SHARE
TWEET

Untitled

a guest Aug 22nd, 2019 77 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  5.    "metadata": {},
  6.    "source": [
  7.     "Python Notebook 1: Reviewing Numpy (1D)"
  8.    ]
  9.   },
  10.   {
  11.    "cell_type": "code",
  12.    "execution_count": 25,
  13.    "metadata": {},
  14.    "outputs": [],
  15.    "source": [
  16.     "import numpy as np"
  17.    ]
  18.   },
  19.   {
  20.    "cell_type": "code",
  21.    "execution_count": 26,
  22.    "metadata": {},
  23.    "outputs": [],
  24.    "source": [
  25.     "a=np.array([1,2,3])"
  26.    ]
  27.   },
  28.   {
  29.    "cell_type": "code",
  30.    "execution_count": 27,
  31.    "metadata": {},
  32.    "outputs": [],
  33.    "source": [
  34.     "b=np.array([2,3,4])"
  35.    ]
  36.   },
  37.   {
  38.    "cell_type": "code",
  39.    "execution_count": 28,
  40.    "metadata": {},
  41.    "outputs": [
  42.     {
  43.      "data": {
  44.       "text/plain": [
  45.        "array([ 2,  3, -5])"
  46.       ]
  47.      },
  48.      "execution_count": 28,
  49.      "metadata": {},
  50.      "output_type": "execute_result"
  51.     }
  52.    ],
  53.    "source": [
  54.     "b[2]=-5\n",
  55.     "b"
  56.    ]
  57.   },
  58.   {
  59.    "cell_type": "code",
  60.    "execution_count": 29,
  61.    "metadata": {},
  62.    "outputs": [
  63.     {
  64.      "data": {
  65.       "text/plain": [
  66.        "0.0"
  67.       ]
  68.      },
  69.      "execution_count": 29,
  70.      "metadata": {},
  71.      "output_type": "execute_result"
  72.     }
  73.    ],
  74.    "source": [
  75.     "mean=b.mean()\n",
  76.     "mean"
  77.    ]
  78.   },
  79.   {
  80.    "cell_type": "code",
  81.    "execution_count": 30,
  82.    "metadata": {},
  83.    "outputs": [
  84.     {
  85.      "data": {
  86.       "text/plain": [
  87.        "3.559026084010437"
  88.       ]
  89.      },
  90.      "execution_count": 30,
  91.      "metadata": {},
  92.      "output_type": "execute_result"
  93.     }
  94.    ],
  95.    "source": [
  96.     "std=b.std()\n",
  97.     "std"
  98.    ]
  99.   },
  100.   {
  101.    "cell_type": "code",
  102.    "execution_count": 31,
  103.    "metadata": {},
  104.    "outputs": [
  105.     {
  106.      "data": {
  107.       "text/plain": [
  108.        "array([  2,   6, -15])"
  109.       ]
  110.      },
  111.      "execution_count": 31,
  112.      "metadata": {},
  113.      "output_type": "execute_result"
  114.     }
  115.    ],
  116.    "source": [
  117.     "a*b"
  118.    ]
  119.   },
  120.   {
  121.    "cell_type": "code",
  122.    "execution_count": 32,
  123.    "metadata": {},
  124.    "outputs": [
  125.     {
  126.      "data": {
  127.       "text/plain": [
  128.        "-7"
  129.       ]
  130.      },
  131.      "execution_count": 32,
  132.      "metadata": {},
  133.      "output_type": "execute_result"
  134.     }
  135.    ],
  136.    "source": [
  137.     "np.dot(a,b)"
  138.    ]
  139.   },
  140.   {
  141.    "cell_type": "markdown",
  142.    "metadata": {},
  143.    "source": [
  144.     "Plotting vectors is cool:"
  145.    ]
  146.   },
  147.   {
  148.    "cell_type": "code",
  149.    "execution_count": 33,
  150.    "metadata": {},
  151.    "outputs": [],
  152.    "source": [
  153.     "import matplotlib.pyplot as plt"
  154.    ]
  155.   },
  156.   {
  157.    "cell_type": "markdown",
  158.    "metadata": {},
  159.    "source": [
  160.     "got this function from IBM online course:"
  161.    ]
  162.   },
  163.   {
  164.    "cell_type": "code",
  165.    "execution_count": 34,
  166.    "metadata": {},
  167.    "outputs": [],
  168.    "source": [
  169.     "def Plotvec2(a,b):\n",
  170.     "    ax = plt.axes()\n",
  171.     "    ax.arrow(0, 0, *a, head_width=0.05, color ='r', head_length=0.1)\n",
  172.     "    plt.text(*(a + 0.1), 'a')\n",
  173.     "    ax.arrow(0, 0, *b, head_width=0.05, color ='b', head_length=0.1)\n",
  174.     "    plt.text(*(b + 0.1), 'b')\n",
  175.     "    plt.ylim(-2, 2)\n",
  176.     "    plt.xlim(-2, 2)"
  177.    ]
  178.   },
  179.   {
  180.    "cell_type": "code",
  181.    "execution_count": 35,
  182.    "metadata": {},
  183.    "outputs": [
  184.     {
  185.      "data": {
  186.       "image/png": "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\n",
  187.       "text/plain": [
  188.        "<Figure size 432x288 with 1 Axes>"
  189.       ]
  190.      },
  191.      "metadata": {
  192.       "needs_background": "light"
  193.      },
  194.      "output_type": "display_data"
  195.     }
  196.    ],
  197.    "source": [
  198.     "a=np.array([1,0])\n",
  199.     "b=np.array([0,1])\n",
  200.     "Plotvec2(a,b)"
  201.    ]
  202.   },
  203.   {
  204.    "cell_type": "markdown",
  205.    "metadata": {},
  206.    "source": [
  207.     "Of course we will see that the dot product of these two vectors is zero, as they are orthogonal:"
  208.    ]
  209.   },
  210.   {
  211.    "cell_type": "code",
  212.    "execution_count": 36,
  213.    "metadata": {},
  214.    "outputs": [
  215.     {
  216.      "data": {
  217.       "text/plain": [
  218.        "0"
  219.       ]
  220.      },
  221.      "execution_count": 36,
  222.      "metadata": {},
  223.      "output_type": "execute_result"
  224.     }
  225.    ],
  226.    "source": [
  227.     "np.dot(a,b)"
  228.    ]
  229.   },
  230.   {
  231.    "cell_type": "markdown",
  232.    "metadata": {},
  233.    "source": [
  234.     "Once final cool thing is generating an array using the np function \"linspace\""
  235.    ]
  236.   },
  237.   {
  238.    "cell_type": "code",
  239.    "execution_count": 39,
  240.    "metadata": {},
  241.    "outputs": [
  242.     {
  243.      "data": {
  244.       "text/plain": [
  245.        "array([11. , 12.5, 14. , 15.5, 17. ])"
  246.       ]
  247.      },
  248.      "execution_count": 39,
  249.      "metadata": {},
  250.      "output_type": "execute_result"
  251.     }
  252.    ],
  253.    "source": [
  254.     "c=np.linspace(11,17,5)\n",
  255.     "c"
  256.    ]
  257.   },
  258.   {
  259.    "cell_type": "code",
  260.    "execution_count": 40,
  261.    "metadata": {},
  262.    "outputs": [
  263.     {
  264.      "data": {
  265.       "text/plain": [
  266.        "numpy.ndarray"
  267.       ]
  268.      },
  269.      "execution_count": 40,
  270.      "metadata": {},
  271.      "output_type": "execute_result"
  272.     }
  273.    ],
  274.    "source": [
  275.     "type(c)"
  276.    ]
  277.   },
  278.   {
  279.    "cell_type": "code",
  280.    "execution_count": 41,
  281.    "metadata": {},
  282.    "outputs": [],
  283.    "source": [
  284.     "d=np.linspace(-5,5,200)"
  285.    ]
  286.   },
  287.   {
  288.    "cell_type": "code",
  289.    "execution_count": 43,
  290.    "metadata": {},
  291.    "outputs": [],
  292.    "source": [
  293.     "e=d**3"
  294.    ]
  295.   },
  296.   {
  297.    "cell_type": "code",
  298.    "execution_count": 44,
  299.    "metadata": {},
  300.    "outputs": [
  301.     {
  302.      "data": {
  303.       "text/plain": [
  304.        "[<matplotlib.lines.Line2D at 0x7f0741bbc5f8>]"
  305.       ]
  306.      },
  307.      "execution_count": 44,
  308.      "metadata": {},
  309.      "output_type": "execute_result"
  310.     },
  311.     {
  312.      "data": {
  313.       "image/png": "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\n",
  314.       "text/plain": [
  315.        "<Figure size 432x288 with 1 Axes>"
  316.       ]
  317.      },
  318.      "metadata": {
  319.       "needs_background": "light"
  320.      },
  321.      "output_type": "display_data"
  322.     }
  323.    ],
  324.    "source": [
  325.     "plt.plot(d,e)"
  326.    ]
  327.   },
  328.   {
  329.    "cell_type": "code",
  330.    "execution_count": null,
  331.    "metadata": {},
  332.    "outputs": [],
  333.    "source": []
  334.   }
  335.  ],
  336.  "metadata": {
  337.   "kernelspec": {
  338.    "display_name": "Python",
  339.    "language": "python",
  340.    "name": "conda-env-python-py"
  341.   },
  342.   "language_info": {
  343.    "codemirror_mode": {
  344.     "name": "ipython",
  345.     "version": 3
  346.    },
  347.    "file_extension": ".py",
  348.    "mimetype": "text/x-python",
  349.    "name": "python",
  350.    "nbconvert_exporter": "python",
  351.    "pygments_lexer": "ipython3",
  352.    "version": "3.6.7"
  353.   }
  354.  },
  355.  "nbformat": 4,
  356.  "nbformat_minor": 4
  357. }
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. OK, I Understand
 
Top