SampleOSMData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd # for batch data loading, in generating sampled dataset\n",
    "from numpy import genfromtxt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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   "source": [
    "def process_chunk_sampling(row, domains, sampled_subset, prob_threshold, total_dims):\n",
    "    prob = random.uniform(0, 1)\n",
    "    row_numpy = row.to_numpy()  \n",
    "    for i in range(len(domains)):\n",
    "        if row_numpy[i] > domains[i][1]:\n",
    "            domains[i][1] = row_numpy[i]\n",
    "        if row_numpy[i] < domains[i][0]:\n",
    "            domains[i][0] = row_numpy[i]\n",
    "    if prob <= prob_threshold:    \n",
    "        sampled_subset.append(row_numpy[0:total_dims].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "source": [
    "def generate_dataset_and_save(original_table_path, prob_threshold,chunk_size = 100000, total_dims = 2):\n",
    "    '''\n",
    "    refer to TPCH tools to generate the original dataset (.tbl)\n",
    "    this function is used to process the .tbl file with given sampling rate to generate a .csv file\n",
    "    consider the possible table size, this function is implemented in a batch processing manner\n",
    "    '''\n",
    "    sampled_subset = []\n",
    "    domains = [[float('Infinity'), float('-Infinity')] for i in range(total_dims)] # indicate min, max\n",
    "\n",
    "    col_names = ['_c'+str(i) for i in range(total_dims)]\n",
    "    cols = [i for i in range(total_dims)]\n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    batch_count = 0\n",
    "    for chunk in pd.read_table(original_table_path, delimiter=',', usecols=cols, names=col_names, chunksize=chunk_size):\n",
    "        print('current chunk: ', batch_count)\n",
    "        chunk.apply(lambda row: process_chunk_sampling(row, domains, sampled_subset, prob_threshold, total_dims), axis=1)\n",
    "        batch_count += 1\n",
    "\n",
    "    end_time = time.time()\n",
    "    print('total processing time: ', end_time - start_time)\n",
    "\n",
    "    sampled_subset = np.array(sampled_subset)\n",
    "    domains = np.array(domains)\n",
    "    np.savetxt('C:/Users/Cloud/iCloudDrive/NORA_experiments/dataset/OSM_6M.csv', sampled_subset, delimiter=',')\n",
    "    np.savetxt('C:/Users/Cloud/iCloudDrive/NORA_experiments/dataset/OSM_6M_domains.csv', domains, delimiter=',')"
   ]
  },
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    }
   ],
   "source": [
    "generate_dataset_and_save(\"D:/Polyfit_Dataset/OSM/100M_Long_Lat_all.csv\", 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "OSM = np.genfromtxt('C:/Users/Cloud/iCloudDrive/NORA_experiments/dataset/OSM_6M.csv', delimiter=',')\n",
    "OSM_boundary = [-180, -90, 180, 90]\n",
    "OSM_block_size = 10000 # 1.1M compared to 6M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MRli9vCNtExqiUJ22RvaxzBgj76xe3sHGvnyGASMTfBFpAUYoTV7+URGZDWwDOoFjwKdU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\nCb5hGEaTYIJvGIbRJJjgG4ZhNAn/H2/r9sUVpsM0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(OSM[:,0], OSM[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}