Figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "326c1ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for the histogram\n",
    "case_labels = ['N-1', 'N-2', 'N-3']\n",
    "percentage_pairs = np.array([[0.8, 0.68], [9.37, 2.34], [18.09, 5.40]])\n",
    "\n",
    "# Transpose the percentage_pairs array to align the dimensions\n",
    "percentage_pairs = percentage_pairs.T\n",
    "\n",
    "# Set up the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define the bar width and positions\n",
    "bar_width = 0.35\n",
    "index = np.arange(len(case_labels))\n",
    "\n",
    "# Define the colors for the bars\n",
    "color1 = '#4CAF50'  # Light green\n",
    "color2 = '#006400'  # Dark green\n",
    "\n",
    "# Plot the bars\n",
    "rects1 = ax.bar(index, percentage_pairs[0], bar_width, label='SCOPF', color=color1)\n",
    "rects2 = ax.bar(index + bar_width, percentage_pairs[1], bar_width, label='NN', color=color2)\n",
    "\n",
    "# Add labels, title, and grid\n",
    "ax.set_xlabel('Outages', fontsize=12)\n",
    "ax.set_ylabel('Percentage of Infeasibility', fontsize=12)\n",
    "ax.set_title('Comparison of Infeasible Cases', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(case_labels, fontsize=11)\n",
    "ax.legend(fontsize=12)\n",
    "ax.set_ylim([0, 20])\n",
    "ax.grid(True, linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Add value labels on top of the bars\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate(f'{height:.2f}%', xy=(rect.get_x() + rect.get_width() / 2, height), xytext=(0, 3),\n",
    "                    textcoords=\"offset points\", ha='center', va='bottom', fontsize=11)\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "# Adjust spacing and font size\n",
    "plt.tight_layout()\n",
    "plt.tick_params(axis='both', which='both', labelsize=11)\n",
    "\n",
    "# Save the plot as a high-resolution image for academic use\n",
    "#plt.savefig('infeasible_cases.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eb4e651",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=========== Redispatching histograms\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for the histogram\n",
    "case_labels = ['N-1', 'N-2', 'N-3']\n",
    "percentage_pairs = np.array([[0.72, 0], [3.63, 0.53], [8.65, 1.99]])\n",
    "\n",
    "# Transpose the percentage_pairs array to align the dimensions\n",
    "percentage_pairs = percentage_pairs.T\n",
    "\n",
    "# Set up the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define the bar width and positions\n",
    "bar_width = 0.35\n",
    "index = np.arange(len(case_labels))\n",
    "\n",
    "# Define the colors for the bars\n",
    "color1 = '#4CAF50'  # Light green\n",
    "color2 = '#006400'  # Dark green\n",
    "\n",
    "# Plot the bars\n",
    "rects1 = ax.bar(index, percentage_pairs[0], bar_width, label='SCOPF', color=color1)\n",
    "rects2 = ax.bar(index + bar_width, percentage_pairs[1], bar_width, label='NN', color=color2)\n",
    "\n",
    "# Add labels, title, and grid\n",
    "ax.set_xlabel('Outages', fontsize=12)\n",
    "ax.set_ylabel('Percentage of Infeasibility', fontsize=12)\n",
    "ax.set_title('Comparison of +10% Infeasible Cases', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(case_labels, fontsize=11)\n",
    "ax.legend(fontsize=12, loc='upper left')\n",
    "ax.set_ylim([0, 10])\n",
    "ax.grid(True, linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Add value labels on top of the bars\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate(f'{height:.2f}%', xy=(rect.get_x() + rect.get_width() / 2, height), xytext=(0, 3),\n",
    "                    textcoords=\"offset points\", ha='center', va='bottom', fontsize=11)\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "# Adjust spacing and font size\n",
    "plt.tight_layout()\n",
    "plt.tick_params(axis='both', which='both', labelsize=11)\n",
    "\n",
    "# Save the plot as a high-resolution image for academic use\n",
    "plt.savefig('infeasible_cases_10p.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42e587a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Generate sample data\n",
    "x_values = ['39-bus', '118-bus']\n",
    "y_values = ['N-1', 'N-2', 'N-3']\n",
    "z_values = np.array([[0.68, 0], [2.34, 0], [5.4, 0]])\n",
    "\n",
    "# Create the heatmap\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "heatmap = ax.imshow(z_values, cmap='YlGn', vmin=0, vmax=1)\n",
    "\n",
    "# Set the tick labels and positions\n",
    "ax.set_xticks(np.arange(len(x_values)))\n",
    "ax.set_yticks(np.arange(len(y_values)))\n",
    "ax.set_xticklabels(x_values, fontsize=12)\n",
    "ax.set_yticklabels(y_values, fontsize=12)\n",
    "\n",
    "# Set the axis labels\n",
    "ax.set_xlabel('Test System', fontsize=14)\n",
    "ax.set_ylabel('Outages', fontsize=14)\n",
    "\n",
    "# Add grid lines\n",
    "ax.set_xticks(np.arange(len(x_values) + 1) - 0.5, minor=True)\n",
    "ax.set_yticks(np.arange(len(y_values) + 1) - 0.5, minor=True)\n",
    "ax.grid(which='minor', color='gray', linestyle='-', linewidth=1)\n",
    "\n",
    "# Create colorbar\n",
    "cbar = ax.figure.colorbar(heatmap, aspect=40, pad=0.02)\n",
    "cbar.set_label('Percentage of Feasibility', rotation=-90, fontsize=12, labelpad=15)\n",
    "\n",
    "# Customize colorbar ticks\n",
    "cbar.ax.tick_params(labelsize=10)\n",
    "\n",
    "# Add colorbar ticks on the right\n",
    "cbar.ax.yaxis.set_ticks_position('right')\n",
    "\n",
    "# Set colorbar tick labels\n",
    "cbar.ax.set_yticklabels(['0%', '25%', '50%', '75%', '100%'])\n",
    "\n",
    "# Adjust the plot layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot as a high-resolution image for academic use\n",
    "plt.savefig('heatmap.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c061547f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate sample data\n",
    "x_values = ['39-bus', '118-bus']\n",
    "y_values = ['N-3', 'N-2', 'N-1']  # Updated y_values with reversed order\n",
    "z_values = np.array([[5.4, 0], [2.34, 0], [0.68, 0]])\n",
    "\n",
    "# Create the heatmap\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "heatmap = ax.imshow(z_values, cmap='Greens', interpolation='bilinear', aspect='auto')\n",
    "\n",
    "# Set the tick labels and positions\n",
    "ax.set_xticks(np.arange(len(x_values)))\n",
    "ax.set_yticks(np.arange(len(y_values)))\n",
    "ax.set_xticklabels(x_values, fontsize=12)\n",
    "ax.set_yticklabels(y_values, fontsize=12)  # Reverse the order of tick labels\n",
    "\n",
    "# Set the axis labels\n",
    "ax.set_xlabel('System size', fontsize=14)\n",
    "ax.set_ylabel('Outages', fontsize=14)\n",
    "\n",
    "# Add colorbar\n",
    "cbar = fig.colorbar(heatmap, ax=ax, pad=0.1, aspect=30)\n",
    "cbar.set_label('Percentage infeasibility', rotation=-90, fontsize=12, labelpad=15)\n",
    "\n",
    "plt.savefig('infeasible_cases_heat.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50260b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#============== plots for reduced computational graph\n",
    "# comp_graph = tot_violation_percentage, tot_time_contflow, tot_memory_contflow\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/reduced graph/Comp_graphN1.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN1 = pickle.load(f)\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/reduced graph/Comp_graphN2.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN2 = pickle.load(f)\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/reduced graph/Comp_graphN3.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN3 = pickle.load(f)\n",
    "    \n",
    "file_path = 'LODF/Computational graph data/IEEE39/reduced graph/Comp_graphN4.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN4 = pickle.load(f)\n",
    "\n",
    "\n",
    "iterations = range(len(comp_graphN1[0]))\n",
    "violation_N1 = comp_graphN1[0, :, 0]\n",
    "violation_N2 = comp_graphN2[0, :, 0]\n",
    "violation_N3 = comp_graphN3[0, :, 0]\n",
    "violation_N4 = comp_graphN4[0, :, 0]\n",
    "\n",
    "print(np.mean(violation_N4))\n",
    "\n",
    "time_N1 = comp_graphN1[1, :, 0]\n",
    "time_N2 = comp_graphN2[1, :, 0]\n",
    "time_N3 = comp_graphN3[1, :, 0]\n",
    "time_N4 = comp_graphN4[1, :, 0]\n",
    "\n",
    "memory_N1 = comp_graphN1[2, :, 0]\n",
    "memory_N2 = comp_graphN2[2, :, 0]\n",
    "memory_N3 = comp_graphN3[2, :, 0]\n",
    "memory_N4 = comp_graphN4[2, :, 0]\n",
    "\n",
    "backward_N1 = comp_graphN1[3, :, 0]\n",
    "backward_N2 = comp_graphN2[3, :, 0]\n",
    "backward_N3 = comp_graphN3[3, :, 0]\n",
    "backward_N4 = comp_graphN4[3, :, 0]\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(8, 12))\n",
    "\n",
    "# Plot the lines for the first graph\n",
    "ax1.plot(iterations, violation_N1, 'r', label='N-1')\n",
    "ax1.plot(iterations, violation_N2, 'b', label='N-2')\n",
    "ax1.plot(iterations, violation_N3, 'g', label='N-3')\n",
    "ax1.plot(iterations, violation_N4, 'k', label='N-4')\n",
    "ax1.set_ylabel('Percentage Non-violated [%]')\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.legend()\n",
    "\n",
    "# Add the average values as text on the graph\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N1), f'Avg N-1: {np.mean(violation_N1):.2f}', color='r')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N2), f'Avg N-2: {np.mean(violation_N2):.2f}', color='b')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N3), f'Avg N-3: {np.mean(violation_N3):.2f}', color='g')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N4), f'Avg N-4: {np.mean(violation_N4):.2f}', color='k')\n",
    "\n",
    "# Repeat the same for the other two graphs\n",
    "ax2.plot(iterations, time_N1, 'r', label='N-1')\n",
    "ax2.plot(iterations, time_N2, 'b', label='N-2')\n",
    "ax2.plot(iterations, time_N3, 'g', label='N-3')\n",
    "ax2.plot(iterations, time_N4, 'k', label='N-4')\n",
    "ax2.set_ylabel('Forward Pass Time [s]')\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.legend()\n",
    "\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N1), f'Avg N-1: {np.mean(time_N1):.2f}', color='r')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N2), f'Avg N-2: {np.mean(time_N2):.2f}', color='b')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N3), f'Avg N-3: {np.mean(time_N3):.2f}', color='g')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N4), f'Avg N-4: {np.mean(time_N4):.2f}', color='k')\n",
    "\n",
    "ax3.plot(iterations, memory_N1, 'r', label='N-1')\n",
    "ax3.plot(iterations, memory_N2, 'b', label='N-2')\n",
    "ax3.plot(iterations, memory_N3, 'g', label='N-3')\n",
    "ax3.plot(iterations, memory_N4, 'k', label='N-4')\n",
    "ax3.set_ylabel('Memory Consumption [GB]')\n",
    "ax3.set_xlabel('Epochs')\n",
    "ax3.legend()\n",
    "\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N1), f'Avg N-1: {np.mean(memory_N1):.2f}', color='r')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N2), f'Avg N-2: {np.mean(memory_N2):.2f}', color='b')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N3), f'Avg N-3: {np.mean(memory_N3):.2f}', color='g')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N4), f'Avg N-4: {np.mean(memory_N4):.2f}', color='k')\n",
    "\n",
    "ax4.plot(iterations, backward_N1, 'r', label='N-1')\n",
    "ax4.plot(iterations, backward_N2, 'b', label='N-2')\n",
    "ax4.plot(iterations, backward_N3, 'g', label='N-3')\n",
    "ax4.plot(iterations, backward_N4, 'k', label='N-4')\n",
    "ax4.set_ylabel('Backward Pass Time [s]')\n",
    "ax4.set_xlabel('Epochs')\n",
    "ax4.legend()\n",
    "\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N1), f'Avg N-1: {np.mean(backward_N1):.2f}', color='r')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N2), f'Avg N-2: {np.mean(backward_N2):.2f}', color='b')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N3), f'Avg N-3: {np.mean(backward_N3):.2f}', color='g')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N4), f'Avg N-4: {np.mean(backward_N4):.2f}', color='k')\n",
    "\n",
    "\n",
    "# Add some additional customizations\n",
    "fig.suptitle('Reduced Computational Graph 39-bus', fontsize=16, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Adjust the layout spacing\n",
    "fig.subplots_adjust(top=0.9, hspace=0.4)\n",
    "\n",
    "# Set the font size of axis labels and tick labels\n",
    "ax1.tick_params(axis='both', labelsize=12)\n",
    "ax2.tick_params(axis='both', labelsize=12)\n",
    "ax3.tick_params(axis='both', labelsize=12)\n",
    "ax4.tick_params(axis='both', labelsize=12)\n",
    "ax1.set_title('Violation Analysis', fontsize=14)\n",
    "ax2.set_title('Forward Pass Time Analysis', fontsize=14)\n",
    "ax3.set_title('Memory Consumption Analysis', fontsize=14)\n",
    "ax4.set_title('Backward Pass Time Analysis', fontsize=14)\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('reduced_comp_graph.png', dpi=300)\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e7a82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#============== plots for full computational graph\n",
    "# comp_graph = tot_violation_percentage, tot_time_contflow, tot_memory_contflow\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/full graph/Comp_graphN1.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN1 = pickle.load(f)\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/full graph/Comp_graphN2.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN2 = pickle.load(f)\n",
    "\n",
    "file_path = 'LODF/Computational graph data/IEEE39/full graph/Comp_graphN3.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN3 = pickle.load(f)\n",
    "    \n",
    "file_path = 'LODF/Computational graph data/IEEE39/full graph/Comp_graphN4.pkl'\n",
    "with open(file_path, 'rb') as f:\n",
    "    comp_graphN4 = pickle.load(f)\n",
    "\n",
    "\n",
    "iterations = range(len(comp_graphN1[0]))\n",
    "violation_N1 = comp_graphN1[0, :, 0]\n",
    "violation_N2 = comp_graphN2[0, :, 0]\n",
    "violation_N3 = comp_graphN3[0, :, 0]\n",
    "violation_N4 = comp_graphN4[0, :, 0]\n",
    "\n",
    "time_N1 = comp_graphN1[1, :, 0]\n",
    "time_N2 = comp_graphN2[1, :, 0]\n",
    "time_N3 = comp_graphN3[1, :, 0]\n",
    "time_N4 = comp_graphN4[1, :, 0]\n",
    "\n",
    "memory_N1 = comp_graphN1[2, :, 0]\n",
    "memory_N2 = comp_graphN2[2, :, 0]\n",
    "memory_N3 = comp_graphN3[2, :, 0]\n",
    "memory_N4 = comp_graphN4[2, :, 0]\n",
    "\n",
    "backward_N1 = comp_graphN1[3, :, 0]\n",
    "backward_N2 = comp_graphN2[3, :, 0]\n",
    "backward_N3 = comp_graphN3[3, :, 0]\n",
    "backward_N4 = comp_graphN4[3, :, 0]\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(8, 12))\n",
    "\n",
    "# Plot the lines for the first graph\n",
    "ax1.plot(iterations, violation_N1, 'r', label='N-1')\n",
    "ax1.plot(iterations, violation_N2, 'b', label='N-2')\n",
    "ax1.plot(iterations, violation_N3, 'g', label='N-3')\n",
    "ax1.plot(iterations, violation_N4, 'k', label='N-4')\n",
    "ax1.set_ylabel('Percentage Non-violated [%]')\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.legend()\n",
    "\n",
    "# Add the average values as text on the graph\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N1), f'Avg N-1: {np.mean(violation_N1):.2f}', color='r')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N2), f'Avg N-2: {np.mean(violation_N2):.2f}', color='b')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N3), f'Avg N-3: {np.mean(violation_N3):.2f}', color='g')\n",
    "ax1.text(iterations[-1] + 5, np.mean(violation_N4), f'Avg N-4: {np.mean(violation_N4):.2f}', color='k')\n",
    "\n",
    "# Repeat the same for the other two graphs\n",
    "ax2.plot(iterations, time_N1, 'r', label='N-1')\n",
    "ax2.plot(iterations, time_N2, 'b', label='N-2')\n",
    "ax2.plot(iterations, time_N3, 'g', label='N-3')\n",
    "ax2.plot(iterations, time_N4, 'k', label='N-4')\n",
    "ax2.set_ylabel('Cont Flow Time [s]')\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.legend()\n",
    "\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N1), f'Avg N-1: {np.mean(time_N1):.2f}', color='r')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N2), f'Avg N-2: {np.mean(time_N2):.2f}', color='b')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N3), f'Avg N-3: {np.mean(time_N3):.2f}', color='g')\n",
    "ax2.text(iterations[-1] + 5, np.mean(time_N4), f'Avg N-4: {np.mean(time_N4):.2f}', color='k')\n",
    "\n",
    "ax3.plot(iterations, memory_N1, 'r', label='N-1')\n",
    "ax3.plot(iterations, memory_N2, 'b', label='N-2')\n",
    "ax3.plot(iterations, memory_N3, 'g', label='N-3')\n",
    "ax3.plot(iterations, memory_N4, 'k', label='N-4')\n",
    "ax3.set_ylabel('Memory Consumption [GB]')\n",
    "ax3.set_xlabel('Epochs')\n",
    "ax3.legend()\n",
    "\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N1), f'Avg N-1: {np.mean(memory_N1):.2f}', color='r')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N2), f'Avg N-2: {np.mean(memory_N2):.2f}', color='b')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N3), f'Avg N-3: {np.mean(memory_N3):.2f}', color='g')\n",
    "ax3.text(iterations[-1] + 5, np.mean(memory_N4), f'Avg N-4: {np.mean(memory_N4):.2f}', color='k')\n",
    "\n",
    "ax4.plot(iterations, backward_N1, 'r', label='N-1')\n",
    "ax4.plot(iterations, backward_N2, 'b', label='N-2')\n",
    "ax4.plot(iterations, backward_N3, 'g', label='N-3')\n",
    "ax4.plot(iterations, backward_N4, 'k', label='N-4')\n",
    "ax4.set_ylabel('Backward Pass Time [s]')\n",
    "ax4.set_xlabel('Epochs')\n",
    "ax4.legend()\n",
    "\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N1), f'Avg N-1: {np.mean(backward_N1):.2f}', color='r')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N2), f'Avg N-2: {np.mean(backward_N2):.2f}', color='b')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N3), f'Avg N-3: {np.mean(backward_N3):.2f}', color='g')\n",
    "ax4.text(iterations[-1] + 5, np.mean(backward_N4), f'Avg N-4: {np.mean(backward_N4):.2f}', color='k')\n",
    "\n",
    "# Add some additional customizations\n",
    "fig.suptitle('Full Computational Graph 39-bus', fontsize=16, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Adjust the layout spacing\n",
    "fig.subplots_adjust(top=0.9, hspace=0.4)\n",
    "\n",
    "# Set the font size of axis labels and tick labels\n",
    "ax1.tick_params(axis='both', labelsize=12)\n",
    "ax2.tick_params(axis='both', labelsize=12)\n",
    "ax3.tick_params(axis='both', labelsize=12)\n",
    "ax4.tick_params(axis='both', labelsize=12)\n",
    "ax1.set_title('Violation Analysis', fontsize=14)\n",
    "ax2.set_title('Forward Pass Time Analysis', fontsize=14)\n",
    "ax3.set_title('Memory Consumption Analysis', fontsize=14)\n",
    "ax4.set_title('Backward Pass Time Analysis', fontsize=14)\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('full_comp_graph_600MW.png', dpi=300)\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "244052a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data from the table\n",
    "categories = ['N-1', 'N-2', 'N-3']\n",
    "cont_case_infeasibility = [0.53, 3.79, 7.73, 0.18, 1.95, 4.57, 0.18, 1.72, 4.07]\n",
    "\n",
    "# Reshape the data for plotting\n",
    "data = np.array(cont_case_infeasibility).reshape(len(categories), -1)\n",
    "\n",
    "# Create the bar plot\n",
    "x = np.arange(data.shape[1])\n",
    "width = 0.2\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "#hatches = ['///', '...', 'xxx']\n",
    "\n",
    "for i in range(data.shape[0]):\n",
    "    ax.bar(x + (i-1)*width, data[i], width, label=f'N-{i+1} loss', color=colors[i])#, hatch=hatches[i])\n",
    "\n",
    "# Adjust font and label sizes\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "\n",
    "# Add legend\n",
    "ax.legend(fontsize=10)\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel('Contingency level')\n",
    "ax.set_ylabel('Infeasible cases [%]')\n",
    "ax.set_title('Contingency level security')\n",
    "\n",
    "# Set x-axis tick labels\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels([f'N - {i+1}' for i in range(data.shape[1])])\n",
    "\n",
    "\n",
    "# Set tick locations and directions\n",
    "ax.tick_params(axis='x', bottom=True, top=True, direction='out', length = 6)\n",
    "ax.tick_params(axis='y', left=True, right=True, direction='out', length = 6)\n",
    "\n",
    "# Set tick intervals and format\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels([f'N - {i+1}' for i in range(data.shape[1])])\n",
    "ax.set_yticks(np.arange(0, max(data.flatten()) + 1, 2))\n",
    "\n",
    "# Adjust layout and save the figure\n",
    "plt.tight_layout()\n",
    "plt.savefig('Contingency_level_security.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86114ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for Forward [s]\n",
    "forward_full = [3.39, 3.57, 5.04, 15.37, 69.28]\n",
    "forward_reduced = [3.40, 3.58, 5.19, 16.76, 77.87]\n",
    "\n",
    "# Data for Backward [s]\n",
    "backward_full = [2.25, 2.40, 4.26, 17.68, 90.81]\n",
    "backward_reduced = [2.24, 2.25, 2.47, 3.98, 11.66]\n",
    "\n",
    "# Calculate the absolute difference for Forward [s] and Backward [s]\n",
    "forward_difference = [-1 * (f - r) for f, r in zip(forward_full, forward_reduced)]\n",
    "backward_difference = [-1 * (f - r) for f, r in zip(backward_full, backward_reduced)]\n",
    "speed_up = [(f + b) for f, b in zip(forward_difference, backward_difference)]\n",
    "\n",
    "forward_difference = np.abs(forward_difference)\n",
    "backward_difference = np.abs(backward_difference)\n",
    "speed_up = np.abs(speed_up)\n",
    "\n",
    "# Create the grouped bar plot\n",
    "x = np.arange(len(forward_full))\n",
    "\n",
    "# Set the width and spacing of the bars\n",
    "bar_width = 0.2\n",
    "spacing = 0.025\n",
    "\n",
    "# Define different green colors\n",
    "colors = ['darkgreen', 'limegreen', 'green', 'black', 'lightgreen']\n",
    "\n",
    "# Create the grouped bar plot\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x - bar_width - spacing, forward_difference, bar_width, color=colors[3], label='Forward')\n",
    "ax.bar(x, speed_up, bar_width, color=colors[1], label='Speed Up')\n",
    "ax.bar(x + bar_width + spacing, backward_difference, bar_width, color=colors[0], label='Backward')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xlabel('Contingency level')\n",
    "ax.set_ylabel('Difference [s]')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['N-1', 'N-2', 'N-3', 'N-4', 'N-5'])\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('Forward vs Backward.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62f31877",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "forward_full = [3.39, 3.57, 5.04, 15.37, 69.28]\n",
    "forward_reduced = [3.40, 3.58, 5.19, 16.76, 77.87]\n",
    "\n",
    "memory_full = [0.0026, 0.0148, 0.1449, 1.0987, 3.2241]\n",
    "memory_reduced = [0.0006, 0.0021, 0.0506, 0.0900, 0.0306]\n",
    "\n",
    "backward_full = [2.25, 2.40, 4.26, 17.68, 90.81]\n",
    "backward_reduced = [2.24, 2.25, 2.47, 3.98, 11.66]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(forward_full)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen', 'green', 'black', 'lightgreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "\n",
    "# Forward Pass\n",
    "axs[0].bar(np.arange(num_cases), forward_full, color=colors[0], width=bar_width, label='Full Graph')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, forward_reduced, color=colors[1], width=bar_width, label='Reduced Graph')\n",
    "axs[0].set_xlabel('Case')\n",
    "axs[0].set_ylabel('Forward Pass [s]')\n",
    "axs[0].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[0].set_xticklabels(labels)\n",
    "axs[0].legend()\n",
    "\n",
    "# Memory Usage\n",
    "axs[1].bar(np.arange(num_cases), memory_full, color=colors[0], width=bar_width, label='Full Graph')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, memory_reduced, color=colors[1], width=bar_width, label='Reduced Graph')\n",
    "axs[1].set_xlabel('Case')\n",
    "axs[1].set_ylabel('Memory [GB]')\n",
    "axs[1].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[1].set_xticklabels(labels)\n",
    "axs[1].legend()\n",
    "\n",
    "# Backward Pass\n",
    "axs[2].bar(np.arange(num_cases), backward_full, color=colors[0], width=bar_width, label='Full Graph')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, backward_reduced, color=colors[1], width=bar_width, label='Reduced Graph')\n",
    "axs[2].set_xlabel('Case')\n",
    "axs[2].set_ylabel('Backward Pass [s]')\n",
    "axs[2].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[2].set_xticklabels(labels)\n",
    "axs[2].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot\n",
    "#plt.savefig('computational_graph_comparison.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('computational_graph_comparison.tex')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb5c099",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13dc362f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for Memory [GB]\n",
    "full_graph_memory = [0.0026, 0.0148, 0.1449, 1.0987, 3.2241]\n",
    "reduced_graph_memory = [0.0006, 0.0021, 0.0506, 0.0900, 0.0306]\n",
    "\n",
    "# Calculate the size reduction\n",
    "size_reduction = [(f / r) for f, r in zip(full_graph_memory, reduced_graph_memory)]\n",
    "\n",
    "# Create the bar plot\n",
    "x = range(len(full_graph_memory))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar(x, size_reduction, color='#4CAF50', width=0.4)\n",
    "\n",
    "ax.set_xlabel('Contingency level')\n",
    "ax.set_ylabel('Reduction Ratio')\n",
    "ax.set_yscale('log')\n",
    "#ax.set_title('Memory Reduction Reduced Graph vs Full Graph')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['N-1', 'N-2', 'N-3', 'N-4', 'N-5'])\n",
    "\n",
    "plt.savefig('Memory_Reduction.png', dpi=300)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b2fe9b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "contingencies_per_iteration = np.array([5, 10, 15, 20, 25, 30, 35])\n",
    "cont_case_infeasibility = np.array([3.03, 3.01, 1.68, 0.72, 0.57, 0.72, 0.57])\n",
    "feasibility = np.array([0.25, 0.24, 0.22, 0.22, 0.22, 0.22, 0.22])\n",
    "time = np.array([67, 112, 166, 214, 257, 372, 386])\n",
    "\n",
    "# Create figure and axes\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot cont_case_infeasibility on the left y-axis (blue)\n",
    "ax1.plot(contingencies_per_iteration, cont_case_infeasibility, color='tab:blue', marker='o', label='Cont case infeasibility')\n",
    "#ax1.plot(contingencies_per_iteration, feasibility, color='blue', linestyle='dashed', marker='o', label='+10% feasibility')\n",
    "ax1.set_xlabel('Contingencies per iteration')\n",
    "ax1.set_ylabel('Cont case infeasibility (%)', color='black')  # Set y-axis label color to black\n",
    "ax1.tick_params(axis='y', colors='black')  # Set tick color to black\n",
    "\n",
    "# Create a twin y-axis on the right\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot time on the right y-axis (orange)\n",
    "ax2.plot(contingencies_per_iteration, time, color='tab:orange', marker='s', label='Time')\n",
    "ax2.set_ylabel('Time (s)', color='black')  # Set y-axis label color to black\n",
    "ax2.tick_params(axis='y', colors='black')  # Set tick color to black\n",
    "\n",
    "# Set axis title colors\n",
    "#ax1.set_title('Contingencies per iteration', color='black')\n",
    "ax1.yaxis.label.set_color('tab:blue')\n",
    "ax2.yaxis.label.set_color('tab:orange')  # Set right y-axis title color to black\n",
    "\n",
    "# Add legend\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "lines = lines1 + lines2\n",
    "labels = labels1 + labels2\n",
    "ax1.legend(lines, labels, loc='best')\n",
    "\n",
    "plt.savefig('Benchmark_CS_N2.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('Benchmark_CS_N2.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e15a2c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nSUtLY/To0QQHB/PNN98QFRVFkyZN+Pbbb4mNjaVJkyYAnDp1is8//5w777yTRo0aceTIEY4dO2auBfrKK69wxx13EBgYyN13341Wq2Xfvn0cPHjwprVGb8XAgQMJDw9nxIgRvP3227Ro0YKzZ8+yatUq7rrrLjp37kxwcDA///wzw4YNQ6PR8PLLL5dqxVu5ciUnT56kd+/euLq68ueff2I0GmnZsmWZr7tlyxbefvttRowYwdq1a1m+fDmrVq0qdY5/xhaY/yT2Fy9QYjSR3vJ+vEe/T9aWq5I+lwCcW/Vmy8nprD+UTvv27bGzszN309495Slefre1+TVF5alex62+6xLkdqmbVMvGI+eY+t0eikoq1jUghBCgJG//PN+f7x/qztwxHfj+oe7883z/ai2+27RpU3bv3k2/fv145plnaNOmDYMGDWL9+vUsWLAAULoBf/vtN1xdXenduzcDBw6kadOm/PDDDxV6rWeffRadTkdISAienp7msVv/tmDBAu6++24effRRWrVqxUMPPUReXh4A//nPfxg5ciT33nsv3bp1Iz09vVTrm52dHYcPHzaXNnn44YeZOnUq//nPfwCIjIxk5cqVREdH06VLF7p3784HH3xA48aNK/PPV24ajYY///yT3r17M3nyZFq0aMGYMWM4ffo03t7eALz//vu4urrSo0cPhg0bRmRkZKkJfi4uLvz888/079+f1q1b8+mnn/L9998TGhpa5us+88wz7Ny5k44dO/LGG2/w/vvvExl5aexlVjIrx9rSOfFzuHiBIteWdF2YR0row2B5bbdxjx49+O9//8u9996Lp6cnb7/9tvlYcHAwPXr0oFWrVnTr1q2K/tUaJo2pouWe67jk5GQCAgJISkrC39+/xl53y/HzPLA4lsISI4NDffj4vo5Y6iRvFqIhKCgo4NSpUzRp0qRS5Y+EqFFGI+xaBGtnQFEO6Kygz/+g55Ogq9jKC5eZTCaCg4N59NFHS5Xo+rcb/a6o9fld20jmUEN6Nvfg8wmdsdJpWROn58kf9lJikJY3IYQQtUj6Cfh6GKx6Wkna/LvAf/6G3s9VOmk7d+4c8+bNQ6/XM3ny5CoOuOGpVWPc6rs+LTz5bHwYD3+7k1X7U7DQanh/dAdZx1QIIYS6jAbY9glsmAUlF8HSDga8Al0fBu2tjcv28vLCw8ODzz//vMx1T0X5SeJWw/q18uKTcWE8smQXv+09i06r4Z2720vyJoQQQh2p8fD7NDizS9lu0geGzQW3JlVy+wY2IqvaSVepCgaFeDPvvo7otBp+3n2G6T/vx2iUH2whhBA1qKQINs2Bz3orSZu1Ewz7CCb8VmVJm6h6kripZHAbX+aO6YBWAz/uTOal3w7KtxIhhBA148wu+LwvbJoNxmJoeTtM3Q5hE0FTP3qAXn31VTQaTanH1cu3FRQUMHXqVNzd3XFwcGDUqFGkpqaWukdiYiJDhw7Fzs4OLy8vnnvuOUpKSmr6rZQiXaUquqNdIwxGE0/9sJel2xOx0GqYeWeoebFjIYQQokoVX4SNb0LMPDAZwc4dhrwNbUbVm4TtaqGhoaxbt868bWFxJe156qmnWLVqFcuXL8fZ2Zlp06YxcuRIc505g8HA0KFD8fHxYevWraSkpDBhwgQsLS158803a/y9XCaJm8qGd/CjxGDi2RX7+CbmNBZaLS/f0VqSNyGEEFUrYYsyli3jpLLd9h4YPAfsPdSNqxpZWFjg4+Nzzf6srCy+/PJLli5dSv/+/QFlKbTWrVuzbds2unfvTnR0NPHx8axbtw5vb286dOjA66+/zvPPP8+rr76KlZVVTb8dQLpKa4VRYf68NVJZkPirLaeYs/qwdJsKIYSoGgXZsPJpWHy7krQ5NoKxy2DUF3UyacvJySE7O9v8KCwse43UY8eO0ahRI5o2bcq4cePMRZ137dpFcXFxqWXTWrVqRWBgIDExMQDExMTQtm1bcwFkUAo0Z2dnExcXV03v7uYkcaslRncJYNZdbQD47K+TvBt9RJI3IUStN2nSJEaMGKF2GKIsx9bCJ+Gw80tlu9NEmLoNWg5RN65bEBISgrOzs/kxe/bs657XrVs3Fi9ezJo1a1iwYAGnTp2iV69e5OTkoNfrsbKywsXFpdQ13t7e6PV6APR6famk7fLxy8fUIl2ltci4bo0xGE288lsc8zeewEKr5alBLdQOSwhRG2QmQX562cft3MEloEpf8mZDNmbMmMHcuXPlS2ZtlJ8Ba6bD/mXKtmuQMmO0aR9Vw6oK8fHx+Pn5mbetra2ve96QIVeS03bt2tGtWzcaN27Mjz/+WKcXuZfErZaZEB5EscHE6yvjmbv+GJY6DdP6B6sdlhBCTZlJMC8MSsruEsLCGqbtqtLkLSUlxfz8hx9+4JVXXuHIkSPmfQ4ODjg4OFTZ64kqEvcr/Pks5J0DNND9Uej/f2Blr3ZkVcLR0REnJ6cKX+fi4kKLFi04fvw4gwYNoqioiMzMzFKtbqmpqeYxcT4+PuzYsaPUPS7POr3euLmaIl2ltdCU25owfYgyZfnd6KN8uvmEyhEJIVSVn37jpA2U4zdqkasEHx8f88PZ2RmNRlNqn4ODwzVdpX379uWxxx7jySefxNXVFW9vbxYuXEheXh6TJ0/G0dGR5s2bs3r16lKvdfDgQYYMGYKDgwPe3t6MHz+e8+fPV+n7qfdy9PDD/bB8opK0ebaCKWth8Jv1Jmm7Fbm5uZw4cQJfX1/CwsKwtLRk/fr15uNHjhwhMTGR8PBwAMLDwzlw4ABpaWnmc9auXYuTkxMhISE1Hv9lkrjVUv/p04znIlsCMGf1Yb74+6TKEQkhqpTJBEV55XuUXCzfPUsulu9+1dy1+fXXX+Ph4cGOHTt47LHHeOSRR7jnnnvo0aMHu3fvJiIigvHjx5Ofnw9AZmYm/fv3p2PHjuzcuZM1a9aQmprK6NGjqzXOesNkgj3fwfyucOgP0FpA7//Bf/6CgC5qR6eaZ599ls2bN5OQkMDWrVu566670Ol0jB07FmdnZ6ZMmcLTTz/Nxo0b2bVrF5MnTyY8PJzu3bsDEBERQUhICOPHj2ffvn1ERUXx0ksvMXXq1DK7Z2uCdJXWYlP7NafYYOTDdcd4Y9UhLLQaJvWUatZC1AvF+fBmo6q951eDy3fei2ertQWmffv2vPTSSwBMnz6dOXPm4OHhwUMPPQTAK6+8woIFC9i/fz/du3dn3rx5dOzYsVRtrK+++oqAgACOHj1KixYy1rdMmYnwx5Nw4lLLkW8HGD4PfNqqGVWtkJyczNixY0lPT8fT05PbbruNbdu24enpCcAHH3yAVqtl1KhRFBYWEhkZySeffGK+XqfTsXLlSh555BHCw8Oxt7dn4sSJvPbaa2q9JUASt1rviQHBlBhMzNt4nFf/iMdCp+X+7o3VDksIIcrUrl0783OdToe7uztt215JJC7PzLvcBbVv3z42btx43fFyJ06ckMTteoxGiP0C1r0KxXmgs4Z+L0L4NNDJRzvAsmXLbnjcxsaG+fPnM3/+/DLPady4MX/++WdVh3ZL5P9uLafRaHgmogXFRiOfbT7JS78exEKrYUzXQLVDE0LcCks7peWrPPT7y9ea9sAa8Gl38/Ms7cr3upVkaWlZaluj0ZTad3m2qtFoBJSxR8OGDeOtt9665l6+vr7VGGkddf4Y/P4YJCr1xggMhzvngUdzdeMSNUIStzpAo9HwwuBWlBhMfPnPKab/cgALnZa7w/zVDk0IUVkaTfm7Ky3KWbrAwrZODkLv1KkTP/30E0FBQaWWJBL/YiiBmI9h42wwFIKVAwx8FTpPAa0MWW8o5P90HaHRaHhpaGsm9QjCZILnVuzjlz3JaoclhBC3bOrUqWRkZDB27FhiY2M5ceIEUVFRTJ48GYPBoHZ4tYP+AHzRX+kaNRRCswHwaAx0fUiStgZG/m/XIRqNhhnDQhjXLRCTCZ75cR+/7ytnV4sQou6yc1fqtN2IhbVyXh3UqFEjtmzZgsFgICIigrZt2/Lkk0/i4uKCtqEnJSWFsOEN+LwvpOwDGxcYsQDu/wlcZMhMQ6QxNbCS18nJyQQEBJCUlIS/f93sajQaTbz4ywGWxSah02r4eGxHbm8r40CEqK0KCgo4deoUTZo0wcbGpnI3UWHlBKGypFj4bSqcv1T0uPUwuP09cPS+8XV12I1+V+rD53dVkMEEdZBWq+HNu9pSbDDx0+5kHv9+DxZaDRGh6lVyFkJUM5cAScwaiqI8pZVt2wLABPZeMPRdCBmudmSiFmjgbdB1l1ar4e272zGiQyNKjCamLt3N+kOpaoclhBDiVpzcDAt6wLZPABO0HwtTt0vSJswkcavDdFoN797Tnjva+VJsMPHIkt1sOpJ28wuFEELULgVZSomPb+6ECwng5A/jVsBdn4Kdm9rRiVpEErc6zkKn5YN7OzCkjQ9FBiMPf7uLf47J+n5CCFFnHFkN87vB7m+U7S4PwtRtEDxI3bhErSSJWz1gqdMyd0xHBrb2pqjEyIPfxBJzomoXmxZC3LoGNhdM3EzeeVgxBb4fAzkp4NYMJv0JQ98Da0e1o1OF/I7cnCRu9YSVhZb54zrSr6UnBcVGHlgcy45TGWqHJYTgykoClxdVFw2cyQQHViiLwh9cARot9HwCHtkCQT3Vjk5VRUVFgLJUmrg+mVVaj1hb6FhwfxgPfbOTv4+dZ/KiHXwzpSthjWV8hBBq0ul0uLi4mNfmtLOzMy/7JBqYnBQso/6H7ng0AEbP1hTf/iEm3w5gAAwFqoanJqPRyLlz57Czs5MVNG5A/mXqGRtLHQsndGbK17FsOZ7OxK9iWfJgNzoEuKgdmhANmo+PUq7ncvImGhiTCZeTv+G172N0xXmYtBacD5nM+VYToMASTp1SO8JaQavVEhgYKF9sbkASt3rIxlLHFxO6MHnxDradzGD8l9tZ+mB32vo7qx2aEA2WRqPB19cXLy8viouL1Q5H1CBNZgIWq59Fd/ofAIy+HSm+/UMcPVvRMEeylc3KykpWy7gJSdzqKVsrHV9O7MKkRTuITbjA/V9uZ+lD3QhtJMmbEGrS6XQyfqehMBpg+2ew4XUozgcLW+j/Etruj2CtlZ8BUTmS1tZj9tYWLJrclU6BLmRdLOb+L7ZzWJ+tdlhCCFH/pR2GryIharqStAX1UiYf9JgGkrSJWyCJWz3nYG3B4ge60j7AhQv5xYxbuJ1jqTlqhyWEEPWToRg2vwOf9YLkWLByhDs+hAm/g3sztaMT9YAkbg2Ak40l3zzQlTZ+TqTnFTF24XZOnMtVOywhhKhfzu6Fz/vBxjfAUATBkcpyVZ0ng4zbElVEfpIaCGdbS5ZM6UZrXyfO5xYy9vNtnDqfp3ZYQghR9xVfhLUzYGF/SD0Atm4w8gu47wdw9lM7OlHPSOLWgLjYWfHdg91o6e1IWk4h9y3cRmK6FAQVQohKOx0Dn94GWz4EkwFCR8LUHdDuHpCSFqIayKzSBsbN3orvHurGmM+3cTwtl7ELt7Hs4e4EuNmpHZoQQtQumUmQX8bygcX5sPtb2Pc9YAIHH7jjfWg1tEZDFA2PJG4NkIeDNUsfVJK3k+fzuO+LbfzwcDiNXGzVDk0IIWqHzCSYFwYlhTc/t+N4iHgDbF2qPSwhpKu0gfJysmHpQ91p7G5HUsZFxi7chj6r4S61IoQQpeSnly9pu/09GD5PkjZRYyRxa8B8nG34/qHuBLjZcjo9n/sWbiMtW5I3IYQoN//OakcgGhhJ3Bq4Ri62LH2wO34utpe6TbdzLqcc3zKFEEIIUeMkcRMEuNnx/UPd8XW24XhaLvd/sZ2MvCK1wxJCCCHEv0jiJgAIdLdj6UPd8XK05khqDuO+2E5mviRvQgghRG0iiZswa+Jhz/cPd8fDwZpDKdnc/+V2svKL1Q5LCCFqXvZZtSMQ4rokcROlNPN04PuHuuFub8XBM9lM+Go72QWSvAkhGpCLF2D1/9SOQojrksRNXCPY25HvHuqGq50l+5KzmPTVDnILS9QOSwghql9JEfwwHrKSbn6uhTXYuVd/TEJcRQrwiutq5ePEkge7cd/C7exOzGTyoh0sntwVe2v5kRFC1FMmE/zxBCT8DVaOcM9isPco+3w7d3AJqLHwhIBa1OI2Z84cNBoNTz755A3PW758Oa1atcLGxoa2bdvy559/1kyADVBoI2eWTOmGo40FsQkXmPJ1LBeLDGqHJYQQ1ePvd2HfUtDolKQteCA06lD2Q5I2oYJakbjFxsby2Wef0a5duxuet3XrVsaOHcuUKVPYs2cPI0aMYMSIERw8eLCGIm142vo7880DXXGwtmDbyQwe/CaWgmJJ3oQQ9cyBFbDhDeX57W8rSZsQtZDqiVtubi7jxo1j4cKFuLq63vDcuXPnMnjwYJ577jlat27N66+/TqdOnZg3b14NRdswdQx05esHumBvpWPL8XQe/naXJG9CiPrjdAz8+ojyPHwadHlQ3XiEuAHVE7epU6cydOhQBg68+bebmJiYa86LjIwkJiamzGsKCwvJzs42P3Jycm455oYorLEbiyZ3xdZSx19Hz/Hod7spLJHkTQhRx6WfgGX3gaEIWt0Bg15XOyIhbkjVxG3ZsmXs3r2b2bNnl+t8vV6Pt7d3qX3e3t7o9foyr5k9ezbOzs7mR0hIyC3F3JB1beLGl5M6Y2OpZcPhNKZ+t4eiEqPaYQkhROXkZ8DS0XAxAxp1gpELQat6e4YQN6TaT2hSUhJPPPEE3333HTY2NtX2OtOnTycrK8v8iI+Pr7bXagh6NPPgiwldsLLQsu5QKo9/v4digyRvQog6pqQQfrgf0o+DcwCMXQZWdmpHJcRNqZa47dq1i7S0NDp16oSFhQUWFhZs3ryZjz76CAsLCwyGa7vhfHx8SE1NLbUvNTUVHx+fMl/H2toaJycn88PR0bHK30tDc1uwB5+PD8NKp2VNnJ4nf9hLiSRvQoi6wmSC3x+H01vA2gnu+xEcvW9+nRC1gGqJ24ABAzhw4AB79+41Pzp37sy4cePYu3cvOp3ummvCw8NZv359qX1r164lPDy8psIWl/Rt6cWC+zthqdOwan8Kzyzfh8FoUjssIYS4uc1vw/5lV8p+eMsQGlF3qFZN1dHRkTZt2pTaZ29vj7u7u3n/hAkT8PPzM4+Be+KJJ+jTpw/vvfceQ4cOZdmyZezcuZPPP/+8xuMXMKC1N/Pu68TU73bz296zWGi1vHN3O7RajdqhCSHE9e37ATa9qTy/431oPkDdeISooFo9CjMxMZGUlBTzdo8ePVi6dCmff/457du3Z8WKFfz666/XJICi5kSG+vDx2I7otBp+2p3M9J8PYJSWNyFEbXR6K/w+TXne8wkIm6RqOEJUhsZkMjWoT9nk5GQCAgJISkrC399f7XDqjT/2neWJZXswmmBct0DeGNEGjUZa3oQQtUT6CfhigLKAfOs74Z6vZQZpHSOf3wr5qRVVYlj7Rrw3uj0aDXy3PZFXf4+jgX0nEELUVvkZ8N3dStLmFwYjP5ekTdRZ8pMrqsxdHf15e1Q7NBr4OuY0b6w6JMmbEEJdJYVKgd2Mk+AcqJT9sLRVOyohKk0SN1Gl7ukcwOy72gLw5T+nmLPmsCRvQgh1mEzw21RIjAFrZxi3HBy81I5KiFsiiZuocmO6BvL6CGXCyGebT/Je9FFJ3oQQNW/TbDiwHLQWMPpr8GqldkRC3DJJ3ES1GN+9Ma8OU2ojzdt4nLnrj6kckRCiQdn7PWx+S3l+xwfQrJ+68QhRRSRxE9VmUs8mvDS0NQAfrjvG/I3HVY5ICNEgJPwDvz+mPL/tKeg0Qd14hKhCqhXgFQ3Dg72aUmww8daaw7wTdQQLrYYHezVlx6kM0nIK8HK0oWsTN3RStFcIURXOH4Nl48BYDCEjoP8rakckRJWSxE1Uu0f6NqPEYOS9tUeZvfow8zYeJ6egxHzc19mGGcNCGNzGV8UohRB1Xt55pexHQSb4d4G7PpWyH6LekZ9oUSMeGxDM7W2VxOzqpA1An1XAI0t2s+ZgyvUuFUKImysuUMp+XEgAl8Yw5nsp+yHqJUncRI0wGE3sTrxw3WOX55vO/CNeFqoXQlSc0Qi/PQpJ28HmctkPT7WjEqJaSOImasSOUxnoswrKPG4CUrIK2HEqo+aCEkLUD5vehIM/XSr78S14tlQ7IiGqjSRuokak5ZSdtFXmPCGEAGDPd/DXO8rzYXOhaR914xGimkniJmqEl6NNlZ4nhBCc3Ax/PK487/UsdLxf3XiEqAGSuIka0bWJG77ONpRV9EODMru0axO3mgxLCFFXnTsCP44HYwm0GQX9/k/tiISoEZVK3M5kXmTHqQw2Hz3HwTNZFJYYqjouUc/otBpmXFpJoazkbcawEKnnJoS4udxz8N09UJAFAd1g+CdS9kM0GOWu45aUkc+S7adZuS+FlKyLXD33z1KnpWuQG2O7BjKkjQ9a+fAV1zG4jS8L7u/EzD/iSblqooKtpY4P7m0vddyEEDdXfFEp+5F5GlybwJilYClDLETDUa7E7dXf4/hpVzK9W3jyTEQL2ge44O1kg42FlsyLxRzV57AjIYP31x5h7vqjvHN3e9oHuFRz6KIuGtzGl0EhPuw4lcGW4+eZt/E4VhYaBrb2Vjs0IURtZzTCr49A8g6wcVHKfth7qB2VEDWqXImbrZWOv/7XD1d7q2uOeThY49Hcmh7NPXhyYAs2HUkjJeuiJG6iTDqthvBm7nQJcuW77ae5kF/MjoQMejSTP8BCiBvY8DrE/QJaS7h3CXgEqx2REDWuXIMCnh/c6rpJ2/X0beklXV6iXCx0WgZcammLjktVORohRK22+xv4533l+Z0fQ5Ne6sYjhEpuaTRnRl4RGw6nsjY+lbRsqb8lKi4y1AeAtfGpmEyyaoIQ4jpObISVTynP+zwPHcaqG48QKqr0IvOrD6Twv5/209TDnmKDiZPnc3lteBtGdw6oyvhEPdcr2ANbSx1nMi8SdzabNn7OaockhKhN0g7DjxOVsh9t74G+09WOSAhVlTtxyysswd76yulz1x/jt6k9aerpAMCGw6m88NMBSdxEhdhY6ujTwpM1cXqi4vSSuAkhrshNg6X3QGEWBIbD8PmgkaoFomErd1fpsI//ITpOb97WaTWk5xWZt8/nFGGpkzo6ouIiQpVxblFX/XwJIRq4onz4fgxkJoJbU7j3O7CwVjsqUYfNmTMHjUbDk08+ad5XUFDA1KlTcXd3x8HBgVGjRpGaWnrMdWJiIkOHDsXOzg4vLy+ee+45SkpKajj6K8rd4vb1A1155beDrNiVzOsj2jBjWCjTlu7GYASD0YhWo+Hde9pXZ6yinhrQyhsLrYajqbmcOp9HEw97tUMSQqjJaIRf/gNndoGtK4xbAfbuakcl6rDY2Fg+++wz2rVrV2r/U089xapVq1i+fDnOzs5MmzaNkSNHsmXLFgAMBgNDhw7Fx8eHrVu3kpKSwoQJE7C0tOTNN99U462Uv8UtwM2ORZO7MrSdL/d+FkP82Sw2P9ePJQ92ZcmD3djyQn/6tfKqzlhFPeVsZ0n3psof5WhpdRNCrH8VDv0OOiulwK57M7UjErVITk4O2dnZ5kdhYeENz8/NzWXcuHEsXLgQV1dX8/6srCy+/PJL3n//ffr3709YWBiLFi1i69atbNu2DYDo6Gji4+NZsmQJHTp0YMiQIbz++uvMnz+foqKisl6yWlW4b3N4Bz9+m3Ybh1JyuPfzbRiNENrIGRtLXXXEJxqIy92l0fFSFkSIBm3XYtgyV3k+fD407qFqOKL2CQkJwdnZ2fyYPXv2Dc+fOnUqQ4cOZeDAgaX279q1i+Li4lL7W7VqRWBgIDExMQDExMTQtm1bvL2vFImPjIwkOzubuLi4KnxX5VehWaUbD6dxPC2X1r5OvHV3O7adTOfJH/bQt6UXTw9qIcmbqLRBId688lscuxMvkJZTgJejLGEjRINzYgOsfFp53nc6tButbjyiVoqPj8fPz8+8bW1d9tjHZcuWsXv3bmJjY685ptfrsbKywsXFpdR+b29v9Hq9+Zyrk7bLxy8fU0O5W9zeWBnPcyv2sS85kxd/OcBH64/Rvak7Kx/rhbWFlts/+puNR9KqM1ZRj/k629Le3xmTSanpJoRoYFLjlbIfJgO0u1ep1ybEdTg6OuLk5GR+lJW4JSUl8cQTT/Ddd99hY1N/GgPKnbit2J3MokldmXdfJ36f1pNf9pwBwMpCyzMRLfns/jA+2Xi82gIV9V/EpWK8soqCEA1MTiosHQ2F2dC4p7IygpT9ELdo165dpKWl0alTJywsLLCwsGDz5s189NFHWFhY4O3tTVFREZmZmaWuS01NxcdH+Tzy8fG5Zpbp5e3L59S0cidudpY6ki7kA3A2swBri9KXBns7svy/MhZBVN7lVRS2njhPdkGxytEIIWpEUT58fy9kJYF7c2UNUin7IarAgAEDOHDgAHv37jU/OnfuzLhx48zPLS0tWb9+vfmaI0eOkJiYSHh4OADh4eEcOHCAtLQrPYpr167FycmJkJCQGn9PUIExbv8b3Iqnf9zLq7/HcbHYwHtS+kNUseZeDjT1tOfkuTw2HTnHne0bqR2SEKI6GY3w80Nwdg/YusF9P4Kdm9pRiXrC0dGRNm3alNpnb2+Pu7u7ef+UKVN4+umncXNzw8nJiccee4zw8HC6d+8OQEREBCEhIYwfP563334bvV7PSy+9xNSpU284tq46lTtxG9HRjz4tPEnMyCfIwx5nW8vqjEs0UJGhPizYdIKoOL0kbkLUd+tegcMrlbIfY7+Xsh+ixn3wwQdotVpGjRpFYWEhkZGRfPLJJ+bjOp2OlStX8sgjjxAeHo69vT0TJ07ktddeUy1mjamBreydnJxMQEAASUlJ+Pv7qx2O+Jc9iRe465Ot2Fvp2P3KIKwtZKayEPVS7Jew6tIM0lFfQtu71Y1H1Hry+a0o1xi3F385QErWxXLd8I99Z/n10sQFISqqvb8L3k7W5BUZ2Ho8Xe1whBDV4dg6+PM55Xm/lyRpE6ICytVV6m5vRcT7fxEW5MqA1t6083PG28kGawstWReLOZaWy86EDP7YdxYvJxtmj2xb3XGLekqr1RAR4sO3204TFaeX1TiEqG/0B2H5JKXsR/v7oPezakckRJ1SrsTtmYiWTAgP4ofYRJbEnOZYWk6p4/bWFtzW3IM3R7alb0v5oBW3JiLUm2+3nWbdoVQMRhM6rZQFEKJeyNHD0nuhKAeCesGwuVL2Q4gKKvfkBE9Ha6b1D2Za/2Cy8os5k3mRghIDbnZWNHa3QyO/fKKKdG/qjqONBedzi9ideIEuQTLLTIg6ryhPSdqyk8E9GO79Fiys1I5KiDqnwmuVgrIoeEgjJzoFuhLkYS9Jm6hSljotAy51kcqi80LUA0YD/PQQpOwFO3cYtxxsXW96mRDiWpVK3ISobpeL8UbFpdLAJj4LUf9EvwxHVoHOGsZ8D25N1I5IiDpLEjdRK/Vu4YmVhZbEjHwO63NufoEQonbasRC2zVee37UAArupG48QdZwkbqJWsre2oHewByBrlwpRZx2NhtX/U54PeAXajFI3HiHqAUncRK0VYe4ulXFuQtQ5+gOwYjKYjNDxfrjtabUjEqJeqHDi9v7aoyRfWmxeiOo0oJUXWg3Ep2STlCE/c0LUGdln4bvRUJQLTfrAHR9K2Q8hqkiFE7e18an0eWcT9y3cxm97z1BYYqiOuITA3cHaXAokOl66S4WoEwpzlbIfOWfBoyWM/gZ0sra1EFWlwonb6id68dvUnrTwdmTmH/F0eWMd//fLAfYlZVZDeKKhk+5SIeoQowF+mgL6/WDnAeN+BFsXtaMSol6p1Bi3Nn7OvHpnKNtfHMDbd7dDn1XA3Z9uZfCHf/HVP6fILiiu6jhFAxUR4g3AzoQM0nMLVY5GCHFDUS/C0TVgYQNjl4FrkNoRCVHv3NLkBJMJig0migxGTCZwsrXkm5gEeszewB/7zlZVjKIBC3CzI7SRE0YTrD+UpnY4QoiybP8Mtn+qPL/rMwjoom48QtRT5V7y6moHkrNYviuJ3/edxUqnZWQnf14f3oYgD3sAFm85xcw/4hjWvlGVBisapogQH+LOZhMdr2d0lwC1wxFC/NuRNbDmBeX5wJkQOkLVcISozyrc4hb5wV/c9ckWkjLyeWtUO2KmD+CFIa3MSRvAnR38SM8ruum9FixYQLt27XBycsLJyYnw8HBWr159w2s+/PBDWrZsia2tLQEBATz11FMUFBRU9G2IOiSyjdJd+tex8+QVlqgcjRCilJR9sOIBpexHpwnQ8wm1IxKiXqtwi9vQdr6M7hyAj7NNmee42VtxavbQm97L39+fOXPmEBwcjMlk4uuvv2b48OHs2bOH0NDQa85funQpL7zwAl999RU9evTg6NGjTJo0CY1Gw/vvv1/RtyLqiJbejgS62ZGYkc9fR88xpK2v2iEJIQCyzigzSIvzoGlfGPq+lP0QoppVuMXNZAJn22undhcUG5i77liF7jVs2DBuv/12goODadGiBbNmzcLBwYFt27Zd9/ytW7fSs2dP7rvvPoKCgoiIiGDs2LHs2LGjom9D1CEajYbIUKXVTWaXClFLFOZcKvuRAp6tpOyHEDWkwonb3PVHySu6trvqYpGBueuPVjoQg8HAsmXLyMvLIzw8/Lrn9OjRg127dpkTtZMnT/Lnn39y++23l3nfwsJCsrOzzY+cHFn3si66vOj8+sNpFJUYVY5GiAbOUKJ0j6YeAHsvuO9HsHFWOyohGoQKd5WagOs1hB9KycbFzqrCARw4cIDw8HAKCgpwcHDgl19+ISQk5Lrn3nfffZw/f57bbrsNk8lESUkJ//3vf3nxxRfLvP/s2bOZOXNmheMStUvHQFc8HKw4n1vE9lPp9Ar2VDskIRomk0mZiHAsGixsL5X9aKx2VEI0GOVO3Nq9GoVGo0ED9Ht3E5qrxjEYjSbyikoY163iv7wtW7Zk7969ZGVlsWLFCiZOnMjmzZuvm7xt2rSJN998k08++YRu3bpx/PhxnnjiCV5//XVefvnl695/+vTpPP30lTXyzpw5U2ZiKGovnVbDoBBvvt+RRFScXhI3IdSy/VOIXQhoYOTn4B+mdkRCNCgak8lkKs+JK3YlYzKZ+N9P+3nljhAcba6MZbDUafB3tSOssestBzRw4ECaNWvGZ599ds2xXr160b17d9555x3zviVLlvDwww+Tm5uLVnvznt/k5GQCAgJISkrC39//luMVNWfj4TQmL47F28mamBcGoNXKIGghatThVbBsHGCCQa9Dz8fVjkg0IPL5rSh3i9vdYco/UoCbkqBZ6m6pdm+ZjEYjhYXXr5Cfn59/TXKm0+kAKGf+KeqwHs3dsbfSkZpdyL7kTDoG3voXBSFEOZ3dAz89CJggbDL0eEztiIRokMqVuOUUFJtb2EIbOVFQbKCg+PqLy1/dEncz06dPZ8iQIQQGBpKTk8PSpUvZtGkTUVFRAEyYMAE/Pz9mz54NKLNQ33//fTp27GjuKn355ZcZNmyYOYET9Ze1hY6+rbxYtT+FqLhUSdyEqCmZSZfKfuRDswFw+7tS9kMIlZQrcWs/M5od/zcQDwdr2s2Mvu7khMuTFk6Wo37bZWlpaUyYMIGUlBScnZ1p164dUVFRDBo0CIDExMRSLWwvvfQSGo2Gl156iTNnzuDp6cmwYcOYNWtWuV9T1G2RoT6s2p9CdLyeF4a0UjscIeq/gmwlactNBa9QuGcx6Cq16I4QogqUa4zbtpPpdG7sioVOy7aT6Tc8t3tT9yoLrjpIH3ndllNQTKfX11JsMLHu6d4093JUOyQh6i9DCXx/LxxfBw7e8OB6cJFl54Q65PNbUa6vTVcnY7U9MRP1m6ONJT2aebD56Dmi4lIlcROiuphMsPo5JWm7XPZDkjYhVFeuxO1QSna5b9ja16nSwQhRHpGhPmw+eo7oOD1T+zVXOxwh6qeY+bDzK0ADo74Av05qRySEoJyJ2+0f/Y0GZRzbjVR0jJsQlTEwxIv/+xX2JWeRknURX2dbtUMSon459AdEv6Q8j5wFre9QNx4hhFm5Ere//9evuuMQoty8HG3oFOjKrtMXWBufyoTwILVDEqL+OLMLfnoIMEGXB6H7o2pHJIS4SrkSN39Xu+qOQ4gKiQz1ZtfpC0TF6SVxE6KqZCbC92Oh5CI0HwSD35KyH0LUMuVK3NbGp9K3pSeWOi1r41NveO6gEO8qCUyIG4kI8eHNPw+z7WQGWfnFONuVv36gEOI6CrKulP3wbgP3LJKyH0LUQuX6rXz4253EXqrj9vC3O8s8T8a4iZoS5GFPS29HjqTmsP5wKiM7Ndyp4UKUW2YS5F+npJOxBFY/D2nx4OAD9/0A1jJjW4jaqFyJ26mrkrFTkpiJWiIi1JsjqTlEx0niJsRNZSbBvDAouf6SgmZ3fgTO8vskRG1VPQuOClEDIkN9ANh89FyZS7AJIS7JT7950gZKoV0hRK1VqQEMW46f58t/TnE8LReA5l4OPNCzCbcFe1RpcELcSGgjJ/xcbDmTeZG/jp4j4lIiJ4QQQtRXFW5x+zYmgYlf7cDe2oLJPYOY3DMIB2sLJi/ewTcxCdUQohDXp9FozJNhom8yaUYIIYSoDyrc4jZ/4wleviOEiT2CzPsm94RvYlyZv/G4lGYQNSoy1IfFWxNYfyiVEoMRC530/gshhKi/Kvwpl11QTJ8Wntfs7xXsSU5BSZUEJUR5dQlyxdXOkgv5xcQmXFA7HCGEEKJaVThxG9jam6g4/TX718br6d/Kq0qCEqK8LHRaBrRWukuv93MphBBC1Cfl6ipdtOWU+XmwlwPzNh5n28l0OgW6ArAnKZOdCRk81Ktp9UQpxA1EhHizYlcya+NTmTEsBI1UehfiWic2qB2BEKIKlCtx+/KfU6W2nW0tOZaWy7FLs0oBnGwt+XFXEo8NCK7aCIW4id4tPLG11HEm8yJxZ7Np4+esdkhC1C57voP1r938PAtrsHOv/niEEJVWrsTtn+f7V3ccQlSajaWOPi08WROnJypOL4mbEFfbsRD+fFZ53vZu6D6t7PVH7dzBJaDmYhNCVJgsRCfqhYhQb9bE6YmOS+WZiJZqhyNE7bBlLqx9RXne/VGIfFMWjReiphmKlTWAiy8qX47s3G7pduVK3F5fGc8zES2ws7Lg9ZXxNzz35TtCbikgISpjQCtvdFoNR1JzSDifR5CHvdohCaEekwk2vwWbZivbvZ6F/i9J0iZETSnMgf0/wMGf4cwuMBQpv5caDTj5QbN+EDYJ/MIqfOtyJW5xZ7MoNpjMz8uiQf4oCHU421nSvakbW46nExWn5z99mqkdkhDqMJmUVratHynbA16BXs+oG5MQDcnWefD3u+DaBFoOgV5Pg6MvWNjAxUxIi4fEGPj2LvDrDLe/A+7l/8wqV+K27OHw6z4XojaJDPVhy/F0ouNTJXETDZPRCKufg9gvlO3Bc6D7I+rGJERDc3Y3TF4NXq2vf9w/DDqNh5IPYM8SOL21QonbLZeZzykoJipOb163VAi1XF7+anfiBdJyClSORogaZjTA79MuJW0aGDZXkjYh1HD3V2UnbVezsIYuU5QkrgIqnLhN/W43X29NAKCg2MCd87YwbeluBn/4F6sPpFT0dkJUGV9nW9r7O2Mywbr4NLXDEaLmGIrhpwdh73eg0cHIz5XxM0KI2qUgGw6thHNHKn2LCidu209l0CVImRERFafHZDKxf0YkM+4M5eMNxysdiBBVISLUB5BVFEQDUlwAP4yHuJ9Bawn3LIZ2o9WOSggB8ONE2P658rz4InzeF5ZPggU9IP63St2ywolbTkExLnaWAGw+co7BbXyxtdLRv5UXCel5lQpCiKoSGap0l249cZ6cgmKVoxGimhXlw/dj4OhqZeDz2O8h5E61oxJCXHZ6KzS+NDfg0ErABC8kwpC34K93KnXLCidujVxs2Z14gfyiEjYfPUfvYA8AsvKLsba45SFzQtyS5l6ONPW0p9hgYuORc2qHI0T1KciGJaPg5EawtIdxyyF4kNpRCSGuVpgNtsryoBxfB63vBCs7CI6E9JOVumWFM60Hegbx5LK9dH9zPV5ONnRvqiyPsuNUOi19HCsVhBBVKVK6S0V9l58B346AxK1g7Qzjf4EmvdWOSgjxb05+kLQDivKUxK3ZpZWoCjKVyQmVUOGVE8aHB9EhwJUzmRfpFeyBVqvUbgt0t+NZqVgvaoGIEG8WbDrBpsNpFJYYsLbQqR2SEFUn95yStKUeBFs3JWlr1EHtqIQQ19P9Efj5IbCyB+dACOql7D+9FbxDK3XLSi151dbfmbb+pdeD7N/Ku1IBCFHV2vu74O1kTWp2IVuPp9OvlZfaIQlRNbLPwjfD4fxRcPCG8b+Ct6xWI0St1fUh8O8MWcnQtB9oL3V0ugYpq5lUQqUSt5Ssi6yLT+VMZgHFBmOpY7LklVCbVqthUIg3S7YlEh2vl8RN1A8XTsM3d8KFBHDyh4m/V6hopxBCJY06Ko+rtYis9O0qPMZty/Hz9H93M0u2JfLF3yeJOZHO8p1J/Lgzifiz2ZUORIiqdHmc29r4VAxGk8rRCHGLzh+HRUOUpM01CB5YLUmbELXV3+8rpT/KI3knHI2q0O0rnLi9veYwD/VuStRTvbG20PLp/WHETB9Atybu3N7Ot6K3E6JadGvijqONBedzi9ideEHtcISovNR4JWnLPgMeLWHyGnAJVDsqIURZzh2BD0Jh5dNwbC3knb9yzFAC+oOwYyF8MQiWTwYrhwrdvsJdpcfTcvlorNLkp9NqKCgxYG9twdODWvDQNzsZ371xRW8pRJWzstAyoJUXv+49S3Sc3lw0Wog65eweZSHqixfAuy1M+BXsPdSOSghxIyM/A/0B2PE5/DQFCnOUFU0srKE4XznHpx10mgAdxoGlTYVuX+HEzdbKwjyuzcvJhtPp+bTwVsqAXMgvqujthKg2kaE+/Lr3LFFxqbx4e2s0Go3aIQlRfonb4Lt7lDpQfp3h/hVX6kEJIWo3n7Zw58dwx1xlBnhWktJ9aueuJG327pW+dYUTt46BLsQmXKC5lyP9Wnoya1U8R/TZrInT0zHQpdKBCFHVerfwxMpCS2JGPkdSc2jl46R2SEKUz8nNyooIxfnQuCfc9wNYS51MIeocrRZ82ymPqrplRS94eWgIHQJcAHhqUAt6NPdg5f4U/F3seGtU1QUmxK2yt7Ywr+wRdTBV5WiEKKej0UpLW3G+Uqxz3ApJ2oQQZhVucQt0tzM/t7Oy4M272lZpQEJUpYgQH9YdSiM6Xs8TA4PVDkeIG4v/DVZMAWMxtBwK9yyqdHV1IUT9VKnFRbMuFrNsRyJvrTlM5qVxbQfPZKHPKqjS4IS4VQNae6HVQNzZbJIy8tUOR4iy7fsBlk9SkrY2o2D015K0CXELFixYQLt27XBycsLJyYnw8HBWr15tPl5QUMDUqVNxd3fHwcGBUaNGkZpauncmMTGRoUOHYmdnh5eXF8899xwlJSU1/VZKqXDidiglm/7vbuLTzSdY+NdJsi8qb2DNQT1vrzlc5QEKcSvcHazNM0qj46W7VNRSOxfBL/8BkxE63A8jF4LOUu2ohKjT/P39mTNnDrt27WLnzp3079+f4cOHExcXB8BTTz3FH3/8wfLly9m8eTNnz55l5MiR5usNBgNDhw6lqKiIrVu38vXXX7N48WJeeeUVtd4SUInE7Y1V8dwd5s+m5/phbXHl8n6tPNl+KqNKgxOiKkRcKsYbLYvOi9oo5hNY+SRggi4PKTPRtLK+rhBlycnJITs72/woLCy87nnDhg3j9ttvJzg4mBYtWjBr1iwcHBzYtm0bWVlZfPnll7z//vv079+fsLAwFi1axNatW9m2bRsA0dHRxMfHs2TJEjp06MCQIUN4/fXXmT9/PkVFFayikX5CWWT+cmFeU+ULw1c4cduflMV93a4t/ujtZMO53Ov/4wmhpogQZR3d2IQM0uVnVNQmf70DUdOV5z2fgNvfubKWoRDiukJCQnB2djY/Zs+efdNrDAYDy5YtIy8vj/DwcHbt2kVxcTEDBw40n9OqVSsCAwOJiYkBICYmhrZt2+LtfWUt9sjISLKzs82tdjeVnwFf3wkfhymTjnIuNSD8Ng2i/q/8b/oqFf4LYWWhJafg2v7dU+fzcLe3qlQQQlSnADc7QnydMJpg/eE0tcMRQvm2vW4mbHhD2e77IgycCVJrUIibio+PJysry/yYPn16meceOHAABwcHrK2t+e9//8svv/xCSEgIer0eKysrXFxcSp3v7e2NXq8kV3q9vlTSdvn45WPlsmY6aC3gqTiwvDK5kzZ3KS1wlVDhxG1ga28+Wn/MXIRXo4EzmReZs/owg9v4VCoIIapbpHSXitrCZII1L8A/7yvbEW9A3+claROinBwdHc0TDpycnLC2LnsST8uWLdm7dy/bt2/nkUceYeLEicTHx9dcsCc2wKCZ4OxXer9bM8hMqtQtK5y4/d8drckvMhD2+loKSozc+1kMfd/ZiL21Bc9FtqxUEEJUt8g2yrekv46dJ69Q3RlBogEzGuCPx2H7p8r27e9Cj8fUjUmIeszKyormzZsTFhbG7Nmzad++PXPnzsXHx4eioiIyMzNLnZ+amoqPj/JF38fH55pZppe3L59zU8X5pVvaLrt4ASwq10tZ4cTNycaSJQ9246tJXXh1WAgTegSxaFJXfvxPOHZWFS4LJ0SNaOntSKCbHUUlRv46ek7tcERDZChRZo7u/gY0WhixALo+pHZUQjQoRqORwsJCwsLCsLS0ZP369eZjR44cITExkfDwcADCw8M5cOAAaWlXhtisXbsWJycnQkJCyveCgeGw7/sr2xoNGI2wZS4E9arUeyhXptV+ZjQbn+2Lm70Vzy3fx4w7Q+kc5EZnWbhb1BEajYbIUG8W/n2KqDg9Q9r6qh2SaEhKCmHFA3B4pTLeZeRCaDPy5tcJISpt+vTpDBkyhMDAQHJycli6dCmbNm0iKioKZ2dnpkyZwtNPP42bmxtOTk489thjhIeH0717dwAiIiIICQlh/PjxvP322+j1el566SWmTp16w+7ZUga9Bt/cCWf3gKEI1r4CaYeVFrcpUZV6X+VqcSs2GMm9NCHhp93JFBYbKvViQqjpclmQ9YfTzGM0hah2xRdh2X1K0qazgnuXSNImRA1IS0tjwoQJtGzZkgEDBhAbG0tUVBSDBg0C4IMPPuCOO+5g1KhR9O7dGx8fH37++Wfz9TqdjpUrV6LT6QgPD+f+++9nwoQJvPbaa+UPwjsEHtsFgd2h5e1QlA+th8F//wa3ppV6XxqT6ebFRO7/Yjvncwtp4+fMT7uTuaNdI2wsrp/zvXNP+0oFUlOSk5MJCAggKSkJf39/tcMRNchgNNHtzXWczy3i2yld6RXsqXZIor4rzFUWi0/4GyxsYexSZf1RIUSFyee3olwtbh/c24G+Lb3ILypBA+QUFJN18fqPirjZchTXk5mZydSpU/H19cXa2poWLVrw559/Vuh1RcOk02oY2FqZpBAls0tFdbuYCd/epSRtVo4w/mdJ2oRoiIoLIHkXHFkDh/8s/aiEco1x83S05oUhrQC47a0NfDC6A65VULPt8nIUwcHBmEwmvv76a4YPH86ePXsIDQ295vyioiIGDRqEl5cXK1aswM/Pj9OnT19Th0WIskSG+rAsNom18am8dmcbtFopwSCqQV46fDsC9PvBxgXu/xn8w9SOSghR046tUyYl5adfe0yjgRkXKnzLCk8D/ef5qvvGOGzYsFLbs2bNYsGCBWzbtu26idtXX31FRkYGW7duxdJSWccvKCioyuIR9V+P5u7YW+lIzS5kX3ImHQNd1Q5J1Dc5evhmBJw7BHYeMOE38GmjdlRCCDWsfg5CR0Cf58HBq0puWan6HVuOn2fL8fOk5xZh/NcQucqOcTMYDCxfvty8HMX1/P7774SHhzN16lR+++03PD09ue+++3j++efR6a6/tl9hYWGpdcxycnIqFZ+oH6wtdPRt5cWq/SlEx6dK4iaqVmaSMoMs4yQ4+sKE38GzhdpRCSHUknsOwqdWWdIGlajj9uG6o4z/cjtbTqSTkV90S2PcoOzlKK7n5MmTrFixAoPBwJ9//snLL7/Me++9xxtvvFHm/WfPnl1qTbNy114R9dblVRRknJuoUuknYNEQJWlzCYTJqyVpE6KhCxkOCf9U6S3LNav0al1mrWP6kFaM7FQ1MzqKiopITEwkKyuLFStW8MUXX7B58+brJlgtWrSgoKCAU6dOmVvY3n//fd555x1SUlKue/9/t7idOXOGkJCQBj8rpSHLLigm7PW1FBtMrHu6D829HNQOSdR1aYfhm+GQqwf35kr3qLP8fRGiKtXJWaVF+bB8ojJswjsEtJalj3f/b4VvWeGu0mKDkbDGVde9dHk5CoCwsDBiY2OZO3cun3322TXn+vr6YmlpWapbtHXr1uj1eoqKirCyunbChLW1dalCednZ2VUWu6ibnGws6dHMg81HzxEVp6e5V3O1QxJ1Wcp+ZSJCfjp4hcD4X8HR+2ZXCSEagoMrlPVKLWyUlrdS8+E0NZO43dslgN/2nuXxAcEVfrHyuLwcxfX07NmTpUuXYjQa0WqVXt6jR4/i6+t73aRNiLJEhvqw+eg5ouP0TO0niZuopOSdsGQkFGSBbwcY/wvYyYoyQohL1r8OfafDbU+DtsKj066rwolbYbGR77ef5J/j52nt44iFrnQgL99R/jFkN1qOAmDChAn4+fkxe/ZsAB555BHmzZvHE088wWOPPcaxY8d48803efzxxyv6NkQDNzDEi//7FfYlZ5GSdRFfZ1u1QxJ1TcI/sPReKMqFgG4wbjnYOKsdlRCiNjEUKSulVFHSBpVI3A7rswlp5ATAkdTSMzQ1VKwm1uXlKFJSUnB2dqZdu3allqNITEw0t6wBBAQEEBUVxVNPPUW7du3w8/PjiSee4Pnnn6/o2xANnJejDZ0CXdl1+gJr41OZEB6kdkiiLjm+DpbdDyUXoUlvGLsMrOzVjkoIUdt0uA8O/gy9n62yW1Z4ckJdVycHN4pq8dnmE8xefZjbmnuw5MFuaocj6orDq2D5JOWbdHAEjP4GLKXFVojqVic/v//8H+xbptRy9A69dnLC4DcrfMtK1XEToj6IDPVh9urDbDuZTlZ+Mc52lje/SDRsB1bAzw+DyQCt74RRX4KFjK8VQpQhLR582116fqhKblmuxO0/3+7k3Xva42hjyX++3XnDcz8b37lKAhOiugV52NPS25EjqTmsP5xaZSVuRD21+1v4/THABO3GwPD5oJPvvkKIG5i0sspvWa7Rco42lmg0GvPzGz2EqEsiQpWyDdFxqSpHImq17Z/D79MAE4RNhhELJGkTQqiiXH953r1qGat3K7mklRC1UWSoDx9vOM7mo+coKDZgY3n9pdNEA/bPh7BuhvK8+1SInKUsDi2EENezbJzy5c7GSXl+I2O+q/Dt5SujaNBCGznh52LLmcyL/H3sPINCpHCquMRkgk2zYfNbynbv56Df/0nSJoS4MRvnK38nqqFEkCRuokHTaDQMCvFm8dYEouL0krgJhckE0S9BzDxle8Ar0OsZdWMSQtQNIz6BTW9Bj8eU51Ws6irCCVFHXR7ntv5QKiUGo8rRCNUZjbDq6StJ2+C3JGkTQlTM5jlQlFctt5bETTR4XYPccLWz5EJ+MbEJF9QOR6jJUAK/TYWdXwEaGPZRpdYSFEI0cNVYIveWEreCYkNVxSGEaix0Wga0VlrdouL0KkcjVFNSBD9NgX1LQaODkQshbKLaUQkh6qpqGg9b4TFuRqOJeRuP893205zPLWLjM30JdLfjvegj+Lvacm+XwOqIU4hqFRHizYpdyayNT2XGsBBz+RvRQBQXwPKJcHSNUtn8nkXQepjaUQkh6rKPO8HNlgJ94XSFb1vhxO3jDcf5aXcy04e05oWf95v3t/B25KstpyRxE3VS7xae2FrqOJN5kbiz2bTxk8XCG4yiPFh2H5zcBBY2cO8SCB6kdlRCiLqu74tKSZAqVuHE7ec9ycwe2ZaezT34v18OmPe39nXiRFpulQYnRE2xsdTRu4UHUXGpRMfpJXFrKAqyYeloSIwBS3u47wdo0kvtqIQQ9UGbUeDgWeW3rfAYN31WAY3d7a7ZbzKZKDE2qPXqRT0TGeoDQJSsotAw5GfAN3cqSZu1M0z4VZI2IUTVqMbhNhVO3IK9HYhNyLhm/58H9IQ2qvomQSFqyoBW3ui0Go6k5pBwvnqmcYtaIjcNFt8BZ/eArRtM/B0CuqodlRCivqjGWaUV7ip9vH8wzyzfhz6rEKMJ1sSlcPJcHj/vPsOXk2SBeVF3OdtZ0r2pG1uOpxMdr+fh3s3UDklUh6wz8M1wSD8GDt4w4Tfwaq12VEKI+uTVzGq7dYVb3CJCffhyYhe2HD+PnZWO99ce5XhaLl9M7Eyv4KrvyxWiJkl3aT13IQEWDVGSNid/mLxakjYhRJ1SqSWvujZxY8mD3ao6FiFUNyjEm1d+i2N34gXScgrwcrRROyRRVc4fg6/vhJyz4NpE6R51kVnwQoi6pcItbmczL5KSddG8vTcpk5l/xLF0e2KVBiaEGnydbWnv74zJBOvi09QOR1QV/UGlpS3nLHi0VFraJGkTQtRBFU7cnli2h5gT6QCk5RRw/xfb2ZeUybvRR5i77liVByhETYswd5fKKgr1wpndsHgo5J0Dn7Yw+U9w8lU7KiGEqJQKd5Ue0efQPsAFgFX7U2jp48hPj/Tgr6Pn+L9fD/DEwOCqjlGIGhUZ6s07UUfYeuI8OQXFONpYqh2SuJHMJMhPv/4x/QFY8wIU5YJfZ7h/Bdi61mx8QghRhSqcuJUYTVjplIa6LcfPM/DSGo/NvBxIyy6s2uiEUEFzL0eaetpz8lweG4+c4872jdQOSZQlMwnmhUHJTf72+HVW6rRZO9ZIWEIIUV0qUcfNke+2J7LjVAZ/HztPnxbKTNLU7AJc7ayqPEAh1BARonSXRkt3ae2Wn37zpA0gcpYkbUKIeqHCidsLg1uxdPtpxnwew53tGxFyqejuuvhU2gfIMkGifogMVVqSNx05R2GJQeVoxC2zkNnBQoj6ocJdpeHN3NnzSgS5BSU4210Z+zO2ayC2VroqDU4ItbT3d8HL0Zq0nEK2Hk+nXysvtUMSQgghKt7iBqDTakolbQABbnZ4OFhXSVBCqE2r1RBxqdUtOl66S4UQQtQOlSrA++eBFFbtT+FM5kWKDcZSx1Y9Los0i/ohMtSHJdsSWRufyhsjTOi01bdosKikoly1IxBCiBpV4Ra3RVtO8dzyfXg4WBF/Npv2AS642lmRmJFP35ay5JWoP7o1ccfRxoLzuUXsSbygdjjiaiYTHFgBP4xXOxIhhKhRFU7cvt12mjdHtmXm8DZY6jT8t3czljzYjck9gsgpKKmOGIVQhZWFlgGXxrZJMd5a5Pxx+HYE/DQFLmaoHY0QQtSoSi15FdZYKWBpY6kjt1BJ1u7q5M/v+85WbXRCqOzyKgrR8amYTCaVo2ngii/ChlmwIBxObgKdNYRPU/57IxbWYOdeIyEKIUR1q/AYN09HazLzi/F3hUYutuxJukBIIyeSMvKRzzVR3/Rp4YmVhZbT6fkcSc2hlY+T2iE1TMfWwp/PwoUEZbv5QLj9HXBrCt3+W/bKCaAkbS4BNRKmEEJUtwonbj2aerDuUCpt/Jy5p7M/r6+MZ/UBPfuTMxncxqc6YhRCNfbWFvQO9mDdoTSiDqZK4lbTspJhzXQ49Luy7dgIhsyB1neC5tJkEZcAScyEEA1GhRO32SPbYrzUtDYhPAgXOyt2n77AwNZe3NetcZUHKITaIkJ8WHcojeh4vazFW1MMxbD9U9g4G4rzQKOD7o9A3xdkBQQhRINW4cRNq9Wg5UpZhDvbN5K1HEW9NqC1F1oNxJ3NJikjnwA3O7VDqt8St8HKpyEtTtkO6AZD3wefNurGJYQQtUCFJyf8uDOJVftTrtm/an8KK3YlV0lQQtQm7g7WdA5yA2BtfKrK0dRjeenw21T4KlJJ2mzd4M55MHmNJG1CCHFJhRO3BZtO4Gpvec1+dwcrPtl4vEqCEqK2ibw0u1TKglQDoxF2fQ3zwmDPEmVfpwnw2C7oNB60lVrgRQgh6qUK/0U8k3mRANdru4r8XGw5k3mxSoISoraJCFGWv4pNyCA9t1DlaOoR/QGlhe2Px+HiBfBuAw9Ew50fg52b2tEJIUStU+HEzcPeisP6nGv2H0rJxtXOqkqCEqK2CXCzI8TXCaMJ1h9OUzucuq8wB9a8CJ/1geQdYOUAkW/Cw5shsJva0QkhRK1V4cRtWIdGvPp7HFtPnMdgNGEwmth6/Dwz/4hnWHvf6ohRiFrhcndptHSXVp7JBHG/wLwusG0+mAwQMhym7oDwqaCr1PLJQgjRYFT4r+Qzg1qSfOEi477YjsWlRbeNJhjZ0Y/nIltVeYBC1BYRod58sO4ofx07T15hCfbWkmRUSPoJ+PM5OLFe2XYNgtvfg+CBqoYlhBB1SYU/eawstMy/rxOnzucRfzYbG0stLX0c8b/OuDch6pNWPo4EutmRmJHPX0fPMaSttDCXS3EBbPkQ/n4fDIWgs4LbnlIelrZqRyeEEHVKpZsMmnjY08TDvipjEaJW02g0RIR488U/p4iOT5XErTxObIBVz0DGSWW7aT8Y+h64N1M3LiGEqKNknr0QFRB5aVm39YdSKTYYVY6mFstOgeWT4Nu7lKTNwQfu/grG/yJJmxBC3AIZpCNEBXQKdMXDwYrzuUVsO5lOr2BPtUOqXQwlELsQNsyCohzQaKHrf6Dfi2Aj67wKIcStkhY3ISpAp9UwsLVS0y06TlZRKCUpFhb2hTUvKEmbX2d4eJOyKLwkbUIIUSUqVYDXdGmR+auZTCYpwCsaBHNZkHg9RuO1vwsNTn4G/PEEfDlIKahr4wJ3fAhT1oJve7WjE0KIeqXCiVuvtzaQnld0zf7M/GJ6vbWhSoISojYLb+aOvZWO1OxC9p/JUjsc9ZhMsOc7mNcZdi0GTNBhHEzbCZ0ny1JVQghRDSr8l9UEaK6zP6+oBGsL3a1HJEQtZ2Opo28rL6ABr12aGg+LhsBvj0J+Oni2hsmrYcQn4CDj/oQQorqUe3LC6yvjASVpe2/tUWwtryRpBqOJvUmZhDSScSyiYYgM9WHV/hSi4vQ8P7gBFZ4uzIXNb8G2T8BYApZ20PcF6P4o6CzVjk4IIeq9cre4xZ3NIu5sFibgiD7HvB13NosT53Jp7evEu/dUbDzLggULaNeuHU5OTjg5OREeHs7q1avLde2yZcvQaDSMGDGiQq8pRFXo29ITS52Gk+fyOJ6Wq3Y41c9kgkN/wPxusPUjJWlrdYeyVFXPJyRpE0KIGlLuFrdlD4cD8OzyfcwYFoKjza3/ofb392fOnDkEBwdjMpn4+uuvGT58OHv27CE0NLTM6xISEnj22Wfp1avXLccgRGU42VjSo5kHm4+eIypOT3Ov5mqHVH0uJMCf/4NjUcq2SyAMeQdaDlY1LCGEaIgqPMbt3XvaV0nSBjBs2DBuv/12goODadGiBbNmzcLBwYFt27aVeY3BYGDcuHHMnDmTpk2bVkkcQlRGROilsiDx9bQsSEkh/PWO0sp2LAq0ltDrGXh0uyRtQgihkgoX4M0vKmHBphNsOX6e9LwijP8qDfL3//pXKhCDwcDy5cvJy8sjPDy8zPNee+01vLy8mDJlCn///fdN71tYWEhhYaF5Oycnp1LxCfFvg0K8eenXg+xLykSfVYCPs43aIVWdk5uVparSjynbQb1g6Pvg2ULduIQQooGrcIvb8z8d4IfYJLo0cWNCeBCTezQp9aioAwcO4ODggLW1Nf/973/55ZdfCAkJue65//zzD19++SULFy4s9/1nz56Ns7Oz+VHWvYWoKC9HGzoFugJKTbd6IScVfnoQvrlTSdrsvWDkQpj4hyRtQog6Zfbs2XTp0gVHR0e8vLwYMWIER44cKXVOQUEBU6dOxd3dHQcHB0aNGkVqaulelMTERIYOHYqdnR1eXl4899xzlJSU1ORbKaXCLW6bjqSxaFIXOge5VUkALVu2ZO/evWRlZbFixQomTpzI5s2br0mwcnJyGD9+PAsXLsTDw6Pc958+fTpPP/20efvMmTOSvIkqExHiza7TF4iOS2VCeJDa4VSe0QA7v4L1r0NhFqCBLg9C/5fA1kXt6IQQosI2b97M1KlT6dKlCyUlJbz44otEREQQHx+Pvb09AE899RSrVq1i+fLlODs7M23aNEaOHMmWLVsApTdw6NCh+Pj4sHXrVlJSUpgwYQKWlpa8+eabqrwvjel6yyDcwG1vbWDx5C4093KsloAGDhxIs2bN+Oyzz0rt37t3Lx07dkSnu1KGxGhUFvnWarUcOXKEZs1uvnh1cnIyAQEBJCUl4e/vX7XBiwbn1Pk8+r27CQuthl0vDcLZrg7OrjyzC1Y+DSl7le1GHZVuUb9OqoYlhBBXu/z5HR8fj5+fn3m/tbU11tbWN73+3LlzeHl5sXnzZnr37k1WVhaenp4sXbqUu+++G4DDhw/TunVrYmJi6N69O6tXr+aOO+7g7NmzeHsr45o//fRTnn/+ec6dO4eVlVX1vNkbqHBX6TMRLXh/7VEuFhmqIx6MRmOpMWmXtWrVigMHDrB3717z484776Rfv37s3buXgICAaolHiBtp4mFPC28HSowmNhypY5MULmYqCdvCAUrSZu0Mt78LD66XpE0IUWuFhISUGgI1e/bscl2XlaWsdOPmpvQY7tq1i+LiYgYOHGg+p1WrVgQGBhITEwNATEwMbdu2NSdtAJGRkWRnZxMXF1dVb6lCKtxVuvCvUyRm5NP5jbX4u9phoSu9jsKqx8tfomP69OkMGTKEwMBAcnJyWLp0KZs2bSIqSik7MGHCBPz8/Jg9ezY2Nja0adOm1PUuLi4A1+wXoiZFhvpwNPU4UQdTuatjHWjFNZlg/48Q/X+Qd07Z1+5eGPQ6OHrf+FohhFDZ9VrcbsZoNPLkk0/Ss2dPc86g1+uxsrIy5xKXeXt7o9frzedcnbRdPn75mBoqnLhdLoFQFdLS0pgwYQIpKSk4OzvTrl07oqKiGDRoEKAMCNTKeoeilosI8eHjDcfZfPQcBcUGbCxr8dJv544os0UTLs3I9mgBQ9+DJr3VjUsIIcrJ0dERJ6eKrdQ0depUDh48yD///FNNUdWcCiduTw6supllX3755Q2Pb9q06YbHFy9eXGWxCFFZbfyc8HOx5UzmRf4+dp5BIbWw1aooX6nJtvVjMBaDhS30eQ7CHwOLmh+jIYQQNWXatGmsXLmSv/76q9TYdh8fH4qKisjMzCzV6paamoqPj4/5nB07dpS63+VZp5fPqWmVbs46kJzFL3uS+WVPMgfPZFVlTELUKRqNxpys1cpF54+sVoro/vO+krS1GAxTtynFdCVpE0LUUyaTiWnTpvHLL7+wYcMGmjQpXbIsLCwMS0tL1q9fb9535MgREhMTzfVkw8PDOXDgAGlpaeZz1q5di5OTk2oVKirc4nY+t5DHlu5h26l0nC6toJBdUEx4U3c+HtsRd4eb9zULUd9EhHqzeGsC6w+lUmIwYqGrBV38mYmw+gU4skrZdg6AIW9By9tBo7nxtUIIUcdNnTqVpUuX8ttvv+Ho6Ggek+bs7IytrS3Ozs5MmTKFp59+Gjc3N5ycnHjssccIDw+ne/fuAERERBASEsL48eN5++230ev1vPTSS0ydOrVcY+uqQ4UTtxm/x5FXVMLap3qbS4IcS83hmeX7ePWPeD4e27HKgxSitusa5IaLnSUX8ouJTbhAeDN39YIpKYJt82Hz21CcD1oLCJ8Gff4HVvbqxSWEEDVowYIFAPTt27fU/kWLFjFp0iQAPvjgA7RaLaNGjaKwsJDIyEg++eQT87k6nY6VK1fyyCOPEB4ejr29PRMnTuS1116rqbdxjQonbn8dOceSB7uVquMW7O3Ia8PbMP7L7VUanBB1hYVOy4BW3vy0O5noeL16iVvCP8rkg3OHle3GPZXJB16t1YlHCCFUUp4ytTY2NsyfP5/58+eXeU7jxo35888/qzK0W1Lh/hyjyXRNCRAAC62GipXyFaJ+iby86Hxcarn+YFSp3HPwy39h8VAlabPzgBGfwqRVkrQJIUQ9UuHELbyZBzP/iCc1u8C8T59VwOsr4+mhZveQECrr3cITW0sdZzIvEnc2u2Ze1GiE2C9hXhjs+x7QQNhkmBYLHcbKWDYhhKhnKtxV+trwUB78eie3vbUBX2dbAFKyLtLC25EPx3So6viEqDNsLHX0buFBVFwq0XF62vg5V/5mmUmQn172cTt3uJgBK59SlqwC8GkLd3wI/p0r/7pCCCFqtQonbo1cbFn1+G38c/w8J9JyAWju5chtweVf+F2I+ioy1IeouFSi4lJ5OqJl5W6SmaS0oJVcu/SbmUYHJiNgAitHZTH4Lg+CrsK/0kIIIeqQSv2V12g09Ar2pFewZ1XHI0Sd1r+VFzqthiOpOSSczyPIoxKzOPPTb5y0AZgurRXcZhREvgmO6hSCFEIIUbPKPcZt6/HzDHx/MzkFxdccyy4oZtD7m9lxKqNKgxOirnGxs6J7U2UB4+j4ai7Ge/u7cPdXkrQJIUQDUu7E7astpxjTJQDHS0V3r+ZkY8l93QL54u+TVRqcEHVRZKiSSEXFpVbvC/l3qd77CyGEqHXKnbgdSsmhb8uyu0Z7BXvK0ldCgHn5q92JF0jLKbjJ2UIIIUT5lTtxO5dbiIW27NMttBrS84qqJCgh6jJfZ1va+ztjMsG6+LSbX/Bv+oNVH5QQQoh6odyJm4+TDUdSc8o8flifjZeTrFMqBEDEpe7SCo1zy0yCFVPg96nVFJUQQoi6rtyJW7+WnrwffZSCYsM1xwqKDXyw9hgDWnlXaXBC1FWXV1HYejz9uhN6SinKgw2zYF5nOLiiBqITQghRV5W7HMi0/sGsifub/u9uYkKPIJpeKnNw4lwe38YkYDCZmNqvebUFKkRd0szTgaYe9pw8n8emI+cY1r7RtScZjXBgOax7FXLOKvsa94SeT8KP99+4JIiFtVKEVwghRINS7sTN09Ganx7pwUu/HuTtNYe5vBKjBmWpn9eHt8HTUbpKhQCl1mFEqA+fbj5BVJz+2sQtKRbWvABndirbLoEQ8Qa0vlNZpmrarpuvnOASUH1vQAghRK1UoQK8/q52LJ7claz8YhLS8zABTdztcba7tkSIEA1dZKg3n24+waYj5ygsMWBtoYOsZKWF7cBy5SQrB+j1DHR/FCxtrlzsEiCJmRBCiGtUauUEZztL2tu5VHEoQtQv7f1d8HK0Ji2nkG1HkumTthS2zIWSi4AGOo6D/i9LAV0hhBDlJgsbClFNtFoNESFe5MR+T/tfn4TiS6VBAsNh8Gxo1FHV+IQQQtQ9krgJUV2Sd/K/5GdwstoLxWByDkAT8TqEjFDGsQkhhBAVJImbEFUt+6wyjm3/DzgB+Vgzr3g4A4bNJKz5dWaXCiGEEOUkiZsQVaUoH7Z+DFs+hOJ8ZV+HcczJuYtv4oooPpIpiZsQQohbUu4CvEKIMphMcGAFzOsCm95UkraA7vDQRhjxCd07tAEgOj4Vk8l0k5sJIYQQZZMWNyFuxZldsPoFSN6hbDsHwKCZEDrSPI6tTwtPrCy0nE7P50hqDq18nFQMWAghRF0miZsQlZF9Fta/Bvu+V7Yt7eC2p6HHNLC0LXWqvbUFvZp7sP5wGtFxqZK4CSGEqDTpKhWiIoovwuZ34OOwK0lb+7Hw2C7o89w1SdtlkZcWnY+Kq8Ci80IIIcS/SIubEOVhMkHcz7B2BmQlKfv8u8LgOeAfdtPLB7T2QquBuLPZJGXkE+BmV80BCyGEqI+kxU2ImzmzG74aDCseUJI2J38Y9SVMiS5X0gbg7mBN5yA3ANbGp1ZntEIIIeoxSdyEKEt2Cvz6KCzsB0nblHFsfV+EabHQ9u4KF9GV7lIhhBC3ShI3If6t+CL89a4yjm3vd8q+dvfCtJ3Q93mwqlw3Z0SINwCxCRlk5BVVVbRCCCEaEBnjJsRlJhPE/wrRr0BWorLPr7Myji2gyy3fPsDNjhBfJ+JTsll3KJXRnQNu+Z5CCCEaFmlxEwLg7F5YdDssn6QkbY6NYORCmLK2SpK2yyJClVa36DgZ5yaEEKLiJHETDVtOKvw6FT7vC4lbwcIW+rwAj+2EdqNBW7W/IpfHuf197Bz5RSVVem8hhBD1n3SVioapuAC2zYe/34eiXGVf23tg4Kvg7F9tL9vKx5FANzsSM/LZfOQcQ9r6VttrCSGEqH+kxU00LCYTxP8G87sqKx8U5YJfmNIlOuqLak3aADQajXmSQrSUBRFCCFFB0uImGo6UfbDmRTj9j7Lt6Ku0sLWt+i7RG4ls48MX/5xi/aFUig1GLHXy/UkIIUT5SOIm6r/cNNjwOuz+FjCBhQ30fEJ5WNnXeDidAl1xt7ciPa+I7SczuC3Yo8ZjEEIIUTfJV31Rf5UUwj8fwEedYPc3gAna3K3UY+v3oipJG4BOq2HQpe5SKcYrhBCiIiRxE/WPyQTxvyvj2Na9CkU50KgjPBANd38JLurXT7s8uzQ6Xo/RaFI5GiGEEHWFdJWK+kV/ANZMh4S/lW0HH2UcW7t7a3Qc282EN3PH3kpHanYh+89k0SHARe2QhBBC1AG155NMiFuRew5+fxw+7aUkbTpr6PUsPLYLOoytVUkbgI2ljr6tvADpLhVCCFF+tevTTIiKKimELXPho46w+2vABKEjlQK6A14Gawe1IyyTuSyIJG5CCCHKSbpKRd1kMsHhVRD9Elw4pezz7aCsK9o4XNXQyqtfKy8sdRpOnMvjeFouzb1qb5IphBCidpDETdQ9+oMQNR1O/aVsO3jDgBnQvvZ1id6Ik40lPZp5sPnoOaLj9TT3aq52SHWawWhix6kM0nIK8HK0oWsTN3RajdphCSFElZLETdQdeedhwxtKl6jJqIxj6zENbnsKrB3Vjq5SIkK92Xz0HFFxqTzaVxK3ylpzMIWZf8STklVg3ufrbMOMYSEMbiPLigkh6o+60zwhGq6SItj6sTKObdciJWkLGQHTdsCAV+ps0gYwKMQbjQb2JWWivyrpEOW35mAKjyzZXSppA9BnFfDIkt2sOZiiUmRCCFH1JHETtZfJBIf/hE+6KWPZCrPBpx1M+hNGfw2uQWpHeMu8HG3oeKkUyNp4maRQUQajiZl/xHO9SniX9838Ix6D1MoTQtQTqiZuCxYsoF27djg5OeHk5ER4eDirV68u8/yFCxfSq1cvXF1dcXV1ZeDAgezYsaMGIxY1JjUOvh0By8ZCxkmw94I758HDmyCop9rRVanLxXij4mTR+YracSr9mpa2q5mAlKwC5m04RlJGPiaTJHBCiLpN1TFu/v7+zJkzh+DgYEwmE19//TXDhw9nz549hIaGXnP+pk2bGDt2LD169MDGxoa33nqLiIgI4uLi8PPzU+EdiArJTIL89LKP27mDpS1sfPNKl6jOCsKnQq9n6nSX6I1EhPowe/Vhtp1MJyu/GGc7S7VDqtVKDEZ2nMogOj6VX/ecKdc1H6w7xgfrjuFkY0EbP+crj0ZOBLnbo5VJDEKIOkJjqmVfQd3c3HjnnXeYMmXKTc81GAy4uroyb948JkyYUK77JycnExAQQFJSEv7+/rcariivzCSYF6bUXSuL1gIsbJUlqgBa3wmDXgO3JjUTo4oiPtjM0dRcPri3PXd1lJ/Lf7tYZDDPvt1wOI3M/OIKXd/Ew54zFy5SZDBec8zeSkdoI2dC/Zxo08iZtv7ONPWwx0InI0mEqE3k81tRa2aVGgwGli9fTl5eHuHh5avDlZ+fT3FxMW5ubmWeU1hYSGHhlWQhJyfnlmMVlZCffuOkDcBYoiRt3m1h8Gxo0qtmYqsFIkN9OJp6nKiDqZK4XXIhr4h1h1KJjk/l72PnKCi+knS52VsxsLUXA1t588rvB0nNLrzuODcN4ONsw7qn+2AwmjiWlsPBM1kcPJPNwbNZHErJJq/IwI6EDHYkZJivs7HU0tr3UiLnpyR1wV6OWFlIMieEUJfqiduBAwcIDw+noKAABwcHfvnlF0JCQsp17fPPP0+jRo0YOHBgmefMnj2bmTNnVlW4orr1eg76TQetTu1IalREiA8fbzjO5qPnKCg2YGPZsN7/ZckX8omOSyU6Xs+OUxlcPafA39WWyFAfIkK86Rx0pUabEROPLNmNBkolb5c7P2cMC0Gn1aDTapSWtUbO3NtFOVZiMHLyfB4HkrM4eDaLuDPZxJ3NIq/IwJ7ETPYkZprvZ6XT0tLHkTZ+Tpe6WZ1p6ePYYP9fCSHUoXpXaVFREYmJiWRlZbFixQq++OILNm/efNPkbc6cObz99tts2rSJdu3alXnev1vczpw5Q0hISINvaq1xZ/fC531uft7Dm6FRh+qOptYxmUz0nLOBs1kFLJzQmUGXlsOq70wmE4f1OeZkLe5sdqnjrX2diAz1JiLEh9a+jmg01x+LVpV13IxGEwnpeRw4k0Xc2exLLXRZZBeUXHOuTqsh2MvBPF6urb8zrX2dsLNS/TuxEPWOdJUqVP/rYmVlRfPmSuHRsLAwYmNjmTt3Lp999lmZ17z77rvMmTOHdevW3TBpA7C2tsba2tq8nZ2dfYOzhVCHRqMhItSHxVsTiI7T1+vEzWA0sev0BaLj9ETHp5KYkW8+ptVA5yA3c8tagJtdue45uI0vg0J8qmTlBK1WQ1NPB5p6OjC8gzLpyWQykXzhIgcuJXEHLyV0GXlFHNbncFifw4pdyvUaDTTzdKBNIyfzJIiQRk442cikEyHErVM9cfs3o9FYqoXs395++21mzZpFVFQUnTt3rsHIRKXlZ8COshNxoYgI9Wbx1gTWHUqlxGCsV4PjC4oNbDl+nui4VNYdSiU9r8h8zMpCS+9gDyJCfRjQygt3B+sb3KlsOq2G8GbuVRVyKRqNhgA3OwLc7Li9rdKCZzKZ0GcXXOpmzSbujNLdmppdyPG0XI6n5fLr3rPmewS52xF6qYu1rZ8zoY2ccLW3qpZ4hRD1l6qJ2/Tp0xkyZAiBgYHk5OSwdOlSNm3aRFRUFAATJkzAz8+P2bNnA/DWW2/xyiuvsHTpUoKCgtDrlYKlDg4OODjIAt21TkEWxHwC2z5RiueKG+oa5IaLnSUX8ouJTbhQbUlITcm6WMzGw2lEx+vZdOQc+UUG8zEnGwsGtvYmItSbXsGe2FvXuu+QN6XRaPB1tsXX2ZaIS7X4ANJyCog7c6mL9awyEeJM5kUS0vNJSM9n1f4rKzn4udgqY+YaXSlR4ulYucRViNpA1gyufqr+tUxLS2PChAmkpKTg7OxMu3btiIqKYtCgQQAkJiaivWrR8AULFlBUVMTdd99d6j4zZszg1VdfrcnQxY0U5iotbFs+goJMZZ9bM8g4oWpYtZ2FTsuAVt78tDuZ6Hh9nUzc9FkFrI1XukBjTqRTctXsAl9nGyJCvIkI9aFrEzcs61GL4tW8HG3wamVDv1Ze5n0X8orMSZwyCSKLhPR8zmRe5EzmxVLFl72drGnTyPlS65wybs7HyabM8X1C1BayZnDNUH1yQk2TwY3VqPgixH4J/3wA+eeVfR4toN+L0KgTzO9y45IgFtYwbRe4BNRMvLVQdJyeh7/dhZ+LLf88369OfFgfT8sl6tJ4tX1JmaWOBXs5KOPVQr1p6+dcJ95PTckuKDbPYr08bu7EuVyu9xfZ3d7KnMi18VO6Wv1dbSv97ymtIqKqXV4z+N8/vpd/qhbc3+mWkzf5/FbUvf4JUfuUFMKur+Hv9yD30nqbrk2g73Roe/eV0h7Tdt185YQGnLQB9Ar2xMZSy5nMi8SdzaaNn7PaIV3DaDSxNznTPBP05Lk88zGNBjoGuBAZ6sOgEG+aesoQhrI42VgS3sy9VMtqXmEJh1KyS02AOJaWS3peEX8dPcdfR89ddf2VVSBCGznR1s+5XKtASKuIqGo3WzNYg7Jm8KAQH/mCUAUkcROVZyiGvd/B5ncgO1nZ5xwAff4H7ceC7l+z6FwCGnxidjO2Vjr6tPAkKi6V6Dh9rUncikqMxJxMJzpOz9r4VNJyrrScWuo09GjmQWSoDwNbe+HlZKNipHWbvbUFnYPc6Bx0pah4QbGBw3qlcHDcpe7WI/ocsgtK2Hoina0nrnwZ+vcqEG38nGnmeWUViLJaRfRZBTyyZHeVtIqIhqe8awbvOJVRJ4eA1DaSuImKMxpg/4+weQ5cSFD2Ofoq64l2mqB0eYpKiwz1URK3+FSejmipWhy5hSVsOpJGdFwqGw+nkVN4pY6Zg7UFfVt6EhnqQ9+WnjhKqYtqY2Opo0OACx0CXMz7ikqMHE3NMSdy5VkFIsTXiZX7U6RVRFSZgmIDv+87y9x1x8p1flpO2cmdKD9J3ET5GY0Q/wtsmgPnjyr77D3htqeh82RlgXhxy/q38kKn1XBYn8Pp9Dwau9vX2GufyylUlpmK07PleHqptT09Ha0ZFOJNRIg34c3csbaQFQPUYmWhNXeTXr0KxIlzeebZrDdaBeJ6pFVElJc+q4Al206zdEciGVeV9rkZL0dpja8KkriJmzOZ4PAq2PgmpMUp+2xdoecT0PVhsKq5xKIhcLGzontTN7YcTycqTs/DvZtV6+udTs9TJhfEpbIr8UKpwfFNPOyJuLRyQccAl5uOnxLqsbi0JFdLH0dGhSkDt41GE6fSlWTut71n2XA47ab3kVYRcT0mk4ndiZks3prA6gMp5hnjjZxtuD+8MV9vTSDtJmsGd21S9rriovwkcRNlM5ng2FrYOAtS9ir7rJ0gfBp0fwRsnFQNrz6LCPFhy/F0ouNSqzxxM5lMxJ3NNidrR1JzSh1v5+9sXrmguZeDzAStw7RaDc08HWjm6YCXo025Ercl207jZGNJ7xae0mUqKCoxsurAWRZvSWBfcpZ5f9cgNyb3DGJQiDcWOi1NPezLtWawuHWSuInrO7kZNrwByTuUbUt76P5fJWmzk29N1W1QiDczfo9jV+IFzuUU3nJR1hKDkR0JGcpM0Dg9Z68aSKzTauje1O3S5AJvGrlIl3d91LWJG77ONuizCq7bKnJZbMIFJi+OxdfZhns6BzC6sz/+ruVbekzUH2k5BSzdnsh32xM5d2kykpWFluHtGzGxR9A1E6cGt/Flwf2drpmx7CMzlqucJG6itNMxSgtbwt/KtoUNdH0Iej4J9h6qhtaQNHKxpZ2/M/uTs1h3KJWxXQMrfI+LRQb+OnaOqDg9Gw6nkZlfbD5ma6nMXo0I9aZ/Ky9c7GTppfpOp9UwY1jIDVtFXr4jhDOZF/l5dzIpWQV8tP4YH284xm3NPRjbNZCBrb2xsqifhZOFYn9yJou3JPDH/rMUG5SfEm8na8Z3b8zYroE3XJKuKtcMFmWTxE0okncpCduJ9cq2zgrCJkOvp8HR58bXimoRGerD/uQslsUmYmelK9cfwQt5Raw/nEZUnJ6/j52joPjK5AJXO0sGtvYmMtSH24I9sLGUyQUNTXlbRf43uCXRcan8EJvEP8fP8/cx5eFub8WoMH9Gdw6guZfU6Ksvig1G1hzUs3hrArtOXzDv7xTowqSeTRjSxqfcK51U55rBlfHXX3/xzjvvsGvXLlJSUvjll18YMWKE+bjJZGLGjBksXLiQzMxMevbsyYIFCwgODjafk5GRwWOPPcYff/yBVqtl1KhRzJ07V7WlNiVxa+hS9iuTDo6uVra1FtDxfuj9HDg33MrUtYGdlZJY7UvK4olle4HrF0pNvpDP2vhUouL0xCZcwHDVMlP+rrZEhPgQGepNWGPXerVwvaic8rSKWFvoGNa+EcPaNyIxPZ8fdyaxfFcSqdmFfP7XST7/6yRdglwZ0yWQ29v6YmslXwLqooy8Ir7fkci3MafRZyuJvKVOwx3tGjGpRxDtrypBU1fl5eXRvn17HnjgAUaOHHnN8bfffpuPPvqIr7/+miZNmvDyyy8TGRlJfHw8NjbKLNhx48aRkpLC2rVrKS4uZvLkyTz88MMsXbq0pt8OIEteqR2OetIOw6Y3If43ZVujVYrm9n4O3JqoG5u46fIx/3dHa/IKDETH64k7m13qnNa+TkSEKC1rrX0dZXKBqBIlBiObjpxjWWwiGw6ncfn7gaONBSM6+HFvl4BaUzBa3Fj82WwWbz3Fr3vPUlSitMp7OFgxrltjxnULrLVFtG/181uj0ZRqcTOZTDRq1IhnnnmGZ599FoCsrCy8vb1ZvHgxY8aM4dChQ4SEhBAbG0vnzp0BWLNmDbfffjvJyck0atSoyt5feUmLW0OTfkKpw3ZgOeaym21GQd8XwCP4ZleLGnCz5WMA3lh5yLxPq4HOQW7mZC3ATQaSi6pnodMyMMSbgSHe6LMK+Gl3MstiE0nKuMi3207z7bbTtPFzYkyXQO7s0AgnKcpcq5QYjKw7lMqiLQlsP3WlQHNbP2cm9wxiaDvfOlObMScnh+zsK19Yra2tsbau+ASuU6dOodfrGThwoHmfs7Mz3bp1IyYmhjFjxhATE4OLi4s5aQMYOHAgWq2W7du3c9ddd93am6kESdwaigun4a+3Ye/3YDIo+1oPg74vgneIurGJUnacyrjh8jGXdQp0YUyXQAa09rrhgGEhqpqPsw1T+zXnkT7NiDmZzrLYJKIO6jl4JpuXzhxk1qpDDG3ny5guAYQ1dpVWXxVl5RezLDaRb2JOcybzIqCMQxvSxofJPYPoFFj3/v+EhJT+zJoxYwavvvpqhe+j1ytra3t7e5fa7+3tbT6m1+vx8vIqddzCwgI3NzfzOTVNErf6LusM/P0u7P4WjJdmFQZHQr8XoVEHVUMT11feAqgTewQxvINfNUcjRNm0Wg09m3vQs7kHGXlF/LLnDMt2JHIsLZcVu5JZsSuZ5l4OjOkSwF0d/eQLRg06lprDoq0J/LL7DBeLlS/rrnaW3NctkPu7N8bXue6W/YmPj8fP78rfvsq0ttVlkrjVVzmp8M8HsPMrMFxaELxpX+j3EgR0UTU0cWPlXRZGlo8RtYmbvRVTbmvCAz2D2J2YybIdiazcn8LxtFzeWHWIt9YcJiLUhzFdAujZzENW4agGRqOJDYfTWLw1gX+Onzfvb+XjyAM9m3Bnh0b1Yja5o6MjTk63XgDex0epmJCamoqv75UJX6mpqXTo0MF8Tlpa6cLVJSUlZGRkmK+vaZK41Tf5GbDlQ9j+OZQozeIE9oD+L0FQT1VDE+Vzs0KpsnyMqM00Gg1hjV0Ja+zKK8NC+GNfCstiE9mfnMWq/Sms2p+Cv6st93YO4O7O/nW65ae2yC4oZvnOZL6JSeB0ej6gjH2NCPFhUs8gujVxq3PdoTWhSZMm+Pj4sH79enOilp2dzfbt23nkkUcACA8PJzMzk127dhEWFgbAhg0bMBqNdOvWTZW4JXGrLy5mQsx82LYAii4tYeTXGfr/HzTtB/JLW2eUp1CqLB8j6gJHG6Vr7r5ugcSdzeLH2CR+2XOG5AsXeW/tUT5Yd5R+Lb24t0sA/Vp5lbtWmFCcPJfL11sTWLErmbwipTvUycaCsV2V7lCZqAS5ubkcP37cvH3q1Cn27t2Lm5sbgYGBPPnkk7zxxhsEBweby4E0atTIPPO0devWDB48mIceeohPP/2U4uJipk2bxpgxY1SZUQpSDkTtcG5dYQ5s/xS2fgwFl9aR82mntLAFR0jCVoetOZhyTaHU69VxE6IuKSg2sPpgCt/vSGLHVbMbPR2tuSfMn3u7BNDY3V7FCGs3o9HEX8fOsXhrApuOnDPvD/ZyYFLPIO7q6IedVf1sk6nM5/emTZvo16/fNfsnTpzI4sWLzQV4P//8czIzM7ntttv45JNPaNGihfncjIwMpk2bVqoA70cffaRaAV5J3OqqonyIXQj/fAgXL/3x82ytTDpoPUwStnrCYDTJ8jGi3jp5Lpcfdibx065kzucWmff3aObOvV0CiAz1qRdjsqpCXmEJP+1OZvHWBE6eywOUP/MDWnkxqUcTejZ3r/fdofXm8/sWSeJW1xQXwK7F8Pd7kHdpwKR7c+g7HULvAq38kRNC1C1FJUY2HE7l+x1J/HXsHJc/lVzsLLmrox9jugTS0sdR3SBVkpiez9cxCfy4M4mcghIAHK0tuKdzABPCGxPk0XBaJ+v853cVqZ/tqfVRSRHsXQJ/vQvZZ5R9Lo2hz/PQ7l7Qyf9KIUTdZGWhZXAbXwa38eVM5kWW70zix9gkzmYVsGhLAou2JNAx0IUxXQK4o10j7K3r9987k8lEzIl0vtqSwPrDqeZEtomHPZN6BDEqzB+Hev5vIMomLW61naEE9v8Am+dAZqKyz8lPWZqqwziwsFI3PiGEqAYGo4m/j51j2Y4k1h1KpeTSGlv2Vjru7NCIe7sE0t7fuV51D14sMvDLnjMs3nqKo6m55v19WngyqWcQfYI9G3QZlTr3+V1NJGWvrYwGOPizkrClX5oR4+ANvZ6BThPBUmp4CSHqL51WQ9+WXvRt6cW5nEJ+3p3MD7FJnDyfx/c7kvh+RxKtfBwZ0yWAER39cLGru19iz2Re5JuYBJbtSCLrolIo3c5Kx91h/kwID6K5lzqD4EXtJC1utY3RCIf/gI2z4dyl9Sjt3KHnk9DlQbCS6d1CiIbJZFIm6yyLTeLPAykUXlog3cpCy+1tfLi3SyDdm9aNmmUmk4nYhAss2nKKqDg9lxoUCXCzZWJ4EPd0DsDZVtZ7vVqt//yuIZK41RYmExyNgo2zQL9f2WfjDD0eg27/BeuGOTBXCCGuJyu/mN/2neH7HUkcSrmy4HiQux33dglkVJhfrVxdpKDYwB/7zrJ4awJxZ6/E3bO5O5N6NKF/Ky+ZOV6GWvv5XcMkcVObyQQnN8KGWXBmp7LPyhG6PwLhU8HWRdXwhBCiNjOZTBw4k8X3O5L4fe8ZcyFaC62GAa29GNMlkN4tPFVPhlKzC/g25jRLdySSkaeUPrGx1HJXR38m9QhqsLNmK6LWfX6rRMa4qSlhC2x4AxK3KtuWdtD1Yej5BNjJckZCCHEzGo2Gdv4utPN34aWhrVl1IIVlOxLZnZhJVFwqUXGp+DrbcE/nAEZ39sffteaGm5hMJvYkZbJoSwKrD6SYJ1g0crZhQo8g7u0cgKt93R2bJ9QhLW5qSIqFjW/AyU3Kts4aukyB254CBy91YhJCiHrkaGoOy3Yk8fOeZDLzlQH/Gg30CvZkTJcABrb2xsqiepbYKiox8ueBFBZtOcW+5Czz/q5BbkzuGcSgEG8sZHmvCqsVn9+1gCRuNensXtj4JhyLUra1ltBpAvR+FpzUWfNMCCHqs8ISA9FxqSyLTWTL8XTzfnd7K0aF+TO6c0CVzdo8l1PI0u2JLNl+mnM5hYAycWJ4+0ZM7BFEGz/nKnmdhkoSN4UkbrcqMwny08s+bueurCe66U049IeyT6ODDvcptdhcG996DEIIIW4qMT2fH3YmsnxnMmmXEitQWsLu7RLA7W19sbW6dvWZmy09dyA5i0VbT7FyXwpFBmWmq7eTNeO7N2Zs10DcHayr/801AJK4KSRxuxWZSTAvDEoKyz5HowWT8fIGtButrHbg3uzWXlsIIUSllBiMbDpyjmWxiWw4nGYuxeFoY8GIDn7c2yXA3Dq25mAKM/+IJyWrwHy9r7MN/ze0NQCLtiSw6/QF87FOgS5M6tmEIW18sJTu0ColiZtCErdbcXYvfN6nfOeGjFDWE/VqdWuvKYQQosroswr4aXcyy2ITScq4aN7fxs+Jtn7OLNuRxM0+JC11Gu5o14hJPYJoH+BSrfE2ZJK4KWRWaU0Y+QW0u0ftKIQQQvyLj7MNU/s155E+zYg5mc73OxKJjkvl4JlsDp7JvuG1Wg1M7dec8d0b4+VU+2rGifpJErea4BGsdgRCCCFuQKvV0LO5Bz2be5CRV8T7a4+wZFviDa8xmqBHMw9J2kSNkg54IYQQ4ipu9lZ0CSpfLc20nIKbnyREFZLE7f/bu/egqO4rDuDfDQqCLIs8XBDl4QuhgkZUunWiGEgApwYf4xMrNpROUrRKSl7NWGRMq4nptCa1pjOdYnR8NDTxUYgRpYKGGEQMaAyiEJSkAkZQFJSHcPqHw63LQ9fAcln5fmaYYe/vl/2dPXtmOPnde71ERETtmPq4rL74WC16vLFxIyIiameqjxPcdYPQ1YOyNLh3d+lUHz7lhnoXGzciIqJ2rJ7QIGm2PwB0aN7aXifN9lf9GajU/7Bx6w47Z2DAQ/5hxQE29+YREZFFiRjvjq3LJsFNZ3w61E03CFuXTULEeHeVIqP+jHeVdofjCGBl/sOfnOA4ovdiIiKiHhMx3h3P+Ls98MkJRL2JjVt3OY5gY0ZE9BizekIDwyieOaG+gadKiYiIiCwEGzciIiIiC8HGjYiIiMhCsHEjIiIishBs3IiIiIgsBBs3IiIiIgvBxo2IiIjIQrBxIyIiIrIQbNyIiIiILES/e3JCa2srAKCiokLlSIiIiMhUbX+32/6O91f9rnGrqqoCAEydOlXlSIiIiOhRVVVVwdPTU+0wVKMREVE7iN509+5dfPnll9Dr9XjiiZ49U3zr1i34+/vj66+/hlar7dH3ftwwV6ZjrkzHXJmOuXo0zJfpzJWr1tZWVFVV4cknn8SAAf1u30nR7xo3c7p58yZ0Oh1qa2vh4OCgdjh9GnNlOubKdMyV6ZirR8N8mY65Mi/enEBERERkIdi4EREREVkINm49yMbGBklJSbCxsVE7lD6PuTIdc2U65sp0zNWjYb5Mx1yZF69xIyIiIrIQ3HEjIiIishBs3IiIiIgsBBs3IiIiIgvBxo2IiIjIQrBx6wHr1q2DRqMx+hk3bpzaYfUJx44dw+zZszFs2DBoNBrs27fPaFxE8Lvf/Q7u7u6wtbVFWFgYLl68qE6wKntYrlasWNGhziIiItQJVmUbNmzAlClToNVqMXToUMyZMwfFxcVGcxoaGhAfHw9nZ2fY29tj/vz5yiPv+hNTchUSEtKhtl544QWVIlbP1q1bERgYCAcHBzg4OMBgMODgwYPKOGvq/x6WK9aU+bBx6yE/+tGPUFFRofx89tlnaofUJ9TX12PChAnYsmVLp+Nvv/023n33Xbz//vvIzc3F4MGDER4ejoaGhl6OVH0PyxUAREREGNXZ7t27ezHCviM7Oxvx8fH44osvcPjwYTQ3N+PZZ59FfX29MichIQH//ve/kZqaiuzsbFy5cgXz5s1TMWp1mJIrAIiLizOqrbfffluliNUzfPhwbNy4Efn5+Th16hSefvppREVF4dy5cwBYU/d7WK4A1pTZCHVbUlKSTJgwQe0w+jwAsnfvXuV1a2uruLm5yaZNm5RjN27cEBsbG9m9e7cKEfYd7XMlIhITEyNRUVGqxNPXXb16VQBIdna2iNyro4EDB0pqaqoyp6ioSADIiRMn1AqzT2ifKxGRGTNmyOrVq9ULqg8bMmSI/P3vf2dNmaAtVyKsKXPijlsPuXjxIoYNG4aRI0ciOjoa5eXlaofU55WVlaGyshJhYWHKMZ1Oh+DgYJw4cULFyPqurKwsDB06FL6+vnjxxRdRXV2tdkh9Qm1tLQDAyckJAJCfn4/m5maj2ho3bhw8PT37fW21z1WbnTt3wsXFBePHj8frr7+O27dvqxFen9HS0oI9e/agvr4eBoOBNfUA7XPVhjVlHgPUDuBxEBwcjG3btsHX1xcVFRVITk7GU089ha+++gparVbt8PqsyspKAIBerzc6rtfrlTH6v4iICMybNw8+Pj4oLS3Fb3/7W0RGRuLEiROwsrJSOzzVtLa2Ys2aNZg2bRrGjx8P4F5tWVtbw9HR0Whuf6+tznIFAEuXLoWXlxeGDRuGM2fO4NVXX0VxcTE+/vhjFaNVx9mzZ2EwGNDQ0AB7e3vs3bsX/v7+KCgoYE2101WuANaUObFx6wGRkZHK74GBgQgODoaXlxc+/PBDxMbGqhgZPU4WL16s/B4QEIDAwECMGjUKWVlZCA0NVTEydcXHx+Orr77idaUm6CpXv/zlL5XfAwIC4O7ujtDQUJSWlmLUqFG9HaaqfH19UVBQgNraWvzrX/9CTEwMsrOz1Q6rT+oqV/7+/qwpM+KpUjNwdHTE2LFjUVJSonYofZqbmxsAdLgrq6qqShmjro0cORIuLi79us5WrlyJtLQ0HD16FMOHD1eOu7m5oampCTdu3DCa359rq6tcdSY4OBgA+mVtWVtbY/To0QgKCsKGDRswYcIEbN68mTXVia5y1Zn+XFM9jY2bGdTV1aG0tBTu7u5qh9Kn+fj4wM3NDZmZmcqxmzdvIjc31+g6Cercd999h+rq6n5ZZyKClStXYu/evfjPf/4DHx8fo/GgoCAMHDjQqLaKi4tRXl7e72rrYbnqTEFBAQD0y9pqr7W1FY2NjawpE7TlqjOsqZ7DU6U9IDExEbNnz4aXlxeuXLmCpKQkWFlZYcmSJWqHprq6ujqj/8MqKytDQUEBnJyc4OnpiTVr1uDNN9/EmDFj4OPjg7Vr12LYsGGYM2eOekGr5EG5cnJyQnJyMubPnw83NzeUlpbilVdewejRoxEeHq5i1OqIj4/Hrl27sH//fmi1WuUaI51OB1tbW+h0OsTGxuKll16Ck5MTHBwcsGrVKhgMBvz4xz9WOfre9bBclZaWYteuXZg1axacnZ1x5swZJCQkYPr06QgMDFQ5+t71+uuvIzIyEp6enrh16xZ27dqFrKwsHDp0iDXVzoNyxZoyM7Vva30cLFq0SNzd3cXa2lo8PDxk0aJFUlJSonZYfcLRo0cFQIefmJgYEbn3T4KsXbtW9Hq92NjYSGhoqBQXF6sbtEoelKvbt2/Ls88+K66urjJw4EDx8vKSuLg4qaysVDtsVXSWJwCSkpKizLlz54786le/kiFDhoidnZ3MnTtXKioq1AtaJQ/LVXl5uUyfPl2cnJzExsZGRo8eLS+//LLU1taqG7gKnn/+efHy8hJra2txdXWV0NBQycjIUMZZU//3oFyxpsxLIyLSm40iEREREf0wvMaNiIiIyEKwcSMiIiKyEGzciIiIiCwEGzciIiIiC8HGjYiIiMhCsHEjIiIishBs3IiIiIgsBBs3IiIiIgvBxo3oMZaVlQWNRtPhwdiWwNvbG3/+85/VDqNHrFu3DhMnTlQ7jA5CQkKwZs0atcMgokfAJycQmVllZSV+//vfIz09Hf/9738xdOhQTJw4EWvWrEFoaGiPrRMSEoKJEycaNTtNTU2oqamBXq+HRqPpsbV6w/fff4/BgwfDzs5O7VC6ra6uDo2NjXB2dgYArFixAjdu3MC+fft6Zf2srCzMnDkT169fh6Ojo3K8pqYGAwcOhFar7ZU4iKj7+JB5IjO6dOkSpk2bBkdHR2zatAkBAQFobm7GoUOHEB8fj/Pnz5t1fWtra7i5uZl1DXNxdXVVO4RH1tTUBGtr6w7H7e3tYW9v32vrmcrJyakHoyGiXqHuo1KJHm+RkZHi4eEhdXV1HcauX7+u/H758mV57rnnZPDgwaLVamXBggVGD5BPSkqSCRMmyPbt28XLy0scHBxk0aJFcvPmTRERiYmJ6fAQ8bKyMuXB9W1rpaSkiE6nk08//VTGjRsngwcPlvDwcLly5YqyVnNzs6xatUp0Op04OTnJK6+8IsuXL5eoqChlTktLi/zhD38Qb29vGTRokAQGBkpqaqoy3rbukSNHJCgoSGxtbcVgMMj58+eNcnDgwAGZPHmy2NjYiLOzs8yZM0cZ8/Lykj/96U9G+YqNjRUXFxfRarUyc+ZMKSgoUMYLCgokJCRE7O3tRavVyqRJkyQvL6/L7waA/PWvf5WIiAgZNGiQ+Pj4GH0GkXsPy16wYIHodDoZMmSIPPfcc1JWVqaMx8TESFRUlLz55pvi7u4u3t7ena7V9v21/d7+uzp69Gi31tu+fbsEBQWJvb296PV6WbJkiVRVVYmISFlZWYf1YmJiRERkxowZsnr1auX9a2pq5Gc/+5k4OjqKra2tREREyIULF5RxU+qHiMyL17gRmUlNTQ0+/fRTxMfHY/DgwR3G205Ztba2IioqCjU1NcjOzsbhw4fxzTffYNGiRUbzS0tLsW/fPqSlpSEtLQ3Z2dnYuHEjAGDz5s0wGAyIi4tDRUUFKioqMGLEiE7jun37Nt555x3s2LEDx44dQ3l5ORITE5Xxt956Czt37kRKSgpycnJw8+bNDqf0NmzYgO3bt+P999/HuXPnkJCQgGXLliE7O9to3htvvIE//vGPOHXqFAYMGIDnn39eGUtPT8fcuXMxa9YsfPnll8jMzMTUqVO7zOeCBQtw9epVHDx4EPn5+Zg0aRJCQ0NRU1MDAIiOjsbw4cORl5eH/Px8vPbaaxg4cGCX7wcAa9euxfz581FYWIjo6GgsXrwYRUVFAIDm5maEh4dDq9Xi+PHjyMnJgb29PSIiItDU1KS8R2ZmJoqLi3H48GGkpaU9cD0ASExMxMKFCxEREaF8Vz/5yU+6tV5zczPWr1+PwsJC7Nu3D5cuXcKKFSsAACNGjMBHH30EACguLkZFRQU2b97caWwrVqzAqVOncODAAZw4cQIiglmzZqG5uVmZ87D6ISIzU7tzJHpc5ebmCgD5+OOPHzgvIyNDrKyspLy8XDl27tw5ASAnT54UkXu7NHZ2dsoOm4jIyy+/LMHBwcrr9rsnItLpjhsAKSkpUeZs2bJF9Hq98lqv18umTZuU13fv3hVPT09lx62hoUHs7Ozk888/N1orNjZWlixZYrTukSNHlPH09HQBIHfu3BEREYPBINHR0V3m5f4dt+PHj4uDg4M0NDQYzRk1apT87W9/ExERrVYr27Zt6/L92gMgL7zwgtGx4OBgefHFF0VEZMeOHeLr6yutra3KeGNjo9ja2sqhQ4dE5N4OmF6vl8bGxgeudf+OW9t/d/8OZk+vl5eXJwDk1q1bItKxDtrcXzMXLlwQAJKTk6OMX7t2TWxtbeXDDz8UEdPqh4jMi9e4EZmJmHjfT1FREUaMGGG0Q+bv7w9HR0cUFRVhypQpAO7dZXn/ReTu7u64evXqI8dlZ2eHUaNGdfo+tbW1qKqqMtr5srKyQlBQEFpbWwEAJSUluH37Np555hmj921qasKTTz5pdCwwMNBoHQC4evUqPD09UVBQgLi4OJNiLiwsRF1dnXJxf5s7d+6gtLQUAPDSSy/hF7/4BXbs2IGwsDAsWLDA6HN2xmAwdHhdUFCgrFlSUtLhwv2GhgZlTQAICAjo1nVmbbqzXn5+PtatW4fCwkJcv35d+a7Ky8vh7+9v0vpFRUUYMGAAgoODlWPOzs7w9fVVdiGBB9cPEZkfGzciMxkzZgw0Gk2P3YDQ/rSfRqNR/kB3931MbTKBe3dIAvdOdXp4eBiN2djYdLlW212tbTHb2to+0pru7u7IysrqMNZ2ynndunVYunQp0tPTcfDgQSQlJWHPnj2YO3euyeu0XzMoKAg7d+7sMHb/jROdnQbvzfXq6+sRHh6O8PBw7Ny5E66urigvL0d4eLjRKdae0t36IaLu4TVuRGbi5OSE8PBwbNmyBfX19R3G2/5tNT8/P3z77bf49ttvlbGvv/4aN27cMHm3BLh3B2lLS0u3YtbpdNDr9cjLy1OOtbS04PTp08prf39/2NjYoLy8HKNHjzb66eq6us4EBgYiMzPTpLmTJk1CZWUlBgwY0GFNFxcXZd7YsWORkJCAjIwMzJs3DykpKQ983y+++KLDaz8/P2XNixcvYujQoR3W1Ol0Jn/OznT2Xf3Q9c6fP4/q6mps3LgRTz31FMaNG9dhB6xth+5B9eHn54e7d+8iNzdXOVZdXY3i4uJHqkMiMi82bkRmtGXLFrS0tGDq1Kn46KOPcPHiRRQVFeHdd99VTtOFhYUhICAA0dHROH36NE6ePInly5djxowZmDx5sslreXt7Izc3F5cuXcK1a9d+0G4cAKxatQobNmzA/v37UVxcjNWrV+P69evKjplWq0ViYiISEhLwwQcfoLS0FKdPn8Z7772HDz74wOR1kpKSsHv3biQlJaGoqAhnz57FW2+91encsLAwGAwGzJkzBxkZGbh06RI+//xzvPHGGzh16hTu3LmDlStXIisrC5cvX0ZOTg7y8vKUJqwrqamp+Mc//oELFy4gKSkJJ0+exMqVKwHcu9nBxcUFUVFROH78OMrKypCVlYVf//rX+O6770z+nJ3x9vbGmTNnUFxcjGvXrqG5ufkHr+fp6Qlra2u89957+Oabb3DgwAGsX7/eaI6Xlxc0Gg3S0tLw/fffK7um9xszZgyioqIQFxeHzz77DIWFhVi2bBk8PDwQFRXVrc9LRD2HjRuRGY0cORKnT5/GzJkz8Zvf/Abjx4/HM888g8zMTGzduhXAvVNN+/fvx5AhQzB9+nSEhYVh5MiR+Oc///lIayUmJsLKygr+/v7K6bIf4tVXX8WSJUuwfPlyGAwG2NvbIzw8HIMGDVLmrF+/HmvXrsWGDRvg5+eHiIgIpKenw8fHx+R1QkJCkJqaigMHDmDixIl4+umncfLkyU7najQafPLJJ5g+fTp+/vOfY+zYsVi8eDEuX74MvV4PKysrVFdXY/ny5Rg7diwWLlyIyMhIJCcnPzCG5ORk7NmzB4GBgdi+fTt2796t7C7Z2dnh2LFj8PT0xLx58+Dn54fY2Fg0NDTAwcHB5M/Zmbi4OPj6+mLy5MlwdXVFTk7OD17P1dUV27ZtQ2pqKvz9/bFx40a88847RnM8PDyQnJyM1157DXq9XmlO20tJSUFQUBB++tOfwmAwQETwySefPPTuXCLqPXxyAhE9UGtrK/z8/LBw4cIOOzmWTKPRYO/evZgzZ47aoRARmYw3JxCRkcuXLyMjIwMzZsxAY2Mj/vKXv6CsrAxLly5VOzQion6Pp0qJyMgTTzyBbdu2YcqUKZg2bRrOnj2LI0eOPPR6MSIiMj+eKiUiIiKyENxxIyIiIrIQbNyIiIiILAQbNyIiIiILwcaNiIiIyEKwcSMiIiKyEGzciIiIiCwEGzciIiIiC8HGjYiIiMhC/A+SJbatcZpWOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "contingencies_per_iteration = np.array([5, 10, 15, 20, 25, 30, 35])\n",
    "cont_case_infeasibility = np.array([4.20, 3.94, 3.13, 3.21, 3.16, 3.06, 3.17])\n",
    "feasibility = np.array([1.27, 0.92, 0.80, 0.85, 0.85, 0.87, 0.87])\n",
    "time = np.array([70, 122, 180, 242, 339, 457, 523])\n",
    "\n",
    "# Create figure and axes\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot cont_case_infeasibility on the left y-axis (blue)\n",
    "ax1.plot(contingencies_per_iteration, cont_case_infeasibility, color='tab:blue', marker='o', label='Cont case infeasibility')\n",
    "#ax1.plot(contingencies_per_iteration, feasibility, color='blue', linestyle='dashed', marker='o', label='+10% feasibility')\n",
    "ax1.set_xlabel('Contingencies per iteration')\n",
    "ax1.set_ylabel('Cont case infeasibility (%)', color='black')  # Set y-axis label color to black\n",
    "ax1.tick_params(axis='y', colors='black')  # Set tick color to black\n",
    "\n",
    "# Create a twin y-axis on the right\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot time on the right y-axis (orange)\n",
    "ax2.plot(contingencies_per_iteration, time, color='tab:orange', marker='s', label='Time')\n",
    "ax2.set_ylabel('Time (s)', color='black')  # Set y-axis label color to black\n",
    "ax2.tick_params(axis='y', colors='black')  # Set tick color to black\n",
    "\n",
    "# Set axis title colors\n",
    "#ax1.set_title('Contingencies per iteration', color='blue')\n",
    "ax1.yaxis.label.set_color('tab:blue')\n",
    "ax2.yaxis.label.set_color('tab:orange')  # Set right y-axis title color to black\n",
    "\n",
    "# Add legend\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "lines = lines1 + lines2\n",
    "labels = labels1 + labels2\n",
    "ax1.legend(lines, labels, loc='best')\n",
    "\n",
    "plt.savefig('Benchmark_CS_N3.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('Benchmark_CS_N3.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9200d4b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define the data for the heatmaps\n",
    "cont_case = np.array([[0.13, 1.15, 3.02],\n",
    "                      [0.20, 0.89, 2.11],\n",
    "                      [0.15, 1.13, 2.12],\n",
    "                      [0.56, 1.13, 3.61],\n",
    "                      [1.54, 2.04, 2.27]])\n",
    "                      #[1.49, 6.90, 4.46]])\n",
    "\n",
    "cost = np.array([[9.62, 5.76, 4.39],\n",
    "                 [8.41, 5.35, 5.56],\n",
    "                 [6.88, 5.57, 3.49],\n",
    "                 [3.25, 5.51, 2.33],\n",
    "                 [1.29, 1.18, 1.40]])\n",
    "                 #[1.38, -0.61, -1.92]])\n",
    "\n",
    "# Rotate the data arrays\n",
    "rotated_cont_case = np.rot90(cont_case)\n",
    "rotated_cost = np.rot90(cost)\n",
    "\n",
    "# Create figure and axes for the heatmaps\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "# Create the heatmaps\n",
    "im1 = ax1.imshow(rotated_cont_case, cmap='Greens',vmin=0, vmax=5)\n",
    "im2 = ax2.imshow(rotated_cost, cmap='Greens',vmin=-5, vmax=10)\n",
    "\n",
    "# Set the tick labels and axis labels\n",
    "yticks1 = ['N-3', 'N-2', 'N-1']\n",
    "yticks2 = ['N-3', 'N-2', 'N-1']\n",
    "xticks1 = [r'$10^{-1}$', r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$']#, r'$10^{4}$']\n",
    "xticks2 = [r'$10^{-1}$', r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$']#, r'$10^{4}$']\n",
    "\n",
    "ax1.set_xticks(np.arange(len(xticks1)))\n",
    "ax1.set_yticks(np.arange(len(yticks1)))\n",
    "ax2.set_xticks(np.arange(len(xticks2)))\n",
    "ax2.set_yticks(np.arange(len(yticks2)))\n",
    "\n",
    "ax1.set_xticklabels(xticks1, fontsize=12)  # Increase the fontsize here\n",
    "ax1.set_yticklabels(yticks1, fontsize=12)  # Increase the fontsize here\n",
    "ax2.set_xticklabels(xticks2, fontsize=12)  # Increase the fontsize here\n",
    "ax2.set_yticklabels(yticks2, fontsize=12)  # Increase the fontsize here\n",
    "\n",
    "ax1.set_xlabel('Cost Penalty', fontsize=14)  # Increase the fontsize here\n",
    "ax1.set_ylabel('Contingency Level', fontsize=14)  # Increase the fontsize here\n",
    "ax2.set_xlabel('Cost Penalty', fontsize=14)  # Increase the fontsize here\n",
    "ax2.set_ylabel('Contingency Level', fontsize=14)  # Increase the fontsize here\n",
    "\n",
    "# Rotate the xtick labels for better readability\n",
    "plt.setp(ax1.get_xticklabels(), rotation=45, ha=\"right\", fontsize=10)  # Increase the fontsize here\n",
    "plt.setp(ax2.get_xticklabels(), rotation=45, ha=\"right\", fontsize=10)  # Increase the fontsize here\n",
    "\n",
    "# Create a colorbar for each heatmap\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, pad=0.02, shrink=0.7)\n",
    "cbar2 = fig.colorbar(im2, ax=ax2, pad=0.02, shrink=0.7)\n",
    "\n",
    "# Set the colorbar labels\n",
    "cbar1.set_label('Percentage [%]')\n",
    "cbar2.set_label('Percentage [%]')\n",
    "\n",
    "# Set the title for each heatmap\n",
    "ax1.set_title('Infeasible Cases', fontsize=16)  # Increase the fontsize here\n",
    "ax2.set_title('Cost Increase wrt Benchmark', fontsize=16)  # Increase the fontsize here\n",
    "\n",
    "\n",
    "# Adjust the spacing between subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig('Sensitivity_correction.png', dpi=300)\n",
    "\n",
    "# Display the heatmaps\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a0845c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Define the data for the heatmaps\n",
    "cont_case = np.array([[0.22, 1.20, 2.50],\n",
    "                      [0.18, 1.50, 2.56],\n",
    "                      [0.22, 1.31, 2.47],\n",
    "                      [0.18, 1.75, 2.92],\n",
    "                      [4.80, 4.12, 4.62]])\n",
    "                      #[44.71, 14.26, 17.05]])\n",
    "\n",
    "cost = np.array([[7.26, 0.30, 1.07],\n",
    "                 [4.57, 2.99, 0.80],\n",
    "                 [3.91, 3.11, 1.27],\n",
    "                 [2.67, 2.14, -0.40],\n",
    "                 [0.86, -0.84, -2.52]])\n",
    "                 #[-4.69, -2.27, -4.75]])\n",
    "\n",
    "\n",
    "\n",
    "# Rotate the data arrays\n",
    "rotated_cont_case = np.rot90(cont_case)\n",
    "rotated_cost = np.rot90(cost)\n",
    "\n",
    "# Create figure and axes for the heatmaps\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,8))\n",
    "\n",
    "# Create the heatmaps\n",
    "im1 = ax1.imshow(rotated_cont_case, cmap='Greens',vmin=0, vmax=5)\n",
    "im2 = ax2.imshow(rotated_cost, cmap='Greens',vmin=-5, vmax=10)\n",
    "\n",
    "# Set the tick labels and axis labels\n",
    "yticks1 = ['N-3', 'N-2', 'N-1']\n",
    "yticks2 = ['N-3', 'N-2', 'N-1']\n",
    "xticks1 = [r'$10^{-1}$', r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$']#, r'$10^{4}$']\n",
    "xticks2 = [r'$10^{-1}$', r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$']#, r'$10^{4}$']\n",
    "\n",
    "ax1.set_xticks(np.arange(len(xticks1)))\n",
    "ax1.set_yticks(np.arange(len(yticks1)))\n",
    "ax2.set_xticks(np.arange(len(xticks2)))\n",
    "ax2.set_yticks(np.arange(len(yticks2)))\n",
    "\n",
    "ax1.set_xticklabels(xticks1, fontsize=12)  # Increase the fontsize here\n",
    "ax1.set_yticklabels(yticks1, fontsize=12)  # Increase the fontsize here\n",
    "ax2.set_xticklabels(xticks2, fontsize=12)  # Increase the fontsize here\n",
    "ax2.set_yticklabels(yticks2, fontsize=12)  # Increase the fontsize here\n",
    "\n",
    "ax1.set_xlabel('Cost Penalty', fontsize=14)  # Increase the fontsize here\n",
    "ax1.set_ylabel('Contingency Level', fontsize=14)  # Increase the fontsize here\n",
    "ax2.set_xlabel('Cost Penalty', fontsize=14)  # Increase the fontsize here\n",
    "ax2.set_ylabel('Contingency Level', fontsize=14)  # Increase the fontsize here\n",
    "\n",
    "# Rotate the xtick labels for better readability\n",
    "plt.setp(ax1.get_xticklabels(), rotation=45, ha=\"right\", fontsize=10)  # Increase the fontsize here\n",
    "plt.setp(ax2.get_xticklabels(), rotation=45, ha=\"right\", fontsize=10)  # Increase the fontsize here\n",
    "\n",
    "# Create a colorbar for each heatmap\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, pad=0.02, shrink=0.7)\n",
    "cbar2 = fig.colorbar(im2, ax=ax2, pad=0.02, shrink=0.7)\n",
    "\n",
    "# Set the colorbar labels\n",
    "cbar1.set_label('Percentage [%]')\n",
    "cbar2.set_label('Percentage [%]')\n",
    "\n",
    "# Set the title for each heatmap\n",
    "ax1.set_title('Infeasible Cases', fontsize=16)  # Increase the fontsize here\n",
    "ax2.set_title('Cost Increase wrt Benchmark', fontsize=16)  # Increase the fontsize here\n",
    "\n",
    "# Adjust the spacing between subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig('Sensitivity_lower.png', dpi=300)\n",
    "\n",
    "# Display the heatmaps\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "44a8ff5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# different test data sets give very similar results. Not surprising since there is only so much variability in load\n",
    "# and you already average over 1000 samples.\n",
    "import matplotlib.ticker as mticker\n",
    "colors = ['darkgreen', 'limegreen', 'green', 'black', 'lightgreen']\n",
    "\n",
    "lower_1 = np.array([[0.19, 1.34, 2.71], #0\n",
    "                    [0.26, 1.54, 2.72], #1\n",
    "                    [0.23, 1.30, 2.65], #5\n",
    "                    [0.29, 1.32, 2.77], #10\n",
    "                    [0.22, 1.83, 2.77], #50\n",
    "                    [1.52, 1.86, 3.53], #200\n",
    "                    [2.91, 3.45, 4.21], #500\n",
    "                    [5.32, 5.56, 5.20]]) #1000\n",
    "\n",
    "cost_lower_1 = np.array([[8.08, 4.13, 1.44], #0\n",
    "                         [6.21, 3.27, 0.78], #1\n",
    "                         [4.53, 3.90, 0.81], #5\n",
    "                         [3.88, 4.14, 0.80], #10\n",
    "                         [2.61, 1.94, 0.60], #50\n",
    "                         [1.69, 0.19, -1.03], #200\n",
    "                         [1.20, -0.37, -2.23], #500\n",
    "                         [0.70, -1.30, -2.84]]) #1000\n",
    "\n",
    "correction_1 = np.array([[0.14, 1.14, 2.06], #0 \n",
    "                         [0.19, 0.82, 2.26], #1\n",
    "                         [0.27, 1.45, 2.16], #5\n",
    "                         [0.21, 1.20, 1.99], #10\n",
    "                         [0.28, 1.22, 2.10], #50\n",
    "                         [0.31, 1.44, 2.08], #200\n",
    "                         [1.91, 1.33, 2.16], #500\n",
    "                         [1.61, 1.60, 2.83]]) #1000\n",
    "\n",
    "cost_correction_1 = np.array([[5.90, 5.67, 4.13], #0 \n",
    "                             [7.99, 5.18, 3.09], #1\n",
    "                             [7.82, 6.72, 4.91], #5\n",
    "                             [4.66, 7.31, 3.96], #10\n",
    "                             [5.44, 6.94, 1.10], #50\n",
    "                             [1.44, 1.67, 1.32], #200\n",
    "                             [1.55, 1.17, 0.55], #500\n",
    "                             [1.15, 1.01, -0.81]]) #1000\n",
    "\n",
    "scopf_n1 = 0.06\n",
    "scopf_n2 = 0.74\n",
    "scopf_n3 = 3.30\n",
    "scopf_cost = 0\n",
    "\n",
    "correction = (correction_1)/1\n",
    "\n",
    "lower = (lower_1)/1\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "# Plot data as bar plots\n",
    "x = np.arange(len(correction_1))\n",
    "width = 0.35\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].bar(x - width/2, correction_1[:, i], width, color=colors[1], label='Correction')\n",
    "    axs[i].bar(x + width/2, lower_1[:, i], width, color=colors[0], label='Lower')\n",
    "    axs[i].set_xlabel('Cost Penalty', fontsize='12')\n",
    "    axs[i].set_ylabel('Infeasible Cases [%]', fontsize='12')\n",
    "    axs[i].set_title(f'N - {i + 1}', fontsize='16')\n",
    "    axs[i].set_xticks(x)\n",
    "    axs[i].set_xticklabels(['0', '0.01', '0.05', '0.1', '0.5', '2', '5', '10'])\n",
    "\n",
    "    # Add horizontal line\n",
    "    if i == 0:\n",
    "        axs[i].axhline(y=scopf_n1, color='red', label='Benchmark') #, linestyle='--'\n",
    "    if i == 1:\n",
    "        axs[i].axhline(y=scopf_n2, color='red', label='Benchmark')\n",
    "    elif i == 2:\n",
    "        axs[i].axhline(y=scopf_n3, color='red', label='Benchmark')\n",
    "    \n",
    "    axs[i].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig('Infeasible_lower_vs_correction.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('Infeasible_lower_vs_correction.tex')\n",
    "\n",
    "cost_correction = (cost_correction_1)/1\n",
    "\n",
    "cost_lower = (cost_lower_1)/1\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "# Plot data as bar plots\n",
    "x = np.arange(len(correction_1))\n",
    "width = 0.35\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].bar(x - width/2, cost_correction_1[:, i], width, color=colors[1], label='Correction')\n",
    "    axs[i].bar(x + width/2, cost_lower_1[:, i], width, color=colors[0], label='Lower')\n",
    "    axs[i].set_xlabel('Cost Penalty', fontsize='12')\n",
    "    axs[i].set_ylabel('Cost Increase [%]', fontsize='12')\n",
    "    axs[i].set_title(f'N - {i + 1}', fontsize='16')\n",
    "    axs[i].set_xticks(x)\n",
    "    axs[i].set_xticklabels(['0', '0.01', '0.05', '0.1', '0.5', '2', '5', '10'])\n",
    "    axs[i].axhline(y=scopf_cost, color='red', label='Benchmark') #, linestyle='--'\n",
    "    axs[i].legend()\n",
    "\n",
    "    \n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig('Cost_lower_vs_correction.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('Cost_lower_vs_correction.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8068c514",
   "metadata": {},
   "outputs": [],
   "source": [
    "# different test data sets give very similar results. Not surprising since there is only so much variability in load\n",
    "# and you already average over 1000 samples.\n",
    "import matplotlib.ticker as mticker\n",
    "colors = ['darkgreen', 'limegreen', 'green', 'black', 'lightgreen']\n",
    "\n",
    "\n",
    "correction_1 = np.array([[0.0519,0,0], #50 4 neur\n",
    "                         [0.094,0,0], #200 4 neur\n",
    "                         [0.2289,0,0], #250 6 neur\n",
    "                         [0.59,0,0], #500 8 neur\n",
    "                         [1.09,0,0], #1000 4 neur\n",
    "                         [0,0,0], #\n",
    "                         [0,0,0], #\n",
    "                         [0,0,0]]) #\n",
    "\n",
    "cost_correction_1 = np.array([[8.37,0,0], #50 4 neur \n",
    "                             [6.89,0,0], #200 4 neur\n",
    "                             [6.43,0,0], #250 6 neur\n",
    "                             [6.38,0,0], #500 8 neur\n",
    "                             [6.00,0,0], #1000 4 neur\n",
    "                             [0,0,0], #\n",
    "                             [0,0,0], #\n",
    "                             [0,0,0]]) #\n",
    "\n",
    "correction_10 = np.array([[0,0,0], #50 6 neur\n",
    "                         [0,0,0], #200 6 neur\n",
    "                         [0,0,0], #250 6 neur\n",
    "                         [0.046,0,0], #500 6 neur\n",
    "                         [0.095,0,0], #1000 6 neur, gestopt na 76 epochs (redelijk geconvergeerd wel)\n",
    "                         [0,0,0], #\n",
    "                         [0,0,0], #\n",
    "                         [0,0,0]]) #\n",
    "\n",
    "cost_correction_10 = np.array([[0,0,0], #50 6 neur \n",
    "                             [0,0,0], #200 6 neur\n",
    "                             [0,0,0], #250 6 neur\n",
    "                             [7.34,0,0], #500 6 neur 1322.8\n",
    "                             [8.18,0,0], #1000 6 neur 1333.2\n",
    "                             [0,0,0], #\n",
    "                             [0,0,0], #\n",
    "                             [0,0,0]]) #\n",
    "\n",
    "\n",
    "\n",
    "scopf_n1 = 0.03\n",
    "scopf_n2 = 0.74\n",
    "scopf_n3 = 3.30\n",
    "scopf_cost = 0 #(1232.4)\n",
    "\n",
    "correction = (correction_1)/1\n",
    "\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "# Plot data as bar plots\n",
    "x = np.arange(len(correction_1))\n",
    "width = 0.35\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].bar(x - width/2, correction_1[:, i], width, color=colors[0], label='Correction1k')\n",
    "    axs[i].bar(x + width/2, correction_10[:, i], width, color=colors[1], label='Correction10k')\n",
    "    axs[i].set_xlabel('Cost Penalty', fontsize='12')\n",
    "    axs[i].set_ylabel('Infeasible Cases [%]', fontsize='12')\n",
    "    axs[i].set_title(f'N - {i + 1}', fontsize='16')\n",
    "    axs[i].set_xticks(x)\n",
    "    axs[i].set_xticklabels(['50', '200', '250', '500', '1000', '-', '-', '-'])\n",
    "\n",
    "    # Add horizontal line\n",
    "    if i == 0:\n",
    "        axs[i].axhline(y=scopf_n1, color='red', linestyle='--', label='Benchmark')\n",
    "    if i == 1:\n",
    "        axs[i].axhline(y=scopf_n2, color='red', linestyle='--', label='Benchmark')\n",
    "    elif i == 2:\n",
    "        axs[i].axhline(y=scopf_n3, color='red', linestyle='--', label='Benchmark')\n",
    "    \n",
    "    axs[i].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig('Infeasible_lower_vs_correction.png', dpi=300)\n",
    "\n",
    "cost_correction = (cost_correction_1)/1\n",
    "\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "# Plot data as bar plots\n",
    "x = np.arange(len(correction_1))\n",
    "width = 0.35\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].bar(x - width/2, cost_correction_1[:, i], width, color=colors[0], label='Correction1k')\n",
    "    axs[i].bar(x + width/2, cost_correction_10[:, i], width, color=colors[1], label='Correction10k')\n",
    "    axs[i].set_xlabel('Cost Penalty', fontsize='12')\n",
    "    axs[i].set_ylabel('Cost Increase [%]', fontsize='12')\n",
    "    axs[i].set_title(f'N - {i + 1}', fontsize='16')\n",
    "    axs[i].set_xticks(x)\n",
    "    axs[i].set_xticklabels(['50', '200', '250', '500', '1000', '-', '-', '-'])\n",
    "    axs[i].axhline(y=scopf_cost, color='red', linestyle='--', label='Benchmark')\n",
    "    axs[i].legend()\n",
    "\n",
    "    \n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig('Cost_lower_vs_correction.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "518888f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALL trained on same dataset\n",
    "# N-1 lower 1000,5,10\n",
    "Index | Low   | Gens  | Up   \n",
    "0     | 1.31  | 10.10 | 10.40\n",
    "1     | 5.27  | 7.25  | 7.25 \n",
    "2     | 6.42  | 6.52  | 6.52 \n",
    "3     | 5.05  | 5.07  | 5.08 \n",
    "4     | 6.71  | 6.82  | 6.87 \n",
    "5     | 2.68  | 2.68  | 5.80 \n",
    "6     | 2.40  | 2.40  | 5.64 \n",
    "7     | 4.80  | 4.80  | 8.65 \n",
    "8     | 10.99 | 10.99 | 11.00\n",
    "9     | 0.03  | 2.91  | 40.00\n",
    "\n",
    "#N-1 correction\n",
    "Index | Low  | Gens  | Up   \n",
    "0     | 0.00 | 6.90  | 10.40\n",
    "1     | 0.00 | 7.25  | 7.25 \n",
    "2     | 0.00 | 6.52  | 6.52 \n",
    "3     | 0.00 | 5.08  | 5.08 \n",
    "4     | 0.00 | 6.87  | 6.87 \n",
    "5     | 0.00 | 3.60  | 5.80 \n",
    "6     | 0.00 | 3.55  | 5.64 \n",
    "7     | 0.00 | 4.10  | 8.65 \n",
    "8     | 0.00 | 11.00 | 11.00\n",
    "9     | 0.00 | 3.64  | 40.00\n",
    "\n",
    "# N-2 lower 100,10\n",
    "Index | Low   | Gens  | Up   \n",
    "0     | 2.50  | 8.41  | 10.40\n",
    "1     | 1.20  | 7.25  | 7.25 \n",
    "2     | 5.64  | 6.52  | 6.52 \n",
    "3     | 4.84  | 4.90  | 5.08 \n",
    "4     | 4.17  | 5.32  | 6.87 \n",
    "5     | 5.35  | 5.35  | 5.80 \n",
    "6     | 1.80  | 1.80  | 5.64 \n",
    "7     | 6.45  | 6.45  | 8.65 \n",
    "8     | 10.88 | 10.88 | 11.00\n",
    "9     | 1.09  | 2.66  | 40.00\n",
    "\n",
    "# n-2 corr\n",
    "Index | Low  | Gens  | Up   \n",
    "0     | 0.00 | 6.26  | 10.40\n",
    "1     | 0.00 | 6.53  | 7.25 \n",
    "2     | 0.00 | 6.52  | 6.52 \n",
    "3     | 0.00 | 5.08  | 5.08 \n",
    "4     | 0.00 | 6.85  | 6.87 \n",
    "5     | 0.00 | 3.95  | 5.80 \n",
    "6     | 0.00 | 2.87  | 5.64 \n",
    "7     | 0.00 | 6.81  | 8.65 \n",
    "8     | 0.00 | 11.00 | 11.00\n",
    "9     | 0.00 | 3.47  | 40.00\n",
    "\n",
    "#n-3 lower 10, 10\n",
    "Index | Low   | Gens  | Up   \n",
    "0     | 4.13  | 8.29  | 10.40\n",
    "1     | 4.56  | 6.95  | 7.25 \n",
    "2     | 4.47  | 6.08  | 6.52 \n",
    "3     | 4.96  | 4.98  | 5.08 \n",
    "4     | 4.05  | 5.48  | 6.87 \n",
    "5     | 5.26  | 5.26  | 5.80 \n",
    "6     | 1.70  | 1.70  | 5.64 \n",
    "7     | 6.25  | 6.25  | 8.65 \n",
    "8     | 10.91 | 10.91 | 11.00\n",
    "9     | 2.11  | 3.66  | 40.00\n",
    "\n",
    "#n-3 correction\n",
    "Index | Low  | Gens  | Up   \n",
    "0     | 0.00 | 5.24  | 10.40\n",
    "1     | 0.00 | 5.20  | 7.25 \n",
    "2     | 0.00 | 6.52  | 6.52 \n",
    "3     | 0.00 | 5.08  | 5.08 \n",
    "4     | 0.00 | 5.18  | 6.87 \n",
    "5     | 0.00 | 5.04  | 5.80 \n",
    "6     | 0.00 | 4.99  | 5.64 \n",
    "7     | 0.00 | 5.23  | 8.65 \n",
    "8     | 0.00 | 11.00 | 11.00\n",
    "9     | 0.00 | 5.24  | 40.00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a5643de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# n-3 lower cost: 50\n",
    "table_data = np.array([[0.04, 8.58, 10.40],\n",
    "                 [7.01, 7.25, 7.25],\n",
    "                 [4.08, 6.52, 6.52],\n",
    "                 [5.05, 5.07, 5.08],\n",
    "                 [3.58, 4.99, 6.87],\n",
    "                 [5.36, 5.36, 5.80],\n",
    "                 [1.69, 1.69, 5.64],\n",
    "                 [6.28, 6.28, 8.65],\n",
    "                 [10.99, 10.99, 11.00],\n",
    "                 [1.64, 3.30, 40.00]])\n",
    "\n",
    "low_lower = table_data[:, 0]\n",
    "gens_lower = table_data[:, 1]\n",
    "up_lower = table_data[:, 2]\n",
    "\n",
    "# n-3 correction\n",
    "data = np.array([[0.00, 6.92, 10.40],\n",
    "                 [0.00, 7.25, 7.25],\n",
    "                 [0.00, 6.52, 6.52],\n",
    "                 [0.00, 5.08, 5.08],\n",
    "                 [0.00, 6.03, 6.87],\n",
    "                 [0.00, 4.27, 5.80],\n",
    "                 [0.00, 3.04, 5.64],\n",
    "                 [0.00, 6.62, 8.65],\n",
    "                 [0.00, 11.00, 11.00],\n",
    "                 [0.00, 3.31, 40.00]])\n",
    "\n",
    "low_correction = data[:, 0]\n",
    "gens_correction = data[:, 1]\n",
    "up_correction = data[:, 2]\n",
    "\n",
    "# Set the column widths\n",
    "col_widths = [max(len(str(row[i])) for row in table_data) for i in range(len(table_data[0]))]\n",
    "\n",
    "x = np.arange(10)\n",
    "\n",
    "# Create the figure and axes for the two plots\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Plot the range bars and values for n-3 lower cost\n",
    "axs[0].bar(x, up_lower - low_lower, bottom=low_lower, width=0.5, align='center', color='limegreen', label= 'Generator Limits')\n",
    "axs[0].plot(x, gens_lower, 'kx', markersize=12, label = 'Generator Dispatch')\n",
    "axs[0].set_xlim(-0.5, 9.5)\n",
    "axs[0].set_ylim(0, 15)\n",
    "axs[0].set_xlabel('Generator Index', fontsize='12')\n",
    "axs[0].set_ylabel('Generator Dispatch [p.u.]', fontsize='12')\n",
    "axs[0].grid(True, linestyle='--', alpha=0.5)\n",
    "axs[0].set_title('N-3 Generator Dispatch Lower')\n",
    "axs[0].set_xticks(x)\n",
    "axs[0].legend()\n",
    "\n",
    "# Plot the range bars and values for n-3 correction\n",
    "axs[1].bar(x, up_correction - low_correction, bottom=low_correction, width=0.5, align='center', color='limegreen', label= 'Generator Limits')\n",
    "axs[1].plot(x, gens_correction, 'kx', markersize=12, label = 'Generator Dispatch')\n",
    "axs[1].set_xlim(-0.5, 9.5)\n",
    "axs[1].set_ylim(0, 15)\n",
    "axs[1].set_xlabel('Generator Index', fontsize='12')\n",
    "axs[1].set_ylabel('Generator Dispatch [p.u.]', fontsize='12')\n",
    "axs[1].grid(True, linestyle='--', alpha=0.5)\n",
    "axs[1].set_title('N-3 Generator Dispatch Correction')\n",
    "axs[1].set_xticks(x)\n",
    "axs[1].legend()\n",
    "\n",
    "# Adjust the spacing between subplots\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "\n",
    "plt.savefig('Dispatch comparison n3.png', dpi=300)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8ace0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tikzplotlib\n",
    "\n",
    "# Define the data\n",
    "N1loss = [0.33, 5.10, 10.80, 13.26, 10.75]\n",
    "N2loss = [0.34, 1.37, 2.88, 4.05, 3.85]\n",
    "N3loss = [0.33, 1.08, 2.19, 3.11, 3.03]\n",
    "N4loss = [0.32, 1.11, 2.24, 3.14, 3.02]\n",
    "N5loss = [0.34, 1.10, 2.20, 3.13, 3.05]\n",
    "\n",
    "#N1loss_soft = [0, 2.84, 6.72, 8.90, 7.70]\n",
    "#N2loss_soft = [0.01, 0.28, 0.83, 1.50, 1.69]\n",
    "#N3loss_soft = [0.01, 0.31, 0.86, 1.47, 1.63]\n",
    "#N4loss_soft = [0.01, 0.27, 0.76, 1.31, 1.46]\n",
    "#N5loss_soft = [0.01, 0.31, 0.84, 1.44, 1.60]\n",
    "\n",
    "# Create a 2D array from the data\n",
    "data1 = np.array([N1loss, N2loss, N3loss, N4loss, N5loss])\n",
    "#data2 = np.array([N1loss_soft, N2loss_soft, N3loss_soft, N4loss_soft, N5loss_soft])\n",
    "\n",
    "data1 = np.rot90(data1)\n",
    "#data2 = np.rot90(data2)\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, (ax1) = plt.subplots(1, 1, figsize=(12, 6))\n",
    "\n",
    "# Create the first heatmap\n",
    "heatmap1 = ax1.imshow(data1, cmap='Greens', vmin=0, vmax=4)\n",
    "ax1.set_xticks(np.arange(len(N1loss)))\n",
    "ax1.set_yticks(np.arange(len(data1)))\n",
    "ax1.set_yticklabels(['5', '4', '3', '2', '1'], fontsize=14)\n",
    "ax1.set_xticklabels(['N-1', 'N-2', 'N-3', 'N-4', 'N-5'], fontsize=14)\n",
    "ax1.set_xlabel('Trained Model', fontsize=14)\n",
    "ax1.set_ylabel('k outages', fontsize=14)\n",
    "cbar1 = plt.colorbar(heatmap1, ax=ax1, pad=0.02, shrink=0.7)\n",
    "cbar1.set_label('Percentage Infeasible[%]', fontsize=12)\n",
    "\n",
    "# Customize color bar ticks and labels\n",
    "cbar1.set_ticks([0, 1, 2, 3, 4])  # Include ticks for 0, 1, 2, 3, 4, and 5\n",
    "cbar1.set_ticklabels(['0', '1', '2', '3', '4+'])\n",
    "\n",
    "#plt.setp(ax1.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "\n",
    "# Save the first plot\n",
    "plt.savefig('Infeasible_Cases_Plot.png', dpi=300)\n",
    "\n",
    "\n",
    "# Create the second heatmap\n",
    "#heatmap2 = ax2.imshow(data2, cmap='Greens', vmin=0, vmax=2)\n",
    "#ax2.set_xticks(np.arange(len(N1loss_soft)))\n",
    "#ax2.set_yticks(np.arange(len(data2)))\n",
    "#ax2.set_yticklabels(['N-5', 'N-4', 'N-3', 'N-2', 'N-1'])\n",
    "#ax2.set_xticklabels(['N1', 'N2', 'N3', 'N4', 'N5'])\n",
    "#ax2.set_xlabel('Model')\n",
    "#ax2.set_ylabel('Violations')\n",
    "#ax2.set_title('Soft Feasibility')\n",
    "#cbar2 = plt.colorbar(heatmap2, ax=ax2, pad=0.02, shrink=0.7)\n",
    "#cbar2.set_label('Percentage [%]')\n",
    "#plt.setp(ax2.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "\n",
    "# Save the second plot\n",
    "#plt.savefig('Soft_Feasibility_Plot.png', dpi=300)\n",
    "#plt.close()\n",
    "\n",
    "tikzplotlib.save('Infeasible_Cases_Plot.tex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e43c9700",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tikzplotlib\n",
    "\n",
    "# Define the data\n",
    "N1loss = [0.33, 5.10, 10.80, 13.26, 10.75]\n",
    "N2loss = [0.34, 1.37, 2.88, 4.05, 3.85]\n",
    "N3loss = [0.33, 1.08, 2.19, 3.11, 3.03]\n",
    "N4loss = [0.32, 1.11, 2.24, 3.14, 3.02]\n",
    "N5loss = [0.34, 1.10, 2.20, 3.13, 3.05]\n",
    "\n",
    "# Create a 2D array from the data\n",
    "data1 = np.array([N1loss, N2loss, N3loss, N4loss, N5loss])\n",
    "data1 = np.rot90(data1)\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, (ax1) = plt.subplots(1, 1, figsize=(12, 6))\n",
    "\n",
    "# Create the first heatmap\n",
    "heatmap1 = ax1.imshow(data1, cmap='Greens', vmin=0, vmax=4)\n",
    "ax1.set_xticks(np.arange(len(N1loss)))\n",
    "ax1.set_yticks(np.arange(len(data1)))\n",
    "ax1.set_yticklabels(['N-5', 'N-4', 'N-3', 'N-2', 'N-1'])\n",
    "ax1.set_xticklabels(['N1', 'N2', 'N3', 'N4', 'N5'])\n",
    "ax1.set_xlabel('Model', fontsize=14)\n",
    "ax1.set_ylabel('Violations', fontsize=14)\n",
    "cbar1 = plt.colorbar(heatmap1, ax=ax1, pad=0.02, shrink=0.7)\n",
    "cbar1.set_label('Percentage Infeasible[%]', fontsize=12)\n",
    "\n",
    "# Customize color bar ticks and labels\n",
    "cbar1.set_ticks([0, 1, 2, 3, 4])  # Include ticks for 0, 1, 2, 3, 4, and 5\n",
    "cbar1.set_ticklabels(['0', '1', '2', '3', '4+'])\n",
    "\n",
    "# Save the TikZ code to a .tex file\n",
    "tikzplotlib.save('Infeasible_Cases_Plot.tex')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86013fb5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# column 1 = scopf, column 2 = NN\n",
    "gens = np.array([\n",
    "       [0.74, 0.43, 40.  ],\n",
    "       [0.74, 0.77, 40.  ],\n",
    "       [0.96, 0.61, 40.  ],\n",
    "       [0.  , 0.06, 40.  ],\n",
    "       [1.61, 2.12, 20.  ],\n",
    "       [1.85, 1.84, 20.  ],\n",
    "       [0.35, 0.99, 40.  ],\n",
    "       [0.52, 0.85, 40.  ],\n",
    "       [0.19, 0.45, 40.  ],\n",
    "       [0.  , 0.1 , 40.  ],\n",
    "       [3.2 , 3.19, 20.  ],\n",
    "       [1.09, 0.07, 20.  ],\n",
    "       [0.  , 0.06, 40.  ],\n",
    "       [1.07, 1.06, 20.  ],\n",
    "       [0.02, 0.09, 40.  ],\n",
    "       [0.01, 0.06, 40.  ],\n",
    "       [0.01, 0.06, 40.  ],\n",
    "       [0.  , 0.06, 40.  ],\n",
    "       [0.  , 0.06, 40.  ],\n",
    "       [1.19, 1.18, 20.  ],\n",
    "       [3.04, 3.03, 20.  ],\n",
    "       [1.48, 1.47, 40.  ],\n",
    "       [0.05, 0.26, 40.  ],\n",
    "       [0.  , 0.08, 20.  ],\n",
    "       [2.55, 2.54, 20.  ],\n",
    "       [2.6 , 2.59, 40.  ],\n",
    "       [0.15, 0.33, 20.  ],\n",
    "       [2.69, 1.79, 20.  ],\n",
    "       [4.92, 4.9 , 40.  ],\n",
    "       [6.72, 2.52, 40.  ],\n",
    "       [0.  , 0.08, 40.  ],\n",
    "       [0.  , 0.1 , 40.  ],\n",
    "       [0.  , 0.64, 20.  ],\n",
    "       [0.  , 0.06, 20.  ],\n",
    "       [0.  , 0.07, 40.  ],\n",
    "       [0.  , 0.06, 20.  ],\n",
    "       [4.45, 5.75, 40.  ],\n",
    "       [0.  , 0.06, 40.  ],\n",
    "       [1.04, 1.03, 40.  ],\n",
    "       [5.66, 5.58, 40.  ],\n",
    "       [0.09, 0.12, 40.  ],\n",
    "       [0.  , 0.28, 40.  ],\n",
    "       [0.  , 0.08, 40.  ],\n",
    "       [0.  , 0.06, 20.  ],\n",
    "       [3.52, 3.51, 40.  ],\n",
    "       [1.4 , 1.39, 40.  ],\n",
    "       [0.  , 0.08, 40.  ],\n",
    "       [0.  , 0.1 , 20.  ],\n",
    "       [0.  , 0.1 , 20.  ],\n",
    "       [0.  , 0.23, 40.  ],\n",
    "       [1.36, 1.35, 40.  ],\n",
    "       [0.  , 0.39, 40.  ],\n",
    "       [1.  , 0.99, 40.  ],\n",
    "       [0.26, 0.71, 40.  ]])\n",
    "\n",
    "gencost =np.array([40., 40., 40., 40., 20., 20., 40., 40., 40., 40., 20., 20., 40., 20.,\n",
    "        40., 40., 40., 40., 40., 20., 20., 20., 40., 40., 20., 20., 40., 20.,\n",
    "        20., 20., 40., 40., 40., 40., 40., 40., 20., 40., 20., 20., 40., 40.,\n",
    "        40., 40., 20., 20., 40., 40., 40., 40., 20., 40., 40., 40.])\n",
    "\n",
    "difference = gens[:,0] - gens[:,1]\n",
    "\n",
    "print(difference)\n",
    "print(difference.sum())\n",
    "\n",
    "print('')\n",
    "\n",
    "cost_difference = difference * gencost\n",
    "print(cost_difference)\n",
    "print(cost_difference.sum())\n",
    "\n",
    "cost_scopf = gens[:,0] * gencost\n",
    "cost_NN = gens[:,1] * gencost\n",
    "\n",
    "print(cost_scopf.sum(), cost_NN.sum(), cost_NN.sum()/cost_scopf.sum())\n",
    "\n",
    "\n",
    "difcost = gens[:,2] - gencost\n",
    "print(difcost)\n",
    "\n",
    "difference[abs(difference)<0.3] = 0\n",
    "\n",
    "#print('')\n",
    "samen = np.array([difference,gencost]).T\n",
    "#print(samen)\n",
    "\n",
    "blabla = np.array([gens[:,0], gencost]).T\n",
    "print(blabla)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "510535ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_with_value_20 = np.where(blabla[:, 1] == 40)[0]\n",
    "\n",
    "# Create a 2D array with the index and the value of the first column where the value in the third column is 20\n",
    "result_array = np.column_stack((indices_with_value_20, gens[indices_with_value_20, 0]))\n",
    "\n",
    "print(result_array[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6609c0df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tikzplotlib\n",
    "\n",
    "colors = ['darkgreen', 'limegreen', 'green', 'black', 'lightgreen']\n",
    "\n",
    "# Data for 39-bus system\n",
    "contingency_cases_39 = [33, 528, 5456, 40920, 237336, 1107568]\n",
    "islanding_cases_39 = [2, 94, 1902, 22623, 178581, 999868]\n",
    "percentages_39 = [6.06, 17.80, 34.86, 55.29, 75.24, 90.28]\n",
    "\n",
    "# Data for 118-bus system\n",
    "contingency_cases_118 = [186, 17205, 1072445, 49332470]\n",
    "islanding_cases_118 = [9, 1705, 145792, 0, 0, 0]  # Use None for missing data\n",
    "percentages_118 = [4.84, 9.90, 13.60, 0, 0, 0]  # Use None for missing data\n",
    "\n",
    "# List of N values (N-1 to N-6)\n",
    "N_values = ['1', '2', '3', '4', '5', '6']\n",
    "\n",
    "# Width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create evenly spaced x-coordinates for each set of bars\n",
    "x_values_39 = np.arange(len(N_values))\n",
    "x_values_118 = x_values_39 + bar_width\n",
    "\n",
    "# Adjust x_values_118 to align with the bars\n",
    "x_values_118 = x_values_39 + bar_width + 0.05 \n",
    "\n",
    "\n",
    "\n",
    "# Plotting the bar graphs\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Plot for 39-bus system\n",
    "plt.bar(x_values_39, percentages_39, width=bar_width, color=colors[0], label='27-bus system')\n",
    "\n",
    "# Plot for 118-bus system\n",
    "plt.bar(x_values_118, percentages_118, width=bar_width, color=colors[1], label='118-bus system')\n",
    "\n",
    "plt.xlabel('k outages', fontsize=12)\n",
    "plt.ylabel('Percentage Islanding [%]', fontsize=12)\n",
    "plt.legend(fontsize=12, loc='upper left', ncol=1) \n",
    "plt.ylim(0, 100)\n",
    "plt.xticks(x_values_39 + bar_width / 2, N_values, fontsize=12)  # Center x-ticks between the bars\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Adding a fancy border\n",
    "ax = plt.gca()\n",
    "for spine in ['top', 'right', 'bottom', 'left']:\n",
    "    ax.spines[spine].set_linewidth(0.5)\n",
    "ax.yaxis.set_ticks_position('right')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.tick_params(bottom=True, top=True, left=True, right=True)\n",
    "ax.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig('islanding_levels.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('islanding_levels.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76b13cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# number of generators, infeasible cases, cost\n",
    "\"\"\"\n",
    "1: [4] weinig lijnen, veel vermogen\n",
    "1: [36] veel lijnen, veel vermogen\n",
    "5: [4,32,38,50,51] weinig lijnen, weinig vermogen\n",
    "5: [11,27,28,29,44] veel lijnen, veel vermogen\n",
    "10: [0,1,2,4,7,32,38,50,51,53] weinig lijnen, weinig vermogen\n",
    "10: [6,10,14,20,21,23,24,30,37,47] veel lijnen, weining vermogen\n",
    "15: [0, 1, 2, 3, 7, 8, 9, 12, 15, 16, 17, 18, 22, 26, 31]\n",
    "\n",
    "Er is wel degelijk een verschil in cost. Je stopt alleen zoveel data in de nodal balance equations die vast is, dat er in\n",
    "de optimalisatie weinig ruimte is voor verandering. Er zijn hele kleine verschillen die worden gemaakt, omdat je tollerantie\n",
    "voor wat infeasible is minimaal is. Kijk of je betere resultaten krijgt als input layer kleiner maakt.\n",
    "\n",
    "Misschien is het niet heel raar want de marges zijn super klein, en je zet de vergelijking redelijk vast met de loads en \n",
    "Pg_fixed die je input. Soms met bepaalde combinaties Pg_variable maak je een nodal balance equation vrij waar speling ontstaat.\n",
    "Vergeet niet ook de lambda term die hard wordt gepenalized. De reden dat Clil helemaal fout is, maar Pg alsnog redelijk, \n",
    "is omdat je de optimalisatie helemaal vast zet met de constraints, en alle fixed variables. Als Clil helemaal fout is, \n",
    "leert dat ding dus gewoon helemaal niks. Test de 4 datasets, kijk wat er met clil gebeurt, als die helemaal fout is gaat die\n",
    "dus niet goed. Probeer morgen ook input layer kleiner te maken.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# Test data (reduced_output)\n",
    "reduced_output = np.array([[1, 0.1176, 1232, 25],\n",
    "                           [1, 0.1176, 1232, 25],\n",
    "                           [5, 1.8509, 1236, 325],\n",
    "                           [5, 34.86, 1233, 100], # clil keeps on decreasing until 0.04\n",
    "                           [10, 1.2315, 1244, 255],\n",
    "                           [10, 0, 0, 0]])\n",
    "\n",
    "# Extract data for the two bar plots\n",
    "first_column = reduced_output[:, 0]\n",
    "second_column = reduced_output[:, 1]\n",
    "third_column = reduced_output[:, 2]\n",
    "\n",
    "# Pair the rows together\n",
    "paired_first_column = np.array([first_column[::2], first_column[1::2]])\n",
    "paired_second_column = np.array([second_column[::2], second_column[1::2]])\n",
    "paired_third_column = np.array([third_column[::2], third_column[1::2]])\n",
    "\n",
    "# Create the figure and two subplots side by side\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Plot the first column against the second column\n",
    "x = np.arange(len(paired_first_column[0]))\n",
    "width = 0.35\n",
    "axs[0].bar(x - width/2, paired_second_column[0], width, color='darkgreen', label='First Pair')\n",
    "axs[0].bar(x + width/2, paired_second_column[1], width, color='limegreen', label='Second Pair')\n",
    "axs[0].set_xlabel('First Column', fontsize=12)\n",
    "axs[0].set_ylabel('Second Column', fontsize=12)\n",
    "axs[0].set_title('First Column vs. Second Column', fontsize=14)\n",
    "axs[0].set_xticks(x)\n",
    "axs[0].set_xticklabels(paired_first_column[0])\n",
    "axs[0].tick_params(axis='both', which='major', labelsize=10)\n",
    "axs[0].legend(fontsize=10)\n",
    "\n",
    "# Add a red striped line for baseline value\n",
    "baseline = np.array([[0.0852]])\n",
    "for value in baseline[0]:\n",
    "    axs[0].axhline(y=value, color='red', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Plot the first column against the third column\n",
    "x = np.arange(len(paired_first_column[0]))\n",
    "width = 0.35\n",
    "axs[1].bar(x - width/2, paired_third_column[0], width, color='darkgreen', label='First Pair')\n",
    "axs[1].bar(x + width/2, paired_third_column[1], width, color='limegreen', label='Second Pair')\n",
    "axs[1].set_xlabel('First Column', fontsize=12)\n",
    "axs[1].set_ylabel('Third Column', fontsize=12)\n",
    "axs[1].set_title('First Column vs. Third Column', fontsize=14)\n",
    "axs[1].set_xticks(x)\n",
    "axs[1].set_xticklabels(paired_first_column[0])\n",
    "axs[1].tick_params(axis='both', which='major', labelsize=10)\n",
    "axs[1].legend(fontsize=10)\n",
    "\n",
    "# Add a red striped line for baseline value\n",
    "baseline = np.array([[1232]])\n",
    "for value in baseline[0]:\n",
    "    axs[1].axhline(y=value, color='red', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d2f3505",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the Excel sheet into a DataFrame\n",
    "df = pd.read_excel('IEEE118.xlsx', sheet_name='line')  # Replace 'your_excel_file.xlsx' with the actual file path\n",
    "\n",
    "# Create a new DataFrame to store the count of lines connected to each bus\n",
    "bus_counts = pd.DataFrame(columns=['bus', 'count'])\n",
    "\n",
    "# Iterate over each row in the DataFrame and count the lines for each bus\n",
    "for index, row in df.iterrows():\n",
    "    from_bus = row['from_bus']\n",
    "    to_bus = row['to_bus']\n",
    "\n",
    "    # Count lines connected to 'from bus'\n",
    "    if from_bus not in bus_counts['bus'].values:\n",
    "        bus_counts = bus_counts.append({'bus': from_bus, 'count': 1}, ignore_index=True)\n",
    "    else:\n",
    "        bus_counts.loc[bus_counts['bus'] == from_bus, 'count'] += 1\n",
    "\n",
    "    # Count lines connected to 'to bus'\n",
    "    if to_bus not in bus_counts['bus'].values:\n",
    "        bus_counts = bus_counts.append({'bus': to_bus, 'count': 1}, ignore_index=True)\n",
    "    else:\n",
    "        bus_counts.loc[bus_counts['bus'] == to_bus, 'count'] += 1\n",
    "\n",
    "print(bus_counts.loc[29, 'count'])\n",
    "\n",
    "# Count the frequency of buses connected to a specific number of lines\n",
    "count_frequency = bus_counts['count'].value_counts()\n",
    "\n",
    "# Plotting the bar graph\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(count_frequency.index, count_frequency.values, color='darkblue', alpha=0.7, edgecolor='black')\n",
    "\n",
    "plt.xlabel('Number of Lines Connected to Buses', fontsize=12)\n",
    "plt.ylabel('Number of Buses', fontsize=12)\n",
    "plt.title('Number of Buses Connected to a Specific Number of Lines', fontsize=16, fontweight='bold')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Adding a fancy border\n",
    "ax = plt.gca()\n",
    "for spine in ['top', 'right', 'bottom', 'left']:\n",
    "    ax.spines[spine].set_linewidth(0.5)\n",
    "ax.yaxis.set_ticks_position('right')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.tick_params(bottom=True, top=True, left=True, right=True)\n",
    "ax.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# Create and print the table\n",
    "table_data = bus_counts.groupby('count')['bus'].apply(list).reset_index(name='buses')\n",
    "print(table_data)\n",
    "\n",
    "#plt.savefig('buses_connected_to_lines.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1170c8be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the 'gen' sheet from the Excel file\n",
    "gen_df = pd.read_excel('IEEE118.xlsx', sheet_name='gen')  # Replace 'your_excel_file.xlsx' with the actual file path\n",
    "\n",
    "# Count the number of generators connected to each bus\n",
    "bus_generator_counts = gen_df['bus'].value_counts().reset_index()\n",
    "bus_generator_counts.columns = ['bus', 'generator_count']\n",
    "\n",
    "# Filter the 'bus_counts' DataFrame to include only buses that have generators connected\n",
    "bus_counts_with_generators = bus_counts[bus_counts['bus'].isin(bus_generator_counts['bus'])]\n",
    "\n",
    "# Merge with 'gen' DataFrame to get generator indices\n",
    "bus_counts_with_generators = bus_counts_with_generators.merge(gen_df[['bus', 'index']], on='bus')\n",
    "\n",
    "# Count the frequency of buses connected to a specific number of lines\n",
    "count_frequency = bus_counts_with_generators['count'].value_counts()\n",
    "\n",
    "# Plotting the bar graph\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(count_frequency.index, count_frequency.values, color='darkblue', alpha=0.7, edgecolor='black')\n",
    "\n",
    "plt.xlabel('Number of Lines Connected to Buses with Generators', fontsize=12)\n",
    "plt.ylabel('Number of Buses', fontsize=12)\n",
    "plt.title('Number of Buses Connected to a Specific Number of Lines (with Generators)', fontsize=16, fontweight='bold')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Adding a fancy border\n",
    "ax = plt.gca()\n",
    "for spine in ['top', 'right', 'bottom', 'left']:\n",
    "    ax.spines[spine].set_linewidth(0.5)\n",
    "ax.yaxis.set_ticks_position('right')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.tick_params(bottom=True, top=True, left=True, right=True)\n",
    "ax.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# Create and print the table for buses with generators\n",
    "table_data = bus_counts_with_generators.groupby('count')['index'].apply(list).reset_index(name='generator_indices')\n",
    "print(table_data)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d564026e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the 'gen' sheet from the Excel file\n",
    "gen_df = pd.read_excel('IEEE118.xlsx', sheet_name='gen')  # Replace 'your_excel_file.xlsx' with the actual file path\n",
    "\n",
    "# Count the number of generators connected to each bus\n",
    "bus_generator_counts = gen_df['bus'].value_counts().reset_index()\n",
    "bus_generator_counts.columns = ['bus', 'generator_count']\n",
    "\n",
    "# Filter the 'bus_counts' DataFrame to include only buses that have generators connected\n",
    "bus_counts_with_generators = bus_counts[bus_counts['bus'].isin(bus_generator_counts['bus'])]\n",
    "\n",
    "# Merge with 'gen' DataFrame to get generator indices\n",
    "bus_counts_with_generators = bus_counts_with_generators.merge(gen_df[['bus', 'index', 'min_p']], on='bus')\n",
    "\n",
    "# Create two groups based on the number of lines connected to generators\n",
    "few_lines_generators = bus_counts_with_generators[bus_counts_with_generators['count'] <= 4]\n",
    "many_lines_generators = bus_counts_with_generators[bus_counts_with_generators['count'] > 4]\n",
    "\n",
    "# Split the two groups further based on 'min_p' values\n",
    "few_lines_low_output = few_lines_generators[few_lines_generators['min_p'] >= -100]\n",
    "few_lines_high_output = few_lines_generators[few_lines_generators['min_p'] < -100]\n",
    "\n",
    "many_lines_low_output = many_lines_generators[many_lines_generators['min_p'] >= -100]\n",
    "many_lines_high_output = many_lines_generators[many_lines_generators['min_p'] < -100]\n",
    "\n",
    "# Print the indices of generators in each group\n",
    "print(\"Generators with 4 lines or less and low output (min_p >= -100):\")\n",
    "print(few_lines_low_output['index'].tolist())\n",
    "print()\n",
    "\n",
    "print(\"Generators with 4 lines or less and high output (min_p < -100):\")\n",
    "print(few_lines_high_output['index'].tolist())\n",
    "print()\n",
    "\n",
    "print(\"Generators with more than 4 lines and low output (min_p >= -100):\")\n",
    "print(many_lines_low_output['index'].tolist())\n",
    "print()\n",
    "\n",
    "print(\"Generators with more than 4 lines and high output (min_p < -100):\")\n",
    "print(many_lines_high_output['index'].tolist())\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a028535e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "few lines, low output: [0,7,15,22,31,40,46,51] \n",
    "few lines, high output: [4, 10, 11, 13, 19, 25, 27, 38] \n",
    "many lines, low output: [6, 14, 23, 30, 35, 37, 42, 47]\n",
    "many lines, high output: [5, 20, 21, 24, 28, 29, 36, 39], 56 epochs, clil = 0.04, correction layer saves it\n",
    "\n",
    "test veel generatoren van low output and 2 van high output om te laten zien dat het gaat om output gens\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "reduced_output = np.array([[1.77, 1232], #clil 0.433\n",
    "                           [3.93, 1232], #clil 0.126 (diverged downwards)\n",
    "                           [0.54, 1233], # clil 0.126 (output also actually low)\n",
    "                           [2.67, 1233]]) # clil 0.006 (diverged quickly)\n",
    "                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e5bb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Area 3: buses = [81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 99, 100, 101, 102, 103, 104, 105, 105, 106, 107, 108, 109, 110, 111]\n",
    "\n",
    "variable_gens = [37,38,39,40,41,42,44,45,46,47,48,49,50,51] \n",
    "input_loads = [65,66,67,68,69,70,71,72,73,74,75,76,77,81,82,83,84,85,86,87,88,89,90,91,92,107, 108]\n",
    "\n",
    "\n",
    "Area 1: buses = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 112, 113, 114, 116]\n",
    "\n",
    "variable_gens = [0,1,2,3,4,5,6,7,8,10,11,12,13,14,52,53]\n",
    "input_loads = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27, 28, 29, 30,31, 93,94,95,97,99,100,101]\n",
    "\n",
    "\n",
    "Area 2: buses = [23, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 95, 96, 97, 115, 117]\n",
    "\n",
    "variable_gens = [9, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 43]\n",
    "input_loads = [23, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 78, 79, 80, 96, 98, 102, 103, 104, 105, 106]\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "data = np.array([[14, 1.53, 0.89], #1245 Area 3; 150 epochs, 0.001 Adam (F = 1000, c = 1) (converged)\n",
    "                [16, 0.79, 1.30], # 1248 Area 1; 300 epochs 0.05 (F = 500, c = 5)\n",
    "                [24, 1.17, 3.65], # 1277 Area 2; 200 epochs 0.05, 0.01 (F = 200, c = 5)\n",
    "                [30, 1.42, 2.92], #1268 Area 1 + Area 3; 300 epochs 0.05 (F = 1000, c = 1)\n",
    "                [38, 0.68, 6.49], #1312 Area 2 + Area 3; 300 epochs 0.05 (F = 200, c = 1)\n",
    "                [40, 0.18, 6.33], # 1310 Area 1 + Area 2; (F = 500, c = 1)\n",
    "                [54, 0.11, 7.31]]) # 1322 Full network \n",
    "\n",
    "# Extracting columns from the data\n",
    "x = data[:, 0]\n",
    "y1 = data[:, 1]\n",
    "y2 = data[:, 2]\n",
    "\n",
    "# Additional labels for each bar\n",
    "labels = ['Area 3', 'Area 1', 'Area 2', 'Area 1 + 3', 'Area 2 + 3', 'Area 1 + 2', 'All gens']\n",
    "\n",
    "# Define colors for each row\n",
    "#colors = ['purple', 'orange', 'blue', 'red', 'green', 'brown', 'teal']\n",
    "\n",
    "# Create two separate plots side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "\n",
    "# Plot for the second column of data (ax1)\n",
    "rects1 = ax1.bar(x, y1, color='green', label = labels)\n",
    "ax1.set_xlabel('Number of generators', fontsize = 12)\n",
    "ax1.set_ylabel('Infeasible cases [%]', fontsize = 12)\n",
    "ax1.set_ylim(0,2)\n",
    "ax1.set_xlim(0,60)\n",
    "\n",
    "# Plot for the third column of data (ax2)\n",
    "rects2 = ax2.bar(x, y2, color='limegreen', label = labels)\n",
    "ax2.set_xlabel('Number of generators', fontsize = 12)\n",
    "ax2.set_ylabel('Cost increase wrt benchmark [%]', fontsize = 12)\n",
    "ax2.set_ylim(0,7.5)\n",
    "ax2.set_xlim(0,60)\n",
    "\n",
    "# Additional labels at the top of each bar at an angle\n",
    "#for rect, label in zip(rects1, labels):\n",
    "#    height = rect.get_height()\n",
    "#    ax1.text(rect.get_x() + rect.get_width() / 2, height+0.02, label,\n",
    "#             ha='left', va='bottom', rotation=45)\n",
    "\n",
    "#for rect, label in zip(rects2, labels):\n",
    "#    height = rect.get_height()\n",
    "#    ax2.text(rect.get_x() + rect.get_width() / 2, height+0.02, label,\n",
    "#             ha='center', va='bottom', rotation=45)\n",
    "\n",
    "# Remove top and right spines and x-axis grid from both subplots\n",
    "#for ax in [ax1, ax2]:\n",
    "#    ax.spines['top'].set_visible(False)\n",
    "#    ax.spines['right'].set_visible(False)\n",
    "#    ax.xaxis.grid(False)\n",
    "\n",
    "#ax1.legend()\n",
    "#ax2.legend()\n",
    "\n",
    "# Adjust the layout and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4784875a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([[14, 1.65, 0.97], #1244 Area 3 100 epochs, lr 0.05, F = 500, c = 1\n",
    "                [16, 1.39, 1.62], #1252 Area 1\n",
    "                 [24,1.17, 3.65]]) #1277 Area 2\n",
    "                #[24,0.10, 0]]) #1314 Area 2\n",
    "    \n",
    "data = np.array([[14, 1.85, 0.57], #1239 Area 3 100 epochs, lr 0.05, F = 500, c = 5\n",
    "                [16, 1.04, 0.97], #1244 Area 1\n",
    "                 [24, 0.60, 3.73]]) # 1278 Area 2\n",
    "        \n",
    "full_results = np.array([1.16, 6.98]) #1318\n",
    "\n",
    "# Extracting columns from the data\n",
    "x = (data[:, 0]).astype(int)\n",
    "y1 = data[:, 1]\n",
    "y2 = data[:, 2]\n",
    "\n",
    "# Additional labels for each bar\n",
    "labels = ['Area 3', 'Area 1', 'Area 2']\n",
    "\n",
    "# Define colors for each row\n",
    "#colors = ['purple', 'orange', 'blue', 'red', 'green', 'brown', 'teal']\n",
    "\n",
    "# Create two separate plots side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "\n",
    "# Plot for the second column of data (ax1)\n",
    "rects1 = ax1.bar(x, y1, color='green', label = '_nolabel_')\n",
    "ax1.set_xlabel('Number of generators', fontsize = 12)\n",
    "ax1.set_ylabel('Infeasible cases [%]', fontsize = 12)\n",
    "ax1.set_ylim(0,2.5)\n",
    "#ax1.set_xlim(0,60)\n",
    "ax1.set_xticks(x)  # Set x-ticks to match the existing bars\n",
    "ax1.set_xticklabels(x)  # Use the labels from your data\n",
    "\n",
    "# Adding the red striped line for the first plot\n",
    "ax1.axhline(full_results[0], color='red', linestyle='dashed', label = 'full 118-bus')\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "# Plot for the third column of data (ax2)\n",
    "rects2 = ax2.bar(x, y2, color='limegreen', label = '_nolabel_')\n",
    "ax2.set_xlabel('Number of generators', fontsize = 12)\n",
    "ax2.set_ylabel('Cost increase wrt benchmark [%]', fontsize = 12)\n",
    "ax2.set_ylim(0,7.5)\n",
    "#ax2.set_xlim(0,60)\n",
    "ax2.set_xticks(x)  # Set x-ticks to match the existing bars\n",
    "ax2.set_xticklabels(x)  # Use the labels from your data\n",
    "\n",
    "# Adding the red striped line for the second plot\n",
    "ax2.axhline(full_results[1], color='red', linestyle='dashed', label = 'full 118-bus')\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "\n",
    "# Additional labels at the top of each bar at an angle\n",
    "for rect, label in zip(rects1, labels):\n",
    "    height = rect.get_height()\n",
    "    ax1.text(rect.get_x() + rect.get_width() / 2, height+0.02, label,\n",
    "             ha='center', va='bottom', rotation=0)\n",
    "\n",
    "for rect, label in zip(rects2, labels):\n",
    "    height = rect.get_height()\n",
    "    ax2.text(rect.get_x() + rect.get_width() / 2, height+0.02, label,\n",
    "             ha='center', va='bottom', rotation=0)\n",
    "\n",
    "# Adjust the layout and display the plots\n",
    "plt.tight_layout()\n",
    "plt.savefig('118bus_area_results.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec2f3a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tikzplotlib\n",
    "def tikzplotlib_fix_ncols(obj):\n",
    "    \"\"\"\n",
    "    workaround for matplotlib 3.6 renamed legend's _ncol to _ncols, which breaks tikzplotlib\n",
    "    \"\"\"\n",
    "    if hasattr(obj, \"_ncols\"):\n",
    "        obj._ncol = obj._ncols\n",
    "    for child in obj.get_children():\n",
    "        tikzplotlib_fix_ncols(child)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eead984d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tikzplotlib\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.06, 0.74, 3.30, 3.70, 3.51]\n",
    "cont_case_infeasibility_nn = [0.31, 1.33, 2.16, 3.16, 3.11]\n",
    "speed_scopf = [60, 248, 289, 302, 388]\n",
    "speed_nn = [5, 5, 7, 20, 101]\n",
    "cost_scopf = [2041, 2099, 2144, 2210, 2209]\n",
    "cost_nn = [2068, 2123, 2155, 2198, 2154]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[0].set_xticklabels(labels)\n",
    "axs[0].tick_params(axis='x', labelsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Time [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[1].set_xticklabels(labels)\n",
    "axs[1].tick_params(axis='x', labelsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[2].set_xticklabels(labels)\n",
    "axs[2].tick_params(axis='x', labelsize=14)\n",
    "axs[2].set_ylim(0, 2600)\n",
    "axs[2].legend()\n",
    "\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('39bus_contscreen_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('39bus_contscreen_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2473dfd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.01, 0.60, 2.87, 3.40, 3.39]\n",
    "cont_case_infeasibility_nn = [0.25, 1.19, 2.00, 3.00, 3.00]\n",
    "speed_scopf = [60, 103, 145, 149, 163]\n",
    "speed_nn = [4, 5, 7, 20, 97]\n",
    "cost_scopf = [2039, 2097, 2153, 2212, 2208]\n",
    "cost_nn = [2067, 2123, 2154, 2178, 2154]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[0].set_xticklabels(labels)\n",
    "axs[0].tick_params(axis='x', labelsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Time [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[1].set_xticklabels(labels)\n",
    "axs[1].tick_params(axis='x', labelsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[2].set_xticklabels(labels)\n",
    "axs[2].tick_params(axis='x', labelsize=14)\n",
    "axs[2].set_ylim(0, 2600)\n",
    "axs[2].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('39bus_heuristic_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('39bus_heuristic_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27b7de00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vXurZs6ckqVatWvrwww/1/fffS7p2t+2cOXM0ceJE9erVS5L0wQcfyNvbWytXrlS/fv104MABrV27Vjt27FDr1q0lSfPnz1ePHj00c+ZM+fn5WTxvAED5VaTGbYUKFXT48OF8Rfev9u3bp127dnGXLQCgVOvevbsWLlyoF198Uc7Ozlq7dm2+2vbTTz8pICDAShkCpYthuMHaKRSJ8Z3883IC+GedOnWSwWDQ8ePHJUn/+te/ZDD8+e/AsWPH9Mcff6hz586mdWfOnFFKSorZuo0bN952Lvfcc4/efvtt/fTTT6pbt652796tb7/9VrNmzTLlkpKSopCQENM27u7uatu2rRITE9WvXz8lJibKw8PD1LSVpJCQENnZ2Wn79u16+OGHbztPAACuK1LjdsiQIXrllVdu2bj94osvFBUVReMWAFCqRUZG6ssvvzR9qPP19dXkyZNN42lpafruu+8UERFhrRQBALBZ0dHRkqSPP/5Yixcv1vjx4+Xi4iJJSkxM1ObNmzVy5EgFBwebtlm3bp1mz56tV155xaK5vPTSS8rMzFT9+vVlb2+v3NxcTZ061XS3b0pKiiTJ29vbbDtvb2/TWEpKiry8vMzGHRwc5OnpaYr5u6ysLGVlZZmWMzMzLXZOAICyrUiN2xs9CfTvcnNzZWdXpCl0AQCwGT4+Ptq/f782bNggSQoODpabm5tp/Ndff9Xrr7+u0NBQa6UIAIDNuu+++yRdq5eLFy/WhQsX1K1bN0nShx9+KEkaOnSoqlevbtpm27Ztql69umlbS/n444+1bNkyLV++XI0aNVJycrJGjx4tPz+/fHPvWlJsbKxiYmKKbf8AgLKrSI3bgvjhhx/k6elZXLsHAKDEuLi46IEHHrjhWMOGDdWwYcMSzggAgNKla9euqlq1qoYPH66tW7fqwoULWrFihUJDQ82attK1aREaN25s8RzGjh2rl156Sf369ZN0bT7dEydOKDY2VmFhYfLx8ZEkpaamytfX17RdamqqmjdvLunaH3TT0tLM9nv16lWdP3/etP3fRUZGasyYMablzMxM+fv7W/LUAABlVIEbt3+dX0iS4uLitGnTpnxxubm5+uWXX3T8+HE99thjt50gAAAAAKB0c3V1VVxcnAYOHKiFCxdKklq3bq133nnHLO7gwYPasGGDli5davEcLl++nO9bofb29srLy5MkBQYGysfHRxs2bDA1ajMzM7V9+3aNHDlSkhQUFKT09HQlJSWpVatWkq41mvPy8tS2bdsbHtfJyUlOTk4WPx8AQNlX4MbtX5u01yeXvz7B/F/Z2dnJ09NTjz76qObMmWOBFAEAKDn9+/dX37599cgjj1hlewAAyqoePXrozJkz2rNnj6pUqaI777wzX0zFihW1YcMGtWnTxuLHf/DBBzV16lQFBASoUaNG+uGHHzRr1iwNHTpU0rXPuaNHj9aUKVN01113KTAwUJMmTZKfn5969+4tSWrQoIG6deum4cOHa/HixcrJyVFERIT69esnPz8/i+cMACjfCty4vf5XSOlaczY6OlpRUVHFkhQAANayYsUK1a9fv8iN19vdHgCAsszJyUl33333TccDAgIUEBBQLMeeP3++Jk2apGeeeUZpaWny8/PTU089Zfa5dty4cbp06ZJGjBih9PR03XvvvVq7dq2cnZ1NMcuWLVNERIS6dOkiOzs79enTR/PmzSuWnAEA5VuR5rj95ptvVKtWLQunAgCAbUhOTtYHH3xg7TQAAIAFubq6as6cObf8ZqjBYNDkyZM1efLkm8Z4enpq+fLlxZAhAADmitS4tfTTPQEAsCUrV67UF198UejtjEZjMWQDAEDptW7dOt15552qXbu2VbYHAKA0K1Dj9vpdRw8//LBcXV0LdRfSoEGDipYZAABWYImHoVx/oAkAAOVd9+7d9corrxR5mr3b3R4AgNKsQI3bwYMHy2AwqF27dnJ1dTUt34rRaJTBYKBxCwAoVcLCwqydAgAAZcb1z4W3sz0AAOVVgRq3S5YskcFgkK+vryTL3I0EAAAAACj7oqOjFR0dXaRtb6fpCwBAaVfgO27/iruRAAAAAAD/JDg4+LabrzwYGwBQXhXp4WQAAAAAAPyTTZs2WTsFAABKLTtrJwAAAAAAAAAAMFegO25r165dpJ0bDAYdOXKkSNsCAAAAAAAAQHlVoMZtXl5ekeYl4gmgAAAAAAAAAFB4BWrcHj9+vJjTAAAAAAAAAABcx8PJAAAogB9++EEffvihDh48qMuXL2v9+vWSpBMnTmj79u0KCQmRp6enlbMEAAAAAJQVPJwMAIB/MG7cOLVu3VozZ87U6tWr9c0335jGjEaj+vfvr3//+9+F2mdCQoIefPBB+fn5yWAwaOXKlWbjgwcPlsFgMHt169bNLOb8+fMaMGCA3Nzc5OHhoWHDhunixYtmMXv27FGHDh3k7Owsf39/zZgxo3AnDwAAAACwigLdcfvBBx9Ikh5++GG5urqalgti0KBBRcsMAAAbsHTpUs2cOVMPPvigpk6dqg8//FCvvfaaabxWrVpq06aNVq1apeeee67A+7106ZKaNWumoUOH6pFHHrlhTLdu3bR06VLTspOTk9n4gAEDdPbsWcXHxysnJ0dDhgzRiBEjtHz5cklSZmamunbtqpCQEC1evFh79+7V0KFD5eHhoREjRhTmxwAAAAAAKGEFatxev+unXbt2cnV1NS3fitFolMFgoHELACjV3nzzTTVo0ED//e9/5eDgIEdHx3wx9evXN02dUFDdu3dX9+7dbxnj5OQkHx+fG44dOHBAa9eu1Y4dO9S6dWtJ0vz589WjRw/NnDlTfn5+WrZsmbKzs7VkyRI5OjqqUaNGSk5O1qxZs2jcAgCsonbt2ho9erRGjRp105iFCxfqjTfe0NGjR0swMwAAbE+BGrdLliyRwWCQr6+vJJnd/QMAQFn2448/avjw4XJwuHnJ9Pb2VlpamsWPvWnTJnl5ealKlSrq3LmzpkyZoqpVq0qSEhMT5eHhYWraSlJISIjs7Oy0fft2Pfzww0pMTFRwcLBZszk0NFTTp0/XhQsXVKVKlRseNysrS1lZWablzMxMi58bAKB8On78uNLT028Zk56erhMnTpRMQgAA2LAC33H7V2FhYcWRCwAANsfBwUHZ2dm3jDlz5owqV65s0eN269ZNjzzyiAIDA3XkyBFNmDBB3bt3V2Jiouzt7ZWSkiIvL698uXp6eiolJUWSlJKSosDAQLMYb29v09jNGrexsbGKiYmx6PkAAFBQGRkZ+aYHAgCgPCpQ47YkLVq0SIsWLdLx48clSY0aNVJUVNQ/fp0UAIDi0KRJE23cuFG5ubmyt7fPN3758mWtX79erVq1suhx+/XrZ5ZD06ZNVadOHW3atEldunSx6LH+LjIyUmPGjDEtZ2Zmyt/fv1iPCQAouxISEsyWjx8/nm+dJOXm5urUqVNatmyZ6tatW1LpAQBgs26rcXv27FmtWLFCP/zwgzIyMuTu7q4WLVqoX79+pmkVCqtGjRp67bXXdNddd8loNOr9999Xr1699MMPP6hRo0a3ky4AAIU2dOhQPfnkk3r66ae1YMECs7HMzEw9+eSTSklJ0dy5c4s1j9q1a6tatWo6fPiwunTpIh8fn3zTM1y9elXnz583zYvr4+Oj1NRUs5jryzebO1e6NrcudzoBACylY8eOpmekGAwGvf/++3r//fdvGHv9WSl/fRAoAADlVZEbtwsXLtTYsWOVlZUlo9FoWv+f//xHL7/8smbOnKlnnnmm0Pt98MEHzZanTp2qRYsWadu2bTRuAQAlbujQoVq/fr3ee+89ffTRR/Lw8JAktWnTRgcOHNClS5c0ePBg9e3bt1jz+OWXX/Tbb7+Z/jAaFBSk9PR0JSUlme723bhxo/Ly8tS2bVtTzMsvv6ycnBxVqFBBkhQfH6969erddJoEAAAsLSoqSgaDQUajUZMnT9Z9992njh075ouzt7eXp6enOnXqpAYNGpR8ogAA2JgiNW5XrFihZ599VtWqVdPLL7+sDh06yNvbW6mpqUpISNDcuXNN44899liRk8vNzdUnn3yiS5cuKSgo6IYxPEAFAFDcli9frk6dOmnBggXat2+fjEajdu7cqQYNGmjUqFF66qmnCr3Pixcv6vDhw6blY8eOKTk5WZ6envL09FRMTIz69OkjHx8fHTlyROPGjdOdd96p0NBQSVKDBg3UrVs3DR8+XIsXL1ZOTo4iIiLUr18/+fn5SZL69++vmJgYDRs2TOPHj9e+ffs0d+5czZ492zI/GAAACiA6Otr035s3b9aQIUM0aNAg6yUEAEApUaTG7YwZM1StWjUlJyebPhxKUr169RQcHKzBgwerRYsWmj59epEat3v37lVQUJCuXLmiypUr6/PPP1fDhg1vGMsDVAAAJWH48OEaPny4/vjjD124cEFubm639UCynTt3qlOnTqbl63PKhoWFadGiRdqzZ4/ef/99paeny8/PT127dtWrr75qNoXBsmXLFBERoS5dusjOzk59+vTRvHnzTOPu7u5at26dwsPD1apVK1WrVk1RUVEaMWJEkfMGAOB2fPPNN9ZOAQCAUqNIjdsDBw5o2LBhZk3bv6pRo4YeffRRxcXFFSmpevXqKTk5WRkZGfr0008VFhamzZs337B5ywNUAAAlycXFRS4uLre9n44dO5pNNfR3X3/99T/uw9PTU8uXL79lTNOmTbVly5ZC5wcAQHE4deqUfv75Z7Vr104VK1aUJOXl5en111/XqlWr5OLioueff149e/a0cqYAAFhfkRq3Hh4eqlSp0i1jKleubJoHsLAcHR115513SpJatWqlHTt2aO7cuXrrrbfyxfIAFQBAScnLy1NqaqpycnJuOB4QEFDCGQEAULpMmjRJX375pVJSUkzrpk6dqldeecW0vHnzZm3dulV33323NVIEAMBmFKlx+9BDD+nLL7/U1KlT5eCQfxc5OTn68ssv1atXr9tOULr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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.91, 1.03, 5.8735]\n",
    "cont_case_infeasibility_nn = [1.08, 1.70, 2.64]\n",
    "speed_scopf = [1423, 1725, 2841]\n",
    "speed_nn = [9, 10, 104]\n",
    "cost_scopf = [956, 1060, 1058]\n",
    "cost_nn = [980, 995, 1002]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[0].set_xticklabels(labels)\n",
    "axs[0].tick_params(axis='x', labelsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Time [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[1].set_xticklabels(labels)\n",
    "axs[1].tick_params(axis='x', labelsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[2].set_xticklabels(labels)\n",
    "axs[2].tick_params(axis='x', labelsize=14)\n",
    "axs[2].set_ylim(0, 1400)\n",
    "axs[2].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('118bus_spyros_contscreen_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('118bus_spyros_contscreen_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a4c32ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.86, 1.01, 4.5184]\n",
    "cont_case_infeasibility_nn = [0.89, 1.53, 2.42]\n",
    "speed_scopf = [609, 1484, 1530]\n",
    "speed_nn = [8, 9, 104]\n",
    "cost_scopf = [936, 1045, 1036]\n",
    "cost_nn = [963, 978, 985]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[0].set_xticklabels(labels)\n",
    "axs[0].tick_params(axis='x', labelsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Time [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[1].set_xticklabels(labels)\n",
    "axs[1].tick_params(axis='x', labelsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_nn, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "axs[2].set_xticklabels(labels)\n",
    "axs[2].tick_params(axis='x', labelsize=14)\n",
    "axs[2].set_ylim(0, 1400)\n",
    "axs[2].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('118bus_spyros_heuristic_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('118bus_spyros_heuristic_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53a357f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.03, 1.08, 2.92]\n",
    "cont_case_infeasibility_nn = [0.73, 1.80, 2.56]\n",
    "cont_case_infeasibility_MLP = [0.28, 1.78, 2.05]\n",
    "speed_scopf = [60, 248, 289]\n",
    "speed_nn = [5, 5, 7]\n",
    "speed_MLP = [5, 5, 7]\n",
    "cost_scopf = [1993, 2052, 2099]\n",
    "cost_nn = [2025, 2089, 2144]\n",
    "cost_MLP = [2021, 2090, 2119]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen', '#FF6347']  # Adding blue for MLP\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.25  # Adjusted to fit three bars\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].bar(np.arange(num_cases) + 2 * bar_width, cont_case_infeasibility_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Cont Case Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[0].set_xticklabels(labels, fontsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].bar(np.arange(num_cases) + 2 * bar_width, speed_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Speed [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[1].set_xticklabels(labels, fontsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].bar(np.arange(num_cases) + 2 * bar_width, cost_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[2].set_xticklabels(labels, fontsize=14)\n",
    "axs[2].set_ylim(0, 2800)\n",
    "axs[2].legend(loc = 'upper left')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('39bus_contscreen_GNN_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('39bus_contscreen_GNN_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bc9c7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.03, 1.22, 2.78]\n",
    "cont_case_infeasibility_nn = [0.90, 2.09, 2.85]\n",
    "cont_case_infeasibility_MLP = [0.40, 2.07, 2.34]\n",
    "speed_scopf = [60, 103, 145]\n",
    "speed_nn = [5, 5, 7]\n",
    "speed_MLP = [5, 5, 7]\n",
    "cost_scopf = [1998, 2056, 2112]\n",
    "cost_nn = [2029, 2093, 2147]\n",
    "cost_MLP = [2025, 2092, 2121]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen', '#FF6347']  # Adding blue for MLP\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.25  # Adjusted to fit three bars\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].bar(np.arange(num_cases) + 2 * bar_width, cont_case_infeasibility_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Cont Case Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[0].set_xticklabels(labels, fontsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].bar(np.arange(num_cases) + 2 * bar_width, speed_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Speed [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[1].set_xticklabels(labels, fontsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].bar(np.arange(num_cases) + 2 * bar_width, cost_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[2].set_xticklabels(labels, fontsize=14)\n",
    "axs[2].set_ylim(0, 2800)\n",
    "axs[2].legend(loc = 'upper left')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('39bus_heuristic_GNN_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('39bus_heuristic_GNN_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6aaca512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tikzplotlib\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.91, 1.03, 5.8789]\n",
    "cont_case_infeasibility_MLP = [1.08, 1.70, 2.64]\n",
    "cont_case_infeasibility_nn = [1.3075, 3.7380, 4.1926]\n",
    "speed_scopf = [1423, 1725, 2841]\n",
    "speed_MLP = [9, 10, 104]\n",
    "speed_nn = [9,10,104]\n",
    "cost_scopf = [956, 1060, 1058]\n",
    "cost_MLP = [980, 995, 1002]\n",
    "cost_nn = [1012,1014,1039]\n",
    "\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen', '#FF6347']  # Adding blue for MLP\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.25  # Adjusted to fit three bars\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].bar(np.arange(num_cases) + 2 * bar_width, cont_case_infeasibility_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Cont Case Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[0].set_xticklabels(labels, fontsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].bar(np.arange(num_cases) + 2 * bar_width, speed_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Speed [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[1].set_xticklabels(labels, fontsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].bar(np.arange(num_cases) + 2 * bar_width, cost_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[2].set_xticklabels(labels, fontsize=14)\n",
    "axs[2].set_ylim(0, 2800)\n",
    "axs[2].legend(loc = 'upper left')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('118bus_contscreen_GNN_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('118bus_contscreen_GNN_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fe7cdba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cont_case_infeasibility_scopf = [0.86, 1.01, 4.5184]\n",
    "cont_case_infeasibility_MLP = [0.89, 1.53, 2.42]\n",
    "cont_case_infeasibility_nn = [1.1812, 3.3082, 3.8613]\n",
    "speed_scopf = [609, 1484, 1530]\n",
    "speed_MLP = [8, 9, 104]\n",
    "speed_nn = [9,10,104]\n",
    "cost_scopf = [936, 1045, 1036]\n",
    "cost_MLP = [963, 978, 985]\n",
    "cost_nn = [997,998,1026]\n",
    "\n",
    "\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(cont_case_infeasibility_scopf)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen', '#FF6347']  # Adding blue for MLP\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.25  # Adjusted to fit three bars\n",
    "\n",
    "# Create bar plots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# Cont Case Infeasibility\n",
    "axs[0].bar(np.arange(num_cases), cont_case_infeasibility_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[0].bar(np.arange(num_cases) + bar_width, cont_case_infeasibility_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[0].bar(np.arange(num_cases) + 2 * bar_width, cont_case_infeasibility_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[0].set_xlabel('Case', fontsize=14)\n",
    "axs[0].set_ylabel('Cont Case Infeasibility [%]', fontsize=14)\n",
    "axs[0].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[0].set_xticklabels(labels, fontsize=14)\n",
    "axs[0].legend()\n",
    "\n",
    "# Speed\n",
    "axs[1].bar(np.arange(num_cases), speed_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[1].bar(np.arange(num_cases) + bar_width, speed_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[1].bar(np.arange(num_cases) + 2 * bar_width, speed_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[1].set_xlabel('Case', fontsize=14)\n",
    "axs[1].set_ylabel('Speed [s]', fontsize=14)\n",
    "axs[1].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[1].set_xticklabels(labels, fontsize=14)\n",
    "axs[1].legend()\n",
    "\n",
    "# Cost\n",
    "axs[2].bar(np.arange(num_cases), cost_scopf, color=colors[0], width=bar_width, label='SCOPF')\n",
    "axs[2].bar(np.arange(num_cases) + bar_width, cost_MLP, color=colors[1], width=bar_width, label='MLP')\n",
    "axs[2].bar(np.arange(num_cases) + 2 * bar_width, cost_nn, color=colors[2], width=bar_width, label='GNN')\n",
    "axs[2].set_xlabel('Case', fontsize=14)\n",
    "axs[2].set_ylabel('Cost [$]', fontsize=14)\n",
    "axs[2].set_xticks(np.arange(num_cases) + 1.5 * bar_width)\n",
    "axs[2].set_xticklabels(labels, fontsize=14)\n",
    "axs[2].set_ylim(0, 2800)\n",
    "axs[2].legend(loc = 'upper left')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('118bus_heuristic_GNN_MLP.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('118bus_heuristic_GNN_MLP.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6661994c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def calculate_num_params_MLP(input_neurons, output_neurons):\n",
    "    hidden_neurons = 8\n",
    "    hidden_layers = 3\n",
    "\n",
    "    num_params_input_to_hidden = (input_neurons + 1) * hidden_neurons  # +1 for bias\n",
    "    num_params_hidden_to_hidden = (hidden_neurons + 1) * hidden_neurons  # +1 for bias\n",
    "    num_params_hidden_to_output = (hidden_neurons + 1) * output_neurons  # +1 for bias\n",
    "\n",
    "    total_num_params = (num_params_input_to_hidden +\n",
    "                        num_params_hidden_to_hidden * (hidden_layers - 1) +\n",
    "                        num_params_hidden_to_output)\n",
    "\n",
    "    return total_num_params\n",
    "\n",
    "\n",
    "def calculate_num_params_GNN(num_features, num_of_outputs, conv_hid_features1, conv_hid_features2, K):\n",
    "    num_of_outputs = 1\n",
    "    hidden_params1 = (num_features + 1) * conv_hid_features1 * K  # +1 for bias\n",
    "    hidden_params2 = (conv_hid_features1 + 1) * conv_hid_features2 * K  # +1 for bias\n",
    "    hidden_params3 = (conv_hid_features2 + 1) * conv_hid_features2 * K  # +1 for bias\n",
    "    output_params = (conv_hid_features2 + 8) * num_of_outputs  # +8 for extra_feature, +1 for bias\n",
    "\n",
    "    total_num_params = hidden_params1 + hidden_params2 + hidden_params3 + output_params\n",
    "\n",
    "    return 457\n",
    "\n",
    "K = 3\n",
    "num_features = 1   # Example number of input features\n",
    "conv_hid_features1 = 16  # Example number of hidden neurons in the first convolutional layer\n",
    "conv_hid_features2 = 16  # Example number of hidden neurons in the second convolutional layer\n",
    "\n",
    "input_size = 16\n",
    "output_sizes = list(range(0, 201, 20))\n",
    "number_nodes = list(range(0, 101, 10))\n",
    "\n",
    "num_params_MLP = [calculate_num_params_MLP(output_size, output_size) for output_size in output_sizes]\n",
    "num_params_GNN = [calculate_num_params_GNN(num_features, output_size, conv_hid_features1, conv_hid_features2, K) for output_size in output_sizes]\n",
    "\n",
    "plt.figure(figsize=(6, 3))  # Adjust the figure size as needed\n",
    "plt.plot(number_nodes, num_params_MLP, marker=\"3\", label='MLP', markersize=20, linestyle='None')\n",
    "plt.plot(number_nodes, num_params_GNN, marker=\"4\", label='GNN', markersize=20, linestyle='None')\n",
    "plt.xlabel('Number of buses')\n",
    "plt.ylabel('Number of parameters')\n",
    "\n",
    "ax = plt.gca()\n",
    "for spine in ['top', 'right', 'bottom', 'left']:\n",
    "    ax.spines[spine].set_linewidth(0.5)\n",
    "ax.yaxis.set_ticks_position('right')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.tick_params(bottom=True, top=True, left=True, right=True)\n",
    "ax.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "# Set custom ticks and labels\n",
    "plt.xticks(number_nodes)\n",
    "\n",
    "plt.legend()  # Add a legend to the plot\n",
    "plt.tight_layout()  # Improve spacing between elements\n",
    "plt.savefig('parameterization_comparison.png', dpi=300)  # Save the plot to a file\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ec975e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "benchmark = [3.5e-5, 6.7e-5, 1.5e-5]\n",
    "method = [0, 4.6e-5, 1.3e-5]\n",
    "\n",
    "# Number of cases\n",
    "num_cases = len(benchmark)\n",
    "\n",
    "# Create x-axis labels for each case\n",
    "labels = ['N-{}'.format(i) for i in range(1, num_cases+1)]\n",
    "colors = ['darkgreen', 'limegreen']\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Create the bar plot\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "ax.bar(np.arange(num_cases), benchmark, color=colors[0], width=bar_width, label='SCOPF')\n",
    "ax.bar(np.arange(num_cases) + bar_width, method, color=colors[1], width=bar_width, label='MLP')\n",
    "\n",
    "ax.set_xlabel('Security level', fontsize=14)\n",
    "ax.set_ylabel('Expected violation [MW]', fontsize=14)\n",
    "ax.set_xticks(np.arange(num_cases) + bar_width/2)\n",
    "ax.set_xticklabels(labels, fontsize=14)\n",
    "ax.legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('HILP_risk_increase.png', dpi=300)\n",
    "\n",
    "# Convert to TikZ code\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('HILP_risk_increase.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1a4cd1c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#======= zonder softeq\n",
    "# N1loss = [1.2984, 2.9561, 4.8512]\n",
    "# N2loss = [0.6726, 1.7470, 3.1262]\n",
    "# N3loss = [0.5849, 1.5746, 2.8737 ]\n",
    "\n",
    "N1loss = [1.0774, 2.5555, 4.3070]\n",
    "N2loss = [0.6425, 1.7028, 3.0781]\n",
    "N3loss = [0.5425, 1.4481, 2.6370]\n",
    "\n",
    "# Create a 2D array from the data\n",
    "data1 = np.array([N1loss, N2loss, N3loss])\n",
    "\n",
    "data1 = np.rot90(data1)\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, (ax1) = plt.subplots(1, 1, figsize=(12, 6))\n",
    "\n",
    "# Create the first heatmap\n",
    "heatmap1 = ax1.imshow(data1, cmap='Greens', vmin=0, vmax=4)\n",
    "ax1.set_xticks(np.arange(len(N1loss)))\n",
    "ax1.set_yticks(np.arange(len(data1)))\n",
    "ax1.set_yticklabels(['3', '2', '1'], fontsize=14)\n",
    "ax1.set_xticklabels(['N-1', 'N-2', 'N-3'], fontsize=14)\n",
    "ax1.set_xlabel('Trained Model', fontsize=14)\n",
    "ax1.set_ylabel('k outages', fontsize=14)\n",
    "cbar1 = plt.colorbar(heatmap1, ax=ax1, pad=0.02, shrink=0.7)\n",
    "cbar1.set_label('Percentage Infeasible[%]', fontsize=12)\n",
    "\n",
    "# Customize color bar ticks and labels\n",
    "cbar1.set_ticks([0, 1, 2, 3, 4])  # Include ticks for 0, 1, 2, 3, 4, and 5\n",
    "cbar1.set_ticklabels(['0', '1', '2', '3', '4+'])\n",
    "\n",
    "#plt.setp(ax1.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "\n",
    "# Save the first plot\n",
    "plt.savefig('118bus_heatmap.png', dpi=300)\n",
    "\n",
    "tikzplotlib.save('118bus_heatmap.tex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0f2fec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.944230000000001, 1.08258, 0.9941329999999999]\n",
      "[13.20013, 1.846485, 1.397192]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for LOLE and EENS plots\n",
    "x_labels = ['N-1 SCOPF', 'N-k SCOPF', 'Proposed Model']\n",
    "\n",
    "# LOLE data\n",
    "N1_scopf_LOLE = [0, 7.96203, 1.9822]\n",
    "Nk_scopf_LOLE = [0, 0.77256, 0.31002]\n",
    "risk_based_LOLE = [0, 0.689143, 0.30499]\n",
    "\n",
    "# EENS data\n",
    "N1_scopf_EENS = [0, 7.26553, 5.93460]\n",
    "Nk_scopf_EENS = [0, 0.994175, 0.85231]\n",
    "risk_based_EENS = [0, 0.704212, 0.69298]\n",
    "\n",
    "sum_LOLE = [np.sum(N1_scopf_LOLE), np.sum(Nk_scopf_LOLE), np.sum(risk_based_LOLE)]\n",
    "sum_EENS = [np.sum(N1_scopf_EENS), np.sum(Nk_scopf_EENS), np.sum(risk_based_EENS)]\n",
    "print(sum_LOLE)\n",
    "print(sum_EENS)\n",
    "\n",
    "plt.figure(figsize=(9, 4))  # Adjust the figure size as needed\n",
    "\n",
    "# Plot LOLE as a histogram\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(x_labels, sum_LOLE, color='limegreen', label='LOLE')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('LOLE [hrs/y]', fontsize=14)\n",
    "#plt.legend(fontsize=12)\n",
    "\n",
    "# Plot EENS as a histogram\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(x_labels, sum_EENS, color='limegreen', label='EENS')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('EENS [MWh/y]', fontsize=14)\n",
    "#plt.legend(fontsize=12)\n",
    "\n",
    "# Improve spacing between elements\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot to a file (optional)\n",
    "# plt.savefig('reliability_comparison.png', dpi=300)\n",
    "\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('reliability_comparison.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4941ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.365303, 13.754011]\n",
      "[28.281559, 22.195610000000002]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_labels = ['N-k SCOPF', 'Proposed Model']\n",
    "\n",
    "# LOLE data\n",
    "Nk_scopf_LOLE_after = [0, 5.731403, 7.6339]\n",
    "risk_based_LOLE_after = [0, 5.6053, 8.148711]\n",
    "\n",
    "# EENS data\n",
    "Nk_scopf_EENS_after = [0, 8.115259, 20.1663]\n",
    "risk_based_EENS_after = [0, 5.93477, 16.26084]\n",
    "\n",
    "sum_LOLE = [np.sum(Nk_scopf_LOLE_after), np.sum(risk_based_LOLE_after)]\n",
    "sum_EENS = [np.sum(Nk_scopf_EENS_after), np.sum(risk_based_EENS_after)]\n",
    "print(sum_LOLE)\n",
    "print(sum_EENS)\n",
    "\n",
    "plt.figure(figsize=(9, 4))  # Adjust the figure size as needed\n",
    "\n",
    "# Plot LOLE as a histogram\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(x_labels, sum_LOLE, color='limegreen', label='LOLE')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('LOLE [hrs/y]', fontsize=14)\n",
    "#plt.legend(fontsize=12)\n",
    "\n",
    "# Plot EENS as a histogram\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(x_labels, sum_EENS, color='limegreen', label='EENS')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('EENS [MWh/y]', fontsize=14)\n",
    "#plt.legend(fontsize=12)\n",
    "\n",
    "# Improve spacing between elements\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot to a file (optional)\n",
    "# plt.savefig('reliability_comparison.png', dpi=300)\n",
    "\n",
    "tikzplotlib_fix_ncols(plt.gcf())\n",
    "tikzplotlib.save('reliability_comparison_pt2.tex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae68c666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.365303, 13.754011]\n",
      "[28.281559, 22.195610000000002]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for LOLE and EENS plots\n",
    "x_labels = ['N-k SCOPF', 'Proposed Model']\n",
    "\n",
    "# LOLE data\n",
    "Nk_scopf_LOLE_after = [0, 5.731403, 7.6339]\n",
    "risk_based_LOLE_after = [0, 5.6053, 8.148711]\n",
    "\n",
    "# EENS data\n",
    "Nk_scopf_EENS_after = [0, 8.115259, 20.1663]\n",
    "risk_based_EENS_after = [0, 5.93477, 16.26084]\n",
    "\n",
    "sum_LOLE_after = [np.sum(Nk_scopf_LOLE_after), np.sum(risk_based_LOLE_after)]\n",
    "sum_EENS_after = [np.sum(Nk_scopf_EENS_after), np.sum(risk_based_EENS_after)]\n",
    "print(sum_LOLE_after)\n",
    "print(sum_EENS_after)\n",
    "\n",
    "plt.figure(figsize=(8, 3))  # Adjust the figure size as needed\n",
    "\n",
    "# Plot LOLE\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x_labels, sum_LOLE[1:3], marker='4', label='Before HILP event', markersize=20, linestyle='None', color='tab:blue')\n",
    "plt.plot(x_labels, sum_LOLE_after, marker='3', label='After HILP event', markersize=20, linestyle='None', color='limegreen')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('LOLE [hrs/week]', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Plot EENS\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x_labels, sum_EENS[1:3], marker='4', label='Before HILP event', markersize=20, linestyle='None', color='tab:blue')\n",
    "plt.plot(x_labels, sum_EENS_after, marker='3', label='After HILP event', markersize=20, linestyle='None', color='limegreen')\n",
    "plt.xlabel('Method', fontsize=14)\n",
    "plt.ylabel('EENS [MWh/y]', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Improve spacing between elements\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot to a file (optional)\n",
    "#plt.savefig('reliability_comparison_afterHILP.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5659b59",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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