "Testing connection 227 / 300 … ✔\n", "Testing connection 228 / 300 … ✔\n", "Testing connection 229 / 300 … ✔\n", "Testing connection 230 / 300 … ✔\n", "Testing connection 231 / 300 … ✔\n", "Testing connection 232 / 300 … ✔\n", "Testing connection 233 / 300 … ✔\n", "Testing connection 234 / 300 … ✔\n", "Testing connection 235 / 300 … ✔\n", "Testing connection 236 / 300 … ✔\n", "Testing connection 237 / 300 … ✔\n", "Testing connection 238 / 300 … ✔\n", "Testing connection 239 / 300 … ✔\n", "Testing connection 240 / 300 … ✔\n", "Testing connection 241 / 300 … ✔\n", "Testing connection 242 / 300 … ✔\n", "Testing connection 243 / 300 … ✔\n", "Testing connection 244 / 300 … ✔\n", "Testing connection 245 / 300 … ✔\n", "Testing connection 246 / 300 … ✔\n", "Testing connection 247 / 300 … ✔\n", "Testing connection 248 / 300 … ✔\n", "Testing connection 249 / 300 … ✔\n", "Testing connection 250 / 300 … ✔\n", "Testing connection 251 / 300 … ✔\n", "Testing connection 252 / 300 … ✔\n", "Testing connection 253 / 300 … ✔\n", "Testing connection 254 / 300 … ✔\n", "Testing connection 255 / 300 … ✔\n", "Testing connection 256 / 300 … ✔\n", "Testing connection 257 / 300 … ✔\n", "Testing connection 258 / 300 … ✔\n", "Testing connection 259 / 300 … ✔\n", "Testing connection 260 / 300 … ✔\n", "Testing connection 261 / 300 … ✔\n", "Testing connection 262 / 300 … ✔\n", "Testing connection 263 / 300 … ✔\n", "Testing connection 264 / 300 … ✔\n", "Testing connection 265 / 300 … ✔\n", "Testing connection 266 / 300 … ✔\n", "Testing connection 267 / 300 … ✔\n", "Testing connection 268 / 300 … ✔\n", "Testing connection 269 / 300 … ✔\n", "Testing connection 270 / 300 … ✔\n", "Testing connection 271 / 300 … ✔\n", "Testing connection 272 / 300 … ✔\n", "Testing connection 273 / 300 … ✔\n", "Testing connection 274 / 300 … ✔\n", "Testing connection 275 / 300 … ✔\n", "Testing connection 276 / 300 … ✔\n", "Testing connection 277 / 300 … ✔\n", "Testing connection 278 / 300 … ✔\n", "Testing connection 279 / 300 … ✔\n", "Testing connection 280 / 300 … ✔\n", "Testing connection 281 / 300 … ✔\n", "Testing connection 282 / 300 … ✔\n", "Testing connection 283 / 300 … ✔\n", "Testing connection 284 / 300 … ✔\n", "Testing connection 285 / 300 … ✔\n", "Testing connection 286 / 300 … ✔\n", "Testing connection 287 / 300 … ✔\n", "Testing connection 288 / 300 … ✔\n", "Testing connection 289 / 300 … ✔\n", "Testing connection 290 / 300 … ✔\n", "Testing connection 291 / 300 … ✔\n", "Testing connection 292 / 300 … ✔\n", "Testing connection 293 / 300 … ✔\n", "Testing connection 294 / 300 … ✔\n", "Testing connection 295 / 300 … ✔\n", "Testing connection 296 / 300 … ✔\n", "Testing connection 297 / 300 … ✔\n", "Testing connection 298 / 300 … ✔\n", "Testing connection 299 / 300 … ✔\n", "Testing connection 300 / 300 … ✔\n", "✔ (0.2 s)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=2 _.jld2] … ✔ (0.8 s)\n", "First pass … Testing connection 1 / 300 … ✔\n", "Testing connection 2 / 300 … ✔\n", "Testing connection 3 / 300 … ✔\n", "Testing connection 4 / 300 … ✔\n", "Testing connection 5 / 300 … ✔\n", "Testing connection 6 / 300 … ✔\n", "Testing connection 7 / 300 … ✔\n", "Testing connection 8 / 300 … ✔\n", "Testing connection 9 / 300 … ✔\n", "Testing connection 10 / 300 … ✔\n", "Testing connection 11 / 300 … ✔\n", "Testing connection 12 / 300 … ✔\n", "Testing connection 13 / 300 … ✔\n", "Testing connection 14 / 300 … ✔\n", "Testing connection 15 / 300 … ✔\n", "Testing connection 16 / 300 … ✔\n", "Testing connection 17 / 300 … ✔\n", "Testing connection 18 / 300 … ✔\n", "Testing connection 19 / 300 … ✔\n", "Testing connection 20 / 300 … ✔\n", "Testing connection 21 / 300 … ✔\n", "Testing connection 22 / 300 … ✔\n", "Testing connection 23 / 300 … ✔\n", "Testing connection 24 / 300 … ✔\n", "Testing connection 25 / 300 … ✔\n", "Testing connection 26 / 300 … ✔\n", "Testing connection 27 / 300 … ✔\n", "Testing connection 28 / 300 … ✔\n", "Testing connection 29 / 300 … ✔\n", "Testing connection 30 / 300 … ✔\n", "Testing connection 31 / 300 … ✔\n", "Testing connection 32 / 300 … ✔\n", "Testing connection 33 / 300 … ✔\n", "Testing connection 34 / 300 … ✔\n", "Testing connection 35 / 300 … ✔\n", "Testing connection 36 / 300 … ✔\n", "Testing connection 37 / 300 … ✔\n", "Testing connection 38 / 300 … ✔\n", "Testing connection 39 / 300 … ✔\n", "Testing connection 40 / 300 … ✔\n", "Testing connection 41 / 300 … ✔\n", "Testing connection 42 / 300 … ✔\n", "Testing connection 43 / 300 … ✔\n", "Testing connection 44 / 300 … ✔\n", "Testing connection 45 / 300 … ✔\n", "Testing connection 46 / 300 … ✔\n", "Testing connection 47 / 300 … ✔\n", "Testing connection 48 / 300 … ✔\n", "Testing connection 49 / 300 … ✔\n", "Testing connection 50 / 300 … ✔\n", "Testing connection 51 / 300 … ✔\n", "Testing connection 52 / 300 … ✔\n", "Testing connection 53 / 300 … ✔\n", "Testing connection 54 / 300 … ✔\n", "Testing connection 55 / 300 … ✔\n", "Testing connection 56 / 300 … ✔\n", "Testing connection 57 / 300 … ✔\n", "Testing connection 58 / 300 … ✔\n", "Testing connection 59 / 300 … ✔\n", "Testing connection 60 / 300 … ✔\n", "Testing connection 61 / 300 … ✔\n", "Testing connection 62 / 300 … ✔\n", "Testing connection 63 / 300 … ✔\n", "Testing connection 64 / 300 … ✔\n", "Testing connection 65 / 300 … ✔\n", "Testing connection 66 / 300 … ✔\n", "Testing connection 67 / 300 … ✔\n", "Testing connection 68 / 300 … ✔\n", "Testing connection 69 / 300 … ✔\n", "Testing connection 70 / 300 … ✔\n", "Testing connection 71 / 300 … ✔\n", "Testing connection 72 / 300 … ✔\n", "Testing connection 73 / 300 … ✔\n", "Testing connection 74 / 300 … ✔\n", "Testing connection 75 / 300 … ✔\n", "Testing connection 76 / 300 … ✔\n", "Testing connection 77 / 300 … ✔\n", "Testing connection 78 / 300 … ✔\n", "Testing connection 79 / 300 … ✔\n", "Testing connection 80 / 300 … ✔\n", "Testing connection 81 / 300 … ✔\n", "Testing connection 82 / 300 … ✔\n", "Testing connection 83 / 300 … ✔\n", "Testing connection 84 / 300 … ✔\n", "Testing connection 85 / 300 … ✔\n", "Testing connection 86 / 300 … ✔\n", "Testing connection 87 / 300 … ✔\n", "Testing connection 88 / 300 … ✔\n", "Testing connection 89 / 300 … ✔\n", "Testing connection 90 / 300 … ✔\n", "Testing connection 91 / 300 … ✔\n", "Testing connection 92 / 300 … ✔\n", "Testing connection 93 / 300 … ✔\n", "Testing connection 94 / 300 … ✔\n", "Testing connection 95 / 300 … ✔\n", "Testing connection 96 / 300 … ✔\n", "Testing connection 97 / 300 … ✔\n", "Testing connection 98 / 300 … ✔\n", "Testing connection 99 / 300 … ✔\n", "Testing connection 100 / 300 … ✔\n", "Testing connection 101 / 300 … ✔\n", "Testing connection 102 / 300 … ✔\n", "Testing connection 103 / 300 … ✔\n", "Testing connection 104 / 300 … ✔\n", "Testing connection 105 / 300 … ✔\n", "Testing connection 106 / 300 … ✔\n", "Testing connection 107 / 300 … ✔\n", "Testing connection 108 / 300 … ✔\n", "Testing connection 109 / 300 … ✔\n", "Testing connection 110 / 300 … ✔\n", "Testing connection 111 / 300 … ✔\n", "Testing connection 112 / 300 … ✔\n", "Testing connection 113 / 300 … ✔\n", "Testing connection 114 / 300 … ✔\n", "Testing connection 115 / 300 … ✔\n", "Testing connection 116 / 300 … ✔\n", "Testing connection 117 / 300 … ✔\n", "Testing connection 118 / 300 … ✔\n", "Testing connection 119 / 300 … ✔\n", "Testing connection 120 / 300 … ✔\n", "Testing connection 121 / 300 … ✔\n", "Testing connection 122 / 300 … ✔\n", "Testing connection 123 / 300 … ✔\n", "Testing connection 124 / 300 … ✔\n", "Testing connection 125 / 300 … ✔\n", "Testing connection 126 / 300 … ✔\n", "Testing connection 127 / 300 … ✔\n", "Testing connection 128 / 300 … ✔\n", "Testing connection 129 / 300 … ✔\n", "Testing connection 130 / 300 … ✔\n", "Testing connection 131 / 300 … ✔\n", "Testing connection 132 / 300 … ✔\n", "Testing connection 133 / 300 … ✔\n", "Testing connection 134 / 300 … ✔\n", "Testing connection 135 / 300 … ✔\n", "Testing connection 136 / 300 … ✔\n", "Testing connection 137 / 300 … ✔\n", "Testing connection 138 / 300 … ✔\n", "Testing connection 139 / 300 … ✔\n", "Testing connection 140 / 300 … ✔\n", "Testing connection 141 / 300 … ✔\n", "Testing connection 142 / 300 … ✔\n", "Testing connection 143 / 300 … ✔\n", "Testing connection 144 / 300 … ✔\n", "Testing connection 145 / 300 … ✔\n", "Testing connection 146 / 300 … ✔\n", "Testing connection 147 / 300 … ✔\n", "Testing connection 148 / 300 … ✔\n", "Testing connection 149 / 300 … ✔\n", "Testing connection 150 / 300 … ✔\n", "Testing connection 151 / 300 … ✔\n", "Testing connection 152 / 300 … ✔\n", "Testing connection 153 / 300 … ✔\n", "Testing connection 154 / 300 … ✔\n", "Testing connection 155 / 300 … ✔\n", "Testing connection 156 / 300 … ✔\n", "Testing connection 157 / 300 … ✔\n", "Testing connection 158 / 300 … ✔\n", "Testing connection 159 / 300 … ✔\n", "Testing connection 160 / 300 … ✔\n", "Testing connection 161 / 300 … ✔\n", "Testing connection 162 / 300 … ✔\n", "Testing connection 163 / 300 … ✔\n", "Testing connection 164 / 300 … ✔\n", "Testing connection 165 / 300 … ✔\n", "Testing connection 166 / 300 … ✔\n", "Testing connection 167 / 300 … ✔\n", "Testing connection 168 / 300 … ✔\n", "Testing connection 169 / 300 … ✔\n", "Testing connection 170 / 300 … ✔\n", "Testing connection 171 / 300 … ✔\n", "Testing connection 172 / 300 … ✔\n", "Testing connection 173 / 300 … ✔\n", "Testing connection 174 / 300 … ✔\n", "Testing connection 175 / 300 … ✔\n", "Testing connection 176 / 300 … ✔\n", "Testing connection 177 / 300 … ✔\n", "Testing connection 178 / 300 … ✔\n", "Testing connection 179 / 300 … ✔\n", "Testing connection 180 / 300 … ✔\n", "Testing connection 181 / 300 … ✔\n", "Testing connection 182 / 300 … ✔\n", "Testing connection 183 / 300 … ✔\n", "Testing connection 184 / 300 … ✔\n", "Testing connection 185 / 300 … ✔\n", "Testing connection 186 / 300 … ✔\n", "Testing connection 187 / 300 … ✔\n", "Testing connection 188 / 300 … ✔\n", "Testing connection 189 / 300 … ✔\n", "Testing connection 190 / 300 … ✔\n", "Testing connection 191 / 300 … ✔\n", "Testing connection 192 / 300 … ✔\n", "Testing connection 193 / 300 … ✔\n", "Testing connection 194 / 300 … ✔\n", "Testing connection 195 / 300 … ✔\n", "Testing connection 196 / 300 … ✔\n", "Testing connection 197 / 300 … ✔\n", "Testing connection 198 / 300 … ✔\n", "Testing connection 199 / 300 … ✔\n", "Testing connection 200 / 300 … ✔\n", "Testing connection 201 / 300 … ✔\n", "Testing connection 202 / 300 … ✔\n", "Testing connection 203 / 300 … ✔\n", "Testing connection 204 / 300 … ✔\n", "Testing connection 205 / 300 … ✔\n", "Testing connection 206 / 300 … ✔\n", "Testing connection 207 / 300 … ✔\n", "Testing connection 208 / 300 … ✔\n", "Testing connection 209 / 300 … ✔\n", "Testing connection 210 / 300 … ✔\n", "Testing connection 211 / 300 … ✔\n", "Testing connection 212 / 300 … ✔\n", "Testing connection 213 / 300 … ✔\n", "Testing connection 214 / 300 … ✔\n", "Testing connection 215 / 300 … ✔\n", "Testing connection 216 / 300 … ✔\n", "Testing connection 217 / 300 … ✔\n", "Testing connection 218 / 300 … ✔\n", "Testing connection 219 / 300 … ✔\n", "Testing connection 220 / 300 … ✔\n", "Testing connection 221 / 300 … ✔\n", "Testing connection 222 / 300 … ✔\n", "Testing connection 223 / 300 … ✔\n", "Testing connection 224 / 300 … ✔\n", "Testing connection 225 / 300 … ✔\n", "Testing connection 226 / 300 … ✔\n", "Testing connection 227 / 300 … ✔\n", "Testing connection 228 / 300 … ✔\n", "Testing connection 229 / 300 … ✔\n", "Testing connection 230 / 300 … ✔\n", "Testing connection 231 / 300 … ✔\n", "Testing connection 232 / 300 … ✔\n", "Testing connection 233 / 300 … ✔\n", "Testing connection 234 / 300 … ✔\n", "Testing connection 235 / 300 … ✔\n", "Testing connection 236 / 300 … ✔\n", "Testing connection 237 / 300 … ✔\n", "Testing connection 238 / 300 … ✔\n", "Testing connection 239 / 300 … ✔\n", "Testing connection 240 / 300 … ✔\n", "Testing connection 241 / 300 … ✔\n", "Testing connection 242 / 300 … ✔\n", "Testing connection 243 / 300 … ✔\n", "Testing connection 244 / 300 … ✔\n", "Testing connection 245 / 300 … ✔\n", "Testing connection 246 / 300 … ✔\n", "Testing connection 247 / 300 … ✔\n", "Testing connection 248 / 300 … ✔\n", "Testing connection 249 / 300 … ✔\n", "Testing connection 250 / 300 … ✔\n", "Testing connection 251 / 300 … ✔\n", "Testing connection 252 / 300 … ✔\n", "Testing connection 253 / 300 … ✔\n", "Testing connection 254 / 300 … ✔\n", "Testing connection 255 / 300 … ✔\n", "Testing connection 256 / 300 … ✔\n", "Testing connection 257 / 300 … ✔\n", "Testing connection 258 / 300 … ✔\n", "Testing connection 259 / 300 … ✔\n", "Testing connection 260 / 300 … ✔\n", "Testing connection 261 / 300 … ✔\n", "Testing connection 262 / 300 … ✔\n", "Testing connection 263 / 300 … ✔\n", "Testing connection 264 / 300 … ✔\n", "Testing connection 265 / 300 … ✔\n", "Testing connection 266 / 300 … ✔\n", "Testing connection 267 / 300 … ✔\n", "Testing connection 268 / 300 … ✔\n", "Testing connection 269 / 300 … ✔\n", "Testing connection 270 / 300 … ✔\n", "Testing connection 271 / 300 … ✔\n", "Testing connection 272 / 300 … ✔\n", "Testing connection 273 / 300 … ✔\n", "Testing connection 274 / 300 … ✔\n", "Testing connection 275 / 300 … ✔\n", "Testing connection 276 / 300 … ✔\n", "Testing connection 277 / 300 … ✔\n", "Testing connection 278 / 300 … ✔\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Testing connection 279 / 300 … ✔\n", "Testing connection 280 / 300 … ✔\n", "Testing connection 281 / 300 … ✔\n", "Testing connection 282 / 300 … ✔\n", "Testing connection 283 / 300 … ✔\n", "Testing connection 284 / 300 … ✔\n", "Testing connection 285 / 300 … ✔\n", "Testing connection 286 / 300 … ✔\n", "Testing connection 287 / 300 … ✔\n", "Testing connection 288 / 300 … ✔\n", "Testing connection 289 / 300 … ✔\n", "Testing connection 290 / 300 … ✔\n", "Testing connection 291 / 300 … ✔\n", "Testing connection 292 / 300 … ✔\n", "Testing connection 293 / 300 … ✔\n", "Testing connection 294 / 300 … ✔\n", "Testing connection 295 / 300 … ✔\n", "Testing connection 296 / 300 … ✔\n", "Testing connection 297 / 300 … ✔\n", "Testing connection 298 / 300 … ✔\n", "Testing connection 299 / 300 … ✔\n", "Testing connection 300 / 300 … ✔\n", "✔\n", "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=3 _.jld2] … ✔ (0.7 s)\n", "First pass … Testing connection 1 / 300 … ✔\n", "Testing connection 2 / 300 … ✔\n", "Testing connection 3 / 300 … ✔\n", "Testing connection 4 / 300 … ✔\n", "Testing connection 5 / 300 … ✔\n", "Testing connection 6 / 300 … ✔\n", "Testing connection 7 / 300 … ✔\n", "Testing connection 8 / 300 … ✔\n", "Testing connection 9 / 300 … ✔\n", "Testing connection 10 / 300 … ✔\n", "Testing connection 11 / 300 … ✔\n", "Testing connection 12 / 300 … ✔\n", "Testing connection 13 / 300 … ✔\n", "Testing connection 14 / 300 … ✔\n", "Testing connection 15 / 300 … ✔\n", "Testing connection 16 / 300 … ✔\n", "Testing connection 17 / 300 … ✔\n", "Testing connection 18 / 300 … ✔\n", "Testing connection 19 / 300 … ✔\n", "Testing connection 20 / 300 … ✔\n", "Testing connection 21 / 300 … ✔\n", "Testing connection 22 / 300 … ✔\n", "Testing connection 23 / 300 … ✔\n", "Testing connection 24 / 300 … ✔\n", "Testing connection 25 / 300 … ✔\n", "Testing connection 26 / 300 … ✔\n", "Testing connection 27 / 300 … ✔\n", "Testing connection 28 / 300 … ✔\n", "Testing connection 29 / 300 … ✔\n", "Testing connection 30 / 300 … ✔\n", "Testing connection 31 / 300 … ✔\n", "Testing connection 32 / 300 … ✔\n", "Testing connection 33 / 300 … ✔\n", "Testing connection 34 / 300 … ✔\n", "Testing connection 35 / 300 … ✔\n", "Testing connection 36 / 300 … ✔\n", "Testing connection 37 / 300 … ✔\n", "Testing connection 38 / 300 … ✔\n", "Testing connection 39 / 300 … ✔\n", "Testing connection 40 / 300 … ✔\n", "Testing connection 41 / 300 … ✔\n", "Testing connection 42 / 300 … ✔\n", "Testing connection 43 / 300 … ✔\n", "Testing connection 44 / 300 … ✔\n", "Testing connection 45 / 300 … ✔\n", "Testing connection 46 / 300 … ✔\n", "Testing connection 47 / 300 … ✔\n", "Testing connection 48 / 300 … ✔\n", "Testing connection 49 / 300 … ✔\n", "Testing connection 50 / 300 … ✔\n", "Testing connection 51 / 300 … ✔\n", "Testing connection 52 / 300 … ✔\n", "Testing connection 53 / 300 … ✔\n", "Testing connection 54 / 300 … ✔\n", "Testing connection 55 / 300 … ✔\n", "Testing connection 56 / 300 … ✔\n", "Testing connection 57 / 300 … ✔\n", "Testing connection 58 / 300 … ✔\n", "Testing connection 59 / 300 … ✔\n", "Testing connection 60 / 300 … ✔\n", "Testing connection 61 / 300 … ✔\n", "Testing connection 62 / 300 … ✔\n", "Testing connection 63 / 300 … ✔\n", "Testing connection 64 / 300 … ✔\n", "Testing connection 65 / 300 … ✔\n", "Testing connection 66 / 300 … ✔\n", "Testing connection 67 / 300 … ✔\n", "Testing connection 68 / 300 … ✔\n", "Testing connection 69 / 300 … ✔\n", "Testing connection 70 / 300 … ✔\n", "Testing connection 71 / 300 … ✔\n", "Testing connection 72 / 300 … ✔\n", "Testing connection 73 / 300 … ✔\n", "Testing connection 74 / 300 … ✔\n", "Testing connection 75 / 300 … ✔\n", "Testing connection 76 / 300 … ✔\n", "Testing connection 77 / 300 … ✔\n", "Testing connection 78 / 300 … ✔\n", "Testing connection 79 / 300 … ✔\n", "Testing connection 80 / 300 … ✔\n", "Testing connection 81 / 300 … ✔\n", "Testing connection 82 / 300 … ✔\n", "Testing connection 83 / 300 … ✔\n", "Testing connection 84 / 300 … ✔\n", "Testing connection 85 / 300 … ✔\n", "Testing connection 86 / 300 … ✔\n", "Testing connection 87 / 300 … ✔\n", "Testing connection 88 / 300 … ✔\n", "Testing connection 89 / 300 … ✔\n", "Testing connection 90 / 300 … ✔\n", "Testing connection 91 / 300 … ✔\n", "Testing connection 92 / 300 … ✔\n", "Testing connection 93 / 300 … ✔\n", "Testing connection 94 / 300 … ✔\n", "Testing connection 95 / 300 … ✔\n", "Testing connection 96 / 300 … ✔\n", "Testing connection 97 / 300 … ✔\n", "Testing connection 98 / 300 … ✔\n", "Testing connection 99 / 300 … ✔\n", "Testing connection 100 / 300 … ✔\n", "Testing connection 101 / 300 … ✔\n", "Testing connection 102 / 300 … ✔\n", "Testing connection 103 / 300 … ✔\n", "Testing connection 104 / 300 … ✔\n", "Testing connection 105 / 300 … ✔\n", "Testing connection 106 / 300 … ✔\n", "Testing connection 107 / 300 … ✔\n", "Testing connection 108 / 300 … ✔\n", "Testing connection 109 / 300 … ✔\n", "Testing connection 110 / 300 … ✔\n", "Testing connection 111 / 300 … ✔\n", "Testing connection 112 / 300 … ✔\n", "Testing connection 113 / 300 … ✔\n", "Testing connection 114 / 300 … ✔\n", "Testing connection 115 / 300 … ✔\n", "Testing connection 116 / 300 … ✔\n", "Testing connection 117 / 300 … ✔\n", "Testing connection 118 / 300 … ✔\n", "Testing connection 119 / 300 … ✔\n", "Testing connection 120 / 300 … ✔\n", "Testing connection 121 / 300 … ✔\n", "Testing connection 122 / 300 … ✔\n", "Testing connection 123 / 300 … ✔\n", "Testing connection 124 / 300 … ✔\n", "Testing connection 125 / 300 … ✔\n", "Testing connection 126 / 300 … ✔\n", "Testing connection 127 / 300 … ✔\n", "Testing connection 128 / 300 … ✔\n", "Testing connection 129 / 300 … ✔\n", "Testing connection 130 / 300 … ✔\n", "Testing connection 131 / 300 … ✔\n", "Testing connection 132 / 300 … ✔\n", "Testing connection 133 / 300 … ✔\n", "Testing connection 134 / 300 … ✔\n", "Testing connection 135 / 300 … ✔\n", "Testing connection 136 / 300 … ✔\n", "Testing connection 137 / 300 … ✔\n", "Testing connection 138 / 300 … ✔\n", "Testing connection 139 / 300 … ✔\n", "Testing connection 140 / 300 … ✔\n", "Testing connection 141 / 300 … ✔\n", "Testing connection 142 / 300 … ✔\n", "Testing connection 143 / 300 … ✔\n", "Testing connection 144 / 300 … ✔\n", "Testing connection 145 / 300 … ✔\n", "Testing connection 146 / 300 … ✔\n", "Testing connection 147 / 300 … ✔\n", "Testing connection 148 / 300 … ✔\n", "Testing connection 149 / 300 … ✔\n", "Testing connection 150 / 300 … ✔\n", "Testing connection 151 / 300 … ✔\n", "Testing connection 152 / 300 … ✔\n", "Testing connection 153 / 300 … ✔\n", "Testing connection 154 / 300 … ✔\n", "Testing connection 155 / 300 … ✔\n", "Testing connection 156 / 300 … ✔\n", "Testing connection 157 / 300 … ✔\n", "Testing connection 158 / 300 … ✔\n", "Testing connection 159 / 300 … ✔\n", "Testing connection 160 / 300 … ✔\n", "Testing connection 161 / 300 … ✔\n", "Testing connection 162 / 300 … ✔\n", "Testing connection 163 / 300 … ✔\n", "Testing connection 164 / 300 … ✔\n", "Testing connection 165 / 300 … ✔\n", "Testing connection 166 / 300 … ✔\n", "Testing connection 167 / 300 … ✔\n", "Testing connection 168 / 300 … ✔\n", "Testing connection 169 / 300 … ✔\n", "Testing connection 170 / 300 … ✔\n", "Testing connection 171 / 300 … ✔\n", "Testing connection 172 / 300 … ✔\n", "Testing connection 173 / 300 … ✔\n", "Testing connection 174 / 300 … ✔\n", "Testing connection 175 / 300 … ✔\n", "Testing connection 176 / 300 … ✔\n", "Testing connection 177 / 300 … ✔\n", "Testing connection 178 / 300 … ✔\n", "Testing connection 179 / 300 … ✔\n", "Testing connection 180 / 300 … ✔\n", "Testing connection 181 / 300 … ✔\n", "Testing connection 182 / 300 … ✔\n", "Testing connection 183 / 300 … ✔\n", "Testing connection 184 / 300 … ✔\n", "Testing connection 185 / 300 … ✔\n", "Testing connection 186 / 300 … ✔\n", "Testing connection 187 / 300 … ✔\n", "Testing connection 188 / 300 … ✔\n", "Testing connection 189 / 300 … ✔\n", "Testing connection 190 / 300 … ✔\n", "Testing connection 191 / 300 … ✔\n", "Testing connection 192 / 300 … ✔\n", "Testing connection 193 / 300 … ✔\n", "Testing connection 194 / 300 … ✔\n", "Testing connection 195 / 300 … ✔\n", "Testing connection 196 / 300 … ✔\n", "Testing connection 197 / 300 … ✔\n", "Testing connection 198 / 300 … ✔\n", "Testing connection 199 / 300 … ✔\n", "Testing connection 200 / 300 … ✔\n", "Testing connection 201 / 300 … ✔\n", "Testing connection 202 / 300 … ✔\n", "Testing connection 203 / 300 … ✔\n", "Testing connection 204 / 300 … ✔\n", "Testing connection 205 / 300 … ✔\n", "Testing connection 206 / 300 … ✔\n", "Testing connection 207 / 300 … ✔\n", "Testing connection 208 / 300 … ✔\n", "Testing connection 209 / 300 … ✔\n", "Testing connection 210 / 300 … ✔\n", "Testing connection 211 / 300 … ✔\n", "Testing connection 212 / 300 … ✔\n", "Testing connection 213 / 300 … ✔\n", "Testing connection 214 / 300 … ✔\n", "Testing connection 215 / 300 … ✔\n", "Testing connection 216 / 300 … ✔\n", "Testing connection 217 / 300 … ✔\n", "Testing connection 218 / 300 … ✔\n", "Testing connection 219 / 300 … ✔\n", "Testing connection 220 / 300 … ✔\n", "Testing connection 221 / 300 … ✔\n", "Testing connection 222 / 300 … ✔\n", "Testing connection 223 / 300 … ✔\n", "Testing connection 224 / 300 … ✔\n", "Testing connection 225 / 300 … ✔\n", "Testing connection 226 / 300 … ✔\n", "Testing connection 227 / 300 … ✔\n", "Testing connection 228 / 300 … ✔\n", "Testing connection 229 / 300 … ✔\n", "Testing connection 230 / 300 … ✔\n", "Testing connection 231 / 300 … ✔\n", "Testing connection 232 / 300 … ✔\n", "Testing connection 233 / 300 … ✔\n", "Testing connection 234 / 300 … ✔\n", "Testing connection 235 / 300 … ✔\n", "Testing connection 236 / 300 … ✔\n", "Testing connection 237 / 300 … ✔\n", "Testing connection 238 / 300 … ✔\n", "Testing connection 239 / 300 … ✔\n", "Testing connection 240 / 300 … ✔\n", "Testing connection 241 / 300 … ✔\n", "Testing connection 242 / 300 … ✔\n", "Testing connection 243 / 300 … ✔\n", "Testing connection 244 / 300 … ✔\n", "Testing connection 245 / 300 … ✔\n", "Testing connection 246 / 300 … ✔\n", "Testing connection 247 / 300 … ✔\n", "Testing connection 248 / 300 … ✔\n", "Testing connection 249 / 300 … ✔\n", "Testing connection 250 / 300 … ✔\n", "Testing connection 251 / 300 … ✔\n", "Testing connection 252 / 300 … ✔\n", "Testing connection 253 / 300 … ✔\n", "Testing connection 254 / 300 … ✔\n", "Testing connection 255 / 300 … ✔\n", "Testing connection 256 / 300 … ✔\n", "Testing connection 257 / 300 … ✔\n", "Testing connection 258 / 300 … ✔\n", "Testing connection 259 / 300 … ✔\n", "Testing connection 260 / 300 … ✔\n", "Testing connection 261 / 300 … ✔\n", "Testing connection 262 / 300 … ✔\n", "Testing connection 263 / 300 … ✔\n", "Testing connection 264 / 300 … ✔\n", "Testing connection 265 / 300 … ✔\n", "Testing connection 266 / 300 … ✔\n", "Testing connection 267 / 300 … ✔\n", "Testing connection 268 / 300 … ✔\n", "Testing connection 269 / 300 … ✔\n", "Testing connection 270 / 300 … ✔\n", "Testing connection 271 / 300 … ✔\n", "Testing connection 272 / 300 … ✔\n", "Testing connection 273 / 300 … ✔\n", "Testing connection 274 / 300 … ✔\n", "Testing connection 275 / 300 … ✔\n", "Testing connection 276 / 300 … ✔\n", "Testing connection 277 / 300 … ✔\n", "Testing connection 278 / 300 … ✔\n", "Testing connection 279 / 300 … ✔\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Testing connection 280 / 300 … ✔\n", "Testing connection 281 / 300 … ✔\n", "Testing connection 282 / 300 … ✔\n", "Testing connection 283 / 300 … ✔\n", "Testing connection 284 / 300 … ✔\n", "Testing connection 285 / 300 … ✔\n", "Testing connection 286 / 300 … ✔\n", "Testing connection 287 / 300 … ✔\n", "Testing connection 288 / 300 … ✔\n", "Testing connection 289 / 300 … ✔\n", "Testing connection 290 / 300 … ✔\n", "Testing connection 291 / 300 … ✔\n", "Testing connection 292 / 300 … ✔\n", "Testing connection 293 / 300 … ✔\n", "Testing connection 294 / 300 … ✔\n", "Testing connection 295 / 300 … ✔\n", "Testing connection 296 / 300 … ✔\n", "Testing connection 297 / 300 … ✔\n", "Testing connection 298 / 300 … ✔\n", "Testing connection 299 / 300 … ✔\n", "Testing connection 300 / 300 … ✔\n", "✔ (0.1 s)\n", "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=4 _.jld2] … ✔ (0.6 s)\n", "First pass … Testing connection 1 / 300 … ✔\n", "Testing connection 2 / 300 … ✔\n", "Testing connection 3 / 300 … ✔\n", "Testing connection 4 / 300 … ✔\n", "Testing connection 5 / 300 … ✔\n", "Testing connection 6 / 300 … ✔\n", "Testing connection 7 / 300 … ✔\n", "Testing connection 8 / 300 … ✔\n", "Testing connection 9 / 300 … ✔\n", "Testing connection 10 / 300 … ✔\n", "Testing connection 11 / 300 … ✔\n", "Testing connection 12 / 300 … ✔\n", "Testing connection 13 / 300 … ✔\n", "Testing connection 14 / 300 … ✔\n", "Testing connection 15 / 300 … ✔\n", "Testing connection 16 / 300 … ✔\n", "Testing connection 17 / 300 … ✔\n", "Testing connection 18 / 300 … ✔\n", "Testing connection 19 / 300 … ✔\n", "Testing connection 20 / 300 … ✔\n", "Testing connection 21 / 300 … ✔\n", "Testing connection 22 / 300 … ✔\n", "Testing connection 23 / 300 … ✔\n", "Testing connection 24 / 300 … ✔\n", "Testing connection 25 / 300 … ✔\n", "Testing connection 26 / 300 … ✔\n", "Testing connection 27 / 300 … ✔\n", "Testing connection 28 / 300 … ✔\n", "Testing connection 29 / 300 … ✔\n", "Testing connection 30 / 300 … ✔\n", "Testing connection 31 / 300 … ✔\n", "Testing connection 32 / 300 … ✔\n", "Testing connection 33 / 300 … ✔\n", "Testing connection 34 / 300 … ✔\n", "Testing connection 35 / 300 … ✔\n", "Testing connection 36 / 300 … ✔\n", "Testing connection 37 / 300 … ✔\n", "Testing connection 38 / 300 … ✔\n", "Testing connection 39 / 300 … ✔\n", "Testing connection 40 / 300 … ✔\n", "Testing connection 41 / 300 … ✔\n", "Testing connection 42 / 300 … ✔\n", "Testing connection 43 / 300 … ✔\n", "Testing connection 44 / 300 … ✔\n", "Testing connection 45 / 300 … ✔\n", "Testing connection 46 / 300 … ✔\n", "Testing connection 47 / 300 … ✔\n", "Testing connection 48 / 300 … ✔\n", "Testing connection 49 / 300 … ✔\n", "Testing connection 50 / 300 … ✔\n", "Testing connection 51 / 300 … ✔\n", "Testing connection 52 / 300 … ✔\n", "Testing connection 53 / 300 … ✔\n", "Testing connection 54 / 300 … ✔\n", "Testing connection 55 / 300 … ✔\n", "Testing connection 56 / 300 … ✔\n", "Testing connection 57 / 300 … ✔\n", "Testing connection 58 / 300 … ✔\n", "Testing connection 59 / 300 … ✔\n", "Testing connection 60 / 300 … ✔\n", "Testing connection 61 / 300 … ✔\n", "Testing connection 62 / 300 … ✔\n", "Testing connection 63 / 300 … ✔\n", "Testing connection 64 / 300 … ✔\n", "Testing connection 65 / 300 … ✔\n", "Testing connection 66 / 300 … ✔\n", "Testing connection 67 / 300 … ✔\n", "Testing connection 68 / 300 … ✔\n", "Testing connection 69 / 300 … ✔\n", "Testing connection 70 / 300 … ✔\n", "Testing connection 71 / 300 … ✔\n", "Testing connection 72 / 300 … ✔\n", "Testing connection 73 / 300 … ✔\n", "Testing connection 74 / 300 … ✔\n", "Testing connection 75 / 300 … ✔\n", "Testing connection 76 / 300 … ✔\n", "Testing connection 77 / 300 … ✔\n", "Testing connection 78 / 300 … ✔\n", "Testing connection 79 / 300 … ✔\n", "Testing connection 80 / 300 … ✔\n", "Testing connection 81 / 300 … ✔\n", "Testing connection 82 / 300 … ✔\n", "Testing connection 83 / 300 … ✔\n", "Testing connection 84 / 300 … ✔\n", "Testing connection 85 / 300 … ✔\n", "Testing connection 86 / 300 … ✔\n", "Testing connection 87 / 300 … ✔\n", "Testing connection 88 / 300 … ✔\n", "Testing connection 89 / 300 … ✔\n", "Testing connection 90 / 300 … ✔\n", "Testing connection 91 / 300 … ✔\n", "Testing connection 92 / 300 … ✔\n", "Testing connection 93 / 300 … ✔\n", "Testing connection 94 / 300 … ✔\n", "Testing connection 95 / 300 … ✔\n", "Testing connection 96 / 300 … ✔\n", "Testing connection 97 / 300 … ✔\n", "Testing connection 98 / 300 … ✔\n", "Testing connection 99 / 300 … ✔\n", "Testing connection 100 / 300 … ✔\n", "Testing connection 101 / 300 … ✔\n", "Testing connection 102 / 300 … ✔\n", "Testing connection 103 / 300 … ✔\n", "Testing connection 104 / 300 … ✔\n", "Testing connection 105 / 300 … ✔\n", "Testing connection 106 / 300 … ✔\n", "Testing connection 107 / 300 … ✔\n", "Testing connection 108 / 300 … ✔\n", "Testing connection 109 / 300 … ✔\n", "Testing connection 110 / 300 … ✔\n", "Testing connection 111 / 300 … ✔\n", "Testing connection 112 / 300 … ✔\n", "Testing connection 113 / 300 … ✔\n", "Testing connection 114 / 300 … ✔\n", "Testing connection 115 / 300 … ✔\n", "Testing connection 116 / 300 … ✔\n", "Testing connection 117 / 300 … ✔\n", "Testing connection 118 / 300 … ✔\n", "Testing connection 119 / 300 … ✔\n", "Testing connection 120 / 300 … ✔\n", "Testing connection 121 / 300 … ✔\n", "Testing connection 122 / 300 … ✔\n", "Testing connection 123 / 300 … ✔\n", "Testing connection 124 / 300 … ✔\n", "Testing connection 125 / 300 … ✔\n", "Testing connection 126 / 300 … ✔\n", "Testing connection 127 / 300 … ✔\n", "Testing connection 128 / 300 … ✔\n", "Testing connection 129 / 300 … ✔\n", "Testing connection 130 / 300 … ✔\n", "Testing connection 131 / 300 … ✔\n", "Testing connection 132 / 300 … ✔\n", "Testing connection 133 / 300 … ✔\n", "Testing connection 134 / 300 … ✔\n", "Testing connection 135 / 300 … ✔\n", "Testing connection 136 / 300 … ✔\n", "Testing connection 137 / 300 … ✔\n", "Testing connection 138 / 300 … ✔\n", "Testing connection 139 / 300 … ✔\n", "Testing connection 140 / 300 … ✔\n", "Testing connection 141 / 300 … ✔\n", "Testing connection 142 / 300 … ✔\n", "Testing connection 143 / 300 … ✔\n", "Testing connection 144 / 300 … ✔\n", "Testing connection 145 / 300 … ✔\n", "Testing connection 146 / 300 … ✔\n", "Testing connection 147 / 300 … ✔\n", "Testing connection 148 / 300 … ✔\n", "Testing connection 149 / 300 … ✔\n", "Testing connection 150 / 300 … ✔\n", "Testing connection 151 / 300 … ✔\n", "Testing connection 152 / 300 … ✔\n", "Testing connection 153 / 300 … ✔\n", "Testing connection 154 / 300 … ✔\n", "Testing connection 155 / 300 … ✔\n", "Testing connection 156 / 300 … ✔\n", "Testing connection 157 / 300 … ✔\n", "Testing connection 158 / 300 … ✔\n", "Testing connection 159 / 300 … ✔\n", "Testing connection 160 / 300 … ✔\n", "Testing connection 161 / 300 … ✔\n", "Testing connection 162 / 300 … ✔\n", "Testing connection 163 / 300 … ✔\n", "Testing connection 164 / 300 … ✔\n", "Testing connection 165 / 300 … ✔\n", "Testing connection 166 / 300 … ✔\n", "Testing connection 167 / 300 … ✔\n", "Testing connection 168 / 300 … ✔\n", "Testing connection 169 / 300 … ✔\n", "Testing connection 170 / 300 … ✔\n", "Testing connection 171 / 300 … ✔\n", "Testing connection 172 / 300 … ✔\n", "Testing connection 173 / 300 … ✔\n", "Testing connection 174 / 300 … ✔\n", "Testing connection 175 / 300 … ✔\n", "Testing connection 176 / 300 … ✔\n", "Testing connection 177 / 300 … ✔\n", "Testing connection 178 / 300 … ✔\n", "Testing connection 179 / 300 … ✔\n", "Testing connection 180 / 300 … ✔\n", "Testing connection 181 / 300 … ✔\n", "Testing connection 182 / 300 … ✔\n", "Testing connection 183 / 300 … ✔\n", "Testing connection 184 / 300 … ✔\n", "Testing connection 185 / 300 … ✔\n", "Testing connection 186 / 300 … ✔\n", "Testing connection 187 / 300 … ✔\n", "Testing connection 188 / 300 … ✔\n", "Testing connection 189 / 300 … ✔\n", "Testing connection 190 / 300 … ✔\n", "Testing connection 191 / 300 … ✔\n", "Testing connection 192 / 300 … ✔\n", "Testing connection 193 / 300 … ✔\n", "Testing connection 194 / 300 … ✔\n", "Testing connection 195 / 300 … ✔\n", "Testing connection 196 / 300 … ✔\n", "Testing connection 197 / 300 … ✔\n", "Testing connection 198 / 300 … ✔\n", "Testing connection 199 / 300 … ✔\n", "Testing connection 200 / 300 … ✔\n", "Testing connection 201 / 300 … ✔\n", "Testing connection 202 / 300 … ✔\n", "Testing connection 203 / 300 … ✔\n", "Testing connection 204 / 300 … ✔\n", "Testing connection 205 / 300 … ✔\n", "Testing connection 206 / 300 … ✔\n", "Testing connection 207 / 300 … ✔\n", "Testing connection 208 / 300 … ✔\n", "Testing connection 209 / 300 … ✔\n", "Testing connection 210 / 300 … ✔\n", "Testing connection 211 / 300 … ✔\n", "Testing connection 212 / 300 … ✔\n", "Testing connection 213 / 300 … ✔\n", "Testing connection 214 / 300 … ✔\n", "Testing connection 215 / 300 … ✔\n", "Testing connection 216 / 300 … ✔\n", "Testing connection 217 / 300 … ✔\n", "Testing connection 218 / 300 … ✔\n", "Testing connection 219 / 300 … ✔\n", "Testing connection 220 / 300 … ✔\n", "Testing connection 221 / 300 … ✔\n", "Testing connection 222 / 300 … ✔\n", "Testing connection 223 / 300 … ✔\n", "Testing connection 224 / 300 … ✔\n", "Testing connection 225 / 300 … ✔\n", "Testing connection 226 / 300 … ✔\n", "Testing connection 227 / 300 … ✔\n", "Testing connection 228 / 300 … ✔\n", "Testing connection 229 / 300 … ✔\n", "Testing connection 230 / 300 … ✔\n", "Testing connection 231 / 300 … ✔\n", "Testing connection 232 / 300 … ✔\n", "Testing connection 233 / 300 … ✔\n", "Testing connection 234 / 300 … ✔\n", "Testing connection 235 / 300 … ✔\n", "Testing connection 236 / 300 … ✔\n", "Testing connection 237 / 300 … ✔\n", "Testing connection 238 / 300 … ✔\n", "Testing connection 239 / 300 … ✔\n", "Testing connection 240 / 300 … ✔\n", "Testing connection 241 / 300 … ✔\n", "Testing connection 242 / 300 … ✔\n", "Testing connection 243 / 300 … ✔\n", "Testing connection 244 / 300 … ✔\n", "Testing connection 245 / 300 … ✔\n", "Testing connection 246 / 300 … ✔\n", "Testing connection 247 / 300 … ✔\n", "Testing connection 248 / 300 … ✔\n", "Testing connection 249 / 300 … ✔\n", "Testing connection 250 / 300 … ✔\n", "Testing connection 251 / 300 … ✔\n", "Testing connection 252 / 300 … ✔\n", "Testing connection 253 / 300 … ✔\n", "Testing connection 254 / 300 … ✔\n", "Testing connection 255 / 300 … ✔\n", "Testing connection 256 / 300 … ✔\n", "Testing connection 257 / 300 … ✔\n", "Testing connection 258 / 300 … ✔\n", "Testing connection 259 / 300 … ✔\n", "Testing connection 260 / 300 … ✔\n", "Testing connection 261 / 300 … ✔\n", "Testing connection 262 / 300 … ✔\n", "Testing connection 263 / 300 … ✔\n", "Testing connection 264 / 300 … ✔\n", "Testing connection 265 / 300 … ✔\n", "Testing connection 266 / 300 … ✔\n", "Testing connection 267 / 300 … ✔\n", "Testing connection 268 / 300 … ✔\n", "Testing connection 269 / 300 … ✔\n", "Testing connection 270 / 300 … ✔\n", "Testing connection 271 / 300 … ✔\n", "Testing connection 272 / 300 … ✔\n", "Testing connection 273 / 300 … ✔\n", "Testing connection 274 / 300 … ✔\n", "Testing connection 275 / 300 … ✔\n", "Testing connection 276 / 300 … ✔\n", "Testing connection 277 / 300 … ✔\n", "Testing connection 278 / 300 … ✔\n", "Testing connection 279 / 300 … ✔\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Testing connection 280 / 300 … ✔\n", "Testing connection 281 / 300 … ✔\n", "Testing connection 282 / 300 … ✔\n", "Testing connection 283 / 300 … ✔\n", "Testing connection 284 / 300 … ✔\n", "Testing connection 285 / 300 … ✔\n", "Testing connection 286 / 300 … ✔\n", "Testing connection 287 / 300 … ✔\n", "Testing connection 288 / 300 … ✔\n", "Testing connection 289 / 300 … ✔\n", "Testing connection 290 / 300 … ✔\n", "Testing connection 291 / 300 … ✔\n", "Testing connection 292 / 300 … ✔\n", "Testing connection 293 / 300 … ✔\n", "Testing connection 294 / 300 … ✔\n", "Testing connection 295 / 300 … ✔\n", "Testing connection 296 / 300 … ✔\n", "Testing connection 297 / 300 … ✔\n", "Testing connection 298 / 300 … ✔\n", "Testing connection 299 / 300 … ✔\n", "Testing connection 300 / 300 … ✔\n", "✔\n", "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=5 _.jld2] … ✔ (0.7 s)\n", "First pass … Testing connection 1 / 300 … ✔\n", "Testing connection 2 / 300 … ✔\n", "Testing connection 3 / 300 … ✔\n", "Testing connection 4 / 300 … ✔\n", "Testing connection 5 / 300 … ✔\n", "Testing connection 6 / 300 … ✔\n", "Testing connection 7 / 300 … ✔\n", "Testing connection 8 / 300 … ✔\n", "Testing connection 9 / 300 … ✔\n", "Testing connection 10 / 300 … ✔\n", "Testing connection 11 / 300 … ✔\n", "Testing connection 12 / 300 … ✔\n", "Testing connection 13 / 300 … ✔\n", "Testing connection 14 / 300 … ✔\n", "Testing connection 15 / 300 … ✔\n", "Testing connection 16 / 300 … ✔\n", "Testing connection 17 / 300 … ✔\n", "Testing connection 18 / 300 … ✔\n", "Testing connection 19 / 300 … ✔\n", "Testing connection 20 / 300 … ✔\n", "Testing connection 21 / 300 … ✔\n", "Testing connection 22 / 300 … ✔\n", "Testing connection 23 / 300 … ✔\n", "Testing connection 24 / 300 … ✔\n", "Testing connection 25 / 300 … ✔\n", "Testing connection 26 / 300 … ✔\n", "Testing connection 27 / 300 … ✔\n", "Testing connection 28 / 300 … ✔\n", "Testing connection 29 / 300 … ✔\n", "Testing connection 30 / 300 … ✔\n", "Testing connection 31 / 300 … ✔\n", "Testing connection 32 / 300 … ✔\n", "Testing connection 33 / 300 … ✔\n", "Testing connection 34 / 300 … ✔\n", "Testing connection 35 / 300 … ✔\n", "Testing connection 36 / 300 … ✔\n", "Testing connection 37 / 300 … ✔\n", "Testing connection 38 / 300 … ✔\n", "Testing connection 39 / 300 … ✔\n", "Testing connection 40 / 300 … ✔\n", "Testing connection 41 / 300 … ✔\n", "Testing connection 42 / 300 … ✔\n", "Testing connection 43 / 300 … ✔\n", "Testing connection 44 / 300 … ✔\n", "Testing connection 45 / 300 … ✔\n", "Testing connection 46 / 300 … ✔\n", "Testing connection 47 / 300 … ✔\n", "Testing connection 48 / 300 … ✔\n", "Testing connection 49 / 300 … ✔\n", "Testing connection 50 / 300 … ✔\n", "Testing connection 51 / 300 … ✔\n", "Testing connection 52 / 300 … ✔\n", "Testing connection 53 / 300 … ✔\n", "Testing connection 54 / 300 … ✔\n", "Testing connection 55 / 300 … ✔\n", "Testing connection 56 / 300 … ✔\n", "Testing connection 57 / 300 … ✔\n", "Testing connection 58 / 300 … ✔\n", "Testing connection 59 / 300 … ✔\n", "Testing connection 60 / 300 … ✔\n", "Testing connection 61 / 300 … ✔\n", "Testing connection 62 / 300 … ✔\n", "Testing connection 63 / 300 … ✔\n", "Testing connection 64 / 300 … ✔\n", "Testing connection 65 / 300 … ✔\n", "Testing connection 66 / 300 … ✔\n", "Testing connection 67 / 300 … ✔\n", "Testing connection 68 / 300 … ✔\n", "Testing connection 69 / 300 … ✔\n", "Testing connection 70 / 300 … ✔\n", "Testing connection 71 / 300 … ✔\n", "Testing connection 72 / 300 … ✔\n", "Testing connection 73 / 300 … ✔\n", "Testing connection 74 / 300 … ✔\n", "Testing connection 75 / 300 … ✔\n", "Testing connection 76 / 300 … ✔\n", "Testing connection 77 / 300 … ✔\n", "Testing connection 78 / 300 … ✔\n", "Testing connection 79 / 300 … ✔\n", "Testing connection 80 / 300 … ✔\n", "Testing connection 81 / 300 … ✔\n", "Testing connection 82 / 300 … ✔\n", "Testing connection 83 / 300 … ✔\n", "Testing connection 84 / 300 … ✔\n", "Testing connection 85 / 300 … ✔\n", "Testing connection 86 / 300 … ✔\n", "Testing connection 87 / 300 … ✔\n", "Testing connection 88 / 300 … ✔\n", "Testing connection 89 / 300 … ✔\n", "Testing connection 90 / 300 … ✔\n", "Testing connection 91 / 300 … ✔\n", "Testing connection 92 / 300 … ✔\n", "Testing connection 93 / 300 … ✔\n", "Testing connection 94 / 300 … ✔\n", "Testing connection 95 / 300 … ✔\n", "Testing connection 96 / 300 … ✔\n", "Testing connection 97 / 300 … ✔\n", "Testing connection 98 / 300 … ✔\n", "Testing connection 99 / 300 … ✔\n", "Testing connection 100 / 300 … ✔\n", "Testing connection 101 / 300 … ✔\n", "Testing connection 102 / 300 … ✔\n", "Testing connection 103 / 300 … ✔\n", "Testing connection 104 / 300 … ✔\n", "Testing connection 105 / 300 … ✔\n", "Testing connection 106 / 300 … ✔\n", "Testing connection 107 / 300 … ✔\n", "Testing connection 108 / 300 … ✔\n", "Testing connection 109 / 300 … ✔\n", "Testing connection 110 / 300 … ✔\n", "Testing connection 111 / 300 … ✔\n", "Testing connection 112 / 300 … ✔\n", "Testing connection 113 / 300 … ✔\n", "Testing connection 114 / 300 … ✔\n", "Testing connection 115 / 300 … ✔\n", "Testing connection 116 / 300 … ✔\n", "Testing connection 117 / 300 … ✔\n", "Testing connection 118 / 300 … ✔\n", "Testing connection 119 / 300 … ✔\n", "Testing connection 120 / 300 … ✔\n", "Testing connection 121 / 300 … ✔\n", "Testing connection 122 / 300 … ✔\n", "Testing connection 123 / 300 … ✔\n", "Testing connection 124 / 300 … ✔\n", "Testing connection 125 / 300 … ✔\n", "Testing connection 126 / 300 … ✔\n", "Testing connection 127 / 300 … ✔\n", "Testing connection 128 / 300 … ✔\n", "Testing connection 129 / 300 … ✔\n", "Testing connection 130 / 300 … ✔\n", "Testing connection 131 / 300 … ✔\n", "Testing connection 132 / 300 … ✔\n", "Testing connection 133 / 300 … ✔\n", "Testing connection 134 / 300 … ✔\n", "Testing connection 135 / 300 … ✔\n", "Testing connection 136 / 300 … ✔\n", "Testing connection 137 / 300 … ✔\n", "Testing connection 138 / 300 … ✔\n", "Testing connection 139 / 300 … ✔\n", "Testing connection 140 / 300 … ✔\n", "Testing connection 141 / 300 … ✔\n", "Testing connection 142 / 300 … ✔\n", "Testing connection 143 / 300 … ✔\n", "Testing connection 144 / 300 … ✔\n", "Testing connection 145 / 300 … ✔\n", "Testing connection 146 / 300 … ✔\n", "Testing connection 147 / 300 … ✔\n", "Testing connection 148 / 300 … ✔\n", "Testing connection 149 / 300 … ✔\n", "Testing connection 150 / 300 … ✔\n", "Testing connection 151 / 300 … ✔\n", "Testing connection 152 / 300 … ✔\n", "Testing connection 153 / 300 … ✔\n", "Testing connection 154 / 300 … ✔\n", "Testing connection 155 / 300 … ✔\n", "Testing connection 156 / 300 … ✔\n", "Testing connection 157 / 300 … ✔\n", "Testing connection 158 / 300 … ✔\n", "Testing connection 159 / 300 … ✔\n", "Testing connection 160 / 300 … ✔\n", "Testing connection 161 / 300 … ✔\n", "Testing connection 162 / 300 … ✔\n", "Testing connection 163 / 300 … ✔\n", "Testing connection 164 / 300 … ✔\n", "Testing connection 165 / 300 … ✔\n", "Testing connection 166 / 300 … ✔\n", "Testing connection 167 / 300 … ✔\n", "Testing connection 168 / 300 … ✔\n", "Testing connection 169 / 300 … ✔\n", "Testing connection 170 / 300 … ✔\n", "Testing connection 171 / 300 … ✔\n", "Testing connection 172 / 300 … ✔\n", "Testing connection 173 / 300 … ✔\n", "Testing connection 174 / 300 … ✔\n", "Testing connection 175 / 300 … ✔\n", "Testing connection 176 / 300 … ✔\n", "Testing connection 177 / 300 … ✔\n", "Testing connection 178 / 300 … ✔\n", "Testing connection 179 / 300 … ✔\n", "Testing connection 180 / 300 … ✔\n", "Testing connection 181 / 300 … ✔\n", "Testing connection 182 / 300 … ✔\n", "Testing connection 183 / 300 … ✔\n", "Testing connection 184 / 300 … ✔\n", "Testing connection 185 / 300 … ✔\n", "Testing connection 186 / 300 … ✔\n", "Testing connection 187 / 300 … ✔\n", "Testing connection 188 / 300 … ✔\n", "Testing connection 189 / 300 … ✔\n", "Testing connection 190 / 300 … ✔\n", "Testing connection 191 / 300 … ✔\n", "Testing connection 192 / 300 … ✔\n", "Testing connection 193 / 300 … ✔\n", "Testing connection 194 / 300 … ✔\n", "Testing connection 195 / 300 … ✔\n", "Testing connection 196 / 300 … ✔\n", "Testing connection 197 / 300 … ✔\n", "Testing connection 198 / 300 … ✔\n", "Testing connection 199 / 300 … ✔\n", "Testing connection 200 / 300 … ✔\n", "Testing connection 201 / 300 … ✔\n", "Testing connection 202 / 300 … ✔\n", "Testing connection 203 / 300 … ✔\n", "Testing connection 204 / 300 … ✔\n", "Testing connection 205 / 300 … ✔\n", "Testing connection 206 / 300 … ✔\n", "Testing connection 207 / 300 … ✔\n", "Testing connection 208 / 300 … ✔\n", "Testing connection 209 / 300 … ✔\n", "Testing connection 210 / 300 … ✔\n", "Testing connection 211 / 300 … ✔\n", "Testing connection 212 / 300 … ✔\n", "Testing connection 213 / 300 … ✔\n", "Testing connection 214 / 300 … ✔\n", "Testing connection 215 / 300 … ✔\n", "Testing connection 216 / 300 … ✔\n", "Testing connection 217 / 300 … ✔\n", "Testing connection 218 / 300 … ✔\n", "Testing connection 219 / 300 … ✔\n", "Testing connection 220 / 300 … ✔\n", "Testing connection 221 / 300 … ✔\n", "Testing connection 222 / 300 … ✔\n", "Testing connection 223 / 300 … ✔\n", "Testing connection 224 / 300 … ✔\n", "Testing connection 225 / 300 … ✔\n", "Testing connection 226 / 300 … ✔\n", "Testing connection 227 / 300 … ✔\n", "Testing connection 228 / 300 … ✔\n", "Testing connection 229 / 300 … ✔\n", "Testing connection 230 / 300 … ✔\n", "Testing connection 231 / 300 … ✔\n", "Testing connection 232 / 300 … ✔\n", "Testing connection 233 / 300 … ✔\n", "Testing connection 234 / 300 … ✔\n", "Testing connection 235 / 300 … ✔\n", "Testing connection 236 / 300 … ✔\n", "Testing connection 237 / 300 … ✔\n", "Testing connection 238 / 300 … ✔\n", "Testing connection 239 / 300 … ✔\n", "Testing connection 240 / 300 … ✔\n", "Testing connection 241 / 300 … ✔\n", "Testing connection 242 / 300 … ✔\n", "Testing connection 243 / 300 … ✔\n", "Testing connection 244 / 300 … ✔\n", "Testing connection 245 / 300 … ✔\n", "Testing connection 246 / 300 … ✔\n", "Testing connection 247 / 300 … ✔\n", "Testing connection 248 / 300 … ✔\n", "Testing connection 249 / 300 … ✔\n", "Testing connection 250 / 300 … ✔\n", "Testing connection 251 / 300 … ✔\n", "Testing connection 252 / 300 … ✔\n", "Testing connection 253 / 300 … ✔\n", "Testing connection 254 / 300 … ✔\n", "Testing connection 255 / 300 … ✔\n", "Testing connection 256 / 300 … ✔\n", "Testing connection 257 / 300 … ✔\n", "Testing connection 258 / 300 … ✔\n", "Testing connection 259 / 300 … ✔\n", "Testing connection 260 / 300 … ✔\n", "Testing connection 261 / 300 … ✔\n", "Testing connection 262 / 300 … ✔\n", "Testing connection 263 / 300 … ✔\n", "Testing connection 264 / 300 … ✔\n", "Testing connection 265 / 300 … ✔\n", "Testing connection 266 / 300 … ✔\n", "Testing connection 267 / 300 … ✔\n", "Testing connection 268 / 300 … ✔\n", "Testing connection 269 / 300 … ✔\n", "Testing connection 270 / 300 … ✔\n", "Testing connection 271 / 300 … ✔\n", "Testing connection 272 / 300 … ✔\n", "Testing connection 273 / 300 … ✔\n", "Testing connection 274 / 300 … ✔\n", "Testing connection 275 / 300 … ✔\n", "Testing connection 276 / 300 … ✔\n", "Testing connection 277 / 300 … ✔\n", "Testing connection 278 / 300 … ✔\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Testing connection 279 / 300 … ✔\n", "Testing connection 280 / 300 … ✔\n", "Testing connection 281 / 300 … ✔\n", "Testing connection 282 / 300 … ✔\n", "Testing connection 283 / 300 … ✔\n", "Testing connection 284 / 300 … ✔\n", "Testing connection 285 / 300 … ✔\n", "Testing connection 286 / 300 … ✔\n", "Testing connection 287 / 300 … ✔\n", "Testing connection 288 / 300 … ✔\n", "Testing connection 289 / 300 … ✔\n", "Testing connection 290 / 300 … ✔\n", "Testing connection 291 / 300 … ✔\n", "Testing connection 292 / 300 … ✔\n", "Testing connection 293 / 300 … ✔\n", "Testing connection 294 / 300 … ✔\n", "Testing connection 295 / 300 … ✔\n", "Testing connection 296 / 300 … ✔\n", "Testing connection 297 / 300 … ✔\n", "Testing connection 298 / 300 … ✔\n", "Testing connection 299 / 300 … ✔\n", "Testing connection 300 / 300 … ✔\n", "✔\n", "Loading [/root/.julia/MemDiskCache.jl/2023-03-14__Nto1_AdEx/calc_all_STAs/_ N=1600 Nᵤ=100 δ_nS=0.08 duration=600.0 seed=1 batch_size=300 part=6 _.jld2] … ✔ (0.4 s)\n", "First pass … Testing connection 1 / 200 … ✔\n", "Testing connection 2 / 200 … ✔\n", "Testing connection 3 / 200 … ✔\n", "Testing connection 4 / 200 … ✔\n", "Testing connection 5 / 200 … ✔\n", "Testing connection 6 / 200 … ✔\n", "Testing connection 7 / 200 … ✔\n", "Testing connection 8 / 200 … ✔\n", "Testing connection 9 / 200 … ✔\n", "Testing connection 10 / 200 … ✔\n", "Testing connection 11 / 200 … ✔\n", "Testing connection 12 / 200 … ✔\n", "Testing connection 13 / 200 … ✔\n", "Testing connection 14 / 200 … ✔\n", "Testing connection 15 / 200 … ✔\n", "Testing connection 16 / 200 … ✔\n", "Testing connection 17 / 200 … ✔\n", "Testing connection 18 / 200 … ✔\n", "Testing connection 19 / 200 … ✔\n", "Testing connection 20 / 200 … ✔\n", "Testing connection 21 / 200 … ✔\n", "Testing connection 22 / 200 … ✔\n", "Testing connection 23 / 200 … ✔\n", "Testing connection 24 / 200 … ✔\n", "Testing connection 25 / 200 … ✔\n", "Testing connection 26 / 200 … ✔\n", "Testing connection 27 / 200 … ✔\n", "Testing connection 28 / 200 … ✔\n", "Testing connection 29 / 200 … ✔\n", "Testing connection 30 / 200 … ✔\n", "Testing connection 31 / 200 … ✔\n", "Testing connection 32 / 200 … ✔\n", "Testing connection 33 / 200 … ✔\n", "Testing connection 34 / 200 … ✔\n", "Testing connection 35 / 200 … ✔\n", "Testing connection 36 / 200 … ✔\n", "Testing connection 37 / 200 … ✔\n", "Testing connection 38 / 200 … ✔\n", "Testing connection 39 / 200 … ✔\n", "Testing connection 40 / 200 … ✔\n", "Testing connection 41 / 200 … ✔\n", "Testing connection 42 / 200 … ✔\n", "Testing connection 43 / 200 … ✔\n", "Testing connection 44 / 200 … ✔\n", "Testing connection 45 / 200 … ✔\n", "Testing connection 46 / 200 … ✔\n", "Testing connection 47 / 200 … ✔\n", "Testing connection 48 / 200 … ✔\n", "Testing connection 49 / 200 … ✔\n", "Testing connection 50 / 200 … ✔\n", "Testing connection 51 / 200 … ✔\n", "Testing connection 52 / 200 … ✔\n", "Testing connection 53 / 200 … ✔\n", "Testing connection 54 / 200 … ✔\n", "Testing connection 55 / 200 … ✔\n", "Testing connection 56 / 200 … ✔\n", "Testing connection 57 / 200 … ✔\n", "Testing connection 58 / 200 … ✔\n", "Testing connection 59 / 200 … ✔\n", "Testing connection 60 / 200 … ✔\n", "Testing connection 61 / 200 … ✔\n", "Testing connection 62 / 200 … ✔\n", "Testing connection 63 / 200 … ✔\n", "Testing connection 64 / 200 … ✔\n", "Testing connection 65 / 200 … ✔\n", "Testing connection 66 / 200 … ✔\n", "Testing connection 67 / 200 … ✔\n", "Testing connection 68 / 200 … ✔\n", "Testing connection 69 / 200 … ✔\n", "Testing connection 70 / 200 … ✔\n", "Testing connection 71 / 200 … ✔\n", "Testing connection 72 / 200 … ✔\n", "Testing connection 73 / 200 … ✔\n", "Testing connection 74 / 200 … ✔\n", "Testing connection 75 / 200 … ✔\n", "Testing connection 76 / 200 … ✔\n", "Testing connection 77 / 200 … ✔\n", "Testing connection 78 / 200 … ✔\n", "Testing connection 79 / 200 … ✔\n", "Testing connection 80 / 200 … ✔\n", "Testing connection 81 / 200 … ✔\n", "Testing connection 82 / 200 … ✔\n", "Testing connection 83 / 200 … ✔\n", "Testing connection 84 / 200 … ✔\n", "Testing connection 85 / 200 … ✔\n", "Testing connection 86 / 200 … ✔\n", "Testing connection 87 / 200 … ✔\n", "Testing connection 88 / 200 … ✔\n", "Testing connection 89 / 200 … ✔\n", "Testing connection 90 / 200 … ✔\n", "Testing connection 91 / 200 … ✔\n", "Testing connection 92 / 200 … ✔\n", "Testing connection 93 / 200 … ✔\n", "Testing connection 94 / 200 … ✔\n", "Testing connection 95 / 200 … ✔\n", "Testing connection 96 / 200 … ✔\n", "Testing connection 97 / 200 … ✔\n", "Testing connection 98 / 200 … ✔\n", "Testing connection 99 / 200 … ✔\n", "Testing connection 100 / 200 … ✔\n", "Testing connection 101 / 200 … ✔\n", "Testing connection 102 / 200 … ✔\n", "Testing connection 103 / 200 … ✔\n", "Testing connection 104 / 200 … ✔\n", "Testing connection 105 / 200 … ✔\n", "Testing connection 106 / 200 … ✔\n", "Testing connection 107 / 200 … ✔\n", "Testing connection 108 / 200 … ✔\n", "Testing connection 109 / 200 … ✔\n", "Testing connection 110 / 200 … ✔\n", "Testing connection 111 / 200 … ✔\n", "Testing connection 112 / 200 … ✔\n", "Testing connection 113 / 200 … ✔\n", "Testing connection 114 / 200 … ✔\n", "Testing connection 115 / 200 … ✔\n", "Testing connection 116 / 200 … ✔\n", "Testing connection 117 / 200 … ✔\n", "Testing connection 118 / 200 … ✔\n", "Testing connection 119 / 200 … ✔\n", "Testing connection 120 / 200 … ✔\n", "Testing connection 121 / 200 … ✔\n", "Testing connection 122 / 200 … ✔\n", "Testing connection 123 / 200 … ✔\n", "Testing connection 124 / 200 … ✔\n", "Testing connection 125 / 200 … ✔\n", "Testing connection 126 / 200 … ✔\n", "Testing connection 127 / 200 … ✔\n", "Testing connection 128 / 200 … ✔\n", "Testing connection 129 / 200 … ✔\n", "Testing connection 130 / 200 … ✔\n", "Testing connection 131 / 200 … ✔\n", "Testing connection 132 / 200 … ✔\n", "Testing connection 133 / 200 … ✔\n", "Testing connection 134 / 200 … ✔\n", "Testing connection 135 / 200 … ✔\n", "Testing connection 136 / 200 … ✔\n", "Testing connection 137 / 200 … ✔\n", "Testing connection 138 / 200 … ✔\n", "Testing connection 139 / 200 … ✔\n", "Testing connection 140 / 200 … ✔\n", "Testing connection 141 / 200 … ✔\n", "Testing connection 142 / 200 … ✔\n", "Testing connection 143 / 200 … ✔\n", "Testing connection 144 / 200 … ✔\n", "Testing connection 145 / 200 … ✔\n", "Testing connection 146 / 200 … ✔\n", "Testing connection 147 / 200 … ✔\n", "Testing connection 148 / 200 … ✔\n", "Testing connection 149 / 200 … ✔\n", "Testing connection 150 / 200 … ✔\n", "Testing connection 151 / 200 … ✔\n", "Testing connection 152 / 200 … ✔\n", "Testing connection 153 / 200 … ✔\n", "Testing connection 154 / 200 … ✔\n", "Testing connection 155 / 200 … ✔\n", "Testing connection 156 / 200 … ✔\n", "Testing connection 157 / 200 … ✔\n", "Testing connection 158 / 200 … ✔\n", "Testing connection 159 / 200 … ✔\n", "Testing connection 160 / 200 … ✔\n", "Testing connection 161 / 200 … ✔\n", "Testing connection 162 / 200 … ✔\n", "Testing connection 163 / 200 … ✔\n", "Testing connection 164 / 200 … ✔\n", "Testing connection 165 / 200 … ✔\n", "Testing connection 166 / 200 … ✔\n", "Testing connection 167 / 200 … ✔\n", "Testing connection 168 / 200 … ✔\n", "Testing connection 169 / 200 … ✔\n", "Testing connection 170 / 200 … ✔\n", "Testing connection 171 / 200 … ✔\n", "Testing connection 172 / 200 … ✔\n", "Testing connection 173 / 200 … ✔\n", "Testing connection 174 / 200 … ✔\n", "Testing connection 175 / 200 … ✔\n", "Testing connection 176 / 200 … ✔\n", "Testing connection 177 / 200 … ✔\n", "Testing connection 178 / 200 … ✔\n", "Testing connection 179 / 200 … ✔\n", "Testing connection 180 / 200 … ✔\n", "Testing connection 181 / 200 … ✔\n", "Testing connection 182 / 200 … ✔\n", "Testing connection 183 / 200 … ✔\n", "Testing connection 184 / 200 … ✔\n", "Testing connection 185 / 200 … ✔\n", "Testing connection 186 / 200 … ✔\n", "Testing connection 187 / 200 … ✔\n", "Testing connection 188 / 200 … ✔\n", "Testing connection 189 / 200 … ✔\n", "Testing connection 190 / 200 … ✔\n", "Testing connection 191 / 200 … ✔\n", "Testing connection 192 / 200 … ✔\n", "Testing connection 193 / 200 … ✔\n", "Testing connection 194 / 200 … ✔\n", "Testing connection 195 / 200 … ✔\n", "Testing connection 196 / 200 … ✔\n", "Testing connection 197 / 200 … ✔\n", "Testing connection 198 / 200 … ✔\n", "Testing connection 199 / 200 … ✔\n", "Testing connection 200 / 200 … ✔\n", "✔\n" ] } ], "source": [ "template = get_template(m, simkw, batch_size);" ] }, { "cell_type": "code", "execution_count": 31, "id": "6bbb0ada", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": { "image/png": { "height": 240, "width": 400 } }, "output_type": "display_data" } ], "source": [ "plotSTA(template);" ] }, { "cell_type": "markdown", "id": "de4e8bb8", "metadata": {}, "source": [ "Aha, here's the problem. We _do_ have an STA. But it's negative.\n", "It's classified as positive cause the heuristic for that -- 'net area above start' -- is..\n", "Ah yes, that is positive, so classified as positive." ] }, { "cell_type": "markdown", "id": "61f5ed9b", "metadata": {}, "source": [ "One ad-hoc solution would be to use a shorter stretch of time for this 'area-above start'.\n", "I.e. first 20 ms.\n", "(Here, first 5 or 10 would be even better; but imagine a spike transmission delay)." ] }, { "cell_type": "code", "execution_count": null, "id": "0808cd4f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.9.0-beta3", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "199.2px", "left": "21px", "top": "167px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 5 }