TestJob.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TestJob.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMwsPyi5zLoFGv/TeQSA4XP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HardRock4Life/snake/blob/master/TestJob.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRR22y8aHH_6"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5PxDsNdHuqm"
      },
      "source": [
        "Задание python\n",
        "\n",
        "1.  Написать функцию аппроксимации некоей математической функции, проходящей через координаты, заданных векторами:\n",
        "\t    X = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 66]\n",
        "        Y = [4.88, 4.73, 4.66, 4.61, 4.52, 4.35, 4.10, 3.74, 3.28, 2.73, 2.11, 1.45, 0.77, 0]\n",
        "\n",
        "\tФункция аппроксимации выбирается самостоятельно.\n",
        "\tПосле получения функции аппроксимации построить график, содержащий точки заданные векторами X, Y и линию функции аппроксимации.\n",
        "\n",
        "2.  Функцию расчета поместить в пакет approx в модуль approx_func.py, главный исполняемый скрипт main.py поместить на уровень выше.\n",
        "\tПокрыть тестом функцию расчета, поместить тест в файл test_func.py папки unittest.\n",
        "\t\n",
        "  Дерево папок\n",
        "  \n",
        "root\n",
        "\n",
        "|\n",
        "\n",
        "\n",
        "approx\n",
        "\n",
        "\t|\n",
        "\n",
        "\tapprox_func.py\n",
        "\n",
        "unittest\n",
        "\n",
        "\t|\n",
        "\t\n",
        "\ttest_func.py\n",
        "\n",
        "mail.py\n",
        "\n",
        "3. Код приложения выложить на GitLab/GitHub, выслать для ознакомления ссылку на репозитарий.\n",
        "\n",
        "4. Срок 3 дня."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSSw9wDtH9KB",
        "outputId": "93c6ed6a-45a1-4be2-8dc2-3fbce5385a23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "X = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 66]\n",
        "Y = [4.88, 4.73, 4.66, 4.61, 4.52, 4.35, 4.10, 3.74, 3.28, 2.73, 2.11, 1.45, 0.77, 0]\n",
        "\n",
        "plt.plot(X, Y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vbCBdl9IBHt",
        "outputId": "943dda03-ab51-4162-8183-5c158c9808af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "x=np.array([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 66], dtype=float)\n",
        "y=np.array([4.88, 4.73, 4.66, 4.61, 4.52, 4.35, 4.10, 3.74, 3.28, 2.73, 2.11, 1.45, 0.77, 0], dtype=float)\n",
        "\n",
        "def lagranz(x,y,t):\n",
        "    z=0\n",
        "    for j in range(len(y)):\n",
        "        p1=1; p2=1\n",
        "        for i in range(len(x)):\n",
        "            if i==j:\n",
        "                p1=p1*1; p2=p2*1   \n",
        "            else: \n",
        "                p1=p1*(t-x[i])\n",
        "                p2=p2*(x[j]-x[i])\n",
        "        z=z+y[j]*p1/p2\n",
        "    return z\n",
        "\n",
        "xnew=np.linspace(np.min(x),np.max(x), 15)\n",
        "ynew=[lagranz(x,y,i) for i in xnew]\n",
        "\n",
        "plt.plot(x,y,'o', xnew,ynew,'o')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkXVAX5CaV28"
      },
      "source": [
        "import unittest\n",
        "\n",
        "def add(x, y):\n",
        "  return x + y\n",
        "\n",
        "class Test(unittest.TestCase):\n",
        "  \n",
        "  def test_add(self):\n",
        "    result = add(10, 5)\n",
        "    self.assertEqual(result, 15)\n",
        "\n",
        "t = Test()\n",
        "t.test_add()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bcnJVTs250-"
      },
      "source": [
        "class "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}