{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "slide_end",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 例3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "subslide_end",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 4、5の場合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "subslide_end",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "以前の意見が次の意見の選び方にどう影響するか\n",
    "\n",
    "４. 一つ前の意見 + 議題\n",
    "\n",
    "５. 二つ前までの意見\n",
    "\n",
    "この二つの場合に共通なのは、参照する点が2つであるというところである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "subslide_end",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "では、この場合についてもシミュレーションを考えていくことにする。意見の選ばれ方は、2,3の場合と同じく、意見の$a$次元ユークリッド距離の大きさによって決める。この二つの意見の間の重みをどうするかというのが議論となる。すなわち、2つ前の意見に影響されるとはいえ、直前の意見よりは影響されないであろう、という考えや、一つ前の意見が参考にされるとはいえ、実際に会議をしている際に望まれるのは議題に沿った発言であるから、むしろ議題の方に重みを置いたほうが良いのではないか、という考えを反映した指標は何を用いるべきかということである。\n",
    "\n",
    "したがって、この3点間の近さの指標として次のようなものが考えられる。\n",
    "\n",
    "$$D(x,(y,z)) \\equiv \\alpha d(x,y) + \\beta d(x,z)\\ \\ (\\alpha, \\beta > 0)$$\n",
    "\n",
    "以下に具体的に同じ長さ$a$に対して$\\alpha$と$\\beta$によってどう変わるかを描いた。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "slide_end",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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XjtObTGSfFCjAjfCVK69/7I1Qpw6we7demXZBnLuXsmKF/pay//zDFVrt2nrl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\nIAiCIAiCIAiCIAiCIAiCIAiCIFyf/wfqt7G7XO+62wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb84c0d0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import euclidean as euc\n",
    "from IPython.html.widgets import interactive\n",
    "from IPython.display import display\n",
    "\n",
    "beta = 1.\n",
    "x1 = (0., 0.)\n",
    "x2 = (0.5, 0.)\n",
    "r = euc(x1, x2)\n",
    "A = np.linspace(1.1*r, 6*r, 5) # larger than r\n",
    "def oval(alpha=1.1):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_aspect('equal', 'datalim')\n",
    "    for a in A:\n",
    "        d1 = np.linspace(r/2., a/alpha, 1000)\n",
    "        d2 = np.linspace(r/2., a/beta, 1000)\n",
    "        theta1 = np.arccos((d1**2 + r**2 - ((a-alpha*d1)/beta)**2)/(2*d1*r))\n",
    "        theta2 = np.pi - np.arccos((d2**2 + r**2 - ((a-beta*d2)/alpha)**2)/(2*d2*r))\n",
    "        ax.plot(x1[0] + d1*np.cos(theta1), x1[1] + d1*np.sin(theta1), 'b')\n",
    "        ax.plot(x1[0] + d1*np.cos(theta1), x1[1] + -d1*np.sin(theta1), 'b')\n",
    "        ax.plot(x2[0] + d2*np.cos(theta2), x2[1] + d2*np.sin(theta2), 'b')\n",
    "        ax.plot(x2[0] + d2*np.cos(theta2), x2[1] + -d2*np.sin(theta2), 'b')\n",
    "    plt.show()\n",
    "\n",
    "w = interactive(oval, alpha=(1., 5., 0.1))\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "また、別の近さの指標として、ベクトル$\\vec{Y}=y-x, \\vec{Z} = z-x$として、\n",
    "\n",
    "$$D(x, (y,z)) \\equiv | t \\vec{Y} + (1-t)\\vec{Z}|\\ \\ (0 \\le t \\le 1)$$\n",
    "\n",
    "とする方法もある。このとき、右辺の括弧の内部があらわすベクトルは、点$y$,$z$を結んだ線分$YZ$を$(1-t):t$に内分する点のベクトルを示しており、$D$はその点からのユークリッド距離をあらわすことになる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jR4DERM/bcgmdRC645ApcQyjZI6cCnhfAt6CI1wHwFAChDGPlRkhJ4RRwT5Oa\nymk/UqGhrVs5fPrhh3lwevEiBy88/rhc+9W8eTkM+eJFz9vKl483KYmmVjfdBCQlydyYXM+fjR2U\nN5JTAb8TwH4AhwEkAfgZwCM5/JmKD3DpEpA/P9/8ErzyCsed9enDPtYFC6ZN/endmwIkgfRqVSK0\n4ecnF9rw9+drRqrboreTUwEvD+Bous+PXfmaoiCP0AnL6dPszf3UU//8t7vvBo4fBxYvlvFl/35e\nEhw7xnMGCc6c4WG7BNHR2tAqq+S0H12WInAffPDB/x4HBQUhKCgoh2YVJY2YGK66M0tXjIiQKyg6\ndEjGDiArdMePy9k6dUpuOpVTCAkJQUhISLb+T04F/DiA9EdiFcFV+FWkF3BFcTeVKrFl7d69PEhM\nz4UL3P736iXjS506nOYuQbVqckOhy5cHmjaVsVWiBIc75Db+vrj98MMPr/t/crrJ3QKgJoAqAPID\neALA3Bz+TMUHCAhgd76UFM/b8vcH3noL6N4dOHs27euxsUDPnkC3bkCpUp73A0i7YUjZkppuJGXL\nGB5iSj2H3k5OV+DJAP4NYAmYkTIOQGROnVI8i0TqWUAAV1InTsikLQ4YwIZLt90GtGnDA9SlS4GH\nHuIgCSnOn5cRutRUCl2RIp63lZQEJCTI1CrExHBcnUSmlC/gjpksi65cihdQvDhXqeUFjpqrVmU8\nWELA8+QBhg1jxklICOcqDhpEQZfizBm5zJuYGGbaSAida1chUZksuYPxBYSGaimZ4ecnE2oA0kRV\nUsClYsIA4+E9esjZS09oKNu8Sgjd+fNyQidpSzIs5AtoKb0DKFdO7oTfJaoS1K8v1xvECYSGspxe\nAun4ty/eLHwBFfAMyJ9frrezpKhK2mrViuPbcguSAn7mjFy3TKm4PqAr8OyiAp4BlSqxFFwCXxXw\nRo3Y8vTECRl7NklMZPtcqXDRzp1yedK6AncuKuAZ4KuiKmkrb172J1m+XMaeTVasYA66VKMwydW+\n9ApcBTzrqIBngK+KqqQtAHjsMWDyZDl7tpgxg3MxpXAdmEogvQLXEErWUQHPAF8V1UqVmC8tNaT2\nX/8Ctm8H9u2TsWeDuDhgzhzerCS4fJmvl7p1ZewdOMDXjQS6As8eKuAZIC2qJ04wd9nT+PsDLVsy\nV1qCAgWA554Dxo6VsWeDH39k0ywpkduxAwgM5EG7BNLZNSrgWUcFPAMqV2a3N4n87Pz5gVtvBY4e\nvf73uoORId/QAAAdTElEQVTWrWWzQ/7v/4CJE32zUX9iIvDFF8DgwXI2JQU1MRGIjAQaNJCxpyGU\n7KECngEFCrB/xrF/tObyDNLpfZIHi9WrA507A0OHytmUYvJkVnveeaeczS1b5OLfERF8bUqN/Dt9\nGihZUsaWL6ACngm+Ggdv2BA4d07u5gQAH38MTJjAeKqvcPEi8MEHwJAhcjZTUoBFi4AHH5SxJ7na\nT0nhWLzc1kY2J6iAZ4KkqNaqxdWOBHnyML1PMoxSpgzQvz/wxhsyzbQkePNNoH17nilIsW4dn0up\nYdGSAr5vH0OJGgPPOirgmSAp4Pfdx0IQKWxUSQ4YwDfp+PGydj3BypWc8vP557J2baQrSgm4pC1f\nQQU8EyQF/M47OR4rfT9rT/LggxSghAQZewC7502fDrz9NrfK3srp08DzzwOjR3MSkBQpKcCvv8oJ\neEoKM15uv13GnmRuu6+gAp4JkgKeLx9T0VatkrFXrRpjjbNny9hzUacOszYef5yzD72N+HgeyD7z\nDMMnkqxfzxBDzZoy9vbsAcqWlek5DugK/EZQAc8E6apF6bBG797Af/8rZ8/Fs88yZNSxI4tSvAVj\nmNNeqRKQhWlXbseXwyfGANu26Qo8u6iAZ0L58sxLlVopSgt4587M8d29W86mi2+/pRB27izX9TEn\npKYCr77KyewTJvAgWJKkJNnwCQBs3iwn4IcPA4ULA6VLy9jzFVTAMyFvXuCuu4A1a2Ts1a/PYhep\nSeP589urksybl4eZxYqx3D4mRt6HrJKQADz1FOP2ixczli/NtGlA7dr/HNrsKYxhe4B27WTsafjk\nxlABvw6SVYt58gAPPCC7Cn/xRRajxMXJ2XTh7w/89BPjrM2bA/v3y/twPS5eZKw7OZniLRUPTk9q\nKvDpp7LVnps3c65p/foy9vQA88ZQAb8O0mGN1q1lqySrVQOaNAFmzpSzmZ58+dhLpE8f5lMvctB0\n1TVrWPQUGMjsmYAAO3789huzXVq1krM5cybDNRLj4QBdgTsZ480kJRlTpIgxUVEy9g4cMKZMGWNS\nU2XsGWPMggXGBAbyd7XJ2rXGVKhgTK9expw+bc+P+Hhj3nyTf4d58+z5YQxfB40aGTN3rqzNypWN\nCQuTs1eqlDFHj8rY8xYAXLfkTVfg18Hfn1NWpIpsqlXjSk8yT7pdO4YxfvhBzua1uPtuYNcuhinq\n1gXGjJEb9gzQ1pQptL1vH9vgduggZ/9aLFpEvyT92LKF5yNSDaxcE5skBm37GirgWUA6jNK1q+wQ\nBD8/YPhwpsZdvChn91rccgvw1VcMI/30E8MX330HxMZ6zmZKCjBrFgtWvvuON7JZs+xnRBgDfPIJ\n8M47cqEMIC1dUTp8Ivk7+goq4FlAWsBd7VclqyRvv52HdcOGydnMjAYNGIMeP567n8qV2Xvk9995\nqOcOwsP5MytWBD77jAeF69ezT4wTmD+fgzekBkUAvGnMmMFCKyk2buQ5jJJ9JO55V8I53osxDDFs\n3MjiHgkefJAFL926ydgDuJWtX59baKnfM6scPMiV8bx5nMj+8MNAmzYMd9Sqdf3UPmPYCXH9emDD\nBmDtWub3d+/OKzBQ5vfIKhcvslI2OFj2hrJlC19ze/bIrIiN4XM/aRJTdpU0/PgHyPSvoAKeRZ5+\nmm+kF16Qsffrr8DIkXI56C4+/pgr019+kbWbHQ4dAhYsAFavZiHSgQOMn1asSCEvWJDnCAkJwMmT\nvE6dAkqUYKZLixa8GjViProTeeklfhwzRtbuW2/x3Eeqd/vOnYzvHz6sIZS/owLuRsaPB5YtY0GF\nBElJDBssX87+IVJcvswBBePHM6XRG0hK4gr92DGKdlwcqzvz5+fOqUwZXoUL2/Y0a6xaBfTowRup\nZN55airb1P72m1wDq/ff5/nG8OEy9rwJFXA38uef7Bh46pTcSuHdd7nNHzlSxp6L5cuBnj2BrVsp\nfIocly8zjDVypHwGzJw5PMjeulUufFKnDlsTNGvmeXveRlYEPCeHmF0B7AKQAsDnU/ArVwZuvlk2\nve/FF5mJId3wqXVrtkt95hnZND4FeO89ipm0eBvDsMm778otUHbt4upbY983Tk4EfCeALgCEo7T2\nkJ4lWbky38zTp8vZdDFkCMvHnZKVkhsICWEe+tdfy9tetoxi2rmznM0ZM5hho7HvGycnAr4bwF53\nOeINtG3LbaYktlq+5s0LTJ0KfP89DwsVz7J3L/DEExTwUqXk7Q8dCgwaJNtlUbo9ri+ieeDZoH17\ntl7ds0fO5sMPM70vNFTOpoty5ZiP/vTTnEKjeIZz5/jaGjpUtt+Ji7VreQD85JNyNnft4vmOhk9y\nhv91/n0ZgGsdY70DYF5WjXzwwQf/exwUFISgoKCs/ldHkb79qtSped68HAQ8eLCdRk8PPcQDza5d\naf+mm+R98GUSEoAuXdhSVypF9e8MHcoxd/7XUwM34gqfSPdVdzIhISEICQnJ1v9xR/RpFYDXAWS0\nRvSJLBQXBw9y1XDkiFxf6KQkFnWMHMkwjjQpKSwqOn2aISRbXfl8DWP4vMbEUNBsiNmWLbyB7N8P\nFCggZ7duXRZmtWghZ9Pb8HQWylW23PRzHI+N9qv58nGO5Ouv82BRmrx5mepVpAhX4omJ8j74IsOG\nARER7HtjayU6bBjbCUiKd0QEK001dTDn5ORl0wXAUQDNACwA4KBOzp6ld2/5CrmOHTnQ9scfZe26\n8PfnAVvevJxOY+NG4isYw74r48YBc+faC0vt2sW2AtKhGw2fuA8t5LkBkpOBKlUYE5aaWAIAYWEM\noezZY2cyDMCYbefOHIU2ebJzS9GdSmoqMGAAG3QtXsyDYls8/ji7AL79tpxNY/ieGTOGbQ2UjJEM\noeQq/P1Z6CK9Cnd1DPz0U1m76SlQgKXWUVGM30p2TPR2EhNZHBUayh43NsV7/nxWXPbtK2t39Wo+\nD82by9r1VXQFfoMcPUpBPXIEKFRIzq5TOgbGxrKL38mTbLxlU4y8geho4NFHGS6ZNs3OYGQXtjod\nAuwg2a0bs7mUzNEVuAepWJETZH7+WdZuuXJAv36y295rUagQD3I7dADuuINtWpVrc+YMh1VXrszn\nzKZ4A8DAgawvkBbv339nwdIzz8jaVXKGjXFyIixYYEzTpvJ2Y2M5O3L9ennb12LBAs40HD1adpan\nN7BokTHlyhnz/vvOeG5WruRr58IFedsdOxrz7bfydr0VZGEmpgS2nwePkZzM4a9btsjbnjLFmLp1\nKeZOYO9eY+rUMeaFFzgUOLcTG2vMK68YU6mSMStW2PaGxMYaU62anUHNYWHGlC1rzOXL8ra9FehQ\nY8+SNy/Hn0kfZgJM5WvYUP4QKiNq1gQ2bQIuXACaNgXWrbPtkT22bGF2x4ULHIz8wAO2PSK2Oh0C\nzDcfMMB++EjJPrZvZB7l5EljihY1JipK3valS8bUqmXMTz/J286I1FRjpk83pnx5Y3r2tPO82CIp\nyZiPPmI46eefbXtzNZs2GXPrrcacOSNve/duPifR0fK2vRnoCtzzlCnDXiHp2r2IcfPNbDXbr59s\ng63M8PNjtWZkJFCyJLMdvv/et/uKG8NZnY0bMz0wNJSdBZ1CQgLTXr/+mn8TaT77DHj1Ve+ZiORN\naBqhG/jrL6B2bfZzrltX3v5//wuMHs0QhtO2qOHhQJ8+HErx3Xe+131u5Uo2GouJYVOojh2d19/6\nnXdYdTl7trxvhw+z9cT+/Sz+UrKOjlQT5OuvgaVLgYUL5W0bw1agxYrZ6R1+PYzhZKFBg3ij69+f\naWzeXEr9++8U7j//BD76iCtuJ/4+U6Zwys6mTWzFIE2fPqwatll85q1kRcAlsBtIEiIhwZgaNYxZ\nvNiO/YsXjale3Xmx1/QkJDBe37gxY/fff29MTIxtr7JOcrIxS5YY06mTMRUrGvPDD8YkJtr2KmPW\nrWPsOTzcjv3jx40pVix3nYO4E2QhBq4rcDcyezZXO2Fhsr2VXYSGsn/3xo2cLu5UjOEQgREjmK3y\n4otsEFapkm3Prk14OKsWp0wBypdnFeFzzzm7re6BAyw0mziRrwkbvP46zz5sjIjzBTSEIowxrG7r\n1o3phTb49ltg/HgKozcMX9i/Hxg1iuJYqRJT3FzVnTZDElFRLHkPDmYlZffuvAID7fmUVc6fZ5/t\nvn2Bl1+248OZMwyX7dgBVKhgxwdvRwXcAqGhbDi1Zw9wyy3y9l1DAk6d8q7hC8nJjNPOn8/r9GnG\nyTt0AFq3BooW9ZxtY4BDh9gOYMMGXocPs+tijx5AUJD3dF1MSmLHygYNuMOxxdNPM0NLanKVL6IC\nbonnnuOL19bBTXIydwHx8ey9kT+/HT9ywqFDwIIFFPN16yjggYFAnTr86HqcnQHACQm8sZ06xSZc\n+/cz3LR+PVf7LVvyatGCjcq87XkzhuGoqCiG82zddObPB157Ddi50zt2gU5FBdwSx49zBbR1K/uG\n2yApiU3z8+Vjwy0bMXl3kZrKro+RkbwiItI+AtzpBATwKlgw7XFAANP7XIIdHQ2ULg2ULcurcmW2\nNW3Rgo+dlv6XXb74Apg6lecLtnKubXY69DVUwC3y4YecYD9tmj0fXMMXihfnG8pbwgBZxRhOdI+J\n4W4jPh6Ii7v6caFCaYJdooQzU/3cwcyZLOjatMluzPmll/jRRnsJX0MF3CKxsTzEmTHDbvP6uDjG\n5KtW5RBZXxWw3MzEiWwvvGgR0KiRPT9WreKZQXi4vYlRvoT2A7dIoUKszBswgCEAWxQsyDLvvXuB\nf/+bq1bFNzAG+PxztnEICbEr3pcvc7bm6NEq3pKogHuQ7t0Ze/78c7t+FCrEA8GtW5mbqyLu/aSm\nAm+8wdDY+vXAbbfZ9cdmp8PcjIZQPMzRo8xpnjmThRU2OX+erU3vv58HXr4WE88tJCYCvXox1XHe\nPPs9Rn7/HXjkEWadZCcrSMkcDaE4gIoVgXHjmNZ37pxdX4oVA5YvZ6Vo+/ZswqV4FzExQKdOwKVL\n7L1jW7wTEngz+fprFW8bqIAL0L49mx317Gk3Hg4wE2PpUuZQ33EHK+UU7+DsWaBVK5bz//abM3Ks\nhw0Dqld3Vvvc3ISGUIRISgLuvZeTyd94w7Y3ZMoUpp59+62+AZ3OkSPsadK5M0XTCTnrO3bwhhIW\nxpuK4l40jdBhHDnCVe+cOTzwcQLbtgH/+heHMAwb5t0FP77KrFnsafL227zhOoHERFatvvQSs08U\n96MC7kDmzmWTodBQFtg4gbNn2U/cz49VmyVK2PZIARjn7tePU34mT7ZbT5AeY9gu4tIl4NdfnbEb\n8EX0ENOBdOrEFe9zzzknna9kSWDxYuYRN23KLbFil7VrObTa359/D6eIN8AeP+HhvKmoeNtFV+AW\nSEwE7rmHq97+/W17czU//8z5hW++ydWftzV08nYSE4EhQ1hdOWYMb/hOYvp0nuFs2gSUK2fbG9/G\n0yvwLwBEAtgO4DcAWn+VRfLnp1B++imwebNtb67mySfZoW/VKnbkCwmx7VHuYdcuzgyNiAC2b3ee\neG/aBLzyCnPPVbydQU4EfCmAugAaAtgLYJBbPMolVK3KFdYTTwAXLtj25mpq1OBsz08+YW+LZ55h\nRz/FM6SmMo86KIgCOXs2uyY6icOHGfqbMIGhHcUZ5ETAlwFwZTX/DkDnbmSTLl24ynriCRZEOAk/\nP75hIyPZ3a5+feCbb9hrXHEfISGs0J0+nSvcF15wXlz54kWWyL/1lpbKOw13vVTmAZgGYOo1/k1j\n4JmQnAw89RRjnzNnsn+3E4mI4OrwwgU2LHJKGqS38scfnGq/fz9bD3fr5szWBsnJLESrUYP1Ak67\nufgy7oiBLwOw8xpXx3TfMxhAIq4t3sp18PdnQY0xHEPl1BVunTrAypU83Hz0Ua4Uo6Jse+V9hIdz\n59W5M3c4u3ez6ZkTxdsYprzmyQOMHKni7URy+id5FsCLAFoBiM/ge8yQIUP+90lQUBCCgoJyaNb3\nSEhgQ6CSJYFJk5z5hnZx8SJXjRMmUIz692eIRcmYAweYXbJsGTBwINCnD1v9OpmRI4Eff2S3Qxvz\nXXMbISEhCEmXNfDhhx8CHizkaQtgOID7AJzN5Ps0hJJFLl9mjLFaNWDsWOcPXzh7lgex330H1K1L\nIW/b1vl+S3L8OPDxxxzs8dprTM30BjGcN49Vlhs3ctycIo+nKzH3AcgPwNXTbiOAPtf4PhXwbBAT\nQxFs2NB7Yo6JicAvv3AKelwcRap7d2c0W7JBSgq7PgYHc0rOiy9y1e0tFa6bNvFwfd48pjUqdtBS\nei/l0iWgTRv2mhg+3DtEHGDMdPVqCvnGjRSuV17JPTnD4eEMf02Zwsydnj2ZYVSypG3Pss6iRUwd\nDQ4G2rWz7U3uRgXci3ENX2jXjqPZvEXEXezbxxjq1Kmc+t6hA7MZKla07Zl7iYri4OrgYODMGe48\nuncHAgNte5Z9Jk/mIfXs2Zpl5ARUwL2cM2c4PeeJJziyyhu5eJF9VubP5+quQgWKeYcO7Mzo5MPa\njIiO5u80aRKwbh0Pn3v2ZCGOt8b/hw8HRo3i7+WNNx9fRAXcBzh1CrjvPuD55xlH9WZSUhhfnT+f\nV1QU8PDDFPMHH3Tm4Z4xwJ9/Ahs2MBtjwwYOiG7RgqGGLl2AwoVte3njpKayQGfBAmDJEt/bIXkz\nKuA+wvHjHAbxwgvsCe1t4ZSMOHyYwjF/PleyDRsym6VOHa4C69ThoADJ3zcxkd3/XGK9YQNvPC1b\nUrRbtmTXxgIF5HzyFElJfE3t28e/gVPaGytEBdyHOHaMq71q1YDx4zlp3peIieFw3MjItCsigqmV\nt912tagHBgJlywIBAdkLwaSmcg7oyZO8Tp26+vHRo2wiVb06xdol2FWr+s5N00VsLPD44/y9pk/P\nvRlDTkYF3MeIi+NkltBQTmmpXt22R57n/Pk0MU//8exZPh958lDIXVfBgld/XqAA4/AnTzJkU7gw\nxb9sWaBMmasfly/PDoxFfLyv5rlzDFvVrg388INz2zfkdlTAfRBjgO+/Bz76iJkPDz1k2yN7GMPW\nA/HxaVdc3NWfx8cztl62LHDrrRT13MzRo3zNdOwIfPaZ7+0sfAkVcB9m7Vpmp/Tty0MofSMq1yMi\ngkVir70GvP66bW+U66EC7uMcO8bGUpUqsS+JN2dDKJ7DGBYXDRjAdMHu3W17pGQFnYnp41SowMrH\nW25h4cW+fbY9UpzGX39xp/bpp0wTVPH2LVTAvZyAAHaM+/e/mTGxcKFtjxSnsGwZUzPLlwe2bmX6\no+JbaAjFh1i/nqlhffoAgwZ5b1WgkjPi4nguMns2Q2utWtn2SLkRNISSy2jZkpNeFixgN7k//7Tt\nkSJNaCjQuDHbMGzfruLt66iA+xjlynGi/F138Y386aesLlR8m5QUYNgwZpm8/z4bbBUrZtsrxdNo\nCMWHOXiQaYb793Pogq7GfJODB3k4GRAATJyo/Ux8BQ2h5HKqVWNT/v/8h82wnnoKOHHCtleKuzCG\nbRXuugvo2pWHlireuQsVcB/Hz4/tTiMiKOgNGnDgglOHJytZY+tWhktGjWLIrF8/PbTOjeifPJdw\n000cDLF+PVMNGzdmB0DFu4iIAB57jIfUjzwCbN4M1Ktn2yvFFirguYzatYGlS4HBg4EnnwSefRY4\nfdq2V8r1OHQobWjEXXexaKtPHyB/ftueKTZRAc+F+PmxOi8ykvMa69XjAOX4eNueKX/nxAkKddOm\nbGu7bx/Hnmn7VwVQAc/V3Hwz8OWXwMqVzB2vUgX48ENdkTuBs2cp1PXrs/f7nj3ABx/4fqtbJXuo\ngCuoV4/zKleu5PSf2rWZtRIebtuz3MelSxTq227j0IUdO4AvvvCuyfaKHCrgyv+oUwcYO5YzH6tU\nAdq04azKRYs4zUbxHJcuUahr1GBe9+bN7PtevrxtzxQno4U8SoYkJAA//8y0w8REpqp1786pN0rO\nSU4Gli/nYI4FCzhoYcgQzgVVFO0HrrgFY5hrPGIE51a+9BIP1sqWte2Zd7JzJzBpEnt0V6zI6fZP\nPqlhEuVqVMAVt7NnDzByJHttdOzICsBWrTQr4npERQFTp3K1ffYsdzLdu3NAs6JcCxVwxWP89RdX\nkXPnAlu2APfey0G57dtzQpDCtMy5cyna69ax8KZHD+Zy581r2zvF6aiAKyJcuMBpL/Pn88CzfHmK\neYcOwJ135i6xio0FNm0Cpk8HZs5kxWuPHkCXLjryTskenhbwjwF0AmAAnAPwLICj1/g+FfBcREoK\nBWzBAgr6yZPAww9TzB980PfymI8eZXuCDRv4cfduTsHp1Al45hmOvVOUG8HT3Qg/B9AQwO0AZgMY\nkoOfJUpISIhtF/6BE30Csu9X3rwcLDFsGHOYt2xh6feECRSzBx5gj/K5c9nmNiXF8z65i6Qk/j6j\nRrGStWJFoEkT4JdfgMTEEHzzDXDuHMX87bfti7cTX1NO9Alwrl/XIycCHp3ucWEAZ3PoixhO/GM5\n0Scg535VrsyMlYULgVOnmIp45gwwejTQujXDCg0aMAvjww+BGTNYQJSQ4DmfskJyMsvYFy4E3n0X\nuP9+oHhx4LnngF27GOtftYqHk7NmAWXKhKBFC/bkdgpOfE050SfAuX5dD/8c/v+hALoDuAygWc7d\nUXyZQoUYWujUKe1rsbEMO0RG8po6lR8PH6b4BwaywCgwEKhenT/j3DngyBGKZUAA89L9/dnj5Xpc\nvsywzqlT/JjR43PnKNj16nFH8dZbQLNmQNGiHnt6FCXbXE/AlwEoc42vvwNgHoDBV663AYwA8Jxb\nvVN8nkKFGIZo0uTqrycmsnFTZCRbqC5cyArFuDiW+8+Zw8fx8bxSU9ME3SXqrsf58zNr5uRJruzL\nlgXKlOFH1+MWLa7+vHRp3hQUxcm4KwulEoCFAK7VmXg/gOpusqMoipJbOACgRmbfkJM1Rk0A+648\nfgTAtgy+L1MHFEVRFHlmAtgJIAzArwBK23VHURRFURRFUZSreB1AKoDith25wscAtoM7iBUAnDDP\n+wsAkaBfvwFwQtlLVwC7AKQAaGzZl7YAdoOhu7cs++JiPIAocDfqFCoCWAX+3cIB9LXrDgAgAMDv\n4PstAsCndt25irxgCHiebUeucBjADtCnzXZdIRUBLAZwCM4R8JvTPX4VwI+2HElHG6Tl5n925bLN\nbQBqgYJgU8DzggfiVQDkA4XACa2g7gHQCM4S8DJggR3AGo09cMZz5Wp55g9gE4C7LfqSngEApgCY\na9uRK2RZJ6UGOnwFYKCQrazixEKkZeAuBeBqxQmF2LsB7LXtBIA7QQE/DCAJwM/g4blt1gI4b9uJ\nv3EKvMEBQAy4qytnz53/cfnKx/zgDfkvi764qADgYXABJ9EbKqtkyRcJAX8EwDFwS+A0hgI4AqAn\nnLHaTU8vMDVTIeVxda+dY1e+pmROFXCH8LtlPwDqTRgYcloFhlJsMwLAm0hbODkBA2A5gC0AXszs\nG91VqpBRwc9gAIMAPJjua5J3OScWIl3PJ1zxKRHAVAF/suqTbbQjWvYpDGaLvQauxG2TCoZ2igBY\nAiAIQIhFfzoAOA3GmoMs+vF3WgI4CaAU+N7cDe70xKkH3m0PXbmSwC2w01IOK4GHPU7gWQDrwUMf\nJ2E7Bt4MPEdxMQjOOcisAmfFwAGeEywB0M+2IxnwHoA3LPswDNzVHQIFMxZAsFWP/skQMAHEETjp\nELNmusevAphsy5F0tAUzB5w4XGsVgCbX/S7P4Q9WplUBY6hOOcQEnCfgfqAQjbDtSDpKAnB1kikI\nYA2AVvbc+Qf3wRm7zZuQlmBRCFzMPZjxt8tyEM4RcCcWIu0D8Ce4pdsG4Hu77gAAuoCrlDjwcGyR\nRV/agRkV+8EVuBOYBuAEgATweXJCP6C7wXBFGNJeS22tegTUBxAK+rQDjDs7ifvgjCyUquBzFAZG\nBZzyOlcURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVGU3Mv/A7elgMRmUci4AAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb82ed86150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import euclidean as euc\n",
    "from IPython.html.widgets import interactive\n",
    "from IPython.display import display\n",
    "\n",
    "x1 = np.array((0., 0.))\n",
    "x2 = np.array((0.5, 0.))\n",
    "def circle(t=0.5):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_aspect('equal', 'datalim')\n",
    "    c = t*x1 + (1.-t)*x2\n",
    "    r = np.linspace(0.1, 3, 5)\n",
    "    theta = np.linspace(0., 2.*np.pi, endpoint=True)\n",
    "    \n",
    "    for _r in r:\n",
    "        ax.plot(c[0] + _r*np.cos(theta), c[1] + _r*np.sin(theta), 'b')\n",
    "    plt.show()\n",
    "\n",
    "w = interactive(circle, t=(0.1, 1.0, 0.1))\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "internals": {
     "slide_type": "subslide"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy.spatial.distance import euclidean as euc\n",
    "import matplotlib.pyplot as plt\n",
    "import mpld3\n",
    "from mpld3 import plugins\n",
    "from mpld3.utils import get_id\n",
    "\n",
    "\n",
    "class Person:\n",
    "\n",
    "    def __init__(self, S, a, p=0.5):\n",
    "        self.S = S\n",
    "        self.a = a\n",
    "        self.p = p\n",
    "\n",
    "    def gather(self):\n",
    "        \"\"\"make person to participate the meeting.\n",
    "        \"\"\"\n",
    "        self.ideas = self.has_idea()\n",
    "\n",
    "    def has_idea(self):\n",
    "        \"\"\"a person has self.S ideas with self.a dimension.\n",
    "        \"\"\"\n",
    "        return list(np.random.rand(self.S, self.a))\n",
    "\n",
    "# ================================================================\n",
    "    def chose_idea(self, idea, idea2=None):\n",
    "        def nearness1(x, y, z):\n",
    "            \"\"\"calculate nearness of x for (y, z)\n",
    "            by calculating a linear combination.\n",
    "            \"\"\"\n",
    "            alpha = 1.\n",
    "            beta = 1.\n",
    "            return alpha*euc(x, y) + beta*euc(x, z)\n",
    "        \n",
    "        def nearness2(x, y, z):\n",
    "            \"\"\"calculate nearness of x for (y, z)\n",
    "            by distance between x and the dividing point of (y, z) with t.\n",
    "            \"\"\"\n",
    "            # t > 0\n",
    "            # t <= 1: interior division\n",
    "            t = 0.5 \n",
    "            x, y, z = np.array(x), np.array(y), np.array(z)\n",
    "            return euc(t*(y-x) + (1.-t)*(z-x), (0., 0.))\n",
    "        \n",
    "        if len(self.ideas) == 0:\n",
    "            return False\n",
    "        # return min(d) and its idea_id\n",
    "        if idea2 == None:\n",
    "            return min([(euc(vec, idea), idea_id) for idea_id, vec in enumerate(self.ideas)])\n",
    "        else:\n",
    "            return min([(nearness1(vec, idea, idea2), idea_id)\n",
    "                        for idea_id, vec in enumerate(self.ideas)])s\n",
    "# ================================================================\n",
    "\n",
    "class Meeting:\n",
    "\n",
    "    \"\"\"Simulate a meeting with \"simple3\" situation.\n",
    "\n",
    "    Give keyword arguments:\n",
    "\n",
    "        K = 20 # Time limit\n",
    "        N = 6 # a number of participants\n",
    "        S = 10 # a number of ideas for each participants\n",
    "        a = 2 # the dimension of an idea\n",
    "        p = 0.5 # probability that a person speak\n",
    "        draw = True # draw image or don't\n",
    "\n",
    "    Output:\n",
    "\n",
    "        self.minutes: list of\n",
    "                      ( idea(which is vector with a dimension)\n",
    "                      , who(person_id in the list \"self.membes\"))\n",
    "        self.k: stopped time (=len(self.minutes))\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, K=20, N=6, S=10, a=2, p=0.5, case=4, draw=True):\n",
    "        self.K = K\n",
    "        self.N = N\n",
    "        self.S = S\n",
    "        self.a = a\n",
    "        self.p = p\n",
    "        self.draw = draw\n",
    "        self.case = case  # case in the above cell: 4 or 5\n",
    "        self.members = []\n",
    "        self.minutes = []  # list of (idea, who)\n",
    "        self.k = 0\n",
    "\n",
    "    def gather_people(self):\n",
    "        \"\"\"gather people for the meeting.\n",
    "\n",
    "        You can edit what ideas they have in here.\n",
    "        \"\"\"\n",
    "        for n in range(self.N):\n",
    "            person = Person(self.S, self.a, self.p)\n",
    "            # person.has_idea = some_function()\n",
    "            # some_function: return list of self.S arrays with dim self.a.\n",
    "            person.gather()\n",
    "            self.members.append(person)\n",
    "\n",
    "    def progress(self):\n",
    "        \"\"\"meeting progress\n",
    "        \"\"\"\n",
    "        self.init()\n",
    "        preidea = self.subject\n",
    "        prepreidea = None\n",
    "        self.k = 1\n",
    "        while self.k < self.K + 1:\n",
    "            # l: (distance, speaker, idea_id) list for who can speak\n",
    "            l = []\n",
    "            for person_id, person in enumerate(self.members):\n",
    "                # chosed: (distance, idea_id)\n",
    "                # ====================================================\n",
    "                chosed = person.chose_idea(preidea, prepreidea)\n",
    "                # ====================================================\n",
    "                if chosed:\n",
    "                    l.append((chosed[0], person_id, chosed[1]))\n",
    "            # if no one can speak: meeting ends.\n",
    "            if len(l) == 0:\n",
    "                print \"no one can speak.\"\n",
    "                break\n",
    "            i = [(person_id, idea_id)\n",
    "                 for distance, person_id, idea_id in sorted(l)]\n",
    "\n",
    "            for person_id, idea_id in i:\n",
    "                rn = np.random.rand()\n",
    "                if rn < self.members[person_id].p:\n",
    "                    idea = self.members[person_id].ideas.pop(idea_id)\n",
    "                    self.minutes.append((idea, person_id))\n",
    "                    if self.case == 5:\n",
    "                        # =================================\n",
    "                        prepreidea = preidea\n",
    "                        preidea = idea\n",
    "                    else:\n",
    "                        prepreidea = idea\n",
    "                        # =================================\n",
    "                    self.callback()\n",
    "                    self.k += 1\n",
    "                    break\n",
    "            else:\n",
    "                self.minutes.append((self.subject, self.N))\n",
    "                self.callback()\n",
    "                self.k += 1\n",
    "\n",
    "        self.after()\n",
    "\n",
    "    def init(self):\n",
    "        self.gather_people()\n",
    "        self.subject = np.random.rand(self.a)\n",
    "        self.minutes.append((self.subject, self.N))\n",
    "        if self.draw:\n",
    "            self.fig = plt.figure(figsize=(9, 9))\n",
    "            self.ax = self.fig.add_subplot(1, 1, 1)\n",
    "            self.labels = ['subject']\n",
    "            self.s1 = [self.ax.scatter(self.subject[0], self.subject[1],\n",
    "                                       c=next(self.ax._get_lines.color_cycle))]\n",
    "            self.ax.text(\n",
    "                self.subject[0], self.subject[1], '0', fontsize=5)\n",
    "            for i, member in enumerate(self.members):\n",
    "                x = [vec[0] for vec in member.ideas]\n",
    "                y = [vec[1] for vec in member.ideas]\n",
    "                s = self.ax.scatter(\n",
    "                    x, y, c=next(self.ax._get_lines.color_cycle), alpha=0.2)\n",
    "                self.labels.append(str(i))\n",
    "                self.s1.append(s)\n",
    "\n",
    "    def callback(self):\n",
    "        if self.draw:\n",
    "            if self.minutes[-1][1] == self.N or self.minutes[-2][1] == self.N:\n",
    "                alpha = 0.2\n",
    "            else:\n",
    "                alpha = 1.0\n",
    "            ix = self.minutes[-2][0][0]\n",
    "            iy = self.minutes[-2][0][1]\n",
    "            jx = self.minutes[-1][0][0]\n",
    "            jy = self.minutes[-1][0][1]\n",
    "            l1 = self.ax.plot([ix, jx], [iy, jy], color='black', alpha=alpha)\n",
    "            self.ax.text(jx, jy, '%d' % self.k, color='blue', fontsize=12)\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "    def after(self):\n",
    "        if self.draw:\n",
    "            plugins.connect(\n",
    "                self.fig, plugins.InteractiveLegendPlugin(\n",
    "                    self.s1, self.labels, ax=self.ax))\n",
    "            mpld3.enable_notebook()\n",
    "        else:\n",
    "            print meeting.minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "internals": {
     "slide_helper": "subslide_end"
    },
    "slide_helper": "subslide_end",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fmMAwjA/KD/G73VQTCTgdKixms/S1cb2ZVtI0jVu3HpJKpTAMg2AweK4S7fl8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\nSZIkSZIkSZIkSZIkSZL61X+8ELKxLC5X2wAAAABJRU5ErkJggg==\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "    .legend-box {\n",
       "      cursor: pointer;\n",
       "    }\n",
       "    \n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el82111397683879085605306859716\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "    mpld3.register_plugin(\"interactive_legend\", InteractiveLegend);\n",
       "    InteractiveLegend.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    InteractiveLegend.prototype.constructor = InteractiveLegend;\n",
       "    InteractiveLegend.prototype.requiredProps = [\"element_ids\", \"labels\"];\n",
       "    InteractiveLegend.prototype.defaultProps = {\"ax\":null,\n",
       "                                                \"alpha_sel\":1.0,\n",
       "                                                \"alpha_unsel\":0}\n",
       "    function InteractiveLegend(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    InteractiveLegend.prototype.draw = function(){\n",
       "        var alpha_sel = this.props.alpha_sel;\n",
       "        var alpha_unsel = this.props.alpha_unsel;\n",
       "\n",
       "        var legendItems = new Array();\n",
       "        for(var i=0; i<this.props.labels.length; i++){\n",
       "            var obj = {};\n",
       "            obj.label = this.props.labels[i];\n",
       "\n",
       "            var element_id = this.props.element_ids[i];\n",
       "            mpld3_elements = [];\n",
       "            for(var j=0; j<element_id.length; j++){\n",
       "                var mpld3_element = mpld3.get_element(element_id[j], this.fig);\n",
       "\n",
       "                // mpld3_element might be null in case of Line2D instances\n",
       "                // for we pass the id for both the line and the markers. Either\n",
       "                // one might not exist on the D3 side\n",
       "                if(mpld3_element){\n",
       "                    mpld3_elements.push(mpld3_element);\n",
       "                }\n",
       "            }\n",
       "\n",
       "            obj.mpld3_elements = mpld3_elements;\n",
       "            obj.visible = false; // should become be setable from python side\n",
       "            legendItems.push(obj);\n",
       "        }\n",
       "\n",
       "        // determine the axes with which this legend is associated\n",
       "        var ax = this.props.ax\n",
       "        if(!ax){\n",
       "            ax = this.fig.axes[0];\n",
       "        } else{\n",
       "            ax = mpld3.get_element(ax, this.fig);\n",
       "        }\n",
       "\n",
       "        // add a legend group to the canvas of the figure\n",
       "        var legend = this.fig.canvas.append(\"svg:g\")\n",
       "                               .attr(\"class\", \"legend\");\n",
       "\n",
       "        // add the rectangles\n",
       "        legend.selectAll(\"rect\")\n",
       "                .data(legendItems)\n",
       "             .enter().append(\"rect\")\n",
       "                .attr(\"height\",10)\n",
       "                .attr(\"width\", 25)\n",
       "                .attr(\"x\",ax.width+10+ax.position[0])\n",
       "                .attr(\"y\",function(d,i) {\n",
       "                            return ax.position[1]+ i * 25 - 10;})\n",
       "                .attr(\"stroke\", get_color)\n",
       "                .attr(\"class\", \"legend-box\")\n",
       "                .style(\"fill\", function(d, i) {\n",
       "                            return d.visible ? get_color(d) : \"white\";})\n",
       "                .on(\"click\", click);\n",
       "\n",
       "        // add the labels\n",
       "        legend.selectAll(\"text\")\n",
       "                .data(legendItems)\n",
       "            .enter().append(\"text\")\n",
       "              .attr(\"x\", function (d) {\n",
       "                            return ax.width+10+ax.position[0] + 40;})\n",
       "              .attr(\"y\", function(d,i) {\n",
       "                            return ax.position[1]+ i * 25;})\n",
       "              .text(function(d) { return d.label });\n",
       "\n",
       "        // specify the action on click\n",
       "        function click(d,i){\n",
       "            d.visible = !d.visible;\n",
       "            d3.select(this)\n",
       "              .style(\"fill\",function(d, i) {\n",
       "                return d.visible ? get_color(d) : \"white\";\n",
       "              })\n",
       "\n",
       "            for(var i=0; i<d.mpld3_elements.length; i++){\n",
       "                var type = d.mpld3_elements[i].constructor.name;\n",
       "                if(type ==\"mpld3_Line\"){\n",
       "                    d3.select(d.mpld3_elements[i].path[0][0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else if((type==\"mpld3_PathCollection\")||\n",
       "                         (type==\"mpld3_Markers\")){\n",
       "                    d3.selectAll(d.mpld3_elements[i].pathsobj[0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel)\n",
       "                        .style(\"fill-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else{\n",
       "                    console.log(type + \" not yet supported\");\n",
       "                }\n",
       "            }\n",
       "        };\n",
       "\n",
       "        // helper function for determining the color of the rectangles\n",
       "        function get_color(d){\n",
       "            var type = d.mpld3_elements[0].constructor.name;\n",
       "            var color = \"black\";\n",
       "            if(type ==\"mpld3_Line\"){\n",
       "                color = d.mpld3_elements[0].props.edgecolor;\n",
       "            } else if((type==\"mpld3_PathCollection\")||\n",
       "                      (type==\"mpld3_Markers\")){\n",
       "                color = d.mpld3_elements[0].props.facecolors[0];\n",
       "            } else{\n",
       "                console.log(type + \" not yet supported\");\n",
       "            }\n",
       "            return color;\n",
       "        };\n",
       "    };\n",
       "    \n",
       "       mpld3.draw_figure(\"fig_el82111397683879085605306859716\", {\"axes\": [{\"xlim\": [-0.20000000000000001, 1.2000000000000002], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"0\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 5.0, \"position\": [0.23714162853034093, 0.05641172676689632], \"rotation\": -0.0, \"id\": \"el8211139768385800016\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"1\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.27192832553776647, 0.08115410050257166], \"rotation\": -0.0, \"id\": \"el8211139768385405520\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"2\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.26343077837718243, 0.03840265586908387], \"rotation\": -0.0, \"id\": \"el8211139768385408272\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"3\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.29353415056156096, 0.02366645969817649], \"rotation\": -0.0, \"id\": \"el8211139768385460176\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"4\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3386400482515677, 0.062200008783161675], \"rotation\": -0.0, \"id\": \"el8211139768385507792\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"5\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2723787000710245, 0.11672694332111488], \"rotation\": -0.0, \"id\": \"el8211139768385510480\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"6\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32412655686308933, 0.1523848209551144], \"rotation\": -0.0, \"id\": \"el8211139768385554192\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"7\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.37461836264834036, 0.1451598759827425], \"rotation\": -0.0, \"id\": \"el8211139768385593808\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"8\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32286604438651845, 0.14957433121875896], \"rotation\": -0.0, \"id\": \"el8211139768385596496\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"9\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.28473632867165866, 0.19909804268010034], \"rotation\": -0.0, \"id\": \"el8211139768385124112\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"10\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3563399147703409, 0.19106061042974953], \"rotation\": -0.0, \"id\": \"el8211139768385167824\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"11\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.36111105895741547, 0.2248711389911041], \"rotation\": -0.0, \"id\": \"el8211139768385170512\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"12\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3875070132315619, 0.2172251129580286], \"rotation\": -0.0, \"id\": \"el8211139768385210128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"13\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3854661589145725, 0.24414255062133428], \"rotation\": -0.0, \"id\": \"el8211139768385253840\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"14\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.40528959146783716, 0.2294397319223005], \"rotation\": -0.0, \"id\": \"el8211139768385256528\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"15\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.39316341718217984, 0.26242159446467284], \"rotation\": -0.0, \"id\": \"el8211139768385304336\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"16\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.46527734531060183, 0.265080724117261], \"rotation\": -0.0, \"id\": \"el8211139768385343952\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"17\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4335921183897553, 0.16615144906842383], \"rotation\": -0.0, \"id\": \"el8211139768385346640\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"18\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.48368453733406, 0.13601588736311943], \"rotation\": -0.0, \"id\": \"el8211139768386021456\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"19\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.47937267493513613, 0.11488671049137889], \"rotation\": -0.0, \"id\": \"el8211139768385984144\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"20\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4833195549328716, 0.0977718203150203], \"rotation\": -0.0, \"id\": \"el8211139768385919312\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"21\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4780197045786919, 0.09594048555114687], \"rotation\": -0.0, \"id\": \"el8211139768382862288\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"22\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5069172346105625, 0.09023286377307604], \"rotation\": -0.0, \"id\": \"el8211139768384876176\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"23\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4934911117932309, 0.03477502904903629], \"rotation\": -0.0, \"id\": 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[0.5051803590323776, 0.8701653805029094], [0.005333108789544405, 0.3533956616050158], [0.8680497619996579, 0.6596567555448603], [0.8014847605749187, 0.10439165831190977], [0.12126130512503486, 0.6135900933865723], [0.24163006560916356, 0.23322730266734837], [0.7588299924442762, 0.32926438796137236], [0.6492333876044366, 0.8739970108438933], [0.1910359263214041, 0.4942244896887018], [0.16519773699240137, 0.11998748324477548], [0.8192269549127114, 0.5533108972609273], [0.5925110234343831, 0.8318876799763587], [0.8061277742896649, 0.4728607470326933], [0.6898055931997152, 0.9311804674773354], [0.42786363009320294, 0.14161240680885834], [0.28976763152485097, 0.5898142629936965], [0.5916500501798232, 0.9115304974967949], [0.7392760754437022, 0.6888750871798488]], \"data25\": [[0.5216694776648446, 0.09327307555754805], [0.509198347759709, 0.005518274288138558]], \"data32\": [[0.6911715563664822, 0.7052368210675457], [0.2758599890200507, 0.8459217742086588], [0.5735507291405292, 0.9851255913373448], [0.27192832553776647, 0.08115410050257166], [0.08816310446445574, 0.5941466578833277], [0.5366848996617591, 0.0044176238289580505], [0.9295645735759896, 0.7643444384843427], [0.23663604841648278, 0.6545731577217926], [0.22691678225003475, 0.8310496524046254], [0.07274479924487431, 0.8694074088795241], [0.7040662582062729, 0.7178554936101222], [0.9320356528300686, 0.8996742524854163], [0.6659978869502505, 0.7740340631579036], [0.3386400482515677, 0.062200008783161675], [0.8020383694882353, 0.637172169776868], [0.5446394774024054, 0.0067639345075600765], [0.3617525714174752, 0.40903851200927177], [0.9998147401923231, 0.40595032247190077], [0.8791425874656902, 0.016743477447895727], [0.203700373627148, 0.11386858882538176], [0.31907314254803376, 0.6393617223055285], [0.49165840213762746, 0.006950181445357195], [0.30124557454408685, 0.9489631976533299], [0.12546636727325622, 0.6565816009420062], [0.24790374261612746, 0.8174544957798002], [0.6761073814470143, 0.6734533713790715], [0.7568897154561073, 0.5887711950550419], [0.7268205183346904, 0.5128928528495856], [0.9232721123186806, 0.8628122693681743], [0.24945413777071423, 0.4867416954135987], [0.7436338072556684, 0.97621203496693], [0.07922805822438461, 0.5097711558460495], [0.13771183332058423, 0.7603689675651301], [0.048093636593000255, 0.9275608863771043], [0.46527734531060183, 0.265080724117261], [0.5094708723139594, 0.7691871311844466], [0.16369015313221214, 0.664526062181427], [0.7234299040595819, 0.7682621212509465], [0.6026484849231424, 0.3550579194090978], [0.741014030262981, 0.06625694840516128], [0.8603591482364592, 0.23494388806983668], [0.005740144518757262, 0.4278130140758031], [0.6838374848716113, 0.9033250513133646], [0.37461836264834036, 0.1451598759827425], [0.7708935186874152, 0.723639897989372], [0.529065739249474, 0.6240616286318342], [0.8782490370946329, 0.7030210363945232], [0.6042629428966572, 0.3750364159486138], [0.5296911748902802, 0.35597140415471495], [0.7976091285799842, 0.9188110403363081]], \"data31\": [[0.23714162853034093, 0.05641172676689632]], \"data30\": [[0.6170543292714906, 0.05292527745663855], [0.5446394774024054, 0.0067639345075600765]], \"data19\": [[0.48368453733406, 0.13601588736311943], [0.47937267493513613, 0.11488671049137889]], \"data18\": [[0.4335921183897553, 0.16615144906842383], [0.48368453733406, 0.13601588736311943]], \"data15\": [[0.40528959146783716, 0.2294397319223005], [0.39316341718217984, 0.26242159446467284]], \"data14\": [[0.3854661589145725, 0.24414255062133428], [0.40528959146783716, 0.2294397319223005]], \"data17\": [[0.46527734531060183, 0.265080724117261], [0.4335921183897553, 0.16615144906842383]], \"data16\": [[0.39316341718217984, 0.26242159446467284], [0.46527734531060183, 0.265080724117261]], \"data11\": [[0.3563399147703409, 0.19106061042974953], [0.36111105895741547, 0.2248711389911041]], \"data10\": [[0.28473632867165866, 0.19909804268010034], [0.3563399147703409, 0.19106061042974953]], \"data13\": [[0.3875070132315619, 0.2172251129580286], [0.3854661589145725, 0.24414255062133428]], \"data12\": [[0.36111105895741547, 0.2248711389911041], [0.3875070132315619, 0.2172251129580286]], \"data29\": [[0.600889462494459, 0.07044942886719041], [0.6170543292714906, 0.05292527745663855]], \"data33\": [[0.5216694776648446, 0.09327307555754805], [0.0101537908764322, 0.1731147795709984], [0.9561078097265406, 0.2597881674288831], [0.01380332337834278, 0.2062213006075494], [0.9038500692956296, 0.9259029973918284], [0.5717888894232105, 0.9219030223439333], [0.7025554606852864, 0.8101346464671207], [0.0482376449561166, 0.1137744719099203], [0.8585288462065549, 0.8632416786728834], [0.10649082191032477, 0.510162127772978], [0.284980356246779, 0.35671775984565146], [0.9580003171339195, 0.09213506082159795], [0.2831089318265546, 0.815196690093884], [0.4623828633654228, 0.38878001304924403], [0.28473632867165866, 0.19909804268010034], [0.4351174695853982, 0.5332252170870508], [0.8870449227160113, 0.30403820413942917], [0.22406514285568424, 0.9198213558874309], [0.13446349926652545, 0.8101648036715909], [0.46731075767342733, 0.6017128592159628], [0.4780197045786919, 0.09594048555114687], [0.4094006104438349, 0.47189700485281827], [0.3875070132315619, 0.2172251129580286], [0.052766949540918096, 0.7533002852274978], [0.6635924038599036, 0.9923352964353602], [0.15322039064503368, 0.6623805927328045], [0.48056571444439844, 0.41866201868744524], [0.02960353036223784, 0.8008520343588575], [0.2500311659339889, 0.3675752380002075], [0.7966901950800198, 0.9696594829385997], [0.613043927731484, 0.8798264817280106], [0.2346623224032226, 0.8901578875238265], [0.3190865889733645, 0.9030753196590705], [0.6424445639922958, 0.4214783860525796], [0.34341553258419444, 0.4849077289669884], [0.274276308281172, 0.4807793671830263], [0.6605552702018392, 0.8169907215976037], [0.7935217888603185, 0.5033901838903576], [0.1952540459751534, 0.9186368373876757], [0.2144120168280771, 0.46551523893071145], [0.6874196093095728, 0.137617271752018], [0.796487827417768, 0.5474978676190868], [0.8257912705713375, 0.587816095893942], [0.3854661589145725, 0.24414255062133428], [0.9178081625493817, 0.43651966917708607], [0.32286604438651845, 0.14957433121875896], [0.9574700093463024, 0.9310671255473607], [0.36111105895741547, 0.2248711389911041], [0.15260442225227022, 0.4547120083820938], [0.39316341718217984, 0.26242159446467284]]}, \"id\": \"el8211139768387908560\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
       "         \n",
       "    mpld3.register_plugin(\"interactive_legend\", InteractiveLegend);\n",
       "    InteractiveLegend.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    InteractiveLegend.prototype.constructor = InteractiveLegend;\n",
       "    InteractiveLegend.prototype.requiredProps = [\"element_ids\", \"labels\"];\n",
       "    InteractiveLegend.prototype.defaultProps = {\"ax\":null,\n",
       "                                                \"alpha_sel\":1.0,\n",
       "                                                \"alpha_unsel\":0}\n",
       "    function InteractiveLegend(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    InteractiveLegend.prototype.draw = function(){\n",
       "        var alpha_sel = this.props.alpha_sel;\n",
       "        var alpha_unsel = this.props.alpha_unsel;\n",
       "\n",
       "        var legendItems = new Array();\n",
       "        for(var i=0; i<this.props.labels.length; i++){\n",
       "            var obj = {};\n",
       "            obj.label = this.props.labels[i];\n",
       "\n",
       "            var element_id = this.props.element_ids[i];\n",
       "            mpld3_elements = [];\n",
       "            for(var j=0; j<element_id.length; j++){\n",
       "                var mpld3_element = mpld3.get_element(element_id[j], this.fig);\n",
       "\n",
       "                // mpld3_element might be null in case of Line2D instances\n",
       "                // for we pass the id for both the line and the markers. Either\n",
       "                // one might not exist on the D3 side\n",
       "                if(mpld3_element){\n",
       "                    mpld3_elements.push(mpld3_element);\n",
       "                }\n",
       "            }\n",
       "\n",
       "            obj.mpld3_elements = mpld3_elements;\n",
       "            obj.visible = false; // should become be setable from python side\n",
       "            legendItems.push(obj);\n",
       "        }\n",
       "\n",
       "        // determine the axes with which this legend is associated\n",
       "        var ax = this.props.ax\n",
       "        if(!ax){\n",
       "            ax = this.fig.axes[0];\n",
       "        } else{\n",
       "            ax = mpld3.get_element(ax, this.fig);\n",
       "        }\n",
       "\n",
       "        // add a legend group to the canvas of the figure\n",
       "        var legend = this.fig.canvas.append(\"svg:g\")\n",
       "                               .attr(\"class\", \"legend\");\n",
       "\n",
       "        // add the rectangles\n",
       "        legend.selectAll(\"rect\")\n",
       "                .data(legendItems)\n",
       "             .enter().append(\"rect\")\n",
       "                .attr(\"height\",10)\n",
       "                .attr(\"width\", 25)\n",
       "                .attr(\"x\",ax.width+10+ax.position[0])\n",
       "                .attr(\"y\",function(d,i) {\n",
       "                            return ax.position[1]+ i * 25 - 10;})\n",
       "                .attr(\"stroke\", get_color)\n",
       "                .attr(\"class\", \"legend-box\")\n",
       "                .style(\"fill\", function(d, i) {\n",
       "                            return d.visible ? get_color(d) : \"white\";})\n",
       "                .on(\"click\", click);\n",
       "\n",
       "        // add the labels\n",
       "        legend.selectAll(\"text\")\n",
       "                .data(legendItems)\n",
       "            .enter().append(\"text\")\n",
       "              .attr(\"x\", function (d) {\n",
       "                            return ax.width+10+ax.position[0] + 40;})\n",
       "              .attr(\"y\", function(d,i) {\n",
       "                            return ax.position[1]+ i * 25;})\n",
       "              .text(function(d) { return d.label });\n",
       "\n",
       "        // specify the action on click\n",
       "        function click(d,i){\n",
       "            d.visible = !d.visible;\n",
       "            d3.select(this)\n",
       "              .style(\"fill\",function(d, i) {\n",
       "                return d.visible ? get_color(d) : \"white\";\n",
       "              })\n",
       "\n",
       "            for(var i=0; i<d.mpld3_elements.length; i++){\n",
       "                var type = d.mpld3_elements[i].constructor.name;\n",
       "                if(type ==\"mpld3_Line\"){\n",
       "                    d3.select(d.mpld3_elements[i].path[0][0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else if((type==\"mpld3_PathCollection\")||\n",
       "                         (type==\"mpld3_Markers\")){\n",
       "                    d3.selectAll(d.mpld3_elements[i].pathsobj[0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel)\n",
       "                        .style(\"fill-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else{\n",
       "                    console.log(type + \" not yet supported\");\n",
       "                }\n",
       "            }\n",
       "        };\n",
       "\n",
       "        // helper function for determining the color of the rectangles\n",
       "        function get_color(d){\n",
       "            var type = d.mpld3_elements[0].constructor.name;\n",
       "            var color = \"black\";\n",
       "            if(type ==\"mpld3_Line\"){\n",
       "                color = d.mpld3_elements[0].props.edgecolor;\n",
       "            } else if((type==\"mpld3_PathCollection\")||\n",
       "                      (type==\"mpld3_Markers\")){\n",
       "                color = d.mpld3_elements[0].props.facecolors[0];\n",
       "            } else{\n",
       "                console.log(type + \" not yet supported\");\n",
       "            }\n",
       "            return color;\n",
       "        };\n",
       "    };\n",
       "    \n",
       "         mpld3.draw_figure(\"fig_el82111397683879085605306859716\", {\"axes\": [{\"xlim\": [-0.20000000000000001, 1.2000000000000002], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"0\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 5.0, \"position\": [0.23714162853034093, 0.05641172676689632], \"rotation\": -0.0, \"id\": \"el8211139768385800016\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"1\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.27192832553776647, 0.08115410050257166], \"rotation\": -0.0, \"id\": \"el8211139768385405520\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"2\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.26343077837718243, 0.03840265586908387], \"rotation\": -0.0, \"id\": \"el8211139768385408272\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"3\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.29353415056156096, 0.02366645969817649], \"rotation\": -0.0, \"id\": \"el8211139768385460176\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"4\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3386400482515677, 0.062200008783161675], \"rotation\": -0.0, \"id\": \"el8211139768385507792\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"5\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2723787000710245, 0.11672694332111488], \"rotation\": -0.0, \"id\": \"el8211139768385510480\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"6\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32412655686308933, 0.1523848209551144], \"rotation\": -0.0, \"id\": \"el8211139768385554192\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"7\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.37461836264834036, 0.1451598759827425], \"rotation\": -0.0, \"id\": \"el8211139768385593808\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"8\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32286604438651845, 0.14957433121875896], \"rotation\": -0.0, \"id\": \"el8211139768385596496\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"9\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.28473632867165866, 0.19909804268010034], \"rotation\": -0.0, 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[0.7976091285799842, 0.9188110403363081]], \"data31\": [[0.23714162853034093, 0.05641172676689632]], \"data30\": [[0.6170543292714906, 0.05292527745663855], [0.5446394774024054, 0.0067639345075600765]], \"data19\": [[0.48368453733406, 0.13601588736311943], [0.47937267493513613, 0.11488671049137889]], \"data18\": [[0.4335921183897553, 0.16615144906842383], [0.48368453733406, 0.13601588736311943]], \"data15\": [[0.40528959146783716, 0.2294397319223005], [0.39316341718217984, 0.26242159446467284]], \"data14\": [[0.3854661589145725, 0.24414255062133428], [0.40528959146783716, 0.2294397319223005]], \"data17\": [[0.46527734531060183, 0.265080724117261], [0.4335921183897553, 0.16615144906842383]], \"data16\": [[0.39316341718217984, 0.26242159446467284], [0.46527734531060183, 0.265080724117261]], \"data11\": [[0.3563399147703409, 0.19106061042974953], [0.36111105895741547, 0.2248711389911041]], \"data10\": [[0.28473632867165866, 0.19909804268010034], [0.3563399147703409, 0.19106061042974953]], \"data13\": [[0.3875070132315619, 0.2172251129580286], [0.3854661589145725, 0.24414255062133428]], \"data12\": [[0.36111105895741547, 0.2248711389911041], [0.3875070132315619, 0.2172251129580286]], \"data29\": [[0.600889462494459, 0.07044942886719041], [0.6170543292714906, 0.05292527745663855]], \"data33\": [[0.5216694776648446, 0.09327307555754805], [0.0101537908764322, 0.1731147795709984], [0.9561078097265406, 0.2597881674288831], [0.01380332337834278, 0.2062213006075494], [0.9038500692956296, 0.9259029973918284], [0.5717888894232105, 0.9219030223439333], [0.7025554606852864, 0.8101346464671207], [0.0482376449561166, 0.1137744719099203], [0.8585288462065549, 0.8632416786728834], [0.10649082191032477, 0.510162127772978], [0.284980356246779, 0.35671775984565146], [0.9580003171339195, 0.09213506082159795], [0.2831089318265546, 0.815196690093884], [0.4623828633654228, 0.38878001304924403], [0.28473632867165866, 0.19909804268010034], [0.4351174695853982, 0.5332252170870508], [0.8870449227160113, 0.30403820413942917], [0.22406514285568424, 0.9198213558874309], [0.13446349926652545, 0.8101648036715909], [0.46731075767342733, 0.6017128592159628], [0.4780197045786919, 0.09594048555114687], [0.4094006104438349, 0.47189700485281827], [0.3875070132315619, 0.2172251129580286], [0.052766949540918096, 0.7533002852274978], [0.6635924038599036, 0.9923352964353602], [0.15322039064503368, 0.6623805927328045], [0.48056571444439844, 0.41866201868744524], [0.02960353036223784, 0.8008520343588575], [0.2500311659339889, 0.3675752380002075], [0.7966901950800198, 0.9696594829385997], [0.613043927731484, 0.8798264817280106], [0.2346623224032226, 0.8901578875238265], [0.3190865889733645, 0.9030753196590705], [0.6424445639922958, 0.4214783860525796], [0.34341553258419444, 0.4849077289669884], [0.274276308281172, 0.4807793671830263], [0.6605552702018392, 0.8169907215976037], [0.7935217888603185, 0.5033901838903576], [0.1952540459751534, 0.9186368373876757], [0.2144120168280771, 0.46551523893071145], [0.6874196093095728, 0.137617271752018], [0.796487827417768, 0.5474978676190868], [0.8257912705713375, 0.587816095893942], [0.3854661589145725, 0.24414255062133428], [0.9178081625493817, 0.43651966917708607], [0.32286604438651845, 0.14957433121875896], [0.9574700093463024, 0.9310671255473607], [0.36111105895741547, 0.2248711389911041], [0.15260442225227022, 0.4547120083820938], [0.39316341718217984, 0.26242159446467284]]}, \"id\": \"el8211139768387908560\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
       "                 \n",
       "    mpld3.register_plugin(\"interactive_legend\", InteractiveLegend);\n",
       "    InteractiveLegend.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    InteractiveLegend.prototype.constructor = InteractiveLegend;\n",
       "    InteractiveLegend.prototype.requiredProps = [\"element_ids\", \"labels\"];\n",
       "    InteractiveLegend.prototype.defaultProps = {\"ax\":null,\n",
       "                                                \"alpha_sel\":1.0,\n",
       "                                                \"alpha_unsel\":0}\n",
       "    function InteractiveLegend(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    InteractiveLegend.prototype.draw = function(){\n",
       "        var alpha_sel = this.props.alpha_sel;\n",
       "        var alpha_unsel = this.props.alpha_unsel;\n",
       "\n",
       "        var legendItems = new Array();\n",
       "        for(var i=0; i<this.props.labels.length; i++){\n",
       "            var obj = {};\n",
       "            obj.label = this.props.labels[i];\n",
       "\n",
       "            var element_id = this.props.element_ids[i];\n",
       "            mpld3_elements = [];\n",
       "            for(var j=0; j<element_id.length; j++){\n",
       "                var mpld3_element = mpld3.get_element(element_id[j], this.fig);\n",
       "\n",
       "                // mpld3_element might be null in case of Line2D instances\n",
       "                // for we pass the id for both the line and the markers. Either\n",
       "                // one might not exist on the D3 side\n",
       "                if(mpld3_element){\n",
       "                    mpld3_elements.push(mpld3_element);\n",
       "                }\n",
       "            }\n",
       "\n",
       "            obj.mpld3_elements = mpld3_elements;\n",
       "            obj.visible = false; // should become be setable from python side\n",
       "            legendItems.push(obj);\n",
       "        }\n",
       "\n",
       "        // determine the axes with which this legend is associated\n",
       "        var ax = this.props.ax\n",
       "        if(!ax){\n",
       "            ax = this.fig.axes[0];\n",
       "        } else{\n",
       "            ax = mpld3.get_element(ax, this.fig);\n",
       "        }\n",
       "\n",
       "        // add a legend group to the canvas of the figure\n",
       "        var legend = this.fig.canvas.append(\"svg:g\")\n",
       "                               .attr(\"class\", \"legend\");\n",
       "\n",
       "        // add the rectangles\n",
       "        legend.selectAll(\"rect\")\n",
       "                .data(legendItems)\n",
       "             .enter().append(\"rect\")\n",
       "                .attr(\"height\",10)\n",
       "                .attr(\"width\", 25)\n",
       "                .attr(\"x\",ax.width+10+ax.position[0])\n",
       "                .attr(\"y\",function(d,i) {\n",
       "                            return ax.position[1]+ i * 25 - 10;})\n",
       "                .attr(\"stroke\", get_color)\n",
       "                .attr(\"class\", \"legend-box\")\n",
       "                .style(\"fill\", function(d, i) {\n",
       "                            return d.visible ? get_color(d) : \"white\";})\n",
       "                .on(\"click\", click);\n",
       "\n",
       "        // add the labels\n",
       "        legend.selectAll(\"text\")\n",
       "                .data(legendItems)\n",
       "            .enter().append(\"text\")\n",
       "              .attr(\"x\", function (d) {\n",
       "                            return ax.width+10+ax.position[0] + 40;})\n",
       "              .attr(\"y\", function(d,i) {\n",
       "                            return ax.position[1]+ i * 25;})\n",
       "              .text(function(d) { return d.label });\n",
       "\n",
       "        // specify the action on click\n",
       "        function click(d,i){\n",
       "            d.visible = !d.visible;\n",
       "            d3.select(this)\n",
       "              .style(\"fill\",function(d, i) {\n",
       "                return d.visible ? get_color(d) : \"white\";\n",
       "              })\n",
       "\n",
       "            for(var i=0; i<d.mpld3_elements.length; i++){\n",
       "                var type = d.mpld3_elements[i].constructor.name;\n",
       "                if(type ==\"mpld3_Line\"){\n",
       "                    d3.select(d.mpld3_elements[i].path[0][0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else if((type==\"mpld3_PathCollection\")||\n",
       "                         (type==\"mpld3_Markers\")){\n",
       "                    d3.selectAll(d.mpld3_elements[i].pathsobj[0])\n",
       "                        .style(\"stroke-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel)\n",
       "                        .style(\"fill-opacity\",\n",
       "                                d.visible ? alpha_sel : alpha_unsel);\n",
       "                } else{\n",
       "                    console.log(type + \" not yet supported\");\n",
       "                }\n",
       "            }\n",
       "        };\n",
       "\n",
       "        // helper function for determining the color of the rectangles\n",
       "        function get_color(d){\n",
       "            var type = d.mpld3_elements[0].constructor.name;\n",
       "            var color = \"black\";\n",
       "            if(type ==\"mpld3_Line\"){\n",
       "                color = d.mpld3_elements[0].props.edgecolor;\n",
       "            } else if((type==\"mpld3_PathCollection\")||\n",
       "                      (type==\"mpld3_Markers\")){\n",
       "                color = d.mpld3_elements[0].props.facecolors[0];\n",
       "            } else{\n",
       "                console.log(type + \" not yet supported\");\n",
       "            }\n",
       "            return color;\n",
       "        };\n",
       "    };\n",
       "    \n",
       "                 mpld3.draw_figure(\"fig_el82111397683879085605306859716\", {\"axes\": [{\"xlim\": [-0.20000000000000001, 1.2000000000000002], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"0\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 5.0, \"position\": [0.23714162853034093, 0.05641172676689632], \"rotation\": -0.0, \"id\": \"el8211139768385800016\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"1\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.27192832553776647, 0.08115410050257166], \"rotation\": -0.0, \"id\": \"el8211139768385405520\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"2\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.26343077837718243, 0.03840265586908387], \"rotation\": -0.0, \"id\": \"el8211139768385408272\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"3\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.29353415056156096, 0.02366645969817649], \"rotation\": -0.0, \"id\": \"el8211139768385460176\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"4\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3386400482515677, 0.062200008783161675], \"rotation\": -0.0, \"id\": \"el8211139768385507792\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"5\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2723787000710245, 0.11672694332111488], \"rotation\": -0.0, \"id\": \"el8211139768385510480\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"6\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32412655686308933, 0.1523848209551144], \"rotation\": -0.0, \"id\": \"el8211139768385554192\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"7\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.37461836264834036, 0.1451598759827425], \"rotation\": -0.0, \"id\": \"el8211139768385593808\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"8\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.32286604438651845, 0.14957433121875896], \"rotation\": -0.0, \"id\": \"el8211139768385596496\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"9\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.28473632867165866, 0.19909804268010034], \"rotation\": -0.0, \"id\": \"el8211139768385124112\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"10\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3563399147703409, 0.19106061042974953], \"rotation\": -0.0, \"id\": \"el8211139768385167824\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"11\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.36111105895741547, 0.2248711389911041], \"rotation\": -0.0, \"id\": \"el8211139768385170512\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"12\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3875070132315619, 0.2172251129580286], \"rotation\": -0.0, \"id\": \"el8211139768385210128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"13\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3854661589145725, 0.24414255062133428], \"rotation\": -0.0, \"id\": \"el8211139768385253840\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"14\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.40528959146783716, 0.2294397319223005], \"rotation\": -0.0, \"id\": \"el8211139768385256528\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"15\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.39316341718217984, 0.26242159446467284], \"rotation\": -0.0, \"id\": \"el8211139768385304336\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"16\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.46527734531060183, 0.265080724117261], \"rotation\": -0.0, \"id\": \"el8211139768385343952\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"17\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4335921183897553, 0.16615144906842383], \"rotation\": -0.0, \"id\": \"el8211139768385346640\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"18\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.48368453733406, 0.13601588736311943], \"rotation\": -0.0, \"id\": \"el8211139768386021456\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"19\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.47937267493513613, 0.11488671049137889], \"rotation\": -0.0, \"id\": \"el8211139768385984144\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"20\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4833195549328716, 0.0977718203150203], \"rotation\": -0.0, \"id\": \"el8211139768385919312\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"21\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4780197045786919, 0.09594048555114687], \"rotation\": -0.0, \"id\": \"el8211139768382862288\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"22\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5069172346105625, 0.09023286377307604], \"rotation\": -0.0, \"id\": \"el8211139768384876176\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"23\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4934911117932309, 0.03477502904903629], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e5d1cb3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meeting = Meeting(K=30, N=6, S=50, a=2, p=0.6, case=5)\n",
    "meeting.progress()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "slide_end",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "見られる性質について(観察)\n",
    "\n",
    "二つ前の結果まで参照するので、全体の描く軌跡の大きさは、一つ前のみの点を参照してシミュレーションを行った場合よりも小さくなる。\n",
    "\n",
    "戻りの効果が強くなるので、2のケースに比べると同じ$p$でもギザギザした軌道になっているように見える。"
   ]
  }
 ],
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