q1.ipynb

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   "source": [
    "#Q1 ASSIGNMENT 3 SOLVING SYSTEM OF EQUATIONS USING GAUSS JORDAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f32e0bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import import_ipynb            #for import ipynb files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad5a4021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from mylibrary.ipynb\n"
     ]
    }
   ],
   "source": [
    "import mylibrary as ml         #importing library\n",
    "import numpy as np             #for loading file\n",
    "import math                    #floor and ceil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a8aacfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "The given equations are:\n",
      "\n",
      "\n",
      "1.0 x +  1.0 y +  1.0 z +  1.0 w =  13.0\n",
      "2.0 x +  3.0 y +  0.0 z +  -1.0 w =  -1.0\n",
      "-3.0 x +  4.0 y +  1.0 z +  2.0 w =  10.0\n",
      "1.0 x +  2.0 y +  -1.0 z +  1.0 w =  1.0\n",
      "\n",
      "\n",
      "The original augmented matrix is: \n",
      "\n",
      "[1.0 , 1.0 , 1.0 , 1.0 | 13.0  ]\n",
      "[2.0 , 3.0 , 0.0 , -1.0 | -1.0  ]\n",
      "[-3.0 , 4.0 , 1.0 , 2.0 | 10.0  ]\n",
      "[1.0 , 2.0 , -1.0 , 1.0 | 1.0  ]\n",
      "\n",
      "\n",
      "The final augmented matrix is: \n",
      "\n",
      "[1.0 , 0.0 , 0.0 , 0.0 | 2.0  ]\n",
      "[0.0 , 1.0 , 0.0 , 0.0 | 0.0  ]\n",
      "[0.0 , 0.0 , 1.0 , 0.0 | 5.99  ]\n",
      "[0.0 , 0.0 , 0.0 , 1.0 | 5.0  ]\n",
      "\n",
      "\n",
      "The solutions are:\n",
      "\n",
      "\n",
      "x  =  2.0\n",
      "y  =  0.0\n",
      "z  =  5.99\n",
      "w  =  5.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#import the matrix A and B in AX = B\n",
    "A = np.loadtxt('q1.csv', delimiter = ',')                    \n",
    "B = np.loadtxt('q1_1.csv', delimiter = ',')\n",
    "\n",
    "###############################################################\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "B is taken a simple list. We want it to be of the form\n",
    "[[],[],[],[]...] and not [...] because the latter is \n",
    "interpretted as a row matrix from my code. Hence we use\n",
    "the following function.\n",
    "'''\n",
    "B = ml.Matrix_operations.make_proper_column(B)\n",
    "\n",
    "##############################################################\n",
    "\n",
    "\n",
    "\n",
    "#augmenting the matrices and keeping the orginal in another matrix\n",
    "\n",
    "aug_mat_original = ml.Matrix_operations.augment_matrix(A, B)    \n",
    "aug_mat = ml.Matrix_operations.augment_matrix(A, B)             \n",
    "##############################################################\n",
    "\n",
    "\n",
    "\n",
    "#printing the matrices and equations\n",
    "\n",
    "ml.Printing.print_eq(aug_mat_original)\n",
    "print(\"The original augmented matrix is:\", \"\\n\")\n",
    "ml.Printing.print_aug_matrix(aug_mat_original)\n",
    "##############################################################\n",
    "\n",
    "\n",
    "\n",
    "#performing gauss jordan elimination\n",
    "\n",
    "ml.Gauss_Jordan.solve(aug_mat)\n",
    "##############################################################\n",
    "\n",
    "\n",
    "\n",
    "#printing the final results\n",
    "\n",
    "print(\"The final augmented matrix is:\", \"\\n\")\n",
    "ml.Printing.print_aug_matrix(aug_mat)\n",
    "\n",
    "ml.Printing.print_sol(aug_mat)\n",
    "##############################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5655aa48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b6ff41b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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