3.wed/Wednesday Exercise Solutions.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:7b78bd3e9a73d4412d3d12391709a8df122ac9bec156e889cfcfb9c4f016a618"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "10. Introduction to Numpy"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "np.logspace(-20, -10, 11)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.repeat(2, 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.empty(10)  # sometimes returns values != 0 because uninitialized"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.zeros(5, dtype=np.float32)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.random(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[1:] - x[:-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.diff(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = \"data/munich_temperatures_average_with_bad_data.txt\"\n",
      "date, temperature = np.loadtxt(filename, unpack=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keep = np.abs(temperature) < 50"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date_good = date[keep]\n",
      "temperature_good = temperature[keep]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "11. Introduction to Matplotlib"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The following code reads in the file and removes bad values\n",
      "date, temperature = np.loadtxt('data/munich_temperatures_average_with_bad_data.txt', unpack=True)\n",
      "keep = np.abs(temperature) < 90\n",
      "date = date[keep]\n",
      "temperature = temperature[keep]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(date, temperature, '.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(date % 1, temperature, '.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gaussian(x, amplitude, offset, sigma):\n",
      "    return 1. / np.sqrt(2 * np.pi) / sigma * np.exp(-0.5 * (x - offset) ** 2 / sigma ** 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = np.random.normal(0, 1, 100000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = plt.hist(values, bins=100, normed=True)\n",
      "x = np.linspace(-5, 5, 100)\n",
      "plt.plot(x, gaussian(x, 1, 0, 1), color='red', lw=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total = np.zeros(10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = np.random.random(10000)\n",
      "total = total + values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = plt.hist(total, bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = np.random.random(10000)\n",
      "total = total + values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = plt.hist(total / 2., bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for iter in range(8):  # already did it twice\n",
      "    values = np.random.random(10000)\n",
      "    total = total + values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = plt.hist(total / 10., bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "12. Files and paths"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import glob\n",
      "for filename in glob.glob('*'):\n",
      "    print(filename, os.path.getsize(filename) / 1024)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "13. String Formatting"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def to_string(year, day, month, hour, minute, second):\n",
      "    date = \"{0:04d}-{1:02d}-{2:02d}\".format(year, month, day)\n",
      "    time = \"{0:02d}:{1:02d}:{2:02d}\".format(hour, minute, second)\n",
      "    return \"{date} {time}\".format(date=date, time=time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_string(2006,3,22,13,12,55)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def to_values(string):\n",
      "    date, time = string.split()\n",
      "    year, month, day = date.split('-')\n",
      "    hour, minute, second = time.split(':')\n",
      "    return (int(year), int(month), int(day),\n",
      "            int(hour), int(minute), int(second))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_values('2006-22-03 13:12:55')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}