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   "source": [
    "# 例3"
   ]
  },
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   "source": [
    "簡単な例の3つ目を考えてみる。今までのモデルの主眼にあった考えは、人を選ぶ確率とそのうえで意見が選ばれる確率は独立であるというものであった。しかし、これまで考えたように、単にそのようなモデルを考えただけだと人数の効果をうまく反映できないように思える。したがって、次に考えるモデルは、人がそれぞれ意見をもっており、前の発言に関して意見が近い、または反対意見を持っている時にはその意見をもつ人が発言する確率が高い、というようなものを考える。"
   ]
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   "source": [
    "アルゴリズムとしては、はじめに参加者たち$P=\\{i\\ |\\ i = 0, 1, 2,\\cdots , n-1\\}$はそれぞれに独自の$s_{i}$個の意見を持っているとする。このときの意見$X$は状態空間$S$上のある一点を指しており、ベクトルで記述されるようなものである。意見すべての集合は互いに重なりあうことはなく(全く同じ点に異なる人の意見が存在することはない。)、それぞれが異なる分布を持っていてもよい。はじめに議題、すなわち時刻$0$における意見が作られ、次に時刻1では、それぞれの参加者の意見のうちから、一番その意見によって発言されやすいものをそれぞれ一つずつ選ぶ。これを大きい順に並べ替えて、一番発言しやすい人に発言の機会が生まれる。それぞれの人には発言力が割り当てられており、その値によって確率的に発言するかどうかが決まる。すなわち、意見の強さが試行の順番を決め、自分の番が回ってきたときの発言の確率は一定であるという場面を考えている。また別の方法としては、この発言力と意見のしやすさを合わせた指標で発言するかどうかを決めるという方法があるが、まずは簡単のために、はじめのそれぞれが自分の中で一番いい意見を持ち寄り、その順番に発言の権利が与えられていくような場面を考えることにする。また、最終的に誰も発言できない場合もあるので、そのときには沈黙があったとして時間は1進めて同じ意見について先程と同じ試行を繰り返すこととする。"
   ]
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   "source": [
    "また、以前の意見が次の発言の意見にどう影響するかの考え方にもいくつかの候補が考えられる。\n",
    "\n",
    "単純な順に並べていくことにすると、\n",
    "\n",
    "1. 影響なし(独立)\n",
    "2. 議題\n",
    "3. 一つ前の意見\n",
    "4. 一つ前の意見 + 議題\n",
    "5. 二つ前までの意見\n",
    "6. 二つ前までの意見 + 議題\n",
    "7. それ以上前の意見"
   ]
  },
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   },
   "source": [
    "## 1の場合"
   ]
  },
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   "source": [
    "1の場合には意見の質を測るものはなくなってしまうので、意見の選び方はランダムになっていると見てもよい。そうすると、発言の順番は単なる組み合わせになり、人$i$が$r+1$($r = 1, 2, \\cdots , n-1$)番目に発言する権利を得たとき、自分までその権利が回ってくる確率は、以下のように期待値であらわすことが出来る。\n",
    "\n",
    "$$p_{r+1}(i) = \\frac{\\sum_{<j_{0}, \\cdots ,j_{r-1}>_{r} = J}\\prod_{j\\in J}(1-P_{j})}{_{n-1}C_{r}}$$\n",
    "\n",
    "ここで$J = <j_{0}, j_{1}, \\cdots ,j_{r-1}>_{r}$は、$i$を除く$n-1$個の要素から$r$個取り出したときの人の組み合わせのうちの1つのパターンをあらわすことにする($j_{0}$などはその実際の値をあらわす)。このときどの組み合わせが起こる確率も等しいので、その$J$すべてに対して和をとって、その組み合わせのパターンの数$_{n-1}C_{r}$で割ることで期待値を求めることができている。\n",
    "\n",
    "また、\n",
    "\n",
    "$$p_{0}(i) = 1$$\n",
    "\n",
    "である。"
   ]
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   "source": [
    "人$i$が発言権の順番で$r$番目になる確率は等しいわけなので、したがって$r$に対しても平均をすることができて、\n",
    "\n",
    "$$p(i) = \\frac{\\sum_{r=0}^{n}p_{r}(i)P_{i}}{n}.$$\n",
    "\n",
    "このとき得られた確率は人$i$によって異なり、毎時刻ごとにその確率でそれぞれの人が発言する確率があるということになる。沈黙の(平均的な)確率は\n",
    "\n",
    "$$p(s) = 1- \\sum_{i}p(i)$$\n",
    "\n",
    "である。\n",
    "\n",
    "このように整理すれば、各時刻でそれぞれの人が意見を発言する確率が求められるので、簡単な確率過程に帰着できた。"
   ]
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   },
   "source": [
    "## 2、3の場合"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "次に2、または3の場合について考える。どちらの場合も参照とするデータは一つだけであるという点で共通であり、違いはそのデータが時系列にしたがって変化するかという点だけである。\n",
    "\n",
    "シミュレーションの設定を考える。\n",
    "\n",
    "意見は$a$次元上の一点であり、意見が近い場合に発言されやすいと考えることにする。意見の近さは、ユークリッド距離\n",
    "\n",
    "$$d(x, y) = \\sqrt{(x_{1}-y_{1})^{2} + (x_{2} -y_{2})^{2} + \\cdots +(x_{a}-y_{a})^{2}}$$\n",
    "\n",
    "の大小で測ることにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=case_2and3.html width=100% height=350></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML at 0x7f806ac9c2d0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=case_2and3.html width=100% height=350></iframe>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "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",
    "    def chose_idea(self, idea):\n",
    "        if len(self.ideas) == 0:\n",
    "            return False\n",
    "        # return min(d) and its idea_id\n",
    "        return min([(euc(vec, idea), idea_id) for idea_id, vec in enumerate(self.ideas)])\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, draw=True, case=2):\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: 2 or 3\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",
    "        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",
    "                chosed = person.chose_idea(preidea)\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 == 3:\n",
    "                        preidea = idea\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"
   ]
  },
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ES9taXyc5P49i2wT6+xmdnr4wHwKmaZLL5bBtm0gkIvUb4tKTpEQI0fIsy8K2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blNX1c7RbEscGqvTaYStk4oFGJx8VUaG/OfXpqmMTi4zN13v63AI9s4mqbx7LPP8rGPfQxJknjq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\nUghKysvLaXE6mbh0CclsRp9Mc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      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "    .legend-box {\n",
       "      cursor: pointer;\n",
       "    }\n",
       "    \n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el81341406539388169764828141852\"></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_el81341406539388169764828141852\", {\"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\": [0.2957251643870865, 0.3517186350468202], \"rotation\": -0.0, \"id\": \"el8134140653937056336\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"1\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3027832109452653, 0.3150995940031973], \"rotation\": -0.0, \"id\": \"el8134140653936674128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"2\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.288097262941026, 0.28477941536890594], \"rotation\": -0.0, \"id\": \"el8134140653936722000\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"3\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.21393434432255531, 0.32426736522559696], \"rotation\": -0.0, \"id\": \"el8134140653936724688\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"4\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2424615313215681, 0.35244657077934693], \"rotation\": -0.0, \"id\": \"el8134140653936760016\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"5\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2501071491010529, 0.4588009750227334], \"rotation\": -0.0, \"id\": \"el8134140653936762704\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"6\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2654431508693852, 0.418836957244923], \"rotation\": -0.0, \"id\": \"el8134140653936814608\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"7\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3226324773060669, 0.4430164085341147], \"rotation\": -0.0, \"id\": \"el8134140653936850128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"8\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3423537561589167, 0.4813579568079882], \"rotation\": -0.0, \"id\": \"el8134140653936852816\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"9\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3446726620796655, 0.4531377669813311], \"rotation\": -0.0, \"id\": \"el8134140653936908816\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"10\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4109405837143316, 0.4627536705692685], \"rotation\": -0.0, \"id\": \"el8134140653936424144\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"11\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.47203884360824744, 0.40808221962063285], \"rotation\": -0.0, \"id\": \"el8134140653936426832\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"12\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4925559489276454, 0.4146001660683232], \"rotation\": -0.0, \"id\": \"el8134140653936474640\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"13\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.487835183991694, 0.38865118235540497], \"rotation\": -0.0, \"id\": \"el8134140653936514256\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"14\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4580357287981798, 0.3694542793931189], \"rotation\": -0.0, \"id\": \"el8134140653936516944\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"15\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5064675774339333, 0.35367048639641907], \"rotation\": -0.0, \"id\": \"el8134140653936568848\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"16\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5114541436383395, 0.33612996705974507], \"rotation\": -0.0, \"id\": \"el8134140653936608464\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"17\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5048811494318495, 0.32832605954916516], \"rotation\": -0.0, \"id\": \"el8134140653936611152\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"18\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4609735216012142, 0.34576531695578083], \"rotation\": -0.0, \"id\": \"el8134140653937281872\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"19\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.4416240443980045, 0.2902526544127546], \"rotation\": -0.0, \"id\": \"el8134140653937244432\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"20\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3789111372669951, 0.30665706930396086], \"rotation\": -0.0, \"id\": \"el8134140653937201808\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"21\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2633284279149335, 0.31971248709119493], \"rotation\": -0.0, \"id\": \"el8134140653937701776\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"22\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.27605398901407674, 0.38719510363942233], \"rotation\": -0.0, \"id\": \"el8134140653936189904\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"23\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.17632462307380736, 0.3596867136066353], \"rotation\": -0.0, \"id\": \"el8134140653936192592\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"24\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.17091672886003917, 0.41947488715907166], \"rotation\": -0.0, \"id\": \"el8134140653936232208\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"25\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.20367784421980029, 0.532324198788909], \"rotation\": -0.0, \"id\": \"el8134140653936284112\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"26\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.15414015580476548, 0.49399719328881564], \"rotation\": -0.0, \"id\": \"el8134140653936286800\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"27\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.18721383941354697, 0.46452788960677827], \"rotation\": -0.0, \"id\": \"el8134140653936330512\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"28\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.11884848642481083, 0.5275425647996925], \"rotation\": -0.0, \"id\": \"el8134140653936374224\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"29\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.10074672381770744, 0.4625075253231755], \"rotation\": -0.0, \"id\": \"el8134140653936376912\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"30\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.07710420142218266, 0.47596708758157136], \"rotation\": -0.0, \"id\": \"el8134140653935896336\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.20000000000000001, 1.2000000000000002], \"ylim\": [-0.20000000000000001, 1.2000000000000002], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 9, \"tickvalues\": null}], \"lines\": [{\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 0.2, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data01\", \"id\": \"el8134140653937056144\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data02\", \"id\": \"el8134140653936675344\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data03\", \"id\": \"el8134140653936723152\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data04\", \"id\": \"el8134140653936725840\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data05\", \"id\": \"el8134140653936761168\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data06\", \"id\": \"el8134140653936813072\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data07\", \"id\": \"el8134140653936815760\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data08\", \"id\": \"el8134140653936851280\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data09\", \"id\": \"el8134140653936907280\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data10\", \"id\": \"el8134140653936909968\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data11\", \"id\": \"el8134140653936425296\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data12\", \"id\": \"el8134140653936473104\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data13\", \"id\": \"el8134140653936475792\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data14\", \"id\": \"el8134140653936515408\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data15\", \"id\": \"el8134140653936567312\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data16\", \"id\": \"el8134140653936570000\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data17\", \"id\": \"el8134140653936609616\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data18\", \"id\": \"el8134140653936649232\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data19\", \"id\": \"el8134140653937280528\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"none\", \"zorder\": 2, \"alpha\": 1.0, \"xindex\": 0, \"linewidth\": 1.0, \"data\": \"data20\", \"id\": \"el8134140653937202704\"}, {\"color\": \"#000000\", \"yindex\": 1, \"coordinates\": \"data\", 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       "   }(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",
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[0.9587378416572754, 0.6660451442395211], [0.7869904275554527, 0.6975254314063837], [0.9852595116169853, 0.14828893774466123], [0.17048998138793192, 0.5970741338702987], [0.9952077503121903, 0.5259647998534646], [0.4609735216012142, 0.34576531695578083], [0.5034358076516656, 0.950252223002649], [0.6389966408001588, 0.34937237348524974], [0.5874479296438637, 0.6583497254805715], [0.3226324773060669, 0.4430164085341147], [0.42016699177588845, 0.827625252926711], [0.42318337854886734, 0.9836885743852167], [0.15413608522123012, 0.9906555727105225], [0.5537653040156985, 0.5434023586942323], [0.8772971750047162, 0.7384701971983646], [0.6199738262071811, 0.13519188483063216], [0.1982750187582777, 0.07073042526823059], [0.6713227587039741, 0.03010520463532418]], \"data25\": [[0.17091672886003917, 0.41947488715907166], [0.20367784421980029, 0.532324198788909]], \"data32\": [[0.8990535161862907, 0.08606623154969784], [0.4123583778627281, 0.8279966758745015], [0.7342548930771519, 0.9132644095196338], [0.004673865187839854, 0.8033213528914128], [0.49072123237889376, 0.8490717095568104], [0.16716203557030973, 0.7941284092161622], [0.3401808094281832, 0.17311426093635995], [0.9865855597049141, 0.2274671438616278], [0.960574230358406, 0.7617748644874696], [0.7640821012024669, 0.5897744946770956], [0.8780245355849575, 0.18798062691633277], [0.13367105925983436, 0.9456429181883916], [0.9106291603944131, 0.15779403728657804], [0.45310891962023125, 0.57080238857022], [0.8422267301929423, 0.2823983548485076], [0.5048811494318495, 0.32832605954916516], [0.5702454363908962, 0.735782500774521], [0.6702481170538311, 0.8468983404107848], [0.9388848840509653, 0.3124667475286894], [0.2488914751975022, 0.12691899642787663], [0.13932030688636676, 0.7720210640776264], [0.7159784075956513, 0.9817695822156243], [0.7430530153320614, 0.015008615900325428], [0.4580357287981798, 0.3694542793931189], [0.3119314070219871, 0.061579446804055804], [0.07853279855388895, 0.17515500695258457], [0.447326327989507, 0.6583076930524574], [0.8952129778277886, 0.939687985046633], [0.5957658561429044, 0.07376373883008347], [0.9644468022389083, 0.882196443852912], [0.8675135802669062, 0.450744615435292], [0.2129686353981124, 0.8969148798374805], [0.21393434432255531, 0.32426736522559696], [0.08049920793634524, 0.28381141109810837], [0.013496277417805236, 0.8364013930593841], [0.3027832109452653, 0.3150995940031973], [0.7331985063152759, 0.4443660914451495], [0.9802123761723809, 0.5858024012017022], [0.05713438007838478, 0.09141473091340202], [0.6575080807534764, 0.8635510888994984], [0.2643125135590205, 0.14835861043868015], [0.3549372234852217, 0.6312470743682571], [0.8454194807259239, 0.4809374084313274], [0.09121182844394715, 0.008904594897639062], [0.6273623614958889, 0.733364215557673], [0.5065294240094559, 0.4750428779257113], [0.8459757382787995, 0.7875803426891631], [0.04497201341729873, 0.8050530685500853], [0.20038835690704027, 0.011494663759785473], [0.5114340110347836, 0.48131919107272003]], \"data31\": [[0.2957251643870865, 0.3517186350468202]], \"data30\": [[0.10074672381770744, 0.4625075253231755], [0.07710420142218266, 0.47596708758157136]], \"data19\": [[0.4609735216012142, 0.34576531695578083], [0.4416240443980045, 0.2902526544127546]], \"data18\": [[0.5048811494318495, 0.32832605954916516], [0.4609735216012142, 0.34576531695578083]], \"data15\": [[0.4580357287981798, 0.3694542793931189], [0.5064675774339333, 0.35367048639641907]], \"data14\": [[0.487835183991694, 0.38865118235540497], [0.4580357287981798, 0.3694542793931189]], \"data17\": [[0.5114541436383395, 0.33612996705974507], [0.5048811494318495, 0.32832605954916516]], \"data16\": [[0.5064675774339333, 0.35367048639641907], [0.5114541436383395, 0.33612996705974507]], \"data11\": [[0.4109405837143316, 0.4627536705692685], [0.47203884360824744, 0.40808221962063285]], \"data10\": [[0.3446726620796655, 0.4531377669813311], [0.4109405837143316, 0.4627536705692685]], \"data13\": [[0.4925559489276454, 0.4146001660683232], [0.487835183991694, 0.38865118235540497]], \"data12\": [[0.47203884360824744, 0.40808221962063285], [0.4925559489276454, 0.4146001660683232]], \"data29\": [[0.11884848642481083, 0.5275425647996925], [0.10074672381770744, 0.4625075253231755]], \"data33\": [[0.062262912015978, 0.4539446247276069], [0.9602509785370608, 0.9742634998340677], [0.12690613764331116, 0.2412034859928377], [0.5043845353833274, 0.8772462264046906], [0.9801124880636661, 0.8521319427066764], [0.38993590197113814, 0.8561447205613181], [0.2595170439965938, 0.6943464599652662], [0.21884167941909882, 0.9625436129248511], [0.7898708134033531, 0.7682446646742572], [0.07103241407138872, 0.9737802744826799], [0.07710420142218266, 0.47596708758157136], [0.154569887472099, 0.8893300106150732], [0.8556131842525835, 0.5478554819576115], [0.4185396230370314, 0.08535711642858246], [0.9981657753556709, 0.8610725945768697], [0.8081994523569396, 0.3712295819121548], [0.7670939489730074, 0.8936100294929396], [0.04099381706393568, 0.25868113269466575], [0.3010894913666923, 0.9131812436848924], [0.4630423182113379, 0.8934016465084075], [0.6289413216049302, 0.07770721831934235], [0.41721251565328055, 0.10099598339147975], [0.3552185886744631, 0.7131843584251369], [0.30398819205191285, 0.14097828441197702], [0.18721383941354697, 0.46452788960677827], [0.6903705743606482, 0.12474913578151925], [0.7559835209183459, 0.9788882654215788], [0.5979268770141369, 0.9998696820915718], [0.5074026590265407, 0.4601717112166579], [0.196010358875096, 0.7420563772152632], [0.8811852036094012, 0.5736627228791094], [0.08458124825234103, 0.3765190504168284], [0.8210141655495794, 0.7383128790370992], [0.7044081420991575, 0.9703640661402323], [0.3175929219994843, 0.9296407107932952], [0.2590960519678436, 0.22746566727421136], [0.09388929656095757, 0.6039451189806043], [0.08706929850787182, 0.6618776589154964], [0.5229243512670234, 0.3201672084126801], [0.3789111372669951, 0.30665706930396086], [0.28850417712917165, 0.5610606697111101], [0.9765003625514821, 0.8924779893515877], [0.24962182824118473, 0.8476866102337761], [0.2501071491010529, 0.4588009750227334], [0.7776197893431012, 0.4802919288657015], [0.28232064767527565, 0.10254597812703392], [0.8644140616551039, 0.9780794221971203], [0.5555708588334324, 0.8251669860340171], [0.4937459434632684, 0.9626616710181322], [0.08940077330249441, 0.028497134088101617]]}, \"id\": \"el8134140653938816976\"});\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_el81341406539388169764828141852\", {\"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\": [0.2957251643870865, 0.3517186350468202], \"rotation\": -0.0, \"id\": \"el8134140653937056336\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"1\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3027832109452653, 0.3150995940031973], \"rotation\": -0.0, \"id\": \"el8134140653936674128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"2\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.288097262941026, 0.28477941536890594], \"rotation\": -0.0, \"id\": \"el8134140653936722000\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"3\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.21393434432255531, 0.32426736522559696], \"rotation\": -0.0, \"id\": \"el8134140653936724688\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"4\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2424615313215681, 0.35244657077934693], \"rotation\": -0.0, \"id\": \"el8134140653936760016\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"5\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2501071491010529, 0.4588009750227334], \"rotation\": -0.0, \"id\": \"el8134140653936762704\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"6\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.2654431508693852, 0.418836957244923], \"rotation\": -0.0, \"id\": \"el8134140653936814608\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"7\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3226324773060669, 0.4430164085341147], \"rotation\": -0.0, \"id\": \"el8134140653936850128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"8\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3423537561589167, 0.4813579568079882], \"rotation\": -0.0, \"id\": \"el8134140653936852816\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#0000FF\", \"text\": \"9\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.3446726620796655, 0.4531377669813311], \"rotation\": -0.0, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec8c0f37d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meeting = Meeting(K=30, N=6, S=50, a=2, p=.6, case=3)\n",
    "meeting.progress()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_helper": "subslide_end",
     "slide_type": "subslide"
    },
    "slide_helper": "subslide_end",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "以下に観察から得られる定性的な性質をまとめてみる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "slide_type": "subslide"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$S$を大きくすると、意見の密度を増加させるため、発言された意見の描く軌跡の範囲は小さくなる。\n",
    "\n",
    "Note:\"意見の描く軌跡の範囲\"をどう数値化する?\n",
    "- 各座標軸における最小値、最大値で描かれる$a$次元立方体の体積?\n",
    "- すべての座標軸に対しての分散?\n",
    "- 慣性半径?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "frag_number": 14
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "$p$を大きくするほどより直線的(?)な軌道となる。$p$が小さい時には軌跡はより\"ギザギザ\"しており、線が重なったり、沈黙で中断されて島になったりする部分が多く現れるようになる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "frag_helper": "fragment_end",
     "frag_number": 15
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "$K$の値を大きくするほど、最後に選ばれた点の座標は開始座標よりも遠くなる。\n",
    "\n",
    "適当な$S$や$N$を考えたときには自己排他的な2次元のランダムウォークに近くなるのかもしれない。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "internals": {
     "frag_helper": "fragment_end",
     "frag_number": 15,
     "slide_helper": "subslide_end"
    },
    "slide_helper": "slide_end",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "TODO: 描画を切って上の性質を確かめる"
   ]
  }
 ],
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