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willycs40/Get Data From MS SQL.ipynb

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Get Data From MS SQL.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:ba2149ddfb8db0f42157d63204c795f4bb9fc8cdc864f1c4f42309d9dd78e375"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sqlalchemy import create_engine # database connection\n",
"import pyodbc\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pyproj\n",
"import datetime as dt\n",
"from scipy import spatial"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print('hello world')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"hello world\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set up the connection to the database\n",
"mssql_engine = create_engine('mssql://will.cubitt-smith:lawfordRM3@127.0.0.1:8788/DTP3')\n",
"sqllite_engine = create_engine('sqlite:///Weather.db')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Go and get the Weather station locations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q = '''\n",
" SELECT SiteCode, Latitude, Longitude\n",
" FROM Weather\n",
" GROUP BY SiteCode, Latitude, Longitude\n",
"'''\n",
"sites_df = pd.read_sql_query(q, sqllite_engine)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(sites_df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"143"
]
}
],
"prompt_number": 40
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Convert Weather station coordinates to UK national grid, and create a KD-Tree"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#spatial_data = np.array(sites_df[['Longitude','Latitude']])\n",
"stations = list(sites_df['SiteCode'])\n",
"lon = list(sites_df['Longitude'])\n",
"lat = list(sites_df['Latitude'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wgs84=pyproj.Proj(\"+init=EPSG:4326\") # LatLon with WGS84 datum used by GPS units and Google Earth\n",
"osgb36=pyproj.Proj(\"+init=EPSG:27700\") # UK Ordnance Survey, 1936 datum\n",
"\n",
"x, y = pyproj.transform(wgs84, osgb36, lon, lat)\n",
"eastingNorthing = np.array(zip(x,y))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tree = spatial.KDTree(eastingNorthing)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Now get the business data, run each line through the tree to return the nearest station, and write the results back to the DB"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q = '''\n",
" SELECT ServiceViewDataID, OBI, UserID, Date, Hour, Distance, deltaT, Speed, Latitude, Longitude\n",
" FROM DTP4.working.X02_Speeds\n",
" WHERE OBI = 910 \n",
"'''"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start = dt.datetime.now()\n",
"chunksize = 50000\n",
"j = 0\n",
"index_start = 1\n",
"\n",
"# Loop through the source data, pulling 'chunksize' rows at a time\n",
"for df in pd.read_sql_query(sql=q, con=mssql_engine, chunksize=chunksize):\n",
" \n",
" df.index += index_start\n",
" \n",
" result_station = []\n",
" result_distance = []\n",
" \n",
" # go through each row in the chunk, converting the coordinates and using the tree to find the closest weather station\n",
" for index, row in df.iterrows():\n",
" x, y = pyproj.transform(wgs84, osgb36, row['Longitude'], row['Latitude'])\n",
" distance, station = tree.query([x,y])\n",
" result_station.append(stations[station])\n",
" result_distance.append(distance)\n",
" \n",
" df = df.drop('Latitude',1)\n",
" df = df.drop('Longitude',1)\n",
" df['SiteCode'] = result_station\n",
" df['SiteDistance'] = result_distance\n",
" \n",
" # write the results back to the database\n",
" df.to_sql('gps_to_station', sqllite_engine, if_exists='append')\n",
" \n",
" j+=1\n",
" print '{} seconds: completed {} rows'.format((dt.datetime.now() - start).seconds, j*chunksize)\n",
"\n",
" index_start = df.index[-1] + 1\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"ERROR:sqlalchemy.pool.NullPool:Exception during reset or similar\n",
"Traceback (most recent call last):\n",
" File \"C:\\Anaconda\\lib\\site-packages\\sqlalchemy\\pool.py\", line 565, in _finalize_fairy\n",
" fairy._reset(pool, echo)\n",
" File \"C:\\Anaconda\\lib\\site-packages\\sqlalchemy\\pool.py\", line 699, in _reset\n",
" pool._dialect.do_rollback(self)\n",
" File \"C:\\Anaconda\\lib\\site-packages\\sqlalchemy\\engine\\default.py\", line 406, in do_rollback\n",
" dbapi_connection.rollback()\n",
"OperationalError: cannot rollback - no transaction is active\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"27 seconds: completed 50000 rows\n",
"46 seconds: completed 100000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"69 seconds: completed 150000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"93 seconds: completed 200000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"133 seconds: completed 250000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"154 seconds: completed 300000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"175 seconds: completed 350000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"194 seconds: completed 400000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"215 seconds: completed 450000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"235 seconds: completed 500000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"259 seconds: completed 550000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"277 seconds: completed 600000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"298 seconds: completed 650000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"315 seconds: completed 700000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"332 seconds: completed 750000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"353 seconds: completed 800000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"371 seconds: completed 850000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"389 seconds: completed 900000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"412 seconds: completed 950000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"432 seconds: completed 1000000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"452 seconds: completed 1050000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"477 seconds: completed 1100000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"497 seconds: completed 1150000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"517 seconds: completed 1200000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"543 seconds: completed 1250000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"564 seconds: completed 1300000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"584 seconds: completed 1350000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"606 seconds: completed 1400000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"629 seconds: completed 1450000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"648 seconds: completed 1500000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"665 seconds: completed 1550000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"687 seconds: completed 1600000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"711 seconds: completed 1650000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"734 seconds: completed 1700000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"759 seconds: completed 1750000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"785 seconds: completed 1800000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"808 seconds: completed 1850000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"825 seconds: completed 1900000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"847 seconds: completed 1950000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"865 seconds: completed 2000000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"889 seconds: completed 2050000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"907 seconds: completed 2100000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"924 seconds: completed 2150000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"944 seconds: completed 2200000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"964 seconds: completed 2250000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"982 seconds: completed 2300000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"999 seconds: completed 2350000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1022 seconds: completed 2400000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1029 seconds: completed 2450000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Take a quick look at the results..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# take a quick look at the new results\n",
"q = '''\n",
" SELECT *\n",
" FROM gps_to_station\n",
" limit 100\n",
"'''\n",
"df = pd.read_sql_query(q, sqllite_engine)\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>ServiceViewDataID</th>\n",
" <th>OBI</th>\n",
" <th>UserID</th>\n",
" <th>Date</th>\n",
" <th>Hour</th>\n",
" <th>Distance</th>\n",
" <th>deltaT</th>\n",
" <th>Speed</th>\n",
" <th>SiteCode</th>\n",
" <th>SiteDistance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>82449274</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>12</td>\n",
" <td>33.803380</td>\n",
" <td>23</td>\n",
" <td>1.469712</td>\n",
" <td>3672</td>\n",
" <td>13865.565864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>82449279</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>12</td>\n",
" <td>23.123716</td>\n",
" <td>43</td>\n",
" <td>0.537761</td>\n",
" <td>3672</td>\n",
" <td>13880.806346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>82449282</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>12.463798</td>\n",
" <td>34</td>\n",
" <td>0.366582</td>\n",
" <td>3672</td>\n",
" <td>14002.569243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>82449283</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>11.571229</td>\n",
" <td>31</td>\n",
" <td>0.373265</td>\n",
" <td>3672</td>\n",
" <td>14004.815460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>82449284</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>21.119235</td>\n",
" <td>30</td>\n",
" <td>0.703975</td>\n",
" <td>3672</td>\n",
" <td>14023.947056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>82449285</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>33.382297</td>\n",
" <td>31</td>\n",
" <td>1.076848</td>\n",
" <td>3672</td>\n",
" <td>14024.509468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>82449286</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>19.951840</td>\n",
" <td>32</td>\n",
" <td>0.623495</td>\n",
" <td>3672</td>\n",
" <td>14042.246096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>82449287</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>20.532177</td>\n",
" <td>31</td>\n",
" <td>0.662328</td>\n",
" <td>3672</td>\n",
" <td>14058.308719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>82449288</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>12.195665</td>\n",
" <td>31</td>\n",
" <td>0.393409</td>\n",
" <td>3672</td>\n",
" <td>14061.231439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>82449289</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>9.694192</td>\n",
" <td>31</td>\n",
" <td>0.312716</td>\n",
" <td>3672</td>\n",
" <td>14070.889234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>82449290</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>14.160167</td>\n",
" <td>32</td>\n",
" <td>0.442505</td>\n",
" <td>3672</td>\n",
" <td>14080.382591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>82449291</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>23.022320</td>\n",
" <td>30</td>\n",
" <td>0.767411</td>\n",
" <td>3672</td>\n",
" <td>14073.176382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>82449292</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>13.413259</td>\n",
" <td>30</td>\n",
" <td>0.447109</td>\n",
" <td>3672</td>\n",
" <td>14066.749583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>82449293</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>19.066580</td>\n",
" <td>31</td>\n",
" <td>0.615051</td>\n",
" <td>3672</td>\n",
" <td>14052.790054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>82449294</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>18.003704</td>\n",
" <td>30</td>\n",
" <td>0.600123</td>\n",
" <td>3672</td>\n",
" <td>14034.855849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>82449295</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>13.032765</td>\n",
" <td>30</td>\n",
" <td>0.434425</td>\n",
" <td>3672</td>\n",
" <td>14029.140951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>82449296</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>16.618881</td>\n",
" <td>33</td>\n",
" <td>0.503602</td>\n",
" <td>3672</td>\n",
" <td>14012.587825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>82449297</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>18.814057</td>\n",
" <td>31</td>\n",
" <td>0.606905</td>\n",
" <td>3672</td>\n",
" <td>13998.608400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>82449298</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>22.850813</td>\n",
" <td>32</td>\n",
" <td>0.714088</td>\n",
" <td>3672</td>\n",
" <td>13975.850142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>82449299</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>32.278173</td>\n",
" <td>31</td>\n",
" <td>1.041231</td>\n",
" <td>3672</td>\n",
" <td>13950.015203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>82449300</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>52.490226</td>\n",
" <td>30</td>\n",
" <td>1.749674</td>\n",
" <td>3672</td>\n",
" <td>13917.701598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>82449301</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>4.154720</td>\n",
" <td>32</td>\n",
" <td>0.129835</td>\n",
" <td>3672</td>\n",
" <td>13913.561428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>82449302</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>28.058218</td>\n",
" <td>33</td>\n",
" <td>0.850249</td>\n",
" <td>3672</td>\n",
" <td>13933.130248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>82449303</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>6.231537</td>\n",
" <td>31</td>\n",
" <td>0.201017</td>\n",
" <td>3672</td>\n",
" <td>13939.339070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>82449304</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>25.085089</td>\n",
" <td>31</td>\n",
" <td>0.809196</td>\n",
" <td>3672</td>\n",
" <td>13952.773789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>82449305</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>16.139646</td>\n",
" <td>31</td>\n",
" <td>0.520634</td>\n",
" <td>3672</td>\n",
" <td>13965.688181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>82449306</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>22.944503</td>\n",
" <td>31</td>\n",
" <td>0.740145</td>\n",
" <td>3672</td>\n",
" <td>13961.884236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>82449307</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>25.378642</td>\n",
" <td>31</td>\n",
" <td>0.818666</td>\n",
" <td>3672</td>\n",
" <td>13985.853004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>82449308</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>11.548960</td>\n",
" <td>30</td>\n",
" <td>0.384965</td>\n",
" <td>3672</td>\n",
" <td>13988.500202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>82449310</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>13</td>\n",
" <td>24.336460</td>\n",
" <td>30</td>\n",
" <td>0.811215</td>\n",
" <td>3672</td>\n",
" <td>13965.856554</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",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>71</td>\n",
" <td>82449520</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>171.678896</td>\n",
" <td>31</td>\n",
" <td>5.538029</td>\n",
" <td>3672</td>\n",
" <td>14627.998307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>72</td>\n",
" <td>82449521</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>390.200026</td>\n",
" <td>31</td>\n",
" <td>12.587098</td>\n",
" <td>3672</td>\n",
" <td>14895.129793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>73</td>\n",
" <td>82449522</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>391.998300</td>\n",
" <td>31</td>\n",
" <td>12.645106</td>\n",
" <td>3672</td>\n",
" <td>15287.672112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>74</td>\n",
" <td>82449523</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>196.167079</td>\n",
" <td>30</td>\n",
" <td>6.538903</td>\n",
" <td>3672</td>\n",
" <td>15426.405133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>75</td>\n",
" <td>82449524</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>260.109024</td>\n",
" <td>32</td>\n",
" <td>8.128407</td>\n",
" <td>3672</td>\n",
" <td>15446.787429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>76</td>\n",
" <td>82449525</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>59.380533</td>\n",
" <td>29</td>\n",
" <td>2.047605</td>\n",
" <td>3672</td>\n",
" <td>15462.613091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>77</td>\n",
" <td>82449526</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>31.281951</td>\n",
" <td>32</td>\n",
" <td>0.977561</td>\n",
" <td>3672</td>\n",
" <td>15482.841805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>78</td>\n",
" <td>82449527</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>42.634698</td>\n",
" <td>30</td>\n",
" <td>1.421157</td>\n",
" <td>3672</td>\n",
" <td>15453.762593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>79</td>\n",
" <td>82449528</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>87.065633</td>\n",
" <td>30</td>\n",
" <td>2.902188</td>\n",
" <td>3672</td>\n",
" <td>15382.871043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>80</td>\n",
" <td>82449529</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>75.630740</td>\n",
" <td>31</td>\n",
" <td>2.439701</td>\n",
" <td>3672</td>\n",
" <td>15336.628638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>81</td>\n",
" <td>82449530</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>14.692382</td>\n",
" <td>31</td>\n",
" <td>0.473948</td>\n",
" <td>3672</td>\n",
" <td>15345.313174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>82</td>\n",
" <td>82449531</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>11.062392</td>\n",
" <td>32</td>\n",
" <td>0.345700</td>\n",
" <td>3672</td>\n",
" <td>15346.992177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>83</td>\n",
" <td>82449532</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>12.057865</td>\n",
" <td>30</td>\n",
" <td>0.401929</td>\n",
" <td>3672</td>\n",
" <td>15341.166877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>84</td>\n",
" <td>82449533</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>11.127523</td>\n",
" <td>30</td>\n",
" <td>0.370917</td>\n",
" <td>3672</td>\n",
" <td>15340.773635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>85</td>\n",
" <td>82449534</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-29</td>\n",
" <td>14</td>\n",
" <td>12.057865</td>\n",
" <td>31</td>\n",
" <td>0.388963</td>\n",
" <td>3672</td>\n",
" <td>15334.948993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>86</td>\n",
" <td>82523877</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>11.383130</td>\n",
" <td>31</td>\n",
" <td>0.367198</td>\n",
" <td>3672</td>\n",
" <td>14237.153625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>87</td>\n",
" <td>82523878</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>30.796542</td>\n",
" <td>31</td>\n",
" <td>0.993437</td>\n",
" <td>3672</td>\n",
" <td>14213.271555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>88</td>\n",
" <td>82523879</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>37.001880</td>\n",
" <td>31</td>\n",
" <td>1.193609</td>\n",
" <td>3672</td>\n",
" <td>14247.184035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>89</td>\n",
" <td>82523880</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>56.070542</td>\n",
" <td>31</td>\n",
" <td>1.808727</td>\n",
" <td>3672</td>\n",
" <td>14239.625301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>90</td>\n",
" <td>82523881</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>11.772059</td>\n",
" <td>30</td>\n",
" <td>0.392402</td>\n",
" <td>3672</td>\n",
" <td>14227.900455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>91</td>\n",
" <td>82523882</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>34.254372</td>\n",
" <td>31</td>\n",
" <td>1.104980</td>\n",
" <td>3672</td>\n",
" <td>14238.471030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>92</td>\n",
" <td>82523883</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>34.401201</td>\n",
" <td>31</td>\n",
" <td>1.109716</td>\n",
" <td>3672</td>\n",
" <td>14234.640297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>93</td>\n",
" <td>82523884</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>3.463462</td>\n",
" <td>30</td>\n",
" <td>0.115449</td>\n",
" <td>3672</td>\n",
" <td>14231.194195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>94</td>\n",
" <td>82523885</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>48.418472</td>\n",
" <td>31</td>\n",
" <td>1.561886</td>\n",
" <td>3672</td>\n",
" <td>14199.832106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>95</td>\n",
" <td>82523886</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>101.079948</td>\n",
" <td>31</td>\n",
" <td>3.260643</td>\n",
" <td>3672</td>\n",
" <td>14114.820595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>96</td>\n",
" <td>82523887</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>56.860793</td>\n",
" <td>31</td>\n",
" <td>1.834219</td>\n",
" <td>3672</td>\n",
" <td>14109.871819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>82523890</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>67.274731</td>\n",
" <td>62</td>\n",
" <td>1.085076</td>\n",
" <td>3672</td>\n",
" <td>14110.750529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>98</td>\n",
" <td>82523891</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>68.080346</td>\n",
" <td>30</td>\n",
" <td>2.269345</td>\n",
" <td>3672</td>\n",
" <td>14049.339534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>99</td>\n",
" <td>82523892</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>11.795056</td>\n",
" <td>31</td>\n",
" <td>0.380486</td>\n",
" <td>3672</td>\n",
" <td>14053.483472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>100</td>\n",
" <td>82523893</td>\n",
" <td>910</td>\n",
" <td>26</td>\n",
" <td>2014-01-31</td>\n",
" <td>11</td>\n",
" <td>8.309440</td>\n",
" <td>30</td>\n",
" <td>0.276981</td>\n",
" <td>3672</td>\n",
" <td>14061.746939</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows \u00d7 11 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" index ServiceViewDataID OBI UserID Date Hour Distance \\\n",
"0 1 82449274 910 26 2014-01-29 12 33.803380 \n",
"1 2 82449279 910 26 2014-01-29 12 23.123716 \n",
"2 3 82449282 910 26 2014-01-29 13 12.463798 \n",
"3 4 82449283 910 26 2014-01-29 13 11.571229 \n",
"4 5 82449284 910 26 2014-01-29 13 21.119235 \n",
"5 6 82449285 910 26 2014-01-29 13 33.382297 \n",
"6 7 82449286 910 26 2014-01-29 13 19.951840 \n",
"7 8 82449287 910 26 2014-01-29 13 20.532177 \n",
"8 9 82449288 910 26 2014-01-29 13 12.195665 \n",
"9 10 82449289 910 26 2014-01-29 13 9.694192 \n",
"10 11 82449290 910 26 2014-01-29 13 14.160167 \n",
"11 12 82449291 910 26 2014-01-29 13 23.022320 \n",
"12 13 82449292 910 26 2014-01-29 13 13.413259 \n",
"13 14 82449293 910 26 2014-01-29 13 19.066580 \n",
"14 15 82449294 910 26 2014-01-29 13 18.003704 \n",
"15 16 82449295 910 26 2014-01-29 13 13.032765 \n",
"16 17 82449296 910 26 2014-01-29 13 16.618881 \n",
"17 18 82449297 910 26 2014-01-29 13 18.814057 \n",
"18 19 82449298 910 26 2014-01-29 13 22.850813 \n",
"19 20 82449299 910 26 2014-01-29 13 32.278173 \n",
"20 21 82449300 910 26 2014-01-29 13 52.490226 \n",
"21 22 82449301 910 26 2014-01-29 13 4.154720 \n",
"22 23 82449302 910 26 2014-01-29 13 28.058218 \n",
"23 24 82449303 910 26 2014-01-29 13 6.231537 \n",
"24 25 82449304 910 26 2014-01-29 13 25.085089 \n",
"25 26 82449305 910 26 2014-01-29 13 16.139646 \n",
"26 27 82449306 910 26 2014-01-29 13 22.944503 \n",
"27 28 82449307 910 26 2014-01-29 13 25.378642 \n",
"28 29 82449308 910 26 2014-01-29 13 11.548960 \n",
"29 30 82449310 910 26 2014-01-29 13 24.336460 \n",
".. ... ... ... ... ... ... ... \n",
"70 71 82449520 910 26 2014-01-29 14 171.678896 \n",
"71 72 82449521 910 26 2014-01-29 14 390.200026 \n",
"72 73 82449522 910 26 2014-01-29 14 391.998300 \n",
"73 74 82449523 910 26 2014-01-29 14 196.167079 \n",
"74 75 82449524 910 26 2014-01-29 14 260.109024 \n",
"75 76 82449525 910 26 2014-01-29 14 59.380533 \n",
"76 77 82449526 910 26 2014-01-29 14 31.281951 \n",
"77 78 82449527 910 26 2014-01-29 14 42.634698 \n",
"78 79 82449528 910 26 2014-01-29 14 87.065633 \n",
"79 80 82449529 910 26 2014-01-29 14 75.630740 \n",
"80 81 82449530 910 26 2014-01-29 14 14.692382 \n",
"81 82 82449531 910 26 2014-01-29 14 11.062392 \n",
"82 83 82449532 910 26 2014-01-29 14 12.057865 \n",
"83 84 82449533 910 26 2014-01-29 14 11.127523 \n",
"84 85 82449534 910 26 2014-01-29 14 12.057865 \n",
"85 86 82523877 910 26 2014-01-31 11 11.383130 \n",
"86 87 82523878 910 26 2014-01-31 11 30.796542 \n",
"87 88 82523879 910 26 2014-01-31 11 37.001880 \n",
"88 89 82523880 910 26 2014-01-31 11 56.070542 \n",
"89 90 82523881 910 26 2014-01-31 11 11.772059 \n",
"90 91 82523882 910 26 2014-01-31 11 34.254372 \n",
"91 92 82523883 910 26 2014-01-31 11 34.401201 \n",
"92 93 82523884 910 26 2014-01-31 11 3.463462 \n",
"93 94 82523885 910 26 2014-01-31 11 48.418472 \n",
"94 95 82523886 910 26 2014-01-31 11 101.079948 \n",
"95 96 82523887 910 26 2014-01-31 11 56.860793 \n",
"96 97 82523890 910 26 2014-01-31 11 67.274731 \n",
"97 98 82523891 910 26 2014-01-31 11 68.080346 \n",
"98 99 82523892 910 26 2014-01-31 11 11.795056 \n",
"99 100 82523893 910 26 2014-01-31 11 8.309440 \n",
"\n",
" deltaT Speed SiteCode SiteDistance \n",
"0 23 1.469712 3672 13865.565864 \n",
"1 43 0.537761 3672 13880.806346 \n",
"2 34 0.366582 3672 14002.569243 \n",
"3 31 0.373265 3672 14004.815460 \n",
"4 30 0.703975 3672 14023.947056 \n",
"5 31 1.076848 3672 14024.509468 \n",
"6 32 0.623495 3672 14042.246096 \n",
"7 31 0.662328 3672 14058.308719 \n",
"8 31 0.393409 3672 14061.231439 \n",
"9 31 0.312716 3672 14070.889234 \n",
"10 32 0.442505 3672 14080.382591 \n",
"11 30 0.767411 3672 14073.176382 \n",
"12 30 0.447109 3672 14066.749583 \n",
"13 31 0.615051 3672 14052.790054 \n",
"14 30 0.600123 3672 14034.855849 \n",
"15 30 0.434425 3672 14029.140951 \n",
"16 33 0.503602 3672 14012.587825 \n",
"17 31 0.606905 3672 13998.608400 \n",
"18 32 0.714088 3672 13975.850142 \n",
"19 31 1.041231 3672 13950.015203 \n",
"20 30 1.749674 3672 13917.701598 \n",
"21 32 0.129835 3672 13913.561428 \n",
"22 33 0.850249 3672 13933.130248 \n",
"23 31 0.201017 3672 13939.339070 \n",
"24 31 0.809196 3672 13952.773789 \n",
"25 31 0.520634 3672 13965.688181 \n",
"26 31 0.740145 3672 13961.884236 \n",
"27 31 0.818666 3672 13985.853004 \n",
"28 30 0.384965 3672 13988.500202 \n",
"29 30 0.811215 3672 13965.856554 \n",
".. ... ... ... ... \n",
"70 31 5.538029 3672 14627.998307 \n",
"71 31 12.587098 3672 14895.129793 \n",
"72 31 12.645106 3672 15287.672112 \n",
"73 30 6.538903 3672 15426.405133 \n",
"74 32 8.128407 3672 15446.787429 \n",
"75 29 2.047605 3672 15462.613091 \n",
"76 32 0.977561 3672 15482.841805 \n",
"77 30 1.421157 3672 15453.762593 \n",
"78 30 2.902188 3672 15382.871043 \n",
"79 31 2.439701 3672 15336.628638 \n",
"80 31 0.473948 3672 15345.313174 \n",
"81 32 0.345700 3672 15346.992177 \n",
"82 30 0.401929 3672 15341.166877 \n",
"83 30 0.370917 3672 15340.773635 \n",
"84 31 0.388963 3672 15334.948993 \n",
"85 31 0.367198 3672 14237.153625 \n",
"86 31 0.993437 3672 14213.271555 \n",
"87 31 1.193609 3672 14247.184035 \n",
"88 31 1.808727 3672 14239.625301 \n",
"89 30 0.392402 3672 14227.900455 \n",
"90 31 1.104980 3672 14238.471030 \n",
"91 31 1.109716 3672 14234.640297 \n",
"92 30 0.115449 3672 14231.194195 \n",
"93 31 1.561886 3672 14199.832106 \n",
"94 31 3.260643 3672 14114.820595 \n",
"95 31 1.834219 3672 14109.871819 \n",
"96 62 1.085076 3672 14110.750529 \n",
"97 30 2.269345 3672 14049.339534 \n",
"98 31 0.380486 3672 14053.483472 \n",
"99 30 0.276981 3672 14061.746939 \n",
"\n",
"[100 rows x 11 columns]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q = '''\n",
" SELECT SignificantWeather\n",
" FROM Weather as w\n",
" GROUP BY SignificantWeather\n",
"'''\n",
"weather_types_df = pd.read_sql_query(q, sqllite_engine)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weather_types = list(weather_types_df['SignificantWeather'].values)\n",
"weather_types = filter(None, weather_types)\n",
"weather_types.append(u'None')\n",
"print(weather_types)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'(Black) Low-level cloud', u'(White) Medium-level cloud', u'Clear sky (Night)', u'Drizzle', u'Fog', u'Hail shower (Day)', u'Hail shower (Night)', u'Heavy Rain', u'Heavy rain shower (Day)', u'Heavy rain shower (Night)', u'Heavy snow', u'Heavy snow shower (Day)', u'Heavy snow shower (Night)', u'Light rain', u'Light rain shower (Day)', u'Light rain shower (Night)', u'Light snow', u'Light snow shower (Day)', u'Light snow shower (Night)', u'Mist', u'Partly cloudy (Night)', u'Sleet', u'Sunny (Day)', u'Sunny intervals', u'Thunder storm', u'Thundery shower (Day)', u'Thundery shower (Night)', u'None']\n"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_bins = np.arange(0,3.1,0.1)\n",
"histograms = {}\n",
"for weather_type in weather_types:\n",
" histograms[weather_type], discard = np.histogram([], hist_bins)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q = '''\n",
" SELECT s.Speed, ifnull(w.SignificantWeather,'None') as SignificantWeather\n",
" FROM gps_to_station s \n",
" INNER JOIN Weather as w\n",
" ON w.SiteCode = s.SiteCode\n",
" and date(w.ObservationDate) = s.Date\n",
" and w.ObservationTime = s.Hour\n",
"'''"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start = dt.datetime.now()\n",
"chunksize = 1000\n",
"j = 0\n",
"index_start = 1\n",
"\n",
"# Loop through the source data, pulling 'chunksize' rows at a time\n",
"for df in pd.read_sql_query(sql=q, con=sqllite_engine, chunksize=chunksize):\n",
" \n",
" df.index += index_start\n",
" \n",
" for weather_type in weather_types:\n",
" \n",
" data = df[df['SignificantWeather']==weather_type]['Speed'].values\n",
" hist, discard = np.histogram(data, hist_bins)\n",
" histograms[weather_type] = histograms[weather_type] + hist\n",
" \n",
" j+=1\n",
" print '{} seconds: completed {} rows'.format((dt.datetime.now() - start).seconds, j*chunksize)\n",
"\n",
" index_start = df.index[-1] + 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"8 seconds: completed 1000 rows\n",
"15 seconds: completed 2000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"23 seconds: completed 3000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"31 seconds: completed 4000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"39 seconds: completed 5000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"47 seconds: completed 6000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"55 seconds: completed 7000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"63 seconds: completed 8000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"71 seconds: completed 9000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"79 seconds: completed 10000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"86 seconds: completed 11000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"94 seconds: completed 12000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"102 seconds: completed 13000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"110 seconds: completed 14000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"118 seconds: completed 15000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"126 seconds: completed 16000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"134 seconds: completed 17000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"142 seconds: completed 18000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"150 seconds: completed 19000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"158 seconds: completed 20000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"166 seconds: completed 21000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"175 seconds: completed 22000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"183 seconds: completed 23000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"191 seconds: completed 24000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"199 seconds: completed 25000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"207 seconds: completed 26000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"215 seconds: completed 27000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"224 seconds: completed 28000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"232 seconds: completed 29000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"240 seconds: completed 30000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"248 seconds: completed 31000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"256 seconds: completed 32000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"264 seconds: completed 33000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"272 seconds: completed 34000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"280 seconds: completed 35000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"288 seconds: completed 36000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"296 seconds: completed 37000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"304 seconds: completed 38000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"312 seconds: completed 39000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"320 seconds: completed 40000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"328 seconds: completed 41000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"336 seconds: completed 42000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"344 seconds: completed 43000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"352 seconds: completed 44000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"360 seconds: completed 45000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"368 seconds: completed 46000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"376 seconds: completed 47000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"384 seconds: completed 48000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"392 seconds: completed 49000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"400 seconds: completed 50000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"408 seconds: completed 51000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"416 seconds: completed 52000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"424 seconds: completed 53000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"433 seconds: completed 54000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"440 seconds: completed 55000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"449 seconds: completed 56000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"457 seconds: completed 57000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"465 seconds: completed 58000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"473 seconds: completed 59000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"480 seconds: completed 60000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"488 seconds: completed 61000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"496 seconds: completed 62000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"504 seconds: completed 63000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"512 seconds: completed 64000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"520 seconds: completed 65000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"528 seconds: completed 66000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"536 seconds: completed 67000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"545 seconds: completed 68000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"553 seconds: completed 69000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"561 seconds: completed 70000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"569 seconds: completed 71000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"577 seconds: completed 72000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"585 seconds: completed 73000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"593 seconds: completed 74000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"601 seconds: completed 75000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"609 seconds: completed 76000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"617 seconds: completed 77000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"625 seconds: completed 78000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"633 seconds: completed 79000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"641 seconds: completed 80000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"649 seconds: completed 81000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"657 seconds: completed 82000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"665 seconds: completed 83000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"673 seconds: completed 84000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"681 seconds: completed 85000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"689 seconds: completed 86000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"697 seconds: completed 87000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"705 seconds: completed 88000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"713 seconds: completed 89000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"721 seconds: completed 90000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"729 seconds: completed 91000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"736 seconds: completed 92000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"744 seconds: completed 93000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"752 seconds: completed 94000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"760 seconds: completed 95000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"769 seconds: completed 96000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"777 seconds: completed 97000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"784 seconds: completed 98000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"792 seconds: completed 99000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"800 seconds: completed 100000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"808 seconds: completed 101000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"817 seconds: completed 102000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"825 seconds: completed 103000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"833 seconds: completed 104000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"841 seconds: completed 105000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"849 seconds: completed 106000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"857 seconds: completed 107000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"865 seconds: completed 108000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"873 seconds: completed 109000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"880 seconds: completed 110000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"889 seconds: completed 111000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"897 seconds: completed 112000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"905 seconds: completed 113000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"914 seconds: completed 114000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"924 seconds: completed 115000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"934 seconds: completed 116000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"944 seconds: completed 117000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"953 seconds: completed 118000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"966 seconds: completed 119000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"975 seconds: completed 120000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"983 seconds: completed 121000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"991 seconds: completed 122000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1000 seconds: completed 123000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1008 seconds: completed 124000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1017 seconds: completed 125000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1025 seconds: completed 126000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1033 seconds: completed 127000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1042 seconds: completed 128000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1050 seconds: completed 129000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1058 seconds: completed 130000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1066 seconds: completed 131000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1075 seconds: completed 132000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1083 seconds: completed 133000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1091 seconds: completed 134000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1100 seconds: completed 135000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1108 seconds: completed 136000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1116 seconds: completed 137000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1124 seconds: completed 138000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1133 seconds: completed 139000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1141 seconds: completed 140000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1149 seconds: completed 141000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1158 seconds: completed 142000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1166 seconds: completed 143000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1174 seconds: completed 144000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1182 seconds: completed 145000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1190 seconds: completed 146000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1198 seconds: completed 147000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1206 seconds: completed 148000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1215 seconds: completed 149000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1223 seconds: completed 150000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1231 seconds: completed 151000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1239 seconds: completed 152000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1247 seconds: completed 153000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1255 seconds: completed 154000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1263 seconds: completed 155000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1271 seconds: completed 156000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1279 seconds: completed 157000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1287 seconds: completed 158000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1295 seconds: completed 159000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1303 seconds: completed 160000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1311 seconds: completed 161000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1319 seconds: completed 162000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1328 seconds: completed 163000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1336 seconds: completed 164000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1344 seconds: completed 165000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1352 seconds: completed 166000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1360 seconds: completed 167000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1368 seconds: completed 168000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1376 seconds: completed 169000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1384 seconds: completed 170000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1392 seconds: completed 171000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1400 seconds: completed 172000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1408 seconds: completed 173000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1416 seconds: completed 174000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1425 seconds: completed 175000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1433 seconds: completed 176000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1441 seconds: completed 177000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1449 seconds: completed 178000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1457 seconds: completed 179000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1465 seconds: completed 180000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1473 seconds: completed 181000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1481 seconds: completed 182000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1489 seconds: completed 183000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1497 seconds: completed 184000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1505 seconds: completed 185000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1513 seconds: completed 186000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1521 seconds: completed 187000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1529 seconds: completed 188000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1537 seconds: completed 189000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1545 seconds: completed 190000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1553 seconds: completed 191000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1561 seconds: completed 192000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1569 seconds: completed 193000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1577 seconds: completed 194000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1585 seconds: completed 195000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1593 seconds: completed 196000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1601 seconds: completed 197000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1609 seconds: completed 198000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1617 seconds: completed 199000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1625 seconds: completed 200000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1633 seconds: completed 201000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1641 seconds: completed 202000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1650 seconds: completed 203000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1658 seconds: completed 204000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1666 seconds: completed 205000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1675 seconds: completed 206000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1683 seconds: completed 207000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1691 seconds: completed 208000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1700 seconds: completed 209000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1708 seconds: completed 210000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1716 seconds: completed 211000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1724 seconds: completed 212000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1732 seconds: completed 213000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1741 seconds: completed 214000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1749 seconds: completed 215000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1757 seconds: completed 216000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1765 seconds: completed 217000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1774 seconds: completed 218000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1782 seconds: completed 219000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1790 seconds: completed 220000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1799 seconds: completed 221000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1807 seconds: completed 222000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1816 seconds: completed 223000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1824 seconds: completed 224000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1832 seconds: completed 225000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1841 seconds: completed 226000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1849 seconds: completed 227000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1857 seconds: completed 228000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1866 seconds: completed 229000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1874 seconds: completed 230000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1882 seconds: completed 231000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1890 seconds: completed 232000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1899 seconds: completed 233000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1924 seconds: completed 236000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1932 seconds: completed 237000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2006 seconds: completed 246000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2014 seconds: completed 247000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2048 seconds: completed 251000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2106 seconds: completed 258000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2115 seconds: completed 259000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2123 seconds: completed 260000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2131 seconds: completed 261000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2139 seconds: completed 262000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2148 seconds: completed 263000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2156 seconds: completed 264000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2164 seconds: completed 265000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2172 seconds: completed 266000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2180 seconds: completed 267000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2196 seconds: completed 269000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"2205 seconds: completed 270000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
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"text": [
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]
},
{
"output_type": "stream",
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"text": [
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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]
},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2400 seconds: completed 294000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"2767 seconds: completed 340000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2825 seconds: completed 346000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2836 seconds: completed 347000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2844 seconds: completed 348000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"2852 seconds: completed 349000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2861 seconds: completed 350000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2870 seconds: completed 351000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"2896 seconds: completed 354000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2905 seconds: completed 355000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2949 seconds: completed 360000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"2984 seconds: completed 364000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"2992 seconds: completed 365000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3001 seconds: completed 366000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"3025 seconds: completed 369000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"3034 seconds: completed 370000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"3042 seconds: completed 371000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3050 seconds: completed 372000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3075 seconds: completed 375000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3100 seconds: completed 378000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3108 seconds: completed 379000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3117 seconds: completed 380000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3125 seconds: completed 381000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"3200 seconds: completed 390000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
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{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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{
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{
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},
{
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},
{
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{
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},
{
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{
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},
{
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},
{
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},
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},
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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{
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},
{
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},
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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{
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{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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"text": [
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{
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"text": [
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{
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"text": [
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},
{
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"text": [
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{
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"text": [
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{
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{
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"text": [
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{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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{
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"text": [
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{
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"text": [
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{
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"text": [
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{
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"text": [
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},
{
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"text": [
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{
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{
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{
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},
{
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},
{
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"text": [
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{
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},
{
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"text": [
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},
{
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"text": [
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{
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},
{
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"text": [
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{
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"text": [
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{
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},
{
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"text": [
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{
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"text": [
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{
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{
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{
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"text": [
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},
{
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"text": [
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
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"text": [
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{
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
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},
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},
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},
{
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},
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},
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},
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},
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{
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{
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},
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{
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},
{
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{
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},
{
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},
{
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},
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},
{
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},
{
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},
{
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},
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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"stream": "stdout",
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},
{
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},
{
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},
{
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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},
{
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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"text": [
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{
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{
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{
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"text": [
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},
{
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{
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{
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{
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"text": [
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"text": [
"\n",
"7488 seconds: completed 900000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
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]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
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]
},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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]
},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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"text": [
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{
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{
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{
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"text": [
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{
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"text": [
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{
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"text": [
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{
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{
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"stream": "stdout",
"text": [
"\n",
"10179 seconds: completed 1223000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10188 seconds: completed 1224000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"10196 seconds: completed 1225000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10204 seconds: completed 1226000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10212 seconds: completed 1227000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10221 seconds: completed 1228000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10229 seconds: completed 1229000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10237 seconds: completed 1230000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"10254 seconds: completed 1232000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10275 seconds: completed 1234000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10288 seconds: completed 1235000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10298 seconds: completed 1236000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10308 seconds: completed 1237000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"10321 seconds: completed 1238000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"10331 seconds: completed 1239000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"10846 seconds: completed 1300000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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},
{
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
"text": [
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{
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"text": [
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},
{
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"stream": "stdout",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
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},
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},
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},
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},
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{
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},
{
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},
{
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},
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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{
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{
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{
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},
{
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},
{
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"text": [
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},
{
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"text": [
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},
{
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},
{
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"text": [
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},
{
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"text": [
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},
{
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},
{
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{
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},
{
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{
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},
{
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{
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"text": [
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},
{
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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"text": [
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{
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{
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{
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{
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{
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"text": [
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{
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"text": [
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},
{
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
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{
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{
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"text": [
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{
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{
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{
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{
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"text": [
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"14262 seconds: completed 1706000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"14270 seconds: completed 1707000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"14279 seconds: completed 1708000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"14287 seconds: completed 1709000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"14295 seconds: completed 1710000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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"14329 seconds: completed 1714000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"14354 seconds: completed 1717000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"14370 seconds: completed 1719000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"14380 seconds: completed 1720000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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"14464 seconds: completed 1730000 rows"
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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"stream": "stdout",
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},
{
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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"14547 seconds: completed 1740000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"14563 seconds: completed 1742000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"14572 seconds: completed 1743000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15034 seconds: completed 1799000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15043 seconds: completed 1800000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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{
"output_type": "stream",
"stream": "stdout",
"text": [
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{
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"stream": "stdout",
"text": [
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{
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"stream": "stdout",
"text": [
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{
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"stream": "stdout",
"text": [
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{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"text": [
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},
{
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"text": [
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{
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"text": [
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{
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"text": [
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{
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"text": [
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{
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"stream": "stdout",
"text": [
"\n",
"15597 seconds: completed 1867000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"15605 seconds: completed 1868000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"15613 seconds: completed 1869000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15621 seconds: completed 1870000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"15629 seconds: completed 1871000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"15638 seconds: completed 1872000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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"15646 seconds: completed 1873000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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"15663 seconds: completed 1875000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15671 seconds: completed 1876000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15704 seconds: completed 1880000 rows"
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},
{
"output_type": "stream",
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"15712 seconds: completed 1881000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
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"15737 seconds: completed 1884000 rows"
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"15754 seconds: completed 1886000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16425 seconds: completed 1963000 rows"
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
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"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16500 seconds: completed 1972000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16525 seconds: completed 1975000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16533 seconds: completed 1976000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16541 seconds: completed 1977000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16566 seconds: completed 1980000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16574 seconds: completed 1981000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16615 seconds: completed 1986000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16640 seconds: completed 1989000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16648 seconds: completed 1990000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16657 seconds: completed 1991000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16665 seconds: completed 1992000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16673 seconds: completed 1993000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16681 seconds: completed 1994000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16715 seconds: completed 1998000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16731 seconds: completed 2000000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16739 seconds: completed 2001000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16805 seconds: completed 2009000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16813 seconds: completed 2010000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16821 seconds: completed 2011000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16863 seconds: completed 2016000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16880 seconds: completed 2018000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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"16896 seconds: completed 2020000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
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},
{
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"stream": "stdout",
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"16979 seconds: completed 2030000 rows"
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},
{
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},
{
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{
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},
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},
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},
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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{
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{
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{
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{
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},
{
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"text": [
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},
{
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},
{
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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"\n",
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]
},
{
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]
},
{
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},
{
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},
{
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"\n",
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},
{
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"\n",
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{
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{
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"stream": "stdout",
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"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
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"\n",
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},
{
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"stream": "stdout",
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"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
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"\n",
"20095 seconds: completed 2400000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"20103 seconds: completed 2401000 rows"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"20108 seconds: completed 2402000 rows"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"condition_count = len(weather_types)\n",
"\n",
"plt.figure(figsize=(10, 80))\n",
"#plt.subplots(nrows=condition_count+1, ncols=1)\n",
"#plt.tight_layout()\n",
"\n",
"bin_centers = (hist_bins[:-1] + hist_bins[1:]) / 2\n",
"\n",
"for idx, weather_type in enumerate(weather_types):\n",
" \n",
" plt.subplot(condition_count+1,1,idx+2)\n",
" plt.title(weather_type + ' ' + str(sum(data)))\n",
" data = histograms[weather_type]\n",
" if sum(data)>0:\n",
" plt.bar(bin_centers, data, align='center', width = 0.1)\n",
" #plt.text(5, 2,condition,horizontalalignment='center',verticalalignment='center')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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6G+jz1y4B28+AyZIOl7QncBZwY9U5NwLvA5D0WmBrRHQPbzFLqd+6kzReedpq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euragtwAXDsDOXeDFwEfHeYlZMkSeqSUQW2VcDtPcsb2nXbJVlFE+LObFfVcKomSZLULbuN6HUH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\nZ2CTJEnqOQObJElSzxnYJEmSes7AJkmS1HMGNkmSpJ4zsEmSJPWcgU2SJKn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lfXwPTWj6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y5mWSVJkhbF1NQUU1NT939+4xvfOK/rjKpL9CLgFe3+K4AL+9JfkmTPJI8FDgXWV9XtwHeT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\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"text": [
"<matplotlib.figure.Figure at 0xb425730>"
]
}
],
"prompt_number": 111
}
],
"metadata": {}
}
]
}
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