Coarse32x32PPE_Copy0_8_NonNewtUpdateMomentum.ipynb

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  "metadata": {
    "colab": {
      "name": "Coarse32x32PPE Copy0.8 NonNewtUpdateMomentum.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Geh_3E8iEQ3"
      },
      "source": [
        ""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RuQrw7vma3il"
      },
      "source": [
        "#!apt-get --purge remove cuda nvidia* libnvidia-*\n",
        "#!dpkg -l | grep cuda- | awk '{print $2}' | xargs -n1 dpkg --purge\n",
        "#!apt-get remove cuda-*\n",
        "#!apt autoremove\n",
        "#!apt-get update"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67zye6Oybjuj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "5adb7eb2-0789-415d-9bc6-5b397a838be1"
      },
      "source": [
        "\"\"\"!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb\n",
        "!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb\n",
        "!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub\n",
        "!apt-get update\n",
        "!apt-get install cuda-9.2\"\"\"\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb\\n!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb\\n!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub\\n!apt-get update\\n!apt-get install cuda-9.2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EM5_bYd7_OjS"
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt, cm\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from numba import cuda\n",
        "from numba import *\n",
        "from IPython.display import clear_output"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_FTArA3_OjV"
      },
      "source": [
        "def plot_contour(x, y, p, u, v):\n",
        "    fig = plt.figure(figsize = (11, 7), dpi = 100)\n",
        "    X, Y = np.meshgrid(x, y)\n",
        "    \n",
        "    #Velocity field\n",
        "    plt.quiver(X[::5, ::5], Y[::5, ::5], u[::5, ::5], v[::5, ::5])\n",
        "    \n",
        "    plt.xlabel('X')\n",
        "    plt.ylabel('Y');"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIjAWYOQ_OjY"
      },
      "source": [
        "@jit(parallel = True, cache = True)\n",
        "def buildB(b, rho, dt, u, v, dx, dy):\n",
        "    dy2 = dy**2\n",
        "    dx2 = dx**2\n",
        "    dx2y2 = dy2 * dx2 \n",
        "    sumdx2y2 = dy2 + dx2\n",
        "    b[1:-1,1:-1] = dx2y2 / (2 * (sumdx2y2)) * (rho * (1 / dt *\n",
        "                   ((u[1:-1,2:] - u[1:-1,0:-2]) / (2 * dx) +\n",
        "                    (v[2:,1:-1] - v[0:-2,1:-1]) / (2 * dy)) -\n",
        "                     ((u[1:-1,2:] - u[1:-1,0:-2]) / (2 * dx))**2 -\n",
        "                       2 * ((u[2:,1:-1] - u[0:-2,1:-1]) / (2 * dy) *\n",
        "                            (v[1:-1,2:] - v[1:-1,0:-2]) / (2 * dx)) -\n",
        "                          ((v[2:,1:-1] - v[0:-2,1:-1]) / (2 * dy))**2))\n",
        "    \n",
        "    #periodic BC pressure @x = 2\n",
        "    b[1:-1,-1] = dx2y2 / (2 * (sumdx2y2)) * (rho * (1 / dt *\n",
        "                   ((u[1:-1,0] - u[1:-1,-2]) / (2 * dx) +\n",
        "                    (v[2:,-1] - v[0:-2,-1]) / (2 * dy)) -\n",
        "                     ((u[1:-1,0] - u[1:-1,-2]) / (2 * dx))**2 -\n",
        "                       2 * ((u[2:,-1] - u[0:-2,-1]) / (2 * dy) *\n",
        "                            (v[1:-1,0] - v[1:-1,-2]) / (2 * dx)) -\n",
        "                          ((v[2:,-1] - v[0:-2,-1]) / (2 * dy))**2))\n",
        "    \n",
        "    # Periodic BC Pressure @ x = 0\n",
        "    b[1:-1,0] = dx2y2 / (2 * (sumdx2y2)) * (rho * (1 / dt *\n",
        "                   ((u[1:-1,1] - u[1:-1,-14]) / (2 * dx) +\n",
        "                    (v[2:,0] - v[0:-2,0]) / (2 * dy)) -\n",
        "                     ((u[1:-1,1] - u[1:-1,-1]) / (2 * dx))**2 -\n",
        "                       2 * ((u[2:,0] - u[0:-2,0]) / (2 * dy) *\n",
        "                            (v[1:-1,1] - v[1:-1,-1]) / (2 * dx)) -\n",
        "                          ((v[2:,0] - v[0:-2,0]) / (2 * dy))**2))\n",
        "    #print(b)\n",
        "    return b"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3pqBW9Y_Oja",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bc22869-d591-4694-e221-a2f46e4dcf5c"
      },
      "source": [
        "cuda.detect()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1 CUDA devices\n",
            "id 0             b'Tesla T4'                              [SUPPORTED]\n",
            "                      compute capability: 7.5\n",
            "                           pci device id: 4\n",
            "                              pci bus id: 0\n",
            "Summary:\n",
            "\t1/1 devices are supported\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJTAvkt8_Ojg"
      },
      "source": [
        "@jit(nopython = True)\n",
        "def gamma_cal(u, v, dx, dy):\n",
        "    S_11 = np.zeros_like((v))\n",
        "    S_12 = np.zeros_like((v))\n",
        "    S_22 = np.zeros_like((v))\n",
        "\n",
        "    # For b[1:-1, 1:-1] \n",
        "    S_11[1:-1, 1:-1]  = ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx))\n",
        "    S_12[1:-1, 1:-1] = (1/2)*(((u[2:, 1:-1] - u[0:-2, 1:-1]) / (2 * dy)) + ((v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dx)) )\n",
        "    S_22[1:-1, 1:-1]  = ((v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy))\n",
        "\n",
        "    ## Boundary conditions x = 2\n",
        "    S_11[1:-1,-1]  = ((u[1:-1,0] - u[1:-1,-2]) / (2 * dx))\n",
        "    S_12[1:-1, -1] = (1/2)*(((u[2:,-1] - u[0:-2,-1]) / (2 * dy)) + ((v[1:-1, 0] - v[1:-1, -2]) / (2 * dx)) )\n",
        "    S_22[1:-1, -1]  = ((v[2:, -1] - v[0:-2, -1]) / (2 * dy))\n",
        "    \n",
        "    #Boundary Conditions x = 0 \n",
        "    S_11[1:-1, 0]  = ((u[1:-1, 1] - u[1:-1, -1]) / (2 * dx))\n",
        "    S_12[1:-1, 0] = (1/2)*(((u[2:, 0] - u[0:-2, 0]) / (2 * dy)) + ((v[1:-1, 1] - v[1:-1, -1]) / (2 * dx)) )\n",
        "    S_22[1:-1, 0]  = ((v[2:, 0] - v[0:-2, 0]) / (2 * dy))\n",
        " \n",
        "    #y = 0\n",
        "    S_11[0,:] = 0 # no slip condition means u = 0 S_11[1,:]#\n",
        "    S_12[0,:] = ((u[1,:] - u[0,:]) / (dx))      \n",
        "    #S_12[1,:]# Forward Difference applied since there is no element before 0th element.\n",
        "    #V terms are removed since impermeable wall BC is used rendering V = 0 at the wall\n",
        "                                                \n",
        "    S_22[0,:] = ((v[1,:] - v[0,:]) / (dy))#S_22[1,:]#\n",
        "    \n",
        "    #y = 2\n",
        "    S_11[-1,:] = 0  # no slip condition means u = 0#S_11[-2,:]#\n",
        "    S_12[-1,:] = ((u[-1,:] - u[-2,:]) / (dx))   \n",
        "    ##S_12[-2,:]# Forward Difference applied since there is no element after -1st element.\n",
        "    #V terms are removed since impermeable wall BC is used rendering V = 0 at the wall\n",
        "                                                \n",
        "    S_22[-1,:] = ((v[-1,:] - v[-2,:]) / (dy)) #S_22[-2,:]#\n",
        "    \n",
        "    #print(S_11, S_22, S_12)\n",
        "    #gamma_dot =  9*np.sqrt(( np.square(S_11) + np.square(S_22) + 2*np.square(S_12)))\n",
        "    gamma_dot = np.sqrt(2*(np.square(S_11) + np.square(S_22) + 2*np.square(S_12)))\n",
        "    \n",
        "    #print(\"gamma_dot:\", gamma_dot)\n",
        "    return gamma_dot, S_11, S_12, S_22\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0cisRaw_Oji"
      },
      "source": [
        "@jit(nopython = True)\n",
        "def nufunc(nu_0, nu_inf, m, n, gamma):\n",
        "    #nu_0 = 0.001 #limiting range of kinematic viscosity at 0\n",
        "    #nu_inf = 0.0014 #limiting range of kinematic viscocity at infinity\n",
        "    #m = 10e-4 # Non-newtonion flow parameter\n",
        "    #n = 1.2\n",
        "    nu1 = np.empty_like((gamma))\n",
        "    #Truncated Power Law\n",
        "    # For certain value of n\n",
        "    h = gamma.shape[0]\n",
        "    w = gamma.shape[1]\n",
        "    \n",
        "    if n == 1:\n",
        "        nu1[:,:] = nu_inf\n",
        "        return nu1\n",
        "    gamma_0 = (nu_0/m)**(1/(n-1))\n",
        "    gamma_inf = ( nu_inf/m)**(1/(n-1))\n",
        "    for i in range(w):\n",
        "        for j in range(h):\n",
        "            if (gamma[j,i] < gamma_0):\n",
        "                nu1[j,i] = nu_0\n",
        "            elif ((gamma[j,i] > gamma_0) and (gamma_inf >= gamma[j,i])):\n",
        "                nu1[j,i] = m*(gamma[j,i])**(n - 1)\n",
        "            elif (gamma[j,i] > gamma_inf):\n",
        "                nu1[j,i] = nu_inf\n",
        "\n",
        "    #print( gamma_0)\n",
        "    #print( gamma_inf)\n",
        "    #print(gamma)\n",
        "    #nu1 = nu1*10\n",
        "    return nu1"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOoV32C5XOGi"
      },
      "source": [
        "@jit(nopython = True)\n",
        "def uv(u, v, p, nu1, dx, dy, dt, F, rho):\n",
        "  un = u.copy()\n",
        "  vn = v.copy()\n",
        "  u[1:-1, 1:-1] = (un[1:-1, 1:-1]-\n",
        "                 un[1:-1, 1:-1] * dt / dx *\n",
        "                 (un[1:-1, 1:-1] - un[1:-1, 0:-2]) -\n",
        "                 vn[1:-1, 1:-1] * dt / dy *\n",
        "                 (un[1:-1, 1:-1] - un[0:-2, 1:-1]) -\n",
        "                 dt / (2 * rho * dx) * (p[1:-1, 2:] - p[1:-1, 0:-2]) \n",
        "                 +(dt / (2 * dx**2) * (nu1[1:-1,2:] - nu1[1:-1,0:-2]) * (un[1:-1,2:] - un[1:-1,0:-2]))\n",
        "                 +(2 * dt / (dx**2) * nu1[1:-1,1:-1] * (un[1:-1,2:] - 2 * un[1:-1,1:-1] + un[1:-1,0:-2])) \n",
        "                 +(dt / (4 * dy**2) * (nu1[2:,1:-1] - nu1[0:-2,1:-1]) * (un[2:,1:-1] - un[0:-2,1:-1]))\n",
        "                 +(dt / (dy**2) * nu1[1:-1,1:-1] * (un[2:,1:-1] - 2 * un[1:-1,1:-1] + un[0:-2,1:-1]))\n",
        "                 +(dt / (4 * dx * dy) * (nu1[2:,1:-1] - nu1[0:-2,1:-1]) * (vn[1:-1,2:] - vn[1:-1,0:-2]))\n",
        "                 +((dt * nu1[1:-1,1:-1 ]) / (4 * dx * dy) * ((vn[2:,2:] - vn[0:-2,2:]) - (vn[2:,0:-2] - vn[0:-2,0:-2]) )) + F * dt )\n",
        "\n",
        "\n",
        "  v[1:-1, 1:-1] = (vn[1:-1, 1:-1]-\n",
        "                 un[1:-1, 1:-1] * dt / dx *\n",
        "                 (vn[1:-1, 1:-1] - vn[1:-1, 0:-2]) -\n",
        "                 vn[1:-1, 1:-1] * dt / dy *\n",
        "                 (vn[1:-1, 1:-1] - vn[0:-2, 1:-1]) -\n",
        "                 dt / (2 * rho * dy) * (p[1:-1, 2:] - p[1:-1, 0:-2]) \n",
        "                 +(dt / (4 * dx**2) * (nu1[1:-1,2:] - nu1[1:-1,0:-2]) * (vn[1:-1,2:] - vn[1:-1,0:-2]))                 \n",
        "                 +(dt / (dx**2) * nu1[1:-1,1:-1] * (vn[1:-1,2:] - 2 * vn[1:-1,1:-1] + vn[1:-1,0:-2]))                 \n",
        "                 +(dt / (2 * dy**2) * (nu1[2:,1:-1] - nu1[0:-2,1:-1]) * (vn[2:,1:-1] - vn[0:-2,1:-1]))                 \n",
        "                 +( 2 * dt / (dy**2) * nu1[1:-1,1:-1] * (vn[2:,1:-1] - 2 * vn[1:-1,1:-1] + vn[0:-2,1:-1]))                 \n",
        "                 +(dt / (4 * dx * dy) * (nu1[1:-1,2:] - nu1[1:-1,0:-2]) * (un[2:,1:-1] - un[0:-2,1:-1]))                 \n",
        "                 +((dt * nu1[1:-1,1:-1]) / (4 * dx * dy) * ((un[2:,2:] - un[2:,0:-2]) - (vn[0:-2,2:] - vn[0:-2,0:-2]) )))\n",
        "\n",
        "  # Periodic BC u @ x = 2     \n",
        "  u[1:-1,-1] = (un[1:-1,-1]-\n",
        "                 un[1:-1,-1] * dt / dx *\n",
        "                 (un[1:-1,-1] - un[1:-1, -2]) -\n",
        "                 vn[1:-1,-1] * dt / dy *\n",
        "                 (un[1:-1,-1] - un[0:-2,-1]) -\n",
        "                 dt / (2 * rho * dx) * (p[1:-1,0] - p[1:-1, -2]) \n",
        "                 +(dt / (2 * dx**2) * (nu1[1:-1,0] - nu1[1:-1, -2]) * (un[1:-1,0] - un[1:-1, -2]))              \n",
        "                 +(2 * dt / (dx**2) * nu1[1:-1,-1] * (un[1:-1,0] - 2 * un[1:-1,-1] + un[1:-1, -2]))              \n",
        "                 +(dt / (4 * dy**2) * (nu1[2:,-1] - nu1[0:-2,-1]) * (un[2:,-1] - un[0:-2,-1]))              \n",
        "                 +(dt / (dy**2) * nu1[1:-1,-1] * (un[2:,-1] - 2 * un[1:-1,-1] + un[0:-2,-1]))              \n",
        "                 +(dt / (4 * dx * dy) * (nu1[2:,-1] - nu1[0:-2,-1]) * (vn[1:-1,0] - vn[1:-1, -2]))              \n",
        "                 +((dt * nu1[1:-1,-1]) / (4 * dx * dy) * ((vn[2:,0] - vn[0:-2,0]) - (vn[2:, -2] - vn[0:-2, -2]) )) + F * dt)\n",
        "\n",
        "  # Periodic BC u @ x = 0\n",
        "  u[1:-1, 0] = (un[1:-1, 0]-\n",
        "                 un[1:-1, 0] * dt / dx *\n",
        "                 (un[1:-1, 0] - un[1:-1, -1]) -\n",
        "                 vn[1:-1, 0] * dt / dy *\n",
        "                 (un[1:-1, 0] - un[0:-2, 0]) -\n",
        "                 dt / (2 * rho * dx) * (p[1:-1, 1] - p[1:-1, -1]) \n",
        "                 +(dt / (2 * dx**2) * (nu1[1:-1,1] - nu1[1:-1,-1]) * (un[1:-1,1] - un[1:-1,-1]))              \n",
        "                 +(2 * dt / (dx**2) * nu1[1:-1,0] * (un[1:-1,1] - 2 * un[1:-1,0] + un[1:-1,-1]))              \n",
        "                 +(dt / (4 * dy**2) * (nu1[2:,0] - nu1[0:-2,0]) * (un[2:,0] - un[0:-2,0]))              \n",
        "                 +(dt / (dy**2) * nu1[1:-1,0] * (un[2:,0] - 2 * un[1:-1,0] + un[0:-2,0]))              \n",
        "                 +(dt / (4 * dx * dy) * (nu1[2:,0] - nu1[0:-2,0]) * (vn[1:-1,1] - vn[1:-1,-1]))              \n",
        "                 +((dt * nu1[1:-1,0]) / (4 * dx * dy) * ((vn[2:,1] - vn[0:-2,1]) - (vn[2:,-1] - vn[0:-2,-1]) )) + F * dt)\n",
        "\n",
        "  # Periodic BC v @ x = 2\n",
        "  v[1:-1,-1] = (vn[1:-1,-1]-\n",
        "                      un[1:-1,-1] * dt / dx *\n",
        "                      (vn[1:-1,-1] - vn[1:-1,-2]) -\n",
        "                      vn[1:-1,-1] * dt / dy *\n",
        "                      (vn[1:-1,-1] - vn[0:-2,-1]) -\n",
        "                      dt / (2 * rho * dy) * (p[1:-1,0] - p[1:-1,-2]) \n",
        "                      +(dt / (4 * dx**2) * (nu1[1:-1,0] - nu1[1:-1,-2]) * (vn[1:-1,0] - vn[1:-1,-2]))                 \n",
        "                      +(dt / (dx**2) * nu1[1:-1,-1] * (vn[1:-1,0] - 2 * vn[1:-1,-1] + vn[1:-1,-2]))                 \n",
        "                      +(dt / (2 * dy**2) * (nu1[2:,-1] - nu1[0:-2,-1]) * (vn[2:,-1] - vn[0:-2,-1]))                 \n",
        "                      +( 2 * dt / (dy**2) * nu1[1:-1,-1] * (vn[2:,-1] - 2 * vn[1:-1,-1] + vn[0:-2,-1]))                 \n",
        "                      +(dt / (4 * dx * dy) * (nu1[1:-1,0] - nu1[1:-1,-2]) * (un[2:,-1] - un[0:-2,-1]))                 \n",
        "                      +((dt * nu1[1:-1,-1 ]) / (4 * dx * dy) * ((un[2:,0] - un[2:,-2]) - (vn[0:-2,0] - vn[0:-2,-2]) )))\n",
        "\n",
        "  # Periodic BC v @ x = 0\n",
        "  v[1:-1,0] = (vn[1:-1,0]-\n",
        "                    un[1:-1,0] * dt / dx *\n",
        "                    (vn[1:-1,0] - vn[1:-1,-1]) -\n",
        "                    vn[1:-1,0] * dt / dy *\n",
        "                    (vn[1:-1,0] - vn[0:-2,0]) -\n",
        "                    dt / (2 * rho * dy) * (p[1:-1,1] - p[1:-1,-1]) \n",
        "                    +(dt / (4 * dx**2) * (nu1[1:-1,1] - nu1[1:-1,-1]) * (vn[1:-1,1] - vn[1:-1,-1]))                 \n",
        "                    +(dt / (dx**2) * nu1[1:-1,0] * (vn[1:-1,1] - 2 * vn[1:-1,0] + vn[1:-1,-1]))                 \n",
        "                    +(dt / (2 * dy**2) * (nu1[2:,0] - nu1[0:-2,0]) * (vn[2:,0] - vn[0:-2,0]))                 \n",
        "                    +( 2 * dt / (dy**2) * nu1[1:-1,0] * (vn[2:,0] - 2 * vn[1:-1,0] + vn[0:-2,0]))                 \n",
        "                    +(dt / (4 * dx * dy) * (nu1[1:-1,1] - nu1[1:-1,-1]) * (un[2:,0] - un[0:-2,0]))                 \n",
        "                    +((dt * nu1[1:-1,0 ]) / (4 * dx * dy) * ((un[2:,1] - un[2:,-1]) - (vn[0:-2,1] - vn[0:-2,-1]) )))\n",
        "\n",
        "\n",
        "  # Wall BC: u,v = 0 @ y = 0,2\n",
        "  u[0, :] = 0\n",
        "  u[-1, :] = 0\n",
        "  v[0, :] = 0\n",
        "  v[-1, :]=0\n",
        "  return u, v\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU5zW0iu_Ojd"
      },
      "source": [
        "@cuda.jit('void(float32[:,:], float32[:,:], float32[:,:], float32, float32, float32[:,:])')\n",
        "def pres_poisson(p, pn, b, dx, dy, pdiff):\n",
        "    dy2 = dy**2\n",
        "    dx2 = dx**2\n",
        "    sumdx2dy2 = dy2 + dx\n",
        "    height = p.shape[0]\n",
        "    width = p.shape[1]\n",
        "    \n",
        "    startX, startY = cuda.grid(2)\n",
        "    gridX = cuda.gridDim.x * cuda.blockDim.x;\n",
        "    gridY = cuda.gridDim.y * cuda.blockDim.y;\n",
        "    for i in range(50):\n",
        "        pn = p\n",
        "        for x in range((startX + 1) , (width - 1), gridX):\n",
        "            for y in range((startY + 1), (height - 1), gridY):\n",
        "                p[y,x] = (((pn[y,(x+1)] + pn[y,(x-1)]) * dy2 +\n",
        "                (pn[(y+1),x] + pn[(y-1),x]) * dx2 ) /\n",
        "                (2 * sumdx2dy2)) - b[y,x]\n",
        "        \n",
        "            # Wall BC pressure\n",
        "            p[0,x] = p[1,x]     # dp/dy = 0 at y = 0\n",
        "            p[-1,x] = p[-2,x]   # dp/dy = 0 at y = \n",
        "        # Periodic BC pressure @ x = 2\n",
        "        for y in range((startY), (height), gridY):\n",
        "            p[y,-1] = (((pn[y,0] + pn[y,-2]) * dy2 +\n",
        "                           (pn[(y+1),-1] + pn[(y-1),-1]) * dx2) / \n",
        "                          (2 * (sumdx2dy2)) - b[y,-1])\n",
        "            p[y,0] = (((pn[y,1] + pn[y,-1]) * dy2 +\n",
        "                          (pn[(y+1),0] + pn[(y-1),0]) * dx2) /\n",
        "                         (2 * (sumdx2dy2)) - b[y,0])\n",
        "        \n",
        "        for x in range((startX) , (width), gridX):\n",
        "            for y in range((startY), (height), gridY):\n",
        "              pdiff[y,x] = abs(p[y,x] -pn[y,x])\n",
        "     \n"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joMKOoRo_Ojl"
      },
      "source": [
        "\n",
        "def channel_flow(udiff_target,F, u, v, dx, dy, p, b, rho, nu_0, nu_inf, m, n):\n",
        "    udiff = 10\n",
        "    stepcount = 0\n",
        "    un = np.empty_like(u)\n",
        "    vn = np.empty_like(v)\n",
        "    pn = np.empty_like(p)\n",
        "    #b = np.zeros((ny, nx))\n",
        "    gamma_dot, S11, S12, S22 = gamma_cal(u, v, dx, dy)\n",
        "    plotpdiff = np.array([])\n",
        "    pdiff = np.empty_like(p)\n",
        "    \n",
        "    while udiff > udiff_target:\n",
        "        un = u.copy()\n",
        "        vn = v.copy()\n",
        "        ## setting time step with max viscosity\n",
        "        gamma_dot, S11, S12, S22 = gamma_cal(u, v, dx, dy)\n",
        "        nu1 = nufunc(nu_0, nu_inf, m, n, gamma_dot)\n",
        "        \n",
        "        nu_max = np.max(nu1)\n",
        "        sigma = .5\n",
        "        dt = sigma * dx * dy / nu_max\n",
        "        \n",
        "        dtx = dt / dx\n",
        "        dty = dt / dy\n",
        "        dtx2 = dt / dx**2\n",
        "        dty2 = dt / dy**2\n",
        "        \n",
        "        \n",
        "        b = buildB(b, rho, dt, u, v, dx, dy)\n",
        "        blockdim = (9,9)#(13, 3)\n",
        "        griddim = (11,11) #(3, 13)\n",
        "        \n",
        "        \n",
        "\n",
        "        d_b = cuda.to_device(b)\n",
        "        d_p = cuda.to_device(p)\n",
        "        d_pn = cuda.to_device(pn)\n",
        "        d_pdiff = cuda.to_device(pdiff)\n",
        "        pres_poisson[griddim, blockdim](p, pn, b, dx, dy, pdiff)\n",
        "        d_pdiff.to_host()\n",
        "        d_b.to_host()\n",
        "        d_p.to_host()\n",
        "        d_pn.to_host()\n",
        "        \n",
        "        u , v = uv(u, v, p, nu1, dx, dy, dt, F, rho)\n",
        "\n",
        "\n",
        "        udiffprev = udiff\n",
        "        udiff = np.sum(np.abs(u -un))\n",
        "        udiffdelta = udiff - udiffprev \n",
        "        stepcount += 1\n",
        "        if stepcount%100 == 0:\n",
        "          plotpdiff = np.append(plotpdiff, np.sum(pdiff))\n",
        "        if stepcount%20 == 0:\n",
        "          A = np.max(u)\n",
        "          clear_output(wait=True)\n",
        "          print(stepcount, udiff, A, dt, flush=True)\n",
        "    print(stepcount)\n",
        "    fig1 = plt.figure(figsize=(11, 7), dpi=100)\n",
        "    plt.plot(gamma_dot[:,20], marker = '.', lw=0.5, label='gamma_dot')\n",
        "    plt.legend()\n",
        "    fig2 = plt.figure(figsize=(11, 7), dpi=100)\n",
        "    plt.plot(nu1[:,20], marker = '.', lw=0.5, label='nu1')\n",
        "    fig3 = plt.figure(figsize = (11, 7), dpi = 100)\n",
        "    plt.plot(plotpdiff, lw = 0.5, label = 'pdiff')\n",
        "    plt.legend()\n",
        "    #print(stepcount * dt)\n",
        "    A = np.max(u)\n",
        "    print(\"U_maximum:\", np.max(u))\n",
        "    return u, v, p, nu1, gamma_dot, S11, S12, S22, plotpdiff"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tp9sreVTUCux"
      },
      "source": [
        ""
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8_O7ow4iIYM"
      },
      "source": [
        "@jit(nopython = True)\n",
        "def nonNewtanalytical(n, H,ny):\n",
        "    #H = 2\n",
        "    b = H\n",
        "    U0 = 0.04#\t\t\t% Avg inlet velocity\n",
        "    #n = 2  #          % power law index\n",
        "\n",
        "    U_max = (2*n+1)/(n+1)*U0\n",
        "\n",
        "\n",
        "    Re = 40            \n",
        "    m = U0**(2-n) * H**n / Re \n",
        "\n",
        "    G = m*U_max**n*((n+1)/n)**n*(2/H)**(n+1)\n",
        "\n",
        "    UMAX = n/(n+1)*(G/m)**(1/n)*(b/2)**((n+1)/n)\n",
        "\n",
        "    y=np.linspace(0,H,ny)\n",
        "    u = np.zeros(ny)\n",
        "\n",
        "    if n<1:\n",
        "        nu_0 = 0.1\n",
        "        nu_inf = 0.001\n",
        "\n",
        "\n",
        "    if n>=1:\n",
        "        nu_0 = 0.001\n",
        "        nu_inf = 0.1\n",
        "\n",
        "    if n!= 1:\n",
        "        z_h = b/2 - (nu_inf/(m**(1/n)))**(n/(n-1))/G\n",
        "\n",
        "        z_l = b/2 - (nu_0/(m**(1/n)))**(n/(n-1))/G\n",
        "    else:\n",
        "        z_h = b/2\n",
        "        z_l = b/2\n",
        "\n",
        "\n",
        "    alpha_1 = G/(2*nu_inf)*z_h*(b-z_h) - n/(n+1)*(G/m)**(1/n)*((b/2)**((n+1)/n)-(b/2-z_h)**((n+1)/n))\n",
        "\n",
        "    if z_h<0:  \n",
        "        z_h = 0\n",
        "        alpha_1 = 0\n",
        "\n",
        "\n",
        "\n",
        "    alpha_2 = (n/(n+1))*(G/m)**(1/n)*((b/2)**((n+1)/n)-(b/2-z_l)**((n+1)/n)) - (G/(2*nu_0))*z_l*(b-z_l) + alpha_1\n",
        "\n",
        "    if z_l<0:\n",
        "        alpha_2=0\n",
        "        z_l=0\n",
        "\n",
        "\n",
        "\n",
        "    for i in  range(ny):\n",
        "\n",
        "        if ((y[i]>=0) and (y[i]<=z_h)) or ((y[i]>=b-z_h) and (y[i]<=b)):\n",
        "\n",
        "            u[i] = (G/(2*nu_inf))*y[i]*np.abs((b-y[i]))\n",
        "\n",
        "\n",
        "\n",
        "        elif ((y[i]>z_h) and (y[i]<z_l)) or ((y[i]>=b-z_l) and (y[i]<b-z_h)):\n",
        "\n",
        "            u[i] = n/(n+1)*(G/m)**(1/n)*((b/2)**((n+1)/n) - np.abs((b/2-y[i]))**((n+1)/n)) + alpha_1\n",
        "\n",
        "\n",
        "\n",
        "        elif ((y[i]>=z_l) and (y[i]<b/2)) or ((y[i]>=b/2) and (y[i]<b-z_l)):\n",
        "\n",
        "            u[i] = (G/(2*nu_0))*y[i]*np.abs((b-y[i])) + alpha_2\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    #u1 = (G/m)**(1/n)*(n/(n+1))*((H/2)**((n+1)/n)-np.abs((H/2-y))**((n+1)/n))\n",
        "\n",
        "    #figure(1)\n",
        "    #plt.plot(y,u1, label = 'Power')\n",
        "    #plt.legend()\n",
        "    #plt.plot(y,u, label = 'Truncated')\n",
        "    return G, m, nu_0, nu_inf, u"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIXatgwd_Ojn"
      },
      "source": [
        "def chn_sim_run(n, nx, udiff_target):\n",
        "    \n",
        "    #nx = 34\n",
        "    ny = nx\n",
        "    dx = 2 / (nx - 1)\n",
        "    dy = 2 / (ny - 1)\n",
        "    x = np.linspace(0, 2, nx)\n",
        "    y = np.linspace(0, 2, ny)\n",
        "\n",
        "    rho = 1\n",
        "    \n",
        "    #F = 1.7881e-4\n",
        "\n",
        "    #nu_0 = 0.1 #limiting range of kinematic viscosity at 0\n",
        "    #nu_inf = 0.001 #limiting range of kinematic viscocity at infinity\n",
        "    #m = 9.1461e-4 # Non-newtonion flow parameter\n",
        "    #n = 1.2\n",
        "    G, m, nu_0, nu_inf, vel_anal= nonNewtanalytical(n,2,ny)\n",
        "\n",
        "\n",
        "    u = np.zeros((ny, nx))\n",
        "    v = np.zeros((ny, nx))\n",
        "    p = np.zeros((ny, nx))\n",
        "    b = np.zeros((ny, nx))\n",
        "\n",
        "    u, v, p, nu1, gamma_dot, S11, S12, S22, plotpdiff = channel_flow(udiff_target,G, u, v, dx, dy, p, b, rho, nu_0, nu_inf, m, n)\n",
        "    plot_contour(x, y, p, u, v)\n",
        "    \n",
        "    y = np.linspace(0, 2, nx)\n",
        "    #a = np.asarray((1 * rho * ( 1 - (y - 1)**2) / (2 * nu)))\n",
        "    #d = a - u[:,int((nx-1)/2)]\n",
        "    #globalerror = np.sum(np.abs(d))/ np.sum(np.abs(a))\n",
        "    #print(globalerror)\n",
        "    \n",
        "    \n",
        "    return u, v, p, vel_anal, nu1, gamma_dot, S11, S12, S22, plotpdiff"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXAB1Ka3_Ojp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "077eeede-e5f8-4868-ff5b-2b670e50737e"
      },
      "source": [
        "%time u, v, p, vel_anal, nu1, gamma_dot, S11, S12, S22, plotpdiff = chn_sim_run(1.4,9)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "U_maximum: 0.0007845522757141283\n",
            "CPU times: user 25.9 s, sys: 282 ms, total: 26.2 s\n",
            "Wall time: 26.2 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So_Sw0Qe_Ojs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a7d5e728-4d59-49c2-8318-5f1f8acc75e5"
      },
      "source": [
        "%time u, v, p, vel_anal, nu1, gamma_dot, S11, S12, S22, plotpdiff = chn_sim_run(0.8,1e-6)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7120 1.010747831577119e-06 0.05850226791995236 0.5652348554221921\n",
            "7127\n",
            "U_maximum: 0.05850227726962194\n",
            "CPU times: user 30 s, sys: 3.3 s, total: 33.3 s\n",
            "Wall time: 32.2 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tpKqS1C2ixb4cBbv7XccAAGQAiigAADEydEU0dZdukSQWbwEAJAJFFACAGAm0dqkmhYfmSpIvx6vO3gHXMQAAaY4iCgBAjBzq6NW0wlzXMSalqoT7RAEA8UcRBQAghowxriNMSjVLuAAAEoAiCgBADPQODCrbm/pvqyzhAgBIhNR/xwQAIAnsO9ytWVPzXceYtOpSnwJtFFEAQHxRRAEAiIGGFF9DdFh5YZ6agz2uYwAA0hxFFACAGKhv7Ur5pVskyeMxLOECAIg7iigAADEQSJMropKU5THqGwi7jgEASGMUUQAAYuDAkW7NKE79e0QlaebUfO0/0u06BgAgjVFEAQCIgcGwTYtZcyWpprRAgVaWcAEAxE96vGMCAOBQOGyV4suHHqWq1KcGZs4FAMQRRRQAgElqau9ReVGe6xgxU13iYy1RAEBcUUQBAJikdJqoSJJmTfVp32GKKAAgfiiiAABMUiBNlm4ZlpPlUf8gi7gAAOKHIgoAwCQF2tLriqgkGQ3d+woAQDxQRAEAmKSGtpCqStKriE4vytPBjl7XMQAAaYoiCgDAJPX0DcqXk+U6RkxVl/pYwgUAEDcUUQAAJsFaq3QcwFpd4lOAJVwAAHFCEQUAYBKOhPpV7Mt2HSPmqkp9amAJFwBAnFBEAQCYhPrWLtWk0Yy5w6pLC1TP0FwAQJxQRAEAmISGNJwxV5Km5GYp1DfoOgYAIE1RRAEAmIRAa/rNmAsAQLxRRAEAmIRAa0jVaTg0V5L8+dkKhvpdxwAApCGKKAAAk3Ak1KepaThZkSRVlfgUaOM+UQBA7FFEAQCYBCvJGOM6RlwMrSXKzLkAgNijiAIAMEGhvgHlZ3tdx4iboSLKFVEAQOxRRAEAmKCGtpCq0nDG3GFVJQVcEQUAxAVFFACACQq0hlSdxjPmlk3JUWtXn+sYAIA0RBEFAGCCGlrT+4pout77CgBwjyIKAMAEBdq60nbplmF52R719A+6jgEASDMUUQAAJqgp2KuKojzXMeJq9lSf9rZxnygAILYoogAATFDYWnk96T18tYolXAAAcUARBQBgAvoHw2lfQiWpuqRA9SzhAgCIMYooAAATcOBIt2YW57uOEXfVpT41MDQXABBjFFEAACYg0BpSdRrPmDus0p+nA0d6XMcAAKQZiigAABMQaMuMIprl9cha6zoGACDNUEQBAJiAhtYuVZWk99Itw4wxGgxTRgEAsUMRBQBgAva2dWt2SfrfIypJM4rzdOBIt+sYAIA0QhEFAGAC+gbDys3yuo6REFUlLOECAIgtiigAAONkrVX6L9zyN9WlBQq0sYQLACB2KKIAAIzToY5eTSvMdR0jYWpKfWrgiigAIIYoogAAjFOgLaSqDJgxd9jsEp/2HqaIAgBihyIKAMA4BVpDqs6QGXMlKS/bq97+sOsYAIA0QhEFAGCcGlq7MmIN0ZFYTxQAECsUUQAAxinThuZKUklBjlq7+lzHAACkCYooAADj1NEzoKK8bNcxEqq61KdAKzPnAgBigyIKAMA4ZdLSLcOqSgtYSxQAEDMUUQAAxqG9p19T8rJcx0i4mlIfRRQAEDMUUQAAxqGhNaTqksy6P1SSqksK1NBGEQUAxAZFFACAcQi0hlRVmjlLtwzz+7LV3t3vOgYAIE1QRAEAGIdAW2Yu3SJJLN4CAIgViigAAOMQaMnMobmS5MvxqrN3wHUMAEAaoIgCADAOLZ29mlaY6zqGEyzhAgCIFYooAADjZEwmLuAiVZcWqIGZcwEAMUARBQBgjHoHBpXtzdy3zuoSnwLMnAsAiIHMfTcFAGCc9rZ1a3ZJvusYzlSXFrCWKAAgJiiiAACMUUNbV0Yu3TJsemGuDrb3uI4BAEgDFFEAAMaoPoNnzJUkj8ewhAsAICYoogAAjFFDW0g1GXxFVJKyPEZ9A2HXMQAAKY4iCgDAGO0/0q0ZxXmuYzg1u8SnfYe5TxQAMDkUUQAAxigctsrK4FlzpchaosycCwCYpMx+NwUAYIwGwzZj1w+NVlXiYy1RAMCkUUQBABiDpvYeVfhzXcdwjiVcAACxQBEFAGAMAq1dqi7J7ImKJGlmcT73iAIAJo0iCgDAGARaQ6oqzdylW4blZHk0EGYRFwDA5FBEAQAYg0ArS7cMMxqauAkAgImiiAIAMAZ720KqKuGKqCSV+/PU3NHjOgYAIIVRRAEAGIOe/kHl53hdx0gK1SU+JiwCAEwKRRQAgBOwlmGo0apLWcIFADA5FFEAAE7gcKhfxb4c1zGSRlVJgQJtXa5jAABSGEUUAIATqG/tUjUz5r6uqpShuQCAyaGIAgBwAg2tIYpolCm5WQr1DbqOAQBIYRRRAABOINAaUjVLtwAAEDMUUQAATiDQ1qVqlm45SnF+to6E+lzHAACkKIooAAAnEAz1q9iX7TpGUuE+UQDAZFBEAQA4ASvJGOM6RlKpLvUp0EYRBQBMDEUUAIDj2HWwU119A2oMdruOklQKcrL0zPZD/LsAACbEsEj33xhjiiQFg8GgioqKXMcBADh23/oGffH+DbJW8hhp1Yo6rTy7ynUs5+5b36Ab7t+gMP8uAIAo7e3t8vv9kuS31rYfb98JXRE1xnzSGFNvjOkxxjxvjDnnBPu/1xizNbL/BmPMFSO+bowxNxljGo0x3caYNcaYeVFfrzHG3GWM2RP5+i5jzFeNMTkj9rHH+Dh3Is8RAJDZGoPduiFSQiUpbKUb79+Y8VcAh/9dwvy7AAAmYdxF1BizUtJtkr4q6UxJr0p62BgzfZT93yzpXkl3STpD0gOSHjDG1Ebt9gVJn5b0cUlvktQVOWZe5OsLI1k/JmmxpM9F9r3lGKe8VFJl1MeL432OAADsael6vWwNG7RW9S2ZfV8k/y4AgFiYyBXR/yvpJ9ban1lrN2uoEIYk/fMo+39G0mpr7bestVustV+S9JKka6Whq6GSPivpZmvtg9ba1yS9X9IMSVdJkrV2tbX2g9baR6y1u621f5D0n5JWHON8rdbapqiP/gk8RwBAhptTViDPiPmJvMaopiyzl3Hh3wUAEAvjKqKRobBnSVozvM1aG458ft4oDzsvev+Ih6P2nyOpYsQxg5KeP84xJckvqe0Y2/9gjDlojFlrjLnyOI8HAGBUlf58feStJ2m4c3mN0S0ralXpz3eay7VKf75WraiTNzKLsJH4dwEAjFvWOPcvk+SV1Dxie7OGhs8eS8Uo+1dEfV0n2Ocoxpi5kj4l6fNRmzslXSfpL5LCkt6toSHAV0WuoB7rOLmScqM2FY7yHAAAGahvMKxffPhN8kSu+FG2hqw8u0oXzJ+m+paQbn9ip/7utBmuIwEAUsx4i6hzxpiZklZL+o219ifD2621LRq6d3XYemPMDEnXSzpmEZV0g6T/iFdWAEDqstZq58FOffkdp7CG6DFU+vNV6c/XrkOdenLbIV1RV+k6EgAghYz3HtEWSYOSykdsL5fUNMpjmk6wf1PUtuMeM1Isn5D0rKSPjiHv85LmHufrqzQ0xHf4Y9YYjgkAyACv7QuqbqafEnoCyxaX65FNo/0KAADAsY2riFpr+zQ0C+0lw9uMMZ7I5+tGedi66P0jlkbtv0dDhTP6mEUamj13XdS2mZKejJz/g5F7U0/kdEmNx3k+vdba9uEPSR1jOCYAIAM8tLFJl9dyle9EphfmKdjdr96BQddRAAApZCKz5t4m6SPGmA8YYxZJukNSgaSfSZIx5m5jzKqo/b8nabkx5jpjzEJjzFckLZH0Q0my1lpJ35X078aYK40xdZLulnRAQ0u9RJfQBg3dFzrNGFNhjHn9HtJInn+InGOhMeZGDc3k+4MJPEcAQAaz1mrTgaBqZxa5jpISzp83TWt3tLiOAQBIIeO+R9Rae58xZpqkmzQ0mdArkpZba4cnG6rS0GRBw/s/a4y5RtLNGlr3c4ekq6y1G6MO+00Nldk7JRVLWhs5Zk/k60s1NMR2rqR9IyJFj5n6kqRqSQOStkpaaa397XifIwAgs21r7tD88kKG5Y7RZYvL9b01O3TJopF32QAAcGxm6IIkpNeHBAeDwaCKivgrOABkqu88ul1vnVemJTUlrqOkjPf/9AXd9YElyvZOZLAVACAdtLe3y+/3S5I/cuvjqHi3AABghJcaDuvMqqmuY6SUc08q0Qt7jrW8NwAAb0QRBQAgyp6WLlWX+uTxMCx3PJYvrtBDG0edHxAAgKNQRAEAiPLQxkZmy52Ak6ZNUaA1pHCYW34AACdGEQUAIMrzu9t0zhzuDZ2IM6qm6qWGw65jAABSAEUUAICI/Ue6VV6Uy4Q7E3R5bYUe2tjkOgYAIAXwTgsAQMTqjU26bHHFiXfEMS2sKNS2pg4xIz8A4EQoogAARPxlZ4vOn1fmOkbKMsZo8cwibdx/3Bn7AQCgiAIAIEkHO3pUlJel3Cyv6ygp7fLaSmbPBQCcEEUUAABJj25u1jKG5U7aabP8em1fkOG5AIDjoogCACDpyW2HdOGCaa5jpDxjjOZOn6IdBztdRwEAJDGKKAAg4x0J9SknyyNfTpbrKGnh8toKPbSB2XMBAKOjiAIAMt6aLQd16aLprmOkjSU1JfproM11DABAEqOIAgAy3mNbmnXxwnLXMdKG12M0a6pPgdYu11EAAEmKIgoAyGidvQMaDFv587NdR0krl9dWaPVGhucCAI6NIgoAyGhPbD2oixcyLDfWzj2pVOt2t7qOAQBIUhRRAEBGe3Rzs5aewrDcWMvJ8qi0IFeNwW7XUQAASYgiCgDIWD39g+rqHVDplFzXUdLS8toKPczwXADAMVBEAQAZ6+nth3TBfNYOjZe3zivTMztaXMcAACQhiigAIGM9vKlZly2ucB0jbeVle1WQm6WWzl7XUQAASYYiCgDISH0DYbV19arCn+c6Slpbekq5Ht3c7DoGACDJUEQBABlp3e5WnXdyqesYae+ihdP1xNaDrmMAAJIMRRQAkJFWb2zS8sWVrmOkvSm5WfJ6jILd/a6jAACSCEUUAJBxBsNW+490q6rU5zpKRrhkUbke28LwXADA31BEAQAZZ319m5ZUT3UdI2MsXVSuNRRRAEAUiigAIOOs3tiky2uZLTdR/L5s9Q9adfUOuI4CAEgSFFEAQEYJh612HerUvPJC11EyyoULpunJbYdcxwAAJAmKKAAgo7y2P6hTZ/ldx8g4y06p0CObm1zHAAAkCYooACCjPLSxUZfXMltuok0rzFVHz4B6+gddRwEAJAGKKAAgY1hrtflAuxbPKHIdJSOdP7dMa3e0uI4BAEgCFFEAQMbY2tShBeWFMsa4jpKRltdW6OFNDM8FAFBEAQAZ5KGNTbq8jtlyXZlRnK+DHb3qHwy7jgIAcIwiCgDIGC83HNYZs1k/1KVzTyrV87vbXMcAADhGEQUAZITdhzpVU1ogj4dhuS4tr63QQxsbXccAADhGEQUAZISHNjbp8lqG5bo2p6xADW0hDYat6ygAAIcoogCAjPDCnjadM6fEdQxIOqt6ql5qOOw6BgDAIYooACDt7TscUnlRrrK8vO0lg+W1FXpoA7PnAkAm4x0ZAJD2Ht7UrOUMy00aC8oLteNgh6xleC4AZCqKKAAg7f1lZ4veMrfMdQxEGGNUO9OvDfuDrqMAAByhiAIA0trBjh7587OVm+V1HQVRli+u0EMbGZ4LAJmKIgoASGuPbGrWslPKXcfACKfO8mvj/iDDcwEgQ1FEAQBp7anth/S2BdNcx8AIxhjNm16o7c2drqMAABygiAIA0tbhrj7lZHnky8lyHQXHsLy2Qg9tbHQdAwDgAEUUAJC21mxp1tJFDMtNVmdVT9WLAdYTBYBMRBEFAKStx7ce1MWLpruOgVF4PUazS3yqb+lyHQUAkGAUUQBAWursHVDYWhXlZbuOguO4vLZCqzcxey4AZBqKKAAgLT2+9aAuXsjV0GR37kmlWrer1XUMAECCUUQBAGnp0c3NWnpKhesYOIFsr0fTCnN14Ei36ygAgASiiAIA0k5P/6C6+wZUUpDjOgrGYPniCj3M8FwAyCgUUQBA2nlq+yFdMJ+1Q1PF+fPK9MyOFtcxAAAJRBEFAKSdhzc1aRnDclNGXrZXhXlZOtTR6zoKACBBKKIAgLTSNxDW4fWsehYAACAASURBVK4+VfjzXEfBOCw7pUKPbm52HQMAkCAUUQBAWnl2V4vefHKZ6xgYpwsXTNMT2w66jgEASBCKKAAgrTy8qUnLaxmWm2oKcrOU7TUKhvpdRwEAJABFFACQNgbDVvuP9Gh2ic91FEzApYvKtWYLw3MBIBNQRAEAaWN9fZvOrp7qOgYm6JKF5XpsK0UUADIBRRQAkDZWb2zS5XUMy01Vfl+2BgatunoHXEcBAMQZRRQAkBbCYatdhzo1d3qh6yiYhIsWTmfSIgDIABRRAEBaeHXfEZ02q9h1DEzS0lPKWcYFADIARRQAkBZWb2S23HRQNiVXnT0D6ukfdB0FABBHFFEAQMqz1mpzY7sWzyhyHQUxcP68Mq3d0eI6BgAgjiiiAICU98z2FhXlZampvcd1FMTA8toK3f/yPj27q0WNwW7XcQAAcZDlOgAAAJNx3/oGffF3G2QlPbSxSatW1Gnl2VWuY2ESnt5+SP+7oUn/u6FJHiO+pwCQhrgiCgBIWY3Bbt1w/1AJlaSwlW68fyNX0VLY8Pd0GN9TAEhPFFEAQMra09KlsD1626C1qm8JuQmESeN7CgCZgSIKAEhZ06bkvmGb1xjVlPkcpEEszCkrkMccvY3vKQCkH4ooACBl/f7l/frQ+XPkNUPNxWuMbllRq0p/vuNkmKhKf75Wrah7/XsqSV/+u0V8TwEgzTBZEQAgJe0/0q1dhzr1439aog+/dY7qW0KqKfNRWNLAyrOrdMH8aapvCam5vUfbmztcRwIAxBhFFACQkr79yDZdt2yBpKGraBTQ9BL9Pf3YPX/VvsMhzZrK8FwASBcMzQUApJxX9h5RfrZX88sLXUdBAly3bIFue3S76xgAgBiiiAIAUoq1Vt9bs12fvXS+6yhIkPnlhcrP9uqVvUdcRwEAxAhFFACQUh7e1KQlNSWaVvjGGXORvj576Xx9b812WWtPvDMAIOlRRAEAKaNvIKy71wX0ofPnuI6CBJtWmKslNSVavbHJdRQAQAxQRAEAKePudfW6esls5WV7XUeBAx86f47ueS6gvoGw6ygAgEmiiAIAUsKRUJ+e2n5IV542w3UUOJKX7dXVS2br7nX1rqMAACaJIgoASAnff2ynPnXxPHk8xnUUOHTlaTP09I4WHe7qcx0FADAJFFEAQNLb09KlQ529OmdOiesocMzjMfrUxXP1g8d3uo4CAJgEiigAIOl9+5Ftum4py7VgyNk1JTrU2avdhzpdRwEATBBFFACQ1J7b3aryojzVlBW4joIkct3S+fr2o9tdxwAATBBFFACQtMJhq9uf2KlPXTzXdRQkmZqyAlUU5em53a2uowAAJoAiCgBIWg++ul8XLpiuYl+O6yhIQp+6eK5uf2KnwmHrOgoAYJwoogCApNTdN6hfr9+nfzq32nUUJKliX44uXDBdD7yy33UUAMA4UUQBAEnprrW79YE31ygni7cqjO6fzq3Wb1/cp+6+QddRAADjwLs7ACDpHOzo0UsNR3TZ4nLXUZDkcrI8ev95Nbpr7W7XUQAA40ARBQAkne+u2aHPXjpPxhjXUZACLltcrhcDh3Wwo8d1FADAGFFEAQBJZVtTh3r7wzp1VrHrKEgRxhh9bul8fXfNDtdRAABjRBEFACSV2x7dpuuWzXcdAynm1FnF6u0Pa1tTh+soAIAxoIgCAJLGk9sOau70KZpRnO86ClLQdcvm69uPbHMdAwAwBhRRAEBSGBgM6yfP7NYnLpzrOgpS1IzifM0vL9ST2w66jgIAOAGKKAAgKfz6r/v09roZmpKb5ToKUtjHLzxZP3lmtwYGw66jAACOgyIKAHCus3dAf95wQFcvmeU6ClLclNwsvb1uhn79132uowAAjoMiCgBw7o4nd+qjF5ysLC9vS5i8q5fM0p83HFBHT7/rKACAUfCODwBw6sCRbu1o7tTb5k9zHQVpIsvr0UcvOFn/9dQu11EAAKOgiAIAnPr2I9t13bIFrmMgzbxt/jTtaO7U/iPdrqMAAI6BIgoAcObVvUeUm+3RgopC11GQhq5btkC3PbLddQwAwDFQRAEATlhr9d012/W5S+e7joI0taCiUHnZHr2694jrKACAESiiAAAnHt7UrLOqp2paYa7rKEhjn710vr67Zrusta6jAACiUEQBAAnXNxDW3evq9eG3nuQ6CtLctMJcLakp0cObmlxHAQBEoYgCABLunucCeu+SWcrL9rqOggzwofPn6O51AfUNhF1HAQBEUEQBAAl1JNSnJ7cd1DtPm+k6CjJEXrZX710yS/c8F3AdBQAQQREFACTU9x/bqWsvmiuPx7iOggzyztNm6sltB3Uk1Oc6CgBAFFEAQALVt3TpYEeP3nRSqesoyDAej9G1F83V9x/b6ToKAEAUUQBAAv3nI9t03bIFrmMgQ73ppFId7OjRnpYu11EAIONRRAEAcdcY7NZda3fLl+PVnLIC13GQwa5btkA3/2mznt3VosZgt+s4AJCxslwHAACkt/vWN+iG+zcobCWPkc6qnqqVZ1e5joUM9cKeVj2+9aAe23pQHiOtWlHHzyMAOMAVUQBA3DQGu18voZIUttKN92/kShScGP55jPw48vMIAA5RRAEAcbOnpev1Ejps0FrVt4TcBEJG4+cRAJLHhIqoMeaTxph6Y0yPMeZ5Y8w5J9j/vcaYrZH9NxhjrhjxdWOMuckY02iM6TbGrDHGzIv6eo0x5i5jzJ7I13cZY75qjMkZcZxTjTHPRM6z1xjzhYk8PwBAbORne9+wzWuMasp8DtIg080pK9DIVYOMxM8jADgw7iJqjFkp6TZJX5V0pqRXJT1sjJk+yv5vlnSvpLsknSHpAUkPGGNqo3b7gqRPS/q4pDdJ6oocMy/y9YWRrB+TtFjS5yL73hJ1niJJj0gKSDpL0vWSvmKM+eh4nyMAYPK6+wZ126Pb9a/LF8hrhn779xqjW1bUqtKf7zgdMlGlP1+rVtRF/TxKZ8+Zqs0H2h0nA4DMY6y1J94r+gHGPC9pvbX22sjnHkl7Jf3AWnvrMfa/T1KBtfYdUduek/SKtfbjxhgj6YCkb1tr/zPydb+kZkn/x1r7q1FyXC/pE9bakyKff0LS1yVVWGv7IttulXSVtXbhGJ9bkaRgMBhUUVHRWB4CADgGa60+d98ruvrs2XrzyWVqDHarviWkmjIfJRTORf88lhTk6KN3v6gvvWOR5k4vdB0NAFJae3u7/H6/JPmttcf9K9+4rohGhsKeJWnN8DZrbTjy+XmjPOy86P0jHo7af46kihHHDEp6/jjHlCS/pLYR53l6uIRGnWeBMWbqKM8n1xhTNPwhiXcgAIiBHz25S2dUTdWbTy6TNHQl6ryTSymhSArRP4+5WV59672n6ksPbFIw1O86GgBkjPEOzS2T5NXQ1cpozRoqk8dScYL9K6K2jemYxpi5kj4l6cdjOE/0OUa6QVIw6mPfKPsBAMZozeZm7TvcrfefV+06CjAm0wvzdMMVC3X9b1/V4MjZjAAAcZFys+YaY2ZKWi3pN9ban0zycKs0dGV1+GPWJI8HABltR3OHfvF8QF+9crGMMSd+AJAkTp1VrLefWqlvrN7qOgoAZITxFtEWSYOSykdsL5fUNMpjmk6wf1PUtuMe0xgzQ9ITkp6VNHISotHOE32Oo1hre6217cMfkjpGeQ4AgBMIhvr15Qc36VvvOU05WSn3d05A7zx9poyR7n+JAVIAEG/j+k0hcv/li5IuGd4WmazoEknrRnnYuuj9I5ZG7b9HQ0Ux+phFGpo9d13UtpmSnoyc/4ORe1NHnucCY0z2iPNss9YeHsPTAwBM0MBgWJ//7au68YpFmlaY6zoOMGFfuGyhHt7UpFf2HnEdBQDS2kT+ZH2bpI8YYz5gjFkk6Q5JBZJ+JknGmLuNMaui9v+epOXGmOuMMQuNMV+RtETSDyXJDk3b+11J/26MudIYUyfpbg3NpPtA5JjDJbRB0uclTTPGVBhjou/9/KWkPkl3GWMWR5aZ+UwkLwAgjr6xeqvecWql6mb5XUcBJsXrMfrme07TN1dv1cH2HtdxACBtZY33Adba+4wx0yTdpKFJgF6RtNxaOzwxUJWkcNT+zxpjrpF0s4bW/dyhoSVVNkYd9psaKrN3SiqWtDZyzOF3gKWS5kY+Ro6XMZHzBI0xyyTdrqGrpi2SbrLW3jne5wgAGLvfvbhPXo9H7zx9pusoQEz487P1tatq9fnfvqY7/+ks5WV7XUcCgLQz7nVE0xnriALA+LzccFh3Pr1bP7zmTHk9TE6E9PLE1oNavbFJt767jsm3AGAM4raOKAAAw5rbe/Sth7fpG+85lRKKtHTRwumaM61AP/1LvesoAJB2KKIAgHHr6R/U9b99TTdfVauivOwTPwBIUR+74CRtOhDU2h0trqMAQFqhiAIAxsVaqy8/uFH//JYanTRtius4QFwZY3TLu+r046d3qb6ly3UcAEgbFFEAwLjctXaP5k6fogsXTHcdBUiIvGyvvvmeU3Xj7zeoo6ffdRwASAsUUQDAmD29/ZC2NHboI289yXUUIKEq/fm6btl8ffF3GxQOM9EjAEwWRRQAMCZ7Wrr0k2d26+vvqmUGUWSks6pLdMH8Mn1nzXbXUQAg5VFEAQAn1NHTr3/7/QZ96z2nsaYiMtrKs6vU0TOgP7/W6DoKAKQ0iigA4LjCYat//d1rum7ZAlX481zHAZz7t7cv0u9f3qdNB4KuowBAyqKIAgCO69uPbtOFC6brrOqprqMASSHb69G33nOabv7TFrV29rqOAwApiSIKABjVH189oFDfoK5eMtt1FCCpTC3I0X9ceYqu/+1r6hsIu44DACmHIgoAOKaN+4N68JUD+rcrFrmOAiSlhRVFunrJbH3tT5tdRwGAlEMRBQC8QUtnr77+5y361ntOVZaXtwpgNMtrK1Q2JVf/83zAdRQASCn8dgEAOErfQFjX/+ZVfeXKxZpakOM6DpD0PnXxXD23u03P7251HQUAUkaW6wAAgOTQGOzWnpYu/eave/UP51RpQUWh60hASvB4jG5dUaeP3fOiZk7Nl9djtKelS3PKClTpz3cdDwCSEkUUAKD71jfohvs3KGwlI+nck0pdRwJSSkFullatqNP/+dl67T7UqbCVPEZataJOK8+uch0PAJIOQ3MBIMM1BrtfL6GSZCXdeP9GNQa7neYCUk2W12hXpIRKUtjyfwkARkMRBYAMt6el6/VfnIcNWqv6lpCbQECK2tPSJcv/JQAYE4ooAGS45vbeN2zzGqOaMp+DNEDqmlNWII85epvHiP9LAHAMFFEAyGB/fPWA1mxp1s1X1cprhn6D9hqjW1bUMskKME6V/nytWlH3+v8lj5EWVRbJn5/tOBkAJB9jR44hyWDGmCJJwWAwqKKiItdxACCu7nkuoI37gvr6u2qV5fWoMdit+paQasp8lFBgEqL/LwVaQ7rjyV36/t+fIb+PQgogvbW3t8vv90uS31rbfrx9mTUXADKMtVY/fHynOnoHdOu762QiV28q/fkUUCAGov8vVfrzNSU3S//yyxf1natP1/SiPMfpACA5MDQXADJIOGz1tT9tUZbXoxuvWPR6CQUQP7Uz/fraO2v1mV+9okBrl+s4AJAUKKIAkCH6B8P6wu9e07zyKfrEhSe7jgNklJOmTdFtK0/TDfdv0OYDxx2tBgAZgSIKABmgp39Qn/nVy7pk4XT9wzlVruMAGanSn6/brzlTt67eqvX1ba7jAIBTFFEASHPtPf36xC9e1DXnVOvyukrXcYCMNrUgR3e870zd+fRuPb612XUcAHCGIgoAaexQR6/+5Rcv6TOXztf588pcxwEgqSA3Sz+85gw9+MoB/f7lfa7jAIATFFEASFN720L69L0v6ytXnqLTZxe7jgMgSm6WV7ddfbr+Wn9YP/vLHtdxACDhKKIAkIa2NXXoC799Tf959WmaO73QdRwAx+D1GN18Va1aO/t02yPbxNruADIJRRQA0syLgcO6+c+b9cNrztDMYtYFBZKZMUafv2yBivKz9R9/2KRwmDIKIDNQRAEgjTy1/ZB+9MRO/eh9Z6p0Sq7rOADG6MNvPUmnzirWdb95VX0DYddxACDuKKIAkCb++OoB/fqve3X7+85UYV626zgAxuk9Z83SFXWV+tS9LynUN+A6DgDEFUUUANLAPc8F9JedLfreytOVl+11HQfABC09pVwffMscfeIXL+lIqM91HACIG4ooAKQwa62+/9gO7WsLadWKOmV5eVkHUt25J5Xq+ssW6JO/fEnN7T2u4wBAXPAbCwCkqHDY6mt/2qKcLI9uuGKRjDGuIwGIkdqZft18VZ0++6tXVN/S5ToOAMQcRRQAUlD/YFhf+N1rml8+RR9/28mu4wCIgzllBfrOytN14+83aNOBoOs4ABBTFFEASBGNwW49u6tF9S1d+vS9L+uShdP19+dUuY4FII4q/Hm6/Zoz9Y3V2/TCnrbXXwcag92uowHApGS5DgAAOLH71jfohvs3aHiJwY+8dY4ur6t0GwpAQkwtyNEd7ztT7/2vddrS1C5rJY+RVq2o08qz+WMUgNTEFVEASHKNwe6jSqgk/XRtPVdEgAzS3tOvrZESKklhK914/0ZeBwCkLIooACS5PS1dR5VQSRq0VvUtITeBACQcrwMA0g1FFACSWN9AWH989YBGzofrNUY1ZT4nmQAk3pyyAnmOMTH2QDic+DAAEAMUUQBIUoHWLn3o5+v1tvnTdeu76+SNLM/iNUa3rKhVpT/fcUIAiVLpz9eqFUe/Dvz72xfpnnUB/c/zAVlrT3AEAEguhheuvzHGFEkKBoNBFRUVuY4DIIM9+Mp+/f7l/br5qlrNmjp05bMx2K36lpBqynyUUCBDjXwdCIetfvLMbm060K6vvbNWfl+264gAMlh7e7v8fr8k+a217cfblyIahSIKwLVQ34Bu+uNmlU3J1WcvnacsLwNXAJzYyw2H9c3V2/T5yxborOqpruMAyFAU0QmiiAJwafOBdt3858269qK5evPcMtdxAKSY9p5+ffmBjZpXXqiPv+1keY91UykAxNF4iih/agcAx6y1untdvb732Hb94B/OoIQCmJCivGx9Z+XpKpuSo4/d81cdbO9xHQkARkURBQCHjoT69Kl7X1bfQFh3vO8slU7JdR0JQAozxmjl2VX61+UL9blfv6Intx10HQkAjomhuVEYmgsgkdbXt+m2R7brhisW6tRZxa7jAEgzPf2D+vqftyg/x6vPL1ugnCyuPwCIL4bmAkASGwxbff+xHbr3+Qbd+f6zKKEA4iIv26uvXVWrM2YX60M/X6+G1pDrSADwOoooACRQc3uPPnr3X1Xpz9O3rz5NhXkstQAgvi6vq9SqFXX60oMb9YdXD7iOAwCSGJp7FIbmAoinx7c266dr6/WVKxdr7vQpruMAyDADg2F9d80OtXT26st/d4p8OVmuIwFIMyzfMkEUUQDx0DswqG+u3qbBsNUXL1+ovGyv60gAMthfdrbo9id26kvvOEWLKvl9B0DsjKeI8qcwAIijPS1d+vKDG/WP51brssUVruMAgN4yt0wLKgr1b7/foPPnlukfz62WMaw5CiCxuCIahSuiACarMditPS1dmlNWoOd2t+rBVw7olnfVaUZxvutoAHCUcNjqrrV79Oq+I7r5qloV+3KOeg2r9PO6BWB8uCIKAA7ct75BN9y/QeHI3/cuWTRd//3+JcryMi8cgOTj8Rh95IKT9OreI/rEL17S6bP9+vHTuxW2ksdIq1bUaeXZVa5jAkhT/HYEADHQGOw+qoRK0pNbD+lQZ6+7UAAwBqfNLtZN71ysO57a/fprWNhKN96/UY3BbrfhAKQtiigAxMCWxvajSqgkDVqr+hbW7QOQ/I71RzNewwDEE0NzAWASrLX63w1NumvtbhkjRd927zVGNWU+d+EAYIzmlBXIY3TUH9SMpKk+1joGEB9cEQWACdp1qFMfvedF7Wnp1L0fPVe3rqiTNzLzpNcY3bKilsk+AKSESn++Vo14Dfv4hSfr5j9v0a9eaFB45JAPAJgkZs2Nwqy5AMYi1DegHz6+U4G2kL64fKFml/ztqmdjsFv1LSHVlPkooQBSzsjXsMGw1f88H9BjWw7q+ssWqHam33VEAElsPLPmUkSjUEQBHI+1Vo9sbtbPn63Xh986RxcvLHcdCQAS4lBHr765eqvyc7y6bukC+RmyC+AYKKITRBEFMJpAa5dW/e9WLaws1MffdrLysr2uIwFAwr2wp03fe2y7rjp9pt595ix5PMZ1JABJhCI6QRRRACP19A/qR0/u0vamDt1wxUJVlxa4jgQATg0MhvXzdQGt3XFIX1i+UIsq+Z0JwBCK6ARRRAFEe2xLs/77mT364FtqtPSUchnDX/4BYFhze4++sXqrivNz9Nml81SUx3BdINNRRCeIIgpAkva2hXTrQ1s1p6xAn7xorvJzGIYLAKN5dleLfvj4Tl29ZLbeefoM/mgHZDCK6ARRRIHM1jswqDuf2q3X9gd1w+ULddK0Ka4jAUBK6BsI62d/2aMX9rTpXy9fqPnlha4jAXCAIjpBFFEgcz21/ZB+/NQu/eO51bq8toK/6APABDQGu3XrQ1tVXpSnT18yT1Nys1xHApBAFNEJoogC6a8x2K09LV2aU1agSn++Dhzp1qqHtmpGcZ4+ffE8FfBLEwBM2tPbD+m/ntqla95UpbfXVcoY84bXXwDphyI6QRRRIL3dt75BN9y/QWEreYy0bHGFBgbD+uLlCzV3OsPIACCWegcG9d/P7NHLDUd02my/vvPo9tdff1etqNPKs6tcRwQQYxTRCaKIAumrMditt9z6uMJRL3nGSH/514s0o9jnLhgApLmXAm1acce6o7Z5jdHaL17ElVEgzYyniHoSEwkA3NrT0nVUCZUka6VAa7ebQACQIXoGwm/YNmit6ltCDtIASBYUUQBpb3tzh366tv4N273GqKaMq6EAEE9zygrkOcb8b6/tO6L+wTeWVACZgSIKIG3tOtSpz933iu56Zo/+4+9O0TfeXSdvZDZcrzG6ZUUtw8IAIM4q/flatWLE6++7alWUn60P/PQF/e7FfRocOWQFQNrjHtEo3CMKpIdAa5d+8PhOWStde/FczSkreP1rjcFu1beEVFPmo4QCQAId6/W3d2BQv16/V6s3NWnl2VV6R12lPMe6fAogJTBZ0QRRRIHUtu9wSLc/sVPdfYO69uK5zIQLACmip39Qv3y+QY9vPaj3valKly2uoJACKYgiOkEUUSA1NQa7dfsTO3U41K9rL5qrRZX8/wWAVBTqG9A96wJau7NF7z+vRpcumi5jKKRAqqCIThBFFEgtBzt69KMndqm5vUf/cuFc1c3yu44EAIiBzt4B/fz/t3fn8V3Vd77HX9+EfQuQAAmbgQAFBTfUFlQUtGOd2qkytbbOvVM7bafa2k4fdx7T6uy9M1PtTKfjTOt08fbWLretXRw7ti7VurApIiqCokAgrGFJAmFJSEjyvX/8ftgkBEgi+Z1ffnk9H4888sg5J/ETv/lyzvuc7/l+V1SwcksNf3JpKVfMGGMglXoBg2g3GUSl3qH6cAPfWrKZiqoj3HplGRdOHpV0SZKkHnDw6DG+s3QLa3Yc4BOXT2V+WaGBVMpiBtFuMohK2e1AXSPfXrKZN3cf4pNXlHHJlNFJlyRJyoADdY3ct3Qzb1T677+UzQyi3WQQlbJDZW09W6qOMKVoKCUFg6mtP8Z3lm1h7Y4DfGLBVOaXFSVdoiQpAdWHG/j2ks1srjrCbe1GxLQ/d0jKPINoNxlEpeQ9sGobdz64lpYIeQGunjWOo00tfOyyKSyYXuSQLEkSew8d5RvPlFN54CifXjiN1ytr25w77lo8h5sunpx0mVKfYxDtJoOolKzK2nouvfspWq9rnhdg2RcWMn7kkOQKkyRlpd21R/mXx9/gFy/tbLM9PwSW3bHQJ6NShnUliOZlpiRJOr2Xt+1vE0IBWiJsra5PpiBJUlYrLhjEH86deML25hipqKpLoCJJndUv6QIk6bVdtdy/vILqI42EAK0HauSHQGmRT0MlSR2bUjSUvMAJNzLX7jzABZNHMqh/fjKFSToln4hKSkRLS+SJ1/fwsftX8dNV2/nUwmn831su5u7Fc8hPvweaHwJfWjzboVWSpJMqKRjMXe3OHf94/WxGDhnAx7/3Iv/2xAb2HWpIuEpJ7fmOaCu+Iyr1vCMNTfx89Q5+8/puFkwfw4cunkzBkP5tjqmsraeiqo7SoiGGUElSp3R07ogxsqK8mh8+v5URg/pzy6WlzCrxGk/qKU5W1E0GUann7DxQz/dWVLBhzyE+MHci7zmnmH75DsqQJGXG5n2HuX9FBbsO1PPhSyaz8B1jyctzJnbpTDKIdpNBVDrzVm/dz/efqwDgI/NL26z5JklSph2oa+THL2xn2aZ9vOecYv5w7kSGDHDaFOlMMIh2k0FUOjOamlt4dN1ufr56BzOLh/PH80uZMNIhtpKk7HGs1blqVslwPjKvlPGeq6S3xSDaTQZR6e2prTvGj1dtY8mGfVxzTjEfmDuRoQO9yyxJyl4xRl7atp/vrdhKXkiN3rnA0TtStxhEu8kgKnVOZW09W6qOMKVoKCUFg9u8d/OhiyezaKbv3UiSep/tNXV8/7kKNu49zI1zJ3HNOePol593wnlPUscMot1kEJVO74FV27jzwbW0RAgBzikZwaySEdxyaSnnjC9IujxJkt62ww1N/OzF7Tzx+h5GDunPY+t20xIhL8Bdi+dw08WTky5RykoG0W4yiEqnVllbz6V3P9Vm0fC8AMvvWOQdYklSztmxv47Lv/w0ra+W80Ng2R0LPe9JHehKEHXtBEmnFWPk+c3VfP7nr7YJoQAtESqq6pIpTJKkHrStpo72j2yaY+S+JZuprT+WSE1SrnAWEUkntefgEeIHAQAAHNZJREFUUX6+egcryquYe9ZoPnf1dJZvqmoTRvNDoLRoSHJFSpLUQ6YUDSUvcMJIoCljhvIXP1vDsIH9+MDcibxraqFzI0hd5NDcVhyaK6Wms3/qjb089PJO+ufn8YdzJ3LZtCLy0yfYB1Zt4y8fXEdzjOSHwJcWz/ZdGUlSzjrVeW/ngXp+/uIOVm6pZt7UQj5w0USH7KpP8x3RbjKIqi/btPcwP31xO6/vOsiimWO5/oIJjB46oMNjK2vrqaiqo7RoiCdcSVLOO915r6UlsqK8ml+8tIO6xibef/4Erp41jgH9fAtOfYtBtJsMouprjjQ08etXK3lkXSUTRg7mgxdN4tyJBYTg8CJJkrrjQF0jv3xlF0+u38OMccO56eJJzBg3POmypIwwiHaTQVR9wfGFu3+6agdVhxt477klXDu7hMED8pMuTZKknLJuZy0PrNrOtpo63jO7mOvOLWH4oP5JlyX1GINoNxlElUvaL75ddbiBB1/awbMb9nHexJF88KJJlBYNTbpMSZJy3tFjzTz+2m4eXrOLUUMGcONFk7i4dNRbI5Dan7Ol3sog2k0GUeWKB1Zt484H19ISIQSYPaGAs0YPYfGFE1gwfQz98n1nRZKkJGyrruNnq7fz0rb9XDZtDP3yAnc9up6WmJqR967Fc5wEUL2WQbSbDKLKBbsO1HHpl58mtptqfvkdi7zLKklSlmhuifzylZ38r5+uabM9PwSW3bHQc7Z6pa4EUR+LSDmifN9h/v3JjXzyB6tpf3+pJUJFVV0yhUmSpBPk5wWKCwadsL05Rn6xegcNTc0JVCVlTr+kC5DUfdtr6vjVq5WsKK+itHAo151bwgfmTuDyf366zeLb+SFQWjQkuUIlSdIJphQNJS/Q5pydF6CpOfLJH6xm7PCBXHfueOaXFfpajXKOQ3NbcWiueoPdtUf59dpKlm7cx7jhg7juvBLmTW17gjrV4tuSJCl7nOqcvb2mjl+vrWRFeTWTRg3mfeeN5+LS0eTnucyaspPviHaTQVTZqvpwA4+s280zb+ylYHB/3ntuCZdPH3PKhbJPt/i2JEnKDp05Z2/ed5hfvVrJqooapo8dzvvOK+H8SSNd+1tZxSDaTQZRZZPa+mM8/tpunnh9D4P653Pt7GIWvmOs631KktSHxRh5c88hHl6zizXba5k9oYD3nVfC2SUjDKVKXI8H0RDCp4G/AIqBNcBnYowvnOL4G4F/AEqBjcAXYoyPtNofgC8CnwBGAsuB22KMG1sd81fAe4HzgcYY48gO/jsd/TIfjjH+pJO/l0FUGdPRmmFHGpp4cv0eHlu3G4Brzinm6rPHMWygr3NLkqS2Yoys3VnLw2t2sb7yEBeeNYo/OK+EaWOHv3WMa5Qqk3o0iIYQbgK+D9wKrAQ+B9wIvCPGuLeD4+cDS4A7gV8BNwNfAC6MMa5LH/OF9P6PAFtIhdY5wNkxxqPpY74IHAAmAh87RRD9KPBYq80Hjv+MTvxuBlFlROt1PvMC3HzJZGqPNnH0WDPvnjWOa84ppmBI/6TLlCRJvURLS+Slbft5eM0uNlcd4V1TCwnAV37zpmuUKmN6OoiuBFbFGG9Pf50HbAe+FmO8u4PjHwCGxhiva7XteeCVGOOt6aehu4B/jTF+Jb2/ANgD3NL+aWYI4RbgnlME0RtijA916Zf63fcbRNXjKmvrufTup9rMkBcC/Oozl3HO+ILkCpMkSTmhuSXyyNpdfObHr7TZ7hql6mk9to5oCGEAMBd48vi2GGNL+ut5J/m2ea2PT3u81fFTSA3xbf0za0k9bT3ZzzyVe0MIVSGEF0IIfxJOMVg+hDAwhDDi+Acw/GTHSm/X3kNH+ckL2/jsj19uE0IBYoSD9U3JFCZJknJKfl6gcNjAE7Y3x8hdj7zBS9v209L+YkTKsK6+eFYE5JN6WtnaHmDmSb6n+CTHF7faz2mO6ay/BZ4C6oDfA/4TGAb8x0mOvxP4uy7+N6ROiTGyae9hnli/hxe21DBqyACumjWWf7phDu+5Z4nrfEqSpB7T0Rql+QFuuGACz7yxl3ue3Mj4gkG8++xxXDqtiEH9nQxRmZVTM6DEGP+h1ZcvhxCGkppU6WRB9C7gq62+Hg7s6KHy1Ac0Nbeweut+nly/hzd2H6JszDB+7+xxfOLyqfRvtc7nXYvnnLBmmMNkJEnSmVJSMLjD642FM8eycOZYILVO6ZPr9/DjH73EwH75XPmOMSyaObbDp6nSmdbVIFoFNAPj2m0fB+w+yffsPs3xu1ttq2x3TNuB7V23EvibEMLAGGND+53pbW9td8prdceRhiaWbtzHk+v3su9QA3PPGsXiCycys3j4Sf+mbrp4MgtmjHGdT0mS1GNOd70xafQQPnrpFD566RRq647xzIa9fPHh1zl09Bjzygq5etY4po4ZllD1ynVdCqIxxsYQwmrgKuAheGuyoquAr5/k255L77+n1bZ3p7dDapbc3eljXkn/zBHAO4FvdKW+DpwP7O8ohEqdcbIpz/cePMqT6/fy7Ia9xAiXzxjDn//ejC4FypKCwQZQSZLUozp7vVEwpD/vP38C7z9/Ag1NzTy/uYbvLq+govoI54wv4N1nj+X8SaPIz2t7k93lYdRd3Rma+1XgeyGEF4EXSC3fMhT4LkAI4fvAzhjjnenj/x14NoTw58CvgQ8BFwF/ChBjjCGEe4C/DiFs5HfLt+wiHXbTP3cyMBqYDOSHEM5P79oUYzwcQngfqaeozwNHSYXdvwS+0o3fUTphiZU/u3o6/fLyeGFLDYVDB3DVrHF85cbzGD7IZVYkSVLuGNgvnytmjOGKGWOIMbJu50GeWL+Hf//tJkpGDOLqs8dx2bQi/nvNzjbXSi4Po67o8vItACGE20m9e1lM6inmZ2OMK9P7ngEqYoy3tDr+RuAfgVJgI/D5GOMjrfYH4IukwulIYBnwqRjjhlbH3E9qndH2FsYYnwkhvIfUO5/TgABsIvVE9b70zL6d+b1cvkXASZZYAf7jw+fzntklbd73lCRJ6iu219Tx2/V7eHL9XpZtqmqzz+Vh1KPriOYyg6i2Vdfx7Ia9PLxmFy9U7D9h/48/8S7mlRUmUJkkSVL2WFFexc33rTxh+1+/dxZ/PK+UAf28ad8XdSWI5tSsuVJXHWlo4vnN1Ty7YR9bqo4wefQQFswYwz/dMIdrXGJFkiSpQx0tD5MXoPpIA7f9cDX98/OYV1bIFTPGUFo0NLlClbUMoupTYoysrzzEko37WLWlhn75gXlTC7llfilTioa2meXWJVYkSZI6drLlYY6/I3q4oYnnyqv5zrItVFQf4azCISyYPob504oYNtAIIofmtuHQ3NxUc6SRpRv3sWRDFfsONzCreDgLZozhotJRDOx36sWbK2vrXWJFkiTpJDp7rVRRdYQlG/exYlM1jc0tzD1rFFfMGMPZJSPIy3MJxVzhO6LdZBDtfTqaMrypuYWXtx9gyYZ9vLqjloLB/bl8ehELZoxh3IhBCVcsSZLUtzU0NbN6636WbKji9cqDFA0dwIIZY7hsehFFwwa+dZxLw/Q+BtFuMoj2Lu2XV7n+/Ak0NLVQ19jEBZNHsWDGGOZMKDhhvStJkiRlj72HjrJ0QxVLN+6jpu4Ys8ePoLklct/SzS4N08sYRLvJINp7bNh9iGvuWULrv968AI99bgEzxg1PrC5JkiR1X0tL5JkNe/nY/S+ecJ237AsLGT/SiSOzmbPmKufUNTaxqmI/K8qreKPyEA1NzbS/hdISofpwI4xLpERJkiS9TXl5gUH98zu8zrv1hy8xpWgo86YWMr+siEmjB7eZaFK9i0FUWamxqYWXt+1nRXk1a3fWMiA/j4tKR/H+8ybwhWuGs+fQUS69+ymXV5EkScoxHS0Nkx8C3/qfcxnYL5/nN1fz7aXlbKupp3jEQOaXFTG/rJCxzgXSqzg0txWH5ianuSXy2q5alm+qZvXW/UDkgsmjmFdWyLkTCuiXf+KiyA+s2nbSKcMlSZLUe3X2Oq+ytp7nyqtZUV7NnoNHmVI0lPllhbxraiEjhwxIoPK+zXdEu8kg2jM6mvEsxsjGvYdZsamKFypqqGtsZvb4AuZPK+TCyaMY1P/Uy6q0/tkuryJJkpR7unqdF2Nka3Udy8ureH5zDQfrjzGzeDjzygq5uHQ0Q9utX+qsvGeeQbSbDKJnXvuZbW+4YAJNLZEDdceYPnYY86cVcsmUQhc2liRJ0hnV0hJ5c88hVpRXs2pLDY3NLcyeUMD8skI27T3M3/5ynbPynmEG0W4yiJ5Z22qOcMW/PEPrP7G8AL/+7OXMKvH/ryRJkjKnqbmFtTtrefy13Xzz2c1t9uWHwLI7Fvpk9G3qShA98cU76W041tzCsxv2cccvXuX2H71M+/scLREO1B1LpjhJkiT1Wf3y895aa7695hj55jPl7Dl4NIHK+ibHQ+ptO9bcwvJNVTy6dje7Dx5lflkhn144jX75wZltJUmSlFU6mpU3L8DZJSP48qNvsL+ukQUzxnDt7BKKC5yJt6c4NLcVh+Z2XmNTC8vLq3h0bSV7DjZw6bRCrp1dwqTRbUOmM9tKkiQp25zqGvXosWaWbkxd5+6va+Ty6WP4/TmG0s7wHdFuMoieWmNT6snnI2sr2Xe4gUvLirh2TjETR536Cacz20qSJCnbdOYa9a1Quq6SmiPHQ2mx17QnYRDtpr4aRE81dXVjUwvLNu3jkbW7qTrcwGXTirh2TgkTRtr5JEmS1Hc0NDWzdEMVj67bTfWR1HXx788pYXwH18V9dWkYg2g39cUg2n55lbsWz+H6CyawbGMVj6z9XSczfEqSJEkpDU3Nb10vVx1u4PLpv7te7uj6uq+8mmYQ7aa+FkQra+tPmEwoAO+cOpqrZ4076R0eSZIkSSkNTc3p19d2s6OmjpVbamidsPrS0jBdCaLOmtuHbak60iaEAkTgz66awbyywkRqkiRJknqTgf3yWTRzHItmjmPJhn08v+WFNvubY6Siqq5PBNGucB3RPuz41NWtubyKJEmS1D3Txw3z+rqTDKJ9WEnBYG69oozjfeX41NXerZEkSZK6rqRgMHctnkN+SF1hB/D6+iQcmtvHbdx7mP++/VIONzS7vIokSZL0Nt108WQWzBhDRVUdP1q5lfMmjUy6pKxkEO3DVm6uprRwCHMm2jkkSZKkM6WkYDAlBYOZOGowdz/2BvfefGHSJWUdh+b2UTFGvrVkM7deUZZ0KZIkSVJOmjR6CCMH92ftjtqkS8k6BtE+avmmamaVDKdw2MCkS5EkSZJy1u2LpnHv05uSLiPrGET7oBgj3166mT+93KehkiRJUk8qKRhMychBrN66P+lSsopBtA96+s29zJ08ioIh/ZMuRZIkScp5t11ZxjeeKU+6jKxiEO1jYox8d3kFf3JZadKlSJIkSX3C2OGDKBszlOfKq5MuJWsYRPuYx1/bzfyyIoYP8mmoJEmSlCmfvKKMby8pJ8aYdClZwSDahzS3RH7w/FY+Mv+spEuRJEmS+pTRQwcwe0IBSzdWJV1KVjCI9iG/enUXi2aOY8gAl4+VJEmSMu3jl03lvqWbfSqKQbTPaGpu4YFV2/mjd05OuhRJkiSpTyoY0p9LSkfz2/V7ky4lcQbRPuKhV3Zx7ZwSBvXPT7oUSZIkqc/66GVTuH9FBS0tffupqEG0DzjW3MIvVu/gposmJV2KJEmS1KcNG9iPy6cX8dhru5MuJVEG0T7gZy/u4PoLxjOgn80tSZIkJe2P55Xyw+e30tyHn4qaTHLc0WPNPLxmF4svnJh0KZIkSZKAwQPyuXrWOB5esyvpUhJjEM1xP3lhGx+8eCL9821qSZIkKVvc/M7JPLBqO03NLUmXkgjTSQ6rb2zmifV7+IPzJiRdiiRJkqRWBvXP573nlvDgyzuTLiURBtEc9oPnK7j5krPIzwtJlyJJkiSpnQ9eNIn/emknjU1976moQTRHHW5oYsmGKq6dXZx0KZIkSZI6MKBfHjdcOIGfvrg96VIyziCao+5fvoWPzC8lz6ehkiRJUtZafMEEfv1qJUePNSddSkYZRHNQbf0xVm6p4epZY5MuRZIkSdIp9MvP40OXTOJHK7clXUpGGURz0HeWbeHjl08lBJ+GSpIkSdnuunPH89s39lDX2JR0KRljEM0x+4808uqOAyyYXpR0KZIkSZI6IT8v8D/eeRbff25r0qVkjEE0x3xryWY+uaDMp6GSJElSL3LNOcUs21jFoaPHki4lIwyiOWTfoQY27T3EvLLCpEuRJEmS1AV5eYFb5pfy3eUVSZeSEQbRHPKNZ8q57cqypMuQJEmS1A1XzRrLi1v3U1uX+09FDaI5YnftUXYeqGPuWaOTLkWSJElSN4QQ+PhlU/g/yzYnXUqPM4jmiHuf3sSnF05LugxJkiRJb8Pl04tYt7OWmiONSZfSowyiOWB7TR01dY2cO3Fk0qVIkiRJehtCCHzyijK+9Wx50qX0KINoDrj36U3c7tNQSZIkKSe8a2oh5fsOs/fQ0aRL6TEG0V6uouoIR481M6tkRNKlSJIkSTpDbrtyGv/5dO4+FTWI9nJfe2oTty+annQZkiRJks6guWeNYnftUXYdqE+6lB5hEO3FNu45RAgwbeywpEuRJEmSdIZ9euE07n16U9Jl9AiDaC/2tac28ZlFvhsqSZIk5aI5EwuorT/G9pq6pEs54wyivdRru2oZOrAfZxUOTboUSZIkST3k9kXT+NpTG5Mu44wziPZS//l0Obf7NFSSJEnKaTOLR9DY1MLmfYeTLuWMMoj2Qmu2H6Bo2AAmjBycdCmSJEmSethnrprO15/KrXdFDaK90L1Pb+JTrhsqSZIk9QllY4aRnxd4c/ehpEs5Ywyivcyqihomjx7CuBGDki5FkiRJUoZ8ZtF0vp5DM+gaRHuZbz5Tzq1XliVdhiRJkqQMmlw4hGED+7FuZ23SpZwRBtFeorK2nvuWlDNh5GCKhg1MuhxJkiRJGXb7otS6opW19awor6Kytj7pkrqtX9IF6PQeWLWNOx9cS0uEvADnTBjBTRdPTrosSZIkSRk0YeRgauuPMf/up4jpbHDX4jm9Mhv4RDTLVdbWvxVCAVoi/OWD63r13Q9JkiRJXVdZW8/zm6uJOZANDKJZbkvVkbdC6HHNMVJRVZdMQZIkSZISkUvZwCCa5aYUDSUvtN2WHwKlRUOSKUiSJElSInIpGxhEs1xJwWDuWjyH/JD6i8sPgS8tnk1JweCEK5MkSZKUSbmUDUKM8fRH9REhhBFAbW1tLSNGjEi6nDYqa+upqKqjtGhIr/xDkyRJknRmZGs2OHjwIAUFBQAFMcaDpzrWWXN7iZKCwVn1RyZJkiQpGbmQDRyaK0mSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmj+iVdQDY6ePBg0iVIkiRJUq/SlRwVYow9WErvEkKYAOxIug5JkiRJ6sUmxhh3nuoAg2grIYQAjAcOJV1LB4aTCskTyc761HW2aW6yXXOPbZp7bNPcZLvmHtu0dxoO7IqnCZoOzW0l/T/rlMk9KamMDMChGKNjh3OAbZqbbNfcY5vmHts0N9muucc27bU61VZOViRJkiRJyiiDqCRJkiQpowyivUcD8MX0Z+UG2zQ32a65xzbNPbZpbrJdc49tmsOcrEiSJEmSlFE+EZUkSZIkZZRBVJIkSZKUUQZRSZIkSVJGGUQlSZIkSRllEO0FQgifDiFUhBCOhhBWhhAuSbomdV8I4e9DCLHdxxtJ16XOCyEsCCE8HELYlW6/69vtDyGE/x1CqAwh1IcQngwhTE+qXnVOJ9r1/g767mNJ1avTCyHcGUJYFUI4FELYG0J4KITwjnbHDAoh3BtCqA4hHA4h/CKEMC6pmnVqnWzTZzroq99MqmadXgjhthDCqyGEg+mP50II17babz/NQQbRLBdCuAn4Kqmpqy8E1gCPhxDGJlqY3q7XgJJWH5clW466aCipvvjpk+z/PPBZ4FbgncARUv12UGbKUzedrl0BHqNt3/1wBupS910B3Au8C3g30B/4TQhhaKtj/g14H3Bj+vjxwIMZrlOd15k2BbiPtn3185ksUl22A7gDmAtcBDwF/DKEcE56v/00B7l8S5YLIawEVsUYb09/nQdsB74WY7w70eLULSGEvweujzGen3QtevtCCBG4Icb4UPrrAOwC/jXG+JX0tgJgD3BLjPEniRWrTmvfrult9wMjY4zXn/QbldVCCGOAvcAVMcYl6b65D7g5xvjz9DEzgfXAvBjj88lVq85o36bpbc8Ar8QYP5dkbXp7Qgg1wF8AP8d+mpN8IprFQggDSN0ZevL4thhjS/rreUnVpTNienr43+YQwv8LIUxOuiCdMVOAYtr221pgJfbbXHBlejjgmyGEb4QQCpMuSF1SkP5ck/48l9QTtdb99Q1gG/bX3qJ9mx73RyGEqhDCuhDCXSGEIZkuTN0TQsgPIXyI1CiV57Cf5qx+SRegUyoC8kk9SWltDzAz8+XoDFkJ3AK8SWq40N8BS0MIs2OMh5IsTGdEcfpzR/22GPVmj5EaCrYFKAO+BDwaQpgXY2xOtDKdVnpE0T3A8hjjuvTmYqAxxnig3eH2117gJG0K8CNgK6nRKecCXwbeASzOeJHqtBDCHFLBcxBwmNSolNdDCOdjP81JBlEpw2KMj7b68tX08OutwAeB7yRTlaTTaTesem0I4VWgHLgS+G0iRakr7gVm4zv5uaTDNo0xfrvVl2tDCJXAb0MIZTHG8kwWqC55Ezif1FPuDwDfCyFckWxJ6kkOzc1uVUAz0H5WsHHA7syXo56QvsO3AZiWdC06I473Tfttjosxbib177R9N8uFEL4OXAcsjDHuaLVrNzAghDCy3bfYX7PcKdq0IyvTn+2rWSzG2Bhj3BRjXB1jvJPU5HF/hv00ZxlEs1iMsRFYDVx1fFt6GMpVpIYuKAeEEIaRGuZXmXQtOiO2kDoxtu63I0jNnmu/zSEhhIlAIfbdrJVeSunrwA3AohjjlnaHrAaO0ba/vgOYjP01K3WiTTtyfHJA+2rvkgcMxH6asxyam/2+SmpowovAC8DnSL28/d1Eq1K3hRC+AjxMajjueFJL8zQDP06yLnVe+uZB6zvrU9LvsNTEGLeFEO4B/jqEsJFUMP0HUu8qPXTiT1O2OFW7pj/+DvgFqRsNZcA/A5uAxzNcqjrvXuBm4P3AoRDC8ffJamOM9THG2hDCd4CvpmfoPAh8DXjOmTiz1inbNIRQlt7/CFBN6h3RfwOWxBhfTaJgnV4I4S7gUVITEA0n1YZXAtfYT3OXy7f0AiGE20lNX10MvAJ8Nsa48tTfpWwVQvgJsIDUk5R9wDLgr3xvpfcIIVwJPN3Bru/FGG9JL+HyReBPgZGk2vhTMcYNmatSXXWqdgVuI3Uj4QJSbboL+A3wNzHG9hNTKUukl+HpyEdjjPenjxkE/CupNWEHkrqx8KkYo0P+stDp2jSEMAn4Ial3R4eSWvLuv4B/jDEezFCZ6qJ00LyK1CSOtcCrwJdjjE+k99tPc5BBVJIkSZKUUb4jKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMsogKkmSJEnKKIOoJEmSJCmjDKKSJEmSpIwyiEqSJEmSMur/A8rXMRuAIqGqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaA6QTwZmPck",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "23b75c0e-1ae3-40ff-fba2-41b09b6c3772"
      },
      "source": [
        "fig = plt.figure(figsize=(11, 7), dpi=100)\n",
        "plt.plot(u[:,20], marker = '.', lw=0.5, label='u')\n",
        "plt.plot(vel_anal, marker = '*', lw=0.5, label='Analytical Velocity')\n",
        "plt.legend()\n",
        "print(\"Maximum Velocity:\", np.max(u))\n",
        "print(\"Maximum Analytical Velocity:\", np.max(vel_anal))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum Velocity: 0.05850227726962194\n",
            "Maximum Analytical Velocity: 0.057755641506445486\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUYUZGJhnJNO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0159e75d-acb4-4d9c-aa53-457b65bce895"
      },
      "source": [
        "np.sum(np.abs(u[:,20] - vel_anal)) / np.sum(np.abs(vel_anal))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017331136082475915"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTTbtO25CoNj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "3b1b8b86-c117-409d-a023-f557c92f3ca8"
      },
      "source": [
        "fig = plt.figure(figsize = (11, 7), dpi = 100)\r\n",
        "plt.plot(plotpdiff, lw = 0.5, label = 'pdiff')\r\n",
        "plt.ylim((0,1e-317))\r\n",
        "plt.legend()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff64fdfb198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbbmAjZCLgHj",
        "outputId": "99706b9a-eb72-4401-94ae-846a6d7ff03c"
      },
      "source": [
        "plotpdiff"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixkQ-IEKLs4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ffaa1b9e-83f1-4eb0-95c2-ac37e73c25e2"
      },
      "source": [
        "%time u, v, p, vel_anal, nu1, gamma_dot, S11, S12, S22, plotpdiff = chn_sim_run(1,1e-6)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1820 1.040204561275149e-06 0.001198578037026756 0.018365472910927456\n",
            "1829\n",
            "U_maximum: 0.001198590845553014\n",
            "CPU times: user 8.52 s, sys: 860 ms, total: 9.38 s\n",
            "Wall time: 9.06 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hmXw28i0HeOGfdddcdeGfHxhjXDbd5ulJbso2v/BPMvmrxS33fTHfcOxRW/If0Vaub/b5rL9d197q9c0+n/XNPp/1zT6f9c0+n/XNPp/1zT6f9bd69gOx0bfLbiYyz0ryziT/Mck1Sc5J8v1JnjHGuKeq3pXkjjHGudPtdyX5punT/yjJb09vnx9j3LyRNafb/GqSFyV5ZZLlJBcnyRjjeRuc+5CNTAAAgEPdln0mc4xxaVU9PskbkhyX5IYkZ67EYJITkzyy6inHJ/nwqvuvmd4+mOT0Da6ZJD85XfeyJEcmuSLJj886PwAAAFtn5jOZ25UzmQAAAJu3Jd+TCQAAAPsiMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGgjMgEAAGizqcisqrOr6paqeqCq9lTVc/az/cuq6qbp9h+tqhet+fnuqnpHVd1ZVV+sqvdX1VPXbHNcVf1mVd1dVV+oqr+qqn+3mfkBAADYGjNHZlWdleTCJOclOSXJjUmuqKonrLP985K8O8nbkzw7yeVJLq+qk6Y/r+lj35jke6fbfCbJlVV19Kql3pXk6Um+J8mzkrwnye9V1bNnfQ0AAABsjRpjzPaEqj1Jrh1jvHp6/7AktyW5eIxxwV62vzTJ0WOM71r12IeS3DDGeFVVPS3JJ5KcNMb461Vr3p3kZ8cYvzF97PNJfmyM8Zur1vlskteubLOfuReSLC0tLWVhYWGm1wwAAPBot7y8nMXFxSRZHGMsr7fdTGcyq2pXklOTXLny2Bjjken909Z52mmrt5+6YtX2R07/fWDNmg8mef6q51yV5KyqelxVHVZVL0/ymCR/vs6sR1bVwsotyTH7f4UAAAAciFnfLntsksOT3LPm8XuSHLfOc47bz/Y3Jbk1yflV9bVVtauqXpvkhCRPXPWc70/yVUk+m0mA/lqSl4wxbl7n956bZGnV7fZ9vzQAAAAO1NyvLjvG+HKSlyZ5WpLPJfliku9I8r4kj6za9I1JHpvkXyf5lkw+F/p7VfWsdZY+P8niqtsJWzE/AAAA/+SIGbe/L8nDSXaveXx3Jp+h3Ju797f9GOP6JCdX1WKSXWOMe6ef/bwuSarqKUlenVWf20xyY1X9qyRnJ3nV2l86xngwkzOema6xoRcIAADA5s10JnOM8VCS65OcsfLY9CI9ZyS5ep2nXb16+6kX7m37McbSNDCfmsnZyvdOf3TU9N9H1jzl4VlfAwAAAFtn1jOZyeRtqu+squuSXJPknCRHJ7kkSarqXUnuGGOcO93+V5J8sKp+KskfJnl5JgH5oysLVtXLktybyWcznzV9zuVjjA9MN7kpyc1Jfq2qXpPJ5zJfnEms/uNVawEAAJivmSNzjHFpVT0+yRsyuXjPDUnOHGOsXNznxKw64zjGuKqqXpHkF5K8KcnfJXnxGONjq5Z9YibxujvJXZl8J+YbV63x5ap6UZILkvxBkq/JJDp/aIzxR7O+BgAAALbGzN+TuV35nkwAAIDN25LvyQQAAIB9EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC0EZkAAAC02VRkVtXZVXVLVT1QVXuq6jn72f5lVXXTdPuPVtWL1vx8d1W9o6rurKovVtX7q+qpe1nntKr606r6QlUtV9VfVNVXb+Y1AAAA0G/myKyqs5JcmOS8JKckuTHJFVX1hHW2f16Sdyd5e5JnJ7k8yeVVddL05zV97BuTfO90m88kubKqjl61zmlJ3p/kA0mek+Rbk7wlySOzvgYAAAC2Ro0xZntC1Z4k144xXj29f1iS25JcPMa4YC/bX5rk6DHGd6167ENJbhhjvKqqnpbkE0lOGmP89ao1707ys2OM31j1nD8eY/zcJl5nqmohydLS0lIWFhY2swQAAMCj1vLychYXF5NkcYyxvN52M53JrKpdSU5NcuXKY2OMR6b3T1vnaaet3n7qilXbHzn994E1az6Y5PnT3/uEJM9N8vdVdVVV3VNVH6yq5+9j1iOramHlluSYDb5MAAAANmnWt8sem+TwJPesefyeJMet85zj9rP9TUluTXJ+VX1tVe2qqtcmOSHJE6fbfOP039cneVuSM5P8VZI/2dtnN6fOTbK06nb7Pl8ZAAAAB2zuV5cdY3w5yUuTPC3J55J8Mcl3JHlf/unzlitz/toY45IxxofHGD+Zydtsf2Sdpc9PsrjqdsLWvAIAAABWHDHj9vcleTjJ7jWP787kM5R7c/f+th9jXJ/k5KpaTLJrjHHv9LOf1003uWv678fXrPM3SU7c2y8dYzyYyVtukyST6wsBAACwlWY6kznGeCjJ9UnOWHlsepGeM5Jcvc7Trl69/dQL97b9GGNpGphPTfItSd47/dEtSe5M8vQ1T3laJleiBQAA4BAw65nMZPL1Je+squuSXJPknCRHJ7kkSarqXUnuGGOcO93+V5J8sKp+KskfJnl5JgH5oysLVtXLktybyWcznzV9zuVjjA8kyRhjVNX/SHJeVd2Y5IYkP5TkGUm+bxOvAQAAgC0wc2SOMS6tqscneUMmF++5IcmZY4yVi/ucmFXfXTnGuKqqXpHkF5K8KcnfJXnxGONjq5Z9YibxujuTt8a+K8kb1/zei6rqMUnenORxmXw/5wvHGJ+c9TUAAACwNWb+nsztyvdkAgAAbN6WfE8mAAAA7IvIBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoI3IBAAAoM0R8x7gYFteXp73CAAAANvORluqxhhbPMqhoaq+Psnt854DAABgmzthjHHHej98NEVmJTk+yf3znmUvjskkgE/IoTkfm2O/7jz26c5jn+5M9uvOY5/uTPbr9nRMkjvHPkLyUfN22en/Etat7Xma9G+S5P4xhvfz7hD2685jn+489unOZL/uPPbpzmS/blv73Vcu/AMAAEAbkQkAAEAbkXloeDDJedN/2Tns153HPt157NOdyX7deezTncl+3aEeNRf+AQAAYOs5kwkAAEAbkQkAAEAbkQkAAEAbkQkAAEAbkXkIqKqzq+qWqnqgqvZU1XPmPRObU1Wvr6qx5nbTvOdiNlX17VX1B1V153QfvnjNz6uq3lBVd1XVl6rqyqp66rzmZf82sE/fsZdj9/3zmpf9q6pzq+raqrq/qv6+qi6vqqev2eYxVfXWqvpsVX2+qi6rqt3zmpl92+A+/fO9HKv/a14zs39V9WNV9ZGqWp7erq6qf7vq547THUhkzllVnZXkwkwu33xKkhuTXFFVT5jrYByIv07yxFW35893HDbh6EyOxbPX+fnPJPmJJK9K8twkX8jkuH3MwRmPTdjfPk2S9+crj90fOAhzsXkvSPLWJN+W5IVJvirJB6rq6FXbvDnJdyd52XT745O85yDPycZtZJ8mydvylcfqzxzMIZnZ7Ulel+TUJN+S5E+TvLeqnjn9ueN0B/IVJnNWVXuSXDvGePX0/mFJbkty8RjjgrkOx8yq6vVJXjzGOHnes9CjqkaSl4wxLp/eryR3JvnlMcYvTR9bTHJPkleOMX53bsOyIWv36fSxdyR57Bjjxes+kUNaVT0+yd8necEY4y+mx+W9SV4xxvj96TbPSPI3SU4bY3xoftOyEWv36fSxP09ywxjjnHnOxoGpqs8l+ekkvx/H6Y7kTOYcVdWuTP6qc+XKY2OMR6b3T5vXXBywp07fkvepqvrtqjpx3gPR6slJjstXHrdLSfbEcbvdnT59i94nqupXq+rr5j0QM1mc/vu56b+nZnImbPWxelOSW+NY3S7W7tMVP1hV91XVx6rq/Ko66mAPxuZU1eFV9fJM3l1ydRynO9YR8x7gUe7YJIdncgZktXuSPOPgj0ODPUlemeQTmbyF5+eT/GVVnTTGuH+eg9HmuOm/eztujwvb1fszeXvWp5M8Jcmbkryvqk4bYzw818nYr+m7gC5K8n/HGB+bPnxckofGGP+wZnPH6jawzj5Nkt9J8plM3lHyzUn+e5KnJ3npQR+SDauqZ2USlY9J8vlM3k3y8ao6OY7THUlkQqMxxvtW3f3I9O3Qn0ny/UnePp+pgP1Z8zbnj1bVR5J8MsnpSf5kLkMxi7cmOSk+A7+T7HWfjjF+fdXdj1bVXUn+pKqeMsb45MEckJl8IsnJmZyd/r4k76yqF8x3JLaSt8vO131JHk6y9gpau5PcffDHodv0L3N/m+RfzHsW2qwcm47bHWyM8alM/m+0Y/cQV1VvSfJdSb5jjHH7qh/dnWRXVT12zVMcq4e4fezTvdkz/dexeggbYzw0xrh5jHH9GOPcTC7E9p/jON2xROYcjTEeSnJ9kjNWHpu+PeSMTN5SwDZXVV+TyVvv7pr3LLT5dCb/j2/1cbuQyVVmHbc7RFWdkOTr4tg9ZE2/SugtSV6S5DvHGJ9es8n1Sb6crzxWn57kxDhWD9hGpUAAAAHuSURBVEkb2Kd7s3KhPcfq9nJYkiPjON2xvF12/i7M5C0D1yW5Jsk5mXwY+pK5TsWmVNUvJfmDTN4ie3wmX03zcJJ3z3MuZjP948Dqv4o/efq5kc+NMW6tqouS/Leq+rtMovONmXw+6PJ/vhqHgn3t0+nt55NclskfEJ6S5BeT3JzkioM8Khv31iSvSPK9Se6vqpXPby2NMb40xliqqrcnuXB6JcvlJBcnudoVKw9Z+9ynVfWU6c//KMlnM/lM5puT/MUY4yPzGJj9q6rzk7wvk4v5HJPJPjw9yb9xnO5cvsLkEFBVr87kMs7HJbkhyU+MMfbs+1kciqrqd5N8eyZnQO5N8n+S/FefE9lequr0JH+2lx+9c4zxyunXmJyX5EeTPDaT/fzjY4y/PXhTMot97dMkP5bJHwiencn+vDPJB5L83Bhj7QWeOERMv4pmb354jPGO6TaPSfLLmXzn6ZGZ/NHgx8cY3oZ3CNrfPq2qJyX5rUw+q3l0Jl/59r+T/MIYY/kgjcmMphF5RiYXRFxK8pEk/32M8cfTnztOdyCRCQAAQBufyQQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKCNyAQAAKDN/wdYD8/EKQtlMQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "zXPuTVnXbKe8",
        "outputId": "7327f56b-b965-4c1a-e2e8-9e099647b963"
      },
      "source": [
        "fig = plt.figure(figsize=(11, 7), dpi=100)\r\n",
        "plt.plot(u[:,20], marker = '.', lw=0.5, label='u')\r\n",
        "plt.plot(vel_anal, marker = '*', lw=0.5, label='Analytical Velocity')\r\n",
        "plt.legend()\r\n",
        "print(\"Maximum Velocity:\", np.max(u))\r\n",
        "print(\"Maximum Analytical Velocity:\", np.max(vel_anal))\r\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum Velocity: 0.001198590845553014\n",
            "Maximum Analytical Velocity: 0.0011988980716253444\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cc_UJRvDbpVO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}