bigsnarfdude/bc_cdc_covid19_data_EDA.ipynb

## bc_cdc_covid19_data_EDA.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'{}'\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import urllib.request\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import lxml.html as lh\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "\n",
    "body = {}\n",
    "\n",
    "cdc_url = 'https://bc-covid-counts-uac6u.transposit.io/api/v1/execute/get_bc_covid_data'\n",
    "req = urllib.request.Request(cdc_url)\n",
    "req.add_header('Content-Type', 'application/json; charset=utf-8')\n",
    "\n",
    "json_data = json.dumps(body)\n",
    "json_data_as_bytes = json_data.encode('utf-8')   # needs to be bytes\n",
    "req.add_header('Content-Length', len(json_data_as_bytes))\n",
    "print(json_data_as_bytes)\n",
    "response = urllib.request.urlopen(req, json_data_as_bytes)\n",
    "data = response.read()\n",
    "\n",
    "pd.read_json(data)\n",
    "df = pd.read_json(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "events = df.iloc[1]['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "flattened_dict = defaultdict(list)\n",
    "\n",
    "for i in range(len(events)):\n",
    "    event = events[i]\n",
    "    for key in ['date', 'confirmed', 'deaths', 'days since zero', 'total cases', 'total deaths']:\n",
    "        flattened_dict[key].append(event[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118877908>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(flattened_dict['total deaths'])\n",
    "df['norm_total_deaths'] = (1+df[0])/2\n",
    "df['lognorm_total_deaths'] = np.log(df['norm_total_deaths'])\n",
    "df['lognorm_total_deaths'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118964048>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(flattened_dict['total cases'])\n",
    "df['norm_total_cases'] = (1+df[0])/2\n",
    "df['lognorm_total_cases'] = np.log(df['norm_total_cases'])\n",
    "df['lognorm_total_cases'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118a3ada0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "china = \"\"\"547\t639\t916\t1,399\t2,062\t2,863\t5,494\t6,070\t8,124\t9,783\t11,871\t16,607\t19,693\t23,680\t27,409\t30,553\t34,075\t36,778\t39,790\t42,306\t44,327\t44,699\t59,832\t66,292\t68,347\t70,446\t72,364\t74,139\t74,546\t74,999\t75,472\t76,922\t76,938\t77,152\t77,660\t78,065\t78,498\t78,824\t79,251\t79,826\t80,026\t80,151\t80,271\t80,422\t80,573\t80,652\t80,699\t80,735\t80,757\t80,921\t80,932\t80,945\t80,977\t81,003\t81,033\t81,058\t81,102\t81,156\t81,250\t81,305\t81,397\t81,496\t81,591\t81,661\t81,782\t81,897\t81,999\t82,122\t82,198\t82,279\t82,381\"\"\".replace(\",\",\"\").split(\"\\t\")\n",
    "numbers_china = []\n",
    "for i in china:\n",
    "    numbers_china.append(int(i))\n",
    "df_china = pd.DataFrame(numbers_china)\n",
    "df_china['norm_total_cases'] = (1+df_china[0])/2\n",
    "df['lognorm_total_cases_china'] = np.log(df_china['norm_total_cases'])\n",
    "\n",
    "df[['lognorm_total_cases_china','lognorm_total_cases']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for key in flattened_dict.keys():\n",
    "    plt.plot(flattened_dict[key])\n",
    "    plt.ylabel(key)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e2ea20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(flattened_dict['deaths']).rolling(2, min_periods=1).sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COUNTING DEATHS\n",
    "Tracking deaths has become a useful statistic to contrast and compare with other countries. Even tracking at the provincial level to see how well we are doing between provinces is interesting. We may even find comfort that it's not as \"crazy\" here as it is in Quebec or Ontario. The rate at which we count the daily deaths have made us ignorant of the facts that ZERO is the only acceptable death count. We have more deaths in Canada than China now.\n",
    "\n",
    "I often hear it's only 5 deaths or 50 deaths a day. Are we really so complacent that we're comfortable trading a few \n",
    "extra deaths in favour of a beer on a patio deck?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18250"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 deaths a day acceptable?\n",
    "5*365*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36500"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 deaths a day acceptable?\n",
    "10*365*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 20 deaths a day acceptable?\n",
    "20*365*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182500"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 50 deaths a day acceptable?\n",
    "50*365*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100 deaths a day acceptable?\n",
    "100*365*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final result: [4537.249170130934, 4.0]\n"
     ]
    }
   ],
   "source": [
    "from math import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def f(mu, sigma2, x):\n",
    "    coefficient = 1.0 / sqrt(2.0 * pi *sigma2)\n",
    "    exponential = exp(-0.5 * (x-mu) ** 2 / sigma2)\n",
    "    return coefficient * exponential\n",
    " \n",
    "\n",
    "def update(mean1, var1, mean2, var2):\n",
    "    new_mean = (var2*mean1 + var1*mean2)/(var2+var1)\n",
    "    new_var = 1/(1/var2 + 1/var1)\n",
    "    \n",
    "    return [new_mean, new_var]\n",
    "\n",
    "\n",
    "\n",
    "def predict(mean1, var1, mean2, var2):\n",
    "    new_mean = mean1 + mean2\n",
    "    new_var = var1 + var2\n",
    "    \n",
    "    return [new_mean, new_var]\n",
    "\n",
    "update(20,9,30,3)\n",
    "\n",
    "measurements = range(1, len(flattened_dict['total cases'])+1)\n",
    "motions = flattened_dict['total cases']\n",
    "\n",
    "\n",
    "measurement_sig = 4.\n",
    "motion_sig = 2.\n",
    "mu = 0.\n",
    "sig = 10000.\n",
    "\n",
    "\n",
    "for n in range(len(measurements)):\n",
    "    mu, sig = update(mu, sig, measurements[n], measurement_sig)\n",
    "    #print('Update: [{}, {}]'.format(mu, sig))\n",
    "    mu, sig = predict(mu, sig, motions[n], motion_sig)\n",
    "    #print('Predict: [{}, {}]'.format(mu, sig))\n",
    "\n",
    "print('Final result: [{}, {}]'.format(mu, sig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21,\n",
       " 27,\n",
       " 32,\n",
       " 39,\n",
       " 46,\n",
       " 53,\n",
       " 64,\n",
       " 73,\n",
       " 103,\n",
       " 186,\n",
       " 231,\n",
       " 271,\n",
       " 348,\n",
       " 424,\n",
       " 472,\n",
       " 617,\n",
       " 659,\n",
       " 725,\n",
       " 792,\n",
       " 884,\n",
       " 900,\n",
       " 970,\n",
       " 1013,\n",
       " 1066,\n",
       " 1121,\n",
       " 1174,\n",
       " 1203,\n",
       " 1229,\n",
       " 1266,\n",
       " 1291,\n",
       " 1336,\n",
       " 1370,\n",
       " 1410,\n",
       " 1445,\n",
       " 1470,\n",
       " 1490,\n",
       " 1517,\n",
       " 1561,\n",
       " 1575,\n",
       " 1618,\n",
       " 1647,\n",
       " 1676,\n",
       " 1699,\n",
       " 1724,\n",
       " 1795,\n",
       " 1824,\n",
       " 1853,\n",
       " 1948,\n",
       " 1998,\n",
       " 2053,\n",
       " 2087,\n",
       " 2112,\n",
       " 2145,\n",
       " 2171,\n",
       " 2205,\n",
       " 2224,\n",
       " 2232,\n",
       " 2255]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattened_dict['total cases'][10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3427.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 06 May 2020</td> <th>  Prob (F-statistic):</th> <td>6.35e-52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:35:34</td>     <th>  Log-Likelihood:    </th> <td> -344.48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    58</td>      <th>  AIC:               </th> <td>   693.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    56</td>      <th>  BIC:               </th> <td>   697.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td> -527.8582</td> <td>   31.461</td> <td>  -16.778</td> <td> 0.000</td> <td> -590.882</td> <td> -464.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   42.9314</td> <td>    0.733</td> <td>   58.543</td> <td> 0.000</td> <td>   41.462</td> <td>   44.400</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.489</td> <th>  Durbin-Watson:     </th> <td>   0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.039</td> <th>  Jarque-Bera (JB):  </th> <td>   2.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.097</td> <th>  Prob(JB):          </th> <td>   0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.007</td> <th>  Cond. No.          </th> <td>    110.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.984\n",
       "Model:                            OLS   Adj. R-squared:                  0.984\n",
       "Method:                 Least Squares   F-statistic:                     3427.\n",
       "Date:                Wed, 06 May 2020   Prob (F-statistic):           6.35e-52\n",
       "Time:                        19:35:34   Log-Likelihood:                -344.48\n",
       "No. Observations:                  58   AIC:                             693.0\n",
       "Df Residuals:                      56   BIC:                             697.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       -527.8582     31.461    -16.778      0.000    -590.882    -464.835\n",
       "x1            42.9314      0.733     58.543      0.000      41.462      44.400\n",
       "==============================================================================\n",
       "Omnibus:                        6.489   Durbin-Watson:                   0.081\n",
       "Prob(Omnibus):                  0.039   Jarque-Bera (JB):                2.474\n",
       "Skew:                          -0.097   Prob(JB):                        0.290\n",
       "Kurtosis:                       2.007   Cond. No.                         110.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "Y = flattened_dict['total cases'][10:]\n",
    "X = range(1, len(flattened_dict['total cases'])+1)[10:]\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-16.77825835,  58.54263928])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.tvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Test for Constraints                             \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "c0          -527.8582     31.461    -16.778      0.000    -590.882    -464.835\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(results.t_test([1, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[6238.98749099]]), p=1.5950624747722174e-66, df_denom=56, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(results.f_test(np.identity(2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -55.61309176,  -12.68171891,   30.24965394,   73.18102679,\n",
       "        116.11239964,  159.04377249,  201.97514534,  244.90651819,\n",
       "        287.83789105,  330.7692639 ,  373.70063675,  416.6320096 ,\n",
       "        459.56338245,  502.4947553 ,  545.42612815,  588.357501  ,\n",
       "        631.28887385,  674.2202467 ,  717.15161955,  760.0829924 ,\n",
       "        803.01436525,  845.9457381 ,  888.87711095,  931.8084838 ,\n",
       "        974.73985666, 1017.67122951, 1060.60260236, 1103.53397521,\n",
       "       1146.46534806, 1189.39672091, 1232.32809376, 1275.25946661,\n",
       "       1318.19083946, 1361.12221231, 1404.05358516, 1446.98495801,\n",
       "       1489.91633086, 1532.84770371, 1575.77907656, 1618.71044941,\n",
       "       1661.64182226, 1704.57319512, 1747.50456797, 1790.43594082,\n",
       "       1833.36731367, 1876.29868652, 1919.23005937, 1962.16143222,\n",
       "       2005.09280507, 2048.02417792, 2090.95555077, 2133.88692362,\n",
       "       2176.81829647, 2219.74966932, 2262.68104217, 2305.61241502,\n",
       "       2348.54378787, 2391.47516072])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from fbprophet import Prophet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.969</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2155.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 06 May 2020</td> <th>  Prob (F-statistic):</th>          <td>1.13e-52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:35:36</td>     <th>  Log-Likelihood:    </th>          <td> -463.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    68</td>      <th>  AIC:               </th>          <td>   928.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    67</td>      <th>  BIC:               </th>          <td>   931.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   31.4936</td> <td>    0.678</td> <td>   46.419</td> <td> 0.000</td> <td>   30.139</td> <td>   32.848</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.342</td> <th>  Durbin-Watson:     </th> <td>   0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>   7.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.637</td> <th>  Prob(JB):          </th> <td>  0.0282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.055</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.970\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.969\n",
       "Method:                 Least Squares   F-statistic:                              2155.\n",
       "Date:                Wed, 06 May 2020   Prob (F-statistic):                    1.13e-52\n",
       "Time:                        19:35:36   Log-Likelihood:                         -463.39\n",
       "No. Observations:                  68   AIC:                                      928.8\n",
       "Df Residuals:                      67   BIC:                                      931.0\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            31.4936      0.678     46.419      0.000      30.139      32.848\n",
       "==============================================================================\n",
       "Omnibus:                       11.342   Durbin-Watson:                   0.015\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                7.133\n",
       "Skew:                          -0.637   Prob(JB):                       0.0282\n",
       "Kurtosis:                       2.055   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "\n",
    "x = pd.Series(range(1, len(flattened_dict['total cases'])+1))\n",
    "y = pd.Series(flattened_dict['total cases'])\n",
    "#print(len(x), len(y))\n",
    "regression = sm.OLS(y, x).fit()\n",
    "regression.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  x1    31.493578\n",
      "dtype: float64\n",
      "Standard errors:  x1    0.678461\n",
      "dtype: float64\n",
      "Predicted values:  [  31.49357814   62.98715627   94.48073441  125.97431254  157.46789068\n",
      "  188.96146881  220.45504695  251.94862509  283.44220322  314.93578136\n",
      "  346.42935949  377.92293763  409.41651577  440.9100939   472.40367204\n",
      "  503.89725017  535.39082831  566.88440644  598.37798458  629.87156272\n",
      "  661.36514085  692.85871899  724.35229712  755.84587526  787.33945339\n",
      "  818.83303153  850.32660967  881.8201878   913.31376594  944.80734407\n",
      "  976.30092221 1007.79450035 1039.28807848 1070.78165662 1102.27523475\n",
      " 1133.76881289 1165.26239102 1196.75596916 1228.2495473  1259.74312543\n",
      " 1291.23670357 1322.7302817  1354.22385984 1385.71743797 1417.21101611\n",
      " 1448.70459425 1480.19817238 1511.69175052 1543.18532865 1574.67890679\n",
      " 1606.17248493 1637.66606306 1669.1596412  1700.65321933 1732.14679747\n",
      " 1763.6403756  1795.13395374 1826.62753188 1858.12111001 1889.61468815\n",
      " 1921.10826628 1952.60184442 1984.09542255 2015.58900069 2047.08257883\n",
      " 2078.57615696 2110.0697351  2141.56331323]\n"
     ]
    }
   ],
   "source": [
    "print('Parameters: ', regression.params)\n",
    "print('Standard errors: ', regression.bse)\n",
    "print('Predicted values: ', regression.predict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (50,) and (68,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-80066374118c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b-'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"True\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfittedvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r--.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"OLS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miv_u\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r--'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miv_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r--'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[1;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (50,) and (68,)"
     ]
    },
    {
     "data": {
      "image/png": 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erPuqFTt2+Z5GbVds3p5pH0S8YAycdJK7LVgA773nbq+8Ao8/7vbp2NEl47ZtYfVqt7LU5tvMbpSsrU95STblJQ0A2K5NEW1P+Y5Z4/5M/TgzTKYWFyoBi4REplTu1mT2bPj59X3I2WM1uYd/t3l7Jn4Q8VqbNnDhhe5WVgbTp7tk/P77bmrL0lK3X8OG0KwZNG0KrZrn8GOjNdiGv1O/8QYa7VfIjs03Mrxvh1ol30wtLtRiDCISCmvXukUGiovh9qeW8tiX36b9B5GgtAzXr3ct3qZNoVGjrZcFTDTGbiOmRLy0kpebwyeDglElnYiaFmOI+RnFGDMO6AWssNa2r9i2M/A80BpYBJxurV3jVcAiIlVZCxdcAPPnw5Qp0L37bvzjWB9nbkiBILUMt9/e3SJJtHo4k4sL46mCfgI4rtq2QcAH1tq2wAcVP4uIJMWDD7p5gm+/Hbp39zua1EjHBSMiSedpQWOJmYCttR8Dq6tt7g08WfH9k0Afj+MSyThhWTYu1aZNg3/9yxUKDRzodzSpkyktw0yYFjSauhZh7WqtXQpgrV1qjIlaqG6M6Q/0B9hjjz3qeDiR9Bak7sYgWbkSTj8d8vLcnMGpXiXHT5ky7CyTiwuTXgVtrR0DjAFXhJXs44mEkWa52lZ5OZxzDixfDp9+GnuFo3STScPOMmFa0EjqmoCXG2N2q2j97gasiPkbIhJVpnQ31satt8LkyfDww3DggX5Hk3qZ3DLMFHVNwK8B/YARFV9f9SwikQyUKd2N8XrjDRg2zLWA+/f3Oxr/ZGrLMFPEvKJijJkA/A9oZ4xZbIw5H5d4exhj5gM9Kn4WkTrK5EKU6r75Bs46Czp1gkce2XrMqUg6idkCttaeGeWuoz2ORSTUEpmQQN2NzqpVcPLJbrKHSZPcV5F0pakoRTzgRRVzpnc3lpa6iueff4apU2H33f2OSCS5lIBFPJBJVczJmh7x6qvdLFdPPAGHHJJ4nCJBpwQs4oFMqWJO1njlMWPcbFdXXw39+nkSqkjgZdCwdpHkyZTp9JIxPeLHH8Oll8Jxx8GddyYaoUh4KAGLeCCeKuZ0mGrS65b+okXwl7/AXnvBhAmQlRXzV0TShrqgRTwQq4o5XaaajGe8crzXiNetg969XfHVa69Bbm5SQxcJHCVgEY/UVMWcLkVasaZHjPeDRnk5/P3vMHs2vP027L13Ck9CJCCUgEVSIExFWjW1YGO19OP5oFFeDpddBq+8AvfcA8cem8KTEwkQJWCRFAjLVJPxtGBraunH+qBRXu4Krh5+GK69Fq680uszEAkPFWGJpEBYpppMtMq5pmrw8nK45BKXfAcNghEjNM2kZDYlYJEU6NMpj+F9O5CXm4MB8nJzGN63Q+Cu/ybaVR7tg8a/erTj4ovd3M6DB8Pttyv5iqgLWiROic4AFYapJhPtKo90jfhfPdrxziN5PPooXH+9W2ZQyVdECVgkLukyjCgWLxaBr/pBo7wcLrwQxo6FIUPglluUfEUqqQtaJA7JmAEqiLzsKi8vd2v5jh0LN9yg5CtSnVrAInEI0zCiRHnRVV5eDv/8J4wbBzfeCMOGKfmKVKcWsEgcMmWuZy8UFbnpJceNg6FD4aablHxFIlELWCQOdb02ai38+CN8+qm7ffIJLFkCu+yy5bbrrlt/36IFdOwI9UP46nz1VdftXFwMd98NV13ld0QiwRXCl7hI6sWaAarSxo3w1Vcu0VYm3SVL3H077AAHHwydO8PKlbB8ORQUuK9r1259vJ12gh493ApBPXtCy5apOMu6Ky52k2o89RTsvz+8/z506OB3VCLBZqy1KTtYfn6+LSgoSNnxROKV6BAja+Hll10Sqky4rVtD167QrZv72r599FZtSYnrul2+HBYsgPfeg3fegcKKBZPat3fJ+Ljj4NBDoWHDxM7XS++/D+edB0uXumFGN9wADRr4HZVIMBhjpltr8yPepwQsma76ECNw3cvxVv8uWODmNn77bdf6GzzYJckWLRKLy1qYM8cl4nfegf/7P9fCbtTItYpPPRV69YIdd0zsOHW1fj1cdx2MHg377ANPPgkHHeRPLCJBpQQsUoNuI6ZEnHwiLzeHTwYdFfX3Nm6EUaPg5ptdy/aWW1wiTta12/XrYepUeOstt5DB0qWupdmjh0vGJ58MO++cnGNXZS18+KEb3/v99zBggJvZKkf1aCLbqCkB6xqwZLy6DDH6+GO46CL49ltX8Xvvvcm/Trv99nDiie72wAMwbZrr9n7pJXjzTZf4jzzSxXPiid7GYy188QW88AK8+CL89BO0auUS8RFHeHcckUyiBCwZrzbTL65c6Vbxefxxd433jTdcsku1evXcdeWuXeGuu2D69C3J+KKL3D4tW7qir8rbAQfUrpVqrXvcF15wtx9/hOxs1/19yy3Qt68rLBORulEXtGS8eK8BT5/uiqCKi+Gaa+Df/3bXY4PEWpg1y3VVT5vmbgsXuvvq13fXqA8+2BV1AWzatOVWWrrl+zVr4PXX3e/Wr+/W7D39dOjdG3JzfTs9kdDRNWCRGGJVQc+c6bp3d9rJJabKBBYGy5fDZ59tSciff+6uJ9ekQQM46qgtSTcV15ZF0pESsEgCZs92yTcnBz76CPbc0++IErNpkyvgyspyrdvsbPe18paV5bq4RSRxKsISqaO5c+GYY1ySmjIl/MkXXJLdfXe/oxARJWCRKObPd92w4JLvH//obzzpItFJT0TShRKwSAQLF7rkW1rqhtrss4/fEaWHTFlXWSQeCV3pMcZcaYyZbYyZY4wZ4FVQIn766SeXfNevd9MshqngKugyZV1lkXjUOQEbY9oD/wQOAjoCvYwxbb0KTMQPhYUu+a5Z4+Zj7tjR74jSSyatqywSSyIt4H2Badba36y1m4CPgFO8CUsk9ZYvd8l3xQqYPBkOPNDviNKP1lUW2SKRBDwbONwY09QY0wg4AdimttIY098YU2CMKSgqKkrgcCLJU14OZ50FP//s5lru0sXviNLTwJ7tyMnO2mpbPOsqi6SjOhdhWWu/NcbcAbwHrANmApsi7DcGGANuHHBdjyeSiFiVtyNHukrnRx91KxlJcsS7rrJIJvBsIg5jzO3AYmvtQ9H20UQc4odYU01+/rlbs7dPHzfnsTE+BisiaaWmiTgSrYLepeLrHkBfYEIijyeSDDVV3q5d67qeW7SAMWOUfEUkdRIdB/yyMaYpUApcaq1d40FMIp6qqfL2ssvcmN+pU6FJk9TGJSKZLaEEbK09zKtARJIl2nKD2Yta89TzcOONcJj+k0UkxTTluqSFSTMK6TZiCnsOepNuI6YwaUbh5vsiVd5mrduBxW/sS7dubllBEZFU01SUEnqxpjesXnn7h8aNWP3WITSoX4/x493iBCIiqaa3Hgm9moqsKpNv1UR8ww1w2yx4/nlo1Srl4YqIAErAEgKxxvDWZnrDjz6C22+H885zi82LiPhF14Al0Cq7lwuLS7Bs6V6ueo033ukNV6+Gc86Btm3h/vuTGbWISGxKwBJo8ayeE+/0hpdc4uZ7njABdtgheTGLiMRDXdASaPF0L8czveG777prvjffDAcckNyYRUTioQQsgRZtDG/17uWqRVbV/f47XHaZ63q+9tqkhBkasa6ni0jqqAtaAs2L1XNGjoT582H0aGjY0OsIwyOe6+kikjpKwBJofTrlMbxvB/JyczBAXm7O5kUU4rFwIdx2G5x2GvTokdxYgy6e6+kikjrqgpbAq6l7uSbWwuWXu4k27rknCYGFTG2Ga4lI8qkFLGnrtdfgzTdh2DDI02XOuIdriUhqKAFLWlq/Hq68Etq3hyuu8DuaYPDierqIeEdd0JKWbrsNfvwRPv4YsrP9jiYY4hmuJSKpowQsaWfuXLjrLvj737XMYHV1vZ4uIt5TF7SkFWvh0kth++3hzjv9jkZEJDq1gCWtPP88TJnixvzuuqvf0YiIRKcWsKSNX3+Fq6+GAw+ECy/0OxoRkZqpBSxpY+hQWLYMXn0VsrJi7y8i4ie1gCUtfPMNPPAA9O8PnTv7HY2ISGxKwJIWrrnGLTF4661+RyIiEh91QUvoTZ4Mb7/thh41a+Z3NCIi8VELWEJt0yb4179gr73ckoMiImGhFrCE2tixMGcOvPxyZi81KCLhoxawhNYvv8CNN0L37nDKKX5HIyJSO0rAElq33w4rV8Ldd4MxfkcjIlI7SsASSgsWwL33uvmeDzjA72hERGpPCVhCadAgqF/frXokIhJGKsKS0Pnvf+HFF2HYMMjTwj5bmTSjUMsNioREQi1gY8xVxpg5xpjZxpgJxpjtvApMJJLycjffc16em3xDtpg0o5DBE2dRWFyCBQqLSxg8cRaTZhT6HZqIRFDnBGyMyQOuAPKtte2BLOAMrwITieTZZ+GLL2D4cLfkoGwxcvI8SkrLttpWUlrGyMnzfIpIRGqS6DXg+kCOMaY+0AhYknhIIpH99hsMHgz5+XD22X5HEzxLiktqtV1E/FXnBGytLQTuAn4ClgK/WGvfrb6fMaa/MabAGFNQVFRU90gl440aBYsXu2FH9VQ+uI0WuTm12i4i/kqkC7oJ0BvYE2gBbG+MOaf6ftbaMdbafGttfvPmzeseqWS0JUvgjjvg1FPhsMP8jiaYBvZsR0721usw5mRnMbBnO58iEpGaJNKOOAZYaK0tstaWAhOBrt6EJbK166+H0lKXhCWyPp3yGN63A3m5ORggLzeH4X07qApaJKASGYb0E3CwMaYRUAIcDRR4EpVIFV98AU8+CdddB23a+B1NsPXplKeEKxISdU7A1trPjDEvAV8Cm4AZwBivApP0kcjYVGthwADYdVcYMiTJgYqIpFBCE3FYa4cCQz2KRdJQ5djUyuExlWNTgbiS8HPPwaefwmOPQePGSQ1VRCSlVEsqSZXI2NT16+Haa91cz+eem6QARUR8oqkoJakSGZs6cqQbdvTssxp2JCLpR29rklR1HZv6889w551w+ukadiQi6UkJWJKqrmNTr7vOFWDdeWcyoxMR8Y+6oCWpKgutalMF/emnMGEC3HADtGqVqkhFRFLLWGtTdrD8/HxbUKChwhJdeTl06eJmvpo3D3bYwe+IRETqzhgz3VqbH+k+tYAlUJ5+GgoK4KmnlHxFJL3pGrAExrp1brWjgw7Sakcikv7UApbAGD4cli6FiRM17EhE0p8SsNQokWkka2PhQrfc4Nlnw8EHe/7wIiKBowQsUSU6jWS8rIUrr4SsLBgxwrOHFREJNHX0SVSJTCNZGy+9BK+/DjfdBC1bevrQIiKBpQQsUSUyjWS81qyByy938z0PGODZw4qIBJ66oCWqFrk5FEZItrGmkayNa6+FlSvhrbegvv4bRSSDqAUsUdV1Gsl4TZ0KY8fC1Ve7FrCISCZRm0Oiqss0kvEqKYH+/aFNGxg2LOGHExEJHSVgqVGfTnlJGXZ0660wfz689x40auT5w4uIBJ4SsKTc11+7VY769YNjjkndWGMRkSBRAg6wdExMZWVwwQXQpImbeCNVY41FRIJGRVgBVZmYCotLsGxJTJNmFPodWkIefBC++ALuuw+aNk3dWGMRkaBRAg6odExMP/4IQ4bA8cfDGWe4bakYaywiEkRKwAGVbonJWrjkEvf9f/4Dxrjvo40p9nKssYhIECkBB1SYEtOkGYV0GzGFPQe9SbcRUyJ2kz/3nJts47bboFWrLduTPdZYRCSolIADKiyJKZ5r1YsWwWWXQefO7mtVfTrlMbxvB/JyczBAXm4Ow/t2UAGWiKQ9VUEHVDInwfBSTdeq+3TKY8MGOPVU2LQJxo93Kx5Vl6yxxiIiQaYEHGBhSEyxrlVfcQVMnw6TJkHbtqmMTEQk2NQFLQmp6Vr144/Do4/C4MHQu3eKAxMRCTgl4CSKpzgp7KJdq+67R3suvhiOPhpuucWn4EREAkxd0EmSKTM8RbpWfdHB+3DjebvQvDlMmBD5uq+ISKarcwI2xrQDnq+yqQ1wo7X23oSjSgOxipPSSdVr1eXlcNJJUFgI//d/0Ly5z8GJiARUnROwtXYesD+AMSYLKARe8Siu0Eu3iTTideutbrzv6NHQpYvf0YiIBJdX14CPBn6w1v7o0eOFXltb63kAABLfSURBVJgm0vDKO++4tX3/9je4+GK/oxERCTavEvAZwASPHisthGUiDa8sWgRnnQUdOsDDD2+ZalJERCJLOAEbYxoAJwMvRrm/vzGmwBhTUFRUlOjhQiOTZnhau9ZNtlFeDi+/DI0a+R2RiEjwGWttYg9gTG/gUmvtsbH2zc/PtwUFBQkdT2on1prCia45vHo1nHACFBTAK6+4AiwREXGMMdOttfmR7vNiGNKZqPs5kGINhUp0qNTy5XDssTB3Lrz0kpKviEhtJNQFbYxpBPQAJnoTjngp1prCiaw5/NNPcNhh8P338MYb0KePd3GLiGSChFrA1trfgKYexSIeizUUqq5DpebPdzNc/fILvPsudOuWWJwiIplIU1GmsVhDoeoyVOrrr13Lt6QEpk5V8hURqSsl4DQWayhUbYdKffYZHHEE1K/vZrnq1CkpYYuIZATNBZ3GYq0pXJs1h6dOdUVWu+wCH3wArVun6ixERNJTwsOQakPDkMKnrAzGjoUBA6BNG3jvPWjRwu+oRETCoaZhSOqClqimT4dDDoGLLnJfP/pIyVdExCtKwLKNNWvg0kuhc2c33OiZZ1y3c7NmfkcmIpI+lIBlM2vhySehXTs3n/Pll8O8eXD22ZrbWUTEayrCEgBmzYJLLoH//hcOPhgmT1aVs4hIMikBZ7CNG13X8vPPu27m3FxXcHXeeVBPfSMiIkmlBOyjRBdCqIuNG+H99+HFF2HSJCguhh13hAsvhJtvhqaa10xEJCWUgH2S6EIItbFxoxs+9OKL8OqrW5Ju795w2mluQYWGDT09pIiIxKAE7JOaFkKoawK21lUtz5kDs2dvuX37LWzYADvttCXp9uihpCsi4iclYJ/UdSEEcJNjLFrklgGcO9cl2Dlz3G3t2i375eVB+/Zw5JFw1FFwzDFKuiIiQaEEXEeJXr9tkZtDYYRkW30hhAUL4NNPtyTbuXPdakQbN27Zp1kzl2j79XNf27eH/faDJk3qfHoiIpJkSsB14MX124E92231GOAWQhhwVDumTIE333S3eRVL82ZlwR//6Mbonngi7LOP+75dOxVOiYiEkRJwHXhx/bbqQgg/LS6n4bI8cte05vz7cli7Fho0cCsPXXKJW3u3bVu3TURE0oMScB0kcv22qpZleTR8N4/FH7qfN+TBmWe6Fu5RR8EOOyQaqYiIBJUScB3Ee/02msWL4frr4emn3fJ+t9zilvr785815aOISKbQfEd1UNuF7CutWwdDh8Lee8MLL8Dgwa6g6oYboGNHJV8RkUyiFnAd1GYhe3DDhp56CoYMgaVL4YwzYPhwLWovIpLJlIDrqE+nvLgKrqZOhauugq++gi5d4OWX3dq6IiKS2dQFnUT33+8mwVi9GiZMgP/9T8lXREQctYCTwFq3sMGwYXDKKTB+POTEV58lIiIZQgnYY+Xlrsv5/vvh3HPh0Uehvv7KIiJSjbqgPbRpk1tL9/77XRJ+7DElXxERiUzpwSMbNsBf/wqvvQa33urG+WpYkYiIRKME7IG1a90yfx9+CA8+CJde6ndEIiISdErACVq1Co4/Hr78Ep55Bs4+2++IREQkDJSAE7BsmVsoYcECmDQJevXyOyIREQmLhIqwjDG5xpiXjDFzjTHfGmMyZpTrpk1uRqtFi+Cdd5R8RUSkdhJtAd8HvGOtPdUY0wBo5EFMoXDzzfDRR26Kye7d/Y5GRETCps4J2BizI3A4cC6AtXYjsNGbsILt/fddpfN558Hf/uZ3NCIiEkaJdEG3AYqAx40xM4wxY40x23sUV2AtXeoKrfbdFx54wO9oREQkrBJJwPWBA4D/WGs7AeuBQdV3Msb0N8YUGGMKioqKEjic/8rKXPJdu9YtJ7h92n/cEBGRZEkkAS8GFltrP6v4+SVcQt6KtXaMtTbfWpvfvHnzBA7nv1tvdWN9R4+GP/3J72hERCTM6pyArbXLgJ+NMZWr0B8NfONJVAE0ZQrcdJO75nvuuX5HIyIiYZdoFfTlwPiKCugFwHmJhxQ8y5e7rud27eChhzTFpIiIJC6hBGyt/QrI9yiWQCorg3POgeJiePdd2GEHvyMSEZF0oJmwYhg+3A07evRR6NDB72hERCRdZGwCnjSjkJGT57GkuIQWuTkM7NmOPp3yttrno49g6FA46yw4/3yfAq1BPOcgIiLBlJEJeNKMQgZPnEVJaRkAhcUlDJ44C2BzAvv9d7jgAmjTBh5+OHjXfeM5BxERCa6E5oIOq5GT521OXJVKSssYOXne5p/vuw++/94tL9i4caojjC2ecxARkeDKyAS8pLikxu3LlsEtt8BJJ0HPnqmMLH6xzkFERIItIxNwi9ycGrdff73rgh41KpVR1U6scxARkWDLyAQ8sGc7crKzttqWk53FwJ7t+OILePxxuOoqaNvWpwDjUNM5iIhI8GVkEVZlkVL1CuLe++fRtSvsuisMGeJzkDFEOwcVYImIhENGJmBwCax6sho/HqZNg3HjYMcdfQqsFiKdg4iIhENGdkFHsm4dXHst5OdDv35+RyMiIukuY1vA1Y0YAUuWwEsvQT19LBERkSRTqgEWLIC77nJzPh9yiN/RiIhIJlACBgYOhPr1XStYREQkFTI+AU+ZAhMnurG/eapnEhGRFMnoa8CbNsGVV8Kee8LVV299nxY6EBGRZMroBDxmDMye7VrA2223ZbsWOhARkWTL2C7otWvhxhvhqKOgT5+t79NCByIikmwZm4AfeghWrXKFV9WXGtRCByIikmwZmYDXr3fDjo4/Hjp33vZ+LXQgIiLJlpEJ+OGHYeVK+Pe/I9+vhQ5ERCTZMq4I67ffYORI6NEj+qQbWuhARESSLeMS8KOPwvLlrgCrJlroQEREkimjuqA3bIA77oAjj4RDD/U7GhERyWQZ1QIeOxaWLoVnn/U7EhERyXQZ0wL+/Xc35Oiww6B7d7+jERGRTJcxLeDHH4fCQnjiiW3H/YqIiKRaRrSAN26E4cNd1fPRR/sdjYiISIa0gJ96Cn76CR55RK1fEREJhrRvAZeWwu23uxmvevb0OxoREREnoRawMWYRsBYoAzZZa/O9CMpL48fDwoVw//1q/YqISHB40QV9pLV2pQeP47lNm+C226BTJzjxRL+jERER2SKtrwE/9xx8/z288opavyIiEiyJXgO2wLvGmOnGmP5eBOSVsjK49Vb485/h5JP9jkZERGRribaAu1lrlxhjdgHeM8bMtdZ+XHWHisTcH2CPPfZI8HDxe+UVmDcPXngB6qV9qZmIiISNsdZ680DGDAPWWWvvirZPfn6+LSgo8OR4NbEWDj4YVq1ySTgrK/bviIiIeM0YMz1agXKd24bGmO2NMY0rvweOBWbX9fG89Mkn8PnncPXVSr4iIhJMiXRB7wq8Ylx1U33gWWvtO55ElaC77oKdd4Zzz/U7EhERkcjqnICttQuAjh7G4onvvoPXXoMhQ6BRI7+jERERiSzthiHdcw9kZ8Ne3ZfSbcS3LCkuoUVuDgN7tqNPpzy/wxMREQHSLAEXFbnVjg4/cT13fDSTktIyAAqLSxg8cRaAkrCIiARCWg3Qeegh2LABVraetTn5ViopLWPk5Hk+RSYiIrK1tEnAJSUwerSbcrK4waqI+ywpLklxVCIiIpGlTQJ++mnXBX3NNdAiNyfiPtG2i4iIpFpaJODychg1Cg48ELp3h4E925GTvfUA4JzsLAb2bOdThCIiIltLiyKsN990w4+efdYtulBZaDVy8jxVQYuISCB5NhVlPJI1FWX37rBokVv5KDvb84cXERGpk6RMRRkUn38OH38MAwYo+YqISHiEPgGPGgU77QQXXOB3JCIiIvELdQJetAheegn694fGjf2ORkREJH6hTsD33uvW+r3iCr8jERERqZ3QJuA1a2DsWDjzTGjZ0u9oREREaie0CXjMGFi/Hv71L78jERERqb3QJuDDD4cbb4SOgVsQUUREJLbQTsRxyCHuJiIiEkahbQGLiIiEmRKwiIiID5SARUREfKAELCIi4gMlYBERER8oAYuIiPgglMOQJs0o1Fq/IiISaqFLwJNmFDJ44ixKSssAKCwuYfDEWQBKwiIiEhqh64IeOXne5uRbqaS0jJGT5/kUkYiISO2FLgEvKS6p1XYREZEgCl0CbpGbU6vtIiIiQRS6BDywZztysrO22paTncXAnu18ikhERKT2QleEVVlopSpoEREJs4QTsDEmCygACq21vRIPKbY+nfKUcEVEJNS86IK+EvjWg8cRERHJGAklYGNMS+BEYKw34YiIiGSGRFvA9wLXAuUexCIiIpIx6pyAjTG9gBXW2ukx9utvjCkwxhQUFRXV9XAiIiJpJZEWcDfgZGPMIuA54ChjzDPVd7LWjrHW5ltr85s3b57A4URERNJHnROwtXawtbaltbY1cAYwxVp7jmeRiYiIpLHQTcQhIiKSDjyZiMNaOxWY6sVjiYiIZAK1gEVERHygBCwiIuIDY61N3cGMKQJ+9PAhmwErPXw8P+lcgiddzgN0LkGVLueSLucB3p9LK2ttxCFAKU3AXjPGFFhr8/2Owws6l+BJl/MAnUtQpcu5pMt5QGrPRV3QIiIiPlACFhER8UHYE/AYvwPwkM4leNLlPEDnElTpci7pch6QwnMJ9TVgERGRsAp7C1hERCSUQpGAjTHHGWPmGWO+N8YMinB/Q2PM8xX3f2aMaZ36KGMzxuxujPnQGPOtMWaOMebKCPscYYz5xRjzVcXtRj9ijYcxZpExZlZFnAUR7jfGmPsrnpevjTEH+BFnTYwx7ar8rb8yxvxqjBlQbZ/APifGmHHGmBXGmNlVtu1sjHnPGDO/4muTKL/br2Kf+caYfqmLOrIo5zLSGDO34v/nFWNMbpTfrfF/MdWinMswY0xhlf+jE6L8bo3vd6kU5Tyer3IOi4wxX0X53aA9JxHff319vVhrA30DsoAfgDZAA2AmsF+1fS4BHq74/gzgeb/jjnIuuwEHVHzfGPguwrkcAbzhd6xxns8ioFkN958AvA0Y4GDgM79jjnE+WcAy3Li9UDwnwOHAAcDsKtvuBAZVfD8IuCPC7+0MLKj42qTi+yYBPJdjgfoV398R6Vwq7qvxfzEg5zIMuCbG78V8v/P7PKrdPwq4MSTPScT3Xz9fL2FoAR8EfG+tXWCt3Yhb+rB3tX16A09WfP8ScLQxxqQwxrhYa5daa7+s+H4t8C2Q529USdUbeMo604BcY8xufgdVg6OBH6y1Xk4Wk1TW2o+B1dU2V309PAn0ifCrPYH3rLWrrbVrgPeA45IWaBwinYu19l1r7aaKH6cBLVMeWB1EeV7iEc/7XcrUdB4V77GnAxNSGlQd1fD+69vrJQwJOA/4ucrPi9k2aW3ep+LF+gvQNCXR1VFFN3kn4LMIdx9ijJlpjHnbGPOnlAZWOxZ41xgz3RjTP8L98Tx3QXIG0d9MwvKcAOxqrV0K7k0H2CXCPmF7bgD+getRiSTW/2JQXFbRnT4uSldnmJ6Xw4Dl1tr5Ue4P7HNS7f3Xt9dLGBJwpJZs9dLtePYJDGPMDsDLwABr7a/V7v4S1wXaEXgAmJTq+Gqhm7X2AOB44FJjzOHV7g/N82KMaQCcDLwY4e4wPSfxCs1zA2CMGQJsAsZH2SXW/2IQ/AfYC9gfWIrrvq0uTM/LmdTc+g3kcxLj/Tfqr0XYlvDzEoYEvBjYvcrPLYEl0fYxxtQHdqJu3T9JZ4zJxj354621E6vfb6391Vq7ruL7t4BsY0yzFIcZF2vtkoqvK4BXcN1nVcXz3AXF8cCX1trl1e8I03NSYXllV3/F1xUR9gnNc1NR8NILONtWXJCrLo7/Rd9Za5dba8usteXAo0SOMRTPS8X7bF/g+Wj7BPE5ifL+69vrJQwJ+AugrTFmz4pWyhnAa9X2eQ2orEo7FZgS7YXqp4prJo8B31pr746yzx8qr18bYw7CPUerUhdlfIwx2xtjGld+jyuWmV1tt9eAvxvnYOCXyq6eAIr6aT4sz0kVVV8P/YBXI+wzGTjWGNOkoiv02IptgWKMOQ64DjjZWvtblH3i+V/0XbX6h1OIHGM873dBcAww11q7ONKdQXxOanj/9e/14ndlWjw3XDXtd7jqwCEV227GvSgBtsN1HX4PfA608TvmKOdxKK7b4mvgq4rbCcBFwEUV+1wGzMFVP04Duvodd5RzaVMR48yKeCufl6rnYoDRFc/bLCDf77ijnEsjXELdqcq2UDwnuA8NS4FS3Kf083H1Dx8A8yu+7lyxbz4wtsrv/qPiNfM9cF5Az+V73LW3ytdL5WiHFsBbNf0vBvBcnq54HXyNe9Pfrfq5VPy8zftdkM6jYvsTla+PKvsG/TmJ9v7r2+tFM2GJiIj4IAxd0CIiImlHCVhERMQHSsAiIiI+UAIWERHxgRKwiIiID5SARUREfKAELCIi4gMlYBERER/8PwmKbLoLbwmDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsample = 50\n",
    "sig = 0.5\n",
    "x = np.linspace(0, 20, nsample)\n",
    "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n",
    "beta = [0.5, 0.5, -0.02, 5.]\n",
    "\n",
    "y_true = np.dot(X, beta)\n",
    "y = y_true + sig * np.random.normal(size=nsample)\n",
    "\n",
    "\n",
    "\n",
    "prstd, iv_l, iv_u = wls_prediction_std(regression)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "ax.plot(x, y, 'o', label=\"data\")\n",
    "ax.plot(x, y_true, 'b-', label=\"True\")\n",
    "ax.plot(x, regression.fittedvalues, 'r--.', label=\"OLS\")\n",
    "ax.plot(x, iv_u, 'r--')\n",
    "ax.plot(x, iv_l, 'r--')\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1b046748>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = mu\n",
    "sigma2 = sig\n",
    "\n",
    "x_axis = np.arange(-2000, 2000, 1.)\n",
    "\n",
    "g = []\n",
    "for x in x_axis:\n",
    "    g.append(f(mu, sigma2, x))\n",
    "\n",
    "\n",
    "plt.plot(x_axis, g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1b0a4390>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 0\n",
    "sigma2 = 10000\n",
    "\n",
    "x_axis = np.arange(-2000, 2000, 1.)\n",
    "\n",
    "g = []\n",
    "for x in x_axis:\n",
    "    g.append(f(mu, sigma2, x))\n",
    "\n",
    "plt.plot(x_axis, g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x1a1b0d5d30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" # refactored webpage to include new CSV file direct instead of webscrape table\n",
    "url = \"https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html#a1\"\n",
    "page = requests.get(url)\n",
    "doc = lh.fromstring(page.content)\n",
    "tr_elements = doc.xpath('//tr')\n",
    "table = tr_elements[:16]\n",
    "\n",
    "\n",
    "col = []\n",
    "i = 0\n",
    "\n",
    "for t in tr_elements[0]:\n",
    "    i+=1\n",
    "    name=t.text_content()\n",
    "    col.append((name,[]))\n",
    "\n",
    "\n",
    "for j in range(1,len(table)):\n",
    "    T = table[j]\n",
    "    i=0\n",
    "\n",
    "    for t in T.iterchildren():\n",
    "        data = t.text_content()\n",
    "        \n",
    "        #Check if row is empty\n",
    "        if i>0:\n",
    "        #Convert any numerical value to integers\n",
    "            try:\n",
    "                data=int(data)\n",
    "            except:\n",
    "                pass\n",
    "        #Append the data to the empty list of the i'th column\n",
    "        col[i][1].append(data)\n",
    "        #Increment i for the next column\n",
    "        i+=1\n",
    "\n",
    "[len(C) for (title,C) in col]\n",
    "\n",
    "canada_dict = {title:column for (title,column) in col}\n",
    "\"\"\"\n",
    "df = pd.read_csv(\"https://health-infobase.canada.ca/src/data/covidLive/covid19.csv\")\n",
    "df['datetime'] = pd.to_datetime(df.date, infer_datetime_format=True)\n",
    "temp_df = df[['datetime','prname', 'numconf', 'numdeaths']]\n",
    "temp_df[temp_df['datetime'] == max(temp_df['datetime'])]\n",
    "\n",
    "\n",
    "from fbprophet import Prophet\n",
    "\n",
    "df['ds'] = df['datetime']\n",
    "df['y']  = df['numconf']\n",
    "df_prophet = df[['ds', 'y','prname']]\n",
    "\n",
    "m = Prophet()\n",
    "m.fit(df_prophet[df_prophet['prname'] == 'British Columbia'][['ds', 'y']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>-17.715619</td>\n",
       "      <td>-46.921732</td>\n",
       "      <td>10.144080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>11.425020</td>\n",
       "      <td>-17.419044</td>\n",
       "      <td>40.816275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-8.861926</td>\n",
       "      <td>-36.722359</td>\n",
       "      <td>19.901300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>0.921463</td>\n",
       "      <td>-27.437335</td>\n",
       "      <td>30.023962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>8.847515</td>\n",
       "      <td>-20.895502</td>\n",
       "      <td>37.281344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.832201</td>\n",
       "      <td>-23.614190</td>\n",
       "      <td>34.342066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>16.042096</td>\n",
       "      <td>-10.340657</td>\n",
       "      <td>45.515479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>19.044295</td>\n",
       "      <td>-8.780297</td>\n",
       "      <td>46.514480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>30.204241</td>\n",
       "      <td>1.185873</td>\n",
       "      <td>60.153437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>3.763234</td>\n",
       "      <td>-25.820461</td>\n",
       "      <td>32.144037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>12.404174</td>\n",
       "      <td>-15.165143</td>\n",
       "      <td>39.980449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>25.749903</td>\n",
       "      <td>-2.954864</td>\n",
       "      <td>53.835876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>13.957237</td>\n",
       "      <td>-13.590430</td>\n",
       "      <td>43.372736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>36.977782</td>\n",
       "      <td>11.319677</td>\n",
       "      <td>66.235335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>10.570742</td>\n",
       "      <td>-17.524233</td>\n",
       "      <td>40.583343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>25.464578</td>\n",
       "      <td>-3.821016</td>\n",
       "      <td>54.156964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>39.439903</td>\n",
       "      <td>9.717136</td>\n",
       "      <td>68.410864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>50.340174</td>\n",
       "      <td>21.645177</td>\n",
       "      <td>79.222018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>46.366083</td>\n",
       "      <td>16.684576</td>\n",
       "      <td>75.283682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>77.210517</td>\n",
       "      <td>48.463618</td>\n",
       "      <td>105.538210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>58.627366</td>\n",
       "      <td>30.595366</td>\n",
       "      <td>85.922428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>78.040601</td>\n",
       "      <td>47.759222</td>\n",
       "      <td>105.588203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>135.955294</td>\n",
       "      <td>104.889369</td>\n",
       "      <td>164.223948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>207.082425</td>\n",
       "      <td>178.076711</td>\n",
       "      <td>236.856939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>271.011633</td>\n",
       "      <td>242.072364</td>\n",
       "      <td>302.623897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>320.066478</td>\n",
       "      <td>290.588541</td>\n",
       "      <td>346.659504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>403.934570</td>\n",
       "      <td>375.350723</td>\n",
       "      <td>433.703036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>438.375077</td>\n",
       "      <td>408.746742</td>\n",
       "      <td>467.530347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>510.811971</td>\n",
       "      <td>481.714508</td>\n",
       "      <td>540.237730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>568.736471</td>\n",
       "      <td>538.700732</td>\n",
       "      <td>596.927987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>639.873410</td>\n",
       "      <td>610.684871</td>\n",
       "      <td>670.210449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>703.802654</td>\n",
       "      <td>674.014801</td>\n",
       "      <td>732.462599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>752.857535</td>\n",
       "      <td>723.732794</td>\n",
       "      <td>782.221389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>836.725616</td>\n",
       "      <td>805.604573</td>\n",
       "      <td>863.610983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>871.166111</td>\n",
       "      <td>843.667090</td>\n",
       "      <td>901.733887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>937.116167</td>\n",
       "      <td>908.853875</td>\n",
       "      <td>965.224957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>988.553830</td>\n",
       "      <td>961.046317</td>\n",
       "      <td>1017.873822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1053.187982</td>\n",
       "      <td>1023.625687</td>\n",
       "      <td>1082.291702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1110.614438</td>\n",
       "      <td>1080.133283</td>\n",
       "      <td>1140.119775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>1153.166532</td>\n",
       "      <td>1125.120171</td>\n",
       "      <td>1183.171683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>1207.943643</td>\n",
       "      <td>1177.475889</td>\n",
       "      <td>1235.854393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>1213.293169</td>\n",
       "      <td>1184.985115</td>\n",
       "      <td>1243.956989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>1256.638754</td>\n",
       "      <td>1227.591319</td>\n",
       "      <td>1286.338525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>1285.471945</td>\n",
       "      <td>1255.096706</td>\n",
       "      <td>1314.553137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>1327.514119</td>\n",
       "      <td>1298.783085</td>\n",
       "      <td>1355.170753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>1362.348596</td>\n",
       "      <td>1334.225512</td>\n",
       "      <td>1391.506102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>1382.308711</td>\n",
       "      <td>1355.416022</td>\n",
       "      <td>1412.118969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>1437.082037</td>\n",
       "      <td>1407.907934</td>\n",
       "      <td>1467.197352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>1442.427778</td>\n",
       "      <td>1414.910040</td>\n",
       "      <td>1470.380091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>1485.769905</td>\n",
       "      <td>1457.203519</td>\n",
       "      <td>1513.559877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>1514.599639</td>\n",
       "      <td>1485.119361</td>\n",
       "      <td>1541.144367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>1556.641813</td>\n",
       "      <td>1529.874156</td>\n",
       "      <td>1585.125999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>1591.476290</td>\n",
       "      <td>1565.067406</td>\n",
       "      <td>1618.917018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>1611.443430</td>\n",
       "      <td>1581.593095</td>\n",
       "      <td>1639.720005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>1666.223780</td>\n",
       "      <td>1636.830273</td>\n",
       "      <td>1693.673553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2020-04-19</td>\n",
       "      <td>1671.576546</td>\n",
       "      <td>1644.041133</td>\n",
       "      <td>1700.356638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>1718.467302</td>\n",
       "      <td>1689.058607</td>\n",
       "      <td>1747.737239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>1750.845664</td>\n",
       "      <td>1723.936614</td>\n",
       "      <td>1777.149066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>1796.436469</td>\n",
       "      <td>1768.381371</td>\n",
       "      <td>1824.943943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>1834.819579</td>\n",
       "      <td>1803.626637</td>\n",
       "      <td>1862.854028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>1858.328326</td>\n",
       "      <td>1829.833282</td>\n",
       "      <td>1886.658000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>1916.650284</td>\n",
       "      <td>1887.364385</td>\n",
       "      <td>1946.122657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>1925.544657</td>\n",
       "      <td>1896.695863</td>\n",
       "      <td>1955.437739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>1972.435417</td>\n",
       "      <td>1943.341087</td>\n",
       "      <td>2002.919159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>2004.813783</td>\n",
       "      <td>1975.456279</td>\n",
       "      <td>2034.435614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2050.404588</td>\n",
       "      <td>2020.488404</td>\n",
       "      <td>2079.923709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2088.787698</td>\n",
       "      <td>2059.869786</td>\n",
       "      <td>2116.672855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>2112.296445</td>\n",
       "      <td>2081.932858</td>\n",
       "      <td>2140.303698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>2170.618403</td>\n",
       "      <td>2141.992182</td>\n",
       "      <td>2198.702427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>2179.512776</td>\n",
       "      <td>2150.065917</td>\n",
       "      <td>2209.142146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>2226.403536</td>\n",
       "      <td>2198.773176</td>\n",
       "      <td>2255.348946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>2258.781902</td>\n",
       "      <td>2230.363609</td>\n",
       "      <td>2288.076197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>2304.372707</td>\n",
       "      <td>2273.611452</td>\n",
       "      <td>2335.155303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>2342.755817</td>\n",
       "      <td>2314.197163</td>\n",
       "      <td>2372.520051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>2366.264564</td>\n",
       "      <td>2337.460231</td>\n",
       "      <td>2395.226342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>2424.586522</td>\n",
       "      <td>2393.657715</td>\n",
       "      <td>2453.388290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>2433.480895</td>\n",
       "      <td>2397.930493</td>\n",
       "      <td>2462.251889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>2480.371655</td>\n",
       "      <td>2445.372457</td>\n",
       "      <td>2514.552367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds         yhat   yhat_lower   yhat_upper\n",
       "0  2020-01-31   -17.715619   -46.921732    10.144080\n",
       "1  2020-02-08    11.425020   -17.419044    40.816275\n",
       "2  2020-02-16    -8.861926   -36.722359    19.901300\n",
       "3  2020-02-21     0.921463   -27.437335    30.023962\n",
       "4  2020-02-24     8.847515   -20.895502    37.281344\n",
       "5  2020-02-25     5.832201   -23.614190    34.342066\n",
       "6  2020-02-26    16.042096   -10.340657    45.515479\n",
       "7  2020-02-27    19.044295    -8.780297    46.514480\n",
       "8  2020-02-29    30.204241     1.185873    60.153437\n",
       "9  2020-03-01     3.763234   -25.820461    32.144037\n",
       "10 2020-03-03    12.404174   -15.165143    39.980449\n",
       "11 2020-03-05    25.749903    -2.954864    53.835876\n",
       "12 2020-03-06    13.957237   -13.590430    43.372736\n",
       "13 2020-03-07    36.977782    11.319677    66.235335\n",
       "14 2020-03-08    10.570742   -17.524233    40.583343\n",
       "15 2020-03-09    25.464578    -3.821016    54.156964\n",
       "16 2020-03-11    39.439903     9.717136    68.410864\n",
       "17 2020-03-12    50.340174    21.645177    79.222018\n",
       "18 2020-03-13    46.366083    16.684576    75.283682\n",
       "19 2020-03-14    77.210517    48.463618   105.538210\n",
       "20 2020-03-15    58.627366    30.595366    85.922428\n",
       "21 2020-03-16    78.040601    47.759222   105.588203\n",
       "22 2020-03-17   135.955294   104.889369   164.223948\n",
       "23 2020-03-18   207.082425   178.076711   236.856939\n",
       "24 2020-03-19   271.011633   242.072364   302.623897\n",
       "25 2020-03-20   320.066478   290.588541   346.659504\n",
       "26 2020-03-21   403.934570   375.350723   433.703036\n",
       "27 2020-03-22   438.375077   408.746742   467.530347\n",
       "28 2020-03-23   510.811971   481.714508   540.237730\n",
       "29 2020-03-24   568.736471   538.700732   596.927987\n",
       "30 2020-03-25   639.873410   610.684871   670.210449\n",
       "31 2020-03-26   703.802654   674.014801   732.462599\n",
       "32 2020-03-27   752.857535   723.732794   782.221389\n",
       "33 2020-03-28   836.725616   805.604573   863.610983\n",
       "34 2020-03-29   871.166111   843.667090   901.733887\n",
       "35 2020-03-30   937.116167   908.853875   965.224957\n",
       "36 2020-03-31   988.553830   961.046317  1017.873822\n",
       "37 2020-04-01  1053.187982  1023.625687  1082.291702\n",
       "38 2020-04-02  1110.614438  1080.133283  1140.119775\n",
       "39 2020-04-03  1153.166532  1125.120171  1183.171683\n",
       "40 2020-04-04  1207.943643  1177.475889  1235.854393\n",
       "41 2020-04-05  1213.293169  1184.985115  1243.956989\n",
       "42 2020-04-06  1256.638754  1227.591319  1286.338525\n",
       "43 2020-04-07  1285.471945  1255.096706  1314.553137\n",
       "44 2020-04-08  1327.514119  1298.783085  1355.170753\n",
       "45 2020-04-09  1362.348596  1334.225512  1391.506102\n",
       "46 2020-04-10  1382.308711  1355.416022  1412.118969\n",
       "47 2020-04-11  1437.082037  1407.907934  1467.197352\n",
       "48 2020-04-12  1442.427778  1414.910040  1470.380091\n",
       "49 2020-04-13  1485.769905  1457.203519  1513.559877\n",
       "50 2020-04-14  1514.599639  1485.119361  1541.144367\n",
       "51 2020-04-15  1556.641813  1529.874156  1585.125999\n",
       "52 2020-04-16  1591.476290  1565.067406  1618.917018\n",
       "53 2020-04-17  1611.443430  1581.593095  1639.720005\n",
       "54 2020-04-18  1666.223780  1636.830273  1693.673553\n",
       "55 2020-04-19  1671.576546  1644.041133  1700.356638\n",
       "56 2020-04-20  1718.467302  1689.058607  1747.737239\n",
       "57 2020-04-21  1750.845664  1723.936614  1777.149066\n",
       "58 2020-04-22  1796.436469  1768.381371  1824.943943\n",
       "59 2020-04-23  1834.819579  1803.626637  1862.854028\n",
       "60 2020-04-24  1858.328326  1829.833282  1886.658000\n",
       "61 2020-04-25  1916.650284  1887.364385  1946.122657\n",
       "62 2020-04-26  1925.544657  1896.695863  1955.437739\n",
       "63 2020-04-27  1972.435417  1943.341087  2002.919159\n",
       "64 2020-04-28  2004.813783  1975.456279  2034.435614\n",
       "65 2020-04-29  2050.404588  2020.488404  2079.923709\n",
       "66 2020-04-30  2088.787698  2059.869786  2116.672855\n",
       "67 2020-05-01  2112.296445  2081.932858  2140.303698\n",
       "68 2020-05-02  2170.618403  2141.992182  2198.702427\n",
       "69 2020-05-03  2179.512776  2150.065917  2209.142146\n",
       "70 2020-05-04  2226.403536  2198.773176  2255.348946\n",
       "71 2020-05-05  2258.781902  2230.363609  2288.076197\n",
       "72 2020-05-06  2304.372707  2273.611452  2335.155303\n",
       "73 2020-05-07  2342.755817  2314.197163  2372.520051\n",
       "74 2020-05-08  2366.264564  2337.460231  2395.226342\n",
       "75 2020-05-09  2424.586522  2393.657715  2453.388290\n",
       "76 2020-05-10  2433.480895  2397.930493  2462.251889\n",
       "77 2020-05-11  2480.371655  2445.372457  2514.552367"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods=5)\n",
    "forecast = m.predict(future)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntable = tr_elements[90:92]\\n\\ncol = []\\ni = 0\\n\\nfor t in tr_elements[0]:\\n    i+=1\\n    name = t.text_content()\\n    col.append((name,[]))\\n\\n\\nfor j in range(1,len(table)):\\n    T = table[j]\\n    i=0\\n\\n    for t in T.iterchildren():\\n        data = t.text_content()\\n        \\n        #Check if row is empty\\n        if i>0:\\n        #Convert any numerical value to integers\\n            try:\\n                data = int(data.replace(\",\", \"\"))\\n            except:\\n                pass\\n        #Append the data to the empty list of the i\\'th column\\n        col[i][1].append(data)\\n        #Increment i for the next column\\n        i+=1\\n\\n[len(C) for (title,C) in col]\\n\\ncanada_dict = {title:column for (title,column) in col}\\ncanada_dict'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "table = tr_elements[90:92]\n",
    "\n",
    "col = []\n",
    "i = 0\n",
    "\n",
    "for t in tr_elements[0]:\n",
    "    i+=1\n",
    "    name = t.text_content()\n",
    "    col.append((name,[]))\n",
    "\n",
    "\n",
    "for j in range(1,len(table)):\n",
    "    T = table[j]\n",
    "    i=0\n",
    "\n",
    "    for t in T.iterchildren():\n",
    "        data = t.text_content()\n",
    "        \n",
    "        #Check if row is empty\n",
    "        if i>0:\n",
    "        #Convert any numerical value to integers\n",
    "            try:\n",
    "                data = int(data.replace(\",\", \"\"))\n",
    "            except:\n",
    "                pass\n",
    "        #Append the data to the empty list of the i'th column\n",
    "        col[i][1].append(data)\n",
    "        #Increment i for the next column\n",
    "        i+=1\n",
    "\n",
    "[len(C) for (title,C) in col]\n",
    "\n",
    "canada_dict = {title:column for (title,column) in col}\n",
    "canada_dict\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 100, \"display.max_columns\", None)\n",
    "\n",
    "\n",
    "def get_covid_cases(countries=None, provinces=None, start_date=None, end_date=None, casetype=['confirmed', 'death', 'recovered'], cumsum=False):\n",
    "    df = pd.read_csv('https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv',\n",
    "            names=[\"province_state\", 'country_region', 'lat', 'long', 'date', 'cases', 'type'], skiprows=1, parse_dates=['date'])\n",
    "    \n",
    "    df = df[df.type.isin(casetype)]\n",
    "    \n",
    "    if start_date is not None:\n",
    "        df = df[df.date >= start_date]\n",
    "    if end_date is not None:\n",
    "        df = df[df.date <= end_date]\n",
    "\n",
    "    if provinces is not None:\n",
    "        for province in provinces:\n",
    "            if province not in df.province_state.values:\n",
    "                print(\"Province: {0} not found in database. Check spelling!\".format(province))\n",
    "            df = df[(df.province_state.isin(provinces))] \n",
    "            \n",
    "    if countries is not None:\n",
    "        for country in countries:\n",
    "            if country not in df.country_region.values:\n",
    "                print(\"Country: {0} not found in database. Check spelling!\".format(country))\n",
    "            df =  df[(df.country_region.isin(countries))]\n",
    "\n",
    "    if cumsum is True:\n",
    "        df.cases = df.groupby('province_state')['cases'].transform(pd.Series.cumsum)\n",
    "            \n",
    "\n",
    "    iso_df = pd.read_csv('https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/slim-3/slim-3.csv')\n",
    "    iso_df = iso_df[['name', 'alpha-3']]\n",
    "    iso_df.loc[iso_df.name==\"United States of America\", 'name'] = 'US'\n",
    "    iso_df.loc[iso_df.name==\"China\", 'name'] = 'Mainland China'\n",
    "    iso_df.loc[iso_df.name==\"United Kingdom\", 'name'] = 'UK'\n",
    "    iso_df.loc[iso_df.name==\"Russian Federation\", 'name'] = 'Russia'\n",
    "    iso_df.loc[iso_df.name==\"Korea, Republic of\", 'name'] = 'South Korea'\n",
    "    iso_df.loc[iso_df.name==\"Macao\", 'name'] = 'Macau'\n",
    "    iso_df.loc[iso_df.name==\"Taiwan, Province of China\", 'name'] = 'Taiwan'\n",
    "    iso_df.loc[iso_df.name==\"Viet Nam\", 'name'] = 'Vietnam'\n",
    "    iso_df.loc[iso_df.name==\"Iran (Islamic Republic of)\", 'name'] = 'Iran'\n",
    "    iso_df.loc[iso_df.name==\"Czechia\", 'name'] = 'Czech Republic'\n",
    "    iso_df.loc[iso_df.name==\"Saint Barthélemy\", 'name'] = 'Saint Barthelemy'\n",
    "    iso_df.loc[iso_df.name==\"Palestine, State of\", 'name'] = 'Palestine'\n",
    "    iso_df.loc[iso_df.name==\"Moldova, Republic of\", 'name'] = 'Moldova'\n",
    "    iso_df.loc[iso_df.name==\"Ireland\", 'name'] = 'Republic of Ireland'\n",
    "    iso_df.loc[iso_df.name==\"Holy See\", 'name'] = 'Vatican City'\n",
    "               \n",
    "    df = pd.merge(df, iso_df, left_on=\"country_region\", right_on='name')\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "def get_covid_cases_wide(countries=None, start_date=None, end_date=None, casetype=['confirmed', 'death', 'recovered'], cumsum=False):\n",
    "    df = pd.read_csv('https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv',\n",
    "            names=[\"province_state\", 'country_region', 'lat', 'long', 'date', 'cases', 'type'], skiprows=1, parse_dates=['date'])\n",
    "    \n",
    "    df = df[df.type.isin(casetype)]\n",
    "    \n",
    "    if start_date is not None:\n",
    "        df = df[df.date >= start_date]\n",
    "    else:\n",
    "        if end_date is not None:\n",
    "            df = df[df.date <= end_date]\n",
    "    \n",
    "    if countries is not None:\n",
    "        for country in countries:\n",
    "            if country not in df.country_region.values:\n",
    "                print(\"Country: {0} not found in database. Check spelling!\".format(country))\n",
    "            df =  df[(df.country_region.isin(countries))]\n",
    "    \n",
    "    df = df.pivot_table(index=[\"date\", 'country_region'], columns='type', values='cases', \\\n",
    "                        aggfunc={'date':'first', 'country_region':'first', 'cases':np.sum})\\\n",
    "            .reset_index().fillna(0)\n",
    "    \n",
    "    if 'death' not in df.columns:\n",
    "        df['death'] = 0\n",
    "    \n",
    "    if 'recovered' not in df.columns:\n",
    "        df['recovered'] = 0\n",
    "        \n",
    "    df.sort_values(by=['country_region', 'date'], inplace=True)\n",
    "    \n",
    "    if cumsum is True:\n",
    "        df.confirmed = df.groupby('country_region')['confirmed'].transform(pd.Series.cumsum)\n",
    "        df.recovered = df.groupby('country_region')['recovered'].transform(pd.Series.cumsum)\n",
    "        df.death = df.groupby('country_region')['death'].transform(pd.Series.cumsum)\n",
    "        \n",
    "        \n",
    "\n",
    "    iso_df = pd.read_csv('https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/slim-3/slim-3.csv')\n",
    "    iso_df = iso_df[['name', 'alpha-3']]\n",
    "    iso_df.loc[iso_df.name==\"United States of America\", 'name'] = 'US'\n",
    "    iso_df.loc[iso_df.name==\"China\", 'name'] = 'China'\n",
    "    iso_df.loc[iso_df.name==\"United Kingdom\", 'name'] = 'UK'\n",
    "    iso_df.loc[iso_df.name==\"Russian Federation\", 'name'] = 'Russia'\n",
    "    iso_df.loc[iso_df.name==\"Korea, Republic of\", 'name'] = 'South Korea'\n",
    "    iso_df.loc[iso_df.name==\"Macao\", 'name'] = 'Macau'\n",
    "    iso_df.loc[iso_df.name==\"Taiwan, Province of China\", 'name'] = 'Taiwan'\n",
    "    iso_df.loc[iso_df.name==\"Viet Nam\", 'name'] = 'Vietnam'\n",
    "    iso_df.loc[iso_df.name==\"Iran (Islamic Republic of)\", 'name'] = 'Iran'\n",
    "    iso_df.loc[iso_df.name==\"Czechia\", 'name'] = 'Czech Republic'\n",
    "    iso_df.loc[iso_df.name==\"Saint Barthélemy\", 'name'] = 'Saint Barthelemy'\n",
    "    iso_df.loc[iso_df.name==\"Palestine, State of\", 'name'] = 'Palestine'\n",
    "    iso_df.loc[iso_df.name==\"Moldova, Republic of\", 'name'] = 'Moldova'\n",
    "    iso_df.loc[iso_df.name==\"Ireland\", 'name'] = 'Republic of Ireland'\n",
    "    iso_df.loc[iso_df.name==\"Holy See\", 'name'] = 'Vatican City'\n",
    "\n",
    "\n",
    "    \n",
    "    df = pd.merge(df, iso_df, left_on=\"country_region\", right_on='name')\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "def get_interval_data(df, interval='30D'):\n",
    "    \n",
    "    df.index = df.date\n",
    "\n",
    "    df = df.groupby([pd.Grouper(freq=interval), 'country_region']).sum().reset_index()\n",
    "    \n",
    "    return df.sort_values(by=['country_region', 'date'])\n",
    "\n",
    "\n",
    "\n",
    "def plot_countries(df=None, grouped_data=False, metric=\"confirmed\"):\n",
    "    \n",
    "    if df == None:\n",
    "        df = get_covid_cases_wide()\n",
    "   \n",
    "    if grouped_data == False:\n",
    "        df = df.groupby(['country_region', 'alpha-3']).sum().reset_index()\n",
    "        \n",
    "    fig = px.choropleth(df, locations=\"alpha-3\",\n",
    "                    color=metric, # lifeExp is a column of gapminder\n",
    "                    hover_name=\"country_region\", # column to add to hover information\n",
    "                    color_continuous_scale=\"OrRd\")\n",
    "    fig.show()\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "def plot_provinces(countries=None, provinces=None, start_date=None, end_date=None, casetype=['confirmed', 'death', 'recovered']):\n",
    "    \n",
    "    province_populations = { 'Ontario': 14446515,  \n",
    "                    'Quebec': 8433301,   \n",
    "                    'British Columbia': 5020302,   \n",
    "                    'Alberta': 4345737,   \n",
    "                    'Manitoba': 1360396,   \n",
    "                    'Saskatchewan': 1168423,   \n",
    "                    'Nova Scotia': 965382, \n",
    "                    'New Brunswick': 772094, \n",
    "                    'Newfoundland': 523790, \n",
    "                    'Prince Edward Island': 154748, \n",
    "                    'Northwest Territory': 44598,  \n",
    "                    'Yukon': 40369,  \n",
    "                    'Nunavut': 38787\n",
    "                    };\n",
    "\n",
    "\n",
    "\n",
    "    df = get_covid_cases(countries=countries, provinces = provinces, casetype = casetype, start_date=start_date, end_date=end_date, cumsum=True)\n",
    "    \n",
    "    if provinces is not None:\n",
    "        for province in provinces:\n",
    "            print(province)\n",
    "            df.loc[df.province_state == province, 'cases'] = (df.loc[df.province_state == province, 'cases'] / province_populations[province]) * 100000\n",
    "             \n",
    "    fig = px.line(df, x=\"date\", y=\"cases\", color='province_state', title=\"Number of confirmed COVID-19 cases over time per 100,000 Canadians\")\n",
    "\n",
    "    fig.update_layout(\n",
    "        yaxis_title=\"cases per 100,000 people\",\n",
    "        yaxis = dict(\n",
    "            showexponent = 'all',\n",
    "            exponentformat = 'e'\n",
    "        ),\n",
    "        xaxis = {\n",
    "            'tickformat': '%m-%d',\n",
    "            'tickmode': 'auto',\n",
    "            'nticks': 30, \n",
    "            'tick0': start_date,\n",
    "        }\n",
    "    \n",
    "    )\n",
    "\n",
    "    fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province_state</th>\n",
       "      <th>country_region</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>alpha-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60685</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>9</td>\n",
       "      <td>recovered</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60686</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>12</td>\n",
       "      <td>recovered</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60687</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>recovered</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60688</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>4</td>\n",
       "      <td>recovered</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60689</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>9</td>\n",
       "      <td>recovered</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60690 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          province_state country_region      lat      long       date  cases  \\\n",
       "0                    NaN    Afghanistan  33.0000   65.0000 2020-01-22      0   \n",
       "1                    NaN    Afghanistan  33.0000   65.0000 2020-01-23      0   \n",
       "2                    NaN    Afghanistan  33.0000   65.0000 2020-01-24      0   \n",
       "3                    NaN    Afghanistan  33.0000   65.0000 2020-01-25      0   \n",
       "4                    NaN    Afghanistan  33.0000   65.0000 2020-01-26      0   \n",
       "...                  ...            ...      ...       ...        ...    ...   \n",
       "60685  Western Australia      Australia -31.9505  115.8605 2020-04-28      9   \n",
       "60686  Western Australia      Australia -31.9505  115.8605 2020-04-29     12   \n",
       "60687  Western Australia      Australia -31.9505  115.8605 2020-04-30      0   \n",
       "60688  Western Australia      Australia -31.9505  115.8605 2020-05-01      4   \n",
       "60689  Western Australia      Australia -31.9505  115.8605 2020-05-02      9   \n",
       "\n",
       "            type         name alpha-3  \n",
       "0      confirmed  Afghanistan     AFG  \n",
       "1      confirmed  Afghanistan     AFG  \n",
       "2      confirmed  Afghanistan     AFG  \n",
       "3      confirmed  Afghanistan     AFG  \n",
       "4      confirmed  Afghanistan     AFG  \n",
       "...          ...          ...     ...  \n",
       "60685  recovered    Australia     AUS  \n",
       "60686  recovered    Australia     AUS  \n",
       "60687  recovered    Australia     AUS  \n",
       "60688  recovered    Australia     AUS  \n",
       "60689  recovered    Australia     AUS  \n",
       "\n",
       "[60690 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_covid_cases()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country_region</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>death</th>\n",
       "      <th>recovered</th>\n",
       "      <th>name</th>\n",
       "      <th>alpha-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17029</th>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17030</th>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17032</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17033</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17034 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date country_region  confirmed  death  recovered         name  \\\n",
       "0     2020-01-22    Afghanistan          0      0          0  Afghanistan   \n",
       "1     2020-01-23    Afghanistan          0      0          0  Afghanistan   \n",
       "2     2020-01-24    Afghanistan          0      0          0  Afghanistan   \n",
       "3     2020-01-25    Afghanistan          0      0          0  Afghanistan   \n",
       "4     2020-01-26    Afghanistan          0      0          0  Afghanistan   \n",
       "...          ...            ...        ...    ...        ...          ...   \n",
       "17029 2020-04-28       Zimbabwe          0      0          0     Zimbabwe   \n",
       "17030 2020-04-29       Zimbabwe          0      0          0     Zimbabwe   \n",
       "17031 2020-04-30       Zimbabwe          8      0          0     Zimbabwe   \n",
       "17032 2020-05-01       Zimbabwe          0      0          0     Zimbabwe   \n",
       "17033 2020-05-02       Zimbabwe         -6      0          0     Zimbabwe   \n",
       "\n",
       "      alpha-3  \n",
       "0         AFG  \n",
       "1         AFG  \n",
       "2         AFG  \n",
       "3         AFG  \n",
       "4         AFG  \n",
       "...       ...  \n",
       "17029     ZWE  \n",
       "17030     ZWE  \n",
       "17031     ZWE  \n",
       "17032     ZWE  \n",
       "17033     ZWE  \n",
       "\n",
       "[17034 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_covid_cases_wide()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ontario\n",
      "British Columbia\n"
     ]
    },
    {
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this._expand(r+1),this.words[r]=e?this.words[r]|1<<a:this.words[r]&~(1<<a),this.strip()},i.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var a=0,i=0;i<n.length;i++)e=(0|r.words[i])+(0|n.words[i])+a,this.words[i]=67108863&e,a=e>>>26;for(;0!==a&&i<r.length;i++)e=(0|r.words[i])+a,this.words[i]=67108863&e,a=e>>>26;if(this.length=r.length,0!==a)this.words[this.length]=a,this.length++;else if(r!==this)for(;i<r.length;i++)this.words[i]=r.words[i];return this},i.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},i.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,a=this.cmp(t);if(0===a)return this.negative=0,this.length=1,this.words[0]=0,this;a>0?(r=this,n=t):(r=t,n=this);for(var i=0,o=0;o<n.length;o++)i=(e=(0|r.words[o])-(0|n.words[o])+i)>>26,this.words[o]=67108863&e;for(;0!==i&&o<r.length;o++)i=(e=(0|r.words[o])+i)>>26,this.words[o]=67108863&e;if(0===i&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},i.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,a,i,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],h=8191&u,f=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,v=0|o[2],m=8191&v,y=v>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],k=8191&w,T=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,z=P>>>13,I=0|o[8],D=8191&I,R=I>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],V=8191&j,U=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,at=0|s[6],it=8191&at,ot=at>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var vt=(c+(n=Math.imul(h,V))|0)+((8191&(a=(a=Math.imul(h,U))+Math.imul(f,V)|0))<<13)|0;c=((i=Math.imul(f,U))+(a>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(d,V),a=(a=Math.imul(d,U))+Math.imul(g,V)|0,i=Math.imul(g,U);var mt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(a=(a=a+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((i=i+Math.imul(f,G)|0)+(a>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,V),a=(a=Math.imul(m,U))+Math.imul(y,V)|0,i=Math.imul(y,U),n=n+Math.imul(d,H)|0,a=(a=a+Math.imul(d,G)|0)+Math.imul(g,H)|0,i=i+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(a=(a=a+Math.imul(h,X)|0)+Math.imul(f,W)|0))<<13)|0;c=((i=i+Math.imul(f,X)|0)+(a>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,V),a=(a=Math.imul(b,U))+Math.imul(_,V)|0,i=Math.imul(_,U),n=n+Math.imul(m,H)|0,a=(a=a+Math.imul(m,G)|0)+Math.imul(y,H)|0,i=i+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,a=(a=a+Math.imul(d,X)|0)+Math.imul(g,W)|0,i=i+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(h,J)|0)|0)+((8191&(a=(a=a+Math.imul(h,K)|0)+Math.imul(f,J)|0))<<13)|0;c=((i=i+Math.imul(f,K)|0)+(a>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,V),a=(a=Math.imul(k,U))+Math.imul(T,V)|0,i=Math.imul(T,U),n=n+Math.imul(b,H)|0,a=(a=a+Math.imul(b,G)|0)+Math.imul(_,H)|0,i=i+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,a=(a=a+Math.imul(m,X)|0)+Math.imul(y,W)|0,i=i+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,a=(a=a+Math.imul(d,K)|0)+Math.imul(g,J)|0,i=i+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(a=(a=a+Math.imul(h,tt)|0)+Math.imul(f,$)|0))<<13)|0;c=((i=i+Math.imul(f,tt)|0)+(a>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,V),a=(a=Math.imul(A,U))+Math.imul(S,V)|0,i=Math.imul(S,U),n=n+Math.imul(k,H)|0,a=(a=a+Math.imul(k,G)|0)+Math.imul(T,H)|0,i=i+Math.imul(T,G)|0,n=n+Math.imul(b,W)|0,a=(a=a+Math.imul(b,X)|0)+Math.imul(_,W)|0,i=i+Math.imul(_,X)|0,n=n+Math.imul(m,J)|0,a=(a=a+Math.imul(m,K)|0)+Math.imul(y,J)|0,i=i+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,a=(a=a+Math.imul(d,tt)|0)+Math.imul(g,$)|0,i=i+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(a=(a=a+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((i=i+Math.imul(f,nt)|0)+(a>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,V),a=(a=Math.imul(L,U))+Math.imul(C,V)|0,i=Math.imul(C,U),n=n+Math.imul(A,H)|0,a=(a=a+Math.imul(A,G)|0)+Math.imul(S,H)|0,i=i+Math.imul(S,G)|0,n=n+Math.imul(k,W)|0,a=(a=a+Math.imul(k,X)|0)+Math.imul(T,W)|0,i=i+Math.imul(T,X)|0,n=n+Math.imul(b,J)|0,a=(a=a+Math.imul(b,K)|0)+Math.imul(_,J)|0,i=i+Math.imul(_,K)|0,n=n+Math.imul(m,$)|0,a=(a=a+Math.imul(m,tt)|0)+Math.imul(y,$)|0,i=i+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,a=(a=a+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,i=i+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,it)|0)|0)+((8191&(a=(a=a+Math.imul(h,ot)|0)+Math.imul(f,it)|0))<<13)|0;c=((i=i+Math.imul(f,ot)|0)+(a>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,V),a=(a=Math.imul(O,U))+Math.imul(z,V)|0,i=Math.imul(z,U),n=n+Math.imul(L,H)|0,a=(a=a+Math.imul(L,G)|0)+Math.imul(C,H)|0,i=i+Math.imul(C,G)|0,n=n+Math.imul(A,W)|0,a=(a=a+Math.imul(A,X)|0)+Math.imul(S,W)|0,i=i+Math.imul(S,X)|0,n=n+Math.imul(k,J)|0,a=(a=a+Math.imul(k,K)|0)+Math.imul(T,J)|0,i=i+Math.imul(T,K)|0,n=n+Math.imul(b,$)|0,a=(a=a+Math.imul(b,tt)|0)+Math.imul(_,$)|0,i=i+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,a=(a=a+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,i=i+Math.imul(y,nt)|0,n=n+Math.imul(d,it)|0,a=(a=a+Math.imul(d,ot)|0)+Math.imul(g,it)|0,i=i+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(a=(a=a+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((i=i+Math.imul(f,ct)|0)+(a>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,V),a=(a=Math.imul(D,U))+Math.imul(R,V)|0,i=Math.imul(R,U),n=n+Math.imul(O,H)|0,a=(a=a+Math.imul(O,G)|0)+Math.imul(z,H)|0,i=i+Math.imul(z,G)|0,n=n+Math.imul(L,W)|0,a=(a=a+Math.imul(L,X)|0)+Math.imul(C,W)|0,i=i+Math.imul(C,X)|0,n=n+Math.imul(A,J)|0,a=(a=a+Math.imul(A,K)|0)+Math.imul(S,J)|0,i=i+Math.imul(S,K)|0,n=n+Math.imul(k,$)|0,a=(a=a+Math.imul(k,tt)|0)+Math.imul(T,$)|0,i=i+Math.imul(T,tt)|0,n=n+Math.imul(b,rt)|0,a=(a=a+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,i=i+Math.imul(_,nt)|0,n=n+Math.imul(m,it)|0,a=(a=a+Math.imul(m,ot)|0)+Math.imul(y,it)|0,i=i+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,a=(a=a+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,i=i+Math.imul(g,ct)|0;var Tt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(a=(a=a+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((i=i+Math.imul(f,ft)|0)+(a>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(B,V),a=(a=Math.imul(B,U))+Math.imul(N,V)|0,i=Math.imul(N,U),n=n+Math.imul(D,H)|0,a=(a=a+Math.imul(D,G)|0)+Math.imul(R,H)|0,i=i+Math.imul(R,G)|0,n=n+Math.imul(O,W)|0,a=(a=a+Math.imul(O,X)|0)+Math.imul(z,W)|0,i=i+Math.imul(z,X)|0,n=n+Math.imul(L,J)|0,a=(a=a+Math.imul(L,K)|0)+Math.imul(C,J)|0,i=i+Math.imul(C,K)|0,n=n+Math.imul(A,$)|0,a=(a=a+Math.imul(A,tt)|0)+Math.imul(S,$)|0,i=i+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,a=(a=a+Math.imul(k,nt)|0)+Math.imul(T,rt)|0,i=i+Math.imul(T,nt)|0,n=n+Math.imul(b,it)|0,a=(a=a+Math.imul(b,ot)|0)+Math.imul(_,it)|0,i=i+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,a=(a=a+Math.imul(m,ct)|0)+Math.imul(y,lt)|0,i=i+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,a=(a=a+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,i=i+Math.imul(g,ft)|0;var Mt=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(a=(a=a+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((i=i+Math.imul(f,gt)|0)+(a>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),a=(a=Math.imul(B,G))+Math.imul(N,H)|0,i=Math.imul(N,G),n=n+Math.imul(D,W)|0,a=(a=a+Math.imul(D,X)|0)+Math.imul(R,W)|0,i=i+Math.imul(R,X)|0,n=n+Math.imul(O,J)|0,a=(a=a+Math.imul(O,K)|0)+Math.imul(z,J)|0,i=i+Math.imul(z,K)|0,n=n+Math.imul(L,$)|0,a=(a=a+Math.imul(L,tt)|0)+Math.imul(C,$)|0,i=i+Math.imul(C,tt)|0,n=n+Math.imul(A,rt)|0,a=(a=a+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,i=i+Math.imul(S,nt)|0,n=n+Math.imul(k,it)|0,a=(a=a+Math.imul(k,ot)|0)+Math.imul(T,it)|0,i=i+Math.imul(T,ot)|0,n=n+Math.imul(b,lt)|0,a=(a=a+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,i=i+Math.imul(_,ct)|0,n=n+Math.imul(m,ht)|0,a=(a=a+Math.imul(m,ft)|0)+Math.imul(y,ht)|0,i=i+Math.imul(y,ft)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(a=(a=a+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((i=i+Math.imul(g,gt)|0)+(a>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),a=(a=Math.imul(B,X))+Math.imul(N,W)|0,i=Math.imul(N,X),n=n+Math.imul(D,J)|0,a=(a=a+Math.imul(D,K)|0)+Math.imul(R,J)|0,i=i+Math.imul(R,K)|0,n=n+Math.imul(O,$)|0,a=(a=a+Math.imul(O,tt)|0)+Math.imul(z,$)|0,i=i+Math.imul(z,tt)|0,n=n+Math.imul(L,rt)|0,a=(a=a+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,i=i+Math.imul(C,nt)|0,n=n+Math.imul(A,it)|0,a=(a=a+Math.imul(A,ot)|0)+Math.imul(S,it)|0,i=i+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,a=(a=a+Math.imul(k,ct)|0)+Math.imul(T,lt)|0,i=i+Math.imul(T,ct)|0,n=n+Math.imul(b,ht)|0,a=(a=a+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,i=i+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(m,dt)|0)|0)+((8191&(a=(a=a+Math.imul(m,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((i=i+Math.imul(y,gt)|0)+(a>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),a=(a=Math.imul(B,K))+Math.imul(N,J)|0,i=Math.imul(N,K),n=n+Math.imul(D,$)|0,a=(a=a+Math.imul(D,tt)|0)+Math.imul(R,$)|0,i=i+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,a=(a=a+Math.imul(O,nt)|0)+Math.imul(z,rt)|0,i=i+Math.imul(z,nt)|0,n=n+Math.imul(L,it)|0,a=(a=a+Math.imul(L,ot)|0)+Math.imul(C,it)|0,i=i+Math.imul(C,ot)|0,n=n+Math.imul(A,lt)|0,a=(a=a+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,i=i+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,a=(a=a+Math.imul(k,ft)|0)+Math.imul(T,ht)|0,i=i+Math.imul(T,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(a=(a=a+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((i=i+Math.imul(_,gt)|0)+(a>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),a=(a=Math.imul(B,tt))+Math.imul(N,$)|0,i=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,a=(a=a+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,i=i+Math.imul(R,nt)|0,n=n+Math.imul(O,it)|0,a=(a=a+Math.imul(O,ot)|0)+Math.imul(z,it)|0,i=i+Math.imul(z,ot)|0,n=n+Math.imul(L,lt)|0,a=(a=a+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,i=i+Math.imul(C,ct)|0,n=n+Math.imul(A,ht)|0,a=(a=a+Math.imul(A,ft)|0)+Math.imul(S,ht)|0,i=i+Math.imul(S,ft)|0;var Lt=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(a=(a=a+Math.imul(k,gt)|0)+Math.imul(T,dt)|0))<<13)|0;c=((i=i+Math.imul(T,gt)|0)+(a>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),a=(a=Math.imul(B,nt))+Math.imul(N,rt)|0,i=Math.imul(N,nt),n=n+Math.imul(D,it)|0,a=(a=a+Math.imul(D,ot)|0)+Math.imul(R,it)|0,i=i+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,a=(a=a+Math.imul(O,ct)|0)+Math.imul(z,lt)|0,i=i+Math.imul(z,ct)|0,n=n+Math.imul(L,ht)|0,a=(a=a+Math.imul(L,ft)|0)+Math.imul(C,ht)|0,i=i+Math.imul(C,ft)|0;var Ct=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(a=(a=a+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((i=i+Math.imul(S,gt)|0)+(a>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,it),a=(a=Math.imul(B,ot))+Math.imul(N,it)|0,i=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,a=(a=a+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,i=i+Math.imul(R,ct)|0,n=n+Math.imul(O,ht)|0,a=(a=a+Math.imul(O,ft)|0)+Math.imul(z,ht)|0,i=i+Math.imul(z,ft)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(a=(a=a+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((i=i+Math.imul(C,gt)|0)+(a>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),a=(a=Math.imul(B,ct))+Math.imul(N,lt)|0,i=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,a=(a=a+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,i=i+Math.imul(R,ft)|0;var Ot=(c+(n=n+Math.imul(O,dt)|0)|0)+((8191&(a=(a=a+Math.imul(O,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((i=i+Math.imul(z,gt)|0)+(a>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ht),a=(a=Math.imul(B,ft))+Math.imul(N,ht)|0,i=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(a=(a=a+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((i=i+Math.imul(R,gt)|0)+(a>>>13)|0)+(zt>>>26)|0,zt&=67108863;var It=(c+(n=Math.imul(B,dt))|0)+((8191&(a=(a=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((i=Math.imul(N,gt))+(a>>>13)|0)+(It>>>26)|0,It&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Tt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=zt,l[18]=It,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),i.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,a=0,i=0;i<r.length-1;i++){var o=a;a=0;for(var s=67108863&n,l=Math.min(i,e.length-1),c=Math.max(0,i-t.length+1);c<=l;c++){var u=i-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),a+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[i]=s,n=o,o=a}return 0!==n?r.words[i]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=i.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,a=0;a<e;a++)n|=(1&t)<<e-a-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,a,i){for(var o=0;o<i;o++)n[o]=e[t[o]],a[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,a,i){this.permute(i,t,e,r,n,a);for(var o=1;o<a;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<a;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=h*v-f*m;m=h*m+f*v,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=l*h-c*f,f=l*f+c*h,h=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,a=0;for(r=r/2|0;r;r>>>=1)a++;return 1<<a+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var a=t[n];t[n]=t[r-n-1],t[r-n-1]=a,a=e[n],e[n]=-e[r-n-1],e[r-n-1]=-a}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var a=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&a,r=a<67108864?0:a/67108864|0}return t},g.prototype.convert13b=function(t,e,r,a){for(var i=0,o=0;o<e;o++)i+=0|t[o],r[2*o]=8191&i,i>>>=13,r[2*o+1]=8191&i,i>>>=13;for(o=2*e;o<a;++o)r[o]=0;n(0===i),n(0==(-8192&i))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),a=this.makeRBT(n),i=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,i,s,l,n,a),this.transform(c,i,u,h,n,a);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,i,n,a),this.conjugate(f,i,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.mulf=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),d(this,t,e)},i.prototype.imul=function(t){return this.clone().mulTo(t,this)},i.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var a=(0|this.words[r])*t,i=(67108863&a)+(67108863&e);e>>=26,e+=a/67108864|0,e+=i>>>26,this.words[r]=67108863&i}return 0!==e&&(this.words[r]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.imul(this.clone())},i.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,a=r%26;e[r]=(t.words[n]&1<<a)>>>a}return e}(t);if(0===e.length)return new i(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var a=r.sqr();n<e.length;n++,a=a.sqr())0!==e[n]&&(r=r.mul(a));return r},i.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,a=(t-r)/26,i=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&i,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==a){for(e=this.length-1;e>=0;e--)this.words[e+a]=this.words[e];for(e=0;e<a;e++)this.words[e]=0;this.length+=a}return this.strip()},i.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},i.prototype.iushrn=function(t,e,r){var a;n(\"number\"==typeof t&&t>=0),a=e?(e-e%26)/26:0;var i=t%26,o=Math.min((t-i)/26,this.length),s=67108863^67108863>>>i<<i,l=r;if(a-=o,a=Math.max(0,a),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=a);c--){var h=0|this.words[c];this.words[c]=u<<26-i|h>>>i,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},i.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.ushln=function(t){return this.clone().iushln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.ushrn=function(t){return this.clone().iushrn(t)},i.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,a=1<<e;return!(this.length<=r)&&!!(this.words[r]&a)},i.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var a=67108863^67108863>>>e<<e;this.words[this.length-1]&=a}return this.strip()},i.prototype.maskn=function(t){return this.clone().imaskn(t)},i.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},i.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},i.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},i.prototype.addn=function(t){return this.clone().iaddn(t)},i.prototype.subn=function(t){return this.clone().isubn(t)},i.prototype.iabs=function(){return this.negative=0,this},i.prototype.abs=function(){return this.clone().iabs()},i.prototype._ishlnsubmul=function(t,e,r){var a,i,o=t.length+r;this._expand(o);var s=0;for(a=0;a<t.length;a++){i=(0|this.words[a+r])+s;var l=(0|t.words[a])*e;s=((i-=67108863&l)>>26)-(l/67108864|0),this.words[a+r]=67108863&i}for(;a<this.length-r;a++)s=(i=(0|this.words[a+r])+s)>>26,this.words[a+r]=67108863&i;if(0===s)return this.strip();for(n(-1===s),s=0,a=0;a<this.length;a++)s=(i=-(0|this.words[a])+s)>>26,this.words[a]=67108863&i;return this.negative=1,this.strip()},i.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),a=t,o=0|a.words[a.length-1];0!==(r=26-this._countBits(o))&&(a=a.ushln(r),n.iushln(r),o=0|a.words[a.length-1]);var s,l=n.length-a.length;if(\"mod\"!==e){(s=new i(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(a,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[a.length+h])+(0|n.words[a.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(a,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(a,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},i.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new i(0),mod:new i(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(a=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:a,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(a=s.div.neg()),{div:a,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new i(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e);var a,o,s},i.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},i.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},i.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},i.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),a=t.andln(1),i=r.cmp(n);return i<0||1===a&&0===i?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,a=this.length-1;a>=0;a--)r=(e*r+(0|this.words[a]))%t;return r},i.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var a=(0|this.words[r])+67108864*e;this.words[r]=a/t|0,e=a%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var a=new i(1),o=new i(0),s=new i(0),l=new i(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(a.isOdd()||o.isOdd())&&(a.iadd(u),o.isub(h)),a.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),a.isub(s),o.isub(l)):(r.isub(e),s.isub(a),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},i.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var a,o=new i(1),s=new i(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(a=0===e.cmpn(1)?o:s).cmpn(0)<0&&a.iadd(t),a},i.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var a=e.cmp(r);if(a<0){var i=e;e=r,r=i}else if(0===a||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},i.prototype.invm=function(t){return this.egcd(t).a.umod(t)},i.prototype.isEven=function(){return 0==(1&this.words[0])},i.prototype.isOdd=function(){return 1==(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,a=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=a,this;for(var i=a,o=r;0!==i&&o<this.length;o++){var s=0|this.words[o];i=(s+=i)>>>26,s&=67108863,this.words[o]=s}return 0!==i&&(this.words[o]=i,this.length++),this},i.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},i.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var a=0|this.words[0];e=a===t?0:a<t?-1:1}return 0!==this.negative?0|-e:e},i.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},i.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],a=0|t.words[r];if(n!==a){n<a?e=-1:n>a&&(e=1);break}}return e},i.prototype.gtn=function(t){return 1===this.cmpn(t)},i.prototype.gt=function(t){return 1===this.cmp(t)},i.prototype.gten=function(t){return this.cmpn(t)>=0},i.prototype.gte=function(t){return this.cmp(t)>=0},i.prototype.ltn=function(t){return-1===this.cmpn(t)},i.prototype.lt=function(t){return-1===this.cmp(t)},i.prototype.lten=function(t){return this.cmpn(t)<=0},i.prototype.lte=function(t){return this.cmp(t)<=0},i.prototype.eqn=function(t){return 0===this.cmpn(t)},i.prototype.eq=function(t){return 0===this.cmp(t)},i.red=function(t){return new w(t)},i.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},i.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},i.prototype._forceRed=function(t){return this.red=t,this},i.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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numbers\"),this.red._verify1(this),this.red.sqr(this)},i.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},i.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},i.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},i.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},i.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new i(e,16),this.n=this.p.bitLength(),this.k=new i(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=i._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new i(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new i(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},a(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=4194303&a,n=10;n<t.length;n++){var i=0|t.words[n];t.words[n-10]=(4194303&i)<<4|a>>>22,a=i}a>>>=22,t.words[n-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},a(x,m),a(b,m),a(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,a=67108863&n;n>>>=26,t.words[r]=a,e=n}return 0!==e&&(t.words[t.length++]=e),t},i._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new i(1)).iushrn(2);return this.pow(t,r)}for(var a=this.m.subn(1),o=0;!a.isZero()&&0===a.andln(1);)o++,a.iushrn(1);n(!a.isZero());var s=new i(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new i(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,a),f=this.pow(t,a.addn(1).iushrn(1)),p=this.pow(t,a),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(h,new i(1).iushln(d-v-1));f=f.redMul(m),h=m.redSqr(),p=p.redMul(h),d=v}return f},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new i(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new i(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var a=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),a=r.isub(n).iushrn(this.shift),o=a;return a.cmp(this.m)>=0?o=a.isub(this.m):a.cmpn(0)<0&&(o=a.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:105}],97:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,a=t.length,i=0;for(e=0;e<a;++e)i+=t[e].length;var o=new Array(i),s=0;for(e=0;e<a;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),h=0;for(n=0;n<c;++n)n!==r&&(u[h++]=l[n]);if(1&r){var f=u[1];u[1]=u[0],u[0]=f}}}return o}},{}],98:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return n=[],c(a=t,a,u,!0),n;case 2:return\"function\"==typeof e?c(t,t,e,!0):function(t,e){return n=[],c(t,e,u,!1),n}(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}var a};var n,a=t(\"typedarray-pool\"),i=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var a=0,i=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[a++]=c[u];n[i++]=o}}return i}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var h,f=a.mallocDouble(2*u*s),p=a.mallocInt32(s);if((s=l(t,u,f,p))>0){if(1===u&&n)i.init(s),h=i.sweepComplete(u,r,0,s,f,p,0,s,f,p);else{var d=a.mallocDouble(2*u*c),g=a.mallocInt32(c);(c=l(e,u,d,g))>0&&(i.init(s+c),h=1===u?i.sweepBipartite(u,r,0,s,f,p,0,c,d,g):o(u,r,n,s,f,p,c,d,g),a.free(d),a.free(g))}a.free(f),a.free(p)}return h}}}function u(t,e){n.push([t,e])}},{\"./lib/intersect\":100,\"./lib/sweep\":104,\"typedarray-pool\":544}],99:[function(t,e,r){\"use strict\";var n=\"d\",a=\"ax\",i=\"vv\",o=\"fp\",s=\"es\",l=\"rs\",c=\"re\",u=\"rb\",h=\"ri\",f=\"rp\",p=\"bs\",d=\"be\",g=\"bb\",v=\"bi\",m=\"bp\",y=\"rv\",x=\"Q\",b=[n,a,i,l,c,u,h,p,d,g,v];function _(t){var e=\"bruteForce\"+(t?\"Full\":\"Partial\"),r=[],_=b.slice();t||_.splice(3,0,o);var w=[\"function \"+e+\"(\"+_.join()+\"){\"];function k(e,o){var _=function(t,e,r){var o=\"bruteForce\"+(t?\"Red\":\"Blue\")+(e?\"Flip\":\"\")+(r?\"Full\":\"\"),_=[\"function \",o,\"(\",b.join(),\"){\",\"var \",s,\"=2*\",n,\";\"],w=\"for(var i=\"+l+\",\"+f+\"=\"+s+\"*\"+l+\";i<\"+c+\";++i,\"+f+\"+=\"+s+\"){var x0=\"+u+\"[\"+a+\"+\"+f+\"],x1=\"+u+\"[\"+a+\"+\"+f+\"+\"+n+\"],xi=\"+h+\"[i];\",k=\"for(var j=\"+p+\",\"+m+\"=\"+s+\"*\"+p+\";j<\"+d+\";++j,\"+m+\"+=\"+s+\"){var y0=\"+g+\"[\"+a+\"+\"+m+\"],\"+(r?\"y1=\"+g+\"[\"+a+\"+\"+m+\"+\"+n+\"],\":\"\")+\"yi=\"+v+\"[j];\";return 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n=t(\"typedarray-pool\"),a=t(\"bit-twiddle\"),i=t(\"./brute\"),o=i.partial,s=i.full,l=t(\"./sweep\"),c=t(\"./median\"),u=t(\"./partition\"),h=128,f=1<<22,p=1<<22,d=u(\"!(lo>=p0)&&!(p1>=hi)\",[\"p0\",\"p1\"]),g=u(\"lo===p0\",[\"p0\"]),v=u(\"lo<p0\",[\"p0\"]),m=u(\"hi<=p0\",[\"p0\"]),y=u(\"lo<=p0&&p0<=hi\",[\"p0\"]),x=u(\"lo<p0&&p0<=hi\",[\"p0\"]),b=6,_=2,w=n.mallocInt32(1024),k=n.mallocDouble(1024);function T(t,e,r,n,a,i,o,s,l){var c=b*t;w[c]=e,w[c+1]=r,w[c+2]=n,w[c+3]=a,w[c+4]=i,w[c+5]=o;var u=_*t;k[u]=s,k[u+1]=l}function M(t,e,r,n,a,i,o,s,l,c,u){var h=2*t,f=l*h,p=c[f+e];t:for(var d=a,g=a*h;d<i;++d,g+=h){var v=o[g+e],m=o[g+e+t];if(!(p<v||m<p)&&(!n||p!==v)){for(var y,x=s[d],b=e+1;b<t;++b){v=o[g+b],m=o[g+b+t];var _=c[f+b],w=c[f+b+t];if(m<_||w<v)continue t}if(void 0!==(y=n?r(u,x):r(x,u)))return y}}}function A(t,e,r,n,a,i,o,s,l,c){var u=2*t,h=s*u,f=l[h+e];t:for(var p=n,d=n*u;p<a;++p,d+=u){var g=o[p];if(g!==c){var v=i[d+e],m=i[d+e+t];if(!(f<v||m<f)){for(var 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n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),a=8;function i(t,e,r,n,a,i){for(var o=2*t,s=o*(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=a[s],u=l,h=o*(l-1);u>r&&a[h+e]>c;--u,h-=o){for(var f=h,p=h+o,d=0;d<o;++d,++f,++p){var g=a[f];a[f]=a[p],a[p]=g}var v=i[u];i[u]=i[u-1],i[u-1]=v}}},{\"./partition\":102}],102:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),a=[];t.indexOf(\"lo\")>=0&&a.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&a.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",a.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2*a,k=j*c,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],103:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4*n?a(0,e-1,t):function t(e,r,h){var 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c=r[l-2],u=r[l-1];if(c<i)break;if(c===i&&u<o)break;r[l]=c,r[l+1]=u,l-=2}r[l]=i,r[l+1]=o}}function i(t,e,r){e*=2;var n=r[t*=2],a=r[t+1];r[t]=r[e],r[t+1]=r[e+1],r[e]=n,r[e+1]=a}function o(t,e,r){e*=2,r[t*=2]=r[e],r[t+1]=r[e+1]}function s(t,e,r,n){e*=2,r*=2;var a=n[t*=2],i=n[t+1];n[t]=n[e],n[t+1]=n[e+1],n[e]=n[r],n[e+1]=n[r+1],n[r]=a,n[r+1]=i}function l(t,e,r,n,a){e*=2,a[t*=2]=a[e],a[e]=r,a[t+1]=a[e+1],a[e+1]=n}function c(t,e,r){e*=2;var n=r[t*=2],a=r[e];return!(n<a)&&(n!==a||r[t+1]>r[e+1])}function u(t,e,r,n){var a=n[t*=2];return a<e||a===e&&n[t+1]<r}},{}],104:[function(t,e,r){\"use strict\";e.exports={init:function(t){var e=a.nextPow2(t);s.length<e&&(n.free(s),s=n.mallocInt32(e));l.length<e&&(n.free(l),l=n.mallocInt32(e));c.length<e&&(n.free(c),c=n.mallocInt32(e));u.length<e&&(n.free(u),u=n.mallocInt32(e));h.length<e&&(n.free(h),h=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;p.length<r&&(n.free(p),p=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,a,h,f,v,m,y){for(var x=0,b=2*t,_=t-1,w=b-1,k=r;k<n;++k){var T=h[k],M=b*k;p[x++]=a[M+_],p[x++]=-(T+1),p[x++]=a[M+w],p[x++]=T}for(var k=f;k<v;++k){var T=y[k]+o,A=b*k;p[x++]=m[A+_],p[x++]=-T,p[x++]=m[A+w],p[x++]=T}var S=x>>>1;i(p,S);for(var E=0,L=0,k=0;k<S;++k){var C=0|p[2*k+1];if(C>=o)d(c,u,L--,C=C-o|0);else if(C>=0)d(s,l,E--,C);else if(C<=-o){C=-C-o|0;for(var P=0;P<E;++P){var O=e(s[P],C);if(void 0!==O)return O}g(c,u,L++,C)}else{C=-C-1|0;for(var P=0;P<L;++P){var O=e(C,c[P]);if(void 0!==O)return O}g(s,l,E++,C)}}},sweepComplete:function(t,e,r,n,a,o,v,m,y,x){for(var b=0,_=2*t,w=t-1,k=_-1,T=r;T<n;++T){var M=o[T]+1<<1,A=_*T;p[b++]=a[A+w],p[b++]=-M,p[b++]=a[A+k],p[b++]=M}for(var T=v;T<m;++T){var M=x[T]+1<<1,S=_*T;p[b++]=y[S+w],p[b++]=1|-M,p[b++]=y[S+k],p[b++]=1|M}var E=b>>>1;i(p,E);for(var L=0,C=0,P=0,T=0;T<E;++T){var O=0|p[2*T+1],z=1&O;if(T<E-1&&O>>1==p[2*T+3]>>1&&(z=2,T+=1),O<0){for(var I=-(O>>1)-1,D=0;D<P;++D){var R=e(h[D],I);if(void 0!==R)return R}if(0!==z)for(var D=0;D<L;++D){var R=e(s[D],I);if(void 0!==R)return R}if(1!==z)for(var D=0;D<C;++D){var R=e(c[D],I);if(void 0!==R)return R}0===z?g(s,l,L++,I):1===z?g(c,u,C++,I):2===z&&g(h,f,P++,I)}else{var I=(O>>1)-1;0===z?d(s,l,L--,I):1===z?d(c,u,C--,I):2===z&&d(h,f,P--,I)}}},scanBipartite:function(t,e,r,n,a,c,u,h,f,v,m,y){var x=0,b=2*t,_=e,w=e+t,k=1,T=1;n?T=o:k=o;for(var M=a;M<c;++M){var A=M+k,S=b*M;p[x++]=u[S+_],p[x++]=-A,p[x++]=u[S+w],p[x++]=A}for(var M=f;M<v;++M){var A=M+T,E=b*M;p[x++]=m[E+_],p[x++]=-A}var L=x>>>1;i(p,L);for(var C=0,M=0;M<L;++M){var P=0|p[2*M+1];if(P<0){var A=-P,O=!1;if(A>=o?(O=!n,A-=o):(O=!!n,A-=1),O)g(s,l,C++,A);else{var z=y[A],I=b*A,D=m[I+e+1],R=m[I+e+1+t];t:for(var F=0;F<C;++F){var B=s[F],N=b*B;if(!(R<u[N+e+1]||u[N+e+1+t]<D)){for(var j=e+2;j<t;++j)if(m[I+j+t]<u[N+j]||u[N+j+t]<m[I+j])continue t;var V,U=h[B];if(void 0!==(V=n?r(z,U):r(U,z)))return V}}}}else d(s,l,C--,P-k)}},scanComplete:function(t,e,r,n,a,l,c,u,h,f,d){for(var g=0,v=2*t,m=e,y=e+t,x=n;x<a;++x){var b=x+o,_=v*x;p[g++]=l[_+m],p[g++]=-b,p[g++]=l[_+y],p[g++]=b}for(var x=u;x<h;++x){var b=x+1,w=v*x;p[g++]=f[w+m],p[g++]=-b}var k=g>>>1;i(p,k);for(var T=0,x=0;x<k;++x){var M=0|p[2*x+1];if(M<0){var b=-M;if(b>=o)s[T++]=b-o;else{var A=d[b-=1],S=v*b,E=f[S+e+1],L=f[S+e+1+t];t:for(var C=0;C<T;++C){var P=s[C],O=c[P];if(O===A)break;var z=v*P;if(!(L<l[z+e+1]||l[z+e+1+t]<E)){for(var I=e+2;I<t;++I)if(f[S+I+t]<l[z+I]||l[z+I+t]<f[S+I])continue t;var D=r(O,A);if(void 0!==D)return D}}}}else{for(var b=M-o,C=T-1;C>=0;--C)if(s[C]===b){for(var I=C+1;I<T;++I)s[I-1]=s[I];break}--T}}}};var n=t(\"typedarray-pool\"),a=t(\"bit-twiddle\"),i=t(\"./sort\"),o=1<<28,s=n.mallocInt32(1024),l=n.mallocInt32(1024),c=n.mallocInt32(1024),u=n.mallocInt32(1024),h=n.mallocInt32(1024),f=n.mallocInt32(1024),p=n.mallocDouble(8192);function d(t,e,r,n){var a=e[n],i=t[r-1];t[a]=i,e[i]=a}function g(t,e,r,n){t[r]=n,e[n]=r}},{\"./sort\":103,\"bit-twiddle\":94,\"typedarray-pool\":544}],105:[function(t,e,r){},{}],106:[function(t,e,r){var n=Object.create||function(t){var e=function(){};return e.prototype=t,new e},a=Object.keys||function(t){var e=[];for(var r in t)Object.prototype.hasOwnProperty.call(t,r)&&e.push(r);return r},i=Function.prototype.bind||function(t){var e=this;return function(){return e.apply(t,arguments)}};function o(){this._events&&Object.prototype.hasOwnProperty.call(this,\"_events\")||(this._events=n(null),this._eventsCount=0),this._maxListeners=this._maxListeners||void 0}e.exports=o,o.EventEmitter=o,o.prototype._events=void 0,o.prototype._maxListeners=void 0;var s,l=10;try{var c={};Object.defineProperty&&Object.defineProperty(c,\"x\",{value:0}),s=0===c.x}catch(t){s=!1}function u(t){return void 0===t._maxListeners?o.defaultMaxListeners:t._maxListeners}function h(t,e,r,a){var i,o,s;if(\"function\"!=typeof r)throw new TypeError('\"listener\" argument must be a 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array shape\")}else{if(\"object\"!=typeof o||!o.offset)throw new Error(\"cwise: Unknown argument type \"+r[i]);e.argTypes[i]=\"offset\",e.offsetArgs.push({array:o.array,offset:o.offset}),e.offsetArgIndex.push(i)}}if(e.arrayArgs.length<=0)throw new Error(\"cwise: No array arguments specified\");if(e.pre.args.length>r.length)throw new Error(\"cwise: Too many arguments in pre() block\");if(e.body.args.length>r.length)throw new Error(\"cwise: Too many arguments in body() block\");if(e.post.args.length>r.length)throw new Error(\"cwise: Too many arguments in post() block\");return e.debug=!!t.printCode||!!t.debug,e.funcName=t.funcName||\"cwise\",e.blockSize=t.blockSize||64,n(e)}},{\"./lib/thunk.js\":150}],149:[function(t,e,r){\"use strict\";var n=t(\"uniq\");function a(t,e,r){var 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n=t(\"./compile.js\");e.exports=function(t){var e=[\"'use strict'\",\"var CACHED={}\"],r=[],a=t.funcName+\"_cwise_thunk\";e.push([\"return function \",a,\"(\",t.shimArgs.join(\",\"),\"){\"].join(\"\"));for(var i=[],o=[],s=[[\"array\",t.arrayArgs[0],\".shape.slice(\",Math.max(0,t.arrayBlockIndices[0]),t.arrayBlockIndices[0]<0?\",\"+t.arrayBlockIndices[0]+\")\":\")\"].join(\"\")],l=[],c=[],u=0;u<t.arrayArgs.length;++u){var 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2:this._point=3,this._x4=t,this._y4=e,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+e)/6);break;default:lt(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},ht.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){switch(t=+t,e=+e,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var r=(this._x0+4*this._x1+t)/6,n=(this._y0+4*this._y1+e)/6;this._line?this._context.lineTo(r,n):this._context.moveTo(r,n);break;case 3:this._point=4;default:lt(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},ft.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var t=this._x,e=this._y,r=t.length-1;if(r>0)for(var 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e=(t=t||{}).shape?t.shape:t.canvas?[t.canvas.width,t.canvas.height]:[512,512],r=t.canvas||document.createElement(\"canvas\"),i=t.font,o=\"number\"==typeof t.step?[t.step,t.step]:t.step||[32,32],s=t.chars||a;i&&\"string\"!=typeof i&&(i=n(i));if(Array.isArray(s)){if(2===s.length&&\"number\"==typeof s[0]&&\"number\"==typeof s[1]){for(var l=[],c=s[0],u=0;c<=s[1];c++)l[u++]=String.fromCharCode(c);s=l}}else s=String(s).split(\"\");e=e.slice(),r.width=e[0],r.height=e[1];var h=r.getContext(\"2d\");h.fillStyle=\"#000\",h.fillRect(0,0,r.width,r.height),h.font=i,h.textAlign=\"center\",h.textBaseline=\"middle\",h.fillStyle=\"#fff\";for(var f=o[0]/2,p=o[1]/2,c=0;c<s.length;c++)h.fillText(s[c],f,p),(f+=o[0])>e[0]-o[0]/2&&(f=o[0]/2,p+=o[1]);return r}},{\"css-font/stringify\":144}],232:[function(t,e,r){\"use strict\";function n(t,e){e||(e={}),(\"string\"==typeof t||Array.isArray(t))&&(e.family=t);var r=Array.isArray(e.family)?e.family.join(\", \"):e.family;if(!r)throw Error(\"`family` must be 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m=p-i(h.getImageData(0,0,p,p))-1+g;d.baseline=d.alphabetic=m,h.clearRect(0,0,p,p),h.textBaseline=\"middle\",h.fillText(\"H\",0,.5*p);var y=i(h.getImageData(0,0,p,p));d.median=d.middle=p-y-1+g-.5*p,h.clearRect(0,0,p,p),h.textBaseline=\"hanging\",h.fillText(\"H\",0,.5*p);var x=i(h.getImageData(0,0,p,p));d.hanging=p-x-1+g-.5*p,h.clearRect(0,0,p,p),h.textBaseline=\"ideographic\",h.fillText(\"H\",0,p);var b=i(h.getImageData(0,0,p,p));if(d.ideographic=p-b-1+g,f.upper&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.upper,0,0),d.upper=i(h.getImageData(0,0,p,p)),d.capHeight=d.baseline-d.upper),f.lower&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.lower,0,0),d.lower=i(h.getImageData(0,0,p,p)),d.xHeight=d.baseline-d.lower),f.tittle&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.tittle,0,0),d.tittle=i(h.getImageData(0,0,p,p))),f.ascent&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.ascent,0,0),d.ascent=i(h.getImageData(0,0,p,p))),f.descent&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.descent,0,0),d.descent=o(h.getImageData(0,0,p,p))),f.overshoot){h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.overshoot,0,0);var _=o(h.getImageData(0,0,p,p));d.overshoot=_-m}for(var w in d)d[w]/=s;return d.em=s,n.cache[r]=d,a(d,c)}function a(t,e){var r={};for(var n in\"string\"==typeof e&&(e=t[e]),t)\"em\"!==n&&(r[n]=t[n]-e);return r}function i(t){for(var e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement(\"canvas\"),n.cache={}},{}],233:[function(t,e,r){\"use strict\";e.exports=function(t){return new c(t||d,null)};var n=0,a=1;function i(t,e,r,n,a,i){this._color=t,this.key=e,this.value=r,this.left=n,this.right=a,this._count=i}function o(t){return new i(t._color,t.key,t.value,t.left,t.right,t._count)}function s(t,e){return new i(t,e.key,e.value,e.left,e.right,e._count)}function l(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function c(t,e){this._compare=t,this.root=e}var u=c.prototype;function h(t,e){this.tree=t,this._stack=e}Object.defineProperty(u,\"keys\",{get:function(){var t=[];return this.forEach(function(e,r){t.push(e)}),t}}),Object.defineProperty(u,\"values\",{get:function(){var t=[];return this.forEach(function(e,r){t.push(r)}),t}}),Object.defineProperty(u,\"length\",{get:function(){return this.root?this.root._count:0}}),u.insert=function(t,e){for(var r=this._compare,o=this.root,u=[],h=[];o;){var f=r(t,o.key);u.push(o),h.push(f),o=f<=0?o.left:o.right}u.push(new i(n,t,e,null,null,1));for(var p=u.length-2;p>=0;--p){o=u[p];h[p]<=0?u[p]=new i(o._color,o.key,o.value,u[p+1],o.right,o._count+1):u[p]=new i(o._color,o.key,o.value,o.left,u[p+1],o._count+1)}for(p=u.length-1;p>1;--p){var d=u[p-1];o=u[p];if(d._color===a||o._color===a)break;var g=u[p-2];if(g.left===d)if(d.left===o){if(!(v=g.right)||v._color!==n){if(g._color=n,g.left=d.right,d._color=a,d.right=g,u[p-2]=d,u[p-1]=o,l(g),l(d),p>=3)(m=u[p-3]).left===g?m.left=d:m.right=d;break}d._color=a,g.right=s(a,v),g._color=n,p-=1}else{if(!(v=g.right)||v._color!==n){if(d.right=o.left,g._color=n,g.left=o.right,o._color=a,o.left=d,o.right=g,u[p-2]=o,u[p-1]=d,l(g),l(d),l(o),p>=3)(m=u[p-3]).left===g?m.left=o:m.right=o;break}d._color=a,g.right=s(a,v),g._color=n,p-=1}else if(d.right===o){if(!(v=g.left)||v._color!==n){if(g._color=n,g.right=d.left,d._color=a,d.left=g,u[p-2]=d,u[p-1]=o,l(g),l(d),p>=3)(m=u[p-3]).right===g?m.right=d:m.left=d;break}d._color=a,g.left=s(a,v),g._color=n,p-=1}else{var v;if(!(v=g.left)||v._color!==n){var m;if(d.left=o.right,g._color=n,g.right=o.left,o._color=a,o.right=d,o.left=g,u[p-2]=o,u[p-1]=d,l(g),l(d),l(o),p>=3)(m=u[p-3]).right===g?m.right=o:m.left=o;break}d._color=a,g.left=s(a,v),g._color=n,p-=1}}return u[0]._color=a,new c(r,u[0])},u.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return function t(e,r){var n;if(r.left&&(n=t(e,r.left)))return n;return(n=e(r.key,r.value))||(r.right?t(e,r.right):void 0)}(t,this.root);case 2:return function t(e,r,n,a){if(r(e,a.key)<=0){var i;if(a.left&&(i=t(e,r,n,a.left)))return i;if(i=n(a.key,a.value))return i}if(a.right)return t(e,r,n,a.right)}(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return function t(e,r,n,a,i){var o,s=n(e,i.key),l=n(r,i.key);if(s<=0){if(i.left&&(o=t(e,r,n,a,i.left)))return o;if(l>0&&(o=a(i.key,i.value)))return o}if(l>0&&i.right)return t(e,r,n,a,i.right)}(e,r,this._compare,t,this.root)}},Object.defineProperty(u,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new h(this,t)}}),Object.defineProperty(u,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new h(this,t)}}),u.at=function(t){if(t<0)return new h(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new h(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new h(this,[])},u.ge=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i<=0&&(a=n.length),r=i<=0?r.left:r.right}return n.length=a,new h(this,n)},u.gt=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i<0&&(a=n.length),r=i<0?r.left:r.right}return n.length=a,new h(this,n)},u.lt=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i>0&&(a=n.length),r=i<=0?r.left:r.right}return n.length=a,new h(this,n)},u.le=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i>=0&&(a=n.length),r=i<0?r.left:r.right}return n.length=a,new h(this,n)},u.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var a=e(t,r.key);if(n.push(r),0===a)return new 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i(r._color,r.key,r.value,r.left,e[u+1],r._count)}if((r=e[e.length-1]).left&&r.right){var h=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var f=e[h-1];e.push(new i(r._color,f.key,f.value,r.left,r.right,r._count)),e[h-1].key=r.key,e[h-1].value=r.value;for(u=e.length-2;u>=h;--u)r=e[u],e[u]=new i(r._color,r.key,r.value,r.left,e[u+1],r._count);e[h-1].left=e[h]}if((r=e[e.length-1])._color===n){var d=e[e.length-2];d.left===r?d.left=null:d.right===r&&(d.right=null),e.pop();for(u=0;u<e.length;++u)e[u]._count--;return new c(this.tree._compare,e[0])}if(r.left||r.right){r.left?p(r,r.left):r.right&&p(r,r.right),r._color=a;for(u=0;u<e.length-1;++u)e[u]._count--;return new c(this.tree._compare,e[0])}if(1===e.length)return new c(this.tree._compare,null);for(u=0;u<e.length;++u)e[u]._count--;var g=e[e.length-2];return function(t){for(var e,r,i,c,u=t.length-1;u>=0;--u){if(e=t[u],0===u)return void(e._color=a);if((r=t[u-1]).left===e){if((i=r.right).right&&i.right._color===n)return c=(i=r.right=o(i)).right=o(i.right),r.right=i.left,i.left=r,i.right=c,i._color=r._color,e._color=a,r._color=a,c._color=a,l(r),l(i),u>1&&((h=t[u-2]).left===r?h.left=i:h.right=i),void(t[u-1]=i);if(i.left&&i.left._color===n)return c=(i=r.right=o(i)).left=o(i.left),r.right=c.left,i.left=c.right,c.left=r,c.right=i,c._color=r._color,r._color=a,i._color=a,e._color=a,l(r),l(i),l(c),u>1&&((h=t[u-2]).left===r?h.left=c:h.right=c),void(t[u-1]=c);if(i._color===a){if(r._color===n)return r._color=a,void(r.right=s(n,i));r.right=s(n,i);continue}i=o(i),r.right=i.left,i.left=r,i._color=r._color,r._color=n,l(r),l(i),u>1&&((h=t[u-2]).left===r?h.left=i:h.right=i),t[u-1]=i,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((i=r.left).left&&i.left._color===n)return c=(i=r.left=o(i)).left=o(i.left),r.left=i.right,i.right=r,i.left=c,i._color=r._color,e._color=a,r._color=a,c._color=a,l(r),l(i),u>1&&((h=t[u-2]).right===r?h.right=i:h.left=i),void(t[u-1]=i);if(i.right&&i.right._color===n)return c=(i=r.left=o(i)).right=o(i.right),r.left=c.right,i.right=c.left,c.right=r,c.left=i,c._color=r._color,r._color=a,i._color=a,e._color=a,l(r),l(i),l(c),u>1&&((h=t[u-2]).right===r?h.right=c:h.left=c),void(t[u-1]=c);if(i._color===a){if(r._color===n)return r._color=a,void(r.left=s(n,i));r.left=s(n,i);continue}var h;i=o(i),r.left=i.right,i.right=r,i._color=r._color,r._color=n,l(r),l(i),u>1&&((h=t[u-2]).right===r?h.right=i:h.left=i),t[u-1]=i,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}}}(e),g.left===r?g.left=null:g.right=null,new c(this.tree._compare,e[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),n=e[e.length-1];r[r.length-1]=new i(n._color,n.key,t,n.left,n.right,n._count);for(var a=e.length-2;a>=0;--a)(n=e[a]).left===e[a+1]?r[a]=new i(n._color,n.key,n.value,r[a+1],n.right,n._count):r[a]=new i(n._color,n.key,n.value,n.left,r[a+1],n._count);return new c(this.tree._compare,r[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],234:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],a=607/128,i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+a+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],a=1;a<9;a++)r+=n[a]/(e+a);var i=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(i,e+.5)*Math.exp(-i)*r},e.exports.log=o},{}],235:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,a=e;try{var i=[t];0===t.indexOf(\"webgl\")&&i.push(\"experimental-\"+t);for(var o=0;o<i.length;o++)if(n=r.getContext(i[o],a))return n}catch(t){n=null}return n||null}},{}],236:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),a=t(\"./lib/lines.js\"),i=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=i(t)}var h=u.prototype;function f(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}h.update=function(t){function e(e,r,n){if(n in t){var a,i=t[n],o=this[n];(e?Array.isArray(i)&&Array.isArray(i[0]):Array.isArray(i))?this[n]=a=[r(i[0]),r(i[1]),r(i[2])]:this[n]=a=[r(i),r(i),r(i)];for(var s=0;s<3;++s)if(a[s]!==o[s])return!0}return!1}t=t||{};var r,i=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),u=!1,h=!1;if(\"bounds\"in t)for(var f=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)f[p][d]!==this.bounds[p][d]&&(h=!0),this.bounds[p][d]=f[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else i(\"tickSpacing\")&&(this.autoTicks=!0,h=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),h=!0,u=!0,this._firstInit=!1),h&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),i(\"tickSize\"),i(\"tickAngle\"),i(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),i(\"labelSize\"),i(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),i(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),i(\"lineTickLength\"),i(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),i(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),i(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=a(this.gl,this.bounds,this.ticks))};var p=[new f,new f,new f];function d(t,e,r,n,a){for(var i=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=i,f=s,p=o,d=l;c&1<<u&&(h=s,f=i,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],a[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l,_ortho:!1};h.isOpaque=function(){return!0},h.isTransparent=function(){return!1},h.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],x=[0,0,0];h.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||l,n=t.view||l,a=t.projection||l,i=this.bounds,s=t._ortho||!1,u=o(r,n,a,i,s),h=u.cubeEdges,f=u.axis,b=n[12],_=n[13],w=n[14],k=n[15],T=(s?2:1)*this.pixelRatio*(a[3]*b+a[7]*_+a[11]*w+a[15]*k)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=h[M],this.lastCubeProps.axis[M]=f[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,h,f);e=this.gl;var S,E=g;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=f[M]:E[M]=0;this._background.draw(r,n,a,i,E,this.backgroundColor),this._lines.bind(r,n,a,this);for(M=0;M<3;++M){var L=[0,0,0];f[M]>0?L[M]=i[1][M]:L[M]=i[0][M];for(var C=0;C<2;++C){var P=(M+1+C)%3,O=(M+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,O,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(M+1+C)%3,O=(M+1+(1^C))%3;this.zeroEnable[O]&&Math.min(i[0][O],i[1][O])<=0&&Math.max(i[0][O],i[1][O])>=0&&this._lines.drawZero(P,O,this.bounds,L,this.zeroLineColor[O],this.zeroLineWidth[O]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(m,A[M].primalMinor),I=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=T/r[5*C];z[C]*=D[C]*R,I[C]*=D[C]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,I,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,a,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,a=(t+2)%3,i=e[n],o=e[a],s=r[n],l=r[a];i>0&&l>0?N(n):i>0&&l<0?N(n):i<0&&l>0?N(n):i<0&&l<0?N(n):o>0&&s>0?N(a):o>0&&s<0?N(a):o<0&&s>0?N(a):o<0&&s<0&&N(a)}for(M=0;M<3;++M){var V=A[M].primalMinor,U=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[M]&&(q[C]+=T*V[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,V,U);for(C=0;C<3;++C)q[C]+=T*V[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)q[C]+=T*V[C]*this.labelPad[C]/r[5*C];q[M]+=.5*(i[0][M]+i[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},h.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":237,\"./lib/cube.js\":238,\"./lib/lines.js\":239,\"./lib/text.js\":241,\"./lib/ticks.js\":242}],237:[function(t,e,r){\"use 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l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:a,colors:i},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":240,\"gl-buffer\":244,\"gl-vao\":329}],238:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,i,p){a(s,e,t),a(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=i[x][2];for(var b=0;b<2;++b){u[1]=i[b][1];for(var _=0;_<2;++_)u[0]=i[_][0],f(l[y],u,s),y+=1}}for(var w=-1,x=0;x<8;++x){for(var k=l[x][3],T=0;T<3;++T)c[x][T]=l[x][T]/k;p&&(c[x][2]*=-1),k<0&&(w<0?w=x:c[x][2]<c[w][2]&&(w=x))}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=C<<M,O=P+(C<<A)+(1-C<<S),z=P+(1-C<<A)+(C<<S);o(c[P],c[O],c[z],h)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<M);else{for(var C=0;C<2;++C){var P=C<<M,O=P+(C<<A)+(1-C<<S),z=P+(1-C<<A)+(C<<S),I=d([l[P],l[O],l[z],l[P+(1<<A)+(1<<S)]]);C?E=I:L=I}L>E&&(w|=1<<M)}}}for(var D=7^w,R=-1,x=0;x<8;++x)x!==w&&x!==D&&(R<0?R=x:c[R][1]>c[x][1]&&(R=x));for(var F=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D){F<0&&(F=B);var S=c[B];S[0]<c[F][0]&&(F=B)}}for(var N=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D&&B!==F){N<0&&(N=B);var S=c[B];S[0]>c[N][0]&&(N=B)}}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^N)]=R&N;var V=7^N;V===w||V===D?(V=7^F,j[n.log2(N^V)]=V&N):j[n.log2(F^V)]=V&F;for(var U=v,q=w,M=0;M<3;++M)U[M]=q&1<<M?-1:1;return m};var n=t(\"bit-twiddle\"),a=t(\"gl-mat4/multiply\"),i=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),h=[0,0,0];function f(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var a=0;a<3;++a)t[n]+=e[a]*r[4*a+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=i.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],a=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-a,h=l[0]/l[3]-n,f=l[1]/l[3]-a;o+=Math.abs(c*f-u*h)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{\"bit-twiddle\":94,\"gl-mat4/multiply\":270,\"robust-orientation\":509,\"split-polygon\":526}],239:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var h=0;h<3;++h){for(var f=o.length/3|0,d=0;d<r[h].length;++d){var g=+r[h][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;s[h]=f,l[h]=v-f;for(var f=o.length/3|0,m=0;m<r[h].length;++m){var g=+r[h][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;c[h]=f,u[h]=v-f}var y=n(t,new Float32Array(o)),x=a(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=i(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function h(t){return t[0]=t[1]=t[2]=0,t}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,a,i,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=a,this.tickOffset=i,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,a){var i=h(s);this.shader.uniforms.majorAxis=s,i[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=i;var o,u=f(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=n,(o=h(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=h(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,a){if(this.tickCount[t]){var i=h(o);i[t]=1,this.shader.uniforms.majorAxis=i,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=a;var s=h(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,a,i){if(this.gridCount[t]){var u=h(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=f(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=h(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=h(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,a,i){var o=h(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=f(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=h(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":240,\"gl-buffer\":244,\"gl-vao\":329}],240:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return a(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return a(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":304,glslify:411}],241:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,i,s,l){var u=n(t),h=a(t,[{buffer:u,size:3}]),f=o(t);f.attributes.position.location=0;var p=new c(t,f,u,h);return p.update(e,r,i,s,l),p};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,h=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var a=this.shader.uniforms;a.model=t,a.view=e,a.projection=r,a.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,a){var o=[];function s(t,e,r,n,a,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return i(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:a,styletags:s}));for(var h=(n||12)/12,f=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var v=p[d],m=2;m>=0;--m){var y=f[v[m]];o.push(h*y[0],-h*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(o.length/3|0)-h[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||a,n[d][g].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=h,this.labelCount=f},u.drawTicks=function(t,e,r,n,a,i,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,a,i,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t(\"_process\"))},{\"./shaders\":240,_process:484,\"gl-buffer\":244,\"gl-vao\":329,\"vectorize-text\":549}],242:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),a=0;n>=0&&(a=r.length-n-1);var i=Math.pow(10,a),o=Math.round(t*e*i),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/i,c=o%i;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),a){for(var h=\"\"+c;h.length<a;)h=\"0\"+h;return u+\".\"+h}return u}r.create=function(t,e){for(var r=[],a=0;a<3;++a){for(var i=[],o=(t[0][a],t[1][a],0);o*e[a]<=t[1][a];++o)i.push({x:o*e[a],text:n(e[a],o)});for(var o=-1;o*e[a]>=t[0][a];--o)i.push({x:o*e[a],text:n(e[a],o)});r.push(i)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var a=t[r][n],i=e[r][n];if(a.x!==i.x||a.text!==i.text||a.font!==i.font||a.fontColor!==i.fontColor||a.fontSize!==i.fontSize||a.dx!==i.dx||a.dy!==i.dy)return!1}}return!0}},{}],243:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,h){var f=e.model||c,p=e.view||c,m=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(h=h||i(f,p,m,x,y)).axis;o(u,p,f),o(u,m,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var k=n(s(u,u));s(u,u);for(var T=0;T<3;++T){var M=(T+1)%3,A=(T+2)%3,S=v;t:for(var w=0;w<2;++w){var E=[];if(b[T]<0!=!!w){S[T]=x[w][T];for(var L=0;L<2;++L){S[M]=x[L^w][M];for(var C=0;C<2;++C)S[A]=x[C^L^w][A],E.push(S.slice())}for(var P=y?5:4,L=P;L===P;++L){if(0===E.length)continue t;E=a.positive(E,k[L])}for(var L=0;L<E.length;++L)for(var A=E[L],O=d(v,u,A,r,l),C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,A[C]),_[C].hi=Math.max(_[C].hi,A[C]),C!==T&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}return _};var n=t(\"extract-frustum-planes\"),a=t(\"split-polygon\"),i=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function h(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var f=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,a){for(var i=0;i<3;++i){for(var o=f,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[i]+=1,l(s,s,e),s[3]<0&&(t[i]=1/0),o[i]-=1,l(o,o,e),o[3]<0&&(t[i]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,h=(o[1]/o[3]-s[1]/s[3])*a;t[i]=.25*Math.sqrt(u*u+h*h)}return t}var g=[new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0)],v=[0,0,0]},{\"./lib/cube.js\":238,\"extract-frustum-planes\":227,\"gl-mat4/multiply\":270,\"gl-mat4/transpose\":279,\"gl-vec4/transformMat4\":400,\"split-polygon\":526}],244:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),a=t(\"ndarray-ops\"),i=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,a){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=a}var l=s.prototype;function c(t,e,r,n,a,i){var o=a.length*a.BYTES_PER_ELEMENT;if(i<0)return t.bufferData(e,a,n),o;if(o+i>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,i,a),r}function u(t,e){for(var r=n.malloc(t.length,e),a=t.length,i=0;i<a;++i)r[i]=t[i];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=i(s,t.shape);a.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var a=t.createBuffer(),i=new s(t,r,a,0,n);return i.update(e),i}},{ndarray:452,\"ndarray-ops\":446,\"typedarray-pool\":544}],245:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,a=t.vectors,i={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),i;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,g=[],v=1/0,m=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),h=Math.min(x[2],h),f=Math.max(x[2],f);var b=a[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(v=Math.min(v,_),m=!1):m=!0}m||(p=x,d=b),g.push(b)}var w=[s,c,h],k=[l,u,f];e&&(e[0]=w,e[1]=k),0===o&&(o=1);var T=1/o;isFinite(v)||(v=1),i.vectorScale=v;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*T),i.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=g[y],P=n.length(C)*T,O=0;O<8;O++){i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.positions.push([S,E,L,A++]),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vertexIntensity.push(P,P,P),i.vertexIntensity.push(P,P,P);var z=i.positions.length;i.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return i};var a=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:a,traceType:\"cone\"})}},{\"./create_mesh\":246,\"./lib/shaders\":247,\"gl-vec3\":348}],246:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e,r,n,a,i,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=a,this.triangleVectors=i,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=h,this._view=h,this._projection=h,this._resolution=[1,1]}var p=f.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var a=e[n],i=0;i<3;++i)r[4*n+i]=a[i];r[4*n+3]=255*a[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,a=t.vectors;if(n&&r&&a){var i=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=a;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var v=0;v<p.length;++v){var m=p[v];d=Math.min(d,m),g=Math.max(g,m)}else for(v=0;v<n.length;++v){m=n[v][2];d=Math.min(d,m),g=Math.max(g,m)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(v=0;v<n.length;++v)for(var y=n[v],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(v=0;v<r.length;++v){var _=r[v];switch(_.length){case 3:for(x=0;x<3;++x){y=n[k=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var k;y=n[k=_[2-x]];i.push(y[0],y[1],y[2],y[3]);var T=a[k];o.push(T[0],T[1],T[2],T[3]||0);var M,A=f;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[k]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),h.push(v)}b+=1}}this.triangleCount=b,this.trianglePositions.update(i),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(h))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:a,inverseModel:h.slice(),clipBounds:i,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/f}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(a),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:a,clipBounds:i,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),a={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?a.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(a.intensity=this.intensity[r[1]],a.velocity=this.vectors[r[1]].slice(0,3),a.divergence=this.vectors[r[1]][3],a.index=e),a},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),u=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=a(t),d=a(t),g=a(t),v=a(t),m=a(t),y=new f(t,h,l,u,p,d,m,g,v,i(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return y.update(e),y}},{colormap:128,\"gl-buffer\":244,\"gl-mat4/invert\":268,\"gl-mat4/multiply\":270,\"gl-shader\":304,\"gl-texture2d\":324,\"gl-vao\":329,ndarray:452}],247:[function(t,e,r){var n=t(\"glslify\"),a=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),i=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha