cohort2/exploration/wiggles_i5s.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import datetime as dt\n",
    "df = pd.read_csv(\"../five_min_frame.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'Timestamp', u'Station', u'District', u'Freeway #',\n",
       "       u'Direction', u'Lane Type', u'Station Length', u'Samples',\n",
       "       u'% Observed', u'Total Flow', u'Avg Occupancy', u'Avg Speed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idx1 = df['Freeway #'] == 5\n",
    "idx2 = df['Direction'] == 'S'\n",
    "idx3 = df['Lane Type'] == 'ML'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29376, 3)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.ix[idx1 & idx2 & idx3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10773178, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Time'],df['Date']= df['Timestamp'].apply(lambda x:x.time()), df['Timestamp'].apply(lambda x:x.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[['Time','Station','Total Flow']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.groupby(['Time','Station']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29664, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Time</th>\n",
       "      <th>Total Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1108417</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>140.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1108417</td>\n",
       "      <td>00:05:00</td>\n",
       "      <td>149.246575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1108417</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>151.482192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1108417</td>\n",
       "      <td>00:15:00</td>\n",
       "      <td>139.030137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1108417</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>125.994521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Station      Time  Total Flow\n",
       "0    1108417  00:00:00  140.400000\n",
       "103  1108417  00:05:00  149.246575\n",
       "206  1108417  00:10:00  151.482192\n",
       "309  1108417  00:15:00  139.030137\n",
       "412  1108417  00:20:00  125.994521"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta = pd.read_csv('I5Smeta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meta = meta.rename(columns={'ID':'Station'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Station</th>\n",
       "      <th>Fwy</th>\n",
       "      <th>Dir</th>\n",
       "      <th>District</th>\n",
       "      <th>County</th>\n",
       "      <th>City</th>\n",
       "      <th>State_PM</th>\n",
       "      <th>Abs_PM</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Length</th>\n",
       "      <th>Type</th>\n",
       "      <th>Lanes</th>\n",
       "      <th>Name</th>\n",
       "      <th>User_ID_1</th>\n",
       "      <th>User_ID_2</th>\n",
       "      <th>User_ID_3</th>\n",
       "      <th>User_ID_4</th>\n",
       "      <th>file_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1121734</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R71.05</td>\n",
       "      <td>70.878</td>\n",
       "      <td>33.383349</td>\n",
       "      <td>-117.578043</td>\n",
       "      <td>2.621</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB Basilone Rd</td>\n",
       "      <td>3109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1121728</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R68.451</td>\n",
       "      <td>68.279</td>\n",
       "      <td>33.362845</td>\n",
       "      <td>-117.540736</td>\n",
       "      <td>2.714</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB Nuclear Plant</td>\n",
       "      <td>4107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1121722</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R65.621</td>\n",
       "      <td>65.449</td>\n",
       "      <td>33.336536</td>\n",
       "      <td>-117.503249</td>\n",
       "      <td>1.415</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB Truck Scales</td>\n",
       "      <td>3106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1121715</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R65.62</td>\n",
       "      <td>65.448</td>\n",
       "      <td>33.336526</td>\n",
       "      <td>-117.503236</td>\n",
       "      <td>1.514</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 NB Track Scale</td>\n",
       "      <td>3105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1121712</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R62.591</td>\n",
       "      <td>62.419</td>\n",
       "      <td>33.303834</td>\n",
       "      <td>-117.470867</td>\n",
       "      <td>2.509</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB  Las Pulgas Rd</td>\n",
       "      <td>3104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1121702</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R60.601</td>\n",
       "      <td>60.429</td>\n",
       "      <td>33.281440</td>\n",
       "      <td>-117.449446</td>\n",
       "      <td>2.045</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB N/O Rest Area</td>\n",
       "      <td>3102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1119997</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R58.501</td>\n",
       "      <td>58.329</td>\n",
       "      <td>33.257953</td>\n",
       "      <td>-117.426547</td>\n",
       "      <td>1.785</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>1M S/O ALISO CREEK</td>\n",
       "      <td>23607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1119978</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R57</td>\n",
       "      <td>56.858</td>\n",
       "      <td>33.240152</td>\n",
       "      <td>-117.413376</td>\n",
       "      <td>1.235</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>.5 N/O STA MARGRITA</td>\n",
       "      <td>13605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1119966</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R56</td>\n",
       "      <td>55.858</td>\n",
       "      <td>33.230196</td>\n",
       "      <td>-117.400904</td>\n",
       "      <td>1.000</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>S/O SANTA MARGARITA</td>\n",
       "      <td>13603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1119954</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R55</td>\n",
       "      <td>54.858</td>\n",
       "      <td>33.218567</td>\n",
       "      <td>-117.391109</td>\n",
       "      <td>0.783</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>S5 N/O CAMP DEL MAR</td>\n",
       "      <td>13601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1122257</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R54.434</td>\n",
       "      <td>54.292</td>\n",
       "      <td>33.210909</td>\n",
       "      <td>-117.387715</td>\n",
       "      <td>0.600</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 @ HARBOR DR</td>\n",
       "      <td>15010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>1115348</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>53322.0</td>\n",
       "      <td>R53.8</td>\n",
       "      <td>53.658</td>\n",
       "      <td>33.203488</td>\n",
       "      <td>-117.381365</td>\n",
       "      <td>0.567</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 @76</td>\n",
       "      <td>15008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17</td>\n",
       "      <td>1108672</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>53322.0</td>\n",
       "      <td>R53.3</td>\n",
       "      <td>53.158</td>\n",
       "      <td>33.200618</td>\n",
       "      <td>-117.373434</td>\n",
       "      <td>0.696</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>WB RTE 76/MISSION</td>\n",
       "      <td>220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>1108459</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>53322.0</td>\n",
       "      <td>R52.408</td>\n",
       "      <td>52.266</td>\n",
       "      <td>33.191277</td>\n",
       "      <td>-117.363281</td>\n",
       "      <td>0.932</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>Oceanside Blvd</td>\n",
       "      <td>15003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>1108461</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>53322.0</td>\n",
       "      <td>R51.435</td>\n",
       "      <td>51.293</td>\n",
       "      <td>33.179734</td>\n",
       "      <td>-117.353651</td>\n",
       "      <td>0.848</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>CASSIDY ST</td>\n",
       "      <td>15001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26</td>\n",
       "      <td>1108463</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R50.71</td>\n",
       "      <td>50.568</td>\n",
       "      <td>33.170920</td>\n",
       "      <td>-117.346811</td>\n",
       "      <td>0.672</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>LAS FLORES DR</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27</td>\n",
       "      <td>1108657</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R50.09</td>\n",
       "      <td>49.948</td>\n",
       "      <td>33.162818</td>\n",
       "      <td>-117.342266</td>\n",
       "      <td>0.730</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>CARLSBAD VILLAGE DR</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>1108560</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R49.25</td>\n",
       "      <td>49.108</td>\n",
       "      <td>33.151704</td>\n",
       "      <td>-117.336331</td>\n",
       "      <td>1.062</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>TAMARACK AVE</td>\n",
       "      <td>158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>34</td>\n",
       "      <td>1108661</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R47.965</td>\n",
       "      <td>47.823</td>\n",
       "      <td>33.134215</td>\n",
       "      <td>-117.329382</td>\n",
       "      <td>1.076</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>CANNON RD</td>\n",
       "      <td>213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37</td>\n",
       "      <td>1108685</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R47.097</td>\n",
       "      <td>46.955</td>\n",
       "      <td>33.122973</td>\n",
       "      <td>-117.322702</td>\n",
       "      <td>0.832</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>WB PALOMAR AIRPORT</td>\n",
       "      <td>227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>1119915</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R46.3</td>\n",
       "      <td>46.158</td>\n",
       "      <td>33.112683</td>\n",
       "      <td>-117.316473</td>\n",
       "      <td>0.830</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 N/O POINSETTIA</td>\n",
       "      <td>13409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>41</td>\n",
       "      <td>1108680</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R45.435</td>\n",
       "      <td>45.293</td>\n",
       "      <td>33.101626</td>\n",
       "      <td>-117.309439</td>\n",
       "      <td>0.799</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>POINSETTIA LN</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44</td>\n",
       "      <td>1119909</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>11194.0</td>\n",
       "      <td>R44.701</td>\n",
       "      <td>44.559</td>\n",
       "      <td>33.092248</td>\n",
       "      <td>-117.303383</td>\n",
       "      <td>0.825</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 N/O BATIQUITOS</td>\n",
       "      <td>13406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47</td>\n",
       "      <td>1108725</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R43.785</td>\n",
       "      <td>43.643</td>\n",
       "      <td>33.080188</td>\n",
       "      <td>-117.296877</td>\n",
       "      <td>0.700</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>La Costa Ave</td>\n",
       "      <td>249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48</td>\n",
       "      <td>1119897</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R43.301</td>\n",
       "      <td>43.159</td>\n",
       "      <td>33.073371</td>\n",
       "      <td>-117.294873</td>\n",
       "      <td>0.547</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 N/O LEUCADIA</td>\n",
       "      <td>23401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>49</td>\n",
       "      <td>1108723</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R42.69</td>\n",
       "      <td>42.548</td>\n",
       "      <td>33.064772</td>\n",
       "      <td>-117.292309</td>\n",
       "      <td>0.635</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>Leucadia Blvd</td>\n",
       "      <td>248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52</td>\n",
       "      <td>1119877</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R42.03</td>\n",
       "      <td>41.888</td>\n",
       "      <td>33.055523</td>\n",
       "      <td>-117.289401</td>\n",
       "      <td>0.581</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 N/O ENCINITAS</td>\n",
       "      <td>13310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>55</td>\n",
       "      <td>1108479</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R41.527</td>\n",
       "      <td>41.385</td>\n",
       "      <td>33.048438</td>\n",
       "      <td>-117.287351</td>\n",
       "      <td>0.702</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>ENCINITAS BLVD</td>\n",
       "      <td>111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>57</td>\n",
       "      <td>1108477</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R40.625</td>\n",
       "      <td>40.483</td>\n",
       "      <td>33.036347</td>\n",
       "      <td>-117.281649</td>\n",
       "      <td>0.850</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SANTA FE DR</td>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60</td>\n",
       "      <td>1108475</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>22678.0</td>\n",
       "      <td>R39.827</td>\n",
       "      <td>39.685</td>\n",
       "      <td>33.026250</td>\n",
       "      <td>-117.274915</td>\n",
       "      <td>0.712</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>BIRMINGHAM DR</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>148</td>\n",
       "      <td>1108417</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R16.11</td>\n",
       "      <td>16.001</td>\n",
       "      <td>32.723898</td>\n",
       "      <td>-117.156068</td>\n",
       "      <td>0.484</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>5th Ave</td>\n",
       "      <td>10512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>150</td>\n",
       "      <td>1117683</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R15.807</td>\n",
       "      <td>15.698</td>\n",
       "      <td>32.723795</td>\n",
       "      <td>-117.151061</td>\n",
       "      <td>0.187</td>\n",
       "      <td>ML</td>\n",
       "      <td>6</td>\n",
       "      <td>SB5 @PARK BLVD</td>\n",
       "      <td>10510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>151</td>\n",
       "      <td>1114318</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R15.737</td>\n",
       "      <td>15.628</td>\n",
       "      <td>32.723198</td>\n",
       "      <td>-117.150182</td>\n",
       "      <td>0.234</td>\n",
       "      <td>ML</td>\n",
       "      <td>6</td>\n",
       "      <td>South of Park Blvd</td>\n",
       "      <td>4009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>153</td>\n",
       "      <td>1117810</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R15.339</td>\n",
       "      <td>15.230</td>\n",
       "      <td>32.718001</td>\n",
       "      <td>-117.147844</td>\n",
       "      <td>0.386</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 @ B ST</td>\n",
       "      <td>10508</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>155</td>\n",
       "      <td>1117789</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R14.965</td>\n",
       "      <td>14.856</td>\n",
       "      <td>32.712581</td>\n",
       "      <td>-117.147751</td>\n",
       "      <td>0.276</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 @ SR 94</td>\n",
       "      <td>10605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>156</td>\n",
       "      <td>1122726</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R14.78</td>\n",
       "      <td>14.677</td>\n",
       "      <td>32.709980</td>\n",
       "      <td>-117.147760</td>\n",
       "      <td>0.220</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>North of Imperial Av</td>\n",
       "      <td>4008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>157</td>\n",
       "      <td>1117774</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R14.519</td>\n",
       "      <td>14.416</td>\n",
       "      <td>32.706200</td>\n",
       "      <td>-117.147722</td>\n",
       "      <td>0.652</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>SB5 @ Imperial</td>\n",
       "      <td>10603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>158</td>\n",
       "      <td>1114268</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R13.475</td>\n",
       "      <td>13.372</td>\n",
       "      <td>32.695740</td>\n",
       "      <td>-117.135196</td>\n",
       "      <td>0.697</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>N/O 28TH ST</td>\n",
       "      <td>4007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>159</td>\n",
       "      <td>1117755</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R13.125</td>\n",
       "      <td>13.022</td>\n",
       "      <td>32.694358</td>\n",
       "      <td>-117.129467</td>\n",
       "      <td>0.312</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB 5 S/O 29th</td>\n",
       "      <td>10601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>160</td>\n",
       "      <td>1114261</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R12.851</td>\n",
       "      <td>12.748</td>\n",
       "      <td>32.694108</td>\n",
       "      <td>-117.124841</td>\n",
       "      <td>0.454</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB N/O SR-15</td>\n",
       "      <td>4006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>161</td>\n",
       "      <td>1118076</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R12.216</td>\n",
       "      <td>12.113</td>\n",
       "      <td>32.688779</td>\n",
       "      <td>-117.116159</td>\n",
       "      <td>0.449</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB I5 S/O I15</td>\n",
       "      <td>10608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>162</td>\n",
       "      <td>1114254</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>R11.951</td>\n",
       "      <td>11.848</td>\n",
       "      <td>32.686001</td>\n",
       "      <td>-117.113072</td>\n",
       "      <td>0.467</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>N/O DIVISION</td>\n",
       "      <td>4005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>163</td>\n",
       "      <td>1118472</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>50398.0</td>\n",
       "      <td>R11.281</td>\n",
       "      <td>11.178</td>\n",
       "      <td>32.676960</td>\n",
       "      <td>-117.109463</td>\n",
       "      <td>0.526</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB N/O 8th St</td>\n",
       "      <td>15511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>164</td>\n",
       "      <td>1118465</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>50398.0</td>\n",
       "      <td>R10.899</td>\n",
       "      <td>10.796</td>\n",
       "      <td>32.671761</td>\n",
       "      <td>-117.111234</td>\n",
       "      <td>0.640</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB  Civic Center</td>\n",
       "      <td>15508</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>165</td>\n",
       "      <td>1114240</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>50398.0</td>\n",
       "      <td>R9.988</td>\n",
       "      <td>9.898</td>\n",
       "      <td>32.658893</td>\n",
       "      <td>-117.107675</td>\n",
       "      <td>1.043</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>Bay Marina Dr</td>\n",
       "      <td>4003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>166</td>\n",
       "      <td>1120603</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.710</td>\n",
       "      <td>32.642724</td>\n",
       "      <td>-117.101267</td>\n",
       "      <td>0.703</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>SB5 N/O E ST</td>\n",
       "      <td>4067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>167</td>\n",
       "      <td>1122601</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>8.581</td>\n",
       "      <td>8.491</td>\n",
       "      <td>32.639722</td>\n",
       "      <td>-117.100176</td>\n",
       "      <td>0.111</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>E St on to 5 SB</td>\n",
       "      <td>25411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>168</td>\n",
       "      <td>1118442</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>8.579</td>\n",
       "      <td>8.489</td>\n",
       "      <td>32.639695</td>\n",
       "      <td>-117.100165</td>\n",
       "      <td>0.336</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB @ E St</td>\n",
       "      <td>15411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>169</td>\n",
       "      <td>1118428</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>7.909</td>\n",
       "      <td>7.819</td>\n",
       "      <td>32.630458</td>\n",
       "      <td>-117.096492</td>\n",
       "      <td>0.644</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB N/O H St</td>\n",
       "      <td>15410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>170</td>\n",
       "      <td>1118413</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>7.291</td>\n",
       "      <td>7.201</td>\n",
       "      <td>32.621946</td>\n",
       "      <td>-117.093171</td>\n",
       "      <td>0.596</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB @ J St</td>\n",
       "      <td>15407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>171</td>\n",
       "      <td>1120356</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>6.717</td>\n",
       "      <td>6.627</td>\n",
       "      <td>32.613849</td>\n",
       "      <td>-117.091173</td>\n",
       "      <td>0.495</td>\n",
       "      <td>ML</td>\n",
       "      <td>3</td>\n",
       "      <td>SB 5 S/O L St</td>\n",
       "      <td>25406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>172</td>\n",
       "      <td>1114749</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>13392.0</td>\n",
       "      <td>6.301</td>\n",
       "      <td>6.211</td>\n",
       "      <td>32.607870</td>\n",
       "      <td>-117.090464</td>\n",
       "      <td>0.651</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>N/O PALOMAR ST</td>\n",
       "      <td>4054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>173</td>\n",
       "      <td>1123134</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>5.414</td>\n",
       "      <td>5.324</td>\n",
       "      <td>32.595070</td>\n",
       "      <td>-117.088901</td>\n",
       "      <td>0.490</td>\n",
       "      <td>ML</td>\n",
       "      <td>1</td>\n",
       "      <td>Main St to 5 SB</td>\n",
       "      <td>35401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>174</td>\n",
       "      <td>1114735</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>5.321</td>\n",
       "      <td>5.231</td>\n",
       "      <td>32.593819</td>\n",
       "      <td>-117.088714</td>\n",
       "      <td>0.660</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>S/O MAIN ST</td>\n",
       "      <td>4053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>175</td>\n",
       "      <td>1118372</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>4.093</td>\n",
       "      <td>4.003</td>\n",
       "      <td>32.576983</td>\n",
       "      <td>-117.083123</td>\n",
       "      <td>0.947</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB @ Coronado Ave</td>\n",
       "      <td>15309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>176</td>\n",
       "      <td>1118365</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>3.426</td>\n",
       "      <td>3.336</td>\n",
       "      <td>32.568952</td>\n",
       "      <td>-117.076731</td>\n",
       "      <td>0.722</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB N/0 905</td>\n",
       "      <td>15306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>177</td>\n",
       "      <td>1114721</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>2.649</td>\n",
       "      <td>2.559</td>\n",
       "      <td>32.561106</td>\n",
       "      <td>-117.067228</td>\n",
       "      <td>0.574</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>N/O DAIRY MART RD</td>\n",
       "      <td>4052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>178</td>\n",
       "      <td>1118341</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>2.276</td>\n",
       "      <td>2.186</td>\n",
       "      <td>32.558188</td>\n",
       "      <td>-117.061806</td>\n",
       "      <td>0.634</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB @ Dairy Mart</td>\n",
       "      <td>15210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>179</td>\n",
       "      <td>1114708</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>1.381</td>\n",
       "      <td>1.291</td>\n",
       "      <td>32.552409</td>\n",
       "      <td>-117.048120</td>\n",
       "      <td>0.522</td>\n",
       "      <td>ML</td>\n",
       "      <td>5</td>\n",
       "      <td>N/O VIA DE SAN YSIDR</td>\n",
       "      <td>3050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>180</td>\n",
       "      <td>1118326</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.140</td>\n",
       "      <td>32.551447</td>\n",
       "      <td>-117.045806</td>\n",
       "      <td>1.216</td>\n",
       "      <td>ML</td>\n",
       "      <td>4</td>\n",
       "      <td>5 SB @ Via de Ysidro</td>\n",
       "      <td>15205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meta_2015_12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Station  Fwy Dir  District  County     City State_PM  Abs_PM  \\\n",
       "0             0  1121734    5   S        11      73      NaN   R71.05  70.878   \n",
       "1             1  1121728    5   S        11      73      NaN  R68.451  68.279   \n",
       "2             2  1121722    5   S        11      73      NaN  R65.621  65.449   \n",
       "3             3  1121715    5   S        11      73      NaN   R65.62  65.448   \n",
       "4             4  1121712    5   S        11      73      NaN  R62.591  62.419   \n",
       "5             5  1121702    5   S        11      73      NaN  R60.601  60.429   \n",
       "6             6  1119997    5   S        11      73      NaN  R58.501  58.329   \n",
       "7             7  1119978    5   S        11      73      NaN      R57  56.858   \n",
       "8             8  1119966    5   S        11      73      NaN      R56  55.858   \n",
       "9             9  1119954    5   S        11      73      NaN      R55  54.858   \n",
       "10           11  1122257    5   S        11      73      NaN  R54.434  54.292   \n",
       "11           13  1115348    5   S        11      73  53322.0    R53.8  53.658   \n",
       "12           17  1108672    5   S        11      73  53322.0    R53.3  53.158   \n",
       "13           19  1108459    5   S        11      73  53322.0  R52.408  52.266   \n",
       "14           21  1108461    5   S        11      73  53322.0  R51.435  51.293   \n",
       "15           26  1108463    5   S        11      73  11194.0   R50.71  50.568   \n",
       "16           27  1108657    5   S        11      73  11194.0   R50.09  49.948   \n",
       "17           32  1108560    5   S        11      73  11194.0   R49.25  49.108   \n",
       "18           34  1108661    5   S        11      73  11194.0  R47.965  47.823   \n",
       "19           37  1108685    5   S        11      73  11194.0  R47.097  46.955   \n",
       "20           40  1119915    5   S        11      73  11194.0    R46.3  46.158   \n",
       "21           41  1108680    5   S        11      73  11194.0  R45.435  45.293   \n",
       "22           44  1119909    5   S        11      73  11194.0  R44.701  44.559   \n",
       "23           47  1108725    5   S        11      73  22678.0  R43.785  43.643   \n",
       "24           48  1119897    5   S        11      73  22678.0  R43.301  43.159   \n",
       "25           49  1108723    5   S        11      73  22678.0   R42.69  42.548   \n",
       "26           52  1119877    5   S        11      73  22678.0   R42.03  41.888   \n",
       "27           55  1108479    5   S        11      73  22678.0  R41.527  41.385   \n",
       "28           57  1108477    5   S        11      73  22678.0  R40.625  40.483   \n",
       "29           60  1108475    5   S        11      73  22678.0  R39.827  39.685   \n",
       "..          ...      ...  ...  ..       ...     ...      ...      ...     ...   \n",
       "72          148  1108417    5   S        11      73  66000.0   R16.11  16.001   \n",
       "73          150  1117683    5   S        11      73  66000.0  R15.807  15.698   \n",
       "74          151  1114318    5   S        11      73  66000.0  R15.737  15.628   \n",
       "75          153  1117810    5   S        11      73  66000.0  R15.339  15.230   \n",
       "76          155  1117789    5   S        11      73  66000.0  R14.965  14.856   \n",
       "77          156  1122726    5   S        11      73  66000.0   R14.78  14.677   \n",
       "78          157  1117774    5   S        11      73  66000.0  R14.519  14.416   \n",
       "79          158  1114268    5   S        11      73  66000.0  R13.475  13.372   \n",
       "80          159  1117755    5   S        11      73  66000.0  R13.125  13.022   \n",
       "81          160  1114261    5   S        11      73  66000.0  R12.851  12.748   \n",
       "82          161  1118076    5   S        11      73  66000.0  R12.216  12.113   \n",
       "83          162  1114254    5   S        11      73  66000.0  R11.951  11.848   \n",
       "84          163  1118472    5   S        11      73  50398.0  R11.281  11.178   \n",
       "85          164  1118465    5   S        11      73  50398.0  R10.899  10.796   \n",
       "86          165  1114240    5   S        11      73  50398.0   R9.988   9.898   \n",
       "87          166  1120603    5   S        11      73  13392.0      8.8   8.710   \n",
       "88          167  1122601    5   S        11      73  13392.0    8.581   8.491   \n",
       "89          168  1118442    5   S        11      73  13392.0    8.579   8.489   \n",
       "90          169  1118428    5   S        11      73  13392.0    7.909   7.819   \n",
       "91          170  1118413    5   S        11      73  13392.0    7.291   7.201   \n",
       "92          171  1120356    5   S        11      73  13392.0    6.717   6.627   \n",
       "93          172  1114749    5   S        11      73  13392.0    6.301   6.211   \n",
       "94          173  1123134    5   S        11      73  66000.0    5.414   5.324   \n",
       "95          174  1114735    5   S        11      73  66000.0    5.321   5.231   \n",
       "96          175  1118372    5   S        11      73  66000.0    4.093   4.003   \n",
       "97          176  1118365    5   S        11      73  66000.0    3.426   3.336   \n",
       "98          177  1114721    5   S        11      73  66000.0    2.649   2.559   \n",
       "99          178  1118341    5   S        11      73  66000.0    2.276   2.186   \n",
       "100         179  1114708    5   S        11      73  66000.0    1.381   1.291   \n",
       "101         180  1118326    5   S        11      73  66000.0     1.23   1.140   \n",
       "\n",
       "      Latitude   Longitude  Length Type  Lanes                  Name  \\\n",
       "0    33.383349 -117.578043   2.621   ML      4      5 SB Basilone Rd   \n",
       "1    33.362845 -117.540736   2.714   ML      4    5 SB Nuclear Plant   \n",
       "2    33.336536 -117.503249   1.415   ML      4     5 SB Truck Scales   \n",
       "3    33.336526 -117.503236   1.514   ML      4      5 NB Track Scale   \n",
       "4    33.303834 -117.470867   2.509   ML      4   5 SB  Las Pulgas Rd   \n",
       "5    33.281440 -117.449446   2.045   ML      4    5 SB N/O Rest Area   \n",
       "6    33.257953 -117.426547   1.785   ML      4    1M S/O ALISO CREEK   \n",
       "7    33.240152 -117.413376   1.235   ML      4   .5 N/O STA MARGRITA   \n",
       "8    33.230196 -117.400904   1.000   ML      4   S/O SANTA MARGARITA   \n",
       "9    33.218567 -117.391109   0.783   ML      4   S5 N/O CAMP DEL MAR   \n",
       "10   33.210909 -117.387715   0.600   ML      4      SB 5 @ HARBOR DR   \n",
       "11   33.203488 -117.381365   0.567   ML      4              SB 5 @76   \n",
       "12   33.200618 -117.373434   0.696   ML      4     WB RTE 76/MISSION   \n",
       "13   33.191277 -117.363281   0.932   ML      4        Oceanside Blvd   \n",
       "14   33.179734 -117.353651   0.848   ML      4            CASSIDY ST   \n",
       "15   33.170920 -117.346811   0.672   ML      4         LAS FLORES DR   \n",
       "16   33.162818 -117.342266   0.730   ML      4   CARLSBAD VILLAGE DR   \n",
       "17   33.151704 -117.336331   1.062   ML      4          TAMARACK AVE   \n",
       "18   33.134215 -117.329382   1.076   ML      4             CANNON RD   \n",
       "19   33.122973 -117.322702   0.832   ML      4    WB PALOMAR AIRPORT   \n",
       "20   33.112683 -117.316473   0.830   ML      4   SB 5 N/O POINSETTIA   \n",
       "21   33.101626 -117.309439   0.799   ML      4         POINSETTIA LN   \n",
       "22   33.092248 -117.303383   0.825   ML      4   SB 5 N/O BATIQUITOS   \n",
       "23   33.080188 -117.296877   0.700   ML      4          La Costa Ave   \n",
       "24   33.073371 -117.294873   0.547   ML      4     SB 5 N/O LEUCADIA   \n",
       "25   33.064772 -117.292309   0.635   ML      4         Leucadia Blvd   \n",
       "26   33.055523 -117.289401   0.581   ML      4    SB 5 N/O ENCINITAS   \n",
       "27   33.048438 -117.287351   0.702   ML      4        ENCINITAS BLVD   \n",
       "28   33.036347 -117.281649   0.850   ML      4           SANTA FE DR   \n",
       "29   33.026250 -117.274915   0.712   ML      4         BIRMINGHAM DR   \n",
       "..         ...         ...     ...  ...    ...                   ...   \n",
       "72   32.723898 -117.156068   0.484   ML      5               5th Ave   \n",
       "73   32.723795 -117.151061   0.187   ML      6        SB5 @PARK BLVD   \n",
       "74   32.723198 -117.150182   0.234   ML      6    South of Park Blvd   \n",
       "75   32.718001 -117.147844   0.386   ML      4           SB 5 @ B ST   \n",
       "76   32.712581 -117.147751   0.276   ML      4          SB 5 @ SR 94   \n",
       "77   32.709980 -117.147760   0.220   ML      5  North of Imperial Av   \n",
       "78   32.706200 -117.147722   0.652   ML      5        SB5 @ Imperial   \n",
       "79   32.695740 -117.135196   0.697   ML      5           N/O 28TH ST   \n",
       "80   32.694358 -117.129467   0.312   ML      4         SB 5 S/O 29th   \n",
       "81   32.694108 -117.124841   0.454   ML      4        5 SB N/O SR-15   \n",
       "82   32.688779 -117.116159   0.449   ML      4         SB I5 S/O I15   \n",
       "83   32.686001 -117.113072   0.467   ML      5          N/O DIVISION   \n",
       "84   32.676960 -117.109463   0.526   ML      4       5 SB N/O 8th St   \n",
       "85   32.671761 -117.111234   0.640   ML      4    5 SB  Civic Center   \n",
       "86   32.658893 -117.107675   1.043   ML      4         Bay Marina Dr   \n",
       "87   32.642724 -117.101267   0.703   ML      4          SB5 N/O E ST   \n",
       "88   32.639722 -117.100176   0.111   ML      5       E St on to 5 SB   \n",
       "89   32.639695 -117.100165   0.336   ML      4           5 SB @ E St   \n",
       "90   32.630458 -117.096492   0.644   ML      4         5 SB N/O H St   \n",
       "91   32.621946 -117.093171   0.596   ML      4           5 SB @ J St   \n",
       "92   32.613849 -117.091173   0.495   ML      3         SB 5 S/O L St   \n",
       "93   32.607870 -117.090464   0.651   ML      5        N/O PALOMAR ST   \n",
       "94   32.595070 -117.088901   0.490   ML      1       Main St to 5 SB   \n",
       "95   32.593819 -117.088714   0.660   ML      5           S/O MAIN ST   \n",
       "96   32.576983 -117.083123   0.947   ML      4   5 SB @ Coronado Ave   \n",
       "97   32.568952 -117.076731   0.722   ML      4          5 SB N/0 905   \n",
       "98   32.561106 -117.067228   0.574   ML      4     N/O DAIRY MART RD   \n",
       "99   32.558188 -117.061806   0.634   ML      4     5 SB @ Dairy Mart   \n",
       "100  32.552409 -117.048120   0.522   ML      5  N/O VIA DE SAN YSIDR   \n",
       "101  32.551447 -117.045806   1.216   ML      4  5 SB @ Via de Ysidro   \n",
       "\n",
       "     User_ID_1  User_ID_2  User_ID_3  User_ID_4     file_date  \n",
       "0         3109        NaN        NaN        NaN  meta_2015_12  \n",
       "1         4107        NaN        NaN        NaN  meta_2015_12  \n",
       "2         3106        NaN        NaN        NaN  meta_2015_12  \n",
       "3         3105        NaN        NaN        NaN  meta_2015_12  \n",
       "4         3104        NaN        NaN        NaN  meta_2015_12  \n",
       "5         3102        NaN        NaN        NaN  meta_2015_12  \n",
       "6        23607        NaN        NaN        NaN  meta_2015_12  \n",
       "7        13605        NaN        NaN        NaN  meta_2015_12  \n",
       "8        13603        NaN        NaN        NaN  meta_2015_12  \n",
       "9        13601        NaN        NaN        NaN  meta_2015_12  \n",
       "10       15010        NaN        NaN        NaN  meta_2015_12  \n",
       "11       15008        NaN        NaN        NaN  meta_2015_12  \n",
       "12         220        NaN        NaN        NaN  meta_2015_12  \n",
       "13       15003        NaN        NaN        NaN  meta_2015_12  \n",
       "14       15001        NaN        NaN        NaN  meta_2015_12  \n",
       "15         100        NaN        NaN        NaN  meta_2015_12  \n",
       "16         211        NaN        NaN        NaN  meta_2015_12  \n",
       "17         158        NaN        NaN        NaN  meta_2015_12  \n",
       "18         213        NaN        NaN        NaN  meta_2015_12  \n",
       "19         227        NaN        NaN        NaN  meta_2015_12  \n",
       "20       13409        NaN        NaN        NaN  meta_2015_12  \n",
       "21         224        NaN        NaN        NaN  meta_2015_12  \n",
       "22       13406        NaN        NaN        NaN  meta_2015_12  \n",
       "23         249        NaN        NaN        NaN  meta_2015_12  \n",
       "24       23401        NaN        NaN        NaN  meta_2015_12  \n",
       "25         248        NaN        NaN        NaN  meta_2015_12  \n",
       "26       13310        NaN        NaN        NaN  meta_2015_12  \n",
       "27         111        NaN        NaN        NaN  meta_2015_12  \n",
       "28         110        NaN        NaN        NaN  meta_2015_12  \n",
       "29         109        NaN        NaN        NaN  meta_2015_12  \n",
       "..         ...        ...        ...        ...           ...  \n",
       "72       10512        NaN        NaN        NaN  meta_2015_12  \n",
       "73       10510        NaN        NaN        NaN  meta_2015_12  \n",
       "74        4009        NaN        NaN        NaN  meta_2015_12  \n",
       "75       10508        NaN        NaN        NaN  meta_2015_12  \n",
       "76       10605        NaN        NaN        NaN  meta_2015_12  \n",
       "77        4008        NaN        NaN        NaN  meta_2015_12  \n",
       "78       10603        NaN        NaN        NaN  meta_2015_12  \n",
       "79        4007        NaN        NaN        NaN  meta_2015_12  \n",
       "80       10601        NaN        NaN        NaN  meta_2015_12  \n",
       "81        4006        NaN        NaN        NaN  meta_2015_12  \n",
       "82       10608        NaN        NaN        NaN  meta_2015_12  \n",
       "83        4005        NaN        NaN        NaN  meta_2015_12  \n",
       "84       15511        NaN        NaN        NaN  meta_2015_12  \n",
       "85       15508        NaN        NaN        NaN  meta_2015_12  \n",
       "86        4003        NaN        NaN        NaN  meta_2015_12  \n",
       "87        4067        NaN        NaN        NaN  meta_2015_12  \n",
       "88       25411        NaN        NaN        NaN  meta_2015_12  \n",
       "89       15411        NaN        NaN        NaN  meta_2015_12  \n",
       "90       15410        NaN        NaN        NaN  meta_2015_12  \n",
       "91       15407        NaN        NaN        NaN  meta_2015_12  \n",
       "92       25406        NaN        NaN        NaN  meta_2015_12  \n",
       "93        4054        NaN        NaN        NaN  meta_2015_12  \n",
       "94       35401        NaN        NaN        NaN  meta_2015_12  \n",
       "95        4053        NaN        NaN        NaN  meta_2015_12  \n",
       "96       15309        NaN        NaN        NaN  meta_2015_12  \n",
       "97       15306        NaN        NaN        NaN  meta_2015_12  \n",
       "98        4052        NaN        NaN        NaN  meta_2015_12  \n",
       "99       15210        NaN        NaN        NaN  meta_2015_12  \n",
       "100       3050        NaN        NaN        NaN  meta_2015_12  \n",
       "101      15205        NaN        NaN        NaN  meta_2015_12  \n",
       "\n",
       "[102 rows x 20 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.sort_values(['Latitude'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.merge(meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.to_csv('I5SmeanVectors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=df.sort_values(['Latitude','Time'],ascending=[False,True])\n",
    "df = df[['Station','Time','Total Flow']]\n",
    "\n",
    "data = df.as_matrix()\n",
    "\n",
    "rows, row_pos = np.unique(data[:, 0], return_inverse=True) #Time\n",
    "cols, col_pos = np.unique(data[:, 1], return_inverse=True)\n",
    "\n",
    "pivot_table = np.zeros((len(rows), len(cols)), dtype=data.dtype)\n",
    "pivot_table[row_pos, col_pos] = data[:, 2]\n",
    "\n",
    "a = pivot_table; # example of what real input will look like\n",
    "\n",
    "# create 2D flattened version of 3D input array\n",
    "d1,d2 = a.shape\n",
    "b = np.zeros([d1,d2])\n",
    "for i in range(len(a)):\n",
    "    b[i] = a[i].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 288)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import analyzeWiggles as aw\n",
    "from scipy.interpolate import interp1d, Akima1DInterpolator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#first,second = aw.interpolate(b,'akima',6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-200-42cb2c6e02a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlist_of_b_diffs\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      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m102\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'akima'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mnew_vec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'meanVector'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smoothedVector'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#list_of_b_diffs.append(diff['diffVector'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object has no attribute '__getitem__'"
     ]
    }
   ],
   "source": [
    "list_of_b_diffs = []\n",
    "for i in range(102):\n",
    "    diff = aw.smooth_vector(b[i],'akima',6)\n",
    "    new_vec = diff['meanVector'] - diff['smoothedVector']\n",
    "    #list_of_b_diffs.append(diff['diffVector'])\n",
    "    list_of_b_diffs.append(new_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         39.26539499,  39.36630386,  39.41993389,  39.42745638,\n",
       "         39.29151763,  38.97010367,  38.54739547,  38.10757399,\n",
       "         37.73482017,  37.51331497,  37.69152671,  38.29960918,\n",
       "         39.09427158,  39.83222307,  40.27017286,  40.16483012,\n",
       "         39.56699932,  38.75614531,  37.83196784,  36.89416667,\n",
       "         36.04244153,  35.37649219,  34.855934  ,  34.3623948 ,\n",
       "         33.87859308,  33.38724734,  32.87107606,  32.31279774,\n",
       "         31.69586938,  31.03196481,  30.34612773,  29.66340185,\n",
       "         29.00883089,  28.40745856,  27.91004883,  27.51805476,\n",
       "         27.18255373,  26.85462312,  26.48534032,  26.02578269,\n",
       "         25.4215427 ,  24.69643055,  23.91885027,  23.15720585,\n",
       "         22.47990133,  21.9553407 ,  21.5578052 ,  21.20291688,\n",
       "         20.87109096,  20.54274269,  20.19828728,  19.81813996,\n",
       "         19.39909   ,  18.95878685,  18.50893602,  18.06124306,\n",
       "         17.6274135 ,  17.21915285,  16.8988317 ,  16.68393453,\n",
       "         16.51883776,  16.34791783,  16.11555116,  15.76611418,\n",
       "         15.30279067,  14.78301052,  14.23251935,  13.67706276,\n",
       "         13.14238635,  12.65423573,  12.19158182,  11.72572584,\n",
       "         11.27090878,  10.84137167,  10.4513555 ,  10.11510129,\n",
       "          9.82183977,   9.5459901 ,   9.27957589,   9.01462077,\n",
       "          8.74314837,   8.45718232,   8.16195539,   7.86707039,\n",
       "          7.57110598,   7.27264084,   6.97025363,   6.66252302,\n",
       "          6.34949882,   6.03222242,   5.71075983,   5.3851771 ,\n",
       "          5.05554024,   4.3839779 ,   4.48618785,   4.51381215]),\n",
       " 'timelabels': array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "        195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "        208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
       "        221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
       "        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
       "        247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "        260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
       "        273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
       "        286, 287])}"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_b = np.array(list_of_b_diffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nw+cPp2yxSlTe1izD+UgZOeAAKD3nMj77+TO27tiaq/VJkrLOQEiSJEnKhKwEQu9MeocB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d4d3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T5J07pZe8RLr5ZulHP5IefFD66Eeb3qqZZdcuCxUPOYQ5SEHo68rrA+UgQALQ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\n9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c7d8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(diff['meanVector'])\n",
    "plt.plot(diff['smoothedVector'])\n",
    "plt.show()\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(diff['diffVector'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "curve_fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.99352297,  0.55479191]),\n",
       " array([[  4.85048727e-07,  -6.95534308e-05],\n",
       "        [ -6.95534308e-05,   1.34006137e-02]]))"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curve_fit(mysine, np.arange(288), diff['diffVector'], p0=[6,0],maxfev=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mysine(x, a, b):\n",
    "    return 1. * np.sin(a * x + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.pi*5/60\n",
    "b = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14e0db9d0>]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YYFC0VGxuzr7vwpXxVO12LtI3N1efDRO9fqLTR19eo6u+Oq6iZXOS9flyNemD2VMtu6Kv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\nAhoMFAqFQgENBgqFQqEA8P8BuFb6Jjx5ZeEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144bd17d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mysine(x, a*2, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x165b54310>]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Eu0Gu2YOKBaN5pms264HLisXJk9GXwQ0b3LOt5YuDB4Ft23jNMy3lC/aXY0v5grWmmexB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ESKwsXn0MITCxmwdID5wC6GW62rZs7+ObzR6w0CPQIZET+CRScX2fjsyNRqqKrR8ePpH4n7Ko4h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\ne1g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x165b68350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(mysine(x, a*2*2, b+np.pi/30*-1))\n",
    "plt.plot(diff['diffVector'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         1.15675812e-02,  -3.27770612e-02,  -2.15830439e-02,\n",
       "        -4.67734786e-03,  -6.91930342e-03,  -4.84207978e-04,\n",
       "        -2.67237849e-03,  -3.11859377e-03,  -3.42773052e-02,\n",
       "         7.60190490e-04,   1.33631761e-02,   2.25596106e-02,\n",
       "        -1.51562037e-02,  -1.23806555e-02,  -2.89790184e-02,\n",
       "        -2.32202731e-02,  -2.39245736e-03,  -1.24949201e-02,\n",
       "         5.38293381e-03,   2.53515089e-02,   1.47117160e-02,\n",
       "         7.53272720e-03,  -4.41642855e-03,  -1.15037392e-02,\n",
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       "        -4.53835042e-02,  -3.47889043e-02,  -3.55398982e-02,\n",
       "        -2.01866852e-03,  -1.19396685e-02,  -5.36560356e-03,\n",
       "        -1.79763329e-02,  -1.14367756e-02,  -2.50970813e-02,\n",
       "        -4.97243982e-02,  -3.61299138e-02,  -1.48057774e-02,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff['diffVector']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_b = pd.DataFrame(new_b)\n",
    "big_df = pd.concat([df_b.reset_index(),meta.reset_index()],axis=1)\n",
    "output = pd.melt(big_df, id_vars=list(meta.columns), value_vars=list(df_b.columns), var_name='Time',value_name='Flow')\n",
    "output.to_csv('I5S Wiggle Output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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