andrewxiechina · December 4, 2017 04:17
diff --git a/03.01a Simple Data.ipynb b/03.01a Simple Data.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# RUN ALL THE CODE BEFORE YOU START\n",
    "import numpy as np\n",
    "from matplotlib.pylab import plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {
    "download%20%281%29.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Plot a Simple Line\n",
    "Plot $y = x^2,  x \\in [-3, 3]$, the result should resumble the following:\n",
    "\n",
    "![download%20%281%29.png](attachment:download%20%281%29.png)\n",
    "\n",
    "**Hints **\n",
    "- `np.linspace(-3,3,100)` can be used to generate 100 numbers between -3 and 3.\n",
    "- `numpy` contains functions for all basic arithmetic operations, use `?command` to look for documentation, here are some examples: \n",
    "\n",
    "```\n",
    "np.add\n",
    "np.square\n",
    "np.power\n",
    "```\n",
    "- You can do scatter plot using the following code, note that you have to run the import code in cell 1 to make it work:\n",
    "\n",
    "```\n",
    "plt.plot(x, y)\n",
    "plt.show()\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.        , -2.93939394, -2.87878788, -2.81818182, -2.75757576,\n",
       "       -2.6969697 , -2.63636364, -2.57575758, -2.51515152, -2.45454545,\n",
       "       -2.39393939, -2.33333333, -2.27272727, -2.21212121, -2.15151515,\n",
       "       -2.09090909, -2.03030303, -1.96969697, -1.90909091, -1.84848485,\n",
       "       -1.78787879, -1.72727273, -1.66666667, -1.60606061, -1.54545455,\n",
       "       -1.48484848, -1.42424242, -1.36363636, -1.3030303 , -1.24242424,\n",
       "       -1.18181818, -1.12121212, -1.06060606, -1.        , -0.93939394,\n",
       "       -0.87878788, -0.81818182, -0.75757576, -0.6969697 , -0.63636364,\n",
       "       -0.57575758, -0.51515152, -0.45454545, -0.39393939, -0.33333333,\n",
       "       -0.27272727, -0.21212121, -0.15151515, -0.09090909, -0.03030303,\n",
       "        0.03030303,  0.09090909,  0.15151515,  0.21212121,  0.27272727,\n",
       "        0.33333333,  0.39393939,  0.45454545,  0.51515152,  0.57575758,\n",
       "        0.63636364,  0.6969697 ,  0.75757576,  0.81818182,  0.87878788,\n",
       "        0.93939394,  1.        ,  1.06060606,  1.12121212,  1.18181818,\n",
       "        1.24242424,  1.3030303 ,  1.36363636,  1.42424242,  1.48484848,\n",
       "        1.54545455,  1.60606061,  1.66666667,  1.72727273,  1.78787879,\n",
       "        1.84848485,  1.90909091,  1.96969697,  2.03030303,  2.09090909,\n",
       "        2.15151515,  2.21212121,  2.27272727,  2.33333333,  2.39393939,\n",
       "        2.45454545,  2.51515152,  2.57575758,  2.63636364,  2.6969697 ,\n",
       "        2.75757576,  2.81818182,  2.87878788,  2.93939394,  3.        ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.linspace(-3, 3, 100) # Create an array of 100 numbers between -3 and 3\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F/Tvx8hwpbXcnxS2Axh0F589JYsqATvz1y1Se+XafnKLjAux2zWPLUnlmxT6u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\nuBgpbiGEcDH/B6gjJZPrEVvvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114534320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = np.power(X, 2) # Calculate Y\n",
    "plt.plot(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "03.01.01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: Change Line Style\n",
    "\n",
    "Change the line style so the plot will look like this:\n",
    "\n",
    "![03.01.01.png](attachment:03.01.01.png)\n",
    "\n",
    "**Hints: **Passing style character to the `plt.plot` function will change the line style. For example, `g-` means green solid line. The detailed documentation for style character is summarized below:\n",
    "\n",
    "**Color Character**\n",
    "```\n",
    "==========  ========\n",
    "character   color\n",
    "==========  ========\n",
    "'b'         blue\n",
    "'g'         green\n",
    "'r'         red\n",
    "'c'         cyan\n",
    "'m'         magenta\n",
    "'y'         yellow\n",
    "'k'         black\n",
    "'w'         white\n",
    "==========  ========\n",
    "```\n",
    "\n",
    "**Line Style Character**\n",
    "```\n",
    "================    ===============================\n",
    "character           description\n",
    "================    ===============================\n",
    "``'-'``             solid line style\n",
    "``'--'``            dashed line style\n",
    "``'-.'``            dash-dot line style\n",
    "``':'``             dotted line style\n",
    "``'.'``             point marker\n",
    "``','``             pixel marker\n",
    "``'o'``             circle marker\n",
    "``'v'``             triangle_down marker\n",
    "``'^'``             triangle_up marker\n",
    "``'<'``             triangle_left marker\n",
    "``'>'``             triangle_right marker\n",
    "``'1'``             tri_down marker\n",
    "``'2'``             tri_up marker\n",
    "``'3'``             tri_left marker\n",
    "``'4'``             tri_right marker\n",
    "``'s'``             square marker\n",
    "``'p'``             pentagon marker\n",
    "``'*'``             star marker\n",
    "``'h'``             hexagon1 marker\n",
    "``'H'``             hexagon2 marker\n",
    "``'+'``             plus marker\n",
    "``'x'``             x marker\n",
    "``'D'``             diamond marker\n",
    "``'d'``             thin_diamond marker\n",
    "``'|'``             vline marker\n",
    "``'_'``             hline marker\n",
    "================    ===============================\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3339P69atadasGTt37gRQacuvqLilWC6//HKeeOIJ3n77ba688krNOimGN954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\nQUgoGtW+c6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184b3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, Y, 'k--')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "download.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3: Do Scatter Plot for Line with Error\n",
    "Plot $y = x^2 + \\epsilon,  x \\in [-3, 3]$, the result should resumble the following:\n",
    "![download.png](attachment:download.png)\n",
    "\n",
    "**Hints **\n",
    "- To introduce error, I use `np.random.normal` and a standard deviation of `0.4`.\n",
    "- You will need to change the style using style character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124d2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(-3, 3, 100)\n",
    "error = np.random.normal(0,0.4,100) # Make an array of 100 numbers, with mean = 0, std = 0.4\n",
    "Y = np.power(X, 2) + error\n",
    "plt.plot(X, Y, 'r.') # r. means red dots\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
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