From 90529da1ac2b7b463c8578f814ce44e6e317ec49 Mon Sep 17 00:00:00 2001
From: Igor Fontana de Nardin <igor.fontana@irit.fr>
Date: Tue, 29 Apr 2025 12:12:03 +0200
Subject: [PATCH] Big bang
---
Analysis/Section_7.1.ipynb | 220 +
Analysis/Section_7.2.ipynb | 230 +
Analysis/Section_7.3.1.ipynb | 2058 +++
Analysis/Section_7.3.2.ipynb | 12800 ++++++++++++++++
Analysis/Section_7.3.3.ipynb | 1330 ++
ExecutionScripts/1_milp.py | 68 +
ExecutionScripts/2_exps_bell.py | 89 +
ExecutionScripts/3_exps_amplitude.py | 54 +
ExecutionScripts/4_exps_real.py | 92 +
ExecutionScripts/__init__.py | 0
GenerateExperiments/__init__.py | 0
GenerateExperiments/create_instance.py | 57 +
GenerateExperiments/expeditions.py | 7 +
GenerateExperiments/experiments.py | 426 +
GenerateExperiments/seaborn_tests.py | 61 +
GenerateInstance/Examples.py | 20 +
GenerateInstance/Instance.py | 93 +
GenerateInstance/README.md | 3 +
GenerateInstance/__init__.py | 0
GenerateInstance/instance_gen.py | 32 +
GenerateInstance/jobGen.py | 233 +
GenerateMetrics/__init__.py | 0
GenerateMetrics/metrics_gen.py | 132 +
.../DatacenterModel/Constraints.md | 0
GenerateProblem/DatacenterModel/Variables.md | 0
GenerateProblem/DatacenterModel/__init__.py | 0
.../datacenter_constraints_gen.py | 88 +
.../datacenter_variables_gen.py | 71 +
.../MarketplaceModel/Constraints.md | 0
GenerateProblem/MarketplaceModel/Objective.md | 0
GenerateProblem/MarketplaceModel/Variables.md | 0
GenerateProblem/MarketplaceModel/__init__.py | 0
.../marketplace_constraints_gen.py | 70 +
.../marketplace_variables_gen.py | 55 +
GenerateProblem/Modeling.md | 0
GenerateProblem/__init__.py | 0
GenerateProblem/problem_gen.py | 124 +
GenerateVisualisation/__init__.py | 0
GenerateVisualisation/experiments_analysis.py | 1585 ++
GenerateVisualisation/visualisation_gen.py | 384 +
Heuristic/Solver.py | 221 +
Heuristic/__init__.py | 0
Heuristic/bidder/__init__.py | 0
Heuristic/bidder/bidder.py | 7 +
Heuristic/bidder/random.py | 53 +
Heuristic/bidder/simple.py | 73 +
Heuristic/common.py | 216 +
Heuristic/scheduler/__init__.py | 0
Heuristic/scheduler/best_fit.py | 204 +
Heuristic/scheduler/scheduler.py | 7 +
README.md | 130 +-
RenewableData/ninja_pv_Canberra.csv | 8764 +++++++++++
RenewableData/ninja_pv_Dubai.csv | 8764 +++++++++++
RenewableData/ninja_pv_Paris.csv | 8764 +++++++++++
RenewableData/ninja_pv_Sao_Paulo.csv | 8764 +++++++++++
RenewableData/ninja_wind_Canberra.csv | 8764 +++++++++++
RenewableData/ninja_wind_Dubai.csv | 8764 +++++++++++
RenewableData/ninja_wind_Paris.csv | 8764 +++++++++++
RenewableData/ninja_wind_Sao_Paulo.csv | 8764 +++++++++++
RenewableData/renewable.ipynb | 277 +
RenewableData/renewable.py | 66 +
default.nix | 22 +
62 files changed, 91703 insertions(+), 67 deletions(-)
create mode 100644 Analysis/Section_7.1.ipynb
create mode 100644 Analysis/Section_7.2.ipynb
create mode 100644 Analysis/Section_7.3.1.ipynb
create mode 100644 Analysis/Section_7.3.2.ipynb
create mode 100644 Analysis/Section_7.3.3.ipynb
create mode 100644 ExecutionScripts/1_milp.py
create mode 100644 ExecutionScripts/2_exps_bell.py
create mode 100644 ExecutionScripts/3_exps_amplitude.py
create mode 100644 ExecutionScripts/4_exps_real.py
create mode 100644 ExecutionScripts/__init__.py
create mode 100644 GenerateExperiments/__init__.py
create mode 100644 GenerateExperiments/create_instance.py
create mode 100644 GenerateExperiments/expeditions.py
create mode 100644 GenerateExperiments/experiments.py
create mode 100644 GenerateExperiments/seaborn_tests.py
create mode 100644 GenerateInstance/Examples.py
create mode 100644 GenerateInstance/Instance.py
create mode 100644 GenerateInstance/README.md
create mode 100644 GenerateInstance/__init__.py
create mode 100644 GenerateInstance/instance_gen.py
create mode 100644 GenerateInstance/jobGen.py
create mode 100644 GenerateMetrics/__init__.py
create mode 100644 GenerateMetrics/metrics_gen.py
create mode 100644 GenerateProblem/DatacenterModel/Constraints.md
create mode 100644 GenerateProblem/DatacenterModel/Variables.md
create mode 100644 GenerateProblem/DatacenterModel/__init__.py
create mode 100644 GenerateProblem/DatacenterModel/datacenter_constraints_gen.py
create mode 100644 GenerateProblem/DatacenterModel/datacenter_variables_gen.py
create mode 100644 GenerateProblem/MarketplaceModel/Constraints.md
create mode 100644 GenerateProblem/MarketplaceModel/Objective.md
create mode 100644 GenerateProblem/MarketplaceModel/Variables.md
create mode 100644 GenerateProblem/MarketplaceModel/__init__.py
create mode 100644 GenerateProblem/MarketplaceModel/marketplace_constraints_gen.py
create mode 100644 GenerateProblem/MarketplaceModel/marketplace_variables_gen.py
create mode 100644 GenerateProblem/Modeling.md
create mode 100644 GenerateProblem/__init__.py
create mode 100644 GenerateProblem/problem_gen.py
create mode 100644 GenerateVisualisation/__init__.py
create mode 100644 GenerateVisualisation/experiments_analysis.py
create mode 100644 GenerateVisualisation/visualisation_gen.py
create mode 100644 Heuristic/Solver.py
create mode 100644 Heuristic/__init__.py
create mode 100644 Heuristic/bidder/__init__.py
create mode 100644 Heuristic/bidder/bidder.py
create mode 100644 Heuristic/bidder/random.py
create mode 100644 Heuristic/bidder/simple.py
create mode 100644 Heuristic/common.py
create mode 100644 Heuristic/scheduler/__init__.py
create mode 100644 Heuristic/scheduler/best_fit.py
create mode 100644 Heuristic/scheduler/scheduler.py
create mode 100644 RenewableData/ninja_pv_Canberra.csv
create mode 100644 RenewableData/ninja_pv_Dubai.csv
create mode 100644 RenewableData/ninja_pv_Paris.csv
create mode 100644 RenewableData/ninja_pv_Sao_Paulo.csv
create mode 100644 RenewableData/ninja_wind_Canberra.csv
create mode 100644 RenewableData/ninja_wind_Dubai.csv
create mode 100644 RenewableData/ninja_wind_Paris.csv
create mode 100644 RenewableData/ninja_wind_Sao_Paulo.csv
create mode 100644 RenewableData/renewable.ipynb
create mode 100644 RenewableData/renewable.py
create mode 100644 default.nix
diff --git a/Analysis/Section_7.1.ipynb b/Analysis/Section_7.1.ipynb
new file mode 100644
index 0000000..13ba02d
--- /dev/null
+++ b/Analysis/Section_7.1.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Objective:** Show that the Heuristic (coop) has a gap to the optimal result, but it finishes faster (and knowing less info)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from GenerateVisualisation.experiments_analysis import Analysis, Analysis_plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_list = []\n",
+ "\n",
+ "base_folder = \"XPs/1_bell/\"\n",
+ "seeds = range(3, 103)\n",
+ "analysis = Analysis(base_folder=base_folder,\n",
+ " files=[\"/simple_aggre_0_\"],\n",
+ " names=[\"Heuristic\"],\n",
+ " nb_dcs=4,\n",
+ " seeds = seeds)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis_plot = Analysis_plots(analysis_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Solution time (s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_f6258 td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_f6258 th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_f6258\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_f6258_level0_col0\" class=\"col_heading level0 col0\" >Value</th>\n",
+ " <th id=\"T_f6258_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
+ " <th id=\"T_f6258_level0_col2\" class=\"col_heading level0 col2\" >Min</th>\n",
+ " <th id=\"T_f6258_level0_col3\" class=\"col_heading level0 col3\" >Max</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">Large</th>\n",
+ " <th id=\"T_f6258_level1_row0\" class=\"row_heading level1 row0\" >MILP</th>\n",
+ " <td id=\"T_f6258_row0_col0\" class=\"data row0 col0\" >295.15</td>\n",
+ " <td id=\"T_f6258_row0_col1\" class=\"data row0 col1\" >209.85</td>\n",
+ " <td id=\"T_f6258_row0_col2\" class=\"data row0 col2\" >141.71</td>\n",
+ " <td id=\"T_f6258_row0_col3\" class=\"data row0 col3\" >1883.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level1_row1\" class=\"row_heading level1 row1\" >Heuristic</th>\n",
+ " <td id=\"T_f6258_row1_col0\" class=\"data row1 col0\" >0.19</td>\n",
+ " <td id=\"T_f6258_row1_col1\" class=\"data row1 col1\" >0.01</td>\n",
+ " <td id=\"T_f6258_row1_col2\" class=\"data row1 col2\" >0.17</td>\n",
+ " <td id=\"T_f6258_row1_col3\" class=\"data row1 col3\" >0.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"2\">Medium</th>\n",
+ " <th id=\"T_f6258_level1_row2\" class=\"row_heading level1 row2\" >MILP</th>\n",
+ " <td id=\"T_f6258_row2_col0\" class=\"data row2 col0\" >111.34</td>\n",
+ " <td id=\"T_f6258_row2_col1\" class=\"data row2 col1\" >25.86</td>\n",
+ " <td id=\"T_f6258_row2_col2\" class=\"data row2 col2\" >71.44</td>\n",
+ " <td id=\"T_f6258_row2_col3\" class=\"data row2 col3\" >202.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level1_row3\" class=\"row_heading level1 row3\" >Heuristic</th>\n",
+ " <td id=\"T_f6258_row3_col0\" class=\"data row3 col0\" >0.12</td>\n",
+ " <td id=\"T_f6258_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
+ " <td id=\"T_f6258_row3_col2\" class=\"data row3 col2\" >0.10</td>\n",
+ " <td id=\"T_f6258_row3_col3\" class=\"data row3 col3\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"2\">Small</th>\n",
+ " <th id=\"T_f6258_level1_row4\" class=\"row_heading level1 row4\" >MILP</th>\n",
+ " <td id=\"T_f6258_row4_col0\" class=\"data row4 col0\" >36.35</td>\n",
+ " <td id=\"T_f6258_row4_col1\" class=\"data row4 col1\" >5.82</td>\n",
+ " <td id=\"T_f6258_row4_col2\" class=\"data row4 col2\" >28.71</td>\n",
+ " <td id=\"T_f6258_row4_col3\" class=\"data row4 col3\" >61.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f6258_level1_row5\" class=\"row_heading level1 row5\" >Heuristic</th>\n",
+ " <td id=\"T_f6258_row5_col0\" class=\"data row5 col0\" >0.06</td>\n",
+ " <td id=\"T_f6258_row5_col1\" class=\"data row5 col1\" >0.01</td>\n",
+ " <td id=\"T_f6258_row5_col2\" class=\"data row5 col2\" >0.05</td>\n",
+ " <td id=\"T_f6258_row5_col3\" class=\"data row5 col3\" >0.08</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x722fcde85190>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Benefits (% gap to MILP)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_fff01 td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_fff01 th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_fff01\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_fff01_level0_col0\" class=\"col_heading level0 col0\" >Value</th>\n",
+ " <th id=\"T_fff01_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
+ " <th id=\"T_fff01_level0_col2\" class=\"col_heading level0 col2\" >Min</th>\n",
+ " <th id=\"T_fff01_level0_col3\" class=\"col_heading level0 col3\" >Max</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_fff01_level0_row0\" class=\"row_heading level0 row0\" >Large</th>\n",
+ " <td id=\"T_fff01_row0_col0\" class=\"data row0 col0\" >46.79</td>\n",
+ " <td id=\"T_fff01_row0_col1\" class=\"data row0 col1\" >1.61</td>\n",
+ " <td id=\"T_fff01_row0_col2\" class=\"data row0 col2\" >42.31</td>\n",
+ " <td id=\"T_fff01_row0_col3\" class=\"data row0 col3\" >50.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fff01_level0_row1\" class=\"row_heading level0 row1\" >Medium</th>\n",
+ " <td id=\"T_fff01_row1_col0\" class=\"data row1 col0\" >45.11</td>\n",
+ " <td id=\"T_fff01_row1_col1\" class=\"data row1 col1\" >1.52</td>\n",
+ " <td id=\"T_fff01_row1_col2\" class=\"data row1 col2\" >42.25</td>\n",
+ " <td id=\"T_fff01_row1_col3\" class=\"data row1 col3\" >48.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fff01_level0_row2\" class=\"row_heading level0 row2\" >Small</th>\n",
+ " <td id=\"T_fff01_row2_col0\" class=\"data row2 col0\" >43.35</td>\n",
+ " <td id=\"T_fff01_row2_col1\" class=\"data row2 col1\" >1.90</td>\n",
+ " <td id=\"T_fff01_row2_col2\" class=\"data row2 col2\" >38.58</td>\n",
+ " <td id=\"T_fff01_row2_col3\" class=\"data row2 col3\" >47.60</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x722fcdc0e4d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_comparison_milp()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analysis/Section_7.2.ipynb b/Analysis/Section_7.2.ipynb
new file mode 100644
index 0000000..2b3f0fa
--- /dev/null
+++ b/Analysis/Section_7.2.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Objective:** Show that in a centralized dc, the moments with low production mainly impact the services. The services have a exact moment to run, so, if a centralized DC does not have energy at this moment, it can not bid the services. This does not impact the jobs, because they can execute any time. \n",
+ "\n",
+ "Also, the decentralized uses better the energy and receives more benefits.\n",
+ "\n",
+ "In experiment LARGE, we don't see a big difference, since we have so many jobs. In MEDIUM and SMALL, we can evaluate the diffs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append(\"../\")\n",
+ "from GenerateVisualisation.experiments_analysis import Analysis, Analysis_plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_list = []\n",
+ "seeds = range(3, 103)\n",
+ "\n",
+ "analysis = Analysis(base_folder=\"../XPs/1_bell/\",\n",
+ " files=[\"/simple_centralized_\"],\n",
+ " names=[\"Central.Equal\"],\n",
+ " nb_dcs=1,\n",
+ " seeds = seeds,)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "names = []\n",
+ "files = []\n",
+ "for dc in range(4):\n",
+ " files.append(\"/simple_centralized_dc_{}_\".format(dc))\n",
+ " names.append(\"Central.Real DC {}\".format(dc))\n",
+ "analysis = Analysis(base_folder=\"../XPs/3_real/\",\n",
+ " files=files,\n",
+ " names=names,\n",
+ " nb_dcs=1,\n",
+ " seeds = range(3, 103))\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis = Analysis(base_folder=\"../XPs/1_bell/\",\n",
+ " files=[\"/simple_aggre_0_\"],\n",
+ " names=[\"Distrib.Equal\"],\n",
+ " nb_dcs=4,\n",
+ " seeds = seeds)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis = Analysis(base_folder=\"../XPs/3_real/\",\n",
+ " files=[\"/simple_aggre_0_dc_0_0_0_0_seed_\"],\n",
+ " names=[\"Distrib.Real\"],\n",
+ " nb_dcs=4,\n",
+ " seeds = range(3, 103))\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis_plot = Analysis_plots(analysis_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_consolidate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Large"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"large\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"large\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"large\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"large\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Medium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"medium\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"medium\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"medium\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"medium\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Small"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"small\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"small\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"small\", tables_type=\"conso\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"small\", tables_type=\"conso\", simplified=True)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analysis/Section_7.3.1.ipynb b/Analysis/Section_7.3.1.ipynb
new file mode 100644
index 0000000..876858a
--- /dev/null
+++ b/Analysis/Section_7.3.1.ipynb
@@ -0,0 +1,2058 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Objective**: The impact of the aggressiveness of one or more aggressive dcs. Here, I show the results by dc, to show the impact of one dc in the others, and consolidate, to show the results globally. \n",
+ "\n",
+ "Coop results in higher global benefits and benefit/energy factor, and lower wasted energy. The aggressive dcs bid for more jobs than the non-aggressive, but they have lower QoS."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from GenerateVisualisation.experiments_analysis import Analysis, Analysis_plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_list = []\n",
+ "seeds = range(3, 103)\n",
+ "\n",
+ "base_folder = \"XPs/1_bell/\"\n",
+ "analysis = Analysis(base_folder=base_folder,\n",
+ " files=[\"/simple_aggre_0_\", \"/simple_aggre_25_\", \"/simple_aggre_50_\", \"/simple_aggre_75_\", \"/simple_aggre_100_\"],\n",
+ " names=[\"Cooperative\", \"25% aggre\", \"50% aggre\", \"75% aggre\", \"all aggre\"],\n",
+ " nb_dcs=4,\n",
+ " seeds = seeds)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis_plot = Analysis_plots(analysis_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_ecd50 td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_ecd50 th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_ecd50_row0_col4, #T_ecd50_row2_col6, #T_ecd50_row4_col0, #T_ecd50_row4_col10, #T_ecd50_row4_col12, #T_ecd50_row4_col14, #T_ecd50_row6_col8, #T_ecd50_row8_col2, #T_ecd50_row16_col4, #T_ecd50_row18_col6 {\n",
+ " background-color: #34FF34;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_ecd50\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_ecd50_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bid</th>\n",
+ " <th id=\"T_ecd50_level0_col4\" class=\"col_heading level0 col4\" colspan=\"6\">QoS</th>\n",
+ " <th id=\"T_ecd50_level0_col10\" class=\"col_heading level0 col10\" colspan=\"6\">Money/Energy</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_ecd50_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Tasks</th>\n",
+ " <th id=\"T_ecd50_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " <th id=\"T_ecd50_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_ecd50_level1_col6\" class=\"col_heading level1 col6\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_ecd50_level1_col8\" class=\"col_heading level1 col8\" colspan=\"2\">Slowdown</th>\n",
+ " <th id=\"T_ecd50_level1_col10\" class=\"col_heading level1 col10\" colspan=\"2\">Benefits</th>\n",
+ " <th id=\"T_ecd50_level1_col12\" class=\"col_heading level1 col12\" colspan=\"2\">Energy wasted</th>\n",
+ " <th id=\"T_ecd50_level1_col14\" class=\"col_heading level1 col14\" colspan=\"2\">Ratio</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_ecd50_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col5\" class=\"col_heading level2 col5\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col6\" class=\"col_heading level2 col6\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col7\" class=\"col_heading level2 col7\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col8\" class=\"col_heading level2 col8\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col9\" class=\"col_heading level2 col9\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col10\" class=\"col_heading level2 col10\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col11\" class=\"col_heading level2 col11\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col12\" class=\"col_heading level2 col12\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col13\" class=\"col_heading level2 col13\" >std</th>\n",
+ " <th id=\"T_ecd50_level2_col14\" class=\"col_heading level2 col14\" >Value</th>\n",
+ " <th id=\"T_ecd50_level2_col15\" class=\"col_heading level2 col15\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_ecd50_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_ecd50_row0_col0\" class=\"data row0 col0\" >17.41</td>\n",
+ " <td id=\"T_ecd50_row0_col1\" class=\"data row0 col1\" >2.46</td>\n",
+ " <td id=\"T_ecd50_row0_col2\" class=\"data row0 col2\" >18.09</td>\n",
+ " <td id=\"T_ecd50_row0_col3\" class=\"data row0 col3\" >2.30</td>\n",
+ " <td id=\"T_ecd50_row0_col4\" class=\"data row0 col4\" >89.08</td>\n",
+ " <td id=\"T_ecd50_row0_col5\" class=\"data row0 col5\" >4.02</td>\n",
+ " <td id=\"T_ecd50_row0_col6\" class=\"data row0 col6\" >97.21</td>\n",
+ " <td id=\"T_ecd50_row0_col7\" class=\"data row0 col7\" >3.81</td>\n",
+ " <td id=\"T_ecd50_row0_col8\" class=\"data row0 col8\" >1.34</td>\n",
+ " <td id=\"T_ecd50_row0_col9\" class=\"data row0 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row0_col10\" class=\"data row0 col10\" >23.87</td>\n",
+ " <td id=\"T_ecd50_row0_col11\" class=\"data row0 col11\" >2.41</td>\n",
+ " <td id=\"T_ecd50_row0_col12\" class=\"data row0 col12\" >32.99</td>\n",
+ " <td id=\"T_ecd50_row0_col13\" class=\"data row0 col13\" >6.45</td>\n",
+ " <td id=\"T_ecd50_row0_col14\" class=\"data row0 col14\" >1.73</td>\n",
+ " <td id=\"T_ecd50_row0_col15\" class=\"data row0 col15\" >0.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_ecd50_row1_col0\" class=\"data row1 col0\" >18.13</td>\n",
+ " <td id=\"T_ecd50_row1_col1\" class=\"data row1 col1\" >2.48</td>\n",
+ " <td id=\"T_ecd50_row1_col2\" class=\"data row1 col2\" >17.75</td>\n",
+ " <td id=\"T_ecd50_row1_col3\" class=\"data row1 col3\" >2.58</td>\n",
+ " <td id=\"T_ecd50_row1_col4\" class=\"data row1 col4\" >88.27</td>\n",
+ " <td id=\"T_ecd50_row1_col5\" class=\"data row1 col5\" >4.37</td>\n",
+ " <td id=\"T_ecd50_row1_col6\" class=\"data row1 col6\" >97.19</td>\n",
+ " <td id=\"T_ecd50_row1_col7\" class=\"data row1 col7\" >2.65</td>\n",
+ " <td id=\"T_ecd50_row1_col8\" class=\"data row1 col8\" >1.33</td>\n",
+ " <td id=\"T_ecd50_row1_col9\" class=\"data row1 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row1_col10\" class=\"data row1 col10\" >24.06</td>\n",
+ " <td id=\"T_ecd50_row1_col11\" class=\"data row1 col11\" >2.56</td>\n",
+ " <td id=\"T_ecd50_row1_col12\" class=\"data row1 col12\" >32.26</td>\n",
+ " <td id=\"T_ecd50_row1_col13\" class=\"data row1 col13\" >6.78</td>\n",
+ " <td id=\"T_ecd50_row1_col14\" class=\"data row1 col14\" >1.74</td>\n",
+ " <td id=\"T_ecd50_row1_col15\" class=\"data row1 col15\" >0.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_ecd50_row2_col0\" class=\"data row2 col0\" >18.13</td>\n",
+ " <td id=\"T_ecd50_row2_col1\" class=\"data row2 col1\" >2.20</td>\n",
+ " <td id=\"T_ecd50_row2_col2\" class=\"data row2 col2\" >17.90</td>\n",
+ " <td id=\"T_ecd50_row2_col3\" class=\"data row2 col3\" >2.45</td>\n",
+ " <td id=\"T_ecd50_row2_col4\" class=\"data row2 col4\" >87.72</td>\n",
+ " <td id=\"T_ecd50_row2_col5\" class=\"data row2 col5\" >4.37</td>\n",
+ " <td id=\"T_ecd50_row2_col6\" class=\"data row2 col6\" >97.54</td>\n",
+ " <td id=\"T_ecd50_row2_col7\" class=\"data row2 col7\" >2.61</td>\n",
+ " <td id=\"T_ecd50_row2_col8\" class=\"data row2 col8\" >1.34</td>\n",
+ " <td id=\"T_ecd50_row2_col9\" class=\"data row2 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row2_col10\" class=\"data row2 col10\" >24.17</td>\n",
+ " <td id=\"T_ecd50_row2_col11\" class=\"data row2 col11\" >2.58</td>\n",
+ " <td id=\"T_ecd50_row2_col12\" class=\"data row2 col12\" >32.63</td>\n",
+ " <td id=\"T_ecd50_row2_col13\" class=\"data row2 col13\" >5.89</td>\n",
+ " <td id=\"T_ecd50_row2_col14\" class=\"data row2 col14\" >1.75</td>\n",
+ " <td id=\"T_ecd50_row2_col15\" class=\"data row2 col15\" >0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_ecd50_row3_col0\" class=\"data row3 col0\" >18.18</td>\n",
+ " <td id=\"T_ecd50_row3_col1\" class=\"data row3 col1\" >2.46</td>\n",
+ " <td id=\"T_ecd50_row3_col2\" class=\"data row3 col2\" >18.27</td>\n",
+ " <td id=\"T_ecd50_row3_col3\" class=\"data row3 col3\" >2.55</td>\n",
+ " <td id=\"T_ecd50_row3_col4\" class=\"data row3 col4\" >88.03</td>\n",
+ " <td id=\"T_ecd50_row3_col5\" class=\"data row3 col5\" >4.51</td>\n",
+ " <td id=\"T_ecd50_row3_col6\" class=\"data row3 col6\" >96.46</td>\n",
+ " <td id=\"T_ecd50_row3_col7\" class=\"data row3 col7\" >4.69</td>\n",
+ " <td id=\"T_ecd50_row3_col8\" class=\"data row3 col8\" >1.34</td>\n",
+ " <td id=\"T_ecd50_row3_col9\" class=\"data row3 col9\" >0.11</td>\n",
+ " <td id=\"T_ecd50_row3_col10\" class=\"data row3 col10\" >24.69</td>\n",
+ " <td id=\"T_ecd50_row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
+ " <td id=\"T_ecd50_row3_col12\" class=\"data row3 col12\" >33.02</td>\n",
+ " <td id=\"T_ecd50_row3_col13\" class=\"data row3 col13\" >6.50</td>\n",
+ " <td id=\"T_ecd50_row3_col14\" class=\"data row3 col14\" >1.79</td>\n",
+ " <td id=\"T_ecd50_row3_col15\" class=\"data row3 col15\" >0.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% aggre</th>\n",
+ " <th id=\"T_ecd50_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_ecd50_row4_col0\" class=\"data row4 col0\" >33.39</td>\n",
+ " <td id=\"T_ecd50_row4_col1\" class=\"data row4 col1\" >3.80</td>\n",
+ " <td id=\"T_ecd50_row4_col2\" class=\"data row4 col2\" >20.72</td>\n",
+ " <td id=\"T_ecd50_row4_col3\" class=\"data row4 col3\" >3.05</td>\n",
+ " <td id=\"T_ecd50_row4_col4\" class=\"data row4 col4\" >81.71</td>\n",
+ " <td id=\"T_ecd50_row4_col5\" class=\"data row4 col5\" >5.42</td>\n",
+ " <td id=\"T_ecd50_row4_col6\" class=\"data row4 col6\" >91.08</td>\n",
+ " <td id=\"T_ecd50_row4_col7\" class=\"data row4 col7\" >5.06</td>\n",
+ " <td id=\"T_ecd50_row4_col8\" class=\"data row4 col8\" >2.16</td>\n",
+ " <td id=\"T_ecd50_row4_col9\" class=\"data row4 col9\" >0.12</td>\n",
+ " <td id=\"T_ecd50_row4_col10\" class=\"data row4 col10\" >35.81</td>\n",
+ " <td id=\"T_ecd50_row4_col11\" class=\"data row4 col11\" >4.25</td>\n",
+ " <td id=\"T_ecd50_row4_col12\" class=\"data row4 col12\" >1.31</td>\n",
+ " <td id=\"T_ecd50_row4_col13\" class=\"data row4 col13\" >0.83</td>\n",
+ " <td id=\"T_ecd50_row4_col14\" class=\"data row4 col14\" >2.59</td>\n",
+ " <td id=\"T_ecd50_row4_col15\" class=\"data row4 col15\" >0.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_ecd50_row5_col0\" class=\"data row5 col0\" >13.08</td>\n",
+ " <td id=\"T_ecd50_row5_col1\" class=\"data row5 col1\" >2.45</td>\n",
+ " <td id=\"T_ecd50_row5_col2\" class=\"data row5 col2\" >17.14</td>\n",
+ " <td id=\"T_ecd50_row5_col3\" class=\"data row5 col3\" >2.70</td>\n",
+ " <td id=\"T_ecd50_row5_col4\" class=\"data row5 col4\" >86.90</td>\n",
+ " <td id=\"T_ecd50_row5_col5\" class=\"data row5 col5\" >4.50</td>\n",
+ " <td id=\"T_ecd50_row5_col6\" class=\"data row5 col6\" >96.88</td>\n",
+ " <td id=\"T_ecd50_row5_col7\" class=\"data row5 col7\" >3.61</td>\n",
+ " <td id=\"T_ecd50_row5_col8\" class=\"data row5 col8\" >1.32</td>\n",
+ " <td id=\"T_ecd50_row5_col9\" class=\"data row5 col9\" >0.12</td>\n",
+ " <td id=\"T_ecd50_row5_col10\" class=\"data row5 col10\" >19.06</td>\n",
+ " <td id=\"T_ecd50_row5_col11\" class=\"data row5 col11\" >2.99</td>\n",
+ " <td id=\"T_ecd50_row5_col12\" class=\"data row5 col12\" >43.88</td>\n",
+ " <td id=\"T_ecd50_row5_col13\" class=\"data row5 col13\" >6.68</td>\n",
+ " <td id=\"T_ecd50_row5_col14\" class=\"data row5 col14\" >1.38</td>\n",
+ " <td id=\"T_ecd50_row5_col15\" class=\"data row5 col15\" >0.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_ecd50_row6_col0\" class=\"data row6 col0\" >12.51</td>\n",
+ " <td id=\"T_ecd50_row6_col1\" class=\"data row6 col1\" >1.96</td>\n",
+ " <td id=\"T_ecd50_row6_col2\" class=\"data row6 col2\" >17.45</td>\n",
+ " <td id=\"T_ecd50_row6_col3\" class=\"data row6 col3\" >2.34</td>\n",
+ " <td id=\"T_ecd50_row6_col4\" class=\"data row6 col4\" >87.02</td>\n",
+ " <td id=\"T_ecd50_row6_col5\" class=\"data row6 col5\" >4.47</td>\n",
+ " <td id=\"T_ecd50_row6_col6\" class=\"data row6 col6\" >96.04</td>\n",
+ " <td id=\"T_ecd50_row6_col7\" class=\"data row6 col7\" >4.53</td>\n",
+ " <td id=\"T_ecd50_row6_col8\" class=\"data row6 col8\" >1.32</td>\n",
+ " <td id=\"T_ecd50_row6_col9\" class=\"data row6 col9\" >0.11</td>\n",
+ " <td id=\"T_ecd50_row6_col10\" class=\"data row6 col10\" >18.78</td>\n",
+ " <td id=\"T_ecd50_row6_col11\" class=\"data row6 col11\" >2.25</td>\n",
+ " <td id=\"T_ecd50_row6_col12\" class=\"data row6 col12\" >45.31</td>\n",
+ " <td id=\"T_ecd50_row6_col13\" class=\"data row6 col13\" >6.74</td>\n",
+ " <td id=\"T_ecd50_row6_col14\" class=\"data row6 col14\" >1.36</td>\n",
+ " <td id=\"T_ecd50_row6_col15\" class=\"data row6 col15\" >0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_ecd50_row7_col0\" class=\"data row7 col0\" >12.94</td>\n",
+ " <td id=\"T_ecd50_row7_col1\" class=\"data row7 col1\" >2.07</td>\n",
+ " <td id=\"T_ecd50_row7_col2\" class=\"data row7 col2\" >17.08</td>\n",
+ " <td id=\"T_ecd50_row7_col3\" class=\"data row7 col3\" >2.36</td>\n",
+ " <td id=\"T_ecd50_row7_col4\" class=\"data row7 col4\" >86.54</td>\n",
+ " <td id=\"T_ecd50_row7_col5\" class=\"data row7 col5\" >4.54</td>\n",
+ " <td id=\"T_ecd50_row7_col6\" class=\"data row7 col6\" >95.86</td>\n",
+ " <td id=\"T_ecd50_row7_col7\" class=\"data row7 col7\" >4.25</td>\n",
+ " <td id=\"T_ecd50_row7_col8\" class=\"data row7 col8\" >1.33</td>\n",
+ " <td id=\"T_ecd50_row7_col9\" class=\"data row7 col9\" >0.12</td>\n",
+ " <td id=\"T_ecd50_row7_col10\" class=\"data row7 col10\" >18.81</td>\n",
+ " <td id=\"T_ecd50_row7_col11\" class=\"data row7 col11\" >2.28</td>\n",
+ " <td id=\"T_ecd50_row7_col12\" class=\"data row7 col12\" >44.49</td>\n",
+ " <td id=\"T_ecd50_row7_col13\" class=\"data row7 col13\" >7.14</td>\n",
+ " <td id=\"T_ecd50_row7_col14\" class=\"data row7 col14\" >1.36</td>\n",
+ " <td id=\"T_ecd50_row7_col15\" class=\"data row7 col15\" >0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">50% aggre</th>\n",
+ " <th id=\"T_ecd50_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_ecd50_row8_col0\" class=\"data row8 col0\" >27.52</td>\n",
+ " <td id=\"T_ecd50_row8_col1\" class=\"data row8 col1\" >2.87</td>\n",
+ " <td id=\"T_ecd50_row8_col2\" class=\"data row8 col2\" >23.47</td>\n",
+ " <td id=\"T_ecd50_row8_col3\" class=\"data row8 col3\" >3.08</td>\n",
+ " <td id=\"T_ecd50_row8_col4\" class=\"data row8 col4\" >86.18</td>\n",
+ " <td id=\"T_ecd50_row8_col5\" class=\"data row8 col5\" >3.58</td>\n",
+ " <td id=\"T_ecd50_row8_col6\" class=\"data row8 col6\" >95.84</td>\n",
+ " <td id=\"T_ecd50_row8_col7\" class=\"data row8 col7\" >4.06</td>\n",
+ " <td id=\"T_ecd50_row8_col8\" class=\"data row8 col8\" >1.70</td>\n",
+ " <td id=\"T_ecd50_row8_col9\" class=\"data row8 col9\" >0.11</td>\n",
+ " <td id=\"T_ecd50_row8_col10\" class=\"data row8 col10\" >10.69</td>\n",
+ " <td id=\"T_ecd50_row8_col11\" class=\"data row8 col11\" >2.84</td>\n",
+ " <td id=\"T_ecd50_row8_col12\" class=\"data row8 col12\" >7.24</td>\n",
+ " <td id=\"T_ecd50_row8_col13\" class=\"data row8 col13\" >3.46</td>\n",
+ " <td id=\"T_ecd50_row8_col14\" class=\"data row8 col14\" >0.77</td>\n",
+ " <td id=\"T_ecd50_row8_col15\" class=\"data row8 col15\" >0.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_ecd50_row9_col0\" class=\"data row9 col0\" >27.88</td>\n",
+ " <td id=\"T_ecd50_row9_col1\" class=\"data row9 col1\" >2.82</td>\n",
+ " <td id=\"T_ecd50_row9_col2\" class=\"data row9 col2\" >23.38</td>\n",
+ " <td id=\"T_ecd50_row9_col3\" class=\"data row9 col3\" >3.33</td>\n",
+ " <td id=\"T_ecd50_row9_col4\" class=\"data row9 col4\" >87.61</td>\n",
+ " <td id=\"T_ecd50_row9_col5\" class=\"data row9 col5\" >3.55</td>\n",
+ " <td id=\"T_ecd50_row9_col6\" class=\"data row9 col6\" >96.09</td>\n",
+ " <td id=\"T_ecd50_row9_col7\" class=\"data row9 col7\" >3.57</td>\n",
+ " <td id=\"T_ecd50_row9_col8\" class=\"data row9 col8\" >1.69</td>\n",
+ " <td id=\"T_ecd50_row9_col9\" class=\"data row9 col9\" >0.11</td>\n",
+ " <td id=\"T_ecd50_row9_col10\" class=\"data row9 col10\" >10.88</td>\n",
+ " <td id=\"T_ecd50_row9_col11\" class=\"data row9 col11\" >2.69</td>\n",
+ " <td id=\"T_ecd50_row9_col12\" class=\"data row9 col12\" >6.96</td>\n",
+ " <td id=\"T_ecd50_row9_col13\" class=\"data row9 col13\" >3.21</td>\n",
+ " <td id=\"T_ecd50_row9_col14\" class=\"data row9 col14\" >0.79</td>\n",
+ " <td id=\"T_ecd50_row9_col15\" class=\"data row9 col15\" >0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_ecd50_row10_col0\" class=\"data row10 col0\" >8.11</td>\n",
+ " <td id=\"T_ecd50_row10_col1\" class=\"data row10 col1\" >1.73</td>\n",
+ " <td id=\"T_ecd50_row10_col2\" class=\"data row10 col2\" >12.50</td>\n",
+ " <td id=\"T_ecd50_row10_col3\" class=\"data row10 col3\" >2.61</td>\n",
+ " <td id=\"T_ecd50_row10_col4\" class=\"data row10 col4\" >83.85</td>\n",
+ " <td id=\"T_ecd50_row10_col5\" class=\"data row10 col5\" >5.68</td>\n",
+ " <td id=\"T_ecd50_row10_col6\" class=\"data row10 col6\" >94.77</td>\n",
+ " <td id=\"T_ecd50_row10_col7\" class=\"data row10 col7\" >6.36</td>\n",
+ " <td id=\"T_ecd50_row10_col8\" class=\"data row10 col8\" >1.37</td>\n",
+ " <td id=\"T_ecd50_row10_col9\" class=\"data row10 col9\" >0.15</td>\n",
+ " <td id=\"T_ecd50_row10_col10\" class=\"data row10 col10\" >12.09</td>\n",
+ " <td id=\"T_ecd50_row10_col11\" class=\"data row10 col11\" >2.58</td>\n",
+ " <td id=\"T_ecd50_row10_col12\" class=\"data row10 col12\" >60.73</td>\n",
+ " <td id=\"T_ecd50_row10_col13\" class=\"data row10 col13\" >7.95</td>\n",
+ " <td id=\"T_ecd50_row10_col14\" class=\"data row10 col14\" >0.87</td>\n",
+ " <td id=\"T_ecd50_row10_col15\" class=\"data row10 col15\" >0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_ecd50_row11_col0\" class=\"data row11 col0\" >8.29</td>\n",
+ " <td id=\"T_ecd50_row11_col1\" class=\"data row11 col1\" >1.78</td>\n",
+ " <td id=\"T_ecd50_row11_col2\" class=\"data row11 col2\" >13.00</td>\n",
+ " <td id=\"T_ecd50_row11_col3\" class=\"data row11 col3\" >2.21</td>\n",
+ " <td id=\"T_ecd50_row11_col4\" class=\"data row11 col4\" >82.89</td>\n",
+ " <td id=\"T_ecd50_row11_col5\" class=\"data row11 col5\" >6.31</td>\n",
+ " <td id=\"T_ecd50_row11_col6\" class=\"data row11 col6\" >93.42</td>\n",
+ " <td id=\"T_ecd50_row11_col7\" class=\"data row11 col7\" >8.09</td>\n",
+ " <td id=\"T_ecd50_row11_col8\" class=\"data row11 col8\" >1.38</td>\n",
+ " <td id=\"T_ecd50_row11_col9\" class=\"data row11 col9\" >0.14</td>\n",
+ " <td id=\"T_ecd50_row11_col10\" class=\"data row11 col10\" >12.36</td>\n",
+ " <td id=\"T_ecd50_row11_col11\" class=\"data row11 col11\" >2.36</td>\n",
+ " <td id=\"T_ecd50_row11_col12\" class=\"data row11 col12\" >60.80</td>\n",
+ " <td id=\"T_ecd50_row11_col13\" class=\"data row11 col13\" >6.61</td>\n",
+ " <td id=\"T_ecd50_row11_col14\" class=\"data row11 col14\" >0.89</td>\n",
+ " <td id=\"T_ecd50_row11_col15\" class=\"data row11 col15\" >0.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">75% aggre</th>\n",
+ " <th id=\"T_ecd50_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_ecd50_row12_col0\" class=\"data row12 col0\" >22.23</td>\n",
+ " <td id=\"T_ecd50_row12_col1\" class=\"data row12 col1\" >2.35</td>\n",
+ " <td id=\"T_ecd50_row12_col2\" class=\"data row12 col2\" >21.19</td>\n",
+ " <td id=\"T_ecd50_row12_col3\" class=\"data row12 col3\" >2.57</td>\n",
+ " <td id=\"T_ecd50_row12_col4\" class=\"data row12 col4\" >88.44</td>\n",
+ " <td id=\"T_ecd50_row12_col5\" class=\"data row12 col5\" >3.55</td>\n",
+ " <td id=\"T_ecd50_row12_col6\" class=\"data row12 col6\" >97.22</td>\n",
+ " <td id=\"T_ecd50_row12_col7\" class=\"data row12 col7\" >3.00</td>\n",
+ " <td id=\"T_ecd50_row12_col8\" class=\"data row12 col8\" >1.43</td>\n",
+ " <td id=\"T_ecd50_row12_col9\" class=\"data row12 col9\" >0.09</td>\n",
+ " <td id=\"T_ecd50_row12_col10\" class=\"data row12 col10\" >4.01</td>\n",
+ " <td id=\"T_ecd50_row12_col11\" class=\"data row12 col11\" >1.41</td>\n",
+ " <td id=\"T_ecd50_row12_col12\" class=\"data row12 col12\" >19.82</td>\n",
+ " <td id=\"T_ecd50_row12_col13\" class=\"data row12 col13\" >5.60</td>\n",
+ " <td id=\"T_ecd50_row12_col14\" class=\"data row12 col14\" >0.29</td>\n",
+ " <td id=\"T_ecd50_row12_col15\" class=\"data row12 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_ecd50_row13_col0\" class=\"data row13 col0\" >22.58</td>\n",
+ " <td id=\"T_ecd50_row13_col1\" class=\"data row13 col1\" >2.44</td>\n",
+ " <td id=\"T_ecd50_row13_col2\" class=\"data row13 col2\" >21.24</td>\n",
+ " <td id=\"T_ecd50_row13_col3\" class=\"data row13 col3\" >2.74</td>\n",
+ " <td id=\"T_ecd50_row13_col4\" class=\"data row13 col4\" >88.45</td>\n",
+ " <td id=\"T_ecd50_row13_col5\" class=\"data row13 col5\" >3.58</td>\n",
+ " <td id=\"T_ecd50_row13_col6\" class=\"data row13 col6\" >96.92</td>\n",
+ " <td id=\"T_ecd50_row13_col7\" class=\"data row13 col7\" >2.91</td>\n",
+ " <td id=\"T_ecd50_row13_col8\" class=\"data row13 col8\" >1.47</td>\n",
+ " <td id=\"T_ecd50_row13_col9\" class=\"data row13 col9\" >0.11</td>\n",
+ " <td id=\"T_ecd50_row13_col10\" class=\"data row13 col10\" >4.16</td>\n",
+ " <td id=\"T_ecd50_row13_col11\" class=\"data row13 col11\" >1.38</td>\n",
+ " <td id=\"T_ecd50_row13_col12\" class=\"data row13 col12\" >19.35</td>\n",
+ " <td id=\"T_ecd50_row13_col13\" class=\"data row13 col13\" >5.32</td>\n",
+ " <td id=\"T_ecd50_row13_col14\" class=\"data row13 col14\" >0.30</td>\n",
+ " <td id=\"T_ecd50_row13_col15\" class=\"data row13 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_ecd50_row14_col0\" class=\"data row14 col0\" >21.70</td>\n",
+ " <td id=\"T_ecd50_row14_col1\" class=\"data row14 col1\" >2.51</td>\n",
+ " <td id=\"T_ecd50_row14_col2\" class=\"data row14 col2\" >20.82</td>\n",
+ " <td id=\"T_ecd50_row14_col3\" class=\"data row14 col3\" >2.45</td>\n",
+ " <td id=\"T_ecd50_row14_col4\" class=\"data row14 col4\" >88.75</td>\n",
+ " <td id=\"T_ecd50_row14_col5\" class=\"data row14 col5\" >3.72</td>\n",
+ " <td id=\"T_ecd50_row14_col6\" class=\"data row14 col6\" >96.75</td>\n",
+ " <td id=\"T_ecd50_row14_col7\" class=\"data row14 col7\" >3.33</td>\n",
+ " <td id=\"T_ecd50_row14_col8\" class=\"data row14 col8\" >1.45</td>\n",
+ " <td id=\"T_ecd50_row14_col9\" class=\"data row14 col9\" >0.12</td>\n",
+ " <td id=\"T_ecd50_row14_col10\" class=\"data row14 col10\" >4.08</td>\n",
+ " <td id=\"T_ecd50_row14_col11\" class=\"data row14 col11\" >1.47</td>\n",
+ " <td id=\"T_ecd50_row14_col12\" class=\"data row14 col12\" >20.69</td>\n",
+ " <td id=\"T_ecd50_row14_col13\" class=\"data row14 col13\" >5.96</td>\n",
+ " <td id=\"T_ecd50_row14_col14\" class=\"data row14 col14\" >0.30</td>\n",
+ " <td id=\"T_ecd50_row14_col15\" class=\"data row14 col15\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_ecd50_row15_col0\" class=\"data row15 col0\" >5.30</td>\n",
+ " <td id=\"T_ecd50_row15_col1\" class=\"data row15 col1\" >1.30</td>\n",
+ " <td id=\"T_ecd50_row15_col2\" class=\"data row15 col2\" >8.85</td>\n",
+ " <td id=\"T_ecd50_row15_col3\" class=\"data row15 col3\" >1.91</td>\n",
+ " <td id=\"T_ecd50_row15_col4\" class=\"data row15 col4\" >78.16</td>\n",
+ " <td id=\"T_ecd50_row15_col5\" class=\"data row15 col5\" >7.58</td>\n",
+ " <td id=\"T_ecd50_row15_col6\" class=\"data row15 col6\" >93.38</td>\n",
+ " <td id=\"T_ecd50_row15_col7\" class=\"data row15 col7\" >8.05</td>\n",
+ " <td id=\"T_ecd50_row15_col8\" class=\"data row15 col8\" >1.45</td>\n",
+ " <td id=\"T_ecd50_row15_col9\" class=\"data row15 col9\" >0.23</td>\n",
+ " <td id=\"T_ecd50_row15_col10\" class=\"data row15 col10\" >7.60</td>\n",
+ " <td id=\"T_ecd50_row15_col11\" class=\"data row15 col11\" >1.74</td>\n",
+ " <td id=\"T_ecd50_row15_col12\" class=\"data row15 col12\" >72.80</td>\n",
+ " <td id=\"T_ecd50_row15_col13\" class=\"data row15 col13\" >6.51</td>\n",
+ " <td id=\"T_ecd50_row15_col14\" class=\"data row15 col14\" >0.55</td>\n",
+ " <td id=\"T_ecd50_row15_col15\" class=\"data row15 col15\" >0.13</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_ecd50_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">all aggre</th>\n",
+ " <th id=\"T_ecd50_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_ecd50_row16_col0\" class=\"data row16 col0\" >17.41</td>\n",
+ " <td id=\"T_ecd50_row16_col1\" class=\"data row16 col1\" >2.46</td>\n",
+ " <td id=\"T_ecd50_row16_col2\" class=\"data row16 col2\" >18.09</td>\n",
+ " <td id=\"T_ecd50_row16_col3\" class=\"data row16 col3\" >2.30</td>\n",
+ " <td id=\"T_ecd50_row16_col4\" class=\"data row16 col4\" >89.08</td>\n",
+ " <td id=\"T_ecd50_row16_col5\" class=\"data row16 col5\" >4.02</td>\n",
+ " <td id=\"T_ecd50_row16_col6\" class=\"data row16 col6\" >97.21</td>\n",
+ " <td id=\"T_ecd50_row16_col7\" class=\"data row16 col7\" >3.81</td>\n",
+ " <td id=\"T_ecd50_row16_col8\" class=\"data row16 col8\" >1.34</td>\n",
+ " <td id=\"T_ecd50_row16_col9\" class=\"data row16 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row16_col10\" class=\"data row16 col10\" >1.95</td>\n",
+ " <td id=\"T_ecd50_row16_col11\" class=\"data row16 col11\" >0.87</td>\n",
+ " <td id=\"T_ecd50_row16_col12\" class=\"data row16 col12\" >32.99</td>\n",
+ " <td id=\"T_ecd50_row16_col13\" class=\"data row16 col13\" >6.45</td>\n",
+ " <td id=\"T_ecd50_row16_col14\" class=\"data row16 col14\" >0.14</td>\n",
+ " <td id=\"T_ecd50_row16_col15\" class=\"data row16 col15\" >0.06</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_ecd50_row17_col0\" class=\"data row17 col0\" >18.13</td>\n",
+ " <td id=\"T_ecd50_row17_col1\" class=\"data row17 col1\" >2.48</td>\n",
+ " <td id=\"T_ecd50_row17_col2\" class=\"data row17 col2\" >17.75</td>\n",
+ " <td id=\"T_ecd50_row17_col3\" class=\"data row17 col3\" >2.58</td>\n",
+ " <td id=\"T_ecd50_row17_col4\" class=\"data row17 col4\" >88.27</td>\n",
+ " <td id=\"T_ecd50_row17_col5\" class=\"data row17 col5\" >4.37</td>\n",
+ " <td id=\"T_ecd50_row17_col6\" class=\"data row17 col6\" >97.19</td>\n",
+ " <td id=\"T_ecd50_row17_col7\" class=\"data row17 col7\" >2.65</td>\n",
+ " <td id=\"T_ecd50_row17_col8\" class=\"data row17 col8\" >1.33</td>\n",
+ " <td id=\"T_ecd50_row17_col9\" class=\"data row17 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row17_col10\" class=\"data row17 col10\" >1.81</td>\n",
+ " <td id=\"T_ecd50_row17_col11\" class=\"data row17 col11\" >0.78</td>\n",
+ " <td id=\"T_ecd50_row17_col12\" class=\"data row17 col12\" >32.26</td>\n",
+ " <td id=\"T_ecd50_row17_col13\" class=\"data row17 col13\" >6.78</td>\n",
+ " <td id=\"T_ecd50_row17_col14\" class=\"data row17 col14\" >0.13</td>\n",
+ " <td id=\"T_ecd50_row17_col15\" class=\"data row17 col15\" >0.06</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_ecd50_row18_col0\" class=\"data row18 col0\" >18.13</td>\n",
+ " <td id=\"T_ecd50_row18_col1\" class=\"data row18 col1\" >2.20</td>\n",
+ " <td id=\"T_ecd50_row18_col2\" class=\"data row18 col2\" >17.90</td>\n",
+ " <td id=\"T_ecd50_row18_col3\" class=\"data row18 col3\" >2.45</td>\n",
+ " <td id=\"T_ecd50_row18_col4\" class=\"data row18 col4\" >87.72</td>\n",
+ " <td id=\"T_ecd50_row18_col5\" class=\"data row18 col5\" >4.37</td>\n",
+ " <td id=\"T_ecd50_row18_col6\" class=\"data row18 col6\" >97.54</td>\n",
+ " <td id=\"T_ecd50_row18_col7\" class=\"data row18 col7\" >2.61</td>\n",
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+ " <td id=\"T_ecd50_row18_col9\" class=\"data row18 col9\" >0.10</td>\n",
+ " <td id=\"T_ecd50_row18_col10\" class=\"data row18 col10\" >1.88</td>\n",
+ " <td id=\"T_ecd50_row18_col11\" class=\"data row18 col11\" >0.79</td>\n",
+ " <td id=\"T_ecd50_row18_col12\" class=\"data row18 col12\" >32.63</td>\n",
+ " <td id=\"T_ecd50_row18_col13\" class=\"data row18 col13\" >5.89</td>\n",
+ " <td id=\"T_ecd50_row18_col14\" class=\"data row18 col14\" >0.14</td>\n",
+ " <td id=\"T_ecd50_row18_col15\" class=\"data row18 col15\" >0.06</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_ecd50_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_ecd50_row19_col0\" class=\"data row19 col0\" >18.18</td>\n",
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+ " <td id=\"T_ecd50_row19_col15\" class=\"data row19 col15\" >0.06</td>\n",
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+ " </tbody>\n",
+ "</table>\n"
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+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_f4359_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col5\" class=\"col_heading level2 col5\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col6\" class=\"col_heading level2 col6\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col7\" class=\"col_heading level2 col7\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col8\" class=\"col_heading level2 col8\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col9\" class=\"col_heading level2 col9\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col10\" class=\"col_heading level2 col10\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col11\" class=\"col_heading level2 col11\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col12\" class=\"col_heading level2 col12\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col13\" class=\"col_heading level2 col13\" >std</th>\n",
+ " <th id=\"T_f4359_level2_col14\" class=\"col_heading level2 col14\" >Value</th>\n",
+ " <th id=\"T_f4359_level2_col15\" class=\"col_heading level2 col15\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_f4359_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_f4359_row0_col0\" class=\"data row0 col0\" >18.45</td>\n",
+ " <td id=\"T_f4359_row0_col1\" class=\"data row0 col1\" >2.98</td>\n",
+ " <td id=\"T_f4359_row0_col2\" class=\"data row0 col2\" >19.43</td>\n",
+ " <td id=\"T_f4359_row0_col3\" class=\"data row0 col3\" >2.93</td>\n",
+ " <td id=\"T_f4359_row0_col4\" class=\"data row0 col4\" >89.30</td>\n",
+ " <td id=\"T_f4359_row0_col5\" class=\"data row0 col5\" >4.75</td>\n",
+ " <td id=\"T_f4359_row0_col6\" class=\"data row0 col6\" >96.91</td>\n",
+ " <td id=\"T_f4359_row0_col7\" class=\"data row0 col7\" >3.21</td>\n",
+ " <td id=\"T_f4359_row0_col8\" class=\"data row0 col8\" >1.21</td>\n",
+ " <td id=\"T_f4359_row0_col9\" class=\"data row0 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row0_col10\" class=\"data row0 col10\" >17.04</td>\n",
+ " <td id=\"T_f4359_row0_col11\" class=\"data row0 col11\" >2.12</td>\n",
+ " <td id=\"T_f4359_row0_col12\" class=\"data row0 col12\" >51.68</td>\n",
+ " <td id=\"T_f4359_row0_col13\" class=\"data row0 col13\" >6.48</td>\n",
+ " <td id=\"T_f4359_row0_col14\" class=\"data row0 col14\" >1.23</td>\n",
+ " <td id=\"T_f4359_row0_col15\" class=\"data row0 col15\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_f4359_row1_col0\" class=\"data row1 col0\" >19.19</td>\n",
+ " <td id=\"T_f4359_row1_col1\" class=\"data row1 col1\" >2.81</td>\n",
+ " <td id=\"T_f4359_row1_col2\" class=\"data row1 col2\" >18.40</td>\n",
+ " <td id=\"T_f4359_row1_col3\" class=\"data row1 col3\" >2.75</td>\n",
+ " <td id=\"T_f4359_row1_col4\" class=\"data row1 col4\" >90.11</td>\n",
+ " <td id=\"T_f4359_row1_col5\" class=\"data row1 col5\" >4.07</td>\n",
+ " <td id=\"T_f4359_row1_col6\" class=\"data row1 col6\" >97.07</td>\n",
+ " <td id=\"T_f4359_row1_col7\" class=\"data row1 col7\" >4.15</td>\n",
+ " <td id=\"T_f4359_row1_col8\" class=\"data row1 col8\" >1.20</td>\n",
+ " <td id=\"T_f4359_row1_col9\" class=\"data row1 col9\" >0.11</td>\n",
+ " <td id=\"T_f4359_row1_col10\" class=\"data row1 col10\" >17.01</td>\n",
+ " <td id=\"T_f4359_row1_col11\" class=\"data row1 col11\" >2.06</td>\n",
+ " <td id=\"T_f4359_row1_col12\" class=\"data row1 col12\" >52.60</td>\n",
+ " <td id=\"T_f4359_row1_col13\" class=\"data row1 col13\" >6.96</td>\n",
+ " <td id=\"T_f4359_row1_col14\" class=\"data row1 col14\" >1.23</td>\n",
+ " <td id=\"T_f4359_row1_col15\" class=\"data row1 col15\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_f4359_row2_col0\" class=\"data row2 col0\" >19.06</td>\n",
+ " <td id=\"T_f4359_row2_col1\" class=\"data row2 col1\" >2.98</td>\n",
+ " <td id=\"T_f4359_row2_col2\" class=\"data row2 col2\" >18.89</td>\n",
+ " <td id=\"T_f4359_row2_col3\" class=\"data row2 col3\" >2.97</td>\n",
+ " <td id=\"T_f4359_row2_col4\" class=\"data row2 col4\" >89.70</td>\n",
+ " <td id=\"T_f4359_row2_col5\" class=\"data row2 col5\" >4.94</td>\n",
+ " <td id=\"T_f4359_row2_col6\" class=\"data row2 col6\" >97.76</td>\n",
+ " <td id=\"T_f4359_row2_col7\" class=\"data row2 col7\" >3.18</td>\n",
+ " <td id=\"T_f4359_row2_col8\" class=\"data row2 col8\" >1.20</td>\n",
+ " <td id=\"T_f4359_row2_col9\" class=\"data row2 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row2_col10\" class=\"data row2 col10\" >17.17</td>\n",
+ " <td id=\"T_f4359_row2_col11\" class=\"data row2 col11\" >2.15</td>\n",
+ " <td id=\"T_f4359_row2_col12\" class=\"data row2 col12\" >51.33</td>\n",
+ " <td id=\"T_f4359_row2_col13\" class=\"data row2 col13\" >6.86</td>\n",
+ " <td id=\"T_f4359_row2_col14\" class=\"data row2 col14\" >1.24</td>\n",
+ " <td id=\"T_f4359_row2_col15\" class=\"data row2 col15\" >0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_f4359_row3_col0\" class=\"data row3 col0\" >19.02</td>\n",
+ " <td id=\"T_f4359_row3_col1\" class=\"data row3 col1\" >2.94</td>\n",
+ " <td id=\"T_f4359_row3_col2\" class=\"data row3 col2\" >19.69</td>\n",
+ " <td id=\"T_f4359_row3_col3\" class=\"data row3 col3\" >2.89</td>\n",
+ " <td id=\"T_f4359_row3_col4\" class=\"data row3 col4\" >89.33</td>\n",
+ " <td id=\"T_f4359_row3_col5\" class=\"data row3 col5\" >5.04</td>\n",
+ " <td id=\"T_f4359_row3_col6\" class=\"data row3 col6\" >97.50</td>\n",
+ " <td id=\"T_f4359_row3_col7\" class=\"data row3 col7\" >3.05</td>\n",
+ " <td id=\"T_f4359_row3_col8\" class=\"data row3 col8\" >1.22</td>\n",
+ " <td id=\"T_f4359_row3_col9\" class=\"data row3 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row3_col10\" class=\"data row3 col10\" >17.57</td>\n",
+ " <td id=\"T_f4359_row3_col11\" class=\"data row3 col11\" >2.04</td>\n",
+ " <td id=\"T_f4359_row3_col12\" class=\"data row3 col12\" >51.65</td>\n",
+ " <td id=\"T_f4359_row3_col13\" class=\"data row3 col13\" >6.10</td>\n",
+ " <td id=\"T_f4359_row3_col14\" class=\"data row3 col14\" >1.27</td>\n",
+ " <td id=\"T_f4359_row3_col15\" class=\"data row3 col15\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% aggre</th>\n",
+ " <th id=\"T_f4359_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_f4359_row4_col0\" class=\"data row4 col0\" >44.93</td>\n",
+ " <td id=\"T_f4359_row4_col1\" class=\"data row4 col1\" >3.92</td>\n",
+ " <td id=\"T_f4359_row4_col2\" class=\"data row4 col2\" >32.28</td>\n",
+ " <td id=\"T_f4359_row4_col3\" class=\"data row4 col3\" >4.87</td>\n",
+ " <td id=\"T_f4359_row4_col4\" class=\"data row4 col4\" >87.06</td>\n",
+ " <td id=\"T_f4359_row4_col5\" class=\"data row4 col5\" >3.35</td>\n",
+ " <td id=\"T_f4359_row4_col6\" class=\"data row4 col6\" >94.52</td>\n",
+ " <td id=\"T_f4359_row4_col7\" class=\"data row4 col7\" >3.51</td>\n",
+ " <td id=\"T_f4359_row4_col8\" class=\"data row4 col8\" >1.80</td>\n",
+ " <td id=\"T_f4359_row4_col9\" class=\"data row4 col9\" >0.12</td>\n",
+ " <td id=\"T_f4359_row4_col10\" class=\"data row4 col10\" >34.91</td>\n",
+ " <td id=\"T_f4359_row4_col11\" class=\"data row4 col11\" >3.61</td>\n",
+ " <td id=\"T_f4359_row4_col12\" class=\"data row4 col12\" >4.98</td>\n",
+ " <td id=\"T_f4359_row4_col13\" class=\"data row4 col13\" >2.71</td>\n",
+ " <td id=\"T_f4359_row4_col14\" class=\"data row4 col14\" >2.53</td>\n",
+ " <td id=\"T_f4359_row4_col15\" class=\"data row4 col15\" >0.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_f4359_row5_col0\" class=\"data row5 col0\" >9.88</td>\n",
+ " <td id=\"T_f4359_row5_col1\" class=\"data row5 col1\" >2.22</td>\n",
+ " <td id=\"T_f4359_row5_col2\" class=\"data row5 col2\" >15.04</td>\n",
+ " <td id=\"T_f4359_row5_col3\" class=\"data row5 col3\" >2.99</td>\n",
+ " <td id=\"T_f4359_row5_col4\" class=\"data row5 col4\" >85.62</td>\n",
+ " <td id=\"T_f4359_row5_col5\" class=\"data row5 col5\" >6.34</td>\n",
+ " <td id=\"T_f4359_row5_col6\" class=\"data row5 col6\" >96.23</td>\n",
+ " <td id=\"T_f4359_row5_col7\" class=\"data row5 col7\" >4.96</td>\n",
+ " <td id=\"T_f4359_row5_col8\" class=\"data row5 col8\" >1.23</td>\n",
+ " <td id=\"T_f4359_row5_col9\" class=\"data row5 col9\" >0.15</td>\n",
+ " <td id=\"T_f4359_row5_col10\" class=\"data row5 col10\" >10.31</td>\n",
+ " <td id=\"T_f4359_row5_col11\" class=\"data row5 col11\" >1.80</td>\n",
+ " <td id=\"T_f4359_row5_col12\" class=\"data row5 col12\" >69.38</td>\n",
+ " <td id=\"T_f4359_row5_col13\" class=\"data row5 col13\" >6.22</td>\n",
+ " <td id=\"T_f4359_row5_col14\" class=\"data row5 col14\" >0.75</td>\n",
+ " <td id=\"T_f4359_row5_col15\" class=\"data row5 col15\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_f4359_row6_col0\" class=\"data row6 col0\" >10.25</td>\n",
+ " <td id=\"T_f4359_row6_col1\" class=\"data row6 col1\" >2.48</td>\n",
+ " <td id=\"T_f4359_row6_col2\" class=\"data row6 col2\" >14.96</td>\n",
+ " <td id=\"T_f4359_row6_col3\" class=\"data row6 col3\" >3.18</td>\n",
+ " <td id=\"T_f4359_row6_col4\" class=\"data row6 col4\" >86.42</td>\n",
+ " <td id=\"T_f4359_row6_col5\" class=\"data row6 col5\" >6.50</td>\n",
+ " <td id=\"T_f4359_row6_col6\" class=\"data row6 col6\" >95.48</td>\n",
+ " <td id=\"T_f4359_row6_col7\" class=\"data row6 col7\" >6.63</td>\n",
+ " <td id=\"T_f4359_row6_col8\" class=\"data row6 col8\" >1.22</td>\n",
+ " <td id=\"T_f4359_row6_col9\" class=\"data row6 col9\" >0.15</td>\n",
+ " <td id=\"T_f4359_row6_col10\" class=\"data row6 col10\" >10.41</td>\n",
+ " <td id=\"T_f4359_row6_col11\" class=\"data row6 col11\" >1.95</td>\n",
+ " <td id=\"T_f4359_row6_col12\" class=\"data row6 col12\" >68.04</td>\n",
+ " <td id=\"T_f4359_row6_col13\" class=\"data row6 col13\" >6.48</td>\n",
+ " <td id=\"T_f4359_row6_col14\" class=\"data row6 col14\" >0.75</td>\n",
+ " <td id=\"T_f4359_row6_col15\" class=\"data row6 col15\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_f4359_row7_col0\" class=\"data row7 col0\" >10.88</td>\n",
+ " <td id=\"T_f4359_row7_col1\" class=\"data row7 col1\" >2.48</td>\n",
+ " <td id=\"T_f4359_row7_col2\" class=\"data row7 col2\" >14.79</td>\n",
+ " <td id=\"T_f4359_row7_col3\" class=\"data row7 col3\" >2.77</td>\n",
+ " <td id=\"T_f4359_row7_col4\" class=\"data row7 col4\" >85.83</td>\n",
+ " <td id=\"T_f4359_row7_col5\" class=\"data row7 col5\" >6.38</td>\n",
+ " <td id=\"T_f4359_row7_col6\" class=\"data row7 col6\" >96.17</td>\n",
+ " <td id=\"T_f4359_row7_col7\" class=\"data row7 col7\" >5.11</td>\n",
+ " <td id=\"T_f4359_row7_col8\" class=\"data row7 col8\" >1.23</td>\n",
+ " <td id=\"T_f4359_row7_col9\" class=\"data row7 col9\" >0.16</td>\n",
+ " <td id=\"T_f4359_row7_col10\" class=\"data row7 col10\" >10.63</td>\n",
+ " <td id=\"T_f4359_row7_col11\" class=\"data row7 col11\" >1.94</td>\n",
+ " <td id=\"T_f4359_row7_col12\" class=\"data row7 col12\" >68.32</td>\n",
+ " <td id=\"T_f4359_row7_col13\" class=\"data row7 col13\" >6.10</td>\n",
+ " <td id=\"T_f4359_row7_col14\" class=\"data row7 col14\" >0.77</td>\n",
+ " <td id=\"T_f4359_row7_col15\" class=\"data row7 col15\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">50% aggre</th>\n",
+ " <th id=\"T_f4359_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_f4359_row8_col0\" class=\"data row8 col0\" >32.33</td>\n",
+ " <td id=\"T_f4359_row8_col1\" class=\"data row8 col1\" >3.82</td>\n",
+ " <td id=\"T_f4359_row8_col2\" class=\"data row8 col2\" >30.43</td>\n",
+ " <td id=\"T_f4359_row8_col3\" class=\"data row8 col3\" >3.46</td>\n",
+ " <td id=\"T_f4359_row8_col4\" class=\"data row8 col4\" >90.31</td>\n",
+ " <td id=\"T_f4359_row8_col5\" class=\"data row8 col5\" >3.24</td>\n",
+ " <td id=\"T_f4359_row8_col6\" class=\"data row8 col6\" >96.78</td>\n",
+ " <td id=\"T_f4359_row8_col7\" class=\"data row8 col7\" >2.94</td>\n",
+ " <td id=\"T_f4359_row8_col8\" class=\"data row8 col8\" >1.42</td>\n",
+ " <td id=\"T_f4359_row8_col9\" class=\"data row8 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row8_col10\" class=\"data row8 col10\" >6.65</td>\n",
+ " <td id=\"T_f4359_row8_col11\" class=\"data row8 col11\" >2.36</td>\n",
+ " <td id=\"T_f4359_row8_col12\" class=\"data row8 col12\" >22.49</td>\n",
+ " <td id=\"T_f4359_row8_col13\" class=\"data row8 col13\" >6.90</td>\n",
+ " <td id=\"T_f4359_row8_col14\" class=\"data row8 col14\" >0.48</td>\n",
+ " <td id=\"T_f4359_row8_col15\" class=\"data row8 col15\" >0.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_f4359_row9_col0\" class=\"data row9 col0\" >32.89</td>\n",
+ " <td id=\"T_f4359_row9_col1\" class=\"data row9 col1\" >3.19</td>\n",
+ " <td id=\"T_f4359_row9_col2\" class=\"data row9 col2\" >30.07</td>\n",
+ " <td id=\"T_f4359_row9_col3\" class=\"data row9 col3\" >4.00</td>\n",
+ " <td id=\"T_f4359_row9_col4\" class=\"data row9 col4\" >89.69</td>\n",
+ " <td id=\"T_f4359_row9_col5\" class=\"data row9 col5\" >3.96</td>\n",
+ " <td id=\"T_f4359_row9_col6\" class=\"data row9 col6\" >97.28</td>\n",
+ " <td id=\"T_f4359_row9_col7\" class=\"data row9 col7\" >2.81</td>\n",
+ " <td id=\"T_f4359_row9_col8\" class=\"data row9 col8\" >1.42</td>\n",
+ " <td id=\"T_f4359_row9_col9\" class=\"data row9 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row9_col10\" class=\"data row9 col10\" >6.66</td>\n",
+ " <td id=\"T_f4359_row9_col11\" class=\"data row9 col11\" >2.02</td>\n",
+ " <td id=\"T_f4359_row9_col12\" class=\"data row9 col12\" >22.02</td>\n",
+ " <td id=\"T_f4359_row9_col13\" class=\"data row9 col13\" >6.24</td>\n",
+ " <td id=\"T_f4359_row9_col14\" class=\"data row9 col14\" >0.48</td>\n",
+ " <td id=\"T_f4359_row9_col15\" class=\"data row9 col15\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_f4359_row10_col0\" class=\"data row10 col0\" >5.25</td>\n",
+ " <td id=\"T_f4359_row10_col1\" class=\"data row10 col1\" >1.53</td>\n",
+ " <td id=\"T_f4359_row10_col2\" class=\"data row10 col2\" >8.26</td>\n",
+ " <td id=\"T_f4359_row10_col3\" class=\"data row10 col3\" >2.36</td>\n",
+ " <td id=\"T_f4359_row10_col4\" class=\"data row10 col4\" >77.59</td>\n",
+ " <td id=\"T_f4359_row10_col5\" class=\"data row10 col5\" >9.92</td>\n",
+ " <td id=\"T_f4359_row10_col6\" class=\"data row10 col6\" >93.91</td>\n",
+ " <td id=\"T_f4359_row10_col7\" class=\"data row10 col7\" >10.66</td>\n",
+ " <td id=\"T_f4359_row10_col8\" class=\"data row10 col8\" >1.28</td>\n",
+ " <td id=\"T_f4359_row10_col9\" class=\"data row10 col9\" >0.22</td>\n",
+ " <td id=\"T_f4359_row10_col10\" class=\"data row10 col10\" >5.00</td>\n",
+ " <td id=\"T_f4359_row10_col11\" class=\"data row10 col11\" >1.28</td>\n",
+ " <td id=\"T_f4359_row10_col12\" class=\"data row10 col12\" >82.96</td>\n",
+ " <td id=\"T_f4359_row10_col13\" class=\"data row10 col13\" >4.43</td>\n",
+ " <td id=\"T_f4359_row10_col14\" class=\"data row10 col14\" >0.36</td>\n",
+ " <td id=\"T_f4359_row10_col15\" class=\"data row10 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_f4359_row11_col0\" class=\"data row11 col0\" >5.45</td>\n",
+ " <td id=\"T_f4359_row11_col1\" class=\"data row11 col1\" >1.84</td>\n",
+ " <td id=\"T_f4359_row11_col2\" class=\"data row11 col2\" >8.40</td>\n",
+ " <td id=\"T_f4359_row11_col3\" class=\"data row11 col3\" >2.17</td>\n",
+ " <td id=\"T_f4359_row11_col4\" class=\"data row11 col4\" >79.95</td>\n",
+ " <td id=\"T_f4359_row11_col5\" class=\"data row11 col5\" >9.52</td>\n",
+ " <td id=\"T_f4359_row11_col6\" class=\"data row11 col6\" >94.76</td>\n",
+ " <td id=\"T_f4359_row11_col7\" class=\"data row11 col7\" >8.13</td>\n",
+ " <td id=\"T_f4359_row11_col8\" class=\"data row11 col8\" >1.24</td>\n",
+ " <td id=\"T_f4359_row11_col9\" class=\"data row11 col9\" >0.21</td>\n",
+ " <td id=\"T_f4359_row11_col10\" class=\"data row11 col10\" >5.22</td>\n",
+ " <td id=\"T_f4359_row11_col11\" class=\"data row11 col11\" >1.45</td>\n",
+ " <td id=\"T_f4359_row11_col12\" class=\"data row11 col12\" >82.33</td>\n",
+ " <td id=\"T_f4359_row11_col13\" class=\"data row11 col13\" >4.75</td>\n",
+ " <td id=\"T_f4359_row11_col14\" class=\"data row11 col14\" >0.38</td>\n",
+ " <td id=\"T_f4359_row11_col15\" class=\"data row11 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">75% aggre</th>\n",
+ " <th id=\"T_f4359_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_f4359_row12_col0\" class=\"data row12 col0\" >24.18</td>\n",
+ " <td id=\"T_f4359_row12_col1\" class=\"data row12 col1\" >3.27</td>\n",
+ " <td id=\"T_f4359_row12_col2\" class=\"data row12 col2\" >23.83</td>\n",
+ " <td id=\"T_f4359_row12_col3\" class=\"data row12 col3\" >3.22</td>\n",
+ " <td id=\"T_f4359_row12_col4\" class=\"data row12 col4\" >90.00</td>\n",
+ " <td id=\"T_f4359_row12_col5\" class=\"data row12 col5\" >3.96</td>\n",
+ " <td id=\"T_f4359_row12_col6\" class=\"data row12 col6\" >97.12</td>\n",
+ " <td id=\"T_f4359_row12_col7\" class=\"data row12 col7\" >3.90</td>\n",
+ " <td id=\"T_f4359_row12_col8\" class=\"data row12 col8\" >1.27</td>\n",
+ " <td id=\"T_f4359_row12_col9\" class=\"data row12 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row12_col10\" class=\"data row12 col10\" >2.22</td>\n",
+ " <td id=\"T_f4359_row12_col11\" class=\"data row12 col11\" >0.91</td>\n",
+ " <td id=\"T_f4359_row12_col12\" class=\"data row12 col12\" >39.49</td>\n",
+ " <td id=\"T_f4359_row12_col13\" class=\"data row12 col13\" >6.87</td>\n",
+ " <td id=\"T_f4359_row12_col14\" class=\"data row12 col14\" >0.16</td>\n",
+ " <td id=\"T_f4359_row12_col15\" class=\"data row12 col15\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_f4359_row13_col0\" class=\"data row13 col0\" >24.36</td>\n",
+ " <td id=\"T_f4359_row13_col1\" class=\"data row13 col1\" >3.41</td>\n",
+ " <td id=\"T_f4359_row13_col2\" class=\"data row13 col2\" >23.87</td>\n",
+ " <td id=\"T_f4359_row13_col3\" class=\"data row13 col3\" >3.46</td>\n",
+ " <td id=\"T_f4359_row13_col4\" class=\"data row13 col4\" >90.22</td>\n",
+ " <td id=\"T_f4359_row13_col5\" class=\"data row13 col5\" >4.92</td>\n",
+ " <td id=\"T_f4359_row13_col6\" class=\"data row13 col6\" >97.30</td>\n",
+ " <td id=\"T_f4359_row13_col7\" class=\"data row13 col7\" >3.09</td>\n",
+ " <td id=\"T_f4359_row13_col8\" class=\"data row13 col8\" >1.28</td>\n",
+ " <td id=\"T_f4359_row13_col9\" class=\"data row13 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row13_col10\" class=\"data row13 col10\" >1.93</td>\n",
+ " <td id=\"T_f4359_row13_col11\" class=\"data row13 col11\" >0.84</td>\n",
+ " <td id=\"T_f4359_row13_col12\" class=\"data row13 col12\" >41.01</td>\n",
+ " <td id=\"T_f4359_row13_col13\" class=\"data row13 col13\" >6.67</td>\n",
+ " <td id=\"T_f4359_row13_col14\" class=\"data row13 col14\" >0.14</td>\n",
+ " <td id=\"T_f4359_row13_col15\" class=\"data row13 col15\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_f4359_row14_col0\" class=\"data row14 col0\" >23.84</td>\n",
+ " <td id=\"T_f4359_row14_col1\" class=\"data row14 col1\" >3.54</td>\n",
+ " <td id=\"T_f4359_row14_col2\" class=\"data row14 col2\" >23.35</td>\n",
+ " <td id=\"T_f4359_row14_col3\" class=\"data row14 col3\" >3.01</td>\n",
+ " <td id=\"T_f4359_row14_col4\" class=\"data row14 col4\" >90.59</td>\n",
+ " <td id=\"T_f4359_row14_col5\" class=\"data row14 col5\" >4.42</td>\n",
+ " <td id=\"T_f4359_row14_col6\" class=\"data row14 col6\" >97.55</td>\n",
+ " <td id=\"T_f4359_row14_col7\" class=\"data row14 col7\" >2.83</td>\n",
+ " <td id=\"T_f4359_row14_col8\" class=\"data row14 col8\" >1.27</td>\n",
+ " <td id=\"T_f4359_row14_col9\" class=\"data row14 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row14_col10\" class=\"data row14 col10\" >1.88</td>\n",
+ " <td id=\"T_f4359_row14_col11\" class=\"data row14 col11\" >0.78</td>\n",
+ " <td id=\"T_f4359_row14_col12\" class=\"data row14 col12\" >39.28</td>\n",
+ " <td id=\"T_f4359_row14_col13\" class=\"data row14 col13\" >7.49</td>\n",
+ " <td id=\"T_f4359_row14_col14\" class=\"data row14 col14\" >0.14</td>\n",
+ " <td id=\"T_f4359_row14_col15\" class=\"data row14 col15\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_f4359_row15_col0\" class=\"data row15 col0\" >3.58</td>\n",
+ " <td id=\"T_f4359_row15_col1\" class=\"data row15 col1\" >1.55</td>\n",
+ " <td id=\"T_f4359_row15_col2\" class=\"data row15 col2\" >5.69</td>\n",
+ " <td id=\"T_f4359_row15_col3\" class=\"data row15 col3\" >2.03</td>\n",
+ " <td id=\"T_f4359_row15_col4\" class=\"data row15 col4\" >72.24</td>\n",
+ " <td id=\"T_f4359_row15_col5\" class=\"data row15 col5\" >12.95</td>\n",
+ " <td id=\"T_f4359_row15_col6\" class=\"data row15 col6\" >92.87</td>\n",
+ " <td id=\"T_f4359_row15_col7\" class=\"data row15 col7\" >15.31</td>\n",
+ " <td id=\"T_f4359_row15_col8\" class=\"data row15 col8\" >1.28</td>\n",
+ " <td id=\"T_f4359_row15_col9\" class=\"data row15 col9\" >0.29</td>\n",
+ " <td id=\"T_f4359_row15_col10\" class=\"data row15 col10\" >3.19</td>\n",
+ " <td id=\"T_f4359_row15_col11\" class=\"data row15 col11\" >1.17</td>\n",
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+ " <td id=\"T_f4359_row15_col14\" class=\"data row15 col14\" >0.23</td>\n",
+ " <td id=\"T_f4359_row15_col15\" class=\"data row15 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f4359_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">all aggre</th>\n",
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+ " <td id=\"T_f4359_row16_col0\" class=\"data row16 col0\" >18.45</td>\n",
+ " <td id=\"T_f4359_row16_col1\" class=\"data row16 col1\" >2.98</td>\n",
+ " <td id=\"T_f4359_row16_col2\" class=\"data row16 col2\" >19.43</td>\n",
+ " <td id=\"T_f4359_row16_col3\" class=\"data row16 col3\" >2.93</td>\n",
+ " <td id=\"T_f4359_row16_col4\" class=\"data row16 col4\" >89.30</td>\n",
+ " <td id=\"T_f4359_row16_col5\" class=\"data row16 col5\" >4.75</td>\n",
+ " <td id=\"T_f4359_row16_col6\" class=\"data row16 col6\" >96.91</td>\n",
+ " <td id=\"T_f4359_row16_col7\" class=\"data row16 col7\" >3.21</td>\n",
+ " <td id=\"T_f4359_row16_col8\" class=\"data row16 col8\" >1.21</td>\n",
+ " <td id=\"T_f4359_row16_col9\" class=\"data row16 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row16_col10\" class=\"data row16 col10\" >0.87</td>\n",
+ " <td id=\"T_f4359_row16_col11\" class=\"data row16 col11\" >0.51</td>\n",
+ " <td id=\"T_f4359_row16_col12\" class=\"data row16 col12\" >51.68</td>\n",
+ " <td id=\"T_f4359_row16_col13\" class=\"data row16 col13\" >6.48</td>\n",
+ " <td id=\"T_f4359_row16_col14\" class=\"data row16 col14\" >0.06</td>\n",
+ " <td id=\"T_f4359_row16_col15\" class=\"data row16 col15\" >0.04</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_f4359_row17_col0\" class=\"data row17 col0\" >19.19</td>\n",
+ " <td id=\"T_f4359_row17_col1\" class=\"data row17 col1\" >2.81</td>\n",
+ " <td id=\"T_f4359_row17_col2\" class=\"data row17 col2\" >18.40</td>\n",
+ " <td id=\"T_f4359_row17_col3\" class=\"data row17 col3\" >2.75</td>\n",
+ " <td id=\"T_f4359_row17_col4\" class=\"data row17 col4\" >90.11</td>\n",
+ " <td id=\"T_f4359_row17_col5\" class=\"data row17 col5\" >4.07</td>\n",
+ " <td id=\"T_f4359_row17_col6\" class=\"data row17 col6\" >97.07</td>\n",
+ " <td id=\"T_f4359_row17_col7\" class=\"data row17 col7\" >4.15</td>\n",
+ " <td id=\"T_f4359_row17_col8\" class=\"data row17 col8\" >1.20</td>\n",
+ " <td id=\"T_f4359_row17_col9\" class=\"data row17 col9\" >0.11</td>\n",
+ " <td id=\"T_f4359_row17_col10\" class=\"data row17 col10\" >0.88</td>\n",
+ " <td id=\"T_f4359_row17_col11\" class=\"data row17 col11\" >0.61</td>\n",
+ " <td id=\"T_f4359_row17_col12\" class=\"data row17 col12\" >52.60</td>\n",
+ " <td id=\"T_f4359_row17_col13\" class=\"data row17 col13\" >6.96</td>\n",
+ " <td id=\"T_f4359_row17_col14\" class=\"data row17 col14\" >0.06</td>\n",
+ " <td id=\"T_f4359_row17_col15\" class=\"data row17 col15\" >0.04</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_f4359_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_f4359_row18_col0\" class=\"data row18 col0\" >19.06</td>\n",
+ " <td id=\"T_f4359_row18_col1\" class=\"data row18 col1\" >2.98</td>\n",
+ " <td id=\"T_f4359_row18_col2\" class=\"data row18 col2\" >18.89</td>\n",
+ " <td id=\"T_f4359_row18_col3\" class=\"data row18 col3\" >2.97</td>\n",
+ " <td id=\"T_f4359_row18_col4\" class=\"data row18 col4\" >89.70</td>\n",
+ " <td id=\"T_f4359_row18_col5\" class=\"data row18 col5\" >4.94</td>\n",
+ " <td id=\"T_f4359_row18_col6\" class=\"data row18 col6\" >97.76</td>\n",
+ " <td id=\"T_f4359_row18_col7\" class=\"data row18 col7\" >3.18</td>\n",
+ " <td id=\"T_f4359_row18_col8\" class=\"data row18 col8\" >1.20</td>\n",
+ " <td id=\"T_f4359_row18_col9\" class=\"data row18 col9\" >0.10</td>\n",
+ " <td id=\"T_f4359_row18_col10\" class=\"data row18 col10\" >0.89</td>\n",
+ " <td id=\"T_f4359_row18_col11\" class=\"data row18 col11\" >0.53</td>\n",
+ " <td id=\"T_f4359_row18_col12\" class=\"data row18 col12\" >51.33</td>\n",
+ " <td id=\"T_f4359_row18_col13\" class=\"data row18 col13\" >6.86</td>\n",
+ " <td id=\"T_f4359_row18_col14\" class=\"data row18 col14\" >0.06</td>\n",
+ " <td id=\"T_f4359_row18_col15\" class=\"data row18 col15\" >0.04</td>\n",
+ " </tr>\n",
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+ " <th id=\"T_f4359_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_f4359_row19_col0\" class=\"data row19 col0\" >19.02</td>\n",
+ " <td id=\"T_f4359_row19_col1\" class=\"data row19 col1\" >2.94</td>\n",
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+ " <td id=\"T_f4359_row19_col9\" class=\"data row19 col9\" >0.10</td>\n",
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+ " <td id=\"T_f4359_row19_col15\" class=\"data row19 col15\" >0.04</td>\n",
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+ "<style type=\"text/css\">\n",
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+ " <th id=\"T_f9677_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_f9677_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_f9677_row0_col0\" class=\"data row0 col0\" >20.15</td>\n",
+ " <td id=\"T_f9677_row0_col1\" class=\"data row0 col1\" >3.97</td>\n",
+ " <td id=\"T_f9677_row0_col2\" class=\"data row0 col2\" >20.18</td>\n",
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+ " <td id=\"T_f9677_row0_col7\" class=\"data row0 col7\" >5.44</td>\n",
+ " <td id=\"T_f9677_row0_col8\" class=\"data row0 col8\" >1.12</td>\n",
+ " <td id=\"T_f9677_row0_col9\" class=\"data row0 col9\" >0.11</td>\n",
+ " <td id=\"T_f9677_row0_col10\" class=\"data row0 col10\" >9.18</td>\n",
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+ " <td id=\"T_f9677_row0_col15\" class=\"data row0 col15\" >0.10</td>\n",
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+ " <th id=\"T_f9677_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
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+ " <td id=\"T_f9677_row1_col1\" class=\"data row1 col1\" >4.16</td>\n",
+ " <td id=\"T_f9677_row1_col2\" class=\"data row1 col2\" >19.93</td>\n",
+ " <td id=\"T_f9677_row1_col3\" class=\"data row1 col3\" >4.47</td>\n",
+ " <td id=\"T_f9677_row1_col4\" class=\"data row1 col4\" >90.20</td>\n",
+ " <td id=\"T_f9677_row1_col5\" class=\"data row1 col5\" >7.97</td>\n",
+ " <td id=\"T_f9677_row1_col6\" class=\"data row1 col6\" >98.01</td>\n",
+ " <td id=\"T_f9677_row1_col7\" class=\"data row1 col7\" >4.09</td>\n",
+ " <td id=\"T_f9677_row1_col8\" class=\"data row1 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row1_col9\" class=\"data row1 col9\" >0.11</td>\n",
+ " <td id=\"T_f9677_row1_col10\" class=\"data row1 col10\" >8.85</td>\n",
+ " <td id=\"T_f9677_row1_col11\" class=\"data row1 col11\" >1.69</td>\n",
+ " <td id=\"T_f9677_row1_col12\" class=\"data row1 col12\" >74.23</td>\n",
+ " <td id=\"T_f9677_row1_col13\" class=\"data row1 col13\" >5.21</td>\n",
+ " <td id=\"T_f9677_row1_col14\" class=\"data row1 col14\" >0.64</td>\n",
+ " <td id=\"T_f9677_row1_col15\" class=\"data row1 col15\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_f9677_row2_col0\" class=\"data row2 col0\" >19.43</td>\n",
+ " <td id=\"T_f9677_row2_col1\" class=\"data row2 col1\" >4.59</td>\n",
+ " <td id=\"T_f9677_row2_col2\" class=\"data row2 col2\" >19.95</td>\n",
+ " <td id=\"T_f9677_row2_col3\" class=\"data row2 col3\" >4.23</td>\n",
+ " <td id=\"T_f9677_row2_col4\" class=\"data row2 col4\" >89.63</td>\n",
+ " <td id=\"T_f9677_row2_col5\" class=\"data row2 col5\" >6.50</td>\n",
+ " <td id=\"T_f9677_row2_col6\" class=\"data row2 col6\" >97.95</td>\n",
+ " <td id=\"T_f9677_row2_col7\" class=\"data row2 col7\" >4.04</td>\n",
+ " <td id=\"T_f9677_row2_col8\" class=\"data row2 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row2_col9\" class=\"data row2 col9\" >0.15</td>\n",
+ " <td id=\"T_f9677_row2_col10\" class=\"data row2 col10\" >8.91</td>\n",
+ " <td id=\"T_f9677_row2_col11\" class=\"data row2 col11\" >1.69</td>\n",
+ " <td id=\"T_f9677_row2_col12\" class=\"data row2 col12\" >74.48</td>\n",
+ " <td id=\"T_f9677_row2_col13\" class=\"data row2 col13\" >5.54</td>\n",
+ " <td id=\"T_f9677_row2_col14\" class=\"data row2 col14\" >0.64</td>\n",
+ " <td id=\"T_f9677_row2_col15\" class=\"data row2 col15\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_f9677_row3_col0\" class=\"data row3 col0\" >19.27</td>\n",
+ " <td id=\"T_f9677_row3_col1\" class=\"data row3 col1\" >3.87</td>\n",
+ " <td id=\"T_f9677_row3_col2\" class=\"data row3 col2\" >19.51</td>\n",
+ " <td id=\"T_f9677_row3_col3\" class=\"data row3 col3\" >3.67</td>\n",
+ " <td id=\"T_f9677_row3_col4\" class=\"data row3 col4\" >90.08</td>\n",
+ " <td id=\"T_f9677_row3_col5\" class=\"data row3 col5\" >7.41</td>\n",
+ " <td id=\"T_f9677_row3_col6\" class=\"data row3 col6\" >95.82</td>\n",
+ " <td id=\"T_f9677_row3_col7\" class=\"data row3 col7\" >8.17</td>\n",
+ " <td id=\"T_f9677_row3_col8\" class=\"data row3 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+ " <td id=\"T_f9677_row3_col10\" class=\"data row3 col10\" >8.82</td>\n",
+ " <td id=\"T_f9677_row3_col11\" class=\"data row3 col11\" >1.45</td>\n",
+ " <td id=\"T_f9677_row3_col12\" class=\"data row3 col12\" >74.73</td>\n",
+ " <td id=\"T_f9677_row3_col13\" class=\"data row3 col13\" >4.55</td>\n",
+ " <td id=\"T_f9677_row3_col14\" class=\"data row3 col14\" >0.64</td>\n",
+ " <td id=\"T_f9677_row3_col15\" class=\"data row3 col15\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% aggre</th>\n",
+ " <th id=\"T_f9677_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_f9677_row4_col0\" class=\"data row4 col0\" >60.94</td>\n",
+ " <td id=\"T_f9677_row4_col1\" class=\"data row4 col1\" >5.21</td>\n",
+ " <td id=\"T_f9677_row4_col2\" class=\"data row4 col2\" >51.84</td>\n",
+ " <td id=\"T_f9677_row4_col3\" class=\"data row4 col3\" >5.48</td>\n",
+ " <td id=\"T_f9677_row4_col4\" class=\"data row4 col4\" >90.39</td>\n",
+ " <td id=\"T_f9677_row4_col5\" class=\"data row4 col5\" >4.02</td>\n",
+ " <td id=\"T_f9677_row4_col6\" class=\"data row4 col6\" >96.87</td>\n",
+ " <td id=\"T_f9677_row4_col7\" class=\"data row4 col7\" >3.35</td>\n",
+ " <td id=\"T_f9677_row4_col8\" class=\"data row4 col8\" >1.40</td>\n",
+ " <td id=\"T_f9677_row4_col9\" class=\"data row4 col9\" >0.10</td>\n",
+ " <td id=\"T_f9677_row4_col10\" class=\"data row4 col10\" >25.62</td>\n",
+ " <td id=\"T_f9677_row4_col11\" class=\"data row4 col11\" >2.41</td>\n",
+ " <td id=\"T_f9677_row4_col12\" class=\"data row4 col12\" >28.77</td>\n",
+ " <td id=\"T_f9677_row4_col13\" class=\"data row4 col13\" >5.71</td>\n",
+ " <td id=\"T_f9677_row4_col14\" class=\"data row4 col14\" >1.85</td>\n",
+ " <td id=\"T_f9677_row4_col15\" class=\"data row4 col15\" >0.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_f9677_row5_col0\" class=\"data row5 col0\" >5.92</td>\n",
+ " <td id=\"T_f9677_row5_col1\" class=\"data row5 col1\" >2.34</td>\n",
+ " <td id=\"T_f9677_row5_col2\" class=\"data row5 col2\" >9.34</td>\n",
+ " <td id=\"T_f9677_row5_col3\" class=\"data row5 col3\" >3.40</td>\n",
+ " <td id=\"T_f9677_row5_col4\" class=\"data row5 col4\" >80.62</td>\n",
+ " <td id=\"T_f9677_row5_col5\" class=\"data row5 col5\" >13.74</td>\n",
+ " <td id=\"T_f9677_row5_col6\" class=\"data row5 col6\" >94.54</td>\n",
+ " <td id=\"T_f9677_row5_col7\" class=\"data row5 col7\" >13.60</td>\n",
+ " <td id=\"T_f9677_row5_col8\" class=\"data row5 col8\" >1.14</td>\n",
+ " <td id=\"T_f9677_row5_col9\" class=\"data row5 col9\" >0.19</td>\n",
+ " <td id=\"T_f9677_row5_col10\" class=\"data row5 col10\" >3.10</td>\n",
+ " <td id=\"T_f9677_row5_col11\" class=\"data row5 col11\" >0.99</td>\n",
+ " <td id=\"T_f9677_row5_col12\" class=\"data row5 col12\" >89.88</td>\n",
+ " <td id=\"T_f9677_row5_col13\" class=\"data row5 col13\" >3.64</td>\n",
+ " <td id=\"T_f9677_row5_col14\" class=\"data row5 col14\" >0.22</td>\n",
+ " <td id=\"T_f9677_row5_col15\" class=\"data row5 col15\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_f9677_row6_col0\" class=\"data row6 col0\" >6.00</td>\n",
+ " <td id=\"T_f9677_row6_col1\" class=\"data row6 col1\" >2.41</td>\n",
+ " <td id=\"T_f9677_row6_col2\" class=\"data row6 col2\" >9.05</td>\n",
+ " <td id=\"T_f9677_row6_col3\" class=\"data row6 col3\" >3.14</td>\n",
+ " <td id=\"T_f9677_row6_col4\" class=\"data row6 col4\" >81.56</td>\n",
+ " <td id=\"T_f9677_row6_col5\" class=\"data row6 col5\" >12.03</td>\n",
+ " <td id=\"T_f9677_row6_col6\" class=\"data row6 col6\" >96.31</td>\n",
+ " <td id=\"T_f9677_row6_col7\" class=\"data row6 col7\" >9.29</td>\n",
+ " <td id=\"T_f9677_row6_col8\" class=\"data row6 col8\" >1.11</td>\n",
+ " <td id=\"T_f9677_row6_col9\" class=\"data row6 col9\" >0.14</td>\n",
+ " <td id=\"T_f9677_row6_col10\" class=\"data row6 col10\" >2.99</td>\n",
+ " <td id=\"T_f9677_row6_col11\" class=\"data row6 col11\" >1.00</td>\n",
+ " <td id=\"T_f9677_row6_col12\" class=\"data row6 col12\" >90.37</td>\n",
+ " <td id=\"T_f9677_row6_col13\" class=\"data row6 col13\" >3.30</td>\n",
+ " <td id=\"T_f9677_row6_col14\" class=\"data row6 col14\" >0.22</td>\n",
+ " <td id=\"T_f9677_row6_col15\" class=\"data row6 col15\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_f9677_row7_col0\" class=\"data row7 col0\" >5.88</td>\n",
+ " <td id=\"T_f9677_row7_col1\" class=\"data row7 col1\" >2.71</td>\n",
+ " <td id=\"T_f9677_row7_col2\" class=\"data row7 col2\" >9.64</td>\n",
+ " <td id=\"T_f9677_row7_col3\" class=\"data row7 col3\" >3.38</td>\n",
+ " <td id=\"T_f9677_row7_col4\" class=\"data row7 col4\" >82.58</td>\n",
+ " <td id=\"T_f9677_row7_col5\" class=\"data row7 col5\" >13.44</td>\n",
+ " <td id=\"T_f9677_row7_col6\" class=\"data row7 col6\" >91.62</td>\n",
+ " <td id=\"T_f9677_row7_col7\" class=\"data row7 col7\" >16.58</td>\n",
+ " <td id=\"T_f9677_row7_col8\" class=\"data row7 col8\" >1.21</td>\n",
+ " <td id=\"T_f9677_row7_col9\" class=\"data row7 col9\" >0.32</td>\n",
+ " <td id=\"T_f9677_row7_col10\" class=\"data row7 col10\" >3.06</td>\n",
+ " <td id=\"T_f9677_row7_col11\" class=\"data row7 col11\" >1.14</td>\n",
+ " <td id=\"T_f9677_row7_col12\" class=\"data row7 col12\" >90.18</td>\n",
+ " <td id=\"T_f9677_row7_col13\" class=\"data row7 col13\" >3.84</td>\n",
+ " <td id=\"T_f9677_row7_col14\" class=\"data row7 col14\" >0.22</td>\n",
+ " <td id=\"T_f9677_row7_col15\" class=\"data row7 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">50% aggre</th>\n",
+ " <th id=\"T_f9677_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_f9677_row8_col0\" class=\"data row8 col0\" >36.79</td>\n",
+ " <td id=\"T_f9677_row8_col1\" class=\"data row8 col1\" >5.23</td>\n",
+ " <td id=\"T_f9677_row8_col2\" class=\"data row8 col2\" >35.55</td>\n",
+ " <td id=\"T_f9677_row8_col3\" class=\"data row8 col3\" >4.92</td>\n",
+ " <td id=\"T_f9677_row8_col4\" class=\"data row8 col4\" >90.23</td>\n",
+ " <td id=\"T_f9677_row8_col5\" class=\"data row8 col5\" >5.06</td>\n",
+ " <td id=\"T_f9677_row8_col6\" class=\"data row8 col6\" >98.04</td>\n",
+ " <td id=\"T_f9677_row8_col7\" class=\"data row8 col7\" >2.31</td>\n",
+ " <td id=\"T_f9677_row8_col8\" class=\"data row8 col8\" >1.21</td>\n",
+ " <td id=\"T_f9677_row8_col9\" class=\"data row8 col9\" >0.10</td>\n",
+ " <td id=\"T_f9677_row8_col10\" class=\"data row8 col10\" >2.39</td>\n",
+ " <td id=\"T_f9677_row8_col11\" class=\"data row8 col11\" >1.08</td>\n",
+ " <td id=\"T_f9677_row8_col12\" class=\"data row8 col12\" >53.22</td>\n",
+ " <td id=\"T_f9677_row8_col13\" class=\"data row8 col13\" >6.04</td>\n",
+ " <td id=\"T_f9677_row8_col14\" class=\"data row8 col14\" >0.17</td>\n",
+ " <td id=\"T_f9677_row8_col15\" class=\"data row8 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_f9677_row9_col0\" class=\"data row9 col0\" >36.54</td>\n",
+ " <td id=\"T_f9677_row9_col1\" class=\"data row9 col1\" >5.02</td>\n",
+ " <td id=\"T_f9677_row9_col2\" class=\"data row9 col2\" >35.73</td>\n",
+ " <td id=\"T_f9677_row9_col3\" class=\"data row9 col3\" >5.20</td>\n",
+ " <td id=\"T_f9677_row9_col4\" class=\"data row9 col4\" >91.11</td>\n",
+ " <td id=\"T_f9677_row9_col5\" class=\"data row9 col5\" >4.66</td>\n",
+ " <td id=\"T_f9677_row9_col6\" class=\"data row9 col6\" >97.27</td>\n",
+ " <td id=\"T_f9677_row9_col7\" class=\"data row9 col7\" >4.18</td>\n",
+ " <td id=\"T_f9677_row9_col8\" class=\"data row9 col8\" >1.22</td>\n",
+ " <td id=\"T_f9677_row9_col9\" class=\"data row9 col9\" >0.10</td>\n",
+ " <td id=\"T_f9677_row9_col10\" class=\"data row9 col10\" >2.61</td>\n",
+ " <td id=\"T_f9677_row9_col11\" class=\"data row9 col11\" >1.20</td>\n",
+ " <td id=\"T_f9677_row9_col12\" class=\"data row9 col12\" >53.70</td>\n",
+ " <td id=\"T_f9677_row9_col13\" class=\"data row9 col13\" >6.15</td>\n",
+ " <td id=\"T_f9677_row9_col14\" class=\"data row9 col14\" >0.19</td>\n",
+ " <td id=\"T_f9677_row9_col15\" class=\"data row9 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_f9677_row10_col0\" class=\"data row10 col0\" >2.55</td>\n",
+ " <td id=\"T_f9677_row10_col1\" class=\"data row10 col1\" >1.71</td>\n",
+ " <td id=\"T_f9677_row10_col2\" class=\"data row10 col2\" >4.38</td>\n",
+ " <td id=\"T_f9677_row10_col3\" class=\"data row10 col3\" >2.10</td>\n",
+ " <td id=\"T_f9677_row10_col4\" class=\"data row10 col4\" >64.98</td>\n",
+ " <td id=\"T_f9677_row10_col5\" class=\"data row10 col5\" >25.36</td>\n",
+ " <td id=\"T_f9677_row10_col6\" class=\"data row10 col6\" >81.26</td>\n",
+ " <td id=\"T_f9677_row10_col7\" class=\"data row10 col7\" >35.09</td>\n",
+ " <td id=\"T_f9677_row10_col8\" class=\"data row10 col8\" >1.17</td>\n",
+ " <td id=\"T_f9677_row10_col9\" class=\"data row10 col9\" >0.32</td>\n",
+ " <td id=\"T_f9677_row10_col10\" class=\"data row10 col10\" >1.16</td>\n",
+ " <td id=\"T_f9677_row10_col11\" class=\"data row10 col11\" >0.64</td>\n",
+ " <td id=\"T_f9677_row10_col12\" class=\"data row10 col12\" >95.54</td>\n",
+ " <td id=\"T_f9677_row10_col13\" class=\"data row10 col13\" >2.58</td>\n",
+ " <td id=\"T_f9677_row10_col14\" class=\"data row10 col14\" >0.08</td>\n",
+ " <td id=\"T_f9677_row10_col15\" class=\"data row10 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_f9677_row11_col0\" class=\"data row11 col0\" >2.85</td>\n",
+ " <td id=\"T_f9677_row11_col1\" class=\"data row11 col1\" >1.84</td>\n",
+ " <td id=\"T_f9677_row11_col2\" class=\"data row11 col2\" >4.19</td>\n",
+ " <td id=\"T_f9677_row11_col3\" class=\"data row11 col3\" >2.59</td>\n",
+ " <td id=\"T_f9677_row11_col4\" class=\"data row11 col4\" >67.02</td>\n",
+ " <td id=\"T_f9677_row11_col5\" class=\"data row11 col5\" >27.19</td>\n",
+ " <td id=\"T_f9677_row11_col6\" class=\"data row11 col6\" >79.70</td>\n",
+ " <td id=\"T_f9677_row11_col7\" class=\"data row11 col7\" >36.24</td>\n",
+ " <td id=\"T_f9677_row11_col8\" class=\"data row11 col8\" >1.21</td>\n",
+ " <td id=\"T_f9677_row11_col9\" class=\"data row11 col9\" >0.50</td>\n",
+ " <td id=\"T_f9677_row11_col10\" class=\"data row11 col10\" >1.24</td>\n",
+ " <td id=\"T_f9677_row11_col11\" class=\"data row11 col11\" >0.73</td>\n",
+ " <td id=\"T_f9677_row11_col12\" class=\"data row11 col12\" >95.62</td>\n",
+ " <td id=\"T_f9677_row11_col13\" class=\"data row11 col13\" >2.32</td>\n",
+ " <td id=\"T_f9677_row11_col14\" class=\"data row11 col14\" >0.09</td>\n",
+ " <td id=\"T_f9677_row11_col15\" class=\"data row11 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">75% aggre</th>\n",
+ " <th id=\"T_f9677_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_f9677_row12_col0\" class=\"data row12 col0\" >25.91</td>\n",
+ " <td id=\"T_f9677_row12_col1\" class=\"data row12 col1\" >4.83</td>\n",
+ " <td id=\"T_f9677_row12_col2\" class=\"data row12 col2\" >25.48</td>\n",
+ " <td id=\"T_f9677_row12_col3\" class=\"data row12 col3\" >4.99</td>\n",
+ " <td id=\"T_f9677_row12_col4\" class=\"data row12 col4\" >90.24</td>\n",
+ " <td id=\"T_f9677_row12_col5\" class=\"data row12 col5\" >6.98</td>\n",
+ " <td id=\"T_f9677_row12_col6\" class=\"data row12 col6\" >97.64</td>\n",
+ " <td id=\"T_f9677_row12_col7\" class=\"data row12 col7\" >3.99</td>\n",
+ " <td id=\"T_f9677_row12_col8\" class=\"data row12 col8\" >1.16</td>\n",
+ " <td id=\"T_f9677_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+ " <td id=\"T_f9677_row12_col10\" class=\"data row12 col10\" >0.64</td>\n",
+ " <td id=\"T_f9677_row12_col11\" class=\"data row12 col11\" >0.48</td>\n",
+ " <td id=\"T_f9677_row12_col12\" class=\"data row12 col12\" >66.10</td>\n",
+ " <td id=\"T_f9677_row12_col13\" class=\"data row12 col13\" >6.57</td>\n",
+ " <td id=\"T_f9677_row12_col14\" class=\"data row12 col14\" >0.05</td>\n",
+ " <td id=\"T_f9677_row12_col15\" class=\"data row12 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_f9677_row13_col0\" class=\"data row13 col0\" >25.52</td>\n",
+ " <td id=\"T_f9677_row13_col1\" class=\"data row13 col1\" >4.12</td>\n",
+ " <td id=\"T_f9677_row13_col2\" class=\"data row13 col2\" >25.82</td>\n",
+ " <td id=\"T_f9677_row13_col3\" class=\"data row13 col3\" >4.59</td>\n",
+ " <td id=\"T_f9677_row13_col4\" class=\"data row13 col4\" >90.99</td>\n",
+ " <td id=\"T_f9677_row13_col5\" class=\"data row13 col5\" >5.20</td>\n",
+ " <td id=\"T_f9677_row13_col6\" class=\"data row13 col6\" >97.17</td>\n",
+ " <td id=\"T_f9677_row13_col7\" class=\"data row13 col7\" >5.41</td>\n",
+ " <td id=\"T_f9677_row13_col8\" class=\"data row13 col8\" >1.16</td>\n",
+ " <td id=\"T_f9677_row13_col9\" class=\"data row13 col9\" >0.10</td>\n",
+ " <td id=\"T_f9677_row13_col10\" class=\"data row13 col10\" >0.62</td>\n",
+ " <td id=\"T_f9677_row13_col11\" class=\"data row13 col11\" >0.48</td>\n",
+ " <td id=\"T_f9677_row13_col12\" class=\"data row13 col12\" >66.28</td>\n",
+ " <td id=\"T_f9677_row13_col13\" class=\"data row13 col13\" >5.89</td>\n",
+ " <td id=\"T_f9677_row13_col14\" class=\"data row13 col14\" >0.04</td>\n",
+ " <td id=\"T_f9677_row13_col15\" class=\"data row13 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_f9677_row14_col0\" class=\"data row14 col0\" >25.73</td>\n",
+ " <td id=\"T_f9677_row14_col1\" class=\"data row14 col1\" >4.75</td>\n",
+ " <td id=\"T_f9677_row14_col2\" class=\"data row14 col2\" >26.06</td>\n",
+ " <td id=\"T_f9677_row14_col3\" class=\"data row14 col3\" >5.06</td>\n",
+ " <td id=\"T_f9677_row14_col4\" class=\"data row14 col4\" >88.64</td>\n",
+ " <td id=\"T_f9677_row14_col5\" class=\"data row14 col5\" >7.09</td>\n",
+ " <td id=\"T_f9677_row14_col6\" class=\"data row14 col6\" >97.37</td>\n",
+ " <td id=\"T_f9677_row14_col7\" class=\"data row14 col7\" >4.97</td>\n",
+ " <td id=\"T_f9677_row14_col8\" class=\"data row14 col8\" >1.17</td>\n",
+ " <td id=\"T_f9677_row14_col9\" class=\"data row14 col9\" >0.12</td>\n",
+ " <td id=\"T_f9677_row14_col10\" class=\"data row14 col10\" >0.62</td>\n",
+ " <td id=\"T_f9677_row14_col11\" class=\"data row14 col11\" >0.56</td>\n",
+ " <td id=\"T_f9677_row14_col12\" class=\"data row14 col12\" >67.34</td>\n",
+ " <td id=\"T_f9677_row14_col13\" class=\"data row14 col13\" >5.42</td>\n",
+ " <td id=\"T_f9677_row14_col14\" class=\"data row14 col14\" >0.05</td>\n",
+ " <td id=\"T_f9677_row14_col15\" class=\"data row14 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_f9677_row15_col0\" class=\"data row15 col0\" >1.64</td>\n",
+ " <td id=\"T_f9677_row15_col1\" class=\"data row15 col1\" >1.48</td>\n",
+ " <td id=\"T_f9677_row15_col2\" class=\"data row15 col2\" >2.45</td>\n",
+ " <td id=\"T_f9677_row15_col3\" class=\"data row15 col3\" >1.60</td>\n",
+ " <td id=\"T_f9677_row15_col4\" class=\"data row15 col4\" >59.84</td>\n",
+ " <td id=\"T_f9677_row15_col5\" class=\"data row15 col5\" >32.49</td>\n",
+ " <td id=\"T_f9677_row15_col6\" class=\"data row15 col6\" >61.37</td>\n",
+ " <td id=\"T_f9677_row15_col7\" class=\"data row15 col7\" >45.82</td>\n",
+ " <td id=\"T_f9677_row15_col8\" class=\"data row15 col8\" >1.15</td>\n",
+ " <td id=\"T_f9677_row15_col9\" class=\"data row15 col9\" >0.34</td>\n",
+ " <td id=\"T_f9677_row15_col10\" class=\"data row15 col10\" >0.67</td>\n",
+ " <td id=\"T_f9677_row15_col11\" class=\"data row15 col11\" >0.46</td>\n",
+ " <td id=\"T_f9677_row15_col12\" class=\"data row15 col12\" >97.41</td>\n",
+ " <td id=\"T_f9677_row15_col13\" class=\"data row15 col13\" >1.69</td>\n",
+ " <td id=\"T_f9677_row15_col14\" class=\"data row15 col14\" >0.05</td>\n",
+ " <td id=\"T_f9677_row15_col15\" class=\"data row15 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">all aggre</th>\n",
+ " <th id=\"T_f9677_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_f9677_row16_col0\" class=\"data row16 col0\" >20.15</td>\n",
+ " <td id=\"T_f9677_row16_col1\" class=\"data row16 col1\" >3.97</td>\n",
+ " <td id=\"T_f9677_row16_col2\" class=\"data row16 col2\" >20.18</td>\n",
+ " <td id=\"T_f9677_row16_col3\" class=\"data row16 col3\" >3.94</td>\n",
+ " <td id=\"T_f9677_row16_col4\" class=\"data row16 col4\" >89.20</td>\n",
+ " <td id=\"T_f9677_row16_col5\" class=\"data row16 col5\" >6.34</td>\n",
+ " <td id=\"T_f9677_row16_col6\" class=\"data row16 col6\" >97.37</td>\n",
+ " <td id=\"T_f9677_row16_col7\" class=\"data row16 col7\" >5.44</td>\n",
+ " <td id=\"T_f9677_row16_col8\" class=\"data row16 col8\" >1.12</td>\n",
+ " <td id=\"T_f9677_row16_col9\" class=\"data row16 col9\" >0.11</td>\n",
+ " <td id=\"T_f9677_row16_col10\" class=\"data row16 col10\" >0.24</td>\n",
+ " <td id=\"T_f9677_row16_col11\" class=\"data row16 col11\" >0.26</td>\n",
+ " <td id=\"T_f9677_row16_col12\" class=\"data row16 col12\" >73.39</td>\n",
+ " <td id=\"T_f9677_row16_col13\" class=\"data row16 col13\" >5.67</td>\n",
+ " <td id=\"T_f9677_row16_col14\" class=\"data row16 col14\" >0.02</td>\n",
+ " <td id=\"T_f9677_row16_col15\" class=\"data row16 col15\" >0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_f9677_row17_col0\" class=\"data row17 col0\" >19.68</td>\n",
+ " <td id=\"T_f9677_row17_col1\" class=\"data row17 col1\" >4.16</td>\n",
+ " <td id=\"T_f9677_row17_col2\" class=\"data row17 col2\" >19.93</td>\n",
+ " <td id=\"T_f9677_row17_col3\" class=\"data row17 col3\" >4.47</td>\n",
+ " <td id=\"T_f9677_row17_col4\" class=\"data row17 col4\" >90.20</td>\n",
+ " <td id=\"T_f9677_row17_col5\" class=\"data row17 col5\" >7.97</td>\n",
+ " <td id=\"T_f9677_row17_col6\" class=\"data row17 col6\" >98.01</td>\n",
+ " <td id=\"T_f9677_row17_col7\" class=\"data row17 col7\" >4.09</td>\n",
+ " <td id=\"T_f9677_row17_col8\" class=\"data row17 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row17_col9\" class=\"data row17 col9\" >0.11</td>\n",
+ " <td id=\"T_f9677_row17_col10\" class=\"data row17 col10\" >0.19</td>\n",
+ " <td id=\"T_f9677_row17_col11\" class=\"data row17 col11\" >0.26</td>\n",
+ " <td id=\"T_f9677_row17_col12\" class=\"data row17 col12\" >74.23</td>\n",
+ " <td id=\"T_f9677_row17_col13\" class=\"data row17 col13\" >5.21</td>\n",
+ " <td id=\"T_f9677_row17_col14\" class=\"data row17 col14\" >0.01</td>\n",
+ " <td id=\"T_f9677_row17_col15\" class=\"data row17 col15\" >0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_f9677_row18_col0\" class=\"data row18 col0\" >19.43</td>\n",
+ " <td id=\"T_f9677_row18_col1\" class=\"data row18 col1\" >4.59</td>\n",
+ " <td id=\"T_f9677_row18_col2\" class=\"data row18 col2\" >19.95</td>\n",
+ " <td id=\"T_f9677_row18_col3\" class=\"data row18 col3\" >4.23</td>\n",
+ " <td id=\"T_f9677_row18_col4\" class=\"data row18 col4\" >89.63</td>\n",
+ " <td id=\"T_f9677_row18_col5\" class=\"data row18 col5\" >6.50</td>\n",
+ " <td id=\"T_f9677_row18_col6\" class=\"data row18 col6\" >97.95</td>\n",
+ " <td id=\"T_f9677_row18_col7\" class=\"data row18 col7\" >4.04</td>\n",
+ " <td id=\"T_f9677_row18_col8\" class=\"data row18 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row18_col9\" class=\"data row18 col9\" >0.15</td>\n",
+ " <td id=\"T_f9677_row18_col10\" class=\"data row18 col10\" >0.22</td>\n",
+ " <td id=\"T_f9677_row18_col11\" class=\"data row18 col11\" >0.24</td>\n",
+ " <td id=\"T_f9677_row18_col12\" class=\"data row18 col12\" >74.48</td>\n",
+ " <td id=\"T_f9677_row18_col13\" class=\"data row18 col13\" >5.54</td>\n",
+ " <td id=\"T_f9677_row18_col14\" class=\"data row18 col14\" >0.02</td>\n",
+ " <td id=\"T_f9677_row18_col15\" class=\"data row18 col15\" >0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f9677_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_f9677_row19_col0\" class=\"data row19 col0\" >19.27</td>\n",
+ " <td id=\"T_f9677_row19_col1\" class=\"data row19 col1\" >3.87</td>\n",
+ " <td id=\"T_f9677_row19_col2\" class=\"data row19 col2\" >19.51</td>\n",
+ " <td id=\"T_f9677_row19_col3\" class=\"data row19 col3\" >3.67</td>\n",
+ " <td id=\"T_f9677_row19_col4\" class=\"data row19 col4\" >90.08</td>\n",
+ " <td id=\"T_f9677_row19_col5\" class=\"data row19 col5\" >7.41</td>\n",
+ " <td id=\"T_f9677_row19_col6\" class=\"data row19 col6\" >95.82</td>\n",
+ " <td id=\"T_f9677_row19_col7\" class=\"data row19 col7\" >8.17</td>\n",
+ " <td id=\"T_f9677_row19_col8\" class=\"data row19 col8\" >1.13</td>\n",
+ " <td id=\"T_f9677_row19_col9\" class=\"data row19 col9\" >0.12</td>\n",
+ " <td id=\"T_f9677_row19_col10\" class=\"data row19 col10\" >0.22</td>\n",
+ " <td id=\"T_f9677_row19_col11\" class=\"data row19 col11\" >0.27</td>\n",
+ " <td id=\"T_f9677_row19_col12\" class=\"data row19 col12\" >74.73</td>\n",
+ " <td id=\"T_f9677_row19_col13\" class=\"data row19 col13\" >4.55</td>\n",
+ " <td id=\"T_f9677_row19_col14\" class=\"data row19 col14\" >0.02</td>\n",
+ " <td id=\"T_f9677_row19_col15\" class=\"data row19 col15\" >0.02</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7a4a85710f50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_consolidate_by_dc()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_5ed03 td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_5ed03 th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_5ed03_row0_col4, #T_5ed03_row0_col6, #T_5ed03_row0_col8, #T_5ed03_row0_col10, #T_5ed03_row0_col12, #T_5ed03_row0_col14, #T_5ed03_row1_col0, #T_5ed03_row1_col2, #T_5ed03_row4_col4, #T_5ed03_row4_col6, #T_5ed03_row4_col8, #T_5ed03_row4_col12 {\n",
+ " background-color: #34FF34;\n",
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+ "<table id=\"T_5ed03\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_5ed03_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bid</th>\n",
+ " <th id=\"T_5ed03_level0_col4\" class=\"col_heading level0 col4\" colspan=\"6\">QoS</th>\n",
+ " <th id=\"T_5ed03_level0_col10\" class=\"col_heading level0 col10\" colspan=\"6\">Money/Energy</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_5ed03_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Tasks</th>\n",
+ " <th id=\"T_5ed03_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " <th id=\"T_5ed03_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_5ed03_level1_col6\" class=\"col_heading level1 col6\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_5ed03_level1_col8\" class=\"col_heading level1 col8\" colspan=\"2\">Slowdown</th>\n",
+ " <th id=\"T_5ed03_level1_col10\" class=\"col_heading level1 col10\" colspan=\"2\">Benefits</th>\n",
+ " <th id=\"T_5ed03_level1_col12\" class=\"col_heading level1 col12\" colspan=\"2\">Energy wasted</th>\n",
+ " <th id=\"T_5ed03_level1_col14\" class=\"col_heading level1 col14\" colspan=\"2\">Ratio</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_5ed03_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
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+ " <th id=\"T_5ed03_level2_col5\" class=\"col_heading level2 col5\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col6\" class=\"col_heading level2 col6\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col7\" class=\"col_heading level2 col7\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col8\" class=\"col_heading level2 col8\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col9\" class=\"col_heading level2 col9\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col10\" class=\"col_heading level2 col10\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col11\" class=\"col_heading level2 col11\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col12\" class=\"col_heading level2 col12\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col13\" class=\"col_heading level2 col13\" >std</th>\n",
+ " <th id=\"T_5ed03_level2_col14\" class=\"col_heading level2 col14\" >Value</th>\n",
+ " <th id=\"T_5ed03_level2_col15\" class=\"col_heading level2 col15\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_5ed03_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_5ed03_row0_col0\" class=\"data row0 col0\" >71.84</td>\n",
+ " <td id=\"T_5ed03_row0_col1\" class=\"data row0 col1\" >3.39</td>\n",
+ " <td id=\"T_5ed03_row0_col2\" class=\"data row0 col2\" >72.02</td>\n",
+ " <td id=\"T_5ed03_row0_col3\" class=\"data row0 col3\" >3.06</td>\n",
+ " <td id=\"T_5ed03_row0_col4\" class=\"data row0 col4\" >88.30</td>\n",
+ " <td id=\"T_5ed03_row0_col5\" class=\"data row0 col5\" >2.28</td>\n",
+ " <td id=\"T_5ed03_row0_col6\" class=\"data row0 col6\" >97.17</td>\n",
+ " <td id=\"T_5ed03_row0_col7\" class=\"data row0 col7\" >1.77</td>\n",
+ " <td id=\"T_5ed03_row0_col8\" class=\"data row0 col8\" >1.34</td>\n",
+ " <td id=\"T_5ed03_row0_col9\" class=\"data row0 col9\" >0.05</td>\n",
+ " <td id=\"T_5ed03_row0_col10\" class=\"data row0 col10\" >96.79</td>\n",
+ " <td id=\"T_5ed03_row0_col11\" class=\"data row0 col11\" >5.05</td>\n",
+ " <td id=\"T_5ed03_row0_col12\" class=\"data row0 col12\" >32.73</td>\n",
+ " <td id=\"T_5ed03_row0_col13\" class=\"data row0 col13\" >3.99</td>\n",
+ " <td id=\"T_5ed03_row0_col14\" class=\"data row0 col14\" >1.75</td>\n",
+ " <td id=\"T_5ed03_row0_col15\" class=\"data row0 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_5ed03_level0_row1\" class=\"row_heading level0 row1\" >25% aggre</th>\n",
+ " <td id=\"T_5ed03_row1_col0\" class=\"data row1 col0\" >71.92</td>\n",
+ " <td id=\"T_5ed03_row1_col1\" class=\"data row1 col1\" >3.20</td>\n",
+ " <td id=\"T_5ed03_row1_col2\" class=\"data row1 col2\" >72.39</td>\n",
+ " <td id=\"T_5ed03_row1_col3\" class=\"data row1 col3\" >3.13</td>\n",
+ " <td id=\"T_5ed03_row1_col4\" class=\"data row1 col4\" >85.53</td>\n",
+ " <td id=\"T_5ed03_row1_col5\" class=\"data row1 col5\" >2.35</td>\n",
+ " <td id=\"T_5ed03_row1_col6\" class=\"data row1 col6\" >93.81</td>\n",
+ " <td id=\"T_5ed03_row1_col7\" class=\"data row1 col7\" >2.71</td>\n",
+ " <td id=\"T_5ed03_row1_col8\" class=\"data row1 col8\" >1.71</td>\n",
+ " <td id=\"T_5ed03_row1_col9\" class=\"data row1 col9\" >0.07</td>\n",
+ " <td id=\"T_5ed03_row1_col10\" class=\"data row1 col10\" >92.46</td>\n",
+ " <td id=\"T_5ed03_row1_col11\" class=\"data row1 col11\" >5.09</td>\n",
+ " <td id=\"T_5ed03_row1_col12\" class=\"data row1 col12\" >33.75</td>\n",
+ " <td id=\"T_5ed03_row1_col13\" class=\"data row1 col13\" >4.03</td>\n",
+ " <td id=\"T_5ed03_row1_col14\" class=\"data row1 col14\" >1.67</td>\n",
+ " <td id=\"T_5ed03_row1_col15\" class=\"data row1 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_5ed03_level0_row2\" class=\"row_heading level0 row2\" >50% aggre</th>\n",
+ " <td id=\"T_5ed03_row2_col0\" class=\"data row2 col0\" >71.80</td>\n",
+ " <td id=\"T_5ed03_row2_col1\" class=\"data row2 col1\" >3.30</td>\n",
+ " <td id=\"T_5ed03_row2_col2\" class=\"data row2 col2\" >72.36</td>\n",
+ " <td id=\"T_5ed03_row2_col3\" class=\"data row2 col3\" >3.10</td>\n",
+ " <td id=\"T_5ed03_row2_col4\" class=\"data row2 col4\" >85.64</td>\n",
+ " <td id=\"T_5ed03_row2_col5\" class=\"data row2 col5\" >2.15</td>\n",
+ " <td id=\"T_5ed03_row2_col6\" class=\"data row2 col6\" >95.61</td>\n",
+ " <td id=\"T_5ed03_row2_col7\" class=\"data row2 col7\" >2.41</td>\n",
+ " <td id=\"T_5ed03_row2_col8\" class=\"data row2 col8\" >1.62</td>\n",
+ " <td id=\"T_5ed03_row2_col9\" class=\"data row2 col9\" >0.07</td>\n",
+ " <td id=\"T_5ed03_row2_col10\" class=\"data row2 col10\" >46.02</td>\n",
+ " <td id=\"T_5ed03_row2_col11\" class=\"data row2 col11\" >5.74</td>\n",
+ " <td id=\"T_5ed03_row2_col12\" class=\"data row2 col12\" >33.93</td>\n",
+ " <td id=\"T_5ed03_row2_col13\" class=\"data row2 col13\" >4.04</td>\n",
+ " <td id=\"T_5ed03_row2_col14\" class=\"data row2 col14\" >0.83</td>\n",
+ " <td id=\"T_5ed03_row2_col15\" class=\"data row2 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_5ed03_level0_row3\" class=\"row_heading level0 row3\" >75% aggre</th>\n",
+ " <td id=\"T_5ed03_row3_col0\" class=\"data row3 col0\" >71.81</td>\n",
+ " <td id=\"T_5ed03_row3_col1\" class=\"data row3 col1\" >3.25</td>\n",
+ " <td id=\"T_5ed03_row3_col2\" class=\"data row3 col2\" >72.10</td>\n",
+ " <td id=\"T_5ed03_row3_col3\" class=\"data row3 col3\" >3.15</td>\n",
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+ " <td id=\"T_2afd9_row0_col7\" class=\"data row0 col7\" >2.82</td>\n",
+ " <td id=\"T_2afd9_row0_col8\" class=\"data row0 col8\" >1.13</td>\n",
+ " <td id=\"T_2afd9_row0_col9\" class=\"data row0 col9\" >0.06</td>\n",
+ " <td id=\"T_2afd9_row0_col10\" class=\"data row0 col10\" >35.76</td>\n",
+ " <td id=\"T_2afd9_row0_col11\" class=\"data row0 col11\" >2.61</td>\n",
+ " <td id=\"T_2afd9_row0_col12\" class=\"data row0 col12\" >74.21</td>\n",
+ " <td id=\"T_2afd9_row0_col13\" class=\"data row0 col13\" >2.53</td>\n",
+ " <td id=\"T_2afd9_row0_col14\" class=\"data row0 col14\" >0.65</td>\n",
+ " <td id=\"T_2afd9_row0_col15\" class=\"data row0 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2afd9_level0_row1\" class=\"row_heading level0 row1\" >25% aggre</th>\n",
+ " <td id=\"T_2afd9_row1_col0\" class=\"data row1 col0\" >78.74</td>\n",
+ " <td id=\"T_2afd9_row1_col1\" class=\"data row1 col1\" >4.99</td>\n",
+ " <td id=\"T_2afd9_row1_col2\" class=\"data row1 col2\" >79.86</td>\n",
+ " <td id=\"T_2afd9_row1_col3\" class=\"data row1 col3\" >4.20</td>\n",
+ " <td id=\"T_2afd9_row1_col4\" class=\"data row1 col4\" >87.46</td>\n",
+ " <td id=\"T_2afd9_row1_col5\" class=\"data row1 col5\" >3.71</td>\n",
+ " <td id=\"T_2afd9_row1_col6\" class=\"data row1 col6\" >96.18</td>\n",
+ " <td id=\"T_2afd9_row1_col7\" class=\"data row1 col7\" >3.30</td>\n",
+ " <td id=\"T_2afd9_row1_col8\" class=\"data row1 col8\" >1.35</td>\n",
+ " <td id=\"T_2afd9_row1_col9\" class=\"data row1 col9\" >0.08</td>\n",
+ " <td id=\"T_2afd9_row1_col10\" class=\"data row1 col10\" >34.77</td>\n",
+ " <td id=\"T_2afd9_row1_col11\" class=\"data row1 col11\" >2.58</td>\n",
+ " <td id=\"T_2afd9_row1_col12\" class=\"data row1 col12\" >74.80</td>\n",
+ " <td id=\"T_2afd9_row1_col13\" class=\"data row1 col13\" >2.52</td>\n",
+ " <td id=\"T_2afd9_row1_col14\" class=\"data row1 col14\" >0.63</td>\n",
+ " <td id=\"T_2afd9_row1_col15\" class=\"data row1 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2afd9_level0_row2\" class=\"row_heading level0 row2\" >50% aggre</th>\n",
+ " <td id=\"T_2afd9_row2_col0\" class=\"data row2 col0\" >78.72</td>\n",
+ " <td id=\"T_2afd9_row2_col1\" class=\"data row2 col1\" >4.98</td>\n",
+ " <td id=\"T_2afd9_row2_col2\" class=\"data row2 col2\" >79.84</td>\n",
+ " <td id=\"T_2afd9_row2_col3\" class=\"data row2 col3\" >4.26</td>\n",
+ " <td id=\"T_2afd9_row2_col4\" class=\"data row2 col4\" >88.53</td>\n",
+ " <td id=\"T_2afd9_row2_col5\" class=\"data row2 col5\" >3.85</td>\n",
+ " <td id=\"T_2afd9_row2_col6\" class=\"data row2 col6\" >97.08</td>\n",
+ " <td id=\"T_2afd9_row2_col7\" class=\"data row2 col7\" >3.02</td>\n",
+ " <td id=\"T_2afd9_row2_col8\" class=\"data row2 col8\" >1.22</td>\n",
+ " <td id=\"T_2afd9_row2_col9\" class=\"data row2 col9\" >0.07</td>\n",
+ " <td id=\"T_2afd9_row2_col10\" class=\"data row2 col10\" >7.40</td>\n",
+ " <td id=\"T_2afd9_row2_col11\" class=\"data row2 col11\" >2.13</td>\n",
+ " <td id=\"T_2afd9_row2_col12\" class=\"data row2 col12\" >74.52</td>\n",
+ " <td id=\"T_2afd9_row2_col13\" class=\"data row2 col13\" >2.47</td>\n",
+ " <td id=\"T_2afd9_row2_col14\" class=\"data row2 col14\" >0.13</td>\n",
+ " <td id=\"T_2afd9_row2_col15\" class=\"data row2 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2afd9_level0_row3\" class=\"row_heading level0 row3\" >75% aggre</th>\n",
+ " <td id=\"T_2afd9_row3_col0\" class=\"data row3 col0\" >78.80</td>\n",
+ " <td id=\"T_2afd9_row3_col1\" class=\"data row3 col1\" >5.02</td>\n",
+ " <td id=\"T_2afd9_row3_col2\" class=\"data row3 col2\" >79.81</td>\n",
+ " <td id=\"T_2afd9_row3_col3\" class=\"data row3 col3\" >4.25</td>\n",
+ " <td id=\"T_2afd9_row3_col4\" class=\"data row3 col4\" >89.29</td>\n",
+ " <td id=\"T_2afd9_row3_col5\" class=\"data row3 col5\" >3.69</td>\n",
+ " <td id=\"T_2afd9_row3_col6\" class=\"data row3 col6\" >97.28</td>\n",
+ " <td id=\"T_2afd9_row3_col7\" class=\"data row3 col7\" >2.82</td>\n",
+ " <td id=\"T_2afd9_row3_col8\" class=\"data row3 col8\" >1.17</td>\n",
+ " <td id=\"T_2afd9_row3_col9\" class=\"data row3 col9\" >0.07</td>\n",
+ " <td id=\"T_2afd9_row3_col10\" class=\"data row3 col10\" >2.56</td>\n",
+ " <td id=\"T_2afd9_row3_col11\" class=\"data row3 col11\" >1.10</td>\n",
+ " <td id=\"T_2afd9_row3_col12\" class=\"data row3 col12\" >74.28</td>\n",
+ " <td id=\"T_2afd9_row3_col13\" class=\"data row3 col13\" >2.51</td>\n",
+ " <td id=\"T_2afd9_row3_col14\" class=\"data row3 col14\" >0.05</td>\n",
+ " <td id=\"T_2afd9_row3_col15\" class=\"data row3 col15\" >0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2afd9_level0_row4\" class=\"row_heading level0 row4\" >all aggre</th>\n",
+ " <td id=\"T_2afd9_row4_col0\" class=\"data row4 col0\" >78.53</td>\n",
+ " <td id=\"T_2afd9_row4_col1\" class=\"data row4 col1\" >5.03</td>\n",
+ " <td id=\"T_2afd9_row4_col2\" class=\"data row4 col2\" >79.56</td>\n",
+ " <td id=\"T_2afd9_row4_col3\" class=\"data row4 col3\" >4.18</td>\n",
+ " <td id=\"T_2afd9_row4_col4\" class=\"data row4 col4\" >89.77</td>\n",
+ " <td id=\"T_2afd9_row4_col5\" class=\"data row4 col5\" >3.76</td>\n",
+ " <td id=\"T_2afd9_row4_col6\" class=\"data row4 col6\" >97.40</td>\n",
+ " <td id=\"T_2afd9_row4_col7\" class=\"data row4 col7\" >2.82</td>\n",
+ " <td id=\"T_2afd9_row4_col8\" class=\"data row4 col8\" >1.13</td>\n",
+ " <td id=\"T_2afd9_row4_col9\" class=\"data row4 col9\" >0.06</td>\n",
+ " <td id=\"T_2afd9_row4_col10\" class=\"data row4 col10\" >0.87</td>\n",
+ " <td id=\"T_2afd9_row4_col11\" class=\"data row4 col11\" >0.62</td>\n",
+ " <td id=\"T_2afd9_row4_col12\" class=\"data row4 col12\" >74.21</td>\n",
+ " <td id=\"T_2afd9_row4_col13\" class=\"data row4 col13\" >2.53</td>\n",
+ " <td id=\"T_2afd9_row4_col14\" class=\"data row4 col14\" >0.02</td>\n",
+ " <td id=\"T_2afd9_row4_col15\" class=\"data row4 col15\" >0.01</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7a4a581d3890>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_consolidate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Large"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"large\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Medium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"medium\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Small"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"small\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "opt",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analysis/Section_7.3.2.ipynb b/Analysis/Section_7.3.2.ipynb
new file mode 100644
index 0000000..5d8e25a
--- /dev/null
+++ b/Analysis/Section_7.3.2.ipynb
@@ -0,0 +1,12800 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Objective:** Similar to the previous one, but with a difference where the aggressive can have less energy than the other dcs. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from GenerateVisualisation.experiments_analysis import Analysis, Analysis_plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_list = []\n",
+ "seeds = range(3, 103)\n",
+ "\n",
+ "# base_folder = \"XPs/1_bell/\"\n",
+ "# analysis = Analysis(base_folder=base_folder,\n",
+ "# files=[\"/simple_aggre_0_\", \"/simple_aggre_25_\", \"/simple_aggre_50_\", \"/simple_aggre_75_\", \"/simple_aggre_100_\"],\n",
+ "# names=[\"Cooperative\", \"25% aggre\", \"50% aggre\", \"75% aggre\", \"all aggre\"],\n",
+ "# nb_dcs=4,\n",
+ "# seeds = seeds)\n",
+ "# analysis_list.append(analysis)\n",
+ "\n",
+ "servers=np.array([(s0, s1, s2, s3) for s0 in [0, 1] for s1 in [0, 1] for s2 in [0, 1] for s3 in [0, 1]])\n",
+ "idx=[0, 8, 4, 2, 1, 12, 10, 9, 6, 5, 3, 14, 13, 11, 7, 15]\n",
+ "name = []\n",
+ "files = []\n",
+ "for s0, s1, s2, s3 in servers[idx]:\n",
+ " aggre_sum = sum([s0, s1, s2, s3]) / 4 * 100\n",
+ " if (aggre_sum > 0):\n",
+ " name.append(\"{:.0f}% ({} {} {} {})\".format(aggre_sum, s0, s1, s2, s3))\n",
+ " else:\n",
+ " name.append(\"Cooperative\")\n",
+ " files.append(\"/simple_aggre_{}_dc_{}_{}_{}_{}_seed_\".format(int(aggre_sum), s0, s1, s2, s3))\n",
+ "\n",
+ "analysis = Analysis(base_folder=\"XPs/2_amplitude/\",\n",
+ " files=files,\n",
+ " names=name,\n",
+ " nb_dcs=4,\n",
+ " seeds = seeds)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis_plot = Analysis_plots(analysis_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_37b7a td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_37b7a th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_37b7a_row0_col4, #T_37b7a_row0_col6, #T_37b7a_row0_col8, #T_37b7a_row0_col10, #T_37b7a_row0_col12, #T_37b7a_row0_col14, #T_37b7a_row2_col2, #T_37b7a_row4_col0, #T_37b7a_row15_col4, #T_37b7a_row15_col6, #T_37b7a_row15_col8, #T_37b7a_row15_col12 {\n",
+ " background-color: #34FF34;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_37b7a\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_37b7a_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bid</th>\n",
+ " <th id=\"T_37b7a_level0_col4\" class=\"col_heading level0 col4\" colspan=\"6\">QoS</th>\n",
+ " <th id=\"T_37b7a_level0_col10\" class=\"col_heading level0 col10\" colspan=\"6\">Money/Energy</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_37b7a_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Tasks</th>\n",
+ " <th id=\"T_37b7a_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " <th id=\"T_37b7a_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_37b7a_level1_col6\" class=\"col_heading level1 col6\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_37b7a_level1_col8\" class=\"col_heading level1 col8\" colspan=\"2\">Slowdown</th>\n",
+ " <th id=\"T_37b7a_level1_col10\" class=\"col_heading level1 col10\" colspan=\"2\">Benefits</th>\n",
+ " <th id=\"T_37b7a_level1_col12\" class=\"col_heading level1 col12\" colspan=\"2\">Energy wasted</th>\n",
+ " <th id=\"T_37b7a_level1_col14\" class=\"col_heading level1 col14\" colspan=\"2\">Ratio</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_37b7a_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col5\" class=\"col_heading level2 col5\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col6\" class=\"col_heading level2 col6\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col7\" class=\"col_heading level2 col7\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col8\" class=\"col_heading level2 col8\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col9\" class=\"col_heading level2 col9\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col10\" class=\"col_heading level2 col10\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col11\" class=\"col_heading level2 col11\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col12\" class=\"col_heading level2 col12\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col13\" class=\"col_heading level2 col13\" >std</th>\n",
+ " <th id=\"T_37b7a_level2_col14\" class=\"col_heading level2 col14\" >Value</th>\n",
+ " <th id=\"T_37b7a_level2_col15\" class=\"col_heading level2 col15\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_37b7a_row0_col0\" class=\"data row0 col0\" >88.50</td>\n",
+ " <td id=\"T_37b7a_row0_col1\" class=\"data row0 col1\" >3.05</td>\n",
+ " <td id=\"T_37b7a_row0_col2\" class=\"data row0 col2\" >84.65</td>\n",
+ " <td id=\"T_37b7a_row0_col3\" class=\"data row0 col3\" >3.64</td>\n",
+ " <td id=\"T_37b7a_row0_col4\" class=\"data row0 col4\" >91.04</td>\n",
+ " <td id=\"T_37b7a_row0_col5\" class=\"data row0 col5\" >1.95</td>\n",
+ " <td id=\"T_37b7a_row0_col6\" class=\"data row0 col6\" >98.60</td>\n",
+ " <td id=\"T_37b7a_row0_col7\" class=\"data row0 col7\" >1.35</td>\n",
+ " <td id=\"T_37b7a_row0_col8\" class=\"data row0 col8\" >1.48</td>\n",
+ " <td id=\"T_37b7a_row0_col9\" class=\"data row0 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row0_col10\" class=\"data row0 col10\" >119.77</td>\n",
+ " <td id=\"T_37b7a_row0_col11\" class=\"data row0 col11\" >5.54</td>\n",
+ " <td id=\"T_37b7a_row0_col12\" class=\"data row0 col12\" >12.89</td>\n",
+ " <td id=\"T_37b7a_row0_col13\" class=\"data row0 col13\" >2.48</td>\n",
+ " <td id=\"T_37b7a_row0_col14\" class=\"data row0 col14\" >2.17</td>\n",
+ " <td id=\"T_37b7a_row0_col15\" class=\"data row0 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
+ " <td id=\"T_37b7a_row1_col0\" class=\"data row1 col0\" >88.76</td>\n",
+ " <td id=\"T_37b7a_row1_col1\" class=\"data row1 col1\" >3.06</td>\n",
+ " <td id=\"T_37b7a_row1_col2\" class=\"data row1 col2\" >84.86</td>\n",
+ " <td id=\"T_37b7a_row1_col3\" class=\"data row1 col3\" >3.67</td>\n",
+ " <td id=\"T_37b7a_row1_col4\" class=\"data row1 col4\" >87.62</td>\n",
+ " <td id=\"T_37b7a_row1_col5\" class=\"data row1 col5\" >1.81</td>\n",
+ " <td id=\"T_37b7a_row1_col6\" class=\"data row1 col6\" >94.71</td>\n",
+ " <td id=\"T_37b7a_row1_col7\" class=\"data row1 col7\" >2.51</td>\n",
+ " <td id=\"T_37b7a_row1_col8\" class=\"data row1 col8\" >1.75</td>\n",
+ " <td id=\"T_37b7a_row1_col9\" class=\"data row1 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row1_col10\" class=\"data row1 col10\" >112.76</td>\n",
+ " <td id=\"T_37b7a_row1_col11\" class=\"data row1 col11\" >5.28</td>\n",
+ " <td id=\"T_37b7a_row1_col12\" class=\"data row1 col12\" >13.92</td>\n",
+ " <td id=\"T_37b7a_row1_col13\" class=\"data row1 col13\" >2.40</td>\n",
+ " <td id=\"T_37b7a_row1_col14\" class=\"data row1 col14\" >2.04</td>\n",
+ " <td id=\"T_37b7a_row1_col15\" class=\"data row1 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row2\" class=\"row_heading level0 row2\" >25% (0 1 0 0)</th>\n",
+ " <td id=\"T_37b7a_row2_col0\" class=\"data row2 col0\" >88.71</td>\n",
+ " <td id=\"T_37b7a_row2_col1\" class=\"data row2 col1\" >3.01</td>\n",
+ " <td id=\"T_37b7a_row2_col2\" class=\"data row2 col2\" >85.11</td>\n",
+ " <td id=\"T_37b7a_row2_col3\" class=\"data row2 col3\" >3.54</td>\n",
+ " <td id=\"T_37b7a_row2_col4\" class=\"data row2 col4\" >87.62</td>\n",
+ " <td id=\"T_37b7a_row2_col5\" class=\"data row2 col5\" >2.00</td>\n",
+ " <td id=\"T_37b7a_row2_col6\" class=\"data row2 col6\" >93.40</td>\n",
+ " <td id=\"T_37b7a_row2_col7\" class=\"data row2 col7\" >2.74</td>\n",
+ " <td id=\"T_37b7a_row2_col8\" class=\"data row2 col8\" >1.77</td>\n",
+ " <td id=\"T_37b7a_row2_col9\" class=\"data row2 col9\" >0.07</td>\n",
+ " <td id=\"T_37b7a_row2_col10\" class=\"data row2 col10\" >112.45</td>\n",
+ " <td id=\"T_37b7a_row2_col11\" class=\"data row2 col11\" >5.23</td>\n",
+ " <td id=\"T_37b7a_row2_col12\" class=\"data row2 col12\" >13.91</td>\n",
+ " <td id=\"T_37b7a_row2_col13\" class=\"data row2 col13\" >2.52</td>\n",
+ " <td id=\"T_37b7a_row2_col14\" class=\"data row2 col14\" >2.03</td>\n",
+ " <td id=\"T_37b7a_row2_col15\" class=\"data row2 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
+ " <td id=\"T_37b7a_row3_col0\" class=\"data row3 col0\" >88.90</td>\n",
+ " <td id=\"T_37b7a_row3_col1\" class=\"data row3 col1\" >2.85</td>\n",
+ " <td id=\"T_37b7a_row3_col2\" class=\"data row3 col2\" >84.69</td>\n",
+ " <td id=\"T_37b7a_row3_col3\" class=\"data row3 col3\" >3.80</td>\n",
+ " <td id=\"T_37b7a_row3_col4\" class=\"data row3 col4\" >87.60</td>\n",
+ " <td id=\"T_37b7a_row3_col5\" class=\"data row3 col5\" >2.10</td>\n",
+ " <td id=\"T_37b7a_row3_col6\" class=\"data row3 col6\" >93.51</td>\n",
+ " <td id=\"T_37b7a_row3_col7\" class=\"data row3 col7\" >2.52</td>\n",
+ " <td id=\"T_37b7a_row3_col8\" class=\"data row3 col8\" >1.77</td>\n",
+ " <td id=\"T_37b7a_row3_col9\" class=\"data row3 col9\" >0.08</td>\n",
+ " <td id=\"T_37b7a_row3_col10\" class=\"data row3 col10\" >112.25</td>\n",
+ " <td id=\"T_37b7a_row3_col11\" class=\"data row3 col11\" >5.51</td>\n",
+ " <td id=\"T_37b7a_row3_col12\" class=\"data row3 col12\" >14.20</td>\n",
+ " <td id=\"T_37b7a_row3_col13\" class=\"data row3 col13\" >2.60</td>\n",
+ " <td id=\"T_37b7a_row3_col14\" class=\"data row3 col14\" >2.03</td>\n",
+ " <td id=\"T_37b7a_row3_col15\" class=\"data row3 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
+ " <td id=\"T_37b7a_row4_col0\" class=\"data row4 col0\" >88.93</td>\n",
+ " <td id=\"T_37b7a_row4_col1\" class=\"data row4 col1\" >3.07</td>\n",
+ " <td id=\"T_37b7a_row4_col2\" class=\"data row4 col2\" >84.62</td>\n",
+ " <td id=\"T_37b7a_row4_col3\" class=\"data row4 col3\" >3.61</td>\n",
+ " <td id=\"T_37b7a_row4_col4\" class=\"data row4 col4\" >87.89</td>\n",
+ " <td id=\"T_37b7a_row4_col5\" class=\"data row4 col5\" >2.03</td>\n",
+ " <td id=\"T_37b7a_row4_col6\" class=\"data row4 col6\" >93.28</td>\n",
+ " <td id=\"T_37b7a_row4_col7\" class=\"data row4 col7\" >2.87</td>\n",
+ " <td id=\"T_37b7a_row4_col8\" class=\"data row4 col8\" >1.75</td>\n",
+ " <td id=\"T_37b7a_row4_col9\" class=\"data row4 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row4_col10\" class=\"data row4 col10\" >112.31</td>\n",
+ " <td id=\"T_37b7a_row4_col11\" class=\"data row4 col11\" >5.29</td>\n",
+ " <td id=\"T_37b7a_row4_col12\" class=\"data row4 col12\" >13.69</td>\n",
+ " <td id=\"T_37b7a_row4_col13\" class=\"data row4 col13\" >2.55</td>\n",
+ " <td id=\"T_37b7a_row4_col14\" class=\"data row4 col14\" >2.03</td>\n",
+ " <td id=\"T_37b7a_row4_col15\" class=\"data row4 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_37b7a_row5_col0\" class=\"data row5 col0\" >88.55</td>\n",
+ " <td id=\"T_37b7a_row5_col1\" class=\"data row5 col1\" >3.05</td>\n",
+ " <td id=\"T_37b7a_row5_col2\" class=\"data row5 col2\" >84.74</td>\n",
+ " <td id=\"T_37b7a_row5_col3\" class=\"data row5 col3\" >3.67</td>\n",
+ " <td id=\"T_37b7a_row5_col4\" class=\"data row5 col4\" >88.03</td>\n",
+ " <td id=\"T_37b7a_row5_col5\" class=\"data row5 col5\" >1.95</td>\n",
+ " <td id=\"T_37b7a_row5_col6\" class=\"data row5 col6\" >95.29</td>\n",
+ " <td id=\"T_37b7a_row5_col7\" class=\"data row5 col7\" >2.61</td>\n",
+ " <td id=\"T_37b7a_row5_col8\" class=\"data row5 col8\" >1.74</td>\n",
+ " <td id=\"T_37b7a_row5_col9\" class=\"data row5 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row5_col10\" class=\"data row5 col10\" >80.97</td>\n",
+ " <td id=\"T_37b7a_row5_col11\" class=\"data row5 col11\" >5.58</td>\n",
+ " <td id=\"T_37b7a_row5_col12\" class=\"data row5 col12\" >13.98</td>\n",
+ " <td id=\"T_37b7a_row5_col13\" class=\"data row5 col13\" >2.58</td>\n",
+ " <td id=\"T_37b7a_row5_col14\" class=\"data row5 col14\" >1.46</td>\n",
+ " <td id=\"T_37b7a_row5_col15\" class=\"data row5 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row6\" class=\"row_heading level0 row6\" >50% (1 0 1 0)</th>\n",
+ " <td id=\"T_37b7a_row6_col0\" class=\"data row6 col0\" >88.79</td>\n",
+ " <td id=\"T_37b7a_row6_col1\" class=\"data row6 col1\" >2.83</td>\n",
+ " <td id=\"T_37b7a_row6_col2\" class=\"data row6 col2\" >84.78</td>\n",
+ " <td id=\"T_37b7a_row6_col3\" class=\"data row6 col3\" >3.75</td>\n",
+ " <td id=\"T_37b7a_row6_col4\" class=\"data row6 col4\" >87.79</td>\n",
+ " <td id=\"T_37b7a_row6_col5\" class=\"data row6 col5\" >1.81</td>\n",
+ " <td id=\"T_37b7a_row6_col6\" class=\"data row6 col6\" >95.44</td>\n",
+ " <td id=\"T_37b7a_row6_col7\" class=\"data row6 col7\" >2.40</td>\n",
+ " <td id=\"T_37b7a_row6_col8\" class=\"data row6 col8\" >1.75</td>\n",
+ " <td id=\"T_37b7a_row6_col9\" class=\"data row6 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row6_col10\" class=\"data row6 col10\" >81.96</td>\n",
+ " <td id=\"T_37b7a_row6_col11\" class=\"data row6 col11\" >4.40</td>\n",
+ " <td id=\"T_37b7a_row6_col12\" class=\"data row6 col12\" >14.07</td>\n",
+ " <td id=\"T_37b7a_row6_col13\" class=\"data row6 col13\" >2.36</td>\n",
+ " <td id=\"T_37b7a_row6_col14\" class=\"data row6 col14\" >1.48</td>\n",
+ " <td id=\"T_37b7a_row6_col15\" class=\"data row6 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row7\" class=\"row_heading level0 row7\" >50% (1 0 0 1)</th>\n",
+ " <td id=\"T_37b7a_row7_col0\" class=\"data row7 col0\" >88.68</td>\n",
+ " <td id=\"T_37b7a_row7_col1\" class=\"data row7 col1\" >2.93</td>\n",
+ " <td id=\"T_37b7a_row7_col2\" class=\"data row7 col2\" >84.80</td>\n",
+ " <td id=\"T_37b7a_row7_col3\" class=\"data row7 col3\" >3.72</td>\n",
+ " <td id=\"T_37b7a_row7_col4\" class=\"data row7 col4\" >88.10</td>\n",
+ " <td id=\"T_37b7a_row7_col5\" class=\"data row7 col5\" >1.89</td>\n",
+ " <td id=\"T_37b7a_row7_col6\" class=\"data row7 col6\" >95.41</td>\n",
+ " <td id=\"T_37b7a_row7_col7\" class=\"data row7 col7\" >2.53</td>\n",
+ " <td id=\"T_37b7a_row7_col8\" class=\"data row7 col8\" >1.71</td>\n",
+ " <td id=\"T_37b7a_row7_col9\" class=\"data row7 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row7_col10\" class=\"data row7 col10\" >79.39</td>\n",
+ " <td id=\"T_37b7a_row7_col11\" class=\"data row7 col11\" >5.13</td>\n",
+ " <td id=\"T_37b7a_row7_col12\" class=\"data row7 col12\" >13.67</td>\n",
+ " <td id=\"T_37b7a_row7_col13\" class=\"data row7 col13\" >2.52</td>\n",
+ " <td id=\"T_37b7a_row7_col14\" class=\"data row7 col14\" >1.44</td>\n",
+ " <td id=\"T_37b7a_row7_col15\" class=\"data row7 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row8\" class=\"row_heading level0 row8\" >50% (0 1 1 0)</th>\n",
+ " <td id=\"T_37b7a_row8_col0\" class=\"data row8 col0\" >88.53</td>\n",
+ " <td id=\"T_37b7a_row8_col1\" class=\"data row8 col1\" >2.99</td>\n",
+ " <td id=\"T_37b7a_row8_col2\" class=\"data row8 col2\" >84.66</td>\n",
+ " <td id=\"T_37b7a_row8_col3\" class=\"data row8 col3\" >3.66</td>\n",
+ " <td id=\"T_37b7a_row8_col4\" class=\"data row8 col4\" >87.86</td>\n",
+ " <td id=\"T_37b7a_row8_col5\" class=\"data row8 col5\" >2.06</td>\n",
+ " <td id=\"T_37b7a_row8_col6\" class=\"data row8 col6\" >95.54</td>\n",
+ " <td id=\"T_37b7a_row8_col7\" class=\"data row8 col7\" >2.18</td>\n",
+ " <td id=\"T_37b7a_row8_col8\" class=\"data row8 col8\" >1.76</td>\n",
+ " <td id=\"T_37b7a_row8_col9\" class=\"data row8 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row8_col10\" class=\"data row8 col10\" >77.04</td>\n",
+ " <td id=\"T_37b7a_row8_col11\" class=\"data row8 col11\" >4.92</td>\n",
+ " <td id=\"T_37b7a_row8_col12\" class=\"data row8 col12\" >14.35</td>\n",
+ " <td id=\"T_37b7a_row8_col13\" class=\"data row8 col13\" >2.70</td>\n",
+ " <td id=\"T_37b7a_row8_col14\" class=\"data row8 col14\" >1.39</td>\n",
+ " <td id=\"T_37b7a_row8_col15\" class=\"data row8 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row9\" class=\"row_heading level0 row9\" >50% (0 1 0 1)</th>\n",
+ " <td id=\"T_37b7a_row9_col0\" class=\"data row9 col0\" >88.66</td>\n",
+ " <td id=\"T_37b7a_row9_col1\" class=\"data row9 col1\" >3.05</td>\n",
+ " <td id=\"T_37b7a_row9_col2\" class=\"data row9 col2\" >84.68</td>\n",
+ " <td id=\"T_37b7a_row9_col3\" class=\"data row9 col3\" >3.68</td>\n",
+ " <td id=\"T_37b7a_row9_col4\" class=\"data row9 col4\" >87.75</td>\n",
+ " <td id=\"T_37b7a_row9_col5\" class=\"data row9 col5\" >2.09</td>\n",
+ " <td id=\"T_37b7a_row9_col6\" class=\"data row9 col6\" >95.59</td>\n",
+ " <td id=\"T_37b7a_row9_col7\" class=\"data row9 col7\" >2.33</td>\n",
+ " <td id=\"T_37b7a_row9_col8\" class=\"data row9 col8\" >1.73</td>\n",
+ " <td id=\"T_37b7a_row9_col9\" class=\"data row9 col9\" >0.07</td>\n",
+ " <td id=\"T_37b7a_row9_col10\" class=\"data row9 col10\" >76.81</td>\n",
+ " <td id=\"T_37b7a_row9_col11\" class=\"data row9 col11\" >4.37</td>\n",
+ " <td id=\"T_37b7a_row9_col12\" class=\"data row9 col12\" >13.94</td>\n",
+ " <td id=\"T_37b7a_row9_col13\" class=\"data row9 col13\" >2.61</td>\n",
+ " <td id=\"T_37b7a_row9_col14\" class=\"data row9 col14\" >1.39</td>\n",
+ " <td id=\"T_37b7a_row9_col15\" class=\"data row9 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row10\" class=\"row_heading level0 row10\" >50% (0 0 1 1)</th>\n",
+ " <td id=\"T_37b7a_row10_col0\" class=\"data row10 col0\" >88.93</td>\n",
+ " <td id=\"T_37b7a_row10_col1\" class=\"data row10 col1\" >2.77</td>\n",
+ " <td id=\"T_37b7a_row10_col2\" class=\"data row10 col2\" >84.82</td>\n",
+ " <td id=\"T_37b7a_row10_col3\" class=\"data row10 col3\" >3.53</td>\n",
+ " <td id=\"T_37b7a_row10_col4\" class=\"data row10 col4\" >87.97</td>\n",
+ " <td id=\"T_37b7a_row10_col5\" class=\"data row10 col5\" >2.28</td>\n",
+ " <td id=\"T_37b7a_row10_col6\" class=\"data row10 col6\" >95.69</td>\n",
+ " <td id=\"T_37b7a_row10_col7\" class=\"data row10 col7\" >1.97</td>\n",
+ " <td id=\"T_37b7a_row10_col8\" class=\"data row10 col8\" >1.74</td>\n",
+ " <td id=\"T_37b7a_row10_col9\" class=\"data row10 col9\" >0.07</td>\n",
+ " <td id=\"T_37b7a_row10_col10\" class=\"data row10 col10\" >77.56</td>\n",
+ " <td id=\"T_37b7a_row10_col11\" class=\"data row10 col11\" >4.86</td>\n",
+ " <td id=\"T_37b7a_row10_col12\" class=\"data row10 col12\" >13.92</td>\n",
+ " <td id=\"T_37b7a_row10_col13\" class=\"data row10 col13\" >2.45</td>\n",
+ " <td id=\"T_37b7a_row10_col14\" class=\"data row10 col14\" >1.40</td>\n",
+ " <td id=\"T_37b7a_row10_col15\" class=\"data row10 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row11\" class=\"row_heading level0 row11\" >75% (1 1 1 0)</th>\n",
+ " <td id=\"T_37b7a_row11_col0\" class=\"data row11 col0\" >88.61</td>\n",
+ " <td id=\"T_37b7a_row11_col1\" class=\"data row11 col1\" >2.93</td>\n",
+ " <td id=\"T_37b7a_row11_col2\" class=\"data row11 col2\" >84.82</td>\n",
+ " <td id=\"T_37b7a_row11_col3\" class=\"data row11 col3\" >3.71</td>\n",
+ " <td id=\"T_37b7a_row11_col4\" class=\"data row11 col4\" >88.83</td>\n",
+ " <td id=\"T_37b7a_row11_col5\" class=\"data row11 col5\" >1.87</td>\n",
+ " <td id=\"T_37b7a_row11_col6\" class=\"data row11 col6\" >97.27</td>\n",
+ " <td id=\"T_37b7a_row11_col7\" class=\"data row11 col7\" >1.57</td>\n",
+ " <td id=\"T_37b7a_row11_col8\" class=\"data row11 col8\" >1.63</td>\n",
+ " <td id=\"T_37b7a_row11_col9\" class=\"data row11 col9\" >0.05</td>\n",
+ " <td id=\"T_37b7a_row11_col10\" class=\"data row11 col10\" >47.85</td>\n",
+ " <td id=\"T_37b7a_row11_col11\" class=\"data row11 col11\" >4.19</td>\n",
+ " <td id=\"T_37b7a_row11_col12\" class=\"data row11 col12\" >13.68</td>\n",
+ " <td id=\"T_37b7a_row11_col13\" class=\"data row11 col13\" >2.44</td>\n",
+ " <td id=\"T_37b7a_row11_col14\" class=\"data row11 col14\" >0.87</td>\n",
+ " <td id=\"T_37b7a_row11_col15\" class=\"data row11 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row12\" class=\"row_heading level0 row12\" >75% (1 1 0 1)</th>\n",
+ " <td id=\"T_37b7a_row12_col0\" class=\"data row12 col0\" >88.51</td>\n",
+ " <td id=\"T_37b7a_row12_col1\" class=\"data row12 col1\" >3.12</td>\n",
+ " <td id=\"T_37b7a_row12_col2\" class=\"data row12 col2\" >84.64</td>\n",
+ " <td id=\"T_37b7a_row12_col3\" class=\"data row12 col3\" >3.65</td>\n",
+ " <td id=\"T_37b7a_row12_col4\" class=\"data row12 col4\" >89.13</td>\n",
+ " <td id=\"T_37b7a_row12_col5\" class=\"data row12 col5\" >1.99</td>\n",
+ " <td id=\"T_37b7a_row12_col6\" class=\"data row12 col6\" >97.02</td>\n",
+ " <td id=\"T_37b7a_row12_col7\" class=\"data row12 col7\" >1.84</td>\n",
+ " <td id=\"T_37b7a_row12_col8\" class=\"data row12 col8\" >1.61</td>\n",
+ " <td id=\"T_37b7a_row12_col9\" class=\"data row12 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row12_col10\" class=\"data row12 col10\" >47.61</td>\n",
+ " <td id=\"T_37b7a_row12_col11\" class=\"data row12 col11\" >4.56</td>\n",
+ " <td id=\"T_37b7a_row12_col12\" class=\"data row12 col12\" >13.34</td>\n",
+ " <td id=\"T_37b7a_row12_col13\" class=\"data row12 col13\" >2.48</td>\n",
+ " <td id=\"T_37b7a_row12_col14\" class=\"data row12 col14\" >0.86</td>\n",
+ " <td id=\"T_37b7a_row12_col15\" class=\"data row12 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_37b7a_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
+ " <td id=\"T_37b7a_row13_col0\" class=\"data row13 col0\" >88.80</td>\n",
+ " <td id=\"T_37b7a_row13_col1\" class=\"data row13 col1\" >2.98</td>\n",
+ " <td id=\"T_37b7a_row13_col2\" class=\"data row13 col2\" >84.87</td>\n",
+ " <td id=\"T_37b7a_row13_col3\" class=\"data row13 col3\" >3.57</td>\n",
+ " <td id=\"T_37b7a_row13_col4\" class=\"data row13 col4\" >89.00</td>\n",
+ " <td id=\"T_37b7a_row13_col5\" class=\"data row13 col5\" >1.85</td>\n",
+ " <td id=\"T_37b7a_row13_col6\" class=\"data row13 col6\" >97.37</td>\n",
+ " <td id=\"T_37b7a_row13_col7\" class=\"data row13 col7\" >1.56</td>\n",
+ " <td id=\"T_37b7a_row13_col8\" class=\"data row13 col8\" >1.61</td>\n",
+ " <td id=\"T_37b7a_row13_col9\" class=\"data row13 col9\" >0.06</td>\n",
+ " <td id=\"T_37b7a_row13_col10\" class=\"data row13 col10\" >47.83</td>\n",
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+ " <td id=\"T_ebd83_row1_col1\" class=\"data row1 col1\" >1.10</td>\n",
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+ " <td id=\"T_ebd83_row1_col3\" class=\"data row1 col3\" >0.89</td>\n",
+ " <td id=\"T_ebd83_row1_col4\" class=\"data row1 col4\" >96.00</td>\n",
+ " <td id=\"T_ebd83_row1_col5\" class=\"data row1 col5\" >1.59</td>\n",
+ " <td id=\"T_ebd83_row1_col6\" class=\"data row1 col6\" >98.50</td>\n",
+ " <td id=\"T_ebd83_row1_col7\" class=\"data row1 col7\" >1.92</td>\n",
+ " <td id=\"T_ebd83_row1_col8\" class=\"data row1 col8\" >1.27</td>\n",
+ " <td id=\"T_ebd83_row1_col9\" class=\"data row1 col9\" >0.07</td>\n",
+ " <td id=\"T_ebd83_row1_col10\" class=\"data row1 col10\" >45.49</td>\n",
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+ " <td id=\"T_ebd83_row1_col12\" class=\"data row1 col12\" >64.72</td>\n",
+ " <td id=\"T_ebd83_row1_col13\" class=\"data row1 col13\" >3.05</td>\n",
+ " <td id=\"T_ebd83_row1_col14\" class=\"data row1 col14\" >0.82</td>\n",
+ " <td id=\"T_ebd83_row1_col15\" class=\"data row1 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td id=\"T_ebd83_row2_col4\" class=\"data row2 col4\" >95.30</td>\n",
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+ " <td id=\"T_ebd83_row2_col7\" class=\"data row2 col7\" >2.44</td>\n",
+ " <td id=\"T_ebd83_row2_col8\" class=\"data row2 col8\" >1.36</td>\n",
+ " <td id=\"T_ebd83_row2_col9\" class=\"data row2 col9\" >0.08</td>\n",
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+ " <td id=\"T_ebd83_row2_col13\" class=\"data row2 col13\" >3.10</td>\n",
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+ " <td id=\"T_ebd83_row2_col15\" class=\"data row2 col15\" >0.03</td>\n",
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+ " <tr>\n",
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+ " <td id=\"T_ebd83_row3_col14\" class=\"data row3 col14\" >0.82</td>\n",
+ " <td id=\"T_ebd83_row3_col15\" class=\"data row3 col15\" >0.03</td>\n",
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+ " <td id=\"T_ebd83_row4_col9\" class=\"data row4 col9\" >0.07</td>\n",
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+ " <td id=\"T_ebd83_row4_col13\" class=\"data row4 col13\" >3.12</td>\n",
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+ " <td id=\"T_ebd83_row4_col15\" class=\"data row4 col15\" >0.03</td>\n",
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+ " <tr>\n",
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+ " <td id=\"T_ebd83_row13_col6\" class=\"data row13 col6\" >99.90</td>\n",
+ " <td id=\"T_ebd83_row13_col7\" class=\"data row13 col7\" >0.39</td>\n",
+ " <td id=\"T_ebd83_row13_col8\" class=\"data row13 col8\" >1.12</td>\n",
+ " <td id=\"T_ebd83_row13_col9\" class=\"data row13 col9\" >0.05</td>\n",
+ " <td id=\"T_ebd83_row13_col10\" class=\"data row13 col10\" >6.08</td>\n",
+ " <td id=\"T_ebd83_row13_col11\" class=\"data row13 col11\" >2.36</td>\n",
+ " <td id=\"T_ebd83_row13_col12\" class=\"data row13 col12\" >63.97</td>\n",
+ " <td id=\"T_ebd83_row13_col13\" class=\"data row13 col13\" >3.15</td>\n",
+ " <td id=\"T_ebd83_row13_col14\" class=\"data row13 col14\" >0.11</td>\n",
+ " <td id=\"T_ebd83_row13_col15\" class=\"data row13 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebd83_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_ebd83_row14_col0\" class=\"data row14 col0\" >98.80</td>\n",
+ " <td id=\"T_ebd83_row14_col1\" class=\"data row14 col1\" >1.20</td>\n",
+ " <td id=\"T_ebd83_row14_col2\" class=\"data row14 col2\" >99.31</td>\n",
+ " <td id=\"T_ebd83_row14_col3\" class=\"data row14 col3\" >0.92</td>\n",
+ " <td id=\"T_ebd83_row14_col4\" class=\"data row14 col4\" >98.75</td>\n",
+ " <td id=\"T_ebd83_row14_col5\" class=\"data row14 col5\" >0.98</td>\n",
+ " <td id=\"T_ebd83_row14_col6\" class=\"data row14 col6\" >99.86</td>\n",
+ " <td id=\"T_ebd83_row14_col7\" class=\"data row14 col7\" >0.44</td>\n",
+ " <td id=\"T_ebd83_row14_col8\" class=\"data row14 col8\" >1.12</td>\n",
+ " <td id=\"T_ebd83_row14_col9\" class=\"data row14 col9\" >0.05</td>\n",
+ " <td id=\"T_ebd83_row14_col10\" class=\"data row14 col10\" >4.25</td>\n",
+ " <td id=\"T_ebd83_row14_col11\" class=\"data row14 col11\" >1.66</td>\n",
+ " <td id=\"T_ebd83_row14_col12\" class=\"data row14 col12\" >63.98</td>\n",
+ " <td id=\"T_ebd83_row14_col13\" class=\"data row14 col13\" >3.07</td>\n",
+ " <td id=\"T_ebd83_row14_col14\" class=\"data row14 col14\" >0.08</td>\n",
+ " <td id=\"T_ebd83_row14_col15\" class=\"data row14 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebd83_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_ebd83_row15_col0\" class=\"data row15 col0\" >98.78</td>\n",
+ " <td id=\"T_ebd83_row15_col1\" class=\"data row15 col1\" >1.26</td>\n",
+ " <td id=\"T_ebd83_row15_col2\" class=\"data row15 col2\" >99.25</td>\n",
+ " <td id=\"T_ebd83_row15_col3\" class=\"data row15 col3\" >0.88</td>\n",
+ " <td id=\"T_ebd83_row15_col4\" class=\"data row15 col4\" >99.40</td>\n",
+ " <td id=\"T_ebd83_row15_col5\" class=\"data row15 col5\" >0.88</td>\n",
+ " <td id=\"T_ebd83_row15_col6\" class=\"data row15 col6\" >99.98</td>\n",
+ " <td id=\"T_ebd83_row15_col7\" class=\"data row15 col7\" >0.13</td>\n",
+ " <td id=\"T_ebd83_row15_col8\" class=\"data row15 col8\" >1.08</td>\n",
+ " <td id=\"T_ebd83_row15_col9\" class=\"data row15 col9\" >0.03</td>\n",
+ " <td id=\"T_ebd83_row15_col10\" class=\"data row15 col10\" >1.31</td>\n",
+ " <td id=\"T_ebd83_row15_col11\" class=\"data row15 col11\" >0.78</td>\n",
+ " <td id=\"T_ebd83_row15_col12\" class=\"data row15 col12\" >63.78</td>\n",
+ " <td id=\"T_ebd83_row15_col13\" class=\"data row15 col13\" >3.10</td>\n",
+ " <td id=\"T_ebd83_row15_col14\" class=\"data row15 col14\" >0.02</td>\n",
+ " <td id=\"T_ebd83_row15_col15\" class=\"data row15 col15\" >0.01</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x70cf40dc3750>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_consolidate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Large"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 2500x1500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.plot_graph(\"large\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
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+ " background-color: #66fc66;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row31_col2 {\n",
+ " background-color: #b1f7b1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row32_col0, #T_fd2f5_row36_col0, #T_fd2f5_row40_col0 {\n",
+ " background-color: #d4f4d4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row32_col2 {\n",
+ " background-color: #72fb72;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row33_col0 {\n",
+ " background-color: #58fd58;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row35_col2 {\n",
+ " background-color: #69fc69;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row36_col2 {\n",
+ " background-color: #78fb78;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row37_col0 {\n",
+ " background-color: #55fd55;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row37_col2, #T_fd2f5_row38_col2, #T_fd2f5_row39_col0 {\n",
+ " background-color: #5efc5e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row38_col0 {\n",
+ " background-color: #c2f6c2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row39_col2 {\n",
+ " background-color: #98f898;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row40_col2, #T_fd2f5_row45_col2, #T_fd2f5_row49_col2 {\n",
+ " background-color: #6afb6a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row41_col0 {\n",
+ " background-color: #c1f6c1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row42_col2, #T_fd2f5_row48_col0 {\n",
+ " background-color: #81fa81;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row44_col0, #T_fd2f5_row55_col2 {\n",
+ " background-color: #7efa7e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row45_col0, #T_fd2f5_row49_col0, #T_fd2f5_row54_col0, #T_fd2f5_row59_col2 {\n",
+ " background-color: #74fb74;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row46_col2 {\n",
+ " background-color: #83fa83;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row47_col0 {\n",
+ " background-color: #eaf3ea;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row48_col2 {\n",
+ " background-color: #3efe3e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row50_col0, #T_fd2f5_row53_col0 {\n",
+ " background-color: #e0f4e0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row50_col2 {\n",
+ " background-color: #94f994;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row52_col0 {\n",
+ " background-color: #80fa80;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row52_col2 {\n",
+ " background-color: #37ff37;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row53_col2 {\n",
+ " background-color: #97f897;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row55_col0 {\n",
+ " background-color: #75fb75;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row56_col2 {\n",
+ " background-color: #9ff89f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row57_col0 {\n",
+ " background-color: #70fb70;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row57_col2 {\n",
+ " background-color: #42fe42;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fd2f5_row58_col0, #T_fd2f5_row58_col2, #T_fd2f5_row59_col0 {\n",
+ " background-color: #77fb77;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_fd2f5\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_fd2f5_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bidded</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_fd2f5_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Jobs</th>\n",
+ " <th id=\"T_fd2f5_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_fd2f5_level2_col0\" class=\"col_heading level2 col0\" >Number</th>\n",
+ " <th id=\"T_fd2f5_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_fd2f5_level2_col2\" class=\"col_heading level2 col2\" >Number</th>\n",
+ " <th id=\"T_fd2f5_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_fd2f5_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_fd2f5_row0_col0\" class=\"data row0 col0\" >20.36</td>\n",
+ " <td id=\"T_fd2f5_row0_col1\" class=\"data row0 col1\" >2.55</td>\n",
+ " <td id=\"T_fd2f5_row0_col2\" class=\"data row0 col2\" >21.73</td>\n",
+ " <td id=\"T_fd2f5_row0_col3\" class=\"data row0 col3\" >2.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_fd2f5_row1_col0\" class=\"data row1 col0\" >22.60</td>\n",
+ " <td id=\"T_fd2f5_row1_col1\" class=\"data row1 col1\" >2.42</td>\n",
+ " <td id=\"T_fd2f5_row1_col2\" class=\"data row1 col2\" >21.05</td>\n",
+ " <td id=\"T_fd2f5_row1_col3\" class=\"data row1 col3\" >3.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_fd2f5_row2_col0\" class=\"data row2 col0\" >23.32</td>\n",
+ " <td id=\"T_fd2f5_row2_col1\" class=\"data row2 col1\" >2.47</td>\n",
+ " <td id=\"T_fd2f5_row2_col2\" class=\"data row2 col2\" >21.43</td>\n",
+ " <td id=\"T_fd2f5_row2_col3\" class=\"data row2 col3\" >3.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_fd2f5_row3_col0\" class=\"data row3 col0\" >22.21</td>\n",
+ " <td id=\"T_fd2f5_row3_col1\" class=\"data row3 col1\" >2.56</td>\n",
+ " <td id=\"T_fd2f5_row3_col2\" class=\"data row3 col2\" >20.43</td>\n",
+ " <td id=\"T_fd2f5_row3_col3\" class=\"data row3 col3\" >2.89</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_fd2f5_row4_col0\" class=\"data row4 col0\" >33.25</td>\n",
+ " <td id=\"T_fd2f5_row4_col1\" class=\"data row4 col1\" >3.66</td>\n",
+ " <td id=\"T_fd2f5_row4_col2\" class=\"data row4 col2\" >19.99</td>\n",
+ " <td id=\"T_fd2f5_row4_col3\" class=\"data row4 col3\" >2.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_fd2f5_row5_col0\" class=\"data row5 col0\" >18.32</td>\n",
+ " <td id=\"T_fd2f5_row5_col1\" class=\"data row5 col1\" >3.01</td>\n",
+ " <td id=\"T_fd2f5_row5_col2\" class=\"data row5 col2\" >22.29</td>\n",
+ " <td id=\"T_fd2f5_row5_col3\" class=\"data row5 col3\" >2.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_fd2f5_row6_col0\" class=\"data row6 col0\" >18.84</td>\n",
+ " <td id=\"T_fd2f5_row6_col1\" class=\"data row6 col1\" >2.29</td>\n",
+ " <td id=\"T_fd2f5_row6_col2\" class=\"data row6 col2\" >21.72</td>\n",
+ " <td id=\"T_fd2f5_row6_col3\" class=\"data row6 col3\" >2.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_fd2f5_row7_col0\" class=\"data row7 col0\" >18.34</td>\n",
+ " <td id=\"T_fd2f5_row7_col1\" class=\"data row7 col1\" >2.59</td>\n",
+ " <td id=\"T_fd2f5_row7_col2\" class=\"data row7 col2\" >20.86</td>\n",
+ " <td id=\"T_fd2f5_row7_col3\" class=\"data row7 col3\" >2.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_fd2f5_row8_col0\" class=\"data row8 col0\" >17.48</td>\n",
+ " <td id=\"T_fd2f5_row8_col1\" class=\"data row8 col1\" >2.50</td>\n",
+ " <td id=\"T_fd2f5_row8_col2\" class=\"data row8 col2\" >22.49</td>\n",
+ " <td id=\"T_fd2f5_row8_col3\" class=\"data row8 col3\" >2.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_fd2f5_row9_col0\" class=\"data row9 col0\" >32.71</td>\n",
+ " <td id=\"T_fd2f5_row9_col1\" class=\"data row9 col1\" >3.56</td>\n",
+ " <td id=\"T_fd2f5_row9_col2\" class=\"data row9 col2\" >17.56</td>\n",
+ " <td id=\"T_fd2f5_row9_col3\" class=\"data row9 col3\" >3.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_fd2f5_row10_col0\" class=\"data row10 col0\" >19.34</td>\n",
+ " <td id=\"T_fd2f5_row10_col1\" class=\"data row10 col1\" >2.24</td>\n",
+ " <td id=\"T_fd2f5_row10_col2\" class=\"data row10 col2\" >22.72</td>\n",
+ " <td id=\"T_fd2f5_row10_col3\" class=\"data row10 col3\" >2.85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_fd2f5_row11_col0\" class=\"data row11 col0\" >19.17</td>\n",
+ " <td id=\"T_fd2f5_row11_col1\" class=\"data row11 col1\" >2.30</td>\n",
+ " <td id=\"T_fd2f5_row11_col2\" class=\"data row11 col2\" >22.34</td>\n",
+ " <td id=\"T_fd2f5_row11_col3\" class=\"data row11 col3\" >2.75</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_fd2f5_row12_col0\" class=\"data row12 col0\" >17.39</td>\n",
+ " <td id=\"T_fd2f5_row12_col1\" class=\"data row12 col1\" >2.52</td>\n",
+ " <td id=\"T_fd2f5_row12_col2\" class=\"data row12 col2\" >22.42</td>\n",
+ " <td id=\"T_fd2f5_row12_col3\" class=\"data row12 col3\" >2.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_fd2f5_row13_col0\" class=\"data row13 col0\" >19.84</td>\n",
+ " <td id=\"T_fd2f5_row13_col1\" class=\"data row13 col1\" >2.73</td>\n",
+ " <td id=\"T_fd2f5_row13_col2\" class=\"data row13 col2\" >22.90</td>\n",
+ " <td id=\"T_fd2f5_row13_col3\" class=\"data row13 col3\" >2.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_fd2f5_row14_col0\" class=\"data row14 col0\" >32.41</td>\n",
+ " <td id=\"T_fd2f5_row14_col1\" class=\"data row14 col1\" >4.16</td>\n",
+ " <td id=\"T_fd2f5_row14_col2\" class=\"data row14 col2\" >16.64</td>\n",
+ " <td id=\"T_fd2f5_row14_col3\" class=\"data row14 col3\" >2.96</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_fd2f5_row15_col0\" class=\"data row15 col0\" >19.26</td>\n",
+ " <td id=\"T_fd2f5_row15_col1\" class=\"data row15 col1\" >2.50</td>\n",
+ " <td id=\"T_fd2f5_row15_col2\" class=\"data row15 col2\" >22.73</td>\n",
+ " <td id=\"T_fd2f5_row15_col3\" class=\"data row15 col3\" >2.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_fd2f5_row16_col0\" class=\"data row16 col0\" >17.49</td>\n",
+ " <td id=\"T_fd2f5_row16_col1\" class=\"data row16 col1\" >2.53</td>\n",
+ " <td id=\"T_fd2f5_row16_col2\" class=\"data row16 col2\" >22.60</td>\n",
+ " <td id=\"T_fd2f5_row16_col3\" class=\"data row16 col3\" >2.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_fd2f5_row17_col0\" class=\"data row17 col0\" >19.58</td>\n",
+ " <td id=\"T_fd2f5_row17_col1\" class=\"data row17 col1\" >2.49</td>\n",
+ " <td id=\"T_fd2f5_row17_col2\" class=\"data row17 col2\" >23.36</td>\n",
+ " <td id=\"T_fd2f5_row17_col3\" class=\"data row17 col3\" >2.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_fd2f5_row18_col0\" class=\"data row18 col0\" >20.05</td>\n",
+ " <td id=\"T_fd2f5_row18_col1\" class=\"data row18 col1\" >2.66</td>\n",
+ " <td id=\"T_fd2f5_row18_col2\" class=\"data row18 col2\" >22.34</td>\n",
+ " <td id=\"T_fd2f5_row18_col3\" class=\"data row18 col3\" >3.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_fd2f5_row19_col0\" class=\"data row19 col0\" >31.82</td>\n",
+ " <td id=\"T_fd2f5_row19_col1\" class=\"data row19 col1\" >3.70</td>\n",
+ " <td id=\"T_fd2f5_row19_col2\" class=\"data row19 col2\" >16.33</td>\n",
+ " <td id=\"T_fd2f5_row19_col3\" class=\"data row19 col3\" >3.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_fd2f5_row20_col0\" class=\"data row20 col0\" >28.14</td>\n",
+ " <td id=\"T_fd2f5_row20_col1\" class=\"data row20 col1\" >2.91</td>\n",
+ " <td id=\"T_fd2f5_row20_col2\" class=\"data row20 col2\" >23.23</td>\n",
+ " <td id=\"T_fd2f5_row20_col3\" class=\"data row20 col3\" >3.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_fd2f5_row21_col0\" class=\"data row21 col0\" >29.26</td>\n",
+ " <td id=\"T_fd2f5_row21_col1\" class=\"data row21 col1\" >3.02</td>\n",
+ " <td id=\"T_fd2f5_row21_col2\" class=\"data row21 col2\" >19.27</td>\n",
+ " <td id=\"T_fd2f5_row21_col3\" class=\"data row21 col3\" >3.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_fd2f5_row22_col0\" class=\"data row22 col0\" >15.93</td>\n",
+ " <td id=\"T_fd2f5_row22_col1\" class=\"data row22 col1\" >2.56</td>\n",
+ " <td id=\"T_fd2f5_row22_col2\" class=\"data row22 col2\" >21.37</td>\n",
+ " <td id=\"T_fd2f5_row22_col3\" class=\"data row22 col3\" >2.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_fd2f5_row23_col0\" class=\"data row23 col0\" >15.21</td>\n",
+ " <td id=\"T_fd2f5_row23_col1\" class=\"data row23 col1\" >2.29</td>\n",
+ " <td id=\"T_fd2f5_row23_col2\" class=\"data row23 col2\" >20.87</td>\n",
+ " <td id=\"T_fd2f5_row23_col3\" class=\"data row23 col3\" >2.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_fd2f5_row24_col0\" class=\"data row24 col0\" >29.11</td>\n",
+ " <td id=\"T_fd2f5_row24_col1\" class=\"data row24 col1\" >3.24</td>\n",
+ " <td id=\"T_fd2f5_row24_col2\" class=\"data row24 col2\" >23.18</td>\n",
+ " <td id=\"T_fd2f5_row24_col3\" class=\"data row24 col3\" >3.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_fd2f5_row25_col0\" class=\"data row25 col0\" >15.71</td>\n",
+ " <td id=\"T_fd2f5_row25_col1\" class=\"data row25 col1\" >2.52</td>\n",
+ " <td id=\"T_fd2f5_row25_col2\" class=\"data row25 col2\" >21.92</td>\n",
+ " <td id=\"T_fd2f5_row25_col3\" class=\"data row25 col3\" >2.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_fd2f5_row26_col0\" class=\"data row26 col0\" >28.90</td>\n",
+ " <td id=\"T_fd2f5_row26_col1\" class=\"data row26 col1\" >3.17</td>\n",
+ " <td id=\"T_fd2f5_row26_col2\" class=\"data row26 col2\" >18.87</td>\n",
+ " <td id=\"T_fd2f5_row26_col3\" class=\"data row26 col3\" >3.35</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_fd2f5_row27_col0\" class=\"data row27 col0\" >15.08</td>\n",
+ " <td id=\"T_fd2f5_row27_col1\" class=\"data row27 col1\" >2.27</td>\n",
+ " <td id=\"T_fd2f5_row27_col2\" class=\"data row27 col2\" >20.81</td>\n",
+ " <td id=\"T_fd2f5_row27_col3\" class=\"data row27 col3\" >2.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_fd2f5_row28_col0\" class=\"data row28 col0\" >28.56</td>\n",
+ " <td id=\"T_fd2f5_row28_col1\" class=\"data row28 col1\" >3.15</td>\n",
+ " <td id=\"T_fd2f5_row28_col2\" class=\"data row28 col2\" >23.41</td>\n",
+ " <td id=\"T_fd2f5_row28_col3\" class=\"data row28 col3\" >3.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_fd2f5_row29_col0\" class=\"data row29 col0\" >15.94</td>\n",
+ " <td id=\"T_fd2f5_row29_col1\" class=\"data row29 col1\" >2.53</td>\n",
+ " <td id=\"T_fd2f5_row29_col2\" class=\"data row29 col2\" >21.68</td>\n",
+ " <td id=\"T_fd2f5_row29_col3\" class=\"data row29 col3\" >2.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_fd2f5_row30_col0\" class=\"data row30 col0\" >16.02</td>\n",
+ " <td id=\"T_fd2f5_row30_col1\" class=\"data row30 col1\" >2.40</td>\n",
+ " <td id=\"T_fd2f5_row30_col2\" class=\"data row30 col2\" >21.05</td>\n",
+ " <td id=\"T_fd2f5_row30_col3\" class=\"data row30 col3\" >2.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_fd2f5_row31_col0\" class=\"data row31 col0\" >28.16</td>\n",
+ " <td id=\"T_fd2f5_row31_col1\" class=\"data row31 col1\" >3.29</td>\n",
+ " <td id=\"T_fd2f5_row31_col2\" class=\"data row31 col2\" >18.65</td>\n",
+ " <td id=\"T_fd2f5_row31_col3\" class=\"data row31 col3\" >3.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_fd2f5_row32_col0\" class=\"data row32 col0\" >14.55</td>\n",
+ " <td id=\"T_fd2f5_row32_col1\" class=\"data row32 col1\" >2.10</td>\n",
+ " <td id=\"T_fd2f5_row32_col2\" class=\"data row32 col2\" >21.19</td>\n",
+ " <td id=\"T_fd2f5_row32_col3\" class=\"data row32 col3\" >2.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_fd2f5_row33_col0\" class=\"data row33 col0\" >29.00</td>\n",
+ " <td id=\"T_fd2f5_row33_col1\" class=\"data row33 col1\" >2.89</td>\n",
+ " <td id=\"T_fd2f5_row33_col2\" class=\"data row33 col2\" >21.92</td>\n",
+ " <td id=\"T_fd2f5_row33_col3\" class=\"data row33 col3\" >3.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_fd2f5_row34_col0\" class=\"data row34 col0\" >29.29</td>\n",
+ " <td id=\"T_fd2f5_row34_col1\" class=\"data row34 col1\" >3.26</td>\n",
+ " <td id=\"T_fd2f5_row34_col2\" class=\"data row34 col2\" >19.97</td>\n",
+ " <td id=\"T_fd2f5_row34_col3\" class=\"data row34 col3\" >3.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_fd2f5_row35_col0\" class=\"data row35 col0\" >15.68</td>\n",
+ " <td id=\"T_fd2f5_row35_col1\" class=\"data row35 col1\" >2.47</td>\n",
+ " <td id=\"T_fd2f5_row35_col2\" class=\"data row35 col2\" >21.58</td>\n",
+ " <td id=\"T_fd2f5_row35_col3\" class=\"data row35 col3\" >2.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_fd2f5_row36_col0\" class=\"data row36 col0\" >14.48</td>\n",
+ " <td id=\"T_fd2f5_row36_col1\" class=\"data row36 col1\" >2.59</td>\n",
+ " <td id=\"T_fd2f5_row36_col2\" class=\"data row36 col2\" >20.96</td>\n",
+ " <td id=\"T_fd2f5_row36_col3\" class=\"data row36 col3\" >2.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_fd2f5_row37_col0\" class=\"data row37 col0\" >29.37</td>\n",
+ " <td id=\"T_fd2f5_row37_col1\" class=\"data row37 col1\" >3.10</td>\n",
+ " <td id=\"T_fd2f5_row37_col2\" class=\"data row37 col2\" >22.03</td>\n",
+ " <td id=\"T_fd2f5_row37_col3\" class=\"data row37 col3\" >3.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_fd2f5_row38_col0\" class=\"data row38 col0\" >16.56</td>\n",
+ " <td id=\"T_fd2f5_row38_col1\" class=\"data row38 col1\" >2.65</td>\n",
+ " <td id=\"T_fd2f5_row38_col2\" class=\"data row38 col2\" >22.04</td>\n",
+ " <td id=\"T_fd2f5_row38_col3\" class=\"data row38 col3\" >2.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_fd2f5_row39_col0\" class=\"data row39 col0\" >28.26</td>\n",
+ " <td id=\"T_fd2f5_row39_col1\" class=\"data row39 col1\" >3.15</td>\n",
+ " <td id=\"T_fd2f5_row39_col2\" class=\"data row39 col2\" >19.66</td>\n",
+ " <td id=\"T_fd2f5_row39_col3\" class=\"data row39 col3\" >3.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_fd2f5_row40_col0\" class=\"data row40 col0\" >14.50</td>\n",
+ " <td id=\"T_fd2f5_row40_col1\" class=\"data row40 col1\" >2.41</td>\n",
+ " <td id=\"T_fd2f5_row40_col2\" class=\"data row40 col2\" >21.49</td>\n",
+ " <td id=\"T_fd2f5_row40_col3\" class=\"data row40 col3\" >2.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_fd2f5_row41_col0\" class=\"data row41 col0\" >16.67</td>\n",
+ " <td id=\"T_fd2f5_row41_col1\" class=\"data row41 col1\" >2.54</td>\n",
+ " <td id=\"T_fd2f5_row41_col2\" class=\"data row41 col2\" >22.70</td>\n",
+ " <td id=\"T_fd2f5_row41_col3\" class=\"data row41 col3\" >2.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_fd2f5_row42_col0\" class=\"data row42 col0\" >29.18</td>\n",
+ " <td id=\"T_fd2f5_row42_col1\" class=\"data row42 col1\" >2.98</td>\n",
+ " <td id=\"T_fd2f5_row42_col2\" class=\"data row42 col2\" >20.58</td>\n",
+ " <td id=\"T_fd2f5_row42_col3\" class=\"data row42 col3\" >2.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_fd2f5_row43_col0\" class=\"data row43 col0\" >28.57</td>\n",
+ " <td id=\"T_fd2f5_row43_col1\" class=\"data row43 col1\" >3.03</td>\n",
+ " <td id=\"T_fd2f5_row43_col2\" class=\"data row43 col2\" >20.04</td>\n",
+ " <td id=\"T_fd2f5_row43_col3\" class=\"data row43 col3\" >3.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_fd2f5_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_fd2f5_row44_col0\" class=\"data row44 col0\" >24.48</td>\n",
+ " <td id=\"T_fd2f5_row44_col1\" class=\"data row44 col1\" >2.52</td>\n",
+ " <td id=\"T_fd2f5_row44_col2\" class=\"data row44 col2\" >23.71</td>\n",
+ " <td id=\"T_fd2f5_row44_col3\" class=\"data row44 col3\" >3.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_fd2f5_row45_col0\" class=\"data row45 col0\" >25.73</td>\n",
+ " <td id=\"T_fd2f5_row45_col1\" class=\"data row45 col1\" >3.03</td>\n",
+ " <td id=\"T_fd2f5_row45_col2\" class=\"data row45 col2\" >21.53</td>\n",
+ " <td id=\"T_fd2f5_row45_col3\" class=\"data row45 col3\" >3.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_fd2f5_row46_col0\" class=\"data row46 col0\" >26.49</td>\n",
+ " <td id=\"T_fd2f5_row46_col1\" class=\"data row46 col1\" >2.36</td>\n",
+ " <td id=\"T_fd2f5_row46_col2\" class=\"data row46 col2\" >20.52</td>\n",
+ " <td id=\"T_fd2f5_row46_col3\" class=\"data row46 col3\" >3.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_fd2f5_row47_col0\" class=\"data row47 col0\" >11.91</td>\n",
+ " <td id=\"T_fd2f5_row47_col1\" class=\"data row47 col1\" >2.17</td>\n",
+ " <td id=\"T_fd2f5_row47_col2\" class=\"data row47 col2\" >19.06</td>\n",
+ " <td id=\"T_fd2f5_row47_col3\" class=\"data row47 col3\" >2.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_fd2f5_row48_col0\" class=\"data row48 col0\" >24.18</td>\n",
+ " <td id=\"T_fd2f5_row48_col1\" class=\"data row48 col1\" >2.47</td>\n",
+ " <td id=\"T_fd2f5_row48_col2\" class=\"data row48 col2\" >23.30</td>\n",
+ " <td id=\"T_fd2f5_row48_col3\" class=\"data row48 col3\" >2.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_fd2f5_row49_col0\" class=\"data row49 col0\" >25.74</td>\n",
+ " <td id=\"T_fd2f5_row49_col1\" class=\"data row49 col1\" >3.00</td>\n",
+ " <td id=\"T_fd2f5_row49_col2\" class=\"data row49 col2\" >21.50</td>\n",
+ " <td id=\"T_fd2f5_row49_col3\" class=\"data row49 col3\" >3.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_fd2f5_row50_col0\" class=\"data row50 col0\" >13.12</td>\n",
+ " <td id=\"T_fd2f5_row50_col1\" class=\"data row50 col1\" >2.27</td>\n",
+ " <td id=\"T_fd2f5_row50_col2\" class=\"data row50 col2\" >19.82</td>\n",
+ " <td id=\"T_fd2f5_row50_col3\" class=\"data row50 col3\" >2.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_fd2f5_row51_col0\" class=\"data row51 col0\" >25.47</td>\n",
+ " <td id=\"T_fd2f5_row51_col1\" class=\"data row51 col1\" >2.80</td>\n",
+ " <td id=\"T_fd2f5_row51_col2\" class=\"data row51 col2\" >20.02</td>\n",
+ " <td id=\"T_fd2f5_row51_col3\" class=\"data row51 col3\" >2.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_fd2f5_row52_col0\" class=\"data row52 col0\" >24.32</td>\n",
+ " <td id=\"T_fd2f5_row52_col1\" class=\"data row52 col1\" >2.55</td>\n",
+ " <td id=\"T_fd2f5_row52_col2\" class=\"data row52 col2\" >23.57</td>\n",
+ " <td id=\"T_fd2f5_row52_col3\" class=\"data row52 col3\" >3.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_fd2f5_row53_col0\" class=\"data row53 col0\" >13.14</td>\n",
+ " <td id=\"T_fd2f5_row53_col1\" class=\"data row53 col1\" >2.36</td>\n",
+ " <td id=\"T_fd2f5_row53_col2\" class=\"data row53 col2\" >19.72</td>\n",
+ " <td id=\"T_fd2f5_row53_col3\" class=\"data row53 col3\" >2.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_fd2f5_row54_col0\" class=\"data row54 col0\" >25.73</td>\n",
+ " <td id=\"T_fd2f5_row54_col1\" class=\"data row54 col1\" >2.91</td>\n",
+ " <td id=\"T_fd2f5_row54_col2\" class=\"data row54 col2\" >20.87</td>\n",
+ " <td id=\"T_fd2f5_row54_col3\" class=\"data row54 col3\" >3.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_fd2f5_row55_col0\" class=\"data row55 col0\" >25.61</td>\n",
+ " <td id=\"T_fd2f5_row55_col1\" class=\"data row55 col1\" >2.72</td>\n",
+ " <td id=\"T_fd2f5_row55_col2\" class=\"data row55 col2\" >20.71</td>\n",
+ " <td id=\"T_fd2f5_row55_col3\" class=\"data row55 col3\" >3.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_fd2f5_row56_col0\" class=\"data row56 col0\" >11.81</td>\n",
+ " <td id=\"T_fd2f5_row56_col1\" class=\"data row56 col1\" >2.56</td>\n",
+ " <td id=\"T_fd2f5_row56_col2\" class=\"data row56 col2\" >19.39</td>\n",
+ " <td id=\"T_fd2f5_row56_col3\" class=\"data row56 col3\" >2.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_fd2f5_row57_col0\" class=\"data row57 col0\" >26.19</td>\n",
+ " <td id=\"T_fd2f5_row57_col1\" class=\"data row57 col1\" >2.73</td>\n",
+ " <td id=\"T_fd2f5_row57_col2\" class=\"data row57 col2\" >23.13</td>\n",
+ " <td id=\"T_fd2f5_row57_col3\" class=\"data row57 col3\" >3.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_fd2f5_row58_col0\" class=\"data row58 col0\" >25.35</td>\n",
+ " <td id=\"T_fd2f5_row58_col1\" class=\"data row58 col1\" >3.00</td>\n",
+ " <td id=\"T_fd2f5_row58_col2\" class=\"data row58 col2\" >21.00</td>\n",
+ " <td id=\"T_fd2f5_row58_col3\" class=\"data row58 col3\" >3.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_fd2f5_row59_col0\" class=\"data row59 col0\" >25.33</td>\n",
+ " <td id=\"T_fd2f5_row59_col1\" class=\"data row59 col1\" >2.57</td>\n",
+ " <td id=\"T_fd2f5_row59_col2\" class=\"data row59 col2\" >21.12</td>\n",
+ " <td id=\"T_fd2f5_row59_col3\" class=\"data row59 col3\" >3.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_fd2f5_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_fd2f5_row60_col0\" class=\"data row60 col0\" >20.36</td>\n",
+ " <td id=\"T_fd2f5_row60_col1\" class=\"data row60 col1\" >2.55</td>\n",
+ " <td id=\"T_fd2f5_row60_col2\" class=\"data row60 col2\" >21.73</td>\n",
+ " <td id=\"T_fd2f5_row60_col3\" class=\"data row60 col3\" >2.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_fd2f5_row61_col0\" class=\"data row61 col0\" >22.60</td>\n",
+ " <td id=\"T_fd2f5_row61_col1\" class=\"data row61 col1\" >2.42</td>\n",
+ " <td id=\"T_fd2f5_row61_col2\" class=\"data row61 col2\" >21.05</td>\n",
+ " <td id=\"T_fd2f5_row61_col3\" class=\"data row61 col3\" >3.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_fd2f5_row62_col0\" class=\"data row62 col0\" >23.32</td>\n",
+ " <td id=\"T_fd2f5_row62_col1\" class=\"data row62 col1\" >2.47</td>\n",
+ " <td id=\"T_fd2f5_row62_col2\" class=\"data row62 col2\" >21.43</td>\n",
+ " <td id=\"T_fd2f5_row62_col3\" class=\"data row62 col3\" >3.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fd2f5_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_fd2f5_row63_col0\" class=\"data row63 col0\" >22.21</td>\n",
+ " <td id=\"T_fd2f5_row63_col1\" class=\"data row63 col1\" >2.56</td>\n",
+ " <td id=\"T_fd2f5_row63_col2\" class=\"data row63 col2\" >20.43</td>\n",
+ " <td id=\"T_fd2f5_row63_col3\" class=\"data row63 col3\" >2.89</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
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+ " background-color: #66fc66;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row12_col2 {\n",
+ " background-color: #40fe40;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row12_col4, #T_8eb4f_row16_col4 {\n",
+ " background-color: #f3e7e7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row13_col0, #T_8eb4f_row26_col2 {\n",
+ " background-color: #84fa84;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row13_col2, #T_8eb4f_row47_col2, #T_8eb4f_row50_col2 {\n",
+ " background-color: #5dfc5d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row13_col4 {\n",
+ " background-color: #f5c7c7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row14_col0 {\n",
+ " background-color: #d9f4d9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row14_col2, #T_8eb4f_row19_col2 {\n",
+ " background-color: #e8f3e8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row14_col4 {\n",
+ " background-color: #ff0000;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row15_col0 {\n",
+ " background-color: #86fa86;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row15_col2 {\n",
+ " background-color: #57fd57;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row15_col4, #T_8eb4f_row23_col4 {\n",
+ " background-color: #f4d8d8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row16_col0 {\n",
+ " background-color: #69fc69;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row17_col2, #T_8eb4f_row52_col0 {\n",
+ " background-color: #63fc63;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row18_col0, #T_8eb4f_row32_col0, #T_8eb4f_row36_col0 {\n",
+ " background-color: #6ffb6f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row18_col2, #T_8eb4f_row30_col2 {\n",
+ " background-color: #61fc61;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row19_col4 {\n",
+ " background-color: #fe1b1b;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row20_col0, #T_8eb4f_row24_col0 {\n",
+ " background-color: #87fa87;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row20_col4 {\n",
+ " background-color: #f88383;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row21_col0 {\n",
+ " background-color: #81fa81;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row21_col2 {\n",
+ " background-color: #94f994;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row21_col4 {\n",
+ " background-color: #fb5151;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row22_col0, #T_8eb4f_row42_col2 {\n",
+ " background-color: #73fb73;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row22_col2 {\n",
+ " background-color: #5afc5a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row22_col4 {\n",
+ " background-color: #f5cccc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row23_col0, #T_8eb4f_row33_col0 {\n",
+ " background-color: #8cf98c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row23_col2, #T_8eb4f_row28_col2 {\n",
+ " background-color: #62fc62;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row24_col2 {\n",
+ " background-color: #68fc68;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row24_col4 {\n",
+ " background-color: #f97c7c;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row25_col0 {\n",
+ " background-color: #7efa7e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row25_col4, #T_8eb4f_row41_col4 {\n",
+ " background-color: #f5caca;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row26_col0 {\n",
+ " background-color: #83fa83;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row26_col4 {\n",
+ " background-color: #fb5555;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row27_col0 {\n",
+ " background-color: #93f993;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row27_col2, #T_8eb4f_row29_col2, #T_8eb4f_row41_col2, #T_8eb4f_row45_col0, #T_8eb4f_row54_col0 {\n",
+ " background-color: #5cfc5c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row27_col4, #T_8eb4f_row35_col4 {\n",
+ " background-color: #f4dddd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row28_col0, #T_8eb4f_row31_col2 {\n",
+ " background-color: #89f989;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row28_col4 {\n",
+ " background-color: #f88686;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row30_col0, #T_8eb4f_row58_col0 {\n",
+ " background-color: #65fc65;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row30_col4 {\n",
+ " background-color: #f5cbcb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row31_col0 {\n",
+ " background-color: #90f990;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row31_col4 {\n",
+ " background-color: #f96f6f;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row32_col4, #T_8eb4f_row40_col4 {\n",
+ " background-color: #f3eaea;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row33_col2, #T_8eb4f_row39_col2 {\n",
+ " background-color: #78fb78;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row33_col4 {\n",
+ " background-color: #fa6b6b;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row34_col0 {\n",
+ " background-color: #85fa85;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row34_col2 {\n",
+ " background-color: #7cfa7c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row34_col4 {\n",
+ " background-color: #fb5353;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row35_col0 {\n",
+ " background-color: #9af89a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row35_col2 {\n",
+ " background-color: #58fd58;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row36_col2 {\n",
+ " background-color: #41fe41;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row36_col4 {\n",
+ " background-color: #f3eeee;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row37_col0 {\n",
+ " background-color: #92f992;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row37_col2, #T_8eb4f_row56_col0 {\n",
+ " background-color: #80fa80;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row37_col4 {\n",
+ " background-color: #fa6666;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row38_col0 {\n",
+ " background-color: #7dfa7d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row38_col4 {\n",
+ " background-color: #f5c8c8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row39_col0 {\n",
+ " background-color: #aef7ae;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row39_col4 {\n",
+ " background-color: #f97373;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row40_col2 {\n",
+ " background-color: #43fe43;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row41_col0 {\n",
+ " background-color: #8bf98b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row42_col0, #T_8eb4f_row53_col0 {\n",
+ " background-color: #88f988;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row42_col4 {\n",
+ " background-color: #fb5858;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row43_col0 {\n",
+ " background-color: #99f899;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row43_col2 {\n",
+ " background-color: #79fa79;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row43_col4 {\n",
+ " background-color: #f97777;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_8eb4f_row44_col0 {\n",
+ " background-color: #56fd56;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row44_col2 {\n",
+ " background-color: #46fe46;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row44_col4 {\n",
+ " background-color: #f6b8b7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row45_col2 {\n",
+ " background-color: #51fd51;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row45_col4 {\n",
+ " background-color: #f79897;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row46_col4 {\n",
+ " background-color: #f88f8f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row47_col0 {\n",
+ " background-color: #a2f8a2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row47_col4 {\n",
+ " background-color: #f3e4e4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row48_col0 {\n",
+ " background-color: #5ffc5f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row48_col2, #T_8eb4f_row57_col2 {\n",
+ " background-color: #44fe44;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row48_col4 {\n",
+ " background-color: #f6bbbb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row49_col0 {\n",
+ " background-color: #6bfb6b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row49_col2, #T_8eb4f_row53_col2, #T_8eb4f_row58_col2 {\n",
+ " background-color: #55fd55;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row49_col4, #T_8eb4f_row54_col4 {\n",
+ " background-color: #f89090;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row50_col0 {\n",
+ " background-color: #7afa7a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row51_col4 {\n",
+ " background-color: #f6abab;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row52_col4 {\n",
+ " background-color: #f6b7b7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row53_col4 {\n",
+ " background-color: #f4d0d0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row55_col0 {\n",
+ " background-color: #64fc64;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row55_col2, #T_8eb4f_row59_col2 {\n",
+ " background-color: #4efd4e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row55_col4 {\n",
+ " background-color: #f6b2b2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row56_col2 {\n",
+ " background-color: #49fe49;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row56_col4 {\n",
+ " background-color: #f3f0f0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row57_col0 {\n",
+ " background-color: #67fc67;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row57_col4 {\n",
+ " background-color: #f7a0a0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row58_col4 {\n",
+ " background-color: #f79595;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_8eb4f_row59_col4 {\n",
+ " background-color: #f6b3b3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_8eb4f\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_8eb4f_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">% of load of jobs/services bidded</th>\n",
+ " <th id=\"T_8eb4f_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\"></th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_8eb4f_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_8eb4f_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_8eb4f_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Slowdown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_8eb4f_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_8eb4f_level2_col1\" class=\"col_heading level2 col1\" >Std</th>\n",
+ " <th id=\"T_8eb4f_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_8eb4f_level2_col3\" class=\"col_heading level2 col3\" >Std</th>\n",
+ " <th id=\"T_8eb4f_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_8eb4f_level2_col5\" class=\"col_heading level2 col5\" >Std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_8eb4f_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_8eb4f_row0_col0\" class=\"data row0 col0\" >91.10</td>\n",
+ " <td id=\"T_8eb4f_row0_col1\" class=\"data row0 col1\" >3.61</td>\n",
+ " <td id=\"T_8eb4f_row0_col2\" class=\"data row0 col2\" >99.09</td>\n",
+ " <td id=\"T_8eb4f_row0_col3\" class=\"data row0 col3\" >1.36</td>\n",
+ " <td id=\"T_8eb4f_row0_col4\" class=\"data row0 col4\" >1.40</td>\n",
+ " <td id=\"T_8eb4f_row0_col5\" class=\"data row0 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_8eb4f_row1_col0\" class=\"data row1 col0\" >90.66</td>\n",
+ " <td id=\"T_8eb4f_row1_col1\" class=\"data row1 col1\" >3.14</td>\n",
+ " <td id=\"T_8eb4f_row1_col2\" class=\"data row1 col2\" >98.58</td>\n",
+ " <td id=\"T_8eb4f_row1_col3\" class=\"data row1 col3\" >2.47</td>\n",
+ " <td id=\"T_8eb4f_row1_col4\" class=\"data row1 col4\" >1.51</td>\n",
+ " <td id=\"T_8eb4f_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_8eb4f_row2_col0\" class=\"data row2 col0\" >91.48</td>\n",
+ " <td id=\"T_8eb4f_row2_col1\" class=\"data row2 col1\" >3.07</td>\n",
+ " <td id=\"T_8eb4f_row2_col2\" class=\"data row2 col2\" >98.57</td>\n",
+ " <td id=\"T_8eb4f_row2_col3\" class=\"data row2 col3\" >2.66</td>\n",
+ " <td id=\"T_8eb4f_row2_col4\" class=\"data row2 col4\" >1.53</td>\n",
+ " <td id=\"T_8eb4f_row2_col5\" class=\"data row2 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_8eb4f_row3_col0\" class=\"data row3 col0\" >91.14</td>\n",
+ " <td id=\"T_8eb4f_row3_col1\" class=\"data row3 col1\" >3.80</td>\n",
+ " <td id=\"T_8eb4f_row3_col2\" class=\"data row3 col2\" >98.21</td>\n",
+ " <td id=\"T_8eb4f_row3_col3\" class=\"data row3 col3\" >2.67</td>\n",
+ " <td id=\"T_8eb4f_row3_col4\" class=\"data row3 col4\" >1.45</td>\n",
+ " <td id=\"T_8eb4f_row3_col5\" class=\"data row3 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_8eb4f_row4_col0\" class=\"data row4 col0\" >84.62</td>\n",
+ " <td id=\"T_8eb4f_row4_col1\" class=\"data row4 col1\" >4.28</td>\n",
+ " <td id=\"T_8eb4f_row4_col2\" class=\"data row4 col2\" >91.56</td>\n",
+ " <td id=\"T_8eb4f_row4_col3\" class=\"data row4 col3\" >5.02</td>\n",
+ " <td id=\"T_8eb4f_row4_col4\" class=\"data row4 col4\" >2.15</td>\n",
+ " <td id=\"T_8eb4f_row4_col5\" class=\"data row4 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_8eb4f_row5_col0\" class=\"data row5 col0\" >87.70</td>\n",
+ " <td id=\"T_8eb4f_row5_col1\" class=\"data row5 col1\" >3.76</td>\n",
+ " <td id=\"T_8eb4f_row5_col2\" class=\"data row5 col2\" >95.67</td>\n",
+ " <td id=\"T_8eb4f_row5_col3\" class=\"data row5 col3\" >5.33</td>\n",
+ " <td id=\"T_8eb4f_row5_col4\" class=\"data row5 col4\" >1.53</td>\n",
+ " <td id=\"T_8eb4f_row5_col5\" class=\"data row5 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_8eb4f_row6_col0\" class=\"data row6 col0\" >88.99</td>\n",
+ " <td id=\"T_8eb4f_row6_col1\" class=\"data row6 col1\" >3.48</td>\n",
+ " <td id=\"T_8eb4f_row6_col2\" class=\"data row6 col2\" >97.12</td>\n",
+ " <td id=\"T_8eb4f_row6_col3\" class=\"data row6 col3\" >3.13</td>\n",
+ " <td id=\"T_8eb4f_row6_col4\" class=\"data row6 col4\" >1.51</td>\n",
+ " <td id=\"T_8eb4f_row6_col5\" class=\"data row6 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_8eb4f_row7_col0\" class=\"data row7 col0\" >88.52</td>\n",
+ " <td id=\"T_8eb4f_row7_col1\" class=\"data row7 col1\" >3.13</td>\n",
+ " <td id=\"T_8eb4f_row7_col2\" class=\"data row7 col2\" >96.68</td>\n",
+ " <td id=\"T_8eb4f_row7_col3\" class=\"data row7 col3\" >3.53</td>\n",
+ " <td id=\"T_8eb4f_row7_col4\" class=\"data row7 col4\" >1.46</td>\n",
+ " <td id=\"T_8eb4f_row7_col5\" class=\"data row7 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_8eb4f_row8_col0\" class=\"data row8 col0\" >88.74</td>\n",
+ " <td id=\"T_8eb4f_row8_col1\" class=\"data row8 col1\" >3.49</td>\n",
+ " <td id=\"T_8eb4f_row8_col2\" class=\"data row8 col2\" >98.34</td>\n",
+ " <td id=\"T_8eb4f_row8_col3\" class=\"data row8 col3\" >2.18</td>\n",
+ " <td id=\"T_8eb4f_row8_col4\" class=\"data row8 col4\" >1.37</td>\n",
+ " <td id=\"T_8eb4f_row8_col5\" class=\"data row8 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_8eb4f_row9_col0\" class=\"data row9 col0\" >85.03</td>\n",
+ " <td id=\"T_8eb4f_row9_col1\" class=\"data row9 col1\" >4.18</td>\n",
+ " <td id=\"T_8eb4f_row9_col2\" class=\"data row9 col2\" >87.43</td>\n",
+ " <td id=\"T_8eb4f_row9_col3\" class=\"data row9 col3\" >5.31</td>\n",
+ " <td id=\"T_8eb4f_row9_col4\" class=\"data row9 col4\" >2.32</td>\n",
+ " <td id=\"T_8eb4f_row9_col5\" class=\"data row9 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_8eb4f_row10_col0\" class=\"data row10 col0\" >88.68</td>\n",
+ " <td id=\"T_8eb4f_row10_col1\" class=\"data row10 col1\" >2.85</td>\n",
+ " <td id=\"T_8eb4f_row10_col2\" class=\"data row10 col2\" >96.43</td>\n",
+ " <td id=\"T_8eb4f_row10_col3\" class=\"data row10 col3\" >4.64</td>\n",
+ " <td id=\"T_8eb4f_row10_col4\" class=\"data row10 col4\" >1.52</td>\n",
+ " <td id=\"T_8eb4f_row10_col5\" class=\"data row10 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_8eb4f_row11_col0\" class=\"data row11 col0\" >87.15</td>\n",
+ " <td id=\"T_8eb4f_row11_col1\" class=\"data row11 col1\" >3.77</td>\n",
+ " <td id=\"T_8eb4f_row11_col2\" class=\"data row11 col2\" >96.30</td>\n",
+ " <td id=\"T_8eb4f_row11_col3\" class=\"data row11 col3\" >4.24</td>\n",
+ " <td id=\"T_8eb4f_row11_col4\" class=\"data row11 col4\" >1.44</td>\n",
+ " <td id=\"T_8eb4f_row11_col5\" class=\"data row11 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_8eb4f_row12_col0\" class=\"data row12 col0\" >89.27</td>\n",
+ " <td id=\"T_8eb4f_row12_col1\" class=\"data row12 col1\" >3.41</td>\n",
+ " <td id=\"T_8eb4f_row12_col2\" class=\"data row12 col2\" >98.30</td>\n",
+ " <td id=\"T_8eb4f_row12_col3\" class=\"data row12 col3\" >2.23</td>\n",
+ " <td id=\"T_8eb4f_row12_col4\" class=\"data row12 col4\" >1.37</td>\n",
+ " <td id=\"T_8eb4f_row12_col5\" class=\"data row12 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_8eb4f_row13_col0\" class=\"data row13 col0\" >87.95</td>\n",
+ " <td id=\"T_8eb4f_row13_col1\" class=\"data row13 col1\" >2.90</td>\n",
+ " <td id=\"T_8eb4f_row13_col2\" class=\"data row13 col2\" >96.49</td>\n",
+ " <td id=\"T_8eb4f_row13_col3\" class=\"data row13 col3\" >3.45</td>\n",
+ " <td id=\"T_8eb4f_row13_col4\" class=\"data row13 col4\" >1.51</td>\n",
+ " <td id=\"T_8eb4f_row13_col5\" class=\"data row13 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_8eb4f_row14_col0\" class=\"data row14 col0\" >84.24</td>\n",
+ " <td id=\"T_8eb4f_row14_col1\" class=\"data row14 col1\" >5.45</td>\n",
+ " <td id=\"T_8eb4f_row14_col2\" class=\"data row14 col2\" >87.63</td>\n",
+ " <td id=\"T_8eb4f_row14_col3\" class=\"data row14 col3\" >6.26</td>\n",
+ " <td id=\"T_8eb4f_row14_col4\" class=\"data row14 col4\" >2.35</td>\n",
+ " <td id=\"T_8eb4f_row14_col5\" class=\"data row14 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_8eb4f_row15_col0\" class=\"data row15 col0\" >87.86</td>\n",
+ " <td id=\"T_8eb4f_row15_col1\" class=\"data row15 col1\" >3.67</td>\n",
+ " <td id=\"T_8eb4f_row15_col2\" class=\"data row15 col2\" >96.82</td>\n",
+ " <td id=\"T_8eb4f_row15_col3\" class=\"data row15 col3\" >3.16</td>\n",
+ " <td id=\"T_8eb4f_row15_col4\" class=\"data row15 col4\" >1.43</td>\n",
+ " <td id=\"T_8eb4f_row15_col5\" class=\"data row15 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_8eb4f_row16_col0\" class=\"data row16 col0\" >89.13</td>\n",
+ " <td id=\"T_8eb4f_row16_col1\" class=\"data row16 col1\" >3.39</td>\n",
+ " <td id=\"T_8eb4f_row16_col2\" class=\"data row16 col2\" >97.86</td>\n",
+ " <td id=\"T_8eb4f_row16_col3\" class=\"data row16 col3\" >2.75</td>\n",
+ " <td id=\"T_8eb4f_row16_col4\" class=\"data row16 col4\" >1.37</td>\n",
+ " <td id=\"T_8eb4f_row16_col5\" class=\"data row16 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_8eb4f_row17_col0\" class=\"data row17 col0\" >88.53</td>\n",
+ " <td id=\"T_8eb4f_row17_col1\" class=\"data row17 col1\" >2.85</td>\n",
+ " <td id=\"T_8eb4f_row17_col2\" class=\"data row17 col2\" >96.11</td>\n",
+ " <td id=\"T_8eb4f_row17_col3\" class=\"data row17 col3\" >4.21</td>\n",
+ " <td id=\"T_8eb4f_row17_col4\" class=\"data row17 col4\" >1.53</td>\n",
+ " <td id=\"T_8eb4f_row17_col5\" class=\"data row17 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_8eb4f_row18_col0\" class=\"data row18 col0\" >88.89</td>\n",
+ " <td id=\"T_8eb4f_row18_col1\" class=\"data row18 col1\" >2.81</td>\n",
+ " <td id=\"T_8eb4f_row18_col2\" class=\"data row18 col2\" >96.18</td>\n",
+ " <td id=\"T_8eb4f_row18_col3\" class=\"data row18 col3\" >4.40</td>\n",
+ " <td id=\"T_8eb4f_row18_col4\" class=\"data row18 col4\" >1.53</td>\n",
+ " <td id=\"T_8eb4f_row18_col5\" class=\"data row18 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_8eb4f_row19_col0\" class=\"data row19 col0\" >83.47</td>\n",
+ " <td id=\"T_8eb4f_row19_col1\" class=\"data row19 col1\" >5.66</td>\n",
+ " <td id=\"T_8eb4f_row19_col2\" class=\"data row19 col2\" >87.62</td>\n",
+ " <td id=\"T_8eb4f_row19_col3\" class=\"data row19 col3\" >5.86</td>\n",
+ " <td id=\"T_8eb4f_row19_col4\" class=\"data row19 col4\" >2.23</td>\n",
+ " <td id=\"T_8eb4f_row19_col5\" class=\"data row19 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_8eb4f_row20_col0\" class=\"data row20 col0\" >87.84</td>\n",
+ " <td id=\"T_8eb4f_row20_col1\" class=\"data row20 col1\" >3.50</td>\n",
+ " <td id=\"T_8eb4f_row20_col2\" class=\"data row20 col2\" >96.70</td>\n",
+ " <td id=\"T_8eb4f_row20_col3\" class=\"data row20 col3\" >3.14</td>\n",
+ " <td id=\"T_8eb4f_row20_col4\" class=\"data row20 col4\" >1.79</td>\n",
+ " <td id=\"T_8eb4f_row20_col5\" class=\"data row20 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_8eb4f_row21_col0\" class=\"data row21 col0\" >88.11</td>\n",
+ " <td id=\"T_8eb4f_row21_col1\" class=\"data row21 col1\" >4.04</td>\n",
+ " <td id=\"T_8eb4f_row21_col2\" class=\"data row21 col2\" >92.95</td>\n",
+ " <td id=\"T_8eb4f_row21_col3\" class=\"data row21 col3\" >6.07</td>\n",
+ " <td id=\"T_8eb4f_row21_col4\" class=\"data row21 col4\" >2.00</td>\n",
+ " <td id=\"T_8eb4f_row21_col5\" class=\"data row21 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_8eb4f_row22_col0\" class=\"data row22 col0\" >88.71</td>\n",
+ " <td id=\"T_8eb4f_row22_col1\" class=\"data row22 col1\" >2.90</td>\n",
+ " <td id=\"T_8eb4f_row22_col2\" class=\"data row22 col2\" >96.66</td>\n",
+ " <td id=\"T_8eb4f_row22_col3\" class=\"data row22 col3\" >4.66</td>\n",
+ " <td id=\"T_8eb4f_row22_col4\" class=\"data row22 col4\" >1.48</td>\n",
+ " <td id=\"T_8eb4f_row22_col5\" class=\"data row22 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_8eb4f_row23_col0\" class=\"data row23 col0\" >87.62</td>\n",
+ " <td id=\"T_8eb4f_row23_col1\" class=\"data row23 col1\" >4.08</td>\n",
+ " <td id=\"T_8eb4f_row23_col2\" class=\"data row23 col2\" >96.17</td>\n",
+ " <td id=\"T_8eb4f_row23_col3\" class=\"data row23 col3\" >4.01</td>\n",
+ " <td id=\"T_8eb4f_row23_col4\" class=\"data row23 col4\" >1.43</td>\n",
+ " <td id=\"T_8eb4f_row23_col5\" class=\"data row23 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_8eb4f_row24_col0\" class=\"data row24 col0\" >87.82</td>\n",
+ " <td id=\"T_8eb4f_row24_col1\" class=\"data row24 col1\" >3.40</td>\n",
+ " <td id=\"T_8eb4f_row24_col2\" class=\"data row24 col2\" >95.74</td>\n",
+ " <td id=\"T_8eb4f_row24_col3\" class=\"data row24 col3\" >3.61</td>\n",
+ " <td id=\"T_8eb4f_row24_col4\" class=\"data row24 col4\" >1.82</td>\n",
+ " <td id=\"T_8eb4f_row24_col5\" class=\"data row24 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_8eb4f_row25_col0\" class=\"data row25 col0\" >88.20</td>\n",
+ " <td id=\"T_8eb4f_row25_col1\" class=\"data row25 col1\" >3.14</td>\n",
+ " <td id=\"T_8eb4f_row25_col2\" class=\"data row25 col2\" >96.70</td>\n",
+ " <td id=\"T_8eb4f_row25_col3\" class=\"data row25 col3\" >4.32</td>\n",
+ " <td id=\"T_8eb4f_row25_col4\" class=\"data row25 col4\" >1.49</td>\n",
+ " <td id=\"T_8eb4f_row25_col5\" class=\"data row25 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_8eb4f_row26_col0\" class=\"data row26 col0\" >88.03</td>\n",
+ " <td id=\"T_8eb4f_row26_col1\" class=\"data row26 col1\" >3.75</td>\n",
+ " <td id=\"T_8eb4f_row26_col2\" class=\"data row26 col2\" >93.95</td>\n",
+ " <td id=\"T_8eb4f_row26_col3\" class=\"data row26 col3\" >4.63</td>\n",
+ " <td id=\"T_8eb4f_row26_col4\" class=\"data row26 col4\" >1.99</td>\n",
+ " <td id=\"T_8eb4f_row26_col5\" class=\"data row26 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_8eb4f_row27_col0\" class=\"data row27 col0\" >87.30</td>\n",
+ " <td id=\"T_8eb4f_row27_col1\" class=\"data row27 col1\" >3.90</td>\n",
+ " <td id=\"T_8eb4f_row27_col2\" class=\"data row27 col2\" >96.50</td>\n",
+ " <td id=\"T_8eb4f_row27_col3\" class=\"data row27 col3\" >3.55</td>\n",
+ " <td id=\"T_8eb4f_row27_col4\" class=\"data row27 col4\" >1.41</td>\n",
+ " <td id=\"T_8eb4f_row27_col5\" class=\"data row27 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_8eb4f_row28_col0\" class=\"data row28 col0\" >87.76</td>\n",
+ " <td id=\"T_8eb4f_row28_col1\" class=\"data row28 col1\" >3.68</td>\n",
+ " <td id=\"T_8eb4f_row28_col2\" class=\"data row28 col2\" >96.15</td>\n",
+ " <td id=\"T_8eb4f_row28_col3\" class=\"data row28 col3\" >4.70</td>\n",
+ " <td id=\"T_8eb4f_row28_col4\" class=\"data row28 col4\" >1.78</td>\n",
+ " <td id=\"T_8eb4f_row28_col5\" class=\"data row28 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_8eb4f_row29_col0\" class=\"data row29 col0\" >87.70</td>\n",
+ " <td id=\"T_8eb4f_row29_col1\" class=\"data row29 col1\" >3.16</td>\n",
+ " <td id=\"T_8eb4f_row29_col2\" class=\"data row29 col2\" >96.53</td>\n",
+ " <td id=\"T_8eb4f_row29_col3\" class=\"data row29 col3\" >3.36</td>\n",
+ " <td id=\"T_8eb4f_row29_col4\" class=\"data row29 col4\" >1.51</td>\n",
+ " <td id=\"T_8eb4f_row29_col5\" class=\"data row29 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_8eb4f_row30_col0\" class=\"data row30 col0\" >89.32</td>\n",
+ " <td id=\"T_8eb4f_row30_col1\" class=\"data row30 col1\" >2.99</td>\n",
+ " <td id=\"T_8eb4f_row30_col2\" class=\"data row30 col2\" >96.18</td>\n",
+ " <td id=\"T_8eb4f_row30_col3\" class=\"data row30 col3\" >4.29</td>\n",
+ " <td id=\"T_8eb4f_row30_col4\" class=\"data row30 col4\" >1.48</td>\n",
+ " <td id=\"T_8eb4f_row30_col5\" class=\"data row30 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_8eb4f_row31_col0\" class=\"data row31 col0\" >87.44</td>\n",
+ " <td id=\"T_8eb4f_row31_col1\" class=\"data row31 col1\" >5.50</td>\n",
+ " <td id=\"T_8eb4f_row31_col2\" class=\"data row31 col2\" >93.65</td>\n",
+ " <td id=\"T_8eb4f_row31_col3\" class=\"data row31 col3\" >5.34</td>\n",
+ " <td id=\"T_8eb4f_row31_col4\" class=\"data row31 col4\" >1.87</td>\n",
+ " <td id=\"T_8eb4f_row31_col5\" class=\"data row31 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_8eb4f_row32_col0\" class=\"data row32 col0\" >88.91</td>\n",
+ " <td id=\"T_8eb4f_row32_col1\" class=\"data row32 col1\" >3.40</td>\n",
+ " <td id=\"T_8eb4f_row32_col2\" class=\"data row32 col2\" >97.90</td>\n",
+ " <td id=\"T_8eb4f_row32_col3\" class=\"data row32 col3\" >2.72</td>\n",
+ " <td id=\"T_8eb4f_row32_col4\" class=\"data row32 col4\" >1.35</td>\n",
+ " <td id=\"T_8eb4f_row32_col5\" class=\"data row32 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_8eb4f_row33_col0\" class=\"data row33 col0\" >87.63</td>\n",
+ " <td id=\"T_8eb4f_row33_col1\" class=\"data row33 col1\" >3.49</td>\n",
+ " <td id=\"T_8eb4f_row33_col2\" class=\"data row33 col2\" >94.76</td>\n",
+ " <td id=\"T_8eb4f_row33_col3\" class=\"data row33 col3\" >4.33</td>\n",
+ " <td id=\"T_8eb4f_row33_col4\" class=\"data row33 col4\" >1.89</td>\n",
+ " <td id=\"T_8eb4f_row33_col5\" class=\"data row33 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_8eb4f_row34_col0\" class=\"data row34 col0\" >87.92</td>\n",
+ " <td id=\"T_8eb4f_row34_col1\" class=\"data row34 col1\" >4.29</td>\n",
+ " <td id=\"T_8eb4f_row34_col2\" class=\"data row34 col2\" >94.49</td>\n",
+ " <td id=\"T_8eb4f_row34_col3\" class=\"data row34 col3\" >4.48</td>\n",
+ " <td id=\"T_8eb4f_row34_col4\" class=\"data row34 col4\" >2.00</td>\n",
+ " <td id=\"T_8eb4f_row34_col5\" class=\"data row34 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_8eb4f_row35_col0\" class=\"data row35 col0\" >87.00</td>\n",
+ " <td id=\"T_8eb4f_row35_col1\" class=\"data row35 col1\" >3.66</td>\n",
+ " <td id=\"T_8eb4f_row35_col2\" class=\"data row35 col2\" >96.77</td>\n",
+ " <td id=\"T_8eb4f_row35_col3\" class=\"data row35 col3\" >3.47</td>\n",
+ " <td id=\"T_8eb4f_row35_col4\" class=\"data row35 col4\" >1.41</td>\n",
+ " <td id=\"T_8eb4f_row35_col5\" class=\"data row35 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_8eb4f_row36_col0\" class=\"data row36 col0\" >88.88</td>\n",
+ " <td id=\"T_8eb4f_row36_col1\" class=\"data row36 col1\" >3.11</td>\n",
+ " <td id=\"T_8eb4f_row36_col2\" class=\"data row36 col2\" >98.25</td>\n",
+ " <td id=\"T_8eb4f_row36_col3\" class=\"data row36 col3\" >2.38</td>\n",
+ " <td id=\"T_8eb4f_row36_col4\" class=\"data row36 col4\" >1.34</td>\n",
+ " <td id=\"T_8eb4f_row36_col5\" class=\"data row36 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_8eb4f_row37_col0\" class=\"data row37 col0\" >87.35</td>\n",
+ " <td id=\"T_8eb4f_row37_col1\" class=\"data row37 col1\" >4.13</td>\n",
+ " <td id=\"T_8eb4f_row37_col2\" class=\"data row37 col2\" >94.24</td>\n",
+ " <td id=\"T_8eb4f_row37_col3\" class=\"data row37 col3\" >4.14</td>\n",
+ " <td id=\"T_8eb4f_row37_col4\" class=\"data row37 col4\" >1.91</td>\n",
+ " <td id=\"T_8eb4f_row37_col5\" class=\"data row37 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_8eb4f_row38_col0\" class=\"data row38 col0\" >88.27</td>\n",
+ " <td id=\"T_8eb4f_row38_col1\" class=\"data row38 col1\" >3.09</td>\n",
+ " <td id=\"T_8eb4f_row38_col2\" class=\"data row38 col2\" >97.05</td>\n",
+ " <td id=\"T_8eb4f_row38_col3\" class=\"data row38 col3\" >3.36</td>\n",
+ " <td id=\"T_8eb4f_row38_col4\" class=\"data row38 col4\" >1.50</td>\n",
+ " <td id=\"T_8eb4f_row38_col5\" class=\"data row38 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_8eb4f_row39_col0\" class=\"data row39 col0\" >86.12</td>\n",
+ " <td id=\"T_8eb4f_row39_col1\" class=\"data row39 col1\" >5.22</td>\n",
+ " <td id=\"T_8eb4f_row39_col2\" class=\"data row39 col2\" >94.75</td>\n",
+ " <td id=\"T_8eb4f_row39_col3\" class=\"data row39 col3\" >4.62</td>\n",
+ " <td id=\"T_8eb4f_row39_col4\" class=\"data row39 col4\" >1.86</td>\n",
+ " <td id=\"T_8eb4f_row39_col5\" class=\"data row39 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_8eb4f_row40_col0\" class=\"data row40 col0\" >89.26</td>\n",
+ " <td id=\"T_8eb4f_row40_col1\" class=\"data row40 col1\" >3.30</td>\n",
+ " <td id=\"T_8eb4f_row40_col2\" class=\"data row40 col2\" >98.12</td>\n",
+ " <td id=\"T_8eb4f_row40_col3\" class=\"data row40 col3\" >2.41</td>\n",
+ " <td id=\"T_8eb4f_row40_col4\" class=\"data row40 col4\" >1.35</td>\n",
+ " <td id=\"T_8eb4f_row40_col5\" class=\"data row40 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_8eb4f_row41_col0\" class=\"data row41 col0\" >87.69</td>\n",
+ " <td id=\"T_8eb4f_row41_col1\" class=\"data row41 col1\" >3.13</td>\n",
+ " <td id=\"T_8eb4f_row41_col2\" class=\"data row41 col2\" >96.54</td>\n",
+ " <td id=\"T_8eb4f_row41_col3\" class=\"data row41 col3\" >3.97</td>\n",
+ " <td id=\"T_8eb4f_row41_col4\" class=\"data row41 col4\" >1.49</td>\n",
+ " <td id=\"T_8eb4f_row41_col5\" class=\"data row41 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_8eb4f_row42_col0\" class=\"data row42 col0\" >87.79</td>\n",
+ " <td id=\"T_8eb4f_row42_col1\" class=\"data row42 col1\" >4.38</td>\n",
+ " <td id=\"T_8eb4f_row42_col2\" class=\"data row42 col2\" >95.06</td>\n",
+ " <td id=\"T_8eb4f_row42_col3\" class=\"data row42 col3\" >3.03</td>\n",
+ " <td id=\"T_8eb4f_row42_col4\" class=\"data row42 col4\" >1.98</td>\n",
+ " <td id=\"T_8eb4f_row42_col5\" class=\"data row42 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_8eb4f_row43_col0\" class=\"data row43 col0\" >87.06</td>\n",
+ " <td id=\"T_8eb4f_row43_col1\" class=\"data row43 col1\" >3.99</td>\n",
+ " <td id=\"T_8eb4f_row43_col2\" class=\"data row43 col2\" >94.67</td>\n",
+ " <td id=\"T_8eb4f_row43_col3\" class=\"data row43 col3\" >4.21</td>\n",
+ " <td id=\"T_8eb4f_row43_col4\" class=\"data row43 col4\" >1.84</td>\n",
+ " <td id=\"T_8eb4f_row43_col5\" class=\"data row43 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_8eb4f_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_8eb4f_row44_col0\" class=\"data row44 col0\" >89.97</td>\n",
+ " <td id=\"T_8eb4f_row44_col1\" class=\"data row44 col1\" >3.19</td>\n",
+ " <td id=\"T_8eb4f_row44_col2\" class=\"data row44 col2\" >97.92</td>\n",
+ " <td id=\"T_8eb4f_row44_col3\" class=\"data row44 col3\" >2.86</td>\n",
+ " <td id=\"T_8eb4f_row44_col4\" class=\"data row44 col4\" >1.57</td>\n",
+ " <td id=\"T_8eb4f_row44_col5\" class=\"data row44 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_8eb4f_row45_col0\" class=\"data row45 col0\" >89.72</td>\n",
+ " <td id=\"T_8eb4f_row45_col1\" class=\"data row45 col1\" >2.96</td>\n",
+ " <td id=\"T_8eb4f_row45_col2\" class=\"data row45 col2\" >97.25</td>\n",
+ " <td id=\"T_8eb4f_row45_col3\" class=\"data row45 col3\" >3.05</td>\n",
+ " <td id=\"T_8eb4f_row45_col4\" class=\"data row45 col4\" >1.70</td>\n",
+ " <td id=\"T_8eb4f_row45_col5\" class=\"data row45 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_8eb4f_row46_col0\" class=\"data row46 col0\" >89.29</td>\n",
+ " <td id=\"T_8eb4f_row46_col1\" class=\"data row46 col1\" >3.89</td>\n",
+ " <td id=\"T_8eb4f_row46_col2\" class=\"data row46 col2\" >97.06</td>\n",
+ " <td id=\"T_8eb4f_row46_col3\" class=\"data row46 col3\" >3.05</td>\n",
+ " <td id=\"T_8eb4f_row46_col4\" class=\"data row46 col4\" >1.74</td>\n",
+ " <td id=\"T_8eb4f_row46_col5\" class=\"data row46 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_8eb4f_row47_col0\" class=\"data row47 col0\" >86.67</td>\n",
+ " <td id=\"T_8eb4f_row47_col1\" class=\"data row47 col1\" >3.82</td>\n",
+ " <td id=\"T_8eb4f_row47_col2\" class=\"data row47 col2\" >96.46</td>\n",
+ " <td id=\"T_8eb4f_row47_col3\" class=\"data row47 col3\" >4.71</td>\n",
+ " <td id=\"T_8eb4f_row47_col4\" class=\"data row47 col4\" >1.38</td>\n",
+ " <td id=\"T_8eb4f_row47_col5\" class=\"data row47 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_8eb4f_row48_col0\" class=\"data row48 col0\" >89.60</td>\n",
+ " <td id=\"T_8eb4f_row48_col1\" class=\"data row48 col1\" >3.59</td>\n",
+ " <td id=\"T_8eb4f_row48_col2\" class=\"data row48 col2\" >98.04</td>\n",
+ " <td id=\"T_8eb4f_row48_col3\" class=\"data row48 col3\" >2.37</td>\n",
+ " <td id=\"T_8eb4f_row48_col4\" class=\"data row48 col4\" >1.55</td>\n",
+ " <td id=\"T_8eb4f_row48_col5\" class=\"data row48 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_8eb4f_row49_col0\" class=\"data row49 col0\" >89.05</td>\n",
+ " <td id=\"T_8eb4f_row49_col1\" class=\"data row49 col1\" >3.83</td>\n",
+ " <td id=\"T_8eb4f_row49_col2\" class=\"data row49 col2\" >96.97</td>\n",
+ " <td id=\"T_8eb4f_row49_col3\" class=\"data row49 col3\" >2.85</td>\n",
+ " <td id=\"T_8eb4f_row49_col4\" class=\"data row49 col4\" >1.74</td>\n",
+ " <td id=\"T_8eb4f_row49_col5\" class=\"data row49 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_8eb4f_row50_col0\" class=\"data row50 col0\" >88.40</td>\n",
+ " <td id=\"T_8eb4f_row50_col1\" class=\"data row50 col1\" >3.29</td>\n",
+ " <td id=\"T_8eb4f_row50_col2\" class=\"data row50 col2\" >96.45</td>\n",
+ " <td id=\"T_8eb4f_row50_col3\" class=\"data row50 col3\" >4.23</td>\n",
+ " <td id=\"T_8eb4f_row50_col4\" class=\"data row50 col4\" >1.46</td>\n",
+ " <td id=\"T_8eb4f_row50_col5\" class=\"data row50 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_8eb4f_row51_col0\" class=\"data row51 col0\" >89.54</td>\n",
+ " <td id=\"T_8eb4f_row51_col1\" class=\"data row51 col1\" >3.68</td>\n",
+ " <td id=\"T_8eb4f_row51_col2\" class=\"data row51 col2\" >96.42</td>\n",
+ " <td id=\"T_8eb4f_row51_col3\" class=\"data row51 col3\" >4.13</td>\n",
+ " <td id=\"T_8eb4f_row51_col4\" class=\"data row51 col4\" >1.62</td>\n",
+ " <td id=\"T_8eb4f_row51_col5\" class=\"data row51 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_8eb4f_row52_col0\" class=\"data row52 col0\" >89.39</td>\n",
+ " <td id=\"T_8eb4f_row52_col1\" class=\"data row52 col1\" >3.14</td>\n",
+ " <td id=\"T_8eb4f_row52_col2\" class=\"data row52 col2\" >98.22</td>\n",
+ " <td id=\"T_8eb4f_row52_col3\" class=\"data row52 col3\" >2.30</td>\n",
+ " <td id=\"T_8eb4f_row52_col4\" class=\"data row52 col4\" >1.57</td>\n",
+ " <td id=\"T_8eb4f_row52_col5\" class=\"data row52 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_8eb4f_row53_col0\" class=\"data row53 col0\" >87.77</td>\n",
+ " <td id=\"T_8eb4f_row53_col1\" class=\"data row53 col1\" >3.38</td>\n",
+ " <td id=\"T_8eb4f_row53_col2\" class=\"data row53 col2\" >96.95</td>\n",
+ " <td id=\"T_8eb4f_row53_col3\" class=\"data row53 col3\" >3.26</td>\n",
+ " <td id=\"T_8eb4f_row53_col4\" class=\"data row53 col4\" >1.47</td>\n",
+ " <td id=\"T_8eb4f_row53_col5\" class=\"data row53 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_8eb4f_row54_col0\" class=\"data row54 col0\" >89.72</td>\n",
+ " <td id=\"T_8eb4f_row54_col1\" class=\"data row54 col1\" >3.52</td>\n",
+ " <td id=\"T_8eb4f_row54_col2\" class=\"data row54 col2\" >96.76</td>\n",
+ " <td id=\"T_8eb4f_row54_col3\" class=\"data row54 col3\" >3.23</td>\n",
+ " <td id=\"T_8eb4f_row54_col4\" class=\"data row54 col4\" >1.74</td>\n",
+ " <td id=\"T_8eb4f_row54_col5\" class=\"data row54 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_8eb4f_row55_col0\" class=\"data row55 col0\" >89.35</td>\n",
+ " <td id=\"T_8eb4f_row55_col1\" class=\"data row55 col1\" >3.94</td>\n",
+ " <td id=\"T_8eb4f_row55_col2\" class=\"data row55 col2\" >97.42</td>\n",
+ " <td id=\"T_8eb4f_row55_col3\" class=\"data row55 col3\" >3.06</td>\n",
+ " <td id=\"T_8eb4f_row55_col4\" class=\"data row55 col4\" >1.59</td>\n",
+ " <td id=\"T_8eb4f_row55_col5\" class=\"data row55 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_8eb4f_row56_col0\" class=\"data row56 col0\" >88.15</td>\n",
+ " <td id=\"T_8eb4f_row56_col1\" class=\"data row56 col1\" >3.61</td>\n",
+ " <td id=\"T_8eb4f_row56_col2\" class=\"data row56 col2\" >97.73</td>\n",
+ " <td id=\"T_8eb4f_row56_col3\" class=\"data row56 col3\" >3.40</td>\n",
+ " <td id=\"T_8eb4f_row56_col4\" class=\"data row56 col4\" >1.33</td>\n",
+ " <td id=\"T_8eb4f_row56_col5\" class=\"data row56 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_8eb4f_row57_col0\" class=\"data row57 col0\" >89.24</td>\n",
+ " <td id=\"T_8eb4f_row57_col1\" class=\"data row57 col1\" >3.20</td>\n",
+ " <td id=\"T_8eb4f_row57_col2\" class=\"data row57 col2\" >98.08</td>\n",
+ " <td id=\"T_8eb4f_row57_col3\" class=\"data row57 col3\" >1.89</td>\n",
+ " <td id=\"T_8eb4f_row57_col4\" class=\"data row57 col4\" >1.67</td>\n",
+ " <td id=\"T_8eb4f_row57_col5\" class=\"data row57 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_8eb4f_row58_col0\" class=\"data row58 col0\" >89.29</td>\n",
+ " <td id=\"T_8eb4f_row58_col1\" class=\"data row58 col1\" >3.42</td>\n",
+ " <td id=\"T_8eb4f_row58_col2\" class=\"data row58 col2\" >96.97</td>\n",
+ " <td id=\"T_8eb4f_row58_col3\" class=\"data row58 col3\" >3.11</td>\n",
+ " <td id=\"T_8eb4f_row58_col4\" class=\"data row58 col4\" >1.72</td>\n",
+ " <td id=\"T_8eb4f_row58_col5\" class=\"data row58 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_8eb4f_row59_col0\" class=\"data row59 col0\" >89.62</td>\n",
+ " <td id=\"T_8eb4f_row59_col1\" class=\"data row59 col1\" >3.84</td>\n",
+ " <td id=\"T_8eb4f_row59_col2\" class=\"data row59 col2\" >97.45</td>\n",
+ " <td id=\"T_8eb4f_row59_col3\" class=\"data row59 col3\" >3.19</td>\n",
+ " <td id=\"T_8eb4f_row59_col4\" class=\"data row59 col4\" >1.59</td>\n",
+ " <td id=\"T_8eb4f_row59_col5\" class=\"data row59 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_8eb4f_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_8eb4f_row60_col0\" class=\"data row60 col0\" >91.10</td>\n",
+ " <td id=\"T_8eb4f_row60_col1\" class=\"data row60 col1\" >3.61</td>\n",
+ " <td id=\"T_8eb4f_row60_col2\" class=\"data row60 col2\" >99.09</td>\n",
+ " <td id=\"T_8eb4f_row60_col3\" class=\"data row60 col3\" >1.36</td>\n",
+ " <td id=\"T_8eb4f_row60_col4\" class=\"data row60 col4\" >1.40</td>\n",
+ " <td id=\"T_8eb4f_row60_col5\" class=\"data row60 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_8eb4f_row61_col0\" class=\"data row61 col0\" >90.66</td>\n",
+ " <td id=\"T_8eb4f_row61_col1\" class=\"data row61 col1\" >3.14</td>\n",
+ " <td id=\"T_8eb4f_row61_col2\" class=\"data row61 col2\" >98.58</td>\n",
+ " <td id=\"T_8eb4f_row61_col3\" class=\"data row61 col3\" >2.47</td>\n",
+ " <td id=\"T_8eb4f_row61_col4\" class=\"data row61 col4\" >1.51</td>\n",
+ " <td id=\"T_8eb4f_row61_col5\" class=\"data row61 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_8eb4f_row62_col0\" class=\"data row62 col0\" >91.48</td>\n",
+ " <td id=\"T_8eb4f_row62_col1\" class=\"data row62 col1\" >3.07</td>\n",
+ " <td id=\"T_8eb4f_row62_col2\" class=\"data row62 col2\" >98.57</td>\n",
+ " <td id=\"T_8eb4f_row62_col3\" class=\"data row62 col3\" >2.66</td>\n",
+ " <td id=\"T_8eb4f_row62_col4\" class=\"data row62 col4\" >1.53</td>\n",
+ " <td id=\"T_8eb4f_row62_col5\" class=\"data row62 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_8eb4f_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_8eb4f_row63_col0\" class=\"data row63 col0\" >91.14</td>\n",
+ " <td id=\"T_8eb4f_row63_col1\" class=\"data row63 col1\" >3.80</td>\n",
+ " <td id=\"T_8eb4f_row63_col2\" class=\"data row63 col2\" >98.21</td>\n",
+ " <td id=\"T_8eb4f_row63_col3\" class=\"data row63 col3\" >2.67</td>\n",
+ " <td id=\"T_8eb4f_row63_col4\" class=\"data row63 col4\" >1.45</td>\n",
+ " <td id=\"T_8eb4f_row63_col5\" class=\"data row63 col5\" >0.09</td>\n",
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+ "<table id=\"T_054b5\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_054b5_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">% of load of jobs/services bidded</th>\n",
+ " <th id=\"T_054b5_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\"></th>\n",
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+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_054b5_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_054b5_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Before due</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_054b5_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
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+ " <th id=\"T_054b5_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_054b5_row0_col0\" class=\"data row0 col0\" >91.04</td>\n",
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+ " <td id=\"T_054b5_row0_col5\" class=\"data row0 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_054b5_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
+ " <td id=\"T_054b5_row1_col0\" class=\"data row1 col0\" >87.62</td>\n",
+ " <td id=\"T_054b5_row1_col1\" class=\"data row1 col1\" >1.81</td>\n",
+ " <td id=\"T_054b5_row1_col2\" class=\"data row1 col2\" >94.71</td>\n",
+ " <td id=\"T_054b5_row1_col3\" class=\"data row1 col3\" >2.51</td>\n",
+ " <td id=\"T_054b5_row1_col4\" class=\"data row1 col4\" >1.75</td>\n",
+ " <td id=\"T_054b5_row1_col5\" class=\"data row1 col5\" >0.06</td>\n",
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+ " <th id=\"T_054b5_level0_row2\" class=\"row_heading level0 row2\" >25% (0 1 0 0)</th>\n",
+ " <td id=\"T_054b5_row2_col0\" class=\"data row2 col0\" >87.62</td>\n",
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+ " <td id=\"T_054b5_row2_col2\" class=\"data row2 col2\" >93.40</td>\n",
+ " <td id=\"T_054b5_row2_col3\" class=\"data row2 col3\" >2.74</td>\n",
+ " <td id=\"T_054b5_row2_col4\" class=\"data row2 col4\" >1.77</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_054b5_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
+ " <td id=\"T_054b5_row3_col0\" class=\"data row3 col0\" >87.60</td>\n",
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+ " <td id=\"T_054b5_row3_col5\" class=\"data row3 col5\" >0.08</td>\n",
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+ " <th id=\"T_054b5_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
+ " <td id=\"T_054b5_row4_col0\" class=\"data row4 col0\" >87.89</td>\n",
+ " <td id=\"T_054b5_row4_col1\" class=\"data row4 col1\" >2.03</td>\n",
+ " <td id=\"T_054b5_row4_col2\" class=\"data row4 col2\" >93.28</td>\n",
+ " <td id=\"T_054b5_row4_col3\" class=\"data row4 col3\" >2.87</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_054b5_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_054b5_row5_col0\" class=\"data row5 col0\" >88.03</td>\n",
+ " <td id=\"T_054b5_row5_col1\" class=\"data row5 col1\" >1.95</td>\n",
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+ " <td id=\"T_054b5_row9_col3\" class=\"data row9 col3\" >2.33</td>\n",
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+ " <td id=\"T_054b5_row12_col4\" class=\"data row12 col4\" >1.61</td>\n",
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+ " <td id=\"T_054b5_row13_col0\" class=\"data row13 col0\" >89.00</td>\n",
+ " <td id=\"T_054b5_row13_col1\" class=\"data row13 col1\" >1.85</td>\n",
+ " <td id=\"T_054b5_row13_col2\" class=\"data row13 col2\" >97.37</td>\n",
+ " <td id=\"T_054b5_row13_col3\" class=\"data row13 col3\" >1.56</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_054b5_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_054b5_row14_col0\" class=\"data row14 col0\" >89.09</td>\n",
+ " <td id=\"T_054b5_row14_col1\" class=\"data row14 col1\" >2.03</td>\n",
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+ " <th id=\"T_054b5_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_054b5_row15_col0\" class=\"data row15 col0\" >91.04</td>\n",
+ " <td id=\"T_054b5_row15_col1\" class=\"data row15 col1\" >1.95</td>\n",
+ " <td id=\"T_054b5_row15_col2\" class=\"data row15 col2\" >98.60</td>\n",
+ " <td id=\"T_054b5_row15_col3\" class=\"data row15 col3\" >1.35</td>\n",
+ " <td id=\"T_054b5_row15_col4\" class=\"data row15 col4\" >1.48</td>\n",
+ " <td id=\"T_054b5_row15_col5\" class=\"data row15 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
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+ "metadata": {},
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+ "source": [
+ "analysis_plot.draw_table_qos(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
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+ "</style>\n",
+ "<table id=\"T_9f1a8\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_9f1a8_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
+ " <th id=\"T_9f1a8_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Benefits / energy available</th>\n",
+ " <th id=\"T_9f1a8_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_9f1a8_level1_col0\" class=\"col_heading level1 col0\" >Value</th>\n",
+ " <th id=\"T_9f1a8_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
+ " <th id=\"T_9f1a8_level1_col2\" class=\"col_heading level1 col2\" >Value</th>\n",
+ " <th id=\"T_9f1a8_level1_col3\" class=\"col_heading level1 col3\" >std</th>\n",
+ " <th id=\"T_9f1a8_level1_col4\" class=\"col_heading level1 col4\" >Value</th>\n",
+ " <th id=\"T_9f1a8_level1_col5\" class=\"col_heading level1 col5\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_9f1a8_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_9f1a8_row0_col0\" class=\"data row0 col0\" >29.05</td>\n",
+ " <td id=\"T_9f1a8_row0_col1\" class=\"data row0 col1\" >2.94</td>\n",
+ " <td id=\"T_9f1a8_row0_col2\" class=\"data row0 col2\" >2.10</td>\n",
+ " <td id=\"T_9f1a8_row0_col3\" class=\"data row0 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row0_col4\" class=\"data row0 col4\" >18.14</td>\n",
+ " <td id=\"T_9f1a8_row0_col5\" class=\"data row0 col5\" >5.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_9f1a8_row1_col0\" class=\"data row1 col0\" >30.10</td>\n",
+ " <td id=\"T_9f1a8_row1_col1\" class=\"data row1 col1\" >2.96</td>\n",
+ " <td id=\"T_9f1a8_row1_col2\" class=\"data row1 col2\" >2.18</td>\n",
+ " <td id=\"T_9f1a8_row1_col3\" class=\"data row1 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row1_col4\" class=\"data row1 col4\" >13.17</td>\n",
+ " <td id=\"T_9f1a8_row1_col5\" class=\"data row1 col5\" >3.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_9f1a8_row2_col0\" class=\"data row2 col0\" >31.22</td>\n",
+ " <td id=\"T_9f1a8_row2_col1\" class=\"data row2 col1\" >3.41</td>\n",
+ " <td id=\"T_9f1a8_row2_col2\" class=\"data row2 col2\" >2.26</td>\n",
+ " <td id=\"T_9f1a8_row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
+ " <td id=\"T_9f1a8_row2_col4\" class=\"data row2 col4\" >8.94</td>\n",
+ " <td id=\"T_9f1a8_row2_col5\" class=\"data row2 col5\" >3.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_9f1a8_row3_col0\" class=\"data row3 col0\" >29.40</td>\n",
+ " <td id=\"T_9f1a8_row3_col1\" class=\"data row3 col1\" >2.96</td>\n",
+ " <td id=\"T_9f1a8_row3_col2\" class=\"data row3 col2\" >2.13</td>\n",
+ " <td id=\"T_9f1a8_row3_col3\" class=\"data row3 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row3_col4\" class=\"data row3 col4\" >11.30</td>\n",
+ " <td id=\"T_9f1a8_row3_col5\" class=\"data row3 col5\" >3.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_9f1a8_row4_col0\" class=\"data row4 col0\" >35.76</td>\n",
+ " <td id=\"T_9f1a8_row4_col1\" class=\"data row4 col1\" >4.11</td>\n",
+ " <td id=\"T_9f1a8_row4_col2\" class=\"data row4 col2\" >2.59</td>\n",
+ " <td id=\"T_9f1a8_row4_col3\" class=\"data row4 col3\" >0.30</td>\n",
+ " <td id=\"T_9f1a8_row4_col4\" class=\"data row4 col4\" >1.46</td>\n",
+ " <td id=\"T_9f1a8_row4_col5\" class=\"data row4 col5\" >0.85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_9f1a8_row5_col0\" class=\"data row5 col0\" >25.83</td>\n",
+ " <td id=\"T_9f1a8_row5_col1\" class=\"data row5 col1\" >3.19</td>\n",
+ " <td id=\"T_9f1a8_row5_col2\" class=\"data row5 col2\" >1.87</td>\n",
+ " <td id=\"T_9f1a8_row5_col3\" class=\"data row5 col3\" >0.23</td>\n",
+ " <td id=\"T_9f1a8_row5_col4\" class=\"data row5 col4\" >20.25</td>\n",
+ " <td id=\"T_9f1a8_row5_col5\" class=\"data row5 col5\" >4.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_9f1a8_row6_col0\" class=\"data row6 col0\" >26.10</td>\n",
+ " <td id=\"T_9f1a8_row6_col1\" class=\"data row6 col1\" >3.05</td>\n",
+ " <td id=\"T_9f1a8_row6_col2\" class=\"data row6 col2\" >1.89</td>\n",
+ " <td id=\"T_9f1a8_row6_col3\" class=\"data row6 col3\" >0.22</td>\n",
+ " <td id=\"T_9f1a8_row6_col4\" class=\"data row6 col4\" >15.39</td>\n",
+ " <td id=\"T_9f1a8_row6_col5\" class=\"data row6 col5\" >3.77</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_9f1a8_row7_col0\" class=\"data row7 col0\" >25.07</td>\n",
+ " <td id=\"T_9f1a8_row7_col1\" class=\"data row7 col1\" >3.17</td>\n",
+ " <td id=\"T_9f1a8_row7_col2\" class=\"data row7 col2\" >1.81</td>\n",
+ " <td id=\"T_9f1a8_row7_col3\" class=\"data row7 col3\" >0.23</td>\n",
+ " <td id=\"T_9f1a8_row7_col4\" class=\"data row7 col4\" >18.58</td>\n",
+ " <td id=\"T_9f1a8_row7_col5\" class=\"data row7 col5\" >4.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_9f1a8_row8_col0\" class=\"data row8 col0\" >25.69</td>\n",
+ " <td id=\"T_9f1a8_row8_col1\" class=\"data row8 col1\" >2.79</td>\n",
+ " <td id=\"T_9f1a8_row8_col2\" class=\"data row8 col2\" >1.86</td>\n",
+ " <td id=\"T_9f1a8_row8_col3\" class=\"data row8 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row8_col4\" class=\"data row8 col4\" >23.74</td>\n",
+ " <td id=\"T_9f1a8_row8_col5\" class=\"data row8 col5\" >6.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_9f1a8_row9_col0\" class=\"data row9 col0\" >33.34</td>\n",
+ " <td id=\"T_9f1a8_row9_col1\" class=\"data row9 col1\" >4.54</td>\n",
+ " <td id=\"T_9f1a8_row9_col2\" class=\"data row9 col2\" >2.41</td>\n",
+ " <td id=\"T_9f1a8_row9_col3\" class=\"data row9 col3\" >0.33</td>\n",
+ " <td id=\"T_9f1a8_row9_col4\" class=\"data row9 col4\" >1.21</td>\n",
+ " <td id=\"T_9f1a8_row9_col5\" class=\"data row9 col5\" >0.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_9f1a8_row10_col0\" class=\"data row10 col0\" >26.86</td>\n",
+ " <td id=\"T_9f1a8_row10_col1\" class=\"data row10 col1\" >2.69</td>\n",
+ " <td id=\"T_9f1a8_row10_col2\" class=\"data row10 col2\" >1.94</td>\n",
+ " <td id=\"T_9f1a8_row10_col3\" class=\"data row10 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row10_col4\" class=\"data row10 col4\" >14.35</td>\n",
+ " <td id=\"T_9f1a8_row10_col5\" class=\"data row10 col5\" >3.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_9f1a8_row11_col0\" class=\"data row11 col0\" >26.56</td>\n",
+ " <td id=\"T_9f1a8_row11_col1\" class=\"data row11 col1\" >2.83</td>\n",
+ " <td id=\"T_9f1a8_row11_col2\" class=\"data row11 col2\" >1.92</td>\n",
+ " <td id=\"T_9f1a8_row11_col3\" class=\"data row11 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row11_col4\" class=\"data row11 col4\" >16.33</td>\n",
+ " <td id=\"T_9f1a8_row11_col5\" class=\"data row11 col5\" >3.56</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_9f1a8_row12_col0\" class=\"data row12 col0\" >25.67</td>\n",
+ " <td id=\"T_9f1a8_row12_col1\" class=\"data row12 col1\" >2.87</td>\n",
+ " <td id=\"T_9f1a8_row12_col2\" class=\"data row12 col2\" >1.86</td>\n",
+ " <td id=\"T_9f1a8_row12_col3\" class=\"data row12 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row12_col4\" class=\"data row12 col4\" >23.95</td>\n",
+ " <td id=\"T_9f1a8_row12_col5\" class=\"data row12 col5\" >6.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_9f1a8_row13_col0\" class=\"data row13 col0\" >27.22</td>\n",
+ " <td id=\"T_9f1a8_row13_col1\" class=\"data row13 col1\" >2.81</td>\n",
+ " <td id=\"T_9f1a8_row13_col2\" class=\"data row13 col2\" >1.97</td>\n",
+ " <td id=\"T_9f1a8_row13_col3\" class=\"data row13 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row13_col4\" class=\"data row13 col4\" >16.37</td>\n",
+ " <td id=\"T_9f1a8_row13_col5\" class=\"data row13 col5\" >3.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_9f1a8_row14_col0\" class=\"data row14 col0\" >32.55</td>\n",
+ " <td id=\"T_9f1a8_row14_col1\" class=\"data row14 col1\" >4.68</td>\n",
+ " <td id=\"T_9f1a8_row14_col2\" class=\"data row14 col2\" >2.35</td>\n",
+ " <td id=\"T_9f1a8_row14_col3\" class=\"data row14 col3\" >0.34</td>\n",
+ " <td id=\"T_9f1a8_row14_col4\" class=\"data row14 col4\" >0.84</td>\n",
+ " <td id=\"T_9f1a8_row14_col5\" class=\"data row14 col5\" >0.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_9f1a8_row15_col0\" class=\"data row15 col0\" >26.81</td>\n",
+ " <td id=\"T_9f1a8_row15_col1\" class=\"data row15 col1\" >3.04</td>\n",
+ " <td id=\"T_9f1a8_row15_col2\" class=\"data row15 col2\" >1.94</td>\n",
+ " <td id=\"T_9f1a8_row15_col3\" class=\"data row15 col3\" >0.22</td>\n",
+ " <td id=\"T_9f1a8_row15_col4\" class=\"data row15 col4\" >15.63</td>\n",
+ " <td id=\"T_9f1a8_row15_col5\" class=\"data row15 col5\" >3.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_9f1a8_row16_col0\" class=\"data row16 col0\" >25.85</td>\n",
+ " <td id=\"T_9f1a8_row16_col1\" class=\"data row16 col1\" >2.61</td>\n",
+ " <td id=\"T_9f1a8_row16_col2\" class=\"data row16 col2\" >1.87</td>\n",
+ " <td id=\"T_9f1a8_row16_col3\" class=\"data row16 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row16_col4\" class=\"data row16 col4\" >23.91</td>\n",
+ " <td id=\"T_9f1a8_row16_col5\" class=\"data row16 col5\" >6.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_9f1a8_row17_col0\" class=\"data row17 col0\" >27.43</td>\n",
+ " <td id=\"T_9f1a8_row17_col1\" class=\"data row17 col1\" >2.62</td>\n",
+ " <td id=\"T_9f1a8_row17_col2\" class=\"data row17 col2\" >1.98</td>\n",
+ " <td id=\"T_9f1a8_row17_col3\" class=\"data row17 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row17_col4\" class=\"data row17 col4\" >17.01</td>\n",
+ " <td id=\"T_9f1a8_row17_col5\" class=\"data row17 col5\" >4.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_9f1a8_row18_col0\" class=\"data row18 col0\" >27.19</td>\n",
+ " <td id=\"T_9f1a8_row18_col1\" class=\"data row18 col1\" >3.39</td>\n",
+ " <td id=\"T_9f1a8_row18_col2\" class=\"data row18 col2\" >1.97</td>\n",
+ " <td id=\"T_9f1a8_row18_col3\" class=\"data row18 col3\" >0.25</td>\n",
+ " <td id=\"T_9f1a8_row18_col4\" class=\"data row18 col4\" >13.03</td>\n",
+ " <td id=\"T_9f1a8_row18_col5\" class=\"data row18 col5\" >3.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_9f1a8_row19_col0\" class=\"data row19 col0\" >31.84</td>\n",
+ " <td id=\"T_9f1a8_row19_col1\" class=\"data row19 col1\" >4.63</td>\n",
+ " <td id=\"T_9f1a8_row19_col2\" class=\"data row19 col2\" >2.30</td>\n",
+ " <td id=\"T_9f1a8_row19_col3\" class=\"data row19 col3\" >0.33</td>\n",
+ " <td id=\"T_9f1a8_row19_col4\" class=\"data row19 col4\" >0.80</td>\n",
+ " <td id=\"T_9f1a8_row19_col5\" class=\"data row19 col5\" >0.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_9f1a8_row20_col0\" class=\"data row20 col0\" >18.53</td>\n",
+ " <td id=\"T_9f1a8_row20_col1\" class=\"data row20 col1\" >3.60</td>\n",
+ " <td id=\"T_9f1a8_row20_col2\" class=\"data row20 col2\" >1.34</td>\n",
+ " <td id=\"T_9f1a8_row20_col3\" class=\"data row20 col3\" >0.26</td>\n",
+ " <td id=\"T_9f1a8_row20_col4\" class=\"data row20 col4\" >4.38</td>\n",
+ " <td id=\"T_9f1a8_row20_col5\" class=\"data row20 col5\" >1.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_9f1a8_row21_col0\" class=\"data row21 col0\" >16.78</td>\n",
+ " <td id=\"T_9f1a8_row21_col1\" class=\"data row21 col1\" >3.32</td>\n",
+ " <td id=\"T_9f1a8_row21_col2\" class=\"data row21 col2\" >1.21</td>\n",
+ " <td id=\"T_9f1a8_row21_col3\" class=\"data row21 col3\" >0.24</td>\n",
+ " <td id=\"T_9f1a8_row21_col4\" class=\"data row21 col4\" >3.40</td>\n",
+ " <td id=\"T_9f1a8_row21_col5\" class=\"data row21 col5\" >1.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_9f1a8_row22_col0\" class=\"data row22 col0\" >23.36</td>\n",
+ " <td id=\"T_9f1a8_row22_col1\" class=\"data row22 col1\" >2.82</td>\n",
+ " <td id=\"T_9f1a8_row22_col2\" class=\"data row22 col2\" >1.69</td>\n",
+ " <td id=\"T_9f1a8_row22_col3\" class=\"data row22 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row22_col4\" class=\"data row22 col4\" >22.06</td>\n",
+ " <td id=\"T_9f1a8_row22_col5\" class=\"data row22 col5\" >4.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_9f1a8_row23_col0\" class=\"data row23 col0\" >22.30</td>\n",
+ " <td id=\"T_9f1a8_row23_col1\" class=\"data row23 col1\" >2.80</td>\n",
+ " <td id=\"T_9f1a8_row23_col2\" class=\"data row23 col2\" >1.61</td>\n",
+ " <td id=\"T_9f1a8_row23_col3\" class=\"data row23 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row23_col4\" class=\"data row23 col4\" >26.08</td>\n",
+ " <td id=\"T_9f1a8_row23_col5\" class=\"data row23 col5\" >5.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_9f1a8_row24_col0\" class=\"data row24 col0\" >19.71</td>\n",
+ " <td id=\"T_9f1a8_row24_col1\" class=\"data row24 col1\" >3.54</td>\n",
+ " <td id=\"T_9f1a8_row24_col2\" class=\"data row24 col2\" >1.43</td>\n",
+ " <td id=\"T_9f1a8_row24_col3\" class=\"data row24 col3\" >0.26</td>\n",
+ " <td id=\"T_9f1a8_row24_col4\" class=\"data row24 col4\" >4.10</td>\n",
+ " <td id=\"T_9f1a8_row24_col5\" class=\"data row24 col5\" >2.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_9f1a8_row25_col0\" class=\"data row25 col0\" >23.53</td>\n",
+ " <td id=\"T_9f1a8_row25_col1\" class=\"data row25 col1\" >2.68</td>\n",
+ " <td id=\"T_9f1a8_row25_col2\" class=\"data row25 col2\" >1.70</td>\n",
+ " <td id=\"T_9f1a8_row25_col3\" class=\"data row25 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row25_col4\" class=\"data row25 col4\" >25.22</td>\n",
+ " <td id=\"T_9f1a8_row25_col5\" class=\"data row25 col5\" >4.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_9f1a8_row26_col0\" class=\"data row26 col0\" >16.73</td>\n",
+ " <td id=\"T_9f1a8_row26_col1\" class=\"data row26 col1\" >3.58</td>\n",
+ " <td id=\"T_9f1a8_row26_col2\" class=\"data row26 col2\" >1.21</td>\n",
+ " <td id=\"T_9f1a8_row26_col3\" class=\"data row26 col3\" >0.26</td>\n",
+ " <td id=\"T_9f1a8_row26_col4\" class=\"data row26 col4\" >2.02</td>\n",
+ " <td id=\"T_9f1a8_row26_col5\" class=\"data row26 col5\" >1.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_9f1a8_row27_col0\" class=\"data row27 col0\" >21.98</td>\n",
+ " <td id=\"T_9f1a8_row27_col1\" class=\"data row27 col1\" >2.65</td>\n",
+ " <td id=\"T_9f1a8_row27_col2\" class=\"data row27 col2\" >1.59</td>\n",
+ " <td id=\"T_9f1a8_row27_col3\" class=\"data row27 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row27_col4\" class=\"data row27 col4\" >24.95</td>\n",
+ " <td id=\"T_9f1a8_row27_col5\" class=\"data row27 col5\" >4.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_9f1a8_row28_col0\" class=\"data row28 col0\" >18.39</td>\n",
+ " <td id=\"T_9f1a8_row28_col1\" class=\"data row28 col1\" >3.83</td>\n",
+ " <td id=\"T_9f1a8_row28_col2\" class=\"data row28 col2\" >1.33</td>\n",
+ " <td id=\"T_9f1a8_row28_col3\" class=\"data row28 col3\" >0.28</td>\n",
+ " <td id=\"T_9f1a8_row28_col4\" class=\"data row28 col4\" >4.31</td>\n",
+ " <td id=\"T_9f1a8_row28_col5\" class=\"data row28 col5\" >2.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_9f1a8_row29_col0\" class=\"data row29 col0\" >23.32</td>\n",
+ " <td id=\"T_9f1a8_row29_col1\" class=\"data row29 col1\" >2.68</td>\n",
+ " <td id=\"T_9f1a8_row29_col2\" class=\"data row29 col2\" >1.69</td>\n",
+ " <td id=\"T_9f1a8_row29_col3\" class=\"data row29 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row29_col4\" class=\"data row29 col4\" >26.17</td>\n",
+ " <td id=\"T_9f1a8_row29_col5\" class=\"data row29 col5\" >5.34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_9f1a8_row30_col0\" class=\"data row30 col0\" >23.15</td>\n",
+ " <td id=\"T_9f1a8_row30_col1\" class=\"data row30 col1\" >2.80</td>\n",
+ " <td id=\"T_9f1a8_row30_col2\" class=\"data row30 col2\" >1.67</td>\n",
+ " <td id=\"T_9f1a8_row30_col3\" class=\"data row30 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row30_col4\" class=\"data row30 col4\" >21.44</td>\n",
+ " <td id=\"T_9f1a8_row30_col5\" class=\"data row30 col5\" >4.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_9f1a8_row31_col0\" class=\"data row31 col0\" >14.53</td>\n",
+ " <td id=\"T_9f1a8_row31_col1\" class=\"data row31 col1\" >3.15</td>\n",
+ " <td id=\"T_9f1a8_row31_col2\" class=\"data row31 col2\" >1.05</td>\n",
+ " <td id=\"T_9f1a8_row31_col3\" class=\"data row31 col3\" >0.23</td>\n",
+ " <td id=\"T_9f1a8_row31_col4\" class=\"data row31 col4\" >2.77</td>\n",
+ " <td id=\"T_9f1a8_row31_col5\" class=\"data row31 col5\" >1.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_9f1a8_row32_col0\" class=\"data row32 col0\" >22.61</td>\n",
+ " <td id=\"T_9f1a8_row32_col1\" class=\"data row32 col1\" >2.65</td>\n",
+ " <td id=\"T_9f1a8_row32_col2\" class=\"data row32 col2\" >1.64</td>\n",
+ " <td id=\"T_9f1a8_row32_col3\" class=\"data row32 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row32_col4\" class=\"data row32 col4\" >29.85</td>\n",
+ " <td id=\"T_9f1a8_row32_col5\" class=\"data row32 col5\" >6.96</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_9f1a8_row33_col0\" class=\"data row33 col0\" >16.67</td>\n",
+ " <td id=\"T_9f1a8_row33_col1\" class=\"data row33 col1\" >3.25</td>\n",
+ " <td id=\"T_9f1a8_row33_col2\" class=\"data row33 col2\" >1.21</td>\n",
+ " <td id=\"T_9f1a8_row33_col3\" class=\"data row33 col3\" >0.23</td>\n",
+ " <td id=\"T_9f1a8_row33_col4\" class=\"data row33 col4\" >2.84</td>\n",
+ " <td id=\"T_9f1a8_row33_col5\" class=\"data row33 col5\" >1.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_9f1a8_row34_col0\" class=\"data row34 col0\" >14.87</td>\n",
+ " <td id=\"T_9f1a8_row34_col1\" class=\"data row34 col1\" >3.39</td>\n",
+ " <td id=\"T_9f1a8_row34_col2\" class=\"data row34 col2\" >1.08</td>\n",
+ " <td id=\"T_9f1a8_row34_col3\" class=\"data row34 col3\" >0.24</td>\n",
+ " <td id=\"T_9f1a8_row34_col4\" class=\"data row34 col4\" >2.30</td>\n",
+ " <td id=\"T_9f1a8_row34_col5\" class=\"data row34 col5\" >1.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_9f1a8_row35_col0\" class=\"data row35 col0\" >22.90</td>\n",
+ " <td id=\"T_9f1a8_row35_col1\" class=\"data row35 col1\" >2.66</td>\n",
+ " <td id=\"T_9f1a8_row35_col2\" class=\"data row35 col2\" >1.66</td>\n",
+ " <td id=\"T_9f1a8_row35_col3\" class=\"data row35 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row35_col4\" class=\"data row35 col4\" >22.41</td>\n",
+ " <td id=\"T_9f1a8_row35_col5\" class=\"data row35 col5\" >5.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_9f1a8_row36_col0\" class=\"data row36 col0\" >22.30</td>\n",
+ " <td id=\"T_9f1a8_row36_col1\" class=\"data row36 col1\" >3.04</td>\n",
+ " <td id=\"T_9f1a8_row36_col2\" class=\"data row36 col2\" >1.61</td>\n",
+ " <td id=\"T_9f1a8_row36_col3\" class=\"data row36 col3\" >0.22</td>\n",
+ " <td id=\"T_9f1a8_row36_col4\" class=\"data row36 col4\" >30.65</td>\n",
+ " <td id=\"T_9f1a8_row36_col5\" class=\"data row36 col5\" >7.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_9f1a8_row37_col0\" class=\"data row37 col0\" >16.97</td>\n",
+ " <td id=\"T_9f1a8_row37_col1\" class=\"data row37 col1\" >3.42</td>\n",
+ " <td id=\"T_9f1a8_row37_col2\" class=\"data row37 col2\" >1.23</td>\n",
+ " <td id=\"T_9f1a8_row37_col3\" class=\"data row37 col3\" >0.25</td>\n",
+ " <td id=\"T_9f1a8_row37_col4\" class=\"data row37 col4\" >3.23</td>\n",
+ " <td id=\"T_9f1a8_row37_col5\" class=\"data row37 col5\" >1.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_9f1a8_row38_col0\" class=\"data row38 col0\" >23.96</td>\n",
+ " <td id=\"T_9f1a8_row38_col1\" class=\"data row38 col1\" >2.80</td>\n",
+ " <td id=\"T_9f1a8_row38_col2\" class=\"data row38 col2\" >1.73</td>\n",
+ " <td id=\"T_9f1a8_row38_col3\" class=\"data row38 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row38_col4\" class=\"data row38 col4\" >19.20</td>\n",
+ " <td id=\"T_9f1a8_row38_col5\" class=\"data row38 col5\" >4.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_9f1a8_row39_col0\" class=\"data row39 col0\" >13.58</td>\n",
+ " <td id=\"T_9f1a8_row39_col1\" class=\"data row39 col1\" >2.87</td>\n",
+ " <td id=\"T_9f1a8_row39_col2\" class=\"data row39 col2\" >0.98</td>\n",
+ " <td id=\"T_9f1a8_row39_col3\" class=\"data row39 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row39_col4\" class=\"data row39 col4\" >2.69</td>\n",
+ " <td id=\"T_9f1a8_row39_col5\" class=\"data row39 col5\" >1.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_9f1a8_row40_col0\" class=\"data row40 col0\" >22.78</td>\n",
+ " <td id=\"T_9f1a8_row40_col1\" class=\"data row40 col1\" >2.92</td>\n",
+ " <td id=\"T_9f1a8_row40_col2\" class=\"data row40 col2\" >1.65</td>\n",
+ " <td id=\"T_9f1a8_row40_col3\" class=\"data row40 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row40_col4\" class=\"data row40 col4\" >29.42</td>\n",
+ " <td id=\"T_9f1a8_row40_col5\" class=\"data row40 col5\" >7.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_9f1a8_row41_col0\" class=\"data row41 col0\" >24.35</td>\n",
+ " <td id=\"T_9f1a8_row41_col1\" class=\"data row41 col1\" >2.93</td>\n",
+ " <td id=\"T_9f1a8_row41_col2\" class=\"data row41 col2\" >1.76</td>\n",
+ " <td id=\"T_9f1a8_row41_col3\" class=\"data row41 col3\" >0.21</td>\n",
+ " <td id=\"T_9f1a8_row41_col4\" class=\"data row41 col4\" >21.75</td>\n",
+ " <td id=\"T_9f1a8_row41_col5\" class=\"data row41 col5\" >4.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_9f1a8_row42_col0\" class=\"data row42 col0\" >15.99</td>\n",
+ " <td id=\"T_9f1a8_row42_col1\" class=\"data row42 col1\" >3.24</td>\n",
+ " <td id=\"T_9f1a8_row42_col2\" class=\"data row42 col2\" >1.16</td>\n",
+ " <td id=\"T_9f1a8_row42_col3\" class=\"data row42 col3\" >0.23</td>\n",
+ " <td id=\"T_9f1a8_row42_col4\" class=\"data row42 col4\" >2.00</td>\n",
+ " <td id=\"T_9f1a8_row42_col5\" class=\"data row42 col5\" >1.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_9f1a8_row43_col0\" class=\"data row43 col0\" >14.44</td>\n",
+ " <td id=\"T_9f1a8_row43_col1\" class=\"data row43 col1\" >3.00</td>\n",
+ " <td id=\"T_9f1a8_row43_col2\" class=\"data row43 col2\" >1.04</td>\n",
+ " <td id=\"T_9f1a8_row43_col3\" class=\"data row43 col3\" >0.22</td>\n",
+ " <td id=\"T_9f1a8_row43_col4\" class=\"data row43 col4\" >2.50</td>\n",
+ " <td id=\"T_9f1a8_row43_col5\" class=\"data row43 col5\" >1.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_9f1a8_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_9f1a8_row44_col0\" class=\"data row44 col0\" >11.47</td>\n",
+ " <td id=\"T_9f1a8_row44_col1\" class=\"data row44 col1\" >2.70</td>\n",
+ " <td id=\"T_9f1a8_row44_col2\" class=\"data row44 col2\" >0.83</td>\n",
+ " <td id=\"T_9f1a8_row44_col3\" class=\"data row44 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row44_col4\" class=\"data row44 col4\" >9.05</td>\n",
+ " <td id=\"T_9f1a8_row44_col5\" class=\"data row44 col5\" >3.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_9f1a8_row45_col0\" class=\"data row45 col0\" >9.57</td>\n",
+ " <td id=\"T_9f1a8_row45_col1\" class=\"data row45 col1\" >2.18</td>\n",
+ " <td id=\"T_9f1a8_row45_col2\" class=\"data row45 col2\" >0.69</td>\n",
+ " <td id=\"T_9f1a8_row45_col3\" class=\"data row45 col3\" >0.16</td>\n",
+ " <td id=\"T_9f1a8_row45_col4\" class=\"data row45 col4\" >7.13</td>\n",
+ " <td id=\"T_9f1a8_row45_col5\" class=\"data row45 col5\" >2.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_9f1a8_row46_col0\" class=\"data row46 col0\" >8.52</td>\n",
+ " <td id=\"T_9f1a8_row46_col1\" class=\"data row46 col1\" >2.29</td>\n",
+ " <td id=\"T_9f1a8_row46_col2\" class=\"data row46 col2\" >0.62</td>\n",
+ " <td id=\"T_9f1a8_row46_col3\" class=\"data row46 col3\" >0.17</td>\n",
+ " <td id=\"T_9f1a8_row46_col4\" class=\"data row46 col4\" >4.46</td>\n",
+ " <td id=\"T_9f1a8_row46_col5\" class=\"data row46 col5\" >1.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_9f1a8_row47_col0\" class=\"data row47 col0\" >18.28</td>\n",
+ " <td id=\"T_9f1a8_row47_col1\" class=\"data row47 col1\" >2.48</td>\n",
+ " <td id=\"T_9f1a8_row47_col2\" class=\"data row47 col2\" >1.32</td>\n",
+ " <td id=\"T_9f1a8_row47_col3\" class=\"data row47 col3\" >0.18</td>\n",
+ " <td id=\"T_9f1a8_row47_col4\" class=\"data row47 col4\" >34.06</td>\n",
+ " <td id=\"T_9f1a8_row47_col5\" class=\"data row47 col5\" >5.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_9f1a8_row48_col0\" class=\"data row48 col0\" >10.63</td>\n",
+ " <td id=\"T_9f1a8_row48_col1\" class=\"data row48 col1\" >2.47</td>\n",
+ " <td id=\"T_9f1a8_row48_col2\" class=\"data row48 col2\" >0.77</td>\n",
+ " <td id=\"T_9f1a8_row48_col3\" class=\"data row48 col3\" >0.18</td>\n",
+ " <td id=\"T_9f1a8_row48_col4\" class=\"data row48 col4\" >9.75</td>\n",
+ " <td id=\"T_9f1a8_row48_col5\" class=\"data row48 col5\" >3.94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_9f1a8_row49_col0\" class=\"data row49 col0\" >9.84</td>\n",
+ " <td id=\"T_9f1a8_row49_col1\" class=\"data row49 col1\" >2.47</td>\n",
+ " <td id=\"T_9f1a8_row49_col2\" class=\"data row49 col2\" >0.71</td>\n",
+ " <td id=\"T_9f1a8_row49_col3\" class=\"data row49 col3\" >0.18</td>\n",
+ " <td id=\"T_9f1a8_row49_col4\" class=\"data row49 col4\" >7.62</td>\n",
+ " <td id=\"T_9f1a8_row49_col5\" class=\"data row49 col5\" >2.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_9f1a8_row50_col0\" class=\"data row50 col0\" >19.80</td>\n",
+ " <td id=\"T_9f1a8_row50_col1\" class=\"data row50 col1\" >2.65</td>\n",
+ " <td id=\"T_9f1a8_row50_col2\" class=\"data row50 col2\" >1.43</td>\n",
+ " <td id=\"T_9f1a8_row50_col3\" class=\"data row50 col3\" >0.19</td>\n",
+ " <td id=\"T_9f1a8_row50_col4\" class=\"data row50 col4\" >29.71</td>\n",
+ " <td id=\"T_9f1a8_row50_col5\" class=\"data row50 col5\" >5.77</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_9f1a8_row51_col0\" class=\"data row51 col0\" >7.35</td>\n",
+ " <td id=\"T_9f1a8_row51_col1\" class=\"data row51 col1\" >1.97</td>\n",
+ " <td id=\"T_9f1a8_row51_col2\" class=\"data row51 col2\" >0.53</td>\n",
+ " <td id=\"T_9f1a8_row51_col3\" class=\"data row51 col3\" >0.14</td>\n",
+ " <td id=\"T_9f1a8_row51_col4\" class=\"data row51 col4\" >6.28</td>\n",
+ " <td id=\"T_9f1a8_row51_col5\" class=\"data row51 col5\" >2.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_9f1a8_row52_col0\" class=\"data row52 col0\" >10.88</td>\n",
+ " <td id=\"T_9f1a8_row52_col1\" class=\"data row52 col1\" >2.35</td>\n",
+ " <td id=\"T_9f1a8_row52_col2\" class=\"data row52 col2\" >0.79</td>\n",
+ " <td id=\"T_9f1a8_row52_col3\" class=\"data row52 col3\" >0.17</td>\n",
+ " <td id=\"T_9f1a8_row52_col4\" class=\"data row52 col4\" >9.62</td>\n",
+ " <td id=\"T_9f1a8_row52_col5\" class=\"data row52 col5\" >3.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_9f1a8_row53_col0\" class=\"data row53 col0\" >19.87</td>\n",
+ " <td id=\"T_9f1a8_row53_col1\" class=\"data row53 col1\" >2.81</td>\n",
+ " <td id=\"T_9f1a8_row53_col2\" class=\"data row53 col2\" >1.44</td>\n",
+ " <td id=\"T_9f1a8_row53_col3\" class=\"data row53 col3\" >0.20</td>\n",
+ " <td id=\"T_9f1a8_row53_col4\" class=\"data row53 col4\" >32.58</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td id=\"T_9f1a8_row54_col2\" class=\"data row54 col2\" >0.69</td>\n",
+ " <td id=\"T_9f1a8_row54_col3\" class=\"data row54 col3\" >0.18</td>\n",
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+ " <td id=\"T_9f1a8_row54_col5\" class=\"data row54 col5\" >1.96</td>\n",
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+ " <td id=\"T_9f1a8_row55_col1\" class=\"data row55 col1\" >2.07</td>\n",
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+ " <td id=\"T_9f1a8_row55_col3\" class=\"data row55 col3\" >0.15</td>\n",
+ " <td id=\"T_9f1a8_row55_col4\" class=\"data row55 col4\" >5.74</td>\n",
+ " <td id=\"T_9f1a8_row55_col5\" class=\"data row55 col5\" >1.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
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+ " <tr>\n",
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+ " <td id=\"T_9f1a8_row57_col5\" class=\"data row57 col5\" >2.68</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
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+ " <td id=\"T_9f1a8_row58_col3\" class=\"data row58 col3\" >0.16</td>\n",
+ " <td id=\"T_9f1a8_row58_col4\" class=\"data row58 col4\" >5.81</td>\n",
+ " <td id=\"T_9f1a8_row58_col5\" class=\"data row58 col5\" >1.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_9f1a8_row59_col0\" class=\"data row59 col0\" >7.55</td>\n",
+ " <td id=\"T_9f1a8_row59_col1\" class=\"data row59 col1\" >2.08</td>\n",
+ " <td id=\"T_9f1a8_row59_col2\" class=\"data row59 col2\" >0.55</td>\n",
+ " <td id=\"T_9f1a8_row59_col3\" class=\"data row59 col3\" >0.15</td>\n",
+ " <td id=\"T_9f1a8_row59_col4\" class=\"data row59 col4\" >6.16</td>\n",
+ " <td id=\"T_9f1a8_row59_col5\" class=\"data row59 col5\" >2.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_9f1a8_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_9f1a8_row60_col0\" class=\"data row60 col0\" >6.41</td>\n",
+ " <td id=\"T_9f1a8_row60_col1\" class=\"data row60 col1\" >2.19</td>\n",
+ " <td id=\"T_9f1a8_row60_col2\" class=\"data row60 col2\" >0.46</td>\n",
+ " <td id=\"T_9f1a8_row60_col3\" class=\"data row60 col3\" >0.16</td>\n",
+ " <td id=\"T_9f1a8_row60_col4\" class=\"data row60 col4\" >18.14</td>\n",
+ " <td id=\"T_9f1a8_row60_col5\" class=\"data row60 col5\" >5.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_9f1a8_row61_col0\" class=\"data row61 col0\" >5.67</td>\n",
+ " <td id=\"T_9f1a8_row61_col1\" class=\"data row61 col1\" >1.52</td>\n",
+ " <td id=\"T_9f1a8_row61_col2\" class=\"data row61 col2\" >0.41</td>\n",
+ " <td id=\"T_9f1a8_row61_col3\" class=\"data row61 col3\" >0.11</td>\n",
+ " <td id=\"T_9f1a8_row61_col4\" class=\"data row61 col4\" >13.17</td>\n",
+ " <td id=\"T_9f1a8_row61_col5\" class=\"data row61 col5\" >3.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_9f1a8_row62_col0\" class=\"data row62 col0\" >5.29</td>\n",
+ " <td id=\"T_9f1a8_row62_col1\" class=\"data row62 col1\" >1.65</td>\n",
+ " <td id=\"T_9f1a8_row62_col2\" class=\"data row62 col2\" >0.38</td>\n",
+ " <td id=\"T_9f1a8_row62_col3\" class=\"data row62 col3\" >0.12</td>\n",
+ " <td id=\"T_9f1a8_row62_col4\" class=\"data row62 col4\" >8.94</td>\n",
+ " <td id=\"T_9f1a8_row62_col5\" class=\"data row62 col5\" >3.07</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_9f1a8_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_9f1a8_row63_col0\" class=\"data row63 col0\" >3.83</td>\n",
+ " <td id=\"T_9f1a8_row63_col1\" class=\"data row63 col1\" >1.30</td>\n",
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+ " <td id=\"T_9f1a8_row63_col3\" class=\"data row63 col3\" >0.09</td>\n",
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+ " <td id=\"T_9f1a8_row63_col5\" class=\"data row63 col5\" >3.21</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
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+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_766ec_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
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+ " <th id=\"T_766ec_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
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+ " <th id=\"T_766ec_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_766ec_row0_col0\" class=\"data row0 col0\" >119.77</td>\n",
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+ " <td id=\"T_766ec_row6_col5\" class=\"data row6 col5\" >2.36</td>\n",
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+ " <th id=\"T_766ec_level0_row7\" class=\"row_heading level0 row7\" >50% (1 0 0 1)</th>\n",
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+ " <th id=\"T_766ec_level0_row9\" class=\"row_heading level0 row9\" >50% (0 1 0 1)</th>\n",
+ " <td id=\"T_766ec_row9_col0\" class=\"data row9 col0\" >76.81</td>\n",
+ " <td id=\"T_766ec_row9_col1\" class=\"data row9 col1\" >4.37</td>\n",
+ " <td id=\"T_766ec_row9_col2\" class=\"data row9 col2\" >1.39</td>\n",
+ " <td id=\"T_766ec_row9_col3\" class=\"data row9 col3\" >0.08</td>\n",
+ " <td id=\"T_766ec_row9_col4\" class=\"data row9 col4\" >13.94</td>\n",
+ " <td id=\"T_766ec_row9_col5\" class=\"data row9 col5\" >2.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row10\" class=\"row_heading level0 row10\" >50% (0 0 1 1)</th>\n",
+ " <td id=\"T_766ec_row10_col0\" class=\"data row10 col0\" >77.56</td>\n",
+ " <td id=\"T_766ec_row10_col1\" class=\"data row10 col1\" >4.86</td>\n",
+ " <td id=\"T_766ec_row10_col2\" class=\"data row10 col2\" >1.40</td>\n",
+ " <td id=\"T_766ec_row10_col3\" class=\"data row10 col3\" >0.09</td>\n",
+ " <td id=\"T_766ec_row10_col4\" class=\"data row10 col4\" >13.92</td>\n",
+ " <td id=\"T_766ec_row10_col5\" class=\"data row10 col5\" >2.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row11\" class=\"row_heading level0 row11\" >75% (1 1 1 0)</th>\n",
+ " <td id=\"T_766ec_row11_col0\" class=\"data row11 col0\" >47.85</td>\n",
+ " <td id=\"T_766ec_row11_col1\" class=\"data row11 col1\" >4.19</td>\n",
+ " <td id=\"T_766ec_row11_col2\" class=\"data row11 col2\" >0.87</td>\n",
+ " <td id=\"T_766ec_row11_col3\" class=\"data row11 col3\" >0.08</td>\n",
+ " <td id=\"T_766ec_row11_col4\" class=\"data row11 col4\" >13.68</td>\n",
+ " <td id=\"T_766ec_row11_col5\" class=\"data row11 col5\" >2.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row12\" class=\"row_heading level0 row12\" >75% (1 1 0 1)</th>\n",
+ " <td id=\"T_766ec_row12_col0\" class=\"data row12 col0\" >47.61</td>\n",
+ " <td id=\"T_766ec_row12_col1\" class=\"data row12 col1\" >4.56</td>\n",
+ " <td id=\"T_766ec_row12_col2\" class=\"data row12 col2\" >0.86</td>\n",
+ " <td id=\"T_766ec_row12_col3\" class=\"data row12 col3\" >0.08</td>\n",
+ " <td id=\"T_766ec_row12_col4\" class=\"data row12 col4\" >13.34</td>\n",
+ " <td id=\"T_766ec_row12_col5\" class=\"data row12 col5\" >2.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
+ " <td id=\"T_766ec_row13_col0\" class=\"data row13 col0\" >47.83</td>\n",
+ " <td id=\"T_766ec_row13_col1\" class=\"data row13 col1\" >4.08</td>\n",
+ " <td id=\"T_766ec_row13_col2\" class=\"data row13 col2\" >0.86</td>\n",
+ " <td id=\"T_766ec_row13_col3\" class=\"data row13 col3\" >0.07</td>\n",
+ " <td id=\"T_766ec_row13_col4\" class=\"data row13 col4\" >13.32</td>\n",
+ " <td id=\"T_766ec_row13_col5\" class=\"data row13 col5\" >2.36</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_766ec_row14_col0\" class=\"data row14 col0\" >45.64</td>\n",
+ " <td id=\"T_766ec_row14_col1\" class=\"data row14 col1\" >3.87</td>\n",
+ " <td id=\"T_766ec_row14_col2\" class=\"data row14 col2\" >0.83</td>\n",
+ " <td id=\"T_766ec_row14_col3\" class=\"data row14 col3\" >0.07</td>\n",
+ " <td id=\"T_766ec_row14_col4\" class=\"data row14 col4\" >13.81</td>\n",
+ " <td id=\"T_766ec_row14_col5\" class=\"data row14 col5\" >2.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_766ec_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_766ec_row15_col0\" class=\"data row15 col0\" >21.21</td>\n",
+ " <td id=\"T_766ec_row15_col1\" class=\"data row15 col1\" >2.69</td>\n",
+ " <td id=\"T_766ec_row15_col2\" class=\"data row15 col2\" >0.38</td>\n",
+ " <td id=\"T_766ec_row15_col3\" class=\"data row15 col3\" >0.05</td>\n",
+ " <td id=\"T_766ec_row15_col4\" class=\"data row15 col4\" >12.89</td>\n",
+ " <td id=\"T_766ec_row15_col5\" class=\"data row15 col5\" >2.48</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7129a32943d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Medium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 2500x1500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.plot_graph(\"medium\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "}\n",
+ "#T_26666_row8_col0, #T_26666_row16_col0, #T_26666_row36_col2, #T_26666_row40_col2 {\n",
+ " background-color: #c6f5c6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row8_col2 {\n",
+ " background-color: #a1f8a1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row9_col0, #T_26666_row28_col2 {\n",
+ " background-color: #34ff34;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row9_col2, #T_26666_row58_col2 {\n",
+ " background-color: #53fd53;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row10_col0, #T_26666_row11_col0, #T_26666_row13_col0, #T_26666_row29_col2 {\n",
+ " background-color: #b3f7b3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row10_col2, #T_26666_row49_col0 {\n",
+ " background-color: #76fb76;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row11_col2 {\n",
+ " background-color: #83fa83;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row12_col0 {\n",
+ " background-color: #c5f5c5;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row12_col2 {\n",
+ " background-color: #9bf89b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row13_col2, #T_26666_row46_col0, #T_26666_row55_col0 {\n",
+ " background-color: #73fb73;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row14_col0, #T_26666_row24_col2 {\n",
+ " background-color: #35ff35;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row14_col2 {\n",
+ " background-color: #5efc5e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row15_col0, #T_26666_row17_col0 {\n",
+ " background-color: #b1f7b1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row15_col2 {\n",
+ " background-color: #81fa81;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row16_col2 {\n",
+ " background-color: #9ff89f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row17_col2 {\n",
+ " background-color: #78fb78;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row18_col0 {\n",
+ " background-color: #b0f7b0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row19_col0 {\n",
+ " background-color: #38ff38;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row19_col2 {\n",
+ " background-color: #65fc65;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row20_col0, #T_26666_row28_col0 {\n",
+ " background-color: #6afb6a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row21_col0, #T_26666_row45_col2 {\n",
+ " background-color: #52fd52;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row21_col2 {\n",
+ " background-color: #50fd50;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row22_col0, #T_26666_row25_col0 {\n",
+ " background-color: #d0f5d0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row22_col2 {\n",
+ " background-color: #abf7ab;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row23_col0, #T_26666_row27_col0 {\n",
+ " background-color: #d4f4d4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row23_col2 {\n",
+ " background-color: #bbf6bb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row24_col0, #T_26666_row51_col2 {\n",
+ " background-color: #67fc67;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row25_col2 {\n",
+ " background-color: #b2f7b2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row26_col0, #T_26666_row54_col2, #T_26666_row57_col2 {\n",
+ " background-color: #54fd54;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row26_col2 {\n",
+ " background-color: #4efd4e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row29_col0, #T_26666_row30_col0 {\n",
+ " background-color: #cdf5cd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row30_col2 {\n",
+ " background-color: #a9f7a9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row31_col0, #T_26666_row34_col0, #T_26666_row42_col0, #T_26666_row44_col2 {\n",
+ " background-color: #5bfc5b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row31_col2, #T_26666_row43_col0, #T_26666_row52_col2 {\n",
+ " background-color: #5afc5a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row32_col0 {\n",
+ " background-color: #dcf4db;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row32_col2 {\n",
+ " background-color: #bff6bf;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row33_col0, #T_26666_row46_col2 {\n",
+ " background-color: #59fd59;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row33_col2 {\n",
+ " background-color: #39ff39;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row34_col2 {\n",
+ " background-color: #4afe4a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row35_col0 {\n",
+ " background-color: #cff5cf;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row35_col2 {\n",
+ " background-color: #acf7ac;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row36_col0 {\n",
+ " background-color: #dbf4db;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row37_col0, #T_26666_row59_col2 {\n",
+ " background-color: #5cfc5c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row37_col2 {\n",
+ " background-color: #3aff3a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row38_col0 {\n",
+ " background-color: #ccf5cc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row38_col2 {\n",
+ " background-color: #a0f8a0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row39_col0 {\n",
+ " background-color: #5dfc5d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row39_col2 {\n",
+ " background-color: #4dfd4d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row40_col0 {\n",
+ " background-color: #dcf4dc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row41_col0 {\n",
+ " background-color: #cef5ce;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row41_col2 {\n",
+ " background-color: #a2f8a2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row42_col2 {\n",
+ " background-color: #40fe40;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row43_col2 {\n",
+ " background-color: #45fe45;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row44_col0 {\n",
+ " background-color: #8bf98b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row47_col0, #T_26666_row47_col2 {\n",
+ " background-color: #e8f3e8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row48_col2, #T_26666_row49_col2 {\n",
+ " background-color: #57fd57;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row50_col0 {\n",
+ " background-color: #e3f3e3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row50_col2 {\n",
+ " background-color: #d7f4d7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row53_col0 {\n",
+ " background-color: #e2f4e2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row53_col2 {\n",
+ " background-color: #d9f4d9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row55_col2 {\n",
+ " background-color: #64fc64;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row56_col0, #T_26666_row56_col2 {\n",
+ " background-color: #ebf3eb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row57_col0, #T_26666_row58_col0 {\n",
+ " background-color: #7dfa7d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_26666_row59_col0 {\n",
+ " background-color: #7afa7a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_26666\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_26666_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bidded</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_26666_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Jobs</th>\n",
+ " <th id=\"T_26666_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_26666_level2_col0\" class=\"col_heading level2 col0\" >Number</th>\n",
+ " <th id=\"T_26666_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_26666_level2_col2\" class=\"col_heading level2 col2\" >Number</th>\n",
+ " <th id=\"T_26666_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_26666_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_26666_row0_col0\" class=\"data row0 col0\" >20.14</td>\n",
+ " <td id=\"T_26666_row0_col1\" class=\"data row0 col1\" >2.59</td>\n",
+ " <td id=\"T_26666_row0_col2\" class=\"data row0 col2\" >21.85</td>\n",
+ " <td id=\"T_26666_row0_col3\" class=\"data row0 col3\" >3.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_26666_row1_col0\" class=\"data row1 col0\" >25.38</td>\n",
+ " <td id=\"T_26666_row1_col1\" class=\"data row1 col1\" >2.86</td>\n",
+ " <td id=\"T_26666_row1_col2\" class=\"data row1 col2\" >24.40</td>\n",
+ " <td id=\"T_26666_row1_col3\" class=\"data row1 col3\" >3.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_26666_row2_col0\" class=\"data row2 col0\" >25.71</td>\n",
+ " <td id=\"T_26666_row2_col1\" class=\"data row2 col1\" >2.98</td>\n",
+ " <td id=\"T_26666_row2_col2\" class=\"data row2 col2\" >25.13</td>\n",
+ " <td id=\"T_26666_row2_col3\" class=\"data row2 col3\" >3.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_26666_row3_col0\" class=\"data row3 col0\" >25.01</td>\n",
+ " <td id=\"T_26666_row3_col1\" class=\"data row3 col1\" >3.21</td>\n",
+ " <td id=\"T_26666_row3_col2\" class=\"data row3 col2\" >24.10</td>\n",
+ " <td id=\"T_26666_row3_col3\" class=\"data row3 col3\" >3.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_26666_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_26666_row4_col0\" class=\"data row4 col0\" >45.20</td>\n",
+ " <td id=\"T_26666_row4_col1\" class=\"data row4 col1\" >3.99</td>\n",
+ " <td id=\"T_26666_row4_col2\" class=\"data row4 col2\" >31.11</td>\n",
+ " <td id=\"T_26666_row4_col3\" class=\"data row4 col3\" >4.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_26666_row5_col0\" class=\"data row5 col0\" >17.03</td>\n",
+ " <td id=\"T_26666_row5_col1\" class=\"data row5 col1\" >2.54</td>\n",
+ " <td id=\"T_26666_row5_col2\" class=\"data row5 col2\" >21.37</td>\n",
+ " <td id=\"T_26666_row5_col3\" class=\"data row5 col3\" >3.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_26666_row6_col0\" class=\"data row6 col0\" >17.35</td>\n",
+ " <td id=\"T_26666_row6_col1\" class=\"data row6 col1\" >2.45</td>\n",
+ " <td id=\"T_26666_row6_col2\" class=\"data row6 col2\" >22.54</td>\n",
+ " <td id=\"T_26666_row6_col3\" class=\"data row6 col3\" >3.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_26666_row7_col0\" class=\"data row7 col0\" >16.76</td>\n",
+ " <td id=\"T_26666_row7_col1\" class=\"data row7 col1\" >2.75</td>\n",
+ " <td id=\"T_26666_row7_col2\" class=\"data row7 col2\" >20.66</td>\n",
+ " <td id=\"T_26666_row7_col3\" class=\"data row7 col3\" >3.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_26666_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_26666_row8_col0\" class=\"data row8 col0\" >13.97</td>\n",
+ " <td id=\"T_26666_row8_col1\" class=\"data row8 col1\" >2.74</td>\n",
+ " <td id=\"T_26666_row8_col2\" class=\"data row8 col2\" >19.64</td>\n",
+ " <td id=\"T_26666_row8_col3\" class=\"data row8 col3\" >3.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_26666_row9_col0\" class=\"data row9 col0\" >45.93</td>\n",
+ " <td id=\"T_26666_row9_col1\" class=\"data row9 col1\" >4.89</td>\n",
+ " <td id=\"T_26666_row9_col2\" class=\"data row9 col2\" >28.45</td>\n",
+ " <td id=\"T_26666_row9_col3\" class=\"data row9 col3\" >4.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_26666_row10_col0\" class=\"data row10 col0\" >18.18</td>\n",
+ " <td id=\"T_26666_row10_col1\" class=\"data row10 col1\" >3.15</td>\n",
+ " <td id=\"T_26666_row10_col2\" class=\"data row10 col2\" >24.52</td>\n",
+ " <td id=\"T_26666_row10_col3\" class=\"data row10 col3\" >3.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_26666_row11_col0\" class=\"data row11 col0\" >18.16</td>\n",
+ " <td id=\"T_26666_row11_col1\" class=\"data row11 col1\" >3.02</td>\n",
+ " <td id=\"T_26666_row11_col2\" class=\"data row11 col2\" >23.01</td>\n",
+ " <td id=\"T_26666_row11_col3\" class=\"data row11 col3\" >3.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_26666_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_26666_row12_col0\" class=\"data row12 col0\" >14.13</td>\n",
+ " <td id=\"T_26666_row12_col1\" class=\"data row12 col1\" >3.56</td>\n",
+ " <td id=\"T_26666_row12_col2\" class=\"data row12 col2\" >20.33</td>\n",
+ " <td id=\"T_26666_row12_col3\" class=\"data row12 col3\" >3.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_26666_row13_col0\" class=\"data row13 col0\" >18.18</td>\n",
+ " <td id=\"T_26666_row13_col1\" class=\"data row13 col1\" >2.98</td>\n",
+ " <td id=\"T_26666_row13_col2\" class=\"data row13 col2\" >24.85</td>\n",
+ " <td id=\"T_26666_row13_col3\" class=\"data row13 col3\" >3.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_26666_row14_col0\" class=\"data row14 col0\" >45.58</td>\n",
+ " <td id=\"T_26666_row14_col1\" class=\"data row14 col1\" >5.16</td>\n",
+ " <td id=\"T_26666_row14_col2\" class=\"data row14 col2\" >27.21</td>\n",
+ " <td id=\"T_26666_row14_col3\" class=\"data row14 col3\" >5.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_26666_row15_col0\" class=\"data row15 col0\" >18.43</td>\n",
+ " <td id=\"T_26666_row15_col1\" class=\"data row15 col1\" >3.20</td>\n",
+ " <td id=\"T_26666_row15_col2\" class=\"data row15 col2\" >23.26</td>\n",
+ " <td id=\"T_26666_row15_col3\" class=\"data row15 col3\" >3.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_26666_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_26666_row16_col0\" class=\"data row16 col0\" >13.93</td>\n",
+ " <td id=\"T_26666_row16_col1\" class=\"data row16 col1\" >3.06</td>\n",
+ " <td id=\"T_26666_row16_col2\" class=\"data row16 col2\" >19.82</td>\n",
+ " <td id=\"T_26666_row16_col3\" class=\"data row16 col3\" >3.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_26666_row17_col0\" class=\"data row17 col0\" >18.63</td>\n",
+ " <td id=\"T_26666_row17_col1\" class=\"data row17 col1\" >3.21</td>\n",
+ " <td id=\"T_26666_row17_col2\" class=\"data row17 col2\" >24.23</td>\n",
+ " <td id=\"T_26666_row17_col3\" class=\"data row17 col3\" >3.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_26666_row18_col0\" class=\"data row18 col0\" >18.74</td>\n",
+ " <td id=\"T_26666_row18_col1\" class=\"data row18 col1\" >2.79</td>\n",
+ " <td id=\"T_26666_row18_col2\" class=\"data row18 col2\" >25.16</td>\n",
+ " <td id=\"T_26666_row18_col3\" class=\"data row18 col3\" >3.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_26666_row19_col0\" class=\"data row19 col0\" >44.96</td>\n",
+ " <td id=\"T_26666_row19_col1\" class=\"data row19 col1\" >5.10</td>\n",
+ " <td id=\"T_26666_row19_col2\" class=\"data row19 col2\" >26.37</td>\n",
+ " <td id=\"T_26666_row19_col3\" class=\"data row19 col3\" >4.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_26666_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_26666_row20_col0\" class=\"data row20 col0\" >34.10</td>\n",
+ " <td id=\"T_26666_row20_col1\" class=\"data row20 col1\" >3.34</td>\n",
+ " <td id=\"T_26666_row20_col2\" class=\"data row20 col2\" >31.55</td>\n",
+ " <td id=\"T_26666_row20_col3\" class=\"data row20 col3\" >3.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_26666_row21_col0\" class=\"data row21 col0\" >39.35</td>\n",
+ " <td id=\"T_26666_row21_col1\" class=\"data row21 col1\" >3.76</td>\n",
+ " <td id=\"T_26666_row21_col2\" class=\"data row21 col2\" >28.74</td>\n",
+ " <td id=\"T_26666_row21_col3\" class=\"data row21 col3\" >3.96</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_26666_row22_col0\" class=\"data row22 col0\" >11.81</td>\n",
+ " <td id=\"T_26666_row22_col1\" class=\"data row22 col1\" >2.62</td>\n",
+ " <td id=\"T_26666_row22_col2\" class=\"data row22 col2\" >18.57</td>\n",
+ " <td id=\"T_26666_row22_col3\" class=\"data row22 col3\" >3.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_26666_row23_col0\" class=\"data row23 col0\" >10.91</td>\n",
+ " <td id=\"T_26666_row23_col1\" class=\"data row23 col1\" >2.47</td>\n",
+ " <td id=\"T_26666_row23_col2\" class=\"data row23 col2\" >16.81</td>\n",
+ " <td id=\"T_26666_row23_col3\" class=\"data row23 col3\" >3.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_26666_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_26666_row24_col0\" class=\"data row24 col0\" >34.82</td>\n",
+ " <td id=\"T_26666_row24_col1\" class=\"data row24 col1\" >3.42</td>\n",
+ " <td id=\"T_26666_row24_col2\" class=\"data row24 col2\" >31.82</td>\n",
+ " <td id=\"T_26666_row24_col3\" class=\"data row24 col3\" >3.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_26666_row25_col0\" class=\"data row25 col0\" >11.73</td>\n",
+ " <td id=\"T_26666_row25_col1\" class=\"data row25 col1\" >2.66</td>\n",
+ " <td id=\"T_26666_row25_col2\" class=\"data row25 col2\" >17.72</td>\n",
+ " <td id=\"T_26666_row25_col3\" class=\"data row25 col3\" >3.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_26666_row26_col0\" class=\"data row26 col0\" >38.81</td>\n",
+ " <td id=\"T_26666_row26_col1\" class=\"data row26 col1\" >3.88</td>\n",
+ " <td id=\"T_26666_row26_col2\" class=\"data row26 col2\" >28.94</td>\n",
+ " <td id=\"T_26666_row26_col3\" class=\"data row26 col3\" >4.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_26666_row27_col0\" class=\"data row27 col0\" >11.00</td>\n",
+ " <td id=\"T_26666_row27_col1\" class=\"data row27 col1\" >2.60</td>\n",
+ " <td id=\"T_26666_row27_col2\" class=\"data row27 col2\" >17.31</td>\n",
+ " <td id=\"T_26666_row27_col3\" class=\"data row27 col3\" >2.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_26666_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_26666_row28_col0\" class=\"data row28 col0\" >34.13</td>\n",
+ " <td id=\"T_26666_row28_col1\" class=\"data row28 col1\" >3.25</td>\n",
+ " <td id=\"T_26666_row28_col2\" class=\"data row28 col2\" >31.91</td>\n",
+ " <td id=\"T_26666_row28_col3\" class=\"data row28 col3\" >4.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_26666_row29_col0\" class=\"data row29 col0\" >12.48</td>\n",
+ " <td id=\"T_26666_row29_col1\" class=\"data row29 col1\" >2.65</td>\n",
+ " <td id=\"T_26666_row29_col2\" class=\"data row29 col2\" >17.62</td>\n",
+ " <td id=\"T_26666_row29_col3\" class=\"data row29 col3\" >3.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_26666_row30_col0\" class=\"data row30 col0\" >12.40</td>\n",
+ " <td id=\"T_26666_row30_col1\" class=\"data row30 col1\" >2.67</td>\n",
+ " <td id=\"T_26666_row30_col2\" class=\"data row30 col2\" >18.68</td>\n",
+ " <td id=\"T_26666_row30_col3\" class=\"data row30 col3\" >2.74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_26666_row31_col0\" class=\"data row31 col0\" >37.29</td>\n",
+ " <td id=\"T_26666_row31_col1\" class=\"data row31 col1\" >4.09</td>\n",
+ " <td id=\"T_26666_row31_col2\" class=\"data row31 col2\" >27.58</td>\n",
+ " <td id=\"T_26666_row31_col3\" class=\"data row31 col3\" >4.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_26666_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_26666_row32_col0\" class=\"data row32 col0\" >9.27</td>\n",
+ " <td id=\"T_26666_row32_col1\" class=\"data row32 col1\" >2.58</td>\n",
+ " <td id=\"T_26666_row32_col2\" class=\"data row32 col2\" >16.33</td>\n",
+ " <td id=\"T_26666_row32_col3\" class=\"data row32 col3\" >3.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_26666_row33_col0\" class=\"data row33 col0\" >37.70</td>\n",
+ " <td id=\"T_26666_row33_col1\" class=\"data row33 col1\" >3.97</td>\n",
+ " <td id=\"T_26666_row33_col2\" class=\"data row33 col2\" >31.35</td>\n",
+ " <td id=\"T_26666_row33_col3\" class=\"data row33 col3\" >4.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_26666_row34_col0\" class=\"data row34 col0\" >37.33</td>\n",
+ " <td id=\"T_26666_row34_col1\" class=\"data row34 col1\" >4.15</td>\n",
+ " <td id=\"T_26666_row34_col2\" class=\"data row34 col2\" >29.42</td>\n",
+ " <td id=\"T_26666_row34_col3\" class=\"data row34 col3\" >4.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_26666_row35_col0\" class=\"data row35 col0\" >12.00</td>\n",
+ " <td id=\"T_26666_row35_col1\" class=\"data row35 col1\" >2.95</td>\n",
+ " <td id=\"T_26666_row35_col2\" class=\"data row35 col2\" >18.38</td>\n",
+ " <td id=\"T_26666_row35_col3\" class=\"data row35 col3\" >3.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_26666_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_26666_row36_col0\" class=\"data row36 col0\" >9.50</td>\n",
+ " <td id=\"T_26666_row36_col1\" class=\"data row36 col1\" >2.69</td>\n",
+ " <td id=\"T_26666_row36_col2\" class=\"data row36 col2\" >15.52</td>\n",
+ " <td id=\"T_26666_row36_col3\" class=\"data row36 col3\" >3.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_26666_row37_col0\" class=\"data row37 col0\" >37.08</td>\n",
+ " <td id=\"T_26666_row37_col1\" class=\"data row37 col1\" >3.52</td>\n",
+ " <td id=\"T_26666_row37_col2\" class=\"data row37 col2\" >31.17</td>\n",
+ " <td id=\"T_26666_row37_col3\" class=\"data row37 col3\" >4.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_26666_row38_col0\" class=\"data row38 col0\" >12.55</td>\n",
+ " <td id=\"T_26666_row38_col1\" class=\"data row38 col1\" >2.86</td>\n",
+ " <td id=\"T_26666_row38_col2\" class=\"data row38 col2\" >19.75</td>\n",
+ " <td id=\"T_26666_row38_col3\" class=\"data row38 col3\" >3.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_26666_row39_col0\" class=\"data row39 col0\" >37.01</td>\n",
+ " <td id=\"T_26666_row39_col1\" class=\"data row39 col1\" >3.80</td>\n",
+ " <td id=\"T_26666_row39_col2\" class=\"data row39 col2\" >29.06</td>\n",
+ " <td id=\"T_26666_row39_col3\" class=\"data row39 col3\" >4.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_26666_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_26666_row40_col0\" class=\"data row40 col0\" >9.19</td>\n",
+ " <td id=\"T_26666_row40_col1\" class=\"data row40 col1\" >2.98</td>\n",
+ " <td id=\"T_26666_row40_col2\" class=\"data row40 col2\" >15.53</td>\n",
+ " <td id=\"T_26666_row40_col3\" class=\"data row40 col3\" >3.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_26666_row41_col0\" class=\"data row41 col0\" >12.25</td>\n",
+ " <td id=\"T_26666_row41_col1\" class=\"data row41 col1\" >2.57</td>\n",
+ " <td id=\"T_26666_row41_col2\" class=\"data row41 col2\" >19.52</td>\n",
+ " <td id=\"T_26666_row41_col3\" class=\"data row41 col3\" >3.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_26666_row42_col0\" class=\"data row42 col0\" >37.41</td>\n",
+ " <td id=\"T_26666_row42_col1\" class=\"data row42 col1\" >4.00</td>\n",
+ " <td id=\"T_26666_row42_col2\" class=\"data row42 col2\" >30.50</td>\n",
+ " <td id=\"T_26666_row42_col3\" class=\"data row42 col3\" >4.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_26666_row43_col0\" class=\"data row43 col0\" >37.50</td>\n",
+ " <td id=\"T_26666_row43_col1\" class=\"data row43 col1\" >3.50</td>\n",
+ " <td id=\"T_26666_row43_col2\" class=\"data row43 col2\" >29.94</td>\n",
+ " <td id=\"T_26666_row43_col3\" class=\"data row43 col3\" >4.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_26666_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_26666_row44_col0\" class=\"data row44 col0\" >26.75</td>\n",
+ " <td id=\"T_26666_row44_col1\" class=\"data row44 col1\" >3.17</td>\n",
+ " <td id=\"T_26666_row44_col2\" class=\"data row44 col2\" >27.55</td>\n",
+ " <td id=\"T_26666_row44_col3\" class=\"data row44 col3\" >3.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_26666_row45_col0\" class=\"data row45 col0\" >30.92</td>\n",
+ " <td id=\"T_26666_row45_col1\" class=\"data row45 col1\" >3.15</td>\n",
+ " <td id=\"T_26666_row45_col2\" class=\"data row45 col2\" >28.47</td>\n",
+ " <td id=\"T_26666_row45_col3\" class=\"data row45 col3\" >3.88</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_26666_row46_col0\" class=\"data row46 col0\" >32.11</td>\n",
+ " <td id=\"T_26666_row46_col1\" class=\"data row46 col1\" >3.14</td>\n",
+ " <td id=\"T_26666_row46_col2\" class=\"data row46 col2\" >27.71</td>\n",
+ " <td id=\"T_26666_row46_col3\" class=\"data row46 col3\" >4.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_26666_row47_col0\" class=\"data row47 col0\" >6.50</td>\n",
+ " <td id=\"T_26666_row47_col1\" class=\"data row47 col1\" >2.16</td>\n",
+ " <td id=\"T_26666_row47_col2\" class=\"data row47 col2\" >11.71</td>\n",
+ " <td id=\"T_26666_row47_col3\" class=\"data row47 col3\" >2.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_26666_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_26666_row48_col0\" class=\"data row48 col0\" >25.81</td>\n",
+ " <td id=\"T_26666_row48_col1\" class=\"data row48 col1\" >3.07</td>\n",
+ " <td id=\"T_26666_row48_col2\" class=\"data row48 col2\" >27.97</td>\n",
+ " <td id=\"T_26666_row48_col3\" class=\"data row48 col3\" >4.25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_26666_row49_col0\" class=\"data row49 col0\" >31.45</td>\n",
+ " <td id=\"T_26666_row49_col1\" class=\"data row49 col1\" >3.30</td>\n",
+ " <td id=\"T_26666_row49_col2\" class=\"data row49 col2\" >27.91</td>\n",
+ " <td id=\"T_26666_row49_col3\" class=\"data row49 col3\" >4.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_26666_row50_col0\" class=\"data row50 col0\" >7.67</td>\n",
+ " <td id=\"T_26666_row50_col1\" class=\"data row50 col1\" >2.28</td>\n",
+ " <td id=\"T_26666_row50_col2\" class=\"data row50 col2\" >13.56</td>\n",
+ " <td id=\"T_26666_row50_col3\" class=\"data row50 col3\" >3.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_26666_row51_col0\" class=\"data row51 col0\" >31.27</td>\n",
+ " <td id=\"T_26666_row51_col1\" class=\"data row51 col1\" >3.81</td>\n",
+ " <td id=\"T_26666_row51_col2\" class=\"data row51 col2\" >26.14</td>\n",
+ " <td id=\"T_26666_row51_col3\" class=\"data row51 col3\" >4.55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_26666_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_26666_row52_col0\" class=\"data row52 col0\" >25.81</td>\n",
+ " <td id=\"T_26666_row52_col1\" class=\"data row52 col1\" >3.01</td>\n",
+ " <td id=\"T_26666_row52_col2\" class=\"data row52 col2\" >27.58</td>\n",
+ " <td id=\"T_26666_row52_col3\" class=\"data row52 col3\" >3.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_26666_row53_col0\" class=\"data row53 col0\" >7.81</td>\n",
+ " <td id=\"T_26666_row53_col1\" class=\"data row53 col1\" >2.41</td>\n",
+ " <td id=\"T_26666_row53_col2\" class=\"data row53 col2\" >13.38</td>\n",
+ " <td id=\"T_26666_row53_col3\" class=\"data row53 col3\" >2.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_26666_row54_col0\" class=\"data row54 col0\" >30.73</td>\n",
+ " <td id=\"T_26666_row54_col1\" class=\"data row54 col1\" >3.64</td>\n",
+ " <td id=\"T_26666_row54_col2\" class=\"data row54 col2\" >28.26</td>\n",
+ " <td id=\"T_26666_row54_col3\" class=\"data row54 col3\" >4.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_26666_row55_col0\" class=\"data row55 col0\" >32.04</td>\n",
+ " <td id=\"T_26666_row55_col1\" class=\"data row55 col1\" >3.67</td>\n",
+ " <td id=\"T_26666_row55_col2\" class=\"data row55 col2\" >26.52</td>\n",
+ " <td id=\"T_26666_row55_col3\" class=\"data row55 col3\" >3.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_26666_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_26666_row56_col0\" class=\"data row56 col0\" >5.92</td>\n",
+ " <td id=\"T_26666_row56_col1\" class=\"data row56 col1\" >2.41</td>\n",
+ " <td id=\"T_26666_row56_col2\" class=\"data row56 col2\" >11.37</td>\n",
+ " <td id=\"T_26666_row56_col3\" class=\"data row56 col3\" >3.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_26666_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_26666_row57_col0\" class=\"data row57 col0\" >29.87</td>\n",
+ " <td id=\"T_26666_row57_col1\" class=\"data row57 col1\" >3.45</td>\n",
+ " <td id=\"T_26666_row57_col2\" class=\"data row57 col2\" >28.26</td>\n",
+ " <td id=\"T_26666_row57_col3\" class=\"data row57 col3\" >3.55</td>\n",
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+ "source": [
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+ ]
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+ {
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+ "execution_count": 9,
+ "metadata": {},
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+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row6_col4 {\n",
+ " background-color: #f5c8c8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row7_col0 {\n",
+ " background-color: #72fb72;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row7_col2, #T_ebc63_row15_col2, #T_ebc63_row20_col2, #T_ebc63_row24_col2, #T_ebc63_row46_col0, #T_ebc63_row50_col2 {\n",
+ " background-color: #59fd59;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row7_col4, #T_ebc63_row11_col4 {\n",
+ " background-color: #f4d7d7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row8_col0, #T_ebc63_row33_col0, #T_ebc63_row39_col0, #T_ebc63_row47_col0 {\n",
+ " background-color: #8bf98b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row8_col2 {\n",
+ " background-color: #3aff3a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row8_col4 {\n",
+ " background-color: #f3e9e9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row9_col0 {\n",
+ " background-color: #d0f5d0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row9_col2, #T_ebc63_row19_col0 {\n",
+ " background-color: #ebf3eb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row9_col4 {\n",
+ " background-color: #ff0808;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row10_col0, #T_ebc63_row22_col0, #T_ebc63_row31_col2, #T_ebc63_row37_col2 {\n",
+ " background-color: #74fb74;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row10_col2, #T_ebc63_row44_col0, #T_ebc63_row56_col2 {\n",
+ " background-color: #4cfd4c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row10_col4, #T_ebc63_row17_col4, #T_ebc63_row29_col4 {\n",
+ " background-color: #f5c6c6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row11_col0 {\n",
+ " background-color: #80fa80;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row11_col2, #T_ebc63_row16_col2, #T_ebc63_row35_col2, #T_ebc63_row47_col2, #T_ebc63_row52_col2, #T_ebc63_row57_col2 {\n",
+ " background-color: #49fe49;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row12_col0, #T_ebc63_row27_col0 {\n",
+ " background-color: #7cfa7c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row12_col2, #T_ebc63_row48_col0, #T_ebc63_row55_col2 {\n",
+ " background-color: #4efd4e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row12_col4 {\n",
+ " background-color: #f4e1e1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row13_col0, #T_ebc63_row23_col0, #T_ebc63_row26_col0, #T_ebc63_row41_col0 {\n",
+ " background-color: #8df98d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row13_col2 {\n",
+ " background-color: #55fd55;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row13_col4, #T_ebc63_row38_col4 {\n",
+ " background-color: #f5caca;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row14_col0 {\n",
+ " background-color: #ddf4dd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row14_col2 {\n",
+ " background-color: #cdf5cd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row14_col4 {\n",
+ " background-color: #ff0000;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row15_col0 {\n",
+ " background-color: #7bfa7b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row16_col0 {\n",
+ " background-color: #7afa7a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row16_col4, #T_ebc63_row40_col4, #T_ebc63_row47_col4 {\n",
+ " background-color: #f3e6e6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row17_col0, #T_ebc63_row53_col0 {\n",
+ " background-color: #94f994;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row17_col2 {\n",
+ " background-color: #53fd53;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row18_col0 {\n",
+ " background-color: #6dfb6d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row18_col2, #T_ebc63_row30_col2, #T_ebc63_row38_col2, #T_ebc63_row49_col2 {\n",
+ " background-color: #50fd50;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row18_col4 {\n",
+ " background-color: #f5c8c7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row19_col2 {\n",
+ " background-color: #cef5ce;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row19_col4 {\n",
+ " background-color: #fe1616;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row20_col0, #T_ebc63_row21_col2, #T_ebc63_row40_col0 {\n",
+ " background-color: #7efa7e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row20_col4 {\n",
+ " background-color: #f89191;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row21_col0 {\n",
+ " background-color: #9df89c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row21_col4 {\n",
+ " background-color: #fb5454;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row22_col2, #T_ebc63_row43_col2 {\n",
+ " background-color: #62fc62;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row22_col4 {\n",
+ " background-color: #f5cdcd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row23_col2, #T_ebc63_row51_col2, #T_ebc63_row54_col2, #T_ebc63_row58_col2 {\n",
+ " background-color: #44fe44;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row23_col4 {\n",
+ " background-color: #f4dada;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row24_col0 {\n",
+ " background-color: #86fa86;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row24_col4 {\n",
+ " background-color: #f88f8f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row25_col0 {\n",
+ " background-color: #85fa85;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row25_col2 {\n",
+ " background-color: #60fc60;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row25_col4, #T_ebc63_row50_col4 {\n",
+ " background-color: #f5cccc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row26_col2 {\n",
+ " background-color: #66fc66;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row26_col4 {\n",
+ " background-color: #fb5858;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row27_col2, #T_ebc63_row44_col2 {\n",
+ " background-color: #41fe41;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row27_col4 {\n",
+ " background-color: #f4d8d8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row28_col0 {\n",
+ " background-color: #88f988;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row28_col2, #T_ebc63_row42_col2, #T_ebc63_row45_col0 {\n",
+ " background-color: #5efc5e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row28_col4 {\n",
+ " background-color: #f89090;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row29_col0 {\n",
+ " background-color: #81fa81;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row29_col2, #T_ebc63_row55_col0 {\n",
+ " background-color: #5dfc5d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row30_col0 {\n",
+ " background-color: #76fb76;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row30_col4 {\n",
+ " background-color: #f4d2d2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row31_col0, #T_ebc63_row34_col0 {\n",
+ " background-color: #92f992;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row31_col4 {\n",
+ " background-color: #f97070;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row32_col0, #T_ebc63_row38_col0, #T_ebc63_row56_col0 {\n",
+ " background-color: #79fa79;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row32_col2 {\n",
+ " background-color: #48fe48;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row32_col4, #T_ebc63_row36_col4 {\n",
+ " background-color: #f3efef;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row33_col2 {\n",
+ " background-color: #68fc68;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row33_col4 {\n",
+ " background-color: #fa6c6c;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row34_col2 {\n",
+ " background-color: #6bfb6b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row34_col4 {\n",
+ " background-color: #fa6262;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row35_col0 {\n",
+ " background-color: #84fa84;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row35_col4 {\n",
+ " background-color: #f4e2e2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row36_col0 {\n",
+ " background-color: #7dfa7d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row36_col2 {\n",
+ " background-color: #3ffe3f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row37_col0, #T_ebc63_row42_col0, #T_ebc63_row43_col0 {\n",
+ " background-color: #95f995;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row37_col4 {\n",
+ " background-color: #f96d6d;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row39_col2 {\n",
+ " background-color: #67fc67;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row39_col4 {\n",
+ " background-color: #f98080;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row41_col2, #T_ebc63_row58_col0 {\n",
+ " background-color: #5ffc5f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row41_col4 {\n",
+ " background-color: #f4d4d4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row42_col4 {\n",
+ " background-color: #fa6767;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row43_col4 {\n",
+ " background-color: #f97a7a;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_ebc63_row44_col4 {\n",
+ " background-color: #f5bcbc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row45_col2 {\n",
+ " background-color: #4afe4a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row45_col4, #T_ebc63_row58_col4 {\n",
+ " background-color: #f7a0a0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row46_col2, #T_ebc63_row59_col2 {\n",
+ " background-color: #45fe45;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row46_col4 {\n",
+ " background-color: #f79898;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row48_col2 {\n",
+ " background-color: #3efe3e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row48_col4 {\n",
+ " background-color: #f5c1c1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row49_col0 {\n",
+ " background-color: #69fc69;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row49_col4 {\n",
+ " background-color: #f79696;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row50_col0 {\n",
+ " background-color: #83fa83;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row51_col0 {\n",
+ " background-color: #5cfc5c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row51_col4 {\n",
+ " background-color: #f6b2b2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row52_col0 {\n",
+ " background-color: #4ffd4f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row52_col4 {\n",
+ " background-color: #f5c2c2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row53_col2, #T_ebc63_row57_col0 {\n",
+ " background-color: #63fc63;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row53_col4 {\n",
+ " background-color: #f4cfcf;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row54_col0 {\n",
+ " background-color: #56fd56;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row54_col4 {\n",
+ " background-color: #f79897;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row55_col4 {\n",
+ " background-color: #f6b4b4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row56_col4 {\n",
+ " background-color: #f3f0f0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row57_col4 {\n",
+ " background-color: #f6a9a9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row59_col0 {\n",
+ " background-color: #5bfc5b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_ebc63_row59_col4 {\n",
+ " background-color: #f6b9b9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_ebc63\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_ebc63_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">% of load of jobs/services bidded</th>\n",
+ " <th id=\"T_ebc63_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\"></th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_ebc63_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_ebc63_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_ebc63_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Slowdown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_ebc63_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_ebc63_level2_col1\" class=\"col_heading level2 col1\" >Std</th>\n",
+ " <th id=\"T_ebc63_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_ebc63_level2_col3\" class=\"col_heading level2 col3\" >Std</th>\n",
+ " <th id=\"T_ebc63_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_ebc63_level2_col5\" class=\"col_heading level2 col5\" >Std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_ebc63_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_ebc63_row0_col0\" class=\"data row0 col0\" >96.86</td>\n",
+ " <td id=\"T_ebc63_row0_col1\" class=\"data row0 col1\" >2.51</td>\n",
+ " <td id=\"T_ebc63_row0_col2\" class=\"data row0 col2\" >99.75</td>\n",
+ " <td id=\"T_ebc63_row0_col3\" class=\"data row0 col3\" >1.14</td>\n",
+ " <td id=\"T_ebc63_row0_col4\" class=\"data row0 col4\" >1.19</td>\n",
+ " <td id=\"T_ebc63_row0_col5\" class=\"data row0 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_ebc63_row1_col0\" class=\"data row1 col0\" >96.98</td>\n",
+ " <td id=\"T_ebc63_row1_col1\" class=\"data row1 col1\" >2.20</td>\n",
+ " <td id=\"T_ebc63_row1_col2\" class=\"data row1 col2\" >99.41</td>\n",
+ " <td id=\"T_ebc63_row1_col3\" class=\"data row1 col3\" >1.69</td>\n",
+ " <td id=\"T_ebc63_row1_col4\" class=\"data row1 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_ebc63_row2_col0\" class=\"data row2 col0\" >96.71</td>\n",
+ " <td id=\"T_ebc63_row2_col1\" class=\"data row2 col1\" >2.42</td>\n",
+ " <td id=\"T_ebc63_row2_col2\" class=\"data row2 col2\" >99.48</td>\n",
+ " <td id=\"T_ebc63_row2_col3\" class=\"data row2 col3\" >1.67</td>\n",
+ " <td id=\"T_ebc63_row2_col4\" class=\"data row2 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row2_col5\" class=\"data row2 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_ebc63_row3_col0\" class=\"data row3 col0\" >97.10</td>\n",
+ " <td id=\"T_ebc63_row3_col1\" class=\"data row3 col1\" >2.44</td>\n",
+ " <td id=\"T_ebc63_row3_col2\" class=\"data row3 col2\" >99.78</td>\n",
+ " <td id=\"T_ebc63_row3_col3\" class=\"data row3 col3\" >0.90</td>\n",
+ " <td id=\"T_ebc63_row3_col4\" class=\"data row3 col4\" >1.22</td>\n",
+ " <td id=\"T_ebc63_row3_col5\" class=\"data row3 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_ebc63_row4_col0\" class=\"data row4 col0\" >90.94</td>\n",
+ " <td id=\"T_ebc63_row4_col1\" class=\"data row4 col1\" >3.31</td>\n",
+ " <td id=\"T_ebc63_row4_col2\" class=\"data row4 col2\" >95.10</td>\n",
+ " <td id=\"T_ebc63_row4_col3\" class=\"data row4 col3\" >3.43</td>\n",
+ " <td id=\"T_ebc63_row4_col4\" class=\"data row4 col4\" >1.78</td>\n",
+ " <td id=\"T_ebc63_row4_col5\" class=\"data row4 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_ebc63_row5_col0\" class=\"data row5 col0\" >94.10</td>\n",
+ " <td id=\"T_ebc63_row5_col1\" class=\"data row5 col1\" >2.52</td>\n",
+ " <td id=\"T_ebc63_row5_col2\" class=\"data row5 col2\" >98.55</td>\n",
+ " <td id=\"T_ebc63_row5_col3\" class=\"data row5 col3\" >3.14</td>\n",
+ " <td id=\"T_ebc63_row5_col4\" class=\"data row5 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row5_col5\" class=\"data row5 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_ebc63_row6_col0\" class=\"data row6 col0\" >94.27</td>\n",
+ " <td id=\"T_ebc63_row6_col1\" class=\"data row6 col1\" >2.65</td>\n",
+ " <td id=\"T_ebc63_row6_col2\" class=\"data row6 col2\" >98.96</td>\n",
+ " <td id=\"T_ebc63_row6_col3\" class=\"data row6 col3\" >2.00</td>\n",
+ " <td id=\"T_ebc63_row6_col4\" class=\"data row6 col4\" >1.27</td>\n",
+ " <td id=\"T_ebc63_row6_col5\" class=\"data row6 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_ebc63_row7_col0\" class=\"data row7 col0\" >94.32</td>\n",
+ " <td id=\"T_ebc63_row7_col1\" class=\"data row7 col1\" >3.06</td>\n",
+ " <td id=\"T_ebc63_row7_col2\" class=\"data row7 col2\" >98.22</td>\n",
+ " <td id=\"T_ebc63_row7_col3\" class=\"data row7 col3\" >3.49</td>\n",
+ " <td id=\"T_ebc63_row7_col4\" class=\"data row7 col4\" >1.21</td>\n",
+ " <td id=\"T_ebc63_row7_col5\" class=\"data row7 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_ebc63_row8_col0\" class=\"data row8 col0\" >93.18</td>\n",
+ " <td id=\"T_ebc63_row8_col1\" class=\"data row8 col1\" >3.52</td>\n",
+ " <td id=\"T_ebc63_row8_col2\" class=\"data row8 col2\" >99.51</td>\n",
+ " <td id=\"T_ebc63_row8_col3\" class=\"data row8 col3\" >1.88</td>\n",
+ " <td id=\"T_ebc63_row8_col4\" class=\"data row8 col4\" >1.15</td>\n",
+ " <td id=\"T_ebc63_row8_col5\" class=\"data row8 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_ebc63_row9_col0\" class=\"data row9 col0\" >90.13</td>\n",
+ " <td id=\"T_ebc63_row9_col1\" class=\"data row9 col1\" >3.38</td>\n",
+ " <td id=\"T_ebc63_row9_col2\" class=\"data row9 col2\" >92.08</td>\n",
+ " <td id=\"T_ebc63_row9_col3\" class=\"data row9 col3\" >4.25</td>\n",
+ " <td id=\"T_ebc63_row9_col4\" class=\"data row9 col4\" >1.96</td>\n",
+ " <td id=\"T_ebc63_row9_col5\" class=\"data row9 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_ebc63_row10_col0\" class=\"data row10 col0\" >94.21</td>\n",
+ " <td id=\"T_ebc63_row10_col1\" class=\"data row10 col1\" >2.45</td>\n",
+ " <td id=\"T_ebc63_row10_col2\" class=\"data row10 col2\" >98.74</td>\n",
+ " <td id=\"T_ebc63_row10_col3\" class=\"data row10 col3\" >2.69</td>\n",
+ " <td id=\"T_ebc63_row10_col4\" class=\"data row10 col4\" >1.28</td>\n",
+ " <td id=\"T_ebc63_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_ebc63_row11_col0\" class=\"data row11 col0\" >93.70</td>\n",
+ " <td id=\"T_ebc63_row11_col1\" class=\"data row11 col1\" >2.77</td>\n",
+ " <td id=\"T_ebc63_row11_col2\" class=\"data row11 col2\" >98.89</td>\n",
+ " <td id=\"T_ebc63_row11_col3\" class=\"data row11 col3\" >3.05</td>\n",
+ " <td id=\"T_ebc63_row11_col4\" class=\"data row11 col4\" >1.21</td>\n",
+ " <td id=\"T_ebc63_row11_col5\" class=\"data row11 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_ebc63_row12_col0\" class=\"data row12 col0\" >93.86</td>\n",
+ " <td id=\"T_ebc63_row12_col1\" class=\"data row12 col1\" >3.37</td>\n",
+ " <td id=\"T_ebc63_row12_col2\" class=\"data row12 col2\" >98.64</td>\n",
+ " <td id=\"T_ebc63_row12_col3\" class=\"data row12 col3\" >2.73</td>\n",
+ " <td id=\"T_ebc63_row12_col4\" class=\"data row12 col4\" >1.18</td>\n",
+ " <td id=\"T_ebc63_row12_col5\" class=\"data row12 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_ebc63_row13_col0\" class=\"data row13 col0\" >93.11</td>\n",
+ " <td id=\"T_ebc63_row13_col1\" class=\"data row13 col1\" >2.44</td>\n",
+ " <td id=\"T_ebc63_row13_col2\" class=\"data row13 col2\" >98.37</td>\n",
+ " <td id=\"T_ebc63_row13_col3\" class=\"data row13 col3\" >3.61</td>\n",
+ " <td id=\"T_ebc63_row13_col4\" class=\"data row13 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row13_col5\" class=\"data row13 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_ebc63_row14_col0\" class=\"data row14 col0\" >89.56</td>\n",
+ " <td id=\"T_ebc63_row14_col1\" class=\"data row14 col1\" >3.72</td>\n",
+ " <td id=\"T_ebc63_row14_col2\" class=\"data row14 col2\" >93.34</td>\n",
+ " <td id=\"T_ebc63_row14_col3\" class=\"data row14 col3\" >4.24</td>\n",
+ " <td id=\"T_ebc63_row14_col4\" class=\"data row14 col4\" >1.99</td>\n",
+ " <td id=\"T_ebc63_row14_col5\" class=\"data row14 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_ebc63_row15_col0\" class=\"data row15 col0\" >93.94</td>\n",
+ " <td id=\"T_ebc63_row15_col1\" class=\"data row15 col1\" >3.02</td>\n",
+ " <td id=\"T_ebc63_row15_col2\" class=\"data row15 col2\" >98.24</td>\n",
+ " <td id=\"T_ebc63_row15_col3\" class=\"data row15 col3\" >3.37</td>\n",
+ " <td id=\"T_ebc63_row15_col4\" class=\"data row15 col4\" >1.22</td>\n",
+ " <td id=\"T_ebc63_row15_col5\" class=\"data row15 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_ebc63_row16_col0\" class=\"data row16 col0\" >93.95</td>\n",
+ " <td id=\"T_ebc63_row16_col1\" class=\"data row16 col1\" >3.48</td>\n",
+ " <td id=\"T_ebc63_row16_col2\" class=\"data row16 col2\" >98.88</td>\n",
+ " <td id=\"T_ebc63_row16_col3\" class=\"data row16 col3\" >3.09</td>\n",
+ " <td id=\"T_ebc63_row16_col4\" class=\"data row16 col4\" >1.16</td>\n",
+ " <td id=\"T_ebc63_row16_col5\" class=\"data row16 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_ebc63_row17_col0\" class=\"data row17 col0\" >92.81</td>\n",
+ " <td id=\"T_ebc63_row17_col1\" class=\"data row17 col1\" >2.78</td>\n",
+ " <td id=\"T_ebc63_row17_col2\" class=\"data row17 col2\" >98.46</td>\n",
+ " <td id=\"T_ebc63_row17_col3\" class=\"data row17 col3\" >2.76</td>\n",
+ " <td id=\"T_ebc63_row17_col4\" class=\"data row17 col4\" >1.28</td>\n",
+ " <td id=\"T_ebc63_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_ebc63_row18_col0\" class=\"data row18 col0\" >94.53</td>\n",
+ " <td id=\"T_ebc63_row18_col1\" class=\"data row18 col1\" >2.40</td>\n",
+ " <td id=\"T_ebc63_row18_col2\" class=\"data row18 col2\" >98.59</td>\n",
+ " <td id=\"T_ebc63_row18_col3\" class=\"data row18 col3\" >2.67</td>\n",
+ " <td id=\"T_ebc63_row18_col4\" class=\"data row18 col4\" >1.27</td>\n",
+ " <td id=\"T_ebc63_row18_col5\" class=\"data row18 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_ebc63_row19_col0\" class=\"data row19 col0\" >88.95</td>\n",
+ " <td id=\"T_ebc63_row19_col1\" class=\"data row19 col1\" >3.77</td>\n",
+ " <td id=\"T_ebc63_row19_col2\" class=\"data row19 col2\" >93.31</td>\n",
+ " <td id=\"T_ebc63_row19_col3\" class=\"data row19 col3\" >3.71</td>\n",
+ " <td id=\"T_ebc63_row19_col4\" class=\"data row19 col4\" >1.91</td>\n",
+ " <td id=\"T_ebc63_row19_col5\" class=\"data row19 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_ebc63_row20_col0\" class=\"data row20 col0\" >93.76</td>\n",
+ " <td id=\"T_ebc63_row20_col1\" class=\"data row20 col1\" >2.30</td>\n",
+ " <td id=\"T_ebc63_row20_col2\" class=\"data row20 col2\" >98.21</td>\n",
+ " <td id=\"T_ebc63_row20_col3\" class=\"data row20 col3\" >2.45</td>\n",
+ " <td id=\"T_ebc63_row20_col4\" class=\"data row20 col4\" >1.47</td>\n",
+ " <td id=\"T_ebc63_row20_col5\" class=\"data row20 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_ebc63_row21_col0\" class=\"data row21 col0\" >92.44</td>\n",
+ " <td id=\"T_ebc63_row21_col1\" class=\"data row21 col1\" >3.08</td>\n",
+ " <td id=\"T_ebc63_row21_col2\" class=\"data row21 col2\" >96.62</td>\n",
+ " <td id=\"T_ebc63_row21_col3\" class=\"data row21 col3\" >3.01</td>\n",
+ " <td id=\"T_ebc63_row21_col4\" class=\"data row21 col4\" >1.69</td>\n",
+ " <td id=\"T_ebc63_row21_col5\" class=\"data row21 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_ebc63_row22_col0\" class=\"data row22 col0\" >94.23</td>\n",
+ " <td id=\"T_ebc63_row22_col1\" class=\"data row22 col1\" >2.73</td>\n",
+ " <td id=\"T_ebc63_row22_col2\" class=\"data row22 col2\" >97.84</td>\n",
+ " <td id=\"T_ebc63_row22_col3\" class=\"data row22 col3\" >6.32</td>\n",
+ " <td id=\"T_ebc63_row22_col4\" class=\"data row22 col4\" >1.25</td>\n",
+ " <td id=\"T_ebc63_row22_col5\" class=\"data row22 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_ebc63_row23_col0\" class=\"data row23 col0\" >93.12</td>\n",
+ " <td id=\"T_ebc63_row23_col1\" class=\"data row23 col1\" >3.37</td>\n",
+ " <td id=\"T_ebc63_row23_col2\" class=\"data row23 col2\" >99.11</td>\n",
+ " <td id=\"T_ebc63_row23_col3\" class=\"data row23 col3\" >2.31</td>\n",
+ " <td id=\"T_ebc63_row23_col4\" class=\"data row23 col4\" >1.20</td>\n",
+ " <td id=\"T_ebc63_row23_col5\" class=\"data row23 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_ebc63_row24_col0\" class=\"data row24 col0\" >93.44</td>\n",
+ " <td id=\"T_ebc63_row24_col1\" class=\"data row24 col1\" >2.82</td>\n",
+ " <td id=\"T_ebc63_row24_col2\" class=\"data row24 col2\" >98.20</td>\n",
+ " <td id=\"T_ebc63_row24_col3\" class=\"data row24 col3\" >2.44</td>\n",
+ " <td id=\"T_ebc63_row24_col4\" class=\"data row24 col4\" >1.47</td>\n",
+ " <td id=\"T_ebc63_row24_col5\" class=\"data row24 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_ebc63_row25_col0\" class=\"data row25 col0\" >93.47</td>\n",
+ " <td id=\"T_ebc63_row25_col1\" class=\"data row25 col1\" >3.36</td>\n",
+ " <td id=\"T_ebc63_row25_col2\" class=\"data row25 col2\" >97.90</td>\n",
+ " <td id=\"T_ebc63_row25_col3\" class=\"data row25 col3\" >5.83</td>\n",
+ " <td id=\"T_ebc63_row25_col4\" class=\"data row25 col4\" >1.25</td>\n",
+ " <td id=\"T_ebc63_row25_col5\" class=\"data row25 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_ebc63_row26_col0\" class=\"data row26 col0\" >93.14</td>\n",
+ " <td id=\"T_ebc63_row26_col1\" class=\"data row26 col1\" >2.85</td>\n",
+ " <td id=\"T_ebc63_row26_col2\" class=\"data row26 col2\" >97.65</td>\n",
+ " <td id=\"T_ebc63_row26_col3\" class=\"data row26 col3\" >2.35</td>\n",
+ " <td id=\"T_ebc63_row26_col4\" class=\"data row26 col4\" >1.67</td>\n",
+ " <td id=\"T_ebc63_row26_col5\" class=\"data row26 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_ebc63_row27_col0\" class=\"data row27 col0\" >93.86</td>\n",
+ " <td id=\"T_ebc63_row27_col1\" class=\"data row27 col1\" >2.82</td>\n",
+ " <td id=\"T_ebc63_row27_col2\" class=\"data row27 col2\" >99.22</td>\n",
+ " <td id=\"T_ebc63_row27_col3\" class=\"data row27 col3\" >2.18</td>\n",
+ " <td id=\"T_ebc63_row27_col4\" class=\"data row27 col4\" >1.21</td>\n",
+ " <td id=\"T_ebc63_row27_col5\" class=\"data row27 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_ebc63_row28_col0\" class=\"data row28 col0\" >93.34</td>\n",
+ " <td id=\"T_ebc63_row28_col1\" class=\"data row28 col1\" >2.70</td>\n",
+ " <td id=\"T_ebc63_row28_col2\" class=\"data row28 col2\" >98.00</td>\n",
+ " <td id=\"T_ebc63_row28_col3\" class=\"data row28 col3\" >3.05</td>\n",
+ " <td id=\"T_ebc63_row28_col4\" class=\"data row28 col4\" >1.47</td>\n",
+ " <td id=\"T_ebc63_row28_col5\" class=\"data row28 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_ebc63_row29_col0\" class=\"data row29 col0\" >93.67</td>\n",
+ " <td id=\"T_ebc63_row29_col1\" class=\"data row29 col1\" >2.61</td>\n",
+ " <td id=\"T_ebc63_row29_col2\" class=\"data row29 col2\" >98.04</td>\n",
+ " <td id=\"T_ebc63_row29_col3\" class=\"data row29 col3\" >4.58</td>\n",
+ " <td id=\"T_ebc63_row29_col4\" class=\"data row29 col4\" >1.28</td>\n",
+ " <td id=\"T_ebc63_row29_col5\" class=\"data row29 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_ebc63_row30_col0\" class=\"data row30 col0\" >94.15</td>\n",
+ " <td id=\"T_ebc63_row30_col1\" class=\"data row30 col1\" >2.78</td>\n",
+ " <td id=\"T_ebc63_row30_col2\" class=\"data row30 col2\" >98.59</td>\n",
+ " <td id=\"T_ebc63_row30_col3\" class=\"data row30 col3\" >4.77</td>\n",
+ " <td id=\"T_ebc63_row30_col4\" class=\"data row30 col4\" >1.23</td>\n",
+ " <td id=\"T_ebc63_row30_col5\" class=\"data row30 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_ebc63_row31_col0\" class=\"data row31 col0\" >92.88</td>\n",
+ " <td id=\"T_ebc63_row31_col1\" class=\"data row31 col1\" >2.96</td>\n",
+ " <td id=\"T_ebc63_row31_col2\" class=\"data row31 col2\" >97.05</td>\n",
+ " <td id=\"T_ebc63_row31_col3\" class=\"data row31 col3\" >2.52</td>\n",
+ " <td id=\"T_ebc63_row31_col4\" class=\"data row31 col4\" >1.59</td>\n",
+ " <td id=\"T_ebc63_row31_col5\" class=\"data row31 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_ebc63_row32_col0\" class=\"data row32 col0\" >94.00</td>\n",
+ " <td id=\"T_ebc63_row32_col1\" class=\"data row32 col1\" >4.04</td>\n",
+ " <td id=\"T_ebc63_row32_col2\" class=\"data row32 col2\" >98.92</td>\n",
+ " <td id=\"T_ebc63_row32_col3\" class=\"data row32 col3\" >3.35</td>\n",
+ " <td id=\"T_ebc63_row32_col4\" class=\"data row32 col4\" >1.13</td>\n",
+ " <td id=\"T_ebc63_row32_col5\" class=\"data row32 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_ebc63_row33_col0\" class=\"data row33 col0\" >93.19</td>\n",
+ " <td id=\"T_ebc63_row33_col1\" class=\"data row33 col1\" >2.49</td>\n",
+ " <td id=\"T_ebc63_row33_col2\" class=\"data row33 col2\" >97.59</td>\n",
+ " <td id=\"T_ebc63_row33_col3\" class=\"data row33 col3\" >2.75</td>\n",
+ " <td id=\"T_ebc63_row33_col4\" class=\"data row33 col4\" >1.60</td>\n",
+ " <td id=\"T_ebc63_row33_col5\" class=\"data row33 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_ebc63_row34_col0\" class=\"data row34 col0\" >92.93</td>\n",
+ " <td id=\"T_ebc63_row34_col1\" class=\"data row34 col1\" >2.63</td>\n",
+ " <td id=\"T_ebc63_row34_col2\" class=\"data row34 col2\" >97.44</td>\n",
+ " <td id=\"T_ebc63_row34_col3\" class=\"data row34 col3\" >3.16</td>\n",
+ " <td id=\"T_ebc63_row34_col4\" class=\"data row34 col4\" >1.64</td>\n",
+ " <td id=\"T_ebc63_row34_col5\" class=\"data row34 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_ebc63_row35_col0\" class=\"data row35 col0\" >93.50</td>\n",
+ " <td id=\"T_ebc63_row35_col1\" class=\"data row35 col1\" >3.34</td>\n",
+ " <td id=\"T_ebc63_row35_col2\" class=\"data row35 col2\" >98.88</td>\n",
+ " <td id=\"T_ebc63_row35_col3\" class=\"data row35 col3\" >2.57</td>\n",
+ " <td id=\"T_ebc63_row35_col4\" class=\"data row35 col4\" >1.17</td>\n",
+ " <td id=\"T_ebc63_row35_col5\" class=\"data row35 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_ebc63_row36_col0\" class=\"data row36 col0\" >93.84</td>\n",
+ " <td id=\"T_ebc63_row36_col1\" class=\"data row36 col1\" >4.30</td>\n",
+ " <td id=\"T_ebc63_row36_col2\" class=\"data row36 col2\" >99.30</td>\n",
+ " <td id=\"T_ebc63_row36_col3\" class=\"data row36 col3\" >2.14</td>\n",
+ " <td id=\"T_ebc63_row36_col4\" class=\"data row36 col4\" >1.13</td>\n",
+ " <td id=\"T_ebc63_row36_col5\" class=\"data row36 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_ebc63_row37_col0\" class=\"data row37 col0\" >92.75</td>\n",
+ " <td id=\"T_ebc63_row37_col1\" class=\"data row37 col1\" >2.85</td>\n",
+ " <td id=\"T_ebc63_row37_col2\" class=\"data row37 col2\" >97.07</td>\n",
+ " <td id=\"T_ebc63_row37_col3\" class=\"data row37 col3\" >3.42</td>\n",
+ " <td id=\"T_ebc63_row37_col4\" class=\"data row37 col4\" >1.60</td>\n",
+ " <td id=\"T_ebc63_row37_col5\" class=\"data row37 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_ebc63_row38_col0\" class=\"data row38 col0\" >94.00</td>\n",
+ " <td id=\"T_ebc63_row38_col1\" class=\"data row38 col1\" >2.95</td>\n",
+ " <td id=\"T_ebc63_row38_col2\" class=\"data row38 col2\" >98.58</td>\n",
+ " <td id=\"T_ebc63_row38_col3\" class=\"data row38 col3\" >3.45</td>\n",
+ " <td id=\"T_ebc63_row38_col4\" class=\"data row38 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row38_col5\" class=\"data row38 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_ebc63_row39_col0\" class=\"data row39 col0\" >93.19</td>\n",
+ " <td id=\"T_ebc63_row39_col1\" class=\"data row39 col1\" >2.73</td>\n",
+ " <td id=\"T_ebc63_row39_col2\" class=\"data row39 col2\" >97.63</td>\n",
+ " <td id=\"T_ebc63_row39_col3\" class=\"data row39 col3\" >2.57</td>\n",
+ " <td id=\"T_ebc63_row39_col4\" class=\"data row39 col4\" >1.53</td>\n",
+ " <td id=\"T_ebc63_row39_col5\" class=\"data row39 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_ebc63_row40_col0\" class=\"data row40 col0\" >93.76</td>\n",
+ " <td id=\"T_ebc63_row40_col1\" class=\"data row40 col1\" >3.93</td>\n",
+ " <td id=\"T_ebc63_row40_col2\" class=\"data row40 col2\" >98.99</td>\n",
+ " <td id=\"T_ebc63_row40_col3\" class=\"data row40 col3\" >3.79</td>\n",
+ " <td id=\"T_ebc63_row40_col4\" class=\"data row40 col4\" >1.16</td>\n",
+ " <td id=\"T_ebc63_row40_col5\" class=\"data row40 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_ebc63_row41_col0\" class=\"data row41 col0\" >93.15</td>\n",
+ " <td id=\"T_ebc63_row41_col1\" class=\"data row41 col1\" >2.66</td>\n",
+ " <td id=\"T_ebc63_row41_col2\" class=\"data row41 col2\" >97.96</td>\n",
+ " <td id=\"T_ebc63_row41_col3\" class=\"data row41 col3\" >4.94</td>\n",
+ " <td id=\"T_ebc63_row41_col4\" class=\"data row41 col4\" >1.23</td>\n",
+ " <td id=\"T_ebc63_row41_col5\" class=\"data row41 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_ebc63_row42_col0\" class=\"data row42 col0\" >92.80</td>\n",
+ " <td id=\"T_ebc63_row42_col1\" class=\"data row42 col1\" >2.64</td>\n",
+ " <td id=\"T_ebc63_row42_col2\" class=\"data row42 col2\" >98.03</td>\n",
+ " <td id=\"T_ebc63_row42_col3\" class=\"data row42 col3\" >2.25</td>\n",
+ " <td id=\"T_ebc63_row42_col4\" class=\"data row42 col4\" >1.62</td>\n",
+ " <td id=\"T_ebc63_row42_col5\" class=\"data row42 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_ebc63_row43_col0\" class=\"data row43 col0\" >92.79</td>\n",
+ " <td id=\"T_ebc63_row43_col1\" class=\"data row43 col1\" >2.68</td>\n",
+ " <td id=\"T_ebc63_row43_col2\" class=\"data row43 col2\" >97.84</td>\n",
+ " <td id=\"T_ebc63_row43_col3\" class=\"data row43 col3\" >2.21</td>\n",
+ " <td id=\"T_ebc63_row43_col4\" class=\"data row43 col4\" >1.55</td>\n",
+ " <td id=\"T_ebc63_row43_col5\" class=\"data row43 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_ebc63_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_ebc63_row44_col0\" class=\"data row44 col0\" >96.03</td>\n",
+ " <td id=\"T_ebc63_row44_col1\" class=\"data row44 col1\" >2.34</td>\n",
+ " <td id=\"T_ebc63_row44_col2\" class=\"data row44 col2\" >99.21</td>\n",
+ " <td id=\"T_ebc63_row44_col3\" class=\"data row44 col3\" >1.69</td>\n",
+ " <td id=\"T_ebc63_row44_col4\" class=\"data row44 col4\" >1.31</td>\n",
+ " <td id=\"T_ebc63_row44_col5\" class=\"data row44 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_ebc63_row45_col0\" class=\"data row45 col0\" >95.21</td>\n",
+ " <td id=\"T_ebc63_row45_col1\" class=\"data row45 col1\" >2.33</td>\n",
+ " <td id=\"T_ebc63_row45_col2\" class=\"data row45 col2\" >98.84</td>\n",
+ " <td id=\"T_ebc63_row45_col3\" class=\"data row45 col3\" >2.40</td>\n",
+ " <td id=\"T_ebc63_row45_col4\" class=\"data row45 col4\" >1.41</td>\n",
+ " <td id=\"T_ebc63_row45_col5\" class=\"data row45 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_ebc63_row46_col0\" class=\"data row46 col0\" >95.43</td>\n",
+ " <td id=\"T_ebc63_row46_col1\" class=\"data row46 col1\" >2.15</td>\n",
+ " <td id=\"T_ebc63_row46_col2\" class=\"data row46 col2\" >99.05</td>\n",
+ " <td id=\"T_ebc63_row46_col3\" class=\"data row46 col3\" >2.27</td>\n",
+ " <td id=\"T_ebc63_row46_col4\" class=\"data row46 col4\" >1.44</td>\n",
+ " <td id=\"T_ebc63_row46_col5\" class=\"data row46 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_ebc63_row47_col0\" class=\"data row47 col0\" >93.20</td>\n",
+ " <td id=\"T_ebc63_row47_col1\" class=\"data row47 col1\" >4.50</td>\n",
+ " <td id=\"T_ebc63_row47_col2\" class=\"data row47 col2\" >98.90</td>\n",
+ " <td id=\"T_ebc63_row47_col3\" class=\"data row47 col3\" >3.19</td>\n",
+ " <td id=\"T_ebc63_row47_col4\" class=\"data row47 col4\" >1.16</td>\n",
+ " <td id=\"T_ebc63_row47_col5\" class=\"data row47 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_ebc63_row48_col0\" class=\"data row48 col0\" >95.94</td>\n",
+ " <td id=\"T_ebc63_row48_col1\" class=\"data row48 col1\" >2.45</td>\n",
+ " <td id=\"T_ebc63_row48_col2\" class=\"data row48 col2\" >99.34</td>\n",
+ " <td id=\"T_ebc63_row48_col3\" class=\"data row48 col3\" >1.29</td>\n",
+ " <td id=\"T_ebc63_row48_col4\" class=\"data row48 col4\" >1.29</td>\n",
+ " <td id=\"T_ebc63_row48_col5\" class=\"data row48 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_ebc63_row49_col0\" class=\"data row49 col0\" >94.73</td>\n",
+ " <td id=\"T_ebc63_row49_col1\" class=\"data row49 col1\" >2.41</td>\n",
+ " <td id=\"T_ebc63_row49_col2\" class=\"data row49 col2\" >98.58</td>\n",
+ " <td id=\"T_ebc63_row49_col3\" class=\"data row49 col3\" >2.93</td>\n",
+ " <td id=\"T_ebc63_row49_col4\" class=\"data row49 col4\" >1.45</td>\n",
+ " <td id=\"T_ebc63_row49_col5\" class=\"data row49 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_ebc63_row50_col0\" class=\"data row50 col0\" >93.58</td>\n",
+ " <td id=\"T_ebc63_row50_col1\" class=\"data row50 col1\" >3.46</td>\n",
+ " <td id=\"T_ebc63_row50_col2\" class=\"data row50 col2\" >98.19</td>\n",
+ " <td id=\"T_ebc63_row50_col3\" class=\"data row50 col3\" >4.13</td>\n",
+ " <td id=\"T_ebc63_row50_col4\" class=\"data row50 col4\" >1.25</td>\n",
+ " <td id=\"T_ebc63_row50_col5\" class=\"data row50 col5\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_ebc63_row51_col0\" class=\"data row51 col0\" >95.31</td>\n",
+ " <td id=\"T_ebc63_row51_col1\" class=\"data row51 col1\" >2.82</td>\n",
+ " <td id=\"T_ebc63_row51_col2\" class=\"data row51 col2\" >99.06</td>\n",
+ " <td id=\"T_ebc63_row51_col3\" class=\"data row51 col3\" >1.60</td>\n",
+ " <td id=\"T_ebc63_row51_col4\" class=\"data row51 col4\" >1.35</td>\n",
+ " <td id=\"T_ebc63_row51_col5\" class=\"data row51 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_ebc63_row52_col0\" class=\"data row52 col0\" >95.86</td>\n",
+ " <td id=\"T_ebc63_row52_col1\" class=\"data row52 col1\" >2.51</td>\n",
+ " <td id=\"T_ebc63_row52_col2\" class=\"data row52 col2\" >98.87</td>\n",
+ " <td id=\"T_ebc63_row52_col3\" class=\"data row52 col3\" >2.57</td>\n",
+ " <td id=\"T_ebc63_row52_col4\" class=\"data row52 col4\" >1.29</td>\n",
+ " <td id=\"T_ebc63_row52_col5\" class=\"data row52 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_ebc63_row53_col0\" class=\"data row53 col0\" >92.81</td>\n",
+ " <td id=\"T_ebc63_row53_col1\" class=\"data row53 col1\" >3.48</td>\n",
+ " <td id=\"T_ebc63_row53_col2\" class=\"data row53 col2\" >97.80</td>\n",
+ " <td id=\"T_ebc63_row53_col3\" class=\"data row53 col3\" >7.06</td>\n",
+ " <td id=\"T_ebc63_row53_col4\" class=\"data row53 col4\" >1.24</td>\n",
+ " <td id=\"T_ebc63_row53_col5\" class=\"data row53 col5\" >0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_ebc63_row54_col0\" class=\"data row54 col0\" >95.59</td>\n",
+ " <td id=\"T_ebc63_row54_col1\" class=\"data row54 col1\" >2.01</td>\n",
+ " <td id=\"T_ebc63_row54_col2\" class=\"data row54 col2\" >99.07</td>\n",
+ " <td id=\"T_ebc63_row54_col3\" class=\"data row54 col3\" >1.73</td>\n",
+ " <td id=\"T_ebc63_row54_col4\" class=\"data row54 col4\" >1.44</td>\n",
+ " <td id=\"T_ebc63_row54_col5\" class=\"data row54 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_ebc63_row55_col0\" class=\"data row55 col0\" >95.27</td>\n",
+ " <td id=\"T_ebc63_row55_col1\" class=\"data row55 col1\" >2.69</td>\n",
+ " <td id=\"T_ebc63_row55_col2\" class=\"data row55 col2\" >98.64</td>\n",
+ " <td id=\"T_ebc63_row55_col3\" class=\"data row55 col3\" >2.52</td>\n",
+ " <td id=\"T_ebc63_row55_col4\" class=\"data row55 col4\" >1.34</td>\n",
+ " <td id=\"T_ebc63_row55_col5\" class=\"data row55 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_ebc63_row56_col0\" class=\"data row56 col0\" >93.99</td>\n",
+ " <td id=\"T_ebc63_row56_col1\" class=\"data row56 col1\" >5.01</td>\n",
+ " <td id=\"T_ebc63_row56_col2\" class=\"data row56 col2\" >98.76</td>\n",
+ " <td id=\"T_ebc63_row56_col3\" class=\"data row56 col3\" >4.01</td>\n",
+ " <td id=\"T_ebc63_row56_col4\" class=\"data row56 col4\" >1.12</td>\n",
+ " <td id=\"T_ebc63_row56_col5\" class=\"data row56 col5\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_ebc63_row57_col0\" class=\"data row57 col0\" >94.98</td>\n",
+ " <td id=\"T_ebc63_row57_col1\" class=\"data row57 col1\" >2.39</td>\n",
+ " <td id=\"T_ebc63_row57_col2\" class=\"data row57 col2\" >98.89</td>\n",
+ " <td id=\"T_ebc63_row57_col3\" class=\"data row57 col3\" >1.92</td>\n",
+ " <td id=\"T_ebc63_row57_col4\" class=\"data row57 col4\" >1.38</td>\n",
+ " <td id=\"T_ebc63_row57_col5\" class=\"data row57 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_ebc63_row58_col0\" class=\"data row58 col0\" >95.16</td>\n",
+ " <td id=\"T_ebc63_row58_col1\" class=\"data row58 col1\" >2.40</td>\n",
+ " <td id=\"T_ebc63_row58_col2\" class=\"data row58 col2\" >99.06</td>\n",
+ " <td id=\"T_ebc63_row58_col3\" class=\"data row58 col3\" >2.12</td>\n",
+ " <td id=\"T_ebc63_row58_col4\" class=\"data row58 col4\" >1.41</td>\n",
+ " <td id=\"T_ebc63_row58_col5\" class=\"data row58 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_ebc63_row59_col0\" class=\"data row59 col0\" >95.34</td>\n",
+ " <td id=\"T_ebc63_row59_col1\" class=\"data row59 col1\" >2.64</td>\n",
+ " <td id=\"T_ebc63_row59_col2\" class=\"data row59 col2\" >99.06</td>\n",
+ " <td id=\"T_ebc63_row59_col3\" class=\"data row59 col3\" >2.16</td>\n",
+ " <td id=\"T_ebc63_row59_col4\" class=\"data row59 col4\" >1.32</td>\n",
+ " <td id=\"T_ebc63_row59_col5\" class=\"data row59 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_ebc63_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_ebc63_row60_col0\" class=\"data row60 col0\" >96.86</td>\n",
+ " <td id=\"T_ebc63_row60_col1\" class=\"data row60 col1\" >2.51</td>\n",
+ " <td id=\"T_ebc63_row60_col2\" class=\"data row60 col2\" >99.75</td>\n",
+ " <td id=\"T_ebc63_row60_col3\" class=\"data row60 col3\" >1.14</td>\n",
+ " <td id=\"T_ebc63_row60_col4\" class=\"data row60 col4\" >1.19</td>\n",
+ " <td id=\"T_ebc63_row60_col5\" class=\"data row60 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_ebc63_row61_col0\" class=\"data row61 col0\" >96.98</td>\n",
+ " <td id=\"T_ebc63_row61_col1\" class=\"data row61 col1\" >2.20</td>\n",
+ " <td id=\"T_ebc63_row61_col2\" class=\"data row61 col2\" >99.41</td>\n",
+ " <td id=\"T_ebc63_row61_col3\" class=\"data row61 col3\" >1.69</td>\n",
+ " <td id=\"T_ebc63_row61_col4\" class=\"data row61 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row61_col5\" class=\"data row61 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_ebc63_row62_col0\" class=\"data row62 col0\" >96.71</td>\n",
+ " <td id=\"T_ebc63_row62_col1\" class=\"data row62 col1\" >2.42</td>\n",
+ " <td id=\"T_ebc63_row62_col2\" class=\"data row62 col2\" >99.48</td>\n",
+ " <td id=\"T_ebc63_row62_col3\" class=\"data row62 col3\" >1.67</td>\n",
+ " <td id=\"T_ebc63_row62_col4\" class=\"data row62 col4\" >1.26</td>\n",
+ " <td id=\"T_ebc63_row62_col5\" class=\"data row62 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ebc63_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_ebc63_row63_col0\" class=\"data row63 col0\" >97.10</td>\n",
+ " <td id=\"T_ebc63_row63_col1\" class=\"data row63 col1\" >2.44</td>\n",
+ " <td id=\"T_ebc63_row63_col2\" class=\"data row63 col2\" >99.78</td>\n",
+ " <td id=\"T_ebc63_row63_col3\" class=\"data row63 col3\" >0.90</td>\n",
+ " <td id=\"T_ebc63_row63_col4\" class=\"data row63 col4\" >1.22</td>\n",
+ " <td id=\"T_ebc63_row63_col5\" class=\"data row63 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
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+ "<table id=\"T_3152e\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_3152e_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">% of load of jobs/services bidded</th>\n",
+ " <th id=\"T_3152e_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\"></th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_3152e_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_3152e_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_3152e_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Slowdown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_3152e_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
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+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_3152e_row0_col0\" class=\"data row0 col0\" >96.90</td>\n",
+ " <td id=\"T_3152e_row0_col1\" class=\"data row0 col1\" >1.43</td>\n",
+ " <td id=\"T_3152e_row0_col2\" class=\"data row0 col2\" >99.59</td>\n",
+ " <td id=\"T_3152e_row0_col3\" class=\"data row0 col3\" >0.85</td>\n",
+ " <td id=\"T_3152e_row0_col4\" class=\"data row0 col4\" >1.24</td>\n",
+ " <td id=\"T_3152e_row0_col5\" class=\"data row0 col5\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
+ " <td id=\"T_3152e_row1_col0\" class=\"data row1 col0\" >93.33</td>\n",
+ " <td id=\"T_3152e_row1_col1\" class=\"data row1 col1\" >1.48</td>\n",
+ " <td id=\"T_3152e_row1_col2\" class=\"data row1 col2\" >97.04</td>\n",
+ " <td id=\"T_3152e_row1_col3\" class=\"data row1 col3\" >1.97</td>\n",
+ " <td id=\"T_3152e_row1_col4\" class=\"data row1 col4\" >1.50</td>\n",
+ " <td id=\"T_3152e_row1_col5\" class=\"data row1 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row2\" class=\"row_heading level0 row2\" >25% (0 1 0 0)</th>\n",
+ " <td id=\"T_3152e_row2_col0\" class=\"data row2 col0\" >92.77</td>\n",
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+ " <td id=\"T_3152e_row2_col2\" class=\"data row2 col2\" >95.78</td>\n",
+ " <td id=\"T_3152e_row2_col3\" class=\"data row2 col3\" >2.13</td>\n",
+ " <td id=\"T_3152e_row2_col4\" class=\"data row2 col4\" >1.57</td>\n",
+ " <td id=\"T_3152e_row2_col5\" class=\"data row2 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
+ " <td id=\"T_3152e_row3_col0\" class=\"data row3 col0\" >92.53</td>\n",
+ " <td id=\"T_3152e_row3_col1\" class=\"data row3 col1\" >1.65</td>\n",
+ " <td id=\"T_3152e_row3_col2\" class=\"data row3 col2\" >95.99</td>\n",
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+ " <td id=\"T_3152e_row3_col4\" class=\"data row3 col4\" >1.59</td>\n",
+ " <td id=\"T_3152e_row3_col5\" class=\"data row3 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
+ " <td id=\"T_3152e_row4_col0\" class=\"data row4 col0\" >92.58</td>\n",
+ " <td id=\"T_3152e_row4_col1\" class=\"data row4 col1\" >1.63</td>\n",
+ " <td id=\"T_3152e_row4_col2\" class=\"data row4 col2\" >96.13</td>\n",
+ " <td id=\"T_3152e_row4_col3\" class=\"data row4 col3\" >1.97</td>\n",
+ " <td id=\"T_3152e_row4_col4\" class=\"data row4 col4\" >1.56</td>\n",
+ " <td id=\"T_3152e_row4_col5\" class=\"data row4 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_3152e_row5_col0\" class=\"data row5 col0\" >93.40</td>\n",
+ " <td id=\"T_3152e_row5_col1\" class=\"data row5 col1\" >1.62</td>\n",
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+ " <td id=\"T_3152e_row5_col5\" class=\"data row5 col5\" >0.07</td>\n",
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+ " <td id=\"T_3152e_row6_col5\" class=\"data row6 col5\" >0.07</td>\n",
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+ " <td id=\"T_3152e_row12_col3\" class=\"data row12 col3\" >1.25</td>\n",
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+ " </tr>\n",
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+ " <th id=\"T_3152e_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
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+ " <td id=\"T_3152e_row13_col1\" class=\"data row13 col1\" >1.50</td>\n",
+ " <td id=\"T_3152e_row13_col2\" class=\"data row13 col2\" >98.75</td>\n",
+ " <td id=\"T_3152e_row13_col3\" class=\"data row13 col3\" >1.35</td>\n",
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+ " </tr>\n",
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+ " <th id=\"T_3152e_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_3152e_row14_col0\" class=\"data row14 col0\" >95.00</td>\n",
+ " <td id=\"T_3152e_row14_col1\" class=\"data row14 col1\" >1.43</td>\n",
+ " <td id=\"T_3152e_row14_col2\" class=\"data row14 col2\" >98.98</td>\n",
+ " <td id=\"T_3152e_row14_col3\" class=\"data row14 col3\" >1.24</td>\n",
+ " <td id=\"T_3152e_row14_col4\" class=\"data row14 col4\" >1.36</td>\n",
+ " <td id=\"T_3152e_row14_col5\" class=\"data row14 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_3152e_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_3152e_row15_col0\" class=\"data row15 col0\" >96.90</td>\n",
+ " <td id=\"T_3152e_row15_col1\" class=\"data row15 col1\" >1.43</td>\n",
+ " <td id=\"T_3152e_row15_col2\" class=\"data row15 col2\" >99.59</td>\n",
+ " <td id=\"T_3152e_row15_col3\" class=\"data row15 col3\" >0.85</td>\n",
+ " <td id=\"T_3152e_row15_col4\" class=\"data row15 col4\" >1.24</td>\n",
+ " <td id=\"T_3152e_row15_col5\" class=\"data row15 col5\" >0.05</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7129a215c890>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
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+ "source": [
+ "analysis_plot.draw_table_qos(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
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+ "<table id=\"T_1a926\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_1a926_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
+ " <th id=\"T_1a926_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Benefits / energy available</th>\n",
+ " <th id=\"T_1a926_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_1a926_level1_col0\" class=\"col_heading level1 col0\" >Value</th>\n",
+ " <th id=\"T_1a926_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
+ " <th id=\"T_1a926_level1_col2\" class=\"col_heading level1 col2\" >Value</th>\n",
+ " <th id=\"T_1a926_level1_col3\" class=\"col_heading level1 col3\" >std</th>\n",
+ " <th id=\"T_1a926_level1_col4\" class=\"col_heading level1 col4\" >Value</th>\n",
+ " <th id=\"T_1a926_level1_col5\" class=\"col_heading level1 col5\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_1a926_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_1a926_row0_col0\" class=\"data row0 col0\" >19.78</td>\n",
+ " <td id=\"T_1a926_row0_col1\" class=\"data row0 col1\" >1.99</td>\n",
+ " <td id=\"T_1a926_row0_col2\" class=\"data row0 col2\" >1.43</td>\n",
+ " <td id=\"T_1a926_row0_col3\" class=\"data row0 col3\" >0.14</td>\n",
+ " <td id=\"T_1a926_row0_col4\" class=\"data row0 col4\" >42.06</td>\n",
+ " <td id=\"T_1a926_row0_col5\" class=\"data row0 col5\" >7.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_1a926_row1_col0\" class=\"data row1 col0\" >23.47</td>\n",
+ " <td id=\"T_1a926_row1_col1\" class=\"data row1 col1\" >2.29</td>\n",
+ " <td id=\"T_1a926_row1_col2\" class=\"data row1 col2\" >1.70</td>\n",
+ " <td id=\"T_1a926_row1_col3\" class=\"data row1 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row1_col4\" class=\"data row1 col4\" >32.02</td>\n",
+ " <td id=\"T_1a926_row1_col5\" class=\"data row1 col5\" >5.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_1a926_row2_col0\" class=\"data row2 col0\" >24.03</td>\n",
+ " <td id=\"T_1a926_row2_col1\" class=\"data row2 col1\" >2.56</td>\n",
+ " <td id=\"T_1a926_row2_col2\" class=\"data row2 col2\" >1.74</td>\n",
+ " <td id=\"T_1a926_row2_col3\" class=\"data row2 col3\" >0.18</td>\n",
+ " <td id=\"T_1a926_row2_col4\" class=\"data row2 col4\" >27.88</td>\n",
+ " <td id=\"T_1a926_row2_col5\" class=\"data row2 col5\" >5.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_1a926_row3_col0\" class=\"data row3 col0\" >23.49</td>\n",
+ " <td id=\"T_1a926_row3_col1\" class=\"data row3 col1\" >2.19</td>\n",
+ " <td id=\"T_1a926_row3_col2\" class=\"data row3 col2\" >1.70</td>\n",
+ " <td id=\"T_1a926_row3_col3\" class=\"data row3 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row3_col4\" class=\"data row3 col4\" >28.03</td>\n",
+ " <td id=\"T_1a926_row3_col5\" class=\"data row3 col5\" >4.64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_1a926_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_1a926_row4_col0\" class=\"data row4 col0\" >35.15</td>\n",
+ " <td id=\"T_1a926_row4_col1\" class=\"data row4 col1\" >3.85</td>\n",
+ " <td id=\"T_1a926_row4_col2\" class=\"data row4 col2\" >2.54</td>\n",
+ " <td id=\"T_1a926_row4_col3\" class=\"data row4 col3\" >0.28</td>\n",
+ " <td id=\"T_1a926_row4_col4\" class=\"data row4 col4\" >5.21</td>\n",
+ " <td id=\"T_1a926_row4_col5\" class=\"data row4 col5\" >2.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_1a926_row5_col0\" class=\"data row5 col0\" >16.96</td>\n",
+ " <td id=\"T_1a926_row5_col1\" class=\"data row5 col1\" >2.02</td>\n",
+ " <td id=\"T_1a926_row5_col2\" class=\"data row5 col2\" >1.23</td>\n",
+ " <td id=\"T_1a926_row5_col3\" class=\"data row5 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row5_col4\" class=\"data row5 col4\" >44.48</td>\n",
+ " <td id=\"T_1a926_row5_col5\" class=\"data row5 col5\" >5.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_1a926_row6_col0\" class=\"data row6 col0\" >17.48</td>\n",
+ " <td id=\"T_1a926_row6_col1\" class=\"data row6 col1\" >2.23</td>\n",
+ " <td id=\"T_1a926_row6_col2\" class=\"data row6 col2\" >1.26</td>\n",
+ " <td id=\"T_1a926_row6_col3\" class=\"data row6 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row6_col4\" class=\"data row6 col4\" >41.11</td>\n",
+ " <td id=\"T_1a926_row6_col5\" class=\"data row6 col5\" >6.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_1a926_row7_col0\" class=\"data row7 col0\" >16.56</td>\n",
+ " <td id=\"T_1a926_row7_col1\" class=\"data row7 col1\" >2.22</td>\n",
+ " <td id=\"T_1a926_row7_col2\" class=\"data row7 col2\" >1.20</td>\n",
+ " <td id=\"T_1a926_row7_col3\" class=\"data row7 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row7_col4\" class=\"data row7 col4\" >43.98</td>\n",
+ " <td id=\"T_1a926_row7_col5\" class=\"data row7 col5\" >5.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_1a926_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_1a926_row8_col0\" class=\"data row8 col0\" >14.74</td>\n",
+ " <td id=\"T_1a926_row8_col1\" class=\"data row8 col1\" >2.39</td>\n",
+ " <td id=\"T_1a926_row8_col2\" class=\"data row8 col2\" >1.07</td>\n",
+ " <td id=\"T_1a926_row8_col3\" class=\"data row8 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row8_col4\" class=\"data row8 col4\" >54.47</td>\n",
+ " <td id=\"T_1a926_row8_col5\" class=\"data row8 col5\" >8.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_1a926_row9_col0\" class=\"data row9 col0\" >33.98</td>\n",
+ " <td id=\"T_1a926_row9_col1\" class=\"data row9 col1\" >4.22</td>\n",
+ " <td id=\"T_1a926_row9_col2\" class=\"data row9 col2\" >2.46</td>\n",
+ " <td id=\"T_1a926_row9_col3\" class=\"data row9 col3\" >0.31</td>\n",
+ " <td id=\"T_1a926_row9_col4\" class=\"data row9 col4\" >3.46</td>\n",
+ " <td id=\"T_1a926_row9_col5\" class=\"data row9 col5\" >1.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_1a926_row10_col0\" class=\"data row10 col0\" >18.66</td>\n",
+ " <td id=\"T_1a926_row10_col1\" class=\"data row10 col1\" >2.40</td>\n",
+ " <td id=\"T_1a926_row10_col2\" class=\"data row10 col2\" >1.35</td>\n",
+ " <td id=\"T_1a926_row10_col3\" class=\"data row10 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row10_col4\" class=\"data row10 col4\" >38.21</td>\n",
+ " <td id=\"T_1a926_row10_col5\" class=\"data row10 col5\" >6.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_1a926_row11_col0\" class=\"data row11 col0\" >18.09</td>\n",
+ " <td id=\"T_1a926_row11_col1\" class=\"data row11 col1\" >2.37</td>\n",
+ " <td id=\"T_1a926_row11_col2\" class=\"data row11 col2\" >1.31</td>\n",
+ " <td id=\"T_1a926_row11_col3\" class=\"data row11 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row11_col4\" class=\"data row11 col4\" >39.96</td>\n",
+ " <td id=\"T_1a926_row11_col5\" class=\"data row11 col5\" >5.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_1a926_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_1a926_row12_col0\" class=\"data row12 col0\" >15.09</td>\n",
+ " <td id=\"T_1a926_row12_col1\" class=\"data row12 col1\" >2.50</td>\n",
+ " <td id=\"T_1a926_row12_col2\" class=\"data row12 col2\" >1.09</td>\n",
+ " <td id=\"T_1a926_row12_col3\" class=\"data row12 col3\" >0.18</td>\n",
+ " <td id=\"T_1a926_row12_col4\" class=\"data row12 col4\" >53.73</td>\n",
+ " <td id=\"T_1a926_row12_col5\" class=\"data row12 col5\" >7.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_1a926_row13_col0\" class=\"data row13 col0\" >18.56</td>\n",
+ " <td id=\"T_1a926_row13_col1\" class=\"data row13 col1\" >2.25</td>\n",
+ " <td id=\"T_1a926_row13_col2\" class=\"data row13 col2\" >1.34</td>\n",
+ " <td id=\"T_1a926_row13_col3\" class=\"data row13 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row13_col4\" class=\"data row13 col4\" >40.44</td>\n",
+ " <td id=\"T_1a926_row13_col5\" class=\"data row13 col5\" >6.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_1a926_row14_col0\" class=\"data row14 col0\" >33.37</td>\n",
+ " <td id=\"T_1a926_row14_col1\" class=\"data row14 col1\" >4.62</td>\n",
+ " <td id=\"T_1a926_row14_col2\" class=\"data row14 col2\" >2.41</td>\n",
+ " <td id=\"T_1a926_row14_col3\" class=\"data row14 col3\" >0.33</td>\n",
+ " <td id=\"T_1a926_row14_col4\" class=\"data row14 col4\" >2.24</td>\n",
+ " <td id=\"T_1a926_row14_col5\" class=\"data row14 col5\" >1.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_1a926_row15_col0\" class=\"data row15 col0\" >18.11</td>\n",
+ " <td id=\"T_1a926_row15_col1\" class=\"data row15 col1\" >2.29</td>\n",
+ " <td id=\"T_1a926_row15_col2\" class=\"data row15 col2\" >1.31</td>\n",
+ " <td id=\"T_1a926_row15_col3\" class=\"data row15 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row15_col4\" class=\"data row15 col4\" >39.62</td>\n",
+ " <td id=\"T_1a926_row15_col5\" class=\"data row15 col5\" >6.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_1a926_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_1a926_row16_col0\" class=\"data row16 col0\" >14.86</td>\n",
+ " <td id=\"T_1a926_row16_col1\" class=\"data row16 col1\" >2.34</td>\n",
+ " <td id=\"T_1a926_row16_col2\" class=\"data row16 col2\" >1.07</td>\n",
+ " <td id=\"T_1a926_row16_col3\" class=\"data row16 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row16_col4\" class=\"data row16 col4\" >54.66</td>\n",
+ " <td id=\"T_1a926_row16_col5\" class=\"data row16 col5\" >7.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_1a926_row17_col0\" class=\"data row17 col0\" >18.57</td>\n",
+ " <td id=\"T_1a926_row17_col1\" class=\"data row17 col1\" >2.39</td>\n",
+ " <td id=\"T_1a926_row17_col2\" class=\"data row17 col2\" >1.34</td>\n",
+ " <td id=\"T_1a926_row17_col3\" class=\"data row17 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row17_col4\" class=\"data row17 col4\" >40.44</td>\n",
+ " <td id=\"T_1a926_row17_col5\" class=\"data row17 col5\" >6.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_1a926_row18_col0\" class=\"data row18 col0\" >19.28</td>\n",
+ " <td id=\"T_1a926_row18_col1\" class=\"data row18 col1\" >2.45</td>\n",
+ " <td id=\"T_1a926_row18_col2\" class=\"data row18 col2\" >1.39</td>\n",
+ " <td id=\"T_1a926_row18_col3\" class=\"data row18 col3\" >0.18</td>\n",
+ " <td id=\"T_1a926_row18_col4\" class=\"data row18 col4\" >36.67</td>\n",
+ " <td id=\"T_1a926_row18_col5\" class=\"data row18 col5\" >6.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_1a926_row19_col0\" class=\"data row19 col0\" >32.48</td>\n",
+ " <td id=\"T_1a926_row19_col1\" class=\"data row19 col1\" >4.50</td>\n",
+ " <td id=\"T_1a926_row19_col2\" class=\"data row19 col2\" >2.35</td>\n",
+ " <td id=\"T_1a926_row19_col3\" class=\"data row19 col3\" >0.33</td>\n",
+ " <td id=\"T_1a926_row19_col4\" class=\"data row19 col4\" >2.35</td>\n",
+ " <td id=\"T_1a926_row19_col5\" class=\"data row19 col5\" >1.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_1a926_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_1a926_row20_col0\" class=\"data row20 col0\" >14.64</td>\n",
+ " <td id=\"T_1a926_row20_col1\" class=\"data row20 col1\" >3.40</td>\n",
+ " <td id=\"T_1a926_row20_col2\" class=\"data row20 col2\" >1.06</td>\n",
+ " <td id=\"T_1a926_row20_col3\" class=\"data row20 col3\" >0.25</td>\n",
+ " <td id=\"T_1a926_row20_col4\" class=\"data row20 col4\" >15.25</td>\n",
+ " <td id=\"T_1a926_row20_col5\" class=\"data row20 col5\" >5.55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_1a926_row21_col0\" class=\"data row21 col0\" >15.09</td>\n",
+ " <td id=\"T_1a926_row21_col1\" class=\"data row21 col1\" >2.66</td>\n",
+ " <td id=\"T_1a926_row21_col2\" class=\"data row21 col2\" >1.09</td>\n",
+ " <td id=\"T_1a926_row21_col3\" class=\"data row21 col3\" >0.19</td>\n",
+ " <td id=\"T_1a926_row21_col4\" class=\"data row21 col4\" >9.13</td>\n",
+ " <td id=\"T_1a926_row21_col5\" class=\"data row21 col5\" >3.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_1a926_row22_col0\" class=\"data row22 col0\" >12.83</td>\n",
+ " <td id=\"T_1a926_row22_col1\" class=\"data row22 col1\" >2.19</td>\n",
+ " <td id=\"T_1a926_row22_col2\" class=\"data row22 col2\" >0.93</td>\n",
+ " <td id=\"T_1a926_row22_col3\" class=\"data row22 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row22_col4\" class=\"data row22 col4\" >53.84</td>\n",
+ " <td id=\"T_1a926_row22_col5\" class=\"data row22 col5\" >6.93</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_1a926_row23_col0\" class=\"data row23 col0\" >11.75</td>\n",
+ " <td id=\"T_1a926_row23_col1\" class=\"data row23 col1\" >2.07</td>\n",
+ " <td id=\"T_1a926_row23_col2\" class=\"data row23 col2\" >0.85</td>\n",
+ " <td id=\"T_1a926_row23_col3\" class=\"data row23 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row23_col4\" class=\"data row23 col4\" >57.27</td>\n",
+ " <td id=\"T_1a926_row23_col5\" class=\"data row23 col5\" >6.37</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_1a926_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_1a926_row24_col0\" class=\"data row24 col0\" >14.69</td>\n",
+ " <td id=\"T_1a926_row24_col1\" class=\"data row24 col1\" >3.18</td>\n",
+ " <td id=\"T_1a926_row24_col2\" class=\"data row24 col2\" >1.06</td>\n",
+ " <td id=\"T_1a926_row24_col3\" class=\"data row24 col3\" >0.23</td>\n",
+ " <td id=\"T_1a926_row24_col4\" class=\"data row24 col4\" >15.01</td>\n",
+ " <td id=\"T_1a926_row24_col5\" class=\"data row24 col5\" >5.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_1a926_row25_col0\" class=\"data row25 col0\" >12.38</td>\n",
+ " <td id=\"T_1a926_row25_col1\" class=\"data row25 col1\" >2.05</td>\n",
+ " <td id=\"T_1a926_row25_col2\" class=\"data row25 col2\" >0.90</td>\n",
+ " <td id=\"T_1a926_row25_col3\" class=\"data row25 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row25_col4\" class=\"data row25 col4\" >55.87</td>\n",
+ " <td id=\"T_1a926_row25_col5\" class=\"data row25 col5\" >6.37</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_1a926_row26_col0\" class=\"data row26 col0\" >15.25</td>\n",
+ " <td id=\"T_1a926_row26_col1\" class=\"data row26 col1\" >2.66</td>\n",
+ " <td id=\"T_1a926_row26_col2\" class=\"data row26 col2\" >1.10</td>\n",
+ " <td id=\"T_1a926_row26_col3\" class=\"data row26 col3\" >0.19</td>\n",
+ " <td id=\"T_1a926_row26_col4\" class=\"data row26 col4\" >6.99</td>\n",
+ " <td id=\"T_1a926_row26_col5\" class=\"data row26 col5\" >2.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_1a926_row27_col0\" class=\"data row27 col0\" >11.94</td>\n",
+ " <td id=\"T_1a926_row27_col1\" class=\"data row27 col1\" >2.02</td>\n",
+ " <td id=\"T_1a926_row27_col2\" class=\"data row27 col2\" >0.86</td>\n",
+ " <td id=\"T_1a926_row27_col3\" class=\"data row27 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row27_col4\" class=\"data row27 col4\" >57.34</td>\n",
+ " <td id=\"T_1a926_row27_col5\" class=\"data row27 col5\" >7.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_1a926_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_1a926_row28_col0\" class=\"data row28 col0\" >14.27</td>\n",
+ " <td id=\"T_1a926_row28_col1\" class=\"data row28 col1\" >3.36</td>\n",
+ " <td id=\"T_1a926_row28_col2\" class=\"data row28 col2\" >1.03</td>\n",
+ " <td id=\"T_1a926_row28_col3\" class=\"data row28 col3\" >0.24</td>\n",
+ " <td id=\"T_1a926_row28_col4\" class=\"data row28 col4\" >16.94</td>\n",
+ " <td id=\"T_1a926_row28_col5\" class=\"data row28 col5\" >5.84</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_1a926_row29_col0\" class=\"data row29 col0\" >12.67</td>\n",
+ " <td id=\"T_1a926_row29_col1\" class=\"data row29 col1\" >2.01</td>\n",
+ " <td id=\"T_1a926_row29_col2\" class=\"data row29 col2\" >0.92</td>\n",
+ " <td id=\"T_1a926_row29_col3\" class=\"data row29 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row29_col4\" class=\"data row29 col4\" >54.64</td>\n",
+ " <td id=\"T_1a926_row29_col5\" class=\"data row29 col5\" >6.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_1a926_row30_col0\" class=\"data row30 col0\" >13.36</td>\n",
+ " <td id=\"T_1a926_row30_col1\" class=\"data row30 col1\" >2.30</td>\n",
+ " <td id=\"T_1a926_row30_col2\" class=\"data row30 col2\" >0.97</td>\n",
+ " <td id=\"T_1a926_row30_col3\" class=\"data row30 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row30_col4\" class=\"data row30 col4\" >53.39</td>\n",
+ " <td id=\"T_1a926_row30_col5\" class=\"data row30 col5\" >7.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_1a926_row31_col0\" class=\"data row31 col0\" >12.88</td>\n",
+ " <td id=\"T_1a926_row31_col1\" class=\"data row31 col1\" >2.75</td>\n",
+ " <td id=\"T_1a926_row31_col2\" class=\"data row31 col2\" >0.93</td>\n",
+ " <td id=\"T_1a926_row31_col3\" class=\"data row31 col3\" >0.20</td>\n",
+ " <td id=\"T_1a926_row31_col4\" class=\"data row31 col4\" >8.30</td>\n",
+ " <td id=\"T_1a926_row31_col5\" class=\"data row31 col5\" >2.71</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_1a926_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_1a926_row32_col0\" class=\"data row32 col0\" >10.85</td>\n",
+ " <td id=\"T_1a926_row32_col1\" class=\"data row32 col1\" >2.47</td>\n",
+ " <td id=\"T_1a926_row32_col2\" class=\"data row32 col2\" >0.78</td>\n",
+ " <td id=\"T_1a926_row32_col3\" class=\"data row32 col3\" >0.18</td>\n",
+ " <td id=\"T_1a926_row32_col4\" class=\"data row32 col4\" >64.08</td>\n",
+ " <td id=\"T_1a926_row32_col5\" class=\"data row32 col5\" >7.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_1a926_row33_col0\" class=\"data row33 col0\" >13.26</td>\n",
+ " <td id=\"T_1a926_row33_col1\" class=\"data row33 col1\" >3.22</td>\n",
+ " <td id=\"T_1a926_row33_col2\" class=\"data row33 col2\" >0.96</td>\n",
+ " <td id=\"T_1a926_row33_col3\" class=\"data row33 col3\" >0.23</td>\n",
+ " <td id=\"T_1a926_row33_col4\" class=\"data row33 col4\" >9.97</td>\n",
+ " <td id=\"T_1a926_row33_col5\" class=\"data row33 col5\" >3.34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_1a926_row34_col0\" class=\"data row34 col0\" >12.32</td>\n",
+ " <td id=\"T_1a926_row34_col1\" class=\"data row34 col1\" >3.11</td>\n",
+ " <td id=\"T_1a926_row34_col2\" class=\"data row34 col2\" >0.89</td>\n",
+ " <td id=\"T_1a926_row34_col3\" class=\"data row34 col3\" >0.23</td>\n",
+ " <td id=\"T_1a926_row34_col4\" class=\"data row34 col4\" >8.47</td>\n",
+ " <td id=\"T_1a926_row34_col5\" class=\"data row34 col5\" >2.94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_1a926_row35_col0\" class=\"data row35 col0\" >12.74</td>\n",
+ " <td id=\"T_1a926_row35_col1\" class=\"data row35 col1\" >2.29</td>\n",
+ " <td id=\"T_1a926_row35_col2\" class=\"data row35 col2\" >0.92</td>\n",
+ " <td id=\"T_1a926_row35_col3\" class=\"data row35 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row35_col4\" class=\"data row35 col4\" >53.41</td>\n",
+ " <td id=\"T_1a926_row35_col5\" class=\"data row35 col5\" >7.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_1a926_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_1a926_row36_col0\" class=\"data row36 col0\" >10.60</td>\n",
+ " <td id=\"T_1a926_row36_col1\" class=\"data row36 col1\" >2.29</td>\n",
+ " <td id=\"T_1a926_row36_col2\" class=\"data row36 col2\" >0.77</td>\n",
+ " <td id=\"T_1a926_row36_col3\" class=\"data row36 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row36_col4\" class=\"data row36 col4\" >65.15</td>\n",
+ " <td id=\"T_1a926_row36_col5\" class=\"data row36 col5\" >8.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_1a926_row37_col0\" class=\"data row37 col0\" >12.93</td>\n",
+ " <td id=\"T_1a926_row37_col1\" class=\"data row37 col1\" >2.75</td>\n",
+ " <td id=\"T_1a926_row37_col2\" class=\"data row37 col2\" >0.94</td>\n",
+ " <td id=\"T_1a926_row37_col3\" class=\"data row37 col3\" >0.20</td>\n",
+ " <td id=\"T_1a926_row37_col4\" class=\"data row37 col4\" >10.59</td>\n",
+ " <td id=\"T_1a926_row37_col5\" class=\"data row37 col5\" >3.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_1a926_row38_col0\" class=\"data row38 col0\" >13.66</td>\n",
+ " <td id=\"T_1a926_row38_col1\" class=\"data row38 col1\" >2.39</td>\n",
+ " <td id=\"T_1a926_row38_col2\" class=\"data row38 col2\" >0.99</td>\n",
+ " <td id=\"T_1a926_row38_col3\" class=\"data row38 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row38_col4\" class=\"data row38 col4\" >49.45</td>\n",
+ " <td id=\"T_1a926_row38_col5\" class=\"data row38 col5\" >7.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_1a926_row39_col0\" class=\"data row39 col0\" >11.16</td>\n",
+ " <td id=\"T_1a926_row39_col1\" class=\"data row39 col1\" >2.59</td>\n",
+ " <td id=\"T_1a926_row39_col2\" class=\"data row39 col2\" >0.81</td>\n",
+ " <td id=\"T_1a926_row39_col3\" class=\"data row39 col3\" >0.19</td>\n",
+ " <td id=\"T_1a926_row39_col4\" class=\"data row39 col4\" >8.95</td>\n",
+ " <td id=\"T_1a926_row39_col5\" class=\"data row39 col5\" >2.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_1a926_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_1a926_row40_col0\" class=\"data row40 col0\" >10.33</td>\n",
+ " <td id=\"T_1a926_row40_col1\" class=\"data row40 col1\" >2.50</td>\n",
+ " <td id=\"T_1a926_row40_col2\" class=\"data row40 col2\" >0.75</td>\n",
+ " <td id=\"T_1a926_row40_col3\" class=\"data row40 col3\" >0.18</td>\n",
+ " <td id=\"T_1a926_row40_col4\" class=\"data row40 col4\" >64.23</td>\n",
+ " <td id=\"T_1a926_row40_col5\" class=\"data row40 col5\" >8.84</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_1a926_row41_col0\" class=\"data row41 col0\" >13.28</td>\n",
+ " <td id=\"T_1a926_row41_col1\" class=\"data row41 col1\" >2.32</td>\n",
+ " <td id=\"T_1a926_row41_col2\" class=\"data row41 col2\" >0.96</td>\n",
+ " <td id=\"T_1a926_row41_col3\" class=\"data row41 col3\" >0.17</td>\n",
+ " <td id=\"T_1a926_row41_col4\" class=\"data row41 col4\" >53.56</td>\n",
+ " <td id=\"T_1a926_row41_col5\" class=\"data row41 col5\" >7.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_1a926_row42_col0\" class=\"data row42 col0\" >12.56</td>\n",
+ " <td id=\"T_1a926_row42_col1\" class=\"data row42 col1\" >2.93</td>\n",
+ " <td id=\"T_1a926_row42_col2\" class=\"data row42 col2\" >0.91</td>\n",
+ " <td id=\"T_1a926_row42_col3\" class=\"data row42 col3\" >0.21</td>\n",
+ " <td id=\"T_1a926_row42_col4\" class=\"data row42 col4\" >8.44</td>\n",
+ " <td id=\"T_1a926_row42_col5\" class=\"data row42 col5\" >3.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_1a926_row43_col0\" class=\"data row43 col0\" >12.04</td>\n",
+ " <td id=\"T_1a926_row43_col1\" class=\"data row43 col1\" >2.80</td>\n",
+ " <td id=\"T_1a926_row43_col2\" class=\"data row43 col2\" >0.87</td>\n",
+ " <td id=\"T_1a926_row43_col3\" class=\"data row43 col3\" >0.20</td>\n",
+ " <td id=\"T_1a926_row43_col4\" class=\"data row43 col4\" >8.82</td>\n",
+ " <td id=\"T_1a926_row43_col5\" class=\"data row43 col5\" >2.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_1a926_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_1a926_row44_col0\" class=\"data row44 col0\" >6.23</td>\n",
+ " <td id=\"T_1a926_row44_col1\" class=\"data row44 col1\" >1.92</td>\n",
+ " <td id=\"T_1a926_row44_col2\" class=\"data row44 col2\" >0.45</td>\n",
+ " <td id=\"T_1a926_row44_col3\" class=\"data row44 col3\" >0.14</td>\n",
+ " <td id=\"T_1a926_row44_col4\" class=\"data row44 col4\" >27.37</td>\n",
+ " <td id=\"T_1a926_row44_col5\" class=\"data row44 col5\" >7.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_1a926_row45_col0\" class=\"data row45 col0\" >6.02</td>\n",
+ " <td id=\"T_1a926_row45_col1\" class=\"data row45 col1\" >1.80</td>\n",
+ " <td id=\"T_1a926_row45_col2\" class=\"data row45 col2\" >0.44</td>\n",
+ " <td id=\"T_1a926_row45_col3\" class=\"data row45 col3\" >0.13</td>\n",
+ " <td id=\"T_1a926_row45_col4\" class=\"data row45 col4\" >19.88</td>\n",
+ " <td id=\"T_1a926_row45_col5\" class=\"data row45 col5\" >4.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_1a926_row46_col0\" class=\"data row46 col0\" >5.69</td>\n",
+ " <td id=\"T_1a926_row46_col1\" class=\"data row46 col1\" >1.76</td>\n",
+ " <td id=\"T_1a926_row46_col2\" class=\"data row46 col2\" >0.41</td>\n",
+ " <td id=\"T_1a926_row46_col3\" class=\"data row46 col3\" >0.13</td>\n",
+ " <td id=\"T_1a926_row46_col4\" class=\"data row46 col4\" >14.87</td>\n",
+ " <td id=\"T_1a926_row46_col5\" class=\"data row46 col5\" >3.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_1a926_row47_col0\" class=\"data row47 col0\" >7.42</td>\n",
+ " <td id=\"T_1a926_row47_col1\" class=\"data row47 col1\" >1.87</td>\n",
+ " <td id=\"T_1a926_row47_col2\" class=\"data row47 col2\" >0.54</td>\n",
+ " <td id=\"T_1a926_row47_col3\" class=\"data row47 col3\" >0.14</td>\n",
+ " <td id=\"T_1a926_row47_col4\" class=\"data row47 col4\" >70.54</td>\n",
+ " <td id=\"T_1a926_row47_col5\" class=\"data row47 col5\" >7.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_1a926_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_1a926_row48_col0\" class=\"data row48 col0\" >6.38</td>\n",
+ " <td id=\"T_1a926_row48_col1\" class=\"data row48 col1\" >2.02</td>\n",
+ " <td id=\"T_1a926_row48_col2\" class=\"data row48 col2\" >0.46</td>\n",
+ " <td id=\"T_1a926_row48_col3\" class=\"data row48 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row48_col4\" class=\"data row48 col4\" >29.67</td>\n",
+ " <td id=\"T_1a926_row48_col5\" class=\"data row48 col5\" >7.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_1a926_row49_col0\" class=\"data row49 col0\" >6.65</td>\n",
+ " <td id=\"T_1a926_row49_col1\" class=\"data row49 col1\" >1.84</td>\n",
+ " <td id=\"T_1a926_row49_col2\" class=\"data row49 col2\" >0.48</td>\n",
+ " <td id=\"T_1a926_row49_col3\" class=\"data row49 col3\" >0.13</td>\n",
+ " <td id=\"T_1a926_row49_col4\" class=\"data row49 col4\" >19.66</td>\n",
+ " <td id=\"T_1a926_row49_col5\" class=\"data row49 col5\" >4.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_1a926_row50_col0\" class=\"data row50 col0\" >8.57</td>\n",
+ " <td id=\"T_1a926_row50_col1\" class=\"data row50 col1\" >2.17</td>\n",
+ " <td id=\"T_1a926_row50_col2\" class=\"data row50 col2\" >0.62</td>\n",
+ " <td id=\"T_1a926_row50_col3\" class=\"data row50 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row50_col4\" class=\"data row50 col4\" >65.17</td>\n",
+ " <td id=\"T_1a926_row50_col5\" class=\"data row50 col5\" >8.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_1a926_row51_col0\" class=\"data row51 col0\" >4.65</td>\n",
+ " <td id=\"T_1a926_row51_col1\" class=\"data row51 col1\" >1.55</td>\n",
+ " <td id=\"T_1a926_row51_col2\" class=\"data row51 col2\" >0.34</td>\n",
+ " <td id=\"T_1a926_row51_col3\" class=\"data row51 col3\" >0.11</td>\n",
+ " <td id=\"T_1a926_row51_col4\" class=\"data row51 col4\" >16.74</td>\n",
+ " <td id=\"T_1a926_row51_col5\" class=\"data row51 col5\" >3.94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_1a926_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_1a926_row52_col0\" class=\"data row52 col0\" >5.82</td>\n",
+ " <td id=\"T_1a926_row52_col1\" class=\"data row52 col1\" >2.01</td>\n",
+ " <td id=\"T_1a926_row52_col2\" class=\"data row52 col2\" >0.42</td>\n",
+ " <td id=\"T_1a926_row52_col3\" class=\"data row52 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row52_col4\" class=\"data row52 col4\" >29.79</td>\n",
+ " <td id=\"T_1a926_row52_col5\" class=\"data row52 col5\" >7.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_1a926_row53_col0\" class=\"data row53 col0\" >8.61</td>\n",
+ " <td id=\"T_1a926_row53_col1\" class=\"data row53 col1\" >2.10</td>\n",
+ " <td id=\"T_1a926_row53_col2\" class=\"data row53 col2\" >0.62</td>\n",
+ " <td id=\"T_1a926_row53_col3\" class=\"data row53 col3\" >0.15</td>\n",
+ " <td id=\"T_1a926_row53_col4\" class=\"data row53 col4\" >66.96</td>\n",
+ " <td id=\"T_1a926_row53_col5\" class=\"data row53 col5\" >6.59</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_1a926_row54_col0\" class=\"data row54 col0\" >6.15</td>\n",
+ " <td id=\"T_1a926_row54_col1\" class=\"data row54 col1\" >1.95</td>\n",
+ " <td id=\"T_1a926_row54_col2\" class=\"data row54 col2\" >0.44</td>\n",
+ " <td id=\"T_1a926_row54_col3\" class=\"data row54 col3\" >0.14</td>\n",
+ " <td id=\"T_1a926_row54_col4\" class=\"data row54 col4\" >17.55</td>\n",
+ " <td id=\"T_1a926_row54_col5\" class=\"data row54 col5\" >4.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_1a926_row55_col0\" class=\"data row55 col0\" >4.73</td>\n",
+ " <td id=\"T_1a926_row55_col1\" class=\"data row55 col1\" >1.78</td>\n",
+ " <td id=\"T_1a926_row55_col2\" class=\"data row55 col2\" >0.34</td>\n",
+ " <td id=\"T_1a926_row55_col3\" class=\"data row55 col3\" >0.13</td>\n",
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+ " <td id=\"T_1a926_row55_col5\" class=\"data row55 col5\" >3.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_1a926_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_1a926_row56_col0\" class=\"data row56 col0\" >7.03</td>\n",
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+ " <td id=\"T_1a926_row56_col2\" class=\"data row56 col2\" >0.51</td>\n",
+ " <td id=\"T_1a926_row56_col3\" class=\"data row56 col3\" >0.16</td>\n",
+ " <td id=\"T_1a926_row56_col4\" class=\"data row56 col4\" >74.06</td>\n",
+ " <td id=\"T_1a926_row56_col5\" class=\"data row56 col5\" >8.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_1a926_row57_col0\" class=\"data row57 col0\" >5.30</td>\n",
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+ " <td id=\"T_1a926_row57_col3\" class=\"data row57 col3\" >0.11</td>\n",
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+ " <td id=\"T_1a926_row57_col5\" class=\"data row57 col5\" >4.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_1a926_row58_col0\" class=\"data row58 col0\" >5.35</td>\n",
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+ " <td id=\"T_1a926_row58_col3\" class=\"data row58 col3\" >0.12</td>\n",
+ " <td id=\"T_1a926_row58_col4\" class=\"data row58 col4\" >19.35</td>\n",
+ " <td id=\"T_1a926_row58_col5\" class=\"data row58 col5\" >4.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_1a926_row59_col0\" class=\"data row59 col0\" >4.52</td>\n",
+ " <td id=\"T_1a926_row59_col1\" class=\"data row59 col1\" >1.60</td>\n",
+ " <td id=\"T_1a926_row59_col2\" class=\"data row59 col2\" >0.33</td>\n",
+ " <td id=\"T_1a926_row59_col3\" class=\"data row59 col3\" >0.12</td>\n",
+ " <td id=\"T_1a926_row59_col4\" class=\"data row59 col4\" >18.91</td>\n",
+ " <td id=\"T_1a926_row59_col5\" class=\"data row59 col5\" >3.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_1a926_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_1a926_row60_col0\" class=\"data row60 col0\" >2.21</td>\n",
+ " <td id=\"T_1a926_row60_col1\" class=\"data row60 col1\" >1.09</td>\n",
+ " <td id=\"T_1a926_row60_col2\" class=\"data row60 col2\" >0.16</td>\n",
+ " <td id=\"T_1a926_row60_col3\" class=\"data row60 col3\" >0.08</td>\n",
+ " <td id=\"T_1a926_row60_col4\" class=\"data row60 col4\" >42.06</td>\n",
+ " <td id=\"T_1a926_row60_col5\" class=\"data row60 col5\" >7.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_1a926_row61_col0\" class=\"data row61 col0\" >2.70</td>\n",
+ " <td id=\"T_1a926_row61_col1\" class=\"data row61 col1\" >1.33</td>\n",
+ " <td id=\"T_1a926_row61_col2\" class=\"data row61 col2\" >0.20</td>\n",
+ " <td id=\"T_1a926_row61_col3\" class=\"data row61 col3\" >0.10</td>\n",
+ " <td id=\"T_1a926_row61_col4\" class=\"data row61 col4\" >32.02</td>\n",
+ " <td id=\"T_1a926_row61_col5\" class=\"data row61 col5\" >5.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_1a926_row62_col0\" class=\"data row62 col0\" >2.24</td>\n",
+ " <td id=\"T_1a926_row62_col1\" class=\"data row62 col1\" >1.03</td>\n",
+ " <td id=\"T_1a926_row62_col2\" class=\"data row62 col2\" >0.16</td>\n",
+ " <td id=\"T_1a926_row62_col3\" class=\"data row62 col3\" >0.07</td>\n",
+ " <td id=\"T_1a926_row62_col4\" class=\"data row62 col4\" >27.88</td>\n",
+ " <td id=\"T_1a926_row62_col5\" class=\"data row62 col5\" >5.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1a926_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_1a926_row63_col0\" class=\"data row63 col0\" >1.56</td>\n",
+ " <td id=\"T_1a926_row63_col1\" class=\"data row63 col1\" >0.80</td>\n",
+ " <td id=\"T_1a926_row63_col2\" class=\"data row63 col2\" >0.11</td>\n",
+ " <td id=\"T_1a926_row63_col3\" class=\"data row63 col3\" >0.06</td>\n",
+ " <td id=\"T_1a926_row63_col4\" class=\"data row63 col4\" >28.03</td>\n",
+ " <td id=\"T_1a926_row63_col5\" class=\"data row63 col5\" >4.64</td>\n",
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+ " </tbody>\n",
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+ "<table id=\"T_2c0eb\">\n",
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+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_2c0eb_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
+ " <th id=\"T_2c0eb_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Benefits / energy available</th>\n",
+ " <th id=\"T_2c0eb_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
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+ " <tbody>\n",
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+ " <th id=\"T_2c0eb_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_2c0eb_row0_col0\" class=\"data row0 col0\" >90.77</td>\n",
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+ " <td id=\"T_2c0eb_row0_col2\" class=\"data row0 col2\" >1.64</td>\n",
+ " <td id=\"T_2c0eb_row0_col3\" class=\"data row0 col3\" >0.06</td>\n",
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+ " <td id=\"T_2c0eb_row0_col5\" class=\"data row0 col5\" >3.48</td>\n",
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+ " <th id=\"T_2c0eb_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
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+ " <th id=\"T_2c0eb_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
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+ " <td id=\"T_2c0eb_row3_col3\" class=\"data row3 col3\" >0.06</td>\n",
+ " <td id=\"T_2c0eb_row3_col4\" class=\"data row3 col4\" >34.01</td>\n",
+ " <td id=\"T_2c0eb_row3_col5\" class=\"data row3 col5\" >3.47</td>\n",
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+ " <th id=\"T_2c0eb_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
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+ " <td id=\"T_2c0eb_row4_col5\" class=\"data row4 col5\" >3.58</td>\n",
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+ " <th id=\"T_2c0eb_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
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+ " <td id=\"T_2c0eb_row5_col5\" class=\"data row5 col5\" >3.50</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row6\" class=\"row_heading level0 row6\" >50% (1 0 1 0)</th>\n",
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+ " <td id=\"T_2c0eb_row6_col2\" class=\"data row6 col2\" >0.98</td>\n",
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+ " <td id=\"T_2c0eb_row6_col5\" class=\"data row6 col5\" >3.58</td>\n",
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+ " <td id=\"T_2c0eb_row9_col2\" class=\"data row9 col2\" >0.87</td>\n",
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+ " <td id=\"T_2c0eb_row10_col2\" class=\"data row10 col2\" >0.87</td>\n",
+ " <td id=\"T_2c0eb_row10_col3\" class=\"data row10 col3\" >0.09</td>\n",
+ " <td id=\"T_2c0eb_row10_col4\" class=\"data row10 col4\" >33.76</td>\n",
+ " <td id=\"T_2c0eb_row10_col5\" class=\"data row10 col5\" >3.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row11\" class=\"row_heading level0 row11\" >75% (1 1 1 0)</th>\n",
+ " <td id=\"T_2c0eb_row11_col0\" class=\"data row11 col0\" >25.37</td>\n",
+ " <td id=\"T_2c0eb_row11_col1\" class=\"data row11 col1\" >3.69</td>\n",
+ " <td id=\"T_2c0eb_row11_col2\" class=\"data row11 col2\" >0.46</td>\n",
+ " <td id=\"T_2c0eb_row11_col3\" class=\"data row11 col3\" >0.07</td>\n",
+ " <td id=\"T_2c0eb_row11_col4\" class=\"data row11 col4\" >33.17</td>\n",
+ " <td id=\"T_2c0eb_row11_col5\" class=\"data row11 col5\" >3.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row12\" class=\"row_heading level0 row12\" >75% (1 1 0 1)</th>\n",
+ " <td id=\"T_2c0eb_row12_col0\" class=\"data row12 col0\" >26.24</td>\n",
+ " <td id=\"T_2c0eb_row12_col1\" class=\"data row12 col1\" >3.55</td>\n",
+ " <td id=\"T_2c0eb_row12_col2\" class=\"data row12 col2\" >0.47</td>\n",
+ " <td id=\"T_2c0eb_row12_col3\" class=\"data row12 col3\" >0.06</td>\n",
+ " <td id=\"T_2c0eb_row12_col4\" class=\"data row12 col4\" >32.81</td>\n",
+ " <td id=\"T_2c0eb_row12_col5\" class=\"data row12 col5\" >3.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
+ " <td id=\"T_2c0eb_row13_col0\" class=\"data row13 col0\" >25.31</td>\n",
+ " <td id=\"T_2c0eb_row13_col1\" class=\"data row13 col1\" >3.92</td>\n",
+ " <td id=\"T_2c0eb_row13_col2\" class=\"data row13 col2\" >0.46</td>\n",
+ " <td id=\"T_2c0eb_row13_col3\" class=\"data row13 col3\" >0.07</td>\n",
+ " <td id=\"T_2c0eb_row13_col4\" class=\"data row13 col4\" >32.86</td>\n",
+ " <td id=\"T_2c0eb_row13_col5\" class=\"data row13 col5\" >3.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_2c0eb_row14_col0\" class=\"data row14 col0\" >22.20</td>\n",
+ " <td id=\"T_2c0eb_row14_col1\" class=\"data row14 col1\" >3.82</td>\n",
+ " <td id=\"T_2c0eb_row14_col2\" class=\"data row14 col2\" >0.40</td>\n",
+ " <td id=\"T_2c0eb_row14_col3\" class=\"data row14 col3\" >0.07</td>\n",
+ " <td id=\"T_2c0eb_row14_col4\" class=\"data row14 col4\" >33.45</td>\n",
+ " <td id=\"T_2c0eb_row14_col5\" class=\"data row14 col5\" >3.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_2c0eb_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_2c0eb_row15_col0\" class=\"data row15 col0\" >8.71</td>\n",
+ " <td id=\"T_2c0eb_row15_col1\" class=\"data row15 col1\" >2.07</td>\n",
+ " <td id=\"T_2c0eb_row15_col2\" class=\"data row15 col2\" >0.16</td>\n",
+ " <td id=\"T_2c0eb_row15_col3\" class=\"data row15 col3\" >0.04</td>\n",
+ " <td id=\"T_2c0eb_row15_col4\" class=\"data row15 col4\" >32.50</td>\n",
+ " <td id=\"T_2c0eb_row15_col5\" class=\"data row15 col5\" >3.48</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7129a0e5e910>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Small"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 2500x1500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.plot_graph(\"small\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_fbfcf td {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_fbfcf th {\n",
+ " border: 1px solid grey !important;\n",
+ " text-align: center;\n",
+ "}\n",
+ "#T_fbfcf_row0_col0, #T_fbfcf_row60_col0 {\n",
+ " background-color: #b9f6b9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row0_col2, #T_fbfcf_row60_col2 {\n",
+ " background-color: #a9f7a9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row1_col0, #T_fbfcf_row61_col0 {\n",
+ " background-color: #a6f7a6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row1_col2, #T_fbfcf_row61_col2 {\n",
+ " background-color: #98f898;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row2_col0, #T_fbfcf_row62_col0 {\n",
+ " background-color: #aaf7aa;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row2_col2, #T_fbfcf_row62_col2 {\n",
+ " background-color: #96f996;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row3_col0, #T_fbfcf_row44_col0, #T_fbfcf_row48_col0, #T_fbfcf_row63_col0 {\n",
+ " background-color: #a4f8a4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row3_col2, #T_fbfcf_row59_col0, #T_fbfcf_row63_col2 {\n",
+ " background-color: #92f992;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row4_col0 {\n",
+ " background-color: #4afe4a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row4_col2 {\n",
+ " background-color: #42fe42;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row5_col0, #T_fbfcf_row6_col0 {\n",
+ " background-color: #ccf5cc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row5_col2, #T_fbfcf_row7_col2, #T_fbfcf_row11_col2, #T_fbfcf_row15_col2 {\n",
+ " background-color: #b8f6b8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row6_col2, #T_fbfcf_row10_col2 {\n",
+ " background-color: #b7f6b7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row7_col0 {\n",
+ " background-color: #cbf5cb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row8_col0, #T_fbfcf_row12_col0, #T_fbfcf_row23_col2, #T_fbfcf_row25_col2 {\n",
+ " background-color: #d6f4d6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row8_col2 {\n",
+ " background-color: #c5f5c5;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row9_col0, #T_fbfcf_row9_col2, #T_fbfcf_row19_col2 {\n",
+ " background-color: #35ff35;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row10_col0 {\n",
+ " background-color: #d4f4d4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row11_col0 {\n",
+ " background-color: #cef5ce;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row12_col2 {\n",
+ " background-color: #c2f6c2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row13_col0, #T_fbfcf_row15_col0, #T_fbfcf_row29_col2 {\n",
+ " background-color: #d1f5d1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row13_col2 {\n",
+ " background-color: #bcf6bc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row14_col0, #T_fbfcf_row14_col2, #T_fbfcf_row19_col0 {\n",
+ " background-color: #34ff34;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row16_col0, #T_fbfcf_row35_col2, #T_fbfcf_row41_col2 {\n",
+ " background-color: #d9f4d9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row16_col2 {\n",
+ " background-color: #c7f5c7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row17_col0, #T_fbfcf_row18_col0 {\n",
+ " background-color: #d0f5d0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row17_col2 {\n",
+ " background-color: #baf6ba;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row18_col2 {\n",
+ " background-color: #b4f7b4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row20_col0 {\n",
+ " background-color: #87fa87;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row20_col2, #T_fbfcf_row31_col0 {\n",
+ " background-color: #68fc68;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row21_col0 {\n",
+ " background-color: #64fc64;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row21_col2, #T_fbfcf_row33_col2 {\n",
+ " background-color: #58fd58;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row22_col0, #T_fbfcf_row30_col0, #T_fbfcf_row36_col2 {\n",
+ " background-color: #e1f4e0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row22_col2, #T_fbfcf_row30_col2 {\n",
+ " background-color: #d2f5d2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row23_col0, #T_fbfcf_row41_col0 {\n",
+ " background-color: #e2f4e2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row24_col0 {\n",
+ " background-color: #86fa86;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row24_col2 {\n",
+ " background-color: #6cfb6c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row25_col0, #T_fbfcf_row40_col2 {\n",
+ " background-color: #e0f4e0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row26_col0 {\n",
+ " background-color: #65fc65;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row26_col2 {\n",
+ " background-color: #4ffd4f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row27_col0, #T_fbfcf_row35_col0, #T_fbfcf_row38_col0 {\n",
+ " background-color: #e3f3e3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row27_col2 {\n",
+ " background-color: #d8f4d8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row28_col0 {\n",
+ " background-color: #88f988;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row28_col2 {\n",
+ " background-color: #6dfb6d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row29_col0 {\n",
+ " background-color: #def4de;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row31_col2 {\n",
+ " background-color: #5afc5a;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row32_col0, #T_fbfcf_row36_col0, #T_fbfcf_row40_col0, #T_fbfcf_row53_col2 {\n",
+ " background-color: #e6f3e6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row32_col2 {\n",
+ " background-color: #dff4df;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row33_col0, #T_fbfcf_row37_col0 {\n",
+ " background-color: #70fb70;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row34_col0, #T_fbfcf_row39_col0 {\n",
+ " background-color: #75fb75;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row34_col2 {\n",
+ " background-color: #59fd59;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row37_col2 {\n",
+ " background-color: #57fd57;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row38_col2 {\n",
+ " background-color: #d5f4d5;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row39_col2 {\n",
+ " background-color: #5dfc5d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row42_col0 {\n",
+ " background-color: #6efb6e;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row42_col2 {\n",
+ " background-color: #56fd56;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row43_col0, #T_fbfcf_row46_col2 {\n",
+ " background-color: #77fb77;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row43_col2 {\n",
+ " background-color: #5bfc5b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row44_col2, #T_fbfcf_row46_col0, #T_fbfcf_row51_col0 {\n",
+ " background-color: #8df98d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row45_col0, #T_fbfcf_row52_col2 {\n",
+ " background-color: #91f991;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row45_col2, #T_fbfcf_row49_col2 {\n",
+ " background-color: #7cfa7c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row47_col0, #T_fbfcf_row47_col2 {\n",
+ " background-color: #eaf3ea;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row48_col2 {\n",
+ " background-color: #90f990;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row49_col0 {\n",
+ " background-color: #93f993;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row50_col0 {\n",
+ " background-color: #e9f3e9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row50_col2 {\n",
+ " background-color: #e5f3e5;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row51_col2 {\n",
+ " background-color: #79fa79;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row52_col0 {\n",
+ " background-color: #a3f8a3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row53_col0 {\n",
+ " background-color: #e8f3e8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row54_col0 {\n",
+ " background-color: #94f994;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row54_col2 {\n",
+ " background-color: #78fb78;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row55_col0 {\n",
+ " background-color: #8ff98f;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row55_col2 {\n",
+ " background-color: #7bfa7b;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row56_col0, #T_fbfcf_row56_col2 {\n",
+ " background-color: #ebf3eb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row57_col0 {\n",
+ " background-color: #95f995;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row57_col2, #T_fbfcf_row58_col2 {\n",
+ " background-color: #81fa81;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row58_col0 {\n",
+ " background-color: #9cf89c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_fbfcf_row59_col2 {\n",
+ " background-color: #7dfa7d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_fbfcf\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_fbfcf_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bidded</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_fbfcf_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Jobs</th>\n",
+ " <th id=\"T_fbfcf_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_fbfcf_level2_col0\" class=\"col_heading level2 col0\" >Number</th>\n",
+ " <th id=\"T_fbfcf_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
+ " <th id=\"T_fbfcf_level2_col2\" class=\"col_heading level2 col2\" >Number</th>\n",
+ " <th id=\"T_fbfcf_level2_col3\" class=\"col_heading level2 col3\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_fbfcf_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_fbfcf_row0_col0\" class=\"data row0 col0\" >19.65</td>\n",
+ " <td id=\"T_fbfcf_row0_col1\" class=\"data row0 col1\" >4.16</td>\n",
+ " <td id=\"T_fbfcf_row0_col2\" class=\"data row0 col2\" >20.66</td>\n",
+ " <td id=\"T_fbfcf_row0_col3\" class=\"data row0 col3\" >4.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_fbfcf_row1_col0\" class=\"data row1 col0\" >26.73</td>\n",
+ " <td id=\"T_fbfcf_row1_col1\" class=\"data row1 col1\" >4.86</td>\n",
+ " <td id=\"T_fbfcf_row1_col2\" class=\"data row1 col2\" >25.36</td>\n",
+ " <td id=\"T_fbfcf_row1_col3\" class=\"data row1 col3\" >4.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_fbfcf_row2_col0\" class=\"data row2 col0\" >25.18</td>\n",
+ " <td id=\"T_fbfcf_row2_col1\" class=\"data row2 col1\" >4.37</td>\n",
+ " <td id=\"T_fbfcf_row2_col2\" class=\"data row2 col2\" >26.14</td>\n",
+ " <td id=\"T_fbfcf_row2_col3\" class=\"data row2 col3\" >4.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_fbfcf_row3_col0\" class=\"data row3 col0\" >27.23</td>\n",
+ " <td id=\"T_fbfcf_row3_col1\" class=\"data row3 col1\" >5.17</td>\n",
+ " <td id=\"T_fbfcf_row3_col2\" class=\"data row3 col2\" >27.09</td>\n",
+ " <td id=\"T_fbfcf_row3_col3\" class=\"data row3 col3\" >4.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_fbfcf_row4_col0\" class=\"data row4 col0\" >61.04</td>\n",
+ " <td id=\"T_fbfcf_row4_col1\" class=\"data row4 col1\" >5.03</td>\n",
+ " <td id=\"T_fbfcf_row4_col2\" class=\"data row4 col2\" >49.55</td>\n",
+ " <td id=\"T_fbfcf_row4_col3\" class=\"data row4 col3\" >5.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_fbfcf_row5_col0\" class=\"data row5 col0\" >12.41</td>\n",
+ " <td id=\"T_fbfcf_row5_col1\" class=\"data row5 col1\" >3.36</td>\n",
+ " <td id=\"T_fbfcf_row5_col2\" class=\"data row5 col2\" >16.55</td>\n",
+ " <td id=\"T_fbfcf_row5_col3\" class=\"data row5 col3\" >4.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_fbfcf_row6_col0\" class=\"data row6 col0\" >12.57</td>\n",
+ " <td id=\"T_fbfcf_row6_col1\" class=\"data row6 col1\" >3.39</td>\n",
+ " <td id=\"T_fbfcf_row6_col2\" class=\"data row6 col2\" >16.81</td>\n",
+ " <td id=\"T_fbfcf_row6_col3\" class=\"data row6 col3\" >4.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_fbfcf_row7_col0\" class=\"data row7 col0\" >12.85</td>\n",
+ " <td id=\"T_fbfcf_row7_col1\" class=\"data row7 col1\" >3.98</td>\n",
+ " <td id=\"T_fbfcf_row7_col2\" class=\"data row7 col2\" >16.43</td>\n",
+ " <td id=\"T_fbfcf_row7_col3\" class=\"data row7 col3\" >4.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_fbfcf_row8_col0\" class=\"data row8 col0\" >8.46</td>\n",
+ " <td id=\"T_fbfcf_row8_col1\" class=\"data row8 col1\" >2.93</td>\n",
+ " <td id=\"T_fbfcf_row8_col2\" class=\"data row8 col2\" >12.85</td>\n",
+ " <td id=\"T_fbfcf_row8_col3\" class=\"data row8 col3\" >3.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_fbfcf_row9_col0\" class=\"data row9 col0\" >69.15</td>\n",
+ " <td id=\"T_fbfcf_row9_col1\" class=\"data row9 col1\" >5.91</td>\n",
+ " <td id=\"T_fbfcf_row9_col2\" class=\"data row9 col2\" >53.00</td>\n",
+ " <td id=\"T_fbfcf_row9_col3\" class=\"data row9 col3\" >6.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_fbfcf_row10_col0\" class=\"data row10 col0\" >9.50</td>\n",
+ " <td id=\"T_fbfcf_row10_col1\" class=\"data row10 col1\" >3.17</td>\n",
+ " <td id=\"T_fbfcf_row10_col2\" class=\"data row10 col2\" >16.76</td>\n",
+ " <td id=\"T_fbfcf_row10_col3\" class=\"data row10 col3\" >4.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_fbfcf_row11_col0\" class=\"data row11 col0\" >11.68</td>\n",
+ " <td id=\"T_fbfcf_row11_col1\" class=\"data row11 col1\" >3.52</td>\n",
+ " <td id=\"T_fbfcf_row11_col2\" class=\"data row11 col2\" >16.69</td>\n",
+ " <td id=\"T_fbfcf_row11_col3\" class=\"data row11 col3\" >4.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_fbfcf_row12_col0\" class=\"data row12 col0\" >8.54</td>\n",
+ " <td id=\"T_fbfcf_row12_col1\" class=\"data row12 col1\" >3.54</td>\n",
+ " <td id=\"T_fbfcf_row12_col2\" class=\"data row12 col2\" >13.79</td>\n",
+ " <td id=\"T_fbfcf_row12_col3\" class=\"data row12 col3\" >4.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_fbfcf_row13_col0\" class=\"data row13 col0\" >10.35</td>\n",
+ " <td id=\"T_fbfcf_row13_col1\" class=\"data row13 col1\" >3.49</td>\n",
+ " <td id=\"T_fbfcf_row13_col2\" class=\"data row13 col2\" >15.54</td>\n",
+ " <td id=\"T_fbfcf_row13_col3\" class=\"data row13 col3\" >4.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_fbfcf_row14_col0\" class=\"data row14 col0\" >69.49</td>\n",
+ " <td id=\"T_fbfcf_row14_col1\" class=\"data row14 col1\" >6.18</td>\n",
+ " <td id=\"T_fbfcf_row14_col2\" class=\"data row14 col2\" >53.56</td>\n",
+ " <td id=\"T_fbfcf_row14_col3\" class=\"data row14 col3\" >6.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_fbfcf_row15_col0\" class=\"data row15 col0\" >10.50</td>\n",
+ " <td id=\"T_fbfcf_row15_col1\" class=\"data row15 col1\" >3.46</td>\n",
+ " <td id=\"T_fbfcf_row15_col2\" class=\"data row15 col2\" >16.43</td>\n",
+ " <td id=\"T_fbfcf_row15_col3\" class=\"data row15 col3\" >3.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_fbfcf_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_fbfcf_row16_col0\" class=\"data row16 col0\" >7.50</td>\n",
+ " <td id=\"T_fbfcf_row16_col1\" class=\"data row16 col1\" >2.99</td>\n",
+ " <td id=\"T_fbfcf_row16_col2\" class=\"data row16 col2\" >12.40</td>\n",
+ " <td id=\"T_fbfcf_row16_col3\" class=\"data row16 col3\" >4.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_fbfcf_row17_col0\" class=\"data row17 col0\" >10.97</td>\n",
+ " <td id=\"T_fbfcf_row17_col1\" class=\"data row17 col1\" >3.75</td>\n",
+ " <td id=\"T_fbfcf_row17_col2\" class=\"data row17 col2\" >16.07</td>\n",
+ " <td id=\"T_fbfcf_row17_col3\" class=\"data row17 col3\" >4.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_fbfcf_row18_col0\" class=\"data row18 col0\" >11.09</td>\n",
+ " <td id=\"T_fbfcf_row18_col1\" class=\"data row18 col1\" >3.63</td>\n",
+ " <td id=\"T_fbfcf_row18_col2\" class=\"data row18 col2\" >17.74</td>\n",
+ " <td id=\"T_fbfcf_row18_col3\" class=\"data row18 col3\" >4.25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_fbfcf_row19_col0\" class=\"data row19 col0\" >69.24</td>\n",
+ " <td id=\"T_fbfcf_row19_col1\" class=\"data row19 col1\" >5.98</td>\n",
+ " <td id=\"T_fbfcf_row19_col2\" class=\"data row19 col2\" >53.10</td>\n",
+ " <td id=\"T_fbfcf_row19_col3\" class=\"data row19 col3\" >6.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_fbfcf_row20_col0\" class=\"data row20 col0\" >38.38</td>\n",
+ " <td id=\"T_fbfcf_row20_col1\" class=\"data row20 col1\" >5.08</td>\n",
+ " <td id=\"T_fbfcf_row20_col2\" class=\"data row20 col2\" >38.83</td>\n",
+ " <td id=\"T_fbfcf_row20_col3\" class=\"data row20 col3\" >5.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_fbfcf_row21_col0\" class=\"data row21 col0\" >51.36</td>\n",
+ " <td id=\"T_fbfcf_row21_col1\" class=\"data row21 col1\" >4.94</td>\n",
+ " <td id=\"T_fbfcf_row21_col2\" class=\"data row21 col2\" >43.40</td>\n",
+ " <td id=\"T_fbfcf_row21_col3\" class=\"data row21 col3\" >5.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_fbfcf_row22_col0\" class=\"data row22 col0\" >4.89</td>\n",
+ " <td id=\"T_fbfcf_row22_col1\" class=\"data row22 col1\" >2.60</td>\n",
+ " <td id=\"T_fbfcf_row22_col2\" class=\"data row22 col2\" >9.18</td>\n",
+ " <td id=\"T_fbfcf_row22_col3\" class=\"data row22 col3\" >3.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_fbfcf_row23_col0\" class=\"data row23 col0\" >4.20</td>\n",
+ " <td id=\"T_fbfcf_row23_col1\" class=\"data row23 col1\" >2.62</td>\n",
+ " <td id=\"T_fbfcf_row23_col2\" class=\"data row23 col2\" >7.95</td>\n",
+ " <td id=\"T_fbfcf_row23_col3\" class=\"data row23 col3\" >3.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_fbfcf_row24_col0\" class=\"data row24 col0\" >38.73</td>\n",
+ " <td id=\"T_fbfcf_row24_col1\" class=\"data row24 col1\" >5.11</td>\n",
+ " <td id=\"T_fbfcf_row24_col2\" class=\"data row24 col2\" >37.76</td>\n",
+ " <td id=\"T_fbfcf_row24_col3\" class=\"data row24 col3\" >5.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_fbfcf_row25_col0\" class=\"data row25 col0\" >5.06</td>\n",
+ " <td id=\"T_fbfcf_row25_col1\" class=\"data row25 col1\" >3.14</td>\n",
+ " <td id=\"T_fbfcf_row25_col2\" class=\"data row25 col2\" >8.12</td>\n",
+ " <td id=\"T_fbfcf_row25_col3\" class=\"data row25 col3\" >3.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_fbfcf_row26_col0\" class=\"data row26 col0\" >50.90</td>\n",
+ " <td id=\"T_fbfcf_row26_col1\" class=\"data row26 col1\" >5.12</td>\n",
+ " <td id=\"T_fbfcf_row26_col2\" class=\"data row26 col2\" >45.88</td>\n",
+ " <td id=\"T_fbfcf_row26_col3\" class=\"data row26 col3\" >5.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_fbfcf_row27_col0\" class=\"data row27 col0\" >4.14</td>\n",
+ " <td id=\"T_fbfcf_row27_col1\" class=\"data row27 col1\" >2.38</td>\n",
+ " <td id=\"T_fbfcf_row27_col2\" class=\"data row27 col2\" >7.60</td>\n",
+ " <td id=\"T_fbfcf_row27_col3\" class=\"data row27 col3\" >3.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_fbfcf_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_fbfcf_row28_col0\" class=\"data row28 col0\" >37.86</td>\n",
+ " <td id=\"T_fbfcf_row28_col1\" class=\"data row28 col1\" >5.60</td>\n",
+ " <td id=\"T_fbfcf_row28_col2\" class=\"data row28 col2\" >37.45</td>\n",
+ " <td id=\"T_fbfcf_row28_col3\" class=\"data row28 col3\" >5.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_fbfcf_row29_col0\" class=\"data row29 col0\" >5.94</td>\n",
+ " <td id=\"T_fbfcf_row29_col1\" class=\"data row29 col1\" >3.19</td>\n",
+ " <td id=\"T_fbfcf_row29_col2\" class=\"data row29 col2\" >9.68</td>\n",
+ " <td id=\"T_fbfcf_row29_col3\" class=\"data row29 col3\" >3.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_fbfcf_row30_col0\" class=\"data row30 col0\" >4.91</td>\n",
+ " <td id=\"T_fbfcf_row30_col1\" class=\"data row30 col1\" >2.54</td>\n",
+ " <td id=\"T_fbfcf_row30_col2\" class=\"data row30 col2\" >9.29</td>\n",
+ " <td id=\"T_fbfcf_row30_col3\" class=\"data row30 col3\" >3.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_fbfcf_row31_col0\" class=\"data row31 col0\" >50.12</td>\n",
+ " <td id=\"T_fbfcf_row31_col1\" class=\"data row31 col1\" >5.87</td>\n",
+ " <td id=\"T_fbfcf_row31_col2\" class=\"data row31 col2\" >42.91</td>\n",
+ " <td id=\"T_fbfcf_row31_col3\" class=\"data row31 col3\" >6.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_fbfcf_row32_col0\" class=\"data row32 col0\" >2.92</td>\n",
+ " <td id=\"T_fbfcf_row32_col1\" class=\"data row32 col1\" >2.34</td>\n",
+ " <td id=\"T_fbfcf_row32_col2\" class=\"data row32 col2\" >5.56</td>\n",
+ " <td id=\"T_fbfcf_row32_col3\" class=\"data row32 col3\" >2.77</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_fbfcf_row33_col0\" class=\"data row33 col0\" >46.96</td>\n",
+ " <td id=\"T_fbfcf_row33_col1\" class=\"data row33 col1\" >5.05</td>\n",
+ " <td id=\"T_fbfcf_row33_col2\" class=\"data row33 col2\" >43.41</td>\n",
+ " <td id=\"T_fbfcf_row33_col3\" class=\"data row33 col3\" >5.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_fbfcf_row34_col0\" class=\"data row34 col0\" >45.05</td>\n",
+ " <td id=\"T_fbfcf_row34_col1\" class=\"data row34 col1\" >4.55</td>\n",
+ " <td id=\"T_fbfcf_row34_col2\" class=\"data row34 col2\" >43.10</td>\n",
+ " <td id=\"T_fbfcf_row34_col3\" class=\"data row34 col3\" >5.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_fbfcf_row35_col0\" class=\"data row35 col0\" >3.85</td>\n",
+ " <td id=\"T_fbfcf_row35_col1\" class=\"data row35 col1\" >2.18</td>\n",
+ " <td id=\"T_fbfcf_row35_col2\" class=\"data row35 col2\" >7.20</td>\n",
+ " <td id=\"T_fbfcf_row35_col3\" class=\"data row35 col3\" >2.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_fbfcf_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_fbfcf_row36_col0\" class=\"data row36 col0\" >2.96</td>\n",
+ " <td id=\"T_fbfcf_row36_col1\" class=\"data row36 col1\" >2.19</td>\n",
+ " <td id=\"T_fbfcf_row36_col2\" class=\"data row36 col2\" >5.30</td>\n",
+ " <td id=\"T_fbfcf_row36_col3\" class=\"data row36 col3\" >2.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_fbfcf_row37_col0\" class=\"data row37 col0\" >46.79</td>\n",
+ " <td id=\"T_fbfcf_row37_col1\" class=\"data row37 col1\" >4.83</td>\n",
+ " <td id=\"T_fbfcf_row37_col2\" class=\"data row37 col2\" >43.60</td>\n",
+ " <td id=\"T_fbfcf_row37_col3\" class=\"data row37 col3\" >5.35</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_fbfcf_row38_col0\" class=\"data row38 col0\" >4.09</td>\n",
+ " <td id=\"T_fbfcf_row38_col1\" class=\"data row38 col1\" >2.57</td>\n",
+ " <td id=\"T_fbfcf_row38_col2\" class=\"data row38 col2\" >8.39</td>\n",
+ " <td id=\"T_fbfcf_row38_col3\" class=\"data row38 col3\" >3.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_fbfcf_row39_col0\" class=\"data row39 col0\" >44.98</td>\n",
+ " <td id=\"T_fbfcf_row39_col1\" class=\"data row39 col1\" >5.21</td>\n",
+ " <td id=\"T_fbfcf_row39_col2\" class=\"data row39 col2\" >42.09</td>\n",
+ " <td id=\"T_fbfcf_row39_col3\" class=\"data row39 col3\" >5.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_fbfcf_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_fbfcf_row40_col0\" class=\"data row40 col0\" >2.62</td>\n",
+ " <td id=\"T_fbfcf_row40_col1\" class=\"data row40 col1\" >2.15</td>\n",
+ " <td id=\"T_fbfcf_row40_col2\" class=\"data row40 col2\" >5.53</td>\n",
+ " <td id=\"T_fbfcf_row40_col3\" class=\"data row40 col3\" >3.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_fbfcf_row41_col0\" class=\"data row41 col0\" >4.28</td>\n",
+ " <td id=\"T_fbfcf_row41_col1\" class=\"data row41 col1\" >2.65</td>\n",
+ " <td id=\"T_fbfcf_row41_col2\" class=\"data row41 col2\" >7.53</td>\n",
+ " <td id=\"T_fbfcf_row41_col3\" class=\"data row41 col3\" >3.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_fbfcf_row42_col0\" class=\"data row42 col0\" >47.73</td>\n",
+ " <td id=\"T_fbfcf_row42_col1\" class=\"data row42 col1\" >4.88</td>\n",
+ " <td id=\"T_fbfcf_row42_col2\" class=\"data row42 col2\" >43.80</td>\n",
+ " <td id=\"T_fbfcf_row42_col3\" class=\"data row42 col3\" >5.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_fbfcf_row43_col0\" class=\"data row43 col0\" >44.17</td>\n",
+ " <td id=\"T_fbfcf_row43_col1\" class=\"data row43 col1\" >4.65</td>\n",
+ " <td id=\"T_fbfcf_row43_col2\" class=\"data row43 col2\" >42.52</td>\n",
+ " <td id=\"T_fbfcf_row43_col3\" class=\"data row43 col3\" >5.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_fbfcf_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_fbfcf_row44_col0\" class=\"data row44 col0\" >27.31</td>\n",
+ " <td id=\"T_fbfcf_row44_col1\" class=\"data row44 col1\" >4.49</td>\n",
+ " <td id=\"T_fbfcf_row44_col2\" class=\"data row44 col2\" >28.64</td>\n",
+ " <td id=\"T_fbfcf_row44_col3\" class=\"data row44 col3\" >4.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_fbfcf_row45_col0\" class=\"data row45 col0\" >34.49</td>\n",
+ " <td id=\"T_fbfcf_row45_col1\" class=\"data row45 col1\" >4.17</td>\n",
+ " <td id=\"T_fbfcf_row45_col2\" class=\"data row45 col2\" >33.34</td>\n",
+ " <td id=\"T_fbfcf_row45_col3\" class=\"data row45 col3\" >4.46</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_fbfcf_row46_col0\" class=\"data row46 col0\" >35.75</td>\n",
+ " <td id=\"T_fbfcf_row46_col1\" class=\"data row46 col1\" >4.02</td>\n",
+ " <td id=\"T_fbfcf_row46_col2\" class=\"data row46 col2\" >34.74</td>\n",
+ " <td id=\"T_fbfcf_row46_col3\" class=\"data row46 col3\" >4.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_fbfcf_row47_col0\" class=\"data row47 col0\" >1.26</td>\n",
+ " <td id=\"T_fbfcf_row47_col1\" class=\"data row47 col1\" >1.48</td>\n",
+ " <td id=\"T_fbfcf_row47_col2\" class=\"data row47 col2\" >2.67</td>\n",
+ " <td id=\"T_fbfcf_row47_col3\" class=\"data row47 col3\" >2.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_fbfcf_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_fbfcf_row48_col0\" class=\"data row48 col0\" >27.69</td>\n",
+ " <td id=\"T_fbfcf_row48_col1\" class=\"data row48 col1\" >4.53</td>\n",
+ " <td id=\"T_fbfcf_row48_col2\" class=\"data row48 col2\" >27.70</td>\n",
+ " <td id=\"T_fbfcf_row48_col3\" class=\"data row48 col3\" >4.37</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_fbfcf_row49_col0\" class=\"data row49 col0\" >33.69</td>\n",
+ " <td id=\"T_fbfcf_row49_col1\" class=\"data row49 col1\" >3.92</td>\n",
+ " <td id=\"T_fbfcf_row49_col2\" class=\"data row49 col2\" >33.38</td>\n",
+ " <td id=\"T_fbfcf_row49_col3\" class=\"data row49 col3\" >4.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_fbfcf_row50_col0\" class=\"data row50 col0\" >1.73</td>\n",
+ " <td id=\"T_fbfcf_row50_col1\" class=\"data row50 col1\" >1.81</td>\n",
+ " <td id=\"T_fbfcf_row50_col2\" class=\"data row50 col2\" >4.00</td>\n",
+ " <td id=\"T_fbfcf_row50_col3\" class=\"data row50 col3\" >2.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fbfcf_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
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+ "<table id=\"T_ca453\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_ca453_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">% of load of jobs/services bidded</th>\n",
+ " <th id=\"T_ca453_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\"></th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_ca453_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Completed service</th>\n",
+ " <th id=\"T_ca453_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Before due</th>\n",
+ " <th id=\"T_ca453_level1_col4\" class=\"col_heading level1 col4\" colspan=\"2\">Slowdown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_ca453_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_ca453_level2_col1\" class=\"col_heading level2 col1\" >Std</th>\n",
+ " <th id=\"T_ca453_level2_col2\" class=\"col_heading level2 col2\" >Value</th>\n",
+ " <th id=\"T_ca453_level2_col3\" class=\"col_heading level2 col3\" >Std</th>\n",
+ " <th id=\"T_ca453_level2_col4\" class=\"col_heading level2 col4\" >Value</th>\n",
+ " <th id=\"T_ca453_level2_col5\" class=\"col_heading level2 col5\" >Std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_ca453_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_ca453_row0_col0\" class=\"data row0 col0\" >99.52</td>\n",
+ " <td id=\"T_ca453_row0_col1\" class=\"data row0 col1\" >1.21</td>\n",
+ " <td id=\"T_ca453_row0_col2\" class=\"data row0 col2\" >99.95</td>\n",
+ " <td id=\"T_ca453_row0_col3\" class=\"data row0 col3\" >0.47</td>\n",
+ " <td id=\"T_ca453_row0_col4\" class=\"data row0 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row0_col5\" class=\"data row0 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_ca453_row1_col0\" class=\"data row1 col0\" >99.29</td>\n",
+ " <td id=\"T_ca453_row1_col1\" class=\"data row1 col1\" >1.65</td>\n",
+ " <td id=\"T_ca453_row1_col2\" class=\"data row1 col2\" >100.00</td>\n",
+ " <td id=\"T_ca453_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_ca453_row1_col4\" class=\"data row1 col4\" >1.09</td>\n",
+ " <td id=\"T_ca453_row1_col5\" class=\"data row1 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_ca453_row2_col0\" class=\"data row2 col0\" >99.31</td>\n",
+ " <td id=\"T_ca453_row2_col1\" class=\"data row2 col1\" >1.46</td>\n",
+ " <td id=\"T_ca453_row2_col2\" class=\"data row2 col2\" >100.00</td>\n",
+ " <td id=\"T_ca453_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_ca453_row2_col4\" class=\"data row2 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row2_col5\" class=\"data row2 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_ca453_row3_col0\" class=\"data row3 col0\" >99.59</td>\n",
+ " <td id=\"T_ca453_row3_col1\" class=\"data row3 col1\" >1.02</td>\n",
+ " <td id=\"T_ca453_row3_col2\" class=\"data row3 col2\" >99.99</td>\n",
+ " <td id=\"T_ca453_row3_col3\" class=\"data row3 col3\" >0.10</td>\n",
+ " <td id=\"T_ca453_row3_col4\" class=\"data row3 col4\" >1.06</td>\n",
+ " <td id=\"T_ca453_row3_col5\" class=\"data row3 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_ca453_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_ca453_row4_col0\" class=\"data row4 col0\" >94.37</td>\n",
+ " <td id=\"T_ca453_row4_col1\" class=\"data row4 col1\" >2.87</td>\n",
+ " <td id=\"T_ca453_row4_col2\" class=\"data row4 col2\" >97.46</td>\n",
+ " <td id=\"T_ca453_row4_col3\" class=\"data row4 col3\" >3.33</td>\n",
+ " <td id=\"T_ca453_row4_col4\" class=\"data row4 col4\" >1.39</td>\n",
+ " <td id=\"T_ca453_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_ca453_row5_col0\" class=\"data row5 col0\" >96.54</td>\n",
+ " <td id=\"T_ca453_row5_col1\" class=\"data row5 col1\" >2.94</td>\n",
+ " <td id=\"T_ca453_row5_col2\" class=\"data row5 col2\" >99.97</td>\n",
+ " <td id=\"T_ca453_row5_col3\" class=\"data row5 col3\" >0.29</td>\n",
+ " <td id=\"T_ca453_row5_col4\" class=\"data row5 col4\" >1.09</td>\n",
+ " <td id=\"T_ca453_row5_col5\" class=\"data row5 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_ca453_row6_col0\" class=\"data row6 col0\" >97.64</td>\n",
+ " <td id=\"T_ca453_row6_col1\" class=\"data row6 col1\" >2.49</td>\n",
+ " <td id=\"T_ca453_row6_col2\" class=\"data row6 col2\" >99.80</td>\n",
+ " <td id=\"T_ca453_row6_col3\" class=\"data row6 col3\" >1.29</td>\n",
+ " <td id=\"T_ca453_row6_col4\" class=\"data row6 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row6_col5\" class=\"data row6 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_ca453_row7_col0\" class=\"data row7 col0\" >97.87</td>\n",
+ " <td id=\"T_ca453_row7_col1\" class=\"data row7 col1\" >2.59</td>\n",
+ " <td id=\"T_ca453_row7_col2\" class=\"data row7 col2\" >99.88</td>\n",
+ " <td id=\"T_ca453_row7_col3\" class=\"data row7 col3\" >0.84</td>\n",
+ " <td id=\"T_ca453_row7_col4\" class=\"data row7 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row7_col5\" class=\"data row7 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_ca453_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_ca453_row8_col0\" class=\"data row8 col0\" >96.85</td>\n",
+ " <td id=\"T_ca453_row8_col1\" class=\"data row8 col1\" >4.36</td>\n",
+ " <td id=\"T_ca453_row8_col2\" class=\"data row8 col2\" >99.95</td>\n",
+ " <td id=\"T_ca453_row8_col3\" class=\"data row8 col3\" >0.49</td>\n",
+ " <td id=\"T_ca453_row8_col4\" class=\"data row8 col4\" >1.03</td>\n",
+ " <td id=\"T_ca453_row8_col5\" class=\"data row8 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_ca453_row9_col0\" class=\"data row9 col0\" >92.95</td>\n",
+ " <td id=\"T_ca453_row9_col1\" class=\"data row9 col1\" >2.76</td>\n",
+ " <td id=\"T_ca453_row9_col2\" class=\"data row9 col2\" >96.68</td>\n",
+ " <td id=\"T_ca453_row9_col3\" class=\"data row9 col3\" >3.35</td>\n",
+ " <td id=\"T_ca453_row9_col4\" class=\"data row9 col4\" >1.49</td>\n",
+ " <td id=\"T_ca453_row9_col5\" class=\"data row9 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_ca453_row10_col0\" class=\"data row10 col0\" >97.54</td>\n",
+ " <td id=\"T_ca453_row10_col1\" class=\"data row10 col1\" >2.63</td>\n",
+ " <td id=\"T_ca453_row10_col2\" class=\"data row10 col2\" >99.81</td>\n",
+ " <td id=\"T_ca453_row10_col3\" class=\"data row10 col3\" >1.56</td>\n",
+ " <td id=\"T_ca453_row10_col4\" class=\"data row10 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_ca453_row11_col0\" class=\"data row11 col0\" >98.22</td>\n",
+ " <td id=\"T_ca453_row11_col1\" class=\"data row11 col1\" >2.03</td>\n",
+ " <td id=\"T_ca453_row11_col2\" class=\"data row11 col2\" >99.42</td>\n",
+ " <td id=\"T_ca453_row11_col3\" class=\"data row11 col3\" >4.09</td>\n",
+ " <td id=\"T_ca453_row11_col4\" class=\"data row11 col4\" >1.05</td>\n",
+ " <td id=\"T_ca453_row11_col5\" class=\"data row11 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_ca453_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_ca453_row12_col0\" class=\"data row12 col0\" >96.55</td>\n",
+ " <td id=\"T_ca453_row12_col1\" class=\"data row12 col1\" >3.97</td>\n",
+ " <td id=\"T_ca453_row12_col2\" class=\"data row12 col2\" >98.82</td>\n",
+ " <td id=\"T_ca453_row12_col3\" class=\"data row12 col3\" >10.03</td>\n",
+ " <td id=\"T_ca453_row12_col4\" class=\"data row12 col4\" >1.08</td>\n",
+ " <td id=\"T_ca453_row12_col5\" class=\"data row12 col5\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_ca453_row13_col0\" class=\"data row13 col0\" >96.19</td>\n",
+ " <td id=\"T_ca453_row13_col1\" class=\"data row13 col1\" >3.13</td>\n",
+ " <td id=\"T_ca453_row13_col2\" class=\"data row13 col2\" >99.64</td>\n",
+ " <td id=\"T_ca453_row13_col3\" class=\"data row13 col3\" >2.19</td>\n",
+ " <td id=\"T_ca453_row13_col4\" class=\"data row13 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row13_col5\" class=\"data row13 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_ca453_row14_col0\" class=\"data row14 col0\" >93.50</td>\n",
+ " <td id=\"T_ca453_row14_col1\" class=\"data row14 col1\" >2.46</td>\n",
+ " <td id=\"T_ca453_row14_col2\" class=\"data row14 col2\" >97.45</td>\n",
+ " <td id=\"T_ca453_row14_col3\" class=\"data row14 col3\" >2.90</td>\n",
+ " <td id=\"T_ca453_row14_col4\" class=\"data row14 col4\" >1.52</td>\n",
+ " <td id=\"T_ca453_row14_col5\" class=\"data row14 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_ca453_row15_col0\" class=\"data row15 col0\" >97.64</td>\n",
+ " <td id=\"T_ca453_row15_col1\" class=\"data row15 col1\" >3.79</td>\n",
+ " <td id=\"T_ca453_row15_col2\" class=\"data row15 col2\" >98.59</td>\n",
+ " <td id=\"T_ca453_row15_col3\" class=\"data row15 col3\" >10.34</td>\n",
+ " <td id=\"T_ca453_row15_col4\" class=\"data row15 col4\" >1.05</td>\n",
+ " <td id=\"T_ca453_row15_col5\" class=\"data row15 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_ca453_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_ca453_row16_col0\" class=\"data row16 col0\" >95.50</td>\n",
+ " <td id=\"T_ca453_row16_col1\" class=\"data row16 col1\" >10.77</td>\n",
+ " <td id=\"T_ca453_row16_col2\" class=\"data row16 col2\" >99.55</td>\n",
+ " <td id=\"T_ca453_row16_col3\" class=\"data row16 col3\" >3.25</td>\n",
+ " <td id=\"T_ca453_row16_col4\" class=\"data row16 col4\" >1.05</td>\n",
+ " <td id=\"T_ca453_row16_col5\" class=\"data row16 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_ca453_row17_col0\" class=\"data row17 col0\" >96.00</td>\n",
+ " <td id=\"T_ca453_row17_col1\" class=\"data row17 col1\" >3.92</td>\n",
+ " <td id=\"T_ca453_row17_col2\" class=\"data row17 col2\" >99.79</td>\n",
+ " <td id=\"T_ca453_row17_col3\" class=\"data row17 col3\" >1.24</td>\n",
+ " <td id=\"T_ca453_row17_col4\" class=\"data row17 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row17_col5\" class=\"data row17 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_ca453_row18_col0\" class=\"data row18 col0\" >97.53</td>\n",
+ " <td id=\"T_ca453_row18_col1\" class=\"data row18 col1\" >2.44</td>\n",
+ " <td id=\"T_ca453_row18_col2\" class=\"data row18 col2\" >99.89</td>\n",
+ " <td id=\"T_ca453_row18_col3\" class=\"data row18 col3\" >1.12</td>\n",
+ " <td id=\"T_ca453_row18_col4\" class=\"data row18 col4\" >1.09</td>\n",
+ " <td id=\"T_ca453_row18_col5\" class=\"data row18 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_ca453_row19_col0\" class=\"data row19 col0\" >93.69</td>\n",
+ " <td id=\"T_ca453_row19_col1\" class=\"data row19 col1\" >2.47</td>\n",
+ " <td id=\"T_ca453_row19_col2\" class=\"data row19 col2\" >98.08</td>\n",
+ " <td id=\"T_ca453_row19_col3\" class=\"data row19 col3\" >1.80</td>\n",
+ " <td id=\"T_ca453_row19_col4\" class=\"data row19 col4\" >1.45</td>\n",
+ " <td id=\"T_ca453_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_ca453_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_ca453_row20_col0\" class=\"data row20 col0\" >97.60</td>\n",
+ " <td id=\"T_ca453_row20_col1\" class=\"data row20 col1\" >1.87</td>\n",
+ " <td id=\"T_ca453_row20_col2\" class=\"data row20 col2\" >99.47</td>\n",
+ " <td id=\"T_ca453_row20_col3\" class=\"data row20 col3\" >1.94</td>\n",
+ " <td id=\"T_ca453_row20_col4\" class=\"data row20 col4\" >1.21</td>\n",
+ " <td id=\"T_ca453_row20_col5\" class=\"data row20 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_ca453_row21_col0\" class=\"data row21 col0\" >96.09</td>\n",
+ " <td id=\"T_ca453_row21_col1\" class=\"data row21 col1\" >2.39</td>\n",
+ " <td id=\"T_ca453_row21_col2\" class=\"data row21 col2\" >98.83</td>\n",
+ " <td id=\"T_ca453_row21_col3\" class=\"data row21 col3\" >2.92</td>\n",
+ " <td id=\"T_ca453_row21_col4\" class=\"data row21 col4\" >1.32</td>\n",
+ " <td id=\"T_ca453_row21_col5\" class=\"data row21 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_ca453_row22_col0\" class=\"data row22 col0\" >97.65</td>\n",
+ " <td id=\"T_ca453_row22_col1\" class=\"data row22 col1\" >3.77</td>\n",
+ " <td id=\"T_ca453_row22_col2\" class=\"data row22 col2\" >98.00</td>\n",
+ " <td id=\"T_ca453_row22_col3\" class=\"data row22 col3\" >14.00</td>\n",
+ " <td id=\"T_ca453_row22_col4\" class=\"data row22 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row22_col5\" class=\"data row22 col5\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_ca453_row23_col0\" class=\"data row23 col0\" >98.83</td>\n",
+ " <td id=\"T_ca453_row23_col1\" class=\"data row23 col1\" >2.60</td>\n",
+ " <td id=\"T_ca453_row23_col2\" class=\"data row23 col2\" >95.90</td>\n",
+ " <td id=\"T_ca453_row23_col3\" class=\"data row23 col3\" >19.60</td>\n",
+ " <td id=\"T_ca453_row23_col4\" class=\"data row23 col4\" >1.04</td>\n",
+ " <td id=\"T_ca453_row23_col5\" class=\"data row23 col5\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_ca453_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_ca453_row24_col0\" class=\"data row24 col0\" >97.63</td>\n",
+ " <td id=\"T_ca453_row24_col1\" class=\"data row24 col1\" >2.13</td>\n",
+ " <td id=\"T_ca453_row24_col2\" class=\"data row24 col2\" >99.49</td>\n",
+ " <td id=\"T_ca453_row24_col3\" class=\"data row24 col3\" >1.31</td>\n",
+ " <td id=\"T_ca453_row24_col4\" class=\"data row24 col4\" >1.21</td>\n",
+ " <td id=\"T_ca453_row24_col5\" class=\"data row24 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_ca453_row25_col0\" class=\"data row25 col0\" >97.75</td>\n",
+ " <td id=\"T_ca453_row25_col1\" class=\"data row25 col1\" >3.70</td>\n",
+ " <td id=\"T_ca453_row25_col2\" class=\"data row25 col2\" >95.92</td>\n",
+ " <td id=\"T_ca453_row25_col3\" class=\"data row25 col3\" >19.60</td>\n",
+ " <td id=\"T_ca453_row25_col4\" class=\"data row25 col4\" >1.04</td>\n",
+ " <td id=\"T_ca453_row25_col5\" class=\"data row25 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_ca453_row26_col0\" class=\"data row26 col0\" >96.57</td>\n",
+ " <td id=\"T_ca453_row26_col1\" class=\"data row26 col1\" >1.90</td>\n",
+ " <td id=\"T_ca453_row26_col2\" class=\"data row26 col2\" >99.41</td>\n",
+ " <td id=\"T_ca453_row26_col3\" class=\"data row26 col3\" >1.37</td>\n",
+ " <td id=\"T_ca453_row26_col4\" class=\"data row26 col4\" >1.29</td>\n",
+ " <td id=\"T_ca453_row26_col5\" class=\"data row26 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_ca453_row27_col0\" class=\"data row27 col0\" >97.42</td>\n",
+ " <td id=\"T_ca453_row27_col1\" class=\"data row27 col1\" >7.08</td>\n",
+ " <td id=\"T_ca453_row27_col2\" class=\"data row27 col2\" >97.64</td>\n",
+ " <td id=\"T_ca453_row27_col3\" class=\"data row27 col3\" >14.41</td>\n",
+ " <td id=\"T_ca453_row27_col4\" class=\"data row27 col4\" >1.03</td>\n",
+ " <td id=\"T_ca453_row27_col5\" class=\"data row27 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_ca453_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_ca453_row28_col0\" class=\"data row28 col0\" >97.41</td>\n",
+ " <td id=\"T_ca453_row28_col1\" class=\"data row28 col1\" >2.33</td>\n",
+ " <td id=\"T_ca453_row28_col2\" class=\"data row28 col2\" >99.45</td>\n",
+ " <td id=\"T_ca453_row28_col3\" class=\"data row28 col3\" >1.56</td>\n",
+ " <td id=\"T_ca453_row28_col4\" class=\"data row28 col4\" >1.20</td>\n",
+ " <td id=\"T_ca453_row28_col5\" class=\"data row28 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_ca453_row29_col0\" class=\"data row29 col0\" >96.84</td>\n",
+ " <td id=\"T_ca453_row29_col1\" class=\"data row29 col1\" >5.44</td>\n",
+ " <td id=\"T_ca453_row29_col2\" class=\"data row29 col2\" >96.82</td>\n",
+ " <td id=\"T_ca453_row29_col3\" class=\"data row29 col3\" >17.08</td>\n",
+ " <td id=\"T_ca453_row29_col4\" class=\"data row29 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row29_col5\" class=\"data row29 col5\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_ca453_row30_col0\" class=\"data row30 col0\" >97.85</td>\n",
+ " <td id=\"T_ca453_row30_col1\" class=\"data row30 col1\" >2.77</td>\n",
+ " <td id=\"T_ca453_row30_col2\" class=\"data row30 col2\" >98.00</td>\n",
+ " <td id=\"T_ca453_row30_col3\" class=\"data row30 col3\" >14.00</td>\n",
+ " <td id=\"T_ca453_row30_col4\" class=\"data row30 col4\" >1.06</td>\n",
+ " <td id=\"T_ca453_row30_col5\" class=\"data row30 col5\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_ca453_row31_col0\" class=\"data row31 col0\" >96.46</td>\n",
+ " <td id=\"T_ca453_row31_col1\" class=\"data row31 col1\" >2.42</td>\n",
+ " <td id=\"T_ca453_row31_col2\" class=\"data row31 col2\" >98.81</td>\n",
+ " <td id=\"T_ca453_row31_col3\" class=\"data row31 col3\" >2.04</td>\n",
+ " <td id=\"T_ca453_row31_col4\" class=\"data row31 col4\" >1.27</td>\n",
+ " <td id=\"T_ca453_row31_col5\" class=\"data row31 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_ca453_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_ca453_row32_col0\" class=\"data row32 col0\" >97.13</td>\n",
+ " <td id=\"T_ca453_row32_col1\" class=\"data row32 col1\" >7.69</td>\n",
+ " <td id=\"T_ca453_row32_col2\" class=\"data row32 col2\" >88.00</td>\n",
+ " <td id=\"T_ca453_row32_col3\" class=\"data row32 col3\" >32.50</td>\n",
+ " <td id=\"T_ca453_row32_col4\" class=\"data row32 col4\" >1.01</td>\n",
+ " <td id=\"T_ca453_row32_col5\" class=\"data row32 col5\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_ca453_row33_col0\" class=\"data row33 col0\" >96.88</td>\n",
+ " <td id=\"T_ca453_row33_col1\" class=\"data row33 col1\" >1.89</td>\n",
+ " <td id=\"T_ca453_row33_col2\" class=\"data row33 col2\" >99.62</td>\n",
+ " <td id=\"T_ca453_row33_col3\" class=\"data row33 col3\" >1.03</td>\n",
+ " <td id=\"T_ca453_row33_col4\" class=\"data row33 col4\" >1.24</td>\n",
+ " <td id=\"T_ca453_row33_col5\" class=\"data row33 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_ca453_row34_col0\" class=\"data row34 col0\" >97.41</td>\n",
+ " <td id=\"T_ca453_row34_col1\" class=\"data row34 col1\" >1.81</td>\n",
+ " <td id=\"T_ca453_row34_col2\" class=\"data row34 col2\" >99.37</td>\n",
+ " <td id=\"T_ca453_row34_col3\" class=\"data row34 col3\" >2.05</td>\n",
+ " <td id=\"T_ca453_row34_col4\" class=\"data row34 col4\" >1.25</td>\n",
+ " <td id=\"T_ca453_row34_col5\" class=\"data row34 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_ca453_row35_col0\" class=\"data row35 col0\" >96.44</td>\n",
+ " <td id=\"T_ca453_row35_col1\" class=\"data row35 col1\" >14.01</td>\n",
+ " <td id=\"T_ca453_row35_col2\" class=\"data row35 col2\" >96.00</td>\n",
+ " <td id=\"T_ca453_row35_col3\" class=\"data row35 col3\" >19.60</td>\n",
+ " <td id=\"T_ca453_row35_col4\" class=\"data row35 col4\" >1.05</td>\n",
+ " <td id=\"T_ca453_row35_col5\" class=\"data row35 col5\" >0.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_ca453_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_ca453_row36_col0\" class=\"data row36 col0\" >93.33</td>\n",
+ " <td id=\"T_ca453_row36_col1\" class=\"data row36 col1\" >20.56</td>\n",
+ " <td id=\"T_ca453_row36_col2\" class=\"data row36 col2\" >86.00</td>\n",
+ " <td id=\"T_ca453_row36_col3\" class=\"data row36 col3\" >34.70</td>\n",
+ " <td id=\"T_ca453_row36_col4\" class=\"data row36 col4\" >1.00</td>\n",
+ " <td id=\"T_ca453_row36_col5\" class=\"data row36 col5\" >0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_ca453_row37_col0\" class=\"data row37 col0\" >96.59</td>\n",
+ " <td id=\"T_ca453_row37_col1\" class=\"data row37 col1\" >1.95</td>\n",
+ " <td id=\"T_ca453_row37_col2\" class=\"data row37 col2\" >99.57</td>\n",
+ " <td id=\"T_ca453_row37_col3\" class=\"data row37 col3\" >1.16</td>\n",
+ " <td id=\"T_ca453_row37_col4\" class=\"data row37 col4\" >1.24</td>\n",
+ " <td id=\"T_ca453_row37_col5\" class=\"data row37 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_ca453_row38_col0\" class=\"data row38 col0\" >98.05</td>\n",
+ " <td id=\"T_ca453_row38_col1\" class=\"data row38 col1\" >3.17</td>\n",
+ " <td id=\"T_ca453_row38_col2\" class=\"data row38 col2\" >92.57</td>\n",
+ " <td id=\"T_ca453_row38_col3\" class=\"data row38 col3\" >25.59</td>\n",
+ " <td id=\"T_ca453_row38_col4\" class=\"data row38 col4\" >1.05</td>\n",
+ " <td id=\"T_ca453_row38_col5\" class=\"data row38 col5\" >0.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_ca453_row39_col0\" class=\"data row39 col0\" >97.40</td>\n",
+ " <td id=\"T_ca453_row39_col1\" class=\"data row39 col1\" >1.91</td>\n",
+ " <td id=\"T_ca453_row39_col2\" class=\"data row39 col2\" >99.52</td>\n",
+ " <td id=\"T_ca453_row39_col3\" class=\"data row39 col3\" >1.83</td>\n",
+ " <td id=\"T_ca453_row39_col4\" class=\"data row39 col4\" >1.19</td>\n",
+ " <td id=\"T_ca453_row39_col5\" class=\"data row39 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_ca453_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_ca453_row40_col0\" class=\"data row40 col0\" >95.94</td>\n",
+ " <td id=\"T_ca453_row40_col1\" class=\"data row40 col1\" >14.26</td>\n",
+ " <td id=\"T_ca453_row40_col2\" class=\"data row40 col2\" >83.00</td>\n",
+ " <td id=\"T_ca453_row40_col3\" class=\"data row40 col3\" >37.56</td>\n",
+ " <td id=\"T_ca453_row40_col4\" class=\"data row40 col4\" >1.02</td>\n",
+ " <td id=\"T_ca453_row40_col5\" class=\"data row40 col5\" >0.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_ca453_row41_col0\" class=\"data row41 col0\" >96.52</td>\n",
+ " <td id=\"T_ca453_row41_col1\" class=\"data row41 col1\" >4.94</td>\n",
+ " <td id=\"T_ca453_row41_col2\" class=\"data row41 col2\" >94.75</td>\n",
+ " <td id=\"T_ca453_row41_col3\" class=\"data row41 col3\" >21.88</td>\n",
+ " <td id=\"T_ca453_row41_col4\" class=\"data row41 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row41_col5\" class=\"data row41 col5\" >0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_ca453_row42_col0\" class=\"data row42 col0\" >97.12</td>\n",
+ " <td id=\"T_ca453_row42_col1\" class=\"data row42 col1\" >1.82</td>\n",
+ " <td id=\"T_ca453_row42_col2\" class=\"data row42 col2\" >99.50</td>\n",
+ " <td id=\"T_ca453_row42_col3\" class=\"data row42 col3\" >1.71</td>\n",
+ " <td id=\"T_ca453_row42_col4\" class=\"data row42 col4\" >1.26</td>\n",
+ " <td id=\"T_ca453_row42_col5\" class=\"data row42 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_ca453_row43_col0\" class=\"data row43 col0\" >97.29</td>\n",
+ " <td id=\"T_ca453_row43_col1\" class=\"data row43 col1\" >1.86</td>\n",
+ " <td id=\"T_ca453_row43_col2\" class=\"data row43 col2\" >99.43</td>\n",
+ " <td id=\"T_ca453_row43_col3\" class=\"data row43 col3\" >1.85</td>\n",
+ " <td id=\"T_ca453_row43_col4\" class=\"data row43 col4\" >1.19</td>\n",
+ " <td id=\"T_ca453_row43_col5\" class=\"data row43 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_ca453_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_ca453_row44_col0\" class=\"data row44 col0\" >99.25</td>\n",
+ " <td id=\"T_ca453_row44_col1\" class=\"data row44 col1\" >1.14</td>\n",
+ " <td id=\"T_ca453_row44_col2\" class=\"data row44 col2\" >99.95</td>\n",
+ " <td id=\"T_ca453_row44_col3\" class=\"data row44 col3\" >0.40</td>\n",
+ " <td id=\"T_ca453_row44_col4\" class=\"data row44 col4\" >1.12</td>\n",
+ " <td id=\"T_ca453_row44_col5\" class=\"data row44 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_ca453_row45_col0\" class=\"data row45 col0\" >98.74</td>\n",
+ " <td id=\"T_ca453_row45_col1\" class=\"data row45 col1\" >1.67</td>\n",
+ " <td id=\"T_ca453_row45_col2\" class=\"data row45 col2\" >99.90</td>\n",
+ " <td id=\"T_ca453_row45_col3\" class=\"data row45 col3\" >0.58</td>\n",
+ " <td id=\"T_ca453_row45_col4\" class=\"data row45 col4\" >1.14</td>\n",
+ " <td id=\"T_ca453_row45_col5\" class=\"data row45 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_ca453_row46_col0\" class=\"data row46 col0\" >98.58</td>\n",
+ " <td id=\"T_ca453_row46_col1\" class=\"data row46 col1\" >1.62</td>\n",
+ " <td id=\"T_ca453_row46_col2\" class=\"data row46 col2\" >99.87</td>\n",
+ " <td id=\"T_ca453_row46_col3\" class=\"data row46 col3\" >0.70</td>\n",
+ " <td id=\"T_ca453_row46_col4\" class=\"data row46 col4\" >1.16</td>\n",
+ " <td id=\"T_ca453_row46_col5\" class=\"data row46 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_ca453_row47_col0\" class=\"data row47 col0\" >80.77</td>\n",
+ " <td id=\"T_ca453_row47_col1\" class=\"data row47 col1\" >37.66</td>\n",
+ " <td id=\"T_ca453_row47_col2\" class=\"data row47 col2\" >57.00</td>\n",
+ " <td id=\"T_ca453_row47_col3\" class=\"data row47 col3\" >49.51</td>\n",
+ " <td id=\"T_ca453_row47_col4\" class=\"data row47 col4\" >1.04</td>\n",
+ " <td id=\"T_ca453_row47_col5\" class=\"data row47 col5\" >0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_ca453_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_ca453_row48_col0\" class=\"data row48 col0\" >99.16</td>\n",
+ " <td id=\"T_ca453_row48_col1\" class=\"data row48 col1\" >1.21</td>\n",
+ " <td id=\"T_ca453_row48_col2\" class=\"data row48 col2\" >99.72</td>\n",
+ " <td id=\"T_ca453_row48_col3\" class=\"data row48 col3\" >1.44</td>\n",
+ " <td id=\"T_ca453_row48_col4\" class=\"data row48 col4\" >1.12</td>\n",
+ " <td id=\"T_ca453_row48_col5\" class=\"data row48 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_ca453_row49_col0\" class=\"data row49 col0\" >98.34</td>\n",
+ " <td id=\"T_ca453_row49_col1\" class=\"data row49 col1\" >1.65</td>\n",
+ " <td id=\"T_ca453_row49_col2\" class=\"data row49 col2\" >99.90</td>\n",
+ " <td id=\"T_ca453_row49_col3\" class=\"data row49 col3\" >0.58</td>\n",
+ " <td id=\"T_ca453_row49_col4\" class=\"data row49 col4\" >1.17</td>\n",
+ " <td id=\"T_ca453_row49_col5\" class=\"data row49 col5\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_ca453_row50_col0\" class=\"data row50 col0\" >88.33</td>\n",
+ " <td id=\"T_ca453_row50_col1\" class=\"data row50 col1\" >29.65</td>\n",
+ " <td id=\"T_ca453_row50_col2\" class=\"data row50 col2\" >63.00</td>\n",
+ " <td id=\"T_ca453_row50_col3\" class=\"data row50 col3\" >48.28</td>\n",
+ " <td id=\"T_ca453_row50_col4\" class=\"data row50 col4\" >1.04</td>\n",
+ " <td id=\"T_ca453_row50_col5\" class=\"data row50 col5\" >0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_ca453_row51_col0\" class=\"data row51 col0\" >98.69</td>\n",
+ " <td id=\"T_ca453_row51_col1\" class=\"data row51 col1\" >1.71</td>\n",
+ " <td id=\"T_ca453_row51_col2\" class=\"data row51 col2\" >99.78</td>\n",
+ " <td id=\"T_ca453_row51_col3\" class=\"data row51 col3\" >1.82</td>\n",
+ " <td id=\"T_ca453_row51_col4\" class=\"data row51 col4\" >1.12</td>\n",
+ " <td id=\"T_ca453_row51_col5\" class=\"data row51 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_ca453_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_ca453_row52_col0\" class=\"data row52 col0\" >98.99</td>\n",
+ " <td id=\"T_ca453_row52_col1\" class=\"data row52 col1\" >1.49</td>\n",
+ " <td id=\"T_ca453_row52_col2\" class=\"data row52 col2\" >99.85</td>\n",
+ " <td id=\"T_ca453_row52_col3\" class=\"data row52 col3\" >0.91</td>\n",
+ " <td id=\"T_ca453_row52_col4\" class=\"data row52 col4\" >1.12</td>\n",
+ " <td id=\"T_ca453_row52_col5\" class=\"data row52 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_ca453_row53_col0\" class=\"data row53 col0\" >86.10</td>\n",
+ " <td id=\"T_ca453_row53_col1\" class=\"data row53 col1\" >32.06</td>\n",
+ " <td id=\"T_ca453_row53_col2\" class=\"data row53 col2\" >73.00</td>\n",
+ " <td id=\"T_ca453_row53_col3\" class=\"data row53 col3\" >44.40</td>\n",
+ " <td id=\"T_ca453_row53_col4\" class=\"data row53 col4\" >1.03</td>\n",
+ " <td id=\"T_ca453_row53_col5\" class=\"data row53 col5\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_ca453_row54_col0\" class=\"data row54 col0\" >98.65</td>\n",
+ " <td id=\"T_ca453_row54_col1\" class=\"data row54 col1\" >1.59</td>\n",
+ " <td id=\"T_ca453_row54_col2\" class=\"data row54 col2\" >99.89</td>\n",
+ " <td id=\"T_ca453_row54_col3\" class=\"data row54 col3\" >0.70</td>\n",
+ " <td id=\"T_ca453_row54_col4\" class=\"data row54 col4\" >1.15</td>\n",
+ " <td id=\"T_ca453_row54_col5\" class=\"data row54 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_ca453_row55_col0\" class=\"data row55 col0\" >98.87</td>\n",
+ " <td id=\"T_ca453_row55_col1\" class=\"data row55 col1\" >1.35</td>\n",
+ " <td id=\"T_ca453_row55_col2\" class=\"data row55 col2\" >99.89</td>\n",
+ " <td id=\"T_ca453_row55_col3\" class=\"data row55 col3\" >0.97</td>\n",
+ " <td id=\"T_ca453_row55_col4\" class=\"data row55 col4\" >1.11</td>\n",
+ " <td id=\"T_ca453_row55_col5\" class=\"data row55 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_ca453_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_ca453_row56_col0\" class=\"data row56 col0\" >67.04</td>\n",
+ " <td id=\"T_ca453_row56_col1\" class=\"data row56 col1\" >45.36</td>\n",
+ " <td id=\"T_ca453_row56_col2\" class=\"data row56 col2\" >47.00</td>\n",
+ " <td id=\"T_ca453_row56_col3\" class=\"data row56 col3\" >49.91</td>\n",
+ " <td id=\"T_ca453_row56_col4\" class=\"data row56 col4\" >1.01</td>\n",
+ " <td id=\"T_ca453_row56_col5\" class=\"data row56 col5\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_ca453_row57_col0\" class=\"data row57 col0\" >98.78</td>\n",
+ " <td id=\"T_ca453_row57_col1\" class=\"data row57 col1\" >1.44</td>\n",
+ " <td id=\"T_ca453_row57_col2\" class=\"data row57 col2\" >99.87</td>\n",
+ " <td id=\"T_ca453_row57_col3\" class=\"data row57 col3\" >0.76</td>\n",
+ " <td id=\"T_ca453_row57_col4\" class=\"data row57 col4\" >1.12</td>\n",
+ " <td id=\"T_ca453_row57_col5\" class=\"data row57 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_ca453_row58_col0\" class=\"data row58 col0\" >98.55</td>\n",
+ " <td id=\"T_ca453_row58_col1\" class=\"data row58 col1\" >1.61</td>\n",
+ " <td id=\"T_ca453_row58_col2\" class=\"data row58 col2\" >99.87</td>\n",
+ " <td id=\"T_ca453_row58_col3\" class=\"data row58 col3\" >0.76</td>\n",
+ " <td id=\"T_ca453_row58_col4\" class=\"data row58 col4\" >1.14</td>\n",
+ " <td id=\"T_ca453_row58_col5\" class=\"data row58 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_ca453_row59_col0\" class=\"data row59 col0\" >99.03</td>\n",
+ " <td id=\"T_ca453_row59_col1\" class=\"data row59 col1\" >1.37</td>\n",
+ " <td id=\"T_ca453_row59_col2\" class=\"data row59 col2\" >99.84</td>\n",
+ " <td id=\"T_ca453_row59_col3\" class=\"data row59 col3\" >0.96</td>\n",
+ " <td id=\"T_ca453_row59_col4\" class=\"data row59 col4\" >1.10</td>\n",
+ " <td id=\"T_ca453_row59_col5\" class=\"data row59 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_ca453_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_ca453_row60_col0\" class=\"data row60 col0\" >99.52</td>\n",
+ " <td id=\"T_ca453_row60_col1\" class=\"data row60 col1\" >1.21</td>\n",
+ " <td id=\"T_ca453_row60_col2\" class=\"data row60 col2\" >99.95</td>\n",
+ " <td id=\"T_ca453_row60_col3\" class=\"data row60 col3\" >0.47</td>\n",
+ " <td id=\"T_ca453_row60_col4\" class=\"data row60 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row60_col5\" class=\"data row60 col5\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_ca453_row61_col0\" class=\"data row61 col0\" >99.29</td>\n",
+ " <td id=\"T_ca453_row61_col1\" class=\"data row61 col1\" >1.65</td>\n",
+ " <td id=\"T_ca453_row61_col2\" class=\"data row61 col2\" >100.00</td>\n",
+ " <td id=\"T_ca453_row61_col3\" class=\"data row61 col3\" >0.00</td>\n",
+ " <td id=\"T_ca453_row61_col4\" class=\"data row61 col4\" >1.09</td>\n",
+ " <td id=\"T_ca453_row61_col5\" class=\"data row61 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_ca453_row62_col0\" class=\"data row62 col0\" >99.31</td>\n",
+ " <td id=\"T_ca453_row62_col1\" class=\"data row62 col1\" >1.46</td>\n",
+ " <td id=\"T_ca453_row62_col2\" class=\"data row62 col2\" >100.00</td>\n",
+ " <td id=\"T_ca453_row62_col3\" class=\"data row62 col3\" >0.00</td>\n",
+ " <td id=\"T_ca453_row62_col4\" class=\"data row62 col4\" >1.07</td>\n",
+ " <td id=\"T_ca453_row62_col5\" class=\"data row62 col5\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ca453_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_ca453_row63_col0\" class=\"data row63 col0\" >99.59</td>\n",
+ " <td id=\"T_ca453_row63_col1\" class=\"data row63 col1\" >1.02</td>\n",
+ " <td id=\"T_ca453_row63_col2\" class=\"data row63 col2\" >99.99</td>\n",
+ " <td id=\"T_ca453_row63_col3\" class=\"data row63 col3\" >0.10</td>\n",
+ " <td id=\"T_ca453_row63_col4\" class=\"data row63 col4\" >1.06</td>\n",
+ " <td id=\"T_ca453_row63_col5\" class=\"data row63 col5\" >0.06</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
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+ "/home/fontana/workspace/market-place/GenerateVisualisation/experiments_analysis.py:1160: RuntimeWarning: invalid value encountered in divide\n",
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+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row1_col0, #T_bc7ac_row1_col2 {\n",
+ " background-color: #9cf89c;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row1_col4, #T_bc7ac_row61_col4 {\n",
+ " background-color: #fa6464;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row2_col0, #T_bc7ac_row2_col2 {\n",
+ " background-color: #9df89d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row2_col4, #T_bc7ac_row48_col4, #T_bc7ac_row62_col4 {\n",
+ " background-color: #fa6a6a;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row3_col0, #T_bc7ac_row3_col2 {\n",
+ " background-color: #98f897;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row3_col4, #T_bc7ac_row63_col4 {\n",
+ " background-color: #f97373;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row4_col0, #T_bc7ac_row4_col2 {\n",
+ " background-color: #47fe47;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row4_col4 {\n",
+ " background-color: #f5cbcb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row5_col0, #T_bc7ac_row5_col2 {\n",
+ " background-color: #c2f6c2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row5_col4, #T_bc7ac_row7_col4, #T_bc7ac_row18_col4 {\n",
+ " background-color: #fc3c3c;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row6_col0, #T_bc7ac_row6_col2, #T_bc7ac_row7_col0, #T_bc7ac_row7_col2 {\n",
+ " background-color: #c1f6c1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row6_col4 {\n",
+ " background-color: #fc3e3e;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row8_col0, #T_bc7ac_row8_col2 {\n",
+ " background-color: #cef5ce;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row8_col4 {\n",
+ " background-color: #fd2626;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row9_col0, #T_bc7ac_row9_col2, #T_bc7ac_row19_col0, #T_bc7ac_row19_col2 {\n",
+ " background-color: #35ff35;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row9_col4 {\n",
+ " background-color: #f3e8e8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row10_col0, #T_bc7ac_row10_col2, #T_bc7ac_row13_col0, #T_bc7ac_row13_col2, #T_bc7ac_row34_col0, #T_bc7ac_row34_col2, #T_bc7ac_row37_col0, #T_bc7ac_row37_col2 {\n",
+ " background-color: #c7f5c7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row10_col4, #T_bc7ac_row11_col4 {\n",
+ " background-color: #fc3838;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row11_col0, #T_bc7ac_row11_col2, #T_bc7ac_row18_col0, #T_bc7ac_row18_col2 {\n",
+ " background-color: #c3f6c3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row12_col0, #T_bc7ac_row12_col2 {\n",
+ " background-color: #ccf5cc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row12_col4 {\n",
+ " background-color: #fd2828;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row13_col4 {\n",
+ " background-color: #fc3333;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row14_col0, #T_bc7ac_row14_col2 {\n",
+ " background-color: #34ff34;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row14_col4, #T_bc7ac_row19_col4 {\n",
+ " background-color: #f3f0f0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row15_col0, #T_bc7ac_row15_col2, #T_bc7ac_row17_col0, #T_bc7ac_row17_col2, #T_bc7ac_row20_col0, #T_bc7ac_row20_col2 {\n",
+ " background-color: #c6f5c6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row15_col4, #T_bc7ac_row17_col4 {\n",
+ " background-color: #fc3535;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row16_col0, #T_bc7ac_row16_col2 {\n",
+ " background-color: #d0f5d0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row16_col4 {\n",
+ " background-color: #fd2323;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row20_col4, #T_bc7ac_row46_col4, #T_bc7ac_row51_col4 {\n",
+ " background-color: #f89494;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row21_col0, #T_bc7ac_row21_col2, #T_bc7ac_row26_col0, #T_bc7ac_row26_col2 {\n",
+ " background-color: #a7f7a7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row21_col4 {\n",
+ " background-color: #f5bcbc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row22_col0, #T_bc7ac_row22_col2, #T_bc7ac_row25_col0, #T_bc7ac_row25_col2 {\n",
+ " background-color: #d9f4d9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row22_col4 {\n",
+ " background-color: #fe1d1d;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row23_col0, #T_bc7ac_row23_col2, #T_bc7ac_row27_col0, #T_bc7ac_row27_col2, #T_bc7ac_row38_col0, #T_bc7ac_row38_col2 {\n",
+ " background-color: #dcf4db;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row23_col4, #T_bc7ac_row27_col4 {\n",
+ " background-color: #fe1717;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row24_col0, #T_bc7ac_row24_col2, #T_bc7ac_row39_col0, #T_bc7ac_row39_col2, #T_bc7ac_row43_col0, #T_bc7ac_row43_col2 {\n",
+ " background-color: #caf5ca;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row24_col4 {\n",
+ " background-color: #f89191;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row25_col4 {\n",
+ " background-color: #fe1818;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row26_col4 {\n",
+ " background-color: #f5c4c4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row28_col0, #T_bc7ac_row28_col2 {\n",
+ " background-color: #cdf5cd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row28_col4 {\n",
+ " background-color: #f88d8d;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row29_col0, #T_bc7ac_row29_col2 {\n",
+ " background-color: #d6f4d6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row29_col4 {\n",
+ " background-color: #fd1e1e;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row30_col0, #T_bc7ac_row30_col2 {\n",
+ " background-color: #d8f4d8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row30_col4 {\n",
+ " background-color: #fe1c1c;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row31_col0, #T_bc7ac_row31_col2 {\n",
+ " background-color: #b0f7b0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row31_col4 {\n",
+ " background-color: #f5bfbf;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row32_col0, #T_bc7ac_row32_col2, #T_bc7ac_row36_col0, #T_bc7ac_row36_col2, #T_bc7ac_row45_col0, #T_bc7ac_row45_col2, #T_bc7ac_row51_col0, #T_bc7ac_row51_col2 {\n",
+ " background-color: #e1f4e0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row32_col4 {\n",
+ " background-color: #fe0d0d;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row33_col0, #T_bc7ac_row33_col2 {\n",
+ " background-color: #c9f5c9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row33_col4 {\n",
+ " background-color: #f6afaf;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row34_col4, #T_bc7ac_row39_col4, #T_bc7ac_row42_col4 {\n",
+ " background-color: #f6b6b6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row35_col0, #T_bc7ac_row35_col2, #T_bc7ac_row49_col0, #T_bc7ac_row49_col2, #T_bc7ac_row54_col0, #T_bc7ac_row54_col2 {\n",
+ " background-color: #ddf4dd;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row35_col4 {\n",
+ " background-color: #fe1414;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row36_col4 {\n",
+ " background-color: #fe0a0a;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row37_col4 {\n",
+ " background-color: #f6adad;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row38_col4 {\n",
+ " background-color: #fe1919;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row40_col0, #T_bc7ac_row40_col2 {\n",
+ " background-color: #e1f4e1;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row40_col4 {\n",
+ " background-color: #fe0c0c;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row41_col0, #T_bc7ac_row41_col2 {\n",
+ " background-color: #dcf4dc;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row41_col4 {\n",
+ " background-color: #fe1313;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row42_col0, #T_bc7ac_row42_col2 {\n",
+ " background-color: #c8f5c8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row43_col4 {\n",
+ " background-color: #f6b2b2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row44_col0, #T_bc7ac_row44_col2, #T_bc7ac_row48_col0, #T_bc7ac_row48_col2, #T_bc7ac_row50_col0, #T_bc7ac_row50_col2, #T_bc7ac_row57_col0, #T_bc7ac_row57_col2, #T_bc7ac_row59_col0, #T_bc7ac_row59_col2 {\n",
+ " background-color: #e5f3e5;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row44_col4 {\n",
+ " background-color: #f96e6e;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row45_col4 {\n",
+ " background-color: #f88686;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row46_col0, #T_bc7ac_row46_col2 {\n",
+ " background-color: #e0f4e0;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row47_col0, #T_bc7ac_row47_col2 {\n",
+ " background-color: #e7f3e7;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row47_col4 {\n",
+ " background-color: #ff0303;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row49_col4 {\n",
+ " background-color: #f88585;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row50_col4 {\n",
+ " background-color: #ff0808;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row52_col0, #T_bc7ac_row52_col2 {\n",
+ " background-color: #e6f3e6;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row52_col4 {\n",
+ " background-color: #fa6969;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row53_col0, #T_bc7ac_row53_col2 {\n",
+ " background-color: #e4f3e4;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row53_col4 {\n",
+ " background-color: #ff0707;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row54_col4 {\n",
+ " background-color: #f88a8a;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row55_col0, #T_bc7ac_row55_col2 {\n",
+ " background-color: #e2f4e2;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row55_col4 {\n",
+ " background-color: #f89090;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row56_col0, #T_bc7ac_row56_col2 {\n",
+ " background-color: #e8f3e8;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row56_col4 {\n",
+ " background-color: #ff0000;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row57_col4 {\n",
+ " background-color: #f97f7f;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row58_col0, #T_bc7ac_row58_col2 {\n",
+ " background-color: #e3f3e3;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row58_col4 {\n",
+ " background-color: #f88282;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row59_col4 {\n",
+ " background-color: #f88989;\n",
+ " color: #f1f1f1;\n",
+ "}\n",
+ "#T_bc7ac_row60_col0, #T_bc7ac_row60_col2, #T_bc7ac_row63_col0, #T_bc7ac_row63_col2 {\n",
+ " background-color: #ebf3eb;\n",
+ " color: #000000;\n",
+ "}\n",
+ "#T_bc7ac_row61_col0, #T_bc7ac_row61_col2, #T_bc7ac_row62_col0, #T_bc7ac_row62_col2 {\n",
+ " background-color: #e9f3e9;\n",
+ " color: #000000;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_bc7ac\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_bc7ac_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
+ " <th id=\"T_bc7ac_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Benefits / energy available</th>\n",
+ " <th id=\"T_bc7ac_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank\" > </th>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_bc7ac_level1_col0\" class=\"col_heading level1 col0\" >Value</th>\n",
+ " <th id=\"T_bc7ac_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
+ " <th id=\"T_bc7ac_level1_col2\" class=\"col_heading level1 col2\" >Value</th>\n",
+ " <th id=\"T_bc7ac_level1_col3\" class=\"col_heading level1 col3\" >std</th>\n",
+ " <th id=\"T_bc7ac_level1_col4\" class=\"col_heading level1 col4\" >Value</th>\n",
+ " <th id=\"T_bc7ac_level1_col5\" class=\"col_heading level1 col5\" >std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">Cooperative</th>\n",
+ " <th id=\"T_bc7ac_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
+ " <td id=\"T_bc7ac_row0_col0\" class=\"data row0 col0\" >9.67</td>\n",
+ " <td id=\"T_bc7ac_row0_col1\" class=\"data row0 col1\" >1.50</td>\n",
+ " <td id=\"T_bc7ac_row0_col2\" class=\"data row0 col2\" >0.70</td>\n",
+ " <td id=\"T_bc7ac_row0_col3\" class=\"data row0 col3\" >0.11</td>\n",
+ " <td id=\"T_bc7ac_row0_col4\" class=\"data row0 col4\" >71.19</td>\n",
+ " <td id=\"T_bc7ac_row0_col5\" class=\"data row0 col5\" >6.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
+ " <td id=\"T_bc7ac_row1_col0\" class=\"data row1 col0\" >12.45</td>\n",
+ " <td id=\"T_bc7ac_row1_col1\" class=\"data row1 col1\" >1.68</td>\n",
+ " <td id=\"T_bc7ac_row1_col2\" class=\"data row1 col2\" >0.90</td>\n",
+ " <td id=\"T_bc7ac_row1_col3\" class=\"data row1 col3\" >0.12</td>\n",
+ " <td id=\"T_bc7ac_row1_col4\" class=\"data row1 col4\" >63.74</td>\n",
+ " <td id=\"T_bc7ac_row1_col5\" class=\"data row1 col5\" >5.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
+ " <td id=\"T_bc7ac_row2_col0\" class=\"data row2 col0\" >12.23</td>\n",
+ " <td id=\"T_bc7ac_row2_col1\" class=\"data row2 col1\" >1.43</td>\n",
+ " <td id=\"T_bc7ac_row2_col2\" class=\"data row2 col2\" >0.88</td>\n",
+ " <td id=\"T_bc7ac_row2_col3\" class=\"data row2 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row2_col4\" class=\"data row2 col4\" >61.52</td>\n",
+ " <td id=\"T_bc7ac_row2_col5\" class=\"data row2 col5\" >6.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
+ " <td id=\"T_bc7ac_row3_col0\" class=\"data row3 col0\" >13.05</td>\n",
+ " <td id=\"T_bc7ac_row3_col1\" class=\"data row3 col1\" >1.79</td>\n",
+ " <td id=\"T_bc7ac_row3_col2\" class=\"data row3 col2\" >0.94</td>\n",
+ " <td id=\"T_bc7ac_row3_col3\" class=\"data row3 col3\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row3_col4\" class=\"data row3 col4\" >58.69</td>\n",
+ " <td id=\"T_bc7ac_row3_col5\" class=\"data row3 col5\" >6.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">25% (1 0 0 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row4\" class=\"row_heading level1 row4\" >0</th>\n",
+ " <td id=\"T_bc7ac_row4_col0\" class=\"data row4 col0\" >25.65</td>\n",
+ " <td id=\"T_bc7ac_row4_col1\" class=\"data row4 col1\" >2.46</td>\n",
+ " <td id=\"T_bc7ac_row4_col2\" class=\"data row4 col2\" >1.86</td>\n",
+ " <td id=\"T_bc7ac_row4_col3\" class=\"data row4 col3\" >0.18</td>\n",
+ " <td id=\"T_bc7ac_row4_col4\" class=\"data row4 col4\" >29.29</td>\n",
+ " <td id=\"T_bc7ac_row4_col5\" class=\"data row4 col5\" >5.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row5\" class=\"row_heading level1 row5\" >1</th>\n",
+ " <td id=\"T_bc7ac_row5_col0\" class=\"data row5 col0\" >6.53</td>\n",
+ " <td id=\"T_bc7ac_row5_col1\" class=\"data row5 col1\" >1.36</td>\n",
+ " <td id=\"T_bc7ac_row5_col2\" class=\"data row5 col2\" >0.47</td>\n",
+ " <td id=\"T_bc7ac_row5_col3\" class=\"data row5 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row5_col4\" class=\"data row5 col4\" >76.85</td>\n",
+ " <td id=\"T_bc7ac_row5_col5\" class=\"data row5 col5\" >6.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
+ " <td id=\"T_bc7ac_row6_col0\" class=\"data row6 col0\" >6.63</td>\n",
+ " <td id=\"T_bc7ac_row6_col1\" class=\"data row6 col1\" >1.33</td>\n",
+ " <td id=\"T_bc7ac_row6_col2\" class=\"data row6 col2\" >0.48</td>\n",
+ " <td id=\"T_bc7ac_row6_col3\" class=\"data row6 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row6_col4\" class=\"data row6 col4\" >76.05</td>\n",
+ " <td id=\"T_bc7ac_row6_col5\" class=\"data row6 col5\" >6.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row7\" class=\"row_heading level1 row7\" >3</th>\n",
+ " <td id=\"T_bc7ac_row7_col0\" class=\"data row7 col0\" >6.67</td>\n",
+ " <td id=\"T_bc7ac_row7_col1\" class=\"data row7 col1\" >1.41</td>\n",
+ " <td id=\"T_bc7ac_row7_col2\" class=\"data row7 col2\" >0.48</td>\n",
+ " <td id=\"T_bc7ac_row7_col3\" class=\"data row7 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row7_col4\" class=\"data row7 col4\" >76.69</td>\n",
+ " <td id=\"T_bc7ac_row7_col5\" class=\"data row7 col5\" >5.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">25% (0 1 0 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row8\" class=\"row_heading level1 row8\" >0</th>\n",
+ " <td id=\"T_bc7ac_row8_col0\" class=\"data row8 col0\" >4.72</td>\n",
+ " <td id=\"T_bc7ac_row8_col1\" class=\"data row8 col1\" >1.26</td>\n",
+ " <td id=\"T_bc7ac_row8_col2\" class=\"data row8 col2\" >0.34</td>\n",
+ " <td id=\"T_bc7ac_row8_col3\" class=\"data row8 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row8_col4\" class=\"data row8 col4\" >84.17</td>\n",
+ " <td id=\"T_bc7ac_row8_col5\" class=\"data row8 col5\" >5.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row9\" class=\"row_heading level1 row9\" >1</th>\n",
+ " <td id=\"T_bc7ac_row9_col0\" class=\"data row9 col0\" >28.32</td>\n",
+ " <td id=\"T_bc7ac_row9_col1\" class=\"data row9 col1\" >2.90</td>\n",
+ " <td id=\"T_bc7ac_row9_col2\" class=\"data row9 col2\" >2.05</td>\n",
+ " <td id=\"T_bc7ac_row9_col3\" class=\"data row9 col3\" >0.21</td>\n",
+ " <td id=\"T_bc7ac_row9_col4\" class=\"data row9 col4\" >19.46</td>\n",
+ " <td id=\"T_bc7ac_row9_col5\" class=\"data row9 col5\" >5.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row10\" class=\"row_heading level1 row10\" >2</th>\n",
+ " <td id=\"T_bc7ac_row10_col0\" class=\"data row10 col0\" >5.72</td>\n",
+ " <td id=\"T_bc7ac_row10_col1\" class=\"data row10 col1\" >1.50</td>\n",
+ " <td id=\"T_bc7ac_row10_col2\" class=\"data row10 col2\" >0.41</td>\n",
+ " <td id=\"T_bc7ac_row10_col3\" class=\"data row10 col3\" >0.11</td>\n",
+ " <td id=\"T_bc7ac_row10_col4\" class=\"data row10 col4\" >77.97</td>\n",
+ " <td id=\"T_bc7ac_row10_col5\" class=\"data row10 col5\" >6.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row11\" class=\"row_heading level1 row11\" >3</th>\n",
+ " <td id=\"T_bc7ac_row11_col0\" class=\"data row11 col0\" >6.28</td>\n",
+ " <td id=\"T_bc7ac_row11_col1\" class=\"data row11 col1\" >1.30</td>\n",
+ " <td id=\"T_bc7ac_row11_col2\" class=\"data row11 col2\" >0.45</td>\n",
+ " <td id=\"T_bc7ac_row11_col3\" class=\"data row11 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row11_col4\" class=\"data row11 col4\" >77.96</td>\n",
+ " <td id=\"T_bc7ac_row11_col5\" class=\"data row11 col5\" >5.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">25% (0 0 1 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row12\" class=\"row_heading level1 row12\" >0</th>\n",
+ " <td id=\"T_bc7ac_row12_col0\" class=\"data row12 col0\" >4.95</td>\n",
+ " <td id=\"T_bc7ac_row12_col1\" class=\"data row12 col1\" >1.62</td>\n",
+ " <td id=\"T_bc7ac_row12_col2\" class=\"data row12 col2\" >0.36</td>\n",
+ " <td id=\"T_bc7ac_row12_col3\" class=\"data row12 col3\" >0.12</td>\n",
+ " <td id=\"T_bc7ac_row12_col4\" class=\"data row12 col4\" >83.52</td>\n",
+ " <td id=\"T_bc7ac_row12_col5\" class=\"data row12 col5\" >5.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row13\" class=\"row_heading level1 row13\" >1</th>\n",
+ " <td id=\"T_bc7ac_row13_col0\" class=\"data row13 col0\" >5.69</td>\n",
+ " <td id=\"T_bc7ac_row13_col1\" class=\"data row13 col1\" >1.46</td>\n",
+ " <td id=\"T_bc7ac_row13_col2\" class=\"data row13 col2\" >0.41</td>\n",
+ " <td id=\"T_bc7ac_row13_col3\" class=\"data row13 col3\" >0.11</td>\n",
+ " <td id=\"T_bc7ac_row13_col4\" class=\"data row13 col4\" >80.02</td>\n",
+ " <td id=\"T_bc7ac_row13_col5\" class=\"data row13 col5\" >5.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row14\" class=\"row_heading level1 row14\" >2</th>\n",
+ " <td id=\"T_bc7ac_row14_col0\" class=\"data row14 col0\" >28.58</td>\n",
+ " <td id=\"T_bc7ac_row14_col1\" class=\"data row14 col1\" >3.02</td>\n",
+ " <td id=\"T_bc7ac_row14_col2\" class=\"data row14 col2\" >2.07</td>\n",
+ " <td id=\"T_bc7ac_row14_col3\" class=\"data row14 col3\" >0.22</td>\n",
+ " <td id=\"T_bc7ac_row14_col4\" class=\"data row14 col4\" >16.84</td>\n",
+ " <td id=\"T_bc7ac_row14_col5\" class=\"data row14 col5\" >4.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row15\" class=\"row_heading level1 row15\" >3</th>\n",
+ " <td id=\"T_bc7ac_row15_col0\" class=\"data row15 col0\" >5.87</td>\n",
+ " <td id=\"T_bc7ac_row15_col1\" class=\"data row15 col1\" >1.32</td>\n",
+ " <td id=\"T_bc7ac_row15_col2\" class=\"data row15 col2\" >0.42</td>\n",
+ " <td id=\"T_bc7ac_row15_col3\" class=\"data row15 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row15_col4\" class=\"data row15 col4\" >79.14</td>\n",
+ " <td id=\"T_bc7ac_row15_col5\" class=\"data row15 col5\" >5.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">25% (0 0 0 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row16\" class=\"row_heading level1 row16\" >0</th>\n",
+ " <td id=\"T_bc7ac_row16_col0\" class=\"data row16 col0\" >4.36</td>\n",
+ " <td id=\"T_bc7ac_row16_col1\" class=\"data row16 col1\" >1.39</td>\n",
+ " <td id=\"T_bc7ac_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+ " <td id=\"T_bc7ac_row16_col3\" class=\"data row16 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row16_col4\" class=\"data row16 col4\" >85.38</td>\n",
+ " <td id=\"T_bc7ac_row16_col5\" class=\"data row16 col5\" >5.34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row17\" class=\"row_heading level1 row17\" >1</th>\n",
+ " <td id=\"T_bc7ac_row17_col0\" class=\"data row17 col0\" >5.91</td>\n",
+ " <td id=\"T_bc7ac_row17_col1\" class=\"data row17 col1\" >1.41</td>\n",
+ " <td id=\"T_bc7ac_row17_col2\" class=\"data row17 col2\" >0.43</td>\n",
+ " <td id=\"T_bc7ac_row17_col3\" class=\"data row17 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row17_col4\" class=\"data row17 col4\" >79.42</td>\n",
+ " <td id=\"T_bc7ac_row17_col5\" class=\"data row17 col5\" >5.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row18\" class=\"row_heading level1 row18\" >2</th>\n",
+ " <td id=\"T_bc7ac_row18_col0\" class=\"data row18 col0\" >6.34</td>\n",
+ " <td id=\"T_bc7ac_row18_col1\" class=\"data row18 col1\" >1.34</td>\n",
+ " <td id=\"T_bc7ac_row18_col2\" class=\"data row18 col2\" >0.46</td>\n",
+ " <td id=\"T_bc7ac_row18_col3\" class=\"data row18 col3\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row18_col4\" class=\"data row18 col4\" >76.68</td>\n",
+ " <td id=\"T_bc7ac_row18_col5\" class=\"data row18 col5\" >6.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row19\" class=\"row_heading level1 row19\" >3</th>\n",
+ " <td id=\"T_bc7ac_row19_col0\" class=\"data row19 col0\" >28.46</td>\n",
+ " <td id=\"T_bc7ac_row19_col1\" class=\"data row19 col1\" >2.94</td>\n",
+ " <td id=\"T_bc7ac_row19_col2\" class=\"data row19 col2\" >2.06</td>\n",
+ " <td id=\"T_bc7ac_row19_col3\" class=\"data row19 col3\" >0.21</td>\n",
+ " <td id=\"T_bc7ac_row19_col4\" class=\"data row19 col4\" >16.77</td>\n",
+ " <td id=\"T_bc7ac_row19_col5\" class=\"data row19 col5\" >3.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">50% (1 1 0 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row20\" class=\"row_heading level1 row20\" >0</th>\n",
+ " <td id=\"T_bc7ac_row20_col0\" class=\"data row20 col0\" >5.85</td>\n",
+ " <td id=\"T_bc7ac_row20_col1\" class=\"data row20 col1\" >2.05</td>\n",
+ " <td id=\"T_bc7ac_row20_col2\" class=\"data row20 col2\" >0.42</td>\n",
+ " <td id=\"T_bc7ac_row20_col3\" class=\"data row20 col3\" >0.15</td>\n",
+ " <td id=\"T_bc7ac_row20_col4\" class=\"data row20 col4\" >47.49</td>\n",
+ " <td id=\"T_bc7ac_row20_col5\" class=\"data row20 col5\" >5.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row21\" class=\"row_heading level1 row21\" >1</th>\n",
+ " <td id=\"T_bc7ac_row21_col0\" class=\"data row21 col0\" >10.68</td>\n",
+ " <td id=\"T_bc7ac_row21_col1\" class=\"data row21 col1\" >1.86</td>\n",
+ " <td id=\"T_bc7ac_row21_col2\" class=\"data row21 col2\" >0.77</td>\n",
+ " <td id=\"T_bc7ac_row21_col3\" class=\"data row21 col3\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row21_col4\" class=\"data row21 col4\" >34.08</td>\n",
+ " <td id=\"T_bc7ac_row21_col5\" class=\"data row21 col5\" >5.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row22\" class=\"row_heading level1 row22\" >2</th>\n",
+ " <td id=\"T_bc7ac_row22_col0\" class=\"data row22 col0\" >3.03</td>\n",
+ " <td id=\"T_bc7ac_row22_col1\" class=\"data row22 col1\" >1.12</td>\n",
+ " <td id=\"T_bc7ac_row22_col2\" class=\"data row22 col2\" >0.22</td>\n",
+ " <td id=\"T_bc7ac_row22_col3\" class=\"data row22 col3\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row22_col4\" class=\"data row22 col4\" >87.18</td>\n",
+ " <td id=\"T_bc7ac_row22_col5\" class=\"data row22 col5\" >5.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row23\" class=\"row_heading level1 row23\" >3</th>\n",
+ " <td id=\"T_bc7ac_row23_col0\" class=\"data row23 col0\" >2.61</td>\n",
+ " <td id=\"T_bc7ac_row23_col1\" class=\"data row23 col1\" >1.07</td>\n",
+ " <td id=\"T_bc7ac_row23_col2\" class=\"data row23 col2\" >0.19</td>\n",
+ " <td id=\"T_bc7ac_row23_col3\" class=\"data row23 col3\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row23_col4\" class=\"data row23 col4\" >89.15</td>\n",
+ " <td id=\"T_bc7ac_row23_col5\" class=\"data row23 col5\" >5.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">50% (1 0 1 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row24\" class=\"row_heading level1 row24\" >0</th>\n",
+ " <td id=\"T_bc7ac_row24_col0\" class=\"data row24 col0\" >5.19</td>\n",
+ " <td id=\"T_bc7ac_row24_col1\" class=\"data row24 col1\" >1.87</td>\n",
+ " <td id=\"T_bc7ac_row24_col2\" class=\"data row24 col2\" >0.38</td>\n",
+ " <td id=\"T_bc7ac_row24_col3\" class=\"data row24 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row24_col4\" class=\"data row24 col4\" >48.46</td>\n",
+ " <td id=\"T_bc7ac_row24_col5\" class=\"data row24 col5\" >6.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row25\" class=\"row_heading level1 row25\" >1</th>\n",
+ " <td id=\"T_bc7ac_row25_col0\" class=\"data row25 col0\" >2.86</td>\n",
+ " <td id=\"T_bc7ac_row25_col1\" class=\"data row25 col1\" >1.19</td>\n",
+ " <td id=\"T_bc7ac_row25_col2\" class=\"data row25 col2\" >0.21</td>\n",
+ " <td id=\"T_bc7ac_row25_col3\" class=\"data row25 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row25_col4\" class=\"data row25 col4\" >88.69</td>\n",
+ " <td id=\"T_bc7ac_row25_col5\" class=\"data row25 col5\" >5.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row26\" class=\"row_heading level1 row26\" >2</th>\n",
+ " <td id=\"T_bc7ac_row26_col0\" class=\"data row26 col0\" >10.67</td>\n",
+ " <td id=\"T_bc7ac_row26_col1\" class=\"data row26 col1\" >1.75</td>\n",
+ " <td id=\"T_bc7ac_row26_col2\" class=\"data row26 col2\" >0.77</td>\n",
+ " <td id=\"T_bc7ac_row26_col3\" class=\"data row26 col3\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row26_col4\" class=\"data row26 col4\" >31.54</td>\n",
+ " <td id=\"T_bc7ac_row26_col5\" class=\"data row26 col5\" >5.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row27\" class=\"row_heading level1 row27\" >3</th>\n",
+ " <td id=\"T_bc7ac_row27_col0\" class=\"data row27 col0\" >2.53</td>\n",
+ " <td id=\"T_bc7ac_row27_col1\" class=\"data row27 col1\" >1.03</td>\n",
+ " <td id=\"T_bc7ac_row27_col2\" class=\"data row27 col2\" >0.18</td>\n",
+ " <td id=\"T_bc7ac_row27_col3\" class=\"data row27 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row27_col4\" class=\"data row27 col4\" >89.15</td>\n",
+ " <td id=\"T_bc7ac_row27_col5\" class=\"data row27 col5\" >4.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">50% (1 0 0 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row28\" class=\"row_heading level1 row28\" >0</th>\n",
+ " <td id=\"T_bc7ac_row28_col0\" class=\"data row28 col0\" >4.80</td>\n",
+ " <td id=\"T_bc7ac_row28_col1\" class=\"data row28 col1\" >1.93</td>\n",
+ " <td id=\"T_bc7ac_row28_col2\" class=\"data row28 col2\" >0.35</td>\n",
+ " <td id=\"T_bc7ac_row28_col3\" class=\"data row28 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row28_col4\" class=\"data row28 col4\" >49.84</td>\n",
+ " <td id=\"T_bc7ac_row28_col5\" class=\"data row28 col5\" >6.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row29\" class=\"row_heading level1 row29\" >1</th>\n",
+ " <td id=\"T_bc7ac_row29_col0\" class=\"data row29 col0\" >3.30</td>\n",
+ " <td id=\"T_bc7ac_row29_col1\" class=\"data row29 col1\" >1.27</td>\n",
+ " <td id=\"T_bc7ac_row29_col2\" class=\"data row29 col2\" >0.24</td>\n",
+ " <td id=\"T_bc7ac_row29_col3\" class=\"data row29 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row29_col4\" class=\"data row29 col4\" >86.79</td>\n",
+ " <td id=\"T_bc7ac_row29_col5\" class=\"data row29 col5\" >5.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row30\" class=\"row_heading level1 row30\" >2</th>\n",
+ " <td id=\"T_bc7ac_row30_col0\" class=\"data row30 col0\" >3.08</td>\n",
+ " <td id=\"T_bc7ac_row30_col1\" class=\"data row30 col1\" >1.10</td>\n",
+ " <td id=\"T_bc7ac_row30_col2\" class=\"data row30 col2\" >0.22</td>\n",
+ " <td id=\"T_bc7ac_row30_col3\" class=\"data row30 col3\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row30_col4\" class=\"data row30 col4\" >87.40</td>\n",
+ " <td id=\"T_bc7ac_row30_col5\" class=\"data row30 col5\" >5.39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row31\" class=\"row_heading level1 row31\" >3</th>\n",
+ " <td id=\"T_bc7ac_row31_col0\" class=\"data row31 col0\" >9.29</td>\n",
+ " <td id=\"T_bc7ac_row31_col1\" class=\"data row31 col1\" >1.80</td>\n",
+ " <td id=\"T_bc7ac_row31_col2\" class=\"data row31 col2\" >0.67</td>\n",
+ " <td id=\"T_bc7ac_row31_col3\" class=\"data row31 col3\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row31_col4\" class=\"data row31 col4\" >33.08</td>\n",
+ " <td id=\"T_bc7ac_row31_col5\" class=\"data row31 col5\" >5.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">50% (0 1 1 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row32\" class=\"row_heading level1 row32\" >0</th>\n",
+ " <td id=\"T_bc7ac_row32_col0\" class=\"data row32 col0\" >1.79</td>\n",
+ " <td id=\"T_bc7ac_row32_col1\" class=\"data row32 col1\" >1.00</td>\n",
+ " <td id=\"T_bc7ac_row32_col2\" class=\"data row32 col2\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row32_col3\" class=\"data row32 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row32_col4\" class=\"data row32 col4\" >92.52</td>\n",
+ " <td id=\"T_bc7ac_row32_col5\" class=\"data row32 col5\" >4.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row33\" class=\"row_heading level1 row33\" >1</th>\n",
+ " <td id=\"T_bc7ac_row33_col0\" class=\"data row33 col0\" >5.41</td>\n",
+ " <td id=\"T_bc7ac_row33_col1\" class=\"data row33 col1\" >1.96</td>\n",
+ " <td id=\"T_bc7ac_row33_col2\" class=\"data row33 col2\" >0.39</td>\n",
+ " <td id=\"T_bc7ac_row33_col3\" class=\"data row33 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row33_col4\" class=\"data row33 col4\" >38.66</td>\n",
+ " <td id=\"T_bc7ac_row33_col5\" class=\"data row33 col5\" >5.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row34\" class=\"row_heading level1 row34\" >2</th>\n",
+ " <td id=\"T_bc7ac_row34_col0\" class=\"data row34 col0\" >5.68</td>\n",
+ " <td id=\"T_bc7ac_row34_col1\" class=\"data row34 col1\" >1.88</td>\n",
+ " <td id=\"T_bc7ac_row34_col2\" class=\"data row34 col2\" >0.41</td>\n",
+ " <td id=\"T_bc7ac_row34_col3\" class=\"data row34 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row34_col4\" class=\"data row34 col4\" >36.44</td>\n",
+ " <td id=\"T_bc7ac_row34_col5\" class=\"data row34 col5\" >5.39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row35\" class=\"row_heading level1 row35\" >3</th>\n",
+ " <td id=\"T_bc7ac_row35_col0\" class=\"data row35 col0\" >2.32</td>\n",
+ " <td id=\"T_bc7ac_row35_col1\" class=\"data row35 col1\" >0.94</td>\n",
+ " <td id=\"T_bc7ac_row35_col2\" class=\"data row35 col2\" >0.17</td>\n",
+ " <td id=\"T_bc7ac_row35_col3\" class=\"data row35 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row35_col4\" class=\"data row35 col4\" >90.12</td>\n",
+ " <td id=\"T_bc7ac_row35_col5\" class=\"data row35 col5\" >4.72</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">50% (0 1 0 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row36\" class=\"row_heading level1 row36\" >0</th>\n",
+ " <td id=\"T_bc7ac_row36_col0\" class=\"data row36 col0\" >1.76</td>\n",
+ " <td id=\"T_bc7ac_row36_col1\" class=\"data row36 col1\" >0.97</td>\n",
+ " <td id=\"T_bc7ac_row36_col2\" class=\"data row36 col2\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row36_col3\" class=\"data row36 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row36_col4\" class=\"data row36 col4\" >93.26</td>\n",
+ " <td id=\"T_bc7ac_row36_col5\" class=\"data row36 col5\" >4.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row37\" class=\"row_heading level1 row37\" >1</th>\n",
+ " <td id=\"T_bc7ac_row37_col0\" class=\"data row37 col0\" >5.71</td>\n",
+ " <td id=\"T_bc7ac_row37_col1\" class=\"data row37 col1\" >1.95</td>\n",
+ " <td id=\"T_bc7ac_row37_col2\" class=\"data row37 col2\" >0.41</td>\n",
+ " <td id=\"T_bc7ac_row37_col3\" class=\"data row37 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row37_col4\" class=\"data row37 col4\" >39.26</td>\n",
+ " <td id=\"T_bc7ac_row37_col5\" class=\"data row37 col5\" >5.88</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row38\" class=\"row_heading level1 row38\" >2</th>\n",
+ " <td id=\"T_bc7ac_row38_col0\" class=\"data row38 col0\" >2.60</td>\n",
+ " <td id=\"T_bc7ac_row38_col1\" class=\"data row38 col1\" >1.08</td>\n",
+ " <td id=\"T_bc7ac_row38_col2\" class=\"data row38 col2\" >0.19</td>\n",
+ " <td id=\"T_bc7ac_row38_col3\" class=\"data row38 col3\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row38_col4\" class=\"data row38 col4\" >88.32</td>\n",
+ " <td id=\"T_bc7ac_row38_col5\" class=\"data row38 col5\" >4.89</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row39\" class=\"row_heading level1 row39\" >3</th>\n",
+ " <td id=\"T_bc7ac_row39_col0\" class=\"data row39 col0\" >5.38</td>\n",
+ " <td id=\"T_bc7ac_row39_col1\" class=\"data row39 col1\" >1.65</td>\n",
+ " <td id=\"T_bc7ac_row39_col2\" class=\"data row39 col2\" >0.39</td>\n",
+ " <td id=\"T_bc7ac_row39_col3\" class=\"data row39 col3\" >0.12</td>\n",
+ " <td id=\"T_bc7ac_row39_col4\" class=\"data row39 col4\" >36.36</td>\n",
+ " <td id=\"T_bc7ac_row39_col5\" class=\"data row39 col5\" >4.88</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">50% (0 0 1 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row40\" class=\"row_heading level1 row40\" >0</th>\n",
+ " <td id=\"T_bc7ac_row40_col0\" class=\"data row40 col0\" >1.68</td>\n",
+ " <td id=\"T_bc7ac_row40_col1\" class=\"data row40 col1\" >0.97</td>\n",
+ " <td id=\"T_bc7ac_row40_col2\" class=\"data row40 col2\" >0.12</td>\n",
+ " <td id=\"T_bc7ac_row40_col3\" class=\"data row40 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row40_col4\" class=\"data row40 col4\" >92.87</td>\n",
+ " <td id=\"T_bc7ac_row40_col5\" class=\"data row40 col5\" >4.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row41\" class=\"row_heading level1 row41\" >1</th>\n",
+ " <td id=\"T_bc7ac_row41_col0\" class=\"data row41 col0\" >2.47</td>\n",
+ " <td id=\"T_bc7ac_row41_col1\" class=\"data row41 col1\" >1.12</td>\n",
+ " <td id=\"T_bc7ac_row41_col2\" class=\"data row41 col2\" >0.18</td>\n",
+ " <td id=\"T_bc7ac_row41_col3\" class=\"data row41 col3\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row41_col4\" class=\"data row41 col4\" >90.41</td>\n",
+ " <td id=\"T_bc7ac_row41_col5\" class=\"data row41 col5\" >4.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row42\" class=\"row_heading level1 row42\" >2</th>\n",
+ " <td id=\"T_bc7ac_row42_col0\" class=\"data row42 col0\" >5.52</td>\n",
+ " <td id=\"T_bc7ac_row42_col1\" class=\"data row42 col1\" >2.02</td>\n",
+ " <td id=\"T_bc7ac_row42_col2\" class=\"data row42 col2\" >0.40</td>\n",
+ " <td id=\"T_bc7ac_row42_col3\" class=\"data row42 col3\" >0.15</td>\n",
+ " <td id=\"T_bc7ac_row42_col4\" class=\"data row42 col4\" >36.45</td>\n",
+ " <td id=\"T_bc7ac_row42_col5\" class=\"data row42 col5\" >5.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row43\" class=\"row_heading level1 row43\" >3</th>\n",
+ " <td id=\"T_bc7ac_row43_col0\" class=\"data row43 col0\" >5.33</td>\n",
+ " <td id=\"T_bc7ac_row43_col1\" class=\"data row43 col1\" >1.95</td>\n",
+ " <td id=\"T_bc7ac_row43_col2\" class=\"data row43 col2\" >0.39</td>\n",
+ " <td id=\"T_bc7ac_row43_col3\" class=\"data row43 col3\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row43_col4\" class=\"data row43 col4\" >37.66</td>\n",
+ " <td id=\"T_bc7ac_row43_col5\" class=\"data row43 col5\" >5.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">75% (1 1 1 0)</th>\n",
+ " <th id=\"T_bc7ac_level1_row44\" class=\"row_heading level1 row44\" >0</th>\n",
+ " <td id=\"T_bc7ac_row44_col0\" class=\"data row44 col0\" >1.13</td>\n",
+ " <td id=\"T_bc7ac_row44_col1\" class=\"data row44 col1\" >0.84</td>\n",
+ " <td id=\"T_bc7ac_row44_col2\" class=\"data row44 col2\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row44_col3\" class=\"data row44 col3\" >0.06</td>\n",
+ " <td id=\"T_bc7ac_row44_col4\" class=\"data row44 col4\" >60.17</td>\n",
+ " <td id=\"T_bc7ac_row44_col5\" class=\"data row44 col5\" >6.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row45\" class=\"row_heading level1 row45\" >1</th>\n",
+ " <td id=\"T_bc7ac_row45_col0\" class=\"data row45 col0\" >1.74</td>\n",
+ " <td id=\"T_bc7ac_row45_col1\" class=\"data row45 col1\" >0.93</td>\n",
+ " <td id=\"T_bc7ac_row45_col2\" class=\"data row45 col2\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row45_col3\" class=\"data row45 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row45_col4\" class=\"data row45 col4\" >52.45</td>\n",
+ " <td id=\"T_bc7ac_row45_col5\" class=\"data row45 col5\" >5.74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row46\" class=\"row_heading level1 row46\" >2</th>\n",
+ " <td id=\"T_bc7ac_row46_col0\" class=\"data row46 col0\" >1.89</td>\n",
+ " <td id=\"T_bc7ac_row46_col1\" class=\"data row46 col1\" >0.96</td>\n",
+ " <td id=\"T_bc7ac_row46_col2\" class=\"data row46 col2\" >0.14</td>\n",
+ " <td id=\"T_bc7ac_row46_col3\" class=\"data row46 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row46_col4\" class=\"data row46 col4\" >47.52</td>\n",
+ " <td id=\"T_bc7ac_row46_col5\" class=\"data row46 col5\" >6.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row47\" class=\"row_heading level1 row47\" >3</th>\n",
+ " <td id=\"T_bc7ac_row47_col0\" class=\"data row47 col0\" >0.79</td>\n",
+ " <td id=\"T_bc7ac_row47_col1\" class=\"data row47 col1\" >0.68</td>\n",
+ " <td id=\"T_bc7ac_row47_col2\" class=\"data row47 col2\" >0.06</td>\n",
+ " <td id=\"T_bc7ac_row47_col3\" class=\"data row47 col3\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row47_col4\" class=\"data row47 col4\" >95.89</td>\n",
+ " <td id=\"T_bc7ac_row47_col5\" class=\"data row47 col5\" >3.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">75% (1 1 0 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row48\" class=\"row_heading level1 row48\" >0</th>\n",
+ " <td id=\"T_bc7ac_row48_col0\" class=\"data row48 col0\" >1.13</td>\n",
+ " <td id=\"T_bc7ac_row48_col1\" class=\"data row48 col1\" >0.77</td>\n",
+ " <td id=\"T_bc7ac_row48_col2\" class=\"data row48 col2\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row48_col3\" class=\"data row48 col3\" >0.06</td>\n",
+ " <td id=\"T_bc7ac_row48_col4\" class=\"data row48 col4\" >61.42</td>\n",
+ " <td id=\"T_bc7ac_row48_col5\" class=\"data row48 col5\" >6.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row49\" class=\"row_heading level1 row49\" >1</th>\n",
+ " <td id=\"T_bc7ac_row49_col0\" class=\"data row49 col0\" >2.35</td>\n",
+ " <td id=\"T_bc7ac_row49_col1\" class=\"data row49 col1\" >1.20</td>\n",
+ " <td id=\"T_bc7ac_row49_col2\" class=\"data row49 col2\" >0.17</td>\n",
+ " <td id=\"T_bc7ac_row49_col3\" class=\"data row49 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row49_col4\" class=\"data row49 col4\" >52.51</td>\n",
+ " <td id=\"T_bc7ac_row49_col5\" class=\"data row49 col5\" >5.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row50\" class=\"row_heading level1 row50\" >2</th>\n",
+ " <td id=\"T_bc7ac_row50_col0\" class=\"data row50 col0\" >1.16</td>\n",
+ " <td id=\"T_bc7ac_row50_col1\" class=\"data row50 col1\" >0.92</td>\n",
+ " <td id=\"T_bc7ac_row50_col2\" class=\"data row50 col2\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row50_col3\" class=\"data row50 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row50_col4\" class=\"data row50 col4\" >94.37</td>\n",
+ " <td id=\"T_bc7ac_row50_col5\" class=\"data row50 col5\" >4.36</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row51\" class=\"row_heading level1 row51\" >3</th>\n",
+ " <td id=\"T_bc7ac_row51_col0\" class=\"data row51 col0\" >1.74</td>\n",
+ " <td id=\"T_bc7ac_row51_col1\" class=\"data row51 col1\" >0.89</td>\n",
+ " <td id=\"T_bc7ac_row51_col2\" class=\"data row51 col2\" >0.13</td>\n",
+ " <td id=\"T_bc7ac_row51_col3\" class=\"data row51 col3\" >0.06</td>\n",
+ " <td id=\"T_bc7ac_row51_col4\" class=\"data row51 col4\" >47.53</td>\n",
+ " <td id=\"T_bc7ac_row51_col5\" class=\"data row51 col5\" >5.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">75% (1 0 1 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row52\" class=\"row_heading level1 row52\" >0</th>\n",
+ " <td id=\"T_bc7ac_row52_col0\" class=\"data row52 col0\" >0.99</td>\n",
+ " <td id=\"T_bc7ac_row52_col1\" class=\"data row52 col1\" >0.70</td>\n",
+ " <td id=\"T_bc7ac_row52_col2\" class=\"data row52 col2\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row52_col3\" class=\"data row52 col3\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row52_col4\" class=\"data row52 col4\" >61.81</td>\n",
+ " <td id=\"T_bc7ac_row52_col5\" class=\"data row52 col5\" >6.46</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row53\" class=\"row_heading level1 row53\" >1</th>\n",
+ " <td id=\"T_bc7ac_row53_col0\" class=\"data row53 col0\" >1.25</td>\n",
+ " <td id=\"T_bc7ac_row53_col1\" class=\"data row53 col1\" >0.96</td>\n",
+ " <td id=\"T_bc7ac_row53_col2\" class=\"data row53 col2\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row53_col3\" class=\"data row53 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row53_col4\" class=\"data row53 col4\" >94.49</td>\n",
+ " <td id=\"T_bc7ac_row53_col5\" class=\"data row53 col5\" >4.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row54\" class=\"row_heading level1 row54\" >2</th>\n",
+ " <td id=\"T_bc7ac_row54_col0\" class=\"data row54 col0\" >2.29</td>\n",
+ " <td id=\"T_bc7ac_row54_col1\" class=\"data row54 col1\" >1.28</td>\n",
+ " <td id=\"T_bc7ac_row54_col2\" class=\"data row54 col2\" >0.17</td>\n",
+ " <td id=\"T_bc7ac_row54_col3\" class=\"data row54 col3\" >0.09</td>\n",
+ " <td id=\"T_bc7ac_row54_col4\" class=\"data row54 col4\" >50.81</td>\n",
+ " <td id=\"T_bc7ac_row54_col5\" class=\"data row54 col5\" >5.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row55\" class=\"row_heading level1 row55\" >3</th>\n",
+ " <td id=\"T_bc7ac_row55_col0\" class=\"data row55 col0\" >1.55</td>\n",
+ " <td id=\"T_bc7ac_row55_col1\" class=\"data row55 col1\" >0.90</td>\n",
+ " <td id=\"T_bc7ac_row55_col2\" class=\"data row55 col2\" >0.11</td>\n",
+ " <td id=\"T_bc7ac_row55_col3\" class=\"data row55 col3\" >0.07</td>\n",
+ " <td id=\"T_bc7ac_row55_col4\" class=\"data row55 col4\" >48.77</td>\n",
+ " <td id=\"T_bc7ac_row55_col5\" class=\"data row55 col5\" >6.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">75% (0 1 1 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row56\" class=\"row_heading level1 row56\" >0</th>\n",
+ " <td id=\"T_bc7ac_row56_col0\" class=\"data row56 col0\" >0.66</td>\n",
+ " <td id=\"T_bc7ac_row56_col1\" class=\"data row56 col1\" >0.73</td>\n",
+ " <td id=\"T_bc7ac_row56_col2\" class=\"data row56 col2\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row56_col3\" class=\"data row56 col3\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row56_col4\" class=\"data row56 col4\" >96.98</td>\n",
+ " <td id=\"T_bc7ac_row56_col5\" class=\"data row56 col5\" >2.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row57\" class=\"row_heading level1 row57\" >1</th>\n",
+ " <td id=\"T_bc7ac_row57_col0\" class=\"data row57 col0\" >1.11</td>\n",
+ " <td id=\"T_bc7ac_row57_col1\" class=\"data row57 col1\" >0.76</td>\n",
+ " <td id=\"T_bc7ac_row57_col2\" class=\"data row57 col2\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row57_col3\" class=\"data row57 col3\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row57_col4\" class=\"data row57 col4\" >54.44</td>\n",
+ " <td id=\"T_bc7ac_row57_col5\" class=\"data row57 col5\" >6.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row58\" class=\"row_heading level1 row58\" >2</th>\n",
+ " <td id=\"T_bc7ac_row58_col0\" class=\"data row58 col0\" >1.38</td>\n",
+ " <td id=\"T_bc7ac_row58_col1\" class=\"data row58 col1\" >0.81</td>\n",
+ " <td id=\"T_bc7ac_row58_col2\" class=\"data row58 col2\" >0.10</td>\n",
+ " <td id=\"T_bc7ac_row58_col3\" class=\"data row58 col3\" >0.06</td>\n",
+ " <td id=\"T_bc7ac_row58_col4\" class=\"data row58 col4\" >53.51</td>\n",
+ " <td id=\"T_bc7ac_row58_col5\" class=\"data row58 col5\" >6.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row59\" class=\"row_heading level1 row59\" >3</th>\n",
+ " <td id=\"T_bc7ac_row59_col0\" class=\"data row59 col0\" >1.10</td>\n",
+ " <td id=\"T_bc7ac_row59_col1\" class=\"data row59 col1\" >0.68</td>\n",
+ " <td id=\"T_bc7ac_row59_col2\" class=\"data row59 col2\" >0.08</td>\n",
+ " <td id=\"T_bc7ac_row59_col3\" class=\"data row59 col3\" >0.05</td>\n",
+ " <td id=\"T_bc7ac_row59_col4\" class=\"data row59 col4\" >50.98</td>\n",
+ " <td id=\"T_bc7ac_row59_col5\" class=\"data row59 col5\" >5.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">100% (1 1 1 1)</th>\n",
+ " <th id=\"T_bc7ac_level1_row60\" class=\"row_heading level1 row60\" >0</th>\n",
+ " <td id=\"T_bc7ac_row60_col0\" class=\"data row60 col0\" >0.25</td>\n",
+ " <td id=\"T_bc7ac_row60_col1\" class=\"data row60 col1\" >0.32</td>\n",
+ " <td id=\"T_bc7ac_row60_col2\" class=\"data row60 col2\" >0.02</td>\n",
+ " <td id=\"T_bc7ac_row60_col3\" class=\"data row60 col3\" >0.02</td>\n",
+ " <td id=\"T_bc7ac_row60_col4\" class=\"data row60 col4\" >71.19</td>\n",
+ " <td id=\"T_bc7ac_row60_col5\" class=\"data row60 col5\" >6.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row61\" class=\"row_heading level1 row61\" >1</th>\n",
+ " <td id=\"T_bc7ac_row61_col0\" class=\"data row61 col0\" >0.41</td>\n",
+ " <td id=\"T_bc7ac_row61_col1\" class=\"data row61 col1\" >0.39</td>\n",
+ " <td id=\"T_bc7ac_row61_col2\" class=\"data row61 col2\" >0.03</td>\n",
+ " <td id=\"T_bc7ac_row61_col3\" class=\"data row61 col3\" >0.03</td>\n",
+ " <td id=\"T_bc7ac_row61_col4\" class=\"data row61 col4\" >63.74</td>\n",
+ " <td id=\"T_bc7ac_row61_col5\" class=\"data row61 col5\" >5.80</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row62\" class=\"row_heading level1 row62\" >2</th>\n",
+ " <td id=\"T_bc7ac_row62_col0\" class=\"data row62 col0\" >0.47</td>\n",
+ " <td id=\"T_bc7ac_row62_col1\" class=\"data row62 col1\" >0.46</td>\n",
+ " <td id=\"T_bc7ac_row62_col2\" class=\"data row62 col2\" >0.03</td>\n",
+ " <td id=\"T_bc7ac_row62_col3\" class=\"data row62 col3\" >0.03</td>\n",
+ " <td id=\"T_bc7ac_row62_col4\" class=\"data row62 col4\" >61.52</td>\n",
+ " <td id=\"T_bc7ac_row62_col5\" class=\"data row62 col5\" >6.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_bc7ac_level1_row63\" class=\"row_heading level1 row63\" >3</th>\n",
+ " <td id=\"T_bc7ac_row63_col0\" class=\"data row63 col0\" >0.17</td>\n",
+ " <td id=\"T_bc7ac_row63_col1\" class=\"data row63 col1\" >0.19</td>\n",
+ " <td id=\"T_bc7ac_row63_col2\" class=\"data row63 col2\" >0.01</td>\n",
+ " <td id=\"T_bc7ac_row63_col3\" class=\"data row63 col3\" >0.01</td>\n",
+ " <td id=\"T_bc7ac_row63_col4\" class=\"data row63 col4\" >58.69</td>\n",
+ " <td id=\"T_bc7ac_row63_col5\" class=\"data row63 col5\" >6.07</td>\n",
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+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_aca75_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Total benefits</th>\n",
+ " <th id=\"T_aca75_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Benefits / energy available</th>\n",
+ " <th id=\"T_aca75_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Energy wasted (%)</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_aca75_level1_col0\" class=\"col_heading level1 col0\" >Value</th>\n",
+ " <th id=\"T_aca75_level1_col1\" class=\"col_heading level1 col1\" >std</th>\n",
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+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_aca75_row0_col0\" class=\"data row0 col0\" >47.41</td>\n",
+ " <td id=\"T_aca75_row0_col1\" class=\"data row0 col1\" >1.61</td>\n",
+ " <td id=\"T_aca75_row0_col2\" class=\"data row0 col2\" >0.86</td>\n",
+ " <td id=\"T_aca75_row0_col3\" class=\"data row0 col3\" >0.03</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
+ " <td id=\"T_aca75_row1_col0\" class=\"data row1 col0\" >45.49</td>\n",
+ " <td id=\"T_aca75_row1_col1\" class=\"data row1 col1\" >1.78</td>\n",
+ " <td id=\"T_aca75_row1_col2\" class=\"data row1 col2\" >0.82</td>\n",
+ " <td id=\"T_aca75_row1_col3\" class=\"data row1 col3\" >0.03</td>\n",
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+ " <td id=\"T_aca75_row1_col5\" class=\"data row1 col5\" >3.05</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row2\" class=\"row_heading level0 row2\" >25% (0 1 0 0)</th>\n",
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+ " <th id=\"T_aca75_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
+ " <td id=\"T_aca75_row3_col0\" class=\"data row3 col0\" >45.09</td>\n",
+ " <td id=\"T_aca75_row3_col1\" class=\"data row3 col1\" >1.75</td>\n",
+ " <td id=\"T_aca75_row3_col2\" class=\"data row3 col2\" >0.82</td>\n",
+ " <td id=\"T_aca75_row3_col3\" class=\"data row3 col3\" >0.03</td>\n",
+ " <td id=\"T_aca75_row3_col4\" class=\"data row3 col4\" >64.88</td>\n",
+ " <td id=\"T_aca75_row3_col5\" class=\"data row3 col5\" >3.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
+ " <td id=\"T_aca75_row4_col0\" class=\"data row4 col0\" >45.08</td>\n",
+ " <td id=\"T_aca75_row4_col1\" class=\"data row4 col1\" >1.72</td>\n",
+ " <td id=\"T_aca75_row4_col2\" class=\"data row4 col2\" >0.82</td>\n",
+ " <td id=\"T_aca75_row4_col3\" class=\"data row4 col3\" >0.03</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_aca75_row5_col0\" class=\"data row5 col0\" >22.16</td>\n",
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+ " <td id=\"T_aca75_row5_col4\" class=\"data row5 col4\" >64.47</td>\n",
+ " <td id=\"T_aca75_row5_col5\" class=\"data row5 col5\" >3.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td id=\"T_aca75_row6_col0\" class=\"data row6 col0\" >21.24</td>\n",
+ " <td id=\"T_aca75_row6_col1\" class=\"data row6 col1\" >2.73</td>\n",
+ " <td id=\"T_aca75_row6_col2\" class=\"data row6 col2\" >0.38</td>\n",
+ " <td id=\"T_aca75_row6_col3\" class=\"data row6 col3\" >0.05</td>\n",
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+ " <th id=\"T_aca75_level0_row7\" class=\"row_heading level0 row7\" >50% (1 0 0 1)</th>\n",
+ " <td id=\"T_aca75_row7_col0\" class=\"data row7 col0\" >20.46</td>\n",
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+ " <td id=\"T_aca75_row9_col0\" class=\"data row9 col0\" >15.46</td>\n",
+ " <td id=\"T_aca75_row9_col1\" class=\"data row9 col1\" >3.02</td>\n",
+ " <td id=\"T_aca75_row9_col2\" class=\"data row9 col2\" >0.28</td>\n",
+ " <td id=\"T_aca75_row9_col3\" class=\"data row9 col3\" >0.05</td>\n",
+ " <td id=\"T_aca75_row9_col4\" class=\"data row9 col4\" >64.30</td>\n",
+ " <td id=\"T_aca75_row9_col5\" class=\"data row9 col5\" >3.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row10\" class=\"row_heading level0 row10\" >50% (0 0 1 1)</th>\n",
+ " <td id=\"T_aca75_row10_col0\" class=\"data row10 col0\" >15.00</td>\n",
+ " <td id=\"T_aca75_row10_col1\" class=\"data row10 col1\" >3.10</td>\n",
+ " <td id=\"T_aca75_row10_col2\" class=\"data row10 col2\" >0.27</td>\n",
+ " <td id=\"T_aca75_row10_col3\" class=\"data row10 col3\" >0.06</td>\n",
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+ " <td id=\"T_aca75_row10_col5\" class=\"data row10 col5\" >3.04</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row11\" class=\"row_heading level0 row11\" >75% (1 1 1 0)</th>\n",
+ " <td id=\"T_aca75_row11_col0\" class=\"data row11 col0\" >5.55</td>\n",
+ " <td id=\"T_aca75_row11_col1\" class=\"data row11 col1\" >2.03</td>\n",
+ " <td id=\"T_aca75_row11_col2\" class=\"data row11 col2\" >0.10</td>\n",
+ " <td id=\"T_aca75_row11_col3\" class=\"data row11 col3\" >0.04</td>\n",
+ " <td id=\"T_aca75_row11_col4\" class=\"data row11 col4\" >64.01</td>\n",
+ " <td id=\"T_aca75_row11_col5\" class=\"data row11 col5\" >3.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row12\" class=\"row_heading level0 row12\" >75% (1 1 0 1)</th>\n",
+ " <td id=\"T_aca75_row12_col0\" class=\"data row12 col0\" >6.39</td>\n",
+ " <td id=\"T_aca75_row12_col1\" class=\"data row12 col1\" >2.26</td>\n",
+ " <td id=\"T_aca75_row12_col2\" class=\"data row12 col2\" >0.12</td>\n",
+ " <td id=\"T_aca75_row12_col3\" class=\"data row12 col3\" >0.04</td>\n",
+ " <td id=\"T_aca75_row12_col4\" class=\"data row12 col4\" >63.96</td>\n",
+ " <td id=\"T_aca75_row12_col5\" class=\"data row12 col5\" >3.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
+ " <td id=\"T_aca75_row13_col0\" class=\"data row13 col0\" >6.08</td>\n",
+ " <td id=\"T_aca75_row13_col1\" class=\"data row13 col1\" >2.36</td>\n",
+ " <td id=\"T_aca75_row13_col2\" class=\"data row13 col2\" >0.11</td>\n",
+ " <td id=\"T_aca75_row13_col3\" class=\"data row13 col3\" >0.04</td>\n",
+ " <td id=\"T_aca75_row13_col4\" class=\"data row13 col4\" >63.97</td>\n",
+ " <td id=\"T_aca75_row13_col5\" class=\"data row13 col5\" >3.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_aca75_row14_col0\" class=\"data row14 col0\" >4.25</td>\n",
+ " <td id=\"T_aca75_row14_col1\" class=\"data row14 col1\" >1.66</td>\n",
+ " <td id=\"T_aca75_row14_col2\" class=\"data row14 col2\" >0.08</td>\n",
+ " <td id=\"T_aca75_row14_col3\" class=\"data row14 col3\" >0.03</td>\n",
+ " <td id=\"T_aca75_row14_col4\" class=\"data row14 col4\" >63.98</td>\n",
+ " <td id=\"T_aca75_row14_col5\" class=\"data row14 col5\" >3.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_aca75_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_aca75_row15_col0\" class=\"data row15 col0\" >1.31</td>\n",
+ " <td id=\"T_aca75_row15_col1\" class=\"data row15 col1\" >0.78</td>\n",
+ " <td id=\"T_aca75_row15_col2\" class=\"data row15 col2\" >0.02</td>\n",
+ " <td id=\"T_aca75_row15_col3\" class=\"data row15 col3\" >0.01</td>\n",
+ " <td id=\"T_aca75_row15_col4\" class=\"data row15 col4\" >63.78</td>\n",
+ " <td id=\"T_aca75_row15_col5\" class=\"data row15 col5\" >3.10</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7129a19c1410>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "opt",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.2"
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+}
diff --git a/Analysis/Section_7.3.3.ipynb b/Analysis/Section_7.3.3.ipynb
new file mode 100644
index 0000000..0170f23
--- /dev/null
+++ b/Analysis/Section_7.3.3.ipynb
@@ -0,0 +1,1330 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Objective:** Similar to the previous one, but with a difference where the aggressive can have less energy than the other dcs. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from GenerateVisualisation.experiments_analysis import Analysis, Analysis_plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_list = []\n",
+ "seeds = range(3, 103)\n",
+ "\n",
+ "# base_folder = \"XPs/1_bell/\"\n",
+ "# analysis = Analysis(base_folder=base_folder,\n",
+ "# files=[\"/simple_aggre_0_\", \"/simple_aggre_25_\", \"/simple_aggre_50_\", \"/simple_aggre_75_\", \"/simple_aggre_100_\"],\n",
+ "# names=[\"Cooperative\", \"25% aggre\", \"50% aggre\", \"75% aggre\", \"all aggre\"],\n",
+ "# nb_dcs=4,\n",
+ "# seeds = seeds)\n",
+ "# analysis_list.append(analysis)\n",
+ "\n",
+ "servers=np.array([(s0, s1, s2, s3) for s0 in [0, 1] for s1 in [0, 1] for s2 in [0, 1] for s3 in [0, 1]])\n",
+ "idx=[0, 8, 4, 2, 1, 12, 10, 9, 6, 5, 3, 14, 13, 11, 7, 15]\n",
+ "name = []\n",
+ "files = []\n",
+ "for s0, s1, s2, s3 in servers[idx]:\n",
+ " aggre_sum = sum([s0, s1, s2, s3]) / 4 * 100\n",
+ " if (aggre_sum > 0):\n",
+ " name.append(\"{:.0f}% ({} {} {} {})\".format(aggre_sum, s0, s1, s2, s3))\n",
+ " else:\n",
+ " name.append(\"Cooperative\")\n",
+ " files.append(\"/simple_aggre_{}_dc_{}_{}_{}_{}_seed_\".format(int(aggre_sum), s0, s1, s2, s3))\n",
+ "\n",
+ "analysis = Analysis(base_folder=\"XPs/3_real/\",\n",
+ " files=files,\n",
+ " names=name,\n",
+ " nb_dcs=4,\n",
+ " seeds = seeds)\n",
+ "analysis_list.append(analysis)\n",
+ "\n",
+ "analysis_plot = Analysis_plots(analysis_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <th id=\"T_119ac_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">Bid</th>\n",
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+ " <th id=\"T_119ac_level0_col10\" class=\"col_heading level0 col10\" colspan=\"6\">Money/Energy</th>\n",
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+ " <tr>\n",
+ " <th class=\"blank level1\" > </th>\n",
+ " <th id=\"T_119ac_level1_col0\" class=\"col_heading level1 col0\" colspan=\"2\">Tasks</th>\n",
+ " <th id=\"T_119ac_level1_col2\" class=\"col_heading level1 col2\" colspan=\"2\">Services</th>\n",
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+ " <th id=\"T_119ac_level1_col6\" class=\"col_heading level1 col6\" colspan=\"2\">Before due</th>\n",
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+ " <th id=\"T_119ac_level1_col14\" class=\"col_heading level1 col14\" colspan=\"2\">Ratio</th>\n",
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+ " <th class=\"blank level2\" > </th>\n",
+ " <th id=\"T_119ac_level2_col0\" class=\"col_heading level2 col0\" >Value</th>\n",
+ " <th id=\"T_119ac_level2_col1\" class=\"col_heading level2 col1\" >std</th>\n",
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+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row0\" class=\"row_heading level0 row0\" >Cooperative</th>\n",
+ " <td id=\"T_119ac_row0_col0\" class=\"data row0 col0\" >87.87</td>\n",
+ " <td id=\"T_119ac_row0_col1\" class=\"data row0 col1\" >2.94</td>\n",
+ " <td id=\"T_119ac_row0_col2\" class=\"data row0 col2\" >83.98</td>\n",
+ " <td id=\"T_119ac_row0_col3\" class=\"data row0 col3\" >3.46</td>\n",
+ " <td id=\"T_119ac_row0_col4\" class=\"data row0 col4\" >90.97</td>\n",
+ " <td id=\"T_119ac_row0_col5\" class=\"data row0 col5\" >1.99</td>\n",
+ " <td id=\"T_119ac_row0_col6\" class=\"data row0 col6\" >98.57</td>\n",
+ " <td id=\"T_119ac_row0_col7\" class=\"data row0 col7\" >1.20</td>\n",
+ " <td id=\"T_119ac_row0_col8\" class=\"data row0 col8\" >1.49</td>\n",
+ " <td id=\"T_119ac_row0_col9\" class=\"data row0 col9\" >0.05</td>\n",
+ " <td id=\"T_119ac_row0_col10\" class=\"data row0 col10\" >118.81</td>\n",
+ " <td id=\"T_119ac_row0_col11\" class=\"data row0 col11\" >5.14</td>\n",
+ " <td id=\"T_119ac_row0_col12\" class=\"data row0 col12\" >15.31</td>\n",
+ " <td id=\"T_119ac_row0_col13\" class=\"data row0 col13\" >2.67</td>\n",
+ " <td id=\"T_119ac_row0_col14\" class=\"data row0 col14\" >2.15</td>\n",
+ " <td id=\"T_119ac_row0_col15\" class=\"data row0 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row1\" class=\"row_heading level0 row1\" >25% (1 0 0 0)</th>\n",
+ " <td id=\"T_119ac_row1_col0\" class=\"data row1 col0\" >88.46</td>\n",
+ " <td id=\"T_119ac_row1_col1\" class=\"data row1 col1\" >2.98</td>\n",
+ " <td id=\"T_119ac_row1_col2\" class=\"data row1 col2\" >85.12</td>\n",
+ " <td id=\"T_119ac_row1_col3\" class=\"data row1 col3\" >3.57</td>\n",
+ " <td id=\"T_119ac_row1_col4\" class=\"data row1 col4\" >87.56</td>\n",
+ " <td id=\"T_119ac_row1_col5\" class=\"data row1 col5\" >2.00</td>\n",
+ " <td id=\"T_119ac_row1_col6\" class=\"data row1 col6\" >94.60</td>\n",
+ " <td id=\"T_119ac_row1_col7\" class=\"data row1 col7\" >2.40</td>\n",
+ " <td id=\"T_119ac_row1_col8\" class=\"data row1 col8\" >1.69</td>\n",
+ " <td id=\"T_119ac_row1_col9\" class=\"data row1 col9\" >0.06</td>\n",
+ " <td id=\"T_119ac_row1_col10\" class=\"data row1 col10\" >112.97</td>\n",
+ " <td id=\"T_119ac_row1_col11\" class=\"data row1 col11\" >4.90</td>\n",
+ " <td id=\"T_119ac_row1_col12\" class=\"data row1 col12\" >16.30</td>\n",
+ " <td id=\"T_119ac_row1_col13\" class=\"data row1 col13\" >2.60</td>\n",
+ " <td id=\"T_119ac_row1_col14\" class=\"data row1 col14\" >2.04</td>\n",
+ " <td id=\"T_119ac_row1_col15\" class=\"data row1 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row2\" class=\"row_heading level0 row2\" >25% (0 1 0 0)</th>\n",
+ " <td id=\"T_119ac_row2_col0\" class=\"data row2 col0\" >88.11</td>\n",
+ " <td id=\"T_119ac_row2_col1\" class=\"data row2 col1\" >3.04</td>\n",
+ " <td id=\"T_119ac_row2_col2\" class=\"data row2 col2\" >84.03</td>\n",
+ " <td id=\"T_119ac_row2_col3\" class=\"data row2 col3\" >3.74</td>\n",
+ " <td id=\"T_119ac_row2_col4\" class=\"data row2 col4\" >87.57</td>\n",
+ " <td id=\"T_119ac_row2_col5\" class=\"data row2 col5\" >2.00</td>\n",
+ " <td id=\"T_119ac_row2_col6\" class=\"data row2 col6\" >94.55</td>\n",
+ " <td id=\"T_119ac_row2_col7\" class=\"data row2 col7\" >2.74</td>\n",
+ " <td id=\"T_119ac_row2_col8\" class=\"data row2 col8\" >1.69</td>\n",
+ " <td id=\"T_119ac_row2_col9\" class=\"data row2 col9\" >0.06</td>\n",
+ " <td id=\"T_119ac_row2_col10\" class=\"data row2 col10\" >112.14</td>\n",
+ " <td id=\"T_119ac_row2_col11\" class=\"data row2 col11\" >5.71</td>\n",
+ " <td id=\"T_119ac_row2_col12\" class=\"data row2 col12\" >16.33</td>\n",
+ " <td id=\"T_119ac_row2_col13\" class=\"data row2 col13\" >2.73</td>\n",
+ " <td id=\"T_119ac_row2_col14\" class=\"data row2 col14\" >2.03</td>\n",
+ " <td id=\"T_119ac_row2_col15\" class=\"data row2 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row3\" class=\"row_heading level0 row3\" >25% (0 0 1 0)</th>\n",
+ " <td id=\"T_119ac_row3_col0\" class=\"data row3 col0\" >88.29</td>\n",
+ " <td id=\"T_119ac_row3_col1\" class=\"data row3 col1\" >2.97</td>\n",
+ " <td id=\"T_119ac_row3_col2\" class=\"data row3 col2\" >84.00</td>\n",
+ " <td id=\"T_119ac_row3_col3\" class=\"data row3 col3\" >3.47</td>\n",
+ " <td id=\"T_119ac_row3_col4\" class=\"data row3 col4\" >87.44</td>\n",
+ " <td id=\"T_119ac_row3_col5\" class=\"data row3 col5\" >1.97</td>\n",
+ " <td id=\"T_119ac_row3_col6\" class=\"data row3 col6\" >93.30</td>\n",
+ " <td id=\"T_119ac_row3_col7\" class=\"data row3 col7\" >2.62</td>\n",
+ " <td id=\"T_119ac_row3_col8\" class=\"data row3 col8\" >1.76</td>\n",
+ " <td id=\"T_119ac_row3_col9\" class=\"data row3 col9\" >0.07</td>\n",
+ " <td id=\"T_119ac_row3_col10\" class=\"data row3 col10\" >111.29</td>\n",
+ " <td id=\"T_119ac_row3_col11\" class=\"data row3 col11\" >5.47</td>\n",
+ " <td id=\"T_119ac_row3_col12\" class=\"data row3 col12\" >16.52</td>\n",
+ " <td id=\"T_119ac_row3_col13\" class=\"data row3 col13\" >2.66</td>\n",
+ " <td id=\"T_119ac_row3_col14\" class=\"data row3 col14\" >2.01</td>\n",
+ " <td id=\"T_119ac_row3_col15\" class=\"data row3 col15\" >0.10</td>\n",
+ " </tr>\n",
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+ " <td id=\"T_119ac_row4_col0\" class=\"data row4 col0\" >88.09</td>\n",
+ " <td id=\"T_119ac_row4_col1\" class=\"data row4 col1\" >2.96</td>\n",
+ " <td id=\"T_119ac_row4_col2\" class=\"data row4 col2\" >84.17</td>\n",
+ " <td id=\"T_119ac_row4_col3\" class=\"data row4 col3\" >4.03</td>\n",
+ " <td id=\"T_119ac_row4_col4\" class=\"data row4 col4\" >87.28</td>\n",
+ " <td id=\"T_119ac_row4_col5\" class=\"data row4 col5\" >2.15</td>\n",
+ " <td id=\"T_119ac_row4_col6\" class=\"data row4 col6\" >93.64</td>\n",
+ " <td id=\"T_119ac_row4_col7\" class=\"data row4 col7\" >2.71</td>\n",
+ " <td id=\"T_119ac_row4_col8\" class=\"data row4 col8\" >1.75</td>\n",
+ " <td id=\"T_119ac_row4_col9\" class=\"data row4 col9\" >0.07</td>\n",
+ " <td id=\"T_119ac_row4_col10\" class=\"data row4 col10\" >111.70</td>\n",
+ " <td id=\"T_119ac_row4_col11\" class=\"data row4 col11\" >5.56</td>\n",
+ " <td id=\"T_119ac_row4_col12\" class=\"data row4 col12\" >16.95</td>\n",
+ " <td id=\"T_119ac_row4_col13\" class=\"data row4 col13\" >2.62</td>\n",
+ " <td id=\"T_119ac_row4_col14\" class=\"data row4 col14\" >2.02</td>\n",
+ " <td id=\"T_119ac_row4_col15\" class=\"data row4 col15\" >0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_119ac_row5_col0\" class=\"data row5 col0\" >88.02</td>\n",
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+ " <td id=\"T_119ac_row5_col2\" class=\"data row5 col2\" >84.93</td>\n",
+ " <td id=\"T_119ac_row5_col3\" class=\"data row5 col3\" >3.47</td>\n",
+ " <td id=\"T_119ac_row5_col4\" class=\"data row5 col4\" >87.84</td>\n",
+ " <td id=\"T_119ac_row5_col5\" class=\"data row5 col5\" >1.93</td>\n",
+ " <td id=\"T_119ac_row5_col6\" class=\"data row5 col6\" >95.77</td>\n",
+ " <td id=\"T_119ac_row5_col7\" class=\"data row5 col7\" >1.98</td>\n",
+ " <td id=\"T_119ac_row5_col8\" class=\"data row5 col8\" >1.67</td>\n",
+ " <td id=\"T_119ac_row5_col9\" class=\"data row5 col9\" >0.06</td>\n",
+ " <td id=\"T_119ac_row5_col10\" class=\"data row5 col10\" >90.54</td>\n",
+ " <td id=\"T_119ac_row5_col11\" class=\"data row5 col11\" >4.73</td>\n",
+ " <td id=\"T_119ac_row5_col12\" class=\"data row5 col12\" >16.40</td>\n",
+ " <td id=\"T_119ac_row5_col13\" class=\"data row5 col13\" >2.73</td>\n",
+ " <td id=\"T_119ac_row5_col14\" class=\"data row5 col14\" >1.64</td>\n",
+ " <td id=\"T_119ac_row5_col15\" class=\"data row5 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td id=\"T_119ac_row6_col0\" class=\"data row6 col0\" >88.08</td>\n",
+ " <td id=\"T_119ac_row6_col1\" class=\"data row6 col1\" >2.80</td>\n",
+ " <td id=\"T_119ac_row6_col2\" class=\"data row6 col2\" >84.47</td>\n",
+ " <td id=\"T_119ac_row6_col3\" class=\"data row6 col3\" >3.39</td>\n",
+ " <td id=\"T_119ac_row6_col4\" class=\"data row6 col4\" >87.93</td>\n",
+ " <td id=\"T_119ac_row6_col5\" class=\"data row6 col5\" >1.97</td>\n",
+ " <td id=\"T_119ac_row6_col6\" class=\"data row6 col6\" >95.63</td>\n",
+ " <td id=\"T_119ac_row6_col7\" class=\"data row6 col7\" >2.02</td>\n",
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+ " <td id=\"T_119ac_row6_col9\" class=\"data row6 col9\" >0.06</td>\n",
+ " <td id=\"T_119ac_row6_col10\" class=\"data row6 col10\" >80.70</td>\n",
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+ " <td id=\"T_119ac_row6_col12\" class=\"data row6 col12\" >16.47</td>\n",
+ " <td id=\"T_119ac_row6_col13\" class=\"data row6 col13\" >2.78</td>\n",
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+ " <td id=\"T_119ac_row6_col15\" class=\"data row6 col15\" >0.09</td>\n",
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+ " <td id=\"T_119ac_row7_col3\" class=\"data row7 col3\" >3.57</td>\n",
+ " <td id=\"T_119ac_row7_col4\" class=\"data row7 col4\" >88.08</td>\n",
+ " <td id=\"T_119ac_row7_col5\" class=\"data row7 col5\" >2.07</td>\n",
+ " <td id=\"T_119ac_row7_col6\" class=\"data row7 col6\" >95.83</td>\n",
+ " <td id=\"T_119ac_row7_col7\" class=\"data row7 col7\" >2.36</td>\n",
+ " <td id=\"T_119ac_row7_col8\" class=\"data row7 col8\" >1.69</td>\n",
+ " <td id=\"T_119ac_row7_col9\" class=\"data row7 col9\" >0.07</td>\n",
+ " <td id=\"T_119ac_row7_col10\" class=\"data row7 col10\" >81.48</td>\n",
+ " <td id=\"T_119ac_row7_col11\" class=\"data row7 col11\" >4.44</td>\n",
+ " <td id=\"T_119ac_row7_col12\" class=\"data row7 col12\" >16.56</td>\n",
+ " <td id=\"T_119ac_row7_col13\" class=\"data row7 col13\" >2.69</td>\n",
+ " <td id=\"T_119ac_row7_col14\" class=\"data row7 col14\" >1.47</td>\n",
+ " <td id=\"T_119ac_row7_col15\" class=\"data row7 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row8\" class=\"row_heading level0 row8\" >50% (0 1 1 0)</th>\n",
+ " <td id=\"T_119ac_row8_col0\" class=\"data row8 col0\" >87.92</td>\n",
+ " <td id=\"T_119ac_row8_col1\" class=\"data row8 col1\" >2.92</td>\n",
+ " <td id=\"T_119ac_row8_col2\" class=\"data row8 col2\" >83.89</td>\n",
+ " <td id=\"T_119ac_row8_col3\" class=\"data row8 col3\" >3.66</td>\n",
+ " <td id=\"T_119ac_row8_col4\" class=\"data row8 col4\" >87.51</td>\n",
+ " <td id=\"T_119ac_row8_col5\" class=\"data row8 col5\" >1.67</td>\n",
+ " <td id=\"T_119ac_row8_col6\" class=\"data row8 col6\" >94.76</td>\n",
+ " <td id=\"T_119ac_row8_col7\" class=\"data row8 col7\" >2.41</td>\n",
+ " <td id=\"T_119ac_row8_col8\" class=\"data row8 col8\" >1.73</td>\n",
+ " <td id=\"T_119ac_row8_col9\" class=\"data row8 col9\" >0.06</td>\n",
+ " <td id=\"T_119ac_row8_col10\" class=\"data row8 col10\" >89.54</td>\n",
+ " <td id=\"T_119ac_row8_col11\" class=\"data row8 col11\" >5.09</td>\n",
+ " <td id=\"T_119ac_row8_col12\" class=\"data row8 col12\" >16.49</td>\n",
+ " <td id=\"T_119ac_row8_col13\" class=\"data row8 col13\" >2.85</td>\n",
+ " <td id=\"T_119ac_row8_col14\" class=\"data row8 col14\" >1.62</td>\n",
+ " <td id=\"T_119ac_row8_col15\" class=\"data row8 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_119ac_level0_row9\" class=\"row_heading level0 row9\" >50% (0 1 0 1)</th>\n",
+ " <td id=\"T_119ac_row9_col0\" class=\"data row9 col0\" >88.06</td>\n",
+ " <td id=\"T_119ac_row9_col1\" class=\"data row9 col1\" >3.00</td>\n",
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+ " <td id=\"T_b1609_row3_col6\" class=\"data row3 col6\" >95.64</td>\n",
+ " <td id=\"T_b1609_row3_col7\" class=\"data row3 col7\" >2.74</td>\n",
+ " <td id=\"T_b1609_row3_col8\" class=\"data row3 col8\" >1.56</td>\n",
+ " <td id=\"T_b1609_row3_col9\" class=\"data row3 col9\" >0.07</td>\n",
+ " <td id=\"T_b1609_row3_col10\" class=\"data row3 col10\" >83.33</td>\n",
+ " <td id=\"T_b1609_row3_col11\" class=\"data row3 col11\" >2.95</td>\n",
+ " <td id=\"T_b1609_row3_col12\" class=\"data row3 col12\" >36.27</td>\n",
+ " <td id=\"T_b1609_row3_col13\" class=\"data row3 col13\" >3.33</td>\n",
+ " <td id=\"T_b1609_row3_col14\" class=\"data row3 col14\" >1.51</td>\n",
+ " <td id=\"T_b1609_row3_col15\" class=\"data row3 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row4\" class=\"row_heading level0 row4\" >25% (0 0 0 1)</th>\n",
+ " <td id=\"T_b1609_row4_col0\" class=\"data row4 col0\" >95.28</td>\n",
+ " <td id=\"T_b1609_row4_col1\" class=\"data row4 col1\" >2.35</td>\n",
+ " <td id=\"T_b1609_row4_col2\" class=\"data row4 col2\" >94.09</td>\n",
+ " <td id=\"T_b1609_row4_col3\" class=\"data row4 col3\" >2.55</td>\n",
+ " <td id=\"T_b1609_row4_col4\" class=\"data row4 col4\" >91.95</td>\n",
+ " <td id=\"T_b1609_row4_col5\" class=\"data row4 col5\" >1.80</td>\n",
+ " <td id=\"T_b1609_row4_col6\" class=\"data row4 col6\" >95.65</td>\n",
+ " <td id=\"T_b1609_row4_col7\" class=\"data row4 col7\" >2.60</td>\n",
+ " <td id=\"T_b1609_row4_col8\" class=\"data row4 col8\" >1.54</td>\n",
+ " <td id=\"T_b1609_row4_col9\" class=\"data row4 col9\" >0.07</td>\n",
+ " <td id=\"T_b1609_row4_col10\" class=\"data row4 col10\" >84.13</td>\n",
+ " <td id=\"T_b1609_row4_col11\" class=\"data row4 col11\" >3.48</td>\n",
+ " <td id=\"T_b1609_row4_col12\" class=\"data row4 col12\" >36.22</td>\n",
+ " <td id=\"T_b1609_row4_col13\" class=\"data row4 col13\" >3.44</td>\n",
+ " <td id=\"T_b1609_row4_col14\" class=\"data row4 col14\" >1.52</td>\n",
+ " <td id=\"T_b1609_row4_col15\" class=\"data row4 col15\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row5\" class=\"row_heading level0 row5\" >50% (1 1 0 0)</th>\n",
+ " <td id=\"T_b1609_row5_col0\" class=\"data row5 col0\" >95.08</td>\n",
+ " <td id=\"T_b1609_row5_col1\" class=\"data row5 col1\" >2.23</td>\n",
+ " <td id=\"T_b1609_row5_col2\" class=\"data row5 col2\" >94.72</td>\n",
+ " <td id=\"T_b1609_row5_col3\" class=\"data row5 col3\" >2.77</td>\n",
+ " <td id=\"T_b1609_row5_col4\" class=\"data row5 col4\" >92.58</td>\n",
+ " <td id=\"T_b1609_row5_col5\" class=\"data row5 col5\" >1.84</td>\n",
+ " <td id=\"T_b1609_row5_col6\" class=\"data row5 col6\" >97.30</td>\n",
+ " <td id=\"T_b1609_row5_col7\" class=\"data row5 col7\" >1.81</td>\n",
+ " <td id=\"T_b1609_row5_col8\" class=\"data row5 col8\" >1.45</td>\n",
+ " <td id=\"T_b1609_row5_col9\" class=\"data row5 col9\" >0.07</td>\n",
+ " <td id=\"T_b1609_row5_col10\" class=\"data row5 col10\" >63.20</td>\n",
+ " <td id=\"T_b1609_row5_col11\" class=\"data row5 col11\" >3.87</td>\n",
+ " <td id=\"T_b1609_row5_col12\" class=\"data row5 col12\" >36.02</td>\n",
+ " <td id=\"T_b1609_row5_col13\" class=\"data row5 col13\" >3.31</td>\n",
+ " <td id=\"T_b1609_row5_col14\" class=\"data row5 col14\" >1.14</td>\n",
+ " <td id=\"T_b1609_row5_col15\" class=\"data row5 col15\" >0.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row6\" class=\"row_heading level0 row6\" >50% (1 0 1 0)</th>\n",
+ " <td id=\"T_b1609_row6_col0\" class=\"data row6 col0\" >94.75</td>\n",
+ " <td id=\"T_b1609_row6_col1\" class=\"data row6 col1\" >2.17</td>\n",
+ " <td id=\"T_b1609_row6_col2\" class=\"data row6 col2\" >94.21</td>\n",
+ " <td id=\"T_b1609_row6_col3\" class=\"data row6 col3\" >2.72</td>\n",
+ " <td id=\"T_b1609_row6_col4\" class=\"data row6 col4\" >92.53</td>\n",
+ " <td id=\"T_b1609_row6_col5\" class=\"data row6 col5\" >1.68</td>\n",
+ " <td id=\"T_b1609_row6_col6\" class=\"data row6 col6\" >97.21</td>\n",
+ " <td id=\"T_b1609_row6_col7\" class=\"data row6 col7\" >2.15</td>\n",
+ " <td id=\"T_b1609_row6_col8\" class=\"data row6 col8\" >1.47</td>\n",
+ " <td id=\"T_b1609_row6_col9\" class=\"data row6 col9\" >0.06</td>\n",
+ " <td id=\"T_b1609_row6_col10\" class=\"data row6 col10\" >53.64</td>\n",
+ " <td id=\"T_b1609_row6_col11\" class=\"data row6 col11\" >4.45</td>\n",
+ " <td id=\"T_b1609_row6_col12\" class=\"data row6 col12\" >36.04</td>\n",
+ " <td id=\"T_b1609_row6_col13\" class=\"data row6 col13\" >3.27</td>\n",
+ " <td id=\"T_b1609_row6_col14\" class=\"data row6 col14\" >0.97</td>\n",
+ " <td id=\"T_b1609_row6_col15\" class=\"data row6 col15\" >0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row7\" class=\"row_heading level0 row7\" >50% (1 0 0 1)</th>\n",
+ " <td id=\"T_b1609_row7_col0\" class=\"data row7 col0\" >94.96</td>\n",
+ " <td id=\"T_b1609_row7_col1\" class=\"data row7 col1\" >2.29</td>\n",
+ " <td id=\"T_b1609_row7_col2\" class=\"data row7 col2\" >94.24</td>\n",
+ " <td id=\"T_b1609_row7_col3\" class=\"data row7 col3\" >2.59</td>\n",
+ " <td id=\"T_b1609_row7_col4\" class=\"data row7 col4\" >92.95</td>\n",
+ " <td id=\"T_b1609_row7_col5\" class=\"data row7 col5\" >1.82</td>\n",
+ " <td id=\"T_b1609_row7_col6\" class=\"data row7 col6\" >97.76</td>\n",
+ " <td id=\"T_b1609_row7_col7\" class=\"data row7 col7\" >1.93</td>\n",
+ " <td id=\"T_b1609_row7_col8\" class=\"data row7 col8\" >1.47</td>\n",
+ " <td id=\"T_b1609_row7_col9\" class=\"data row7 col9\" >0.06</td>\n",
+ " <td id=\"T_b1609_row7_col10\" class=\"data row7 col10\" >54.61</td>\n",
+ " <td id=\"T_b1609_row7_col11\" class=\"data row7 col11\" >4.74</td>\n",
+ " <td id=\"T_b1609_row7_col12\" class=\"data row7 col12\" >36.13</td>\n",
+ " <td id=\"T_b1609_row7_col13\" class=\"data row7 col13\" >3.28</td>\n",
+ " <td id=\"T_b1609_row7_col14\" class=\"data row7 col14\" >0.99</td>\n",
+ " <td id=\"T_b1609_row7_col15\" class=\"data row7 col15\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row8\" class=\"row_heading level0 row8\" >50% (0 1 1 0)</th>\n",
+ " <td id=\"T_b1609_row8_col0\" class=\"data row8 col0\" >95.13</td>\n",
+ " <td id=\"T_b1609_row8_col1\" class=\"data row8 col1\" >2.29</td>\n",
+ " <td id=\"T_b1609_row8_col2\" class=\"data row8 col2\" >94.04</td>\n",
+ " <td id=\"T_b1609_row8_col3\" class=\"data row8 col3\" >2.80</td>\n",
+ " <td id=\"T_b1609_row8_col4\" class=\"data row8 col4\" >91.90</td>\n",
+ " <td id=\"T_b1609_row8_col5\" class=\"data row8 col5\" >1.85</td>\n",
+ " <td id=\"T_b1609_row8_col6\" class=\"data row8 col6\" >97.04</td>\n",
+ " <td id=\"T_b1609_row8_col7\" class=\"data row8 col7\" >2.38</td>\n",
+ " <td id=\"T_b1609_row8_col8\" class=\"data row8 col8\" >1.51</td>\n",
+ " <td id=\"T_b1609_row8_col9\" class=\"data row8 col9\" >0.07</td>\n",
+ " <td id=\"T_b1609_row8_col10\" class=\"data row8 col10\" >62.85</td>\n",
+ " <td id=\"T_b1609_row8_col11\" class=\"data row8 col11\" >3.35</td>\n",
+ " <td id=\"T_b1609_row8_col12\" class=\"data row8 col12\" >36.10</td>\n",
+ " <td id=\"T_b1609_row8_col13\" class=\"data row8 col13\" >3.38</td>\n",
+ " <td id=\"T_b1609_row8_col14\" class=\"data row8 col14\" >1.14</td>\n",
+ " <td id=\"T_b1609_row8_col15\" class=\"data row8 col15\" >0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row9\" class=\"row_heading level0 row9\" >50% (0 1 0 1)</th>\n",
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+ " <td id=\"T_b1609_row9_col1\" class=\"data row9 col1\" >2.19</td>\n",
+ " <td id=\"T_b1609_row9_col2\" class=\"data row9 col2\" >94.14</td>\n",
+ " <td id=\"T_b1609_row9_col3\" class=\"data row9 col3\" >2.74</td>\n",
+ " <td id=\"T_b1609_row9_col4\" class=\"data row9 col4\" >92.50</td>\n",
+ " <td id=\"T_b1609_row9_col5\" class=\"data row9 col5\" >1.69</td>\n",
+ " <td id=\"T_b1609_row9_col6\" class=\"data row9 col6\" >97.36</td>\n",
+ " <td id=\"T_b1609_row9_col7\" class=\"data row9 col7\" >2.14</td>\n",
+ " <td id=\"T_b1609_row9_col8\" class=\"data row9 col8\" >1.48</td>\n",
+ " <td id=\"T_b1609_row9_col9\" class=\"data row9 col9\" >0.06</td>\n",
+ " <td id=\"T_b1609_row9_col10\" class=\"data row9 col10\" >64.84</td>\n",
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+ " <td id=\"T_b1609_row9_col13\" class=\"data row9 col13\" >3.23</td>\n",
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+ " <td id=\"T_b1609_row9_col15\" class=\"data row9 col15\" >0.07</td>\n",
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+ " <tr>\n",
+ " <th id=\"T_b1609_level0_row10\" class=\"row_heading level0 row10\" >50% (0 0 1 1)</th>\n",
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+ " <td id=\"T_b1609_row10_col2\" class=\"data row10 col2\" >94.25</td>\n",
+ " <td id=\"T_b1609_row10_col3\" class=\"data row10 col3\" >2.62</td>\n",
+ " <td id=\"T_b1609_row10_col4\" class=\"data row10 col4\" >92.53</td>\n",
+ " <td id=\"T_b1609_row10_col5\" class=\"data row10 col5\" >1.77</td>\n",
+ " <td id=\"T_b1609_row10_col6\" class=\"data row10 col6\" >97.30</td>\n",
+ " <td id=\"T_b1609_row10_col7\" class=\"data row10 col7\" >2.04</td>\n",
+ " <td id=\"T_b1609_row10_col8\" class=\"data row10 col8\" >1.46</td>\n",
+ " <td id=\"T_b1609_row10_col9\" class=\"data row10 col9\" >0.07</td>\n",
+ " <td id=\"T_b1609_row10_col10\" class=\"data row10 col10\" >48.54</td>\n",
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+ " <td id=\"T_b1609_row10_col12\" class=\"data row10 col12\" >36.01</td>\n",
+ " <td id=\"T_b1609_row10_col13\" class=\"data row10 col13\" >3.29</td>\n",
+ " <td id=\"T_b1609_row10_col14\" class=\"data row10 col14\" >0.88</td>\n",
+ " <td id=\"T_b1609_row10_col15\" class=\"data row10 col15\" >0.08</td>\n",
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+ " <tr>\n",
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+ " <td id=\"T_b1609_row11_col1\" class=\"data row11 col1\" >2.16</td>\n",
+ " <td id=\"T_b1609_row11_col2\" class=\"data row11 col2\" >94.04</td>\n",
+ " <td id=\"T_b1609_row11_col3\" class=\"data row11 col3\" >2.81</td>\n",
+ " <td id=\"T_b1609_row11_col4\" class=\"data row11 col4\" >93.82</td>\n",
+ " <td id=\"T_b1609_row11_col5\" class=\"data row11 col5\" >1.67</td>\n",
+ " <td id=\"T_b1609_row11_col6\" class=\"data row11 col6\" >98.39</td>\n",
+ " <td id=\"T_b1609_row11_col7\" class=\"data row11 col7\" >1.60</td>\n",
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+ " <td id=\"T_b1609_row11_col11\" class=\"data row11 col11\" >3.96</td>\n",
+ " <td id=\"T_b1609_row11_col12\" class=\"data row11 col12\" >35.58</td>\n",
+ " <td id=\"T_b1609_row11_col13\" class=\"data row11 col13\" >3.27</td>\n",
+ " <td id=\"T_b1609_row11_col14\" class=\"data row11 col14\" >0.68</td>\n",
+ " <td id=\"T_b1609_row11_col15\" class=\"data row11 col15\" >0.07</td>\n",
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+ " <tr>\n",
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+ " <td id=\"T_b1609_row12_col2\" class=\"data row12 col2\" >94.06</td>\n",
+ " <td id=\"T_b1609_row12_col3\" class=\"data row12 col3\" >2.54</td>\n",
+ " <td id=\"T_b1609_row12_col4\" class=\"data row12 col4\" >94.42</td>\n",
+ " <td id=\"T_b1609_row12_col5\" class=\"data row12 col5\" >1.61</td>\n",
+ " <td id=\"T_b1609_row12_col6\" class=\"data row12 col6\" >98.71</td>\n",
+ " <td id=\"T_b1609_row12_col7\" class=\"data row12 col7\" >1.50</td>\n",
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+ " <td id=\"T_b1609_row12_col13\" class=\"data row12 col13\" >3.28</td>\n",
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+ " <td id=\"T_b1609_row12_col15\" class=\"data row12 col15\" >0.07</td>\n",
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+ " <tr>\n",
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+ " <td id=\"T_b1609_row14_col13\" class=\"data row14 col13\" >3.21</td>\n",
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+ " <td id=\"T_f92a8_row9_col4\" class=\"data row9 col4\" >96.03</td>\n",
+ " <td id=\"T_f92a8_row9_col5\" class=\"data row9 col5\" >1.76</td>\n",
+ " <td id=\"T_f92a8_row9_col6\" class=\"data row9 col6\" >98.61</td>\n",
+ " <td id=\"T_f92a8_row9_col7\" class=\"data row9 col7\" >2.36</td>\n",
+ " <td id=\"T_f92a8_row9_col8\" class=\"data row9 col8\" >1.25</td>\n",
+ " <td id=\"T_f92a8_row9_col9\" class=\"data row9 col9\" >0.07</td>\n",
+ " <td id=\"T_f92a8_row9_col10\" class=\"data row9 col10\" >30.49</td>\n",
+ " <td id=\"T_f92a8_row9_col11\" class=\"data row9 col11\" >2.38</td>\n",
+ " <td id=\"T_f92a8_row9_col12\" class=\"data row9 col12\" >64.96</td>\n",
+ " <td id=\"T_f92a8_row9_col13\" class=\"data row9 col13\" >2.99</td>\n",
+ " <td id=\"T_f92a8_row9_col14\" class=\"data row9 col14\" >0.55</td>\n",
+ " <td id=\"T_f92a8_row9_col15\" class=\"data row9 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row10\" class=\"row_heading level0 row10\" >50% (0 0 1 1)</th>\n",
+ " <td id=\"T_f92a8_row10_col0\" class=\"data row10 col0\" >98.53</td>\n",
+ " <td id=\"T_f92a8_row10_col1\" class=\"data row10 col1\" >1.41</td>\n",
+ " <td id=\"T_f92a8_row10_col2\" class=\"data row10 col2\" >98.61</td>\n",
+ " <td id=\"T_f92a8_row10_col3\" class=\"data row10 col3\" >1.35</td>\n",
+ " <td id=\"T_f92a8_row10_col4\" class=\"data row10 col4\" >95.56</td>\n",
+ " <td id=\"T_f92a8_row10_col5\" class=\"data row10 col5\" >1.64</td>\n",
+ " <td id=\"T_f92a8_row10_col6\" class=\"data row10 col6\" >98.96</td>\n",
+ " <td id=\"T_f92a8_row10_col7\" class=\"data row10 col7\" >1.88</td>\n",
+ " <td id=\"T_f92a8_row10_col8\" class=\"data row10 col8\" >1.23</td>\n",
+ " <td id=\"T_f92a8_row10_col9\" class=\"data row10 col9\" >0.07</td>\n",
+ " <td id=\"T_f92a8_row10_col10\" class=\"data row10 col10\" >17.54</td>\n",
+ " <td id=\"T_f92a8_row10_col11\" class=\"data row10 col11\" >2.92</td>\n",
+ " <td id=\"T_f92a8_row10_col12\" class=\"data row10 col12\" >64.99</td>\n",
+ " <td id=\"T_f92a8_row10_col13\" class=\"data row10 col13\" >2.90</td>\n",
+ " <td id=\"T_f92a8_row10_col14\" class=\"data row10 col14\" >0.32</td>\n",
+ " <td id=\"T_f92a8_row10_col15\" class=\"data row10 col15\" >0.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row11\" class=\"row_heading level0 row11\" >75% (1 1 1 0)</th>\n",
+ " <td id=\"T_f92a8_row11_col0\" class=\"data row11 col0\" >98.40</td>\n",
+ " <td id=\"T_f92a8_row11_col1\" class=\"data row11 col1\" >1.54</td>\n",
+ " <td id=\"T_f92a8_row11_col2\" class=\"data row11 col2\" >98.51</td>\n",
+ " <td id=\"T_f92a8_row11_col3\" class=\"data row11 col3\" >1.38</td>\n",
+ " <td id=\"T_f92a8_row11_col4\" class=\"data row11 col4\" >97.38</td>\n",
+ " <td id=\"T_f92a8_row11_col5\" class=\"data row11 col5\" >1.39</td>\n",
+ " <td id=\"T_f92a8_row11_col6\" class=\"data row11 col6\" >99.59</td>\n",
+ " <td id=\"T_f92a8_row11_col7\" class=\"data row11 col7\" >0.83</td>\n",
+ " <td id=\"T_f92a8_row11_col8\" class=\"data row11 col8\" >1.20</td>\n",
+ " <td id=\"T_f92a8_row11_col9\" class=\"data row11 col9\" >0.07</td>\n",
+ " <td id=\"T_f92a8_row11_col10\" class=\"data row11 col10\" >14.14</td>\n",
+ " <td id=\"T_f92a8_row11_col11\" class=\"data row11 col11\" >2.11</td>\n",
+ " <td id=\"T_f92a8_row11_col12\" class=\"data row11 col12\" >64.48</td>\n",
+ " <td id=\"T_f92a8_row11_col13\" class=\"data row11 col13\" >2.98</td>\n",
+ " <td id=\"T_f92a8_row11_col14\" class=\"data row11 col14\" >0.26</td>\n",
+ " <td id=\"T_f92a8_row11_col15\" class=\"data row11 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row12\" class=\"row_heading level0 row12\" >75% (1 1 0 1)</th>\n",
+ " <td id=\"T_f92a8_row12_col0\" class=\"data row12 col0\" >98.55</td>\n",
+ " <td id=\"T_f92a8_row12_col1\" class=\"data row12 col1\" >1.41</td>\n",
+ " <td id=\"T_f92a8_row12_col2\" class=\"data row12 col2\" >98.64</td>\n",
+ " <td id=\"T_f92a8_row12_col3\" class=\"data row12 col3\" >1.39</td>\n",
+ " <td id=\"T_f92a8_row12_col4\" class=\"data row12 col4\" >98.01</td>\n",
+ " <td id=\"T_f92a8_row12_col5\" class=\"data row12 col5\" >1.21</td>\n",
+ " <td id=\"T_f92a8_row12_col6\" class=\"data row12 col6\" >99.78</td>\n",
+ " <td id=\"T_f92a8_row12_col7\" class=\"data row12 col7\" >0.66</td>\n",
+ " <td id=\"T_f92a8_row12_col8\" class=\"data row12 col8\" >1.17</td>\n",
+ " <td id=\"T_f92a8_row12_col9\" class=\"data row12 col9\" >0.05</td>\n",
+ " <td id=\"T_f92a8_row12_col10\" class=\"data row12 col10\" >14.99</td>\n",
+ " <td id=\"T_f92a8_row12_col11\" class=\"data row12 col11\" >2.15</td>\n",
+ " <td id=\"T_f92a8_row12_col12\" class=\"data row12 col12\" >64.42</td>\n",
+ " <td id=\"T_f92a8_row12_col13\" class=\"data row12 col13\" >2.97</td>\n",
+ " <td id=\"T_f92a8_row12_col14\" class=\"data row12 col14\" >0.27</td>\n",
+ " <td id=\"T_f92a8_row12_col15\" class=\"data row12 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row13\" class=\"row_heading level0 row13\" >75% (1 0 1 1)</th>\n",
+ " <td id=\"T_f92a8_row13_col0\" class=\"data row13 col0\" >98.49</td>\n",
+ " <td id=\"T_f92a8_row13_col1\" class=\"data row13 col1\" >1.37</td>\n",
+ " <td id=\"T_f92a8_row13_col2\" class=\"data row13 col2\" >98.42</td>\n",
+ " <td id=\"T_f92a8_row13_col3\" class=\"data row13 col3\" >1.32</td>\n",
+ " <td id=\"T_f92a8_row13_col4\" class=\"data row13 col4\" >97.88</td>\n",
+ " <td id=\"T_f92a8_row13_col5\" class=\"data row13 col5\" >1.27</td>\n",
+ " <td id=\"T_f92a8_row13_col6\" class=\"data row13 col6\" >99.77</td>\n",
+ " <td id=\"T_f92a8_row13_col7\" class=\"data row13 col7\" >0.77</td>\n",
+ " <td id=\"T_f92a8_row13_col8\" class=\"data row13 col8\" >1.15</td>\n",
+ " <td id=\"T_f92a8_row13_col9\" class=\"data row13 col9\" >0.05</td>\n",
+ " <td id=\"T_f92a8_row13_col10\" class=\"data row13 col10\" >8.16</td>\n",
+ " <td id=\"T_f92a8_row13_col11\" class=\"data row13 col11\" >2.07</td>\n",
+ " <td id=\"T_f92a8_row13_col12\" class=\"data row13 col12\" >64.49</td>\n",
+ " <td id=\"T_f92a8_row13_col13\" class=\"data row13 col13\" >2.88</td>\n",
+ " <td id=\"T_f92a8_row13_col14\" class=\"data row13 col14\" >0.15</td>\n",
+ " <td id=\"T_f92a8_row13_col15\" class=\"data row13 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row14\" class=\"row_heading level0 row14\" >75% (0 1 1 1)</th>\n",
+ " <td id=\"T_f92a8_row14_col0\" class=\"data row14 col0\" >98.50</td>\n",
+ " <td id=\"T_f92a8_row14_col1\" class=\"data row14 col1\" >1.48</td>\n",
+ " <td id=\"T_f92a8_row14_col2\" class=\"data row14 col2\" >98.74</td>\n",
+ " <td id=\"T_f92a8_row14_col3\" class=\"data row14 col3\" >1.26</td>\n",
+ " <td id=\"T_f92a8_row14_col4\" class=\"data row14 col4\" >97.10</td>\n",
+ " <td id=\"T_f92a8_row14_col5\" class=\"data row14 col5\" >1.41</td>\n",
+ " <td id=\"T_f92a8_row14_col6\" class=\"data row14 col6\" >99.45</td>\n",
+ " <td id=\"T_f92a8_row14_col7\" class=\"data row14 col7\" >1.59</td>\n",
+ " <td id=\"T_f92a8_row14_col8\" class=\"data row14 col8\" >1.18</td>\n",
+ " <td id=\"T_f92a8_row14_col9\" class=\"data row14 col9\" >0.06</td>\n",
+ " <td id=\"T_f92a8_row14_col10\" class=\"data row14 col10\" >11.59</td>\n",
+ " <td id=\"T_f92a8_row14_col11\" class=\"data row14 col11\" >2.46</td>\n",
+ " <td id=\"T_f92a8_row14_col12\" class=\"data row14 col12\" >64.63</td>\n",
+ " <td id=\"T_f92a8_row14_col13\" class=\"data row14 col13\" >2.91</td>\n",
+ " <td id=\"T_f92a8_row14_col14\" class=\"data row14 col14\" >0.21</td>\n",
+ " <td id=\"T_f92a8_row14_col15\" class=\"data row14 col15\" >0.04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f92a8_level0_row15\" class=\"row_heading level0 row15\" >100% (1 1 1 1)</th>\n",
+ " <td id=\"T_f92a8_row15_col0\" class=\"data row15 col0\" >98.46</td>\n",
+ " <td id=\"T_f92a8_row15_col1\" class=\"data row15 col1\" >1.45</td>\n",
+ " <td id=\"T_f92a8_row15_col2\" class=\"data row15 col2\" >98.50</td>\n",
+ " <td id=\"T_f92a8_row15_col3\" class=\"data row15 col3\" >1.26</td>\n",
+ " <td id=\"T_f92a8_row15_col4\" class=\"data row15 col4\" >98.91</td>\n",
+ " <td id=\"T_f92a8_row15_col5\" class=\"data row15 col5\" >0.95</td>\n",
+ " <td id=\"T_f92a8_row15_col6\" class=\"data row15 col6\" >99.89</td>\n",
+ " <td id=\"T_f92a8_row15_col7\" class=\"data row15 col7\" >0.46</td>\n",
+ " <td id=\"T_f92a8_row15_col8\" class=\"data row15 col8\" >1.12</td>\n",
+ " <td id=\"T_f92a8_row15_col9\" class=\"data row15 col9\" >0.05</td>\n",
+ " <td id=\"T_f92a8_row15_col10\" class=\"data row15 col10\" >4.84</td>\n",
+ " <td id=\"T_f92a8_row15_col11\" class=\"data row15 col11\" >1.51</td>\n",
+ " <td id=\"T_f92a8_row15_col12\" class=\"data row15 col12\" >64.20</td>\n",
+ " <td id=\"T_f92a8_row15_col13\" class=\"data row15 col13\" >2.95</td>\n",
+ " <td id=\"T_f92a8_row15_col14\" class=\"data row15 col14\" >0.09</td>\n",
+ " <td id=\"T_f92a8_row15_col15\" class=\"data row15 col15\" >0.03</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7c31db9db9d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analysis_plot.draw_table_consolidate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Large"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"large\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"large\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Medium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"medium\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"medium\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Small"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.plot_graph(\"small\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_bidded(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_qos(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_plot.draw_table_money_and_energy(\"small\", tables_type=\"both\", simplified=True)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "opt",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ExecutionScripts/1_milp.py b/ExecutionScripts/1_milp.py
new file mode 100644
index 0000000..0d919c8
--- /dev/null
+++ b/ExecutionScripts/1_milp.py
@@ -0,0 +1,68 @@
+import sys
+sys.path.append(".")
+
+from GenerateExperiments.experiments import Experiments, Runner
+from GenerateExperiments.create_instance import create_large_instance, create_medium_instance, create_small_instance
+from RenewableData.renewable import renewable_production_bell
+import os
+import argparse
+
+os.makedirs('XPs/1_bell/large', exist_ok = True)
+os.makedirs('XPs/1_bell/medium', exist_ok = True)
+os.makedirs('XPs/1_bell/small', exist_ok = True)
+
+parser = argparse.ArgumentParser("1_exps_milp_bell.py")
+parser.add_argument("size", help="The size of the exps", type=str, choices=['small', 'medium', 'large', 'all'], default='all', const='all', nargs='?')
+args = parser.parse_args()
+
+small = False
+medium = False
+large = False
+if args.size == "small":
+ small = True
+elif args.size == "medium":
+ medium = True
+elif args.size == "large":
+ large = True
+else:
+ small = True
+ medium = True
+ large = True
+
+# Dcs
+for seed in range(3, 103):
+ if large:
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/large/MILP_seed_" + str(seed) + ".json",
+ log_file="XPs/1_bell/large/MILP_seed_" + str(seed) + ".log",
+ runner=Runner)
+ exp.start()
+
+ if medium:
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/medium/MILP_seed_" + str(seed) + ".json",
+ log_file="XPs/1_bell/medium/MILP_seed_" + str(seed) + ".log",
+ runner=Runner)
+ exp.start()
+
+ if small:
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/small/MILP_seed_" + str(seed) + ".json",
+ log_file="XPs/1_bell/small/MILP_seed_" + str(seed) + ".log",
+ runner=Runner)
+ exp.start()
diff --git a/ExecutionScripts/2_exps_bell.py b/ExecutionScripts/2_exps_bell.py
new file mode 100644
index 0000000..04f4630
--- /dev/null
+++ b/ExecutionScripts/2_exps_bell.py
@@ -0,0 +1,89 @@
+import sys
+sys.path.append(".")
+
+from GenerateExperiments.experiments import Experiments, HeuristicRunner
+from GenerateExperiments.create_instance import create_large_instance, create_medium_instance, create_small_instance
+from RenewableData.renewable import renewable_production_bell
+import os
+
+os.makedirs('XPs/1_bell/large', exist_ok = True)
+os.makedirs('XPs/1_bell/medium', exist_ok = True)
+os.makedirs('XPs/1_bell/small', exist_ok = True)
+
+for seed in range(3, 103):
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_bell(0, cDC=[16])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/large/simple_centralized_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_bell(0, cDC=[16])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/medium/simple_centralized_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_bell(0, cDC=[16])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/small/simple_centralized_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ for s0, s1, s2, s3 in [[0,0,0,0], [1,0,0,0], [1,1,0,0], [1,1,1,0], [1,1,1,1]]:
+ aggre = 0
+ aggre_sum = sum([s0, s1, s2, s3]) / 4 * 100
+ if (aggre_sum > 0):
+ aggre = [s0, s1, s2, s3]
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/large/simple_aggre_" + str(int(aggre_sum)) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/medium/simple_aggre_" + str(int(aggre_sum)) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_bell()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/1_bell/small/simple_aggre_" + str(int(aggre_sum)) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre},
+ seed=seed)
+ exp.start()
\ No newline at end of file
diff --git a/ExecutionScripts/3_exps_amplitude.py b/ExecutionScripts/3_exps_amplitude.py
new file mode 100644
index 0000000..d9cb457
--- /dev/null
+++ b/ExecutionScripts/3_exps_amplitude.py
@@ -0,0 +1,54 @@
+import sys
+sys.path.append(".")
+
+from GenerateExperiments.experiments import Experiments, HeuristicRunner
+import os
+from GenerateExperiments.create_instance import create_large_instance, create_medium_instance, create_small_instance
+from RenewableData.renewable import renewable_production_syntactic
+
+os.makedirs('XPs/2_amplitude/large', exist_ok = True)
+os.makedirs('XPs/2_amplitude/medium', exist_ok = True)
+os.makedirs('XPs/2_amplitude/small', exist_ok = True)
+
+for seed in range(3, 103):
+ for s0 in [0, 1]:
+ for s1 in [0, 1]:
+ for s2 in [0, 1]:
+ for s3 in [0, 1]:
+ aggre = 0
+ aggre_sum = sum([s0, s1, s2, s3]) / 4 * 100
+ if (aggre_sum > 0):
+ aggre = [s0, s1, s2, s3]
+
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_syntactic()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/2_amplitude/large/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
+
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_syntactic()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/2_amplitude/medium/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
+
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_syntactic()),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/2_amplitude/small/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
\ No newline at end of file
diff --git a/ExecutionScripts/4_exps_real.py b/ExecutionScripts/4_exps_real.py
new file mode 100644
index 0000000..91bb7a6
--- /dev/null
+++ b/ExecutionScripts/4_exps_real.py
@@ -0,0 +1,92 @@
+import sys
+sys.path.append(".")
+
+from GenerateExperiments.experiments import Experiments, HeuristicRunner
+import os
+from GenerateExperiments.create_instance import create_large_instance, create_medium_instance, create_small_instance
+from RenewableData.renewable import renewable_production_real_norm_total
+
+os.makedirs('XPs/3_real/large', exist_ok = True)
+os.makedirs('XPs/3_real/medium', exist_ok = True)
+os.makedirs('XPs/3_real/small', exist_ok = True)
+
+for seed in range(3, 103):
+ cities = ["Canberra", "Dubai", "Paris", "Sao_Paulo"]
+ for dc in range(len(cities)):
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=[cities[dc]])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/large/simple_centralized_dc_" + str(dc) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=[cities[dc]])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/medium/simple_centralized_dc_" + str(dc) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=[cities[dc]])),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[16]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/small/simple_centralized_dc_" + str(dc) + "_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':0},
+ seed=seed)
+ exp.start()
+
+ for s0 in [0, 1]:
+ for s1 in [0, 1]:
+ for s2 in [0, 1]:
+ for s3 in [0, 1]:
+ aggre = 0
+ aggre_sum = sum([s0, s1, s2, s3]) / 4 * 100
+ if (aggre_sum > 0):
+ aggre = [s0, s1, s2, s3]
+
+ exp = Experiments(create_large_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=cities)),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/large/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
+
+ exp = Experiments(create_medium_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=cities)),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/medium/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
+
+ exp = Experiments(create_small_instance(seed=seed, renew=renewable_production_real_norm_total(total_energy=55296, cities=cities)),
+ phase=[True],
+ pow_variable=[0.6],
+ cDC=[[4,4,4,4]],
+ do_viz=False,
+ do_save=True,
+ output_file="XPs/3_real/small/simple_aggre_" + str(int(aggre_sum)) + "_dc_" + str(s0) + "_" + str(s1) + "_" + str(s2) + "_" + str(s3) + "_seed_" + str(seed) + ".json",
+ runner=HeuristicRunner,
+ runner_arg={'aggressivity':aggre})
+ exp.start()
\ No newline at end of file
diff --git a/ExecutionScripts/__init__.py b/ExecutionScripts/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateExperiments/__init__.py b/GenerateExperiments/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateExperiments/create_instance.py b/GenerateExperiments/create_instance.py
new file mode 100644
index 0000000..bec1eb5
--- /dev/null
+++ b/GenerateExperiments/create_instance.py
@@ -0,0 +1,57 @@
+from GenerateInstance.Instance import Instance
+
+# demand of ~30% (30.010502047110858%) of all servers on all the time
+def create_large_instance(seed=42, renew=[]):
+ total_time = 72
+ jobs = 238
+ services = 238
+ return Instance(jobs, services, total_time, seed=seed, renewPow=renew)
+
+# demand of ~20% (19.994626128183818%) of all servers on all the time
+def create_medium_instance(seed=42, renew=[]):
+ total_time = 72
+ jobs = 159
+ services = 159
+ return Instance(jobs, services, total_time, seed=seed, renewPow=renew)
+
+# demand of ~10% (10.073523014606495%) of all servers on all the time
+def create_small_instance(seed=42, renew=[]):
+ total_time = 72
+ jobs = 80
+ services = 80
+ return Instance(jobs, services, total_time, seed=seed, renewPow=renew)
+
+def verify_instances(total_cpu_power = 115200):
+ # Evaluate the instances
+ charge = []
+ for seed in range(3, 103):
+ instance = create_small_instance(seed=seed)
+ total_workload = 0
+ for j in instance.jobs:
+ total_workload += j.w * 100
+ for s in instance.services:
+ total_workload += sum(s.work) * 100
+ charge.append(total_workload / total_cpu_power)
+ print((sum(charge) / len(charge)) * 100, "%")
+
+ charge = []
+ for seed in range(3, 103):
+ instance = create_medium_instance(seed=seed)
+ total_workload = 0
+ for j in instance.jobs:
+ total_workload += j.w * 100
+ for s in instance.services:
+ total_workload += sum(s.work) * 100
+ charge.append(total_workload / total_cpu_power)
+ print((sum(charge) / len(charge)) * 100, "%")
+
+ charge = []
+ for seed in range(3, 103):
+ instance = create_large_instance(seed=seed)
+ total_workload = 0
+ for j in instance.jobs:
+ total_workload += j.w * 100
+ for s in instance.services:
+ total_workload += sum(s.work) * 100
+ charge.append(total_workload / total_cpu_power)
+ print((sum(charge) / len(charge)) * 100, "%")
\ No newline at end of file
diff --git a/GenerateExperiments/expeditions.py b/GenerateExperiments/expeditions.py
new file mode 100644
index 0000000..3146e5e
--- /dev/null
+++ b/GenerateExperiments/expeditions.py
@@ -0,0 +1,7 @@
+from collections import namedtuple
+
+from GenerateExperiments.experiments import Runner
+
+
+if __name__ == '__main__':
+ pass
\ No newline at end of file
diff --git a/GenerateExperiments/experiments.py b/GenerateExperiments/experiments.py
new file mode 100644
index 0000000..97fbacd
--- /dev/null
+++ b/GenerateExperiments/experiments.py
@@ -0,0 +1,426 @@
+from collections import namedtuple
+
+from pulp import GUROBI_CMD, COIN_CMD
+import random
+import numpy as np
+import pandas as pd
+import json
+from colorama import Fore, Style
+
+from GenerateInstance.Instance import Instance
+from GenerateInstance.Examples import example1
+from GenerateProblem.problem_gen import problem_gen
+from GenerateMetrics.metrics_gen import Metrics
+from GenerateVisualisation.visualisation_gen import Visualisation
+from Heuristic.Solver import Solver
+
+
+class Experiments:
+ def __init__(self, instance, flex=[0], energy_dist=[[None]],
+ pow_variable=[1.0], phase=[True], curve = ['bell'],
+ cDC=[[2, 2], [4], [1, 1, 1, 1], [1, 3]], do_viz=False, do_save=True, runner=None, runner_arg=None,
+ output_file="Data.json",do_test=False,bidder_param={'bidder':'Simple'}, model_file="", log_file="test.log", seed=42):
+ self.instance = instance
+
+ self.flex = flex
+ self.pow_variable = pow_variable
+ self.phase = phase
+ self.curve = curve
+ self.cDC = cDC
+ self.energy_dist = energy_dist
+ self.do_viz = do_viz
+ self.do_save = do_save
+ self.do_test=do_test
+ self.output_file = output_file
+
+ if runner is None:
+ self.runner = Runner(instance, runner_arg, self.do_viz, bidder_param, model_file, log_file, seed)
+ else:
+ self.runner = runner(instance, runner_arg, self.do_viz,bidder_param, model_file, log_file, seed)
+ self.storage = []
+
+
+ def start(self):
+ for nrg in self.pow_variable:
+ for c in self.cDC:
+ for phase in self.phase:
+ for curve in self.curve:
+ for flex in self.flex:
+ for dist in self.energy_dist:
+ self.storage.append(self.runner.run(phase, curve, c, nrg, flex, dist))
+
+ # Final_DF = pd.concat(self.storage, copy=True)
+ # Final_DF.reset_index(inplace=True)
+ # Final_DF.drop(labels=['index'], axis=1, inplace=True)
+
+ #Pour faire tests
+ if self.do_test:
+ #print('ICI',self.runner.test_money)
+ return(self.runner.test_money)
+
+
+ if self.do_save:
+ # Final_DF.to_csv
+ with open(self.output_file, "w") as outfile:
+ json.dump(self.storage, outfile, indent=4)
+ # Final_DF.to_json(self.output_file, indent=3)
+
+
+
+class Runner:
+ def __init__(self, instance, runner_arg=None, do_viz=False,bidder_param={'bidder':'Simple'}, model_file="", log_file="test.log", seed=42):
+ self.instance = instance
+ self.runner_arg = runner_arg
+ self.bidder_param=bidder_param
+ self.do_vis = do_viz
+ self.model_file = model_file
+ self.log_file = log_file
+
+ def create_dict(self, metrics, phase, c, nrg, flex, tag=None):
+ return metrics.solution
+
+ def run(self, phase, curve, DC, power_var, flex, dist, tag=None):
+ prob, snap_instance = self.instance.create_problem(phase, curve, DC, power_var, flex, dist)
+ # print(prob.to_dict())
+ # prob.writeMPS(self.model_file)
+ if (self.model_file != ""):
+ prob.to_json(self.model_file)
+ result = prob.solve(GUROBI_CMD(msg=1, timeLimit=7200, logPath=self.log_file, gapRel=0.05))
+
+ print(result)
+ #if prob.sol_status > 0:
+ if prob.status > 0:
+ sol = Solution(prob, snap_instance)
+ # A changer
+ self.test_money=sol.solution['TotalMoney'] + sol.solution['TotalPenalty']
+
+ #metrics = Metrics(sol, snap_instance)
+ #print('Metrics', metrics)
+
+ #print(sol.values)
+ if self.do_vis:
+ Visualisation(sol)
+ return self.create_dict(sol, phase, curve, power_var, flex, tag)
+ else:
+ print('Infeasable')
+ return {}
+
+
+class Solution:
+
+ def __init__(self, prob, instance, heuristic=False):
+ self.instance = instance
+ self.jobs = instance['jobs']
+ self.services = instance['services']
+ self.totalTime = instance['total_time']
+ self.nbJobs = instance['nbjobs']
+ self.nbServices = instance['nbservices']
+ self.pmin = instance['pmin']
+ self.pdyn = instance['pdyn']
+ self.cDC = instance['cDC']
+ self.renewPow = instance['renewPow']
+
+ self.prob = prob
+ self._raw = {}
+ self.values = {}
+ self.solution = {}
+ self.parse_solution()
+ self.compute_run_summary()
+
+ def add_value(self, variable, params, value):
+
+ def find_entry(current, _params, _value):
+ # get next key
+ key = _params.pop(0)
+ # check for last key
+ if len(_params) == 0:
+ current[key] = _value
+ return
+ # Create new dict if doesn't exist
+ if current.get(key, None) is None:
+ current[key] = dict()
+ # Go down the tree
+ find_entry(current[key], _params, _value)
+
+ # If variable has no params
+ if len(params) == 0:
+ self.values[variable] = value
+ return
+ # Check if it is stored yet
+ if self.values.get(variable, None) is None:
+ self.values[variable] = dict()
+ find_entry(self.values[variable], params, value)
+
+ def parse_solution(self):
+ distinct_vars = set()
+ for v in self.prob.variables():
+ name = v.name
+ value = v.varValue
+ # print(name, value)
+ # add to raw storage
+ self._raw[name] = value
+ # Get the variable name and its parameters
+ var = name.split('_')
+ if len(var) > 2:
+ print(Fore.GREEN + 'NO UNDERSCORES IN THE VARIABLES NAMES PLEASE OR EDIT ME!!!' + Style.RESET_ALL)
+ print('Also use commas to seperate parameters to the variables')
+ print(name)
+ continue
+ elif len(var) == 1:
+ params = []
+ else:
+ params = var[1].split(',')
+ pair = (var[0], len(params))
+ # add to distinct vars
+ distinct_vars.add(pair)
+ # add value to the variables
+ self.add_value(pair[0], params, value)
+
+ def compute_run_summary(self):
+ # print(self.values)
+ self.solution['jobs'] = []
+ self.solution['services'] = []
+
+ for j in range(self.nbJobs):
+ job = {}
+ job['id'] = j
+ job['arrival'] = self.jobs[j].a
+ job['workload'] = self.jobs[j].w
+ job['duedate'] = self.jobs[j].due
+ job['deadline'] = self.jobs[j].dead
+ job['min_price'] = self.jobs[j].pmin
+ job['max_price'] = self.jobs[j].pmax
+ job['min_penalty'] = self.jobs[j].penmin
+ job['max_penalty'] = self.jobs[j].penmax
+ job['deadline_penalty'] = self.jobs[j].pendead
+ self.solution['jobs'].append(job)
+
+ for j in range(self.nbServices):
+ job = {}
+ job['id'] = j
+ job['arrival'] = self.services[j].arrived
+ job['start'] = self.services[j].start
+ if (type(self.services[j].work) == list):
+ job['workload'] = self.services[j].work
+ else:
+ job['workload'] = self.services[j].work.tolist()
+ job['min_price'] = self.services[j].pmin
+ job['max_price'] = self.services[j].pmax
+ job['penalty'] = self.services[j].pen
+ self.solution['services'].append(job)
+
+ self.solution["dc"] = []
+ total_money = 0
+ total_penalty = 0
+ for dc in range(len(self.cDC)):
+ sol_dc = {}
+ sol_dc['dc'] = dc
+ sol_dc['money'] = self.values['money'][str(dc)]
+ sol_dc['penalty'] = self.values['penalty'][str(dc)] * -1
+ total_money += sol_dc['money']
+ total_penalty += sol_dc['penalty']
+ schedule = []
+
+ for c in range(self.cDC[dc]):
+ core_schedule = {}
+ core_schedule['core'] = c
+ scheduling = []
+ for j in range(self.nbJobs):
+ if (float(self.values['Ejob'][str(dc)][str(c)][str(j)]) <= 0.1):
+ continue
+ job_core = {}
+ job_core["job"] = j
+ job_core["bidded"] = -1
+ job_core["money"] = self.values['moneyByJob'][str(dc)][str(j)]
+ job_core["penalty"] = self.values['penaltyByJob'][str(dc)][str(j)] * -1
+ job_core["alpha"] = [0 for _ in range(self.totalTime)]
+ for t in range(self.totalTime):
+ job_core["alpha"][t] = self.values['AlphaJob'][str(dc)][str(c)][str(j)][str(t)]
+ if (self.values['BidJob'][str(dc)][str(j)][str(t)] > 0):
+ job_core["bidded"] = t
+ scheduling.append(job_core)
+ for j in range(self.nbServices):
+ if (float(self.values['Eserv'][str(dc)][str(c)][str(j)]) <= 0.1):
+ continue
+ job_core = {}
+ job_core["service"] = j
+ job_core["bidded"] = -1
+ job_core["money"] = self.values['moneyByService'][str(dc)][str(j)]
+ job_core["penalty"] = (self.values['penaltyservicehard'][str(dc)][str(j)] + self.values['penaltyserviceregularise'][str(dc)][str(j)]) * -1
+ job_core["alpha"] = [0 for _ in range(self.totalTime)]
+ for t in range(self.totalTime):
+ job_core["alpha"][t] = self.values['AlphaServ'][str(dc)][str(c)][str(j)][str(t)]
+ if (self.values['BidService'][str(dc)][str(j)][str(t)] > 0):
+ job_core["bidded"] = t
+ scheduling.append(job_core)
+ core_schedule['scheduling'] = scheduling
+ schedule.append(core_schedule)
+
+ sol_dc['cores'] = schedule
+ sol_dc['Energy_use'] = [v for i,v in self.values['energy'][str(dc)].items()]
+ sol_dc['Energy_available'] = [self.instance['renewPow'][dc][int(i)] for i,v in self.values['energy'][str(dc)].items()]
+ self.solution["dc"].append(sol_dc)
+ self.solution['SolutionTime'] = self.prob.solutionTime
+ self.solution['TotalMoney'] = total_money
+ self.solution['TotalPenalty'] = total_penalty
+ self.solution['Benefits'] = total_money - total_penalty
+
+class HeuristicRunner(Runner):
+
+ def __init__(self, instance, runner_arg=None, do_viz=False,bidder_param={'bidder':'Simple'}, model_file="", log_file="test.log", seed=42):
+ super(HeuristicRunner, self).__init__(instance, runner_arg=runner_arg, do_viz=do_viz,bidder_param={'bidder':'Simple'})
+ self.runner_arg = runner_arg
+ self.bidder_param = bidder_param
+ self.seed = seed
+
+ def create_dict(self, metrics, phase, c, nrg, flex, tag=None):
+
+ summary_solution = {}
+ summary_solution['jobs'] = []
+ summary_solution['services'] = []
+ # print(metrics)
+ job_id = 0
+ for j in self.instance.jobs:
+ job = {}
+ job['id'] = job_id
+ job['arrival'] = j.a
+ job['workload'] = j.w
+ job['duedate'] = j.due
+ job['deadline'] = j.dead
+ job['min_price'] = j.pmin
+ job['max_price'] = j.pmax
+ job['min_penalty'] = j.penmin
+ job['max_penalty'] = j.penmax
+ job['deadline_penalty'] = j.pendead
+ summary_solution['jobs'].append(job)
+ job_id += 1
+
+ job_id = 0
+ for j in self.instance.services:
+ job = {}
+ job['id'] = job_id
+ job['arrival'] = j.arrived
+ job['start'] = j.start
+ if (type(j.work) == list):
+ job['workload'] = j.work
+ else:
+ job['workload'] = j.work.tolist()
+ job['min_price'] = j.pmin
+ job['max_price'] = j.pmax
+ job['penalty'] = j.pen
+ summary_solution['services'].append(job)
+ job_id += 1
+
+ summary_solution["dc"] = []
+ dc_index = 0
+ total_money = 0
+ total_penalty = 0
+ for dc in c:
+ sol_dc = {}
+ sol_dc['dc'] = dc_index
+ sol_dc['money'] = metrics['dc'][dc_index]['money']
+ total_money += sol_dc['money']
+ sol_dc['penalty'] = metrics['dc'][dc_index]['penalty']
+ total_penalty += sol_dc['penalty']
+ schedule = []
+
+ for core in range(c[dc_index]):
+ core_schedule = {}
+ core_schedule['core'] = core
+ scheduling = []
+ job_id = 0
+ for j in self.instance.jobs:
+ find_job = False
+ for (id, jobcore, time, alpha) in metrics['dc'][dc_index]['jobs_history']:
+ if (id == job_id and jobcore == core):
+ find_job = True
+ if (not find_job):
+ job_id += 1
+ continue
+ job_core = {}
+ job_core["job"] = job_id
+ job_core["bidded"] = -1
+ job_core["money"] = 0
+ job_core["penalty"] = 0
+ for (id, jobcore, time, money) in metrics['dc'][dc_index]['money_jobs']:
+ if (id == job_id):
+ job_core["money"] += money
+ for (id, jobcore, time, penalty) in metrics['dc'][dc_index]['penalty_jobs']:
+ if (id == job_id):
+ job_core["penalty"] += penalty
+ job_core["alpha"] = [0 for _ in range(self.instance.total_time)]
+ for t in range(self.instance.total_time):
+ for (id, jobcore, time, alpha) in metrics['dc'][dc_index]['jobs_history']:
+ if (id == job_id and jobcore == core and t == time):
+ job_core["alpha"][t] = alpha
+ for (isjob, id, jobcore, time) in metrics['dc'][dc_index]['bid_history']:
+ if (id == job_id and isjob):
+ job_core["bidded"] = time
+ scheduling.append(job_core)
+ job_id += 1
+ job_id = 0
+ for j in self.instance.services:
+ find_service = False
+ for (id, jobcore, time, alpha) in metrics['dc'][dc_index]['services_history']:
+ if (id == job_id and jobcore == core):
+ find_service = True
+ if (not find_service):
+ job_id += 1
+ continue
+ job_core = {}
+ job_core["service"] = job_id
+ job_core["bidded"] = -1
+ job_core["money"] = 0
+ job_core["penalty"] = 0
+ for (id, jobcore, time, money) in metrics['dc'][dc_index]['money_services']:
+ if (id == job_id):
+ job_core["money"] += money
+ for (id, jobcore, time, penalty) in metrics['dc'][dc_index]['penalty_services']:
+ if (id == job_id):
+ job_core["penalty"] += penalty
+
+ job_core["alpha"] = [0 for _ in range(self.instance.total_time)]
+ for t in range(self.instance.total_time):
+ for (id, jobcore, time, alpha) in metrics['dc'][dc_index]['services_history']:
+ if (id == job_id and jobcore == core and t == time):
+ job_core["alpha"][t] = alpha
+ for (isjob, id, jobcore, time) in metrics['dc'][dc_index]['bid_history']:
+ if (id == job_id and not isjob):
+ job_core["bidded"] = time
+ scheduling.append(job_core)
+ job_id += 1
+
+ core_schedule['scheduling'] = scheduling
+ schedule.append(core_schedule)
+
+ sol_dc['cores'] = schedule
+ sol_dc['Energy_use'] = [v for i,v in metrics['dc'][dc_index]['energy_use']]
+ sol_dc['Energy_available'] = [i for i,v in metrics['dc'][dc_index]['energy_use']]
+ summary_solution["dc"].append(sol_dc)
+ dc_index += 1
+ summary_solution['SolutionTime'] = metrics["Running_time"]
+ summary_solution['TotalMoney'] = total_money
+ summary_solution['TotalPenalty'] = total_penalty
+ summary_solution['Benefits'] = total_money - total_penalty
+
+ return summary_solution
+
+ def run(self, phase, curve, DC, power_var, flex, dist, tag=None):
+ snap_instance = self.instance.create_problem(phase, curve, DC, power_var, flex, dist, heuristic=True)
+ solver = Solver(snap_instance, runner_arg=self.runner_arg,bidder_param=self.bidder_param, seed=self.seed)
+ solution = solver.run()
+
+ #Pour faire les tests sur les BENEFICES globaux
+ self.test_money=solution['TotalMoney']
+
+ #Faire le test sur les BENEFICES de DC 0
+ #self.test_money=solution['dc'][0]['money'] - solution['dc'][0]['penalty']
+
+
+
+
+ #metrics = Metrics(snap_instance, solution)
+ #print("Metrics", metrics)
+ if self.do_vis:
+ Visualisation(solution, True)
+ return self.create_dict(solution, phase, DC, power_var, flex, tag)
\ No newline at end of file
diff --git a/GenerateExperiments/seaborn_tests.py b/GenerateExperiments/seaborn_tests.py
new file mode 100644
index 0000000..f114b87
--- /dev/null
+++ b/GenerateExperiments/seaborn_tests.py
@@ -0,0 +1,61 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+if __name__ == '__main__':
+
+ data = pd.read_csv('../Data.csv')
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="money", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="penalty", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="money_jobs", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="money_services", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="penalty_jobs", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
+ g = sns.catplot(
+ data=data, kind="bar",
+ x="core", y="penalty_services", hue='power',
+ ci="sd", palette="dark", alpha=.6, height=6
+ )
+ g.despine(left=True)
+ # g.set_axis_labels("", "Body mass (g)")
+ # g.legend.set_title("")
+ plt.show()
\ No newline at end of file
diff --git a/GenerateInstance/Examples.py b/GenerateInstance/Examples.py
new file mode 100644
index 0000000..13c4c6d
--- /dev/null
+++ b/GenerateInstance/Examples.py
@@ -0,0 +1,20 @@
+from collections import namedtuple
+
+def example1():
+ Job = namedtuple('Job', 'a w due dead pmin pmax penmin penmax pendead')
+ # Inputs
+ # Input Case: a service, a batch. The batch can be executed.
+ renewPow = [[400, 200, 200], [300, 309, 392]] # Profile of renewable power or each DataCenter
+ cDC = [2, 2] # Number of cores per server for each DataCenter
+ pmin, pdyn = 100, 100 # Power model of cores/nodes
+ servReq = [[.2, 0.3, 0.0], [0.1, 0.2, 0.0]] # Requested load over time for each service
+ jobs = [Job(0, .7, 1, 1, 1, 2, 4, 4, 10), Job(0, 0.4, 2, 2, 1, 2, 4, 4, 10)] # Characteristics of each batch
+
+ nbDC = len(cDC)
+ totalTime = len(servReq[0])
+ nbJobs = len(jobs)
+ nbServices = len(servReq)
+ # list of id (dc,c) of all cores c of all data centers dc (unique identifier)
+ allCores = sum([[(dc, c) for c in range(cDC[dc])] for dc in range(nbDC)], [])
+
+ return renewPow, cDC, pmin, pdyn, servReq, jobs, nbDC, totalTime, nbJobs, nbServices, allCores
\ No newline at end of file
diff --git a/GenerateInstance/Instance.py b/GenerateInstance/Instance.py
new file mode 100644
index 0000000..d714fdb
--- /dev/null
+++ b/GenerateInstance/Instance.py
@@ -0,0 +1,93 @@
+import math
+
+import numpy as np
+from collections import namedtuple
+
+from GenerateInstance.jobGen import create_all_jobs, create_all_services
+from GenerateProblem.problem_gen import problem_gen
+
+
+class Instance:
+
+ def __init__(self, jobs, services, total_time, renewPow=None, seed=42):
+ if type(jobs) is int:
+ self.jobs = create_all_jobs(nbSteps=total_time, nbtasks=jobs, seed=seed)
+ else:
+ self.jobs = jobs
+ if type(services) is int:
+ self.services = create_all_services(nbSteps=total_time, nbtasks=services, seed=seed)
+ else:
+ self.services = services
+ self.total_time = total_time
+ # Useless for now
+ self.renewPow = renewPow
+
+ self.pmin = 20
+ self.pdyn = 140
+
+ def create_energy(self, phase, cDC, pow_variable, energy_dist, curve='bell', offset=0.6, bell=3):
+
+ if curve == 'flat':
+ renewPow = [[(self.pdyn + self.pmin) * cDC[0] * pow_variable] * self.total_time] * len(cDC)
+ for index, value in enumerate(energy_dist):
+ if value is None:
+ continue
+ if len(renewPow[index]) == len(value):
+ renewPow[index] = [renewPow[index][i]*v for i, v in enumerate(value)]
+ elif curve == 'bell':
+
+ renewPow = []
+ if phase:
+ offset = 0
+
+ curr_offset = 0
+ for dc in range(len(cDC)):
+ renewPow.append([(self.pdyn + self.pmin) * cDC[0] * pow_variable*(math.cos(t/2 + curr_offset)+bell)/(bell+1) for t in range(self.total_time)])
+ curr_offset += offset
+ else:
+ raise Exception
+ return renewPow
+
+ def apply_flex(self, flex):
+ if flex == 0:
+ return self.jobs
+ Job = namedtuple('Job', 'a w due dead pmin pmax penmin penmax pendead')
+ new_jobs = []
+ for job in self.jobs:
+ new_deadline = min(self.total_time-1, int(np.ceil(flex * job.w) + job.a))
+ new_due = min(self.total_time-1, int(np.ceil(flex/2 * job.w) + job.a))
+ if job.a > new_due:
+ new_due = job.a+1
+ if new_due > new_deadline:
+ new_deadline = new_due
+ new_jobs.append(Job(job.a, job.w, new_due, new_deadline, job.pmin, job.pmax, job.penmin, job.penmax, job.pendead))
+ return new_jobs
+
+ def create_problem(self, phase, curve, DC, power_var, flex, energy_dist, heuristic=False):
+ jobs = self.apply_flex(flex)
+ nbJobs = len(jobs)
+ nbServices = len(self.services)
+ nbDC = len(DC)
+ allCores = sum([[(dc, c) for c in range(DC[dc])] for dc in range(nbDC)], [])
+ if self.renewPow is None:
+ renewPow = self.create_energy(phase, DC, power_var, energy_dist, curve=curve)
+ else:
+ renewPow = self.renewPow
+
+ snap_instance = {
+ 'jobs': jobs,
+ 'nbjobs': len(jobs),
+ 'services': self.services,
+ 'nbservices': len(self.services),
+ 'cDC': DC,
+ 'total_time': self.total_time,
+ 'pmin': self.pmin,
+ 'pdyn': self.pdyn,
+ 'renewPow': renewPow,
+ }
+ if heuristic:
+ return snap_instance
+ prob = problem_gen(nbDC, DC, nbServices, self.total_time, jobs, nbJobs, allCores, self.services, renewPow,
+ self.pmin, self.pdyn)
+
+ return prob, snap_instance
diff --git a/GenerateInstance/README.md b/GenerateInstance/README.md
new file mode 100644
index 0000000..a374713
--- /dev/null
+++ b/GenerateInstance/README.md
@@ -0,0 +1,3 @@
+#Warnings
+
+Don't put underscores in your variables names in the LPproblem or it will break the
\ No newline at end of file
diff --git a/GenerateInstance/__init__.py b/GenerateInstance/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateInstance/instance_gen.py b/GenerateInstance/instance_gen.py
new file mode 100644
index 0000000..d9b1323
--- /dev/null
+++ b/GenerateInstance/instance_gen.py
@@ -0,0 +1,32 @@
+import random
+
+from GenerateInstance.jobGen import create_all_jobs
+
+def generateServLoad(T, S, seed):
+ random.seed(seed)
+ return [[random.uniform(0, 1) for _ in range(T)] for _ in range(S)]
+
+def instance_gen(DC, T, S, J, P, seed=None):
+ # and number of cores
+ cDC = [2]*DC
+ # gen the datacenters power usage (idle and dynamic)
+ pmin, pdyn = 100, 100
+ # Gen renewable power
+
+ cpu_capacity = sum(cDC)
+
+ renewPow = []
+ for i in range(DC):
+ renewPow.append([400*P]*T)
+
+ # services gen
+ servReq = generateServLoad(T, S, seed)
+ # jobs gen
+ jobs = create_all_jobs(seed, nbtasks=J, nbDays=T, flex=2)
+
+ allCores = sum([[(dc,c) for c in range(cDC[dc])] for dc in range(DC)], [])
+
+ return renewPow, cDC, pmin, pdyn, servReq, jobs, DC, T, len(jobs), S, allCores
+
+
+
diff --git a/GenerateInstance/jobGen.py b/GenerateInstance/jobGen.py
new file mode 100644
index 0000000..1867960
--- /dev/null
+++ b/GenerateInstance/jobGen.py
@@ -0,0 +1,233 @@
+#!/usr/bin/python3
+
+import numpy.random as nr
+import numpy
+
+def generic_montecarlo_random(distrib_func, fromval, toval, max_distrib=1.0,
+ min_distrib=0.0):
+ """
+ Get a pseudo-random number between `fromval` and `toval`, according to
+ a given custom distribution function.
+ `distrib_func` should be a callable with signature (float) -> float,
+ and should return a value, for a given input, proportional to the
+ probability of this input. The return values require to be in
+ [min_distrib, max_distrib] (default to [0, 1]).
+
+ This function's compute time isn't deterministic, and should be used
+ carefully. Its average complexity depends on the distribution function
+ used : the more the average distribution value is small compared to
+ (`max_distrib` - `min_distrib`), the more average time to find a valid
+ candidate grows.
+ """
+ ret = None
+
+ isok = False
+ while not isok:
+ x1 = nr.uniform(fromval, toval)
+ y1 = nr.uniform(min_distrib, max_distrib)
+
+ if distrib_func(x1) > y1:
+ isok = True
+ ret = x1
+
+ return ret
+
+def generate_random_int_dates(rand_func, fromdate, todate, number = None,
+ hourly_density = None):
+ """
+ Generate a list of dates using a given random date generator (like those
+ provided by `get_custom_date_generator`).
+ The dates generated are in [fromdate ; todate].
+ By default, the list is a single item, and there are two ways to set the
+ size of the returned list :
+ - by providing an explicit size, using `number` parameter
+ - using an implicit size, using a 'mean hourly density', which is the
+ average number of dates per hour (the size is then the number of
+ hours between `todate` and `fromdate`, times `hourly_density`)
+ """
+ nb = 1
+ if number is not None:
+ nb = number
+ elif hourly_density is not None:
+ nb = (todate - fromdate) * 24*hourly_density
+ nb = int(nb)
+ # list comprehension to generate the output
+ ret = [ rand_func(fromdate, todate) for x in range(0, nb) ]
+ return ret
+
+def get_custom_generator(distrib_func, max_distrib=1.0, min_distrib=0.0):
+ """
+ Return a callable that generate a number (float) from a custom distribution
+ function using `generic_montecarlo_random`.
+ """
+ return lambda fromval, toval : generic_montecarlo_random(distrib_func, fromval, toval, max_distrib, min_distrib)
+
+
+
+# probability density function for jobs over time
+# for now, keep a uniform distribution (but may be used to define periods with
+# more or less jobs, like day/night and week/weekend variations)
+def jobs_density(date):
+ return 1.0
+
+def lognorm_duration():
+ isok = False
+ while not isok:
+ ret = nr.lognormal(6.17, 2.32)+3600
+ if ret < 12*3600:
+ isok = True
+ return ret
+
+# generate a job at a given date
+def create_job(real_submit, nbSteps, flex):
+ # get a 'priority', from 0 (low priority) to 2 (high priority)
+ prio = nr.choice(3, p=[0.42, 0.31, 0.27])
+ nbdays = nbSteps // 24
+ if (max(1,nbdays//4) == max(1,nbdays//2)):
+ nphases = 1
+ else:
+ nphases = nr.randint(max(1,nbdays//4),max(1,nbdays//2))
+ duration_sec = lognorm_duration()*nphases
+
+ if prio == 0:
+ # nbtasks = nr.choice([1, 2, 4, 8], p=[0.4, 0.3, 0.2, 0.1])
+ flexibility_base = numpy.clip(nr.normal(3600, 600), 2000, 7200)
+ elif prio == 1:
+ # nbtasks = nr.choice([1, 2, 4, 8], p=[0.3, 0.25, 0.35, 0.1])
+ flexibility_base = numpy.clip(nr.normal(1200, 300), 300, 2000)
+ elif prio == 2:
+ # nbtasks = nr.choice([1, 2], p=[0.9, 0.1])
+ flexibility_base = 0
+ # nbtasks = 1
+ # duration = duration_sec
+
+ flexibility_sec = flex * flexibility_base + 5 * 60
+ period = duration_sec + flexibility_sec
+
+ price_base = nr.choice([200, 300, 400, 500], p=[0.4, 0.3, 0.2, 0.1])
+ price_scale = nr.choice([30, 60, 90, 150], p=[0.4, 0.3, 0.2, 0.1])
+ pmin = numpy.clip(nr.normal(price_base, price_scale), 0, 2000)
+ pmax = pmin*nr.uniform(1,3)
+
+ pen_base = nr.choice([200, 300, 400, 500], p=[0.4, 0.3, 0.2, 0.1])
+ pen_scale = nr.choice([30, 60, 90, 150], p=[0.4, 0.3, 0.2, 0.1])
+ penmin = numpy.clip(nr.normal(pen_base, pen_scale), 0, 2000)
+ penmax = penmin * nr.uniform(1, 3)
+
+ # Le Pb c'est que des fois pendead <= pmax pas normal !!!
+ #pendead = penmax * nr.uniform(1, 3)
+ #Correction :
+ pendead = pmax * nr.uniform(1, 2)
+
+ # workload = nr.uniform(0, 2)
+ workload_per_phase = [max(1, ((duration_sec / nphases) // 3600)) * nr.uniform(0, 1) for _ in range(nphases)]
+ # print(duration_sec, max(1, int((duration_sec / nphases) // 3600)), workload_per_phase, sum(workload_per_phase))
+ workload = sum(workload_per_phase)
+
+ ret2 = [(real_submit, workload, period, period+flexibility_sec, pmin, pmax, penmin, penmax, pendead)]
+ return ret2
+
+
+from collections import namedtuple
+Job = namedtuple('Job', 'a w due dead pmin pmax penmin penmax pendead')
+Services = namedtuple('Service', 'arrived start work pmin pmax pen')
+
+def create_all_jobs(seed=42, nbtasks=4, nbSteps=4, flex=2):
+ nr.seed(seed)
+ #print("Initialized with seed '{}'".format(seed))
+ # get the random submission dates
+ date_generator = get_custom_generator(jobs_density)
+ dates = generate_random_int_dates(date_generator, 0, nbSteps-1, number=nbtasks)
+ dates.sort()
+
+ # for each date, generate a job and add it to the workload
+ wl2=[]
+ for d in dates:
+ tmp2 = create_job(d, nbSteps, flex)
+ wl2.extend(tmp2)
+
+ cte = 60*60
+
+ jobs=[]
+ for (arrival, workload, due, deadline, pmin, pmax, penmin, penmax, pendead) in wl2:
+ dep = int(arrival)
+ if dep == nbSteps-1:
+ dep -= 1
+ due = min(nbSteps-1, dep+int(due)//cte+1)
+ dead = min(nbSteps-1, dep+int(deadline)//cte+1)
+ #print(dep,workload, due, dead)
+ j=Job(dep, workload, due, dead, pmin, pmax, penmin, penmax, pendead)
+ jobs.append(j)
+ return jobs
+
+def create_service(arrival, flex, nbSteps):
+ # get a 'priority', from 0 (low priority) to 2 (high priority)
+ prio = nr.choice(3, p=[0.42, 0.31, 0.27])
+
+ nbdays = nbSteps // 24
+ if (max(1,nbdays//4) == max(1,nbdays//2)):
+ nphases = 1
+ else:
+ nphases = nr.randint(max(1,nbdays//4),max(1,nbdays//2))
+ duration_sec = lognorm_duration()*nphases
+
+ if prio == 0:
+ # nbtasks = nr.choice([1, 2, 4, 8], p=[0.4, 0.3, 0.2, 0.1])
+ # flexibility_base = numpy.clip(nr.normal(1200, 300), 300, 2000)
+ flexibility_base = numpy.clip(nr.normal(5400, 1800), 300, 7200)
+ elif prio == 1:
+ # nbtasks = nr.choice([1, 2, 4, 8], p=[0.3, 0.25, 0.35, 0.1])
+ # flexibility_base = numpy.clip(nr.normal(600, 150), 150, 2000)
+ flexibility_base = numpy.clip(nr.normal(2700, 1800), 300, 7200)
+ elif prio == 2:
+ # nbtasks = nr.choice([1, 2], p=[0.9, 0.1])
+ flexibility_base = 0
+
+ flexibility_sec = (flex * flexibility_base) + 3600
+ start = min(max(1, arrival - 1 + (flexibility_sec // 3600)), nbSteps-1)
+ workload = nr.uniform(0, 1, size=max(1,min(int(nbSteps-1-(int(start-1))), int(int(duration_sec)//(60*60)))))
+
+ price_base = nr.choice([200, 300, 400, 500], p=[0.4, 0.3, 0.2, 0.1])
+ price_scale = nr.choice([30, 60, 90, 150], p=[0.4, 0.3, 0.2, 0.1])
+ pmin = numpy.clip(nr.normal(price_base, price_scale), 0, 2000)
+ multiplier = nr.uniform(1, 3)
+ pmax = pmin*multiplier
+
+ pen_base = nr.choice([200, 300, 400, 500], p=[0.4, 0.3, 0.2, 0.1])
+ pen_scale = nr.choice([30, 60, 90, 150], p=[0.4, 0.3, 0.2, 0.1])
+ pen = numpy.clip(nr.normal(pen_base, pen_scale) * multiplier, pmax / (int(int(duration_sec)//(60*60))), 2000)
+ # print(prio, arrival, start, flexibility_base, flexibility_sec)
+ ret2 = [(arrival, start, workload, pmin, pmax, pen)]
+ return ret2
+
+def create_all_services(seed=42, nbtasks=4, nbSteps=4, flex=2):
+ nr.seed(seed)
+ #print("Initialized with seed '{}'".format(seed))
+ # get the random submission dates
+ date_generator = get_custom_generator(jobs_density)
+ dates = generate_random_int_dates(date_generator, 0, nbSteps-1, number=nbtasks)
+ dates.sort()
+
+ # for each date, generate a job and add it to the workload
+ wl2=[]
+ for d in dates:
+ tmp2 = create_service(d, flex, nbSteps)
+ wl2.extend(tmp2)
+ jobs=[]
+ for (arrival, start, work, pmin, pmax, pen) in wl2:
+ arrived = int(arrival)
+ start = min(nbSteps-1, int(start))
+ if arrived == start:
+ arrived -= 1
+ # Services = namedtuple('Service', 'arrived start work pmin pmax pen')
+ j=Services(arrived, start, work, pmin, pmax, pen)
+ jobs.append(j)
+ return jobs
+
+if __name__ == '__main__':
+ days = 10
+ jobs=create_all_jobs(nbSteps=days, nbtasks=10)
+ print(len(jobs), jobs)
+ services=create_all_services(nbSteps=days)
+ print(len(services), services)
+
diff --git a/GenerateMetrics/__init__.py b/GenerateMetrics/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateMetrics/metrics_gen.py b/GenerateMetrics/metrics_gen.py
new file mode 100644
index 0000000..683fefe
--- /dev/null
+++ b/GenerateMetrics/metrics_gen.py
@@ -0,0 +1,132 @@
+from colorama import Fore, Style
+
+from GenerateInstance.Instance import Instance
+
+
+class Metrics:
+
+ def __init__(self, instance, prob):
+ self.instance = instance
+ self.jobs = instance['jobs']
+ self.totalTime = instance['total_time']
+ self.nbJobs = instance['nbjobs']
+ self.nbServices = instance['nbservices']
+ self.pmin = instance['pmin']
+ self.pdyn = instance['pdyn']
+ self.cDC = instance['cDC']
+ self.renewPow = instance['renewPow']
+
+ self.prob = prob
+ self._raw = {}
+ self.values = {}
+ self.metrics = {}
+
+ # call functions which fill in the previous values
+ self.calculate_metrics()
+
+ def calculate_metrics(self):
+ # Calculate meta metrics
+ self.metrics['SolutionTime'] = self.prob.solutionTime
+ # TODO can't find the sol gap right now. when using hard instances find it
+ # self.metrics['RelativeGap'] = self.prob.solver.solverModel.solution.MIP.get_mip_relative_gap()
+
+ # Calculate Objective metrics
+ self.metrics['TotalMoney'] = self.values['TotalMoney']
+ self.metrics['Money'] = self.values['money']
+ self.metrics['MoneyJobs'] = sum(self.values['moneyjob'].values())
+ self.metrics['MoneyServices'] = sum(self.values['moneyservice'].values())
+ self.metrics['TotalPenalty'] = sum(self.values['penalty'].values())
+ self.metrics['penalty'] = self.values['penalty']
+ self.metrics['PenaltyJobs'] = sum(self.values['penaltyjob'].values())
+ self.metrics['PenaltyServices'] = sum(self.values['penaltyservice'].values())
+
+ # Calculate utilisation Core use, performance load and energy load
+ self.metrics['DatacenterCoreUse'] = dict()
+ self.metrics['TotalDatacenterCoreUse'] = 0
+ self.metrics['DatacenterPerformanceLoad'] = dict()
+ self.metrics['TotalDatacenterPerformanceLoad'] = 0
+ self.metrics['DatacenterEnergy'] = dict()
+ self.metrics['TotalDatacenterEnergy'] = 0
+ for datacenter, dcvalue in self.values['Pon'].items():
+ self.metrics['DatacenterCoreUse'][datacenter] = []
+ self.metrics['DatacenterPerformanceLoad'][datacenter] = []
+ self.metrics['DatacenterEnergy'][datacenter] = []
+ for core, jobs in dcvalue.items():
+ self.metrics['DatacenterCoreUse'][datacenter].append((core, 100*(sum(jobs.values())/len(jobs))))
+ self.metrics['TotalDatacenterCoreUse'] += 100*(sum(jobs.values())/len(jobs))/(len(dcvalue)*len(self.values['Pon']))
+ try:
+ self.metrics['DatacenterPerformanceLoad'][datacenter].append((core, 100*(sum([sum(i.values()) for i in list(self.values['AlphaJob'][datacenter][core].values())]))/self.totalTime))
+ self.metrics['TotalDatacenterPerformanceLoad'] += 100*(sum([sum(i.values()) for i in list(self.values['AlphaJob'][datacenter][core].values())]))/self.totalTime/(len(dcvalue)*len(self.values['Pon']))
+ except Exception:
+ print('exception occured when calculating Performance load')
+ self.metrics['DatacenterPerformanceLoad'][datacenter].append([0]*self.totalTime)
+ self.metrics['TotalDatacenterCoreUse'] += 0
+
+ self.metrics['DatacenterEnergy'][datacenter] = self.values['energy'][datacenter]
+ self.metrics['TotalDatacenterEnergy'] += sum(self.values['energy'][datacenter].values())/self.totalTime/len(self.values['Pon'])
+
+ try:
+ self.metrics['ViolatedJobs'] = list()
+ self.metrics['Violation%'] = 0
+ for job, v in self.values['v'].items():
+ if v > 0:
+ self.metrics['ViolatedJobs'].append(job)
+ self.metrics['Violation%'] += 1
+ self.metrics['Violation%'] = 100*self.metrics['Violation%']/len(self.values['v'])
+ except Exception:
+ print('Violation variable not initialised')
+
+ self.metrics['JobsDetails'] = dict()
+ self.metrics['JobQuality'] = dict()
+ self.metrics['JobDeadlineQuality'] = dict()
+ self.metrics['JobsLate'] = 0
+ for job in range(len(self.jobs)):
+ job_done = False
+ for datacenter, dcvalue in self.values['AlphaJob'].items():
+ for core, jobs in dcvalue.items():
+ if sum(jobs[str(job)].values()) > 0:
+ start = 0
+ end = 0
+ can_stop = False
+ for temp in jobs[str(job)].values():
+ if temp == 0:
+ if can_stop:
+ break
+ start += 1
+ else:
+ can_stop = True
+ if end == 0:
+ end = start + 1
+ else:
+ end += 1
+ end -= 1
+ self.metrics['JobQuality'][str(job)] = end - self.jobs[job].due
+ if end - self.jobs[job].due > 0:
+ self.metrics['JobsLate'] += 1
+ self.metrics['JobsDetails'][str(job)] = (datacenter, core, start, end)
+ job_done = True
+ if job_done:
+ break
+ if job_done:
+ break
+ for job, late in self.metrics['JobQuality'].items():
+ if late > 0:
+ self.metrics['JobDeadlineQuality'][str(job)] = self.jobs[int(job)].due - self.jobs[int(job)].dead
+ try:
+ self.metrics['JobsLate'] = self.metrics['JobsLate']*100/self.nbJobs
+ except Exception:
+ print('There are no jobs')
+ self.metrics['WastedEnergy'] = 0
+ self.metrics['WastedPerformance'] = 0
+ self.metrics['ServiceDetails'] = dict()
+ for serv in range(self.nbServices):
+ serv_done = False
+ for datacenter, dcvalue in self.values['AlphaServ'].items():
+ for core, jobs in dcvalue.items():
+ if sum(jobs[str(serv)].values()) > 0:
+ self.metrics['ServiceDetails'][str(serv)] = (datacenter, core)
+ serv_done = True
+ if serv_done:
+ break
+ if serv_done:
+ break
diff --git a/GenerateProblem/DatacenterModel/Constraints.md b/GenerateProblem/DatacenterModel/Constraints.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/DatacenterModel/Variables.md b/GenerateProblem/DatacenterModel/Variables.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/DatacenterModel/__init__.py b/GenerateProblem/DatacenterModel/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/DatacenterModel/datacenter_constraints_gen.py b/GenerateProblem/DatacenterModel/datacenter_constraints_gen.py
new file mode 100644
index 0000000..1594898
--- /dev/null
+++ b/GenerateProblem/DatacenterModel/datacenter_constraints_gen.py
@@ -0,0 +1,88 @@
+from pulp import lpSum
+
+
+# Constraints
+def addUnubiquitousConstraints(prob, nbServices, eServ, nbJobs, eJob, allCores):
+ for idServ in range(nbServices): # Service on one single core
+ prob += lpSum([eServ[dc][c][idServ] for (dc, c) in allCores]) <= 1, "Service " + str(
+ idServ) + " on max a single Core"
+
+ for idJob in range(nbJobs): # Job on maximum one single core
+ prob += lpSum([eJob[dc][c][idJob] for (dc, c) in allCores]) <= 1, "Job " + str(idJob) + " on a max single Core"
+
+
+def addWorkloadConstraints(prob, nbServices, alphaServ, services, nbJobs, alphaJob, jobs, totalTime, allCores):
+ for idServ in range(nbServices):
+ for t in range(services[idServ].start, services[idServ].start + len(services[idServ].work)):
+ prob += lpSum(
+ [alphaServ[dc][c][idServ][t] for (dc, c) in allCores]) <= services[idServ].work[t - services[idServ].start], \
+ f"Service {idServ} cannot execute more than necessary workload at time {t}"
+ for t in range(0, services[idServ].start):
+ for (dc, c) in allCores:
+ prob += alphaServ[dc][c][idServ][t] == 0, " Before arriving service " + str(idServ) + " on " + str(
+ dc) + "," + str(c) + " at time " + str(t) + "is not executed"
+ for t in range(services[idServ].start + len(services[idServ].work), totalTime):
+ for (dc, c) in allCores:
+ prob += alphaServ[dc][c][idServ][t] == 0, " After service finished " + str(idServ) + " on " + str(
+ dc) + "," + str(c) + " at time " + str(t) + "is not executed"
+
+ for idJob in range(nbJobs): # Job might be served but do not run more than possible
+ prob += lpSum([alphaJob[dc][c][idJob][t] for t in range(jobs[idJob].a, totalTime) for (dc, c) in allCores]) <= \
+ jobs[idJob].w \
+ , "Job " + str(idJob) + " cannot execute more than workload"
+ for t in range(0, jobs[idJob].a):
+ for (dc, c) in allCores:
+ prob += alphaJob[dc][c][idJob][t] == 0, " Before arriving job " + str(idJob) + " on " + str(
+ dc) + "," + str(c) + " at time " + str(t) + "is not executed"
+
+
+def addAllocationConstraints(prob, totalTime, allCores, nbServices, alphaServ, eServ, nbJobs, alphaJob, eJob, jobs):
+ for t in range(totalTime): # Job/services are takinkg CPU only if running on the host
+ for (dc, c) in allCores:
+ for idServ in range(nbServices):
+ prob += alphaServ[dc][c][idServ][t] <= eServ[dc][c][idServ], "Service " + str(idServ) + " on dc " + str(
+ dc) + "," + str(c) + " at time " + str(t) + " charge only if running"
+ for (dc, c) in allCores:
+ for idJob in range(nbJobs):
+ prob += eJob[dc][c][idJob] == lpSum([alphaJob[dc][c][idJob][t] for t in range(totalTime)]) / jobs[idJob].w, \
+ f"execute_all_workload_in_{dc}_core_{c}_job_{idJob}"
+
+
+def addPowerConstraints(prob, nbDC, cDC, nbServices, alphaServ, nbJobs, alphaJob, renewPow, totalTime, pmin, pON, pdyn):
+ for t in range(totalTime): # Checking if servers do not consume too much
+ for dc in range(nbDC):
+ prob += lpSum([pmin * pON[dc][c][t] for c in range(cDC[dc])]) + \
+ lpSum(
+ [pdyn * alphaServ[dc][c][idServ][t] for c in range(cDC[dc]) for idServ in range(nbServices)]) + \
+ lpSum([pdyn * alphaJob[dc][c][idJob][t] for c in range(cDC[dc]) for idJob in range(nbJobs)]) <= \
+ renewPow[dc][t] \
+ , "Power of DC " + str(dc) + " at time " + str(t) + " matches renewable production"
+
+
+def addOverloadConstraints(prob, nbServices, alphaServ, nbJobs, alphaJob, totalTime, allCores):
+ for t in range(totalTime): # checking host are not overloaded
+ for (dc, c) in allCores:
+ prob += lpSum([alphaServ[dc][c][idServ][t] for idServ in range(nbServices)]) + \
+ lpSum([alphaJob[dc][c][idJob][t] for idJob in range(nbJobs)]) <= 1 \
+ , "Host " + str(dc) + "," + str(c) + " is not overloaded at time " + str(t)
+
+
+def addPowerONConstraints(prob, nbServices, alphaServ, nbJobs, alphaJob, totalTime, allCores, pON):
+ for t in range(totalTime): # Node are switched on if something runs on it
+ for (dc, c) in allCores:
+ for idServ in range(nbServices):
+ prob += pON[dc][c][t] >= alphaServ[dc][c][idServ][t], "Server " + str(dc) + "," + str(
+ c) + " on at time " + str(t) + " if workload from Service " + str(idServ)
+ for idJob in range(nbJobs):
+ prob += pON[dc][c][t] >= alphaJob[dc][c][idJob][t], "Server " + str(dc) + "," + str(
+ c) + " on at time " + str(t) + " if workload from Job " + str(idJob)
+
+
+def regular_constraints_gen(prob, nbServices, eServ, nbJobs, eJob, allCores, alphaServ, servReq, alphaJob, jobs,
+ totalTime, nbDC, cDC, renewPow, pmin, pON, pdyn):
+ addUnubiquitousConstraints(prob, nbServices, eServ, nbJobs, eJob, allCores)
+ addWorkloadConstraints(prob, nbServices, alphaServ, servReq, nbJobs, alphaJob, jobs, totalTime, allCores)
+ addAllocationConstraints(prob, totalTime, allCores, nbServices, alphaServ, eServ, nbJobs, alphaJob, eJob, jobs)
+ addPowerConstraints(prob, nbDC, cDC, nbServices, alphaServ, nbJobs, alphaJob, renewPow, totalTime, pmin, pON, pdyn)
+ addOverloadConstraints(prob, nbServices, alphaServ, nbJobs, alphaJob, totalTime, allCores)
+ addPowerONConstraints(prob, nbServices, alphaServ, nbJobs, alphaJob, totalTime, allCores, pON)
diff --git a/GenerateProblem/DatacenterModel/datacenter_variables_gen.py b/GenerateProblem/DatacenterModel/datacenter_variables_gen.py
new file mode 100644
index 0000000..377adb3
--- /dev/null
+++ b/GenerateProblem/DatacenterModel/datacenter_variables_gen.py
@@ -0,0 +1,71 @@
+from pulp import LpVariable, LpInteger
+
+
+# service is executed and how much resource is used
+def serviceVariables(nbDC, cDC, nbServices, totalTime):
+ eServ = [] # eServ[dc][c][s]
+ for idDc in range(nbDC):
+ eServPerCore = []
+ for idCore in range(cDC[idDc]):
+ eServPerCore.append([LpVariable("Eserv_"+str(idDc)+","+str(idCore)+","+str(i),0,1,LpInteger) for i in range(nbServices)])
+ eServ.append(eServPerCore)
+
+ alphaServ = [] # alphaServ[dc][c][s][t]
+ for idDc in range(nbDC):
+ alphaServPerCore = []
+ for idCore in range(cDC[idDc]):
+ alphaServPerServ = []
+ for idServ in range(nbServices):
+ alphaServPerServ.append([LpVariable("AlphaServ_"+str(idDc)+","+str(idCore)+","+str(idServ)+","+str(t),0,1) for t in range(totalTime)])
+ alphaServPerCore.append(alphaServPerServ)
+ alphaServ.append(alphaServPerCore)
+ return eServ, alphaServ
+
+
+# Jobs are executed and how much they use
+def jobVariables(nbDC, cDC, nbJobs, totalTime):
+ eJob = [] # eJob[dc][c][j]
+ for idDc in range(nbDC):
+ eJobPerCore = []
+ for idCore in range(cDC[idDc]):
+ eJobPerCore.append([LpVariable("Ejob_"+str(idDc)+","+str(idCore)+","+str(i),0,1,LpInteger) for i in range(nbJobs)])
+ eJob.append(eJobPerCore)
+
+ alphaJob = [] # alphaJob[dc][c][j][t]
+ for idDc in range(nbDC):
+ alphaJobPerCore = []
+ for idCore in range(cDC[idDc]):
+ alphaJobPerJob = []
+ for idJob in range(nbJobs):
+ alphaJobPerJob.append([LpVariable("AlphaJob_"+str(idDc)+","+str(idCore)+","+str(idJob)+","+str(t),0,1) for t in range(totalTime)])
+ alphaJobPerCore.append(alphaJobPerJob)
+ alphaJob.append(alphaJobPerCore)
+ return eJob, alphaJob
+
+
+# which cores are active
+def infrastructureVariables(nbDC, cDC, totalTime):
+ pON = [] # pON[dc][c][t]
+ for idDc in range(nbDC):
+ pONPerCore = []
+ for idCore in range(cDC[idDc]):
+ pONPerCore.append([LpVariable("Pon_"+str(idDc)+","+str(idCore)+","+str(t),0,1,LpInteger) for t in range(totalTime)])
+ pON.append(pONPerCore)
+ return pON
+
+
+def metricVariables():
+ energy = LpVariable("TotalEnergy",0)
+ totalMoney = LpVariable("TotalMoney",0)
+ return energy, totalMoney
+
+
+def variables_gen(nbDC, cDC, nbServices, totalTime, jobs, nbJobs):
+ eServ, alphaServ = serviceVariables(nbDC, cDC, nbServices, totalTime)
+ eJob, alphaJob = jobVariables(nbDC, cDC, nbJobs, totalTime)
+ pON = infrastructureVariables(nbDC, cDC, totalTime)
+
+ total_energy, totalMoney = metricVariables()
+
+ return eServ, alphaServ, eJob, alphaJob, pON, total_energy, totalMoney
+
diff --git a/GenerateProblem/MarketplaceModel/Constraints.md b/GenerateProblem/MarketplaceModel/Constraints.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/MarketplaceModel/Objective.md b/GenerateProblem/MarketplaceModel/Objective.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/MarketplaceModel/Variables.md b/GenerateProblem/MarketplaceModel/Variables.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/MarketplaceModel/__init__.py b/GenerateProblem/MarketplaceModel/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/MarketplaceModel/marketplace_constraints_gen.py b/GenerateProblem/MarketplaceModel/marketplace_constraints_gen.py
new file mode 100644
index 0000000..2925e2f
--- /dev/null
+++ b/GenerateProblem/MarketplaceModel/marketplace_constraints_gen.py
@@ -0,0 +1,70 @@
+from pulp import lpSum
+
+def qos_constraints(prob, jobs, nbJobs, alphaJob, nbDC, bidJob, eJob, cDC, bidService, services, eService, nbServices, totalTime, percentage_finished):
+ for j in range(nbJobs): # percentage finished
+ for dc in range(nbDC):
+ for t in range(totalTime):
+ prob += percentage_finished[dc][j][t] == lpSum([alphaJob[dc][c][j][t_1] for c in range(cDC[dc]) for t_1 in range(0, t)])/jobs[j].w \
+ , f"percentage_finished_dc_{dc}_job_{j}_at_{t}"
+
+
+ for j in range(nbJobs): # if not executed in DC, fixed arbitrary at a_b
+ for dc in range(nbDC):
+ # prob += bidJob[dc][j] <= jobs[j].a + lpSum([eJob[dc][c][j] for c in range(cDC[dc])]) * (jobs[j].dead - jobs[j].a), \
+ # f"Fixed bidJob of job {j} at {jobs[j].a} if the service doesn't run on dc {dc}"
+ # for c in range(cDC[dc]):
+ # prob += eJob[dc][c][j] == lpSum([alphaJob[dc][c][j][t] for t in range(totalTime)]) / jobs[j].w, f"execute_all_workload_in_{dc}_core_{c}_job_{j}"
+
+ # prob += bidJob[dc][j] >= jobs[j].a, f"Make sure the job {j} on {dc} isn't bid on before it arrives ..."
+
+ for t in range(jobs[j].a):
+ prob += bidJob[dc][j][t] == 0
+ for t in range(jobs[j].due + 1, totalTime):
+ prob += bidJob[dc][j][t] == 0
+ prob += lpSum([eJob[dc][c][j] for c in range(cDC[dc])]) == lpSum([bidJob[dc][j][t] for t in range(totalTime)])
+
+ for j in range(nbServices): # if not executed in DC, fixed arbitrary at a_b
+ for dc in range(nbDC):
+ # prob += bidService[dc][j] <= services[j].arrived + lpSum([eService[dc][c][j] for c in range(cDC[dc])]) * (services[j].start - services[j].arrived), \
+ # f"Fixed bid_service of serv {j} at {services[j].arrived} if the service doesn't run on dc {dc}"
+
+ # prob += bidService[dc][j] <= services[j].start, f'Service {j} on {dc} bid needs to be bid before the start of the service'
+ # prob += bidService[dc][j] >= services[j].arrived, f'Service {j} on {dc} bid needs to be bid after the arrival of the service'
+
+ for t in range(services[j].arrived):
+ prob += bidService[dc][j][t] == 0
+ for t in range(services[j].start + 1, totalTime):
+ prob += bidService[dc][j][t] == 0
+ prob += lpSum([eService[dc][c][j] for c in range(cDC[dc])]) == lpSum([bidService[dc][j][t] for t in range(totalTime)])
+
+
+def only_runs_after_bidded(prob, nbDC, cDC, nbJobs, totalTime, alphaJob, bidJob):
+ for j in range(nbJobs): # runs only if started
+ for t in range(totalTime):
+ for dc in range(nbDC):
+ prob += lpSum([bidJob[dc][j][t2] for t2 in range(t+1)]) >= lpSum([alphaJob[dc][c][j][t] for c in range(cDC[dc])]), \
+ f"Job {j} in dc {dc} is not running on any core if it is not bidded at any step before or equal to t {t}"
+
+def end_job_time(prob, nbJobs, totalTime, percentage_finished, finished, nbDC, finished_in_step, cDC, eJob):
+ for j in range(nbJobs):
+ for dc in range(nbDC):
+ for t in range(totalTime):
+ prob += (1 - finished[dc][j][t]) >= (1 - percentage_finished[dc][j][t]) \
+ , f"Job_{j}_indc_{dc}_finished_at_t_{t}"
+ for t in range(1, totalTime):
+ prob += finished[dc][j][t] >= finished[dc][j][t-1]
+
+ for j in range(nbJobs):
+ for dc in range(nbDC):
+ for t in range (1, totalTime):
+ prob += finished_in_step[dc][j][t] == finished[dc][j][t] - finished[dc][j][t-1] \
+ , f"finished_{j}_indc_{dc}_in_step_{t}"
+ prob += finished_in_step[dc][j][0] == 0, f"finished_{j}_indc_{dc}_in_step_{0}"
+ prob += finished_in_step[dc][j][totalTime] == lpSum([eJob[dc][c][j] for c in range(cDC[dc])]) - finished[dc][j][totalTime - 1], f"finished_{j}_indc_{dc}_in_this_server"
+
+def marketplace_constraints_gen(prob, jobs, nbJobs, alphaJob, nbDC, bidJob, eJob, cDC, totalTime, eService, nbServices,
+ services, bidService, percentage_finished, finished, finished_in_step):
+ qos_constraints(prob, jobs, nbJobs, alphaJob, nbDC, bidJob, eJob, cDC, bidService, services, eService, nbServices, totalTime, percentage_finished)
+
+ only_runs_after_bidded(prob, nbDC, cDC, nbJobs, totalTime, alphaJob, bidJob)
+ end_job_time(prob, nbJobs, totalTime, percentage_finished, finished, nbDC, finished_in_step, cDC, eJob)
diff --git a/GenerateProblem/MarketplaceModel/marketplace_variables_gen.py b/GenerateProblem/MarketplaceModel/marketplace_variables_gen.py
new file mode 100644
index 0000000..43d09cd
--- /dev/null
+++ b/GenerateProblem/MarketplaceModel/marketplace_variables_gen.py
@@ -0,0 +1,55 @@
+from pulp import LpVariable, LpInteger
+
+
+# Bidding, when jobs start ect
+def marketPlaceVariables(nbJobs, nbDC, totalTime):
+ # bidJob = [] # bid[dc][j] #TODO: changer avec due date ?
+ # for idDc in range(nbDC):
+ # bidJob.append([LpVariable("BidJob_" + str(idDc) + "," + str(i), jobs[i].a, jobs[i].due, LpInteger) for i in
+ # range(nbJobs)])
+ bidJob = [] # bid[dc][j][t]
+ for idDc in range(nbDC):
+ bidJobDc = []
+ for idJob in range(nbJobs):
+ bidJobDc.append([LpVariable("BidJob_" + str(idDc) + "," + str(idJob) + "," + str(i), 0, 1, LpInteger) for i in
+ range(totalTime)])
+ bidJob.append(bidJobDc)
+
+ percentage_finished = [] # percentage_finished[dc][j][t]
+ for idDc in range(nbDC):
+ percentage_finished_in_dc = []
+ for idJob in range(nbJobs):
+ percentage_finished_in_dc.append([LpVariable("PercentageFinished_" + str(idDc) + "," + str(idJob) + "," + str(i), 0, 1) for i in range(totalTime)])
+ percentage_finished.append(percentage_finished_in_dc)
+
+ finished = [] # finished[dc][j][t]
+ for idDc in range(nbDC):
+ finished_in_dc = []
+ for idJob in range(nbJobs):
+ finished_in_dc.append([LpVariable("Finished_" + str(idDc) + "," + str(idJob) + "," + str(i), 0, 1, LpInteger) for i in range(totalTime)])
+ finished.append(finished_in_dc)
+
+ finished_in_step = [] # step_finished[dc][j]
+ for idDc in range(nbDC):
+ finished_in_step_dc = []
+ for idJob in range(nbJobs):
+ finished_in_step_dc.append([LpVariable("FinishedInStep_" + str(idDc) + "," + str(idJob) + "," + str(i), 0, 1, LpInteger) for i in range(totalTime + 1)])
+ finished_in_step.append(finished_in_step_dc)
+
+ return bidJob, percentage_finished, finished, finished_in_step
+
+def service_marketplace_variables(nbDC, services, totalTime):
+ bidService = [] # bid[dc][j][t]
+ for idDc in range(nbDC):
+ bidServiceDc = []
+ for idJob in range(len(services)):
+ bidServiceDc.append([LpVariable("BidService_" + str(idDc) + "," + str(idJob) + "," + str(i), 0, 1, LpInteger) for i in
+ range(totalTime)])
+ bidService.append(bidServiceDc)
+ return bidService
+
+def marketplace_variables_gen(nbDC, totalTime, nbJobs, services):
+ bidJob, percentage_finished, finished, finished_in_step = marketPlaceVariables(nbJobs, nbDC, totalTime)
+ bidService = service_marketplace_variables(nbDC, services, totalTime)
+
+ return bidJob, bidService, percentage_finished, finished, finished_in_step
diff --git a/GenerateProblem/Modeling.md b/GenerateProblem/Modeling.md
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/__init__.py b/GenerateProblem/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateProblem/problem_gen.py b/GenerateProblem/problem_gen.py
new file mode 100644
index 0000000..3cb2e3d
--- /dev/null
+++ b/GenerateProblem/problem_gen.py
@@ -0,0 +1,124 @@
+from pulp import LpProblem, LpMaximize, lpSum, LpVariable
+from GenerateProblem.DatacenterModel.datacenter_variables_gen import variables_gen
+from GenerateProblem.DatacenterModel.datacenter_constraints_gen import regular_constraints_gen
+from GenerateProblem.MarketplaceModel.marketplace_constraints_gen import marketplace_constraints_gen
+from GenerateProblem.MarketplaceModel.marketplace_variables_gen import marketplace_variables_gen
+
+
+# create problem + objective (maximize money)
+def createProblem(totalMoney):
+ prob = LpProblem("The scheduling Problem", LpMaximize)
+ prob += totalMoney, "Total Money"
+ return prob
+
+
+def create_obj(prob, nbDC, jobs, nbJobs, alphaJob, cDC, bidJob, totalMoney, total_energy, totalTime, pmin, pON, alphaServ, pdyn, nbServices, services, eServ, bidService, finished_in_step):
+ # Define money calculation
+ money_service = [LpVariable("moneyservice_" + str(idDc)) for idDc in range(nbDC)]
+ money_job = [LpVariable("moneyjob_" + str(idDc)) for idDc in range(nbDC)]
+ money_by_job = []
+ for idDc in range(nbDC):
+ money_by_job.append([LpVariable("moneyByJob_" + str(idDc) + "," + str(j)) for j in range(nbJobs)])
+
+ money_by_service = []
+ for idDc in range(nbDC):
+ money_by_service.append([LpVariable("moneyByService_" + str(idDc) + "," + str(j)) for j in range(nbServices)])
+
+ money = [LpVariable("money_" + str(idDc)) for idDc in range(nbDC)]
+
+ penalty = [LpVariable("penalty_" + str(idDc)) for idDc in range(nbDC)]
+ penalty_job = [LpVariable("penaltyjob_" + str(idDc)) for idDc in range(nbDC)]
+
+ penalty_by_job = []
+ for idDc in range(nbDC):
+ penalty_by_job.append([LpVariable("penaltyByJob_" + str(idDc) + "," + str(j)) for j in range(nbJobs)])
+
+ penalty_service = [LpVariable("penaltyservice_" + str(idDc)) for idDc in range(nbDC)]
+ penalty_service_hard_by_service = []
+ penalty_service_regularise_by_service = []
+ for idDc in range(nbDC):
+ penalty_service_hard_by_service.append([LpVariable("penaltyservicehard_" + str(idDc) + "," + str(j)) for j in range(nbServices)])
+ penalty_service_regularise_by_service.append([LpVariable("penaltyserviceregularise_" + str(idDc) + "," + str(j)) for j in range(nbServices)])
+ energy = [] # energy[dc][t]
+ for idDc in range(nbDC):
+ energy.append([LpVariable("energy_" + str(idDc) + ',' + str(t)) for t in range(totalTime)])
+
+ for dc in range(nbDC):
+
+ for j in range(nbJobs):
+ prob += penalty_by_job[dc][j] == -(
+ lpSum([finished_in_step[dc][j][t] * jobs[j].pendead
+ for t in range(jobs[j].dead+1, totalTime+1)
+ ]) + \
+ lpSum([finished_in_step[dc][j][t] * (jobs[j].penmin + (jobs[j].penmax - jobs[j].penmin) * (t - jobs[j].due) / (jobs[j].dead - jobs[j].due))
+ for t in range(jobs[j].due+1, jobs[j].dead+1)
+ ]))
+ prob += penalty_job[dc] == lpSum([penalty_by_job[dc][j] for j in range(nbJobs)])
+ for j in range(nbServices):
+ prob += penalty_service_hard_by_service[dc][j] == -lpSum([services[j].pen * \
+ lpSum([(1 - ((1 / services[j].work[t]) * (
+ alphaServ[dc][c][j][t + services[j].start]))) for c in
+ range(cDC[dc]) for t in
+ range(len(services[j].work))])])
+
+ prob += penalty_service_regularise_by_service[dc][j] == lpSum(
+ [len(services[j].work) * services[j].pen * (cDC[dc] - lpSum([eServ[dc][c][j] for c in range(cDC[dc])]))])
+ prob += penalty_service[dc] == lpSum([penalty_service_hard_by_service[dc][j] + penalty_service_regularise_by_service[dc][j] for j in range(nbServices)])
+ prob += penalty[dc] == penalty_job[dc] + penalty_service[dc]
+
+ # prob += money_job[dc] == lpSum([jobs[j].pmin * lpSum([eJob[dc][c][j] for c in range(cDC[dc])]) + \
+ # (jobs[j].pmax - jobs[j].pmin) * (
+ # 1 - (jobs[j].dead - bidJob[dc][j]) / (
+ # jobs[j].dead - jobs[j].a))
+ # for j in range(nbJobs)])
+ for j in range(nbJobs):
+ prob += money_by_job[dc][j] == lpSum([bidJob[dc][j][t] * (jobs[j].pmin + (jobs[j].pmax - jobs[j].pmin) * (t - jobs[j].a) / (jobs[j].due - jobs[j].a))
+ for t in range(totalTime)
+ ])
+ prob += money_job[dc] == lpSum([money_by_job[dc][j] for j in range(nbJobs)])
+
+ for j in range(nbServices):
+ # prob += money_by_service[dc][j] == lpSum([services[j].pmin * lpSum([eServ[dc][c][j] for c in range(cDC[dc])]) + \
+ # (services[j].pmax - services[j].pmin) * (
+ # 1 - (services[j].start - bid_service[dc][j]) / (
+ # services[j].start - services[j].arrived))])
+ prob += money_by_service[dc][j] == lpSum([bidService[dc][j][t] * (services[j].pmin + (services[j].pmax - services[j].pmin) * (t - services[j].arrived) / (services[j].start - services[j].arrived))
+ for t in range(totalTime)
+ ])
+
+
+ prob += money_service[dc] == lpSum([money_by_service[dc][j] for j in range(nbServices)])
+ prob += money[dc] == money_job[dc] + money_service[dc]
+ prob += totalMoney == lpSum([penalty[dc] + money[dc] for dc in range(nbDC)])
+
+ # define energy use
+ for dc in range(nbDC):
+ for t in range(totalTime):
+ prob += energy[dc][t] == lpSum([pmin * pON[dc][c][t] for c in range(cDC[dc])]) + \
+ lpSum(
+ [pdyn * alphaServ[dc][c][idServ][t] for c in range(cDC[dc]) for idServ in range(nbServices)]) + \
+ lpSum([pdyn * alphaJob[dc][c][idJob][t] for c in range(cDC[dc]) for idJob in range(nbJobs)]), \
+ f'Energy consumption of dc {dc} at time step {t}'
+
+ prob += total_energy == lpSum(
+ [energy[dc][t] for dc in range(nbDC) for t in range(totalTime)]), "Total energy consumption"
+
+
+def problem_gen(nbDC, cDC, nbServices, totalTime, jobs, nbJobs, allCores, servReq, renewPow, pmin, pdyn):
+ eServ, alphaServ, eJob, alphaJob, pON, total_energy, totalMoney = variables_gen(nbDC, cDC, nbServices, totalTime,
+ jobs, nbJobs)
+
+ bidJob, bidService, percentage_finished, finished, finished_in_step = marketplace_variables_gen(nbDC,
+ totalTime,
+ nbJobs,
+ servReq)
+
+ prob = createProblem(totalMoney)
+
+ regular_constraints_gen(prob, nbServices, eServ, nbJobs, eJob, allCores, alphaServ, servReq, alphaJob, jobs,
+ totalTime, nbDC, cDC, renewPow, pmin, pON, pdyn)
+ marketplace_constraints_gen(prob, jobs, nbJobs, alphaJob, nbDC, bidJob, eJob, cDC, totalTime, eServ, nbServices,
+ servReq, bidService, percentage_finished, finished, finished_in_step)
+ create_obj(prob, nbDC, jobs, nbJobs, alphaJob, cDC, bidJob, totalMoney, total_energy, totalTime, pmin, pON, alphaServ, pdyn, nbServices,
+ servReq, eServ, bidService, finished_in_step)
+ return prob
diff --git a/GenerateVisualisation/__init__.py b/GenerateVisualisation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/GenerateVisualisation/experiments_analysis.py b/GenerateVisualisation/experiments_analysis.py
new file mode 100644
index 0000000..396b61a
--- /dev/null
+++ b/GenerateVisualisation/experiments_analysis.py
@@ -0,0 +1,1585 @@
+import json
+from matplotlib import pyplot as plt
+import numpy as np
+import matplotlib.patches as mpatches
+import pandas as pd
+import seaborn as sns
+from itertools import chain
+from typing import Literal
+
+class Analysis:
+ def __init__(self,
+ base_folder,
+ files,
+ names,
+ nb_dcs,
+ seeds = range(3, 103)):
+ self.base_folder = base_folder
+ self.seeds = seeds
+ self.nb_dcs = nb_dcs
+ self.names = names
+ self.files = files
+
+ def get_money(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_money_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ services_money_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ job_money = 0
+ service_money = 0
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ job_money += scheduling["money"]
+ else:
+ service_money += scheduling["money"]
+ if (abs((job_money + service_money) - (dc_results["money"])) > 0.1):
+ print("Error! Seed {} total benefits by jobs {} total benefits dc {}".format(seed, job_money + service_money, dc_results["money"]))
+ jobs_money_by_dc[exp_nb][dc_results["dc"]].append(job_money)
+ services_money_by_dc[exp_nb][dc_results["dc"]].append(service_money)
+ exp_nb += 1
+ return np.array(jobs_money_by_dc), np.array(services_money_by_dc)
+
+ def get_benefits_total(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ benefits = [[] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ benefits[exp_nb].append(data[0]["Benefits"])
+ exp_nb += 1
+ return np.array(benefits)
+
+ def get_execution_time(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ solutionTime = [[] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ solutionTime[exp_nb].append(data[0]["SolutionTime"])
+ exp_nb += 1
+ return np.array(solutionTime)
+
+ def get_benefits(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_benefits_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ services_benefits_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ job_benefits = 0
+ service_benefits = 0
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ job_benefits += scheduling["money"] - scheduling["penalty"]
+ else:
+ service_benefits += scheduling["money"] - scheduling["penalty"]
+ if (abs((job_benefits + service_benefits) - (dc_results["money"] - dc_results["penalty"])) > 0.1):
+ print("Error! Seed {} total benefits by jobs {} total benefits dc {}".format(seed, job_benefits + service_benefits, dc_results["money"] - dc_results["penalty"]))
+ jobs_benefits_by_dc[exp_nb][dc_results["dc"]].append(job_benefits)
+ services_benefits_by_dc[exp_nb][dc_results["dc"]].append(service_benefits)
+ exp_nb += 1
+ return np.array(jobs_benefits_by_dc), np.array(services_benefits_by_dc)
+
+ def get_penalty(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_penalties_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ services_penalties_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ job_penalties = 0
+ service_penalties = 0
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ job_penalties += scheduling["penalty"]
+ else:
+ service_penalties += scheduling["penalty"]
+ if (abs((job_penalties + service_penalties) - (dc_results["penalty"])) > 0.1):
+ print("Error! Seed {} total benefits by jobs {} total benefits dc {}".format(seed, job_penalties + service_penalties, dc_results["penalty"]))
+ jobs_penalties_by_dc[exp_nb][dc_results["dc"]].append(job_penalties)
+ services_penalties_by_dc[exp_nb][dc_results["dc"]].append(service_penalties)
+ exp_nb += 1
+ return np.array(jobs_penalties_by_dc), np.array(services_penalties_by_dc)
+
+ def get_energy(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ energy_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ energy_by_dc[exp_nb][dc_results["dc"]].append(np.sum(dc_results["Energy_use"]))
+ exp_nb += 1
+ return np.array(energy_by_dc)
+
+ def get_energy_available(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ energy_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ energy_by_dc[exp_nb][dc_results["dc"]].append(np.sum(dc_results["Energy_available"]))
+ exp_nb += 1
+ return np.array(energy_by_dc)
+
+ def get_jobs_finished(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_finished_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ services_finished_by_dc = [[[] for _ in range(nb_dcs)] for _ in range(len(files))]
+ exp_nb = 0
+ for file in files:
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ for dc_results in data[0]["dc"]:
+ job_finished = 0
+ service_finished = 0
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ job_finished += 1
+ else:
+ service_finished += 1
+ jobs_finished_by_dc[exp_nb][dc_results["dc"]].append(job_finished)
+ services_finished_by_dc[exp_nb][dc_results["dc"]].append(service_finished)
+ exp_nb += 1
+ return np.array(jobs_finished_by_dc), np.array(services_finished_by_dc)
+
+ def get_job_times(self, job_id, data):
+ for job in data[0]["jobs"]:
+ if job["id"] == job_id:
+ return job["duedate"], job["deadline"]
+ raise RuntimeError('Job not found')
+
+ def get_job_workload(self, job_id, data):
+ for job in data[0]["jobs"]:
+ if job["id"] == job_id:
+ return job["workload"]
+ raise RuntimeError('Job not found')
+
+ def get_job_arrival(self, job_id, data):
+ for job in data[0]["jobs"]:
+ if job["id"] == job_id:
+ return job["arrival"]
+ raise RuntimeError('Job not found')
+
+ def get_service_alpha(self, service_id, data):
+ for service in data[0]["services"]:
+ if service["id"] == service_id:
+ return sum(service["workload"])
+ raise RuntimeError('Job not found')
+
+ def get_total_workload(self, data):
+ total_service = 0
+ total_jobs = 0
+ for job in data[0]["jobs"]:
+ total_jobs += job["workload"]
+ for service in data[0]["services"]:
+ total_service += sum(service["workload"])
+ return total_jobs, total_service
+
+ def get_load_finished(self, exp, data, nb_dcs=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ jobs_bidded_load_by_dc = np.zeros(nb_dcs)
+ jobs_before_due_by_dc = np.zeros(nb_dcs)
+ jobs_before_dead_by_dc = np.zeros(nb_dcs)
+ jobs_after_dead_by_dc = np.zeros(nb_dcs)
+ services_bidded_load_by_dc = np.zeros(nb_dcs)
+ services_qos_by_dc = np.zeros(nb_dcs)
+
+ jobs_before_due_number_by_dc = np.zeros(nb_dcs)
+ jobs_before_dead_number_by_dc = np.zeros(nb_dcs)
+ jobs_after_dead_number_by_dc = np.zeros(nb_dcs)
+ services_qos_number_by_dc = np.zeros(nb_dcs)
+
+ for dc_results in data[0]["dc"]:
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ duedate, deadline = self.get_job_times(scheduling["job"], data)
+ finish_time = len(scheduling["alpha"])
+ while(finish_time > 0):
+ finish_time -= 1
+ if (scheduling["alpha"][finish_time]>0):
+ finish_time += 1
+ break
+ jobs_bidded_load_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ if (finish_time <= duedate):
+ jobs_before_due_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ jobs_before_due_number_by_dc[dc_results["dc"]] += 1
+ elif (duedate == deadline or finish_time < 0):
+ jobs_after_dead_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ jobs_after_dead_number_by_dc[dc_results["dc"]] += 1
+ elif (finish_time <= deadline):
+ jobs_before_dead_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ jobs_before_dead_number_by_dc[dc_results["dc"]] += 1
+ else:
+ jobs_after_dead_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ jobs_after_dead_number_by_dc[dc_results["dc"]] += 1
+ else:
+ services_bidded_load_by_dc[dc_results["dc"]] += self.get_service_alpha(scheduling["service"], data)
+ services_qos_by_dc[dc_results["dc"]] += sum(scheduling["alpha"])
+ services_qos_number_by_dc[dc_results["dc"]] += 1
+ if (jobs_bidded_load_by_dc[dc_results["dc"]] == 0):
+ if (jobs_before_due_by_dc[dc_results["dc"]] > 0 or
+ jobs_after_dead_by_dc[dc_results["dc"]] > 0 or
+ jobs_before_dead_by_dc[dc_results["dc"]] > 0):
+ raise RuntimeError('More jobs in cores than bidded')
+ if (services_qos_by_dc[dc_results["dc"]] > 0 and services_bidded_load_by_dc[dc_results["dc"]] == 0):
+ raise RuntimeError('More executed than demanded')
+
+ return jobs_bidded_load_by_dc, jobs_before_due_by_dc, jobs_before_dead_by_dc, jobs_after_dead_by_dc, services_bidded_load_by_dc, services_qos_by_dc, jobs_before_due_number_by_dc, \
+ jobs_before_dead_number_by_dc, jobs_after_dead_number_by_dc, services_qos_number_by_dc
+
+ def get_jobs_qos_by_dc(self, exp, bidded=True, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_before_due_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_before_dead_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_after_dead_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_qos_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ jobs_bidded_load, jobs_before_due, jobs_before_dead, jobs_after_dead, services_bidded_load, services_qos = self.get_load_finished(exp, data)[0:6]
+ job_demanded, service_demanded = self.get_total_workload(data)
+ for dc in range(nb_dcs):
+ if bidded:
+ job_demanded = jobs_bidded_load[dc]
+ service_demanded = services_bidded_load[dc]
+
+ if job_demanded > 0:
+ jobs_before_due_by_dc[exp_nb][dc][seed_nb] = jobs_before_due[dc] / job_demanded * 100
+ jobs_before_dead_by_dc[exp_nb][dc][seed_nb] = jobs_before_dead[dc] / job_demanded * 100
+ jobs_after_dead_by_dc[exp_nb][dc][seed_nb] = jobs_after_dead[dc] / job_demanded * 100
+
+ if service_demanded > 0:
+ services_qos_by_dc[exp_nb][dc][seed_nb] = services_qos[dc] / service_demanded * 100
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_before_due_by_dc, jobs_before_dead_by_dc, jobs_after_dead_by_dc, services_qos_by_dc
+
+ def get_jobs_qos_consolidate(self, exp, bidded=True, files=None):
+ if files is None:
+ files = self.files
+ jobs_before_due_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_before_dead_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_after_dead_conso = np.zeros((len(files), len(self.seeds)))
+ services_qos_conso = np.zeros((len(files), len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ jobs_bidded_load, jobs_before_due, jobs_before_dead, jobs_after_dead, services_bidded_load, services_qos = self.get_load_finished(exp, data)[0:6]
+ job_demanded, service_demanded = self.get_total_workload(data)
+ if bidded:
+ job_demanded = np.sum(jobs_bidded_load)
+ service_demanded = np.sum(services_bidded_load)
+
+ if job_demanded > 0:
+ jobs_before_due_conso[exp_nb][seed_nb] = np.sum(jobs_before_due) / job_demanded * 100
+ jobs_before_dead_conso[exp_nb][seed_nb] = np.sum(jobs_before_dead) / job_demanded * 100
+ jobs_after_dead_conso[exp_nb][seed_nb] = np.sum(jobs_after_dead) / job_demanded * 100
+
+ if service_demanded > 0:
+ services_qos_conso[exp_nb][seed_nb] = np.sum(services_qos) / service_demanded * 100
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_before_due_conso, jobs_before_dead_conso, jobs_after_dead_conso, services_qos_conso
+
+ def get_jobs_qos_by_dc_number(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_before_due_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_before_dead_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_after_dead_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_qos_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ total_jobs = len(data[0]["jobs"])
+ total_services = len(data[0]["services"])
+ job_before_due_executed, job_before_dead_executed, job_after_dead_executed, service_executed = self.get_load_finished(exp, data)[6:10]
+ for dc in range(nb_dcs):
+ jobs_before_due_by_dc[exp_nb][dc][seed_nb] = job_before_due_executed[dc] / total_jobs * 100
+ jobs_before_dead_by_dc[exp_nb][dc][seed_nb] = job_before_dead_executed[dc] / total_jobs * 100
+ jobs_after_dead_by_dc[exp_nb][dc][seed_nb] = job_after_dead_executed[dc] / total_jobs * 100
+ services_qos_by_dc[exp_nb][dc][seed_nb] = service_executed[dc] / total_services * 100
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_before_due_by_dc, jobs_before_dead_by_dc, jobs_after_dead_by_dc, services_qos_by_dc
+
+ def get_jobs_qos_consolidate_number(self, exp, files=None):
+ if files is None:
+ files = self.files
+ jobs_before_due_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_before_dead_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_after_dead_conso = np.zeros((len(files), len(self.seeds)))
+ services_qos_conso = np.zeros((len(files), len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ total_jobs = len(data[0]["jobs"])
+ total_services = len(data[0]["services"])
+ job_before_due_executed, job_before_dead_executed, job_after_dead_executed, service_executed = self.get_load_finished(exp, data)[6:10]
+ jobs_before_due_conso[exp_nb][seed_nb] = np.sum(job_before_due_executed) / total_jobs * 100
+ jobs_before_dead_conso[exp_nb][seed_nb] = np.sum(job_before_dead_executed) / total_jobs * 100
+ jobs_after_dead_conso[exp_nb][seed_nb] = np.sum(job_after_dead_executed) / total_jobs * 100
+ services_qos_conso[exp_nb][seed_nb] = np.sum(service_executed) / total_services * 100
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_before_due_conso, jobs_before_dead_conso, jobs_after_dead_conso, services_qos_conso
+
+ def get_workload_bidded(self, exp, data, nb_dcs=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ jobs_number_bidded_by_dc = np.zeros(nb_dcs)
+ jobs_load_bidded_by_dc = np.zeros(nb_dcs)
+ services_number_bidded_by_dc = np.zeros(nb_dcs)
+ services_load_bidded_by_dc = np.zeros(nb_dcs)
+ for dc_results in data[0]["dc"]:
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ jobs_load_bidded_by_dc[dc_results["dc"]] += self.get_job_workload(scheduling["job"], data)
+ jobs_number_bidded_by_dc[dc_results["dc"]] += 1
+ else:
+ services_load_bidded_by_dc[dc_results["dc"]] += self.get_service_alpha(scheduling["service"], data)
+ services_number_bidded_by_dc[dc_results["dc"]] += 1
+ return jobs_load_bidded_by_dc, jobs_number_bidded_by_dc, services_load_bidded_by_dc, services_number_bidded_by_dc
+
+ def get_jobs_bidded_by_dc(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_number_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_load_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_avg_load_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_number_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_load_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ jobs_avg_load_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_number_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_load_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_avg_load_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_number_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_load_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ services_avg_load_not_bidded_by_dc = np.zeros((len(files), nb_dcs, len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ number_jobs_total = len(data[0]["jobs"])
+ number_services_total = len(data[0]["services"])
+ job_load_total, service_load_total = self.get_total_workload(data)
+ jobs_load_bidded, jobs_number_bidded, services_load_bidded, services_number_bidded = self.get_workload_bidded(exp, data)
+ for dc in range(nb_dcs):
+ jobs_number_bidded_by_dc[exp_nb][dc][seed_nb] = jobs_number_bidded[dc] / number_jobs_total * 100
+ services_number_bidded_by_dc[exp_nb][dc][seed_nb] = services_number_bidded[dc] / number_services_total * 100
+ jobs_load_bidded_by_dc[exp_nb][dc][seed_nb] = jobs_load_bidded[dc] / job_load_total * 100
+ services_load_bidded_by_dc[exp_nb][dc][seed_nb] = services_load_bidded[dc] / service_load_total * 100
+ if (jobs_number_bidded[dc] > 0):
+ jobs_avg_load_bidded_by_dc[exp_nb][dc][seed_nb] = jobs_load_bidded[dc] / jobs_number_bidded[dc]
+ if (services_number_bidded[dc] > 0):
+ services_avg_load_bidded_by_dc[exp_nb][dc][seed_nb] = services_load_bidded[dc] / services_number_bidded[dc]
+ jobs_number_not_bidded_by_dc[exp_nb][dc][seed_nb] = (number_jobs_total-jobs_number_bidded[dc]) / number_jobs_total * 100
+ services_number_not_bidded_by_dc[exp_nb][dc][seed_nb] = (number_services_total-services_number_bidded[dc]) / number_services_total * 100
+ jobs_load_not_bidded_by_dc[exp_nb][dc][seed_nb] = (job_load_total-jobs_load_bidded[dc]) / job_load_total * 100
+ services_load_not_bidded_by_dc[exp_nb][dc][seed_nb] = (service_load_total-services_load_bidded[dc]) / service_load_total * 100
+ jobs_avg_load_not_bidded_by_dc[exp_nb][dc][seed_nb] = (job_load_total-jobs_load_bidded[dc]) / (number_jobs_total-jobs_number_bidded[dc])
+ services_avg_load_not_bidded_by_dc[exp_nb][dc][seed_nb] = (service_load_total-services_load_bidded[dc]) / (number_services_total-services_number_bidded[dc])
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_number_bidded_by_dc, services_number_bidded_by_dc, jobs_load_bidded_by_dc, services_load_bidded_by_dc, jobs_avg_load_bidded_by_dc, services_avg_load_bidded_by_dc, \
+ jobs_number_not_bidded_by_dc, services_number_not_bidded_by_dc, jobs_load_not_bidded_by_dc, services_load_not_bidded_by_dc, jobs_avg_load_not_bidded_by_dc, services_avg_load_not_bidded_by_dc
+
+ def get_jobs_bidded_consolidate(self, exp, files=None):
+ if files is None:
+ files = self.files
+ jobs_number_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_load_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_avg_load_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_number_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_load_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ jobs_avg_load_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_number_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_load_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_avg_load_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_number_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_load_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ services_avg_load_not_bidded_conso = np.zeros((len(files), len(self.seeds)))
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ number_jobs_total = len(data[0]["jobs"])
+ number_services_total = len(data[0]["services"])
+ job_load_total, service_load_total = self.get_total_workload(data)
+ jobs_load_bidded, jobs_number_bidded, services_load_bidded, services_number_bidded = self.get_workload_bidded(exp, data)
+ jobs_number_bidded_conso[exp_nb][seed_nb] = np.sum(jobs_number_bidded) / number_jobs_total * 100
+ services_number_bidded_conso[exp_nb][seed_nb] = np.sum(services_number_bidded) / number_services_total * 100
+ jobs_load_bidded_conso[exp_nb][seed_nb] = np.sum(jobs_load_bidded) / job_load_total * 100
+ services_load_bidded_conso[exp_nb][seed_nb] = np.sum(services_load_bidded) / np.sum(service_load_total) * 100
+ if (np.sum(jobs_number_bidded) > 0):
+ jobs_avg_load_bidded_conso[exp_nb][seed_nb] = np.sum(jobs_load_bidded) / np.sum(jobs_number_bidded)
+ if (np.sum(services_number_bidded) > 0):
+ services_avg_load_bidded_conso[exp_nb][seed_nb] = np.sum(services_load_bidded) / np.sum(services_number_bidded)
+ jobs_number_not_bidded_conso[exp_nb][seed_nb] = (number_jobs_total-np.sum(jobs_number_bidded)) / number_jobs_total * 100
+ services_number_not_bidded_conso[exp_nb][seed_nb] = (number_services_total-np.sum(services_number_bidded)) / number_services_total * 100
+ jobs_load_not_bidded_conso[exp_nb][seed_nb] = (job_load_total-np.sum(jobs_load_bidded)) / job_load_total * 100
+ services_load_not_bidded_conso[exp_nb][seed_nb] = (service_load_total-np.sum(services_load_bidded)) / service_load_total * 100
+ if (number_jobs_total-np.sum(jobs_number_bidded)) > 0:
+ jobs_avg_load_not_bidded_conso[exp_nb][seed_nb] = (job_load_total-np.sum(jobs_load_bidded)) / (number_jobs_total-np.sum(jobs_number_bidded))
+ if (number_services_total-np.sum(services_number_bidded)) > 0:
+ services_avg_load_not_bidded_conso[exp_nb][seed_nb] = (service_load_total-np.sum(services_load_bidded)) / (number_services_total-np.sum(services_number_bidded))
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_number_bidded_conso, services_number_bidded_conso, jobs_load_bidded_conso, services_load_bidded_conso, jobs_avg_load_bidded_conso, services_avg_load_bidded_conso, \
+ jobs_number_not_bidded_conso, services_number_not_bidded_conso, jobs_load_not_bidded_conso, services_load_not_bidded_conso, jobs_avg_load_not_bidded_conso, services_avg_load_not_bidded_conso
+
+ def get_slowdown(self, exp, data, nb_dcs=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ jobs_slowdown = [[] for _ in range(nb_dcs)]
+ for dc_results in data[0]["dc"]:
+ for core in dc_results["cores"]:
+ for scheduling in core["scheduling"]:
+ if "job" in scheduling:
+ arrival = self.get_job_arrival(scheduling["job"], data)
+ finish_time = len(scheduling["alpha"])
+ while(finish_time > 0):
+ finish_time -= 1
+ if (scheduling["alpha"][finish_time]>0):
+ finish_time += 1
+ break
+ start_time = 0
+ while(start_time <= len(scheduling["alpha"])):
+ if (scheduling["alpha"][start_time]>0):
+ break
+ start_time += 1
+ waiting_time = start_time - arrival
+ execution_time = finish_time - start_time
+ jobs_slowdown[dc_results["dc"]].append((waiting_time + execution_time) / execution_time)
+ return jobs_slowdown
+
+ def get_jobs_slowdown_by_dc(self, exp, nb_dcs=None, files=None):
+ if nb_dcs is None:
+ nb_dcs = self.nb_dcs
+ if files is None:
+ files = self.files
+ jobs_slowdown_by_dc = np.full((len(files), nb_dcs, len(self.seeds)), 1.)
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ jobs_slowdown = self.get_slowdown(exp, data)
+ for dc in range(nb_dcs):
+ if (len(jobs_slowdown[dc]) > 0):
+ jobs_slowdown_by_dc[exp_nb][dc][seed_nb] = np.mean(jobs_slowdown[dc])
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_slowdown_by_dc
+
+ def get_jobs_slowdown_consolidate(self, exp, files=None):
+ if files is None:
+ files = self.files
+ jobs_slowdown_by_dc = np.full((len(files), len(self.seeds)), 1.)
+ exp_nb = 0
+ for file in files:
+ seed_nb = 0
+ for seed in self.seeds:
+ json_file = open(self.base_folder + exp + file + str(seed) + ".json")
+ data = json.load(json_file)
+ jobs_slowdown = self.get_slowdown(exp, data)
+ if (len(jobs_slowdown) > 0):
+ jobs_slowdown_by_dc[exp_nb][seed_nb] = np.mean(list(chain.from_iterable(jobs_slowdown)))
+ seed_nb += 1
+ exp_nb += 1
+ return jobs_slowdown_by_dc
+
+class Analysis_plots:
+ def __init__(self,
+ analysis_list : Analysis):
+ self.analysis_list = analysis_list
+
+ def plot_metric(self, function, exp_dir, ax, ylabel, title, ylabel2="Sum"):
+ names = []
+ width_jobs = 0.12
+ first_point = None
+ last_point = None
+ x_ticks = []
+ previous_step = 0
+ for analysis in self.analysis_list:
+ jobs_metric, services_metric = function(analysis, exp_dir)
+ jobs_metric_mean = [[np.mean([jobs_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_metric_mean = [[np.mean([services_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_metric_min = [[np.min([jobs_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_metric_min = [[np.min([services_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_metric_max = [[np.max([jobs_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_metric_max = [[np.max([services_metric[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ step = (width_jobs * 2 * analysis.nb_dcs) + (width_jobs / 2)
+ last_step = 0
+ if (len(x_ticks) != 0):
+ last_step = x_ticks[-1] + (previous_step / 2) + (step / 2)
+ previous_step = step
+ x = np.arange(last_step, last_step + (len(analysis.names) * step), step)
+ x_ticks.extend(x)
+ ax.plot(x, [np.sum(jobs_metric_mean[exp] + services_metric_mean[exp]) for exp in range(len(analysis.names))], 'x', label="DC Sum", c="tab:blue")
+ x_point = -(width_jobs * (analysis.nb_dcs - 1) + (width_jobs / 2))
+ if (first_point == None):
+ first_point = x_ticks[0] - (step / 2)
+ last_point = x_ticks[-1] + (step / 2)
+ color = ["tab:green", "tab:red", "tab:purple", "tab:orange"]
+ for dc in range(analysis.nb_dcs):
+ ax.bar(x+x_point, [jobs_metric_mean[exp][dc] for exp in range(len(analysis.names))], width_jobs, label="DC {}".format(dc),
+ yerr=[[jobs_metric_mean[exp][dc] - jobs_metric_min[exp][dc] for exp in range(len(analysis.names))],
+ [jobs_metric_max[exp][dc] - jobs_metric_mean[exp][dc] for exp in range(len(analysis.names))]],
+ color=color[dc], hatch="..")
+ x_point += width_jobs
+ ax.bar(x+x_point, [services_metric_mean[exp][dc] for exp in range(len(analysis.names))], width_jobs, label="DC {}".format(dc),
+ yerr=[[services_metric_mean[exp][dc] - services_metric_min[exp][dc] for exp in range(len(analysis.names))],
+ [services_metric_max[exp][dc] - services_metric_mean[exp][dc] for exp in range(len(analysis.names))]],
+ color=color[dc], hatch="xx")
+ x_point += width_jobs
+ names.extend(analysis.names)
+ ax.set_xlabel("Execution", size=16)
+ ax.tick_params(axis='x', labelrotation=90)
+ ax.set_ylabel(ylabel, size=16)
+ ax.set_title(title, size=18)
+ ax.set_xticks(x_ticks, names, size=16)
+ ax.set_xlim(first_point, last_point)
+ ax.tick_params(axis='both', labelsize=14)
+ ax.grid(axis='y')
+
+ def plot_graph(self, exp_dir, simplified=False):
+ fig = plt.figure(figsize=(25, 15))
+ ax = plt.subplot2grid((3,6), (0,0), colspan=6)
+ # Money
+ self.plot_metric(Analysis.get_benefits, exp_dir, ax, "Money - Penalty", "Benefits")
+ job_patch = mpatches.Patch(facecolor='w', label='Jobs', hatch="..")
+ service_patch = mpatches.Patch(facecolor='w',label='Services', hatch="xx")
+ ax.legend(handles=[job_patch, service_patch], fontsize=16)
+ if not simplified:
+ #Penalties
+ ax = plt.subplot2grid((3,6), (1,0), colspan=6)
+ self.plot_metric(Analysis.get_penalty, exp_dir, ax, "Penalty", "Penalty")
+
+ #Jobs and services
+ ax = plt.subplot2grid((3,6), (2,0), colspan=6)
+ self.plot_metric(Analysis.get_jobs_finished, exp_dir, ax, "Number", "Jobs/services")
+
+ plt.tight_layout()
+ plt.show()
+
+ def draw_table_comparison_milp(self, fmt : Literal["default", "latex"] | None = "default"):
+ table_time = []
+ table_benefits = []
+ index_time = []
+ index_time.append("MILP")
+ index_time.extend(self.analysis_list[x].names[y] for x in range(len(self.analysis_list)) for y in range(len(self.analysis_list[x].names)))
+ index_benefits = []
+ columns = ["Value", "std", "Min", "Max"]
+ exps = ["Large", "Medium", "Small"]
+ for exp in exps:
+ execution = self.analysis_list[0].get_execution_time(exp.lower(), files=["/MILP_seed_"])
+ benefits_milp = self.analysis_list[0].get_benefits_total(exp.lower(), files=["/MILP_seed_"])
+ table_time.append([np.mean(execution), np.std(execution), np.min(execution), np.max(execution)])
+ for analysis in self.analysis_list:
+ execution = analysis.get_execution_time(exp.lower())
+ benefits = analysis.get_benefits_total(exp.lower())
+ difference = [((benefits_milp[0] - benefits[x]) / benefits_milp[0]) * 100 for x in range(len(analysis.names))]
+ for x in range(len(analysis.names)):
+ table_time.append([np.mean(execution[x]), np.std(execution[x]), np.min(execution[x]), np.max(execution[x])])
+ table_benefits.append([np.mean(difference[x]), np.std(difference[x]), np.min(difference[x]), np.max(difference[x])])
+ index_benefits.append(exp)
+ table_time_pd = pd.DataFrame(table_time, index=pd.MultiIndex.from_product([exps, index_time]), columns=columns)
+ styler = table_time_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ styler.format(precision=2)
+ print("Solution time (s)")
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ table_benefits_pd = pd.DataFrame(table_benefits, index=index_benefits, columns=columns)
+ styler = table_benefits_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ styler.format(precision=2)
+ print("Benefits (% gap to MILP)")
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ def draw_table_bidded(self, exp_dir, tables_type : Literal["both", "by_dc", "conso"] | None = "both", simplified = False, fmt : Literal["default", "latex"] | None = "default"):
+ title_jobs = "Jobs"
+ title_services = "Services"
+
+ subtitle_1_bidded = "Bidded"
+ subtitle_1_not_bidded = "Not bidded"
+
+ subtitle_2_number = "Number"
+ subtitle_2_number_std = "Number std"
+ subtitle_2_load = "Load"
+ subtitle_2_load_std = "Load std"
+ subtitle_2_avg_load = "Average Load"
+ subtitle_2_avg_load_std = "Average Load std"
+ if simplified:
+ subtitle_2_number_std = "std"
+ title = [
+ (subtitle_1_bidded, title_jobs, subtitle_2_number),
+ (subtitle_1_bidded, title_jobs, subtitle_2_number_std),
+ # (subtitle_1_bidded, title_jobs, subtitle_2_load),
+ # (subtitle_1_bidded, title_jobs, subtitle_2_load_std),
+ (subtitle_1_bidded, title_services, subtitle_2_number),
+ (subtitle_1_bidded, title_services, subtitle_2_number_std),
+ # (subtitle_1_bidded, title_services, subtitle_2_load),
+ # (subtitle_1_bidded, title_services, subtitle_2_load_std),
+ ]
+ else:
+ title = [
+ (title_jobs, subtitle_1_bidded, subtitle_2_number),
+ (title_jobs, subtitle_1_bidded, subtitle_2_number_std),
+ (title_jobs, subtitle_1_bidded, subtitle_2_load),
+ (title_jobs, subtitle_1_bidded, subtitle_2_load_std),
+ (title_jobs, subtitle_1_bidded, subtitle_2_avg_load),
+ (title_jobs, subtitle_1_bidded, subtitle_2_avg_load_std),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_number),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_number_std),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_load),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_load_std),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_avg_load),
+ (title_jobs, subtitle_1_not_bidded, subtitle_2_avg_load_std),
+ (title_services, subtitle_1_bidded, subtitle_2_number),
+ (title_services, subtitle_1_bidded, subtitle_2_number_std),
+ (title_services, subtitle_1_bidded, subtitle_2_load),
+ (title_services, subtitle_1_bidded, subtitle_2_load_std),
+ (title_services, subtitle_1_bidded, subtitle_2_avg_load),
+ (title_services, subtitle_1_bidded, subtitle_2_avg_load_std),
+ (title_services, subtitle_1_not_bidded, subtitle_2_number),
+ (title_services, subtitle_1_not_bidded, subtitle_2_number_std),
+ (title_services, subtitle_1_not_bidded, subtitle_2_load),
+ (title_services, subtitle_1_not_bidded, subtitle_2_load_std),
+ (title_services, subtitle_1_not_bidded, subtitle_2_avg_load),
+ (title_services, subtitle_1_not_bidded, subtitle_2_avg_load_std),
+ ]
+ columns = pd.MultiIndex.from_tuples(title)
+
+ if tables_type == "both" or tables_type =="by_dc":
+ table = []
+ index_by_dc = []
+ for analysis in self.analysis_list:
+ jobs_number_bidded_by_dc, services_number_bidded_by_dc, jobs_load_bidded_by_dc, services_load_bidded_by_dc, jobs_avg_load_bidded_by_dc, \
+ services_avg_load_bidded_by_dc, jobs_number_not_bidded_by_dc, services_number_not_bidded_by_dc, jobs_load_not_bidded_by_dc, \
+ services_load_not_bidded_by_dc, jobs_avg_load_not_bidded_by_dc, services_avg_load_not_bidded_by_dc = analysis.get_jobs_bidded_by_dc(exp_dir)
+ jobs_number_bidded_by_dc_mean = [[np.mean([jobs_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_number_bidded_by_dc_std = [[np.std([jobs_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_mean = [[np.mean([services_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_std = [[np.std([services_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_load_bidded_by_dc_mean = [[np.mean([jobs_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_load_bidded_by_dc_std = [[np.std([jobs_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_load_bidded_by_dc_mean = [[np.mean([services_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_load_bidded_by_dc_std = [[np.std([services_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_avg_load_bidded_by_dc_mean = [[np.mean([jobs_avg_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_avg_load_bidded_by_dc_std = [[np.std([jobs_avg_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_avg_load_bidded_by_dc_mean = [[np.mean([services_avg_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_avg_load_bidded_by_dc_std = [[np.std([services_avg_load_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_number_not_bidded_by_dc_mean = [[np.mean([jobs_number_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_number_not_bidded_by_dc_std = [[np.std([jobs_number_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_not_bidded_by_dc_mean = [[np.mean([services_number_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_not_bidded_by_dc_std = [[np.std([services_number_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_load_not_bidded_by_dc_mean = [[np.mean([jobs_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_load_not_bidded_by_dc_std = [[np.std([jobs_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_load_not_bidded_by_dc_mean = [[np.mean([services_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_load_not_bidded_by_dc_std = [[np.std([services_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_avg_load_not_bidded_by_dc_mean = [[np.mean([jobs_avg_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_avg_load_not_bidded_by_dc_std = [[np.std([jobs_avg_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_avg_load_not_bidded_by_dc_mean = [[np.mean([services_avg_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_avg_load_not_bidded_by_dc_std = [[np.std([services_avg_load_not_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ for dc in range(analysis.nb_dcs):
+ if simplified:
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb][dc], jobs_number_bidded_by_dc_std[exp_nb][dc],
+ # jobs_load_bidded_by_dc_mean[exp_nb][dc], jobs_load_bidded_by_dc_std[exp_nb][dc],
+ services_number_bidded_by_dc_mean[exp_nb][dc], services_number_bidded_by_dc_std[exp_nb][dc],
+ # services_load_bidded_by_dc_mean[exp_nb][dc], services_load_bidded_by_dc_std[exp_nb][dc],
+ ])
+ else:
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb][dc], jobs_number_bidded_by_dc_std[exp_nb][dc],
+ jobs_load_bidded_by_dc_mean[exp_nb][dc], jobs_load_bidded_by_dc_std[exp_nb][dc],
+ jobs_avg_load_bidded_by_dc_mean[exp_nb][dc], jobs_avg_load_bidded_by_dc_std[exp_nb][dc],
+ jobs_number_not_bidded_by_dc_mean[exp_nb][dc], jobs_number_not_bidded_by_dc_std[exp_nb][dc],
+ jobs_load_not_bidded_by_dc_mean[exp_nb][dc], jobs_load_not_bidded_by_dc_std[exp_nb][dc],
+ jobs_avg_load_not_bidded_by_dc_mean[exp_nb][dc], jobs_avg_load_not_bidded_by_dc_std[exp_nb][dc],
+ services_number_bidded_by_dc_mean[exp_nb][dc], services_number_bidded_by_dc_std[exp_nb][dc],
+ services_load_bidded_by_dc_mean[exp_nb][dc], services_load_bidded_by_dc_std[exp_nb][dc],
+ services_avg_load_bidded_by_dc_mean[exp_nb][dc], services_avg_load_bidded_by_dc_std[exp_nb][dc],
+ services_number_not_bidded_by_dc_mean[exp_nb][dc], services_number_not_bidded_by_dc_std[exp_nb][dc],
+ services_load_not_bidded_by_dc_mean[exp_nb][dc], services_load_not_bidded_by_dc_std[exp_nb][dc],
+ services_avg_load_not_bidded_by_dc_mean[exp_nb][dc], services_avg_load_not_bidded_by_dc_std[exp_nb][dc],
+ ])
+ if (analysis.nb_dcs == 1):
+ index_by_dc.extend([(analysis.names[n], "") for n in np.arange(len(analysis.names))])
+ else:
+ index_by_dc.extend([(analysis.names[n], x) for n in np.arange(len(analysis.names)) for x in range(analysis.nb_dcs)])
+
+ table_pd = pd.DataFrame(table, index=pd.MultiIndex.from_tuples(index_by_dc), columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([[subtitle_1_bidded], [title_jobs, title_services], [subtitle_2_number]]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([[title_jobs, title_services], [subtitle_1_bidded], [subtitle_2_number, subtitle_2_load]]))
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ if tables_type == "both" or tables_type == "conso":
+ # Consolidate
+ table = []
+ index_conso = []
+ for analysis in self.analysis_list:
+ jobs_number_bidded_by_dc, services_number_bidded_by_dc, jobs_load_bidded_by_dc, services_load_bidded_by_dc, jobs_avg_load_bidded_by_dc, \
+ services_avg_load_bidded_by_dc, jobs_number_not_bidded_by_dc, services_number_not_bidded_by_dc, jobs_load_not_bidded_by_dc, \
+ services_load_not_bidded_by_dc, jobs_avg_load_not_bidded_by_dc, services_avg_load_not_bidded_by_dc = analysis.get_jobs_bidded_consolidate(exp_dir)
+ jobs_number_bidded_by_dc_mean = [np.mean([jobs_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_number_bidded_by_dc_std = [np.std([jobs_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_mean = [np.mean([services_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_std = [np.std([services_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_load_bidded_by_dc_mean = [np.mean([jobs_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_load_bidded_by_dc_std = [np.std([jobs_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_load_bidded_by_dc_mean = [np.mean([services_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_load_bidded_by_dc_std = [np.std([services_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_avg_load_bidded_by_dc_mean = [np.mean([jobs_avg_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_avg_load_bidded_by_dc_std = [np.std([jobs_avg_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_avg_load_bidded_by_dc_mean = [np.mean([services_avg_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_avg_load_bidded_by_dc_std = [np.std([services_avg_load_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_number_not_bidded_by_dc_mean = [np.mean([jobs_number_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_number_not_bidded_by_dc_std = [np.std([jobs_number_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_not_bidded_by_dc_mean = [np.mean([services_number_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_not_bidded_by_dc_std = [np.std([services_number_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_load_not_bidded_by_dc_mean = [np.mean([jobs_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_load_not_bidded_by_dc_std = [np.std([jobs_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_load_not_bidded_by_dc_mean = [np.mean([services_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_load_not_bidded_by_dc_std = [np.std([services_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_avg_load_not_bidded_by_dc_mean = [np.mean([jobs_avg_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_avg_load_not_bidded_by_dc_std = [np.std([jobs_avg_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_avg_load_not_bidded_by_dc_mean = [np.mean([services_avg_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_avg_load_not_bidded_by_dc_std = [np.std([services_avg_load_not_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ if simplified:
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb], jobs_number_bidded_by_dc_std[exp_nb],
+ # jobs_load_bidded_by_dc_mean[exp_nb], jobs_load_bidded_by_dc_std[exp_nb],
+ services_number_bidded_by_dc_mean[exp_nb], services_number_bidded_by_dc_std[exp_nb],
+ # services_load_bidded_by_dc_mean[exp_nb], services_load_bidded_by_dc_std[exp_nb],
+ ])
+ else:
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb], jobs_number_bidded_by_dc_std[exp_nb],
+ jobs_load_bidded_by_dc_mean[exp_nb], jobs_load_bidded_by_dc_std[exp_nb],
+ jobs_avg_load_bidded_by_dc_mean[exp_nb], jobs_avg_load_bidded_by_dc_std[exp_nb],
+ jobs_number_not_bidded_by_dc_mean[exp_nb], jobs_number_not_bidded_by_dc_std[exp_nb],
+ jobs_load_not_bidded_by_dc_mean[exp_nb], jobs_load_not_bidded_by_dc_std[exp_nb],
+ jobs_avg_load_not_bidded_by_dc_mean[exp_nb], jobs_avg_load_not_bidded_by_dc_std[exp_nb],
+ services_number_bidded_by_dc_mean[exp_nb], services_number_bidded_by_dc_std[exp_nb],
+ services_load_bidded_by_dc_mean[exp_nb], services_load_bidded_by_dc_std[exp_nb],
+ services_avg_load_bidded_by_dc_mean[exp_nb], services_avg_load_bidded_by_dc_std[exp_nb],
+ services_number_not_bidded_by_dc_mean[exp_nb], services_number_not_bidded_by_dc_std[exp_nb],
+ services_load_not_bidded_by_dc_mean[exp_nb], services_load_not_bidded_by_dc_std[exp_nb],
+ services_avg_load_not_bidded_by_dc_mean[exp_nb], services_avg_load_not_bidded_by_dc_std[exp_nb],
+ ])
+ index_conso.extend(analysis.names)
+
+ table_pd = pd.DataFrame(table, index=index_conso, columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([[subtitle_1_bidded], [title_jobs, title_services], [subtitle_2_number]]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([[title_jobs, title_services], [subtitle_1_bidded], [subtitle_2_number, subtitle_2_load]]))
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ def draw_table_qos(self, exp_dir, tables_type : Literal["both", "by_dc", "conso"] | None = "both", simplified = False, fmt : Literal["default", "latex"] | None = "default"):
+ title_bidded_number = "% of number of jobs/services"
+ title_bidded_load = "% of load of jobs/services bidded"
+ title_workload_load = "% of load of jobs/services of all workload"
+
+ subtitle_1_completed_service = "Completed service"
+ subtitle_1_before_due = "Before due"
+ subtitle_1_after_due = "Between due and dead"
+ title_slowdown = "Slowdown"
+
+ subtitle_2_value = "Value"
+ subtitle_2_std = "Std"
+ if simplified:
+ title = [
+ (title_bidded_load, subtitle_1_completed_service, subtitle_2_value),
+ (title_bidded_load, subtitle_1_completed_service, subtitle_2_std),
+ (title_bidded_load, subtitle_1_before_due, subtitle_2_value),
+ (title_bidded_load, subtitle_1_before_due, subtitle_2_std),
+ # (title_bidded_load, subtitle_1_after_due, subtitle_2_value),
+ # (title_bidded_load, subtitle_1_after_due, subtitle_2_std),
+ ("", title_slowdown, subtitle_2_value),
+ ("", title_slowdown, subtitle_2_std),
+ ]
+ else:
+ title = [
+ (title_bidded_load, subtitle_1_completed_service, subtitle_2_value),
+ (title_bidded_load, subtitle_1_completed_service, subtitle_2_std),
+ (title_bidded_load, subtitle_1_before_due, subtitle_2_value),
+ (title_bidded_load, subtitle_1_before_due, subtitle_2_std),
+ (title_bidded_load, subtitle_1_after_due, subtitle_2_value),
+ (title_bidded_load, subtitle_1_after_due, subtitle_2_std),
+ (title_workload_load, subtitle_1_completed_service, subtitle_2_value),
+ (title_workload_load, subtitle_1_completed_service, subtitle_2_std),
+ (title_workload_load, subtitle_1_before_due, subtitle_2_value),
+ (title_workload_load, subtitle_1_before_due, subtitle_2_std),
+ (title_workload_load, subtitle_1_after_due, subtitle_2_value),
+ (title_workload_load, subtitle_1_after_due, subtitle_2_std),
+ (title_bidded_number, subtitle_1_completed_service, subtitle_2_value),
+ (title_bidded_number, subtitle_1_completed_service, subtitle_2_std),
+ (title_bidded_number, subtitle_1_before_due, subtitle_2_value),
+ (title_bidded_number, subtitle_1_before_due, subtitle_2_std),
+ (title_bidded_number, subtitle_1_after_due, subtitle_2_value),
+ (title_bidded_number, subtitle_1_after_due, subtitle_2_std),
+ ("", title_slowdown, subtitle_2_value),
+ ("", title_slowdown, subtitle_2_std),
+ ]
+ columns = pd.MultiIndex.from_tuples(title)
+
+ if tables_type == "both" or tables_type =="by_dc":
+ table = []
+ index_by_dc = []
+ for analysis in self.analysis_list:
+ # Jobs percentage load only bidded
+ jobs_before_due_bidded, jobs_before_dead_bidded, _, services_qos_bidded = analysis.get_jobs_qos_by_dc(exp_dir)
+ jobs_before_due_bidded_mean = [[np.mean([jobs_before_due_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_due_bidded_std = [[np.std([jobs_before_due_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_bidded_mean = [[np.mean([jobs_before_dead_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_bidded_std = [[np.std([jobs_before_dead_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_bidded_mean = [[np.mean([services_qos_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_bidded_std = [[np.std([services_qos_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # Jobs percentage load all workload
+ jobs_before_due_workload, jobs_before_dead_workload, _, services_qos_workload = analysis.get_jobs_qos_by_dc(exp_dir, False)
+ jobs_before_due_workload_mean = [[np.mean([jobs_before_due_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_due_workload_std = [[np.std([jobs_before_due_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_workload_mean = [[np.mean([jobs_before_dead_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_workload_std = [[np.std([jobs_before_dead_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_workload_mean = [[np.mean([services_qos_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_workload_std = [[np.std([services_qos_workload[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # Jobs number executed
+ jobs_before_due_number, jobs_before_dead_number, _, services_qos_number = analysis.get_jobs_qos_by_dc_number(exp_dir)
+ jobs_before_due_number_mean = [[np.mean([jobs_before_due_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_due_number_std = [[np.std([jobs_before_due_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_number_mean = [[np.mean([jobs_before_dead_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_dead_number_std = [[np.std([jobs_before_dead_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_number_mean = [[np.mean([services_qos_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_number_std = [[np.std([services_qos_number[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # Slowdown
+ slowdown = analysis.get_jobs_slowdown_by_dc(exp_dir)
+ slowdown_mean = [[np.mean([slowdown[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ slowdown_std = [[np.std([slowdown[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ for dc in range(analysis.nb_dcs):
+ if simplified:
+ table.append([
+ services_qos_bidded_mean[exp_nb][dc], services_qos_bidded_std[exp_nb][dc],
+ jobs_before_due_bidded_mean[exp_nb][dc], jobs_before_due_bidded_std[exp_nb][dc],
+ # jobs_before_dead_bidded_mean[exp_nb][dc], jobs_before_dead_bidded_std[exp_nb][dc],
+ slowdown_mean[exp_nb][dc], slowdown_std[exp_nb][dc],
+ ])
+ else:
+ table.append([
+ services_qos_bidded_mean[exp_nb][dc], services_qos_bidded_std[exp_nb][dc],
+ jobs_before_due_bidded_mean[exp_nb][dc], jobs_before_due_bidded_std[exp_nb][dc],
+ jobs_before_dead_bidded_mean[exp_nb][dc], jobs_before_dead_bidded_std[exp_nb][dc],
+ services_qos_workload_mean[exp_nb][dc], services_qos_workload_std[exp_nb][dc],
+ jobs_before_due_workload_mean[exp_nb][dc], jobs_before_due_workload_std[exp_nb][dc],
+ jobs_before_dead_workload_mean[exp_nb][dc], jobs_before_dead_workload_std[exp_nb][dc],
+ services_qos_number_mean[exp_nb][dc], services_qos_number_std[exp_nb][dc],
+ jobs_before_due_number_mean[exp_nb][dc], jobs_before_due_number_std[exp_nb][dc],
+ jobs_before_dead_number_mean[exp_nb][dc], jobs_before_dead_number_std[exp_nb][dc],
+ slowdown_mean[exp_nb][dc], slowdown_std[exp_nb][dc],
+ ])
+ if (analysis.nb_dcs == 1):
+ index_by_dc.extend([(analysis.names[n], "") for n in np.arange(len(analysis.names))])
+ else:
+ index_by_dc.extend([(analysis.names[n], x) for n in np.arange(len(analysis.names)) for x in range(analysis.nb_dcs)])
+
+ table_pd = pd.DataFrame(table, index=pd.MultiIndex.from_tuples(index_by_dc), columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ styler.format(precision=2)
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_load],
+ [subtitle_1_completed_service, subtitle_1_before_due],
+ [subtitle_2_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ # [title_bidded_load],
+ # [subtitle_1_after_due],
+ # [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_tuples([("", title_slowdown, subtitle_2_value)]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_number, title_bidded_load, title_workload_load],
+ [subtitle_1_completed_service, subtitle_1_before_due],
+ [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_number, title_bidded_load, title_workload_load],
+ [subtitle_1_after_due],
+ [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_tuples([("", title_slowdown, subtitle_2_value)]))
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ if tables_type == "both" or tables_type == "conso":
+ # Consolidated
+ table = []
+ index_conso = []
+ for analysis in self.analysis_list:
+ # Jobs percentage load only bidded
+ jobs_before_due_bidded, jobs_before_dead_bidded, _, services_qos_bidded = analysis.get_jobs_qos_consolidate(exp_dir)
+ jobs_before_due_bidded_mean = [np.mean(jobs_before_due_bidded[exp]) for exp in range(len(analysis.names))]
+ jobs_before_due_bidded_std = [np.std(jobs_before_due_bidded[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_bidded_mean = [np.mean(jobs_before_dead_bidded[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_bidded_std = [np.std(jobs_before_dead_bidded[exp]) for exp in range(len(analysis.names))]
+ services_qos_bidded_mean = [np.mean(services_qos_bidded[exp]) for exp in range(len(analysis.names))]
+ services_qos_bidded_std = [np.std(services_qos_bidded[exp]) for exp in range(len(analysis.names))]
+
+ # Jobs percentage load all workload
+ jobs_before_due_workload, jobs_before_dead_workload, _, services_qos_workload = analysis.get_jobs_qos_consolidate(exp_dir, False)
+ jobs_before_due_workload_mean = [np.mean(jobs_before_due_workload[exp]) for exp in range(len(analysis.names))]
+ jobs_before_due_workload_std = [np.std(jobs_before_due_workload[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_workload_mean = [np.mean(jobs_before_dead_workload[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_workload_std = [np.std(jobs_before_dead_workload[exp]) for exp in range(len(analysis.names))]
+ services_qos_workload_mean = [np.mean(services_qos_workload[exp]) for exp in range(len(analysis.names))]
+ services_qos_workload_std = [np.std(services_qos_workload[exp]) for exp in range(len(analysis.names))]
+
+ # Jobs number executed
+ jobs_before_due_number, jobs_before_dead_number, jobs_after_dead_number, services_qos_number = analysis.get_jobs_qos_consolidate_number(exp_dir)
+ jobs_before_due_number_mean = [np.mean(jobs_before_due_number[exp]) for exp in range(len(analysis.names))]
+ jobs_before_due_number_std = [np.std(jobs_before_due_number[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_number_mean = [np.mean(jobs_before_dead_number[exp]) for exp in range(len(analysis.names))]
+ jobs_before_dead_number_std = [np.std(jobs_before_dead_number[exp]) for exp in range(len(analysis.names))]
+ services_qos_number_mean = [np.mean(services_qos_number[exp]) for exp in range(len(analysis.names))]
+ services_qos_number_std = [np.std(services_qos_number[exp]) for exp in range(len(analysis.names))]
+
+ # Slowdown
+ slowdown = analysis.get_jobs_slowdown_consolidate(exp_dir)
+ slowdown_mean = [np.mean([slowdown[exp]]) for exp in range(len(analysis.names))]
+ slowdown_std = [np.std([slowdown[exp]]) for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ if simplified:
+ table.append([
+ services_qos_bidded_mean[exp_nb], services_qos_bidded_std[exp_nb],
+ jobs_before_due_bidded_mean[exp_nb], jobs_before_due_bidded_std[exp_nb],
+ # jobs_before_dead_bidded_mean[exp_nb], jobs_before_dead_bidded_std[exp_nb],
+ slowdown_mean[exp_nb], slowdown_std[exp_nb],
+ ])
+ else:
+ table.append([
+ services_qos_bidded_mean[exp_nb], services_qos_bidded_std[exp_nb],
+ jobs_before_due_bidded_mean[exp_nb], jobs_before_due_bidded_std[exp_nb],
+ jobs_before_dead_bidded_mean[exp_nb], jobs_before_dead_bidded_std[exp_nb],
+ services_qos_workload_mean[exp_nb], services_qos_workload_std[exp_nb],
+ jobs_before_due_workload_mean[exp_nb], jobs_before_due_workload_std[exp_nb],
+ jobs_before_dead_workload_mean[exp_nb], jobs_before_dead_workload_std[exp_nb],
+ services_qos_number_mean[exp_nb], services_qos_number_std[exp_nb],
+ jobs_before_due_number_mean[exp_nb], jobs_before_due_number_std[exp_nb],
+ jobs_before_dead_number_mean[exp_nb], jobs_before_dead_number_std[exp_nb],
+ slowdown_mean[exp_nb], slowdown_std[exp_nb],
+ ])
+
+ index_conso.extend(analysis.names)
+ table_pd = pd.DataFrame(table, index=index_conso, columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ styler.format(precision=2)
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_load],
+ [subtitle_1_completed_service, subtitle_1_before_due],
+ [subtitle_2_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ # [title_bidded_load],
+ # [subtitle_1_after_due],
+ # [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_tuples([("", title_slowdown, subtitle_2_value)]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_number, title_bidded_load, title_workload_load],
+ [subtitle_1_completed_service, subtitle_1_before_due],
+ [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([
+ [title_bidded_number, title_bidded_load, title_workload_load],
+ [subtitle_1_after_due],
+ [subtitle_2_value]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_tuples([("", title_slowdown, subtitle_2_value)]))
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ def draw_table_money_and_energy(self, exp_dir, tables_type : Literal["both", "by_dc", "conso"] | None = "both", simplified = False, fmt : Literal["default", "latex"] | None = "default"):
+ if tables_type == "both" or tables_type =="by_dc":
+ table = []
+ index_by_dc = []
+ for analysis in self.analysis_list:
+ energy_used_by_dc = analysis.get_energy(exp_dir)
+ energy_available_by_dc = analysis.get_energy_available(exp_dir)
+ job_money, service_money = analysis.get_money(exp_dir)
+ job_penalty, service_penalty = analysis.get_penalty(exp_dir)
+ job_benefits, service_benefits = analysis.get_benefits(exp_dir)
+ total_benefits = job_benefits + service_benefits
+
+ total_money_mean = [[np.mean([job_money[exp][dc] + service_money[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_money_std = [[np.std([job_money[exp][dc] + service_money[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_penalty_mean = [[np.mean([job_penalty[exp][dc] + service_penalty[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_penalty_std = [[np.std([job_penalty[exp][dc] + service_penalty[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_benefits_mean = [[np.mean([total_benefits[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_benefits_std = [[np.std([total_benefits[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_used = np.nan_to_num(total_benefits / energy_used_by_dc)
+ ratio_benefits_energy_used_mean = [[np.mean([ratio_benefits_energy_used[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_used_std = [[np.std([ratio_benefits_energy_used[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available = np.nan_to_num(total_benefits / energy_available_by_dc)
+ ratio_benefits_energy_available_mean = [[np.mean([ratio_benefits_energy_available[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available_std = [[np.std([ratio_benefits_energy_available[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ energy_wasted_percentage = (energy_available_by_dc - energy_used_by_dc) / energy_available_by_dc * 100
+ energy_wasted_percentage_mean = [[np.mean([energy_wasted_percentage[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ energy_wasted_percentage_std = [[np.std([energy_wasted_percentage[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # energy_wasted_absolute = (energy_available - energy_used_by_dc)
+ # energy_wasted_absolute_mean = [[np.mean([energy_wasted_absolute[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ # energy_wasted_absolute_std = [[np.std([energy_wasted_absolute[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ for dc in range(analysis.nb_dcs):
+ if simplified:
+ table.append([
+ total_benefits_mean[exp_nb][dc] / 1000, total_benefits_std[exp_nb][dc] / 1000,
+ ratio_benefits_energy_available_mean[exp_nb][dc], ratio_benefits_energy_available_std[exp_nb][dc],
+ energy_wasted_percentage_mean[exp_nb][dc], energy_wasted_percentage_std[exp_nb][dc],
+ ])
+ else:
+ table.append([
+ total_money_mean[exp_nb][dc], total_money_std[exp_nb][dc],
+ total_penalty_mean[exp_nb][dc], total_penalty_std[exp_nb][dc],
+ total_benefits_mean[exp_nb][dc], total_benefits_std[exp_nb][dc],
+ ratio_benefits_energy_used_mean[exp_nb][dc], ratio_benefits_energy_used_std[exp_nb][dc],
+ ratio_benefits_energy_available_mean[exp_nb][dc], ratio_benefits_energy_available_std[exp_nb][dc],
+ energy_wasted_percentage_mean[exp_nb][dc], energy_wasted_percentage_std[exp_nb][dc],
+ # energy_wasted_absolute_mean[exp_nb][dc], energy_wasted_absolute_std[exp_nb][dc]
+ ])
+ if (analysis.nb_dcs == 1):
+ index_by_dc.extend([(analysis.names[n], "") for n in np.arange(len(analysis.names))])
+ else:
+ index_by_dc.extend([(analysis.names[n], x) for n in np.arange(len(analysis.names)) for x in range(analysis.nb_dcs)])
+ if simplified:
+ columns = pd.MultiIndex.from_product([[
+ "Total benefits",
+ "Benefits / energy available",
+ "Energy wasted (%)"],
+ ["Value", "std"]]
+ )
+ else:
+ columns = pd.MultiIndex.from_product([[
+ "Total money",
+ "Total penalty",
+ "Total benefits",
+ "Benefits / energy used",
+ "Benefits / energy available",
+ "Energy wasted (%)"],
+ ["Value", "std"]]
+ )
+
+ table_pd = pd.DataFrame(table, index=pd.MultiIndex.from_tuples(index_by_dc), columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total benefits", "Benefits / energy available"], ["Value"]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Energy wasted (%)"], ["Value"]]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total money", "Total benefits", "Benefits / energy used", "Benefits / energy available"], ["Value"]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total penalty", "Energy wasted (%)"], ["Value"]]))
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ if tables_type == "both" or tables_type == "conso":
+ # Consolidate
+ table = []
+ index_conso = []
+ for analysis in self.analysis_list:
+ energy_used_by_dc = analysis.get_energy(exp_dir)
+ energy_available_by_dc = analysis.get_energy_available(exp_dir)
+ job_money, service_money = analysis.get_money(exp_dir)
+ job_penalty, service_penalty = analysis.get_penalty(exp_dir)
+ job_benefits, service_benefits = analysis.get_benefits(exp_dir)
+ total_benefits = job_benefits + service_benefits
+
+
+ energy_by_exp = []
+ for exp in range(len(analysis.names)):
+ energy_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ energy_by_exp[exp].append(np.sum([energy_used_by_dc[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+ energy_available_by_exp = []
+ for exp in range(len(analysis.names)):
+ energy_available_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ energy_available_by_exp[exp].append(np.sum([energy_available_by_dc[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+ benefits_by_exp = []
+ for exp in range(len(analysis.names)):
+ benefits_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ benefits_by_exp[exp].append(np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+
+ energy_by_exp = np.array(energy_by_exp)
+ energy_available_by_exp = np.array(energy_available_by_exp)
+ benefits_by_exp = np.array(benefits_by_exp)
+ total_money_mean = [np.mean([np.sum([job_money[exp][dc][seed] + service_money[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+ total_money_std = [np.std([np.sum([job_money[exp][dc][seed] + service_money[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+
+ total_penalty_mean = [np.mean([np.sum([job_penalty[exp][dc][seed] + service_penalty[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+ total_penalty_std = [np.std([np.sum([job_penalty[exp][dc][seed] + service_penalty[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+
+ total_benefits_mean = [np.mean([np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+ total_benefits_std = [np.std([np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+
+ ratio_benefits_energy_used = benefits_by_exp / energy_by_exp
+ ratio_benefits_energy_used_mean = [np.mean([ratio_benefits_energy_used[exp]]) for exp in range(len(analysis.names))]
+ ratio_benefits_energy_used_std = [np.std([ratio_benefits_energy_used[exp]]) for exp in range(len(analysis.names))]
+
+ ratio_benefits_energy_available = benefits_by_exp / energy_available_by_exp
+ ratio_benefits_energy_available_mean = [np.mean([ratio_benefits_energy_available[exp]]) for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available_std = [np.std([ratio_benefits_energy_available[exp]]) for exp in range(len(analysis.names))]
+
+ energy_wasted_percentage = (energy_available_by_exp - energy_by_exp) / energy_available_by_exp * 100
+ energy_wasted_percentage_mean = [np.mean([energy_wasted_percentage[exp]]) for exp in range(len(analysis.names))]
+ energy_wasted_percentage_std = [np.std([energy_wasted_percentage[exp]]) for exp in range(len(analysis.names))]
+
+ # energy_wasted_absolute = (energy_available_by_exp - energy_by_exp)
+ # energy_wasted_absolute_mean = [np.mean([energy_wasted_absolute[exp]]) for exp in range(len(analysis.names))]
+ # energy_wasted_absolute_std = [np.std([energy_wasted_absolute[exp]]) for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ if simplified:
+ table.append([
+ total_benefits_mean[exp_nb] / 1000, total_benefits_std[exp_nb] / 1000,
+ ratio_benefits_energy_available_mean[exp_nb], ratio_benefits_energy_available_std[exp_nb],
+ energy_wasted_percentage_mean[exp_nb], energy_wasted_percentage_std[exp_nb],
+ ])
+ else:
+ table.append([
+ total_money_mean[exp_nb], total_money_std[exp_nb],
+ total_penalty_mean[exp_nb], total_penalty_std[exp_nb],
+ total_benefits_mean[exp_nb], total_benefits_std[exp_nb],
+ ratio_benefits_energy_used_mean[exp_nb], ratio_benefits_energy_used_std[exp_nb],
+ ratio_benefits_energy_available_mean[exp_nb], ratio_benefits_energy_available_std[exp_nb],
+ energy_wasted_percentage_mean[exp_nb], energy_wasted_percentage_std[exp_nb],
+ # energy_wasted_absolute_mean[exp_nb], energy_wasted_absolute_std[exp_nb]
+ ])
+ index_conso.extend(analysis.names)
+
+ if simplified:
+ columns = pd.MultiIndex.from_product([[
+ "Total benefits",
+ "Benefits / energy available",
+ "Energy wasted (%)"],
+ ["Value", "std"]]
+ )
+ else:
+ columns = pd.MultiIndex.from_product([[
+ "Total money",
+ "Total penalty",
+ "Total benefits",
+ "Benefits / energy used",
+ "Benefits / energy available",
+ "Energy wasted (%)"],
+ ["Value", "std"]]
+ )
+
+ table_pd = pd.DataFrame(table, index=index_conso, columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ if simplified:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total benefits", "Benefits / energy available"], ["Value"]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Energy wasted (%)"], ["Value"]]))
+ else:
+ styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total money", "Total benefits", "Benefits / energy used", "Benefits / energy available"], ["Value"]]))
+ styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Total penalty", "Energy wasted (%)"], ["Value"]]))
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+
+ def draw_table_consolidate(self, fmt : Literal["default", "latex"] | None = "default"):
+ for exp_dir in ["large", "medium", "small"]:
+ # Consolidate
+ table = []
+ index_conso = []
+
+ for analysis in self.analysis_list:
+ energy_used_by_dc = analysis.get_energy(exp_dir)
+ energy_available_by_dc = analysis.get_energy_available(exp_dir)
+ job_benefits, service_benefits = analysis.get_benefits(exp_dir)
+ total_benefits = job_benefits + service_benefits
+
+ energy_by_exp = []
+ for exp in range(len(analysis.names)):
+ energy_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ energy_by_exp[exp].append(np.sum([energy_used_by_dc[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+ energy_available_by_exp = []
+ for exp in range(len(analysis.names)):
+ energy_available_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ energy_available_by_exp[exp].append(np.sum([energy_available_by_dc[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+ benefits_by_exp = []
+ for exp in range(len(analysis.names)):
+ benefits_by_exp.append([])
+ for seed in range(len(analysis.seeds)):
+ benefits_by_exp[exp].append(np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]))
+
+ energy_by_exp = np.array(energy_by_exp)
+ energy_available_by_exp = np.array(energy_available_by_exp)
+ benefits_by_exp = np.array(benefits_by_exp)
+
+ total_benefits_mean = [np.mean([np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+ total_benefits_std = [np.std([np.sum([total_benefits[exp][dc][seed] for dc in range(analysis.nb_dcs)]) for seed in range(len(analysis.seeds))]) for exp in range(len(analysis.names))]
+
+ ratio_benefits_energy_available = benefits_by_exp / energy_available_by_exp
+ ratio_benefits_energy_available_mean = [np.mean([ratio_benefits_energy_available[exp]]) for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available_std = [np.std([ratio_benefits_energy_available[exp]]) for exp in range(len(analysis.names))]
+
+ energy_wasted_percentage = (energy_available_by_exp - energy_by_exp) / energy_available_by_exp * 100
+ energy_wasted_percentage_mean = [np.mean([energy_wasted_percentage[exp]]) for exp in range(len(analysis.names))]
+ energy_wasted_percentage_std = [np.std([energy_wasted_percentage[exp]]) for exp in range(len(analysis.names))]
+
+ jobs_number_bidded_by_dc, services_number_bidded_by_dc = analysis.get_jobs_bidded_consolidate(exp_dir)[:2]
+ jobs_number_bidded_by_dc_mean = [np.mean([jobs_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ jobs_number_bidded_by_dc_std = [np.std([jobs_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_mean = [np.mean([services_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_std = [np.std([services_number_bidded_by_dc[exp]]) for exp in range(len(analysis.names))]
+
+ jobs_before_due_bidded, _, _, services_qos_bidded = analysis.get_jobs_qos_consolidate(exp_dir)
+ jobs_before_due_bidded_mean = [np.mean(jobs_before_due_bidded[exp]) for exp in range(len(analysis.names))]
+ jobs_before_due_bidded_std = [np.std(jobs_before_due_bidded[exp]) for exp in range(len(analysis.names))]
+ services_qos_bidded_mean = [np.mean(services_qos_bidded[exp]) for exp in range(len(analysis.names))]
+ services_qos_bidded_std = [np.std(services_qos_bidded[exp]) for exp in range(len(analysis.names))]
+
+ slowdown = analysis.get_jobs_slowdown_consolidate(exp_dir)
+ slowdown_mean = [np.mean([slowdown[exp]]) for exp in range(len(analysis.names))]
+ slowdown_std = [np.std([slowdown[exp]]) for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb],
+ jobs_number_bidded_by_dc_std[exp_nb],
+ services_number_bidded_by_dc_mean[exp_nb],
+ services_number_bidded_by_dc_std[exp_nb],
+ services_qos_bidded_mean[exp_nb],
+ services_qos_bidded_std[exp_nb],
+ jobs_before_due_bidded_mean[exp_nb],
+ jobs_before_due_bidded_std[exp_nb],
+ slowdown_mean[exp_nb],
+ slowdown_std[exp_nb],
+ total_benefits_mean[exp_nb] / 1000,
+ total_benefits_std[exp_nb] / 1000,
+ energy_wasted_percentage_mean[exp_nb],
+ energy_wasted_percentage_std[exp_nb],
+ ratio_benefits_energy_available_mean[exp_nb],
+ ratio_benefits_energy_available_std[exp_nb],
+ ])
+ index_conso.extend(analysis.names)
+ subtitle_value = "Value"
+ subtitle_std = "std"
+ title = [
+ ("Bid", "Tasks", subtitle_value),
+ ("Bid", "Tasks", subtitle_std),
+ ("Bid", "Services", subtitle_value),
+ ("Bid", "Services", subtitle_std),
+ ("QoS", "Completed service", subtitle_value),
+ ("QoS", "Completed service", subtitle_std),
+ ("QoS", "Before due", subtitle_value),
+ ("QoS", "Before due", subtitle_std),
+ ("QoS", "Slowdown", subtitle_value),
+ ("QoS", "Slowdown", subtitle_std),
+ ("Money/Energy", "Benefits", subtitle_value),
+ ("Money/Energy", "Benefits", subtitle_std),
+ ("Money/Energy", "Energy wasted", subtitle_value),
+ ("Money/Energy", "Energy wasted", subtitle_std),
+ ("Money/Energy", "Ratio", subtitle_value),
+ ("Money/Energy", "Ratio", subtitle_std),
+ ]
+ columns = pd.MultiIndex.from_tuples(title)
+
+ table_pd = pd.DataFrame(table, index=index_conso, columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Bid"], ["Tasks", "Services"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Completed service", "Before due"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Benefits", "Ratio"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Slowdown"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Energy wasted"], [subtitle_value]]))
+
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["Bid"], ["Tasks", "Services"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Completed service", "Before due"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Benefits", "Ratio"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_min(axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Slowdown"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_min(axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Energy wasted"], [subtitle_value]]), color="#34FF34")
+
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
+
+ def draw_table_consolidate_by_dc(self, fmt : Literal["default", "latex"] | None = "default"):
+ for exp_dir in ["large", "medium", "small"]:
+
+ subtitle_value = "Value"
+ subtitle_std = "std"
+ title = [
+ ("Bid", "Tasks", subtitle_value),
+ ("Bid", "Tasks", subtitle_std),
+ ("Bid", "Services", subtitle_value),
+ ("Bid", "Services", subtitle_std),
+ ("QoS", "Completed service", subtitle_value),
+ ("QoS", "Completed service", subtitle_std),
+ ("QoS", "Before due", subtitle_value),
+ ("QoS", "Before due", subtitle_std),
+ ("QoS", "Slowdown", subtitle_value),
+ ("QoS", "Slowdown", subtitle_std),
+ ("Money/Energy", "Benefits", subtitle_value),
+ ("Money/Energy", "Benefits", subtitle_std),
+ ("Money/Energy", "Energy wasted", subtitle_value),
+ ("Money/Energy", "Energy wasted", subtitle_std),
+ ("Money/Energy", "Ratio", subtitle_value),
+ ("Money/Energy", "Ratio", subtitle_std),
+ ]
+ columns = pd.MultiIndex.from_tuples(title)
+ table = []
+ index_by_dc = []
+ for analysis in self.analysis_list:
+ jobs_number_bidded_by_dc, services_number_bidded_by_dc = analysis.get_jobs_bidded_by_dc(exp_dir)[:2]
+ jobs_number_bidded_by_dc_mean = [[np.mean([jobs_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_number_bidded_by_dc_std = [[np.std([jobs_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_mean = [[np.mean([services_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_number_bidded_by_dc_std = [[np.std([services_number_bidded_by_dc[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # Jobs percentage load only bidded
+ jobs_before_due_bidded, _, _, services_qos_bidded = analysis.get_jobs_qos_by_dc(exp_dir)
+ jobs_before_due_bidded_mean = [[np.mean([jobs_before_due_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ jobs_before_due_bidded_std = [[np.std([jobs_before_due_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_bidded_mean = [[np.mean([services_qos_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ services_qos_bidded_std = [[np.std([services_qos_bidded[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ # Slowdown
+ slowdown = analysis.get_jobs_slowdown_by_dc(exp_dir)
+ slowdown_mean = [[np.mean([slowdown[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ slowdown_std = [[np.std([slowdown[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+
+ energy_used_by_dc = analysis.get_energy(exp_dir)
+ energy_available_by_dc = analysis.get_energy_available(exp_dir)
+ job_benefits, service_benefits = analysis.get_benefits(exp_dir)
+ total_benefits = job_benefits + service_benefits
+
+ total_benefits_mean = [[np.mean([total_benefits[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ total_benefits_std = [[np.std([total_benefits[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available = np.nan_to_num(total_benefits / energy_available_by_dc)
+ ratio_benefits_energy_available_mean = [[np.mean([ratio_benefits_energy_available[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ ratio_benefits_energy_available_std = [[np.std([ratio_benefits_energy_available[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ energy_wasted_percentage = (energy_available_by_dc - energy_used_by_dc) / energy_available_by_dc * 100
+ energy_wasted_percentage_mean = [[np.mean([energy_wasted_percentage[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+ energy_wasted_percentage_std = [[np.std([energy_wasted_percentage[exp][dc]]) for dc in range(analysis.nb_dcs)] for exp in range(len(analysis.names))]
+
+ for exp_nb in range(len(analysis.names)):
+ for dc in range(analysis.nb_dcs):
+ table.append([
+ jobs_number_bidded_by_dc_mean[exp_nb][dc],
+ jobs_number_bidded_by_dc_std[exp_nb][dc],
+ services_number_bidded_by_dc_mean[exp_nb][dc],
+ services_number_bidded_by_dc_std[exp_nb][dc],
+ services_qos_bidded_mean[exp_nb][dc],
+ services_qos_bidded_std[exp_nb][dc],
+ jobs_before_due_bidded_mean[exp_nb][dc],
+ jobs_before_due_bidded_std[exp_nb][dc],
+ slowdown_mean[exp_nb][dc],
+ slowdown_std[exp_nb][dc],
+ total_benefits_mean[exp_nb][dc] / 1000,
+ total_benefits_std[exp_nb][dc] / 1000,
+ energy_wasted_percentage_mean[exp_nb][dc],
+ energy_wasted_percentage_std[exp_nb][dc],
+ ratio_benefits_energy_available_mean[exp_nb][dc],
+ ratio_benefits_energy_available_std[exp_nb][dc],
+ ])
+ if (analysis.nb_dcs == 1):
+ index_by_dc.extend([(analysis.names[n], "") for n in np.arange(len(analysis.names))])
+ else:
+ index_by_dc.extend([(analysis.names[n], x) for n in np.arange(len(analysis.names)) for x in range(analysis.nb_dcs)])
+
+ table_pd = pd.DataFrame(table, index=pd.MultiIndex.from_tuples(index_by_dc), columns=columns)
+ styler = table_pd.style
+ if fmt == "default":
+ styler.set_table_styles(
+ [
+ {"selector": "td, th", "props": [("border", "1px solid grey !important"), ('text-align', 'center')]},
+ ]
+ )
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Bid"], ["Tasks", "Services"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Completed service", "Before due"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("#34FF34", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Benefits", "Ratio"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Slowdown"], [subtitle_value]]))
+ # styler.background_gradient(cmap=sns.light_palette("red", as_cmap=True), axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Energy wasted"], [subtitle_value]]))
+
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["Bid"], ["Tasks", "Services"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Completed service", "Before due"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_max(axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Benefits", "Ratio"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_min(axis=0, subset=pd.MultiIndex.from_product([["QoS"], ["Slowdown"], [subtitle_value]]), color="#34FF34")
+ styler.highlight_min(axis=0, subset=pd.MultiIndex.from_product([["Money/Energy"], ["Energy wasted"], [subtitle_value]]), color="#34FF34")
+
+ styler.format(precision=2)
+ if fmt == "default":
+ display(styler)
+ elif fmt == "latex":
+ print(styler.to_latex(position_float="centering", convert_css=True))
\ No newline at end of file
diff --git a/GenerateVisualisation/visualisation_gen.py b/GenerateVisualisation/visualisation_gen.py
new file mode 100644
index 0000000..f3e01cf
--- /dev/null
+++ b/GenerateVisualisation/visualisation_gen.py
@@ -0,0 +1,384 @@
+import matplotlib.pyplot as plt
+from matplotlib.axes import Axes
+import matplotlib as mpl
+import numpy as np
+
+
+class Visualisation:
+
+ def __init__(self, solution, heuristic=False):
+ self.solution = solution
+ self.colors = ["darkblue", 'blue', 'green', 'yellow', 'red', "purple", "pink", "grey", "Brown", "BurlyWood",
+ "Chartreuse", "CadetBlue", "Coral", "cyan", "Gold", "hotpink", "khaki", "lavender", "Indigo",
+ "darkblue",
+ 'blue', 'yellow', 'orange', 'red', "purple", "pink", "grey", "Brown", "BurlyWood",
+ "Chartreuse"]
+ self.h = list("*+-./OX\ox|")
+
+ # if self.metrics.nbJobs > 0:
+ # self.job_load_graphs()
+ if not heuristic:
+ self.total_load_graphs()
+ self.energy_load_graphs()
+ #Affichage résumé du run
+ self.run_summary()
+
+
+
+
+ else:
+ self.total_load_graphs_h()
+ self.energy_load_graphs_h()
+ self.get_power_curve_h()
+ #Affichage résumé du run
+ self.run_summary_h()
+
+
+
+ return
+ # self.jobs_graph()
+ def get_power_curve_h (self,nbc=1) :
+ #nbc représente le nombre de coeurs actifs en permanence dans le calcul de puissance
+
+ energy=self.solution['instance']['renewPow'][0]
+ power=[]
+ #print('solution',self.solution)
+ #print(self.solution['instance'])
+ for e in energy:
+ if e-nbc*self.solution['instance']['pmin'] < 0 :
+ power.append(0)
+ else :
+ power.append((e -nbc*self.solution['instance']['pmin'])/ self.solution['instance']['pdyn'])
+ print('Workload équivalent disponible pour chaque time step (',nbc,'coeurs allumés ) :',[round(i,3) for i in power])
+
+
+ def run_summary (self) :
+ #Faire l'affichage avec détails dans le cas plusieurs DCs
+ print('******************************')
+ numdc=0
+ for result in self.solution.solution["dc"]:
+ print('Results DC',numdc,':')
+ print('Money',result['money'])
+ print('Penalty',result['penalty'])
+ print('Benefits',result['money'] - result['penalty'] )
+ numdc+=1
+ print('******************************')
+ #print('Penalty',self.solution['dc'][numdc]['penalty'])
+ #print('Benefits', self.solution['dc'][numdc]['money'] - self.solution['dc'][numdc]['penalty'])
+
+ #if numdc>=1 :
+ #print('******************************')
+ #print('Global Results')
+ #print('Total Penalty', self.solution['TotalPenalty'])
+ #print('Total Benefits', self.solution['TotalMoney'])
+ #print('Running Time', self.solution['Running_time'])
+
+ if numdc>=2 :
+ print('Global Results')
+ print('Total Money', self.solution.solution['TotalMoney'])
+ print('Total Penalty', self.solution.solution['TotalPenalty'])
+ print('Total Benefits', self.solution.solution['TotalMoney'] - self.solution.solution['TotalPenalty'] )
+ print('Running Time', self.solution.solution['SolutionTime'])
+
+
+
+
+ def run_summary_h (self) :
+
+ #Faire l'affichage avec détails dans le cas plusieurs DCs
+ print('******************************')
+ numdc=-1
+ for dc in self.solution['dc']:
+ numdc+=1
+ print('Results DC',numdc,':')
+ print('Money',self.solution['dc'][numdc]['money'])
+ print('Penalty',self.solution['dc'][numdc]['penalty'])
+ print('Benefits', self.solution['dc'][numdc]['money'] - self.solution['dc'][numdc]['penalty'])
+ print('******************************')
+
+ if numdc>=1 :
+ print('Global Results')
+ print('Total Money',self.solution['TotalMoney'] + self.solution['TotalPenalty'])
+ print('Total Penalty', self.solution['TotalPenalty'])
+ print('Total Benefits', self.solution['TotalMoney'])
+ print('Running Time', self.solution['Running_time'])
+
+ def job_load_graphs(self):
+ # Load vs timesteps with histograms
+ time = list(range(self.solution.totalTime))
+
+ jobs_names = ['j' + str(j) for j in range(self.solution.nbJobs)]
+
+ for dc, cores in self.solution.values['AlphaJob'].items():
+ for core, jobs in cores.items():
+ values = [list(timesteps.values()) for timesteps in jobs.values()]
+
+ print(values)
+ width = 0.95
+ bars = []
+ bottom = [0] * self.solution.totalTime
+
+ for j in range(self.solution.nbJobs):
+ if j == 0:
+ bars.append(
+ plt.bar(range(self.solution.totalTime), values[j], width, color=self.colors[j], hatch=self.h[j % len(self.h)],
+ bottom=0,
+ align='edge'))
+ else:
+ bars.append(
+ plt.bar(range(self.solution.totalTime), values[j], width, color=self.colors[j], hatch=self.h[j % len(self.h)],
+ bottom=bottom,
+ align='edge'))
+ bottom = [bottom[l] + values[j][l] for l in range(len(bottom))]
+
+ for t in range(self.solution.totalTime):
+ mytext = ""
+ for j in range(self.solution.nbJobs):
+ if values[j][t] > 0.000001:
+ mytext = mytext + "j" + str(j) + " "
+ plt.text(t + 0.3, bottom[t] + 0.05, mytext, ha='center', va='bottom')
+
+ plt.xlabel("timesteps")
+ plt.ylabel('Load Jobs')
+ plt.axis([0, self.solution.totalTime, 0, 1.1])
+ plt.xticks(time)
+ plt.title("DC {0}".format(dc) + " Core {0}".format(core))
+
+ # to let space on the right for the legend
+ plt.subplots_adjust(right=0.8)
+ # to place the legend out of the figure
+ plt.legend(bars, jobs_names, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.5)
+
+ plt.show()
+ plt.close()
+ return
+
+ def total_load_graphs(self):
+ # for i in range(nb_tasks):
+ # colors[i] = [ random.uniform(0,1), random.uniform(0,1), random.uniform(0,1) ]
+
+ # Load vs timesteps with histograms
+ time = list(range(self.solution.totalTime))
+
+ jobs_names = [f'S{i}' for i in range(self.solution.nbServices)]
+ jobs_names.extend(['j' + str(j) for j in range(self.solution.nbJobs)])
+ try:
+ dict_to_use = self.solution.values['AlphaJob']
+ except:
+ dict_to_use = self.solution.values['AlphaServ']
+ for dc, cores in dict_to_use.items():
+ for core, jobs in cores.items():
+ values = [list(timesteps.values()) for timesteps in jobs.values()]
+ try:
+ valuesServ = [list(timesteps.values()) for timesteps in self.solution.values['AlphaServ'][dc][core].values()]
+ except Exception:
+ valuesServ = []
+ print("Jobs Load ")
+ print(values)
+ print("Services Load ")
+ print(valuesServ)
+
+ width = 0.95
+ bars = []
+ bottom = [0] * self.solution.totalTime
+
+ for j in range(self.solution.nbServices):
+ if j == 0:
+ bars.append(
+ plt.bar(range(self.solution.totalTime), valuesServ[j], width, color=self.colors[j], bottom=0, align='edge'))
+ else:
+ bars.append(
+ plt.bar(range(self.solution.totalTime), valuesServ[j], width, color=self.colors[j], bottom=bottom,
+ align='edge'))
+ bottom = [bottom[l] + valuesServ[j][l] for l in range(len(bottom))]
+
+ for j in range(self.solution.nbJobs):
+ bars.append(plt.bar(range(self.solution.totalTime), values[j], width, color=self.colors[(j + self.solution.nbServices) % len(self.colors)], hatch=self.h[j % len(self.h)], bottom=bottom,
+ align='edge'))
+ bottom = [bottom[l] + values[j][l] for l in range(len(bottom))]
+
+ for t in range(self.solution.totalTime):
+ mytext = ""
+ for j in range(self.solution.nbJobs):
+ if values[j][t] > 0.000001:
+ mytext = mytext + "j" + str(j) + " "
+ try:
+ for s in range(self.solution.nbServices):
+ if valuesServ[s][t] > 0.000001:
+ mytext = mytext + "s" + str(s) + " "
+ except:
+ pass
+ plt.text(t + 0.3, bottom[t] + 0.05, mytext, ha='center', va='bottom')
+
+ plt.xlabel("timesteps")
+ plt.ylabel('Load')
+ plt.axis([0, self.solution.totalTime, 0, 1.1])
+ plt.xticks(time)
+ plt.title(f"DC {dc} Core {core}")
+
+ # to let space on the right for the legend
+ plt.subplots_adjust(right=0.8)
+ # to place the legend out of the figure
+ plt.legend(bars, jobs_names, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.5)
+
+ plt.show()
+ plt.close()
+ return
+
+ def energy_load_graphs(self):
+ # Load vs timesteps with histograms
+ for j in range(len(self.solution.cDC)):
+ time = list(range(self.solution.totalTime))
+
+ values = [[v for i, v in sorted(timesteps.items(), key=lambda x: int(x[0]))] for timesteps in self.solution.values['energy'].values()]
+
+ for value in values:
+ value.append(value[-1])
+
+ plt.plot(range(self.solution.totalTime + 1), values[j], drawstyle='steps-post', color=self.colors[0])
+ available_pow = self.solution.renewPow[j]
+ available_pow.append(self.solution.renewPow[j][-1])
+ plt.plot(range(len(available_pow)), available_pow, drawstyle='steps-post', color='green')
+
+ plt.xlabel("timesteps")
+ plt.ylabel('Energy Load')
+ plt.axis([0, self.solution.totalTime, 0, max([max(pow) for pow in values])+50])
+ plt.xticks(time)
+ plt.title(f'Energy Consumption for DC {j}')
+
+ # to let space on the right for the legend
+ plt.subplots_adjust(right=0.8)
+ # to place the legend out of the figure
+ plt.legend([f'Energy use', 'Energy available'])
+ # plt.legend()
+
+ plt.show()
+ plt.close()
+
+ def total_load_graphs_h(self):
+ # for i in range(nb_tasks):
+ # colors[i] = [ random.uniform(0,1), random.uniform(0,1), random.uniform(0,1) ]
+
+ # Load vs timesteps with histograms
+ timestep = self.solution['instance']['total_time']
+ time = list(range(timestep))
+ nbjob = self.solution['instance']["nbjobs"]
+ nbservice = self.solution['instance']["nbservices"]
+
+ jobs_names = [f'S{i}' for i in range(nbservice)]
+ jobs_names.extend([f'j{j}' for j in range(nbjob)])
+ for dc in range(len(self.solution['instance']['cDC'])):
+ for core in range(self.solution['instance']['cDC'][dc]):
+ width = 0.95
+ bars = []
+ bottom = [0] * timestep
+
+
+ for j in range(nbservice):
+ values = [0]*timestep
+ for index, i in enumerate(self.solution['dc'][dc]['cores']):
+ for t in i[core]:
+ if not t[0]:
+ if t[1] == j:
+ values[index] += t[2]
+
+ if j == 0:
+ bars.append(
+ plt.bar(range(timestep), values, width, color=self.colors[j], bottom=0, align='edge'))
+ else:
+ bars.append(
+ plt.bar(range(timestep), values, width, color=self.colors[j], bottom=bottom,
+ align='edge'))
+ bottom = [bottom[l] + values[l] for l in range(len(bottom))]
+
+ for j in range(nbjob):
+ values = [0] * timestep
+ for index, i in enumerate(self.solution['dc'][dc]['cores']):
+ for t in i[core]:
+ if t[0]:
+ if t[1] == j:
+ values[index] += t[2]
+
+ bars.append(plt.bar(range(timestep), values, width, color=self.colors[(j + nbservice) % len(self.colors)], hatch=self.h[j % len(self.h)], bottom=bottom,
+ align='edge'))
+ bottom = [bottom[l] + values[l] for l in range(len(bottom))]
+
+ # for t in range(timestep):
+ # mytext = ""
+ # for j in range(nbjob):
+ # if values[j][t] > 0.000001:
+ # mytext = mytext + "j" + str(j) + " "
+ # try:
+ # for s in range(nbservice):
+ # if valuesServ[s][t] > 0.000001:
+ # mytext = mytext + "s" + str(s) + " "
+ # except:
+ # pass
+ # plt.text(t + 0.3, bottom[t] + 0.05, mytext, ha='center', va='bottom')
+
+ plt.xlabel("timesteps")
+ plt.ylabel('Load')
+ plt.axis([0, timestep, 0, 1.1])
+ plt.xticks(time)
+ plt.title(f"DC {dc} Core {core}")
+
+ # to let space on the right for the legend
+ plt.subplots_adjust(right=0.8)
+ # to place the legend out of the figure
+ plt.legend(bars, jobs_names, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.5)
+
+ plt.show()
+ plt.close()
+ return
+
+ def energy_load_graphs_h(self):
+ # Load vs timesteps with histograms
+ for dc in range(len(self.solution['instance']['cDC'])):
+ timestep = self.solution['instance']['total_time']
+ time = list(range(timestep))
+
+ used = [x[1] for x in self.solution['dc'][dc]['energy_use']]
+ used.append(used[-1])
+ available = [x[0] for x in self.solution['dc'][dc]['energy_use']]
+ available.append(available[-1])
+
+ plt.plot(range(timestep+1), used, drawstyle='steps-post', color=self.colors[0])
+ plt.plot(range(timestep+1), available, drawstyle='steps-post', color='green')
+
+ plt.xlabel("timesteps")
+ plt.ylabel('Energy Load')
+ plt.axis([0, timestep, 0, max(available)+50])
+ plt.xticks(time)
+ plt.title(f'Energy Consumption for DC {dc}')
+
+ # to let space on the right for the legend
+ plt.subplots_adjust(right=0.8)
+ # to place the legend out of the figure
+ plt.legend([f'Energy use', 'Energy available'])
+ # plt.legend()
+
+ plt.show()
+ plt.close()
+
+ def jobs_graph(self):
+ data = []
+ for j in range(self.solution.nbJobs):
+ # get the arrived
+ arrived = self.solution.jobs[j].a
+ # get the started
+ if self.solution.solution['JobsDetails'].get(str(j), None) is None:
+ data.append([])
+ continue
+ started = self.solution.solution['JobsDetails'].get(str(j), None)[2]
+ # get the ended
+ ended = self.solution.solution['JobsDetails'].get(str(j), None)[3]
+ # get the deadline
+ deadline = self.solution.jobs[j].dead
+ data.append([arrived, started, ended, deadline])
+ plt.title('Job Description')
+ plt.boxplot(data, vert=False, meanline=False)
+ # plt.boxplot(, vert=False, meanline=False)
+ plt.xticks(list(range(self.solution.totalTime)))
+ plt.show()
+ plt.close()
+
diff --git a/Heuristic/Solver.py b/Heuristic/Solver.py
new file mode 100644
index 0000000..7b102e1
--- /dev/null
+++ b/Heuristic/Solver.py
@@ -0,0 +1,221 @@
+import time
+from random import randint, seed
+
+import numpy as np
+
+from Heuristic.bidder.random import RandomBidder
+from Heuristic.bidder.simple import SimpleBidder
+from Heuristic.common import Heuristic
+from Heuristic.scheduler.best_fit import BestFitScheduler
+
+
+class Solver:
+
+ def __init__(self, instance, heuristic=None, runner_arg=None,bidder_param={'bidder':'Simple'}, seed=42):
+ self.DC = instance['cDC']
+ self.jobs = list(enumerate(instance['jobs']))
+ self.services = list(enumerate(instance['services']))
+ self.totaltime = instance['total_time']
+ self.bidder_param=bidder_param
+ self.seed = seed
+
+ #Choix du système de bid, aggressif ou pas
+ self.runner_arg=runner_arg
+ self.cooperation=False
+ if runner_arg is None or runner_arg=={'aggressivity':0} :
+ self.cooperation = True
+ self.bidded_job = set()
+ self.bidded_service = set()
+
+
+ if heuristic is None:
+ heur = []
+ if self.bidder_param['bidder']=='Simple' :
+
+ for i in range(len(self.DC)):
+ #i est le numéro du DC
+ heur.append(Heuristic(instance, i, BestFitScheduler(runner_arg), SimpleBidder(runner_arg,i)))
+ self.heuristic = heur
+
+ elif self.bidder_param['bidder']=='Random' :
+ for i in range(len(self.DC)):
+ #i est le numéro du DC
+ heur.append(Heuristic(instance, i, BestFitScheduler(runner_arg), RandomBidder(self.bidder_param)))
+ self.heuristic = heur
+
+ if RandomBidder(self.bidder_param).arg['Coop']==False:
+ self.cooperation = False
+ else :
+ raise NotImplemented
+
+ else:
+ self.heuristic = heuristic
+
+
+
+
+
+ def get_available_workload(self, t):
+ jobs = []
+ services = []
+ for i, j in self.jobs:
+ if j.a <= t <= j.due and i not in self.bidded_job:
+ jobs.append((i, j))
+ jobs = sorted(jobs, key=lambda x: (Solver.get_price(x[1],t),), reverse=True)
+ for i, j in self.services:
+ if j.arrived <= t <= j.start and i not in self.bidded_service:
+ services.append((i, j))
+ services = sorted(services, key=lambda x: (Solver.get_price(x[1],t),), reverse=True)
+ return jobs, services
+
+
+ def get_price(task,t):
+ #Task :: NamedTuple Job or Service from main expected
+ #Job
+ if type(task).__name__=='Job':
+ if task.due==task.a:
+ #print('Warning, the arrival date and the due date are equals, price will be pmax : ',task)
+ price=task.pmax
+ else:
+ if t>=task.due:
+ price = task.pmax
+ else :
+ price= task.pmin + (task.pmax - task.pmin) * (t - task.a) / (task.due - task.a)
+
+ #Service
+ else :
+ if task.start==task.arrived:
+ price=task.pmax
+ #print('Warning, start=arrived, price will be pmax',task)
+ else :
+ price=task.pmin + (task.pmax - task.pmin)* (t - task.arrived) / (task.start - task.arrived)
+
+ return(price)
+
+
+ def run(self):
+ start = time.time()
+ seed(self.seed)
+
+ for t in range(self.totaltime):
+ jobs, services = self.get_available_workload(t)
+
+ print(f'Running Timestep {t} with workload {jobs, services}')
+
+ for iden, j in jobs:
+ bids = []
+ #print('J',j,'iden',iden)
+ for index, h in enumerate(self.heuristic):
+ bids.append((index, h.bid_workload(j, jobs, services, t)))
+
+ bids = [b for b in bids if b is not None]
+ #print('Bids',bids)
+ pre_bids = [i for i in bids if i[1] is not None]
+ #print('Pre',pre_bids)
+
+ #No jobs
+ if len(pre_bids) == 0:
+ print('No bids for Job')
+ continue
+
+ if len(pre_bids) == 1:
+ price= Solver.get_price(j,t)
+ self.heuristic[pre_bids[0][0]].accepted_bid(jobs, services, j, price, t)
+ self.bidded_job.add(iden)
+ # print("\tJob: ", iden, "\n\tBids: ", bids, "\n\tWinner: ", pre_bids[0][0], "\n\tPrice: ", price)
+ for h in self.heuristic:
+ h.end_timestep(t)
+ continue
+
+ # find the lowest bidder
+
+ #Si on veut trouver le plus petit reverse = False
+ #bids = sorted(pre_bids, key=lambda x: x[1], reverse=True)
+ bids = sorted(pre_bids, key=lambda x: x[1], reverse=False)
+ #Pourquoi faire ça c'est déjà trié ??? -> Cas égalité dans sorted
+ winner_bids = [b for b in bids if b[1] == bids[0][1]]
+ #winner_bids=bids[0]
+ #print('winner_bids',winner_bids)
+
+ #Cas de l'égalité dans le sorted
+ if len(winner_bids) > 1 :
+ winner = winner_bids[randint(0, len(winner_bids) - 1)][0]
+ else:
+ winner = winner_bids[0][0]
+ #print('Winner',winner)
+
+ # calculate price to receive
+ if self.cooperation:
+ price = Solver.get_price(j,t)
+ # get the price of the second to last
+ else :
+ price = bids[1][1]
+ self.heuristic[winner].accepted_bid(jobs, services, j, price, t)
+ self.bidded_job.add(iden)
+
+ for h in self.heuristic:
+ h.end_timestep(t)
+
+ for iden, j in services:
+ bids = []
+ for i, h in enumerate(self.heuristic):
+ bids.append((i, h.bid_workload(j, jobs, services, t)))
+
+ bids = [b for b in bids if b is not None]
+ pre_bids = [i for i in bids if i[1] is not None]
+
+ #No services
+ if len(pre_bids) == 0:
+ continue
+
+ if len(pre_bids) == 1:
+ price = Solver.get_price(j,t)
+ self.heuristic[pre_bids[0][0]].accepted_bid(jobs, services, j, price, t)
+ self.bidded_service.add(iden)
+ # print("\tService: ", iden, "\n\tBids: ", bids, "\n\tWinner: ", pre_bids[0][0], "\n\tPrice: ", price)
+ for h in self.heuristic:
+ h.end_timestep(t)
+ continue
+ # find the lowest bidder
+ post_bids = sorted(pre_bids, key=lambda x: x[1], reverse=False)
+ winner_bids = [b for b in post_bids if b[1] == post_bids[0][1]]
+ # print('winner_bids',winner_bids)
+ if len(winner_bids) > 1 :
+ winner = winner_bids[randint(0, len(winner_bids) - 1)][0]
+ else:
+ winner = winner_bids[0][0]
+
+ # calculate price to receive
+ if self.cooperation:
+ price = Solver.get_price(j,t)
+ else:
+ # get the price of the second to last
+ price = post_bids[1][1]
+ self.heuristic[winner].accepted_bid(jobs, services, j, price, t)
+ self.bidded_service.add(iden)
+
+ for h in self.heuristic:
+ h.end_timestep(t)
+
+ end = time.time()
+ print()
+ print('*******************')
+ print('Bidded Job',self.bidded_job)
+ print('Bidded Service',self.bidded_service)
+ print('*******************')
+ print()
+ return self.get_values(end - start)
+
+ def get_values(self, running_time):
+ values = []
+ for h in self.heuristic:
+ values.append(h.get_values())
+
+ solution = {'dc': values}
+ solution['instance'] = self.heuristic[0].instance
+ solution['TotalPenalty'] = sum([i['penalty'] for i in values])
+ solution['TotalMoney'] = sum([i['money'] for i in values]) - solution['TotalPenalty']
+ solution['Running_time'] = running_time
+ print('Les variables de values :')
+ print(values)
+ return solution
diff --git a/Heuristic/__init__.py b/Heuristic/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Heuristic/bidder/__init__.py b/Heuristic/bidder/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Heuristic/bidder/bidder.py b/Heuristic/bidder/bidder.py
new file mode 100644
index 0000000..3739900
--- /dev/null
+++ b/Heuristic/bidder/bidder.py
@@ -0,0 +1,7 @@
+class Bidder:
+
+ def __init__(self):
+ self.scheduler = None
+
+ def bid_workload(self, heuristic, w, jobs, services, t):
+ raise NotImplemented
diff --git a/Heuristic/bidder/random.py b/Heuristic/bidder/random.py
new file mode 100644
index 0000000..39c5732
--- /dev/null
+++ b/Heuristic/bidder/random.py
@@ -0,0 +1,53 @@
+from random import randint
+import numpy as np
+
+from Heuristic.bidder.bidder import Bidder
+from Heuristic.common import Job, Service
+
+
+class RandomBidder(Bidder):
+
+ def __init__(self, bidder_param={'bidder':'Random','arg':{'p': .7,'Coop':False,'seed':41}}):
+ super(RandomBidder, self).__init__()
+
+ if len(bidder_param)<=1:
+ self.arg= {'p': .7,'Coop':False,'seed':41}
+ else :
+ self.arg = bidder_param['arg']
+ np.random.seed(self.arg['seed'])
+
+
+ def bid_workload(self, heuristic, w, jobs, services, t):
+ if self.scheduler is None:
+ raise Exception('Scheduler not set')
+ if np.random.random() <= self.arg['p']:
+ # if w in [i[1] for i in jobs]:
+ if type(w).__name__=='Job':
+ iden = [i[0] for i in jobs if i[1] == w][0]
+ temp = Job(w, None, t, iden)
+ heuristic.wanted_bid_history.append((True, temp.iden, t))
+ #gain et price sont inversé
+ #gains, price = self.scheduler.predict_bid(heuristic, t, w, True)
+ price, gains = self.scheduler.predict_bid(heuristic, t, w, True)
+ #elif w in [i[1] for i in services]:
+ elif type(w).__name__=='Service':
+ #iden = [i[0] for i in services if i[1] == w][0]
+ iden = [i[0] for i in services if i[1] is w][0]
+ temp = Service(w, None, t, iden)
+ heuristic.wanted_bid_history.append((False, temp.iden, t))
+ #gain et price inversés
+ #gains, price = self.scheduler.predict_bid(heuristic, t, w, False)
+ price, gains = self.scheduler.predict_bid(heuristic, t, w, False)
+ else:
+ print(f'Cannot recognise job: {w}')
+ return None
+ if gains is None or gains < 0:
+ return None
+
+ if self.arg['Coop'] :
+ #Laisse le bid au DC qui fait le plus de bénef dans solver
+ return price - gains
+
+ #Donne le bid de manière aléatoire dans solver
+ return price
+ return None
diff --git a/Heuristic/bidder/simple.py b/Heuristic/bidder/simple.py
new file mode 100644
index 0000000..55fcaa3
--- /dev/null
+++ b/Heuristic/bidder/simple.py
@@ -0,0 +1,73 @@
+import random
+import numpy as np
+
+from Heuristic.bidder.bidder import Bidder
+from Heuristic.common import Job, Service
+
+
+class SimpleBidder(Bidder):
+
+ def __init__(self, runner_arg = None,numdc=0, seed=42):
+ super(SimpleBidder, self).__init__()
+ self.coop=False
+ if runner_arg is None or runner_arg=={'aggressivity':0} :
+ self.aggressivity=0
+ self.coop = True
+
+ elif runner_arg['aggressivity'] == 'random':
+ random.seed(seed)
+ self.aggressivity = random.uniform(0, 1)
+
+ else:
+ if type(runner_arg['aggressivity'])==list :
+ self.aggressivity=runner_arg['aggressivity'][numdc]
+ #Cas où c'est un chiffre
+ else :
+ self.aggressivity = runner_arg['aggressivity']
+ #print('Aggressivité',self.aggressivity)
+
+
+
+ def bid_workload(self, heuristic, w, jobs, services, t):
+ if self.scheduler is None:
+ raise Exception('Scheduler not set')
+ #print('w class',type(w))
+ if type(w).__name__=='Job':
+ iden = [i[0] for i in jobs if i[1] is w][0]
+ temp = Job(w, None, t, iden)
+ price, gains = self.scheduler.predict_bid(heuristic, t, w, True)
+ job = True
+
+
+
+ elif type(w).__name__=='Service':
+ iden = [i[0] for i in services if i[1] is w][0]
+ temp = Service(w, None, t, iden)
+ price, gains = self.scheduler.predict_bid(heuristic, t, w, False)
+ #heuristic.wanted_bid_history.append((False, temp.iden, t))
+ job = False
+ else:
+ print(f'Cannot recognise job: {w}')
+ return None
+ # print('price - gains',price, gains, price-gains)
+ if price is None or gains is None:
+ return None
+ if gains > 0:
+ # Store as job or service
+ heuristic.wanted_bid_history.append((job, temp.iden, t))
+
+ #Donner la priorité au meilleur bénéfice dans le bid : coopératif
+ if self.coop :
+ #print('Prix pas Agressif',price - gains)
+ return(price - gains)
+
+ #Donner le bid au plus agressif
+ else :
+ if price - gains * self.aggressivity < 0:
+ print('Error negative price accepted')
+ return(None)
+
+ #print('Prix Agressif',price - gains * self.aggressivity)
+ return price - gains * self.aggressivity
+
+ return None
diff --git a/Heuristic/common.py b/Heuristic/common.py
new file mode 100644
index 0000000..61ab856
--- /dev/null
+++ b/Heuristic/common.py
@@ -0,0 +1,216 @@
+import numpy as np
+from random import randint
+
+
+class Heuristic:
+ def __init__(self, instance, i, scheduler, bidder):
+ self.instance = instance
+ self.timestep = instance['total_time']
+ self.pmin = instance['pmin']
+ self.pdyn = instance['pdyn']
+ self.powerdist = instance['renewPow'][i]
+ self.corecount = instance['cDC'][i]
+
+ self.money = 0
+ self.penalty = 0
+
+ # Simulation of the run
+ self.cores = [[[] for _ in range(self.corecount)] for i in range(self.timestep)]
+ self.poweron = [[False for _ in range(self.corecount)] for i in range(self.timestep)]
+ self.jobs_flexible = []
+ self.services_flexible = []
+
+ # History tracking
+ self.jobs_history = []
+ self.services_history = []
+
+ self.money_jobs = []
+ self.money_services = []
+ self.penalty_jobs = []
+ self.penalty_services = []
+ self.wanted_bid_history = []
+ self.bid_history = []
+
+ self.scheduler = scheduler
+ self.bidder = bidder
+
+ self.bidder.scheduler = self.scheduler
+
+ self.bid = bidder.bid_workload
+
+ def get_available_power(self, t):
+ power_available = self.powerdist[t] - sum(self.poweron[t]) * self.pmin
+ power_available -= sum([sum(tup[2] for tup in i) for i in self.cores[t]]) * self.pdyn
+ #for i in self.cores[t]:
+ #for tup in i:
+ # print('tup',tup[2])
+
+ if power_available < 0:
+ print('ERROR POWER IS NEGATIVE', power_available,t)
+ return 0
+ return power_available
+
+ def get_available_perf(self, t, c):
+ power_available = self.get_available_power(t)
+ #On retire pmin que si le coeur n'est pas allumé
+ if not self.poweron[t][c]:
+ if power_available <= self.pmin:
+ return 0
+ power_available -= self.pmin
+ perf_available = min(1, power_available / self.pdyn)
+ core_load = sum([tup[2] for tup in self.cores[t][c]])
+ if core_load > 1:
+ print("Core load is greater than one")
+ perf_available = min(perf_available, 1 - core_load)
+ if perf_available < 0:
+ print('Error negative Performance')
+ return 0
+ return perf_available // 0.00001 * 0.00001
+
+ def get_available_perf_all_cores(self, t):
+ total = []
+ for c in range(self.corecount):
+ total.append(self.get_available_perf(t, c))
+ return total
+
+
+ def get_values(self):
+ energy_use = []
+
+ for t in range(self.timestep):
+
+ energy_use.append((self.powerdist[t], self.powerdist[t] - self.get_available_power(t)))
+
+ final = {
+ 'money': self.money,
+ 'penalty': self.penalty,
+ 'cores': self.cores,
+ 'pon': self.poweron,
+ 'energy_use': energy_use,
+ 'bid_history': self.bid_history,
+ # DEBUG
+ 'jobs_history': self.jobs_history,
+ 'services_history': self.services_history,
+ 'money_jobs': self.money_jobs,
+ 'money_services': self.money_services,
+ 'penalty_jobs': self.penalty_jobs,
+ 'penalty_services': self.penalty_services,
+
+
+ }
+ return final
+
+ def bid_workload(self, j, jobs, services, t):
+ #print('BIDDER',self.bidder)
+ return self.bidder.bid_workload(self, j, jobs, services, t)
+
+
+ def accepted_bid(self, jobs, services, workload, price, t):
+ if type(workload).__name__=='Job':
+ iden = [i[0] for i in jobs if i[1] is workload][0]
+ temp = Job(workload, None, t, iden)
+ self.bid_history.append((True, temp.iden, temp.core, t))
+ self.jobs_flexible.append(temp)
+ self.money += price
+ #Ici avant il y'avait la formule du prix au lieu de price, pourquoi ?
+ self.money_jobs.append((temp.iden, temp.core, t, price))
+ elif type(workload).__name__=='Service':
+ iden = [i[0] for i in services if i[1] is workload][0]
+ temp = Service(workload, None, t, iden)
+ self.bid_history.append((False, temp.iden, temp.core, t))
+ self.services_flexible.append(temp)
+ self.money += price
+ #Ici avant il y'avait la formule du prix au lieu de price, pourquoi ?
+ self.money_services.append((temp.iden, temp.core, t, price))
+ #print('Service',iden,'accepté au temps',t)
+ else:
+ print(f'Cannot recognise job: {workload}')
+
+ def end_timestep(self, t):
+ # Schedule the tasks you bid on
+ self.scheduler.schedule_workload(self, t)
+
+
+class Job:
+ def __init__(self, job, core, bid, iden, work_left=None):
+ self.job = job
+ self.core = core
+ self.work_left = work_left
+ self.bid = bid
+ self.iden = iden
+ self.done = None
+ self.started = False
+ if self.work_left is None:
+ self.work_left = self.job.w
+
+ def remove_work(self, w):
+ self.work_left -= w
+ self.started = True
+ if self.work_left <= -0.0001:
+ print('Error work left is negative')
+
+ def price(self, t):
+ if self.job.due==self.job.a:
+ return self.job.pmax
+ else :
+ if t<=self.job.due :
+ return self.job.pmin + (self.job.pmax - self.job.pmin) * (t - self.job.a) / (self.job.due - self.job.a)
+ return self.job.pmax
+
+
+ def isdone(self, t):
+ if self.work_left <= 0:
+ self.done = t
+ return True
+ return False
+
+ def late(self, t):
+ return t >= self.job.dead
+
+ def penalty(self, t):
+ if t <= self.job.due:
+ return 0
+ if self.job.dead==self.job.due :
+ return self.job.pendead
+ if t <= self.job.dead:
+ return self.job.penmin + (self.job.penmax - self.job.penmin) * (t - self.job.due)/ (self.job.dead - self.job.due)
+ return self.job.pendead
+
+
+class Service(Job):
+ def __init__(self, service, core, bid, iden, work_left=None):
+ super(Service, self).__init__(service, core, bid, iden, work_left=-1)
+ self.work_done = [0]*len(service.work)
+
+ def price(self, t):
+ if self.job.start==self.job.arrived:
+ return self.job.pmax
+ return self.job.pmin + (self.job.pmax - self.job.pmin) * (t - self.job.arrived) / (self.job.start - self.job.arrived)
+
+ def remove_work(self, w):
+ return
+
+ def work_at_t(self, t):
+ if t - self.job.start < 0:
+ return 0
+ if t - self.job.start >= len(self.job.work):
+ return 0
+ return self.job.work[t - self.job.start] - self.work_done[t - self.job.start]
+
+ def store_work_done(self, t, w):
+ if t - self.job.start < 0:
+ return 0
+ self.work_done[t - self.job.start] = w
+
+ def isdone(self, t):
+ return t >= self.job.start + len(self.job.work)
+
+ def service_penalty(self, w, t):
+ if t - self.job.start < 0 or t - self.job.start > len(self.job.work):
+ return 0
+ if self.work_at_t(t) == 0:
+ return 0
+ penalty = self.job.pen * (1 - min((w / self.work_at_t(t)), 1))
+ if penalty < 0:
+ print('Error Negative penalty')
+ return penalty
diff --git a/Heuristic/scheduler/__init__.py b/Heuristic/scheduler/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Heuristic/scheduler/best_fit.py b/Heuristic/scheduler/best_fit.py
new file mode 100644
index 0000000..1163653
--- /dev/null
+++ b/Heuristic/scheduler/best_fit.py
@@ -0,0 +1,204 @@
+import copy
+import random
+
+from Heuristic.common import Heuristic, Job, Service
+from Heuristic.scheduler.scheduler import scheduler
+
+
+class BestFitScheduler(scheduler):
+ def __init__(self, runner_arg):
+ pass
+
+ def schedule_workload(self, heuristic: Heuristic, t):
+
+ for j in heuristic.jobs_flexible:
+ # find core which suits the most if equal then random
+ score = []
+ for c in range(heuristic.corecount):
+ temp = j.work_left
+ time = -1
+ notexceed=True
+ while temp > 0 and notexceed:
+ time += 1
+ if t+time == heuristic.timestep:
+ notexceed=False
+ else:
+ temp -= heuristic.get_available_perf(t + time, c)
+ if notexceed :
+ score.append((c, time))
+
+ #Normalement on devrait jamais avoir cette erreur mais si c'est le cas c'est que predict_bid a biddé un job problématique
+ #Par exemple un job où la pendead<=pmax
+ if len(score)==0:
+ print('\nERROR : A JOB WAS BIDDED BUT CANT BE PLACED','\nThis job has a problem :', j.job,'\n')
+ heuristic.penalty+=j.penalty(t+time+1)
+ heuristic.penalty_jobs.append((j.iden, j.core, t+time+1, j.penalty(t+time+1)))
+ #Du coup on regarde pas les autres jobs mais le programme tourne
+ break
+
+
+
+
+ best_fit = [i[0] for i in score if i[1] == min(score, key=lambda x: x[1])[1]]
+ winner = random.choice(best_fit)
+ j.core = winner
+ # print('winner',winner)
+
+ time = -1
+ work_left = j.work_left
+ while not work_left <= 0:
+ time += 1
+
+ if heuristic.get_available_perf(t + time, j.core) == 0 :
+ work_done=0
+ else :
+ heuristic.poweron[t+time][j.core] = True
+ work_done = min(work_left, heuristic.get_available_perf(t+time, j.core))
+ work_left -= work_done
+ heuristic.cores[t+time][j.core].append((True, j.iden, work_done))
+ heuristic.jobs_history.append((j.iden, j.core, t+time, work_done))
+ if work_left < 0:
+ print("Error cannot work job more than required")
+ j.started = True
+
+ #Ici rajouté +1
+ temp = j.penalty(t+time+1)
+ if temp > 0:
+
+ heuristic.penalty += temp
+ heuristic.penalty_jobs.append((j.iden, j.core, t+time+1, temp))
+
+
+ # Remove from the list the jobs which could be started
+ heuristic.jobs_flexible = [i for i in heuristic.jobs_flexible if not i.started]
+ if len(heuristic.jobs_flexible) != 0:
+ print('Jobs not all scheduled properly')
+
+
+ # Late penalties for jobs which are past their deadline
+ penalties = [(j.iden, j.core, t, j.penalty(t)) for j in heuristic.jobs_flexible if j.late(t)]
+ for p in penalties:
+ heuristic.penalty_jobs.append(p)
+ heuristic.penalty += p[3]
+
+ # Remove the deadline jobs
+ heuristic.jobs_flexible = [i for i in heuristic.jobs_flexible if not i.late(t)]
+ # remove the completed jobs
+ heuristic.services_flexible = [i for i in heuristic.services_flexible if not i.started]
+
+ for s in heuristic.services_flexible:
+ score = []
+ price=s.price(t)
+ for c in range(heuristic.corecount):
+ time = -1
+ penalty = 0
+ available_work = 0
+ for w in s.job.work:
+ time += 1
+ work_can_do = heuristic.get_available_perf(s.job.start + time, c)
+ available_work += work_can_do
+ work_done = min(work_can_do, w)
+ penalty += max(s.service_penalty(work_done, s.job.start+ time), 0)
+ score.append((c, penalty, available_work))
+
+ # Sort by penalty then space
+ best_fit = [i for i in score if i[1] == min(score, key=lambda x: x[1])[1]]
+ best_fit = [i[0] for i in best_fit if i[2] == min(best_fit, key=lambda x: x[2])[2]]
+ winner = random.choice(best_fit)
+ s.core = winner
+
+ #print('NEGATIF ? ',score)
+
+ time = -1
+ start = s.job.start
+ for w in s.job.work:
+ time += 1
+
+ #ICI PB ERR NEG
+ # ERR NEG POWER venait du fait qu'on allumait par défaut le coeur sans regarder au préalable la puissance
+ #En soit ce bug n'influançait pas les résultats de prix car get_available_perf renvoyait 0 mais
+ # dans les historiques des coeurs il y avait des incohérences.
+
+ #Cas où il n'ya pas la puissance pour allumer le coeur
+ if heuristic.get_available_perf(start + time, s.core) ==0 :
+ work_done=0
+
+ else :
+ heuristic.poweron[start+time][s.core] = True
+ work_done = min(w, heuristic.get_available_perf(start + time, s.core))
+
+ #Cas où le service ne tourne pas réellement
+
+ heuristic.cores[start + time][s.core].append((False, s.iden, work_done))
+ heuristic.services_history.append((s.iden, s.core, start + time, work_done))
+ penalty = s.service_penalty(work_done, start + time)
+ if penalty > 0:
+ #print('Iden',s.iden,'pena au temps',start + time,penalty)
+ heuristic.penalty += penalty
+ heuristic.penalty_services.append((s.iden, s.core, start + time, penalty))
+ s.started = True
+
+ # Return the money gain from taking this job
+ def predict_bid(self, heuristic, t, work, job):
+ if job:
+ work = Job(work, None, t, None)
+ # find core which suits the most if equal then random
+ score = []
+ for c in range(heuristic.corecount):
+ temp = work.work_left
+ time = -1
+ notexceed=True
+ while temp>0 and notexceed:
+ time += 1
+ if t+time == heuristic.timestep:
+ notexceed=False
+ else :
+ temp -= heuristic.get_available_perf(t + time, c)
+ if notexceed:
+ score.append((c, time))
+ if len(score)!=0:
+ best_time = min(score, key=lambda x: x[1])[1]
+ #print('Best Time',best_time)
+ # A vérifier mais best_time est le meilleur* temps d'éxécution de tâche (*en terme de gestion de coeur)
+ # J'ai rajouté un +1 à t+best_time qui n'était pas là avant -> devient le temps de rendu
+ price = work.price(t)
+ penalty = work.penalty(t + best_time+1)
+
+ else:
+ price = work.price(t)
+ penalty = work.penalty(heuristic.timestep)
+ #Formule Pénalité
+ #print('Test pénalité au temps',t,'bid',work.bid)
+ return price, price - penalty
+ else:
+ work = Service(work, None, t, None)
+ score = []
+ for c in range(heuristic.corecount):
+ time = -1
+ penalty = 0
+ available_work = 0
+ for w in work.job.work:
+ time += 1
+
+ work_can_do = heuristic.get_available_perf(work.job.start + time, c)
+ available_work += work_can_do
+ work_done = min(work_can_do, w)
+ penalty += max(work.service_penalty(work_done, work.job.start+ time), 0)
+ score.append((c, penalty, available_work))
+
+ # Sort by penalty then space
+ best_fit = [i for i in score if i[1] == min(score, key=lambda x: x[1])[1]]
+ best_fit = [i[0] for i in best_fit if i[2] == min(best_fit, key=lambda x: x[2])[2]]
+ winner = random.choice(best_fit)
+ work.core = winner
+
+ price = work.price(t)
+ time = -1
+ penalty = 0
+ for w in work.job.work:
+ time += 1
+ work_done = min(w, heuristic.get_available_perf(work.job.start + time, work.core))
+ penalty += work.service_penalty(work_done, work.job.start + time)
+
+ #print('Predict',price,penalty)
+ return price, price - penalty
diff --git a/Heuristic/scheduler/scheduler.py b/Heuristic/scheduler/scheduler.py
new file mode 100644
index 0000000..967cccf
--- /dev/null
+++ b/Heuristic/scheduler/scheduler.py
@@ -0,0 +1,7 @@
+class scheduler:
+
+ def schedule_workload(self, heuristic, t):
+ raise NotImplemented
+
+ def predict_bid(self, heuristic, t, work, jobs):
+ raise NotImplemented
\ No newline at end of file
diff --git a/README.md b/README.md
index ce8d22f..3c43449 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,89 @@
-# Cooperative vs. non-cooperative marketplace for computing jobs to be run on geo-distributed data centers only supplied by renewable energies
+# MarketPlace
+Marketplace distributed scheduling of cloud data centers.
+This repository shows how to run the experiments and analysis of the article "Cooperative vs. non-cooperative marketplace for computing jobs to be run on geo-distributed data centers only supplied by renewable energies".
-## Getting started
+The experimental campaign can be re-executed as follows:
+1. Run the experiments;
+1. Put the results of the experiments inside XPs folder;
+1. Re-run the jupyters notebook for each analysis.
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+## Directory organization
+This directory has the following folders:
+- [Analysis](Analysis): All notebooks for the article's analysis of each article's section;
+- [ExecutionScripts](ExecutionScripts): The scripts to run the experiments;
+- [GenerateExperiments](GenerateExperiments): Python classes for managing the experiments;
+- [GenerateInstance](GenerateInstance): Python classes for creating the instances (jobs, services, and power);
+- [GenerateMetrics](GenerateMetrics): Python classes for organizing the metrics;
+- [GenerateProblem](GenerateProblem): Python classes for solving the problem using LP;
+- [GenerateVisualisation](GenerateVisualisation): Python classes for creating some graphs;
+- [Heuristic](Heuristic): Python classes for solving the problem using heuristic;
+- [RenewableData](RenewableData): Data and scripts to generate renewable production from real traces;
+- [XPs](XPs): The results of the experiments.
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+## Installing the environment
+It is also possible to install all dependencies using [nix](https://nixos.org/). The file [default.nix](default.nix) provides all dependencies (see buildInputs variable inside this file).
-## Add your files
+To install it using nix, execute the following command:
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+> nix-shell
-```
-cd existing_repo
-git remote add origin https://gitlab.irit.fr/sepia-pub/open-science/cooperative-vs-non-cooperative-marketplace.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.irit.fr/sepia-pub/open-science/cooperative-vs-non-cooperative-marketplace/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
+Besides the dependencies, we executed our experiments using Gurobi. So, it is necessary to have a Gurobi license to have the same results. It is possible to execute it using another solver, by modifying the file *GenerateExperiments/experiments.py* when it calls prob.solve(). However, in this case, you can have different results (mainly concerning execution time).
-## Test and Deploy
+The list of dependencies are *at least*:
+- pandas
+- numpy
+- matplotlib
+- jupyter
+- ipython
+- pulp
+- colorama
+- termcolor
+- tabulate
+- seaborn
+- cbc
+- gurobi
-Use the built-in continuous integration in GitLab.
+## Time window
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+We defined 72 time steps. Imaging that each time step is 1 hour, this corresponds to a three-day experiment.
-***
+## Workload
-# Editing this README
+We defined three workloads' size:
+- Large: This workload has 238 jobs and 238 services. This demand corresponds to ~30% (30.010502047110858%) of all servers on all the time;
+- Medium: This workload has 159 jobs and 159 services. This demand corresponds to ~20% (19.994626128183818%) of all servers on all the time;
+- Small: This workload has 80 jobs and 80 services. This demand corresponds to ~10% (10.073523014606495%) of all servers on all the time.
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
+We create 100 different workloads for each size. So, we have 100 large, 100 medium, and 100 small workloads.
-## Suggestions for a good README
+## Renewable energy production
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+We propose different renewable energy productions. The file [renewable.ipynb](RenewableData/renewable.ipynb) presents all productions.
-## Name
-Choose a self-explaining name for your project.
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+## Experimental campaigns
+We have the following experimental campaigns:
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+1. MILP and Heuristic comparison: Comparison between MILP and heuristic coop. The power production comes from bell generation. This is the Section 7.1 of the article;
+1. Centralized and distributed comparison: We compare the executions of a centralized data center with a distributed approach. This is the Section 7.2 of the article;
+1. Non-cooperative and cooperative comparison: We compare non-cooperative and cooperative approaches under different power productions. This is the Section 7.3 of the article (and subsections 7.3.1, 7.3.2, and 7.3.3).
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+## Running the experiments
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+From the root folder (with nix activated), execute the following commands
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+```bash
+python ExecutionScripts/1_milp.py
+python ExecutionScripts/2_exps_bell.py
+python ExecutionScripts/3_exps_amplitude.py
+python ExecutionScripts/4_exps_real.py
+```
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+This can take time to execute everything. For this reason, we make available the experiments outputs in [Zenodo](https://doi.org/10.5281/zenodo.15166838).
-## License
-For open source projects, say how it is licensed.
+## Analyzing the results
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Folder [Analysis](Analysis) shows all the analysis of each section of the article.
\ No newline at end of file
diff --git a/RenewableData/ninja_pv_Canberra.csv b/RenewableData/ninja_pv_Canberra.csv
new file mode 100644
index 0000000..cd42dc1
--- /dev/null
+++ b/RenewableData/ninja_pv_Canberra.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Solar photovoltaic power (PV) (Point API) - -35.298, 149.101 - Version: 1.1 (using GSEE v0.3.1) - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
+# Units: time in UTC, local_time in Australia/Sydney, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Australia/Sydney", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "-35.2975906", "lon": "149.1012676", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "system_loss": "0.1", "tracking": "0", "tilt": "35", "azim": "180", "raw": false}}
+time,local_time,electricity
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diff --git a/RenewableData/ninja_pv_Dubai.csv b/RenewableData/ninja_pv_Dubai.csv
new file mode 100644
index 0000000..83cbfca
--- /dev/null
+++ b/RenewableData/ninja_pv_Dubai.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Solar photovoltaic power (PV) (Point API) - 25.074, 55.189 - Version: 1.1 (using GSEE v0.3.1) - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
+# Units: time in UTC, local_time in Asia/Dubai, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Asia/Dubai", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "25.074282349999997", "lon": "55.18853865430702", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "system_loss": "0.1", "tracking": "0", "tilt": "35", "azim": "180", "raw": false}}
+time,local_time,electricity
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diff --git a/RenewableData/ninja_pv_Paris.csv b/RenewableData/ninja_pv_Paris.csv
new file mode 100644
index 0000000..1947220
--- /dev/null
+++ b/RenewableData/ninja_pv_Paris.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Solar photovoltaic power (PV) (Point API) - 48.853, 2.348 - Version: 1.1 (using GSEE v0.3.1) - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
+# Units: time in UTC, local_time in Europe/Paris, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Europe/Paris", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "48.8534951", "lon": "2.3483915", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "system_loss": "0.1", "tracking": "0", "tilt": "35", "azim": "180", "raw": false}}
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diff --git a/RenewableData/ninja_pv_Sao_Paulo.csv b/RenewableData/ninja_pv_Sao_Paulo.csv
new file mode 100644
index 0000000..1b4a1ee
--- /dev/null
+++ b/RenewableData/ninja_pv_Sao_Paulo.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Solar photovoltaic power (PV) (Point API) - -1.204, -47.158 - Version: 1.1 (using GSEE v0.3.1) - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.060
+# Units: time in UTC, local_time in America/Belem, electricity in kW
+# {"units": {"time": "UTC", "local_time": "America/Belem", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "-1.2043218", "lon": "-47.1583944", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "system_loss": "0.1", "tracking": "0", "tilt": "35", "azim": "180", "raw": false}}
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diff --git a/RenewableData/ninja_wind_Canberra.csv b/RenewableData/ninja_wind_Canberra.csv
new file mode 100644
index 0000000..f9e66a6
--- /dev/null
+++ b/RenewableData/ninja_wind_Canberra.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Wind power (Point API) - -35.298, 149.101 - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.068
+# Units: time in UTC, local_time in Australia/Sydney, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Australia/Sydney", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "-35.2975906", "lon": "149.1012676", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "height": "80", "turbine": "Vestas V90 2000", "raw": false}}
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diff --git a/RenewableData/ninja_wind_Dubai.csv b/RenewableData/ninja_wind_Dubai.csv
new file mode 100644
index 0000000..3f3c864
--- /dev/null
+++ b/RenewableData/ninja_wind_Dubai.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Wind power (Point API) - 25.074, 55.189 - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.068
+# Units: time in UTC, local_time in Asia/Dubai, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Asia/Dubai", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "25.074282349999997", "lon": "55.18853865430702", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "height": "80", "turbine": "Vestas V90 2000", "raw": false}}
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diff --git a/RenewableData/ninja_wind_Paris.csv b/RenewableData/ninja_wind_Paris.csv
new file mode 100644
index 0000000..e3428e6
--- /dev/null
+++ b/RenewableData/ninja_wind_Paris.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Wind power (Point API) - 48.853, 2.348 - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.068
+# Units: time in UTC, local_time in Europe/Paris, electricity in kW
+# {"units": {"time": "UTC", "local_time": "Europe/Paris", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "48.8534951", "lon": "2.3483915", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "height": "80", "turbine": "Vestas V90 2000", "raw": false}}
+time,local_time,electricity
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diff --git a/RenewableData/ninja_wind_Sao_Paulo.csv b/RenewableData/ninja_wind_Sao_Paulo.csv
new file mode 100644
index 0000000..fb11dc8
--- /dev/null
+++ b/RenewableData/ninja_wind_Sao_Paulo.csv
@@ -0,0 +1,8764 @@
+# Renewables.ninja Wind power (Point API) - -1.204, -47.158 - Version: 1.1 - License: https://creativecommons.org/licenses/by-nc/4.0/ - Reference: https://doi.org/10.1016/j.energy.2016.08.068
+# Units: time in UTC, local_time in America/Belem, electricity in kW
+# {"units": {"time": "UTC", "local_time": "America/Belem", "electricity": "kW"}, "params": {"local_time": true, "header": true, "lat": "-1.2043218", "lon": "-47.1583944", "date_from": "2019-01-01", "date_to": "2019-12-31", "dataset": "merra2", "capacity": "0.64", "height": "80", "turbine": "Vestas V90 2000", "raw": false}}
+time,local_time,electricity
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diff --git a/RenewableData/renewable.ipynb b/RenewableData/renewable.ipynb
new file mode 100644
index 0000000..11117e2
--- /dev/null
+++ b/RenewableData/renewable.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "from matplotlib import figure, pyplot as plt\n",
+ "import pandas as pd\n",
+ "from renewable import *\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "color = ['tab:green', 'tab:red', 'tab:purple', 'tab:orange']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Same production"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2500x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cDC = [16]\n",
+ "renewPow = renewable_production_bell(0, cDC=cDC)\n",
+ "fig = plt.figure(figsize=(25, 10))\n",
+ "for x in range(len(cDC)):\n",
+ " plt.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "plt.legend(fontsize=16)\n",
+ "plt.xlabel(\"Time\", size=16)\n",
+ "plt.ylabel(\"Power\", size=16)\n",
+ "plt.title(\"Power production for centralized DC\", size=18)\n",
+ "plt.tick_params(axis='both', labelsize=14)\n",
+ "plt.xlim(0,71)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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XSnwm+5alf/zxR4kdr4YNG1YmWxd27NiRm266qci4u7s7w4YNA+C3337j1KlTxZ7fp0+fQlvGnu3bb78FzqzDrbfeWmTebDYzZswYAGJjY9m6datjbv78+VitVtzd3UvshBYZGVnyg/1Ln3/+OQDNmjVj8ODBl+0+59qyZQvbtm0DYNSoUcW+xnfccQft2rUDir5nz/baa68VO37XXXcBZ7adzczM/LeRCynL/CNHjrzkHF999RWZmZmEhYUV+/6GM++/hx56CMDxc+Ji3X777QDs2rWLQ4cOFZqzv3d69OhBjx49ShsdNze3Et/z9tduy5Ytpb7upbB3xjx+/Lhj7Pjx446tYO3zIiIiIiIiIiJmowOIiIiIiIiIiJRHJpOpxLlHH32UV199FTizbam9IOPmm28u8Zxu3boxd+5ctmzZQl5eHi4uLgB06dIFZ2dnTp06xYYNG2jXrh3Z2dmsXr2aSpUq0a5dO7p27cqKFSv466+/CAsLA/63lem525auWLECgIKCAho1alRiHntxW0ZGBseOHaNatWpFjunYsWOJ55fGjTfeeMG5goICNm7cWOR5LpTDvvXq+da+a9euODs7k5+fz/r16x3bxdrPDQsLc2wRe66GDRsSGBhIcnJyide/VPatZ++4444yv/b52J/bbDbTpUuXEo/r1q0ba9euLXF7Wz8/P0JCQoqdq1mzpuPzEydOFFsAeqnKKr+7uztt2rS55Bz277XY2FiqV69e4nFZWVkA7N27t8hceno6U6dO5eeffyY+Pp6TJ0+Sl5dX5Ljk5GRq1KgBnNm2dN26dcClv3eaNWtGpUqVip2zv3ZnF55dTvafnyWNne9nsYiIiIiIiIhcW1ToJiIiIiIiIiJSjICAAMfnbm5u+Pv707p1ax555JFCxVhHjhxxfB4YGFji9ewdyaxWK8ePH3dc38fHh9atW7N+/Xr++usv2rVrx6pVq8jJyeHGG2/EbDZz4403Mm7cOP766y9efvllsrKyWLNmDVC00M3ecS4/P5/Dhw9f1LOW1HGruOK3S3G+dTl77uy1vNgc9nPOdw+LxYK/vz+HDx8udI+LORfOvHaXo9AtJSUFgDp16pT5tc/H/tz+/v64ubmVeJz9PVvS6+Ll5VXiuWbz//7sWFzh1r9RVvmrVKmCk9Olb3hh/17LyspyFLOdz7nfZwkJCdx0000cOHDAMebh4UHlypUduezfwxkZGY5jjh075ljTS33vXMxrZ7VaL+napXXixAngzOthV6VKFUwmEzabjWPHjl2RHCIiIiIiIiJS/qnQTURERERERESkGPYipMvh3A5FXbt2dRS6jRgxwtGtzd7tzL496YoVK8jLy2PlypXk5OTg4eHBddddV+ha9k5tjRs3Jj4+/l/lLIttS+H8HZkuplvTxeS42K5PxR1nVMco+32Nvn9ZHXel/dv8//b9bf9eGzRoEB9//HGpz+/fvz8HDhwgODiYt99+mxtvvLHQNp35+fmOorPiup5B+X1tSsO+RWr9+vUdY2azmYYNG7Jjxw42bdpkVDQRERERERERKWcu/Z8sioiIiIiIiIhIoW5jZ3dmOpd9zmw24+vrW2jOXtC2cuVKcnNzixS6ubm50aFDBzIyMlizZo1jvlOnTo4tUO3sWyju3r27UBcoI13MusCldZCzn7N///4Sj8nOznZ0hapatWqRc8+XDyixm9vZXcuys7NLPP/UqVPFjttfq6SkpPPev6zZnzs1NZWcnJwSj7Ovy9lrVh6Ul/z212/r1q2lPnf//v2OrWu//vpr7r333kJFblBysW2VKlUc3/dX+r1T1tavX+/oWhcREVFo7qabbgJg06ZNxW77KiIiIiIiIiLXHhW6iYiIiIiIiIj8C23atHFsM/jnn3+WeNwff/wBQKtWrYoUp9kL1jIzM/njjz9Yt24dvr6+hIaGOo6xF7399ddfLF26FCi6bSlAx44dAcjNzeW777679AcrQ/a855tzcnKidevWpb52WFgYcP61X7ZsmWMbxvDw8CLnrl+/nvT09GLPTUxMLLEQ7uyCxZIK7RISEjh58mSxcx06dADgp59+KjF7cezvt5K6fF2I/bmtVivR0dElHmd/z569ZuVBeclv/177559/Sl2Idfb7paT3vT3/ucxmM+3atQNK/94pb8aOHQuc6a73yCOPFJobMmQIJpOJ/Px8oqKiLvqaBQUFZZpRRERERERERMoPFbqJiIiIiIiIiPwLlStX5tZbbwXg7bffJjMzs8gxMTExLFiwAICHHnqoyHylSpUcxThRUVFYrVYiIiIcBU3wv6K2H3/8kfXr1xcaO1tYWJijcObVV18lNTX1vPmPHz9+wWf8t1asWMGyZcuKjGdnZ/POO+8AcOutt1K5cuVSX/vBBx8EYPXq1SxZsqTIvNVqdRTJNG/enObNmzvmevfujdlsJisry5HjXOcrsPH09HRst2h/fc/1+uuvl3j+wIEDAYiLiyvV1pfe3t4AJRbQXUjLli1p2rQpAOPHj3dswXm2X375hTVr1gDFv2eNVF7y9+nTB3d3d/Lz83n66aeLzWFXUFBQ6PXy8fFxfB4TE1Pk+PT0dMaPH1/i9ezvnV9++YVffvnlEtIbKz8/n5deeomff/4ZgCeeeIIGDRoUOqZZs2Y88cQTAHz++ee89957F7zu999/f951ExEREREREZGKTYVuIiIiIiIiIiL/0uuvv46Liws7d+7k1ltvdWxlWFBQwC+//EKPHj2wWq3Ur1+fp556qthr2IvW7MU59g5udu3ataNSpUps2LABq9WKl5cXbdu2LXIdk8nE1KlTcXNzY9++fVx33XXMnz+/UAFecnIys2fPplu3bgwfPrxM1uB8fHx86N27N/Pnz3d0Vtu+fTu3334727dvx9nZuVQdm87Wu3dvrrvuOgDuv/9+5syZQ15eHgB79uyhd+/erF69GoC33nqr0LmBgYEMGTIEgHHjxjFhwgRHZ7fU1FSeeeYZZs+eXago6Vz2IqrPP/+cjz76iKysLOBMx67HH3+cb7/9Fg8Pj2LP7dq1q6NQ75lnnmHkyJGO7nE2m42DBw8yffp0R1GTnb1YLy0tjblz517MMhXx5ptvAvD3339z7733smfPHgDy8vL46quvHM/VoUMH7r777ku6x+VUHvJXr16diRMnArBo0SK6devGypUrHQVvNpuN7du3M2nSJJo3b+4o6gJo2rQptWvXBmDAgAFs2LDBMbd69WoiIiI4ceJEiffu06cPnTp1wmaz0bt3b95++22OHj0KnCkiS0pK4t13370i398Xy2azkZiYyKeffkqbNm2YNGkScOZn3eTJk4s95/3336dz584AvPDCC/To0YPFixc7vs/gTLHuvHnzuPHGG7nnnnuuSPGuiIiIiIiIiBjEJiIiIiIiIiIiNpvNZhszZowNsF3Kn0y++eYbm6urq+N8b29vm8VicXxdq1Yt27Zt20o8/88//3QcC9ji4uKKHNO9e3fHfI8ePc6bZ8mSJbYqVao4jnd2drZVqVLF5uHhUeg+jz/+eKHzli5deslrcK46derYANukSZNsjRo1sgE2Nzc3m4+Pj+MeJpPJ9umnnxZ7vv2YpUuXnvc+Bw4csDVr1sxxvKurq61y5cqOr52cnGyTJ08u9tysrCzbzTffXGidfH19bSaTyQbYhg8fbuvSpYsNsI0ZM6bI+enp6bamTZsWupf93i4uLravv/7asQ4zZswocn5GRoatV69ehV4Tb29vm5ubm+PrVq1aFTnvpptucsx7eXnZ6tSpY6tTp47t3XffdRxjfz936dKl2GefNGmS4zkBW+XKlQu9h1u0aGFLTk4uct6MGTNsgK1OnTrFXtdms9n27NnjuM6ePXtKPK4kF/PaX878pfHWW2/ZnJ2dC73/qlSpYnNxcSn0us6ePbvQeT/99JPNbDY75j08PBzfnx4eHrY//vjjvOuQmppq69y5c6HvpcqVKxe671133VXonAu9J2y2f/czwL62gM3f398WEBBgCwgIsPn5+RVaI/v7dvz48ba8vLzzXjMnJ8f29NNPF1orwObj42Pz9PQsNBYUFGT77bffSp1bRERERERERCoGdXQTERERERERESkDDzzwAHFxcTz11FPUr1+fnJwczGYzoaGhjB07ltjYWJo0aVLi+R06dMDNzQ2AgIAAx9aMZzu7y1tx25aerVu3buzcuZMJEybQqVMnfHx8OHnyJE5OTjRt2pSBAwfy448/MmXKlEt84ovn6+vL2rVrGTFiBLVr1yYnJwc/Pz/uuOMOVq5c6die8FIFBgayfv16Jk2aRPv27XF3dyczM5NatWrRp08fNmzYwLPPPlvsuRaLhV9//ZXJkycTGhqKq6srNpuNzp07M3fuXEfHrpJUqlSJFStW8OKLL1K3bl3MZjMuLi6OTnL2jm0l8fDwYMGCBfz888/cc8891KxZk+zsbCpVqkTLli159tln+fTTT4ucN3/+fF544QUaNmxIXl4ee/fuZe/evaXazvSFF15g/fr1PProo9SqVYvMzEzc3d1p3749kyZNYu3atdSsWfOir3ellZf8w4YNY/v27bzwwgu0bNkSi8XCyZMnHVsSv/zyy6xatYqHH3640Hk9e/Zk+fLl3H777VSuXBmr1Yq/vz/9+/dn48aN3HTTTee9r7+/P8uWLWP27NncdtttVK1alYyMDHx9fWnbti0jRozgjTfeuJyPfl5Hjx7l8OHDHD58mOzsbKpWrUrbtm0ZOHAgX3zxBYcOHeLVV1/FbDaf9zqurq588MEH7Nixg9dee40OHToQEBDg6FIZEhLCgw8+yDfffOPoqikiIiIiIiIiVyeTzWazGR1CRERERERERESuPsHBwezdu5cZM2bQr18/o+OIiIiIiIiIiIhIBaaObiIiIiIiIiIiIiIiIiIiIiIiIlKuqdBNREREREREREREREREREREREREyjUVuomIiIiIiIiIiIiIiIiIiIiIiEi5pkI3ERERERERERERERERERERERERKddMNpvNZnQIERERERERERERERERERERERERkZKYjQ5wtSkoKODgwYN4eXlhMpmMjiMiIiIiIiIiIiIiIiIiIiIiIlIu2Ww20tPTqVmzJk5O59+cVIVuZezgwYPUqlXL6BgiIiIiIiIiIiIiIiIiIiIiIiIVwv79+wkKCjrvMSp0K2NeXl7AmcX39vY2OI2IiIiIiIiIiIiIiIiIiIiIiEj5lJaWRq1atRw1V+dTIQvd3nrrLYYPHw7A6tWrad++fZFj0tLSiIyMZMGCBaSkpFC9enV69+5NZGRkiQVoc+bM4b333iMuLg5XV1euv/56oqKiCAsLu+hs9u1Kvb29VegmIiIiIiIiIiIiIiIiIiIiIiJyAfaaq/M5/8am5VB8fDyjR4/G09OzxGMyMjLo0qUL7777Lo0aNeKFF16gadOmvPvuu3Tp0oWMjIwi57zxxhs88sgjHD58mEGDBnH//fezcuVKOnbsyLJlyy7jE4mIiIiIiIiIiIiIiIiIiIiIiMj5VKhCt/z8fB577DFatWrFPffcU+Jxb731Fps3b+bll19myZIlTJw4kV9//ZXRo0ezefNm3nrrrULHJyYmMmbMGBo2bMiWLVt45513+OSTT1i1ahVms5nHH38cq9V6uR9PREREREREREREREREREREREREilGhCt3efPNNYmJi+Pzzz3F2di72GJvNxvTp06lUqRKjR48uNDdy5Eh8fX357LPPsNlsjvEZM2ZgtVp59dVX8fHxcYw3a9aMvn37smvXLv7666/L81AiIiIiIiIiIiIiIiIiIiIiIiJyXhWm0C02NpaxY8fy2muv0axZsxKPS0xM5ODBg3Ts2LHI9qYWi4UbbriB5ORkdu7c6Ri3b016yy23FLnerbfeCkB0dHSx98vJySEtLa3Qh4iIiIiIiIiIiIiIiIiIiIiIiJSdClHoZrVa6devH02aNGHEiBHnPTYxMRGABg0aFDtvH7cfZ/+8UqVKVK9e/aKOP9uECRPw8fFxfNSqVevCDyQiIiIiIiIiIiIiIiIiIiIiIiIXrUIUur3xxhuOLUtdXFzOe+ypU6cACm1BejZvb+9Cx9k/L83xZxs5ciSnTp1yfOzfv//8DyMiIiIiIiIiIiIiIiIiIiIiIiKlYjY6wIXExMQwfvx4/vOf/9CmTRuj4xTh5uaGm5ub0TFERERERERERERERERERERERESuWuW+0O2xxx6jfv36REZGXtTx9s5sJXVgS0tLK3Sc/fPSHC8iIiIiIiIiIiIiIiIiIiLXHpvNRl5eHgUFBUZHEREpV5ycnHBxccFkMl22e5T7QreYmBgALBZLsfPXX389AN999x133303DRo0ACAxMbHY4+3j9uPsn69evZqUlBSqV69+weNFRERERERERERERERERETk2pGfn8/Ro0dJT08nLy/P6DgiIuWSi4sLXl5e+Pv74+zsXObXL/eFbgMHDix2fPny5SQmJnLnnXdStWpVgoODgTMFaTVr1mTlypVkZGTg6enpOCc7O5vly5dTs2ZNQkJCHONdunRh9erVLFmyhL59+xa6z+LFix3HiIiIiIiIiIiIiIiIiIiIyLUlPz+f/fv3k5OTg4+PD5UqVcLZ2fmydi0SEalIbDYb+fn5nD59mpMnT5KVlUWtWrXKvNjNZLPZbGV6xSukX79+fPHFF6xevZr27dsXmhszZgxRUVG8/PLLvPnmm0XGR48ezdixYx3jCQkJNGvWjHr16rF27VrHNqVxcXG0a9eOGjVqsH37dszmC9cFpqWlObZC9fb2LqOnFRERERERERERERERERERESMcPnyYkydPUrt2bdzd3Y2OIyJSrmVlZbFv3z4qV65MQEDABY8vTa1Vue/odilefvllfvzxR9566y02bdpE27ZtiYmJ4ddffyU0NJSXX3650PENGzYkMjKS1157jZYtW3LvvfeSkZHB119/TV5eHtOmTbuoIjcRERERERERERERERERERG5ethsNtLT0/Hx8VGRm4jIRXB3d8fb25v09HSqVatWpt0vncrsSuWIp6cny5Yt44UXXmD79u288847xMbG8sILL7Bs2bJC25navfrqq8yePZtq1arx8ccf880339ChQwdWrlxJ165dDXgKERERERERERERERERERERMVJeXh55eXlUqlTJ6CgiIhWGl5eX4+dnWaqwW5eWV9q6VERERERERERERERERERE5OqQnZ3Nnj17CA4OVkc3EZGLlJWVRVJSEnXr1sVisZz32NLUWl2VHd1EREREREREREREREREREREykpZbr0nInK1u1w/M1XoJiIiIiIiIiIiIiIiIiIiIiIiIuWaCt1ERERERERERERERERERERERESkXFOhm4iIiIiIiIiIiIiIiIiIiIiIiJRrKnQTEREREREREREREREREREREZFCkpKSMJlMBAcHGx3lvEwmEyaTyegYcgWo0E1ERERERERERERERERERERELllwcLCj2Mj+4e7uTv369RkwYABxcXFGR5QKaubMmURGRpKUlGR0lH/l3O8PZ2dnKleuTHBwMD179uT1119n3759F329lStX8uSTT9K4cWN8fHxwc3MjMDCQnj17Mn36dDIyMkqdMTs7m6ioKJo2bYq7uztVq1blrrvu4p9//in1tS4Xk81msxkd4mqSlpaGj48Pp06dwtvb2+g4IiIiIiIiIiIiIiIiIiIicomys7PZs2cPdevWxWKxGB2n3AoODmbv3r00aNCAatWqAXDy5EkSExPJzc3Fzc2NefPmcccddxicVEojKSmJunXrUqdOHcMKzSIiIoiOjmbp0qVEREQUe0zjxo0B2L59+xVMVjr2jnPNmzfHx8cHgMzMTI4cOUJycjIATk5OPPnkk7zzzjt4eHgUe53MzEz69+/P3LlzAbBYLNSvXx93d3eSk5M5dOgQADVq1GDx4sW0aNHiovJlZGTQpUsXNmzYgKurK82aNXNkc3Z2Zvbs2Tz44IMX/byl+dlZmlordXQTERERERERERERERERERERkX/tlVdeYcWKFaxYsYLY2Fj27dvHzTffTE5ODv379+f06dNGR5Sr0Pbt28t1kdvZpkyZ4vge2bhxIwcOHODAgQNERUVhsViYOnUqt912G3l5eUXOzcvL45ZbbmHu3LlUr16dL774guPHjxMbG8u6des4ePAgcXFxPPXUU6SmprJr166LzvXSSy+xYcMGGjduTEJCAhs3bmTfvn28+eab5OfnM2DAAPbv31+WS3FJVOgmIiIiIiIiIiIiIiIiIiIiImUuICCAWbNm4ebmxrFjx/j999+NjiRS7gQGBjJq1ChWrFiBp6cny5cv54033ihy3NixY1m5ciUBAQGsXr2avn374u7uXuiYpk2bMnXqVJYuXerornghhw4d4rPPPgPg888/p06dOsCZDnMvv/wy3bp1Iysri//+97//8kn/PRW6iYiIiIiIiIiIiIiIiIiIiMhlUb16dRo0aABAYmJioblFixbRvXt3/P39cXNzo27dugwZMqTYzlGtWrXCZDKxZcuWQuOHDx/GZDJhMpkYNWpUkfMiIiIwmUwsW7asyNzixYu58847CQgIwM3NjaCgIPr3719sJ6ykpCRMJhPBwcEATJs2jfDwcLy8vBzbUl7I2VnWrl3L7bffjp+fH56ennTo0IHvv/++2PP69euHyWRi5syZ7Nmzh379+hEYGIjZbCYyMtJxXEZGBuPHj6dly5Z4enri7e3Nddddx4cffojVai0xV3R0NDfffDPe3t74+PjQtWvX8xYlLlu2DJPJVOJWoueu1bkyMzP573//S/v27alcuTIeHh40aNCAPn36EB0dXege9q+7du3qeJ3ta2FnHytOadfk7GcrKChg8uTJNG/eHIvFQkBAAAMHDiQ1NbXEtfk3Wrduzfjx4wGYPHkymZmZjrlTp07x/vvvA/Dee++VuLZ2nTp1okOHDhd13x9//BGr1UqTJk24/vrri8wPHDgQgPnz51/U9S4nFbqJiIiIiIiIiIiIiIiIiIiIyGVjs9mKjI0cOZKePXuyePFi3N3dadGiBUeOHOHjjz+mVatWrF+/vtDxN9xwA4Cj8Mnu7K/PncvJyWHNmjW4ubnRvn37QnPPP/883bt356effgKgWbNmpKenM3PmTNq0acOqVatKfJ7Bgwfz5JNPcvjwYRo3bkzlypUvvAhn+fvvv+ncuTPLly+nfv36+Pj4sHr1au655x4mTZpU4nk7duygTZs2fPPNN44CQnuBV2pqKtdffz2jRo0iLi6OkJAQgoKCWLt2Lc888ww9evQgOzu7yDW/+eYbbrzxRv78809cXV1p2LAhW7ZsoXv37nz77beleq6LsW/fPsLCwhg2bBhr1qyhWrVqNGnShOPHjzN79mzGjBkDgI+PDx07dsTb2xuA5s2b07FjR8dHQEDABe91qWti16dPH55//nlyc3MJCQnh+PHjfP7553Tt2pWcnJyyWZBzDBw4EBcXF06cOMHq1asd44sWLSI9PZ2qVaty7733luk9//nnHwA6duxY7Lx9/ODBg4ZvX6pCNxERERERERERERERERERERG5LFJSUti5cycAISEhAPz8889MnDgRs9nM7Nmz2b9/P+vXr+fQoUPcc889nDhxgvvuu4+srCzHdbp06QKUXOgWGBjI2rVrCxUu2b9u164dFovFMf7JJ58wefJk6taty9KlSzl8+DAbN27k+PHjjB8/nrS0NB544IFii6AOHDjArFmz+OGHH9i3bx/r1q3j4MGDpVqTqKgoevXqRUpKCuvWrSM5OdnRrWv48OHExMQUe97bb7/NDTfcwMGDB9mwYQPx8fEMHz4cOFN8t3XrVpo1a0ZCQgIxMTFs27aNdevWERAQwO+//+4oIrNLTk7miSeeoKCggBEjRjjypKSk8NxzzxXbIe/fyM/Pp1evXsTHxxMWFsa2bdtISEhgw4YNHDt2jE2bNvHAAw8AZ7qbrVixgtatWwMwZcoUVqxY4fi47bbbLni/S1kTu1WrVrFs2TLWrFlDQkICsbGxxMXFERQURFxcHDNmzCi7hTmLl5cXLVu2BGDdunWF8sCZojOz2Vym97R3WqxXr16x84GBgbi6uhY61igqdBMRERERERERERERERERERG5BDabjcy8zAr5UVyXtbJ25MgR+vTpQ05ODr6+vnTr1g2AiRMnAvD000/zyCOPOI739vZm9uzZ+Pv7k5SUxNdff+2Ys3d0W758eaF7REdH4+fnx8CBA8nJyXF0p7LPwf+K5AByc3OJjIzE2dmZBQsWFNp+09nZmVdffZXevXtz4MAB5s2bV+SZ8vPziYqK4s4773SMubu7l2pd/Pz8mDFjBp6ensCZrTeHDh1Kr169sFqtJXZ1q1q1KnPmzMHPz88xZrFYSExMZOHChQDMmjWL+vXrO+bDwsKYMmUKAB9++CHp6emOualTp3L69GnCw8OZMGGCo4DKxcWFSZMm0aRJk1I914UsXLiQDRs2UK1aNX777bci1w8NDWXw4MFlcq9LXRO7vLw8pkyZQrt27RxjDRs25OWXXwbg119/LZOcxalVqxZw5vvHLjk5GYC6deuW+f1OnDgBgK+vb7HzJpPJ0bXQfqxRyrbET0REREREREREREREREREROQakWXN4ro51xkd45KseXgNHi4eZXrNN954g+nTpwNw8uRJEhMTyc3NxcXFhWnTpuHl5cXp06cdWzIOHTq0yDU8PDx44oknmDBhAkuWLGHAgAEAVKtWjcaNG7N9+3bi4+Np0qQJx44dY9u2bdx1111EREQQFRVFdHS0o3jNXhRnL5IDWL16NSkpKYSHhzu6hZ3rzjvvZMGCBURHR9OnT58i83379r30ReLM9pRnd5izGzJkCAsXLmTx4sXFnte7d29HcdzZfv/9d2w2G506dSr2mXr37k1QUBAHDhxg5cqVdO/eHcBxn5KKy4YMGcKgQYMu+rku5IcffgBgwIABVKlSpcyuW5xLXRM7X19fevXqVeS88PBwAHbv3n15goPjNT67AM/+eXGv/79l71xo79pWHDc3N4BCXRaNoEI3EREREREREREREREREREREfnXEhMTHVsburq6Ur16dW644QZeeuklQkNDAdi5cycFBQW4ubmVuFVis2bNAEhISCg0fsMNN7B9+3aio6Np0qQJy5cvx2az0aVLF9q3b4+rq6uji5vVamXVqlWYzWY6dOjguMbWrVsBSEpKolOnTsXe/+TJk8D/umidzd/fH39//4tckeKV1CnNPn748GHS0tLw9va+qPPs69S0adNi552cnGjcuDEHDhwgISHBUdRlP+9CecpKfHw8AO3bty/T6xbnUtfE7uwOcGerVq0aAKdPny7DtIXZr3326+/l5QVARkZGmd/PXnSZm5tb4jE5OTlA6bsXljUVuomIiIiIiIiIiIiIiIiIiIhcAnezO2seXmN0jEvibi77gpUZM2bQr1+/8x5jL+KpWrUqJpOp2GMCAgIAimwp2aVLFz799FOio6MZNGhQoa1J3d3dCQ8P559//iE3N5eNGzeSkZFB+/btC3XBOnXqFACpqamkpqaeN2tx3avKoqOWvVjqfOPp6elFCt1Kurd9TUu6LhS/pme/Fuc7p6ykpaUBOLbBvJwudU3sSlprJycngMu69e++ffuAwtkDAwMB2LNnT5nfz75laUnbktpsNkfxZ0nbm14pKnQTERERERERERERERERERERuQQmk6nMt/+82lWqVAk4U2hms9mKLXY7fPgw8L8uVnZdunQBcBS4RUdH4+PjQ6tWrRzzK1euZO3ataxcubLQOefe/5FHHmH27Nll9VilUlKB3dnj5z77+dif6ciRIyUeU9yaVqpUiVOnTpGamlpsB7OSrmd/zUoq9iqp65j93vaiqcvpUtfEaKdOnXJ0HWzXrp1jvEOHDnz44YesWrUKq9WK2Vx2JV8NGjRg5cqVJW7Hmpyc7Oj21qBBgzK776VwMvTuIiIiIiIiIiIiIiIiIiIiInLNCAkJwcnJiZycnBILa+Li4gBo2LBhofHAwEDq1avHoUOHWL9+PVu2bKFz586OLlv2orZly5axfPly4Mx2p2ezb2UZGxtbdg9VSvYtPEsaDwgIKNLN7Xzs67Rt27Zi5wsKCti+fXuhY8/+3D53sTnt3c5KKtjbuXNnseP2LWn/+eefYueLU1LXvwu51DUx2meffYbVasXPz6/QFq89evSgUqVKHDlyhPnz55fpPa+77joAR3HouezjNWvWpFatWmV679JSoZuIiIiIiIiIiIiIiIiIiIiIXBGVKlWiQ4cOAEyZMqXIfFZWFtOnTwfg1ltvLTJvL1wbP348BQUFhTq2dejQAbPZzF9//cWKFStwdnamU6dOhc7v3Lkz/v7+xMTEsGzZsrJ6rFL57LPPyMnJKTL+0UcfAXDLLbeU6nq33HILJpOJFStWsGnTpiLzCxcu5MCBA3h6etKxY8dC5wFMnTq12Ot+/PHHxY7Xq1cPgN27d3Ps2LEi8/bX71x33303AJ9//jnHjx8v+YHO4u5+Zovd4raRPZ9LXRMjbdq0idGjRwPwwgsvOJ4dzmz3OnToUACef/55kpKSznutlStXsmrVqou675133onZbCY+Pp7Vq1cXmf/ss88A6N2790Vd73JSoZuIiIiIiIiIiIiIiIiIiIiIXDHDhw8HzhR2zZkzxzGenp5O3759SU1NJTg4mAcffLDIufbCth9//LHQ13CmiK5NmzYsW7aMtLQ0QkNDi3RGs1gsREVFAXDffffx3XffFdmCMzY2luHDh5fY4erfOnbsGAMHDnRs8Wmz2fjoo49YuHAhzs7OvPjii6W6XkhICL169QKgb9++hTrlbdy4kWeffRaAZ555ptA2nYMGDcLT05M1a9YwatQorFYrAHl5eQwbNszRWe9cfn5+tGvXjpycHF588UXy8vIAyM/PZ+LEiSxevLjY8+6++27CwsI4cuQIPXr0YMeOHYXmY2JiihTX2Yvq7NvVXqxLXRMjJCcnM27cODp16kRGRgYRERGMGDGiyHGRkZFcf/31HD58mOuvv55Zs2aRnZ1d6JiEhASefvppIiIizrtt69lq1qxJ//79ARgwYAB79+4Fzrwv3377bX7//XcsFgv/+c9//uWT/nsqdBMRERERERERERERERERERGRK6Znz56MGDGCvLw8HnnkEWrXrk14eDg1atRg/vz5+Pr6Mnfu3EIdrezsHd1sNhteXl60adOm0HyXLl0chWtnF8GdbfDgwYwYMYKjR4/Sq1cv/P39adeuHW3btqVKlSq0aNGCt956i/T09DJ+8jNGjx7NvHnzqFGjBuHh4QQFBfH0009js9mYMGECoaGhpb7mxx9/TIsWLYiNjaVhw4aEhobSrFkz2rZty6FDh7j55puJjIwsdE5QUBCffPIJJpOJ8ePHU6NGDdq1a0f16tWZNGkS48aNK/F+b775JmazmS+//JJq1aoRHh5OQEAAo0aNYtKkScWe4+zszIIFC2jUqBFr1qyhcePGNGrUiLCwMPz9/QkNDeXbb78tdM4DDzzguF+jRo3o0qULERER/Pbbb5dlTS63oUOH0qlTJzp16kTbtm0JCgoiKCiI0aNHk5OTw5AhQ/jll18wm81FznV1dWXJkiX07t2blJQU+vbti5+fHy1atKBdu3YEBQXRqFEjPvroI6pXr05ISMhF53rnnXdo3bo127dvp2HDhrRp04ZatWrx8ssv4+zszPTp06ldu3ZZLsUlUaGbiIiIiIiIiIiIiIiIiIiIiFxREyZM4KeffqJbt26cPn2aLVu24O/vz6BBg4iJiSE8PLzY8+rVq0dQUBAAHTt2xNnZudD82cVt9qK4ku6/cuVKHn74YTw9PYmJiSEpKYmgoCAGDBjAokWLuOmmm8rgSYvq3Lkzf//9N506dWLnzp2cOHGC9u3bs3DhQoYNG3ZJ16xatSqrV68mKiqKJk2akJCQwN69ewkPD2fKlCn88ssvWCyWIuc98sgj/PXXX3Tt2pXs7Gy2b99OixYt+PXXXx1FZsWJiIhg8eLFdOrUidzcXBISEhzd9Hr27FniebVr12bDhg1MmDCBNm3acPDgQeLj4/Hz8+Oxxx4rUlzXuXNn5syZQ7t27UhOTmb58uVER0eTkpJy2dbkcoqNjXVsK7pz507MZjM9evRg/Pjx7N69mw8//LDYAk+7SpUqMX/+fJYvX87AgQOpVasWSUlJxMTEYLPZuP322/nss89ISEigefPmF53Ly8uLlStXEhkZSd26ddm2bRvZ2dnccccd/P333zzyyCNl8fj/msl2bv9F+VfS0tLw8fHh1KlTRdpfisgZR/fEk/T1e5hr1KVFn//gXEwlsoiIiIiIyJWSb7WyZeZE8lMPUO/Rl/Cr1cDoSCIiIiIico07sHkFKT/PxNIojOb3DTI6jsg1LTs7mz179lC3bt0rXhAjV5+IiAiio6NZunQpERERRscRuWxK87OzNLVWqi4RkStmx+JvsP35GfUtMYQ55cBeODpqFkme19P06Ql4+FY1OqKIiIiIiFxD0o+lkPDhCIKz19LaNRmA3Gnfsy27JS7dB9PgxnsMTigiIiIiItea2LkfYlk3l7oesQSZrBA3j0MbPiHZ/wZaPj0BV3cPoyOKiIiIGEaFbiJyWeVbrWz+ZAzV9v5OI48d8P+/f53Kq4an+QT+bgfwt84jc9IStuSF4vfgywSFdjI2tIiIiIiIXNX2rVvKqQXvEOKymbbO6eAKeQVuZOZ74+OSSlOPNdii17Lvtzc5Wv82Wg18VZ2oRURERETkssnNymTLByMIOvY3zS27wfPM+IncGni7HKGGZTc1Tu8m/fWfiaMtQf1GUzXk4rciExEREbla6K+0InJZnErZz86pr1Avdx1tXQ+BB9hsJvZnNyI1+FZCnxhN6o5NpHw1gRCnjVQyn6ClczT5361k55zmZLW9jxYPPGP0Y4iIiIiIyFUkZva7VNqykGD3OGpb8gFIt/qx09aWwH6j8avTiA1TxxCQ/CdB7gnU9oin9qF4jo3+liS3djQY8gbeVWsa/BQiIiIiInK1SN0Zy4EZkYSYNhJmPgYWyLc5k5TVjNMte9Hq0RdI+mcJp7+bTH3XzXiZj9KaxeR9uYzt2S0x3fQ4jW590OjHEBEREbliTDabzWZ0iKtJafaNFbkaJa1azOkfJhPiGoPF+TQAuQUWdmW3wtztCRp0u6/IOdnpJ4j94BVqn1pJNctex3hKdj0O+HWixdNv4ObpdcWeQURERERErh723zdqnVpFgCXJMX6h3zfiF83GFD2D+patuDjlAJCV78XO3FB87n2J2uFdr9QjiIiIiIjIVWb7r3Pgr8+p777lrN83KrEztzXevZ6nznU3FzknLfUgiR+NpG7OGvxcDznG92c24kjtmwkdFKVO1CKXSXZ2Nnv27KFu3bpYLBaj40gFFxERQXR0NEuXLiUiIsLoOCKXTWl+dpam1kqFbmVMhW5yLcq3Wtk66x184n+kjvs2nEwFAKTlVWWXqS21BkbiX7fJRV3r7A4LziZ7h4Uq7LS1IbDfaKo1aHnZnkNERERERK4eKds2kjJ7vKODNEC+zcyerNJ1kD6SuIXkmVGEmDbgZT7+/9cp3GFBRERERETkQvKtVmI+HkXVA39Sy32HY/x4bk32uF130R2k861WNk+LomrSYmpZdmAynflPvSdya7DbNZyQQW/gU73WZXsOkWuRCt1EREpPhW4VhArd5FqSdfI4cR+NoE76aqq67XOMH8yuz8GqXWg55HVc3T0u6dp71/xB2sL3CHHdhPv/d4bLK3BjV1ZLuHEAjW97uEyeQUREREREri7xP8zAacUs6rlvxcUpF4DMfG925rXG7/5hBLXpfEnXzclIZ+uHrxB0fAXVLbsd44ezg9nv04Hmz7yBxcu3TJ5BRERERESuHicOJrHnk9eom7cOX9cUAGw2E/uzGnO03q20enzUJXdiS/x9Htbfp1HfEoOrUzYA2QWe7MwJpdJdzxHc4dYyew6Ra5kK3URESk+FbhWECt3kWnBw6xpS50wgxHkTnuaTAFhtLuzObE5u+4dp3vvJMrvXmVbcr/x/K+6DjvH9WY1IDbqJVoPHqRW3iIiIiMg1zpqbQ8xHo6h+6C8C3RMd40dzgtjr0Z4mz0zEw7dqmd1v67cf4L5hHnXdY3E2WQE4bfVlZ0Ebqj/6GtWbtimze4mIiIiISMW0Z8UvZPw0hRC3GCxOGQDkFrizM7slLrc8RYObe5fZvY7uiWf/Z5HUt23A2yUVgAKbE3uzmnKqyZ206POS/luKyL+gQjcRkdJToVsFoUI3uZrFLvgU13/mUM8jFrMpD4AMa2V25rem6sMjqdniust273yrlZjp4/DfvZha7tsdrbhP5lVntzmcuk+Nx7dm8GW7v4iIiIiIlD/H9yeSNG0M9fPX4eNyBDjzH3P2ZTXhRMMetOw34rL+x5wDG//m+Ny3CXHZhIdzGgB5Ba7szmpBQac+NLmr/2W7t4iIiIiIlD/5VitbZ/0Xn/ifqOO+DSdTAQCn8qqyy6ktdQZGUSW40WW7f25WJls+epWaqdHUtOxyjKfm1Gav1/U0GzIR98p+l+3+IlcrFbqJiJSeCt0qCBW6ydXG/ktR4JFoarif+0tRB5oNmXDFfylK/GMBeUs+IcSyBVenLAByCjxIzAnF846h1O3U44rmERERERGRK2vX0u/J/u1jQtxicDvrd4Kd2a2w3P409bvccUXzZJ5IJf6DEdTJ/Ad/twOO8eSsBqTUuJFWQ8ZhdnW7oplEREREROTKyTp5nLgPh1Pn9Gqquu13jB/MCuFQ9QhaDBqHq7vHFc1kZPMCkauNCt1EREpPhW4VhArd5Gpxps31GOrbNhZtc930Llo8+qLhba6PJe1g32eR1CtYh8+5GRv1pMVjwwzPKCIiIiIiZSPfamXLjDfwS/yVWu7bHZ0RTuYFsNs5nLpPjcM3sJ7hGbfOnEjlhF+o7R5/VveGauxyCiP4ySj8ajUwNKOIiIiIiJSd5C2rOTpnIiEum/B0PgWA1ebC7qwW5LV/mGa9njA4IRzcuobUORMIcd6Ep/kkcCbjnqzm5LR7iOb3PmVsQJEKQIVuIiKlp0K3CkKFblLRJSz5lvw/plPfsgVXp2wAsgs82ZkTSqW7niO4w60GJywqNyuTrVNHUSNlGTXddzrGU3Nqsa9Se5o+/ZZacYuIiIiIVFDpx1JI+OgVgrP+oYprsmP8QFZDDte8kVaDo8plt7Rd0T+RvehDQiwxuDllApBT4M6u7Fa43TaY+l3vNjagiIiIiIhcsth5U3Fb+w31PLbibLICZ7qlJea3ofqjr1C9WbjBCYvKOnmcuI9GUid9FVXd9jnGD2XVJ7naDbQc8sYV7zonUlGo0E1EpPRU6FZBqNBNKqJ8q5WYT8fiv3cJtSw7MJnO/Fg4kVuD3a7tCBn0Oj7Vaxmc8uLELZyGyz9zqOe+9X+tuPN92JnXGv+HRxDY8nqDE4qIiIiIyMXYv34ZJ+e/Q4jLJtyd0wHIK3Bld3ZLCm54jCY9+xqc8OKcSN7Nnk9GUS9/HZVdDgNnOlHvz2rM8ZDbaDngFXWiFhERERGpAHKzMtny4UgCj/5NDcsux/iRnDrs8+5A82cmYPHyNTDhxcm3Wtk6exLe234k2D3O0Yk6Lc+fXaa21Bo4Fv+6TQxOKVK+qNBNRKT0VOhWQajQTSqStNSDJH44grq5a/FzPeQY35/VmCO1byb0qbEV9j+4pMStI2X2GzRw3uhoxZ1vM7M7swU57R6k+X2DjA0oIiIiIiLF2vL1ZDw3LSDYPRZnUz4Ap61+7LS1oUbfUQQ0CjU24CWy5uYQ8/FoAg7+RZB7gmP8WG4gSe7taTjkDbyqVDcwoYiIiIiIFOfonnj2fzaGEDbgZT4KQL7Nmb1ZTUlvfhet+g4zOOGlS1q1mNM/TCbENQaL82kAcgss7MpuifPNj9PwlgcMTihSPqjQTUSk9FToVkGo0E0qgqR/lnD6u8mEuG52/OKSV+DGruxWmG4aSKNbHzQ4YdnJTj9B7AcjqZ22impuex3jh7Lrkex/Ay2fnqBW3CIiIiIiBsvJSGfrByMIOrGS6pY9jvGU7Loc8O1Ii2cm4ubpZWDCshX/85c4Lf+CepatuDjlAJCV78XOvNZUvvclaoVFGBtQRERERETYsfgbbH9+Rn1LjOP/t2fnV2JnbiiV7nmO4Pa3GJyw7JxK2c/Oqa9SL3ctvv/fGMFmM3EguxGpdW6h1ZNjKmxjBJGyoEI3EZHSU6FbBaFCNynPcrMy2T36Thp6bHC0ok63+rOTtgT1G03VkOYGJ7y8Yr58G6/YH6jjvs3RGSLd6s+u+k8S2n+4welERERERK5Nmz6NosG+GVQyHwfOdEZIympORuvetHzoOYPTXV6Hd2zm0JfjCDFtLPT8O7LCaDj+J8yubgYnFBERERG59qSlHuTY2/dR1yPWMXY8twZ7XNsRMuh1fKrXMjDd5ZVvtbL5kzFU2/cHtdy3O8aP59bkcIeRNOnZ18B0IsZRoZuISOmp0K2CUKGblGdxL3enmcdqAA5l1ye56g20HPLGNdfRbO+aP0hb+C4hrptxdz5Ndn4l9nWepBbcIiIiIiJXWPxPX1Bv3XDcnLKu6Y5mORnpbP1wJEHHVzg62m3NvoEWE38yOJmIiIiIyLXFmpvDvtduop7HVmw2E/uzG5EafCuhT4y+5jqaJSz5lvw/phPy/x3t0vL8Of3gHGq2uM7oaCJXnArd5FqWlJRE3bp1qVOnDklJSUbHKZHJZAJAJVDlx+UqdHMqy5AiUn5tGPuYo8htvcv91Ji4kbCX3rvmitwA6lx3My3eXET24JUcyamDxfk01aJHcXjHZqOjiYiIiIhcMw5uXUPgP1G4OWWRkl2XvGfX0mLiT9dckRuAm6cXYS9/QPWJm9lgugeAFpblrH/9CYOTiYiIiIhcW+JH30s9j63k25zZWP1Zar+5hraDo665IjeAhrc8QJO3fufIvT9wKq8q3i5Hcf7qSdKPpRgdTUTKqeDgYEwmU6EPd3d36tevz4ABA4iLizM6olRQM2fOJDIyslwX2l2Mc78/nJ2dqVy5MsHBwfTs2ZPXX3+dffv2XfT1Vq5cyZNPPknjxo3x8fHBzc2NwMBAevbsyfTp08nIyChVvmXLljFhwgTuueceAgMDHTkPHDhQ2ke9rNTRrYypo5uURxunjqHVoSk4m/LZkt2FlhN/NDpSuXFg499UXtiXSubjHMyuj9+rf2Lx8jU6loiIiIjIVS39WAqZb99KgCWJtLyqZD78NdWbhRsdq9yIHd6D5u4rybeZ2Ro8jND+I4yOJCIiIiJy1Vv/xpOE5X4LwAbn3rQd9bnBicqP+EWzqbdmGG5OmezKaEnwhKXXZPGfXLvU0e3iBAcHs3fvXho0aEC1atUAOHnyJImJieTm5uLm5sa8efO44447DE4qpVEeOrpFREQQHR3N0qVLiYiIKPaYxo0bA7B9+/Zi58sDe9e55s2b4+PjA0BmZiZHjhwhOTkZACcnJ5588kneeecdPDyKb1qUmZlJ//79mTt3LgAWi4X69evj7u5OcnIyhw4dAqBGjRosXryYFi1aXFS+ypUrc+rUqSLj+/fvJygoqHQPizq6icgl2v7rHJod/BRnUz67MlvSbPxCoyOVK0FtOpPUeiS5BRZqWnaxb1wv8q1Wo2OJiIiIiFy18q1Wjrx5PwGWJHIKPEjuEKkit3M0HvcdezOb4myy0mj3ByT+9Z3RkURERERErmqbZ0ykdc4CAGKzOqjI7RxNbn+UuOqPU2Bzor7nFmJH9TY6koiUY6+88gorVqxgxYoVxMbGsm/fPm6++WZycnLo378/p0+fNjqiXIW2b99erovczjZlyhTH98jGjRs5cOAABw4cICoqCovFwtSpU7ntttvIy8srcm5eXh633HILc+fOpXr16nzxxRccP36c2NhY1q1bx8GDB4mLi+Opp54iNTWVXbt2XXSuZs2a0a9fPz766CPWr19flo9cplToJnIVS9m2kRorRuPmlMnh7GCqDZ+nf2FTjOa9n2Sr32MU2Jxo6LGRLWMeNDqSiIiIiMhVK3ZUb+p7xFBgcyKu+uM0uf1RoyOVO2ZXN7yf/5qjOUG4O6fj/8cIUnfGGh1LREREROSqlPjXdzTa/QHOJit7M5vSeNz3Rkcql9oMGccm05kuTK3clrF+wmCDE4lIRREQEMCsWbNwc3Pj2LFj/P7770ZHEil3AgMDGTVqFCtWrMDT05Ply5fzxhtvFDlu7NixrFy5koCAAFavXk3fvn1xd3cvdEzTpk2ZOnUqS5cudXRXvBgrV65kxowZDB48mLZt2/7rZ7pcVOgmcpXKPJGK7cuB+Likkm71x/rwVLyqVDc6VrnV9vm32FxwCwCh5j9Y/87zxgYSEREREbkKrZ8wmFZuywDYZLqDNkPGGRuoHPOtGUxmrw84bfXF1zWF3Gn9yE4/YXQsEREREZGrSurOWPz/GIG7czpHc4Lwfv5rzK5uRscqt9pGfklcVnsAQrPnEfPl2wYnEpGKonr16jRo0ACAxMTEQnOLFi2ie/fu+Pv74+bmRt26dRkyZAj79+8vcp1WrVphMpnYsmVLofHDhw9jMpkwmUyMGjWqyHkRERGYTCaWLVtWZG7x4sXceeedBAQE4ObmRlBQEP379y+2E1ZSUhImk4ng4GAApk2bRnh4OF5eXo5tKS/k7Cxr167l9ttvx8/PD09PTzp06MD3339f7Hn9+vXDZDIxc+ZM9uzZQ79+/QgMDMRsNhMZGek4LiMjg/Hjx9OyZUs8PT3x9vbmuuuu48MPP8R6np3NoqOjufnmm/H29sbHx4euXbuetyhx2bJlmEymErcSPXetzpWZmcl///tf2rdvT+XKlfHw8KBBgwb06dOH6OjoQvewf921a1fH62xfCzv7WHFKuyZnP1tBQQGTJ0+mefPmWCwWAgICGDhwIKmpqSWuzb/RunVrxo8fD8DkyZPJzMx0zJ06dYr3338fgPfee6/EtbXr1KkTHTp0uCw5jaRCN5GrUL7VysE37qWGZTc5Be4cCH+NwJbXGx2r3Gs15iu2Z4ZhMtlodWoOW7/9wOhIIiIiIiJXjZgv3yY0ex4AcVntaRv5pcGJyr/a4V1JajmMvAI3At0T2TvuXvLP8wdJERERERG5eNnpJ8id1g9f1xROW33J7PUBvjWDjY5V7jWI/I59mU0wm/JokPg+u6J/MjqSiFQQNputyNjIkSPp2bMnixcvxt3dnRYtWnDkyBE+/vhjWrVqVWT7xBtuuAHAUfhkd/bX587l5OSwZs0a3NzcaN++faG5559/nu7du/PTT2d+ljVr1oz09HRmzpxJmzZtWLVqVYnPM3jwYJ588kkOHz5M48aNqVy58oUX4Sx///03nTt3Zvny5dSvXx8fHx9Wr17NPffcw6RJk0o8b8eOHbRp04ZvvvnGUUBoL/BKTU3l+uuvZ9SoUcTFxRESEkJQUBBr167lmWeeoUePHmRnZxe55jfffMONN97In3/+iaurKw0bNmTLli10796db7/9tlTPdTH27dtHWFgYw4YNY82aNVSrVo0mTZpw/PhxZs+ezZgxYwDw8fGhY8eOeHt7A9C8eXM6duzo+AgICLjgvS51Tez69OnD888/T25uLiEhIRw/fpzPP/+crl27kpOTUzYLco6BAwfi4uLCiRMnWL16tWN80aJFpKenU7VqVe69997Lcu+KQIVuIlehraPvJ8Rz85mtgKr2p8ld/Y2OVCE4m83UHfM9B7Ia4uKUQ72tb7N3zR9GxxIRERERqfB2/72IBonvYzblsS+zCQ0ivzM6UoXR/P6nifF5BJvNRCOP9cSMfcToSCIiIiIiFV6+1crecfcS6J5IXoEbSS2HUTu8q9GxKgRXdw+8npvDsdxAPJzT8F38MseSdhgdS0TKuZSUFHbu3AlASEgIAD///DMTJ07EbDYze/Zs9u/fz/r16zl06BD33HMPJ06c4L777iMrK8txnS5dugAlF7oFBgaydu3aQoVL9q/btWuHxWJxjH/yySdMnjyZunXrsnTpUg4fPszGjRs5fvw448ePJy0tjQceeKDYIqgDBw4wa9YsfvjhB/bt28e6des4ePBgqdYkKiqKXr16kZKSwrp160hOTnZ06xo+fDgxMTHFnvf2229zww03cPDgQTZs2EB8fDzDhw8HzhTfbd26lWbNmpGQkEBMTAzbtm1j3bp1BAQE8PvvvzuKyOySk5N54oknKCgoYMSIEY48KSkpPPfcc8V2yPs38vPz6dWrF/Hx8YSFhbFt2zYSEhLYsGEDx44dY9OmTTzwwAPAme5mK1asoHXr1gBMmTKFFStWOD5uu+22C97vUtbEbtWqVSxbtow1a9aQkJBAbGwscXFxBAUFERcXx4wZM8puYc7i5eVFy5YtAVi3bl2hPAAdO3bEbDZflntXBCp0E7nKrH9rKKGufwKw2XYbbYZOMDhRxeLm6YXbEzM4kVsdT/NJPL9/jhMHk4yOJSIiIiJSYR1L2kHlX/+Dh3Max3ID8XpuDq7uHkbHqlDCXnqXzdabAQh1WsKG9142OJGIiIiISMUWM/YRGnmsx2YzEePzCM3vf9roSBWKb2A90nu+S4a1Mn6uB8n8uA85GelGxxIxjM1moyAzs0J+FNdlrawdOXKEPn36kJOTg6+vL926dQNg4sSJADz99NM88sj//mGft7c3s2fPxt/fn6SkJL7++mvHnL2j2/LlywvdIzo6Gj8/PwYOHEhOTg7//PNPoTn4X5EcQG5uLpGRkTg7O7NgwYJC2286Ozvz6quv0rt3bw4cOMC8efOKPFN+fj5RUVHceeedjjF3d/dSrYufnx8zZszA09MTOLP15tChQ+nVqxdWq7XErm5Vq1Zlzpw5+Pn5OcYsFguJiYksXLgQgFmzZlG/fn3HfFhYGFOmTAHgww8/JD39fz+zp06dyunTpwkPD2fChAmOAioXFxcmTZpEkyZNSvVcF7Jw4UI2bNhAtWrV+O2334pcPzQ0lMGDB5fJvS51Tezy8vKYMmUK7dq1c4w1bNiQl18+87e5X3/9tUxyFqdWrVrAme8fu+TkZADq1q172e5bEVy7JX4iV6EtX0+m1elvwAniM9vR5q05RkeqkKqGNCfx5olYlg7F3+0Ae997CK/xyzC7uhkdTURERESkQsnJSCfz4z7Ucj9IhrUy6T3fJTiwntGxKqSWY78h4ZVuNPTYSIvjXxC7IITmvZ80OpaIiIiISIWz4b2Xae20BIDN1psJe+ldgxNVTMEdbmXL3hdpEj+eWu47iB/biyZv/W50LBFD2LKy2NGmrdExLkmjjRsweZTtP0h84403mD59OgAnT54kMTGR3NxcXFxcmDZtGl5eXpw+fdqxJePQoUOLXMPDw4MnnniCCRMmsGTJEgYMGABAtWrVaNy4Mdu3byc+Pp4mTZpw7Ngxtm3bxl133UVERARRUVFER0c7itfsRXH2IjmA1atXk5KSQnh4uKNb2LnuvPNOFixYQHR0NH369Cky37dv30tfJM5sT3l2hzm7IUOGsHDhQhYvXlzseb1793YUx53t999/x2az0alTp2KfqXfv3gQFBXHgwAFWrlxJ9+7dARz3Kam4bMiQIQwaNOiin+tCfvjhBwAGDBhAlSpVyuy6xbnUNbHz9fWlV69eRc4LDw8HYPfu3ZcnODhe47ML8OyfF/f6X0vU0U3kKpG0ajH1t72Di1Mu+7MaUW/MQqMjVWgNbryHHfWeId9mpo7HNraPutvoSCIiIiIiFc7usb2o5b6DvAJXdjV7keAOtxodqcJyNpupPWohB7NCcHXKJnjTBPavX2Z0LBERERGRCiV2wae0OP4FTqYCEjLb0HLsN0ZHqtBaPvQcMV4PAdDEYy0bRz9scCIRKQ8SExNZuXIlK1euJDExkerVq/Poo4+ydu1aevfuDcDOnTspKCjAzc2NevWK/0eRzZo1AyAhIaHQuL1gzd6pbfny5dhsNrp06UL79u1xdXV1zFmtVlatWoXZbKZDhw6Oa2zduhWApKQkOnXqVOzHW2+9Bfyvi9bZ/P398ff3v+Q1AkrslGYfP3z4MGlpaRd9nn2dmjZtWuy8k5MTjRs3LnTs2Z9fKE9ZiY+PB6B9+/Zlet3iXOqa2J3dAe5s1apVA+D06dNlEbNY9mt7e3s7xry8vADIyMi4bPetCNTRTeQqcCJ5N14/v4Cn6ymO59bEY/As3Dy9jI5V4YX2H8H6N3YTlvstzd1XsWFsP9qOmWl0LBERERGRCmHj6Idp47EWgBivhwh76DmDE1V8Fi9fzI/P4OTMe6nscpisBUM5FfQLPtVrGR1NRERERKTc279+GcGbJuBqzuZgVgi1Ry3E2az/VPpvhQ17n82vHCDU9U9CTb+y4f0RtH12otGxRK4ok7s7jTZuMDrGJTGVcrvNizFjxgz69et33mPsRTxVq1bFZDIVe0xAQABAkS0lu3Tpwqeffkp0dDSDBg0qtDWpu7s74eHh/PPPP+Tm5rJx40YyMjJo3759oS5Yp06dAiA1NZXU1NTzZs3KyioyVhYdtezFUucbT09PL1TodL5729e0pOtC8Wt69mtxvnPKir14r3LlymV63eJc6prYlbTWTk5neopdzq1/9+3bBxTOHhgYCMCePXsu230rAnV0E6ngcrMySZ/8MFVck8nM9+Hkbf+lSnAjo2NdNcJe+ZQt2Wf+VUBowY9s+jTK4EQiIiIiIuXfhvdHEGr6FYDNuTcRNux9gxNdPao1aElqxOtk51eiqts+jk96EGtujtGxRERERETKtbTUg1gWDKWS+Tgn8wIwPz4Di5ev0bGuGi2i5pKY2RonUwEtjs5k23efGx1J5IoymUw4eXhUyI+Siswut0qVKgFnCs1KKhY6fPgw8L8uVnZdunQB/tfRLTo6Gh8fH1q1auWYz8rKYu3atYWK4Iq7/yOPPILNZjvvx7Jly8rgiYsqqcDu7PFzn/187M905MiREo8pbk3Pfi2KU9L17O+dkl6/krqO2e998uTJEnOWlUtdE6OdOnXK0XWwXbt2jnF7V8JVq1ZhtVoNyVYeqNBNpIJLjLyH2h7xWG0uJDZ8jnqdbzc60lWn2fjv2J3ZAmdTPk0OTGXHYrUyFxEREREpSfwPM2h+dCZOpgISM1rTImqu0ZGuOg263Ud8ncHk25yp6xFL/KjeRkcSERERESm3rLk5HHv7Pqq67SM7vxKpEa9TrUFLo2NdVZzNZoJeWcih7Hq4OmVRa8PrJG9ZbXQsESnHQkJCcHJyIicnh927dxd7TFxcHAANGzYsNB4YGEi9evU4dOgQ69evZ8uWLXTu3NnRZcte1LZs2TKWL18O/G+7Uzv7VpaxsbFl91ClZN/Cs6TxgICAIt3czse+Ttu2bSt2vqCggO3btxc69uzP7XMXm9Pe7aykArmdO3cWO27fkvaff/4pdr44l1qQealrYrTPPvsMq9WKn59foS1ee/ToQaVKlThy5Ajz5883MKGxVOgmUoFtiOxLM/cz/wOw2XIfrfq8ZHCiq5Oz2Yz/sLkcya6DxSmD6stHcXjHZqNjiYiIiIiUO8lbVhO0bjxuTlkcyq5H0KvaCuhyaf34a2w23wNAC/e/WT/+cYMTiYiIiIiUT/GjelPXI5Z8mzPxdQbToNt9Rke6KrlX9sPU9zNO5VXDy3wU85xBpB9LMTqWiJRTlSpVcnSnmjJlSpH5rKwspk+fDsCtt95aZN5euDZ+/HgKCgoKdWzr0KEDZrOZv/76ixUrVuDs7EynTp0Knd+5c2f8/f2JiYm5bB3bLuSzzz4jJ6dol/6PPvoIgFtuuaVU17vlllswmUysWLGCTZs2FZlfuHAhBw4cwNPTk44dOxY6D2Dq1KnFXvfjjz8udrxevXoA7N69m2PHjhWZt79+57r77rsB+Pzzzzl+/HjJD3QW9//fYre4bWTP51LXxEibNm1i9OjRALzwwguOZ4cz270OHToUgOeff56kpKTzXmvlypWsWrXqsmU1igrdRCqojR+NorXtJwBiciIIG1n8/8BI2fCuWpPcBz8i3VoFH5cjFMwYQNbJi/sfXhERERGRa0H6sRTMcwbhZT7KqbxqmPp+hntlP6NjXdXajvqM2Kwzf6htnbeQzdPHG5xIRERERKR8WT/+cVq4/w3AZvM9tH78NYMTXd2qN23DoU5jySnwIMCSxJE37yP/Gt5aTUTOb/jw4cCZwq45c+Y4xtPT0+nbty+pqakEBwfz4IMPFjnXXtj2448/FvoazhTRtWnThmXLlpGWlkZoaGiRzmgWi4WoqCgA7rvvPr777rsiW3DGxsYyfPhwVq5cWQZPW9SxY8cYOHCgY4tPm83GRx99xMKFC3F2dubFF18s1fVCQkLo1asXAH379i3UKW/jxo08++yzADzzzDOFtukcNGgQnp6erFmzhlGjRjm2xMzLy2PYsGGOznrn8vPzo127duTk5PDiiy+Sl5cHQH5+PhMnTmTx4sXFnnf33XcTFhbGkSNH6NGjBzt27Cg0HxMTU6S4zl5UZ9+K9mJd6poYITk5mXHjxtGpUycyMjKIiIhgxIgRRY6LjIzk+uuv5/Dhw1x//fXMmjWL7OzsQsckJCTw9NNPExERcd5tWysqFbqJVEDxP39Js5TpOJkK2JXZiubjFhgd6ZoQFNqJ/W1fI7fAnRqWXRx4o5d+QRMRERERAfKtVo68eR8BliRyCjw41Gks1Zu2MTrWNaHxuIXsyWyOsymfRns/JvH3eUZHEhEREREpFzZPH0/rvIUAxGZ1ou2ozwxOdG1ofNvDxAUOIt/mTH2PLcS91svoSCJSTvXs2ZMRI0aQl5fHI488Qu3atQkPD6dGjRrMnz8fX19f5s6dW6ijlZ29o5vNZsPLy4s2bQr/HapLly6OwrWzi+DONnjwYEaMGMHRo0fp1asX/v7+tGvXjrZt21KlShVatGjBW2+9RXp6ehk/+RmjR49m3rx51KhRg/DwcIKCgnj66aex2WxMmDCB0NDQUl/z448/pkWLFsTGxtKwYUNCQ0Np1qwZbdu25dChQ9x8881ERkYWOicoKIhPPvkEk8nE+PHjqVGjBu3ataN69epMmjSJcePGlXi/N998E7PZzJdffkm1atUIDw8nICCAUaNGMWnSpGLPcXZ2ZsGCBTRq1Ig1a9bQuHFjGjVqRFhYGP7+/oSGhvLtt98WOueBBx5w3K9Ro0Z06dKFiIgIfvvtt8uyJpfb0KFD6dSpE506daJt27YEBQURFBTE6NGjycnJYciQIfzyyy+Yi9kpw9XVlSVLltC7d29SUlLo27cvfn5+tGjRgnbt2hEUFESjRo346KOPqF69OiEhIaXK5e/v7/iwa9mypWPsrrvuKpM1+DdU6CZSwaTErSNwdRRuTpmkZNelxsgF2groCmp6zwC2+vejwOZEA49NbB3zgNGRREREREQMF/daL+p7bCHf5kxc4CAa3/aw0ZGuGWZXN/xe/IbUnNq4O5+m6rJXOZK4xehYIiIiIiKGSvxjAY32foyzKZ89mc1pPG6h0ZGuKW2eGsNmpzsBaGmJZv0bTxmcSETKqwkTJvDTTz/RrVs3Tp8+zZYtW/D392fQoEHExMQQHh5e7Hn16tUjKCgIgI4dO+Ls7Fxo/uziNntRXEn3X7lyJQ8//DCenp7ExMSQlJREUFAQAwYMYNGiRdx0001l8KRFde7cmb///ptOnTqxc+dOTpw4Qfv27Vm4cCHDhg27pGtWrVqV1atXExUVRZMmTUhISGDv3r2Eh4czZcoUfvnlFywWS5HzHnnkEf766y+6du1KdnY227dvp0WLFvz666+OIrPiREREsHjxYjp16kRubi4JCQmObno9e/Ys8bzatWuzYcMGJkyYQJs2bTh48CDx8fH4+fnx2GOPFSmu69y5M3PmzKFdu3YkJyezfPlyoqOjSUm58BbZl7oml1NsbKxjW9GdO3diNpvp0aMH48ePZ/fu3Xz44YfFFnjaVapUifnz57N8+XIGDhxIrVq1SEpKIiYmBpvNxu23385nn31GQkICzZs3v+hc6enpHDt2zPFhd+LECcfYqVOn/tWzlwWT7dz+i/KvpKWl4ePjw6lTp4q0vxT5tzJPpJL2ZjeqW/aQlufP6QfnULPFdUbHuiZtHP0wbZwWYbOZ2OD1GGH/mWx0JBERERERQ6yfMIiwnK8B2GC6m7ZjvjA40bVp//pl+H7fj0rmExzMCsHvtT+wePkaHUtERERE5IpL3RmLy4zeVHZJITWnNq7P/YJP9VpGx7omxQ2/jWbuq7DaXIgNHkZo/+FGRxK5JNnZ2ezZs4e6dete8YIYufpEREQQHR3N0qVLiYiIMDqOyGVTmp+dpam1Ukc3kQoi32rl4ITeVLfsIafAneT2o1XkZqA2UXPYnhmOyWSjVdoctn4zxehIIiIiIiJX3OYZbxKaPR+AuMzrVeRmoFphESSFjiSvwI2a7jvZG9WbfKvV6FgiIiIiIldUdvoJ8qY9RmWXFE5bfcnuPUVFbgZqELmAvZlNMZvyaLR7CruWfm90JBEREangVOgmUkHEjrqXEI8YCmxOxAYMpMkdjxkd6ZpXd8x3HMhqiItTLvVi/0vSP0uMjiQiIiIicsXsWvo9jXZPwWzKY29mUxqM1VZARmt+71Ns8e2DzWaikecGtkRqC1kRERERuXbkW63sjepNTfed5BW4kRQ6klphEUbHuqa5unvg/fzXHM0Jwt05Hb/fR3B0T7zRsURERKQCU6GbSAWw/s0htHJbCsBmbqft068bnEgA3Dy9sAz6kuO5NfA0n6TSDy9wInm30bFERERERC67o3vi8ft9BO7O6RzNCcL7+a9xdfcwOpYAbV94h835twAQ6ryEDZNeNDiRiIiIiMiVsSXyYRp5bsBmM7HFtw/N733K6EgC+NYMJuPuyWRYK+PreojsqX3JyUg3OpaIiIhUUCp0EynnYma/S2jmXAC2ZV5Hm7GzDU4kZ/Ov24QTt75NZr43/m4HSJ/8MLlZmUbHEhERERG5bHIy0sme2hdf10NkWCuTcfdkfGsGGx1LztIycg47MsMwmWy0PDmb2HlTjY4kIiIiInJZbXj3JUKdz+y6sjn/Ftq+8I7BieRsda67md0thpFX4EaQewJ7xt5jdCQRERGpoFToJlKO7VnxCw12vIvZlMe+rMaEjP3e6EhSjPpd7iCh/lCsNhdqe8STOKaX0ZFERERERC6bPWPvIcg9gbwCN3a3GEad6242OpKcw9lsps6o+SRnNcDFKYfgmInsX7/M6FgiIiIiIpdF7LyptDwxC5PJxo7MMFpGzjE6khSjxQPPEOPzMDabicYe69g46iGjI4mIGGLZsmXYbDYiIiKMjiJSIanQTaScOr4/EZ9fXsLD+RTHcgOp9MxsbQVUjoX2e5nNlvsAaOaxmg1jHzM4kYiIiIhI2ds46iEae6zDZjMR4/MwLR54xuhIUgKLly+uT8zkZF51KplPYFkwlFMp+42OJSIiIiJSpvavX0ZwzERcnHJIzmpAnVHzcTabjY4lJQh76T02W8/8Y6lQp9/YMHmYwYlERESkolGhm0g5lJuVScYHj+LnepDMfB/SevwXv1oNjI4lFxA28mO2ZHcBILTgJzZ8PNrgRCIiIiIiZWfD5GGEOv0GQIz1JsJees/YQHJBVUOak9r1DbLyK1HVbR8nJj2ANTfH6FgiIiIiImXiVMp+LAuGUsl8gpN51XF9YiYWL1+jY8kFtBz7DQkZrXEyFdDi2CziFk4zOpKIiIhUICp0EymHdo65m1ru27HaXEhs9AJ1O/UwOpJcpGbjF7IrsyXOpnyaH5pG/KLZRkcSEREREfnX4hZOo8WxWTiZCkjIaE2Lsd8aHUkuUoObe7Mj+BnybWaCPeLYPuoeoyOJiIiIiPxr1twcTkx6gKpu+8jKr0Rq1zeoGtLc6FhyEZzNZmq9upCD2fVxdcqi9sY3OLDxb6NjiYiISAWhQjeRcmbD6Edp6rEGgM2eD9Lq0RcMTiSl4Ww2U234PA5nB+PmlEnNVZGkbNtodCwRERERkUt2YPMKam98A1enLA5m16fWqwu1FVAFEzpwJJtcewHQ3H0l68cNMDiRiIiIiMi/s31UL4I94si3mdkR/AwNbu5tdCQpBffKfjj3/5yTeQF4mY/jOu9p0lIPGh1LREREKgAVuomUIxs+eIXWpkUAbM65kbCXPzA4kVwKryrVsT48lXSrPz4uqdi+HEjmiVSjY4mIiIiIlFpa6kFcvx2Cl/k4J/MCcO7/Oe6V/YyOJZcg7NVpbM3uDEBr6/dsmhZlcCIRERERkUuzftwAmruvAGCTay9CB440OJFcioBGoRzpMo7s/EpUc9vL0bfvx5qbY3QsERERKedU6CZSTsT/9AXNj3yOk6mAnZmhtBg3z+hI8i8EtryeA+GvkVPgTg3Lbg6+cS/5VqvRsURERERELpo1N4ejb99PNbe9ZBd4cqTLOAIahRodS/6FJlEL2JPZHGdTPk32fULCEm1BKyIiIiIVy6ZpUbS2fg/A1uzOhL06zdhA8q80vOUB4ms/Rb7NmXoeW4kffa/RkURERKScU6GbSDlwcOsaAv+Jws0pi5TsetQcOV9bAV0FmtzVn7iq/SmwORHiuZmto+83OpKIiIiIyEWLH30v9Ty2km9zJj5oEA1vecDoSPIvmV3dqDJsHkdy6mBxPk216FEc3rHZ6FgiIiIiIhclYcm3NNn3Cc6mfPZkNqdJ1AKjI0kZaP3EaDab7waghWU5619/wthAIiIiUq6p0E3EYOnHUnD+6km8XY5yKq8q9PkUD9+qRseSMtJm6AQ223oAEOr6J+vfesbgRCIiIiIiF7b+9SdoYVkOwGbz3bR+crTBiaSseFetSe59H5Ju9aOyy2HyZwwg6+Rxo2OJiIiIiJzX4R2bqRY9CovzaY7k1KHKsHmYXd2MjiVlpO2oz4nN6ghA69yFbP5sgsGJREREpLxSoZuIgfKtVo5MvI8ASxI5BR4c7BBJ9WbhRseSMtYm6iviM68DoNXpb9ky5z1jA4mIiIiInMfmGRNpnbsQgNisjrQd9bnBiaSsBbXpzN7WI8ktsFDTsov9r/cm32o1OpaIiIiISLGy00+QP2MAlV0Ok271I/e+D/GuWtPoWFLGGo/7jqTMZjibrDRK+pDEv74zOpKIiIiUQyp0EzFQ7Kje1PfcQr7NmdgaT9Dk9keNjiSXSf2x37M/sxEuTrnUj59E0qrFRkcSERERESki8a/vaLT7A5xNVpIym9F4nP7DwtWqee8n2VqlLwU2Jxp6bmTLmAeNjiQiIiIiUkS+1cq+cb2oadlFboGFva1HEtSms9Gx5DIwu7rh++K3pObUwt05Hf8/RpC6M9boWCIiIlLOqNBNxCDrJwymldsyADabetJ2cJSxgeSycnX3wHPoVxzPrYmn8ym8fn6BE8m7jY4lIiIiIuKQujMW/z9G4O6cTmpOLXyen6OtgK5ybZ97m5iC7gCEmv9g/TvPGxtIREREROQcW8Y8SEOPjRTYnNjq9xjNez9pdCS5jHyq1yKr1xROW33xdU0hd1o/stNPGB1LREREyhEVuokYIObLtwnNngdAXFZ72kZ+aXAiuRL8ajXg5G3/JTPfhyquyaS//xC5WZlGxxIRERERITv9BLnT+uHrmsJpqy9ZvabgWzPY6FhyBbQcM4vtmWGYTDZanZrD1m8/MDqSiIiIiAgA6995nlDzHwBsLriFts+/ZXAiuRJqh3clqeUw8grcCHRPZO+4e8m3Wo2OJSJyzUpKSsJkMhEcHGx0lPMymUyYTCajY8gVoEI3kStsV/RPNEh8H7Mpj32ZTWgQqa2AriX1Ot9OYsPnsNpcqO2+nZ1j7jE6koiIiIhc4/KtVvaOu5dA90TyCtxIajmM2uFdjY4lV4iz2UzdMd9zIKshLk451Nv6NnvX/GF0LBERERG5xm399gNanZqDyWRje2YYrcZ8ZXQkuYKa3/80Wyo/is1mopHHerZEPmJ0JBG5CMHBwY5iI/uHu7s79evXZ8CAAcTFxRkdUSqomTNnEhkZSVJSktFR/pVzvz+cnZ2pXLkywcHB9OzZk9dff519+/Zd9PVWrlzJk08+SePGjfHx8cHNzY3AwEB69uzJ9OnTycjIuOhrpaenM3v2bB599FEaNmyIu7s7Hh4eNGvWjGHDhnHo0KFLeeTLwmSz2WxGh7iapKWl4ePjw6lTp/D29jY6jpQzx5J2YPr0bvxcD3I0JwjnZ37CN7Ce0bHEAOvfHEJY1plfzDdwJ20jZxmcSERERESuVRtHPUAb59+w2Uxs9BlA2xcnGR1JDHB0TzzO0+7B1/XQmd9Xhy5SVz8RERERMcTeNX/g/9NAPM0nOZDVkKqj/8LN08voWGKATa/dR2vzkjO/r/o+Ttvn/2t0JLlGZWdns2fPHurWrYvFYjE6TrkVHBzM3r17adCgAdWqVQPg5MmTJCYmkpubi5ubG/PmzeOOO+4wOKmURlJSEnXr1qVOnTqGFZpFREQQHR3N0qVLiYiIKPaYxo0bA7B9+/YrmKx07B3nmjdvjo+PDwCZmZkcOXKE5ORkAJycnHjyySd555138PDwKPY6mZmZ9O/fn7lz5wJgsVioX78+7u7uJCcnO4rSatSoweLFi2nRosUFs/Xp04fZs2cD4OXlRf369cnIyGD37t3k5+dTpUoVfv31V8LDwy/6eUvzs7M0tVbq6CZyheRkpJP5cR/8XA+SYa3M6bveVZHbNSxs+EfE5JzpktHa9jMbPxplcCIRERERuRZteO9lQp2WALA5v5uK3K5h/nWbcLzbRLLyvfB3O0Daew9hzc0xOpaIiIiIXGNOHEzC8/vn8DSf5ERuDSyDvlSR2zWsZeTXJGS2xWSy0eL4LGIXfGp0JBG5CK+88gorVqxgxYoVxMbGsm/fPm6++WZycnLo378/p0+fNjqiXIW2b99erovczjZlyhTH98jGjRs5cOAABw4cICoqCovFwtSpU7ntttvIy8srcm5eXh633HILc+fOpXr16nzxxRccP36c2NhY1q1bx8GDB4mLi+Opp54iNTWVXbt2XXSuu+++mz/++IPjx4+zadMmEhIS2LFjB+3atePYsWP07t2brKysslyKS6JCN5ErJDHqAWq57yCvwJXdzf9DcPtbjI4kBms+bj47M1vhZCqgWcp09qz4xehIIiIiInINSfxjAS2Of4GTqYCEzLa0jPza6EhisPpd72ZHvaFYbS7U8dhG3Oj7jY4kIiIiIteY4+/1wd/tAFn5XhzvNhH/uk2MjiQGcjabqT1qAQezQnB1yiZ40wSOJe0wOpaIlFJAQACzZs3Czc2NY8eO8fvvvxsdSaTcCQwMZNSoUaxYsQJPT0+WL1/OG2+8UeS4sWPHsnLlSgICAli9ejV9+/bF3d290DFNmzZl6tSpLF261NFd8UImT57Md999x0033YTZbHaM169fn/nz5+Pq6sr+/fv57bff/t2DlgEVuolcAbuWfk8Tyz8AxHjeT4sHhxqcSMoDZ7OZmiMXcCSnDm5OmVh/eMvoSCIiIiJyDTEvmYSrUzaHsutRe9QCnM/6A4Zcu0L7D2ezy90ANHVdxd41fxgbSERERESuGZtnvkV9jy0U2JzYUe8Z6ne92+hIUg5YvHwxPz6DU3lVqWQ+TvLU/xgdSUQuQfXq1WnQoAEAiYmJheYWLVpE9+7d8ff3x83Njbp16zJkyBD2799f5DqtWrXCZDKxZcuWQuOHDx/GZDJhMpkYNaroTloRERGYTCaWLVtWZG7x4sXceeedBAQE4ObmRlBQEP379y+2E1ZSUhImk4ng4GAApk2bRnh4OF5eXo5tKS/k7Cxr167l9ttvx8/PD09PTzp06MD3339f7Hn9+vXDZDIxc+ZM9uzZQ79+/QgMDMRsNhMZGek4LiMjg/Hjx9OyZUs8PT3x9vbmuuuu48MPP8RqtZaYKzo6mptvvhlvb298fHzo2rXreYsSly1bhslkKnEr0XPX6lyZmZn897//pX379lSuXBkPDw8aNGhAnz59iI6OLnQP+9ddu3Z1vM72tbCzjxWntGty9rMVFBQwefJkmjdvjsViISAggIEDB5Kamlri2vwbrVu3Zvz48cCZ4rPMzEzH3KlTp3j//fcBeO+990pcW7tOnTrRoUOHi7qvn59fiXO1atVybA2bkJBwUde7nFToJnIl/PIOzqZ8krMaEDb8Q6PTSDni4VuVlBYDAAjx2My27z43OJGIiIiIXAu2fD2Zuh6x2GwmjrcbgsXL1+hIUo60HjGVlOy6uDjlkrOg6L8cFREREREpa/lWK9W3n+kyvTMzlND+IwxOJOVJtQYtSazcA4Amrms4sHmFwYlE5FLYbLYiYyNHjqRnz54sXrwYd3d3WrRowZEjR/j4449p1aoV69evL3T8DTfcAOAofLI7++tz53JyclizZg1ubm60b9++0Nzzzz9P9+7d+emnnwBo1qwZ6enpzJw5kzZt2rBq1aoSn2fw4ME8+eSTHD58mMaNG1O5cuULL8JZ/v77bzp37szy5cupX78+Pj4+rF69mnvuuYdJkyaVeN6OHTto06YN33zzjaOA0F7glZqayvXXX8+oUaOIi4sjJCSEoKAg1q5dyzPPPEOPHj3Izs4ucs1vvvmGG2+8kT///BNXV1caNmzIli1b6N69O99++22pnuti7Nu3j7CwMIYNG8aaNWuoVq0aTZo04fjx48yePZsxY8YA4OPjQ8eOHfH29gagefPmdOzY0fEREBBwwXtd6prY9enTh+eff57c3FxCQkI4fvw4n3/+OV27diUnJ6dsFuQcAwcOxMXFhRMnTrB69WrH+KJFi0hPT6dq1arce++9l+XeJbGv0bnd44ygQjeRyyx2/ifU89gKwPE2/YwNI+VSy4efZ09mc0wmG5VWf2p0HBERERG5Bvhv/hKAXVktadbrCYPTSHnjbDaT0ughAELcN7H91zkGJxIRERGRq93mD16humU3VpsLrr1HGh1HyqHWL0ziSE4dXJxyOD0n0ug4IoXYbDbycvIr5EdxxWeXQ0pKCjt37gQgJCQEgJ9//pmJEydiNpuZPXs2+/fvZ/369Rw6dIh77rmHEydOcN9995GVleW4TpcuXYCSC90CAwNZu3ZtocIl+9ft2rXDYrE4xj/55BMmT55M3bp1Wbp0KYcPH2bjxo0cP36c8ePHk5aWxgMPPFBsEdSBAweYNWsWP/zwA/v27WPdunUcPHiwVGsSFRVFr169SElJYd26dSQnJzu6dQ0fPpyYmJhiz3v77be54YYbOHjwIBs2bCA+Pp7hw4cDZ4rvtm7dSrNmzUhISCAmJoZt27axbt06AgIC+P333x1FZHbJyck88cQTFBQUMGLECEeelJQUnnvuuWI75P0b+fn59OrVi/j4eMLCwti2bRsJCQls2LCBY8eOsWnTJh544AHgTHezFStW0Lp1awCmTJnCihUrHB+33XbbBe93KWtit2rVKpYtW8aaNWtISEggNjaWuLg4goKCiIuLY8aMGWW3MGfx8vKiZcuWAKxbt65QHoCOHTsW2l70couJiXF0cuvYseMVu29JtC+JyGXms+4zTO42dme2oMUDzxgdR8qp3JuGUrBqMLU94tn82QRCB+oXeRERERG5PDZ8+Cpt3XeSbzNjun2Y0XGknArtP5y9L39PHY9tWJZ+ALc9bHQkEREREblK5WZlUvfIT+AK27PDad7+FqMjSTnkbDaTXO9eqiW/QwP3jST+sYAGN/c2OpYIANbcAj59LvrCB5ZDT07ugoub82W9x5EjR+jTpw85OTn4+vrSrVs3ACZOnAjA008/zSOPPOI43tvbm9mzZ1OnTh2SkpL4+uuvGTDgzA5Z9o5uy5cvL3SP6Oho/Pz8GDhwIFFRUfzzzz+OLTXtRXD2IjmA3NxcIiMjcXZ2ZsGCBY5CKgBnZ2deffVVNm3axIIFC5g3bx59+vQpdL/8/HyioqK48847HWOl7XTl5+fHjBkzHMV3JpOJoUOHsmzZMhYuXMikSZP44osvipxXtWpV5syZg6enp2PMYrGQmJjIwoULAZg1axb169d3zIeFhTFlyhTuv/9+PvzwQ1577TW8vLwAmDp1KqdPnyY8PJwJEyY4znFxcWHSpEn8+eefRbaK/TcWLlzIhg0bqFatGr/99htVqlQpNB8aGkpoaGiZ3OtS18QuLy+PKVOm0K5dO8dYw4YNefnll3n22Wf59ddfGTRoUJlkPVetWrXYsGEDR44ccYwlJycDULdu3ctyz+Lk5+czdOhQAG688Ubatm17xe5dEnV0E7mMNn0aRS33HeTbnMnv9pzRcaQca3TrgyRmtQGgRuI35J9nf3QRERERkUtlzc2hzoHvAdiR1Zb6Xe4wNpCUa5mdB1FgcyLYI46YWe8YHUdERERErlJb3nkWP9eD5BR4UO3xN42OI+VY6ydGsz+rEc6mfJyXvGt0HBEpwRtvvEGnTp3o1KkTzZs3p1atWvzxxx+4uLgwbdo0vLy8OH36tGNLRnsRzdk8PDx44okzuxAsWbLEMV6tWjUaN25Mamoq8fHxABw7doxt27Zxww03FClug/8VxdmL5ABWr15NSkoKbdq0KVTkdjZ7Edu53ePs+vbte1HrUZKBAwcW6jBnN2TIEAAWL15c7Hm9e/cuVORm9/vvv2Oz2ejUqVOxz9S7d2+CgoLIyMhg5cqVjnH7fQYPHlzs/ex5ysoPP/wAwIABA4oUuZW1S10TO19fX3r16lVkPDw8HIDdu3eXfej/Z3+N09PTHWP2z4t7/S+XV155hb///hsvLy8+/bR87E6njm4il0m+1UrgnvngBomZbWjc7T6jI0k5537fa+T9dD8BliQ2vP8ybV8see91EREREZFLsfmd5wlzO0BugTt+j71udBwp55rc8RiJf8+ggccmqsV+Rb71OZyv4LYIIiIiInL1yzyRSqPsv8AM8dbrCW3Q0uhIUs6dCh9I0Nbh1PPYytZvP9BuSlIumF2deHJylwsfWA6ZXcu+N1JiYiKJiYkAuLq6Ur16dW644QZeeuklR6eunTt3UlBQgJubG/Xq1Sv2Os2aNQNwbJlod8MNN7B9+3aio6Np0qQJy5cvx2az0aVLF9q3b4+rq6ujOM1qtbJq1SrMZjMdOnRwXGPr1q0AJCUl0alTp2Lvf/LkSeB/XbTO5u/vj7+//0WuSPGaNGly3vHDhw+TlpaGt7f3RZ1nX6emTZsWO+/k5ETjxo05cOAACQkJdO/evdB5F8pTVuwFiu3bty/T6xbnUtfE7uwOcGerVq0aAKdPny7DtIXZr33262/vOJeRkXHZ7nu2qVOn8tZbb2E2m/n6669LXI8rTX+dFLlMNr37ImFue8krcKPSQ8Xv6SxyttrhXYmd347m7iuoe/QXcrPG4+ruYXQsEREREblKZJ08ToPTv4MLxOddR6tm4UZHkgrA5e6RWBc/Qg33XWz46DXaPjvR6EgiIiIichXZ8c4ztDYfI8NambrPTjY6jlQAze99il1rZlPfcwu+G78AFbpJOWAymS779p8VyYwZM+jXr995j7EX8VStWhWTyVTsMQEBAUDhjlZwZgvSTz/9lOjoaAYNGlRoa1J39/9j787Dqi7z/48/D+fA4bCIICoKKqCIKCiuZWpq0zJZM9O0T022T7bYOpUtmpllq01jTZqVbT+nve9MY43VlFpalrmCqCigiKKoyCKHA2f5/cHgRGqpAfc5h9fjurguONvneQ5XhJz3574dDB06lG+++Ya6ujpWrlzJgQMHOPHEE5usglVRUQFAWVkZZWVlP9nqdDoPuaw5VtRqHJb6qcurqqoOGXQ70rEbX9MjPS4c/jX94ffip+7TXCorKwFo3759sz7u4Rzva9LoSK91SEjDgKjP5/uliUe0bds2oGl7YmIiAIWFhS123EZvvfUWN954IxaLhVdeeYWzzjqrxY95tLR1qUgLcB2ootf+fwOwoe4EkgaNMlwkgaLztY9R64kiLmwna5/UP85EREREpPnkzZxITGgZNZ4Yut+kLV7k6CSfdAYbnUMaPi/9J+46l+EiEREREQkWewrz6GP5CoANoWOISehmuEgCxrg78PisJDk28f3zU0zXiMhxiIqKAhoGzY40LLRr1y7gf6tYNRo9umH1vMYBt8WLFxMTE8OAAQMOXu90Ovn222+bDMEd7viXXnopPp/vJz8WLVrUDM/4UEcasPvh5T9+7j+l8Tnt3r37iLc53Gv6w+/F4Rzp8RoHFI/0/TvSqmONx25cMa8lHe9rYlpFRcXBVQeHDRt28PLGVQmXLVuG2+1useN/9NFHXHbZZXi9Xp599lkuvfTSFjvW8dCgm0gLyHniRtqH7sLpiabrhCdN50gA6dgrkzxfw/+g+ri+oGpvqeEiEREREQkG5SUF9PEtAWCDZRSxiYffEkLkcOKvfhSXN4IOYSWsfnKi6RwRERERCRI759yJw1rN/voEMv/8rOkcCSA9x57Dpv+ekNN92wd4WvDNfhFpGb169SIkJASXy0VBQcFhb5ObmwtA7969m1yemJhIamoqO3fuZMWKFaxdu5ZRo0YdXGWrcaht0aJFLFnS8Pewk08+ucljNG5lmZOT03xP6hg1buF5pMs7d+58yGpuP6XxdVq/fv1hr/d6vWzYsKHJbX/4eeN1R9vZuNrZkQbkNm/efNjLG7ek/eabbw57/eEcadW/n3O8r4lpL730Em63m7i4uCZbvI4bN46oqCh2797Nu+++2yLHXrJkCeeffz719fXMmDGDG264oUWO80to0E2kmVWUFtPH3TAZvsE3gg7J6YaLJND0um0W1e44omz72Py03kQSERERkV9u27O3EWGtpKK+I33v1BtIcmw6p2eT5x4OQO8D/8G5f5/hIhEREREJdNtXfkmfsG8B2Nz+DOyR/rOKigSGdn98kDpvOB3t21j11C2mc0TkGEVFRR1cnWrWrFmHXO90OnnxxRcBOOOMMw65vnFwbfr06Xi93iYrtp100knYbDY+//xzvvrqK6xWKyNHjmxy/1GjRhEfH8+aNWtabMW2n/PSSy/hch26cv7f/vY3AE4//fRjerzTTz8di8XCV199xapVqw65/v3332f79u1ERkYyYsSIJvcDmD179mEf9/nnnz/s5ampDSfSFhQUsHfv3kOub/z+/dg555wDwMsvv8y+fUf3NyaHwwEcfhvZn3K8r4lJq1atYsqUhtVKb7vttoPPHRq2e504sWF+4NZbb6WoqOgnH2vp0qUsW7bsqI/9/fff85vf/Aan08k999zDpEmTjv0JtAINuok0s8K/3kKkbT9V7g6k36E3kOTYRXdIYIN9LAAZlmWUbTZ3JoGIiIiIBL4d65aTEbocgPyo0wiPjjVcJIEo5eZnOOBuT7vQPeQ9daPpHBEREREJcNV/f5DQEBe7XT0YeNtM0zkSgBL7DyevrmGVm7TKT6itKjdcJCLH6u677wYaBrvmz59/8PKqqirGjx9PWVkZycnJXHzxxYfct3Gw7Z///GeTr6FhiG7QoEEsWrSIyspKsrOzD1kZLTw8nGnTpgFwwQUX8MEHHxyyBWdOTg533303S5cubYZne6i9e/dy9dVXH9zi0+fz8be//Y33338fq9XK7bfffkyP16tXL84991wAxo8f32SlvJUrV3LzzTcDcNNNNzXZpnPChAlERkayfPlyJk+efHBLzPr6eu68886DK+v9WFxcHMOGDcPlcnH77bdTX18PgMfj4dFHH2XhwoWHvd8555zDkCFD2L17N+PGjWPjxo1Nrl+zZs0hw3WNQ3WNW9EereN9TUwoKSnhoYceYuTIkRw4cIAxY8YcdtBs6tSpDB8+nF27djF8+HBef/11amtrm9xm06ZN3HjjjYwZM+Ynt239oY0bN/LrX/+ayspKbrjhBh555JFmeV4twe8H3fbv38/NN9/M8OHDSUhIwG63k5iYyCmnnMJ77713yA+bqVOnYrFYDvsRHh5+xOPMnz+fYcOGERkZSWxsLOPGjWPFihUt/fQkyOzauJoM29cAbHKcQkRsR8NFEqj6//lZ9tV1Idxaza65d5vOEREREZEAtv/1yYSFONnjSiL7jr+YzpEAFZPQjQ22hj8aZ/Al+4rzDReJiIiISKDK//Qd0iJWAlCSej5Wm81wkQSq7hOfpsbTjpjQ3ax/UjvkiASas88+m0mTJlFfX8+ll15K9+7dGTp0KF26dOHdd98lNjaWt99+u8mKVo0aV3Tz+XxER0czaNCgJtePHj364CzJD4fgfuj6669n0qRJ7Nmzh3PPPZf4+HiGDRvG4MGD6dChA1lZWTz++ONUVVU18zNvMGXKFN555x26dOnC0KFDSUpK4sYbb8Tn8zFjxgyys7OP+TGff/55srKyyMnJoXfv3mRnZ9OvXz8GDx7Mzp07OfXUU5k6dWqT+yQlJTFnzhwsFgvTp0+nS5cuDBs2jISEBGbOnMlDDz10xOM99thj2Gw2XnvtNTp16sTQoUPp3LkzkydPZubMww+yW61W3nvvPdLT01m+fDl9+vQhPT2dIUOGEB8fT3Z2Nm+99VaT+1x00UUHj5eens7o0aMZM2YM//73v1vkNWlpEydOZOTIkYwcOZLBgweTlJREUlISU6ZMweVyccMNN/DRRx9hO8zvSGFhYXzyySecd955lJaWMn78eOLi4sjKymLYsGEkJSWRnp7O3/72NxISEujVq9dRNd18883s2bMHi8XC6tWrD/b9+OPll19u7pfjmPn9oNuePXt4+eWXiYyM5JxzzuGOO+7gzDPPJDc3l/PPP5/rrrvusPe7/PLLeeCBB5p83H///Ye97SOPPMKll17Krl27mDBhAhdeeCFLly5lxIgRxpaplMC05+V7sIfUsK+uK1m3/9V0jgSwMEcEhfHjAEi3f8u2774wXCQiIiIigWjL4g9Jd3wPwNakc7CF2Q0XSSDLvPM59td3xmGtYvtzd5jOEREREZEAZf30GawWD8XOdAZeO8V0jgSw2MRUNlhGAZDuXUL5jiKzQSJyzGbMmMGHH37IaaedRnV1NWvXriU+Pp4JEyawZs0ahg4detj7paamkpSUBMCIESOwWq1Nrv/hcFvjUNyRjr906VIuueQSIiMjWbNmDUVFRSQlJXHVVVexYMECfvWrXzXDMz3UqFGj+PLLLxk5ciSbN2+mvLycE088kffff58777zzuB6zY8eOfP3110ybNo2MjAw2bdrE1q1bGTp0KLNmzeKjjz467AJRl156KZ9//jljx46ltraWDRs2kJWVxccff3xwyOxwxowZw8KFCxk5ciR1dXVs2rTp4Gp6Z5999hHv1717d77//ntmzJjBoEGD2LFjB3l5ecTFxXH55ZcfMlw3atSog4tXlZSUsGTJEhYvXkxpaWmLvSYtKScn5+C2ops3b8ZmszFu3DimT59OQUEBzz333GEHPBtFRUXx7rvvsmTJEq6++mq6detGUVERa9aswefzcdZZZ/HSSy+xadMmMjMzj6qpcRtdn8/HsmXLWLp06WE/tm3b1iyvwS9h8f14STQ/4/F48Pl8h0wqVlVVceKJJ7J+/XpycnLo168f0LCi24MPPsgXX3zBmDFjfvbx8/Pz6du3L6mpqXz77bfExMQAkJuby7Bhw+jSpQsbNmw47KTk4VRWVhITE0NFRcUhy19KcCtatpCkhZdis9TzfeyfGHzLE6aTJMB53G7K7h9CQnghG2uGkP74f0wniYiIiEiA2XzXyfSKWEOJM43Ex7RqufxyKx6fyJCa16jzhrP73PdIyh5pOklEREREAsi6N2eRteF+fD4LuVmPkXn+4Re0EDlazv37qH9iGO1Cy1jtOoXsGR+YTpIgVFtbS2FhISkpKa0+ECPBZ8yYMSxevPioZ1pEAtWx/Ow8llkrv1/RzWq1HnbILDo6mjPOOAOAzZs3H/fjz5s3D7fbzX333XdwyA2gX79+jB8/ni1btvD5558f9+NL21H3wQxslnp21qaSfeMM0zkSBKw2G6XpDXvOpzlWsuHj+T9zDxERERGR/8l57wV6OtYCsG/g5YZrJFgMvP1pdrt6EBZSS/X8B03niIiIiEiAiVv1KgAFzv4acpNm4Wgfx6aoUwHICP2a0vUrDReJiIhIS/L7Qbcjqa2t5fPPP8disdC3b99Drv/yyy95/PHHeeqpp1iwYMHBZfZ+rHFr0tNPP/2Q6xoH6RYvXtx84RKU8j58lV4RawDY3fcSrEe5AqDIz8m+chLbajIIsXixf/Gc6RwRERERCSAxy1/EYvFRWJNJ1sUTTedIkLDabGxPPg+ANMf35H+u1RJERERE5Ois/NtkEh35eHxWOPN20zkSRLLveIY9riTsIU72vnqP6RwRERFpQQEzjbN//37+8pe/4PV62b17Nx999BHFxcU88MADpKWlHXL7KVOmNPm6S5cuvPrqq5x22mlNLs/PzycqKoqEhIRDHqPxcfPz84/Y5XK5mgzRVVZWHtPzkuAQ8eVsQiK8bK3py4Dxx7dftsiRVA//E77Vt5MSkcOaN55mwB9vM50kIiIiIn5u1dxpDIzYiMdnpf7Um03nSJAZdN0DbL/7XyQ5NhHy75lwyu9NJ4mIiIiIn3PXuehW/AHYYZNzCBljzzGdJEHEFmZnW9ffEr/3b/QJX0HBlwtIHXWW6SwRERFpAQGzotv+/ft58MEHeeihh5gzZw6lpaU88cQTPPDAA01ul52dzauvvkpRURFOp5P8/Hweeugh9u/fz29/+1vWrFnT5PYVFRVNtiz9ocZ9XysqKo7YNWPGDGJiYg5+dOvW7Rc+Uwk0q+c9Ro+I9Xh9IdScfL3pHAlCfX9/FZtrsgHomPM6HrfbbJCIiIiI+DWP201iwbsA5DsH0vv0iwwXSTDaP+RqAHpGrCXnba0+LSIiIiI/bfVTt9LRXkyd10HMZQ+ZzpEgNOD6h9jh7IXV4sb7z8dN54iIiEgLCZhBt+TkZHw+H263m8LCQqZNm8Z9993Heeedh/sHQx/nnHMO48ePp0ePHoSHh9OrVy/uv/9+nnnmGWpra5k+fXqzdt1zzz1UVFQc/CguLm7Wxxf/5nG7Sdj4JgCba7LJOHu84SIJVrbf3YXbF0rX8C2seX6y6RwRERER8WOr/nIHnexbqffaibp4qukcCVKZF0xgS01/ANp//4rZGBERERHxa7VV5aRVfwZAXt0JdM06wXCRBCOrzcaeAZcB0DNiDes/eNlwkYjI4S1atAifz8eYMWNMp4gEpIAZdGtktVpJTk5m0qRJTJ8+nQ8++IC5c+f+7P0uv/xybDYbS5cubXJ5TEzMEVdsa9yG9EgrvgHY7XbatWvX5EPajtXP3ktCeAFuXyhh591jOkeCWMrIcWysHQxA9x3/xF3n+pl7iIiIiEhb5DpQRa/yfwOwwTWMpEGjDBdJMPP++nY8PitJjk2snPOg6RwRERER8VO5T0wkJnQ3NZ52dJ/4tOkcCWL9L7mVwppMLBYfUV+/YDpHREREWkDADbr90Omnnw40TLz+nLCwMKKjo6mpqWlyeVpaGtXV1ZSWlh5yn/z8/IO3EfmxOmcNKbs/BGBD7VCSTzzdcJEEuw6Xz8DldRBv387qp24xnSMiIiIifijnyYm0Dy3F6Ymiy4QnTOdIkEs75fdscjackNOt6F08P1hxX0REREQEoLykgAzfEgA2WEYRm5hquEiCXd2vJuL1hdA9Io/VL80wnSMiIiLNLKAH3Xbs2AGAzWb72dvm5+dTXl5OcnJyk8tHjx4NwCeffHLIfRYuXNjkNiI/tPapm4kL24HLG0Gnax4znSNtQELfQeTVDwegd/VnOPfvM1wkIiIiIv6ksmwH6fWLANjgG0F8SobZIGkT2v1xGnXecDrat7Fq5q2mc0RERETEz2x79nYirBVU1nck4/ZnTedIG5B+xsXkOwcB0CX/TZ2QIyIiEmT8ftBt9erVh91adN++fdx7770AnHnmmQBUVVWxdu3aQ25bXl7O1VdfDcAf/vCHJtddeeWV2Gw2Hn744SbHyc3N5bXXXqNnz56ccsopzfZ8JDjUlJeRXvs5AHnu4XRK62+4SNqKHhOf5oAnhnahZeTNvMl0joiIiIj4kYK/TCTKVk6VO45et/3VdI60EYn9h7Oh7gQAelV8gutAleEiEREREfEXpbnfkRH6DQCbok7F0T7OcJG0FY4L7qfeG0bn8CJW//Uu0zkiIiLSjH5+KTTDXnnlFV588UXGjh1Ljx49iIyMZOvWrSxYsIDq6mrOO+88LrnkEgD27t3LgAEDGDJkCFlZWXTq1ImSkhI+/vhj9u7dy2mnncZtt93W5PF79+7N1KlTuf/+++nfvz/nn38+Bw4c4O9//zv19fXMnTv3qFaMk7Zl41M3MdC2l2p3LKm36QwkaT2xXZP53jqawfyTPr4v2VecT1w3ba8sIiIi0tbtzl9LhnUZABvDf8WQDgmGi6QtSbrxKZwvjKV96C6+f+JGBk99zXSSiIiIiPiBfa/eT0KEkz2uJLLvfcZ0jrQh3YeOZd27w8hyfEXqno+oc04nzBFhOktERESagd+v6Hb++edzwQUXsHnzZl5//XVmzpzJF198wciRI5k/fz7vvPMOISENTyMuLo4bb7wRn8/Hhx9+yFNPPcWHH35IRkYGs2fP5uOPPyYsLOyQY9x333288cYbdOrUieeff54333yTk046iaVLlzJ27NjWfsri5/YU5tHH8hUAG0NH065jV8NF0tb0u+OvVNR3IsJayfbn7jCdIyIiIiJ+YPeLd2MPqWFfXVf636HV3KR1xXVLI49RAPRxL6aitNhwkYiIiIiYVvDlAtIdKwDYmvg7bGF2w0XS1nSZ8CS1nihiw3ay7kntkCMiIhIsLD6fz2c6IphUVlYSExNDRUUF7dq1M50jLWDdpLPJCv+S/fUJOO5dgT0y2nSStEErHruRIc43qPOGU3b+/5HYf7jpJBERERExpOibT0j6+BJslnq+b38tg2990nSStEHO/fuof2Io7UL3sLruV2Q/8r7pJBERERExaPNdo+kVsZodzl50fng5Vu2eJAasuv8CBto+ododh++2r4nW6udynGprayksLCQlJYXw8HDTOSIiAeFYfnYey6yV36/oJuJPtq/8kj5h3wKwuf0ZGnITYwbe8Qxlru6EhdRS+cYDpnNERERExKC692dgs9RTWptC9k2Pms6RNsrRPo5Nkb8CIMP2NaXrVxouEhERERFTct+fS0/HGgD2DLhMQ25iTK/bZlHtjiPKto/NT080nSMiIiLNQINuIseg+s2phIa42O3qwcDbZprOkTbMarNR3ONcAHo7VpD/+QeGi0RERETEhLx/vUYvx2oAStP/oDeQxKjsP89ib10i9pAa9r5yr+kcERERETGk3fIXsVh8FNZk0v+SW03nSBsW3SGBDfaxAGSELKVsc47hIhEREfmlNOgmcpTyP32HNMcqAEpSz9cbSGLcoAkPst3ZG6vFQ8jHGrwUERERaYsiljxPiMXL1pq+ZF95t+kcaeNsYXaKEn4LQLpjBYVffWS4SERERERa2+oXp9PNsQGvL4S6X2kFLTGv/5+fZV9dF8JDDrBrrv7dLCIiEug06CZylKyfPoPV4qHYmc7Aa6eYzhEBYP/gKwBIjVhHzrtzzMaIiIiISKta89oT9IhYj9cXQs2oCaZzRADIvmE6O509sVnqcf/zcdM5IiIiItKKPG43XTa/A0C+cxDpZ1xsuEgEwhwRFMaPAyDd/i3bvvvCcJGISGApKirCYrGQnJxsOuUnWSwWLBaL6QxpBRp0EzkK696cRWrEOnw+CxVDrzadI3JQ5oU3sqWmPxaLj5jvXjKdIyIiIiKtxON202n9fAA21wwg4zeXGy4SaWC12diddRkAPR1ryPvHPMNFIiIiItJaVj1zJ53Di6j32om4cLLpHJGDsm9+nNLaFEJD6nC+M910jkjQSk5OPjhs1PjhcDjo2bMnV111Fbm5uaYTJUC98sorTJ06laKiItMpv8iP//uwWq20b9+e5ORkzj77bB5++GG2bdt21I+3dOlS/vSnP9GnTx9iYmKw2+0kJiZy9tln8+KLL3LgwIGjfqzq6moef/xxzjvvPNLT04mNjSUsLIzExETOP/98Fi9efDxPuUVo0E3kKMStehWAAmd/Ms+/znCNSFPe02/F47PSzbGRVS9MM50jIiIiIq1g9XP30CW8ALcvlLDf32M6R6SJAX+8jaKafoRYvEQu1crTIiIiIm2B60AVPfc2bF2/wTWUbkPGmA0S+QGrzUZpesMKg2mOlWz4eL7hIpHglpaWxogRIxgxYgQ9e/Zk+/btzJs3j8GDB/Phhx+azpMA9Morr/Dggw/+5KBbeno66enprRf1C2RmZjJixAiGDx9OamoqbrebBQsWcP/995OSksL1119PTU3NEe9fU1PDRRddxMiRI5k7dy5bt26lW7du9O/fH5/Px4IFC7j22mtJS0tj3bp1R9VUWlrK3Xffzfvvv09ZWRmJiYlkZGRQVVXFe++9x5gxY5g2zT9mETToJvIzVv5tMomOfDw+K5x5u+kckUOknXoe+c5BACQWvovH7TZcJCIiIiItyV3nInnXAgA2OoeQfNIZhotEDlU79ia8vhC6R+Sxet6jpnNEREREpIXlPDWR2LBSaj1RdJnwpOkckUNkXzmJbTUZhFi82L94znSOSFC79957+eqrr/jqq6/Iyclh27ZtnHrqqbhcLq688kqqq6tNJ0oQ2rBhAxs2bDCdcVRmzZp18L+RlStXsn37drZv3860adMIDw9n9uzZnHnmmdTX1x9y3/r6ek4//XTefvttEhISePXVV9m3bx85OTl899137Nixg9zcXK677jrKysrYsmXLUTW1b9+eWbNmsWnTpoOPt2bNGvbs2cPjjz8OwNSpU/nuu++a9bU4Hhp0E/kJ7joX3Yo/ABreQOo59hyzQSJHEHXJVOq9djrZt7LqaQ1kioiIiASz1U/cRIewElzeCOKv1gCR+Kc+Z15Cfs1AABI2vqkTckRERESCWGXZDtLrFgGQ5xtBfEqG2SCRI6ge/ie8vhBSInJY88bTpnNE2ozOnTvz+uuvY7fb2bt3L59++qnpJBG/k5iYyOTJk/nqq6+IjIxkyZIlPPLII4fc7sEHH2Tp0qV07tyZr7/+mvHjx+NwOJrcpm/fvsyePZsvvviCTp06HdXx4+Pjuemmm0hLS2tyeVhYGHfeeSfjxo3D5/Pxf//3f8f9HJuLBt1EfsLqp26lo72YOq+D9pc9ZDpH5IiSskeSV3cCAL32/xvXgSrDRSIiIiLSEmrKy0h3/geAPPdwOqdnmw0S+QmOCydT7w0jIbyQ1bPuNp0jIiIiIi1ky19uJspWTrU7jl63/dV0jsgR9f39VWypGQBAx5zXdUKOSCtKSEg4OECTn5/f5LoFCxbw61//mvj4eOx2OykpKdxwww0UFxcf8jgDBgzAYrGwdu3aJpfv2rULi8WCxWJh8uTJh9xvzJgxWCwWFi1adMh1Cxcu5Le//S2dO3fGbreTlJTElVdeediVsIqKirBYLCQnJwMwd+5chg4dSnR0NBaL5aheix+2fPvtt5x11lnExcURGRnJSSeddMRBoiuuuAKLxcIrr7xCYWEhV1xxBYmJidhsNqZOnXrwdgcOHGD69On079+fyMhI2rVrxwknnMBzzz2H+yd+7i1evJhTTz2Vdu3aERMTw9ixY39yKHHRokVYLBbGjBlz2Ot//Fr9WE1NDU8++SQnnngi7du3JyIigrS0NC677DIWL17c5BiNX48dO/bg97nxtWjUeNnhHOtr8sPn5vV6eeaZZ8jMzCQ8PJzOnTtz9dVXU1ZWdsTX5pcYOHAg06dPB+CZZ55psoVpRUUFf/1rw+9af/nLX4742jYaOXIkJ510UrN09enTB+Ant1RtLRp0EzmC2qpy0qo/AyCv7gS6Zp1guEjkpyVOeBKnJ5r2obvIeeJG0zkiIiIi0gI2zLyJaNteDrjbk3LzM6ZzRH5S96Fj2egaCkBK2QLqnOb/ECYiIiIizatscw4Z1mUAbLCPJbpDguEikZ9m+91duH2hdA3fwprnDx2GEZGW4/P5Drnsnnvu4eyzz2bhwoU4HA6ysrLYvXs3zz//PAMGDGDFihVNbn/yyScDHBx8avTDr398ncvlYvny5djtdk488cQm19166638+te/5sMPPwSgX79+VFVV8corrzBo0CCWLVt2xOdz/fXX86c//Yldu3bRp08f2rdv//Mvwg98+eWXjBo1iiVLltCzZ09iYmL4+uuv+f3vf8/MmTOPeL+NGzcyaNAg3nzzzYMDhI0DXmVlZQwfPpzJkyeTm5tLr169SEpK4ttvv+Wmm25i3Lhx1NbWHvKYb775Jqeccgr/+c9/CAsLo3fv3qxdu5Zf//rXvPXWW8f0vI7Gtm3bGDJkCHfeeSfLly+nU6dOZGRksG/fPt544w0eeOABAGJiYhgxYgTt2rUDIDMzkxEjRhz86Ny5888e63hfk0aXXXYZt956K3V1dfTq1Yt9+/bx8ssvM3bsWFwuV/O8ID9y9dVXExoaSnl5OV9//fXByxcsWEBVVRUdO3bk/PPPb5FjH47P5+Obb74BYNCgQa123CPRoJvIEeQ+MZGY0N3UeNrRfaKWLxb/1yE5nTxGANDHvZiK0kPPchARERGRwLW3aCMZLAVgg200MQndDBeJ/LzO1z5OrTeSuLCdrHtyoukcEREREWlmu+beRXjIAfbVdaH/n581nSPys1JGjmNj7WAAuu/4J+66lhlSkDbG54O6A4H5cZjhs5ZQWlrK5s2bAejVqxcA//rXv3j00Uex2Wy88cYbFBcXs2LFCnbu3Mnvf/97ysvLueCCC3A6nQcfZ/To0cCRB90SExP59ttvmwwuNX49bNgwwsPDD14+Z84cnnnmGVJSUvjiiy/YtWsXK1euZN++fUyfPp3Kykouuuiiww5Bbd++nddff51//OMfbNu2je+++44dO3Yc02sybdo0zj33XEpLS/nuu+8oKSk5uFrX3XffzZo1aw57vyeeeIKTTz6ZHTt28P3335OXl8fddzespH/99dezbt06+vXrx6ZNm1izZg3r16/nu+++o3Pnznz66acHh8galZSUcO211+L1epk0adLBntLSUm655ZbDrpD3S3g8Hs4991zy8vIYMmQI69evZ9OmTXz//ffs3buXVatWcdFFFwENq5t99dVXDBw4EIBZs2bx1VdfHfw488wzf/Z4x/OaNFq2bBmLFi1i+fLlbNq0iZycHHJzc0lKSiI3N5d58+Y13wvzA9HR0fTv3x+A7777rkkPwIgRI7DZbC1y7B+qqalh7dq1jB8/nmXLljF8+HD+8Ic/tPhxf07LP3ORAFReUkCGbwkAGyyjGJSYarhI5Oj0uf1Zqp46kWjbHlb/9RayH3nfdJKIiIiINJMds/9MVngV++s7k3nvc6ZzRI5Kx16ZrPKcxMCQT0l3fU7V3lKt8iEiIiISJLYu/4w+4d8CUNjxLAY7IgwXiRydDpfPwPXmOOLt21nx5M0MuXeO6SQJdPU18EhX0xXH594dEBbZoofYvXs3l112GS6Xi9jYWE477TQAHn30UQBuvPFGLr300oO3b9euHW+88QY9evSgqKiIv//971x11VXA/1Z0W7JkSZNjLF68mLi4OK6++mqmTZvGN998c3BLzcYhuMYhOYC6ujqmTp2K1WrlvffeOzhIBWC1WrnvvvtYtWoV7733Hu+88w6XXXZZk+N5PB6mTZvGb3/724OXORyOY3pd4uLimDdv3sHhO4vFwsSJE1m0aBHvv/8+M2fO5NVXXz3kfh07dmT+/PlERv7v+xYeHk5+fj7vv9/w3vDrr79Oz549D14/ZMgQZs2axYUXXshzzz3H/fffT3R0NACzZ8+murqaoUOHMmPGjIP3CQ0NZebMmfznP/85ZKvYX+L999/n+++/p1OnTvz73/+mQ4cOTa7Pzs4mOzu7WY51vK9Jo/r6embNmsWwYcMOXta7d2/uuusubr75Zj7++GMmTJjQLK0/1q1bN77//nt279598LKSkhIAUlJSWuSYjbKzs5sMWkZFRTF16lTuuuuuVhmw+zla0U3kMLY9ezsR1goq6zuScbvOQJLAERHbkY2OUwDIsH3Nro2rzQaJiIiISLPYvvor+oQtB2BzzBnYI6N/5h4i/qPX7c9S7Y4lyraPzU/fbDpHRERERJqJ671HsFnqKa1NIXviY6ZzRI5aQt9B5NUPB6D3gf/g3L/PcJFIcHnkkUcYOXIkI0eOJDMzk27duvHZZ58RGhrK3LlziY6Oprq6+uCWjBMnHroCfEREBNdeey0An3zyycHLO3XqRJ8+fSgrKyMvLw+AvXv3sn79ek4++eRDhtvgf0NxjUNyAF9//TWlpaUMGjSoyZDbDzUOsf149bhG48ePP6rX40iuvvrqJivMNbrhhhsAWLhw4WHvd9555zUZcmv06aef4vP5GDly5GGf03nnnUdSUhIHDhxg6dKlBy9vPM71119/2OM19jSXf/zjHwBcddVVhwy5NbfjfU0axcbGcu655x5y+dChQwEoKCho/uj/avweV1VVHbys8fPDff+b08CBAxkxYgRZWVlERkZSXV3NO++885Nb+bYm86N2In6mNPc7MkIb9hfeFHUqQ9rHGS4SOTbZdz7L3qlL6RBWwp6X76HzYx+bThIRERGRX6h6/lSSIlzsdvVg4P1Pm84ROSbRHRL4PmwMg70fkGFZyp7CPOJTMkxniYiIiMgvsOHj+fR2rAKgNP1iEvxgdQ+RY9Fj4tMceP5k2oWWsXLmTQyaNt90kgSy0IiGldECUWjzr8aZn59Pfn4+AGFhYSQkJHDyySdzxx13HFypa/PmzXi9Xux2O6mph99drV+/fgBs2rSpyeUnn3wyGzZsYPHixWRkZLBkyRJ8Ph+jR4/mxBNPJCws7OBwmtvtZtmyZdhsNk466aSDj7Fu3ToAioqKGDly5GGPv3//fuB/q2j9UHx8PPHx8Uf5ihxeRsbh/zbSePmuXbuorKykXbt2R3W/xtepb9++h70+JCSEPn36sH37djZt2sSvf/3rJvf7uZ7m0jigeOKJJzbr4x7O8b4mjX64AtwPderUCYDq6upmrG2q8bF/+P1vXHHuwIEDLXZcoMmWrPX19cydO5c77riDX//613zxxRdH/G+mtei3TpEf2ffq/SREONnjSiL73mdM54gcM1uYnaLOv6FD+WzSw7+jaNlCkk86w3SWiIiIiByn/M/eI82xEoCSlPPppDeQJABl3jGL8oe/ITZsJ/mz7yT+sX+ZThIRERGRXyD8i2cJifCyrSaD7Csnmc4ROWaxXZP53jqawfyTPr4v2VecT1y3NNNZEqgslhbf/jOQzJs3jyuuuOInb9M4xNOxY0csFsthb9O5c2eg6YpW0LAF6QsvvMDixYuZMGFCk61JHQ4HQ4cO5ZtvvqGuro6VK1dy4MABTjzxxCarYFVUVABQVlZGWVnZT7Y6nc5DLmuOFbUah6V+6vKqqqpDBt2OdOzG1/RIjwuHf01/+L34qfs0l8rKSgDat2/frI97OMf7mjQ60msdEtKweabP5/uliUe0bds2oGl7YmIiAIWFhS123B8LDQ3lhhtuwOl08uc//5mpU6fy2WeftdrxD0dbl4r8QMGXC0h3rABga+LvsIXZDReJHJ/sGx9mZ21PbJZ66j6Y8fN3EBERERG/Zf3kaawWD9udvRn4pymmc0SOiz0ymi0dzgSgj305xSsWmQ0SERERkeO25vWnSI7IxesL4cCI60zniBy3fnf8lYr6TkRYK9n+3B2mc0TalKioKKBh0OxIw0K7du0C/reKVaPRo0cD/9tSdPHixcTExDBgwICD1zudTr799tsmQ3CHO/6ll16Kz+f7yY9FixY1wzM+1JEG7H54+Y+f+09pfE67d+8+4m0O95r+8HtxOEd6vMYBxSN9/4606ljjsRtXzGtJx/uamFZRUXFw1cFhw4YdvLxxVcJly5bhdrtbtemss84CYOXKla163MPRoJvID3g/fByrxc0OZy+yJ0wznSNy3Kw2G7v7/gGAXhFryPvwVcNFIiIiInI81r31LKkRDX/U2D/kasM1Ir/MwFueYFdtMqEhddS8/ZDpHBERERE5Dh63m045/w+ALTUDyPjdlYaLRI5feHQs+e1OB6BP2HJK1n5tuEik7ejVqxchISG4XC4KCgoOe5vc3FwAevfu3eTyxMREUlNT2blzJytWrGDt2rWMGjXq4CpbjUNtixYtYsmSJUDDdqc/1LiVZU5OTvM9qWPUuIXnkS7v3LnzIau5/ZTG12n9+vWHvd7r9bJhw4Ymt/3h543XHW1n42pnRxqQ27x582Evb9yS9ptvvjns9YdzpFX/fs7xviamvfTSS7jdbuLi4pps8Tpu3DiioqLYvXs37777bqs2NQ7WtfaA3eFo0E3kv3Lfn0tPxxoA9gy4DKu2A5IAN2D8nRTV9CPE4iXiy9mmc0RERETkOMSubDhhYcuB/mReMMFwjcgvY7XZ2NnrAgDSHCvZuPBNw0UiIiIicqxW/+1+uji24PaFYvvdXaZzRH6xgXc8Q5mrO2EhtVS+8YDpHJE2Iyoq6uDqVLNmzTrkeqfTyYsvvgjAGWecccj1jYNr06dPx+v1Nlmx7aSTTsJms/H555/z1VdfYbVaGTlyZJP7jxo1ivj4eNasWdNiK7b9nJdeegmXy3XI5X/7298AOP3004/p8U4//XQsFgtfffUVq1atOuT6999/n+3btxMZGcmIESOa3A9g9uzDv5/8/PPPH/by1NRUAAoKCti7d+8h1zd+/37snHPOAeDll19m3759R35CP+BwOIDDbyP7U473NTFp1apVTJnSsKvHbbfddvC5Q8N2rxMnTgTg1ltvpaio6Ccfa+nSpSxbtqxZuv7v//4PgOzs7GZ5vF9Cg24i/9Vu+YtYLD4KazLpf8mtpnNEmoVz9A14fSH0iFjP6nmPmc4RERERkWPw/fNTSHJswuOzwjhtoSLBIfua+9nm7EOIxUvYfw79Q7aIiIiI+C93nYvk0n8CsNE5hJSR4wwXifxyVpuN4h7nAtDbsYL8zz8wXCTSdtx9991Aw2DX/PnzD15eVVXF+PHjKSsrIzk5mYsvvviQ+zYOtv3zn/9s8jU0DNENGjSIRYsWUVlZSXZ29iEro4WHhzNtWsMObxdccAEffPDBIVtw5uTkcPfdd7N06dJmeLaH2rt3L1dfffXBLT59Ph9/+9vfeP/997Fardx+++3H9Hi9evXi3HMbfp6NHz++yUp5K1eu5OabbwbgpptuarJN54QJE4iMjGT58uVMnjz54Ipd9fX13HnnnQdX1vuxuLg4hg0bhsvl4vbbb6e+vh4Aj8fDo48+ysKFCw97v3POOYchQ4awe/duxo0bx8aNG5tcv2bNmkOG6xqH6hq3oj1ax/uamFBSUsJDDz3EyJEjOXDgAGPGjGHSpEmH3G7q1KkMHz6cXbt2MXz4cF5//XVqa2ub3GbTpk3ceOONjBkz5ie3bf2hmTNn8ve///2QLWerqqp47LHHDv730jhoZ5IG3USA1S9Op5tjA15fCHW/Mv8fpkhzyTjrj2x2ZgOQsPFNPH6wlKiIiIiI/DyP2033bQ1/XN/kHEzPseeYDRJpRlUnXIPPZyElIoe1f3/GdI6IiIiIHKXVT06kQ1gJLq+DDlc8YjpHpNkMmvAg2529sVo8hHw803SOSJtx9tlnM2nSJOrr67n00kvp3r07Q4cOpUuXLrz77rvExsby9ttvN1nRqlHjim4+n4/o6GgGDRrU5PrRo0cfHFz74RDcD11//fVMmjSJPXv2cO655xIfH8+wYcMYPHgwHTp0ICsri8cff5yqqqpmfuYNpkyZwjvvvEOXLl0YOnQoSUlJ3Hjjjfh8PmbMmHFcK2c9//zzZGVlkZOTQ+/evcnOzqZfv34MHjyYnTt3cuqppzJ16tQm90lKSmLOnDlYLBamT59Oly5dGDZsGAkJCcycOZOHHnroiMd77LHHsNlsvPbaa3Tq1ImhQ4fSuXNnJk+ezMyZh/95arVaee+990hPT2f58uX06dOH9PR0hgwZQnx8PNnZ2bz11ltN7nPRRRcdPF56ejqjR49mzJgx/Pvf/26R16SlTZw4kZEjRzJy5EgGDx5MUlISSUlJTJkyBZfLxQ033MBHH32E7TC7EIaFhfHJJ59w3nnnUVpayvjx44mLiyMrK4thw4aRlJREeno6f/vb30hISKBXr15H1bR27VouueQSYmJiSE9P54QTTqBXr17Ex8czadIk3G43U6ZM4YILLmjul+OYadBN2jyP202Xze8AkO8cRPoZh06EiwQy+3n34faFkhBewOpn7zWdIyIiIiJHYdVTt9DRvo06bzjt/jjNdI5Is+p37rVscQ4AIH71azohR0RERCQA1JSXkV7zHwDy6oeT0HfQz9xDJLDsH3wFAKkR68h55/Db94lI85sxYwYffvghp512GtXV1axdu5b4+HgmTJjAmjVrGDp06GHvl5qaSlJSEgAjRozAarU2uf6Hw22NQ3FHOv7SpUu55JJLiIyMZM2aNRQVFZGUlMRVV13FggUL+NWvftUMz/RQo0aN4ssvv2TkyJFs3ryZ8vJyTjzxRN5//33uvPPO43rMjh078vXXXzNt2jQyMjLYtGkTW7duZejQocyaNYuPPvqI8PDwQ+536aWX8vnnnzN27Fhqa2vZsGEDWVlZfPzxxweHzA5nzJgxLFy4kJEjR1JXV8emTZsOrqZ39tlnH/F+3bt35/vvv2fGjBkMGjSIHTt2kJeXR1xcHJdffvkhw3WjRo1i/vz5DBs2jJKSEpYsWcLixYspLS1tsdekJeXk5BzcVnTz5s3YbDbGjRvH9OnTKSgo4LnnnjvsgGejqKgo3n33XZYsWcLVV19Nt27dKCoqYs2aNfh8Ps466yxeeuklNm3aRGZm5lE13XzzzUyaNIlhw4ZRXV3NqlWrKC0tJTU1lWuuuYZvv/2WBx98sLlegl/E4vvx+ovyi1RWVhITE0NFRcUhy1+Kf1rx1K0MqZpHvddO6W/fptuQMaaTRJpdzt3jyHQsZV9dV6Imf0+YI8J0koiIiIgcQW1VOa5HhxETupu1taPp/+g/TSeJNLuCLxfQ47PxWC1uvo+/kcE3aUUQEREREX+2avIfGGj9iAPu9tTdsJjYrsmmk0Sa3Za7RtEzYi3bnb1Jeuw70zniR2prayksLCQlJaXVB2Ik+IwZM4bFixfzxRdfMGbMGNM5Ii3mWH52HsuslVZ0kzbNdaCKnns/BmCDa6iG3CRodbrmUVzeCOLCdrD2qZtN54iIiIjIT1j/5ERiQnfj9ESTdONTpnNEWkTqqLPY6BwCQI+Sf+CucxkuEhEREZEj2VecTx++BGCD7WQNuUnQ8p5+Kx6flSTHJla9oNXVRURE/JEG3aRNy3lqIrFhpdR6ougy4UnTOSItplNaf/I8JwGQXvsfqvb+/DKuIiIiItL6yncUke5dAkAeo4jrlma4SKTlxF0+HZfXQbx9O6ufutV0joiIiIgcwfbn7sBhraKivhP97vir6RyRFpN26nnkOxu25U0sfBeP2224SERERH5Mg27SZlWW7SC9bhEAeb4RxKdkmA0SaWGpt86i2h1LtG0fm5/Wqm4iIiIi/mjbrFuJtFZQWR9Pxh3Pmc4RaVEJ/Yayof5EAHpXf4pz/z7DRSIiIiLyY9tXf0VG2DcA5MecQXh0rOEikZYVdclU6r12Otm3smrmbaZzRERE5Ec06CZt1pa/3EyUrZxqdxy9btMZSBL82nXsysbQMQD0sSxlT2Ge2SARERERaaJ0/Ur6hDa8gbQp8lQc7eMMF4m0vO4T/8IBTwztQsvIm3mT6RwRERER+ZHq+Q8SGuKizNWdgbf/xXSOSItLyh5JXt0JAPSqWIjrQJXhIhEREfkhDbpJm1S2OYcM6zIANtjHEt0hwXCRSOvI/PMs9tcn4LBWs3POnaZzREREROQH9r56D/YQJ3tcSWT/WSfjSNsQ2zWZDZaTAejj+5LykgLDRSIiIiLSKP/zD0hzfA9AcY9zsdpshotEWkfihCdxeqJpH7qLnCduNJ0jIkFm0aJF+Hw+xowZYzpFJCBp0E3apF1z7yI85AD76rrQ/8/Pms4RaTX2yGg2x50JQJ+wb9m+8kvDRSIiIiICUPDlAvqErwBgW9ffYguzGy4SaT397pxFRX0nIqyVbHtWWwOJiIiI+IuQf8/EavGw3dmbQRMeNJ0j0mo6JKezgVEA9HEvpqK02HCRiIiINNKgm7Q5W5d/Rp/wbwEo7HgWYY4Iw0UirWvgLU+yqzaZ0BAX1W9ONZ0jIiIiIoD3n49jtbjZUduTAdc/ZDpHpFWFR8eS3+40ADJCv2HHuuWGi0REREQk5+3n6BmxFoD9g68wGyNiQPrtf6XKHU+kbT+Ff73FdI6IiIj8lwbdpM1xvfcINks9pbUpZE98zHSOSKuz2mzs6HUBAGmOVeR/+o7hIhEREZG2bf0HL9MzYg0AZZmXaTsgaZOyb3uaMlc3wkJq2f/6ZNM5IiIiIm1e++9fAWBLTX8yL9TWjdL2RMR2ZKPjFAAybF+za+Nqs0HiF3w+n+kEEZGA0VI/MzXoJm1K3oI36OVYBUBp+sV6A0narIHX3E9xTTpWiwfrp8+YzhERERFp06K+fgGLxUdRTT8G/FHbNkrbZAuzU9zt9wCkO1aw5Yv/MxskIiIi0oatnP0ASY5NeHxWvKffajpHxJjsO59lb10i9pAa9rx8j+kcMSgkpGGswuPxGC4REQkcjT8zG3+GNhcNukmb4lj8N0IsXrbVZJB95STTOSJGVZxwDT6fhdSIdax7c5bpHBEREZE2afVLM+gekYfXF0Lt2JtM54gYNeiGhyhxpmG1eODjmaZzRERERNokj9tNt63vA5DvHEzaqecZLhIxxxZmp6jzbwBID/+OomULDReJKaGhoYSGhlJdXW06RUQkYFRVVR38+dmcNOgmbcaa158iOSIXry+EAyOuM50jYlzmeX9ii7M/AHGrXjVcIyIiItL2eNxuuuS/CUB+zUD6nHmJ4SIR8/YNugKAVMdact57wWyMiIiISBu0auatdLRvo95rJ+qSB0zniBiXfePD7Kztic1ST90HM0zniCEWi4Xo6GgqKipwOp2mc0RE/J7T6aSyspLo6GgsFkuzPrb2bZQ2weN20yn3/0E4bKkZQMbvrjSdJOIXLGfdiefzK0h05PP9c/cx+MaHTSeJiIiItBmr/3oXg8OLqPeG4bhwsukcEb+QddFNFNz1JqkR64hZ/iKc9yfTSSIiIiJtRm1VOb0qPoFQyKs7gf7ZI00niRhntdnY3fcPdCmYTq+INeR9+CoZv7ncdJYYEB8fj9PpZNu2bbRr147o6GisVmuzD3CIiAQqn8+Hx+OhqqqKyspK7HY78fHxzX4cDbpJm7D6ufsYHL4Fty8U2+/uMp0j4jd6jv4N6xcMpm/Ecnps/z/cdVOwhdlNZ4mIiIgEvTpnDal7PoIw2OgaRubQsaaTRPyG57Rb8Hx1Hd0iNrJq7jQGXjvFdJKIiIhIm5D71M0MDt2F0xNN4oQnTeeI+I0B4++k6K4PSI7IJeLL2aBBtzbJarXSrVs39uzZQ1VVFfv37zedJCLil0JDQ2nfvj3x8fFYrdZmf3wNuknQc9e5SN71YcMbSM4h9Bs5znSSiF+Ju/xh6t76DfH27ax46laG3PO86SQRERGRoLfuyZsYHLaTWm8kna99zHSOiF9JO+0CNnwyhz6R35FY8C4e971YbfoTloiIiEhLqigtpo97CdhgA6MYmJxuOknErzhH34D324n0iFjP6nmPkX3l3aaTxACr1Urnzp3p1KkT9fX1eL1e00kiIn4lJCSE0NDQFl3tUn8llKC3+qlbGRJWgsvroMMVj5jOEfE7Cf2Gsqb+BAbYF5FW/Rl1zhrCHBGms0RERESCVm1VOemuL8AGeZ6TGNgr03SSiN+J+sMD1P/feXSyb+X7v97F4Ntnmk4SERERCWoFs25jYOh+qtzxpN/xV9M5In4n46w/smnxS/SOWEnnDW8BGnRryywWC2FhYaYzRETapBDTASItLaniawA21g0joe8gwzUi/qn7TU9T640kJnQ361540HSOiIiISFDLmf0AUbZ91Hja0fNWvYEkcjhJg0ax0TUYgIRdSwzXiIiIiAQ3d52LXpZVAGwKG0VEbEfDRSL+Kezce/D6Quji2ELeP+aZzhEREWmTNOgmQS3vH/NICC/E6wuh/Xm3mc4R8VuxiakU1DasJBJfojeRRERERFpSwp6Gk3EK6rJo17Gr4RoR/+X49XX4fBYSHfnkf/ae6RwRERGRoLVm9gNE2/bg8kaQdr12xhE5kuQTT6fY2afhi6XzzcaIiIi0URp0k+C29O8AFDv70H3oWMMxIv7NcvIfAejm2MDW5Z8ZrhEREREJTvmff0CSYxMA9jOuNVwj4t96jj2Hkto0AFyfvmS4RkRERCR4xW9fDEBBrU7GEfk5e7uNASA1PIeK0mKzMSIiIm2QBt0kaFWW7SA1fB0Ae5JGG64R8X8ZZ4+ntDaVEIuX/R/MMp0jIiIiEpRcC+cCsN3Zm7RTfm+4RsT/lXY8CYDUsHU49+8zXCMiIiISfLZ99wXdHBsavhh1qdkYkQDQ/7qpVNbHYw+pIX/uA6ZzRERE2hwNuknQyp89GXtIDVXueAZMeNB0jkhA2N5uKAA9bWupc9YYrhEREREJLs79+0gNazgZpzR+uOEakcCQed1UDrjbE2GtJHfO/aZzRERERIJO+ft/IcTipbQ2hYzfXG46R8Tv2cLsFPgGAND9wArDNSIiIm2PBt0kaCX995fLLZ7+2MLshmtEAkP6ddNweqKIsu1j3ZwppnNEREREgkrunMlEWCs54IkhUyfjiByV8OhYCtxZAHTdt9xwjYiIiEhwqXPW0NO6FoDt0UMN14gEjvjzb8Pjs9IpfCs5771gOkdERKRN0aCbBKXc9+fSObwIj89K/AW3m84RCRjRHRIoqMsEoOPOLw3XiIiIiASXrvu+AaCgrj/h0bGGa0QCR+RZ1+PzWejq2MymT94ynSMiIiISNNbNmUqUbR+1nijSJzxkOkckYCQNGsU2ZwYAtm/eNlwjIiLStmjQTYKS9ZuGP3wXO/uQNGiU4RqRwGId07A8fVL4JoqWLTRcIyIiIhIc8j99h66Ozfh8FiLPvt50jkhASR11FtudvQGo/2ye4RoRERGR4NFxxxIAClyZRHdIMFwjEljKk08BIMWRQ/mOIrMxIiIibYgG3SToVJQWkxKeA8DeHmMN14gEnj5nXsJOZ09CLF4q//mc6RwRERGRoFD32csAbK/tTeqoswzXiASe3QkjAUi151BTXma4RkRERCTwFS1bSJJjIwCW0ZcZrhEJPP2vuZ+K+o7YQ5wUvDjVdI6IiEiboUE3CTqb5z6APcRJZX1HBlw7xXSOSEAqaX8CAKm2tbgOVBmuEREREQlsNeVlpIatA2BXpxGGa0QCU+Z1D1LtjsVhrWL9bP1bX0REROSXqvznc4RYvOys7UnGWX80nSMScGxhdgoZAEA350rDNSIiIm2HBt0k6HSrWQFAga8/tjC74RqRwNR3wjScnmiibOXkzHnAdI6IiIhIQFs/ewoOaxXV7liyJkwznSMSkOyR0RS4+wOQuH+54RoRERGRwOY6UEWqbS0AJTHDDNeIBK6OF/0Zj89KJ/tWct6ZbTpHRESkTdCgmwSVnHfn0Mm+FY/PSseL7zSdIxKwImI7UlCXBUCn0q8M14iIiIgEtsahnAJ3FvbIaMM1IoGr3Tk34fWF0MWxhQ0fzzedIyIiIhKwcuY8QJStHKcnmr4THjKdIxKwEvsPZ6uzLwCh371juEZERKRt0KCbBBXbt28DsM2ZQWL/4YZrRAJb2KlXAZDk2ETBlwsM14iIiIgEpg0fz6eLYwteXwjtfnuT6RyRgJZ84ulsd6YD4Fn0quEaERERkcDVeHJzgSuTiNiOhmtEAtv+nqcBkBKeS3lJgeEaERGR4KdBNwka5SUFpIbnNnyeeqrhGpHAl3baBexw9sJi8XFgwfOmc0REREQCUuMwzvba3iSfdIbhGpHAV9b1ZAB6huVQtbfUcI2IiIhI4Cn4cgFJjk0AhJ56peEakcA34Or72F/fmbAQJwVzp5rOERERCXoadJOgUfDig4SFOKmo78SAayabzhEJCjviTgQg1baO2qpywzUiIiIigaVqbyk9w3IAKOsyynCNSHDIum4qVe44wq3VbJwzxXSOiIiISMA5sOB5LBYfO5y96H36RaZzRAKe1Waj0DIAgO6ulYZrREREgp8G3SRodK9t+OWxgAFYbTbDNSLBod91D1HjaUekbT85c6aazhEREREJKBvnPEC4tZpqdxxZ100znSMSFMIcERR4+gOQVPmd4RoRERGRwOI6UEWqbR0AO+JOMFwjEjy6/OEuPD4bHe3FrHtzlukcERGRoKZBNwkK6956lo72bXh8NrpccrfpHJGg4WgfR0FdFgAJZcsM14iIiIgElsTKbwHY4u5PmCPCcI1I8Ig991a8vhASwgvI+/BV0zkiIiIiAWPd7MlE2vZT42lHv+umm84RCRoJ/YZS5OwLgH3lB4ZrREREgpsG3SQohK14H4AiZ18S+g01XCMSXOxnXAtAYng+W774P7MxIiIiIgEi71+v0SW8AK8vhPa/n2g6RySodB86lmJnHwB8X/4/wzUiIiIigSNh91IACuqycLSPM1wjElwqe58BQEp4DnuLNhquERERCV4adJOAt684nxRHLgCVaacZrhEJPmmn/J7tzt5YLD6c/55rOkdEREQkIPiWvAFAsbMPPU441XCNSPDZkzQagJ7h66gs22G4RkRERMT/bVn8IYnh+QDYT7vacI1I8Ol/xSTK6xIIDXGxdd5DpnNERESClgbdJOAVvjSNsJBa9tcn0P/Ke03niASl0vjhAKSGraO2qtxwjYiIiIh/qyzbQWp4DgB7Ek82XCMSnAZMeJAqdzz2kBo2zZliOkdERETE79V8NBuLxUeJM420U88znSMSdKw2G0Uh2QB0r1uFx+02GyQiIhKkNOgmAc3jdtPDtRKAQssArDab4SKR4JQ54UEOeGKIsFaQ8/xk0zkiIiIifi3/hSmEhxygyt2BrD89YDpHJCjZwuxs8fQHoFv1d4ZrRERERPxbbVU5qWHrANjZYbjhGpHg1fWye3D7Qom3byfnzVmmc0RERIKSBt0koOW+/Szx9u24faF0vUyruYm0lPDoWArqGt5E6rL3G8M1IiIiIv4tsWoFAAWe/oQ5IgzXiASv+Atux+Oz0jm8iPUfvGw6R0RERMRv5Tw/hUhrBQc8MWReP810jkjQ6pyeTZGzLwCONf8wXCMiIhKcNOgmAS18VcMviVudfemcnm02RiTIRZ59PT6fhURHPvmfvmM6R0RERMQv5f1jHgnhhXh8VuLOu9V0jkhQSxo0imJnHwBCvv674RoRERER/9Vl79cAFNRlER4da7hGJLhVZ4wDIMWxnj2FeYZrREREgo8G3SRg7SnMI8WRC0BV+pmGa0SCX+qos9he2xsA12daLUFERETksJbOB6DY2YduQ8aYbRFpA/b2GAtASngOFaXFhmtERERE/E/+Z++R6MjH57MQMW6C6RyRoJd12Z/ZV9eF0BAX2+Y9ZDpHREQk6GjQTQLWtnnTCQ1xUV7XhazL7zSdI9Im7Oo0AoCeYetw7t9nuEZERETEv1SUFpMangPA3u5jzMaItBEDrp1CZX1H7CE1bJ77gOkcEREREb/j+vQlAEpq0+g5+jeGa0SCn9Vmo8g6EIBk9yo8brfhIhERkeCiQTcJSB63mx7uVQAUWQditdkMF4m0DVkTpnHA3R6HtYrc5+8znSMiIiLiV/LnPoA9pIbK+ngG/EkDNyKtwRZmp8DXH4BuNSsM14iIiIj4F+f+faSGrQOg9L8nMYtIy+t2+X24faHEhe1g3RszTeeIiIgEFQ26SUDKmf80HcJKcPtCSRx/j+kckTbDHhlNgbvhTaSuFcsN14iIiIj4l+4HGoZsCnwDsIXZDdeItB3xF96Ox2elk30rOe/OMZ0jIiIi4jdy59xPhLWSA+72ZOpkHJFW07FXJkU1/QCIyl1guEZERCS4aNBNAlLEuoZfCouc/eiU1t9wjUjbEnX29Xh9IXQN38LGhW+azhERERHxCznvvUCn8K14fFbiz7/NdI5Im5KUPZJtzgwAbN++bbhGRERExH903ddwsnKBO4vw6FjDNSJtS3W/swBIjshld/5awzUiIiLBQ4NuEnDKNueQ7MgFoDpjnOEakbYnZeQ4Smp7A+D+zytmY0RERET8hO2bhuGabc4MkgaNMlwj0vbsS/kVAKnhuZSXFBiuERERETFv0ydv0dWxGZ/PQuRZ15vOEWlzsv54O/vqumKz1FPy2gzTOSIiIkFDg24ScIpffZjQkDr21XUl67I7TOeItEm7E0YCkGpfR9XeUsM1IiIiImaV7ygixZHT8HnyKYZrRNqmAVffR0V9J8JCnBS8+KDpHBERERHj6j+bB8B2Z29SR51luEak7bHabBTZBgKQ7FmFx+02XCQiIhIcNOgmAcXjdpPsWQVAkW0gVpvNcJFI25T5p6lUu2NxWKvZOOcB0zkiIiIiRhXMnYo9xElFfUf6X3O/6RyRNskWZqeAAQB0r11puEZERETErJryMlLtDSfjNJ60LCKtr/uVk6n32okN28m6V58wnSMiIhIUNOgmAWXd608SF7aTem8Y3cbfazpHpM2yR0ZT4O4PQGLlt4ZrRERERMzqXvs9AIUMwBZmN1wj0nZ1vvhOPD4bHe3bWPfWs6ZzRERERIxZP3syDmsV1e5YMq/TarcipsSnZFDo7AtA9MaPDdeIiIgEBw26SUCJyvsIgKLafnTslWm4RqRta3fOTXh9IXQJLyDvX6+ZzhERERExIuft5+ho34bHZ6XjRX82nSPSpnXNOoGtzgwAwla8b7hGRERExJzEioaTkwvc/bFHRhuuEWnbnAN+B0APx3pK12v1aRERkV9Kg24SMHZtXE2yYz0ANVm/MVwjIsknns52ZzoAviVvGK4RERERMSP0v8M0W519Sew/3HCNiFT0Oh2AFEcu+4rzDdeIiIiItL68BW/QJXwLXl8I7c65yXSOSJuXefFE9riSsFnq2Tn/UdM5IiIiAU+DbhIwdrw+A5ulnr11iWT+4RbTOSIClCWOAiA1PIfKsh2Ga0RERERa177ifFLCcwDY3/M0wzUiAtD/qnvZX9+ZsJBaCl+aZjpHREREpNX5Fr8OwHZnOsknnm64RkSsNhvbwgYCkOJbjcftNlwkIiIS2DToJgHB43aT4lsNQJFtEFabzWyQiACQ9acHqXLHER5ygPwXppjOEREREWlVhS89RFhILfvrOzPg6vtM54gIDW8iFVqyAejhWqk3kURERKRNqdpbSqq94WScsq4nG64RkUY9rpxMvddO+9BdrJ33iOkcERGRgKZBNwkIa195lPahpdR77SRfNdl0joj8V5gjggLPAAASq1YYrhERERFpXT1c3wNQaBmgk3FE/EiXSybh9oUSb99O7tvPms4RERERaTUbZ08m3FpNlTuOrOumms4Rkf/qkJxOYW0mAO3yPzVcIyIiEtg06CYBod2mhQAU1vajQ3K64RoR+aHYc2/B6wshIbyQvH/MM50jIiIi0irW/v0Z4u3b8fhsdPnDXaZzROQHEvoOYmtNBgDhq/5huEZERESk9SRVfQdAgac/YY4IwzUi8kOuQb8HINmxntLc7wzXiIiIBC4NuonfK839jmTHegBqB55jNkZEDtF96FiKnX0avlg632yMiIiISCsJX/V/ABQ5+5LQb6jZGBE5RFX6GQCkOHLZU5hnuEZERESk5eX9Yx4J4YV4fSHEnnur6RwR+ZGsiydS5uqG1eJm5/zHTOeIiIgELA26id/b+ffHsVrc7HEl0f8Pt5jOEZHD2NttDACp4TlUlBabjRERERFpYXuLNpISngtAZe8zDNeIyOFkXTGJ8roEQkNcbJs33XSOiIiISMtb+ncAip196D50rOEYETmcbfZBAKSyBo/bbbhGREQkMGnQTfyax+0mxbcGgK32wYZrRORI+l83lcr6eOwhNeTPfcB0joiIiEiLKnr5IUJDXJTXJdD/ikmmc0TkMKw2G4XWbAB6uFfpTSQREREJapVlO0gNXwfAnqTRhmtE5EhSr51KnddBTOhu1rz4kOkcERGRgKRBN/Fra156mPahu6jzhpNy9WTTOSJyBLYwOwW+AQB0P7DCcI2IiIhIy/G43SS7VwJQFJKN1WYzXCQiR5I0/j7cvlA6hJWQM/9p0zkiIiIiLSZ/9mTsITVUueMZMOFB0zkicgSxiakU1vZr+LzgM8M1IiIigUmDbuLX2m/5FIDC2kziuqUZrhGRnxJ//m14fFY6hW8l570XTOeIiIiItIicvz9Dh7AS3L5Qul52j+kcEfkJndL6U+RseBMpYt0CwzUiIiIiLSfpvycfb/H0xxZmN1wjIj+lfugFAHR35FGy9mvDNSIiIoFHg27it0rWfk0Px3oA6oeca7hGRH5O0qBRbHNmAGD75m3DNSIiIiItI2LdhwAUOfvSOT3bbIyI/KzqjHEAJDtyKducY7hGREREpPnlvj+XzuFFeHxW4i+43XSOiPyMzAsmsNvVA6vFQ9mbT5jOERERCTgadBO/VfbWkw2/5Lm6k3nhjaZzROQolCefAkCKI4fyHUVmY0RERESaWdnmHJLDc4H/Dc+IiH/LuuwO9tV1JTSkjuJXHzadIyIiItLsrN+8BUCxsw9Jg0YZrhGRo1HsGARAqmUt7jqX4RoREZHAokE38UvuOhcprAVgW/hgwzUicrT6X3M/FfUdsYc4KXhxqukcERERkWZV/NojhIbUsa+uC1mX/dl0jogcBavNRpFtIADJntV43G7DRSIiIiLNp3xHESnhDavW7u0x1nCNiByt1Gum4vI6aBdaxtoXp5vOERERCSgadBO/tOalh4kJ3Y3L6yD12qmmc0TkKNnC7BQyAIBuzpWGa0RERESaj8ftJtm9CoAi60CsNpvhIhE5Wt3G30u9N4y4sB2se/0p0zkiIiIizabgxanYQ5xU1ndkwLVTTOeIyFGK7ZpMYW1mw+dFnxuuERERCSwadBO/FFf4HwAKnZnEdk02GyMix6TjRX/G47PSyb6VnHdmm84RERERaRbrXn+KuLAd1HvD6Hb5faZzROQYdOyVSVFtPwCi8j4yXCMiIiLSfBpPNi7w9ccWZjdcIyLHwnPiRQB0d+SxfeWXhmtEREQChwbdxO9sX/0V3R15ALhPvNBwjYgcq8T+w9nq7AtA6HfvGK4RERERaR6NwzFFzn507JVpuEZEjlVN1m8ASHbksjt/reEaERERkV8u553ZdLJvxeOz0vHiO03niMgx6nfuteyqTcZq8bDn3adN54iIiAQMDbqJ39nz9kysFg+7a3uQed6fTOeIyHHY3/M0AFLCcykvKTBcIyIiIvLL7M5fS7IjF4CarLMM14jI8cj8wy3srUvEZqln+2sPm84RERER+cUaTzLe5swgsf9wwzUicjy2Rw4BINWyBnedy3CNiIhIYNCgm/gVd52LVEvDmdXb/vvLnYgEngFX38f++s6EhTgpmDvVdI6IiIjIL7L9tYexWerZW5dI5iW3mc4RkeNgtdkosg0CIMWzGo/bbbhIRERE5PiVlxSQEt5wMk556qmGa0TkeKVNeAiXN4J2oXtYO2eq6RwREZGAoEE38StrXniQdqFluLwRpF37oOkcETlOVpuNQssAALq7VhquERERETl+HrebFM9qALbaBmK12cwGichxS75qMvVeO7Fhpax75VHTOSIiIiLHrWDuVMJCnFTUd2LANZNN54jIcWrXsSsFtVkAdCheZLRFREQkUGjQTfxKh22LACiozSQmoZvZGBH5Rbr84S48Phsd7cWse3OW6RwRERGR47LulUeJDSul3mun+5X3m84RkV+gQ3I6hbX9AIjeuNBwjYiIiMjxazy5uIABOhlHJNCNuhSAbo4NbPvuC8MxIiIi/s/vB93279/PzTffzPDhw0lISMBut5OYmMgpp5zCe++9h8/nO+Q+lZWV3H777fTo0QO73U6PHj24/fbbqaysPOJx5s+fz7Bhw4iMjCQ2NpZx48axYsWKlnxq8iPFKxbR3ZHX8MWIS8zGiMgvltBvKEXOvgDYV35guEZERETk+DQOwxQ6+xGfkmG4RkR+qdqB5wDQIyKP0vVafVpEREQCz7o3Z9HRXozHZ6PLJXebzhGRXyjjN5dTWptCiMVL+fvPmM4RERHxe34/6LZnzx5efvllIiMjOeecc7jjjjs488wzyc3N5fzzz+e6665rcvsDBw4wevRonn76adLT07ntttvo27cvTz/9NKNHj+bAgQOHHOORRx7h0ksvZdeuXUyYMIELL7yQpUuXMmLECBYtWtRKz1T2vfcXQixeSmtTyPjdlaZzRKQZVPY+A4CU8Bz2Fm00XCMiIiJybErXr6RHRMPJOLUDf2e4RkSaQ/8/3MIeVxI2Sz0752v7UhEREQk8jScVFzn7ktBvqOEaEWkO26Mb/ltOta6hzlljuEZERMS/+f2gW0pKCvv37+ezzz5j9uzZPPLII7z44ots3ryZvn37MnfuXHJzcw/e/vHHH2f16tXcddddfPLJJzz66KN8/PHHTJkyhdWrV/P44483efz8/HweeOABevfuzdq1a3nqqaeYM2cOy5Ytw2azcc011+B2u1v7abc5dc4aUq1rASiJHmK4RkSaS/8rJlFel0BoiIut8x4ynSMiIiJyTHbOfxSbpZ49riT6XXiT6RwRaSZb7YMBSPGtxqO/+YiIiEgA2Vu0kZTwHAAq004zXCMizSV9wkPUeqKItu1j3QsPmM4RERHxa34/6Ga1WrHZbIdcHh0dzRlnNKwUtHnzZgB8Ph8vvvgiUVFRTJkypcnt77nnHmJjY3nppZeabHc6b9483G439913HzExMQcv79evH+PHj2fLli18/vnnLfHU5AfWvfAg0ba91HojSfvTNNM5ItJMrDYbRSHZAHSvW6U3kURERCRgeNxuUnyrAdhqH4T1MP8uFZHAlHL1ZOq84bQP3cXalx8xnSMiIiJy1LbOe4jQEBf76xPof+W9pnNEpJlEd0igwJUJQMeSLw3XiIiI+De/H3Q7ktraWj7//HMsFgt9+/YFGlZn27FjByNGjCAyMrLJ7cPDwzn55JMpKSk5OBgHHNya9PTTTz/kGI2DdIsXL26hZyGN4kuWAFBQm0m7jl0N14hIc+p62T24faHE27eT8+Ys0zkiIiIiR2Xty4/QPnQXdd5wUq6e8vN3EJGAEdctjcLahjeRYjZ/YrhGRERE5Oh43G66160CoNAyQCfjiAQZy+jLAEhybKToG/07RURE5EgCZtBt//79TJ06lSlTpjBhwgR69+7NmjVrmDJlCmlpaUDDoBtw8Osf+/HtGj+PiooiISHhqG7/Yy6Xi8rKyiYfcmy2Lv+Mbo4NAFhO/qPhGhFpbp3TsylyNgwkO9b8w3CNiIiIyNFpHH4pdPYjrtvh/40pIoGrfsi5APRwrGfHuuWGa0RERER+Xs6bs4i3b8ftC6XrZVrNTSTYZJz1R3bW9iTE4qXy/541nSMiIuK3AmrQ7cEHH+Shhx5izpw5lJaW8sQTT/DAA//bp7yiogKgyRakP9SuXbsmt2v8/Fhu/2MzZswgJibm4Ee3bt2O7YkJ+z+YRYjFy87aVDLOHm86R0RaQHXGOABSHOvZU5hnuEZERETkp+1Yt5wejvUA1P13GEZEgkvmhTdS5uqO1eJh15tPmM4RERER+VmNJxFvdfalc3q22RgRaRElMcMASLWtxXWgynCNiIiIfwqYQbfk5GR8Ph9ut5vCwkKmTZvGfffdx3nnnYfb7TbWdc8991BRUXHwo7i42FhLIKpz1tDTthaAknbDDNeISEvJuuzP7KvrQmiIi23zHjKdIyIiIvKTdr35BFaLhzJXd7Iuusl0joi0kG3hgwFIZQ3uOpfhGhEREZEj21OYR8p/T8apSj/TcI2ItJS+Ex7C6YkmylZOzgtTTeeIiIj4pYAZdGtktVpJTk5m0qRJTJ8+nQ8++IC5c+cC/1vJ7UgrsDVuK/rDFdxiYmKO6fY/ZrfbadeuXZMPOXrr5kwhyrYPpyeKvtdPN50jIi3EarNRZB0IQLJ7FR6DA8oiIiIiP8Vd5yKVNQBsCx9kuEZEWlLqtVNxeR3EhO5mzUsPm84REREROaJt8x4iNMRFeV0Xsi6/03SOiLSQiNiOFLgyAehU+pXhGhEREf8UcINuP3T66acDsGjRIgDS0tIAyM/PP+ztGy9vvF3j59XV1ZSWlh7V7aV5ddz5JQAFriwiYjsarhGRltTt8vuo94YRF7aDdW/MNJ0jIiIiclhrXnqYmNDd1HkdpF7zgOkcEWlBsV2TKXQ2vIkUV/gfwzUiIiIih+dxu0l2rwKgyDoQq81muEhEWlLoqVcCkBi+icKvPjJcIyIi4n8CetBtx44dANj++0t9WloaXbt2ZenSpRw4cKDJbWtra1myZAldu3alV69eBy8fPXo0AJ988skhj79w4cImt5HmVbRsIUnhmwCw/eoKszEi0uI69sqkyNkPgKjcBYZrRERERA6vcdiloLYfsYmphmtEpKW5T7wQgO6OPLav1ooJIiIi4n/WvTGTuLAduH2hJI6/x3SOiLSw3qdfxA5nL0IsXqr/9bzpHBEREb/j94Nuq1evPuzWovv27ePee+8F4MwzzwTAYrFwzTXXUF1dzbRp05rcfsaMGZSXl3PNNddgsVgOXn7llVdis9l4+OGHmxwnNzeX1157jZ49e3LKKae0xFNr8yr/+SwhFi87anuSfsbFpnNEpBXUZJ0FQHJELrvz1xquEREREWlq++qv6O7IA8A97ELDNSLSGjLP+xO7a3tgtXjY87ZWnhYRERH/03jScFFNPzql9TdcIyKtYUfcCQCk2tbiOlBluEZERMS/+P2g2yuvvEJiYiK/+c1vuOmmm7j77ru5+OKL6dGjB6tXr+a8887jkksuOXj7u+66i+zsbB5//HFOP/107rnnHsaNG8e0adPIzs7mrrvuavL4vXv3ZurUqWzatIn+/ftzxx13MGHCBE466STq6+uZO3fuwRXjpPm4DlTR09Yw5LIj5gTDNSLSWjIvuY29dYnYLPWUvDbDdI6IiIhIE3venonV4mG3qweZ519nOkdEWsm2yCEApFrW4q5zGa4RERER+Z/d+WtJjsgFoLrfWYZrRKS19LtuOjWedkTa9rNu9mTTOSIiIn7F7wfdzj//fC644AI2b97M66+/zsyZM/niiy8YOXIk8+fP55133iEk5H9PIzIykkWLFnHbbbexYcMGnnrqKXJycrjttttYtGgRkZGRhxzjvvvu44033qBTp048//zzvPnmm5x00kksXbqUsWPHtubTbTPWzZ5CpG0/Tk80/a5/2HSOiLQSq83GVttAAJI9q/C43YaLRERERBq461ykWhpOximOGGK4RkRaU9q1D+LyRtAutIw1LzxoOkdERETkoJLXZmCz1LOvritZf7zddI6ItBJH+zgK6rIASNi91HCNiIiIf7H4fD6f6YhgUllZSUxMDBUVFbRr1850jt8qvnsY3RwbyXGeROZjH5vOEZFWtKcwj5h5owkNcbG62ySyr77HdJKIiIgI3z97L4P3PIfLG0HtDd8Sk9DNdJKItKK8u04jI+Jbimr6kfz4MtM5IiIiInjcbiqnZBIbtpOV3rMYNG2+6SQRaUX5n71H2ldX4fNZKDjldXqO/o3pJBERkRZzLLNWfr+imwSfLYs/JCl8EwD2U68yXCMirS0+JYNCZz8Aojdq0FVERET8Q4dtiwAorM3UkJtIWzTiEgC6O/IoXrHIbIuIiIgIsO7VJ4gN20m91073K7V1oUhbk3bqeZQ407BYfNR8NNt0joiIiN/QoJu0upqP5mCx+ChxppF22gWmc0TEgNqBvwOgh2M9petXGq4RERGRtq54xSK6O/IA8A7/g+EaETEh43dXUlqbQojFy773/mI6R0REROTgScKFzr7Ep2QYrhERE3Z2GA5Aatg6aqvKDdeIiIj4Bw26SauqrSonNWwtADs7nGi4RkRM6XfhTexxJWGz1LNz/qOmc0RERKSN2/feXwixeNlVm0zf32vVaZG2qiR6CACp1rXUOWsM14iIiEhbVrp+JT0c6wFwDvid4RoRMSXz+mnUeGKItFaQ8/wU0zkiIiJ+QYNu0qpyZj9ApLWCGk87+v1pmukcETHEarOxLWwgACm+1XjcbsNFIiIi0lbVOWtItTacjLM9cojhGhExKe1P06j1RhJt28u6Fx40nSMiIiJt2M75j2Kz1LO3LpHMiyeazhERQ8KjYymoywKgy96vDdeIiIj4Bw26SatK2NPwS1hBXRaO9nGGa0TEpB5XTqbea6d96C7WznvEdI6IiIi0UeteeJBo215c3gjSJjxkOkdEDGrXsSsFtZkAxJcsMVwjIiIibZXH7SbFtxqAItsgrDab2SARMcrx62vx+SwkOvLJ/+w90zkiIiLGadBNWk3+5x+Q5NgEgP2Maw3XiIhpHZLTKfzvm0jt8j81XCMiIiJtVeMwS0FtFu06djVcIyKmWU7+IwDdHBvYuvwzwzUiIiLSFq2d9wjtQ3dR77WTfNVk0zkiYljPsedQUpsGgOvTlwzXiIiImKdBN2k1roVzAdju7E3aKb83XCMi/sA1qOFnQbJjPaW53xmuERERkbZm6/LP6ObY0PDFqEvNxoiIX8g4ezyltamEWLzs/2CW6RwRERFpgxpPCi6s7UeH5HTDNSLiD0o7ngRAatg6nPv3Ga4RERExS4Nu0iqc+/eRGrYOgNL44YZrRMRfZF08kTJXN6wWNzvnP2Y6R0RERNqY/R/MIsTipbQ2hYzfXG46R0T8xPZ2QwHoaVtLnbPGcI2IiIi0JaW535HsWA9A7cBzzMaIiN/IvG4qB9ztibBWkjvnftM5IiIiRmnQTVpF7pzJRFgrOeCJIXPCg6ZzRMSPbLMPAiCVNXjcbsM1IiIi0lbUOWvoaVsLwPbooYZrRMSfpF83DacniijbPtbNmWI6R0RERNqQnfMfw2pxs8eVRP8/3GI6R0T8RHh0LAXuLAC67ltuuEZERMQsDbpJq+i67xsACur6Ex4da7hGRPxJ6rVTqfM6iAndzZoXHzKdIyIiIm3EujlTiLLto9YTRfoE/Q4iIv8T3SGBgrpMADru/NJwjYiIiLQVHrebVNYAsNU+2HCNiPibyLOux+ez0NWxmU2fvGU6R0RExBgNukmLy//0Hbo6NuPzWYg8+3rTOSLiZ2ITUyms7dfwecFnhmtERESkrWgcXilwZRLdIcFwjYj4G+uYhu2Mk8I3UbRsoeEaERERaQvWvPgQMaG7qfOGk3L1ZNM5IuJnUkedxXZnbwDqP5tnuEZERMQcDbpJi6v77GUAttf2JnXUWYZrRMQf1Q+9AIDujjxK1n5tuEZERESCXdGyhSSFbwLAMvoywzUi4o/6nHkJO509CbF4qfzns6ZzREREpA1oX/AfAAprM4nrlma4RkT80e6EkQCk2nOoKS8zXCMiImKGBt2kRdWUl5Eatg6AXZ1GGK4REX+VecEEdrt6YLV4KHvzCdM5IiIiEuQq//kcIRYvO2t7knHWH03niIifKml/AgA9bWtxHagyXCMiIiLBrGTt1/RwrAegfsi5hmtExF9lXvcg1e5YHNYq1s/Wyo8iItI2adBNWtT62VNwWKuodseSNWGa6RwR8WPFjkEApFjW4q5zGa4RERGRYOU6UEWqbS0AJTHDDNeIiD/rO2EaTk80kbb95Mx5wHSOiIiIBLGyt55sOAnY1Z3MC280nSMifsoeGU2Buz8Aifu/NVwjIiJihgbdpEV1rVgOQIE7C3tktOEaEfFnqddMxeV1EBNaxtoXp5vOERERkSCVM+cBomzlOD3R9J3wkOkcEfFjEbEdKajLAqBT6VeGa0RERCRYuetcpLAGgG3hgw3XiIi/a3fOTXh9IXRxbGHDx/NN54iIiLQ6DbpJi8n/9B26hm/B6wuh3W9vMp0jIn4utmsyhbWZDZ8XfW64RkRERIJVx11LAShwZRIR29FwjYj4u7BTrwIgybGJomULDdeIiIhIMFr78iPEhJbh8jpIvXaq6RwR8XPJJ57Odmc6AJ4vXjNcIyIi0vo06CYtpuY/bwCwo7YXySedYbhGRAKBe8jvAejm2Mjeoo2Ga0RERCTYlK5fSWJ4PgAhJ19iuEZEAkHaaRews7YnFouP8gVzTeeIiIhIEIrashiArbV9ie2abDZGRALC7s7DAegRtgF3nctwjYiISOvSoJu0CI/bTQ9rHgC72g0wXCMigSLj3OvYX5+AzVJP0f97wnSOiIiIBJmSt5/GavGwx5VExtnjTeeISIDYEd6w8nQ3b57hEhEREQk2NeVl9HA0nPB7IG2M2RgRCRh9r7oXlzeCKFs56159zHSOiIhIq9Kgm7SIvPfn0D50F25fKCnj7zadIyIBwmqzsdXTB4DOlWsM14iIiEiw6VqbA0BxSIbhEhEJJIkX3orHZyXevp28f2lrIBEREWk+619+GHtIDdXuWLIu13spInJ0ImI7stXZ8F5KxKZFZmNERERamQbdpEVYVnwIQLEznbhuaYZrRCSQOE5p2Easa/hmdqxbbrhGREREgkXRsoV0CS8AIPasaw3XiEggSeg7iJLahr9tuL96x3CNiIiIBJMOu74DYGtdH2xhdsM1IhJIqnueDEAPxwac+/cZrhEREWk9GnSTZueuc9EjbAMAezsNMVwjIoGm9+kXUebqTojFS+k7fzWdIyIiIkGifMFcAEprU0k+6QzDNSISaHbHZAPQ3boBj9ttNkZERESCwr7ifLr9d9tS76CzDNeISKDJvPxuDrjbEx5ygPWvPGI6R0REpNVo0E2a3brXniDKVo7LG0H6lfeYzhGRALTd2rCdWNe6XMMlIiIiEiy6efMA2GHvZ7hERAJRz8vvpt5rJyZ0N7lvP2s6R0RERIJA4WuPYbPUs7++M33Pv950jogEmDBHBFvr0gFov0O744iISNuhQTdpdhGbvgBgW2060R0SDNeISCDq8Lvr8fksJIQXUvDlAtM5IiIiEuDyFrxBvH07Hp+VrhfeYjpHRAJQbGIqxbW9AbCt/thwjYiIiASDTpVrAdjm6YPVZjNcIyKBqL7/mQB0D99I+Y4iszEiIiKtRINu0qxqq8rpbm/YtrQqeZThGhEJVN2HjmWnKxWAyo9fMlwjIiIiga7+y7cA2OHsRUK/oYZrRCRQlXc5AYAeYRuoc9YYrhEREZFAtmPdchLD8wEIG32x4RoRCVSZF0+kor4joSEutrz6qOkcERGRVqFBN2lWuS8/gsNaTY0nhkxtWyoiv8DO8EwAurHBcImIiIgEMo/bTQ9rw+8Tu2IGGK4RkUDW98r7qPVGEmnbT85rT5jOERERkQC2851ZhFi87HEl0efMS0zniEiAstpsbPP0AaBT+WqzMSIiIq1Eg27SrNpv/waAra4+hDkiDNeISCBL+sPteHxWOoSVkPfhq6ZzREREJEDlvvMcMaG7qfeGkTr+LtM5IhLAHO3j2Fbb8CZS1JZFZmNEREQkoCXW5QBQbO1ruEREAp1t5IUAJDo2sWvjarMxIiIirUCDbtJsKkqL6e7YCEBd5mmGa0Qk0HVOz6bE2RsA99J3DNeIiIhIoLKu/giA7bW9ieuWZrhGRAJddc/RAHQP30hNeZnhGhEREQlEhV99REJ4IT6fhdizrzWdIyIBLuPs8eytS8Rq8bD97zNN54iIiLQ4DbpJs8mf9wihIS4q6+PJvOQ20zkiEgR2xw4EoId1Ax6323CNiIiIBJo6Zw3JoQ3blu7tPMxwjYgEg8zxd3LA3Z7wkAPkzZthOkdEREQCUMXHLwFQ6kol+cTTDdeISDAotmQA0NWZY7hERESk5WnQTZpNp/JVAGx1Z2C12QzXiEgw6Hn53dR77bQLLSPnzVmmc0RERCTA5L7+JJG2/bi8EfS96l7TOSISBMIcERTVN2xfGlv6reEaERERCURJ3oaTcXaEZxouEZFgEXPm1QAkhBewdflnhmtERERalgbdpFnszl9LomMTALaTzjNcIyLBIrZrMttq0wEIXfux4RoREREJNJGbFwGw1dmHiNiOZmNEJGh4sscB0C18I+UlBYZrREREJJDkffgq8fbteHxWEi+81XSOiASJlJHjKK1NwWLxse/DF0zniIiItCgNukmzKJ7/FFaLh711iWT87krTOSISRPZ3HQ5AD/sG6pw1hmtEREQkUDj376N7eMNKCdU9TzZcIyLBpN8FN1JR34nQkDq2vPqo6RwREREJIO6l7wBQUptGQt9BhmtEJJiUhDWsEpnkWW+4REREpGVp0E2aRZeahj3fi+ljuEREgk3fKyZR64ki0lrBulf0JpKIiIgcnfXzHiY85AAH3O3JvPxu0zkiEkSsNhtbPQ1//+hcscZwjYiIiAQKj9tNd2vDyTi7Y7LNxohI0OlywUS8vhA62ovZuPBN0zkiIiItRoNu8ott++4LuoRvASD6DK3mJiLNy9E+jq2uhjeRoguWGK4RERGRQBG7czkAW+vSCXNEGK4RkWATOuoiALqG51O6fqXhGhEREQkEOW/OIia0jHqvnZ46GUdEmlnXrBMoqU0DwPXFfMM1IiIiLUeDbvKL7f3HbCwWH7tqk+k5+jemc0QkCNWkjQagh2MjNeVlhmtERETE35WXFNAtfBMA9f3PNFwjIsEo46w/sseVhNXioeTtp03niIiISAAIXfsxAMW1vYlNTDVcIyLBaHe7/gB0t27A43YbrhEREWkZGnSTX6xxr/eSsH6GS0QkWGVdfjfV7ljsITWsf/lh0zkiIiLi57a8+hihIS4q6juSefFE0zkiEqSKQzIA6FqbY7hERERE/F2ds4Ye9oZtS8u7nGC4RkSCVcr4u3H7Qmkfuov17802nSMiItIiNOgmv0j+p+/Q0b4Nry+EzufrDSQRaRm2MDtb6xq2L+2w6zvDNSIiIuLvOlWsBmCbpw9Wm81sjIgErdizrgUgwV5I0bKFhmtERETEn+W8+hiR1gpqvZH0vfI+0zkiEqTiuqVR7EwHIOT7fxmuERERaRkadJNfpObzNwDYUduLxP7DDdeISDDzDj4bgG6OjewrzjdcIyIiIv6qdP1KEsMbflewjbzQcI2IBLPkk85gZ20qFouP8gVzTeeIiIiIH4vasgSAbbV9cLSPM1wjIsFsb+ehAPQI24C7zmW4RkREpPlp0E2Om8ftpkdIw1Lbu6L6G64RkWDX97wJ7K/vjM1ST8Frj5vOERERET9V8vZfsFo87K1LJOPs8aZzRCTI7bT3A6CbN89wiYiIiPirmvIyejga3kup7jnacI2IBLu+V92HyxtBlK2cda89YTpHRESk2WnQTY5b3gcv0j60FLcvlOTL7jKdIyJBzmqzsc3TsH1p58o1hmtERETEX3WtzQGg2JJhuERE2oKuF96Cx2cl3r6dDR/PN50jIiIifmj9y49gD6nhgLs9mePvNJ0jIkEuIrYjW/+7fWnEpi8M14iIiDQ/DbrJcbOs+AcAxc50OiSnG64RkbbAPvYSABLD89mxbrnhGhEREfE3Rd98QoK9AICYM682XCMibUFCv6HsqE0DoG7xm4ZrRERExB/F7foOgKL6PoQ5IgzXiEhbUJV6MgA97Btw7t9nuEZERKR5adBNjou7zkWP0Ialtvd2HGy4RkTaivQzLqbM1Y0Qi5ed78wynSMiIiJ+pvxfc7FYfJTWppAycpzpHBFpI3bFDACghzUPj9ttuEZERET8yb7ifLqFbwTAk61/o4hI68i6YhIHPDGEW6tZ/8qjpnNERESalQbd5LjkvPYEUbZ9uLwO0q+613SOiLQh2619AUisyzFcIiIiIv6mm2c9ACVhmYZLRKQt6Xn5JOq9YcSE7ib3nedM54iIiIgfKXjtcUJD6qio70S/C240nSMibUSYI4Ktrj4AtN/xteEaERGR5qVBNzku4f/d032bsw/RHRIM14hIWxL3mz/h81lICC+k8KuPTOeIiIiIn9jw8Xzi7dvx+kLocsFE0zki0obEJqZSXJsOgHW1/o0iIiIi/9O5Yg0AWz19sNpshmtEpC2pzzoDgO7hGynfUWQ2RkREpBlp0E2OWW1VOT3sDduWViaPMFwjIm1NjxNOpbQ2FYCKj18yXCMiIiL+om7xmwCU1KbRNesEwzUi0taUJwwDIDl0A3XOGsM1IiIi4g9Kc7+jq2MzAKGjLjJcIyJtTeYfbqGyviOhIS62vPqY6RwREZFmo0E3OWa5Lz+Kw1pNjacdmVdMMp0jIm3QDkfDdmRJ3g2GS0RERMQfeNxuulsbfi/Y3a6/4RoRaYsyrrwHlzeCSNt+cl9/0nSOiIiI+IEdbz+D1eJhjyuJjLP+aDpHRNoYq83GVnfD9qWdylcZrhEREWk+GnSTY9a+pGEv962uPtgjow3XiEhblHjhrXh8VuLt28n712umc0RERMSw9e8+T/vQXbh9oaSMv9t0joi0QRGxHdla2/AmUuTmRWZjRERExC90deUCUBySYbhERNoq28gLAEh0bGLXxtVmY0RERJqJBt3kmFSUFtM9vGGlBFff0wzXiEhbldB3ECW1aQC4v3rHcI2IiIiYFrJyAQDFznTiuqUZrhGRtqo69WQAuodvwLl/n+EaERERMalo2UISwgsAiD3rWsM1ItJWZfzmcvbWJWK1eNj+5tOmc0RERJqFBt3kmOS/OoPQEBdV7niy/ni76RwRacN2tx8IQHdrHh6323CNiIiImOKuc9EjrOFknL2dhxquEZG2LPPyuzngbk94yAHWz3vYdI6IiIgYVL5gLgA7a1NJPukMwzUi0pYV07DydBfnOsMlIiIizUODbnJMOu5t2MN9a30frDab4RoRact6Xj6Jeq+dmNAyct9+1nSOiIiIGLLu1ceIspXj8kbQ96r7TOeISBsW5ohga106AO13fmu4RkREREzq5s0DYKe9n+ESEWnr2p15NQBd7AVs++4LwzUiIiK/nAbd5Kjtzl9LkmMTACEnnme4RkTautiuyWyrbXgTybb6I8M1IiIiYkrEpkUAbHWmExHb0WyMiLR57uxxAHQP30j5jiKzMSIiImJE3oI3iLdvx+Oz0vXCW0zniEgblzrqLEprU7BYfOz9x/Omc0RERH4xDbrJUSuePxOrxc2+uq70/f1VpnNERNjf9QQAeoRtpM5ZY7hGREREWptz/z56hG8EoCr1ZMM1IiLQ78KbqKjvRGiIiy2vPmo6R0RERAyo//ItAHbUppHQb6jhGhER2BHWsLpkkifPcImIiMgvp0E3OWpdahr2bt9GhuESEZEGfa+4l1pvJJG2/eS8+pjpHBEREWll6195hHBrNQc8MWRdMcl0jogIVpuNbZ4+AHTav8pwjYiIiLQ2j9tND+sGAHbFDDBcIyLSIOGCm/H6Quho38amT94ynSMiIvKLaNBNjkrxikV0Cd8CQPRp4w3XiIg0cLSPY6uz4U2kqC1LDNeIiIhIa2u/YzkAW119CHNEGK4REWlgG3kBAInh+ZSuX2m4RkRERFpT7tvPEhO6m3pvGD0v18k4IuIfumadwI7aXgA4P59vuEZEROSX0aCbHJWyfzyPxeJjV20yPceeYzpHROSgmrQxAPRwbKCmvMxsjIiIiLSa8h1FdP/vtqX1WWcYrhER+Z+Ms8ezx5WE1eKh5O2/mM4RERGRVmRb/TEAxbXpxCamGq4REfmf0uiGVSZ7WPPwuN2Ga0RERI6fBt3kqCS5G/ZsL/nvHu4iIv6i32V/ptodiz2khvUvP2I6R0RERFrJllcfJTTERWV9RzL/cIvpHBGRJopDMgDoWptjuERERERaS52zhh5hDduWlicMM1wjItJUyh/vxO0LpX3oLvLen2M6R0RE5Lhp0E1+Vv6n79DJvhWvL4RO595gOkdEpIkwRwRb69MBiNv1neEaERERaS2dylcDsNXdB6vNZjZGRORH2p95FQAJ9gKKvvnEcI2IiIi0hpzXniDSth+XN4KMK+8xnSMi0kSH5HSKnQ3vpVi+/5fhGhERkeOnQTf5WTWf/z8AdtT2Iil7pOEaEZFDeQeeDUC38I2UlxQYrhEREZGWtmvjahIdmwCwjbzAcI2IyKFSRo6jtDYVi8VH+b/mms4RERGRVhC1ZREAW2v7EBHb0WyMiMhh7O00BIAeoXm461yGa0RERI6PBt3kJ3ncbrpbGrYtLY3KMlwjInJ4fc+/nor6ToSG1LHl1UdN54iIiEgL2/73mVgtHvbWJZLxm8tN54iIHFZJWD8Akjx5hktERESkpdWUl/H/2bvz8KjLe+/jn1kyM9kXspGFBLJACFCqiK2Ka9XjVnetClhRW62tVqq21j5tj61al6K19tgdBUS01WLVWtS6oa244AIhkGELWUgI2UMyM5nl+WMEpYICSbhneb+uK9fB5JeZd/oHOczvO/d3jGudJKlv3NGGawBgz8ZfdrO8wUSl2Du1esHdpnMAADggDLrhM9Uu/ZMyHS3yhxJUOvNG0zkAsEc2u131gSpJUl7X+2ZjAADAiCsYWC1JatAEwyUAsHeFF16nQMimHGeD1i1bYjoHAACMoNr5d8hl3aEd/gxNuvT7pnMAYI9SR+Vriyf8Wkpi3cuGawAAODAMuuGzvb1UktQ4UKnssVVmWwDgMziO+ZokqSBxvVpq3jZcAwAARkr9iheV7wqvKk875XLDNQCwd/nVh6nZUy5J8r682HANAAAYSZktb0mS6n3j5UhMMlwDAHvXU3KkJGmMc608vZ2GawAA2H8MumGv/D6vShLWSpK2Zx9quAYAPtuEUy7Wdm+RbJaAmh//lekcAAAwQjqe/r0slpBaPGM1bsZppnMA4DO1pn1BkjTGtlYBv99wDQAAGAmdTRtV/NHaUv/UUw3XAMBnm/T1H6g/kK5EW59q/ny76RwAAPYbg27Yq9UL5ynV3iFvMFGVl91sOgcAPleDbaIkabS3xnAJAAAYKUWBWklSs6PacAkAfL5xs2/SYNChjIRWrXnit6ZzAADACNjw8J1KsPrUPZir6gu+bToHAD6TMzlV9d7w+tKMphWGawAA2H8MumGvXGv/JUlqGBivtJwCwzUA8PkyT79SoZBFo10btfnfy0znAACAYbZu2RLlOLcoGLIq//xrTecAwOfKKq5Qo6dSkmR99xnDNQAAYCTkdb8vSdoSmCCb3W42BgD2gW/SiZKkMa616m5pMFwDAMD+YdANe+Tp7VSJK7y2tPujXe0AEOlKv3SSWrzjJEmdz/7BcA0AABhu3pcXS5KaPeUqmHy44RoA2DftedMlSSWOtfL7vIZrAADAcGpZs1IFLrckyX7U+YZrAGDfTLr4evUMZivB6pV7PutLAQDRhUE37FHNn3+hRFuv+gNpmsTaUgBRpNkVXmNWHKw1XAIAAIZTwO/XGFv4zTgtqV8wXAMA+27inB/KG0xSir1Tqx6+03QOAAAYRk2P3yubJaDt3iJVnT7bdA4A7BOb3a56f3h9aU7n+2ZjAADYTwy6YY/Sm/4jSdrinSBncqrhGgDYd4UXXK9AyKZsZ6Nqn11kOgcAAAyTNU/8VhkJrfKHEjR25o2mcwBgnyVl5qh+IHwTKanuFbMxAABgWBV4VkuSGqxVhksAYP/YjzhPklSUuE7b3B8argEAYN8x6IZP6W5p0BjXOkmSp+orhmsAYP/kTzxEzZ4KSdLg8scM1wAAgOFiffcZSVLDwHiNKh1vuAYA9k/vuBmSpBLXOg10dRiuAQAAw2Hzv5cp37lJkpRxyhzDNQCwf6rOvEwdvgLZLAE1LJ5nOgcAgH3GoBs+xf3wL+SwetTrz9bkWd8znQMA+601faokqcS2VgG/32wMAAAYMr/PqxJHeG1pe+40wzUAsP8mf/0H2hFIl8vWpzUP3W46BwAADIPOZ/8giyWkFs84jT3qVNM5ALDftih8GuXo/lWGSwAA2HcMuuFTctpXSpLqB8fLZrcbrgGA/Vd26fc1GHQoPWGbav7yG9M5AABgiFYtuFsp9k55g4kaf9nNpnMAYL85EpNU7w2vL81oXmG4BgAADIfiYK0kqclRbbgEAA5M6smXSZJGuzao4Z1XzMYAALCPGHTDbtrWr1ZRYp0kyXL42YZrAODAZBaOU4MnvNLM/t4/DNcAAIChSqp7WZK0xTNBqaPyDdcAwIEZnHKKJGmMa506mzebjQEAAEOy9rnFynY2KhCyqfDC60znAMABKTvmDLV6SmWxhLR96YOmcwAA2CcMumE3WxbfI5vFrw7faFWfc6XpHAA4YJ350yVJJY618g30G64BAAAHaqCrQyXO8NrSnpIjDdcAwIGb9LXvqHswRwlWrzY8fKfpHAAAMAS+V5dIkpo95cqvPsxwDQAcuJ2nUhYGag2XAACwbxh0w27yd6yWJG0JVRkuAYChmTjnR/IEk5Vs79LqBXebzgEAAAdozUO/kMvWp/5AuiZ9/QemcwDggNnsdm0JhNeX5na+Z7gGAAAcqIDfrxJbeCCkNe0LhmsAYGhyz/mWgiGrcp31cr/wF9M5AAB8LgbdsEvDO69otGuDJCn1pEsN1wDA0CRmZGmLJ3wTKWXDq4ZrAADAgcpo+o8kqd47Qc7kVMM1ADA09qMukCQVJtapdd37ZmMAAMABWfPXB5WesE2DQYfGzb7JdA4ADEnR1KPU7CmXJPW/9IjhGgAAPh+Dbtil7akHZbUEtc1TorLjzjKdAwBD1ld2jCRpjGut+jvbDNcAAID91dm8WWMS10mSfJNONFwDAENXdfpstfsKZbME1PjoPNM5AADgAFjfe0aS1OipVFZxheEaABi61pQpkqQSa60Cfr/hGgAAPhuDbtilyB8+arsxodpwCQAMj0mzb9QOf4Zc1h2qnX+H6RwAALCfNjx8pxKsXvUMZmvSxdebzgGAYdFgqZIkFQysNlwCAAD2l2+gX6UJayVJ7XnTDdcAwPAonXWT/KEEZSS0qPZvfzSdAwDAZ2LQDZIk94tPKNdZH97Bfu41pnMAYFg4EpNU7wuvL81sectwDQAA2F+5ne9Jkur9E2Sz2w3XAMDwSD/lcklSvmuj6le8aLgGAADsj5qF9yjZ3iVvMEkT5/zQdA4ADItRpePVOFAZ/o93njIbAwDA52DQDZKk/n8tlCRt9ZSpaOpRhmsAYPj4v3iqJKnYtU6dTRsN1wAAgH3Vuu59FSbWSZLsR5xnuAYAhs/Yo05Vi2esLJaQOp7+vekcAACwH5LXvyJJqh+YoKTMHLMxADCMtudMkySVJqyV3+c1XAMAwN4x6AYF/H6NsYSP2m75aAc7AMSK6vOvUfdgrhKsPm14+E7TOQAAYB81LrlXNktAHb4CVZ15mekcABhWzY5qSVJRoNZwCQAA2FcDXR0a4wrfS+kdN8NwDQAMr/FzfihvMFEp9g6tXjjPdA4AAHvFoBtU+9SflenYKn8oQSUzbzSdAwDDyma3qz4QXl+a1/2+2RgAALDPRg+skiRtUZXhEgAYfvnnX6tgyKoc5xbVPf+Y6RwAALAP1sy/TS7rDu0IpGvy139gOgcAhlXqqHxtGQjfS3Gte9FwDQAAe8egG6S3lkqSmgYqlD2Wm0gAYk/CjAslSQUut1rWrDRcAwAAPs+Wt1/WaGd45XjqyZzmBiD2FEw+XM2ecknSwEuLDdcAAIB9kbH1LUlSvXeCHIlJhmsAYPj1lBwhSSpxrpOnt9NwDQAAe8agW5zz+7wqSQgftd2WfajhGgAYGVWnzdR2b5FsloCaHr/XdA4AAPgc7U89KIslpFZPqcqOOcN0DgCMiNa0L0iSSmy1Cvj9hmsAAMBn6WzerDGudZKkwSmnGK4BgJEx6bKb1R9IU6KtVzV//oXpHAAA9ohBtzi3+pF7lWpvly+YqMrLfmg6BwBGTIM1fGJlgafGcAkAAPg8RYFaSVKTo9pwCQCMnLGzvy9/KEEZCa2qffJ3pnMAAMBn2PDwL5Rg9ap7MEeTvvYd0zkAMCKcyana4g2vL01v+o/hGgAA9oxBtzjnWhPesb7FM15pOQWGawBg5GSedqUkKd+5UZvffN5wDQAA2Ju65x9TjnOLgiGrcs/5lukcABgxWcUVahgYL0myvPO04RoAAPBZcrvekyRtCUyQzW43XAMAI8cz8URJ0hjXOnW3NBiuAQDg0xh0i2Oe3k6VuMJrS3uKjzBcAwAjq/SIk7XVM04W0NwyhwABAABJREFUS0idz/zBdA4AANiLgZcXS5KaPeUqmnqU4RoAGFnteYdJkkoca+X3eQ3XAACAPWlZs1KFLrckyX7UBYZrAGBkTZ45V73+bDmsHrkfZn0pACDyMOgWx2rm36lEW68GAqmqnsPaUgCxb6szvP6sOLDGcAkAANiTgN+vEmt4bWlryhTDNQAw8ibOuUXeYJJS7J1ateBu0zkAAGAPmh6/TzZLQO2+QlWdPtt0DgCMKJvdrvrB8PrSnPaVhmsAAPg0Bt3iWHpjeLd6vXeCnMmphmsAYOQVXHCdAiGbsp2NWvvcYtM5AADgv9Q++TtlJLTKH0pQ6aybTOcAwIhLysxR/UfrS5PqXjZcAwAA9qTAs1qS1GCpMlwCAAeH9UvnSpKKEuvUtn614RoAAHbHoFuc6mlr1piP1pZ6qr5iuAYADo786sPUPFAuSfK9usRwDQAA+G+Wd5+RJDUOVGpU6XjDNQBwcPSNnSFJKnGu1UBXh+EaAADwSZvffF75zo2SpPRTLjdcAwAHx8Sz56jDVyCbxa8ti+8xnQMAwG4YdItTdfNvl8PqUa9/lCZdcr3pHAA4aFozpkqSSmy1Cvj9ZmMAAMAufp9XJQnhtaXbc6YZrgGAg2fSZTerP5Aul61Pax76hekcAADwCZ3P/EEWS0gtnrEae9SppnMA4KDZovAplqN3rDJcAgDA7hh0i1M529+VJNUPTpDd4TRcAwAHT9mlP9Bg0KH0hG1a89cHTecAAICPrF5wt1LsnfIGEzV+zg9N5wDAQeNITFK9d4IkKaPpP4ZrAADAJxUFwm/GaXZUGy4BgIMr9cTZkqR810Y1vPOK2RgAAD6BQbc41LZ+tQoT3eH/mH6W0RYAONgyC8epwRNehWZ97xnDNQAAYKfEupclSVsGJih1VL7hGgA4uHyTT5YkjUlcp+6WBsM1AABAktYtW6IcZ4OCIavyz7/WdA4AHFRlx52lVk+prJag2p7i0AAAQOSI+EG3pqYm3XfffTrppJM0ZswYORwO5efn69xzz9WKFSs+df1Pf/pTWSyWPX64XK69Ps/ixYs1ffp0JScnKzMzU6eeeqreeeedkfzRjNny6DzZLYPq9I1W1ZlzTOcAwEHXkXeYJKk0Ya18A/2GawAAgKe3U2OcayVJPSVHGK4BgINv0kXXqWcwRwlWr9zzbzedAwAAJHlfXixJavaUq2Dy4YZrAODga0qYKEkq8tcaLgEA4GN20wGf59e//rXuvPNOlZWV6cQTT1Rubq7cbreWLl2qpUuX6tFHH9UFF1zwqe+79NJLVVpautvn7PY9/7i33367brnlFo0ZM0ZXXXWV+vr6tGTJEh155JFatmyZjj322BH4yczJ7/tQSpS2hCboC3v53wQAYtnEOT+U994lSrZ36b2F9+iL3/ix6SQAAOJazZ9v16G2PvUH0jTpsptN5wDAQWez21Xvn6DJCW3K7XzPdA4AAHEv4PdrjC38ZpzWtC+oyHAPAJiQe+41Cv7tn8p11sv94hOq+Mq5ppMAAIj8Qbfp06frtdde04wZM3b7/PLly3XCCSfo6quv1plnnimn07nb17/+9a/v04Ca2+3WT37yE1VWVuqtt95Senq6JOnaa6/V9OnTdcUVV2jt2rV7HZKLNo0rl6vAtUGSlHTCLMM1AGBGUmaO6gYmqDJ5pZLcr0hi0A0AAJMymlZISdIW7wRNSE41nQMARtiPOFd6b7kKE+u0zf2hciummE4CACBurXnit5qc0Cp/KEFjZ3/fdA4AGFE09Sg1PVqmwkS3+v+1UGLQDQAQASJ+dek555zzqSE3SZoxY4aOO+44dXR0aNWqVQf8+PPnz5ff79ctt9yya8hNkqqrqzV79mxt2LBBL7300gE/fqRp+9v/yWoJapu3hKl7AHGtr+xoSVJJ4loNdHUYrgEAIH51tzRojCt8UoJn4omGawDAnKozL1O7r1A2S0ANi39pOgcAgLhmffcZSVLDwHhlFVcYrgEAc7amhN+AM8ayVgG/33ANAABRMOj2WRISEiTteSXp8uXLddddd+mXv/ylnn32WXm93j0+xiuvvCJJOumkkz71tZNPPlmS9Oqrr+61wev1qqenZ7ePSFY4WCNJarJXGy4BALMmXfp97fBnyGXdoTUP3W46BwCAuOV++A4lWL3q9Wdr8sy5pnMAwKgGTZAkje5fbbgEAID45fd5VeIIvxmnPe8wwzUAYFbpzBvlDyUo07FVtU/92XQOAADRO+i2ZcsWvfjii8rPz9fkyZM/9fUf//jH+v73v68bbrhBp59+usaOHasXXnjhU9e53W6lpKQoPz//U1+rqKjYdc3e3HHHHUpPT9/1UVxcPISfamS5X/qbcl31Coasyjn7W6ZzAMAoR2KS6n3jJUkZzSsM1wAAEL9y2t+TJNUPTpBtD29iAoB4knbK5ZKk0a4N2vL2y4ZrAACIT6sevlMp9k55g0maOOcW0zkAYFT22Co1DlSG/+OtpUZbAACQonTQbXBwULNmzZLX69Vdd90lm82262tTp07Vww8/rM2bN2tgYEBut1s/+9nP1NXVpa9+9av64IMPdnus7u7u3VaWflJaWtqua/bm5ptvVnd3966PhoaGYfgJR8aOFxZIkrZ6ylR0yKfXwQJAvPFPPVWSNMa1Tp3Nm83GAAAQh7a5P1RR4jpJkvVL5xquAQDzxs04TS2esbJYQmp/6kHTOQAAxKWkulckSfUD45WUmWM2BgAiwPbsQyVJJQm18vv2vEUNAICDJeoG3YLBoObMmaPXXntNV155pWbNmrXb18866yzNnj1bJSUlcrlcKi8v149+9CP96le/ksfj0c9//vNh7XE6nUpLS9vtIxIF/H6VWGolSS3JkwzXAEBkqL7g2+oezFGC1asND//CdA4AAHGnYfE82SwBdfgKNPHsOaZzACAiNDsmSpKKArWGSwAAiD8DXR0qcYXfjNM3lgMDAECSKi+7Wb5golLtHVr9yL2mcwAAcS6qBt1CoZCuvPJKLVq0SDNnztRvf/vbff7eSy+9VHa7XW+88cZun09PT9/riW09PT27rol2tU/9WZmOrQqE7Bpz8Q2mcwAgItjsdm0JVEmScrveM1wDAED8Gd2/SpK0RVWGSwAgcuSd9x0FQ1blOLeo7vnHTOcAABBX1jx0u1y2PvUH0jXpsptN5wBAREjLKdCWgfGSJNeaFw3XAADiXdQMugWDQV1++eX685//rIsuukgPPfSQrNZ9z3c4HEpNTVV/f/9un6+oqFBfX59aWlo+9T1ut3vXNVHvo53pjQOVyinnRDcA2Ml+1PmSpEKXWy1rVhquAQAgfjS884pGuzZIklJPnG24BgAiR+GUL6vZUy5JGnh5seEaAADiS0bzCklSvXeCHIlJhmsAIHL0jDlCklTiWitPb6fhGgBAPIuKQbdgMKgrrrhC8+fP14UXXqiFCxfKZrPt12O43W51dnaqtLR0t88fc8wxkqTnn3/+U9+zbNmy3a6JVgG/XyUJayVJbaMOMVwDAJGl6vTZ2u4tks0SUNPj95nOAQAgbmxf+qAslpBaPaUqO+4s0zkAEFFaUr8gSSqxrlXA7zdcAwBAfOhs3qwxH60t9U0+2XANAESW6jk/1EAgVYm2XtXMv9N0DgAgjkX8oNvOk9zmz5+v888/X4sWLdrrkFtvb68+/PDDT32+s7NTl19+uSTpoosu2u1rl112mex2u2677bbdVpjW1NRowYIFKisr0/HHHz+MP9HBt2rhL5Vqb5cv6FLlnFtM5wBAxGm0TpAkFQysNlwCAED8KAzUSpKaEiYaLgGAyDN25o3yhxKUkdCi2r/90XQOAABxYcPDdyrB6lXPYI4mXXSd6RwAiCjO5FTVe8P3UtIb/2O4BgAQz+ymAz7PrbfeqoceekgpKSmqrKzUz3/+809dc9ZZZ2nq1Klqb2/XF77wBU2bNk2TJ09Wbm6umpqa9Nxzz6m9vV0nnniirr/++t2+t7KyUj/96U/1ox/9SFOmTNF5552nHTt26NFHH9Xg4KD+8Ic/yG6P+P+ZPpOr9gUpSdriGa/ynALTOQAQcdJPuVx68UXluzaqfsWLKjn8K6aTAACIae4X/qIKZ72CIatyz73GdA4ARJxRpeO1aWC8xiatluWdp6TzrzKdBABAzMvtfE9Kkur9EzQ5yu8LAcBI8FSdKNW/rTGuteppa1Ya950BAAZE/P+nvnnzZklSX1+fbrvttj1eU1paqqlTpyorK0vXXHON3nzzTT399NPq6upScnKyJk+erJkzZ+qKK67Y42lwt9xyi0pLS3XffffpwQcflMPh0BFHHKFbb71Vhx122Ej+eCPOu6NXJc7w2tLuwi8brgGAyDT2qFPV8sxY5bs2qePp3zPoBgDACOt/6RHJKW31lKlo6lGmcwAgIrXnHKqxO1arJGGt/D6v7A6n6SQAAGJW67r3VZhYJ0myH3Gu4RoAiEyTZ31PvT/9nVLt7fpw/u2adtMDppMAAHHIEgqFQqYjYklPT4/S09PV3d2ttLQ00zla+eubdUj7/2kgkCrLTavkSs00nQQAEendH8/Uodan1eYtVs4drDAFAGCkBPx+9fy4WpmOFr1jO1fT/t+fTScBQETqbW+R41dT5bQO6P2iGzX1ih+ZTgIAIGa9+9PZOlRPqd1XqFG3rzGdAwARa/X3T9WkxDdU3z9RJXexwhQAMDz2Z9bKepCaYEjaln9Lkuo9ExhyA4DPUHjhdQqEbMpxNmjdsiWmcwAAiFm1S/+kTEeL/KEElc680XQOAESs1FH52uKZIEly1b1suAYAgNg22hN+42uDJhguAYDIZjn8bElSYaJbbes5NAAAcPAx6BbDetqaNSZxnSTJM+EEwzUAENnyqw9Ts6dckuR9ebHhGgAAYtjbSyVJjQOVyh5bZbYFACJcT8mRkqQS51p5ejsN1wAAEJvqV7yo0c6NkqS0Uy43XAMAka36nCvV4Rstu2VQWx6dZzoHABCHGHSLYXXzb5fDOqBef5YmzZprOgcAIl5r2hckSWNsaxXw+w3XAAAQe/w+r0oT1kqStmcfargGACLfpK//QP2BdCXa+lTz59tN5wAAEJM6nv69LJaQWjxjNW7GaaZzACDibQmF37iY3/eh4RIAQDxi0C2GZW9fKUmqH5wgu8NpuAYAIt+42TfJH0pQRkKr1jzxW9M5AADEnNUL5ynF3iFfMFGVl91sOgcAIp4zOVX13vGSpIymFYZrAACITUWBWklSs2Oi4RIAiA7JJ86WJI12bVDjyuWGawAA8YZBtxi1fVOtihLrwv9x2FlGWwAgWmQVV6hhIHwTyfruM4ZrAACIPa51L0qStgyMV1pOgeEaAIgOvkknSZLGuNaqu6XBcA0AALGl7vnHlOPcomDIqrzzvmM6BwCiQsXxZ2ubp0RWS1Btf/s/0zkAgDjDoFuM2rzobtktg+r05avqrMtN5wBA1GjPO0ySVOJYK7/Pa7gGAIDY4entVIlznSSpZ8wRhmsAIHpMuvh69QxmK8HqlfvhO0znAAAQUwZeWixJavaUq3DKlw3XAED0aEqoliQVDtYYLgEAxBsG3WLU6I92om8JVclmtxuuAYDoMXHOLfIGk5Ri79Sqh+80nQMAQMyo+fMvlGjr1UAgVdVzfmg6BwCihs1uV71/giQpp/09wzUAAMSOgN+vElt4bWlL6hcM1wBAdMk5+1sKhqzKddXL/dLfTOcAAOIIg24xqKXmbY12bZAkJR1/ieEaAIguSZk5qv9ofWmi+zXDNQAAxI60phWSpHrPBDmTUw3XAEB0sX3pXElSUWKdOhrchmsAAIgNa/8+XxkJrfKHEjR25o2mcwAgqhQdMkNbPWWSpB3/WmS4BgAQTxh0i0FNTz4oqyWodl+hKk4833QOAESd3qLw+tJih5v1pQAADAPvjl4VO8ODGf3lMwzXAED0GX/GbHUN5slm8WvjonmmcwAAiAmBt56WJDUPlGtU6XjDNQAQfVpd4ZOnC4K8GQcAcPAw6BaDcvvCu9AbQxWGSwAgOlXNvkneYKKS7V1avfhXpnMAAIh6qxfcLZetL7y2dOZc0zkAEHVsdrsa/eHXeUZ1fmi4BgCA2FBkDQ9mtKVWGy4BgOiUe+Y3FQpZlOus1+Y3nzedAwCIEwy6xZj2zetU4FovSXJ86SyzMQAQpZIyc9ToCd9ESljzkuEaAACiX/LGf0uSGjwVrC0FgAMUnHqSJKnI5VZPW7PhGgAAolvd848py9GsYMiqogu+YzoHAKJS0SEz1OotlSR1PDvfbAwAIG4w6BZjNj16n2wWv7oG81R52izTOQAQtTqzp0qSihPWK+D3m40BACCK+X3eXWtLe4umGa4BgOhVff416vVnKcHqlXvhPaZzAACIav2vPi5J2uopU974qWZjACCKbbVXSpJG+9YZLgEAxAsG3WLMzvUVjf4K2ex2wzUAEL3KZn5Pg0GH0hLaVPu3P5rOAQAgaq1+5D4l27vkDSaqavZNpnMAIGrZ7HY1+MInT6e3vGu4BgCA6FYQDL8Zp9U1wXAJAES39JMvlSTluTapedUKwzUAgHjAoFsM6WlrVpEr/I+znessAAAHJrOgVM2ecklS6N1nDdcAABC9EmpfliQ1eiqUlJljuAYAopuv8mhJUrGrTp7eTsM1AABEp83/XqZcZ71CIYtyzrjCdA4ARLVxM05Tm7dYVktQW5/8P9M5AIA4wKBbDKlbcJcSrF71+rNUff41pnMAIOq1pU+SJBVZ3YZLAACITgG/X2Mc4d+jO9eCAwAO3MRL5qo/kCantV81D99tOgcAgKjU+dxDkqRWb6mKpx1rtAUAYkGTwidP5/WvMVwCAIgHDLrFkIzWlZKkBh9rSwFgOJRceJ0CIZsyHVtV++wi0zkAAESd2id/p1T7dg0GHSqb+T3TOQAQ9RyJSWrwVkqSUrawFggAgAORP7hOkrTVXmm4BABig+vo8yVJo10b1LZ+teEaAECsY9AtRgx0daj4o7WlO9dYAACGJqd8krZ6yiRJ3jeeNFwDAED0Ca18TpLU7ClXZkGp2RgAiBF9Yw6XJBU73fIN9BuuAQAgujR9+B/lOTdJktJOnGW4BgBiw4RTLlanb7RsloDqH7/fdA4AIMYx6BYjahfeLae1X/2BdE28ZK7pHACIGa1JEyVJhaE6wyUAAESfneu/2zImGy4BgNhRfemN8gaTlGTr1ppH5pnOAQAgqrQ8+aCslqDavMUqO+YM0zkAEDMag+H1pTldqwyXAABiHYNuMSL5o3UVDd4KORKTDNcAQOzIP/sqBUNW5TgbtHH5s6ZzAACIGrXPLFCmo0WBkE0lF1xrOgcAYoYrNVMNnvCqNUfda4ZrAACILnkDtZKkJlUYLgGA2GI59DRJUoFrvTqbN5uNAQDENAbdYoBvoH/X2tKd6ysAAMOjcMqX1eodK0nqXrbAcA0AANHD+8bfJElbPWXKKZ9kuAYAYktX3iGSpGKHWwG/33ANAADRoW39ao12bZAkOY8613ANAMSWqrOvUM9gthKsPm3g5GkAwAhi0C0G1DwyT0m2HnmDSaq+9EbTOQAQc7YmjJckjfavM1wCAED0KFJ47ffONeAAgOFTOftGDQadSrV3qOYvvzGdAwBAVKh/7FeyWQLq9I1W1WkzTecAQEyx2e1qGAyflpnZ9p7hGgBALGPQLQY4P1pT0eCplCs103ANAMSerNMukyTlOTerceVywzUAAES+Da8+rWxno4Ihq0af8y3TOQAQc9JyCtToCd9Esr7/vOEaAACiQ073aklSY7DccAkAxKbBicdLkopcbvV3thmuAQDEKgbdolzA79cYR3htaXf+oYZrACA2lX7pJG3zlshiCWnbU78znQMAQMTreWGhJKnVM1YFkw83XAMAsak9c4okqcjO+lIAAD5PZ/NmFbjWS5Ish55uuAYAYtOki6/TDn+GnNYB1S64y3QOACBGMegW5Woef0Ap9g4NBp2qmHWD6RwAiFnN1vBpCXmetYZLAACIfKP94bWlWx3jDZcAQOwaN3OuAiG7MhJaVffsQtM5AABEtA2LfqkEq089g9mqOvsK0zkAEJPsDqcafOF7KamN7xiuAQDEKgbdopz1g/B6ikZPhdJyCgzXAEDsSj72a5KkfNdGta5732wMAAARrOGdV5Tn3Czp4/XfAIDhl1VcoSZPePWa782lZmMAAIhwmdvflyQ1DFbIZrebjQGAGNY/7khJUrHLLe+OXsM1AIBYxKBbFAv4/Sq2h9eW7lxXAQAYGRUnnq92X6FsloAaH/+16RwAACLWtr//Mbzu21ui0i+dZDoHAGLatpRqSVKRxW24BACAyNXf2aYiV/h35eDE4w3XAEBsq551gwYCKUq09apm0TzTOQCAGMSgWxRb9/QCpSdsUyBk17iZc03nAEDMawqGT0vI6a0xXAIAQOTK94bXfO9c+w0AGDlF535bwZBVoxxNcr/4hOkcAAAiUu2Cu+S0DmiHP0OTLr7OdA4AxDRncqoaPOHXhJI2vG64BgAQixh0i2KDK56SJDV5ypVVzE0kABhptulnSJIKEtero4ETEwAA+G8ta1ZqtGuDpI/XfgMARk7+xEPU4hknSep7+VHDNQAARKbUxrclSQ3eCtkdTsM1ABD7egunS5KKHXXy+7yGawAAsYZBtyhWZA0PWexcUwEAGFkTvnqZugdzZbcMauPie03nAAAQcRqfeEBWS1DtvkJVnHi+6RwAiAtbnVWSpMJAneESAAAij3dHr4o/WlvaX3ak4RoAiA9Vl94oXzBRyfYu1Sy533QOACDGMOgWpdwv/EWjHE0KhqwqOvfbpnMAIC7Y7HY1+MMnaGa1f2i4BgCAyJPbu0bSx+u+AQAjL/erVygUsijXVa/6FS+azgEAIKLULJqnRFufBgIpqp51g+kcAIgLSZk5u9aX2mteMlwDAIg1DLpFqR2vLJEkbfWUKX/iIYZrACB+BL9wkiSpyOVWb3uL4RoAACJHR4NbBYnrJUn2w88yGwMAcaR42rFq9ZZKktqf/bPZGAAAIkzShuWSpAZPhZzJqYZrACB+dGZPlSQV290K+P1mYwAAMYVBtyhVEAwftd3inGC4BADiS/UF31afP1MOq0d1C+42nQMAQMTYuGie7JZBdQ/mavwZs03nAEBc2WqvlCTl+9YaLgEAIHL4fV4VO8L3UnoLpxuuAYD4Ujbze/KHEpSW0KbapX8ynQMAiCEMukWhzW8+r1xnfXgtxVevMJ0DAHHFZrerwRe+iZTW/K7hGgAAIseozvBa7wZ/hWx2u+EaAIgvaSdcLEnKd25SS83bhmsAAIgMNUvuV7K9S75goqouvdF0DgDElcyCUjUNhNeXBt95xnANACCWMOgWhTqenS9JavWWqnjasWZjACAOeSqPliQVJ9bJ09tpuAYAAPN62ppV5AqflLBzzTcA4OApO+4sbfcWyWoJqumvvzGdAwBARLCv/pek8NrSpMwcwzUAEH/a0idJkoqsbsMlAIBYwqBbFBrtWyfp47UUAICDq/qSuRoIpMpl3aE1C+eZzgEAwDj3wnuUYPWqz5+p6gu+bToHAOJSk8KnJeTuWGO4BAAA8wJ+v4oT1kuSOrO/YLgGAOJTyYXXKRCyKcuxVWufW2w6BwAQIxh0izLNq1Yoz7VJkpR24izDNQAQnxyJSWrwhoeNkzf/23ANAADmpbeE13k3+CpZWwoAhjiOPEeSVJC4Xts31RquAQDArNq//VFpCW3yhxJUdslc0zkAEJdyyidpq6dMkuRZ/lfDNQCAWMGgW5TZ+uT/yWoJaru3SGXHnGE6BwDiVl/x4ZKkYqdbvoF+wzUAAJjj6e1Usasu/OeP1nsDAA6+qtNnq9OXL5sloM2P3mc6BwAAo0LvPitJahqoUGbhOMM1ABC/WhOrJEmFIdaXAgCGB4NuUSa3P/yO3J3rKAAAZlTNulHeYJKSbN1as+R+0zkAABhTs+AeOa396g+kqZqTEgDAqMZA+PWi7K5VhksAADCr0BoeqNieXm24BADiW/45VysYsirHuUWbXv+H6RwAQAxg0C2KtK1frQLXekmS46jzDNcAQHxLzMhSgyd8E8lR+7LhGgAAzEmpf1OS1OCpkCMxyXANAMQ3y6GnSJIKXevV3dJguAYAADPWPrdYWY6tCoRsGnPhd03nAEBcK5zyZbV6x0qSuv75sOEaAEAsYNAtitQ/fr9sloA6ffmqOm2m6RwAiHtdeYdIkoodbgX8fsM1AAAcfL6BfhU7wycl7BhzuOEaAEDVOd9Urz9bCVav3IvuMZ0DAIARnuV/lSRt9ZQpp3yS4RoAQEvCeEnSaH+d4RIAQCxg0C2KZHetlvTxGgoAgFkVl3xPg0GnUu3tWvPXB03nAABw0K15ZJ6SbN3yBpNUNetG0zkAEPdsdru2DJZLkjJbVxquAQDAjMJQeJCiNbHKcAkAQJIyT/m6JCnPuUmNK5ebjQEARD0G3aJEZ/NmFbrCJyXsXEMBADArPb9YjR+tL9V7/zQbAwCAAY661ySF15YmZmQZrgEASNLghOMlSUWJbg10dRiuAQDg4Nq4/FnlOBsUClmUf87VpnMAAJJKjzhZ27wlslhCav37703nAACiHINuUWLDI/OUYPWp15+tqnO+aToHAPCR9szJkqRi+3rDJQAAHFwBv1/FjvCbcXau8wYAmDfpku9qhz9DTuuA1iy4y3QOAAAHVfeyBZKkFu9YFU75suEaAMBOzZbwydN5A2sNlwAAoh2DblEis+09SdIWX4VsdrvhGgDATmMvul6BkF0ZCS2qffph0zkAABw0NX/5jVLtHRoMOlU5+ybTOQCAj9gdTjV4wydPpzauMFwDAMDBNdq/TpLUkjDBcAkA4JOSjr1QkjTatUHb3B8argEARDMG3aJAf2ebij5aWzpYdZzhGgDAJ40qHa9mT/idSL5//81wDQAAB4/1/eclSY2eCqXlFBiuAQB80o5xR0iSip1u+Qb6DdcAAHBwNK5crjznZklS5ilfN9oCANhd5UkXqsNXIJsloC2P3W86BwAQxRh0iwK1C+6R0zqgHf4MTbrku6ZzAAD/ZVtylSSpUG7DJQAAHBwBv19F9vDvvZ1rvAEAkaN65vXyBFKUaOtVzaJ7TOcAAHBQtP7997JYQmrzjlHpESebzgEA/JfGYPjk6ZyeGsMlAIBoxqBbFEhtfEuS1OCtkN3hNFwDAPhvhedeo2DIqmxno9wvcaobACD21T27UBkJrQqE7Bp70fWmcwAA/8WVmrlrfanLvdxwDQAAB0f+QK0kqclSYbgEALAntulnSJIKE93qbNpouAYAEK0YdItw3h29Kv5obenOtRMAgMiSX32YWjzjJEl9Ly02XAMAwMjzvblUktTsKdeo0vFmYwAAe9RTME2SVOxwy+/zGq4BAGBkta57X/mu8NBE4jEXGK4BAOzJhK9epu7BXNktg9qw6JemcwAAUYpBtwi35pF7lWjrlSeQokmzbzSdAwDYixZn+CZ/gb/OcAkAACOvyBJ+M87O9d0AgMhTOesG+YIupdg7VfPYA6ZzAAAYUY2P/1o2S0AdvgKNP/lrpnMAAHtgs9vV4C+XJGW1f2C4BgAQrRh0i3CJ68PrJRq8FXImpxquAQDsTfYZV0qS8lybteXtlw3XAAAwctwvPqFRjiYFQ1YVnnuN6RwAwF6kjspXoye8us22+kXDNQAAjKyc3hpJUmOQtaUAEMkCk0+SJBW53OptbzFcAwCIRgy6RTC/z6siR/ikhJ3rJgAAkWnMYcep1VMqSdr+9B/NxgAAMIL6Xn5UktTiGaf86sMM1wAAPkvHqC9Ikort6xXw+w3XAAAwMjqbNqogcb0kyXrY6YZrAACfpfqCb6nPnymH1aO6hfeYzgEARCEG3SJYzZL7lWLvlC+YqMpZN5jOAQB8jmZ7pSQp37vWcAkAACOnMBBe093inGC4BADwecpmfk/+UILSE7ap9qk/m84BAGBEbFh0j+yWQXUP5qrqzDmmcwAAn8HucKrBF76Xktb0juEaAEA0YtAtgtlrXpIkNXrKlToq33ANAODzpBx/sSQp37VRLTVvG64BAGD41a94UbmueklS9hlXGK4BAHyezMJxahoIr3ALvv2M4RoAAEZGVvuHkqQGf7lsdrvhGgDA5/FUHCVJKna55entNFwDAIg2DLpFqIDfryJ7eG3pzjUTAIDIVnH82druLZLVElTTkw+azgEAYNi1Pxs+DajVU6oxhx1nuAYAsC/a0qolSUVWt+ESAACGX297i4pc4d9xgUlfMVwDANgX1TNv0EAgVS5bn2oW3Ws6BwAQZRh0i1C1S/+k9IQ2+UMJKpv5PdM5AIB91KTwaQm5fTWGSwAAGH75vvB67p3rugEAkW/MhdcqELIpy9GsuucfM50DAMCwqltwtxxWj/r8maq+8NumcwAA+8CRmKQGb/i1peRNbxiuAQBEGwbdIlTwnfA6iaaBCmUWjjNcAwDYV44jzpYkFbjWq33zOsM1AAAMn5aat5Xv3CTp43XdAIDIl1sxRVs9ZZKk/lcYdAMAxJa05nclSQ2+CtkdTsM1AIB91Vs0XZJU7HTL7/MargEARBMG3SJUkXW9JGl7erXhEgDA/qg85RJ1DebJZvFr06McuQ0AiB1Nf/2NrJagtnuLVHH82aZzAAD7oTVxgiSpMMT6UgBA7PD0dqo4sS785/IZhmsAAPtj4uyb5A0mKdnWrdWPcC8FALDvGHSLQOuWLVGWo1mBkE3FF1xrOgcAsB9sdrsa/OH1paM6VxmuAQBg+OTuWCPp4zXdAIDokXfWNxUKWZTj3KJNr//DdA4AAMNizcJ5cll3aCCQqupZN5jOAQDsh8SMLDV6wq8xJax92XANACCaMOgWgQZefVyStNVTptyKKYZrAAD77Yv/I0kqcrnV09ZsOAYAgKHbvqlWBYkbJH28phsAED2Kph6lVu9YSVLXsoWGawAAGB5Jm/8tSWrwVsiRmGS4BgCwvzpzvyhJGpOwXgG/33ANACBaMOgWgXaukWhNrDJcAgA4EBPPu1q9/lFKsHpVt+Au0zkAAAzZ5kfvk83iV9dgvqrOuNR0DgDgADQnVEqS8gfXGi4BAGDo/D6vip3heym9RYcbrgEAHIiKmTdoMOhUqn271jzxW9M5AIAowaBbhNn0+j+U49yiUMiivLO+YToHAHAAbHa7GnzhI7czWlcargEAYOiyu8LruBv85YZLAAAHKvPk8KByvnOTGt9/3XANAABDs/qRe5Vs65Y3mKiJs28ynQMAOADp+cVq8nz0WtPKf5qNAQBEDQbdIkznsoclSS3esSqaepThGgDAgfKNP1aSVOxya6Crw2wMAABD0N3SoELX+vB/fLSeGwAQfcYedaravGNksYTUuvT3pnMAABgSR+3LkqTGgQolZmQZrgEAHKjtGZMlScV2t+ESAEC0YNAtwhQM1kmStn60TgIAEJ0mzZqrHYF0Oa39ql14t+kcAAAOmHvRPUqwetXrH6WJ511tOgcAMARNlvDJ03kDtYZLAAA4cAG/X8WO8EBEZ94hhmsAAEMx9uK5CoTsykhoUe0zC0znAACiAINuEaTx/deV59wk6eN1EgCA6GR3ONXoDd9ESt6ywnANAAAHLnPbe5KkBl+FbHa74RoAwFAkHnOBJGm0a4Pa1q82XAMAwIFZ88RvlWpv12DQqYqZN5jOAQAMwajS8WoeKJMk+d540nANACAaMOgWQVqX/l4WS0ht3jEae9SppnMAAEO0o/QISeH1pb6BfsM1AADsv4GuDhW5wicl+KqOM1wDABiq8Sd/TR2+AtksAW157D7TOQAAHJiVz0mSmjzlSs8vNhwDABiqbSkTJUmFYn0pAODzMegWQXaujdi5RgIAEN0mzporTzBZSbYe1Twyz3QOAAD7bc2Cu+S09mtHIF2TLrnedA4AYBg0BsslSdndNYZLAAA4MMX29ZKk7RmTDZcAAIZD4bnXKBiyKtvZqA0vLzWdAwCIcAy6RYht7g812rVB0sdrJAAA0c2VmqnGgfDwsrPuNcM1AADsv9TGtyRJjd4K2R1OwzUAgOFgnXa6JKkw0a3O5s1mYwAA2E+1zyxQRkKLAiGbSi/6rukcAMAwyK8+TC3esZKknn8tNlwDAIh0DLpFiC2P3S+bJaAOX4HGn/w10zkAgGHSXTBNkjTG4VbA7zdcAwDAvvMN9KvYWSfp43XcAIDoV3XW5eoezJHdMqgNi+4xnQMAwH7xvfGkJKl5oFzZY6sM1wAAhkuLY4IkqcC/znAJACDSMegWIXJ6wusidq6PAADEhsrZN2ow6FSKvUM1jz9gOgcAgH1Ws+geJdp65Qkma+KsuaZzAADDxGa3q9EfPnk6c/sHhmsAANg/hXJLkrYlTzRcAgAYTqNOmyNJynNtVsM7r5iNAQBENAbdIkBn00YVJob/cWY97AzDNQCA4ZQ6Kl+NnvBNJOsHzxuuAQBg37ncyyVJjQMVcqVmGq4BAAwnf/XxkqRil1v9nW2GawAA2Dful/6mbGejgiGrRp9ztekcAMAwKjn8K9rmKZEktf39D4ZrAACRjEG3CLBh0S9ltwyqezBHVWfOMZ0DABhm7aO+IEkqtrO+FAAQHfw+r4od4Tfj7FzDDQCIHdVfu1Z9/kw5rAOqffhu0zkAAOyTvpcWS5JavGNVMPlwwzUAgOHWZKuUJOV71xouAQBEMgbdIkBWe3hNRKO/Qja73XANAGC4jbv4evlDCUpP2KZ1Ty8wnQMAwOeqefz/lGLvlC/oUuXsG03nAACGmd3hVKMvfPJ0atNbhmsAANg3Bf46SVKLY4LhEgDASEg54RJJUr5ro1rWrDRcAwCIVAy6Gdbb3qIiV/ikBP+krxiuAQCMhKziCjUPlEuS/CuWmo0BAGAf2FaF1203eiqUOirfcA0AYCQMlM+QFF5f6t3Ra7gGAIDPtuXtl5Xn2ixJGnUam3EAIBZVHH+2tnuLZLUE1fTEA6ZzAAARikE3w+oW3iOH1aM+f6aqL/y26RwAwAhpS62WJBVa1xsuAQDgswX8fhXbw7+vOkZNMVwDABgpEy+5XgOBVCXa+lSz8B7TOQAAfKbtT/9RkrTNU6KSwzk0AABiVZPChwbk9NUYLgEARCoG3QxLa35HktTgq5Dd4TRcAwAYKUUXfEeBkE2jHE1yv/AX0zkAAOzV2r/PV3rCNvlDCRp38fWmcwAAI8SZnKoGT3h9adLGNwzXAADw2fK9ayVJTbZKwyUAgJHk+NJZkqRC13p1NLjNxgAAIhKDbgZ5ejtV7Az/gvZ8tC4CABCb8sZPVYtnnCRpxytLDNcAALB3gbeeliQ1D5Qrq7jCcA0AYCT1Fk2XJBU73PL7vIZrAADYs5Y1K5Xv2ihJSjnuIsM1AICRVHnaLHUN5slm8WvjonmmcwAAEYhBN4NqFt0rl61PA4FUTbyEkxIAINa1uiZIkgqCvAsJABC5iqzh31M7124DAGJX1ewb5A0mKtnepdWLf2U6BwCAPWp64gFZLUG1+wpV8ZVzTecAAEaQzW5Xoz/8xstRnR8argEARCIG3QxK3vhvSVKDt0LO5FTDNQCAkZZ75jcVClmU66zX5jefN50DAMCn1D3/mLIczQqEbCq64DumcwAAIywpM0eNA+GbSAlrXjJcAwDAnuX21UiSGkOcOA0A8SD0xf+RJBW53OppazZcAwCINAy6GeL3eXetLe0tnG64BgBwMBQdMkOt3lJJUsez883GAACwB/2vPCZJavGMU974qWZjAAAHRWfuFyVJxQluBfx+wzUAAOyuffM6FbjWS5IcXzrLbAwA4KCYeN7V6vVnKcHqVd2Cu03nAAAiDINuhqx+5D4l27vkDSaqavYNpnMAAAfJVnulJGm0b53hEgAAPq0gFL6BtHPdNgAg9pVdMleDQYfSErar9m9/NJ0DAMBuNj16n2wWv7oG81R52izTOQCAg8Bmt6vBFz7FM6N1peEaAECkYdDNkIS14XUQjQMVSsrMMVwDADhY0k++VJKU59qk5lUrDNcAAPCxzf9eplxnfXjN9pnfNJ0DADhIMgtK1eQJ30QKvfuM4RoAAHY3qvNDSVKjv0I2u91wDQDgYPGNP06SVOyq00BXh+EaAEAkYdDNgIDfrzEJ4ZMSdq6HAADEh3EzTlObt1hWS1Bbn/w/0zkAAOzS+dxDkqRWb6mKDplhNgYAcFBtz5gkSSqyrjdcAgDAx3ramlXkckuSglNPMlwDADiYJl58nfoD6XJa+7Vm0T2mcwAAEYRBNwNqn/ydUu3bNRh0qGLWjaZzAAAHWZPCpyXk9a8xXAIAwMfyB9dK+njNNgAgfpRccK0CIZsyHVtV++wi0zkAAEiS6hbcpQSrV73+LFWff43pHADAQeRITFKDN3wvJaX+TcM1AIBIwqCbAaF3n5MkNXkqlJ5fbLgGAHCwuY4+X5I02rVBbetXG64BAEBqfP915Ts3Sfp4zTYAIH7klE9Ss6dckuR9/QnDNQAAhGW0rpQkNfhYWwoA8aiv5EuSpGKXW76BfsM1AIBIMeRBN6vVquzsbHm93uHoiQtFtvBR2zvXQgAA4suEUy5Wp2+0bJaA6h+/33QOAABqXfp7WSwhtXmLNW7GaaZzAAAGbEuqkiQVym24BAAAaaCrQ8UfrS31VR5tuAYAYEL17BvkCSYrydajNY/MM50DAIgQQx50S0lJUVlZmZxO53D0xLzaZxYo09GiQMim0ouuN50DADCkMRg+cjuna5XhEgAApLyBWklSk4W1pQAQr0af8y0FQ1blOBu04dWnTecAAOJc7cK75bT2qz+QromXzDWdAwAwwJWaqcaB8L0UR91rhmsAAJFiyINuEyZMUGtr63C0xAXfG09Kkpo95coeW2W4BgBgiuXQ8Gk5Ba716mzebDYGABDX2tav1mjXBkmSa8Z5hmsAAKYUTD5crZ6xkqSeFxYargEAxLvkLSskSQ3eCjkSkwzXAABM6R59qCRpjMOtgN9vuAYAEAmGPOh25ZVXasuWLXr22WeHoyfm7Vz/sC1pouESAIBJVWdfoZ7BbCVYfdrAkdsAAIPqH/uVbJaAOn2jNeGUi03nAAAM2uoYL0ka7a8zXAIAiGe+gf5da0v7xhxuuAYAYFLFrBs0GHQqxd6hmr/8xnQOACACDMug21VXXaWLLrpIv/rVr9TR0TEcXTFpw8tLle1sVDBk1ehzrjadAwAwyGa3q2EwfOR2Ztt7hmsAAPEsp3u1pI/XagMA4lfWaZdJkvKcm9W4crnhGgBAvKp5ZJ6SbD3yBpNUfemNpnMAAAal5RSo0RN+zcr6/jLDNQCASDDkQbdx48bpn//8pwYGBjR37lzl5OQoLy9P48aN2+NHWVnZcHRHpZ5/LZYktXrGqmAy70ICgHg3OPF4SVKRy63+zjbDNQCAeNTZvFmFieGTEnau1QYAxK/SL52kbd4SWSwhbXvqd6ZzAABxyln3miSpwVMpV2qm4RoAgGntmVMkScV21pcCAIZh0G3z5s3avHmzAoGAQqGQQqGQ2tradn1+Tx/7o6mpSffdd59OOukkjRkzRg6HQ/n5+Tr33HO1YsWKPX5PT0+P5s6dq5KSEjmdTpWUlGju3Lnq6enZ6/MsXrxY06dPV3JysjIzM3XqqafqnXfe2a/Wz7Nz7cNW54RhfVwAQHSadPF12uHPkNM6oNoFd5nOAQDEoQ2L7pHdMqiewWxVnX2F6RwAQARotoZPS8jzrDVcAgCIRwG/X2Mc4TfjdOUdYrgGABAJxs2cK38oQekJ27Tu6QWmcwAAhtmH+gCbNm0ajo69+vWvf60777xTZWVlOvHEE5Wbmyu3262lS5dq6dKlevTRR3XBBRfsun7Hjh065phj9P777+vEE0/URRddpA8++ED33nuvXn75Zb3++utKTk7e7Tluv/123XLLLRozZoyuuuoq9fX1acmSJTryyCO1bNkyHXvssUP+ORreeUVFzs2SpKxTvz7kxwMARD+7w6n1vgpNsL+t1MbhHa4GAGBfZG7/QEqSGgYrVG0f8j8PAQAxIPnYr0lvvKjRrg1qXfe+8sZPNZ0EAIgjNY8/oCn2Dg0GnaqczdpSAICUVVyhLQPlGpNUq8EVT0lnzzGdBAAwaMh3MkpKSoajY6+mT5+u1157TTNmzNjt88uXL9cJJ5ygq6++WmeeeaacTqck6a677tL777+vm266SXfeeeeu63/yk5/o1ltv1V133aX//d//3fV5t9utn/zkJ6qsrNRbb72l9PR0SdK1116r6dOn64orrtDatWtlH+JNn21//6OKrSFt85ao9EsnDemxAACxo3/ckVLL2yp2ueXd0StncqrpJABAnOjvbFOxK3xSws512gAAVJx4vtpf/olGOZrU+Pivlff//mQ6CQAQR6wfPC8lSo2eCo3NKTCdAwCIENtSJ2pMoFZFVrfpFACAYUNeXTrSzjnnnE8NuUnSjBkzdNxxx6mjo0OrVq2SJIVCIf3xj39USkqKfvzjH+92/c0336zMzEz96U9/UigU2vX5+fPny+/365Zbbtk15CZJ1dXVmj17tjZs2KCXXnppyD9Hvje87qHJWjnkxwIAxI7qWTdoIJCiRFuvahbNM50DAIgjtQ/fLYd1QDv8GZp08XWmcwAAEaQxGF5fmtNbY7gEABBPAn6/iu3hAYb2zCmGawAAkaT4gmsVDFk1ytEk94tPmM4BABg0bINuPp9PjzzyiL7xjW/otNNO0wknnLDb1//zn//o+eefVyAQGK6nVEJCgiTtOm3N7XarublZRx555KfWk7pcLh199NFqamrS+vXrd33+lVdekSSddNKnT1k7+eSTJUmvvvrqXhu8Xq96enp2+/hvLWtWarRrgyQp5biL9uMnBADEOmdyqho84ZtISRteN1wDAIgnqU1vSZIafBWyO5yGawAAkSTh8DMlSQWJ69XRwIkJAICDY93TC5SesE2BkF3jZs41nQMAiCB546dqq6dMktT38qOGawAAJg3LoNubb76piooKzZ49W3/84x/13HPP7Rog2+mpp57SKaecohdffHE4nlJbtmzRiy++qPz8fE2ePFlSeNBNkioqKvb4PTs/v/O6nX9OSUlRfn7+Pl3/3+644w6lp6fv+iguLv7UNY1PPCCrJah2X6EqvnLuPv6EAIB40Vs4XZJU7KiT3+c1XAMAiAfeHb271pb2jzvScA0AINKMP2O2ugdzZbcMaiMnTwMADhL/iqWSpCZPubKK93yfBwAQv1qcEyRJhcE6wyUAAJOGPOi2ceNG/c///I8aGhp0zjnn6OGHH1Z1dfWnrps5c6ZCoZCeeGLoR4kODg5q1qxZ8nq9uuuuu2Sz2SRJ3d3dkrTbCtJPSktL2+26nX/en+v/280336zu7u5dHw0NDZ+6JrcvvOZh59oHAAA+qerSG+ULJirZ3qWaJfebzgEAxIGahfco0dangUCKqmfdYDoHABBhbHa7Gvzh17FGdX5ouAYAEC8KreFtPNtSPn2PCQCA3K9eoVDIolxnvTa/+bzpHACAIUMedPv5z3+unp4e3XbbbfrLX/6iWbNmKSMj41PXTZo0SVlZWXr77beH9HzBYFBz5szRa6+9piuvvFKzZs0a0uMNldPpVFpa2m4fn9TR4FahK/yPs51rHwAA+KSkzJxd60vtNS8ZrgEAxIOkjW9Ikho8FXImpxquAQBEouAXTpIkFbnc6m1vMVwDAIh17hf+olGOJgVDVhWd+23TOQCACFQ87Vi1ekslSR3/eMhoCwDAnCEPur3wwgtKT0/XD37wg8+9trS0VI2NjQf8XKFQSFdeeaUWLVqkmTNn6re//e1uX995MtveTmDr6enZ7bqdf96f6/fXxkXzZLP41TWYp/FnzD7gxwEAxLbO7KmSpGK7WwG/32wMACCm+X1eFTvCa0t7iw4zXAMAiFTVF3xbff4sJVi9qnv4LtM5AIAYt+OVJZKkrZ4y5U88xHANACBSbbVXSpJGe9caLgEAmDLkQbe2tjaVlZXJYrF87rU2m019fX0H9DzBYFCXX365/vznP+uiiy7SQw89JKt19/yKivBpOG63e4+PsfPzO6/b+ee+vj61tHz6nal7un5/7Vzv0Ogvl81uP+DHAQDEtrKZ35M/lKC0hDbVLv2T6RwAQAxbvfhXSrZ3yRdMVNXsm0znAAAilM1u1xZf+DWx9K3vGq4BAMS6gmD4fkyLc4LhEgBAJEs7MbztLc+1Sc2rVhiuAQCYMORBt4yMDDU1Ne3TtRs2bFBeXt5+P0cwGNQVV1yh+fPn68ILL9TChQtls9k+dV1FRYUKCgr0xhtvaMeOHbt9zePx6LXXXlNBQYHKy8t3ff6YY46RJD3//Kf3eC9btmy3a/ZXT1uzilzhf5wFp558QI8BAIgPmQWlahoI30QKvvOM4RoAQCxLWBNek93gqVBSZo7hGgBAJPNWHi1JKkp0y9PbabgGABCrNr/5vHKd9QqFLMr96hWmcwAAEazsmDO03VskqyWorU8+aDoHAGDAkAfdpk+frm3btmn58uWfed3SpUvV0dGhGTNm7Nfj7zzJbf78+Tr//PO1aNGiPQ65SZLFYtEVV1yhvr4+3Xrrrbt97Y477lBnZ6euuOKK3U6fu+yyy2S323XbbbfttsK0pqZGCxYsUFlZmY4//vj9at7JvfAeJVi96vNnqfr8aw7oMQAA8aMtfZIkqci655NJAQAYqoDfr+KE8O+ZnWuzAQDYm+pL5qo/kCaXdYdqFtxjOgcAEKM6np0vSWr1lqp42rFmYwAAEa9R4fWluf1rDJcAAEwY8i7Na665Rs8884zmzJmjJ554QlOmTPnUNa+99pq+8Y1vyGKx6Jpr9m/g69Zbb9VDDz2klJQUVVZW6uc///mnrjnrrLM0depUSdJNN92kv//977rrrrv03nvv6dBDD9UHH3yg5557TlOnTtVNN+2+mqeyslI//elP9aMf/UhTpkzReeedpx07dujRRx/V4OCg/vCHP8h+gCtH01velZKkLb4KTWRtKQDgc5RceJ0CC59QlmOr1j63WBNOudh0EgAgxtT+7Y+alLBd/lCCymZ+z3QOACDCORKTtMlTofHJ7yql/k3TOQCAGDXat05KlLbaK5VvOgYAEPGcR50rvf2SClzrtX1TrbLHVplOAgAcREOevjr55JN17bXX6v7779e0adN0yCGHaNOmTZKk2bNna/Xq1frggw8UCoV0880360tf+tJ+Pf7mzZslSX19fbrtttv2eE1paemuQbfk5GS98sor+t///V/99a9/1SuvvKL8/Hxdf/31+slPfqLk5ORPff8tt9yi0tJS3XfffXrwwQflcDh0xBFH6NZbb9Vhhx22X707eXo7VeyqkyT5PlrzAADAZ8kpn6RGT5mKEuvkWf5XiUE3AMAwC737jOSSmgYqVFJQajoHABAFdow5XGp/V8VOt3wD/XIkJplOAgDEkOZVK5TvCt9TSjuB18IAAJ+v6rSZ6nzjZ8p0tGjzo/cq+4e/N50EADiILKFQKDQcD/S73/1OP/3pT9Xa2vqpr2VnZ+vWW2/VVVddNRxPFdF6enqUnp6uV+6aq2N2/FH9gTTZf7iOFwEBAPvk3Z/O1qF6Sm3eMcq5Y5XpHABAjOn84QRlOrbqnYQLNO2WP5jOAQBEgYGuDlnnVctp7df7xT/Q1MtvNp0EAIgh7/7vpTo0tFTbvUXKvqPGdA4AIEqs+sHpmuxars391Sq969+mcwAAQ7Rz1qq7u1tpaWmfee2w7dP85je/qTlz5ug///mPVq1ape7ubqWkpGjixImaMWOGnE7ncD1VVEhpfEfKlBo8FRrPkBsAYB/ln3O1gk88rRznFm16/R8ae9SpppMAADGi9tlFqnJsVSBkU8mF15nOAQBEicSMLK33VKg86QM51r0iiUE3AMDwye2vlRKlJlUo23QMACBqWA45RVqzXIUut7pbGpSeX2w6CQBwkAzboJskJSQk6Oijj9bRR7Oqs9C5QZLUV7J/q1oBAPGtcMqXtXXxWI12bVDXPx+WGHQDAAwT7+tPSE5pq6dMReWTTOcAAKJIV96hUu8HKna4FfD7ZbMP60uKAIA41bZ+tQpc6yVJjqPOM1wDAIgmVed8Uz0fzFNawna5F96taTfebzoJAHCQWIf6AK+99pp8Pt9wtMSUJFuPvMEkTZx5g+kUAECUaUkYL0ka7a8zXAIAiCWFckuSWhOrDJcAAKJN5ewbNRh0KtXerjV/fdB0DgAgRtQ/fr9sloA6ffmqOm2m6RwAQBSx2e1qGKyQJGVue89wDQDgYBryoNuxxx6rjIwMHXvssfrJT36if/3rXxoYGBiOtqjX4KlUYkaW6QwAQJTJPOXrkqQ85yY1vv+62RgAQEzYuPxZ5TgbFAxZlX/O1aZzAABRJi2nQI2e8E0kvfdPszEAgJiR3bVaktQYqDBcAgCIRoNVx0mSihLdGujqMFwDADhYhjzotnNN6Wuvvaaf/exnOumkk5SRkaEjjzxSP/zhD/XPf/5TfX19Qw6NRl15h5hOAABEodIjTtY2b4kslpBal/7OdA4AIAZ0L3tYktTqHavCKV82XAMAiEbtmVMkScX28PpSAACGorN5swpd4VOnLYeeYrgGABCNJl3yXe3wZ8hpHdCah+80nQMAOEiGPOj2yiuvqKura9eg21e+8hU5HA795z//0S9+8QuddtppysrK0vTp03XjjTfq6aefHo7uiDcYdKhy9o2mMwAAUarZUi5JyhtYa7gEABALdq7D3rkeGwCA/TX2ou8qELIrI6FVdc89YjoHABDlNjwyTwlWn3r92ao655umcwAAUcjucKrBGz4VNLXpLcM1AICDZciDbpLkcDh01FFH6ZZbbtGyZcvU1dW1a9DtlFNOUVJSkt59913NmzdPZ5999nA8ZcRr8pQpLafAdAYAIEolHXuhJGm0a4Na171vNgYAENUaVy5XnnOzpI/XYwMAsL9GlY5Xsyf8hhzfv580XAMAiHaZbe9JkrYMlstmtxuuAQBEqx3jjpAkFTvd8u7oNVwDADgYhmXQ7b/ZbDYdeuihOuqoo3TEEUfo0EMPlSSFQiGFQqGReMqI05ExyXQCACCKVZ50oTp8BbJZAmr4ywOmcwAAUWzbU7+TxRJSm3eMSo842XQOACCKbUupliQVar3hEgBANOvvbFPRR2tLByccZ7gGABDNJs2+UZ5AihJtvVrzyL2mcwAAB8GwvU3G5/PpzTff1KuvvqpXX31Vb775pgYGBhQKhWS323XYYYfpmGOO0THHHDNcTxnRxl78XdMJAIAo1xisUJaaldNTYzoFABDF8jxrpUSpyVKhHNMxAICoVnjutxV87G/KdjbK/dLfVHF8fGxuAAAMr9oF9+hQ64B2+DM06ZLrTecAAKKYMzlVbm+FKpLek2v9ctM5AICDYMiDbj/5yU/06quv6q233pLX61UoFJLD4dhtsO2II45QcnLycPRGjczRJaYTAABRzjb9DOnDV1WY6FZn00ZlFo4znQQAiDKt695XvmujpI/XYgMAcKDyJx6iZs84FSSuV9+/HpEYdAMAHIDUxrekZKnBW6EJDqfpHABAlOspmC51vadih1t+n1d2frcAQEwb8urSn/3sZ1q+fLmys7P14x//WC+99JK6urq0fPly/fznP9eJJ54Yd0NuAAAMhwlfvUzdg7myWwa1YdEvTecAAKJQ4+O/ls0SUIevQJUnMegGABi6FucESVJBwG24BAAQjbw7elX80drSHeOOMFwDAIgFlbPmyhdMVIq9UzWPPWA6BwAwwoY86CZJoVBIjY2NeuCBB/TrX/9af/jDH/Thhx8Ox0MDABC3bHa7GvzlkqSs9g8M1wAAolFOb3j9dWOwwnAJACBWZJ9xhSQpz7VZ9SteNFwDAIg2NYvmKdHWK08gRdUzWVsKABi61FH5avSE76XYV/NvFACIdUMedGtpadFjjz2mb33rW8rPz9fSpUt13XXX6Ytf/KKysrJ05plnat68eXr33XcVCoWGoxkAgLgRmHySJKnI5VZve4vhGgBANOlocKsgcb2kj9ZhAwAwDMYcdpxaPaWSpPZn/2w2BgAQdZI2vC4pvLbUlZppuAYAECs6sqdKkorsbgX8frMxAIARNeRBt9zcXJ1//vl64IEHtGrVKrW1temJJ57Qd77zHZWUlOjZZ5/VjTfeqOnTpyszM1Onn376cHQDABAXqi/4lvr8mXJYPapbeI/pHABAFNm4+F7ZLYPqHszVhK9eZjoHABBDmu2VkqR87zrDJQCAaOL3eVXkCK8t7SmYZrgGABBLyi6ZK38oQekJbap9ijfkAEAsG5bVpZ+UlZWls88+W/fdd5/ee+89rVixQl/96lclST09PXruueeG+ykBAIhZdodTDb7wTaS0pncM1wAAosmoj9ZeN/grZLPbDdcAAGJJ2gmXSJLyXRvVUvO24RoAQLSoWXK/Uuyd8gUTVTnrBtM5AIAYklk4Tk0DFZKk4NtPG64BAIykYR90W7dunX7/+9/rkksuUVFRkaZPn66///3vu9aWFhYWDvdTAgAQ0zwVR0mSil1ueXo7DdcAAKJBb3uLilzhkxICk080XAMAiDVlx52l7d4iWS1BNT3xG9M5AIAoYa95SZLU6ClX6qh8wzUAgFizPb1aklRkXW+4BAAwkoY86LZ69Wr95je/0QUXXKD8/HxNnDhRV199tR599FE1Nzdr7NixuvTSSzV//nxt2LBBW7ZsGY5uAADiRvXMGzQQSJXL1qeaRfeazgEARIG6BXcrwepVnz9T1Rd8y3QOACAGNSl8WkLujlrDJQCAaBDw+1VkD78Zp2PUFwzXAABiUfEF1yoQsinL0ax1y5aYzgEAjJAh76+ZMmWKLBbLrhPbJkyYoKOPPlrHHHOMjjnmGBUUFAw5EgCAeOZITFKdt1KVSe8qedMbpnMAAFEgvfkdKVlq8FWqyuE0nQMAiEGOI8+R3nlZBa71at+8TqNKx5tOAgBEsNqlf9KkhDb5Qwkqm/k90zkAgBiUWzFFjZ4yFSXWaeDVx6WTv2Y6CQAwAoY86DZ58uRdQ21HH320cnJyhqMLAAB8Qm/RdKnjXRU73fL7vLIztAAA2AtPb6eKEsMnJexcfw0AwHCrOn22uv5zmzISWrTp0Xs16ubfmk4CAESw4DvPSC6paaBCJYXjTOcAAGJUa2KVilSnwpDbdAoAYIQMeXXpBx98oPvvv1/nnnsuQ24AAIyQibNvkjeYpGRbt1Y/wvpSAMDe1Sy4Ry7rDg0EUlU98wbTOQCAGNbgL5ckZXd+aLgEABDpiqzhgYO2tGrDJQCAWJZ31jcUClmU49yiTa//w3QOAGAEDHnQDQAAjLzEjCw1eiokSQlrXzZcAwCIZCn1b0qSGryVciQmGa4BAMS0Q06RJBW61qu7pcFwDAAgUq19brGyHFsVCNk05sJrTecAAGJY0dSj1OIdK0nqXPaw4RoAwEgYtkE3r9erhx56SBdffLGmTZum8ePHa9q0abr44ov18MMPy+v1DtdTAQAQlzpzvyhJGpOwXgG/33ANACAS+Qb6VewMn5TQWzTdcA0AINZNPPcq9fqzlWD1yv3IL03nAAAilGf5XyVJWz1lyq2YYrgGABDrtiZUSpJGD9YZLgEAjIRhGXRbuXKlJk6cqMsvv1xLlizRypUr5Xa7tXLlSi1ZskRz5sxRdXW1Vq5cORxPBwBAXKqYeYMGg06l2rdrzRO/NZ0DAIhAaxb/Skm2bnmDSZo4+ybTOQCAGGez27VlMLy+NLOV1/0AAHtWGAq/Gac1scpwCQAgHmSdMkeSlO/cpMb3XzdcAwAYbkMedGtsbNSJJ56oTZs2adSoUbr55pu1aNEivfDCC1q0aJFuvvlmZWdna+PGjTr55JPV1NQ0HN0AAMSd9PxiNXnCN5G08p9mYwAAEcmx7hVJUqOnQokZWWZjAABxYXDCcZKkIpdbA10dhmsAAJFm0+v/UI5zi0Ihi/LO+obpHABAHCg94mS1ecfIYgmpdenvTecAAIbZkAfd7rjjDnV2duqcc85RfX29brvtNl188cU64YQTdPHFF+u2225TfX29zj33XLW3t+uOO+4Yjm4AAOLS9ozJkqRiu9twCQAg0gT8fhU7wr8fdq67BgBgpE265HrtCKTLae3XmgV3mc4BAESYrn8+LElq9Y5V0dSjDNcAAOJFk6VCkpQ3UGu4BAAw3IY86Pbcc88pOTlZDz30kBITE/d4jcvl0vz585WcnKx//OMfQ31KAADi1tiL5yoQsisjoUW1zywwnQMAiCBr/vqgUu3tGgw6VTHzBtM5AIA4YXc41eAN30RKbXzLcA0AINKM9tdJkpoTKg2XAADiSeIxF0iSRrs2aJv7Q8M1AIDhNORBt+bmZlVVVSklJeUzr0tJSVFVVZW2bt061KcEACBujSodr+aBMkmS740nDdcAACLKe+G11k2ecqXnFxuOAQDEk/7SIyRJxc46+Qb6DdcAACJF4/uvK8+5SZKUefKlhmsAAPFk/MlfU4evQDZLQA2P3286BwAwjIY86JaamqrW1tZ9ura1tVXJyclDfUoAAOLatpSJkqRCsb4UABAW8Pt3rbXenjnFcA0AIN5MnDVXnkCKEm29qll0j+kcAECEaF36O1ksIbV5x2jsUaeazgEAxJnGYPjk6ezuGsMlAIDhNORBt0MPPVSNjY1asmTJZ1736KOPqqGhQdOmTRvqUwIAENcKz71GwZBV2c5GbXh5qekcAEAEqHvuEWUktCoQsmvsRdebzgEAxBlXaqYaPOGbSC7364ZrAACRIm9grSSpyVJhuAQAEI+sh50uSSpMdKuzaaPhGgDAcBnyoNt3vvMdhUIhXXrppfre976nTZs27fb1TZs2ae7cubrssstksVh07bXXDvUpAQCIa/nVh6nFO1aS1POvxYZrAACRwPfvv0mSmgfKNKp0vOEaAEA86ik4VJJU7KhTwO83XAMAMG2b+0ONdm2QJCUec4HhGgBAPKo6c466B3NktwxqwyPzTOcAAIbJkAfdTjvtNH3/+9/X4OCg7rvvPpWXlyspKUklJSVKSkpSeXm5fvWrX8nn8+kHP/iBTj2V46kBABiqFscESVKBf53hEgBAJNi5znrnemsAAA62ytk3yhd0KcXeqdVLfm06BwBg2JbH7pfNElCHr0DjT/6a6RwAQByy2e1q8IdPFc3a/r7ZGADAsBnyoJsk3XHHHXr66af15S9/WRaLRR6PRw0NDfJ4PLJYLDryyCP19NNP67bbbhuOpwMAIO6NOm2OJCnPtVkN77xiNgYAYJT7pb8p29moYMiqwnOvMZ0DAIhTqaPy1fjR+lLbqhcM1wAATMvpqZEkNQbLDZcAAOJZYNJXJElFLrd621sM1wAAhoP9QL+xv79fL7zwgtzu8MkB5eXlWrZsmSRp/fr16uvrU0pKisrLy5WcnDw8tQAAQJJUcvhXtO1vJcp11avt739Q8bRjTScBAAzp+9cjUoLU4h2rgurDTOcAAOJYx6gpGjewSsV2twJ+v2z2A37pEQAQxTqbNqowMXzvyDrtdMM1AIB4Vn3ht9V366+VYu/UqoX36NDv3mM6CQAwRAf0atOzzz6ryy67TO3t7bt9PisrS3/4wx901llnDUcbAAD4DE22SuWqXvnetaZTAAAGFQTc4UE3xwQVmI4BAMS1spk3yP/7x5WesE01f5+v6nOuNJ0EADBgw6JfapplUN2DOao663LTOQCAOGZ3ONXgq1CV/S2lNb9jOgcAMAz2e3XpmjVrdN5552n79u1yOByqrq7WxIkT5XA41N7erq997Wv68MMPR6IVAAB8QsoJl0iS8l0b1bJmpeEaAIAJW95+WXmuzZI+XmsNAIApmYXj1DwQXlEXeOtpwzUAAFOy2j+QJDX6KzjdEwBgnKd8hiSp2OmWd0ev4RoAwFDt96DbL3/5S3m9Xp144onavHmzPvzwQ61atUqbNm3SCSecIJ/Pp3nz5o1EKwAA+ISK48/Wdm+RrJagmp54wHQOAMCA7U//QZLU6ilVyeFfMVwDAIC0LW2SJKnI6jZcAgAwobe9RUWu8O8A/yT+jQIAMK961g0aCKTKZevT6gV3m84BAAzRfg+6vfrqq3I6nVq0aJHy8vJ2fT4/P1+PPPKIHA6HXn311WGNBAAAe9ak8GkJOX01hksAACbke9dJkpptFYZLAAAIKz7/2wqEbMpyNKvu+cdM5wAADrK6hffIYfWoz5+p6gu/bToHAAA5EpPU4A2/dpa88d+GawAAQ7Xfg27Nzc2qqKhQTk7Op76Wm5uriooKtbS0DEscAAD4bI4vnSVJKnStV0cDJyYAQDxpqXlb+a6Nkj5eZw0AgGl546eqxTNOktT/6uOGawAAB1ta8zuSpEZfhewOp+EaAADCegunSwqvL/X7vIZrAABDsd+Dbh6PRxkZGXv9ekZGhnw+31CaAADAPqo8bZa6BvNks/i1cRGrwwEgnjQ98RtZLUFt9xap4vizTecAALBLS2KVJKkgyJtxACCeeHo7VewM/90/UD7DcA0AAB+beOn35Q0mKtnepdWP3Gc6BwAwBPs96AYAACKHzW5Xoz985Paozg8N1wAADqbcHbWSPl5jDQBApMj76jcUClmU66zX5n8vM50DADhIahbdK5etTwOBVE285HrTOQAA7JKYkaXGgfC9lITalw3XAACGwn4g37Rt2zYtWLBgr1+TpIULFyoUCu3xmtmzZx/I0wIAgD0ITj1JWve6ilxu9bQ1Ky2nwHQSAGCEtW9epwLXekkfr7EGACBSFB0yQy2PlyrftUmdzz2k0iNONp0EADgIkje9ISVJDZ4KVSanms4BAGA3nblflHZ8qDEOtwJ+v2z2AxqVAAAYZgntbRptL6xWqywWy4E/ocUiv99/wN8f6Xp6epSenq7u7m6lpaWZzgEAxIGA36/+n1Yo1d6hd1LnaNr37jWdBAAYYe/ccZWmeR9V12CeUv93DS/MAQAizns/ulBftP9TWz1lGv2LlaZzAAAjzO/zyvuzCiXbuvVu5lU69Lo7TScBALCb7pYGJf3fIUqw+rR64u2adME1ppMAAB/Zn1mr/b4bMmbMmCENugEAgOFls9vV4KvQRPsKZbRyAwkA4sGozlVSktTor9AkhtwAABEo/eTZ0r/+qTznJjV9+B8VTvmy6SQAwAha/ch9mmrrljeYqKrZN5jOAQDgU9Lzi7XZU6HSpBqFVj4nMegGAFFpv++IbN68eQQyAADAUPjGHyc1rFCxq04DXR1KzMgynQQAGCHdLQ0qcrklSaEv/o/hGgAA9mzcjNPU9o8xynFuUcuTDzLoBgAxLmHtS1Ki1DhQobLMHNM5AADs0faMySr11ajI6jadAgA4QFbTAQAAYOgmXnyd+gPpclr7tWbRPaZzAAAjyP3IL5Vg9arXn6WJ511tOgcAgL1qslRIkvIGag2XAABGUsDv15iE9ZKkztwvGq4BAGDvSi/6rgIhmzIdLap9ZoHpHADAAWDQDQCAGOBITFKDN3wTKaX+TcM1AICRlPnRmuoGX4VsrC0FAEQw14zzJEmjXRvUtn614RoAwEhZ88RvlWrfrsGgQ2WXzDWdAwDAXmWPrVKzp1yS5HvjScM1AIADwaAbAAAxoq/kS5KkYpdbvoF+wzUAgJEw0NWxa22pb/xxhmsAAPhsE065WB2+0bJZAqp/7FemcwAAI2XlPyVJTZ4KZRaUmm0BAOBzbEuaKEkqFOtLASAaMegGAECMqJ59gzzBZCXZerTmkXmmcwAAI6B24d1yWvvVH0jXxIuvM50DAMDnagqGT57O6eZENwCIVUW28KDA9oxJhksAAPh8o8+5WsGQVdnORm149WnTOQCA/cSgGwAAMcKVmqnGgfBNJEfda4ZrAAAjIaVhhSSpwVshR2KS4RoAAD6fZdrpkqQC13p1Nm82GwMAGHa1zyxQpqNFgZBNpRddbzoHAIDPVTD5cLV4x0qSel5YaLgGALC/GHQDACCGdI8+VJI0xuFWwO83XAMAGE6+gX4VO+skfbyuGgCASFd11uXqGcxRgtWnDYt+aToHADDMfG88KUlq9pQre2yV4RoAAPZNi2OCJGm0v85wCQBgfzHoBgBADKmYdYMGg06l2DtU85ffmM4BAAyjmkfmKdHWK08wWdWzbzCdAwDAPrHZ7WoYLJckZW5/32wMAGDYFSq8tnRbEkNuAIDoMeq0OZKkPOdmNbzzitkYAMB+YdANAIAYkpZToEZPeH2p9f1lhmsAAMPJ9dFa6saBCrlSMw3XAACw7/yTTpAkFbnc6u9sM1wDABguG15eqmxno4Ihq0af8y3TOQAA7LOSw7+ibZ4SWSwhbfv7H03nAAD2A4NuAADEmPbMKZKkYjvrSwEgVgT8fhU7wqsUdq6pBgAgWlR/7Vrt8GfIaR1Q7YK7TOcAAIZJz78WS5JaPWNVMPlwwzUAAOyfJlulJCnfu9ZwCQBgfzDoBgBAjBk3c678oQSlJ2zTuqcXmM4BAAyD1Ut+rRR7pwaDTlXMYm0pACC62B1ONfjCJ0+nNr5tuAYAMFwK/OskSVsd4w2XAACw/1KOu0iSNNq1QS1rVhquAQDsKwbdAACIMVnFFWoeKJckDa54ynANAGA42Fa9IElq9FQoLafAcA0AAPuvv2yGJKnY5ZZ3R6/hGgDAUDW884ryXJslSVmnXWY2BgCAA1DxlXPV7iuU1RJU4xMPmM4BAOwjBt0AAIhB21InSpKKrG7DJQCAoQr4/Sq2h/8+37meGgCAaFM9c64GAilKtPWpZtE80zkAgCHa9vc/hv+vt0SlXzrJcA0AAAemMRg+eTq3d43hEgDAvmLQDQCAGFR8wbUKhqwa5WiS+8UnTOcAAIZg7d/nKz1hm/yhBI2bOdd0DgAAB8SZnKoGT6UkKWnDcsM1AIChGu2tlSQ1WSsNlwAAcOAcXz5LklSQuF4dDRwcAADRgEE3AABiUN74qdrqKZMk9b38qOEaAMBQBN56WpLUPFCurOIKwzUAABy43qJpkqRih1t+n9dwDQDgQLWsWal810ZJUsqxFxquAQDgwFWeNktdg3myWwa1kZOnASAqMOgGAECManFOkCQVBuoMlwAAhqLQul7Sx2upAQCIVlWzb5I3mKhke5dqltxvOgcAcIAan3hAVktQ7b5CVZx4vukcAAAOmM1uV6O/XJI0qvNDwzUAgH3BoBsAADEq96tXKBSyKNdVr81vPm86BwBwANwv/EWjHE0KhqwqvuBa0zkAAAxJUmaOGj3h00ntq/9luAYAcKBy+2okSY1BTpwGAES/4NSTJUlFLrd62poN1wAAPg+DbgAAxKjiaceq1VsqSer4x0NGWwAAB2bHK0skSVs9ZcobP9VsDAAAw6Aze6okqThhvQJ+v9kYAMB+62hwq9AVPnU64fAzDdcAADB01edfoz5/lhKsXrkX3mM6BwDwORh0AwAghm21V0qSRnvXGi4BAByIgqBb0sfrqAEAiHZlM7+nwaBDaQltqv3bH03nAAD208ZF82Sz+NU9mKvxZ8w2nQMAwJDZ7HZt8YVPKU1veddwDQDg8zDoBgBADEs7cZYkKc+1Sc2rVhiuAQDsj83/XqZcZ314DfVXrzCdAwDAsMgsKFWzp1ySFHr3WcM1AID9NarzQ0lSg79CNrvdcA0AAMPDV3m0JKnYVSdPb6fhGgDAZ2HQDQCAGFZ2zBna7i2S1RLU1icfNJ0DANgPHc89JElq9ZaqeNqxRlsAABhObemTJEmFVrfhEgDA/uhpa1aRK/x3d/ALJxmuAQBg+Ey8ZK76A2lyWvtVs4D1pQAQyRh0AwAgxjUqvL40t3+N4RIAwP4YPbhO0sdrqAEAiBUlF16nQMimLMdWrX1usekcAMA+qltwtxKsXvX5s1R9wbdN5wAAMGwciUlq8IZfg0upf9NwDQDgszDoBgBAjHMeda4kqcC1Xts31RquAQDsi6YP/6M85yZJH6+hBgAgVuSUT9JWT5kkybP8r4ZrAAD7KqN1pSRpi4+1pQCA2NM35nBJUrHTLd9Av+EaAMDeMOgGAECMqzptpjp9+bJZAtr86L2mcwAA+6DlyQdltQS13VuksmPOMJ0DAMCwa02skiQVhuoMlwAA9sVAV4eKXeG/s72VRxuuAQBg+E2ceYO8wSQl2bq15pF5pnMAAHvBoBsAAHGgMVghScruWm24BACwL/IGwidw7lw/DQBArMk/52oFQ1blOBu0cfmzpnMAAJ9jzaJ75LT2qz+QpupL5prOAQBg2CVmZKnBE34tzlH3muEaAMDeMOgGAEAcsBxyiiSp0OVWd0uD4RoAwGdpW79ao10bJH28fhoAgFhTOOXLavWOlSR1L1tguAYA8HlStqyQJDV4KuRITDJcAwDAyOjKO0SSVOxwK+D3G64BAOwJg24AAMSBqnO+qZ7BbCVYfXIvvNt0DgDgM9Q/9ivZLAF1+kar6rSZpnMAABgxWxPGS5JG+9cZLgEAfBbfQL+KneG1pX0lXzJcAwDAyKmcfaMGg06l2jtU85ffmM4BAOwBg24AAMQBm92uhsHw+tLMbe8ZrgEAfJac7vCa6cZgueESAABGVtYpX5ck5Tk3q3HlcrMxAIC9WvPIPCXZeuQNJmnizBtM5wAAMGLScgrU6AnfS7G+/7zhGgDAnjDoBgBAnBisOk6SVJTo1kBXh+EaAMCedDZvVoFrvaSP104DABCrSo84Wdu8JbJYQmr9++9N5wAA9sJR95qk8NrSxIwswzUAAIys9swpkqQiO+tLASASMegGAECcmHTJd7XDnyGndUBrHr7TdA4AYA82LPqlEqw+9Qxmq+qcb5rOAQBgxDVbw6cl5A/UGi4BAOxJwO9XscMtSerKO9RwDQAAI2/czLkKhOzKSGhV3bMLTecAAP4Lg24AAMQJu8OpBm/4JlJq01uGawAAe5K5/X1JUsNghWx2u9kYAAAOgqRjLpAk5bs2qnXd+2ZjAACfUvOX3yjV3qHBoFOVs280nQMAwIjLKq5Qk6dckuR7c6nZGADApzDoBgBAHNkx7ghJUrHTLe+OXsM1AIBP6u9sU5ErfFLCznXTAADEusqTLlSHr0A2S0CNj//adA4A4L9Y318mSWr0VCgtp8BwDQAAB0dbSrUkqcjiNlwCAPhvDLoBABBHJs2+UZ5AihJtvVrzyL2mcwAAn1C74C45rQPa4c/QpEu+azoHAICDpjEYPnk6p7fGcAkA4JMCfr+K7eEb/O2ZUwzXAABw8BSe+20FQ1aNcjTJ/eITpnMAAJ/AoBsAAHHEmZy6a32pa/1ywzUAgE9KbXxHktTgrZDd4TRcAwDAwWObfoYkqSBxvTqbNhquAQDsVPfsQqUnbFMgZNfYi75rOgcAgIMmf+IhavGMkyT1vfyo4RoAwCcx6AYAQJzpKZguSSp2uOX3eQ3XAAAkybujV8WuOklSf9mRhmsAADi4Jnz1MnUP5spuGdSGRfeYzgEAfMT3n6WSpCZPuUaVjjcbAwDAQbbVWSVJKgzUGS4BAHwSg24AAMSZyllz5QsmKsXeqZrHHjCdAwCQVLNonhJtffIEUlQ96wbTOQAAHFQ2u10N/vDJ01ntHxquAQDsVGQNry1tS6k2XAIAwMGX+9UrFApZlOuqV/2KF03nAAA+wqAbAABxJnVUvho95ZIk22r+cQYAkSBpw+uSwmtLncmphmsAADj4ApNPlCQVudzqbW8xXAMAcL/4hEY5mhQMWVV47rdN5wAAcNAVTztW27wlkqT2Z/9suAYAsBODbgAAxKGO7KmSpGK7WwG/32wMAMQ5v8+rYkd4BcLO9dIAAMSbSV/7jvr8mXJYPapbcLfpHACIe30vPypJavGMU/7EQwzXAABgRrM9vLo737fWcAkAYCcG3QAAiENll8yVP5Sg9IQ21T7FO5EAwKSaJfcr2d4lXzBRVZfeaDoHAAAjbHa7GnyVkqS05ncN1wAACgPhN+O0OCcYLgEAwJy0Ey6WJOU7N6ml5m3DNQAAiUE3AADiUmbhODUNVEiSgm8/bbgGAOKbffW/JEkNngolZeYYrgEAwBxP5dGSpOLEOnl6Ow3XAED82vzm88p11SsUsijnq1eazgEAwJiy487Sdm+RrJagmv76G9M5AAAx6AYAQNzanl4tSSqyrjdcAgDxK+D3qzgh/PdwZ/YXDNcAAGBW9SVzNRBIlcu6Q2sWzjOdAwBxq+MfD0mStnlLVDztWKMtAACY1qTwoQG5O9YYLgEASAy6AQAQt4ovuFaBkE1ZjmatW7bEdA4AxKXapX9SWkKb/KEElV0y13QOAABGORKT1OANry9N2vxvwzUAEL/yfWslSc32SsMlAACY5zjyHElSQeIGbd9Ua7gGAMCgGwAAcSq3Yoq2esokSQOvPm64BgDiU+idZyRJTQMVyiwcZ7gGAADz+ooPlyQVO93y+7yGawAg/jSvWqF85yZJUtoJlxiuAQDAvKrTZ6vTly+bxa/Nj95nOgcA4h6DbgAAxLHWxCpJUmHIbbgEAOJToTX89+/OddIAAMS7qlk3yhtMUrKtW6sfudd0DgDEna1PPiirJajt3iKVHXeW6RwAACJCYyC8vjS7a5XhEgAAg24AAMSxvLO+oVDIohznFm16/R+mcwAgrqx9brGyHFsVCNlUfMG1pnMAAIgIiRlZavSEbyI5al82XAMA8Se3f40kqUkVhksAAIggh/yPJKnQtV7dLQ2GYwAgvjHoBgBAHCuaepRavGMlSZ3LHjZcAwDxxbP8r5KkrZ4y5VZMMVwDAEDk6Mw7RJJU7HAr4PcbrgGA+LF9U60KXOslSY4jzzFcAwBA5Jh47lXq9WcrweqVe9E9pnMAIK4x6AYAQJzbmlApSRo9WGe4BADiy8610TvXSAMAgLCKS76nwaBTqfZ2rXnit6ZzACBubH70PtksAXUN5qvq9NmmcwAAiBg2u11bBsslSZnb3jNcAwDxLSoG3RYtWqRvfvObmjZtmpxOpywWix566KE9XvvTn/5UFotljx8ul2uvz7F48WJNnz5dycnJyszM1Kmnnqp33nlnhH4iAAAiR9YpcyRJ+c5Nanz/dcM1ABAfNi5/VjnOLQqFLMo/52rTOQAARJT0/OJd60u18jmzMQAQR7K7VkmSGvysLQUA4L8NTjheklTkcmugq8NwDQDEL7vpgH3xox/9SPX19crOztbo0aNVX1//ud9z6aWXqrS0dLfP2e17/nFvv/123XLLLRozZoyuuuoq9fX1acmSJTryyCO1bNkyHXvsscPwUwAAEJlKjzhZbU+PUY5zi1qX/l5FU48ynQQAMa972QLJLrV4x6pwypdN5wAAEHHaMydrrHe1/j97dx4eZ1kvbvyezCQzkzZt0zVNm6QL6ZqWsonignhYjtsBN1QUEASBn8sRBJRFZFFAFHDBA8hSQBSUg+hB5CAeRVmUReiSNm3TpnubNt3TZrLMZH5/hEVk65LkyUzuz3XluprJO+/c8Q+kPN/nfSpiS0OnSFK/sL1xNWMSXU+d5sB/DxsjSVIfVPOZr7Lrip8wILqdf9x1DQd95erQSZLUL+XEoNutt95KdXU1VVVVXH311VxwwQVv+Z7Pfe5zuzWgVl9fz7e+9S0mTZrEM888w+DBgwH4yle+wtve9jZOO+00Fi1a9IZDcpIk5YO1kWpGsIpRqbrQKZLUL4xOL4FY1/HRo0PHSJLUB43/9NlkZt/HkMJG6n53l0foSVIPq7/7+xxc0E5zejjTPnZm6BxJkvqcWFGcpW3VTCl+jpI1z4TOkaR+KyeOLj3yyCOpqqrqkXvPnj2bdDrNRRdd9PKQG8D06dM56aSTWLZsGX/605965LMlSeorkocfD8DoxDI21s8LXCNJ+W3N848zKr4ceOX4aEmS9GrDxk1mXet+ALQ/+evANZKU/0o3PA/Aqo79iLrxX5Kk17Vr/DsBqIgvoT3VErhGkvqnnBh02xuPP/4411xzDddeey0PPfQQbW1tr3vdY489BsDRRx/9mp8dc8wxAPzlL395w89pa2tjx44dr/qSJCnXTD7mU2xpLycaybD6Vz8KnSNJeW3D//yUSCRLU1sl4w47JnSOJEl91sYBUwEYQ33gEknKb6ltWxib7PpnbceUIwLXSJLUd03/7Nm0ZgaSjDaz4O7vh86RpH4pbwfdLrnkEr7+9a9z7rnn8qEPfYjx48fz6KOPvua6+vp6Bg4cSFlZ2Wt+Vl1d/fI1b+Sqq65i8ODBL39VVFR03y8hSVIvWtPZ9bSE4dsXBC6RpPxW9uIx0Wsj1YFLJEnq28Z87It0ZgsYHl9D/Z8eCJ0jSXlr4Z3fJV6QYldmMDWfOTt0jiRJfVaipJTVrV3/TS9R/3jgGknqn/Ju0G3WrFnceeedrFixglQqRX19PVdccQXbtm3jP/7jP5g7d+6rrt++ffurjiz9Z4MGDXr5mjdywQUXsH379pe/Vq9e3X2/jCRJvajgkA8DMCZZz9a1DYFrJCk/bVg8h7JE1z9jXzo2WpIkvb6y6YfQ2DoBgJ1/+kXgGknKXyVrnwFgdVs1saJ44BpJkvq2HWMOBqCiqJ50++ufKidJ6jl5N+h23HHHcdJJJ1FVVUUikWC//fbj4osv5oc//CGtra18+9vf7tbPi8fjDBo06FVfkiTloqnHnsr2jhHEIh0s+/l1oXMkKS+tvu8GopEMW9rLmXzMp0LnSJLU5zXGJwNQnl4SuESS8lN7qoWKeNepNi3jDgtcI0lS3zfpxHNp70wwMLaVBb/6r9A5ktTv5N2g2xs5+eSTicViPPnkk696ffDgwW/4xLYdO3a8fI0kSfkuGouxOt31yO2hm+aEjZGkPDVyRy0Aazo9tlSSpN0x/MOnAzAqsYJVz/45cI0k5Z8FP/s+yWgzrZmBTDvxnNA5kiT1eSXDyljz4vGl0fl/CFwjSf1Pvxl0KyoqoqSkhJaWlle9Xl1dzc6dO2lsbHzNe+rr61++RpKk/iBTcyQAYxP1NG9+7f83SpL23ta1DZQnlwJQcMiHAtdIkpQbKg85gg2t4wDY9OCtYWMkKQ8llj4OwOrWahIlpYFrJEnKDVuG7Q9ARWwpmXQ6cI0k9S/9ZtCtvr6erVu3Mm7cuFe9fvjhhwPwhz+8dtr6kUceedU1kiTlu+mf/BI706UUFbSy5GceXypJ3WnZ3dcSi3SwvWMEU489NXSOJEk5Y120axNqWduiwCWSlF/S7W1UFHVt+N9RflDgGkmScsfEz36NdLaQwYUbWfQ/s0PnSFK/kleDbs3NzcybN+81r2/dupXPf/7zAHz6059+1c9OOeUUYrEY3/nOd151hOmCBQu46667mDhxIu973/t6NlySpD4iVhRndXvXItKgdc8ErpGk/DJ081wAVqericZigWskScodA//tMwCUJRpoXPh84BpJyh8LfnkDA2Nbae9MMOmk80LnSJKUM0rHTGBtqmstJfPMg4FrJKl/yYnVlVtvvZUnnngCgPnz57/82mOPPQbAcccdx3HHHcfmzZvZf//9Ofjgg5kxYwYjR45k7dq1PPzww2zevJmjjjqKs88++1X3njRpEpdeeikXX3wxM2fO5OMf/zi7du3innvuoaOjg1tuuYWYi1CSpH6kdb93w7pnqIjX07armfiAktBJkpTzmjc3MjbR9aSEl46JliRJu6f6fR9h06MXMzy+hrX330DZtNtDJ0lSXojW/hGSsKa1mgnDykLnSJKUU5oGTacqvZCxBfWhUySpX8mJJ7o98cQT3Hnnndx55508/3zXrs0nn3zy5dfmzJkDwNChQ/niF79INpvlwQcf5Nprr+XBBx9k6tSp3HTTTTz88MMUFRW95v4XXXQRd999NyNHjuTGG2/k3nvv5bDDDuPJJ5/kiCOO6M1fVZKk4KafeC6pTAmJ6E5q7/pe6BxJygtL7voeRQWt7EyXMv2TXwqdI0lSzllL19MSRu5cELhEkvJDJp2mIta1ML9l2P6BayRJyj2Vn/wKmWyUoUXrWPKHX4bOkaR+IyceVXbHHXdwxx13vOV1gwYN4oYbbtirz/jMZz7DZz7zmb16ryRJ+aQoWcyStmomFT/PgIanQudIUl4YtO4fMABWt1cztSgeOkeSpJxTdNhH4B9/pjyxlM0rFjNs3OTQSZKU0+p+ezs1hU2ks4VM/OzXQudIkpRzRlbPZE3rRMYml9Dy2C/h6E+GTpKkfiEnnugmSZJ6V/OYtwFQEa8n3d4WuEaScltr81YqXjy2tHW/dweukSQpN016/2fY1jGKaCTN8nt+EDpHknJe57O/A2Bdaj9Kx0wIXCNJUm7akJwCQHl2aeASSeo/HHSTJEmvMe3kr9PWmWRAbBu1P/9B6BxJymkLf3YdiehOUpkSpp94bugcSZJyUjQWY3W66/jSYVvnBa6RpNw3tqBrM87GQTWBSyRJyl2jjjuDbDbCyPhKVjz1SOgcSeoXHHSTJEmvkRwylDWprkWkwro/B66RpNxWvKLrGOjVbdUUJYsD10iSlMMO+HcAxibq2dG0LnCMJOWuxY/cy9CidWSyUSo+8aXQOZIk5ayxs97FhrbxAGx9+I6wMZLUTzjoJkmSXtfWkQcAUFlUTyadDlwjSbkp3d5GRbzrSQkvHQstSZL2zrSPn0VzehiFBW0sueua0DmSlLNSf/kVAI2tExg1eVbYGEmSctz62CQAyjoWBS6RpP7BQTdJkvS6qk88j47OIkpim6j79c2hcyQpJ9X+/HoGRLfT1plk2slfD50jSVJOi8ZirG7vevL0kA3PB66RpNw1Jtu1GacxOTVwiSRJuW/Iv58MQFl8OWvmPBG4RpLyn4NukiTpdQ0uq2Bta9ciUvb5hwPXSFJuKlzUdfzzmlQ1ySFDA9dIkpT72ie/F4CKRD2pbVvCxkhSDlr+xO8ZEV9FNhth1H98IXSOJEk5b/y7PkBTWyWRSJYNv/lp6BxJykkv3Hjxbl/roJskSXpDm4bMAKAi6vGlkrSnMuk0lYVdT0rYOurAwDWSJOWHmhPPYVdmMPGCFhZ6fKkk7bGtj9wJwIa28Yw98N2BayRJyg9rI10PDRiVqgtcIkm5Z3vjamq2/GK3r3fQTZIkvaHxJ5xDOlvIkMJG6n5zW+gcScop8+++jpLYZjo641R/9tzQOZIk5YVYUZzVrZMAGLz6b4FrJCn3jE13LcCvL5wSuESSpPwx4IhPA1CeWMraef49RZL2RP3sKyksaN/t6x10kyRJb2jYuMmsSXUtIvHsb4K2SFKuSSx8FIBVrVMYXFYRuEaSpPzROu1IACqTi9nRtC5wjSTljvo/3s/I+Eo6swWM+Mj/C50jSVLeqD7qEzS1VVIQ6WTDf/84dI4k5ZSRW1/Yo+sddJMkSW9q0/CDAKgqXES6vS1wjSTlhrZdzVTGFwGwbcyhgWskScovNZ85m+b0UIoKUiyZfVXoHEnKGS3/9zMA1rdO9NhSSZK62ZroVADK2xcGLpGk3LFh8RzGJJfs0XscdJMkSW9q0ikX0NaZpCS2hdqfXRc6R5JyQu3sqyiO7iCVGcj0Uy8MnSNJUl6JFcVZ2dG1iDR80z8C10hSbsik01RGujbjNA6cGbhGkqT8M+zYs8hmI5QlltPw+EOhcyQpJ6y593qikQxb2st2+z0OukmSpDc1aEQ5q1JTAEgs+r/ANZKUGwatfAqAlW1TSJSUBq6RJCkPve04AMYml7BpeV3YFknKAXW/vZ3SovWks4VUffa80DmSJOWdykOOYH3rRAB2PHxb4BpJyg2jU/MBWMPk3X6Pg26SJOkt7ag6DICqxCJam7cGrpGkvm1H0zqqkl1PSmiddETgGkmS8tPUY09la/toYpEOVtz9vdA5ktT3PfMbANamqhk+fmrYFkmS8tT64hoAKlgUuESS+r5Vz/6Z0fEGAAb+22d3+30OukmSpLdUc8oFtGQGkYw2s+D2q0PnSFKftmT2VRQVpNiZHkrNST4pQZKknhCNxViV7Xry9Oid8wLXSFLflm5vo6qw6+mXTcMPClwjSVL+qjjha2SyUYYVraXut7ND50hSn7b5tzcSiWTZ0DqOCe96/26/z0E3SZL0luIDSljV1rWINHjt3wLXSFLfNnzTPwBY2TGFWFE8cI0kSfmr+N9OBGB0Yhlr5jwRuEaS+q7an19PSWwL7Z1JJp1yYegcSZLy1sjqmaxNTQIg/dT9gWskqW8bm+najLO2aPoevc9BN0mStFtapx0FQGViMdsbVweukaS+adPyOsYmlwCQPfjYwDWSJOW36iM/xsa2KgoinWy8/yehcySpz0os/CMAq1onM2hEeeAaSZLy28bSAwCoii0ik04HrpGkvmnJH37JiPgqOrMFjPzo/9uj9zroJkmSdsuMz55Dc3o4RQWt1N/p8aWS9HpW3v19YpEOtnWUMfUjp4XOkSQp762JTQVgTMfCwCWS1De1Nm+lKrEIgO1j3xG4RpKk/Fd9yoV0dMYZVNhE7T0/DJ0jSX1S6s+/AGBd636MnfWuPXqvg26SJGm3RGMxVnZ0HV86YvPzgWskqW8atXMeACs7pxCNxQLXSJKU/0Ycexad2QJGJVaw7M+/CZ0jSX3OgtnfJRltJpUpYfopXw+dI0lS3htcVsGq1GQAiuY/ErhGkvqeTDpNVUHXZpwNA2fu8fsddJMkSbstcuhxAIxNLqFpaW3YGEnqY9bMeYLyxFIAit/32cA1kiT1DxUHv5fG1gkAND96V+AaSep7Bq/5GwArW6eQKCkNXCNJUv+wbUzXU1Sr4otoT7UErpGkvqXugVsZUthIOlvIuBPP3+P3O+gmSZJ22/SPns6W9nKikTSrfvH90DmS1Kds/PV/URDppKmtkuqjPhE6R5KkfmP9gBkAVFIXuESS+pYdTeuofPHY0tZpRwaukSSp/5j++QtIZQZSHN1O7eyrQudIUp8See63AKxJTWLYuMl7/H4H3SRJ0h5Zle06vrRsl090k6R/NqZ9AQBrolMDl0iS1L9UnnAumWyMoUXrWPDrW0LnSFKfsWT2lRQVtNKcHkrNZ84OnSNJUr+RKCllVVvXWsrA5Y8HrpGkviPd3kZVYddGxU0jDt6rezjoJkmS9kjJ0ScDMDqxjDXP+xc0SQJY9pcHGZVYQTYbYdixZ4XOkSSpXxmxXw1rUpMAyD79m7AxktSHjNj0DwBWdkwlVhQPXCNJUv/SMukIAKqSi2nZ2hS4RpL6htq7vsfA2FbaOpNMPvXCvbqHg26SJGmPTDziODa0jus6nu+B/wqdI0l9QvMjswFY3zqRykOOCFwjSVL/0zTsQACqCheRSacD10hSeE1LaxmTrO/65m3HBW2RJKk/mnHSeexMlxIvaGHh7d8JnSNJfUJyyZ8BWJWaQsmwsr26h4NukiRpj60tnAbAmPSCwCWS1DdU0vWo7fXFNYFLJEnqnyadehHtnQlKYpuYf/d1oXMkKbhV91xHLNLB1vbRTD321NA5kiT1O7GiOCvbu44vHbbh2cA1khRea/NWKuOLANhRddhe38dBN0mStMdGfuyLdGYLGBlfSf0f7w+dI0lBLXzgdoYWrSOTjVJxwtdC50iS1C8NGlHOqtbJACQWPhq4RpLCK9s5D4BV2SlEY7HANZIk9U/Zgz8MQEVyMZtXLA5cI0lhLbj9SpLRnbRkBlFzygV7fR8H3SRJ0h4bO+tdrG+dCEDL//0scI0khZX5e9fA79rUJEZWzwxcI0lS/7V9zDsAqIwvom1Xc+AaSQpnzfOPMzqxDIDifzsxcI0kSf3X1I+ewbaOUcQiHaz4+fdC50hSUEPWPg3AqrYpxAeU7PV9HHSTJEl7Zf3ArmGOysgiMul04BpJCiOTTlMV63rU9sbSAwLXSJLUv00/9RukMiUUR3dQO/uq0DmSFEzTA/9FQaSTjW1VVB/5sdA5kiT1W9FYjJWZqQCM2jE3cI0khbO9cTWVia61lNZpR+3TvRx0kyRJe2XcZ88jnS2ktGg9db+9PXSOJAVR+4vrGVS4iY7OONWnXBg6R5Kkfi1RUsrKtq7jSwetfCpwjSSFM6ZjAQBrYlMDl0iSpOT7TgCgPLGUdfOfDlwjSWHU33kVhQVtNKeHM+Oz5+zTvRx0kyRJe2X4+KmsSU3q+uaZ3wRtkaRQimr/AMCq1GQGl1UErpEkSa2TjwSgMrmI5s2NgWskqffV/+kBRiZW0pktYMSxZ4XOkSSp35t09CdpaqukINJJ430/Cp0jSUGM2PwCACs7phCNxfbpXg66SZKkvbZp+IEAVBUuIt3eFrhGknpX265mquKLAdg25h2BayRJEkDNieewMz2UeEGKxbdfGTpHknrdrkfvAqCxdQIVB783bIwkSQJgTbTrKavl7QsCl0hS79tYP4+xySUARA49bp/v56CbJEnaa5NOuZD2ziQlsc3U/vz60DmS1Ktq77ia4uh2UpmBTP/8BaFzJEkSECuKs6JjCgDDm54LXCNJva8yUgfA+gEzApdIkqSXDP3wF8hmI5QllrP8id+HzpGkXrX6F9cRjaTZ0l7O9I+evs/3c9BNkiTttUEjylnVOhmARN0fA9dIUu8atPJJAFa1TSFRUhq4RpIkvezgYwEYm1zC5hWLA8dIUu9Z8OtbGFq0nkw2RuUJ54bOkSRJL6o69EgaWycAsP3h2wLXSFLvGt0yH4BVTO2W+znoJkmS9smOisMAqIovom1Xc+AaSeodzZsbqUwsAqBl0hGBayRJ0j+b+pHT2NpeRizSwYqfXRM6R5J6TfbpBwBYk5rEiP1qAtdIkqR/ti7Z9f/NFdm6wCWS1HtWP/cYoxPLACg56qRuuaeDbpIkaZ9MP/VCUpkSktFmFtx+ZegcSeoVi2dfRbwgxc50KTNOOi90jiRJ+ifRWIxV2a5dwqN2zgtcI0m9I5NOU1XYtRmnadiBgWskSdK/Gvvpc8hkowwrWkvdg3eGzpGkXrHpNzcSiWTZ0DqOiUcc1y33dNBNkiTtk/iAEla2TQFg0OqnAtdIUu8YtvE5AFa2TyFWFA9cI0mS/lXx+z4DQHliKWvmPBG4RpJ63vyfXUtJbDPtnQkmnXpR6BxJkvQvRk2exdrUJADST94XuEaSeseYTNdTLNcWTuu2ezroJkmS9lnr1KMAqEwsYkfTusA1ktSzNq9YTEVyMQDZgz8cuEaSJL2e6qM+wca2KgoinWz89X+FzpGkHpeoexSAVa2TGTSiPHCNJEl6PRtLDwCgKrqITDoduEaSelb9o/cxMr6SzmwBIz/2xW67r4NukiRpn8048Ws0p4dRVNDKktkeXyopvy2/+3vEIh1s6xjF1I+eETpHkiS9gbXRruNLx7QvCFwiST2rbVczVfGuY0u3j3lH4BpJkvRGJp78dTo64wwqbKL23h+HzpGkHtXyp58DsL51ImNnvavb7uugmyRJ2mfRWIyVHV3Hl47Y9I/ANZLUs8qa5wKwMjOVaCwWuEaSJL2R4cedRTYbYVRiBcv+8mDoHEnqMQtuv5JktJlUpoTpp34jdI4kSXoDpeXjWNU6GYDCeQ8HrpGknpNJp6mMdB1bun7gzG69t4NukiSpe7ztOADGJOtpWlobtkWSesjaeX+jPLEUgOT7TghcI0mS3kzFwe9lfetEAJofmR24RpJ6zqBVTwGwsm0yiZLSwDWSJOnNbCs/FICq+CLaUy2BaySpZ9T95jZKixpJZwsZ99nzuvXeDrpJkqRuMfXYU9nSPppYpINV91wXOkeSesSG//4xBZFOmtoqmXT0J0PnSJKkt7C+eAYAldQFLpGknrGjaR2VycUAtE4+MnCNJEl6K9M+dyGtmYEMiG5n/h1Xh86RpJ7x7G8AWJOaxPDxU7v11g66SZKkbhGNxVid7Tq+tGznvMA1ktQzytsXArAm2r1/MZMkST2j4oRzyGSjDC1ax8IHbg+dI0ndbsnsqygqSLEzPZSaE88JnSNJkt5CcshQVr54fGlJw+OBaySp+6Xb26gqXATApuEHdvv9HXSTJEndpviokwEYnVjGmuf9C5qk/NLw+EOUJZaTzUYY+uEvhM6RJEm7YWT1TNakuhaRMn+/P3CNJHW/4Zv+AcCKjinEiuKBayRJ0u5omfReAKqSi2jZ2hQ2RpK6We3PrqMktoX2ziSTTrmw2+/voJskSeo21e/7CBvbqiiIdLLxgf8KnSNJ3WrHw7cB0Ng6gapDPRJIkqRc0VQ6C4Cq2CIy6XTYGEnqRpuW1zE2uaTrm4OPDRsjSZJ224yTv87OdCnxghYW3n5l6BxJ6laJRf8HwKrUZAaNKO/2+zvoJkmSutXa2HQAxnYsCFwiSd2rgq5Hba9L1gQukSRJe6L6lAvp6IwzqHATtb+4PnSOJHWbFXd/j1ikg63tZUz9yGmhcyRJ0m6KFcVZ2T4FgGEbnglcI0ndp7V5K1WJrrWUHZWH9chnOOgmSZK61YiP/D86swWMTKyk/k8PhM6RpG5R99vZDCtaSyYbZeynzwmdI0mS9sDgsgpWtXYtIhXVPhq4RpK6T9nO+QCsyk4lGosFrpEkSXui86APATA2sYQtq+sD10hS91hw+9Uko82kMiVMP7X7jy0FB90kSVI3G3vgu1nfOhGAXY/eFbhGkrpH+qn7AVibmsSoybPCxkiSpD22bcyhAFTFF9G2qzlwjSTtuzVznqA8sRSA4vd9JnCNJEnaU9M+dibbOkZRWNBOw13XhM6RpG4xeO3fAFjZNoX4gJIe+QwH3SRJUrdrHDgTgMpIXeASSdp3mXSaqljXo7Y3lh4QuEaSJO2N6adeSCozkOLodmrvuDp0jiTts433/4SCSCcb26qoPuoToXMkSdIeisZirMp0PXl61I65gWskad9tb1xNZWIxAK1Tj+qxz3HQTZIkdbvKT59DJhtjaNF6Fvz6ltA5krRPau/5IYMKm+jojDPx5K+HzpEkSXshUVLKqrauRaRBK58MXCNJ+25Mx0IA1kanBi6RJEl7K37ECQCMSdSzbv7TgWskad/U33k1RQWtNKeHMePEr/XY5zjoJkmSut2I/WpYk5oEQPbpBwLXSNK+KZr/CACrWidTWj4ubIwkSdprqUlHAFCZWETz5sbANZK095b9+TeMSqwgm40w/LizQudIkqS9NPmYT9HUVkFBpJP19/04dI4k7ZMRm58HYGXHFKKxWI99joNukiSpRzQNOxCAqsJFZNLpwDWStHfaUy1UxbuOLd1WfmjgGkmStC9qTjqPnelS4gUpFs++KnSOJO215kfvAmB960QqDn5v2BhJkrRP1rz4dNYx7QsCl0jS3mtaWsvY5JKub952XI9+loNukiSpR0w69SLaOxOUxDYz/2fXhs6RpL1SO/sqiqPbac0MZNrnLgydI0mS9kGsKM7Kjq5FpGEbnwtcI0l7r5I6ANYXzwhcIkmS9lXph04nm41Qlmhg+RO/D50jSXtl1S++TzSSZkv7aKYee2qPfpaDbpIkqUcMGlHOqtbJACTq/hi4RpL2zsDljwOwsnUyySFDA9dIkqR9lT3oQwBUJBezecXiwDWStOcW/PoWhhatI5ONUnHCOaFzJEnSPhr39qNpbJsAwLaHbw9cI0l7p2xXLQCrsz17bCk46CZJknrQ9rGHAVAZX0TbrubANZK0Z1q2NlGV7FoAb5n03rAxkiSpW0z96Bls6ygjFulg+d3fC50jSXss+/RvAFiTmszI6plhYyRJUrdYl6gBoKKzLnCJJO25Nc8/zujEMgCKjzq5xz/PQTdJktRjpp/ydVKZEoqjO6idfVXoHEnaIwtv/w7xghZ2pkuZcfLXQ+dIkqRuEI3FWJmZAkBZ89zANZK0ZzLpNFWFiwBoKp0VNkaSJHWbMcd/lUw2yvD4Gup+d1foHEnaI00P/BcFkU42tlZR/b6P9PjnOegmSZJ6TKKklJWtXYtIg1c+GbhGkvbMsA3PArCyfQqxonjgGkmS1F2S7zsBgPLEUtbNfzpwjSTtvvl3X0dJbBMdnXGqT7kwdI4kSeomZdMOZG1rNQDpJ+4LXCNJe2ZMegEAawun98rnOegmSZJ6VOu0IwGoTC5mR9O6wDWStHu2rK6n4sVjSzsP+lDgGkmS1J0mHf1JmtoqKYh00njfj0LnSNJuiy98FIBVrVMYXFYRuEaSJHWnjUMOAKAyuohMOh24RpJ2T/0f72dkfCWd2QJGfOT/9cpnOugmSZJ6VM1nzqY5PZSighRLPL5UUo5Yftd3iUU62NYximkfOzN0jiRJ6mZrolMBKG9fELhEknZP265mquJdx5ZuG3No4BpJktTdJp78DTo64wwu3MiCX90QOkeSdkvL//0MgPWtExl74Lt75TMddJMkST0qVhRnZUfXItLwTf8IXCNJu2fUjrkArMpMIRqLBa6RJEndbdixZ5HNRihLLKfh8YdC50jSW6qdfRXF0R2kMgOZfqrHlkqSlG9Ky8exqnUyALE5vw9cI0lvLZNOUxnp2ozTOHBmr32ug26SJKnnHXIsAGOTS9i0vC5wjCS9uXXzn6Y8sRSA+BEnBK6RJEk9ofKQI1jfNgGAHQ/fFrhGkt7aoJVPAbCqbQqJktLANZIkqSdsG/02AKqKFtOeaglcI0lvru63t1NatJ5MNkblp8/ptc910E2SJPW4qcd9nq3to4lFOlhx9/dC50jSm2q870cURDppaqtg8jGfCp0jSZJ6yPrkDAAqWBS4RJLeXPPmRiqTXf+sSk06InCNJEnqKdNOuYjWzgEMiG2j9s7vhs6RpDf3zG8AWJOaxIj9anrtYx10kyRJPS4ai7EqOwWAsp3zA9dI0psrb18AwJro1MAlkiSpJ1Wc8DUy2SjDitZS99vZoXMk6Q0tvv1K4gUpdqZLqTnpvNA5kiSphySHDGVVa9daysCGvwaukaQ3lm5vo6qwazNO07ADe/WzHXSTJEm9ovjfTgSgPLGUNXOeCFwjSa9v+RO/pyyxnGw2QumHTg+dI0mSetDI6pmsTU0CIP3U/YFrJOmNDW96DoCVHVOJFcUD10iSpJ60a7/3AlCVWETL1qawMZL0Bmp/fj0lsc20dyaYdOpFvfrZDrpJkqReUX3kx9jYVkVBpJONv/6v0DmS9Lq2P3wbAI1tExj39qMD10iSpJ62sfQAAKpii8ik04FrJOm1Nq9YzNjkEgCyB30ocI0kSepp0088l13pIcQLWqibfVXoHEl6XYm6PwKwqnUyg0aU9+pnO+gmSZJ6zZpY1zGAY148FlCS+pqKbB0A6xI1gUskSVJvmHjy1+nojDOosInae34YOkeSXmPFz64hFulgW0cZUz96RugcSZLUw4qSxazo6Dq+tLTxmcA1kvRabbuaqYp3HVu6fexhvf75DrpJkqReM+LYs+jMFjAqsYJlf/5N6BxJepW6B+9kWNFaMtkoY47/augcSZLUC0rLx7GqdTIAhfMfCVwjSa81auc8AFZmphCNxQLXSJKk3pCZ9QEAKhKL2bq2IXCNJL3agtuvJBltJpUpYfopX+/1z3fQTZIk9ZqKg99LY+sEAJofvStwjSS9WvrJ+wBY21pN2bQDA9dIkqTesq38HQBUxRfRnmoJXCNJr1g772+UJ5YCkHzfCYFrJElSb5n+iS+yvWMkhQXtLLvz6tA5kvQqg1Y/BcDK1ikkSkp7/fMddJMkSb1q/YAZAFRSF7hEkl6RSaepjHY9anvjkAMC10iSpN40/fMXkMoMZEB0O/PvcBFJUt+x4b9/TEGkk6a2SiYd/cnQOZIkqZdEYzFWZqYCMGr73MA1kvSKHU3rqEx0raW0TjsySIODbpIkqVdVnnAumWyMoUXrWPDrW0LnSBIAtff+mMGFTXR0xpl48jdC50iSpF6UKCllVdsUAEoa/hq4RpJeMaZ9AQBrolMDl0iSpN5WdPinAChP1NO44NnANZLUZcnsKykqaKU5PZSaz5wdpMFBN0mS1KtG7FfDmtQkALJP/yZsjCS9qHDewwCsap1Mafm4sDGSJKnXtUw6AoCq5GJatjYFrpEkWPaXBxmVWEE2G2HYsWeFzpEkSb1syvtPYFPbWKKRDOt+9cPQOZIEwIhN/wBgZcdUYkXxIA0OukmSpF7XNKzrWMCqwkVk0unANZL6u/ZUC1Xxrkdtbxv9tsA1kiQphBknncfOdCnxghYW3v6d0DmSRPMjswFY3zaBykOOCFwjSZJCWF3Q9VTX0W0LApdIEjQtrWVMsr7rm0OODdbhoJskSep11SdfQHtngpLYJubffV3oHEn93Pw7rmZAdDutnQOYdspFoXMkSVIAsaI4K9u7ji8dtsFjgSSFV0kdAOuTMwKXSJKkUEo//AWy2QijEw2seOqR0DmS+rlV91xHLNLB1vbRTD3u88E6HHSTJEm9bnBZBatbJwMQX/ho4BpJ/V1Jw+MArGqdQnLI0MA1kiQplM6DPgRARXIxW1bXB66R1J8tfOB2hhatI5ONUnHC10LnSJKkQMa9/Wga28YDsPWhWwLXSOrvynbOA2BVdgrRWCxYh4NukiQpiG1j3gFAVXwRbbuaA9dI6q9atjZRlew6tnTXfu8NGyNJkoKa9rEz2dYxilikg+V3fTd0jqR+LPP3+wFYm5rEyOqZgWskSVJI6xI1AFR01gUukdSfrXn+cUYnlgFQ/G8nBm1x0E2SJAUx/dRvkMoMpDi6g9o7rg6dI6mfWnj7lcQLWtiVHsL0E88NnSNJkgKKxmKsynQdXzpyx7zANZL6q0w6TVWsazPOxtIDAtdIkqTQxhx/NplslOHxNdQ9dHfoHEn91MYH/ouCSCcb26qoPvJjQVscdJMkSUEkSkpZ2da1iDRoxZOBayT1V8M2PAPAio4pFCWLA9dIkqTQ4kecAMCYRD3r5j8duEZSf1T7i+sZVLiJjs44E0/+eugcSZIUWNm0A1nXWg1Ax+O/DFwjqb8a27EAgDWxqYFLHHSTJEkBtU46AoDK5CKaNzcGrpHU32xZXc/YxBIAMrM+ELhGkiT1BZOP+RRNbRUURDpZf9+PQ+dI6oeKah8FYFXrZErLx4WNkSRJfcKGwfsDUBVdRCadDlwjqb+p/9MDjEyspDNbwIhjzwqd46CbJEkKp+ak89iZHkq8IMXi268MnSOpn2m46xoKC9rZ3jGS6Z/4YugcSZLUR6yJTgNgTHtt4BJJ/U3brmaq4l3Hlm4rf0fgGkmS1FdMPPkbdHQWMbhwIwvu+0noHEn9zK5H7wKgsXUCFQe/N2wMDrpJkqSAYkVxVnR0HV86rOkfgWsk9Tejts8FYGVmKtFYLHCNJEnqK4Z++AtksxHKEstZ/sTvQ+dI6kcW3HkNxdHtpDIDmf75C0LnSJKkPqJ0zARWt04GIDrHv6NI6l2VkToA1g+YEbiki4NukiQprIOPBaAiuZjNKxYHjpHUX6yb/zRjkvUAFB3+qcA1kiSpL6k69EgaWycAsP3h2wLXSOpPSlY8DsCqtikkSkoD10iSpL5ka9nbABhXuIj2VEvgGkn9xYJf38LQovVksjEqTzg3dA7goJskSQps6kdOY2t7GbFIByt+dk3oHEn9xPr7fkxBpJNNbWOZ8v4TQudIkqQ+Zl2yBoCKbF3gEkn9RfPmRioTXceWtkw6InCNJEnqa6adejGtnQMYENtG7V3fC50jqZ/IPv0AAGtSkxixX03gmi4OukmSpKCisRirsl3Hl47aOS9wjaT+Ykz7AgBWF0wNXCJJkvqisZ8+h0w2yrCitdQ9eGfoHEn9wOLZVxEvSLEzXcqMk84LnSNJkvqY5JChrHrx+NKBy/4SuEZSf5BJp6kq7NqM0zTsgMA1r3DQTZIkBVf8vs8CUJ5Yytp5fwtcIynfLX/i95QlGshmI5R++AuhcyRJUh80avIs1qYmAZB+8r7ANZL6g2EbnwNgZfsUYkXxwDWSJKkv2jnxvQBUJhaR2rYlbIykvDf/Z9dSEttMe2eC6pMvCJ3zMgfdJElScNVHfYKmtkoKIp1s+O8fh86RlOe2PXw7AI1t4xn39qMD10iSpL5qY+ksACqji8ik02FjJOW1zSsWU5FcDEDnQR8KXCNJkvqqmpPOY1d6CImCXSyc/Z3QOZLyXKLujwCsbp3M4LKKwDWvcNBNkiT1CWui04BXjhOUpJ5S0VkHwLpETeASSZLUl008+Rt0dMYZXNhE7b1uyJHUc5bf/T1ikQ62dYxi2sfODJ0jSZL6qKJkMSvbpwBQuv7pwDWS8lnbrmYq413Hlm4b847ANa/moJskSeoThh17JtlshFGJFSz7y4OhcyTlqbrf3cXw+Boy2Shjjj87dI4kSerDSsvHsap1MgCF8x4OXCMpn5U1zwVgVWYK0VgscI0kSerL0rPeD0BFYglb1zYErpGUr2pnX0VxdAepzECmn/qN0Dmv4qCbJEnqEyoPOYL1rRMBaH5kduAaSfkq/cR9AKxrraZs2oGBayRJUl+3rfxQAKrii2hPtQSukZSP1s1/mvLEUgDiR5wQuEaSJPV104//Ets7RlJY0MayO78bOkdSnhq88kkAVrZNIVFSGrjm1Rx0kyRJfcb64q5jBCtYFLhEUj7KpNNURrv++bJh8P6BayRJUi6Y9rkLae0cwIDodmpdRJLUAxrv+xEFkU6a2iqYfMynQudIkqQ+LhqLsSrTdXzpyO1zwsZIyks7mtZRmVwMQOvkIwPXvJaDbpIkqc+oOOFrZLJRhhWtpe63PtVNUvda8KsbGFy4kY7OIiae3LcetS1Jkvqm5JChrEx1LSINXPbXwDWS8lF5+wIA1kSnBS6RJEm5IvauTwAwJlFP48LnA9dIyjdLZl9FUUGKnemh1Jx4Tuic13DQTZIk9Rkjq2eyJjUZgPRT9weukZRvYnMeBmB162RKx0wIXCNJknJFy6T3AlCVXETL1qawMZLySsPjD1GWWE42G2Hoh78QOkeSJOWIqR86iU1tY4lGMqz91Q9C50jKM8M3/QOAFR1TiBXFA9e8Vk4Mut19992cccYZHHzwwcTjcSKRCHfccccbXr9jxw7OOeccqqqqiMfjVFVVcc4557Bjx443fM8vfvEL3va2tzFgwABKS0v5wAc+wHPPPdcDv40kSXozTaWzAKiK1ZFJp8PGSMob7akWqoq6ji3dWva2wDWSJCmXzDj56+xMlxIvaGHh7VeGzpGUR3Y8fBsAja0TqDq07x0JJEmS+q7VBVMBKG+tDVwiKZ9sWl7H2OSSrm8OPjZszBvIiUG3iy++mJ/+9KesXLmS0aNHv+m1u3bt4vDDD+f6669n8uTJnH322UybNo3rr7+eww8/nF27dr3mPVdeeSWf+cxn2LBhA2eeeSbHH388Tz75JO985zt57LHHeui3kiRJr6f6lAvp6IwzqHATtb+4PnSOpDxRe+d3GRDbRmvnAKadenHoHEmSlENiRXFWtncdXzpswzOBayTlkwq6NuOsS9YELpEkSbmm9IOnA1AWb2DF3/8QuEZSvlhx9/eIRTrY2l7G1I+cFjrndeXEoNutt97KihUraGpq4swzz3zTa6+55hrmzJnD+eefzx/+8AeuvvpqHn74YS655BLmzJnDNddc86rr6+vr+da3vsWkSZOYN28e1157LTfffDNPPfUUsViM0047jbRPk5EkqdcMLqtg1YvHlxbVPhq4RlK+GNjwVwBWtU4mOWRo4BpJkpRrOg/8IABjE0vYsro+cI2kfFD329kMK1pLJhtl7KfPCZ0jSZJyzLjDjqGxdQKRSJatv7sldI6kPFG2cz4Aq7JTiMZigWteX04Muh155JFUVVW95XXZbJZbb72VgQMHcskll7zqZxdccAGlpaXcdtttZLPZl1+fPXs26XSaiy66iMGDB7/8+vTp0znppJNYtmwZf/rTn7rvl5EkSW9p29i3A1AVX0TbrubANZJyXcvWJqoSXU9K2DnxvWFjJElSTpr28bPY1jGKwoJ2Gu665q3fIElvIf3U/QCsTU1i1ORZYWMkSVJOWhefDkBFZmHgEkn5YM2cJyhPLAWg+H2fDVzzxnJi0G131dfXs27dOt75zncyYMCAV/0skUjwnve8h7Vr17J06dKXX3/paNKjjz76Nfc75phjAPjLX/7Sc9GSJOk1pp96IanMQIqj21lwp4tIkvZN3eyriBe0sCs9hJqTzgudI0mSclA0FmNVpuv40lHb5waukZTrMuk0VbGuzTgbS2eFjZEkSTmr/Pj/JJONMjy+hkUP/yJ0jqQct/HX/0VBpJOmtkqqj/pE6Jw3lHeDbgDV1dWv+/OXXn/pupf+PHDgQMrKynbr+n/V1tbGjh07XvUlSZL2TaKklFVtXYtIJSseD1wjKdeVNj4DwMr2KRQliwPXSJKkXFV0+KcAKE8upXHBs4FrJOWy2nt+yKDCJjo640w8+RuhcyRJUo4qm34I61L7AdD+l3sD10jKdWPaFwCwJjotcMmby6tBt+3btwO86gjSfzZo0KBXXffSn/fk+n911VVXMXjw4Je/Kioq9qpdkiS9WsukIwCoTCymeXNj4BpJuWrr2gYqEosBSM96f+AaSZKUy6a8/wQ2tY0lGsmw7lc/DJ0jKYcVzn8EgFWtkyktHxc2RpIk5bQNg/cHoDK6iEw6HbhGUq5a9uffMCqxgmw2wrBjzwyd86byatAthAsuuIDt27e//LV69erQSZIk5YUZJ53HznQp8YIWFs++KnSOpBy17M6rKSxoZ3vHSKYf/6XQOZIkKcetfnFXc3nbgsAlknJVe6qFqnjXsaXbyg8NXCNJknLdhJPOp6OziCGFG1j43zeGzpGUo5ofvQuA9a0TqTzkiMA1by6vBt1eejLbGz2B7aVjRf/5CW6DBw/eo+v/VTweZ9CgQa/6kiRJ+y5WFGdlx1QAhm18LnCNpFw1avtcAFZmphCNxQLXSJKkXFf6odPJZiOUJRpY8dQjoXMk5aD5d1zNgOh2WjMDmfa5C0PnSJKkHDe0oprVrZMBKHj+ocA1knJVJXUArC+eEbjkreXVoFt1dTUA9fX1r/vzl15/6bqX/rxz504aG197JNrrXS9JknpP9qAPAVCRXMzmFYsD10jKNY0Ln6c80fXv9IXv/mTgGkmSlA/Gvf1oGtsmALD1oVsC10jKRSUNfwVgZetkkkOGBq6RJEn5YMuoQwCoKlpEur0tcI2kXLPwgdsZWrSOTDZKxQnnhM55S3k36FZeXs6TTz7Jrl27XvWz1tZW/vrXv1JeXs5+++338uuHH344AH/4wx9ec79HHnnkVddIkqTeNfWjZ7Cto4xYpIMVP/9e6BxJOWbtr64nGsmwqW0sUz/42dA5kiQpT6xL1ABQ0VkXuERSrmnZ2kRVsmsjX8uk94aNkSRJeWPaqRfS1lnMwNhW5t/53dA5knJM5u/3A7AmNZmR1TMD17y1vBp0i0QinHbaaezcuZPLL7/8VT+76qqr2Lp1K6eddhqRSOTl10855RRisRjf+c53XnWE6YIFC7jrrruYOHEi73vf+3rtd5AkSa+IxmKszEwBYNSOuYFrJOWa8tZaAFYXTA1cIkmS8smY479KJhtleHwNdb+7K3SOpByy8PbvEC9oYWe6lBknfz10jiRJyhPFpSNYmepaSyle8ljYGEk5JZNOM65wEQBNpbPCxuymWOiA3XHrrbfyxBNPADB//vyXX3vssccAOO644zjuuOMAOP/88/mf//kfrrnmGl544QUOOugg5s6dy8MPP8ysWbM4//zzX3XvSZMmcemll3LxxRczc+ZMPv7xj7Nr1y7uueceOjo6uOWWW4jFcuJ/JkmS8lLyfSfAU49RnljKuvlPUz7j0NBJknLAiqceYVyiAYDSD54euEaSJOWTsmkHsqq1msrkItJP3AcfOil0kqQcMWzDs1AMK9unML0oHjpHkiTlkZ0T3wONz1OVXERq2xaPSJe0W+bffR2zYpvo6IxTfcqFoXN2S0480e2JJ57gzjvv5M477+T5558H4Mknn3z5tTlz5rx87YABA3jsscc4++yzWbRoEddeey21tbWcffbZPPbYYwwYMOA197/ooou4++67GTlyJDfeeCP33nsvhx12GE8++SRHHHFEb/2akiTpdUw6+pM0tVVSEOmk8b4fhs6RlCO2PnQLAI2tExh32DGBayRJUr7ZOHgWAJXRRWTS6bAxknLCltX1VLx4bGnngR8MXCNJkvJNzclfZ1d6CImCXSyc/Z3QOZJyRHzhowCsSk1mcFlF4JrdE8lms9nQEflkx44dDB48mO3btzNo0KDQOZIk5YUXLv4kB8T+l6a2CkZcVRs6R1Ifl0mn2XHJdEqLGnk++2EOvOzu0EmSJCnPbF3bwMCb305hQRtzxp7HrNMuDp0kqY/7x6UncRC/ZVvHKEouW0jUk2QkSVI3W3j+MUwr/jsrW6ZRdc3fQudI6uOaNzdS+IODSER38o/SL3DQf34vWMuezFrlxBPdJElS/zb60+eSycYYEV/N/Ht/HDpHUh83/46rKS1qpKMzTuXnLgqdI0mS8lDpmAksb50OQMniRwLXSMoFlW1dp9UsZ6ZDbpIkqUdE3n0CABXJRax69s+BayT1dYtvvoREdCc700OZ8YXLQufsNgfdJElSn1c2/RBWpKYBEH/+gcA1kvq6lxabl6emM3z81MA1kiQpX7UecBwAVcV1NC58PmyMpD5t/r0/ZkR8NZlsjNEnfD10jiRJylNTP3wyja3jKYh0svXXPwydI6mPG7vjWQCWpWdSlCwOXLP7HHSTJEk5YcekYwAYn6hl84rFgWsk9VWNC5+nqrgOgNYDjg1cI0mS8tnMT/8nm9rGEot0sP4XV4fOkdSHvbRpb0VqGmXTDwlcI0mS8tmakq5/15gQnUt7qiVwjaS+qu7BOylLNNCZLWDIR74cOmePOOgmSZJywszPfYOt7WUUFrSxcvYVoXMk9VHrf3E1sUgHm9rGMv34L4XOkSRJeW5l/CAAxmfnkEmnA9dI6os2r1jM+EQtADuqjwpcI0mS8t3kM6+gNTOQktgW5v/0W6FzJPVR2cd/DsDq1BSqDj0ycM2ecdBNkiTlhGgsxoqCWQBUtr/gIpKk18ik04zPzgFgZfxAorFY2CBJkpT3xn/+m7R3JhhSuIF5t18ZOkdSH7Ry9hUUFrSxraOMmadcGDpHkiTluZJhZTS01QAwYu3jgWsk9UU7mtYxMTEfgE1j3hO4Zs856CZJknJG+YkXkM4WMjy+htp7fxw6R1IfM+/2KxlSuIH2zgTjP39J6BxJktQPDK2oZnlr1yLS4KV/CFwjqa/JpNNUtr8AwPLI/m7GkSRJvSJy+IkAjE0uZsXf/XuKpFdbcvMlxAtaaE4PY8YXcu/Jjw66SZKknDFq8ixWpKYBkJz728A1kvqalxaXl6emM7SiOnCNJEnqLzoO/igAVcmFrJv/dOAaSX1J7b0/Znh8DelsIeUn+jQ3SZLUO6Z+8LOsb51IQaSTHb+5IXSOpD6mYuezADRkZlKULA5cs+ccdJMkSTll59QPADA+uZBNy+sC10jqK9bNf5qq5EIA2l9cbJYkSeoNNcd/kaa2SqKRDBvu/V7oHEl9yEub9FampjFq8qywMZIkqV9ZO/htAEyIzaNtV3PgGkl9xcIHbmdUYgWZbJShH/tq6Jy94qCbJEnKKTNOPJct7aMpLGhj1ewrQudI6iM23Ps9opEMTW2VzPjkl0LnSJKkfmZV4iAAJjCXdHtb4BpJfcGm5XWMf3EzTvPk9weukSRJ/c20M68glSlhYGwrtT+9NHSOpD6i4G/3ALA6NYWKg98bNmYvOegmSZJySjQWY0X0AADGpV8gk04HLpIUWrq9jQnMBWBV4sDANZIkqT+acPqltHUmGVy4kbm3fSd0jqQ+YNXsKygsaGNr+2hmnHxe6BxJktTPFJeOoKGtBoCRjU8ErpHUF2xvXM34RC0AmyvfGzZmHzjoJkmSck7FyRfR0VnE0KJ1zL/7utA5kgKbe9t3GFy4kfbOJBNO+1boHEmS1A+Vlo9jeaprEWno8v8LXCMptEw6zbj0CwCsiB5ANBYLXCRJkvqjwiNPAWBMYgnLn/h94BpJoS295VvEC1rY0TGC/b+Qu2spDrpJkqScM2K/GlakpgMwcMFDgWskhfbSYnJD63RKx0wIXCNJkvqr9NuPB6AyWceaOT4xQerP5t99HUOL1pHOFjLmpAtC50iSpH5q0tGfZF1qPwoinez83Y2hcyQFVtHyHAAN2ZnEiuKBa/aeg26SJCkntcz4IADjihewsX5e4BpJoayZ8wSVyToA0m87PnCNJEnqz2o+9gU2tlYRjWTY9CufPC31Zy9tylvRMp2R1TMD10iSpP5s3dBDAZgQm0fbrubANZJCqf3vmxkZX0kmG2X48eeEztknDrpJkqScVHPC2WxuH0Ms0sHau64KnSMpkE2/uo5oJMPGtipqPn5G6BxJktTPrRpwMAATIvNIt7cFrpEUwsb6eYwrXgDAzukfDFwjSZL6u+lnfJuWzCAGxLYx/6Zvhs6RFEjsmV8BsCo1lbGz3hW4Zt846CZJknJSNBZjZewAAMZlXiCTTgcuktTb0u1tTIh0PdFxdfHBgWskSZKg+vTLaOssZlBhE3N/elnoHEkBrL3rKmKRDra0lzPjs7n9pARJkpT7kkOG0tA+A4CyjU8GrpEUwta1DUxIdG3G2TL+3wLX7DsH3SRJUs6qPOViOjrjlBatZ/6d3wudI6mXzf3pZQwqbKKts5j9TnchWZIkhTe4rIKG1hoAhq16LGyMpF6XSacZl3kBgBWxA4jGYoGLJEmSIH7U5wEYk6hn2V8eDFwjqbc13HoZRQUptneMZP/PXxQ6Z5856CZJknLW8PFTWZ6aBkDJ4ocD10jqbS8tHi9vrWFwWUXYGEmSpBdl3/FpACqTdax+7rGwMZJ61fw7v0dp0Xo6OuNUnuLRYJIkqW+oPvJjrE1VE4lkafn9TaFzJPWyytbnAWhgf2JF8cA1+85BN0mSlNNS+x8LQFVyIY0Lnw9cI6m3rH7uMSqTdQB0vriYLEmS1BdM+8ipbGgdR0Gkky33/yB0jqRe9NImvOWpaQwfPzVwjSRJ0ivWD3s7ABOK5tPavDVwjaTeMv+XNzAivopMNsaoT50XOqdbOOgmSZJyWs2nvsymtrHEIh2s/8XVoXMk9ZIt9/+AgkgnG1rHMe0jp4bOkSRJepXVAw8BYEJ0Hu2plsA1knpD48LnqUouBF7ZlCdJktRX1Jx1BS2ZwQyIbqf2xktC50jqJUXP/RqAlamplM84NHBN93DQTZIk5bRoLMaqogMAGJ+dQyadDlwkqae1p1qYEJ0HvLKILEmS1JdMOuNyWjsHUBLbzPyfXhY6R1IvWP+Lq4lFOtjUNpaaT305dI4kSdKrJEpKaWifAcDozX8LXCOpN2xZXc/45AIAtu93dOCa7uOgmyRJynlVp3yTjs44Qwo3MG/2laFzJPWw+T+9jJLYZlo7BzDpjMtD50iSJL3GoBHlNLTWADB87V8D10jqaZl0mvHZOQCsKjqAaCwWNkiSJOl1JP/9dLLZCGOS9dT/8f7QOZJ62PLbLqeooJVtHaOYeeqFoXO6jYNukiQp5w0bN5nlLy4iDap/NHCNpJ720mJxQ2sNg0aUB66RJEl6fZH3fBaAiuQiVj79x8A1knrSvNlXMqRwAx2dcapO+WboHEmSpNc18YjjWNtaDUDbo7cFrpHUkzLpNFVtzwOwPDIrrzbjOOgmSZLyQtuBHwFgXHIhjQueDVwjqaesfPqPVCQXAa8sHkuSJPVFUz90Eo2tEyiIdLLtgR+HzpHUg17adLe8tYZh4yYHrpEkSXpjjSMOA2BC0XxS27YErpHUUxb86gaGx9eQzhYy+oRvhM7pVg66SZKkvDDjU1+mqa2CaCTN+l98N3SOpB6y7YEfUxDppLF1AlM/dFLoHEmSpDe1ZtAhAEyMzaM91RK4RlJPaFzwLOOSC4FXNuFJkiT1VTVnXMqu9BCKoztYcPPFoXMk9ZDEC78FYGXLVMqmHRi4pns56CZJkvLGqnjXv6hNYC6ZdDpwjaTu1p5qYWJsHvDKorEkSVJfNvmMy0llBjIwtoX5N18SOkdSD1j/i+8SjaRpaqtgxqe+HDpHkiTpTSVKSmlIzwCgfMvTgWsk9YRNy+sYn1wAQPPkYwLXdD8H3SRJUt6YcPqltHcmGVy4kbm3XhE6R1I3m3/zJQyMbSGVGcjkMy4PnSNJkvSWSoaV0dBeA8CI9Y8HrpHU3TLpNBOYC7yy+U6SJKmvG/DBs8hmI5Qnl7LkD78MnSOpm62a/W0KC9rY2l7GjM/l17Gl4KCbJEnKI6VjJrC8dXrXnxv+GLhGUnd7aXG4ob2GkmFlgWskSZJ2T/S9JwMwNrGEFU89ErhGUneae+sVDC7cSHtnggmnXxo6R5IkabdMePcHWZOaBEDHH2cHrpHUnTLpNFXpFwBYHp1FNBYLXNT9HHSTJEl5peOQTwBQmaxj7by/Ba6R1F1WPPUIYxNLACh4z4mBayRJknbflPefwPrURAoinez4nxtC50jqRi9tslveWkPpmAmBayRJknbfxrJ3ATAhXkvL1qbANZK6S+0vrmdY0VrS2ULGfPaC0Dk9wkE3SZKUV2o+cSYb26qIRjI03fu90DmSusmO//kJBZFO1rdOZOoHPxs6R5IkaY+sHXIoABNj82jb1Ry4RlJ3WDvvb1Qm6wDoOPijgWskSZL2TM0Zl7EzXUoy2szCm74ZOkdSNymufQiAFalpjJo8K2xMD3HQTZIk5Z3VyQMBmBCZR7q9LXCNpH3VtquZCbF5AKwd/LbANZIkSXtu2pmXk8qUMCC2jfk3XRI6R1I3aPrl97s22bVVUnP8F0PnSJIk7ZH4gBIa0jMBGLP9mcA1krpD09JaxiUWALBz6gcC1/QcB90kSVLemXDapbR1JhlU2MTcWy4PnSNpH9Xe/C0GxraSypQw7cwrQudIkiTtseLSETS0zwBg1MYnA9dI2lfp9jbGMxeAVYmDAtdIkiTtnUHHfYnObAGjE8uoe+ju0DmS9tHqu66ksKCdLe2jmXHiuaFzeoyDbpIkKe+Ulo9jeWsNAMNW/jlwjaR9NbLxCQAa2mooLh0RuEaSJGnvFB15KgBjE0tY9pcHA9dI2hfzbv02gwubaOtMMuH0S0PnSJIk7ZVxbz+aNanJAGT/8rPANZL2RSadZlz6BQBWRA8gGosFLuo5DrpJkqS8lHn7JwGoSC5izfOPB66RtLcaHn+IscklABQeeUrgGkmSpL1XfdQnWJfaj0gkS8vvbw6dI2kflK74EwDLUzWUlo8LGyNJkrQPmsrfA8CEeC3NmxsD10jaW/N/di1Di9bR0VlExckXhc7pUQ66SZKkvDT9o6ezoXUc0UiGTfddFzpH0l7a9dCNRCJZ1qX2Y9LRnwydI0mStE/WDX07ABOK5tHavDVwjaS9seb5x6lM1gGQfvvxgWskSZL2zYwzLqU5PZREdCeLb/pm6BxJe2lg3e8BWJGazoj9agLX9CwH3SRJUt5aM+BgACZG55FubwtcI2lPtTZvZUJsPgDrhh4auEaSJGnfTT/jCloygxgQ3U7tTd8KnSNpL2z67+uJRjJsbK2i5mNfCJ0jSZK0T4qSxTRkZgIwtvnZwDWS9sbG+nmMSy4AoGXGBwPX9DwH3SRJUt6qPvMK2jqLKYltYq6LSFLOqb35UgbEttGSGcT0M74dOkeSJGmfJYcMpaF9BgBlm/4WuEbSnkq3tzEhMheAVS9urpMkScp1pR/9Kp3ZAsoSy6n77ezQOZL20Jq7vkMs0sHm9jHUnHB26Jwe56CbJEnKW4NGlNPQ2rWINHzNXwLXSNpTZU1PAdDQPoPkkKGBayRJkrpH/JjTARibXEL9nx4IXCNpT8y7+VIGFW6irbOY6tMvC50jSZLULSoPOYLVqSld3zx5T9gYSXskk04zPjMHgJWxA4jGYmGDeoGDbpIkKb+9+zMAVCQXserZPweOkbS7lv35N4xJ1AMQP+rzgWskSZK6T/X7PsKa1CQA2h65JXCNpD0xbPVjADS01jC4rCJoiyRJUnfaNPZwACYk5rOjaV3gGkm7a/4dV1Na1EhHZ5zKUy4OndMrHHSTJEl5beqHT6axdTwFkU62/voHoXMk7abU/95CJJJlbaqa6iM/FjpHkiSpWzUOfwcAE4rmk9q2JXCNpN2x6tk/U5Fc1PXNO08IGyNJktTN9j/zMprTw4kXtFB/0zdD50jaTSWLHwFgeWo6w8dPDVzTOxx0kyRJeW9NySEATIzOoz3VErhG0ltpbd7KhKL5AKwf9o7ANZIkSd2v5szL2JUZTHF0BwtudhFJygVbf/1DCiKdNLaOZ+qxp4TOkSRJ6laxojjLMjMBGLvrucA1knZH48LnqSquA6D1gGMD1/QeB90kSVLem3zmFbRmBjIwtoX5N18aOkfSW6i98ZsUR7ezKzOYmrMuD50jSZLU7RIlpTS0dy0ilW/5e+AaSW+lPdXChOhcANaWHBy4RpIkqWcM/8Q5ZLJRRiVWsODXt4TOkfQW1v/iamKRDja1jWX68V8KndNrHHSTJEl5r2RYGQ1tNQCMWPfXwDWS3srozV2LvQ3tM0iUlAaukSRJ6hkDPnQW2WyE8uRS6h+9L3SOpDcx/6ffoiS2hdbOAVR/wc04kiQpP4098N2sTk0BIPr3XwaukfRmMuk047NzAFgZP5BoLBY2qBc56CZJkvqFyOEnAjA2uZgVTz0SuEbSG6l/9D7GJOvJZiMUf+DM0DmSJEk9ZsK7P8ia1kkAtP/x9sA1kt7MiLWPA9DQWsOgEeWBayRJknrO5qojABifqGXruhVhYyS9oXm3X8mQwg20dyYY//lLQuf0KgfdJElSvzD1g59lfetECiKd7Pifn4TOkfQG2l5c5F3bWs3Ewz8cuEaSJKlnbRj5TgAmFM2nZWtT4BpJr2fF3//A2ORiACLv+WzgGkmSpJ61/+mXsKNjBPGCFA23Xho6R9IbGLz0DwAsT01naEV14Jre5aCbJEnqN9YOfhsAE2LzaNvVHLhG0r9KbdvCxKL5ADS+uOgrSZKUz2aceTk706Uko80svKl/7cCWcsWO39xAQaST9a0TmPqhk0LnSJIk9ahYUZyG7EwAKlLPB66R9HrWzX+aquRCANoP/mjgmt7noJskSeo3pp15BalMCQNjW6m9+VuhcyT9iwU3XkQy2syu9BBmnHlF6BxJkqQeFx9QQkN6BgBjtj0duEbSv2rb1cyE2DwA1g56W+AaSZKk3jHiU+eRyUYZGV9J7X03hc6R9C823Ps9opEMTW2VzPjkl0Ln9DoH3SRJUr9RXDqChrYaAEY2PhG4RtK/Kt/etbjbkJ5JfEBJ4BpJkqTeMeg/vkRntoDRyWUsevgXoXMk/ZPan17KwNhWUpmBTDvr26FzJEmSesWYme9gVWoqAIXP3he4RtI/S7e3MYG5AKxKHBi4JgwH3SRJUr9SeOQpAIxNLqHh8YcC10h6yeJH7qU8sYzObAEDP3RW6BxJkqReM+6wY1jTOgmAzGN3Bq6R9M9e2iTX0DaD4tIRgWskSZJ6z9YJRwIwPrGArWsbAtdIesnc277D4MKNtHcmmXBa/zy9ykE3SZLUr0w6+pOsS+1HJJJl10M3hs6R9KL0/90BwNrWSYx/1wfCxkiSJPWyptHvBmBiUS3NmxsD10gCWP7E7xmTWAJA7N8+FzZGkiSpl+1/2jfZ3jGSooIUDbdcGjpH0ouGLv8/ABpap1M6ZkLgmjAcdJMkSf3OuqGHAjAhNp/W5q2BayQ1b25kQnw+ABvL3hW4RpIkqffNOONydqaHkojuZPHNl4TOkQTs/N2NFEQ6Wdc6kcnHfCp0jiRJUq+KxmI0sD8AlW3PB66RBLBmzhNUJusASL/t+MA14TjoJkmS+p3pZ3yblswgBsS2UXvzpaFzpH5v8c3fIhndyc50KTVfuDR0jiRJUq8rShazLD0TgLE7ng1cI6ltVzMTYvMAWDf40MA1kiRJYYw+4etksjFGxFcz/94fh86R+r1Nv7qOaCTDxrYqaj5+RuicYBx0kyRJ/U5yyFAa2mcAUNb0VOAaSWN2PANAQ3om8QElgWskSZLCGPKRL9OZLaAs0UDdg3eGzpH6tfk3fZMBsW2kMiVMP+s7oXMkSZKCKJt+CCtS0wCIP/9A4Bqpf0u3tzEh0rUZZ3XxwYFrwnLQTZIk9UvxI08BYEyinmV//k3YGKkfq/vdXYxONNCZLWDQcV8KnSNJkhRM1aFHsiY1GYDs4z8PXCP1b2UbnwRgWfsMkkOGBq6RJEkKZ8d+RwEwPlHL5hWLA9dI/dfcn17GoMIm2jqL2e/0y0LnBOWgmyRJ6peqj/oEa1PVRCJZUv97S+gcqd/K/vVuANakJjPu7UcHrpEkSQqracy7AZiYmM+OpnWBa6T+adlfHmRMoh6A+JGnBq6RJEkKa+apF7KtYxSFBW2snH1F6Byp3xq26jEAlrfWMLisImxMYA66SZKkfmv9sLcDMKFoPq3NWwPXSP3PjqZ1TEjUAq8s6kqSJPVnM75wGc3pocQLWlhy8yWhc6R+qeX3NxGJZFmbqqb6qE+EzpEkSQoqGouxPDILgMr2F8ik02GDpH5o9XOPUZmsA6DzHZ8OXBOeg26SJKnfqjnrCloygymObqf2xm+GzpH6nfqfXkKiYBfN6aHM+EL/ftS2JEkSQFGymIbM/gBU7Hw2cI3U/7Q2b2VC0Xzglc1xkiRJ/d3oE75BOlvI8Pgaau/9cegcqd/Zcv8PKIh0sqF1HNM+4lOnHXSTJEn9VqKklIb2GQCM3vz3wDVS/zOm+TkAGjL7U5QsDlwjSZLUN5R+9D/pzBYwKrGChQ/cHjpH6ldqb7yEAdHttGQGU3OWR3NJkiQBlE07kJUtUwFIzv1t4Bqpf2lPtTAhOg+A1QMPCVzTNzjoJkmS+rXkv59ONhthTLKe+kfvC50j9Rt1v51NWWI5ndkCSj/6n6FzJEmS+ozKQ45gdWoKAAV/uydwjdS/jN78NwAa2meQKCkNXCNJktR3NE8+BoDxyYVsWl4XuEbqP+b/9DJKYptp7RzApDMuD53TJzjoJkmS+rWJRxzH2tZqANr+6NMSpF7z5C8AWJ2aQuUhRwSOkSRJ6ls2V7wXgPGJWrY3rg4bI/UT9X+8nzHJerLZCMl/Pz10jiRJUp8y43PfYGt7GYUFbaya7ZNvpd4yfO1fAWhorWHQiPLANX2Dg26SJKnfaxxxGAATi+aT2rYlcI2U/7Y3rmZCohZ4ZRFXkiRJr5h5xqXs6BhOvKCFpbd8K3SO1C+0PXobAGtbq5l4xHFhYyRJkvqYaCzG8ugsAMalXyCTTocNkvqBlU//kYrkIgAi7/ls4Jq+w0E3SZLU79WccSm70kNIRptZcONFoXOkvFd/y7eIF7Swo2M4M8+4NHSOJElSnxMritOQ3R+AipbnAtdI+S+1bQsTiuYDr2yGkyRJ0quNPeki0tlChhatY/7d14XOkfLetgd+TEGkk8bWCUz90Emhc/oMB90kSVK/lygppSE9A4Dy7U8HrpHyX+WursXahuz+xIrigWskSZL6puEfP5tMNsrI+Epq//vm0DlSXltw88UUR3ewKz2EGjfjSJIkva6R1TNZkZoOwMAFDwWukfJbe6qFibF5AKwZdEjgmr7FQTdJkiRgwAfPIpuNUJ5YxuJH7g2dI+Wt2vt/ysjESjLZKMM/fnboHEmSpD5r7IHvZlVqKgCxZ34VuEbKb+Vbuja9NaRnkCgpDVwjSZLUd+2c+gEAxhUvYGP9vMA1Uv6af/MlDIxtIZUZyOQzLg+d06c46CZJkgRMePcHWZOaBED6/+4IGyPlsdjfuxZpV6WmMvbAdweukSRJ6tu2jnsfABMSC9i6bkXYGClPLfnDLylPLiWbjTDgg2eFzpEkSerTZpz4Nba0lxOLdLDmrqtC50h5a8T6xwFoaK+hZFhZ4Jq+xUE3SZKkF20sexcAE+K1tGxtClwj5Z+t61YwPlnb9ecXF20lSZL0xmaedjHbO0ZQVJCi4ZZLQ+dIeanjj7MBWJOaxIR3fzBwjSRJUt8WjcVYETsAgHGZF8ik04GLpPyz4qlHGJtYAkD0vScHrul7HHSTJEl6Uc0Zl7EzXUoy2szCm74ZOkfKOw23XEq8IMX2jhHMPO3i0DmSJEl9XqwoznL2B6Cy9R+Ba6T807K1iQnxrs04L21+kyRJ0purOOlCOjqLGFq0nvk/+37oHCnv7PifGyiIdLI+NZEp7z8hdE6f46CbJEnSi+IDSmhIzwRgzPZnAtdI+eelxdnl7E+sKB64RpIkKTeM+OS5ZLJRRsRXUfurn4TOkfLKwpu+STLazM50KTVnXBY6R5IkKSeM2K+GFa3TARhY9/vANVJ+advVzMTYPADWDjk0cE3f5KCbJEnSPxl03JfozBYwOrGMuofuDp0j5Y3aX/2EEfFVXYu0nzw3dI4kSVLOGDPzHaxMTQOg8LlfB66R8stLm9wa0jOJDygJXCNJkpQ7WmZ8GIBxyYVsWDwnbIyUR+bfdAkDYttIZUqYdubloXP6JAfdJEmS/sm4tx/NmtRkALJ/+VngGil/vLQouzI1jTEz3xG4RpIkKbdsm3gUAOMTtWxZXR+4RsoPdQ/dzejEMjqzBQw67kuhcyRJknJKzaf/k83tY4hFOlj3s6tC50h5Y9TGJwFoaJ9BcemIwDV9k4NukiRJ/6Kp/D0ATIjX0ry5MXCNlPu2rK5nfKIWeGWRVpIkSbtv/89fxLaOURQVtLL8titC50h54aXNbWtSkxn39qMD10iSJOWWaCzGitiBAIzPziGTTgcuknLfsr88yNjEEgCKjjw1cE3f5aCbJEnSv5hxxqU0p4eSiO5k8U3fDJ0j5bzlt11BUUEr2zpGsf/nLwqdI0mSlHOisRjLI/sDUNX2j8A1Uu5r3tzIhHjXZpyXNrtJkiRpz4w79Zt0dMYZUtjIvDuuDp0j5byW399MJJJlXWo/qo/6ROicPstBN0mSpH9RlCymITMTgLHNzwaukXLfS4uxyyP7E43FAtdIkiTlptGfPp9MNsbw+Brm3fPD0DlSTlt80zdJRHfSnB7KjDMuDZ0jSZKUk4aNm8zy1ukADFrySOAaKbe1Nm9lQtE8ANYNfXvgmr7NQTdJkqTXUfrRr9KZLaAssZy6384OnSPlrHn3/JDh8TVksjFGf/r80DmSJEk5q2z6IaxITQMg8cJvwsZIOe6lTW0NmZkUJYsD10iSJOWu1gOOA2BcciGNC3xwgLS3am/6FgOi22nJDGL6GVeEzunTHHSTJEl6HZWHHMHq1JSub568J2yMlMNeWoRdkZpG2fRDwsZIkiTluB2TjgFgfGIBm1csDlwj5aa6386mLLGczmwBpR/9augcSZKknDbz0//JpraxRCNp1t9zTegcKWeVbfobAA3tM0gOGRq4pm9z0E2SJOkNbBp7OAATEvPZ0bQucI2UezavWMz4xALglUVZSZIk7b2Zn/sGW9vLKCxoY8Xt7vCW9kb2xc1sq1NTqDzkiMA1kiRJuW9l/CAAxmfnkkmnA9dIuWfZn3/D2OQSAOLHnB64pu9z0E2SJOkN7H/mZTSnhxMvaKH+pm+GzpFyzsrZV1BY0MbW9jJmfu4boXMkSZJyXjQWY0XBLADGpZ93EUnaQzua1jExMR94ZXObJEmS9s34z3+T9s4EQwo3MPe274TOkXJO6n9vAWBNahLV7/tI4Jq+z0E3SZKkNxArirMsMxOAsbueC1wj5ZZMOk1l+wsArCiYRTQWC1wkSZKUH8pPvIB0tpBhRWupveeHoXOknLLk5kuIF7TQnB7O/mdeFjpHkiQpLwytqGZ5aw0AQ5Y9GrhGyi2tzVuZUNS1Gadx+DsC1+QGB90kSZLexPBPnEMmG2VUYgULfn1L6BwpZ9Te+2OGx9eQzhZSfuIFoXMkSZLyxqjJs1iRmgZA8fwHA9dIuaVi57MALMvMJFYUD1wjSZKUPzoO/igAVcmFrJ33t8A1Uu6ovfGbFEe3syszmBo34+wWB90kSZLexNgD383q1BQAon//ZeAaKXck5/4WgBWpaYyaPCtsjCRJUp7ZOfUDAIxLLKBpaW3gGik3LHzgdkYlVpDJRhn+iXNC50iSJOWVmuO/SFNbJdFIhqZffj90jpQzRm/+OwAN7TNJlJQGrskNDrpJkiS9hc1VRwAwPlHL1nUrwsZIOWDT8jrGJxcCryzCSpIkqfvMOPFctrSPprCgndV3XRk6R8oJBX+7B4DVqSmMPfDdgWskSZLyz6rEQQCMZy7p9rbANVLfV//ofYxJ1pPNRhjwobNC5+QMB90kSZLewv6nX8KOjhHEC1I03Hpp6Bypz1s1+woKC9rY0j6aGSeeGzpHkiQp70RjMVZEDwBgXPoFMul04CKpb9veuJrxia6nH760mU2SJEnda8Lpl9LWmWRwYRPzbv126Bypz2v74+0ArGmdxIR3fzBwTe5w0E2SJOktxIriNGRnAlCRej5wjdS3ZdJpxqVfAGBF9ACisVjgIkmSpPxUcfJFdHQWMbRoHfN/dm3oHKlPW3rLt4gXtLCjYwT7n35J6BxJkqS8VFo+juWpmq4/r/hT4Bqpb0tt28LEovkAbBj5zsA1ucVBN0mSpN0w4lPnkclGGRlfyfx7fxw6R+qz5v/s+wwtWkdHZxEVJ18UOkeSJClvjdivhhWp6QCU1D0UuEbquzLpNJUtzwLQkJ1JrCgeuEiSJCl/pd9+PACVyTpWPfvnwDVS37XgxgtJRpvZmS5lxpmXh87JKQ66SZIk7YYxM9/x8iLSkBfuDlwj9U2ZdJqRi38FwPJUDSP2qwlcJEmSlN9SBxwHwPjkfOp+d1fYGKmPmnPDNxgRX0U6W8iIT50XOkeSJCmv1XzsCzS2jicaydByn8eXSq+nbVcz++3seurhsvT+xAeUBC7KLQ66SZIk7ab0v32RzmwBFclFvHCrf0GT/tXcG79JeWIZmWyM2H+cGzpHkiQp78084ausbJlGQaST4r/eGDpH6nPaUy2M3/g7ABa1HsKYme8IXCRJkpT/1k/8KACTks+z5A+/DFwj9T213/siQwo3kMoMZOyZ14TOyTkOukmSJO2mycd8ivrUgQCMWforMul04CKp70i3t1G57n8AWJw6iAnv/mDgIkmSpP6h5d1n0pktoKp4IXPu8D+QS/9s/ve/zNCi9bR1FjPytO+GzpEkSeoXDjj9ElanJlMQ6aTwjz8KnSP1KdsbVzMl/RcAFmXfybBxkwMX5R4H3SRJkvZA8fHfpKMzzsjESl74oU+skl4y59r/ZHh8De2dSYae/J3QOZIkSf3G1A+fzNKW/QEYtegeN+RIL2re3Miktq7jgOoyhzGyembgIkmSpP5j+yGfJ5uNML64lvn3/jh0jtRnLP/RfzIgto3m9DAmf+0noXNykoNukiRJe6Di4PeyqO0QACZufpi2Xc2Bi6TwUtu2MGnnHwGo63g7ZdMPCVwkSZLUvxR95ALS2UJGJxqY85MLQudIfcLS679CSWwLO9OlTPiqi6uSJEm9qebjZ9CQ6tpoMPSFOwPXSH3DhsVzmBr7GwCLE++juHRE4KLc5KCbJEnSHhp95vdpzQyktKiR2u9/OXSOFFzddV9iUGETLZnBVH75B6FzJEmS+p1xhx3D4tTBAIzf8DvaUy2Bi6SwNi2vY2rkSQAWFx7OoBHlgYskSZL6ofefQyYbZUyynn/85KLQNVJwm26/gHhBC1vay5n5NY/13VsOukmSJO2h4eOnUpd9JwCTOx5jR9O6wEVSOFvXNjAl+zgAdZH3UFo+LmyQJElSPzX881fT1lnM0KJ1zL/uK6FzpKDW33QeiehOtnWUUXPuDaFzJEmS+qWJRxz38oacqjW/Id3eFrhICmfF3//A5MSzACwf+WGKksWBi3JXXg66jRs3jkgk8rpfZ5555muu37FjB+eccw5VVVXE43Gqqqo455xz2LFjR4B6SZKUC/Y7+0fsTA9lYGwry37gIpL6r1U/Ppvi6A62d4xk+nkeByRJkhTKqMmzqEu/A4BJqT/RsrUpcJEUxurnHmNK/BkAlpb+O/EBJYGLJEmS+q8hJ15Be2eS4fE1zLn2q6FzpGDa77+KWKSDxtYJzPrSlaFzclosdEBPGTx4MF/96ldf8/rBBx/8qu937drF4Ycfzpw5czjqqKP49Kc/zdy5c7n++uv585//zBNPPMGAAQN6qVqSJOWKkmFlPBc/goMz9zMt+iQb6+cxsnpm6CypV62d9zemFv0dgPqBR3JwSWngIkmSpP5t/Fd+yK4b3kVJbDMvXPslDvj2L0MnSb2u5VdXUFjcxsbWKg64+NrQOZIkSf1a+YxDmfvzQ9k/8RjVOx8ltW0LySFDQ2dJvaruwTuZXDwHgMYpn6YslrejWr0ib//XGzJkCJdeeulbXnfNNdcwZ84czj//fL773e++/Pq3vvUtLr/8cq655houu+yyHiyVJEm5aua5N7DliqcYWrSejbd+g5Hf/X3oJKlX7bj7W4wpbqWprYJZF/4gdI4kSVK/N7isgn/EDucgfsuUyJNsXrGYYeMmh86Ses2SP/yS6uTzAKyd+HFGuoAkSZIUXOWXr6flpsMZXNjE89d9mQMv/3noJKlXFT9+EwXFnaxsmcasz50fOifn5eXRpbsrm81y6623MnDgQC655JJX/eyCCy6gtLSU2267jWw2G6hQkiT1ZUXJYpaP+CAAUxLPsPLpPwYuknrPsj//hknJ5wBYXfERYkXxwEWSJEkCqDnvJ2zrGEUy2sy6m84NnSP1qsI//oiCSCerWyZzwOmXvPUbJEmS1ONKx0xgUeTdAEzJPs7WtQ2Bi6TeM+eOa6gqXkhntoCW95wVOicv5O2gW1tbG3feeSdXXnklN954I3Pnzn3NNfX19axbt453vvOdrzmeNJFI8J73vIe1a9eydOnS3sqWJEk5ZtaXv0tj63hikQ7a7r8ydI7Ue35/LdFIhrWpag78f1eErpEkSdKL4gNKWDr4GACmFD3NmucfD1wk9Y559/yQ8cW1ZLMRth96WugcSZIk/ZOp59zAjo4RFEe3s+qGc0LnSL0ik05TtugeAJa27M/UD50UuCg/5O2gW2NjI5/73Oe46KKL+H//7/8xa9Ys3v/+97Np06aXr6mvrwegurr6de/x0usvXfd62tra2LFjx6u+JElS/xGNxWic/CkA9ku+QN1Ddwcuknpe7X/fzITi+QBsOeDkwDWSJEn6Vweccz0b26ooLGhj5z2Xhc6ResXwOXcB0JCaSc3HvhC4RpIkSf8sOWQoSwYeCcDUwr+zbv7TgYuknjfnhgspSzSQzhZS9JELQufkjbwcdDv11FN57LHHaGpqYseOHfz973/n/e9/P//7v//Lf/zHf7x8FOn27dsBGDx48OveZ9CgQa+67vVcddVVDB48+OWvioqKbv5tJElSXzfrlG+wqmUqBZFOkn/5r9A5Uo8b/OxtRCJZGlpmMONTXw6dI0mSpH8RjcVYO/7jAFQXP0/9o/cFLpJ61j9+chHlyaVksjH44HmhcyRJkvQ6Zn3th2xqG0tRQYptP/tm6BypR7WnWhi/8UEAFrcewrjDjglclD/yctDtkksu4fDDD2f48OGUlJRw6KGH8rvf/Y53vetd/O1vf+P3v/99t33WBRdcwPbt21/+Wr16dbfdW5Ik5Y5d7zyDzmwB44oXMPdn14bOkXrMCz+9nIrkYjLZKJmj/jN0jiRJkt7AAV+4hDWpSUQjGaKP/jB0jtRj0u1tVK35DQCLUwcx8fAPhw2SJEnS64oVxVk55lgAJif/QcPjDwUuknrO/Ou+wtCidbR1FjP81KtC5+SVvBx0ez0FBQWccsopADz55JPAK09ye6Mntr10DOkbPfENIB6PM2jQoFd9SZKk/mfqsaewrGV/AEYu+DmZdDpwkdT9Muk0Y5b/NwD1qQOoPuoTgYskSZL0ZrYd/HkAJhTPZ/4vbwhcI/WMOdd+leHxNbR3Jhly4hWhcyRJkvQmZp15OetS+xGNpOl88JrQOVKPaNnaxKTUnwCoS7+DUZNnhQ3KM/1m0A1g+PDhALS0tABQXV0NQH19/ete/9LrL10nSZL0ZmLHnk86W8joxDLm/OSi0DlSt3vh+nMYGV9JR2ecgZ+6NHSOJEmS3kLNJ85k2a6ZAAx9/o6wMVIPSG3bQvXORwGo6ziU8hmHBi6SJEnSm4nGYmza/0QAJibnUnv/TwMXSd1v8bVfoiS2mV3pIYz/ik9Y7279atDt6aefBmDcuHFA1wBbeXk5Tz75JLt27XrVta2trfz1r3+lvLyc/fbbr7dTJUlSDhr/rg+wOHUwAOM2PEi6vS1wkdR92nY1s9+2/wVgUfvbGHvguwMXSZIkabd84GtkslHGJOv5x42XhK6RulXddV9mcGETLZnBVH7p+tA5kiRJ2g0zT/gqy1tqiESyDH7mttA5UrfatLyOKZEnAFgUO5zBZRWBi/JP3g26LVy4kG3btr3m9SeeeILrrruOeDzORz/6UQAikQinnXYaO3fu5PLLL3/V9VdddRVbt27ltNNOIxKJ9Ea6JEnKA8M+dyVtnUmGFa1lzve/HDpH6ja13/siQwo3kMoMpPzMa0PnSJIkaTdNPOI4lqQOAqBy1a/dkKO8sXVtA1OyfwVgUeRdlI6ZELhIkiRJu6v9375MZ7aAiuQiXrj126FzpG6z/ubzSEZ3sq1jFDXn/SR0Tl7Ku0G3X/3qV5SXl/PhD3+YL3/5y5x77rn8+7//O+95z3vo6OjghhtuoLKy8uXrzz//fGbNmsU111zD0UcfzQUXXMAHPvABLr/8cmbNmsX5558f8LeRJEm5pmzagdSlDwNgcsv/0bK1KXCRtO+2N65mSvovACzKvoth4yYHLpIkSdKeGPTZy2nvTDAivpo5158dOkfqFqtuOJvi6A62d4xg6jk3hM6RJEnSHph8zKeoTx0IwJilvyKTTgcukvbdmucfZ0rRMwAsHfLvxAeUBC7KT3k36HbEEUfw4Q9/mEWLFnHnnXfyox/9iAULFvDJT36Sp556itNOO+1V1w8YMIDHHnuMs88+m0WLFnHttddSW1vL2WefzWOPPcaAAQMC/SaSJClXjf/KD9mVHkJJbBOLr/tK6Bxpny3/0X8yILaN5vQwJn/NBSRJkqRcM2bmO1jUfigA1TsepbV5a+Aiad+sm/80UwufBqB+4FEkhwwNXCRJkqQ9VXz8N+nojDMysZIXfnhu6Bxpn+285zIKC9rY2FbFAWdfFzonb0Wy2Ww2dEQ+2bFjB4MHD2b79u0MGjQodI4kSQrkH5eeyEH8D6lMCakv/JmhFdWhk6S9smHxHIb8/P3EC1p4LvYJDr741tBJkiRJ2gtbVteT/OkRJKPNPJ/9MAdednfoJGmvLTz/aKYVP82mtrEMuex5YkXx0EmSJEnaC/O//kFmJJ9ga3sZxRc95xOwlLPqH72PCU+cQTSS4YXyr3HAFy4JnZRT9mTWKu+e6CZJktQXTP/aj9jeMZJktJk1P/la6Bxpr226/QLiBS1saS9n5td+FDpHkiRJe2loRTV1vBuAyZ1/Zeu6FWGDpL207C8PMjn5DwBWjjnWITdJkqQcNvrM79OaGUhpUSO11345dI6016KP/pBoJMOa1CSH3HqYg26SJEk9IFFSSv3gYwCYWvR31sx5InCRtOdW/P0PTE48C8DykR+mKFkcuEiSJEn7YurXfsKOjuEMiG5n1Y+/GjpH2ivZh75HNJJmXWo/Zp15eegcSZIk7YPh46dSl30nAJPbH2NH07rARdKem3/vj5lQPJ9sNsK2Q04LnZP3HHSTJEnqIQec8wOa2iopLGhj5y8uC50j7bH2+68iFumgsXUCs750ZegcSZIk7aPkkKEsGXAkAFMK/07jwucDF0l7pvb+nzIxOQ+ATbNOIhqLBS6SJEnSvtrv7B+xMz2UgbGtLPvBV0LnSHts6At3AtDQMoOaj58RuCb/OegmSZLUQ6KxGKurPgpAdfIf1P/x/sBF0u6re/BO9iueA0DjlE+7gCRJkpQnZp37Iza1jSVekGLznReEzpH2yOCnbyUSybK8pYaZn/7P0DmSJEnqBiXDylgUPwKAqdGn2Fg/L3CRtPue/69vMiZZTyYbhQ98LXROv+CgmyRJUg868MzLWJOaRDSSoeAPPwidI+224sdvoiDSycqWacz63PmhcyRJktRNYkVxVpX/BwBTEs/R8PhDgYuk3fPCrd+mongxndkCOo70SR+SJEn5ZOa5N7ClfTSJgl1svPUboXOk3ZJub6Ni9QMALEkdzMQjjgsb1E846CZJktTDth30OQAmFs+j9lc/CRsj7YY5d1xDVfFCOrMFtLznrNA5kiRJ6mb7n3UF61onEo2k6fyfa0LnSG8pk04zZumvAKhPHcikoz8ZuEiSJEndqShZzPIRHwRgSuIZVj79x8BF0lubc/3ZjIivpr0zwaDPXhY6p99w0E2SJKmH1Rz/RZa1zARgyD/uCBsjvYVMOk3ZonsAWNqyP1M/dFLgIkmSJHW3aCxGU82JAEwsnsvCB24PXCS9uRd+8DVGJlbS0Rmn+Phvhs6RJElSD5j15e/S2DqeWKSDtvuvDJ0jvanW5q1U73gUgEXthzJm5jsCF/UfDrpJkiT1gs5/P4dMNsrY5BKev+lboXOkNzTnhgspSzSQzhZS9JELQudIkiSph+z/2bNZ3lJDJJJl4N9+GjpHekNtu5rZb+v/ArCo7W1UHPzesEGSJEnqEdFYjMbJnwJgv+QL1D10d+Ai6Y0t+N6XGVy4kZbMIMZ+8drQOf2Kg26SJEm9oPp9H6E+dRAAFSt/TSadDlwkvVZ7qoXxGx8EYHHrIYw77JjARZIkSepJ7f/2ZTqzBVQW1zHntqtC50ivq/b7X2ZIYSOpzEBGn/m90DmSJEnqQbNO+QarWqZSEOkk+Zf/Cp0jva6taxuYmv0rAIsi72ZoRXXgov7FQTdJkqReMvCEb9HRGWdEfBUvXPfV0DnSa8y79isMLVpHW2cxw091oVOSJCnfTT7mU9S3HADA6Pp73ZCjPmdH0zomdzwGwKLsOxk+fmrYIEmSJPW4Xe88g85sAeOKFzD3Zz4pS33PqhvOoTi6nR0dI5h6zg2hc/odB90kSZJ6ydhZ76Ku/e0A7Lf9D7Q2bw1cJL2iZWsTk1v/BEBd+h2MmjwrbJAkSZJ6RfL4b9LRWcSoxArm/Oj80DnSqzT84MsMjG2lOT2U/c7+UegcSZIk9YKpx57CstT+AIxc8HM35KhPaVzwLFML/w7AkoFHkhwyNHBR/+OgmyRJUi8a+8VrSWVKGFK4gQXXfiV0jvSyxdd+iZLYZnalhzD+Kz8MnSNJkqReUnnIESxuexsAEzb9nvZUS+AiqcvG+nlMjT4FwJL4+ygZVha4SJIkSb2l8LgLSGcLGZ1YxpyfXBQ6R3rZljsvpqggxaa2scz6mmspITjoJkmS1IuGVlSziHcDMCX9V7Y3rg5cJMGm5XVMiTwBwKLY4QwuqwhcJEmSpN406vTv0to5gNKi9cz//pdC50gAbLz1G8QLWtjSXs6Mc38cOkeSJEm9aNxhx7A4dXDXnzc8SLq9LXCRBA2PP8Tk5HMArCr/D2JF8cBF/ZODbpIkSb1s8jk/ojk9nAGxbSz/0X+GzpFYf/N5JKM72dYxiprzfhI6R5IkSb1sxH411GUOA2By259p3twYuEj93Yq//4EpiWcAWD7iAxQliwMXSZIkqbcN+9yVtHUmGVa0ljnf/3LoHInOB68hGkmzrnUi+591ReicfstBN0mSpF5WXDqC/9/enYdVWef/H3+dBQ4HQQRFcUFQIEVFcWmxbDFLp5lfjUuW7VraZvs2aea+VGbLtFlZ2uTY6tJ3pmW0xdSyxQUERQUFRQ1XEITDOZzl94cTk2mmpn4O8Hxcl39037fwpOl6j8L7fM6G8F6SpFT7chWtW2W4CHXZtlVL1Tb04A+Q8hr8SY56kYaLAAAAYELyAy/qgDdGEfZ9ynuWHyLBLM+8KbJbqlRU2Urpdz9pOgcAAAAGxLXrohzvf1+QU/GFKop3Gy5CXbZ23utKcmZKkvZ0vFE2u91wUd3FohsAAIAB6Q+9oL2e5nJYK7R31kjTOajDDrwzTiFWt3a5E9T5/mdM5wAAAMCQyIZx2uDoKUlKtX6j3XnZhotQV+V8PFvJzgxJUlGba/gBEgAAQB3W6p7nVe5toEj7Hm145h7TOajDIr+fIYsloPyKDup47X2mc+o0Ft0AAAAMsIc6VNDkcklSG+cK5S/7xHAR6qLcRR8oJfzgiYLbW13JD5AAAADquLSHXlSxp6nCrOXa+frfTOegjgr/+iVZLX5trUhV+hD+OwQAAKjLouLitd5+gSSprZZqX2Gu4SLURRkzJqqlc738Aas8vTgB3TQW3QAAAAxJHz5JP7mSZLdUyft/T5nOQR1kW/S8bBafCl1t1PnW0aZzAAAAYFioM1ybG/1ZktTG8YO2/viV4SLUNZn/mKqE8HXyB6wqP+820zkAAAAIAu0f/LtKqprIaSvTtpceNJ2DOsbn9app3geSpFxXF7XpM8hwEVh0AwAAMMRmt2tXh+skSUnOTOV8NNNwEeqSrHdfUOvwLAUCFu0/8xbTOQAAAAgS6fc8paLKVgqxeuR6f4LpHNQhPq9XjdfNkSRtcnVS6l+HGC4CAABAMAiLjFZeVG9JUmrod9qWscxwEeqS1c8/rCZhBaryh8o5cJTpHIhFNwAAAKM63fCgCiray2rxq943r5rOQR0Ss/otSdLmijR1uJKTEgAAAHCQzW5XUcrVkqSU8NVa/+kcw0WoKzJeGqGmYZvlDYQopO8I0zkAAAAIIp0feE673S0VYnXrwJxxpnNQR7jLy5S09xNJ0gb3WWp5Zk/DRZBYdAMAADCusudd8gesahmeo4yZT5jOQR2w6uXH1dyZK1/AJv2Zo94BAABwqPRbRmhrRaqsFr8cX71kOgd1gNfjVuLOjyVJG1zdlHhuH8NFAAAACCY2u12FiVdKklKcK5X75XzDRagLsqfdrejQIlX6ItRk2JOmc/BfLLoBAAAY1vaya5Xn6ixJitvwrnxer+Ei1GZej1vxhQe/CbDR1U1JPfuaDQIAAEBQOtD9VgUCFrUKz1bm7GdN56CWy5h6lxqGbpfbH65Gt/ACMAAAAByuy21jtM11hmwWn6yfPWM6B7Vc6e4dauNZLEnKCZyr2OQOZoNQjUU3AACAIOAYMFLeQIjiwvKV8cLfTOegFst49n7FOgrl8Yep/vUc8Q4AAIAja9fvZm2q6CRJis1+mxfk4JSpKN6tNq4vJUk53u5q0ibdbBAAAACCVknXwZKkpPA1yn6f06dx6mx67h5F2It1wBuj5PtfMJ2DX2DRDQAAIAgknH2J1leeJUlqtftjeVwVhotQG1WWFSuldJEkab3nbDXv2N1wEQAAAIKZ9YpH5AvY1SxskzJfedx0Dmqp9c/cpUj7HpV7G6jVPc+bzgEAAEAQ63DVcG2q6ChJarByltkY1Fq787KVavtWkrTB0VORDeMMF+GXWHQDAAAIEo2HPqFKfz3FhP6krKfvNp2DWmjt1LsVFbJLFb76ajF8mukcAAAABLnW5/9F6yu7SZJa7vg/eT1uw0WobfYWbFCqvpEkrbdfqKi4eMNFAAAACHb+Pz0gX8CmFs6NWjV9jOkc1EI7X39EYdZy7fM0VdpDL5rOwa+w6AYAABAkGqd0VI7vXElSG/eXKttbZLgItUnx9s1KDSyRJK23nK+Y+BTDRQAAAKgJGt40RW6/U40c25Tx9D2mc1DLbJ/+kJy2MpVUNVGHh3nrKQAAAPy+lIv7KdfVVZIUv2WefF6v4SLUJlu+/1xtHD9KkvIb/VmhznDDRfg1Ft0AAACCSNJ9f9cBb7Qi7PuU9yw/RMLJs/XFBxRu26/SqlilPsArkAAAAHBs4tp10fqqcyRJZ5R/IVfJPsNFqC22ZSxTauj3kqS8qN5y1Is0XAQAAICaIuLaMaryOxTr2KrVz9xnOge1iHvuZIVYPSqqbKX0e54ynYMjYNENAAAgiNSPbaYNoRdJklIt32hPfo7ZINQKRWt/VGrId5KkjRGXyNkgxnARAAAAapKWdz+ncl+U6ofsVs4zd5nOQS1xYM5YhVjd2uVOUOcHnjOdAwAAgBqkRXoP5XgOviAnef9CucvLDBehNlj/6RwlO1dLkopSrpbNbjdchCNh0Q0AACDIdHjwBRV74hRmO6Cfpj9sOge1wL63RinU6tIedwulP/i86RwAAADUMNHNErXBeoEkqW1gqfYV5houQk2X+/lcpThXSZK2JQ7gB0gAAAA4bi2GT5PLF6kGITuVPXW46RzUAmFfvSirxa+tFalKv2WE6Rz8BhbdAAAAgoyjXqQ2NbxMktTW8b0KVyw2G4QabfPSj9XGuUKStLXZFbKHOgwXAQAAoCZq99AL2l/VWOG2Um176UHTOajhbAuflc3i0zbXGepy2xjTOQAAAKiBYuJTtF7nS5Laer/W/qJCw0WoyTLfnqbE8LUKBCw60P1W0zk4ChbdAAAAglDne5/WzspEhVg9qnh/gukc1GD+fz0lm8WrHa5kdbqD/5YAAABwYsIio5Vbv7ckqW3o99q+ZrnhItRUWe+9qNbhWZKkkm63GK4BAABATdbmgb+rtKqR6tlLlP/3e03noIbyeb1qnP1PSVJeRbra9bvZcBGOhkU3AACAIGSz2/VT8kBJUopzlTb8513DRaiJ1s57XUnOTEnSnk438HZAAAAA+EM6P/i8drtbKtRaqdLZnMKFExO96i1J0qaKjuow8HbDNQAAAKjJwqNjtbFeL0lSqn25itatMlyEmijj5VFq6twkbyBE9r8+YjoHv4NFNwAAgCCVPnSUCl1tZbX4FfrFC6ZzUANFfj9DFktA+RUd1PHa+0znAAAAoIaz2e3a2rKfJOkM5wrlfjnfcBFqmpWvjFYL50b5Ajb5//SA6RwAAADUAukPvaC9nuZyWCu0d9ZI0zmoYbwetxKL/k+StKGyq1r1+LPhIvweFt0AAACCWOnZQxUIWNQqPFtr3nnedA5qkIwZE9XSuV7+gFWeXnebzgEAAEAt0fWO8druSpHN4pP102dM56AG8Xm9arn14HJkrqurUi7uZ7gIAAAAtYE91KGCJpdLkto4Vyh/2SeGi1CTZDx9txqGbpfb71TDm6aYzsExYNENAAAgiLXvP0ybXJ0kSY0y/mG4BjWFz+tV07wPJEm5ri5q02eQ4SIAAADUJvu6DJYktQ7PUvaHr5qNQY2xetq9inVslccfpsjrx5vOAQAAQC2SPnySfnIlyW6pkvf/njKdgxrCVbJPZ5R/IUnKqequuHZdDBfhWLDoBgAAEOSslz8iX8CuZs48rXzpMdM5qAFWP/+wmoQVqMofKufAUaZzAAAAUMukXX2XNlekyWIJKOrHN0znoAaoLCtWSulCSdJ6z9lq3rG74SIAAADUJja7Xbs6XCdJSnJmKuejmYaLUBPkTBuu+iF7VO6LUsLdz5rOwTFi0Q0AACDItT7/L9rg6ipJSti2QF6P23ARgpm7vExJew8ezb7BfZZantnTcBEAAABqI9+l98oXsCneuUGrX+N0LhzduqfvVlTILrl8kWoxfJrpHAAAANRCnW54UAUV7WW1+FXvG06extHtK8xVqpZKkjZYL1B0s0SzQThmLLoBAADUADE3TZLH71QjxzZlTLvPdA6CWPa0uxUdWqRKX4SaDHvSdA4AAABqqZRLByrXdfBtXZrnfyif12u4CMGqeEeB2viXSJJydL5i4lMMFwEAAKC2qux5l/wBq1qG5yhj5hOmcxDEtr30oJy2Mu2vaqx2D71gOgfHgUU3AACAGiCu/ZnKqTpHkpRyYJFcJfsMFyEYle7eoTaexZKknMC5ik3uYDYIAAAAtVrEtWNV5XeosWOLVj/7gOkcBKktL9yverb9Kq1qpNQHXzKdAwAAgFqs7WXXKs/VWZIUt+FdXpCDI9qWsUxtQ7+XJOXW762wyGjDRTgeLLoBAADUEC3vfk4VvihFhexWzjN3mc5BENr03D2KsBfrgDdGyffzCiQAAACcWi3Se2i952xJUnLJZ3KXlxkuQrApWrdKqSHLJUkb6/WSs0GM4SIAAADUdo4BI+UNhCguLF8ZL/zNdA6C0IE54xRqrdRud0t1fvB50zk4Tiy6AQAA1BDRzRKVY7lAkpQaWKKtP35luAjBZPPSj5Vq+1aStN7RU5EN4wwXAQAAoC5odvvTcvki1SBkp9ZNHmI6B0HE5/WqdOaDclhd2utprnTeDggAAACnQcLZl2h95VmSpKQ9/9Legg2GixBMst9/SSnOlZKkwoT+stnthotwvFh0AwAAqEHaP/yC9nmaymkrk3Pe3dpfVGg6CUFgb8EGNfj0IYVZy7XH3UIdH3rRdBIAAADqiIaJbbTO1lOS1Mn+hVY+/7DhIgSLNeNu0Bn1VikQsKgg/krZQx2mkwAAAFBHNBn2lMq9DdQgZKcqXr5BHleF6SQEga0/fqXENVNls/i0tSJVXW4fZzoJJ4BFNwAAgBokLDJaey+ZIpcvUrGOQhU/c7W8HrfpLBjkLi9Txcs3KCZ0h8q9DXTg8mcU6gw3nQUAAIA6JH3UTG0s7yKrxa+0vW9r7bzXTSfBsJXPPaJO1s8kSRneS9T1jvGGiwAAAFCXxCZ30KZ296vKH6r48A3KG9PXdBIMK95RIOe8uxVhL1axJ07OW2eYTsIJYtENAACghkm5uJ82JA6XL2BXYvharX+8n+kkGLR53ADFh29QlT9Um9o/oMRz+5hOAgAAQB1js9sV/9hc7ahMUqjVpZarJmvbqqWms2BI9tzXlLbvLVktfm2s6KKO4941nQQAAIA6qOO19ykz4mpJUrvw77Vy9PWGi2CK1+PW/ueuVayjUC5fpPZc8oRikzuYzsIJYtENAACgBkq/ZYRWh/aXJHVwfqOVE242XAQTVo2+Tqnh30uSMiOuVsdr7jVcBAAAgLrK2SBGtiFvqqSqiSLt+xT6wXCV7t5hOgun2bZVS5W4eopCrZXaUZmk+JFzZbPbTWcBAACgjur2yIvKcF8sSeps+VirXhhhuAgmrH+8nxLD18oXsGtD67uUcjEHSNRkLLoBAADUUN0ee11ZledLktK9C7T6dd4Kpi5Z9cIIpVs+kSRleHqp2yMvGi4CAABAXdekTbp2XThBlb4INXZs0d6pA+X1uE1n4TQp3b1DoR8MV4R9n0qqmsh+yyw5G8SYzgIAAEAdlzbhA+WVp8tq8av97pnK+Wim6SScRisn3KwOzm8kSasdA5Q+5FHDRfijWHQDAACowVLHz1V+RQfZLD6lbn1VGxe+ZzoJp0HORzPVfvdMWS1+5ZWnK238+6aTAAAAAEnSGb2vVk7L2+QL2NQqPFs5oweYTsJp4PW4tXfqQDV2bFGlL0K7L5qkxikdTWcBAAAAstntajbyQxVVtpbD6lKLHydq+5rlprNwGqx+bbzSvQskSVmVF6jbyNfMBuGkYNENAACgBrOHOtTw4Q+0y52gMNsBNf76ce3ckGE6C6fQ9jXL1eLHiXJYXfqpsrWaPzaftwICAABAUOk8bLRW2/tKktLClmrFpGFmg3DK5YweoFbh2fIFbMpJuEMplw40nQQAAABUC4+OlW54TfurYhVp3yP7nNtVtrfIdBZOoY0L31PqtumyWXzaXJGmdhPnm07CScKiGwAAQA1XP7aZPANfUpk3Rg1Cdso382a5SvaZzsIpULa3SPY5tyvSvkf7q2JlufEN3goIAAAAQanb428q23WeJKmzZ54y3phiuAinyspJQ5UWtlSStNreV52HjjJcBAAAABwurv2Z2nHuWLn94WoSVqBdTw6Uz+s1nYVTYOeGDDX5epTCrOXa5U5Qo4ff58CAWoRFNwAAgFqgRZfztaXzCHn8YWoWtkmFkwbwF7Raxuf1ateTA9UkrEBuf7h+6jFece26mM4CAAAAflPbCfNVUNFeNotXbQpeUu6XvIK+tsl4Y4rSPQf/d812naduj79puAgAAAD4bal/uV7ZTYfJF7ApKXyNskcNMJ2Ek8xVsk/+N29WVMgulXlj5Ln6ZdWPbWY6CycRi24AAAC1RIcBtyqr4Y3yB6w6o94qrRkzyHQSTqLsUQOUFL5GvoBNa5vdqraXXWs6CQAAADgqe6hD0Q+8p93ueDltZYr94lHtzss2nYWTJPfzuWpT8KJsFq8KKtqr7QQWGQEAABD8ut4xXhmW/ydJ6hS2WCum3GG4CCeLz+tV4aT+aurcJI/fqa1dRqpFeg/TWTjJWHQDAACoRbreO1WZ/j9JktLtn2vFtPvMBuGkWDHlDnUKWyxJyrBeri63jzMbBAAAAByjqLh4ufq/oAPeaDUIKZLn9cGqLCs2nYU/aHdetmK/Gimn7YB2u1sq+oH3ZA91mM4CAAAAjknXsf/Q2orukqT0yg+UMespw0U4GbLGXK0z6q2WP2BVVsOb1L7/MNNJOAVYdAMAAKhlOo55W+vLz5TFElCn/XOU9d6LppPwB2TMekrplR9IktZWdFfXMW8ZLgIAAACOT8sze6qg06Oq8jvU3JmrLROulM/rNZ2FE1RZVizP64PVIKRIB7zRqhzwgqLi4k1nAQAAAMclZdw8ba1Ild1SpTM2vaBNX//LdBL+gBVP36tO9i8kSRn+P6nrvU8aLsKpwqIbAABALWOz29Vq7Hxtc52hEKtbrbOmasv3n5vOwgnY9PW/1GbT32W3VGlLRTuljJtnOgkAAAA4IR0G3q410TcoELCoTfgKrRl7nekknACf16stE65Uc2euqvwOFaSPUHy3i0xnAQAAAMct1BmuyHvnaI+7hcJtpYr+zyPaW7DBdBZOQNZ7L6pT6TuyWAJaX3Gmukx4x3QSTiEW3QAAAGohR71Ihd3+DxV7mqqevUT1Ftyr4h0FprNwHPbk5yj6Pw/LaSvTHncL1b/vHYU6w01nAQAAACes6/3TlOHrLUlKt/1HK597yHARjteasdeqTfgKBQIWrYm+QR2uvM10EgAAAHDCopu31oG/PqtybwPFhO6Q65Xr5S4vM52F47Dl+8/VOmuqQqxubXOdoVZj5ptOwinGohsAAEAt1ahVqvZd+oRcvkg1cmxT6XPXyOtxm87CMXCXl6ly+o2KCf1J5d4GOvDXZxXdLNF0FgAAAPCHdRw7RxsruspiCajjvn8o+8NXTSfhGK189kGl2xZKkjJ8vdX1/mmGiwAAAIA/LvGc3trc4SFV+UPVwrlR+eP6m07CMSrevln1FtyrevYSFXuaKuz2f8hRL9J0Fk4xFt0AAABqsaSefbWh9d3yBkKUEL5OGx7vazoJxyB/XH+1cG5Uld+hzR0eUuI5vU0nAQAAACeFzW5Xy8fnaocrWSFWtxIzpqhwxWLTWfgd2R++qo7Fb8tiCWhDeVd1HDvHdBIAAABw0qQNuluZkddIktqG/6BVo681XITf43FVqPT569TIsU0uX6T2XfqEGrVKNZ2F04BFNwAAgFoufcjflOEYIElq7/xWK8cNNhuEo1o1+lq1Df9BgYBFmfWvUdqgu00nAQAAACdVWGS07ENnqqSqiSLsxQqbe7f2FxWazsJvKFyxWIkZUxRidWu7K0UJo+fKZrebzgIAAABOqm4P/10Znl6SpHTLp1r590cNF+FocscOUEL4OnkDIdqQdI+SevY1nYTThEU3AACAOqDbyFe1pvJCSVK6//+06tVxhotwJCuf/5vSLZ9KkjK9vdTtoecNFwEAAACnRuOUjtrdc4pcvgjFOrZq3zOD5PW4TWfhV/YXFSps7t2KsBerpCpOocNmKSwy2nQWAAAAcEqkjX9fG8s7y2rxK23PLK2b/6bpJBzBynE3qb3zW0lSRtiVSh/8iOEinE4sugEAANQR7SfO06aKjrJZfGq3/VVt+M+7ppPwC+vmv6m0vW/JavFrY3lnpY17z3QSAAAAcEqlXDJAGxLukC9gU6vwbOU83t90En7B63Gr+JmrFevYKpcvQrt7TlZscgfTWQAAAMApY7PbFf/YPP1UmaRQq0vxKydqW8Yy01n4hVXTxyjd/y9J0prKC9VtxHTDRTjdWHQDAACoI2x2uxr/7QPtqkxQmLVccUseV9G6VaazIGlbxjLFr5yoUKtLP7mSFP/YPN4KCAAAAHVC+tBRWh1ycMEtzblMKyfcYrgIP8t5vL8Sw9fKF7BpQ8IdSrlkgOkkAAAA4JRzNoiR5cYZ2l/VWJH2vQp9906V7t5hOguS1n86R+13vCabxadNFR3VfuI800kwgEU3AACAOiSyYZw8g15WmbehokJ2KfCPoXKV7DOdVaeV7t6h0HfvVKR9r/ZXNZb15jflbBBjOgsAAAA4bbqNmqEsVw9JUrp3vlbPmGi4CCsn3KI058GTK1aH9Ff60FGGiwAAAIDTJ65dFxVdMEGV/npqHLZFe6ZeJZ/XazqrTitat0pNl42Ww1qhnZWJavy3DzgwoI5i0Q0AAKCOaZHeQ4VdR8njd6pp2CZtm9Sfv6AZ4vN6tWfqVWoctkWV/nraeeFENWmTbjoLAAAAOO1SJ8xTfkUH2Sw+pW55RbmLPjCdVGetnjFR6d75kqQs1/nqNmqG4SIAAADg9GvTZ5DWNb9NvoBNrcOztHZUP9NJdVZF8W4F/nGLokJ2q8zbSN5rpyuyYZzpLBjCohsAAEAd1K7fzcpqNFj+gFUp9VYra/RVppPqpLWj+ql1eJZ8AZtyWtyuM3pfbToJAAAAMMIe6lDMA+9qt7ulwmwHFLv4Me3KXWM6q87JXfSBUre8IpvFp/yKDkqdMNd0EgAAAGBMl9vGKMN2hSSpY9gSrZh8q+Giusfn9WrH5CvVNGyz3H6ntp05Ss07djedBYNYdAMAAKijut7zhDICl0mS0kO/0Iqp9xguqltWTL5VHcOWSJIy7H3V+dbRhosAAAAAs6Li4uUe+LIOeGPUIGSnvDOGqLKs2HRWnbErd41iFz+mMNsB7XInqOHDH8ge6jCdBQAAABjVdfQsZbvOlSR1ds9VxswnDRfVLVmjr1JyvQz5A1atjR2i1L8OMZ0Ew1h0AwAAqMO6jJ+j9RVnSZI6lb2jNe88b7iobsiY+YQ6uw+ejJDtOlddH3/TcBEAAAAQHFp0OV8F6Y/K4w9TM2eetk4YIJ/Xazqr1qssK5Z3xhA1CNmpA94YeQa+pPqxzUxnAQAAAEGh7YQF2lLRTjaLV202v6BNXy0wnVQnrHjqbqWHfiFJyghcpi53TzFchGDAohsAAEAd12rMPBW62ijE6lHS2mdU8N1C00m12qavFqjN5hdls3i1paKd2k5YYDoJAAAACCodrrxNWTE3yB+w6ozwlVoz9hrTSbWaz+vV1gkD1MyZJ48/TAWdR6hFl/NNZwEAAABBwx7qUP373tEedws5bWWKWfSo9uTnmM6q1da887w6HXhXkpRTcZa6jJ9juAjBgkU3AACAOs5RL1Lhd7ytfZ5mqmcvUcRH96t4+2bTWbXSnvwcxSx6VE5bmfa4W6j+fe/wVkAAAADAEXS972ll+npLktJti7Ri2v2Gi2qvNWOv0RnhK+UPWJUVc5M6DLjVdBIAAAAQdKKbJaq87/Mq9zZQdOhPqpx+o9zlZaazaqWCb/+jpHXTFGL1qNDVRq3HzDOdhCDCohsAAADUMLGNSi57WhW++mrk2Kay56+Vx1VhOqtWcZeXqXL6jYoO/UkHvNGq6P+iopslms4CAAAAglbHsf/UhopuslgC6rT/n8p+/yXTSbXOimn3K922SJKU4e+trvc9ZbgIAAAACF4JZ1+izWkPq8rvUAvnRuWP6yuf12s6q1Yp3r5Zkf++X/Vs+7XP00zhd7wtR71I01kIIiy6AQAAQJLU+vy/KDflHnkDIWoZnqPcMf1NJ9UaPq9X+eP6qoVzo6r8DhV0fFgtz+xpOgsAAAAIaja7XQmPf6jtrhSFWN1KXDNVW3/8ynRWrZH9/kvqtP+fslgCWl/RTZ3G/NN0EgAAABD00q6+S5lR1yoQsKht+AqtGXeD6aRaw+OqUNnz16ph6HZV+Oqr5LKn1TCxjeksBBkW3QAAAFCt040PKyNsoCSpffhyrRx7o+Gi2mHNuBvUNnyFAgGLMqOuU4erhptOAgAAAGqEsMhohQ6bpWJPnCLsxXLOu1vFOwpMZ9V4W3/8SolrpirE6tZ2V4oSH/9QNrvddBYAAABQI3R78DlleC+RJHWyfqaVzz1iuKh2yB3bTy3Dc+QNhCg35R61Pv8vppMQhFh0AwAAwCG6jXhFmZUXSZLSA//WyldGmw2q4VY+94g6WT+TJGV4L1G3B581XAQAAADULLHJHbTnkifk8kUq1lGo/c9dK6/HbTqrxireUSDnvLsVYS9WsSdOocNmKSwy2nQWAAAAUKN0HPeuNpZ3kdXiV9q+t5Q99zXTSTXayrE3qr3zO0lSRthAdbrxYcNFCFYsugEAAOAwHSbO1abyjrJZfOrw0+vK+Xi26aQaKXvua0rb95asFr82VnRRx3Hvmk4CAAAAaqSUi/tpQ+u75AvYlRi+Vusf72c6qUbyetza/9y1inUUyuWL1J5LnlBscgfTWQAAAECNY7PbFf/YXO2oTFKotVKJq6do26qlprNqpFUvP67OgX9JkjLdF6nbiFcMFyGYsegGAACAw9jsdjV+9APtrEyUw1qhZt+OVdHaH01n1SjbVi1V4uopCrVWaocrWS0fn8dbAQEAAAB/QPqQR7XaMUCS1MH5jVZOuNlwUc2z/vF+SgxfK1/Arg2t71LKxSwMAgAAACfK2SBG9ltmqaSqiSLs+xT6wXCV7t5hOqtGyfl4ttoXzZDV4temik7qMGGu6SQEORbdAAAAcESRDePku+41lVY1UlTIbuntW1VRvNt0Vo1QunuHQj8Yrgj7PpVUNZF96EzeCggAAAA4CbqNfE1ZlRdIktK9C7T69fGGi2qOlRNuVgfnN5Kk1Y4BSh/yqOEiAAAAoOZrnNJRuy6coEpfhBo7tmjv1IHyetyms2qEorU/qvm3Y+WwVqiospWajpjLgQH4XSy6AQAA4Dc1Sztb288ZLbffqbiwzdox5Ur5vF7TWUHN63Fr79SBauzYokpfhHZfNEmNUzqazgIAAABqjdTxHyq/ooNsFp9St76qjQvfM50U9Fa/Pl7p3gWSpKzKC9Rt5GtmgwAAAIBa5IzeVyun5W3yBWxqFZ6tnNEDTCcFvYri3dLbw1Q/ZLdKqxrJf92rCo+ONZ2FGoBFNwAAABxV6uU3KbvxzfIHrEoOz1DW4wNNJwW1nNED1Co8W76ATTkJdyjlUv59AQAAACeTPdShhg9/oF3uBIXZDqjJ16O0c0OG6aygtXHhe0rd+qpsFp82V6Sp3cT5ppMAAACAWqfzsNFabe8rSUoLW6qVk4aaDQpiPq9XO6YMUFxYvtx+p7afM1rN0s42nYUagkU3AAAA/K6ud03W6sBfJEnpji+14snhhouC08pJQ5UWtlSSlGHvp85DRxkuAgAAAGqn+rHN5Ln6ZZV5YxQVskv+N2+Wq2Sf6aygs3NDhpp8PUphtgPaVZmgRg+/z1sBAQAAAKdIt8ffVLbrPElSume+Mt6YYrgoOGU/fqWSwzPlD1iV3eQWpV5+k+kk1CAsugEAAOCYdB0/W+sqDr6iJr3iPWXOftZwUXDJeGOK0j0HT0bIdvVQ18ffMFwEAAAA1G4t0ntoa5eR8vidaurcpMJJ/eXzek1nBQ1XyT7537xZUSG7VOZtKM+gl1U/tpnpLAAAAKBWazthvgoq2stm8apNwYvK/Xyu6aSgsuLJO9XJ8ZUkKUN/UdfhkwwXoaZh0Q0AAADHLHncAhW62spuqVLKhme1eenHppOCQu7nc9Wm4EXZLF4VVLRX2wnzTCcBAAAAdUL7/sOU1fAG+QNWnVFvtbLGXG06KSj4vF4VThqgps5N8vidKuw6Si3Se5jOAgAAAGo9e6hD0Q+8p93ueDltBxT71Ujtyl1jOisoZL49TekV70uS1lWcrS7jZhsuQk1kCQQCAdMRtUlpaamioqK0f/9+1a9f33QOAADASbevMFd65QrFhO5Qld+hTZWdZOl1i9r0GWQ67bTL+fc/ZF3yllqHrVGI1aPd7pYKvfcTRcXFm04DAAAA6pTVj1+jzrZPJEk7KxNVGN1DaXc9IUe9SMNlp9f+okLlTX9MrT0/KDr0J/kDVq2OuVVd733SdBoAAABQp2z98SvFfDREEfZiVfoilOdJV0S/e5V4Tm/Taadd5uxnFbFmnhKda2Wz+FToaqsmY79SqDPcdBqCxPHsWrHodpKx6AYAAOqC/GWfqP4nD6lh6Pbqa4UVbbSr5SVKv328bHa7wbpTy+txK/OV0Wqy40u1cG6svr7b3VKu/n9XyzN7GqwDAAAA6iaf16t1o/qqneNb2Sw+SdIBb4zy/J3V9KbRatIm3WzgKVbw7X904KPnlRyaqTDbAUmSxx+mLPtl6jp6ltk4AAAAoI7K/vBVtVw9VfVDdkuS/AGrClztVdbhr+p048OG606tyrJiZb84UvH7v1WTsILq69tdKXLe9Y5i4lPMxSHosOhmEItuAACgrvB5vcp8bZxityxUvHN99fViT1NtDj1TybdPrlUnmxVv36z8V0eptW+FGoTslCQFAhZtdaVqX/Jl6njzyFq94AcAAADUBIUrFqvkw2lKDlktp61MklTlD9Xmyo7yn3+jUi+/yXDhyePzepU1+xlFrftICc51slr8kqTSqkbaZOmq+FvGqVGrVMOVAAAAQN3mcVVozUsj1HzPEjUN21x9fZc7QVsju6v9nVPkbBBjsPDkKlq3SkWzJyrZukoR9mJJki9gU76rg1xdBipt0N2GCxGMWHQziEU3AABQF21c+J58n89QUtgahVorJem/R3F3UsRf71XiuX0MF564TV//S5Ufv6TksEw5rBWSJLffqU2VnRTyp9uVcnE/w4UAAAAAfq1sb5E2vjxSia7vDjmJepvrDO1sdpE63TFR9lCHwcIT5yrZp7UvP6qEsuWKdWytvv6TK0nbG1+gjndO5i2AAAAAgCCU/f5Lcvz4gVqHZ8lm8UqSyr0NlOfrrNhrR6hZ2tmGC09czkczZV32tlo7sxRi9UiSKnz1lVeVrugrH1R8t4vMBiKosehmEItuAACgLtuTn6PCN8YoKbBS9UP2SDp4FPcWVzvtT71CaTc8WCNOPfN5vVoz6wlFb/xELZ051Scj7K9qrE3Wbkq8dTzHagMAAAA1gM/r1Zo3Jysm71O1dObIYjn47fCSqibabOumVrdNVHTz1oYrj82OrO+1e84UJdtWq569RJLkDYQo39VB7rOuUYcrbzMbCAAAAOCYbMtYpr3vTlVKyGqF2/ZLOvhn+80Vaarqfq3a9x9muPDYeD1uZb78uOJ++lLNnbnV1/e4W6gg/Gy1uXOyIhvGGSxETcGim0EsugEAAPz3KO6XR6r5riVq6g3PwQsAACD0SURBVNxUfX23u6W2RHZX+zufCMqjuCuKdyv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+ "text/plain": [
+ "<Figure size 2500x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cDC = [4, 4, 4, 4]\n",
+ "renewPow = renewable_production_bell(0)\n",
+ "fig = plt.figure(figsize=(25, 10))\n",
+ "for x in range(len(cDC)):\n",
+ " plt.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "plt.legend(fontsize=16)\n",
+ "plt.xlabel(\"Time\", size=16)\n",
+ "plt.ylabel(\"Power\", size=16)\n",
+ "plt.title(\"Power production for each DC\", size=18)\n",
+ "plt.tick_params(axis='both', labelsize=14)\n",
+ "plt.xlim(0,71)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Different offsets and amplitudes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2500x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cDC = [4, 4, 4, 4]\n",
+ "# Bell example\n",
+ "renewPow = renewable_production_syntactic()\n",
+ "fig = plt.figure(figsize=(25, 10))\n",
+ "for x in range(len(cDC)):\n",
+ " plt.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "plt.legend(fontsize=16)\n",
+ "plt.xlabel(\"Time\", size=16)\n",
+ "plt.ylabel(\"Power\", size=16)\n",
+ "plt.title(\"Power production for each DC\", size=18)\n",
+ "plt.tick_params(axis='both', labelsize=14)\n",
+ "plt.xlim(0,71)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Real production normalizing the total energy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2500x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cDC = [4, 4, 4, 4]\n",
+ "# Raw\n",
+ "fig = plt.figure(figsize=(25, 10))\n",
+ "x = 0\n",
+ "cities = [\"Canberra\", \"Dubai\", \"Paris\", \"Sao_Paulo\"]\n",
+ "renewPow = renewable_production_real_norm_total(localdir = '', total_energy=55296, cities=cities)\n",
+ "for x in range(len(cDC)):\n",
+ " plt.plot(renewPow[x], label=\"Power production DC \" + cities[x], c=color[x])\n",
+ "\n",
+ "plt.legend(fontsize=16)\n",
+ "plt.xlabel(\"Time\", size=16)\n",
+ "plt.ylabel(\"Power\", size=16)\n",
+ "plt.ylim(0,)\n",
+ "# plt.xlim(72,72)\n",
+ "plt.title(\"(Adjusted) Power production for each DC\", size=18)\n",
+ "plt.tick_params(axis='both', labelsize=14)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Article's Figure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total energy of bell curve 55296.0\n",
+ "Total energy of variable curve 55296.0\n",
+ "Total energy of normalized real curve 55296.0\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x800 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cDC = [4, 4, 4, 4]\n",
+ "fig = plt.figure(figsize=(10, 8))\n",
+ "ax = plt.subplot2grid((3,1), (0,0), colspan=1)\n",
+ "renewPow = renewable_production_bell(0)\n",
+ "print(\"Total energy of bell curve\", np.sum(renewPow))\n",
+ "for x in range(len(cDC)):\n",
+ " ax.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "ax.legend(fontsize=12, loc=\"upper right\")\n",
+ "ax.set_ylabel(\"Power (W)\", size=16)\n",
+ "ax.set_title(\"Equal\", size=18)\n",
+ "ax.tick_params(axis='both', labelsize=12)\n",
+ "ax.set_xlim(0,71)\n",
+ "ax.set_ylim(0,)\n",
+ "\n",
+ "ax = plt.subplot2grid((3,1), (1,0), colspan=1)\n",
+ "renewPow = renewable_production_syntactic()\n",
+ "print(\"Total energy of variable curve\", np.sum(renewPow))\n",
+ "for x in range(len(cDC)):\n",
+ " ax.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "ax.set_ylabel(\"Power (W)\", size=16)\n",
+ "ax.set_title(\"Variable\", size=18)\n",
+ "ax.tick_params(axis='both', labelsize=12)\n",
+ "ax.set_xlim(0,71)\n",
+ "ax.set_ylim(0,)\n",
+ "\n",
+ "ax = plt.subplot2grid((3,1), (2,0), colspan=1)\n",
+ "cities = [\"Canberra\", \"Dubai\", \"Paris\", \"Sao_Paulo\"]\n",
+ "renewPow = renewable_production_real_norm_total(localdir = '', total_energy=55296, cities=cities)\n",
+ "print(\"Total energy of normalized real curve\", np.sum(renewPow))\n",
+ "for x in range(len(cDC)):\n",
+ " ax.plot(renewPow[x], label=\"Power production DC \" + str(x), c=color[x])\n",
+ "ax.set_xlabel(\"Time (h)\", size=16)\n",
+ "ax.set_ylabel(\"Power (W)\", size=16)\n",
+ "ax.set_title(\"Real\", size=18)\n",
+ "ax.tick_params(axis='both', labelsize=12)\n",
+ "ax.set_xlim(0,71)\n",
+ "ax.set_ylim(0,)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/RenewableData/renewable.py b/RenewableData/renewable.py
new file mode 100644
index 0000000..067b7b5
--- /dev/null
+++ b/RenewableData/renewable.py
@@ -0,0 +1,66 @@
+import math
+import pandas as pd
+import numpy as np
+from itertools import cycle
+
+def renewable_production_bell(offset=0, cDC = [4, 4, 4, 4], pmin = 20, pdyn = 140, pow_variable=0.6):
+ # Bell power production
+ renew = []
+ bell=1
+ total_time = 72
+ curr_offset = 0
+ for dc in range(0, len(cDC)):
+ renew.append([round((pdyn + pmin) * cDC[0] * pow_variable*(math.sin((2 * np.pi * t * 1/24) + curr_offset)+bell)/(bell+1), 0) for t in range(total_time)])
+ curr_offset += offset
+ return renew
+
+def renewable_production_syntactic(cDC = [4, 4, 4, 4], pmin = 20, pdyn = 140, pow_variable=[0.6, 0.45, 0.4, 1]):
+ # Bell power production
+ renew = []
+ offset = 2
+ bell=1
+ total_time = 72
+ curr_offset = 0
+ for dc in range(0, len(cDC)-1):
+ renew.append([round((pdyn + pmin) * cDC[0] * pow_variable[dc]*(math.sin((2 * np.pi * t * 1/24) + curr_offset)+bell)/(bell+1), 0) for t in range(total_time)])
+ curr_offset += offset
+ bell += 1
+ renew.append([192 for _ in range(total_time)])
+ return renew
+
+# Raw production
+# These productions were extracted from renewable.ninja (https://www.renewables.ninja/)
+def get_production_raw(city, localdir = 'RenewableData/'):
+ pv_production_data = pd.read_csv(localdir + "ninja_pv_"+city+".csv", header=3)
+ wt_production_data = pd.read_csv(localdir + "ninja_wind_"+city+".csv", header=3)
+ return (pv_production_data['electricity'] + wt_production_data['electricity']) * 1000
+
+def renewable_production_real(localdir = 'RenewableData/', total_energy = 13824.0):
+ result = []
+ for city in ["Canberra", "Dubai", "Paris", "Sao_Paulo"]:
+ prod = get_production_raw(city, localdir)[0:72]
+ new_prod = (prod - prod.min()) / (prod - prod.min()).sum()
+ new_prod = new_prod * total_energy
+ result.append(new_prod)
+ return result
+
+def renewable_production_real_norm_total(localdir = 'RenewableData/', total_energy = 55296.0, cities=["Canberra", "Dubai", "Paris", "Sao_Paulo"]):
+ result = []
+ for city in cities:
+ prod = get_production_raw(city, localdir)[0:72]
+ result.append(prod)
+
+ result = np.array(result)
+ result = (result - result.min()) / (result - result.min()).sum()
+ result = result * total_energy
+ return result.tolist()
+
+def renewable_production_real_max_power(localdir = 'RenewableData/', max_power=640):
+ result = []
+ for city in ["Canberra", "Dubai", "Paris", "Sao_Paulo"]:
+ result.append(get_production_raw(city, localdir)[0:72])
+
+ result = np.array(result)
+ max_found = result.max()
+ result = result / max_found * max_power
+ return result.tolist()
\ No newline at end of file
diff --git a/default.nix b/default.nix
new file mode 100644
index 0000000..3c1d5b9
--- /dev/null
+++ b/default.nix
@@ -0,0 +1,22 @@
+{
+ pkgs ? import (fetchTarball {
+ url = "https://github.com/NixOS/nixpkgs/archive/23.11.tar.gz";
+ sha256 = "sha256:1ndiv385w1qyb3b18vw13991fzb9wg4cl21wglk89grsfsnra41k";
+ }) {}
+}:
+
+pkgs.mkShell {
+ buildInputs = [
+ pkgs.python311Packages.pandas
+ pkgs.python311Packages.numpy
+ pkgs.python311Packages.matplotlib
+ pkgs.python311Packages.jupyter
+ pkgs.python311Packages.ipython
+ pkgs.python311Packages.pulp
+ pkgs.python311Packages.colorama
+ pkgs.python311Packages.termcolor
+ pkgs.python311Packages.tabulate
+ pkgs.python311Packages.seaborn
+ pkgs.cbc
+ ];
+}
--
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