{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect interaction graphs\n",
    "\n",
    "Inspecting interaction graphs by counting the components, determining the connectivity, clustering and various other metrics. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "casename = '...'\n",
    "case = '...'\n",
    "\n",
    "# Import the graph\n",
    "G = nx.read_graphml(f'../datasets/{case}-dtc-graph.graphml')\n",
    "\n",
    "# Print nodes, edges and density of the graph\n",
    "print(f'Nodes: {G.number_of_nodes()}\\nEdges: {G.number_of_edges()}\\nDensity: {nx.density(G):.5f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove isolated nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.remove_nodes_from(list(nx.isolates(G)))\n",
    "print(f'Nodes: {G.number_of_nodes()}\\nEdges: {G.number_of_edges()}\\nDensity: {nx.density(G):.5f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute number of connected components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.number_connected_components(G)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute metrics of each component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "components = list(nx.connected_components(G))\n",
    "\n",
    "component_metrics = []\n",
    "\n",
    "for component in components:\n",
    "\n",
    "    subG = G.subgraph(component)\n",
    "\n",
    "    num_nodes = subG.number_of_nodes()\n",
    "    num_edges = subG.number_of_edges()\n",
    "    diameter = nx.diameter(subG)\n",
    "    average_degree = (2 * num_edges) / num_nodes\n",
    "    density = nx.density(subG)\n",
    "    connectivity = nx.node_connectivity(subG)\n",
    "    clustering = nx.average_clustering(subG)\n",
    "    degree_assortivity = nx.degree_assortativity_coefficient(subG)\n",
    "    sentiment_assortivity = nx.numeric_assortativity_coefficient(subG, 'sentiment')\n",
    "\n",
    "    metrics = {\n",
    "        'num_nodes': num_nodes,\n",
    "        'num_edges': num_edges,\n",
    "        'diameter': diameter,\n",
    "        'average_degree': average_degree,\n",
    "        'density': density,\n",
    "        'connectivity': connectivity,\n",
    "        'clustering': clustering,\n",
    "        'degree_assortivity': degree_assortivity,\n",
    "        'sentiment_assortivity': sentiment_assortivity\n",
    "    }\n",
    "\n",
    "    component_metrics.append(metrics)\n",
    "\n",
    "df_metric = pd.DataFrame(component_metrics)\n",
    "df_metric.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "upper_limit = 1e5\n",
    "\n",
    "plt.subplot(3, 3, 1)\n",
    "sns.histplot(df_metric['num_nodes'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of nodes')\n",
    "plt.xlabel('Number of nodes')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "sns.histplot(df_metric['num_edges'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of edges')\n",
    "plt.xlabel('Number of edges')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "sns.histplot(df_metric['diameter'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of diameter')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "sns.histplot(df_metric['average_degree'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of average degree')\n",
    "plt.xlabel('Average degree')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "sns.histplot(df_metric['density'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e1, upper_limit)\n",
    "plt.title('Distribution of density')\n",
    "plt.xlabel('Density')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "sns.histplot(df_metric['connectivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of connectivity')\n",
    "plt.xlabel('Connectivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 7)\n",
    "sns.histplot(df_metric['clustering'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of clustering')\n",
    "plt.xlabel('Clustering')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "sns.histplot(df_metric['degree_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of degree assortivity')\n",
    "plt.xlabel('Degree assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 9)\n",
    "sns.histplot(df_metric['sentiment_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of sentiment assortivity')\n",
    "plt.xlabel('Sentiment assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.suptitle(f'Metrics {casename} case', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "\n",
    "plt.savefig(f'../datasets/{case}-metrics.png')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out the outliers in the dataframe, based on number of edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metric_ro = df_metric[df_metric['num_edges'] < 400]\n",
    "\n",
    "df_metric_ro.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the metrics of the outlier filtered dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "upper_limit = 1e5\n",
    "\n",
    "# Plot distributions\n",
    "plt.subplot(3, 3, 1)\n",
    "sns.histplot(df_metric_ro['num_nodes'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of nodes')\n",
    "plt.xlabel('Number of nodes')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "sns.histplot(df_metric_ro['num_edges'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of edges')\n",
    "plt.xlabel('Number of edges')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "sns.histplot(df_metric_ro['diameter'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of diameter')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "sns.histplot(df_metric_ro['average_degree'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of average degree')\n",
    "plt.xlabel('Average degree')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "sns.histplot(df_metric_ro['density'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e1, upper_limit)\n",
    "plt.title('Distribution of density')\n",
    "plt.xlabel('Density')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "sns.histplot(df_metric_ro['connectivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of connectivity')\n",
    "plt.xlabel('Connectivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 7)\n",
    "sns.histplot(df_metric_ro['clustering'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of clustering')\n",
    "plt.xlabel('Clustering')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "sns.histplot(df_metric_ro['degree_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of degree assortivity')\n",
    "plt.xlabel('Degree assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 9)\n",
    "sns.histplot(df_metric_ro['sentiment_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of sentiment assortivity')\n",
    "plt.xlabel('Sentiment assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.suptitle(f'Metrics (edges < 400) {casename} case', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "\n",
    "plt.savefig(f'../datasets/{case}-metrics-ro.png')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter metrics on number of nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metric_ls = df_metric[df_metric['num_nodes'] > 10]\n",
    "\n",
    "df_metric_ls.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the metrics of the large structures set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Plot distributions\n",
    "plt.subplot(3, 3, 1)\n",
    "sns.histplot(df_metric_ls['num_nodes'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of nodes')\n",
    "plt.xlabel('Number of nodes')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "sns.histplot(df_metric_ls['num_edges'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of edges')\n",
    "plt.xlabel('Number of edges')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "sns.histplot(df_metric_ls['diameter'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of diameter')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "sns.histplot(df_metric_ls['average_degree'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of average degree')\n",
    "plt.xlabel('Average degree')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "sns.histplot(df_metric_ls['density'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e1, upper_limit)\n",
    "plt.title('Distribution of density')\n",
    "plt.xlabel('Density')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "sns.histplot(df_metric_ls['connectivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of connectivity')\n",
    "plt.xlabel('Connectivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 7)\n",
    "sns.histplot(df_metric_ls['clustering'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of clustering')\n",
    "plt.xlabel('Clustering')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "sns.histplot(df_metric_ls['degree_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of degree assortivity')\n",
    "plt.xlabel('Degree assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 9)\n",
    "sns.histplot(df_metric_ls['sentiment_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of sentiment assortivity')\n",
    "plt.xlabel('Sentiment assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.suptitle(f'Metrics (nodes > 10) {casename} case', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "\n",
    "plt.savefig(f'../datasets/{case}-metrics-ls.png')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out the outliers in the large structures dataset, based on number of edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metric_ls_ro = df_metric_ls[df_metric_ls['num_edges'] < 400]\n",
    "\n",
    "df_metric_ls_ro.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Plot distributions\n",
    "plt.subplot(3, 3, 1)\n",
    "sns.histplot(df_metric_ls_ro['num_nodes'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of nodes')\n",
    "plt.xlabel('Number of nodes')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "sns.histplot(df_metric_ls_ro['num_edges'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of edges')\n",
    "plt.xlabel('Number of edges')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "sns.histplot(df_metric_ls_ro['diameter'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of diameter')\n",
    "plt.xlabel('Diameter')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "sns.histplot(df_metric_ls_ro['average_degree'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of average degree')\n",
    "plt.xlabel('Average degree')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "sns.histplot(df_metric_ls_ro['density'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e1, upper_limit)\n",
    "plt.title('Distribution of density')\n",
    "plt.xlabel('Density')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "sns.histplot(df_metric_ls_ro['connectivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of connectivity')\n",
    "plt.xlabel('Connectivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 7)\n",
    "sns.histplot(df_metric_ls_ro['clustering'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of clustering')\n",
    "plt.xlabel('Clustering')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "sns.histplot(df_metric_ls_ro['degree_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of degree assortivity')\n",
    "plt.xlabel('Degree assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.subplot(3, 3, 9)\n",
    "sns.histplot(df_metric_ls_ro['sentiment_assortivity'], bins=20)\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "plt.title('Distribution of sentiment assortivity')\n",
    "plt.xlabel('Sentiment assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in',top=True, right=True)\n",
    "\n",
    "plt.suptitle(f'Metrics (nodes > 10, edges < 400) {casename} case', fontsize=16)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "\n",
    "plt.savefig(f'../datasets/{case}-metrics-ls-ro.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "8c75c0fdd1a718867cdcb84b32adcfdbeaad00b3a4e00a59385211aeed084d4c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}