syntheticdatasetgeneration.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandapower.networks as pn\n",
    "import pypower.api as pp\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphs = [pp.case300(), pp.case118(), pp.case57(), pp.case39()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyse_graph(pp_graph, plot=True):\n",
    "    node_ids = pp_graph['bus'][:,0].astype(np.int64)\n",
    "    edges = pp_graph['branch'][:,0:2].astype(np.int64)\n",
    "\n",
    "    G = nx.Graph()\n",
    "    G.add_nodes_from(node_ids)\n",
    "    for edge in edges:\n",
    "        G.add_edge(edge[0], edge[1])\n",
    "\n",
    "    degree_sequence = sorted((d for n, d in G.degree()), reverse=True)\n",
    "    dmax = max(degree_sequence)\n",
    "\n",
    "    if plot:\n",
    "        fig = plt.figure(\"Degree of a random graph\", figsize=(8, 8))\n",
    "        # Create a gridspec for adding subplots of different sizes\n",
    "        axgrid = fig.add_gridspec(5, 4)\n",
    "\n",
    "        ax0 = fig.add_subplot(axgrid[0:3, :])\n",
    "        Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])\n",
    "        pos = nx.spring_layout(Gcc, seed=10396953)\n",
    "        nx.draw_networkx_nodes(Gcc, pos, ax=ax0, node_size=20)\n",
    "        nx.draw_networkx_edges(Gcc, pos, ax=ax0, alpha=0.4)\n",
    "        ax0.set_title(\"Connected components of G\")\n",
    "        ax0.set_axis_off()\n",
    "\n",
    "        ax1 = fig.add_subplot(axgrid[3:, :2])\n",
    "        ax1.plot(degree_sequence, \"b-\", marker=\"o\")\n",
    "        ax1.set_title(\"Degree Rank Plot\")\n",
    "        ax1.set_ylabel(\"Degree\")\n",
    "        ax1.set_xlabel(\"Rank\")\n",
    "\n",
    "        ax2 = fig.add_subplot(axgrid[3:, 2:])\n",
    "        ax2.bar(*np.unique(degree_sequence, return_counts=True))\n",
    "        ax2.set_title(\"Degree histogram\")\n",
    "        ax2.set_xlabel(\"Degree\")\n",
    "        ax2.set_ylabel(\"# of Nodes\")\n",
    "\n",
    "        fig.tight_layout()\n",
    "        plt.show()\n",
    "    return np.unique(degree_sequence, return_counts=True)\n",
    "\n",
    "# (array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11]), array([69, 76, 84, 42, 14,  6,  5,  2,  1,  1], dtype=int64)) \n",
    "\n",
    "# (array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([ 7, 56, 19, 15, 11,  6,  2,  1,  1], dtype=int64)) \n",
    "\n",
    "# (array([1, 2, 3, 4, 5, 6]), array([ 1, 32, 12,  7,  3,  2], dtype=int64)) \n",
    "\n",
    "# (array([1, 2, 3, 4, 5]), array([ 9, 12, 14,  3,  1], dtype=int64)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16731518 0.34241245 0.25097276 0.13035019 0.05642023 0.02723735\n",
      " 0.01361868 0.00583658 0.00389105 0.         0.00194553]\n"
     ]
    }
   ],
   "source": [
    "def analyse_graphs(graphs):\n",
    "    degree_frequencies = [analyse_graph(graph, plot=False) for graph in graphs]\n",
    "    maxdegree = max([np.max(x[0]) for x in degree_frequencies])\n",
    "    aggregated_degree_frequencies = {d: 0 for d in list(range(1, maxdegree+1))}\n",
    "    for ds, cs in degree_frequencies:\n",
    "        for d, c in zip(ds, cs):\n",
    "            aggregated_degree_frequencies[d] += c\n",
    "    frequencies = [x for x in aggregated_degree_frequencies.values()]\n",
    "    # now we need to determine random degree sequences\n",
    "    print(np.array(frequencies) / sum(frequencies))\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "analyse_graphs(graphs)"
   ]
  }
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