analyze_responses-N.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This script analyze the minimum distance among the agents in a \n",
    "# Alpha Alpha flock, as a function of the number of agents:\n",
    "# d(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import csv\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from numpy.linalg import norm\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('distance_data-N.csv')\n",
    "df = df.set_index('N')\n",
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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8H9hW0mWSFkl6x0gFJc2VtFDSwpUrV65DlRER0UmVO4UvAYslXQaIYuDa5yRtDvx0HeveGzgQ2Ay4UtJVtn83tKDtecA8gP7+fq9DnRER0UGVp4/OkPQTYJ9y1ydt/7H8/LF1qHs5cLftB4EHJS0AdgeelBQiImJiVBrRDDwM3AH8GXiupG5Mc3E+cICkTSRNBV5MBsVFRDSqyiOp76YYxbwDsBjYF7gSeOUo550NzAamSVoOHE/x6Cm2T7O9TNJFwHUUy3x+0/bS8X+ViIhYV1X6FI4GXgRcZfsVkmYBnxvtJNuHVyhzEnBShRgiImICVGk+etj2wwCSnmr7RmDnesOKiIgmVLlTWF4ONDsPuETSn4Fb6g0rIiKaUOXpo0PLjydI+jmwNXBRrVFFREQjqj59NGhn2z+0/Ugt0URERKPGmhTeW0sUERHRE8aaFFRLFBER0RM6JgVJG0n627Zdb6g5noiIaFDHpGB7DfBPbdvLa48oIiIaU6X56KeSjpW0o6SnDb5qjywiIiZclXEKby3f29dlNvCc7ocTERFNqjJO4dkTEUhERDRv1OYjSVMlfUrSvHL7eZIOqT+0iIiYaFX6FL4FPAK8pNy+HfhsbRFFRERjqiSFnWx/CXgUwPYqMl4hImKDVCUpPCJpM4rOZSTtxLqtzRwRET2qytNHJ1BMgLejpAFgf+CIOoOKiIhmVHn66GJJiyhWXBNwtO27ao8sIiImXJWnjy61fbftH9u+wPZdki6diOAiImJijZgUJG1ajlyeJmnbttHMfcD2o11Y0pmSVkgadt1lSbMl3Stpcfn6l/F+iYiI6I5OzUfvAY4BngUs4oknju4DTqlw7flluW93KPML2xnzEBHRI0ZMCra/CnxV0gdtf22sF7a9oLyriIiI9USVjuavSXoJ0Nde3nanO4Cq9pN0LfBH4Fjb1w9XSNJcYC7AjBkzulBtREQMZ9SkIOk/gZ2AxcDj5W7TuVmoimuAmbYfkPR64DzgecMVtD0PmAfQ39/vdaw3IiJGUGWcQj+wi+2u/jK2fV/b559I+rqkaXncNSKiOVVGNC8FntntiiU9U5LKz/uUsdzd7XoiIqK6KncK04AbJF1N2/QWtt/Y6SRJZwOzKR5pXQ4cD0wpzz0NeAvwPkmPAQ8Bb+v23UhERIxN1Wkuxsz24aMcP4Vqj7ZGRMQEqfL00eUTEUhERDRvxKQg6X7KmVGHHgJse6vaooqIiEZ0Gry25UQGEhERzavy9FFEREwSSQoREdGSpBARES1JChER0ZKkEBERLUkKERHRkqQQEREtSQoREdGSpBARES1JChER0ZKkEBERLUkKERHRkqQQEREtSQoREdGSpBARES21JQVJZ0paIWnpKOVeJOkxSW+pK5aIiKimzjuF+cBBnQpI2hj4InBxjXFERERFtSUF2wuAe0Yp9kHgHGBFXXFERER1jfUpSNoeOBQ4takYIiJibU12NH8F+Gfba0YrKGmupIWSFq5cuXICQouImJw2abDufuC/JAFMA14v6THb5w0taHseMA+gv7/fExplRMQk0lhSsP3swc+S5gMXDJcQIiJi4tSWFCSdDcwGpklaDhwPTAGwfVpd9UZExPjVlhRsHz6Gsv9QVxwREVFdRjRHRERLkkJERLQkKUREREuSQkREtCQpRERES5JCRES0JClERERLkkJERLQkKUREREuSQkREtCQpRERES5JCRES0JClERERLkkJERLQkKUREREuSQkREtCQpRERES5JCRES0JClERERLbUlB0pmSVkhaOsLxN0m6TtJiSQslHVBXLBERUU2ddwrzgYM6HL8U2N32HsC7gG/WGEtERFRQW1KwvQC4p8PxB2y73Nwc8EhlIyJiYmzSZOWSDgU+DzwDOLhDubnA3HLzAUm/HWeV04C7xnlunXo1Lujd2BLX2CSusdkQ45pZpZCe+GO9+yT1ARfY3nWUci8D/sX2q2oLpqhnoe3+OusYj16NC3o3tsQ1NolrbCZzXD3x9FHZ1PQcSdOajiUiYjJrLClIeq4klZ/3Ap4K3N1UPBERUWOfgqSzgdnANEnLgeOBKQC2TwP+BniHpEeBh4C3us62rMK8mq8/Xr0aF/RubIlrbBLX2EzauGrtU4iIiPVLT/QpREREb0hSiIiIlkmRFCTtKOnnkm6QdL2ko5uOCUDSppKulnRtGddnmo6pnaSNJf1G0gVNxzJI0s2SlgxOj9J0PIMkbSPp+5JulLRM0n49ENPO5c9p8HWfpGOajgtA0ofL/+eXSjpb0qZNxwQg6egypuub/lkNN1WQpKdJukTS78v3bbtd76RICsBjwEdt7wLsCxwlaZeGYwJYDbzS9u7AHsBBkvZtOKZ2RwPLmg5iGK+wvUePPUf+VeAi27OA3emBn5vt35Y/pz2AvYFVwA8aDgtJ2wMfAvrLMUwbA29rNiqQtCvwj8A+FP8ND5H03AZDms+Tpwr6OHCp7edRTBX08W5XOimSgu07bF9Tfr6f4h/s9s1GBS48UG5OKV890fMvaQeKUeaZk2oUkrYGXgacAWD7Edt/aTaqJzkQ+F/btzQdSGkTYDNJmwBTgT82HA/AC4Bf2V5l+zHgcuCwpoIZYaqgNwFnlZ/PAt7c7XonRVJoV46y3hP4VbORFMommsXACuAS2z0RF/AV4J+ANU0HMoSBiyUtKqc/6QXPBlYC3yqb274pafOmgxribcDZTQcBYPt24N+BW4E7gHttX9xsVAAsBV4q6emSpgKvB3ZsOKahtrN9R/n5T8B23a5gUiUFSVsA5wDH2L6v6XgAbD9e3t7vAOxT3sI2StIhwArbi5qOZRgH2N4LeB1FM+DLmg6I4q/evYBTbe8JPEgNt/XjJekpwBuB/246FoCyHfxNFMn0WcDmkt7ebFRgexnwReBi4CJgMfB4o0F1UI7r6nrLwqRJCpKmUCSEAdvnNh3PUGVzw8/pPN34RNkfeKOkm4H/Al4p6TvNhlQo/8rE9gqK9vF9mo0IgOXA8ra7vO9TJIle8TrgGtt3Nh1I6VXAH2yvtP0ocC7wkoZjAsD2Gbb3tv0y4M/A75qOaYg7Jf0VQPm+otsVTIqkUE6ncQawzPbJTcczSNJ0SduUnzcDXg3c2GxUYPsTtnew3UfR7PAz243/JSdpc0lbDn4GXkNxy98o238CbpO0c7nrQOCGBkMa6nB6pOmodCuwr6Sp5b/NA+mBjnkASc8o32dQ9Cd8t9mInuSHwDvLz+8Ezu92BY1OnT2B9gf+HlhStt8DfNL2TxqMCeCvgLMkbUyRoL9nu2ce/+xB2wE/KKfM2gT4ru2Lmg2p5YPAQNlUcxNwRMPxAK3k+WrgPU3HMsj2ryR9H7iG4snA39A700qcI+npwKPAUU0+MDDCVEFfAL4n6UjgFuBvu15vprmIiIhBk6L5KCIiqklSiIiIliSFiIhoSVKIiIiWJIWIiGhJUoiIiJYkhYiIaElSiNpJsqQvt20fK+mELly3r32u+TpJ+lC5TsLARNQ3pO5tJL1/jOe8p/y5v6Bt3zJJzx5Srk/SQ22DOtuPfV7SKyS9WdInyn2blWszPCJp2ni/U/SuJIWYCKuBw3rtl4gKVf8NvB94te05dcY0gm3K+sfihRQTuh0MxYJOFCPCbx6m7P+WkzIO9WLgKuDlwAIA2w+VZXthquuoQZJCTITHKKYx+HD7zqF/6Q/eQZT7b5Q0X9LvJA1IepWkK8oVp9onwdukPL5MxcpnU8trvV3FqnaLJX2jnEpksM7fSvo2xbxJOw6J6SPlyltLB1feknQa8BzgQklrfYfy+HnlVN7Xt0/nLenTZV2/VLG62LEjxVbGtUzS6eV1Li7nw4JiaoOdyvInlXNA/VjFin1LJb11mJ/5bhQzfh5cbu8C3OgKUxiUdVwHvAi4Eng3cKqkfxnt3NgA2M4rr1pfwAPAVhR/pW4NHAucAPQBS9vKte9/jOKv3Y2ARcCZgCimXD6vLN9HMXXw/uX2meU1XgD8CJhS7v868I62c9YA+w4T597AEmBzYAvgemDP8tjNwLQRvt/TyvfNKBLN0yl+oS4GNgW2BH7fKba277xHuf97wNvbYm7/Of0NcHrb9tbDxHRXGc/15c/8ncBpw5Rb69pt+18EfI1i4acrhjk+4s8jr/X7NVkmxIuG2b6v/Ov8Q8BDFU75g+0lAJKup1iC0JKWUPwiG3Sb7SvKz98pr/8wxS/4X5eT523G2lMM32L7qmHqPAD4ge0Hy3rPBV5KMWFbJx+SdGj5eUfgeRTLvp5v+2HgYUk/Ko8fOEJsC8rvPNi2v2jI92y3BPiypC8CF9j+RftBSTsCd9t+SNIlwGsp7hyWjPI92u0FXAvMokdmMI2JkaQQE+krFDNjfqvcfoy1mzDbF29f3fZ5Tdv2Gtb+/3Zoc4gp7ijOsv2JEeJ4cAwxdyRpNsX6APvZXiXpMtb+Hk86ZbjYVKwI2P6dH6dIGE9i+3eS9qJYGeyzki61/a9tRV7IEwngJ8Acihl5z6vwffagWBt4B4q7janFbi0uv2OVhB7rsfQpxISxfQ9Fs8iR5a47gWeoWP7wqcAh47jsDEn7lZ//DvglxYLmb9ETc+M/TdLMCtf6BfBmFfP8bw4cWu7rZGvgz2VCmEVxhwBwBfAGSZuqWPFv8LuNJ7b7KZqgKM95FrDK9neAk3jygj7tdwWXU6wfXelOwfZiFx3Jv6Poh/gZ8FrbeyQhTA65U4iJ9mXgAwC2H5X0r8DVwO2Mb4Gh31Isy3kmxcI2p5a/oD9FsZbzRpRz41PMPz8i29dIml/GA/BN26M1HV0EvFfSsjKWq8pr/VrSD4HrKJLfEoq1iG8YIbY/dYjr7rKTfSlwIfBT4CRJa8rz3zfklBdSrDKI7dVlp/Gerrg2gKTpFIlujaRZtntpwaCoWdZTiKiJpC1sP1A+EbUAmGv7mqbjalc2W11ge0xrg6tYqrXf9l01hBUNSvNRRH3mlW3x1wDn9FpCKD0ObK1hBq8NZ3DwGsVTSWtqjSwakTuFiIhoyZ1CRES0JClERERLkkJERLQkKUREREuSQkREtCQpRERES5JCRES0/H/Gmqgm1aGwZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the inter-agent distance with std-dev\n",
    "# for N <= 10\n",
    "\n",
    "N = df.index.values\n",
    "\n",
    "cut_at_idx = 11\n",
    "idx_list = df.index.values < cut_at_idx\n",
    "idx_values = df[idx_list].index.values\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "for k in idx_values:\n",
    "    ax.errorbar(k, np.squeeze(np.asarray(df.ix[k,'avg_min_d'])), np.squeeze(np.asarray(df.ix[k,'std_min_d'])), \n",
    "                linestyle='None', marker='o', color='b');\n",
    "\n",
    "ax.set_xlabel('Number of agents $N$ [#]')\n",
    "ax.set_ylabel('Inter-agent distance [m]')\n",
    "\n",
    "plt.ylim([1.3,2.1])\n",
    "\n",
    "plt.savefig('figures/dist_std_zoom.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the inter-agent distance with min-max\n",
    "# for N <= 10\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "plot1, = plt.plot(idx_values, df.ix[idx_list,'min_min_d'],'ro')\n",
    "plot2, = plt.plot(idx_values, df.ix[idx_list,'max_min_d'],'go')\n",
    "plot3, = plt.plot(idx_values, df.ix[idx_list,'avg_min_d'],'bo')\n",
    "\n",
    "ax.set_xlabel('Number of agents $N$ [#]')\n",
    "ax.set_ylabel('Inter-agent distance [m]')\n",
    "ax.legend((plot2, plot3, plot1), ('max', 'average', 'min'))\n",
    "\n",
    "plt.ylim([1.3,2.1])\n",
    "\n",
    "plt.savefig('figures/dist_min_max_zoom.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the inter-agent distance with std-dev\n",
    "# for N > 10\n",
    "\n",
    "cut_at_idx = 10\n",
    "idx_list = df.index.values > cut_at_idx\n",
    "idx_values = df[idx_list].index.values\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "for k in idx_values:\n",
    "    ax.errorbar(k, np.squeeze(np.asarray(df.ix[k,'avg_min_d'])), np.squeeze(np.asarray(df.ix[k,'std_min_d'])), \n",
    "                linestyle='None', marker='o', color='b');\n",
    "\n",
    "ax.set_xlabel('Number of agents $N$ [#]')\n",
    "ax.set_ylabel('Inter-agent distance [m]')\n",
    "\n",
    "plt.ylim([1.3,2.1])\n",
    "\n",
    "plt.savefig('figures/dist_std.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the inter-agent distance with min-max\n",
    "# for N > 10\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "plot1, = plt.plot(idx_values, df.ix[idx_list,'min_min_d'],'ro')\n",
    "plot2, = plt.plot(idx_values, df.ix[idx_list,'max_min_d'],'go')\n",
    "plot3, = plt.plot(idx_values, df.ix[idx_list,'avg_min_d'],'bo')\n",
    "\n",
    "ax.set_xlabel('Number of agents $N$ [#]')\n",
    "ax.set_ylabel('Inter-agent distance [m]')\n",
    "ax.legend((plot2, plot3, plot1), ('max', 'average', 'min'))\n",
    "\n",
    "plt.ylim([1.3,2.1])\n",
    "\n",
    "plt.savefig('figures/dist_min_max.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}