DMR_Multilayered_Torus.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DMR_Multilayered_Domain.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "vS_YdSbqFOln",
        "9SJXe6I5JUwF",
        "r6GW2fwLE042",
        "pfryWRtY-FU-"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/van-dang/FEniCS-Colab/blob/master/DMR_Multilayered_Torus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "vS_YdSbqFOln",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# About the code"
      ]
    },
    {
      "metadata": {
        "id": "CiQ9r51xKgWh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This demo solves the Bloch-Torrey equation applied to computational diffusion MRI using \n",
        "# the finite element method coupled with the theta-method for the spatial discretization.\n",
        "\n",
        "# The scope of usage: \n",
        "# (1) multilayered structures, (2) membrane permeability, (3) pure homogeneous Neumann \n",
        "\n",
        "# Copyright (C) 2017 Van-Dang Nguyen (vdnguyen@kth.se)\n",
        "\n",
        "# This file is part of DOLFIN.\n",
        "\n",
        "# DOLFIN is free software: you can redistribute it and/or modify\n",
        "# it under the terms of the GNU Lesser General Public License as published by\n",
        "# the Free Software Foundation, either version 3 of the License, or\n",
        "# (at your option) any later version.\n",
        "\n",
        "# DOLFIN is distributed in the hope that it will be useful,\n",
        "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
        "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
        "# GNU Lesser General Public License for more details.\n",
        "\n",
        "# You should have received a copy of the GNU Lesser General Public License\n",
        "# along with DOLFIN. If not, see <http://www.gnu.org/licenses/>.\n",
        "\n",
        "# First added:  2017-10-10\n",
        "# Last changed: 2017-11-18\n",
        "\n",
        "# This demo is maintained by Van-Dang Nguyen\n",
        "# Please report problems to vdnguyen@kth.se"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9SJXe6I5JUwF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Setting a working environment with FEniCS"
      ]
    },
    {
      "metadata": {
        "id": "4jWzVxURFVLj",
        "colab_type": "code",
        "outputId": "f39621e5-b1f3-4a04-abf3-3b5bd7528541",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "import platform, sys\n",
        "python_version=platform.python_version()\n",
        "from distutils.version import LooseVersion, StrictVersion\n",
        "\n",
        "if ( LooseVersion(python_version) < LooseVersion(\"3.0.0\")):\n",
        "    print(\"Python3 is needed!\");\n",
        "    print(\"How to fix: Runtime/Change_runtime_type/Python 3\");\n",
        "    sys.exit()\n",
        "    \n",
        "try:\n",
        "    from dolfin import *; from mshr import *\n",
        "except ImportError as e:\n",
        "    !apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "    !add-apt-repository -y ppa:fenics-packages/fenics\n",
        "    !apt-get update -qq\n",
        "    !apt install -y --no-install-recommends fenics\n",
        "    from dolfin import *; from mshr import *\n",
        "    \n",
        "import matplotlib.pyplot as plt;\n",
        "from IPython.display import clear_output, display; import time; import dolfin.common.plotting as fenicsplot \n",
        "import time\n",
        "\n",
        "import os, sys, shutil\n",
        "\n",
        "dolfin_version = dolfin.__version__\n",
        "print ('dolfin version:', dolfin_version)\n",
        "\n",
        "!rm -rf * # clean up all files\n",
        "# Useful commands\n",
        "# Remove an empty folder      : os.rmdir(\"my_results\")\n",
        "# Remove a folder with files  : shutil.rmtree(\"results\")\n",
        "# Make a folder               : os.mkdir(\"my_results\")\n",
        "# Runtime/Change_runtime_type/Python3"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dolfin version: 2018.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r6GW2fwLE042",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Define a mesh"
      ]
    },
    {
      "metadata": {
        "id": "ksCOgH6e6d8e",
        "colab_type": "code",
        "outputId": "e70de5d8-6f89-42e7-8a86-f91f835694a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "cell_type": "code",
      "source": [
        "# # # # # # # # Create .geo file for gmsh # # # # # # # #\n",
        "\n",
        "R=[5, 7.5, 10.];\n",
        "\n",
        "geo_file = open(\"multi_layer_torus.geo\", \"w\")\n",
        "\n",
        "geo_file.write(\"i = 0;\\n\")\n",
        "geo_file.write(\"N = 6;\\n\")\n",
        "geo_file.write(\"lc = 2.0;\\n\")\n",
        "\n",
        "geo_file.write(\"L = 20.0;\\n\")\n",
        "\n",
        "geo_file.write(\"xc[]={0,-L,0};\\n\")\n",
        "\n",
        "geo_file.write(\"Point(1) = {xc[0], xc[1], xc[2], \tlc};\\n\")\n",
        "geo_file.write(\"R[] = {\");\n",
        "for ir in range(0,len(R)-1):\n",
        "    geo_file.write(str(R[ir])+\",\")\n",
        "geo_file.write(str(R[len(R)-1])+\"};\\n\")\n",
        "geo_file.write(\"ncomp = #R[];\\n\")\n",
        "geo_file.write(\"nlayers = 20;\\n\")\n",
        "\n",
        "geo_file.write(\"For comp In {0:(ncomp-1)}\\n\")\n",
        "geo_file.write(\"  r = R[comp];\\n\")\n",
        "geo_file.write(\"  Point(2+i) = {xc[0], r+xc[1], xc[2], \tlc};\\n\")\n",
        "geo_file.write(\"  Point(3+i) = {xc[0], xc[1], r+xc[2], \tlc};\\n\")\n",
        "geo_file.write(\"  Point(4+i) = {xc[0], xc[1], -r+xc[2], \tlc};\\n\")\n",
        "geo_file.write(\"  Point(5+i) = {xc[0], -r+xc[1], xc[2], \tlc};\\n\")\n",
        "\n",
        "geo_file.write(\"  Circle(1+i) = {2+i, 1, 4+i};\\n\")\n",
        "geo_file.write(\"  Circle(2+i) = {4+i, 1, 5+i};\\n\")\n",
        "geo_file.write(\"  Circle(3+i) = {5+i, 1, 3+i};\\n\")\n",
        "geo_file.write(\"  Circle(4+i) = {3+i, 1, 2+i};\\n\")\n",
        "geo_file.write(\"  Line Loop(1+i) = {1+i, 2+i, 3+i, 4+i};\\n\")\n",
        "geo_file.write(\"  i += N;\\n\")\n",
        "geo_file.write(\"EndFor\\n\")\n",
        "\n",
        "geo_file.write(\"Plane Surface(1) = {1};\")\n",
        "geo_file.write(\"out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{1}; Layers{nlayers}; };\\n\")\n",
        "geo_file.write(\"out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{out[0]}; Layers{nlayers}; };\\n\")\n",
        "geo_file.write(\"out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{out[0]}; Layers{nlayers}; };\\n\")\n",
        "\n",
        "geo_file.write(\"i = 0;\\n\")\n",
        "geo_file.write(\"For r In {1:(ncomp-1)}\\n\")\n",
        "geo_file.write(\"  Plane Surface(2+i) = {1+i*N, 1+(i+1)*N};\\n\")\n",
        "geo_file.write(\"  out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{2+i}; Layers{nlayers}; };\\n\")\n",
        "geo_file.write(\"  out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{out[0]}; Layers{nlayers}; };\\n\")\n",
        "geo_file.write(\"  out[] = Extrude { { 0,0,1 }, { 0,0,0 }, 2*Pi/3 } { Surface{out[0]}; Layers{nlayers}; };\\n\")\n",
        "geo_file.write(\"  i += 1;\\n\")\n",
        "geo_file.write(\"EndFor\\n\")\n",
        "\n",
        "geo_file.write(\"Mesh 3;\\n\")\n",
        "geo_file.write(\"Coherence Mesh;\\n\")\n",
        "\n",
        "geo_file.close()\n",
        "\n",
        "\n",
        "# # # # # # # # # # Run with Gmsh # # # # # # # # # # # #\n",
        "!apt-get install gmsh\n",
        "!gmsh --version\n",
        "!gmsh -v 0 -3 multi_layer_torus.geo -o multi_layer_torus.msh\n",
        "!dolfin-convert multi_layer_torus.msh multi_layer_torus.xml\n",
        "# Save to your local computer\n",
        "#from google.colab import files\n",
        "#files.download('multi_layer_torus.msh')\n",
        "\n",
        "# # # # # # # # # # Convert mesh by meshio # # # # # # # # \n",
        "# !pip install -U meshio\n",
        "# !apt-get install python-lxml\n",
        "# !meshio-convert multi_layer_torus.msh multi_layer_torus.vtu"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "gmsh is already the newest version (3.0.6+dfsg1-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 111 not upgraded.\n",
            "3.0.6\n",
            "Converting from Gmsh format (.msh, .gmsh) to DOLFIN XML format\n",
            "Expecting 8160 vertices\n",
            "Found all vertices\n",
            "Expecting 42840 cells\n",
            "Found all cells\n",
            "Conversion done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pfryWRtY-FU-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Setup phase-function for multilyered structures"
      ]
    },
    {
      "metadata": {
        "id": "eaxy_-rr9-SY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mesh = Mesh(\"multi_layer_torus.xml\");\n",
        "\n",
        "gdim = mesh.geometry().dim()\n",
        "\n",
        "\n",
        "V_DG = FunctionSpace(mesh, 'DG', 0)\n",
        "dofmap_DG = V_DG.dofmap()\n",
        "phase = Function(V_DG)\n",
        "cellmarker = MeshFunction(\"size_t\", mesh, mesh.topology().dim())\n",
        "\n",
        "L = 20.;\n",
        "for cell in cells(mesh):\n",
        "    p = cell.midpoint();\n",
        "    d = (L-sqrt(p.x()*p.x()+p.y()*p.y()))**2+p.z()*p.z()\n",
        "    for i in range(0, len(R)):\n",
        "        if d<R[i]*R[i]:\n",
        "            phase.vector()[dofmap_DG.cell_dofs(cell.index())] = i%2;\n",
        "            cellmarker[cell.index()] = i%2;\n",
        "            break;\n",
        "\n",
        "# plot(mesh);\n",
        "# plot(cellmarker, cmap=\"coolwarm\");\n",
        "mesh0 = SubMesh(mesh, cellmarker, 0)\n",
        "mesh1 = SubMesh(mesh, cellmarker, 1)\n",
        "File(\"Phase.pvd\")<<phase\n",
        "File(\"mesh0.xml\")<<mesh0\n",
        "File(\"mesh1.xml\")<<mesh1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vR0qSxavFNzl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Define parameters"
      ]
    },
    {
      "metadata": {
        "id": "E7kARxlvFSRI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "bvalue = 4000;             # bvalue\n",
        "delta, Delta = 40000, 40000 # PGSE sequence\n",
        "g0, g1, g2 = 1, 0, 0;       # gradient direction\n",
        "theta = 0.5;                # theta=0.5: midpoint method\n",
        "K = 3e-3;                   # Diffusion coefficient\n",
        "kappa = 5e-5                # Permeability\n",
        "nskip = 5;                  # Output frequency (for visualization only)\n",
        "porder = 1;                 # order of basis functions of FEM\n",
        "Nsteps = 200;               # number of time intervals"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aFEvif9PF2yH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Solve the Bloch-Torrey equation"
      ]
    },
    {
      "metadata": {
        "id": "DYZ5SWlw69tQ",
        "colab_type": "code",
        "outputId": "43c03659-44c3-454a-b032-8975363c96da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1418
        }
      },
      "cell_type": "code",
      "source": [
        "if (Delta-delta/3.0<=0):\n",
        "    print('Check time sequence!');\n",
        "    exit(0);\n",
        "gnorm = sqrt(bvalue)/sqrt(delta*delta*(Delta-delta/3.0));\n",
        "t, T = 0, Delta+delta;\n",
        "\n",
        "#################################################################################\n",
        "# FUNCTION SPACES\n",
        "if dolfin_version=='1.6.0':\n",
        "    V = FunctionSpace(mesh , \"CG\", porder);\n",
        "    W = MixedFunctionSpace([V, V, V, V])\n",
        "else:\n",
        "    # For FEniCS 2016, 2017\n",
        "    Ve = FiniteElement(\"CG\", mesh.ufl_cell(), porder)\n",
        "    TH = MixedElement([Ve,Ve,Ve,Ve])\n",
        "    V = FunctionSpace(mesh,Ve);\n",
        "    W = FunctionSpace(mesh, TH)\n",
        "\n",
        "v = TestFunction(W)\n",
        "v0r, v0i, v1r, v1i = v[0], v[1], v[2], v[3]\n",
        "\n",
        "w = TrialFunction(W);\n",
        "u0r, u0i, u1r, u1i = w[0], w[1], w[2], w[3]\n",
        "#################################################################################\n",
        "\n",
        "#################################################################################\n",
        "# Initial conditions\n",
        "one = Function(V);\n",
        "one.vector()[:] = 1.0;\n",
        "u_0 = Function(W);\n",
        "assign(u_0.sub(0), one)\n",
        "assign(u_0.sub(2), one)\n",
        "u0r_0, u0i_0, u1r_0, u1i_0 = u_0[0], u_0[1], u_0[2], u_0[3]\n",
        "#################################################################################\n",
        "\n",
        "if (gdim==2):\n",
        "  GX=Expression(\"x[0]*g0+x[1]*g1\", g0=g0, g1=g1, domain=mesh, degree=3);\n",
        "if (gdim==3):\n",
        "  GX=Expression(\"x[0]*g0+x[1]*g1+x[2]*g2\", g0=g0, g1=g1, g2=g2, domain=mesh, degree=3);\n",
        "\n",
        "#################################\n",
        "## output files \n",
        "file_u0r = File(\"results/u0r.pvd\")\n",
        "file_u0i = File(\"results/u0i.pvd\")\n",
        "file_u1r = File(\"results/u1r.pvd\")\n",
        "file_u1i = File(\"results/u1i.pvd\")\n",
        "#################################\n",
        "\n",
        "def FuncF(ft, gnorm, GX, ur, ui, vr, vi, K):\n",
        "    Fr = ft*gnorm*GX*ui*vr - K*inner(grad(ur), grad(vr))\n",
        "    Fi = - ft*gnorm*GX*ur*vi - K*inner(grad(ui), grad(vi))\n",
        "    return Fr + Fi\n",
        "  \n",
        "def icondition(kappa, u0rm, u1rm, v0r, v1r, u0im, u1im, v0i, v1i):\n",
        "    F_bcr = kappa*(u0rm-u1rm)*(v0r-v1r)\n",
        "    F_bci = kappa*(u0im-u1im)*(v0i-v1i)\n",
        "    return F_bcr + F_bci\n",
        "\n",
        "def SubMeshSave(ur, ui, file_ur, file_ui, mesh, n, stepcounter, dolfin_version):\n",
        "  if dolfin_version=='1.6.0':\n",
        "    V = FunctionSpace(mesh, \"CG\", porder)\n",
        "  else:\n",
        "    # For FEniCS 2016, 2017\n",
        "    Ve = FiniteElement(\"CG\", mesh.ufl_cell(), porder)\n",
        "    V = FunctionSpace(mesh, Ve)\n",
        "  if stepcounter % n == 0:\n",
        "    ur_p = project(ur, V);\n",
        "    ui_p = project(ui, V);\n",
        "    ur_p.rename(\"Real\", \"label\");\n",
        "    ui_p.rename(\"Imag\", \"label\");\n",
        "    file_ur << ur_p;\n",
        "    file_ui << ui_p;\n",
        "\n",
        "def ieval(u,omega, phase):\n",
        "  if omega==1:\n",
        "    return u('+')*phase('+') + u('-')*phase('-');\n",
        "  if omega==0:\n",
        "    return u('+')*(1.-phase('+')) + u('-')*(1.-phase('-'))                      \n",
        "  \n",
        "def FT(t, delta, Delta):\n",
        "    ft1 = 1.0*(t>=0 and t<delta) \n",
        "    ft2 = -1.0*(t>=Delta and t<=Delta+delta);\n",
        "    return ft1 + ft2;  \n",
        "  \n",
        "stepcounter = 0;\n",
        "\n",
        "def ThetaMethod_L(ft, gnorm, GX, u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, u0r_0, u0i_0, u1r_0, u1i_0,k, kappa, K, theta, phase):\n",
        "    L0 = (u0r_0/k*v0r + u0i_0/k*v0i+(1-theta)*FuncF(ft, gnorm, GX, u0r_0, u0i_0, v0r, v0i, K))*(1-phase)*dx\n",
        "    L1 = (u1r_0/k*v1r +u1i_0/k*v1i+(1-theta)*FuncF(ft, gnorm, GX, u1r_0, u1i_0, v1r, v1i, K))*phase*dx\n",
        "    L_bc = avg((1-theta)*icondition(kappa, u0r_0, u1r_0, v0r, v1r, u0i_0, u1i_0, v0i, v1i))*abs(jump(phase))*dS;\n",
        "    return L0+L1-L_bc\n",
        "\n",
        "\n",
        "def ThetaMethod_a(ft, gnorm, GX, u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, u0r_0, u0i_0, u1r_0, u1i_0,k,kappa, K, theta, phase):\n",
        "    a0 = (u0r/k*v0r   + u0i/k*v0i  -theta*FuncF(ft, gnorm, GX, u0r  , u0i  , v0r, v0i, K))*(1-phase)*dx\n",
        "    a1 = (u1r/k*v1r   + u1i/k*v1i  -theta*FuncF(ft, gnorm, GX, u1r  , u1i  , v1r, v1i, K))*phase*dx\n",
        "    a_bc = avg(  (theta*icondition(kappa, u0r  , u1r  , v0r, v1r, u0i  , u1i  , v0i, v1i)))*abs(jump(phase))*dS;\n",
        "    return a0+a1+a_bc\n",
        "\n",
        "\n",
        "def NoTimeMatrices(u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, K, GX, kappa, theta, phase):\n",
        "    m0 = (u0r*v0r   + u0i*v0i)*(1-phase)*dx\n",
        "    m1 = (u1r*v1r   + u1i*v1i)*phase*dx\n",
        "    M = assemble(m0+m1);\n",
        "        \n",
        "    j0 = -GX*(u0i*v0r   - u0r*v0i)*(1-phase)*dx\n",
        "    j1 = -GX*(u1i*v1r   - u1r*v1i)*phase*dx\n",
        "    J = assemble(j0+j1);    \n",
        "    s0 = K*( inner(grad(u0r), grad(v0r)) + inner(grad(u0i), grad(v0i)) )*(1-phase)*dx\n",
        "    s1 = K*( inner(grad(u1r), grad(v1r)) + inner(grad(u1i), grad(v1i)) )*phase*dx\n",
        "    S = assemble(s0+s1)\n",
        "\n",
        "    im = avg(  icondition(kappa, u0r  , u1r  , v0r, v1r, u0i  , u1i  , v0i, v1i) )*abs(jump(phase))*dS;\n",
        "    I = assemble(im)\n",
        "    \n",
        "    M.ident_zeros();\n",
        "    \n",
        "    return M, J, S, I\n",
        "\n",
        "def ThetaMethod_A(ft, gnorm, theta, k, M, J, S, I):\n",
        "    return 1./k*M + ft*gnorm*theta*J + theta*S + theta*I\n",
        "\n",
        "# Theta method\n",
        "\n",
        "stepcounter = 0;\n",
        "\n",
        "k = T/Nsteps; # Time-step size\n",
        "\n",
        "hmin = mesh.hmin();\n",
        "\n",
        "M, J, S, I = NoTimeMatrices(u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, K, GX, kappa, theta, phase);\n",
        "\n",
        "while t < T + dt: # Time-stepping loop\n",
        "    if stepcounter % nskip == 0:\n",
        "        print('t: %f '%t, 'T: %.1f'%T, 'dt:%.1f'%k,'gnorm: %e'%gnorm,'Completed %.2f%%'%(float(t)/float(T+k)*100.0));\n",
        "\n",
        "    ft = FT(t, delta, Delta);\n",
        "\n",
        "    L = ThetaMethod_L(ft, gnorm, GX, u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, u0r_0, u0i_0, u1r_0, u1i_0,k, kappa, K, theta, phase);\n",
        "    # a = ThetaMethod_a(ft, gnorm, GX, u0r, u0i, v0r, v0i, u1r, u1i, v1r, v1i, u0r_0, u0i_0, u1r_0, u1i_0,k,kappa, K, theta, phase);\n",
        "    # a = a + 1e-6*(u0r*v0r+u0i*v0i)*theta*dx + 1e-6*(u1r*v1r+u1i*v1i)*(1-theta)*dx\n",
        "    A = ThetaMethod_A(ft, gnorm, theta, k, M, J, S, I);\n",
        "    # A = assemble(a);\n",
        "    b = assemble(L);\n",
        "\n",
        "    # print J.is_symmetric(1e-10);\n",
        "    # stop\n",
        " \n",
        "    u = Function(W)\n",
        "    solve(A,u.vector(),b, \"gmres\", \"ilu\");\n",
        "    \n",
        "    u0r_0, u0i_0, u1r_0, u1i_0 = split(u)\n",
        "    \n",
        "    SubMeshSave(u0r_0, u0i_0, file_u0r, file_u0i, mesh0, nskip, stepcounter, dolfin_version);\n",
        "    SubMeshSave(u1r_0, u1i_0, file_u1r, file_u1i, mesh1, nskip, stepcounter, dolfin_version);\n",
        "    \n",
        "    t += k;\n",
        "    stepcounter += 1;\n",
        "\n",
        "signal = assemble((phase*u1r_0+(1-phase)*u0r_0)*dx)/assemble(one*dx);\n",
        "print('b:',bvalue, 'Signal: %.3e'%signal,'kappa: %.3e'%kappa,', dt:',k,', hmin:',hmin)\n",
        "\n",
        "\n",
        "V0 = FunctionSpace(mesh0,Ve);\n",
        "V1 = FunctionSpace(mesh1,Ve);\n",
        "u0r_0p = project(u0r_0,V0)\n",
        "u1r_0p = project(u1r_0,V1)\n",
        "plot(u0r_0p, cmap=\"coolwarm\")\n",
        "plot(u1r_0p, cmap=\"coolwarm\")\n",
        "\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "t: 0.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 0.00%\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "t: 2000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 2.49%\n",
            "t: 4000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 4.98%\n",
            "t: 6000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 7.46%\n",
            "t: 8000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 9.95%\n",
            "t: 10000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 12.44%\n",
            "t: 12000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 14.93%\n",
            "t: 14000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 17.41%\n",
            "t: 16000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 19.90%\n",
            "t: 18000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 22.39%\n",
            "t: 20000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 24.88%\n",
            "t: 22000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 27.36%\n",
            "t: 24000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 29.85%\n",
            "t: 26000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 32.34%\n",
            "t: 28000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 34.83%\n",
            "t: 30000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 37.31%\n",
            "t: 32000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 39.80%\n",
            "t: 34000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 42.29%\n",
            "t: 36000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 44.78%\n",
            "t: 38000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 47.26%\n",
            "t: 40000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 49.75%\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "t: 42000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 52.24%\n",
            "t: 44000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 54.73%\n",
            "t: 46000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 57.21%\n",
            "t: 48000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 59.70%\n",
            "t: 50000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 62.19%\n",
            "t: 52000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 64.68%\n",
            "t: 54000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 67.16%\n",
            "t: 56000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 69.65%\n",
            "t: 58000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 72.14%\n",
            "t: 60000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 74.63%\n",
            "t: 62000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 77.11%\n",
            "t: 64000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 79.60%\n",
            "t: 66000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 82.09%\n",
            "t: 68000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 84.58%\n",
            "t: 70000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 87.06%\n",
            "t: 72000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 89.55%\n",
            "t: 74000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 92.04%\n",
            "t: 76000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 94.53%\n",
            "t: 78000.000000  T: 80000.0 dt:400.0 gnorm: 9.682458e-06 Completed 97.01%\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
            "b: 4000 Signal: 5.253e-02 kappa: 5.000e-05 , dt: 400.0 , hmin: 1.9876796914809614\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fe3b38e6a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wfOA7SnangT/5M/j2dwgvve4pc+H97xbsspYnpAT8dHFWtbPVNPiw2cD+3O3JzAbWr/U9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\n437S0VvUqWVd6QWqCmKA0UBiPdhlxLaB4VAzn/tu6ysxuSKEpJC7ttF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c7K66FtFVS1gi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vcA0N9diypRAbN76D55+fBACkMMaR8CsLCYBGcxsnT/4V775bAplMBgDElsIrlFjcunUbeXnL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\nPRULde/ePQwcOFDIJRLCmP8HboxJggCNVfQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2PFBYd0QMw4U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Reference solutions"
      ]
    },
    {
      "metadata": {
        "id": "3paAgRxbM3yW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# The results were generated by the Matrix Formalism method propsed in\n",
        "# D.S. Grebenkov, Pulsed-gradient spin-echo monitoring of restricted \n",
        "# diffusion in multilayered structures, J. Magn. Reson., 205 (2) (2010)\n",
        "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
        "# # # R=[5,7.5,10], kappa=5e-5m/s delta=Delta=40000\n",
        "#  bvalue  # signal attenuation\n",
        "# # # # # # # # # # # # # # #\n",
        "# # # # # # # # # # # # # # #\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}