demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook documents how to impletement the \"DataPublishTest\" package to generate and visualize data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def hist(path):\n",
    "    # path = path to file containing numbers\n",
    "    # this creates a histogram for those numbers\n",
    "    \n",
    "    f=open(path)\n",
    "    data=[]\n",
    "    for i in f.readlines():\n",
    "        i=i[:-1]\n",
    "        data.append(float(i))\n",
    "    f.close()\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist(data)\n",
    "    plt.title('Histogram')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first step, we will invoke the \"genRandom.py\" Python script to generate some random integers. Due to pre-specified/hard-coded parameters, 1000 random integers ranging from 0 to 100 inclusively will be generated. The integers will be saved in a text file called \"data_x.txt\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run genRandom.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below is showing the distribution of the random numbers by a hostrogram. The x-axis is showing the value of the numbers and the y-axis is showing their quantity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path='data_x.txt'\n",
    "hist(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the second step, we will invoke the \"LinMap.py\" Python script to linearly transform the integers we just created from the first step. To do so, the code reads the file containing the random integers that we just created and generates new numbers for all the original integers by using the equation: y=3x+6. In this euqation, y is the output and x represents a random integer from the first step. The numbers after the linear transform will be saved as a text file called \"data_y.txt\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run LinMap.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below is showing the distribution of the random numbers by a hostrogram after the linear transform. The x-axis is showing the value of the numbers and the y-axis is showing their quantity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path='data_y.txt'\n",
    "hist(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last step, we will invoke the \"Vis.py\" Python script to visualize the results from the previous steps. The code reads both the output files from the last two steps, so the code has the original data and and numbers after the linear transform. The code will make a scatter plot where the orinal integers are on the x-axis and the numbers after the linear after the linear transform are on the y-axis. The code will save the scatter plot as a file called \"Scatter_Plot.png\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run Vis.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.6rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}