Graficar_vectores.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Graficar vectores.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP/cgXiK2p6FrT7T6Y782dr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mgobeaalcoba/Deepnote_Repository/blob/main/Graficar_vectores.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_dODD6XX2-a"
      },
      "source": [
        "Queremos estudiar las combinaciones lineales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4JiNtWCX8lx"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0tLBzRQYZei"
      },
      "source": [
        "v1 = np.array([2,5])\n",
        "v2 = np.array([3,2])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_QsqIxmYiIH",
        "outputId": "06ea1221-396c-4bc1-cf88-e6d531edfcb5"
      },
      "source": [
        "% run \".\\\\funciones_auxiliares\\graficarVectores.ipynb\"\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:File `'.\\\\funciones_auxiliares\\\\graficarVectores.ipynb.py'` not found.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "yD77iEx2acQM",
        "outputId": "03e14503-9c4c-46d5-a262-9faad4ae83a8"
      },
      "source": [
        "graficarVectores([v1,v2],[\"orange\",\"blue\"])\n",
        "plt.xlim(-1,8)\n",
        "plt.ylim(-1,8)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 8.0)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}