{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layered Seawater Intrusion and Melt Under Grounded Ice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter notebook is meant as a companion to the manuscript by Robel, Wilson and Seroussi on the intrusion of seawater under grounded ice sheets. This notebook will produce equivalent versions to figures 2-4 and has an interactive cell that shows how changes in various parameters change the numerically-calculated intrusion distance. Figures 6-7 use ISSM MATLAB functions to load and plot ISSM model output, so are available separately as MATLAB scripts (in the same GitHub repository where this is available). GitHub repository: XXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp1d\n",
    "#ipywidgets is loaded further down for the interactive intrusion calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define function for numerically calculating intrusion distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def intrusion(Fr0,Ci,Cd,theta,gamma):\n",
    "    h=np.empty([100000, 1])\n",
    "    x=np.empty([100000, 1])\n",
    "    #Initial conditions\n",
    "    h[0] = Fr0**(2/3)\n",
    "    x[0] = 0\n",
    "    i=0\n",
    "    int=1e16\n",
    "    while h[i] < 1: #stop when freshwater occupies entire layer\n",
    "        dx = 1e-16 + (1e-1-1e-16)*np.tanh(-1/int)\n",
    "        #start with very small O(machine precision) grid spacing and then increase as gradient decreases\n",
    "        if i==0 :\n",
    "            Fr = 1 - 1e-8  #avoid singularity due to Fr=1 at boundary while maintaining accuracy (has been tested for sensitivity)\n",
    "        else:\n",
    "            Fr = Fr0/(h[i]**1.5)\n",
    "    \n",
    "        int = ((Fr**2)*((Ci/(1-h[i])) + Cd*(1+gamma*h[i])) - theta)/(Fr**2 - 1)\n",
    "    \n",
    "        h[i+1] = h[i] - dx*int\n",
    "        x[i+1] = x[i] - dx\n",
    "        i = i+1;\n",
    "        if i > 100000-2:\n",
    "            break\n",
    "    return x, h, i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Small Fr0 and large gamma\n",
    "Fr0 = 1e-2;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    "theta = 0;\n",
    "gamma = 100;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "\n",
    "\n",
    "plt.plot(x[0:i],1-h[0:i],linewidth=2,color='black')\n",
    "\n",
    "np.tanh(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Small Fr0 and large gamma\n",
    "Fr0 = 1e-2;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    "theta = 0;\n",
    "gamma = 100;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,figsize=(18,18))\n",
    "\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('h')\n",
    "ax1.plot(x[0:i],1-h[0:i],linewidth=2,color='black')\n",
    "ax1.set_ylim([-0.05, 1])\n",
    "ax1.set_xlim([x[i], 0])\n",
    "\n",
    "#without C_i and  with small Fr0  and large gamma\n",
    "h=[];\n",
    "x=[];\n",
    "Ci = 0;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax1.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='red')\n",
    "\n",
    "l_p = 1/(3*gamma*Cd*Fr0**2);\n",
    "ax1.plot(-l_p,0,linestyle = 'None',marker='.',markersize=30,Color='m')\n",
    "ax1.legend(['C_i=0.1','C_i=0','Theory (l_p)'])\n",
    "ax1.set_xlim([-l_p-1, 0])\n",
    "ax1.set_title(r'$Fr_0<<1$, $\\gamma>>1$')\n",
    "\n",
    "\n",
    "#Integration from ocean upstream with all terms and large Fr0\n",
    "Ci = 1e-1;\n",
    "Fr0 = 5e-1;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "\n",
    "ax2.set_xlabel('x')\n",
    "ax2.set_ylabel('h')\n",
    "ax2.plot(x[0:i],1-h[0:i],linewidth=2,color='black')\n",
    "ax2.set_ylim([-0.05, 1])\n",
    "ax2.set_xlim([x[i], 0])\n",
    "\n",
    "#without C_i and large Fr0\n",
    "Ci = 0;\n",
    "Fr0 = 5e-1;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax2.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='red')\n",
    "\n",
    "l_p = 1/(3*gamma*Cd*Fr0**2);\n",
    "ax2.plot(-l_p,0,linestyle = 'None',marker='.',markersize=30,Color='m')\n",
    "ax2.legend(['C_i=0.1','C_i=0','Theory (l_p)'])\n",
    "ax2.set_xlim([-l_p-0.001, 0])\n",
    "ax2.set_title(r'$Fr_0=0.5$, $\\gamma>>1$')\n",
    "\n",
    "#With all terms and medium gamma\n",
    "Ci = 1e-1;\n",
    "Fr0 = 1e-2;\n",
    "gamma=2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "\n",
    "ax3.set_xlabel('x')\n",
    "ax3.set_ylabel('h')\n",
    "ax3.plot(x[0:i],1-h[0:i],linewidth=2,color='black')\n",
    "ax3.set_ylim([-0.05, 1])\n",
    "ax3.set_xlim([x[i], 0])\n",
    "\n",
    "#Without C_i and medium gamma\n",
    "Ci = 1e-2;\n",
    "Fr0 = 1e-2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax3.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='blue')\n",
    "\n",
    "#without C_i and medium gamma\n",
    "Ci = 0;\n",
    "Fr0 = 1e-2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax3.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='red')\n",
    "\n",
    "l_p = 1/(3*gamma*Cd*Fr0**2);\n",
    "ax3.plot(-l_p,0,linestyle = 'None',marker='.',markersize=30,Color='m')\n",
    "ax3.legend(['C_i=0.1','C_i=0.01','C_i=0','Theory (l_p)'])\n",
    "ax3.set_xlim([-l_p-0.1, 0])\n",
    "ax3.set_title(r'$Fr_0<<1$, $\\gamma=6$')\n",
    "\n",
    "#with all terms and zero gamma\n",
    "Ci = 1e-1;\n",
    "Fr0 = 1e-2;\n",
    "gamma = 1e-2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "\n",
    "ax4.set_xlabel('x')\n",
    "ax4.set_ylabel('h')\n",
    "ax4.plot(x[0:i],1-h[0:i],linewidth=2,color='black')\n",
    "ax4.set_ylim([-0.05, 1])\n",
    "ax4.set_xlim([x[i], 0])\n",
    "\n",
    "#Small C_i and zero gamma\n",
    "Ci = 1e-2;\n",
    "Fr0 = 1e-2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax4.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='blue')\n",
    "\n",
    "\n",
    "#Without C_i and zero gamma\n",
    "h=[];\n",
    "x=[];\n",
    "Ci = 0;\n",
    "Fr0 = 1e-2;\n",
    "x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "ax4.plot(x[0:i],1-h[0:i],linewidth=2,linestyle='dashed',color='red')\n",
    "\n",
    "l_u = 1/(4*Cd*Fr0**2);\n",
    "ax4.plot(-l_u,0,linestyle = 'None',marker='.',markersize=30,Color='m')\n",
    "ax4.legend(['C_i=0.1','C_i=0.01','C_i=0','Theory (l_p)'])\n",
    "ax4.set_xlim([-l_u-0.1, 0])\n",
    "ax4.set_title(r'$Fr_0<<1$, $\\gamma<<1$')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in power\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'im' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-7401c631ffa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0mcbar_ax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.85\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.7\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;32m---> 94\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcbar_ax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'im' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=10\n",
    "n2=11;\n",
    "#Zoomed out Parameter sweep for gamma = 0 and Ci = 0.1\n",
    "gamma = 0;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    " \n",
    "Ls = np.zeros([n1,n2])\n",
    "Fr0s = np.linspace(0.01,1,n1)\n",
    "thetas = np.linspace(-1,1,n2)\n",
    "for a in np.arange(n1):\n",
    "    Fr0 = Fr0s[a]\n",
    "    for b in np.arange(n2):\n",
    "        theta = thetas[b]\n",
    "        x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "        Ls[a,b] = -x[i];\n",
    "\n",
    "#plot\n",
    "FR0, THETA = np.meshgrid(Fr0s, thetas, sparse=False, indexing='ij')\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2,figsize=(18,18))\n",
    "\n",
    "ax1.pcolormesh(FR0, THETA,Ls, vmin=10, vmax=10000)\n",
    "ax1.set_xlabel(r'$Fr_0$')\n",
    "ax1.set_ylabel(r'$\\theta$') \n",
    "\n",
    "#Zoomed in Parameter sweep for gamma = 0 and Ci = 0.1\n",
    "gamma = 0;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    " \n",
    "Ls = np.zeros([n1,n2])\n",
    "Fr0s = np.linspace(0.01,0.1,n1)\n",
    "thetas = np.linspace(-0.1,0.1,n2)\n",
    "for a in np.arange(n1):\n",
    "    Fr0 = Fr0s[a]\n",
    "    for b in np.arange(n2):\n",
    "        theta = thetas[b]\n",
    "        x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "        Ls[a,b] = -x[i];\n",
    "\n",
    "#plot\n",
    "FR0, THETA = np.meshgrid(Fr0s, thetas, sparse=False, indexing='ij')\n",
    "\n",
    "ax2.pcolormesh(FR0, THETA,Ls, vmin=10, vmax=10000)\n",
    "ax2.set_xlabel(r'$Fr_0$')\n",
    "ax2.set_ylabel(r'$\\theta$') \n",
    "\n",
    "#Zoomed out Parameter sweep for gamma = 2 and Ci = 0.1\n",
    "gamma = 2;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    " \n",
    "Ls = np.zeros([n1,n2])\n",
    "Fr0s = np.linspace(0.01,1,n1)\n",
    "thetas = np.linspace(-1,1,n2)\n",
    "for a in np.arange(n1):\n",
    "    Fr0 = Fr0s[a]\n",
    "    for b in np.arange(n2):\n",
    "        theta = thetas[b]\n",
    "        x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "        Ls[a,b] = -x[i];\n",
    "\n",
    "#plot\n",
    "FR0, THETA = np.meshgrid(Fr0s, thetas, sparse=False, indexing='ij')\n",
    "\n",
    "ax3.pcolormesh(FR0, THETA,Ls, vmin=10, vmax=10000)\n",
    "ax3.set_xlabel(r'$Fr_0$')\n",
    "ax3.set_ylabel(r'$\\theta$') \n",
    "\n",
    "#Zoomed Parameter sweep for gamma = 2 and Ci = 0.1 \n",
    "gamma = 2;\n",
    "Ci = 1e-1;\n",
    "Cd = 1;\n",
    " \n",
    "Ls = np.zeros([n1,n2])\n",
    "Fr0s = np.linspace(0.01,0.1,n1)\n",
    "thetas = np.linspace(-0.1,0.1,n2)\n",
    "for a in np.arange(n1):\n",
    "    Fr0 = Fr0s[a]\n",
    "    for b in np.arange(n2):\n",
    "        theta = thetas[b]\n",
    "        x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "        Ls[a,b] = -x[i];\n",
    "        \n",
    "#plot\n",
    "FR0, THETA = np.meshgrid(Fr0s, thetas, sparse=False, indexing='ij')\n",
    "\n",
    "ax4.pcolormesh(FR0, THETA,Ls, vmin=10, vmax=10000)\n",
    "ax4.set_xlabel(r'$Fr_0$')\n",
    "ax4.set_ylabel(r'$\\theta$') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in log\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb6ae6e0e90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=10\n",
    "alphau_over_k_0 = 0.1\n",
    "theta = np.linspace(-0.1,0.1,n)\n",
    "mults  = np.linspace(1e-2,2,n)\n",
    "\n",
    "LSOH = np.zeros([n,n])\n",
    "\n",
    "for i in np.arange(n):\n",
    "    mult = mults[i]\n",
    "    \n",
    "    alphau_over_k = alphau_over_k_0*mult\n",
    "    Ls_over_H = -(1/np.tan(theta))*(1 + (alphau_over_k/np.tan(theta))*np.log(1 - (np.tan(theta)/alphau_over_k)))\n",
    "    \n",
    "    LSOH[i,:] = Ls_over_H;\n",
    "\n",
    "np.nan_to_num(LSOH, copy=False, nan=10000)\n",
    "MULT, THETA = np.meshgrid(mults,theta, sparse=False, indexing='ij')    \n",
    "\n",
    "plt.pcolormesh(MULT*alphau_over_k_0, THETA, LSOH, vmin=10, vmax=10000)\n",
    "plt.xlabel(r'$\\alpha U_{in} / K$')\n",
    "plt.ylabel(r'$\\theta$') \n",
    "\n",
    "\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive seawater intrusion calculation (for hard beds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an interactive widget where various physical parameters can be changed and the intrusion distance from theory is interactively calculated and plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def intrusionplot(Fr0,theta,gamma):\n",
    "    Ci = 1e-3\n",
    "    Cd = 1\n",
    "    x, h, i = intrusion(Fr0,Ci,Cd,theta,gamma)\n",
    "    plt.plot(x[0:i],1-h[0:i],linewidth=4)\n",
    "\n",
    "    l_p = 1/(3*gamma*Cd*Fr0**2)\n",
    "    l_u = 1/(4*Cd*Fr0**2)\n",
    "    plt.plot(-l_p,0,linestyle = 'None',marker='.',markersize=30,Color='r')\n",
    "    plt.plot(-l_u,0,linestyle = 'None',marker='.',markersize=30,Color='b')\n",
    "    plt.legend(['Numerical','Theory (l_p)','Theory (l_u)'])\n",
    "    plt.xlim([-l_u-1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "471aef102b9c479ba7db81182aeea244",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='Fr0', max=1.0, min=0.01, step=0.01), FloatSlider(val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive_plot = interactive(intrusionplot, Fr0=(0.01,1.0,0.01), theta=(-0.1,0.1,0.01),gamma=(0,10,1))\n",
    "interactive_plot"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}