TriQ_Q_3_Incomplete.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QCWinter_Q3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "_DI9Q3YoSW0r",
        "outputId": "f6877973-8e45-4664-fea1-d652bb53d4ff"
      },
      "source": [
        "#L1_Norm_CPU\r\n",
        "%time\r\n",
        "\r\n",
        "import matplotlib.pyplot as plt \r\n",
        "import numpy as np\r\n",
        "from timeit import default_timer as timer\r\n",
        "from datetime import timedelta\r\n",
        "from __future__ import division\r\n",
        "from scipy import *\r\n",
        "from mpl_toolkits.mplot3d import Axes3D  # 3d images\r\n",
        "from matplotlib import cm                # colourmap\r\n",
        "from scipy import linalg  \r\n",
        "\r\n",
        "u_an = np.ones(41) \r\n",
        "\r\n",
        "u= np.array([ 1.00000e+00, -4.09500e+03, -2.81500e+03,  2.79050e+04, -4.73500e+03,\r\n",
        " -8.95990e+04,  3.28010e+04 ,1.79681e+05, -6.20590e+04, -2.47359e+05,\r\n",
        "  5.19940e+04,  2.37294e+05 ,-4.44600e+03, -1.51326e+05, -2.89260e+04,\r\n",
        "  5.59380e+04,  2.32980e+04 ,-7.42200e+03, -6.14200e+03, -1.02200e+03,\r\n",
        "  2.00000e+00,  2.00000e+00 ,2.00000e+00,  2.00000e+00,  2.00000e+00,\r\n",
        "  2.00000e+00,  2.00000e+00 ,2.00000e+00,  2.00000e+00,  2.00000e+00,\r\n",
        "  2.00000e+00,  2.00000e+00 ,2.00000e+00,  2.00000e+00,  2.00000e+00,\r\n",
        "  2.00000e+00,  2.00000e+00 ,2.00000e+00,  2.00000e+00,  2.00000e+00,\r\n",
        "  2.00000e+00,]) #Sample U from broken Q_\r\n",
        "\r\n",
        "dx = 2 / (41-1)    #numpy function ones()\r\n",
        "u_an0=np.ones(41)\r\n",
        "u_an0[int(0.5 / dx):] = 2\r\n",
        "x = np.linspace(0,2,41)\r\n",
        "t=10\r\n",
        "\r\n",
        "for time in range(10):\r\n",
        "  for i in range(1,41):\r\n",
        "    u_an[i]=u_an0[i]*(x[i]-time)\r\n",
        "print(u_an)\r\n",
        "\r\n",
        "plt.subplot(122)\r\n",
        "plt.plot(x,u_an)\r\n",
        "plt.xlabel('$x$')\r\n",
        "plt.ylabel('$u$')\r\n",
        "plt.title('$Analytical test$')\r\n",
        "plt.rcParams['font.size'] = 15\r\n",
        "\r\n",
        "#L1_Norm\r\n",
        "l1 = np.abs(np.subtract(u,u_an))\r\n",
        "print(l1)\r\n",
        "\r\n",
        "plt.subplot(122)\r\n",
        "plt.plot(x,l1)\r\n",
        "plt.xlabel('$x$')\r\n",
        "plt.ylabel('$l1$')\r\n",
        "plt.title('$L1\\_norm$')\r\n",
        "plt.rcParams['font.size'] = 15"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n",
            "Wall time: 4.77 µs\n",
            "[  1.    -8.95  -8.9   -8.85  -8.8   -8.75  -8.7   -8.65  -8.6   -8.55\n",
            " -17.   -16.9  -16.8  -16.7  -16.6  -16.5  -16.4  -16.3  -16.2  -16.1\n",
            " -16.   -15.9  -15.8  -15.7  -15.6  -15.5  -15.4  -15.3  -15.2  -15.1\n",
            " -15.   -14.9  -14.8  -14.7  -14.6  -14.5  -14.4  -14.3  -14.2  -14.1\n",
            " -14.  ]\n",
            "[0.0000000e+00 4.0860500e+03 2.8061000e+03 2.7913850e+04 4.7262000e+03\n",
            " 8.9590250e+04 3.2809700e+04 1.7968965e+05 6.2050400e+04 2.4735045e+05\n",
            " 5.2011000e+04 2.3731090e+05 4.4292000e+03 1.5130930e+05 2.8909400e+04\n",
            " 5.5954500e+04 2.3314400e+04 7.4057000e+03 6.1258000e+03 1.0059000e+03\n",
            " 1.8000000e+01 1.7900000e+01 1.7800000e+01 1.7700000e+01 1.7600000e+01\n",
            " 1.7500000e+01 1.7400000e+01 1.7300000e+01 1.7200000e+01 1.7100000e+01\n",
            " 1.7000000e+01 1.6900000e+01 1.6800000e+01 1.6700000e+01 1.6600000e+01\n",
            " 1.6500000e+01 1.6400000e+01 1.6300000e+01 1.6200000e+01 1.6100000e+01\n",
            " 1.6000000e+01]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K51kLh40Sbcr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}