Aula_20220309.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b3c1a6ea",
   "metadata": {},
   "source": [
    "# Aula de 09 de março de 2022\n",
    "\n",
    "## Funcionamento do Jupyter Notebook\n",
    "\n",
    "A imagem abaixo foi buscada da pasta ``images``:\n",
    "\n",
    "<img src=\"images/heaviside.png\" alt=\"Heaviside\" width=\"240px\"/>\n",
    "\n",
    "Sabendo-se que $a = b^2$, pode-se concluir que:\n",
    "\n",
    "$$ c = \\int_0^L {x^2 \\; dx = \\frac{L^3}{3} }$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31e988bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2], [3, 4]] <class 'list'>\n",
      "[[5, 6], [7, 8]] <class 'list'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3492/1168541710.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'list'"
     ]
    }
   ],
   "source": [
    "a = [[1, 2],[3, 4]] \n",
    "b = [[5, 6],[7, 8]] \n",
    "\n",
    "print(a, type(a))\n",
    "print(b, type(b))\n",
    "\n",
    "print(a*b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab30a5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]] <class 'numpy.ndarray'>\n",
      "[[5 6]\n",
      " [7 8]] <class 'numpy.ndarray'>\n",
      "[[ 5 12]\n",
      " [21 32]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array(a)\n",
    "b = np.array(b)\n",
    "\n",
    "print(a, type(a))\n",
    "print(b, type(b))\n",
    "\n",
    "print(a*b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2d031d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "palavra <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "a = 'palavra'\n",
    "\n",
    "print(a, type(a))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf052e9",
   "metadata": {},
   "source": [
    "Abaixo temos um gráfico de uma senoide:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a8333c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'velocidade (mm/s)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.grid(True)\n",
    "plt.xlabel('tempo (s)')\n",
    "plt.ylabel('velocidade (mm/s)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c44dc1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.06346652 0.12693304 0.19039955 0.25386607 0.31733259\n",
      " 0.38079911 0.44426563 0.50773215 0.57119866 0.63466518 0.6981317\n",
      " 0.76159822 0.82506474 0.88853126 0.95199777 1.01546429 1.07893081\n",
      " 1.14239733 1.20586385 1.26933037 1.33279688 1.3962634  1.45972992\n",
      " 1.52319644 1.58666296 1.65012947 1.71359599 1.77706251 1.84052903\n",
      " 1.90399555 1.96746207 2.03092858 2.0943951  2.15786162 2.22132814\n",
      " 2.28479466 2.34826118 2.41172769 2.47519421 2.53866073 2.60212725\n",
      " 2.66559377 2.72906028 2.7925268  2.85599332 2.91945984 2.98292636\n",
      " 3.04639288 3.10985939 3.17332591 3.23679243 3.30025895 3.36372547\n",
      " 3.42719199 3.4906585  3.55412502 3.61759154 3.68105806 3.74452458\n",
      " 3.8079911  3.87145761 3.93492413 3.99839065 4.06185717 4.12532369\n",
      " 4.1887902  4.25225672 4.31572324 4.37918976 4.44265628 4.5061228\n",
      " 4.56958931 4.63305583 4.69652235 4.75998887 4.82345539 4.88692191\n",
      " 4.95038842 5.01385494 5.07732146 5.14078798 5.2042545  5.26772102\n",
      " 5.33118753 5.39465405 5.45812057 5.52158709 5.58505361 5.64852012\n",
      " 5.71198664 5.77545316 5.83891968 5.9023862  5.96585272 6.02931923\n",
      " 6.09278575 6.15625227 6.21971879 6.28318531]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179bac54",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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