2017-10-02-uw-python-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap on indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "horrible_list_name = [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt('data/inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of first row\n",
    "data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Third row, second column\n",
    "data[2,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "row0 = data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexing: row0[start:stop:step]\n",
    "row0[0:5:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  1.,  3.,  1.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row0[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last element\n",
    "row0[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second to last element\n",
    "row0[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0.,   1.,   3.,   1.,   2.,   4.,   7.,   8.,   3.,   3.,\n",
       "         3.,  10.,   5.,   7.,   4.,   7.,   7.,  12.,  18.,   6.,  13.,\n",
       "        11.,  11.,   7.,   7.,   4.,   6.,   8.,   8.,   4.,   4.,   5.,\n",
       "         7.,   3.,   4.,   2.,   3.,   0.,   0.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row0[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0.,   3.,   2.,   4.,   3.,   7.,   5.,   4.,   4.,   8.,\n",
       "         8.,   6.,   4.,   7.,   7.,  11.,  11.,  13.,   6.,  18.,  12.,\n",
       "         7.,   7.,   4.,   7.,   5.,  10.,   3.,   3.,   3.,   8.,   7.,\n",
       "         4.,   2.,   1.,   3.,   1.,   0.,   0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse order of columns in row\n",
    "row0[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap on for loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o\n",
      "x\n",
      "y\n",
      "g\n",
      "e\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "word = 'oxygen'\n",
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oxygen\n",
      "o\n",
      "x\n",
      "y\n",
      "g\n",
      "e\n",
      "n\n",
      "nitrogen\n",
      "n\n",
      "i\n",
      "t\n",
      "r\n",
      "o\n",
      "g\n",
      "e\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "words = ['oxygen', 'nitrogen']\n",
    "for wrd in words:\n",
    "    print(wrd)\n",
    "    for char in wrd:\n",
    "        print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "char\t data\t horrible_list_name\t numpy\t row0\t word\t words\t wrd\t \n"
     ]
    }
   ],
   "source": [
    "who"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable             Type       Data/Info\n",
      "-----------------------------------------\n",
      "char                 str        n\n",
      "data                 ndarray    60x40: 2400 elems, type `float64`, 19200 bytes\n",
      "horrible_list_name   list       n=1\n",
      "numpy                module     <module 'numpy' from 'c:\\<...>ges\\\\numpy\\\\__init__.py'>\n",
      "row0                 ndarray    40: 40 elems, type `float64`, 320 bytes\n",
      "word                 str        oxygen\n",
      "words                list       n=2\n",
      "wrd                  str        nitrogen\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Recap on conditionals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num = 0.5238239844329"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 is positive\n"
     ]
    }
   ],
   "source": [
    "if num > 0:\n",
    "    print(\"%d is positive\" % num)\n",
    "elif num == 0:\n",
    "    print(\"%.2f is zero\" % num)\n",
    "else:\n",
    "    print(num, \"is negative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap on functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_num(num):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print_num(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Default arguments:\n",
    "def print_num(num1, num2=6, num3=17):\n",
    "    print('num1:', num1, 'num2:', num2, 'num3:', num3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num1: 1 num2: 2 num3: 17\n"
     ]
    }
   ],
   "source": [
    "print_num(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num1: 1 num2: 6 num3: 17\n"
     ]
    }
   ],
   "source": [
    "print_num(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num1: 1 num2: 6 num3: 22\n"
     ]
    }
   ],
   "source": [
    "print_num(1, num3=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahr_to_kelvin(t_fahr):\n",
    "    t_kelv = (t_fahr - 32.0) * (5/9) + 273.15\n",
    "    return t_kelv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255.3722222222222"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_kelvin(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.15"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Better example\n",
    "fahr_to_kelvin(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kelvin_to_celsius(kelv):\n",
    "    return kelv - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The naive way:\n",
    "def fahr_to_celsius(t_fahr):\n",
    "    t_cels = (t_fahr - 32.0) * (5/9)\n",
    "    return t_cels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 C\n"
     ]
    }
   ],
   "source": [
    "# More meaningful print:\n",
    "print(fahr_to_celsius(32), 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Better: Reuse code\n",
    "def fahr_to_celsius_v2(t_fahr):\n",
    "    t_kelv = fahr_to_kelvin(t_fahr)\n",
    "    t_cels = kelvin_to_celsius(t_kelv)\n",
    "    return t_cels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 C\n"
     ]
    }
   ],
   "source": [
    "print(fahr_to_celsius_v2(32), 'C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing test functions\n",
    "\n",
    "Use assertion statements:\n",
    "\n",
    "    assert condition, msg\n",
    "\n",
    "`condition` has to evaluate to True or False:\n",
    "- If condition is True, nothing happens\n",
    "- If condition is False, throws error message `msg`\n",
    "\n",
    "`condition` could be `a > 3`, `func(x) == 7`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "-3 is not greater than 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-a93728b77f3c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-3 is not greater than 0'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: -3 is not greater than 0"
     ]
    }
   ],
   "source": [
    "assert -3 > 0, '-3 is not greater than 0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_fahr_to_celsius_v2():\n",
    "    assert fahr_to_celsius_v2(32.0) == 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# No output == every assert passed == everything good\n",
    "test_fahr_to_celsius_v2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defensive programming\n",
    "\n",
    "Usual approach:\n",
    "1. Write function\n",
    "2. Write test function\n",
    "\n",
    "Defensive programming\n",
    "1. Write test function\n",
    "2. Write function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thus write test function first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# A dummy function\n",
    "def range_overlap(ranges):\n",
    "    \"\"\"Returns the common overlap among a set of (low, high) ranges\"\"\"\n",
    "    return 0\n",
    "\n",
    "def test_range_overlap():\n",
    "    assert range_overlap([(0.0, 1.0)]) == (0.0, 1.0)\n",
    "    assert range_overlap([(0.0, 10.0), (2.0, 3.0)]) == (2.0, 3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-cf9215c96457>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_range_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-39-365d16466402>\u001b[0m in \u001b[0;36mtest_range_overlap\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtest_range_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "test_range_overlap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "def range_overlap(ranges):\n",
    "    \"\"\"Finds the common overlap among a set of (low, high) ranges\n",
    "    \n",
    "    This is a longer description of the function etc.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ranges : list\n",
    "        List of ranges with (low, high) bounds\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Returns the common overlap etc.\n",
    "    \n",
    "    \"\"\"\n",
    "    # Initialize lower/upper bounds\n",
    "    highest_of_lower_bounds = -numpy.inf\n",
    "    lowest_of_higher_bounds = numpy.inf\n",
    "    \n",
    "    for (low, high) in ranges:\n",
    "        # Iteratively find the max of all lower bounds\n",
    "        highest_of_lower_bounds = max(low, highest_of_lower_bounds)\n",
    "        \n",
    "        # Iteratively find the min of all upper bounds\n",
    "        lowest_of_higher_bounds = min(high, lowest_of_higher_bounds)\n",
    "        \n",
    "    return (highest_of_lower_bounds, lowest_of_higher_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_range_overlap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test the example from the lecture notes\n",
    "r1 = (-3.0, 5.0)\n",
    "r2 = (0.0, 4.5)\n",
    "r3 = (-1.5, 2)\n",
    "ranges = [r1, r2, r3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_overlap(ranges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with edge cases, improving the tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_range_overlap():\n",
    "    assert range_overlap([(0.0, 1.0)]) == (0.0, 1.0)\n",
    "    assert range_overlap([(0.0, 10.0), (2.0, 3.0)]) == (2.0, 3.0)\n",
    "    assert range_overlap([(1.0, 0.0)]) == None\n",
    "    assert range_overlap([(0.0, 1.0), (2.0, 10.0)]) == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-f2236e633213>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Edge cases break the function:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_range_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-45-395cbf84d0a0>\u001b[0m in \u001b[0;36mtest_range_overlap\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Edge cases break the function:\n",
    "test_range_overlap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to fix?\n",
    "# Investigate what happens for nonsensical input, then fix the function:\n",
    "range_overlap([(1.0, 0.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "def range_overlap(ranges):\n",
    "    \"\"\"Finds the common overlap among a set of (low, high) ranges\n",
    "    \n",
    "    This is a longer description of hte function.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ranges : list\n",
    "        List of ranges with (low, high) bounds\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Returns the common overlap ....\n",
    "    \n",
    "    \"\"\"\n",
    "    # Initialize lower/upper bounds\n",
    "    highest_of_lower_bounds = -numpy.inf\n",
    "    lowest_of_higher_bounds = numpy.inf\n",
    "    \n",
    "    for (low, high) in ranges:\n",
    "        if low >= high:\n",
    "            return None\n",
    "            #raise ValueError('low must be lower than high')\n",
    "\n",
    "        # Iteratively find the max of all lower bounds\n",
    "        highest_of_lower_bounds = max(low, highest_of_lower_bounds)\n",
    "        \n",
    "        # Iteratively find the min of all upper bounds\n",
    "        lowest_of_higher_bounds = min(high, lowest_of_higher_bounds)\n",
    "        \n",
    "    if highest_of_lower_bounds > lowest_of_higher_bounds:\n",
    "        return None\n",
    "        \n",
    "    return (highest_of_lower_bounds, lowest_of_higher_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Now the errors are gone: no output\n",
    "test_range_overlap()"
   ]
  }
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