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traffic_dataproc/nb_011p2.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# nb 011p2: Open ./data as timeseries (cont)\n",
"\n",
"Memory leaks in previous notebook. Let's start over. Our goal is to take our big OD dataframe, and create one series for each plateID from it. Then, do the same for GPS.\n",
"\n",
"See: https://stackoverflow.com/questions/19790790/splitting-dataframe-into-multiple-dataframes\n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from math import floor, ceil\n",
"from matplotlib import path\n",
"\n",
"# Time string to unix time converter\n",
"formattime = lambda timestr: int(time.mktime(time.strptime(timestr, \"%Y-%m-%dT%H:%M:%S.000Z\")))\n",
"# Headers\n",
"od_headers = [\"id\", \"ptime\", \"dtime\", \"plon\", \"plat\", \"dlon\", \"dlat\", \"pRID\", \"dRID\"]\n",
"gps_headers = [\"id\", \"color\", \"lon\", \"lat\", \"time\", \"speed\", \"noMeaning\"]\n",
"\n",
"# Filenames of our pickles\n",
"gps_fns = [\"./data/gps_part-r-{x}.pkl\".format(x=str(x).rjust(3, \"0\")) for x in range(2)]\n",
"od_fns = [\"./data/od_part-m-{x}.pkl\".format(x=str(x).rjust(3, \"0\")) for x in range(12)]\n",
"\n",
"# Start, end times\n",
"start_time = 1404360000\n",
"end_time = 1405828798\n",
"\n",
"def time_to_index(tt, st = 1404360000, divisor=60):\n",
" return floor((tt-st)/divisor)\n",
"\n",
"NN = ceil((end_time - start_time)/60)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"OD = pd.read_pickle(\"./data/OD_combined.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'ptime', 'dtime', 'plon', 'plat', 'dlon', 'dlat', 'pRID', 'dRID'], dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OD.keys()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"OD.sort_values(by='id', kind='mergesort', axis=0, inplace=True)\n",
"# use mergesort because it is stable, will keep each part sorted by id"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"OD.set_index(keys=['id'], drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
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" ptime dtime plon plat dlon dlat \\\n",
"id \n",
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"... ... ... ... ... ... ... \n",
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"\n",
"[5893689 rows x 8 columns]"
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"OD"
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{
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"IDs = OD.index.unique().tolist()"
]
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{
"cell_type": "code",
"execution_count": 8,
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{
"data": {
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{
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"... ... ... ... ... ... ... \n",
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OD.loc[\"粤B000H6\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"OD.to_pickle(\"./data/OD_combined.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# There we go!\n",
"# To use:\n",
"# IDs = OD.index.unique().tolist()\n",
"# OD.loc[\"粤B000H6\"]\n",
"\n",
"# Now for GPS..."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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" <td>22.543716</td>\n",
" <td>200</td>\n",
" <td>177</td>\n",
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" <th>粤B000H6</th>\n",
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" <td>22.553267</td>\n",
" <td>114.131104</td>\n",
" <td>22.579533</td>\n",
" <td>212</td>\n",
" <td>191</td>\n",
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" <tr>\n",
" <th>粤B000H6</th>\n",
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" <td>1404390022</td>\n",
" <td>114.123848</td>\n",
" <td>22.579384</td>\n",
" <td>114.140366</td>\n",
" <td>22.557983</td>\n",
" <td>200</td>\n",
" <td>179</td>\n",
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" <td>22.538799</td>\n",
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" <td>58</td>\n",
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" ptime dtime plon plat dlon dlat \\\n",
"id \n",
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"粤B000H6 1404385673 1404386603 114.122681 22.578699 114.126518 22.543716 \n",
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"粤B000H6 1404388859 1404390022 114.123848 22.579384 114.140366 22.557983 \n",
"... ... ... ... ... ... ... \n",
"粤B000H6 1405734598 1405735663 114.094383 22.559999 114.106651 22.541500 \n",
"粤B000H6 1405735726 1405737003 114.108398 22.541384 114.139297 22.614866 \n",
"粤B000H6 1405739981 1405740392 114.112984 22.538799 114.113434 22.531700 \n",
"粤B000H6 1405740461 1405741294 114.114487 22.532000 114.049721 22.545633 \n",
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"... ... ... \n",
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"\n",
"[397 rows x 8 columns]"
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" ptime dtime plon plat dlon dlat \\\n",
"id \n",
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"粤B005V8 1404540290 1404540592 114.055748 22.624283 114.058731 22.637917 \n",
"... ... ... ... ... ... ... \n",
"粤B005V8 1405726709 1405726978 114.135635 22.612717 114.131149 22.614483 \n",
"粤B005V8 1405729575 1405730173 114.126747 22.613068 114.130280 22.641167 \n",
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"[93 rows x 8 columns]"
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