{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio\n",
    "\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "                        {},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3100d9be-f563-49b0-9aac-820b17e72a92');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "sz = np.random.rand(N)*30\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_scatter(x=x,\n",
    "                y=y,\n",
    "                mode='markers',\n",
    "                marker={'size': sz,\n",
    "                        'color': colors,\n",
    "                        'opacity': 0.6,\n",
    "                        'colorscale': 'Viridis'\n",
    "                       });\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('images'):\n",
    "    os.mkdir('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.write_image(fig, 'images/fig1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')\n",
    "\n",
    "fig = {\n",
    "    'data': [\n",
    "          {\n",
    "          'x': df.gdpPercap, \n",
    "            'y': df.lifeExp, \n",
    "            'text': df.country, \n",
    "            'mode': 'markers', \n",
    "            'name': '2007'},\n",
    "    ],\n",
    "    'layout': {\n",
    "        'xaxis': {'title': 'GDP per Capita', 'type': 'log'},\n",
    "        'yaxis': {'title': \"Life Expectancy\"}\n",
    "    }\n",
    "}\n",
    "\n",
    "iplot(fig, filename='pandas-multiple-scatter')"
   ]
  },
  {
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   "metadata": {},
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