{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAGD - Membrane-Aided Gas-Diffusion\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First import numpy, since all arrays are based on numpy\n",
    "import numpy as np \n",
    "\n",
    "#Import matplotlib, for plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the main software library\n",
    "import simex_lib as simex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All parameters must be adopted in the same units of space/time/mass/volume\n",
    "here we adopt:\n",
    "- space units: centimeters (cm) \n",
    "- time units: seconds (s)\n",
    "- mass: Micrograms (mug)\n",
    "- volume: cubic-centimeters or milliliters (cm^3, mL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Experiment basename (for filenames and figures)\n",
    "name = \"magd\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to set the domain, that is, the position of the interfaces and boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Domain definition (position of interfaces) \n",
    "# x0   x1   x2  x3 \n",
    "# |----|----|---|\n",
    "#x=np.array([initial_position, interface_1, interface_2, final_position])\n",
    "x=np.array([0, 0.3, 0.305, 0.355]) # cm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we set the names of the compartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Names of compartments\n",
    "#xnames = [\"\", \"\", \"\"]\n",
    "xnames = [\"sample\", \"headspace\", \"extract\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set initial concentrations, constant in each compartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initial concentrations in each space\n",
    "#  Assumed constant for each space\n",
    "#   C0   C1   C2   \n",
    "# |----|----|----|\n",
    "#C=np.array([C0, C1, C2])   # (mug/mL)\n",
    "C=np.array([2, 0.0, 0.0]) # (mug/mL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diffusion coefficients for each compartment, constant at each one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Diffusion coefficients for each space\n",
    "#   D0   D1   D2   \n",
    "# |----|----|----|\n",
    "#D=np.array([D0, D1, D2])           # (cm^2/s)\n",
    "D=np.array([219e-6, 0.11, 219e-6]) # (cm^2/s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the partition coefficients. Always enforce zero at external boundaries for no flow condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Partition coefficients K for each interface\n",
    "# Set 0.0 for beggining and endpoints    \n",
    "# K0  K1  K2   K3\n",
    "# |---|---|----|\n",
    "#K=np.array([K0, K1, K2, K3]) # 2 boundaries and 2 interface coefficient, non-dimensional\n",
    "K=np.array([0.0, 13.2, 1/13.2, 0.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now configure runtime options for this simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Max time definition (how long to run the simulation)\n",
    "maxtime = 6000 # seconds\n",
    "\n",
    "#Time step size\n",
    "dt = 0.1  # seconds - depending on your diffusion coefficients, this may be required to be small\n",
    "\n",
    "#Plotting time spots (snapshots of the concentrations are saved in these time instants)\n",
    "iplot_time=np.array([0.0, 30, 60, 120, 300, 600]) # seconds\n",
    "#iplot_time=np.linspace(0, 50, 21, endpoint=True) #Equally spaced plots, for animations\n",
    "\n",
    "#Space discretization (number of grid points) - depending on your device, this may need to be incresed\n",
    "N = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build the device with these parameters (we have given the same names of the variables as the device function arguments, but we could have called the variables differently)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        --------------------------------------------------------------\n",
      "        Simex - Simulation of Extraction Processes\n",
      "        --------------------------------------------------------------\n",
      "        \n",
      "You defined a device with 3 compartment(s).\n",
      "Mechanism layout/interfaces (x):  [0.    0.3   0.305 0.355]\n",
      "Initial concentrations: [2. 0. 0.]\n",
      "Diffusion coefficients: [0.000219 0.11     0.000219]\n",
      "Interface coefficients: [ 0.         13.2         0.07575758  0.        ]\n",
      "Output basename: output/magd/magd\n",
      "\n",
      "Compartment 0  setup\n",
      " Name:                   sample\n",
      " Local Domain:           [0.  0.3]\n",
      " Difusion (neigbours):   [0.       0.000219 0.11    ]\n",
      " Border/Interfaces Coef: [ 0.  13.2]\n",
      "\n",
      "Compartment 1  setup\n",
      " Name:                   headspace\n",
      " Local Domain:           [0.3   0.305]\n",
      " Difusion (neigbours):   [0.000219 0.11     0.000219]\n",
      " Border/Interfaces Coef: [13.2         0.07575758]\n",
      "\n",
      "Compartment 2  setup\n",
      " Name:                   extract\n",
      " Local Domain:           [0.305 0.355]\n",
      " Difusion (neigbours):   [0.11     0.000219 0.      ]\n",
      " Border/Interfaces Coef: [0.07575758 0.        ]\n",
      "\n",
      "Proposed number of control volumes (grid points):  500\n",
      "Adjusted number of grid points:  500\n",
      "Number of degrees of freedom:  496\n",
      "\n",
      "\n",
      "Time-space info (dx, dt, Nt, maxD, dx/maxD):\n",
      "0.0007099999999999999 0.1 60000 0.11 0.006454545454545454\n",
      "\n",
      "Equilibrium concentrations:\n",
      " [1.7124324324324325, 0.12972972972972974, 1.7124324324324325]\n",
      "------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "p = simex.device(\n",
    "        D = D, \n",
    "        K = K, \n",
    "        C = C, \n",
    "        xspace = x, \n",
    "        xnames = xnames, \n",
    "        name = name, \n",
    "        N = N, dt = dt, maxtime = maxtime, iplot_time = iplot_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Now that the device structure is built, we can actually use in a few ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets loop in time the model to see how the concentrations evolve in time. The main function here is run(), which calculates all time steps of an implicit solver for the model of the device. It will save and return snapshots at all time set in iplot_time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " It:  0  Time:  0.0  Mass:  0.59928  %Dif Eq:  100.0000 %\n",
      " It:  300  Time:  30.0  Mass:  0.60162  %Dif Eq:  23.1960 %\n",
      " It:  600  Time:  60.0  Mass:  0.60186  %Dif Eq:  13.4087 %\n",
      " It:  1200  Time:  120.0  Mass:  0.60199  %Dif Eq:  5.7680 %\n",
      " It:  3000  Time:  300.0  Mass:  0.60205  %Dif Eq:  1.1937 %\n",
      " It:  6000  Time:  600.0  Mass:  0.60206  %Dif Eq:  0.4138 %\n",
      " \n",
      " Saving concentrations as \n",
      "  output/magd/magd_data.csv\n",
      "\n",
      " %Eq   Time \n",
      "50.0 %   9.00\n",
      "60.0 %   13.30\n",
      "70.0 %   20.60\n",
      "80.0 %   34.60\n",
      "90.0 %   70.00\n",
      "95.0 %   112.20\n",
      "99.0 %   334.90\n",
      "99.5 %   529.90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "concentrations = p.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The snapshots were saved in the csv files and also returned to the list of snapshots given at \"concentrations\". We can use this output to plot the concentrations, as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 1, constrained_layout=True, figsize=(15,10))\n",
    "plt.xlabel(\"x-distance (cm)\")\n",
    "plt.ylabel(\"concentration ($\\mu g/mL$)\")\n",
    "plt.title(\"membrane-aided gas-diffusion\")\n",
    "\n",
    "for i, c in enumerate(concentrations):    \n",
    "    #Plot\n",
    "    t = iplot_time[i]\n",
    "    axes.plot(p.x, c, label=str(t)+\"s\")\n",
    "    \n",
    "for i, name in enumerate(xnames):\n",
    "    xlabel=0.5*p.xspace[i]+0.5*p.xspace[i+1]\n",
    "    plt.text(xlabel, -0.3, name)\n",
    "    \n",
    "axes.plot(p.x, p.u_equi_ext, 'k--', label=\"Theoretical \\n Equilibrium\")\n",
    "axes.legend()\n",
    "plt.savefig(p.basename+\"_final.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}