cmp-mps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9066924",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utz import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5c9f3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.91 s, sys: 137 ms, total: 10.1 s\n",
      "Wall time: 10.2 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRI</th>\n",
       "      <th>MP</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09061720__</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.040478</td>\n",
       "      <td>40.736641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09061720__</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-74.040446</td>\n",
       "      <td>40.735928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09061720__</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-74.040561</td>\n",
       "      <td>40.735207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09061720__</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-74.040673</td>\n",
       "      <td>40.734481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09061720__</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-74.040774</td>\n",
       "      <td>40.733785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>554583</th>\n",
       "      <td>15210018__</td>\n",
       "      <td>1.10</td>\n",
       "      <td>-74.126725</td>\n",
       "      <td>39.925199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554584</th>\n",
       "      <td>15210018__</td>\n",
       "      <td>1.15</td>\n",
       "      <td>-74.125753</td>\n",
       "      <td>39.925170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554585</th>\n",
       "      <td>15210018__</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-74.124795</td>\n",
       "      <td>39.925142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554586</th>\n",
       "      <td>15210018__</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-74.124003</td>\n",
       "      <td>39.925121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554587</th>\n",
       "      <td>21191168__</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-75.191445</td>\n",
       "      <td>40.692384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554588 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               SRI    MP        LON        LAT\n",
       "0       09061720__  0.00 -74.040478  40.736641\n",
       "1       09061720__  0.05 -74.040446  40.735928\n",
       "2       09061720__  0.10 -74.040561  40.735207\n",
       "3       09061720__  0.15 -74.040673  40.734481\n",
       "4       09061720__  0.20 -74.040774  40.733785\n",
       "...            ...   ...        ...        ...\n",
       "554583  15210018__  1.10 -74.126725  39.925199\n",
       "554584  15210018__  1.15 -74.125753  39.925170\n",
       "554585  15210018__  1.20 -74.124795  39.925142\n",
       "554586  15210018__  1.25 -74.124003  39.925121\n",
       "554587  21191168__  0.00 -75.191445  40.692384\n",
       "\n",
       "[554588 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from nj_crashes.sri.mp05 import get_mp05, get_mp05_map\n",
    "mp05 = get_mp05()\n",
    "mp05m = get_mp05_map()\n",
    "mp05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "898ee5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nj_crashes.sri.mp10 import get_mp10, get_mp10_map, make_mp10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b28a950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time\n",
    "#make_mp10()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "002b00a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 450 ms, sys: 9.94 ms, total: 460 ms\n",
      "Wall time: 467 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRI</th>\n",
       "      <th>MP</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18466</th>\n",
       "      <td>00000001BS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.238610</td>\n",
       "      <td>-74.741773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18467</th>\n",
       "      <td>00000001BS</td>\n",
       "      <td>0.1</td>\n",
       "      <td>40.240074</td>\n",
       "      <td>-74.739503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18472</th>\n",
       "      <td>00000001BS</td>\n",
       "      <td>0.2</td>\n",
       "      <td>40.242223</td>\n",
       "      <td>-74.739730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18464</th>\n",
       "      <td>00000001BS</td>\n",
       "      <td>0.4</td>\n",
       "      <td>40.244703</td>\n",
       "      <td>-74.742177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18470</th>\n",
       "      <td>00000001BS</td>\n",
       "      <td>0.5</td>\n",
       "      <td>40.245579</td>\n",
       "      <td>-74.743258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615</th>\n",
       "      <td>00000700__</td>\n",
       "      <td>51.5</td>\n",
       "      <td>40.097889</td>\n",
       "      <td>-74.728225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14601</th>\n",
       "      <td>00000700__</td>\n",
       "      <td>51.6</td>\n",
       "      <td>40.098976</td>\n",
       "      <td>-74.727144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>00000700__</td>\n",
       "      <td>51.7</td>\n",
       "      <td>40.100068</td>\n",
       "      <td>-74.726071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14585</th>\n",
       "      <td>00000700__</td>\n",
       "      <td>51.8</td>\n",
       "      <td>40.101166</td>\n",
       "      <td>-74.725013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14607</th>\n",
       "      <td>00000700__</td>\n",
       "      <td>51.9</td>\n",
       "      <td>40.102264</td>\n",
       "      <td>-74.723953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46596 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              SRI    MP        LAT        LON\n",
       "18466  00000001BS   0.0  40.238610 -74.741773\n",
       "18467  00000001BS   0.1  40.240074 -74.739503\n",
       "18472  00000001BS   0.2  40.242223 -74.739730\n",
       "18464  00000001BS   0.4  40.244703 -74.742177\n",
       "18470  00000001BS   0.5  40.245579 -74.743258\n",
       "...           ...   ...        ...        ...\n",
       "14615  00000700__  51.5  40.097889 -74.728225\n",
       "14601  00000700__  51.6  40.098976 -74.727144\n",
       "14621  00000700__  51.7  40.100068 -74.726071\n",
       "14585  00000700__  51.8  40.101166 -74.725013\n",
       "14607  00000700__  51.9  40.102264 -74.723953\n",
       "\n",
       "[46596 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "mp10 = get_mp10()\n",
    "mp10m = get_mp10_map()\n",
    "mp10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7d1ea7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34479, 291)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mp05m), len(mp10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b51016c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRI</th>\n",
       "      <th>MP</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [SRI, MP, LAT, LON]\n",
       "Index: []"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp10[mp10.LON.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f846cdda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRI</th>\n",
       "      <th>MP</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
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       "Empty DataFrame\n",
       "Columns: [SRI, MP, LON, LAT]\n",
       "Index: []"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp05[mp05.LON.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ce0cf7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lon05</th>\n",
       "      <th>lat05</th>\n",
       "      <th>lat10</th>\n",
       "      <th>lon10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sri</th>\n",
       "      <th>mp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">00000001BS</th>\n",
       "      <th>0.00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.238610</td>\n",
       "      <td>-74.741773</td>\n",
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       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.240074</td>\n",
       "      <td>-74.739503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.242223</td>\n",
       "      <td>-74.739730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.244703</td>\n",
       "      <td>-74.742177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.245579</td>\n",
       "      <td>-74.743258</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">21231071__</th>\n",
       "      <th>0.15</th>\n",
       "      <td>-75.074255</td>\n",
       "      <td>40.805299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>-75.073876</td>\n",
       "      <td>40.805943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>-75.074580</td>\n",
       "      <td>40.806391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>-75.075426</td>\n",
       "      <td>40.806403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.35</th>\n",
       "      <td>-75.075825</td>\n",
       "      <td>40.805732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>571797 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lon05      lat05      lat10      lon10\n",
       "sri        mp                                              \n",
       "00000001BS 0.00        NaN        NaN  40.238610 -74.741773\n",
       "           0.10        NaN        NaN  40.240074 -74.739503\n",
       "           0.20        NaN        NaN  40.242223 -74.739730\n",
       "           0.40        NaN        NaN  40.244703 -74.742177\n",
       "           0.50        NaN        NaN  40.245579 -74.743258\n",
       "...                    ...        ...        ...        ...\n",
       "21231071__ 0.15 -75.074255  40.805299        NaN        NaN\n",
       "           0.20 -75.073876  40.805943        NaN        NaN\n",
       "           0.25 -75.074580  40.806391        NaN        NaN\n",
       "           0.30 -75.075426  40.806403        NaN        NaN\n",
       "           0.35 -75.075825  40.805732        NaN        NaN\n",
       "\n",
       "[571797 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lower(df):\n",
    "    df = df.copy()\n",
    "    df.columns = [ col.lower() for col in df.columns ]\n",
    "    return df\n",
    "\n",
    "mp05i = lower(mp05).set_index(['sri', 'mp'])\n",
    "mp10i = lower(mp10).set_index(['sri', 'mp'])\n",
    "m = mp05i.merge(mp10i, left_index=True, right_index=True, how='outer', suffixes=[\"05\", \"10\"])\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8545f53",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lon10</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon05</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>17209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>519080</td>\n",
       "      <td>35508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lon10   False  True \n",
       "lon05               \n",
       "False       0  17209\n",
       "True   519080  35508"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has05 = ~m.lon05.isna()\n",
    "has10 = ~m.lon10.isna()\n",
    "pd.crosstab(has05, has10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "345743f4",
   "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></th>\n",
       "      <th>lon05</th>\n",
       "      <th>lat05</th>\n",
       "      <th>lat10</th>\n",
       "      <th>lon10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sri</th>\n",
       "      <th>mp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">00000001B_</th>\n",
       "      <th>0.0</th>\n",
       "      <td>-74.740087</td>\n",
       "      <td>40.239449</td>\n",
       "      <td>40.239441</td>\n",
       "      <td>-74.740085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>-74.739083</td>\n",
       "      <td>40.240842</td>\n",
       "      <td>40.240834</td>\n",
       "      <td>-74.739081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>-74.739721</td>\n",
       "      <td>40.242357</td>\n",
       "      <td>40.242349</td>\n",
       "      <td>-74.739719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>-74.740984</td>\n",
       "      <td>40.243667</td>\n",
       "      <td>40.243659</td>\n",
       "      <td>-74.740983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>-74.742229</td>\n",
       "      <td>40.244826</td>\n",
       "      <td>40.244818</td>\n",
       "      <td>-74.742228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">00000700__</th>\n",
       "      <th>51.5</th>\n",
       "      <td>-74.728227</td>\n",
       "      <td>40.097897</td>\n",
       "      <td>40.097889</td>\n",
       "      <td>-74.728225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.6</th>\n",
       "      <td>-74.727146</td>\n",
       "      <td>40.098984</td>\n",
       "      <td>40.098976</td>\n",
       "      <td>-74.727144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.7</th>\n",
       "      <td>-74.726073</td>\n",
       "      <td>40.100076</td>\n",
       "      <td>40.100068</td>\n",
       "      <td>-74.726071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.8</th>\n",
       "      <td>-74.725014</td>\n",
       "      <td>40.101175</td>\n",
       "      <td>40.101166</td>\n",
       "      <td>-74.725013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.9</th>\n",
       "      <td>-74.723954</td>\n",
       "      <td>40.102272</td>\n",
       "      <td>40.102264</td>\n",
       "      <td>-74.723953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35508 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lon05      lat05      lat10      lon10\n",
       "sri        mp                                              \n",
       "00000001B_ 0.0  -74.740087  40.239449  40.239441 -74.740085\n",
       "           0.1  -74.739083  40.240842  40.240834 -74.739081\n",
       "           0.2  -74.739721  40.242357  40.242349 -74.739719\n",
       "           0.3  -74.740984  40.243667  40.243659 -74.740983\n",
       "           0.4  -74.742229  40.244826  40.244818 -74.742228\n",
       "...                    ...        ...        ...        ...\n",
       "00000700__ 51.5 -74.728227  40.097897  40.097889 -74.728225\n",
       "           51.6 -74.727146  40.098984  40.098976 -74.727144\n",
       "           51.7 -74.726073  40.100076  40.100068 -74.726071\n",
       "           51.8 -74.725014  40.101175  40.101166 -74.725013\n",
       "           51.9 -74.723954  40.102272  40.102264 -74.723953\n",
       "\n",
       "[35508 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both = m[has05 & has10]\n",
    "both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50e6d74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sri         mp  \n",
       "00000001B_  0.0     0.914448\n",
       "            0.1     0.914486\n",
       "            0.2     0.914519\n",
       "            0.3     0.914544\n",
       "            0.4     0.914571\n",
       "                      ...   \n",
       "00000700__  51.5    0.899595\n",
       "            51.6    0.904965\n",
       "            51.7    0.918105\n",
       "            51.8    0.935235\n",
       "            51.9    0.911380\n",
       "Name: meters, Length: 35508, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from geopy.distance import geodesic\n",
    "meters = both.apply(lambda r: geodesic((r.lat05, r.lon05), (r.lat10, r.lon10)).meters, axis=1).rename('meters')\n",
    "meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0398fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "from nj_crashes.utils import show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fafa153b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_meters = meters.sort_values().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d3045ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show(\n",
    "    px.line(\n",
    "        sorted_meters,\n",
    "        log_y=True,\n",
    "    ),\n",
    "    #i=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aee37115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show(\n",
    "    px.line(\n",
    "        sorted_meters[sorted_meters >= 0.937],\n",
    "        log_y=True,\n",
    "    ),\n",
    "#     i=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lon05</th>\n",
       "      <th>lat05</th>\n",
       "      <th>lat10</th>\n",
       "      <th>lon10</th>\n",
       "      <th>meters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sri</th>\n",
       "      <th>mp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00000003E_</th>\n",
       "      <th>9.5</th>\n",
       "      <td>-74.052637</td>\n",
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       "      <td>40.790150</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">00000007__</th>\n",
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       "    <tr>\n",
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       "      <td>89.040380</td>\n",
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       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>-74.076538</td>\n",
       "      <td>40.739214</td>\n",
       "      <td>40.738727</td>\n",
       "      <td>-74.075653</td>\n",
       "      <td>92.188413</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">00000295__</th>\n",
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       "      <td>-74.841120</td>\n",
       "      <td>1.330577</td>\n",
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       "    <tr>\n",
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       "      <td>40.262902</td>\n",
       "      <td>-74.842096</td>\n",
       "      <td>4.932749</td>\n",
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       "    <tr>\n",
       "      <th>76.3</th>\n",
       "      <td>-74.843104</td>\n",
       "      <td>40.261695</td>\n",
       "      <td>40.261726</td>\n",
       "      <td>-74.843148</td>\n",
       "      <td>5.047586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.4</th>\n",
       "      <td>-74.844471</td>\n",
       "      <td>40.260706</td>\n",
       "      <td>40.260755</td>\n",
       "      <td>-74.844503</td>\n",
       "      <td>6.008754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.5</th>\n",
       "      <td>-74.845926</td>\n",
       "      <td>40.259826</td>\n",
       "      <td>40.259937</td>\n",
       "      <td>-74.846028</td>\n",
       "      <td>15.092262</td>\n",
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       "<p>1070 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lon05      lat05      lat10      lon10     meters\n",
       "sri        mp                                                         \n",
       "00000003E_ 9.5  -74.052637  40.790149  40.790150 -74.052650   1.051653\n",
       "00000007__ 0.0  -74.074607  40.739215  40.738769 -74.073731  89.040380\n",
       "           0.0  -74.074607  40.739215  40.738769 -74.073731  89.040380\n",
       "           0.0  -74.074607  40.739215  40.738769 -74.073731  89.040380\n",
       "           0.1  -74.076538  40.739214  40.738727 -74.075653  92.188413\n",
       "...                    ...        ...        ...        ...        ...\n",
       "00000295__ 76.1 -74.841121  40.264170  40.264158 -74.841120   1.330577\n",
       "           76.2 -74.842051  40.262929  40.262902 -74.842096   4.932749\n",
       "           76.3 -74.843104  40.261695  40.261726 -74.843148   5.047586\n",
       "           76.4 -74.844471  40.260706  40.260755 -74.844503   6.008754\n",
       "           76.5 -74.845926  40.259826  40.259937 -74.846028  15.092262\n",
       "\n",
       "[1070 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fars = sxs(both, meters)[meters >= 1]\n",
    "fars"
   ]
  }
 ],
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