TM351 VM Installation Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TM351 VM Installation Test\n",
    "\n",
    "This notebook provides a series of tests to ensure that the virtual machine is running correctly.\n",
    "\n",
    "Run each cell in turn by clicking the play button or keyboard shortcut `shift-return`. (A full list of keyboard shortcuts can be found from the *Help* menu or via the keyboard shortcut `ESC-h`.)\n",
    "\n",
    "The cells should run without error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions\n",
    "\n",
    "Display the VM build version and build time, as well as database service versions and `pandas` version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat: /opt/version.txt: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "!cat /opt/version.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "psql (PostgreSQL) 11.2\r\n"
     ]
    }
   ],
   "source": [
    "! psql --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "db version v3.6.15\r\n",
      "git version: 18934fb5c814e87895c5e38ae1515dd6cb4c00f7\r\n",
      "allocator: tcmalloc\r\n",
      "modules: none\r\n",
      "build environment:\r\n",
      "    distarch: x86_64\r\n",
      "    target_arch: x86_64\r\n"
     ]
    }
   ],
   "source": [
    "! mongod --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.25.3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Test Core Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#When this cell is run, a simple line chart should be displayed\n",
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database tests\n",
    "\n",
    "Check that the database services are running as required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SET DATABASE CONNECTION STRINGS\n",
    "PGCONN='postgresql://testuser:testpass@localhost:5432/testdb'\n",
    "MONGOHOST='localhost'\n",
    "MONGOPORT=27017\n",
    "MONGOCONN='mongodb://{MONGOHOST}:{MONGOPORT}/'.format(MONGOHOST=MONGOHOST,MONGOPORT=MONGOPORT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PostgreSQL\n",
    "\n",
    "Check the connection to the PostgreSQL server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "engine = create_engine(PGCONN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>table_schema</th>\n",
       "      <th>table_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>information_schema</td>\n",
       "      <td>administrable_role_authorizations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>information_schema</td>\n",
       "      <td>applicable_roles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>information_schema</td>\n",
       "      <td>attributes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         table_schema                         table_name\n",
       "0  information_schema  administrable_role_authorizations\n",
       "1  information_schema                   applicable_roles\n",
       "2  information_schema                         attributes"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run a simple query on a default table\n",
    "from pandas import read_sql_query as psql\n",
    "\n",
    "psql(\"SELECT table_schema,table_name FROM information_schema.tables \\\n",
    "    ORDER BY table_schema,table_name LIMIT 3;\", engine)\n",
    "#A table containing three rows should appear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SQL Cell Magic\n",
    "\n",
    "We can use cell magics to allow the writing of SQL statements within a code cell flagged appropriately.\n",
    "\n",
    "To invoke the cell magic in a cell, we need to run the following (though we could perhaps autoload this in every notebook?)\n",
    "\n",
    "In the following example, magic SQL cells will be configured to run as a the root user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: testuser@testdb'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext sql\n",
    "%sql {PGCONN}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://testuser:***@localhost:5432/testdb\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>table_schema</th>\n",
       "        <th>table_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>information_schema</td>\n",
       "        <td>administrable_role_authorizations</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('information_schema', 'administrable_role_authorizations')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT table_schema,table_name FROM information_schema.tables ORDER BY table_schema,table_name LIMIT 1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the ability to pull the result of a SQL query directly into a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://testuser:***@localhost:5432/testdb\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>table_schema</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>public</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>pg_catalog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>pg_catalog</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('public',), ('pg_catalog',), ('pg_catalog',)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo=%sql SELECT table_schema FROM information_schema.tables LIMIT 3\n",
    "demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MongoDB\n",
    "\n",
    "Test that the mongoDB database is running... This example also shows how to connect to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo\n",
    "from pymongo import MongoClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If connecting to the default port, you can omit the second (port number) parameter\n",
    "# Open a connection to the Mongo server, open the accidents database and name the collections of accidents and labels\n",
    "c = pymongo.MongoClient(MONGOCONN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, this database should contain an accidents database along with any default databases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['admin', 'config', 'local', 'test_database']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.list_database_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = c.accidents\n",
    "accidents = db.accidents\n",
    "accidents.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Chart Tests\n",
    "\n",
    "Viewing data in charts provides a handy way of actually looking at your data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "from numpy.random import randn\n",
    "\n",
    "data = randn(75)\n",
    "plt.hist(data);\n",
    "#Running this cell should produce a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot();\n",
    "#Running this cell should produce a line chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maps\n",
    "\n",
    "Several of the data investigations may benefit from displaying data on a map. Test that the mapping functions work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe 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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fa43f7c79d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "#Note - this will not display a map if you are offline.\n",
    "\n",
    "#A network connection is required to retrieve the map tiles\n",
    "osmap = folium.Map(location=[52.01, -0.71], zoom_start=13,height=500,width=800)\n",
    "folium.Marker([52.0250, -0.7056], popup='The <b>Open University</b> campus.').add_to(osmap)\n",
    "osmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of how to explicitly save map as HTML file\n",
    "osmap.save('test.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other VM Services\n",
    "\n",
    "- [OpenRefine - by default on host path `openrefine`](../openrefine)\n",
    "\n",
    "From the notebook home page (`/tree`) you should also be able to launch a terminal as well as a new notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
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 },
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}