Kelvinrr/query_data.ipynb

## query_data.ipynb
{
 "cells": [
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "from pymongo import MongoClient\nfrom collections import OrderedDict\n\nimport numpy\n\nimport matplotlib.pyplot as plt",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": false,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "# client = MongoClient(\"192.168.0.21\", 27017)\nclient = MongoClient('192.168.0.21')\nprint(\"Authentatied:\", client.selene.authenticate('owner', 'data'))",
   "execution_count": 8,
   "outputs": [
    {
     "text": "Authentatied: True\n",
     "output_type": "stream",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": false,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "point_collection = client.selene.points\npoint_data = point_collection.find()",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "point_data.count()",
   "execution_count": 12,
   "outputs": [
    {
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "1000011"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "longitudes = [point['loc']['coordinates'][0] for point in point_data]",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "latitudes = [point['loc']['coordinates'][1] for point in point_data]",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "print('Longitude min/max: ', (min(longitudes), max(longitudes)))\nprint('Latitude min/max: ', (min(latitudes), max(latitudes)))",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": false,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": false,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "scrolled": false,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "import numpy\n\nplt.figure(figsize=(10,10))\n\nfor point in point_list:\n    plt.scatter(x=point[1], y=point[0], color='b')\nplt.xlabel('Latitude')\nplt.ylabel('Longitude')\nplt.show()",
   "execution_count": 71,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<matplotlib.figure.Figure at 0x10dc5ec50>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2kAAANSCAYAAAD/NYbIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "language_info": {
   "pygments_lexer": "ipython3",
   "version": "3.5.2",
   "file_extension": ".py",
   "name": "python",
   "nbconvert_exporter": "python",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "version": 3,
    "name": "ipython"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}