pdf1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'programdata.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-4a7c6d92d75c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mrawData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'programdata.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0myData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrawData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-1-4a7c6d92d75c>\u001b[0m in \u001b[0;36mreadFile\u001b[0;34m(fileName)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mreadFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileName\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mfileObj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#opens the file in read mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfileObj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#puts the file into an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mfileObj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'programdata.txt'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def readFile(fileName):\n",
    "        fileObj = open(fileName, 'r') #opens the file in read mode\n",
    "        words = fileObj.read().splitlines() #puts the file into an array\n",
    "        fileObj.close()\n",
    "        return words\n",
    "    \n",
    "\n",
    "rawData = readFile('programdata_bin.txt')\n",
    "yData = rawData\n",
    "\n",
    "for count in range(10):\n",
    "    yData[count] = float(yData[count])\n",
    "\n",
    "yData = np.array(f)\n",
    "lenData = yData.size\n",
    "maxData = np.max(yData)\n",
    "minData = np.min(yData)\n",
    "\n",
    "yData = np.sort(yData)\n",
    "yProb = np.arange(0, lenData, 1)\n",
    "\n",
    "print (lenData)\n",
    "print (yData.size)\n",
    "print (yProb.size)\n",
    "\n",
    "p1, = plt.plot(yData, yProb, 'b', label='theoretical')\n",
    "p2, = plt.plot(yData, yProb, 'r+', label='numerical') #karsilastirirken oburunu ayni yap ama 'ro' de.\n",
    "plt.grid()\n",
    "plt.ylabel('Demon')\n",
    "plt.xlabel('time')\n",
    "#plt.xlim([0,lenData])\n",
    "#plt.ylim([minData, maxData])\n",
    "\n",
    "plt.legend(handles=[p1, p2], loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "40\n",
      "40\n"
     ]
    },
    {
     "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": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}