3DFEM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3DFEM",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMfe36WLE2ditmK66IX1JZN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/adsk2050/btp-iitg/blob/master/3DFEM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6mX4ERqBp4u",
        "colab_type": "code",
        "outputId": "2a18c441-6b23-45dd-9f72-092d76fe6d54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGghbSzsdsT6",
        "colab_type": "code",
        "outputId": "13fd77e1-2bb9-4aa8-cbc5-f05c09e7036b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "from google.colab import files\n",
        "import math, shutil, os, csv\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "import pandas as pd\n",
        "from scipy.stats import norm\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from scipy.spatial import ConvexHull\n",
        "np.set_printoptions(precision=1, linewidth=10000)\n",
        "import time\n",
        "\n",
        "\n",
        "class Material:\n",
        "    def __init__(self, c11, c12, c13, c14, c15, c16, c21, c22, c23, c24, c25, c26, c31, c32, c33, c34, c35, c36, c41, c42, c43, c44, c45, c46, c51, c52, c53, c54, c55, c56, c61, c62, c63, c64, c65 ,c66):\n",
        "        \"\"\" \n",
        "        creates C matrix \n",
        "        sigma = [sig11, sig22, sig33, tau12, tau23, tau31]T\n",
        "        epsilon = [e11, e22, e33, gam12, gam23, gam31]T\n",
        "        \"\"\"\n",
        "        self.C = np.array([[c11, c12, c13, c14, c15, c16],\n",
        "                           [c21, c22, c23, c24, c25, c26],\n",
        "                           [c31, c32, c33, c34, c35, c36],\n",
        "                           [c41, c42, c43, c44, c45, c46],\n",
        "                           [c51, c52, c53, c54, c55, c56],\n",
        "                           [c61, c62, c63, c64, c65, c66]])\n",
        "        \n",
        "class Node:\n",
        "    def __init__(self, x, y, z, gi):\n",
        "        \"\"\" gi is the global index of the node \"\"\"\n",
        "        self.x = x\n",
        "        self.y = y\n",
        "        self.z = z\n",
        "        self.gi =gi\n",
        "\n",
        "class Element:\n",
        "    \"\"\" \n",
        "    We don't have body force, so \n",
        "    nodes: 8 Nodes per element with three coordinates (x, y, z):Global index\n",
        "    u    : x-disp for 8 nodes\n",
        "    v    : y-disp for 8 nodes\n",
        "    w    : z-disp for 8 nodes\n",
        "    \"\"\"\n",
        "    on_boundary=False\n",
        "    nodes = np.zeros((8,4)) \n",
        "    u = np.zeros((1, 8)) \n",
        "    v = np.zeros((1, 8)) \n",
        "    w = np.zeros((1, 8)) \n",
        "    def __init__(self, node_list, on_boundary):\n",
        "        \"\"\" Assuming node_list contains 8 nodes in anticlockwise order \"\"\"\n",
        "        self.on_boundary=on_boundary\n",
        "        for i in range(0, 8):\n",
        "            self.nodes[i][0]=node_list[i].x\n",
        "            self.nodes[i][1]=node_list[i].y\n",
        "            self.nodes[i][2]=node_list[i].z\n",
        "            self.nodes[i][3]=node_list[i].gi\n",
        "    \n",
        "    def isOnBoundary(self):\n",
        "        self.on_boundary=True\n",
        "    \n",
        "    def sizni(self, e, n, z):\n",
        "        \"\"\" \n",
        "        Approximation function for x and y in terms of e, n and z \n",
        "\n",
        "        \"\"\"\n",
        "        si1enz = (1/8)*(1+e)*(1+n)*(1+z)\n",
        "        si2enz = (1/8)*(1+e)*(1-n)*(1+z)\n",
        "        si3enz = (1/8)*(1-e)*(1+n)*(1+z)\n",
        "        si4enz = (1/8)*(1-e)*(1-n)*(1+z)\n",
        "        si5enz = (1/8)*(1+e)*(1+n)*(1-z)\n",
        "        si6enz = (1/8)*(1+e)*(1-n)*(1-z)\n",
        "        si7enz = (1/8)*(1-e)*(1+n)*(1-z)\n",
        "        si8enz = (1/8)*(1-e)*(1-n)*(1-z)\n",
        "        self.si = np.array([si1enz, si2enz, si3enz, si4enz, si5enz, si6enz, si7enz, si8enz])\n",
        "\n",
        "    def dsiide(self, e, n, z):\n",
        "        \"\"\" Derivative of approximation fn wrt z \"\"\"\n",
        "        dsi1de = (+1/8)*(1+n)*(1+z)\n",
        "        dsi2de = (+1/8)*(1-n)*(1+z)\n",
        "        dsi3de = (-1/8)*(1+n)*(1+z)\n",
        "        dsi4de = (-1/8)*(1-n)*(1+z)\n",
        "        dsi5de = (+1/8)*(1+n)*(1-z)\n",
        "        dsi6de = (+1/8)*(1-n)*(1-z)\n",
        "        dsi7de = (-1/8)*(1+n)*(1-z)\n",
        "        dsi8de = (-1/8)*(1-n)*(1-z)\n",
        "        self.dside = np.array([dsi1de, dsi2de, dsi3de, dsi4de, dsi5de, dsi6de, dsi7de, dsi8de])\n",
        "        # print(\"\\n dNi/de: {}\\n\".format(self.dside))\n",
        "    \n",
        "    def dsiidn(self, e, n, z):\n",
        "        \"\"\" Derivative of approximation fn wrt n \"\"\"\n",
        "        dsi1dn = (+1/8)*(1+e)*(1+z)\n",
        "        dsi2dn = (-1/8)*(1+e)*(1+z)\n",
        "        dsi3dn = (+1/8)*(1-e)*(1+z)\n",
        "        dsi4dn = (-1/8)*(1-e)*(1+z)\n",
        "        dsi5dn = (+1/8)*(1+e)*(1-z)\n",
        "        dsi6dn = (-1/8)*(1+e)*(1-z)\n",
        "        dsi7dn = (+1/8)*(1-e)*(1-z)\n",
        "        dsi8dn = (-1/8)*(1-e)*(1-z)\n",
        "        self.dsidn = np.array([dsi1dn, dsi2dn, dsi3dn, dsi4dn, dsi5dn, dsi6dn, dsi7dn, dsi8dn])\n",
        "        # print(\"\\n dNi/dn: {}\\n\".format(self.dsidn))\n",
        "\n",
        "    def dsiidz(self, e, n, z):\n",
        "        \"\"\" Derivative of approximation fn wrt z \"\"\"\n",
        "        dsi1dz = (+1/8)*(1+e)*(1+n)\n",
        "        dsi2dz = (+1/8)*(1+e)*(1-n)\n",
        "        dsi3dz = (+1/8)*(1-e)*(1+n)\n",
        "        dsi4dz = (+1/8)*(1-e)*(1-n)\n",
        "        dsi5dz = (-1/8)*(1+e)*(1+n)\n",
        "        dsi6dz = (-1/8)*(1+e)*(1-n)\n",
        "        dsi7dz = (-1/8)*(1-e)*(1+n)\n",
        "        dsi8dz = (-1/8)*(1-e)*(1-n)\n",
        "        self.dsidz = np.array([dsi1dz, dsi2dz, dsi3dz, dsi4dz, dsi5dz, dsi6dz, dsi7dz, dsi8dz])\n",
        "        # print(\"\\n dNi/dz: {}\\n\".format(self.dsidz))\n",
        "\n",
        "    def Je(self, e, n, z):\n",
        "        \"\"\" Jacobian and its inverse \"\"\"\n",
        "        x = []\n",
        "        y = []\n",
        "        z = []\n",
        "        for i in self.nodes:\n",
        "            x.append(i[0])\n",
        "            y.append(i[1])\n",
        "            z.append(i[2])\n",
        "        X = np.array([x, y, z])\n",
        "        self.Nprime = np.array([self.dside, self.dsidn, self.dsidz])\n",
        "        # print(\"\\n N`:\\n\", self.Nprime, \"\\n\")\n",
        "        self.J = np.dot(self.Nprime, np.transpose(X))\n",
        "        try:\n",
        "            self.Jinv = np.linalg.inv(self.J)\n",
        "        except np.linalg.LinAlgError:\n",
        "            self.Jinv = np.linalg.pinv(self.J)\n",
        "            print(\"The element level Jacobian is singular:\\n\", self.J)\n",
        "    \n",
        "    def makeB(self):\n",
        "        \"\"\"\n",
        "        creates B matrix for [K] = integral [B]t[D][B]det([J])dzdn which is stiffness matrix (8*8)\n",
        "        B=R1*R2*R3\n",
        "        \"\"\"\n",
        "        self.R1 = np.array([[1 ,0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                            [0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
        "                            [0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
        "                            [0, 1, 0, 1, 0, 0, 0, 0, 0],\n",
        "                            [0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "                            [0, 0, 1, 0, 0, 0, 1, 0, 0]])\n",
        "        zero33 = np.zeros((3, 3))\n",
        "        xaxis = np.concatenate((self.Jinv, zero33, zero33), axis=1)\n",
        "        yaxis = np.concatenate((zero33, self.Jinv, zero33), axis=1)\n",
        "        zaxis = np.concatenate((zero33, zero33, self.Jinv), axis=1)\n",
        "        self.R2 = np.concatenate((xaxis, yaxis, zaxis), axis=0)\n",
        "        zero38 = np.zeros((3, 8))\n",
        "        uaxis = np.concatenate((self.Nprime, zero38, zero38), axis=1)\n",
        "        vaxis = np.concatenate((zero38, self.Nprime, zero38), axis=1)\n",
        "        waxis = np.concatenate((zero38, zero38, self.Nprime), axis=1)\n",
        "        self.R3 = np.concatenate((uaxis, vaxis, waxis), axis=0)\n",
        "        self.B = np.dot(np.dot(self.R1, self.R2), self.R3)\n",
        "        # print(\"\\nR1R2:\\n\", np.dot(self.R1, self.R2), \"\\nR3:\\n\", self.R3, \"\\nB:\\n\", self.B, \"\\n\")\n",
        "\n",
        "    def makeK(self, mat):\n",
        "        \"\"\"\n",
        "        Will do numerical integration with only one quadrature point\n",
        "        n=z=0 and W=4\n",
        "        \"\"\"\n",
        "        # start = time.process_time()\n",
        "        pts, wts = np.polynomial.legendre.leggauss(2)\n",
        "        _K = np.zeros((24, 24))\n",
        "        for i in range(0, len(wts)):\n",
        "            for j in range(0, len(wts)):\n",
        "                for k in range(0, len(wts)):\n",
        "                    e = pts[i]\n",
        "                    n = pts[j]\n",
        "                    z = pts[k]\n",
        "                    wi = wts[i]\n",
        "                    wj = wts[j]\n",
        "                    wk = wts[k]\n",
        "                    self.sizni(e, n, z)\n",
        "                    self.dsiide(e, n, z)\n",
        "                    self.dsiidn(e, n, z)\n",
        "                    self.dsiidz(e, n, z)\n",
        "                    self.Je(e, n, z)\n",
        "                    self.makeB()\n",
        "                    detJ = np.linalg.det(self.J)\n",
        "                    # print(\"\\ne:{}, n:{}, z:{}; wi:{}, wj:{}, wk:{}\\n\".format(e, n, z, wi, wj, wk))\n",
        "                    _K += wi*wj*wk*detJ*(np.dot(np.transpose(self.B), np.dot(mat.C, self.B)))\n",
        "        self.K=_K\n",
        "        # print(\"Time spent for calculating K_local: {}\".format(time.process_time() - start))\n",
        "        # print(self.K)\n",
        "        # print(np.shape(self.K))\n",
        "\n",
        "def load_data():\n",
        "    node_list=[]\n",
        "    with open('/content/drive/My Drive/Colab Notebooks/nodes.csv', 'r') as f:\n",
        "        csv_reader = csv.reader(f, delimiter=',')\n",
        "        for row in csv_reader:\n",
        "            tmp = Node(float(row[1]), float(row[2]), float(row[3]), int(row[0]))\n",
        "            node_list.append(tmp)\n",
        "        f.close()\n",
        "\n",
        "    _CM = []\n",
        "    with open('/content/drive/My Drive/Colab Notebooks/connectivity.csv', 'r') as f:\n",
        "        csv_reader = csv.reader(f, delimiter=',')\n",
        "        for row in csv_reader:\n",
        "            _CM.append([int(ri) for ri in row])\n",
        "        f.close()\n",
        "\n",
        "    no_elements = len(_CM)\n",
        "    no_nodes = len(node_list)\n",
        "    CM = np.zeros((no_elements, 8))\n",
        "    mval=0\n",
        "    # from operator import attrgetter\n",
        "    for element in _CM:\n",
        "        CM[element[0]-1] = element[1:9]\n",
        "        # val = max(element, key=attrgetter('gi')).gi\n",
        "        # val = max(element[1:9])\n",
        "        # if mval < val:\n",
        "        #     mval = val\n",
        "    # print(mval) #396\n",
        "    # ----------------\n",
        "    # for example\n",
        "    # CM =  np.array([[1, 2, 4, 5, 10, 11, 13, 14],[2, 3, 5, 6, 11, 12, 14, 15],[4, 5, 7, 8, 13, 14, 16, 17],[5, 6, 8, 9, 14, 15, 17, 18]])\n",
        "    # print(np.shape(CM))\n",
        "    # (250, 8)\n",
        "    # ----------------\n",
        "    L = np.zeros((no_elements, 24))\n",
        "    a=[]\n",
        "    j=1\n",
        "    for i in range(0, no_nodes):\n",
        "        a.append([p for p in range(j, j+3)])\n",
        "        j+=3\n",
        "    # print(np.shape(a))\n",
        "    # (395, 3)\n",
        "    for i in range(0, no_elements):\n",
        "        k=0\n",
        "        for j in range(0, 8):\n",
        "            L[i][k:k+3]=a[int(CM[i][j])-1]\n",
        "            k+=3\n",
        "    return node_list, CM, L\n",
        "\n",
        "def Assemble(CM, L, node_list, mat, length, breadth, height):\n",
        "    \"\"\"\n",
        "    For rectangular linear element (=> with 4 nodes per element), No. of nodes = 4*ne\n",
        "    Connectivity matrix CM\n",
        "\n",
        "    \"\"\"\n",
        "    K_list = []\n",
        "    # print(\"Finding element level stiffness matrices\")\n",
        "    for idx, elt in enumerate(CM): #as CM=matrix, elt=row of matrix which is itself a list which means, for all elements in connectivity matrix\n",
        "        # print(\"Element no. {}\".format(idx+1))\n",
        "        is_onboundary=False\n",
        "        local_node_list = []\n",
        "        for gi in elt: # gi is the index of the nodes of element  \n",
        "            ni = node_list[int(gi)-1]\n",
        "            if ni.x==length or ni.y==breadth or ni.z==height:\n",
        "                is_onboundary=True\n",
        "            local_node_list.append(ni)\n",
        "        element = Element(local_node_list, is_onboundary)\n",
        "        element.makeK(mat)\n",
        "        K_list.append(element.K)\n",
        "        # print(element.K)\n",
        "        \n",
        "    no_nodes = len(node_list)\n",
        "    K = np.zeros((3*no_nodes, 3*no_nodes))\n",
        "    no_elements = len(K_list)\n",
        "    # print(\"Assembling\")\n",
        "    for i in range(0, no_elements):\n",
        "        Ki = K_list[i]\n",
        "        for j in range(0, 24):\n",
        "            for k in range(0, 24):\n",
        "                l=int(L[i][j])-1\n",
        "                m=int(L[i][k])-1\n",
        "                K[l][m]+=Ki[j][k]\n",
        "    # print(K)\n",
        "    return K\n",
        "\n",
        "def ApplyBC(K, node_list, F0, length, breadth, height):\n",
        "    \"\"\"\n",
        "    Apply Dirichlet (displacement) and Neuman(force) boundary conditions\n",
        "    Situation is: The composite is attached to a wall at x=0 with the corresponding surface.\n",
        "    Along the opposite surface, two distributed forces are acting in opposite directions along upper and lower halves of the curve.\n",
        "    Also, a distributed force along x direction is acting on whole oppsite surface.\n",
        "    \"\"\"\n",
        "    no_nodes = len(node_list)\n",
        "    F = np.zeros((3*no_nodes, 1))\n",
        "    for i in range(0, no_nodes):\n",
        "        node = node_list[i]\n",
        "        if node.x==length:\n",
        "            F[i]=-F0\n",
        "        # if node.x==length:\n",
        "        #     if node.y<=breadth/2:\n",
        "        #         F[i]=-F0\n",
        "        #         # F[i+1]=F0\n",
        "        #     else:\n",
        "        #         F[i]=-F0\n",
        "        #         # F[i+1]=-F0\n",
        "        if node.x==0:\n",
        "            K[i, :]=0\n",
        "            K[:, i]=0\n",
        "            K[i, i]=1\n",
        "            K[i+1, :]=0\n",
        "            K[:, i+1]=0\n",
        "            K[i+1, i+1]=1\n",
        "            K[i+2, :]=0\n",
        "            K[:, i+2]=0\n",
        "            K[i+2, i+2]=1\n",
        "    return F, K\n",
        "\n",
        "def savePlots(node_list, U):\n",
        "    pts = np.zeros((len(node_list), 3))\n",
        "    disps = np.zeros((len(node_list), 3))\n",
        "    for i in range(0, len(node_list)):\n",
        "        node = node_list[i]\n",
        "        pts[i]+=np.array([node.x, node.y, node.z])\n",
        "        disps[i]+=np.array([U[3*i], U[3*i+1], U[3*i+2]])\n",
        "\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(1,1,1, projection='3d')\n",
        "    ax.scatter(pts[:, 0], pts[:, 1], pts[:, 2], marker='.', s=10, c=\"black\", alpha=0.5)\n",
        "    # ax2 = fig.add_subplot(1,1,1, projection='3d')\n",
        "    # ax.scatter(disps[:, 0], disps[:, 1], disps[:, 2], marker='.', s=10, c=\"magenta\", alpha=0.5)\n",
        "    ax.set_xlabel('X')\n",
        "    ax.set_ylabel('Y')\n",
        "    ax.set_zlabel('Z')\n",
        "    plt.show()\n",
        "\n",
        "node_list, CM, L= load_data()\n",
        "# print(L)\n",
        "# print(CM)\n",
        "length = 15\n",
        "breadth = 10\n",
        "height = 5\n",
        "F0 = 1\n",
        "E=210e9\n",
        "nu=0.3\n",
        "# for plane stress\n",
        "c = E/((1+nu)*(1-2*nu))\n",
        "c11 = c*(1-nu)\n",
        "c12 = c*nu\n",
        "c13 = c12\n",
        "c21 = c12\n",
        "c22 = c11\n",
        "c23 = c12\n",
        "c31 = c12\n",
        "c32 = c12\n",
        "c33 = c11\n",
        "c44 = c*(1-2*nu)/2\n",
        "c55 = c44\n",
        "c66 = c44\n",
        "\n",
        "mat = Material(c11, c12, c13, 0, 0, 0, c21, c22, c23, 0, 0, 0, c31, c32, c33, 0, 0, 0, 0, 0, 0, c44, 0, 0, 0, 0, 0, 0, c55, 0, 0, 0, 0, 0, 0 ,c66)\n",
        "start = time.process_time()\n",
        "_K = Assemble(CM, L, node_list, mat, length, breadth, height)\n",
        "df1 = pd.DataFrame(_K)\n",
        "F, K = ApplyBC(_K, node_list, F0, length, breadth, height)\n",
        "print(K)\n",
        "print(np.shape(K))\n",
        "U = np.linalg.solve(K, F).flatten()\n",
        "print(\"Time spent: {}\".format(time.process_time() - start))\n",
        "# print(\"K\\n\", np.shape(K), \"\\n\", K, \"\\nF\\n\", np.shape(F), \"\\n\", F)\n",
        "no_elements = len(CM)\n",
        "# print(\"\\nNo. of elements:{}\\n\".format(no_elements), \"\\nNo. of nodes:{}\\n\".format(len(node_list)))\n",
        "savePlots(node_list, U)\n",
        "usq = np.zeros(len(U))\n",
        "for i in range(int(len(U)/3)):\n",
        "    usq[i] = np.sqrt(np.power(U[3*i], 2)+np.power(U[3*i+1], 2)+np.power(U[3*i+2], 2))\n",
        "print(usq)\n",
        "print(U)\n",
        "\n",
        "np.savetxt(\"foo.csv\", K, delimiter=\",\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-e53529f75c5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m \u001b[0mnode_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m \u001b[0;31m# print(L)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;31m# print(CM)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-e53529f75c5b>\u001b[0m in \u001b[0;36mload_data\u001b[0;34m()\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0mcsv_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsv_reader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m             \u001b[0mnode_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogxT9t3Y7TiR",
        "colab_type": "code",
        "outputId": "91f0c48e-3726-4252-e575-f72fef733535",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# np.savetxt(\"foo - foo.csv\", K, delimiter=\",\")\n",
        "K_ = np.genfromtxt('foo - foo.csv', delimiter=',')\n",
        "U = np.linalg.solve(K_, F).flatten()\n",
        "print(U)\n",
        "print(\"{} zero rows out of {}\\n\".format(sum([ int(sum(pow(K[i, :], 2))==0) for i in range(len(K))]), len(K))) \n",
        "print(int(True))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 1.0e-09  1.2e-09  2.3e-09  2.2e-09 -1.7e-11  1.3e-11 -2.3e-09 -2.3e-09  0.0e+00  0.0e+00  0.0e+00 -4.2e+00  4.0e-23  0.0e+00 -7.2e-11 -1.4e-09 -3.9e-10  1.6e-09  7.4e-10  1.1e-09  9.8e-10 -6.4e-11 -1.4e-09  5.1e-10 -2.6e-09  7.7e-10  2.2e-10 -3.1e-09 -2.5e-09  7.6e-10 -2.5e-09 -3.9e-09 -3.9e-09 -3.1e-09  2.2e-10 -2.5e-09 -2.0e-11 -1.1e-11 -1.4e-09 -1.4e-09 -1.9e-09  7.2e-10  7.5e-10 -1.9e-09 -2.8e-09 -5.1e-11 -2.8e-09 -8.2e-11]\n",
            "0 zero rows out of 48\n",
            "\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMeSS2kSkKy4",
        "colab_type": "code",
        "outputId": "d8294642-39df-44bf-d1ff-4ccced3a7c08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# a, wt = np.polynomial.hermite.hermgauss(3)\n",
        "np.set_printoptions(precision=10, linewidth=10000)\n",
        "\n",
        "a, wt = np.polynomial.legendre.leggauss(3)\n",
        "print(a, wt)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.2247448714  0.            1.2247448714] [0.2954089752 1.1816359006 0.2954089752]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FA5xeZyNckBk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(F[:100])\n",
        "print(sum(F))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGvVhLnyhW7t",
        "colab_type": "code",
        "outputId": "712ca90d-6f61-4f6c-b07b-c2caed354d7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from scipy.spatial import ConvexHull\n",
        "\n",
        "# 8 points defining the cube corners\n",
        "pts = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0],\n",
        "                [0, 0, 1], [1, 0, 1], [1, 1, 1], [0, 1, 1], ])\n",
        "hull = ConvexHull(pts)\n",
        "colors = np.array([50]*len(hull.simplices))\n",
        "plt.tripcolor(pts[:,0], pts[:,1], hull.simplices.copy(), facecolors=colors, edgecolors='k')\n",
        "plt.draw()\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(1,1,1, projection='3d')\n",
        "x=pts[:, 0]\n",
        "y=pts[:, 1]\n",
        "z=pts[:, 2]\n",
        "ax.set_xlabel('X')\n",
        "ax.set_ylabel('Y')\n",
        "ax.set_zlabel('Z')\n",
        "ax.scatter(x,y,z, marker='.', s=10, c=\"black\", alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXb0lEQVR4nO3de2hk53nH8e8jyVrtrq9raS9aaSx5\nd12yJIUY4aQEGpdcWPsPLyRtsI1xU0yWpLUpTVpwSXGD81damkKo22RLXTeBxHEcCIJsMDS1MYQ4\n9QanTrzBQat1otFlLrquNLrOPP1jZtdjWVqNpDNz5pzz+4BgLmdnnlcz89tXZ877HHN3REQk+lrC\nLkBERIKhQBcRiQkFuohITCjQRURiQoEuIhITbWE9cWdnp/f19YX19CIikfTzn/887+5dG90XWqD3\n9fVx/vz5sJ5eRCSSzOy3m92nXS4iIjGhQBcRiQkFuohITCjQRURiQoEuIhITWwa6mT1tZlkz+9Um\n95uZfc3MhszsdTO7M/gyRURkK7XM0J8BTl3j/nuAE5WfM8C/7b4sERHZri2PQ3f3l82s7xqbnAa+\n6eU+vK+Y2c1mdsTdxwOq8R1WV1c5e/ZsPR5aRKTuisUiZ86coaOjI/DHDmJh0VFgpOp6unLbuwLd\nzM5QnsWTSqV29GTf//73eebR59nD3h39exGRsFxmmkmymBmPPfZY4I/f0JWi7n4WOAswMDCwozNr\ntLa2soe97LX9gdYmIlIvRS+SYYQO9tHH79HSUp/jUYII9FGgt+p6T+U2EZHEm/VJ5pjhKP20WAuL\nvlC35wriv4lB4OHK0S4fBGbrtf9cRCQqVn2FtA8D0GvHaLH6HyW+5QzdzL4D3A10mlka+HvgOgB3\n/zpwDrgXGAIKwJ/Vq1gRkSiY9hwLzNFdmZU3Si1HuTywxf0O/EVgFYmIRNSKL5FllJvopMeONfz5\nQ2ufKyISF+7OJBmWWQwlyK9QoIuI7MKSF8gxzq0cotMOh1qLAl1EZAfcnRxjlCjSG+KsvJoCXURk\nmwo+T55xDtNLuwW/4nOnFOgiIjVydzKkMYyUnQi7nHdRoIuI1GDB55giwxFuo83awy5nQwp0EZFr\nKHmJDCNcxx56m3BWXk2BLiKyiTmfZoZJuumjzZo/Lpu/QhGRBlvzVTKk2ct+UnY87HJqpkAXEalS\nbqY1zVFub+iy/SBEq1oRkTp5u5lWC712PHJhDpqhi4gw5VkKzNNNXySD/AoFuogk1rIvkWOUm+ni\ngB0Mu5xdU6CLSOKUm2lNsMJKqM20gqZAF5FEWfQCecboopsO2xd2OYFSoItIIpSbaY1SwumN0KGI\n26FAF5HYKzfTmuAwPU3VTCtoCnQRia2Sl8iSppXWSC0Q2ikFuojE0rzPMkWWbvojsWw/CMkYpYgk\nRsmLTDBCOx1N2eK2nhToIhIbcz7NLHmOcizSC4R2KnkjFpHYWfVVRv0SRYr02olEhjlohi4iETft\neRaYpZv+xAb5FckevYhE1oovk/aLtNFKjyVzF8t6mqGLSORMeYYlCpqVr6NAF5HIWPJF8oxxCwc5\nYIfCLqfpKNBFpOm5O3nGWWM1Vs20gqZAF5GmtugL5BmPZTOtoCnQRaQpuTtZRgFi20wraAp0EWk6\nC36ZSTIcJkW7tYddTmQo0EWkaZS8RIY0bVyXiGZaQavpeB8zO2Vmb5rZkJk9vsH9KTN70cxeM7PX\nzeze4EsVkTi77DOkGaaLbrrsSNjlRNKWgW5mrcBTwD3ASeABMzu5brO/A55z9/cD9wP/GnShIhJP\nRS8y5pdYYZmUHU9MZ8R6qOU3dxcw5O7DAGb2LHAauFC1jQM3Vi7fBIwFWaSIxNOsTzHHFEe5XQuE\nAlDLb/AoMFJ1PV25rdqXgIfMLA2cAx7b6IHM7IyZnTez87lcbgflikgcrPoKaR+GyungFObBCOq3\n+ADwjLv3APcC3zJ79yvk7mfdfcDdB7q6ugJ6ahGJkmnPkWGEbvq4yW4Nu5xYqSXQR4Hequs9lduq\nPQI8B+DuPwU6gM4gChSReFjxJUb8Im20q5lWndTyG30VOGFm/WbWTvlLz8F12/wO+AiAmb2HcqBr\nn4qIlJfte4Yc4/TaMW6wm8IuKba2/FLU3dfM7FHgBaAVeNrd3zCzJ4Hz7j4IfAH4dzP7K8pfkH7a\n3b2ehYtI81vyAjnGuJXDdKqZVt3VdHyQu5+j/GVn9W1PVF2+AHwo2NJEJKrcnRxjFFnTsv0G0gGf\nIhKogs+TZ5yD9NJhHWGXkygKdBEJhLuTIY1hpOxE2OUkkgJdRHZtweeYIssRUrSpmVZoFOgismPl\nZlojXEe79pU3AQW6iOzInE8zwyTd9Kn/SpPQqyAi21L0NSYYYS/71eK2ySjQRaRmsz7JHNNqptWk\n9IqIyJbebqaFmmk1Mc3QReSapjxLgXm66VOQNzkFuohsaNmXyDHKzXRxwA6GXY7UQIEuIu/g7kwy\nwQor9NixsMuRbVCgi8hVi14gzxhddNNh+8IuR7ZJgS4iV5tplShpgVCEKdBFEu5KM63D9NKuZlqR\npkAXSaiSl8iSppVWNdOKCQW6SALN+yxTZOmmX8v2Y0SvpEiClLzIBGna2aNZeQwp0EUSYs6nmWVS\ny/ZjTK+qSMyt+Sqjfunq6eAU5vGlGbpIjE17ngVm6aZfQZ4AeoVFYmjFl0n7MG200mPHFOYJoRm6\nSMxMeYYlCmqmlUAKdJGYWPJF8oxxCwc5YIfCLkdCoEAXiTh3J88Ea2qmlXgKdJEIW/QF8oyrmZYA\nCnSRSHJ3sowCqJmWXKVAF4mYBb/MFBkOkaLd2sMuR5qIAl0kIkpeIkOaNq7TrFw2pEAXiYDLPsM0\nebrpUzMt2ZTeGSJNrOhFMoywh72kNCuXLdS06sDMTpnZm2Y2ZGaPb7LNp8zsgpm9YWbfDrZMkeSZ\n9SnGeIvDpLhVx5VLDbacoZtZK/AU8DEgDbxqZoPufqFqmxPA3wIfcvdpM50iXGSnVn2FLGn2cxO9\nOq5ctqGWXS53AUPuPgxgZs8Cp4ELVdt8BnjK3acB3D0bdKEiSVBupjWnZfuyI7W8Y44CI1XX05Xb\nqt0B3GFmPzGzV8zs1EYPZGZnzOy8mZ3P5XI7q1gkhlZ8iRG/SBtt9Jj6lcvOBPWlaBtwArgb6AFe\nNrP3uftM9UbufhY4CzAwMOABPbdIZLk7k2RZoaDdK7JrtUwDRoHequs9lduqpYFBd19190vAbygH\nvIhsYskLpBlmH/vptv6wy5EYqCXQXwVOmFm/mbUD9wOD67b5AeXZOWbWSXkXzHCAdYrEhruT9VGm\nydFrx9hn14ddksTElrtc3H3NzB4FXgBagafd/Q0zexI47+6Dlfs+bmYXgCLwN+4+Wc/CRaKo4PPk\nGecgvXRYR9jlSMzUtA/d3c8B59bd9kTVZQc+X/kRkXXcnQxpDCNl2hsp9aGVoiJ1tuBzTJFVMy2p\nOwW6SJ2Um2mN0Ea7mmlJQyjQRepgzmeYUTMtaTC900QCVPQ1JhhhL/vVTEsaToEuEpBZn2SWGXro\n10pPCYXedSK7tOorpMutjkjZMYW5hEYzdJFdmPIsBebVTEuaggJdZAeWfYkco9xMJwfULVqahAJd\nZBvKzbQmWGaZHjXTkiajQBep0aIXyDNGJ0fotP1hlyPyLgp0kS24OznGKFHSAiFpagp0kWso+DyT\nTHCIHtrVTEuanAJdZAMlL5ElTQutmpVLZCjQRda57LNMk6Wbfi3bl0jRu1WkouRFJkjTzh61uJVI\nUqCLAHM+zQyT9KATNEt06Z0ribbmq4z6JYqskbLjCnOJNM3QJbFmPM88s3SrmZbEhN7Fkjgrvkza\nh2mhlR4105IY0QxdEmXKsyyyoGZaEksKdEmEZV8kxxi30KVmWhJbCnSJNXcnzwRrrKiZlsSeAl1i\na9EXyDNOF9102L6wyxGpOwW6xI67k2UUx7VsXxJFgS6xsuCXmSTDYVK0W3vY5Yg0lAJdYqHkJTKk\naaONlGblklAKdIm8yz7DNHm66VMzLUk0vfslsopeJMMIe9irWbkICnSJqFmfYo4pjqqZlshV+iRI\npKz6KqM+fPUIFoW5yNs0Q5fImPY8C2qmJbKpmj4VZnbKzN40syEze/wa233SzNzMBoIrUZKu3Ezr\nIm20qZmWyDVsOUM3s1bgKeBjQBp41cwG3f3Cuu1uAP4S+Fk9CpVkmvQMyxS0bF+kBrVMde4Chtx9\n2N1XgGeB0xts92XgK8BSgPVJQi35IiN+kb3sp9v6wy5HJBJqCfSjwEjV9XTltqvM7E6g191/eK0H\nMrMzZnbezM7ncrltFyvx5+5kfYxpsvTaMfbZ9WGXJBIZu94ZaWYtwFeBL2y1rbufdfcBdx/o6ura\n7VNLzBR8nhGGuJEDHLHbwi5HJHJqOcplFOitut5Tue2KG4D3Ai+ZGcBhYNDM7nP380EVKvF1pZkW\nQMpOhFyNSHTVEuivAifMrJ9ykN8PPHjlTnefBTqvXDezl4C/VphLLRZ8jimyHFIzLZFd23KXi7uv\nAY8CLwC/Bp5z9zfM7Ekzu6/eBUo8lbzEuP+WAgv02nGFuUgAalpY5O7ngHPrbntik23v3n1ZEmdz\nPsOMmmmJBE6fJmmYK820OtinZloidaBAl4aY9UlmmaFHy/ZF6kafLKmrVV8h7cMApLRsX6SuNEOX\nupnyLAXm6aZPQS7SAAp0CdyyL5FjlJvp5IAdDLsckcRQoEtg3J1JMiyzpGZaIiFQoEsglrxAjjE6\nOUKnHQ67HJFEUqDLrrg7OcYoUaJXhyKKhEqBLjtW8HkmmeAQPbRbR9jliCSeAl22zd3JMEILrZqV\nizQRBbpsy4LPMkWWI9xGm/qviDQVBbrUpOQlJhihnT30qsWtSFNSoMuW5nyaGSbp4XYtEBJpYvp0\nyqbWfJVRv0SRNVJ2XGEu0uQ0Q5cNzXieeWbpVjMtkcjQJ1XeYcWXSfswLbTSo2ZaIpGiGbpcpWZa\nItGmQBeWfZEcY9xCl5ppiUSYAj3Bys20JlhlRc20RGJAgZ5Qi75AnnG66KbD9oVdjogEQIGeMO5O\nllEc17J9kZhRoCdIwS+TJ8NhUrRr2b5I7CjQE6DkJTKkaaONlGblIrGlQI+5eZ9hijzd9NFmerlF\n4kyf8JgqepEMI+yhQ7NykYRQoMfQrE8xxxRH1UxLJFH0aY+RVV9l1IevHsGiMBdJFs3QY2La8yyo\nmZZIoumTH3HlZloXaVMzLZHE0ww9wiY9wzIFLdsXEaDGGbqZnTKzN81syMwe3+D+z5vZBTN73cx+\nbGa3BV+qXLHki4z4EHvZT7f1h12OiDSJLQPdzFqBp4B7gJPAA2Z2ct1mrwED7v77wPPAPwRdqFSW\n7fsY02TptePss+vDLklEmkgtM/S7gCF3H3b3FeBZ4HT1Bu7+orsXKldfAXqCLVMWfYERhriRmzmi\nP4BEZAO17EM/CoxUXU8DH7jG9o8AP9roDjM7A5wBSKVSNZaYbFeaaQGk7ETI1YhIMwv0S1EzewgY\nAD680f3ufhY4CzAwMOBBPnccLfhlpshwSM20RKQGtQT6KNBbdb2ncts7mNlHgS8CH3b35WDKS6a3\nm2ldpxa3IlKzWgL9VeCEmfVTDvL7gQerNzCz9wPfAE65ezbwKhPkss8wrWZaIrIDW34p6u5rwKPA\nC8Cvgefc/Q0ze9LM7qts9o/A9cD3zOwXZjZYt4pjquhFxvwtVlkhZccV5iKybTWlhrufA86tu+2J\nqssfDbiuRJn1SeaY4aiW7YvILig9QrTqK6R9GIBeLdsXkV3S3/UhmfYcC1ymmz4FuYgEQknSYMu+\nVGmm1U6PqV+5iARHM/QGcXcmybDMopppiUhdKNAbYMkL5BijkyN02uGwyxGRmFKg15G7k2OMEiUt\nEBKRulOg10nB58kzzmF6abeOsMsRkQRQoAfM3cmQpgVTMy0RaSgFeoAWfJYpshzhNtrUTEtEGkyB\nHoByM60RrmMPvZqVi0hIFOi7NOfTzDLJETXTEpGQKYF2aM1XyZBmH9frCBYRaQoK9B0oN9Oa5iha\n6SkizUNptA1vN9NqodeOK8xFpKlohl6jKc9SYF7NtESkaSnQt7DsS2RJcwsHOWAHwy5HRGRTCvRN\nlJtpTbDKir70FJFIUKBvYNEXyDNOF9102L6wyxERqYkCvUq5mdYoJVyzchGJHAV6RcEvkyfDYXrU\nTEtEIinxgV5etp+mjTZSmpWLSIQlOtDnK820uunXsn0RibxEpljJi0wwwh461OJWRGIjcYF+pZmW\nlu2LSNwkJtFWfZVRv0SRopbti0gsJWKGPu15Fpilm34FuYjEVqzTbcWXSftF2milx44pzEUk1mI7\nQ5/0DMsUNCsXkcSIXaAv+SI5RjnAIW61Q2GXIyLSMLEJ9PKy/XGKrGrZvogkUiwCfdEXyDHOQTXT\nEpEEq2nnspmdMrM3zWzIzB7f4P49Zvbdyv0/M7O+oAvdiLuT8TRzTJOy4wpzEUm0LQPdzFqBp4B7\ngJPAA2Z2ct1mjwDT7n4c+GfgK0EXut6CX2aEi9zCQQ5ZT72fTkSk6dWyy+UuYMjdhwHM7FngNHCh\napvTwJcql58H/sXMzN09wFoBKBaLjLe8RXupgy6OUGSVRV8N+mlEROpimcW6PXYtgX4UGKm6ngY+\nsNk27r5mZrPArUC+eiMzOwOcAUilUjsq+BOf+AQr/7mCme3o34uIhKlUKvHggw/W5bEb+qWou58F\nzgIMDAzsaPbe3t7Oww8/HGhdIiJxUMuXoqNAb9X1nsptG25jZm3ATcBkEAWKiEhtagn0V4ETZtZv\nZu3A/cDgum0GgT+tXP5j4H/qsf9cREQ2t+Uul8o+8UeBF4BW4Gl3f8PMngTOu/sg8B/At8xsCJii\nHPoiItJANe1Dd/dzwLl1tz1RdXkJ+JNgSxMRke1Q1yoRkZhQoIuIxIQCXUQkJhToIiIxYWEdXWhm\nOeC3O/znnaxbhZoAGnMyaMzJsJsx3+buXRvdEVqg74aZnXf3gbDraCSNORk05mSo15i1y0VEJCYU\n6CIiMRHVQD8bdgEh0JiTQWNOhrqMOZL70EVE5N2iOkMXEZF1FOgiIjHR1IHerCenrqcaxvx5M7tg\nZq+b2Y/N7LYw6gzSVmOu2u6TZuZmFvlD3GoZs5l9qvJav2Fm3250jUGr4b2dMrMXzey1yvv73jDq\nDIqZPW1mWTP71Sb3m5l9rfL7eN3M7tz1k7p7U/5QbtV7EbgdaAf+Dzi5bps/B75euXw/8N2w627A\nmP8I2Fe5/LkkjLmy3Q3Ay8ArwEDYdTfgdT4BvAbcUrl+MOy6GzDms8DnKpdPAm+FXfcux/yHwJ3A\nrza5/17gR4ABHwR+ttvnbOYZ+tWTU7v7CnDl5NTVTgP/Vbn8PPARi/bJRrccs7u/6O6FytVXKJ9B\nKspqeZ0Bvgx8BVhqZHF1UsuYPwM85e7TAO6ebXCNQatlzA7cWLl8EzDWwPoC5+4vUz4/xGZOA9/0\nsleAm83syG6es5kDfaOTUx/dbBt3XwOunJw6qmoZc7VHKP8PH2Vbjrnyp2ivu/+wkYXVUS2v8x3A\nHWb2EzN7xcxONay6+qhlzF8CHjKzNOXzLzzWmNJCs93P+5YaepJoCY6ZPQQMAB8Ou5Z6MrMW4KvA\np0MupdHaKO92uZvyX2Evm9n73H0m1Krq6wHgGXf/JzP7A8pnQXuvu5fCLiwqmnmGnsSTU9cyZszs\no8AXgfvcfblBtdXLVmO+AXgv8JKZvUV5X+NgxL8YreV1TgOD7r7q7peA31AO+KiqZcyPAM8BuPtP\ngQ7KTaziqqbP+3Y0c6An8eTUW47ZzN4PfINymEd9vypsMWZ3n3X3Tnfvc/c+yt8b3Ofu58MpNxC1\nvLd/QHl2jpl1Ut4FM9zIIgNWy5h/B3wEwMzeQznQcw2tsrEGgYcrR7t8EJh19/FdPWLY3wRv8S3x\nvZRnJheBL1Zue5LyBxrKL/j3gCHgf4Hbw665AWP+byAD/KLyMxh2zfUe87ptXyLiR7nU+Dob5V1N\nF4BfAveHXXMDxnwS+AnlI2B+AXw87Jp3Od7vAOPAKuW/uB4BPgt8tuo1fqry+/hlEO9rLf0XEYmJ\nZt7lIiIi26BAFxGJCQW6iEhMKNBFRGJCgS4iEhMKdBGRmFCgi4jExP8DISol3DfWrHoAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCei_lDYwiHM",
        "colab_type": "code",
        "outputId": "7fe87494-35a0-4100-810a-7d320306b08c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.tri as mtri\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "a, b=cmesh(11)\n",
        "\n",
        "points = 1331\n",
        "data = np.zeros([points,3])\n",
        "x = a[:, 0]\n",
        "y = a[:, 1]\n",
        "z = a[:, 2]\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(1,1,1, projection='3d')\n",
        "\n",
        "ax.scatter(x,y,z, marker='.', s=10, c=\"black\", alpha=0.5)\n",
        "ax.set_xlabel('X')\n",
        "ax.set_ylabel('Y')\n",
        "ax.set_zlabel('Z')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1mLgnO5YG8vry/8DRdBah16O+TAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}