{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measuremments with Representative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from GraphType import GraphStat\n",
    "from GraphType import GraphCollection\n",
    "from scipy import stats\n",
    "from ipywidgets import interact, fixed, interactive\n",
    "import readCSV as reader\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import numpy as np\n",
    "import constants\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Record the distances of different metrics using a representative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GraphDistanceWithRep:\n",
    "    #init with a graph stat and a collection of graph stats\n",
    "    def __init__(self, graphStat, rep):\n",
    "        self.graph = graphStat\n",
    "        self.rep = rep\n",
    "        self.out_d_distance, _ = stats.ks_2samp(graphStat.out_d, rep.out_d)\n",
    "        self.na_distance,_ = stats.ks_2samp(graphStat.na, rep.na)\n",
    "        self.mpc_distance,_ = stats.ks_2samp(graphStat.mpc, rep.mpc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Find the median ks distance of the same number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_median(x, metric_distances):\n",
    "    distance_dic = {}\n",
    "    for index, num_of_nodes in enumerate(x):\n",
    "        if num_of_nodes[0] not in distance_dic:\n",
    "            distance_dic[num_of_nodes[0]] = []\n",
    "        distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
    "    median_x = []\n",
    "    y = []\n",
    "    for num_of_nodes, distances in distance_dic.items():\n",
    "        median_x.append(num_of_nodes)\n",
    "        y.append(np.median(distances))\n",
    "    order = np.argsort(median_x)\n",
    "    median_x = np.array(median_x)[order]\n",
    "    median_y = np.array(y)[order]\n",
    "    return median_x, median_y\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Plot Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# metric_selector: GraphDistance -> float\n",
    "def plot(infos, lines, id, metric_selector,colors, title):\n",
    "    metric_distances = retrive_info_from_list(metric_selector, list(infos.values()))\n",
    "    x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values()))\n",
    "    graph = plt.figure(id,figsize=(18, 10))\n",
    "    plt.title(title)\n",
    "    plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
    "    #plot ks distance median\n",
    "    median_x, median_y = find_median(x, metric_distances)\n",
    "    plt.plot(median_x, median_y, color='black',marker='o')\n",
    "    for i in range(0, len(lines)):\n",
    "        line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
    "        line_y = retrive_info_from_list(metric_selector, line_infos)\n",
    "        line_x = retrive_info_from_list(lambda a : a.graph.num_nodes, line_infos)\n",
    "        plt.plot(line_x, line_y, marker='o', color=colors[i])\n",
    "    #graph.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Retrieve information from a list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrive_info_from_list(selector, distances):\n",
    "    return list(map(selector, distances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readStats(path, numModels):\n",
    "    names =  reader.readmultiplefiles(path, numModels, False)\n",
    "    stats = []\n",
    "    for name in names:\n",
    "        stats.append(GraphStat(name))\n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calDistanceDic(stats, rep):\n",
    "    dic = {}\n",
    "    for info in stats:\n",
    "        info = GraphDistanceWithRep(info, rep)\n",
    "        dic[info.graph.id] = info\n",
    "    return dic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read Models\n",
    "#read representative\n",
    "human_rep = GraphStat(constants.HUMAN_OUT_D_REP)\n",
    "human_na = GraphStat(constants.HUMAN_NA_REP)\n",
    "human_mpc = GraphStat(constants.HUMAN_MPC_REP)\n",
    "\n",
    "# assign rep distributions to human_rep\n",
    "human_rep.na = human_na.na\n",
    "human_rep.mpc = human_mpc.mpc\n",
    "# Read generated models\n",
    "viatra_no_con_stats = readStats('../statistics/viatra_nocon_output/', 5000)\n",
    "viatra_con_stats = readStats('../statistics/viatra_con_output/',5000)\n",
    "random_stats = readStats('../statistics/random_output/',5000)\n",
    "con_viatra_stats = readStats('../statistics/controled_viatra/',300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate distribution distantces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)\n",
    "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)\n",
    "random_dic = calDistanceDic(random_stats, human_rep)\n",
    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = reader.readmultiplefiles('../statistics/viatra_nocon_output/trajectories/', 15, False)\n",
    "trajectories = {}\n",
    "for name in filenames:\n",
    "    trajectories[name] = reader.readTrajectory(name)\n",
    "\n",
    "w = widgets.SelectMultiple(\n",
    "    options = trajectories,\n",
    "    value = [trajectories[filenames[0]]],\n",
    "    description='Trajectory:',\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "#generate random color for each line\n",
    "colors = []\n",
    "\n",
    "for i in range(0, len(trajectories)):\n",
    "    color = \"#%06x\" % random.randint(0, 0xFFFFFF)\n",
    "    colors.append(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "868a437468d24144926f1390cbf2acb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
    "interact(plot_out_degree, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8b74fe96a45445f8062468ddf2597bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_na(lines)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_na(lines):\n",
    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
    "interact(plot_out_na, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6e7e31f454a48169dac12c8aac70eef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_mpc(lines)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_mpc(lines):\n",
    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
    "interact(plot_out_mpc, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cebc359548f74cc8b7540ecc3876c9ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(viatra_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
    "interact(plot_out_degree, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "682beae42eef4676b11b6fe23127a44e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_na(lines)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_na(lines):\n",
    "    plot(viatra_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
    "interact(plot_na, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6893b8c6e03441f89fc35bf784992ae9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_mpc(lines)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_mpc(lines):\n",
    "    plot(viatra_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
    "interact(plot_mpc, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff0e1991c69a4d77a40f57225f90295a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(random_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
    "interact(plot_out_degree, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "838570f20bed4d8d9c618305984d19ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(random_dic, lines, 0, lambda a: a.na_distance, colors, 'out degree')\n",
    "interact(plot_out_degree, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4825f6257a74bce9dd22aac8a98effa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(random_dic, lines, 0, lambda a: a.mpc_distance, colors, 'out degree')\n",
    "interact(plot_out_degree, lines=[[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_viatra_stats = readStats('../statistics/controled_viatra/',5000)\n",
    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trajectories for controlled viatra solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = reader.readmultiplefiles('../statistics/controled_viatra/trajectories/', 25, False)\n",
    "trajectories = {}\n",
    "for name in filenames:\n",
    "    trajectories[name] = reader.readTrajectory(name)\n",
    "\n",
    "w = widgets.SelectMultiple(\n",
    "    options = trajectories,\n",
    "    value = [trajectories[filenames[0]]],\n",
    "    description='Trajectory:',\n",
    "    disabled=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b60ae3859e343299badf29272f67d21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_out_degree(lines)>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_out_degree(lines):\n",
    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree')\n",
    "interact(plot_out_degree, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e7965d793a146d4bbc268554262eb58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_na(lines)>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_na(lines):\n",
    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity')\n",
    "interact(plot_na, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55a1209d0b924a39b4729228e81ee3ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_mpc(lines)>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_mpc(lines):\n",
    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc')\n",
    "interact(plot_mpc, lines=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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