{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "..  ..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...    ...    ...    ...   \n",
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      "120  0    0    0    0    0    0    0    0    0    0  ...      0      0      1   \n",
      "121  0    0    0    0    0    0    0    0    0    0  ...      0      0      0   \n",
      "122  0    0    0    0    0    0    0    0    0    0  ...      0      0      0   \n",
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      "\n",
      "     0.118  0.119  0.120  0.121  0.122  0.123  0.124  \n",
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      "2        0      0      0      0      0      0      0  \n",
      "3        0      0      0      0      0      0      0  \n",
      "4        0      0      0      0      0      0      0  \n",
      "..     ...    ...    ...    ...    ...    ...    ...  \n",
      "119      0      0      0      0      0      0      0  \n",
      "120      0      0      0      0      0      0      0  \n",
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      "123      0      0      0      0      0      0      0  \n",
      "\n",
      "[124 rows x 125 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel (r'C:\\Users\\vicen\\Desktop\\UDEM\\PEF\\Matrices\\Bij_Centralizado.xlsx')\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "     0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  ...  0.115  0.116  0.117  \\\n",
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       "\n",
       "     0.118  0.119  0.120  0.121  0.122  0.123  0.124  \n",
       "0        0      0      0      0      0      0      0  \n",
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       "..     ...    ...    ...    ...    ...    ...    ...  \n",
       "119      0      0      0      0      0      0      0  \n",
       "120      0      0      0      0      0      0      0  \n",
       "121      0      0      0      0      0      0      0  \n",
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       "123      0      0      0      0      0      0      0  \n",
       "\n",
       "[124 rows x 125 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cij=100*df\n",
    "Cij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-10-87e74db16df6>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-10-87e74db16df6>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    CV = [2; 3; 4]\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "CV = [2; 3; 4]\n",
    "Cij= CV * df\n",
    "Cij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "d = np.arange(10)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=d*10\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 2 2 2 1 2 2 1 1 1 1 2 1 1 2 2 2 1 2 1 2 1 1 2 2 1 2 1 1 2 2 1 2 1 1 1\n",
      " 1 2 1 1 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 1 2 1 1 1 2 2\n",
      " 2 2 1 2 2 1 1 1 1 2 1 1 2 2 1 2 1 2 2 1 2 2 1 1 1 1 2 1 2 1 1 1 1 2 2 2 1\n",
      " 1 2 2 2 2 2 2 2 2 1 2 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "n = np.random.randint(1,3,125)\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[1, 2, 3],\n",
      "       [4, 5, 6]]), array([[ 2,  4,  6],\n",
      "       [ 8, 10, 12]])]\n"
     ]
    }
   ],
   "source": [
    "a = [[1,2,3],\n",
    "     [4,5,6]]\n",
    "cv =[1,2,3]\n",
    "cij=[]\n",
    "for i in range(0, 2):\n",
    "    ci = np.dot(a,cv[i])\n",
    "    cij.append(ci)\n",
    "\n",
    "print(cij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  4,  6],\n",
       "       [ 8, 10, 12]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "di=[]\n",
    "for i in range(0, 125):\n",
    "    ci = np.dot(a,cv[i])\n",
    "    cij.append(ci)\n",
    "\n",
    "print(cij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_i = [4,0,2,1,5,3,6,0,1,0,0,0]\n",
    "Vc = []\n",
    "for i,d in enumerate(d_i):\n",
    "    if d>0:\n",
    "        Vc.append(i)\n",
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_kij = pd.read_excel (r'C:\\Users\\vicen\\Desktop\\UDEM\\PEF\\Matrices\\R-kij.xlsx')\n",
    "type(r_kij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-97-c5989f011314>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-97-c5989f011314>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    for i in range(len(speeds))\u001b[0m\n\u001b[1;37m                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "k = [5,5,5,5,5,4]\n",
    "speeds = [15,20,25]\n",
    "\n",
    "for i in range(len(speeds))\n",
    "    sk = speeds[i] * k\n",
    "sk[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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