post-analysis/thrustAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "INITIAL_PROPELANT_WEIGHT = 0.30  #Kg\n",
    "SAMPLE_RATE = 80\n",
    "time_constant = 1/SAMPLE_RATE\n",
    "dataFile = 'isiolo_practical.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dataFile, newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    data = list(reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(data)\n",
    "a = a.astype(float)\n",
    "filtered = a[np.where(a > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.      ,   0.354141,   0.993753,   1.222326,   3.528657,\n",
       "         4.902057,   4.465512,   3.957354,   3.912228,   6.550137,\n",
       "         8.374797,   8.963397,  10.145502,   9.577503,  10.546731,\n",
       "        13.087521,  14.178393,  18.45261 ,  21.375009,  26.391843,\n",
       "        30.887766,  35.39448 ,  42.626412,  53.593011,  63.854271,\n",
       "        71.296137,  82.534473,  90.873954,  98.800434, 105.315255,\n",
       "       108.834102, 112.248963, 116.657577, 119.065932, 121.974597,\n",
       "       126.593145, 129.422349, 132.263325, 136.239318, 140.193729,\n",
       "       143.852859, 145.683405, 149.475951, 152.564139, 155.612106,\n",
       "       158.670864, 163.275678, 167.783373, 171.904554, 181.572309,\n",
       "       189.227052, 193.31586 , 199.432395, 203.903793, 208.444842,\n",
       "       212.74947 , 214.275906, 216.314424, 218.189115, 221.98068 ,\n",
       "       224.773587, 226.037115, 228.226707, 229.197897, 233.449551,\n",
       "       234.61596 , 238.592934, 240.226299, 239.083434, 240.582402,\n",
       "       242.416872, 245.530566, 247.861422, 246.997161, 249.666462,\n",
       "       250.295283, 251.70498 , 254.071152, 254.846142, 257.310414,\n",
       "       257.050449, 257.661612, 258.475842, 259.733484, 259.364628,\n",
       "       260.97543 , 262.73142 , 263.582928, 264.865095, 264.532536,\n",
       "       264.441303, 265.859829, 265.955967, 265.992264, 267.10668 ,\n",
       "       267.600123, 269.069661, 269.144217, 267.929739, 270.10854 ,\n",
       "       271.723266, 269.462061, 270.604926, 270.987516, 272.108799,\n",
       "       273.499857, 273.516534, 276.101469, 274.703544, 275.011578,\n",
       "       275.842485, 275.174424, 274.119849, 272.714076, 274.695696,\n",
       "       273.707829, 271.119951, 271.205298, 270.831537, 271.584945,\n",
       "       270.458757, 270.197811, 269.975124, 269.597439, 268.753779,\n",
       "       269.145198, 268.464384, 269.560161, 268.382961, 267.073326,\n",
       "       265.495878, 264.800349, 266.501403, 266.344443, 263.600586,\n",
       "       263.735964, 263.461284, 261.88776 , 260.71056 , 259.595163,\n",
       "       257.383989, 255.175758, 252.876294, 249.902883, 249.514407,\n",
       "       247.149216, 244.909593, 244.162071, 240.045795, 238.472271,\n",
       "       234.562986,   0.      ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = np.append(filtered, 0)\n",
    "filtered = np.insert(filtered, 0, 0)\n",
    "filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtered = np.delete(filtered, [1,2,3,4,10])\n",
    "burnTime = filtered.size * time_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0, burnTime, filtered.size)\n",
    "thrust = filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(t, thrust)\n",
    "fig.suptitle('Thrust against time')\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('thrust (N)')\n",
    "fig.savefig('graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#total impulse\n",
    "#TODO --> Use trapezoidal rule for getting the total impulse\n",
    "totalImpulse = np.sum(thrust) * time_constant\n",
    "#specific impulse\n",
    "specificImpulse = totalImpulse / INITIAL_PROPELANT_WEIGHT\n",
    "#average thrust\n",
    "avgThrust = totalImpulse / burnTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def motorClass(totalImpulse):\n",
    "    if totalImpulse > 5120:\n",
    "        return 'M'\n",
    "    elif totalImpulse > 2560:\n",
    "        return 'L'\n",
    "    elif totalImpulse > 1280:\n",
    "        return 'K'\n",
    "    elif totalImpulse > 640:\n",
    "        return 'J'\n",
    "    elif totalImpulse > 320:\n",
    "        return 'I'\n",
    "    elif totalImpulse > 160:\n",
    "        return 'H'\n",
    "    elif totalImpulse > 80:\n",
    "        return 'G'\n",
    "    elif totalImpulse > 40:\n",
    "        return 'F'\n",
    "    elif totalImpulse > 20:\n",
    "        return 'E'\n",
    "    elif totalImpulse > 10:\n",
    "        return 'D'\n",
    "    elif totalImpulse > 5:\n",
    "        return 'C'\n",
    "    elif totalImpulse > 2.5:\n",
    "        return 'B'\n",
    "    elif totalImpulse > 1.25:\n",
    "        return 'A'\n",
    "    elif totalImpulse > 0.625:\n",
    "        return '1/2A'\n",
    "    elif totalImpulse > 0.3126:\n",
    "        return '1/4A'\n",
    "    else:\n",
    "        return 'micro'\n",
    "    \n",
    "Class = motorClass(totalImpulse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Impulse:\t\t367.20\tN-sec \n",
      "Specific Impulse:\t1224.01\tN-sec/Kg \n",
      "Average Thrust:\t\t193.26\tN\n",
      "Motor Class:\t\tI\n"
     ]
    }
   ],
   "source": [
    "st = \"Total Impulse:\\t\\t{:.2f}\\tN-sec \\nSpecific Impulse:\\t{:.2f}\\tN-sec/Kg \\nAverage Thrust:\\t\\t{:.2f}\\tN\"\n",
    "print(st.format(totalImpulse, specificImpulse, avgThrust))\n",
    "print(\"Motor Class:\\t\\t{}\".format(Class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate Rasp File\n",
    "filename = input(\"Enter rasp file name\")\n",
    "filename = filename + \".eng\"\n",
    "f = open(filename, \"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "motorName = Class + str(int(avgThrust))\n",
    "diameter = input(\"Enter Motor Diameter (mm): \")\n",
    "Length = input(\"Input motor Length (mm): \")\n",
    "delays = input(\"Input motor delays separated by dashes or P(plugged) for none: \")\n",
    "pWeight = input(\"Propellant Weight (Kg): \")\n",
    "totalWeight = input(\"Motor Total Weight (Kg): \")\n",
    "manufucturer = \"NKJ\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write rasp file header\n",
    "f.write(\"; NakujaProject\" + motorName + \"\\n\")\n",
    "f.write(\"; from \" + dataFile + \"\\n\")\n",
    "f.write(\"{} {} {} {} {} {} {} \\n\".format(motorName, diameter, Length, delays, pWeight, totalWeight, manufucturer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "thrustATime = np.array([t, thrust]).T\n",
    "np.savetxt(f, thrustATime, delimiter=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "df95319d8ce4e1d89f5365ae10992bc1f65da593082b1d264e8f529830ec2f02"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}