A01_Views_und_Kopien.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# A01 Views und Kopien\n",
      "Wann werden Daten kopiert? Wann werden ist es nur ein View?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(6)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([0, 1, 2, 3, 4, 5])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a is b  # es wird gar nichts kopiert, a und b zeigen auf das selbe Objekt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[0] = 99  # wird Daten in b ver\u00e4ndert, so \u00e4ndert sich auch a\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([99,  1,  2,  3,  4,  5])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.shape = 2, 3  # wird die Form von b ver\u00e4ndert, so \u00e4ndert sich auch a (gilt auch f\u00fcr alle anderen Attribute)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([[99,  1,  2],\n",
        "       [ 3,  4,  5]])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "auch beim Funktionsaufruf werden keine Daten kopiert (wie in Python bei anderer \"mutable\" Objekten (Listen, etc.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def foo(x):\n",
      "    x[-1, -1] = 99\n",
      "foo(a)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array([[99,  1,  2],\n",
        "       [ 3,  4, 99]])"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Views"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(6)\n",
      "c = a[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c is a  # c ist ein \"View\" auf Daten aus a (hier alle)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print id(c)   # (der Header) liegt an unterschiedlichen Stellen im Speicher\n",
      "print id(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "47013648\n",
        "41505936\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c[2] = 99  # auch hier: werden Daten in c ver\u00e4ndert, so \u00e4ndert sich auch a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([ 0,  1, 99,  3,  4,  5])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(10.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = a[:6]  # ein View auf einen Teil der Daten in a\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([ 0.,  1.,  2.,  3.,  4.,  5.])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.shape = (2, 3)\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([[ 0.,  1.,  2.],\n",
        "       [ 3.,  4.,  5.]])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a  # wird der Header von b ge\u00e4ndert, so \u00e4ndert sich a NICHT"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[1, 0] = 99\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([[  0.,   1.,   2.],\n",
        "       [ 99.,   4.,   5.]])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a  # werden Daten in b ge\u00e4ndert, so \u00e4ndert sich die entsprechenden Daten in auch in a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "array([  0.,   1.,   2.,  99.,   4.,   5.,   6.,   7.,   8.,   9.])"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.may_share_memory(a, b)  # gibt an ob a und b den gleichen Speicher benutzen *k\u00f6nnten*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## mit view() den Datentyp \u00e4ndern"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = a.view(dtype=np.int8)  # erzeugt einen View auf a mit Typ int8\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
        "         0, -16,  63,   0,   0,   0,   0,   0,   0,   0,  64,   0,   0,\n",
        "         0,   0,   0, -64,  88,  64,   0,   0,   0,   0,   0,   0,  16,\n",
        "        64,   0,   0,   0,   0,   0,   0,  20,  64,   0,   0,   0,   0,\n",
        "         0,   0,  24,  64,   0,   0,   0,   0,   0,   0,  28,  64,   0,\n",
        "         0,   0,   0,   0,   0,  32,  64,   0,   0,   0,   0,   0,   0,\n",
        "        34,  64], dtype=int8)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[1] = 2   #VORSICHT, es wird ein int8 in die von a als float interpretierten Daten geschrieben\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([  2.52961611e-321,   1.00000000e+000,   2.00000000e+000,\n",
        "         9.90000000e+001,   4.00000000e+000,   5.00000000e+000,\n",
        "         6.00000000e+000,   7.00000000e+000,   8.00000000e+000,\n",
        "         9.00000000e+000])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Kopien"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(6)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([0, 1, 2, 3, 4, 5])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.copy(a)  # es entsteht eine Kopie von a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a is b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a[0] = 99\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([0, 1, 2, 3, 4, 5])"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.may_share_memory(a, b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "<p align=\"center\">Tutorial: Einf\u00fchrung in NumPy von Henning Dickten und Christian Geier, PyCon.DE 2013</p>\n",
      "<p align=\"center\"><a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/3.0/deed.de\"><img alt=\"Creative Commons Lizenzvertrag\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png\"/></a></p>\n",
      "\n",
      "<p align=\"center\">Dieses Werk bzw. Inhalt ist lizenziert unter einer <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/3.0/deed.de\">Creative Commons Namensnennung - Nicht-kommerziell - Weitergabe unter gleichen Bedingungen 3.0 Unported Lizenz</a>.</p>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}